From bacd765178322dc6e9ae43a22ede6a5208d33add Mon Sep 17 00:00:00 2001 From: Wes Bonelli Date: Mon, 7 Aug 2023 11:33:13 -0400 Subject: [PATCH] feat(PRT): add scripts/notebooks for PRT examples --- .doc/test_build.py | 2 +- .github/workflows/ex-rtd.yml | 16 +- .github/workflows/ex-workflow.yml | 19 +- etc/requirements.usgs.txt | 4 +- notebooks/ex-prt-mp7-p01.ipynb | 1384 ++++++++++++++++++++++ notebooks/ex-prt-mp7-p02.ipynb | 1834 +++++++++++++++++++++++++++++ notebooks/ex-prt-mp7-p04.ipynb | 1373 +++++++++++++++++++++ scripts/ex-prt-mp7-p01.py | 961 +++++++++++++++ scripts/ex-prt-mp7-p02.py | 1102 +++++++++++++++++ scripts/ex-prt-mp7-p04.py | 958 +++++++++++++++ 10 files changed, 7636 insertions(+), 17 deletions(-) create mode 100644 notebooks/ex-prt-mp7-p01.ipynb create mode 100644 notebooks/ex-prt-mp7-p02.ipynb create mode 100644 notebooks/ex-prt-mp7-p04.ipynb create mode 100644 scripts/ex-prt-mp7-p01.py create mode 100644 scripts/ex-prt-mp7-p02.py create mode 100644 scripts/ex-prt-mp7-p04.py diff --git a/.doc/test_build.py b/.doc/test_build.py index 8a350453..3b9cb3a1 100644 --- a/.doc/test_build.py +++ b/.doc/test_build.py @@ -10,7 +10,7 @@ is_CI = "CI" in os.environ or os.environ.get("READTHEDOCS") == "True" # -- update flopy classes ---------------------------------------------------- -flopy.mf6.utils.generate_classes(branch="develop", backup=False) +flopy.mf6.utils.generate_classes(owner="aprovost-usgs", branch="PRT", backup=False) # -- update notebooks and tables --------------------------------------------- pth = os.path.join("..", "scripts") diff --git a/.github/workflows/ex-rtd.yml b/.github/workflows/ex-rtd.yml index 43f77200..a522b7fa 100644 --- a/.github/workflows/ex-rtd.yml +++ b/.github/workflows/ex-rtd.yml @@ -4,17 +4,22 @@ on: schedule: - cron: '0 2 * * *' # run at 2 AM UTC push: - branches: + branches: - master - develop - ci-* + - PRT paths-ignore: - 'README.md' - 'DEVELOPER.md' pull_request: - branches: + branches: - master - develop + - PRT + paths-ignore: + - 'README.md' + - 'DEVELOPER.md' jobs: rtd_build: @@ -68,16 +73,17 @@ jobs: - name: Update flopy MODFLOW 6 classes run: | import flopy - flopy.mf6.utils.generate_classes(branch="develop", backup=False) + flopy.mf6.utils.generate_classes(owner="aprovost-usgs", ref="PRT", backup=False) shell: python - name: Install MODFLOW executables release uses: modflowpy/install-modflow-action@v1 - - name: Install MODFLOW nightly-build executables + - name: Install MODFLOW 6 PRT build uses: modflowpy/install-modflow-action@v1 with: - repo: modflow6-nightly-build + owner: aprovost-usgs + repo: modflow6 - name: Run scripts without model runs run: pytest -v -n=auto --durations=0 ci_build_files.py diff --git a/.github/workflows/ex-workflow.yml b/.github/workflows/ex-workflow.yml index 29a04133..a5812929 100644 --- a/.github/workflows/ex-workflow.yml +++ b/.github/workflows/ex-workflow.yml @@ -8,13 +8,15 @@ on: - master - develop - ci-* + - PRT paths-ignore: - 'README.md' - 'DEVELOPER.md' pull_request: - branches: + branches: - master - develop + - PRT paths-ignore: - 'README.md' - 'DEVELOPER.md' @@ -55,7 +57,7 @@ jobs: - name: Update flopy MODFLOW 6 classes run: | import flopy - flopy.mf6.utils.generate_classes(branch="develop", backup=False) + flopy.mf6.utils.generate_classes(owner="aprovost-usgs", ref="PRT", backup=False) shell: python - name: Install MODFLOW executables release @@ -130,25 +132,24 @@ jobs: - name: Update flopy MODFLOW 6 classes run: | import flopy - flopy.mf6.utils.generate_classes(branch="develop", backup=False) + flopy.mf6.utils.generate_classes(owner="aprovost-usgs", ref="PRT", backup=False) shell: python - name: Install MODFLOW executables release uses: modflowpy/install-modflow-action@v1 - - name: Install MODFLOW nightly-build executables + - name: Install MODFLOW 6 PRT build uses: modflowpy/install-modflow-action@v1 with: - repo: modflow6-nightly-build + owner: aprovost-usgs + repo: modflow6 - name: Run scripts - run: | - pytest -v -n=auto --durations=0 --run=True ci_build_files.py + run: pytest -v -n=auto --durations=0 --run=True ci_build_files.py working-directory: ${{env.etc-directory}} - name: Run processing script - run: | - python process-scripts.py + run: python process-scripts.py working-directory: ${{env.script-directory}} - name: Build mf6examples LaTeX document diff --git a/etc/requirements.usgs.txt b/etc/requirements.usgs.txt index fdb7861a..3add7993 100644 --- a/etc/requirements.usgs.txt +++ b/etc/requirements.usgs.txt @@ -1,2 +1,2 @@ -git+https://github.com/modflowpy/flopy.git@develop -git+https://github.com/MODFLOW-USGS/modflowapi.git@develop +https://github.com/w-bonelli/flopy/archive/prt-utils.zip +https://github.com/MODFLOW-USGS/modflowapi/archive/develop.zip diff --git a/notebooks/ex-prt-mp7-p01.ipynb b/notebooks/ex-prt-mp7-p01.ipynb new file mode 100644 index 00000000..4ede1a30 --- /dev/null +++ b/notebooks/ex-prt-mp7-p01.ipynb @@ -0,0 +1,1384 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2705a063", + "metadata": {}, + "source": [ + "## Particle tracking through a steady-state flow field on a structured grid\n", + "\n", + "Application of a MODFLOW 6 particle-tracking (PRT) model to solve example 1 from the MODPATH 7 documentation.\n", + "\n", + "This example problem involves a flow system consisting of two aquifers separated by a low conductivity confining layer, modeled on a nearly square structured grid with uniform square cells. There is a single well in the center of the grid, with a river running along the grid's right-hand boundary.\n", + "\n", + "In part A, 21 particles are released at the water table in layer 1, all along the grid's third column, and tracked until discharge locations are reached. Some of the particles discharge to the well, while some discharge to the river.\n", + "\n", + "In part B, 9 particles are released from points evenly distributed over the top faces of all cells in layer 1, then the capture zones of the river and the central well are computed.\n", + "\n", + "### Problem setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a4bbe53d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "from warnings import warn\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import flopy\n", + "import numpy as np\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "\n", + "try:\n", + " # append the common/ subdirectory to the system path\n", + " # (assumes running one level down from project root)\n", + " sys.path.append(os.path.join(\"..\", \"common\"))\n", + " import config\n", + " buildModel = config.buildModel\n", + " writeModel = config.writeModel\n", + " runModel = config.runModel\n", + " plotModel = config.plotModel\n", + " plotSave = config.plotSave\n", + " figure_ext = config.figure_ext\n", + " base_ws = config.base_ws\n", + " timeit = config.timeit\n", + "except:\n", + " warn(f\"Failed to import config\")\n", + " # default settings\n", + " buildModel = True\n", + " writeModel = True\n", + " runModel = True\n", + " plotModel = True\n", + " plotSave = False\n", + " figure_ext = \".png\"\n", + " base_ws = Path(\"../examples\")\n", + " def timeit(func):\n", + " return func" + ] + }, + { + "cell_type": "markdown", + "id": "e74ab516", + "metadata": {}, + "source": [ + "Define figure properties." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "384314e1", + "metadata": {}, + "outputs": [], + "source": [ + "# figure_size\n", + "figure_size = (12.5, 4.5)\n", + "\n", + "# Pathline and starting point colors by capture destination\n", + "colordest = dict.fromkeys(['well', 'river'], \"blue\")\n", + "colordest['well'] = \"red\"" + ] + }, + { + "cell_type": "markdown", + "id": "c3fee142", + "metadata": {}, + "source": [ + "Base simulation and model name and workspace" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "25d48a53", + "metadata": {}, + "outputs": [], + "source": [ + "sim_name = \"mp7-p01\"\n", + "gwf_name = sim_name + \"_gwf\"\n", + "prt_name = sim_name + \"_prt\"\n", + "mp7_name = sim_name + \"_mp7\"\n", + "example_name = \"ex-prt-\" + sim_name\n", + "\n", + "sim_ws = base_ws / example_name\n", + "mf6_ws = sim_ws / \"mf6\"\n", + "mp7_ws = sim_ws / \"mp7\"\n", + "\n", + "mf6_ws.mkdir(exist_ok=True, parents=True)\n", + "mp7_ws.mkdir(exist_ok=True, parents=True)\n", + "\n", + "headfile = \"{}.hds\".format(gwf_name)\n", + "budgetfile = \"{}.cbb\".format(gwf_name)\n", + "budgetfile_prt = \"{}.cbb\".format(prt_name)\n", + "trackfile_prt = \"{}.trk\".format(prt_name)\n", + "trackhdrfile_prt = \"{}.trk.hdr\".format(prt_name)\n", + "trackcsvfile_prt = \"{}.trk.csv\".format(prt_name)" + ] + }, + { + "cell_type": "markdown", + "id": "e959201b", + "metadata": {}, + "source": [ + "Define model units." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "84a134b8", + "metadata": {}, + "outputs": [], + "source": [ + "length_units = \"feet\"\n", + "time_units = \"days\"" + ] + }, + { + "cell_type": "markdown", + "id": "45881d02", + "metadata": {}, + "source": [ + "Define time discretization parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e1d1376b", + "metadata": {}, + "outputs": [], + "source": [ + "nper, nstp, perlen, tsmult = 1, 1, 1.0, 1.0" + ] + }, + { + "cell_type": "markdown", + "id": "0cc66083", + "metadata": {}, + "source": [ + "Define MODFLOW 6 flow model parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a75cd601", + "metadata": {}, + "outputs": [], + "source": [ + "# Grid\n", + "nlay, nrow, ncol = 3, 21, 20\n", + "delr = delc = 500.0\n", + "top = 400.0\n", + "botm = [220.0, 200.0, 0.0]\n", + "\n", + "# Layer types\n", + "laytyp = [1, 0, 0]\n", + "\n", + "# Conductivities\n", + "kh = [50.0, 0.01, 200.0]\n", + "kv = [10.0, 0.01, 20.0]\n", + "\n", + "# Well\n", + "wel_loc = (2, 10, 9)\n", + "wel_q = -150000.0\n", + "\n", + "# Recharge\n", + "rch = 0.005\n", + "rch_iface = 6\n", + "rch_iflowface = -1\n", + "\n", + "# River\n", + "riv_h = 320.0\n", + "riv_z = 317.0\n", + "riv_c = 1.0e5\n", + "riv_iface = 6\n", + "riv_iflowface = -1" + ] + }, + { + "cell_type": "markdown", + "id": "1a07d2f3", + "metadata": {}, + "source": [ + "Define data for the MODFLOW 6 well and river packages." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d117464c", + "metadata": {}, + "outputs": [], + "source": [ + "# Well package\n", + "wd = [(wel_loc, wel_q)]\n", + "\n", + "# River package\n", + "rd = []\n", + "for i in range(nrow):\n", + " rd.append([(0, i, ncol - 1), riv_h, riv_c, riv_z, riv_iface, riv_iflowface])" + ] + }, + { + "cell_type": "markdown", + "id": "4cd091a8", + "metadata": {}, + "source": [ + "Define shared MODFLOW 6 PRT and MODPATH 7 particle-tracking model parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9749bd55", + "metadata": {}, + "outputs": [], + "source": [ + "# Porosity\n", + "porosity = 0.1\n", + "\n", + "# Zones\n", + "def_zone = 1\n", + "wel_zone = 2\n", + "zones_lay1 = def_zone\n", + "zones_lay2 = def_zone\n", + "zones_lay3 = np.full((nrow, ncol), def_zone, dtype=np.int32)\n", + "zones_lay3[wel_loc[1:]] = wel_zone\n", + "\n", + "# Starting location template size for example 1B;\n", + "# in the original example, there is initially a\n", + "# 3x3 array of particles in each cell in layer 1;\n", + "# in this example, there is initially one\n", + "# particle in each cell in layer 1; the original\n", + "# 3x3 particle arrays can be restored simply by\n", + "# setting sloc_tmpl_size below to 3 instead of 1.\n", + "sloc_tmpl_size = 1" + ] + }, + { + "cell_type": "markdown", + "id": "aa848b3c", + "metadata": {}, + "source": [ + "Define MODPATH 7 model parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6c461d2a", + "metadata": {}, + "outputs": [], + "source": [ + "# Zones\n", + "zones = [zones_lay1, zones_lay2, zones_lay3]\n", + "\n", + "# Default iface\n", + "defaultiface = {\"RCH\": 6, \"EVT\": 6}" + ] + }, + { + "cell_type": "markdown", + "id": "87da36c9", + "metadata": {}, + "source": [ + "Define particle release point data for the MODFLOW 6 PRT particle-tracking model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "56d77331", + "metadata": {}, + "outputs": [], + "source": [ + "# Example 1A\n", + "releasepts = {}\n", + "releasepts['1A'] = []\n", + "zrpt = top\n", + "k = 0\n", + "j = 2\n", + "for i in range(nrow):\n", + " nrpt = i\n", + " xrpt = (j + 0.5) * delr\n", + " yrpt = (nrow - i - 0.5) * delc\n", + " rpt = [nrpt, k, i, j, xrpt, yrpt, zrpt]\n", + " releasepts['1A'].append(rpt)\n", + "\n", + "# Example 1B\n", + "releasepts['1B'] = []\n", + "ndivc = sloc_tmpl_size\n", + "ndivr = sloc_tmpl_size\n", + "deldivc = delc / ndivc\n", + "deldivr = delr / ndivr\n", + "k = 0\n", + "zrpt = top\n", + "nrpt = -1\n", + "for i in range(nrow):\n", + " y0 = (nrow - i - 1) * delc\n", + " for j in range(ncol):\n", + " x0 = j * delr\n", + " for idiv in range(ndivc):\n", + " dy = (idiv + 0.5) * deldivc\n", + " yrpt = y0 + dy\n", + " for jdiv in range(ndivr):\n", + " dx = (jdiv + 0.5) * deldivr\n", + " xrpt = x0 + dx\n", + " nrpt += 1\n", + " rpt = [nrpt, k, i, j, xrpt, yrpt, zrpt]\n", + " releasepts['1B'].append(rpt)" + ] + }, + { + "cell_type": "markdown", + "id": "e6f7cd40", + "metadata": {}, + "source": [ + "Define particle data for the MODPATH 7 model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5311a5b9", + "metadata": {}, + "outputs": [], + "source": [ + "# Example 1A\n", + "plocs = []\n", + "pids = []\n", + "for idx in range(nrow):\n", + " plocs.append((0, idx, 2))\n", + " pids.append(idx)\n", + "# issue(flopyex): in the flopy example this notebook is based on,\n", + "# localz is not set to 1.0 like in the MODPATH examples doc,\n", + "# so it defaults to 0.5, but it shouldn't really matter because\n", + "# the particle gets placed at the water table anyway\n", + "part0 = flopy.modpath.ParticleData(\n", + " plocs, drape=0, structured=True, particleids=pids, localz=1.0\n", + ")\n", + "\n", + "# Example 1B\n", + "divs = sloc_tmpl_size\n", + "locs1b = [[0, 0, 0, 0, nrow - 1, ncol - 1]]\n", + "sd = flopy.modpath.CellDataType(\n", + " drape=0, columncelldivisions=1, rowcelldivisions=1, layercelldivisions=1\n", + ")\n", + "sd = flopy.modpath.FaceDataType(\n", + " drape=0,\n", + " verticaldivisions1=0, horizontaldivisions1=0,\n", + " verticaldivisions2=0, horizontaldivisions2=0,\n", + " verticaldivisions3=0, horizontaldivisions3=0,\n", + " verticaldivisions4=0, horizontaldivisions4=0,\n", + " rowdivisions5=0, columndivisions5=0,\n", + " rowdivisions6=divs, columndivisions6=divs\n", + ")\n", + "p = flopy.modpath.LRCParticleData(\n", + " subdivisiondata=[sd], lrcregions=[locs1b]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4642880e", + "metadata": {}, + "source": [ + "Define information used to extract and plot model results." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a311a2c1", + "metadata": {}, + "outputs": [], + "source": [ + "# Get well and river cell numbers\n", + "nodes = {}\n", + "k, i, j = wel_loc\n", + "nodes['well'] = ncol * (nrow * k + i) + j\n", + "nodes['river'] = []\n", + "for rivspec in rd:\n", + " k, i, j = rivspec[0]\n", + " node = ncol * (nrow * k + i) + j\n", + " nodes['river'].append(node)" + ] + }, + { + "cell_type": "markdown", + "id": "b49f2ce5", + "metadata": {}, + "source": [ + "Define functions to build, write, run, and plot models. This example employs a shared MODFLOW 6 GWF + PRT simulation, and a separate MODPATH 7 simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fddbae14", + "metadata": {}, + "outputs": [], + "source": [ + "def build_model(example_name):\n", + " print(\"Building models...{}\".format(example_name))\n", + "\n", + " # ===============================\n", + " # Create the MODFLOW 6 simulation\n", + " # ===============================\n", + " \n", + " # Instantiate the MODFLOW 6 simulation object\n", + " sim = flopy.mf6.MFSimulation(\n", + " sim_name=gwf_name, exe_name=\"mf6\", version=\"mf6\", sim_ws=mf6_ws\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 temporal discretization package\n", + " flopy.mf6.modflow.mftdis.ModflowTdis(\n", + " sim, pname=\"tdis\", time_units=\"DAYS\", nper=nper, perioddata=[(perlen, nstp, tsmult)]\n", + " )\n", + "\n", + " # -------------------\n", + " # Build the GWF model\n", + " # -------------------\n", + "\n", + " # Instantiate the MODFLOW 6 gwf (groundwater-flow) model\n", + " model_nam_file = \"{}.nam\".format(gwf_name)\n", + " gwf = flopy.mf6.ModflowGwf(\n", + " sim, modelname=gwf_name, model_nam_file=model_nam_file, save_flows=True\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf discretization package\n", + " flopy.mf6.modflow.mfgwfdis.ModflowGwfdis(\n", + " gwf,\n", + " pname=\"dis\",\n", + " nlay=nlay,\n", + " nrow=nrow,\n", + " ncol=ncol,\n", + " length_units=\"FEET\",\n", + " delr=delr,\n", + " delc=delc,\n", + " top=top,\n", + " botm=botm,\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf initial conditions package\n", + " flopy.mf6.modflow.mfgwfic.ModflowGwfic(gwf, pname=\"ic\", strt=top)\n", + "\n", + " # Instantiate the MODFLOW 6 gwf node property flow package\n", + " flopy.mf6.modflow.mfgwfnpf.ModflowGwfnpf(\n", + " gwf, pname=\"npf\", icelltype=laytyp, k=kh, k33=kv,\n", + " save_saturation=True, save_specific_discharge=True,\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf recharge package\n", + " flopy.mf6.modflow.mfgwfrcha.ModflowGwfrcha(\n", + " gwf, recharge=rch,\n", + " auxiliary=[\"iface\", \"iflowface\"], aux=[rch_iface, rch_iflowface],\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf well package\n", + " flopy.mf6.modflow.mfgwfwel.ModflowGwfwel(\n", + " gwf, maxbound=1, stress_period_data={0: wd}\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf river package\n", + " flopy.mf6.modflow.mfgwfriv.ModflowGwfriv(\n", + " gwf, auxiliary=[\"iface\", \"iflowface\"], stress_period_data={0: rd}\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf output control package\n", + " head_record = [headfile]\n", + " budget_record = [budgetfile]\n", + " saverecord = [(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")]\n", + " flopy.mf6.modflow.mfgwfoc.ModflowGwfoc(\n", + " gwf,\n", + " pname=\"oc\",\n", + " saverecord=saverecord,\n", + " head_filerecord=head_record,\n", + " budget_filerecord=budget_record,\n", + " )\n", + "\n", + " # -------------------\n", + " # Build the PRT model\n", + " # -------------------\n", + "\n", + " # Instantiate the MODFLOW 6 prt model\n", + " prt = flopy.mf6.ModflowPrt(\n", + " sim, modelname=prt_name, model_nam_file=\"{}.nam\".format(prt_name)\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt discretization package\n", + " flopy.mf6.modflow.mfgwfdis.ModflowGwfdis(\n", + " prt, pname=\"dis\",\n", + " nlay=nlay, nrow=nrow, ncol=ncol,\n", + " length_units=\"FEET\",\n", + " delr=delr, delc=delc,\n", + " top=top, botm=botm,\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt model input package\n", + " flopy.mf6.ModflowPrtmip(prt, pname=\"mip\", porosity=porosity)\n", + "\n", + " # Instantiate the MODFLOW 6 prt particle release point (prp) package for example 1A\n", + " flopy.mf6.ModflowPrtprp(\n", + " prt, pname=\"prp1a\", filename=\"{}_1a.prp\".format(prt_name),\n", + " nreleasepts=len(releasepts['1A']), packagedata=releasepts['1A'],\n", + " perioddata={0: [\"FIRST\"],},\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt particle release point (prp) package for example 1B\n", + " flopy.mf6.ModflowPrtprp(\n", + " prt, pname=\"prp1b\", filename=\"{}_1b.prp\".format(prt_name),\n", + " nreleasepts=len(releasepts['1B']), packagedata=releasepts['1B'],\n", + " perioddata={0: [\"FIRST\"],},\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt output control package\n", + " budget_record = [budgetfile_prt]\n", + " track_record = [trackfile_prt]\n", + " trackcsv_record = [trackcsvfile_prt]\n", + " flopy.mf6.ModflowPrtoc(\n", + " prt,\n", + " pname=\"oc\",\n", + " budget_filerecord=budget_record,\n", + " track_filerecord=track_record,\n", + " trackcsv_filerecord=trackcsv_record,\n", + " saverecord=[(\"BUDGET\", \"ALL\")],\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt flow model interface\n", + " flopy.mf6.ModflowPrtfmi(prt)\n", + "\n", + " # Create the MODFLOW 6 gwf-prt model exchange\n", + " flopy.mf6.ModflowGwfprt(\n", + " sim, exgtype=\"GWF6-PRT6\",\n", + " exgmnamea=gwf_name, exgmnameb=prt_name,\n", + " filename=\"{}.gwfprt\".format(gwf_name),\n", + " )\n", + " \n", + " #--------------------------------------------------\n", + " # Create solutions for the models in the simulation\n", + " #--------------------------------------------------\n", + "\n", + " # Create an iterative model solution (IMS) for the MODFLOW 6 gwf model\n", + " ims = flopy.mf6.ModflowIms(\n", + " sim, pname=\"ims\",\n", + " complexity=\"SIMPLE\",\n", + " outer_dvclose=1e-6, inner_dvclose=1e-6,\n", + " rcloserecord=1e-6,\n", + " )\n", + "\n", + " # Create an explicit model solution (EMS) for the MODFLOW 6 prt model\n", + " ems = flopy.mf6.ModflowEms(\n", + " sim, pname=\"ems\",\n", + " filename=\"{}.ems\".format(prt_name),\n", + " )\n", + " sim.register_solution_package(ems, [prt.name])\n", + "\n", + " # ===============================\n", + " # Create the MODPATH 7 simulation\n", + " # ===============================\n", + "\n", + " # Instantiate the MODPATH 7 simulation object\n", + " mp7 = flopy.modpath.Modpath7(\n", + " modelname=mp7_name, flowmodel=gwf, exe_name=\"mp7\", model_ws=mp7_ws,\n", + " budgetfilename=budgetfile, headfilename=headfile\n", + " )\n", + "\n", + " # Instantiate the MODPATH 7 basic data\n", + " flopy.modpath.Modpath7Bas(mp7, porosity=porosity, defaultiface=defaultiface)\n", + "\n", + " # Instantiate the MODPATH 7 particle groups\n", + " pg1a = flopy.modpath.ParticleGroup(\n", + " particlegroupname=\"PG1A\", particledata=part0, filename=sim_name + \"a.sloc\"\n", + " )\n", + " pg1b = flopy.modpath.ParticleGroupLRCTemplate(\n", + " particlegroupname=\"PG1B\", particledata=p, filename=sim_name + \"b.sloc\"\n", + " )\n", + "\n", + " # Instantiate the MODPATH 7 simulation data\n", + " mpsim = flopy.modpath.Modpath7Sim(\n", + " mp7,\n", + " simulationtype=\"combined\",\n", + " trackingdirection=\"forward\",\n", + " weaksinkoption=\"pass_through\",\n", + " weaksourceoption=\"pass_through\",\n", + " budgetoutputoption=\"summary\",\n", + " referencetime=[0, 0, 0.0],\n", + " stoptimeoption=\"extend\",\n", + " timepointdata=[500, 1000.0],\n", + " zonedataoption=\"on\",\n", + " zones=zones,\n", + " particlegroups=[pg1a, pg1b],\n", + " )\n", + " \n", + " #=======================\n", + " # Return the simulations\n", + " #=======================\n", + "\n", + " return sim, mp7" + ] + }, + { + "cell_type": "markdown", + "id": "c795b97b", + "metadata": {}, + "source": [ + "Define a function to write model files." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "64ce608e", + "metadata": {}, + "outputs": [], + "source": [ + "def write_model(sim, mp7, silent=True):\n", + " sim.write_simulation(silent=silent)\n", + " mp7.write_input()" + ] + }, + { + "cell_type": "markdown", + "id": "4da0be74", + "metadata": {}, + "source": [ + "Define a function to run the models." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bdf4cf5d", + "metadata": {}, + "outputs": [], + "source": [ + "@timeit\n", + "def run_model(sim, mp7, silent=True):\n", + " # Run MODFLOW 6 simulation\n", + " success, buff = sim.run_simulation(silent=silent, report=True)\n", + " assert success\n", + " for line in buff:\n", + " print(line)\n", + " \n", + " # Run MODPATH 7 simulation\n", + " success, buff = mp7.run_model(silent=silent, report=True)\n", + " assert success\n", + " for line in buff:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "id": "9024a16c", + "metadata": {}, + "source": [ + "Define functions to load pathline and endpoint data from MODFLOW 6 PRT's particle track CSV files. Note that unlike MODPATH 7, MODFLOW 6 PRT does not make a distinction between pathline and endpoint output files — all pathline data is saved to track files, and endpoints are computed dynamically." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1db272f9", + "metadata": {}, + "outputs": [], + "source": [ + "from flopy.plot.plotutil import to_mp7_pathlines, to_mp7_endpoints\n", + "\n", + "\n", + "def load_mf6pathlines(p):\n", + " # read pathlines from CSV\n", + " pls = pd.read_csv(p)\n", + "\n", + " # convert pathlines to mp7 format and split by particle group\n", + " mp7_pls = to_mp7_pathlines(pls)\n", + " mp7_pls_a = mp7_pls[mp7_pls[\"particlegroup\"] == 1]\n", + " mp7_pls_b = mp7_pls[mp7_pls[\"particlegroup\"] == 2]\n", + "\n", + " # select endpoints, convert to mp7 format and split by particle group\n", + " mp7_eps = to_mp7_endpoints(pls)\n", + " mp7_eps_a = mp7_eps[mp7_eps[\"particlegroup\"] == 1]\n", + " mp7_eps_b = mp7_eps[mp7_eps[\"particlegroup\"] == 2]\n", + "\n", + " # determine which particles ended up in which capture area\n", + " # (for now, use x coordinate of endpoint, todo: use izone)\n", + " wel_pids_a = mp7_eps_a[mp7_eps_a[\"x\"] <= 9000][\"particleid\"].unique()\n", + " riv_pids_a = mp7_eps_a[mp7_eps_a[\"x\"] > 9000][\"particleid\"].unique()\n", + " wel_pids_b = mp7_eps_b[mp7_eps_b[\"x\"] <= 9000][\"particleid\"].unique()\n", + " riv_pids_b = mp7_eps_b[mp7_eps_b[\"x\"] > 9000][\"particleid\"].unique()\n", + "\n", + " # return a dict keyed by subproblem (particle group) and capture area\n", + " return {\n", + " (\"1A\", \"well\"): mp7_pls_a[mp7_pls_a[\"particleid\"].isin(wel_pids_a)],\n", + " (\"1A\", \"river\"): mp7_pls_a[mp7_pls_a[\"particleid\"].isin(riv_pids_a)],\n", + " (\"1A\", \"all\"): mp7_pls_a,\n", + " (\"1B\", \"well\"): mp7_pls_b[(mp7_pls_b[\"particleid\"].isin(wel_pids_b))],\n", + " (\"1B\", \"river\"): mp7_pls_b[(mp7_pls_b[\"particleid\"].isin(riv_pids_b))],\n", + " (\"1B\", \"all\"): mp7_pls_b,\n", + " }\n", + "\n", + "\n", + "def load_mf6endpoints(p):\n", + " # read pathlines from CSV\n", + " pls = pd.read_csv(p)\n", + "\n", + " # select endpoints, convert to mp7 format and split by particle group\n", + " mp7_eps = to_mp7_endpoints(pls)\n", + " mp7_eps_a = mp7_eps[mp7_eps[\"particlegroup\"] == 1]\n", + " mp7_eps_b = mp7_eps[mp7_eps[\"particlegroup\"] == 2]\n", + "\n", + " # determine which particles ended up in which capture area\n", + " # (for now, use x coordinate of endpoint, todo: use izone)\n", + " wel_pids_a = mp7_eps_a[mp7_eps_a[\"x\"] <= 9000][\"particleid\"].unique()\n", + " riv_pids_a = mp7_eps_a[mp7_eps_a[\"x\"] > 9000][\"particleid\"].unique()\n", + " wel_pids_b = mp7_eps_b[mp7_eps_b[\"x\"] <= 9000][\"particleid\"].unique()\n", + " riv_pids_b = mp7_eps_b[mp7_eps_b[\"x\"] > 9000][\"particleid\"].unique()\n", + "\n", + " # return a dict keyed by subproblem (particle group) and capture area\n", + " return {\n", + " (\"1A\", \"well\"): mp7_eps_a[mp7_eps_a[\"particleid\"].isin(wel_pids_a)],\n", + " (\"1A\", \"river\"): mp7_eps_a[mp7_eps_a[\"particleid\"].isin(riv_pids_a)],\n", + " (\"1A\", \"all\"): mp7_eps_a,\n", + " (\"1B\", \"well\"): mp7_eps_b[mp7_eps_b[\"particleid\"].isin(wel_pids_b)],\n", + " (\"1B\", \"river\"): mp7_eps_b[mp7_eps_b[\"particleid\"].isin(riv_pids_b)],\n", + " (\"1B\", \"all\"): mp7_eps_b,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "46046dfb", + "metadata": {}, + "source": [ + "Define a utility function for reading MODPATH 7 pathlines from output files." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4ecae8ef", + "metadata": {}, + "outputs": [], + "source": [ + "def load_mp7pathlines(plf):\n", + " # Extract pathline data to a dictionary keyed on subproblem (1A or 1B)\n", + " # and capture destination (well or river)\n", + " mp7pathlines = {}\n", + " for dest in ['well', 'river']:\n", + " pdata = plf.get_destination_pathline_data(nodes[dest], to_recarray=True)\n", + " for pgidx, subprob in enumerate(['1A', '1B']):\n", + " mp7pathlines[(subprob, dest)] = np.array([point for point in pdata\n", + " if point['particlegroup'] == pgidx],\n", + " dtype=pdata.dtype)\n", + "\n", + " return mp7pathlines" + ] + }, + { + "cell_type": "markdown", + "id": "9107ac3a", + "metadata": {}, + "source": [ + "Define a utility function for reading MODPATH 7 endpoint data from output files." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1035c351", + "metadata": {}, + "outputs": [], + "source": [ + "def load_mp7endpointdata(epf):\n", + " # Extract endpoint data to a dictionary keyed on subproblem (1A or 1B)\n", + " # and capture destination (well or river)\n", + " mp7endpointdata = {}\n", + " for dest in ['well', 'river']:\n", + " epd = epf.get_destination_endpoint_data(dest_cells=nodes[dest])\n", + " for pgidx, subprob in enumerate(['1A', '1B']):\n", + " mp7endpointdata[(subprob, dest)] = np.array([point for point in epd\n", + " if point['particlegroup'] == pgidx],\n", + " dtype=epd.dtype)\n", + "\n", + " # Merge endpoint data for particles that terminate in the well\n", + " # and particles that terminate in the river\n", + " for subprob in ['1A', '1B']:\n", + " mp7endpointdata[(subprob, 'all')] = np.concatenate((mp7endpointdata[(subprob, 'well')],\n", + " mp7endpointdata[(subprob, 'river')]))\n", + "\n", + " return mp7endpointdata" + ] + }, + { + "cell_type": "markdown", + "id": "6902f76c", + "metadata": {}, + "source": [ + "Define a utility function for plotting pathlines, optionally colored by capture destination." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "251a9468", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_pathlines(ax, gwf, pathlines, subprob, plottitle, **kwargs):\n", + " ax.set_title(plottitle, fontsize=12)\n", + " ax.set_aspect('equal')\n", + " mm = flopy.plot.PlotMapView(model=gwf, ax=ax)\n", + " mm.plot_grid(lw=0.5)\n", + " \n", + " for dest in ['well', 'river']:\n", + " label = None\n", + " if 'colordest' in kwargs:\n", + " # Use colordest to color pathlines by capture destination\n", + " color = kwargs['colordest'][dest]\n", + " label = \"captured by \" + dest\n", + " elif 'color' in kwargs:\n", + " # Use the specified color for all pathlines\n", + " color = kwargs['color']\n", + " else:\n", + " # Use a default color for all pathlines\n", + " color = \"blue\"\n", + " mm.plot_pathline(\n", + " pathlines[(subprob, dest)], layer=\"all\",\n", + " colors=[color], label=label)\n", + " if label != None:\n", + " ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "a51007ce", + "metadata": {}, + "source": [ + "Define a utility function for plotting particle starting points, colored by capture destination or travel time to capture." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e2df29a6", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_endpoints(ax, gwf, pointdata, subprob, plottitle, starting, **kwargs):\n", + " ax.set_title(plottitle, fontsize=12)\n", + " ax.set_aspect('equal')\n", + " mm = flopy.plot.PlotMapView(model=gwf, ax=ax)\n", + " mm.plot_grid(lw=0.5)\n", + " startingpoint_markersize = 5\n", + "\n", + " if 'colordest' in kwargs:\n", + " # Use colordest to color starting points by capture destination\n", + " for dest in ['well', 'river']:\n", + " color = kwargs['colordest'][dest]\n", + " label = \"captured by \" + dest\n", + " if \"PRT\" in plottitle:\n", + " # plot points\n", + " ax.scatter(\n", + " pointdata[(subprob, dest)]['x0' if starting else 'x'],\n", + " pointdata[(subprob, dest)]['y0' if starting else 'y'],\n", + " s=startingpoint_markersize ** 2, # todo: why does flopy plot_endpoint square s?\n", + " color=color,\n", + " label=label\n", + " )\n", + " else:\n", + " mm.plot_endpoint(\n", + " pointdata[(subprob, dest)],\n", + " direction=\"starting\" if starting else \"ending\",\n", + " s=startingpoint_markersize,\n", + " color=color,\n", + " label=label\n", + " )\n", + " ax.legend()\n", + " else:\n", + " if \"PRT\" in plottitle:\n", + " # plot points\n", + " ax.scatter(\n", + " pointdata[(subprob, 'all')]['x0' if starting else 'x'],\n", + " pointdata[(subprob, 'all')]['y0' if starting else 'y'],\n", + " s=startingpoint_markersize ** 2, # todo: why does flopy plot_endpoint square s?\n", + " c=pointdata[(subprob, 'all')]['time'],\n", + " )\n", + " else:\n", + " # Color starting points by travel time to capture\n", + " mm.plot_endpoint(\n", + " pointdata[(subprob, 'all')],\n", + " direction=\"starting\" if starting else \"ending\",\n", + " s=startingpoint_markersize,\n", + " colorbar=True,\n", + " shrink=0.25\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "eb0de708", + "metadata": {}, + "source": [ + "Define a wrapper function to plot all results." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "014d7941", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_results(sim, mp7, idx):\n", + "\n", + " # Load MODFLOW 6 PRT pathlines and endpoints\n", + " mf6pathlines = load_mf6pathlines(mf6_ws / trackcsvfile_prt)\n", + " mf6endpoints = load_mf6endpoints(mf6_ws / trackcsvfile_prt)\n", + "\n", + " # Load MODPATH 7 pathline and endpoint data\n", + " mp7pathlines = load_mp7pathlines(flopy.utils.PathlineFile(mp7_ws / f\"{mp7_name}.mppth\"))\n", + " mp7endpoints = load_mp7endpointdata(flopy.utils.EndpointFile(mp7_ws / f\"{mp7_name}.mpend\"))\n", + "\n", + " # Get gwf model\n", + " gwf = sim.get_model(gwf_name)\n", + "\n", + " # ==========\n", + " # Example 1A\n", + " # ==========\n", + "\n", + " # --------------------------------------------------------\n", + " # Pathlines colored by capture destination (well or river)\n", + " # --------------------------------------------------------\n", + "\n", + " # initialize plot\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size)\n", + " plt.suptitle(t='Example 1A - Pathlines colored by capture destination (well or river)',\n", + " fontsize=14, y=1.05)\n", + " axes = axes.flatten()\n", + "\n", + " # MODFLOW 6 PRT\n", + " ax = axes[0]\n", + " plot_pathlines(ax, gwf, mf6pathlines, '1A', 'MODFLOW 6 PRT', colordest=colordest)\n", + "\n", + " # MODPATH 7\n", + " ax = axes[1]\n", + " plot_pathlines(ax, gwf, mp7pathlines, '1A', 'MODPATH 7', colordest=colordest)\n", + " # issue: the pathlines in the right-hand plot appear not to go\n", + " # go quite as far, despite their being the same as the ones\n", + " # on the left; axis scaling issue?\n", + "\n", + " # Save figure\n", + " fpth = os.path.join(\n", + " \"..\", \"figures\", \"{}-conc{}\".format(sim_name, figure_ext)\n", + " )\n", + " if plotSave:\n", + " fig.savefig(fpth)\n", + "\n", + " # ==========\n", + " # Example 1B\n", + " # ==========\n", + "\n", + " # --------------------------------------------------------\n", + " # Pathlines colored by capture destination (well or river)\n", + " # --------------------------------------------------------\n", + "\n", + " # initialize plot\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size)\n", + " plt.suptitle(t='Example 1B - Pathlines colored by capture destination (well or river)',\n", + " fontsize=14, y=1.05)\n", + " axes = axes.flatten()\n", + "\n", + " # MODFLOW 6 PRT\n", + " ax = axes[0]\n", + " plot_pathlines(ax, gwf, mf6pathlines, '1B', 'MODFLOW 6 PRT', colordest=colordest)\n", + "\n", + " # MODPATH 7\n", + " ax = axes[1]\n", + " plot_pathlines(ax, gwf, mp7pathlines, '1B', 'MODPATH 7', colordest=colordest)\n", + "\n", + " # Save figure\n", + " fpth = os.path.join(\n", + " \"..\", \"figures\", \"{}-conc{}\".format(sim_name, figure_ext)\n", + " )\n", + " if plotSave:\n", + " fig.savefig(fpth)\n", + "\n", + " # -----------------------------------------------------------------------\n", + " # Particle starting points colored by capture destination (well or river)\n", + " # -----------------------------------------------------------------------\n", + "\n", + " # initialize plot\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size)\n", + " plt.suptitle(t='Example 1B - Particle starting points colored by capture destination (well or river)',\n", + " fontsize=14, y=1.05)\n", + " axes = axes.flatten()\n", + "\n", + " # MODFLOW 6 PRT\n", + " ax = axes[0]\n", + " plot_endpoints(ax, gwf, mf6endpoints, '1B', 'MODFLOW 6 PRT', colordest=colordest, starting=True)\n", + "\n", + " # MODPATH 7\n", + " ax = axes[1]\n", + " plot_endpoints(ax, gwf, mp7endpoints, '1B', 'MODPATH 7', colordest=colordest, starting=True)\n", + "\n", + " # Save figure\n", + " fpth = os.path.join(\n", + " \"..\", \"figures\", \"{}-conc{}\".format(sim_name, figure_ext)\n", + " )\n", + " if plotSave:\n", + " fig.savefig(fpth)\n", + "\n", + " # ----------------------------------------------------------\n", + " # Particle starting points colored by travel time to capture\n", + " # ----------------------------------------------------------\n", + "\n", + " # initialize plot\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size)\n", + " plt.suptitle(t='Example 1B - Particle starting points colored by travel time to capture',\n", + " fontsize=14, y=1.05)\n", + " axes = axes.flatten()\n", + "\n", + " # MODFLOW 6 PRT\n", + " ax = axes[0]\n", + " plot_endpoints(ax, gwf, mf6endpoints, '1B', 'MODFLOW 6 PRT', starting=True)\n", + "\n", + " # MODPATH 7\n", + " ax = axes[1]\n", + " plot_endpoints(ax, gwf, mp7endpoints, '1B', 'MODPATH 7', starting=True)\n", + "\n", + " # Save figure\n", + " fpth = os.path.join(\n", + " \"..\", \"figures\", \"{}-conc{}\".format(sim_name, figure_ext)\n", + " )\n", + " if plotSave:\n", + " fig.savefig(fpth)\n", + "\n", + " # --------------------------------------------------------------------------\n", + " # Particle terminating points colored by capture destination (well or river)\n", + " # --------------------------------------------------------------------------\n", + "\n", + " # initialize plot\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size)\n", + " plt.suptitle(t='Example 1B - Particle terminating points colored by capture destination (well or river)',\n", + " fontsize=14, y=1.05)\n", + " axes = axes.flatten()\n", + "\n", + " # MODFLOW 6 PRT\n", + " ax = axes[0]\n", + " plot_endpoints(ax, gwf, mf6endpoints, '1B', 'MODFLOW 6 PRT', colordest=colordest, starting=False)\n", + "\n", + " # MODPATH 7\n", + " ax = axes[1]\n", + " plot_endpoints(ax, gwf, mp7endpoints, '1B', 'MODPATH 7', colordest=colordest, starting=False)\n", + "\n", + " # Save figure\n", + " fpth = os.path.join(\n", + " \"..\", \"figures\", \"{}-conc{}\".format(sim_name, figure_ext)\n", + " )\n", + " if plotSave:\n", + " fig.savefig(fpth)\n", + "\n", + " # -------------------------------------------------------------\n", + " # Particle terminating points colored by travel time to capture\n", + " # -------------------------------------------------------------\n", + "\n", + " # initialize plot\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size)\n", + " plt.suptitle(t='Example 1B - Particle terminating points colored by travel time to capture',\n", + " fontsize=14, y=1.05)\n", + " axes = axes.flatten()\n", + "\n", + " # MODFLOW 6 PRT\n", + " ax = axes[0]\n", + " plot_endpoints(ax, gwf, mf6endpoints, '1B', 'MODFLOW 6 PRT', starting=False)\n", + "\n", + " # MODPATH 7\n", + " ax = axes[1]\n", + " plot_endpoints(ax, gwf, mp7endpoints, '1B', 'MODPATH 7', starting=False)\n", + "\n", + " # Save figure\n", + " fpth = os.path.join(\n", + " \"..\", \"figures\", \"{}-conc{}\".format(sim_name, figure_ext)\n", + " )\n", + " if plotSave:\n", + " fig.savefig(fpth)" + ] + }, + { + "cell_type": "markdown", + "id": "ce977511", + "metadata": {}, + "source": [ + "Define a function to wrap all of the steps for each scenario:\n", + "\n", + "1. build model\n", + "2. write model\n", + "3. run model\n", + "4. plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "869c9464", + "metadata": {}, + "outputs": [], + "source": [ + "def scenario(idx, silent=True):\n", + " sim, mp7 = build_model(example_name)\n", + " write_model(sim, mp7, silent=silent)\n", + " if runModel:\n", + " run_model(sim, mp7, silent=silent)\n", + " plot_results(sim, mp7, idx)" + ] + }, + { + "cell_type": "markdown", + "id": "f379afaa", + "metadata": {}, + "source": [ + "We are now ready to run the example problem. Subproblems 1A and 1B are solved by a single MODFLOW 6 run and a single MODPATH 7 run, so they are included under one \"scenario\". Each of the two subproblems is represented by its own particle release package (for MODFLOW 6) or particle group (for MODPATH 7)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9bc54db1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building models...ex-prt-mp7-p01\n", + "writing simulation...\n", + " writing simulation name file...\n", + " writing simulation tdis package...\n", + " writing solution package ims...\n", + " writing solution package ems...\n", + " writing package mp7-p01_gwf.gwfprt...\n", + " writing model mp7-p01_gwf...\n", + " writing model name file...\n", + " writing package dis...\n", + " writing package ic...\n", + " writing package npf...\n", + " writing package rcha_0...\n", + " writing package wel_0...\n", + " writing package riv_0...\n", + "INFORMATION: maxbound in ('gwf6', 'riv', 'dimensions') changed to 21 based on size of stress_period_data\n", + " writing package oc...\n", + " writing model mp7-p01_prt...\n", + " writing model name file...\n", + " writing package dis...\n", + " writing package mip...\n", + " writing package prp1a...\n", + " writing package prp1b...\n", + " writing package oc...\n", + " writing package fmi...\n", + "FloPy is using the following executable to run the model: ../../../venv/bin/mf6\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/14/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 14 2023 19:46:34 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/08/15 13:26:58\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/08/15 13:26:59\n", + " Elapsed run time: 0.217 Seconds\n", + " \n", + " Normal termination of simulation.\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/14/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 14 2023 19:46:34 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/08/15 13:26:58\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/08/15 13:26:59\n", + " Elapsed run time: 0.217 Seconds\n", + " \n", + " Normal termination of simulation.\n", + "FloPy is using the following executable to run the model: ../../../../../.local/share/flopy/bin/mp7\n", + "\n", + "MODPATH Version 7.2.001 \n", + "Program compiled Mar 07 2022 14:08:27 with IFORT compiler (ver. 20.21.5) \n", + " \n", + " \n", + "Run particle tracking simulation ...\n", + "Processing Time Step 1 Period 1. Time = 1.00000E+00 Steady-state flow \n", + "\n", + "Particle Summary:\n", + " 0 particles are pending release.\n", + " 0 particles remain active.\n", + " 302 particles terminated at boundary faces.\n", + " 0 particles terminated at weak sink cells.\n", + " 0 particles terminated at weak source cells.\n", + " 139 particles terminated at strong source/sink cells.\n", + " 0 particles terminated in cells with a specified zone number.\n", + " 0 particles were stranded in inactive or dry cells.\n", + " 0 particles were unreleased.\n", + " 0 particles have an unknown status.\n", + " \n", + "Normal termination. \n", + "\n", + "MODPATH Version 7.2.001 \n", + "Program compiled Mar 07 2022 14:08:27 with IFORT compiler (ver. 20.21.5) \n", + " \n", + " \n", + "Run particle tracking simulation ...\n", + "Processing Time Step 1 Period 1. Time = 1.00000E+00 Steady-state flow \n", + "\n", + "Particle Summary:\n", + " 0 particles are pending release.\n", + " 0 particles remain active.\n", + " 302 particles terminated at boundary faces.\n", + " 0 particles terminated at weak sink cells.\n", + " 0 particles terminated at weak source cells.\n", + " 139 particles terminated at strong source/sink cells.\n", + " 0 particles terminated in cells with a specified zone number.\n", + " 0 particles were stranded in inactive or dry cells.\n", + " 0 particles were unreleased.\n", + " 0 particles have an unknown status.\n", + " \n", + "Normal termination. \n", + "run_model 464.41 ms\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7kAAAHSCAYAAAApCwxwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3hURReHf3d3s7vJpndCSQKE3nsvUqWDggRFQIqNpiKKIEVBQAEFGwpKJyBSBaVIld57h4RQ0/tms22+P4a520sKgnzzPs8+kLv3zp1bdk6ZM+cIhBACDofD4XA4HA6Hw+FwngMkT7sDHA6Hw+FwOBwOh8PhlBTcyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwSpB9+/ZBEARMnTr1aXeFA0AQBLRp08bt/QcPHgxBEJCQkCBuS0hIgCAIGDx4cIn377/Kf/E9L8y7MHXqVAiCgH379j3RPnH+XZYuXQpBELB06dKn3ZVn9jdU2DHz36JXr16oWrUqDAbDU+uDo3HhWb1nrmjTpg0EQXja3bDgtddeQ2RkJDQazdPuCuc5gBu5nGLDjABnn6ioqKfdzeeC5ORkzJw5Ey+//DKio6PF++sIR89GpVKhVq1amDZtGnJzc59on+31QS6Xo2zZshgwYADOnz9f5LbtGaUcDqf4PEsG4X+VZ9H4eRYNG1fs378fmzdvxpQpUyCVSp92dzhPkMmTJ+P+/fv45ptvnnZXOM8BsqfdAc7zQ4UKFfDaa6/Z/c7f3//f7cxzyuXLl/HJJ59AEATExMTAy8sLarXa5XHmz4YQgpSUFPz111+YOnUqtm/fjoMHDz5x5cG8D7m5uTh69Cji4uKwYcMG7N69G82bN3+i5y8qpUuXxpUrV+Dn5/e0u8LhcJ4TGjVqhCtXriA4OPhpd8WCK1euwMvL62l3w4JPP/0UkZGR6Nev39PuynPF8uXL3dIf/k0qVaqEnj17YtasWRg1ahRUKtXT7hLnPww3cjklRsWKFZ+50KvnjapVq2L//v2oW7cufHx8UKVKFVy7ds3lcfaeTUFBAZo2bYqjR49i//79eOGFF55Qrx33YdKkSZgxYwYmTpz4zIaGenh4oEqVKk+7GxwO5znCy8vrmRxXnrU+Xbp0Cf/88w8mTpwIiYQHH5Yk5cqVe9pdsMtrr72GDRs2YM2aNRg6dOjT7g7nPwwfMTj/OrNmzYIgCHjrrbccfvf222+L2x48eIApU6agSZMmCA0NhUKhQFRUFN555x0kJyfbtMFCWG/fvo05c+agUqVK8PT0RLVq1bBmzRoAgFarxcSJExEVFQWlUolatWrhr7/+smmLhXZpNBp8/PHHKFeuHJRKJapWrYpvv/0WhBC3rzs5ORnvvfceKlasCIVCgeDgYLz00ku4ePGi222EhYWhVatW8PHxcfsYRygUCrRt2xYAkJqaWuz2isKoUaMAACdOnABQuGcdFRWFZcuWAYBF6La98MCkpCQMGjQIwcHB8PT0RJMmTdw2qh2tyWXvhk6nw9SpUxEVFQWFQoFKlSrhhx9+sNsWIQS//vormjdvDl9fX3h5eaFBgwb49ddfbfbVaDSYO3cuateuDT8/P6hUKkRFRaFfv344d+6cW30HgHPnzuHVV19FmTJloFAoUKpUKXTu3Bl//PGHxX56vR7z5s1D7dq14enpCT8/P7Rt29ZmP1dcvHgR/fr1E59fdHQ0xo4di7S0NJt9o6KiEBUVhczMTIwcORJly5aFTCazCJE9f/48+vfvj1KlSkEulyMyMhKjRo2y2x4ALF68GDVq1IBSqUTZsmUxfvz4Yq3v+uWXX1CzZk0olUqULl0a7733HnJycsTvb9y4AYlEgi5dutg9PicnB97e3oUyHjZv3oyOHTsiKCgISqUSUVFRGDhwoMVYcf36dYwfPx716tUT96tUqRI+/vhju0sQCjOWDR48GEOGDAEADBkyxGKpAYM9O3vYC4k1X8+4dOlS1KtXD15eXha/15ycHEyZMgXVq1eHp6cn/P390alTJxw8eNDtewcA6enpeOuttxAWFgYvLy80bNgQGzdudHpMYd6zvXv34sUXX0RERAQUCgXCwsLQsmVL/PzzzwBM620BGmprfv/Yu+1oTS67r7m5uRgzZox4jlq1auH333+36Uth3gNBELB//37x/+xjPrY5GkNTU1MxduxYREdHQ6FQIDQ0FP369bMrv5gMjo+Px4IFC1ClShUoFApERkZi2rRpMBqNDp+DNUuWLAEA9O3b12L7/PnzIQiCzT0ZO3YsBEFAixYtLLazcZy91wytVot58+ahXr16UKlU8PHxQcuWLbFlyxa3+1hYCjPWmi8b+OOPP9C8eXP4+Pi4tQSMPcv79+/j9ddfR3h4OCQSiSj7rH+nK1asgCAI+Oyzz+y2d/r0aQiCgFdffdVie2F0G3fG/K5du8LLy4svleAUGz6Ty/nXGT9+PHbt2oWffvoJnTt3Rq9evQAAx48fx+TJk1GtWjXMmzdP3P/AgQOYO3cu2rVrh8aNG8PDwwNnzpzBjz/+iB07duD06dN2Q0nff/99HDt2DN27d4dUKsWaNWswYMAABAQE4Ntvv8Xly5fRtWtXaDQarF69Gj179sSVK1dQoUIFm7b69euHM2fO4KWXXgIArF+/HqNHj0ZCQgLmzp3r8ppv3bqFNm3a4N69e+jYsSN69eqF5ORkrF+/Hjt27MDu3bvRuHHjIt7RoqHVakVFq06dOv/qua1hgrYwz3rs2LFYunQpzp07hzFjxogh8dbCPzMzEy1atICfnx8GDhyI5ORkrF27Fp06dcKpU6dQo0aNYvU9NjYWx48fx4svvgipVIrffvsN7777Ljw8PDB8+HBxP0IIXn31VcTFxSEmJgYDBgyAXC7Hrl27MHToUFy+fBlz5swR9x80aBB+++031KpVC0OGDIFCocDdu3exd+9enDhxArVr13bZt/Xr12PAgAEghKB79+6oXLkykpOTcezYMfzyyy/o3r272LeXX34ZmzdvRqVKlfDuu+8iLy8Pa9euRY8ePTBv3jy89957Ls938OBBdOrUCVqtFi+//DKioqJw5MgRzJ8/H1u3bsXRo0dtwjMLCgrwwgsvIDc3Fz169IBMJkNYWBgAYMuWLejXrx8kEgl69uyJsmXL4vLly/juu++wY8cOHDt2DAEBAWJbn3/+OSZPnoywsDAMHz4cHh4eWLt2La5cueKy7/aYN28edu/ejVdeeQVdu3bF33//jW+++QZHjx7FgQMH4OHhgZiYGLRt2xY7duzA3bt3UbZsWYs2Vq9ejby8PAwbNsytc37wwQeYN28eAgMD0atXL4SGhuLu3bv4+++/Ub9+ffF93bBhA3755Re0bdsWbdq0gdFoxNGjRzF79mzs379f7J817oxlvXr1QmZmJjZv3oyePXuW6Pjw1VdfYe/evejZsyc6duwoLpNIT09Hq1atcOnSJTRv3hxvvfUWsrOzsXnzZrRt2xbr1q0TZYUz1Go12rRpgwsXLqBp06Zo3bo17t69i1deeQUdO3a0e0xh3rNt27ahe/fu8Pf3R8+ePVGqVCmkpKTg3LlzWLFiBUaMGIGoqChMmTIF06ZNQ2RkpIUR6c691Ol06NixIzIyMvDSSy9BrVZjzZo16NevH7Zv325xHYV5D6ZMmYKlS5fizp07mDJlitt9SklJQdOmTUU51r9/f8THx+P333/Htm3bsGPHDhujEgA+/PBD7N+/H926dUOnTp2wadMmTJ06FVqtFjNmzHB5HwBg9+7dUKlUNuM0c9Du3bsXL7/8srh97969AKg+kZeXJ4a7su3sOICOPZ07d8a+fftQp04dDB06FDqdDtu2bUPPnj3x7bffYuTIkW71012KOtauW7cOO3fuRLdu3fDOO+8gOzvbrfOlpaWhadOmCAwMRP/+/aHRaODr62t33z59+uDtt9/GqlWrMHnyZJvvV6xYAQAYOHCguK0ouo2zMR8A5HI56tevjyNHjlg8Qw6n0BAOp5jEx8cTAKRChQpkypQpdj9//fWXxTH37t0jQUFBJDAwkNy7d49kZ2eTChUqEIVCQc6dO2exb1JSEsnJybE577JlywgAMn36dIvtgwYNIgBIpUqVSHJysrj92LFjBADx9/cnLVq0ILm5ueJ3a9euJQDIqFGjLNpq3bo1AUAqV65MMjMzxe2ZmZmkcuXKRBAEcuLECXH73r17CQAyZcoUi3aaNWtGpFIp2b59u8X2a9euER8fH1KzZk17t9YllStXJs5+xvaezeTJk8k777xDKlSoQJRKJfnqq6+KdG53YX3o1KmTzXeTJ08mAEjbtm0JIUV/1vHx8XbPDYAAIO+88w4xGAzi9sWLFxMA5M0333TZHuv/oEGDLPZl70bjxo1JVlaWuP3q1atEJpORypUrW+z/888/EwBkyJAhRKvVitsLCgpI9+7dCQBy8uRJQgh9vwRBIPXr1yd6vd6iHb1eTzIyMuxerzmPHj0iKpWKqFQqcvr0aZvv7969K/6f3d/WrVuTgoICcfudO3dIcHAwkclk5NatW+J2e++5wWAgFSpUIABs3vMPP/yQACBvvPGGxfbIyEjx3VCr1RbfpaamEl9fX1K6dGmSkJBg8V1cXBwBQEaOHCluu3HjBpHJZKR06dIkKSlJ3J6VlSX+Tlq3bu3kjpmYMmUKAUDkcrnFeGQ0GsmAAQMIADJnzhxxOxs/pk6datNWgwYNiFwutxiLHPHHH38QAKRmzZokNTXV4judTkcePXok/n3v3j2LZ8WYNm0aAUBWrlxpsb2wY9mSJUsIALJkyRK7fY2MjCSRkZF2v2PnMofdU5VKRc6fP29zDLuvixYtstielJREypYtS0JCQkh+fr7d89k7z/Dhwy22b9++XRwPzK+psO9Znz59CABy9uxZm3NbPzNn75wjWcF+Ez179rR4vn///bfdcbSo74Ej7PV5yJAhBACZMGGCxfZt27YRAKRixYoW4ysbR6Ojo8mDBw/E7SkpKcTf35/4+PjY7bM1OTk5RCKRkObNm9t8ZzQaSVBQEKlataq4LTU1lQiCQNq1a0cAkB07dojfDRw4kAAgiYmJ4rZPPvmEACCffvopMRqN4vbs7Gzxd3v//n1xO3u39u7da9GXwowthR1r2e9QIpGQXbt2uXUO834xmWMtRwix/y689tprBAA5duyYxXa9Xk/CwsJIeHi4RVuF1W2cjfnmvPfeewQA2bNnj9vXy+FYw41cTrFhRoCzz5gxY2yO27RpEwFA2rRpIw6s8+fPd/u8RqOR+Pr6kjZt2lhsZwJ22bJlNseUL1+eACD79++32K7X64mHhwdp1aqVxXYmBKwVBUIIWbFihY0CZE9xOX36tF0Fn/H+++8TAOTChQsur9kad41cR59u3bqRM2fOFPq8hcGeoT1u3DjSsmVLAoAolUpy+PBhp224etbOjFyVSmVjOOt0OiKTyUi9evVctufKyLUnhNl32dnZ4rZatWoRlUplV7CfP3+eACAffPABIYQaZgBI8+bNLZSvwjB79mwCgEyePNnlvi+88IJdxYYQQmbMmEEAkM8++0zcZu89P3DgAAFAXnzxRZs2cnJySGBgIFEqlRaKHVN4rB1bhBAyb948AoAsX77cbp/r1atHgoODxb+ZUj937lybfdlvtbBG7rBhw2y+S0hIIFKplNSoUUPcptVqSVhYGImMjLRQ9s+dO0cAkL59+7p13hdffLHYil1aWhoBQAYPHmyxvbBj2ZMyct977z2b/VNSUohUKiUvvPCC3fYWLFhAAJA//vjD7vfmREdHE7lcTh4+fGjzHTN+zK+psO8ZM3KvXbvmsi/FMXJv375tc0xkZCQJDAx0eV5CXL8H7va5oKCAKJVKEhQURPLy8mz279ChAwFADhw4IG5j4+ivv/5qsz/7zp6jw5pr164RAKRPnz52v3/ppZcIAPFZ//7776IRqlAoyEcffSTuW6ZMGVK+fHnxb4PBQAICAkiFChXsjrFbtmwhAMi3334rbisJI7ewYy37Hfbu3dut9q37JZfLSUpKit3v7b0LO3bsIICtw//PP/8kAMjYsWPFbUXRbZyN+ebMmjXL6e+Sw3EHHq7MKTE6deqE7du3u71/z5498dZbb2HhwoUAgC5dumD06NF2992wYQN++uknnD59GhkZGRa18h48eGD3GHshWKVKlcLt27dtvpNKpQgNDXXYVsuWLR1uO3PmjN1jGEePHgVA14XaS8x19epV8d/ihs46wvrZpKWl4dChQxgzZgyaN2+OPXv2uAyXPnv2LDZt2mSxLSoqyu36sbdu3cK0adMA0GROYWFhGDBgAD7++GPUrFlT3K8oz9oZlSpVgre3t8U2Fh6VmZlZ6PasqV+/vs22MmXKAKCh0j4+PlCr1bhw4QIiIiIwe/Zsm/11Oh0A07vg6+uLLl264M8//0S9evXQt29ftGnTBg0bNrQbgmqP48ePA4DDEE1zzpw5Ay8vLzRq1MjmOxbed/bsWZdtALC7ns/b2xsNGjTAzp07ce3aNYvnrVQqLf5msN/NsWPHcOvWLZvvNRoNUlNTkZqaiuDgYHGdsrPfamGxd1xkZCTKli2LS5cuQavVQi6Xw8PDA0OGDMGsWbOwc+dOdO7cGQCwaNEiALAIW3fG8ePHoVAo0Lp1a5f7EkKwZMkSLF26FBcvXkRWVpbFWscnMZaVBPbesRMnTsBgMKCgoMDuGHnjxg0A9PfRrVs3h21nZ2cjPj4e1apVQ3h4uM33LVu2xO7duy22FfY969+/PzZs2IAmTZpgwIABaNeuHVq2bFmiWZL9/f0RHR1ts71MmTI4cuSIxbaivgfucvXqVWg0GrRt29Zu1uW2bdti165dOHv2rM275WpsdAVbD+2oOkPbtm2xfv167N27F7Gxsdi7dy98fHzQokULNGnSRAxRvnnzJu7du2eRxOjatWvIyMhARESEKJfMSUlJEa+/JCnqWGtvf3eIjo4u1LvZrl07lCpVCmvWrMG8efMgk1EzYeXKlQAsQ5WLqts4GvPNCQwMBPD08oVwng+4kct5qvTu3Vs0ch2tfZk7dy7GjRuHkJAQdOzYEWXKlIGnpycA4JtvvkFBQYHd4+ytO2EDtqPvmLFhjfl6EettWVlZdo9hpKenA6BrubZt2+Zwv7y8PKftlCRBQUHo0aMHvLy80KFDB0yaNAm7du1yeszZs2dtlIHWrVu7beS64wQp6rN2hqP1RzKZzMKALirO3jPWfkZGBgghuH//vl2FimH+Dqxbtw5ffPEFVq9ejYkTJ4rnGjJkCL744guXZT7Ye1m6dGmX15CdnW2zlpRRqlQpcR9XbQD2fyvO2gkNDbVbt5P9br7//nun583Ly0NwcLB4vaGhoTb7OOqTKxwdFxYWhoSEBOTk5CAoKAgAMGLECMyePRuLFy9G586dodFosGrVKkRHR6N9+/ZunS8rKwulS5d2K4vs6NGj8d1336Fs2bLo0aMHSpUqBYVCAQCYNm2aw99KccayksDe+dmzPnToEA4dOuTwWFdjJHu37L0Drs7t7nvWt29fbNq0CfPmzcPChQvx/fffQxAEtG3bFnPnzi2R9cuOypXJZDKbpE1FfQ/cpai/a8C9sdEZbOx3lDjOfF0uM3JbtWoFmUyGtm3b4vPPP0d2drbd9bjsuV+6dAmXLl1y2IeSlstFHWtLegxzhFQqxYABAzB37lzs2LEDXbt2RW5uLjZt2oRq1aqhXr164r5F1W0cjfnm5OfnA8AzV86K89+CG7mcp0ZmZiaGDx8OlUoFg8GAUaNG4cyZMxaZg/V6PT7//HOUKlUKZ8+etVBeCCH48ssv/5W+JiUl2aTbT0pKAuBYIWEwQf8kklgUFzZ7y7IbO2Pw4MFuG7RF4Vl51k8C9g7Ur18fJ0+edOsYLy8vTJ8+HdOnT0d8fDz27t2LhQsXYv78+cjPz8dPP/3k9Hg2+3H//n2XmTh9fX3tZioHgEePHllcg7M2ANPvwt12HCk7bL8LFy64FeHAfofJycmIjIy0+M5Rn1zh6LikpCQIgmAxVkVHR6Njx47YsmULkpOTsWvXLmRkZOCDDz5wqdAx/P398ejRIxiNRqeGbnJyMr7//nvUqlULR44csVAEHz165NSRUpyxzByJRAKtVmv3O2fGsr17wZ71Bx98YJF8rbCwdhy9y/aeZ2HfM4BGIfXs2RM5OTk4dOiQmPypc+fOuHr16r9WF74474G7FPV3XRKEhIQAMBlT1lSrVg1hYWHYu3cvkpOTcfnyZbzxxhsAqEE7depU/PPPP2I2YXMjl/X3pZdespu1+klR1LHW3TGkJI4bOHAg5s6di5UrV6Jr165Yv3491Gq1xSyueT8Lq9u40yf2zNk7wOEUBV5CiPPUGDFiBBITEzF//nx89dVXuHXrFt59912LfVJTU5GVlYWmTZvaeOdPnjwpevueNP/884/DbXXr1nV6LDMkrcPMngUyMjIAoFAlHZ4URXnWLDNrSczIPkl8fHxQtWpVXLlypUgh0tHR0XjjjTewf/9+eHt7u1XegoW37dy50+W+devWhVqtFkOczWEKoqsZKvY7sFeaKS8vDydPnoSnpycqV67ssj9A4X83LNu0s99qYbF33J07d3D37l1Ur14dcrnc4rs333wTOp0Oy5Ytw+LFiyGVSm1KljijUaNGKCgoEMu8OOL27dsghKB9+/Y2Mx2urtXdsczVbysgIADJycnQ6/UW2/Py8sTwYndp2LAhBEEo9hjp6+uL6Oho3Lx5UzQYzLF37cUZn318fNC5c2f8/PPPGDx4MJKSknDs2DHxe4lE8kTHpqK8B4UdM6tUqQKlUokTJ05ArVbbfO/u+FAUIiIiEBQU5LQWfJs2bXDz5k0x8y+r996kSRN4enpiz5492Lt3L2JiYhARESEeV7VqVfj6+uLkyZMOI7ieBCUx1j5pateujZo1a2Lz5s3IycnBypUr7ZYOepK6DXvmrsKaORxncCOX81T45ZdfsG7dOvTt2xdDhw7FyJEj0a1bN6xYsQKrV68W9wsNDYWnpydOnz5tIWAzMjLEGqv/Bp9//rnF7ERWVhamT58OQRAwaNAgp8c2atQIjRs3RlxcHNauXWvzvdFodKnUPilYqaZWrVo9lfObU5Rnzdbt3L1791/pY3EYPXo01Go1hg8fbjcELj4+HgkJCQDoejB7NQYzMjJQUFAApVLp8nyDBg2Ct7c35s6da3eN1/379y32BYAJEyZYKHx3794V12VZKzjWNG/eHBUqVMBff/2Fv//+2+K76dOnIy0tDbGxsTaGoSOGDBkCHx8fTJw40W44oVqtFteEAcCAAQMglUoxb948i5mS7OxsTJ8+3a1zWrN8+XKcP39e/JsQgk8++QQGg8FuVEP37t0RERGBr7/+Gvv370fXrl0tFGtXMCffmDFjbGav9Hq9OJvGZqoPHz5s4aC6d+8eJkyY4PQc7o5lrn5bDRs2hE6nw6pVq8RthBBMmDCh0CGe4eHh6NevHw4fPoyvvvrKbv3xY8eO2TWyrBk4cCC0Wq1NCZSdO3farMcFCv+eHThwwK6ByN45899mYGAg7t2757LPRaUo70Fhx0y5XI7Y2FikpqZi5syZFt9t374dO3bsQMWKFdG8efOiXIJTBEFAy5YtER8fL66RtYbNzs6ePRuBgYGis0sul6N58+ZYsWIFHj58aDGLC9Cw6bfffht37tzBuHHj7Bq6Fy9edDjrWlRKYqz9Nxg4cCDy8/OxYMEC7NmzB61bt7YJs36Sus2xY8dQqlQpxMTEFOl4Dgfg4cqcEuTmzZt2kw8wPv74YyiVSly/fh1jxoxB2bJl8fPPP4vf//rrr6hVqxbefvttNG3aFNHR0ZBIJHjnnXcwd+5c1K5dG927d0d2djb++usvREZGFkqBLA6VKlVCjRo1LGpL3rt3D++//z4aNGjg8vi4uDi0bdsW/fv3xzfffIN69erB09MTiYmJOHLkCFJSUhyuO7LGXLl++PChzbaPP/4YVapUsTjG+tmkp6fj0KFDOH36NAICAuwmQ/q3KcqzfuGFFzBnzhyMGDECL730ElQqFSIjI23Cqp4F3nzzTRw9ehTLli3DoUOH0L59e0RERCApKQlXr17FsWPHsHr1akRFReH+/fuoW7cuateujVq1aqF06dJIS0vD5s2bodPpMG7cOJfnCw0NxfLly9G/f380atQIPXr0QOXKlZGamopjx44hKipKTCQ2cOBAbNiwAZs3b0atWrXQrVs3sXZjeno65s6di/Llyzs9n0QiwdKlS9GpUyd06dIFffv2RWRkJI4cOYJ9+/ahQoUKmDVrltv3KyQkBHFxcejbty9q166Nzp07o0qVKigoKEBCQgL279+PZs2aieu8K1asiMmTJ2PKlCmoVasW+vXrB5lMhvXr16NWrVpOZ4Mc0alTJzRt2hT9+/dHSEgIdu/ejZMnT6JJkyZ2HS8ymQxDhw7F559/DsD9hFOMLl26YNy4cZgzZw5iYmLQu3dvhIaG4v79+9i9ezfGjRuHsWPHolSpUnjppZewfv16NGjQAO3atUNSUhK2bt2Kdu3a2U2gxHB3LGvatCk8PT3xzTffICMjQwwbnDRpEgCaQ2HJkiUYNmwYdu3ahZCQEPzzzz/IzMxE7dq1xURg7vLDDz/g2rVrGD9+PFasWIGmTZvC398fd+/excmTJ3Hjxg08fPjQ5Rq98ePHY8OGDVi0aBEuXbqEVq1a4e7du/jtt9/QtWtXm7WDhX3PRo8ejQcPHqBFixaIioqCIAg4ePAgjh8/jiZNmljUi33hhRfw22+/oVevXqhbty6kUil69OiBWrVqFereOKIo78ELL7yA33//HS+99BJefPFFKJVKcbx1BKu5O336dBw+fBiNGzdGQkIC1q1bBy8vLyxZssStdeRFoXfv3ti0aRN27dqFAQMG2HzPjNeUlBT07t3boh9t27YVHW7WRi5A1yyfPn0aCxYswLZt29CqVSvx93bhwgWcO3cOR44ccbjGuyiUxFj7b8CSQk6bNg1Go9GhTC1J3YZx69YtxMfH4+233y6JS+H8P/O00jpznh/cKSEEgGRkZJCCggJSr149IpFIbMr4EELIzp07iSAIpEmTJkSn0xFCaHmOGTNmkJiYGKJQKEi5cuXIBx98QHJycuyWsXBWVsZZ+QR7bbH98/Pzyfjx40nZsmWJXC4nlStXJgsWLLApPeCoLAQhhKSnp5NJkyaRGjVqEE9PT+Lt7U1iYmLIgAEDyIYNGxzfYCtc3Wfz8gaOno1CoSAVKlQgb7/9Nrlz547b5y4KzurkWlPYZ00IIV9++SWJiYkhHh4eNqUcrP82x913x1UJIXs4ewfXrl1L2rdvTwICAoiHhwcpXbo0adOmDZk7d65Y6iEjI4NMnTqVtGrVipQqVYrI5XISERFBOnfubFNz2hVnzpwh/fr1I2FhYcTDw4OUKlWKvPjii2Tr1q0W++l0OjJnzhxSs2ZNolAoiI+PD2ndujXZvHmzTZvO3vPz58+Tl19+mQQHBxMPDw8SGRlJxowZY7eMhbMyNIyrV6+SoUOHksjISCKXy0lAQACpWbMmGT16NDl+/LjN/osWLSLVqlUjcrmclClThowbN46o1eoilRDau3cvWbRoEalevTpRKBSkVKlSZMyYMRaloay5efMmAUBKly5ttzalO6xfv560bduW+Pn5EYVCQaKiosjAgQPJxYsXxX1ycnLIBx98QKKioohCoSAxMTHk888/J1qt1u61FnYsI4TWQW3YsCHx9PQUxw5z9uzZQxo3bkwUCgUJCgoiAwcOJElJSU5LCFmXXzFHrVaTL7/8ktSvX5+oVCri6elJoqOjSa9evcjy5ctFmeCKtLQ0MmLECBISEkKUSiWpX78+2bBhg9OySO6+Z2vWrCH9+vUjFSpUIF5eXsTPz4/Url2bzJ4926ZU2cOHD0m/fv1IcHAwkUgkFud2VkKoMKWZCvse6HQ6Mn78eFKuXDkik8lsxjZHv5OUlBQyevRoEhkZSTw8PEhwcDB5+eWX7Za+czb+ufMemJOfn08CAwPtliZjlC5d2qbcDyGEHD58WHxv7ZWUIoSWD/zpp59I8+bNia+vryh3OnfuTH788UeSm5vrsu+FGVsIKdxY66qUlzNc9ctVOan27dsTPC7zZ14L3prC6DbujPlTp04lgP1a1BxOYRAIsRMXxOFwAND1Pvv377cbPsfhcDjW/P777+jbty8+/fRTfPbZZ0+7OyJ8LOP8V/n0008xa9Ys3Lx50yapHOf5Qq/XIyYmBtHR0dizZ8/T7g7nPw5fk8vhcDgcTglACMHcuXMhk8kKHarM4XDsM378eAQGBmLGjBlPuyucJ8yyZctw586dYmVZ53AYfE0uh8PhcDjF4MKFC9i6dSsOHz6Mo0eP4s0333RYC5PD4RQOHx8frFixAidPnoTBYBAzRHOePwRBwKJFiyzq8XI4RYUbuRwOh8PhFINTp07hk08+gZ+fHwYOHMhnITicEqZjx47o2LHj0+4G5wnD6hxzOCUBX5PL4XA4HA6Hw+FwOJznBr4ml8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1cDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznMDN3I5HA6Hw+FwOBwOh/PcwI1czlNl6dKlEAQBgiDg4MGDNt8TQlC2bFkIgoBu3brZfJ+Xl4fPP/8ctWrVgpeXF/z8/NCyZUssX74chBCb/dm5BEGATCZDYGAg6tevjzFjxuDy5cs2+yckJFgcY/5p0qSJuN/gwYPh7e3t8nrd7a/BYICvry969uxp08bXX38NQRAwaNAgm+8mT54MQRBw/fp1l325desWBgwYgNDQUHh6eiImJgYTJ050edzUqVMt7oOXlxeqVauGSZMmITs7W9zP/Nmy+126dGkMHjwY9+/fB0Dvm6P7a/4ZPHiwy35xOBwOh1Mc/ms6iVQqRbly5dC7d2+cPXvWZv/MzEwolUoIgoArV66I263luKNPmzZtALjWcQRBwMiRIx1+b6/v1p/hw4c7PZ7DKSyyp90BDgcAlEolVq9ejRYtWlhs379/P+7duweFQmFzTFJSEtq1a4crV66gf//+GDlyJDQaDdavX49Bgwbhzz//xKpVqyCVSi2O69ChA15//XUQQpCVlYVz585h2bJl+OGHHzB79my8//77NueKjY1Fly5dLLaFhIQU6hoL01+pVIomTZrg8OHDNu0cOnQIMpkMhw4dsvtdaGgoKlWq5LQvZ8+eRZs2bVC6dGl88MEHCAoKQmJiIu7evev29fz444/w9vZGbm4udu7ciRkzZmDPnj04dOgQBEEQ9/vss88QHR0NjUaDo0ePYunSpTh48CAuXryIN998E+3btxf3jY+Px+TJkzFixAi0bNlS3F6hQgW3+8XhcDgcTnH4r+gkBoMBV65cwY8//oi//voLR48eRZ06dcT91q1bB0EQEB4ejlWrVmH69OkAgD59+qBixYrifrm5uXj77bfRu3dv9OnTR9weFhZWpPtnj5CQEKxYscJm+/bt27Fq1Sp07NixxM7F4QAACIfzFFmyZAkBQPr06UOCg4OJTqez+H748OGkfv36JDIyknTt2tXiu06dOhGJREI2b95s0+64ceMIADJr1iyL7QDIu+++a7N/amoqadq0KQFAtm3bJm6Pj48nAMhXX33l9DoGDRpEVCqV030K299p06YRAOTy5csW+4aHh5MBAwYQAOThw4fidp1OR1QqFendu7fTfhgMBlKjRg3SuHFjolarne5rjylTphAAJCUlxWJ7nz59CABy+PBhQojp2Z44ccJiv48++ogAIGvXrrVp+8SJEwQAWbJkSaH7xeFwOBxOcfiv6iRbtmwhAMiIESMstrdq1Yr06dOHvPfeeyQ6OtrhdaekpBAAZMqUKXa/d6XjOLoOd2jXrh3x9fUl+fn5RTqew3EED1fmPBPExsYiLS0Nu3btErdptVr8/vvvGDBggM3+R48exY4dOzB48GD06NHD5vuZM2ciJiYGs2fPRn5+vsvzBwUFYc2aNZDJZJgxY0bxLsYORekv8yCbz9jevn0bjx49wsiRI6FUKi2+O3v2LPLy8mw8z9bs3LkTFy9exJQpU+Dp6Qm1Wg2DwVDsa3zhhRcA0NlYZ7AZ2lu3bhX7nBwOh8PhlDT/NZ3EnvxNTEzEP//8g/79+6N///6Ij4+3Gx32NHn48CH27t2LPn36QKlUPu3ucJ4zuJHLeSaIiopC06ZNERcXJ27766+/kJWVhf79+9vs/8cffwAAXn/9dbvtyWQyDBgwABkZGXbDeu1Rrlw5tG7dGkePHrVYWwoAarUaqampFh+dTufu5RWpv02aNIFMJrNYF3To0CGoVCo0bNgQDRo0sLg29n9XRu7ff/8NAFAoFGjQoAFUKhW8vLzQv39/pKenu31N1jCjNSgoyOl+CQkJAICAgIAin4vD4XA4nCfFs66TWGNP/sbFxUGlUqFbt25o1KgRKlSogFWrVrl1bkdY60HsU1TWrFkDo9GIV199tVj94nDswY1czjPDgAEDsGnTJtHLuWrVKrRu3RoRERE2+7KEDLVr13bYHvvOPNmCK2rUqAGj0SgaYowpU6YgJCTE4uOuoCpqf728vFC3bl0bI7dRo0aQyWRo1qyZxXcHDx6El5cX6tWr57QvN27cAAD069cPVapUwe+//46PPvoI69evR/fu3e0mx7BHeno6UlNTkZCQgJ9//hk//PADwsLCLNbSAkBWVhZSU1Nx7949rF+/HtOmTYNCobCbtIPD4XA4nGeBZ1knYY73pKQk7N+/X0zO2LdvX3GfVatWoWfPnvD09AQAvPLKK/jtt9+g1+vdPr85eXl5NnoQ+xSVVatWoVSpUuJMNIdTkvDEU5xnhn79+mHs2LHYunUrOnfujK1bt2LBggV2983JyQEA+Pj4OGyPfefKA2oOyx7I2meMGDHCQngAzoWZNUXtb4sWLfD111/j0aNHCA8Px6FDh8SMy82bN8e8efOgVqvh5eWFQ4cOoXHjxpDJnP+sc3NzAQANGzbEypUrAQAvvfQSvLy8MGHCBOzevdsiGZQjKleubPF39erVsWzZMnh5eVlst24rKioKK1euRJkyZVyeg8PhcDicp8GzrJNMmTIFU6ZMEf/29fXF7NmzxaRR58+fx4ULFzBz5kxxn9jYWHzxxRfYsWMHunbt6nYfGEqlUpyxtqZDhw6Fbu/69es4deoU3nvvPUgkfM6NU/JwI5fzzBASEoL27dtj9erV4jrRl19+2e6+TFjk5OTA39/f7j7uCB1rmAFofUxMTIxbhp8jitpfZuQeOnQI7dq1w6VLl/Dll18CAJo1awa9Xo/jx48jMjISDx8+xLBhw1z2hXl1Y2NjLbYPGDAAEyZMwOHDh9261vXr18PX1xceHh4oU6aMwwzI33//PSpVqoSsrCz8+uuvOHDggN3MlBwOh8PhPCs8yzoJc7xLJBL4+/ujevXqFnJ15cqVUKlUKF++PG7evAmAGqlRUVFYtWpVkYxcqVRaLD3IGhY6zUOVOU8KbuRynikGDBiA4cOH49GjR3jxxRcdCouqVati06ZNOH/+PFq1amV3n/PnzwMAqlWr5vb5L168CKlUiujo6EL33RlF7S9bX8tCkQGgadOmAIDg4GDExMTg4MGDYukfV+txAYihVtalAUJDQwEAGRkZbl1Tq1atEBwc7HK/Ro0aoUGDBgCAXr16oUWLFhgwYACuXbvmVm1hDofD4XCeBs+qTuLM8U4IQVxcHPLy8uyeKzk5Gbm5uU9d/q5evRqVK1dG/fr1n2o/OM8vPD6A80zRu3dvSCQSHD161G4GQwZbz7l8+XK73xsMBqxevRoBAQFo3ry5W+dOTEzE/v370bRp00J5Wt2hqP0NDQ0VDdlDhw6hWrVqFkK2WbNmOHToEA4dOgSpVCoawM5gAuX+/fsW2x88eACg8PV/C4NUKsXMmTPx4MEDfPfdd0/sPBwOh8PhFJf/ok7Cavl+9tlnWLduncXn559/hlqtxqZNm9xu70lw7Ngx3Lx5k8/icp4o3MjlPFN4e3vjxx9/xNSpU9G9e3eH+zVr1gzt27fHkiVLsHXrVpvvJ06ciOvXr2P8+PFieK4z0tPTERsbC4PBgIkTJxbrGkq6vy1atMDZs2exc+dONGvWzKbdI0eO4J9//kGtWrXcEoQ9e/aEQqHAkiVLYDQaxe2LFy8GULS1NYWhTZs2aNSoEb755htoNJonei4Oh8PhcIrKf1EnYaHKH374IV5++WWLz/DhwxETE1PsLMvFZfXq1QDg1HHA4RQXHq7MeeYYNGiQW/stX74c7dq1Q8+ePTFgwAC0bNkSBQUF2LBhA/bt24dXXnkFH374oc1x169fx8qVK0EIQXZ2Ns6dO4d169YhNzcX8+bNQ+fOnYvUb51Oh+nTp9tsDwwMxDvvvFPk/rZo0QJLlizBiRMn8O6771p816xZM2RlZSErKwujRo1yq5/h4eGYOHEiJk+ejM6dO6NXr144d+4cFi1ahNjYWDRs2LBI118YPvzwQ/Tt2xdLly7FW2+99cTPx+FwOBxOUfgv6SQFBQVYv349OnTo4LDubI8ePTB//nwkJyeLy5T+TQwGA9auXYsmTZo4zOXB4ZQE3Mjl/GcpVaoUjh8/jrlz52LdunVYv349ZDIZatWqhaVLl+L111+HIAg2x+3atQu7du2CRCKBr68voqOjMWjQIIwYMaJQa2Ws0Wq1+PTTT222V6hQAe+8806R+2u+ztZ6Jrd69erw9/dHZmamW+txGZMmTUJAQAC+/fZbjB071sLw/Tfo06cPKlSogDlz5mD48OGQSqX/ynk5HA6Hw3kSPAs6ybZt25CZmel01rl79+6YO3cu1qxZg9GjRxf6OovL33//jaSkpCcSNcfhmCMQd4ticjgcDofD4XA4HA6H84zD1+RyOBwOh8PhcDgcDue5gRu5HA6Hw+FwOBwOh8N5buBGLofD4XA4HA6Hw+Fwnhu4kcvhcDgcDofD4XA4nOcGbuRyOBwOh8PhcDgcDue5gRu5HA6Hw+FwOBwOh8N5bvi/rpNrNBrx4MED+Pj42K1dxuFwOP/vEEKQk5ODiIgISCTcL8rhAFx/4HA4HFc8bf3h/9rIffDgAcqWLfu0u8HhcDjPPHfv3kWZMmWedjc4nGcCrj9wOByOezwt/eH/2sj18fEBQG++r69vkdpYt24d+vbtW6x+FLcN3gfeB94H3ocndXx2djbKli0rjpccDqdk9Afg6f++eR94H3gfeB+eVBtPW3/4vzZyWYiRr69vkYWUl5dXsQRcSbTB+8D7wPvA+/Ak+wCAh2RyOGaUhP4APBu/b94H3gfeB96HJ9nG09If+AIrDofD4XA4HA6Hw+E8N3Ajl8PhcDgcDofD4XA4zw3cyOVwOBwOh8PhcDgcznPD//WaXFcYjfTf4mS91mrp8bIi3mmjEdDraRvsU5Q+AEVvw2g0XUdR29BqaTtFbeNZ6gNQ+DYMBgN0Oh20WoAQgC1PsNeGh4cHNBqNwz7odPT/1m2w/zs7HoDYB+s2zP91tw/mbVi24wG1WmOz3V4f7PUDAGQyyzbM27Lug3U/6H4K5OY6vg/Wx9vrp0Ti4bANe32wxmBQICvLeR/YOOMIQhTIyCh6H3Q6T2RlaeDhQf+2vkYPDw9IpVLnjXA4nELD5E1xjtfrqf5Q1Has5WZR+qDVmvrAdZCn0weDwYCCAp2F/LeW/YD78t+e7C+s/HekP8hk7usg9tphbTD5b32PiqKDWH9v3QfrdlgbzuS/wWC/bROudRBX8h9wrEO4K/8d6Q/u9oEQBbKzNXbHoWddfxAIsX7M/z9kZ2fDz88PWVlZdhdV794NtG9v+lupBMqUAWrXBtq1A3r3BvbujUNsbKzDc0RFAXfuOO4D/VEReHgIUCoBlQrw8wMCA4GQEPoC/vmnqyshAEy/TvMfqiC48yOybaPwFPf457MPgkAwZMgj9OiRCbncdhDlcJ4mhFBhv2WLP5YsCQchAjw9gcxMQC6n+7gaJzmc/0fc+V0EBQHp6fT/UikQGgpUrw60bAl06wbUqQOsXetYh/juO2DUKFc9IZDJBCgUgI8P4OtL9YfQUKBUKWDZMsCJvSG2weSWtYxyT4dwLDcFwdaYKGwb7vHsyf+SaIPrEJxnFXv6g4cH8O67wNdf032etv7AZ3KdkJtr+bdGA9y8ST/r1wPvvANIpa9gzBggIgKoWBGoVQto3hxo2hTw8nLHQwIAArRa+rJkZwMPHxa2p5ajnrlAcd+FUdyRsyRG3uevD0OGPEJsbCb8/UMBeJVA+xxOSUIAqBEbmwwA+PXXUsjPf7o94nCeF/R60/8NBirbHz4E/v4bmDKFGiyeni9h1ixq/DZuTB3rVavSGRPz4x0jQK+n++blAY8eFaWnJrlkrTO4p0M4lmtcByleG1yH4Dy72OoPOh1w//7T7ZU53Mh1QqdOwIULQE4OkJYGHD4MHDoEXLsGpKSwcAUJUlLo3+fOUeOX4eEBeHsDlSoBZctSI7haNfqRyegsrU4H7Nx5EOXKtcC9e8CDB0ByMj1fZiY1etVqU7hPSWAdIkMIYDAY8d9fol0SXtSSQ6UyoEcPJpyCCnHk03kWzENMPe9GCIL9PhBSGMXl6WB+DdaRDZb7OG/HaDRAIrEfiuPOPSDEAEFwHMpTEm0U9nhBsAwPEwRPhIQAr7ySDI0mFJUrS4u8vILD4Zg4exZITaVy/PJlYOdOqlM8fGgKl1Sr5Th/Hjh/HoiLo8cJAnWSBwYCDRpQ/aFSJWr8RkebQiW1WmD37v2IiWmNhAQgMRFISqLnzMykuktu7pPVHwCqPxDyX9cfgOdDh6By72nIaCZbrGWv9cTLf0F/AFzrEK5wpj/Q9l234Uz+/xv6g3Ub5noi/XgiOBjo2zcZ6emhqFBBit69i3W6EoWHKxdjGv3iRWDWrNOQSOrh6lXg3j0amlRQ4PpYmYyGJUdEADLZfbRsWRrVqtHwpdq1aWi0NUYj9dLevAnExwMJCdRjcurUfSgUpZGRQYVpXh6ddba35qCwSKW0r1Ip/VClmLbLBK3BAOh0RhiNxRVyBIIgWBlbJTcosh+ltYFvNJZM+3RZggEeHlIIAlC2rAbz58cjJCQKhHgW/wSPMb8Oc4OF/Wsw0AHJ/NpK+ldu3geJxPR+sI9Op4FSqbToH3WmWH6MRlMfzftK+/vvKRymAdvy3hKih4eHTHz3zX8L7P8eHvT/7F9zIZiWloagoMI4OGwpbhvuHJ+fn4+EhARER0dDaTX4PO1wIw7nWaS4v4v0dLoU6ZdfbiEnpwISE4GMDPdmb2UyGpocHAzIZElo3jwM1atTg7hBA8f6Q2oqcOsWcPs2cPcu1R+SkoCLF5Mgk4UhM5Maxfn5pvWjxYHJBvNxk42P5voDnYkumVBh83EcKFkdwlrmmrdd3PZNzgMqc4Ano0PYk3NsO8DktK0OUdKY6w/W74lEAmi1Gnh6KsV9rXVO9n9HOsS/7bBwpj+w67O+TnMdgn3Mnwnw78j/4rbxLOsP3F9fDGrUALp2vYbY2HoW2/V66sE9eBA4cwYWBjBbG6PX09natDQAKI0zZyzblkoBT08gIAAICwPKlaMzwdWrA/XqAc2amZJZxcUdcLimR6+ngowZxomJ1Iv86BEVeMwwzszUwmCQQ6ezHNDYgOIa6u2SSgGFgn7kcvpha9IJoTPXWi29DwUFsDqfUGhhwc4pkVBvk0QiFY0p68GZte2u4DY37JlhbD3ImkPvk1S8X8zZQYjjtUrWApmeg87kOroXrq/D0mtnfo/MP7bnNV2fXl/cPiiRk2P/G2sD2dxIlslMBqNWmwd/f29xH5MyRD/WBrO5wKPXYfJou3qvHH8vK/IsCL23gUhMtP+c3cVo9C9W+I87xxMiICUFGDaMRpMAwJEjQHh40c/L4XAcExgIvPYaIJUeR2xsBXF7ejqwfz9w9Cid3U1IoIZoTo7JANbrqezOyACAMFy5Ytm2VEpngv396W84MpLqD1WqADVrAn37mtbcA0Bc3B67OgRzrN++bWsYp6TQ2eKsLCAtrQAGg8LGMGZjsavkOAxBoAa6XG7SIViyGybXtVoqW/Pzabsm/YQOroXRIazlkMGgh1QqszCgrO+HuzC5yz6sbyyZmLV+wu4VIBOfszMdwpn+4EzumW9zrNtZRv5Y6w7mOoR5uyb9wQBA6tRANnd628c9HcLaaDTpD2r4+akskiVZ6w/meoS10Uw/zvUg6+uxva8yt999R9cJBCAx0fZ+u0tx9Qd32rCnP8TGAl98UbzzlgTcyH0CyGQmj6o1RiNw+jQNez59moY+37qVC63WG2q1SYgZDNSjmptLBcvJk/bPo1IBcnl3LFhgGRJdpw4VaDIZFXCRkc77HBe3XhRybO3xrVsmw/jBAyrYWBi1PW8vIbBYG+QKDw/qjVapaLIMjSYNERFB4myYXk8FOwu7YuezNlxNnm/nIRlspo0NeiyhhvnAZzSavH/uGviCQNtWKACDgXofBYEqGKbwHdvjHA+cljPi5p5BZ55kg4H939KD6VqY2F6PuYBm98z8vjHoLL6tocnC34tmIDO8kZLiuH/mBrL5LCt7zlqtGv7+3pDLTdvY+fV62m/zZ2/PcNZqdZBIPMR7az3b7MpwBgS377tjipu50L3jmUOMJcrja3M5nH+fwECa1NJeyJ9GA5w6RXWHS5eonL58OQsFBX7IyTEZkwYDlZk5OVR/OHHCti2JhDrS/fwAmawzVq0CKlSATURZRAT9tGjhuM9xcRtE/cFopOe8fp0axnfuUP3B3LGek0OXYRUUmMtv6uTOz3dv7GEOdZWKLgvT6TIQEREAudyUgT8/nxrhWVlUJykosJSDtrJRBkcz6UwWstk2c8PbJENMstdcH3KFeTSQ0UijoAihepEjHcJd/QGwNErtGafmBqr1LGhhJwcAQBAkkEpNkxxMf2Az+eaTBtby12RgEocTBOZ9cnx/VY8nkez1z76T3VxX8PAANJo8BAT4iNtZv60d7o6M54ICHaRSDwvD2Vp/cK1DSAqlv9lSEpmPXbdhrT/Ys1meBoU2cg8cOICvvvoKp06dwsOHD7Fx40b06tVL/J4QgilTpmDRokXIzMxE8+bN8eOPPyImJkbcJz09HaNGjcIff/wBiUSCl156CfPnz4e3t7e4z/nz5/Huu+/ixIkTCAkJwahRozB+/HiLvqxbtw6ffvopEhISEBMTg9mzZ6NLly5FuA3/HhKJrQEcF/eHKCC0WhoGfeYMXcNz6xYVGElJ1NjLzzcNNno9HbyZMXD0qO35ZDKTRzc0lBrCFSpQA7h2bZooy9yjC1DBVqMG/biD0QgsWLAZlSr1xPXr1DC+d88k1JhRbC1g2JpkU7KtILuZqD086DWEh9PwrIgIOvirVHRgYmFYFy4kg5BQi/OZD8zsfK5QKulHoTANbMwbrdGYjG0GIRAThwFKqNV0u0plO4g584KaZqDtG6nuQNum2bqtZ2zZOayFiX3hKXHbA29tFHt40DAdlUoODw9Lbzwrw8AMTHsGsqlf9sONStJI3rJlKebMGYujRzMthLFSSf+9efMaWrSoiWPHzqBhwzoOPanmCo258ZyTo4Zc7iVeX1HCvrRaLeTWP1IrXn21DapWrYNJk74BALRpE4VBg8ZiyJCxbh1vMFAlsF07iIpBQEDh+8rhOIPrD8VDqaSJLZs3N22Li/tT1B/0ejr7y/SHmzdN+kNWlqX+YDTS3zx1SNPZImtYRJmfH5W9pUvTihGVKtEZ4Tp1qFFujkTinmPdnPR0YMGCnYiM7Cj2meUnSU83ObnN5ZHBQA1ltRqPx/kA3Ltn27ZEQu8byzodFkYrV3h7QzSIc3KoDnLtWgaMxgBkZ9PzWScOo4aL82th8oPNSLNZXL2eHqvR0I893YQa90ox6am3t6UOweSs9bIrwLlxaJoldo4gMGewER4eUvFcjpYcOWqXEMFtA9/8uiQSk5FpNGrh7a2wMDAFwXSvrJ3UtrOwjsOV3Xf8+yA52X5/JRLgjz+o/nDkSKbFpICHB/3dJCVdR8uWNXDkyBk0aFDHaa4Lpm9Z6xC5ufnw8PC0G1ngDsXVH9xpw57+0LNn4fv6JCi0kZuXl4fatWvjjTfeQJ8+fWy+//LLL7FgwQIsW7YM0dHR+PTTT9GpUydcvnxZjNV+9dVX8fDhQ+zatQs6nQ5DhgzBiBEjsHr1agA0hrtjx45o3749Fi5ciAsXLuCNN96Av78/RowYAQA4fPgwYmNjMXPmTHTr1g2rV69Gr169cPr0adRw1zp7BpHLaThyvXqO98nNpeHQZ88CV64Ahw49ACERSEmBODibG8LZ2fSTmGjfuyKRAHL5y/joI1NoU6VK1ABu0IAmunBWy00iAcLC1OjSBXClIzAv7+XL1NObkED/fvgQuH07Bzqdj5gogw3sOp3JG3vnDvVkW0MH50AxvLt+fTqrXbEiNfANBnquhARqgCclmYSnRmMZ8sSEkLPr9fIyzUJ7eUFM7Z+cnA65PNCiBIv19bsaqNiAb68uoSkcyL6BStuWuBy8WbvMkDdfCyMIdI2FVOppM8Pp2Ci2DkuTi8a+o/NPnToYubmZ+O67TZDJqFBgRrFCAeTnZyE01B+AKczd3EC2N/taWCOZGZ6O+pqWRhXnGzcsn4O9EC7rsCl6X43w8zOF7he2riLtQ47LNTWenvQ9r1iR/i2TUUWuYkX3jmdK1/ff21/Px+GUBFx/eLLIZK71B+ZIv3CBLqW6dQs4cyYVOl0wMjKossrkh3lE2f37NLmmNVT29sPYsXTMKVOGOtKrV6dy2FGOEXMCA4HKldPgpBqjSGYmncVmM8Us0uzatQzo9dRANZfpRqPJGH70iOoe9pDJALlchfBwqvswY75sWTqWJyfTe8UShKammmaITU5v4bFhYludwxyWlNTXl/6rUFC5QQiQnJwJwB9ZWSYDmWEZueYYc3lu7lC3Z6Tab1/q9DxM/pmHB5vP1Go01DAzl9Xm+oM9HYKdz3QvFQ7voSCY9Idvv90EqZS+Y+b6g0aTjdBQP8hkJsOR6Q/WRrJ9/cG5DmHuuHYUeZCergJA3xtzA9d6/a6jJVs0QlCPwEDTZEFhQ5aLqz+408azrD8U2sh98cUX8eKLL9r9jhCCb775BpMmTULPx2b88uXLERYWhk2bNqF///64cuUKtm/fjhMnTqDB4+nMb7/9Fl26dMGcOXMQERGBVatWQavV4tdff4VcLkf16tVx9uxZzJs3TxRS8+fPR+fOnfHhhx8CAD7//HPs2rUL3333HRYuXGi3fwUFBSgwc8NlZ2cX9vKfCby9aegQCx+Ki9tvs55GraYC6fx5Kshu36aDM1tHo1ZbCgGNxgN37zoObZLLqVEXGEhnUpkhXKMGFWTuYu7ltX6N4uK2WlxHejo15JlBfOcOFbTm18AGRrreV4bkZCqMLl60PbdUSo3SwEBaP7BOHZqtsk4dqhRkZQE//HAI/v7NceMGFZ6PHtHwqtxc07nMhaYtgeK5wsJgYbyxQY2F2LBZTFtB49oLygSMdQg2AOTnF0AQFGYh2PbXFrF+2DfoTQkf2ODLroOtuWaDtrnxafJE0jU5jmYvzYWJY2PYX/TMW6/BMTck5XIDFAoBnp4ScR2XIABpaekICgoSZ9qZkDP3lKpUdF9W7stayNGwLVvc9wQ7nk12JtzM16T9m8kzOJwnCdcfnj72HOlxcbssZC8zhM+dMxnC9+5R2coMYebQpLJXKsreS5dsz2m+Rjg0lOYYYaHRtWu7HzUG0DasZ7PpNWy3uAa9nuoOFy6wZWGmCLP0dFO0F5NPVF7KxfXHhw5Zts/WC/v5meodsxnt+vXptgULdiM0tB2uXaMRbQ8eUH0lK4vKWXPHPYtis3OFEAQ6/gcEmCLZmA7BZI95FJSljHWtPzC5bh1pxdpXq7UQBLmNccowl3/2c1Z4iudhMo7NyJrLNpZrw54OodfTjN3O1hUT4mxCwk9cH2rdF/OoMw8PA7y9BSiVVH9QKk3OBaZDsD5a6xDe3rRdb297M8mAM/3B9bpohg+Skiy32DOQzZeVMae6QgEUPyHsf5sSXZMbHx+PR48eoX379uI2Pz8/NG7cGEeOHEH//v1x5MgR+Pv7iwIKANq3bw+JRIJjx46hd+/eOHLkCFq1amUxPd6pUyfMnj0bGRkZCAgIwJEjR/D+++9bnL9Tp07YtGmTw/7NnDkT06ZNs9m+bt06eHl5Fema79+/jziW97+IFLcNZ8f7+gKNGtGPNXq9BPfu+SAhIRBXr0qQlxeBtDQvZGUpoVbLodVKxbAXrdaUKOvGDXtn6o9Bg/Tw8tLB3z8fISF5iIjIRlRUBipWTEVwsOtFNo6uIyiI1h1u2tT2GKMRSEnxQmJiAC5fFpCbWwYpKSpkZHgiL08BjUYGo1EAWxvJZrUTEmhiHUsI5PKG8PHJQ2CgGqVK5aBly3RUrJiGqKh0SCTAw4c+uHMnAHfv+iEpyRupqd7IylIiL09uc668PDoY2q4xIpDk50EiIZBJCKRSI2QyA6RSAkEgMBiMMBo9oNdLYDRKYDQKj9sFzA0ew+OPLQSADhIJgVRK4CExQuanhExmhFRKrV29XgK9Xgq9XgKDQRDPwwZeo5Fg5cqvsHHjz0hKuovAwDD06fMm3nhjIgDg228/wr59G5GUdA9BQeF48cUBGDHiUygUUkilBIsWTcWePX9g4MBh+O67L5GZmY4OHTpi1qxv4ekZgHnzZmLbtmUAgIYN6TX9/PNuGI3AW2+1w5496fDxofGy166dxWuv1cXmzfGIiIjCxo1LMW/eWEyduhzff/8xEhOvY8OGmwgOLoUffpiInTvjkJOTiQoVamD06Jlo2LA1JBICiYRgy5Zl+P77acjISEOrVu3QqFETCAJBWJj9xTtGYyoAIC/vJN5++01cuHAOkZHlMWXK12jQoBUMBgGdOlVH374jMGjQB4/voYArV87i1VfrY8OG6yhbtqLFc7t58yIGDKiFHTuSEBAQgtTUdHToEIz27fvhiy/WAAB++WU6jhzZjsWLDwIIwK5dF7BgwXicPfsPPD1VaNKkAz74YB4CAoIBUIUtOZngzBn6fLVa4N49+jch/khMdB4+QIgRKSkE/fppcPcufQlCQ3MxbdrfUDubkudwSoj/R/0BeLLyv7htKJXUCVynju0xRiOQluaFO3f8cfmyBLm5ZZCcrEJmpidyc21lr/kaYdtoLAKJpB+GDdPC17cAgYF5CA/PQblymShfPg1RUVmQyZyPYY6uQSKhzuyqVe0fl5srw927AUhM9MeNGxLk5YUjLc0L2dlK5Od7QKeTwHq98KNHdAJh2zbLa5BK28DLSwN//3yEhuYhOjoL7dunoVKlFPj5aaFWyxAfH4C7d/3x4IEvkpJ8kJHhiexsxeNzScVzaTR0tlins3UES/JzHxs5BHKJETIZ1SEkEvI4H4bssWyXWMl2kxwyPv7YX5FEIAgFdOmTlEDqq3x8DiMEgcBoFMz0B6ZDmPQUqj/McVt/6NyZ6Q8ySKUECxdOw549m/Haa8Pw/fcm/eGrrxbA0zMAc+bY6g+LFv0No1HAm28y/cEfgGCjP2za5L7+MGrUTDRs2MZKf5iKzMw0tGzZXtQfgoIyIJHYvp9Mf1CrT+Dtt0fgwoXziIwsj2nT5on6Q4cONdCv33BRfzAaBVy9eg4DBpjrD6ZnZ60/ZGW51h927y66/gDApQ5hT38AgA8+2I+IiKdbNLdEjdxHj6uQh4WFWWwPCwsTv3v06BFCQ0MtOyGTITAw0GKf6OhomzbYdwEBAXj06JHT89hjwoQJFoItOzsbZcuWRd++fYuc2jouLs5hZuN/q42S64OtJZyeTgXS2bPUG8rKFqWlUYFl8oQK0OlkyMqSISvLE3fuWC7UYV5Qf386ixoVRdcF160LNGlCQ5xK5j60tNmu1VKBdPo09U6zNT/JyRBDmx73ElqtHGlpcqSlqXDjRggOHCgvtiOXUy9uqVLUC92hAw3nbtHCtC5pxYo41KwZi1On6L3y9GQJqcw9rgLqtfIp8nUWhZMnbD2K5rPBzIPp6Um9xh98MBarV6/A1Klfo06dFrh//yFu3LgKDw96HV5ePpg8eSlCQiJw8+YFzJgxHF5evnj99fGPvbEeuHPnNjZu3IyvvtqKvLxsTJ8+FO+//zFmzlyFV1+dgBs3bkKtzsbXXy+BXA5ERATizJnDAIC6dQXo9elQqQJFB0FICHV4eHoCGo0aK1fOxuTJi+HrG4SgoFB8+eVIxMdfxowZaxASEoG9ezdi1KguiIu7gHLlYnDx4jFMnvwm3n13Jlq37oUjR7Zj/vwpIERAfDwNxTFfXyyRUE8uAEydOg1Tp36DatWqYdGieXjrrb64dSseISFBeOutoVi1ajnmzv1IvLdLly5Dq1at0KpVIIKCBNHzrtUCkZHVERAQhFu39qNjx5dx/Pg/8PcPwpkz+x+vRQJOn96P+vXbAABycrLwzjvt0LPnMLz//tcoKMjHt99+hI8+6o8ff9wDgHmGBRgMJq+t0Wj5t3MkIERATo4SWVlsFt8TsbGxyM7OxrBhw9x+1zicovD/qD8Az5L8fzKyl+UYuXTJFFHGZoTz89lMlgCjUQq1Wgq1Wo5Hj3xw+bJlaneWqDIkhM4Ex8TQsOKGDem/69aV1H2wjfVOTKR60LlzEGdpHz40zWrTWTsBBoMUOTlS5OQocfduAE6dKiO2IZHQyKHgYBoCXbkyTS7WogWdyWaRWN9/vxFlyvTG2bP0PrEZUPOor2dBfzBfVsX0HE9Peo0ffzwWq1atwGefWeoPCgV1/NvTH1Qqqj/QWVIp7ty5jU2bLPWHMWMmYMaMVejf/xNcu3YLanU25s6l+kN4eCDOnTPpDwZDOry9g8Q11OHh9N3x8gIKCqj+MGWKc/1h9GhL/WHKlBEW+sOCBVR/uHPHlMDCfAlTamoGAGDy5M8wZYpJfxgxoh+uXYtHeDjTH1ZgzpyPxTaY/tC7dwzS0tIQGBgEvZ7+nsqXr47AwCDcvr0fnTq9jBMnTPoDTVxmrT9kPhX9AQDq1++MFi2erv5Qokbus45CoYBCoXja3fhPERhIjbkOHRzvk5wMfPXVfvj4tMaVK1SQPXhABYBabQrNYF7Qhw+pwWmOIAByeV9MmEAN4GrVqPBq04auCS4OcrnjbNeMxESauGvlysswGqshIYF6a7OyLNeKpKTQz/nzwMaNpuMlEhqy4uXVTfR8t2pFBVqFCqZ1Cs7Da54cMpltWBNLAqXVWnqK8/JysHjxTxg//ju0bj0IHh5AqVIV0KJFC9EI/uGHSQDotbRoEYXMzHHYuHEN3n2XGrmCQKDVajBt2nKEhpYGAIwb9y3ee68rxoyZi+DgcEilnhCEAhiN4dBo6Htz8ybtw9mzgK+vv7gWCqD99/Sk0Ql6vQ5LlvyAOnVqAwASExOxdesSxMcnIjAwAgUFQK1a7+DUqe3YtWsJxo79AuvWzUfz5p3xxhvjYTQCUVGVcP78YRw5sl28duukVlqtBwDgpZdGol69lwAAb7/9I7Zv347PP/8Fr78+HvXqDcbkyZOxcuVx1K7dCITosGLFanzyyRxkZHjBaDQlIvHxAXx9BbRp0woXL+7DW2+9jBs39mHYsCFYvHgxlMqrqFChAi5ePIzp08ejXj1gwoQ5qFu3LmbN+kIMlYqO/hXNm5dFVtZ1lC9fCR4etH22bIYpVEFBNMzS1bhnMNDfZu/erCxJ8X93HM7zBNcfCo87OUY0Gmo8Llx4AgpFQ9y4QZ3QKSmWy4R0Oup0T0+nhuauXZbtSKV0XXDp0jSMuHZtGtLcpIn9/BiFoVw5+rGX8Rqg/Tx5Eli8+Awkkrq4ccM0GZCfb5IpbDY7Ph44cMCyDaWS6lteXi3RsiXVf/r1o8eWL2/SIdxN5lSSWBvZgGWyRY0GYrmfvLwcLFr0Ez788Du0aDEIUikQHFwBjRq1gFJJ9Yf58ydBLmfJtqKQnT0OGzaswejR46HVUuNZq9Xgs8+WIyTEvv4gk3lCIimAIIRDp6PvTHw87cPZs4CPTwBkMqrHAbSPLGxep9Nh0aIf0KCBpf5w504igoKo/tCs2TicPPkndu9egvfe+wK//071h6FDHesPgOXyMJ2OmlgvvzwSDRpQ/eHdd3/Ezp3bMXMm1R/q1qX6w4oVx1GrViMYjVR/mDBhDhITAb3eUwxfp9nEBbRu3QoXLuzDm2++jOvXTfqDQmGpP9SvD0yY8BXq1q2Lr776QtT3CqM/AK51CHv6A0BD+p82JWrkhj8uqpiUlIRSpUqJ25OSklDncbxLeHg4kq3Slen1eqSnp4vHh4eHI8kqCJ397WqfcF7Y8V8nNBSoV++Bw6QRjx4Bx46ZMj7evm1a68oEACFAQYEMd+7Qtbf79wM//kiPFwT6gwsNpYp39eo0/LptW7o+uCRgQsxgOIfY2GoW32k0wPHjprqFTIClp5vCkI1GFgrtg+3bge3bgbg4YOFC0+Aql5tmSpGSC6XSNokAIUBSUhYEwU9MgKXV2l+76whBIJDJBDHLo5cXUDGU3kN2PWo17Ttr33x9cELCFWi1BWjQoJ0oxPLzLdcP7dy5FmvXLsD9+7egVufCYNDDx8cXfn70fL6+GpQrVw5dupSG0Uhn/IODm2LMGCMyM68hOjpcTKLAsi5b3wejUSJmowTo/WZJwzw85NDpauHkSXr8oUMXYDAYUKVKJYt2tNoClCoVhMqVgQcPrqB3794WoXdduzbF8ePbUa0a7aN1cquUFKpR1KnTVCwZIZPJUK1aA8TH0+KUwcERaN68KzZu/BVVqjTCnj1/QKstQJMmfZGZ6YnMTNtnVL58a2zc+DMuXAB27NiPceO+QIMG17F58z7Urp0OnU6HJk2aQyIBrl69gH/+2YsKFbxt2tFqbyE6upK4VowZpjIZFVDR0UBaWi6Cgpwr5xoNveaZM5+9xBGc/w+4/vD/iVIJNG4M3L59E7GxDW2+12io7nD8OJ0VZvI3NZUaVqYkWaZ1wWfOAGvXmtqQy+n61jJl6AxqvXp0BrV+fcuEQEXF25s65B8+vIrY2Lo238fHm8pGXrliv/axRsNqjAbj5k1gyRKau2ThQip/2RpLT09AlZgLX1/7fU9OzoBMFiDKd1amqTDZ/SUSI2Qy0xrV0sHUQcui0vLyqA7B2mc6Ck3wSfWHhg3bWazbNS8raU9/8Pb2FdchBwUBZcqUwYsvUgNXpwPCwqj+kJNzDRUrhosZl+058FmFCFYRA6B5XFjFDw8POQih+oMgmPSHmJhKFjoZ1R9CUL48cP8+1R9q1zZ9z/SHmjUt9QemQ6Sm0mDwunWbipVArPWHkBCqP2za9CuqVjXpD02b9n3s4PdCerrl84mObo1Nm37G2bPA9u378d57X6B+/evYsGEfatak+kP9+nTR+pUrF/HPP3sRFVU0/QFwrUM40x+eduqCEjVyo6OjER4ejt27d4tCKTs7G8eOHcPbb78NAGjatCkyMzNx6tQp1H+csWjPnj0wGo1o3LixuM/EiROh0+ng4UFnUnbt2oXKlSsj4HFdi6ZNm2L37t0YO3aseP5du3ahqb2Fm5ynSng4TSfuKKX4vXvA4cPA8uWXoddXw+3bVADk5poSHrBshbdvA7t3m45lM6hhYYCXV2tcvkzTmLdoUTLCC6A/2lat6Mcao5EK3YMHqQDbt+8R8vLCLdLOm3s81WqWYl0l9p+FC3t60muR+hYgJERlty9aLb0PeXnOBZgBQAGAPA2QpgHweJBkJQKY506lovdOpTKFS0kkNGlEeDibCTQZfQYDcP78EUye/CpGjJiGJk06wdvbDzt3rsGqVXPFRFFZWZ4oKKD3hCV4YGFDnp7UoeDvT/9mXn5CgMxMyeNzE0gkGkgkSshkVFCwzI0AoFB4QhDY+h8gJycXUqkUy5adgtQqJaWnpzdOnqT3/sEDOmvAEjSkptLz5uXR++HtTfvKyMuj7umYGMvZCD8/KvQrVqTXNXjwMIwZMxBTpnyNP/9cgk6dXoG3txcMBpqd0Vq5qFevDebOHYsbN27g1q3LqFixBWrUuIrdu/chMTEDVao0wLVrdJ1faqoWLVt2x7hxs8WkEmxmODq6FDic5wGuP3DsoVQ6zskB0HH95Elg4cIzAOri+nWTE5olRdJqqU6RlETDjh8n4hbbDwykMsnTsyG0Whq5VlIOdIAaC9HRwGuv2X6Xm0sN4KNHqWw6cSITarX/4/KQFDoJQD/UaFABSaaQYeZAV6kAo6cOgeH29Qcm63JzTU5uJtvNHc1GAHoAGj2QlQvALMsx01nYOYMfG8Bstlwmo/pD6dJ0YoIZf0wHOnfOsf7A/E7JyYBOJ8GpU6YkUcxYpTPD9JyEWK4Xz8mRPD43gSDkQyLxhFyue9wvk45jrj9Q/ZLqD8uX29cfTp826Q9nzpjCtFNS6H1LTzfpU0FBJj0l93F66IoV6dI8hr8/jUirUoXenzfeGIbRowfis8++xl9/LUHnzq/Ax8fLYUmo+vXbYN68sbh9+wZu376MKlVaoGbNq9i3bx/u36f6w+3bVH9ISaH6w9ixs22SW0ZFlXoqkQH/JoU2A3Jzc3GTxRSCJos4e/YsAgMDUa5cOYwdOxbTp09HTEyMWAIgIiJCrIVXtWpVdO7cGcOHD8fChQuh0+kwcuRI9O/fHxGPR5UBAwZg2rRpGDp0KD766CNcvHgR8+fPx9dffy2ed8yYMWjdujXmzp2Lrl27Ys2aNTh58iR+/vnnYt4Szr9NmTI0LMfeLOqDB3RWl3lx4+PpAJibawoBYsmkgAicOwdMn06PZbV1K1emM7/t29PQpZIyfgE6aFauTD9DhwJxcXvFdUFqNc0KHRRkqrHLPJ6mBE+WwosO8rRmoUxGjV8vL2p8sVI0gYG2tQkBk4c1JUUNwMvCw8rORzNh2k9mYarBFwOl0hP//LMbb745zMIABoB9+w6jXLlITJ06Efn5tO+rVt0R22DnSkpKRFLSA4SERECnA44cOQqJRAKFojIuXQKysuRITzfgzBnTwEtICAAgO/shoqNLITRUiX37zgKgM/hRUTRbplRKBQSb7W7cuC4MBgPy8pJRp05Lu/WGo6Or4uLFYxb1ko8fPwqjETb1mdkM84MHdK3dH38cRdmyrR7XPdTj1KlTePfdkaKh/vrrXTBpkgp///0jDh3ajgMHDqBOHVN2RsD0Dmg0QHh4Tfj5BWDFiumoUqUO/P290ahRGyxfPhs5ORniehoAqFy5HvbsWY+AgCjIzF5enY6+XwD9PaSm0lkCuRxiopekJECtVroUZCzT58qVlknSevTg9XI5JQfXHzgljZcXdUDfv287i6rVUt3h4EEawsoM4IwMkwxgM6h0FrUi9u6l26VSKmejomjoc8uWQOfO1HArSby9gU6d6AcA4uL+EnWIhw9pvwIDaX+Zw5kZpPYd6LQqAXMuM+PL19ekS3jbTuqJhnRODpCRoQEhStE4Nc8AbK6zsBBlBo3KovrD7t27MXToMISFUT2GwfSHL76YKM4Gr159xyaqKykpEcnJVH/Q64GjR6n+4OVVGTdvAjk5cqSlGXDqlMlprdFQ/SEl5SEqVCiNsDBP7NlzFgBdAhcVRaPxpFKaFVujofKuaVOqP+TnJ6N27ZZiVJt5CSGmP5jPTp84cRSE0HfKGqo/+AEAtmw5ilKlTPrDiROn8PbbI6FS0Wfx2mtd8MknKuzY8SMOHqT6A5sxtq4SUVAAlCtH9YdVq6ajatU6CAryRpMmbbBiha3+UKUK1R9CQiz1B8D03ufkUL36/Hn6zrDSo8zp4EqHcKQ/REfbnxz6Nym0un/y5Em0bdtW/JslYhg0aBCWLl2K8ePHIy8vDyNGjEBmZiZatGiB7du3izXuAGDVqlUYOXIk2rVrJxZzX7Bggfi9n58fdu7ciXfffRf169dHcHAwJk+eLKb/B4BmzZph9erVmDRpEj755BPExMRg06ZN/+kadxxbIiKA2FjYDYVOTAT27aMljy5dAi5cyEVurrfo8VOrIZYD+OsvgCXGVKlo8ihm/HboQMOlilLD1BmsDm1IiG0Ih15PBxbmUbUUXoKFMUoz39Hj2EysufHLwpakUvp/nS4fQUGW2T6NRsceXHMDmA5kSrz++kf4/PPxyMuTo3bt5sjMTEF8/CW88spQ+PjE4O7dRGzdugYtWzbE9u3bsG/fRkilNPyLEBqurFQqMXPmIIwfPwcZGdmYO3c0OnToh+BgGhIYERGFo0d34Nata/D3D4K3tx98fCoiLKwsJk2airffnoHExMP45pu5AOg6rIIC0+yrwUAN/+BgoEyZSnj11VcxderrmDt3LurWrYubN2/ixImTqFatFtq27YrRo0eje/fmWL9+Dlq37okDB3bYrKdhmEokUK/uypXfIzQ0BtHRVbF69ddIT89AgwZviOFOEokUXboMxscfT0BkZAzCw5viwQNAp1PAy8tUw48lTwHoutw//liFcePGoVYtoEaNWnjnnQKcOLEbU6a8jxo16LMaNuwNbNmyCFOnxmLQoPFQqQKRmHgTO3aswaRJiyGVSsX+5uWZ6lxmZNB1SoBKLNDujIwM6iAyN/h1OoDnnOKUFFx/4PybyOWW5RbNUatphYVDh+gMKq27q4VGIxdrqLMcHCdOAIsX0+PMQznr1gVat6YGKnN4liQBATTMtlQpSx2C5fZgOoQ9BzozxjQa2gYzxNhMLItcYsYvSw6qVAISSR6CgiyVFq2Wno8tdbI2uAH2f6o/fPHFeGg0VH/IyKD6Q9++Q+HlRfWHdevWoHnzhvj7723Yu3cjJBJTtNSWLYBCocSsWYPw0UdzkJmZjXnzqP4QEhIOQkz6Q3y8SX8ICKD6w9SpTH84KOoPV67Qvicl0XuUnU2vOzQUCA+n+sPkySb9ISUlBX/88QcaNWqCdu264r33RqNLl+bYuHEO2rTpiX37TPqDuXPf/F6w8j2rVn2PsDCT/pCRkYHGjd8Qs4wLghQvvkj1h3LlYhAS0hR371I9T6v1gFZrWb6R5fXYvJnqD9WrA1Wr1sJbb1H9YerU91G3Lr3eN98cjC1bFmHatFi88QbVHxISbmL79jWYOHGxOHPNjGitlvY9M5PpD4A7OoQ9/aFsWfvlPP9NCm3ktmnT5rF3wz6CIOCzzz7DZ5995nCfwMBAsXC7I2rVqoV//vnH6T59+/ZF3759nXe4ODx6BAwZQkeYyEigf39qGRWC8eNpeAPLXMvWZHp5UWPLywu4das82rWz7yG8cYMqmabC0Ja1xhQKID6+Hs6cMX2nVJqy5QYE0JnQS5cs66qyf1nmvjt3GuDgQcttrH5aVhawYYNtLS6ZjHoYAwKAu3ebYOdO2+Lg7P979lCvpHmNMmaYsX0yMtrh559N1+rhQe9XaCgdXHftMtUEs653JpUC3t4FqFDBGxKJaXY3N5f+0LVa0yCUl0cTHN28SUsATJlCt9OZzL54910qsIKD8djzxryEdKA0r0/GasuxPqjV3fDpp/T/pUsDEydSw5GF2rIBhGG+/oO1YTAYIQim+nDWa03sGb/mCEIg7twxGe3m5xAES8PW+nu2fejQSZBKZfjpp8lISXmA4OBS6NPnLajVQP36PRAb+x7ef38kdLoCNG/eFYMGfYpFi6bi5El6fHa2AqVLV0Tz5n3wxhtdkJ2djhYtuuHjj38QvbW9eg3HqVP7MGhQA6jVuVi4cC/q12+DGTPiMGvW2xgwoBaqVWuIt9+ejo8/7it6jnNyTGHi5owcuQSentMxatQHSE6+D3//YNSo0QTly3fDjRtAaGgTTJy4CD/9NAVffz0ZjRq1xxtvTMIvv3xuexNNd+Rx27OwbNksXL9+FmXKVMTcuVvg7x8s3jODAejadSgWL/4CXbsOMRMI9uvkAnRdjcGwCRERbR7fNwlq1WqFgwe3wcuruSgc5PLy+PnnQ/j224/w5psdH68TikTTpp0hccszYzmj7Qz2+2f4+bl1GIfjFv9X+gMAfPghtS7KlKEe1ZdfLtThx44BEyaYciuwf1Uqky5x7VolxMQ4Tqw4fDh19DKZal6nlP3/9u06uHDBtJ3pD6we+i+/WMp0phuwNhMTG+HAActzsH/lcmD9ehrSaV0P3NOTLpeRy4G7dxtj926TXmB+jgcPgL17TceztZjmukRWVjv89JOtnhP6OB/Frl1UcXekP3h4AOHh2QgLC4ZWS/WenBzT0iA2k6XXm0Kfjx415Q4BAImkH0aMoI5MFlJrrudcvkwNRGsdwlyvUau7YupU+v8yZYCPP6byhclNrdYks61zetDs/LRGvT39AbCcic3Ksp2FpG3SaDJWbcD8O+YAsNzfBCGO9YeCAqBhQ6o/fPihY/3h0SOgdOmKaNasDwYPNukPH330g3i+wuoPLHpNrYbd6C1H+kP16v1w+TIQFGTSH+bOtdQfHA9prvUHds+6dRuKX375At26DbHK4+ELe0nfnekPnp7NceYM3U8qrSjqD0OHFkV/YNfhWodgv23WbEiIm80/QQTiTOI852RnZ8PPzw9ZWVn2SwD89RfQpYvltg4dgDVrxHhRV6n3IyPpjKM7eHnRrIBt29K1G/XqAX/+CXTt6u4VcZ4lIiM1WLgwHsHB0QD+v7L5/PzzVOzbtwmrV5992l351zhz5h+88047bN16F0FBYa4PeKbQIDU1Hm+9FY1795TIzTXNHLgcJzmc/0Pc+l2wqThG2bLU4mtoSrDkTIeYPx8wWzbsFA8Pmsn/hReo/sDWsFp3gfPf4f9Vh+D6w39Xf0hMVOKNN0yRD09bf/i/KiFUaFQqmrs9K8tUS2bXLuoaHD4c+PZbl020bk3XhLBsa+zD1jkYDEBBgRGESKBW03UjZ88CX39NPSIhIXQ2RaWiSic7xmAwtaHRaCGVysXU5WytKvO4uXJj0FlGAxQKKWQy0/HsX7b+oCiYr7MgxH5Yhwn3Z5yezPElwbPQB86/iVZbgIyMFPz881S0a9f3PyigLDH30nM4nGJQuzZdZ8GmBO/epTO6bdrQ9PsusjnHxABVq1pmbLXWH7RaAwwGKXQ64OpV+vnhByrXy5ShugOb+WV6g/nxRqNrHcIZNJrJALlcKibtM2+DrfUs6nQKmxUyGp+0/lBSbXA47vO86Q80gejT7oUJbuQ6o1Ur4NYt+n+9Hhg9Gvj5ZzrqL1wILF+OSn36AK+84nBB5/Llrk+zatVaREXFYvVqWjvt5k1TeMzDh3QflmkvKIgWPn/xRWDgQCoj4+LWO/QEG410Mfm2bXT9yZUrNPTHvFYrFWJSMRxHqaShtrVr00zFvXo5zjSYnAzs3An8+utNZGZWRHw87av5Ok/r0JbgYJptrnFjoGNHeg65HIiLW2NzHbm5tBzP7t00g2J8PI39t19OR4AgUMdAjRrUo92/P/VuO4IV2M7OBv7+G/jpp3ikpkYjMdE2MZMzBIGGXQUGJmHAgHD070+v8949OpvPEi+YhxCxNbBZWaaQKJYMw9E5WOF1b28aUm3dLgBkZGTAzy8AarVpDQ3LwuxK0WDrdQSBZiY1Gk2KlTtKiimETS86afR6ywzN7io7gkAglQo2GYXpmiHTdVvDtmVnZ8PX19ci6Zd5lsfClGWy7ZspxGzHjjhMnjwU1arVwfTpy1GunCmEKy8vFyqVZZYPR9dvMFg6wpgSqtNRJ5i7mIffyeUAIWp4e3s5PYadj4VGFreuJIfDAU0awVi7FhgxggqbffuoUO3eHfJu3Rwe3qWLbTCZNXFxv6FZs1j88guN/Lp61bQu3zokMyCAGs1MNrI6ls50iNRUqj/s2UMd8Hfu0EswT54ISJGfT0NxpVI6D1CtGl0L26uXZQZcc/R6Wllh1y5g48ZHyMwMf5xZ17SPdW1WlYpmQq5dm0a99ejB9CBb/cFopBn+//zTpP88emTK9G8PLy+63rZRI6B7dxpJ52g8NK81f+ECMHv2JWRlVcfVq/S+FUa+sDmV4OAb+PDDGLRtS+/ngwf0epVKS92BnT83l8r5/Hy2HMqxoc7yhNAyf/RjLkvZJzMzAypVgEW4Nlvz6wy2dEsi0UGh8BD1Pyb7XcHC0OkyMYKQEJOTxF4SLOdQ/YHJQra8z9PT9Dyd6Q8A1SG8vX1tygOZO4mK47yRSIDt2+MwZcpQVK1aB1OnLkfp0pbJUd3VIczvtbmew/SHwvSTPQe2bMFgyIOXl/2M3YBJfxg/nt7rZylJPQ9XLuw0eno6zYK0c6dpm5cX8OqrwJw5dNQoJPbCle7dA1asoAbehQuWBZbN8fQEQkIy0KNHAF55BWjWzL0ESmo1FSx//80SNxUgL0/h8Icgl9OlyTVqUMHSp4+phpb1NRiNtM2tW6lguXoVNoLLGpUKCAzMRJcu/ujdm0aFO7uOixdp+4cO0XUu1HC3P7jLZNRor1uXZkfs29d+dmLr68jOpgkQtm+nZQfu3LHMHOeKmBgNfvghHpGR0QgKUsLf371n8+hRJgjxR06OyfB19itl67B9fKjhm5+fJmb0tYYVpM/OpsLLXeOXDXjUADat62GDmyusSyUx45xlNyysAGNGJjPk2Fo1b2/atiAAaWmO74M1tCg9LGoLUmFhACHSIhnDbIZDoZBa9FGlon22XsvkCHYdLHkYU2jYu+Fa0BIIggC53PL+WF6/Bg8exOPo0Wjk5ZnC4mJigM6debgyh2NNkfQHoxF47z26kPOxQCSCAKFHDxrfFxzsogFb7OkPjx5R/eHPP6mT27rOJkMmo3I9PDwRo0eXw8svu1cn22ikBu9ff1EZf+xYLrKzvR1GfEkk1EFfqRLVUbp1owawuTw0v44HD4A//qDrcM+doxPg5nVWraEJ/fLQrJkKnTpBdDI7IjvbZLifPk2d55mZjsdRX186FjZrRpdVW/fd3jUA9N5v2EDzoly+XHjDt04dDebNi0eFCtEIDVW69WxSU9MhkQRayHhn55RIqDxiGZj9/YHMTMey03y9MpPd7sz2M1nNKvSYG2Ou9A/zUklMjkqlJrldNCe6ySkv1h9+nO2Y9dFdHYIlAjNPykUzIZv0h8JaWqZqFwYolVKLOsleXpYZq51hfg2E0P7l5Zn0HaYDOn8vCWQyQZxgsX73HekPADB0aDZKlXp6+gM3couqvF28CMTGgly8aDKrBIGOft98Y1lU0wWu1vUC9Ae0fj2wcSMNf37wwP7AIpHQGcU6dagHeMAAxwaddR9eeSUWJ04AmzdT7+rVq6Y6YPaQyUwe24CA85g5s5bTWdPCCC5BoP2uXp0a1bGxrnN+LVq0HoS8hB07qOC6f9+xYe3pSWdYGzemArdbNzp4unoW2dnApk3Ajh3U8E1MdGz42ltPI5XS8/j60uvz9LQ9zt7AWlBAHR1MsDg3fAk8PATR8A0MdK24sBTwbOZXozGKmQEdIQiWHlKJhA6WtEyOe95Da+HFwur0eiA9PR+Ap4UxXRgBJpGYDEwmGKzr4LrC/FkYDLaGsLnHtLCGMLt2c0Pd/B4wQ9QdQctKBuXlmWbu3XVAUExrau7cMb0skZHA+fPcyOVwrCmW/qBWA2+/DaxaZfqRCgJd3/TDD3S61U3c1R+2bqWy6/hxKrcczWYGBNDyKp0702ixMmXc78O9e1RP2bOHOucfPHB8HkGgS7EqVKBJswIC9mLGjLYOHcFaLY3o2r6dOtFv3qQlcxyNu0olLRnTpAmd7XU2IwvQdiZP/hs5Oe1x6BBt37xWrXXfWcRY+/bUqI6Odv0s2Mzyxo3AP/9AnPF1JNesdQgmc1Uqeu8CAkwGGcOevGDO7cxMk+HrTDYIghFKpcTC8HXmoGf1d03JPmkIvTtL5ZjsY7OXJiezbX1Ye5g70JmMl0iA1NQ8SCQqcRa6sDKazkhTA9PcCPbycr8Sh/Wz0Gohlk1i/TJfflAUHcK87q25I50ZwYU11JkeyPRM9/pkX38AgIsXs1GjBjdynwolsSD6j7lz0Z1NiZqPGpGRwKRJbtXfcEdI2ePYMSojN2/OQGpqgMPwWi8vGh7cqhUczvY668PFi1Q4HjhAvZFJSY5DT+RymlujUSOgZ0/6cWZgMcH155/A1q3pSEsLtKm9xmAzsvXqUQP+5Zct0/bbu4b4eBoltns3vY7kZMc/Wl9fIDQ0Fa+9FozBg+kjdIfMTOoY2L4d2Ls3B+npPtDp3EsawYSWlxcVWoGBzr2o5mi1loaveQZpe7Dz+PhQ4ejME8gGRo2GCnpWesgdw4mG8Rrg5SUVBYJe77jsgCMEgRrr5oXuvb1NIVrM2Cx8GJMpHIcJR2ZcqlSWoUKFmQ0G2Po2kyDLySkAIQrxvhVGiJlCv/Tw9paJio073nxzHj3KhMHgL74n9kOdqJAaM4YmjmDUqQNs386NXA7HmhJJqJKbi9tduqD8kSOWQrVKFWDyZPu186woqv6QmAisXk1l47FjGuTk2B9YFAoqC5s0oaHH9oxFZ31ITaX6w99/09lfZ45htpypRg0azTVggGs5fOUKdZ6vWvUAKSkRSEpyPM76+1NneevWVBeynouwvg6tls5W//EH1bfi4x075j08gMDAHHTq5INXXqFOAneMIaORtr1xI9WxLlzQQq2mN9gdHUIisXScO4vkMocQKkczM01liJzJUBbqzOSQn5/j6zOPPmIVEZhx7SoyjRluEokOvr4e4rtmHvXlnsObzj5aG8BKpSnLclFngQFTxmxmXLJZzsI6qK1hNY+ZDpGXpwMhHqKRXhgdgoZEU0PdPNrNvI+uMBiAe/dyodd7Iy/Pvu7vSH8AgDNnslGlCjdynwolIaTEQVGtpkbtL79QVwhDpQJefx348kv7FbhRdCFlfXx6Ok38vG0bLVvkaLC3N9u7Y0fh+hAfTwflvXupx/bePYNYU9QaPz8qWNq0oTLb0focdh1qNQ3x2bqVemzv3XOc+MrLi3qCmzUDAgL2YMaMF5wKFqORzsCuW0cFyrVrjhfJK5U0RKldO2DQIMf9tncNV64AW7Zo0KhRPPz8omE0um+ZCIIRXl4S+PjQEC97s72O0OmA+/fzoNeroFY7FyjmBravLzV82Syns8GZEPq6W68ldjX4Mo+rebiNXm+75sedEck6/Fmlosa7IJhqxWZlFd3AZEawIOigUnmIfbY2gl1h7z7qdKYZV/PQ6MJ4ms094KzWoaP3xB1Bq9FoEB8fj+joaIuapMDTz47I4TyLlNTvIi4uDrG9ewMffURDls291SoVnSb84gv7NQZRfP2BtdG3byw2b6YzsUeP0kgrR85stra3TRu6/OfKlcL1ITeX6iksIio+HsjJsb/cSC6nhm7DhtRp3qOHfUef+X04eRL47TcaJnz9umMZL5XS9bx16lCjVKFYh+HDnZeVyswEfv+dGr+nTtGIMXv3SRBo2w0bUufAK69QGeKKuLg49O5Nn8XevRq89FI8AgIKk12ZQKEQoFLR5+Tv775Bw+rHJiVpoNcrXc74MgPb25uey9vbvaVCLBcJixxjjmpnMONXLjfJYkFwXrfXWVvW4c/e3vRfVqpQrQZycy0NzMJEkjEHtaenTIzSYuVD3X0ewJPTIczrJTPHhaO+uXqez7L+wI3ckjJyzVm1Cpg6lca7MCQSOpX61Vc2Be1Kysi1xmikYce//WZKaOXIC6lQ6FCrlocYohQTU/g+1KsXizVrqGf40iXH64CkUppzo149mkCLrZF1dh/u3KHJKP/+mxrVKSn2BxyJhM72NmtGhUr37q6NEr2ezsJu2AD89Vc20tJ87YY5y2Q0/KlFC1qioW1b5zPi5j98Dw8lMjKogGSDursIAsT1lIGB1KBxNkhaD0jmoc5qtXOPJTuXTFaA4GAFAgLcN+rM1/tmZelgMHi4nezKPFzZx4f2MTU1H4R4Fjr0loUASyRa+PjIxTY9POi9sBYOLNy4KIkZrGeCzdfzAEXz5BoMtH9s3Uxurh4Gg8ztTOnmxm9gIJCdzY1cDqekKVEjl8k9o5GGK3/1lW3twZgYYNQoGuZsNiiXlJFrr42LF6nc3buXzpg6MhYlEiMiIyVo0YLa5O7OYpqzZMk6SKV98ccf1Ei9d8+x4ePnRye727al+kO9es7vg1ZLjerNm+msaUKCZfJNEwS+vgJq1qRLmAYPdpkAGwCt2R4XB6xalYykpFCHYc6BgTQ3SK9edO7D3mvjSIcwGJRIT4eYq6MwTluWsNLPjzrOXS3ZsV7HmZ3tfqgzrZWqQ0CAB/z9qVx0F43GNLOcl6eH0ShzK9kVyxnCItU8PICUlDwAluHK7sh48/YEQQN/f6VoAAOWs8DmIcdFMYLNZ4LNZ5rNKYoOodOxewhkZ2thNMrd7qNUSq+dRfx5e3Mj9z/LEzNyGZcu0SJ3e/ZYjkg+PnR68OOPgcaNn5iRaw822/vnn3S299Ej+4Ml8542bUqTTHXt6tzYsdcHvZ6eZ+NG6hl2LFjoABwcnIG+fQPwxhumrI+OMBqBgwep1/ngQSpk7HmCmTe1aVNq9Pbp4951nD8PLFtGjeobN+yHV0kkNDS7VSuad6xDB2DtWvsCyvqHD9BBiAktV8khrDE3ev38LI1edwbF/HyT4esqq7N5mFJAgGsj27oPbL2oeciSe8avEQqFRJypZeFRzFhnzgL3143AItMiCy/y9oZYOqugwDIUOj/fJGiLIsAkEi38/EyGtvX6KXcwv5c6nWX4OFM4nId/GeHrK0F4OO2DPZ5lIcXhPIs8ESPXnIsXgfffpxamubUnkdBFs8OGASNGIG6948zIxe6DFVotDd3dvJnKdEdrewWBTjzXqUONRXdyg9jrw5Ur1HjcvZv+31ECTokE8PNTo21bL/TrB/Tu7TpD/KNHdCnT9u00R4ijyDeVis5ad+4MvPGGZcJNR9eQnU31rE2b6GyvI6e8vz+9Rz17UoPa39+xkWs9LtO8FdQoZEuJ3EUiMRm9wcG298qVDmFu+ObmutZfWAQXM5zcWXJj3of8fNN6X3eXTbHlTkx38fGhsp46jk2O7sJEkJnPJlvPAAsCbYdFktEET3QmmGVidhfzLNOCoEFAgFJcD1yYWWDA9lkaDKaM3O6GkMtkBgQESBESYj8a4VnWH7iR6+eHxYsXw8udOBI73L9/H6VLl3a6j0SjQZ21axF94ADkVtaSTqHA3ago3O3eHQ/q1Cm8+9PNPjjCaASuXw/Gjh0hSEyMRnKyt4OwYwJv7wKUK5eJWrUeonnzBAQFmSxWd/uQnq7EkSNROH8+HImJ/sjOVtpNLiCTGRAWlovq1ZPQuvVtREc7kG5m3LiRglu3muHkyTK4cycAeXly2IY/Efj5aVChQhoaN05E06Z3IZOZRmdH15Ga6okDB8rj3LlSuHfPD/n5HjZtC4IRfn45qFs3BR06XEdMjBpRUVEoXbo05G7UZDEYJMjNlSMvTwadzgNGo2C3//a2SaVGKJV6eHtrIZPlQS4vRHalx2i1MuTlyaHRyKDVSh4nnrI3ohJIJAQeHgZ4euqhUmkhl1u63HU6WoLIGUajAI3GA/n5HtBqpdDpJDAY2PvvaCSn55bJjPDwMECp1MPTUwcPDwN0Oik0GhkKCmTQ6aTQ6QQYjZLH75czyUCHQNauTGaEXK6HUqmHVJoPudzkFdHrpY/vjxQ6nRR6Pe2z0Sg8FhKFO49CoYdCQc8lCPaHYnfupcEgQX6+h9g3vV4Kg8H2ugWBQKHQw89PAy8vUziBVqvF/fv3kZCQAJ2VtqRWqzFs2DBu5HI4ZpSE/gC4ITuNRsTs2oXKO3bA+9Eji180EQSkhobiTqdOuNW+PYyFWUtRmD44IT1diT//DMCtW9WQmOhvVzbSEFoDwsJyUKVKCpo0uYNKlVIt1B13+qDXS3D2bDhOnCiLmzeDkJrqDZ3Onpwi8PUtQPnyaWjU6C6aNbsDudy5J9RoBPbtk+LatQa4di0EqakqO8kXCeRyAyIislGr1kO0bXsTYWGm8HJH16DXA6dPl8GRI+Vw40YwMjK87Og9BF5eOpQq9QitWj1Cy5bx8PGRFkqHyM/3QHa2DDqdwuF9sbdNEOh1eXnp4ONTAKOxwKXMscZoFJCXJ0d+vgcKCqRmiafs6xBM/nl56aBSaW3knztyT6uViXJPp5NAr3cl78ljJ7RJ/jIdAoCoP1AdQgKDQTB7B5zLdprs0ggPD5NuIpXmw9PT9A4RIjxuX/pYR6E6hNEoOND3HJ2HiNfAdBVHOoQ79xGgzy8/3wNqtRwFBVSHsPf8mA7h758v3rdnWX/gRu6TnMm1x8mTwKxZtH6P+dpdgLq7atWisbBvveV2hpmSDle6do1GXP/9t/MQJaWSejVbtABCQ3fjs8/aFdpGZ0kXfvsNWL8+E8nJ/nY9wzIZPVebNjTMp0UL59cAUG/VqlU0DPnUKZqJ0R6hoTSC/OWXAZlsLQYOfMVlv1NT6Uzvli3UC2wvPKlSJQ1+/DEeMTHRiIhQuj2LZ560IS2Neq9ZmLF7UA8mW9PrzsyrvT4EBgYhL8804+sqTMl8fa8gZCA0NKBwJ30Mm61MS9PAYFC6tV4HsM3SDGQhLMwPgmBaZ8M8rKzNzZuXYt68sdi7N9Numw8exKNnz/JYs+YMatSoY5FgwjqVPkvPb+4l1mgMMBrdK0EkCED37lF4/fWxGD58rOgl1moLH67E+pObC9y7VwCNRmHz7ATB9LwMBg3u34/H4cOWJQCkUmDUKD6Ty+FY88Rncu2RmQnMnUunN2/ftpx+kUjolOOQIcC77xYqQ11JRpPl5tIZUhZ2/PCh89wgdevSSDGFYh2GDnW+HtYebEb2l18e4uHDUkhNtb9fUBBQvz4NFR440H6KFOv7cOgQsHIlLWt865b92VJPT5pzpHNnICxsM8aO7emyz0YjnUFetYpGot27Z/8e1aunwddfx6NaNVqG0B05bj5zl5dHdRUmv92PRqJ5QRzN9LrbB52O6g/Z2XCZI8R8fW9gIFBQUHS5l5MDJCfT5U7u5gwxD1M2LZtKR3BwoDhDax7lpdMBmzYtxdy5zvSHBPTsGY3Vq8+gevU64hpYNqttPTNrHmZsKkXkfp3bHj2i8OqrYzF06FhRTzEYiqeLpaUBKSm6xxMvlt9LJDT6QCazrz8AQMuW2XjhBT6T+1R4KkauOWfPArNnQ7NtG5TWKYUFgcYL9+oFfPCB0zz+T3JNDmAqX7RpE00E5SjRglRKu9y6NQ3rsWeIutOHGzeAX3+lSSmuXrUfKszOxdbHtmtnGSrs6Dri4mjSiJMnaaZlWwgiIwW8+CIwerT7lRzS04Hly4Eff0zGvXuhUKttMyPKZHTQCQlxbng6ChUyGOh5Cm/0UiHl40MFFksO4QxHfTAaqZ7F1hY7Fpym2qwsc7R1uYPBgwcjMzMTmzZtcqsPRQnVBSwTLLA1O8xAXbJkKd57byyuX88U19mYr99hQmrlyjOoXLmOTdvmCSzMMziqVLR9dg3mBrB5oi3rsO2MjBR4eqqgVJrPDNGi9kwB8Pd37xla30eNhiqcWVn23h3HJQCysriRy+FY81SMXHNyc4FvvkHOd9/BJznZciARBFpS4aWXaF1eB0mrit0HN443Gmkt3bVrXeUGIQgMFFC/PnU4v/aae4marPug0VAZv24dLZeUlGRfRgQEmEKFBw2yDRW2x9mzVDf5+29q9Nrm1iDw9qbXMGAAdci7W3d4927qOP/rrzxkZKhAiKUOIQi0Tq6/P32cjibnnIUa5+ZSoyU723U1BnPMw5uDglzXaHXWh4IC0zIt53oMzY7MnNbWcq+w+oN5mSP3E2ZSPcY8RJmVVVIqgaVLl2Ls2LFITMy0cG6zcGpX+gNgGZpsnkTTugSQea4OpntZ6xD29QeTnmK+btnX1/18K+Y6RFIS1UEtn5tj/eH117OxfPnT0x+KFtvCKRnq1AHi4rAxLg6xrVsDX39Npx3j4+lbm5BAa+5+8w11bb3wAl3j27z5v9pNpZKuOX31VdO2s2dp+YE9e+jMb24ugcEg4PZt6mBessQ0+/rCC8DQoTTLoDvExAAzZ9IPQNf9/PorXd97+TL9kRsMEM+1fDkdKAIDu+Off4AxY+zX1FUqqZN7yBD6t1ZLBeFvv5kLQwF37gALF9KPSkX7PXgwvX5Hg0JgIH00YWG7xezKq1ZZ7q/XmwxE1h8/Pyqw3CnsLZVSAzkkhP5tMFjO9DqbZdVq6b5sNpslFmBGr7vQ+2y5voqVMjL31gKCuMaVJb5KSLAsPF8YzzJAhXpwMP2Yo9EYkJMjQK2WWGRqpP47AUajqR9ZWdTQA+jAf/cuFXL5+fR+lC5tUh4IAYxGGm3BEmhYCxVCTImr1GrLNWM0rCgASUmmNcAymalUkTm5uVoQIodKFWInS6JgsdYnKcn0LNj6YpXKsbc9L08htqVSESiVBgiCDFlZtM/mQt7Dw3T97FlxOJxnEG9vYNIkbK1QAbHduwPz51NheOMGHZhu3KBRY7NmUaHRrh0VUo0b/6vdlEioymKutlhXgnj0iMre9HQa5LZrF/Dmm3Ssb9iQ5tOIjXVvPFIqqYH82mv0b72eqlVr19J1xA8f0tuTkUGXO+/dS2+Lnx9QtmwbSCQ0mZW9qLQ6dYAFC0x/nz9PC2rs3k1vt1YrIDeXZnXev58G5EVEAB070uXVNWo4vkcdOtBPXNwW9O0bi3XraAZn1g/mKM3Pp9cgldJXIDjY/QzKLAKJkZtrmul1ZvQajSb58+CByej196fnL0xks0IBlCpFP+y6cnLo87Bc3ytAr6fbcnNNck8mM5UBKsw6V0GwvX4A0OsNUKsF5OVJLBzQrA+EUJ2GZTQ2jwhMSKD7JSVRGRwWZmmInzxJQ/zYMzJPosnutdFIz6fV0utMSTG1TzM0+yMtjd5vhYLee+vr0Gqp/uDnFyLqKI70lLw8y3NYG9j23iOmQxBCw9pLl5aJs/TmeXas9QcWrfE0KfwCUM6TISKCZlS8dYu+Nd9/T0d39sakp1P3ZIsW9G1s1YoKtMJM55UgderQqkgnT9IB6scfN2DmTJrgif349Ho68P/0E62bK5fTmdFRo6hwcJdy5Wiy6uPH6SDw8CGtqNC8uSk7odEIpKZ648cfadZFlttr1SrnNX1ffZUm0Xj4kA6aQ4YcR7NmpnIseXk0VGnwYLp/hQrUiI6Pd97nqlVpRanSpWkSrfLlqRBlM5ks8VNCAr2uQ4eosE9IAPLyBFGgOPtoNHRQKlMGqFSJOgcCAwGjUYeCApNAtDcTXlBAhdvVqzSU++JF6kxg1SuMRiO+/PJLVKxYEQqFAuXKlcOMGTPE4z/66CNUqlQJXl5eqFKlPL777lNERelQuzYN/Y6L+xiDBtXBtm0/oVu3smjRwgsTJvRDdnYWNBpg5sypWLNmGbZs2QxBECAIAtas2YetW/dBEARkmsXInz17FoIgICEhAQD1nvr7+2PLli2oVq0avL0VyMtLRHh4AX75ZRy6dCmNVq1UePPNBnjwYB9CQ+n7IJcDW7cuRbdu5dCihRfGjeuN9PQ0EEIH/fh4Gnp+8iR14ly5AuTkUO9DWtpVDB3aDI0bKzFoUA3k5u5HlSpA6dIEL71UEWvXzoGHh0lAXLt2Fg0aCEhIuC0avw8e0HucmAi88cZg9OvXC1OnzkD16hFo2bIyMjKAdu2isHTpNzAagUmTBmDCBMvQeb1eh/btg7Ft2/LHxrkR8+fPRKNG0Shb1hPNm9fGr7/+jjt3aEbyDRv2ITraB6tX/4VWreqjdGkFtmw5iHv36O+WCVsmDJkw1+no9VuvqOBwOM8g3t7AxInM4wzMng3Urm3ysqakUKuySRNqITRoQI1fR2uRnjCBgcA771Aj98EDKqM/+GA/+valqhAbR1NTqaE3eDBV8MPC6MxrXJz7qo9MBvTrR6PRWBTapk00C3TZsiYjMisLuHixFPr3p2pXpUrAhx/aJrk2p1Yt6lu4eJHK1M8+24FBg6hMFgQ6rt6/T53+NWvSx9S2rWvVTSajRv3PP9M+Vq5MjUJW35RFBT16RM998CBd9nXtGpCVJXVLf8jLo22FhFD9pEoV2m9PT0CnMzjVH5jRe/8+lZlnztBSTamprJar+/pDhQrl8dVXnyIiQofq1Wmm7C1bpuL112th+3ZL/SE3Nwt6PTBv3lSsW7cMW7ea9IeVK/dhw4bC6w9KpQLp6YkIDCzAwoXj0KlTabRsqcK77zZGQsJWlClDZ/09PYE//zTpDx9+2BuZmVR/yMqi7/G1a1SfOn2aVgFJT6eK5N27VzFwYDPUr6/Ea69R/aFuXaBiRYKXX66I33+fA6XSpB9eu3YWDRsKSEi4CZ1O+risk2P9oXHjykhKAlq3jsIvv3wDnQ6YONG5/kCfoxGLFs1Ehw7RqFrVEy1a2OoPoaECNm/+x0J/uHOHXm9+vmP9oVUrU2XVpwmfyX0WkcupBHjnHfr3X39Ro/eff6jWqdHQ///zDzBkCLqEh9Nf1OjR7uW7fwL4+Wnx1ls0YTRAfwCLF9Nat5cumWb4rl6ln+++o5cZE0O9nMOHu3+u8HBgwgT6Aaj9v3Qp8OOPqbh3LxgaDRUAe/bQz+uvU+O0e3dqoJYrZ79dpRLo0OEWfv21EQBq7Hz3HfUqP3hAf8i3b1NP7oIF1HBq0oTOUjvy/AJUkJjPfup0rjzSLtJQusTWrXrypGMvLSFs/SgN4RYE4LvvvsTGjb9gzpyv0bZtCzx8+BBXr14Vj/Hx8cHSpUsRERGBCxcuYPjw4fDx8cH48eMB0MQOd+7cxIEDv2H79j+QlZWNoUOHYv78d/DFF6swaNA4xMdfQV5eNiZPXgIA8PMLxPnzhwFQJ0hAgJ+YBdAatVqN2bNnY/HixQgKCkJoaChGjhyJy5cvY82aNYiIiMDKlSvRr19nXLhwAZUrx+DYsWP4/POh+OKLmejQoRe2bduOOXOmiKE85l5hk9eTGrkffvghPvjgG3zwQTWsXj0P3bp1x9Gj8ShXLggjRryB1auX4KuvxgGgQv7XX5egSZNWqFq1LIxGUw07hiAAJ07shkrli++/3yVuY/8KAvDii6/io4/6Qq3OgUpF0yIfPboDGo0abdr0BgAsXToTf/21Eh9/vBBly8bgzJkDmDz5Nfj7h6B+/dbi+b7//mOMGTMHZcqUh49PgEOvPy3/gMfP0P4+HA7nGcbLCxg/nn6MRro49scfaT1Btjjz1Cn6mTABCAlB04oVqTC2Km/4byGRAPXrP8CcOfRvNvsaF0dnXx89otuTk2kejC1bqIM6PJzK4AED3Dd6JRJqKPd8vHTWaKQyftky4M8/85GV5QmjkTro58yhH39/WpZwxAiqRziS9RUrpuPTT+n/tVpqzK5YQeUvCzXdt49+hgyheknPnlQvcbIiDR4e1GFeujSV185zn/i7dyOcYjn4nznj/P4aDFQ1zc6mjvrvvvsKmzYtxowZX6NjxxZISiqc/iCRAImJt7F3L9UfsrOp/vDdd+/gyy9XYejQcUhIuILcXPv6w9mzgL+/PzIyYHeNtjv6w8aNG/Haay/jwoULiImh+sO0aUMxc+ZMdO3aC1u2bMesWVPEsovm641Z9JhGQxW9iROp/vD++9UQFzcPXbt2x7598ShTJghDhryB335bglmzxon9W7p0CZo2bYV69SoiN1cHo9HDov3i6g9t2z55/cHf3/H78q9C/o/JysoiAEhWVpb9HQwG+nHC6tWrnZ/ExfEu27Duw9WrhAwfTkjp0syBYvkJDCSkVy9Ctm83HVfMPrhxG1zeh5s3CfnkE0Lq1SPE09N+1+VyHWndmpBlywjR6Wz7YL3NUR+OHydk4EBCwsPtn8ffn5AuXQjZtMn2uhxdR04OIXPnElK/PiEKhW2bEgkhlSoR0r37JZKWRo/Jz88nly9fJvn5+WI7RiP92OvXk/wQQkhuLiEJCYScO0fIiROOP/v2ZRO5XEEmTlxETpwg5NQpQi5dIuT+fUIKCmj/DQbTtRBCyFdffUXq168vXueHH35IpFIpuXfvnrjtr7/+IhKJhDx8+JAYDIS8/vog0qVLT5KQQNs/dYqQhQv3EgBkz54MsT8rV54hAMgff8STixcJ+eqrJQQAOXnyrNiPhIQ7RCqVkvv374vnS01NJe3atSMTJkwghBASGxtLunTpYvFO9ev3CvHz8xPbUasJSUoi5PZt2qetW28RAGTkyFlif44c0ZHQ0DJk1KjZ5MQJQv788z6RSqVkxYpj5PJlQm7c0JKgoGCyePFSkpqaavd9GjRoEAkLCyP5+QXi78tgICQyMpLMm/c1MRoJ0Wp1JDg4mPzwww/ivY6NjSWvvPIKMRoJyc/XEC8vL3Lw4GFSUED7feMGIb17DyWdOsWSEydM93POnE3kzBlCHj40PTNz7L2rDJfjJIfzf4hbv4uSlv+FPd66D1evEjJqFCEVK1KhZS0oPD0JadWKkF9/NQncEuiDG7fBaRv5+VQv6NqVkJAQ+zJOEIykQgVC3nuPkDt3bNvQ6dzTY7KyCJk5k5C6dQmRy23PI5USUq0aIZMm0THX3Ws4c4aQ115zrJcEBBDSsychEyfuMrtuy3H5aeoPej293qtXqax2V384cYKQs2epbEpNNb1OzvSHKVOmONUfjEaqP3Tv3pM8ekTI9ev0HM70h82b48nZs4R88QXVH44cOSv2xZ7+QAghrVq1cqg/GI22+oNeT0hGBiF379L7tG3bTbf1h2XLjpHz5wm5dElLAgODycKFS4nRSOzqEEx/0Gjs6w8GQ+H0B4OB9j0zk5B+/YaSF1+01R9Y/y9dIiQlxVKPeJb1Bz6T64wrV+giCk9Putq+b19g3rzCtRETQ6f/zItneniYMtV4eqKTwUBjA95/33aKb98+GncLmFw0Eglty9MTMBhg1OkgYS6e9HQai8MW46tU1M3GAueZC0YiMfVJIsHLRiNdQML6yAqKymTYXdAKHR8sBR6XQKEtEAggkAiAIBVA0A9Dh5ri8KVSy09ysuv1E1qtTFzLMmgQ3SaVmjLQuQ6d7C+uG2aXKZVaDtcAjdD680/6cdaGeTss9MhRkgJaigm4fr0aWK6DyEi6rtdeko0DB5xdB4HrVPKFO/7kSfePTki4Aq22AA0b0vfOaDTVpX3wgO6zc+darF27APfu3UJ+fi4MBj1UKl/xPJmZnggLK4eHD0uLa2CVyqYwGo3YvPka6tcPR1qa7foQZ9fB1s7m5AAeHnIYjbVw+jT97uDBCzAYDKhYsZJFC1ptAQQhCCdPAqdOXUGbNr0t7kWZMk1hMGwX27GGluEBatZsKm6TyWSoVq0B4uOvAABCQiLQvHlXbNz4K6pUaYQ9e/6ARlOAypX7Ij7ey25oe2oqEBlZExcvWv7etVq6TvjUKQCQoU2bfli+fDMaNnwb+fl52LhxM2bMWINTp4Bbt25CrVajQ4cOFm3odFpUrlzXYlvVqg2g19MsnvfuWfaFZZXMyqKzFz162L8XHA6nkFSoQAdNPz8ac7puXeGirX79lYY5Mblvrj881iG66HQ07nfWLPvTgSEhVC8ALPUHpmvo9SB6PR1p8/OpcDpwgGaPVCio7sMGftYGa+fxpy8hdBEt0x/MPx4eCL51HBlGP1MTIBBAy5FIJALVIUhfDBsmqiSi7iCTUdljr4KBOYQIuHWLpjb5+mtTV1nSxbQ0x9FMJvqLl8eOl0rxeD0i3WYw0Nwgly8D06fbHj9ggOXx5v8604EyMujSqc2b24NF9jrSIZ6s/mDbRnH0B4DOcrJ8JPHxrvWHBw/glv6Qm0vlpTvXwPqh0VD9QSp1T3+Qy8OLrD/o9XRG3B39YfPmX1GtGtUfCgoKUL1638d6QJCNDsH0hwsX7OsPtD8lqz8w1Go6W/84+hsA/b1mZtLEtO3b0yUHzwrcyHXGkSP03/x8qhl+/TVd+LB/v/tpydiCBvOV3+YrtfE4OHXCBLqmpnZtmgXhtdfom2M+sjNLzWi0iB1xGrliPTqaj9SA2I4HYL+qO4B8VH/8P2be0v8TAEYCQG/RVIliMNgPV7WPIF6ea2Hmuo3iteMctubXUR+KR/GOVyicdg7nzx/B5MmvYsSIaWjSpBO8vf2wc+carFo1t9h9EAT6NhNiElB6vW3dBoXCE4JZzIxanQupVIrly09BahVn6+lZiMxatj1ya6+ePYdhypSBeO+9r/HHH0vQvv0rNhkOrfH0VDn9HgA6d34Vb77ZGunpyTh2bBcUCk80a9YZAJCfnwsA+PrrbQgNtazN6OFhmcnM2bkIocJfraYrHnr2pArXMxNuxOH8V3n0iGqeKSn0U6kSXVoUGene8SzWkhAqDA0GGzntB9DkE6tW0YzK771HHdYsptVcMJu3Y4bDUa6gwNLAZW1YeXxlbF8HGK3OQh7rD3jcHRhoK/ZK9BQHQkxJB91DsHCIFx5BPK/5vyXNk9UfitdGyegPRePZ0x/c47+sPwCm5FmTJ9MJqj596Br4ZwFu5Dqjf3/qBT1+nC4CuXiRrmspX54G/ZunmHXE0aN0pXhKCnUlpqZSDTIri7o+srKQdf48/FgxuTNn6FsybBjNrDR+PBWKmZl0KjMnh35yc8Vc6NfPnEGlMmWo8cwyBWRlmc7rSHKw9Mddu+JiQgJqlC9P26CLCcQc5R1z9bic8zryNFKotTLk66RQaz2g0UqhTs9Hvl6GAiihgQIaKOn/fUOhjayEgiq1UCDzRmamKXOeTmf61/yjVmvg4aEU/QDW5VWsUalMHiO9HsjKyoNCoYLBQG+l+b/Mx8Bks/m/zpBKqRdYIqHHszbZsdazxAaDAdbrWQpPyc/kFoayZWOgUHjixIndKF16mM33588fRnh4JN54Y6K47dGjOzZ9SEpKRErKA4SE0Id04cJRSCQSREbS1NceHvLH98tEQABNHZ2a+hC+vv4ABFy/ftZlnytXrguDwYCMjGTUrdvS7j7R0VVx6dIxi20XLx512Tbbr169VgAAvV6PK1dOoV+/keL3zZt3gaenCuvX/4gjR7bj55+dutrdpnbtZggLK4tdu9bi8OG/0L59X8hkHo+vpxrkcgWSkhIt1s9wOJxnhH37qFP8n39ogoqcHDqju3cvzdLoipEjaSRXUhL9sBpyZvpA8tWrCL1zhwrVmzdpjdwxY2j7n35KdZXkZLp/VhbtA0uF/zh17Y0LFxBTqpQpOUNeHo0uS0qyn3kIoNOTwcFA69a4YDSiZoUKJh2EZT58XE/lXNZbyMhXIl8rgVrngXytlP6brYcmT4c8qFAAJQqgQD6U0Mp9oSkVDU2lmtCGlEFeHu26mVoiZpFl+Q7y8vIhk3mKCrcpS699ZDJTOT/WTk5OHuRylag3mMt7a/3BWWSXOZ6e9MOciUwfsdYhALbdiOLlhC35mdzCUDL6A7j+UAI8Lf3Bnd/FvwU3cp3h7W3KUDBjBg0bWryYxgNERlID1hXlyjnOdPSYP+PiaAmAL7+k2Q8SE+loyDIUqFRAp07AtGl2c9CfiotDJWc17rRa4KOPaAYE8/znLP3xN9+guiBQg3f2bFqkzgwlAKflYq9dw/WRI1Hp+nVTKsJsABcefwICaPHccaNoPSEHxMVttKlRZzQCixbR23/vnqVRmpdHuy+RAFFRQGzsGXz3XQtnPXWI0Qh8/jnw5ZcFUKtNXiyDgeoTUilNdh0XR8/l+Bp+E69BraZJt5RKm8l7ADSMys+PXlNWlrkvggoXlvK+VClTFmlGQQHVPzIz7dXqoxFtAQE0Mq6wZWAIUeKdd8biu+/GQ6GQo2bN5sjISMHt25fQs+dQlC0bg0ePErFz5xpUq9YQBw9uw759G61aESCXKzF16iC8//4cGAzZmD9/NPr27YfOnWm4XsOGUfjppx3w8bmGoKAg+Pn5oXbtiihbtix+/30qxo0bh5SUFKxfTz28tWrRe3/xIn0e5jlSGjSohFdffRUzZryOiRPnIjq6Lh48SMKxY3sRE1MLLVp0xSuvjMawYc2xYsUctG7dE0eP7sCRI9shCPReeXtbZsCm0EiKzZu/R8uWMahcuSoWLvwaeXkZ+OijN8zKGUkxZMhgfPPNBMTExGDAAKrAZmZmwv/xlCh7dzUaqvRoNPTZWJYLsqVz51hs2LAQiYnX8fvve1G1Kgt/88GYMeMwf/57CA83omnTFv9j77rjo6qy//e96ZNOCAkECL1KkSIWlKIoimtviGDDVdy1rG3Fzqq4Ki6KylqQVWEDCCI2sCCoICLV0ESkGkMMMaRnMpm8Ob8/Tt68PiUJhv0538/nfTKZee+8c++7755yzz0HFRXl+Pbbb5CcnIwJE64NhYL36KFdmQ0EeJGpqkp7r65d2Yd3/fXKQtDdd1vzFkcccYTBsGF83Hcf16obP54n7+HDWZeQ69lZITExYu29LxYswPgrr2R6M2awUKyvZ8P67LNZfzjvPNYfdKGJMjYtWIDu4XSIV15h3eDnn5XJioid6EuW4ASAhdSkSZzkShfpltNwmKKmBpg7F789/zxa5+c3WLAADjUcDgdP/ldfzSvUFsVzFyxYZlrj9uBBVt3WrNGu6NbXc1WFwkJ2ZF98MTBy5Ee4/vorDTSiQV4ecMEFR/HLL60087m87pCSAtxzD/sdwiWOWrBgUagdBw+y3qNPjAiwDEhKYlki12hlGaMYp04nr8W0bauVa0TsLykuZj1K7+yXdY/MzNgjeojcuO22O/DSS/fB63Wib9/TcPRorPoDQvrDnXfOQF1dBWbOvB2XXHJs9Yfp0yfhkUdk/aEY69atRLduA8LqDwD3lVx/VluiqGn6A6DoEOpnlJTEr0lSEo/pcDqEWn94663VaNeOX1VRTMKdd0anP7RtW44OHVINtCWJx6dclYP7knm79FL+v18/c75+N7TITuDjBI3aEP3880SCwM43u52+vPvuJvNhSFZQWEh0yy1ErVsbMwCkp3PiKdUG+YiJJ/T49luioUOJHA7rTAPp6ZxZIZqMEWoeqquJZs7kDE1mWRucTqIhQ7gfdZvUo2nHjh1Ew4ebk5aPzp2JPv88IinLdkgS0d13EyUmmtO324lGjTImndC3Qb0ZPxgkOnKEN+2bJWvIy+NkGQcPEm3ZUm/4ffNmTq5QWWm8Z309D5kdO8xpf/890YEDRLW10ffDkSNH6IknnqCcnBxyOByUnd2R7rxzOm3axDQnTryXUlLSyetNpDFjrqS//W0mJSamhO55002PUPfuA+jvf59NGRntyOVy05lnXkZffHGUtm4l+vFHol27jtBZZ42hxMREAkCrV68mIqK1a9dSv379yO120+mnn06LFy8mAHTgwAEiIvrPf/5DKSkpBp7r6urokUceoU6dOpHD4aDMzEy6+OKLadOmbfTrr5z4Ytq0Nygzsz25XB46/fQ/0R13zNDwvXEj0aZN/Dx+/JHoyy93EQD6739z6aSTTiKn00l9+vShVatWGe6/bx8nqXrmmWdC34VLPHXhhReG/g8GiSoqiNq3z6GpU2dSXh6F+vqdd5iHtm1zaMOGoCaRx5EjQZo583nq2bMnORwOysjIoHPOOYe++uorIiJavZoTR5SWlpryUV/PSbY2bfLRihW7KCfHZxjvL78cTzwVRxx6NEp/WL9em7nw6qspd/78JvFhkJvFxUR//at5hqa0NKKrr2ZhEY5GOBQXE11xBVFSkrUQdruJ/vQnPjfWdnzxBdHFF3MSTTPaHTqwfrRnT8xtCASI/v53Vm/MWQ+S18vkIyW6DNeGrVuJ+vc3z+0lq1ezZkXoB9LqELW1LMe3bjXK+M2bWV798gvRtm11IdmhPrZtY7Wxvt54z4oKlidbthiv27SJ6IcfOGmUWdJCM+j1hw4dOtK9904PJb2MrD88aqk/bNrEbdm8+QiNHn3s9Ie2bdvSuHHj6Pvvt1FZGetnTzwRWX/YuJH7cedOopUrdxMAmjevcfoDkXXiKbX+QMSqeocOOfTwwzNp507lWZrpD/KY2b49SI888jx17x5ef9i3b1+E5+2jTz4x1x+6dGlZ/UEgOla7Bo5/VFRUICUlBeXl5UjWL5WFw6efch75QICDOv76V+DFFxvNx4IFC0w9kADY7fTYY8Annxj313boAFx7LRZ36YLLI3mErdCwyut/7TW41O4YNbxeYOxYXlK1CNG2bMPnn3P5o6+/5jArNQSBV7nHjQPuugsLNmyw7gcL1u+5B5g/30haJn/CCfx7//7R0dS3o66Oo79yc7XeKhlOJy+yz5/P3jH19bW1tThw4AA6d+4Mt9sduoaIo9aLi81p2u0SWre2ob6eV2r1e51tNr5Xu3bGvTlEwM8/V6GmJlHl2VXTZo9yVlb4fT0lJSVIlzNo6VBTo0S/ma0iA8Brrz2GL79chtzc761vouLJ62WPcXq64nEOx0M0CHe9uuRBdbUS+hYONhvnYfF6uf/1q75r1qzBmWeeifz8fGQ2VEBvahvq64Gff65GIJCAmhrrxCVWfRgtDzU1tdi69QD+/vfO+Plnt+a3l16qwIUXNmKejCOO/8dotP7wyy+8utuw17WyTRsk5eU1uvxfRP3h4YeBzz4zCpvUVBZeDzyABTt3xiR7Nfj0U5Rffz1SiorMl5NsNl7O+fe/ud5PLO345RcuRLtsGbBvn1GgJSVxbZ+bb8aCmhqMV2eNjALLl3Okyk8/mc+tycmcp2DatEhle6zb8M03vPvsxx/Nt0e1a8clCeWVr2h0iNpaXoEuLzfKLUEIIiVFhNfL+oOZjuF2s5zIzDS2q7YWOHjQj9palwlt1hsyMjhS3aqUTDiZEwxyQOHRoyx7zYZMLPqDILBcTk5mnuSF/qbK3kg0amsR2obn83F0VLiILDnBo9vNwRVJScpWOMBcf2hqO4iAwsJyEKWgslLZBmjFn9vNek1GBvdptDzU1tZi794DeOihztiyRas/nHRSBd59t+X0h7iR2xghBfCMNWyYkhiqa1c25BqRViyskFJj1SqO2127VmNdEAChTx+2xv785+iTYpnx8NZbvHs8P998RrbbgYEDuUr5iScarw+HQ4eA55/n/c0HDhjo+xMT4br8cuDRR6NPzKHCdddtwEcfnaSJyJZhs/EWpUWLwj+icO2oqeHosvffN09k4XYDgwcfwKpVneF0WgsoNSIZvF4v+xWqq9kg0wtiu511lbZtlUlJnpCIeBI+coQnYv3jlI3lrCyedNWIdmKVw57kTMkyf7EIKT3kjNoORzU6dEiAw1j2NyrEKhyIWFjJgou3kxGIrPcmsZD3o6amGA88cC2ys7OwcOF/G82DGdQ0JIn7u6yM+9tKYMnJ11NTAVEsRZs2aWHvEW6sNmmejCOO/6do0nsRDALnnw+sWMH/O53sSZUtnRgQk/4wYwbvEdYJG39CAsveadMibq8Ky8OuXWzRbdxoPjkJAhd7ffBBDj2OpR1y4dk33+T087o9QEFRhHjyyVylohH9uHcvcM45xTh4MMPUWGndmrc06diOqQ2ffgpMmQLTjPuCwMU4rr12BR544FwA0ekQPp9i8JrpB2lprBuUlJhvr3a7gTZt2LCRjVZZ5vj9vK2lrMw8tYvHw/2SkaE1lmORe7W1rKOUlyt6VVP0B9nodTpr0batG4mJ1sZ4JMQqvyWJ2yE7z/3+IILB8N6R+no/qquL8fDD16JdO9Yf1Cp8czv7iVi/KS1V9Byz8S6KbIRnZgKBQGQj93jVH5qyu/2PjZ49gV9/RVGvXvz/vn0c8P/228funqNHA198wTPVvHnAoEGAKPIOjF272Mh1uzlh1QcfNO4e117LxmgwyHnjhwzRGs319fx9w73RqRPzEg1ycjhD9b59/GbNns2e3QbrzFVVBfznP0yzbVtOnmFWydsC55yzD7/9xhPN/ffzCypDktg3kJ3Nt7vwQuN+xEjwetlIrq3lCeK886AxwGprgW++6QyXiyd+uYpTOAgCC4g+fYDBg7mLnE5FOaipYWd2WRkbojk5suHCv9fXcxdt3857gn7+GZAkMUQ7LY2H6uDBLEB5L4bSJ6WlXClryxYW8rH2iSCwR7hHD/Z5DBgg7/8JNlqwyBm1jx5NQF4e87ZzJ7fNzFhvLggCP+N27bg9vIfnKAYO5CGZns5CXb+36YMPFmDkyBz89lsZJk16Bps2ca6XH34ASkoScPRo5PJZ0cJm4/HSvTv7mgYN4j20aWnasShJ3Fe//AL8/HMqtm0DCgqaj4844oijCRBFXkacNQtBQWAD7rLL2Dg7FmUKANYfli9n7XvNGjayG7ybrupqNh5zcrgM0QMPxC4MABZk69axRVReDlxxhXYPLRFPSlOm8ISbnMyeY6soMjWcTjag165lHWj9et7j3KYNAEAMBvnel13GetCYMWzYR4lu3YAnnlgJSWIVp29frXH0228K25068eJ4rDjnHK4oSWR0uBNxGcIHHxwLh4P3CEeTFdrjYRlw4onc/QkJfo1+UFzMXR4M8v3at+fukVFby7J1yxZg925tqUaXi4fEgAF8ZGZqc3z4fErJmh07ODghVhnjdrNfpV8/1lO6dOH7NlZ/IOI2VVS48eOP7A/Jy2P9prj42MpAm40XJTp14vGTk1OKwYOB3r1Z90xJ4bapHQIrVizA2WfnoLS0DDfc8Ay+/5553r6deS4vd5vmdGks5L3cHTvyeBk0iPteju6T+z0Y5Fd4zx7g4MFW2LOncVNCSyO+kttED8OCBQswvqSEy/7Ib8/pp7NX1qxmnRWNxoYK1dVh67XX4sRNm/iNUMPj4VCev/yFrbow8TYReTh6lAXMp5+aCiQCILRpwx7a22+PvR1ffIHCO+9E259+Ms7sOTmcdOK++8JmQTBrQ10dL26/8465F9PjYbv+xRfZlm/Ms/j1V07EvXYtIEkEOfFDTk4tXnnlADp27IwePdxRL7CXlJQgNTUdv/7K3ld9SLDNxkZNSorVKi3B6RTQpg0LJTNhUVnJfFdWGr14ogi43XXo2NGpS6IQPWTvoc/HgqWigh9rbLON0pd6OJ2c4CE9nfUkszYey3AlIoQyfsre0EjZwOWajV4vP7u0NH2Sq9h4MAMR9/XRo0o2cz1SUlihU/fZ8eyJjSOO4xHN9V58NGMGzn/mGaVgeGoqCyyLJFF6NEl/AIC1a1E4ZQra7tmjFTaCwN6+a65hmR6mjRF5CAY5Cu3FFy0Lo0t2O2zy3p9Ysx0dOYIfJ01Cz7w8FmxqJCQAo0ax4R4ho7VZOz7/nHUIdV1QGYLABkJuLhs1jX0WzzwDPPUUO7PVyMmpxWuvHUD//p2RlWW+kquHLC8qKniF18wxnJDAzmifj413vZwQBEJKioDsbPNtTfX1SrJvMxnjcNQjM9OONm2iC/O2akdaWjpKStgZr44Uiw7W+oM60ik9HZbRYsdyy1QwyPpXebm86hvZvyWHOycmsv7g9UZ2BjSmDVVVypY0PU92O/uV2rZV7n086w9xIzclBXPmzIHXImNfJBQUFCA7OxveoiKM+cc/4G3YHEoADg8ciPW33IK6CA9WptFYyNc7q6rQd+lS5Hz7Ldzl5ZrXO2izobRjR+wfMQL7R49GUGdxxcRDMIgT3nkH3VetgquqyjCNEDj0+Ifzz8fu88+PepaTeWi/YQN6ffwx0vftY++sim51RgYOnnYafjj/fNTrnlmkNtTU2PHSS6dg+/a2Daudas4JXm8dzjnnS1x++dGo+DXDt9/W4Z13LkZRURJycvx45ZUDaN26MwAXRJGQnl6NxESLjawNCAQCcKhmXUkSUVrqQXW1E8GgoOKb4HRKSEnxNRTjdsPvtxva5XbXIy3NB7fbvJRUXZ0dZWVu+HwOHX3e3+P1BpCaWqtZYY4EfRtk+P12VFa64PM5UF+vfwbMr/l3ahh/t9uDcLvrkZjoh8cTCMtDLIiVhiSJqKlxoLbWgbo6GwIBsSHc2UwSEQSB4HAw7wkJdabPqKntKCuzobo6EXV1thAfDkc92revgNy3dXV1KCgowMGDBxHQxaXV1NRg8uTJcSM3jjhUaA79AWiQW23b4pSXXkLO+vUQwG9lSbduWHPHHaiNoKA2VX9Q89Bx/Xr0+vhjpB08CFGlGhKAmlatUDB4MH44/3zUZGQ0iYfMrVsx6L//RXJhIQQincQCgnY7fu3TB+tuvRX1Uc45IV2suBj93n0X2Zs3w1ldraEdcLlQ1Lcvdl1wAUp69LCkYYXVqztj4cIBqKx0Qy9nRZHQufPPmDr1O3i9jauhUlcHvPBCf2zb1guSJKp0iE4A3PB4AsjMrAxr2JjJi8pKFyoq3BoZAACiGERiYh2Sk2tRUeFGVZVRx7DZCImJfqSl+SAIRnOBSEBFhRuVlU4EAlr6so6SmOhHSoofRlkeWzuCQRGVlS5UV7N8NcpWmX44HcL4mygyn15vAElJfohi0JKHWNCY6/1+O2pqnKittSMQsEGSrPQH5t1mIzgcUoP+UweHQ6unNbUN9fU2lJQ4UVvr1oReCwIhLa0GKSm1x7X+EDdym2MlV/bcBYNcqmfWLMUjKoocsvP66/rc4uY0msqDjJ9+Ap54ghNWHTmi/U3e/HHllbwC3apV03h45x1UTZmCxKMWxmGrVhx6/NBDsa0mB4Mcvvzvf3P8p96N16ULlyy4+24gMTGmNhw8yJFOGzcayYoiR3a9/75ltYKo2jBrVi169JAFlNYdmpLC27jNuiOc5626msNOKyu13llRZM9edjZQWFiFqqpEw8q1HEqTnW29bVve31NWZtxLYrMx3+3aacOdzBCN91BedSwuZs+h0Ytp7YmVhbzV7OV0Ak6nH5mZLqSm/n57csyub9UqPbTqGyn5A8DPxuPhkCLej9083uRgkPeCyUnaHA5efbDbAZ/Ph4MHDx6Xntg44jge0VzvhUZurVrF+oKcWEIQWFC98YblpNvklVwzGmxtcQjz7t3GcJ+0NK7fe9ddwCmnNI2Hw4eBSZMgffklbGZLdQ4HlyCcNy9sci5THnbsAP7xD44tlvOnyEhMBM46i6PPGurJRNuOYJAXhmfP5jldj9RU4F//ilwhKlw7du4Ebr21FvfffwAZGZ2g1iHsdg5wSzNJtRAp4WJhIa/e6mWQvFXH5eLEUz6fy/DYPR6ODktPN5epwSCv8B45Uo9AwKhkWO3hNUM0stfvZ/2hrMy8TGM4CIK1/qDkBalB+/bemMswymiuaLLk5HSUlfFYkxNlWvEuR4wlJPD4CAabL6KtspLD09XBnA4H0L69D0eOHJ/6Q3xPbnNCFIFnn+XJdPJkflOCQWDhQh5tf/6zdTra5kb37pxEqqiIZ7V77uE6uPKbvWcPZ1FITweyszH4zTeNYT7R4oor8OFLLzHd997jDQnqGfDoUU4mJcfZTp0aXbVoUQRuvJE3yNTWAq++yhtP5Nlx/37OPJ2cDPTogd7vvx/1fqZOnYBvv+XTN2wA5K3VALO2ciVPEpmZ/Lkx+MtfHA1bnGoMIT/l5byPJS/PKHvDISGBI8gGDeJoeHnylTMWbtvGK7pZWbxns00bxaCVJBYI33/Psv+334wTpby/JyenFL17a/f/ykmPduxgGgcPRrdnyAqCoITODhyo7OdV9oVYW6Zygno1LfWQq6sDqqpc2LeP97d8/z3nivv1V/MEGscScs3B7GweZwMH8t6jHj1Y2Hs8WoFfX8/C7PBh7usDB1ph+3Ye7qWl/BysjmDQeBAJodetc2ce0wD3w7Zt/GpVVdU09KmDy1TW/X5TVRxxxNGA0aPZKf3QQ6w9EnEsbGoqG5THar+uHk4ncO+9nAzB7wfmzuVQXzm7YWkpsGQJb4fyejHqySc5rrcxaNcOWLkS78ybxwLqnHO0Gz8DARbCbdsqBu/PP0dH+4QTOPS7rAzYuhW4/HIWOgB7Vpct4zrEKSnA5ZcjsbAwKrKiCPzzn+yk9fk4opu7hoVSWRlwww2s8owZo93nGi369gVWrXKgUycgJaVGI9/q6zm1yaZN5kmnrWCzsd4wcCDLInXekpoa3vH2ww+AKBL692fZrE5K6fOx3N+yhVVIfcEPUeTH1L59OU48UcmBor5e3sO7cycP9aYss7lc3J4TTlD28yYny9uAwhM2qw0sQ8kL4sW2baxD7NjBbS8vP3Z5QazgcLCu0KWLsnd54EDF0aHew0zEr+zRozw2Dhxohe+/Z19VYSH/Fov+wLoD6xAJCTxu+vRRfG6BAHDgQE2DPqnVH6JR84814iu5zbmSa7wBG7vvvqs8bZcLuO02LqzeoNkek5VcK9TUcEmf+fN5ltF7TrOz2ZP8wANQVapuHA8rVnBb5UwLeiQnsyR47jlAFKNvR309Z3d+9VWeedRvkt3O3tkZM1hKxIhLLtmOFSv6GbyCDgezOnt2pELu2jYUFhairKwMbdq0gdfrxa+/Cjh61HyCTU7mCbusrBRpZi5aC9TWsvGmL+ouCKwbZWbyxHbkiFEoqYu+qx1wpaVaHqqrrQvH2+3Me0aGsrdFf31jUFRUhUAgEdXVsRum7MsJvxIs7+tNSbFesW9qO2K5Xi5rJK/2BgK/hzAlADUoKzuCBQtSMXdu29AvaWksKFvaExtHHMcjjslKrho1NRyptHSpMhG4XMCtt/Imzgbv5TFZyQ2HL77gKglff2203rxe3v/68MNcfaIpPBw9ygkzPvvM3ONms/EK7FtvAT17xtaGLVuA6dOZtmoplgAInTqxQ+Evf4l5Q+nMme9jzpwLsWuX8bfWrZnV884LTyOcDiGKXuTnC6bd4XBwMqHa2thkVjDIDu+jR40+FLebdYOEBD6ntNQoi+12jjhq3VrpLr3ckyR2wpeVmT9KuaSROuqqqbK3pKQckqSUzolVlkbSIWw2ZX9sSgpMV3ubQw+KhUZdnbY6xLE3Mq31B4Bfr2HD4ntyWwzH3MiV8dtvLKw++UR507xeNiSnTsWCRYt+PyNXjWAQWLAA+Pe/EVy/HqLe4O3YkTMqTZ0aMRFERB6+/JJz7//0k/mbl5iI7aNHo9/778fWhro6NnZffRW0c6d2OurYkVewYxBWcju2bQMuusg81X/Pnmy/d+5sfb0MIsKvv/6KMl1Gibo6NhrNHPOiGES7dmJUSYnUIJITGRhDjZ1OxeMnG1L6x22zKRN2TU01EvR1hRrg8zENs/Agm40Fot1ejaQk8+ujRXW1woNc3kdO0NCYDInhwpMAbR1cOaGDmofGoKnXBwJAWVkdJMkZsQ5fY0DEY/GDD1Lxn/9kacolxY3cOOKwxjE3cmUcOsTG3ldfKd+5XBwL+9xzWPD++7+vkavGDz8AzzyD2iVL4NanXk1O5lXZRx7hpbam8FBRAVx3HWeHNgsfstnwW8eOaP3111En/Axh40ZOivXZZ9rslE4nMHYsOxR69oyKlLoNDz7IIctmDvObb+ZocDO1JFodoqTEKtstITVVCC1Yx4K6OjZka2u1xp0oslxPTWUZJNff1ctTt5vlRiBgLfeCQaXEjpn+I9dqlaTmlb319dqato0zesP/7nAohq/D0XT5DzSNBhFQWlqLYNAdVWKrxtC30h+AuJHb4vjdjFwZP//McS1r1ijfJScj75xzMGDhwkanoWsWT+5//4vxosgz7+bNxrehc2fm/b77TPcWx8TDt98CN93EAtJMa+/WjUOhBgyIqQ3vvvoqLv3uOw5PUi9ZOp1cLmHGDHPLVAV9O+rrudlLlhgNq4QE3u5z113W18uQJMmwIR/g5j/4IPDhh8Yub9WKtyPH2A346KOP0KPH+Xj6aa6yoOY7LY0jtv7yF/a9zJzJ28DUeyxEEejQoRSPPJKG006zvo8c1v3mmxz2quWfkJ0t4NxzOZgh1mSZcjvOP/9809/8fu6zTz7hVPvmId/WXliHg43a+nrriT8lBWjVqgTjxqXjggsaVT4ybBsaQ8Pvt66qZTWTf/HFKowaNdr0tx07gOuuc6Cykj0qTz7JW/UBJRQsbuTGEYcRv5uRK8PM2LXZ8MuAAWi/YkWolM4x5SHc9QMHskBcscI4IbdqBVxwARu8FjI4ah5qalh/eO8965IJt93G6Ypj1Km+vfVWnLJ6Ncd2qpGTw/lLbr895ioVW7dyZSgzh/nAgdxd6q3GseoQGzZwWhJ90mpR5FXjZ56JXbV8//3lyM8/DwsXauWNIDDPd93FUd6rVnHC7N27tfInKakWV1zhxq23asOd9aiq4nQ1H37I23PUcDoDGDbMgWuvBYYPj41/ILLs3beP9bq1a/nVMt+eY65DyJFggPX+WKcTSE2txLBhSRg1igMczDJVN7UdsV5/5Ih5ZJyV/hAMAqtXW+sQouiAINhCOW4Afv3uuAPo0AGorW1h/YH+wCgvLycAVF5e3mgaubm5sV+0axfR4MHytkI+nE6iK68kKiz8fXgIR0OSiN58k3m02bR8CgJRt25Ejz9OVF3ddB62bCEaMIBIFCmovo/cJ9dfz/zE2ob584l69dLSA4i6dyeaO9eSZrh2vPceUatWRpKCQDRiBFF5edOexRdfELVuTQQENfQTE4kWLIiejpoHv5/ooYeIMjK0PNtsRKNHE23dyuctWUI0aBC3RX1ecjLR5MlERUXh7ylJRPPmEQ0ZYhwyAFGHDkR/+1tkOlbtiITSUqLnniM67TTuL/39Ix02G5HXS+RyWZ/jdBJ17Up09dVEixdz3zZnG44VjUjX79pFlJCgtPOxx7S/N8c8GUcc/9/QXO9FzO/3wYNEZ51FJIpaIXT22USHDv0+PES6fsMGogsv1E4s8pGRwXJ9796m8+DzEd14I5HXa9QfAKLOnYk2bYq9HQUFRBMmEHk8WnoOB9H55xPt3h3+ehP4/USXXaZ9bPKRlsYyJRKNcPD5eAjo9QeAVbnKyuhpqXlYt47o9NONfLduTTR1Kt+3pITo5ptZX9DrRv36Eb31VmQ1Lj+fdY20NGP/uFxEZ5xBtHBh1OpgzP24cSPfPyfHqAdFc7hcrEOY6T9qXW7AAKLbbuN+PRbtaO7rY6HRr5/y3PPy+LuW1h/iK7nhPAx1dRxmPHw48Kc/sTdPh4jex4cf5mWutDTetCgXmGrbluNC/vEP0PbtWl/R4MG86jhyJLuYbriB4yeTkjgEKCmJl5lSU4HUVKzetg2jLr6YvaXyxgZ1+ty//53diR4P0/F4eDU2IYH/Jibiu507Mezss5V7JCfzPRIT2VX39NPGDZ0At2vwYOSlpWHAGWcwTRVdJCYyzZkz2d3ndiuHy8W8eDyA240DX3yBztu3m9bh3ZcyCDdmfgRnVhqcXjtcHhFujwiXSyG3b99ODB7cV00SHl8JvIv+A++GL/Fm4GrsQ1e44IdH8MPTLg3eoX3hTvXA7WbP24EDuzBwYJ8QazKbbjc7p2fP5hXA/fvNQ4UcjnpcdZU9lHBBDl9xOvn/d9/lxAsOBx/6czwe4MCBQ9i9O8eQtVEUOc/H4cPKSqTdzp/lv04nUFx8GB07tgvR50zD7Oldv96YbNtm4/00ck1dzo4oIRjUxksnJHA+s9ateTjZbHy+KPJn9d/iYh/8fo9FDT0+5PPlpFGiqHicRRGora2B1+vV/C7/ravjcC11sgj5WjmsyO+vB5G9iSG+QUSbn88s2yRREGIYN3p0vJl7k6NHEIIghvYQpadzZu9hw4CLL+at61VV/Ff22t94IzBnDn+Or+TGEYcRUb0XzzzDL9c55/ALZ5LWPqwO8f33wP3380srFz7PzOSlv5QU4IUXEFy8WFPuB4MGcYUHOQzn1lt5GVGWxampfK18pKZi5ebNOOuqq5TNleoNht99x1uWZP3B6zXI+Q27duGks85S6Ccnh/QTbNrEelRhoXHCc7k4A9/o0dhUWoohp5+u0E5OVnjes4eTenq9fI0soOW/Xi92bN+OE3buNC9oa7Phz9kfYn/7kaw/eG1wNegPah1i797tOOmkfiEdwusFEjZ+Cfe783GwwIY5uAku1MKNWng8IhJO6Ax3r84hPeHAgV0YMKBPiDW3W6WLeHhnlVx/9NAh8/m/Y8ejuPLKViHeZN3B7eZr3nuPh5Es1+XfnU4+p7DwACorO2P7diPt5GRWY30+o+4g03Q4gCNHCpCTkx3SKZxO/n3LFo7+0a8Eejys0iYl8XDPz69DXZ12k6og8NDq25eHY00NfyfrADaboj8EAkBxMdMw6yM50ZKc1EqtG8j6gt9fEyrtpdYt5PP0GabVvwMcAVdXJ4HIFnNos4Lo9Qf1vfU0BMGcxu+jPzAPoijC4eBn3L4966IjRvC0JptGFRU8Nfl8HOG/e3fL6w9xIzdc569Yoc0SkJnJT00VexnRyO3Yka2aMCAAQmIijwx1bGlWFt9/7tzoGqSHIChC9fdOKdvMWI6xGIcVLc1GHHH8v4QgsL/K51MiAHNyOAv5VVdVYMqUuJEbRxxqRKW8paXxBkaAtej//IcNPhXC6hAvvsghsmFAAASHgzVetf7QoQNXULjzToWHWCBbCx6PuYP7fwxpOIoyNC0JUBxxxGEOu50N3s6dOa8twNmgTzyxAu++23L6g0W1zDgA8HJRmza8ZCRJvMTVsycH81vUvDXg1FPZE1pTw9pjXR27j1TCSACUZUE5aN/n45S5soEre03r6xUaDXQoGDT30xAZjVt5uVAQFD4kCcH6evY3yZEVxxmy8Cv6YgcCcKAedgRgR33DZwk2SLChHjYEISLY8JcgIAgB1PBZQVO9WnHE8f8LRJxkSo1Dh/jIyGgZnuKI438eHTqwLPf72Qi9/nrWIaLNPNy+PeeoqKpiHaKujmW6Xn+Q5bwcIuP3s3P9uuuUAucJCXyeXN9DVTeEiIxSUdYF1Aauzcb6g8Oh6CDBIOsP6sjM4xBn4GvsQXcE4EQ97KrDBqnhbzCkQ4gqHYJX0ZQeiusPccShR3098M03fDgcPNXs3w8UFLQsX3EjNxwuvJAPgMvc3HMPx5r06sUFxSyKs2uwcKH1b7W1QH4+tk6bhhNXreJwHnkZxW5noSQncKiuZiE3ZAiH/qoyAi2UPcF1dayplpTwUVrKoULz5rG2CigCzuvlUjuPPgoMGoRFem+ynIquvJyPigolLW9lJf9WVsbZDrdssU5327Mne6IvuYTbIF8rV7WWj6oqbNuwAf27dlWWk3w+dihs2IBBwe+xA/2s+7JbN2w8+WQMPeUU7fW1tXz4/aG/dbVB1FQTqnwifD//huqiKlRLTlQhUQ5EQq2YgJrR4+AfexFqAzb15fD7uQvV5OUC3fn5JSgqSjckMZDL+QCKw12SjHkzcnKA9PSfkZ3dMXQf2acRCLDeYpZ4yGZj+kRAba0fdrsLRHydrHcEg4r+odZFotFLOAkT81BSYp7vQxRZ/7HbmQeHw2V6H/l/na/HBOZhOtHrUM0RpnM8oKntiP36G27ghCYAcNJJnKAjjjjiiBHbtvHfffs4TtPvB844g2VmNCXuLr6YDzPU1wO//opVL7+M0Z9/zgkjZWPXbmf9pKqKJ1lZhp90EpfOGa1NIrNwwQKMv/JKluklJaxHlJUBv/zCdW/kovKSxDRFkQt23norMHkyFukrRASDfF5pqZzyX5H7sg5QXc2F4j/9lM8zQ0oKcNllvOXKbtdeL6fKbdAhtq1fj/7du2vlf0UFsHo1UFaG93GRdT8nJgITJ2KD34+T+vXT6g+y0G/4HPT5UeMTUFUjorq0Dr6DRaiuDqIKSfDBjVq4UQMvalt3hG/8DfC37xryc8gyXf5cU6PoELW1/NuePdUoKzNma5JL3qnrmeoz5iYmAh06HEbPnu0095LXRurquMCF2TYieSebJAF+P8vvYFCrQ+gPIDr9wW7niHuvVxkWZgkf5ZBrIqCujvUY9T309w2fJZlDhfUhwLH5YP4/6BDN0YbYaAwbpvjkMjL4FWwxtMhO4OMEMW+IfuIJ5T3r0oUoEGjeTd2rVxMNHGjcuZ+WZtz1364d0fPPE0lSdDwUFRHdeitRmzamWQ/2nHkmZw9oDCSJNl1zDVGnTtY77h0Oogsu4Cw3kfpBj+pqovvuI0pPD7vzPwhw+7Zsib0NeXlEgwYZk1fYbES3304UCERFRm5DcTHn59KTy8rS5qwoKCA67zxtogNRlGjSJE1eLwPefJOTHOjpZ2YSPfHE8tjb3wBJInrhBSKPp9a0mwcNUpJVLVpE1L+/MUlDWhrRmDE/UnFx9Pdctoxo5EjzxE8JCURnnsmJOaJNOkHUtIQLkkT08cdEp5++j7p14+FrNfTCJalwOALUqpUxIYf6cLuJTjiBk1Fs3Ni87WiO61s6cUQccRyPiPm9WLdOyUrj8XACKWrG93vXLqKTTzZOTmaTT6tWRPfcExIyEXmQJKJXXiHq29c44dlsdKR7d9ZfGouNG6mwb9/wWXt692bB14ikkfTJJ5x9KUJGIUkUie64I3b+fT6iu++merOsUu3aEa1cGTUpuR2zZnGyQ/3jvOoqpQskiWjmTH2ypiD16hU+sVEgQHTOOUZWRZHVxKaMye3bibp0OWLa1S4Xq1M+HyfqvO02Y6IpUSQaPpzo/vu/iPqexcVEd91FlJ1tLp9zcoimTCHasye2tjSlH/bvJ7r88jwaMYKTdFkNvXBDkhNi+iktzZgDTX14PDy8H32UE3g1Vxuag0ZL6w9okbseJ2hU50+dqoysPn0od/78JvNhGECHDnFmQrtdO5JTUozZCt1u2nf66bEZqLt2cSbnpCTj2zZgANHSpY1vw969RJdeypq71RuZnc0pXHXpaaN6kVavZkEeKf1dcjJbKTFi2cyZRCedZD77T5kSMaWuvg1FRZyRV0+ubVvthFtcTHTxxVpfht3OiSN9Puv7rVkjZ2TWCrnkZKLljbd1KTc3l7Zv52zJVt377LMsZEtLWViZZZ7u2pXo6aejy0Qs48MPOXmoy1VnOun37880I2WLbO7JPS+P9cJBg8yThkYjtNxufl5padb6nN3OPrSJE3kIz58fN3LjiON4Q6Pei08+USYIj4do9+7md2Ll5xNddJFxgklLM6aeF0WioUNp5dSp0d+wspLo4YfNndrp6UT33hveQxuuHYEA0fTp5taKfDgc3D5dhuOo+rG0lK24lJTw+oMgsECO0rkd4mH+fF4MMfPWtmvHJRSi6QcVXnrJ3Ni94QatvT97tlEGn3CCueNUjWuvNa6hCEJQY0zHitzcXPL7eZhYVTsYPJhoxw4+/7PPiIYONcrOhAR+1LJjPRoUFhL99a9ErVtXmsri5GROkB2N36E5381AgJ35V1/NSb/16n20OkRiIq/ltGplrUMkJhKdeirRM88Qvfbakia1Qd+OWNHS+gNa5K7HCRrd+X/9a2g0lXTq1PiZoAGWA8jvZ4Gh98R6PMbZTBCIhg2LPi+5jK++Iho9mj2Y+rfk+uujrvliaIMkcakeszI+6uO000Ie4JhepMpKrkljlm9eb1W8+mrUZEM8HDpk9IrLSkEYy9OqDYWFbLjoybVtq62kUFJCNGTIz5pJzuGIbF8fOGBO3+MhevvtqJtv2g6fj72kZnJbX7lizRoupySKkqHbhgzh1d9YeNi6lY29zEzzx9uhA+ss+/eHb0NjEY5GURHRjBlEo0aZG/h8BMMKLLudX+/k5HBCT6LsbKJLLuFSDB9/zMfy5cbjk0+Mx/33r6LPPqOYD3mIt7SQiiOO4xGNfi8WL1a0WJeLVjz5ZJP4sJyjfD6WkSaOcX1NuVAU1PTpsekzxcVEd95JtWbG82mnEa1d2/h27NhBNG6c0cJTH9nZRNOmEfn9sc/3H37IkXOqCdq0DNHw4VEb7RoeZs0y94a2axd21duqHc88Y5QRZgvP1123waAWDRgQ2VC8/35zo+mss2JzUpu1YcMGY5CifKSkcJCAJClqHQftaUshtWrFZYqirbKZm5tLPh/Ryy+zKme27uJy8TB9801zf8axjqLasIEXCPr1C7cuFDQtO6XWD5OTeaiZ6xpB8nqJTjyR6O67id5/31p/sNYhvohZf5AXcVpaf0CL3PU4QZM6/7rrlFHUq1fss4AKUb1I8+ax+0c9em02ojZtSDKznJ57LiZhtfDtt9kD2a6d8S3p25coAo9h21BQwO7CcMtfqam08/zzG9ePDaFIpgJKbU088khEUqZe8eHDza27iRMNwi/SsywoMHeAt2unGGq5ublUVMTyXT1pOZ2RI6crK4k6dSo20Hc6WUhGC6t2fPaZcRjKR1aWMkzmz8+lV14h6tPHOPG63UTnnssTfCw8FBcTPfAAUY8e5pN5aip7fWX94fcO0/H5uP2XXsrGdzjB9L9wyF72lhZSccRxPKJJ78XHH4cmCEkUo1rhs0JUc9Srr/KkpH/JMzKMTnSnkyO9orUmZB6soqzS09lyidBPlu2QJLZUzEKiVMevvXpxrGysKC5mh3646DOAl0UjRMyZtmHOHPOQ8exsXmSIth8a8PjjRmNUFFk2qq+fOdN4W/XqqRWmTyey2eoN7A4aFP0CvVUbZL+LmcPcZiO64gqli59++iO67DLzleAuXVifCacumvGwbh3XKjZbGxFFVnWnT1eixH7vrUKHDrHP5uSTw6vL/wvHqFHcppbWH9Aidz1O0OTOnzhRMazateNQmEYgphdh82aOQ9B7H7OyjLOBy8WxEQUFsfGQl8cbNvRuQ6+XC6SbFJyPug1Llhi8p4aZbvx4inpDpwrvz5zJGzvDLZ2JIgu0WPf1FBQwbT09eZNMw6wYbT/k5/M+ETO5N2vWe6HzCgt5pVTdJJeLw2atfBi5ubkUCPA+VrPuve++yPxFakdxMQskM0PObicaPnxf6BGWl7OX2CwCLS2NH4eZPhWOh0CA6PXXiU45xVxgulxEXbsW09NPR9StmtQP4SBJRI8++inddht70hMSuL9EkZ9n9AXng1Gep9BVH7Fcrz7k7fMtLaTiiON4RJPfizVrFBkrCOzIbgRimqN27CAaO9Yg2+vcbvNwmYEDecUzFh6soqwEgS0li+1QUbXj4EGWt+E2KPbqxY7vWCFJtO3ii63ja+UjJ4dDp2Jtw8KF5sZux448FqKhocLUqUb5a7cTjR+/Wd0kevppY5OGDQu/PzU3N5fmzjXv5l69Iu+Oi6YNX31lHn0m30O9J3f1aqLRo425MWw2NgjNdqZF4mH/fo6Qs4qM79CBaOTIvRHDvcOhqUZyYSHRhAmbacwYfj1ttsboEE3VHxqnQ4wdy21oaf0BLXLX4wTN0fk7zz9feapOJ4eoxIhGvQjFxWxw6kN5kpLMYydzcthFFEuYbSDA7jIzD3B2NodSNxj2MbehtJTDvsOFG598cuTlPrM2yNmcwi2lCQK/hbpl0YjtKCw0tx4Fgej663lFPAbk57OM009KY8dqjdj8fJ7k1eclJ7PctOwHYhqXX26cDB2O8GHMsTzPuXPN9gXz0bu31lm9fz8vgJvJ+n79OJSmMTx88QWv4loNp3bt2JiO1dH/e3pyd+1iB8TAgdY6XHIyv8pW4dFmQRfxPblxxNH8aJb3Ii+PAmrN/dZbYybRqPdb3vuqj9yy2xVtWv2918v7JSyWAS15WL2a40H1stjtZvmrCmeOuR3z5/PqqpWMb9OG6KmnYopoy83N5fNnzIi8Fap1a6L166PrBzXeesvckO7WjWjz5pj74c479d3L4alffqmcI0m8AqxeHRQETv1ilttCzcPy5eby+vTTraPKYmlDcTHnJDUz2NxudujLaqskcVCCWYRYaipvq5JXm2PhobSU08T06mWuNno8PIxffTW2YMPfO5pswwbWc3JyzPvTZuNAz5wccx0jIYGDONRb6JrajpbWHwQiot8vl/PxBbmY+5w5c+D1ehtFo6CgAKPy8jDov/+F0NCVFVlZ+PLvf0d1ZmbUNLKzsxt1f9TXo+O8eRiybh1cqnp2BCDg8cBWWwub6hGTIKA0Jwe7zz0Xh047jUsBRMFDUkEBTlywAFnbtsGmyv1OAKoyM7F5yBAUXnkl54qPEW22b8eJCxci7cABQ5JyAlCTno4dF1+M/bqyB3ro2+CsqsKQN95Ah40bIQaDIXqAkgydAJR06YIvHngAQa836mfhrKjAKbNno+22bRqeg4KALRMm4KfzzotIQ43ffnNh2rRzUFLiDXEnikHceus6nHpqfui84mIvZs8+BT/+mNFwHiE7uxx33bUGbdtWmfaDjDfeGIxVq7qBSC7LQ/B4Anjkkc+Rk1OhObcxY7KwMBGvv34Sdu/OAKAu/UNwu+txwQU7ccEFP8hDDrt3t8bSpSfghx8yIUnK+V5vHUaM2IdTT/0UXbtG9w6pUVTkxfLlvbFlSwaOHk0FkXZUuVwBdOtWglGj9uLkk/ND/JihSe9mE68vLvbiyy+7YMOGDBw5koFAwKb6lZCQUIeMjCoEAjYUFiYjGFQa4nLVY8iQfIwf/z18vn1NakNNTQ0mT57cYsXc44jjeERz6A8AUL5zJ65/4QW4qnj+rszKwuePPAK/XHMuApo6R7XevRv958xBm8OHNXKx3u0GEcHh92u+9ycn49DJJ2PHZZehLjExKh7Eujr0+eADdF21Cp6yMo3MrPN4cHjgQHw6ciSS+4UpE2gBZ0UFBixahJw1a+AwqUsjiSJ+PuUUbJk4EXUR5i99O7qsWoX+77wDT4UiHwla/aHe5cK6W27B4WHDYnoWXVeuxKB582CXyz410CvKzsa399+P2vT0qOjImDNnEFat6qHhrnXravzjH58hNZVrBgWDwLvvnoCPPuqNQIB1NZtNwiWX7MTFF++07AcA2LcvFU8/PQpVVS7NPUaO3Ic//3mj5tzGjMlgEHj//V54//0TUFfn0P1K6N79N0yevAEdOvCzqKqy4/33+2LNms6oqHCHeBKEIHr2LMaYMZ/hlFNi10fr64GvvuqCr7/ugkOHUhp4UUasIBBat67CoEGHcd55u5GRUWNJq6nvZlNo1NcDGzZ0wKpVWfjll/aoqFA/NwAgJCfXwuMJoKrKiepq7XPNyqrCJZdsx/Dhh5rUjhbXH1rEtD5O0BwehpCHY/9+XrZSu8mmTInKi9hsq0WrV7N7Te+FlXem6902TifHwq5dGz0PksTLh0OHmt/noovMswBFgaUvvsjXW2XgSUhgT7dFWt2wJYiuvz58HZgGT+q7s2fHxnRhoXkYc3a2IftjNOAItnoDKX0k/Pr12hVgQWAPHO8LDf8sr77ayG7Xrtqw3qaMSZ+P6Nxzd5qGEYsiZzZUp7kPBNjh3rat/vwg9e3L3tPG5HaTw7bnzeNHZFZySRT5tX34YfMQrJYu36OmsXw5JwEx2zrWqhUnljCLOOzY8WjETNTh0NKe2DjiOB7RXO9Fbm4uLw+pcz84HJwNJ9rrm4jc3FzOpHfjjcZVxobcH6YTT/fuRM89R7mxRDDt3Us0aRIvvaloBQFeSjMLT4qmDfPncy4SqyyFAMfp6lZfDf1ghiVLrJNRyIfLRd9dd13sjD/zjLluIgv0GBAIEPXq9auB1A03aM/z+/kRqFct27ZVoqbDjakvvzTKUpuNk0fJaOqY/OQToqysMtNuTk/nbc5qrFvHr49+RTslhdvZUK0rZuTm5tKuXZzwyiygEeDEWeedx1H9ej2lpcv3qK8vKeHV6t69zVers7KIevY0BocmJhKdffaPsQ7FEFpaf4gbuc1l5MqYPVu7SbBVq4i5yptdkfb7ORTJ7K2UU7nqvq9NSOB9NLFow4EA0YsvEvXubUz61KNHzHuMQu3w+zluM1xx0bPOMtTcjdiPfj/H9oRJMBEE2PCP1ao6eJBo4EBjP0yaFDOt3NxcmjzZyJ5eUBFxqWR1c9xuokmTIm8i8fnMsx2ecw6z21xjcvFi6z0vXboY99KsX89+GrO9RmecwYmvYuVBjc2b+ZFkZZnz1KYNOwHkKPnjychV47PPiMaMMQ85Sk421uVzOHhYn3debMfmzS0vpOKI43hEsxq5Mp5/Xus8PuOMiLk+jskcs2SJeRH05GSePHXfS6LI+2k2bza/gRU2buRYVb1HNDGR6JZbYspzomnDZ5/x/hcr/aF9e9bVdLI5Yl+uWROWbhAgQ6xwNJAkzhylF3wOB4dlx4Dc3Fzav9+QQJtcLs3WXyJiZ/OgQdrzRo8meu21dyLe5+WXjescyclEW7Y035gsKSG65hrzrM92O8ty9RCprOTknGYVotq25dDnWFLn6NtRXc2v6NCh5rlA7HbWq2bOjG7BoTE8NMf1gQBHzZ9+urEdosg6m15NFgReWBk7Nnr9Ydq0ltcf4kZucxu5RPymjRljNMpiXYFsCg8y9u7ldHJ6bVgUWaPXp3ATBH6DY0zcsPSll9g60L8ZCQlEkydHVcfXtB1mWaXVR/fubElF6gc1JCl8ATe5fxqxv/rLu+82zhoeT1TJO2TI7SgvZ1msF1SrVmnP9/mM+267dDH4AExx4AAPA/0QGDcuQgrGKNsgY/9+fgXM9ol4PJyYSr3Xxe8nuuqqraaJKRISeEhHal+k8VBSwpPwCSeYC1GPh6h79yP0/POx+X5i4aE5aKxcyc6JcLlYGnvMmdPyQiqOOI5HHBMjl4gnS7Vn0OHgPaLRXt8cPMgoKeFlLDOHc0aG0ZICeJ/v9Omx1ZiVJNo4aZLRMS+XRjTJQhxVG/bv5zIFVgVFvV5uX4yJI2nrVk5VHG7ybN8+6hKMIVRX088nnmik1bt3o8o5vv660W7u18+4r3TpUq1hKIoSTZ0anX/ezCHfpk1FNCpf1O0gYlVQr6vIR//+xmpVjz32KY0da26M9ujBi/6Rhmik8fDVV6yLmOUkEQSijIxKuvHGsMEDEXGsHe2SxH6UAQNiSYYZ3dGtW8vrD2iRux4nOGZGroyVK7UZYlwudn/FQqOpPGhPNPfOJiZStZnrKzWVk0NF0T8hHiSJPaT61MGCwIVSw5RKCNsOk6zSel63XXxxbKumksQznSrBhGElNiXFuAs/DHJzc3nmHD/eyOPQoY0y9t95xyije/c2Cqrdu+WI+WCoyydNik7XWL7c6J9wODipVGNg9Sz9fnZYmxlkgsBeZDnro0yjqIiHoZk+1aYN15kzS8Ydy3slSbx4MWaMte8jM5OdCdEUkW8MD81B44svuDxT167KkZDgUykvXMKqS5foji++aHkhFUccxyOOmZFLxBOSPqNQ586miZ9+tznmk0946Ue/nchmo+rUVGMMq93Ok1GkmjV6HvLyOLmjXui1acPxlhaZf8K2wefj/gzn2B49mpbNnBkVryHs2sV6SThN/5JLYk9+tWuXsdagIDSqDKIkGXNlCgJXjNSfd9tt2iGXns66QST4/eY2/wknRF92KFI7ZOzda17VUVbXZs40RqMtXkx00knGIWWzsVrWsE4SNQ9mKCrijNfdu5urqQ4HJ4a8556YVMrfNZqsuprooYdYj5T1hy5diFq3rjTobDYb+3HC6Q+TJrW8/hA3co+lkUvEb9uUKdpR36uXZt/q7x4SWVrKMR1ccVs787VrZ9grE0r9H2Y10pSHHTs4RlI/s7RuzW+STlhF1Q6rrNLykZxs3LARDebMMbZbfQwbFvv+6u3bjfuDRNEoXcLRaIAksUNaz9aDDxqvnzx5vcZgTUqyrNpgwJNPGj2/iYnGMKdIiOZZfvyxdQmB7GyiP//5W0OXb9/OOoPZ/tpu3bS185ryXsn7cDIyKk0Flt3OAuv++7X7ixvTD5HQHPPDc88pvLdqFVuFrpYWUnHEcTzimBq5Mg4dMobHjh6tqbn2u88x8jalXr2ME2N6ujFjM8Bxjs8/H1aGGnjw+XiC1aeSl41nXc6LqNswZ45ZSQMiNDi5L7gg9rpz+/cTnXGG0Umutgii3LesaUdurlHXad06rOPAqh9++smo4iQnE+3bpz2vsJCoW7dizXlDhoSXczIOHZJXW7UlZ0aMiG1hP1w7ZPh8bJSbbWe224lGjNhrKE0YCLAR3LOn8RqXi4eVOuK+se+Wz8eBgD16FFnWu/V62WcUKWPz8bRlKj9f62AQBH5Fw6Gl9Ye4kft7GLlE7LpRv1mCwMtTkhSehiRFNK6i5sEM69YRjRxJkl6TT0lhQWC2J+fmmw0rkmF58PvZGtMvxdlsbAQ3TNgx9UMgwDnx9Ya6fHToYFg1jmijShLtOfNMy9ViCQJJj04LS8K0DdOmhWhKMr3MTA55ipZGAw4cMO43SUzUyrzc3Fzy+bg8gPq8k05ib2MgELkvTjttv6EL2rblSU6S+JGGoxFpTPr9Ch/5+ZyMyqzbRZGHiL70jyQRvfceO9H1RrkoslD+61/XhOXR72dhpObFrB2BAIfznH22tR8kMZF1z//8RyuwoukHn0/hQ+ZF5ifS/mhJUq7X05GPt9/m62fNUvhNS4s+kq6lhVQccRyP+F2MXHlSevNNrWdPFDmspLKyafI/Wh6saDQ4zP16r6PLZV6nxOnkzXomtdzC8rB8OW921AuJLl1CmQlj7of161koWgmeW2/VWGZRqGL06WOPWWcoAkhq1ZpLHIaBoR2SxIkzG/SHkA5xzTWmDEXqhyefNLJ21llGGp99pg0PFkVW/WQZFQ5PPPGxaQWm8eOj0x8itUNPY+5c80gvgLeQm4UmFxVxGhqz/BwZGbzw/8orS8LyEI3+QMTG/wMPcCClWfi0rLLedJPRfxGt/LfSH+bPj14HsdIh9DQWLtS+2hdeaP08W1p/iJcQSkmxTm39889A585AejrQuzdw223AZZdpTlmwYAHGjx9vfZMePYCffuLSOk4nf1ejSjnucKCgd29kL10KdO1qvP7rr4ERI/izKDIdmZbTCbjdKA8EkJKVBSQkAImJfKSk8JGZCbz8MrdFEPiw2fiw2wGHA3A4UB0IIIEIqKjgXO5qOJ1AXZ15+xp48AeDcCUmMj0Vb3C5+O933wE+n3U/AZAcDtgSEpiGy6Vc63bztbt2aS+Q2wMAwaAmtb+6f5GVBSQlofPez3Cwrh1fCkAAQRAIouoQUA+HTYC9zgcb1cGBethRDwcC8MOJfOQAUL8yQogF/huEKIoQRaWrRRGwCUHYaipQJiUiCBuMUOgABFEUQjRlWqIISJL1o1BAGnqNe8NNe/N3vP54wf+Hdli3oXVrYPRoYPx4YORIwKxqScR5Mo44/oCI6r3o0wf45RfWI0aOBJ57zlBmL6wO8dZbwHXXsSBxOHgy9/uV3wUBFZmZSJ4xA5gwwZxG69ZASUmDIFLJfaeTZazHg1K/H2nZ2aw7JCXxIesQPh/zDTANNZ0GWjWSBK/DAZSVGQWU2w3U1przZrMBHg/gcKBWkuBOSlL4kg+PBzhyBPjhB3MaDQiKIsTEREX/UOsPLhewaRPXVVH1XeivrK+bIS0NyMzE7MqJ+EvBA5DlvwCCAIIoAKIQbNAfJDjsAuzBAGwBH+wIwNGgPzgQwB50N5H/QkiVYVaCsNnEUFfL3W1DPeoqfKhGErQ6CNNQmkQQBC1NtR4RQQ1rANMAWkp/aC4aTcXxoAcdv/0gvzoA0K8fsGWLsYpoS+sPsReR+iNh6VI2+IqL+fj6a2DePOCaa6KnUVnJf+vrtROsjEAA2du2Ad26sZG7dCnQv7/y+5EjyudgkAVIXZ3GUE4BgMLCyLzIE3kwCKhqswFAQrjrwllVDfy4AKChzl9jYQsEWEhGC51gMp0GAgEgn+vMBhAMnUUACAJAgKSZxJ1APQB4wtxYeyeZBf4rGnwEDBFAahia6qYIkKSwp0ZAU4STlkbLXX+84P9DO6zb8NtvwDvv8CEIrM/deCPw0ENA3J6NI44mYu9elkHbtvGRlwd8+WX01zfILkgSTIUCEZJ//ZV1kltvBRYuBM49V3uObBQHg4rs11k6aep7hYNMo75eY2yHrRJsZeAC3KYGvcENsJO9kRCDwdiu1wpua5SWAqWlqMbRhi9kHUIAgQNzQYrh6pMAbk1STKwobFjpEHYVTfM5nWkITZT9zUGjOeTm8SB7jwc96PjtB/UY2b6dfWfnnw9MmQKcd97vxFoExI3ccLjuOsDrBT7/HFi+nA3LSZOAdu14+SMarF0L7NnDAuTwYeDXX9lwLSnh7w4dUlZO9+0DBgwAsrOBRYuA007jEbN1K1BUxBrp0aN8bWkpG4QVFfh1zx5kJSUxfz4fH3V1LISqqrQrs04nMGiQwn9lJVBRgUM//ICc1q21NGpq+L6yoR4NPB5gyBB25/h8zENtLVBdzX8DAeZNFtqy0CSK7K8SRfYs22xMp75eoUMESZLYR2oxO6/HyTiCNqhCIqqR0HDIn72ogRc+eOAX+G8tXKglF/xwoRYu1MCDCiTDDxf8cCIAZ4N/ltd7JdggwYYgRBDEhr9C6ECDUGSoW3o8TGJx/NFBxNPLs8+yoRs3cuOIo4n48ENg2TLgs8+A/fuBr74CHnwQePLJ6K6//XbgjDOAAwdYV/j1V3a4l5SwPrB3L8jnYwlSUcGaZdu2wAcfsBwGgO+/Z13jt9+Ua48e5fPLy4HKShw5cABtvF6W+bW1fMg6hH7pr2tXNqTT0/n6qioc3LULnVq3Zjmv1iGKi/mcKLy2BPDqYY8eQPv2Wv1B1kf8fmXBQK0/WEVy6eHx8Gq1rD/IBjsRIEkIBoMQLS6dgn/jXHyCSiSiComogTekS9Q06BA+uOFDAnxCAnxww0+sO9TChTq4UI4k1MADP9wa3UE+ZP0hCBuCylqxif4AVWvj+kMcxw8++ohf1+PFyI2HK0e7jP7LLzz5+nxswH3/PdC3b+Rw5Siw5o47cPqbbxq9kK1bc7hShNESloe6OtZa//1voKBA+1uvXsD99wMTJ2LBokXh2/H++8DDD7O7Ro1WrYDqapDfr51qU1KA+fPZSI8Sq++7D6PeeYeFuYy0NDZuS0qU77KyAJPwLE0/1NUBkycD//2vMfza5QKuvRYYM4aFcnU1hzK9/bYijLt1Az75RAkh37YNuOACLW9q2O3Agw9i3ZEjOHXQIEXIqwW1fPj9fBw4AGzZgnqIGoG5bcwF6Pjkv0KX+nxavUMmIfsMZHKrV/OwVKNPH26C38/XBgLst9i7V3teejrL/9pa1oXU3990E/td6uuVRYncXG23CgJH4/XuzV3444/70KlT19DCgaxP1NVpdQvZR6HXWQIBoLT0KJKTW2mCD4qK+HGZQRCUiH0iwOfzweXyhLzkso5k5iUXBH6Ebjf/L8+KgUAdHA6nwW8S7n/15/r6ehDZI+p46nAyPd1gUIIomoW4Rwer6xt8SyHIYXFuN7/mOTn8fUuHG8URx/GImN+L/v0V+Tl3LnD99QCi2PIUAR8+9xz+9NJLwMGD2h86dGAZfMYZEWmE5eHrr4GpU4H167WTZ3o6y+Bp07BgxYrwbfjxR+Cee4BPP9VGkXm97HjXR3CJIjBxIveTaGV2avHuq6/i0hUr2LEg8ymKbPQfPqxMdjYbcMUVwCuvGDx5mn746CPWIYqKjDfr0wf4299YaFZVsdBU61g2G4fDPPaYcs2iRUzPKuKtf3/g/vux/ssvcfLAgYr+oNYd1MK/rg749FMEa2tRCzdq4EUlknCkTSc4/rsUPk8rjY/ATH+Q/Rh1dez3mDfP+HhGjVJkv7xGIa/ZyLDbeWccEa/hyEGLogiceSarTrKcr6vj++ijz1NSgCuv5Gvq64GffmIdQtYN5HsTKTqEeq0jGFT+1tcDJSVHkZLSKiTngkEeZkePmgdVAjwUk5O5PcEg6xBuN+sQcgCEVWCjzcbXy+G6fE1k/SGSblFXJ4HIFnFlXd4ep4ckNU1/AKLTQdQ6zmWXAYsX8+cW1x9aZCfwcYKYN0Tn5Snp871eosLC5s1s+NFHxkyCAGcaClMQPGoetm/n9HH6dHRuN+07/fToqmQXFXFecF1CiXqbzVhzFw0JKMLU+DNtx7JlxuQN6enGzAJt2mhq3Jj2Q0EB0Wmnme/0b9OG6P33lXMLC+moOuuiKBLddZd2R/2MGdaZnQGqyMjgnfvRYvt2837r3TuqckN6lJcTpaVVaUhlZBhJBQL8GNU5N/r2JTp4kPNhZWVVaGiMGmXMyPvUU8akT14v1/E91hk/lyyRyyUZD7m04oMPmtf68fn4MfbrZ+Qf4ARbN9/MCTObM7Oh38/DdcQI80cOcGLSSZOINm6Mrh9i5UGPefOUMZCQwM9fj5ZOHBFHHMcjYn4vqquVRImCEKpF32zv91dfmSdizMiIWKc9Kh6qq7n+iV5HEQSWm6tXR6YRCPDkqy8AD5DP6zVPAjVyZFQyNdSG4mJjJkNRZJ1CPeGLItGll2r0HtN+eO456yLkl16qKaKed9ll2moS2dnadL2SRDRxoqX+QDYbfXXHHZH7UU3vkktM6TS2/t899xCpsyMLAtELLxjPKyjQJv4WRaVoxsiRezVdnZxM9NZb2usrK7nEkJ71ceMiJ1yMBuGuLy4m+vOfrR9rairnC33zzYWm1+/dy/WBzer2Op2cgXjx4shJn2JtR1ER51sdPNhY8lF+Br17M+/ytPR7ZV8vL1eSanXqpP4+nl25xdCozv/kE2XybN2aFja2kKgKhgH0xRfG0jOyBfHii5GvjwRJ4nT+ZnXYRo82pOa3xJtvclEw/VtmloLWZiO68cbYSggsWcJCQi+w9X3TujXRq6+G74d164ztlY9evdiBIfPwyitaQ7ZtW62gKi/ndIRWgkoQmPdoIUmcKVNPx27nNHYxIjc3l2bONOoL991nPHfXLq6Fpn58t97Kk/O8eVx+SP0Ib7vN+Aivu87IenJyjdyljUY047qykoWymk99F555pnVt20CAk3MOGWIs/8iGXy2NG8e+kFhKMEfThl27uLpYp07mup3bzck/b7jhu5jLL0TLA/+u3N/r1VQ3I6KWF1JxxHE8olHvRWGhkiXZZiNas6b5lflFi8wnxJQU9mpFQyMSVq/m+if68oDZ2dGX8MvL45T1+om3VSsjXYB1jUOHLMkZ2lBUxBaTenK12XjC1Ru7l1xCVFJi3Q+BAHs+zfiy20PO8NzcXL7v0KHac669VitANm40T+uLhjJGJ58cm8D54gtz5/uZZ4avT2OBV15ZYlhTaN/evLLSrFnadZOcHKIZMz6gwkKiM87Q0ujShWjDBu31P/3EKpxejfzTn6Krr2yFaMf06tXsFDdX54LUoQPRww9bV5U6eJCHhpnKLooS9e3L9XP15Yyaox15eUS33GLqMwq9jqNH76E9exp372h4UOOtt5R7yypwS+sPaG6C9fX19NBDD1GnTp3I7XZTly5d6B//+AcFg8HQOcFgkB5++GHKysoit9tNZ555Ju3RPYWSkhK6+uqrKSkpiVJSUuiGG26gSpXHjIgoLy+Phg8fTi6Xi9q3b09PP/10TLw2uvPnzAk9yYo2bWIvAKaD5QBat848Hb3Lxe6chkmwSUJy716i888nSb+k1bcv0WefRUdj/34q7NvXqKlnZsbkmbVsx6JFbGjqaeu+q01MjOixprlzrS2iUaNoySuv8Hnl5SzE1b9PmqQVPF99ZXDlaerkDR8eXf/JWL3aPL/8uefGNMbkfvT5iDp31pJKSzOfbGfO1MpIr9dPS5Zwc2+/XSvbk5LYv6FGIGBeoD09nejbb2PrBn07osXmzeyjsagA1VA7z7q4vSSxT+H0080fg83Gr8VDD0VffifaNsirvCNHmq/yiiJ7vZ95hhdUYkE0PCxapPSbx6MtVt/SQiqOPw7+EPrDjh2KVWCz0edmxc5jgOX7vXSp0XqQ9Yf77tMYP43WIfx+oieeIJ9epiYlcQHNaAysQIDo0UepTr80lZ5ubri1bm1auN2yDUVFLEP1xm7XrlrBJoq074wzwsvakhKiMWPMBUxqKq1Rr8IuWqSdzFu31lp4ksQ1bKwEltMZth6uAT4f0cknG2v1JiRwuaQYIPflY48Z2XrqKfNu0dr1Qbr6an7869cb9ZARI4zRZR9+aJS7stO9uR3MZvD7iaZPt65KKaud991nHWR36BA7ri38F9SqFdFFF0WvWsfSjvJyNgv69jWPUEtNJbrsMqOToTl5IFIM7g4dZL7+nxm5Tz75JKWnp9NHH31EBw4coMWLF1NiYiK9oIp3+Oc//0kpKSm0bNkyysvLowsuuIA6d+5MPpXhM3bsWBowYACtX7+e1qxZQ926daPx48eHfi8vL6fMzEyaMGEC7dixgxYsWEAej4deffXVqHltUuc/+qgyenr0aJKhG3EAbd5M1K2budZ+992UG2WR8XBYOHcu1+3Vx29kZfHqcTS1eouK+A1Wv2GCwKPezAPaq5emZlzEfpg/3+gua9vW6MY66aTwbjNJ4uU/k6U7yWYjevll5dzFi7X1CdPStPV3JYlnPbP2yQpFtCvjREQ+Hx0xe9ZJSVHPTvp+fOMNoxy95RbjdWYL1P36scFTUsIGpPq3rl21C9xEHPnFi/vaYvApKYayxTG3I1pIEi/GZ2WVWQorm42j2Jcts6bz2WdEQ4b8bCn00tN5uH/yifXr0dg27N7Nwj0nx9iXgsDTwcMPR7fDIFoelizRGrrysG1pIRXHHwd/GP1h8+aQoSuJYnShvhaI+H5/9ZX5Uo8g8IS/f3/zrCYvWWLUUxwOoquuMu51MaMxfz4vC+on3FatuBC5nn+Ph+i116Lvh8JCorFjtcLQbifq2VMrvxMTObwlHLZvZ2tCx1NQtuBkh4rfz3WB1eddd51WYOzZowmnMhipZsI6DL674QZzw/nOO6Omoe7L8nLeRqM39sxkz9y5WkPV4yGaPZt/e/NNDltWd/399xtl59SpRiNNEIgmTIjN2G3KmN6+nYMAbLZ6Sx0iM5P5t5LBBQVEF120nfr1M48Sczi49PNTT1mvEje2HZLEq6rDhxM5HAHDvRMSiM45J7RjIiJi4WHRIuU+O3a0vP6A5iY4btw4uuGGGzTfXXLJJTRhwgQiYi9sVlYWPfvss6Hfy8rKyOVy0YIFC4iIaNeuXQSANqo2pq1YsYIEQaCCBqNo9uzZlJaWRn6Vp/Dvf/879ezZ05K32tpaKi8vDx35+flN6/wpU5QJqWNH8w1tUSDqAbRjh+nEKgkC0QUXRCwyHhUPksTLevrNBgkJHHJjcQ9NG8rLedVTv/e3fXvzJbJWrYgWL46+H956y8hfu3ZUqzZGbTae1MPNiuXlvHfHbAYbPFiZeXw+43kXXaR1bBQW8jVWM+JDD0XXNrkvX3nFXFBNmxbd9ToEAkR9+mhJpaaarwquWUOUllatETBTpvBvGzdyyJGazoUXGn08r7yyxHTPbEoK0bZtMfRDE5Cbm0uBAHel1f5deaiMHm3u7JZ5KCpio7JvX+uItcGDOchDPeSaYz/MnDnv0LRp7EszGxJduoTdsh8TD8uWKfdwu1mAt7SQiuOPgz+U/rB1q7JSKYrsUG0Eon6/8/KMQqDhqMjIMF0dbRQPmzcTnXKKdrISBJ4gw0RaaWjMm2fcqpScbJ6zxGYjuvLK6J39hYUcJq3mz+kk6txZa2DKHt5weP99c548Hu2WpY8/1hrqGRlGD/ETT5gvwQG82BDlQkpubi5Rfr7RMgXYCaGLaLCkocOzzxrlz0svGa+triYaOvSQQUbt3cuy8c47tTI0I0Obg0LGuHE7DLJWEDiyLOp+aCJyc3Np40aiiy823wMrH9nZHGWlf0QyD5LEEWQXXMDrJGY02rZlPSs/v3nbkZubS6tX873VTgb58HrZgRBuXShWHuQAhgsuaHn9odmzK0+fPh2vvfYaPvvsM/To0QN5eXk4++yz8a9//QsTJkzA/v370bVrV2zduhUDBw4MXTdixAgMHDgQL7zwAubOnYu7774bpaWlod/r6+vhdruxePFiXHzxxZg0aRIqKiqwbNmy0DmrV6/G6NGjcfToUaSlpRl4e+yxxzBt2jTD93PmzIHXG7bSmyV6vPwyBn3zDQQAQUHA7vPPR16MmRILCgqQnZ0d9fmJhYU45cUXkX7woCajMQGoadUKWydORP6wYU3mIWvrVgyaPx/JhYWh+xCAyqws7D73XOw788xQ5kOz68X6egz873/RddUq2FUp+/weD8T6etgDAQ1dSRSx5dprsW/MmKh47rJ6NQYsXAh3Q4kjAuBLTYW7ooJr5gGo83qx/uabUTB0qCWdxMOHMfyFF5Can6/pz3qHA9/ccQcODxoEAGizcydOnzkTzoYaxXVeL7687z6U9OgRuqbt/Pk4Y8UKiEShdP9CA2+/9u2LLx98MGK75L70/PYbznn4YXjKy0O/EYADp5+O76ZMiXi9Gb79NhsvvTQcRPzcBCGIp55agY4dtZm9CwoKsGXLmVi8uD/q6zmrXkZGJR59dCVatarF6tVd8Pbbg+D3OwAACQl+TJ26Gl26lGp4qKmx4/HHz8ShQ2mA6ml37FiKf/zjczidpgUBI7YjGuivr68H1qzpghUreuCXX1IAQ7EIgttdj9NPP4DLL9+OxMQ6Ux6CQeD779th1apu+PHH1qiudkJdxkEUg8jJKcVZZ+1Ft25r0KFD49ugb0ddnYiVK7vhq6+64JdfUkLPEQC83jqMGbMHl166XVOQPdZ+3LKlHWbMOAOAgEsu2Y7zztuAyZMnx7Mrx3HM8UfTH1IOHsTZDz8MuySBAOw+7zx8f801MdGI9f12lZVhyJw5aL9li2YGJAABjwfbLr8cP519dtRZja14cJWVYcibb6L9pk0heQwAAbcb+UOHIu/KK1HbqlVYGllbt2LI228jsagoNMMGAdQlJsJVVWXQf37r1g2rHnoIQaczIs+e337DKbNno83u3SE6AZsNQbcbroYU/gTg0Mkn47tbbrGmGQxiwKJF6PXxxxBVpYwIQMGgQVhz552A3Q6xvh7D//UvtPv++9DvP44di62TJoVIJRYWYuS0aUhqqLQh0wJYP/ro2WdR07Zt2Hap+3Hw3LnovnKlpp/qPB6smD4dNZmZUdFQo65OxD33nIfffksMtXLIkHzcddc3huuBXvjXv05HYWEyAAGCEMRVV+XhT3/ajfJyJ/71rzPw00+tQ3TGjfsBEybkGXh4773eWLq0HyRJye5rt0u47bY1GDq0MOY2xAI9jUOHUrBsWR9s3ZqNujo79OWbBIHQrVsxrrhiO/r2PWLJQ0mJG59+2hObNrVHUVGiRo4DhJSUWpx0Uj4uvHAnfL59zaoHHTqUgo8+6o28vLaoqnJBrZd16FCOyy/Pw5Ahh8PSiITnnz8VGzbkwOutw6xZ81pWf2huq1mSJPr73/9OgiCQ3W4nQRBo+vTpod+/+eYbAkCHDx/WXHf55ZfTFVdcQUQcstSjRw8D7YyMDJrdEPswZswY+vOf/6z5fefOnQSAdu3aZcpbs3tiqcHD8fLL2lXL7Gz20sZCozE4eJDonHN4Jddsaenaa6OLZ4zEw44d5lmZnU4OxcnLC3+9JPFmAX0mA1G0zsgcSwj2668bV3b14VkDB0ZeaZ8zx7g3GSC6+mpleU6SiMaP1/5+003a/dE7dljv+23XLrrQbzUuvthIZ8QISzqRxpMkGffIqKK+NDR8Pt4nKp/ncChZEiWJu0btZb3rLnMefD5+BPpmhIvEOpbZFSWJw6d69bL2znbrRjRhwuaIiT0LCjj6XZ2YWxniEvXvzwk6Y0m6HU075FVqDmnWPqPLL1f2DTemH2WaY8e2vCc2jj8O/mj6AxHR+zNnalcDTzklpo33jZ4nAwGiJ58kSkoyhsiKIi/D6JeVGsOD30/0wAPmGxW7d+d4VjlpkxXWreNtSHpdJyXFPLRm+PDot5Dt2aMVcoBxP7DbzZN4OBQWUpk+dwjAS3fqMKEPP9RugcrJ0WT7y50/n+jKK60FU4TEXoZ+XL3a2Ed2O4exR0tDh+ef15LLydF2t/r6OXO0Xake3h9/rO2K7t2VFUU9D889Z1zozsy0DmA81lmFd+1iOWtVbMPjITr99H1kMZ2EEAhwJNbpp5vTSkry0SWXWCfPbGobLrjAqNqnp2tz3Mbal6tXK7T27Pl/Fq68YMECat++PS1YsIC2bdtGb7/9NrVq1YrebMhU05JCSo/mUN5CD7+0VFuqRhCMGfUi0WgsD/Pn86xjltVYnjki7HSPigdJYoNen1EZoOq0NGXzRTisXMlhS3phZSZoPR6i996LrhOIKO/yy7WbH5KStMqDIBBdf31Y4bf0xRfNszC3bUuaFHWffaYNP2rXjmj3bu140Fsf8uF0hs1cZPos/vY38+caSwIvHfSVB665xprGyy9r5eR55ynduHy5UVC99NJS03vu2mUcpg4H0bvvRtkPMSCWhA333GP9+gC8ZerxxyNHepWUEN17r7nBKwhMZ+rUqLaoxdSODRs4Q6Q+QnDYMKInnlgR/c0aIOdW6dw5buTG8fvhj6Y/EDW83z6fdrtLQkLUG+aaQ5lf+eCD5pMWwE77CFn+o+Zh61b2nJk4zA+cckrksnnFxaYlDMnt5jKGet7HjIl+I+f27VShN8TbtdNOqtnZYcO6c3NzeaOm2cLDbbcpJ1ZXa1MPiyJRQ+KzUF++9Zb5Zk6AMwiF40GPQ4fM9zVbVAeJ5nl+/73W6HS5FINTf31xsdahnJio2NiVlWz4yr/Z7WwYm/FgVTHJzOf/e5XOIeJtPmZOfLUqe8EFkbfeSxJHufNeWiMdeR/vzJnRO82jaUMgwHuD9T6a5GTeIdeYUkjy6/jkk//PjNz27dvTS7pA/ccffzy012Xfvn0EgLbqVjrPOOMMur0h2P6NN96g1NRUze+BQIBsNhstXcrK88SJE+nCCy/UnLNq1SoCQEePHo2K12Y1cmUsWqTV+FNTI7pgmlWZ375dO2PoBAHdcYeplzhmHgoKWNjoJ87EREPmRlMUFXG9ONVGh6B8vZ7vhISo0tHl5uaycan3yubkaGeMhARjemB9P9x8s5EPUdTW/PX5tJmaRJG2XXKJ8rskcYFZtdWh/vz55+F50GPWLCNP6emGzRSxPMt//UtLrlcv6xp1+/drt0m1ahWqvGQQVKIohd0+/MorRuey3iv7exm5amzdykELVnnEAM4aOHVqZH2spITowgu3U7du5jpP27YcBBBp21cs7SgsZP1HKyCD1L8/xVTSaepURTjHjdw4fi/80fQHIt37ffvt2sniqqtij/xpCg/797P8tIoOu+Ya04kvZh6sHOaCwIZppKgrSWKnulkYi5l1cNFF0S84vP66VodzOIza//Dhpo7qUD/k5Rkj12RdRN22OXO0/PbvT0vUYVX795vXppEFkUmbLJ+Fz6etFygfJrUFo32elZXafZ6CwD4Aq+v1vnp1LqxZs7Ryt3Pn3ywDCcrLjU0RBN7WHGsbwiFWGj4f5+4we/Tq12j4cM5tFm5IShInSB848BdTB7wg8OvzwAPhdZHGrMLqc5i4XAGaPj22xF9yYZhRo/6fGbmtWrUKeUtlTJ8+nbp3705ESuKIGSpjoby83DRxxKZNm0LnfPrpp6aJI+rq6kLnTJ06NWziCD2OiZFLxCP9ggu0o+S88yxdL8dEmff7OdmRerJWHwMHakJomsTDsmXGMB2Hg2NZI1kCksSzmz7c2GyHfHJyZC+qjC++0M40ckpaNb2ePUkfS6KhsXKleTj1sGHaJb3587UJtXTKE912m7mhC3CO/nDt0GPpUiMNt1tTaiDWZ7lhg5ZkYiLRa6+9Y3quvpa9IGhzYekFVXa2dfUCSWKDTN+9I0ce+2LwkcD3J+ra9YhlLhCAI/DuvNM6aYM67HvmTH7tzAzotDTr9P6NaYffz4aq/jXyenlIRzrUSTaKi+NGbhy/D/5o+gORyfu9fr02O03r1mEd5cdEmZeXdlJSzCe+Ll00qembxENBAdFllxm3Cg0eHF3pm7w8ohEjtJFgbre5sTtpUlhSoXb4/Szo1IIxPV27DUkUuSKFSvPX9IMkmYcd22za0OeiIq4P1/B7wOnk8Gw1HX2GZrWepTO2Iz6Lc8810rnwQut2RIAkGVcxJ040ySTVgLVrtd3Ys6fShIMHtX4LUQwffLd+vVHFTUgg+u67ljFy1dizh2jUqD1hSxIJAvedPmGlGQ/5+RwlZuU0z87m7V96/1Bj27BxozGXqsMRnf6QkKDoOdnZ/8+M3GuvvZays7NDJQCWLl1KrVu3pvtU3qJ//vOflJqaSu+//z5t27aNLrzwQtMSACeeeCJ99913tHbtWurevbumBEBZWRllZmbSxIkTaceOHbRw4ULyer2/XwmABoQdQKtXa8NlPR7TsJ9jrsyvXWualTlk0Tz6aJPLEOXm5rKwOfVU44rl6NEUTTXqDdddZ9TKzYRsq1ZEW7aY86CGJHHokNqqSEvTemUFga2LhrFnoFFZyXuB9Dx4vdpwspISbebKoUO1M/Prryv9op+hzjknfDv0WL/eGMZks4VWuxsznkpLtcJCEIJhAxDef19rCA0apCSjLihgL6yavXHjrLeZlZYa9wgLAmdXbAqaS8hJEutzI0ZYR4/JOtDNN2uFjFXIVW4u78Exy9jo9fJixvvvN4+xP2HCFtMAiWiPd9+NG7lx/D74o+kPRBbvdyDAq4/qF/HUU029acdcmc/LM2ZLlg+7nej88+mDSPtVo8DCuXM5vax+UuzUiSfMCHh/5kyjrE5MNE7agkD0l7+Y0jD0w969nGVZz4+aZlpaqPi6aT9+/LExvBrQVnAg4mVItY4wa5aWzvPPmz8DQWCrzqoNZnjoISOdfv2s9aAocN11anJBGjnS+lyfT7u7z+nUJhefNk1bvicxkYMkrfDoo8auycwsD5sxOBo0l25eWspdLq9uWhm8J57IQYaRKjT4fOwnGTDA3Gmemcn+l8LCprdhxw6iTp1KGq0/OJ3/z4zciooKuuOOO6hjFDoslQABAABJREFUx46hYu4PPvigJlW/XMw9MzOTXC4XnXnmmfTjjz9q6JSUlND48eMpMTGRkpOT6frrrw9bzD07O5v++c9/xsTrMTdyiXi03nij9g08+WTNCufvtmJVWcmuHhMPZxDgtPqHDjWdh8JCTpakf/v69Yuc7ECSeHVTL+jMjN2MDKKdOyP3Q1ER97n62h49tPdwu4lmzbKm8fTT5un91TXvJIkK+vdXfuvSRWvZrVljXkIJ4ERZ6uRVkbB/v/kq/Zw5jR5PksTdoiZ3663W55eWEqmb6/GEZD3l5ubSkiVaj63LpU1moMeaNUZdQBT59fk9isFHS+Ozz9gINVsoUA/XSZOInn3WumSGjJUruUKVWa4yh4OoW7dig+CLtQ2SxH6Whx6K/pD1uNtvjxu5cfw++KPpD0QR5qmPP9Y6yiOtHh4LHmQEAiybTTxmQYCXkj7+uOk8SBJbOfpaK2lpbAhGSri4ebPRqW+WoEoU2QkeTT8sXKh1wIuisZbe8OHaUGM1KivZSaGf4N1urXW3di0F1ILlyiu17V261LrMUIODJurxsGSJuR5SXd3oMfXqq1pyCQnh80/MmKFtzuWXK82dO/cdGjdOS+/EE61zoQUC5l3coYNS8z1WHAvdvLqaaPp0Y2Ch+pArbs2bF3k/rCRxMOHw4VYVOqvor3+NOoecZTs2b45Nf5gwQebh/1kJof8lVFRUICUlxTq1dX09cMMNwDnnABdfDJiUCViwYAHGhysZ9MgjwPbtgM0GfPYZ0FDuBjYb8M9/AvfcE57GoUPAjTcCyclASgqQmgqkpQGtW/PRpg0+2rQJ599wA/9mlvZ/6lRg61YgIQFISuLjyBHmp6zMeH779sC//w2cfz7/f+AA8+DxMI2EBCAxUaGVlIRvd+3CKWPHMg/y4fUCjz4K/Oc/gM+n0Pd6gSFDgAEDmE5CApCcjG937sQpY8Yo1778MrBgAddskWG383NRIzERuOUWbDpyBENGj+b/k5OVo6Hf7pt4GFs/yIe7vhJu1MKLWnhaeeA9mo8EVMELH8QUO1pPfxBJrd0hMnJTj27eh7snFMFTXwE3/PDABzd88Hht8Fx1ATxZqfjppzycfOgHJGxYBQ98SEiywfvKv5DUsRW8XmD2M1XY/e4OeOvL4IUPbvjhgh8u1MIt1sPzl+vx0y/7MHhwP3g8gNuN0F+3GygtBV54AXC5AJctANemdfDUl8MDH7yohht1qOzSHlkXnQ+XC3A6AYeDr3U6+XjvPR5WDgd3p9PJ9BwO/rtzJ3DoEEFOLZ+czK+BfF5pKfDhhzyEbTbg6FGgpER5HK1bA2lpR9CuXRuIIvDjj8BhVUb6hAQgIwOoqeHhKopMR/5cXs409UhLA7p25XP9fmDfPkAQ+JCHvSgq3/n9NfB4vKH/1Ycocjvq6hT68m/yZwCQpAAcDofmHBnBIA/roHUVJKChCIR8T/3rWV/PYuDYIghAhCBw37lcPKZTUwFVBQ8Dduzg6er00yuwZk2YeTKOOP6AiKg/ADxZ19UBV14JdOxoekpY+b9tG3D//cAvv/ALKU8WXi/w2GPAvfdG1kFuu41leEoKv/BpaUB6uqJDZGTgvXXrcPEtt0BTe0zGxo2sQ6jlfm0t8MUXzJcebjfrPX//uzLhTZkC7N/PNLxehY78NyUFa7dvx/A//Yn5lHn88EPg4YdZYMkQRaBLF2DkSJ7EGmh8t2sXhp19tqIn7d4NPPSQlkcz/UEQgDPPBM49l2mMGaPoDqmpoX77bqsTU6/YC9fhA/DABw9q4HEDXqpGgv8oPKiBV6hB8pSJSDh1IJKSFP0hJYWPBy7aiUPrC+EiH9yohRu1TKtLNjyXngtvgoi9277FsFXLkVCWDy98SMhpjcQ3XkBCsh0//QS8+FQFPDs3wUvVIRpuWYfo1wP7urTDoEEnhPQGl4u7XNYhZs8Gfv214beaErjzvgvpDx7Uwumx49Dwoeh7Yj9T/SE/n3UIu51/czgUPcPlAgIBYNUqljkyRo8GBg9Wzv3gA5a/osjy8+efAUlSHlG/foDPV4SsrEzU1PBrUFurPLL27VkH0OsPNhvT+eUXIBhUdBh52PTowc9EFHk41tYadQNZVgsCUFvLOoRar1D0C1af1TqDWj8QBNYf7HaH4Xv5b00N8xteB+B2yG3V6yByvx1b8PMURX6GCQnKq+F2W1wRBL77DgAqALSc/hA3csMJqRUrgPPOU/7v3p3fNtVTjShgOnbkWSEMgjYbxFNOAV57DejdW/vj8uXAuHHRNEeBKPJMYbfzrFNbqzUyY4H85obX5P8nkIOD+Bk5Lc1GHHH8T0DWB9u1q8Dhw3EjN4441IjKyG3VirV5gLX7d99VnMcNCKtDvPACcOedYfkgQYCQlQU8/zxwxRXGE9LSzJ3ZVpC1admKcbuB4uLor9fDzLD8H8SL+Ctux4stzUYccfyPoWWN3Oirff8RUVnJQkp2nfz0E7uYYpmwhw0DOnRgOh4Pu5p0ECUJWLsW6NMH6N+fP8tITwd69WJjuU0bdp14PCx8rIq1B4PsPa6pYQErG7g2m7KEk5DAwstuB0QRlp4OoqgM3P8FT8kIfIWe2I0u2If2yEcmfkU6fkMKypCAKrjhgxN+2BGACAkCguCW/S+0Lo44mhedO/Pfw4fDnxdHHHFYoE0bZXW0rg645BJeVY0W2dm8apmRwctxJiutAhFQWMirxZ06AatWaU845RSm0bYtG7wJCWxwm+giAFje19ezc7yyUjFwBYF1htRUXn1V6SFhJWSU+tLxLmW7Yi96Yye64Sd0xCG0RQEycASpKEUSKuBBDVyohR11EFEf1x/i+EMjIYGniJZGfCU3kicW4En/7rvZUwoAgwZxCI8oRl7Jtb458OSTwMyZHN+hR+fOwHPPcZh0BCyaNw9Xnnkmx6AUFwO//abEkO7axavB1dXKBXY7cNZZwLPPAiecAEDlTa6p4RXsu+/WhgjJSEoCTj6ZjfbKSmDDBr6fjORk4L77ODypvJyN7LIy/lxRwUd5OcewfvqpxkMcBCBmZ3MIld/PxnlRkTZOZeBApnfwoPKd0wkEg6D6eiU4xeXittXW8uH38xEI8CFJfOjiRbUBLgAyM4HOnVFbC5T5nKisdaK8zo2qOhcq6lyoriRUIgE1SEAVElADb0MAkRyM7EQtPPDDCT/cqIUTvoYgJT9cqIMTAThRDzsCcKAeNgQhgmCDZHOCBJvGz/D7va3acKM4/lh4+mngjDNYP+7TpwK7dsVXcuOIQ42o9QcA+OYbYMQIljmpqWzopqYCiCIazAy1tbwqfO+9oMJCrcwCgL59gVdeAYYPj0wrGMS7b7yBS089VdEfSkpYhzhyhGNL9+/XXtOrF4cCT5igbUNtLe93ue024NtvjfdyuYATT2Rj3Ofjfti+XRFsggCMHQu89BLL9bIy5qOsjHWHsjLWO0pKWE/ZvTskHAmAkJjIupO8l6S6mnUIGenp3Dfr1mmN74QErY4EMI9JSUzH72cnRV0dX1dfz89SdghYweEATjgBdeRARa0D5bXukP5QVe9Cld+JSp+IGiSgGl7UNBzyZqhaDkJGLdyoC+kOLtTCE/ouACcCsKO+4ZAa9IcgbAjaXfIjDu3S/H1g0KTi+AOhZ0+ens49F8jPj4crtxhiElIA8Oc/A6+/zp/POgv4/PPGG7kygkFsv+wy9PvkE/OQ4jZteO/NlCmWJKLiYdEi4IkneF+PGtnZwF/+ggXt22P8xIna344c4fsuW2ZczfV4gLvuYt7Wr0fZ1VcjVR2WnZCg/G614gywIT1lCvDJJ8oMnJ4OzJ0LXHAB3/fhh4EZM5QNlA4HcPXVwKZNLFAb4Pd44FL3oSAAc+bwhtJwqK8HLr+c2ylD3twBsED/7js24M1w333MH5EytWdlAT/8wIrMli1sLcj8338/cOmlvFd59mwDOY14ePttQP9cTCAv3Fc9/i/U/Gs26hqEoR8u1LbtCv/sN+AnJ2pr+VzZ5pfltt/PvofXX5dlPWH8eAHz5/PjmzmTH6eM1FSOwk9MVLqwrg4h+vPnf4CPP74AX36pXDNkCG9Dd7vZz/Djj7x168gR5ZyzzgKuvZa7ft269Rg69OSQLiH7JTZvBhYuNPqG0tP5Wrebz9+xYxd69eqjEfDqz/JemHDf799/AJ07dw79DmjTOgBGBUL9OT8/H+3bdwj9L+8lPnzYqFMBrNdlZvLCi9vNvBQVHUZmZrsQbTUi/Q8AhYWFyMpqa/zBAnY7D8327YEHHwRcrgo8+mjcyI0jDjVi1h/eeYdXWwGWD/v2AV5vk3WItXfeieHz5pknMsjJAZ55xjyMWYWIPPz0E3DvvewwV0+8Hg8wbhw+OO00XKAPq66rAx54AJg1yzhZiyJvw3rpJRYmd9yB4NtvQ5T1DEEAzj6b9YB27az5CgaB6dNZ/paXK9decw3LfqeTZfeECdzfMk48kZ3gCxYoRqooImC3w6FO1NCtG8txs73Kanz2GbfHzOC12/nZWy1YHD0KDB1qdCTMnQtcfz1//uc/eV80wLrV4sUsbCdNAqqqNJdp9IeePdkREAHBIOsPNT8cQvUZ56K2NghfgyPehwT4pz2F2n4nhXQFtayXj0CAu2HDBqaZns5d37Ur0z/hBO5KGRMnsnoj31+mI9O9777NWLp0MPx+pdmPP85rLPJaxauvAkuWKHIvNZXXZzp2BL75Zj1OOunkkN4gy/baWk45ow+oEEXemj1smKJr7Nql6BDyEYsusX//fnTq1CXURjMdQr2AYaZH/PJLPrKzO4T4rK4G9u7l4A25b9TweLj9OTn8Khw+rOgPMl01ovn/11+j1yFEEfjTn3iXxXffAY8+WoFPP21B/aFF0l0dJ2hUdsSLL9Zkvmu2zIaSxBl8rerSJSdznvRYin+boaiIC4/pMiTW2+1E116rTWkvQ5KI/vlP06yKJAhE55xD782axVkNTzxR+7vHw8XGrQqdycjPp2J9de9+/ZTSQz4f0fjx2izVSUlcXVxff0afdbhHj8j3JyL64ANtrT112r/ERG6fFdavN6bHTU1VUtodPKh9tnfdxd9Pn27o06C+j2Moa0HUUIpJTyMzk9MhR0BRkTbNfY8eymW7d2srJwiCdflGeUx+9RWXeZSvcTqJXn5Ze+6MGfy9fE5KCpfPiTSuP/rIvJxyp05EBw60fJ28SNfLZQD69TNPmJmRwa/kc899cMx4iAbNlUU2jjj+P6FR78XMmcoLnpND5Pc33xzz9NPmKdoBnoRnzoycmTgSJInpdOxolFnhqifMn0/Upo05bx06EC1axCWEbrzRKGTOOity1QdJou0XXaRNee92M68yFi7UZqpu0F3okku0E3BGhlHHCVe/RkZ1NVWlphqvlT//7W/h+1VfMgogeuYZ5ZxXX1XoeTxEu3ZxJQt9Fmr90aVLZN7VKCqiWrPKDW+9FdXld95JBAQJ4Eep7jr+TTm6d7dWzXJzc8nnI0OG5REjtGpqYSFXbFSfc9llRG+/bSzXqYbfb14y2G4nevJJhYem4FjrIMXFrF7rVWBZfR08mGjKlHWNrswQDQ+R0NL6A1rkrscJGt35Z5wRGkk/jhnTZD4MA2jWLLKsIO12c10XVU3ARg/AJUu0NWDkN2PsWGNFaRmrVxvrzMhCbtAgvm77dmO9OpeL6PbbeWYJ1w9ffGGsY3vJJUopnoICLjCqpp2dTXT//VSvFnBmwiaKfnpv1izz4qVy38yebX1xSQnV6J0UXi8LIyK2Ftu1U3678kr+/plnzIWi+njhhYi8a/px0SIjreTkqPLISxLRgAEFocsSEog2bODfAgHjhGpWZkg9JiWJq16o2enVi6sgyais5ApWWt2nNHTfcFizRqu7KM2tCZU0aix+LyEXCBDNncuVroyliYKUkcHVvwoKjh0PVmhpIRVHHMcjGv1ePPqoxsB7Z+7cJvGheb8liT1nevmnnszvuccghxs1R+zezbJZLy87dmSj1uqakSNN5VzA5eIioT4fFxzXT4TDh4etBZObm8uC5KKLtPTbtWO9Rcb06VqebTYuBajWhUSR+0p9/0GDItZxy83N5Xpw6uvUpYuGDQurA9Httxud3Koa0bRwodI2p5No40YuR6nSFw3XA1waKAa8M3euVleRD7XRHQZ33LFG46u47Tblt5UrtY/H7TaXa+oxuWaN1vfgdHLZYDWWLtWuw7hcAZo2LXLpvUCA1670Q9JuJxo5ck/YxxUJv6ejvbKSK2x17258bDYbrz+9+GJ06z2N5cEMLa0/oEXuepyg0Z0vSaEC4UGAtc8mwHIAzZnDK3Bmk5bdzkZSSUnTX6T8fPpl0CDjctKQIWRpZRQWGoWJfJxyCs9au3drK37Ls9Mtt2iMdNN+mDFDK4icTq6dJ2PzZoOxfbRDB6KBA7UGo94jecIJYWe93NxcngX0RczUfXPVVZY0Fs6dyx56fZvXr+cT/H6iPn2U3047TfGOhxNSAK+mR4FQP65caXymbjdX946CxvTp2tr06hq3N92kJdutW+QC5nv3EvXsqe3S22/XXrdmDVFWlpZ2375E69ZFbvf33xuvlSf4c881D1KIph+agsZcL0m8kn3WWca6wQD7dO69N6qF+UbzoEZLC6k44jge0aT34p57Qi+0Lzk5fDHRCLB8v2fNsl7ls9nYq9ggC5o0R0gSbRk/3uiYb9WK6KmnzGVlIMDy3GzlOTmZa8f6/URTphiLfw4ZQpSXF74fdu821so9+WTFmvL7iSZP1hqgXi/tP+UU7Xd6gWKzsSPeAiEeXnjBWn9IT2dhaIG8Sy819sn11ysnLF+u8Gi3swFfUhLy9Gr0B7V+1rZt1MXWc3NzuY/UAls+7rknquv37tUOiSFDFLWvuFjrQxAElnmmfdkASeKVYHVXdu+u7cpAgGvtqtlNSCB6+OHITZckoquvNldp27WjRjnMWyqarLycS0C3b08kr6qrh/AppxB9+OGx5UHhJW7kthia1PmBAJE6vPbSSxvNR8QBtHAha7ZmwkoQ6PAJJ2iXxRrLQ2UlWy96odK1K6/6mkGSiB57jOr1hdYB9tgWFfHq7qhR2tnDbmfvqarvDf3g93NFafV1rVtrZ8MlS7QuPoeDaOJEbciTPkxKFI0zqhkP11yjvU7tWe7e3dTKCBnJJ5xgFI7yLClJRKNHK7/16MGz/6xZ5sJJfTz6aHTPUsbmzdq+kPt+7dqoaKxerfU1qIu1f/KJlk2nUxmG4cb0rFna8OSMDDZu1Zgxg8jj8WvY7tUrIttERLRvn+xnCBq6LzWVfUfR4ngIV7r33lU0YoTZCi9Hoj3xRHjvbNzIjSOO5keT34tp05QXOSXFOnoqAiK+37NnG0Nw1UdODn1jFo7TGB4WLuT9Imr6Hg+H+1RWml+8bh1R165G525aGtGyZTy53Xuv0ePXr59GIJj2w5IlWkNfFNlglJfnSkqIxowJ/R4EOFJP3Qan03jv0aPD9wMRy161XqTXfyxCoHNzc9mjrO+PCy5QTlq7VpHrsj5TXGy+gq++b0ZGVIZuqB2SxM4BPc2JE6O63u9ng0r9SOXFeI4Y05L9y18s+lKFgweJevfWNu+227TNyssj6tz5Nw1tr5fogQeiM3ZvuEE2po0G4rhxSmBhJBwPW6Zee20JTZ3KuoL+MXq9vIotBxseCx5aWn9Ai9z1OEGTOz8QoKPqvSlt2hB99lnMZKIeQO+/bx58Lx+dO4fddxM1Dw2Gq2F/cEIC74s1Eca58+ezu8zM2O3WjSfl/Hz2HqsnXUEgOvVUonXrrPvh0CF2A+q1e3VfP/44SWq6J52kXY212YyCqndvg3Vg4OE//7E2dJOSDLODRjiceqr2WlHU7uudOFH5rXt3vubFFxVhb7ZRE7De92TVhr17jW2328OGLqtpFBVpt2CNGqWcV1qq3RcrCGzoRhrT5eXsA1GzpLffc3Nz6fnnjXL79NOjW/h4++2FNHmy1qBWD4ebbor8qhwPRq5MQ5I44n7oUONr5nTywoSZ8I0buXHE0fxolvfi5ZeV+d5uj3rfoxpRv9/Ll4ci0EyP5GTeNxqtBh+Oh7VrtVFVsnAYPJjjSk2w4h//0CaEkA+Ph4WD3896iT43SFYW0VNPUe7bb5szJ0lEU6dqnb0uF7dVlv/bt2sXEhITeeFCrVPoneV2O9Hnn4fvh9JSo4NBLddN9umGaMyfb3R2X3SRcuLWrcqChCiys6C4mPxm+2nVdLp2Ne+ncO3Qb4wFeA9SlNffdZe269Wrr7ffriV73XUWPOgwe7Z2PUadP0S+fscO437d1FTrNRs97rtvlWnUtqzWyiljou2HxqA5dZDSUh5yZtvje/Ui+vjj5uehpfWHeHbllBTMmTMHXq+3UTQK8vNx9X//i7bbtkEAQABKunXD13fdBX9DiYCINAoKkJ2dHfU922zfjiFvvolks9IBAII2G4r69MHW8eNR3qlTk3josno1+i1ZAk9paeheBKC6dWv8dNZZ2H3eeYDdrlwfDKL/f/+L3p98AlE1tAhAwO3GtiuuwKFhw3DyG2+g7datoXMIQFl6OjbedhtKevQw5TFz2zac/Mor8DYUticAVVlZ+O7GG1Hcty+q8vJw9Zw5SCgpAQBIDgcKBgxA+82bIRJnPvYnJMBVXa1py8brrsO+s8+27IfEw4dx3n33wdaQBi8oihCCQQgNff3V3Xfj14EDTa8f9eSTyNq5M5TxMOByYdmLL6K+IS3xgNxc9P7oIwgACgYOxNf33Yc2Cxdi9AcfQABQb7fDXl8PyW6H2FAiKSiKWPT225ZZq83a4CorwwV33gm7KnNkVXo6PnzhBVM6ehrBIPD00yOxfTtn2Dv11IP461+VEhHTpo3Gjz+2ASBAEIJ46KF/o3fvVqb8qbF1axZefHE4amsdAIDevYswdeoq2O1aHj79tDsWL+6PmhonAEAUgzj77D245pqtlsm71df//HMiZs06A4cPJ0Nb2oDQvXsx7r33KyQmGjNjxvpuNvf1VjTq64HVq7ti5coeyM9PgdwmUQxiyJBfcMMNG5GcXNcsPNTU1GDy5Mnx7MpxxKFCc+gPAJDw0Uc4f+FCiMEgCMAvQ4Zg7Z13hq9KoEKs77e3uBiD33oL7fLyIMoVBFQICgKK+vbF1okTUd6hgwmF6HlIKCrC0DfeQObOnRp9oN7lQsHAgci76ipUZ2ZqaGT88ANOnTULnvJy7UwtCPhl4EBsvPFGdPr2W/RdtgwuVYr6epsNP44bh21XXGHad/aaGpz60kto9/33IbqSw4Efx45FXkPW676zZqHfhg0hXe5Ir15IOnwY3ooKPl8QAFGEraHfCEBpTg4+ffJJQBTN+yEYxOjHH0ebH38M0ZUcDtgbMk7/2qcPVj/wQIhnNY2srVsxcsYMroXcgD1nn43N110HAEgsLMS5998PeyAAyeHABzNnoqC4GDc//TQctbUaXUWy2WCTJBCAn0aPxubJk02fpZ4HGSe/9BI6r1unPA9wZu9fTjopqus3bszG888PB5EIlyuA55//ACkpLKO2bs3Es8+OAhp66Nxzd2P06I8jjuvaWjueeeYM7N7NuofLVY+pU1ehR48SDQ8FBUl49dWTsXdveugeHTuW4W9/+xqZmTUR+6G+XsTcuYOxZk1nSJIIqDRIr7cON9+8HkOHGgvKHyv53xzXFxQkYfHifvj++2zU1SnZw1NSfLjoop0YM+an0GvUFB5aXH9oEdP6OEFzeBhCHo7ly7VpZEWRQ3RiCQ2JFQUFRNdfT359eLH6yMzkGA2TPbAx8bBjB9GFFxpDmW02otNPp88fflh7viQRPfusMXmDfM2ECbyv9557jEt1gweHd5G99ZbRFdWzJ30qLwXedZfWczlkiDZWQxCMK5tt2xKVl1v3Q3m5dmVbEBSPrCBwDK5ZP0qSMXyrSxftuFDvwbn/fqZx5pnGflNvcBk0yLJ7LNuwdq2RpnqvTxQ01Dm/9E7oESPUw1+KJscVEXEUm3qBoU0bDhYw25Nzzz3aVcyUFN6+FW0bAgFuslnAQYcOxq1ex9NKrhUOHeJcceoFAjkpaTSr6pHQ0p7YOOI4HtFc70Vubi6/qOqEixkZSh6HaK5vDCSJ6JVXzEOF1frDvfdGTGgQkQefj/UQdRvVE++0acaV2I0btXGp6qNHD97j8uGHxuSZSUnGFP5qHDxoDCPyeIgeeogj0tav1+pyCQkc06meYPV7iQWB6I03wvfDffdpr1GvSPfsGVpBN9DIzTW2/6mnlN/Xr1d4a9OGFr79NvedrAOZrewCYatFWLZDXwHD6TQNq7K6ftkyha30dG0Eu149ufDC7Zb86fHUU9r8ITNmmPOwdq12wV4QWBW1Si5lRuOTT7TDQ90VDz4YOTdJrDjWOogk8eui336emMgJ2zmC7H93JRctctfjBM1q5BLxaHjoIW1YTFJSxKy+zTKIP/7YGNKrPkSR91ZYhFPHVELgrbd4z6nKkAwCPHu8/rrxmtWrrcOsTzyRLYs33zSG2Zx9dvi41DlzjFkFL7uMedy1Sxv65HIRXXGF1roxScm79Yorwrddn7pOHQt7yy3m/VhSYhQ048Zpz1FZed/ceivPunIcsDpEWn2/heYp8sM+S3XckHyYZCCwoiFJWr3j6ae1v+vzfn36qTUrekyerFxrt3PqezMUFxt1lH79jLk8Io3pl182l/+JiUrU4P+CkSujtJR3E+i3YHfrVtyorMwyWlpIxRHH8YhmNXKJeHK98krtyzt2rPVeVv31TcDH06ezY9UqF4Qg8CT71ltNL2O4axeH3eqyMtfbbOZlDAsLeT+qGW9JSbxF6+BBKtJXfcjMtAyNJiJ2LAwfrrmmNjGRw34lib2hanojRhgrS+ic/hWtW4df2Hj/fe31amd769ZEBQXmfTljhrHt6tD2uXND35fk5PB36qoNst6jNs7tdstkDpbPs7DQKGD69In+emIVUb60Y0ctC8uXq0kHacQISzIGrFmj7c7+/Qss13ZmzNA+Oo/HvHBGuHbk52tziKpflXHj2K/zv2DkqrF8udGvlJJCdO21Gxt9/5bWH+JGbhM733QAlZZqkhmEJgKL1clmHcSVlbzcZeZqUo/aW25h46spPJSW8o5/vbGYnMx7YPQTaH4+94uZsGrdmr68807OoqMWfqLIe1fDrUTPnq1dZW3dmverEBlXdYcONb7Fqk2lQYAzOoaDvtaNyiNb2KePuZDbsMG4x1adLdrnCxnskihyX61bZxSm+hqCJi7IiM9SnzHR7daMhUg0/H6tN1S/jYyTSSgJGx56KDw7asyfr/ZDBKlXL+uqEWZe2fHjlS6JdkyvW2eeldluJzr11P2RdMywaAkh5/NxEIn+NbrnnkZt129xIRVHHMcjmt3IlbFsmTbRgdPJWnm01zeFh0CAZbBVoktZFp13niJjG8uDnGSgf3+tjBZFpq8PBQoEeE+uWcp5m432nnEGy1m9fO/WLXx6/t27ubSP+prx4/l+GzZo99QmJLA3Vu141ucucTo5kYUV9u3TynG149rjoY/Vq7Rq6IvMCoJ20eJvf1N+mzCBvzvrLGNfqVfTTQxUogjP02xl+d57o7+eeIjJl/brp1/91JLOyIh+m3hJiTZoz+XSlkpWw+czbrvOyeFF8GjbIdOx8sG0aVNOX34ZHe9WaAlHe16eMSlYRkajUg61uP4QN3KPhZErY/16bcYeQWBPrc5gO2aDeN06y5p0oeOEE4jmz28yD1/edZdxldPh4JzsOgOK/H7WuHUZgUKrwd9/z4amXhDcd5+1lh4I0CF1bV5BUOrL7d5tXNWdOFErqPSG+mWXhW/wHXdoz1cLussvN79m9mxj/6t3+h88qFh4nTvzd+qsDOo4H/WsrEPEZ1lcbEzV27dvTDTKy5WklYLAITxqjBy5R0NenawqEvbs0UZ4y6+NVUiR3ivrdnNyyljHdFERR8qbvSZJSWysx1pjriU9uXK+FVGUNIJKXTIyGrS0kIojjuMRx8zIJeKX96abtLI7O9s0Y84xm2O2bzfug9AfGRlEt99O77z2WtMYKCgwL2N48smm5YLok0+0upXK2KXp03lJSm+oDxkStnQPffIJ1ak9g2lpbPFIkjbMSBZo+sRaeuNbl5RKA3WkFqBpd8DlMupMMi67THsPu13bP+ptTjNnssCSjXT1WFILTLWzvQERx9RFFxn7XhVeH82YvO025dKRI7W/7dpFJIr1mmaGKZGsgSRx8iq1o71jR63xqr9Xr17appx9Nq/hxPpuPfKIeaJLUWTdIorqjQa0ZDTZ+vVy3yh9eeqp4X04erS0/hA3co+lkStj1iztsorbHdrDGTWNpvDg90f0zgacTqJJkzgcpSk8bN3KOeP1GZRHjTKfpRYvNk/1NmwYezz1VboTEzV9Z+Dh/fe1wqZ7d6U23j33aGmddJLBqtHsTYpkmc2Zo+VZHftqVTRdnVEZYGNTna169myFB7n+suyaVPOubqOufm5U42nJEmOfq+rfRUOjoEBpss1m9IC+/baWfFZWxK3hGlx77UbNa2MVUkTEdC+7TNtFrVpV0bx50d9PRiDAjnD2NxjLELVtS/Tf/0ZH63gIV3rttSX6qDw666zo6wa3tJCKI47jEcfUyJVx8KAxI3J2tqb8zDGfYwIB9iSaGZVquTl0aNgaslHxUFnJYcJ6S6F3b/NlpEOHeDLTO/K9Xg5VnjPHuNI6dqylEZn79ttEl1yi1V1kZ/nGjVpdxesl+vOfNQ7jOj3fJgZkCJIkFzI1yveOHa0d+vrJPDlZEaySRJVyFJ8ossd4717FeS7fQ58rRWf8RxxTkmTMGp2UFPJERzsm1TVt9esKc+cuNAQkzp8fFVkiInruuQ8MpZLHjrWWe/PnG6O5R4zYG3U9ejU+/FBeNzHqD3Y70TnnRG8oHg9bpu6/f5VmbcVms9bF9Ghp/SFu5B5rI1eeqHw+Xo7ST2QbNoSnIUkRYwyj5oGIXUnjxhn3VaiPE07QZvNpDA+FhWyg6jP89O1rLqzWrKFasw2SEyYQHThAdNpp2u/btDF4tUM8VFZqw4/sdk6wIUnmq7o33RRyQhgScAwcGL4bvv5ac36ojJE+lEj9KPRKS3q6ZpmyUL3R44sv2JpseF6SzUGSLMDUNGIIPQ8EGvjQ7/8CQgVrw9GQJGZXkng4yTJeXRpAvn7XLu0QcDh4K5Tfr+LDArm5ueTzsSBUvzZdunDODJ/PSGPXLvNo9PvuM64E+/1MIxwvjz/+MfXpYx0M0bMnDwE1jVgST8g8yHzIdNS0Ij0L+Xo9Hfl4+22+fvlyY8nItLTwR7duLS+k4ojjeMTvYuTKk8nixUYndVYW0bx5scn/xvIg09i9m2MzdftpDfLs/vu1Hs1YeQgEmIZZuSCzMonV1VRgVh6pXTt2vE+bpjWcRZH3/1pF1S1frnVa9+rFOk0gQHTzzVqBMGKENrZTr8eoasqadoOVo/2ss8wfRUAybjk6/fTQOUteeUVpa3a2kkdF1lHka7iYvKJPWT0LEx4CAWLhr9dDGhYGopF7cl+o82uoqxLJNPSq3+TJWh3ECvL1ubla49XhIHr8cUU+qmlIEgcR6pM4jhjBgQ36foikP7z55kK69Vbr3F8uF9GNN7LKakUnWvlvpT/Mnx+9DmKlQ8yfn0uSxHqUWp9LTIysQ/znP/ESQi0GuQSAZWrrn38GOncGWrUCevYE/vxnYNIkzSkLFizA+PHjrW/Sowfw00+A3Q44HIDTCVRXcw2QBtQmJsL99NPArbcar//6a2DECP4sikzHbmc6TifgdqM8EEBK27aA1wskJgJJSUBKCpCayrz/+99Afj4gCHzYbHwQAYEA14cxg80GdOgAdOoEfPml8n1DGn3YbCFeaiUJ7uRk5snl4sPt5r+HDvGhh0yjgY6fCC6bDWhI169BdjZQUGDOZ0OfBIjg8Hq5n91uwOcDiou1fFsN93C/ORzAwIH4wncqztrxPAA+TwRBEAiiQLAFA7BBgg0S7KgPHY6MVDjcdjhcApwuET/8ZIOqig/fGgQBgGATIQgAIMFWz/REBGFLSYS9vha26nJIEFGCNiEetE2Q09oHIYpi6HHLhyjyX59PbmqkV1+ATFJQ1XKQp7jwIMC0wFUsaCqN44GH4wGNb4PTCRQXR5gn44jjD4iI+gMA9O4N/PIL6xEnnQS89BLLJhXC6hBvvQVcdx3LSKeTJ3GfTyOzJbsdtosuAhYtMi871Lo1UFKildmyLuJyAR4PSv1+pLVvr+gPsg6RkgL89hvrEADTkOkArMeYlCEKISMDGDYM+PxzwO/n79QCyWZjHhwO1EgSvCkpWv3B42EZvmeP8T5qXcZuR10wCKfHA5SXG89NTgZqajR6VwiiyP1ht6OOCM6EBKVvCgu5v6OBKIIayvUYkJoK9O2L9O+W42h9ErMP4m4QCDaqh0j1sOt0CIfHAUerRDhdIhwuAWWVIvJ/sUEruxv0BwEQRBGABLsAiPV+iJBgc9hgS/TCXlMOu78KvyILBPPyVIIgNKhCWh1C1h1EkbtQ0WHCKQKCRm9Q6xLhhoy6Xf/78v944KE50Hge/vnPCtx/f8vpD/bIp/yB8d57LEx++42Pb74Bdu4Enn46ehqVlfy3vp4PkwnTXVUF/OUvwG23AWPGAB98wJMsABw5opwYDPLsUlfHE3YDUgCejCNBtk6CQTZuI0GSgIMH+dDTkSQ+Gnhxq9saLYJBDS+ucOdaGbhAqG8dgCJIzRDOMgv3WyAAbNyIKmQ1fMEvexACQIBEQMDqVSo2/1pzawgsKkITvw31sCknlAOAp+GQYZxwlCaIUQqRyJOWTDN2V1hzTMpNpXE88HA8ILo22GxA+/bA+efz/6LIPrI44oijkdi3j+XH9u18fP01sHt31DVwkZ/PfyXJ0tiy1dcDS5bwC3z66cDKlYr+ACgyUS1vdbTS1PcKB5mGmbFohuJi4KOPtN+p9ZD6+hB/XgAoK4uOrkxH1qv8fjgBa4PUzHEuIxhkHmQaNdZ1U8PCysAFuF3ffIN6lbFAELgbCA3y3kQD8gEwVX0EzWdCg4yWAMDWoEo01G4OACgFWFNMCdsExYHdPDqEWm/4/XWI40H+Hw88NAei46FNG2DIEPbpyVPcyJHHjqtoEDdyw+Haa9mzuXIl8OmnQGkp8Mwz7Cl85ZXoaKxbx17I/Hw21H79lQ3Xo0eZ3qFDoLIyHkLBIN/H5eKVy/feY41z61agqIgN7aNH2StbWsoTZ0UFft2zB1lJSTw5+3x8+P2KMawXSO3aASefDHTrBlRVAZWV+Hn3bnRs1QrYu5d51S83RgNBYNrduvH1tbV8+P3MR20tC1i/31RIxuQrcjoVQ7sx11sgHI1z8Cm24wSUIxlVSEQlklCNRFQiEVVIRA28qEYCfPDABw9q4UEt3KiFC3644YcLlfCiBl7UwYU6OBCAAxIckBoM2yBsCEIAQWQjOrTOK0PQ/Y0jjuaBJHHAxcsvA8uXA+eey9+H0w/jiCOOMPjwQ+D994G1a9nI/eknYOhQYOPG6Azd228HzjhDcTYXFbHhKOsPR44gePgwRHlld80a1h/692enfGIi8P33LNN/+42vLSnh6ysqWJeprMSRAwfQxuMx6g86hzoApnniiaxD+P1AZSUO/vADOrVqBRw+zPpOIwxFAiAkJQG9enEbfD5Ff5D5CgSYJxNndkzy325nfUXn7D/Wa2bb0B/FaI0KJKMSSahq0B2qkIRqnf5QA2+D/uAO6Q81cKMKiaiFC3VwNegP9oa1XxFB2CE16A1BjjVraJeZ3hDXIeJoXhw5wrrDwIFstgAtrz/Ew5WjDcOrqwNOOIGFFABceSWwcGHkcOUosGDBAoz/4Qc2oPWTt8sF3HMP8MQT4a+34iEYBBYvZtrff68NTXY4WIA+9hgW5Odrafz4I3D33cAnn5jHlggCC+tLLwU++AD0zTfKlCmKwNixwOuvs9Frhe+/5xXsb79V3HwJCcDjjwOjRrERv2ED8I9/cIi3HsOG8ar3a68BTz7JAhFgAf/hh0yztJTfsrIyIC8PeOopxdvr8bATwenkcGz1anHnzsCBA0pbZf7sduDVV1k4VlXxUV3NR00Nft2wAVm7dhl5TU4G0tP5+QYCrASYtQnA/uHD0eXEE7k9CxcqK+QjRvA1mzYhCKC2QeBVIxE18KAGCahGAkq9rQF7ImqDDtQFHaiVHKgjB2rJCT85URd0wA8nAmSHn5wIwBEyuOvgRABO1MMGadAw1Ldqg0AAoUN2nMtRapKkfA4GWWdSNys5Wem+YFCJkNf7UBwOo9eXSAIaVrTNZqnfJ2T6jwmtkRsPV44jDj1ifi9uuAH4z3/4c48eLI/c7ibrEAsWLMB4ux24/nqjTMnIAN54A/jTnyLTsOLhp5+ARx7hFdmqKu1vvXoBN9+MRenpuHLiROX7335jveWdd6xXV7t0AW65hR3rubla2t27Ay++CJxzjjXTVVXAvfdySLf6HuedxzqE38+LCtOnA5s2Ga/3evm+HToAEybwCjvAOtf8+cCpp7JAa1hMQFkZ8/TNNwqNAQPY4D98GPjsM+X7zEx2SACsD6n1rr592XlRU6PoDzU1IZ1Aeucd2Mx0rs6doRHGR4+adktFmzZInjCB9YeDB4EVK/iH1q2BQYM46m/7dtTBjhp4QzpENbysQyS0QXnQCdgTUCs5UBt0wh90oo5Yj/AHWU/wkwMB4s91DTpEfUiHsENKy4A09BTU1bF+IMt9WWeQ9Qa9LqEELxLcbgEul6I/yDqEfkjJUfRqBIMSBMEWNhrt2OsQf0wdRGvktrD+0CI7gY8TxJw4IhDQJhg455zmz2y4aZM24576OOkkMkv1FjUPPh/vuFcnHGg4ahMTiR54wJidJxDgTMHh6ua1akW7xozhhBTqXemCwJkAd+0Kz1dhIdFZZ2mTPvXooeRblySuAadPYiUfEyYQlZZSSefO2uQPZpkeAwGiCy/U8igXch01SktXXfhdnajLbrfOzpibS/SPf5jzqU9Hd8oppuf5PR7lORQVKUkksrK4L9S591XPQP58aPDg8P1thupqI02bzZhtIQrcf78chMX5ScxKFO7dq61iZLMZh0m043rxYm0CTbud6OGHI1+/eLGx6pXNxiUa5YTXahpyIodAQJusobqaE0eUl/NRWsqVoTgptjG7otwvV1zBeVwKCvjIz+fj0CHlOHiQ6IUXltHBg/x5/37tsXev9tizx3jMmPEB7d5NMR3qfCzxxFNxxGFEo94LdXb9rCyikpLmzZ76xRdEqanmc/n551umt4+ah08+YZmuk8WSKBKNGWOskSJJLPfCZGYmh4PorLNo+/nnazPjAXydSdkkwz2eeYYCaoHichE9/bRyzurV1jpMdjaX4XnmGQqqE0pdd515NqHPPtNmJ5YrOHz8sVaHychQElSJojZZ1RlnWDbnnddeM9d1unfX8rNypfKbKlNSECBNWYGbblLOk7+fNMn4DFS0ct9+O3yfm2HoUCPP114bM5ldu7TdO3my+XnjxmlvddVV2t+jHdNFRcbkVkOH8iMNR6OwkDNC66sx5uQo3ay/XtYf1DpEdbVWhygt5Xtfdpms+pnrEGlpRH//O+sJav1Br0Oo9YfG6BC7d8euQ6iLtLS0/oAWuetxgkZ1viRpUrgXd+sWe/FMHUxfpECAtW2zGnWtWhF98EH46yOhoIBnD71Qsdv5vnv2GK/Jy2NBZpVq1uXilPrXXmssAdC/f/iC7ES04h//MBrgF16oVAIvLzfObCohvmX8eDZY1fzdfrv5zZYs0WaI7NWLqKiI8vW179SZjvXZGfftM5ANPQu1wazOuLx2rXJyebnCg/45jx2rnLdwoXEmNysr1CAYgwDRN9+E7WtTzJ1r7NeEBOuafWEwZcq6UJNEkejNN43n+HxEmZna26llayzjWpI4M6O6GxMTfTRjRuTEntu3c/ZE/bDu14/o9tvXRJMY1BK5ubm0ahUPI6tyk243Z5AuLram0RQ09fqWFlJxxHE8otHvBXsBQ/PriieeaBIfpu/3zp1ca91swsnIIFq2LDKNcAgEiJ5/nh3B+omzSxfzCX/3bi7PY1ZIVJZbgwcTPfqosaxgZmbEmiUL336baPx4LT9t22oLg8+ebZ3qduhQ+vTRR4116PPzjTerrtZaRnY70Zw5tPLBB7X3T0szpuqVP59yimk7cnNzOSO0GY8XXaQ9mQvCGg+nUxEo6lJF8veBgDFztaok0NEOHcL2tSXMqnZMnx4zmZISLgEokzjtNHM1+6mntLfq1UuR97GO6ZUrtcNOFImGDPnZ9PGr4ffz+pC+UlVKCtG55+5qjPqkwZtvLqR77jGq6uojNZUd+2Z91NJlDFtaf0CL3PU4QZM6/9xztROwutZpjIg4gF57zTghyRPrdddRbizFw8zw1Vf0W5cuRmHVowcbWHr4/USPPcaeaLM3zuHgyffOO40CpUsXrmUbrh9mz9bWgXU6uc6vjF27uMyR2b179CD68kttofVevcwNtdJSbfp+h4PW/PWvvMSmt3bUbVP/tmWLeRuItBac3Ldut9bNtXixuQAEOPe9DHXx9aVLWfDKAkUWomqj3W6PqmyDAepIBfnIzo7ZkZObm0vr12tZUpXh1WDECO3trr9eoRErCgu11aPkbrnuOtMgCA1KS/k8l8uoL4wcyauzsULfhm+/5apUVgavy8V+HLVgjRu5ccRx/KFJ78WsWaH5PigIRC+/3Gg+Ipb/mTLF3LAURZ58i4qaNkeUltKeUaOMsj4hgcvt6PtHknipy0qGA+wUf/JJY1RbUhLR1Kmm8ijUhv37+Xr1daedphQmlaOhTCZhSRCIPvqIncxqmW+lY82apVl1Lejfn8vwqWknJ2v1BrWcHzrUuh2vvWbeN2pdSJK4TJJKDwitJvfrp5y3d6/C0wkn8Hdq3UMtgGQar70WzdPXYv16c54tdL5wePvtXI0foX17czVu5UptlyYlsSxv7Jh+9FGjrX7iiVpfiRXmzzf3LeXksCHcmKlCH03273+HD6xMS+NXp7HGfiQeYkVL6w9okbseJ2hy5994ozKh2O3mBmEUiHoAHThgrI8mT0h9+vDqbCORm5vLHr7rrtMamPKscfvtyoqqGuvXEw0fbqwvK/fJhAkcvqt3Q7Vty4XarfrB7+eQGvXslZnJM5qMDz80FiSXrZKvv9Z6Wl0u64l2xgztKuibbxLdcIOWptr40xu6qnq4mjZUV5t7Ntu21Qrps882n62cTmVWVxdfd7n4+9dfN16jrp6uFnKxwMyhMmxYTCTkfigo0D6ic84xt70ffFB7u379Itd3C4eVK4k6dCjV0BQELie4dWv4ayWJI91Yd9CGCrnd3IavvoqOj3Dv9rZt3K1WUfgOB9Ho0UT/+tey6BseIw/RoKWFVBxxHI9o8nvxxRdaL+BllzXKMRn1+/3990oEkO6oc7mIXn015nsbeJg713gPQWBZvGGD8cKSEqJbbzXqHPLRuzfRs88a9R6nkyPGVH1v6IelS42FwW+9VenjkhKezM3uO3ky0SuvaCfnSy4xfz779xN16KCcN2QI0caN2msTErTeU7URPHCgeV8SEV1zjTl/ao/rnj0KPb1u8vjjynlPP618/9hj/N2gQdrz9cKoMWP73nuN+qDdHnnbmg5yP0yerO1Gsx1UhYXaV0kUiZ54ohFe6QaUlxNdfTWR3V6vaYZVmWY9Nm4kOvVUIlGUDK9C9+68Am2xc8CAcO/30qVEXbtaB1dmZRHddNO6JkWjReIhElpaf0CL3PU4QXN0/jdTpmgNmXPOMQ9vCYOYB5AksRFmZkAlJPCsoN9bGwsPVvtoRJE9j/PmGd7ypS++aG2siSL3y7RpbOCpf0tN5TjT0lLzfjh40DgRDxqktVSeecbcU33HHTyxqwXK1VdbCyp1xfC5c9mwV9NThzLrhcnixcZ+JOKJ3axPRo5UzvH5FMNS3w61AFQXXx8wgL8bPdqosKjHxVtvRXz2Bvh87NHW83zjjVGTUPeDz6f1EfTqZb4wvGKF9nYOR4Dy8mJnX83D/v28MqqX3V268PaySHjxxaV0883mvpSEBI4eDxfEEe27vXcvb9Mye6WBYGivsLwYEQviRm4ccTQ/muW9KCqiKnV4bFZWxG09ejTq/b7zTq1VoJbVw4Y1XYfZsYPorLOMq6Vt2xLddZdxSU6SaOuVV2o3Y6qPzp15Bffkk438nnEG0VdfmfeDJBHdd592Yk1KYp1B1gN27GBjWn/Pdu14249aZ8nKMreyJImNYPm8/v15e5f6vh6Ptn3qvpFXV836Up0bRG3Mqp/RvfeGzgnq+2fvXuW8E09Uvt+zh73Q+meUkaHQyMyM4ukbcbhvX2N/JiVFb9np+uH55xVDzmYzX1UNBPQqa9AyeixazJ+fS9OnG+W/x8NpYiIFuM2fn0tz5nC367tZEFgvWrQoPI1o3+8PP7TeodDwWOmFFxoX4Pe/bOTGsyunpGDOnDnwer2NolFQUIAedjvOfvRRuBtyZROAI7164bubb0Z1ZmZUNLKzsxt1/7abNmHYv/8Nt8+nyeFGAOpdLhw87TRsmjRJWzcvBh5SDh7E4LffRpsff4SgGipBUcTRLl2we+xY5J98MgoKC5GdnQ330aMY9vrraJuXZ8gpRwB8qako6tMHrffuReKRI6FzCMDhnBxsuf12VLVta+Cj3ZYtGPbKK1xTuOH88g4dsOHGG1HSowfsNTUY+cADyFDXFQZQm5iIdbfcgtP+/W+4GjJP+hMSsObOO1Hct6/mXPfRozj37rvh9vtBAL7561+ReugQ+n74IYSGexZ364aMvXshAAjY7bDX14d+W3PbbdjQsaOhH3O+/hqnvPIKBACSzQabJIEArJg+HeWdOgEAMrdtw6h//hMCgDq7Hc6GEksE4ONnn0VlA82+S5ei/5IlIADLXnwRdUlJuOTmm+Hw+0O0y9PSkFxayrQ8Hrz7xhuG/oyEqu+/x+RnnjGMqWUvvYTaKIqnmo2n558/FRs2dAQgoHv3YkybttJwXUmJG3/7259QXy9XNyOMG/cDJkzIi7kNah7q6kS8805/rFrVDbW1jhDtjIxqXHfdRpx44q8RaRQWJmLZsr7YsiUb1dVOQDV6MzKqcc45P2Ls2D2aLI+NebeLi914442TsHNnFiRJBHRPweutw0UX7cR55/0YVRWSpswvAFBTU4PJkyfHsyvHEYcKzaE/AEBBfj6ueucdZG/eHJIlRX37Ys0dd6A+MTHy9U14v+0VFRjx3HPI+Oknw1wftNuRP3gwvp0ypdH6g1hXh36LF6Pb6tVwqsoKEYCa9HQcOvVU/PCnP6EuMZFptG2LXitWoO/SpXCaZGWW7Hb82qcPRElC5g8/KKWTANQkJGD71Vdj/6hRxnZWVWH4rFnI2rEj1M6Ay4Xd48ZhxyWXAKKIPu+9h/6LFxv6YccFFyD511/RccOG0PP5acwYbL72WkNK3/7PPou+DWllq1u1wpd33YWxjz4aypYccDgQdDjgqqnh2raCALFBryrLzsaKZ5819KVYX49Lb7wR9kCArwEgAijt2BGf/POfofMuvPVWeMvKUOd0wllXF8rrW9S7N1Y9/HCoHy659VbY6utxeMAAfPX3v2PQW2+h56efhs4nABVJSUiprGSZ/8ILqM3IMPRpOBQUFGDKU08hoSEDtEx776hR2HjTTVHTUPfDtm1ZePbZMyBJNtjtEp577iNkZBhLVT399OnIy8sOtSY7uxxPPbUC9kYUTFXzsHVrFhYsOBG//JICWSbb7RLGjNmD8eO/N6Wvvr6+XsTKlV3xxRfdUFCg0AAAp7Megwf/gquuyjO0qTHv91df5WDRooEoK/PAmN2ZkJFRhZtu+g4nnFAcFb2mzDEtrj+0iGl9nKA5PAwhD0cgwFns9CtxQ4dSpOWoZtlzV1jIYTJWsY8JCZwUymKFNyIPlZW8qVIdlqMsuVFRz55aD3RRESeB0G9wlA+bjd1bJ55o5Hn4cF5Z1UOSOPxGveIKsKdz5Upuw333md/vzju1WZUBoksvNbji3p09W6EvCBzi/PDD2uvUm0h13vCvbrvNvP/UaXzla/RZkBv2Bwf0q8TnnaftA3mM3XILfzdlCv/f4DUOArxcKl9fWRn+2ZogNzeXx4u+H88+O/rrTXD99Qqpq682v9bvl1d+lVDhjh1jj5yy4mHuXGNek44dzaPZrWhs387bpPXD227n3GwbN4a/PlqUlBCdfPIBQ+CAfJx4ojaXmRniK7lxxNH8aK73IvR+mqWJv+km821CZtc3lYf77rNOyiTvr22s/kDEsZVW+zM6dqStV12llccvv2yub8hHTg6v4qoqCxDAk7vVHtqNG437dT0e1m0CAU4CZpbhp3VrXk5Uh1VnZioTvbof/vpX5ZzkZN6jq+7XhARjhiP5c58+5n2Zl2euc6gTfC1fbn6OIGhXzq+9NqS3kSTx+FIvkwK0/9RTletPOinys9UhNzeXn6W+H+32iONZQ0OHzZuV7kpLs1ZtZs3S6g8uV6MKRZjysHcvB+Kpm+VysYqpX9m1ei+qqzmw0WxvbefO2pDoprzfkkT05z+vC23bNlPDzzorcoTY//JKLlrkrscJmtXIleH3cxyDPhSoXz/LMKRmTyyzcyeH9FpltzERWDHxUFTEb7TZG9qmDe/cl2lLEofMqo0u/ZGeTnTyyUbjbsQI81hQSeJZQCeMKtq04RCcXbuMYdGyoJo3T6tIJCVp4lZzc3M577oslESRN3jqjWd1mLDqWQcB3vukx9692hlR/rx7t3LOhg3K92pnicOhnT1lI1sOJTp40NhW9Z7iSy6J/tmq+4HIuNlDEKIKZQs3ntQ+AnlrkBmuuWaD5taiGFv+ikhjev58Y+60Dh1YF4uWhiTx1nKz4d2mDWdXVOcZawxkHr7+mvffmL1CLhdHk5tlaI4buXHE0fxodiOXyDwpkt3O+yIsMuc1e2KZykp2rJrvnWB5d/75mskmJh7kSXPwYKPB63BwKUJ1iO3WrSxvrTYeOhxEJ51Elep8FADrAFblh7ZvZ+NNfb7TSVuuuor5kw1B/XHTTURnnqn9TlVqKNQPzz2nleUffqg1kJOStAa86nkf6dbNnGf1dik5xN3r1cafytaM3vBXe5QLCpTv5YRn8j7qhm1TtYmJCr+CEP2zbUCoH844w9iHUZYVshpTubkKqW7drMNvZ8z4wKCGP/poI9thgoMHjVUnXS7e5Sarv9G8F7t28fqLlcP8/vtNSmE2og2SRPSvf2mTh6uPtDSi//wnPI3GoKX1B7TIXY8THBMjV4Yk8RulT+LTvbsmUVFYGk3lgYj3m0Rh8DaqLhoRryDfeivV6vfSyC4i9Sp2QQEnojLbC4SGzIYDB2rfdkHgmeTQIfP7v/660SsqZx+8807zNt99NysN6u/OO4/I51P6cs8ehQ+bjZfL7rhDe82YMcpntQDzes15VVtCskE/fLj2HLmEkn61Wl3zb9ky5XvZCSDPXHJW6bZtlXs4HLE+VaUf1DX25GP06OivN4EksXCSyalL+ulpHDqkTZQN8B7baPaVRPteLVxIBk9ndjYvrMTybhYVsaGp5xfg/TBXXx056VU07fD5WJBavEYmh3mdvWiOtWtbXkjFEcfxiGNi5MooLOQMv2rDThRZTukSTB7T7Kk//mi+oVCtiZ90En3w1FONu7EkcWhNv37GZEXdumlXKv1+Lkdj5sBGg4O5a1fjZN6hg3Vq/D17jIbYwIG88rlunbEuDMAG5CuvaPfXtmpFtHatth+XLlWMeFEkevddrW6TmqpN0qXOjmwmFNUro2rngLpEz4svmp/jcGhX4eXIsu7d+f+pU408qJNevf56TI811A9+v/mYiSK6LNy4njYtsjqSm5tLgYCxYESfPs2T9EmGbOyqX1Wnk9eR3nwz+mS04RzmTicHQbz6auwVS83a4Pdz6h6Lal7NpkOcemrL6w9okbseJzimRq4MSeLsvfoQmJyc0JLR71YiZNs2S4M3CLB2/sADjc/w+Mkn7J3Ve1zbtydDwdJ588yTPciT4KBBRmP3rLOsM0jPnUv1as9zv37sad6+3RibCrDVsWSJ1q2VkEBf3H+/QnP7dsVQtNs5VuaWW7R01KUG1Pe/804jj+oVV/WqtdqAN6tVC7DVpYbcNzfdxP9PmMD/q/tMvdq8Y0fsz1OGmQc9QsmsSGOyulpxNosiR3RZ0ZAkY6R5UlLkql2xvleLFxuDE7xeP117bewVwpYv54UCfXZF/SEI0RzBsL83RvhEe8SN3DjiMMcxNXJlFBdz1mX9imfv3iFD6HcrEfLDD5wl2WTvRFCWf5dcEnPSSxnvzp7Nckyfadnr5dVSdZjKjh28mmy12tytm7EIe5cu2uoMahw6RKROluR0cqiPJPG2K7N7/O1v7HFVfbfv9NO1dDdsUGSyw0H0+efa/ktPN4+IEwTzfpw0STknNVWhIUOSKCBbLnpngFq3mT1b+b6ggI16+X/Ze6rOPK2+x/+xd51hUhRb++2euLM5J/KScxIEJAkIhiuiKCIqBjBgxKxXBTN6DVfMin6AyJqviCgGVARFkkjOYVkW2F02p8n1/ThT090z3TOzARa13+fpZ6ZTdXV1d533nDp1TgRQvE9Dhwbf35Qp9StDBVdcIRV3442hz3/qKeXlTaYgT/MG1UGOvLxghwNR9LABA7QdCrTADeakNmgrmJHwByA0fziZHGLgwObnD2iWq54mOCVKrhxvvRWscGVkUFTBekSda1QdOEIovAwg195XX21YHUpKqNcJHI3krkh79kjHHjtG7itqkw5NJppnLDc3CQJ1vio+oJ++/bZyvo1cUM2YoX6fDzygSKjuEQRlfph16yRBajaTb0lgAvbzzlMKe76ozT2RC1Je7pgxikOcXMgEegHIR8W5n0xaGq1v3eo/zsXLlWuGPXtG/CyDnmdgOiWArN+Rnq+B/HxJnprNwdOwA8v48svg1/U//2lcHdTw+efqU8Gys8ngXZ8pzosWLWZffEGPWCtw6Om6vPsudUvNLaR06DgdcUqUXI6qKmK9gUMvUVHs4ODBqvKwyesgR1kZKbRaqX8SEurFHxR18HiIK6mlIurThyyIHB4Pudy2bq2exrBjx+DQuB07qltVGWObJ05UCpkxY0jZXLlS3UUnPZ2xDz5Qcp3A6UEbN0pGCpuNIjbLjRZpaYrUf/77aNEiuIIej/qcZpkWtYe7UwcKy9hYZTmcc3H3Ya5s8/Yym5V5iuvxngelgwysr8EQtrxI3km5x/kLL4Q+f9u2YJfgmTPrcR8RIj8/2AkDIK5z7rnqmbRCYe7c/7HbbgtOdnK6L927k+Lf3PwBzXLV0wSnXMnl+PDD4CTngkCjj2++We+R1EZbcv/8k5W2aKFt0uG+mw2pwyefqCd9b9dOea8uF9s5bpy6ZdZioQBegQnVzz1XMWPeX4cnn1QXVJs2KfPIyoXM0qVSD2g0Ks18K1dK5UVFkTYW6O58wQXB5bZpE9we+fnSfn6vgqC4jx3nnittl5cnD/q0dKm0nWuHPiFczb0GoqKUQbTqgaDnqfZuyOdNhTtfA+vWSU0bH6+Ue2plVFVJHt3yzlTN6N3Y7+K33xjr2bMgSDAKAg2kvPZaeNehwDrs2EEOD8uXE1fjy7Jl0rJ0qbQsWcLY3Xf/zJYsYYrliy+Cl88/p+Xpp4OdJASBscsuIy4U6cKV+eYWUjp0nI44pUouh8tFXlGBnSBACqCWW25T1iEQDgc7NHCguqEaIIthuDwpWnXYtUt9fnBiIgWLkhmSf3zgAe1AVZ06Bc9T7dCBchUH1mHXLqXLc2ws5axxuShYpVr5jz9Ovpl8/dprlffx9deSDE1OZmztWqWympnpd432yMt97LHgNnn0UWk/b3PZPN6P33tPEqqBPFOeC5l7ocXE0DoPmGUySYr2LbdI5zYwhSBjjDwAAtvs8svrV4YKXC7lI5fH7NBy0w10X27VSnO6e6O+i7Iyxi64YLuqd31CAo2VaM3C06pDVRXNdqwPh4iUP8g5xD33BDtCWCz0yCLlD3xMprn5g8AYY6culvPpBZ4CQDO0td0OXHUVcOaZwNixQNeuQSHjc3NzMXnyZO2LPPII8OefQFISkJwMpKYCaWlAZiZw4ADw6qtgu3crg3wbjUD//sA999DvddcBNhsQGwvExADx8fQ/MRGIj8fKrVsx/KKLgJQUukZCAhTxzO+/H9i0icqw2YDoaPqNiaFy4uLw27ZtGDx2LLBvH/Dii8DRo+r3Y7MBQ4YAbdooytmwaxf6n302lRkXR0tCAtU1IQG46y7gf/8DiooAWdh/GAxAp07AhRdi/fHjOGPoUODjj4HvvqNvSw5RpPKrqpT7cnKASy/FH/n56DtiBN1TZSXw0EPAiRN0THQ0cMEFtL5nD5CfH3xv/fvDu2EDRF6vt98GunWje9m0CQenPITr8H+wii5YxwxF1Pb1sB3ZgyjUwYYaRGfEI/r4PvrvW+IemYmYc4ciNpaq9fzzwI7/+x3WmhOwwg4rHLDAjqi2mbBdch6iooB9OzdgwGeLEcWqEWX0wOYuhw21sIlOlH70LZ551girFbD++gNsrBrWNlmwDhsA88/fwXp4N4wGD6yeOljggKl7J1i3bYAZTliuvxKWoQPx0UdAXh5lhTCZaLFaad1iAaKigCNHDqBTp3b+baYfvoFp20aY4EYZ4rAEE2CwmGBo1QJGIzWXwUCvndEIlJUVIT09DSaTtI8vZjOweTNQXk6P1OGg/7zZW7Wi35qaCsTHx8NgAASByhUE2nfokPRoOWJi6Bj+iTqddlitVv+rA9D5fKmsBFwuZRl8H4fH44LRaILHQ8dq9ZaB53Ew5oWokueHH+vxaJcpKwXBaQDqAy8o6QShXTugXz9g+HBg0qTg7iIQYftJHTr+gYjou3jmGZJXI0cCQ4dSRxuAkBzijz+ABx+UuAPnD+npgNsNvPUWPD/8AIO8E7HZgDFjgNmzgd69gRkzgIMHJXnP+YNMPq/YtAmjLruMOERKijJl0Nq1VIeoKIk/REdTedHRQFwc1u7YgYHnnEOd6hNPUAetBqsV6NMH6NxZ4g++stbu3ImBo0dL/IFzh4QE4i9TpwIFBYDTqSwzKwsYNQobGUO/ESOAXbuAefMkoSKHzUbn+1L0AaDrXHAB0LMn1u/ciTNGjaL2eecdYNky6bhx44gT1tYCGzdS5y1HXBxqRBHR/LoXXUQcjt/H0qW4aYaAA2gLS5SIqMF9EbViGWyooSXGiOjqY4hGLWyoQSyqEINqxCzJRVyrBMTG0q0996wXUau/g5XZfRyiDlGww3rFJYhqnYZ9+7Zg4KbfELVvC6JMHthc5bCijjhEsg0vDv8SR48CUe4qWDf8AhtqYRk7EpZ4K6wfL4AFTogmBpurGqa0JFiLDhN/MHpgfvMVHD5MFM1kotdEvnAecfz4AXTsKPEHs8EN40v/gQlOGOHGl7gQpUiCoW0bGMwGBXfgfKKsrBAZGekKbmEw0HVLS4Ft2yS5XlwsydHsbKpHTU0lEhLi/JxBFOHnEkVFwJEjyscXFUV1BehYQQAcDuIQcvnO/zud9GkHyn35sZw/MEb8IfCVkUM7HaAHgmAI2ioIRKXldFodjeUPyjIEgWjx5MnA2WeTWhSKFjQ3f9CV3FCN/803wHnnSes2G/Dll8CoUf5NYZXc1q2Bw4dD1oMBECwW+op9uVz9MBqVHXJ9IAj05TAWyZegIwy+wVich+XNXQ0dOpocWVnE82bNov9yNLeQ0qHjdERE30ViolLZuu02YO5cxSEhOcTLLwN33hm+MpzBB1rtkpKA6upgxTAScLauVq6OBiERpShHYnNXQ4eOJoXZDAweDLzwAtmv5GnDm5s/aNoOdIA69owMMh0BZMEbPRq4447Iyxg8GGjbliyvcXFkYuLmJB8EgIazuIJrsUgW30AF12wmy2dUFP03GKBppWCMTEeBCq7JROdHR9N1TCZ4uemqgfgnWErSUYiu2I722IvWOIRMFCAVRUhEKWJRCRtqYIEdJjhhgBsCvIA/fbsOHacvjh4lx4XsbKBXL+DXX5u7Rjp0/A3QqhXJWo5XXiEOEanhulUroEMH8vyKj6eyuEuLHF6vpIiazZJrRmmppOCKIrHPuDj65YZ1QVCXUNw4HqjgGgzEG2Ji/DyksfwB+GdIyWH4BZ2wC21xANnIRzqOIwknEIcKRKMaVtTBDAcMcEOER+cQOv4ScDqBn38mD7GWLYFbbmnuGkkI4aSmAxdeSAtALjvcHWfuXGDFCmD16vBl5OZq7/N6gePH8dMrr2Dktm3ATz+RoutwSMdwtyEuqJxOWhITqW6zZuHD338nS7DTSUKtpAQoK6PfggLg88+B33+XlGiXixZRBDp2BCZOxP9atcIl06dL9aqtJQs0XyorgYoK+v/RR8BvvymEn0K8iSL5Qj79NNC+PZ1XUSGVUVVFS3W19LtqFbwbN0KUOxbEx9MSZiQcFgsQHw9WVCTVo0MH+tpqa4G6Orr3I0fI3YiDkwU1Q0BcHNUXoGO6dEFfZzW228dJ98BhMoW1dHsBVCMGlYhDFWJR7XNGov8xqEE0ahGNamMC6rwmOGGF3WuGHRY4YIUTZjhggQO0jRyVpH0umPyLB0Z4YYDHJyYZRDAY4IUALwQAApjsPwAwf8txu1dTuLjo+CtiyxaatTBkSHPXRIeOvzg2b6Zfu50+qD/+IO6QlgZ8+y1wxhmhz58wgRY1eL1AeTm+fvttnJefT1yjrEw5amu10jr3a6ytpe1paeSRdscdwMCB+DA3F5MnTSL+UFZGv3xZtYrcdrl/p8cj+V2mpACjRuGHLl1wzqxZ0nVra+lczh+qqkieVlXRfS9fTnJfBoW06dSJ3ErOPVfiD5yH8LJqaqiMmhrgwAE4li2Dpa5OKsNkIreUgoLwRoX0dJr/wu8rNpYYu8tF91JbS3wqcI6M2Qy/L2og5LygRQsgKgpL7LcRvystlerEDQS+a2tJXjvMqEQcKhDv5w2ViEMNYlDt4w81QgxqhSi4ZPyBeIK0OP0cIgp2mOEkZ2Q4YYIbZrhl/MELER4FfwAAEczHIZjOH3QEoLQU+P775q6FBN1duT7D6HY7CYbffqP1qCj8eMcdOPuZZxpVD4W70i+/AM8+S2YRLpA4TCbqUAM67Lr4eERNnAjcey8JBy1s2QK89BIJmWPHFLsYAKFNG+CKK2gObyTt4XQC06YBn3wCZrerd2np6cDNN9OcHvk8HxXkfvABJv/5JxkR5NbnkSNp0sWWLSHPd9hssLjd0rl9+5LhQH4vubnANddIx3TvTsfs3Uuj7nKceSYZBwASlvv3SyPsL74I3H03/W/ZkrSCtm215zLHxRHB4QL6hhuA3buDj7v+euSOGkXvQ6C7PMeIEcDSpSTgL7mE3se4OJrzvHs32IwZ9CySk0kwx8cTSQBIsZ85k4Svw0G/ublEFOLj/a51gS50Xq/PvnLdTXDmfuJTrM1wwgRnl95wLPrUbztxOoHvv/8JQ4eOhNstcSKPB4p1PkclcP+zz1JHCZAtZ+pUGnjgjglavzU1wIcfym1EDEajgDPOIJsHH5gIDAOhto0vR44cQXZ2C78NhPeWfL/attJSelXKy9WnldtsNDCTmakc/AgsD6D7OnGiECkp6Ypy1HptrZ68qKgIqalpim0VFTQ1XW7b4XOCeB25A8uaNZXo2FF3V9ahQ44GueFdcw2wYAH9FwTgttuQO2AAJk+Z0uB6BPGHhx8mmRA4AVBr6pPViuM5Och4+WXFVKwgVFcDr78OLF4MbN8eXFZ0NI1S//vf4ZV3jvfeo/oG8BFFmVdfDcyZE5aT5ObmYnJmJnD99RTvhKNVK5qb/M03YY3RtQkJsHH3cquVDPp8oAOgTn30aJqTC1CbvvoqXbNdO2WsD5OJ6l9eTs/6s8+UBov+/aVynniCvAb5QAOHIEgde+vWZGyorKRBl5kzg48zGvHRe+9h0lVX0fakJDJatGihnIT61VfAoEHER9q1I0EwZQot114LFBaS8C0ro+PbtyeOA1CcmpQUiUMUFwNffEH7li4FLrhA1QXf7QaclXbYkzN9ijUZ552wwLViFZxxKX5K4nIBP/xAHELOG3hcDDXuwJcffwS+/pquKYrAv/4F9OgRmjtwWr10KdlEJDC0aiVg8GB6nPXhDwCQn0/8AUBYDsH/OxzEH44f17adxMXRay3/JOTyX/6/sDAy/qC1nTGguDiYQ9TWUjegxiG4wykATJxYibfeakb+0Czhrk4TNDjq15NP+iPleQHKA6aVvzUCaEZw++knioCnFqbfZFJP/5OQwNiECZTgMhQqKug+evRQD0mfk8PY7NkR50v5/KWXKMu2lr4gipREbOPG8O3gcFD+V3lERYOBsX/9i+qrrZPQkpEh/Y+KojB0cpSUKEPsmc2Uc3DFiuA0BPJE8e3aKcPpyjOS5+QwtmOHehoDvjz1lHTu5s3B7eN7rh8uXCgdx8P5y0P8GgxSOOHly6XtvtRMtTzVQfv20j75/coiOTPG6F3h+3z5dENGFVSLtBzwXBsTlbCujgdjpPxwRiMlSY8U8lSC8s/l7rubJpF6pPB4KLLhueeqZ58QBHqlZs4MHWXxZOXR9ngooGa4PHlbt+rRlXXoCESD+cPy5Yp8YnVxcSTrGwjV79vhYOyVVyhvvVrGAqNRXe7bbJT79bffQl/U46EwrePGMRYXFyz3YmMpEvGmTZHdhMfD9p91VujOaNAgzdQ/Qe3wxRfKCMkApQ666CLt3Lp8SUtT1mPKlOCMFy+8oGy/IUMYq6hgdYFp/6KjpVSAoqjM0+tyUQYGfuy8ecQj1J4V/5+fL52vEUl6+7/+JR3zyCOSsJEfN3KkdAxPu9ixI60//7ySPwGKNImsa9fgxuc8ZfTo4GcRiDPPDK73uHGhn2c98dpryhz1F1wQuez/8kv1T2PAgOA0h+HQWNmdl0cRmjt3Vq+TxULN+dpr2llIT2Ye7bo6ZdZOteXss/UUQs2GRoW2Xr9eSsbNO7ArrmhQvtuIXsIVKyg/rIrC6zEY1L+AqCjqzD79NHRaIpeL/X799ZSCIFBxFgQKtf/00+p5X9Xu4cEHpQSoakt6OimIAXlfgtqhqooSscvrZDJRLhR5vlm1JTtb2bFPnx7cBk8/rSx79Gi26uab1b5S6X+PHspy7rtP2tetG6VSkLddYFvK77ltW9W675KnCnrmGfX7mzVLOoa/E758d/4URAaDlGJAnjN31KjgB2iz0b6rr1Z/FnJcdVVwfQLy8DZFx3r11esVr/Xo0eqpgtSweTNj6ekVQdUUBGoKrZQBgWhKBXPPHsZuvlk7311iInEwOQ9q6jqoYd8+xm67jT4RtaWgQFdydegIRKP4Q00NY8OHB8uZSDsmGcL2Dx4PpfIbMULdYC6KzKNmMI+OpjotXhw2reGyZ55hbOJEdWtefDxjkyZRLvdI7uOzz6gz1JLt8fGU976kJHw7vP9+cNrA3r1pICCUsmsyKXPfZmcHaziHDilz+UZHs+/vvz84v31iosSHDAbGfv9dKqOqilIKceH07ruhDeUtW0rnvvlm0HNkAHNardIxLpfEAeR81WSSninnGKJIx7tc0vvQqRP9Wq2h0xFOnSpxTq1nwVFXF3xfgqBU4MOVEQFeeukLf+pfgJqZp7UJB4eDUvwIgieoqq1bhx9D4mhKBdPjofQ+PruSahO2a8fYvfcqxzFOppLLMXeuNn9YuFBXcpsNjc7f5PGw7eefr8wPZzaT9aweuW7r/RJ+9x3lflVTJE0m9Xx1RiNZdV97TVVT8NfB5aJjevRQV3g7dyZLXzgFlTHGdu4k66BWhy2KpHD5RgE126GkhLELL1QqjVYr9UKdO/u3BQkHq1UpcNq0CepI2Z49TN4T1iQmMvbii8F1lSu6gwYpy+D55QB2onVr7XyBACV15Zg/X1VIuSwW6RiPRxrFlWt86enSMeecI/XijLGP335bOo4nTI2JUZYTiDFjaF9qauhnwaFmcZdlOW+qjvXQIaXBOi4u/CCD/PyCgmAuyZe+fUOm+m2S+9A6v6aGsf/+lz5Jszm4bmYz7XvpJcbee+/Dk1KHSNHcee506Dgd0STfxbJlzC4b1WVGIykMIQzKgaj3971qFVnTfHlZg2S8msFcFEnWPvwwY8XFoeuwfj2VH6jscYVvyhTKRRvuPsrKSNaGUvq6diU56vGEbodXXgnWDAYOZOziixX3G8Qh5MLHYCDFMhAzZyo9+3JzWVBy9ZYtJV5gMvk9phhjjB07Jo3sGwzscN++yuch/wUY276dzvN41I0WACn3HBMnBvMH+TEVFdK2+fMZY4wV8xHltDRpnzxHcGBb79gh7Vu9Ovw7qTaaG2B8bwrZ6/HQ6yZvzgceiLyMRYsWs8cfD36cAH0+8mbWqkNjoVXGvn1EPVu3Vqdj6elEkZ99tmG5syOpQyRobv6AZrnqaYKmaPzFixeTNe7ii5VvWkICY++8E5Gy26gPYflydrR7d/XOzmxWV4QFgSx0jzzid7PWdHnibshqo5Ldu9P+qqrw93D33eo9heyLXHvddaHLKChQKpsACYfp05VuP4FL69ZKEvHee8pyPR4qgx/TuTNj998fXM6IEdJ/+WgrY9Sb8H0XXxwspOTLypXSeXKCI1/kdeQjp4FlcYvwDz9I23btomfBLbDyYcPRo6X/v/6qrP/SpdK+vLzwz1MuOfjSrZt/d1NbMOWPB2Ds9tsjP58xMh5fe626h3+LFspHolVGY+4hHL77jnih+gCGl2VkEFd55x0SrGrLokXqy4wZv7LFi1m9lxUrqG7NLaR06Dgd0VTfxeJFi8iVQt45GY3U70dQdqP6qH37GJsxg4y7ajxBy2CbmsrY5ZdTJxFKwVy9mnxF1eRccjLJTd8Ib8j7eP11daWcLyYTOzRgQOiRcI+HpmhxryV+jyNHknuPlqt0aqpy1HfUqOCBgt9+k8oVRca++ipYqZT7nFqtypHhHTv8bV0bExPaE85niGaMMTZtWvAzA8j7juPYMWm//B3r21c6hhv6hw5ljDG26o47FNyMATQCzrdlZAS3L38+F10U/p3UGs09dMh/SFPK3i++UDZphw7B4x3hysjNVQ6Gy149zalQp2IUlTFqzlCzE6xWcrd++mlt/hCKQ9x66+oGcYiqqubnD2iWq54maDIll+PQIXqTAr+AYcNIiYikjMbU4YcfyKVZ3onLOzctgWWzseL27f0Kqyrq6hh79llSnlSEQV1cHGkR4YbGtm1TzhVV6zGmTw8trPbtIyus/DyzmVXIrY6BS0yMsoPv0kVpTWWMsbvukiy5Z5whueDIF7kFcvx45fn9+0v3IHdjClxiY6VzZsxQF1KdO0vHlJSoC+Bzz5WO4UaO6dPpfZg7V0kmAKWBIMC9mDEmDSnee29kLnBqdfIJ7pPRua9YoRwc6NUr9ICHWh08HvLOUuMQKSmkbIYrozH3EAny8mhwoF278PNlT+bCnQWaW0jp0HE6osmUXN5HFBeTx5JcTgkCTSOaN0/TYN5kfe2xY4zdeSdZ/dQ6BC33XlFklampNHVHZZTXj1AxRiwWVtixI2Mffhh6YKCqSvJc0lr69dO2WjJG5d93X7DRvVUr5lJzq5HLbf7faiWPNjn27WNu3kYmE2PLlgV34N27S8/XZlPyJbmh+corpf9qI+ucH5WVaQsJ+bPo14+2ye9P7tHFleXoaOl94O3DR5ZFkbHMTOn8QHDjflJSZO+k2mjuBRf4dze17K2oUNJzi0XpOR5JGYyRPUNtppkoEp2TK7unSsmVw+OhGYpnn6090H+qlvXrm58/6NGV4+Mxb9482OTZi+uBgoICZGdnK7Yl796NQW++iZjCQkXEYbfJhOJOnbBj/HgUdesWsozG1iF15050WboUaTt3wuhwBEU+5g9dbbs9Ph6FXbti39lno1hWTw7RbkeXb75B219+QUxREQTZK8QAOGNicLR3b2wfPx5VWvfl9aLvggVo/+OPED0e1XqUtG2LTVddhROdO6sWEZ+Xh0FvvIGEw4f953sEARBFGAIjS/rK9BqNMPiiQjIAR3v3xm+33gq37/n3fP55dPvjDwBAUadOcEZFIfvPP/3lMwBlrVohyZfWKL9/f6y+6y4AgLW0FBfdeisEALtHjULHFSsgAHAbDDB6PPCCAuwzAB8uXkznlJRgwm23KeooAHDabPhs3jz/9gk33QRrZSXcRiOMvvqXtGuH7558EgAwfsYM2MrLcWjwYHw6YQL6lpTg7DlzwABUZmUh/uhRFHbpgrSdOyEAqMjKwtfPP69on4unT4elpgZ7Ro/Gl2PHhn0nz585E3GFhYp6b7j6auwdN67R7zSg/l7b7SLmzBmJPXso0p/N5sTjj3+LrKzqiM6XY926LLzzzkDU1FgA2ROOjnbixht/R//+R0/Kt1kfuN3AN9/EYvv2vti3LwV2e/2zvjW0h09Lq8FLL32F2tpaTJs2TY+urEOHDE3BH4DgPsJcWYmB77yDrE2bIMpCl3pFESU5Odh1/vk40r+/P5TpyehrzRUV6PLVV2j9+++wlZRo8gdAySG4/D/RsSMODBumqKccGVu2oPNXXyF1zx4Y5SmPAHgNBpS2bYs9Y8Ygb8gQ1fMBIGPTJgx85x1ElZer1s9ps2H3eedh+4UXSnmDZRDdbvRZuBA5P/+s4AQuqxUmX7YILtf8dRMECExKmFMXH481N9+Mwp49AQDuNWsw5bXXIHq98BiNWD1jBobNnasooyw7GwlHj0JgDG6TCcufftrPk8Y98AASDx+GIyYGbpMJ0WVlCq7G67N/6FCsu/lmAMB599yDeFlWB37MyrvvxtF+/QAA3T7/HD0//RRegwGiLFUR5yFdvvwSvT/8EG6TCZ8sWICCggLcNns2LDU1ONa9OzK3bQMTBJS0aYOUgwcV53L0WrwYXb/6Ci6rFS8/8UTYd1KsrcVl06Yp2qYqNRVfvfwygJPDiwHg2287YOHCvmBMhCB4cfvtv2HgwHyVEkLXoaTEitdeG4Rdu9Ihf0sEwYsRIw7g2ms3orAw/6TwoPrgjz/s2LJlCDZvzsSJE9H15gON0RCfemo50tKONS9/aBbV+jRBk4/kBqKggCLNqI0wRkeT1XbdupM/WpSfT4GezjhD0z1WM9iByUSjfrNmBUflZYzMVp98wo5166Y+PJaYSG5NoWb8l5QwNnKkdh3i4xl74gnt8Hj5+YxNnMi8gXN2Qw2Dya2jRiNZdrnbldzdePRock8KPF8e5XnCBH9Vijp0kKy+POKg2vLll1L91eppNCrvkbsdy9u4Tx9pPx+tnTaN7uGVV6Tnx62vkyZJ5w4fHtyOfJj0gQcieyflwaz4cumljLGTb8F84AFlU33ySf3Ol2PVquBAnABN4Zo8+Y+GxJKrdx1OZhn6nFwdOpoeTT6SG4i6OpoDK59uwxeLhabPLFjAFsuj8Td1HRgj19z58ylug0ZAKE3ZLYrkkjJjRrDnFMeuXYzdfDOrUfMFNRrJQyrESDZjjLHnn2dOLU81UaQRQi0vM4+H5GVSkvKcUC7DgSO+/foxdugQtePKlUq35CVLgs/v1k0a0TWbpSjUK1dKbXn33crnHcirOAI8ztycTzz2mHTMW29J9yUvh+PBByVeygJGcseNk+6FD2GqBZ/ic4qSkyOXOYHt4ru+vw6NQKjzf/tN2aTPPVf/MjgqKuj1CrwVQWCsf/88nvSiwWhu+d/YMpqbP6ibyHQ0DbKyKK9cYSHlGJs6lfKWAZTU88svgQEDcOnUqZRX7qmngpONNwVatAAefRRYt45y3eXnU6L1/v0pISZCpO12uSg/7WOPUc7bpCTgggsoJ6vXSxbSiRPx07//DdTVAd99B4wd6y8XZWWUvLRXLyAhAZg4EdiwQXmNpCTgxx/JMjh3LhAVpdxfUQE88ggl37rwQkoiFnh/n3yCJS+/TLlxAcppzJiUrCsQHo+UhN3tBp57DkhORvqWLZTPjuen/eEHqs+wYcrzt26l5GsAtcUllwAA1l97LW2rqlKeE2hJfuQR6b9aHd1uZR5C3p6MKY8J/B8bS788T2BUFD1zAJBbA1u2VL8mQPlyI8HYscHbtm+P7NxG4plnKDUfT/146aVS2uL64qyzKDfe77/Tq8RRWQnk5vZBVBS9+rNmSU2pQ4cOHScVVivlTz10iDjE7bdTcm2Akmn+/DMwdSounzoV6NIFePxx6rSaGmYzcZfvvqMk4DU1wLx5JCPTyKNGkz94vSSLXn+dctLbbJR//oUXpLp26gS8/jqWvP465cu9806pI3a7iS9Mm0b16NsXeOON4By9d9+NTxcsoNzzOTnBdfjqK8r1mpMDLFqk3C+KwK23AsXF2HneeYDBQOfY7fQMDIbg++KjzzyZ+MaNQNu2GPjGGyT3ly2jcu124PLLgZUrlUnRt28HunWjY5xOYMAA4M8/gWHDUMs54v/+J/EGKfk7geevBYA+fZT7+HV275a2ZWVJbaEGLtj4/cjvkXOOqCjpmam1SVUV/ZrN6teIBDU12nVsQgwaBOzcKVGd++4DLrqoYZeOi6P8uhUVypTKjAEbNrRCx47UdJMmAXv3Nkn1ddQDupJ7qpCTA8yfD5SUkII0aZI/k7PR5aKO/OGHgdRUSsI9Zgzw7rvUSTY1WrQAZs8G1q+nTuXAAey44AISlCpuPQqUlVEHfvHF1CF27AjcdRdsPrdVjBkDLF9O5a5cCZx/vqTEVVSQAnnGGXTvF10ErFmjLP+22yjL9MGDQKCbMmPUm7RvD7RpA3zwgWJ3XUoKCZslS5TGBEEgYRUInsWbd8rl5Rjx7LPAihV0j6NG0favviJhPmiQ8ny5ovv558DEiaho0wbo0IG2/fabJHAC3ad37JD+pymTbDO1Y7QUYQ6eNZy7g/jcqREbK71DGRnS8Wou4Lw83nbhcOmlwdvkCedPMsaPB3btkqr74ovA0KHB/CdSDBxI9p9Nm+j1kqOoiDhkbCxd7+67T449SocOHTqCkJYGvPwycPQoKb3XX+9XeAXGqCOcNYtYe4sWwIwZwcbgpoLNRtdftoyUb4cDa6dNo85Xy22by8G6OmDtWuCee6iuaWnAhAlk8Pd6SUa99BJ1xMXFwL33Aq1b07keD3XOM2YAFgvQsye1idzduWNHGlBwucigHogDB4CrriKt46abgPJyaZ8o4s8rr6Q2PvNM2ma303W13Cy53DUYAMbQbtUqGjwYNYqUVEGgez7vPOJ4ckV361ZS/A0GKufMM4G9e7GFy9UDB4BzztF+Dps30y+vK29q/ufgQWmjXPbLwbW6AAXVyAcJAKUBva6O/qvxRF6GGtfSglo5vuliJxtt2xJN4rJ+yRKy/XPqVF/ExVEZNTXA5MnKR223Ax9/DL/Ce8klShuEjpMHXcltDnTvTqObFRXA1q3YM2YMKUbcOlZeTiOI06bRF5GVBVx2GfDNNyfHytW2LTZfcQUpVS4XKXmXXy5ZjbXg9ZJp6qWXcOHMmaScT5gA/PIL7R82jBTE6mpS+C66SBpprKqiHmHwYCAmBjjvPCTu2yeV3aYNmdo8HhJGgZbDvDzgyitJ2N18s9KCfeGFJCAfeIA6Ucaol7FYQltlBQEiYzRC+euv9AyGDKF9n34KtGtHAkwOuaL72WcY/N//Av/5D60fOyYpvPIRWIDamT/LVq3U23fjRul/TEzwfrnizP/z9j12jH6TkiQhJScggdZfeRmRKrkpKcHbqqpOiSWWIyeHRmF9U4+wejUJqsbo2r17Ez8oLgbOPXd7UHOUlZFCnZpKgu2GG4gX6dChQ8dJR+vWNJJ69ChQUYHNl14qjQoC1CG+8QYZg6OiSBmcOZNGCk8GzGYcOPtskvs1NSQTp01TegvJ5Z+c/RcXk0vO+PG4bOpUYPhwUngBki/PPUdKfVkZ8NBDJIMBkjFbt9Kor9UKdOuGDsuXS7LHaAQ++YSu+957wUZiux146y3iLAMHkuDgSEsj4/sPP0jKIecXanIYINnJucXGjST3R48mzUYQqF1GjgS2bVPOMd6yhfiDKNJobe/eON69uzTEePy4ersBdO9AkCz3x0iRCyUtJZcbwANGcmPlArSmhn4TEiSlXk2R5cfVR8kN9NoDgO+/j/z8RiIujmxBkybR+tGj9Nl8/HHDy7TZgMWLiXbNmPEbOncOVng//5zGGSwWopvr1zfuPnRoQ1dymxvdu2PjtdcCe/aQsvXdd6RgZmVJX8axY9Rhn3ceWdo6dCBr5pYtJ6dOZ58N5ObSF19XR8JgxAhJgVKBAJBy/sUXJKiiosiyu3AhCZ5Bg8iyWVlJX/TEiVJHXlMDfPMNxj76KCnWs2dLiqcoSu5J338frFg5ncCbbwLx8Rj16KNSxy6K5NNaWEj3A5AQ8Xi074MxGkX1eOh+N2wgwc01qA8+IA2oVy/leTJFt9W6dSQIeD1DDStyZbhLF2U1+HOXj+TytpKTBTUllx9XXEy/KSnBFllAUr4VF/YdF6mSqwbGTpkllsNqpUc1fTqtHz9Oyu+WLemNKjclBbjqqi0oKSHdffZscluWo6oKeOcd8gS32ejTPVkDKDp06NChQFwcdkyYQMqTw0HeYoMHE3sGiFFv3Qr897+kDJnNNJw0fTrJtpNhkOzenTrFw4eJP7z2Gsl/rvwEGnx9MHg8VKfx4yX+8O67JLcSEmg61/79NDgwezbJMEGg8nbsQP+FC0mZnTqV3Ko5rr2WlLiDB8k1OhDr1tG1EhPRWq7sjhpF3OuJJyRvr+pqUgTVXHI9HskL69AhoGtXMuwvXEjbKitpbsz27Uqt588/yUghCEBtLUY+9xx5tAEkS7mrcWC7rV1Lv2az0s2YHyd3aQ7wFvODK7m1tfTre2/iuZFcFKVyUlMlDqGm7HMltz5B2NQM5fy+ThFEkcac3ntPGlSfNAm47rrGl3vWWXnYuZOa7cMPierJHz2n/AMGkF1m0CDg228bd10dSuhKbih4vY0XAvU5XxTJ3Tc3l6yxdjt9eaNHk8URIGVm3z5S/Hr1AqxWjHvgAeCOO7TNQY25B6uVrLIrVlAnnZdHI6Tduys71kDY7WQdnTqVjuvSBXjySSqjf39S2svLyeVm8mQgLo4U5ePHaf6vzUbKKb8nr5fWi4tJcI4cGXTJtH37SNu47TbpnpOSqO6//SbNSeVuNSodtb//cbtpFHfnThKCPXrAC8A7bx6NUAdGnd66FUhNpfPvvZcmeQDkciQTiIon8dpr9MvnEQdCPrLtU169HgY3/2zlSiu/X+5Wxd2w5PdYVESnQYS3ZWvlteTviJrgCTjU6fSdovYORNBLO52yMhoAPmXK6aRm8HqBt98mjsenOc2ZMxJdumhbZZ1OqQx5OWqIiSFvwOPHiUu+/HLwtOa6OuCjj8gSbDaTTerAgdDzm3kd+CKvC194N6S2uN3K89UWt1vv5nXoaBY0BX+ItAyjkeTtr7/Sh795M3DXXSSruYLpcpH31bx5ZIw2mYDWrckD6eOPla6/DalDIKxWMsivXk0d5LZtVKeePUOP+HH+wOfi5uTQHJEjR0jGzZpFAwPV1cDTTwMdOpCCabeTUpmSQhrDzz9LZbZqRW7dHg+5Wwd6dZWXY9Drr9NosdxS+fDDNM3sggukNnQ6yVgeMLqqWMvLI8PCFVeQ0g/AW1ZGCjV3NebYupX8ZwHE5+fTVCDOG2RzjL2QcQjuOgyou1NzpRNQcBA3RKkMDSU3ho8gW61kVADIwssYvADcCSocgZehFQdFBi7jguZPA8S5woBzkFAyO5I6yMu49lq6dGoq7f+//wOmTbsEjz+ufg3OQQJ5iNqxokiK844dtH/p0mDa5/FQXJBx4+i16tCBXptQ8lteBzl/qA+HCMcf7PZT6pzX5NBTCMXHa4e23rKFFEmLhTqRvn1J6eSWNQC5ubmYPHmy9kVyckjR8aW2gcFAwohbA81mVLndiO3bF/jXv4ApU5Qd1ooVpORycMtlKBgMZAlNSKAvdvduqQPinTIPumQwAKIIl9cLk8VCdTMaabvJRP9dLpojEwhZB88Y0w4+EQq8DEGAlzFyFw6EKNL9yDvtSGCx0MLvqaQkeG5sJEhIACwWrCjuidHe78BnzQpgEMAgwutfDPBAhAdGsxFGZw2McMMoeGFiDpjgwgG0hQvBVmBelrR4IQqAaLVAFAHRWQeDyw4PBFQhAVAkFoCsTvClEODlAIAXAgBBECAwD5wwIdi+pfZOBT/R4Ncv1LvYoDcioOzGltFYnA51OHkwm4lbjR1LMyK4d74cYftJHTr+gYjou2jVikYDY2LIZfSFF6Sghj6E5BBvvw3ceKOSP3DZ7OMQ1W43Ylq3Jv5w003Bo3bJycqRzXAQRVJu4uOJP9TVKSPmCIJUH9/iZgxGs1mStXzh9dy7V50pNxWH4PyBCgreHxtLCnF92LrBQMZ2fi8OR8MCexkMpHQ7HEgu34dSkKeUXN5LHMIDAzwwCgwG0QujxwEjXDDBDROcqEIMjiMbgXJXAAPAIAZyCKsFoijQq1NdDgM8KEMimD+ZIZ0tL08QBIB5fWVK9YQoQvC64YUAjwqHCeYC6k8zMg6hlsCpvvh7y+7TAaJI4289e5L3/NlnB48dNTd/qH/SxX8S+Ciiw0EjiN9+S0Edzj0XWLAg7IgXAMnaxhgpWB5PkLU0FqD5tt98Q1bP2FhydRkzRqFQ+8sJB4+HOvTq6uDJifx8ruz5RgNN/D7rA1ldGtyV8DK4gFKD11t/BReg+6nvPanBNypaDW55prv1df3wQm2eLwD4LJoRPDLmFymKjYDfWBvlWzgCW5zXSa08g6weWp98ZE8w+PU7mULkdBBQp0MdTh6cTrKB7d5Ngc1NJvJkvPFGElw6dOhoBE6cIBlbXk7L+efT9JcFC4K9gdTAlVM5fwAUo3gxAPGTDRtohDMhgbylrriCvKTqG4nP6yWjeG2tFNtBDp4lRaYwGoGGydqm4hCh+ANjDVNOPR7J66sx8Hho2hQAN4wI5A8AEGR6Z2ob5VC2FjcPBJ2iiFuaEKIMubEBAMRgPuIFoMZ1NOqkhcg5RGNl799bdp8O8Hpp7Oinn2gRRRoXvO46Clh+OkAfyQ1lYaiupjkimzbR8uWX0sR7UQTGj8cn556LS/lkQDXk5ZGiWVJCcxvKykjYVVTQUlWFY7t2IbO0lASK2uMwGknZ7dqVnPdbtSIBVFMD1NRg14YN6Ny6NQnE3bv9ASn8dQ0HqxXVNhtiunQhVx2DgQSW3U6/NTXU2fN17q7jdpOwVVNARZGsoIxRXSN4zRgAgVuruQANNfIaG0sWZ7cb8HjgdDhg9ngaHl4XoW1/dpixDx1QAxuqEYtqRKMaMahDFGoQjVpEoxZRsCMKtYhCHaLggAV2WGGHFU5YUIFY1CIKDljhhBlOn42WFgM8MMAL0W/bleyzALepStD6r0NH4/Dss5LHfXNbYnXoOB0R0XexYQP5IG7fTjEpeBYCgOI4vP46cvPztUdyq6vJLbe4mBbOISoqSHGrrkbBnj3Irqmh6S1q8jImhozz3bqRgp2V5ecOfNmzeTM6ZmTQtQ4fJi4RqdJqMMBus8Havj3xh4QE4gXc19HhIP5QV0f/nU7iEHyprFSvd3Q0cZ+6OnU36gAw+EYhecRe7o+pxT2sVmnYyRcI0l1XB+NJ4g8AkIdWKEYKamFDNWJQjRjUIBp1sKEGNtT6ljpE+XhDFOp8/MEBC2oRhUrEwuFbd8IMN/mLwQOjbxzYAK9vfDjQPwxBxnSdQ+hoemRl0YxLoPn5g67k1qfx7Xaa7zl/vl+RYoIAISeHAgzMnBk88hoB/O5KXi+ZQ3JzaT7KwYPqnbsoUoCm/v2B8ePxEYBJPD+rHG43pfP59FMqLy8vMgXQaqUJASNGULj9M84Ifbzbjd/uuAODd++moAGByUQFgVyoYmJIQEfiOtWhA7XnjTfSPTz6qDLasBwDBwIvv4zcAwekdhwyhMiFHGYzuZtPmULrTicJ2Pffp3nG4QRpQgLN1bFayQ2dz1u55x6yzPNgTxw5OSRwQ8WKj4+nSI4jRvjnzCowdy69dxs2BE8utVgoFn1FBbzLlpGAHDQaR/NLEH3kOCnS0Ulw1DjhMETD5TXCwYxwwAJ3ajYcxeWkaMenw3XJJDjfex9OWOCa9RQ2b96GLl26c/uBf15H0Pqe/fCu+R1uGOGFCJfv93CPfkjPaAG3WzL683PkgxL8P9/OeQkZ0SsRHR0nH+wPGkSQbwtcr6urg9UapdhXU6PO3Uwm8ojn3nO8DLfbAaPR4j9OrbcM1YO63S4YDDRvmbdbpB7zfDYB4IYxXGqvEHC73TAYIj/fZgNeeUX6TJpbSOnQcTqiQd/Fk08Cc+YoDMO1iYmw3XcfyZEGfOdB/GHePErfpzYKC1DHkppKc3XPPhu4/HLkrlsXrGh7vTRdavFi4g8HDkTm7iuKFP9i4EBKNThhQviIu9u24cAtt6Dd3r3q9U5Lo6WiggYNwtHWxETiLk88QUb2Bx6gyD9qnX9qKkVuvv125H70EbXDu+/SfOBAjB9P5XDjem0tuWJPmRJZTpg776QUSQ88QJZEgKJlT51KOeoCcfvtxAEAElJqAxfbtlGMk8cek7Zxn+AhQ+gZOBzKgJ4cY8cCCQnwfvElnA4PapNbobZDL9Su3QwPM8ARkwpHtQN1iIIrJRuOExVwwQRnYgZcogWOkipStB+aBecLr8LlcMNx5khszsxGly49tHmDW+IAnvcX+RR0ER4Y4YYIZ4euOBYVjbS0bP85apxBbeH8oKJCmz9ocYbAfXV1dlitVv+2+vAHAHC5lPxBvk9rPXCfx0McgvgI/JwqEhgMgCA0jj9QHSLnECYT2dL4dPhm5w/sH4yKigoGgFVUVNTvxKoqxqZMYcxgCP42kpMZmziRsVWrIi5u8eLF2jt37WLsoYcYGzCAsdhY1e/RCzAWE8NYz56MTZ/O2JIljDkc2uU98ghjgwZplhe0iCJjrVszNmkSY598olq24h527WLs5pvpHEEILs9mY6xVK8bS08Nf22xm7LzzGNu6lbG6Oqp7UpLqsfaoKMZWrJDqsXu3+j22a8fYnj3KG3C5GMvMDF+flBTGKiroHUhLk7a/+mrw+yAIjG3aFL5Mxuh5qO2rqaH9K1YE70tKon2bN0vbPB72y513StcfMoT+9+hB7wjAmMVC551zDq136MDYO+/Qf6Mx/DspR0WFar2///e/Izs/BCKuQwPO//BDxjp1Cn5c/fox9sMPp6YO27YxdtttjGVn0yem/Yp4mcXCWJ8+jL35pvRKNEUdIkGD+0kdOv7GaPB34fEw9vzzwXLMaGRs3DjGNm6sV3Ga37fLRVzg+usZ69aN5K5GJ+M2GhnLySEZP38+9etq2LiRsZkziY9oyGHVJSmJsREjGHv2WcYKCkLfR0kJY7NnM9a1q3rHGB3NWMeOxC+MxtDXFQTG+vdnbPlyKnv5csZ699Y8du/w4fR8OM47L/g4g4GxuXODb+C555g3krZ47DE6fuZMaVvXroxdd13wsfzZAYzFx6uX9/DDxGcC6wgwNnq0VL+UlOBzf/qJ9nHuM3kyY4wxp9VK61OmSMe+/bb0Pz+fsQMHpPWKCsaiovz3Vy+Zo/YML7zwpMrexpTh8TD2wguMtWwZXO0ePRj79NOTXwfGiIZ/9BF9VtHR4T+DuDjGRo5kbMGCU8shmps/oFmuepqg0Y1fU8P+mDKF2Kfah2qxkPLy5pvaSier5wtUVsbYa6+RMExLU1ci+RIfT6z9ttuIucs7b9k9sPnz2eH+/Rlr0SJyoZWcTB3of//LWHGx9j04HHT/Q4ZInWDg15eWxlh6engBkZlJHXpdHWkI552nbmjo318ppF98Ub28yy9XPheHg9WFIAKKetTU0LNITlZ0zEHHTphA9ZELnsBlyxbGrrlGfR83lmzbFrwvIYH2rVwpbWOMbZo0SXr/Onem/yNHksGAvxeMMTZwIK336cPYc89J59T3nVSp99bx4yM/XwOnQsjt28fYBRcEf76pqcSz5s//8KTXgSM/n2w47dppvyp8MZmI+7z0kjYfbUgd1NDcQkqHjtMRTfJdfPIJK1Njy9HRjI0aRUZlNbktQ735wzvvMHbxxYy1aUMdiVYnEx1NrH3aNMaWLiWlORAeD2OrVrHdY8aQohaqPPkSFUVl3347Y+vWad+Hx8PYokXE5NX4A8BYYiJjLVsyB5dvWktCAmO33kodZl0dY7Nm0TY13rZsmVSHwkJ1BTE9PcggsVWNA6gtr7xCJ9x8s7StZ8/g48xmxr7+WlpXU/qzsqid5Ns4L+zcWaqcGr/7+mvax3nM9OnU7FwAcV4iivSs+Hm+5y7nHf5n/8or9Xsn4+KC69W+/Wmr5MqxbRtR8UD+YLMxdumljD3//JcnvQ4cHg99puPGRTZ+ZTTS+Mbjj9Mr3hR1UENz8wecjEKPHDnCpkyZwpKSkpjVamXdu3dn69ev9+/3er3skUceYRkZGcxqtbJRo0axPQEjayUlJeyKK65gsbGxLD4+nl133XWsqqpKcczmzZvZWWedxSwWC2vRogV79tln61XPpmh8/8P3eKhjPO88dYubIJCl9K67iM2qldEQuFxsxQMPMDZjBmN9+6p3GPI6JCUxduaZjN17L2O//eYXoIr7WLWKyuveXVKMwixOi4WE8qJF6sKQY+NG6jizsoLK8AIkTMJ9oaJI9/Ddd1Tf+fMZy85WKsmCIFlM+X0NH67+pb/zjv+wj997L7xZDKCRaIeDseJiSVAKQrCibjAw9vPPocsaOZKMBaGEYVlZ8L64ONr3xRfSPTPG9owcSetJSUoLLReQmZl0HrcQDx9O3gIA3Xt930kVQ8vR7t0jP18Dp1LIORzEeYK5jJelptJnPX8+caOTVYdAlJQw9p//MJadXRqWOxqNNLjx9NN0XlPVgbHmF1I6/ln4J/EHxnzfZ2EhY9deqy77DAbqq2+9lWS22vmNwa5dbMuECYwNG0YdYCjDeUICjeLecw9jq1cH8wfGaFTxoYfIuKulmKrI9PLsbOqEi4u167p+PWNXXEHuL2r1jI5Welhp8aC+fWkYzOMhzzO1Edtu3ZR1ef999WuOHUueXbwdbropsnvmbSY3cKvxtwcflOQ4H2ENvB/G1PelpUn179AheP8XX9A+ft2ZMxljTOIx114rXff886XzGCMDjPz6nF8sWlS/d7JLl+B6Wa1/CSWXw+Vi7Jln6LUM5A9RUTTW9MgjQdS/SesQCI+HHBcGDjwQdjyMdzPt2pGzRWCXpiu5MpSWlrLWrVuza665hq1du5YdOHCAffvtt2zfvn3+Y+bMmcPi4+PZF198wTZv3swuvPBC1rZtW1YnY5Djxo1jvXr1Yr///jtbtWoVa9++PZvsc6VgjBouPT2dTZkyhW3bto3l5uayqKgo9tZbb0Vc1yZVcgOxbx9Z6Vq1Un+jkpLImrpyZdN/zA4HdV7XX0/W0lBKmyAwlprKjnXrRopWIENmjL7MZ54hZUjN8qlWZk4OdZiHDmlXvKqK/D769w82hVmtjGVkhPPjJIPC9OmMFRezVbfcEmxJTkxUWlsPHlQ3QrRowdi2bdSWJSVkzQ13n+3bU+927JifnKiORt94I1nNtcqJiiLhHdiGABkbGAu21ALkfswY+Z/wXooxVtCrF623bSuRpnvukc7r2pXOa92a1i+8kK7D20vtnQoFFUNIVUpK5OdroLmE3HffEQfSEgppaTT6u2hRSAeNRtVBrYyqKnLi6N07vO3JYKDH/8gjjL3++meNun5zCykd/xz80/gDYyp9xObNjE2dqj2dx2AgXjFxImOLF7MP589v1PWD6uDxkBvrbbcROw9lOAcYi41lpS1b0kifmqt1YSG5Z48YQfIlnFzl/Oiii2ikUWsk2+EgxfPccxlLSgo2cqenh6+7xUJDX7//zlhhISnageXcf7/yuhdfHFyOKDL2zDNSO152WWQ8iY+kXn65f3sQh4iJYez110OXVVioPt3KZpPqzXmBfPnoI9rHPdhmzSI+w/dffbXEC/r0kbYzRgZ4gDgXY9K+r7+un9y78krVe/r07bcjL0MFp1LJlWPbNnp1tew70dGMnXEGeYpFovQ2JQ8qKyNP+3791G0i8sVsZmzwYHIC1ZVcGe6//3521llnae73er0sIyOD/ec///FvKy8vZxaLheXm5jLGGNuxYwcDoLDefvPNN0wQBFbgm8/x+uuvs8TEROaQscz777+fderUSfPadrudVVRU+Jf8/PxGN35ED7+mhvwK+/VTdefxiCIJrTFjyB1XZiFtsjpUVZHlcMoUcmEJZWGNiaFR3CuuYOytt6jTk8PhoEmNl15KExPCmYiiokiRvecev2tSEFwu9sfkyeoTHZKSpPmkIZaKjAzG3nhDXQi1ayfNP2FMU2jk9+pFbV9QEH6+D0CKbnEx9VZamofFQhpRqHJcrmABCJCVmCOwnbkAmztXIWxKuWFFbjzgxwA0cswY+eQCRKiuuIL+p6dH/k5xyF22fYubC75GoLktuQ4HY7feupqNH6/NNwWBbDETJpBhO9CJ4WTPyVm4kLzOQwssLzMY6DW0Wuu3TJ/e/EJKxz8H/zT+wFiYPqK4mEbyevfWnFPr5TJy0KCgEdYmqQNj5MLyySckK8LxB6OR5Pg55xCTX79eWZ+aGpKHV11FZYUzKPOYIJdfTudpTChc/vjjpEgHzvWw2ah9wvGU+Hi2f+hQxv7v/4LrZLEwdt99klWzpERVKDgtFinex5gx4fmDwUBcijGa/gUwj9pxr7wSmgNNmkSWWbXyOXh8Dvny/vu0j3OX558nj0S+/4IL6Ld1a8ndmY/cPvyw1L5yI/z69fWTe0uXqt7T79OmRV6GCppLyZXjmWeWsdtuo9dcyxvLZiMZ/swz6k4MJ5MHcRfnMWNCTtlngJcZjfXnEGlpzc8fmjy6cteuXTF27FgcOXIEK1euRHZ2NmbMmIHpvjQ7Bw4cQE5ODjZt2oTevXv7zxs+fDh69+6Nl19+Ge+99x7uvvtulJWV+fe73W5YrVZ88sknmDBhAq6++mpUVlbiiy++8B/z008/4eyzz0ZpaSkSExOD6jZ79mw8Jo9A58O8efNgs9kadL8FBQXIzs6u1zkZW7ag43ffIXXXLphqa1UDtzMAbqsVNcnJKG/ZEic6dUJB376oTU1tkjoAgLm6Gi3Wr0fmn38iYc8exFRWQtR4HbwGA+yxsajKzERJ+/Y41rMnijp18keDNPz4Iwb9+SfStm+Hua4uZDB6BsARG4vSdu1wrEcP5A0eDEdCgv8+4vLz0Ts3F5lbt0KUhaJlAJgoQvB6Q5bvNplQ2Lkzkg8dglWW54757uPAWWfhz6lT4bZaMfzpp5G5bZuiPLfJhG/mzIFXFPGvu+7SbBMGCrrvMRqxdvp0pOzdi44//AAv4M/Zx49ZeffdGPzaazDZ7appBnIXLsTlU6dC8F2LH3O0Vy+svP9+ZG3ciGEvvKA4zxEdjc/feQejZ81C6t69cNps2D5hAnp/8AEEAHVxcYiqrAQDYI+KQpQvt+Kfl14KS00NOn/9NQQAO889F52+/Rai14uyVq2wfM6ciN+pqOPHMf6uu4JSt3tEER8vWhT2/FBo6HvdVOcHluF2i1izpiV+/7019u9PRmWlBYFPUhAYEhLq0KlTEYYOPYiUlI1o2bLp6hAKbjewcWM2vvuuA/bvT4HTKeVkbCj69j2CGTO+xbRp0/ToyjpOOv5p/AGoXz9lKS9H219/ReamTUg4fBiW6mpN/uCMiUFlZiaKO3ZEYdeuKOrSBV6NKMcN6SuNdjvSt25FxvbtiN+6FcklJTA4ndr1iY5GTWoqStu2xfFu3XCsd2+4fe1mLSlB9qefovPOnYgpKtLOeesry2MyoTIrC0VduuDwwIEo6dABBceOITs7G6Lbja5ffIH2K1bAWlGhqE8kHIIBqMjKgiM2Fmm7dwedX56VhbUzZqCsXTtkr12Ls15+WVFfBmDrRRdh+2WXYfTDDyP1wAH/9sCyuNw81qsXfr3xRky85RYIjCn2CQDKWrZEUdeu6PTtt0H7AKAyNRW1qanI2LFDUbZXFPHRokWA14tLbrgB5tpaxXk/PvAAvAYDRj31FAQAv950EwbMnw+T3Q63yQSD2w2BMVRkZiLu2DEIIH70vzfewEW33AKTw4HaxEQc7d0b7X/6CQzA52+/jYMVFRG/T+2//RZnLFgQ1C6/jR6NvOuui6gMNTS1/G+K8/ftS8LKle2wfXs6iotj4PEEvukMUVEutGpVjv79CzB8+AFUVBw8ZTzI7QbWr2+Jr77qgsOHE3z1aziHMBg8eOut+c3LH5paa7ZYLMxisbAHH3yQ/fHHH+ytt95iVquVzfe51Pz6668MADt69KjivEsvvZRddtlljDHGnnrqKdaxY8egslNTU9nrr7/OGGNszJgx7IYbblDs3759OwPAduzYoVq3ZhvJDYX8fPbnpZeSy0ybNuF9CAwGGoEbOJDcoT/5hOaRNhKLFy8ms86KFVTugAE0Pyecu3BMDGNdurCDgwZJURkLCsjKN2hQRCOwDGT9LWvRguby5OVRpTweGoEcMCB8u2gtqanaVudWrWjSQkEBuVcH7n/kEYogGGkU6uHDtYNnXX21+twTvpSUqG/n77LaUOLs2crIyjNmSNZquSVa7q6enCwFmQJoJJqbGM1mmp/EInyvPR5N82RBz55N80424/nhyqirY+y99+jTVRnMZgBjguBlSUk0qH7TTRR58VRFNuRzclq1KgmqV0YGzWZ49tnQy+rVzW+J1fHPwT+NPzDW+Jgcq26/nTxxOnUKPwfWZKKPf8AAmm/55puM7dvXdH1tfj4FBhg3jkb/wo3SWix03KhRbMvFF1MWBR5sasIE7bm3KktdXBx5qq1eLVVs3Tpy69aapxuJF5pa0Cle99tvJ0Fw443BZWVlER+aMCEy/hAXpz5vFqBhtl27tM/t3Jm81QK3X3wxtcPs2cH7+JSitm0lbtCjB2PweQdw7hfoobZ8uRTQUhCUXnE+z7N6vU8aHPHL559vmneyGcsId/769cQL2rfXDjZpMrlY+/bkAv3SS0RJm7IOoXDsGGN33MFYXFyNat1ataJQAVr84c03m58/NDx5kga8Xi/69++Pp59+GgDQp08fbNu2DW+++SamTp3a1JerFywWCywWS/gDTyVatMCOCRPQS56jrroa+P574McfKT/qgQNASYmUKIwnhl+7FnjjDUwEKJ9sYiLQsiXQuTPl0B0xAujZk/LWRQJRpLx5Z5+t3L5zJ/D115Qrb9s2yvLsGxVEdTWwcyfaAJTv9ZprKGFY69ZA377AjBnAeedR3r4lS7Tz9brdSDhyBHj6aVqsVkq2NWkS5eqLiQH++AN4/XXgu+9Uc+UxqNiceO5as5ny3BYXS+cdPgyMG0ej0ZddBixcCM9ZZ8HA9z/xBOXLO3wYmD6d8g2HwsqVUn66QKxZA7RtS22phtWrg+9j7FigSxdKWlpYqDw+NhZ45BGAj2ZERwNbtgCMUe5mnswtNhbgo9k2G/Dii5STD6Bcy4WFlHtPFKmdO3UKfY9y5OSo5+0TBKydPh0TIi/pLwmrFbj2WloA+hTefx/47DN6VcvKAMYElJZSaugNG4A336Rjo6IolWR6upRbTwvFxaPwxhva+8Odb7O5MGQIsG8fpWJmjFI8v/su5bRr2RJo0SK4nGefpVSXlZWhy9eho6mg84d6wmhE/plnAnL+UFkJfPEF8O231BEdPkz5XAHqr48fp2XdOuD//g8AMEkQKCdru3bEGfr3B4YNI3kQKX8AqCN58EFaOMrLga++IvmyaRNw6BDVkTFKOpqXB+TloQcAfP45JfdMTQW6dQNuuolywXu9lJ92xQpg61bqbANgrawEPviAFqORzr/4YuCNN4CUFLrmW29RTtmtWyn/fDgOUVcncZ20NCrDbqd1h4Ny2M6dC7RvD6xcieIrr0Tq4cO0/+hR4hyffQbceivJ80DeI0dlpXZnW1sLxMdr58q1WoGDB5X3YDIBCxZQbtxnngk+57XXqG6+83DzzZTHGYBXEGDweqkd5bl1X3iB2nfXLlqfPZsWgPiFzDMiIvz8s/JZiiI9a6sV1VlZ9SvrL4j+/WkB6LZ//pke2S+/0Gfr9QIulxH79pH8/uILYOZM+kRSUohmm83a5QsCUFQUmj/w40IhK6sKvXvbcPw40X+e5vvwYeDVV+n169yZXlGOAQOA++5rfv7Q5EpuZmYmunbtqtjWpUsXfPbZZwCAjIwMAEBhYSEyMzP9xxQWFvrdjzIyMlBUVKQow+12o7S01H9+RkYGCgOIP1/nx/xlERNDCdQnBKgJeXnAsmXAqlXUSefnA5WV1KG5XMRgi4qAjRupI+Kw2aiDbteOEsAPHgyMGkVfSSTo0oWWu++WttntpGz+8AOwfj3s27bBWlNDQqOujjrBXbsokTyvQ+vWlJz8iSeAM84AvvySytiwgZR4Oex24Ndfabn9dvp6hg8Hrr4aePttEhbvv0/3uX49oOG25YfTSW0DUFvU1EhfqttN9Vy8GCXt2yPNZiOFESBBlZhIwve226jdQggqQU3BBejZjRlD96uG9eulMgDqxRYvpl5OThg4XnyRFOOtW2n9lluA556j/7wO8fFARQX9N5mAjz8G/vUvWk9IoF+uAOfmAmedpXlfQbjmGiIrchgMZIQ56yzYk5MjL+tvgpgY4gk330zr5eXAQw+tQU3NIGzaRK8A7/Dr6uAXXOGRht27G1OzdM09LhfZ0HwedQr8+ScpuTp0nCro/KEJEBdHcvLqq6VtXi8pvD/9RLJm1y5iqxUVgMdDU3JOnKBl3Tpg3jzpXJuNlM7WrYGuXSUDek5OZPVJSACuvJIWeX3WriXj+fr1wJ498OTnw+B2kwzhiviKFWTMNRjIItitG/DQQ8BFFwF79wL/+x/JwUOHlHLZ7QY2b6Zl1izqnIcPB66/Hvj9d1KmNmwgo/kPP/iN5iE5BH+nbDYyKp84IcnaffuAYcOQYDYDTz0F/PvftJ0xUrSHDqW2HjRI4hYaUDXWA8QHUlOJkwTCZiOlW4477qD7njYteF+7dmTY599Qdjbw0Uf+3QbGqM3lbXrTTfRuvf8+rY8ZQ3Xh7bJoEWk79cHFFwffR3V1/YztfxMEjjF5vaTsvvjiDlRWdsWePTRGwz+RwsLgsQ91NJY/AEC6366hBrud+IIchw9LlLQ50eRK7pAhQ7A7oEX37NmD1q1bAwDatm2LjIwMrFixwi+UKisrsXbtWtzsY4eDBg1CeXk5Nm7ciH79+gEAfvzxR3i9Xgz0sa5Bgwbh3//+N1wuF0wmEwDg+++/R6dOnVTn0/wt0Lo1jYzOmCFt83qxfM4cjLNaqdPetYs67LIyqYOqrSUhcOgQjQ7PnUvbDQZShLKyMCQqCti/nxTgwYPDd1ZWK3DhhbQA+F9uLiZPmkQjll98Qcrp7t18OIvqsHMnLQsXUhnR0UCbNqR4nXsuVv76K4YfOULmrNJS5fUqKkgp/vJLWs/IAM4/nyyUAwcC+/fj4NSpaLt5s6qVVwHeKZtMQHIyrXu9AIC0ffvINPbEEyRcOS6+mJTAqipS1P/4I/Q1AuF0koDSwrZtyvVp04CkJDIscGWcIyuL9nOSkZREz9UHAVAquKJIngGjR9OziIoiwsC/0xdeIIEXKT7/nMyNcths0mjBu+/Su/gPR0ICMHToIUyePMi/ze0mTvbNN8TxZNMGAag7AZSXlyOBGyUagLIy9fOrqohHBvIfs5lemf/8h0aeH364wZfWoaNe0PnDSYIoKoeN5Dh6FGsffRQD6+pIKSwooM6Bx8OorfWPtuKXX6TzBMGvAI+MjiaD66hRJB/D8QdRJIVvkNQ3fpybi8nDh5N8+eEHqsvRoyQ7PR76f/QoybKHHqJRxvR0oEcPYPp0/FRcjJEFBcDy5cEda3U1DRAsW0brmZnAxInAnXcC771H1/jvf1E7Zw5sgecGorZWknXp6dRWvnWT00kK7g030IgxL2vVKhIIf/wB/PYbed5pQFPR/vpr4itqSq5sOE8AyIPr2WeJR82fH3z8ggU0WHD8OK3fdJOC7zBBUBrsx4wh622fPrSelQXMmSO9T2PGBA/KhMPXXwc/Jy6Mzj23fmX9DSGKZEs6dmwzJk+WDH95eUSDV64kyu6jrkHgj6+x/IGx0GWUlQHHjinrYbEQ/ezTh+xSzYqm9n9et24dMxqN7KmnnmJ79+5lH3zwAbPZbGzRokX+Y+bMmcMSEhLYkiVL2JYtW9j48eNVUwD06dOHrV27lq1evZp16NBBkQKgvLycpaens6uuuopt27aNffjhh8xms50+KYROYRma51dUMLZkCUUFHDuW5nvExkY2x8Vsprk7/ftTFMSXXqL0ABpRGzXr4PEwtnIlpRMaMCBkCgEvnxcyciSFmnvxRYo4GDrsG80d6dCBbb3oIrrnZcsoj264+cTyJT09OJrytGkUlVE5QYLmDc2bF3nZfLnuOu19Xbv6/7tFkcL01tSoz3n9+mspNy4QNN/GLZ/cIQg0R4nPaTYaqW34/jvvrN/zLCxUf3/4M+rXL/T59cDpPif371KHfftoCp3W5/LYY/qcXB2nBv80/sDYadzHFBdTEIH77qM0PZ06Ufq9SOSqyUTxMHr1ojmxTz5J2Q1CJBrXvI/8fEpveMEFNAlQIw6EF6D4EwMGUCqc//yH+EO4mBqCQHN2J01i3zz+OGObNjE2apT2JEm1JTo6OI1RRgZxiMBj77iD5GhGRv34Q2oqRaxW2zdokHKdv8fjxgUf26cP8TKeDrJDh6BUly75M+7UibJz8HRMZjM9E15/my3ouUb0TgamcpRzwwMHTt/vQq9DEDweovhq1DAz82+WQogxxpYuXcq6d+/OLBYL69y5M3s7IN8VT+aenp7OLBYLGzVqFNu9e7fimJKSEjZ58mQWExPD4uLi2LXXXhsymXt2djabM2dOver5t1dyQ2HPHpoVfv31jJ15JqtJTIw8uFNUFKUIOOssCrgwbx774qWXIr+2x0PJt26/nZToULl3bTbqlG+9lbG77qKAW+GShGZnU/CLigrGnnhCPS2RxuIOFOBpaZRHJfDY228nEpCVFbmQGjAgtJD0/S/s0IHaadKk4OM6d6Z9PCdeVpY/WARfFIL2p58k4SSKSqF3ySX1e6c8HvXAJr17S/83bdI+v574K3Tuf6c61NSQzWPwYOWyZImu5Oo4dfgn8QfG/qJ9TGEh5Vi95x7Gxo5llaECPAYuJhPJ1T59KA3h008ztmoVW7xwYeTXP3SI0t2cdx7Jd610f3FxjA0bxticOWQ0Hz48fD0tFqrXH39QAE2eai+CJSj9jyBQPQPrl5JC/OHOOyPnD4KgbSjv1k0p/xkjjqd27K5dNIjA1598Uvuaqakk9+Uc4+uvKcglX//00/q/T3PmBF+LB9dMSoqsjDD4K8nev0sdNm0iO4ycP0yb9jdLIfRXQmVlJeLj47VDWx85Alx3HbmDJieTa8bkyQpXnNzcXEyWB30IxL33kouK2Uxj+CYT/Vqt9GuxYPv+/eh2/vkUqKl3b39aHgA07+T662kbX8xm+jWZAJMJ+w8fRk7nzv51/zXMZlp+/plccuPipDIMBjrO93swPx9tO3SgfSUl5Ox/4gT5IlRUBPs0asFkonkgSUnkUtyuHc33+Pln8mkwGKTry+/JaET54cNIcLmUwR3UYDSSy60o0nGh6hYTQ23asSO5UQe6QUeK0aOx94eDuB7vwQQXjHDBZGAw9ekO07HDMBUchAkumOGEBU6Y4YBFtljhgMUCLHGMw2G0hBlO37F8vx0WOBCFOjgyk5AxdhjM89+CBXaY4YYJTpjgRsnVd+LTb2JgLD4KAzwwpqfCWJgPI9y+erlhhAtGeGDs3xuGLX/C6Kyh+rbMgjH/ADaiH0qMGRDTUmEwUDOKIj0SvlRWliExMRGCQPsEATDs3AbBXgMHTDiIHAi+2UMivAAYIIoQExIgCIDDYYfVaoUgSEENePwSUaRXKjB+hvxYAPB4XDAaTUHHACQVw72SjHkhqgRN4WV4fIwkTCmQO5DJ6xdZz6k8v2FobBnK8/nnk5AA9OpFLlHDh1O8GbUgFmH7SR06/oGI6Lu44w6KnZGSQkGZrruOfmUIySHWrCHXXC7LfZwBZjNxCLMZOw4eRNezzqJIL2eeGewqPG0a+TUGcgiZ/N97+DA6dOlCZXNuwa9ZXEzuw/HxVHYgfzAacfDIEeIPBgN17sePE48oKyN33sApNypgAASjkaYxpaZSO3XoQNzr2DGaTyznDwEcovT4cSS5XBQMgbsTq4G3ndFI/KGuTrszNxhoWlCHDjSXNzB2SKTIzgZEEdPyH8V+5PjktROmdq1hSrDBtGk9zMzu4xAOP4eI8vECC+ywwoH8bmPxxfYOMq7hgBkOWE0MFlcVolBHx954HaL+9wHMRYdhgRNGuGGGE6YObfFJ0g0oWbsfRrhgsJpgctthcNv9x1Dd3DCYDTCMORvGrZtgOrwfBrhg7NYVJSwRf+ywQIQXBosJYnqaCn8oR2Jigp87+DmEARAELw6uK4YdVh+HgJ9DCGAQo2wQom0KDgEo459xmiiXx/y/JN/dMMq5dMAxKnHHVOCBIBiCtgoCuedquQpLCJbdp55DNC0HEUUpjmurVqS2nH02MH48fVKBlKu5+YOu5IZq/G++ocjAgcjIoLkkV12F3NpaTJ4yRfsirVvTDOz6gHf0ycnUGftyn+loXnyNcTgf3zR3NXToOGkwGmnq9u23S9OimltI6dBxOiKi7yIxkZSuwG19+lAsiquvRu6332oruS+/TPNG6wOjkRTSjAziHz/8oIyQq6PZkIhSlONvOOdbhw6QgtumDSm8L75I25qbP+hKbqjG5ylxqqvJMlhREWR6YQCE6GiK2Nu2LQ2HnHkmMHIkbbvmGopW6HaT+cntlsKjeTyA2w2XwwET3xYp+LCXKMIDwGA0Kk1mjJGZibHQo6KRXIM7lJxkNIW96WTiT/TEVVgEN42hwgMD3DDCAxEeGOGFCA8M8MDg+y/CCxHM9+uFADeMUL/L0/nOdfzTcOedwEsv0f/mFlI6dJyOiOi7OOccCgbJ+YNKZH63xQJju3Y0JHLOORTMkQd5+eYb8gZzOok/+DiD/NflcMDk9dZfkeV8QRThYQwGk0naxmU+Lzf8kJV6+XzhXOQk4nTwmQmHCfgce9DRxxsMcMMAr+yX+IPo+0+/Xj+PEOCGCIbgkUWCziF0nB5ISJBiijU3f9CV3Po0vtNJudo+/JAU13CuK6JILsJZWeTu0rs3RR4cMoSiEvrgd1ey2ymi4B9/ULTd/fvJZbq4uH4uwxxmM40IJySQ9TgmhrYxRvdSVUX3UF4Ob00NxEgFmSCQv2N8PEUXzMpCXnExWrdoQT4kBQXkXlRV1TDhCFC7tW1Lgr9lS3IzXreO3Lc1oh8GCShBoPqZzdSOWnXp0oUs6x9/HDqPnRxjx1J43AArfUOFpBeAHVbUwoYqWyLEWhfssMAFM/2mZMHVsx8cP62Bi4lwWePhcBvgcjM4YYYLJp8DlBl1higIHi8JTLMNXqcbHohwSo7W5Nx82RR4bAlwuST7i8dD/wsKjiA9vQW8The8P/8MDwxgEOA1mOH1eOGFCZ52OWAHDpAS364jvLHxfl5UVlaG+PjEsBONAO19tbXVsNliFMfJjwekRyrvxfj/uro6WK1RqvsCobadPhMHzGb13JiB56iV4XQ6YDI1PLcmY4DL5YTJFCIZXhjU53xRpK7ikUfIyxFofiGlQ8fpiAZ9F99+S+l51q4lOaklk4xGktty7jBoEOWOD4hy6ucPbjflof3tN8rnsWcPXaO8nNyFI5VtHCYT8ZS4OPIqi48nd2ZBIP5QV0dyuaQEnvJyGOpjpDebKQJwSgqNOCcl4dDRo2jDU+Tk5yszRNQXZjNFUO7WjQYeRJG41ZYt5EqtlmtWDbGxNFhRVCSl2wtEfDy5v2zcCHbokLr85+n15OcMGwYsXao8TisPbgRwwoha2HAiuQXMJZVwwQQ7rHDBBAcscJ19Lpw79sJxvBQumODKaAXn8RK4fLzBzyEMNjg9ACDCBSO8ZhtcXhFutxceiHDAAg9McPfoB1e/MxXjNnwsJz+f+ANj0rQgz+Ej8O7fD+YfABCIO8TEg1XXkDLfrQeYaFLlEHJZH45DAEBNDfEH+X7+X/4baH+R77fb62CxRAXtkyOUBuVwaPMHtXPV1l2uxnEIp7Nx/AGoH4eIjqZpUDxbZrPzh2aZCXyaoNGBIyoq2C933knBF845h7H27esXvTg9nbF+/Vh+v34UWOmFFxhbupTCnWpEMWZ5eTTR/5FHKDDRgAGsKjWVgjeFC8iktggCY1Yrq4uJYSwnhwIM9OlDwYS6dWOsbVsKkmCx1K/MqCgKitSzJwVcOuMMxrp3Z6xFi8gDVAQuFgtFArz4YsZuu42CMPTrFzpwlXxJTqaATVpREwWB1cbEaJ8fGNUxJYVCysm2ecPVIVxkSlEMLkMQGCsrY+yBB6Rtzz+vfr48EvWwYVIwMfk7mZBAgTtCwB9oQB7xsGdP6f/770sBvVq21D6/EfgrBVz4O9ehqQLs6NDxd0KjvwuPh7GffmI7zj2XIt4nJ0fGHbh8TU9nrHt3VtCzJwUCeuklCgqUn6/OHzwe4haffkoBGa++mrERIxjr1InZY2Iaxh98ctERFUUBHzt1Ig7RsydlDGjblupps0V2b/z+oqPp3EGDqI5nnUWcpFWr8NkWQpWbkUHBp266ibFbbqFAjC1aaAeuCuQfWVmKIJGBS024SM6By7PPNrzdNRanWgDRe+6hQJyc+5x7rnbUafn93XZbUGBLBlDgqhAIkjn//a/6tSZPljjK2LGhy6gn/g6y9+9Qh+bmD02eJ/cfhbg4HBkwgIJRBWL/fgqUsG4dzanNy6NATtx12On0Z3NuAQAbNwaXYTDQnNyYGBpeSU0ly27LlhTQacQIoHt3LF2xQjmn5+hRslru3ElJyvPyaGT1xAkaEa6tlSykjAF2O6xA6PyygiCNDPOgDQCV43DAXV0No8sldV91dbSo5XQTRSonLo7KokhFcJaUwOxw0PlqcDhoJHfvXmUbZWUBZ5yBQ5WVaAOQFVtt1Fs+8h4TQ1bp0lLpeowhqrqaLOWB86iAYAvriRPk0zlnDvDoo4DTGX4UN4KR7aAyrr+eLOrcf3TIEOD559VP5gE3evakduDvG7/Hli3JSyASi9rIkdKoecuWwPbt9P+MM8iyn59P63zyhQ4dOnToiAy+RJh/HjuGLlx+e73kybV6Nf3yvPelpZJMk8vXwkJkATRCGQiTSfK4Sk6mEdPsbJKXrVrRyHD79kDLlvj8o48kDlFURDlvd+yQ+MPRo5JHWU2N0jXa5YKZ3H9C36/JRPI+Kor+i6LkDl1XB29tLXmTMUbXOHiQFrV2s9kkGW4wAF4v7KWlsDoc2iOhjNEo7vHjlGSUIzGRhp7atcPRXbuQdfiwlGNeDodD4jMGA51XXa2YDmbjo73yvPFq7cDreP/9NDqfmEhzp4FGu0ybAqenRUVRztxLL6VhVVGk+/1GJb6I2QzU1FAdnn+eOIf8uYoisGiROufVwp130tzyQLRvLwX/AoA33oi8TB06IoSu5J4s5OTQwn3+OLhL0S+/kGK7Zw+q8/IQ43ZTp8gVRYA6pJoaWgoLSeCp4HIAuOoq6vCjokiBSUggwZaWRi4748ZREAoeKdBoJAGyZQuwYwf2//ADckwmKSpiZSUJUe5iwxgJI415P/4XSRCkKNIGA53ndktzigASbPy+ZPA7Q0RF0WIw0Hm8XdTg8ZCylZ9PCi5AClnv3tR5rl+vrrBWV0tKPY9WyQUUPz4qitpADWaz1BYPPEDuzn/+qa2gRwqvVynkTCbgtdeAW28lISsINKv/vvuCz/UJT5fFAhN/hnL060cRIo3hP/uOy5ZRRGxebkwMtbXRSEncBw2ifenpwMSJDbxZHTp06NDhhyhSFof+/YP3eb0kszdtIoPj3r1AXh6q9uxBrNNJ8lQuJ/k8lMpKySCpgUmCIBlT4+MpO4JvKhJ69qQAnNy43rYtyT+uDO/ciV3LlqGz1Sopw9xFOkAZhsul6fYrACTfrFaSOfyeuQ8s5yJeL3GCACXSH1PaaCR5ZTLROdwgoIWyMuJiGzciE6D7HzeO6rJ2rXpGBo+HjNy8zrGxdL+8jrW1ZFQ4fjz4XJdL6b68cSPV9fzzgWXLmn5m7dNP07P63/9ofdIkdaUT8D+vTZMno++TTyq5k9kMrFgBnHVW5NceNQr48Udpncd4iYqieDUPP0zbR4+m90qHjiaGruSeahiNNBJ2xhn+TUsDUwhUVpIQ27NHsqQWFFBHVVpKQqKuzj8aKwDUYfKOP5Iw94JAdbFagehopAKkBLduDQwcSBbfli1p3WCga+/eDRw4QJZlrgz76sK8XqqHb2Q4ZLArebx5wC/I/GVoCSWeGgkgQaE1R9mn9AIg4TNyJAnvLVvUhb3D4S/LC8AfAb2uTltQOZ1KQbVpkzI2fFPhgQfoWv/3f7Q+bhyNHKvBR3BcVitMgVb18eMphVIk2L4dfT/4QFmHJ56g/88+S+/Bvn20/tRTkZWpQ4cOHToaDlGUjOcyw+JXgfwhcCT28GFJ+eTGa6dTMd9VlI8Ol5SQnA8Fng/Glw6xhSjS6HBaGinFrVuT0tKxIxndjxyh+vgU86CRYZdL4g/hlFJASiPElSa3G8zjoTLcbnXDtigS3+Fy224P8qwSAKrT8uW0wWKhuCBWK7VlUVFwuYz5DcoKA/Xx43SeWuAuzht4XVwuYNmy0PccIRR1SE2lkdTRo6meJhMZL9Talxvuk5PR89NPlQaTmBjyLOjQIbJKOJ3EnQoLpW0Wi2Sof/xxyVDfqhXNVdeh4yRAV3JPR8TF0UgZHy0LhePHseLVVzGqbVtS4LgCynPcVlaSELHbqfOXuebKLatxvrJCgifIioqiTi81FejUCUhNxa6KCnTho6dVVSTAjh4loVBeTp0q7+h5grGA0VlBfh0+2sijUANSfdXqZTLB63IFB8+qqiK3cYAESo8epMAfOEAu5QHBMoKyqnJBpeZGzc/lbleMNUl0Rv/5MTHA7NnkZuR203UGDVJ3M+IuUAYDbIGuVrffrm25DcTRo0CvXlId5syhKEQAPes776Q2BMjF6vrr63NrOnTo0KHjZCItjUbQRo0Kf2xlJbBnD377v//D4PR0Uoi5QZ0bse32IKXY76HldgM1NYgBSOaHAs+t6zOsIy6OZHFSEpCWhh3HjqFrmzbEFyoqaIpVURH9l3uVAdK1ZVDIXe4OzaMhAdIIsBp8kaVZ4JQjh4OUO46MDPKQKy6mJYATBMl+buzXmgLl8TQq2JQaFHV49VWatrZiBa1fd522WzAfdS8pUSoGmZk0xSkpKfzFvV5g1ixc/uST0jbOj/igxMyZlPuZMXoPNm0KTq6qQ0cTQVdy/+rIyEBRt26Rz5Gw20m527+fXJ+OHAEKCnB8yxZkmEzUEVdVkTBwOpWCxeuVRmnLyhSjol0A4PvvldfyKZ+IiqIOMi5Ois7o8UiuyDU1QHU1WF0dddCh0iGoZdL2daBhu0mPhyzcW7dK23gyebk7lFqbhUJDI0iHQ3o6jUDLR6wffVT9WC4kA+/hpZfC51n8/HPgwQfJUi2/F4uFRnE5oqKIoPBrcVcjHTp06NDx10NcHNC/P/L27sXgSDhEba00VzY/n4yix46hYONGZBsM5GnGDeuBI5jcYG23E88I8DbqGngtQZAiPGdlUV1tNv8cXD9/8E098lZV0Yg0EFpp5BxCrqD6jg9roOZzeuVlGY2S0q8FNQU34NocjTKUc4USICP5bbcpR5/feqt+5XXrRkq+OURk3fJyYO5cYPFi8vJCQP15fYxGGrXlMTwMBooEHonyrENHA6GnEIqPx7x582CTpfSpDwoKCpCdnd2oejS2jJNdB3NlJWKPH0dMURFsJSWwlZYiqqwMlspKmGtqYK6thaG2Fia3GwJ3GaonmG/xGgxgPjckweuF6PFA8HWSkZQbSkBEIjwaOxJbkJWFdQ89BNHthqm8HHUpKfDabBDtdjj5XKEQuPCWW2ArK2v0aLDbYsGam2+mwGgBEJ1O9Hn/fbReswbm2tqgawW2gdp6WZs2+PbJJzUtsP+E7+KfUofa2lpMmzZNTyGkQ4cMTcEfgOb/vk9qHbxeRJWWIqawENGlpbCdOIGo8nJYKypgqaqCuaYGptpamOx2GOrqYPB5RDWUQ3gFAcxoBPMpsqLH4w9m1Vj+UJ96NLQMBuDdO+5AdL9+sB47BqMgoDYjA6LdDtHphDMhIeSoZ+q2bRj19NNAI+rA63G0b1/8ctddqtdL3LcPnb79FhnbtsFaURHyWlzBkB/jMRjw6+23o0A2bS8Qf+vv4h9Uh2bnD80S0/k0QVOEtm7u8NynZR0qKhhbv56xDz9k7OmnGbv5ZsYuuoixwYMZ69yZ0vjExlJKnkjTCgQugkDpeESx4WWchGX/gAGNexDp6Q2/fnQ0Y5deyr556qngcjdsoDROgWmQNBbVVEgtWzJ2++2MFRTU731oIP5238VftA7NnQJAh47TEU31XTT3933a1cHhYGzbNsY+/5xS5d12G6UNHDyYUhRlZJCsawx/OI05RKPaMjc3fBpDrUUUKc3jvHnsw4ULleU6HIy9+SaldIog5ZFmHdLSGHvyScZcrsjfhwbib/dd/EXr0Nz8QXdX1tH08LlAqUaIVENtLbBnD1a/+y7OSk0FDh2S5hZz9ydZoC0AUrd5miFoTnB9oRbYIhTS02le7N13+91+ynJzyUVozhxyI5IHf4gQ/kiX7dpRNEZZ+Tp06NChQ8dJgdlMbrLdukV2vNsN5Ofjp7ffxsiMDIq3cfgwzenlkZ5ra4PjapymHKJR+OGH+o3gWiwUmPPRRxUxYLy5uRQgbNYs4MsvpVSCEUKRHaJzZ+Cii4AZM2hOsw4dpxC6kquj+WGzAb17I3/w4NBzi71eUny3bpUiT8uEmaOwEBaXS3s+7ymAi0d/bijCCV1RJKHx0EPUVnwOzvffA59+CmzciEu3bQOuuKJh109IANq3x6b27dHn3Xfp2ejQoUOHDh2nI4xGoG1bHO/ZM3xsktpaylyxcydFej54kOYVFxYCpaVwl5bC6HafvDgbJxs5OeHdpRMTiR88/LCkdNbWUmrA+fOBVatwebggpFowGIAWLXAoOxttnnkGGDasYeXo0NFE0JVcHX8diCIFoMjKAsaODdr9OU+l4PVSUIw//lDkE8TRozQyXF2tnX6okWh0EiF5WiIOi4Xy/nboQAHD9u0Drr0WuPJK1SIi+qgFgRTaDh2AwYOBf/0LGDHCP/9mV24u+ugKrg4dOnTo+LvAZgtK4SjHJ5xDVFdT3vstW4Bdu0juFhRIaY/k2SJOJzCVuceCQOkgL7uMuNOGDcB33wELFpByq3IfEfMYg4FSWp1zDinOvtHgNbm5aKMruDpOA+hKro6/H0RRyvk7YYL2cUeOSIrwnj3AoUOo2L0b8W43RZhWSxsUBpWZmY2re4sWYHl5wWkM1q6lpSEQBLLeduxICu348ZTQXQ/br0OHDh06dCgRE0My8qyztI+prSWvsj//JEV43z6Ubd6MRB49uiFpgYRGmskvuwzs3/9W8gfGyOPt+ecbVzYAxMZSHuRx44CrriKOpUPHaQxdyQ2HMHMkzRUVoY8JlzsOQEx+PilaWigpCXl+0s6dwC+/aB8QwXyK9PXrQ7umhgqBDyB7zZrQbsK+ZOmh0Hr9esrvq4WqqpDn52zaRO5HWqiu1t6XmQlkZmJ/TAz68jywAFlsjx+nZ1xaiqITIlBTDdTWAY7ghOoHUruixS87NS9TjOSQ94A8K4BOIQ8pQWKIvQLcohnGNi2BDh2Bfv2ADu1JeLZqCxiM9DosDV2N9evTG/M6YM2a7Ma+Dli/vnVjXgds2pTT4NeBY+vWToqMU4GoqQl9/o4d3fD779r7tdI2yrFnTw/8+GPDyzhwoBd+/JE+7+hoKcB3bCxNn09IIBsIQCkgU1Mp1aYOHToaiQhiLDSWQzSWPwCN5xCN5Q9A4zlEY/kD0AgOERUF9OiBgwASOX9wu8l77MgRer4nTqCo0qrKG/yw2ODeeQJFWhwiORnFoR7n+j1oHH8AACMcRiMsSUlAfByQmAB07Q5cegkQkwDA9zr86VvUqtFI/gA0nkM0lj8AjecQjeUPQOM5RGP5AwAcOdLDXwfOITh/ACQOwfkDcPpwCD2FUHx86NDWjbWs6fhbQcA/9nPR8Q+CXCpE1E/q0PEPg84fdNQXOn/Q8U8B5xDNzR90f0UdOnTo0KFDhw4dOnTo0PG3ge6uHA5h0q989vnnuOTii7UPiMBdeemyZfjX+edrHxDG3ejbH37A2NGjtQ+IwF35x5Urcfbw4doHhPEv+WXNGgyThaAPQgT+qb+tX4/BIZKDh/MvWbdpEwb06aN9QAT+qX9s3ap0V/Z6KVhVURFQUoLCorOAyiqgrpZcll1OQGad/XnGLRgxaZJm+WHdlYcPV5SnBm13IwGAAQ6LBZacHKBrF0rjlJFOu+XuymGwcuWPGD78bM394d2Vf8GgQdqBJyJzV/4NZ5wxWHN/eHfldejTZ4Dm/sjclf9Ajx59NfeHd1fegq5de2ruj8xdeRs6duze4DIOHNiB7t271stdWYcOHU2ACNK3NZZDNJY/AI3nEI3lD0DjOURj+QPQeA4RxB/KyqR0iCdOoLCsA1BnJ1dmrzu4gLh4fHvHHdrPIpy78h9/AjNvD1nH0O7KImA0o85kRFRaGhAbB6QkAz17USwPo9l/W6HQWP4ANJ5DNJY/AI3nEI3lD0DjOURj+QMAHDmyDb17UxmRuiufLtCV3HAI41jujI8PfUwEjunVW7ZEnhNOBaUFBY0O1V5YW0udWANRYDaHD98fBnm5uRjciDL25+ZiQH3Ot9spcMSmTRQ44sABtNm+Hdi8mSIo1tYqc/MCCPc0OxRuQ9qw2Zr7w74NLarBjhxpXJRmB4AdK4AdAD4FucxlZtI7Nnw4MHEi0Cn0vJ3a2sLGvA4wmwsa+zogNzcPkydrC6nw5+/H5MnaAiqyMnZj8mRtIRX+/O2YPFlbQEVWxlZMnqwtpMKfvxmTJ3dtVB106NDRAEQg/xvLIRrLH4DGc4jG8geg8RyisfwBqCeH8HopawMPXrlvH1ru3k38oaSE4nkEzAYM+zZUnoCxSwrShnXRPCRkGWmtwGbubhx/cPuWPNm2nwHMBWkw/ftTNoYpUyQtJwCN5Q9A4zlEY/kDldE4DtFY/kBlNI5DNJY/NFUZzQVdydXx90RtLSmvPAXAgQNkUeXJ4TVSACQ18rKxDc0vx6Gm4FosFEAqJYWiQB85ohn6XxWMUQCMo0cpn+7DD1Po/7Q0oGtXIjeXXNJooqRDhw4dOnT85eH1UtqgTZuAHTtI7h4+TCOypaU0BKcSFanZB7GWLQvmD6JIRu3x48ng/euvwO7dxIPqm0qxuBj45htaZswAzGagRw/gvPOAqVMpnZAOHacRdCVXx18LTicJnZ07SfAcPEj56woLMT4/H5g+vXlz2DU20IggBKctcjiA336j/0Yj5fh74glg1CjaVloKLF5Mue927IDnwAEYwsWT83iAY8doWbECmDWLhKFP8W3bvj1w6aV0PR06dOjQoeOvDq+XjMTbt5Oit38/kJ9PBuATJ3BJYSEpaw1J/3M6oK4ODAF5br1e4ks7fRGbMzOBW28F7rxTmruSlwd8/TXw0UfApk1glZWRjQY7ncDGjbQ88QQZ5Nu2Rb8WLSjVkG4419HM0BmsjtMDRUVI274deP11Ejx5eX7Bg7Iyspw6HCGV1xAR608ZjPW1jNYXbjewZg3A5+y0bQvccQdw440kuAB8nJuLyRMmADNnAh9+GNkEGIDa9vhx4PhxnPnjj8C775KQuvZa4KabAKv15NyTDh06dOjQ0VDY7Yg/dAj44ANg3z6F8RulpTSBs64uaPpRIMynprYnD3l54ZXTY8eARx6hJSYGmDCB/t98My0APszNxeQOHWj7ypXUdpHA4QB27ULHXbuA7t0ptVLv3sCkScD110tKtQ4dpwi6kquj6eH1koK6Zw+5CeflScEXiotJaa2qkhRXjwcAMKq+1xEEaeSUMTDGGjcXpQnQ6AQBiYkklCPFwYNkkb3zTpr9f/nlsHXpQgrpG2/QAgD/+x/w4IPA3r0RjXIzAILHQ+7eM2cCd90FdOgAXHEFreupZHTo0KFDx8lAebnSW+vwYVLOiotpvitXWp1OP384r77XCOAPQR5Uf0UMHQo2b17kPKi6Gnj/fVqMRmDgQOC++4gj9O9PbskAtfdrrwGLFtH0rzAcwj+aXFdHRvk1a4ijtG5NPGT6dPIc06HjJENXcnWEh9dLLj3cQnr4MCmthYX+kdYJhYVkqZMJnfqCARBEkTpbg4GEjsdDi1qnGiCYmlvBBYCooiKaL+t0UmCruDi6H6+X5q+E69it1mB3o0hRXg68+SYuBIDHHqNR3DFjaN+ECbQAFCn6hhtofq5GaL2g6zNGhGP2bFqGDwd+/rkhtdShQ4cOHf8U2O3EGw4dkrgDV1i5p1ZlJSaWlABXXx12tFULfrlpMknTbDh/UOMkp6liaywvJ77Af5OSfNGYveG9qbKyGn5ht5vm644fj0kA8O23wMKFtC8ujpTTBx+keixfTl53v/6q6immyV/y8sgr7K67gC++kPiJDh0nCQJjp+FXforAkxTPmzcPNlvDnF0LCgqQnZ3dqHo0toz6nm+srkbc8eOIKSpC9IkTsJWWgh09ikSXC+aaGphqa2Gy22FwuSC63UADR0gZACaK8JpMcJvNcFkscFss8BqNEBiD4PHA6HLB4HTCaLfD6HBADPM6Bu7VqhcLs19ePyaKgNcL0etVHB+obIYqk8l+A/eHU1oD9/P/1cnJsJWVQfQp+F4oE1vz87TKZ4KAtdOn4+CIEdoX93rRcflydF62DDZfXgBelsdohOh20zUEARUZGYg7ftz/jNZddx32a6Q6+Ct+F3od1FFbW4tp06Y1WzJ3HTpORzQFfwCa//uubxnG6mrEcu5w4gSiysuBI0eQ5HTCWlkJc00NjHY7DE4nRI+nwcZnP38wGuG2WOCKioLTagUzGsEAiF4vcQfOH+rqYAhzPTV2oXa8V7Y9Ig7hq08k1w7HWcIhkAfI4REEGH3y2WWxgBkMMNXW+nlCqOur4URODr6fNStkbA5bcTE6ffMNWqxbh+jSUn/5gdfzGAyoi4tDdFkZBABegwHLnnsO1ZmZquX+1b4LvQ7qaHb+wP7BqKioYABYRUVFg8tYvHhxo+vRqDIcDvbVs88ytmQJY3PnMnbvvYxdcQVjo0cz1qcPY23bMpaSwpjNxpjBwG2XDV+MRiorNZWxdu0Y69uXsTFj2P6hQxmbOZOx229nbNo0xiZOZGzoUMY6dGAsKYkxsznyawgCYyYTYxYLXS/C492CEP64jh0ZGzuWsa5d6RqR1CfccbJ29Ta2feVLUhJjHg9jY8ZI17nrrtB1FMXgfbNmRf4+bdhA7cTPff556Rn07MmYy8VYTg6tp6aenHe6icrQ69A05zdFP6lDx98NTfVdNOv37fEwlp/PVjzwAGOvvcbYPfcwNmkSY8OGMdalC2PZ2YwlJDBmtarLlobyh+Rkxlq3ZqxHD8aGD2fskkvYrtGjGbvxRsauvJKxiy6S+ENyMnGB+lzHYKBzzGalPAtVL7M5vPxOTKR2ycysX3skJDScXzRmWbaMsd9/l9ZvuSX89QLbq00bxqqqInufHA7GrrtO2Y4Gg1SmIDB2330SZ0pI0Cy7ueWeXoemKaO5+YPurnw6gudf43NSDhwgd+Fjx8i9p7xcCmHv9SJEGvjQEEVyobVaUSOKiM7KIteYtDSKwNeiBc2hSE0ll6O8PJrTeegQBXU4cYLckLZtQ1unE1i1KvJrc5dkHk3Y7QbjVljG1KMbGgwUvc9goP12O233HW9Qu47VCvTpQ+l3du4kl+s9ezSrFWQlTUsj9141GAxBrlBN4TLNeDmlpcBzz5HbcWoqXefAAXIdCsyEztvL64U9NhZWeabzxx6j57RgQfiL9+sHrFkDduaZVId77gEeegh4+mman/vee8Bbb1Hgq+JiIDe30fmRdejQoUNHE8HtpnmTPILwwYPS9KKyMpqHabf7+QMAnN2Q6wgCuQZbLKgTRURlZpKczcgAWrYk7pCTQ/8dDgoouXu3lM7v+HGSrfv3A3Y7Onm9wA8/RHZtg4EWUST57/WCuVyS/NVyUbZaiXswRm3Aj/G5SAfJb4OB0u8kJkq8x+fxpAa/7OYwmSTXYzUYjU0eyVlRh2nTKD7KWWcBq1cD8+ZRIMm33w4+0WymdyIlBZ7SUhh42xw6RM9y82bihKFgNgPvvotPBw/GpXffDVRUSG1ssdB78J//AHPmAPffT+3Srx9xM32Oro6TAF3JbQ7k5dF8xrVrgR07cN7evTRHoapK2fHWB4JAHabVCkRHkyKUmEhCJzMTyM4mYdO2LdCxI83dqK2ljmv7duR9+SW6RkVRJ75pE/Djj5IizVj4y8tXzGZpXowg0P04ncqy3O6guTeC/Hybjc73eKiePECVxhxSBVJTKc0OQKHt16wJfbzRSG1WUaFUcG02bQWXCwSO9u1JeDd16qLHHydFc8oUCg6xZAlte+QRzTqJTmewcr5wIc2HWrEivDAZOBBbL7oIPb/4gtZffJHIyv79wG23kXLbujW9x/feqyu5OnTo0HEqUFkJ/PknGRx9KXDO3baNggFy/tAAGcQACJw/xMZSEMPUVJIjGRnEF7KzgTZtaGnRgq6zezewbRv2fvopekZHk/K6cycpVFVVDUvnx/mDwWe29nhIEXS5JP6gosT6+YMgkDy3Wul4h4MUe0AyjIeCzUYBmGJigA0bKGVhOMTGAnY7BLnCGomBnHMggwG48EIKENlIKLjYsWOk0H7wAT03h4OuyRVOOTifKS7GpquuQv/PPycOCJDBvWNHSmXYu3fYOrhtNlJg+/YlPgnQ9fh9P/EEze995hkadLjkkia5dx06AqEruScLlZXATz9Rp7BlCykIhYXU8QcojfFaZXBLqc1GnWhyMpCeTgKmTRuKdtulCz7atAmTpk6VznM6gW3baOGW3B9/lCy5VVXU4cjq0TWSe+JCMCpKEkJeL+B0wlVVBRNXXLlCGwpWKwkRrsy6XHCUlsLCgzaFO59DFKldOnRAntuN1lFRpNh+/XVk1y8vpzpXVEj7YmJIKKop1Dwao7x+//43MHdukym4CiFVV0cGkDffpBFdl4veq+RkijIph69OZoeDjBm1tZJwB8iw0rUrkaQwASy2X3YZeubnU1va7XTfokjX+Ne/gJdeAi6+mIwiX30FXHBBU9y6Dh06dPyzceQI9dXr1tFo7KFDpCzV1qrKmAStcgSBlBmutKalEX/IyqKlVSugXTsgJwcfrliBydxYyUeCt26VPMl27KCR19JSVWN8z0juSxQlPmOxSCOqbjfgcMBTXQ1DpPzBYqFybDYqkzHUlZUhigd8rK5Wyr5QsNmoLTp2xJH8fLQoKCAZGw7JyZICLfecAkjJjtRA3qEDjWZ++CEAldHgxuLee2lEd9w4ipa8YAEZq//73+BjTSbA5UK/RYuAV1+lIJPFxbSvro4iLn/5JXBehLGs//gDuPJKUrIB6Z2pribeMGECKbdffEHvfbiRYh066gldyW0M3G6k7NpF1qg//iCBUFBAClO4CIGiSMInIwOFooj0Pn0k957OnSk/aUJC8Hm1tXStTZuoI543D2N37gQeeIAU64ZYcg0GuEwmmBITgfh4KQqwx0Pl1dRQ2VxpUhEepqANJrq/1FTq8I1GEghVVaScVVWRu7MMlnD1NJlISHftSlZFLox37gR+/x2tw913bCzdX1ER3VegVVcQUBsTA1ugwOIIFE6xsSRAHn3UvymsgPIFuQq8rt/gYDBIbtscb7xB7sI33QS88goZLObMoWceCJuNntPatcA55xBZcjql6+7eTe/Z1q1EekJh3Tpqr+pqcvPu04feu19+oXQAmZlkKb7jDl3J1aFDh476YvdudP3f/4D/+z9SJAsLI4suzL2PkpJwwmBASv/+pLB27EjcoWtXkgWBqK4mI+fWrdS/f/wxcOQIxh88SGldGjLyKghwG40wJiSQgZi7BPsM4H7ewFMGauSSV0w3Mptp6lR6upSurq6OjNInThAfKStTuA5HRVLXhATiWD17kvw+epS82Q4dAnbtQkgVSxRJ5tXUUD0CjcwA6mw2RNXVSSOgapBziIcfJmXTp+ACjVdwnSYTzPIR5cpK8v5auJDa0+MhhZJzBTlcLsBohOB2A7fcQlOVPvqIBmkAOvf888nofuONkVVo0SJ6L2fNUm7fuhUYPFjiVbfcQp5qOnQ0IfToyvHxkUf98nqlsOqrVlEHGa75bDZS9Nq2peTYZ54JjBypCPWem5srWVEPHybhs2WLlCPu+HHqzGtq6ieARJFGXWNjyXU5MZEEkCiSIK2tJYW8pASe8nKyokYKrsQmJwOZmcivqUHLVq1IEBUVUSdaVtbw+SZWK1n1evYEunQhwbhpE1m2i4tVXbpVFcyUFFLUCgq0XZXataOUOB98oG1B5m42HCNGUMefnx++DvWEPSYGxupa2GGFE2bYYYWrVQfYB42A6+MvYGcmuOJS4Kpzw+ES4IIJDljggomON1gBD+CBAZ6oOHjqHPDAABeMtA0GeAQTvHfcBU9MPJxOyRvM7abfPXv2oU2b9vDU2uH97HPfeQKY0Qq32wsmmODp1gOebdvAIMLT+wywxGR/tqfi4kIkJ6fD66X1hkTNKC8vR3x8gv8Tk/9Gsq2qqgoxMbGKT1Trv9b+uroaREVFqx4fuK72adbV1cJqjSzyqlZ97PY6WK0RUThV1Od8UaRP5qGHyAAPNKCf1KHjH4AGfRd//EFGy1WrSLZryRtBIIUxPV3iDv360Uhahw6KKSdB/GHVKvLA2bGD1rlR2W6PaOqRH6JII6YxMaRwJiUppxE5HKRAlZTAXVwMY4RTm/xl22zESdLTgfR05BUXo3V2Nimxhw6RnI/EvVgNBgNxk06dqN1iYshQ++efNM2mri6ycsxmmuZVWSmNaAYiOpoMynv2gG3fri7/fSOkinMmT6Y5sqGOawAqMjIQe/y4nzs4YYZdsMF1+VWwr94AV/4x4gutO8KRdwxOmPzcwQUTnOY4uJweeCH6OYTbDXhcHok/QITn7DFwDx6h4A582b17H1q1au8fqPd6Ae/27fBs3wkPRHhhgBcCPDDAm5AKVl5OZZ41AkwwwusFioqKkJycFsQfAOm/FrcAgLIy4g/y4/n/SLdVV2tzCK1XXb69tpb4QyTHAsEcguR/ZBxC6xp1dY3jD0D9OERsLHm0c+/z5uYPupIbqvHXrydrVWUlKZhFRUFvIQMgcKtjy5ZkQe3fn5Sgrl3JLWTdOkl7kGsSbjfg8cBRUwOLz2WnXhBFQBThAWAwmWidB2MQBPg1jOrq+gk3efncPdfjkf4rGqDpXp8md9NpYmxDN0zDO3DDCA+McMMIt7/TJwXSLfvPO3OpUxfhgBle/8xfrbs9nVtBxz8Bd95JHulA8wspHTpOR0T0XVx8MSmb1dXk6quiXHkMBhhatAB69CAD+IQJpNgC5LXz4IPEF7g1kmsSvjmd9poaWBkjo3V95DGfgiKKcAMwms1K/sC1CB6kqr7g5fO4HGo4zfjDyeYgV2Ih9qAj3DD5uYJbbnz2cwiJV5CyKcILEW4Y4fL7zYWqqc4hdDQfEhIkJ4vm5g+6u3IoFBdLk+blSEgg6+Bll+FjkwmTrr1Wu4ylS4NG+wIR5KYrCDSSmZBAvwcPqp/oU2INQIMTqIeEmlnpJOJ075YPoyXWYlBzV0OHjpMGUQQGDSIbnQ4dOhqJH39UxnsAaBSva1dg7Fjgmmvw8bp10khsILhbcQgERVfggZdSU8kb6vff1UcHGfMHcDICTR7l11/+KUJT8IeTzUGW4QKUI/EkX0WHjuZDZiZ5tJ8u0JXcUOjWjdyLExJopLZXL5rAn5TkP8Sbmxu6jPPPpwh9ZrMUNdBsJjcgiwUwm7E3Px8dRo+m8gcMIH9Bjp07KbKuwUBuQkajFLnY9z//6FG0zMmR9vnC+vuPW72aFPbYWCngAy/PF4r/SGEhWrRpQ+fU1ZHLUEkJCWg+HzcSGAxSUKeUFHLLbtmSBO2JE37LsT8FAF8EAZWlpYjzeqW5O1pKtShK988DVWjBZKI5ODk5wK+/0v00RFnv1QtttxxGH/YHjHDBCDdM8MDYvjVM1WUwHs+HCW6f0w8tFjhhhgMWvpgFLHOORgGyfOe7YILLd6wDZrhggR2ulHgk9e8B0/IlsPj2WWGHBXZUTJyGpd9aIVaVka03IR6G8mK/TZjswAwGuGHs3AGGvbtg8DggwgtjSiKMJ45hO7qi0pAMMTZGYWznj0YQALu9BjEx0cr9+Qchuh1wwoTjIHd7AYAABoBBEMg9XhAAt9sFk8mkGPzn/wWBXjFul1E7BgC8Xg9E0RC0nfaF5k80EMEgqHkf+BDZa9BY2/7pML6gPJ8HYo+OpgGjgQOBUaNoepRsFoUOHToai4svJvfYxESKTjx9OrkeyxFKie3ThwzqXKZzHiFb9ufnI+eMM8iD7Kyzgj/iyy8nV10uM+U8wmAATCbkHTuG1jk5En/gi9lMHmy//EIdRkyMKn8oKCxEdps2tM3pJL5RVkbylgdmCtPhMgCCKBJ/iI+nmBGtW5M7c3ExyW8t/mAwoKK8HPFeb3jXbF53k0lyu9aaBiYIksv4nj2q83AjQkICEB+PsXnfYh9yfPLfDWNqIkyJsTDu2wmz1w6jjw+ocQgr7ChoPxzL9nXycQe35Ghs8MLsqYUFdpjghnX8ubD8tBymymJYYJc4RE4rfGmeiLKdx4krmE0wuOwwMGfAuLJvX9+eEPfsgrG0kPZ3aIeyWjN2F8RAAINgNEKMi1VwB1GkaTqxsTZVfiEKXhzdUwUnzD7uQBzC/99sBkxmeDxOmExmxaPgv2635BQRCX8I3AdQGVqvCD82FIc4NfyhKcpo+joYDDTbICWFQgiddRZ532dnU5dxWqFZsvOeJmiKJMXNnWi5weeXlTH26aeU/H3MGFaZns5YbGzkidNTUxnr3ZsSxz/7LPt29mzGXK7Ir791K2OzZzM2ciRj6enaidXNZsY6dmRs6lTG7r2XsbFjGYuODl2/pCTG7r6bseJixt58k7HOnSOeCuoOrEd8PF038NjLL6ck5u3bRz7NtG9f7X1RUf7/he3bUxtdeGHwcT170r7UVFpv3ZraR3aMYprK6tWM2Wz032Cg9ub7rr22fu+Ux0PPI7BO/fpJ/3ft0j6/nvirJ0H/q9XB5WLsiScYGzdOuSxf3rzJ3HXoOB3RFPyBsb9oH+NwMLZyJWPPPMPYpEmsrEULxuLiIuMPokhytWNHkuf33MPYJ5+wj+fNi/z6FRWMzZ/P2OTJVI7VylSnZ1qtjPXpw9hDDxEXmDCBsYSE8Pxm1Ci6v5deYqxly4hlvGodHn+c6iHfFh3NWF4edbiR8geAsWnT1Ld36aKsA2PEsdSOPXSIsYcfVtZP63otW5Jg6NCB1gWBsVWriI/xY77+uv7v07//HXyttDT6TU2NrIww+CvJ3r9LHfbtY2ziRCV/mDmzefmDPpL7d4bXS/OKV66kgBe7dlFAKJXoz7GB54oiRTXMyKAcsL1701DP0KFkzQ1ASW4uWUfVsHMn8NlnFOl3xw6yDGsFjoqJIdPQqFE0Avzzz+TytWBB6HvNzMSe7t3Rcf58cg9/5BGyaEfqgpWaCpSXwyA/fvx4arf//EfaJoo0Mn78OFmb6xMIrHt3Kk8NLVuSpRhA8sGDVO7775PXgLyttmyhNnn5ZeCKK2iU4IknFHlzvaIIA6/X0KH0/EeNorb47TcpSvL//R+lTpg9O7L6iyKwdy9Z1+X44w+yvtvtwPXXU/vo+MugqIgCZH/+ubpTxCDdQ1+Hjn8e3G6K0v/rryQvePaIsrKgjiIh8FxRDA6cNXAgMGwYcQq1y2l5xVVXU9Tdb76hgFoagaMEgIaROnQAxowh762VK2l5+unQ9xoXBwwciJ9698bIm28G7r6byoh0LjJP1STPGJGQQCP3suwLAGhk/a23KIcsj1ocCZKTib+pIT7e/1cAKIjmlCk0xzswHdLkySSj//tfatuPP5YyJfjgFUWIXi9xqUGDyNsgO5s8+s45hwKDffUVjWxPmkT3XZ8hvCefpOvLo1BzzlJcTAHTdPyl8NRT9KoHUuLNm5unPhy6kvs3gLWkhCI+//475cY9dIg6nUgiCEZHA2lpOG6zIWPMGHLPHj5cUxCFxd69wKefkiK2bRulRNDyKzWbac5Qr17A6NH46fBhnF1UBHz3HfDcc2Tf00J8PHD22cB111HOttJSUvp69KD/kcBoJJeo48eVkRMNBvpiH3xQWYdu3cj1/LzzIsujF4g2bbT3de7sV3INHg9wzz3Aiy+SkHzzTeWx11xDz/j++0kIvfIKRaHesoXO93qpfbhr9tlnU97gcePINWvPHqrLoUPAY4/RM5g2LbJ7aNWKlGP5PHTGyPXLbidClJcXYYP8M7F7N6UaXL06KItWEBgDTpwYjVde0d4fDidOjMHcucHbq6roUQVmBOMZxFJSKDh7x47hr6FDh46/KGprkb1+PXVIXIksLY1MwfMF3TwRHY2UMWNIiR0+vOHzHmprKY7J11+TrD10SHuqlCCQEbhzZ2DIEPzs8WBEbS11rmo5YOWIj6epZNdcQwZgAFiwAP0fekhp1A6HtDTiWVVVgMMhOXROnEh8TM1AfvQoua/XN1VTjx4KRVQBS0Bkl1tvJWV28WJSTuXXWrOGDNNPPkkRBrdtoxRDcmXc65XSDm7YQDzkxx9J4a2rI4PFkiXkp1pVBVx2GeW6rQ8WLKB24jhxQspi8c47NG9dRxAqK4Fly4AVK4hLhHuNiou1+QPQOA4BUFdx8KByPMliIe6Qmkq0ub6vRpOiWcaPTxP85dyVq6oY++QTxm66ibEBA8itw2AI7+JiMpFLcP/+jF19NWNz55Iri8fT8PvweBhbt46xWbMYO+ccVpOYSG4+WnUwm8m1dvx4xl55hbHduxlbsICxSy8llxgtd2W529GwYeRyVFND11+yhMpLTq6fy09yMrlWqewry85WuvTy5a23GNu0KdjtKNLFaGTsttu09z/4YPDxVVXkKiRzZfYvixcztny5tK7m/iO/R6uVsY8/ltzJUlJo4S5Iy5bV75286KLg6/FnOHr0P85NRw0OB2P33fcju/12xgYNos813Gt+Oi1vvkn30VRumTp0/J3wl3NXzstj7L33GJs+nTqkzEzGLJbwHYHZTK6kffvSNJ1nn6VpMA5H4+7hwAFyB770Usa6dWPOUHURBJqGdOaZ5N78yy+M/fYbYzNnUr2iotRdheX3MHgwyfG6Orr+oUOM3XUXuTtHwqPkZaWnq+5zGwzUPoFu2927E28ZNixs+Zr38eyzjGVnq+8bMiT4/Fmz6D4nTw4+vnNn2se5U9u2xM/kxxgMyvt48EF6Xnx9wgTGpkyR1r/7rv7vQ2ys8poxMfTbt+9fXv43xfm7djF2+eV/sAsuYKxdu4bTz+ZY2rale2hu/qCP5J6O8HrJ2vbtt2QN3L2bRkRDpRgSRXKXycwkN50+fcjKNnSoelL4+uDECTId/fQTuS3l5ZE5iTH/IYormExUjx49gNGjgYsuIjcnPsJ7772UWikUDAay5F18MXDDDWQZzssDXn2V3Hz27QsaIWYIMb3eaCR3n6Ki4OARokjW3NmzETNsGNWVIyGB2v/RRyNKfq5Zh1ataJRbC2ecoSzD7QauuoqSjT36KI0qy3HbbfRcOnWi+r36KgUe2bABXkGAyBg9o5gYGqqz26kdX3mFrLwnTlCdeEL4Cy8kl6S+fcPeIwCqV2YmjYL7K+57H1asgPmSSyIr528Et5uM2x98QJ9vYSHA2EjVY00mGgRISVHPzCVHeXkpEhOTNPeHO7+0tBQJCUk4epQeu9zyK4pkbc3MVKTeBEDpqXXo0PEXg9sNfP89uZOuXUsusZWVIYd8GAAhNpZkQpcuND1pyBDy7LIGxW+uH5xO4g7ffksyZu9eksEB8tskX0lMJLfjIUNINnXsSG61334LzJ8PvPCCgn8AKnI3Jwf4179I3uXkUD0++IBGcNevpxFItXbQuo/UVJKldXXUucuRmQnMnYvC2bORdf/9yn3cNTkpKaI0kZrXv+IKxbQkBWprg3PszpkDPPAA5eL99FPlvl27aLT85ZcpIfrBg8QzHn8cAMAEAYLHI42sAsAzz5AX10UX0dDc//5H3mbffEPDeRMn0nPVmramhsWL6RlxcO/DnTsjL+NvhG3bgHffJWfGffu4M0WfoOMEgTzsIwn0VFZWiqQkbf7AywsFziEqK+nVl3uZA/TqtWxJdJlj4MDQZZ4q6Epuc6OyEjk//EDseOtWcj9V6Xz9EARSZlu1IiVy6FAscbkw/vbbm6Y+27YBubkkHLlyHWpeq8UCZGTgaEICsq66ijq65GTqWL/5htx+Z84Mf11BQFVSEmIvu4yUsd69SSh/9BFw9dUkHNXahefzg4ZwSE4mwVJdHSyYUlLInWjiRJq7MmSI8oO44QZy5+3WLbxfKUCCTGvO7Zlnhp6cMHRo8LYlS0j4PPAA1VPuhl1SQtsWLKCyKyrIqLFhAym4fI5sdTX5jdTVAeXlJKgeeYTm8R4+TIaEvXvpGQ8ZQs+8Vavw9wrQu2q1SkKQkw7GMHDePOCmmyIr5y8KrxdYvpymTq9eTbaRAN4FgCE6WkDLlvRKjxhBMr0+3ny5ud9qpxgJgz//BK64woUtW5QcNzWV0nGeeab2ufv3kyBt165Bl9ahQ8fJhteL1O3bgVtuIZm9b19wyiI5eKyN7GwykPbtC4wYgQ8PHcLkKVMaX5/qauDDD4EffiB5l58fzIjlMBhIRrdpg90JCeh0333k7rxyJbBoESlir7wSUYpEh8UCy+jRFBdi/Hi61/37Sdn77juqS3AHrUAQhzCbibkXFSmnNAGkzF1yCU0nmjsXmDwZWfJ6cgP5Qw9FZCBHVJQiNocCVivxGC0Xcrudppjl50v34HDQNKRFi4iDPfec8pwbbqA5vg88QL9vv02u37t2UTvx/MZms3Tda68FVq0iwXLoEM1dfuMNkvWVlTQ/97PPwt8rxwUXSPwEkLhEXR2iA/na3xB5eWQDWbaMXhU1G4jB4EV6uojOnUleX3ABKZCBRmktNIY/1NbS+MkLL5iQnx888zA7G7j0Upotp4ZPP6Up3M2KZhk/Pk3QbO7KGzeSmwePJqe1WK2MtWlDIcpmz2bs998VLsaNqgNj5G60ZAlj11/PKjIyyK1Zqy7cXahfP3J3+vBDinDow7Knn6aIe5mZkfkyxMSQy9TDD5PrtPw+8vPJxUbNTVcQwvt8Go2MDR+u7tpjMDB2wQWMFRbSteTuNvJjNm1irKAgtAu2/Pi5c8l1SuuYzz+niJJa+10u9e0XXMAfcvC+5GTax6M2JyYy9sgjkruS3I1bHpH6pZeUURrvvFNq03796vdOaURwdBsMDXsnZWhuV6HAMjwe8siaMoU8u7Rew+Rk+mTnz2ds/vwPm7QO4bB6NXn/JSY2jcvRhRc2v7uRDh2nI5rFXbmmhqb5XHQRybdQsjAmhrEePSiK/vz5JFebog4cHg91OI88wgo7dgwt37gM6tSJwq++8gq5T3NUVbE/Lr+cIiFHInONRsZycmj61aefMuZwSPfg8ZA8btNGW16HK79tW8ZGjFDf17KlNL3nl1/UXb5vuIH2jxkTWUc7ZAhjx45pP8/u3dWzPISrr9EotUmgazDA2E8/0b3w9QULJN5ltUruyjy6MkDZGoqLpeNatSI3dr6/rKx+79OcOar3tPmSS+r/TspwOrorHzhAgb59Hvaqj9JqpcQlDz5I0YpPNQ9auZKxc87Rrl99FpOp+fmDwBhjzatmNx8qKysRHx+PefPmwdZAl96CggJkZ2eHPsjtRus1a5Dz009IPnAAxgBrHAPgiItDdVoaStu2xfHu3VHYowfcEboJRVQHrxdp27ej5caNSNm7FzHHj8NUVxdkuWQAvEYjahMTUdmiBYo7dsTRvn1R0bKloqzUnTvRes0apO7ejdjCQhhCWFrdoojq9HSUdOqEI/3742jPnsEuLV4vEpYswZBVqxB7/Li/XgyAMyoKAqCoL39p5etV8fE4MHYsogsL0X7lSsU+e3w8Nl59NfJ9oWKz1q3D0LlzKYKg7LiCFi2w6umnYbbbMf6WW2DUGMVmvmvXJibi28cfh9dkwvjbboPR5QpyefIYDFh9660Y/vLLqu5QDMCHixbh8iuv9O/zAhABHOvRAz/7XJUvv+IKxblusxmfzJ+PDsuXo//ChfAYDPj4/fcxeuZMpBYW4kTbtkjKy4Po9WL91Vej/8KFEAAc69oVPz/8MC6+4QZYqqtxYNgwiC4X2qxZg7r4eHzxxhuRvVM+TLjhBlirqxX35jYY8Mn770d0vhbqU4eTcb7XC/zyi4CdOwdg1640nDhhA2OB5lMGm82FNm1KMXDgYQwdmgerVfoWGluHUGV4vcC2belYvrwTdu9ORV2dCdrObvSeR2r95Rgy5BCmTv0J06ZNQ0VFBeLi4upXgA4df1M0BX8AQvcRotOJ9j/8gJbr1yP+yBGYa2pU5YfbYiH+kJODgj59cKxXL3jrEe02bD/l9SJ19260WL+eZL4GfwAAJghwxMaiKj0dpe3a4WjPnijq0QNeLvO9XqTt3IkWGzYgZe9eKqu2VrPnYgDssbEoa9cOR3v1wuEzz4RD7hfpQ+2mTRi7YgUyN2+m4I0+uA0GuK1WmOvqguS9Qp6aTDjQsSOKhg3DwPfeg0k2rOYVRRwcMgR/TJ0Kt80G0enEufffj9jCQmUZoojvnnwSFW3aYPDLL6P12rWq1+LrTBCw/tprsX/0aAx94QW02LhRcSz/v+nyy9Htyy9hrq1V5RCH+/VDdGkpZWaQwSsI+OiDDwAAo2bPRtqePYrz19x8Mw4NHYrLrroKBo8Hf0yZguq0NAx76SUwAMd69ULW5s2oTkmB6HbDVl7uL7PHRx+h+5IlcFss+OSdd3D51VdDALD8ySexzWKJWO6lbtmC0XPmBLXNdxMmoOTSSyMqQw0nU/ZGiu3bK7Bz5xBs2pSFgoJ4OJ0GBD49g8GDzMwq9Op1FCNH7kdWljIS5MnmQQcOJOLrrzth8+Ys1NSYg+pHYOCsuz4cwmp1Y+7c95uXPzSLan2a4KSO5BYX0yhl587q1rnoaMZGj2bso4/Y4kWLGnx91Tp4PBSU4Z57KFBDYmLo/HVxcaw4J4exRx4hU1MgqqoYe+cdGtbJyoosF15mJmPXXEOj1lpwuchyOHJk8Ix6o5HKUMvLKl+iomjo6sAB9uMDDwSXY7MxtmKFdM2SEsYyMoLLSUxkbPVqasuqKikAQqilRQsKYlFWRqPc0AgacfXVSkuomulu0yblNt7GN90kPdPA82JiaN/8+bTuGz0t6NWL1tu1k+5DbgXu1o3O44Emxo9nbMYMqR3U3qlQUHlG1UlJkZ+vgeYayd24keKDaBn84+PJUeCll+jRn4w6qJXhcpFDwNlnh08VLQjkKDJ9OmP/+c/SRl2/uS2xOnScjjhpI7n5+eRd06aNuqwVBIpiN2YMY//9L/uUR4hryjps20aBi4YPp2uFkvkWC6tMTWXsqqtoVDDQ26yujuT8+PEk0yPhDzYbdXTz51PHpwbuWjN5MmNZWcGyNzXVL5c1F4OBAlItWcJYRQUryskJPua665T3dMMN6mXde6/E5bSOCXyOn3xCx990k3970H1ERVHAsFBlFRQQN1NrR47evYP3f/QR7bPZaH3WLHr/5NwFoHaUn88YjcYDNFzHmLRv+fL6yb1rrlG9p0/feCPyMlTQXCO5+fnk1EiODd6gWzObGevalbHbbyfadzLqoHW+w0GOD+ecoz6wH/hpdO1Kn+DChQ2vQ3PzB31OblNi/XrgpZcotndRUfD+rCxg7FjgrrsobxyHVn64CBF/6BDNs/zlF5qwf+IEvadqiI6mSXYDB5Jz/7nnAmYzvs/Nlfz29+6llETff0/lVVaGrYPHYIChXz8KW3/DDdrBrvLyKLz/0qXAgQOKejIAQmIizQUpLdUOly8I1H4PPkjX86XHGRE4l+XWWymwAjc9XXIJJQMNLOvpp2leCgDxwAGakxqYVyUQ6enUNm43zW3yzZdlCLCDiSKF31+4ULus/v2Dc8vyduncmX7VngG/L27V9lmpa3mQgYoKKfBUQYGUEqCsjPZHR0vH8XD9keYFlENltLsqIwPR9S+p2eD1Aq+9RtOc8/Plexji4gT07EnxNq69luKHnAo4nRRr5YknRuH660NnBBMEin1yySWU8zYnR9qXmxtijr8OHTqaH9XVwKxZJCcC4z8IAuUmHzKEJsCdf77CE8rZSP6AoiJ0WL6cgjJt3kzBBLU8s2w2ynt75pnEHcaOBWJi8JWcP5SW0n0sXUpzNyNM6VedlISYK66gtDbyDkyOgweJYy1fTvxBNmIrABQjJCGB2jBwDq0cnToBt99OXKWoiIJhXnQRUuW8qU0bCpTJ88KvWEHpAwNl5MCBFOQrJYW43IMP0vzWcHjzTYoHMnOmlCYwOhpC4BzmG2+kdwOQYm3IIQjELblclyMmRvqvNjeapx7i71NVFZCVJfEYvr22lq7x55/SuWlp9MufAecXkaZw5Fi/XrVeTlnu39MdXi/w3nvAs8/StHgJAoxGep1HjSIq2Lv3qavXli3AggV98dBDNO063NT2tDRK43zvvZRZkqOxXUxzQldyGwO3G21WrqSJ9xs2BLNQHiF40iSKhtsUQ/VOJ0W2+/xzirx85AjO08pDGxVFHfQZZ5BA+te/lJ0eQF/n119j4JtvUkd66FDoQFMc0dGkaI4ZA1x9NT7esEF9crvXSwGU3n6bQs6qBcVISgIsFrDCQghqHTVHYiIptU88QZ3u/fcDU6f66+tXLjt2pGiOPLrP4sUUvCqwnUaPpgiBvE3cbvzrtttCB+7g9d21izr19u0lg8b48RCXLFEeO3Ys1RNQRiqU48UXKfqAGvr4IuupKfxcyeXCwCega1JSaL2mhp7/8eMUdMtgoOfBhR2/78pKSVGOIMBHEFQMKqU5OWhgpuVTiqIisjl99pmSO8TF0avVtevnuOmmUxMpurKSYoTMmwfs2CEPQpEWdKwokhCaPBmYMSPyWGE6dOg4jfDzzxjz6KMU4VYeGc5sJjZ8zTUUSKkersch4XSSgvjllySPDx4E6urQX+1Ynse+b19S7iZMUIZP5di7Fz0+/piM1zt2hDcQAySL2rSRFPdx47D0k0/UOcSKFRR8auVKCp4YiOhoIDERzpISmNUiH3PYbHQPzz1HQZpef506zkDZajRSiNurr6b16mriOoH532NiiNucfbZ/U6elSyPTCJ55hhTsBx+Ucvp27BgcdMpoJN7Gj1EzQnNlU84/eUBO+fMKVI4B6b0y+WJbV1UBoggmihC8XumddDqVEQi9Xolf8WM4vygpoWBikeLIkeBtjXQzPlU4coQUwi++UDav2UyxTAcO/B6PPTbmlNVn/Xr6VL75hh4DUbNOqscKAn0GF11Eceu6dTtl1Tyl0JXchsDpJOvbO+9gUKBCGBMDDB5MHdiECfWfBBeIoiIpyuCffwanv4Fv9NBikQTS2LE0pKMmkE6cIIstL6+4GGAMYQOopqTQiOOFFxKzDix7wwbp//Hj9KX973/UaQcqdkYjfV2M0f35LH+qLWUwUHs++SRFD37lFYoqffRo0KFukwnGxYulBONFRXRs4Kh6airdf3+ZaPd6gY4dYQun4MbHUxg8q5WEEheQzz4bnOYHoHrz62gZI844QzsCNU/poya4+bslH1r0elGVmUn/HQ5J2BQXk9XW5ZK0J64cV1VJZWjVUQsao/zHu3XD6ZzKfcUK4L77goNhd+5MAbUvu4zWc3MbMLIdIY4fJ+vvwoUUBFTbvsBgMAho0waYMgW4+Wb6fHTo0PEXxcKFFHW3oAApfJsoAoMGkVfW2LFNc53164FPPiEvr9271ZVE0NxNMTOTwqSOHk2Kp5rl7MgRiuj/88/A9u0kWz0edA8+UomoKDL4jxpFqfG6hzjD6SRL34IFxFECFTuerwQg+VtTA9TUQNMM0KULtfWVVxJPmTyZIgSrGGeP9O2LFmvWSMrfNddQPQLxyCP+dDt+vPMO+kSi4N5zD3mOPfYYRX4GaHR8zJhgJfeyy+h4gCyvavL2qqvIWCEHV3Llw3FqSi4fyeX36zOAe8xmiHa71PZer1ILOn6cXIc4qquJ17lc9I7VR8lVG2E+zfPXLVpE1G73buX2du3ocd14I33OubkRZOVoIChmCI2xff+9+kC+HFYrOTBccQXZb/4pHEIPPBUfH/mEaK+XOqe5c/2KAgMgZGfTSOmddzbIHJIrd/XZsoVykvz4I31BWuH3k5NJIJ17Lj6PicHFN9+sftz69fRFrlxJHWgon0cOUSQr66BB1Mmed17Y3Gc/Pvggzt69m746FUUciYmkSFVXk2AM99plZ1NY+vvuo9y8Dz5IglXtvC5dgOefR25FhdSO559Pimzgfb34IvlzyvHll6Q9hLNAx8TQM0lLo97iwAHafv/95FcaKGhyckgwbdqkSHWkQHw8jZ53767MzwvQOR988P/sXXd8FcX2/+7emptK6B3pvfcq2GjPghUbFnx2xd6f8lQsT54VbIgoGEAQQQURQUQQkSIixUgJiSGEEEJIvbll7/z+OHfu9luSYPg97/fzmU9udndmz8zuzilz5hxiTj/+SNZlJRISyH/25ElK2xDsd96Bw0jdnw0v7Kiq1wye4gp4rUnwSFZ4mA1e2OBp3RmenKPwwQZvelP4rrsZ3tdnwwMH/LPew88/b0fPnv0hScS3JImUMP43EKC//r1/QPp2LfywIgAL/LAgAAuyhgxHy9ZtIUnUbX49Y9SGsjAmt8nPBwLAiRMnUK9eg9Bx/lf5G6DfynP8eFlZORITk0L/SxLp8+Xl+kfhcFBmLouF/uftVFW54XQmhK7j9ZT1jR4rP+bxeOAIChJ+P00bPl/k158/fpsNEAQPnE6H4TXRtOPzeWC1Gtc3QlISLRxwRT/meTKOOP4GqNZ38fbbwBNPqCRST1ISHLffDjzzTLVy2uvkh3feoZQ52dnmRsvUVDLSjhgBTJqEhWYphH77jfLSrltH8oORomSE+vWBfv1oS9Q110Te43HkCH6/9VZ0ycwkPqqd2FJTSWFzu2lFNZIxNjGRjN0vvkhj+vTTpDgb8fiUFNra9NRTWPj55zSWX34JTJqktzyOHEmrt0oj/4kTlLJo8+bwNAGkNH/4Ia0mcw+vZs0oR6025Z4gqJVph8M4x0xBAckfd9+trssYrTJPmULP7d579c+Pp3xcsIBkzaZNEeg3ABWrvoMUEFGV3hyekxWoggOe5u3gyTtB8kPj1vDZXPAcOU4yxCtvwffEM/B6JPjOHY8tzVqjV6/+OrlB+9fvB6Q334IEKySIshzRow+yk1PQsmUblQzBZQPOQ5VyA//NrysqOoG0tAY62UArS2iP8YVpxoDyclmGiEZ+sFrVMojbrZYfeLvKv9rf2v+5DMH77fVG73BnsQAWC9U3W2+LJEPQ2EYvQ9jtwMCBsvhd1/JDXMkNN/h+P1kSd+wgZTEjQ60knnMOPr/kElxy553mN6mspEmI7xE5eZIYXHExucWWluLP335Dq6IimryN3l6+4a5LF1rN7NOHJqvgF/fb5s3o2a4dWdD27SMmceJEdAotAFgsqEhJQWKXLrSUlZZGdauqqLjd9GXz3x4P/fb5iIbiYv2XIgjEaESR6kThAs0ACBaL7DrDZ0KjV1QQiPGlpISuqyovh5PPAtWEbk+tAgEAp5CGciShDMkoRxLKkYwKuFCBxFBxIwGVcMENFyqRAA+ccMOJKjhRDheqkIBKJMINB7xwwgM7vLDBDzv8sEKCBQGIkCCCQUQAIhgEMAiQIAQp1FIpmPyOI47Y8dJLZF8C6p5JxRHHmYiovos//yS34N9+I4Ol0uW1bVvg1VexsKLCPI+l309G4ePHSY5Qyg+lpUBZGQ7/9hvOcruJ95ut1jVvTobUQYPob1UVKTXl5UBFBfb8/DO6t25NGwozM+leFRXRWdEAVCUmwtmhAynOLVrIskJVFckLFRX0u7KS/nq9dNzjIaO4Ec9OTCTa3e6oZBkGQBBFbhmUtR0zhdjlIuO7KNI4+3zwlJTAEc12rXA0hDlfCSdOIQ0VSFTIEEmohAsVSArKEC5UBosbCaiCE1VIgBtOVMIZlDFccMMJD5zwwAEfbPDBCj9sIYUxEJId+F8Ef3MKzWSGuPwQR83QrJm8ZlPX8kPcXTkc5s2jneJaDB9O7kZnnYWqSO4p7dubB1AKQucU5HRSvZEjiWE8/ji55x49ShZVDUzyMEcPSUJicTFZIqOxRkYDMoPFVEUI0hKVyyxjpNQr3K+iS7gUBQ0m+BL/wMX4ohbuEkccZyZEkUIIjB5d15TEEcf/ADp10iuenTqRV86wYfR/OBnilVeMt8EocJb2QEICeXlNmgTcdBN5FGVlUfnCmH9FdDeOAGdFBS0IKAMT1RRBN+RoIQCk2BqtdhqhspKKAtH7u4ShIQya4hhK8f8noFIcccSKjh3l7exnAuJKbjjwvZQWC1kVu3YlxbeT8UZuQyhz3QoCFYuF/BpsNsBmQ2UgAFe3brQP5/rr1XthDJTaiODWzKQkslQeOWJs4RUEulYQIAGw2Gz0P/k4EI18n4U24qMGkSyY0cC0DUEgH4homReHxUL1eJ/Ky9XBPaKF0wnYbHCVewCVYZv+EYLrrLLtNACLRYBF8sASdMCxwgcrfMhHM/hhU9VXtico2gMA0WalxyT5IEpeBABUGcYtNrK4M4XNVqZVgj5Xm3Eb1X2ivJ24RfhMh8VCe3NGjaKtahMn1jVFccTxP4R69Wj/YkICuYXOmEF7Q6OF1s1Xy5/tdrglCQlNmwLnn09R6LTySYStRjoIAtXhK52SpA05L18XLDr5gcsOvOTlReS9NZUhwtY3ikocDex2kqUsFpKDovWOU4J7tTEGS4XSU08tPyhlCAsCEEUGa8ALC/ywwQ8r/HDDiSI01LURulVwvTYkR1gtEEWBZAhPJUQEUAEX9BFQmKoVJW2hv4IAgQUQAMAMVYfakh+U7cVliDMZgkDTRIcOFLzqssuMQwHVJeJKbjj07EmTmrMGa4Tbt9NEqY1qrMAK5Z4aJQIBUswmTiR3aaPgQxYLyho0QPIFF4SiFOqYGldQk5JM+/KpGQ0AuRIdP071AwHaU7JkCblgBSf9sFORINCK9PjxwAMPkGWZbxD480/aN7N0qTrkvyBQ0KVnn6X9zpyGlBTqw5QpRIPRqu8VV9C+ZmVEyr17ad/MoUPyseRkcrc2g9VKK9v9+gHDh2PUT98hF82RctVEJGX9BnHrFvX1jRoRjS1akPWdu7Er9tKcAAktSSiHs1kDYv4ffkgW9yAkQYCFMQryxaM1T5kKfPwxvKITxwINyJmpZUM4szNpKC1WejZLlmChz4fJ995L4zlxIqU3AGgP0Esv4TgawJuVB2eyHU4nDZMVfoiOoPK9ezcW7t5t+j54veScYLEA9jbNYJcqYIUfdnipnenTsbBDB/P3CTRMbrcsD4miLA+JIpXPPluMyZOvDP2vpYFnReK2GlEEvvmGYr6R1z9DixYC7r+fApTyz4Jfe/w4PRb+v7Idft3y5Z9h0qRLVccCAVoQefll2qZm5NEnikCvXsCAAZvxyCNDVfQrP8/jx/We/NpAqt98sxLjx0/Q3UMQaByUn43RvpsffvgG11xzAZo3J4YUq8wbRxxxVBNZWTWTH66/nvhZSoppEMvlZrz76FEKmtS+PaW7M+J1ggA0aID8xo3R9NZbSQHXSqlVVeQmnZJCE4gBHWHlB4AUfauV5JkFCyj86+7dKprCyhDNmpFn27330uIDn8T8flp4ePttfRTB7t1pLy4PRMlpSEqi6Mb/+Y+x0tqoEdHHgz4CNNHeeCOtuvMJPzGR6odT3tu2pTgen30GTJ6MfegKa3IiUjauhL23JjRjixbAkSOk2m3bRh6DHg/FRvnpJwBAFew4ifSgk3M5RE5LgwahWCgh1VAUAZ9CPhLIOH4sGK3fhUq4cv6AtVUzYPQYinnStSuwdy/2XDIJ3Zcvp+edkkJjd/U1wCefoBJOnOgyEs7vv4HdjpAMIfbsQXLWyJFYeNttYd+Ho0fp0dmvvQLWjetghzckP1g7tAP278fChYtM26isJL7H7TFGMsSSJYtxzTXG8gOngfN/i4X+FhSQyMTjmtrtftx8szW0XZ63JYoyDfy+XH7gQy+KwOefL8Oll04KHeN/d+wAnn+eQq2Y7a9t3Ji2szdosBKTJ8v8X8m/Kyv1Aae08sPXX6/CxInjde0LwQ+uoEBNg9FY7djxJaZM+QfS0qoVNqBOERd3wkEUa8aggNiTav76K6WT+fZb2lurnUB5TrSRI0lyHz0aXy1eHJ7B8JQy1UEgQBP+hx/SF2myoquzuVmtxGSuu44CKii/jMOHyaL9xReqyMcCQAz2uusodJ3Sf99up6955Eh9gKcgTjZtivQNG8isxFFZSYz788/lY4mJ5AZlwPRD/bBYaBV9wAAK3vDTT7ADaHH5MOBoJqBVcPv3lyNMv/ACxZUHKIUP34climgQUOSQmzqV/mqYs8AZV/v28sGgW7bdIqFVIBhy35YmG2S59nXqFPUvLY1mYKU7VjCcXiOcAAK5QANlLkLFVBBh1d5ul1MHQjJwxb/gAjkolwl41oNwsFoDpgqZ3U4BKZV45BFSPKkucNddm/DqqyNM21cGnjRDaqoXjRoRE/jgA5KLNOmdQ7DZaEV0xgx6bQBg4cIctG07tEY0ZGaWomcN9iQUFZ0804NVxhHH/yZqKj84nbG1sWoVZSDYtMl4u5AokvwweDDtS7j4YsBqxffhlFSnU04XEysCATJgf/ABpTw0icavA5cfeI405SKB1wu89Ra1uXt3yNAdyus6YQIFB9VGh3Y6abXbKC9r8Pz2K65Af2005bffJuM8V4i5JmXgSq2Sg5o0IaVv5UrqB2NoklQJ7NikX23v0CFkgC9t2hSp33wje64pUuw44UUzHJP7w2E0rokKjy+FFtMEimwTScE2EoLBkYKr3WWNG9P/Ho+s5BYUAKIIV6AKrcp/12e2421E4WIeep3ydgDQ5NWNwlPS5VLIICaw283lBxUNQQTtECGj86RJwKRJS3DNNcbfhd0eedUyLc0TknW++47iiP38s/HajCBQ1x97jERWrmwuXFiKNm3C3yccMjNLwsoPPXpEbqOoqLzaU0BdI67k1jWOHkX3JUtIOv/9d2OX3Hr1KFzZ1VdTpubayplnhj17KIfcmjXGirYBBIBmnsGDKcfJpElqk9Aff5Dyt3KlXomy24EBA7B+xAiMfuEFfePPPkuag5nL9WWXAa+9hm82bMBkpYL7n//QrMLH1GYj7STMJByygK5cSQr1uHGUcxegleCiIoogrUS/fiEF92jPnmhWvz4xBUCe+AH9OD71FP3dt09PA0BWVQ7OxMyWBC0WYmSlpcTcGjQADhxQR7pWXr9/P62oq24cjMgYQcmNiAEDIiq5tQm/n7IvfP89/V+vHjGS7dsN8u/FgMpK4IsvOuOhh/SBrzkcDnJQePbZ/908c3HEEccZCr8f7daupXDoO3fq3UKsVlL2RoygZaFzzql5WsNIOHKElMwVKyiQVZRbhAKCALFPH4p0PGWKmk6u2H74IfFLbZutW2N3377osWiRkSsMaQ1mfK1zZ/Imu+giHFi4UM4ZvH07ecdlZ8vX8jQ+ZgFCufWzSROS5zZsIFmIMZKPtm6lAKJKK2nHjsQvAwHA4cCGhx/Ghc8+S+d69QJ27dK3D8jb6YJBsziYIJChXLnAok2jyMEVZa4QBwOAlfI8tZIkt1NYKOfCNTKg8DZicelWuiFxcAvxX4jbbqO1JYC6OHs2eYRFkxEqHHbvboTXXye7itFnIIokPj7zDMkRcdQ+4kpuXeDIEfJVWLYMOH4cOkOKy0XmlUmTaLUv1tXgWHHqFDp/8QXw6quk4EY7SQkCRW0891ysbt8eY594Qn1+zx5SbFevDuXCDcHpJDecadPILRfAMeWMUlpKVteffza+d3o6hd2/+2490960iazU3OdEEEjZ1ASZCMFqDTEtBkBYtoxWIy+5hGgHiBaPh8xxSihXcM87DxtuvBGTH3iA/m/cWE6kpmVQ3BoM6JPMc/TpI/82cjXj+W6Uv0tLKRI3z2F38iQp99r9RDt3kgKvbc/vpzrJycY0KaF9pgDVO91ClAJHj9Ij4LHdevcmzy6nU526ORYcOkSf3m+/AUAf3XmXi16NZ5/VrybHEUcccZxWnDxJStlnnwGHD2Og1q2kfn3iV3ffTTz2dCMQQLPt28kNefNm41y8Zin0UlJoAp88GYsdDky+7jr5XGUlKcsffUR8VFu/bVsy/D/wAJCWhj0LF6KHUsG9+27SXIyiJVssNMn/9796d5rSUlJu16yRj/GtTWYr0RYLIFHuA3TuTPx182bSXAIBYkibNtHqtFLb6dSJFhL8fpIHtmxB+gcfyLy1dWtZydX2/7//pb87d6oOMz7WykSo3OiuhZmSq1y246vop07JsoTRggNvI5a9z0ay5nnnRV+/hqiqotdv7176v359cliMJeyOFn4/5cqdNQvw+8/RnbdayRN9xoy/5vP8u+O0S6MvvvgiBEHAtGnTQseqqqpw5513on79+khKSsKll16KAs1+0z///BMTJkyAy+VCo0aN8NBDD8GvsZ59//336Nu3LxwOB9q3b4958+ad7u5UHydOAPffT0phy5aUyy5oXQtYLGTdu/9+WmGrqCDXnocfPj0KbmkpWUXPOYfar1cPfRYtInNTJAU3MZFWON99l5hQbi7w4YcobtuWzv/yCzGItDRS1DMy5Ak7IYEmsFWr6D7ffQBTZ20AAQAASURBVBdScENYtoxoSk01VnCHDaMNDUVFtD9HoVDZS0uBs88mqzVXcJOSaMI3U3AFQbbKulzYNG0ardhOngwsX07HR40i6zBXeDkUK7g491xgzRqk5OaSBRcgOjhj4mmROJSzqMbKGlrJ7a6IeclXnwVBPqZcmeW/uTLMXbVKS2VGpryPNos5ICvKRkKKEZYu1R/jFuC/AN99R3IOV3Bvvpn4fXU9BP/4gz7D9u25gisjJYUcFPLz6VEsWBBXcOOI43QjLj8EUVlJ+VLbtSNJXLFvggGkDE2bRvz4xAniu6dLguZBCa6+GmjTBrDZMOq//yW+bsY7lHywe3fyyczJoTSK69aRMZ/v2f33v2l1MymJrsvMpPqCQG6906cTnzt0iCyN2py1ffrQtW+9pVdwGzcmY77XS7llNQput88/Jy8oruA6HESXWewOzneD/qdH+vUjjWnHDjI0BFdn8e23tMdXqeC2b0998HqJ965fD/Tujd6LF9P5Vq1oq5gZ+Iqn2UKAkkGZKblcfuJG7eB4+V0uWdbgfSwtlT3TjAwHfHtZLEqu0Yr4oEHR168Bjh2jIeYK7tlnk9hYXQXX66XX2OEAXn9d3TWrlcTtLVto6Navjyu4fxVO60rutm3b8O6776KnxiH8vvvuw8qVK7FkyRKkpqbirrvuwqRJk/Djjz8CACRJwoQJE9CkSRNs3rwZ+fn5uP7662Gz2TBjxgwAwOHDhzFhwgTcdttt+OSTT7Bu3TpMnToVTZs2xQUXXHA6uxU9Sksp4aQ2Px5AStPQocBDD2FxSUn4PbU1RXk5pTxatoyUUO1OdYSJYyeKpEmMG0eWUaU7MMcPP2D4f/9LPh9aSydXih98kPa2GsHrBaZMwZWLFxtbex0O0jCef9541/upU8Cjj+KS99+XmYjDQe2apTHie1gZo9833QS8/TaOLFlCASYWLaLrBg0ioWLZMnX9fv2IkQE0e337LQCg/4cfyv1WujVr8wA+84z828h9Ohg5MwQjJV2p5HIlmjNjbnSorCT6y8rUPrd//qlvz2aj1epTp8gQEwnffKM/9hf57L78MvDoo/Lje+89UnKrgz17aGX24EH18YQEYPTovZg/v9tpd6aII4441Pjbyw+BAO2vffddWdHjsFjIbeXqq7G0Xj1cfuONp5eO1auBuXNpdfLYMR2fDhsHt2lT0iD++U/6q8Xx48Cbb2Lie++R4qwE36g4ZQpwzz3mUW+WLMGkqVPNjdlDh5LmwV18tVi8GLjvPvTkFtNI3l/BlduQJtO5M7BkCTbu3o3Jv/xC/ZQk4qnLlpHBW4m2bWkFV5KIj3//PRnwf/4ZidyF97LL5NVabXToVEUaIk3aJoHLQJ07yweVXm1GMpZGyQUgZ7Tgyq7bTavDRUXGm0p5G7FmwVAiMfEv8QTbs4dEO/54H3+cxMvqwOsl0XfePP3Qdu5cgHnzGv9VenscRmCnCWVlZaxDhw7s22+/ZaNGjWL33nsvY4yxU6dOMZvNxpYsWRK69vfff2cA2E8//cQYY2zVqlVMFEV27Nix0DVvv/02S0lJYR6PhzHG2MMPP8y6deumuueVV17JLrjgAlOaqqqqWElJSajk5uYyAKykpKTa/czIyNAf3LOHsZ49GaN3Xi5WK2ODBzO2eDFjkhS+jZrSsHs3Y5MnM1a/vp6OSCUtjbFx43R0huDzMfbOO4z17k190tZPSmLswgsZ27gxPOEzZjDmcpnT0aEDY8uWmdfPzmZs+HDGBEGuI4qMORzmbWrpHTGCsfz8UJN/nHeefK5PH8auvlrfRr9+8u8xY2R6CgtZgNMydap8jc2mb4MjP9+YzpQUdV8bNaLjTqd8Tf/+8vmmTenY5Mn0PqxeLV/XuTP9HT1aPtali34869Wjc7ffHt07ydtVlpkzGWM1f6fN2vD56NXkt0tMZGzHjujrK7FxI2MtW+q74HIx9sEH0bVRnT7EAkli7NFH17Err2SsSRN6tY2K3W5erFZ/2PNmpXt3oqGkpKTG82QcccSCv4v8wJjBHFFRwdj119NHqJyYBIGxHj2I9/p85vVri4YZM2iOF0VjHqXku1r+P2oU0el2G99w507GLr2UsdRUfX1RpMln5kzz+owx9ssvjLVrZ87rnU7G7rmHsbIy4/qSxNjjjxMTCdYJAIwlJ5u3qe1zWhpjixaFmvzqpZfk52a1MvbZZ/o22raVx9RmY2zrVpkmLlukpTHWpo35fa+6Sq7TrZvqXID/VspOzz5rLP9wzJghjxl/H7g8cO658nNRyj5aPPggHU9Oju6ddLv1Y9OmTej06eK9CxbIwyAIjM2ZE3sbjDFWXMzYxRcbfwYXX8xYScnpk4NiwezZn7GnnqKpIyHhr5Uhdu+ue/nhtK3k3nnnnZgwYQLOPfdcPPfcc6HjO3bsgM/nw7nnnhs61rlzZ7Rq1Qo//fQTBg8ejJ9++gk9evRAYx7hDcAFF1yA22+/HXv37kWfPn3w008/qdrg1yjdmrR44YUXMH36dN3xJUuWwFXNuNh5eXlYGNxLai8txdC33kKTPXtCVs2AIOBU69bYf/75ODxyJFmpJIkshwZt1ISG1OxsdFuxAk1374atslJlWWUAAqIIMRDQWVwliwUnGzRAwZAh2H/++fBw9x8lnYEAzvrhB3T89luk5eRAVLjdMAAehwPHe/bEvn/8A8U8KnBurm7nvr24GGe//DLSc3IMLb8SgCP9++OXKVNQVb8+WS81bThOncKQ2bPV4yyKKElLQ9rJkxA8Hp1l2We1QpQkWIKW1/IGDbD5rrtQ1LFjKLBUt2XL0CO4InuqeXMcbdwYXTIyQu0wAMc7dkTj4Arusa5dsX7q1BB950yfjkaMISCKKF25EmmcNkmCGKwvAPA5HFgarNN7wQJ0UZwLgPYQlLpcWBm8xlpZicsKCyl7XVVViJ58jwffB6+5/ORJWAEcys9HXl4etq5di4HBdqvy85EA4Fh+PhoH71NcWorVmnG9orwcFgC/Z2cjr23biO/klX/8odrvwAB8BaB84cIav9OA/rvIyqqHF14YjYoKBwCgceMyPPvsavzxh9/Q+9qMhvXr22DBgn5wu21QviVOpxc33bQVw4dTTsiFC2vv24wF5eVWfPttR2zZ0hp5eSkIBEw8IKKGJfIlBsjNdWPhwuWoNFvNiCOO04S/i/wAyHOEtaoK/efORevNm0P8lQEoa9IEh84+G/vHjkWAe/csWaKrXxPk5eVhyYcfotPXX6PNxo1IPnZMJz8wUYTAmBz9P/iXAahITkZ+//7Yd+GFqFSMuzKrQWpuLrp+/jma/fabTj4JiCIKGzdG7vnn49A55yDAvZSUWREAwO/HoHffRestWyDyPbAaOksbN8ZvkyfjyMCBdPDLL3X97fzll+i+bBlswVVHBqAqNRWC2w2nSdYFb0ICHMHtXAGLBb+PH4/frrySVroXLoSzqAgTn3wS8PkQEEVsuPdejLr0UhWPLGnUCMnZ2RADAUhWK775979RcvAgcPAgGu3dizE7dkAAcLhbN7T58UeSCQQBImMhGYEBWDpqFPzBd+bSfftC91Bes6ygAN7ge3Huxx+jIUi+4twgIIpYHDzff906dADgZwxLgvzbX14OK4AThw6hAYAAY3AfPozEYPuLNO/ckE2b0AaAV5KieidHP/ssmihoBoCjqanYEKxX27zX6xXxyisjsWdPEwACLJYAHn54PVyu46YBpoxoyMtLxptvDsWff9aDWsoMYODAP3HbbT/D6Qxg5cra+zZjbeP33xvi6687Yd++xqisvKRG9ydUT4ZYsWI1mjQxidr5V+F0aM4LFy5k3bt3Z+6gFU5pif3kk0+Y3W7X1RkwYAB7+OGHGWOM3XLLLez8889Xna+oqGAA2KpVqxhjjHXo0IHNmDFDdc3KlSsZAFZZWWlI12lbyfV4GLv5ZrXF0+Vi7IUXVBbXsG1UF9u2sZyBA2nlT2tOslrJdGNkkWzZkrG77iJTixkNkkRmr4EDjVdszzqLscceYywvL3IfXn1VZTHVleRktuvyy41XjjmMTGcOB2NTpjDWtat520qLqCiGVhtVePhh9fXPP69vZ8wY+feQIer699yjNuOZWWABxoLvMGPM+LkBjM2aJV/DV4W1bW3YQOc3bpSP7dpFz4Jbd1u0kM+NHSv/XrtWTf+aNfK5rKzIz/P22/U0d+gQOl3bFsyHH1Z3//rrw78q2vo+H7VhtKhevz5jS5dGbqOmfQiHnTvpNeYL8uoSYGlpjJ1/Pk0pr75qXF57zbhcf/0203PhCl8IqGtLbBx/L/yd5AfGgnPE00+reawokjdUXl509auLoiLGnn6alTZubMyrjCZMgPjWuHGMBeUfUxoyMxm75hp5VVBZkpKIV65bx5gkhe/HF1/I3kwm5Wj37ozl5ITv77vv0iqpsu6gQYxdeaX5inVamnocBg6kZToldu+WebkgMLZ8uV5mat1absdmC8lejDHySgt6oFUlJqo9trQlPV2ud9dd6nP8GSpWRFlhoXxcSVOPHvI13KUpKNd8z1dlAcaaNaO/yhXjhg31Y5ueTufGj4/8Tvp8xn3LzAxdUpu8d8MGet34bZo3Z2z//tjaWLvWmD+LIskjRk4Hf9VKriSRQ8GoUcavjtVKIvL995vLD6dDhigpqXv5odZXcnNzc3Hvvffi22+/hbOmOeJqGQ6HAw6Ho1bb7LJiBe3n5PslLBbaP/rqqwibpKsm+OUXCs327bdAaSlU2eB4Jmyvl/aL8D0jfG/L5MkUoEKZg1YJnteOxz3XBhho1Yr2zTzySOQkYSdOUHTB7dvpWzPCwIGUE/e887B34UL0NNqPUVlJmx4yMuS9IFYr7Vn54w+KvmgEm432ePAUAE2a0N4XZWSBo0dp/8yBAwCAquRkOB9/nPYPKXHOORQgA6CAWps2yecef5yiQAIoadYMqStXyue0/W7fXo5qvHixeg9zcL+M32aD9bbb6FggQHu6ATn6MUD7bEeOpN887VJaGtCzJ6w7dshpiVJTKZq3yyXn2BFF6o8Sr75Kf9PTKWDFFk0eYC14vH0l5swJX6caOH6c4n5lZtL/Lhe9ntrA0GYoLaUt1p9/bvwoFi4036Z1OhE0/uODDyirhHZbtihS7JVJk4AWLT7H7bdPqva9Fi48gMmT66CTccQRI/5u8gN+/BEX3347BWAC6MO/8ELg/fdrlt8+HH7/nYIafP015T4FoIqlz/ebAjL/FwTi/RMmUIBMbfo5JQ4dIp7+5ZfqFHYA8ePRoymgwrBh4en0eoFLL6WAVmZpiJo3pwCU992H75cswWRtblyOxYtpT68y+GLPnsTLlywJBW9i0OwtbtVKjl8hitSvxx5Tt/3cc8DTTwOBANX/8EOSs5SRhxo1oqiFPh+N78aNcmDJkyeJFo8HEEUc69oVrXkuXx47RLmXdu1auV1tsDR+jTLTxbRpdFwU1TTdcw/95QFEAeCGGwAAXb74gv5PT5f383brJkdp0qZ6PHRIDjB6//3mKYs4zj9ff2z48JqFNTbB3XdTDDKOW2+lFEHRbv19910KKaMN75KQQHHP7rvvL00oEcKJEyR2Ll1K8Wq126TT0uhT69p1HZ57Th/lORbURIaINjX26UKta2E7duzA8ePH0bdv39AxSZLwww8/4K233sI333wDr9eLU6dOIU2hJBUUFKBJMOR5kyZNsHXrVlW7PHqi8hptRMWCggKkpKQgQZmb9HThjz+As89Gbx61ThBIoVuwILLyVx389htNpmvWyAwxiIAoQuQTmDakW+/eFPLtxhvD5tdttn07JT3/+Wd9kKQWLYArrqDJPRrGO3s2MTGziITJyRQlaPp0c2UbIDqmTSOGz/tlsRCjzcuTg0MZoVEjmmh5tMfLLiNGp5yN3n6bJnre9sCB2DJkCM7WKrijR8sKbtu2ZGTg7Tz/vDzht22LwhYtkMqZglJY4Ni9W/7N8+RyBBnU4eHD0YG3/+abcsRr5bNVRlnasIH+BjW/rlyjE0U5X+2oUXKkRqOwfjxIVjRBV+68Uy90dO4sK921hI0bW2PKFFnOGjSI+DvPaBAOhw8DTzxxvi6OCUBkLlyoTwZ/unHiBNmOOFPSDmFiItl8brmFMmDJyeBrEMgjChw7RozcLCjmvfee1tvHEUcIfxv5we8nBe6LLxC624ABFLVYmfqltvDDDxTEaP16vdQpCOQSyyckzrPsdsrTet11NCmFMToknDhB16xYoc99mpBA/OeRR4wDT2mxeTPxax4ESguHg6x/L7xAUaXD4euvSavhShxA1sP+/SkFkyaMfkjBFUWSTbiCW78+GYmVGQ+OHydj8Z49Ibp+uOMOjJo2TZ2lIiWFZCGvl9r95hs5grDXS23yZzJrFlrefrtclz8Trrw2biynFvzoI7XmxY3kDgesN90k1+eu7ampctBRq5UWZwDK9MHr33ADEAigAY/C2KgRKa92uzqlkjbK44sv0l+nk8YknHut3x/aIqbCxx+b16kGTp1yoEMHOaBkcjLZS4YPj1w3ECBxd+bMK3QiXIMGZJzWJgb5K/DTT6TYrltnnGK4aVMSjx98ULYXLFwYweBQC1i8WBf/LIQ6SHusRm0vDZeWlrLdu3erSv/+/dm1117Ldu/eHQocsVThH5iZmckAfeCIgoKC0DXvvvsuS0lJYVVVVYwxChzRnUdGCWLy5MlhA0doUe1l9FmzQq4tAYCxvn2j830wgak7wu7d5Eajda8BGLNYjF2IExIYO+cc8jUM58/p8VBgiCFDjAM1NW1KLriKoEzh8Ol771FbZsEoAMYGDCC32EjjIEnkW6qkSxDIX3PgwLAuSyFfFP7bbqcAWkpUVDA2bJh6LF97jbEdO+SgDbwMHap221H6pLz2mvqef/yhr68sjzwi183MNHYzslrZorlz5eu4f4xyLESR+sAY+eHw43v2UPf4+9K2rXyuf3/5d26uejy2bZPP7dypfhZGMHLr2rzZ+FlWA3JwqUCou0Ye5mZ44AFzlyI+bNGipu5G//73ajZ5Mnl3Gb0STZsydsMNjO3adfpoCFf/qafo9Q/32u7eHXdXjuOvwd9CftiyRcXTPQkJ4QMsRoDpVqNw/ouiaOyKbLMxNnIkY19+GV0/rrqKsYYN9XzP6aSARWH4vRZ7Jk7UB9tSlm7dwo6Tahw2bmSsfXt1/VatSJ4K5woMkNutUo6ZOFG/7WzuXPX49erFWGEhK+Muu0p5TOnGrKRfktTBG199lQJCGsgEoWMKd14Vf1eUTOU7zANKcTmH/x4+XL6mb1861q4d/f/mm/J13MdXGZBTsS0phCZN6NyoUfpnoYUyACYvZ58d/nnGiAULGLNY/KHmR44MH8NMiU2bjEXijh1D4lHUqCnvLi5m7KqrdrJ+/YxpsljI63z6dNp5cDpoCNeGz6cWkY3KmDH/Y+7KycnJ6K60dgFITExE/fr1Q8dvvvlm3H///UhPT0dKSgruvvtuDBkyBIMHDwYAnH/++ejatSuuu+46vPzyyzh27BiefPJJ3HnnnSF3odtuuw1vvfUWHn74Ydx000347rvv8Omnn2Kl0lW0tuH3AxMnyqlT7Hb8ePvtGP7aa7V3j717aXXw66/1OecsFrIE+nyqFUKvywX7uHGUFD1c8q0jR8iau2IFLXUxFjrFAAhNmlAulSefjG6ZKxCga996C5eZrdomJZHF8Nlnw6/a8vZeeIH6r8wLO3w49VlpSTSAz2aDzWKRU+V06kQW7EaN5ItWraI8vjyYTtu2ZFUsKwO6d1e7KvXrR1ZlgCy5v/8uW7PnzKFVZoDaz8wEevaU62tD9ScmAv/6Fy2blZQA11yjJp5fe8UVCPB7fP+9bM1WhuUfNoz8dgMBOe49TymwbBkS+HvDxzAtTU53ZLWSt4HXS236fGSWBMjU2bu3blxVuOceIBBAAIAfVnhhh7dzb3jPGgLvn9Sc1wvk5qbil1/kLAv8r88n/688x19pn4+yKx05AgACUlOBu+6iW8+cSdcEAvSXMfXfykpa9Fe+ihYLWRL796dH8sgjcuYoXpT/a88dPjwIX3+tN6jz88pjvO6pU/QZHz8OSJJ6ZVwQaJibNyevcJuNjOv/+pe6HQ7GgKNHR4Y81rVQvmJmyMsbhfnz1cdKSsghQbl6y9MkA7RYwleStSme44jjdOF/Wn4AyLdRKS9ccw0+GzcOky+pjeAwoFXFRx4hlxetR5bFIk9yvACA1YoTrVujwbPPql1ItPD7gfnzKZXQ9u2qyUMAaNIYOpSWkcaPj47etWtpe9fBgzBMPme1Er/+73+jW+H+9Vfg+uvVHlONG5MMsWqVKuCnESrS0pDI3W5tNuLz118vX1BVRX3jK5GiSDz44YeBbt2QxOty2hMTyYUHAD78kOQrjjFj5H04Dz5IMtfvv8vn+eTOPbhatQIaNiTZbds22UsLkOUNiwW/Tp6MkNMv34bUvLk6heAjj8ht87EaOZKYwssv0//p6bILstLrYcwY8irkMkRhoZyHlzNrMyhWcQMAvLCjCgnwzpwP7xFZfvD5gOzsVGzfLssGWjlCeUxZ1q1DkN9ZIAg05CNGALNm6WUH5afg8wHLl8uPJPgQ0Ly5gLFjSeT64IPo5QcAyMoaFHKgiyRD8PM+H9GQk8MdAnqrhtBqpUfTpg292qJIj02ZRUwpQ+TlmcsPSnrCnT96VC9DVFbSrjYumoqinG3KYpEdR6PJRnk6ITAWjZhUM5x99tno3bs3XgtO7lVVVXjggQewcOFCeDweXHDBBZg9e3bIlQgAcnJycPvtt+P7779HYmIipkyZghdffBFWxT7X77//Hvfddx/27duHFi1a4KmnnsINwT0F0aC0tBSpqakoKSlBSiQF7I8/aKLkE1abNsBPP2Hh+vU1y3Hr92PVf/6D8Xv30iSszWFrtdIbo809lpgInHce8NRTWPjHHzINgQBJ2UVFRGteHu3p3LDBMD8u32ezonNnXMT3aChoQ2kp1SsuJsm4tJT8LZcvN88hB5BW8dxz5P5aVUWTJa9/6hRpImVl9H95ObBxI6R160LRjwGQ4ta0qXbW0cNqBRo1Ajt6VFYyO3YEunalWaKqiu514IBaA7LbiZHxWVWJlBTZhchiof74/dReYaHsKxIpn54GAQDlSMIppKEMyShHEkqRgnIkoQKJqHA0QLnfBq/ogttnhQd2VMEJD5yoggMeOOGBA17YUAU7vHCiEk544SCFM3hOgiVYrAhAhAQLAhAQgAgW+i0AEMBCv0VAEIOTHoNx5kM+XZhmRYzjfwDz5lFqSiDGeTKOOGoZ/xPyQyBAygHfWpKYSMbmc87BwoULqy9DBAL47P33cemRIySBa118HQ51PleO1FQylt5+OzB+PBYuXkw0BALEy4qK5PLdd+Tae+iQfo9FcjIwdCg2dOuGUTNnqs/5/cTrS0qocL6/aRNNMFyWMkKzZuQvescdcjvFxcSTuQxRUUGyQ3k5kJODyk8+gUu5lctmI0tiXp5xLnolmjYlergckJhILtYAyQ8VFWS1zM6WtQJBIM3H7zfODavMbdu2LWkmlZV0LD9fdmlWxlGBOecFSH6ogjMkP5QhWS0/iKkoExPhFxLglmyoCthQBWeoeGEPyg+OoGzhgAeOkGzhhR0+2OGDFRKsITmCZAgxKD/QX3oTRLCgDMEgBOUHZZ4ILZTqRlyG+F9Fhw60JQuoe/nhL1Fyz1REHPyvv6ZJtqBAvcdiyhSyZopiZAY1dSqZO8rL5QmOB4UySqgN0ORstarvyWG3k1IFhJbFAn4/RKVZKBJsNpqArdYQHZLXS0HCtaaoWMHNObyd/yH8hu64Be8HmQQplD7YQsUPK/whpmCBpGAILDShxyf2OM5stGtHCxh8+1xdM6k44jgTEdV3MXQoBQEsLZX54YAB5GEU9NgJK0OsWkWBDcvKSNGqqiKFissPRjxWozSFIIokOzgc8tJVUAFmkiSnBooEi4XasNvp/n6/LD9wmqrB+xkAQRDkVcn/MfkBAK7BfPyBzvAElUxSKJXyg1VhoLaEZIeQEgkgLkPEcSYjJYVWz3lMtLqWH05bntz/CUiSHJkXoEk9I4MCRkSLNWvUQQ/MYLUS06uoIObDLYo8uh6H16tjXmEDu3H/ASXDULYfRPWyYBnALAri/wCOoAW2YnBdkxFHHLWOtDRSap96ClDE/Ikjjjhqgn371IEir7/ePBuAEfbvB3btiu5au102oHMkJspKcSBA8oXBqmZYtUkrg/C9IQrvpdqQHwTgf1a55ViFCTiFenVNRhxx1CosFnKcnDEDGDz49AWGrw7iSm44jBlDLsH9+1PEuBEjwkYoNsSllwI7d5IUWa8ePf2GDcmZ/tQp4IMPENi7F6LfL0fKEwQK137HHUTDtdeSW0xiIu1xTUoid6PUVCAlBT9nZmLQuHG0bzQ9nUqDBjKtd95JezITEqi4XLr2fjlwAH1HjiSL8axZarccJex2oEsX8kfg7bhc2H34MHoMGULtJSdTCdKH1FQKsf/tt+T+pFzB5qkJRo/Gb+Xl6DloEG2qWLdO70IMEM1aJt20KTB2LPacPInugwcTDX4/8NJL8l4Rh4P2K5eX0zHa9KlGx44IHDxIUSZFEfj3vynFQEoKcPgw2t30EvphOxyCF45BveHMPYCEvANwogouVMLVMAmuwhwkoBKJqIQLFUi+5WokjhsZemyvvw5kfvEHEtxFcKEy6IjshrNpOhLGj0FCApBzaA96rV4BJ6tAgtWPBH8pXHDDKXhx8rWP8N837WRI37cTTlYJZ+N6cPbtCteuzXAePQSLEEACc8MOD2yd2sHxxy7Y4YPj4nGwDx+EZcuo+1YrLepbLPSXe2/bbMDJk/lo2bKpfHzXNjj374IdHpQhEWswDqLVAkujBhBFGi6LRS5lZaeQnp4W+p+f54sMBw/Sq8aDgnN5SRDo9bVYAK+3Ei6XC9y4z+8jCLL3uxJ8Hwi/3u/3wWq1hf7n7fPfbjfdW9BIeMr/AwEJomiploMDLUgwWp0wQXTthXNgi71+/foUDHvwYJK527enzziOOOKoZdxxB7nZDh9OMkSskZOHDKFSrx59uFx+aNiQJuolS+BfswZWvpkRoOPDhtEK8Hnnkfxw+LDM75OTiadx3pycjE379mH4hRfSfRo2pHu5XDThbtpE8SfM5AeXC79mZaH3iBE0ob35JinnZqvMZ51FEnFioqqtX7Oy0Hv4cKKL08jlnN27iYbjx/WuwenpwMCB2GO3o/uwYeQKnJFhnMbGyH3b5aLn06sXfjt8GD2HDaP7f/EFpUFSrsB7vaTw79un98RLSEC5w4EkHhNjxAjag837sXkzxt27ClloC4cNcPbrBueW75GASioOIMFTjMSg87GLHJCRPPs/SGzTECkptGvu1VeBhD3b4EJ50AmZNjM5x5+DhFYNkZ2diT77f4Uzay8SRB8SAmVwogoJcMOVYsfrfeahoACwMzccf/xG5/r3gKuBC87Vn8MOLyy2ABJ8FbDXS4a9+Bg5OVsk2GdMx59/0tBwfs7lCC4/2O1AUVE+WrVqGnJKtNsDcCz6KLh+7cEaXIASpEFs1BCizaKTH0iGKEZ6ej3VMX5dWRl5z/NF/4oK+TGlptJjrqpyIzExISQzKP+WluqzW4ki1VPKCH6/DzabTSU/8L8+n+yNrmTxtSk/AOFliL9GftC30awZhSc65xySI8wyd50RqJNwV2cIaiNJcbUil61dS6HatGHI2rZl7Lnnog8DVxMalMjLY8c6dTIPs5qeTpH/qkPD+vWUbF0bdblpU+orj1ooSWzXZZdRNEKjEHL9++vDyw0fzlhWlp6G2bPVEQkHDWKsrIwiYCsjL/OSksLYV18x5nLR/6JIUYs5tm2T27PbKYrxP/+pbmPiRH27TZrox6OgQN0vgMZGEfF4z4UXGj8HZQTCNWvk4zziYjBiZ3m9enTc6WQsNVW+RwzQPU+jiMrBaM5R1TfB7t3y0CYmUt76cG14PIx1764mo3VrerzVpcEM+/YxNmhQtiqJvPKe06dHjtaspaGggCI07tzJ2I4dctm2TS5bt8plyxaK0LxlC1OVzZv1ZdMmKgsWUKBz5ScgCIxdfjljP/wgXxep8GmorpO5xxHHmYja+i5imqckibF589RReXnp0IGi/obLqlAbNBjQtHfsWGPeDTBWvz5jL78ckS5DGvLyGJs8WR8ROTGRsZtvVoWUXf3vfxuPC0CRgxs3Vh9r0YKxJUv0NOTm0ljy6xISGFu+nOi/+Wbj9qdN4ykBqEyapO7Hxo0yD01JYeyXX9QTdHo6Y40aMQaoI1Xff79+TF56ST7Po1G3bh06vWjuXFm2aNlSTedrr8ntXHQRHXO56P/77gvJJSEa7rhDrnvNNWGfX9hnee65+jG76KLY2jCAJKkf1YIF4etLEmMjRqjJaNDAPIlITb4Lj4ciIxsFwXa5GLvkkvBZFYxo8PmoTiwyRLTyg1KGeOMNfXBym42xK66IXobgonldyw+ok7ueIfjLldxly2gyUr45aWls3/jx5vG/a5sGjoICSjVklEYAoNDxDz0UNcNU0eB2M/boo8TclG2KIilqW7fK15aUUKqixEQ9DaLIWL9+egY3dKhhyqZP585VxzO3WOhrZYyxxx837ufNNxMjCSrhAUFQp0/Ys0dmJFYrzSD33qtuQ8HcVAyquFg/UP36yec5kxs5UnWJhyvbycnq+yjHjYf0r1+f/lekJPLxZzp2rFy3c+eonqPh85w2TT9ugwZFX98EBQXyY7da9Tqzto3Nm/Wv60MP1YwGI6xfz1inTsay2q23ktwVLTIyMtjmzYxddhnJMUav4Jlali8nplrXTCqOOM5E/KVKrs9HfIrzBoXkmderV41SGEZNg5aeO++U0+MYSfExyA86GpYt01szAVocmD9fXXHxYsa6dTNO33fWWZT6T3msRQua3AywbcoUtfI5eDBZUHfsIG1I235KCt1fee7cc9WNZmbKjMvhIA1AychSU4kmrQzRsKHxQBmljlRod78r+b7SOO1yyc9DkuRFg8mT6VirVvJ9g++WSkOLQUZVPUtJ0tMrihHbi+adHDNGbvKZZ8LXz8piOoP1ddfF0I8oUVxMmaq0a0ZWK63LrFwZW3vvvfcpe+YZEuGM1hrO1NKnD41FXcsPqJO7niH4y5TcefPkPGK8NGrE2LvvRt9GTWlgLKxiGwDIYnnDDWSCqg4NW7dSnjTtl1i/PmOPPaZeoV69mlZnjfLqCgJ9IVrL8KBB6jxxSqxYwXzKHHvt2jGWk0MzG5+8lSU5mbHPP1ev7Doc7Idp0+Q2Dx6UFWyLhayxjzyibuf88+U+KGe1G2/U05iXp57x+G+lgLJokXoc+G/tqjAfmylT6P8bbpAZE69zwQXy7+3bY3+eHEYzq9lzMKpvAI9H/iQEgbFVq8K3oTWgO51hF5KjokGLdevUVmGAMbvdxy6+ODqLqxLbtpEdwmbz1QLDCNQZo9q0qe6ZVBxxnIn4S5TcigrGpk7V549t3Zq8qyTptObBVKGwkLELLzTMMxsAiE+MGFFtg/2nc+cydtddesXZZqNlL4XXFsvNpZVFrdLPS5s2KsUxxEMXLza+eXExY4MHywqm1crYrFmkoN1+u/E9pkxh7OqrVcdy+/ZVK/b79sk0WiyMffON2iMtKYlkFaP2jdyE7rxTPs+9tNLSVJd4+fPRypxK2WbuXPl4djYtNvD/Ob3KfLapqTE9S9X7pJRDeLn00tjaMMBtt8nNcT3drP4776hvL4qMffttjP2IgKIixi6+WCsuBVi3bnT/WBwsysrIRtS0aW3w/7qTHwYNqnv5AXVy1zMEp13JfeMN/Wpm8+aMaeqcViW3oIAsi2Yrtg4HYxddxBa99171bl5WxthTT7FKPuHyIgj0hq9fL19bUkJMTHttsEgAJVXXMq4BA4hZGEG7Oi4IjD34IJ2bPt2cOf3zn2olcsQIxkpK5LHMzZWXGUWRlPJ//UvdzpgxchtKxu9wGNPau7d8DRdaBgxQX8N9RLQmx+nT5WtWr5aPcwU56OoUEhAaNVKvQMeI0Djcf79+/IYNi76+ASSJEpjz5mbPNm+jsFDumvL23Mu9ujQosWaNXs6oX58YUyzfJmdM3BCuLCkpjE2YQPeKFVoaJImxGTPMF1Fqs8SV3DjiMMZpVXJLSvTLQYJAfEpjYDytSm5hISk7ZvKDKDLWsSNb+9hj1b/5l18yNmQIeVFpZSWlm7Mk0cKA1hIZLAGAFNs2bdTnGjZk7KOPjO+dn09bjZSaSYcOxP937zaezBMTGZszJ7Q9iAFkNF+1Sj2Oa9fKxmxBILlPKdu4XIx17Sr/H3zWAYCxt97S0+rzyfKGkt6nnpKvmTNHL2NwGUC5yMDdus86i/5/5hl9P7nhHIi4XU2L0DiYreIaebmZtWGAV19Vi4dm9SWJ1l2Ut2/dmj6vmPoRBkbKrcVC24Pee29pdDcK4ssvydlP+ymIIjk2zJhhvDUr1j5IEr02Rg6UtVniSm4d47QouZLE2LPP6iXQtm3VbrDh2qgpDYWFUSm2yi89JhoqKkjpat9e/zWmpjJ2993qWWTlSuMvl5ekJMYGDWIe7Z7bfv2I0Rhh/nxu5gqVytRUcivKyWGGGyGSkhhbuFCtNSUk0OqpchwKCuTnJwikSD/3nLqtUaPk/ijoDgC04qtFbq5cV/lclEuEu3bJx5VKs8WiXl3n+1saNKD/lSvEfKZVMqgJE6J/tspxYMx4FffgwejrG0C5fdloyxHH7bdv1L0yc+dWow8mWLWKeLyy/YYNGXv//ejbYMycMaWkMDZy5CFT+0ys/dizhxwcjD4ji4UWWfbsoU9PWebM+VR3LNoiSXXPpOKI40zEaVNyn3pK7ekjirQlRhG3IWz9mtLg85EnksGKbYh/DRnC2N691adhxQrauqTl+XxL07Zt8rVZWaRFaFezeQl6fpVoraH16oW85XTIziY+qpxMLRa25x//oPNGW3QA0lwuvlh97JJLQvw5NA5z5shtW62MffaZ2nDtdKo3PSqMGSebNzemWbkVi/fV4VBbfLnXGo/LwYtyj3BhoXx85kw6xmPEBGn0uFwyvYIQ8z7v0Dgo9ynzcuWVsbWhwZdfykPbooW5wfvNN5fpdnzddVdM3Qj7XhcWkhitFJGsVnKB5opoNN9FXh5j11+vX9cQRVIS77jjx+psszfsw5IlxmFpAFJ4n32WuxjXjgzhdte9/IA6uesZglpVciWJ9qFqVyG7dlUHMQrXRk1oqKggt5Bwiu2FF5qasCLS4PNRwIOOHQ3NTEVt2qiV+LIycvMJt9zUrRuZ4bSMrndv2lVvhHfe0VtXW7RgbNEi6sMLLxjf69prafZRHjv3XJ1L0KfvvafePLlgAVmTlfWGDZPHQMN4fzZyU2ZMvb+Iuxr36KG+JsjEvNo9N9o9Plz44Pe65x55VkRQ0e7VS64fhdVUi4yMDMYeeEA/jqNGRV/fAErZ4cILjevK1lfZzaZBA7I9xNwHA6xYod8a36iRsQJt1kZuLtkRzBjT6tXh60cLt5uxceP2mcZ0Oess0+1lEfsQLeqaScURx5mIWldy165V8zZRJKWqFvYuRkXDa6+pVye18sNFF5nSEhUNGzcSL9Py++AEvHfiRDU/XrTIOEAnLw0bEqPQuuWmpKgDLCmxfz/xb+X1Nhvx0rIy9sUrr+jbA2il9s031XEy0tJoj4t2HJTxP1wuxr7/Xi0H2e3qgFBKLal1a+Ox3L9fXZ//njVLvmb9evV9+W9BUEdWuvVWWSPz+YjJKBVygB0eOFCu36tX5GerQUZGhvEqrsUS9TKq0ThkZsr2n+Rk809j4UK1/GCxUEyPavVDg4ICY+V2yhS9h7nZdyFJ5MFm5JTQpAljTz4pL7zX9Pt+7LFvWadO5jsD+/aNvKW/JjTUtfyAOrnrGYJaU3Lz8vRR7Pr2VVsjI7VRXWzdyirCMaYwim1UNGRlURtaxUsUSUl74w3GfD65fkkJY1ddZR6pOS2NQrQp3XSC5WTLlrQSa4RNm/R7bJSr4243KzRbvV21Sr3qm5horBkUFTG3kom9+65eaVbuI9YaFN55x3gcP/9cPRvy38pZd+PG0HGvVghQKvxKP52sLGJS3P07WK9M+T4kJoZ/8CZY9uabxjNidnZU9Y3GYepUuamePY2Nw0VFesWRbzuOFUoaJIkW47WxQ5o00ccxMWujoICUdI0DQaidxx/XB0avzrddUkIKtNl2s4QEsj9EG4Q9ruTGEUfto1aV3ClT1B/5gAFRR7ir0fftdjM2cSKTzNxDRo+umWupz0c+ltrIxgBZFm+5JcRTQm3Mnavfo6LkQaNGMTZ+vI7/ep1O4tdGjKWiglYzlf10OGhpj0+ks2bRliltueUWxm66SX3s6qsNlxD/4AEhAVLCd+xQr4jbbOq+KeWkNm2Mx9LnU/uVcu2qY0f1dUHLrVcrlwwdKl9TViYb2c85h449+qiuz6VKRlmNoGYZGRnG0Ruvvjq2NhTYvFkeSqvVPCzINdeob9mqVeQsCNHQsHkzYwMHql8hM+XWqL4kkcf6wIF68dhmozihRms71fm+1641drTkpXFjxj7+OPr2/j8ruQJjjP01yYrOPJSWliI1NRVz5syBq5qJIu3ffouL58+Hxe8HA3CiQwf8/M9/oqx586jbyMvLQ/MYrgeAbkuWoOuXX8Li94eyVzEAAasV+T164Mfbb0cgKanaNLT8+Wf0WLIEKUePqtovbd4ch84+GwcuuAABq5xmufDAAVy0ciVabtsGUfNK8XHJ690b7X74AUkFBaE2/XY7cgcMwK4rr8RBj0c3DvbSUgx/7TU0ysyEEGyrrEkTbLv5Zhzv1g0A0Hb9evSdNw82RV5dBuDw0KEo7NQJAz76CGIgAAbgaO/e2HT//SraAcBx6hQmPPwwHOXlYAB+ufZa2MvL0X358hCtJ1u0QL0jRyAAkEQRYiAQomnrTTch69xzdeOYcOIELrrnntB1AGUbO9GuHb599tngIPhx6W23wV5ZCcligUWSQlnJSpo3x6r//AcAkFhQgIkPPAAxEEBJ06ZYNXMmRr7yCpr/8kvoegbAZ7PB7vOBAdg2ZQoOXXCB4TM3hd+PK264AZZAIDSWAoDswYPx0z33RNWEchz8fuC5587B/v2NAAD161fg1Ve/gtUaUNXZv78+pk8/F4yJAABRlPDEE+vQpYsmoV2UyMvLQ3LyWZg/vy9+/rkV/H5L8AxDWpob1167E0OH/hm2jQMHCrF9+znYvLkNiopcUOaKs1oldOtWgCuv/BVt2pQY1o/22y4ttePDD/vjl1+aw+ezQJ/XjqFt20LceefPaNq0PGJ71aHBDJWVlZg6dSpKSkqQkpJS7XbiiON/CbUhP1grKzHmscdQv7AQAOBJTMTmO+/Esd69o26jOt93x5Ur0WPZMtjcbtVMwwCUNGuGzXfeiZKzzqo2Dc6iIvSbPx/Nd+yARZFP1pOYiCP9+2PPpEmobNhQ1Ub6Z59h1Jo1cJaV6dp3Jycje/hwpOXkoMnvv0MIyhgMQHGbNth78cX4uXlzw3Ho/MUX6Ll0KSzB3Lh+mw37x47FrssvB6xWuAoLMfzVV5Gena0aC4/Lhe8feghDZ89GsuL5bHjoIRR17Ki7z4D330e79eshAChr3Bjf338/xj3+OKzB/kuiCL/TCUdlJRgAJgghWamsYUN89frrhmN5/mOPoX5ODgAgAEAEEBBFfDVzJioaNwYAdFu2DD2XLlXJGHx8Vr30EkpbtgQAnPv002h44AAYgK9fegnu1FRccscdIflIAOBOSoKzvBxCsL/L3n9f19dI6PP88+i0d69qPP12Oz5/5x34nc6o2lCOw4YNbfDee4PBmABBYHjggR/Qt+9R1fWBAPDII+OQl5cW6v3IkYdw223bYqafIzc3D/v3j8Dnn3fHyZMy/7dYAhg6NBs33rgNTmfAtH5eXh4KCgbgq68648CBBpAki+IsQ+PG5Rg79g+cd94BiKJ5G9F834cPp+GddwbhyJF6YEwvPyQk+HDOOQdwxRW/QSP+RkRNZIg6lx/qRLU+Q1BjC8P998vR+ETRfP9HBERtJSkuJl9IjVkmwM1Vv/1WrfuHaKiooE2S2sBQTie5+hr5i+7fz9iECfqgEbxceCG5DmmXv+rVI/dnhdVVF3Z+2jS1ySs1VZ3XbtMm/Qo6t46uXq3eD2K1mi/ZzZkTsgoHAFqd5jnjeFG6G2tXtRURG1V98HiMXbPq11cvw2n3+Cjvw92MJElOhWCz0fGMDH0dpQW2Q4foXwAljHLd9OhRrXQQRUXqR2QWNGrmTPXt2rRh7OOPq2893LaNsfbtC3WWzD59dB5mhpgzhwejCugeyZAh9BpGMxzhvu3cXNoubfSK8Ne4d2/Gnnnm6xh6HhsN0aCuLbFxxHEmosbfxbZt6hW688+PLqKeBlF/33l56uCHiuK3WtUBjKpLw5dfGqf+6dXLOIS+202+mWb51Vq3pu1CffvqJ8eLL1atduvGYcMGtdwhCLSSyONbVFSQO7jRfS+5hPirclX0nHOMM08UFKjz83bpQrKYUk5wONQuSkpf1/btjceSMXU0ZROZg+XkyHKSdhX34Yfl65TeWTz3nnKfr1GpTqTsGTOM0zlF6dmoHYcHH1SLokZux8XFak9yQWDsiSeiCJ9sArebFvkdDq9ObH388cgJSA4epJVZm82vG4bmzalP0Q5tuO97yxb6pM1SC6WlMTZp0q/VSZgSNQ2RUNfyA+rkrmcIajT4kyer36RI+UzCIOILtHKlPkoznyjHj2eL5s2r9r0ZY4zt3s3yu3XTfyktWpDCp5XmJYn2gmij9ihnmMsuI6apDYDQtKlp9KDQOCxapFa0LRZSOjkdubnk82F07xYtaBZUjlerVsZBO9xuCnKhuM+Wm2+myMtaBY//1jIRoz05HNqozwDtp1G6/K5cqe6nsm2l0UTpgzN/PkU94Htz+HNT1hfFaglMuv1KALldResbqxiH3bvVMtzUqcbXanV8vle3ujnqtHnnrVaSWUzitoTgdtNrpnWXtlgosNS8eTHH3zDM1TdmjPnWeYuFUjMq7VV/WYoxE9Q1k4ojjjMRNfouPvpIHUPhueeqTUfE7/v5543DqAoC8bY9e2o2R3g8FLBJy+vtdgoyZOR2vWoVGezNpPP27Rl78UX9nlynk3LHGPiHhvpQUKDnY716ySmIJImxp582zDcrAYx98ola+RVFUhCNMH++ajI/3qED+ZwqebHLpU6HqOyzQf76UD8WLzYeG6XiypgshwVljJCCqTRy5+bKNHGlWil7KGnlbfCgVLFgzx5jmhV5fKPFggUZTJnyt2FD41dpxw71kDqdfA2geu/0G2/oY6516KBeXzHD6tUUakbb/UaNKERNlDsQVND246uvwufMTUyke9XWnt6atlHX8gPq5K5nCKo9+OPHh96o4pYtY1YAtDB9gW6+2Tjxd1KSKu9KtV7AkhJlIi414xsxwtjqlp1NyqvZ0pMo0n7cO+7Qbyhs1840ujTHF6+8ot+re/bZpNAxRuOsyUunpHvvhAm0Z1X59V9/vbFmsnatmvG3bs1YVhY7pLVsmim4gmCYPDX0LIxWZwVBnUPG7VanKVJeO2KEfN2KFfJxHinZYE8zf1cCgBz5KBYsW6Zv026v1sx8//3fh3iqIBjHAfH59HaS//5XPh/re62VW5xOL3vsschW15wcivistTG0a8fYDTdsrZatQNmHPXto9ddsm7rVSq+5WdDquJIbRxxnHqr9XSjjPDid7NsnnqgRHYbfd0GBOvigsiQn61LUxDxHSBIpLn376nlX06aMvfKKnu/m51OAJ23I22AJcKXv2Wf1kQHT0uh4GCtjxoIFJHso6alXTx1/Y/Fi0xSGrHNn9vUzz8geU1w7Mdr86fEwlQYmiozNnMlW//vf6vsnJ6sDRSnPde9u3I+MDLqnEY18Hy3H3XebMxUl3+Y58qxWUnglSR9kTOEFVtKoUfjnbwYj62010kuVlTHWpElJqImePY3F7HffVd+qZUuZ38f6Tm/bpl+X6NYtP2J2BEkie4B2G3lCAmMjRhxSpXeuDhYsyGCvvaZ+LbXF5SJ1wWhvcFzJ/Rsj5sGXJMaGD5ffrKFDaWKtIVQvUE6O2vVFWTp1YuzQofD1I9E/eza502j8OL0OB5l/tGMhSbSEZZKbjgGMJSaygyNHUgRj7STXq1fksHYlJYxdeqnaxaVlS3W9Z581X/4aO5ax4mJ2rEsXtXK2bJnxGFx/vbr+HXfQuUsuUR9XKpLKe4siY4cPG3YlIyODsddfN6bzpZfUF48ZY3idz+GQZ6viYtmsWL8+aYaPPaavp3D3yu/aNfx4G8HnM6Z569aYm5oxgzHu5muzGevb+flq47YgMPbjj+pron2v165VMxhRJFejBQvC11+3Ti8HchsPt19Ud3Lfv59ix4iiZDisdju9tjk5ch1JkovPR8XjYezjjxcxj4epitutLxUVxmXu3E9ZWRmLqShlybpmUnHEcSaiWt/FXXep5+y8vNo1Yu3ebRwdWBDI0sYNxuHaCIdt22gbkMbQHQDIBWXTJn2dJUvMZRpeBg1iuyZN0hvdGzakjAqRMG8eBZ5SKnkPPyxPZDt36pOh81KvHgV+fP99JimV0IsuMlaqt25VK4jNm5OFcsMGdeCq1FR9Kij+u3dv064smjvXOF1Sq1ZqejZvNmw7AKjHTLn1ii+O3H67um0lbYLAFsUSlYhj9Gg9zZdcEnMz2dlqp4DLLjO+TivGXXCB+ny073RJidpewUW//fvDt1FSQjHJtJkPGjcmpZcCTVXv2/b56PUle1BAN6wArXXdcYdasdXKDz5f9PKDmQxRVha7DKE0SNS1/BAPPJWaGt2GaL8f6N8f2LWL/h87Fvj6ayxcuBCTJ0+uER0LFy7E5PJy4P77gXJNUBmrFbj2WuCDD2C2Mz0iDevWAf/+N7B5M/WDQxSpT48+ioVVVeo2jh0DHnwQWLYMcLuN2+3alWj74QcEvvlGDjglCMDw4cD77wOdOpnTdfgwcPvtwLffUtQAAHA6gRkzgGuuAYqKiOaHHgKKi/X1O3cGvvkG+PpropWPXYcOwKZNQFoacPIkUFICnDoF7NlD1506Jd/rssvoutWrgYMH5bY7dQL++IN+W63yuFkswKefUh/LyuievFRUIOeHH9B6+3Y9rfXqAa1aAVVVgMcDlJYSbQY4MHo0OowcSeM+dy5w4gSdGDuW6q5fH7o2AMALOyrhQjmSUIFEFKa1gMWRgiq/DR7JCnfADm/AiqqAHVWSDV5mg4fZ4Q0E/8IGb8BKf2GHDzb4YIM0eDh8zdpAkqj7fj8gSYDPB3i98jGfj45LEpHKuyUIQP369JoxRo+YMRqCykp1n51Oup6/QowBfr8EUbSE/ldC2Z4RqK0ABEE0vSY6MOiDQP3vY9UqYNw4+h3TPBlHHH8TxPxd3Hcf8Npr9LtlS2DfPiApqcYyxMKFCzG5RQvgoov0fDIlBXjxReKzkdowo+H4ceCZZ4DFi/U8q2VL4LrrsKRtW1x+883y8cpK4MkniX+VGAflQ9OmwD//SXzugw+I33M0bw785z9AuHHx+4GnnwZmz5Z5OgCccw4wZw4xh2PHiI7vvtPXt9uBt94Czj6b7rNjBx23Womea68lPl1cLMsQr70GrFght9GrF9XPywOWLpWPp6dTHUkiZgTIzKpTJ+Cll3SyAyoqgMpK+N97D1avV02rKAI9elCfuQxx5AidUzJOAGWNGiH57rtpXLOzgYwMOtGgATB6NHD0KPDjj/IwQkQlklAOFyqQiIqUZigSXLDYU+D2W1ElyfKDJ2CFJ2CHl9lQxezwBazwwAFvwBKSHTzBv1JaQ/jPHauSHbi84PPJv/l5v5/kipwc3h2GpCQBiYnE6zm/lyQSvbjICJBYZrer5QdJ8kMUrSG1UItwMoT8yHior+ri7yk/9O4N7NxJv+tafogrueEG3+sl5efrr4Hly2nCBIArrwQWLQIQhYJZWgpkZdGXm5tLE8yxYzShFxYChw8jUFCgi0iM9HRiEP/4BzGZY8fo+hMnZE0iqMDl7t2LlmlpNFlWVtLk5nbTRFherldSGzUiJbR/f7q+tBSHdu1Cu0aNgAMHgP379VqIEoLio9XS3bw5TcaBgJqOykp5cq6ooN+aujFNB4mJsrZVnfomCNdGAMAppKEEqShDMkqRijIkoQzJKEMyKpGIciShEgmohAtuJMCNBFSF/jpQCRcq4UQVElAFJzxwhJRKP2yQIEKCBQwWSBAAiAhAAAsWGWa/44ij5ogruXHEER5RfRe//QYsWQKsXQts2ULHOncGdu8GD3EaVobweskYnJUF/PknKVPHjskywLFjkA4dCkUODuGss4DvvyfDamkp1SssJFmisJDqnjpFpawMRzMz0Sw5OaRooapK5tdKBRIAEhKAPn2Ac88l7aKsDAd++QUdGjcm+WbXLn0dLbjBXqGpMABCUhLQsyeQmirLMtwq6nbTeLjd9L9Sy0GM/N9qpeL16mmIto1qoBLOkAxRihSUIxmlQfmhIiQ/kLLpDskQTlTBGfpdicSgDOGEF46QcinBCj+skCAiAEtQbhDBIIJ6qJQh4vJDHKcPZ5KSG2Mg6b8Z3nyTVv6UuPVW4J13om+jUydZOTaByk40YACwZg2tLgJkHbz88rD1W0ZPDeH4cVqhXbYsdKhdLPXD2UXy8qhUAzFNtRUVNatfDRpW4CJMwvJauEsccZx5EEVaXLn8cnKg2LqVpqEbb6xryuKI4/8p+vVTe0+1bElKYLQ5PF54gVZRw0CZlAQ9egA//CDLDwAZnrUeYho0i44agttNE8TmzaFDHWKpD+gUVCDIe8vLVe3Ggpj4P186rEkb1UBTHEMpUk/zXeKIo26Qng4MGQJ07w5Mn07HLrywbmmKK7nhMGkSKbnJyUDbtiTt3XtvbG0kJtJfQZCth9xHIwifwwHbAw8Azz+vr9+ggfp/UZTbsdkAhwMVkoTEBg3IwpqYCCQlEc2pqVQWL1YrnqIoW1IlKbzS2rgxMclfflEfFwQqFgtgs8HLGOwuF9FktwMOBxWbjazOubnm97BaAYsFfsZgFUWy3GqRnm7q3svbkABY7HZqz26ncVa6SmnceqKGKAJt2yIl0AjI4gepHQEI2kvJfko2VAkWbldNSoDVIcJmA2w2Adn5Nvj9oqoNJXmCIICxACxMCrYbgOh0QGR+WD0VkCCgXMUklW3I2fGE4Go7f0zKovWEMqJF3Z6axuiG8HSvq8dR2wgEaJrgHpUAfUZxJTeOOKqJli1pdbN5c1re+Ogj+qiiRatW8m+rlXiR369SEgOiCHH4cNr2Y9S2wyEruZz3c/khyKvLJAnJjRoBLpcsP3AZorgY+OQTuT1BoDYEQfYjNUNKCtCtG7kCaxkPb8dmA6xWeBiDIzGR6OUyREICrUQfPmyolAIgGUQU4QdgtdtJflDk5g2NgcdjTqcgADYbySBOZ4gmlJYayyMmMOVYCQlAhw6w7gUgKa+GzOeN5AebCGuSE1YbYLMLKHeLOF5k09xR2Q0BAIMoMAgBP7UpCrAkOCB63bD43ChBGphqacVchuBOe0r5IRDQD28s8gPwV8oQcfyVOHkSWLmSCkeUaZFPH+pkJ/AZgqg2REcIrRpxY3lJibwD/NZb1Rv8GzVibM2a8G34fJRQK1xUwUg0FBXJ0WRycylCsTb6sbK0bUuBC/g9PR6KFBQmTK2OhpISxm64QR+JuU0bShGkxa5drEIb7Q+gIBe5ufrIACkpjL3/vjENHo86j4woUkTLwkIKcd+pkzpAxdSpobwxgWDghdD5du1C7Xs8FLCwokLxOHbuVD1Tv5JGZVTHIAoLGSvscx7zQBGQIjWVnk8QR/r0kc99+aUqXZDHksBy0YwVI4V5oAiGFS5foAYFBXR58bV3Mjfs6mAZwejP4drweCg4RH4+Y7/8Ij9im43inijrZ2frg0v++ivRkJNDdBQWUnytsjJqm49tRkYG8/kouIPys2nalIJNZWXJbRQUyI9Xm1IxIYHa0H7m+fnURna23E5+vtxWURFjzz//VdhMF02bUjYtTktuLhXe1uzZn7GCArlN3i4vmZk0Hrt2UdqgPXsY27ePyv79VF59dTnLymK6kpPD2IEDFOuFl82bKXeessyY8QXLy6PYKsqYNIJA00C40rx53QeOiCOOMxG1IT8wFmG+9niI4axbpw/+WK8eY6+9Fh3/jxBiPmwbbjdNXD4fTWrXX0/810x+SEoiWae4WG6joIAm4GjlGEmi/Lja/LlpaZRXVzuuPh/LGjJET0taGk18b76pzqYgCJQBQfPsQjRs2qTuY8uWNFnn51OSUyVD6N+fgnrx/7X5Z3hGhOAwFBdryNcEoAwF4BwyxPBR5OVKrKz7YDXf7tcvdM2nc+bINDRqRGO5dGno2nw0YgVowCpadpLbqFfP/FkY0JCTw1jh9mxWhkTmg2IsBg2KWJ8x4o+5uTQeStHu+uv1NEyYoB7OSZPok8jOltsoKqJH6XbT2CqDPq1apc4oabEwdv/9VJe3kZ8v8+cHHtAnNenbl14jJSoq1PKDku9zfv/OO0vZk0+aB/S2Whk77zySAZQyRF6e3Nbbby9VyQ9KGSIvTy0//PabXn545RVz+SEnh2SESDLEa68tZSUlFFRUKc85HJFliPfei0dXrjPUhvAWVfS0+fPVyTdtNsp3EksbNaFBkkhpDRMh2S8IFE1w//6a0ZCVRUnttVpB//70tWixbh0p1VqaunShiIU33qjOveJw0NgZMMuMjAxKAK9MUcAjHzJGX6iyre7dGRs50pjB8JktHBSMQ8fcnnzSuI4yuiafcZUx6ufPl2m45ho6ZpQuSPk+Pf648bMIh1Wr9G3edltMbRQWykMtijT0yvpffaVuPjVVr2iGw113bVI9SpuNgmwbwecjW4U6e4Obvfpq7LltJYmYIE3m+siGaWkUKDuadus6fD9jjM2du4iNG6fuw6BBxIijQVzJjSMOPWrruwj7fRcUqBUogCT2WbOiq18bNEgS5fUNEyE5wHn24sU1o8HjoclXa4Rv3ZoxIxqLihi74gq9vGGzMfbee2RobtxYfW7YMHVYeyUNCxaQzKG8/tZbaQwyM9U5ZhwO4pkKvu/TZoC4667wnTYb0yZNzA0kWoO/yyUbySWJneIRqgWBrM65uXJ2CG7A1yri3DqtfBbhIEkkWynbSEgIhfqN9p2cOlWuro2O/PHHi1jLlupbvP56VM0yxsjAPHCg/tGb8b2VK9XKsCgy1r//n9XKbbt1Kx8evfxA7RpnpjJCXacQZIyxf/97NWvRQt2HaFN717X8EFdyT6eSu2+ffhIbO9bceljbNOzbx9iFFxqHo+elbVuyBtcwFdKaf/2LwuJrv+YJE/QMRZIY+89/1IqakoFv3EiKm3IV2GKhGdHMIi1J7ODZZ6vbuuUWefVau3p73XXq2O9aU5s2H50Wzz6r1+D473HjjOvMn6/vr1IoyM+XmVGzZkT744+rx5NrWUpmqEFU3gXaFXbFinU0bVRUqNP2aBOlT5y4V9V8nz7RK5u5uTyhuswgDD6bEObNUxvpbTbGnnkm9u+qrIysqprsWiGZwCwPXTjUpZLL8/fZbP5QP1JSDB0MwqKumVQccZyJOK1KriRRDhGlUbZ+fZ33kmn92qAhJ4exK6/U50hRlpQUxq6/ni17882aEVBczLKGDdOnCezRQ2095di6lYzUWnoEgbFp02g5SmvU79KFsR07zGnYvJm5lTJJUhJj69fTuYceUjOGgQMZGzpU3X5SktpIHk7hlyRKj6SkO/jbb7OZ56a/+Wa9jKVMtXjxxfK56dONlVFAbUS47z7dbSK+U9dco29z3bro6zNaB+BV+/VTywc5OYxZrX5VN7dti9hkCPffz5ggyOn7GjYk7y8j5ObS/ZVdGTKExLFYv61331XLIsrH2749Y999F1NzjLG6VXIzMykVolIW69mTVq+jRV3LD6iTu54hOG1KrtvN2KWXqifFNm1Mv9JafYl9PlqxVJpdtMXhYOyqq1RvarVocLtpVbVZM/Xk7nSShVPhgssYIz+dq64y9v1MSmKbb72V8rgpl+8EgRR1peuTFosXqzWulBTZt0S7etutG/9q5fYVSdkCADH2cLjySh3t/PeJtm2Ntbndu9V0AOT2xOHzhXIESqJIq887d5pbXnnRjjGL4llqk8NarTSjR9mGJKlTDmrlm1Gj1JPi7beHJ0eJlSvVNpnWrc2Z2+7dejlm/Hh5SKJ9p/fvJwakHVpRZKxbt7xqWXI56kLJ9flIgFDKa4JA8lGsq9qM1T2TiiOOMxGnTcldv16tAFkslBvd5OOt1TmGe30ZTYjKyWTECJXWUG0a1qwhZVGTp5WNGKH2cFLSpkyiqqjzZ58+5K/Zv7/6XLNm5Llkhrw8sm4q64wbR/LNvn1MtZzocDD2z3+q+bHWddtikb3HjFBQoDfg8992O/vmmWeM6z39tP45LFtmfP7CC+nYJZfox0qZi7hNG8NbhX2ey5fr29Qw+UjvwxtvyFXbtVMvWn/9tbrplJTw4p8SZWWkhClFm6eeMr7W56NFe+Wr17ixWhmO5r2WJFpP0Yp3AGOpqRVs/vzoaDdDXSi5WVm06q19DtVx1Khr+SGu5Na2kjtzpnoCS0hQuRZF1UZ1aNiyhZJxG31pvPToYezyEysNq1Yxw42K9evT3lctM965Uz3zKEv79mSpXbGCVWpXU4cMCW8yWrZMZaUMAOQq7fGQKVC7envllWoNKi1Nt2y3z2wVljHql3K/rFb5vOoq43EsK1Mr7gBjZ5+tvmbQoNC5n2+6iWZgvh9JuUFEeT9lwncFwj5LLaMEDPdIh2tDaSPQemVrvL/ZJ5+Yk6LFww+r+f611243vK6igni48j5t29JrFm0fGCNebbQN3OFg7Pnno2sjEv5KJdftJtuSdpG+Zcvi6u5CYIzVPZOKI44zEadFyb3tNvXHO2wY7QuJtn41sfz110kh0k4e2kl25kxDN9qYaMjLY2zKFB1PlESRViI1BldWVkYWOu1GSc4Pn3iCsfx8lqeVMVJTGZszx5yOggJSZhUygNfplBViWg6U2xswQO+tplnlrkxJoYnYDBs2qNvUKssHDxqP5bvv6vuutC4vWRI6XtKkiWwQUMo/gHoFVxRNaTV9nkVFes9AA0U53PuwaJFaqVSS8OOPyqYDrGfP6A2zO3eq7Q0dOhw3VY7nztV7f02frr8uXD+Ki/WKIC+DB9NQnQnblWKpv2WL3kaUkMDYpZfuqpaBnLG6lx9QJ3c9Q1CrSu7mzerVU0FgbPLkiAEfVG3EipISxu6/n7m1CpSypKWRla2oqGY05OZSVACti7EgMNanD9tg4PLCPvpIvclBWWfsWFJEZ8xQR8MByMU7nG/KypWMtWqlrtOqFfuer4w+95xa2e/aVe/apPUpqVePsfx883Fwu/V7e5RKfnCG1NWXJL3LevPmakFBuQfo9tupDaUWx5miMghH586mw2Pah5079c/i8stjamP8eLnq1Knqc2qSpagVK49Hve0sLY0CMBnRoA184HIR/4+2D5JEMpGRB3+DBqG4W2HbiAV/BZMrKiL7jVYGHDSIAlLUlIa6ZlJxxHEmolaV3Px8dWyK1FTGVq+Ovn51sXQpYz16qD2xlCUlhRTMCO4sEWnw+UhBNoq/0bAhY9OmsU/nzlXXOXiQtAUjupo2JSvl5s20EqxVHLmrrhEKC4lZKevYbIzdfjtb9PHHtHqrlOUcDuKTygnWaKvVCy+EH4eXXjKXQVq2DC1X6tpYsUK/h+axx+Tzu3bJ8k5aGo3jnj2yfKJUpJUyi5EbeBCm/aB9RHKxWAzfDbP669bJJKSkqMXS3bvVTY8Z84cpfVq88466a08+aUzDrl16J4UJEwwd4kz7sXMn0+0V5kNx441q0e7/i5K7aBFjZ52l7o/Dwdijj6qDeFUHdS0/oE7ueoag1pRcrftqjx7h3VWM2ogFO3ZQUCSjzYO8DBlivgkhFhqWLzcOWNWoES29BWcHlbvTiy8aaxHc6lpURHs6NPtvKtLSSIE1w9ateibZvHnIZeez2bPVCqXVSm7jSuZktHSn8GcxHIfcXDWjEAR57EVR5cOhq691F3I61Zbq556Tz40cyRhj5LatvF7LnEQxbNROwz4UF+sZc+PGMbnAKV9zRcBIxpheJ3/ttc9N6VNi0ya17t6vn2zZVdKQkaG2MQgCLQaEC16qrJ+XR0qf0SfTrZtpHJIzVsmVJDLeDx6s9/Q77zy1A0RcyY0jjtpHbX0X3z38sJoXjh4dlXGcI+bvu7BQH5NCWwYNCqsERU1Dfj6Fw9Va4Ox2ijSkMGaH2ti1S70fRlmGDqX9JfPmkRuy4pwkihQkymzsPB7KLKGcMHl2hSDT2Ttxon71tk0b9QSrlW3atQvVNx2Hiy7SywH8d//+KkamamPDBr133nXXyedzc2Vl2WZjLCuLoitzl25lX5V0T54c+/O84w798/joo6jrr10rvwZOp5pHbdmiHvYpU6J7rz0exiZOVCtm3FCtrJ+bq064wR/brl3h2+dtSBKJiUZJSVwuc2fNM1nJzcujV18rEqelUUyT2lLW61p+EBhj7K9JVnTmobS0FKmpqZgzZw5cLlfsDQQCGPHkk2iRnQ0A8CYkYOsttyB38OCYmsnLy0Pz5s0jXlf/jz8w8IMPkHrkiC57GANQlZqKQ6NHY+/FFyMQSy4+LQ2BADp8+y26ff45EkpLQ9dIViuOde+OXVdcgZI2bXT1R+3Zgz4ZGbD6fCq63Glp2HbzzTjeqRMGzpmDltu3Qwzm1mMATrVqhV+vvBI7GjUyHAd7aSmGvfEGGu/bF+p3ZVoafrn++tBYt/v2W/T76CNYgu0Wt2gBJoqo9+efEAAEAEgOB6weD4TgfSvS07HylVcQUCTy0j6Lpjt2YNTMmaH7SqIIMRCAAMBvt2Ptv/6F4rZtDeuPevFFNPvtN3ksBAFrn3oKJzp3BgD0mzcPHdasgQCgon59fPH662jz448Y/PbbRLMgQGQslIGO0/39Qw/hWJ8+unEy64Pz5ElMeOAB2BV5AgMAvnr1VVQ0bhyxjcpKK/71r/Nx9GgqAKBt2yI899waAIDXK+Keey5EaWkC7yVeeGElrNbMsO+01yvijTeG4Zdfmod6dv75+3HDDXI+5ry8PGRlDUVGRh+UlMjtt2lTjPvu24iGDStN2weA3Nw8/Pzz+Vi5sgs8HivUOfcY+vbNxT33/AS73TzPY7Tf5umqr21j9+5GWLGiG/bvbwi/3xK6RhQD6N8/FzfdtB0pKV7T+tVBZWUlpk6dipKSEqSkpFS7nTji+F9CjeUHAK03bsQQxXz/y3XX4cDYsTG1Ee33Xe/gQfT/+GPUP3jQMPuo32LBn0OHYud118GblFQjGlJyczHggw/QcP9+ReZVoLRpU2SOH4+s0aMpR64CFb/+issWLEDq0aMq+iSLBQdGj8auq69Guw0b0P2zz+DkeX8BeBITcWj0aKweMgRNzzrLkL7OK1ei56efwhKUTQKiiOxhw7DtxhsRcDrhKizEmBdeQPKxYzQWNhuO9OuHVj//HOLB7uRkJJSVhWgLCAI233EHcocNMx0H0evFhAcfRNKJE6ExYADE4N/DI0fi59tuMxzLZjt2YMTMmaqMtse6dsX6J58EACTl52Pc44/D6vGAAVj/6KMoat8e46dNQ2J5uUpuCATvCQAVaWn4YvZsw3Ey68fgt95Cm82bVc8la8QI/Hz77VHVX7asG5Yu7QFAgCgG8Pzzq9G6dQkA4PPPu2DJkl7gckDfvkfw4IObIr7Xmze3xPvvD4LHQ3mD09Mr8Nxz3yAtzROiweFoj3feGYx9+xqD83+Hw4/rrtuBMWOywo4BAGzZ4sGnn07CsWPJ0MoPaWluTJv2Azp2LDatX9v8v6b1vV4RK1d2wfr1bXHiRCKUeZHr16/EFVf8hhEjsmuVhjqXH+pEtT5DUCMLgyTRii03f5x7bvWiurAorCRr1oRN/1PYvr1+Q2J1aPB4aHVWu+LXrBkFhDLr37vvMp/Rym3v3rSiXVhIlkztauQll6hWNQ1dfe+6S23JTE4mKy7H+vVq3xHazKneW9SggZouQaD0AmbjwDFtmrqe0hrdooV+7xCvL0mqPbah/ip37Sstuy1bkuv5Rx/J5kyzVfq77w7/ILV92LdPb3kWhPCBOBRt7Nihfh3OO09+DX7+Wf1ILRbKm6ujQYP589WLCCkp+gX8efMYS0lxq8hu3ZrSBkdCYSH3TteH73e5GPv3vyO3oR2H6qJ2wvd/zSZONLYiN21KwTHDRX2Or+TGEUfto8bfhTL6jstFLqbVQMTve8kSY/9KXho1Ytuvvbba8ouKhrVr9SnvbDZaOTTL3ZKZyVjfvnqXab4XRZIo1oc2ZkfbtuTKG24c1q1TB1oSBFpZ5v6pFRWqIKEBgNyJlG65Fot+e1PfvuZpDDn27zff4yyKpvtsMjIySBbQ1hk1Sr7nzp1y24JATLWkRB2AU9lnpfwUxXNW9eP88/Vt8sBWEepLkjqmV1KSOsC1NoaHMuan2XtdUKDfN3rNNepuZWcz1rnzMdU1djvt3IsidTV74gnuXBHQDeXgwdGn3zsTVnLnzVvEXnmF1BWjTFtjxqgDdNc2DXUtP6BO7nqGoEaDH9wrEgAiun5EQlhXYWVeNm254ALGsrNr/iEVF7Os4cP14fu7dDHfF1RWRr4ORsEgunalCT4vj/beKidZi4UiLBtEBFD1Y948tXZlsTB2zz3yTLZ/vy4IVEmjRvpIANr9ym3ahA0MEVJStQGmlIr2uHGmjGLRxx/TuCnrWq2y67gkMVXytl69aNadMyd0zHR/1COPRPEwFeO4ZYve1clqJbfvKNqYPVueFAVBnaNWmdmIK1tKLzGjdzI/n2QDJcO4/no109m1S+0dxh9ZOC92jpwc4y1fgkBDnpsbuQ2jcagJqlt/82aSxUiuUzPa+vVpm5yZi3Vt0cBR10wqjjjORNTou5g+PfRBe1yu6k1OQZh+3xkZagVPW4YMCWUhqOkc8dM//6lPVZOYSKl3jNyHeWAkrQEaIF9WblR/8km9gtmli6ErtaoPublMlyS1Rw8WChQhSZTdQCnz2O0sa8gQ9TFtvBBR1OfLM6Lhk0/CK5phFiW2XX+9fkwmTZIv2LhRlrtEkRT9srIQrQGz+6alRW3ICMlBSobNyy23RFU/L0/tUd65s5wGsKxMH65FuXahGksFpk/XrzUolWYe6VjZbbud1kqi2QHwwAPGsVvT08PHMAs3DjVFddrweBh75RXGOnbUyw+CQJ/C3LnR27XiSu7/U1R78BXRd7KGD68xHboXKCPDOLcZV1ImT1bt2K/2C7hhg3H4/mHDdMnBQ9i1Sx1eV6mYdehA9XJyKM+scqaxWkmjMdvhz/uxbZteUznnHLm/xcXq6Edc6n/sMeZVWk21Yf0BspxHwNJ33tErxsqxmTHDvHJFBSvXztwOh7zvqKJC3bcLLtBHQTRbwf3XvyLSrhrHVav0bSUksGiiQUkSY0OGHFZ1gafAkyR9oOwrrjChQYGnnlIzj1at1PthPB7GLrtM3W7DhqVRxV7JzDT+XBISPEZpJWPCX6nkrlxJVm+jFduUFLJyV2exJ67kxhFH7aPa38XMmfKH3agRW2riVRQtdN/3vHn6IIlKif/mm3VJx6s1R+TnU6QdLb9s1Ih4rZEEnZdHCpuBJuG3WBj7z3+o3qOP6vcM9+4d1kAb8kabMkUt09Svr04OruXxgkCruUOHqhVErQzRvn3EZcCMjAzGbrhBXU+bwjCMDMSeekpv5L71Vvn8ypVy36xW8mQrK1Npk4ZG8gYNYlqpX/Txx8b7orXpFEzw6KPrVIvY11wjn9uxQz0kVquxWKJ8J3ft0jvsaVMDrV+vju9htfrZPfdEVm4libYba1c5Kabqn2EfVyT8lUquz0evdp8+xn3p2JGSa4Tz+qopDUaoa/kBdXLXMwTVGvwpU+Q3Z+zY2n2J33/f2N0EIOvmXXcZrkLGRENZGYXGV84GCAZt0LgPqzBvnrHVFWBswAC28qWXyDV5xAj1OZuNTGvhwuozxlhREcvXRu5r21Y20/l85GuinB35mGjraZlu8+bRfdkbNrCAUjHURmzkieGNsGePbkxZYqI8e+fnq8fv5pvp+KxZuvvpmNQLL0SmXYEfjQJEpKWZP1sFSkrUnvEtW8rV8vL0csennxq3w9/JnTvVgSqtVvWKMGNkUVS263KRt1ak93rbNnU6SaWMtX37meEqFK6+JFE/hw41jtOWkkJeYc88881poyEa1DWTiiOOMxHV+i4UHjusSRPGKipqb46ZM8d4QuQ88fHHTZW0mGiYP1+XMSAAkFKkzNuqxOrV+vCtvNSvz9ibb7KMefNodVWbE37gQHOjuwLbr71WzUhsNtKEuHK3Zo3eGjpsGG1vUi4Pavm4IERMA8kYY8znY6e0K7/KMmVK+PpK2ZKXxx+Xz2dkyDKJ3U4MsLjYXGZUvmexuKKXlLCKai4SMMazFNLqodYrWxlnEyDd3EwJzcjIYD4fKchKUaxfP7UoU1JCLrfKds8/n7G5c/WpEZWQJLJHaNcCRJGxO++seVRh3oeaIlwbFRWMvfwybc8yUmw7d2bsmmt2RBS/a0JDJNS1/GD963cB/z/GQw8BH31EvwcOBL7+Gli4sMbNdli9GrjrLuDkSf3J1FTg/vuBJ5/UBWqICd9+S21s20bfAEf9+sCNN2JJ16648sYb1XXKy4EHHgA+/BBQBJMCAFitwE03ATNnAjk5GHjxxcAjj8jnHQ7gn/8EXnkFCBcE68QJ4O67gSVL0ESS6FhyMvDmm8CUKfT/f/8LPPUUUBkMNCSKwKWXAn/+Cbz1Vqgpr9MJe1UVUFYmt//ii2q6jBAIALfeCsyZI4cWsFgATk/r1sDWrUCjRsb1X3iBxjYQAENwK3+9esCePUCzZsDvvwMDBgAVFXT9o48Cd94JTJ8OPPOM3I7yuXC8+iowbVp4+pVdefV19Jo9FyeQjio44YED7oZtULXkC3gOJaFqL+DxUKmqArxe+f+SEnpcJSUAwDBunICvvqLh/uQT4Npr5fu4XMDhw+ZDUlUFXHkl8Omn8rHevYE5c4C0NBqS7GwahsOH5WtGj6ZXThSB775rBpcL8PtpaCSJfu/ZA7zxhvw6cKSmAnfcATRoAHz3HfDLL52RnU2PlzH5L/8tSer/+W9+PBAAMjP74qef5P/5Y+IFUNdV/g8Ahw4NwjffyP/7fMCBA8ChQ8CpU/pH7nAATZoAXbrQODEGrF7dEb//rnnOBrGyjF4fAPjzz2H47DPjc2Z46y2i47XX9J9+HHHEUQ2sWgXccgv9Tk+nSbCaAauUaLR3L9CiBZCXpz/ZpAnwr38BJgGCosbx4yT/LF2qnnhtNmD0aHwzciTGPvGEuk4gQAxl+nT9ZA0Aw4cDb78NdOwIPPoornjrLfVkM3QoMHcu0KlTeNrefhuYPh39CgrkYxdeSEwrKYnG+corgd275fMdOgBnnw3Mnw/8+GPosNfphF0phzVrRhN2pOe0bRswciRSq6r050SRGJ9WvuIoLweGDCHGpoSS97/7LsADVCUkAKtX04Tfrp1ObgzJIADQsiXJSVEicPQYvJ16wFsOnEITeOBAJVzwzHgV7r4XwPMdyQpeL/F4/puXr74C1q0DAAHJycAPPxDfB4DBg4Gff5bvNX48sHKlCR0B4OefW+DWW2VxLiGBRK0LLyQZpagIeO89YPZskgsAEh2feILuuW5dI3zxBfFzSZJ5vsdD/G3HDvU9BQEYNQq46CK67pVXgJ07u4RkCCN5gf81kyV+/53kB8BchtAe0/6flTUIq1fLdHo8wN69JD8p4q+F4HIBbdqQDGGzAYcOpeOGG/TjawQjGYIxIDc3Nhni/PNJ/N+7F3j22ejrnQ7EoyunpkYX9eu550jRAmjS3bcPEEUsXLgQkydPjv3mgQB9nY8+Kis/SjRpQgrQrbdGbGrhwoWYfPnlxIiOHwcKC0l5PHGC3rJPPwWKi+UKokiT6owZwMiRchu8H7/8AkydCuzcqb+Zy0V1WrcGSkuBjRuBI0fk804nKewvvEB9PHmS7n3yJM1MJSVUb/9+YpiKCZgBEFq0oImZz6JHjnCti9CrF7WrZFgWC2C1ggUjJwMgxXrAALrW4wHcbmqPa3Y+H82MXq88Q0LDIACgcWOge3fA50PA7UGlW0Cpx44SjxPlVTaUFXlQJiWgHEkoRxIqkAg3ElApJKEKTriZA1VwhhTOKjjhhQNuOOCGE57gOS/s8MEOP6zwwwoJIiS7C8xiD03QWtOqHjrqa4AALBYxpOfH8ffCBx8AffsCffoA3bqVYu/eKOfJOOL4myAm+eH334l3+XykeB06FLIQVluG2LoVmDIFLDNTP+u3aQO8/jppBFFg4SefYPK4cUBBAckPhYWkRRw/DixerFfAWrcG7ruPDNRaOejkSVLmly/XS9M2G00svXoRLz50CNiyBSpGM3w4MG8e3ePUKaLj1Cm5lJSQTPHFF6RcBhVjBkBITCTpXhSp/dJS0gY4UlOBYcOA9etJJuBITlYbxwHgrLOA5s2pHa38wOUGn4+KmRhttdKCiM2GgNuDKq+AUxV2lHntKPM6UF5pQXmJHxVIRDkSUYlElMMFj5CISiTAzRJQBTvcSAjJEJ6g7FAFB6qCx32wwwsb/LDJ8gNsCCQkIRDQK2WAEcn8QG3IEAwWi6BS2uL4+6BHDzJ4nH02cPhwKYC6kx/iSm44JvX556SwFRTIk3CLFjQxB1cnIzKoG26gSby0lKyZ3PxlhrZtaeXyoovo/19+IZNIRYW6Pp9kJQksmNImIiwWIDGRTGJ8cvb5AElCwO+HWINXgQEQ+ErzGTqr3YG3sBWD4IEDXtiDiqUNviBj4CUAERIsCEAEg4CAKoh/bSmRccRxZqNHD25LqlsmFUccZyKiUnL79AEyM4lvA8SDd+0CunULXRJWhvjiC1qaKimhZRu3m/i/Ga9u0oSWqS69VD52+eVEQ2UlFS4/BHk/AgEwxqLjbAkJpBAGArL84PeT/FBDvh+SIcytuHWKdRiDxzADXthDMoQvJENYgwqmBQFYQvIDlyHk0Y3LD3H8PZCaStNDZWXdyg9xd+VwsNmAo0fl/5s3p5XRWHLQfvcdkJsb9pIAALFnT1rZVeRcA0D31/pVaGA4bYoiWRFtNnrTvF5iaKWlVLSXh+9FRAjAGavccnyFichF67omI444/l8gM5P+Nm0K5OfXLS1xxPH/EocPywquKJJ7rELBjYisLP0qqgYMgFCvHvD888ZuyWvX0gpoGOhkCEEIeUjB4ZC9qdxu9QpoEDWVH0I0nMEyxB50wzYMqmsy4ojj/wVKSmq2w7K2EFdyw2HsWHL3GTOGrKHNmsXexuWXA7/+ShbQH3+UmY0oAvfcA8ycicWLF5tbcrt1I3eX5GQgJYVKvXpUGjQAGjTAut9+wzmTJ5NrbXq6/s26+25SlF0ucpVKSiKr8ObN5JakRb16wL//TXtArFbakzJ5MvXB5ZJLcjKtDCcnY+fBg+hz9tlEX2oqbShMSSG/x/ffVyvWNhvQuTPQsye1EWxr58GD6DNiBNVLTASWLaM9z16vXFe5V5bD4QAuvxy7KyvRY+hQuY/KMUtNxcR7GHas2QGnvxzOoLNwQqoDCSVH4UIlXHDDmsBQ76E74WqYqOwekpKAE/sK8OQdxUjwl8CFypATstPO4Bx7NlzN6yE7+3f0KToC19b1SIAbiU6GhOefRGLrBnC5gHffqMKBtTlw+svgRBVdgwo44IFT8MFxxYX4s/gYunfvCKeTupaQgNDv4mLanmOzATZrAPYDe+DwlYcclxLghrdxPTQcNwZOZ/A6G9W12+n3l1+S7cRiocKvsVrpmiNHgLw82fXZ6QQmTCA6HA7y6lq/nl4zi4WcDJQe5TT0xWjYsB4EgRSk48fl83Y7janXS7KUKFIRBCpVVcZ7TZxOUrisVrLb5OfLdYSglKb87fd7YbfbDc+JIvVD+Sopr+MIBCRYLBYdLYIgL2bECuXn+dfIdPKz5M/M6aTnlJpK/xsxo8OHqX9nnRVXcuOIo1p48EGaICdNAvr1i13qGz6cyqlTtEWKTxh2O3l4zZyJRZ99Ft6b7KKL6GNOTia+zOWH+vWppKdj9c6dGDtlCrlQa434P/5Irskul8wQAwFyFc7J0a+68ngdr74q72W95hpS2F0uWY7gfDrIq7ceOICB551Hk1JqKskyv/5Ke3r/+ENuXxBoxXrQIKI/MRFITMSvWVnoPXKkzPOPH6dtU1lZcl1RNJ50e/UCxo+nNoYPp/rJyURHUH7oszcd/W7LhbPgz+DGIzcSbAG4xCo4PcVIDMoEKZePhWtYn5AIkpQkk/TkDbnI23kcCahEQtDh2IkquJqkwTnhHCQkisg5uAt9fv4BrqIckg8aJCLx1efgSrPj0CHgnder4Mj+Aw7mDsotXBapgqNNM/zZriV69OgQkhm4DOFw0Lz//vs0NHY7YPdXkAwBDxLghhNuuKwSCvp3RYceXWCzBa+zy/JDXh7tpbVa9cVuJ566c2cAjMnverduNMQ2G9Gydi2JhBYLPY4TJ2ReLIrkqe73FyM9vR58PnqEyu3O9eqR/GCxyDIE/xsI0OcSCKi3bwkC0LAhPVJRJBmIyyBmcoTf74XNZlfJDfw8rVKqXyOtDBEISBBFi0qmUJ4P59QpQ92Pv15+UNNgscgyXFISvVMGIhICAVoTrHPUSbirMwS1EfUrqqhjDz6oDn3WrZsqL95fFsHN4zHOO4dgdMQBA6KKYhiRhpISClunDRkbLvqiso0339RHSK5XTx/tLyVFlS/PdBxKStQZyQGK3KzMw2u1Mvb00+ZtvP++cU7gSy5RRazMHjRIHTpQmQ94507DsWcARZgMthPV88zP50lU1WXmzBq9T9rsUJddZn5tRQVFBubX2u2MLQoGNMzIyGBr16oDSlutjD3zjHl7+/bpuyQIjP3jH9ElcNfidEU23LmT0vkoXx9tcTqJ7n/9a03Ee+zaRREkjYKXiyJjLVoUs5dfrl7of2Ufli9n7LXXoi/8873jjnh05Tji0KK2ooaGnac2bVJHBBYEmiwUIWn/sijykkT5aw0mqgDn0R9+WHMa+H20kYoTEykzhEk43lD9gwf1OXKTk9X5b/lY3nBDdOOwZo06crUo6lPs9O7Nlr35pnF9j4exsWP1E7zNxlR57nbtYl5ldOmxY9WRkdeuNZZDAMaefz58H7RYt07fRoMGjBUXV/udWrJE3ZzDwdihQ+bXz52r7s7558uP9+OPF+kiK3fsSCkDjSBJxHONRKtgOuiYcTpkc5+PIk737auPiKwsHTpQtrFIUaIZo9dz3DhjmSQpyc2uuUadsrE6/cjKik1+uPNOTkM8hVCd4bQruTt2qJOx2+2GoehPu5K7Zw+FyjfKwWq3M3bHHezTuXNrTkN2NuV+1X65/fuHzW/HsfGuu/QpEIwUucREygNrRIMWL7+sVraTk9UJ1wBSgIPKqK4Nj4exc8/V0+BwMLZ4sXxdRYWasXbtqmbGixcbZxkHqF6kfiixZ49xzpmlS6OrbwC3W2tHCIQUViNs2KCeUDt2ZKyggM4VFzPWpcsxFWkjR6pSO6vg8TDWq5e+O0OHZsWU+UCL2hT+Nm9m7KKL9CmUtK/llVeSEhyJhrVrKd2zUUpmm42xQYMoI4jPV/N+3HrrZl1WjFjK4sVxJTeOOLQ4rUouz2+i/BB79mQsKyu6+rVBA0d2Nk1WRhK5IDA2bBj7+rnnak7Dxx9TyhztpNikCRm+IzCDz2bPZuycc9R1nU5jXnnZZYbt6cYhP5+xwYPVdVu1UivMiYmU3seoPmOkYRml5OncWZ0LZ/Zs9RhPn65uZ948YxkOUMlDUb0Pr76qb6NNm1AO3+q8Uw88oJYfevc2f2Q+Hyll/HqLRZ1qaNYsxux2n+oxzp5tfu+339a/nmlpFYb5d2NBbcnmHg8pfl26mD9CgOwmr7yizrhpRIMkUSavYcOYKh+x3HeyhWVm1rwPOTmMde58zJTmSMVujyu5dYbTpuT6fIxdd536SQ8frl7Vi9RGTWmQJPqqjFZAAVrJXLmydmjYtIkVtWmjbl8UiTHm5ETTAWIcyvpG0n9CQkiRM25G0YcdO9TKrChSMndley1bMrZli3kbW7fSbKGlo1s3xgoL5eu+/JKSu/Lzo0erZ/ennlILBcq27r47fD+0WLdOP5vbbKp+xPosMzPV+rfDwdisWebjfPfd6ttPmyafmz9fLVOkp5OV0QyytU8uvXrRJF/XOerWrWOsR488QxmJl9RUSm9oZl3mNHCmNHy4MVNKTCQr9pdf1k4/JImswNoUijYbPZ9IRWldz8+PK7lxxKHFaVNy9+1TG8eTk6Pne7VFgySRla1xY+OJr3Fj0kqCfK5GNJSUMDZ1KvNrVyg7d1bJKKbIzmZs4kR1jnur1Xii1a6Mmo2DJBGjU/La1FS1PCUIjE2ebL6qLkmM3XabngZBII86jrIylSLtt1opr7CyncmTjZ+DxaIzfER8Ftdeq29HI7PE+jxHjVI3d9FFv5leu2uXOhVxy5b0CBkjQ3mXLuq2LrmEmeZ53b9fL6LZ7Yx99dVf6OFggoICxv7xj726NRVtadeOsZdeMu8jp6GkhK7r2dN4vSQ9nbGrriLRtzb6cPAgY2efrRZZRTE6+cFul+s1axZXcusMp0XJXbNG/dUlJpIPRyxt1ISGnBxzq6vFQuYdpYJWXRpKShi75x594nSHg7GpU0MWQVOUlRET0SqzRu68DgdjH38c3ThUVDB24YXq+s2bqxmew0EagFkbjDH26KPGzEmZnN3nY2zSJNX5zAsukM9LEmMXX2yu4CpXgo1o0OKjj/Q0JSXJHCJSfQN8/LG6uZYtzVcP8/PJhUZ5a+4G5PGQkiZ3VWLTppnLEytW6L3HUlNJvqtOP4xQnfo5OYxdfTVZjs2YUv36JLtohl0Ht5sSsZsxpXr1GLv88shODrH0o6yMsVtv1a44B1j79irP/ojgdhmns+6TuccRx5mI06LkPv20mk+MG2fqnmtYv6Y0FBQQTzOasESR6DEwXFeLhsWLyaKp5YuDB0fnW7lkCSnCWhqNlNvhw6Pa85KRkUEGBaUHmcWidhkHiO5wq+pZWXqPMYCMA3v2yBWWLVMzm3bt1C7P+fmMtW5tzIgaNDB8N0yfhc9HGpK2nVtvjb4NDTwevR1kxQrz+s89p37c118vywjz56tlgqZNT6nkAe19e/fWd+Wuu2LvQzjE2gZfW9Ku2WjFyJ49yTkhwqfNNm1ibOjQwzrPfaVoe/vt4WWRWPuwcyc5X6pVBz+7//6IzhQq8Nd21Ki4kltnqFUlt6JCv+fioosiv8WsloT5jAy19VdZmjZlbMGC2qHBhDFVJSaSe02kr2DHDjL7aZXwpCSmSwVrsxm6d5th6w03qBlGQoJ+KWvSJHOTGWNs6Xvvkauxlpb0dLWJbMsWtVW3fn3Gtm1Tvw9Kk6RyvCwWxg4cMKXB8Fk895yepmbNyNgQTX0DTJ2qbm7cOPM2FixQM6ARI+Rh3LxZLRM0b87Yq6+uMLxnXp5+q5UoGj/mv0rJ9XgYe+EFGk4zxtS4MWP33af2LjOC203W1s6djd2SmjUjmcJAPqpRP/bvJ6977WfVvTtjjz66LvqbBXHBBVS/deu4khtHHEaoVSXX51Mvh2m3w0SqX0N8/fzz5KFkNPnVq0eSezQroJGQnU3LTUrPpyBPzOvVK7LnV0UFuQ5p3X+tVuYz2qvar1/0wQyys9lJrXaiVW7T0ym4QbhxePll40WG666Tx9DjYWzCBLV88PDDchuMkbZo5kY0Zkx4GrQoKNAvSADkthxtGxocPqz2+LFaZb6mrV9WplacnE7Za0lrIBdFGkIzGv75T303evbUi9p/pZK7aZOxWKscmxEjyC4TSUTesIFkMe0nwsemQwdyBDDb/lWdPkgSbQ1v21Z9P6eTPrePP468L1gL/m48/XRcya0z1JqSu2CBeukkPT2mZZNqf4yFhYxdfTWTjL4sUWRs4sTo3IUj0ZCba8qY2PDhjK1fH7kP77xjbNk0cku2WmmWixbLlqmtnYLAWIsW6jY7dFBbUI2wdKneZQogZsStwJJEpjPl+SuvVLttZWcbMxQuMMRqmb/5Zn07ffqYWqYjPQtJ0u+BnTHDuA2fj2w1yteK80VJIsupsp2bb6bjWhp8Pv12KYBckcwm/dOt5K5eTXtfzfbINGnC2EUX7TbbZRBCWRkpycaKLa2iPv549Ewpln4sX663yYgiKakHD0aubwa+++D88+NKbhxxGKG2votlb72lNlD36GG6tckI1Z4nuUtygwZ6A7MgEG+PxDOjoUGSiGlotzRxJfLZZxnzeMK3sW8fMRCtrJOcbByIqVs3QwOwIfLzickpJ+969dQGc6uVscceC6+hFBezorPO0tOSmKjes7Nxo9oq3KiRauU6IyPD2M2ZlxdfDNsd3Thu26Z3mxJF470xZm1o8Mkn6uYaNjTfR7psmVo8Vr7emzap7RXNm5vzrYULjb2/9u6tXh+iQbg28vLIYG0WS9RqZaxPn9yogl7xGB1GbTmdXnbuubSOVdsBOPPyyPaijTWSnExBQvnrHutYbt4st7VvX1zJrTPUBpP67ZJL1Ixh6tTY1vRZNT7G/Hyy5Bl9WenpjL31Vu3QsHkzWULDMKaw9SWJvhStIutyGX7NkiCQn2S0WL5cr8w2aqQPChEpqFZuLmNDhuj7abOpV8C3bVMvQxoEwNpy883m0Q/79YuqW6GxLCzUm9YAcseOpr4BvvtOTZ4gGEcezMjIYHPnqh9T06YsFMhh2TK1Hp+URHtYtTR4PLTdR9uFNm0iy3GnQ8n98kuyqJrFALPbyZ7D3X/MaNi6lZiDkfOEIJDC+9JLjM2bF7sFNFw/+N7eoUP1Rv6EBJKNtDsFYh3HDRtkWfLxx+NKbhxxGKFWvov9+5lP+SHfeGPMTVRrq9FNNxnzKaeTlomi8ECLSENJCVk9tXs/HA7axqPxRTWVQbQutqJIrroai2IAoIk3WgNBURHxUkU7fotFH/zyggsiK8wvvWS86nr22bL2J0m0F0Z5XumvyxhjeXms1GwftCiSDBIBqnF84gl9OwkJEbNomL1TPh95BymbGzXKuH5Ojlp8FATaBcYYDSf3FuKFG8i1NHzwgf4V0gaqiqUPsUDbRk4O8VizUDcAjY8yy4QRSkrIMN6nj15xB6j9q68mG1Nty0E5ObQ4oRWdARI3339frz7ESgN/zZOT615+EBhj7K9JVnTmobS0FKmpqZgzZw5cPJ9bDOi5cCG6fvklBABVKSn47oknUNKyZczt5OXloXnz5hGvSzhxAsPefBMNDhxQJW8PACjs1Albb7kF5dXJ5auhofXGjei5ZAkST5wI3UeyWnG0d2/8etVVhvdQ1hf9fvTKyED7detgDSYSZQAq0tLgqKyE1esNtcsA+B0O7Lr8cnzfq1dU49Dsl1/Q/8MP4SoqCrVTlZICye9HYjBxGRMEHB4+HD/fcgslcDOA6PVi4Pvvo82PP0KTxgwlTZti7b/+BW9qKuD3Y/C774auYwAKunbFhocfRiCYU9BaWYmRr7yCRpmZoWuUfTxwzjnYcfPNEfsG0FgOzsnBkFmzYFF8ngxA5rhx+PW66yLW145jIAA888y5OHiwQYgyu92HN974AikpXtW1OTmpmDlzME6cSA/deciQHNx55084diwJr702AkeOpAbbYejS5TgeeeR72O1y0rZDh/Lx0UfXqe4HMNhsEh5++Ht062aQnzmKfsSCvLw8NG3aHNu3t8BXX3VGVlZ9BAJGeSoDaN36JC67bC/69TtqSENVlYjvv2+HzZvbICenHnw+dWI4QWBo0qQUo0ZlYezY/aGxqGkfACAnJx9//DEC33/fDn/+mabpA0NamhsXXrgP559/wDANZyw07N3bCC+8MBqBgAiLRcKsWcthtZ7C1KlTUVJSgpSUlBr1JY44/ldQU/khKT8f4x59FFafD0wQsPXmm5E1ZkzM7UT7fbsKCzH0rbd08gMDUJGYiB233oqj/fvHfH8tDa6CAgycOxdN9uyBEORfDEBps2b4feJEHB450jBfsLKNJr/+iv4ff4ykY8dCtPqtVvicTjjLy9X0CwIKunTBosmT0bhdu4i0WisrMfC999By2zaIQfoCFgsq0tORVFgYaru8YUNsmjYNxWedZdpWg8xMDHvzTbiKi1XHJYsF22+6CVmjRwMA6v/xB0a98gocFRUAAG9CAjbefz+Od+sWqtPu22/R/+OPIUoSONfntHhcLiyfPTskb4RDXl4ezkpNxXlPPYWUggLVuYq0NKx+8UV4I8zjRu/Uxo2t8M47Q8GYzM8vu+w3TJq0T3Wd3w+8+WZ3bN/ePXRtaqobjzyyHm3alODzz7ti2bLukCTioQkJXtx//0Z063Zc1c6iRY2xatUo+P0ilDLEkCHZuPvuLVGNQ015b15eHqzWDvjii274+edWqKy0ATppkSEx0YsxYw7ikkv2wen0G9KQlVUPq1d3wp49jXHqVIKmHWqjZ898XHzxPrRsWVJr/cjLy0Mg0BVfftkFu3Y1RVmZQ3Vvi0VCr17HcM01v6Bp03LTNmKh4aabLkNVlQ29eh3F3Xd/XbfyQ52o1mcIamRheOUV2fzRsmX1k1iyKKwkBw+SycdoyejGG9miGqb/YYxR+P6nntKHqqtXj1ZjI/hJZGRk0BLSlCl601SLFsbmqhYtVO5QEcdh1Sp9EIaGDfVuUN26yT4vZpgxwzBAhSQItErNsXatPpCYNtfvSy8Z9w8ga/mmTeFpUcLnY3lGwSEcDqIlCmjH8csv9SuXRvlv3W51HC2AIv/t3Emv98UXq43nzZoxtn69vg2y3gZ0xuP586MfBqN+xIKlSxlr27YwbB66bt3IGmz2au/axdioUQd127J4SUoiD7r5883bqG4fCgpoQYVSDgR0927enKyx0QUvj46GNWvk98Rul1Mh1bUlNo44zkTU6LvIzAwtTwUEgfZfVhMRv+9du4zjTACUGH3nztpZLVq/3njVddy4qIIQhGKLaJeYkpONUwnabBSoK9pxKCkhNx0lM7RYaPVXuartdFJUoHDIztbn4kVwNblfP3lvitHq7cUXq1fK8/LIh1fZRiRmHQYb77nHeHOodtU4DJRj6fGoA04C9Dhyc/X1li9Xe3/ZbHImpBUr9KmGb71VT9Jbbxk7GZx3XmzuujV5p3NzSZR1Or2m8kNKCmO33GLOg8vKGLv22u1s8GDjQJZ8f+1DDxmPZU37sWoVfXoJCR7dve128gabPz+6VyIWGlaskO+zY0fdyw+ok7ueIaj24C9eHHqKFfXqhQ1kFA1MX6A9e/Tx1PnEPG1aaKKsEYMqKGDsmmv0e1HPOosYTpRt/Nm/v5p5CAK59hpNtr17G25QNO3H2rV6t9369XV7fL1OZ2RNavlyw2T2DGCsf3+2/PXX6Tq3mzZJKM9rA4nt3KkLo6diUD16xOY2vmOH8aaMoUNjMqIo99NqIxC6XMbpbubNU+/LcDh8bM4cOvfMM2pPLKdTH69Ckmh/rZEiGCZOR1T9iBZZWSQPmNkbeFTDefPMH8v69bQF2yipuiDQZ3HHHeYpg6rbB0kiY8SFF5pv527blty9DIKj15iGVavkT9XhUAc3rWsmFUccZyKq/V1kZclSryiy7x98sEZ0mH7fGzYYh3m1WMg3VEF3jfb1vvMOK9dOWk4naTCRsizwNmbNYm7ttqa0NOPE5MnJhhkrTPtQVkYpeJTyiSiSBVezd+fP/v3Dy3MVFYxdeqnxBO1ysS1Tp8rXrl6tNpAnJ+vz6U2fbr53xmIx3ktkBp+PsbFj9Uqy3R4+j58B+Fi+844+zsR99xkPi3oHXYBNmEBDn5mpd3Hu10+v2G3dque7gkCG9xh336n6EC0kiYze4VL+pKaSYmsWzbi4mLb4GO0yA0i8GzOGdsFFq7DHEkBz9myyWxnJQC4XGQrCbMWuMQ2MkYwE0FYuxupefkCd3PUMQbUGf+NGWRpMTWWfvvdejenQvUA7dxp/JXY7WS41X3y1GNT8+cbW3QEDIuc04fjyS9KitLOgkZQuCOoATgbQ9WP9en1u2/R0fcTD5GTGZsxgGeEiSO/ZY27Nbt06xEwyMjLIiKEMslWvnnrJ0uMxZ3S8vP12dGPIYZZP7513Ymsn2IeFC/X2hRtu0F+bk6MyIjNBoCxTH3+cwb78Um0PEATah6rdrvXII/pXwOn0RLvwHLYfkeDz0UK6Noi2kua+fYmpGDFKSSIlfPRoY2urw+FjI0dSfJYYt6lF7EN+PjHErl2N5RyrlVabL7/816hkxerQwBilolQquNpYM3XNpOKI40xEtb6LoiI50o4oMrZyZe3HHli71nhCTEggDzSDiTBmGnbtIiOwdh9q/fo0IUejlWRnk1VSO/HWq2cspTdrFja1kK4PZWXEzLTKbZs2eoP8OecwlpNjPg6SRBZGI7pEkfZS+3yyR5s2Zop29Xb/flNNKLS3OBbNbts243C8AwZUy8tw7txFOqe59HRaE9Fi/nz1I2zcmLHnnvualZSoA0hzxUerbxcWGttiRow4WC3lliPad3rXLtofbOz1FWDp6eQ1ZbbaWlREr4ZRXDVBCLCzziJ7j1k6pJr0IzeX4qF16GAcQLN+fcYGDsxmmzdX797R0KDE0qXyvfl6U13LD6iTu54hiHnw9++XJ3WHg7Hs7Nrd3L55s16B48yJrzCGqx8JOTnkrqO1jlqtLNfItGaEsjLKj6txa5a4smkkqd97b1TkhfqxcSNjHTuq20lL0+eeSU1VMW3DcSgu1kc4UNb/6CPVtceVfjlBd3DVTDt3rrF1OVgqU1Kis15zZGUZRzFo2TK656GBx0P55ZRNpaToXWokiR6jcmI/6yyKR3HwIGOtWp1UtdG/v76NefP0PN9up6ALpzuy4YYN4SMjd+7M2F13bTJVbD/6iNIxmgV9uPJKWlivTQE0mtXa9HRyGFi5svqRDcPRoMWKFfI74HQaM+K6ZlJxxHEmIubvgiZn+WNfupQxVovf9+7d5hkMImxpiooGn4+2+RjkWitt3NhwdVUHSSLGofV/BViVy2WsafTqFVVo+lAfKirIGqtVblu1UrcvCLS0peCzhuMwb56xuzTA2LnnqjS/rTfcoFb869dXG8g54zVjXILAtsQafEybC5D3zSQ9UCTQTjz1FpnHH9dfV1xMPFR5y7vuotdkwoS9qkXyhAQ9OR4PBYHUkj5oELVxOvleRQX1ySyAVEIC9eWdd5Ya1i8sJDdjI+VcFGntZ/ZsWiyoKZT9qKigdkeNMha3AVqzmTZNfq3/qlRKFRWynaV5c/l4XcsP8cBTqanRbYg+cQI46yygvBywWIAtW4D+/bFw4UJMnjy5RnSsffJJnPv++8Bx9cZ7JCUBs2YB118ftn5YGgIB4P33gVdeAQ4eVJ9r3Rq45x7gnnuwcMmS8P3YtAl4+GHqt/KVSUkBfD4wt1u9HT8hAXjtNeCf/wxLuxJrpk/H+YsWAZmZ6vadTvXY1KsHPP00cO+9qvqqcQgEgGnTgLfeUtMLADYbjem11wKVlfRMt20D3ngD8AaDMDVrBqxcCfTuTf8fPgxMnAjsUwdZCMFiAZ58Et95PBgzeDC1W1UFuN3q4vXS8aoqICsL+O67UBMBAF7Y8evZE9Fi/mehKh6PXJ1X9Xj0f7//Hli3Tk1W587AwIGAz0fF7wfy8oCdO+k3AAgC0LgxDWtlJZCTI9dPS6NXpGtXuY3MTOD11+meHIIAjBgBjBxJ7e7Zsw8dOnSFJNGjUP7lv71eujYQkGkLBOivJAFHjx5DgwZNwBj97/MBf/4JFBXpHymnITkZaNCA/i8rK0ViYkrovuXl1L9gLDRdXYeDXluLRW7f46mCw+FU3Y//NjrGf8v1vQDs8PmMaeb3tljoteTxWJTX+v0+WK0248oKmLVvVt/vV4+Fw0F0JCYC27cDrVrR8ZjmyTji+Jsg5u9i8GDg55/p96uvEn9CBP4dBT6fNQuXvPce8Ntv6hP16xPvv+SSiG2EpeG334CHHiJe5ZeD6iApCZg0CXj+eSzcuDF8H44dAx58EFi2jBgZh9VKk7YmcBMEARg3DlixwjRwpBaLP/wQV27YAHzyiUynKAJNmwJHj8oTJG/7gw+AJk3Mx+HHH4FrrlEzRI6OHYF//Qto1IgYS2EhMHMmsH+/fI877yRGySf1b74BrruOrjVCx47ArFnY8M03GDVoEDErrfzABQCvl/7//HOgrCzUhB8iCtJbwbL6B7gbtAw1weUE3oSR/FBaSiJQMHYnAOKHF15IfIHzaI+HRJc//pCH1GYjcdJiAbKz1bLBuecCN99M1/p8dG7+fGDjRnX3k5NJXHQ46D579+5Dx45dQ/KAUjZQ/uZ8THssPz8fDRo0hSQhJEMUFwO5udRnI9jtJD+4XHR9WVkZEhOTwRjRXV5OYxgI6OtaLDReDgc9fqpjLj8of5sdIznJi0DADkkyphmg+1mtVESxevKDlg4lommjqkquf8UVwOLF9Luu5Ye4khtu8L1e4Ouvga++Aj79lGYBQQCWL6cvH1EwqFOnaEbIyQGOHKHJ9tgxmuhycoA//gDz+dQKYno6TcAXX0xv+fHjcp0TJ6icPEmlpAS5e/eiZVoaUFFBRTkZlpTIihtAs1Hv3sT4HA7qU2kpDv76K9o3aUL1KyvlcuQI3cfoqzaCwwEMHUrKKVf0qqrUSp/XK8+YihmKQR+3TgVBABo2pJnE45G1pOCsJvn9sAAwmw2qYMcppKEMyShDMsqRFCoVSIQbLpTDhSoxCW4xEVXMCXfAgSpmRxWccMOBciSiCglwIwEeOFAFB3ywwwcbfLBBghV+WBCAiEDorwAGASz4W99LQfM3jjjqFpmZQKdO9LuumVQccZyJiOq7+PVXkh2++grYvZuO3X03aRNBhJUhvF4ysGZlkfbA5YcTJ0gu2LMHrLRUzTnq1SMJ87zzOKFk2SwspDqFhcTTT52iUlaGo5mZaJacLMsPSutqSYmaptatSTZp357OlZZi/y+/oGPTpqQFcBnC7SZajx83tiwaQRCo3e7d1TJDJPkhGJE4IgdNSiL5SpKof8G6IRlCkmAxkXX8EFGKFJQhJShDqOUHLkNUIBGV1hS4mRNVAQfczIEqOOGBA+VIgBuJcCMBVbDDCye8QdnBBxv8sEKCFZJOfhCDvxHspbKnZr/jiKNucM45wNq19Luu5YfoTGR/V7z2GvDII+pjs2aFFNyo0KULTfRhEJqWmjYFFi2i5TCOZcuAyy8PWz+mpEU+H61abtumOtw+ljbCweMB1q+vVtWI0zNj+tVuBSymZwjtcRB50YxWIFjiiONvhsRE4KqraGU/jjjiqCEGDFCvfvbrp1JwI+KFF4Bnngl7SYhvOp3Ae+/RaqESzZuT8hkGMSUezMmhFUoFOsZSPxwYAw4coBIjolLvysvDjkU4GeJV3IeH8Up0xPgjXxJHHP+LGDkyJgfO0464khsOl19OSm5KCtCuHXDHHcDUqbG1kZREf7k/gSiqfTksFpxo3RoNPvtMdo1Vgvtecoii7JdgswEOByokCYn165OPhctF90xOptKwISnOublyG9w/kherFVV+P5wWC1lMtSuhNpu5JVYUAZsNPgC2hAS6lheHg4rdDuzZo+63ASRRhMXplH03bTZi3Hzl9vDhsPUNLbmiCKSmAomJsB6DgvmQAwPZRBkEgUEUGAQEYBEBi+SFhfmD67J+2OCHD1Ych9K9SekEIQRdVBhEkX7zIoqAKDCIXjcqAw4wFStlofrKY4JA/yvb4S4w/ogMNCqb9mluozZoiON0IzGR5PDLLgPGj6cdGXHEEUctoVUrWkVt0QIYNIg8tGKtzyGKVDQMoDItDa4nnwQeeMC4DYdDVux4G1x+sNsBhwNlkoTkRo1IfkhMlGWIlBSSB957T26PMzWF/OAJBOCw2+k+WlkhnPzAz9vt8AYCsLtcKrpgt5MMcPJkRP4fACDa7VTHaqW/djvJD04nbTUK5/NphoQEIC0Nid4EoEh5gssQQT8tARAgwWoBRCbBInlhhV9VjqAFAiH+r3WiJLmB838+zLIMEYDf7YUXTh0N4eQH/peX6BbVzwT+H5ch/j+hTRuyTUW5u+CvQ53sBD5DENWG6Gjyw4a/CbWRn6/PdXvNNXJEvnD3LyoKG2kvIg1FRRQMyaiNzEzGLryQ+bXhXV0u2sGujZ7odFJUHk0M9bA0SBJjzz2nD6olCIwNGaKObGwGj4fyyfF+SBLtwDdLYtqgQSiwB0dhIZVwUXJ3X3SRaUh/D6ws97pHWXFxjHlRZ80KtVmABiwfjVhZclPmW7ch+jaCKCig6IVK0hwOCsSkrO/zUewNZXyLbt3osRUUUFCCwkIaTo9H/2qMG7dXFxsjLY0iEXo81E5eHrVVXExBB5RjEumdLCigQFZ5eYz98QdjV1xhnBsPoHSEGzRD5fEw9vnnFBVZm+5YEBjr1Imxq6/ewUpK6FqfT9/H/HyK+5WdLdOSn0+0FRbSZ/Pee5+y4mKKwHzJJeZRnB0Oio/y5JOM7d0rtzV79mesoEBuk7fLS2YmY7/+SuO6axdFNt6zhwJAZWZS+e9/l7ODB1moZGVROXyYxm7TJnXZskVdZsz4guXlUYRo/kyTkqLLsctY3QeOiCOOMxG1IT8wFgXfq6igCUibw/788xkrLIyO/0cIDR+2DbebJi6jvrjdjE2fTgEXtdF3WrWSI0nzYrFQtCHthB6Jhh9+oChH2sBUTZtSMKxIchRjNAkXFcn9yMykwFNGE7ooUn5bRToht1stghjhq5de0gfNVJT8xLascMMe5nabt2HYj4cfZgxgbthZLpqxYqQwz7iLDJ9rpHF4/XX9MPbrp+ffW7eqAysJAol+RUXEOzjvN+rLf//7uSrGGi/nnkv3ycsjGSQ/XxZPtXJIpMBR2dlyG598YhwECqCYYY8+qg8yffgwDas2ijRAr+3VVzP2wgtfMrfbWIaoqFDLD7m5ehnivfeWsuJiOv/ssxRUyyhLJEAxyiZOpPhteXlyW2+/vVQlPyhliLw8tfzw22+yDMHlh//8Z4VKfuAyxOHDVDZvjixDvPba0pAqwxhFyFZ+2mefbf4+17X8EFdyazj4UUUue/xxteLUqpUqFP7pjCJnCI+H8rMZKYiNGxvnke3UiSI0mrzJhjTk5FCKAK0WkpxMyUY14x5VPyoqKOyd2UyRkEApDGLF8uX66M3K0rlzVPmQVX3IypKThinLtGnVMlrcdJO+qVtu0V83bdoPqsh7CQkslPM2Et5/X69sOhykUMaCaJ7l5s2Ut9ZouNPSGLvqql90clVuLgW81sp7AL3Ojz4qv1Y1+a7y8yk0f4sWxYbKtyBQyoBbbw2b1eIvi2wYqf6cOerPL5bA3XXNpOKI40xEbX0XYb/vwkJ1CFuAQt3v3x9d/dqgQQtJohD6ffvqtaWUFMZatNAfb9SILIBheKiOBkli7OWX9dGcRZEU5S1bqteHFSuInxswngCX2PPzox8PxkirmTBBn6NWyTBefDGqplT9WLVKnzw2KUnXd9P6GhK1Ik5CAmURUKKkhLEePY6qruvSJbrc8MXFXORRR2fu2TO2pBPh+sHh85EYa5Q5iVIH5ig/E8YYvVILFhi/ulYrY8OGMVXqw+p+W5LEWEYGY7165ZlmUrDZKArzjBk0btUdh0iozfmhuJgyhyn7cdtt4evWtfxwpi0s/29h+3bgoosoWARA6/hPPw08+WTd0LNuHUUE3LJFHUjK6USl0wlXeTlQUCAfd7koQNWLL5K7VbT49FPaR/T77+rjXbrQ8SuuiJ32Q4coWuE33xifd7mwf9gwdFy1KjZ/iQULKGp0fj4A6B1kEhJo3IYMib7NQIBCEs+apT7eqRPtxo9lLEFbnMeNU3t7t2xJQS/T0uRjR47Q49q+fXjo2EUXkbe6U+nhZIAdO4DRo1WBGiGKwLPPAo8/HhO5YREI0Ja06dMp5okWffsC77xDLrQLF2bCau2Dykp6BefNU3vdA+SJfuGF1F5NXW03baKtZuvXUwRnQlrofFoaeRxOmUI7Gc44txzQO/HCC+og4Lm5F4aCl4oiBQ4fPz669mbPBnr2rH0644gjjgj4979pAubuyU2aAEuXAsOG1Q0927eT/LJunW7rUWVKClyCEApEBYAmmyFDgJdeio3mnBzgvvsou4EyaGZqKk2+zz8vbwOLFoEAMGMGxVkpKjK+pn9/fH3ppRj/6KPRt3vkCG1hW7MGYMzYuXbkSGIqPMpyNPjzT8rmwIOVcdx6K03KMbQVCABXXy1Hu+W45x7d1mo88wwNr9/fFAB5rL/7LhAp+HcgQDLKmjX8CI1EmzYUULxRo6jJjYjjx4FbbqFYbtoYYQkJ9OpMnw4sWfIjOnQgd/8ffqDPaeNG9SslCCSW3nUXDW0sj0iLykrg7bcpcvSePdwzXt7tLooUs23MGKJ/0KDq3+t0we8nsfWTT+RPnDHg5MmxuP12dQw6i4X68+OP5jLCsGH0+dcp6kS1PkNw2lZyPR5awVSaOwYPJqtstG3UlAaOggLKo2aU561hQ+Mlsa5dydwVAz6dM4dWZ7XJu+x2GosofCMN+7FqFfnZmllIW7Qgt2VJim0c33iD/EPCWV6feCKGESB8+9RT+jGwWtX5eCOA98Pj0RucLRbGPv1Ufb3PRyuKSstks2Zhjb0hFBcbJzEfPvxQDL027wNHQQG5JBt5gluttCKt/AwlibGpU7ewHj30KQXtdvLU27QpNhq08Hjo1Rk8WO9swOlq2fIke+qp2A370dJQG23s2sXY5Zebp3KsSZkzp+4tsXHEcSbitK3k5uer88haLOSiEm392qCBo6KC7t2woX5ySEsjzy/tklh6OmMPPqj3D42AjXfdZbzC2r07Y8uWVa8PhYXkd2q2F8ZuJx/c4AQf9VhmZdGKr1m+Wz4Oh2Lkoz4fOzR8uLGck5UVVRPKPixfrs8F36aNflV1/Xr1NihBkNhtt4V1OAvhscf0w5CaWhFz18P1gzESBdu2NR7qNm1ogV6JV175gl11lXE+2ZYtycEy0upypPdhzx4Sr1u0MKYrNbWSXXop0RbNWFaHhtqov3o1Y8OHm+7Uq3Zp377u5QfUdoMzZsxg/fv3Z0lJSaxhw4bsoosuYpkaPwe3283uuOMOlp6ezhITE9mkSZPYsWPHVNfk5OSw8ePHs4SEBNawYUP24IMPMp/Gd3H9+vWsT58+zG63s3bt2rEPP/wwJlpPi5K7aJHalTYpSbc3NGIbNaVBkhh7911yMda+dYmJhhmwvQ4HY1OmxCbNc7el/v1ZQDvLtWhB2b9j+LJD/ZAkcjk2Yqw0A9NGzc2bw4+DEb3PPqvfJ6QoAYCxAQNin5EKChgbNUrvrjRhQlRuztp+PPGEnryJE/VkzZ+v9may2RibNOm3iPeQJMbGjNHfY8CA2k3Evn69qWcYa9yYsXnz1PUyM8kdRsuYRZFcjBYsiP7RGPUhO5uxe+4hlyoj+SQ1lR7ZypXc5ejMcRVSYscOxi691FixTUgg+41S9rRY6JMZPjy2smVL3TOpOP4++LvJD4xpvu+PPlJPfn360Ma7aOvXBg2MUZyMYcP0CmxCAu2D1cTqCPFNpa9nNDh4kIJHaF1yHQ7GrroqYt9N+7BtG8X7MJO+69cnWUDzTkQcy927w7cL0NgsXhzLKBBmzjRmfK+9FlMzGRkZrKBAb7y2WvW2gqIixkaN0ssAb70V2aiweLFxyJZvv629d7KignZ3OZ3GYuD48eqtNx4PY888w2NnqF2m09NJIY1lq462H9zdecwY4x1zgkD2qSefpLE9U7YrGWHVKho/7afHZbOEBP3xBg1oUSBa+eGhh+pefqh1JfeCCy5gH374IduzZw/79ddf2fjx41mrVq1YeXl56JrbbruNtWzZkq1bt45t376dDR48mP1fe9cdHlWV9n/TS3oHQodQA1INvQjSEQFRWexlLbh22fVbXdct6lp27WV17RIUlI6gIF1675BCGqT3TCaTmTnfHy9n7pQ7NYEgnN/z3GeSmXvPPefcc8/b33fYsGGO361WK0tNTWXjx49nBw4cYGvWrGHx8fHs2WefdZyTlZXFjEYje/LJJ9nx48fZO++8w1QqFVu7dm3AfW1WIbeoiGJlnFfErbc2PfFEMH04dIhWrfvOo1QSN+yuplGpaMNevjy4PqxfT2+53H2uu46xAwdCGsd3//0vCdruG70z8fjd72iufc2DOxob6W2TC95wPlq1Ysteey24TldVMXbjjZ5txcYytnt3cG0xSjik01k8mnI3hB8/7ik8TppE3fH3LJ94wlPAa93alZ9oypq02Ri7+eYDspsnQLzT8eOu53/8sZyW1s46daKYlSD1BC5jWLuWsRtukHdaUCgo8cQjj8gryi8nIXfPHsZmzZLX0RgMFPckl3yjVatqb04kAaGliZTA1YOrjX9g7ML7bbO5BrupVKQkDvT6JmLhwoVkeX3mGXkPp/h4eW44OZmx//s/9t2nnwZ+s5IS0jS6BYjaAdrA3nknJLPXwoULSUkg55rEj969GVu50ncbcti40bdH2QUp8vCNNwbdb/bpp/IS0/XX+w7WlIHNxtigQTkeTd1wg+uU2mwkPDobuGNipKnxtaaOHvWkpQoFxcdyNHVN7tnDWOfOpbLTHB7O2D//6TqekycZmzzZ02BvMNDY3eOOA8XChQvZ+fPkzNC9u6fOh99j5Eh6jO7s/uUk5NbX06s1aJA8ix0TQ7HX7pZvnY6x4cOzgl2KDrQ0/9DsQq47iouLGQC2+UI2vcrKSqbRaNjixYsd55w4cYIBYDt27GCMMbZmzRqmVCpdtLMffPABi4yMZA0XssktWLCA9e7d2+Vet9xyC5s4caLXvpjNZlZVVeU48vLymkfI/fRTVwGyXbugBL0mLWKbje2+6y4mm8ouPNxTEAUo+99//uPyRvrtQ0EBZYOW24yTk9nR6dNDk0QYo7kaO9Z70oaEBJJ0/BA+jzHU15Mfr9wcOB8pKcG7K9XUMDZ3roe0aANCcnOuqKANxp1wuPM5JSXECznftmNHIgpe5+EC/v53eSKwYYPnuaGsyaIiykAsRwh0Osaeesp1iRw9SkKbu8bQYCBdx/vvfx90HxgjDerzz5OrsdxmrtWSVfOdd/wv2ZYUcisqSInfo0ehrMuV0UgZnfv189R0a7Vk+T9+vOljaGkiJXD14krnHxhjbMmHH7omgUxOJgtngGgyI330KCvs0UPeaiunUYuMJOurU4WFgLyo3n5b3t9Ur2ds4kS2+qWXQuv/BeG8Qc70BBBvNnVqQO6+HuP44Qf59LvuxO3ttwObB2csXiyrea1MSgosy5Mb/vIXz0eYkMA8ki+9+aarIKNSkeLbX2bjjAx5t9zp0z1Zs1DX5KefyjoaMoB0DM4hSg0NjL38sqdOQ6GgbNHPPPNLSH2w2eixT5rEWFiYWbYvrVszdscd/oXnlhZyN2xgbMSILNa+vbznWkwMuRS7V+zg2xB3xmxKH1qaf1AwxtjFjPnNyMhASkoKjhw5gtTUVPzyyy8YN24cKioqEO2UNadDhw54/PHH8cQTT+Avf/kLVqxYgYMHDzp+z87ORufOnbF//370798fo0aNwoABA/Dmm286zvnss8/w+OOPo8o5OtoJf/3rX/Hiiy96fP/JJ5/AaDSGNL62X36JEWvXQgHArlTi6MyZODZ7dlBtFBQUIDk5OahrlGYz+i1ahM6bN0PjlASiUauFTauFrrbWkQSBATBHRSFn6FAcmzULFpmkDbJ9sNvRacsW9Fy5EpHnz7u0ZwkPR97gwTg6cybq4+ODHoPSYkGvFSvQdcMG6KuqPBI2MABVbdviwO9+h0K5+sEy4H1Qm0wY9Nln6LBjB5QXMhPwRe48hrLOnbHh//4Pdqdn728capMJ1/73v2i/e7dbZTogd8AALJ85EwldugTUXwAwmdR44YXxKCiIduldly6lePHF9Y5ECLW1Wvz3v9di3762YIzO02ismDfvACZMyJCdB47PP++P9eu7wW6XsiooFHbMnXsA06adlu1XMM/zwIFW+PTTwSgrC4N7vb7Y2Drceed+DB5cAACortbihx9SsXNnB1RX61zGnJRUixkzjmHUqGwolcH1IS8vCqtW9cDhw61RVaX36Ed4uAU9exZhwoQz6N27OKA2gdDezVCvr6rSYvPmzti/Pxl5edGor9fAvU6gVtuIyEgzGhvVsuNMSKjD5MknMWHCGcfaaeoYTCYT7rvvPlRVVSEyMjLkdgQEgsWVzj+EFRVh4p/+BF1DAxiAs8OHY+dDDwWVASek99tuR+eNG9Fr5UqEFxe78gqRkdCaTFA51eO1qVQo7tkTR2fNQmmPHgH3ISonB/0WLULS0aNQOdWotSuVKO3aFSemTcO5QYNCGkfS4cPou2QJ4jIyZBM+WQwGZFx3HY7MmQO7VhtQm7wPHTdvRr9Fi2BwWgsMrvxDo16PX+fPx/mBAz2u94U2+/cj7cMPoee1iy+0Z4qKws5HHsGB6Oig5mHFiu747rt+LvRdpbJh/vztGDKkwPHdzz93wXffXYO6Op3jrr16FeHRR7cjMtLi0qbzOLKzo/HKK2NQU+NMbxjatavECy/8BKPRLftTgPPAYTYr8cUXA7F1a2eXMQCAUmnH8OFZuP32AwgPp/W4Z08yli/vjezsGDAmna/VWjFkSC5+97sDiIy0BNUHq1WJDRu6YMuWzsjNjYbN5tmPtm2rMHRoDq6//gyMRquXllzRVNobbBtFRWH4+ecUHDzYBoWFER7zCTBERJih11tRV6eFyaSFMw+hVtvQu3cR5sw5jM6dK5plHC3OP1xMCdpms7GpU6ey4cOHO7775ptvmFar9Th38ODBbMGCBYwxxu6//342YcIEl9/r6uoYALZmzRrGGGMpKSnsJTfN3+rVqxkAZjKZZPvT7JrYRx6RrI9xcSFnpwlKS5KRQSomJ8uxnavs3FVgajVpMJ19QwPpw9mzlMnG3Tyk0VB7R46EPoatW8m3w5tWVKkkv5MAkyw4Y8kHHzA2Y4a8KdH5mDLFqxu513FUVJCZUk4ddqFeYTDzQGn6PZuKjaXabM7n3XST65DUasbuvdd76cOFCxcym43yabh3V6EgDaQ/+BsHr+QkZyRXKBjr2zfXoehvaKBKEF27ep4bFkbWXDnFta8+2GyUVGPCBHkvOpWKyv8880xwMTjBzoM/vPHGCvbCC+SiHRNDr5BGQ89QqfSdt0Q67AGcQ22pVJ6HUmmT/d7fwZ9JS2tiBa5OXPH8w759UsY7hYJcS0JAUHtUTg4RBjfabtFoKFuh+4aUkuKzdKBsHxobyW1Izruse3fKFyLTXkDjKCtj7KGH5GNP+NGhAyWrCBY2G9tzxx3Ma80XfiQkeA1F8jmGn36SL7QeH0/ELJA2nPDOO55upwoFYxMnuvJ7H3/s6YGemurq/SU3jjVr5A35yclUX9UXAhnDkSMUci43xW3aMPbII9tczr3xRk+vL4WCxiK3RP31oayMkk+lpMjT4YQExq69NseRnyMUNJV/qKtj7KGHtrObbqJlrdOFwkMEzj80Jw/BPfdbmn+4qEUw5s+fj6NHj2Lbtm0X8zYBQ6fTQafT+T8xEDz2GPDuu6QD6dCBcoYHm9Y+GPz4I/DMM8CxY67fJybCXlYGVUmJ9F1sLPDgg5TuP0ANJux2yhX/2mtUrscZnTpRXvb580PLsV5bS6WLvvzSe/p+oxGZ116LLitXBj+Pe/cCTz+NWVu20PsqB6USuOceGmMwY6isBO64A1i50vO3adOAL76g+Q4QtbWUVv3wYdfvY2KoOhKVzqlBbS2ltP/2W56KnlK2z5tHaeq9GQ6sVuCll0bjd79z/V6lAv7wB+A//wm4q7LYv5+m8dAhz9+ioqgEwaOPAt9+uw17987FnDlUnsg51b9aDQwZQqWJJk8O/N4mE5UX+vprqqxgdVOmGo3U7gMPADfdBHz77Y+Y66/2QTPCbqdKEenpVKrg7FnAYpneDC3LFqbwAGPSWnFFaHUR3OdXQOBS4ormH7ZsAcaNA6xW2BUKKJcupXpvFwvffw/8+c/AqVOu38fFAYxBXV4ulTpUqYDx46m+TPfugd/j8GHg6adpE3TePMLDaUN++WUqgxQKvvsO+Mc/PEvqcCgUKO/QAbFffQWMGCF/jjeYTMDzzwMff4xBzjX03NGhA/DLL0DnzsG1v3w51Yxx5tEA4hs++YTq/gWBTz4hVsy9FM4999Bv6ekHAfTEl1/S43C+bY8ewKef+q6I+MUXwO9/P8elfQDo2pXoWqiPECAa+frr9CjlpnrcOOpf+/bAu+8W4/77gaVLPdnG1q2B22+nJR2McfDMGeDVV6k61YWqkQ4oFFTyaPZs4KmnaJzp6dsxZUr7oMcZKqqrqXzP0qXEN5WXA8CwJrYaOP8ANB8P4cUh5pLjogm5jzzyCFatWoUtW7agrVNd0FatWsFisaCystLF3aioqAitLrw9rVq1wu7du13aK7pQv9X5nCLnmq4XzomMjITBYLgYQ5Lw/PNU7BNAVevWiMrIuDhFM/mO8PrrrjuVRkMSRWkpUFwMFf8+NZUIybRpgd/j2DHguedw86pVroRJpwOmTqUdIQj3WxesWkVSz7598r8rFFRg67nngJtuwu70dHQJVMC1Wql46nvvAYWF1JzceRoNtf+XvwTX9/JykijXrvX8bcYMKtrqXKTWD2prifa6C4fR0cCaNRLRoVprabj9dmmzUSqBW24B/vtf7/J/eTnVX6P2pdpsGg2VWvzjHwPuqgf8EaZBg4DPPqPld+wY0ewff5yDxkbpHIWCCOwjj5D+JVA9Q04O8MYbwIoVVDrQXYeRkABcfz3pYC54vV0ymEzA4sVEkPbsoWXoXrePQ6Hwrn8xGmn7aGjwKD95AQyxsQqkphIT0K4dfSs3hwqZl2Dnzh0YNiyIOs8X0NS6wwICoeKK5h/WraNC1XY7oFbjl2efxfiLIeDy2rD/+Q/nlglqNQlY5eUO6UEBEE9x//1EMPwVVucwmYB//xsz/v1vOIpxAxJtf/HF0IX3/HzgT38iAd1slj8nLo6knRdfxLrVqzE3GAF3xw5gwQLg118dGzeDDB/RuzcpJYJQZgMgaeXRR13nHiCi/8knJE0FgYULgXvvdZ0KhYJ4g2++kejBr7+2w5NPOtgiAEBKCvEPY8Z4b//dd0m4I+FW4mcHDAA2b26aDefMGVI+b9rkSQcNBnoMzz1Hj+HVV8kWkZ9/o8t5YWHE2v797zSeQGC3U2npTz4Bdu705F9UKuJbbr8deOgh78aDi4XsbLL9rF1Lte15iWlXMCiVCq+8hVJJc6hQ0NrwVE4zaLUKtGtHRpQRI2ic3ngwbzzEkCHeeQi5tnr18nr6pUVzm4btdjubP38+a9OmDTvtHvHOpMQRS5zK6pw8eZIBnokjipyy6H700UcsMjKSmc1mxhgljkhNTXVpe+7cuT4TR7gjJDP6yy9L9v1OndjCL78M/Fov8HBpKCujzDvuxTvDwz39R9RqljtoUEB1aB0oKKCETPHxnj4LXbsy9uGHQftnOMZQUkJ52uUy5fAjNpayK7qlawvItePQIco66K3uHT8MBiryGSSWfPAB+cDKtXnTTa7FXH3NwwXU1Mi75ERHu1ZAqqtj7K67XIelVFKZGF9Z7U6epDxn7u4pBgO58ISKhQsXspwcxsaPl3eH0esZW7CAXJF52v42beTdjhYs8DttLti5k1yY5ZI+KBS0RBcs8B8d0NxJH/LyKP/ZqFHek2PwPnr7Ta2m5R8fL1+bl1/fujVlhfz8c8a+/HJRs40hFLS0u5HA1YMrnn/Ytk0KNdLpGNu3r/mT21VUyPMPkZHybr49e1KN2kDR2EjZi3r18tzswsOJ/peVhTYOXv7QuU6w3CY6dmzwJQQZI2L13HPybsPux4gRIZX/Yx99JM//REdTsqlA2nDCkiXyhSFuuME16mrxYtf8ZQDl+frpJ9/3+7//k2On7GzcOO8hUYGMwWZj7L335NlMgLEuXSh6jTHy3B83zrMAiFZLBTuCqUxVU8PYCy8w1qZNpWzdV72eyibx5eZvHE2B+/W//srYww9TEi25skiB8BB6PblSJyR4b0Ono2oLCxZQyFRT8VtOPIXmbvChhx5iUVFRbNOmTez8+fOOwznO5cEHH2Tt27dnv/zyC9u7dy8bOnQoGzp0qON3XgJgwoQJ7ODBg2zt2rUsISFBtgTAM888w06cOMHee++9i18C4J13pFWUnMxYfX3zMtKHDlG8qvsKj472DL6IjaUUsg0NgfWhpoY2d7kaI0Yjyx4yxCVbYrDYzt9cb2+mSkXF053T43mbB3c0NlJQp/sO7nbYASqVFMQacODsWcauu84zw7NCQVmU/VUNdxtDXR1l+HNvLipK2tgZI+Fv7lzXzV2hsLMZM3zzCWvXyvMrUVGMLVggky45CCxaREXMvREmno15wwbGhg3zDIEOC6OU88GEVW/bRkRbLr6WZ0N+//3giG5T3819+xibNOkE69tXvmZcIARJpWpkMTEkFHvTy6jVxIzcfjuTjf9p6TJGLU2kBK4eXNH8w6FDEh3Xaim9PGvG9zsvjxTA7htSZKTn5hMeTokdgskl8cMPjKWleUoiCgUrb9eOpLFQkZHBcgYP9l4+EKCgxFdfDT6fBmMUQztmjGff3Q6bQsHYzTcHH4RpszH28susUS5JRXQ0EdUAwcfx3Xfywu348a508Kuv3MOf7ax9e6IlvnDHHfLpS2bNYuzrr0Nfkzk5jPXtmy/btkpF/E5FhaQcd6skxRQKqhjwyCPbAn4MdXWM/eMf3uNrExPpvm56Eb9oanWEe+7ZxW64gQwRvpaeb6G2gcXGkt7E23lqNYW7P/IIbTPNNYbmaKOl+Ydmz66skLN1gzIX3nXXXQAAs9mMp556Cunp6WhoaMDEiRPx/vvvO1yJACAnJwcPPfQQNm3ahLCwMNx555145ZVXoHZyC960aROeeOIJHD9+HG3btsXzzz/vuEcgqK6uRlRUVGBZvz77jIIeAPKRzMoCwsORnp7etLi/2lr8umABhq1bR21yKBTkPlRVReuYo1cvckm+4QbHVy59sNspjrSsjNxkdu4k34+TJ13b0WiAYcOAZ58FJk70Pg6LhXwoqqroqK6mo6aG3JOWLgW2bYNHAAdHu3bAww8DTz5JfhT82poa8uHlR10d9m7ejEG9epELlMkE1NcDp09TjI+z76sc2rfHrnHjkDZpEl3Hr6+vJx8O56OhATCbYTeZYcoqRG1WMeoalKhDGOoQhnoYYIYBptFTYL79PpiZHmYzNcVdSi80AYuF/ua3PHPmHPLz28ApcaLjcSYkkKeYzUZHY6Nn3EKnTkD79hno1q2ro7uNjXQPi4WWyNmzro8SILebxET6rK2tg1YbBrsdjoMx6dNq9dwmbTbPNp0RHQ20bUvnnT9Pj9HdfUajASIiyNPdZKqHTmdwtMvPdf7bYqGxeXPDAexQq5UO9xneV3d4/96OUONRLy8wBBpX0xzXKxQUy7V1K/0/b141/vCHAPdJAYEm4IrlH4qLaXM3mYgI/Por+Q4CTeMhrFasef11TFm7llxq+UaoUJB/qbt/ZmoqhVvdfLPL144+2O3Ux/JyOvLzKRHChg2eLsMpKcDddwNPPIH0pUvlx2C1Ej9SVUV94fxDXR39v38/sGyZZ4Akh8FAPqr/+hcFYrrzD3V1js/dmzbh2t696TtOkKuryRfU2XfX231uvRVbo6IwcuBAT/6Bf3LC39AAe30DzBX1MB3PRl1pPWoRBhPCUA896hAGc1RrmO6ZD3PvQQ7egTfhzj84syeHDpXh/Pk4D5qm1QLx8fSIOA9RV+ca4hIRAXTpko+0tLYuvEpDA9Fak4lCp+XcYsPCiM4zBtTVmaDVGl14CF/8gzN/IQeVih5fbCw9uqIi6oszFApyo42IoP9NpnpoNAavGgnOQ/H+eILcfBUKcqfl57if6/v7K4GHaCr/EHwb118vre34+GqsWtVy/MNFLyF0OcMvkVqzhgS0wkJpJ4mJATIyHPEZfgnUPfcAu3bRRmwySTub1SrP3Wu1dLhLSeHh0g7Ed6wLb7jdaoXSG6cv1354OH3yNmw22CwWiu113s0uIxxFb9yHj9EILRqhQSM0sEADKzSwQg0rVLBBDRtUjsMOpeNgUMAGJVxf1Ka++AICVz5uuqkaS5YIIVdAwBkBCblpacCJE5KwqVAAP/9MwfUX4JOHWLOGFNE1NZLwxvkHuQwxKhXXMrp+FxFBvIvVKmlMuYRgs4HZbFAEQvNVKpKGjEa69sJhbWiAWqGgPjUn/+ArmUGQuA1f4hS6w3KBh7Be4COIf6DDmX+QeAgFGJSwQQFXgUfwDwICctDpJJFJp6tGQ0PL8Q8XNbvybx52O2W+4YiKoujwYBIQrF8P5OX5PIUBUISFEeHhB0BC7fXXU3YbbvGUgU89k0JBhIlHo1ssnokQACl51WWKXLTDLgSfPEdAQCAw8OQVnD9OTCTr/fDhlLxDQEAgSJw65WpNffddFwHXL86c8UzFLweVisxV3FwHkOvQH/9IyZ8qK+nwAp/imjPHarNJ1lgnXDRGshmV7asxDZWIabb2BAQEJCiVlIy9Vy/KFQdQhuq+fYGffmq5fgkh1xfGjwcmTKDUtxMnklY22BI6s2cDBw+SwBoXR4QnKYkOmw14801y2amrk67p0YNS2c6eTeV8srNJcxoWRlbYyEjpiI7GzhMnMGTKFNLUxsZKB8+QOH8+ZTjW64mTNRrpMzzc0e6BjAz0HzWKNL78iIykz++/pyyNJpMn0QkLA7p3x8mEBPQYOlTqp/MREUHZqE+dovvyQ6+nQ6cDDAZk7d+PzidOyPrSdDEWYkBEATQJUdDpldAalNAa1dAZlNDpldDrgdzcM+jbN8W1WZhg+PEHGHdvwlfmm5CNTtDCAh0s0EUbYOjRAYa4MMeU5OWdQe/eKdBqpTb4Z2UlpbdXqcj7zPmRcSiVNowZo0KXLnSdRkPXarX0uWIFUFBAHmv80GrpPH6UlJxDQUEbj2lQKGgTqaykZahS0fX8kx9VVWVITIyDRkP/83br6sioUFzs2W54OC0fuh6oqLDBbndVfWi1tGnFxpIzg0IhHUqlpE9RKICaGjNsNr2sFzvvOyCd73zwPlmtFmi1Wpfv+etnt0vLxNnDkZ9LLl02AKom8kmX1lX4YvZBoaDnGxEBJCcT8bn5Zsq4abeTJ2NBAV1x002kWwNonp94omkjEBC4KvGHP9CGPWYMKayDtWQMG0ZHTAz5qiYmSjxEXBywaBFs334L1QWPLABAt25UCpCHNB04QDEuzvxDRAQp7S/wEduOH8eIadOoTX7wFKzbtgGPP+5Ku515iPBwHMrIwDUjR7ryJ9HR9FlQANx5J5UocvdeU6tpMxoyBEcbGpCaliZZiiMiHO3j1CkqacTv7cY7QKfDqawsdM/LI+Lkfh+FApOidyEzaRi0BjW0egW0BjX0RiW0BhX0eqJveXmncc013RysksEAGDKPwLByMbJzGL7CHRf4hwboNAz6donQpyQjLIx4kIKCTPTq1cXRNc4/8PY+/ZQKZVgs5CAoZ4yPja3B9OkRCAuj6zkPodORvWT1aonWO9N4zkeUlZ0H0BonT3pOg15Py8hm8+QfVCq6XqUCqqpKkZQU73Gfs2dJ7+JO1zUaetzh4fRbSUkjLBaNx9giIqg8UFEROTU68w5KpXRYrUBtbSOsVo2sw6Na7UhO7sFDXHjcDv4BkPgG5/Oqq2ke3PkHQGJzbTY7GGuKu/LlQ/+bow9KpbSG+venbe2664iXAEhESEqivzt2JNGlupq2mhZDi0QCXyZojoDokAKyc3IoRat7RP6kSYzJZJS8KH3w18bKlZSoQS6RQnIyY0884Uha0aQ+FBRQpkS12jPpk0rF2OTJlFUg2DH89BNlfnKP1E9ODjl5xY4d3nNf9e1LeT+a8iyOHqXcGu7Fu3U6xt54I/B2nPtgs1EiTPcMzAoFY4MGSVkLN2+mJemeHMFgoKTSGRn+77tmDT1KuazB8fGUPTqY5R3MXDY0MPbFF/QKuRe+D+RQKGisBoP35A5KJWWLnjqVskYGkkC0pZM+BHJ9Tg4lLOPjfPxx199bOnGEgMDliOZ6L4J+v0tKKO2+ezKpIUMYO3Lk0vTB3/UZGZTZSC4NfWQkYzNnUjKopvbBZmPsL39hLDrak38AGEtKYmxF4NllHX2oqqIsPs4bIycCo0dTCQBf13vp6iOPyOfWMhoZe+st/234gs1GyQsVCptH+ykpxJ8ECuc+HD/O2JQpnsstIoKxBx+kBEv19Yw9+yxl/HW/d5cuxL94YbkcqKpi7Jln3JNoSaxg//6U+zXQ5JPBzuPZs3T/nj39F++QO9Rqeo6+cqfpdFQd4vbbGVu2zP+chDKO5r4+mDbS0qSx8lekpfkHYcm9lCgtJY3mjz9KqiKDASfGjkXP778PvDbdxcKGDVR7dts2zyQTrVoBs2ZR9e02beSvDxS5ucDvfkexyk5FvRy6ptatgf/9D5g8OahmlWYzFXr77DPXmn0qFamcXn8d6NcvqDbtdkrC8957ntpLrZbqvr72WvAGfmc8+ywZ9KUpp5mIjAS+/hqYPj34NnNzaSpWrnRNTBEeTuV/X3mFpv7554E5czxLHCYnV+KVV6Ixb57v+2zbBvzzn1RHr77e+ReGxEQFbrgB+L//a/6aq3Y7lWH+6itg+3bSintabL1rMJVK0ozbbPRcGXPvPxkM4uMrMGNGDG65hRw6mvKcmwN2u1wdPOk3OVgsSq9lJg8cIE0s//1f/6LSjmYzjfWCIlxAQKClUVpKhVJXrZJedoUCRd27I2nVqtDr2TcXzp0jD7Tvv/d0FwoLI0v2n//c9ILmdju5Yf/3vy5eX46dXqOh4rGffUamviDQ6sABKsy+f78rQUlMBH7/e+p/kHxafj7lzqIa9q7o0oVodM+eQTXpghMnqH0pbykRKYWCPONXrw5+H7fbybP+9dddI/YAoE8f4IUXyNFw+3biT5xKDQMAjEYL5s3T4m9/I9bRGywW4P33KTfqqVOuU65W23DttSo89BCxi81Ne4uLgY8/Ju+6o0c9E2ERvPMQWi0tLx4izw8OpRKIjq7DsGFhmDCB+Cxfc3Gp4C0dkPcEoL55CI6bbiKWHiAHlmuu8d3mJUOLiNaXCS6ZJbemhrFbb3XN167VMvbkk4w1NraspuannxibNEk+9X1CAmP33x9QDV6/fTh9Wr78gJOKK2PkyMBUW86w2ajgWVoas7ub4BITHWWWAgUfR0EBY336yHc1Odl7KvpAn0Venvf2o6PrPNLAB95/qg/n3maPHlTFwGaj+rk9e3reNzaWNM1lZb7HsWcPYzfeSKWC5Cy2993H2FtvLQttAC5jce3Dtm1kDe7YUb70gT8tq07nffkpFLRcrr+eLN/cSaGltahlZYzdddduNnkyabh9lRpo7iMmhvrQ0ppYAYHLEZfMkltSQvXVnF9+tZp4ipKSlvUWKShg7MknWW1srOcGotdTzfmNG5veh8ZGKjAqV2PuwgZe2aqVZ/2UQHD6NFmd3a22vOyhF6utvzF88IF8uVylksr0eGNLAn0WL70kX9pOqbSyRx8NuMsuOHuW+qZWW13aNBqp9HJREVlun3jC02qrUFCpv59+8j2GhgbG3n6b+B93Oq5SEZu4ZEnz096KCrIojxwp/1z8HTqdvGMjPzQaxrp1I/5n82bOmrasJ5fNxtif/7yePfQQzbev2rwX4/jpp5bnH4Ql92LCYiFT32efSSoelQq46y5Sk7WE5dZqJfPXJ59QnO4FM59jIcTFkVrw+eebrhk+dgy44w5SYcoFn+j1FDf0v/8B4eHYnZ6OLoFoXu12yoTz1lvAnj2O8kIKgNRnQ4eSSWr48KC7vH59ZzzwgGflBaWSpmXx4qZZt/7zH5pa93hepZJCwJcuBZYvX46+fQMvKXHuHLBgAV1L2sgoAGSpnD6dFNM1NWRRvfNOV8uuWg2MHAn8/e++p+vECeBvfyMnBPeyR9HRwNSpNK7u3em79HRZtWhQyMuLxGOPUdKCjAzvFkw50DKywWajmFx3LatGA3ToQOFuc+YAkyYFrfRvdlitNNZly4AdO0gzT89zcMt2TEBA4NLDagUefJD4B24SUauB224j1yKjsWX6dfAgmfh++okCTAGE8d80GqK/Tz3lUuYwJJjNxD8tWuQtAQbFH3/4ITB6NNakp2MuDw70h5wcInrLl5OF/AIYAEVSElltn3suaGJfXa3G8OG0fzPm+ltEBNHi++8PqkkXlJaSg9u+fZ7tJyYCX34JlJd/F1RJKrudpvCNN5ytwZQ0o3t34htuu41YxjFjPKtRxsQQX/Hii1LIeXq66z2sVjK8f/QRWU2dLXwKBdC1Ky31Rx+V6LB7G8HCbFbio4+IZ9u/39VbzR8UClrKVqsddjuZkJ35JoDG2qMHeULNm0c5LVoalZU0b6tW0ZiLigDGgkh0dwVCCLkXA3Y78Kc/UbIl/mYoleTf8cknwSefaCpqa4F33gG++YakFXcfgpgYZPfqhU4ff9w0vxmAdt977vHcyTgMBpqHjz4Kjkjb7USQ/vMf8olw9x1OTsbxfv3Q67vvgib+Z88Cc+cCu3cDdnuay29RUfQY77gjqCZdUFlJAvKvv3oSpqgoGtLddwfXpt0OfPEFyfKnTrn+FhdXhxdeCMO995Jb8pAhnmUIO3Yk/uGxx7wLd9nZJNiuWOGZkDsignKyPfdc0B7gXmEykfvQwoWUUNRsnhrwtWo1ESZeTpkEWilxVkQELe3x44lgN3WZNweOHiWCtHkzvZYySc8BAAoFQ+vWCgwYQEqL0aODE8hXrlyBGTMkhvPdd4F//5v+NhhIOdKtm+d1Ol0QgxEQEGg+vPkmSRc8hqIlhVu7nUoZvfMO+ae6C5waDcratUPcP/5BbsJN8Ss1m4EHHiAltpz/qFIJ9O5Ngv/AgcG1nZtL7tTLl3u6U+v1wKhRWDdiBCY9/3xQzdrt5MX87rtAbe1NHr8PGEAuyU2J8nr7bbqHe4ENhYIEz1WrpGURqHB46hQpx9eudWWndDqgf/+z+PbbjsjLA/76V+C++yTaCpCtJi2Nfrv+evn2rVayX3z4IdFzd3awa1fg9tuBJ5+kMKrmwLZt9Bw2bgSKi2/2f8EFSEKtVAGL5oTWskpF+dEGDSLdzezZzdfnUGGxkNFh9WpiibOyvBVgYQgPVyAlBRg7lpJNxscHd6+VK1dg+vQbHK/2+fPEPx44IJ1z113AX/7iel1ysmfk4yVHi9iPLxNcFHfll14i3w5nH45Jk8jPI9A2mtoHxhg7f54yyFAmI8+jbVty/zl7tul92LGDVSQne/chNRoZu+ceyk4Q7DhWrqRED3J+Im3aMPboozTWIMdQVUUJhLwlCRgwwOcjC2gMX3/NWHS0rGcVS0vznrjI1ziyshibM8fT7USrJY+2kycZe/rpTSwtzfNxhIUxNneu45HLoqCAlkVUlEn2MU6d6t1VO9AxOGPPHsbuvdd7Qi9vh0rlO8FDYiJj/fvns48+omcdKprr3czKIjfoqVPp1fPmNg1QXpaBA+n13bqVsa+/bp4+2Gw01/w+cXGOV8cnWtrdSEDgcsRFcVfes4c2COcNYepUn5vYRXGJtNkY+/xz8j+V22jDwii2Y/lyxmy2pvWhro6x22+XD5kCiJANGOA3qZZsH/LyKDtSUpK8/+nYsZQx0VcbXvDBBxSe443l+etfA25KdhxFRTT9ciEq4eHk8uvrejk0NjL2+uuMtW/v2WZKCmMff0xTNnbsGVnepXNnn3k7HeFQHTuWydK4zp0Ze+GFwGhyIM+iooL6M3Cgb1diuUOj8c6y6vW05KZNO8YOHPDf16aMIZA26uspUdWDD1ICrshI7+NSKCi0ado0xj76iLFPPvmuWfrAGL2Co0e7rsnYWMbWrvV+bUvzD8KS21woLCS/z4wM6buRI8k1uEOHS9MHsxl49VXKU++eLYC79sydSz4h0dFNu9d//0vuPgUFAGPwaC08nNR0b78dvB/oli1Usmjz5mZNgGW1khfSokWeSYYAsmpNmHAUy5aF7ndSVqbHgAHk0eVutTUaKWHDggXBtVlZSdPx9deeFtlOnUgT2rcvndOvH2A2j3b8rlCQJvnPfwZmzpRvPzubrl25ktxbCAYApNUdOZIcE4Ip7+gNZ87Q0vnxR/lSBN7Aywxwr3ebzdUDPiEBGDyYDAm33kpeZunpW4Jy22oOmM3kxffzz8DevcDx4zNx++3y3voAzW/79tT3G24gS627ocZPmW2/OHCgFd5/H9i5U3LZ7tgROHKk5bXRAgICIPPRww+ThxNHairw3XeX1u1kwwaydm7f7mq6A8j8c/31lIlxwICm3efwYSLG+/c77uPCJahUdI8vvySf0GBQXk7ZEBctolgeZ2i1FKPyxBMhuVOvW0eGZnf2CiAa1b59GbZvjwvZamu3A998cw3uvdeTR1EoyHi9ejW5JgeD5cuBl18mmuRMiwwG4MYbydX4q6/Ic4vcqbs6zomOpqRCf/+7fOIk7lX24Yf0OInGxDp+79SJ3HmfeabpTowWC1mqFy6kSLVgXJBVKslKC7gub72eltmkSeSImJJC36enH0K/fr2a1ukgkZ1N3lUbN1LEX37+HPzud97Pj4wkq/iQIcQajx3r6kyRnh5EnJcMiouBr77qh6eecuU/NRriC//615ZPyOkLCsbcWfGrB9XV1YiKisInn3wCY4guQAUFBRiWnY20jz6CymYDA1DZvj1+nT8f1e3aBdxGcnJySPeH3Y7o5csxZNcuROflQeH0OO0qFco7dkTmmDHI8uPj6LcPFgsGfPMNOm7bBm19vUe+OQagUa9H5pgxOHjbbUGv+qizZ9Fl4UJ0PnMGGqfgBwbAHBmJ/MGDcWzGDNT78LOQG4PdDnz3XR/89FM3mM0auGbKY1Cp7OjXrwAPPbQDRqM9pGdhNivxwQdDcOBAW1itSo97JCdXYcGCjUhICMxvo6CgAElJyVi/PgU//5yC8+cjXdrUaKzo1+8cRo3KxJYtXXD4cGs0NDjXpGOIjDRj9OhszJx5DHq95yaXlxeJFSt64dChNqit1bq0r1LZ0L79ecycmYlBg855XBvoGJKTk1FZqcMvv3TFvn3JKCiIgsWigvv8EOS+c/9e+j08vAFdupQjLS0Xw4blQKv1dI1v0nsVQBtFRUbs29cWJ08mIj8/CuXlRpnxSVAq7YiMNKN9+0r06VOIIUNyEBfnf00EO46srBhs2dIJx48nobAwAlarc71jhm7dSvDccxsC1j2ZTCbcd999qKqqQuSlDrUQELhM0Rz8AwBUHT2K2z/8EGEX4hUadTrsuv9+5A0bFtD1Td3nIvPy0PXrr9H59GkP2muKjUX+4ME4Pm0azHFxTepDh82b0XfJEoSVlQHw3PHtCgXKO3fGr/PnwxRkGlql2Yy26em45uBBhJWUuLRtU6lQmpKCk1Om4JyfzM5y48jNjcS77w5DQUH0BQHJtecJCbW4775d6NOnJORnsWNHW3z11UBUVho82tfprJg9+xCmTTsTUFu8D2fPRmHJkr44cqQVGhudN3uG5ORqTJ9+DFarCj/91A15edFgzJUH6NGjBDfddBjdu5d53MNqVWL9+i7YtKkL8vOjHHGrvP3o6GqMHJmP6dOPIzw8NCGroKAArVsn4+DBNtiypSNOn06QmR+6n9x3CgW7MCbP3zQaG9q0qUbfvucxZkwWWreW9fNt8rvl63q7HcjNjXLwEOfORaKqSu82l6791uutSEysRdeupRgw4Bz69i2EWu07hXEoYygqCsOqVT2xf38yKipc51yna8TYsZm49dZDsnyXO1qcf2gR+/Flgiab0RsbWf6AAa4+lG++GXQzIbk0rF1LhU3dXYkUCira+skn5DvSlD7k5VF2RLnCp/xISGDsb39jC7/+Orj+22yMLV5MLk9yGRNjYijjYSAFWmXG8N573l2JVCrGhg+XrxsX6LNoaGDs7rvlsxsClL3vH/8IuOsO/PADY127lni4+qhU5Lr0zjtUY02u/GBMDP32xhvydQH37KGat3JuSHo9JZL84YemZQWsqyM3qh49CkPKYOjNrahLF8bmzaOsixerTp63NhoayKPt8cdp3SQm+nY35u5kvXoxNnRoNnvvPd8u4k0Zh81GyUvvvZfczeTr+9lZYiJlzg7FBb+l3Y0EBC5HNMt78cUXzObsM3n99X7DetwR0j539ChtqHJZkePiGHvgAYpdaUofGhtp05QjOM6EZ/JkxgoKgh/H6dOMPfQQ+d66+/RygslLCgQ5jrw82uu97fMJCcRiBTQPXrBtG9VMlduvlUq6P8/yHyhychgbMyZD9rG2akUFPT78kLHBgz3HplCQK+zXX8uHyNTVMfbyy1SZQc7Nt107yrpcVNQ02rt1Ky3N+PiaoKoo+Ko8EBdH/M3LL4deLzgUOK+njz6icfXtS6+Er/6q1RSNN24cYzNmHGEbNwZfeCSYMXC37yFD5KtmKJU2lpZGLtPBoqX5B7TIXS8TNGny8/Jcc6i3bh2UQOaMgF+kAweodotzzC9ARdA7dmTsxRdpJ2pKHzZtYiw11fvurlRSnvTly4MfQ0UF9dFL+xadjvLXhxgE8eSTmzzCmZw3wNRU/xUG/AkUjz/uvYqBWs1YampB0DGgy5ZRnIOcLqFbN4rteeABEq7khCn3KXMew9atFJsh12ejkbGJE+XjKYLZ3NevZ+yWW4iINkWY5UdYGBHhG288wo4fD24uQx0DR0EBY++/T3HPPXowptNZfPZVpaJxjxzJ2NNP01w4E6PmjpdrbKT1cuutxNt5YwIMBsb69aPi9t6UHoGipYmUgMDliCa9FzYbJUhw3kjkpKYAEPAec+gQbRwyGtJGnY6xmTNDK8Hj3IeyMsamT/ddqyQ2lrE//tGDa/c7DpuNNr9Jk2SDEu0KBdVJ+fDDkCSCsjLG+vbN95rrISKCsWef9S0z+xvD4cMk5Hjbt5OTGXv66Y1B9Zvn0ZCjvxER9MjffpuxYcPklaAdOhBb5qxb4eOoqGDsuedIyewulCkU9P1zz9F5wcyDM0pK6P7XXBN8XK03FrVjR8aGD88KSikuh2Dpt81G5YOefpqxoUMZi4ys96sQ1+kY69SJcqq88Qbl72hKHwIZw9GjFBs9apS8wYT3a8QIxr76qml5QVqafxAxuaFg+3YKUGxoIEeJ3/2OAhouhmN6fj45vS9d6pl+NSEBuOkmLO3TB7Meeii09u124L33MOMvf4FXx3+tFrj2WkqVJ5eG1RcOHqT0wT/9RHHL7khMpJS3jz+OJRkZQae+f+MNar6wEGBstMc5HTtSrMjEicF12/ke//gHZaN1L50DUJzHtddSjEjHjkB6+mZERvofw4oV1ObOnZ6p6aOjTbjtNiMUCnrsf/2r6+96PWXY/b//A0aN8mz78OFW+PxzyjTonqAyIoJiNp55Bhgxwm83ZZGbS0k+V60CTp8OrrSPHMLDKfxs6lSKh+HxTOnpR9Cz58XJy2+3U5mH1avpGZw6RbEnnmOR3MDDwoC2bYE+fWj+b7iB4mkvJsxmYOvWDvj2W8r+Tevc87yICEo6OnkyZTl07ld6urwrljPsdorZWrTIcw54JmYBAYFmQHk5EY3MTACAKToaxv37KXixuZGbSylPly+n5A7OMBqJgDz9NBYXF4eev+DYMVz//PNUgkCOGCgURBxfeYVSuwaD6moiNosWAcePe7av11Ps7p134juDAbfcfntQzR8+TDG2+/bxGE1Xt069ntKYfPhh6KUDs7MpT8S+ffK5GWJjqUrBE0/Q/+np5z1PckNxMYUdL1niGXasVtswerQKY8cS2/XDDzR9zmjblvr07LN0f2cUFlIM5h//6JkPQqGg2NXbb6fKDKF46dvtlP/j00+JR/FWVSBQKJU0npEjqV/XX0/fpafvxOzZF+GduoDycuKBNm6kLMNnz9JydaXPUplQpZLKLXXsCPTvTzzEpEnBZzoOFiUlRrz1FuUJOXSI4mrl1qFSSX0bN47C5Z09+wPJ2F1ZSTlRcnNdvx/cwhUQhZAbLL74gjhxux1QKrHjgQcw7P33m/ce1dXEcX79NQm5zggPJ072xRcdCSkagi0olptLRck2bXJkN/DYq8LDKU/6++8HX+pn4UISiHfv9pSylEoqvnbTTZQAy/kNd07a5QXFxdT1devkKwwAQFIS0dO77gq82+546y0Sbp1K6DmgVALXXEOPp1cQOQlWrCCBfMcOT8E2MRGYMoUIztdf2/Duu66/8/KDzzxD5Yic4Vn6eKzL79HRtPH/6U+h5QuxWKh9nuzBvYawPBi8xaaGh5OgOH06lU4KMvwqaFRWkjC7fr0vYiRBqaRy0V27AlFRJ/GHP/TA+PFNq48cCE6coEodv/5K/Fx+Pi8J4BmbFxNDa3D6dOItQyWUFgutqY8/lk/GBhCTJiAg0Aw4epQyxPAyPFOmYPnvfoe5zSngmkzEP3zxhaeUEhZGNWcWLHDVkAbDQ5jNREwWLiQCyRg8th+1mrjbTz4JjkgCNEdvvEFE3j3TIkDK/XHjiH8YOtTxtT2AMdjtlPjwpZdof/WkAQxarQJTphDNCzUxX34+VXzavl1e7o+IIOH6X/8K3DZSWkrnL1rkyRZqNLSsxo4Fvv++BNu2tcKGDa7ntG5NbNf//Z8nzT16FHjtNSonRNWVpGRnKhUpoe+9l/ocCh3MyaEcpKtXE5vnLRGjK+R5CIWCStOMGEFC7aRJFzfxkd1OCbVWrybe7cQJUgT4SpppMFC+2aioHMyb1wFTpgBduly8PvJ+7tlDgvf27VTPuKQEsFpnyJ6v1QLt2pEwe9ttxH+GOo87dhCPKVfOOtDkohcLQsgNBs88Q0XQAVLxbdqEnKwsGRY0BFitpLH88EMyKznvvlotqamee44IVCh46y0iHAUFsvVrGS4UQX/6aUrXG8xqLy5Gn2+/pdR8p097th8WRkXVfv97YM6coN+kdeuotvzJk943x6QkYMyYA1i4sH/IL+qXXwKPPTZD1qDNNZiffkrEJFD4E2wnTyaC9+OPxJPQY48AQMSlf3/g8cdJm+w8rspKeqTffkvLxXXKGeLiiFD/8Y9k4QsWGRmxuPNO4JdfHAm0g4REnCIiJKH2nnuCzwwZDLKzSXO9aROwa9d03H2357w7Q68ny3FqKhHNG24gHQxHevoBTJlC2T35HPv7dP7bZFKjutr1+/PnSau6YwcxF3l5voVu/jyvuYbWy6xZrnonq5WIrlxZagAoL9d7aPurqijj9sqVrkxY167EAGm1tOaB4HlUAQEBGfz4I20wViu9XK+9RoQtWCW1N3z/PWlmDx1y3Ux0OuIbnn8eGD48tLbXrSP+58QJWamNAVAYDLRB/e9/wVVvsNuBJUsw9p//pNS+7pwyrwwxaxaZD4MkINXVpBhfudJbHVFHmVzceOMPeOih2UG1z3HuHPC3v431atA2GinL8LvvBi4oVlaSYJue7pnRmesRRowg4WbnTmDrVgCQJNjERKqq8NxzZO10xs8/UznmrVs9FddKpR0DByrxwAPAnXcGXyCjtlaN114jT7TDh+WFH/9QXOgLCbVpaSSMTZ9+8YRas5le0x9/pEzUp0/Pwrx53mmzQkFLvVMnMiCMH08eaVw5kp7+K+bO7eA4P1AewvnTmYfg32dmkgV59256JfPziaZ766dKRYrwXr1onU+fTnPqjOLi4HgIgPRpDz1EBgQ+H337khJedSHfZb9+JHq0FER25ago71m/rFbaPXbsoFT+u3bR9wkJ5Ibbpg3S09N9u/nU1hJXW1xMqriSEvJzqKigo6oKZbt2IS4311WCUypJwnnsMSIcVVV0VFbSjlRTQ7t3bS2O7NyJPp06kSnGZKKjpobehLw879WYVSra+W66CfuKijCwVy9qo76erjGbgYYG2E1mVJvUMNUDdWY16swq1DcoYDKrUFdYDXO9HfUwwgyd49McnoCGTj3RcM21MEckwGSiqXBrGhYLHY2NQFVVLbTacDQ20hAaGnxr/MLDgc6diYY3NgLFxZUIC4uGzUaPjh92Oxzf2Wy0EfDv7HbvLzafopgYIoIWC51rtVIbztfz1PQ2mw2MqUIQCp1hh1qt9Eh5z/vuH96tqJcOzdGHy2EcVycqKiQ+1e8+KSBwFSKg9yI7mzRuW7ZIWky1mswtF2JofPIQVitJT0VFxEeUlRH/UFlJR1UVzh0+jDYZGa6SBHc3euop4mj5+dXVrjxEbS1QW4vje/eiV4cO1IbZTJ/Z2SRZOWvp3BEdDYwYgb3R0RjUuzddazLRZ309EXGzmeh/vRJ19SqYGpTEO1i1qC+vh6nUhHroYYIRDdCjHgaY1REwt+mEht4DYO7QA2aLEjU1UrNmM9FjzkM0NgKVlTXQ6SJgtdL3JhN9741majQkBLZtS+dZLEBZWRUMhigHr+DMP/C2nHkI/rsvGAxAVJT0OJ35Bue2eDRkY6MNCkVTeAg7lEriHxQKqV3ne/hHU2nv5UD/Bf/QUrjxRlJwAC3PPwhLri/873+kAnRG374k7Or18te4o1s3eZcbJ7gk5k9JIZ+QP/yB1H1Ll5JQ7QN9AuuJJ2w2ImJvvIGBPk5biem4ESuCa7sWwJELR8AIzjeotpa0hBKig7o+ENhs8i7L3qHyf4pfKB0Cemi4HDb25ujD5TAOAQEBgRDRs6erK4nBQIrzQN1rXnuN/Et9wKUka5s25DH1xz9KPEpUlG9BFUDIzhqVlcCqVfBdmAdojUpUIyrwdq0Aci8cASMimJPR2EheSgUFzt8G0ccAwe0GgaOpSnKlX+W9fzSV9l4O9F/wDwJCyPUN7tqj0RChmDYN+Oyz4NowGKS/FQoyDarVdGi1gE6HasYQedtt5D/o7uYjp/lQKCR/DYUCzGZzqY/rAu4XI+cYz9tRKGADoNJo6H+VSuqnSgVjYxjgUipNupcCDAqFAlAwqFRKR5P84E1VVoa+6Wq11IZ/QuGqueNazODgqf3jbpuXzudBaDAFLh14QozUVGDGDHJPEwZbAYFmQHw8KbnDwykAbvVqCtYLFM4Kbk5cOf+g0QA6HeqsVoSlpZFfa6pMoj53X1OFwoX2w2YDs9vlKYZaTZuBXHYgp3ZsjHnyD068juo8A9xCaoALVErBaSyDWq10dEulkpqzWEJ1eaXr9Xqy7Pqn4RLtDI3ue+cfgm8rVFwOVlSBqwk87GvuXIpIdA75amkIIdcXevUiLWxTMs7s20crwIfld7Uvd6WRI0nVGBtLbRQXU5Dn999TBD9jrltReDhlbrz7bkqfp1ZLpsjwcK/9+M5HH0ZbgLxiutxoBLRa5zvS3+npi3y6bWdkUFzJunXAkSPyimWVyoZ+/VT43e/IgO4cd2ixUNyh0ShNpzvt5n3IzKR41dWryePKnbAYDBQ/cccdlJzK+fF6G4fVSommPv6YQp7kCG67dkDXrmfw2WcpXvmY4mIai1ZLh15PxvT336c4kMxMwG53JS7R0ZRQ4vHHKR74/Hnib7ieRKuV+B7KKig/hpwcCvlevpyeB2WT9A+NhpImXH89GQm6daMl6ayvcea9lEpg8eJvMW/eLQ5lhzsKC+nVOnaMkl3s2kV9qqjwzgjo9RRH0q8fZSZMS6NYbGfFCiD9vXTp95gzZ7bH9/zv4mJSnPA+K5Xkwffrr2RsOXECOHnSBJPJ6DOuF6C5iIig/nTpQjHIw4cDJ08ux003SYkfnPVTvA8NDa5zxPvDsW7dakyZMlX2vhYLvd4WC201u3ZRjHZhoWeUQlgY8MgjFLYXbIyVgIBACMjKahr/cMcdlC0oMtJrIOIKfyFTJ07Q5hkeTpvF+++Th9iJEw4i4KA4ajVtXnPnUrBdeDhtJOXl1AejUbYfvvgHADheSJdxFkSp9BSgfPEQJhN1edkyij09d05Oac7Qpo0CEyZQlFe/fq6/8uIOej3nYzzvk56+CLNnz8VXX1GOjr17PRNcKhRE6ydPpozIrjkcvI8hN5f23p9+or/d6VxYGLFuffpsw3/+M0L2cZtM9Ag53ddqaV4XLiT+ZO9e+ZDmHj2oeMYf/kAecFarK+/g3Ja3cdjt9Aw+/5yeQTAebtHR9DxmzKAlbTbT0guUh5Cbh6Iiamf9esodcugQrQtvtJrHz6akEB81bBjxgTz/hLNxxpmHmD17tix/YTJR9CH/X62mdo4fp8RPBw4ABw9Wob4+CjU1/pNtabXE5rdrRw4gQ4bQsXnzcsyYMcMxBuf5MJspgoH3C/DkH378cQ2mTZvi9b5FRVLfzp4FNm+mRFs5OZ5zqVDQer/nHnrHLnYyzuaAYHV8Qals+lMMJgGDHLRa2p2feYbeZkp9J0GhQF1sLMLmzKFsgz17erbRxBzlWq1n0gJ/qK2l7ITff08vTEWF5zlKJSm1R48mmbyg4DuvBEKr9V6uxWymVPqvvjoGDzwgn/03ORmYMIGmyJ34eUNODgnLK1cSr+JOVPV6amvePOC+++j/9PS96NAhxWubiYnU308/JaH/wAE5gZkhOVmB6dMpD5h7Vr5ADQHV1XSfJUsohDxQTbhSCcTH1+CGGyJwzz0uCSwd8JcMVKu1ewhSZ85QaPvGjSTc+kp0YDQC0dFVGDMmChMmEHEM9lWKjLR4lEfgOHUK2LCBiPXx48R0VFTIuYi7ZhbX6eh16tyZeMFhwyjRp3u2ysxMCsP7+eee+PFHWkslJRIxCVSjTzFgE/Hii9J3PLbKORbcTytQqxVobKT1/NZbgd37zJng33sBAQEnNJV/4NJHU5CZSYrxX36ROGJnREbiXKdOaPPSS/KparmZpgkINoO+3U775+efE9OdmytPK2JiSFCZPRvQ65fg7rvnhNSHgwdJgb148XTZREMGAzBwIGXzdVeM+xrDqlXU7rZtnhWcAOJLJk8mBTb3YE9Pz/OaWMloJD7o4EFKYPXTT3JZohWOykr33Ud9dqbFwXjpbNlCUXubNlF6l0DpVlhYA9LSdLjxRrp/KGywWm13mQerlWj2ihWkiM7I8J5IjJTOJvTvb8SQIZQMaujQ4BNWhYfL8xAWC4XKbd1KPNypU6T4r611nyNX93eFgp5hYiIJ2wMHEv87erSr/clkoraXLwdWrx6ATz8l44azwiXQZ2G1TsDf/+75vXM+Gf9elgwqlQIqFQnCf/kLHb5w882UhqClIYTcyxEXsg3io4+IC3dXJXJt6223AQ8+iBXLl4de466ZYLEo8b//AYsXkzZRjpY61zO7804SDpw3nUCTTJpMpLn8/nuyXpWU8F9aO87R6Sh8et48StgYSBUkux3YtasdvviCco3JWZtbtyaL5iOPBFf/a9s24IMPSMCTC9HW6cjTbPZsIDFxCe691zux9gaLhebktddG4Q9/kH8GcuCa6ZEjSdkwdizw7bermrSmysv1ePNNyuB45AiN2VuMsU5HhHvAACL4M2cSIU5PX9OkPlitNO+//ELr5PRp0vTW1PgmEAoFWRxatQLCwwswbVoyxoyhLJbOzE1ODjFg//wnCe1nz5Lg7uoWF2RdaVmoA7a6y0MRUnx30+4pICDQIrDbpZpye/d6unSoVMRhT59OhKx9e2xOT8fcKd6tPZcCGRmx+P3vab8+e1be8hUVRfk4Z83yDKtITw98k9uxgyy1GzeSAlva66S8IG3bEq0PRjFeVGTEk0+SF1lmpucYtFpi3eS81XyhtpYe56JFZLF0f6S8rM64cUCXLmvx/POTAmvYDceOEdu5ZMkUr1mi5WA0Er81axaVGlq37ocm0W6LBThwoBW2biXvpMxMyrsqB4WCLKA9e1IS8TlzqC/p6U3ni0tLDfjsM1ovx44RzS8t9V2xASDvt/h4IDy8BKNGJWDwYOpbSorE89rtpKzYsoVe15MnSZFQUeFOe9s1aQxN5x8AQAGbLdDSTwQ/qYguGYSQe7mgtpZUcwsXkmnJfTWFh5PZ6OGHL24O9QCRn09dXbuWhJjSUs8i7woFKYBHjGhaHa7qaiJIy5aR1sxb8fCoqHpMmWLA/PmBVUqw20kz+NVXpDUjbfEIl3M4UZo7l/KBBVo3r7CQvMKWLaPNy32TUSiIiI4fT4/UtfB2YJTFapUKve/YQW4nJFwl+7xOoSBhfdgw0rJOm9a05WQyUT9WryaeKjcXsFhulD1Xo6E10a8fWdZvuqnpJYUsFhI2uTB75gzNRX39rT6vU6lIw9y+veQedN119Defj3ff3Y3o6JlYvpxqK2ZnkyBbV+dfk6pUAjqdBe3aadGxI0U/xMTIn1dZSW0XFFD7lZV0D1815rh7U/v25I6WkiLvhnzo0EFcc00/l+/MZlrzhw9714b370/tZWX5HqeAgEALw2olk9t//0tSkDv/EBlJRPHBB5u+4TcDLBaijcuXkxCTmws0Nk70OC8igpJE33gjKWC9eeb4ArcKf/klfebkyAtvBgPQpk0pHnssHvfeG5gAmp9P3lKrV5MQVFd3g8c5cXFkqZs/n+hLoH3euJEe6caNkqu1M8LCSDE8bx4J/NwSmJ4u4zbnBZmZtGTWrSNrpCQ8+07ApdFQ2NLkybSkmloD9uhRMnJs3Ehe9GT1Hit7blgYlbobNowU4u7GklCQk0O87K+/Ej+bk8PzyNzo8zq9noTZjh2JXo4aRXwNV7589dUv6Nz5FuzcSfzD6dNE40tLA0tGptcDBoMJ3bsb0b073cd9rPz/0lJ6nvn5xENUVdE97Hbv8dUKBc1nq1Y0p9dcI291l+MhKispxC4jQ94SvGMH8TuPPOJ/nBcTQshtSeTmAq+8ghu+/RayxVlbtaIyA08+SaqpFgIXBhcvJstYdrZcVSIFFArq8tChNJwZM0KL/SstJTeHFSuIXstp8Pi9rr2WNHdz5gDff7/Mp+bObgfWrCHhfPt22gzk4noSExW47joiSiNGyLXkCYuFXHFff300Hn5Y3jUpMpKsv7ffTkJzsF5odjsxBd98QxvI+fOBu6wkJtJzmTuXrMWhxmRyhuH772kOz5zxJiSRa0urVmShHjeO3FeCybniDrOZiODGjeQCn5EhxeXIgzZ2dxfjESOoP1y4Li0ld6wdO+hVy86mdsniO9Nnn3icWVISuXCnplKs8Jgx1H56+veONVlYSML4rl1E1Pl9PF2c3MEQFqZAx4603qdOpSPQBO/p6Scwd24/FBZSstYlS2jr8Qfv8yogINDiuCDYTnz5ZdIiuxOztm1po3APHG0BnDlDdGv9etLhy4Uv8X0uNZX0+PfeG7ybM0DT8PPPdL+tW8k6JmeBCgsjhebkyXSvDh2A9PSfffIQubkk1K5ZQ+PwDAFSuHhlPfBA4IL5wYMk1C5ZMhW33+7ZZ4WCaMykSSQ4yEWm+UNmJlmE164l5Xuge7xWS0LQuHEUjxmoZVsOlZVEg378kZTSBQXeLMYMERES3Zs8OTi6J4fMTBLot28npURenv+kqEol8W5t2tBrNGAA0fchQ4iPsttJMN+4kULb/vUvare8HGhs9DT+uEOjIcGybVtqf9AgEpa5kplbpO12UkT8+ivxP6dO0XqUeBVvdyDePDKSPPZSU0nfNXly4MoJzkMUF1O401df0RgDQSC8xsWEEHIvNbKzSaWzYoUjvjaM/8aLnt90E0V1NzGWNlSUlhKB+PFH2niLi+VfILWaXGSuvRZIStqO//xneEjCU2YmafG+/HK815ha7o4zdChwyy2BCdBWKwmF3NJ57pz8OKKiSIcwfToQHb0E99/v31XYbAa+/ZYE/z17nEOlpdgltVryCps/33tMsTdwofzrr0m54K3/nmCIi1Ng8GASLOfODZ0wnDkjaViPHaO1IdcH7jLUqxcRgPDwNViwIDT3N55M4ptvrsHHH5MwyxM0+YLBQExRt24U62K3/4IXXrgOej15A2zZQgTim2+Av/2NhM7qav/xKNx9OSmJNKmpqbTmR4/2DFUrLqb7vP46CbIHD07BQw/RmvZ3H2eBmQvkaWlATY3vWDNfOHMG+OSTQXjqKU/3IaWS4sBGjCBCKweRoEpA4DKCxUIuQp9+Shuy3Q4XGapjR9rwFyxoej6QEGG1khC1eDHttzk53kMfYmNpDxo/HoiNXYZHHvGtVJSDyUTCxVtvDcMf/0hCk9xeGxFBe/eUKSSoBRJmnJlJU71uHQky7pFjAO2RnTqRlTY5OXBX4b17qVjHL7/QfaQ5knywIyOJBtx5J/E9we7H2dkUE8yF2kBLGanVNvToocL48WRBD9XGYjYTq7t6NbB7N60Fb33Q6UjRMGQIcMMNgMn0HW6//ZaQ7nvsGLBmTTesWEF/5+eTscQXDXb27Orbl8K36uuX4pFHZsJuJ6XGli0kYP7pTyS8+bfIkqKdC7KtWhFt79WL6O6oUa7egRYL8ZObNgFvvkmW34yMGbjnnsAUEnwMPHlVWhpQX0+8WCjWbpOJ+Nx//3uMrPFGp6N1n+I9DQ0mTSIvipaCYGEuBaxWEmzfe88zcZRWi+KOHZH4pz95Zgi4RDh1CkhP74vXX6eNUG4jB4hIdO9Oi3rePNeNLz09F2q1fx/h2lqyAq5aRZt8QYHz5i6VS1Aq6UUdMYLuNXGif5cUq1WJb74hLeGuXSTEyAlk7pn+nDWt3lyFq6tJ2Fy+nFympThgV0RG1mPCBAN+//vg3WjMZspe+Oabw30SaznExZFgN2sWoNWGJhTl5hJjsmEDsHv3DT5jcsLCSAs4bBjd0zO+2ksAjROsVgo5X7+eCODp0+7JFeSrNxqNkjA7eDCtx2HDaP42baI2Dx4EDh0aiP/+l56dv9gi54QQzoJsRcUy/OEPNzrOq66mtXXgAPDss8ScnDtH8c+1tXLPyzPxRFgY3adTJ2LwuMDsLclTIO7rZrPksr1/Pwm3589zd2eJAqnVpCH+/e8pgUog242fEpsCAgIXG999B7z8Mrk2uRG12oQEhD/wAPDUUy0i2JpMwMaNnR3ZfUtK5OkuTx6ZlkZuptOnu1c28M/FU94Mmo4tW2ifk5Tirm5Czsrru+8OzGZw9mwUnniCaOCZM/KChUZDgsrYsbSHpqU5j0HeVdhuJ4H/yy9pn87Olhf8DQagdesy3H13HH7/++BDeQ4eBL7+uh9efZX4ukCFWp2OBJXrryer9uHD3pOAegNXbixfDqxbN8mrwQIgOhgfT89nwgQqBOJuBEhP98/8nDtHxpitWyU344oKTocHyl6jUklhPtyza+JEUnocP048xJ49FJuckTERCxYENo9qNa25tm0pfGjgQMBk+gnPPjvBsc7tduIZdu2iNfb++9TnoiISwuXDk1z95lUqEowTEkgp0L27p3eaM9LTqwLiQ61WWqPLl5NR5fRpZ6FWynejUtFze+YZUrz4a7ul+QcFY5eu+uflhurqakRFReGTTz6BMdAMAG4oKChAcrJ8DKS+rAwDv/oKyfv2QeXke2LTaFCSkoJTkyfj3MCBPttoah/cYbUqsW9fMvbuTUZGRjxKS42w2VQe5ykUdkRHm9G5czn69y/AkCG5MBq9M9tyfbDbgaysWOzY0R4nTybh/PkImM1qyMUHqFR2REdXITW1HCNGZKNnzxK/L8+5c+HYsaMDjh1LQn5+FGprdTJtM4SHW9CxYzkGDcrHiBFnAxpHWZkeW7Z0xsGDbZCfH4X6eo1s21FRZnTpUoa0tFwMHZqHoqK8gJ9FUZER27d3wuHDrVBQEIW6Oq3sPeS+i4hoQOfO5bj22jwMHXoWer1EEAJZD9XVWuza1Q5HjrRGTk4MyssNsusAoNJOsbEmdOpUjv79z2Hw4Hyfc+jeB7sdyMiIw9GjrZCZGYdz5yJRWWlAQ4NKZmzSGLXaRsTENKB162p07lyO1NRCtG9fjuzseJw6lYCcnGgUFkaiosIAk0lzofSSt/Z4mzZERDQgPr4OyclV6Ny5HD17FqN1a/K5rq1VIyMjHtnZsTh3LhJ5eTrU1dGzaWhQgbFAtBYMOp0VkZENCA+vQJcu9RfuU4SkJC8aJB9wnkuLRYnjxxNx/HgSsrNjUVgYgepqHRobvc+lWm1F165lmDDhDK691nvmTm8wmUy47777UFVVhUhRQFdAAEDz8A+A9/3aUFqK/l9/jeQDB6B2kogYgNpWrXB22DCcnDIFORUVTeIffPXBHXY7cPp0PHbubI9TpxJQVOSNpjMYjY1ITq5Cnz6FGD78rGOPDaYPVVVa7NjRAYcOtUFOTjSqqvRe9mAGo9GMzp0rMXBgAYYPz0Z4uG8aZTYrsXdvO+zf3wZZWXEoK5PnhdRqG5KSatGzZxFGjcpC167eY175GKxW4MCBZOza1R5nzsSjrMwIu92930SPkpOr0K/fOYwZk4WEBFPAz8JqVWL37mTs3dsWmZne7iEPjcaGpKQa9O5dhOuuy0S7dq5KaX99sNuBs2djsHt3O5w4kYhz5yK98C80TqOxEa1bV6N79xIMHpyHlJQyv3TIuQ/V1VocOtQaJ08mICcnFqWlYait1foYL4NSaUd4eCMSEmrRvn0lunUrQZ8+hWhsVOH48SRkZcUiPz8KpaVhqKnRwWLxxY/wNhkMhkZER9cjKakW7dpVomvXMnTvXorwcAvsdqCgIBJnzsQjJycGOTka1NXForpah/p6DaxWpZ970H1UKjvCwhoRE2NCVFQJUlIa0LlzGVJSSv2ua1/zCNCzy8mJwZEjScjIiEd+fhQqKgxoaJDnzYnfrEPv3qUYMyYLqalFQfEQLc4/sKsYVVVVDACrqqoKuY2FCxd6fvnTT4xdcw1jpNCUjoEDGVu9OrA2mtqHCzh9mrEXXmBs+HDGYmI8u8QPtdrKevVi7OGHGfv1V8ZstuD7UFLC2DvvMDZlCmNt2jCmVMrfS6FgLCGBsbFjGfvHP6iP/sZRUcHYxx8zNnMmYx06MKZWex9LQgJjkyYx9tFHjNXV+e+7zcbY7t2MPf00Yx06lDGDwXu/k5OpDwsXMtbQID8P3u6xejVjd93FWPfujOl03vvvfiiVdN8ZMxj7/HPG6uv9Pwtn1NQw9sUXjN16K2MpKYzp9b7WAd2rb98C9tJLjGVl+Z8/5zHu20fPdMCAfJaSwlhYmP/x6XSMtWtH6+Hppxlbs4axbdsYmzdvP5s7l16bxETGtNrA5stoZKx9e8a6dSti995La/LIEepfXR1jGzcy9uqrjN15J70XHTsyFhHhfb1663NCAmO9ezM2eTJjjz5Kc+w+X6G82zYbYydPMvbVV4w9+SRjvXqdZ+3a+X5u/DAYGOvUibGJExl7/nlqp6n7S3PskwICVxqa673weD8XLWKsRw/Plzs5mYi5G1Fr6vvtq42CAsbeeIOxCRMYa9XK1x5pZ61aMXbDDYx9+mlgdNcdX3+9kK1dy9j999O+6o0O830uNZXOXbOGscZG3/NgsxFNefxxoicREd7HYTAw1q8f0aKjRwPre0kJY++9x9g11xSwxETiFeTaDw9nLC2NHmNOjnxb3saRk0O0ddQoxmJjA6dVAI138GDGnn1W4rd8wb0PBw4QPRk9mmixL1oZGclYp05l7MEHiedpbAxsDvk8fvEFY/fey1jnzqUsMdE3r8f5o5gYema330584qFDjD388Hb2+OOMjRvHWOfONPfenovzoVIxFhfHWN++jA0enMOef57WGH/N8/IYW7KE5uOmmxgbMIDejUDos3Ofw8OJlx02jLE77iCeZNs2T74ylPe7oYHaevVVxoYPz2KDBjHWunXgPETPntSnZcv8v1v+0NL8g3BXbi5YrRRx/tZbrr6sWi35c/7nP6FlUQgC8hkL5c+NiZFiYW67Ddi9Ozj3lMpKirVYv56SB2RkzPGaCdZgIPeeYcMoGcP11/t2ceCJI5YuJfeJzEzvLtS8hu/gwUBc3K/4z3+G+U3olJ1NcQYbNlDcpGu9Vsl3Wa0md5ARIyjMyV+/nVFcTAmu1qwhDzN3L3Vf0Ghs6NpVhVGjyI1n1KjA72s2A7t3t8VPP5HLTXa297lTKsm9pVcvcr2aM0fKT5KevtnnesjJoTibbdvITSgvj9xSGONneGqBdTpysenalZI39O9P63P/fmojM5NcjV9/nV/R3+v9dTpy0eaxJwMHUixwt26UMXjnTuCHH6qQmZmIzZuB554jd+JAU+DrdOR6pNdXoG/fGPToIcXIOpcBCAUWC62JvXtp/WVk0PyVlNAcer5HnvuGnMv2iBHy7sf794feVwEBgUsAq5Vqkb35pmvgm05H2ZBfftl34FszgLvULlxIrsCZmd7jAMPCJJo+cyZQXLwIt98eOP9gt5Nb6OrVxKucPg2UlMhnw+cJDPv1o3jam2/273qck0O5JNavpz22pEQ+9Ifnkujdm/bQmJgVePTRGT7btlqp38uXE505e9bZndU10Dcmhlw7p06luNpA3Y/5/Cxa5CvZpzwUCoakJAUGDKDY1rlzg6uNe/QosGRJKj7+mNx3vc0dIMXRpqXRMr3hBsr/kZ6+zif/YLXSGlu3jviU06eJR3LlV+PcxuWaPCktjVyMMzOJlp4+TWFw33zD+zvM6/2VSgq/a92a1nHfvpTvZdQoor9bthBfu369AitXUrKuykp6BhKP4x3cpVivr0H37hHo1IlcmK+5hvodSpZwZ3AeYs8e4p1OnyYeorSUXMRdw7M6ybah0Uj82MCBtP6vuy7wsla/FQght4nQVVaSJLJ0qSt3mphIWQ0XLLho6fqzsynGY+HCcZg/31vGQv+xMADFRHpDcTENb/16erHy8uQ2XFpKSqVEkCZPpqnxRZB4rbAVK4BFi8bhD3+grHRyG4lCQS9lnz4knN9yC8U1cqSn50Crdd3YnDP5HThACQi8Cf6UDr4SM2dG4447XMv6+ILVSkXZV60CVq2ahPvu8y5Y+hvT3LnAr78GpnDgCTfWrqUNOTubZzoeKXsfXktu5EjKbTZggP/+8TTxmzeTsHT2LD0fX8KiVguEhZnQt68R/foRIamvJ+J5+rRURN2fwKlS2RAXp0LbtiTMDRhAglxyMq3XffuICO/bR0zHU0+5C4jemUKtlghmUpJUgueaayjphbMQm56+NujYpMJCWmtHjwKrV1+Ljz6SygbU1gZee1CpJAVReHgNBg6MwODBtEZ4VsdAUVmpw5tv0jo5fDjwtQkQ8Vu6NPDzBQQEgoDFggFffEFBo86Z9dq1o6C3+fMvGv9QXS3lx9i06QbZBM0AMext2kh1zOfM8WTSfdW4N5tJmPnxR2LKvdc8JVfJyEja78eMIYHWXz360lISOD/+eBiee45ovDeFu9FIQs2QIWR7uP561700Pd11c+QxwD/8QDTr9GnvfBZl4TVh9GgjZs6keQpUYMjMpHwYv/wC7N59o2yxDW/Q6YgPGjGC+K3CwkWYNy8wmnXqFO3vv/wiKfyJLveRvU/btlL5v1mz/Csb7HZq9+efSYHCk0B5ZqWWoFBQaHl4eAWGDYtB797Em+XmUltZWVJCUV9QKChbN698wJXUw4dTvopdu4genjlDMd7vvy+XV0O+Rq1CQX2KiiJ+pHNnErwHD6b2uVIhPX1VSLHNnK85ehTYsmUE/v1vqbSgyRQ4D6HVAnq9GZ0769GlCwnzfA4CLYdptwNHjiRi40ZSuART1QOgd74lIYTcUJGTA9xyC2bu2uX6ff/+VKdj3LhmvR2vm+as2ZNooqt60N1KG0wNs5wcInwbN5KGSEpe4wmFgu6VkgLExp7ACy/0xODB3mmyyUSEbv16ycpYUeH8wriOIyKC2h4+nISyESN803urVYnly2kD3L3btxWTZ4bu35+yv82eTRt2evqPfjWQP/9MjMHOnXIEO8Z7B0FEr0sXaUxjx3qO6ddfPa/jlvOffqLNLzfXl7DCEB2tcMzdzJn+585ioWe+YQNpRQ8enIG77vJdp5UncOjUiYTn6Gjq0+nTwNGjDLt3k3DsCzxzcatW9Kz79aNETOHhwGefHYJaPQBnzlB27JUrqf1ANli1GtDpGtC2rQ7t2hHTxDf41NTQ8rvZ7aTg2b+fiPWZM/S+nD8vJZ7yzADt/eVTq8kiEhcnEcpevWhNDh4s5ZAJhFDa7bQutm6lz1OniJmoqAAsluAzlnJkZoZ8qYCAgDdYraQAf/99dHfeNAYOBN55h8xKzQi7nfgGXgbwzBl3QSNM+ivMNcFkMOViamuJ/nI6dfasb4HGaCR5vl8/IDZ2B15/fahXwdBup7139WqikSdOUMIeiUZ1cDmf0/gBA4jG33yz/7xc586F429/IysqrybgzYoZFUV0ZfRoouWDBwPffrvc716dm0uK9w0bJB7LVWgxeL2We1/xhE233OKZsFBO4cDnbvlyKp9z4gRZaL0pmtVqG9q3V6FfP+IjZ8/2b4XOzia+aMsWYMeOKXjwQX+lbXhtYqJ7bdvS+PLyOG0Nw5Il5HXnC1ot0dAOHYi2DxlC63fhwh0wGIbhxAmi0wcOAO++693I4QyeiFKnq0PnzmEellhvSSL9wWymZ374MNHorCwaLxdg5ZXg8oI2QGucJ6Fq1454KC5s9+9Pc5OevjRgQbu4mPjbDRtovs6eJf6WsdDlGV885KWAEHKDhclEKfWWLAEYI92jRkOpet96K7C89AGgtpYE2uXLiVgUFclvthoNEBtbg/HjI7xaaeXANZTr1hHB2Lt3hs9sukolCYE9epDgNGMGXATa9PSDSEuTCrfl5JDr9ObNpI3Kz/edoY7cQ2swdmwEpk0jLaEvLWhxMQk9mzeTJTgnB6iulq9Jxq2lqalEuG++OTDPL6uVXvZVq0jIysjwpoGWB3ez6t+f3KxuuSUwN5Xqai0++YQYhYMHfc8d13p26UJ80dSpvl3HOLFbt47GdPIkZSj0bF+afGc3oY4d6blYrTTnubnUnqcnQJjLf5wQtW9PBK1vX2ozN5c2/YwMUnysX++8KXo3NXMiFBtLjExKiqSpvfZa7jL1Q8Cbu9VKc3HoEGlRs7KobxkZ0zB/Pr2PgRBHZ+h0gEZDgnbbtuQW1Ls38bH9+wdf1onXydu0SbJi5+aSgO3blY1qFrduTQxf5840f4Ggl3yCawEBgVDxj3+Qa/KFl5YBUIwdS/VkmlJI3Am89MeyZbSveqs0wLPcJiWdx733tsZttwVeuTA/nwTOLVuAzZun+PVgiogghejgwSSgTZvmSuPT08/CaBzq6P+PPxLDvXcv7ceVld4FJqUSCAszY8AAPcaOJYumr9LA3F127Vrig86cIaHPap0ue77BQI9m8GByyZ02LbD9+9w5MhpwL7j8/MBDZgBJ0T9qFAnSQ4f6N+zb7WSZXb2aaPzp09694wDiIVu3hkOgnTMH2LjRuzeZyUT8Aw9Vy8qS8+6K8rhHXBzRnqQk+r+0lISo8+e9KVMlRlZOGd6hAymVjx0j2s0tvf/7Hx+rd3dlgATEiAgS3nmW4kGDyNONewimp68ImIcoLSXBkLsPZ2cDx49PxpNPkteE2Rx4tQyA+EeDAdBq69CxYxjatiX+q0cP4p8GDgytNGRxseQJd/Agrf1z56iP3tcm1Szu3p3egWDuGxfn/5yLCSHkBgq7HfjrXynulnPhWi2OTp6M1CVLmlz6JzMT+OIL2tSPH/eedjsighb5uHFkpe3d27+l59w5EtQ2byYNUm6uXPsStVGraSPirq033ui9TprFQu2mp1+DDz/ksTW+N/LISNpUBgwgoXPaNBJW5MbB44TWrCFidOqUXOwGB3HuXMM6ciQJy4EQBquVLJmffz4Ab7xBz8O9JpgvKBR039jYMsyaFefVzcpup3tZLPR5/ryk/Tx2jJ5VQ8Msr/eIjaVNftgwUmjIxet+8w21nZkpEbqjR8lltrbWt2aVXLYBvb4EHTokoLGRGKTCQurf0aO+5yA8nAhmWFgBxoxJhsFA9zx1igjaqVPEtARC6FUqOyIjlUhIIM1pSgqt9/796R58Pvkn/5sTzLy8KBw9SpaE06clQpifT0qjigp6DxoafPUnQnacGg0xaFFRtIknJUlKgK5dSakCAOvXb8N117lqQU0m0qa7w2Kh55+dTf08f57mPS/vRg9vRu9zRs8gPp6E/y5dAKt1D265xY/PnxPUatf485YuASAgcEVg3ToqE+ics2P0aKyYNQszHn20SU1XVhL/sHQp0XhvLrW89NuIEWSdGzOG3vP09E1eeQizmYSmdeto787IIMWaN6GGK167diWl45QpxK94U76fOkUK3a++Go4XXghMId6qFSnghg8nOti3r3eLVW6uRGMPHybLWa2PRM9areTlNXEiCZeBKKeJ3vbAwoUk6BQUBO5WyseVnAwkJOTgkUc6yCr67XaJd7Ba6dls2ULzx5UBVVXysc3O9+jbl3ivmTPlrZJ2O83Rxo2SUjUzk5auLzqkUhFNDAurQNeuMVAqyZrLw3XkvNScwZXhnToBGs0ZDBpE1oiCAlp3BQW0Ftes8d0OQO7KRqMCMTG0Xjp0kATEPn3oXs78A/80mYjXYYx4iMOHiec8dYr6kJND/Sgpofeuro74UXm+Klq2b0olPYuwMJqv+HjqY8eO9H6mpBAvRgqLnR48hNnsyUPwtZGZSbxWbi71MyuLFPW+++k8b1LOjy5dyHrd2LgF48eP9n2h09h69JAUBS3NP4gSQlFR3lNb79gBPPAAUYyyMmnnVSjILPfZZ0hf6tsV4OGHSWvS0EAH36AaG+mwWgGTyealjA9trlYrCYYREbRArVYiMI2N9FlXVw+NxgCbTWL2+cHv4QsqFaBUWmEwqKHV0n3d23APuA901SgU1L6zBsv9Wul/Bv/p1X2hqdc3By6HPggINA319ZK21u8+KSBwFSKg92LaNNJaV1a6Sp4DB5JfaUoK0tPTvfIQGzZQPW6zmXgHfnAewmYDzGbrhfIfrlAoyEqlVEo5CLiSlV/LeQmTyQy1Wu+g+c68hD/lmlIJKBQ26PUqaLWkJHPnQ0wm3zyANygUvH3qE/dAkedFmoP2Xg70+3Log4BA6Jg9m5xdgZbnH4Ql1xcqKkgl54whQ8gPyL1ytResWkWaQ98gAVerJW3T8OHkUjtTYa4+AAA0jElEQVRxImmspk/35ybrPYYjENhsgM2mDtodMxBwoTwwNHVjvxwIw+XQBwGB0BGoK7OAgIAfbNvmSrzj4ki4vf76gC4/ejSQxC1S0se2bYl/uOUWCl1Rq8mqev68vzZC8Hu8ABJeVair8x1/GwoYc7UY+xaOm2Pjuhw2v8uhDwICoSMUN+qLBSHk+kK/fkQxkpLIx2PePPK/CQLTppGrh14vHQYDHUYjHXl5B/Hii/1kY0i6dCG3GY3G89DpSDAuKMhG9+6doNVK3+l0dMTEkDtLRgZZVNVqOjQa6W+1Gjh/PgddunSARkPnabXSfaqrSVhXqTyPiAhytSguLkCHDslQqeBow/k+W7aQu4pSKR3ubdXWViAuLgZqtXS9Xi9l5928WdLq8uu5pVilAkymakRHR0KhIG23yUTXcS14IPEQSqUder3S4Yqq13NLN2m0c3Lonvzg/eF/Wyz1MBgMLmN0HjN39/HWBgBYLA1QKnUOLbvdHpzmG7BDrVY65pF/z+9jMrkyDtxyD0j3IQePphBbOwCl4/7uCCwu6XKw7F8OWvWLNw/Oa5C/RzxOD6CMkzfe2IRbCwhczZg7l/wG27Qhv7+HHw4qW3L//mT0NRjk+Qe9HsjKOob77++NKVPkm540iVwYNRpXus7/1mqB/Pws9OzZ2cE/cB5Cr6c2v/nGlWfgf3N6f/58Lrp0ae/4nrfN/165kozZnB7ya7VasjYbDEBhYT46dWrr0i6/V2Eh6Qv49c6fEv9Qibi4aBc+R60mvUJMDLnd1tS40mN3XqSurgrR0VFgjOgk5yEslkATFtmh1Sqh1xNvFB4u8UNKJYWiWCwSrXfmZ5x5CKPR4LIv836azeRUSPeSruHXA0BDgwVqtfaC8ULiIQKBxIfYoFarXGgCX1vcTdr5GrudPqVqq81BN+1QKJSya5qxQMZ0OdD/K4F/8N2GO/+gVFIugPBw2vJ+/LGJt24ihJDrC23a0M7aBLz/vv9z0tNPoHv3frK/9ezpv85levpOzJ3byevvDz8cSB9+xdy5oSe9SE/fEnSqdM82givXUltL2WR37KBYmz17WEBxp1otxUx26UJxwWPGUMxQeDiQnv5tk8aRnr4soOutVoox5vXYTp+mWCQK/td5vU6pJOG7fXuKTx0+nCz+zhm05cZgtxOjs2ULxe7wjIMlJd4yFrtuaEolzU9iIsVa9O5NCZ5GjpSP6fnf/75Hp05zcPAg3SsrS4rLqakJVMilPqjVxNDFxEglf3gSp/79KWGEXEh8evoizJ07F2YzxePymJqzZ6kvxcXUn+pqmgOLxf88yIHH14SHk9WEx9e0awdUVu7FHXcMQp8+gddI5KispLn7+OM9CA+/FhkZtEaKi8k41NAQqPLD+xg4U8LdGN1RWhpcnwUEBJzwwQdNunzUKNqvfSE9/TCmTevt9Xd/pVaojV2YO7ez19+fftrf9dsxd65377Z//SuQPmxtIu31XRnBHXY7eZJzmkg1YbUoLfUt0PL8G7xe67BhVFqpS5em8w80jsB4iNJScmffto34n+xsrkTX+HQx5/wPr486bhzF5zpb39LTPRNP8TjQ7dspiRZPWFRVJSdwetIcbnRxLwk4cKAn/bbbgddfX4tWrabg2DEpFra4mBwsfcVsu/dBoaB7R0SQwsM9PnfQIO+0mfMQxcXEQ5w5Q7xMTg6NvbiYkm7V1ND8eNLQwIRLbtCJiIAjhrhdO+K1Sku34+67h6NHj+Bq2NrttCbee28rEhNHITOTvEoLC0lRwucxMAWIbx6CK1Pc4d+L9eJDCLkClzV8bayeG4pnRr/4eMkaPno0eYldyrCA/HwiRDt3kuvZ2bNEnPwVdlcqSWDihHT4cMpI6asclN1OQtGGDZ2xcaOUaKmkhDYzfwIRF2STkgC9/jzGj2+NtDQSZN2ThufkkJD+wQckoJ89S5untHHOCWB2iAAZDDRWLsCmpFBSkaKiNXj66SmyAqzdTn04dowyZGZl0Vzzcj6VlUBNzc2YNy+4mm7Oc0HxZQ1ITNQhLo48CniGw27diEh26eI751x6+hmMGzcIgJQMjJcOyMkhIlBURGuiqooUNGazO5N1rd/+6vW0rnmSrs6dJWvs0aNHkJrqWfcwEEydGtJlAgICApcFMjMpgRIXZLlyV15QkkK/FAoSOtq2JXo0dCgplHt71yc0OywW4h22biVjx5kzpKCtqvKlKCaBxGAg4a1rVxIoR40iYdaXoFReTvf67rs++O47mrvCQqKngViy9XrKI2MwlGLIkHhccw0JsoMHu9JJs5n4uf37gS+/JCGW8yrV1fxeUwKaI15Ghydb7NyZnlFp6S946qnrvGYLN5tpPfz0k8TD8KSUXHCtr78lqJrFziALfiNiYjSIjqZn0bo18XQ8uVSvXiTQ+kJ6ei4GDBju6HNmJh1ZWSTEcsHVmYdwTaY5KqD+6nTEQ/AM1t260RiA0HmIbt2CvqTZIYRcgRaH2UzJuZYv74mlS6WNPJiNNS4OMBpLcP31CRg9mgRCfzXxmgvl5aRN3bsXWLNmJP72NxK2amr8a8ncS+ukpQH19cvx+OMzZM83mYjo8ULmPONgRYWz4OzdpZ67mPMC6ampdM8RI6TN1moFXn31EOLjW2P7duDzzz0tiIFCq/UkQD17UiTAwIGez6i6msLgT5wAfv21A+64Q9KYVlRIGtPALMGuvtJccDUa6b6xsVJW5A4diBno2ZM2eJ4J1FcZotJSipfjte4KCui5l5bSmqisBMrLZ+GeewJ3l5eDUmlHRIQSsbFEJDt2JOt1nz40h/7SA6SnH8XcuaEJuUDLZ0cUEBAQ8AbupbRpUyds3eoqyAZSV12tJua+TRvAaMzBzTd3wLhxZOULwrO8Sf0/fJh4iP37ga1br8dTTxEN8UdruSDeujXRhAEDgMbG9fjrX8d7Vb7m5JAgu2cP0dmzZ4m+uvIrqV7vqdMR7WzXjhS9/fsT/zBggDRf7723FW3azMThw1T2mZcKKi+nuO1Aw5WUSoWLEpwLiL16Ee1zr3nP1wJ59sXiueeIdykspHtXVdGaCNwLynUBqFSSVTgmhpTKbdoQDe7ShZ5Br16SZTg9fYlX/sFqJb5hwwZJwOY8BOd3qqqAqqqbcOeddH5oaYIZdDoFwsLgELbbtCFejNf97dLF91pvCg/R0vyDEHIFLjosFnLJ3buXBJgzZ+iFLi11L37dz2sb7hvrgAFkYezXz7lW7/omuwrJwW6nPm/bBof7LXedqa11F148fXeVSokQcRehkSPJOisXoP/uu3akpxPBO3aMtHXFxbRZBJLES6m0IypKiaQk2rz69JFci+PjSfjatUuqCfvOO8Cf/kQWUOl5TPJ7H26FjYqSXIBSUog5KCxchSefnOZ4NqWlRHiOHycitH07Ca/Oafg93YX9q8y9lfNp3x6orDyIm27qhx49SKPorYSF3U7zm5FBde6WLaO+FRUBx45dh5dekjSk9fVSZtLA4Ol6zvtsMJACICZGcnFu25bmsUsXWucdOgDfftt0FzgBAQGB3yK4187OnbQ/83J0RUW0L0vK3SFe21AqJStVly5Eo4YMIeums6K1qWFb3lBbSzRvzx7y6MrIIBrjqpzm8DQ9cv6HK8MHD6Ywq549Pe/1xRdlDkU457d4qFAgQj8AaLVWJCSoHa7F/foR/9C/P83liRPE0x05QrHOX34pWXypEsfMgOZFraYyOvHxcNSST00l/i4zcwnuvps8wngd++PHyeq6fTvw3XeS6211NdFmV/6oX0B94OFGXHBNTCSFfMeOQEnJLvzud2no0YPWjjdB0GwmPi0zk/qVl0fC6qFDo/Hvf9M6rakhPqehIbBSPhI0Ht848xDO/W7Thnjkzp1J4E5NBX78cdFVzT8IIVegybBYgIyMGLz/vrSp5uZK8ZeBZldWq22Ii1M5Ntb+/YkIycVsNDdMJhLCV6zogVWrJAtpeXmg8R88OYgJvXoZHRbSceM8LW3FxUSw//UvKV6VEzxyK/ZPIHQ6Is5cI8cF2aFDgU8/XY3Wrafj8GFiCH7+GfjqKyJAgRI5gEGlIu1fXBzdp2NHIqr9+0txLM7C65kzdL9ffgFycsbgn/+UYl2DBSUPIVcfZ8LjLAD27u1pCbbbaS4zMoD0dDOOHaOi9dydh2tH6+oCTUiW5Lev3EKs10t177iWt6bmDMaNS0GXLrSmu3ULLq4GAGprtdi1S3JxLikJTqN78mS/ADK0eqJ9e+Dxx4O/TkBAQCAY5OYCO3e2w5EjJMxkZ7sKTYFAoaC66klJRBM5DR41Kvh8CMGCK8I3b+6InTtdFeGB5qDgFuWwsEoMHhzt4H+GDHFV0FqtpGzftAl4+205futmv/dSKolWJSQQTe3ZU3JpjosDXn99M2JixuHECXoWO3cC//gHCeuBVuFQKIgmRkXRfdq1I/rXuzfdKzWV+nHqFAn+J0+SkLhsGfDhh8D589Mxfz4JhcF6QSkUxE9GRKgQGSkpv5OTyYON8xA9engqv2trSZDOygKWL1fjxx/Jm43H31ZWklDNBVbfFtY23n5wAU/CZjRKQitXfFdWHseECb0cNXQ7dQoue7HVqsSpU7ROeD6SYLzxgNB5iCefvLThgXIQQq6AT1itJMDs2ydZ4XJz6YXnQhNt4P4tf2o1vcAJCVLsZZ8+RIj69gUWL/ZMdtBcyM8nLerhw65ElLvPSJtof69tOCdf4oShXz9y1UlJod/T05dj7Ni52L2bCNEf/+gqxAYqZPKC3PHxNFdcm5qaSkTm2DF6HllZ9DfPHEkC5fSA5oTHYMTHS25AqakkxO7fvxR9+szCyZMkMHIt5Y4dviyvzgiX/ZYnkeKEhydY4K4+qamS60x6+hJMnDgXmZlS7El+PgntX38txd5yC6unwOpds+8N7gIrUIlOnaKRkCARyfbtSeDv2tW3dhcA0tP3Yu5cKmhfXS25Np87R5rec+fkBXA+Hnq3Zgc9Dlf0DCnDYXS0EHIFBASahnPnJC+u06dJ+OO5E3j8IGGEz3aUSlIkx8ZKoS+9epHAlJYGrFt38TxeTCayIu/fT3SXJwEsKXGmuwAw1Gc7PPkSV06nppIQO3w48RYA8MUX69Ct2y3Yt4+qTf3979K9pHhVX6CYXI2GBMykJOJXuHCZmEjtHTsmCcjLlwNffOGciGic3zlRKqWEkNyTiyeDPHt2KcaNm+mwvHK6t349VeCsrg4k7MhTkuNJpMLCJEGQK+C5AN23L/EX6enfYfbsucjOpvvn5tK4uVeeM801mbwljhrodx7c+6dWS30EatC2bQRiYyXX5uRkKalU167+hcD09EOYO7cXrFZ6l/btc3VtPn+e1gZ3x/a0GPtXevhHaDzE+PHEH7ckhJB7laOwkAjQ8uU9sXYtCRPnzrm7rgYCBrVagfBwSWuXkkKb+ODBtPF5cxdtKkwmcr1dv74LduyQ3KG5i2/g8Rek/YuNVTncmlJTyWo5YgQR1/JyInaHDtHm/e23wH/+IxG7+vpbA+63Xk+CBI+zbN+elADHjx9GYmJfh3B8+jQ9o08/DXwcnCHgGYk5EejQge5bWSkpLPg99uyRhFdgVsDjAFxdhhMSALu9AEOHJjs0pqmpRFyzs+n55ORI987OpvFVVNCa49Zfmy3wuZSfAzt0OqXDLTgqip5hfDzNOY+l6diR1mp8vKfAyjN22u307HmfT54kxUJpqWsMbk2NNAazGTCbb8Ztt4Uej+s6Hqm8RzC1bBsbLdBogn/5OnYM+hIBAYGrCCYTCa+c9nLhr6goeNqrUDAYDAoXIbBnT/LkGjIEXhMINRU8jvPQIWDx4lQsXy7FkHLFdKDhKQoF5U+Ijyda2727VImyVy8658wZEpaPHqVjzRqar4oKHq8amFDC82vwZINdu5Li+PTpQ+jc+RpkZ0sGiW3bKMFSMPycVqtwab9LFzpiYuiM3FyaJ548cds2GovZDNjtNwZ6owvzJmUX5rknGhuzMXZsJ0eSx1696FnwDMe5uZJb8M6ddG/X5I1N4x+oPKUNBoMKRqPEQ8THE0/Fk0h17ChZh90trOnpqxyKF4uFBNOCAuIhtm8Hvv+eeG2e7ZhbibnivqEBaGi4JeQEmhdG4hgPF8C9lXb0hlB5CL5WWhIKxkKfut86qqurERUVhU8++QTGYH0IL6CgoADJyclN6kdT25C73m4HSkqMyMmJRX5+FAoLI1BaakRlpQG1tVqYzRpYrUoEluKcQaFg0GptCAuzIDKyAXFxJiQm1iA5uRrt21dApTqOTp1ahzwGuXFYrUBRUQRycqJx/nwkiooiUFZmRGWlHnV1OtTXq2G1qsBYoBw/g0Zjg9HYiMhIM+LjTWjVqhrt2lWhU6dyREfX4cABBpOpC86di0RJSTgqKgyoqdHBZNKEfK+ICDMiIxug1zdCqbSDMSVqa3WoqtKjtlaLhgYNGhsDfRbUtkplh15vRUREA6Ki6hEdbYZe3wi12o7q6nrYbPGoqNBf6LsWDQ0q2GzB3IPuo1TSczcYGhER0YCYmHrExdUhKakWCQm10GqtMJm0KCsLu/BsDKiq0qOyUonGRgMaGtRobFRdWGsI8v5SP4jg2KHR2KDXW2EwNCIszIKoKDOiosyIjTUhPp761apVDSIjLS7ryWpVoqxMj/LyMFRU6FFZaUB1Nc1Pba0WdXVa1NdrUV+vRkODGhaLClarCo2NCjCmvEBgmqPeHW23SiU9Q63WBp3OCqORxkPP0+yY5/j4OgAZ6NkzKuQkKE3dX0wmE+677z5UVVUhsqV9jwQELhM0B/8AXBz63xxtlJfrcfZsLPLzI1FYGOngH2pqdKiv16CxURXEvsig0dhhMDQiPLwB0dH1SEysu0B/K9GpUwXq6rKafR6qq7XIyYlGQUEUzp+PRGlpGCoqDKiuJrposahgtysCHoNSyRx0NybGhMTEOiQnV6FDhwq0b1+GjIxa1Nd3R0FBFIqKwlFWFoaqKj3q6rRoaFDDZgvtXhERJhgMNmg0digUDBaLClVVEm/S2BjcOJz5uagoM6Kj62A0WqHV2qBQMJSX21BfH4fqah3q6nQwm9VBPm9+HzIc6PXWC7wj0eqEhDokJdUgMrIeKhVDeXkYSkvDUF5udPBc1dXEQxAtVgYxPu/zqdHYodVaHf2htSjR28TEWgdvo1a7rqnaWjXKysJQXm5AZSXxoTU1ugs8BD2H+noNzGY1LBbiIRobFbDblRf4xubgH6R5VansUKvt0OloPEajxYMnSkysBZCFvn2N0GpD17Q3ZY9paf5BCLlRUU2a/PT09GaoixZcG1zzyLWBGzachULR0VHCJZi4CQ6NxoboaJWL1q5XL7LA9u/vP44wmDGYTGQJO3FCsiYWFADHj1fAZotBVRVpsQJP7kNwzkLLXVh69CD3EJ2OrK3O1svSUsnl2rfrrSe45TIykjR8Oh1pyUpLy2A0xqG6WnKDCXYcKpUNkZEqREeTZtNoJOsdYyT4V1W5ZhoOXDvr2X9ueW3Viiyt4eF0rxMnjiM5uReKiqRYFOdY1lDuycE1iu4xrImJpCHlyZcyM9fjvvvGo21bej55ea6uvsXF9Ey52zLX4vI+1tfbYLergp7/QPqvUtEc6nRkMQ8Lo2cVFUXW+bg4mte8vP2YPn2Aw0UpOTn4+PKm7jFNvb459kkBgSsNzfVetMT7fe4chdNwl9UdO87DZmvtqBsebMyetwzw11xDnlzuJeiaMg5e//PECSmUJj8fOHKkBDZbQoDhNJ6gZD5WxMaqHZl8U1JoHJGR1F5mpnypmcAz/hOUSslyGRZGh0ZDfSgpqYBWG+Pg5YIfhx1GoxKRkUSLwsLoXioV9dFkkqyGofA+zv0PDyfLKy+PExVF3506dRxduvRCcbFUj557OsmHFgV/fx5WFBHhWp+e5+7IydmMu+4a7YhhLS+n58ZdfQsLJR7C2YpaVydlYG5osF8QUkPvqxw4D6TVuvIQkZHEC3H+ITERyM3dhZkz09CuHfFGwVYNaQkZxRktzT8Id+XLEO4xLNx1pqyMNglPAbaj17Z4DAOPgXSOX0hNJVeaNm3ki38HA7NZjT17KIlAZqZrsWzn7He+N1R53wZ3V5ZWregwGiXh22QC9u0rhFLZBqWlNHe7dwcv7POES5REShJeuYBpsdA4zGae3t39+jivLSuVknDJ21epaHw2G7VdV0fPuLKSNt5gwDPuqdUWJCRoHQTHYJAEK6uVnkVFBX2WlUlxHq7oFfB9+bh4pr/oaMBqLULfvkkOl+B27WjzVqmIqPBYEr4+ystpzRw9Krkb1dePxUsvBa8kkCDvk6NU0nxoNPQMjEZXIZUTmfh4ID9/D6ZNG4y2bWkMvmJw7XZ6bkVFUu3b4mIrsrJofgMtieUMxoCsrDSsWxfcdc4I9fq33275pBECAgLBwZ1/yM6WyrdwockT3r2weHyhc+b6Ll0o/jE1lYTAxYubFg9rtwNFRWFYtcq1Zqlz3XOuvPQuHCV4bV+jkRSqXCCLjiaapdUSXSwpAQ4dqgCQgIIC4mVWrw5WAGQAXPkHjYZohs1G+z/RNuJZTCa5Nrz7eKpUEv9gMEhtM0bP1WwGKittaGhQOmhsMFCpqN8ajQmtWxsREUH30enoPnY79Z0LhbW1tL5On3Z/LoHzD4DkRuscVmSx5GPgwLaOPBjJydQX7vbLKyBwHqKigkLJtmyhea2rG44336R5D11IlSf27kpuZ3dmXuUhLg44d+4AJk/u7wiPatfON021WEj45jxEWRlw8qQGv/4qzXewyMxsGv8AhMZDTJ4M3HJL0+7bHBBCbgsgPx/YvJmEsBMngOPHp+CxxySNYKDgwp9WW4/27Q1o04ZiA3gNzf79abMIBaWlpOF11ly6B7g7W/UYmxP0Pbg2jgsYVms52rSJhV4vbajOwmRtLd375ElvLfpWF1PGXungcY02G43BapXKw9TWBrehcK0cYw0wGnUOwsMJm8VCn6Qd9CfAeq/vqtfzZ0795/doaJASFpnNauTkBN53dyiVgEplRUSE2kVLmpBAGzQn3kol3beszDWJAwno0cjNpXGHXt9NXkjlz1Gnk6zBnMA4W1Hz8yUCw4VUntyDx9gWFdGaLi2VhG1OwM+cAU6eTEZGhqTd5QoOntSBrxvvjNe1oQzcDZ2xdeulv/6FF4SQKyBwOYFbMXfvJiH21CngwIGJePppqRxNMHstr2Ou1dagW7cIhwK8Tx/KRRGIBVYOZjMJPjyjK7eeOXveeCYMvCHo+3DlKiX5qUKbNlHQ612VujU1kudTYSF5cnmfI++CMiDReZVKimu02yU6z0vD1NcHXpUBkBTFWi1gt5thMOihUFDbvF2eiLCmhg4fs+LyH/c8claua7USD2S3S8p7ztPV1RlQWRl4/93vp1TaYDSqHIoFbu2Ni6NnxY0HAPEM3FuMK9+zsoCamkQcO0bPMDRFt7x4w+eDW1G5gMp5HS6gJiQAubm7MXXqtY4EmW3aSP02m+GwVHMeiPMRlZW01s+cSUBxsSv/wNc85yEaG+kZeOchBoQyeCc0lX8IrY1z54SQe0XDWZA9flyqdVpbK7fBRsm24WyBbduWBNgePcgFaOBAyW0hPX2ZTy2qyUSbBhdYuRaMv5z0Qs7EfffRi9dU906dThJAtFpXbSPfUBsapJedB94DscjPD/5+KhVv3wq1Wu24Dyc+HDabJBB6hxQ3wTdDZ4LG2+Zt8WfJhR1AFzBx02ikQ62WLIT19Wao1XoHYbNaeRKjACdEpoA5F5K5QMgFZrWafufCMlcqFBc3AFCjpITWS/DaUM/6sBzc4swZFJ42nwup8fF0nD9/AFOm9EerVvS7RkME0Nn6yxUuVVX0G08OVV8PlJV1w4YNrgKpzRasm1SIXB7487RDq1U6GJhgEz4AgNlcD73eEHI/Qr1e5/0RCggIXCRYrVTjdPt2CknyXVMVAOQ12c7Z83nplJQUssByDy4O5wQ5cv3hGWqdBVZn/qGwcAYefFCqVRqq5YzTXGf+ge+bXCBz96iqqaG9H4gK2nLpfE/OQyiVahc67zwWic57bc3lP+41xPsPuPImnD+x2yUFOKBHXZ3/fnNrIj+USrpHQ0M91GqDQ4DitK+6ms9TQLMi/aWQvJ64a3V4uCt/x8fQ2EjP49w5ExSKCIdr9Jkzobgne0905G5JDQ+Hwz2bZzIuKjqC8eP7oFUregd44tOyMknZ4hzqxA0pmZlUhcNkAior+2DZMokP4wrtwNd322AH7QApCwCFwgadTuUy18GgqfxDqG307t2kWzYbRExuE3zFi4uBv/71V2g0w3DsGBEA74KsKxQKODIRazTFSEtLDEiDarFI5Vx4WvZff82G0djJ8cI6pxBvSuwkh7P1k2/W3IooER2Kf2xaFlly9eHtc6LAmHQ0FdLG4XRXFuzG5b1thcIOtVoJpVIS7LkA6S50N6X/XJHAhScuJCsUQHV1DfT6CIflmDINNtVtxxVc8+weTxIdTUdJSSZ69epywfVJEu6cLfPV1dJa5dmIGxokgtLYSEm6LgXIei25MXMtr9Vag8TECAdhj4x0HWdsrKSl5jE0zgS1peNhmuP6lo6pERC4HNHU98JiATZuBN599zjq6nohI4OY70CUmbzEXHQ0oNWWYeDAOPToQcJrWhopxeVgtRLPwAXWvDyybu7Zkw+drq2Ll5ZUgiToobnA2YOK77PO/AMX9iwWin9sKtz5B0C6V3O27c6fUPvEw4QKouFWaDRqh9Lbm2K9KWPgygOdztUQwXmW6upa6HThDhdo7onWHHPI4RwyxOlrdDSFC0VHA+fPn0Hv3ikuymG7XQrn4vwDr2bg7mXF+YdLJeFwhQa39uv1gNVai/j4cEdoXXi45HUWGSnxD/HxUk6S1q3pN/78W5r+N7WNluYfhJDrY/J5jB1P9LNpExGlI0fIdVc+tkWCc63TDh0oxXvfvlKtMm6pW778Z6SkXO+wsjq7BfO08k1N9uPeLy4QSS6vNgCqS7YhXBw0jcC0JDiBUSoBq5XStXvTJl8McE02Jyg2Ww3CwyNcGAWuCODKE+7efbmsGT4GbnlvbKxDWFiYg7niRJX/7sxwycFmAyorixEdnej1d38oLy9FZKT32hf+mAZiOLy3EQjTUV1diqioeAfRdJ4P55rAvHRHr17A/fdLRLaliZSAwOWIQN4LHmJTU0P5BlaupPj8vDz4tdZptcQMt25NMYndu0txsOHhUpjNypVrkZo6CRkZUhJHbmXldTMvJv8ASPxDcwpBLYPfJg/BnwXRbol/cOYhLvb9uXWVPMNqER4e7qFocA6v4or35lqXzQHuws0NOlYrjYPzZu5Wc2fDjxwfVFYmzz/wc/29L2SBbzr9j4hoGg9SUyPxEM78E1eW8Jw5bdvSPjVuHCnc6P5CyG0x+Jv8pUuBWX7KhSoUDOHhCkctUl5XdfRoeuDt2xNBExAQEPitoKFBska3NJESELgcEch7ERXl30VUp7OibVs1unenMKQxY4Bhw4hxfPNN4Iknmr3rAgICAhcNc+YA331Hf7c0/yBicn3A/Xmo1eRO0LMnMHIkcOONwLFji3ya8b1ZiVzBoFQqoNVKGWp50WmrFc0QNC4gICAQGEKJ+xEQEPCEe7mwmBhK7DR0KDBhAlk8vv9+sVceIrAYfgaFQuIfuNtnXBx5jK1YEXxJIAEBAYFQoFLR3nO5QAi5PjByJLn+REfDkfHXHceO+W5jzx5yFeJmfe4iqNU6+9x7F5QtFnJf5q4Rzgd3OVy8+FvMm3eL19ImhYVE5Hgb/DrnT+c2+OHch4ICVxcN5/OUSk6o53h8z4/CQoqZ8NfGLbfIt2G10jzw6537yP9euvR7zJkz22sfiotpHrjLhfv17m24/8b7ALjGKDuf88MP3+Ommzyv53+XlFD8iPP4ecwSb3fZsu8xa5ZrG/zTaqUkJM5zwJ8jQG2tWLEcs2bNcLTn/KlUUkZh7mrv7MLq/Ll69XLMmDHD0SaHWk3XlpRI33GLn3N7q1evxIwZ012uc/6bx565M3HO561fvxpTpkx1/M8VRiqVZx/483DG+vXrMGnSRLiDt1NW5uoqJff+/PrrBowZM87zB1Af/FlpduzYitGjR3r9vbLSPwO6b99ODB48RPY3q9V/qanjx/di9OhBSEqi+J+4OCnuiSvyvO0dAgICoePECdofnWPsgsG99wI33CDlCXDmI/h+54t/AIheWK2uMYPc1VCi/+mYN0++DZOJaAande60W62W6L/zb+59aGyUp1vu9F+ON+B9cKf/7rT7llu803/OB/lrwxsPYTYTzeH/y7XhTP/lDh7m5s4/AK59mD1basP5N5PJtQ+8DUBqc+nSHzB79ixZ/gGgPjQ2evIunBcBgJUrf8DMmbNk+QeTiScK9c4/OPMgnN66zwNfk4DEQzivm7VrV2LatOku7XLwhKWAJ3/CsW7dWkyfPsnlOu5WDdB6cqb/cjzEL7+sw4QJ8jwEzy/CIfd+b968EddfP9bzhwsoK5Ncyb3tD7t2bcbIkaNlf3Pvgxz27NmJtDR5/gEg/sGfy3hGxk5MmDDEJfN0XByFYzrLMpcjhJDrA1pt6Cn0ORLlw/mC6kOHDr7PUavtPhdZq1b+76PX212EFfc+dOrk+/rwcKujPEuofQgPt3otWaJW++9DZKTFZ8kkb8k4gmnDn7dFVJQF8d7DH3zOkXMbvtaNr/YBICHB5HOsgZSVSkoy+Zzvjh19X3/0aC169vT+u79nCQBZWdXo39//ed5w/nw5RowI/XoAqK8vxpQpoV+v0eTj1lub1of09GzMneudSPm//gzmzh3UtE4ICAgEjabSf6PRP/33h0B4GF/8g9Hof7/W663Q65vWB6PROw8RSB/Cwy0+6XMgfQgPtziqVriDKyt8wR/9D4TuRUZ6byM83P+aio5u8HmOt/E5Iz6+wSsPERvrn5dq08Y3/xDIs/DHQ/hDVlaFT/6hTx//bTSVh6ivL2wS/wAAGs25JvEQTeUfmquNlsJlLH8LCAgICAgICAgICAgICAQHIeQKCAgICAgICAgICAgIXDH4zQu57733Hjp27Ai9Xo+0tDTs3r27pbskICAgICAgcJlD8A8CAgICVy5+00Lut99+iyeffBIvvPAC9u/fj2uuuQYTJ05EcXFxS3dNQEBAQEBA4DKF4B8EBAQErmz8phNP/fvf/8b999+Pu+++GwDw4YcfYvXq1fj000/xpz/9yeP8hoYGNDilMq26kJas2l+KVB8wmUxNur452hB9EH0QfRB9uFjX82uv4pLqAlcgLgf+AWj591v0QfRB9EH04WK10dL8g4L9RjkXi8UCo9GIJUuW4MYbb3R8f+edd6KyshLLly/3uOavf/0rXnzxxUvYSwEBAYErA5mZmejcuXNLd0NAoMkQ/IOAgIDApUNL8Q+/WUtuaWkpbDYbkpKSXL5PSkrCyZMnZa959tln8eSTTzr+r6ysRIcOHZCbm4uoqKiL2t/LGdXV1WjXrh3y8vIQ6S9H/hUMMQ8EMQ8EMQ+EqqoqtG/fHrGB1J4SEPgNQPAPzQexTxLEPBDEPBDEPBBamn/4zQq5oUCn00Gn03l8HxUVdVUvQo7IyEgxDxDzwCHmgSDmgaC8nCu+CwhcZAj+wTfEPkkQ80AQ80AQ80BoKf7hN8u1xMfHQ6VSoaioyOX7oqIitGrVqoV6JSAgICAgIHA5Q/APAgICAlc+frNCrlarxcCBA7FhwwbHd3a7HRs2bMDQoUNbsGcCAgICAgIClysE/yAgICBw5eM37a785JNP4s4778SgQYNw7bXX4s0330RdXZ0jW6I/6HQ6vPDCC7IuSFcTxDwQxDwQxDwQxDwQxDwIXIkQ/EPzQMwDQcwDQcwDQcwDoaXn4TebXZnj3XffxWuvvYbCwkL069cPb7/9NtLS0lq6WwICAgICAgKXMQT/ICAgIHDl4jcv5AoICAgICAgICAgICAgIcPxmY3IFBAQEBAQEBAQEBAQEBNwhhFwBAQEBAQEBAQEBAQGBKwZCyBUQEBAQEBAQEBAQEBC4YiCEXAEBAQEBAQEBAQEBAYErBletkPvee++hY8eO0Ov1SEtLw+7du1u6SyHj5ZdfxuDBgxEREYHExETceOONOHXqlMs5ZrMZ8+fPR1xcHMLDwzF79mwUFRW5nJObm4upU6fCaDQiMTERzzzzDKxWq8s5mzZtwoABA6DT6dC1a1d8/vnnF3t4IeOVV16BQqHA448/7vjuapmHgoIC3HbbbYiLi4PBYECfPn2wd+9ex++MMfzlL39B69atYTAYMH78eJw5c8aljfLycsybNw+RkZGIjo7Gvffei9raWpdzDh8+jJEjR0Kv16Ndu3Z49dVXL8n4AoHNZsPzzz+PTp06wWAwoEuXLvj73/8O51x7V+I8bNmyBdOnT0ebNm2gUCiwbNkyl98v5ZgXL16MHj16QK/Xo0+fPlizZk2zj1dAoCUgeIgrk3ZyCP5B8A9XI/8AXGE8BLsKsWjRIqbVatmnn37Kjh07xu6//34WHR3NioqKWrprIWHixInss88+Y0ePHmUHDx5kU6ZMYe3bt2e1tbWOcx588EHWrl07tmHDBrZ37142ZMgQNmzYMMfvVquVpaamsvHjx7MDBw6wNWvWsPj4ePbss886zsnKymJGo5E9+eST7Pjx4+ydd95hKpWKrV279pKONxDs3r2bdezYkfXt25c99thjju+vhnkoLy9nHTp0YHfddRfbtWsXy8rKYuvWrWMZGRmOc1555RUWFRXFli1bxg4dOsRuuOEG1qlTJ1ZfX+84Z9KkSeyaa65hO3fuZFu3bmVdu3Zlc+fOdfxeVVXFkpKS2Lx589jRo0dZeno6MxgM7KOPPrqk4/WGf/7znywuLo6tWrWKZWdns8WLF7Pw8HD21ltvOc65EudhzZo17M9//jP74YcfGAC2dOlSl98v1Zi3b9/OVCoVe/XVV9nx48fZc889xzQaDTty5MhFnwMBgYsJwUNcmbSTQ/APgn+4WvkHxq4sHuKqFHKvvfZaNn/+fMf/NpuNtWnThr388sst2KvmQ3FxMQPANm/ezBhjrLKykmk0GrZ48WLHOSdOnGAA2I4dOxhjtKiVSiUrLCx0nPPBBx+wyMhI1tDQwBhjbMGCBax3794u97rlllvYxIkTL/aQgkJNTQ1LSUlhP//8Mxs9erSDSF0t8/DHP/6RjRgxwuvvdrudtWrVir322muO7yorK5lOp2Pp6emMMcaOHz/OALA9e/Y4zvnxxx+ZQqFgBQUFjDHG3n//fRYTE+OYF37v7t27N/eQQsLUqVPZPffc4/LdrFmz2Lx58xhjV8c8uBOoSznmm2++mU2dOtWlP2lpaeyBBx5o1jEKCFxqCB7iyqSdjAn+QfAPBME/EH7rPMRV565ssViwb98+jB8/3vGdUqnE+PHjsWPHjhbsWfOhqqoKABAbGwsA2LdvHxobG13G3KNHD7Rv394x5h07dqBPnz5ISkpynDNx4kRUV1fj2LFjjnOc2+DnXG7zNn/+fEydOtWjr1fLPKxYsQKDBg3CnDlzkJiYiP79++Pjjz92/J6dnY3CwkKXMURFRSEtLc1lHqKjozFo0CDHOePHj4dSqcSuXbsc54waNQpardZxzsSJE3Hq1ClUVFRc7GH6xbBhw7BhwwacPn0aAHDo0CFs27YNkydPBnD1zIMzLuWYL/f3REAgFAgegnAl0k5A8A+CfyAI/kEevzUe4qoTcktLS2Gz2Vw2IQBISkpCYWFhC/Wq+WC32/H4449j+PDhSE1NBQAUFhZCq9UiOjra5VznMRcWFsrOCf/N1znV1dWor6+/GMMJGosWLcL+/fvx8ssve/x2tcxDVlYWPvjgA6SkpGDdunV46KGH8Oijj+KLL74AII3D1ztQWFiIxMREl9/VajViY2ODmquWxJ/+9Cfceuut6NGjBzQaDfr374/HH38c8+bNA3D1zIMzLuWYvZ1zuc2JgEAwEDyEhCuNdgr+QfAPHIJ/kMdvjYdQB3ymwG8C8+fPx9GjR7Ft27aW7solR15eHh577DH8/PPP0Ov1Ld2dFoPdbsegQYPw0ksvAQD69++Po0eP4sMPP8Sdd97Zwr27dPjuu+/wzTffYOHChejduzcOHjyIxx9/HG3atLmq5kFAQEAgUFytPITgHwiCfyAI/uHKwFVnyY2Pj4dKpfLIiFdUVIRWrVq1UK+aB4888ghWrVqFjRs3om3bto7vW7VqBYvFgsrKSpfzncfcqlUr2Tnhv/k6JzIyEgaDobmHEzT27duH4uJiDBgwAGq1Gmq1Gps3b8bbb78NtVqNpKSkq2IeWrdujV69erl817NnT+Tm5gKQxuHrHWjVqhWKi4tdfrdarSgvLw9qrloSzzzzjEMb26dPH9x+++144oknHFr6q2UenHEpx+ztnMttTgQEgoHgISRcSbRT8A8EwT8QBP8gj98aD3HVCblarRYDBw7Ehg0bHN/Z7XZs2LABQ4cObcGehQ7GGB555BEsXboUv/zyCzp16uTy+8CBA6HRaFzGfOrUKeTm5jrGPHToUBw5csRlYf7888+IjIx0bHhDhw51aYOfc7nM27hx43DkyBEcPHjQcQwaNAjz5s1z/H01zMPw4cM9yj+cPn0aHTp0AAB06tQJrVq1chlDdXU1du3a5TIPlZWV2Ldvn+OcX375BXa7HWlpaY5ztmzZgsbGRsc5P//8M7p3746YmJiLNr5AYTKZoFS6bnEqlQp2ux3A1TMPzriUY77c3xMBgVAgeAjClUY7Bf9AEPwDQfAP8vjN8RABp6i6grBo0SKm0+nY559/zo4fP85+//vfs+joaJeMeL8lPPTQQywqKopt2rSJnT9/3nGYTCbHOQ8++CBr3749++WXX9jevXvZ0KFD2dChQx2/89T3EyZMYAcPHmRr165lCQkJsqnvn3nmGXbixAn23nvvXVap7+XgnB2RsatjHnbv3s3UajX75z//yc6cOcO++eYbZjQa2ddff+0455VXXmHR0dFs+fLl7PDhw2zGjBmyKeD79+/Pdu3axbZt28ZSUlJcUsBXVlaypKQkdvvtt7OjR4+yRYsWMaPReNmUALjzzjtZcnKyowTADz/8wOLj49mCBQsc51yJ81BTU8MOHDjADhw4wACwf//73+zAgQMsJyeHMXbpxrx9+3amVqvZ66+/zk6cOMFeeOEFUUJI4IqA4CGuTNrpDsE/CP7hauMfGLuyeIirUshljLF33nmHtW/fnmm1WnbttdeynTt3tnSXQgYA2eOzzz5znFNfX88efvhhFhMTw4xGI5s5cyY7f/68Sztnz55lkydPZgaDgcXHx7OnnnqKNTY2upyzceNG1q9fP6bValnnzp1d7nE5wp1IXS3zsHLlSpaamsp0Oh3r0aMH++9//+vyu91uZ88//zxLSkpiOp2OjRs3jp06dcrlnLKyMjZ37lwWHh7OIiMj2d13381qampczjl06BAbMWIE0+l0LDk5mb3yyisXfWyBorq6mj322GOsffv2TK/Xs86dO7M///nPLinrr8R52Lhxo+x+cOeddzLGLu2Yv/vuO9atWzem1WpZ79692erVqy/auAUELiUED3Fl0k5nCP5B8A9XG//A2JXFQygYYyxwu6+AgICAgICAgICAgICAwOWLqy4mV0BAQEBAQEBAQEBAQODKhRByBQQEBAQEBAQEBAQEBK4YCCFXQEBAQEBAQEBAQEBA4IqBEHIFBAQEBAQEBAQEBAQErhgIIVdAQEBAQEBAQEBAQEDgioEQcgUEBAQEBAQEBAQEBASuGAghV0BAQEBAQEBAQEBAQOCKgRByBQQEBAQEBAQEBAQEBK4YCCFXQEBAQEBAQEBAQEBA4IqBEHIFBAQEBAQEBAQEBAQErhgIIVdAQEBAQEBAQEBAQEDgisH/A7wtYR3tYGA3AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scenario(0, silent=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/ex-prt-mp7-p02.ipynb b/notebooks/ex-prt-mp7-p02.ipynb new file mode 100644 index 00000000..88abe38f --- /dev/null +++ b/notebooks/ex-prt-mp7-p02.ipynb @@ -0,0 +1,1834 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c454beff", + "metadata": {}, + "source": [ + "## Backward particle tracking through a steady-state flow field on an unstructured (DISV quadtree) grid\n", + "\n", + "Application of a MODFLOW 6 particle-tracking (PRT) model to solve example 2 from the MODPATH 7 documentation. This example problem demonstrates a steady-state MODFLOW 6 simulation using a quadpatch DISV grid.\n", + "\n", + "In part A, 16 particles are evenly distributed around the 4 side faces of a cell containing a well in a locally-refined region in the center of the grid, then tracked backwards to recharge locations at the water table.\n", + "\n", + "In part B, 100 particles are evenly distributed around the 4 side faces of the well's cell, with 16 additional particles distributed over the cell's top face, then particles are again tracked backwards to recharge locations at the water table.\n", + "\n", + "### Problem setup\n", + "\n", + "First import dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 888, + "id": "f41bc8b5", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "from warnings import warn\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "from shapely.geometry import MultiPoint, LineString\n", + "import flopy\n", + "from flopy.utils.gridintersect import GridIntersect\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "try:\n", + " # append the common/ subdirectory to the system path\n", + " # (assumes running one level down from project root)\n", + " sys.path.append(os.path.join(\"..\", \"common\"))\n", + " import config\n", + "\n", + " buildModel = config.buildModel\n", + " writeModel = config.writeModel\n", + " runModel = config.runModel\n", + " plotModel = config.plotModel\n", + " plotSave = config.plotSave\n", + " figure_ext = config.figure_ext\n", + " base_ws = config.base_ws\n", + " timeit = config.timeit\n", + "except:\n", + " warn(f\"Failed to import config\")\n", + " # default settings\n", + " buildModel = True\n", + " writeModel = True\n", + " runModel = True\n", + " plotModel = True\n", + " plotSave = False\n", + " figure_ext = \".png\"\n", + " base_ws = Path(\"../examples\")\n", + "\n", + " def timeit(func):\n", + " return func\n", + "\n", + "\n", + "try:\n", + " from figspecs import USGSFigure\n", + "\n", + " styles = True\n", + "except:\n", + " warn(f\"Failed to import figure styles\")\n", + " styles = False" + ] + }, + { + "cell_type": "markdown", + "id": "e584ad01", + "metadata": {}, + "source": [ + "Define simulation and model name and workspace." + ] + }, + { + "cell_type": "code", + "execution_count": 889, + "id": "50fee8ce", + "metadata": {}, + "outputs": [], + "source": [ + "sim_name = \"mp7-p02\"\n", + "example_name = \"ex-prt-\" + sim_name\n", + "gwf_name = sim_name + \"-gwf\"\n", + "prt_name = sim_name + \"-prt\"\n", + "mp7_name = sim_name + \"-mp7\"\n", + "\n", + "sim_ws = base_ws / example_name\n", + "gwf_ws = sim_ws / \"gwf\"\n", + "prt_ws = sim_ws / \"prt\"\n", + "mp7_ws = sim_ws / \"mp7\"\n", + "\n", + "# We only need 1 working directory for the GWF model\n", + "# since it can be reused for both subproblems A & B.\n", + "# The PRT and MP7 models each need separate working\n", + "# directories for each subproblem.\n", + "gwf_ws.mkdir(exist_ok=True, parents=True)\n", + "\n", + "headfile = f\"{gwf_name}.hds\"\n", + "headfile_bkwd = f\"{gwf_name}_bkwd.hds\"\n", + "budgetfile = f\"{gwf_name}.cbb\"\n", + "budgetfile_bkwd = f\"{gwf_name}_bkwd.cbb\"\n", + "trackfile = f\"{prt_name}.trk\"\n", + "trackhdrfile = f\"{prt_name}.trk.hdr\"\n", + "trackcsvfile = f\"{prt_name}.trk.csv\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "b9bd20b0", + "metadata": {}, + "source": [ + "Define model units." + ] + }, + { + "cell_type": "code", + "execution_count": 890, + "id": "9ce1e49c", + "metadata": {}, + "outputs": [], + "source": [ + "length_units = \"feet\"\n", + "time_units = \"days\"" + ] + }, + { + "cell_type": "markdown", + "id": "f43b0255", + "metadata": {}, + "source": [ + "Define time discretization parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 891, + "id": "62f66234", + "metadata": {}, + "outputs": [], + "source": [ + "tdis_rc = [(1000.0, 1, 1.0)]\n", + "nper = len(tdis_rc)" + ] + }, + { + "cell_type": "markdown", + "id": "99fef511", + "metadata": {}, + "source": [ + "Define MODFLOW 6 flow model parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 892, + "id": "a4d03533", + "metadata": {}, + "outputs": [], + "source": [ + "# Cell types by layer\n", + "icelltype = [1, 0, 0]\n", + "\n", + "# Conductivities\n", + "k = [50.0, 0.01, 200.0]\n", + "k33 = [10.0, 0.01, 20.0]\n", + "\n", + "# Well\n", + "# issue(flopyex): in the original flopy example, the well\n", + "# coordinates were at the edge (not the center) of the cell,\n", + "# but it apparently worked out anyway\n", + "wel_coords = [(4718.45, 5281.25)]\n", + "wel_q = [-150000.0]\n", + "\n", + "# Recharge\n", + "rch = 0.005\n", + "rch_iface = 6\n", + "rch_iflowface = -1\n", + "\n", + "# River\n", + "riv_h = 320.0\n", + "riv_z = 318.0\n", + "riv_c = 1.0e5\n", + "riv_iface = 6\n", + "riv_iflowface = -1" + ] + }, + { + "cell_type": "markdown", + "id": "55c805d4", + "metadata": {}, + "source": [ + "Initialize globally available lists of well and river cells, which will be filled when the MODFLOW 6 flow model is built." + ] + }, + { + "cell_type": "code", + "execution_count": 893, + "id": "56df4e25", + "metadata": {}, + "outputs": [], + "source": [ + "welcells = []\n", + "rivcells = []" + ] + }, + { + "cell_type": "markdown", + "id": "73fb7847", + "metadata": {}, + "source": [ + "### Grid refinement\n", + "\n", + "[GRIDGEN](https://www.usgs.gov/software/gridgen-program-generating-unstructured-finite-volume-grids) can be used to create a quadpatch grid with a central refined region.\n", + "\n", + "The grid will have 3 refinement levels. First, create the top-level (base) grid discretization." + ] + }, + { + "cell_type": "code", + "execution_count": 894, + "id": "4530616d", + "metadata": {}, + "outputs": [], + "source": [ + "Lx = 10000.0\n", + "Ly = 10500.0\n", + "nlay = 3\n", + "nrow = 21\n", + "ncol = 20\n", + "delr = Lx / ncol\n", + "delc = Ly / nrow\n", + "top = 400\n", + "botm = [220, 200, 0]\n", + "\n", + "ms = flopy.modflow.Modflow()\n", + "dis = flopy.modflow.ModflowDis(\n", + " ms,\n", + " nlay=nlay,\n", + " nrow=nrow,\n", + " ncol=ncol,\n", + " delr=delr,\n", + " delc=delc,\n", + " top=top,\n", + " botm=botm,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "054f07d3", + "metadata": {}, + "source": [ + "Refine the grid." + ] + }, + { + "cell_type": "code", + "execution_count": 895, + "id": "b6073d90", + "metadata": {}, + "outputs": [], + "source": [ + "from flopy.utils.gridgen import Gridgen\n", + "\n", + "# create Gridgen workspace\n", + "gridgen_ws = sim_ws / \"gridgen\"\n", + "gridgen_ws.mkdir(parents=True, exist_ok=True)\n", + "\n", + "# create Gridgen object\n", + "g = Gridgen(ms.modelgrid, model_ws=gridgen_ws)\n", + "\n", + "# add polygon for each refinement level\n", + "outer_polygon = [[(3500, 4000), (3500, 6500), (6000, 6500), (6000, 4000), (3500, 4000)]]\n", + "g.add_refinement_features([outer_polygon], \"polygon\", 1, range(nlay))\n", + "refshp0 = gridgen_ws / \"rf0\"\n", + "\n", + "middle_polygon = [\n", + " [(4000, 4500), (4000, 6000), (5500, 6000), (5500, 4500), (4000, 4500)]\n", + "]\n", + "g.add_refinement_features([middle_polygon], \"polygon\", 2, range(nlay))\n", + "refshp1 = gridgen_ws / \"rf1\"\n", + "\n", + "inner_polygon = [[(4500, 5000), (4500, 5500), (5000, 5500), (5000, 5000), (4500, 5000)]]\n", + "g.add_refinement_features([inner_polygon], \"polygon\", 3, range(nlay))\n", + "refshp2 = gridgen_ws / \"rf2\"" + ] + }, + { + "cell_type": "markdown", + "id": "df6cd820", + "metadata": {}, + "source": [ + "Build the grid and plot it with refinement levels superimposed." + ] + }, + { + "cell_type": "code", + "execution_count": 896, + "id": "5fbafd91", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 896, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g.build(verbose=False)\n", + "grid_props = g.get_gridprops_vertexgrid()\n", + "disv_props = g.get_gridprops_disv()\n", + "grid = flopy.discretization.VertexGrid(**grid_props)\n", + "\n", + "fig = plt.figure(figsize=(15, 15))\n", + "ax = fig.add_subplot(1, 1, 1, aspect=\"equal\")\n", + "pmv = flopy.plot.PlotMapView(model=ms)\n", + "grid.plot(ax=ax)\n", + "\n", + "flopy.plot.plot_shapefile(refshp0, ax=ax, facecolor=\"green\", alpha=0.3)\n", + "flopy.plot.plot_shapefile(refshp1, ax=ax, facecolor=\"green\", alpha=0.5)\n", + "flopy.plot.plot_shapefile(str(refshp2), ax=ax, facecolor=\"green\", alpha=0.7)" + ] + }, + { + "cell_type": "markdown", + "id": "7c1165b6", + "metadata": {}, + "source": [ + "### Model creation\n", + "\n", + "We are now ready to setup groundwater flow and particle tracking models.\n", + "\n", + "Define shared MODFLOW 6 PRT and MODPATH 7 particle-tracking model parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 897, + "id": "213de8ff", + "metadata": {}, + "outputs": [], + "source": [ + "# Porosity\n", + "porosity = 0.1" + ] + }, + { + "cell_type": "markdown", + "id": "89c9fd16", + "metadata": {}, + "source": [ + "Define particle release points for the PRT model. Note that release points for part A and B are not identical, with more particles released in part B." + ] + }, + { + "cell_type": "code", + "execution_count": 898, + "id": "1a02c551", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "releasepts = []\n", + "\n", + "# retrieve GRIDGEN-generated gridprops\n", + "ncpl = disv_props[\"ncpl\"]\n", + "top = disv_props[\"top\"]\n", + "botm = disv_props[\"botm\"]\n", + "nvert = disv_props[\"nvert\"]\n", + "vertices = disv_props[\"vertices\"]\n", + "cell2d = disv_props[\"cell2d\"]\n", + "\n", + "\n", + "def set_releasepts(part):\n", + " from math import sqrt\n", + "\n", + " global releasepts\n", + "\n", + " welcellnode = welcells[0]\n", + " xctr = cell2d[welcellnode][1]\n", + " yctr = cell2d[welcellnode][2]\n", + " vert0 = cell2d[welcellnode][4]\n", + " vert1 = cell2d[welcellnode][5]\n", + " x0 = vertices[vert0][1]\n", + " y0 = vertices[vert0][2]\n", + " x1 = vertices[vert1][1]\n", + " y1 = vertices[vert1][2]\n", + " dx = x1 - x0\n", + " dy = y1 - y0\n", + " delcell = sqrt(dx * dx + dy * dy)\n", + " delcellhalf = 0.5 * delcell\n", + "\n", + " # Reinitialize for the current scenario\n", + " releasepts = []\n", + "\n", + " if part == \"a\":\n", + " # Example 2A\n", + "\n", + " zrpt = 0.5 * (botm[1][welcellnode] + botm[2][welcellnode])\n", + " npart_on_side = 4\n", + " delta = delcell / npart_on_side\n", + " baseoffset = 0.5 * (npart_on_side + 1) * delta\n", + " xbase = xctr - baseoffset\n", + " ybase = yctr - baseoffset\n", + " nrpt = -1\n", + " for idiv in range(npart_on_side):\n", + " i = idiv + 1\n", + " xrpt = xbase + i * delta\n", + " yrpt = yctr + delcellhalf\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)\n", + " yrpt = yctr - delcellhalf\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)\n", + " yrpt = ybase + i * delta\n", + " xrpt = xctr + delcellhalf\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)\n", + " xrpt = xctr - delcellhalf\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)\n", + "\n", + " else:\n", + " # Example 2B\n", + "\n", + " # 4x4 array of particles on top of well cell\n", + " zrpt = botm[1][welcellnode]\n", + " npart_on_side = 4\n", + " delta = delcell / npart_on_side\n", + " baseoffset = 0.5 * (npart_on_side + 1) * delta\n", + " xbase = xctr - baseoffset\n", + " ybase = yctr - baseoffset\n", + " nrpt = -1\n", + " for idivx in range(npart_on_side):\n", + " ix = idivx + 1\n", + " xrpt = xbase + ix * delta\n", + " for idivy in range(npart_on_side):\n", + " iy = idivy + 1\n", + " yrpt = ybase + iy * delta\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)\n", + " # 10x10 arrays of particles on the four sides of well cell\n", + " zwel = 0.5 * (botm[1][welcellnode] + botm[2][welcellnode])\n", + " npart_on_side = 10\n", + " delcellz = botm[1][welcellnode] - botm[2][welcellnode]\n", + " delta = delcell / npart_on_side\n", + " deltaz = delcellz / npart_on_side\n", + " baseoffset = 0.5 * (npart_on_side + 1) * delta\n", + " baseoffsetz = 0.5 * (npart_on_side + 1) * deltaz\n", + " xbase = xctr - baseoffset\n", + " ybase = yctr - baseoffset\n", + " zbase = zwel - baseoffsetz\n", + " for idivz in range(npart_on_side):\n", + " iz = idivz + 1\n", + " zrpt = zbase + iz * deltaz\n", + " for idiv in range(npart_on_side):\n", + " i = idiv + 1\n", + " xrpt = xbase + i * delta\n", + " yrpt = yctr + delcellhalf\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)\n", + " yrpt = yctr - delcellhalf\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)\n", + " yrpt = ybase + i * delta\n", + " xrpt = xctr + delcellhalf\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)\n", + " xrpt = xctr - delcellhalf\n", + " nrpt += 1\n", + " rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt]\n", + " releasepts.append(rpt)" + ] + }, + { + "cell_type": "markdown", + "id": "87e9047c", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define particle release points for the MODPATH 7 model." + ] + }, + { + "cell_type": "code", + "execution_count": 899, + "id": "5a59d9b0", + "metadata": {}, + "outputs": [], + "source": [ + "def get_particle_data(part):\n", + " nodew = ncpl * 2 + welcells[0]\n", + "\n", + " if part == \"a\":\n", + " # Example 2A\n", + " pcoord = np.array(\n", + " [\n", + " [0.000, 0.125, 0.500],\n", + " [0.000, 0.375, 0.500],\n", + " [0.000, 0.625, 0.500],\n", + " [0.000, 0.875, 0.500],\n", + " [1.000, 0.125, 0.500],\n", + " [1.000, 0.375, 0.500],\n", + " [1.000, 0.625, 0.500],\n", + " [1.000, 0.875, 0.500],\n", + " [0.125, 0.000, 0.500],\n", + " [0.375, 0.000, 0.500],\n", + " [0.625, 0.000, 0.500],\n", + " [0.875, 0.000, 0.500],\n", + " [0.125, 1.000, 0.500],\n", + " [0.375, 1.000, 0.500],\n", + " [0.625, 1.000, 0.500],\n", + " [0.875, 1.000, 0.500],\n", + " ]\n", + " )\n", + " plocs = [nodew for i in range(pcoord.shape[0])]\n", + " pgdata = flopy.modpath.ParticleData(\n", + " plocs,\n", + " structured=False,\n", + " localx=pcoord[:, 0],\n", + " localy=pcoord[:, 1],\n", + " localz=pcoord[:, 2],\n", + " drape=0,\n", + " )\n", + "\n", + " else:\n", + " # Example 2B\n", + " facedata = flopy.modpath.FaceDataType(\n", + " drape=0,\n", + " verticaldivisions1=10,\n", + " horizontaldivisions1=10,\n", + " verticaldivisions2=10,\n", + " horizontaldivisions2=10,\n", + " verticaldivisions3=10,\n", + " horizontaldivisions3=10,\n", + " verticaldivisions4=10,\n", + " horizontaldivisions4=10,\n", + " rowdivisions5=0,\n", + " columndivisions5=0,\n", + " rowdivisions6=4,\n", + " columndivisions6=4,\n", + " )\n", + " pgdata = flopy.modpath.NodeParticleData(subdivisiondata=facedata, nodes=nodew)\n", + "\n", + " return pgdata" + ] + }, + { + "cell_type": "markdown", + "id": "c5e9a6cc", + "metadata": {}, + "source": [ + "Define some variables used to plot model results." + ] + }, + { + "cell_type": "code", + "execution_count": 900, + "id": "7694e269", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "# colormap for boundary locations\n", + "cmapbd = mpl.colors.ListedColormap(\n", + " [\n", + " \"r\",\n", + " \"g\",\n", + " ]\n", + ")\n", + "\n", + "# time series point colors by layer\n", + "colors = [\"green\", \"orange\", \"red\"]\n", + "\n", + "# figure sizes\n", + "figure_size_solo = (8.0, 8.0)\n", + "figure_size_compare = (15, 6)" + ] + }, + { + "cell_type": "markdown", + "id": "cf51c1ab", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define functions to build, write, run, and plot models.\n", + "\n", + "Below we use three distinct simulations:\n", + "\n", + " 1. MODFLOW 6 GWF (groundwater flow)\n", + " 2. MODFLOW 6 PRT (particle-tracking)\n", + " 3. MODPATH 7 particle-tracking" + ] + }, + { + "cell_type": "code", + "execution_count": 901, + "id": "9070eda0", + "metadata": {}, + "outputs": [], + "source": [ + "def build_mf6gwf():\n", + " global welcells, rivcells\n", + "\n", + " print(\"Building GWF model\")\n", + "\n", + " # ====================================\n", + " # Create the MODFLOW 6 flow simulation\n", + " # ====================================\n", + "\n", + " # Instantiate the MODFLOW 6 simulation object\n", + " sim = flopy.mf6.MFSimulation(\n", + " sim_name=gwf_name, exe_name=\"mf6\", version=\"mf6\", sim_ws=gwf_ws\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 temporal discretization package\n", + " flopy.mf6.ModflowTdis(\n", + " sim, pname=\"tdis\", time_units=\"DAYS\", perioddata=tdis_rc, nper=len(tdis_rc)\n", + " )\n", + "\n", + " # -------------------\n", + " # Build the GWF model\n", + " # -------------------\n", + "\n", + " # Instantiate the MODFLOW 6 gwf (groundwater-flow) model\n", + " gwf = flopy.mf6.ModflowGwf(\n", + " sim,\n", + " modelname=gwf_name,\n", + " model_nam_file=\"{}.nam\".format(gwf_name)\n", + " )\n", + " gwf.name_file.save_flows = True\n", + "\n", + " # Instantiate the MODFLOW 6 gwf discretization package\n", + " flopy.mf6.ModflowGwfdisv(\n", + " gwf,\n", + " length_units=length_units,\n", + " **disv_props,\n", + " )\n", + "\n", + " # GridIntersect object for setting up boundary conditions\n", + " ix = GridIntersect(gwf.modelgrid, method=\"vertex\", rtree=True)\n", + "\n", + " # Instantiate the MODFLOW 6 gwf initial conditions package\n", + " flopy.mf6.ModflowGwfic(gwf, pname=\"ic\", strt=riv_h)\n", + "\n", + " # Instantiate the MODFLOW 6 gwf node property flow package\n", + " flopy.mf6.ModflowGwfnpf(\n", + " gwf,\n", + " xt3doptions=[(\"xt3d\")],\n", + " icelltype=icelltype,\n", + " k=k,\n", + " k33=k33,\n", + " save_saturation=True,\n", + " save_specific_discharge=True,\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf recharge package\n", + " flopy.mf6.ModflowGwfrcha(\n", + " gwf,\n", + " recharge=rch,\n", + " auxiliary=[\"iface\", \"iflowface\"],\n", + " aux=[rch_iface, rch_iflowface],\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf well package\n", + " welcells = ix.intersects(MultiPoint(wel_coords))\n", + " welcells = [icpl for (icpl,) in welcells]\n", + " welspd = [[(2, icpl), wel_q[idx]] for idx, icpl in enumerate(welcells)]\n", + " flopy.mf6.ModflowGwfwel(gwf, print_input=True, stress_period_data=welspd)\n", + "\n", + " # Instantiate the MODFLOW 6 gwf river package\n", + " riverline = [(Lx - 1.0, Ly), (Lx - 1.0, 0.0)]\n", + " rivcells = ix.intersects(LineString(riverline))\n", + " rivcells = [icpl for (icpl,) in rivcells]\n", + " rivspd = [\n", + " [(0, icpl), riv_h, riv_c, riv_z, riv_iface, riv_iflowface] for icpl in rivcells\n", + " ]\n", + " flopy.mf6.ModflowGwfriv(\n", + " gwf, stress_period_data=rivspd, auxiliary=[(\"iface\", \"iflowface\")]\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 gwf output control package\n", + " headfile = \"{}.hds\".format(gwf_name)\n", + " head_record = [headfile]\n", + " budgetfile = \"{}.cbb\".format(gwf_name)\n", + " budget_record = [budgetfile]\n", + " flopy.mf6.ModflowGwfoc(\n", + " gwf,\n", + " pname=\"oc\",\n", + " budget_filerecord=budget_record,\n", + " head_filerecord=head_record,\n", + " headprintrecord=[(\"COLUMNS\", 10, \"WIDTH\", 15, \"DIGITS\", 6, \"GENERAL\")],\n", + " saverecord=[(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")],\n", + " printrecord=[(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")],\n", + " )\n", + "\n", + " # Create an iterative model solution (IMS) for the MODFLOW 6 gwf model\n", + " ims = flopy.mf6.ModflowIms(\n", + " sim,\n", + " pname=\"ims\",\n", + " print_option=\"SUMMARY\",\n", + " complexity=\"SIMPLE\",\n", + " outer_dvclose=1.0e-5,\n", + " outer_maximum=100,\n", + " under_relaxation=\"NONE\",\n", + " inner_maximum=100,\n", + " inner_dvclose=1.0e-6,\n", + " rcloserecord=0.1,\n", + " linear_acceleration=\"BICGSTAB\",\n", + " scaling_method=\"NONE\",\n", + " reordering_method=\"NONE\",\n", + " relaxation_factor=0.99,\n", + " )\n", + " sim.register_ims_package(ims, [gwf.name])\n", + "\n", + " return sim" + ] + }, + { + "cell_type": "code", + "execution_count": 902, + "id": "cc213cc9", + "metadata": {}, + "outputs": [], + "source": [ + "def build_mf6prt(part):\n", + " print(f\"Building PRT model for {example_name}{part}\")\n", + "\n", + " prpname = f\"prp2{part}\"\n", + " prpfilename = f\"{prt_name}_2{part}.prp\"\n", + "\n", + " prt_ws_scen = prt_ws.parent / (prt_ws.name + part)\n", + " prt_ws_scen.mkdir(exist_ok=True)\n", + "\n", + " # Instantiate the MODFLOW 6 simulation object\n", + " simprt = flopy.mf6.MFSimulation(\n", + " sim_name=prt_name, version=\"mf6\", exe_name=\"mf6\", sim_ws=prt_ws_scen\n", + " )\n", + "\n", + " # Create a time discretization for backward tracking;\n", + " # since there is only a single period with a single time step,\n", + " # the time discretization is the same backward as forward\n", + " tdis_bkwd = tdis_rc\n", + "\n", + " # Instantiate the MODFLOW 6 temporal discretization package\n", + " flopy.mf6.ModflowTdis(\n", + " simprt,\n", + " pname=\"tdis\",\n", + " time_units=\"DAYS\",\n", + " perioddata=tdis_bkwd,\n", + " nper=len(tdis_bkwd),\n", + " )\n", + "\n", + " # -------------------\n", + " # Build the PRT model\n", + " # -------------------\n", + "\n", + " # Instantiate the MODFLOW 6 prt model\n", + " prt = flopy.mf6.ModflowPrt(\n", + " simprt, modelname=prt_name, model_nam_file=\"{}.nam\".format(prt_name)\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt discretization package\n", + " flopy.mf6.ModflowGwfdisv(\n", + " prt,\n", + " length_units=length_units,\n", + " **disv_props,\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt model input package\n", + " flopy.mf6.ModflowPrtmip(prt, pname=\"mip\", porosity=porosity)\n", + "\n", + " # Set particle release point data according to the scenario\n", + " set_releasepts(part)\n", + " nreleasepts = len(releasepts)\n", + " particle_data = get_particle_data(part)\n", + " prp_pkg_data = list(particle_data.to_prp(grid))\n", + "\n", + " # Instantiate the MODFLOW 6 prt particle release point (prp) package\n", + " pd = {0: [\"FIRST\"], 1: []}\n", + " flopy.mf6.ModflowPrtprp(\n", + " prt,\n", + " pname=prpname,\n", + " filename=prpfilename,\n", + " nreleasepts=len(prp_pkg_data),\n", + " packagedata=prp_pkg_data,\n", + " perioddata=pd,\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt output control package\n", + " budgetfile = \"{}.bud\".format(prt_name)\n", + " trackfile = \"{}.trk\".format(prt_name)\n", + " trackcsvfile = \"{}.trk.csv\".format(prt_name)\n", + " budget_record = [budgetfile]\n", + " track_record = [trackfile]\n", + " trackcsv_record = [trackcsvfile]\n", + " flopy.mf6.ModflowPrtoc(\n", + " prt,\n", + " pname=\"oc\",\n", + " budget_filerecord=budget_record,\n", + " track_filerecord=track_record,\n", + " trackcsv_filerecord=trackcsv_record,\n", + " saverecord=[(\"BUDGET\", \"ALL\")],\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt flow model interface\n", + " # using \"time-reversed\" budget and head files\n", + " pd = [\n", + " (\"GWFHEAD\", headfile_bkwd),\n", + " (\"GWFBUDGET\", budgetfile_bkwd),\n", + " ]\n", + " flopy.mf6.ModflowPrtfmi(prt, packagedata=pd)\n", + "\n", + " # Create an explicit model solution (EMS) for the MODFLOW 6 prt model\n", + " ems = flopy.mf6.ModflowEms(\n", + " simprt,\n", + " pname=\"ems\",\n", + " filename=\"{}.ems\".format(prt_name),\n", + " )\n", + " simprt.register_solution_package(ems, [prt.name])\n", + "\n", + " return simprt" + ] + }, + { + "cell_type": "code", + "execution_count": 903, + "id": "8a94fb6b", + "metadata": {}, + "outputs": [], + "source": [ + "def build_mp7(part, gwf):\n", + " print(f\"Building mp7 model for {example_name}{part}\")\n", + "\n", + " # Set parameters according to the scenario\n", + " pgdata = get_particle_data(part)\n", + " nm_mp7 = f\"{mp7_name}{part}\"\n", + " pgname = f\"BACKWARD2{part.upper()}\"\n", + " fpth = nm_mp7 + \".sloc\"\n", + "\n", + " if part == \"a\":\n", + " simtype = \"combined\"\n", + " timepointdata = [500, 1000.0]\n", + " pg = flopy.modpath.ParticleGroup(\n", + " particlegroupname=pgname, particledata=pgdata, filename=fpth\n", + " )\n", + " else:\n", + " pg = flopy.modpath.ParticleGroupNodeTemplate(\n", + " particlegroupname=pgname, particledata=pgdata, filename=fpth\n", + " )\n", + " simtype = \"combined\"\n", + " timepointdata = None\n", + "\n", + " # Instantiate the MODPATH 7 simulation object\n", + " bfile = gwf_ws / budgetfile\n", + " hfile = gwf_ws / headfile\n", + "\n", + " # create workspace for this scenario (part A or B)\n", + " mp7_ws_scen = mp7_ws.parent / (mp7_ws.name + part)\n", + " mp7_ws_scen.mkdir(exist_ok=True)\n", + "\n", + " mp7 = flopy.modpath.Modpath7(\n", + " modelname=nm_mp7,\n", + " flowmodel=gwf,\n", + " exe_name=\"mp7\",\n", + " model_ws=mp7_ws_scen,\n", + " budgetfilename=budgetfile,\n", + " headfilename=headfile,\n", + " )\n", + "\n", + " # Instantiate the MODPATH 7 basic data\n", + " flopy.modpath.Modpath7Bas(mp7, porosity=porosity)\n", + "\n", + " # Instantiate the MODPATH 7 simulation data\n", + " flopy.modpath.Modpath7Sim(\n", + " mp7,\n", + " simulationtype=simtype,\n", + " trackingdirection=\"backward\",\n", + " weaksinkoption=\"pass_through\",\n", + " weaksourceoption=\"pass_through\",\n", + " referencetime=0.0,\n", + " stoptimeoption=\"extend\",\n", + " timepointdata=timepointdata,\n", + " particlegroups=pg,\n", + " )\n", + "\n", + " # Return the simulation\n", + " return mp7" + ] + }, + { + "cell_type": "code", + "execution_count": 904, + "id": "3194579d", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def build_models(part):\n", + " sim = build_mf6gwf()\n", + " gwf = sim.get_model(gwf_name)\n", + " simprt = build_mf6prt(part)\n", + " mp7 = build_mp7(part, gwf)\n", + " return sim, simprt, mp7" + ] + }, + { + "cell_type": "markdown", + "id": "ed76c4dd", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Function to write simulation/model input files." + ] + }, + { + "cell_type": "code", + "execution_count": 905, + "id": "668e0fba", + "metadata": {}, + "outputs": [], + "source": [ + "def write_models(sim, simprt, mp7, silent=True):\n", + " sim.write_simulation(silent=silent)\n", + " simprt.write_simulation(silent=silent)\n", + " mp7.write_input()" + ] + }, + { + "cell_type": "markdown", + "id": "ba602863", + "metadata": {}, + "source": [ + "Because this problem tracks particles backwards, we need to reverse the head and budget files after running the groundwater flow model and before running the particle tracking model. Define functions to do this." + ] + }, + { + "cell_type": "code", + "execution_count": 906, + "id": "e307782e", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "import flopy.utils.binaryfile as bf\n", + "\n", + "\n", + "def reverse_budgetfile(fpth, rev_fpth, tdis):\n", + " f = bf.CellBudgetFile(fpth, tdis=tdis)\n", + " f.reverse(rev_fpth)\n", + "\n", + "\n", + "def reverse_headfile(fpth, rev_fpth, tdis):\n", + " f = bf.HeadFile(fpth, tdis=tdis)\n", + " f.reverse(rev_fpth)" + ] + }, + { + "cell_type": "markdown", + "id": "058f199c", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function to run all three simulations and their respective models." + ] + }, + { + "cell_type": "code", + "execution_count": 907, + "id": "8e01a121", + "metadata": {}, + "outputs": [], + "source": [ + "@timeit\n", + "def run_models(part, sim, simprt, mp7, silent=True):\n", + " # Run MODFLOW 6 flow simulation\n", + " success, buff = sim.run_simulation(silent=silent, report=True)\n", + " for line in buff:\n", + " print(line)\n", + " assert success\n", + "\n", + " # Process budget and head files for backward tracking\n", + " prt_ws_scen = prt_ws.parent / (prt_ws.name + part)\n", + " reverse_budgetfile(gwf_ws / budgetfile, prt_ws_scen / budgetfile_bkwd, sim.tdis)\n", + " reverse_headfile(gwf_ws / headfile, prt_ws_scen / headfile_bkwd, sim.tdis)\n", + "\n", + " # Run MODFLOW 6 PRT particle-tracking simulation\n", + " success, buff = simprt.run_simulation(silent=silent, report=True)\n", + " for line in buff:\n", + " print(line)\n", + " assert success\n", + "\n", + " # Run MODPATH 7 simulation\n", + " success, buff = mp7.run_model(silent=silent, report=True)\n", + " for line in buff:\n", + " print(line)\n", + " assert success" + ] + }, + { + "cell_type": "markdown", + "id": "8805539e", + "metadata": {}, + "source": [ + "Define functions to load pathline and endpoint data from MODFLOW 6 PRT's particle track CSV files. Note that unlike MODPATH 7, MODFLOW 6 PRT does not make a distinction between pathline and endpoint output files — all pathline data is saved to track files, and endpoints are computed dynamically." + ] + }, + { + "cell_type": "code", + "execution_count": 908, + "id": "aab08ddb", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "from flopy.plot.plotutil import to_mp7_pathlines, to_mp7_endpoints\n", + "\n", + "\n", + "def load_mf6pathlines(p):\n", + " pls = pd.read_csv(p)\n", + " pls = to_mp7_pathlines(pls)\n", + " return pls\n", + "\n", + "\n", + "def load_mf6endpoints(p):\n", + " pls = pd.read_csv(p)\n", + " eps = pls.sort_values(\"t\").groupby([\"imdl\", \"iprp\", \"irpt\", \"trelease\"]).tail(1)\n", + " return eps" + ] + }, + { + "cell_type": "markdown", + "id": "2aa0822b", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function for plotting flow model grid, boundary conditions, and head results." + ] + }, + { + "cell_type": "code", + "execution_count": 909, + "id": "cee48dcd", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n", + "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n", + "\n", + "def plot_nodes_and_vertices(gwf, mg, ibd, ax):\n", + " \"\"\"\n", + " Plot cell nodes and vertices (and IDs) on a zoomed inset\n", + " \"\"\"\n", + "\n", + " ax.set_aspect(\"equal\")\n", + "\n", + " # set zoom area\n", + " xmin, xmax = 2050, 4800\n", + " ymin, ymax = 5200, 7550\n", + " ax.set_xlim([xmin, xmax])\n", + " ax.set_ylim([ymin, ymax])\n", + "\n", + " # create map view plot\n", + " pmv = flopy.plot.PlotMapView(gwf, ax=ax)\n", + " v = pmv.plot_grid(lw=0.5, edgecolor=\"black\")\n", + " t = ax.set_title(\"Node and vertex indices (one-based)\\n\", fontsize=14)\n", + " ax.set_xlim([xmin, xmax])\n", + " ax.set_ylim([ymin, ymax])\n", + "\n", + " # plot vertices\n", + " verts = mg.verts\n", + " ax.plot(verts[:, 0], verts[:, 1], \"bo\")\n", + " for i in range(ncpl):\n", + " x, y = verts[i, 0], verts[i, 1]\n", + " if xmin <= x <= xmax and ymin <= y <= ymax:\n", + " ax.annotate(str(i + 1), verts[i, :], color=\"b\")\n", + "\n", + " # plot nodes\n", + " xc, yc = mg.get_xcellcenters_for_layer(0), mg.get_ycellcenters_for_layer(0)\n", + " for i in range(ncpl):\n", + " x, y = xc[i], yc[i]\n", + " ax.plot(x, y, \"o\", color=\"grey\")\n", + " if xmin <= x <= xmax and ymin <= y <= ymax:\n", + " ax.annotate(str(i + 1), (x, y), color=\"grey\")\n", + " \n", + " # plot well\n", + " ax.plot(wel_coords[0][0], wel_coords[0][1], \"ro\")\n", + "\n", + " # create legend\n", + " ax.legend(\n", + " handles=[\n", + " mpl.patches.Patch(color=\"blue\", label=\"vertex\"),\n", + " mpl.patches.Patch(color=\"grey\", label=\"node\"),\n", + " ],\n", + " loc=\"upper left\"\n", + " )\n", + "\n", + "def plot_bc(sim, mg, ibd):\n", + " if styles:\n", + " fs = USGSFigure(figure_type=\"map\", verbose=False)\n", + " fig = plt.figure(figsize=figure_size_solo)\n", + " fig.tight_layout()\n", + " ax = fig.add_subplot(1, 1, 1, aspect=\"equal\")\n", + " gwf = sim.get_model(gwf_name)\n", + " pmv = flopy.plot.PlotMapView(gwf, ax=ax)\n", + " pmv.plot_bc(\"WEL\", plotAll=True)\n", + " pmv.plot_grid(lw=0.5)\n", + " ax.set_xlim(0, Lx)\n", + " ax.set_ylim(0, Ly)\n", + " pc = pmv.plot_array(ibd, cmap=cmapbd, edgecolor=\"gray\")\n", + " t = ax.set_title(\"Boundary conditions\\n\", fontsize=14)\n", + "\n", + " # create inset\n", + " axins = ax.inset_axes([-0.76, 0.25, 0.7, 0.9])\n", + " plot_nodes_and_vertices(gwf, mg, ibd, axins)\n", + " ax.indicate_inset_zoom(axins)\n", + " \n", + " # create legend\n", + " ax.legend(\n", + " handles=[\n", + " mpl.patches.Patch(color=\"red\", label=\"Well\"),\n", + " mpl.patches.Patch(color=\"green\", label=\"River\"),\n", + " ],\n", + " loc=\"upper left\"\n", + " )\n", + "\n", + " # set title\n", + " plt.suptitle(\n", + " t=\"Example 2: model grid\",\n", + " fontsize=14,\n", + " ha=\"center\"\n", + " )\n", + " \n", + " plt.show()\n", + "\n", + "def plot_head(mg):\n", + " # Import simulated head values from the binary head file\n", + " fname = gwf_ws / (gwf_name + \".hds\")\n", + " hdobj = flopy.utils.HeadFile(fname)\n", + " head = hdobj.get_data()\n", + "\n", + " # Prepare a plot of heads in layer 3\n", + " ilay = 2\n", + " cint = 0.25\n", + " if styles:\n", + " fs = USGSFigure(figure_type=\"map\", verbose=False)\n", + " fig2 = plt.figure(figsize=figure_size_solo)\n", + " fig2.tight_layout()\n", + " ax = fig2.add_subplot(1, 1, 1, aspect=\"equal\")\n", + " mm = flopy.plot.PlotMapView(modelgrid=mg, ax=ax, layer=ilay)\n", + " mm.plot_grid(lw=0.5)\n", + " ax.set_xlim(0, Lx)\n", + " ax.set_ylim(0, Ly)\n", + " pc = mm.plot_array(head[:, 0, :], cmap=\"jet\", edgecolor=\"black\")\n", + " hmin = head[ilay, 0, :].min()\n", + " hmax = head[ilay, 0, :].max()\n", + " levels = np.arange(np.floor(hmin), np.ceil(hmax) + cint, cint)\n", + " cs = mm.contour_array(head[:, 0, :], colors=\"white\", levels=levels)\n", + " plt.clabel(cs, fmt=\"%.1f\", colors=\"white\", fontsize=11)\n", + " cb = plt.colorbar(pc, shrink=0.5)\n", + " t = ax.set_title(\n", + " \"Example 2: Head in layer {}, hmin {:6.2f}, hmax {:6.2f}\\n\".format(\n", + " ilay + 1, hmin, hmax\n", + " ),\n", + " fontsize=14,\n", + " )\n", + " plt.show()\n", + "\n", + "def plot_gwf(sim, mg, ibd):\n", + " plot_bc(sim, mg, ibd)\n", + " plot_head(mg)" + ] + }, + { + "cell_type": "markdown", + "id": "82ea2371", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function for plotting pathlines and time series points colored by layer." + ] + }, + { + "cell_type": "code", + "execution_count": 910, + "id": "b60e7f7e", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def plot_pathlines_and_timeseries(ax, mg, ibd, pathlines, timeseries, plottitle):\n", + " ax.set_aspect(\"equal\")\n", + " mm = flopy.plot.PlotMapView(modelgrid=mg, ax=ax)\n", + " mm.plot_grid(lw=0.5)\n", + " v = mm.plot_array(ibd, cmap=cmapbd, edgecolor=\"gray\")\n", + " mm.plot_pathline(pathlines, layer=\"all\", colors=[\"blue\"], lw=0.75)\n", + " # issue(mf6): no way to output time series in prt\n", + " if timeseries != None:\n", + " for k in range(nlay - 1, -1, -1):\n", + " mm.plot_timeseries(timeseries, layer=k, marker=\"o\", lw=0, color=colors[k])\n", + " ax.set_title(plottitle, fontsize=12)" + ] + }, + { + "cell_type": "markdown", + "id": "67b83c9b", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a utility function for plotting particle endpoints, colored by travel time to capture." + ] + }, + { + "cell_type": "code", + "execution_count": 911, + "id": "5c2e7fb2", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_endpoints(ax, mg, ibd, endpointdata, plottitle):\n", + " ax.set_xlim(0, Lx)\n", + " ax.set_ylim(0, Ly)\n", + " mm = flopy.plot.PlotMapView(modelgrid=mg, ax=ax)\n", + " mm.plot_grid(lw=0.5)\n", + " v = mm.plot_array(ibd, cmap=cmapbd, edgecolor=\"gray\")\n", + " mm.plot_endpoint(endpointdata, direction=\"ending\", colorbar=True, shrink=0.25)\n", + " ax.set_title(plottitle, fontsize=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 912, + "id": "62d2748b", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def plot_results(part, sim, simprt, mp7):\n", + " # Get model grid\n", + " fname = gwf_ws / (gwf_name + \".disv.grb\")\n", + " grd = flopy.mf6.utils.MfGrdFile(fname, verbose=False)\n", + " mg = grd.modelgrid\n", + "\n", + " # Workspaces\n", + " prt_ws_scen = prt_ws.parent / (prt_ws.name + part)\n", + " mp7_ws_scen = mp7_ws.parent / (mp7_ws.name + part)\n", + "\n", + " # identify the boundary locations\n", + " ibd = np.zeros((ncpl), dtype=int)\n", + " ibd[np.array(welcells)] = 1\n", + " ibd[np.array(rivcells)] = 2\n", + " ibd = np.ma.masked_equal(ibd, 0)\n", + "\n", + " if part == \"a\":\n", + " # =========================\n", + " # MODFLOW 6 flow simulation\n", + " # =========================\n", + "\n", + " plot_gwf(sim, mg, ibd)\n", + "\n", + " # ==========\n", + " # Example 2A\n", + " # ==========\n", + "\n", + " # Load MODFLOW 6 PRT pathlines\n", + " mf6pathlines = load_mf6pathlines(prt_ws_scen / trackcsvfile)\n", + "\n", + " # Load MODPATH 7 pathlines\n", + " plf = flopy.utils.PathlineFile(mp7_ws_scen / (mp7_name + f\"{part}.mppth\"))\n", + " mp7pathlines = plf.get_alldata()\n", + "\n", + " # ------------------------------------------------\n", + " # Pathlines and time series point colored by layer\n", + " # ------------------------------------------------\n", + "\n", + " # Initialize plot\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size_compare)\n", + " plt.suptitle(\n", + " t=\"Example 2A: Pathlines and time series points tracked backward from well\",\n", + " fontsize=14,\n", + " )\n", + " axes = axes.flatten()\n", + "\n", + " # MODFLOW 6 PRT\n", + " ax = axes[0]\n", + " # issue(mf6): no way to output time series\n", + " plot_pathlines_and_timeseries(ax, mg, ibd, mf6pathlines, None, \"MODFLOW 6 PRT\")\n", + "\n", + " # MODPATH 7\n", + " ax = axes[1]\n", + " plot_pathlines_and_timeseries(\n", + " ax, mg, ibd, mp7pathlines, mp7pathlines, \"MODPATH 7\"\n", + " )\n", + "\n", + " # Save figure\n", + " if plotSave:\n", + " fpth = os.path.join(\n", + " \"..\", \"figures\", \"{}-paths{}\".format(sim_name, figure_ext)\n", + " )\n", + " fig.savefig(fpth)\n", + "\n", + " else:\n", + " # ==========\n", + " # Example 2B\n", + " # ==========\n", + "\n", + " # Load MODFLOW 6 PRT endpoint data\n", + " mf6endpoints = load_mf6endpoints(prt_ws_scen / trackcsvfile)\n", + "\n", + " # Load MODPATH 7 endpoint data\n", + " epf = flopy.utils.EndpointFile(mp7_ws_scen / (mp7_name + f\"{part}.mpend\"))\n", + " mp7endpoints = epf.get_alldata()\n", + "\n", + " # -------------------------------------------\n", + " # Endpoints colored by travel time to capture\n", + " # -------------------------------------------\n", + "\n", + " # Initialize plot\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size_compare)\n", + " plt.suptitle(\n", + " t=\"Example 2B: Endpoints of particle paths tracked backward from well\",\n", + " fontsize=14,\n", + " )\n", + " axes = axes.flatten()\n", + "\n", + " # MODFLOW 6 PRT\n", + " ax = axes[0]\n", + " plot_endpoints(ax, mg, ibd, mf6endpoints, \"MODFLOW 6 PRT\")\n", + "\n", + " # MODPATH 7\n", + " ax = axes[1]\n", + " plot_endpoints(ax, mg, ibd, mp7endpoints, \"MODPATH 7\")\n", + "\n", + " # issue(flopy): the center \"dot\" looks to be in a slightly different location\n", + " # in the two plots -- true even if you plot the very same endpoint array in both plots\n", + "\n", + " # Save figure\n", + " if plotSave:\n", + " fpth = os.path.join(\n", + " \"..\", \"figures\", \"{}-endpts{}\".format(sim_name, figure_ext)\n", + " )\n", + " fig.savefig(fpth)" + ] + }, + { + "cell_type": "markdown", + "id": "44524ecf", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function to wrap all of the steps for each scenario.\n", + "\n", + "1. build model\n", + "2. write model\n", + "3. run model\n", + "4. plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 913, + "id": "70eaa732", + "metadata": {}, + "outputs": [], + "source": [ + "def scenario(part, silent=True):\n", + " sim, simprt, mp7 = build_models(part)\n", + " write_models(sim, simprt, mp7, silent=silent)\n", + " if runModel:\n", + " run_models(part, sim, simprt, mp7, silent=silent)\n", + " plot_results(part, sim, simprt, mp7)" + ] + }, + { + "cell_type": "markdown", + "id": "19cd1bb5", + "metadata": {}, + "source": [ + "Run the scenario for problem 2A." + ] + }, + { + "cell_type": "code", + "execution_count": 914, + "id": "aababcb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building GWF model\n", + "WARNING: Unable to resolve dimension of ('gwf6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building PRT model for ex-prt-mp7-p02a\n", + "WARNING: Unable to resolve dimension of ('prt6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\n", + "Building mp7 model for ex-prt-mp7-p02a\n", + "writing simulation...\n", + " writing simulation name file...\n", + " writing simulation tdis package...\n", + " writing solution package ims...\n", + " writing model mp7-p02-gwf...\n", + " writing model name file...\n", + " writing package disv...\n", + " writing package ic...\n", + " writing package npf...\n", + " writing package rcha_0...\n", + " writing package wel_0...\n", + "INFORMATION: maxbound in ('gwf6', 'wel', 'dimensions') changed to 1 based on size of stress_period_data\n", + " writing package riv_0...\n", + "INFORMATION: maxbound in ('gwf6', 'riv', 'dimensions') changed to 21 based on size of stress_period_data\n", + " writing package oc...\n", + "writing simulation...\n", + " writing simulation name file...\n", + " writing simulation tdis package...\n", + " writing solution package ems...\n", + " writing model mp7-p02-prt...\n", + " writing model name file...\n", + " writing package disv...\n", + " writing package mip...\n", + " writing package prp2a...\n", + " writing package oc...\n", + " writing package fmi...\n", + "FloPy is using the following executable to run the model: ../../../venv/bin/mf6\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/29/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 29 2023 20:39:23 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:18\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:18\n", + " Elapsed run time: 0.130 Seconds\n", + " \n", + " Normal termination of simulation.\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/29/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 29 2023 20:39:23 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:18\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:18\n", + " Elapsed run time: 0.130 Seconds\n", + " \n", + " Normal termination of simulation.\n", + "FloPy is using the following executable to run the model: ../../../venv/bin/mf6\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/29/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 29 2023 20:39:23 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:18\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:18\n", + " Elapsed run time: 0.043 Seconds\n", + " \n", + " Normal termination of simulation.\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/29/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 29 2023 20:39:23 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:18\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:18\n", + " Elapsed run time: 0.043 Seconds\n", + " \n", + " Normal termination of simulation.\n", + "FloPy is using the following executable to run the model: ../../../../../.local/share/flopy/bin/mp7\n", + "\n", + "MODPATH Version 7.2.001 \n", + "Program compiled Mar 07 2022 14:08:27 with IFORT compiler (ver. 20.21.5) \n", + " \n", + " \n", + "Run particle tracking simulation ...\n", + "Processing Time Step 1 Period 1. Time = 1.00000E+03 Steady-state flow \n", + "\n", + "Particle Summary:\n", + " 0 particles are pending release.\n", + " 0 particles remain active.\n", + " 16 particles terminated at boundary faces.\n", + " 0 particles terminated at weak sink cells.\n", + " 0 particles terminated at weak source cells.\n", + " 0 particles terminated at strong source/sink cells.\n", + " 0 particles terminated in cells with a specified zone number.\n", + " 0 particles were stranded in inactive or dry cells.\n", + " 0 particles were unreleased.\n", + " 0 particles have an unknown status.\n", + " \n", + "Normal termination. \n", + "\n", + "MODPATH Version 7.2.001 \n", + "Program compiled Mar 07 2022 14:08:27 with IFORT compiler (ver. 20.21.5) \n", + " \n", + " \n", + "Run particle tracking simulation ...\n", + "Processing Time Step 1 Period 1. Time = 1.00000E+03 Steady-state flow \n", + "\n", + "Particle Summary:\n", + " 0 particles are pending release.\n", + " 0 particles remain active.\n", + " 16 particles terminated at boundary faces.\n", + " 0 particles terminated at weak sink cells.\n", + " 0 particles terminated at weak source cells.\n", + " 0 particles terminated at strong source/sink cells.\n", + " 0 particles terminated in cells with a specified zone number.\n", + " 0 particles were stranded in inactive or dry cells.\n", + " 0 particles were unreleased.\n", + " 0 particles have an unknown status.\n", + " \n", + "Normal termination. \n", + "run_models 267.92 ms\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scenario(\"a\", silent=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 915, + "id": "3c6f401b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building GWF model\n", + "WARNING: Unable to resolve dimension of ('gwf6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\n", + "Building PRT model for ex-prt-mp7-p02b\n", + "WARNING: Unable to resolve dimension of ('prt6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\n", + "Building mp7 model for ex-prt-mp7-p02b\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/29/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 29 2023 20:39:23 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:24\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:25\n", + " Elapsed run time: 0.140 Seconds\n", + " \n", + " Normal termination of simulation.\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/29/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 29 2023 20:39:23 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:25\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/07 13:17:25\n", + " Elapsed run time: 0.288 Seconds\n", + " \n", + " Normal termination of simulation.\n", + "\n", + "MODPATH Version 7.2.001 \n", + "Program compiled Mar 07 2022 14:08:27 with IFORT compiler (ver. 20.21.5) \n", + " \n", + " \n", + "Run particle tracking simulation ...\n", + "Processing Time Step 1 Period 1. Time = 1.00000E+03 Steady-state flow \n", + "\n", + "Particle Summary:\n", + " 0 particles are pending release.\n", + " 0 particles remain active.\n", + " 416 particles terminated at boundary faces.\n", + " 0 particles terminated at weak sink cells.\n", + " 0 particles terminated at weak source cells.\n", + " 0 particles terminated at strong source/sink cells.\n", + " 0 particles terminated in cells with a specified zone number.\n", + " 0 particles were stranded in inactive or dry cells.\n", + " 0 particles were unreleased.\n", + " 0 particles have an unknown status.\n", + " \n", + "Normal termination. \n", + "run_models 2,165.04 ms\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABJ0AAAIrCAYAAABF8+gVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUVfrA8e+9UzKpJCGh915EEOkdpYlSBAt2say4uuKuva2AqD/LWlgsYAFX1rIWEBVRVFC6iICAIL3XQHqdmXt+f0zJTKYGEknk/TxPHsgt7z33ziTz5r3nnqMppRRCCCGEEEIIIYQQQlQg/Uw3QAghhBBCCCGEEEL8+UjRSQghhBBCCCGEEEJUOCk6CSGEEEIIIYQQQogKJ0UnIYQQQgghhBBCCFHhpOgkhBBCCCGEEEIIISqcFJ2EEEIIIYQQQgghRIWTopMQQgghhBBCCCGEqHBSdBJCCCGEEEIIIYQQFU6KTkIIIYQQQgghhBCiwknRSQghqqABAwagaVrIr0mTJp3pJka0ZMkSNE1j/fr1pxWnoKCAu+66i9q1a2Oz2ejevTvLly8POI7vV3x8PN27d+err74q9/EmTZoU9toPGDDgtM4HQNM0XnrppdOO4+vGG2+kU6dO5d5v3759FdqOYH744QeaN29ObGwszzzzTKUfLxjf8xwwYACjR4+Oar+Keh9XlJKSEo4ePer9vjLeSwA5OTlkZWVVeNyyyvNalEekn4dJkyaRnJxc4cctbzvOpGja9r///Y/69esTHx/Pe++998c07A9U9n3QpEkT7r777jPWHiGE+DMyn+kGCCGECK5NmzY88sgjQdede+65f3BrzpzrrruOL774gokTJ9KwYUPeeOMNhg0bxrZt26hbt653uyeffJJGjRqhlCI7O5u5c+cycuRIVqxYQdeuXct93HfffTfo8tq1a5/yuVSm2267jczMzHLtc8MNN5CSklIpRQtfd911F5qm8fLLL9OzZ89KPVYwZc/zkUcewWq1/uHtOF05OTn06dOHKVOmVEqhxmPPnj0MGDCAefPmVdmCiah8TqeTCRMm0Lx5cyZNmkSvXr3OdJOEEEJUQ1J0EkKIKqp27dpce+21Z7oZZ9TWrVv59NNPee2115gwYQIA119/PS1btuTFF1/k2Wef9W47fPhwvz+QJ0yYQJs2bXj55ZeZM2dOuY9d3a79qRRzfvjhh0otXnhs3bqVxx57jL/85S+Vfqxgyp7n4MGDz0g7TtfJkyfZuHFjpR9nz5497N27t9KPI6q2Y8eOkZmZycSJE6vd70MhhBBVhzxeJ4QQospatmwZAJdddpl3WY0aNejbty9r164Nu6/ZbKZr167s2LGjUtsoIispKSEhIeFMN0MIUQ7FxcUA8rMrhBDitEjRSQghqrELL7yQmjVr+o29Mnr0aGrXru1dtmrVKoYMGUJaWhrx8fH07NmTH374wbv9pEmT6NKlC3PnzqV169bExcUxcuRIMjMz+eSTT2jZsiU1atTguuuuIz8/H3D1hNA0jc8//5zBgwcTFxdHx44d+frrr8O2d9WqVfTt25fY2Fjq16/P448/jtPpDLn9VVddxbp160hLS/NbfvLkyaiuz6FDh2jcuLH3e8/4PLNnz45q/3A81+C7777j0ksvJT4+noYNG/Lmm2/6bXfgwAFGjhxJQkICbdu25fvvv/dbP3v2bBISEli+fDnt2rUjPj6eoUOHsnPnTr/ttmzZwkUXXUR8fDxpaWlMmDCB7Oxs73rf8VmiaVuTJk3Yu3cvL7/8MpqmAZCZmclNN91EnTp1iIuLY+DAgaxbty7sdcjKyuIvf/mL9/01fPhwfv/9d6D0egP8/e9/9/6/rGivwVdffUXv3r1JTk4mKSmJQYMG+fX8ufHGGxk7dizjx4/HZrMxatSooOdZdhyh/Px87rzzTmrVqkWNGjUYNWoUu3fvDnnO5X0fe67DsmXL6NatG3FxcfTq1Yuff/45IG6on9U9e/bQtGlTAC699FK/scXy8vK49dZbSUpKol69ejz66KMYhuFdv2zZMnr16kVCQgK1a9fmjjvuoLCwMGRbBw4cCMB5553HjTfe6H0/vfnmmzRo0IDk5GSWLl0KwIsvvki7du2Ii4sjPT2dG2+8MWAsqA8++ID27dsTGxvLOeecw8cffxzyWr355ptomsarr77qXfbll1/SuXNnbDYbzZo1Y/r06X77KKV44oknqFevHsnJyTz00EMopUIew9fcuXNp1qwZSUlJXHbZZRw/ftxv/Zw5c+jcuTOJiYmkpKQwevRo9u/f77fNt99+S9euXYmNjaVFixZ+bS9r4cKFmM1m7r//fqZOnUpCQgIOh8O7/uKLL6ZGjRp+r99FF13E9ddfD8DBgwe54YYbqF+/PjabjdatWzNjxgzvtp732n//+19q1qxJeno6O3fuLPc1mj17tt/7rUmTJkDpGGJt2rQhISGB999/H4AVK1bQp08fYmNjqVevHg8++CAlJSXeeAMGDOCee+7hiSeeoHbt2iQnJ/PAAw/gdDqZPHkyaWlp1K1bl6eeeipkmwYNGsTQoUO93xuGQVJSEiNGjPAuczgcJCYm8vbbbwOuz4Bx48ZRo0YN7+dYRkZGyGMIIYSoBEoIIUSV079/f9WrVy91/PjxoF9Op1MppdTGjRuVyWRS999/v1JKqQULFihAvfPOO0oppfbs2aOSkpLUoEGD1Ouvv66eeeYZ1aBBA5WQkKCysrKUUko9/vjjqmbNmiotLU09+eST6uGHH1a6rqt+/fqp+vXrq+eee05NnDhRAerJJ59USim1e/duBah69eqpQYMGqZdffll17dpVWSwWtX79eqWUUosXL1aAWrdunVJKqfXr16vY2FjVr18/NX36dDVx4kRlNpvVHXfcUa5rs2vXLmUymdQDDzzgd5zFixd7r8+ePXvUc889p8xms1q+fLl33yNHjqh3331X7dy5M2T8xx9/XAEhr31RUZHfNahfv7667LLL1Ouvv6769OmjAPX9998rpZQqKSlR7dq1U7Vq1VLPPfec+vvf/65q1KihAPXiiy8qpZSaNWuWslqtKi0tTU2YMEE9++yzqk6dOqpJkyaqoKDA+zqmpKSoZs2aqeeff1499NBDymazqV69eim73a6UUuqGG25QHTt2jLptc+fOVWlpaWro0KHq3XffVUopdeWVV6o6deqoZ555Rr366quqXbt2qnbt2t52lGW321X37t1VXFycevTRR9Wzzz6rGjdurGrWrKn27dvnvd6Auvbaa73HKSuaa7B69WplsVjU5ZdfrmbOnKkmT56sUlJSVKNGjZRhGN5rEBcXp7p27apee+01NWfOnKDn2b9/fzVq1Cjv8S+66CJltVrVvffeq6ZNm6YaNWqk2rZtq4qKiirkfeyJUa9ePXX55ZerF198UbVq1UrVqFFDHT582Psah/tZzcvLUy+++KIC1N13362++eYbpZRSgIqNjVUDBgxQr7zyiho7dqwC1OzZs5VSrve8J+6MGTPUY489pmJiYtTf/va3oG09cuSIevjhh70/7ytWrPC+nxISEtSkSZPUww8/rHJzc9ULL7ygLBaLuu+++9Qbb7yhbr/9dmUymdR1113njffBBx8oQA0ZMkS98soravTo0UrXdbV48eKA12Lp0qXKYrGov//97979FyxYoHRdV6NGjVKvvvqquvHGGxWgnnvuOe82U6dOVYC69dZb1b/+9S/VrFkzlZCQ4P15CObxxx9XZrNZxcfHqwcffFA98cQTKjExUXXt2tX7fvr0008VoG677Tb1xhtvqPvvv1/Fxsaqvn37euMsXbpUmc1m1a1bNzVt2jR10003+f0O9v253Lp1q0pOTlZjx45VhmGoZcuWKUD9/PPPSimlDMNQqampClCbN29WSinlcDhUYmKimjVrlnI6napTp06qZcuW6sUXX1TTp09XvXr1UoD67rvv/N5raWlp6vnnn1ePPPKIMgyj3Ndo586dfu+3uXPnet9vcXFx6q677lJTp05VO3fuVD/99JOKiYlRnTp1Ui+//LK68847la7r6vLLL/fG69+/v6pTp45q166dmjZtmrriiisUoAYOHKi6du2qXnnlFXXRRRcpwO93tq+pU6eqGjVqeD//NmzYoACVnp7u3Wb16tUKULt371a5ubmqZcuWqkmTJur5559XU6dOVWlpaeq8885TJSUl3vdBjRo1vPs3btxYTZw4MeT7RgghRPlJ0UkIIaqg/v37KyDk1+7du73b3nnnncpms6ldu3apli1bqn79+nnXTZ06VdWtW1cVFxd7l3377bcKUD/88INSqrTIMm/ePO82w4YNU4BaunSpd1nv3r3V0KFDlVKlRY3OnTt7ix75+fmqbt26aty4cUqpwKLTiBEjVLdu3bx/MCil1GuvvaZ0XVd79+6N6ro4nU41ePBgZbFYvIUjz3GCfV133XV+x4uG53qE+po1a5bfNRg5cqR338zMTBUbG6sefPBBpZRS7733nt8flUop7x9yvkUnQN11113ebdauXasA9frrryullLrttttUfHy8OnTokHcbzx/EH3zwgVIqeNEpXNuUCvwDKzExUb300kve79evX6/atWunNm3aFPRavf/++wpQ8+fP9y47cOCAio2NVRMmTPAu8z3fYKK5Brfccovq0qWL335vvvmmArzvnxtuuEEBatu2bX7blT3PsoUOQL333nve9T///LMC1IIFCyrkfeyJ4ft6HDp0SNlsNu/rEc3Pqud19RQAlHJd244dOyqHw6GUchUpGjVqpK6++mqllFKffPKJArxFZqWUeuSRR9TYsWODttW3vZ5z9hzXE9OjRYsW6p577vFbdu2116qmTZt6v2/evLkaNmyY93o5nU7VsWNHdcUVVyilSl+LvXv3qlq1aqmRI0f6XdsOHTqoyy67zO8YDzzwgEpKSlL5+fmquLhYJSYm+hXRDhw4oOLj4yMWnQD173//27ts/vz5ClBffvmlUkqpQYMGBVynRx99VJnNZm8bL7zwQtWhQwdvMVoppS655BLVrVs3pVTpz2VWVpZq3bq16tatm7eQWlJSouLj49W0adOUUkpt2bJFmUwmlZ6ert5++22llFI//fSTAtS+ffu8Rapff/3Ve6y8vDxls9nU5MmTlVKlr93DDz/s3eZUr1Go91uvXr38ths6dKhq2LChysvL8y574YUXFKBWrVqllHK9zjExMero0aPeNiUlJamUlBSVk5PjPZeYmBj19NNPB23P8uXL/c7/tddeU3Xq1FGA2rVrl1JKqWeffVY1a9ZMKaXUv/71L5WUlOT3e3PTpk3KZDJ5C9BSdBJCiMonj9cJIUQVde6557Jo0aKgX3Xq1PFuN2XKFOLj4xkwYAC7d+/2e7Tj4YcfZtOmTd6ZupRS3nE6fB/N0jSNIUOGeL9v3rw58fHx9OnTx7usadOmHDt2zK+NEyZMwGx2zUkRFxfHVVddxbfffhtwLk6nk2+//ZZhw4Zx8uRJMjIyyMjIYNCgQRiGweLFi6O6Jv/4xz9YtGgRkyZNolmzZn7rXn/9de/1mTt3Ln/729+YM2cOd9xxR1Sxywp17X0f7wC45JJLvP9PTk6mSZMm3sc3vvvuO9q1a8f555/v3eamm24K+pjZnXfe6f1/586d6dy5s/daLlq0iLFjx/rN1nfppZfSqFEjvvnmm5DnEK5twdSsWZM333yTr7/+GofDQceOHdm8eTPt27cPuv2iRYto3Lix3+Mt9evX57LLLgvbrlDCXYPXX3/dL6bT6fQ+luT7Xq5Tpw4tW7aM+pjff/89CQkJXHHFFd5l559/PmvXrvV7hM1zzNN5H/ueX926dRk+fLj3/KL9WQ1m+PDhmEwmAEwmE+3atfP+rNasWROA+++/n+3btwMwderUsI+4hdKvXz+/7zdu3MjkyZO93+fn52M2m73t3bdvHzt37mT8+PHouivl1HWdTz75xG/GxIKCAkaNGkVCQgLvvfeed9vDhw+zceNGhg0b5r3WGRkZDBkyhJycHNasWcNPP/1Ebm4u1113nTde/fr1A35Og7Fardxyyy3e7y+55BLS09P57rvvAFiwYIH3MS1wjXGklMLhcJCfn4/dbmfp0qVce+21xMTEeLd79dVX+d///uf93jAMxo0bx5EjR5g/fz6xsbEAWCwWevfuzYoVKwBYuXIl7du3p1u3bqxevRpwDYLfokULGjZsSO/evTl+/DgdOnTwxj5+/DiJiYkB7xHf1+p0rlEwvrEdDgdLlixh/PjxxMfHe5fffvvtWK1Wv5/ZTp06UatWLcB17Rs2bEjPnj1JTEwEID4+nvT09IDPGY+uXbsSHx/vd70uu+wyEhMT/a7XBRdcAMDXX39N165dsVgs3vdO7dq1ad26tfc1FkIIUflk9johhKiiUlJSGDRoUFTb/fOf/2TixIncdtttfgUCTdPYtWsXjz/+OGvWrOG3334jNzcXwG8Mmvj4eO8fQuAahLvsOEomk8lvnBGAVq1a+X3ftGlTMjIyvH8sexw/fpzCwkKmTJnClClTAs7h4MGDEc9z8uTJvPzyy4wZM4aHHnooYH337t39Zq8bPXo0JSUlzJgxg/vuuy+gSBVJNNceCLhOVqsVu90OBI4pBZCUlER6errfMpPJFNC+pk2beq/LgQMHaN68ecCxmzdvzoEDB06pbcHMnDmTESNGMGzYMGrWrMmYMWO46667OOecc4Juf+DAgaDXtUWLFnz44YchjxNMpGtgMplYt24d77//PmvXrmXr1q3ecYl838tlzzmSgwcP0qhRI2/RxqNz584B257u+zjYz8vKlSuB6H9Wgyl7zjExMd5r069fP26++WZmzpzJzJkzOffcc7n22mu5/fbbyz1AdNnjWCwWPvjgA7766ivWr1/Pzp07cTqd1KhRAyi9Hp6xgTzKvpcXLVqEruuYzWaOHj3qfR94ZtC75ZZb/IpDHgcPHvQWvcv+nLVo0SJgTLCy6tWrh81m836vaRqNGjXi0KFD3vP7/PPPmTdvHuvWrWPbtm3ecYqcTicZGRmUlJQEnF/Dhg39vt+4cSObN2/GMAy2b99O7dq1vesGDhzovVGwcuVKunfvTr169Zg3bx7gX0QBV+Hr8ccfZ/ny5WzcuNFboCn7HvF9rTzncyrXKBjf2MePH6e4uDjgNbXZbNSvX9/v91PZ33tmszno78KynzMeFouFPn36sHz5cm677TZWrlzJpEmT2LhxI6tWreKKK65g2bJlvPbaa4Dr/fP7778HHANcRTchhBB/DOnpJIQQfwKeu7zfffed3+CtX3zxBT169GDJkiV069aN6dOne+8S+yr7B3e0LBaL3/eePxbK9uRR7gFrH3rooaC9h8aNGxf2OC+++CKTJk2ib9++zJkzJ+SA1GVdcsklKKUiDoZ9OsK1xTAMv9cjFJPJFPAalP3DK9Rxwr120V4nj8GDB7Nz506eeeYZWrduzVtvvcX555/PmjVryn2M8r6nIl2D1157jQsvvJCNGzdywQUXMGvWLD755JPTPm55nO77ONTPC0T/sxqMp2dQMJ4BwH/88UfuuOMOMjMzuf/++xkwYEDIP+5DKXttr7nmGm699Vby8/O5+uqr+fLLL3nwwQfLFRNcs6MtWrSI+Ph4v/091/ull14Ker0HDhzoPYdofs7K8hSsfBmG4X1PP/jgg4wdO5aDBw8ycuRI/ve///Hvf/+73McxmUzMmzePFi1acO+99/qtGzBgAPv37+fAgQOsXLmSHj160LNnTzZt2kRubi7Lli3jwgsvBGD//v106tSJmTNn0qhRIx577DE2b94cUOTyHNP3nODUrlGo8ykrmt8DFfGzOWDAAFasWEFGRgbbt2/3Xq/Vq1ezfv16srOzvUU6pRTDhw8P+t55+umnT7stQgghoiNFJyGEqOaWL1/Oe++9x8MPP8yePXv8Hlt59tln6dq1K+vXr2fatGlcf/315S5EhLNv3z6/73fv3k3t2rW9jwh5pKWlYbVavTOOeb769OlDTk4OcXFxIY/x5Zdfcs8999CpUyc+//xzvx5ZkXju/gf74/KP0LBhw4BZ0AoLCwNmyCopKeHIkSN+y3bv3k2jRo28cYL1SNi5cycNGjSokLaWlJSwcuVKYmNjuf/++1m+fDmbN2/GarUyZ86coPs0bNiQXbt2BSzfsWNHudsV6Ro8/fTTXH755axatYrnn3+eK6+8skL+iK5Xrx779u0L6CkyYcIEPvvsM79lp/M+huA/L57zq6yf1ePHj7N8+XL69u3L9OnT2bt3L08++SRr165lw4YNpxx3165dfPjhh7z00kt88cUXPP744wwdOpTMzEzvNvXq1QMI+Bl47733uOeee7zfX3jhhVxwwQU89NBDfPTRR6xatcpv/9q1a/td706dOlFYWEhsbKy34FL2fejpJRXOkSNH/F53wzDYu3cvjRo1orCwkJdeeol7772X7777jieffJJRo0aRl5fn3d7zfih7ft9++y0333yzt1fhOeecw4gRI3jqqadYvXo1H3zwgXfbLl26kJCQwDfffMNvv/1Gjx496N69O4Zh8Pbbb5Odne2dTfCNN96gsLCQDRs28Pbbb3PnnXfSunVrTpw4EfY8T+caRZKenk5MTEzA76eioiIOHjxYYb+fPAYOHMjOnTv54osvSE9Pp1mzZvTs2ZP169ezaNEi2rdv7+1JVq9ePZxOp997Z9CgQdjt9og/q0IIISqOFJ2EEKIaMwyDu+66i06dOvHEE09wyy23MHXqVO8f75mZmTRr1szvDvM777wD4DdN96maPXu29/95eXm8//77QccJsVgs9OvXj7ffftvv0bs33niDsWPHBvwx7pGbm8vNN99MvXr1+Oqrr7yP7UTrs88+Q9d1unTpUq79KsrgwYPZtWuX3zhXb7/9dtCpyn2v5Zo1a1i/fr33Wg4ePJiPP/6Yo0ePereZP38++/bti/oxwGDMZrP3j+6CggL69evnfX8AtGzZksTExJCPYQ0ePJg9e/bwxRdfeJcdPnyYTz755JTaFe4aZGZm+o3VpJTiP//5DxD5vex7nmVdcMEF5OXlMXfuXO+yTZs2MWPGjICeQKf6Pg52fgcOHOCrr77yO79IP6ue4mmkx+18ffjhh1xwwQXe946mad7HUEO9rtEcx1Nc8n1kMCsri/nz53vb27hxY5o1a8a7777r3UYpxcsvv8yOHTsCYv7tb3+jYcOG3t5AjRs3pkWLFgGvxdNPP82ll15KYWEhXbp0ISUlhZkzZ3rXHzt2zO89GUpeXh6ff/659/uPPvqIkydPMnToUAoKCiguLvY7v5KSEm/ByOFweB/3ev/99/0eW50xYwY///xzQM+2yy+/nK5du/Lwww97C6Zms5m+ffsybdo0EhISaNOmDcnJybRp04YXXniBDh06eB8Py8zMJCUlxe9xsY8//piCgoKwPwOnc40iMZvNDBw4kFmzZlFQUOBdPnPmTEpKSk7r91Mw559/PomJibz00kt0794dgB49elBUVMSrr77q7RUGrkekv/vuO7Zt2+ZdtmHDBi655BK/110IIUTlkjGdhBCiijp69GjIHiapqakMHz6ct99+m19++cU7JsqkSZOYM2cODz74ILNnz6Z///7MmDGDhg0b0rx5cxYsWMCCBQvQNM07Xszp+P777xkzZgwDBw7knXfeoaCggMceeyzotlOmTKF///707t2bG264gf379zNt2jRGjBhBt27dgu7z7rvvcvToUW644YaAAco918BjwYIFbNq0CXCNe7J48WL++9//cuutt3rH7zh69CiLFi2iV69eEcd4CnXtAa688sqw+3qMHTuWrl27cvnll3PfffeRn5/PK6+8ErS31pQpUzh+/Dh16tTh+eefp2PHjlxzzTWA63GuDz/8kJ49e/LXv/6V7OxsXnjhBbp27cqYMWOiaksw6enpfPPNN7z55puMHz+eq666ikcffZTjx4/TtGlTPvvsM06cOOE3yHbZ8+vevTtXXnkld999NzVr1mT69OlYrVYeeOCBcrcn3DXo378/06ZNw2azkZ6ezocffugdDynSe7nsefrq27cvQ4YM4brrrmPdunXUqlWLF154gfPPP59LLrmE5cuXB7SxvO9jj1mzZuFwODjnnHN45ZVXSEtLY+LEid7zi/SzWrNmTXRdZ/bs2VitVr8B3EO54oormDx5MoMGDeK2226juLiYl156iR49etCiRYuQ1wtg+vTpXH/99QFjFgG0bduWWrVq8de//pWJEydSVFTE66+/zsmTJ7Hb7TidTkwmE1OmTOHaa69lxIgRDB8+nIULF/LLL78EXFdwjQM0ZcoUxo8fzyeffMLYsWN5+umnueKKKxgyZAhjxoxh48aNzJgxgzvvvNM7sP7kyZO56667AOjWrRuvv/6638DeoSQmJnLzzTezceNGNE3j//7v/7jooou8PYvat2/PY489Rk5ODlarlVmzZrF161bA9Z5LS0vj8ccf54ILLuCCCy5g3Lhx/Pzzz3z88cdBH/0EV4+2gQMH8u9//9vb22vgwIHcf//9DBo0yPuoZM+ePXn77bf9fr779+/P9OnTGTduHIMHD2bjxo3ecw33MxATE3PK1ygaTzzxBH369KFXr16MHz+ePXv28O9//5vRo0dH/JkoL0+RbsGCBVx++eUA1KpVi2bNmrFr1y6/8a/++te/MmPGDPr168fEiROxWCy88MILNGzYMOgYYUIIISrJGZo1TwghRBj9+/dXQMgvzxTctWrVUhdddJHfvpMnT1aapqnVq1er7OxsdcMNN6jU1FQVHx+vBg0apFatWqXatm2rbr75ZqVU4JTRSik1ceJE1bhxY79lnqm/lSqdSvuFF15Qffr0UTExMapbt27qp59+8m5fdtp1pZRasmSJ6tWrl7LZbKpevXrqrrvuUrm5uSGvw5gxY8JeA9/j+H7ZbDbVqlUrNXnyZFVSUhLQplmzZoU8pmcq9XBfmZmZQacTV0qpjh07qhtuuMH7/fHjx9W4ceNUXFycatq0qfrss89U7dq11YsvvqiUUmrWrFkKUB9++KFq3ry5io2NVWPGjFFHjhzxi7t582Y1bNgwFRsbq1JTU9XNN9+sTp48Gfb1idS2efPmqTp16qj4+Hi1f/9+lZubq+644w5Vr149lZCQoHr06KG+/fbbkNdKKaUyMzPVrbfeqlJTU1VsbKwaMmSI2rhxo982gPd8g4nmGhw8eFCNGjVKJSYmqho1aqjRo0erTZs2qcTERPXEE08EXANfZc+zf//+atSoUd71ubm5asKECSo1NVWlpqaqq666Sh0+fFgpVTHvY0+Mt956S5177rnKZrOpCy+8UG3bts27TTQ/q0op9dhjj6n4+Hh17rnnhry2o0aNUv379/d+//PPP6sBAwaoxMREVadOHXX99dcHvL/KGj9+vLLZbGrkyJEh308rVqxQXbt29V6HO+64Q33zzTcKUEuXLvVuN2fOHNWmTRsVGxurzj//fPX1119715V9LZxOp+rQoYNq0aKF92f3k08+UZ06dVIxMTGqSZMmatKkSX4/10op9fLLL6sGDRqohIQEdeedd6p77rkn6HvB4/HHH1etW7dW7733nmrQoIFKTExUN998s9/ruGXLFjVw4EAVFxen0tLS1HXXXafWrVunAPXuu+96t1u4cKE677zzlM1mU+3atVP//e9/veuCvSeHDx+uUlJS1IkTJ5RSSq1Zs0YB6tFHH/Vu88YbbyhAzZ8/32/fp556SjVo0EBZrVbVqVMn9Z///Efdfvvtqnnz5kqp4O/XU71GwV73UD/Ly5YtU71791YxMTGqTp066p577lGFhYXe9WVfZ6UCfx8ppVTjxo3VxIkTQ7ZJKaWee+45Bfj9brrmmmuUyWRSmZmZftvu2bNHXXbZZSopKUmlpKSo0aNHqx07dnjXl/38i+b4QgghykdTKkgffyGEECKMPXv20LRpU+bOncvo0aPPdHOqtdmzZzN+/HgyMzNJTk4+0805I/7s12DJkiUMHDiQdevW+c2wKIQQQgjxZydjOgkhhBBCCCGEEEKICidFJyGEEEIIIYQQQghR4aToJIQQQgghhBBCCCEqnIzpJIQQQgghhBBCCCEqnPR0EkIIIYQQQgghhBAVTopOQgghhBBCCCGEEKLCSdFJCCGEEEIIIYQQQlQ4KToJIYQQQgghhBBCiAonRSchhBBCCCGEEEIIUeGk6CSEEEIIIYQQQgghKpwUnYQQQgghhBBCCCFEhZOikxBCCCGEEEIIIYSocFJ0EkIIIYQQQgghhBAVTopOQgghhBBCCCGEEKLCSdFJCCGEEEIIIYQQQlQ4KToJIYQQQgghhBBCiAonRSchhBBCCCGEEEIIUeGk6CSEEEIIIYQQQgghKpwUnYQQQgghhBBCCCFEhZOikxBCCCGEEEIIIYSocFJ0EkIIIYQQQgghhBAVTopOQgghhBBCCCGEEKLCSdFJCCGEEEIIIYQQQlQ4KToJIYQQQgghhBBCiAonRSchhBBCCCGEEEIIUeGk6CTOepqmoes6GRkZAeseffRRNE1jzpw5fsu/+OILevfuTXx8PPXq1eOWW27h0KFDfts0adIETdO88ZOTkxk2bBi//vpryO18v6699loABgwYwNSpU8Oew4EDB7jhhhtIT08nMTGRfv368fXXX3vX//zzz2iaxoEDB/z2q127No0bN/ZbtmXLFjRNY+/evUGP9fLLL9OgQQNSU1OZMGECxcXFQbebNGmS3/mYzWbq1q3LP/7xD5xOJwCzZ88OOO/Y2FjOOeccPv3005Db+H7Nnj077LURQgghhDhdZzpfLJv/xMfH06NHD1avXh1wTE3TePXVV73LosmlZs+eTYsWLYKeu81mY8mSJQHLlyxZEjJmqDxSCHH2MZ/pBghRVSxatIirrrrKb9lXX30VsN3rr7/OnXfeyR133MGUKVM4fvw4zz77LD169GDZsmU0atTIu+2kSZMYOnQohmFw5MgRXnzxRfr378+GDRuCbucrPT09qnbv3r2b3r1706RJE1577TWSkpL49NNPGT58ODNnzuTmm2+mY8eO2Gw21q9fT4MGDQDYsWMHhYWFZGRkcPDgQerXrw/A2rVrqVu3bkAxCmDWrFlMmjSJl156ifj4eCZOnEjt2rWZPHly0LZ17dqVadOmAeBwONi0aRP/+Mc/SElJ4bHHHgOgQYMGfPTRR959MjMzmTZtGldddRVbtmzh4osvZuXKlQAcPnyYMWPGMH/+fO/1ad68eVTXSQghhBDidJ3JfHHGjBmce+65AGRnZ/PSSy8xfPhwdu7cSXJyMgDz5s2jZcuWfPTRR/z1r38FiCqX+vLLL8t9LTp37uyN63HvvfdiNptp2LBhueMJIf6klBBnOUB17txZ3XjjjX7Ljx49qmrUqKHq1aun3n33XaWUUrt27VIxMTHq5Zdf9ts2Pz9fde7cWY0cOdK7rHHjxt79PAoLC1WLFi3U+PHjw27nq3///uqJJ54IuX7w4MGqe/fuqri42G/5//3f/6n4+Hi1f/9+pZRSPXv29Isze/ZsNWjQINW+fXv14YcfepfffffdasyYMUGP1bRpUzVz5kzv92+//bYaMmRI0G0ff/xxdeGFFwYsnzhxomratKlSSqlZs2ap5s2bB2xTUFCgbDZbwHXevXu3ArznJIQQQgjxRzjT+SKgli5d6rfd8ePHlclkUu+8845SSimn06lq1aql3n33XaXrujpy5EjAeYTKpULlZEopFRMToxYvXhx0na9ly5apmJgYtW3btojbCiHOHvJ4nRDA0KFDWbRokd+yhQsXcsEFF2CxWLzLZs2aRVpaGnfeeafftnFxcUyePJnPP/88oNu0L5vNxq233sonn3yCYRin3e5du3axaNEipkyZgtVq9Vt37733kpCQwLvvvgtAjx49WL9+vXf98uXL6dGjBz169GDFihXe5WvXrqVnz55Bj7Vnzx7Gjh3rXTZ+/Hi/x/ii0bZtWw4fPhx2G6vVSkxMDJqmlSu2EEIIIURlqWr5YlpaGunp6d68asWKFeTl5XH55ZfTpk0b71AFf5RJkybxl7/8hZYtW/6hxxVCVG1SdBIC17hJGRkZbN682bts4cKFXHTRRX7brVixggEDBqDrgT86F154IZqmBX3m3VevXr3Iyclh586d3mWGYeBwOLxfnjGPIlmxYgUmk4n+/fsHrDOZTAwcOJDFixcD0L17d9atW+e3b69evejVqxfLly8HQCnF+vXrgxadtm3bRnJyMsuXL6dZs2bExsZy++2343A4omqrx549e7yP+Hn4nvvRo0e54447KCgoCHjkUAghhBDiTDnT+WJZ+fn5ZGRkePOqefPmMXToUGJiYhg1apTf8AXR8s3JPF/RWL9+PUuWLOG+++4r9zGFEH9uUnQSAtedp759+3p77RiGwaJFiwKSiEOHDgUUTDxiY2OpWbNmxF48aWlpAJw4ccK77IYbbsBisXi/atasGVW7Dx06RFpaGjExMUHXN2zY0Nue7t27s3v3bnJycsjMzGTr1q306NGDnj17sn79egoKCti2bRtFRUWcf/75AbEyMzOx2+387W9/4+mnn+att97io48+4vnnnw/ZPqWUN2HJy8tjwYIFzJgxw28shJ07d/qde506dVi8eDGffvoprVq1iuo6CCGEEEJUtjOdLzqdTm9etW/fPiZMmEB8fLz3+J999hmjR48GYNSoUfz4448cO3Ys6vMrm5N5vkJNGuNr1qxZjBgxQsZyEkIEkIHEhXAbOnQo33zzDf/4xz9Ys2YNdevWDUgYnE5n2G7Ouq5jt9vDHsfTi8lsLv3xmzJlil/C4rsuUqxo29OkSRNq1arFhg0byM3NpVWrVqSkpJCcnExSUhJr165l//79nHfeedhstoBYnsLRW2+9xRVXXAFATk4OU6ZM4cEHHwx6/O+//96vu7mmaYwePZoHHnjAu6xBgwbMnTsXh8PBZ599xvTp05kxYwYDBgyI6hoIIYQQQvxRzmS+WDY3qlWrFrNnzyY1NZVNmzaxa9cu+vTpQ1ZWFq1ataJmzZp8+umnTJgwIapz8+RkZfXu3TvsfkopPvzwQ78Z84QQwkOKTkK4DR06lH/+858UFxcH7SoNUK9ePfbt2xd0/+LiYk6cOOGdBS4Uzx0n39npmjZtSpcuXcrd5nr16nH8+HEKCwuJjY0NWO87Kx24ejutX7+eo0eP0qtXL8BVCOrRowdr1qzh4MGDQR+tA4iPjwegX79+3mW9e/fm8OHD5ObmkpiYGLBPt27deOWVVwCwWCw0bNiQ1NRUv21iYmK8596jRw+OHj3K1VdfzdatW0lKSirP5RBCCCGEqFRnMl9888036dixI5qmkZiYSIsWLbyP8M2bNw/DMAJm9f3oo4+iLjr55mS+Io2xuWHDBrKyshg2bFhUxxFCnF3k8Toh3Dp06EBqaipLly4NmUT079+fZcuWUVJSErDuhx9+wOFwRLwbtHLlSurUqeM3Be6p6tevH5qm8d133wWsMwyDJUuW0KdPH++y7t27s3HjRlavXu0tOoFr3IBffvkl5CDiAI0bNwagsLDQu8zznH+oZCQxMZEuXbrQpUsXOnbsGFBwCubJJ58kMzOTf/7znxG3FUIIIYT4I53JfLF169Z06dKF888/n1atWvmNGTVv3jzuuOMOFi9e7P3617/+xQ8//MDx48dP44wjW7JkCV26dCEuLq5SjyOEqJ6k6CSEjyFDhvDRRx/x+++/B00GbrnlFjIzM5k2bZrf8uLiYh599FGGDx9Os2bNQsYvLi5m1qxZXH755RUyM1vTpk0ZMWIEjz/+eMDz9i+99BIZGRncfPPN3mU9evRg8+bN/PLLLwFFp99//50NGzaELDqde+65JCUlsWDBAu+y77//npYtW5KQkHDa5+JRt25d/vKXv/D666+HndlFCCGEEOJMqGr54oEDB1i7di0333wzAwYM8H7dfvvtxMTEVPosdhs2bKBjx46VegwhRPUlj9cJ4WPo0KFce+21jBw50m8sIo8GDRowffp0br31Vvbv38+oUaPIzMzkueee48CBAwGzhOzcuZNVq1bhdDo5cOAA06dPJzc3l0cffbRc7Vq5ciUvvfSS37JLL72Uxo0b88orr9CjRw8GDBjAvffeS0pKCvPmzeOVV17h9ddf97tD1rVrV9auXUtCQgKtW7f2Lu/WrRubNm0iNTU1ZA8si8XCxIkTefDBB7FarVitViZPnsxTTz1VrnOJxv3338+MGTN45plnePnllys8vhBCCCHEqapq+eK8efOoU6cOnTp18lseGxvLoEGD+Oijj7jttttO+Xwj2b59u3cAcyGEKEuKTkL4GDx4MIZhBO0q7TF+/Hhq1arF1KlTeeONN4iLi+Oiiy7i448/DhhIctKkSUyaNAlN00hPT6d///7MmjWLWrVqlatdCxYs8OthBNCmTRsaN25MgwYN+Omnn3jwwQe57bbbKCgooGPHjnz22Wdccsklfvt4nv9v0qSJ352z+Ph42rVrR9OmTcO2Y9KkSdjtdh588EE0TePvf/87d9xxR7nOJRr169dn/PjxzJw5kwcffJC6detW+DGEEEIIIU5FVcsX582bx9ChQ4P2iho5ciS33XYbx48f9xsfqiKdOHFCxuEUQoSkKaXUmW6EEEIIIYQQQgghhPhzkTGdhBBCCCGEEEIIIUSFk6KTEEIIIYQQQgghhKhwUnQSQgghhBBCCCGEEBVOik5CCCGEEEIIIYQQosJJ0UkIIYQQQgghhBBCVDgpOgkhhBBCCCGEEEKICmc+0w0IJj09ndTU1DPdjD8Vp9OJyWQ6083405DrWfHkmla8s/maKhQaWoXGzM/P58CBAxUaU4izkeR5Fe9s/n1fWeSaViy5nhXvbL6mkudVL1Wy6JSYmMiSJUsqLN7x48eZO3cuY8aMIS0trUrHrayY7733Htdcc02Vb2d1iVnR19MTt7qcf3W4ptXp3Cvr99PZfE0/nPshs5hVIfEA0kgj+7XsCosnxNmsovM8qF6/n+QztGrH9MSVayrv0aoa0xP3bL6mkudVL1Wy6KRpGnXr1q3wuGlpadUmrsQ8O2NWVlyJeXbGrKy41SGmQnGYwxUWTwhRcSorz4Pq8ftJYlaPmJUVV2JKzKoetzrElDyvepExnYQQQgghhBBCCCFEhZOikxBCCCGEEEIIIYSocFJ0EkIIIYQQQgghhBAVTopOQgghhBBCCCGEEKLCSdFJCCGEEEIIIYQQQlQ4KToJIYQQQgghhBBCiAonRSchhBBCCCGEEEIIUeGqZNHJZrOd6Sb86ZjN5jPdhD8VuZ4VT65pxZNrWrHq169/ppsgxJ+C5HkVT37fVzy5phVLrmfFk2tasSTPqzxSdDpLWCyWM92EPxW5nhVPrmnFk2tasSQZEaJiSJ5X8eT3fcWTa1qx5HpWPLmmFUvyvMpTJYtOQgghhBBCCCGEEKJ6k6KTEEIIIYQQQgghhKhwUnQSQgghhBBCCCGEEBVOik5CCCGEEEIIIYQQosJJ0UkIIYQQQgghhBBCVDgpOgkhhBBCCCGEEEKICidFJyGEEEIIIYQQQghR4cpddLLb7QwYMACAuXPn0rt3by699FKKi4spKipi7NixdO/enc8//zzqbYQQQgghxJkneZ4QQgghKlK5ik55eXmMGDGCXbt2ATBr1ix++OEHhgwZwty5c5k7dy6jR4/mxx9/5N///nfU2wghhBBCiDNL8jwhhBBCVDRzeTYuLCzk//7v/3jwwQfJy8ujRo0amM1m+vbty5w5c3A4HNx+++3ExMSQlJREbm5uxG0KCwuJjY31O45hGGzcuLHCTjIzMxOA7du3k5GRUaXjSsyzM2ZlxZWYZ2fMyopbnWLq6HSgQ4XEA0gmucJiCVFVVdc8D6rX7yeJWbVjVlZciSkx5T0qed7ZqlxFp/T0dNLT0wHIysoiLi4OgLi4OPLy8igqKvJblp2dHXGb/Pz8gGQkPz+f//73v5EbbzZjsViibv/ixYuj3rY8KiOuxDw7Y1ZWXIl5dsasrLjVIaYJE2MZG9W2drsdh8MRcbtFBxedbrOEqNKqe54H1eP3k8SsHjErK67ElJhVPW51iCl5XvVSrqKTL08iAa7kITk5mZycHO+ygoICrFZrxG2Sk5MDYsfHx3PNNdecatMCZGZmsnjxYgYOHEhKSkqVjisxz86YlRVXYp6dMSsrbnWK+fXir5nFrOh2sLi/wkgjjSb1m5xu04SoNqpTngfV6/eTxKzaMSsrrsSUmPIelTzvbHXKRafU1FQOHjyI3W5n2bJldO7cmRMnTrBs2TIaNmxIUVERtWrViriN2RzYBF3X6dCh4rrLHT58mMWLF9OyZUvq1q1bpeNKzLMzZmXFlZhnZ8zKiludYi5cvJDDHK6QeB5NaFKh8YSoyqpTngfV6/eTxKzaMSsrrsSUmPIelTzvbHXKRSeAO++8k379+tGwYUPvc/zXXHMNM2fO5JFHHol6GyGEEEIIUbVInieEEEKI03VKRaeFCxcCMHbsWMaOLX2W0mq1MnfuXL9to9lGCCGEEEJUDZLnCSGEEKKi6Ge6AUIIIYQQQgghhBDiz0eKTkIIIYQQQgghhBCiwknRSQghhBBCCCGEEEJUOCk6CSGEEEIIIYQQQogKJ0UnIYQQQgghhBBCCFHhTmn2uurO6YRjx1z/r10bdCm9CSGEEEL8KRiGK88zDKhVC8xnZbYrhBBCVA1nVbklO1tj8mSoXx/q1XN9NWgAU6dCTg5kZBTwf/+3jBYtphEX9yS1aj3H7bd/waZNx85004UQQgghRBhKwauvxtO0KdSt68r3ateGRx6B48chJ6eYadNW0779K8THP0XNms9y/fVzWbPm4JluuhBCCPGndVbc+zmRpWN3mOg3PJ2MQ6CKS9cdPgyPPw5vvWUnK+tNcnKyMAwFQGGhgzffXMeMGWt59dWLmTChyxk6AyGEEEIIEUxWDnz+fQwFhXE8Py0RVVS67uRJeOYZePttJzCHo0cPAK4CVUGBnfff38S77/7KlCkDeOyx/mfmBIQQQog/sT91T6fDx+Dae+G8MbUotts4bjahGgENgNjS7QwD9uwxkZV1ibfg5OFwGCgFt9/+JV99tf0Pbb8QQgghhAguMxsm/BPq9oLbJqeiNA3VUINGQHzpdk4nHDmiceTIJSjlKjh5OBwGAP/85xL+858Nf+wJCCGEEGeBP23R6eAR6DoWPvwSHE7Nf6UNqI9fQuK6FM2A9KDxdF3jySeXVkpbhRBCCCFE9DKzofc4ePMjKCops9IK1AWSfBfqQG2gSciYU6b8gFIq5HohhBBClN+fsuh0MgcGT4SDGjhq4aoj+dadPP+vQ5kr4ATaB41pGIrly/eze3dmJbRYCCGEEEJEI7cALrkXtuSB05Pnxfls4MnzalFmIInQeR7Azp2ZrF4t4zsJIYQQFelPV3SatxzqXQVbTgIx7q84XGfqW2TS3F+JZSPEll3g5+DB3IpsrhBCCCGEiNIPv0KDq2HFHlw91z15Xi1cOZ7JvaGn8OTX20kjcp6XU4GtFUIIIcSfquj0469w2RNQbKe0qITPvzG4elb78nvETgMKwh4jMdF6+g0VQgghhBDlsnE3DHsIcgsJnudpuG4w+n7vl+cpIud5MRXVXCGEEELwJys6PTILDAuuO17xuO6AlX2sznNHzPO933BPOrApZPyGDZM455xaFdhiIYQQQggRjSfmQIkGKpbgeR64HqdL8PneL9M1ARtDxk9KiqFPn0YV01ghhBBCAGWedK/OZi+GZYdwPS7nOwZkPP43tZR7mwL3/+2eFQawAzgRNL6mwd1398Bk+lPV6YQQQgghqrxFv8JHm3A9Llc2zyvEP9dLBHIpk+c5gaPA/qDxdV1jwoTziYuzVGzDhRBCiLPcn6KCsuQ3uPlNAsdr8nzF49/V2uLz/1xXQalBg0JgLrpe9paZa/2oUW2YOLF7JZ6FEEIIIYQoa/0eGPE8ofM8T88nzzrfPC8bdB1q13ai6x9iMgXmebqu0bt3QyZPHliJZyGEEEKcnapkTyfDMNi4MXT3Z19KwY0zW6GU1VUdCkXDlawYuO58KaAQ6tcs5uqrTnDZZSdZu7YzM2duZf36k97d6tSJ5dprW3DNNS347bfNfiEzM10z2W3fvp2MjIxynWMoEvPsjFlZcSXm2RmzsuJWp5g6Oh3oUCHxAJJJrrBYQpztypPnAdwypyl2Z3zoPK/s+OCePK8E0uPtXH3LCS6//AQ7dnRixoytrFx5zLtpamoM48Y146abWrF9+xa/sNXpd57ElM9QiXn2xKysuNUppuR51YumlFKRN/tjtW3blnHjxkW1rcPQKTLCz0QCYNWLKHHavImIjhObpQhNC8xhDMP1pWmuu2PhallCCCGqHgMDvYI7865du5b58+dXaEwhzkblyfOcSqPQGRd5Q8CqleZ6mjKItRahaUryPCGE+JORPK96qZI9nWw2G2PGjIlq26e+rcsHv9V0ZQwK190uz79OXM/5A/c2f57nt94LOfDgRQe5cuBJTKaQYaOSmZnJ4sWLGThwICkpKacXTGKe1TErK67EPDtjVlbc6hTz68VfM4tZFRIPII00mtCkwuIJcTYrT5735qp0pq2u4+qtHizPK8I7xtO9zZ7n+TX38pe+R7ntkmNYTjPLrU6/8ySmfIZKzLMnZmXFrU4xJc+rXqpk0UnXdTp0iNxdbuMB+Ph3nwVlp87VcXW3LizdZNJ18Ph19YH6p93Ow4cPs3jxYlq2bEndunXLvb9CsY4SjuAkEZ1uxHDy8JHTilkZ7ZSYlR+zsuJKzLMzZmXFrU4xFy5eyGEOV0g8D0lGhKgY0eZ5e0/AmxsIzO/K5nk+g4hff0E+rz9QG02rfdrtrIg8bzN29uMgDo0uxJBz+Gi1+T16tsasrLgSU2LKe1TyvLNVlSw6Reuf88Hw3O0KRsM1O677LC9sX8Tj19n+mMZF8CF5vEAOe3B4lyWjc0WCq8lCCCGEEGezZ7+GYgfh8zxwDRxeotA1g6evz0HT4kPs8MdZQAHPkM3W0unziEfj0kSNxDPYLiGEEOKPVm2LTkeyYf4Gd9EpHIV3FpNnxmUDf0zR6SiFfMF+1nESBwZNSWQEDWlLDZ4nm+fJCcihsjB4IwHG26zYqXJDbQkhhBBC/CEKimHWcnAaUWxsAewaMaaiP2x8ppMUs4ADrCGDYpw0IJ6LaUAnUplNHg+SGZDn5aN4L15xY6yVQk3yPCGEEGeHalt02n4sioITeGets2gl1EuOJnM5fR+zh9fYCrgmywPYQS4LOcg5pPERaYAetKykNHCadN6LN3gwwnGUgh8Pwnvb4HghpMbAFS1hUCPQZVBMIYQQQlRTB7Kg0B5xM1eep8FV3fMxZf0xhZxFHOJZNuJEeXO5HeTyHYdpTTLzqQ2YguZ5hgaGrvNagsFzEY6jFKw5Cv/ZCofyIckKlzaDi5uCuWLHzxVCCCEqTbUtOv33l+i3tVkUVj2azOXUFGsODpKFCZ21ZPOKu+Dky+lOPTaRQX1KOECDsDHfize4D4UpRJ/yA7kw8gtYdxzMGjgVmDR46zdomwJfjIRmNU7/3IQQQggh/mif/Fq+7Z8ek8PstyunLXacHCILDY2dFPIUgY3z5Hm/k0VDSthDY0I9F6iAT+IMJmMQF2L2pROFcNkCWHLQP897Zws0SoT5l0DH9Io6QyGEEKLyVMui08/7Ycaq6LY16XBByyK0nIpvR4YpH6dV4/Ha3+Fw92lSmEggjjwSCJVsJJFDDEUUh3nU76gJduCgtefZQB9ZxdD/E9iX6/reofz/3Z4FfT+GX66C2tHNMiyEEEIIUSXsPgGPfEXpTHVh6Bp0bwrWSshos/Uib55XgtNzRJKII5cEVIiCURwFxJNPPgkhY+frsJYS+gbJBYscMGQebMhwfV82zzuYBwM+gZ/HQfPkUzs3IYQQ4o9SLTvnvrIMzCYIUo8J4DRgfPeCyBuW024yeDFtBcqi4dB8H9tzkkguaWSgEfxxPgWkkBnxGIUh9n/tV9iTW5p8lOVQcLQAXloX8RBCCCGEEFXKjJXuYQKiyPMMBXddUPFtOEw2L6YtR1k0SnSnzxqDePJI4zg6zqD7RpvnFYWoqL33O/xy3NW7KRingjw7TF0T8RBCCCHEGVcti06fbwaHAVgJPaOJ26WdoVeTkgo9vh0nz7GIEs0RsM49tAAW7CSRHXR/DbASvk2agnpBOqIpBa/8Gnk8K6eC1zeBPXg+JIQQQghRJc3f7C64WIiYqfZoDpd3qdjjGyhe4FsKQgzNoAFmnCSTFXJ9DMURj9MwxHzFr/waOUF3KFdxKivyYYQQQogzqloWnbwDS2pAHIT4zAYr3D2YCpvJRKHYw1HeYyn55IAWemByV9MKg94FU4AKUy3TgL7FGrWCnFieHQ7mR9ferGJXjychhBBCiOoiz1NI0YBYQg8GYXHleaYKzGYPksEnrOQYGSgt9J07DbBRjInAG5AARoQ8r20JtMEadP3mk4To6+6vxICdwe9vCiGEEFVGtRvT6WC260PWy5OQGIATV0XHdQsKNGiaChSd/nF/Yy8fs5SDnADAhmeYAYWGEfS5fs+drkICB1YqJD7ocTwz6N6SpxNsyCdTOQtoFZmICSGEEEJUpsxC1yDaXq7qTsg8r0UFDaa9k8N8xI/s5gjgm4IpdJwYQW4EKiCWQvJIDFheEGo8J+Vaf3ueCVKDb1KeGYjLmxcKIYQQf7RqV5K46VMwgrVax9UN2+r616TDoJbQMOX0j/kL25nGPA65C04enkfpYskPOn6TAvQgyy3omNyZhm+uoOPKoaxFJZxrDzE4pQXap0Z8qhBwzW4iA4kLIYQQorr4+xdQGKybT5k8T9OgfR3oHH4y4Kj8zn7+xcfs4WjAOleeVxBy/CYtyLhMOhBPuvf/Hib39zHFdvoVh07Be9eNrpiUZIXWFZDnCiGEEJWpWhWdtmfAN9tDFJ3KMBT8c+jpH7OQYmbxjfuRuOBcPZoCu1NpgOFziT35w4Ocw0oa8hwpdMJKHUy0wsw/SOKLY2bMzvCdqv/WMXK7deDOc8t3t0wIIYQQ4kzJyIf/bgClE/HumgKeGHb6Qyg4cfIGX2GgUGGmyrNRSLBM0AiSSv+VNiymMf8mla5YqYuJ5pi5nUTmHzdjdoQfcPPOc0MPIu5h0uDW9hBb7Z5ZEEIIcbapVh9VC353FVEMcN3pCjMWd/dm0LfZ6R9zFVspCfG8vodnQEkNJ8qn+7WBRpFPB+0GxHE7behJLQCuJ5Hry3TJPmwUEsn4dvD+Nlh6CMymYpLjT+JwWsjMS0WhY9KgUzrccW7gvnbsFFGIDRuWEGMJCCGEEEL80b7b6Z4oxjXjiivPC1F8aVYbLg2S55TXenaRS/jcy9Wz3fOYnX/qXEis9/+1sHELrRhMPQCuIIEryjxmd9gZecyHEc1gTHOYuxNMJjup8ScwlM6JvJooZcKkQZMkeLhr4L4O7BRSSAwxWImJeCwhhBCislWrolOB3V10cj23BjG4nu/3fcbf/YxaamLoONFw4GQTW/mOxcSRD2g4MGPHEnT8JoWr8GT3KToNoi31aYATRSMSOIdktKgejAvPaoKPRh5m9tF51K6zBKvZVX07ll2LhRsuIalgGDMHWojznWpYV3yT/Dl72Y1CoaHRijb0pC+NqYDqnBBCCCHEacj3vZnoKTwZgAP/PM8ECbGB+5eHgcEWdvAli4gnHwVR5Xkl7tRZQ6M7TWlDS0owqEccnUhFr4A8T9dg1rCT9Dk8j+Ra3xJrdRXFTual8M2vw3GevJi3Logl1XfsT13xfY2F7GI7hntoh2a0oAd9aUnr026TEEIIcaqqVdGpbqL7DpiHZyDJMmdh1qFRjVM/zkmymMF/OUEm4HoGX6GwUoKVEgqJxRni0unuYcV70JQb6YapEp5gPMF2frA8TqMGxSifMaPSk45xfd9Z1NJ+Ip7HwN2T6be4XyEW9tp2e7uOKxTb+J3f2cJFjKIbPSu8nUIIIYQQ0aqfVGaBhisJKzOGt0mDpqcxllEu+bzJ+xx0DxruydR0d55XhA0HliB7um7aKRTtqMME+mCthFQ6h4N8Z3qEeg1y/fK8lPhMxvV8j2RtOclMBfekNLts2yEWdsZu83tEcDe72MUO+nMhAxhc4e0UQggholFtik5Kwce/R7etw4CbupzacYoo5jXeJZscv+We+1aemUoKiPObyUTDNb9JK2ozjHacT+PTuNulyLeuZzMvkMNGABJoSz3GksT5/MhUnPgXnMAzroHiGJvZwH/ozC3sZy/LkhYDNpSmyhzFtf9XfEZtakuPJyGEEEKcMdHmeU4FtwR5tCyqfXHyBu9xhGMB61y5HNgoohAt4AajQqcpNRlCO3rR7DRuLCoKLb+xhVfI4hcUTuJpTj3GkkoffuRJSsgNmedls481vEpv7iODY3yXvBCwhszzfuA7alGHdnQ4xfYKIYQQp67aFJ0W7YIvd+BqcZghljRgUAvoeoqzmaxlI5lkh42vgBiKKaR0argEbLzCTZhP85IqHJhiHByqOQnXrT3XYJOZrCaTFdhoTTE5hB9hU7GTRZzDVaxkqfuuXGgaOitZJkUnIYQQQpwR64/Am+uImOcBdKwLF7U6teNsZhuHgsxS5+Gb5xX45HQWTLzIjcRjC7lvNBQGJquDA2kPo2FCufO8bNaRzVpsNCOfgqCP+PnG2M9K8sngJ1YSeqobzzlprOBHKToJIYQ4I6rN7HWvrHE9NuedbzYEpcG/R5z6cVayNuI2rt7eTjSfO1Cj6HXaBSeA40lvoZs9cX1nN3H9v5DfSSYrYhwnJRzkZ7ayOeDOV1kKg21swR5uZHYhhBBCiEry+lowhxg2wY8GL10CplPMYFfxS8TxNV15noHuk4ddRNfTLjgBnEz4CN3iyvOUX57nWlbEblLcwztEcpBVbGRdFHme4iD7yY4ifxRCCCEqWrXp6bTmUJkZTTwDS3rqM55ERYe9OdA6vfzHyOA4xzgR1bYaoCvXh/yFOR3ol1T+u0cKRR6ryOZHFEXo1CAn7ivgwrDHjaWQXBJDjivlUUBW2Ol/y7aliCKZ0U4IIYQQf7jVB8DhSVnc+Zx3shjwy/P2557aMbLIZB8Ho86NTO48r2deKy5O6Fbu4ykUBWwgi29xkoeZFLLiPwL6h93LRjEWSrCHyck0dIrJpYjIs+F5FJBPDZKj3l4IIYSoCFWy6GQYBhs3bvRb5nC0Ad9BHXUI9Vm8e/duNhbkeb/PzHTdMdq+fTsZGRkB2ysM1tddx4607RjK5nloPqImR1MpKMilznozG1M2Rt7B95i2vTgbvQgxh0GZvO2IR7kfhvNM0xJsZ4jVCskj/BR9J/cVoDXUIt4B88TcsXknZhXdWyLSNT0VZ3PMyoorMc/OmJUVtzrF1NHpUIGPkiTLH2pCVJhgeV5RUQvAZ0o63f0VZDzv/fv3s1HL8lsW7neJQvFbrc38VmsTxcSAFl03qbonkrAX5NHsl3g2pWyKah/vMS1HcTZ+AWL3+OV5cVrkPE8piNMKyA5TdFLKyclDBVhqW7Cb7VG1ad/W/WTYT0a1bXX6fV/RMSsrrsSUmPIelTzvbKUppaK73fMHatu2LePGjfNbVkhMxJ49HnHkl+u5QWVV3qTGdTGiKDop0Aqi2jKQZmCNLYEQxSB7kQWLzYEKE92VroR7zhCcBWaIwVVaLFJgC9NaB1B86tP8KlyPNoLrtE5/wmAhhDh1BgZ6BT9BvnbtWubPn1+hMYU4GwXL84qxYg86Y1ygWAowRdlbCUBZlN+NynD5lS8t/1TzGYU1rvi08jwAI8LvMGe+2XVeFiLneU6gSPI8IcSfg+R51UuV7Olks9kYM2aM93ul4M6fGrH0UI2w+5k0xYX1snm+236/5ZmZmSxevJiBAweSkuI/x26huYAv2sz3fno60V13wcLQFHQ43oomW+uEjBuOo9HzkLQWNCPo+oXPj2TYvfMpIDZooU0pyCeBHK3s3MKeDSD92ABqHbuAE3HH+aHZN/AvG9wbogu2gn67BpNWUCvqc/Bc056DBvJji2QW1oRcd1MtBgzIhEuPQy179GlJuNfpVFWXmJUVV2KenTErK251ivn14q+ZxawKiQeQRhpNaFJh8YQ4m5XN8wCe+LUuH+1MC7ufjqJTzQJm99sVsC7U7xK7bufztvNw6q7n9Aw0ioghXNlEUxotMhvRdlPjU/r95Kz3Fir124h5XiE2HEEKbUpBEbFkaiGOqTSST3am/qFR5Fpz+LblF6gXYkLneUD3vf2on9Mw6nPwXM8+FwxkeYtkFqbBSXdTTQp6ZcGY49CwHDcsq8tnSGXFlZgSU96jkuedrapk0UnXdTp0KO0u98EWWHoCVzfr4J/fAMRaNF4bk0yzlGS/5YcPH2bx4sW0bNmSunXr+q1bwmL37G6uu1EmDMzY3UlAYNdnDY2GWl3G1bqUE86MkHFDsXOMTawNfyLuI1uwBy06aRoY1MTVPcn3LprrAjXS+tCj9p3otV3duY0sJ0tZjqb8H7XT0TEwuFgbTZfmPaJqv8fhw4f5fvFiXuxWkz0Wp9/Z2HX4viasrmniXZrRxre7fISY5b2ef5aYlRVXYp6dMSsrbnWKuXDxQg5zuELieUgyIkTFKJvnLd4LHx3Ed9LeoEy6xptj4ulQN/CRilC/S35mDU6foDoKC3b3eEmBeZ6ORk0thetSLyO7ZVa5fz85yWcjPxJdnlcStOjkGuWhNmDHP89zza1XWzuXfjXvx1TT3X0r02AR3wXkeZ78dgCD6d849HihwXjyvNe7pbMxxu7XCqcGK1JgTYrGWzSjC/FRx6wOnyGVFVdiSkx5j0qed7aqkkWnsl76GXQdDAuuz99gn+MadKgHzcpZQM3geMAyCw40VEA3bx2NnpzPcC7AGmUXcACDQnL4nCzep4RdxJCPExNOzCEfkfPMkBdIx0Y9ujGTHXzDDhZS5J7lJI3WtOJiGtITzSdu28JzWFq4nCZFzdkTuxOFQkOjNe3oQR8aneIPmD3GzB6zM+jL4QTycTKBPXxLG8zSEVsIIYQQQbzsnrnO4clKg6U/GjRMg051yhc7gwx0TBg+QS040SjBjtnvETcN6EwHRjKYOGKjnu1NUUIuC8nkvxSzDStZGJhwRMzzgmVQOmYSGcAMdvMD21lAPscASKEpLbmYJvRD90nhmxa1gILvaFnYhp1x27xFtua0oge9aU6rqM6jLIfVxCarPeiDjE5cY2VNYDdLaEsCplM6hhBCiD+/Kl90OlkIqz1FTM/MdQrPp52LybVu1REodkBMlGdVQjFZnAyYxUTDlZCYcWKgo9DQ0ehDT4YxrFztL2E/+7kaO/vx9ETSAQ0HZhzYsUY9VhWYMGGjHf+HjWTO4QrO4QqclKBhQg/3gW9oDM66mPTYdIopIoYYzCEKZwondpZjcBCNBCz0Q8f/0cZjuhOn2YQRYfiAw9hZTA6DCf9opBBCCCHOPkrBl7vcM9e5EjBXduqb57kHFd+VAwfzoEH4eVS8HDg4yXG/gpOH2X37zzfPO48OjGVkudrvIIP9XEcxWwie51mC9mYKTkfDTHueIYZk2jCKNozCiR0Nza/QFEBpDMgewhVx11BEEVZisITM8xQOVuFkLxqxWOiNjv+jjfmagcMSPs8zgFwMPieLq6gZ5TkKIYQ421T5olN+sAk5PNPmlqGAwiiLTpmcYA6vc5Jsgk6NQuBdqFa0jKLFpQwK3QWnQ94lvrEVYKWEYjSMsgUjpeHUfE9EJ40BNGECcTTy29QUZnYTn4DY+BRz1s+YVRGYmkHcTWBu7rOFophZFPAMhrfNADZsXEc8U9BIAGC5rSSKY7rqgQvJlqKTEEIIIQLYDXCU7fATIs8DyI8u/SCPXN5jJkc4QqjpjsvmeS3LmecpnOznBorZ5l7in+eBa6gEhRZ4g1FpGJpv7qeRSk+aMIGEMj2TTFEVrRQxfIUpaznxKh9MDSH2BrCc47dVMZ+QzxQMfMfFshDD5cTzFLq7eLQmxk7ZRw+D0YCvpOgkhBAijCpfdEqLBYvuSkoiibdAYhT1FzslzOF1cshGx8DpvZUW/MNVQyOFFJrSLPqGAznMd/dwCs5TeLJQQnHZcY80RUteBJIARTzNsJ7iB7qVZcTZ8klhMhSacSVFGuQ/BbHjocZroMVQwFQKeTZIhCKKeBs7a0nmKzTiyA8xOGZZTiAn3AANQgghhDhrWU2QaoOTocfA9jJpUDuK4YMMDD7gTTI4iuae79cldBElllja0T6qNnvk8wPFbA67jQLM2HF6uuV7aIomTMJMYxRO4mhMDLXLdXwPCxuIsxWQys0+eZ4O+c9DzGhIfhf0BAqZQT73Engd7BTzIXZWk8z36KRGnecpIEvyPCGEEGFU+aJTrAWubgvv/gZGmNlxzRrc1AFMUcycuIl1ZLvHQXLdTHPgwEyogcNNmBjL5WjlHJcoi/cpLS0FpwEaCg3D77n/mML+JMcOLPcxA5SsJJVr0LSJ7gUO//WF74DKx558D4VasIKThxMn6yngeeL5JymGzgmftbU4ykAWcy6/YsHOYeqymIFsoiPpVf9tJoQQQogz5JZz4fk1kfO80S0h2RY53k62coSDgCfPCz1BjGsbjbFcjrmc+UoWHxBp9HNPnueauqW0Z5O1uBM1Y0aglWOM0KDsW0jlMjTtdvcCT57nLhoVfw6ZI3CmziRfu8+9LvgoTQZ7yOefJDKdFMM/oU7hJANYQmd+IYZijpPGDwxgHedT+3TPQQghxJ9ala8GKAXo4RMRDdedsonnRxdzPavxLQbp3hnrzPgXiTTSSGMMl1GfBtG1lyLymU8Ri9x3v8I03O8cXCkJSuG0m0nOehgtNsqCk1JQuBKyXoOitYAOcX0h+a+Qdy9hp4LBgKL/UWIvBKuZgKJUmW2LeIM4HqBfUQw73Oc2hK+5mvcw3EN1AqSRQSc2sINmtODf0Z2HEEIIIc46FnPkPA8NHugeXbz1/OQ3M7GOcvc28gwcXprn1aAGoxlLc5qHjOdLYaeQhRTwBYUsI3yO5XsOrof5UE4Mh4nkzKlodcpRrClaD5mvQuEKV/ttXSHldih4Fo1w3cScULIEe/FDYNMjtNdJMe8TzxN0Kbbys3Jdp14s5xbeBBQm97VL4zjn8BuHqEssL0d/HkIIIc46Vb7o9O4WeKd0bMagFPD2RdA8ypnrssikbDHIM4Wucvc7AtedqWEMibrgVMRKMhiPQaa7wRb3v5HVYAA2OuM41h1nyVy0aF8aVQKHrofcD3G9nO6iUcnvkPU6WEGLeFfQjDn/O7BGbqsiCwebSFT1MNud9FIruEZ7D/AfF8Hz/2bsIYGHUMzym1FPCCGEEGLhbnjyJ8J2GFLAswOgS5SzbQebJEZHoWPHKJPnDaRf1AWnEjZxnGtxchgwoTCBe9jwSBLpTiw9sB/vhqP4C3QVG3EfAJQBR/8GWa/in+dtg5x3wAJaxFAmTPnfgC2aG6ElOFiFlU6Y7U7OURu5VXvD56p5Irpi1eYocdyHwYfoxER3TkIIIc4qVboKoBT830/uj3Idyj4Oj3u5boZfTwTsHlKo2Tw0XEmJCQOTez4TS1SDdLsSkWNciUG2e4nhLrxE/oDXSaQRL1KHWzEZ6dGdhMeR2yH3I/c3vr2UPEkJUBwpiAOTI+JGPlx31MwlTq5Wn4Q9Qx2DPH4lm9XliC+EEEKIs8GzP7vGavJ0BArITDUwmWFjOfI8a5jiR9k8LybKQomDPRzlUpwccy9x+t1sC89MQ16kDrdjdtaLch+3jMfcBSdXK3xbBIAdKIwUxInJXhD1IZU7cTTZndxkzAVCl9VMGBSzlxMsijq+EEKIs0uVLjptOen68hY1PAmJ2edLd3WA+s9v0cXcxVac2ImmGBSDjfo0jipuFk/jSgBKExBz2EfVPHSSuRqdKAYpKKtkF2TPImQXMI+o6klWorlTBxo6TQDQdSeJekYUe5k4xqfRNEIIIYQQZ4mMQli8H5y+43zr+Od5JlcHqPe3uodciOCY5QD5ZBPdTT8TTcvMFBdKNtNQ5OPbHas0zwt3LBNJjMJMalTH8eM8CSeej7xdsJmey9JMUHam5BBMNHXtohnUNO1Ej3gtdY7ycVSxhRBCnH2q9ON1JyLeuSkVzawn22I3sJKv8QzrGGpASXA9e9+ZHiF7RflS+lGK+I5Qj+zZsYY4lo6NjqRxd+TGA9gPQ8bbUPgrrsThBCgNtCiysLCbmMHaHfg5QhATFgZgoj5wGE2PbrwqcFLEvii3FUIIIcTZIJrczaPICUUO1wQzIZkUC2u+7055IuV5OufQiTgiT4entHzy+Ziyz/9pgJUSSkLmeSasNKE2/4x4DAAcJ+DEbMj/2RVPK3INoxDNPcEIeZ6ynAcRZtoDDRPtMXEucKQceZ5BcZjZmoUQQpzdqnTRqWaUj7uDa7rdsDTFyhrfuL9R7iGidELNWFePRvRjaFTHVuY9hPq0t+BEoxg7Fr+n4XUSSOYa0rgbnQgnqhQcngSHnyxzHHcPJyuEq41FThkcmOL+D51bMNhP8EEVXG2P4yGfwNHPrBfxHIUQQghxVomYu/mwmcAWJmst1PPBpvwGDw+X59UkjcGMiu7gpsOE6jZuxkCjBDtmv9npNGzU4HLSuRcTNSIf49g0OHAfKN9e8u48z+L+CpF2RZPn6fFTMPEETjYSbjDxeB4vnTk52poTnFqPfSGEEGeFKl10apsKbVLh95MROi5rcF27CMEs/vOVuLoKO90Jib8e9KcfQyP3ctLyMZntkPhc2M3MGJgpxkADUkjnLWLpHP0H9OFJcHhK6PUl7n9Pdcba+HvQLN2pwZfkMAonO/Af0dM1KHoi/8FC6dQxhlMHpYMWaUwDnRT6nGLjhBBCCPFnlBYLAxvCjwd8HrELwqzBVW1AC3Ova3vsr67/uLdxDe9t4AyS53WiOxdyMbaIN8SKMZkckDQp7FauMaJK3IOUm0jnA2LphInECPHdjr0K+yeGXm/HdV6nlOdpYLsazTqYGpxHNmNxso7APE8ngelYGebdUxk6mmFD6ZG6pJlIod+pNE4IIcRZoEqP6aRp8ECX8AUnDbDoMOHcCMHMoLSyj7/hvi/lRMeJyf3Vlg4RC04lrIFaF2KNKUGz/EZ0YwfoJNCPeHpFX3CyH3H3cIqgJHQTNMDw9koyU3q7LA4SpnqLZiYakcxqEnkHCwMx0RozXYhjEqlsIYbhAZHjivoQaYwADZ10Rkc+ByGEEEKcVe7rEr7gBK705q7zwm+z17YtYJmGf57nyfVa0SZiwcnOFqg1FKutGKzriDbPi6MrCfSNvuDkzIeD90feLuo8z0RpnmeFuH9A8mzQXNlYMotJ4n9YGIqJ1pjoRCwPkMJmbFwTEDmhYBiR/1wwqM3lkc9BCCHEWalK93RSCtafAL8uSmWYdfh0JDRKOvXjeEZ48ijxdh0KzsFOMhkNmmvQKV1T6BjuXlPhHjlzksBN5WtcxttE3b/ZSZBX1IRdb0VhcRwZfEFawi+gisDcDGxXgO6fFGlYiWEMMYyJ6pCpORNwxu6kmCMEdtd2XYtmTMJKWnTnIIQQQoizxvoMcI95EJQOvDMMOtUKH8ehh87dyuZ59gh5npOjZDIC9ExXGzQDHaf78bnweV4it4RvaFmZH4CRH922DoL0dtJxaukUFsdxnG9JT1gFKh9MDSH2StD9BzDXMGHlIqxcFNUhk/OuxZnwGwXsIPBFciXojfkHsTSL7hyEEEKcdap00Wn6r/DyBlw3bQyCftalxsOAhlEEU4QbT9JPYoRn7/N5GUURms9jZSYcGCEHknSJ5wZi6BY2tubMxqyXkHLwOjhmQHkG4A5I2Eygx5MV9yrwA3Y6Q+LF0ccLiO+AQ5/BzleplfUrcabxpG79J3XMd7Gn5lJOsBDlM2NfHC1oyN9Ioe+pH1MIIYQQf0r/2wYPr6S0I02QPC8+BoY1iRwr3pnEcTKjOm5ChDyvgDcwyETTSm+mmfyGZAiW52nEMpzYgF7hZbYyCjDrdlIO3QjHikE7hisdj2LG42B5nmYhM24GsB4H7SDxwshxQlEGHFnoyvNOrHbleVsmUkf/K/vS13NMm4/yGdvKRkMacDtpPo/kCSGEEGVV2aKTw4Cn1/gs0N1fvp1+NDhaCB9th+vbho5l10pC3kHzpaFRmwbUJPTtNIN8CvmAYDOYWCjBgdk9TLlnKWjEk8SdJHF36eCMwWR8Sq3d1xJjuoOYgu8BBTEamFR0M5eUbVH8YKj1Eo6TScAP0e1Wkg0Fh8AUCwmNSwdQKMmCZcPh5ErAhAknGorY43PRFn9MixZ307jj1+RqG1A4sNGQOGdDtOMfQuZ0MIrA1hTq3AzxHcp7MkIIIYT4E1EKpq7x6cweIs/Ld8Dbv8F954eO5cBOkqMGRFF0SiKFhjQN3S4UBbxN8DzPHjTPgxgSuYlkHkEL9yha1mJq7R5NjOkvxOQvAhRYNTBHk+cF6fYf2xtqv4A9sx6wPlIAF3se5B8A3erK83T3EAnOQlh5GRxZAJoJk3LlebYTC9F+/JKmja6hYZeF5Oq/YlCElTokGK3RMj6FE1eDMw9iGkDtGyGxa/gBuIQQQpxVqmzR6ceDcLggyIoyn2G6BrN+C110yuEEX6XNBLOJ0g/r4B+ECkVvhoRtl8FRCNEt25WQOFDgfdSuBq8SxyXokabkzfwWfrsczTvRr7utThVpyKRS9d8ASwKgg60rWD1J1eHI+578FTY8Dbs/Lp05JakFtJ8IrW+DlWMg8yf3xqWJmOb5/46XsNjqkNrmAXe8r2FLD3BmU9pv3gwHX4a0sdD6P2CKi/LEhBBCCPFnsuUkbDwRZEWZFM0A3gpTdCokj4+ZxrH4g0Aakbq192ZI2MKQIg/FyaDrguV5iUwhgevQiTDOQ+4vsPEiNGV3x/LJ86IaIFxBnecgtpHr/zEdIaaNe10UeV7ODleet+O/YLh7K8XVh3Z3Qvu7Ye3Nrl5OACpInrfvPczWNFI6veSOtwo2NwL7MbyDkmtmOPwaJF8IbT8CS0o0JyaEEOJPrsoWnY4EKzgFYSg4mBd8nRMnnzCdAlM2kIpZc+DATNnik4aGQjGEsbTknLDH06LIDDRcM5m4+jhdhhZN1WjX/e59y9zFckCEp/YAHayNIPUm0E5hbPgD38Cika4kw3eq3pydsPIu2PU+FK6IfBdu69PQ8i7IWwubL3F10wZKu5m5Y2fMBeMKaP+53AkTQgghzkJBbyyGcCTEkEcKxTxeJ4PD7hRFede4aD7/KnozlI70CHssDWvE9pTmeRDPZZELTgB7HgPlQCvb9d4J7pQxTJ6lgakGpN8JepQT0fg6/hN8NQgchf55XsFB+PkR2P0/sK+LkOcp2PkKtH0EHEfh1wtLi1eewpQndtYS2HQRdPzR1aNKCCHEWa1KFp0Mw+DkoT1Akyi2VtiMQjZu3Bmw5mjSTjIbH/OZPldhwY7hnsPEvTsmQ6PHzlFYimuwkY0hj2QyHyU+dS4x6SbQnGE/nJXSsed3YNPu3yKegc2xlZb560JvUALEuLqil63RKPedtt3Go+Rv2hx098xMV3fz7du3k5GR4X9O9pO02TAKzSgJLHi5v1dHV0CMhmaLMKC5I5t9q6ZTk/8Sp4zAxMrLgJNfsmvd2+RbSse4CtfOU1VdYlZWXIl5dsasrLjVKaaOTgcq7lHeZJIrLJYQZzvDMDi2bwfQIqrt47QSNm78PWB5ZtxhDjff7bfMk+cZ6N6sxmQouu4eTnJB3bB5nm7KJD71M2y1LKDZI+R5Gs7iBvy2/ShwLGz7zcYR2mR/FSTPcgub57kW7NMeJWfz9qC7h/s9qhlFtFk/DJMjP0ReZqBOrAcLaBE6oCvl5PDqfxFv+Zkko7i0F1QAJ+SuZt+6F8m2lo5xVV0+QyorrsSUmPIelTzvbKUppaKcGu2P07ZtW64cN458LY5oBjOyqhKs2ANX2AzXeEgARRqEK5oU6eAMfSyTyeGaNreM4iIrMbbgj9uVFMXgdEaq6ynMmoMYc2nsIocVm7lMzBBNU0qjyBGDoSLXD3WcmLFjdicJBhoKHbMexeCVQdpQ5LRiM/m3064sWExBXosgHIaJYmf4KYuFEOJUuOYTPYWen2GsXbuW+fPnV2hMIc5GnjyvQIvzFlXCsSg7McGGNrAaYPHJ7cLlehHyPF13EGMrDsh1wuZ5xVacjkg94BUmzYnNXFTalPLkeWgU22NwRpHnaTixlMnzDHQsFZrnmbGYoovnVDpFDhlKQQhR8STPq16qZE8nm83G2DFjeGFHHf6zPw0jxCexhiJGV3zTayvJlsC7LStafki+zVVdzX2+Jon3Bhs8wKX9/gHUy2oTdJ0ldjO1mt8KGGiaQgF2zCg0/vP8bVx/70y/7ZWC4qyh5B54hFBZhNmZQc2CD0jN/x9mLQvf3tzPL7uXe/s8H3S/YnNdsmJHgWYiX+tIvtYt4iN1mSdPsnzJ1zyU+DRKM6G5n9VX6GgYKB20eIj4cxuP3zvm+a33cm+b0nYqNDJrjibVNDdCIPe56I3ZVuPz0nZmZrJ48WIGDhxISkrwcQByrHkUWIqwOM2kFNVAj5CsRhOzvCojZmXFlZhnZ8zKiludYn69+GtmMatC4gGkkUaTqHrfCiEi8eR57+xL41876xD6BqPCpMHn3X+nQWzgDa1fmnzJicT93u9D5Xqa0mh+pBtNM84LehSzdR+1Wl6LppV48zwHJgz0EHmeRkleV3L2PEOoVFo3cqhZ8CE18z/Eoo5BTOm6cHmew5TAifjrACjU2pKr9QUt/DANmZmZLFv8NQ8nPlUmz3MPPh5tnheLfz4akOeZyEq+mBRrdH+UObUEfkte4dfOSL/v8ywF5FkKMCsTqYU1Iv5RKZ+hEvNsjFlZcatTTMnzqpcqWXTSdZ0OHTrwelvYNQ+WHgqYrwOzBrqm8dkIjb6N2wWNs5lvyI9y+tymDVvQqmHwLnp5POHuR+UZ5Ns1kKQzyFhNGjVI0O6kTso/0FJCJAm5G+Hny8GeBThdYcOPe+lmIqb2ldRu/mIUZ1QqZ/FkfnKfgeY3OKSrm7VmAAW4ikrh2hChT5wGpLYYDLujKzrF2OLp0KH0mh8+fJjFixfTsmVL6tSpDc4NoLJAr8Nvus632jL2+wyWmUwS/ehOH7qGTEp8Y9atWzeqdkVSGTErK67EPDtjVlbc6hRz4eKFHI5mcN1ykGREiIrhyfOeaQ97voJPAkdIwKS5iiZzhsJFrYLfFNzLak5yABUhQVEaNKrbhA51g+d5+bxBCU588zwzTpxB4mrEEa/dQkLio2gdQoxXVLgH1lwCRQfwjmsZTZ6nmTGnXkLttq9G2NBf5spX+MndE8w/z3O33wDygYQIbYiY5zlJaTUC9kRXdDKZrGHyvDrg3AQqA7Sa7DQl8o22lF3s826fQBy96cpAemIO8SeLfIZKzLMxZmXFrU4xJc+rXiq2T1oFW34E1p90JQtlJcXAwtEwpHHo/VvQkWgezzNhphGtg64zyMDOAoJNnWvGNZ2s65E1BxYSqcU2Ergv9ODhzgJYO6S04FR6oIgf9uCEurdHPB8/jmISNr8UeTsnZU8xULhBvzUz1BkOtUYR3dvKDDUGBFmuiNPfguxmkNMZci+A7HYk5/QntXiR35ZZ5DCfRcxhLkbI8aOEEEIIURWtz4AfjxA0VYs1w/8ugnGtQu/fnHMjFpw8mtE+6HKFnRLm4J3sxM2V5xl+eZ4ZnXR+I5EpoQccV074ZTgUHwLf3KS0phWackC9O6M6n9J9FIkbg/ea8mNAsJEo/Ohh8jfNDMmdod6VoEcxNIIWOs+L1T+E7LaQc64rz8vpSFJ2V9KLPnU9LuCWRwHf8CNv8D4OonxEUAghRJVTZYtOa4/BRZ9DngNXK8t8ZTvg3uVQEqZQ0oKOrh4wYT7kNTTa0wMbwZ85NyiTNARhQrk7YZ+ESLOeHPkASo4QUOHxJALhEpImT0JcmOwLwHDA9i9g9fOw5mVY/yp6SVb4fTyCD1ngYksDizl4F2/NBLH1ocubEFMPao4icic6B9QrW0AziLEWk2h+DIy9fmvqOI9xbf6HXFC4JCDSr2xhBWsjHE8IIYQQVcWOLLhgHpwsJmieV6Tg/pWQHTicplcT2mHxfW4tCA2d5nQgiZpB1ytOAkVB13l48jwTRYRPloCMryF/i/8sceCqaXl6todS7y6o0Tt8fGXA7m9h9b/gpxfh1zcx5+0Jv49HuKab48AWGyLPM4MlGXp8COZEqD0eIs3MrBxQ768Bi62WEmpY/g7GNr/lacYJLiv4jFEFX/oVnhSKXexjEUvDH08IIUSVVSUfrwP450/gVGCUnfXWzanglwz4eCdcHaQOc5BtzOcVXJ+wwWtrGhq1aEg/Lg3ZDo34crQ6Bi1Sz6pD77rbE6SQVQx4xqNUuMdqMsCSDo2fgHq3hY+9aQ58fz/kH3YlDUqBySCKsSddwtXWzpsMdbrB5kfh6Dd4Z7bTYtCaXA/tp4Ktlmvb5i9AzlKwZxKy+1TDhyD+HL9FsfoHmM2OoB2qPHPQXFT4HbvNTdhtaeK3/kdW04suEcd4EkIIIcSZ9/QvUOBw5XNAQJ7nULA7B97eAn/vFLj/cfYzl5dxkk+oAoiGTg1qMpirQ7ZDC3HT8ZS3P/Keuz1B8p8SvHmea6Y695hLphrQ6CFocH/42Nu/gEV/g+w9peN5akZp7hhJyDxPgw73QotxsOkROPSZd2OlmdEaXAHnPAXx7scLGk+Ck19C8YHg54kGtW+E5Av8lsboi7BYgt9l9bz8fYpXs8vShI3W0hxRoVjBWgbTN+RjdkIIIaquKtnTyVDw1V6fRCQEXYNXgsx8e5IjzGMadopd87Npng9E/4Cd6McV3I0VW8hjaKoOmqoVxaNvZiwMj7QRlBwlbHXH81nsANLGQfvPoPvByAWnX16Dz69zFZzA1b0bI/JdNV9l6zWeu13t74a2t0NqF+i7EC7azYl271HotHG023o4f2ZpwQnA1gQ6rYKknu4FuusuGYApCZo+5+q15UMpRZxpZsS2OtHpXbQqYPlJsjhO6IHihRBCCFE1KAX/3eYqLEUSLM/LI4tPeIEi8tFwzc4bLIFoS1eu4j7iSAzTGCu6ahV8LAc/Jkz0QiMh/GbFQXqz+/LmeRokXwxtP4KeR6DhA+GHMdj6MXw8ErLdPcGV4foqj1B5XrOroNM/Iakt9PoULt7PifbvU+S0cazrOuj+39KCE4A1HTqthJRB7gWePE9zPXrX6BFo9UbA+UST5xlo9C1aGbC8kCL2cKBcpyuEEKJqqJK3CxwqujqJoWBLkHHC1/K1e/jH0gEhS5MS3Mt0rFhCdstWykmxepIS9TJo+e4PznAJiYMYJoRvcPFx0KzuOBGrK5B+FdS8JPx2APlHYdFdwdcZ0Yxq5db0Ysj9BQoPu5KHBkOh3V1Qf7B/4hDfmJIUKwbbUOYawWPFNodOSyF/E2R+DUYR2JpCzUvBFGQsAGM/Fn1bxMaaMGhv3+q5Rei3rjhSl3chhBBCnHFOBcWRxpHElSntygGnASaf26QbWEwJRSjPhCi48gNXrudwL9MxoYgN0WNdKUWJepli9QxKyww/npGr1dgIfFzMT8lJSp8RjFAQcipIHQ3pl0U4LmAvgC9v8rTcf11UY4Li6hnV+AIo2Q55e13f1+nryvMaj/KfCTm2HiXJ/XHyO4Yl+GOJxNSFDguhcAec+AKceRDTANLGuh7BK0OpQmL05aD1DBKslI6iqWMfNqOIIt3/prDkeUIIUT1VyaJTeVjK5Ah2Svidn7yJiC//EoXBJpbSi9EBj8QppSg0bsHB+4BCU6A0LWihw8PGQ1joE7yR2T/DjqlwbD5oqrQKFoahJ6CnXhh+I48Nb4W/2xXxRpgOljgY+j5YE13jQmmm8HfcohV/TsBjdEGp3KhDmnGiY2CU6U5fI9LdRyGEEEKcceXJLnTN9eWhUGxiadA8zz+2wVZWM4BxQW8wFqsHKVEvufZRoAzDVXgKketZuQlLqOEYcn+DnVPhyEeusYwiDHfkYoZaI6PZEH57H0rC5EnRdHjSTDBoNiTWr9g8L7YFNLg78naqoFxhrZRQVOZJhGSSyhVDCCFE1VAli04WDeLMrmf9wzFrcGED/2UF5OCMcoaLIvKxUxzweJ2Dz3Hwnvd7DdANA6W5J5/1+ZA2DJ2SrOdISb4j+EGOfQm/jHYPiqhKp8yNMHVuQc2bSAjWI8jD6YAj66AkD3YuCF90cl+OoHmUZnLd3RrxsavgBKCfwtvCUQLr58L2H1z/r90aetwASbX8t8s/AZs+gbyjYKsB7UdDciPQ66JUdE975mlxGD4DXWpotKAxNSQZEUIIIao8kw514uBIhDqESYO+9fxzFwd2isiP6jgGTvLJIZl0v+UOtdpbcAJ3nqcUyp3r+cVQGvbsx0mucX/wcTszV8CawWCUlA4eHmKcKl+FKZcRZ00PvYFhwNENUJQF2+a7x+oM0T0sbJ7nLqQNne0qOMGp5XmGEzYtgN++Bnsh1GzqyvNSG/pvV5TjyvOyD4A1HtpcAumtQKuBUqGHs/A/HRMFmn8OXJs06lG7/O0WQghxxlXJopOmwU1t4bVN4cd1cii4s4P/MnPUoym6mIJsX2K8StlBIDVAUwrlM6OGYaRSVBiLUTgGkoMELz4G6y5zJwk+J2Lg6nkdUHhyFbWchonc2vcE77fjtMPy52DVNNdjdeAaQDKau2oGYLGA4TNnbsOB0Hsq1O0eRYAQfvsGZl0DeRmliYwy4LOHYfB9MHKq667al/fATzNcBTPd5Nrmi7uh3aVoY9+kyBgc1bP+P8WcX2ap4sJQvcyEEEIIUeXc0QEe/8lnwpggnAruOtd/mSm6bkRewfLCEuM1XClw6U3KYHmeUjaKCuJwFlyPViNIBclZAGtHgLMIv+5GIfM8UGg4DZ2celODD0luGPDTK7DiX6XjN0Wd5+mumYYNn8fQ6vaAnpOh8aDQ+0WyezXMvAyyDvjkeQq++Cf0/guM+7eruPXtJFj6L7AXleZ5X94DLQajXTGbAuOKqMbu/MV6Lg7N/3UbTL/Ik/UIIYSokqpk0QlgcjdYuM81c0mowtPEc6FXXf9lcSSRQh0yOUq4TzYNnfq0DEhelFI4WUqoQSB9P+5M+kk0Lcyn54G33B/8QbYxfIJ5/o1rRU7yDRQ5i0ELUjxz2uG9UbDza/+eTZ7BwiN8FmsOYNRHYEtyFZ5SWkKNpuF38igpgjUfw/blYDiJS2oISmHdswLmjCu9+2b49DJTBnz9tOuOWP4O+N2nR5bvdr99BhnbKBj7T+AnlNKCXlcDjRIsrIhxFch0d5HuCi6hBU2iOw8hhBBCnHH/6Ajzd8Mvx4PneRpwZQsYVSZN0THRgDYcZFvIR+w8UqhDPIFjTzr5DkL0ivdNpTStCF0PM/jUoQ/AfjL4umB5nq0xucnXU+TUUXqQkpNhwLwbYcO7gbF0osjzDBjyGtRsAY5CV46XEmSK52CcDvhlPvz2HThKiI+vA0rHfGQzzBrp6sUO/vkbwPKZUJwDiTZYOxtvzuu73a7F8GpPCq74N4qlYfI8V1HuR1tvAHT3YAqXMIhOtIvuPIQQQlQ5VbbolGqDFWNhwhKYt8v9eau57ojVsMLD58N95wXup6FxHhfyPf8NG19h0IkLgq4JlYiEihTS4Y8I+6C978xyHf8Lda+i4MgRYGbw7Zc/H1hwAld9LIpX0hHfAHPLEf6DRUbj57nw5s1QkAkmC6BIMpzEN7qH5A8muLpch7sO378ECYS+S6eccGwL1rWrKCqJdW+o8C38KXQMYvk48TYKTWkkEkNH2tKLLtQixCCXQgghhKiS4izw3Si4aynM+d3Ve90z/Ha8GSZ2dN2A1IMUWjoxkANsjXiM87gwaO8YhT3I1qfg6KeEHTTcN89r8wI0mUj+kaOEzPPWzw4sOEHUeZ5hSURvfzWYo3uMzeu3xfDaVZB91JvnJRoG8Y3+Qcq748FRHHoYB6VgzfsQH6aNhgNyDhG34mOKixsDMbim8fPN80yAic8SbiXL3JB4zJxDa3pxPvWpU77zEUIIUaVU2aKTUvDlHvg9q/Sj3FDQNhme7gmjmoXYD4MEkogjgQLyQsY/hz40o2PAck3T0WiKYncUbbSEH4fIkRUxRumBzeEHdHQ6YPW04B/6nvpMiLtgnnwnt+vjpJS34LTuc/j32NIgTlei5jmMKTPD1e3bGiGOXQNTmMKUchL36zs41Z1klCwlvcYnUPwBnDgJuQloMYMwN3yI6yxRDEouhBBCiCrvm/2w+aSr4ASufK9poqvYdF2b4PsoFFZiSCKVHEL0MkKjGefSPsSj9yZa4+QnIo3ArZSGES7Ps2dFjOGiu/O8MLGUgpUvEHKGYyeue3JhUsXc8x+mRnkLTtuWw3ND3TcQCcjzzBkHXH8tBJ/s2U2DEsAcJs8zHMRt+Qin5QGOlyynVo3PoOQ/cPIYZMeiWfuj1b+Py6w9iGI+PyGEENVIlS06/fUHeH1z4GfrtmwY/RVM6wt/K/OcvxMHi3iDnazF1SlXuSfQLY0SSyJdGMp5DAr5bLhVu55iNYXwD56bcRSNJuynv60+FO4lqoQkpm749UfWQ96R0OvtuF7NgFdUA91CMTEUNbk4cjucDldhy2x1JSDv3OG+DGGuhefY4epZjnDX0sVUeALNZuCkMdrxwbD8W8g4ABQA74H2P2gxFvq+AAn1Ip+LEEIIIaqkyT/BpDWBPZn25sH137luOk7t4b9OYfAj77GJJYTK8yyGjc76ILoxHD1EYmLRrsepVkVooQlnSR8IV3SyNaDsGKDBGZHzvJwDcGxz6PWeTvieXuPeR/d0FBolWCloc0OQhwnLNsXp+jK77xbOucv1faQJaSyEyfNU5EsAaM4idIsTg7po2cNg2bdweA+uPO9TYC40uRj6vQDJLSMHFEIIUS1UyaJTocNVcILAUofnuf+7lkKP2tDVZyKLFXzETn5xf2e4O/4obwyTMqirNaAzg4Me1zC2U+J8AofxYWmvnKC9j0yADXvB7cC3/qvs2bDvP7DnDSjaAXoUBSdbQ0jpHX6bcFPlejhwfehbrdBsIJis0HgAR2sNw/HOR6H3czpg+fvw1b9h5xrXslrNoOMFcHJ/5ON6jh2pt1OUbPvnw6rbCRx90wE7PoFDS+GK1ZDYIOj+QgghhKi6SpyughMEDiTu+f7JtdCzDlzcpHTdLyx0F5wgMM9z5QxJjiS6Wy9GC1IhMdRB7I4nsBv/iZDn6YAJe/4/gNX+qxwFcOB92P065G8FUxTVFlMS1Ipw46+4HHmeCWg5xDWod/1eHKtzCfY580LvpxT8NBe+mga//QgoSKkH3UfDnl9C7+fLToTeTtGzHl0Ky64NMhufgr1fwYfL4fIVkBqiu5sQQohqpUoWnQocYZ+QB8CswbRf4V13/aiQXHci4p+9+N7/0jTFPjaRwX7S8J/i1Wmsp8g+EChAw4HuBMPkvp3knYPW1SqNFOL0eeQ7yxQ9crbA8guhyNMjSZV+QIcb/LH5o8G7XNsLYeP/YMtnkHs4TAAfSoOkFjBuYemiw2H2tRfDs6Nhw0L/NhzbDYvfjP4dEqm2FkUcZbaBppH808TQQZUDCo7B4tth5OdRNk4IIYQQVUW+A0xa+BmKTRq8tKG06OSghF/4KmC70jzPFSzbepgDbKVhmYGnDbWTwpK+wIkgeZ4niCdZiydO/4B8R3v8ik4F+2DZha7JUTyZqqcB4fK8pveAKTZwudMOv82DTR+F783uSwGWVBj3dem5hcvzDCe8ciMsneOaUc6TJ2cegkWvEfWkz5HyvChm11OajkInZZVnPNBgeZ4TSnLgm2th3M9RNk4IIURVVs4Bfv4YDiPyZ5tDwSc7S7/fwc8YUTzGpqGzlRV+y5RyUGwfDeTj6b+sAbrTie50uopOCjSVgk17kwR9Byatq39gezYsuwCKj+E3cqRn1lrfwSTB9Ww/QLMHoeGtAe20HP4JnmkIn9zoKjrtXxUYI5TOt0Sxkdvsu+HXb9xt9L1+KvrjRSNSLyjdTEG7KzHjCD3jn7dpDtjzJeTsqaDGCSGEEOKPUuIMX3AC1/pvD0CuO4/ay0ZKKIwYW1M6W1jut0wpRbH9SuAEwfM8w53zxBGjTSNR341ZG+Qf2CiB5UOgYI9ngftkKM2X/M7JXYWpPx5aPBrQTnPGFni+GXxwBWz+BPYsjS7v0kxwfmDeGNInU2Gpe3IdI0jPoooSMc8zUdR0CLrmRC/JJPxEO044thaOStFJCCH+DKpk0SlahU5wuj+z8sgM+ey+P0U+WX5LnMaXKPZT9oF0DdCUwmQYmJwOdOdRzPRA04Lcrdr3DhQfDewqrIBiXDmO9zm/eKh9KXT/AVo/HdC1W1NOUj8bB0WZ7hjuk/SdfjcYzQTx6XDejWE28pFzHL5/M8yMJBGO58sUZsMhD0DtNq5u4MHoJrAlkXf+HZiIMBOeb+P2fxdl44QQQghRHeW6J5rLI5NokhKlGeRxwm+ZoVZjqHWUnZ24bJ5ncmZjpj2alhgY+NBcyPvddeOrrBL88zzdBmlDoMsC6PBWQG92TRnUnDu2tBe7Jw/zpJCh0iBNB0scdP1riA3KtqsQvnghdMBoxkD3CJnnadDrJmjW05WHBt3EBKYY8nrejxknKtR2ZffZ9005GiiEEKKqOq2iU0FBAcOGDaN79+588cUXzJ07l969e3PppZdSXFxMUVERY8eOpXv37nz+uetRqLLbnI7UGDC5z8CKzWf0pnA0rPjP7OE0PiO658hMOI35wVfteZOwxRInrqSkCOj5C5z3P0jtF3RTKyVoTntgMcgzS13AnTB3v+64mnDDdxCbErwNSsHmH2D2PfDaX+DVm8AZZiwCw/0V6bLqJqjpHtjbZHFPt6uBxQajn3Z9/WUx1DvPvb25dD+ApAZw248YSQ0AFWWdSwNHUVRbCiGEEKL8znSeZ9FduR6ABRtR3ZRSGlbi/BZFn+eZcRjzgq/a+xZhnyHzzfPO/wa6LoD0i4KOGWXBjlaSG2RMI0oLQWVPVdPBmgDXLYTkRqHbsWMN/Od+V5437XoozAm9rSK6PA+gdhPXv7rZledpuuvfIffBNTNh/AJofkHpNlBahIqvCbd8iyOtjetgKooDajo4Jc8TQog/g9Ma02nhwoUMGTKEO+64g5EjRxITE8MPP/zAG2+8wdy5c1FKMXr0aK644gpGjBjBiBEjmDVrlt8248aNC4hrNUX3rP9NbUu/b0JHVvFpxDYrDJrSqcyyHKKadgMdRYiBHgujHHAboOgAJLYKfoSCDMw40YIlIlBaeNJxfdhbYl1Fm/NvhU43Qmxy0N005STtqQvg8O9gcr/syl3BClfl8cxYEs6Vz8LQu+G3r2H7EnCUQJ020OUqiE1ybZNYB+5YDXuWwbp3IfcI2JKhw2XQ5mJXAerwYRQ6SjOFPn/fC1GjWYRthBBCCHGqKivPs5ldY3OGm9jWrMEVLVzbAjTmHDT3fHXhKZpxnv8SlUN0Xbc1UCHyvPy9RJcrEj4ndBRixh5dnqebXD2bEmrDeeNdwyck1Aqxn0HN5y9xDQzuzfPc1yrcqduJ/Gjc8Pvgymdg+4/w20JXD6q0ZtDtakhIc20Tmwy3fAMHfoa1syFrP1jjod1IaD/GNVueO89D0yIXugwHJEmeJ4QQfwanVXRq27Yt27dvp6SkBKfTSY0aNTCbzfTt25c5c+bgcDi4/fbbiYmJISkpidzc3IBtgiUjsSYDw3sXJPCTUkNh0RQXWrexcaPdu7xG0/pkxx8CLcQnmdKIsSeQ8ztsZKN3cc10G0nJOpoWPplQysGhQ4rcbNe+mZmux9+2b99OmmGJeizGHXsOU3hkY9B1zl1LowtigNMw89uIZT6B9wOBiU7B3i3EOgoxHdnuPoi7a3ikgS/dx1EOHWWzoDuLMdx3rzTDgQJ29LyLwgaDYfNmoAG0uNanPXuDBEyGln8r/dYJbP4NcF1PO+aIBScFOKxpbM2uAxuDX0cP39coIyMj7LbRqoyYlRVXYp6dMSsrbnWKqaPTgQ4VEg8gmeQKiyVEdVFpeZ7u6l6jEbx/s2dGupEJO9i4sbS3S1rDFhyvsT10ngfoDgslv8ezUZXmB8mpJlJqOoNPVOdDKYNjx8xknQzM85IdJmxEV7racyCD3Kzg+UnhwU3R9eg2AMPJptGLUSZ3VWj3UeBowKa5h/YQ5yzEvHeDa4FvnhfpmQYFqgRUXAy6sxilm12jK7jHf9rTeTy57a6FTZuAVGh+dem+uw8DZQcxj4Fmt/kv2vI7EH2eB+DUbWwtbotxBvK8yoorMSWmvEclzztbaUpF08c1uB07dnDRRRdRUlLCrbfeyv79+5kxYwa7du3ihRdeoKioiCeeeIK6dety/fXX89RTT/HEE0/4bTN9+vSAuK1ateLSMZdhBJvRDQCFrgwsZhMWi8VvuRZnBM0IVJGGFqNQhbprhjffi6A5iY2NPDglQEFBPMEOYNWLsWj2wB0CWq5R4IwLGgNAV65eTjatJOh6/1hQoCVE3M7qLMKJjs2IHDOUIpPrkUSTO1EwNB0HJtArelgwhY0iTBEKgMUqBkfUZT4hxNnGwIhynD+w2+04HEHGaSlj0aJFrF279nSbJkS1Ubl53liMMGP76MoZPM+LNUIWUlSR5srxjLJ5nkFsbEFU51xYGIdSgQewaMVY9ch5HkC+M3iuCK7xnHTliCrPA8gnPugjer6szmKcaKeV5xWbYlBorrE1FRiahgNzJeR5EEMRZi3879wSZcUesQuWEOJsJXle9XJaPZ2mT5/OtGnTGDRoEMOHDycx0TXwYn5+PsnJyeTk5JCfnw+4xgWwWv+fvfMOk6LK+vB7qzpMzjMw5JwUASOooCiKooKKopgVcw6YV9c1rauYM+aIERQTCgoIRsRAzhkGmIHJqbur7vdHdU/nrh4Bhc/77tMPUzecutWwUz/PPfccV9N1YEws0tPTOevM0SyrdfPupny+2JpDnaGR6/RxYstyRhVvpzgl/MVvCoOtuQvZkD+X+pQK/86ZCHRS9mArBhx2IC1T28e8Z3Gb80hJnYcQscO2pRRUVpzB9tIbm9rKy8uZPv0bzuhfTdvGD3H71ibUBRLB1rzL2Zp3dlSfu2I5+cveJX3ddJ4S5zLWOy6+IUAKnapWh7Fu4GMJx2n1VfR86HAe6X4tYxfHsGlT6lcKHU92Mcuu+CSYg4nAs09n8ODB5ObGySHVTAI2Dx10GH3W/5uMijlIdIQ/nF2iAyabO11DWftzmmVzV6xzZ9rcVXaVzX+mzV1ld0+y+eX0L3mFV5Kb4MT2GHEBBXRo3WFHl6ZQ7FHsWp13BmvrXby7KZ9PtuRQ7dPJdBgcV1TB6a220SEt3IEiMSnNWcLG/LnUpJWF6zwpyNrWllXPmAw+/IiYv0uKim8gPeMbRJwoKSk1aqqPonTz/5raAjrvtP4e2nkmkepdAsT3AUk0tmePYlPhbVF9rup15C1/j4zVX/G0OIsbvOMSRjxJoVGXtzerjnozwSgQPi/dHzqcxztd8id1noaRksWSa75EOoOFcnbl7/tDDjuCfTb9j+yyr8NSKgR+Lm17Lps7X2/rbNtV69xVdpVNZVP9G1U675/KDjmd0tPTyc7Oxul04nQ6rbBZr5fZs2ez7777sm3bNmbPnk3btm1paGigqKiIjRs3ho2JhaZp9O5tBcyNDOtxAkX+TxAvjXzOo5SwFPwn/gNJxQWCVDObskbB3m33o7i4OGyuaS7E57kV0/zN79oQEVXbdMDAoY+kVYuXaN0y+C926+o5/KTX0aX6EQQi+FKPWfVNQxQeQYuDH6GFFrFzM+N/MOUWK/Gi6YMUK8dionetkAbZQ2+jd2ebsMKlP1g24xFYa6w1aw5Eeg7uf0+ld+seYV0lJSVMnz6drl27Rn2nzaZkNZRuoNRRz3Qp6dSjLxmH/wjrpiEWPAfbFoHuRLQbCr0voTinK7Z3NBoBk5KtFTtvnYHl7sxn38V2lc1/ps1dZXdPsjll+hRKoo597Bgd6LBT7SkUuzt/hc47PqzHART4P0EMfEzjWVbzKyKGznOLDA4yR7BKfhT1u0Saa/B5b8EwvsEIlAEOE1mWztO1w2lR8AEtC4NJyDevW8CPej09qh+kKbxKI7ZmEjoiuw/5A18g3xERhT7nJfjkYuuepkFTTvSEOs8k/cib6d3bRudtXgWNcfJQQbjOI+KemgPhSsVxx5fs3eXAsGk79Xfz1g2weQ1lWi3TpaRL973JPmwqlHyHmPcslP5qfX+tB0Hvyygs6E2hnU3TC6Z3l+g82LPed8rmP8/mrrK7J9lUOm/PYoecTldeeSXnnHMOdXV1DB8+nC5dujBo0CDatm3bdNb/zDPPZPz48dx+++1Nc0LHxGN7A7y8FF5cAhtrId0JIzvCFXtBrwgn6SzeYDPL/FcBEULTdYNehUjNjKpuZ5pz8TYcBjQgkOgmSCExhWg6gqdp/XHqN6FrwxChx/1ML3kLT0dwsv9eMjz5YyiuQuh8JXS7BSIdTnNftxxOEOYcEoFHiSdI9r8QOh0Ru88wYO4U+GESlK6OYyAEvwazBJj/O0rJgMEXwPAbIb+NvY14rJgHH78AK+eDwwn7Hg7DL4S8FvDLNHj9bphn5bEqBNIOuZH0j5+EC/8D7Y+2PsliemDDG7DqCaieZ9l0tcUpzkYY1WDvqlIoFAqFQuFnV+q8ag+8vhyeXwyrqyFFh+PbwZV7wX4RHodfmMRqfgMIcTYFkHio5Ye8V4HssHmmuRxvwwCgEoFh6TwkphZMZK2JvXE6bkXXTkKIEFksJXmLz0fj8IC14B+ROs+RDR0vgR53giM9vG/pF/DhRTTpRD+JdZ6AHidA79NidVpOs3kzYPZ7ULou9phQmnReSJszBQadDSNugpZd7G3EY/1y+Gg8LJlrHcXbuz+MuBhatoOFP8Krd8GcrwBJAZB2yFgy3v0fXHo/tDrU+iSLNGDTe5bOq/gRgCJHEU5tDMK7DaXzFAqFYvdkh5xOxcXFTJ06Naxt5MhgbJLL5WLSpElR/aFjYuEzoft7luMpcNitxgfjF8Ozi+DZQ+GSXlZ7HZUs54coh1IoUpigSba5VtOKVlabNPA2noxV27YpxgkhQZMSSwnoCFmF7jwOERl2tOkjHA2rYt8w9ISeqxCOWQf+nEjh40yY9u/4NsJ2ozSrCokrAwbeCIf/K3Yo1JoFcPdw2LLaql5iGshkjsQLHXoeDle8YO3C5bUBV4w1J4vXA/+9CL543VpHIKnl3G/gxbtg6Gj4+k3QIvIuSEnmuw/Aql/h/k8sR1Uy+Org52GwbSahalD3rMeleyhYdCwUfAcpLf/8MykUCoVC8Q9iV+k8Q0LvD2BdjXUtgRovvLkcXl0G9+wP//IHSXlpZD5fk6jcmcSk3lEBelZYu69xNFBBmM4DNDOo82AruivC4QRQNgtX9S/Q5HQKIVTn6alw9Epw58de3LT/+Ku1Raw/ns5zpMBBl8PQB8LSGjSxZY2l89bMb57OQ0BxD7jjC6vKcG4rSEm3nxYP04Qnx8I7j4KuWxueAL/OgNf+a+m8me/4BwefXUjI+PhJWPIDPPoNpCa5BtMHc0+DzRMJ03m+rbg0L4WLjoL8WZC+Aw40hUKhUOwSdsjptKvY3gjljUQVxQ2U1r10NrTLgGPbwSrmJHQ4hbIh5Q96cwgApvkFSLvdIQMp/0CaPyP0g8K7Vr9slX21w1MK276HohhRSWtmQ/ma+HMDmkgIyOsGQ/4NPYaDKy32+M2r4eZBUFflX77l6LHJyW1hGjDsCijqmMTgELwe+PI9WPiLJah69IOjRsL/LoEpb4atw7qPCZjw+Rv+f33Rf3dCSpgzFd7+H5x9O/zxDUx9xRJa6Vlw0HAYfBakhoSvz78ctgUq/wX/5TQFzzeuhTkj4NAfk8oRoFAoFAqFYtdQ3gibaqMVQEDn3fELtM2Ac7vBOubho9HeqBQIR9CiacxBSruEsAawEdP4DN0xIrxr7Wv+XJJ2Juphy1fQbnR0X+lSWP9TgjUTjHZKbwnH/Bd6joCU7NjjK0vhpoGwfbP/3s3QeRI47moojJ3bNC6GATM+gd+/A58XOu8FQ0+DV++xHE6BMQH8Ve+Y8pal82J8hUKasGwujL8Frn4CFv8AX70EG5dBShrsewwMOQ8yQ442LL0DNgccnNH5VzVvKfw4FAYvtdJVKBQKhWK3Ybf8rSyxdsHioQm49zfL6VRPNRoaJvZvXI9e2/SzaXyO9fh2mewdmMZnaJFOp7p1iBgvvZjUb4jdXplESDRYzhwpYJ/ossNhvHMP1FUHX/gBAo8YL4xbaNB3KOw/PLn1+NENHy3O6AdV5cGIJJ8X7r8MzCp7Az6szapYa5ImfPg4/PoRLJ8bzHclBPwyBV65GW77EPoNgYYS2PAmsURI0yNiQMXPUP4D5B3crOdUKBQKhUKx8zBk0MEUj7vnwjldoV4kyFcUipBhx95M83MC+ZoS44jtdKpb11TIJPF99fg6ryJZnQd4GqCfTZGUjx6D7SXN13maDp33h8HnJbceP7rho+jsA2Db5nCd97+rQEui6nMinWca8NlLsHkRzPsmqPMAfpsKr90GN7wOA08FXw2sfpJE0W4CA+pWwZZPoPikZj2nQqFQKHYtO78O6l+AKeH7LbCyCtykI5N0/jjNkAghWUuil1cQAdRGNzszk4yvAiITSjbZiBOxFAuXTfhxXRXMeDt+0vDAbloA3WmJECHgsLPhpolWeHSSpMz6lBRvA6Kqwmrwea0PQGNVcl8tJPITQWUZLPvdP87/XIGjj/U1cNdxsPwX2PQ+Sd1QOGDjW0kuTKFQKBQKxd/Fqmr4cSukkOTxKynCpYCsIzmZK4G66GZnVnIR7dKMr/PstFvYWBtNaPjgi+eiHU5N6yDi2J8zeDxv/xPgji+blTbB9ft3pHgb0LZvtRpCdZ63zhLjyZBI53nq4bcZ/nEh+lVK8DbC/06DX7+CLZ+BEUOLR6H7NyEVCoVCsTuxRzqdAmyshU7sl7R/o3V9sAKIEMmGFxsQa2zrkSQsOxJAS4GiI2P3tdoP9CQO4gsN9jox8ZhNK6wXdCI8QCNg6nDUxTD6HnhuLVz1qr0QqdgGv3xrfUo3k/34zdbSYn37CUrzRmH3lxdXXJlW35t3gmertdNoey8DGrcmuTCFQqFQKBR/JxtqoS290e1qXQMIifQGxYel8+yi2UPHRtBqRPIR7cXHx27P7wYpOfbzNQfsZROdU74FqrcnHuPF0nk+4PBz4dQ74anlcPMkSI9zZC9ATRXMnQ1zZlo674mbAP9RuEiak6XAVufF+479YVuv3GylqkjqpgY07txqVgqFQqHYcXbL43XJkuGEDPLpzAGs4pe4EU9CamAKCj2dm9p0x7kYvnuTuIuO7jgjurnD+bDgjsRThQ4dLwBnxIt+xXSY8RAsmQKatHHSCGun6oAxsbtLVsF3E2HDEpvn8CMBnHDRU8mN37gGnrgDvng3uMPl0NHsQs5tSgEnTSIbpmEdtTthP8uhZGtLRzqz2cJsqliFQCOHnhTQD7Fn+18VCoVCofh/R4YD3KTRi8EsYGrcHJ4CDZeRBkZQNGiO08F7HdaOWyIMNMd50c1tRmH+dm3iqUKH4uGQ1ja8fd3PMP0hWDDJSrhktxlnGtD/sth9ZRth1vtWgZhkkFhOpwseSy5ReNkWeOrf8PFr0NhgtWkChyahb3K33CESyS9pwqrfoaSC5ELoNaQrn1J+ppKlSCTZdKGQg9CSyc+lUCgUil3CHut0apUGffKsnw/jPKrYSilriXwpCTTcZjqyQQtzLAitM5p+JqbxNvFjfwWa40qEKAg2GQ2w9m1Y/jT4LEdHbP+KDjn9oPf/wptnPwUfXeUPeZbWrfU4RoR/vaPegMyIqmvV2+HhC+DHyVaSK6El5+jRdOgxwGaQn1VL4MxDrN2vsGTgBjLRfQxiJo6MvR6bPltfkASzF0mJEenj11ZL2cxdCP8CJQaptGQfxlLwl6grhUKhUCgUdmQ4YFCx9XN/TqGcDWxgUdQ4gYaTFAZsP491fBpsF3lojmswfeOIrxE0NP00NK1rsMn0wfoPYPlTCI91pCumvBI6pHeC/Z4Pb//tHXj7LH/FOsOaHNAysXSeNGHEk1DUM7yvoRaevAy+8acF0LTgYyTSYEJAmx7JOZw2b4AzDobSTeHJwKWMKrYXhgHJBJ8BiXWcsOkPUN8SNDeYdgnlTRa2KmMtt4XpPDd57M3VtOTQJBetUCgUip3JHhneIYBr9gbdv3oXqZzATfRjGGnkNI1zkUZfjuGwsiuJ5SXRHfcgxAGBq5Aeyxen6WfhcD4YbG7cBlMPhp/HQPnvCOl3xJiE6xlnLvS4GQ6bEX7Of/Vsy+EE4cfGDKJzLgG0GwBjpkKf08Lb66ph7CD4+VNrkmlaTqFYNiIxDTj+SptBWOfprz0FairDHU5+Evq2QhNaJsJObCQraNLbQssTEx6xk2hUZ6SxucDlvzaQ/miterbwMzdTxm9J3lChUCgUCsWOkEhHaAIu7gnpfh2g4+QYrmV/TiKT4EagAzd7cySncjfZvlZRdnTHjQgxJHAV0mPpPKEdg8P1UrDZVwszjoIfRsO2HxGmP0oqUuc5MqDzVXDEj+AuDLZvXQITzracTaE5igLzI3VRy95w9kcw4Irwdq8H/nUsTH/LckrJEJ1nhwROuCqJgcCt50JpSbjDyU9CnRdPt8YyksiQ0+5GflJyoe0FJBKNEo0Gt4v1xWn+66DOa2Q7c7mLEmYmcTOFQqFQ7Gx2y0inLCdsxBIdkXkKBXB8O7h+H+vah4f5TGUBX1NLOQAaDjqzP/sxnByKKTHDz3dLcxVmwy1I30QEBkKAKQwr/xLZaPrh6I7LEdpAhPC/DaWE2SOhcp7fSjA6SoQGSrUbDQe9Aro7+sG+fSy8OkcofhtSaJhSY+s531LUa2DsL2jSo7B+cfQ5+MBWXJyIJykE4sATYMCJse2GMncWrFgYuy901y4eHiDGVwBY0VauFEh3Q205kdtpEoHotS9s+COmwyuMlHTo1Bf0l+D7w6ByATT47aXS9D00up38cmAfawcwxgNJTOYxjsG8oY7aKRQKhUKxC8l2wWb/6ziyWrEmoH8R3OvfEzQxWMR05jGVKqy8jAKN9vRlP4ZTiJWPqYag1pNmCWbDbUjf2wg8CMDUDBBuEFkIrT8O5xUI7SiECHnn/zQGtn7rNxJ0xDTpPKFBiyPh0EngiBFJNDtB6oImnSeQUlB65jcU9h4ce+zUV2HBbKK8OrY6T0P0HABHXxB/HQFWLoafvonfb+cMasSv8wILCkHTQHdAfj5UbPYvLmLM3gfBmjmWQy0Rmg49D4bsYVC9EMq+Bf8pQFJo0qM+h86cg/pi6vEXPo9HKKI/elyBqlAoFIpdwW75X9epDvj8GDioMLy9VRo8cCBMPBocGnhpZDIP8hMfNjmcAEx8rOBnJnIPpawJsyGNpRg1+yN9kwDD2oSRoJug+7zovhocjrFo+qCgwwlg+89QOtM+d9CGiWDEqILi88CCj+JXl/MjpImGiZleHHuA4YNPnomfeDGyWXci/c9Rd9g5cOv7lhiIR3UlrFsFU963BEMsktnd8gGGC1q0s65FSM3cvQfASz/BhBVw2Tho2x1S0jEy8/DqDsoemApXPmHvcNJ0OHoMpGZAXQOUHA6fu2AKTR9zuQufdDB74AHUpSeqDCOpZwtlzLV5MIVCoVAoFDtCig7Tj4fBEcFJ+W64vR9MO87SggY+pvAEs3mLKkqbxklM1vI7H3EfG1kcZkMTJRi1ByJ9b4Lf4STw6zzDh+6rwOG4Ck0fGu5wql4J698jYbk1acLmadBQGrv/twRVhP0IKRFIpCM1/qCPn4jv9ImMmtKdSP9z1B84Au6eAs4ETpXaGkvnffFusLpdJMnoPAPwCGjtz5cqRHBjr/M+8Oy38NYyuPZp6NgbUjMwM3Lx6Q7K7v4Ebn0j2hEVieaAQ0ZCXjHU18Pmg+HL9KDO+xzkYgc+qTPrsAOoys5MaM5HrYp2UigUir+B3TLSCeDYdtZnZRVsqIFMl5XDSQ/RB9/xNqWsItabUWLipZHPeZQjGdvUatSfBlQTu6KJATRg1J2MnrEaEXpca/VrIBwgbRwhpsfKBdD5ovD2xurkkl37EY2VsTtKN0DFFps1BIwAp95AlTOD2uUeqs76L+nOONXyfp4JLz4EM7+wRJWGtd1oc4+EaaSufxRGXQpzp8OaRZYTq89A6Lx3cMyo660PsLWkBM/48fja94LiYjjxOvjo0di2NR2KO8MZd8Hm5fDfgVBTFn5ssR7EAh9aWwPDtP+nLtApZxGFHGA7VqFQKBQKxZ9nYDFMPQ7W1cCaakjVoU8+uEKk11wms475/qvIqGgTE8kUHudMxjW1Z7huALmZ+DpPYtadgsjchBAh0Upr3/TnWLLRakKzxu71r+i+hjjaLZaZeDqvsR7Wxok0DxBwCgnghEupTs2ndrVJ5UVPk5aaEXvOgrnwwoPw5YeWVkpW58k4QeIAY+6Ay++Ced/Bst+tgXsdBD33D44ZcZn1AbaUlNA4fjzervtZOu+8/8Krt8S2remQUwgXPgIVJXD/obBtbbjO8wBLTPS2JqbHCYn2FgnqvDYcnXigQqFQKHYqu63TKUDnLOsTST3VLOO7uJVMwBIk9VRRkrIAAIf2K5h/2NzRALke6fsC4QwpgVu/yd7hBJZjqn5TdHtKFmhOML32NgAZr8SuXRhy2FjgzP9QV7YNVoyPP+6d8fDvS60XvAzxJkkZ36MkCRc9gago0wB3Clz/EJx+udV2wJHWp7lc+DDkt4Z374Oa8mDCTU2HQ06By5+2SgDfd1C0w8mPkCaaKen92iJ+u7y3zQ1F0/l/hUKhUCgUu552GdYnEh9eFjCNRCE3EokXD0v5jiL2QQgTt2OGzR1NoArpfQfhCqkMXL8JyxNjpwO02DoPIC0Pasts5vtXkZoXu6O5Om/kjdT6HDA+gc77ciJc688PGtBKyeq8AA6HdW2aoOtwyR1w8b8sR1OfQ61Pczn1ZsjKh9f/ZW2oBnQeAvYdClc9DwWt4f6BsG1dXJ0nTEm/Z//gx1v3s72l0nkKhULx17PbO53isY4/MJN6cQg2plq7ZC79a6xHtnMeOZC+zyDU6eTMshJV2+2AScMaG4nuhD6nwh/vJQy9lkLHh450xtmuycyD1Eyor7Z5BgEtO0K8yKYA8+ZYDicpw4+zJZOsEiztdv1/Yf1Ky0bPfeH4syAjxnfQXISAk2+AE66EOZ9D2QYrh9N+x0C+PyZ/3hewZbmtqeJftrJ4ewMNeSlxx0h8ZNJpx9etUCgUCoVih9jEEjzUJzFSsoKfKGIfdN2HlAIh7DNcS99nEOp0cmaRdKbuWDoPYL9zYPbjMZ0jTbOFhoGGmdUmztJ0KGhjaR47MnIgtyWUJnB0rVsJ151urSn0OFuSOk8YwNV3w9YNlk7svBcMPxdy8pMzYMfQC2HIefDrV1Cy0sr52XeIpWEB1syF5bNtzeQvqyBrXTVV7eIfsZOYSucpFArF38Bu63QyTPhiA8wrt/ad+hfBYS2DIb6N1CGsU/E2liQerQ4QCFFPcmUyJBCRl6n1ibDmjeTmthoe3mQasORLSM1PKESs6SZeEVG2zeeFr16Dj56EVfP8IdE2yxDAiCSql7z2mBU5FCt/UhKleeuPPo20MXFCo3cWTjccfFLsvjnvx0/OHkHLuaWsOapt/NuQSUsO+bOrVCgUCoVCkSSmhG82wZwyS270y4OjWwfTKHgidVgCGqkBAnIlmWgliZS14U2tT4Ql42KODp/qs8aGtUlYPh00F8GSbbH1qZAmXhGxIWiaMP0dS+ct/tEfQW6zDk2HYZeCw6bU79vP+ivgxVhPEjqvYf/DSbn0DpvF7CC6Aw4YFrtvzgdJ6TxTExTP2ZrQ6aThoA1H7chKFQqFQvEn2C2dTg0GtHsPNtWBQ1jvRENC1yx48VAY1BJSyUrC4WRVOEkxsoBqDLMN9kIkMLFD+HXr4ZDaBhpK4kc7CQe0HAqZnYNtv78PE6+Fqk3BhI2xSshqDpAGlUMexPwxRAg11sPtx8Hv0/3JuLGiiwLzYwkFzQGtu8DQMTE6/UhpJZOc8n78hN0mlnaLrJSi62AY+HSdymsfsDtCv2up3Z6Uw0kKDWdt4qONvbgMDRvxplAoFAqFYofwmNDlfVhdA7qw5IVPQtt0eHoAnNAOUkmcFDqIII0cAEwpECIZnedAaB3DmwoOhpw+ULkwfjoF4YCcfSD/oGDb0qnw3qWwbZUVpYQELcaxNb/jpOrQWzH+CJHfhg/uGw3ffhAs9BJ6tC2WztMdkNMCTr4u/iNKCQ318MlbYMT5TuLpPE0H08DQNCrueI6W8e+y66krT5BUKoQkdF43zsNJnJxXCoVCodhl7JbV6yo9lsMJLBESKKe7sgqO/AJmlkB7+uDA5ugYViht2/q+ADT6TsR+6wjARHOdG9EmoM8DoKX4RUVktw7pHeCgl4NtP78Or46yHE5gRTmZ0vJ7hR7Z15zQZxRc9SP1+5wZbvfJq2Cev9JG6Dn/SP0gtGBepe4HwIMzIC2GYFu5BO68HHpnQJ8s8NrkmDIJ5N2EzGzIzoMBR7H9vtdpdKaA628uO5tVZAk5G4Qpyc46lGCYWOAj0HDRm2tVYkmFQqFQKP4CyhthrRWchCEtrQewoRZGTINJa6CY7qSSnYQ1SXd/lLJhODBlMs4qH5rz/Ojm3veAIzOOznOAuwAO+SDoBFn0OTx3DGxf7V+KYWm1gM5rchxp0PM4uHQ6tQdFRKG//h/49kPrZzNJndemOzw8C3KKote5YQ3cfwP0y4W902Hr5rjfgnVPgjovIxuycmHfQym/83kaXKnInZEuYUfILLSvcodViTor8xAEDoK7u5bOE+h05wI6MWoXL1ahUCgUsdgtI53iYQJIOG8WrDw1ld5iCL/xBXFDmNHIpogWjd2BWUjyEc6rkN5H484BDeE4E6H5z3ybPlj6KCx7LCJxZDB02tQz0bpcDL1uA7c/MWR9Jbx/aeKHkRoUdoOb5weFRElJcEz5Fpj6WrgICcUg+F7tMxA67A1HnAU9Doq5K+T+bir86yLLXiC6KWH5OYJjEPDVasjOBaCxpATmrbKZ+CfxNCLXrwOXC4paIBLtcPU/A2a+YG9T02ix/ziOxMF6vqCaVQg0cuhJG45WO18KhUKhUPyFxFI2AUlywWwoaaPTzzGM75kQ14ZAI4UMutCfMrYDgjrvNWS47k1wZx30waAf6L+phJXjYfGDUBuqa4I6T2opiE7nWxXrUv05JX0eeOtcv0MkhqYMRKWnF8Kd66xcRRCu8xrqYOLjsedD0PGkAT0PhE77wGGnQZ/BMXWec97PMPYsK0o+XnRTLAKP8O7P0LGbtbSSEliaIDn5juD1IjestyLnW7REaAn2wA8aDZ8k+vu0EKZBbv+7GUJLNvAVFSwFIJsutGEobnJ31uoVCoVC0Uz2KKcTWO/wNTXw4ner6Z/djfy2y9mWsxyksLY5oOnd7fKm03nVcazYuhKA5cuXU5Z7FsX5K8jJmIyUelMYduDn6rpBbCy9Gsl8kAbt1o8lq/obQIb5ZgLCyGs6+b7oQ7IdLWHZRmAjAPkL36bY25DYnyNN2LqEldMnUNeiLwDl5eVNazUXfkkr07CxYSUfL3cXsHHQJVaO9AULwoaUl5cjTJOc28cgDQMRK5FkgptITae6zwDWrtsAbIhaZ1lZcpVaAERdHZlffkbmV5+jV2zHl5tPzTHHUX3UsdSsX4ersZHcA3via2wAwNO6LeWnnEn5iFPBGePom8yjS1FPUkqXIRIked/c+RjK1m7xX+2Dk30AqAGWsDrp9cOff/a/w66y+c+0uavs7kk2NTR6Y1exMnly/Md3FArFrkUCFR54ZNZ6ji9oSctWfdic/0e4zgOQAt1w033VCJY0Lmv6XTJv0RC6d1xPfvZrETpPQwiT2oY+bNj6H0y5AKSkVcm95Je/j0RE6Dzp13kOviuaRLa7NazYBmwDIHvVl7Szq1QngZpS1n75LFUdhgDhv/M8636hvW1hGGvt1dLF2iOutBpi6DykSc71ZyAbGxChkfHJ6DwhaOjQnRU1jTB/ftQ6m6XzGhvJ+OYrMj+fjKNsK0ZWNjVHDqV62HCqtm7F6WkkZ8Be+GqtUDdvYQvKR46m/OTTkamxEza073AomWt/SKjztrUZwKYyD5StA3rgpAdgZWhdRlC/Jsue9L5TNv95NneV3T3JptJ5exZCyiRiVv9iOvTch1PPOCfhGKf04JZeQIIuEU7rTwCkQHoFeGMlTzJx6AaabqAJE02zMkOZhobP58Qw9aY5DuHBrXkSrqPBcCHRMSOO7bnNehxJ5o/yCFd0UknA6WvEZdocf/Pj03QaHalx+4XhI83TkJStWNS7UjD1HfNRaoZBSkPiSjSNLhcpnuB3HvjHaeoajS43Dp8PzV9O19A0fA4nIEkz68OdaSHUaSlIoSeXE0ChUPy/wMRE28knyOfOncvkyZN3qk2F4p9IMjrPIb2kSA+xdR5IrxZH50l03YeumWiagaZZcwzTr/OMoM7T8ZKiNyZcR4PhAjSMiH1al9mIk+Q0mhcnHi06HYHD8OA2EuvMAKbQqI9X2RjAMEj3JFPtLzYNTjeGXVJyG4RpkNLQEFePIQSNTmdsnadpNLpTcBg+NNMK5Tc1Da/DOi6Xatb79V809ZobUziUzlMo/kEonbdnsVtGOvmEk3Gtx8btdwiT81uWcVWbLWHtEonERItwAJWXlzN9+jeMOfs3WuR/jhUOpGPFLTuoqBnBlvKbkDLEaSNNui8/Fqe3JGGk0bgVYzmr90JKO/0vrL3dtGvJWjsdYZPsXAqdLfteRmnfi0PWOp3BgwfTZf7nFH/0cPyXd8CGplO+/wlsHHV3zP7y8nJ++vwTxr6foCpL4P+zIQ8rNR1Mk00X3cr2Y8LPwYeuMzc3ImS5sZG0Lz4j/d13cK5aCZqGt2dPMlctQXg9iBjHBXW/Bnzi4rFcMz56nRIrr6Xu0qxQdiHANDFTUtl4w11UHzyUvHnvk//7O7iqrfwF9YXdWddtBO9s0Bl8xBHR6/yTJHz23cyusvnPtLmr7O5JNr+c/iWv8MpOsQdQQAEd6LDT7CkU/2TsdJ6OyYiCCu7quDGsXSKRwkST0TmXystLmT17Gjdc/jCa8GDpPGuTqqruKDZvuwNThucn6rzqDFLrFyFiHvazGLdiLBfs9S0bu7wY1t7qu3vIWzoJES/puB9Tc7C9x6mUDLjVv87g77wOa36m3es3JpwPVjGUmu4DWHPJszH7y8vL+fGLT7nh/XHxNWscnSdMgy2nXcbWUeFOwIS/m71eUr+eSvo7E3AtWWw1delKxua1aPX1iBiVmm11nhBomkR3BhbqP9qoOyi57EbKh40kb8FH5P/+Nu7ytQA05rZnXfeTeLskfafqPNvnVzaVzb/Z5q6yuyfZVDpvz2K3dDrZ4ZMaR3QvonunDJYxm+V8Rz2VuMmgCwPoyiBSQqqelJSs44fvG2iR/ylCBIRF4IXoIzdzErk5pehpXyGEfyeqchEsKiEZijzf0bJ3RHjfugGwbmb8Snd+hDRo2eeIpvklJSVMnz6drl27UtzjCvjoIeLnn/LbMA3yTrqUvMg1+CnZuJGf7R4isiKew4kYOhLOvYbWffvTOtJm6DqLi4NmysqoPmYIxh9/WFVY/A6m9IV/IISMuQklhP3mlMD/VRqmNdbviNMbG2h3/y3wyiS44FHgUWisA00n1ekmq6QExo+PWueOEO/Zd0e7yuY/0+ausrsn2ZwyfQolJPc7PFmUGFEo/hpMNAZ2zqPnXums4AeW8i21bMdFKh05kB4cTnpIjh4pTWrLT+Lnn7qhN0WoB/SXJDt9GtmZm9DTv0MIvz5s3AYLw4+pxSO3cQ55vbqDHhKVXj4Qln5gO1czDQp6DaQgls7btw+8cyfYRCgJaZI5/CJ6x9N5JSX89LmM73CCaJ2naYjDh8E519Di4CNpEcNmrN/NsraW6hHH45s5o6maMUDaskVo2g7oPCktnaebYWOFz0vrJ++ndfsOcM791sdTD0LgdqaQsQt0HuxZ7ztl859nc1fZ3ZNsKp23Z7FbVq+zI9cFA9uv4h1u4AfeoozV1LKd7azjZ95jAtezkUVN41McE9B1I8ThFIkJxiykN2QXy1eT9HqEGUMs9L8wvNpcPDIKYa/jw9ukScbEcXD5/vY2NAe06Qb7HRV/fdWVNm6rwH2xRMkXi2F+PTw6Afr2T2amNV1KakaNxAjkGgiJaNLjOJzAEiLJHvKM+joCE/91TfB+7jRw/s1V9RQKhUKhUPwpnBqc1LWE97iZWbzMVlZSy3bK2chvfMQ73MBq5jSNl77JpDgSHYkwwFyI2fhgsKk5Og8JRoTW2/+scCdUPBxu2D+iMrGUpH/6NFy8j63DCU23qtQdFr/ymqipSu5oWUDnvTETFjTCc5Ph4CPt54VQc+EF+GbPsi5CkpU7tPhCrjk6L0aQlMV/boR6/3flSgVnSnIGFQqFQvG3s0c6nR48eBvT9AfxUEd0FJDEh4cveZhyNiKlJNX5UlJOF9PzBE0prlIjY3viY7gi94eA7Fawz8n2k4ePAz14hl7ftJw0bz0ZHz0GFVsTz9V0yCmA+z6zoooi+XkWnH88LY7Z28q9afclaBr0PgA69/DHQSeP9Hjwff8dvlnfxqyYkkgLJbMD1nSfmI0SNqyFWV8nZ0ShUCgUCsVuy1371zLb9V/qqPC3BN/+ViIFg695mi0sB8D0PImMceQuHBPpfRYp/XmY3AUgksthZGpp4MwMb0zJggPOs5887G5rrB9t+ybSvHVkvvdf2Lou8VzNAakZ8N8vwB0jb+f8X+HyUbQ4ei+EKe11HkCrdnDAoeBo3mEH6fXiW7IY7wfvxdV5uzSlUnUlfPbhLryBQqFQKHYVu6XTKc0BmgBdWBHAAmuhTg2eHgC9O03FSwMy7tvVEiTz+AKowqEtTxxy7J+DuQzkdv8iWkPRESDsnS/1xSE7WIYXptwD/2oNv31o7ShFLlNolqPp1GfhwJAz9F4PeQ+egUCGVx6JRXo2nHoDPPcbtO4S3f/+qzDqMJg5JXa1uliYJlx4Q+L7hiIlvPwyZT16sNXtpmLQwOQiqmKYSXYHLH6uAh3efgFGHQqHdYQT9oXnHkAr3/YnVqRQKBQKhWJXkeEEh7C0XZPO8+u+//SDYXt9Sx2VyLi5lizR8CsfWZfG7KYqdQmR28C0HFU40qHdKBD2zpf64tMt7QaWVpr+GNzZHmY/H0fn6dZn2D0wOCR3lZTkPXxucjrPnQYnXArP/w5d943un/YJnHgQTJkUnkPJTk+NuSH2RmUspIQJE9jWty9bXS7Ke/WKr7x3VlmieELP6YSP34Ezj7B03rG94bF/o23dtJNurFAoFIpdxW6Z0ynTCWtHwYtLYV65JUT6F8L5XSEvRfI6MxIIEQuJyQq+5xA5rJl3D0kIude/YPr0BPfQkEBd8ZlWBinDBy+cBAs/p+mtH4gwCqiqou4wYAwceD5kFIQbnD0Rx7aNEMOHFEZKBry1FjKyY/cvngc3jbEUQOhulIml8ALrCaDpVjzzJTfD8afZ3NzP9u246+sRd9/dlDVBBJ43hmAwzfi7YFImr39EvHGmAZ+/D27NfwZvDSz+g8In7kY/JYEjLdHCFAqFQqFQ7HTSHbDxdHh5GczxV9Dulw9jukFxGrzLdOy8JxKTDcynVm7HTeJk3uGEjO1xI6x7D3/myBj3sERHbesxpIMlWN4eAz+9GjooXOfltYeDzocBF1lR76H8MQPnuoVQfGziJWo6vLoUCtvE7i/ZAJedamm8UG9PPJ0n/Nro5HPhnCsT3ztAXR2u+nrEf/6DLyDSAl9TLJ0n/U7EOBow2cCquHrQ64Xpn0FqMF8oKxZT9Mz96KfdHN+g0nkKhULxt7NbOp0A2qTDXTE2drw0+o/V2WPio07opMhkM+XngsgPXrYYDAe9Cj9fYF2HJQXXkI5sGoxUTJffeTTzyXCHUygBUbJ5KXQ4ONrhBDD1DWRcr0oIDTXwx3Q45MTY/a895ffORDjmJFZezcgKJv36w5jrYWgSxwEDUy+9FK1/f9vKegF8Bjjj/GtLNtJJ0200g0540idpIjwNpHgacCybD4HkdfUVMPcFmPMsVKy2wtc7HA79r4ZuxytholAoFArFLqYoFW7pE7uvluSjlGvEdtxad6SxJInRLtA6BC9z+8ChE+G7U0D6wnWe0JHCRb2RgpHa0WqbOyHc4RRKQOeVrYXifaIdTgBfv2VVBrZDmvDTZ3D8JbH733oefL5o8RRP5/XcB86/Dk46O2mNI266Cb1TJ8tEjKrDkfgMcCc4rZiMzhNaEjovdC3+CK8UbyOuX2bBCf68V55a+O1V+PkpKFtqGW5zEBx0Fex1qiUoFQqFQvGXsds6neKhN3PJDpFCvffcJI596QjXpYhAmHXNalj2HKyZADIVdAcEQrdTW0GnMWxNH4a50F+5xDRh5uPYxjVrDpj5FHQ6JLpv+2b7cOsA5QnyPX3yrhV1FY/QW3z6C/TeL6lbehcvxvvbbxjr1iFmz4b+4UnGTQmOOD4zwwTdtHawYgkKwwQt1R397fkr4AlhoxE0Yh4WDTjFMt54Eg47GravglcOh+qNQQeV6YPV02HVNOh7Hox4KfnQK4VCoVAoFDsVHQcG3qTGOnChuS7HqL/adqRwnIEQ/vxKdSWw/HlY/QaYLtBTsBJgmuAugo7nUpoxHHPxx0ET0x8LRg3FQ+gw43Hoc1J0X0VpgkzZIWh64ryekyckthO6vFc+gyOSi/r3rVqF5+efMUtLYdIkuCE8Ulya8U8jmqbleHLE0WqGFE06LyxYyp9lXAhLasdFYDmdYjQDZL40znI61WyFVwdD6eLAqi1n4oafYP33MO8tOO0DK8G7QqFQKP4S9rj/stZw0IqeCNulC/JoSypZ1HvHgBQJkkzqIArQXH7BsvYD+Lg7LH4Y6tZbFU4aK6ChFgwBB74CPW9EOvOCJrYug+1r7R/A9MHCT2P3ZecnF+kEkBknektKqK1OzgYkOK8WxPPzz5Qeeihbe/Wi/Mwzqbn11piutUDEUrzdLI8PfFIgI1VFbi6O2++mfOqPeJ1OzLyQaLM++6Gffha602b3y02ChE+QMvML2LYV3hgKNSXRgjGwu/n7qzDr/gQ3UigUCoVCsStpR98kdB6kkUMurRHO8zBkpwTbfjqQhpZyu3VZ8jV83AUW3AM1q8BXDY2V0FhriZV9n4K978R0hxSKqSuHdXPsqwpLA1bMjF2VLiMnuSgb04SMBFH6VRX2NgIksYnmXbyYsqFD2dK5M+WjR1N19dUxxZzE2mCMp/O8PvCaWrTOS8/AcfVYyqf9hNfpwmwRUja9Ry/0c8bssM5zzZ8Dq5fBhBNh21KCoWeBxft13vLPYUoz8pcqFAqFYofZ4yKdAPbiaDax2GaUZG+ORiCQ5FPfkIoh2+EQq7HEhxH8U3RAT/scobWEsp9g9ul+URH5VjXBWwNfD4UTFhL29XmTO/JnjY1THvfw02HuVPv57lQ44JjYfUJATh4km0C7MEblvRAav/2WsqOOilmpJBaBXS4pY4gHXcds3xH3V1/DwvnI7dsQBYVohx2BcLuRJSV4XW62z55Hy8xMcDoRKf6SuIcMgntvhopycDj9+ap8lgBJIebuVyjCNODHN2D7CvuH+H4cHDxWleNVKBQKheJvoBdHsYIfbEYJejEEDR1EBpUNH2CaE/x9DqzcTQGdV4Se9ilC6wJVy2DG8WA0EqXzpGHpv5knwrC5QMgmmKcZOg8sreeKqDh32CjE1NeTm39ojEipAAUtktd5BYl1nnfePEoPPRRZF3y+RL4fw0bnGfmFpH09E7F+DXLLZkR2DtrhRyLS0/06z0X59F9omZUFuo5I9X9HRx0Dt10FWzdbOg9pHSHUNXCayf0Xy6+fwQabfzfShLnjYfBdkB4j1YVCoVAodjp7pNOpPfvSkyNZzNdxx3TkQLoxsOlaSo3y+m9pUfAH0jcBaZYiRAHCeTrCMQwRqFK34L/ESyhpYYJRB8uegZYhodw5bZpChG3Jbh3dtnohrF9uH+4rNDj+UkiLKN1bvh3efRnefgmqk4h00nQ44BBoESPngB/p9bJ91CjrpZ/Eef4APsPSCBAUJCIrG/eFF5Fy861oeXnQtl18A0IgMiOe7+yLYNQ58MVHsHyxJUj6D4TzjwKvJ7mFrfrCCnuXNg60hkpY8QX0TCD4FAqFQqFQ7BJa0IX9OJm5TIwzQtCKHuxD8NiYKVvQ0JBKRf175GZ9hjRLECIb4RyJcJyEEC5r4JLHrKjzBBWQkSYsehg6hkQ+pxeAIwV8DfYP4EqH1IhiL5tWwZJfMFMyY88JoOkw6BQoiNCKNdXwwevw1guwepX9GoSADl1hr75xh0gp2X7GGZbDKWRzMU6u8CZ8hhVApYmQcWlpuM89n9RbbkNr1Qq6dUu4NpGREd52wilw7Ikw9TNY9Ie1/n4Hwm0XQLIV6jbMsNJYmDaJ5U0fLPoADrg0ObsKhUKh2CF2W6fT4gr4ZB1Ue62k4qM6Qq7fHyMQHMI55FDMH3xGHeVN81LIpDfHsA/HNYVma2zG6Wgkz3kqog6E3gtS7gH9AEToNk3jdtjwCVEJuCORBqx4IdzplFkEvYbB4imJz9kLDQ4JSQxZuQ3uPRPmfAl6SLhOVOUR/0XfwTDmv+E2F/wOpw+xooACYd8BU/FUg2nAZbfEXyfQMHky5pYt0VOx/4dj+JeR8/576F26ovfoEYxa+rO43XBiRHW9w4+Fbz5LnMMK8LVsg8NZYe9wClAT/dwKhUKhUCh2DqurYeIaqPBAi1Q4taP1Z4B9OZEM8vmNj6kimN/ISSp7MYR9ObEpz6c0y0jTnyHF7SFTfwDN6AXuW8FxWLjOM32w8lUraXgipA/WvA3t7gy2Od1wwJnw02uJnRqaAwZcEDxGV1cDD46BGe+DpgWjg2J5doQGnfrAdeMjvqwVMOoIq2odWBucdjpPSrjytoRn1jzff49v4cLoqQnMBjBN65P1wnic++2P3rVrtCOpuTgccOwI6xNgyHB490VbnWemZ6LlStieRCVDzQE1m3dsrQqFQqFImt3S6VTeCL0mgi6sXRSfCdf8CFf2ggf2t5JVCwR7czS9GMJWllNPFW7SaUk3tJDHkg3PUui6CpfzOlzaj9apOuMX8LwMzpOQGW8hhF/l1G/G1uEUoHFbtOgYejss/pK4kVKaDqm5cPBF1nVdDVx7OKz1HxU0DOslH3oMPfDWL+4EJ11tRTk5XUGb28vg9COhqjI8z0C8srm6w3px//sxGJy4ZG/D1KmWAPBFv8Bt47kcDlyHH477lFPtRu4Y51wNUz+2HVZ3yhiy0n5IPhotJdt+jEKhUCgUimZT4YHO71uvZF1YG1XX/QTndYUn+0OKX8Z1YyBdOZRSVlHLdlyk0oJuOAjqINn4LtSeS6buQdduwKX9Cp554HkTHIcjMyYhtBxrsLcSjDgpDiIxPWje8vC2I8bCL29ZeitWbiehWUfzD7/WuvZ54dbjYP53gAQzgc4raA0jroQTr4LU9KDN+jprY3HLpnD9EsgSEU/nXXk7nHJuwkds/OqruDrPVg07HDh69CBlzIXhjr2dzVmXw9vP2Q6rG3E2GZm1yUU6SQNScnbO+hQKhUJhy26ZSNzjf9MZErym9T5tNOGRBXDut+HvXA2NlnSnIwfQil7hDqfGt6DucoQwIrZs/C8j78dQc3aw2dmMHRrNaR3VCrBuLvz2AXQ8xFJRYQm6hfVJz4erv4HMQqt58nOwZlHsyCgTv4NMwKib4bXlltMp1OEEVph1ZUV0zqVA2dyQhI/S6YLjToVJP8AF18R9NCklnt9/x1i5Mq6DJnC3mL26jt6pE9lvvRX3HjuNg4+AC8fG7ZZCYGg6tadcYJXJTcbhpLuhS2KHnEKhUCgUij9HoxFMSu01Lcnjk/DyMhg+zdpsDCAQFNGZjhxAa/YOdzh5v4La0YAHIWSI1vPrPN8sqDkB2RQFntasdUpHiPOnZBH89Cp0GmhpvEhHi9DAnQGXfQEFnay2b96BP75NrPNMDY6+AN5aC6NvCXc4AXz8DmxYGzu3ZqTO03WrUt1b0+DGexM+m3fRIryL4+dHDfwVxNN5WlER2R9/vGsdTgDde8MtD1k/x7iX1DRMTaPm/Ouh10h7hxNYX5hKoaBQKBR/Gbul0ykeEnh7FUz1H+324aGcjZSzER/heX2kNKDuZhuLJng/RPp+tS7T2kJ2L2yDioUD2ozwR82Y5L15Mjy0P8x8AlbPtuZL08rPlFEIbfrBqU/CHcuhVe/AAmHSU0lUQZHw1ZvxnSVvv5A435IJwq9Ttr43G558G/btH3d43ZtvsrVXL0r79aNx2jTbBOIRtUEQBQWk33YbeT/9hFZUlHDuTuOWB+GeZ6Flm/D2zGxqzr6aBleKlQOq92hIzfOXO45jS+jQ73xIzdnVq1YoFAqFQhGCiaXxJvhTFhn4KGcj29mAl8boCXW3kDgPpwG+2eD90rp0pEKLI8M3DWOiQeEhSEcWSEnu++fB/XvBNw/D8m+CpXp1l7WhWNwbhj8Ad62CzocGzUx80r56nGnC9PfAG+P5AN5+MXFZtxCdV/bCp/Dix3DokXGH10+ezNZ992XrXnvR8P77MaOcQonSednZpF13HXm//oqjU6eEc3caF42FR9+C9l3C21PTqDv5fOpdqcjUNOg6DHI6WicLEum8HiMgp/0uX7ZCoVAoLHbL43WJcAh4YUUFma0/ZQkz8flFiJMUejCYPhxHKlngnQZyYzIWofEFcDxrvdR7XAc/XZR4ivRB96sQDbWkynpc6+dY7ZG7K6ZhhVlfPiUY3RSgpgK2rE3qmSnbCFXbIKcwum9zkskVAb0scZ6iqrvuovo//7GpWRuOCeB2U/Tjj2jZ2Wht2yIcf/E/KyHgjEvhtIvg1++hdDNkZsOBg6jZXg7j/bkRFv0Gm/rAT9OthbuAdv5PCpYzqs1BMPThv3b9CoVCoVAoACutwvPLaunR5XMW8w2N1AKg46Ibh9KXE8ggH+n7HYzfkrCoQ+Nz4PJHMPe8BrbEL0RjYUKPa8DwkiLrca/wj4/UedKwnE+XTIb8DhF9Epb/llwhlvoa2LQSOvWO7tu0PrkobUDfWpKwv+aZZ6i84gp7R1gIgdUXzJqFXlyM3qYNwm1T9GZXMPwMOGE0/PEzbFpnRYQdOIiqquqgzlv2B2zdB6avtqLAnEAboD2QhuVwKugOI17869evUCgU/2D2qEgngOz0Ug464E4WMq3J4QTgpYEFfMlH/JsatoO5lOQezwfGouBllwugw5lxxvrt7fMfaDGItN/fQpMSES85temDik0w/dFkHu3PkZZuP8aPTI0fVt747beWwwmSFjcBMm++GWffvugdO/71DqdQdB0OGAjDToWBR4M7mLg8451H4aKB8POsoILyACuAbwFvIRx5H5z7NbiaF36vUCgUCoVi55DurmLoIf/hD/lZk8MJwMDDEmYwiTupYBMYy5K0aIARcoys9fHQ8wb/RVQmb+uPbpdDu1NIWfIpujQT6DwDGqpgyt1JruVPkJ586odEOs+7eDGVV15pXTSjIjFCkHbRRbgPPRRH585/j8MpZC30PcjSeYOHhX03aV+8DufsD9M/C+aA8AJrsHRedRYMvBUu/AHS8v/6tSsUCsU/mD3M6SS57MjHyUipQsZIcSgxqaWcb3gaK4wlWedJSFU1ocEhr8P+j0Fau/BhOb3g0Amwj1XNJO2Xl5NYsgHfPQeGN7w9IweK2sWcEkV+K8iK8YKsrIDe+9ruWEms3Ea+Tt3jjql54gkrmWSyBCrtXXghmf/+d/Lz/gZ0w0vm2/58ALGqn5ga/OSD3hdZkWkKhUKhUCj+Fs4bOJ7CzK1IEVvnNVLLlzxqpTpIFhGSD1MI2PchGPAyZHYNH5fRCQ56Hg54CoQgfe6r9rZNH8x5C+oqIu4poEvfiByfcUhJh1ado9vraqHnPknZkICnzwFx+2uffjq8SrIdgbEnnUTO008nP+9vQDd8ZD97i7VpGqnzLBEMP3uhxwWQkvW3rFGhUCj+yexRTqfORctpl78OXYu/QyMx2cJyyp1dSc7ppIHTf/a9cTssehg+39/6050PPa+Hwz6C4xfAcfOgw+nWWE89jop1yS28rhwqI47BCQEnX2kvJIQGJ0WEQldWwE0XQ79imDXNfsdKCLwOZ1znlJSShk8/tT3XH0Dv3h3OOouG1FS0u+9GNCNM+y9HSlw+DzJhPgQTqivhk1f+unUpFAqFQqEIoyhrM73bzrPVeVVsYZMjj+SyROjgHGr96K2GZc/ClP7wx79Bc0P3q2DgBzDsNxixHLpe3JRmwLF5QXILNzywNUbk1clX2efu1HQ4bgykhEQp1dfDf26APi3hk/dsbUhNw+dwQkpq3DH1H32UvM7r1AlGjrR03lNPIZzOpOb9XTgNL1JL4FCTErwe+OCZv25RCoVCoWhij8rp1K/DLximjq4lTm4t0Fill7Of43CrcgmJxuvgvgC2zobpx4G3hqbzV3Xrofx3WPEyHPF5eK6jZHauIlYVhs8HLTtBbhGUb40tKHSHtfN14hXBtsoKOPEQWLnUNsm3dVtB46CjLadTHKTHA41xEljGIP+DDyjLz0cGztDvBKSUNP7+O8Lno37GDIzhw9FcLio+/piqKVMwGxpwd+xI/vnn425G4krHmsX+I5A2DkhpwqevwNnxK+EpFAqFQqHYdfRrPxfD1BI6ncCv87RVtHKNAs97NFWri4kJ7kuhfB58fRQ0lPrbJbAeKhfCihdh0IeQ1zfiRs2ozBY51jQhpwUUd4LNa+LrvJxCOOPWYFtDA5xxNPzyfXLH4DQN7z7744msbhyCNAxkdXVyzwHkjB9Pea9eO1XnAXgWLbJ03tdf4zv+ePTMTKqmTKFy8mSMmhpcbdqQd+65pPbqlbRNrXwrumkgYlUIDMU0rM3Fax7awadQKBQKRXPZo5xOac46kopeMqFk+waWbR1Lp6Jf0URt1BApBUJINpTfSd2qn+i69BSE9CCiju1JpLcKc+oQlnf7AK87WCGtU1aHpNbtTc1nyfrtsLESgNzvJtJi8lM4q8qQ+P1XGkgEMiRqqKZjX9Zd8BDG6mBEVaun7iVvxVL7lyvgzS+k7MSzWD74ePh2FsuXL6esrCw4oK4O16uv4nz3XVzY1uwDrN20peXlbN++HSDa5p/AN2UK3meeQa5Zg3PsWErPOIMylwtd0xANDVaIt5QgBJvvuw8xfDj6HXcgXPEFVhMLf09+HVs2sHj+fISoJTtzMtnZ7+JyrkdKJ7V1B1FRNZr6+oMoL68Ads6zh1JeXr7T7Sqb/0ybu8runmRTQ6M3MZLy/klyyNlpthQKRWzSXHXWUSgbpJRsrSxhycYL6VT4DU69FCGMiDGWH2hz5bVUrNlI16UnoRtViCgdaSKNBuSMEazs8iYNaT2betrk9rTyAtlgOlJYvM3ErJoPQNavX1H84cO4tpdYd9OChXNDI3LqW3Vl7ZhH8G4qhU2WM6zozWcpmvM9wi5CCvBl5bDt+NNYNnQkfPd99O9Rrxfnm2/ievttnFVVCJLTeivr6ti2fDmwc343G7Nn43nySeTixZbOO/tsSh0OHC4Xoq4uTOdtefBBxODB6Pffj0i3z1vqWTg3+YVUbmP+vHkIzUNmxmfkZL+Dy7kS0Khv2JeKytHU1g0CtD3qfads/vNs7iq7e5JNpfP2LISUzcwa/RfQsWdvTjnj3Kj2FEc9LqcnKRumRwOvhhAmblcjXq9OSorHeuMLME2Bx+vGMBy4RANOzT7k2Gs68chgAkWH9CAlpMjEa/IIJ17Nmuf0eXAZ8cdLoN6ZAojoUGEpSa+PdqDFwqc7aHS54+/SSYmrvh4tZBfNTohIwNR1PKnxw7eTQkqEaYKUaIaB7g0qOq/LhdNj/3ds6jq+QDJLIeI+p+bf/Urx2duUQlDvTiUltR4hYv/fwudz4vEk66JTKBR/FyYm2k4+QT537lwmT568U20qFP9E4uk8t6MBtzO5yGvpFUiPDlg6z6EbNDS6SHF7QIA0BR6vC5/hxCkacWn23iOf6aBRBnM76tIH0rTVeV7hxOPXeQ7Di9uX+BnqnCkIBKbQwvWLlKQ11NlHZwOGrtPgSkms8xoa0EKi4pPSeZqGJ20HC6pICaaJ2AGdJzUNb4r/7yKBzhOmiWb6ktR5UOdOIyWlAS1ONJ3P58DjcaN0nkKxe6N03p7FbhnplJ3ioPVhpzK9PIt6U6PI5WV4fjkd8jbyR7dkkndD36UXk+LNBixv6IwZX3PCUB/p6S48vo7UevYHBML00mvBIWiywdasoaWxaO/ZTckrK8q28P2MqdxQ8UjMHSkpdBqz27NyxJuYrgxcW9bQ7a4RMXbZgpiazoODbuTgo48lNzc3rC9jziw63nFFnJnhNLTpwPIXJzc9//Tp0xk8eHCTTfe//43z448t54+fwD+GWK9ZKQRoGlWvv47Ru3dMm3ZIKfFOmEDjq68iN28GQI+43zdjx3LUuHEJd+MEVjKypn6nE044Ac47D9GhQ9jY8m1l/PTVF9wwbVxC+SA1jdJjzyXlmhm4nOuidkxDWbv+Mia806JZz54Mf+Y7VTaVzb/S7p5k88vpX/IKOy9PWwEFdKDDTrOnUPyTyUlx0HnwSL7ank21oZPn8HF8fgW9ckr5teezEGfjJ5S9V5xFRn1x03VtzVK+/vp3hh9r4nR3paaxP5bKgB4Lj8Dps99hl2gs2utbTIeVbLp8exnff/MlN5Q/HFO7SaHjS81nxcnv4EsrQK/eTo9bhyAMX1zNYQqNhw4by4Cjj4v6nZeyfBFdrzrddp0AvowsFn8w21pnjN+jriefxPXii2EOrIQ6z/9nzdNP4x048E//bvZMnkzj+PHIdVaUfmD7NHDPP6XzdB2OPhrOPx/Ro0fY2PLt2/nxy8+54euHEzrrpKZTPuB4xA0bSE35Pa7Ok1JQXnk2y1eM2WPed8rmP8/mrrK7J9lUOm/PYrd0OumaxrWD2nNtWGsB0JVyfmU982JWrwMQCDqKAzmgx6FNbSUlJUyfPp2swmspLi4On1BXAvPtHU4AullH725tIaXAb7eAb2bOorHzEaSs+NqKndY06wy+NBA9jiLlnDfYK8Maz4xX/P3xHRqaaeAwfXQvyqVFj4iQwQ0rklonQIo06d27t3+d1vN37dqV4uJijG3bWP/pp1G5AgwscRB4ZYe+7IWuk/fOO7Q56aSYNuPhKSuj/Kuv8FVXUz5pEg1ffhl81jhzEvmsw0RIAK8XJk9GTJlCpy+/JP3Q8L/72V9P88+KI0aEQGg6+WP643PZ//Jq2/od4GrbZ28uyX6nyqay+XfZ3ZNsTpk+hRJKdoq9AEqMKBQ7B03TuPTQjlwa1poPdKaORSxhJvHe2QKNFnRlQJejw9pLSgrwGQvJLLgi/HeJacAfyR3pEJjs1TEHcvby2yxh+vSZ1Pc6kbTFk601af6jYKYP0aE/zvMn0DO3rWXgzf/Z5mHSpIlumvRIg6LeETqvIfm8Sw6fN67OMxsaWP/ee0QeZvBhCf+YOk9Kcp5/njYXXhjTZjx8lZVs//JLfBUVVE6bRtX77zdFJQXsR+q2RA4nf8aJ8L99w4CpU2HaNNpPnEjW8cc3dZWUlPDt11Nto8OENMm5+CS8qeclHickeTnv0q3rrUyfzh7xvlM2/3k2d5XdPcmm0nl7Frul0ykRg7mUz3mIUlYS7kiwfm5BNwZhvTClby40PEWR6wPSUy+l0LUfsu5iSLkUobW0pjmaeVQsML5sNVmf/4d0s5CUJdOstqxcaN0Huh4G+46CFt3D5/78VUKHUyjOJXMg0unUtmNya9R06NAlbnf9lCmWoyYCiSVIQl/4IjWVzOuvJ+3ii3G0a5fc/bFEyIrrrmPLm28ivV4E0f/Yks0tECDgcIo5x+dDGgYrDz+c9MJC9IwMMk46CfPkk/HqThoOOZ7U7z4hyvmk+2Ot7puAUfgmmDqJE8+DppWj68n9PSoUCoVCoUieAZxFLdtZzzwEWsgmo/X+zqU1R3E1ANJYBA1PgecdWriqSEu5ngz9XqRxE0Jv75+mgXCCTCI5E4Du13mVJWROvZs0M4O0eZOstvQcaLMPdB4I/U6BNn3D58792r5anR/3wh9g/0HhjW3aWw4bu+N1QiTUhI2zZyMrK2P2BXRek0PI6ST9yitJv/RSnN26JbV2AKOhgVW33ELJ889jNjSE6zz/+mNuFMZpC7THc1QFKu+tGT6ctKIi9PR00o89FvPUUzE0B7XHnU/6Z68EN38DaLp1fcuzGF1+BJ+DxInnATw4nZ/ZjFEoFApFsuzGte5j4yKNE7iNQVxIPm0R/v8V0IHDuJjjuBknbmTDo1C1P3jeRBM1IEAXJdBwD1T2RPrm+A3mQP4B2H8VGhQeAo50WDsX7utH2q9vhg+p2gaLv4EV30N+DDHgTS4fFYAwYoijvfpAz33sq6mYBpx1SfzuysqENkys17EPEL17k3Xvvc1zOFVX89ugQWx+/XWk37kVtWv1J7B1UkkJhoFn82a8K1ZQ/sgj1B1yCLrPR8WNz8LNz0C7riEGBRxyHLwwC44YCXI9dg6n4Fp2u1RoCoVCoVDs8ThwcTTXcQSXU0gnhN91kUsrDuU8RnAnKWQiG9+Ayn2g8QWQ5QhhIDRJuv68pfO8Uy2DQkCrY5pSI8RHQEZnyOgIW5bBff1I//GFcN1RWwHLZsOiqVDQOdqEN/lKwMTSecWtYdDR/g0xG865NG6XWVWVcKqJpXZ8gNmyJTmPPNIsh5Pp8TD/2GPZ+OSTmA3WaYGdofOS+o8SKfFs2YJv1Soqn3+eusMPR/d6qbr4XvjP69B57/Dx+w+Gp6fCyZeA3IS9wwlAR9O2NP8BFAqFQhGTPS7SCUDHSXcG0Z1BSP8rToTIAun5GOqu919FvlxMkNVQfTQyexlCK4Qe18B3Z9nc1YQeV4OnDp46FhprEDLSQeF/3S7+Cj6+HUZGlGXt2AtKVoNh/8LztYoQM1LCdzMgKy/xDpjugK494JgTY3Yb5eU0zpljv4sGoOs42rSxHxfB2vvuo3bhQisc2k88IeHP627b1pyoqED4eOD+zsZGfNOnw1mXwchLYeNqaKiFgmLIKQi5SY6/ao79d6NcTgqFQqFQ7Bo0dDrTn870j63zvN9B7bnEehtbuXoaoHo4Mns+Qu9i6beNn9jfuMfVVqTSM8dDbVkMnYfVv3YuTLgMzo/YfOzQC+Z/n5zOK46h8+b+BLozTD9FoevQohWcGp2IHcCsqaHxp59s7w+AEH9K5218+mkqZs4M05KxdFrUMb6Q9uZEQEViAE5oin5yejx4338frrkGjj0LNq+DmkrIawH5LUJmZmElkrDbYDSRMgOl9hQKhWLnsMdFOkUSiHQKo/5eEj+aAbIKGl+0LjuMho5nJ75R5/Oh3anwyztQUwqxhEgAacK3z0JDTXj7iEtthYgUVjUTX4e9go3l22H4QBh5BPw423pXSsLfhYFdsW494e2vwOWKsm1MmsSGVq2offXVhGsITjDIODe2qIk7paGBTc89l1gw+YkXgB75aDsDz7hx1g9CQJtO0KV3uMMJ0PWTk7IlpRPDSGIXUqFQKBQKxQ4RU+c1/I9giupY+BMGNDxlXRYPgZ43JLwLrY+DbpfDwimwdXnidAjSgDnvQGVEPpETLrLXeVhaz9N7YLCxthbOHg7HDYCvp8TWeZpf17ZqC+9Ph8ysKNvmjBmsb9WKqgcfTLiG4GIkGeefn9zYwBTTZOMTT0S1x0tOHqvdJFrnNSfdQiyN6HnwQaRpWjqvuD103SfC4QSafiLJRbRLvN6j7YcpFAqFIin2eKdTJNJYDsYvxHdpBDCh8SXrR6HBwa9CvwchJfwFRWox7Psw9H/RepHNeccab4enFhZNCW878GjoO8g6Xx4LYe0TeRwhDiOfD0YfC3N/tK4Nn/W2DRUkEui9H4z/AL6YCy2ik7RpPh++K69ENiYZ+u1w4OzZk9TjjktuPFbS8JU334wRI49ALIEg4/QZcdqTJabA+eMPGhctSjhPc5wFZJL4/xY6jZ5RNmMUCoVCoVDsCqRZDt7PsD8m5YPGV4LJtPd9CA58DtIi0gW4cqH3nXDYRNAcMPc9609bTPhtYnhT931h8KkJdaIAGnVXcLNQSrhoFHz9hXUdqvMC3hkJdO0Fj78OMxZDh+ijfcIw8J5zDrK2Nom1A7qOXlxM+hlnJDceK1/nmnvvpWHNmqiI+Xg6L9ZGohnSn2h+PGI6uDZton7mzITzNH0E0JrEDksdoR2HKds3Y0UKhUKhSMQeebwOwMTEQy0aOi7SQjqakcXe3Bz8WWiw143Q8zoo/Q4at4G7AAoPDhcfNaVJJ4mkrjz8umo7HDLcCvvdvMaf3NDvYhECnG4qrnwCY/6m4JwpH8NvP8dZv/9PTQdnChw3MuYwKSXOgLMpmeSUgKNdO1pMmYJIIq+AlJJ199/PmrvuQsaJcDKJnVAyUDEPwgWHT9PQTdMa77C+f+mLX4I4lHgr9q1di7tXr7jzhMjC6f4Ib+MwwEv0bpiGEP2oa7gLeCuJlSgUCoVCofgzSEw81AECF2nBaCe5FfuNxQBVQCOQYumbbpdA14ug9Ado2ALObCg6FHR3cEpdOZhJ5P0ROtRtD2+rqYR9B8OqBbB2cbjO0zQQGpVj7sNYXR+c8/N38PXnse8RcD4JAQ0eGHlW3JyczsZGf1W9JL4bTUMrKKDFtGlo6en244GNzz3Hyuuvx6yvj9m/QzpP1xFCIP3H5ey0XiKdlwghHDhTPsHbcDhQR7TjUgfRCaf75Rh9CoVCofiz7HFOpzoqWMSXLOFrPFi7Obm0pRdD6coghIgON46LyLT+LJ8Pi5+GNe+BtwZSi6DLedD90ujdrqyWloMqGcdTRqH1p9cDT14Pk8dbO1iabokG04CMHOi8Dww6EY49j4baBpg/Pmjj1Wet3bBEx9VMA374FlavgI7RVevk3LlWKdkk8jiJzExy772XjPPOQ8vMtH9GYO2997LmzjsTjgmIkVih1paeslKFSiFwdO1K0ahRFF50EcbmzVR+8QWyvh7P8uVUT5pku554YkSkpcXpCaLpg3Gm/IjhvRfTmETQ8VSA7rgc3XkTlohVKBQKhUKxs2mklsV8xWKmUo8VOZ1JEb0YSneOQG+OzsMJuKFqBSx5Bla+CZ4KcOdCpzOhx+XhDieAjAJL+9k5nkwDMov8P5vw0r9hwjgrmbjusLSiaUBaJnTcGw4+Do4fQ51HwvgQnff689b4RMfypISVy2DO93DgIdHda9agmWZSqQ1ESgo5d91FxoUXoufn244H2DR+PMsvuyzhGFud5/9IIXB07kzBCSdQdNllyPp6KidPxqypwbdxI5Vv2W/qxfuPF5FqX5Fa0/rhTPnFr/MmYG0yAmShOy5Gd96GELmwk0uxKxQKxT+Z3dbptKAcFlaAQ4P+hdA6DSrYxOfcQyM1ISV0oZwNfMeLrGEOQ/Rr0ERbfyWyROjgOg2WvwzfXeR3JPlf+HUbYf4DsOgxGPI5tAwpaXvQWdHH5mKRkgW9hlpC5M5RMPuToKMqVFjU10DpBjjmHMjKhdqIl9yyxUmJCABWLI3tdFqxIrn5gLNzZ7Kuuirp8b6tW1l7991JjQ3sdkUlltQ0RFoaRa+9hnf+fFrNnElxsf+IYPv2pB90EGDlEdhw4YWUv/JKXEecm/g7ZFsGD0bPyyPj7LPJvOIKnF2ivytrOX3Q3O8jZSnSXAPCjRA9ECJw7FE5nRQKhUKh2BGWV8Hv/kCh/fKhUybUso3PuIdaypoSiANUU8pPvMkqvucY7VYc+v5g/EriiCcHOE9CrPsYZo6yNFggH2fDVlj8BCx5Gg5/H9oND0474Az44RX7B9B06Huy5RB6+DL4+AWaFI4vpDJdYz1sXAFDz4b8llASofOWLkwq8Thg6bwYTidz5crk5gNaXh7ZN9+c9Hizro5VNyTKhxUkUMglSufpOug6hc8+i3f9elrNmhXUeUBanz6AFTm/uW1bSh94wIpy90V/Ly7i67yy0aPZfsklpJ92GllXXYVr771jjtO0rmju15DyMaS5EoTu13n2TiuFQqFQNJ/dMjHNtkbo/QmcPgtOmQntPoCTZ/j43PxflMPJwnq9bWQev4j3IfV67INzJVT0g+8uxKpoF/Fikwb46mHaMKhZF2zvdwrktI6flwmsex9xDbhSYdbH1ideZJThg81r4fX7Y/e7nDbPETo2Onk40HQ8LSmczbgfUP7yy1bixiTwp/XE1DSEy4WjsJDUXr1of9997LdyJakDBiScLzSNNi+9RPuJE0kfODAsxNwBpBD/H7QGCCkxt22j6skn2bj33tR9+mni+4lCNP0ANG2fEIeTQqFQKBSKHWG7B7p9BKO+tT6dJ8HQqZJPfI9Qy7Ywh5OFlRmojNV8x0uQcj32R+x8UD8MZpxqRS1FFoCRBphemHGKFfEeoPsR0Kp34rxOQoMB50NmISz4AT4eT9ysRIYPqsvh+Vtj97tTbJ4jhDgaLZlUCE00RxMCFW+/jVFTYz/Qjw8whUA4HDgKCkjp2pU2t93GfitXkn7ssQnnCiEo/u9/6fjll2QOHRpMno6l89wkiGbH0nmyspKal19mU58+1Lzxhs39ctH0/dG0fsrhpFAoFLuQ3dLp5IvQESawQcylQSuL4XAKRbKEaXjdY8B5KrELuOpWW/rLiAVv2iQFN8FogKXPBpucbrj6K0jLQ0bODVzveyoM8x83+/BJGwcVVvj1Jy9au2GRHHZUcgLBnQJ9D4jZpR14oP18AF0n9cgjbYdJKamcPBm9ro6yJ55I2ukUcBKl9OzJ/qtWcdDWrey7cCFtbrkFV1FRkiYE2SedROfp0+nt8bB3bS1tn3oKp78vHmF/U4YBHg9bR47Es3hx+EDThNpKKzxeoVAoFArFTscbI4B7rbmUOseahDpPYrKKH6lzHQXuK/2t4VpMSv916jjE0m+aZsaziJSw8JFgk6bBFZ9Cbpv4Oq/bYBj1uPXzpGes43GJMHzwzXtQXhrdN/BIe50IlobqPyh2V79+9vMBHA5SktB5ANVff41eV8eWO+9MPsm3X4c527Wj38KFHFRayn7LltH+7rtxt2mTrBUyjz6ajp9+2qTzOrzzDk5NQ9Pia/awb9DnA9Ok7Nxzafj++/CBUkJdVWzNrVAoFIpdwm7pdIrFAR2+xzTt00gbeFkv5kHGBEh7ErRO4QMcgyHzG4QxFEqmRe98RSIN6wheKMW94F/zqDn06vAXcYcD4YK3YcyEoABZ8EPisrsB6qpg3bLo9vOviBleHIauw6lnQXZO+NK9XqreeIP6Sy+1ru3WICWZl1xiM0Sy7Prr2XDppVb+gGaQ1qMHe0+ezAF//IG7detmzY2FcDjQ0tLIveIKiidNihlGHfgHHvUvx59sszpQ9rd0HbwyFs7MgzNy4JQUuP1w+GFSUrmwFAqFQqFQ/Hn2b/8DhmnvfBHAajEH0p6A9NdA6xnW75X7QsZkhOtKWD0hOpI9EumDVW+D4Qm25bWDW3+l+vCbkKEKotXecOYLcNUXVjQ7wO/fJnc8zvDBsl+j28++GFuFpjvgyGHQNryimjRNqj/8kPoLLrCu7dbg85F15ZV2o1jzwAOsO/vsZus8d7t29Hr3XQ5atoy0bt2aNTcWQtfR0tLIOu00Wn/1Fe4Ym6hxdR6ArlP54IPWzxVb4a074ZwiGJ0No9LghgNhxpvJp7FQKBQKxZ9it83pFElmShWalsx//AsaqEYIDVKuQLovp3TLLOrrp7G18Vda5Pl3g6r/SP7mDaWW40EI2LgAZj0Ha37G7fXgE8Mou+BTCvoeaVWQi6Q5DotI59Sq5TBpgpWnaXWcvEy6Dq3bwa3hx/PM2lo2HXecVT5W02DIEGs5xD94mPvQQzjaJy4Ru/ntt1n/2GNN14nsRdLl0UfJGzo0ydHNI+PEE0kfMQLPvHl4li6ldPRoRKAqSjx8Pmpee428a89D3DUUGmrC/w4WzYYFM+Hoi+Hy5+JWjFEoFAqFQrFjZKRUI5KoSifQaKDKim52n4N0nQ3mSspKV1BX9wPbvZMpdhVD3Wbr+FwymB4rwXhqEZSuhG+fgxXfktLYgE8MY9s5H5K/37FBR1PY3GY4ZiLHblwPb70I3XrBkgWx5+g65OTCA0+HNUuvl5LTT6d24kRrzCFWrqdEuizrpptw77tvwiVumzqVFbcGjwJKkt+h7nj33RSNGpXk6OaRduSRtDvySDxLluBZvpzSUaOgocFW59V/8gnGwp/RHxwOVWXhOm/lXHj0bPhhItz4Ljial2JCoVAoFMmxx0Q61TRkYSQR6QQSNxlNV0IIDNkVU+qYtAwOcyZXmQ0AR5rlPHr3Kri3N8x6HtbOwbXpD5zSS8HLx8OkW2ILj4572RzhC6zHDa07+x9BknX/rTCgGzz7cAyHk/970DQ4ZgR8/iMUFIaN2HLhhdTPmmVd+NflI7gLFuoK01q0IP/ll8m+/vqES5RSsvbBB8PO2CflUtM03O3bk3vUUcmM/tMIIXD36UPKAQeg2Tmc/Mj6euQdx0Y7nCB4/dV4+OTxnb5ehUKhUCgUFrWNmZhJyFKJGaXzhN4Fn+yDDJ3vzIgxOwGOdPjsbrizK3zzKKz5GVfJPJzSR/7rI+HNi8DniZ7Xta/l9LFFWJoQQEoynnkQ9msPj90X3+EkBAwcAlN+jopyKrv5ZmoDFX39kToBnRf4BNByc8l95BFyH3jAdpVrH3kk7Hli2Yu1Tj07m8Jd5HAKxdWjB2lDhiDsHE4BTBPzrpOiHU4QzLf600fwduIqzAqFQqH48+wxTqef1/ZHTyLSScdJW/pZx+K8v4FnJjqrowdmdITsXtjG6QgHtB8Jk2+HGf5dplhldKc/Dp/EeGGNvDJ+EvGmRTvg6DMhIxsAl6eRtPdft/pihvxKOOcSmLsWXv4QCsPzIXnXrKHm3XdjOsEMrOKwpv/nvGeeoe2GDWSef37iNQINa9ZQM29elN3AVcy/HU0DIej+4ouIBGfxdyZaVjPKKQuBaCi3PwI58UEVfq1QKBQKxS7i5zX9cWj271kJdOQgazPQNx88M8G3mCgV4syA4iNB2DiEhA6tjoJZL8Kn/7bsxNIEv0yACZdHt598ub0+0HTofyy0bGctzeshc/xj1jPEm3vi6fDjcnh3CrTrENZllJdT+cwzMaPpDfzJvP0/Z919N21LSsi+7rqEuS8BfDU1bP/yy6g1JdR5AFLS/fnn0VOakRR9BxBud7MSomtVmxLrPCnh0yehoXYnrE6hUCgUkewxTqdf1x3Attp8DDPRkgXd5WG4656F7e2hYl+oPJwixyGkuutwi6khQwXsfQO2sTrSgHZnwdRx9mOnPgg128LbhoyG7vvFTxKp65CaCef+y7pcuwqnz4ewO5b31SfQojhmV/Xbb4dFI8XCBEyHA8+GDYgkXtzbvvqKeaedFrNPYgmb0OvA6t2tWrHPF1+Q5z/e91eg5+fjPvRQ+51Hh4O0Ttm2IgyA8hJY8t3OWaBCoVAoFIowlm3pwbrt7RLqPIFGB3kAGfUfwvauUL4PVB4O5b0o0Ieg6xHH6Xpdl1zuzq6Xx944DBtnwvcvWcfvQjnoGDjg6Pi6S9PA4YJLrDQIonwbLl8Sx/6mfgqFLWN21UyciPTEiLoKIaDzvGvXWk4aGyq+/54/TjoppiNLEl4rMFTnOQsL6fXeexTF0Yi7AqFppJ10kr3jSdNwt8lFT0viP3caa2Hu5ztngQqFQqEIY49xOhmmg0en3US9Jx0hI5dtOQ1ayZ4cVPUx1N4E5sawEZpmkqefC/UhZ+K7nA9dLwizETTpd1gc9Dgsn28frQTWLspPEeVZ3SnwyJewz6HWte6wHF6BROOFbeGZb6FVRwDSJr6VXJKkzZtg+pcxu4wtW2ydTk1jN2+2HbPuySf5behQqufOjTsm4Hgy/D+L1FR6f/op/desIW8XH6uLRdb119vvPPp8ZHVzkuQhQaiMUXVGoVAoFArFTkDw5PQbqKjLhSidZ/XnyXYcVjMPai4Fc1VYr4OlpLgaydAeCja2PQ72uc1/EWnTf73Pv6C0Fhqq7Jeo6fDdixFtGtz3IRwy3LrWHVZahYDOyy6Ax6dB1z4ApH3yfnKyo7YGPn43ZpexZUtyR/p8PnwlJbbDNk+YwC8DB1L+zTdxx0TqPKlp9JwwgQEbN1J06qn2a9nJZF1zjX2hHdMkq29+knm3hNJ5CoVCsYvYY5xOACWVbTDW/Ze9xTBcpDW1Z1PMwZzP0fUuhGcyCd/mNVeBb571sxBw8Itw8HjIiqiyUXQwDPkcel4FW5cnV85W06E0JP/SxpXw+LVwakf4faYlQlq0hz6D4Jhz4MFP4d0V0ClYdc2xYmlyYkTXYenC2MvIyko6gbmWnZ2wv/LHH1l29dXWRRIvbQmYuk6ryy8n/7jjEEnlOdj5pJ90Elk33RS70++Qy33gAVK6J1/Cl4y8nbAyhUKhsEeIPgjRPexz9tln/93LUih2KeW1BWxfcR99xYmkEMy9mUEBB3IGxzd2Q28IVBQO1zlCWBolU3sUPNOCHfveB4e9A3l9wm+W3xcOexf2vcfSeXoSSaSlaY0NULoRnrsVTmkPsz4CBBS2gd4Hw1Fnwt3vwaQN1rUfx8qlyW0uOp2JdV4yR/51HT0nJ+GQuuXLWXjOOZbGS1LnSYeDotGjaXH66WjOvyf5dsohh5D78MOxO/06L/Paa0nbv2tyuVWRkJG78xaoUCgUCfin6bzdtnqdQ4BPhv98e2+4tmsugtHsxygaqEbHgYt06zha/QXYe2x0K9op83nrUgjodhF0vRCqlvurl7SEjHYhi3Elt2gZMvaXr+GmE8DwBkvpShO2rIVNK+HAo+Hg46JtJPvyljK4ixZBxsiRbL/3XnsbPh8ZI0cmHLLu8ccRDgfSbjcpgBBoTidtLo+R9+AvJveBB6jr0AFzzZqwdnf//mTffDNpw4fDJB1W/2EfyZZVCL0O3XWLVSgUijC8wJiwlsrK2X/PUhSKXUAsnXdRV7h3n0x0RtKXk2ikGoGGmwwEAur3x9ovjf/OllJH1D8OrpBj/R1Psz7Vq6ChDFIKILNTyGLcyUW0C80aC7BkLlw7BOqqQ/IFSdi6Hjavge77wuBToirfymQrpEniVlNLHzGC0sCGYCIMw1bnrX/mmeTWE7o0KWl33XXNnrezyb7+eqoLCzEXhCdid/buTfaNN5J+xhmIGW/Cr1/YG3OlwH7DdtFKFQqFIpJ/ls7bLZ1O2U6TvbIrWFafgi7gwIwaRhVsp7OjkQVxCny4HcvoVrA+Ces+fHXvsnjNldallDi8W9HMRnzOAkw9DagE5jfNyEzpTAcjifP3ppc17s40zJxKt7uGI3yN0bmZAg6o8bezVqZQtW/40bP0tp1ICtNkRX5L6ufPD2+vqECfMAGHw4FI4CiSuo7s0oUVmZkQaSMwxjTZ+sEH9uHLAXQddJ2sRx5hZW1tXLvxKC8vB2D58uWUlZU1a25cm3vthWfrVja+8w45gMzLo7plS8p8PtyPPkrKV1PJ/EOguyGlDThzo/QhEthy0GhKFy/5U+v0+Xw4ksib1Ry7/2SbyfJPtrmr7O5JNjU0etN7p9gDyCFnp9lKDh2IUZ5dofh/QI7TZN+ccubXpaIBfdLrOK1wG73SGlgUO7gHXdtGr6L4x/wDCGEgGz9jwfzfCMhch7cMzajF58zH1DOAWkJ1XqqjHV3sCooA0jTYmNGdyp+/p/sdw9DrqhGRzqqAnfefYKOewfbDTg/rdha3g+rE+ZgA8HlZ06I11ZFaqrYW/b33cLhciMbG+GvVNCgsZHXHjgn1WOmbbya/seiPIMq6917Wuly7h85r3x7PqlVsfP99cgwDmZWFbNOG7aaJ69lnSZnyBZm/utB1DymtwVUQQ+cJjW37jaRk1bo/tdY9RT/tKk2WjN09ST/sCTZ3ld09yabSeXsWQsokz2H9hfTs2ZPTTz/dfmAImuYj1d0Qt7+h0UWKO/iSr61Px4EXp+ZFC4mO8kkHHulEEnIsTErSzDpEjCiqBuEiRVp2JYI6LQ2X4cGZhJPKFBr1rrTwRmmi+3ykJEoQKfxzUyPmmibu+nqQMipyu9Hlwu0JrBMQgsbU1MS5n6TEWRu/kofX5cIZsk7T4cBwuZLOJ/V3IQwDd0NDzGTtpiZwOGRY6LtPOGjU3dEqRaFQ7LaYmGg7+QT53LlzmTx58k61GQ8h+gJXhrWdcMLkv+z+CsWu5M/oPCFM0lLqEo4J1Xq19eno+HBpXrSQyCif1PFKF2aEzks168PGNdkM03lQp6XjMLy4DXvHkRSCOmdauH6QEt3wkZLAYYTwz02JmGuauOrr0WLolyidB3hSU5E2aQ6cNTVx+6J0nq5bOu9vSp2QLCLB9xRL5xlCp0FPUTpPodiDUDpvz2K3jHRKSUnh5JNPbtYcp1ZCj6KhcfvHvTKWseePQ0rweFvT8HM3siqnAyLMmSTRkcLBmq7PUJt5QFN7WsmvdJx8EcI0wna1xhWO5YbSR5C6g9XDX6SuuC89bhmCs3JrUuteevdneArbNl2Xl5cza+pXjH1tnJWQO2K81DSk08XKx16loWuvYIdp4jr5ZMTatYgY5/wnjh3LyePGIXUd47TT8I0ZA4WFCdfmWb2a8pEj40Y6/Tx2LAeOG9d0XTh7tpVn4E9SXl7O9OnTGTx4MLm5O+dcfaRNfd06ck8/HVFXh4iRu+C1sWM5/6lx5A2Ehtbd2DbgLMr7DQ9zpDVnnbti7D/ZZnP4J9vcVXb3JJtfTv+SV3hlp9gDKKCADnTYafbscUBITpt4rF27lvPOO4+6ujquuuoqPvzww6Zdxeeffx5d17ngggswTZPXX3+dTp06cfvtt/Ptt99y+OGHc88997BixYqoMQrFruTP6DxN1NKr6FCEiB+RFNB6hpFK5Zxjyds2EYmGCHEmBTYV13V6iKrc4BE897bldJ54DpqvARFS8c7SeQ+DEKw99jGqOxxO5/+eTuq6xTE3IyNZOfZ16jr3bbouLy/n26lTueH1h0ESZUMKAZrG6geeo7bfQWF9zjFj0H79NbHOEwLj5JMxxoxBtkmct9JXUsK2kSMhjuMpUuflf/IJjvbt7R45Ln/FO0Rs20beqaeilZfH/J5eGzuWcx8dR/5h4C1ux7YBZ7LtgFOicnr9f9NPe5J2TZZ/ss1dZXdPsql03p6l83ZLp5OmafTu3dxwud5QMRC832PV1oiNEBrujT1wVwYqdEQkosRASEmnVdfAqWvB7U8e3bs3dOsJ710Dq38Mm+Nt0w/Xmc/SuYPfSVWfRAUUP92LcmHv4LNuf/NlfvR6wfRvuAS8TkJYEUw9eyMee5mu++wbZqf2q6/YtCq8kkssBND10UdxFBUlHLfxrbeYd+656KZJ6DJiousUHHssfQ45xPb+iSgpKWH69Ol07dqV4uLiHbIVz2blww/T0NCQMFmmrwoahj5K6pXX0EYIIiVbc9a5K8b+k202h3+yzV1ld0+yOWX6FEqwr9rUHP5aMaIDabajnnjiCcaNG0ffvn0ZMmQIBQUFzJgxo6n/0ksv5Y033qCuro4HH3yQO+64gy1btjBr1ixGjx7Npk2bGDduXNiY5557btc9lkLBn9V5QOUp4PkQSJA+QOroW/qTt20iQJjDybo2AEH7NTdDv6WQ2dHf0xu694B3r4Kl4VXcfC164Dz9KTr0OMJqqK8i2cq3nXMzLA3pZ9vEd/nB50H4pJWeKlLndeoKDz1Pp0MOD7PT+PvvrJszx/Z+Qko633svrh49Eo4r/eILfj35ZGhsDFtGTDSNzL596Xf88bb3T8Rf8Q6pvuUW6srLEyZaNxs16va7iYx77qeVELTagbXuKfppT9KuyfJPtrmr7O5JNpXOs9hTdN7ufQ4qBhJJKStZxgyWM5NKNgU70+7ALrkkZjas+IXEYsEEXy2seDW8ucOBcNMPcPsfcM4rVAx/lDotlW0XfAIdglFRpDcj2iczxOP7+APkXTsGzTT9ZeCw/GcmYEgYdjJ8/RtEOJwAqidMgCTOaWOa1E6alHDI9tmzmXfOOWAYGP7Q5ITSyjRpf+ON9vf+mzErKmiYMME2R5XUNOpefk2FWSsUir8RDeusf+gnmttvv51+/fohhKC+vp558+YxePBgrvMn+d28eTPt27enZ8+erF+/nt9++40BAwYAcMghhzBv3ryoMQrF38121rGMGSxjBttYG+xIC1Sljf9+ltIJy1ckHAPSShy+NEJ4F/eCa7+Gu5bCOa9SccLD1GuplF08DQIOJwjXbnaEjn3zJfIuGY1u+LVqpM47aBB8uxAiHE4A1e+8k5zOcziofvfdhEOqFy5k7oknYjY2Yiap8zrcdpv9vf9mpNdL/fPP21b2k6ZJ/cuvJek2VCgUil3BP0vn7ZaRTvHYxEJ+4g3KCf+yWtKDAZxPrusoyHgBai7G+ou0nAuBI92SLETjveC5Iom7SVg1Afa63kr+/ftEmPEUrP/N6u5wIGafM5FURE8dMhomPh1MGh4LIaBdd+sD8MMsuOfWuEsB4JOJ8OEEOOWMqCFGWVlyCb91HaO0NOGQlfffH+ZwMbB8sbGO+yEEPV94gdxBg+zv/TdjrFwJiXJl+RGmiW/Ror9gRQqFQgETJkxgwoQJEa1LgbvCWjZurI+am5dnReP+5z//4bjjjqNly5ZcdNFF3HTTTUyZMgWvN5hfUEpJRUUFaWnWzlpaWho1NTVRYxSKv4tSVvIjr1PKirD2AjrRn3Mocu4LWe9B1WkEPTYWUloZOquq7iWnZqz9zaQBq96G/f9nRT8v+AxmPAmrfrCEY5s+yD5nYKJFb0IdNRrGL7SveJdTCPv4o8CXLYbrLw6K0rC1+P/8YRa88CRcFl0Zzkg2Aa8QtmPXPPKI9cz+tSTUeUCXBx6ghU0VvN0Bc8sWZEWF7TgBmCUlyOpqxA6khVAoFIpkUDpvD3I6reNXvuZRZIx9iS0s4xP+zfH8m7zUMeA8GBqehYb3QNZiUITH62KrMYuWRpyyKLFo3AYNNfDM8bB8JgjdEikAy2aSt/QbUopuBl9EMsiTLrecTomQEkaPDQqZ5x8H3ZHYUaVp8OwjMZ1Oen6+tQNm53gyDLT8/LjdjVu3UjZlSpgokljuO41gJLj0fzpNmULro49OfM/dheYkvtzNk6ErFIr/P4wePZrRo0eHtQlxGPBoWFvr1nfFnD9+/HhWr17NSy+9hMfvWD/iiCNYtmwZZshRYqfTSVpaGtu3bwegtraWdu3aRY1RKP4OtrCML7gPGSNFwjZW8zn3cgy30NJ9EuQthfrnoPEtMCtBK6DGO5K6+jQMT8cY1uPgqQCfB146HX6fBJoerD63+kdyV31PStFNiMZqIORYyPFj4LV7obEhvuNJCBh1LTj8/596+RkQWtRxvzCkhOcehUuuidIhegLtFmlD9/9HSixMj4dNb70VVrEuns4DaP/BB3TYAxxOQHKRYCGI3TwhukKh+P+B0nl7yPE6H43M5BkkJrECgCUmBh5m8qzllHL0hIwnoGAzFFZTaszG63MhyYOUxMmzgwhIbQmvnQcrZvtvFCKE/D/r0iD7i9vDp7brBne+aYkXPeIFKPxf+YmXwXEXWD+bJkz5OLHDKTDuj7mwOfr8auaoUclFOmkaGSedFLe7cfPm2LtwWHuKPsBLMJtCyl572d9zN8HRowciO9t2nNR1XIceGt5oeGD9p7DkOVI3fUiiY5wKhUKx4wQSTIZ+opk5cyaffvopL774IgsXLuSKK6xI3p9++olevXpRUFDA6tWrWbx4Me3bt6d3797Mnm2903788Uf69OkTNUah+KsxMZnBU5gYMTcXJRKJwQyexsQAvSNk/A/yN0BhNeSvpkbeAGiYroLkb5xSCB9cD3987F9IqM6z3vO6NMmZGBEhn1sE//3IcihF6Tz/ZuJhJ8OZNwfbJ79vr/MANq6HhfOimjNOOy05nefzWWPj4C0vx4xTOS9S50nA3bWr/T13E7SiIrQkfodJIXDsvTciPT3YaBqw8StY+jyseA2tYVN8AwqFQrHD/LN03h4R6bSKH/ESHW4WisSknHWUsoIiErwgWwy0nEn1m+1vnDcUPr3Ldljq7+9AxUOQE5KK8MjTrEoYE5+C32YGd8K672ftfB01OihMPJ7khESAmogdNyD1qKPQW7fG2LQprtMITSPzzDNxtGwZ17QjIyP5dQBiF0UEVS5bRsXChWhOJ6nFxZTPm4fR0EBqcTHe6mo85eW48/Joc/zxuHNykltrSgqpF19M3SOPJDzvLwyD1Kuusi6khEWPw7z7oNEKV88B0rWxZM+/BvLGgzu5+ysUCkXyJJdg8sEHH2Tjxo0MGTIEXdfp1asXgwYNokePHgwZMoS2bdtyzjnnoGkar7/+Ou3bt6dly5YMHDiQIUOGUFhYyL/+9a+wMQrFX81G5lHLtoRjJJI6ylnPb7Rn/7jjfOldILc3lC8k4QaR0KD1KfDmo7bH5FKWT4ONC6D13sHGA46CBz+Fdx+BOVODDqX2PeDUa61oqNBImjgV4mJSUx3V5O7XD1fv3ngWLIiv83Sd1COOwN2rV+x+QA91tCSBlmb/e+jPUL1mDeV//AFCkNa6NRULF+KrrSWlqAjT46GhtBRnVhZthg0j1ab4TQChaaRddRU1N94Y/zsCkDKo8wCWvwq/3g71QUdTERpucSNa41YiNbdCoVDsOP8snbdHOJ1KWIhA80c6xUegUcKixE4nzQF73wxzos/LBw3pVtW6zbXhodaJmPM2HDXWeslNfQfe/B8s/yPY36U3jLoGThgTPdfthswsqE6i6p2mQWH4y9e7Zg2bjjsOY+PGOM9jObe0ffel6JlnEpqvmDsX4XIh7XIf6TqymWHMSWEY/HTSSVT89FPicf4qL5rbTbcLL2T/ceNwpKTYmk+/9VYaP/7Yyu8Ux/Ekjz8ed6BCyy83wsKHY45L3TQRvlgMw74Dl8oJoFAodiY68ZJKhvLZZ58l7O/evTuzZs0Ka3vggQdsxygUfyWbWIBAj3m0LhSBTgkLEzqdEAJ63wbfjo4/Bg30NKjKss/LBEhNR/z4Gox8yGqY/Sm8/l+Y/31wUNsucPLlltaLtSHXshhWrYhuj0XL8Hpqvq1b2TRiBJ758xNO0zp3pvjttxOOKf/+e/TMTIzqaMdWGLqO1LRmO6lsMQx+OeMMts2cmdgxFKjm53DQ6YwzOOjJJ3ElkX8p7fLLaXjvPXxz58bfYDz4YFLPO8/6ecFD8MtNUUMEJg5hUPDjcdBiLqS2SOLhFAqFIln+WTpvjzheZ+CNGW4djcDAC8YaqH8a6h6AhjcR1IYP63UNdL/UPyXiPLfQwZUNR30F1aXYFJENzqnwO3yevRX+fQasiBAGqxbC/RfCK/fGmC/gjPOjQ7Qj0R1wzHDIzmlqMrZtY8OgQXiWLUuwPoHX6ST1vffQEoiH1Y89xh+jRtk7nMCqbLeTz4SunzABrb6eip9/th8cECqNjSx/+mkmtm7Ngvvvp8EmSbqWm0ve7Nm4hg2zvndNs3IACIF0ufA5ncinn7YiuLZ+H9fhBP6yy5WL4Y97mvOYCoVCkQTJVTVRKP4/YGIko7YAMPCBUQL1z/t13itglocP6nQ69Pm39bOIPP6mgyMVhnwKdTXW5qIdUgZ13oRH4MYTYOGP4WM2rITHr4dHr47tTDnjAvt8kZoG+/WHjp2bmsy6OjYeeSSNc+bEnycEPqeT1E8/RS+If7xw45tv8ssxx2AkE3VlGJgul/24ZrDlyy/R6uvZ9u23iR1O0NQvfD7WvP46E1u35rfbbqMu3garH5GaSu60abhPO82KNBMiqPMcDnwOB/KNNxAuF1Qth19uTmhPa9wMc5JITK9QKBTN4p+l8/aISKcsihEIW8eT26ylW/Vj4AnsPOmAjyI9DafjGprCrIWA/s9Am2Gw6Ako+RqQ4M6H7pdAjysgrRW4Y5+tjMaElEz49mN4439WU+TOWSCB1/g7YO8BcMCR4f0XXgWvj0c2JEowacLV4S/HiqefxrdxY9B+HATW8bJ4VM+fz5LrEkR/NRmydp5a/etfzUvMnYCSb79lzi23sO2HH2DsWHshgvU3qxGstOLZvp0//vUv5t91Fwc+/zydzz8/bPzy5cspC63mct99aJddhnvGDERtLWZBAVsOOADvL7+wfM0ayiorabvqXrLRLedSPKSBsfg5FrtGIbXo77e8vDz2/WOQ7Nj/zzYVCkWA5HbAFIr/D2TRAjOJXIkOs5FeNW9C4yVYmk7Hqr12GVnaWUCb4OB+d0HLQdYR+Q2fWhrKmQVdx0DPqyCzI6TMTirSCaFZOm/+D/DEDVZbpO4KaJcPnoa9D4ahEUVfzroQnhqHrKqIfx/ThOvD84RWv/kmnoULk4sKSpAioWHjRuaff35SGgug8JJLdlpEe9lvv/HzLbew5auvLJ1no1nB0nihFfWMmhoWPfAAi/73P/r973/0GhvuCIrSGrfcgnbBBbi/+QZRUYHMzbV03u+/s3zDBspqayle/xD5aAl1npAGctU7LM64EMMZnaB9d9FPf4fNP3N/hUIR4J+l83Zbp1OdD8o9kOWEbs7DmMfHCce7zUaOL59KullHMNm4db5eE3W4nF6ytRtBvmW9nIWAtidYH9MA0wN6SnhZ3D4jrPK5NgjTgD4nwn+vsz+Op+vw7mPRTqc27eDqW+DhGFEzgQioZ16H/fs3NUspqXzmGfuXt2mie71hlUoiWfv00wiHI+EYADSNvhMmwKGHwvjxiccmwcr33mPG6NHWUbkk5+gE48/C/pQS0+vlxwsuwJmZSbtTTmmaM3369NjG0tKsD8Avv4SNTdP2QhA7UXqD6WLc+qDgqV/9CSbxnXBx778DY/8/2iwrK6O4WOVOUCgUiv/vNBiwrRHSHdDZdQhzmIBJfA3ikD6OrZxGjq8SmpwEgfGNpImXcbtuCukDio+wPtIEozFa5+0zAib/y3atwvRZOu+dJCoNCw3eeSTa6ZSbB7f8B/59IxB0pgCWTdOA/z4BQ48Pm1bxtE01ZLB0ns+HrKyEOO/Q9ePHJ1cqWwh6Pf00zhEj4IUX7MfbsPHrr/ly2DCk15tAJYUTqKIHEecNpAQp+e3GG3Gkp9PtssuauuJqDZcLAjmhfv89aWDegwABAABJREFUbGyq1hKN+BuuoVqvYe00jAT/2fR366e/02Zzxiqdp1D8M9ktnU4VHsiaAIb/3TikuAVjDh5MXfoMYlWvA+hbN59MszZhKdo0bQJ4LwTXEeEdmg5aDE9j9yOgRXcoXZHQkeRp3Q9Xbif4/VubJ8M6X/795+D1gNMftvz2K3D3LVC2NVjdDixhlJ0Do86G8y+Hrt3DTJmVlRhbttjfE3/527IyaNs2Zv/Wjz+2dzj515/arp1NWnd7atav59e77mLZyy8DyZ/zFEmO/f6cc9DT0xH77APA4MGDyc3NTTinvLyc6dOnN43t9dsAdLM25thx68cytu24puuV3V6mLjM6x0Skzebcf0fH7Uk2161bxy9+p59CocD6ZbdbvqEVih2n0q/zvH7JdnBhJpcNOAFP9qS4WQ161S0l31eOiKMDhZA4dIMUMRm4PKJTs47URdJ6b+gyCFZ9D2Z8DeTLaYej59Ewc4R9BTppwpK5ULoJCv25mT6bBLddY1Wm8+s8YS0a0jPgpNNgzJWwd58oc57Fi5OKThKAuXYt9OgRs3/L5MkJi6gE1y9Jad0aU8T5i0iS+tJSfv/vf1n42GNNG4vJxVhZOs/u7r9cdRXu/HwcAwYAf07ndVtwHO7G9XHHh2q9tZ3GUZV7tK3N5tx/R8f93TabM1bpPIUign+YztstH7XRCDqcAKZvhukTz+Ox47yk5c0OSyou0NClhx71axM6nACk1BH1T1tOp+rVsPJ1qF1nJZNsezy0Oira6XPJJHj4UKivihIkUtORCCpGPkdRnU1CxrCJJtTXWk6n5x+H268N3jI0zFvTrCimGA4nANHcnEoJwqSN+uTdSEZdXfPuG0HFkiV8OnAgDdu3N3uuIGJ3MA5GfT0zhg0j/6ij4OCD6dq1q+3OSklJCdOnTw+OXdMTtv1Kwuo3/lV17nM0pLe2t9mc++/guD3JJqDEiEIRigbY10ZQKPZIGoygwwngpzL4YfLJ3H9UA0XFX0TpPKSPferXkIzbIl17Cbgc6kpgxWtQvdyKcCoeYkW3axFa6IK34MEBUFUStcEohY4Eyk97hULTAJ83+YesrbKcTh+8DZed1dQcpvOEBl4vnH1RTIcTgNB1pDfJ+ybSebWxN9Fijq2rSzrHVixqN2xg8iGHULt+fdLH+QIkuwkpDYPZp51G9gEHwLBhf06/bOkLGzeBtHfGtd/rSMjvbW+zOfffwXF/t83mjlU6T6EI4R+m8/aMROISDOngqk8vI2fzfXTjcAroRCFd2JthjPSdi06jrR0hDGiYDrPOhQ87WwmgV7wOS5+DqcfAh11h2+/hk4p7wq1z4aCzQA9JqOhMoa7fGdRpqRg5bSErzz4ReABXCqRnwaYNcMf1CR7cgNoauOnymN1aejquvfYKDxWP/eCYQiDy8+MO+T/2zjs8iqr74587sy0JCQmh9yIdVJAmWGg2QEQBFcWG2BEbltfeXwt2UUCBV1FBRVFQpAoqTYo06T2UQArpZbM7M78/ZpPsZstMgAD5MZ/n2YfduefeubO77Hxz7rnnuBo2NDPzctuWRVUU5g0YgDsjw9Se/rIITKV2LyF90SKE2/i7EZJW92HocBIy1LsqpMPJwsLC4riR0cWI/8PC4v8pigYaEv9ZMBx131u05nKq04zqNKM1fblOfRCHlmXq/m9X/4G/H4LvG8C6Z3w6byIsvg6+bwhHykSlJ9SH/6yBi+4Bh1/5atlOQftrKZCi8dZqrS8UxlQ1d0FCQEJNvSrxI3eVbAsLQlX0yPdRt4d1zrguushUDk0NvXpdOKKaNDFOZF5sewI6D2DR9deTf/hwwDWZdT2V19mV9c8/SIWF5rYOlqXlPYYOJw0B1TpA4vnlH9/CwsIiHGeZzjsjI53CIQt4f31j/rzyzsAGzWQJQA34JxuOfqW/KHujydsPcy+BAauhql9kUWJjuHUKDHkPjm7XxUStVmRn5pXmNYqKgZ6DYckPkUOvZRtcdYsuIL78DMPbq6LAX7/Drh1wToug5qr33UfqqFGGl67Y7YgQzilN00j69FMK9u41HANJIqF7d2KaNSM7OdnYvgyqorD6qafI2b07uA3zX0YzkU6lA6tIXi/5u3eHzXNQjLRpIw53IVGXdCVH05DatsXRvj62Zsm6wzJoHhJCyNDhJbOzsbCwsDCHDQifE9jC4v8lAnhrXT22N7olcD1Ni1Cht+wYm4EDH1Hq5vBbPCo8CvMvg6v+hBpdS4/H1YJh4+DaN+HIFn1RrFYLsrLdaP75KwfcATM+irxNTbZBtyuhajWY/AkUGkSSqyrs2Ap/L4NuFwU1V733XgoWLow8hiTpOs/pDNl8+JtvyF6zxnixTwiimzUj/sILOXLkSGTbEGiaxqZ33yVlxYrgtnKPZhJFQSgKWatXU/eaayKaih3bsbvdRPW8kByvF6l5CxwXtMDWdBdCjvDeXPBG+DYLCwuL4+Es03mVItKpGEWDv1LgaNn7t60FZi5FSxVwVCFs9IqmgDcf/vGrHOLOhxVTYeYzsPB9KCqARp0hKi64//DH9VWdcJFHQuirTDc8rL9eujhy0nF/VgY61pSMDFIff5y0p5+O3E+WkTp2RAmzFW/XSy+x5YEHzJXP1TTOeeEFc/Mtg+rx8PvQoWwaOza8jYlxjle0rL7qKnZ88knIlTBN0yh8agxR11yJzetFOngA7dBBlEULKXjnIPkfu9AK0aOa8K16AZocBX1mQ/XgXE4WFhYWJ0Rx2PVZsgJmYQH6PX5nDmzKLNMgNwCigzuEGuBA8ZNQ7aqeKmGVX/JobxGs+R5mPgvz3oKcdGjcGWKCK5UxZBTYHJEjzFUFbvFVGl7xl3E0OugLkSsCI7DU/HzSX3iBo/fcY9hXNG2K1+EI2bz/00/ZcPPNeMykNNA0mr/8cshFSuOuGsvuvZdVZarKBdiYGKf8MfA66264gc1vvIEaxiHoHvsm0Vf2wu7xICXtRzt8CGXpnxS8u4O8sVGo2YAoXf4s1nqZ534C9YJzOVlYWFicEGeZzqtUkU7FHHNDLf98kFItcFwLRT9BpLKn+zV9/3yk8riaAvtnQt5hWPkD/PQsFGaDbNcdSrNfglotYMSXEFUm/LjVBfDyNHjBV7HEP+JJlvU8Aq/PgCZt9GPlyQ3gl+Tbm5rKwR498OzZE361TQiQZWJvvhn1uefgq6+CTHI2bWLXSyaidHzOsnOnTKF6377m5+zHmueeY99PP0W08f9UwkUzqRyfp1TJzWX1Aw+QtW0bnT74IEBQFb37NkUfvFN64pJO+nurbC8k/6dOxDzZGHL3U6Q5ce93kNLzH2rXC44+s7CwsDhhzrK9/hYW/qSX3RUvosB1BxROgAgV7lBAE1Jg3qQgVEhdARn/wvb1MP1hyEvXdR6art0SGsDtkyChXWDX+s3grVnwxEB9W5z/wqFs03XiM5PhPF/EktdjLqeRkAJ0npqby8HevXGvXRs+OsmnY2IGDoRXXoEZM4JMCg8fZsuDD5o4vz5Wq3feoe6wYcb2Ifj3/ffZZlDVOJzOK/u87DEzaG43659+mmPr1nHRtGkIv62ERVP/h/u5p4I7+XSeesBN/tQWxLzVEZG3G2zR5MRdTN5+icI6kaOnLCwsLI6Ls0znVapIp2ISQ0UPx7wIOIh0SVqWLbLDqQQVfnkOpo/WHU4Aiqc0kXjKbni7J/ZDG4K79hkKX/8Lg++Hqol6Zbz4GnD9wzBtC1zkVwq3dXvzeaBatil5mjJypKHDScTF0XjPHmr/73+I6NCrg0mffoqIkHTSf7zuq1ZR75ZbzM21DJ7cXDZ/8IHpZJIKutgofsQ0akRip05U79KFWj17EtO8+XHNA2DHRx9xeM6cktdaQQHuN18zmJCC8scalLgn4epVHOv8HV4caLbY456HhYWFRUTOsr3+Fhb+1Ail86KfABGL/p8jGE2TQcXA4eTHkg9g0i26wwl0nVe8WJh5ED64CseuJcH9uvSF6dvg5sehWm1d58UmwDV3wdQN0O/WUtsWbczlUfJ6oEXrkpepjz2G+59/IjucHA4abtpE3R9/RFQLEZUFHPjsM+NzA2ganX77jSaPPGJsGwLV62X966+btvcSqPOi6tQhsVMnErt0oeYllxDbtu3xJTLXNJK++47dU6aUHlJV3C8/H7mf4kX9dwde9y0wYBVcuYS8JvdTSf9MsrCwqAycZTqvUkU6yQIurgk1Q1S9xdYO4hdC1jWgpaHfKIozBXlRVAmVBGRSjU/kBpZPCd+uKaBA3KwnQB5cejwrHX6ZAr9/D7lZ0Px8ff9/ryHgCKGgbrsHphoIAiGgyTkl+/w9e/eSN3t2ZAeOpqFlZVG4ahX2Bg3CmqXNn4/mjbBiWIyq4j6Ovf0AitvNr1dcgVJYaLpPsQgB6PD883R88cWgUO+933zD2ocfxp1q4vP0Q8gy2z/6iHr9+wPgnf2znujTCJuNoi8nE3WBtZXOwsLiFCBzVu31t7AAPbKlZRy0jQ/RKDeE+D8hqx+oB9D/kygU6zwvLVA1k64KLzD/y/DtmgaoxP80BmLvKj2elw2/TYUF0yAzDRq2gLtfhstv0nN7lmX4nfDuq8bzSagGV+kRNUpGBjlffBE5b5SmQVERBUuW4GzbNqxZ+qJFkcfxw334sCm7sqiKwoKhQ3GnpZWvn+/f1vfdR/ePPw6ITAI4NGcOq+6/n/z9+8s3IUli+wcf0GzECIQQKH/9gXbwgHE/WcYz5XPsV/Yr3/ksLCwsjoezTOedkAs/JyeHwYMH061bNz777DPGjRvHRRddxJ136om+09PTufzyy+natSurVq0CCLIpD4oGT7SLYGDvDokHIHYqOK4D+xXgup00708UuqMoqto5YL92WI6aqJGmKTiSNyEVJyNfMReuaQjjnoQtqyBpO/yzGF4cDje2ggO7gsc4/wK49sbwq2DFjpaX3yl5njd7tun8AHk//hjRRC0qMh7Hh+lyvWVYfOedHF2xoty5mOJbt+ba9eu54KWXQuYWaHLTTVx7+DBVmjUzXY0F9BK7yQsWlOz5V/fvi1hiuASvV7e1sLCwOBWcZStgFmcmp1rnacCT7SLIHFs7qLYb4n4A51Bd5zlvgqrzSVMWoGg2NGFc6Y1UjFMcaBpy9mHk4rQNm1bAtY3h3Qdh03Jd5234C964GwY3gR3rg8do0AjuHm2s2158G3w5mfLnz0czWXU3N8SWOn/Ko/PKY+vPyqeeYq9B+oRQRNevz8AVK+jxySdBDieAev36MWjvXhK7dCmXzkNVydy0icKjR/WXZrWboqDuCS50Y2FhYVEhnGU674QinT755BPuvPNOrrrqKj788EOWLFnC0qVLefLJJ1m5ciWLFy/mueeeo02bNowcOZLp06ezcOHCAJtu3boZnkdGQ0HwSN1kEvL/Zf6RvajCi7MonoTslshq2eSJ5/keOhkZGYBgp3wZ52pziISGjLcgHruWbjgvDYGkKSQv/oVan9yPUBWEfwSSLyxaO3oAzz0XsfOFH1CjAxOQi3vHUC87i4RFv6FJEqK4jxBodjsHH3+JrHqNYdMm/b3YuRObEIZhx5qikHnoEKmbNvmuH3bu3Ema30qU1rAhHDpkXM0EOASk+OYAhB3Tn7wdO9j59deYcOEFEN2tG80//pjDksRhv3OGou6777L71ltRcnJMXQcAqsra2bNxNWtGbFYmCYpi/H4KQbbHw54I7yeA1+vF5ufECmdb1q48tqdyzPLamjl3KJKSkgL+PRmU5/z/38asqHEr05gSEu1pf1LGA4gn/qSNZYpQe/3NB4xaWJwUTrXOu71mKq2VzcxP3oUqFWH3xFItqxU2tex/huZAYCGVjIzdeDQbomxl4jJoSHgL47GJLENbVchImsKBlUuo+cFIpKLCQJ3ne65lpaPcdyk7X/gBb0KtwEGG3k6dlBSqz5wWpPOQZJIfeJz0czuX6rwtWwhd+qXshWjkHTnCpgi6xFO7tp5T1ES0U4rDQVY5dZ47JYUN775b7q1wrjZtaP355xyNieGogc6r+frrZN98M560NNNRWwDrZs8mtls3otPSqGHCXgPyhWCvbz6VXZOdCp1XHltL51k6z9J5ZTjLdJ7QQpXzMsmQIUNo164dv//+O6+88goLFy7klVde4ZdffuHgwYMsWrSIb775BrvdTv/+/XnzzTf59ttvA2zuvffeoHGbtmjF4MFDSieJikBDktQA74XdJWN3yqgeCTxmXBsaTuHGJsJvKdMA1SMhm6yfkSeicCpF2ExUoSuSHXhsfg4yTcPuKcLu9QaImAKnE1lV8diDK6TIHg92EytgGqDYbHhdYdymmoZcUFAifiKOJUkoYfJCRcTtRvgipMwKEtXl0gVSeSqnqCrC7UYKI0YUhwM5xAqeEhWFEBCVX1oOsdDpwOUOvdrndjrDVgG0sLA4s1BRkUwG83o8HrwmthovWLCAtWvXnujUTCEaDIReswKOXZ05kFmzZoXpYWFx8qlYnTeYYnUg0BCooXWeS0bzCLQiCTNqwiHc2EXkKCbFKyMbOJyKyRdR2BUPdtX4N8Ij2ymy+aVT0DTsXg92jydI59lUlSKbPSiKR/J6cZhISaABqizjiQqVc0I/t1xYiDDhqNGE0HVeeavWnUqdV1SEFOZ3OqzOc7lAkoguyC/J2xBJ5xU5HGGrAFpYWJxZWDqvcnFCkU4ZGRm0aNGChx56iCFDhtDXV9ksOjqa3Nxc8vPzsfv+SNc0jczMTKJ9zotim1BUjYli2LAbyFVkYiQV1ZnBlnP+hyK7QYT2kdVK7UzDI33CznPx4sX06tWbhPgq1DkwlsTU7wDNV+VEQ6BQ4GpOUrOxVFv9A9XXf224Agbwat1nefL3twJXvkKgISiq2YAdr/4CgJyVSdOHbsd5YF+Q42fsiDE8+ONk9n08laJ6ZXIyZWfj7N0bYSIM2v3ll2jnn+93/b1ISEhA83hIHTmSovXrI+eGEgKEoMbnn+PsFJjLqOyYZSlKT2dNv36oBbpDR/I9IkkMuVo1kh58kF69e4cc04h9jz1G9qJFQRFPe8eMocnYsaWXBciShBQTQ/M5c6jzzOO41q5GKAoTRo3hno/HBvTXhECNqcKhOQvRXFFhrz3UcbPHTrT/6Z5TKMzaldfWLGfzmBU1bmUac97ieUwhQm4+f+y+RwSqU53G9Rqf6NTMc5ZVNbE4M6lInXfTsBvJVWRckopsz2XLOV9QZM8JrfM0qJbZhqYHryZcrHfxb0mPSy+nZeF0ahyZjNC8aEJGaCDw4nY24ECTt4jZ9je1l39oKun4q3Wf4Yk/30P2GC/4eaNj2fruXyBJiIJ8mjx+D9FbNoKmBcx67IgxPDz9U/a+P5nC5q0DB/F4cPbpg8jMNDxf0XvvofbpE6zzVJX0Rx+l8PffDccASHzvPaL6BGpoo99mb14ea/v3x3PsGGBO54noaA6OGXPcOu/Qf/9L+rffmtN5QiDsdpr9/DN1PhtHzLzfIuo8bDYOzp6PWi0x7PVXFk12qnReeWwrk36oDGNW1LiVaUxL51UuTsjpFBUVxaWXXkpCQgKyLJOXlwdAXl4e8fHx2Gw2PB4Pdrsdu91OdHR0kE0oJEmi47ml4XKLeA+VIoiQGehojdV0q3EtiTQKaktOTmbx4sU0b96cOnXqwLnfQP67sOdrRN4BsEVDgwFE1biQlkJAg5awLkKCSQAh8CY0RkMYOpxAX8VzHkumfXvfdd10NRzcH3ZLmCMni5avPA5L/w1aDUodPZrMd94J7zCy2XB17co5w4cjhAi6/gOffcahdeuM52yz0WHGDGoNHBjUFvSe+qFpGj92717icAI9YaT/Vfg/1wBbVBQXTp9O0vLlIcc0Q4N33+W3zp1R3e6IzjQJQFVRc3OJWrWKGl99R96l3VBTjgYbyzJCkoj9bibtOncBwl97qONmj51o/9M9p1CYtSuvrVnO5jEratzKNObcxXNJJvmkjFdMYxqf1PEiYqNSJJjUNI3U1FRq1qx5uqdiUQFUpM7r4KfzljMFD7mE1XkCjiVsoXPC1dQLs52i5LekRUtq1RkP7tdhzzeInN0gOaDuZTjr9OEcIaBNb1jxsWFFYzUqARXZlMMJwJafQ/tmjaFKVXjkLti6KawekQsLaf7sQ7BuHzgDi81kPPssaWPGhD+RLGM/5xzOGTUKYbMF/Y4enT2bQ2YcTpJE+0mTqH/77UFNRr/Nvw0ahMe3bQaMdZ5ks9F16lQObtp03L/3Td98k1/nzMGbm4sWIYJLAv19VxRsixdT57MvyOt5IUqonE2ShACiJn9F20t7lhyuzJrsVOm88thWJv1QGcasqHEr05iWzjs1nCydd0KJxM877zz+/vtvCgsL8Xg8rF69GoClS5fSsWNH2rVrx7Jlyzh27BhRUVG0bNkyyMaIPI6xn7VoBtvdBBK71J90Z9Kx8yCtNhxrBXkvIRGi8lp0bWj3GHR9Hy54HWp2L3Xu1G4JvR4g/HqNfjy7/6vlS5Bt94mKPbtg/i8R96YLRYHtW+CvYNGQ+MorOC64AAgtzxxt21L3p59CJuAG2PfRR6aSMtoSEqjpq/JWHpL/+ouUlSuDjisQ9ClqvmN9Z86kartIWeKNiW/Xjp6zZiE5Q9Va1pEJ/FT3fvklUoMGxCxdjXfgtUEfuXzxpUQv/Atbr9BRdBYWFhYVQvEK2BmaYPLQoUNce+21NGjQgGbNmrF7926aNm3K8uXLT/fULE4ip0LneShgJ3+a0nnbtV+g4FPI6KTrvPRzIPcpUPYGd3BWg9ajoMt70OlNqNu3VOfF1YSBLxrOLbvfy5gtiFeC3QnpaTD9i4i5JoWqQMoRmP1DUFvVRx4h6vLLgRA6TwhsDRtSb948RJhCKPs/+gghGydUF04ntYcMMbQrS8bWrez7+ecgh5rie/hTXJG4x4QJJF54YbnP5U9Mgwb0WbAAW5Xwf6n5R1tpisK+L79EVKtGzB8r8d5wc7DO69SF6F/mYx9y/QnNzcLCwqJcnGU674ScTo888ggTJkzg4osv5tFHH2XAgAH06NGDrKwsOnbsyOjRo3nttde4+uqrefzxx4mJiQmyMSKV3USKcCom0ZNCh2NPQt4YUDaBdhSU7ZD/MjXlC5El432cAdz4AVz2MAgJhKw/JN8NPCoO7vsRd/NeaEJCiTeRolC2Qdcr9Oe//Fg6ViRsNvj5+4BDWlERB4cMIX/NGhRKb+bFDzU6mhrjxyNXrx5ySLWoiNxNm0wl3fakpFB46JDxPMuw/YsvwgohFb1Sscf38ApBje7daXDFFeU+TyjqXHYZ3SZMCPpiSwQ7nNA03KmpenvduhS98yH5UdEUTvmaqB9mU2XzLmJ+W4Tt3LawYAK8cCk80ppqHw3GpnoQ7vyTMmcLCwuLIExWNdm/fz+9evWia9eufPXVV8dVXSyUjRE33ngjgwcP5uDBg9hsNpo1a8aECROOq2KZxZnLqdB5xziAgnHKgDhvBt2OvQa5D4D3H13nqbuhYCwca0GUCHbeRKTf0zDoVZBsutaTinWeAEcU3DaJgvP0vFOehq10m0hIMrTvDk4XLPgVzFT9lST4+buAQ5qqcmTECHLmz0cRIljn2WxUHzcOe6PgyP5iMlesiBgJVHKuggJyt2wxnmcZtk+dGtappVFG5wExzZrR8o47yn2eUCR27swlM2aE1Xllj7vT9cJAIiGBolffID86hsIvviFqxixi1m0h5o8V2Hr0gD+nwiuXwSNt4LmLiP7j88gpKCwsLCxOhLNM553Q9rrq1aszb968gGMPPvhgyfNatWqxYMGCoHZ/G2OMf/CjlXyuzFqMTfOGsFeBIlzOQmxsBuqA4ob9P0K6L1FYYidodC3IfhEykgzXvwt9H4XlUyB5G8h2aHkpdL5RFyTJySAE+f1GEDvt7chh2ooXhvquOysDZAmMko+rKmRlBhxKefZZ8n77DSgVIAG43RwcNIhz9u5FCpFcUjNb4e147TWNI8uXo5lI1gYgO530+vzzcp3DiCpNmpTkFigmnFS0x8YGHpAklEt7YS8O/9y1Gl6/CnKP6R4rTcMhBM7mPaj2Qid23jyBwjp6PoZQ1RnMHjvR/hUxppGthYVFBWJyr/+HH37I2LFjOf/88+nbty/x8fHlri5W1mbmzJmG592+fTtDfBESxVG1l1xyCenpxpVfLSoPp0LnaSZ0nkMt4qqsRbhUN8HKR9dSVaXRSNIz+iHVCwd+gZRlgArx7aDJDXo6hWKEgP7PwCV3w7IpcHCj7lhq2g26DdcXGH06L6//ncR/+njkSaoKXP+Q/jwrU3coGWkoVYVjgf9njr33HllffAHomiro3VEUDl1/Pefs3YstzAJjeZwl5dV5AEeWLjXl1AIQskyfr74KG31/PMQ0anT8Ok8IlIt7luq8Q9vg1csh/YD++Wv6RsG47cuJbvkYKYu/I619b+DM1mSWzrOwqGScZTrvhJxOp4J46hvatCnYgU3zIoURLsKXlLKKNA72pMHK+6EoAyRfRjHVA44EuPBTXZT4U60+DHgu4vlzB95D7OZlsGl5eIFx0xjoeKn+vEYtc2VfJQlqlO6fVHNzyRg3LrKIURSUo0fJ/v574m+9NahZdrmIatqUgr17DUWJrWpVXHXrGs/Tjw3vvEPmtm2m7c9//HESWrc2NiwHiRdeiKt2bQqPhNhWWYac7dvZ/OqrtHnmmeDGlH3wSl8ozAO0Ep0rNA1VCD5sMBLtr9UUyJvR/FZAFy9eHDSU2WMn2r8ixgx3PC0t7aTuTbewsPCjeAXMgGeeeaYkb86mTZu47777ALj44otZv349//zzD2PGjMFut1NUVMTOnTtp59vKHM7GDLfffjsDBw7kkUceQVEUli1bxvvvv8/w4cOP52otzmKqUgeBFHF7XfPCPUSphQYV0gQOWxHO1MXw52NQcASEXV8wUj3w90P6NrtW9wV2i60BVz4RceSC3jcSv3kZ/BW8payEK2+BPkP159VrmoooR5ahdqnO0rxejr3zTuQ+qoqWn0/WlCkkPh7aERZ7/vlkrlhhOAdht1OlZUvjefqx/csvSf7rL9P2LW+/ndrdupXrHEZUOeccqrZrR9bmzYZatujYMf55+GE6vPtucGN2GrzUC7L1qPfSxWM98buKYMHWg+Tv+B5NlEZ2nYmazNJ5FhaVjLNM51UCp1NdatGSFHaEXg3TNFoU7grrcPLHdfhn2PBT6QHVL/S5KBP+uFG/4TQdph/zFMKa72HtDMg7BvH19NWv9lcFbo9zuOD9+TDhGfhpos9J4aN6Xbj9GbjOT+Rccz08/5jxxXu9MLT0g82dOxct38SWLkki+5tvQjqdABqNGsW2xwzOL8s0vPdepHKUjs0/coSV//mPaXuEoPXIkebtTSLZbLR87DE2hBFjZfn3ueewx8YSe32Z/fy/vAvuPAhRxVDSNMbsGIsmZNK6DedI38cqdVWS8vRPSkpizZo1pt5bCwsLY6ZNm8a0adMCDyatgsmBRRwOycHbnatVqwbASy+9xEMPPYTNt7W5PNXFytqY4a233mLGjBl8++23dO/enQkTJjBo0CCGDRtm/sItLIAo4mhEZ/azOqzjqWXhLsNxhFCRhUrCP7dQskqkeUoDo7w5+qKjWgRtfBFJXg9s+BlWTYPsFN0B1WUYnD8IbH6ljmQZXvseprwC330AuVmlbVUTYdhjcMuTpTmjLh8AUdFQYKDZFCVA5xWsXIk32URiXFUl66uvwjqdGj3wAJnLlkUcQths1LnpJuzlqCblycvjrwceMG0P0K6c9mYQQtDqiSf4O4zOLcvODz7AFhVFjdGjAxsWToCslLA7FWRN49Gd75HV7ioODnr9jNRkls6zsDjzsXReJXA6AXRmGHN4xSdGAt8oGRWXZs5jJzZHepM1QMCK+6HhtXBgE3zUH3JSS8NtJRnWfAt1WsNDcwmoveiKgofeheFPwJKZUFQI55wLHXvqYsWfOnV1kfH9V2FXoTRZRnS6EDp0Ljmm+MrSGqKqeH25ikIiROSVIUnCWbs2jR991Nz5fGydNMncyh4gJInGgwYR27Bhuc5hlpaPPkrmhg3s/+orU/abnn+ergMGlB7wFsHiyYZbIIWmUGPDT9R4aBLJR1MqbVWS8vQHLDFiYXESGTZsWNBNXHQcCI/NCjhW79vgSqIAEydOZO/evYwePZoff/wRKF91sbI2Zunduzft2rVD9fvd37ZtG23atDE9hoUFQEeGcJANKBSFdDzFKPkGUU6UJhMKnXyglDWPQ9PhkJEKH1wJ6fv1vJ2aouu9dTOhWkN4eC4QX9rPZoO7XoIbHobfZ0BeNjRuDV36gr3MAl1sLNz1IHz0Vli9pckyonEzuKxfyTGlHNsWFL8tUuVGkpBjY2n+/PPl6rZr+nQ8ubmmbIUkUbNrV2p06HA8MzSk0fDhZKxfz45QEUwh2DZ2LFVvuinw4PzxhhUMJU0hYctcEh77iuSs6mecJrN0noXFmY+l8yqJ06kmzbmcJ/idDykizxeGrSEQKCXPDLx2Cj4xEgkNPJmw7mP48mUoThRdfEMqdkAc3QljeyLu/q20a9IOmPoGzPsGikvr1myg53Ea+qCeWNKftz+Fg0mwbEnAvn/Nt0rmbdQU+/9+KF01A+TERKML0JEkbLVqhWxKX7KEbUbOJFWl3Wef4SxnacSU1atN5wZwJibSc+LEco1fHoQk0fWLL8hNTWWnCXtvdjZpc+aUHshJ16OczJCfBXmZxzNNCwsLi9DIQHBaviD++OMPfvnlF3788UfcbndA5bAbbriBvXv3smzZMs4999yQ1cVC2Zhh9OjRfPfdd7Rq1QohBEIINE1DCMHvZkq1W1j4EU9d+vEMC3mXfDJKttsV/6sJJ2gGIi4N0DDWg6oCGz6AbydAns/JUxzRXKz3Mg/B25ci3eOXzyrlIEx9E36ZDIU+fRhfAwbfr0c6xZTJHfT0q7B3l16dzl/nFU+jRi3k7+YGLEyGKwITCjmMRsv59182GkUAqSqtxo4lumlT0+cDXecJm81U3k45OprLv/22XOOXByEE548dS/aRI6Z0nqaqHP3++9L3W1Xg2EFzJ1O8cOwQyOajwiwsLCwicpbpvErhdAKoSzuGMY59rOIgG/BSRCw1aSEuRdizwDOP4EKtfmigCRBGEWWSXY9wKSoIua0K0BNUpu8j+p/pANi3r4WXh+nRTYrfjTjlAHzyFCydBe/PA5dfAsvoaPh+Hvz3eZg2BdJSAFDq1MPtcJA+bQ61awQKiipXXomoUgXNaJVJVal6880hm/a8/TZCkiIngJRljs6YQc2rrop8njJk795tzlAI2t1/Py6zTrTjREiS+e2BNhuH1qyBpk3ZuXMnGS6J8vhwN+/YRXqe7mysLAkij7d/UlJSwL/hCDdmJFsLCwsfEuA0tOKtt97i0KFD9O3bF1mWGTRoED169KB9+/Z07NiRevXqMXz4cPLz8/nwww8DqouFszHDF198QVJSElWrVj2x67Sw8FGdplzP+ySxjiTW4qGQGKpxDhfjcLjA/SURVw8zy3Gy5d/rDqdw0cyqAnnHiFn5OVAT28Gd8Nx1+rY6f52XmQpTXoXFP8Cnf0Kcn1PCZoNJ38H7b8CUTyBZ3zahJtagyOEgdcYCajdqEnDaqG7dsNWrh9eocrAkUTVMXo19779vcPF6/yMzZtBgxAhjWz+ydu40VyhGkmh+001UadCgXOOXFyFEyKI54eZ0ZN066NRJ1yWpqbSTZIRRUR8f2/bsJwVdy5xJmux06rxI44azs7Cw8HGW6bxK43QCkLHTjB40o0dgQ/RoyJoTulMAEkRIVAmAR4Pd2423iWkQ/fcUiL6daq/fCu6C0OJFU/UE4x8/DmPGlR7fsRXuGALbt+jCRLaBqiInH0JSVbQQpWilmBiqPfgg6W+8EX57nCxjq1WLWF+2eX+8WVmk/fabcVUTReHw11/T7rPPEJJBiWAfh5Ys4di//5qyRdOo3aOHsd1JQDgcqEKwc8wYQ1vVF264ePFi0DSiWj6BFCbsulB2MLaFPqYqJAp+mVvSVlkSRJ5o/zVr1pgKvw43ZiispJUWFj5sQKyhFb/++mvQsfJWFwtlY8Rdd93FpEmTuOWWW4iJiQloK84lYGFRXiRsNKYzjekc2BA1CtyTI/bVNJPV0TQNdu02UUFYIXrN15D4MAmv3Qo5maH7qArs2wr/HQn//aH0+KEDus77Z1WpztNU5PRUJFXRX5dByDKJjz/O0YcfDj8vSUKKjaXq7beHuDSNw998Y+wYUlXS5s7Fk5FhOqdT+qZNHDJ7P1dV6l58sTnbE0Sy249P5wFzmo9BDrfATKnW04Qg//e/S3YfnIma7HTqvEjjlsXSeRYWPs4ynVepnE5hcVwBUWOgYCy+uvYBzZomUDXJcN82AG6voV/KNypyRhI2lxeRY+C9V1WYPRnueQ1i42H/Xuh/EWT7ElH6iQMB2L1eEh67W4+EKuP0qfHyy7g3biQ3xBcQWUZOSKDBvHlIzmDXqZKZabqMrup2o+TlYStbajYM68eONVceGIht3Jj6ffqYGvdEie/RA+nwYZqPHWtom/jxxyxLSytJppiwfib1Zr8QMo/E2BZjGLNDH/PAoP+S2b5/pUkQWRH9Q2HWDqyklRYWQZisanK6SEhI4JlnnuGDDz5A8rtPCSHYs2fPaZyZxf9L7B0g5m3Ie5xQOg8kvHEmq7ApQJHH0AxAKsxCVr3Yju6PbKgq8MdMSN4PdRpBWir06wFHfEnByziBbF6FavfeBHOWQRm9ljB6NAUbNpA9ZUrweWw2hMtFgzlzsIWIFlcLClALCkxdG5pGUXq6aafTpg8/1HWeierLjqpVaRpi8bMiiO/RA2nDBlM6L/6VV1ipKCW6JHbnXzSeHj7R+dgWY3hsxzscuewx0roNrjSa7FTpvPLYWjrPwqIMZ5nO+//hdAKIeQvklpD/X1D93ghRmxxlJAVeGU2OQSgGeXps0YCJCnGg505SvMaJuUHferdyLlx2I/z3OcjOjnjjdv2xEBb+Bpf3DzieM3MmuQsXhuxjb9CABvPm4WzRImS7HBcXeY5+CLsduYxXMxye3FySfvvNdBLxHu+/bzqC6kSpMXAgjB9v+Bm56tal9cCBLJs8uTSZYts2cHQ9rPqJUElJNUB0v5EGNz5BA0mqNAkiK6J/KMzaFWOJEQsLPyTOaDHy1ltvsXv3burWrWtsbGFxMogeA3IjyHsJlM2lx0UCRI0iLfE2NCYY5/kUEhHTMZTBpipoprZhCd3xdOPD8OGbcORwRJ1n37AWZnwNNwducctftIicGTNC9pETE2kwezZRnTuHbJdcLoTdjuYx51Sz+8pwG6FpGju/+caUwwmg88svY3Odmh+whJ49YeNGw4VPW2wsbYcNY+VXX5XqknbtIH0TLBgftl9Rix7UueM16tgclUaTnSqdV15bS+dZWPhxlum8U/OX/6lACIgaCdV2QfwqiJsN8Ush8QB52v2AILvVi8bjXPIuxIVOwh2AJFPUqBsCDWEyeoicTDiWDj99G5gTIASaLMOkcQHHcufP59ANN0BR6Gp9ngMHOHL//WGTedsSEqjWq1dwNb0yCJuN2kOGmHYMuTMyTDucAOqcopBr0BNZKi6X/v3wS8pelsLDh9l2332BjilVg/PuhNr9ISMK/N52TUDuVWNg9FdB0WgWFhYWJ0yxGPF/nEFcddVV/PXXX6d7GhZnG86hkLAJEtZB3C9Q9Q9ITIaYl0FE41aLo4YibLXr9BLUaxdRE+hDCDw1WgCa8VY80LVVbiYUFsLUz4wdNJIEn30UcKhgzRoO9O+Plhd6gVRJTyf5rrtQ3e7QU5Ykag8dirAZrCnLMtV69sRhMnG56vHgzTe5IAs0uOwy07YnipBlFKdTv+YIesybk8O/t94aqPM0DVoOgYZDISsW/N5W1RGDR7Jz7J6pYDOZH9TCwsLCLGeZzjsj/1pWNZi4A8Zuhm/2QK65BRsdIcDeGZwDwN4DROmNt6D+zXDhBF80k9DbhE1/bouG7hOh1T3Qa5RvJSzSJBXyuo1AFQLNrNOhWi3Y+m9QmHXIy1AUWLcq4FjKk09GjthRFPIXLSI/Qkb5+rffbiiENEWh8UMPGc6xGIfJlTLQHVp2k1v2Tho2G20mTTIUmGlz5iCKivT3d9I4aF8fhg2Ab36BdQWwUgbtIjKGTyBfjiH3ykdBiuzAs7CwsDguZA1c3sDHGcSGDRsYNmwYDoeD6tWrU6NGDWrUqEHNclY9tTg7UTWYvFPXeV/shozQPpTQCAG288HZHxyXgCjdnqZgI/O8CWD3RXb76zzJCRe8Aec+Db1HG0eoa5DfbQQaImT+pSAUL1SrDQf2QU628WWoKmzeELBol/r883qhl3ALeV4v7g0bwkZCAdS/4w7jnE6KQpPHHjOcYzGS3Y5UjjLbzmrVTNueFGw2zv3+e0NnW9bKlYhih933X0PHJnBdX/jf97A2B5YLyO8EI/9HyqsbKJKdYDOR6dfCwsKivJxlOu+M3F6XWgj3rARZgKJBjA0eaQ0vngfyibrJWt4NTYfBnm8gfa1+LPECaHoz2Kvory97FDbMgv3/hKlgJ6DLMNwtr8C7ZI8uHIyIqQrdroS1q4xtQ1C4bh3u9euNDW02MiZMIKZv36CmosOH2f2f/xhuNWv76afEd+1qem5mE0sKWabZkCHI5RAuJ4ui5GQTyeE1JI+H6Hdfg/99EtzuVeDPFVQ5mAZX3FBpq5KcrP4WFhYVh7Ap2KuYjyw41WzduvV0T8GiEpNaCHeuKNV5Tgnuag5jO4HzBNdyCmsPgPbDYd/3kLJM13EJ7aHZreD05ZzpcQf88yNsmR8636eQoFVv8jsOw7v2M4RBdDqgO6Z6D4HUyNVay5yo5JnnwAHy5s41doZJEhnjxoWsUuzNzGTnqFGGOq/5K69Qc8AA07M8unIlqpkte5JEnYsuIrqWiR0DJ5milBS0MDsBSlBVJK8X28QP4aM3gts1DVavgyeeR0ydDVQeTWbpPAuLysXZpvPOSKdTMYrvfpnnhdc2wf48+KKHcUS0IfZYaHlP+HZnNDy2CKY/BCumgurVBYimgrMK9H0YBr4IR1NQJBveWo2wpR2MEEEkYNgj4IqC1u30KiYGq1CaLCM6dCl57d62zdy1eb24w1SRO/if/1B09GhkQWOzUXvwYHPn8rHx3XdNJRHXFIVzI1VkqUBSZs40l3sLyJ/4CVXDRVIrCrbdO7F5iip9VZIT7W9VILGwqDgkScXhMvgD6jRw22238cUXXzB06FBEmJvxd999d4pnZVFZKdZ5bhU+2Q47c+CX3mA70QVGWxScc6v+CIVsgwd+gh+ehD8mgLewVOfZXHDJXTDkbUg7hipkipp3wLFnU/jUCEKCq++E+OoQFQuxcYbRTpokIdqcW7IlrGjXLnPFXlQ1rCZMfv118k2MU+fGG43P48emDz/Utw8abRlUVc5//PFyjX2ySJs1CyHLeqSYATlj36BGuAAmRYHkw8S9/SI0O6/SaDJL51lYVC7ONp13RjudAlH5p/BfvilYhDP6EDI26tGeVvQhjton/3SuWLh9Mgx+Ezb+CgVZULU2nDsAnH4JtoXg2AvTqPni9ZB6OHDFTJL1PABXDofbn9WPVUuEQTfAzMh5nYSiwMhRpa/LER0U0lZVyV640FjQqCoHJ0+m6RNPmDqXJyeHw3/8YXpuieeea9r2ZOI5dsx05T63CFUZpxShKji8Hi7t2YuqtUu/e5W5Kkl5+lsVSCwsKh5JUnG6yrPn6NQwcuRIAEaNGmVgaWFRPlQ09ni382XuIqrG7UUgUZvWtKIP1Wh48k9od8KN78PAl2DjbMhNhyqJus6Lji+1E4KMp6ZQ6+UbIWm7fqxYTxQvuHW7Ah5+Xz/mdMKtd8P49yI7aVQV7iotaX3COk/TOPb994ZOFyHLJH36Ka3fecfUuTRNY+/MmaaTiFc/7zxTdiebovR0Uw4ngKKQFRD9ULy45s+Ge9rTq0+fM1qTWTrPwqJycrbpvErhdHLZ83mw57u0qrOVAlXCje7YyeQQ/zKHzgyjvdYPiuZC4afg3QDYwd4Toh4AynjpvfmQsRlUD1RtAa4IiRRja0CP2yPOT6ndGL7cAD9PhB8/haNJuhDpcAkMHQ2XXBMYnvX0q7DoN8jOCnsTL7y0L64+V5a8jurRw9wqkywTc/nlQYeFopheQUudN8+008mbm2vKrhhPXh62qKhy9TkZ2BMTKdizx9R74DRhIzRo5S2gevv2Jccqc1WS8vQHqwKJhUVFIwkVh3TmrYD179+f7OxsLr300tM9FYv/R9ikIu6+eBwXNFqDqkpk+XReFslsYyHt6U8n7UaE508o+AS8qwAB9gsh6n6w9QgcUHHDsU2gFkFsE4iOEK0RXRW6DY84PzWhJkxeDb9Mhhnj4MAOPbqpdRcYOgr63KBHsRfz4BN60ZgjyWEXGD3nXYBjSOkWOdf55yOio9GMEnbbbCFTKAhFQQuTYNwfTVFInTPHvNNJUVBMjFtMUbZxPquKwFG9uulIJ2ckh5MP4fUiq+oZr8ksnWdhUTk523ReJXA6adx/6Qc0r6WvLkmS6teiP1+nfUnTrBeI8fwDyJSUwnUngXsysdI9QG1EUQasfBO2TwKvz1kibNB4MHR8AeJbl57WnQ+rp8P6n6AgG6o1hO63Q6teoff3xSVA577QpC24ouHcHuAMk4a+YWOYswzuGALbNvuEigBNRVNVPDYbGe9+Rh2/BOX2OnWIve46cmbOjLw1T1VJuCfE1kFNM729TC1HhRJbTIzpcWWXC0fVqqbHPpnUHDyY7NWrzdmaXGwURrkDLCwsLI4TCRUH5v/QO1VoZqu1WliUg9sv/JwODfU8m6F03mZtFs2zxxFftBxduvp0kPsguKeB8zbgFUAjdvsbsHAqFBXnpRHQoD90eAFqdCo9qbcI1v4Aa2dAXjpUrQNdb4b2V4UuEhIVAx17Qp0m4HBC++4QXSX0BVWvAb8t13Xe2r8DdB6Kgtcmc2zCNGo7S/d4SVWqEH/nnWR88knkBUavl4QHHojwbhqjFhSYthWShORwoJrRPEIQdRryOQHUGDSI1FmzTNnWMx1UZv3eWVhYVAxnm847451O59TcQdu6oXMUFdMzaxlRnmTfK/8btS5KqkgTsNv+Q9U/LkPzHEX422hetL0zUPfPYm+rzymo0p7oQ2tp9MtDyO5sQCDQ0ISMWDmV/Frt2Hf1RyjRiSWJ9rK++4Rqi6fiPLq/9MwxVUnvNYzUfneh2YLvbq5d26l2TmuqHDuGXJCHEluV3A6d2TXwRor+3cLO/UmkZecEdrrrLuQlS+DYMT1yyQ8NPR2l8tBDbC8shE2bStoyMjL0yCszXyJZpqhmTTb59Q9F8bWvef99c+NKEtX79WNLhNxU4ZIcngjFY+acey5yfDxKVlbE3FOyEEiSOSfaLq/G4bLvMycnmeORI0ew2WxBtl6vl5ycHFPHQvU/kTkVH09KSgr4Nxzl+TzNjlkeKvL7dKaPWVHjVqYxJSTa097Y2CTxxJ+0scwgoxCN+T8MTxVut5sRI0ZEtJk8efIpmo3F/wdqxR3mwmbLItpcmLuGqkW7fa/8F97051rhl6jpWUSJFsTs/hjwv89raAd+Qzs4l/3NPyI3vgeu1O00/vl+7PmpaEJCaKqu81ZPpzChCfuu+QRPXL2S36f0n/9H/KIviDq4vWRUxRnNsUuGcHTg/WjO6KA5Ow7sI7FJc2IPH0TOyUGpUoX8dh3Yec2NuHfsZsfhZFILCgM7XX898qxZcPBgkM4rRh0+nN2xsUE6TzOb8FSSUOvWNa/zxo0z53CSJBIuuohdycmQnBzSpCLvIccaNcLeoAGew4fDO+2EQBICm804DymAJqQKS9pdXp1X1jZc/xOZU3l1XqRxy2LpPEvnWTovkLNN5wntDFy2bNz6XIberCd/dNnzccjhK2ZIqDhV45thYYEDl0EIm4agUHERpUb+AqgICqRoPcJHVYjxhLdXhEyh3VUaHaVpODxF2MNUAfHaZNx2Z0liycATq9jdbqRQN1Mh8DocKOFyAqgqtoICU84Ur8sVGCYeDlVFmBwTQIuK0rcIni4UBdlgdU9xuYhxF0a0Ach3utDMvEcWFhanBRUViRPNRhzI2rVrmWVyJf1EqTrwYprOejvgWIOBr5+y84cjOjqaGTNmRFwJ69+//ymckUVlxF/nOW2FOG3hV3sFGi7VxGqwFwo9kbWeBhQoUUSpBURy0WgI8k3qPFVIFNijAqLg7Z4iHGEcNYosUehwhdV5tqIi5DAR7SU6L4yDSc7PN1VRWXGZ1zAiP9+UgwZAM6sfKwpV1XVehN8nszpPlQQFruiTUL3IwsKiIrB0XsVQUTrvjPyr2SvsjG04BoD/XPki9WruCWvbLWc1rQp3IUUKgc2DsVPHMKbuWMNz5x9sS9SRbQgt8p7wA31fIa0okUVb9zBmcfhxNSE4ct3DpF1xBwA1vppI7UkfR7R/864n6X7lVQGJ/jhwAHn4cMjODo5ykiRwOlGmTIE2bYLGLE4S2DUvD88nn4S/KEnC2aUL1SdMCJutPmDMhQup8fbbEe1KkGUuWrfOeMwQSQ5PhLJjupOSSLrzTjxHjwZODz1SbOuYMfT+YiIdRF7IVUZNCDRJ4rV7nqZnBSWI7NSpE2vWrKFXr14AJbbFz0O1G/Wp6KSVZt77SJyKz/5sGrOixq1MY85bPI8pTDkp4wFUpzqNaXzSxjNCD7s+87bwyrJMv379Tvc0LCo5/jrv3ks+5IJGq5BEaB13Xt6/dMjfFFnnFYE2D945FFnraQjyU88jes+miDpPQ3Ck+0McqNqZP9esM9B5Eml9h3NkqH49Cb/+QP2PXg9vL0m8defjXHjlVSRUq1bakJam67yjR4N1nhAgy6iffILWrVvQmMW/o92dTtyvvBL23Mgy9ubNqfn114aJyzMyMlj8++/UeOutiHb+dFu+HFuVMFsOOTX3EE9qKkl33ol7374AO3+d1/2Lz+mmZSNpwc40/Vsm2Pz0f/khp7BCknYfj84z036qdV55bCuTfqgMY1bUuJVpTEvnVQwVpfPOSKeTP26vsyQdUSiqKHkIoz3XRyM3l+CRiD642dhOSDTYNYvqWfVZZGsW2VTTqPPX99R55G3Iz4dvIoejCU3D5vXSyiFT0y9J9d4RIyjMyQkZMixUFYqKcD3xBOfs2YMoE02UnJzM4kWLYObM0korIbBVrcpFv/6Kw18EhSE5OZnFCxca2pWgqrRr1y6iMytcMsMToeyY22bNgtTUgC9+2RntS82m4blNqX5oT2DydiEQrigyxo5H3banwhJEVgQVnbQyFOX5PE/FZ382jVlR41amMecunksyobd4HC+nXoyceXv9L7744tM9BYv/Z3gUO5om9AodIaii5hkPkhq2ewBC0YjZtzGw0nAoOzTqbP2B2IQC/iQ2sq2mUmPZTGo8/SnYHHDDhMj2qoqsKLTJy6S6X6LWAwMHkpuSElrnaZoe7f744zQ/dAgpJiagPTk5mcW//w7Tp0fUeZLDQfe5c4lu1CjiHEvGXLTI0M6fls2aEV2zZuQxK/gesm/SJPbt2xdR5x1JzeRwx1bU378NZFtpsndJ0jX0B5NJvLgPTJxYIUm7K4LTofPKY1uZ9ENlGLOixq1MY1o6r2KoKJ13xjqdJPQd+VsOn0vrOpvDOpYUyVaSzygsEfJuB1BgLnwYTYXkrTi3bIVukZ1OAKQchH1b4e/VYDJ5Y/TM6dBNr8ZSsHo1hUZVJBQFb1ISub/9RuyAAUHNkseDJyUlYoi0NyOD9IULqXP99abmiCQhJAnNRNh1lYYNDaOnIqGpKpumTWP+mDHkHTlSclzIMi0HDuSqDz8krn79iGOoXi+7P/5Y3xYYyVCW2d2iI9XffB+mTYED+yAmFvoNgmG34y4ohG0Tj/tajCiuGOIvTPyfh2o36pOWlnZSb3QWFhYVy5m613/OnDmnewoW/0+QBSgabE1uT/dmS8PaKcjG6ZzN6rwiQDGp9TIO4No5F1oPNbbNz4FNKyDTDSlHjO2B6B++gX7X6NPat4/cX36JnK5AVVGzssiaNo0EX0lrfySPB/fu3RF1nlpQQMrPP9N49GhTc0QI5KgoFBPa1V6lCi4Ti5bh0DSN7bNn89vo0WTvL82RiiTRtHdv+o0bR2KLFoZj7Hr/fV2fRtKmksTu6OrU/3EhfPkZ7NmhFwG6rB/cfCfUrBU2L9XJ4Hh0nlG7pfMsLCoXZ5vOO7kbIU8ScQ4Y1BB614Y63kuRkMMWkDjoqBPZgQAQpohcEKGqlYRDtiOKyuGdLMyH/XvAbs7PJx8oveHmzpljbo+8zaaLljIo+flIHk/kaigAssz+j8Nv/QuF5Fd5JRJtQ1XUM4m3sJCJnTszc/jwAIcT6KV8t82cyXsNG7J99uyI4+Rs24b7iAkxqCgcmTsXrrwavvgRfv8HZv8B9z0C8ScvLDYcnTrp1XV69epVEjrt/zxUe7g+xcctLCwqFxIqTtwBDwuL/y9UdcBgn85rrHVBKFVAC63mDjrqGLudokyeOFQepQiIwnL8QVCs80wusNn27y15njd3rskJiZA6T1MUXecZLQIKwf6PPzZfnUgI5DJRVeFoeccdSMeZz0lVFL7u149vr7km0OEEoKrsWbiQj1u25J9JkyKOU5SWRva//xq/D6pK+tKlKF16wOfTdZ03Zyk88rTucKpgjkfnhWu3dJ6FReXkbNN5Z2SkU5QMP/QsflWFPdzHEoqdIYE3yr3OJnTNXYdd8yIIfZPRapsUGVEKOKKhKD+ynWSDVr1R9q82Ny4CajUAp6t8yRh9qHl55kSMpum2ZXDv3h3COASKQtZqs9ek25tZ/QJICJFryizTrrmGI//8E9lI05g+cCD3bdpEzXbtQpqohcaJI0ts3cb/8c1UADmeCiIVgX+1kBOtalKR87SwsNAJtdf/TNr5n5GRETI3Q1JSEg0bNjwNM7KoTLhk+LZkZ5mDQzzIAt5CQ0Uro/MOO+qRK1UhRi0IrD7sT3UJzWFCJzlUiE2EnPTIdkKGJl1QwiT0DkmdxrA/xXRxFTWq1FOm5uXpDjGjxUFNQ83NDTrsTUszd15NI3/nTpS8vIi5l0pQFIpMVpuKN4hCisQv997LbhOOt9kjR1KteXMaX3JJyHalHDoPdK0nuyKvTFdE9bqKwNJ5FhaVi7NN552RTqeyNKUbTqJZzTSOUfqjKpBoILqhxl2PyLoBfZNd2Ru2DdVeDY9mj7wNT8hQtwfEXwCLPgQ1wo1f9UKvB8hXF8Ihg6+HJEPXy6F6Heh1Bbz+rOH1Arh79KS4AK+9YUNjIQIghG5blvIUKCyPraIgbDY0I1EmyxxZvpwm11xjfmwfGXv3smf+fNP2s++5hzuXhS69HNWwoe68M3GNZvIdhNuXH+q42WNQMdvr1qxZU2JzPHMKddwK5bawqDiKV8D8Od1iJCcnhwMHDqBpGl27dmX16tUBEROZmZkMGDCAY8eOncZZWlRG6tGOfjzHKr4mhZ1+LYK64jy0uFsRmYOBQoJ1ngxyFfLOuQX2RTiJsEF8c7jsVpj5TOS8TpoCfUaT3/AIbD4UefJCgnPOhXPagzPOtM4ouqh3SSC+aZ1ns2EPpU/KW4jarL2iIGQZzWhukkSqURqIMBRmZrLu889N2/982208tHdvyDZnjRpIUVGoZrYDxsdji42crwtOTNOdTJ1nRgdaOs/CovJwtum8SuF0AqjHudSlPensI5sjSMjUpAXRxIMDiP8T8v4DniV+vWzgHEpa3mMUabMpqDeE6EMzdOGhFTtKfNmjqp0HfX8EVYZNcyBlV3jH06X3Qsue5Es10f73BZokI0LZFkcn3faM/m+HTnBeR/h3Q1hxoUkSmoDCvqVZ4+NuvJGjjz5qHCXl9RJ/++1Bh51Nm0buV4wkEduhgzlbMC1aJEkyHRFVlj9efrlc9geXL8dTUIA9KjjW3lWzJnWuvpojv/4aWUAJQdP77jM81/FWADGqINKoUSP2799Py5YtAdi+fXvA81DtRn1OZlWTpKSkIGFjYWFxcjkTq5rIsszTTz9NVlYWhYWF3H///QHtDoeDl8v5m21hUUxNmjOAF8ngIBkcQCBRg2ZUoTrYgYSVkPcUFM2hNOpdAscAiHmLnEZV8Gi+qPgAnefTYrGN4Yp54KgO63+Gvat151IQAjoMgk7XU1BzH9rm8eF1HujOq7te0p83aASX9YdFc0uTU5c192nD/GuGEuc7VuXqq5GqVkXNyor8Jnm9xI8YEXTYVqOG6W19UY0bI5uJcgJd55mIwBKAN99gl0AY/i5nWofMffvIPnyYuLp1g9pkl4tGt97KvkmTIi+IyjJN7r4bYWK7ZUVUrzsenWfUbuk8C4vKxdmm8yqN0wlAIKhOE6rTJLjR3g3iF4OyC7xbABvYO4NUAzUvGRBknfsB0e1GwpaP4cifutiIbwNtRkHTG0D25Sd6chl8fT+snaGLCSHp/0bFwRVPwFX/ASHQYqtRYHehxiUiZ6XqfYsdMZKs36hf+gbO61E6z/Ffw5XdITc7+CYuSSDLFDpd4CjNlWSrXp2EBx4g44MPwjt6JIm4oUNxNG8e1CRXqYJqswVWYguFqtJo1Kjw7SHOaSaJuKooxDZubH5cP3YeRzKzY7t2Ucuv8p8/rZ57jiO//aY78MK8l45atWgUQtSVpaKq1+335TPYvn17SVvJc1UN2R6xD5CQkEB733tyolVNAEuMWFhUMHqCycA/4iJtCPJ4PFx22WUsWbKEUaNGsXHjRiRJ4qWXXqJdu3YMGzaMrKwsPvroI7p06cK4ceOYNm0aLVu2ZNKkSaSnpwfZlCU6OpqffvoJgMmTJzPCxO+khUV5SaA+CYQoDGJrB1V/ASUJvBsAAbYOINfT20UyRZqTtG4zqX50GhyaD6oHYptB6/vgnFvA7stP9MgCmP4QrPhSj14Xsq4J7VHQexQMek3XZM4oChxRKDUbYDuyr1QPgq7zNA0e/wQuHlg6z3fGw+VdIeVosOPJ5+QodDrRqpb+4S+5XFR/+mlSnnwy/Bsjy0RfdBFR3bsHNQlZ1nVehMp1xedvNGqU+cIukmQcze4jtkkIbW6CrT/8UO4+SX/+SbsbbwzZ1nzMGJK++gpFVcO+F3KVKjR98EFT56qI6nXHo/OMNJ+l8ywsKhdnm86rVE4nU8jn6I9QCAH1r9AfkaiSCPd8CxmH9Kgndy4k1IdzB4AjMIJGk2RSxy2l9vpFMHsSpB7SK531uQGuuUvP5eRP81awYBU89yjML1OppEsP0kc9gfr32qAp1Xr7bZS0NLK/+iowdNv3PLpvX+pMnhz2klSHAzkuDiXCPu3YDh2oM9RElZZibDZzOapUlaaDB5sf14/8zMxy98k+fDis0ymhUye6/fgjK6+7Ds3jCTYQgjYzZuCIjy/3eU8WnTp1Ys2aNSUJI4tXoACWzpvHgNmzmTl4cEB7uD45OTmWcLCwqIRIaKZXwHJzcxkyZAh79uwBICUlhT///LOk/b///S/PPfccbdq0YeTIkUyfPp2FCxeydOlSnnzySVauXMnixYsDbGbOnBnxnL1792bMmDEcOnQIpcxixnfffVfOq7WwKAdyQ/0RBk+1rtB2UOQxXFXg9kkw+A3Y+AvkZUBcLTh/ILgCt1xpQiL1vUXU2fwX/DwRDu/VK51dMgiuvRcalNGcderB/FXw/GMwa0ag46nd+Rx74HGUf7dTlmqPP473yBGOvfdeSOeR64ILqD9zZliHkWq3Y69bF8+hQ2EX1aIaNaLB3XdHfm/8sdn0KsUGkU6aqtJk0CDz4/qRffBgufukbQ9+/4qpcs459Jg3j2WXX44SJvqq7XffEW1Q8bgiOR6dF07zWTrPwqJycrbpvP9/TqeTSUI9uOQuQzMtqgpcew+07AhHk3Qxct7FuvMpFI2bwtDhcCQZNvhuFInVodfleBs1gRBOJ2Gz4eraleyvvw4UE8XPVdVwy5gUExPR6VSwbx/uo0eJatAgrE2occ1sszv8xx+0vPVW8+P6kGU5XNrQsNhDJIUsTpCoeb0kv/9+WIcTmsbeDRvIi4sLbvdxPMkYT1aCSVUIMo/DIXYyE0wWj+U/ZijCjRnJ1sLibGTatGlMmzYt4NiRVUn8MnBKwLG4Q6G3xBQUFPDGG2/w1FNPAbB7924uu+wy6taty8SJE/nnn38YM2YMdrudoqIidu7cSTtfwYWLL76Y9evXB9kYce2119K3b19GjhyJ7TgrVllYnHZia0CPO4ztnFHQ/zZocT4c2AkOF7TvDlWrhbavXUfXeUeTYfmfgAZxVaH3FXjPaQkhnE5CCFxduoSNStcUJXLUkRDIcXF4Ijhx3EeOUJCURGzbtgYX7IdRlLyP5L/+ovaFF5oft3h4k1WQ/XGGyMVUovM0jZT339cdTmU1qs9ht2/9egoMchWdiKY7W3RepHHD2VlYnI1YOs9yOoVH02DP3/DPD1CQBVVrQ9fhUDu4Oodr2Sz4/j1I8hMRrmi4+k6493WI9vsCeb1w73D46Vs9NLuY9DR48wVqTBqHGBq8CpX1zTekRAgFzl+8mENDh9Lgt99CroJJHo+++hUBJSeHvW+9RZuPPopoV9pBMZfXSQh2f//9cTmd4hs3Jn3r1nL3KUtxgkThdiN16ABhclcpDgcr9u5FSUkxzI9wWhKJ2+0s7tMnqP10JBIPNabZvuGwklZanI0MGzaMYcOGBRxrMbAFN84aEnBs/cBNIfvXqFGDGjVqAKBpGjfffDMPP/ww48ePZ8qUKeTn52O320vaMzMziY7WS1VER0eTm5sbZGNEcnIyr776Ks7j+IPRwuKM4cAGWD0dctOhSnXofAM0OC/IzPnP7/DtWNi5vvSgzQFX3gwPvAXx1UuPaxo89SBMGqc7bIrzT2VnwUdvUWPyp0jDg/Vc7vz5HL7pprC6yr1+PQeuuorGK1YgfP9X/RFeL4VbtkS8XK2oiJ0vvkjH77+PaFeCoqCZ+OMEYNe339LhiSfMjetHrfbtyTXQp2Wp07Fj0LESnVdUhNS4MYwZE7Kv4nCwMjUVZcaMku2OkTjlicTD6LzTkUjcrM6LNG5ZLJ1ncTZi6byz0elUmA47p0DyEn2vf3xraHkXJPit+qTtg/FDYP9akGx6hkRNg19egfMHwYgv9PxOgM1bRMI79wY7KArz4YdPYOMy+OSPUsfT2y/Bz76QtLJJKVUVKT2VKHcB+EXiaKpKis+zGRZFIW/ePAqWLye6R4+gZuHxGDqINK+XA5Mn0/Ktt5BDJOIO7qCZi3TSNAqPs5pR5/vvZ67JffegR4RVDRGp1atXL6pGR7O3d2/UnJyw/beMGUObsWOp8fTTxIfJF1CeZIzHk8wxUth1uHajPiczwWQ4W7PvUyispJUWFoHYQuz1N8s999yDJEn07t2biRMnYrPZ8Hg82O127HY70dHR5OXlAZCXl0d8fHyQjREjRozg2muv5eabbw76/92vX78wvSwsKh7hyYEtP+o5nbz5ENcMWoyEGp1KjbJTYOKNsH2xn84DfvsvtOwFd0+HuJoAyIqHhFdvCdZ53iL4bSqs+xM+X1nqeJo0Tn9AcISQoiByc3C5CxF5uQFNqc88E1lTKQqFa9eSM3s2cdddF9QseTyGOZ00ReHozJm4jx7FWatWWLvSDuar4rlPQOftnju3XH3qde0adKxXr17EV63Kvn798CYnh537ljFjaPPuu1S76y4SH3gg7DlORNNVhM4z036qdV55bC2dZ2ERyNmm8/5/OZ20fHDPAO8mQAZ7d3D0L2mOOvgtzH1STxqJ76Z8+HfY/AGccytc9BnkHoM3e+iCBHy2fmycDR9cBWMWYzu4E6fiWwEKdXNTFdi5AT5/AUa/A3l5MP79iDdxoSgIVcP1xwK45U4A8n//He+BA8bXb7OR8dlnQU4nzeNBmBQOan4+hQcPEhMiIXnwZM1trUOSiAlRZcQMF4wcydzRo00LnwsffTRkNZLmzZsj1q9ndwSHUwlCoPzxB+1fey1kc3mSMR5PMseK4ETnZMa2LGbtirHEiIVFKeI4q5pkZmZy/fXXs2DBAv7++2/atGlDVFQUy5Yt49xzzyUqKoqWLVuyevVqAJYuXcoNN9zA3r17A2yMSE5OplatWixcuDBw3kJYTieLikMrAvdP4PXdL2wXgPNaEA4AZLzU/P18UAqLO+iLjNsmQIMB0GsaeFV4+1JI2amblNV5O/+CsT3hPyuRMlJwed36OKF0iOKFI/vgvYfgpa91J9MHb0S8BKEqCE0j6pcf4GF9QbHw338pNHMPlGUyx48P6XQSERJnB85ZIW/HDnNOJ7MJx4Ugpl49c7ZlaNG/P3JUlOkqx22uvz5kheLmzZvjSk5m1+HDxoOoKu4FC2g/fnxYkxPRdGeLziuvraXzLCxKOdt03v8fp1PBBMh7ArRs9Nq6QMGbINXFIcYi4yF+4yPB/YpL6u76ShcUR6pB9tHgKKRiVAV2L4dV04he+AeQGHleqgI/fwZ3vwKLfoMyK1vhiJo9o8TpVLRrlzkHj9dL0bZtwcdNhA/7I8zu27TZkFwu1MLCyHaqSt1LLinXHEpO4XJx7dSpzBw+3NC2St269HzhhbDtRWZX4TSNotRUs1M86ZjZKmfmmP9zK5zZwqJyIaHixF3ufgkJCfTp04fu3btTr149pk6dSlZWFsOHDyc/P58PP/yQmJgYBgwYQI8ePWjfvj0dO3akXr16ATbhKCgoICoqinHjxp3I5VlYlJ/C7yD3ftDSKdF5eCA3EaqMw5Eu45IKEUohJVvaoFTnHfwNFg0GtScc3VFaha4sqheObIclnxK9U9cNEV0vigKLvoOH3oPt2yHZ3FaxqJ+/K3E6eXbtMtUHRcEdIYm2WUzrPFnGkZhIUXqkmkqAplHrOPI5AQhJYtisWXx12WWGto7YWPp/8knYdk85oq1Ma8IK4Hh0nlG7pfMsLCoXZ5vOq1ROp2yOsJ0lZJGMhExtWnEOF+EomAy5/luw/JJEq8lUE8NxSo+hEUk4qLBzKqyPDu9wKkZIsOhDnGtSoF3oLVgB5OfAllWQlmI6OkhKPVp6OqfTdKSPFCKJtpBlNEkyLqULOOvUIaph+KowgQMLXDVrkm8i0WDqunXmxgzBuTffjJBlfhw+PGwyy7qdO3PLggXYfftXQ+GsXj1sWwBCmFsBrCBO5vY6q6qJhUXlRDqOFbC5vi0qTz31VEmySQCXy8WCBQsCbB988EEe9Nu6XKtWrSCbUHTp0oVNmzbRtm3boPyBmqYhhCiprmJhUV7ySGc7S8ggCYGgBs1pziW4CudAjr/e8tN5Wjrk3EjVbY2AoQjC6CVNgYPzYcs/4R1OJbYqLP4Y55GaUL+38cQVL6z9HQqNtywUI6WmlDwXDof5fmHya2iybCrptxQdTWyYCr9BCEF0w4bGTicgfVPoXCRmaNa3L8PnzePrAQNCF3oBElu25PY//iA6Mfxir8OX88QMDrOasAI4mdvrLJ1nYVE5Odt0XqVwOql4WcEXbOd3BBIaKiDYx99sVKdwQ+4PEZxJGqSBQIu8UgXglqHIxN5KTYWDGxBFkfc5B45dAHHxpp1HatXSsaN79TLnrJIkYq64IvR4drtx2LUk0fD++xGyHNmuZFDVlMMJYNf06fSaNClsqV8j2t94I22HDmX9//7H3x98QPbBg9icThr17Emf118noUkTwzFq9OqFvVo145UwTaPhLbfo2yF/+ha2bQa7HbpeBH2vKjE7HdXrjpeTWdXEqkBiYVHx6GKk/CtgFc0m3x+We/fuBcDr9ZKenk6NGjWQyhlVa2FRjIbKWmawiVnoiq1Y561hvfYNN+XOQi5JvBSCLLBl7Tc+kSJDTuRqqiVkHEQUhl/ICsJdAFVrmjbXqsaXPI/q3h3hdKK5Df7P22zEhNnWoNrthg4nIcs0GDECW5XQFZKCJ6mRadKZdHD+fNxZWTirVjU3dhmaXX45zxUWsuWHH1j25ptk7N2LbLNRr1s3+r7+OjVMVNyreu65xDRvTt6uXZE1syTR6LbbwO2G2T/ApnW6zu7QGfoN0jWfj1Ndve54sXSehUXl4mzTeZXC6bScKezgDwCfENGfATQt3E7AilcIRK7JRIiacUlYf5RaJiOCAOo0hjYXgsMJRcZfsMKrrqE4ZsnRuDEx/fqRN2+eXv0uHLJM/J13hmzSbDZievQgb9mysN2dderQ+JEQWxDDUY4Ek0pBAUphITYzCcrDIMkyHe+8k45hrtGwv8NBizFj2PzMMxHnbktMpIE7C9rU1rdD2u361+3DN6FufRwvvwucpup1YdpPR/U6K5TbwqLikFGIxlyOk9PBgQMHuOeee1i/fj01atQgNTWVDh068Omnn9LQbLSshYWPf5jBRn72vdIC/m3o3ousGaQmMJe5oGJ1Xt2m0O5CqJYIx4wjgwr6X1uySVCOjyfullvImjIlsuNIUUi4996QTZosEz9oEJk//RS2u1ylCs2ef95wbgFE0p0BE9BwZ2Qct9MJ9K12bYcOpe3QocfXXwhaPvUU/xjoRMnlonHNeGhTB7IySp1MHg8kVod3JsAF+nbBU1697jjbLZ1nYVG5ONt03hnvdMrgIDtYEra9ujfdYNucUaMfLsAmg9fE9rqGHclvfBNsNti7L0nQqjM0bq2/Hn4n/G982KgjTZLRhKDg8gHE+x2v8+mn7O3SBSUtLVgA+KKH6nz+Obaa4VfZig4ejBgx5T50iNRff6XO9ddHvqYy5zWDZLcjh9j6d6qJbtoUW2ws3uzs4EZfhFfHYddie/Hx0uP+od5HDlPtnmFI9/yHS/teViHV6xo1asT+/ftp2bIlANu3bw94HqrdqM/JrGpiVSCxsKh4Qu/1Pzlla08Gt956K4MHD+bXX39FCIGmaYwbN45bbrmFP/7443RPz6ISkU8GG5kdtr26Nx0FgRwuygnM6zwb4LJBoQlHSkID8i+5DZZvMLat0xjOv1jXfHc/BG++EFZraUJCE5A/6Abi/I7XfOMN8v/4A8+ePWEdTzXfegtnq1Zhp1G4Z09EnefNyiJ5+nQal7MqsGbS8eSMjzc9bkXhqlcPZ61auI8eDW706bzz7hmB62m/98Bf5x1LhzuG4Hr3M4AKqV53PDrPqN3SeRYWlYuzTeed8U6nwC11wZjSGQa5vkuQJGjbDTatjJzXSVOh1ygK6l6EumU8miQjwtlrGtz1Uunrl8bC1n9h5dLgnAKyDc3lotDpAldgRJC9QQOarFrFoRtuoGDFiqBzRF16KVXCbK0DEF4vnv0GoedCsPOFF6g9dKi5bXCSROKFF5K+cqWprX+K243tNDqedn3wARsffjhsuz0xETXKRb0vp4QfRFX1nE9Fbpqfcw51/KrynayqJvt9n9N2v2Sh/s9DtRv1SUhIoL0vh8OJVjUBqwKJhUVFE3qv/5kjRjZu3Mj8+fNL7hVCCO6++25eiFDMwcIiFDv5k7Db5gBhJqi6WqQR/JBkaN8V1qyInNdJSNB7FO72vVBXbIqs80AvFlO87eDh/+jbteb8pL/210eyDWQZt9OFlhAoTm2JiTRevpxDw4eTP39+kK5yduxI7ODB4aesKBRu3Bh+jj52v/IKDe+9F8lEyWyEoG7//hyaNcuUzlPNRkVVEAe++YbVw4eHXRSVY2JQXC4affFp+EE0DYSg6kuPw/DRFVK97nh0nlG7pfMsLCoXZ5vOOyMTMBSp8ODfcOtSWJ2dFNbhBJAum8irFAcIfXUpIkKG6ydA1boghfHHCRmaXwxdbgRnFIX2KJTajfQ2/32OkqyLi2f/B938nEFRUTBjPrz0NjRoXHrc4YDBw0j7fgFqmJxK+UuXUrByZcibacGyZezt0gXvkSMh+0oej3EVO00jb9s2steujWznR6Nhw0xts1PdbnZ//73pcU82+Tt2sNFg66AnLQ17UZHh9QhNQ1I1HGtWnswpltCpUydAX10rThzp/zxUe7g+xcctLCwqF8UrYP6PM4kBAwZw5513snXrVjIzM9m6dSt33XUXV1999ememkUlwKPCI6t1nbco/UDE2266LQHJyKXkAszsAtIUGPgB1Gmt67RQSDLUbQM97wdJosDuwtOkbWlbMbKs67HR78CVfhV2bTaY/D28/Smc09LP3gZXXk3a9N9Q5NAas/Cff8hftChkm3vjRvZ16qRXNA41ba+3JJInEkWpqaTNn29oV0zjW24xl05B09g2JcKiXQVTdPQoa26/XZ9rmN0ESl4eclERIj8v8mCahpSZgaxUjBPteHReuHZL51lYVE7ONp13RkY6Zbjh0+36Da5mU4k2ceFtd0Y1pWP+RiKtcWkaaDaBgguZQkQZJ1bx9rxDjZ7k2FEV+6BJNPhtDDFHNqIJfbub0FTQNLKa9eFQn5dRt24nIyMDTUiseGACjfasodpfP+BIT0Z1RpHdoTfHLh6Cp1ptCJGE0da2A7XanU988kFdKBQVoX3/FdLePYjOvYMS/ZGejnzbbaCFSYju9eI5dIjtt96K+s47ge9nRgYiwk24LNv/+ouoMNVR/McEOHLggKkxEYL1U6ZQdP75hmMGXfsJUDzm9g8+0J1ukXIlqCqyqpp+n7KWLOBwk+ZB5/r/nmCyeKwkgyTy5fk8zY5ZHiry+3Smj1lR41amMSUk2mOyQpMJ4gM2PFc8x1PV5FQyYcIE3nvvPe655x5SU1OpWbMm/fr146GHHjrdU7OoBBxzw0dbdd3mqiPROUEghwlp2utqRLfctdjwRoxuV9s40dYKVGQkQt/rj9a7n5RUB3L/T2gw7ylik1b46TwNoSnk1O/CgSveRNm5V/99EhIr73yPRgc2Ue3P73Ec3Ydmc5Db7iLSL72eolqNQuo8qWU7arXrQELSPmR3oV7h7teZ2A4kIV3UL/g3r7AQecgQUBRds5XF68WblcWuIUNQpk4NaMrIyNC1i0Ei8WJ2r1jBUYOcHMW/zQd27DA1JprGv19+ie3KKw3HrIh7yNaPPkIzun5FAVXFLSRcBhUMVdmGrKpnZSLx8mgys5+ppfMsnWfpvEDONp0nNK0c2aBPEY1bn8vQm28FwGkrxGmL7PmzaV7sWuTViLyCaAQaTuFGFmWdTgK36kAhMNRY0hRsvnE1wCvskaOlNA1Z9SJpGhqgSDa0UNFFqkp0YUFoUQEUOpxokhQQ8SQXFWErMvfFdEdHB0c1eTzYjKqi+FBcLjSbSX9kURHC5LyQJLToclSCOYnIeXmmVuo0p4MoE9dT6HAgAR67+TLHFhYWpw4VFekkB/OuXbuWWbNmndQxw3HpwFjen9Ug4NgLA885Zec3Q25uLsuWLSMjI4O6devStWtXnAYLFhYWEKjz7HIRUfbIyVRtmoJdi1w0xl3kxKvIuIQbWQQ6HzSgSHXgxY5/Ygbh03nFdfG8woYmIkQLaRqyqiBpqk/nyWihIqY0lajCAiQ1nM5zgJBQ/LSW5PFgN6nTiqKi0MpGNXm92AoLTfVXnE40M9vrADwehMl5IQRaTIw525OMnJ9vatFQcThweYowU6c53+VCCxOVZmFhcXqxdF7FczJ13hn5S+oVdsY2HANAfFQGbw0ejSxFuJGo0Cn7AO2KViBQKN01qKBqMWw/PIbvZwl6+pLkufJ3EJO9BqF5cUc1Iadqd33bXDkpSb7XsyfNV/1EzTmfIRfm6QJEU0GDnHY9OHTrS3jja5T0a/LoSGI2rkWEWZEZO2IMo3+YxI7vF5aIAumOOxBr15rKYaW89hqaX+hbRkYGf8ydS8d33zW8IYvoaOr8/juSgXOo+No72mwcfP11E7MCOS6OLvPnI4cZO1ySwxOheMy2EyaghUoeXoYdY8bw2P/eCesQLGbsiDFcXr82otflQec6Gckcq1Spgt1uD7L1eDzk5uaaOhaq/4nMycg23Htv5vOsyM/+bByzosatTGPOWzyPKZy8rR7VqU5jGp+08Yw401fAvvvuOx555BEuueQSatasSXJyMnfccQcTJ06kT58+p3t6Fmc4/jrPaSvk3aH347S5w9cn0QRtcw/QuWAVAjcakk8PqWg4SM4ew+7Dl7N48WIu7NmPWq4sqmSvRFLdFDnrkxN/CZpk0snih//vU7ONC6n100fY8jL1BUVNQ2gauc0v4NBtL1FUszRyqP5r/yH+97lh80CNHTGGR77+mG0zFqFWiQVAeuIJxPz5CAOdpkkS6sMPo91+e8A8l8ybR8d33jFeYJNl6ixYgFy9uqlr71K9OvtMVjYWDgddFizAHua3vCLvIe2/+w5l3z5D+y1jxnDj+LE0MvF306v3PcMlFZBI/Hh0XlnbcP1PZE7l1Xnlsa1M+qEyjFlR41amMS2dV7GcbJ13Rjqd/MksSOC7tTcxrPNXIdsFErJko178VCS1ChR+CcomQAZ7D2TnDSSomcBEvyR57YHwyRhL0DTYsxLW/QTuHD3XU9eboUYToDT5Xsdl31Bl9sTSOfmJjLitK4l75w6YvAqq1YIdW2HdKsNTO7IyaJe0CwbpleT2CoG59SuoV706Ce1Lww2Tk5NZvGiRqWpzVTt04NxOnRAGeQGKr/38W2/l6Lvv4sk1rles5OTg+e03zn/xxYhjlk1meCIUjxndpAl5GzcaijEzgemaEGhCUGPwjdSpVy/oXCeaYLIiklZWdP9QlOfzrMjP/mwcs6LGrUxjzl08l2SST8p4xZxqMXKm7e/3Z/To0SxZsoRWfpW0Nm/ezNVXX82ePXtO48wsKhtur4svV97J3Rd/UpzDuQwCISQaxo5HiqkH7q8RXl+SY1snhPNm6tWMQ4op+1tiMu9E0npY+z3kZ0CVGnrOzjp6xeESrbNpHnFfly6w+TuGquxZT8u3boXJq6FeU0g5CovnRS5IA0hFbtr+uxbu0quoHXA6yTURqSMkiVrx8dQoq/N+/x3J5UItLIyodaq0bEn7Cy80rChcfO3nDRlCyttvk3/4sOHcNI+HnG++4dLx4yOOWRH3kLjmzck8cMB4ix0Q7XSAwR97apVYFNlWKTXZqdJ55bGtTPqhMoxZUeNWpjEtnVexnGydd0YmEi/Lwq1X8b/lI8kprAKA0CSEb+oJ1Kc/L5BII5ASIfoRiJ0MsZ+B63YQURFGjkDqHnj1AnijOyx4F/76HGa/CE83g89uAnc+AJKqBDicglC8kHYYxj+jv57/q3FCb9DDpueVlhB2NG+uJ6c0gaNp0+CDkkTD997T1VwE51PmsmWsu/56UzdtAFt0NOc++qgpWzSNfz/5BMUTOUS+Iqh5882mKq9okkTuXRH2qvreO7fDaepztLCwsDgeilfA/B9nErVq1SK3zGKDEILERLPlYi0sSvl7bw8+WTKazAJ9Fdxf58VRi6v4D3VoDVIcRN0HsZP0R9R9+rHjIesIvHUpvNIB5r6l67w5r8PzbeCDfpCn5yERqkrsV/8NP46iQF4WvDtaf71kvq79zPDbzyVPHU2bmtN5Xm9onScEjSdO1BcNI+i83K1bWXPllSgmt8wJWeaCZ54xZYumsf2LLygyEVl+sql5003mtKsQ2J981tAs+6lXTC3WWlhYWBwPZ5vOO+MjnYr5a1cvlu+5mI4N1jLhksPI2KhDG6rTFGFq01kZjm2EdN9KWeIFUO280raMQ7qzKTddf62WEQ+rv4PsFLhhCnbFgybbEJEEhuKFuV/Bg2MhP0+vfmK0mqWquq2P+JEjyZ42zfCybA0aEO2rblGWakOG4MrPZ8dTT0Uc4+jMmRz47DMa3nuv4fkAOj3/PGtfecVUzqTC1FSydu6kWps2psY+Kagqh7/+uiTVfNhvi6ahOBzk3vcQsQkJ8O6rupgsdi55vRBblYyX30HZbTKBuoWFhcVxIKMQTf7pnkZYmjZtykUXXUSvXr2oX78+KSkpLFy4kPPPP5/rr7++xO677747jbO0qEysTerKPwc6077uesZfcoBou6Am51Cb1sen87J2QMpyvWJdQjuo3qXUiZCfCW9dDGn79Ndldd6W+fBOH7jte+yqr/pvpMglRYHlcyB5H+QZVEbzITQNcnNKXsffeSfH3nvPsJ8UF0fstdeGbIvr04d2kyez6dZbww+gaRz76y/2vPEGzU2Wvm57332seOIJvCauTSksJHXtWuqF0aIVgqZx8H//Q0XXeBETzjscFAwfSXxCArz4BLgLS6v+eRW9yvQbH1HQ+yqYGGFR2cLCwuIEONt0XqVxOgFomg0prysdTmThIXUV/D0a0v4OPJ7YGbp9BDW6wq+v6g6nsiKkZCIKbFuEa+scZFWJ7HAqxuOGbWuhXgPdeWGEJOu2PqJ79SLq4ospWL48YnWSGq++GnFrXOby5QibDc1gDvvef58G99yDMLHKI8oZ8aOYTHR5MlDy85ELCsj991+g9AtfXLGw5Lkk0ezdd9mUmamL0sefhxH3w/QvYPtmkATUrQYdWwF22KWZqgByohVETueYRrYWFhYVh6SpOJQza9XLn4cffpiHH3444NijZqNeLSzCoGkS2Zkd6WbreDxuJgBsuTth7k1wZElgQ3wb6PI+1L0MFr4PqXt1PRcKVYGD64n+Z5qu8wy2yvlmDxuXB2i3iNayjGjQuOS1s21bYocOJeeHHyIuTFZ/9tmIeTczly0z1nmqyv6PP6bZ008jmUgoLoTAFh1tyukEUGQi5cLJQlMUpIICMpcuBUCGksTwATpPCBo99xybijXryFFw/S3w/VewcZ1uVbsqdGwN1RIRhfo1VBZNZuk8C4vKxdmm8yqV00nVYFQrY7tw2DNWwZobQA2xvevYWvjtEug5C5b/L7zDqRghE/33FOAS8xNQvDBwCDz5ABg4XoTihWF3lL4WggY//8yBq6+mYNkyfVWmjPPJ1aULro4dw46paRqpv/1m6HBC08jbvp3CpCSiGjUyvCwhBPEtWpC5Y4epaKfNEybQc8IEQ7uTQcqPP+pz8r1XXnQR4u8m04Sg1jXXUPP66wNXtRKrw32PwKy3YPZYWJMOa6Aa4Gr5BEsXzsUjOYLCrxcvXhw0D7PHTrR/RYwZ7nhaWtpJ3ZtuYWFRiqRqON1nnhj58ssvDW1ujRRlYWERAYGu8453V5NAIXFlf1BDVMPL3Arzr4RLv4Mln4R3OPkRs3IS2K43tCvB64HLL4fEGpCeGnmuigI3jwg4VveLLzhUUEDuL7+E1HmONm2IuvTSiOOm/Pyzsc4DPGlpZK9bR3yXLoa2ANXatiX5r79MbWHbPH48TfwK2lQk6QsW6Dm2fPNSAJVgnRffrRt17703UOfFVdUXGBeMhx9ehXWHYZ3eVMvuwtbsERb//vsZpfNOtL+l8ywszgzONp1XaZxOkoBeteCGxgaGykFwTwc1GURVcF4HJAIaCRvv9zmcQqwgaSpoXph3C3hMROJoCo5DG1Cr9USTZHOrYI1aQmwc3P8YvPd6RAdN4UW9cbU/P+CYnJBAoz//5MiDD5L5ySfBff75h70dOlBv+nTiBodIlO71opUjn5KSbz7kr+3997OsjDc0HNsmTaLLSy8RXbu26fGPl8NTpkD//gHHNMokDNc0Dv/yCw1fey2ws6bB+Dthyf+CxpU1lSe2v01O84vZf/37INlOWQWRUzVmuONJSUmsWbPG+M23sLA4biQVHKcuKNQ0mzZtAmD37t0sXbqUIUOGUKdOHdLS0pgxYwaXX3655XSyOC5kAefGw70tDAzVVCj8BtQDIGLAMQDsnQFwSW6EWhDGoeTTXItuhRwT+kbTsB3bh1pLMq/zmrQBux3GPAf/GR3RtKh1exw9Lws4JkVFUX/WLNJefJG0V14J7rNjB/u7daP2+PEk3H13yHHLo90Uk5FLAG3vvZfDS5aYsj0wZw7Htmw5JakUkr/8Ejp1CjgWSuelrVhBQVJS8ADf/Ad+fjPosPAU4lQ9jMqcxYHbp6DZXWekJrN0noVF5eRs03lnrNNJFrqjSdEADW5uAuO7gT3cTi4tH3LuBffXxSMAKuS/QDWpO7LohVxoVHlDhcLIK1Nl8ch2YyEiydCpD9RprL9+8iU4mgxfTwbZVppw0reqpcgSmW9/SiiXTP6SJSEdToC+bU8IDt14I87163G2bRvQLOx2HDVrUpSSYnxhsoyzHKsbre64g7WvvEKhX8huODRVZfvUqXR4/HHT4x8v+bt2mbLTPB4Ky4qRv38M6XAqRqARt/Mv2h/9Gy6/t1JXJSlPf8ASIxYWFY0C4gwsavL2228D0KpVK1atWkXjxo1L2h5//HF69ux5eiZmUeko1nmqpj/614cvekBMuN1emgdyx0DhJ+iLhzKgQf7LYLsAZ9Z9SKiIiBFMGijly6HhkW3GOk9I0LQttPY5P0aOgiOH4YM3AnWeJIGqokqCjE++olaI9ATuTZtIez3MwqQvgunIvffibNWK6EuCo+1djRrpKQVMRJ6biWYvpsl11xHXtCnZZqoWyTLbJk+m+9ixpsc/XvK2bg1yOoWjYOfOwAPbloZ0OPkTd3Qr7XbPgaEvnJGazNJ5FhaVlLNM552R5bcSnfDCeTC6FbzZEfYPhi8vguhwLjLNC1kDfQ4n1ffwULzO4RB/47S50YQJH1u0rTShYCSETFG981AkmaKWF+iOpZB2kj7evX5RNLIM738OPy+By/tDTBW9YokriqJ25+OxOdBcoavupb/9duT5+UTGsQ8/DNnc4O67Da9PyDK1Bw/GHh8f0c4fR2wsDa+6ypyxppGyapXpsU+ESPmtDG1/+zD85+rPnPdNiTsLCwsL0yhAXpnHGURBQQEpZRYwdu3ahcPhOE0zsqhMVHfBK+fDg630f3deCz/3gvhwXx9Ng5xbofAj9I3yxTrP58zxbiA+40FzJ3cIcDqN7YTAW60xqpAp7Hx5+Iq1Quj7Ah8cW7oNSwh47r8wfxUMul7fxmWz6zqvRRuK7A7UqlVDDmcmmTiyTHoYh07DMBFQAUgSCRdfTHSoKnjhTmm303TIEHN7HxWFo6dK55ms7AwE69+5H4MUub/QVJg3Tt86aWFhYXGyOMt03hkZ6WST4Llzy9HB/S14FoVtFkLxJRM0qBgHYBNwTgvYuSNyXidNIb/L7bDiKMee/YraH4yCNYtKV7SE0EVSTBz894fS1a/SSYGnCP5cVFqlLi8X+5ZNuLr0pfqQvjBzEdStX9JFOXaMvHnzjB0cXi9ZU6dSe/z4oETgDe+7j/3jxuHNygqbqFJTFNN7/P1xVa9uKkk5wN6ffiJtwwaqn3eeoe3xUpSZid1kWUcpNpbiOLidO3eSdjSZ9lv/NNFTg8Pb2bpiMaleW2n/SpAg8nj7J/kiwpJChan7EW7MSLYWFhY+VOAMDLsuZvLkydxwww3UrVuXmjVrcvjwYQoKCvj666+NO1uc9cgC/tO+HB08i/TUCWHxmkz2je4gat4ENu80zOuU120EbFTJePRT6nz2JPw+I1DnATii4IUvoevlIUbQYPE8yM7S7fM92Hdtw3WRm5r9e8CPC6F5abJSTVH0SsVGOsrrJffXX1GyspDLOK/q3noru994A/eRI+ELz6gqVY9H5yUmlupbA44sXcqhJUuoV4HRj96CAuTYWFO2wm4n1WdbrEvarP0V2SiHK0B2KjsX/0Sys1ZAfzj9mux06rxI44azs7Cw8HGW6bwz0ulkhJciFIqwE42EBAUfowdtRXAqCfS8TUZoClw8BJInQG5a6BK5QoIWl1LYpj+smIwWUxU+XACb/4ZfJkPyfoiuAj0GQN8bwBWiysj6tTBsgC5c/G7exaLJtm83DOoNi9dBTAwASnq66YgaraAAraAAUabCiatuXTr99ht/9+hBpJG2PfkkcZ06kWiQsNKfhldeyUYzK3ToW+z+vPderluxwvT45aEoI4NFPXrgTk5GFYKdY8ZEtFftdrYsWwb4kilqGvNahO9TKDsY69eeP/8PNCGV9i/DmZgg8kT7r1mzxlT4dbgxQ2ElrbSw8KEA5Qi79ng8XHbZZSxZsoSZM2cyduxYatasyfTp09E0jZtvvpmDBw/y7LPPcvXVV5uyiUSfPn3Ys2cP27dvJyMjg9q1a9OkSZMTu2YLCx9eivDixkE0EjIUjEOXrOEdBKKKyYhjIUOny+CoB9L3h15glGSo1478jjfBxq/AGQWvfQ87N8Csz+HATnC4oHNf6HervsBYlr274bq+UJxjyaffhG/BT0o5Atf0gj83QvUaAKh5eWhuk//xVRXl2LEgp5M9Lo4uixax7PzzUSMk/d73zjskdO9O7euuM3c+oMEVV7DyySdN2y8eMYKbd+0qd5VjM3jz81nSty/527ejXn21oc7TbDa2rl2rz8unS+Y1vpdIYthf6xUs3YQqbQno78/p1mSnU+dFGrcsls6zsPBxlum8SuN00tDYzxq2MJcjbAPAhpMW2iV09a5GGEUxyb5FKSNNIiQ4bxQ0uRU+HQwHN+iht0LoTitVhQuGwu2T4Fi2Xz8B55wH7S6EvVt8j826MBl0D9Qps2/+7Zd0h1aYaCOhKLBnl17K9fZ7AJDKsd1NOByIqNBb9NyHDhlXH9E0djzzDBf6StCaoX7fvvp+/717jZ1jqsrRlSsrLNrpn9GjydmxA1QVSdNoHi6vgCThaNSIJl99RbbXW5pMMT6e1u9ciq0gM2S3sS3GMGaHPqYqO9jy+F8cyy2sFAkiK6J/KMzagZW00sIiiHKsgOXm5jJkyBD2+HKtTJkyhT/++IPPPvuMmTNnomkagwYN4vrrr+fqq6/m6quvNmUTiX379vHxxx9z6NAhlDL3k+++++54rtjCgkNsYjO/cZANAMjYaUYPunuWIkVwOAFQCzBT9U7zQrtR0PY/MOEG2PWXn87TdCdU675w1zeQXeYvgsat4byLdH23Z7P+76HdcO29erEYfz56CwoKQi9e4tN5aakw5VN4/HkApJgYPd2CiYhxADmMLvSkpaEaVElGCLY98QS1rr02KCo+HNXPO49aF17I0ZUrjXWeppGzdy8HFy2iwWWXRbY9Dv594QWO/f03aJqhzrNVr06TadPItdkCdEnzTwfhTNuLCPPHQbHW0xBse3ghqV5bpdBkp0rnlcfW0nkWFmU4y3RepXA6aWis4H9sYyHCLw2VFzdbWUgXVFM6g2aAUV7pdo+Dq4b+eH4d7F4B62ZCYQ7E14Vuw6FG8R54P6fTwd0wui8k79MdV8VRVV+9BV+/Dc9Mhqtu0Y8dPQLzfzG+YQtg8iclTidbjRpEXXwxBcuXhw+ZBrDZiL3++rAiIunTT0OW4g1AVclctozcbduo0qpVeDv/6UoSl3//PTM6dzYdkbXquee46uefTQseMxSmpJA0fbqpsr5V27Wj1++/40xMDE6meOX9eoLJSCH7kg2p522069il0iSIrIj+oTBrV4wlRizOVqZNm8a0adMCjq3aAgPL1Fo45D4Usn9BQQFvvPEGTz31FLm5uVStWhWbzcbFF1/MV199hdfr5b777sPpdBIXF0dOTo6hTUFBAVFhFi4Arr32Wvr27cvIkSOxlSenioVFGNbxA+v4MUDnKXjYyZ9cQA7hv40+ZN8jIhI0uxWq+krkPfknJK2DNd9B3jGIrQldhkFdX9W17OTSrulH4KHLYfemkoTgAMz4GL77AB58B4Y9oh/Lz4fvvixNIB4OVdF1ns/pJGSZ2MGDyfnhh8iOJ1kmumdP5DB/5Cd99plxugNNo2D3bjL++otqIRKSh6PP1KlMb9MGtchcqe9Vzz1H/T59Tmq0kzc/n90TJqCFWbj1J6ZhQ3r/9RfR9esH65IBD8EXD0dckNYkGdHhKlp37018JdFkp0rnldfW0nkWZyuWzqskTqdtLGQbCwHQykQ0aUIjQ65KNSXT0PGkNnchOUbClo9B2PTVLih93no0dHy1tIMQcE53/REBUZAHj/WF1IO+SfnNsdhh8cptkFgHuvSF/XvMOWU0DfYGeskSx4zh4DXXRO6nKFR76KGwzbnbtkV2OPmRt2OHaacTQI2OHanRoQOpvhBmI/bPns2/48bRftQo0+cw4siCBabySgE4EhNxhsv7dOUoWDgB8jJDOp40ISHszuBfDAsLC4tyMGzYMIYNGxZwbGBXwaz/BNoN/KxeyP41atSgRg19e05mZibRvm3V0dHR5ObmUlhYGHAsKyvL0CYvLy+iGElOTubVV1/FaSYhs4WFAftYxTp+BELoPFTSbdWo6zmCZBSuLkNO81HE7hznWwD03buLdV6TG6H7hMA+DTvoj0goXnh4AOzbqr/2d3YUO5Y+fBQSa8Plw/QKxUaRRsWkHtUjonz/3xIffpgco1VkRSHxscfCNudu3mxaB+Vt314up1PVZs1ocOWV7J81y5R9yt9/s+r55+n66qvGxiZJW74cb06OKVvJ6SS6fv3QjT1vh1/fhfRD4fO4CgkGP398E7WwsLDA0nlwhlav80dDZSOzI9psjWphaqwChkGXj2DQFmj1AFTvqj9a3acf6/qBfnPxZ+8qmHwbPNkIxtSDD/rBhtkBToioP3+AI0mRHTlCwKQX9eeOcnx4ZTLExw4cSPUXfeOEWTWq+e67REUoHyuVI+t8eWyLqd29e7mqifz97LN4zYozA5SiIg58+61p+yDRommI1BTUAwfQqlSHFxZDnP6fvPi7ofncm5qrCjw7H+o0Pylzt7CwsCiheK+//8MExUICIC8vj/j4+IBj+fn5OBwOQ5t4g+3cI0aM4Nprr+Xrr79mzpw5AQ8Li/KygVlE2hu3Naq5ocNJ02S8ikxu06dh8G49cr3GhZDYGc65HQasgUu/BrmMrjm4Cb66D55qouu8sb1h9XcB1cqcqxfAro3GkUsTn9MXDMsr0u32kqdR3bpR6+OP9RdhKvAmPvUUVSJUDJbKcX5xHDqvVpcu5YpcWvfWWxSmp5f7PKFQFYWkMhEDkfDmlSkJpWmQmqrrPNmp67wavhQYfn8DaL6qhBl3ToZzOp+MqVtYWFiUcpbpvDM+0imV3eQR+Ua129WEloW7SPRmhBQlmiajaYJcdRQxAPGtoev7kU+saTD9Ifj9I32vf/EKSM5R+Pc3aNkTMWQiAFHzv/IlKo8wnqrCxmVwYBe0bgdVEyDLoJKDbINLg/fB13jhBbyHDpH52Wchu6W/+SZVLr8cZ5s2IdurX345Bz//3HAVTDgcxHftGnmOIWhzzz1s+ugj0/aerCz+uPdeWr/+ernP5Y9SVMSf/fqR8vvvpuyFzUZM48aAnnjd9vl4XPkFRHc9n1yAuKo4RtyF45mlSLv/gD+nQuYRPM5Y3LKDFbdMJ64oFjZtAipPVZKT1d/CwqICOc6qJtWqVePQoUN4PB6WLl1Kx44dSU9PZ+nSpTRo0IDCwkJq1qxpaGMUSp2cnEytWrVYuHBhwHEhBP369Sv/xC3OWnJIIZ29EW0OOOpx0F4nQrSTjEYURR6fAyW2CVzwX+OT//oa/PRsoM7LPgrbF0ODDkg3fQlA9MKv9eTiRhXyDu2Gjcvh3O7QuJlxZLskQ+cL9TxOflS7/368hw6RHkoXCUHG+PHEXnMNUd26hRy2+mWXkblypXFUuxBUK0fBmGJajRjB6hdeMG2veTwsHD6cjp9/Xu5zBYyjqqwcPpwD0yNVMvRDkohppDuUNK8X29df4irIJ6brebrOi47GfsvtOB/6FenoOlg8BdKSIKYqOW0uJ2+n4F9HQxLOAJ13ov0tnWdhcYZxlum8M97pVIhx+KwiZOZV7U3v7L+p6zmAflkauifIi5dmFLijUClHtYQ5r+sOJwgMuS0WHDv+Iv77+4GLsKUcMJ3DiCP7ocE5cMe9epLJSIJA8cLI4G1n2TNnhnU4ASipqST17UvTbduQ44IrqjS6/34OjB9vPFchyNu1i/jO5Vvhqda2La3uvJNtkyaZ7rPjiy/QYmOhevVyncufra+/Toqv8pwZNK+XpiNGoOXmktevL441q5Ae8AtXz86i6KP38HwzleiFfyK/OAKA9ORkvBMnsnDZKmBV0LiVpSrJifa3KpBYWFQgKuWqauLPqFGjuOSSS2jQoEHJPv6bb76ZiRMn8swzz5i2icSUKVNCHt++ffvxTdrirMWMzkMIFlW9mIty/qaZez9ldR5SA9KLJqJpy82f+K/PdYcTBOq84i15hzaRMHU4iEHYjiYZO5yKObIPzusBdz0Izz4S2VZV4O7RQYfzly4l/Y03QvfRNNTsbJKuvJKmmzdjrxe8HaPBXXex+5VXDGvnIEnk79xJtG8BziwxderQ4amn+Oe110z3OTB3LuKxx6AcKRvKsnviRPMOJwBVpenIkWhFReRfPwjH/LmBOi8/H8+kiXimfU3MnIXIz84racpLToZdEyuNJrN0noVFJeMs03lnvNPJRawpuyLJwcr4G7nOewsUfgVqMkjx4LiOtPQWaFoZJ43qhUPzIHcPyFFQp4++MgbgzoM5BitkmoJrx0KkeheiOVyQl2Xugpy+vZMPPw2LfoMtm8I7nu59BLoH77NPe+mlwCSWZVEUvEeOkDV1KtUeeCCoObZ9e8556SV2GaxSaR4Pq/v2pcf69USXs0TipePHc3jJErJ3745oJygNEtv58cfw3HPlOk8xXreb7e+9F/49KXtem434c8+l9hVXUHD3Haj/rEGEclYpClp6GvmD+lFl43aEX6j7/6eqJOXpb1UgsbA4BShAnqFVAHPnzgVg8ODBDB48uOS4w+Fg5syZAbZmbEIRFxdHdnZpEY3rrruOH3/8seR1586dA9otLIxwmtR5irCxMu4KmnnHgPtLUJJAxIBjADiuwHskBfBzOmkqJP8OWdtAskOtSyHe5/BQFZhlEKmjenEc3oBc72pUZ/i8F8EXpOfM4I77YN5sWLo4pDbRADH4Jrh6cFBb2uuv+0ouh5ubipqbS8b48dR85ZWgZlfdurQZN47N994bea6qytqrr+bClSuJO//8yLZl6PLKKxyYN49UAz3gr/OSvv0Wnn22XOcpRtM0tpQjIl7YbEQ3aECDG27A/dJzKAvmhdZ5Xi/k5ZI/6CqqbN+P8OU8KeZM12SWzrOwqKScZTrvjHc6VacZ0SSQj1Gop6AZPcDWHqq8WaYtOfDljs/hn2eh8Ch6WisVEFDvKj3B5KYlUGT8LdAkGZvmpbDLFcQs/MZ4r39sArS6QH9epQrM+gOeexS+nQqe0iogmoCc+x4j9uW3g4Zwb96Me8MGw7kBZH7+eUinE0Dz558nc8UK0nxf3pCoKkp+PnvfeYe2xfkFTCLZbHR48kn+uPvukO0CvchMsRApllaisJD8/fuhHCsrKX/+yarbbsNbjv8Aca1bc8mcOWhHj+Kd/rVBxJmCtmc33nm/Ye83oOTw/6eqJOXpD1YFEguLCkfhuMKuKxqtzB9tS5YsidhuYWFEHDWpRiOOkUSkPAUCiWZcBLbmYAt2tASwbwasfgzykgjIf1DrEug+EQ4lQeZhw7npOs+Du9NlOPZtNl7YsjuhY0/9ucMB3/wCrz4NX4zXk4X7kXfzSKq8Oz4oP6c3JYW8uXONo7YVhcxJk0I6nQAa3nMPGcuXc/jLLyNcoIbm9bLr1VfpOGNG5POVQQjBBc89x9wwxW3K6rziY1JhIdlbtpQrgiZj/Xr+vvVWCg4cMN0nun59ei5ciKyq5E8YF/mzUxS0tDQ8303DcfudAU1nuiazdJ6FRSXlLNN5Z3wicQmJ9vQ3sBLYcNCCnsYDbnoTlt/lczgBJVVSNDg8H37pAskb9f39RqgKQlPJv/I2Y4eTJMHg+wOTiMfGwbsT4Z6HS5JOakJCaFBlwnsw8gbIOBYwjOfgQeN5AWga3gg3Z01VyVoVvDUsyM7r5eCUKSju8sf/Nb/pJuyxwSuYgkBvp/9antA0/rriCjJ37jR1jqO//86SPn3I27/f9LzkmBguW70aV61aeGf/ZC46SpbxzjCfoNzCwsLihCgOuy5ngsmKRkSKvjDRbmERivYMIHJiTABBa/oajhV1cBosGepzOBE4bsoy+LUbHFhpal5CVZA0jfzLbgYROql3CZIMVw6HuNIoElwueOUdGPM8RMfos/Elq475ZhLcdDUkB5bI9h4+bDpNgHLkSMQ/AEzpPEXh6MyZuFNTTZ3Tn0b9+xNTr15QVFaxw8n/tb/FsgEDSF292tQ5MtatY1GPHmRt3lyuuV2xaRNVmjbFu3A+lE0mHgoh8HxrPkG5hYWFxQlxlum8MzLSSVVVNvmS9gFo1KN6vfakVdukhwEJvxusJhCaTLN917GrRGAEUpwQ7+Dm36l98D/h66NoXrSCFAoPz8KlqRHqqPgQEgjB1iIbnqGPU/f7t9GECArf1YREfpNz2dvpGjS/60LTqPfWCyTM/ak00kfTHSBCVdFm/4h7/Vp2fzwVtYrPeZOSYvpD8zocbNq0KWSSQDUnB8+xY5G6l6Dm57Pxzz+x1a5dcixcQsKynPPqq2x9+OEAASUTGN0UNO/sbH5o25b6999P7VtuCVlBT1NVsv/8k0Njxuih0WYRAlv9+mzZsQOAuG1biZckhFGyTUUha/8+doV5P6HyJYg83v5JSUkB/4bD7HekPGOWh/Kc///bmBU1bmUaU1+waH9SxgOIJ/6kjWWK40wwaWFRGSir8yCW2rW6caTmypA6D6Bp0tUkZacBoX8n9N8SlbjNT4bXGJqCVpSDe/tkXCbmqfke24/lUnTbS9Sf/AwIgnWeJOOu1Yjdfe5ADbguqPX5h9T8ujSBdonO0zS0JfPx9urIrvHT8Cb6KuUePmxa52lOJ//++2/Y39G8XbvMDaSq/LtoEY62bUsOmf1tbvrqq2y6664ALVbscAqn89TCQn7u1o26I0dSd8QI5DJb2kBfTc9ds4aDDz+Mlp9v7jqKz5+YyLa9enL6Kps2kmimk6ZRcPAAe05C0vDKrhPLo8nMfk8snWfpPEvnleEs03lCOwNj4Vu3bs2NN95Y5qgGsoZwqP4VTdE8As0jlYiSAPsS2aG3OYQbu/BgiAaY9GPkiSh9dQuQFQ8OpQjJ7y3VAK9sp0h2BK0EyV4vLnfwt63Q4cBVVLrdzmO3U1QcIaVpOM2s2ACK3Y43XNlcTcNmchwAb3R0UAi4aTwehC9SysiRpzocSEX+Ww0FWlRU6XsnBKgqUmEhwmT+JsXhQPYbU3U40HyOLNnjwekXxVXodOByFwWNgQCvbKPIZUamWlhYnG5UVKSTHMy7du1aZs2adVLHDMfAZoJZ15Y5tuPqU3b+cNjtdq69Vp+YpmnMmjWLa3zbazRN4+eff6aoKMRvqIWFH6F1HiCrCLuGkP10lNen81RjnQcKMVIBpjAhB6GszvP6dF6g/vBINopszuCIH0UhujB4PmV1ntdmw+306QtNw5GfHzr/UBkUmw1vBF1iy801HKNkDlFRIBtEc4Xt7EUUlurZSFqvrM5DCFR/nQegaUhut/GCoI8gnWe3o/n0r+z14vSbW1idByiyjDuqHPm7LCwsThuWzqsYKkrnnZGRTi6Xi3OvGkaax0aMrNI6ugCb373IKxWiSh5sShSSFngJMfY1JMZ8QZzzT4TQ0DQbKdm9mPhNN0Y3m06MZ6+pORTsOgdXyl6EFvqGpwkJrz2G12s8GpiUT9NwJW3FdWAbUUnbUGKq4kmoRXaH3iixCQFjNHr6QWJXLQ26qY4dMYYxk8eWvFZiYtn64+9oPseT+PRTpPHjwwoSDUAIlJkzoWnTsMkDU4YPp+jff423l/0fe+cdJkWx9eG3uidtDiw5ShIQRAVJAoqCqIiimFHMOSOKes0BvYoRvYoJFXMAQUUQCSIiSlByzjluTjPTXd8fPbM7s5N6ERQ+6n2eeXan+vTp6tll+8epU+e43WQ//TRJffpUpNTF8hmLxdddR/68eeimGZFmHcr2oUOpP2JEzD8hwulE8/nCagUkCmStHjqUFiNGgK7jyM6m2YQJ6ClWmruWl0uDs05DBFboRt02lBtfGxHVz86Royjr3DXuvft8PpxOZ8X7WLZV7apj+0/6rK6tnWv/XVu7HMk+D5bfw8nn5OmTGU30zhv7Qw45NKHJAfOXkP0oMPlPULV17m233Rb3vUIRDY/HwwlnXcIOr5MkXdI6qRSXVqlpDK0cQysP6Lzw51qSYwk5KR+S4ZmCEAZSahSUn8L6HQOY/MNW7mk4IqEukAhKt7QhadOK2DoPgdQcPFn3gYi/T+6tq/FsXEbyxmWYnlR8mTUpOL4H/sxaYT7qvfAE2T+MS6jzpMPB8q+mYmRY1xBffIH21FMJ78P/zjvQqVPMv6N77r6bshkz4tetBNB1Mh9+mJQBAxCBBcbq/m1e+cAD7P7hB9s6L5aNcDjQ/H6r/lPgZVvnaRpaSgrNxo3DWcv6WYiyMhqceSpasRWAi6fz9jw+nOIzrdqdh7sm+yd0XnVsDyf9cDj4PFh+DyefSucdHA6Wzjskg065Po3zV7SseF83Ce5qDUPagCNeQLPkRSi+h8pWuiCEn1rp00jytMfjLLa9spU08E74eDgU7g5vpQug6QjNQcGg0TBlSXjxvaJ8+HI4TPvSmoKmWV1SPnsGzrkO7nypsq7TovmJhQCgFxfS1qFBOyuFUL78MptWr6Zk2rSoASMBaFLivuYasu+/nxqDBkUtErjt/vtZeNlliT8Mr5d9Q4fS6OabafP66wghYhYejEXNl1/mm549wYy9bVFU+RoVn2+/Y9pJtWtzytSppFdp11t61bX43ns7dvBNd6AdfTTNr7kOoWnVuveDYXsk+6wOR7LPg+X3cPI5afoktldtIvE3+UfFyCGadn3yySf/21NQ/D8gz6dxzvKjK95nu+DWVvBgO/DES7Yp+xAKr8YKRVj6SQiTDM9MjmsyjR+1exOXRgAEguQzL4Ox78OedZZOCzPQEEDeRW/ArC3hf5/KSuH7kfDdaOs8TQfToP7nz0LvS+C+NyElUBJh8QJbOk/4/bQpLYLuVsdiecwxbFu7loJPY9cY0oCku+8m6847qXHDDVH/ju79z3/4Y+rUxB+IaZL32GO4Fy/muM8+Q3M4qv23ucHzz/PVjBlIG1n0cRcM/f791nnOjAxOnjSJGp06hY2X3XUP3uFPxK6VpeuIWrU56o67EYHMsf9v+ulw0q52OZJ9Hiy/h5NPpfMODgdL5x2ShcT9Vf7vv70U7l8AF/4MRqykHO/kQMAJqu6NE8J64GtpuxIXggzSqDv8Zy50uhT08JUKju4Fw2bhPapb+HhpMdxyCkz7ygpgSNMqMC4l+H0wbhQMG1C5791m2rB1S5XRMuFy0ej776n5xBPotWpFmAZXhsx9+9hz332UP/RQ1Adt3Ysvpu4ll0Skg0cQOHfTG2+wedQo+3MOoU7Xrpw5YQJC12OWCg3+MsabjUZlSdDq7AutceaZnLFsWUTACcDz/MvoJ58a3Z+mIerXJ3ns9xWrfwqFQnHQOUQLTCoUBwJfFS23zwtPL4LTp0BZLGnkmx8IOJlE1kCw3muarCjUHR8T6nSAB+dAj+vBWWWLWrOucM9UytqcVeUyPri3P3z7bqW+C341Tfjpc7j9NCswFbS3S0hNJKFp1PvoI2q//DKOhg0jTIM6TxYWsu/ppym75ZaoOi/7lFNofOedia8dOHfn2LGsfeop+3MOIat1a/pPmYIWZ3taolpPEK7zEtmGktGtG/1WrYoIOAG4H3gYxwUXx5iUjsjKIvm7HysCTgqFQnHQOcJ03mHzv2gJjN8Mr6+MYVDyHOG9MqLQSIMYadSVaJB9PNQ4DrLqw7UfwvPb4M5JcPt3MHwdDJkCTTpGnvr5y7B2UeSKWcVNmDBnEkwJrFw1P9penSTdAU2ahQ0Jl4uc//yHrGuvRdc0dKy7dxD5Q/WPHh11X7zQNNp/9BHNH3sMLVbtp7ATBOv++1+kzVpKVWl85pmcOXly9HRqmz5CV8eqE3Rq+fzzuDIyovv0eEgePxHvsy9ghvw8RJ26uB95gtRfZqF5p8GMi2HqOaQvfxhBNQKGCoVCUV0MjigxolCYwK+74MlFMQxKXyahbNUrC3XHJbkB1O0NKdlw+RswYgfc/aOl855cAcNmwdGnRJ438QOYNzV2ZrRpwIr58NVr1vtWbe3XSWoRvigmNI3sO+8kZ+hQdKw7j6rzpMT47ju0KE1VhBC0fuklWr30EnqUbsIRSMmGl1/GKNu/5fc6Xbty3h9/xNR51dF6UFm1y47eazliBO6cnOj+dJ2k9z+m7JX/Yeghn152Nq6h95Py+3z0pCXw82XwU3/47WacefNtXlmhUCj2gyNM5x02QacgLy8Hs+ozwNwDvmmQIBAgappQQ4uT7RR4zHX8b/hwWg4cczokZ8DCcTBlBKyYagWRghgGfPV64vpImgZfjbS+v/bWxPa6Dv0HQo3IB6n0eikYNQqRYP88uo7DF321Teg6LR55BE+UlbTIC0pKN2ygYMGCxLYxqH/qqTQ699yIrKH9bbJtJ/xlOp04s7Pj2ginE/9Fl1KWnEzxsrWk7Sogdd1W3Fd0QvzUDn69BjZ8BZu/JXnz+yRrpWT+eQ34DsHNuAqF4vAnuNc/9KVQ/D/HBP63Mkq2kzSg/HMSdnkRIOtCQnnb8bmK4uCApe/a9IGMOrB0Ivz4PCz+PnIR8cuRkCiTSppW0Mk04eqbE2e1axp07QnNW0YcklKSP3IkQoj4tY00LbbOE4Kj7rqL9OOPT5zZDvjz89lrZ0teDGq0bUurG26ILKqO/RBOqJ1JAo0oBKbDQVKTJnF9Ck3D6D+A8qRkipevI21nPmlb9uC59Ry0mZ3g50tgwxew5TtY9Q45v/fHI0oR3lybs1YoFIpqcITpvEOyplMsJLC+CFbmQ+vMkAPmPnsOBNDBhCW9YdtPIBwg/ZUHdTf0GAP1+oSft3omfHwj7FxhiQ0hwDSomdkI3T3Ystm5Cfba2FdqmrBsrpVGfcEgGPUyrF5ppWdXRdOs+k9DH47qyrtsGeY+G/duGGgJRI/Pjp/gdffuhfr1bduHIoTg1E8+Ydpll7Fp/PjwYzbONwkvKikJFySh40IIGt52GysCneps40lCpKXBrt9gylkhwcVgm2Prs/Ts+hGmD4Q+ExOLUIVCoagO0fb6V/NPmUJxOJLnhTm74ZQ6IYOyCLtFOUV7wN0PNnwbrvNEIFeoy2vQ9NLwkzb/BWOugc1/hum8Wmm10ZOvs2zKSmFNrDSsKuzcBPt2Qp+zoEsPmDs7evBJCEvrPTQ8qhtjxw58a9Ykvp5popkmsjz2Url3z57YNY2i2O5nHzsAur32Gqbfz6r33qv2ucF+hKHvDcLDiCLErs6gQaywk60fituDSE+HvOUw+VQwAn9sg7shAr8zujDJnn8ZNPjd+j+CQqFQHCiOMJ13WP5Pead/D+uYwzrmkM920KpRCd/pgd6T4azZ0Hww1O4B9frCiS/CRdugyQXh9iunw8unwa5V1ntpVqx86Xmb8ZhluFdOrl59pqCfpCQYOxXaHWeN6VVigLoOJ3aDFUshygqWPIBtqZ0xUpKj4apR429dy5GcTJ9x4+g3cyaOQBc5sLcCFi2zycQSJMEAlAQy2rXj7HXrOOqBB2yt7EVl3n0hXiMRmLBtMmzf/xVBhUKhiMoRttdfoQilQO5lPb+zjt/YxyYQKYAz4XkAUhPQcwz0/xNaXg+1e1pb6Y5/Ei7aAkffEH7C5j9hxEmwJRBQCtF5WuFOPGY5SX9+En1xMB6G39Jxn3wL3XtZY45KnSfBCjh17ALr10BpaeS9VFfnRdliF8Rds6ZtPeSqhiaMhuZ00vPddznvr79ibnmLRVBxhWrCYOApqPEkkNy4MWcuX07L557bf53312NglMctveEqWAgbvtw//wqFQhGLI0znHYZBJ8mC5EeZwUhmMJKvGcpEbRReZxcS1XSSUgf3JdZDvlZXOOldOHMmnD4JjrkL3FWCV6YB7w+2BEiUGgEi8EjMHH83ZNUET0qETVTqNgFnIJRZqzZM+QPGTYP+AzFTUq25CmEFmn6dDtddDMc1hoXh29ocRx1lryaUEJa/ONS/4grbvnZ+8w0yjrCxgxCCuj16cN6CBSTXq4cphO0tdtEESXBcOhwkNW1Kr6lTSUmQah2XvBWwa1biGmDCASv+t//XUSgUimgYWCtgoS+F4v85Oam7uL3XC+ypcyfTeZUZvMY3PMC34glK3adjJ0G/XJ4MWoZVm7Pr/+DMn6HvFDj2QUiKbL7CmGvBFz3wENQlGRMfBOmFnHr2biQlA2oEUrXSM+CrH2HSbzDwMoyMzIBzYS1Y/jEbbrsK2jWAX6aHudFr10YkJ9u6pBQC4tjWGzTIdqbT7okTMfezrlMoNdq35/yFC8lo2TLxNrkQgj+JqrOVgNR1nLVqcerPP0dtDmObst2w8euQHQ/RkWiw/LX9v45CoVBE4wjTeYdV0EkTBm3rLSYzOS9sfCcrmZmUQqKaTiAh2UYXjyBLJ0HelqgBp7B5leXB0m+h/zWJC0YKDS64rcqYgA6dYeVSRKDjiQgKg2AG1Z5dMKAXrF1dcZqjZk1SBgxIfE0pMZzxVwgbXncduseTOPAkJWuHD2fjbbfZFi/xyGjZkoFLlnDif/+LsJkeHa1vDVj1qRpddBF958zBU7NmXB/SNCn/8UcK77uPgjvvpGTkSMgN2befv8zeDUg/7Ftoz1ahUCjscoStgCkUtdJ28PBZD3Ns/b9AhOuL3azjpyQDaaOJR7G8yf5FN86zMp0SLTCZPpjzPgy8NfF2ek2HATeAI0R3CQHHnwibNqAVFlpDQQ0VrO1ZkAcXnwl/zqt05fGQfvXVYRlSURECv8OBiLPAWPeyy6ysdhuFzTe9+SbrrrjigOi8lHr1GDB/Pt1eew0tNdXWOcHMpgiEoP7ZZ9P3jz9Ibdw4vg8p8f7yC4UPPEDBHXdQPGIE7NpVaZC/ykZzoUBWe95SW/NWKBQK2xxhOu8wqukkkVJwVrvxUY6YbHbXZWFKV9oX/4aV8VT5IJHSesDmmy+R6Tiu8sT8lVaWyoYvwV8ESfWgxTXQ4lrw1IA1M0F3ghG/joDUHIg1v8DlD1ud6Qpyo3ew0x1WltO510ce+/IjWL60InsqAsOAkhJ4aTi8NrpiuMYjj1AycSIy2K43GkIgTBNz40aoWzeqibtOHTp8+y3zzj4bs7w8foFzKcmbMAHRrl1sm2rgzsqi/b33ktW/P++PDtybpsWdg8QKPAldB9Ok1a230v7hh/HUirKKCaxevZo9e/YA4Fi8mIz77kPfuhUZFHKGYRVcv/deVq9cSbm+hSY251/uM1m1eHHUY7mBQFbo9WNh1/b/s0+FQhEgWGBSoThCuPakN0lylaBp0Z79kt3OGsxJPYkuRb8i0AlffnIAfrw+F17Zs3K4aCOsfBPWfQzePHDnQLPBcPSNkFwX1vxiBZESdbyTwOpfYNBomPA27NoSfbudrkNGDlxyd+Sxyd/BbzNjZ/uYpuVz+EPw5aSK4ayhQyn86CPMoqLYpRykRDNNjJUrY+o8R0oKJ06axB+9e+PPz48fUDJNimbPRpx8cmybauBMTaXNrbeSfuaZvPfWW9agrsctTVGh8wJ6sNkVV3D800+TEqPxTajW0NetI+Oee3CsXYvU9UCNLhMxbBjOYcNYvXw5xUnbaG5z/oYULFM674BcX6FQBDjCdN5hEXTShURicu1Jb3J07RVRbSQm85OPoq5jMLVKxoNvMtYjS6dMnkVpWRKl8kIygyesfh9mXwtolam1vpUw/wFY8l84fUr19u+bfqjVAN6YCfecDdvWWUEmw1/5tVk7eP5bSEmPPP/d1xO39jD88PXH8PRLEEjPdrdvT72JE9k+YABmQUH086RE9/sp6dOHsunT8XTsGNWsxqmn0m3+fH7r3BkjsBIXE01Di9EpZX9xZmQgk5I47t132fXZZ+QuXYq/pATp8+EvLq64bjAYJXSdxgMHcsw991CzU6e4vqdPt1LWhWHgKS2FSy+NamcKwawff8TndpKiDY3pr8x0MWKzddwnHXhXjbV1fTvYtf3/6HPPnj3UjSGYFYojimDatULx/5RQydM4axPNa62OZw5Ilic1or7jYhqVzADvOCpaibj6srf0Snz+kCzlTRNgxoVWNkswo8VXCIuegqUj4LRvrUVFIWwUlZRgeCE9y9J5Q8+2iopX1Xn1msIL30NOlOfY6P8lDLRgGDDjR9i0ARo1AcDZpAn1p01j25lnYoRm6lRBMwxKzziDku++I7l376g2GR060H3hQmZ37ox3x45EN43m81mLmgcIPSkJ6fHQ4ZNP2PXZZ+yZPx+juBjTMPAHdacQlQExTaP+WWdxzJAh1O3VK67vCp1nmrhLShDnnRfVzhCCX6dMwetxk6wNjbvlL6j1/FKnfJ3SeQfCVuk8hSLAEabzDsmgU6oT2mTAthJId0HPJstp2/JdaqbFf0AKdNa5kqnl+sHqdGLmg5ZF3o58TPlWpeG2qfDrNUQvEm2CNx8m94F6D9sLPEkT6raxvm/SGr5YBbMnwrQvYfdWKNgLaRlQsz7M/RFOuxg8Vfbdr15uL43Z54MN66D9CRVDyb16cdSWLWwbMIDSadOiniYASkvZdtZZNNm4ES0pKaqdIzU1ccAJrBUj0/zbtZ0iEILaZ57JcddcUzEkTZOSrVsx/X6S6tZF+nx4CwpwZ2XhsFnroFevXmRlZZF51VU4//oLESOL6oOhQ7lyxAj2jh1LPfEGGXnTEFGSvEdsHsrQhiMAWN36S8qSj47qLzc3l+nTp1dcPx52bf8/+ty0aRPz5s2La6NQHEmYplVmRqH4/0iaE9pnwcZiSHbANccuBikittVFIljrNGmU8RXIEjBzrfpNIhVvyXYgEHTa+5fVXVYG24yEIA3wl8JP/aDlK9Ez0yMuq0PdY6zvazeED/+CedPgx0+sTnV5uyE5FWrVhz9+hOzakQuMyxbbazojJaxaXhF0AvCccAJNNmxg5+DBFH31VYxPBvB62XbuuTRZvx5HjMxvV+3atgJOSImQEv+uXVDPZi0rOwhBzimn0C5k8U9KSen27RhlZSTVqWNtjcvLw5WRgdPmlryg1kgfOhT31KmIGJ91UOflvv02OfWmU2PX51F1HlRqvfUtRlGU3jWqzb+tn/5Nn9WxVTpPoQjnSNN5h2TQKcUB88+tfP8bc1nJ7hg9xMIxgi11RSrowQdVfrjRoqcC6dSx0pQNKw07LQ9cSeAtiX9RoUOXKyvf6zp06QuzxsNf06z9/dK0rvnTp/Dq3fDIR3DS2eHn2M0cirK3X0pJ2ezZ8c8zDIzduyn6/HPSr7oqqolpo1NK6OrkAQ86RbuepoWnU3s8ONPSquWjRYsW1MzLY++CBQltpa7TYNo00p95G747EcpzYxeabHsvLTpeEP0YsH37dqZPn06LFi0SruzYtf3/6BNQYkShCME0ofQIEiOKI4tkB8zvX/n+L7z8iUAmTDmSITovGfQYC09Ln6+wj45p1WkyF0FGXcjfHveqQhrQI6TrnRDQoRfMnQyT3wcC2TnLsBYc/3cv3DsKzrii8pxEdZlCiWIrHA5Kf/45/nlSIsvKKHjvPbLvvz+6iQ2tGabzDnBWe9TrCUFylcCWM8Vmc54ALVq0oJamsWfq1ITBPanr1Jk0icyPRsF3c6B4U0ydV1L3Ao7qdn3MDnn/tn76N31W11bpPIWikiNN5x0WhcTTqIVpK+Rkkkb0lZ0KijfDjhk2igeasP4DOOfphFct6n47pFZpCTv8GpgYqE9kGpYYCa6mlRTAAwNgfkhWUufutoo7kpEJzSOzakp++AFpp9OIplH42WcxD3vq1UPzeCLGBValLEfg5QyM7X3rLasG1GGAz+bDThgGvtmzIbUx9Pvd6nQIVtBQWEJQAgUt/wMd/nuQZqtQKI5kDBNKy8Jf0dizZw+NGjXilFNO4eqrr+b111+ne/fuXHvttQDs3buX008/nc6dO/PHH38ARNgoFP82adRG2tB5Ao00ase38ZfA+i8SdiVD+mHt+3D+8/HtgOIOV0CtFuGDbwyDT563/udgGtbiomkCEspL4anBVgAqyEmn2As8OV3QvkPEcOns2Ri7dyc+3zQp/PTTmIf1lBRcMbKgoum8fR98ULn17RDHv3ChrWwyYRj4fvvNqt/abzbU64t1t0GdJ5CaB590kt/upZgBJ4VCodhf7Oo8+P+h9Q6LoFMzTkLYmqqgOd2tb2UJlI+H0vdwi0lUrNkUb7V/4ZLtcOqdMOBZK1sptGOJ0JEIfMJJ0cn3hJ+3Yh5M+Th2YUoprderd1duqbvu9sQPSl2HK2+EKF3ezLw8e/dkmhhxCv3pSUnUv+oqRIgwCgacBJHtbnc99xwL+vbFOACtdQ861alLELRNOwrOnAnnLoGOz8Nxj5Hb7jVKzBSKj7pVCRGFQnFQ8AMFVV7RWL58OUOHDmXGjBm8+eab/PTTT8yaNYucnBzmzJnDW2+9xcMPP8zEiRN55plnKC8vj7BRKP5tGtMRJ9G3/YciMWnJKYE3XiifCKXvQflXCKzAiObdkzjgFMRfBMefA4PeBt0VrvM0ayHQJxwUnFllAXL7Bvj0hcT+X72rUttdeyskyg7XHTDwMsjKjjhkVqMQs7F3b8xjQgga3XxzWLdigRVoiqbz9r7xBvO6d7eKjx/q7E/9qaTa0Oc7OH81dHoRjnsUTnqXnb0W4pVuazeDQqFQHGDs6jz4/6H1DsntdVVJIp22nMFivo9r15o+JMs0KHkYSl8Faf34snVIThpKqvZfcFxl/8KOZCuo0HcYdBkMv74D638HJDQ8gV0tz8H7+cTIwMM3b1YWlYyFNGHtIlg5H1p1hL5nw4CLkeO/iG6va9D4KLgzerq0XrOmvXvSdfQ6deKaNL33XrZ/8olVvNswCD5uo4ZXTJO8X35h3cMP0+L5xKuF/yaO44+3ZSd1HWfnzuGDWcdYL6Bs+3bgrcgTFQqFYj/49NNP+bRKZsJCoGqf08KtkYsmK1as4KuvvuKrr75i4MCBtG3bFoAePXrw119/sWDBAoYOHYrT6cTr9bJ69eoImy5duhyM21IobOPAxXGcx1w+iWkjEDShE1myPpQ+DyXPgtxXcby27sHlvBvTUZ0ggQa6B7pfB8edB7+NtjraGT6oewy7jh6A9+ufQKsil797N9DYJMFi4Z5t8Psk6NYPOnSGG+6At16NbqtrkF0DHhoe/XCM7KQIhMBRO342WKNbb2XTqFH4du9G2tB5RUuXsuK222g7Zoy9OfxLONq1S9j9GCyd56jaVCe9GbS5s9Jme/wtlwqFQmGXv6Pz4P+H1jssgk4AHbgEL6WsZBoCrSINO/h9C3rSSV4CBReB9xuq7uMXQKp4FbSNkNLI2r8dD+GAxiGdLzLqwlkPW/v+Z78H25aQtuW/OMxWCG+VfoerF9rvfLdmkRV00jR48yOKs2pAYPbhiysmbN0AD9wGDz0L9RqEuUk+4wy09PTYHeyCGAbpV1wR1yS5aVM6TZ3KvLPOwh9I5Y6bz2OabBk1iqaPP45us7D3v4GzXTscXbrgnzs3blaZMAySbrrpH5yZQqE4krn00ku5tEpHzR5CUHXz7tP160ec26xZM1588UVatWpFRkYGTz31FADJyckUFRVRUlKC0+kErNp/eXl5JAf+TgdtFIpDgbachZdiFjI+qs5rwPH0kDdC0c1QNirifCHKcDp8ZHlugxodYN+fsTPOwdJ59U4Hzfr3QWoN6DMUul4Nc96HTQtIm/mipfNK84CQejVrF9srQK47rAXGbv2s90+/TIEnGVnsr9R5koDIMq2C5PffAg//F5q1DHPl6dwZR8OG+Ddvjn9NKUm/8sq4Ju6aNek8YwZz+/alfONG6+OId4JhsPOzz2gxYgTuBAGtfxO9Xj3c55xD+bffJtR5ybfe+g/OTKFQHMn8HZ0H/z+03mGxvQ5AQ+MkruUcnqI5PcikPpnUpznd6c8T9OBGtPJPAi10o6fXCiHB9w207EaCx6uVmt3qtsr3pgnj/wMPNoRvH4H5X5C05BvcspxaL7SHP0JW57RqfKyhdZwcDgqHPESZ2wMuPTKDyu+Hbz6HvifC5o1hh7TkZDLvuivxdi+nE7OwEJlgFSijY0dO2biR5MaNE31SABiFheTOmGHD8t8l/ZVXrM88zs9IXn45zvbt/8FZKRQKRTgmUFrlFY3OnTtzwgknkJqaymmnncbuwEJBcXExmZmZOBwOfIFCwE6nk+TkZIqLi8NsFIpDAYGgAxdxHv+lFb3JpAEZ1KMJnTmLh+jNEBy+6VEDTqG4xFxo0Tx+wAksnReS2YKU8NMLcH9dGHsfzPscz5LxuKWX2i8eBzNeq7TVNHvb66Ws2KZn3aSg+Po7LJ2X5A7UigyxN0348Vvo28nqdheC0HWyYhQHD0PTMMvLExYATz36aE5evZrMDh1s6Tzp97N34kQblv8uqc88g0hOjlsnVfbti6t3739wVgqFQhGOXZ0H/z+03gEJOg0ZMoTPPvuMcePGcdJJJ3HeeedRXl5OWVkZAwcOpHPnznz77bcAETbVJYej6MENnM9znM9z9OBGatLMOljyio1b0qDBcmg0gOiBp8D5nV6GGiHbsSb8ByYNrywWKU2rmwkgfCUwehAs+NqyPa5nuMiIxzFVWrCWl+H2llXWfaqK4Ye9e+COqyIOZT/yCKkXXxy4jRifg2Gw6/rr2XnVVQkDT3pSEpqd4uYBVg0Zwr6pU23b/xs4O3Ui66ef0IIrdQ6H9dI0pK7jdzqRzzzz705SoVAc8fiBwiqvaDz88MPMmjULn8+Hz+dj6dKlAMyaNYsTTjiBtm3b8uuvv7Jv3z6SkpI4+uijmTt3bpiNQpGIf1LnZdGArlzJ+fyXgTxPL26jDq0RCCgZCSTSJSbk/AItgxsXqmq9wPtjH4T6p1cOT3sFvh5qba2TJkijUucZXvj8dvj5Dcu2rY3FS7A0Y9tuVcZMPN5yayExWmDMMKCkCK6/KEIHZtx8Mxm33269iaXzTJO9w4ax7ZxzkAk6EmtOJ3pS4lpaQdY+/DC7vvnGtv2/gaNVK7J++QW9cePAQEDn6TpSCPwOB/LNNxHVWSBWKBSKA4xdnQf/P7Te3/6LO3fuXKZPnw7A6NGj+fnnnzn99NMZN24c48aNY8CAAcycOZORI0dGtTlgmPlg/AUJu5+YYC6Enm9Dx+cguUoaW40OcOr48NWvfZvgx9hdyipkx5d3WgJjwE0J95Oj6XBCL2gUnj6d9OO3CAki3vmGH2bPgFXLw+eh69T5+GNqvvFG7HMDfgvHjCHvlVfizxFwN2xoO3OrZPVqFvTpw444XVP+FaTEXLaMspkz8a1ahatHD3I2bSJj3DiSbryRpKuuInX4cOT8+fhcLrTCfeA/+C2CFQqFIhYGUFLlFY27776bRx55hJ49ezJkyBDOPvtsTjrpJPLz8znhhBO44447ePrpp+nfvz/33nsvKSkpETYKRTwOGZ0nJfh+wvrXERshALkNThwCXd+EtKbhBhmtoccYOCGkOHhpPox/IPEcxt0H5cXQ7+rEneg0DZq0gWNPCht2zZmJMCUi3vY8w4A1K2D2z1XuTVDzlVeo88UX8bWZlJT8+CN7H3kk0R3hadjQXvdkoHzbNhaddx6bAj/rQwYpMVetomzmTLxLl+I49lhqrF5N5o8/knTLLXiuvJKURx9F/v47PrcbrSQffPEDcgqFQnEwsavz4P+H1vtbNZ38fj+vvPIKt9xyC36/n4yMDBwOBz169OCjjz7C7/dz880343a7SU9Pp7CwMMLmkksuifBrmiaLFy+OcsXY6GIfbaqxzXzZysUYsi+06o2nZCW6WYzPWROvpwnkAyHXr/XH69RCIGJs26sgbysbvn+TwiY9qdXvJmp/Hz34IzUd0+lh7dl3UF7lPmtPGg8tEv9iSE1jx0fvsufCyH37+qJFFV1IYp4P7H72Wbb06hVXbJi9e8PPP8c8Hm5sBbSWDB7Mjpo10au57z830Jll9erV7InTYc82ponv/fdxO534evdmR2DYaN0a7/XX4+/dG5o1AynJnDuRjGcuIaXp2dS+vj2my0Nu1wHsPm0wvprh9bOqM8+DYfv/0eemTZvCvh4IDvjv02Hk82D5PZx8ami0o90B8QeQSeYB82UHg/ip1kEaNmzIjCpbm28PZkIAtWvXZsqUKRHHQ20UilgcSjoPJG1r+2w3jV21Zinl/m7QYiye0lXoRj5+RzblnmZQIsJ0Xvbiz6jnK0+YuyTLi9g6/gVyW59H9gX3Uf/TpypLMoXaCQ0pdNZf+AAlS5aEHcv6bizUapJw/lLX2fPZB+xIrxFxTF+0CIffn7De5r6RI9l+3nkQp95meY8eYHexMJB5terOO9lTrx7Oli0TnBDOAf97LyXezz7D7ffjO+WUSp131FF4r7kG/znnwHXXgZSkLZpB5qs3kNKwN7VvOA7T4SLvxLPY03sw5fWa7/dc/2399G/6rI6t0nlK5ymdF45dnQf/P7SekHJ/+otaPPvss5x88smsXLkSh8PBL7/8wqhRo1i3bh0vvvgiZWVlPPnkk9StW5fBgwczfPhwnnzyyTCb1157LcJvy5YtOf/88xNe3+FwVBTNAklyUnHMB3BZuQuP2xuwhJLSFCJlgokAZJWmsW6zFIeMviJVJlx4ZOVqiVe48GkukBKn4cNpeKOIEUG57sLQHBE1Adzlpfg1HU+ClGgEeJ1OfE53xCFXcTFalR9rucuFO4rP8qQkZLwVLilxlJQgovya+FwunDHmaTidmO7Iuf1jSImzvBzd74957z6XC8PpxO0vx2Fahd/NQMnSCjdAmTMJ0+52SYVCcUhgYqLZTOb1+Xz4E7UyB6ZMmcL8+fP/7tRscawQ3Fxl7If+/ZkwYcI/cn2FAg41nQfJnmKrPmcMQrVecVydB6HJ/i6zDKeM/jegqs7zCSdezdI3DsOHyx8ZrJIIvA4nfs0ZofNc5WUYmmZL5/kcTryuKDqvpAStSkZ8LK3j9Xgw42VlSYmjtDRqhn08nWc6HBgeT/x7OMg4ystx+Hwx793vdOJ3uXAZXpyGlcFeVecBlDk8GPph01tJoVCgdN7hxt/6C/vjjz8yadIkduzYgaZp1KtXD6gsWFVQUFBRxKqkpASXy2WrqFVKSgqDBg2q9nzqpI0gJ/ljhIgMEI0YPZShV49ASp09JYPYUTi04lh67lRydn5ASvFfgCUWCjN6srv2VZSkdaDRpLtJXzctaqbTiJpDGbp7hHWe0NjR+Q72nFC5qie8pWT8OZXUJTNJXT0fZ/7uimP+lEz2nnIJu8+4HumwRFX2C4/zae2jGPrBiLj3KoEtw54mr0//iGPuk09GBKLKQcYOHcr5IyJ9el9/HbNHj7jX8m/YQO5112Hu2hU2/sfQoXSq4lPDkngiKYmkq6/GM3AgWs2acf0Hyc3NZfr06fTq1YusrCxb58TCOXYsnmefBeDzoUO5OMq9A3iGXEzt3z+PGayUQsP0pLDiqR8wk9P3a54+ny9MNMeiOn6PZJ92OZJ9Hiy/h5PPydMnM5rR9k5wBl5xyCGHJvWb/N2p2aY6K2AKxcHiUNN5OSnvUSf1lZiBpxGjhzLkqpcoKDudzfmVZRFSC34jZ8do0grnVIwVpXVid+0rKcroQb2fnyJr+Vg0M/I/JaE6z9Qc7Gt3KdtPurfiuPD7SFs4nbQlP5O6ah6ufdsrjhmeFPZ1H8iufrdgeqxso9R3X+Vrd2Zinadp7LjuLvacf2nEMdf556OtWRM2Fkvn+Z54AmPAgLjXMnbtIve66zA2bAgbj6fzcDhIGjwYzwUXoDcIzwiPxYH8e6///DPJgVX8eDpPu/lSGi2KncklEUiHk1WPf4svu7JD4b+tdQ4Xn3b9Hk764XDwebD8Hk4+lc47vPhbQadp06YB8P777+PxeBg1ahQ+n6+iYNXevXuZNWsWDRs2pKysjFq1arF169Ywm2gITWNpSjv+3AuagM414eyG4NBAItnNGpbzE3tZj0BQm1a0pjdZxlOQ+y3IAqLt+ZdSR2jp1Gz4FDX1htbg/Adh3TOErngJJOkFs0jPnwnd3ob2fWH99OiFvUPnLU3qdjuPui2qpPptmg/zJlldSkJwFOdRe+Jb1N6xGkZMBKeL3VfeCD/8GP+DB0RKKg1vvJOGKSkRxzbWq4e3StApFs06dcLTLkFqYrt2+FauZOUdd7BjzJioJlrgVZFmXlpK6ahRlI4aRYtXX6X+LbcknMv27duZPn06LVq0oG7dugntYyGlZNsll+ATIv7PzKlTa+43cdPThTTRy4o4ZtN8uPCOAzrPqhwMv8rnkenzYPk9nHxOmj6J7WxPbFwNmtDkgPqLR7DApELxb3KwdJ6maaxNb8fvu8GUcFw2nN8E3IGk4r1sYAVT2ckqQFKTZrSiNzXNRyD3KzB3YP0riUTgJLPe82Q2OsYaWPoyrL4bRHjGcmrRfFIL/4ATnoGO/WDpFwk/D830k3Nif3Kq6qbctfDe9xH2elkxNad9TM1NS+C1GZCcxs6rboJPP0t4LSE06t5+L3Vr1Yk4tqVJE0rXrUtcPxRofMIJpCTSeYCxZAlrH3qITS+/HPV4hM7z+yn74APKRo+myaOP0viRRxAJ9j4eyL/3O+66izJdt+pfxULXcX79DbSMrQcFVn2tVitmwI3DD8pclU/lU+k8pfOqcqTpvAOaS3rbbbfRs2dPGjZsWLHXf9CgQbz11lv85z//iWoTjd2lcOkMcAaebj4JdZLgjZMMMhr9j/XMCSTHWg/bPLaxgp9oq/fjxMypiLwzQO7CejyaSGkFe0yy0DOnQDDgtOErWBzsVFblwR3cTjf7ejj1J6vwtxHn4S40qNUCmlfJGlq/DF68NeAzyvnShAXT4aNn4epH8Ldqi3/KVKSmxS8mfvkNMffopw0ezN4HHogrRjRAy8lBFhQgpUwoFJyZmTS4+eaoQaeKVa+Qr0CFEFh9663o6enUufzyuNc4UPg3bsS3bFlCO0+ygRavkGcQCfz4UUXQSaFQKA42nS6/nPn5+WFjGRkZ/9JsFAqLA6bzyuC8qeAMVDPwmZD1G7zcxaR58w9ZwZQwnZfPdlYzk+ZaD7pn/oSWdyaY6wnqPBBW019gn/ExNRyBgNOOmTD3buv7qmUSgu8XPACnfgdJmVZB8Zj1OwWk1oDjBoQP79kOTwyK3XXYNGDNQnjjfrjndcza9fDpDqQQUUsXVDDgYsipFfVQ+uWXU/rTT7HPtWaLlpQEpok0DESCYuF6SgoNbr89atApkc7b8Nhj6KmpNLznnrjXOFCYJSWUBQKicTEMyneVYh4FWrz/8ZgGTB4TFnRSKBSKg8mRpvMOSNDpqquuqvh+4MCBFd+7XK6IziUDBw4Ms4lG8BHsC4mZ7CyF83/SuK23l+MaUSFELPtAAWu+x+VI5rgaG6D8cyj7GMydeH2ZlHvd7DJ+p64jpIvJkhFWsChaMCiI0GHTGLjgJfj8tujzFRpC0+HydyL27jPuf4GAVZx9pNKEr1+DK6zOKeUuD96O3XD/Mcsq8h1tFWfUi/DTd3DTELj8+rAuJhnXXEPu8OGYhYURgSedkJWqvXvZedppOJo2JfvFF0k+99zYcwQyunQhqVkza3UtRCjZ2U27btgwal96aULRcyCQhfbixnEFSLhHyN2V2EyhUCgOEGNiZJUqFP8GB1rnmQEJ4ZNUiL5cL3xT/BVnyykgouu8NczCqSfRNXsFlI+Dsg/A3AJaNgVlfSkpLcdL18oLLXsJhANi1GsCLJ234jUY9Ba8czGEVH2qvL6w5N3l74BeZY/Gd+9YQYt4ASTTgO/fqwhqeF1uynr3I2nKd6A7omvErz+GOb/A9XfAdXdAyPal1IsuYs+DD2Ls3BmhEcN0Xmkpu885B71ePbKeeYbUwYNjzxFIbtqUjJNOIv+338L0Y3il0+hseOwx6t10E3qUDPwDjQxs4bSLadrQqgX2dggoFArFgeBI03n2qm8dAli6RPLBr9dimLGnvZDx+ATguQoyp0D2IvaZn+M3nEBSpWHJNtjze/yAE1hCZf3ncMqtlthIDuxF1Z3IQNTCyGwAd06F5t0jz585Ln7AKUjebli5wPpeCPa98Sl8/D2ceiZkZleMh7FuNdx7E9w2OEwc6Dk51Js4EZGSEtaZLrSjnYAKgeRfv55d551H0SefxJ2iEIKmjz8eEXCyU4neu20be76PTD0/kPjWrWPvE0+Q+8ILtuyjlG6IgYCs6KuNCoVCoVAo/j4p7kLOaPt9guiGZAVTKBZF4LkYMidC9iLInEGJvIqwk41y2DwhfsAJrIynbZPh2DPghq8gLfC8D9F5ZmotuGk8tI+yODfzG1vb3PCWwV+BbsBCkPfcKPh6Gpx1HtTIqRgPY+smePxeuKI/hBTK1pKSqP/jj+hZWfF1XgBj2zb2XHklBa+8knCaTR99NEzn2WwWiFFUxI4EOvLv4t+6lX3PPsuehx6K23k5DGHF9RKSfuBq7SgUCoUinMMm6GShUVCaycJN0WsEABh4Wc/viV158+xf1ii1IhQnXQvPbodrPoU+91HU/Q7KhYuik+6A3atha5T2v2Ul9q9TFrJyo+vQ+yx45DkoCMy16ipa8P3XH8P7b4QdSurWjcaLF5N5112QllYxHlU8BFLC9153HWaVNL+q1B00iObPPRc2ZleQLD3/fJZfdRVFixbZPMMeZlER2y++mA3Nm7PviSco+OQTTBIHw8qKNUxn7DbCFQjg9H9ma6BCoVAoFEcinZvORtNsbHlHsJqZic18RYkXFiuQ4M2H48+HZ7bATd9A3/spOuk2yoWLwlOGQsFO2DA3UouVFtm8BlAaovOEgO69YPirUFRkvY+WLSUl/DwFXnoqbNh9zDE0WryY7AcfhOzsSrdxLr9vyBD8CVrW1+jThzbvvluRQW9X4wGsvvFGll50Eflz5iQ2rgbS62XnTTexvlEj9j70EAWjR2OaZuJFT4eDlCYZCD3BXWg69I2fBaZQKBSK/ecwCzqBLvys290s5nENnQJ2AiClH+kdS4bjFjyuUjIcdyF9U5DSBHeO/Ys60yv3YjndcOIl0PZM3Kt+xC29ZH43FMZcC08dC891g40hrRZrNcT2I7tWw8ix0f+LKEAegRDw5osRYsXZuDE1R4wg6csvLbMEl5dlZRR98EHCaTa5916aTZ2K6XCguVwJ7SswDHZ+/DHzO3Zk9/jxmOXlyATF2WPO1e+nfPVqShcuZOuZZ1L09dfW/RsG+HwVSfkxvWsaIikFecGd8S+kaZCaCWcoMaJQKBQKxcGiVvouzDiZ7JUICrG2vEtpIr2TkEWDyXIMwu0qw61NQkoDXOmgJe7UZbnUwRXMZHdYGU3HD8S9YXZA590LH18P/+0ETx8Hq2ZUnlunsRW0sHWTUXTeR++Az5tge54J746E8vKwYUedOtR44gmSf/jBuo1E1xeCwlGjEk6z3tVX03zmTAynEy052X7gSUr2jBvHn926sW306L+n80yT8jVrKF26lG0XXkjB229bn0NA5xkBvzG9CwGaRsa998b/bIUAhxPOvXG/5qlQKBSKxBx2QSdrn3/sx59EouNE+pdCfnMoGohH+xbdYeDRvobC06HgOKSrHOqeGtHNJPJ6DmhWJeCwYhq8eArOnVGKVW/4A17oAesD2VbnXJ9YBWgaHNMFGraIPDb+88Tb86SEjetgxdLoh3/7LcEEKimbMcOWnad1awyPh5r33RdWTyoRwu9H8/lYOWAAv3k8/J6Rwbrbb6d01Spb5xtFRex8/HGW1a/PypYtWX3cceTOmoXXMMJKwUui9S8MoGmI1FRqT5qEfvNTlQGlqvehO8CTAs9PhLRM2/eoUCgUCoWievgMp+01Oh0n0tgA+e2g6Ezwfopbn45D95PlvAbyWyLlWmhykaXj4iEc0Og8cIbUItq0AJ7vhnPL/Ej7bUvglT6wdJL1/uzrrJpNiah7FLTrFjk+4XN72/Py86waT1GQc+cmPh/AMBIWIA/ibtoU0+2mzjPPJDYOnYvfjy4l6665ht88HuakpLD6mmsoXrjQ1vlmeTm7R4xgRZMmrGzRgtVt27J3wgTKTbNqy5/YOk/XweWi1rhxuG56EC4dao1XDQ7qDnC44OmxUKdRdW5ToVAoFNXgsAs6GaaDRtkbYx6XmNQzakFhT6u4JCCEEfYVYzkUnoI85rbIbiZhCGsFpHVIAXHDB+9eAtJERO1GZ4DfC+9dZomIM6+E7Drx956bEq56JPqx4mo0UyyMsTXO57N3vpSYpaX2rwdkXHIJwmGvIreGVeAytCClUVjI9jff5M927SgaPx4MA/+WLVFXxoy8PNaedBI7n3gCY1d4YW8DKIeIwJM/5HuRnIzepAmZjz1Gg1Wr8HTrZgWa/vM+PPoJtOpUebInBc67Gd5fCG272Lo/hUKhUCgU+8fSre1w2NheJzFoaDaCwpPBDC5YBZ72QXFhboSCnsjWV5Bws7004JghIe8ljB4EvnJENI0oTev13mXgK4OeA6DR0YkLB13zWPRFugRlDcIoKog+blfnQbV1Xlr//jiysiLrTUVBUFlXquJHUVrKrjFj+OuEEygYM8bSeRs3IqME2syyMtb37cv2YcPwbd4cfgxL54X+RKLqvAYNyLjvPhqsXEnyWWdZ8771eXh2PBzXs/Jkp9vS6O8tgK5n2vw0FAqFQrE/HJDudf8ckmRXMSc0ib6iI9DIogE1yz4DWUDsNRA/mBugxlro9Ar8cWdkdxPhsB5Up3wFGUdXjv/1DRTtTjBNA/asg5VToXUfeHUa3Hka7N0e0D4BAaTplnA5/1Zo1TG6r5q1YXPsIFsYtepGHRbNmsHy5bZclP34I7uvuIKMoUNxtW+f0F7Pzubot99mxZVXxq5HgCU+Yobd/H4EsPfmm3ENHcq2Tp3Y16oVOYMHQ3ExhZMnWynaeXkYW7fGXREsBzxELpb6gUazZ+OOdk9CQJ9LrVdJIZSXQlo22AymKRQKhUKh+Hus2N6GHfl1qJm2C12L9ZwXJJNJ/fJfAwuLsewMkPsg5Sfo8SH8MhgrVb6KzpMmnPQu1ArpeLfmF9ixIv5kpQklubDgK+h8Obw0xdJ5W1aHd0XWdCsLqu8V0PG06L7q1IPtW+JvAQsSS+c1bQpbtyY+H/AvXsyuCy4gfcgQa/EtAZrHQ+sxY1h87rnWHONosKDOiwhP+f1oQO6wYZbO69qVPY0bU/Pqq9E1jfzvv8csLoaSEvzr18f9LLzE1nn1xo0j5fTTo5/Y/RzrVVps1VBNzQRnNUpEKBQKhWK/OcwynQSDunyES48MJgk0HLjpKa9FlI+mcu0jFiaUvwZt7oCzZkPjC0AE9v47kqHl9XDOQmh0TvhpK6dV1neKh+awbAEat4KPl8NdI6F5e0hOs9J5gy12v34NBtSDxweh7dsR7ufSaxJvX9M06NAFmjSNfrh3b6SNFSoATJPizz5jW8eOFFdpgxyLOoMHc8zYsXiOOir2FIm+1hgMRlVtx+tdsYIdDz7IzuHDKZk3j7LFi/Fv3mwrBd1u2dCoJKdZnepUwEmhUCgUin8Qwajpt6NJJyKKPBVo6DjoJW9DlL1J4qe9AeVvI4+6APovgGZXgO6xDmkuaDoI+s+FFleFn7Y/Oq92Q/hgITzwnrWImJphZdIEt91NHgMDG8EDA9C3rw/3c+nViTufCAENm0CHztEPd+xo6TybWq9k/Hh2dO9O4Tvv2LKv0a8f7SdPJrl165g2MQNORNd5vo0b2fnYY2x/5BFKfv+dsiVL8K1bZyv4ZqfcfEySUiydpwJOCoVC8Y9xyAadBOAQ1ksAyQ54rzs81fw0atI8wr4ebTmHJ8gyAWx2jDM3IqXXWuE65VMYXAaDCuG0b6FWJ8hfDGV7ws/xl0f3FXEDItw2JR0G3go3Pm1l0phVgmKGH6Z9Qc79Z4V3W7niBkhJjR54EgR+gibUyIb5c6I+rIXTib86Bb/9fjAMdl98Mb7Vq22dUvO88+i8Zg2tP/oo6nGN6EIkeFehx0IDUcH7qU73lKhiJCkJZ/PI35toSGliGj/i9z2B3/sohn8sUtpPXVcoFAqFQhGfqjrPpcEdzZpwnvY4dYgMbtSiJf14lFrUB7nd3kVkPpg7IftY6P4eXF4Cg4rgjGlQrxcUrISSKhlCfq+94I2U4TrPnQT9roY7XwavN7Iep2nC7O/IeeAsRGgNqIGDoFbt2GUYNEBIaNQIZs+IrvOEwOd2J55zEL/f6lp8ww2U/26j4zOQddppnLh4McdOmRL1eNWgUpBowaho2e+xzo9GVJ2n67iPPdbW+VJKTOMX/L4n8XsfwfB/gpRlNq+uUCgUiupySKZz1PTA0M6wcJ/1AOpUEwY1gzQnQAvO5jFy2UIumwFBTZqRRk0AJBuqebWQx966MbDwESgJaScrnHDUpdDhJXBnQ60W9lrwGn6o1TJ8rKQQHrm4MsMpyjla/m48oSKmVh34dBJc0hdKS6yuHcFpC6zVMU3AzB9h2kToeBK8OxZyaoW7djrRn3gC4/HHE6ZHA5aNlBT+739kv/RS4vvFEj21LruMTc8/T/HixQmvES3gFLw1GWXcLlU/WQk4L7kELSUlmnkYpjEVn/c6kBuorEzgA2ricL2C7rh0P2elUCgUCoUCoFYSPNwNft9tlbVsnw1XtoBsN0BDzuRBCtjJHtYBUIPGZFAPAEn1ahKFFRLfMh4WDIPC0OYlGjQcACe+Csn1LZ2XqIELWPKgqs7z++GhC6zAVbTi4oaBKC2ydF5QB6akwpc/wcBTYd+eSu0UqvOEgPmz4eLToFU7eH+ClfkUgulw4Bg5Ev+QIdY87BQn13XyX3qJWp99ltgWS+dl9+5N1hlnkDtlSqUmJbpmEzGOxdJ/domm8/QzzsBRp07Cc01jHn7vYKRcTrjOy8DhHI7uvGU/Z6VQKBSKWBySmU6agLuOgdE94L0ecFOrYMCpkiwa0JSuNKVLRcDJOrkhiHp2rgJ6Z0Swe93S5+C3q8IDTgDSB+s/hsndoDwXul6Frcek0w0nVglQTP7I2kseJ3VYmAa6aaDv2FA5eGJXmLUc7njACiZVpAEFvkppCQyAP3+HC3tBSXGEb8d119Fg0ybS7rgj8fwB/H6KYmQuxZy/EDQaNsyW2Im1qlWd1a5YfivQdRACp417No2p+MrPABn8HfBjCRGA3fi9l2H4P/gbM1MoFAqFQiGAG1tZGu/9nnB322DAqZJ0agd0XteKgBOAEEmgtyexhBWgNQFR23q7djT8fB4UVs3gNmHLBPihExRvgRMuBFdy4puQJnS7Onzs129hz7a43eyEaaBJE+fqBZWDrY6BmUvh/qegXoNInUeIzlu9HM7vCXsj64vq559Pw23byHz88cTzB/D7Kfn6a2Q1CpEDNBo6NCzgFJhhBLG0XKzsd7tE6DzA/cADCc8zzQX4ynsiZWgB+uC95+P33Yrf9/zfmJlCoVAoonFIBp3+DkLo4LmNxLdmgud269u8ZfDnsNim0oDCNfDXg5BRF069k4SPyzP+A0kZ4WNzfrD9lHUv/Dl8oG59uP9JGHgJOOJ0wjP8liD5fHTUw4569UgdNMjeJAAzN9e2bZDal15KowcftN4ExIBJ4pIF8ajOubquV9Rk0tq0oTwpCS0zDcaOgkvbQxcndPPAzafC9HFgmkhp4vNeG5hp7ICZ33szUPQ37kShUCgUCsXfwnMHiWs6CXDfjhAalO6AOTcGxqMoCumHsl0w9zbwpEK/RxO4FtDzFshuFD4+54fEXewCuP+cFj5QIwfufACuuQX0OBrW8MPObfDOK1EP6zVqkHrllbbmAIDfj1lYjU7JWFvtmr/8svUmoLckkZ9sdRYRo50fi1CdJxo3xpuUhNawAUwcA1d2gq4u63V1V/jhY/BbgSV/+U1YpchjBwUN3/1I015RdoVCoVDY4/9d0AkAz52gn0DsfmkaOPuB6xLr7eo3wtOvoyENWPc+eAvg/Oeg501R3Aaud/owOPM/kcfLSux1JwGEN0r6eGkpfPZexOpSVN4bGfOQVqNG3FM1rIRjB+AwTfZdeSXeudE7Bsai6dNP0+7778nq1QuwpGE04VGdYFJCQaLrOOvWJfPaa8m6+24azplD0qRJICBn6NnwzM2wdrEl2HzlsGAm3Hc+PHAR0jcR5EYSi9gy3K6vqzFrhUKhUCgUBxTXFeA4ndgyVge9E3gCW6XWvGPpuHhIv5XxVLwZ+gyFsx6JtAkWGO96DVz4cuRxb2k1dF6UGkJSwvuvJ84WNwwY82Zl9lPVaWZlxW1CE6bzgLzrr6f855+RNucO0ODOOzluxgxq9OsHmhZV51UnkAQ2Fih1HT0zk+xrriHzjjuoP20ayb/8gtQ0sh++GB4dDCvmW0Emvw+W/QGPXA63nY5ZPBsp52KnDLnhf7sas1YoFApFIg7boNNu1jGDt/iMIXzCXUzmJTazCImJEMmQPjUQVNKRUoAM6gA3uG+F1LGVW+u2/hDeRjcWRhnsmWMFly79H7tvmoZPOPHWbQc1GlrFwtMyYNFXMPExyKuyUlK/me0VMKNWo8jBbZuh2EaWjZSwblVMMeI86ihcHTpEFSQOwvfaCykp/eQTdnfqROEzz9iae5AaZ51F+ylT6FFURJcdO6g3ZIh1IHDdWJIqluAIFSNhNoGOLa5mzWg+bx51Ro0i57nn8HTujAA8vjIcm1cT8ksQcBgQHtPHYs55CnslzjQc+jwbdgqFQqFQKPaXfWzhVz7gM4byCXcxkedYz1xMDIRwQtp4cN8IWI1SpNQDj3kdXJdD+hSECHSr2zYZe71tJeycbumK/o+z67ZfLZ1Xrz3UaGTpvNQMWPMTfPsA7FkXfnrd2F18qxJV5xUWwA6bWTa5e2HPrqiHtNRUks46K2on3gidB5RNmMCeU04hf8iQagWeMk8+mXbffEOPoiK67thB42eftQ4Estxj1eeMdYXQIFWEjabhqF2b5n/8Qe233qLmCy+Q3KsXQgjc/jJcKwKLo6EBu+D3f86Ep+6IMZuqmJjmLBt2CoVCobDLYRd0kpjMZgzf8BhrmE0heyhmH5tZyCRG8AMj8FGOEOmI1I8gczMF/mfw+lwU+F+AzO2IlFcRIqSbm1GNjhVmZZFvf61W+IWOI389FGyF0lwozYc9a2Hy0/BEC1j6Q+W5/a6xVZxSAmUnnBZ5IM6qVQRCxLXPiFJ3KShNItKhA8GrggcfpOTTT+3PIYCekoK7dm2OeuEFjv7yS1I7dQLsiY6qmFhrVBKs+xMCV/Pm1HvpJVrMm4ezXng9L+fyueimGd4pJuKCElbMA2k3CdyOcFUoFAqFQrE/LGA8X/Mgy5lOIbsoZh/bWMZPjGQCT1BGIUJ4ECn/g8xtkPIORcZ9lHvd7PYuQKS+jxCplQ6ro/MMb+W32UfhEw70oi1QsNnSeWX5sG8jTH8Jnjwa5n1See6ZV9or4A2UnnRu5GCsDnaxiGOfMXRoxOJjIp1X/PLLFL8Sfdte3GkkJeGqXZsGw4bR5ocfyDj5ZCC2nouX0RSm8wL352zUiDpPP03LRYtwt2gRfu1t63AYBiLe526aiJ/mw3a7ATWl8xQKheJActgFnf5kAkux2rXKkIdC8PttLGUGb1aMC60upeZgfH4Xpeb5gIas2n0uvSUImw/61GYV3+q5G/GYZQhfSWRHO2mAvwzeHgDbFltjbTpBp9Mrt+HFwKc7rULkVWnQGGrUjByvihDQ+ti4QaeUCy8k45FA6riu2+skIgSFTzxRrVWwquRccAHtf/uNE7dv5/jly2n144/oGRlhwikYHop6FSGQQMP33qOd3087v59Wq1aRc+ed6GlpEeZJM762tbAl1hgg7BTSlBhmKxt2CoVCoVAoqstypjMfaxt7NJ23h41M5iVkQCUIrQbCfS3Fxu34DQcSF7LqVrr0oxOXUQiSVqnzRMlekmQpWmleIFO6Sra06YcPr4A1M62x+s3gjCtAxNZfEvDpDmRKRuTB5BRodrSl4xJRpx5k58Q87Dn5ZLJHBkotOBwVgaZEnguHD692YfFQss44g7ZTp9Jp1y6OX76cNr/+irNOnTBNGvypxlt8rPPss7Tz+Wjn99N640Zq3X8/jijlIezqPDQN/Sc7d+BA09rbMVQoFAqFTQ6roJOPMhbyXVwbiWQD89nHZuu9lLi1H/C4S6jjPgryMiE3C1lyD9LYaJ3U8qbEe/3RoEZnyGxTMZIy953AFrQYKyJSWsGoqSOs90LAk19C++7W+2CgRWCVnwpsrncZPmo83A9mjQ/353TC4JsTZzxJCcsWQr9OMOGLmPUFsh5/nFo//ICnd297hR6lxL9iBb4//7RjHRdXnTokt2pFZp8+HLt0KfXuv7+y1pTDQUrfvqT27Bmxiudu2ZKm48ZR4+qrEUIgEnwW+r6dtgoKaLN1KE+yMXONcu+lic0UCoVCoVBUCxOD+YyNayMx2cUatrGscsz3E5mOK0hJKqa2uzXkpiGLb0YaKy2DFtfbK6OQ0gRqn1zxNvmvTxASRFyNKGDyU5Vv73sLegSymIIlFUJ0nnCA0/CTM+w0+HFMuEYTAq6x2WF4xzY4/Tir1meMLJ/0226jzi+/kNy/Pxr26iuZu3dT/pOt6ExcnDVrktyqFendutFu8WIaPPkket261kFdJ7lXL9JOOw3hDG9P7WzUiMbvv0+dYcMsnZcg+0vfF32LYQSajtibQeIIlR/NEaVuq0KhUCj2G5vLPv8spmmyePHiiPHdmUvwN/Amfl5Iwa97x9Jkey/qZgynRurn6NrQEIMCZOkrmCWj2LBnFKXe1jT3HI2nbA0iSoFBGbjg+oxrKQ7OS5q0/usTSLs1wc34Med9wrK2tyIdgaDGdS+Ttmw22T9/Tsraeei+koh97861C+GhAWw/9y729L6mYlzrfgbNvv4I95aN8beMAXLhfMTNF+PvdxHUa83q1avZs2dPuFH9+vD882TOm4fYuzf+vQRY9+uv7GpvrQJF9bk/XHwxpaefjnfaNMp/+glfdjYAjl27kPPng88HjRphtG/PZiHYHOX3Ixq1DGlvBcxrUjjrVNJO+x4pYy8y7tl3PWvXWR39Dti9B8jNPfB+lc8j0+fB8ns4+dTQaEe7A+IPIJPMA+ZLoTjSiaXz8lLXU3pUfmIHUvB7/nhabDapmfYGtdPfwKXpII4PGJQiy95Glr3Hpr2vUFTWjSapnUktmouIs3VqU42byV+ytOJ98z9Gg/vyBHMxYMUUls+Zij+lljV2yWOkdDib7J8/I3XVHBxlhRE6z7F5JQwfzO45P7FjwD0VwkO068xRrduTvGJR/C1jgFy5FO65Fq17H2jdLfrf0YwMePxx0pcswbF6dfx7CX4Ov//OjpQU4AD+be7fn5Lu3S2dN3Uq/uxs/ICel4f84w+rWU69esgOHdimaWyzqfMyvQY4E9thmhSYnUmSUwCBEJEhOCkF+QUXsmutl9xc67M6HJ53yueR5/Ng+T2cfCqdd3gh5N/ZK3WQaNW6NQMvuQK/cCCFQEiJQ/pwOb1oLnv7rKVfoPsN3G5rb35ZmQuPx1vFyFr1KSlNASRJWhmaiO6/zHBjhD7VpCTFLKZMuPBIb9RzQinRkpFV0q11w4fHXx5hW+Zw4fFX+ix1ejC1kPigNHF7y3HY6WIXoNiTFHdbn7u4GM3mr4IETE3D6/FUFPE+VNENP0gz7POMRYkrGc1p4HZH/kwAfD4nPp8L+w2AFQrFv4WJiXaAk3nnz5/PhAkTDqhPheJIxNJ5l+MXzhCd58flKEdz29R5pkDzmnhCntkRWi+g80pLk5EIPFoZuoiuncpNF37pChtLMYps67xSLQmzSqkG3fTj8UXWk6qq88ocHozQRjNSBnSejeysAMXupLg1nlwlJeg2601JQGoa5W53Rf3MQxXN9CNMezqv1JWEcErc7ug1vvx+J16v0nkKxeGA0nmHF4dkplORSObZhg8E1qIEIrBzP8NVxh1nPk3DGpviO5CCGnkt6ecbiVPfiRDwwrtDuefaEZGmUrAjfyh7i68AaZBW+CtZe7/G5d2CKTyUe5pTnHIsPlcDilNPAGEFnoTh45h3O/JC1j0MzY30W5Vlg3/G8GSFjTX/78V4tq5AVAn2jOg2lKGzLZ9S08lvfxqbr4m8hmvbZho+O4yk1UsjfIR/HILnr7iXLmf2IysrK6pN0siReN59N+GqWoVPTeOTIUMY8MsvlL/2Grij1KCqJrm5uUyfPp1evXrFnGe1fe7Zze9TJnHP1BdjboOUmk7BsT3ZdIdVPFPTCklP/RaPZxFgUu5tSUHhAAwj56DN82D5VT6PTJ8Hy+/h5HPy9MmMZvQB8QeQQw5NaHLA/CkURzLFIplnGj4YrMpEMKe8Z4tfGNTjncQOJKQXN6R/0Tg8zhUVWSvRtJ6UGnuKrmZnwZ3WgmHRH2Tv/Qp3+XokTso9TSlJaY/XVZ/i1A5IzVNxbuvRXXk59WZbOm/VBWMpz24eNtb4jVtJWzE7IjM9TOcJjZKmx7HurvcjfDr27KTBCw+T+tfv8XUeMOKKe+l8xplk1Yhe58n9ySckP/98XD9hPoXgk3vuYeDEiZS+/TYySt3M6nJQ/t7v28ecHycyZNpLaDF2AEhNp7Rxa9Y+bBV9F6KEtNRJJCfNQwgfXm8T8gvPx++ve3Dnqnwqn0rnHTCfSucdXhySQScAM2SVIbi9rcjn4cVJw3jsvAfJSI6Tfi0knVJr4CrcmfA6Qkjq1viJek2fC4wcB/JmWPY8LHuBlNzFZOeOsw65a0LrIXDMfSA0ypp0h0RZ4EKDhh1oc2LP8PHdW2HL8sTzMw0yF08ns23byJWmdu3gtoti1myq8CElmmlydFoStdtFT0P0P/QQO99/P1CHKrEgCQanUubOpeZbb5H11lsJz0nE9u3bmT59Oi1atKBu3bqJT7Dpc8aMGcjkVERZSWT3QE1HNGxOxvNf0C4zVKh1+0fnebD8Kp9Hps+D5fdw8jlp+iS2s/2A+AuixIhCcWCwSnKHahqrSciire24VAq0KFufwhCC9klNSfLa0FHCpGb6D9RqHAxmHQvyWlg9CpYMJznvW7LyvrUOOTPg6Fuh3aOguyg5+kzYauOGshrRsse54TU3fV5Y/mtko5mq85MmKWsX0K5JQ0jLrHK0HTx4Q2Kdh6X12vjLyImh88z772fHa68hS0psddgLBqc8K1eSOXw4Nb6LX1PVDgdN502bhsyoAQX7InWe7kBk1yb51e9pV6dRyIHOYWbhvY8Pr+ed8nnk+TxYfg8nn0rnHV4cVoXEDSko9aYwc8VpMW0EAjep1JWxO3pEYIYUIZQSZl8Ffz4A5bvD7cp3w18Pwq9XgDQp6XR9Yt/ShFPujBwvKbQ/P7/PekXM24SSYttutMKCmMccjRqRNWaMFdiqTste06TkvfconzkT/5o1yGps+TvgFBXCpK/h83dg6rdQbqVPS01nz0uTod/g8K6AaZlwxb0weg5kVuP3RaFQKBQKxQEntySLPzd0RMp48lSg4+Aos4l9x7JKzcoF98EfN0PJ5vBxXz4seRZm9AfTR0nHa0iMsHRe1cYmZcUJA05hlMbQhQV5tl2Iwti2WkYG2WPHgsNRPZ1nGJR9/z1l332Hf9Wqv9XZ7m9TVgpTxls6b/JYKC4CrOz7PS9NhgtuhqSUSvukVLjgFhgzH8ICTgqFQqH4JzlkM51iYUqNmSt7cc4J34S10gUQaAg0+nAHmrDZzQJAC2nBuuEzWD8mjrGEDZ9A/bMob94b30/BVTYrPTyCo/tAzRZWgChUkGTVsrKg7AiS1ExwuiLHNQ0ysiA/N7EPwMyKbDUbSvLFF6PXq0fhk09SPmWKLZ8aIAyDvJOtbi9avXok33oryXfdhUhOtuXjb1NeBi/8Bz55E0pLKsfTM0m96AaQSRh1GsPD78LdL8HmlbB1Nsh8cCVB7kpI7XRI1yxQKBQKheJI4JPfrqRxzgZqpu5Fiqo6TwCCXtyES0u371RkVH6/Yxosj7ddzoTtU2DFSHz1LsUrZgV8iOgZR41PhEYnWhk2oXWZktMtjeEtTTw/3QHpMTRajVqQZ1PnZdeMe9zTpw81f/uNwieeoGzCBFuZ7RqWws3v3x8AkZ1N8s03kzx0KFpmpq15/W0MA157Et57GYpCthgkpZB23mDQa2Fm14ahr8Ktz8CmVbBpjrWo7PZA3nLIrqV0nkKhUPxLHFaZTkHySzNoZHZGVJl+XVpzDg9Rl1bg6Aaitg1vGrgGVb5d8QqJPxYtYAde4SLvrBGQ0yzcJFhMcuUUeKEzPN4MZo2qfMCnZ0PXfqAliPtpOpwVZ6Xtwivjr1gJkIHDOVf2hUduh9Wx09HdPXqQ8+OP1Nm8GXefPpErd5HuwxLkzW3bKHr4Yfb16oVZVBT33AOC1wvX9oPRL4cHnAAK8kh953ncvvLKz33lN/BVP/jpLpj+JEx6AP7XBV45DrYuOPjzVSgUCoVCEZPCsnSGT3iMBkYPtCprozVpxlkM4yhOBK0laG1IXPRZr6LzXgWRaM1VwspXQZr4NBe5542Cum3DTYI6b+Mf8EpPeKQRTB0BwbpCug59B4cHoqJOzwGnXAieGAt1F10VX4uF6LzsOy+Be6+BxbH1jOuEE6jxzTfU2b6dpIsvtjKf4lBV58l9+yh+9ln2demCuXt3rNMOHKYJQy6HkU+EB5wASotJ+fRNPN4y8Ae21a2fCmPPgam3wM9PwuT/wNu9YERLWDvj4M9XoVAoFBEclkEnh4A+4mYG8Sp9uYfTuZuLGUE/hlGTpgAI4QDPPcQXIxqQBO6rrbe+Qtj7O8Rpp2thwt65CH8hCEHpcZfBI6vg3vnQqo9lIqtsM9u3ET6/Cb6+qzIActn98TOdNEAz4bdvYMS1sDqKiLj6NnA4o6/e6JaP4BF97y746E3o0xY+fTvuHeoNGqA3bRpT6ATDXFE/XdPEP38+hXfcEfcaB4SP/wdzpsesTyCQOAw/npk/wJw34MsroTggkkx/5c9p51J4szts/fPgz1mhUCgUCkVMisrT6Gxcy+WM5AyGcjp3cQHPcC6PUI/WAAghIGkYUbPMKxCABp5bKoe2TQJpoytc8Ub00vUAlLXuD/cvhAcWw/EXW8er6reC7fDNffDB5ZWa5IK7rMXDWBk2GoDfqv00fBAsnhWZfXTZdZCSFl2PVdV5uXvh6zFwdgd4bXjc29Nr18bRqlXs44GvUWduGBhr1pA/eHDcaxwQvvscvvssZlaWkBLdNEj+9mNY9CV8OADyA4W4TKNS5+1bB+/2gTVTD/6cFQqFQhHGYRd0cgg4s4H1/E4inUa0pzHHk06tSGPPPeC6xPo+4lnlAFyQ9i1CC6QjGzZSoEMQRkjLVSFg53JYEWtbWmACP78KSwLFGNt2gwc/tARJ1ZUwB4GfjoTt62DKh3BzBxh5W3iApUkzeGestf0u1EeoWghVDIbfEkoP3Ag/T457f0nnn1+5chR63yReV8QwKBvzIWWvj6T8448wVq9OdEb1MU14/1Vbpslj34QJUWprBZEG+L3w9XUHaHIKhUKhUCiqiyagYw3IcoObFBpyLI05gSzqRxq7rgC39WyPrAPlAHRI/RShB7rKSQmm1/ZcInRewQ748/PAQLQgiIQFn8Gc96y3jVvBU+OsepJalaz0oM4TwK5N8PMXcHcPePoyqwh5kOwc+OgHSE4Jz2yPp/MAnv8PjI1XLgKSzjvvb+k876RJlIx4jvIxH+JfsiTRGfvHB68mzLoHSPnibfjq2sC7KD8baVqvL6+uzEZTKBQKxT/CYRd08ku4vU3lexOTrSxhJTNZy++UUVmIUQgNUj4iz/cyphl6q25wDYaMBQhnr8phVzY4QgoQxkNPxnRmho/NeMWq0xQPoVt2QfoMgtFL4NybISMH6RDRn/RBETH+dfjwsfBjp54JUxfDlTdDalqlWoinGIQGI5+KO1V37944WraM2L4Xo3pV2HFdBycGJXfdQfFVV5DfpiUFp/fGWLYs7jXjsnYVPHk/XH8x3H4lvDsStqy3VZPAVTwvMvusKtKAbQtgy7z9n6NCoVAoFIr9xpRwxzGV7yWS7axkFb+wht8oIa/imBACkl+C1K/wyRNCvDjAeQGk/45wDSTkBEhuYG8iQsfwVOlp9vOricsiCAHTX6rUJp3PhA+Ww8X3QHYdpEOLr/N+/gJevTX8WMeuMG0p3DQUMrNt6jwBLz8eVyM527fH1aNHxBa7RDoPQNfA6YCyB4ZRfM2VFBzfjoIe3fD/8UeCM+OwZRM8/xjccAncegW8/wb8NcdWtz3H5nVQUBh/5tKE/M2wKv6iq0KhUCgOLIddIfF72kKfwGLXCmYwn3GUUFlgUUOnOd3owmW4SUEIjTLzIkrL32Jn+WJq1XSDuQDYDcbvIDIRWqB9o+aAZtfAqv/FD1AIBzS/BjRn5VjxXtg0N/ENSANWTQVfeWUntcat4I5X4cK74Ypm8c8H+Px5uGCIVWA8SNMW8OSr8MQrcEpL2LAmvg/TgLmzYNN6aHRUVBOhaWSPH8+e7t0x8/PDVsNi6RwhLCESDf/MGeR370LaR5/C8sWQuw9q5KCffxFa4yaVPgoLkIX5kJQMDRoiysvhnuvhq4+sbC5pWkGzLz8EmzFCUQN7RduFBht+hQYd7TlWKBQKhUJxwBjUFAYFpNA6/mAuX1BAZXMYgUYTOtCNK0gm0wo8uQayz9eN4pI32VU+m5o520FuB/NPMLMRWpPKC7S4ERY+QtxSCsIBDQcgnVmVY1LCsomJs2SkhB3LIG8rZAUCXHWbwI3/tcoqXJig3qg0YdK7cPlDULtx5Xj9hvDgs9brwp4w79f4wRgpYeNa+PN3OKFLTLPsTz9ld7duGFu3WgW7ExBcWIyGf+4fFPTqQepHHyM2bYDduyAjE/3cgWgtj640LC5Grl5pZek3aoyQEh4dAu+MtLKapLQE5VcfQWrCKYXcsw4kuAfNARtmQauzquFYoVAoFH+HQzLolOKAbDfsK68ca5QCD7SHGwPPrPmMYwHjIs41MVjNr+xmHefwMC6ChRklHscXmCWvAblUruPoCMeFaEmvI0Q2tL4b1o4GfwnRBYkGugda3Q2hdbLLi6t3k77SyqBTkMmjbaUQ4y+H6Z9B/5sijwkBO7fbn8eOLTGDTgDOVq2o+eefFA0fTvH770OZlWoeax0pGHCKWr7AMHCVFeK/6GxLsWgaGCb+R+5HG3AB+iWX4y4vI7vrMfiDwqdZS7TUJLSVi6xAV3AlMPizkdjIAQfpdiKw0+ZXWLWeFAqFQqFQHBRSnVA3CbaHVDWo5bEWFoe2s7bYLWMav/J+xLkSkw3MZxdrGcBjJJNZccyhG2QmDcAs3UWlzhPg6IfueROh1beCTitfhfK9MRYYhSVijnkAQnfiGb7qbcvyRtGF0z4N0TFxEBpMfh8GPxr9+LbNtrJ/ANi+Je5hvX59as6bR+Hw4ZS8+y6ysDC+fSDgFEvnOTUD/xUXIjTNMjZN/I//B613X/Qbb8dVXkaNbsfg9wY+3AaN0BrUR/vzt4DOq/IZ29V5Th3hSWyndJ5CoVD88xyS2+tSnbDtEph6Bnx9KszqB+svgptaWQ+5PWyIGnAKIjHJYzvzGVsx5nJ6SXU/CRVZUcGwiYH0f4lRfBJS5kHqUXDaFHBlEDV32ZEGvSZCWtMqk64JusveDbpTwROl1e+O9fbO1x1WnadYJMfogBKN4qKE29McDRuS+cYb1N21i1p//UX6a69Fff4LUfmKhttpCUnAEhW+gICTEjnhazIvH4Du9yNCBcfaVZgLF2KUy+jT9JM4Bxzw1W2fOCUeLAFa+5jEdgqFQqFQKPaLFAdsuhh+PsvSedPPhC2XwH3HWjqhiD3M5oOY50tMSsjjNz6uGEtyjsTtLkcTuyqsKr76J2EUd0aa28CTA72ngydYCzRUCgvQ3dDja6gRul0PcLggpYa9G9QckF4ncnzHOntaBOLrvNQoGjIWJUUJM5j0mjXJfOkl6u7cSa1Fi8j85JP90nkuJ+jBKhGmaem8wLXN6T+ReVl/HH4/whsSzduyCXPObxjeGHLUhz2d1+V40GwYmj6l8xQKheIf5pAMOgG4dTi1HpzfBE6qHRKsAJYxFZFg6hKTFfyMj3Ic2h84nfGyXAwwV2OWP269rdkFBqyHjq9AjY6QVA+ScqzN6+TDjF4w6wKc+b9XunAlQcfLEosJzQFdr42e0eT0xH6Sh92cBHdS7ONnDozfolfDKkDpAK47C05pBqNfBm/84ppaWhrO9u1JvukmZMuWkcdF7PiVQ48vVDRMMGXMxSwpYywwBqcc47pS15FCUHDmI/ZWtjIaQos+ie0UCoVCoVDsNw4NetaxdN4pdcEZIouWM4NE6S0Sk/XMpYQ8pLGKFOezQCyd4Qe5E7PsHuttZhs4dzV0eQdqdoXk+gGd5wRRBr8OgBln4to3I9xNt+sjC4JXRXPAcQMhKSPymCvJVh1KhEig886PnxkfqvPuvxq6N4L/DYeS+Fn5IikJZ7t2JF16KbJr16jTijV9XbN0YCydp2Mk1nnRZFow6BRL52k6Esi/7onEdVUB3OnQ7sLEdgqFQqE4YByyQad4bGYRMt5e/AB+ytnDepKco20skhhI7ztIWWK9dWVAq9uh7TDw7wJ/XmWLXWnA1vHk/HUeDhESqDltqCU2Yj30hLCCQZ2vin78xL4IO2nXhh9OPCP28cG3xk4B14lM4Nq6AZ4eAlefAeVl0c8LQeg68qOPkEIg7QTJsIJOMf2J8K+xCCRFhSOB0KaDQScBMWbk1KHU5cHXpCN0vomEOdpnv5RYUCoUCoVCoThobLGp8yQm21mJ6XuDxJLWj/R/hTR3Wm8dKdD8WjjuCTD2BXReUNOZsHMKNRZfhkuE1HrocSu4UuMHNwRWcCoaHU9H2FkAM/zQsW/s45deb21diyacoum8XdvgpYfh4u5QmJ/4+oB86y3MoM6zofUOiM4z4+i84LgWXsfBTM+gzJWEv9lxcOpDCefJmc+Cqxo7AhQKhULxtzksg04m9vdiG/hx6b/Y2Q4OFIHxV+Xb/GUw+xIryCSrXDPw3q15K1fC6h4DN04AhydckAisT1qTYJbBiyfCmMthx/Jwn93OxciomXiaDhe89xBM/QS85ZHHj24LT7xmfR+6EhbaXjfsXqT1+uNneP6BxNcHaNCAsuRk5MMPoze1thrKOLokbpZTNX4Lo66wmUAxcPGN0PVUaN0eup8OL33C7k9nIYNBpHNGQpdA4EkLLUogwJEEF30I7QZGuYBCoVAoFIp/Cr+tGowWJn6kfwpC2Km35Ecasyvflm6Dmf3BKI+i8yx/Ts1H0s6vrbGsBnDrj+DJiK7zdOsavHk6vHc+bJ4f7rNtd3z1IzPFI9B0+PIFmPgOlEbJTqpdD17+yNIwoQtlsXQeWBGdlYvhwRsSXx+gRg3Kk5ORTz+N3qpVpV6Ko+XiaUC7RO35Egw8nTMYTupj6bxup8Gz77Lrqz8wg4WmTnvECjwJLfDzCc5Zs5r/nP0SdLnZ/mQUCoVCcUA4LINO6dTGVlVBIJ2aUA3xEma76tXAN/HzpFI3v1H5plUfeGwtnPU41GxhLf0En3tBTD/8+Tm80BHWh4gfh5Pc29+yrhgv28bvhUUz4JlBcF1b2LEh0mbwLfDed3DsieHj8T4204RPR0FhQRyjUF8CbrqJnLVrqeX3k7VyTfWURRVXtoj1oxACbnkQxvwE3/0Fo3+Acy4FV0ixdt0BA/4H966GnvdCm3PhmPOh/8vw0HY44Yr9mrtCoVAoFIoDRyZ1E5ZRCJJOLcIrficixHbNW2CWE7eTHZCyaWTlqleTTvDoGjj3OajTxtqSV1XnSROWTICXu8LS7yvHhSDv1jes3WLxdJ5pwLLf4MXr4coWsH5JpM3ZF8Fn06FLzxD/xNd5hgGTvkpYXDx0vlx1FTnLllHL5yN75y6Ey2b90ipu7Oq8mIpbAtfcBe9PsnTeh1PgwmvAE5K1JASc/iTcv8kKQLU5F44ZAH2Hw4Nboftd1Z67QqFQKP4+h3TQaXcpTN1mvXaGbKFqzakkCgQJBHVpRTq1MczmdmoQYmXANLe+lRI2jIlc+YqCO+8XKNtdOZBeB854CFr0jH2S6Qd/GbzdP6zzne/ozpQ6kvAe3Tn+RYNdS3ZsgKGnQmlRpM1p/WD8HHZ9MQtT05CajSd+WSn8MjmxXRWEruNo1gzX1ddGVRaJShjYKXFgXSjKmK5D737QoJE9HzWawRnPwOBv4PKv4KQ7rFVLhUKhUCgU/xh55TBtG0zZCltCEnpacYqt7XUZ1KUWzUFrg5T2tsYLrVXlmw0fxOhgF46zZBUUhGSnp2TDaffAcefFPt80rG1yoy+Awp0Vw/6GrSl1JFHe7mTiRoiCKT95uyydl7sr0qZzT/h0GrvG/o6h60g7qeMSmPJNYrsqCF1Hz8nBM/S+qMejbo0LXlLa13lRPxFNh/YdoN3x9pxk1Ifej8LgcXDFWDhlmNXwR6FQKBT/Codk0MmQcOl0qPcZ9J5kvep/BhdNgzUF0JROZFI/ziqYtdTTgfMBKPVfaSMvSgf9LKudLljdLYwS+5Mu3xP+vngvzPswvpiRJpTsgz8/Cxs2NZ19D34F76+EJm3j1w4w/VbgaerHMU2MRtb2N5HoiR9MD7/3cujRAEY8AFs3xj+nCikjX8d16SDrjaOymLlfipiCw27XX4gSz9J1SEmDJ16s1jwVCoVCoVD8O5gSrv0F6nwKp02C0ydDo8/h7B9h4V5oQFvq0DJhttOJXIhAoLlusrG9TgPtBITevnKoqnaLR1mVoI+vDH55LcZ+sCASDC/MeTd8VNPJHfoRfLwe2vVIoPMMKNwH370Z08So19BaXE0kqASgmfDsEOhWB566E9atjH9OFZIefRz3HXdZb0J1XpyflZT2M50iPgpNB5cLnh9VrXkqFAqF4tDhkAw67S2DLzeAPyRIYUgYuxFOnAArcl30YxhZWAEiUaXdrYbOadxGXazVrHL/OZimiLMKZm3E1z2Phww5QfPYn7Q7O/z98h/AsLGtTwj466vox5xu2LAkgaDBEhHfxhYjgFX0O1Gnk2Bats8LO7fCu8/DGa1gxsT41w+distF6gdjSJ/9B67Lr0Bvfxx6h47oV9+AqFcvale9YDAq0SKYluRECGGlsgf37x/THr6fDUc1hxU/w6hB8FhHePokGP8EWsHO+E4VCoVCoVD8o+wthw/XQHmIvJHApK3Q5TuYvVPjdO6mNlb2eajOE1aYiR5cy1F0tMb00/EaXePoCCvioXmeDR92ZdmfdFWdt+4XKLNRlFuaVkmFaKRmWdvoEuk807Cl8xJ2Lg4Gfvw+2LMTPv4fnHUMjP8o/vVDEJpGygsvkf7nYtzX34h+/Anoxx+PY9BViOYtKvVZ6Nxk3AZ0lVNMdlvBKYez8l6atoBxM6xMp3Vz4b1rLZ33VFf48gH03M22565QKBSKf4c4T6d/D4kVZKqKIaHQB+dPhZUDMzlPPMlm/mIlP1PIbhx4aEIHjqYnHtJCzvRQWpaE3zwGp74Iq9KiQeXTNwUteSxC71B5ihDQ6BLY+FHCLXbe9E64PLXDB0vzA/5t7CsrzY1+bPv6+OeG+ti+Lq6JT3cgYq2ABbVc1VUow7BWzW49D75ZAC2OsTcfwHHiiaSeGF5Pytz8IN5LBiAX/mmtjgWXvvx+zDbHIrZsiPy0dAcYfrTb7kG7/1H49ivYuA7cHujVF47rCKUFMKIPLJtqdQ8MdoZZO4daE57A0eB+2/NWKBQKhUJxcDFl+MJiEENa8ZcBP8GWS1I4W3+QbSxjOdPJZwc6LhrSjlb0IoXKgJEQGgVlozGN0YERB1Q0nRGAGy3pQzRHn/ALNh4EK55PuMXO72mCI6Nd+GCZzfqXACV50cf3bLW24Nlh33bLNkZgya87YndAjqnzAvb3DYZ6jeHEHvbmAjjatsXx6mthY3LPHrxXXIj5y4xIndekBaIoryL4VDEVXQfDQFx0OdpLb8Kk8bBmhbXA2PVk6NLDqmX6v4th7hfhOm/9XGr+8F+cDW02wVEoFArFv8IhGXSKhyFhdQFM2w6n1dNozAk05gRMTPLYjp9yDKKJB428sonUrrkM6X0Paa4DWQwiCbQGSP/PSK05QmtSecrRd8CGDxPOqajhTWRXHUyvS+I1Hay04Yz60Y+5kxKfH8QVPyvL0B346zTAsXu7FUwKIqp8rYqUVuBp9Isw/N0YRvbQGjbCPWs+5pzZGF9/Dvv2Qk5NHBddhtaxEzsW/oXviy8ws3PQ9u0BXUf07ot2w+1opwVaB196deT8XjsfVsyw3oe2IpYmSHCb5XiWTIS61/6t+SsUCoVCoTi4mBL2lMPXG+CyZhr1aUt92iKR5LEDL6VR6z1J0ikr95Bb+g3Zad8gzRUgC0F4QKuHNOYitXYIPaSmU/ObYOWLgUyj2JqtqOEtZFbdH5ZWx+YdCcg8ADpP08M71VXB1HS8LdviWrsiPJCVSOeB1Xpu1DPVCjpFQ+Tk4P5hOuaf8/F//jHs2gmZWejnXYjWvSc7VyzH99HHmHXqoe/YBkJDdO2BduMdiLMHWNnsAy+LdPzeNTAvsCsgVOeZBgJwmV6Sf/8QBgz7W/NXKBQKxcHhsAs6AehC8v6fe6m1dzsmBttz/mRbjQV4XYFi2hIyC5vQcHcX0kvqkZtrZRKtXr2WPXvqkJZ8InVrTEATJYAGxnwkEzDLnya38CJ25g7D+mgcZNZ7nAbbHgE0REgwSyIQSAyps2lHCVvlorAN68JsSGtnKrovSoHvUEyDDbVOpnDxYoCQua5mb1oKrZLScJQWxnUhNUFpeg773n2cgmNOxUjJDDuem5sLQvDnHc9w3PN348jbhwimcifqdAJg+DG/+YhlF9yCdLoi5rlnTzVqIgCkpsOV14ePLV5Mbn4BPpeb39//mqy0VGs1L/iZBj6fqqRsnkvTZVNjXip4a55vH2Vx7RP3u7teKH/r3v9hv8rnkenzYPk9nHxqaLSjXWJjm2SSecB8KRSK+GhIxizOp13JZiSSHdmL2JYznzJ3XoVNelEDGuzuRFZREyD4t0SwfGUW9et2o37OOBx6PtLUwFiAZCLS+zz5RWeyfd/jSGkt1qU1GEHjTUOwlF00naexaa/O5kULwwsOyVSOTqmLs3h7XBklkWyt34fcKDpvT0YGLWs0wLV3KyJO0EtqAm/NBux5+1EKjjkFf3p4Ueygzltw02Mc//wQXDs2g5TWvGzpPAM5cxLLZ83AyKgROc/q/m12uGBQlUXCJUvIzc3F53Lxx1ufWjpP062AV+B4NNx719JyzicJL5k8aThLmvRB6s7qzTUGh9PzTvk88nweLL+Hk0+l8w4vhJS2+4b9YzRpfSwXXjY4ro1D+vDIcoTHBD3Og7pMA6NSJOiaH4+nLDy1two+vwOvtzJzSMOPU/PhiFKkssxw4dG9mFJQbroxQ+J4TlmOS8av6yQBn3Dix4EUkStYLn85TtNGbajQ+WsOvLo7eoBFmjj9PpyGP3Fh8aoE3BlCw6c7MULFwr+E2yjDkWD7Y5nmwmN6KdU9mOKwjLMqFIpqYmKiHeCyhfPnz2fChAkH1KdCcSRiR+fp0k+SLEO4TXDE0XnlGvgr/61rmoHHY7U8jqbzJGD4dcq9ngoLgYFL8+EQkXoiqPMkgnLDjRGi8xzSh1uWx70PCfgDOs9Ei9BmDsOL2/DG9VEVv+agPKbOkwGd59tvnWcGdJ7/ENB5LjOxDg7qvDLNjaEdmKCTQqE4tFE67/DikPwfuF84GVF/aMzjGpJb6u+k+3Hj2VxrTpytYSCkRvO5A5k1aS69ep1Cx2OuwOnYghDxH8Rrt36D1980bCy18Fcab7odgVGxIjVizVCGNh+BDCwlbWj0GkVp3QPXN6n/y2Nkr/4GKXREoGaADCnaLYUDgURIg6LaHVjS/j9M/m0RvXr1IisrC+Etpenr15K0eVlldpL1ISBFDL0hBMXNTmT9dW+A7iQ3N5fp06dX+KxwUZDHMVf3jPs5oAVeIVE6qWkI0+S/A4Zx0qmnkVHbbop5fGLNMx7Nx1xE0u4VcW1GNBjK0C0j2HL6E+xp3wfDkY+QLhzeGggbfQ0PxDz/Lb/K55Hp82D5PZx8Tp4+mdGMTmxskxxyaEKTA+ZPoTiSSaTzdCQX1trH1cf/wLp6U+Nn6kg4bs0VeLc7mD59Orde+w1pKUsRIn5x7o073qOkvGPYmKdkEc02XI+Q5YjAFr5QnSeQbK4/nLzMswPXltSe9yq1Fr0bR+fp1rfSoDSrFYuPf5gf5q6u/Jtn+Gj8zp2krfw1PEgUV+dplNVtybpb3sd0J8f8Oyp8XtoMPgnNGycwFk3nCQ0hTZ4bcB/de/QkrUGjuJ+lXfbn733jcbeRtmlm3F+BEQ2GMmTrS+w66TZ2drkMvzMPIXUc3pyEHRAP5FyVT+Xzn/J5sPweTj6Vzju8OCSDTomQCIaclMOM1OhbrioIPPBFmz0wCdocvReX006XCwfNj5qO7jk35KImfHs2EH3fvwg8rY/a8TB03ga6tQ2NdmNh+Q+ImSNhzTSQ3jABIUKydFJ3L6TjnDuZLK+mRYsW1K1b1zrw+hx470GY+A6Ul1TeV4wnsJCS1DVzabdtHpx1A9u3b2f69OnhPoN06QV/zLQ6o1QltNNJ6JwDBcl10+CECaNIfjVG971qEneesUjLhN2JzfKOSmPH6evZWeNhgj+/ZOpxFOfTmP4IYtdJOCDz/Jf8Kp9Hps+D5fdw8jlp+iS2s/2A+AuixIhC8c9gILinSzarchLoPKwi4uUtNtEytS8zZvxEeiJtCICDJg0moydX2QI2+WrAC1FqRgUXGxtuf4KGnW8GV6Z14Nh3YPXliF9eg2XfgyyrovMq9VVS/ho6/HY7k/Tbwv/mvTQVxjwB41+D4nxLc2mxY21CmiTtWMUxK3+EQQ/H/zvadyBM/CJ6wfJYOi+wyKmZJh0+/i/uj2bGmEn12K+/9z/nwAZR2eo4BgKJv8Eqth7zMDKwTdJNDkcxgCacj47r4M9V+VQ+/yGfB8vv4eRT6bzDi383Z3Y/0IDBzcGduoZyihPaS0y2eSwBomvLSbyxHcCPNP4KH9o+GUo2EU2IVGKCdy9sGVs5JAS0OQtu+gEuejP+5U0/euE2XFRJI05KgVtfgS93wJMTIKdO4tpEAhj/anwbgKvujh5wgnAxEoOkqV/Daw/Bfy6Hh66AT16FwrzE160OJYXwyxcw8U2Y9RWUhfzc254OUbYlhmI6BbMf7Miu7C2EBgxL2M5SXmM+T2BGLT6vUCgUCoXin0QX0Lc+NMvZQQG7EtpLTNYyFwBNi5/dVIkfaf4ZPkPJaKQAAHIYSURBVLRvAeTOT9jJDrMc1r8fPtbiFLjmK7h2bEKdp5Xl46TKdjqnC655Cr7YDsN/gEZHh9ePiurLhG9fT9z9bvAdsW1s1HtyL/gFXrkfHhpsab33n4N9iX8u1aK8FGaPs3Tez59ZgbcgbU6z1ZdHSJP1rfMqAk4A5exhBe/yO8MwiL8NUqFQKBQHj0My6OQOxBD0kAehI/B9v4bwZjfwUmbbn08LPmjsZ7NEJIHtmgHCxj5x4YDdP0c/9uvIhCJCSAOH9IE/ysMxOQ3adIXcHSR8AksJG5daAZt4nNofrrs3cPGQDzyYah13soATeHc4TPoMJn0KI+6C3nXhizcSnGwDXzm8PQQG1YZnL4bXb4ZnLoRBdeCDBy0RdcoNcQVTWaYbX7IDqWvIiFR76wZ3Mpt1fP7356tQKBQKhSIhSQE55gh5fgc1X9da8EUv8FJq25+/IqBQnS3zVTThrhnYlsW7Yui82a/H7TAHls5zSj+iNDfyoDsJTuwL21cHOuolIG8n7Fgf36Z9Zxj2vPV9aH0mOx+VAOkC3v8vTPrE0nojH4DT68PbTyXMPkqIYcDHj8HldeDp8y2d99ylls574zYrGNX1CnAlJVxszW2aTkGj5ChHJLksYQXv/L25KhQKhWK/OSSDTpkumNwX+jWA2h7rdUYD+OF0+KY3eByQUo0K80lGOgA+syO2lkvQEHr38KEEBcHDiFYQ0jRh20JbIkIAekGMbYD+6hUVZ/4kHJuXxhcG9/4XnvsQmrWOnEgsAprK0gDSypYyDOs63jJ45hb4+i3w+2HWd/DFq/DNW7BlbXR/ZpXPxe+DJ86B8a+At4rwLCuCL5+FZy6C9Dpw+esxp7mxVwPrRuJqFcl6xmKSYLVQoVAoFArF3ybdBb+cBRc0gbpJUNMDp9SBsafC9DOt4ylk2PaXhKXzDENDSjvRFAdCr1LTUvpsdriVYMYo/L3xt9jZ41Vw7l0Z/YBpRmqieCyajnPDwvg679qh8Po4OKZD5ViiLKeq/0MwDOveTNNa9Pvfw1bgyTThjynw5UgY+wasWxrdX9V7khJevBI+eQJKCsKP+crg+zfg4b5WJ7zrPyRWXQmpWfsQF17XJs7NSDYxEZ+NHRIKhUKhOPAcsjWdTq9vvWKRQ2MyqE0+OxP6alTagZUswjCPAa0zmPMgwXYqzXV9+EBaK0jQJQ2wgkrprWIcq8aKUCzbzJqQmglFebHPFSGvZy+iJpB0/L14fh8HA26JYi9gwBVw7uWwdgXk7oGx78J3H8dOybaTNPbCnfDuw5C7y8rwCgbcOveF+98C0w/jXoNJ70NhLrVdSbg634lj/SJYsA4WTCFmkFBK+G0c/PwpnHojpGbDF8NgT8iKnxBs694EViWeqpc88lhO9gFsvalQKBQKhSI63etYr1ikUoO6tGQHa5BxShsIBK3oEXin4TVOx+34ifg6z4/mqqKH0lol3loHVkZ7TJ2X+PRK2xjGug51m8H2dbEdhuq8124kB0g+/l6SZ3wIl9wXPXjWZ4D12rgGdm2Hn8bCxyNjB8kC/0OIG4d7+zH4bhTs3hrQedKa87Hd4f5RkJoB49+A796GvF3Ucbhwd7kb54rfYZ0JMz6O7VuasOxXmDASBg6FuyfCZ0Ng27Iws7yW9fAKJ8V1U+JMFEzK2cN86pKggY5CoVAoDjiHbNApEQJBB85hGm/HsdFIIo0GpccDiwDQk97EKO6GVSgy+oNWc/8XoVWJeDW+BP68C4wE6d5Cg6OujOJUgzrHwI6lJFIlEjAyGkY/qDvgrBvhqxGxi39DxOqVJiVZb98KZblwyX9izF1A80C2k5Aw/oPodnby43RAlkFuYBtkaIbXvJ9g8HFglliBp8B9aN5SnIafnP+cAfVqW/cQ76MSmpUJderlcOKF0GEgrPoFdq2xVsaOPgVvxp02JmuhVsAUCoVCoTh0OIH+fM8LcSwEDty04RQKAmUXiryP4nbMBfKJpfOE6x6Efmz4YL2zwF0LyhPUK5J+aHZD9GMNO8DqabaCV74aLWMf7H8rvH1PdA0UKDIeMSwlGR/dD/lb4MZXY0eLGje3XjVqwpiXo9vYqOmJDjhMK+AE4TpvyW9wTSdwCytbPaDzhN+LwzTIefw8aNLE2ooYLzNMmvDtSDhvCLTrC22XwLo/YOsS0J3QvBt/Zj+NfMXetkql8xQKheLf4ZDcXhfKliKYtQPm7wZvledSS7pxIucBRGmJKvCQytnci1O6K0f149BTZoEWXKXSkTgwBRiajuE4CoPlmMbvyNBVKGc6tH0s9kSDIqBmF8idF11wdL89/s1qIHXLV87n/WHWK1CaF2k34C7IyImsGxClA0kEYx6C5b/FnwdAhx7Qsp0V5Ioyz7gI4oczDQMKc6G0PKrYEBKrTkGizDBpwpp51p5/Xz6sHQkbb4Y9t8HuO2DNMFw++91KPGTbtlUoFAqFQvH32VkCv+6AP3ZBaZXk6oa0pSeDsZYaw8WHQODETT+GkEJlG25TNkFP+Q20EwIjoTpPw3A0wGAHpjEjXOdpTjjuudgTDeq87OOgcCmYUcoddLs1fsBJVOq8Gl8PhOnPQFGUIFffa61sJ80RcX5CDfbtazBnfAIjoGkr6No7us5LtOgHls6LZWMaVtOXgqLYQaVdG+xtRdy9yXr5S2D9O7DuJth7O+y+DdYOpeaOgsQ+ArhDfk8UCoVC8c9xyAadZu+Avt9Dw0+gxwToOA7qfwyPzgsXJR04h/N4iOZ0wokHgYabVBrRjq5cTAa1I3wL/QT0lMXoybOQzn6YDhOpC9AMYD2m8SG+8i74ys9DypDMplb3BgJPIqxjmtSxVnw0DXLnwK/9YGIT2DEp/MIdBkODDpHd1kTg/MB2dQE49iyF7+6G55vDlvnh9tl14PmZUKux9T4YfLKz0KM7rFWjRAgBr4yFjKyogiSuFrFbr92M4ag6dUAB8lfCj+1g4d1QuALMMvDlwZYvabJiti0XydQnnRbVvLBCoVAoFIr9YeFeOO9HqPcxdJ8Anb+BOmNg6BzIDymZ1IZeXMBjtKIHLpIRaLhIpj5t6MJF5NA4wrfQW+JI/QM9ZQG4LsZ0yIDOM4EtSPNzfOW98JWfgpR5lScedSV0eM3aQodWIVEqdZ6wAk6/DYTv68Pmz8Iv3OZsOPqM6E1jdMIyiBz7VsGPD8FzTWHNtHDblHR4bgY0OSZwbnCvG4mDQZpuZYHb4dkxULehtaWvCrZ0XiK9Jond9Lk6Wq9oC0w7ERbcAPkLrV0H/kLY/j3tfh+Lw+dPuFDpJJ2adIhro1AoFIqDwyEZdCo3oOe3MHVb+PieMnjqTzjtOygJCTzVphmduYA6NEdiUk4Rm1nMNN5mDHezPnkOVR+fQghMNmPKCURGPyzn0vwWX/lloSdB20eh/3pofT++lKMD40EDs7LuU+lWmNUPtv9Qeb7TAzdOgaNPt95rDkscRNkSZ30rrUynd/pAwfbwD6NBS3h3FTw6Hk6+GNqcZKv1LYYffp+QwChA4+bw1QK44DpweyrHdVfi4pN2xUQ0MRIoCWCLjCyYdx6UbYs8UfppsHE7QkpIUFi0BYMQ1Y52KRQKhUKhqC5eE7p8A99uBDPksV3gg5cXQ9dvIDekiW8OjejEQOrTGpB4KWEry/iFD/mAO1nAd1HrPkn2YpifEVvn/Yqv/GxkaHZSi1vhnM3Q7gl8aYE6j6LSY0VjmfLd8PulsHFM5bmaDleNheMusU7SdOsVEqQJS0qXJvhK4f1+sGtF+ORz6sNrC2D4FOh1GbTvZU/nmQYsnmFlgSeiZh34/A+4/A5ISQu5D0f8Wk7V+d9DzGyoOMdCcbph5a1QGCzQGa7zAHTDpPnyjXHdNONiNGx0oVYoFArFAeeQDDrleS0RYkR5GJkSft8N//mjcqyIXMbyJFuoLC4oAw+lMopYlDEBnOHOpDTx++5LMBMTaX6DacwNH05pDG0eRPdb+9ijP5cDAZD511h1i4IkZcJ1E2HoEjjlXshuUJneFNWNAeUFMOd/kcd0HbqeA8M+hlteTXAvIfhL4LtXYMYYKIrSsjeUOg3g0Tdg1i74ch58NR9+2oxMTrV/vf3Bj73VvH49oGRDzHR2p88fePlAVk3Nt1Rgcy6nAaf//TkrFAqFQqFISF65FXiKpvMMCavy4ZZZlWPlFPMNw9nAnxX6LvjVRxl/8DWL0r+lqnDwex8IfBdLUBhI81dMs0pmelIdaHM/urEHSBDnWXAT+Isq3zuTYNDH8MAaOO0hqN0ygc4zLZ0468XIY5oGJ/SGoR/AfR/Fm0WV84BvX4JpoyE/QY2qrBwY9iL8stNaaPxyHszchVGzXvzz7K7Txfro7TQM1hzQpReULErYzKfZqo1oRnjgMbglsyH9aMqFNi6oUCgUioPBIRl0gvjxBlPC2yugKLDY9BufU0ph/O4mLpNCR+WDV5pTQW62MRMHhj9KsfLNnyKMosjxMCSU7YAd30ceqnOMFXTK30LC6Io04Pe34tvUqE9CBeAA3IBDwvtDYORguLYuvH0reMvin5uSZrXabXMC1KhF/u1W3YOoM69OplKsKQdLJVStW0XIeFoNqL2HRL/GwpScPPm3/2vvvuPjKM4/jn9m9+506pLVLBv3hnuhGDCObXrHmI7pJYXQcUhCDT8SkoBJKCEFCCRAaEmA0E0TxaYYbIqxDRjjgm25yJJstdOVnd8fe5LudG1ln2wJPe/X6wLa23t2bnXovpmdnWFY41QyKLJfjpsyDmA//sAIznXYWCGEEELsLE30CKf2Qhr+/S1UNto/f8xzbGdz0py3OvvDqDhgWUvQ+mMS39/VwiQU+Gvs5sqXMf2VsdtjGtsEa+OswlY0GA65HratJWUosoKw+BEINifeJ6/IHvWTjImd8zzAEzfAvefDhX3hrrOhYVvy13ozYdREO+vlF1J7uT2Bu453q2C6cp4mec7zeGGcO3y7Y3KuYJCJlePJpDf2jF8uitmLfbiVsVwRZ+5XIYQQu0q3/QvcEIS3K6GRbXzLR0mDSItVWR+2/ru2vsbZZZogWi+P3Vz9AY4W/1Nu2Jpg4u66SmfL8wI0bLZvjUuksAz2PjLxl3fEvFFA2yojwWaY91f4zVEQ8Md/bRy+aTPxuTOwCortDS63/QDI7uXs1CYdJm5A2UjIC9dvCT1G+J9FfeB3b0FgE6kDJWQ0+xlePYxDeJKjeJUjeZm9uIkixqV8rRBCCCF2rZCGl9dCgGaW83bKnKe0gXK37aOtr5weCa2XxW6u/gDtoLMDZcLWD+I/59sGfocrpgV90FCV+Hl3Bsw4M/7E39C2iEv7nGcFYf5jcOM0aEp1sbSNf9I0fG4vVnFve4Ppast5mfnORzol+38a2b3jzE8afkFOAfzmdciqTTnKCUBj0LtpIAfxKEcxjyN5mX35LaXsK9MnCCHEbubg27TrqvPDJlY66nAC2OL5pu0H5cH5ZZo4V5Z0yPnrdYL2ubMcHh97iHG8q02RTr0OFs8jZqbJVBM+agu+eAtevx+O/KnjJoVMN5sfXkT51x/Dl4vtA4zeBw48Cq46EhZVJFiZJNy+yHAUr01n3gBTT4D5/4a3H4dtW6CgzA5dB8wCtwe+znfcXly54aN3275WIYQQokcwgLoAbOU7giQZARSmlQVmZC5zvnqtirev04uC6MT7ejqQ8wBcmcmfP+FqqPiXna2iVt4L/zNRprJCsPYLePb3cPotjpsTMl1sfmgh5as+h8/ft7PZsHEwYyb85gJ49bHEGReS5zwUnHoNnHCJvdpexaOwdb09iv0Hp8LUU8GbBVsLwm8wVda3JOcJIUQX1a07nfpmg4XTUABate2rjOkOX2VgmAfHbs4fh5MRNugA5I+N/1zhACgeDlUrSNiB1TIaSAXh5hwYdxpMuQJ6xxmhM+oAuOZxuH12eI4A+/1qJ6vJKeClu+GwH9ttSXQlrT2XGw6eZT8i/e4ZuO4U+OBlu1bLKC2l7Kt1R50JLz9gX9mK1zF14mVw0Gn2/gedZT/i6Xsi1H5Kqt+FZWRhlEx39p6EEEIIsVtZdDznRTLMAwA3bffrJ2JimIfHbs4fh3IwwgatE+c8dyYM+gGsXpC4Y0pFPH5fDqNmwQGXQf/9Y/ftPxJueh5uOR4CPrDs7OMo51khmPcXOPF6e9S4y+Gk2oYBBx5pPyJd+wCgYd6/2uU8w37NsefCvIfajh1BA+qgU+HEy8P1T7If8fSZCZXPO2ioCeVHOXtPQgghdqlueylgQA5M6Q2FlDt+TW6gd+u/G8YwlHEIbcOAEjExXRfEacDZye8xbwkQpgeUBivOrWtKwYFXJH59yypwLVeJgk3w6SPwp0nwaYIJJaeeDA+sgJN+Dr0HQ25RygFSgB2aNn4NZ7jhdDdcPBD+dxs01Dp4cRxZOfCHF+GBD+Doc2HCD2C/I+DSO+D5DfDz+2HuazBhetTLLGVQc8mf4JI7Sb50StigC8DwkGqcd2Pvs8CVvWPvRQghhBC7VL4bjukP+ZTh6F4uraJWqlWqGMM8ldQ5L4Th+kns5r6zsMwko6lb8pmhwcywF2mJZ8oViTuc2ue8kB+W/gf+dgAsuDP+ayYeAg+shNk3Q9/h9lxPpsPbxxq2wllZcIYHflgOT/0Kajc5e217ngz41aPwyGcw80cwcRrseyj88BZ4bp2d8+55F/Y7KirPWUqx/YLfw/X/apsyIZl+p4G7F6n+L4uv+Fjw9k66jxBCiN2jS450MhW4FAST3L12wyQwFPRiD0oYxBZWk+p2t0GN+0LEyGW356/4fZOBbUCwdV5Ey4gMLUMIWW9jGiegIjuZPL2o638NLG835DtmnqIgLDoHllwFez8KZUdE77/vD2HF67D0mej2q3b/bNGyEt5/zoGi4dBv39g3WjoAzvmN/WiohbML452O6GMZ4cO3DNeuWgOP/RJe/TPc9BaUDkxeI25dBaMn24949j7EflRvgtrNbGrw0fT0S/imzLJf21AJXz8BjRvBkwdDZkGvkdE1vGWw31Pw/iziDXHXGIS0QV3/a+jk9faEEEII4YDLQc67ZgJ4XQAFDGA8a/k8+XQKSqP90aHJ5ZmL3/cO6A205DywOz5a+qeU6oehP0DpASjlbXuxmcH2wTfD0nX2yJzW4xCdzZSCJZfD8htgwl+h3+nR7Ro9Eyb/GD5sN1l5olviWnLeS1dC8QgY0W6EEUCv3nDa9fYD4OQUo5aicl74HNZuhKdvgXn3wk1vQv8Eo7VSGToO5vwp/nOj94dbn4PaKqjeyOa6BpqeeZnGQ84i3zCgaaud8+rX2hcGBx0DpZOia7iyYMr/4N3D7Au4MTnPxEKxbcitpLg5UQghxG7SJUc6FWbYQ6rbf6+7wj/cOAku2LNt+36cknSSQKUVhBQl/qHR240heLwfooyD7M4mpbDM8ClRCpRCs5Lm4Gn4AoegdV3U6xv6Xozf8oQnmjSih0i3Nj785e6vgfePgS1vRjfOMGH2U3DErZBd2q7hCd+SPXx5QZzlddvLzCOU3StJHRJ3cGnLvr/+1iMSzM2UJr3KYPBYrOI97J9DPnj9QniwH8yfA5/8ET64CR4dBc8cAo3trsr1ORamzw936EW8iYxS6vtfjc/KBFOiiBBCCNEVFGbAiAL73yODaEvO+/FI+MWEtu37cAIGZsKspzDIC/SGUPTzSpWFc94xaBSWUoRMw+5was15G/AHz6cpsB9ab456fVPpKTRbGaAyAHv/qNFJkTkvuB0+PgPW/7td4xTM/DMc/yfI79e+4YkpE975bZId2gRLByepQ5IOLgsaauDXh4HP4YTnO6KgGAaPIVTa3z4fVhDevRr+Xg5vX2rnvIW3wBN7wZOTYdu30a8vPhAOWgh9ZtnnpYW7gIa+P6YplIV2FXRe+4UQQuyULtnpZCpYchLcMwVGF0K2C3plwOlD4YOZcPPe0fv3ZU8O5xJc4YkgNWChsDAIYZARKkD7jbgTC9odT/PAdT66ZYRT1G1ddmeLpd+jOXBmuxcrAtrD5n0+gRFXtwWRuCx7FNFnl0ZP/gh2x9P0X8C166ia9VS4doqTZAVh6X8h0JR8P8OgcerZiZ9PuoJc+DgbvoJFL8AXr8OLt8NLc/F8+37s+0gLTeGC82D5Q/bVLG2BFWi7srXubXjqAPBVR7+saDIc+AIcvR5mvAeHfAJHr6O+35Up3qAQQgghdiVDwUcnwN9/ABOKIccFBR44bgC8eQz8+UB7nxbF9OdorsIdHsuiUVE5z0Muo7YfSbzve6V64/E+g+H+ZYKcF54XSS/HFzgO3S7bBLWbTft+CmN+E92oRD67xM4t0Y2A/X8KP19N1SnP2yOuUpXSIVj9Lmxbn/KQjVPPSfxku7VlYlghe9TT/Mfg6wXw8h3w4u14vnqr03Je/kdX2h1NVsBunBVoW6Fu82J4an+o+y76ZfljYP+n4JhKmPE+HLwIjqmkbuB1SM4TQoiurUveXgeQ64GfjoaLR8FXtVDVBL28MDLBnWIDmchZ/JEP+A9LmR81BLvRrEdl5vBhwRMcw09bO6daaL2VkP5XihaFCOkXsKwvMIwxUc9Y7iJA21dfkq52YkHdMqh+H4oOiH3adBMqHgksTNGWlnJBaKqxJ6pMonHa+ehHHkEbJipyxFKqDqfWdhnwt9n2sr/hJW2LrBCZA36GZ+V7UH6is/Y6ORRBvJveTryDDkLdGvj4d3DgbbHPZ5bbDyGEEEJ0WZkuOH9P+/HtNtjQAHkeGFMUf0rHPuzJWfyBRTzPJ7yGjphgvIlGFhQ9jPL2wa9iL8Zp7SNg/SVFi4JY+iMsXYGpDop+vSvfnqPTiebN9sTXfWfFPmcYBHpPQpEk57TXsBny+ybdpWm/U7CW3heb88BZzjMUPH65fSFTGaAURVaIrAE/I2PpK1B+nvP2pjoUIbLW/jfxDjpoX1h8/zo47OHY5zNK7IcQQohuo0t2OlmWxZIlS3hpcz5//66EFQ1tnSoDM32c16+KmWU1MaGkNms9Swe/i0ZHfclqZXdArfcu5d/b5zJ6zTFRQ7Tz8p+kqDSYct5qrU0qN91BddVVANTU1ACwYsUKchvfJsfB8roaReWyF9hakBv3+dqqjSlrtNYyoOY/FxPI6k1t/6Px5w6Mu19NTQ1NphdfTimZ2yvRykBpC41CaZ08kBiAy0L7G8KjyEMRT2kK7z+Vb2v/QkP/yWSvX0jm5qUoNE3Fe1Lfb3+iZjHXmsytn5O37i2MQCPBrFJqBx5NILt3azvdBLAwMJLN26BDhD7/K8tzT0UbyUNg5O+oqqoq6b5OdUbNzqorNXtmzc6q251qGhiMZQfnKImjgIK01RKip2vJeW9vzeX+tSV8Xte20Ed5hp+z9qjijD5bYwYW1Xu38OmQ17GUFZ3zWjKDCW9k3s9eS07FiJhAPDvndcr6bEvZLq1Ntmy9ky0b7U6NyL9P3uYKCnAwEB0XVSvmsal6WNzna2pqUsxAGsGA2hevxZ/dl239jsRXMDxhzSYzk4bCAeRs/RatTJQOoZVCpTqaAtwaHWiy35u2WkdGKTSFD5/P2pqtbBtxGFmbPiercjHKCuLrNZS6AVPBiP6/Et6ar8hf+xqGv46Qt5DaAUfizxvQ2k4755kYyVYl1EGsrx7ny+KLCLkLkjZfvkOlZk+s2Vl1u1NNyXndi9LtxxF3ASNHjuSE084ioBJ3KLh1AA/+6LkcM0MJbxjUPoXy2m9VNxlgtb3S7W7G7U61pK4tGHTh93tjtnvNJkyHy/o2Wx6COvF7U1aQLOVzVCuqbZg040286pvWmDqEywqg0OFFV5J07kDSdOUzPHgtv11JERNsNIpmI4OQcqG0hRdf3M6kAC78ZIACkwBeFWelvzgadSY65ao0QoieyAp3X6fTokWLeO6559JaU4ieaOTIkcw67Uz8KiPhPi4dJIPm6BiSEUq6cHBL1tM+BaG2//5dLj8ej7NsEbIMmn1ZMdszDB8uFXRUw2+5CejE721Hc14IEx8ZJFyWuDXnBVHhvGWmJeeBVgojbs7zEFJuCOc8M07Oi8ynds5rjtknnibtxeqa18eFELuZ5LzupUv+JTc8Xn5bfG3K/eaOXMNhJdsBaMioYtHwxLfIbZ3bh6I5G0ArSrYNY+R3bauBFPR6gMKiP6NU8i9mrU3qth9H1aabALuXtaKighkzZrCn9TAlNQ+1fsnHFb6drbLoKmpyDiNkxk7wXVNTw9tvvsacwNz4NVr6WOKEBK0MGnuN59tpD4DRtpJJazsPOojCwrb7E83GGkb+8WCUlSBEGUCSgURzB8xhzhq7nTqDmCCow41cP+Mmypb9CVfzNlSc0WAag7o+B/DpuJtZ+M7LzClI8N7b+Xqf/9KcE/9KYovI31Hke98ZnVGzs+pKzZ5Zs7Pqdqea8yrm8RAPpaUeQDHFDGRg2uoJ0ZO5wjlPJ+vxQPPLIZWc3ncrAM2uej7c8+9JO0m2zu1D0dWV5Df0YfyqttvbcvOeobjsdw5GtCsaGw5k04Z7gOi/T0PVS5RX3ZF85FA4520uvJCteacRNEtjdqmpqeGtN1/n6sAfsGcgbSdpzjNpzh3EyoMexXK1dYwlynkq2MzIP8zAbK6P316HOU8DZCiUq32nk93M9QdeQ/Gqx/E0bIif85RBU69RLNr7D3zw7muOc9634++jodd+SfeR71Cp2RNrdlbd7lRTcl730iU7nZpCBqaCUJLvdVPB0zUDuDp82/3XfOisuNIEC+oYW9A2HM+yLqQpkGC518iXqhAlRedQXmq/trKykoqKCoYNG0Zp/g0w78H4LzSwz3S4M7a87g+U198N5afBnnMho6x115aa2w+4nrz3fh09T1Si1Uda35pF9tZPGKuWw9jZMTWHDRtGeXm7+Y6mnwNv/SP+6nQty+s6mA8gXn+dCr94j49vBVco4XxXCou8DfMZM2k1H2gDjZG88w7AlcnwvQ8Hd3bS3ZK+9x3UGTU7q67U7Jk1O6tud6r5SsUrVFKZlnotJIwIkR5NloGpFMGk/TeKJ7f04deH98FQsI7lzoorjS+nlrFjI3KeLqLJfwukyBZKaQoLzqS0KDbnlRf9Al6+C6w4I5QU4KY1p5U2PEBpw4NQdjzseQdkDWrdtaXmtoPmUvDm1WAYbRks0WrCrW8thLfuW0Y3zocpV8fUjPt39Ogr4JnfxJ8U3GHOU2CHclec7UDfRXMhQyXOedoia+tSxgU+4T1ttN7+l/ygBoP3Ohpy+iTdTb5DpWZPrNlZdbtTTcl53UuXXL3OH0re4QT28+9thOrwd7/Rgdus2g/FM4yxGOpA2n+btgwnDhkGIdMkZGYSZBGWXhdbNGsA7HlTvIPZQaT9F7oOQuUT8N6+0Bw7j1PDpIvh7Jdg4NS2jU4m/lYGfHBPip0inHsnDNkn/lBtpZwvCJLw96VB+eN3akUdyyT7s4cI4CZVKES5YOR5KTuchBBCCNH1NIdI2uEEdqz4djt8ZU8HsnM5T/XBNE6EdjV0+NGW8zwE+RJLr4wt6imAsX+IdzB7tFBMXrJg83Pw3j7Q8E3My5pGnQYXvQ3Djmh7cdJVkFsabcGH9zhfWe6kG2FCeGW/9kO9Ug39ijpukudcVoqFdGxZnz5IALeDDicTBh+fssNJCCFE99AlO506oj48FVMZg3HSQ6Iw6MueMdsz3I+gKKWl40kDlmlimWa480WBCuDnNuqt4fitOMP59rwRRv8ezEy7LcpldziRoGk6CM0bYOkl8Rs74ki4sAJ+uQnOfMZZB5C2YNMiWHQvLP4rbF6SfH9vDtz0Jpz+G+gVsTqK2wtDJzsPJIk+SSYO2x3CvfFTgrgIFIyzA0c8ygXeQtj7l87aJYQQQogupSOzidaFc14x/TBxJ98ZUNqgDyNitme47kWpobR0PGnAMgwslysi51kE+DP11miarTi3gA3+CUy8H1x54YOlynkhCNbC52fHb+zAqXD2C3BdFVz0jvMLfdvXwMI/wuK/wIaFyU+oyw3X/A/OuwtKB7dtN10wdH/nx0yU81SS56Jo3NVfEdImvtKpSXKeCa5M2P83DhsmhBCiq+uSt9c5/QJ0GVAcntM7l14MZBxrWNK2ikkcGosxTI/Zbqj+eD0LCQRvJmA9jGVGzHMU1fFiX53x6R9jev7Wrt0Khl8Dg34M6x6HDY/C9gUkvTykg7DpGfCtB2+CJXFzSqFwUPznot4E4dv4gvDqpa3HLSrZC8VRiV/nyYSZv4DjfgZb1kDQD0X97KVzL+sDIQeTrKflk2SP8a6e9gS9F18G370enihK2+fWCkL+EDj2OcjdIx0HFEIIIcQu1n5VumT6hAc1e8hkTw5gGe8mz3nKYiwzYrYrVUSmewH+0C0EQ3/HMhrb8l2cnNesr8PljdPQgRdCv9mw7imofBxq5iV/AzoEte/D9s8gb3z8fbJ6QVHyOSrtdtJ2G9+bc1oOQHHhKAxmJX6d6YIjL4UjLrFznr/JvtDo8sClvaEp9cp+6fx/DLVTHqL3kl/AN/+JzXnZfeCYZ6DXyPQdUAghxG7VJUc6ZZrgShFIXApOGQJZERe9pnIaHjJR4belsadoDIZ/DmJQyrCEV8oMVU6G+69kuB9tu+qVkMKTcztxO5TceTDoR+Ay4z8fw4KtbyTfpdew5LeTxdzG13Zcd9UnZNKEa+sXyY9hmFA2GPruCd5syC2G469P3fy4w8rDQjg7BcokUDrObrmnAE54DU7/BCZcBsNPhdEXwczX4KzlUBh/yWAhhBBCdH2ZrtQdT6aCGX1hj5y2bftyPNkUxM15LVkvO1CGl7y4NZUqJMP1BzLcr9hzKaUYze3OmUvcEGNmwoBzwNuL9rfsJXg3UPVq8l2ySyE3ye1kMbfxtdwcCK6aL8nEh6fyveTHUApKB8IeIyErDzxeOPX3qZsfMV9VjLZmpKAIFg4BpdCuLDjq33Dmcpg0x855I8+DY56Dc1dB6V5OCgohhOgmumSnU7bLHsWUKAqo8P/MmRi9PY8STuJayhhs36OP0W5dEEUlq/kHN/AVCxMeP6AfJ/UlHY3h+gbDSDL/UKgxRY3IfZuSP+/Jgknn2x1D8SQZ3q20fRYK5l/RsTHtAMffAMddbweViGPr8BxQlted+lQFVeJh1K0FQzSOPx8Ag03Q/Abk1cKU6+Hwf8GMP0P/Qzo2/4AQQgghupwsF+S57Y6lRCwN17Xre8gij5O4lj0YGZHzoldZ2e6q5lF+xae8mbB2gMdwMnTHMKoxzSTzD1lNpJyHEux5M1PlPMOAfX8af45NSJ7zwm0oePeS1HNotnfQj+CMP4Lhijp2a87LyiLlXY3NpM55QMP4C8Lt3QrNFZC9Afa70s55B98Hg49NnHOFEEJ0W12y08k04LmjweuKDSSmArcB/zkcJpbEvraAMo7mUtzk0TYjY1sRjYXGYh4Pso6v4x7f4msgGPe59lS8ZdtaZA0LDxtOViD8qH7Vnljc8ifed+q1kN3bDgaRkiyvG8lds6zt3n/tICSB3clz0i3whzVw7LUw4RiYcCz1h86hwcyi7qgbUt8OeeDVkFkQ2+7WY5gw5GCCQwaS4WmilL2g+hCongGbekPteQRD37GJLWxkM36SnCMhhBBCdGmGgnnHQa47dsSTS9nbHpgBB/eLfW02BRzFJWRR2jriKSrnhXPZ2zzJChbFPb6lv8FJztPaSJ7zMgen7mxRAAHYtgDW/wOsJBck970EikbE1oyNs3GZjRth5Ysdy3kAR1wBd6+Hk34NE4+D8UfTMP0SGs0stp04N8VxFexzIeTvkTzn9ZlI86gpZLh9lDERqg+C6oNhcx+oOZVQ8Gs2U8VGNuGj2XnbhRBCdHldc04n4NB+8PVs+P1i+OeX9kSSCjugXDAK9uud+LXLeA8fjSQf76tYyIvsQbxbtTI60NIk38T9LoTKxxK/LDJAbHkeNj9NmVmIqS6O/5rc3nDhAnjqFFi/MPzlruw5nByMbdamgXrtLKhfbd83nzsAxv3EvnXNW5j8xUX94MT/a/2xvrISVt5H474XkJ+dCc/+0g44LSOpVHgd3qNuhKNvhAMvhEdmweZlEaFEg2XB2JPg+Isp2n4ELvOnrVfsABqVwduu9Xyo/oIv/Htx42IvJjCdAykgP+X7FkIIIUTXsm8ZfDUb/vgp/G0Z1Ib7GTJdcPowOCROh1OLb1hEHVtTHEHxAc8zlEmomKwWXvAlZXbSJM15e5wPq+OsaNcicjW6mgqofpUyIxuXuiL+/t48OP9t+M8Z8O3r0TlP65SdTto0UPOvgnkn2xcxs/vAmB/C2B9DVlnyF+eVwrFti7TUVVai77sP35iZkO2Fx38CofBFP63tEUmWBdMvgZP/CHWV8OiJ8N3C6M4nKwjDDoWTb6a4/ghcrgtQUR1+ISzffwg0P8e/is5jk7sME5MJjGE6UymhKHm7hRBCdHldttMJ4OPN8Lel9hBrsL/6a/3wx8/gvqXw0jFwYJzb35fwLqmChMbiO76kjmpy6RX1nEsdgV9/QctkktGvC/9TKdAGISvJYLFe06HwB1C7IHop2ZaVPiKbqO3JulWoBq+rGe+2F6D8otiaBQPghx/C+o9g+TPQvB3WVUD1sqTvF0B5Ldj+TduB61bDgl/CZ3+CE9+C/MGJX5ywqIJD58Dks2DB32HNR/b2fhNhyoVQEP4FlYyAK7+AVe/Asv9Bcx3k9YVJZ0Gv/rB5AKrdla16I5u/FJ1PtVnYOswbIECQhSxmCcv4MedRQnHH2y2EEEKI3WpZDdyzBJpCbZGoLgB/Xw7//Ar+fTgcG2cdlS+Yj0Khk2Y9TTWVbGYtZQyIesalDiKkX0rwqvA/lX2EpDkvdzT0Phk2/peY2+zav6wl51kNZLiaydr6Tyj/RWzN7BI45zXYvBS+eBIat8KmD2HLZ/biM0korwV137a9i4YNsPAW+OweOOENKEkwkXkqB5wH42fCB/+Ab+bbHUnlo+HAi6A4/AvK3wN++iF89xEs+Tc0VkNuGUw4A0pHQdV4FNvjljewcOtmzqx5ijtKLiGkQnzCEpawnIs4m34kWGhHCCFEt9BlO50WbYaT50HIiu0+sjQ0BOHIF+CL02BAu/ki66l2fJw6amI6nTzqQvz69qhtGjuA6HYTjGdmNuHK/Q2auSi80cWVgr3+Bx8fY3c8KdPufGq5uBb3vnxb3vpfwPBzwUhwI33ffewHQMUv4MOvoju2IrmB1gXoYs4mNFTCs4fZEzqaqZcjjiuvDI68Nvk+SsHgafYjUtN/waqMOR1P5p9AjVkQ1eHU1mqLJpr4J09wFT/FcLzmrxBCCCF2t5Xb4KgXoDnUdnGxRUjbUxOd+Ap8dDKMb3dtaTtVKTqc2tRRHdPp5FFn0ayvB3xE5iKrXc7TgDfThyv/WjR3ouKNrh73T3u+pi0v2FMq6CBx7vpr1ZrzKn8FI34C7gQjtktHw0HhEeYL/whvfJL4TbpInPN0CJpr4dlD4exvICP+JOspZRfCwVfaj2T67WM/IvkXQHBJ0qRmoikJbWWIfxUrMwZjYREgwD94jF9wJe6u+39ZhBBCpNAl53QC+7Y6dOLxSpaGpiD8aUnsc4lWp4vHjSdmm6H641V3hX9S9uooSqHjrHSilMbM/gf1HI+Odw+6uwD2exv2egGKj4SMYmf35YeqYfOzzt7E+AsTdzhB6gkgdRC2rYRFv4GKM+B/+8KL0+Hz28FX5awNiYTqoeELaFyeeL6q5hdo3/+5xSxihXcoVpK5Eiw0VWxlJd/uXBuFEEIIsUvd9Rn443Q4tdDYd3HdHqevxdWBaRDi5Tyl8sk0Hmz5CYjIee33RWNm/oftHIqON1LHzIS9noN934SyEyCj3FHOQ/uh8hFnb2LMWckn2E6Z80LQVAUf/x+8cx48Nxle+AEsvtkeDbUzQk3QsNR+JJos3fc8Tq5zhzAY6fuqrdloGmhkCUt3ro1CCCF2qy7Z6aQ1PP0tBFNcxAppeGBZ7IJsQ5gQMblkYtnkU5RgyK7H+CGZxuMowrecxQkiLZSyCLKAZu5NsIMJpUfD3s9D2fGpJxcHNC7Y/lHK/QDoNRQmXETchGPG3xzXRzfDqn9D1Uew8W346Bfw+B7w7VOx+1rRVwdj+NbAih/D+yWwaCx8PAo+KIdV10FwW/S+uoH2w9KXekeiHEyCaWDwBctTvzchhBBCdBkPfWnnuGSCGp78BhoC0dud5jwXHsoZEvc5t5pFlvE8BqPtDq6WnBdnlVylQlgsp4lb4x9IKSiaAROfgn7ngXJy8dOAbQ5zXlYx7P/L+M8ZOEzzGj65A755FLYshE3vwqe3wJP9YflfYne3mkma8/ybYeVV8EEZLBpjPz4og5VXgn9Tu0M34DSMenT0L1uhWCI5TwghurUu2elk6dRBpEWt3x6aHWkc09ERnRgtpUIYhDBbH/0ZE2dyyTZuNYscYynKOAB0qlNl4ePP6DjzQHVIS3MygrDpLlg4BNbcAv6NyV932L0w7txwjYhOLaViOuUS0rSbL8CyRydVnAbrXwPfRlh6IzxfRvn7g8k2G+j1xSmw4X/RPX8NS2HxJKj8e7hzKixYDd/9Hj7ZDwIRI6jMfrT/KPqMDAwnk6OjZZUTIYQQohvRGuoDqfcDCFpQ1W4AzVimxtYMPyJz3h7siSvJMCCXOpRs42NM43gHOS9EMw+iSbL6XEd4QlDzGHzYH1b90r5Yl8zUm2Dyz+x/jxwFnuSiaIz2OU+H7Md7F8PKx8FfA1/+Fl7sT/n7g8g2Gyj6/FhY+3j0iHrfWli8F6y/G0J1bdtDdbD+Hvs539q27WZ/4s2T2p5CU2tG326o0TSRYASVEEKIbqFLdjrFuciUkKnA027EcRkDmMIsADQKq7X7QkU9vuBDnuN+giROPkopQuoTSLZkbphmA1aqW71yJya+Fc6AllHgSmEPvfZ9C2t+BR8Nh23vJq5ruuHoB+HCJTDpx9BvKvSfASNmdeh8xgpPPvXRFfDqWPjyVmje3PqsZ9v78N5MWPzj8Op1IfjimPBopngTXoagaQV8dUHbpsxzY/bNCdVjObgqplDkkN3xtyWEEEKI3UIp54OwAbLb9RvlUczBnAnYOS+EgdV6+1ZbzlvJMv7D3TQn6bRQShFUixzlPKgnSJK5lQDyJrVOGh7DoHWBZHu6yiA0fwff3Q4fjYCtzyeuqww46Db40QrY90roP81+7HmKg3Y78NHV8NoE+OJ6aPqudbO77hNYeAa8fzJY4fe1/OTwxdB4eTZkj3RadmLbBcnM2Y6aoNAszoye7NxAkUdux9+PEEKILqNLdjoZyp40MlXjXAqOHmDv397eHM6RXIiHbNqiTeyO3/I5b/BkwmPYE1U6vBwHkGrUTfmZYHhjtyuS3JNvQagBlhwJvtXJ65eMgcPugTPfgdlvwuEPoI3Y+QziSnjXnwX+ZfYVsHYdZqolcKy6D1b8EapfgubVJL+iFYLq56Fplf2jeyxkHIeO+I2P9zm7f9/CYhI7uBqLEEIIIXaLw/vbFw6TMRTsWwrFmbHPjWYKx3MpWRSQLDGuZyUv8mDC520dGTHtS/50yXHgjrOqbtKcF7IvNC47Eeo/S16/11A46HaY/Zb9OOqfWJ7ClK0Gkk+rpCqhaR3tpztQLT9veBa+uA7qPoK6hcS/sNgiCPUf2/sCmH0g8yJ0kq5GC8XizPHUugrabdfsJTlPCCG6tS7Z6QRw5fiYxWdjBDVcnuR7qC/DaaaZZNfTNJqlfMB2tsZ9XqEw6Je8HRgEMQniIkRD8ka782H47bHbIy/QxWXZt6ptSDBvVCIZBTQOOt3ZvonCiLulXSmGRn91G2z5j6M5q0DB1mfbfiz4F372A0BjkmvVs3fTJ0nndTJQDGEgfSl3cDwhhBBCdBWXj0s9lYKl4YqkOW9YOOclptGsZhmbWJtwH4MhJIvEbTnPJJTqVi/DDSP/HP4hItSZsZvatxStYd3c5PXbMz00DLvQ2b6J4pmLcPuSJW8NK/8Emx53lvOUC6r+2/Zz/t34ODJcqe0WhVD4hHyVMYxn8o+JKmGgKKOEYQxNfTwhhBBdVpftdDp7BJy3Z/gHjf09GIp4WPDziXDQHolrLMfZBI0KxTIWJnw+g4tof6rs8U9meFi32TpfVDWHs5XjCLE5bi0A+v8U9rwXjExA2V/MBg7Gmoeg8gEINbebeym57eNuIKSNqJFEMbxJju9woBTNm6FxZfKV9FooE4K1bT8bOVTzJL5mL80cAEYJx9UtYljQ7sRTOrZxvSnjDE522DghhBBCdBVHDIBfTAr/kCDnnT0cThuWuMYKPiVAgpVxIygMvuD9hM9ncCHtO1wS5bxazqSKgwiyOvEBe58MYx8HV164AS6HC7sEYcsTdj5KdIteHPV7XkpQm+GRRO0PEv45g8Sp303SOcNbhZqg7vM4x4hHQShi4RjloZb7wznvYDBK0UYJmzIm8mDhbB4pPJ1guwnYCyjgPGZjdOhmTCGEEF3NTnU6rVmzhhkzZjB58mQeffRR7r33Xg488EAuuMCer2fr1q0cdthhTJ48mYUL7U6d9vskohQ8cBCcNwI7B7T/MtTwz2WwLP4AJQC2U+1odRMV3jcRD+ehKKPlMpUdRFxYrbVbvuTtRgZYQDVHYFGb+KD9L4bpm+yrYb1PdPb97QI8tfCpFxa7YfnesPXh1B1Qrix8ZFI39ueQHbFan+GGopGQZYKZ+OWOlv5t3TczeoLLRHQQPO1HKJmELBc1PAllm3GXrecc922cxiz6qb4YGCgUvSnlBI7hJ5xPNlkOGyaEEEKIjujMnAdw637wswnh6ZTa5Tyl4bmV8GGSdVTqqMFwkPM0VoqcdzIGI0id82xBPqOawwixIfFBy0+DaZUw+h9QfpbznOcNwmeFsNgDS8fAlr9FL8oSj+GiGS/bJ/wf5A9u265MKB4NXpX81jpHFz4BTNCmPYdnKtoCd+92G1U45/0DyjahyjZT3utjJnuvZ7AajBnOeUX04hgO5zJ+RAH58aoLIYToRpzcB5XQ3Xffzdy5c5kwYQKHHHIIBQUFzJ8/n5///Od88MEHVFRUcMMNNzBq1CguvPBCnnjiCV5//fWoffbbb7+E9RdsgH8uj+zOibalCQ56BpadCb3iTJPkxpPglYn2jc+gF7nMo55jsFiLRaKrSS1ChFhDA38il+sTH9SVC/1+DKXHwYeJ55VCEX8kUuMnsPocqH4chjwTf66oiCINe15K3rRbYPu39mipnH7QtBH+OzLJ63B6Cm35R0Dd66n3Uy4oOSnlbiYGExjLBMaG59ci6YqDQgghhEiPzs55y6rh7k/j5zwN1AXgsGdhyWwYkBf7ejee1myQjELhaZnBO+7zXnJ5kTqOw2JZOOcl68wKYbGVen5HPncn3s3MhL7nQPkZMP+fJL19zUvspWDfMlj7E6h6EIa/CmayDhhF47ALyJ96HWz7FoKNkNPXHn3+RN+2ScDjcZzzQpA7A+pfdbZv2Zkp91IoRrMno9lTcp4QQnxP7VSn03XXXUdBQQEAS5Ys4Sc/+QkAU6dO5dNPP2Xx4sXMmTMHt9uN3+9nxYoVjBkzJmqfeGHEsiyWLFnCzz8dCDon4cSDIQ2bGzW/eWMj5/avinleZ2VhDYn9grdHcRutdRUavSqTJfVLkr9h9S8y898ku88dKMOX4qpQiLrg/axefjQpT7PWDDVH4A2taJuwMVIGCUYb2fvq7fOo/uxsNrhuiFu+pqYGgBUrVlBVFXme7PkNivtdTvl3d7asUxfdNExCVjYmdagUqSRk5LB8+1QGmRPICi1pm2Q8pqZiq/tEKr+sBCodtHPHdZeanVVXavbMmp1VtzvVNDAYy9i01AP7Ng8heprOznnXL9uDYKgg4Wq1IQ2NAc1Nb1Rx9dDYIU9+jxs9IjabxMt5al0OS2pS5Dzux5s3n5w+t6Nc21PmvEbrCb5bfjrayklRFwa69icn+EH8bNRy61vM8ez3phs/ZvtnJ7DWfVfc2on/jq4HoGDAjfRbdQMaFZPlNCZWKAPT3ZjyPWhcLK8/gL7uQ8gLvBk/swIag+3uGaz9pgloO+fd5Tuks+pKTakpn1HJeT2V0lrHflt30M0334xhGLhcLn75y1/y5ptvsnjxYioqKnjxxRcBOOqoo7j22mt59913o/aZM2dOTL3hw4cza9YsQqkmKnR5we1FYZGt403sqCFTg6HBp8Abr1ulbVeaFYRSXV3RZGa3fTE3+zxkeBPPJ+BrzETr1EO/XUaADNOeDNMX9OB1hWt24GJPgz+LHb1j0kUAj+GPCSNBbeLXHjLNppim+EIevGbbe/dbbgI6A7DIdDVhqPgfraBl0hxKNomUEELsHAvL0W03AIFAgGAw9Tx5r732GosWLdrZpgnR7ezunAeabN0YPzV4LTDDeSNZ1nOc88Cb1YAK75Yy5zV50VbqaQVMFcTrsm+T29Gc1+jPSjECK8nxCZJhNMfkvJA2adYeMk1fzHPtc15Au/Bb9u/D62rCVPE7nULawBcMz1sqhBCdQHJe97JTI50A7rvvPlatWsVll13G008/DUBDQwMFBQW4XC4CgQButxu3201WVhYNDQ1R+8STnZ3NmGMu5KzFzlar8BgWH09bGve5+owaFg56mtq78sicUxu+Pz9BGEExedURFDckWw0tSMnYGa0/PTz3h5w9576oMhojfIUNmmtn4K8+jlDD+PjHbX2hpm/jjfTy/4+58+cw58Dw6iUe0C5aw0/ClwPN7gG43LVoTBr03my1TqNR70VNTS0VFRXMmDGDwsIky+rqEDnbP8TTvB7LyKA+bzJBTxkAWY0fM2jdxSgdaL1KN/fLOVy951xAUZd9IGv63gnhSSCVbqLQ/xxFvsfJsFYDikZzHFu9Z7DNfUjceZ9qamqctbMDukvNzqorNXtmzc6q251qzquYx0M85OwFbpIsZW4rppiBfQfubNOE6HY6K+dNm3UuR32wZ9zn43l/6lKyXbGdHD53PQuHPE2z2UTdH3qRMacufoFwX9SkNTMo3z4o6bGKRh2BYdrvI37OU+Fb8MC/fT/81TMJ1u1Dqgt/ZU13Uer7e3TOc4N2O8l5Bs2uPXB57PfXqMex1TqVen0ANTXbHOY8TXbdIjy+1aBc1OfuTcBrr8jj9X3FoO/Ox7Qa4+a8Ru84VvX7G9qw59JUOkC+/yWKmh8jM/QVAD5zBFUZZ7DNcxRaxf5R7S7fIZ1VV2pKTfmMSs7rqXaq0+ntt9/mhRde4Omnn6a5uZmPPrJXi5s/fz6nnnoqq1atYsGCBYwbN47MzExGjBgRs088hmEwbs+hsNhZOzJdBmPHJh5eV7S5iMdDT2Elmy07/GW/cvAnTOfQpPeTb2E4IVbQ/iZ4jSKACzt02OnGU7AAT8E7uBlHCY/gov2kipEFnmbbl79HUxPVLodrhOBlrT3zJlCg3qTAeBXyL6Ky6CYqKioYNmwY5eXJOtQAJiTYPhZG7gdf3QbfPQaWfeUr5B2Ea8+ryR58BsOMahQZeOgTnsB9X+DX9hLAQLZSZCc5cmVlZQfa6Ux3qdlZdaVmz6zZWXW7U81XKl6hMuL23XQYyMC01hOiq+vMnDdpzJ7wgbN2KGCvcaNxJ4hxIxnFy42PsizZysHhMPXVgI85hKMxkmTCrUwkwPvQ7lY4e4JxN205D9x5i3HnfYSLwZTwL9wMbl8uwgPUfjUeS38X1S5nOc/Cy3etOS9PzSfPeBtyTqCy6I9UVODw7+i4BNvHwqi94evbYfU/IGSP6rc8fTBHXI536AUMNbehcOGhLwoTmARc35rzMpWiH9AvwRG6y3dIZ9WVmlJTPqOS83qqnep0uu2221i/fj2HHHIIpmkyc+ZMpkyZwtixY5k0aRJ9+/blzDPPpLGxkbvvvpvs7GyOOeaYqH0SGd0L+mTDhoYUb0DBccm+34HcUC+wQGmFTnDLl01TxUYqWUsfBiTcK4uLqOOadq9sCSItWiKEHVgCLGUzJ9CbVzHIjV9YGTQWnEtj4G9s6f8qJQUu2HInbH8JSD0kkKj3Ft5/2/3kZOQCeXYrQ5vsNhml9mTeHZE3EvZ5CCbew+a1n9OwdB4b9n0Eq/RZapjWekwPe1DCWZRwGgoHl++EEEII0eV0Zs4rzYRxxfBFVdLptTEVzNiDhB1OADnkkxcqh2SdTmEN1LGS5QxjTMJ9srmIWuZHbWvLeS2ZJjrnBVnDJo6nnDcwKU1YuynvJJqCf2NL/wpKCjTUPAzVj9LxnBfuEKv/H3nubGCo3UqrCnQzGCWgEi+SE1f2QJh4L4yby+Y1n9K49BU2TH4WXfYC1RyMxr7g6KaUYk6nlHMw8ErOE0IIkdROdTq13Mcf6dJLL23997KyMl577bWY5yP3ScQ04NLxcN174TBizwoZPbhIQdCASxJdtIlkkKLDqc1m1qfodJpNEw8T0Mtat4Var5glXtEuyCrq+Rd5/DhFCxTBjDFQUA40wPbnHLU70UW77OY/43ZdRQn7w+a14UMUQtYPIftKMMui9reoIcibaOow6IuLGajIj4orh1DmIJSh2VpyMfYvpu1qoJ/1rOd3bOddBvMnjCQrAwohhBCia+rMnKcUXDEBzm9Z8FbTlvVad4KQAZdPSN3WalfqDicAA4PNbEja6ZTB0XiYSrNe0LrNHi2ffOVii61s528UEn9hlzaKYMYIO+e5c6D6H47aHj/nWWQF/oXb/CXFzIBNX4cPkQ2ZF0D2VeCKzrSaOgK8gaYWRSluDkZFru5nZhLKHAgGbC25GI2PyJwXYDOV3MM23mQoD2ImHccuhBCip9ux2Qh3kasmwsH9QbX0acRZT9etYVvzzh1Ht3uoFKdFkUUvngP/vgBY2owII8nV8WDHGpd/JHgGk7BHqYVJwt+myvbhcfsxiRjOrWugYS5UTYTgN/YmttPAT9nGIBo4i0Yupp7j2cZQfPwlalliS23H7Q1gX5lrvxKLfSbrWMBG/tyx9yuEEEKIHuGckXD6cOzYECJ2yJMGIwQNDnJesgQWm/OS5zWFiwKeAN8hgJ3zQqlyGAAh6nkYTcDBvmHZ+0D2fqS8DmyQOApmaTwePy5WtG3TDdD4Z6iaAIFP7E34aOQX1DKIBs6gkYtp4CS2MZgmfo+O+AVo/Li9frRqIjbnAVg0spR13Or8vQohhOiRunSnk8eE3+9vLz6XSMiC456Fr6pTFLMUSkeHDPuCmiKEQQgz/DCopppgimHOBoVQ/SC+Ri80nkCqDid7WLZBExvYzG9o4J2oL/eElAnDXgRXIQkDiQGRF6iiZNIaUtqvSgIhsDZD9dFovZ06DsfPw0D0Ki2azTRxNU3c2LqtKWuePcw7xe2KW3gMC1+SfYQQQgjRExkKbt0/cYQBOz/Nfhk+2JC8VnGgT9zXts95AaCWWvwk78kyyIbau/A1ZkL92SQf5WQfK4hJM41s4lfU8SrayS1zAIP/DZ6+JOxVUiQ+SV5a42FszguCroPqI9B6G/XMopk/AY1Re2lq8HEzjfy49QKjz/tOOOcly6oW1TxPgFQhXAghRE/WpTudAO7+JHl3jgUELbgz1aTjAaJG6tgX1QysmBChWMCb/Iu/pQwkAFqb0JD4djm7s8lFE14CuAliUs1DfMfZfMs0mnCwLKN3Txj5KZT+FIyctu2GF9zKDhzxTpKC1FMqhSD0Nb7QxYT4gvhXs2zN3EGQDwFo8lakbjdgUU9d+DVCCCGEEJH++rmd4xIJLzrH7z5KXmdoY/TtctE5L5LiExbyEPfQSIqJQwGtDahPfrtgAJMmvPhxE8RFLU+ynh/yDQdQz9spj4FnDxj5MfS+BsyI1Z1acl4miRO7x0HOszbTHLiUIG+TbAYtP48S4HkAfJnvpG43AEG285bDfYUQQvREXbrTyReEx76EYIqpmIIa/rkseWhBK8bXH2D/K3YQscX/pl7Hal7hGWcNDfXGSDBpZABXxMSTLY9g+Ln1rOUMmvg89TE8faHfnTB+C4z5BsasgkH3g0cn7pVLsTRkC42LZuN5knU42Vz4+Kv9GqPeWXEgRILli4UQQgjRoz2wBEIOct7zK6G6KfE+GToTmtsCUXTOaz/SXVPFZp7lX47aqMjEzZ4xdcDucArgIV7OC7GFdZxHAw46cFzF0PdWGLcRxqy0H8OeTZ7zXHGbFENj0Gw+n3pHTJr5CwCWcprdDMl5QgghkurSnU5bm8Dv4A40gKZg6rmdxtdPYQbHYkZ1AsWn0SxhMfVsT3lshUku59H+dFoogkl7fiw0ATZF3LaWkuGFjCGQMRDyTgazJOa4EQ1zJOQOoQ0n8w8ECWJPGGqEegHwCRO4m0u4mRu4jTlUMB1fuzHgboqdNUQIIYQQPUbQgq0O78C3gI2NKXYKKvavPRw3GaTOeRbf8jVb2Ojo+LlcSPvJRWNXLo49Cmg2cq2zKRUADA9kDLYf2YeBZzhJb7tzwDI1ltlM7OSo7YUI8i4aC8MqclYcC5fkPCGEEEl06U6nnA4uepaVYg5GhWIyBzGCiSknkbRpvmSJo2Pn8iPcjCQyGAQxSf0Fb+HjU3wsd3ScKEYG9H0GVAZxA4l2lkZ0BxYxbFkut953NPXk8EeuZDGTWMlQljKahziXK/gjXzICABfF5LC34/pCCCGE6BlMBZ4OJNFsByO4hzeNZwIHOsp5BgZL+dTZsTmNDKYQGZ2DjvKTJsA6Gpnv6DhRlIK+/wUjl7jzemqnJ68jcd8CguA7yGFlL/lM70B9IYQQPU2X7nTKz4ApfexQArQtpRuMeITsN3FIf8h0eDtZM76o+Z0itdx6F8JAYzi63x/sCSfLeIZMjqbl6prTFe0AvuEMlnMCDVn/JXVHVYSsKTDgA8hpOW6Y8kLmbHsi8hTMUMDhIRUmQ2ggxE3Zo8Pvj9Z/agxA0UQmtzOHdfSljItQHejUEkIIIUTPoBQcPwRcKXIeGkYXQf9cZ3V9+BJ2OrXlPBMLRZPDnKdwU8K/yOZ0Wu5rsxytaGcfczWXsJxjqc9+jA7lvIwxMPAjyD2J6I4nF2SdDGSnLGFYIdDOspiinCAubswehBVeYCcRjaKEMzEdtEEIIUTP1aU7nQCunBS+1z/JcrpWECZ2YGRvJtkY7d66Bvx48JGJHy9+vDSRwcd8wRrWOKprkE8J99OHRRTya0xiV1KJx85YDfj4mrq8v+DJaibgWpHyda2842CP/8GQ76Df69D/LRi6Ecofgcxzw51BibgwjNG4OIzEa/G2tTSDi/gfVWwwEw8T1+Ew9wqXUsKZzt+HEEIIIXqUyyaF5+5MkvMIwfiiVBNmt8kkK2abnfPcETkvgyYy+Jxv+BpnmcsgkyL+QF8+pZDf42Y4Ti8uWjThYyX1uQ/iyWrG7/nM2ZsB8AyFvo/D0A3Q7037MbQS+jwBOVegk7bBRKk+eDiVhKsgtzLI4CIqqOUrT4gGsqimF1a7dfFaOqK+Zn/Kucz5+xBCCNEjdflOp1nD4MLRpJzj+o6P4J3vnNUcxXisiFSjAR9eQnFGJlWzjQd4iK/42nGbXfQllwvJ4QhSd+TYR7RXV9Hh5Wk11UU/I0CV42MC4O4L2QdD1jQw8+1t+XfjZzJAnFBigNEHCl8kU11n/5wwuJgYDMXDqTzJ5pRNsTBZQDnbUk5OLoQQQoie6sC+8Kv9SJnzHlsGTzuMYqOZEJPzmvESijPz9nYa+CeP8InD2+wATErI5RxyOAEnUdrOeeH9WnJe4S/xsdrxMQFwlUD2DPvhCl9tzfkVPo4EiHOR0QRVCL3m4VXXABlJ2muiKCGDC3iSzRjarnc9t/AEp7IlPG+TheJrhnE3l3ArF7E2PGm6EEIIkUiX73RSCgbnp76OpIDfvO+s5mCGUUxp62gnf9SqI9E0GguLJ3gKHw5nuwwr4ExSpSh7lJNCt+ucslQD33IrX/NbvuZW1vMEAQeTmsdQWVTzOM3+DIIMb9tulEHOr6BkMbgG4GIfcngKe13eyPNgt8tgGLm8hCKbtTQ7urAXAtaTYnZ3IYQQQvRoTm6bU8BN80E7uDOtjD4MYAgqnPMCuMMX3hKHl6d5lm1sc9bgsAJOTloTWqYSj+h0atmugqzi96zg93zFr/mOR/CztUPHB0C5qOVv+PwZBBgbsb0Qsq+Bks/BPQaTYeTyPIo8ojOv3S5FH3KZh0EJq/BhhZ9uIotXOJI53MG5PMh5PMhvuZaP2QeNwZoOZmMhhBA9T5fvdAK4/zMH621oeHU1bHCwaqvC4FQuIJtcNIajuZf8+PmUz502GYAMhlLA2Qmfb3lP9lK70S2sV1lsYgGbeJ5NvMBK7uQDjuI7Hkk4H1ViHoIhN1VUQFkdlNVAaSXk3gBG2+okbg4nnxVk8ntMDsBkDC6OIJsnyGMhBnsA4HK6XEoH9xVCCCFEz3P/Z2CkiAsa+KIKPks92BqAWZxJL4oAFXeEU2x9zUcsclY8zEUpxVyRpKat7eJmC0W98rKFxWzkf2zmJVZxLx9wLKu41/lKd61MQiE3W3kZyuqhrBrKqiDvVjDLI9q7H/l8TRZ34WJqOOcdRDYPkc/nmOGLk+4E58rCjBlNJTlPCCFEKt2i02l9vfN91znct5AiLuRKBjGKZEHEwh6JZKFYugMrzJVxE734EfapNgAzqsvIT0bURJQBXFG3wWlC6PAsmpoAq/gT3/EwACECbOBjVvIaa1mAP8VkmCGC+AwDy8hLODGCQSFeLiGP18ljIbn8Gw/HRU0Gvh+5GA76vQpwMRhv6h2FEEII0WOt2Q6Ww+tp3zm4uAiQTS7nczkj2ZtkOa9lxHkIWMpSZ8UjFHEJJVyDwh0+Tvuc58GKyFBBzKhRV5E5D0J8x8N8y10AWITYyKes5HXW8C6+FCOxQgRpMgjnvPgRX5FDBheSyyvhnPccHk5FkdG6zwHkYzr4fXhQjJVJxIUQQqTQLZYVy3SBP/IutfaTTdp9OaAgqwPvKJscBjOCL1kVM3rIQoWDQduX9pes41nmcQTT8UZ8OSejMCnll/TiArbxH5r4glpexQpPtk1UBxM0t9ZNHJBW8ze24+ZrXsZPWy+biYfBHMx4zsEVrqPRrM1YAV6LR8v/AIAbD+OYzD78gAI6MAN72OmU8ZZKHnwM4FRKcXePfk0hhBBC7CbZ7VcfTpbzHK5UDJCBl2GMYnGczqR4Oe87tvEUL3AkM8h12JmiUBRxMQWcwTb+i4+lVPPcTuW8dTxBM+Ws4FWaqIk4lskApjKJC/CQ07p9g2e1nfN62znPxMVo9mIfplFCefvyKZ1GKf9RW5LuYwLHUUxu9/i/EkIIIXajbtEjcNxQcBnY39YBoBl7GV2LtqV1m6HYA6M62IdSQEFMh1MIFTPiCOwOnAV8zJ/4B00dvIfdRSlFXExf7kUxmFDrFbE2QVwpVpqzT0EtOXzBv6M6nOx2+1nBK7zFrwjhR6N5jaep6PVM1HzmAfwsZgF/53a+49sOvQ+A/cnjqIbYlWFaGMAIsjif3h2uLYQQQoieZeYwMMPrqbRkung5zwvs72xh4FYFFMRssxLkPICP+Yy7+DvbcTikKsykgF5cQB/+gId9CcXcUmfP62SReoGZOvL4nCejOpzAHhW1hnd4nWtbR7cv4FVeK3qKyP6tEEG+4CMe4g5W8EWH3gfYGe7MusQTbZlAORlcRt8O1xZCCNHzdItOp0snQdDC7nBKMi/31u3wegf7UIYzjEwyW3+2807LVZv4E4tvoorneb1jB2qtqCjhrLjP2ZNMJh/P3EwGjUmvvmmq+IoveY5PeI/FzE+wl0WQAP/mPhrpwP2L2O/hsu2FuPwhcsIzTbacKReK4yjmIfYky0GwEkIIIUTP9pMJ4fQTgmSLofl88OyXHavdjz0oolfrz/b1y8Q5z0KznTqe4oWOHSiCnfNi52WyRz4lz3l+3NRHjGJqT2NRx3q+4HG+4nPe5eW4+1lYWIR4ln9Q09HVkIGz6vNwNQcpCEX/XwUDOJhCHmUkhXRg2JkQQogeq1t0Ou1TDlftRbzv7xiXv+JsZZMWLlwcxIzWn0OtpyTZ/f+aRSzBp3ZsZbYSziCfGfGPkaLtDWSn3gnN17zEB7yRYi9NAD+f82GKerEMFK5AiCc29eFOhnItA7iVQbzJeG5hENnS4SSEEEIIBwYVwNxpJO1wanHFPAgkXxg4ikJxCAe3/mw5yHkWmi9ZSa25A6sGAwUcQREnx38yZc7LSrmTxmIlr/M+r6FSTORtoVnMguQHjUOhcAUtHttczr0M4zoGcAuDeI3x3MFQiqTDSQghhENd8kZsy7JYsmRJ1LZNG8sxKMJKMRnkl1vh4bdWMqm4sXV7TY09PHnFihVUVcVe7ckhi1FlI1lWuhxLG6kWOAEgRIjH3a+iVeK6yWguxCgpxSp+CVx2qDEJJZzgu4U9F0DqBm6nge3thmXHbYfWfNw8n+wVpY7a3aLlnK5Z8Q2lVYW0vHodlazrUKXYmjtyPrt7zc6qKzV7Zs3OqtudahoYjI1cPnwnxbtFRwixY+LlvNVrS1CUxb3lLdKWRrjntTUc2je6QyjZ3xIFTCyawCfln9qdTk4WXNPwlOfNnch5J2IUZWMVPw+erYDTnOfFSQObCbLRQeLSWHwafJ/S5YMdtbtFy/lcteIbCqsKKQxv30QlmzpUKbZmV/8O6ay6UlNqymdUcl5PpbTuyLigXWPkyJGcdtppUdsa8Tq6Dx7AQzMeJ5fL2tFKozOVszCC3cmlfIAJyu/4ZTFVlGHZL9YKlzdAoNnE7Y1/Gc9yODhNo6L39UHCheQ00NgtBr0JIYQjFhZGmgfzLlq0iOeeey6tNYXoieLlPB8ZEdMbJOfGTwaBDh939+U8DUqDVpgZQYJ+Y9fmPIAGyXlCiO8PyXndS5cc6eT1epk1a1bUtvPfHcTHVeF73O31be1h2JFdZiZgaq6ftIFZg6pbN9fU1FBRUcGMGTMoLCwkmTcGfMD63M1olbovrpFMiuc2sGlOPvttHMzeWwY6eXtJ1fA+8+et5sCr34ibbjZRSki7UiYfn/ayUbVN5N08N4+MOXGGiWvIac5n2opZsc8la2cHzqnU3H11pWbPrNlZdbtTzXkV83iIh9JSD6CYYgYyMG31hOjJ4uW8X3zUj1fW5beNaG+Z3ykyjhlguDU/GrmZn4zcHPV6p39L3uv7Kd8UrnWU85rwUjS3kU1X5zN2a1+mVY5w+A4Tqwl+xvw3lnHg1W/aHVHtbKGIgM5ImfMCuFjPHq0/J8x5gDuYwWHLZ3esnd3o7718h0rNnlazs+p2p5qS87qXLtnpZBgGY8dGD5c7fAt8sgBCLROKx5vfKQSEFEP69WXs2LYVNSorK6moqGDYsGGUlydfOlbh4R/8O+k+GvsKUyhi5NXHvddwcu9DyImYlHxHVFYW85bvL5hWASGzFhX+FWmCmGTRn31ZpT4h2f3+CoNham/qqaOebUmPp5RikndKzPlO3U7n51Rq7r66UrNn1uysut2p5isVr1BJZVrqtZAwIkR6xMt5RzTBy+toW8Eu3kAgC6xmRe+SMsaOLYt6yunfklwKWcE/UrYxamEZBUuK13Nq8aGUkJ/ytclUVhbzVtMKXKFigq4tqPCyc5oQBh4GsD/fqMVJaygM+rInAfLZwoaYVZjb7zvOta/kvC5QV2pKTfmMSs7rqbpkp1M8P9wLfjsfO4SkmFD8iufh+D2hcAf6f0YyjHJK2cQWrARf4gp7GLhGobEn99YY/IjHGUQRhzOKAxiCuYND/nTIZNDmv+Mp/4btfI5Gk80QSjiEECE2cQVNVKPjngiFgYsxnIyHVbzBs0mOpHDhZjz77VA7hRBCCCHS4axxcM3r4Gsm6UrFALe+CedMgAE7cNF8AH0ZykC+ZU3CnAfgxxOR87LQmFzBf+lLAYcxih8wFM8OxmhtGQzc8je85WupZRGaAJkMoJTDUGSwhTlsZ13CnAcwltPpjY/neCTpsRQwiQN3qJ1CCCFEOnSbG7z758OvHK5sst0H/0x+kSghE4OLOJ0SimKea4kmzXjw46GRLOyrU21joFezlb/yLrfzKv4dmFeqhcKkmOkM5jKGcDm9OQYTLx6yOYhbyKYUf9DNZ2smseDrA/niu3GELAMXXqZxA/n0Z2+mMpZ949Y3MDAxOZELyCZ3h9sphBBCCLGzCrxwzxE4ynlBC/66cMeOo1Ccw0n0o0/Mcy05z4+bZjw0tea8tri8gVoe4j1u4SUa8e9YI7AvEfZifwZzCUO4kj7MwkUOJm6m8yvyGRBulNHWOG1fWJzCNZQwipFMZD8OilvfwEBhcBxnUUTHFosRQggh0qnbjHQCmNovwRM6/Aj3/WgFj3wCV0zZsePkkcuVXMhnLOc53qSe+tZh1gHchDDxkRkRQto6nVoCyxds4E7eYwj9sYCB5LAXRRg7OA1lpIxQbz74+F7u+dxiu7/tV1iU2czPJxmcON4Nyg40R3EaBTWlvG6907qfiYvR7MW+zKCYsniHEEIIIYTYpQ7cg/izB7TLeSHg4U/gt4fv2HEy8XIx57CMr/kfb1BNbbiuiR8PFiY+vBETdcfmvNVs5TbeYjwjCGCxB9lMpniHR7lHytCFLPnkdl6s+oRJQ16nNG8zTYFMln63L+NcM5g1MQ97ET7FdI4lszqfV0JvtL5eYTCC8UxmBr1JFJ6FEEKIXaNbdTpVN7bbYBGexylimwJM2Fy/c8dy4WIvxqLJ4H5ebndYM+lKegFc1FLAyzSi+BIVbmopXi5hJFN3oqMnaMGsV+DF1UbUlTeArU0ZXLMAvqmBv063V+VVKAb7RkHTfE7beCm9evcii2xcuHe4DUIIIYQQ6ba1qd2GRPM7uWBrw84dy8RgLHuSQS538J+o5ywUoSQ5KYhJLQWsw+IdlrfmvEI8XMRwjoyY4LujtIaLKuDB5SawN2+v2Dvq+aeBxZvgv0eAGY6B/ZuHga+C0zdeRmHvAjLJxo1nh9sghBBCpFO36nQqyor4wYK4o5rDAWVbHTQHIWMn3+EEhpBLJvU0tV7dCuAi6pJbhAAuqihuveWu5eIcwGZ83MgnXMs4+tKLJ6jla5pxodifLE6hIGV77l8GL65ONo043LcMjh4Ixw2K3p6hM8lzcAwhhBBCiF2tKHIuzkQ5DyBoP73dB3nenTvmMPrQm0I2U9s6x1MAN4lyXhCTLZTEzXk1+LmNL6gnyF704XFq+QIfBjCRTE6nIOU4qGe+hQeXJ35eA8+tsve5aHT0cx7tJY/0rY4lhBBCpEO3mdMJYMoAKMvB/sZNcRt9fRNc99LOH9OFyQUcgcJAtQaM+LfIaaCGwvDzife5ikqOYjX/opYPaGI+jdxBFfvxDU9nNSdsi9Zw52ep22wquMvBfkIIIYQQXcWIEhhdih2WAsn3DQbhJ/9Jvo8TCsX5HI4LM2XOA9hGfsqcdwubOIhveZBq3qeRBTTyZ7ZyICt5KMeX9MLhXZ/bOS6VOz+zc6EQQgjR1XWrTieXCXOmknJVE7C/9P+yAOoT9+E4NpL+XM0s9qAYSBQz7CtjQdxJ9oAtFFOHPWQr8m203Cl4e76PoBn/17KhAb6uTT7KCSCkoWI9hFKs8ieEEEII0VUoBT+fhh2KUoQdDTz5CWzYtvPHHUAZ13AKg+lttyPBwYOYNJNBspxXQyHbyQNic54G7sttJuiOP0WDPwTvbLBzXDIaWFYDm9pPOyGEEEJ0Qd2q0wngqinQO6vdxpYrYo1AQ/ifAWj0w7wv03PcofTles7gWk7jMEYTL3DYQSTZ8rtuGsiJ+9pWGgIZLkJx6jR1YDE8Dfil00kIIYQQ3ciZE2BUcbuNLTmvibac57c7Z579Ij3H7UcJ13AKN3EmM9mLeFnNjyfu9hZBTLaRl3QfgKDbRZOKzXl+BxdVI/k6uL8QQgixO3S7TifDCN9i1yKEHT6aabuMZIV/boRPv0vv8QdQxukcQBl5GLpjK9HVkUvKS3cKtFJ8kBHbw1SeDR6Hv7FiL3gTz3UuhBBCCNHlKAWDCiI2WLTlvBBtOc8P1MOi1ek9fh+KmMm+DKM0Juclu+0OoJ6cpM+31lHwujf2/sFsNxRmOGun14Sy9hdhhRBCiC6o23U6AZTngdGyVEgTiftxNPzxFVhXm963aWAwh0PJtKJXNnERJNnVLX+KW+8irXXFDlPKdsMZw8GVooSp4Mdj7OAmhBBCCNGdlOaCy8DOd8lyHvDIfFiyLv1tuIwZ5IeiZyl3p5hoKuBwZWAFrHHFDlNSCn40OvWcTi4FZ46AzG61HJAQQoieqlt2Op25F1gOJpkE8AXhgQ+y096GPuRzVdUUVEDjsewhRV58mCS+p01FrXGSnCvBbj+fBG4z3OkWh6mgIAMuHuPoMEIIIYQQXcoZkyBoYee8FLHJ0nDry+lvQy+yuaLqAFRAt15kdIdn70zMWcbTgDvBaPlLxkKeJ3HHk6HAY8LPJjo6lBBCCLHbdctOp5PGwx75OOp0Clnw6MdZnbLCR77lxfRrbt50MHdwEndyMlcxNuH+mfgc157oj3/5as9CeOVYyHZFj5lq+UUWe+HN4+1b8YQQQgghupuDh8G4chznvH8vgtqm9A/vztYeTL/mpk0H8QdO4o+cxE3snXB/Lz6cjmhPlPP65tg5rpeXqDXyWv49xw3zjoXhBR14I0IIIcRu1C07nTJc8MRZzvdvDBiEdOdNcOTGpDd5lJLLUfTjMkZGLLxrM1DkUJ/yhBsajJBmaDBxe3/QB9aeA388ECaXwdB8mFIO98+AlWfBuPYTcAohhBBCdBNKwfMX4HTgECELXvjcm3rHHeTCoIw8yshjCr25gfF4MKJynokim0Y8KWZ+MjQYWrO3P3HOm1AC354Jf5tu57uh+bBvGdw1FdaeDQf2Sdc7E0IIITpft70bfGx5x/b3WRlsa1J08GU75AQGMJ3evMQ6PqeGEJpB5HAs/fiQID9jY9zXmYBXg6c59aW9ggy4fLz9EEIIIYT4PunfCzwu8Dtcuff6Z/P4xcBdM5nlQZSzD8XMYz2L2Iofi35kcwx7sAaDi7AnmWrfZ2ZgZz23L9CuyypWjgcuGm0/hBBCiO6s23Y65WXCxP7w2Xfh+Z0SaZn7SSseXpDJrYN3TfsKyWA2Q5jdbnt/IBuD37CZ9QRbI4cGJpHJlVtNXuuMewGFEEIIIbqRQ0fCvKXh+Z0S0UAI/NsUAcvZRN7pkIubkxjISQyM2j4U+Cf9uIlNfIs/KueNJIMrt7pYkDS4CiGEEN8v3bbTCeDyg+DcfzjYcTug4bbH8jh5f5g4tJMblsJR5HEEubxHI9/QjIliMlkMJ4PKYOXubZwQQgghRBdw6UHw4hIHO9YBDYpAyM17yz2cuCuGtScxlWzeYBAf08QymlHABLyMI5PKQCULdm/zhBBCiF2qW87p1OLM/WD/QSS+518DjcA2+99D22DaJVC5dZc1MSEDxYFkcy69OItChpOxu5skhBBCCNFlHDYKTpyQZAcNNAPVQIP98+m/LmT5ml3RuuQUin3I4hwKOZtCxpG5u5skhBBC7BbdutPJNODW44m/pK4F1AJLge/CP1cp6r6F0UfBvztheV0hhBBCCJEeSsGtM7E7luLlvO205bwt9rbAOoN9ZsL9T+7KlgohhBAikS55e51lWSxZ4mQ8NeRb0Ms1kuo6FzRhz9AYwr7itQFoPwGlBTVrNKecqZi+7zZ+fO5mRo3yAVBfH+CFF9bxzjubaGwMssce2cyc2Z+99uqFUrETPtbU1ACwYsUKqqqqdvTtSk2p2Wl1pWbPrNlZdbtTTQODsYxNSz2AAgrSVkuInq4jOU9rGJw/jG+3ZECjsnOeRVvOa7/2igUNGzU/vFhx/1/qufjcTUya1IhS0NQU5JVXNvDGG5XU1QUoL8/k2GP7sf/+JRiG5Dyp2bl1pabUlM+o5LyeSmnd9WatHjlyJKeddprj/f2WG3/IE30VzCLqZ4/bh7/ZG/dWPMOwcLl8+P3xT4VhQGamfcVNCCFE12dhYaR5MO+iRYt47rnn0lpTiJ6oozkvYLlotjLsbNeiXc4D8LjCWa8dpSw8Hh/NzYlzntdr/1MIIUTXJzmve+mSI528Xi+zZs1yvH/IgkufHMD8j/PCG7CHWkeYM3Muc2+fE/f1hrEWy3oUpTTxuuBMUzF6dAH/+McUXK62D3dNTQ0VFRXMmDGDwsJCx+1NRmr2zJqdVVdq9syanVW3O9WcVzGPh3goLfUAiilmYLtVqoQQO6ajOU9ruOWlcv7zZrG9wcLOeZGZrQbmnDuXuXNjs55Sm9H6IZQKJsx5/fpl8cQT08jKaovG3elvntSU71Cp2XNqdlbd7lRTcl730iU7nQzDYOzYjg2Xe2sMlJ8OW2oBX7sn480FEMGy3gKIG0QAQiHN55/XsGaNl5kzR7Rur6yspKKigmHDhlFenp6lUqRmz6zZWXWlZs+s2Vl1u1PNVypeoZL0rgYqYUSI9NiRnPfUWBj5DXy1FvATneuCQJK7NrSeD4SS5rw1axr49FPNj37U1q7u9DdPasp3qNTsOTU7q253qik5r3v53gwkNk246UxQENvBtC3ZK7cSe7ksXn3Fn/+8aGeaKIQQQgghdoBS8Ouzwz+0j2zbk72yEfgyzoti3XvvxzvUNiGEEEIk9r3pdAL48dFw/AGAp90TTcleVe2odiikWb48fZO/CSGEEEII506cCj88ithx+klzXi1OOpy0hq+/dpYJhRBCCOHc96rTyTTh3zfALReBmeH0Vc5Pgdv9vTpdQgghhBDdhlLw18vh7svBk+v0VZLzhBBCiN3pe/ft6jLh+tnw5NyIjUk7oPpgr7+boq5Lceihg3eydUIIIYQQYkcpBZfOhFf/ErHaXPsR7lGKgdgV7dpzuQxmzBi40+0TQgghRLTvXadTixMPh7/fandCqYJkw6ozgTGEZ4NKKBjUXHzxXulsohBCCCGE2AHT9oX/3gPeDFAFyfZ0AZNInfMsLrlk7/Q1UAghhBDA97jTCeD8k2B1BVzxo3pQmkT39Cs1A8jDNBMHkuuum8L48WWd01AhhBBCCNEhMw+FtW/D766F4kGhJHseAJRgGIlz3gUXTODQQwelvY1CCCFET/e97nQC6NsbfnZ+PdlZjZx4oj3TpMtlD882w3fVFRRk8/jj53LMMcNQ4TzSEkyKijK5++7DuOWWabuj+UIIIYQQIoGSXnDNRfDJO5txuwMopTFN+9Y707TzXlZWBg8+eBZnnDEal8vOdy05Ly/Pw//93w+4776jUCr5aCghhBBCdFz79T++t5SCe+7Zxq23ZvHgg7ByJXi9cOihcMop4PXmcNppJ7N27TbmzfuWpqYgAwbkceSRQ/F4Us/5JIQQQgghdg/ThIwMPx9+uIUXXihl+XL7IuO0aTB7NuTkeDnvvOO5/faDefnllWzf3kyfPrkcc8xQMjPdu7v5QgghxPdWj+l0ajF8OPzud4mf798/n4sumrjrGiSEEEIIIdJijz1C3Hxz4ud7987hvPPG77oGCSGEED3c9/72OiGEEEIIIYQQQgix60mnkxBCCCGEEEIIIYRIO+l0EkIIIYQQQgghhBBpJ51OQgghhBBCCCGEECLtpNNJCCGEEEIIIYQQQqSddDoJIYQQQgghhBBCiLSTTichhBBCCCGEEEIIkXbS6SSEEEIIIYQQQggh0k46nYQQQgghhBBCCCFE2rl2dwPisSyLJUuWpK1eTU0NACtWrKCqqqpL15WaPbNmZ9WVmj2zZmfV7U41DQzGMjYt9QAKKEhbLSF6unTnPOhef5+kZteu2Vl1pabUlM+o5LyeSmmt9e5uRHsjR47ktNNO293NEEII0U1ZWBhpHsy7aNEinnvuubTWFKInkpwnhBBiZ0jO61665Egnr9fLrFmz0lavpqaGiooKZsyYQWFhYZeuKzV7Zs3Oqis1e2bNzqrbnWrOq5jHQzyUlnoAxRQzkIFpqydET5bunAfd6++T1OzaNTurrtSUmvIZlZzXU3XJTifDMBg7Nn3D5SorK6moqGDYsGGUl5d36bpSs2fW7Ky6UrNn1uysut2p5isVr1BJZVrqtZAwIkR6pDvnQff6+yQ1u3bNzqorNaWmfEYl5/VUMpG4EEIIIYQQQgghhEg76XQSQgghhBBCCCGEEGknnU5CCCGEEEIIIYQQIu2k00kIIYQQQgghhBBCpJ10OgkhhBBCCCGEEEKItJNOJyGEEEIIIYQQQgiRdtLpJIQQQgghhBBCCCHSTjqdhBBCCCGEEEIIIUTaSaeTEEIIIYQQQgghhEi7Ltnp5PP5dncTvncCgcDubsL3ipzP9JNzmn5yTtNr/fr1u7sJQnwvSM5LP/l7n35yTtNLzmf6yTlNL8l5nUc6nXqIYDC4u5vwvSLnM/3knKafnNP0kjAiRHpIzks/+XuffnJO00vOZ/rJOU0vyXmdp0t2OgkhhBBCCCGEEEKI7k06nYQQQgghhBBCCCFE2kmnkxBCCCGEEEIIIYRIO+l0EkIIIYQQQgghhBBpJ51OQgghhBBCCCGEECLtpNNJCCGEEEIIIYQQQqTdLu908vl8nHjiiUyePJnnn39+Vx9eCCGEEEJ0Esl5QgghhIjk2tUHfOaZZ5g5cyannHIKxx57LMcee2zMPlprKisr03bMLVu2AFBVVZW2mp1VV2r2zJqdVVdq9syanVW3O9VUKMopT1vNYorZzOa01RPi+2p35DzoXn+fpGbXrtlZdaWm1Eyn7tJWyXkCQGmt9a484Jw5c/jJT37CkCFDOOmkk3jkkUfIzMyM2qeoqAi3252yVt++fenbt29nNfV7Zf369XKu0kjOZ/rJOU0/OafOrF+/nvXr16fcLxAIsHXr1l3QIiG6L8l5u4f8vU8/OafpJecz/eScOiM5b/fb5SOdamtrycrKAiArK4uGhoaYMCK/bCGEEEKI7kdynhBCCCEi7fI5nVoCCEBjYyMFBQW7uglCCCGEEKITSM4TQgghRKRd3uk0duxY5s+fT3NzMz6fD5drlw+2EkIIIYQQnUBynhBCCCEi7fI5nRobG5k9ezabNm3iuuuu4+ijj96VhxdCCCGEEJ1Ecp4QQgghIu3yTichhBBCCCGEEEII8f23y2+vS8bn83HiiScyefJknn/++d3dnG5jzZo1zJgxg8mTJ/Poo49y7733cuCBB3LBBRcA9oSdhx12GJMnT2bhwoUAMfuI+K666iqeeOIJnnnmGaZMmcIJJ5zQestA+89q+31Em7q6Ok488UT2228/7r//fvmMpkFjYyNHHHEEkydP5oUXXpDP6E4KBAJMnz4diD1PTs6lfH8J4Yz8t9JxkvM6j+S89JCcl36S89JLct5upruQxx57TD/88MPa5/PpQw89dHc3p9u46qqr9Mcff6yDwaCePn26njlzptZa62uuuUa///77+tZbb9XvvPOOrqqq0jNnztQ+ny9mHxFr4cKFesKECfrxxx/Xxx57rA4EAvrPf/6zfvzxx+N+VtvvI9r87ne/0y+++KK2LEvfeeed8hlNg//+97/6jjvu0D6fTx922GHyGd0JdXV1+vDDD9f9+vXTWseeJyfnUr6/hHBG/lvpOMl5nUNyXvpIzks/yXnpIzlv9+tSI50WLVrEAQccQEZGBnl5eTQ1Ne3uJnUL1113HRMnTkQpxZIlSxgzZgwAU6dO5dNPP2Xx4sXst99+FBUV4ff7WbFiRcw+IlowGOSuu+7i4osvJhgMkp+fj8vlaj1f7T+rdXV1MfuINh999BELFy5k2rRpTJgwQT6jaTBy5EgCgQB+v59QKCSf0Z3Q1NTE7373O0aNGkV9ff0OnUv5/hLCGflvpeMk56Wf5Lz0kpyXfpLz0kdy3u7XpTqdamtrycrKAqKX3BXJ9erVC8MwuOWWW7j88sujzmF9fT2NjY243W4AtNYx57m+vn63tb2rmjt3Lj/96U9bz1v789X+HG7btk3OaRI1NTUMHz6c//3vf/zf//2ffEbTwO1288ADDzBmzBimT58un9GdUFJSwoQJE4DY7yGn51K+v4RwRv5b6TjJeeknOS+9JOeln+S89JGct/t1qXVsI3+BjY2NFBQU7N4GdSP33Xcfq1at4rLLLuPpp58GoKGhgYKCAlwuF4FAALfbjdvtjjrPLfuIaK+++iqvvPIKGzduxDAM+vTpA7Sdr+3bt0d9Vj0ej5zTJDIzM5k2bRqFhYWYphlzruQz2nF/+tOfuPvuuznkkEM46qijyM3NBeQzurPiffacnMv2+8j5FSI+yXo7RnJeeknOSy/JeeknOa9zSM7bPbrUSKexY8cyf/781sm6XK4u1SfWZb399tu88MILPPDAA4wYMYKPPvoIgPnz5zNp0iTGjBnDggULqK6uJjMzM+4+Itqbb77JW2+9xS9+8QtuvPFGQqEQgUCg9Xy1/6yWlpayfv36qH1Em/Hjx/Phhx/i8/kIBALyGU2D7Oxs8vPzWwNcTU2NfEbToFevXjHnycm5lO8vIZyR/1Y6TnJe+knOSy/JeeknOa9zSM7bPbrU2Zo9ezazZ8/mvvvu47rrrtvdzek2brvtNtavX88hhxyCaZrMnDmTKVOmMHbsWCZNmkTfvn0588wzaWxs5O677yY7O5tjjjkmah+R3CWXXMIPfvAD+vXrx6OPPkowGIz5rLbfR7S58sormT17Nr/97W+58cYbWb16tXxGd9Ill1zC2WefTWNjI8cddxxDhw6Vz2ia7Mh/7/H2EULEkqzXcZLzOp/kvJ0jOS/9JOd1Hsl5u57SWuvd3QghhBBCCCGEEEII8f3SpW6vE0IIIYQQQgghhBDfD9LpJIQQQgghhBBCCCHSTjqdhBBCCCGEEEIIIUTaSaeTEEIIIYQQQgghhEg76XQSQgghhBBCCCGEEGknnU5CCCGEEEIIIYQQIu2k00kIIYQQQgghhBBCpJ10OgkhhBBCCCGEEEKItJNOJyGEEEIIIYQQQgiRdtLpJIQQQgghhBBCCCHS7v8B/HTtt6oGe5gAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Scenario for example 2B\n", + "scenario(\"b\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/ex-prt-mp7-p04.ipynb b/notebooks/ex-prt-mp7-p04.ipynb new file mode 100644 index 00000000..6649df04 --- /dev/null +++ b/notebooks/ex-prt-mp7-p04.ipynb @@ -0,0 +1,1373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "209968cd", + "metadata": {}, + "source": [ + "## Particle tracking through a steady-state system with lateral flow boundaries and a refined vertex grid.\n", + "\n", + "Application of a MODFLOW 6 particle-tracking (PRT) model to solve example 4 from the MODPATH 7 documentation.\n", + "\n", + "This notebook demonstrates a steady-state MODFLOW 6 simulation using a quadpatch DISV grid with an irregular domain and a large number of inactive cells. Particles are tracked backwards from terminating locations, including a pair of wells in a locally-refined region of the grid and constant-head cells along the grid's right side, to release locations along the left border of the grid's active region. Injection wells along the left-hand border are used to generate boundary flows.\n", + "\n", + "### Problem setup\n", + "\n", + "First import dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "21879ba8", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "from warnings import warn\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "from shapely.geometry import MultiPoint, LineString\n", + "import flopy\n", + "from flopy.utils.gridintersect import GridIntersect\n", + "import numpy as np\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "\n", + "try:\n", + " # append the common/ subdirectory to the system path\n", + " # (assumes running one level down from project root)\n", + " sys.path.append(os.path.join(\"..\", \"common\"))\n", + " import config\n", + "\n", + " buildModel = config.buildModel\n", + " writeModel = config.writeModel\n", + " runModel = config.runModel\n", + " plotModel = config.plotModel\n", + " plotSave = config.plotSave\n", + " base_ws = config.base_ws\n", + " timeit = config.timeit\n", + "except:\n", + " warn(f\"Failed to import config\")\n", + " # default settings\n", + " buildModel = True\n", + " writeModel = True\n", + " runModel = True\n", + " plotModel = True\n", + " plotSave = False\n", + " base_ws = Path(\"../examples\")\n", + "\n", + " def timeit(func):\n", + " return func" + ] + }, + { + "cell_type": "markdown", + "id": "4124404b", + "metadata": {}, + "source": [ + "Define workspace paths and model/file-names." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "f551fd05", + "metadata": {}, + "outputs": [], + "source": [ + "sim_name = \"mp7-p04\"\n", + "example_name = \"ex-prt-\" + sim_name\n", + "gwf_name = sim_name + \"-gwf\"\n", + "prt_name = sim_name + \"-prt\"\n", + "mp7_name = sim_name + \"-mp7\"\n", + "\n", + "sim_ws = base_ws / example_name\n", + "gwf_ws = sim_ws / \"gwf\"\n", + "prt_ws = sim_ws / \"prt\"\n", + "mp7_ws = sim_ws / \"mp7\"\n", + "\n", + "gwf_ws.mkdir(exist_ok=True, parents=True)\n", + "prt_ws.mkdir(exist_ok=True, parents=True)\n", + "mp7_ws.mkdir(exist_ok=True, parents=True)\n", + "\n", + "headfile = f\"{gwf_name}.hds\"\n", + "headfile_bkwd = f\"{gwf_name}_bkwd.hds\"\n", + "budgetfile = f\"{gwf_name}.cbb\"\n", + "budgetfile_bkwd = f\"{gwf_name}_bkwd.bud\"\n", + "trackfile_prt = f\"{prt_name}.trk\"\n", + "trackcsvfile_prt = f\"{prt_name}.trk.csv\"\n", + "budgetfile_prt = f\"{prt_name}.cbb\"\n", + "pathlinefile_mp7 = f\"{mp7_name}.mppth\"" + ] + }, + { + "cell_type": "markdown", + "id": "64927ca6", + "metadata": {}, + "source": [ + "### Grid refinement\n", + "\n", + "[GRIDGEN](https://www.usgs.gov/software/gridgen-program-generating-unstructured-finite-volume-grids) can be used to create a quadpatch grid with a refined region in the upper left quadrant.\n", + "\n", + "We will create a grid with 3 refinement levels, all nearly but not perfectly rectangular: a 500x500 area is carved out of the corners of the rectangle for each level. To form each region's polygon we combine 5 rectangles.\n", + "\n", + "Create the top-level grid discretization." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "d8561918", + "metadata": {}, + "outputs": [], + "source": [ + "nlay, nrow, ncol = 1, 21, 26\n", + "delr = delc = 500.0\n", + "top = 100.0\n", + "botm = np.zeros((nlay, nrow, ncol), dtype=np.float32)\n", + "ms = flopy.modflow.Modflow()\n", + "dis = flopy.modflow.ModflowDis(\n", + " ms,\n", + " nlay=nlay,\n", + " nrow=nrow,\n", + " ncol=ncol,\n", + " delr=delr,\n", + " delc=delc,\n", + " top=top,\n", + " botm=botm,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "dd8a18ac", + "metadata": {}, + "source": [ + "Refine the grid. Create a Gridgen object from the base grid, then add refinement features." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "ca673ccd", + "metadata": {}, + "outputs": [], + "source": [ + "from flopy.utils.gridgen import Gridgen\n", + "\n", + "# create Gridgen workspace\n", + "gridgen_ws = sim_ws / \"gridgen\"\n", + "gridgen_ws.mkdir(parents=True, exist_ok=True)\n", + "\n", + "# create Gridgen object\n", + "g = Gridgen(ms.modelgrid, model_ws=gridgen_ws, exe_name=\"gridgen\")\n", + "\n", + "# add polygon for each refinement level\n", + "outer_polygon = [\n", + " [\n", + " (2500, 6000),\n", + " (2500, 9500),\n", + " (3000, 9500),\n", + " (3000, 10000),\n", + " (6000, 10000),\n", + " (6000, 9500),\n", + " (6500, 9500),\n", + " (6500, 6000),\n", + " (6000, 6000),\n", + " (6000, 5500),\n", + " (3000, 5500),\n", + " (3000, 6000),\n", + " (2500, 6000),\n", + " ]\n", + "]\n", + "g.add_refinement_features([outer_polygon], \"polygon\", 1, range(nlay))\n", + "refshp0 = gridgen_ws / \"rf0\"\n", + "\n", + "middle_polygon = [\n", + " [\n", + " (3000, 6500),\n", + " (3000, 9000),\n", + " (3500, 9000),\n", + " (3500, 9500),\n", + " (5500, 9500),\n", + " (5500, 9000),\n", + " (6000, 9000),\n", + " (6000, 6500),\n", + " (5500, 6500),\n", + " (5500, 6000),\n", + " (3500, 6000),\n", + " (3500, 6500),\n", + " (3000, 6500),\n", + " ]\n", + "]\n", + "g.add_refinement_features([middle_polygon], \"polygon\", 2, range(nlay))\n", + "refshp1 = gridgen_ws / \"rf1\"\n", + "\n", + "inner_polygon = [\n", + " [\n", + " (3500, 7000),\n", + " (3500, 8500),\n", + " (4000, 8500),\n", + " (4000, 9000),\n", + " (5000, 9000),\n", + " (5000, 8500),\n", + " (5500, 8500),\n", + " (5500, 7000),\n", + " (5000, 7000),\n", + " (5000, 6500),\n", + " (4000, 6500),\n", + " (4000, 7000),\n", + " (3500, 7000),\n", + " ]\n", + "]\n", + "g.add_refinement_features([inner_polygon], \"polygon\", 3, range(nlay))\n", + "refshp2 = gridgen_ws / \"rf2\"" + ] + }, + { + "cell_type": "markdown", + "id": "d2e6caae", + "metadata": {}, + "source": [ + "Build the grid and plot it with refinement levels superimposed." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "b1930b4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g.build(verbose=False)\n", + "grid = flopy.discretization.VertexGrid(**g.get_gridprops_vertexgrid())\n", + "\n", + "fig = plt.figure(figsize=(15, 15))\n", + "ax = fig.add_subplot(1, 1, 1, aspect=\"equal\")\n", + "mm = flopy.plot.PlotMapView(model=ms)\n", + "grid.plot(ax=ax)\n", + "\n", + "flopy.plot.plot_shapefile(refshp0, ax=ax, facecolor=\"green\", alpha=0.3)\n", + "flopy.plot.plot_shapefile(refshp1, ax=ax, facecolor=\"green\", alpha=0.5)\n", + "flopy.plot.plot_shapefile(str(refshp2), ax=ax, facecolor=\"green\", alpha=0.7)" + ] + }, + { + "cell_type": "markdown", + "id": "de29c2a4", + "metadata": {}, + "source": [ + "We are now ready to set up groundwater flow and particle tracking models.\n", + "\n", + "Define shared variables, including discretization parameters, idomain, and porosity." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "dc75054d", + "metadata": {}, + "outputs": [], + "source": [ + "porosity = 0.1\n", + "nper = 1\n", + "tdis_rc = [(10000, 1, 1.0)]\n", + "# fmt: off\n", + "idomain = [\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n", + " 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0\n", + "]\n", + "# fmt: on\n", + "disv_props = g.get_gridprops_disv()\n", + "\n", + "# from pprint import pprint\n", + "# pprint(idomain, compact=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b77fb792", + "metadata": {}, + "source": [ + "Define well locations and flows." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "952ed5f0", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "wells = [\n", + " # negative q: discharge\n", + " (0, 861, -30000.0, 0, -1),\n", + " (0, 891, -30000.0, 0, -1),\n", + " # positive q: injection\n", + " (0, 1959, 10000.0, 1, 4),\n", + " (0, 1932, 10000.0, 3, 3),\n", + " (0, 1931, 10000.0, 3, 3),\n", + " (0, 1930, 5000.0, 1, 4),\n", + " (0, 1930, 5000.0, 3, 3),\n", + " (0, 1903, 5000.0, 1, 4),\n", + " (0, 1903, 5000.0, 3, 3),\n", + " (0, 1876, 10000.0, 3, 3),\n", + " (0, 1875, 10000.0, 3, 3),\n", + " (0, 1874, 5000.0, 1, 4),\n", + " (0, 1874, 5000.0, 3, 3),\n", + " (0, 1847, 10000.0, 3, 3),\n", + " (0, 1846, 10000.0, 3, 3),\n", + " (0, 1845, 5000.0, 1, 4),\n", + " (0, 1845, 5000.0, 3, 3),\n", + " (0, 1818, 5000.0, 1, 4),\n", + " (0, 1818, 5000.0, 3, 3),\n", + " (0, 1792, 10000.0, 1, 4),\n", + " (0, 1766, 10000.0, 1, 4),\n", + " (0, 1740, 5000.0, 1, 4),\n", + " (0, 1740, 5000.0, 4, 1),\n", + " (0, 1715, 5000.0, 1, 4),\n", + " (0, 1715, 5000.0, 4, 1),\n", + " (0, 1690, 10000.0, 1, 4),\n", + " (0, 1646, 5000.0, 1, 4),\n", + " (0, 1646, 5000.0, 4, 1),\n", + " (0, 1549, 5000.0, 1, 4),\n", + " (0, 1549, 5000.0, 4, 1),\n", + " (0, 1332, 5000.0, 4, 1),\n", + " (0, 1332, 5000.0, 1, 5),\n", + " (0, 1021, 2500.0, 1, 4),\n", + " (0, 1021, 2500.0, 4, 1),\n", + " (0, 1020, 5000.0, 1, 5),\n", + " (0, 708, 2500.0, 1, 5),\n", + " (0, 708, 2500.0, 4, 1),\n", + " (0, 711, 625.0, 1, 4),\n", + " (0, 711, 625.0, 4, 1),\n", + " (0, 710, 625.0, 1, 4),\n", + " (0, 710, 625.0, 4, 1),\n", + " (0, 409, 1250.0, 1, 4),\n", + " (0, 407, 625.0, 1, 4),\n", + " (0, 407, 625.0, 4, 1),\n", + " (0, 402, 625.0, 1, 5),\n", + " (0, 402, 625.0, 4, 1),\n", + " (0, 413, 1250.0, 1, 4),\n", + " (0, 411, 1250.0, 1, 4),\n", + " (0, 203, 1250.0, 1, 5),\n", + " (0, 203, 1250.0, 4, 1),\n", + " (0, 202, 1250.0, 1, 4),\n", + " (0, 202, 1250.0, 4, 1),\n", + " (0, 199, 2500.0, 1, 4),\n", + " (0, 197, 1250.0, 1, 4),\n", + " (0, 197, 1250.0, 4, 1),\n", + " (0, 96, 2500.0, 1, 4),\n", + " (0, 96, 2500.0, 4, 1),\n", + " (0, 98, 1250.0, 1, 4),\n", + " (0, 101, 1250.0, 1, 4),\n", + " (0, 101, 1250.0, 4, 1),\n", + " (0, 100, 1250.0, 1, 4),\n", + " (0, 100, 1250.0, 4, 1),\n", + " (0, 43, 2500.0, 1, 5),\n", + " (0, 43, 2500.0, 4, 1),\n", + " (0, 44, 2500.0, 1, 4),\n", + " (0, 44, 2500.0, 4, 1),\n", + " (0, 45, 5000.0, 4, 1),\n", + " (0, 10, 10000.0, 1, 5),\n", + "]\n", + "\n", + "assert len(wells) == 68" + ] + }, + { + "cell_type": "markdown", + "id": "f37f587f", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function to build the groundwater flow model." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "df971e94", + "metadata": {}, + "outputs": [], + "source": [ + "def build_gwf():\n", + " print(\"Building GWF model\")\n", + "\n", + " # simulation\n", + " sim = flopy.mf6.MFSimulation(\n", + " sim_name=sim_name, sim_ws=gwf_ws, exe_name=\"mf6\", version=\"mf6\"\n", + " )\n", + "\n", + " # temporal discretization\n", + " tdis = flopy.mf6.ModflowTdis(sim, time_units=\"days\", nper=nper, perioddata=tdis_rc)\n", + "\n", + " # iterative model solver\n", + " ims = flopy.mf6.ModflowIms(\n", + " sim,\n", + " pname=\"ims\",\n", + " complexity=\"SIMPLE\",\n", + " outer_dvclose=1e-4,\n", + " outer_maximum=100,\n", + " inner_dvclose=1e-5,\n", + " under_relaxation_theta=0,\n", + " under_relaxation_kappa=0,\n", + " under_relaxation_gamma=0,\n", + " under_relaxation_momentum=0,\n", + " linear_acceleration=\"BICGSTAB\",\n", + " relaxation_factor=0.99,\n", + " number_orthogonalizations=2,\n", + " )\n", + "\n", + " # groundwater flow model\n", + " gwf = flopy.mf6.ModflowGwf(\n", + " sim, modelname=gwf_name, model_nam_file=f\"{sim_name}.nam\", save_flows=True\n", + " )\n", + "\n", + " # grid discretization\n", + " disv = flopy.mf6.ModflowGwfdisv(\n", + " gwf, length_units=\"feet\", idomain=idomain, **disv_props\n", + " )\n", + "\n", + " # initial conditions\n", + " ic = flopy.mf6.ModflowGwfic(gwf, strt=150.0)\n", + "\n", + " flopy.mf6.modflow.mfgwfwel.ModflowGwfwel(\n", + " gwf,\n", + " maxbound=len(wells),\n", + " auxiliary=[\"IFACE\", \"IFLOWFACE\"],\n", + " save_flows=True,\n", + " stress_period_data={0: wells},\n", + " )\n", + "\n", + " # node property flow\n", + " npf = flopy.mf6.ModflowGwfnpf(\n", + " gwf,\n", + " xt3doptions=True,\n", + " save_flows=True,\n", + " save_specific_discharge=True,\n", + " save_saturation=True,\n", + " icelltype=[0],\n", + " k=[50],\n", + " )\n", + "\n", + " # constant head boundary (period, node number, head)\n", + " chd_bound = [\n", + " (0, 1327, 150.0),\n", + " (0, 1545, 150.0),\n", + " (0, 1643, 150.0),\n", + " (0, 1687, 150.0),\n", + " (0, 1713, 150.0),\n", + " ]\n", + " chd = flopy.mf6.ModflowGwfchd(\n", + " gwf,\n", + " pname=\"chd\",\n", + " save_flows=True,\n", + " stress_period_data=chd_bound,\n", + " # auxiliary=[\"IFLOWFACE\"]\n", + " )\n", + "\n", + " # output control\n", + " budget_file = f\"{gwf_name}.cbb\"\n", + " head_file = f\"{gwf_name}.hds\"\n", + " oc = flopy.mf6.ModflowGwfoc(\n", + " gwf,\n", + " pname=\"oc\",\n", + " budget_filerecord=[budget_file],\n", + " head_filerecord=[head_file],\n", + " saverecord=[(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")],\n", + " )\n", + "\n", + " return sim" + ] + }, + { + "cell_type": "markdown", + "id": "399b8a25", + "metadata": {}, + "source": [ + "Next we can define the particle-tracking model. This example problem uses a reverse-tracking model, in which termination and release zones are swapped: we \"release\" (terminate) particles in the constant head boundary on the grid's right edge and the two pumping wells, and track them backwards to \"termination\" (release) locations at the wells along the left boundary of the active domain.\n", + "\n", + "Define particle release locations, initially in the representation for MODPATH 7 particle input style 1." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "4ace439d", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "# define particles in MODPATH 7 input style 1\n", + "# each particle is a tuple (node number, localx, localy, localz)\n", + "rpts = [\n", + " (1327, 0.000, 0.125, 0.500),\n", + " (1327, 0.000, 0.375, 0.500),\n", + " (1327, 0.000, 0.625, 0.500),\n", + " (1327, 0.000, 0.875, 0.500),\n", + " (1545, 0.000, 0.125, 0.500),\n", + " (1545, 0.000, 0.375, 0.500),\n", + " (1545, 0.000, 0.625, 0.500),\n", + " (1545, 0.000, 0.875, 0.500),\n", + " (1643, 0.000, 0.125, 0.500),\n", + " (1643, 0.000, 0.375, 0.500),\n", + " (1643, 0.000, 0.625, 0.500),\n", + " (1643, 0.000, 0.875, 0.500),\n", + " (1687, 0.000, 0.125, 0.500),\n", + " (1687, 0.000, 0.375, 0.500),\n", + " (1687, 0.000, 0.625, 0.500),\n", + " (1687, 0.000, 0.875, 0.500),\n", + " (1713, 0.000, 0.125, 0.500),\n", + " (1713, 0.000, 0.375, 0.500),\n", + " (1713, 0.000, 0.625, 0.500),\n", + " (1713, 0.000, 0.875, 0.500),\n", + " (861, 0.000, 0.125, 0.500),\n", + " (861, 0.000, 0.375, 0.500),\n", + " (861, 0.000, 0.625, 0.500),\n", + " (861, 0.000, 0.875, 0.500),\n", + " (861, 1.000, 0.125, 0.500),\n", + " (861, 1.000, 0.375, 0.500),\n", + " (861, 1.000, 0.625, 0.500),\n", + " (861, 1.000, 0.875, 0.500),\n", + " (861, 0.125, 0.000, 0.500),\n", + " (861, 0.375, 0.000, 0.500),\n", + " (861, 0.625, 0.000, 0.500),\n", + " (861, 0.875, 0.000, 0.500),\n", + " (861, 0.125, 1.000, 0.500),\n", + " (861, 0.375, 1.000, 0.500),\n", + " (861, 0.625, 1.000, 0.500),\n", + " (861, 0.875, 1.000, 0.500),\n", + " (891, 0.000, 0.125, 0.500),\n", + " (891, 0.000, 0.375, 0.500),\n", + " (891, 0.000, 0.625, 0.500),\n", + " (891, 0.000, 0.875, 0.500),\n", + " (891, 1.000, 0.125, 0.500),\n", + " (891, 1.000, 0.375, 0.500),\n", + " (891, 1.000, 0.625, 0.500),\n", + " (891, 1.000, 0.875, 0.500),\n", + " (891, 0.125, 0.000, 0.500),\n", + " (891, 0.375, 0.000, 0.500),\n", + " (891, 0.625, 0.000, 0.500),\n", + " (891, 0.875, 0.000, 0.500),\n", + " (891, 0.125, 1.000, 0.500),\n", + " (891, 0.375, 1.000, 0.500),\n", + " (891, 0.625, 1.000, 0.500),\n", + " (891, 0.875, 1.000, 0.500),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "05131e24", + "metadata": {}, + "source": [ + "For vertex grids, the MODFLOW 6 PRT model's PRP (particle release point) package expects initial particle locations as tuples `(irpt, (k, j), x, y, z)`, where `irpt` is the release point index, `k` and `j` are the layer and cell indices, respectively, and `xyz` are coordinates. While MODPATH 7 input style 1 expects local coordinates, PRT expects global coordinates. The user must guarantee that release point coordinates fall within the cell with the given ID.\n", + "\n", + "FloPy's `ParticleData` provides a `to_prp()` utility method (a generator) for conversion to PRT particle release point (PRP) package input data." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "886d18c2", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "particle_data = flopy.modpath.ParticleData(\n", + " partlocs=[p[0] for p in rpts],\n", + " structured=False,\n", + " localx=[p[1] for p in rpts],\n", + " localy=[p[2] for p in rpts],\n", + " localz=[p[3] for p in rpts],\n", + " timeoffset=0,\n", + " drape=0,\n", + ")\n", + "prp_pkg_data = list(particle_data.to_prp(grid))" + ] + }, + { + "cell_type": "markdown", + "id": "38c75f0f", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function to build the PRT model/simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "b8e857c3", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def build_prt():\n", + " print(\"Building PRT model\")\n", + "\n", + " simprt = flopy.mf6.MFSimulation(\n", + " sim_name=prt_name, version=\"mf6\", exe_name=\"mf6\", sim_ws=prt_ws\n", + " )\n", + "\n", + " flopy.mf6.ModflowTdis(\n", + " simprt, pname=\"tdis\", time_units=\"DAYS\", nper=1, perioddata=[(10000, 1, 1.0)]\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt model\n", + " prt = flopy.mf6.ModflowPrt(\n", + " simprt, modelname=prt_name, model_nam_file=\"{}.nam\".format(prt_name)\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 DISV vertex grid discretization\n", + " disv = flopy.mf6.ModflowGwfdisv(prt, idomain=idomain, **disv_props)\n", + "\n", + " # Instantiate the MODFLOW 6 prt model input package\n", + " flopy.mf6.ModflowPrtmip(prt, pname=\"mip\", porosity=porosity)\n", + "\n", + " # Instantiate the MODFLOW 6 prt particle release point (prp) package\n", + " flopy.mf6.ModflowPrtprp(\n", + " prt,\n", + " pname=\"prp\",\n", + " filename=\"{}_4.prp\".format(prt_name),\n", + " nreleasepts=len(prp_pkg_data),\n", + " packagedata=prp_pkg_data,\n", + " perioddata={\n", + " 0: [\"FIRST\"],\n", + " },\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt output control package\n", + " budgetfile_prt = \"{}.cbb\".format(prt_name)\n", + " budget_record = [budgetfile_prt]\n", + " trackfile_prt = \"{}.trk\".format(prt_name)\n", + " trackcsvfile_prt = \"{}.trk.csv\".format(prt_name)\n", + " flopy.mf6.ModflowPrtoc(\n", + " prt,\n", + " pname=\"oc\",\n", + " budget_filerecord=budget_record,\n", + " track_filerecord=trackfile_prt,\n", + " trackcsv_filerecord=trackcsvfile_prt,\n", + " saverecord=[(\"BUDGET\", \"ALL\")],\n", + " )\n", + "\n", + " # Instantiate the MODFLOW 6 prt flow model interface\n", + " flopy.mf6.ModflowPrtfmi(\n", + " prt,\n", + " packagedata=[\n", + " (\"GWFHEAD\", headfile_bkwd),\n", + " (\"GWFBUDGET\", budgetfile_bkwd),\n", + " ],\n", + " )\n", + "\n", + " # Create an explicit model solution (EMS) for the MODFLOW 6 prt model\n", + " ems = flopy.mf6.ModflowEms(\n", + " simprt,\n", + " pname=\"ems\",\n", + " filename=\"{}.ems\".format(prt_name),\n", + " )\n", + " simprt.register_solution_package(ems, [prt.name])\n", + "\n", + " return simprt" + ] + }, + { + "cell_type": "markdown", + "id": "a3c2dd7c", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function to create the MODPATH 7 model/simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "a940daf0", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def build_mp7(gwf):\n", + " print(\"Building MP7 model\")\n", + "\n", + " pg = flopy.modpath.ParticleGroup(\n", + " particlegroupname=\"G1\", particledata=particle_data, filename=f\"{sim_name}.sloc\"\n", + " )\n", + "\n", + " mp = flopy.modpath.Modpath7(\n", + " modelname=mp7_name,\n", + " flowmodel=gwf,\n", + " exe_name=\"mp7\",\n", + " model_ws=mp7_ws,\n", + " )\n", + " mpbas = flopy.modpath.Modpath7Bas(\n", + " mp,\n", + " porosity=porosity,\n", + " )\n", + " mpsim = flopy.modpath.Modpath7Sim(\n", + " mp,\n", + " simulationtype=\"pathline\",\n", + " trackingdirection=\"backward\",\n", + " budgetoutputoption=\"summary\",\n", + " particlegroups=[pg],\n", + " )\n", + "\n", + " return mp" + ] + }, + { + "cell_type": "markdown", + "id": "9ef64619", + "metadata": {}, + "source": [ + "-" + ] + }, + { + "cell_type": "markdown", + "id": "752f3799", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function to build the three models: MODFLOW 6 GWF, MODFLOW6 PRT, and MODPATH 7." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "3233cc44", + "metadata": {}, + "outputs": [], + "source": [ + "def build_models():\n", + " gwfsim = build_gwf()\n", + " gwf = gwfsim.get_model(gwf_name)\n", + " return gwfsim, build_prt(), build_mp7(gwf)" + ] + }, + { + "cell_type": "markdown", + "id": "048ab608", + "metadata": {}, + "source": [ + "Define a function to view the grid and boundary conditions. Also highlight the vertices of well-containing cells which lie on the boundary between coarser- and finer-grained refinement regions (there are 7 of these). Note the disagreement between values of `IFLOWFACE` and `IFACE` for these cells. This is because the cells have 5 polygonal faces." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "808fde74", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "from matplotlib.collections import LineCollection\n", + "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n", + "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n", + "\n", + "\n", + "def sort_square_verts(verts):\n", + " \"\"\"Sort 4 or more points on a square in clockwise order, starting with the top-left point\"\"\"\n", + "\n", + " # sort by y coordinate\n", + " verts.sort(key=lambda v: v[1], reverse=True)\n", + "\n", + " # separate top and bottom rows\n", + " y0 = verts[0][1]\n", + " t = [v for v in verts if v[1] == y0]\n", + " b = verts[len(t) :]\n", + "\n", + " # sort top and bottom rows by x coordinate\n", + " t.sort(key=lambda v: v[0])\n", + " b.sort(key=lambda v: v[0])\n", + "\n", + " # return vertices in clockwise order\n", + " return t + list(reversed(b))\n", + "\n", + "\n", + "def plot_well_cell_ids(ax):\n", + " xc, yc = grid.get_xcellcenters_for_layer(0), grid.get_ycellcenters_for_layer(0)\n", + " for well in wells:\n", + " nn = well[1]\n", + " x, y = xc[nn], yc[nn]\n", + " ax.annotate(str(nn), (x - 50, y - 50), color=\"purple\")\n", + "\n", + "\n", + "cells_on_refinement_boundary = [10, 43, 203, 402, 708, 1020, 1332]\n", + "\n", + "\n", + "def plot_well_vertices_on_refinement_boundary(ax):\n", + " for nn in cells_on_refinement_boundary:\n", + " verts = list(set([tuple(grid.verts[v]) for v in grid.iverts[nn]]))\n", + " verts = [\n", + " v\n", + " for v in verts\n", + " if len([vv for vv in verts if vv[0] == v[0]]) > 2\n", + " or len([vv for vv in verts if vv[1] == v[1]]) > 2\n", + " ]\n", + " for v in verts:\n", + " ax.plot(v[0], v[1], \"go\")\n", + "\n", + "\n", + "def plot_well_ifaces(ax):\n", + " ifaces = []\n", + " for well in wells:\n", + " nn = well[1]\n", + " iverts = grid.iverts[nn]\n", + "\n", + " # sort vertices of well cell in clockwise order\n", + " verts = [tuple(grid.verts[v]) for v in iverts]\n", + " sorted_verts = sort_square_verts(list(set(verts.copy())))\n", + "\n", + " # reduce vertices to 4 corners of square\n", + " xmax, xmin = max([v[0] for v in sorted_verts]), min(\n", + " [v[0] for v in sorted_verts]\n", + " )\n", + " ymax, ymin = max([v[1] for v in sorted_verts]), min(\n", + " [v[1] for v in sorted_verts]\n", + " )\n", + " sorted_verts = [\n", + " v for v in sorted_verts if v[0] in [xmax, xmin] and v[1] in [ymax, ymin]\n", + " ]\n", + "\n", + " # define the iface line segment\n", + " iface = well[3]\n", + " if iface == 1:\n", + " p0 = sorted_verts[0]\n", + " p1 = sorted_verts[-1]\n", + " elif iface == 2:\n", + " p0 = sorted_verts[1]\n", + " p1 = sorted_verts[2]\n", + " elif iface == 3:\n", + " p0 = sorted_verts[2]\n", + " p1 = sorted_verts[3]\n", + " elif iface == 4:\n", + " p0 = sorted_verts[0]\n", + " p1 = sorted_verts[1]\n", + " else:\n", + " continue\n", + "\n", + " ifaces.append([p0, p1])\n", + "\n", + " lc = LineCollection(ifaces, color=\"red\", lw=4)\n", + " ax.add_collection(lc)\n", + "\n", + "\n", + "def plot_map_view(ax, gwf):\n", + " # plot map view of grid\n", + " mv = flopy.plot.PlotMapView(model=gwf, ax=ax)\n", + " mv.plot_grid(alpha=0.3)\n", + " mv.plot_ibound() # inactive cells\n", + " mv.plot_bc(\"WEL\", alpha=0.3) # wells (red)\n", + " ax.add_patch( # constant head boundary (blue)\n", + " mpl.patches.Rectangle(\n", + " ((ncol - 1) * delc, (nrow - 6) * delr),\n", + " 1000,\n", + " -2500,\n", + " linewidth=5,\n", + " facecolor=\"blue\",\n", + " alpha=0.5,\n", + " )\n", + " )\n", + "\n", + "\n", + "def plot_inset(ax, gwf):\n", + " # create inset\n", + " axins = ax.inset_axes([-0.76, 0.25, 0.7, 0.9])\n", + "\n", + " # plot grid features\n", + " plot_map_view(axins, gwf)\n", + " # plot_well_cell_ids(axins)\n", + " plot_well_ifaces(axins)\n", + " plot_well_vertices_on_refinement_boundary(axins)\n", + "\n", + " # zoom in on refined region of injection well boundary\n", + " axins.set_xlim(2000, 6000)\n", + " axins.set_ylim(6000, 10500)\n", + "\n", + " # add legend\n", + " legend_elements = [\n", + " mpl.lines.Line2D([0], [0], color=\"red\", lw=4, label=\"Well IFACEs\"),\n", + " mpl.lines.Line2D(\n", + " [0],\n", + " [0],\n", + " marker=\"o\",\n", + " color=\"grey\",\n", + " label=\"Well cell vertices on refinement border\",\n", + " markerfacecolor=\"g\",\n", + " markersize=15,\n", + " ),\n", + " ]\n", + " axins.legend(handles=legend_elements)\n", + "\n", + " # add the inset\n", + " ax.indicate_inset_zoom(axins)\n", + "\n", + "\n", + "def plot_grid(gwf):\n", + " # setup the plot\n", + " fig = plt.figure(figsize=(13, 13))\n", + " ax = fig.add_subplot(1, 1, 1, aspect=\"equal\")\n", + "\n", + " # add plot features\n", + " plot_map_view(ax, gwf)\n", + " plot_well_ifaces(ax)\n", + " plot_inset(ax, gwf)\n", + "\n", + " # add legend\n", + " ax.legend(\n", + " handles=[\n", + " mpl.patches.Patch(color=\"red\", label=\"Wells\"),\n", + " mpl.patches.Patch(color=\"blue\", label=\"Constant head boundary\"),\n", + " ]\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "bb651b9f", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Because this problem tracks particles backwards, we need to reverse the head and budget files after running the groundwater flow model and before running the particle tracking model. Define functions to do this (FloPy has a utility that can do most of the work)." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "4fbcf9c7", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def reverse_budgetfile(fpth, rev_fpth, tdis):\n", + " import flopy.utils.binaryfile as bf\n", + "\n", + " f = bf.CellBudgetFile(fpth, tdis=tdis)\n", + " f.reverse(rev_fpth)\n", + "\n", + "\n", + "def reverse_headfile(fpth, rev_fpth, tdis):\n", + " import flopy.utils.binaryfile as bf\n", + "\n", + " f = bf.HeadFile(fpth, tdis=tdis)\n", + " f.reverse(rev_fpth)\n", + "\n", + "\n", + "# Define a function to run the models, reversing the GWF model's head and budget output files before running the PRT model.\n", + "\n", + "\n", + "@timeit\n", + "def run_models(gwfsim, prtsim, mp7sim):\n", + " success, buff = gwfsim.run_simulation(silent=True, report=True)\n", + " assert success, f\"Failed to run MF6 GWF model.\"\n", + " for line in buff:\n", + " print(line)\n", + "\n", + " # tdis is required for output file reversal\n", + " tdis = gwfsim.tdis\n", + "\n", + " # reverse gwf head and output files (for backwards tracking)\n", + " reverse_headfile(gwf_ws / headfile, prt_ws / headfile_bkwd, tdis)\n", + " reverse_budgetfile(gwf_ws / budgetfile, prt_ws / budgetfile_bkwd, tdis)\n", + "\n", + " success, buff = prtsim.run_simulation(silent=True, report=True)\n", + " assert success, f\"Failed to run MF6 PRT model.\"\n", + " for line in buff:\n", + " print(line)\n", + "\n", + " success, buff = mp7sim.run_model(silent=True, report=True)\n", + " assert success, \"Failed to run MODPATH 7 model.\"\n", + " for line in buff:\n", + " print(line)\n", + "\n", + "\n", + "# Define functions to plot heads and particle paths over the grid, comparing the PRT and MODPATH results." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "599b7c7e", + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], + "source": [ + "def plot_pathlines(ax, grid, hd, pl, title):\n", + " ax.set_aspect(\"equal\")\n", + " ax.set_title(title)\n", + " mm = flopy.plot.PlotMapView(modelgrid=grid, ax=ax)\n", + " mm.plot_grid(lw=0.5, alpha=0.5)\n", + " mm.plot_ibound()\n", + " mm.plot_array(hd, alpha=0.5)\n", + " mm.plot_pathline(pl, layer=\"all\", lw=0.3, colors=[\"black\"])\n", + "\n", + "\n", + "def plot_results(grid, heads, prtpl, mp7pl):\n", + " fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(14, 14))\n", + " plt.suptitle(\n", + " t=\"Example 4: Particles tracked backward on a locally refined vertex grid with lateral flow boundaries\",\n", + " fontsize=14,\n", + " y=0.7,\n", + " )\n", + "\n", + " plot_pathlines(axes[0], grid, heads, prtpl, \"MODFLOW 6 PRT\")\n", + " plot_pathlines(axes[1], grid, heads, mp7pl, \"MODPATH 7\")\n", + "\n", + " # for ax in axes:\n", + " # ax.set_xlim(2000, 6000)\n", + " # ax.set_ylim(6000, 10500)" + ] + }, + { + "cell_type": "markdown", + "id": "fbde07e8", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Define a function to wrap the entire problem scenario." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "ff3fb845", + "metadata": {}, + "outputs": [], + "source": [ + "def scenario():\n", + " # build models\n", + " gwfsim, prtsim, mp7sim = build_models()\n", + "\n", + " # plot grid with active domain and boundary conditions\n", + " gwf = gwfsim.get_model(gwf_name)\n", + " plot_grid(gwf)\n", + "\n", + " # write input files\n", + " gwfsim.write_simulation()\n", + " prtsim.write_simulation()\n", + " mp7sim.write_input()\n", + "\n", + " if runModel:\n", + " # run models\n", + " run_models(gwfsim, prtsim, mp7sim)\n", + "\n", + " # extract grid\n", + " grid = gwf.modelgrid\n", + "\n", + " # load mf6 gwf head results\n", + " hf = flopy.utils.HeadFile(gwf_ws / headfile)\n", + " hds = hf.get_data()\n", + "\n", + " # load mf6 prt pathline results\n", + " prt_pl = pd.read_csv(prt_ws / trackcsvfile_prt)\n", + "\n", + " # load mp7 pathline results\n", + " plf = flopy.utils.PathlineFile(mp7_ws / pathlinefile_mp7)\n", + " mp7_pl = pd.DataFrame(\n", + " plf.get_destination_pathline_data(range(grid.nnodes), to_recarray=True)\n", + " )\n", + "\n", + " # plot results\n", + " plot_results(grid, hds, prt_pl, mp7_pl)" + ] + }, + { + "cell_type": "markdown", + "id": "c9ea154d", + "metadata": {}, + "source": [ + "Now run the scenario for example problem 4." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "6d48051c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building GWF model\n", + "WARNING: Unable to resolve dimension of ('gwf6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\n", + "Building PRT model\n", + "WARNING: Unable to resolve dimension of ('prt6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\n", + "Building MP7 model\n", + "writing simulation...\n", + " writing simulation name file...\n", + " writing simulation tdis package...\n", + " writing solution package ims...\n", + " writing model mp7-p04-gwf...\n", + " writing model name file...\n", + " writing package disv...\n", + " writing package ic...\n", + " writing package wel_0...\n", + " writing package npf...\n", + " writing package chd...\n", + "INFORMATION: maxbound in ('gwf6', 'chd', 'dimensions') changed to 5 based on size of stress_period_data\n", + " writing package oc...\n", + "writing simulation...\n", + " writing simulation name file...\n", + " writing simulation tdis package...\n", + " writing solution package ems...\n", + " writing model mp7-p04-prt...\n", + " writing model name file...\n", + " writing package disv...\n", + " writing package mip...\n", + " writing package prp...\n", + " writing package oc...\n", + " writing package fmi...\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/29/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 29 2023 20:39:23 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/05 21:28:24\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/05 21:28:24\n", + " Elapsed run time: 0.089 Seconds\n", + " \n", + " Normal termination of simulation.\n", + " MODFLOW 6\n", + " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", + " VERSION 6.5.0.dev0+prt (preliminary) 08/29/2023\n", + " ***DEVELOP MODE***\n", + "\n", + " MODFLOW 6 compiled Aug 29 2023 20:39:23 with Intel(R) Fortran Intel(R) 64\n", + " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", + " Build 20220726_000000\n", + "\n", + "This software is preliminary or provisional and is subject to \n", + "revision. It is being provided to meet the need for timely best \n", + "science. The software has not received final approval by the U.S. \n", + "Geological Survey (USGS). No warranty, expressed or implied, is made \n", + "by the USGS or the U.S. Government as to the functionality of the \n", + "software and related material nor shall the fact of release \n", + "constitute any such warranty. The software is provided on the \n", + "condition that neither the USGS nor the U.S. Government shall be held \n", + "liable for any damages resulting from the authorized or unauthorized \n", + "use of the software.\n", + "\n", + " \n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2023/09/05 21:28:24\n", + " \n", + " Writing simulation list file: mfsim.lst\n", + " Using Simulation name file: mfsim.nam\n", + " \n", + " Solving: Stress period: 1 Time step: 1\n", + " \n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2023/09/05 21:28:24\n", + " Elapsed run time: 0.087 Seconds\n", + " \n", + " Normal termination of simulation.\n", + "\n", + "MODPATH Version 7.2.001 \n", + "Program compiled Mar 07 2022 14:08:27 with IFORT compiler (ver. 20.21.5) \n", + " \n", + " \n", + "Run particle tracking simulation ...\n", + "Processing Time Step 1 Period 1. Time = 1.00000E+04 Steady-state flow \n", + "\n", + "Particle Summary:\n", + " 0 particles are pending release.\n", + " 0 particles remain active.\n", + " 52 particles terminated at boundary faces.\n", + " 0 particles terminated at weak sink cells.\n", + " 0 particles terminated at weak source cells.\n", + " 0 particles terminated at strong source/sink cells.\n", + " 0 particles terminated in cells with a specified zone number.\n", + " 0 particles were stranded in inactive or dry cells.\n", + " 0 particles were unreleased.\n", + " 0 particles have an unknown status.\n", + " \n", + "Normal termination. \n", + "run_models 273.11 ms\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scenario()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/ex-prt-mp7-p01.py b/scripts/ex-prt-mp7-p01.py new file mode 100644 index 00000000..fff5081d --- /dev/null +++ b/scripts/ex-prt-mp7-p01.py @@ -0,0 +1,961 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.14.6 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# ## Particle tracking through a steady-state flow field on a structured grid +# +# Application of a MODFLOW 6 particle-tracking (PRT) model to solve example 1 from the MODPATH 7 documentation. +# +# This example problem involves a flow system consisting of two aquifers separated by a low conductivity confining layer, modeled on a nearly square structured grid with uniform square cells. There is a single well in the center of the grid, with a river running along the grid's right-hand boundary. +# +# In part A, 21 particles are released at the water table in layer 1, all along the grid's third column, and tracked until discharge locations are reached. Some of the particles discharge to the well, while some discharge to the river. +# +# In part B, 9 particles are released from points evenly distributed over the top faces of all cells in layer 1, then the capture zones of the river and the central well are computed. +# +# ### Problem setup + +# + +import os +import sys +from warnings import warn +import matplotlib as mpl +import matplotlib.pyplot as plt +import flopy +import numpy as np +import pandas as pd +from pathlib import Path + +try: + # append the common/ subdirectory to the system path + # (assumes running one level down from project root) + sys.path.append(os.path.join("..", "common")) + import config + + buildModel = config.buildModel + writeModel = config.writeModel + runModel = config.runModel + plotModel = config.plotModel + plotSave = config.plotSave + figure_ext = config.figure_ext + base_ws = config.base_ws + timeit = config.timeit +except: + warn(f"Failed to import config") + # default settings + buildModel = True + writeModel = True + runModel = True + plotModel = True + plotSave = False + figure_ext = ".png" + base_ws = Path("../examples") + + def timeit(func): + return func + + +# - + +# Define figure properties. + +# + +# figure_size +figure_size = (12.5, 4.5) + +# Pathline and starting point colors by capture destination +colordest = dict.fromkeys(["well", "river"], "blue") +colordest["well"] = "red" +# - + +# Base simulation and model name and workspace + +# + +sim_name = "mp7-p01" +example_name = "ex-prt-" + sim_name +gwf_name = sim_name + "-gwf" +prt_name = sim_name + "-prt" +mp7_name = sim_name + "-mp7" + +sim_ws = base_ws / example_name +mf6_ws = sim_ws / "mf6" +mp7_ws = sim_ws / "mp7" + +mf6_ws.mkdir(exist_ok=True, parents=True) +mp7_ws.mkdir(exist_ok=True, parents=True) + +headfile = f"{gwf_name}.hds" +budgetfile = f"{gwf_name}.cbb" +budgetfile_prt = f"{prt_name}.cbb" +trackfile_prt = f"{prt_name}.trk" +trackhdrfile_prt = f"{prt_name}.trk.hdr" +trackcsvfile_prt = f"{prt_name}.trk.csv" +# - + +# Define model units. + +length_units = "feet" +time_units = "days" + +# Define time discretization parameters. + +nper, nstp, perlen, tsmult = 1, 1, 1.0, 1.0 + +# Define MODFLOW 6 flow model parameters. + +# + +# Grid +nlay, nrow, ncol = 3, 21, 20 +delr = delc = 500.0 +top = 400.0 +botm = [220.0, 200.0, 0.0] + +# Layer types +laytyp = [1, 0, 0] + +# Conductivities +kh = [50.0, 0.01, 200.0] +kv = [10.0, 0.01, 20.0] + +# Well +wel_loc = (2, 10, 9) +wel_q = -150000.0 + +# Recharge +rch = 0.005 +rch_iface = 6 +rch_iflowface = -1 + +# River +riv_h = 320.0 +riv_z = 317.0 +riv_c = 1.0e5 +riv_iface = 6 +riv_iflowface = -1 +# - + +# Define data for the MODFLOW 6 well and river packages. + +# + +# Well package +wd = [(wel_loc, wel_q)] + +# River package +rd = [] +for i in range(nrow): + rd.append([(0, i, ncol - 1), riv_h, riv_c, riv_z, riv_iface, riv_iflowface]) +# - + +# Define shared MODFLOW 6 PRT and MODPATH 7 particle-tracking model parameters. + +# + +# Porosity +porosity = 0.1 + +# Zones +def_zone = 1 +wel_zone = 2 +zones_lay1 = def_zone +zones_lay2 = def_zone +zones_lay3 = np.full((nrow, ncol), def_zone, dtype=np.int32) +zones_lay3[wel_loc[1:]] = wel_zone + +# Starting location template size for example 1B; +# in the original example, there is initially a +# 3x3 array of particles in each cell in layer 1; +# in this example, there is initially one +# particle in each cell in layer 1; the original +# 3x3 particle arrays can be restored simply by +# setting sloc_tmpl_size below to 3 instead of 1. +sloc_tmpl_size = 1 +# - + +# Define MODPATH 7 model parameters. + +# + +# Zones +zones = [zones_lay1, zones_lay2, zones_lay3] + +# Default iface +defaultiface = {"RCH": 6, "EVT": 6} +# - + +# Define particle release point data for the MODFLOW 6 PRT particle-tracking model. + +# + +# Example 1A +releasepts = {} +releasepts["1A"] = [] +zrpt = top +k = 0 +j = 2 +for i in range(nrow): + nrpt = i + xrpt = (j + 0.5) * delr + yrpt = (nrow - i - 0.5) * delc + rpt = [nrpt, k, i, j, xrpt, yrpt, zrpt] + releasepts["1A"].append(rpt) + +# Example 1B +releasepts["1B"] = [] +ndivc = sloc_tmpl_size +ndivr = sloc_tmpl_size +deldivc = delc / ndivc +deldivr = delr / ndivr +k = 0 +zrpt = top +nrpt = -1 +for i in range(nrow): + y0 = (nrow - i - 1) * delc + for j in range(ncol): + x0 = j * delr + for idiv in range(ndivc): + dy = (idiv + 0.5) * deldivc + yrpt = y0 + dy + for jdiv in range(ndivr): + dx = (jdiv + 0.5) * deldivr + xrpt = x0 + dx + nrpt += 1 + rpt = [nrpt, k, i, j, xrpt, yrpt, zrpt] + releasepts["1B"].append(rpt) +# - + +# Define particle data for the MODPATH 7 model + +# + +# Example 1A +plocs = [] +pids = [] +for idx in range(nrow): + plocs.append((0, idx, 2)) + pids.append(idx) +# issue(flopyex): in the flopy example this notebook is based on, +# localz is not set to 1.0 like in the MODPATH examples doc, +# so it defaults to 0.5, but it shouldn't really matter because +# the particle gets placed at the water table anyway +part0 = flopy.modpath.ParticleData( + plocs, drape=0, structured=True, particleids=pids, localz=1.0 +) + +# Example 1B +divs = sloc_tmpl_size +locs1b = [[0, 0, 0, 0, nrow - 1, ncol - 1]] +sd = flopy.modpath.CellDataType( + drape=0, columncelldivisions=1, rowcelldivisions=1, layercelldivisions=1 +) +sd = flopy.modpath.FaceDataType( + drape=0, + verticaldivisions1=0, + horizontaldivisions1=0, + verticaldivisions2=0, + horizontaldivisions2=0, + verticaldivisions3=0, + horizontaldivisions3=0, + verticaldivisions4=0, + horizontaldivisions4=0, + rowdivisions5=0, + columndivisions5=0, + rowdivisions6=divs, + columndivisions6=divs, +) +p = flopy.modpath.LRCParticleData(subdivisiondata=[sd], lrcregions=[locs1b]) +# - + +# Define information used to extract and plot model results. + +# Get well and river cell numbers +nodes = {} +k, i, j = wel_loc +nodes["well"] = ncol * (nrow * k + i) + j +nodes["river"] = [] +for rivspec in rd: + k, i, j = rivspec[0] + node = ncol * (nrow * k + i) + j + nodes["river"].append(node) + + +# Define functions to build, write, run, and plot models. This example employs a shared MODFLOW 6 GWF + PRT simulation, and a separate MODPATH 7 simulation. + + +def build_model(example_name): + print("Building models...{}".format(example_name)) + + # =============================== + # Create the MODFLOW 6 simulation + # =============================== + + # Instantiate the MODFLOW 6 simulation object + sim = flopy.mf6.MFSimulation( + sim_name=gwf_name, exe_name="mf6", version="mf6", sim_ws=mf6_ws + ) + + # Instantiate the MODFLOW 6 temporal discretization package + flopy.mf6.modflow.mftdis.ModflowTdis( + sim, + pname="tdis", + time_units="DAYS", + nper=nper, + perioddata=[(perlen, nstp, tsmult)], + ) + + # ------------------- + # Build the GWF model + # ------------------- + + # Instantiate the MODFLOW 6 gwf (groundwater-flow) model + model_nam_file = "{}.nam".format(gwf_name) + gwf = flopy.mf6.ModflowGwf( + sim, modelname=gwf_name, model_nam_file=model_nam_file, save_flows=True + ) + + # Instantiate the MODFLOW 6 gwf discretization package + flopy.mf6.modflow.mfgwfdis.ModflowGwfdis( + gwf, + pname="dis", + nlay=nlay, + nrow=nrow, + ncol=ncol, + length_units="FEET", + delr=delr, + delc=delc, + top=top, + botm=botm, + ) + + # Instantiate the MODFLOW 6 gwf initial conditions package + flopy.mf6.modflow.mfgwfic.ModflowGwfic(gwf, pname="ic", strt=top) + + # Instantiate the MODFLOW 6 gwf node property flow package + flopy.mf6.modflow.mfgwfnpf.ModflowGwfnpf( + gwf, + pname="npf", + icelltype=laytyp, + k=kh, + k33=kv, + save_saturation=True, + save_specific_discharge=True, + ) + + # Instantiate the MODFLOW 6 gwf recharge package + flopy.mf6.modflow.mfgwfrcha.ModflowGwfrcha( + gwf, + recharge=rch, + auxiliary=["iface", "iflowface"], + aux=[rch_iface, rch_iflowface], + ) + + # Instantiate the MODFLOW 6 gwf well package + flopy.mf6.modflow.mfgwfwel.ModflowGwfwel( + gwf, maxbound=1, stress_period_data={0: wd} + ) + + # Instantiate the MODFLOW 6 gwf river package + flopy.mf6.modflow.mfgwfriv.ModflowGwfriv( + gwf, auxiliary=["iface", "iflowface"], stress_period_data={0: rd} + ) + + # Instantiate the MODFLOW 6 gwf output control package + head_record = [headfile] + budget_record = [budgetfile] + saverecord = [("HEAD", "ALL"), ("BUDGET", "ALL")] + flopy.mf6.modflow.mfgwfoc.ModflowGwfoc( + gwf, + pname="oc", + saverecord=saverecord, + head_filerecord=head_record, + budget_filerecord=budget_record, + ) + + # ------------------- + # Build the PRT model + # ------------------- + + # Instantiate the MODFLOW 6 prt model + prt = flopy.mf6.ModflowPrt( + sim, modelname=prt_name, model_nam_file="{}.nam".format(prt_name) + ) + + # Instantiate the MODFLOW 6 prt discretization package + flopy.mf6.modflow.mfgwfdis.ModflowGwfdis( + prt, + pname="dis", + nlay=nlay, + nrow=nrow, + ncol=ncol, + length_units="FEET", + delr=delr, + delc=delc, + top=top, + botm=botm, + ) + + # Instantiate the MODFLOW 6 prt model input package + flopy.mf6.ModflowPrtmip(prt, pname="mip", porosity=porosity) + + # Instantiate the MODFLOW 6 prt particle release point (prp) package for example 1A + flopy.mf6.ModflowPrtprp( + prt, + pname="prp1a", + filename="{}_1a.prp".format(prt_name), + nreleasepts=len(releasepts["1A"]), + packagedata=releasepts["1A"], + perioddata={ + 0: ["FIRST"], + }, + ) + + # Instantiate the MODFLOW 6 prt particle release point (prp) package for example 1B + flopy.mf6.ModflowPrtprp( + prt, + pname="prp1b", + filename="{}_1b.prp".format(prt_name), + nreleasepts=len(releasepts["1B"]), + packagedata=releasepts["1B"], + perioddata={ + 0: ["FIRST"], + }, + ) + + # Instantiate the MODFLOW 6 prt output control package + budget_record = [budgetfile_prt] + track_record = [trackfile_prt] + trackcsv_record = [trackcsvfile_prt] + flopy.mf6.ModflowPrtoc( + prt, + pname="oc", + budget_filerecord=budget_record, + track_filerecord=track_record, + trackcsv_filerecord=trackcsv_record, + saverecord=[("BUDGET", "ALL")], + ) + + # Instantiate the MODFLOW 6 prt flow model interface + flopy.mf6.ModflowPrtfmi(prt) + + # Create the MODFLOW 6 gwf-prt model exchange + flopy.mf6.ModflowGwfprt( + sim, + exgtype="GWF6-PRT6", + exgmnamea=gwf_name, + exgmnameb=prt_name, + filename="{}.gwfprt".format(gwf_name), + ) + + # -------------------------------------------------- + # Create solutions for the models in the simulation + # -------------------------------------------------- + + # Create an iterative model solution (IMS) for the MODFLOW 6 gwf model + ims = flopy.mf6.ModflowIms( + sim, + pname="ims", + complexity="SIMPLE", + outer_dvclose=1e-6, + inner_dvclose=1e-6, + rcloserecord=1e-6, + ) + + # Create an explicit model solution (EMS) for the MODFLOW 6 prt model + ems = flopy.mf6.ModflowEms( + sim, + pname="ems", + filename="{}.ems".format(prt_name), + ) + sim.register_solution_package(ems, [prt.name]) + + # =============================== + # Create the MODPATH 7 simulation + # =============================== + + # Instantiate the MODPATH 7 simulation object + mp7 = flopy.modpath.Modpath7( + modelname=mp7_name, + flowmodel=gwf, + exe_name="mp7", + model_ws=mp7_ws, + budgetfilename=budgetfile, + headfilename=headfile, + ) + + # Instantiate the MODPATH 7 basic data + flopy.modpath.Modpath7Bas(mp7, porosity=porosity, defaultiface=defaultiface) + + # Instantiate the MODPATH 7 particle groups + pg1a = flopy.modpath.ParticleGroup( + particlegroupname="PG1A", particledata=part0, filename=sim_name + "a.sloc" + ) + pg1b = flopy.modpath.ParticleGroupLRCTemplate( + particlegroupname="PG1B", particledata=p, filename=sim_name + "b.sloc" + ) + + # Instantiate the MODPATH 7 simulation data + mpsim = flopy.modpath.Modpath7Sim( + mp7, + simulationtype="combined", + trackingdirection="forward", + weaksinkoption="pass_through", + weaksourceoption="pass_through", + budgetoutputoption="summary", + referencetime=[0, 0, 0.0], + stoptimeoption="extend", + timepointdata=[500, 1000.0], + zonedataoption="on", + zones=zones, + particlegroups=[pg1a, pg1b], + ) + + # ======================= + # Return the simulations + # ======================= + + return sim, mp7 + + +# Define a function to write model files. + + +def write_model(sim, mp7, silent=True): + sim.write_simulation(silent=silent) + mp7.write_input() + + +# Define a function to run the models. + + +@timeit +def run_model(sim, mp7, silent=True): + # Run MODFLOW 6 simulation + success, buff = sim.run_simulation(silent=silent, report=True) + assert success + for line in buff: + print(line) + + # Run MODPATH 7 simulation + success, buff = mp7.run_model(silent=silent, report=True) + assert success + for line in buff: + print(line) + + +# Define functions to load pathline and endpoint data from MODFLOW 6 PRT's particle track CSV files. Note that unlike MODPATH 7, MODFLOW 6 PRT does not make a distinction between pathline and endpoint output files — all pathline data is saved to track files, and endpoints are computed dynamically. + +# + +from flopy.plot.plotutil import to_mp7_pathlines, to_mp7_endpoints + + +def load_mf6pathlines(p): + # read pathlines from CSV + pls = pd.read_csv(p) + + # convert pathlines to mp7 format and split by particle group + mp7_pls = to_mp7_pathlines(pls) + mp7_pls_a = mp7_pls[mp7_pls["particlegroup"] == 1] + mp7_pls_b = mp7_pls[mp7_pls["particlegroup"] == 2] + + # select endpoints, convert to mp7 format and split by particle group + mp7_eps = to_mp7_endpoints(pls) + mp7_eps_a = mp7_eps[mp7_eps["particlegroup"] == 1] + mp7_eps_b = mp7_eps[mp7_eps["particlegroup"] == 2] + + # determine which particles ended up in which capture area + # (for now, use x coordinate of endpoint, todo: use izone) + wel_pids_a = mp7_eps_a[mp7_eps_a["x"] <= 9000]["particleid"].unique() + riv_pids_a = mp7_eps_a[mp7_eps_a["x"] > 9000]["particleid"].unique() + wel_pids_b = mp7_eps_b[mp7_eps_b["x"] <= 9000]["particleid"].unique() + riv_pids_b = mp7_eps_b[mp7_eps_b["x"] > 9000]["particleid"].unique() + + # return a dict keyed by subproblem (particle group) and capture area + return { + ("1A", "well"): mp7_pls_a[mp7_pls_a["particleid"].isin(wel_pids_a)], + ("1A", "river"): mp7_pls_a[mp7_pls_a["particleid"].isin(riv_pids_a)], + ("1A", "all"): mp7_pls_a, + ("1B", "well"): mp7_pls_b[(mp7_pls_b["particleid"].isin(wel_pids_b))], + ("1B", "river"): mp7_pls_b[(mp7_pls_b["particleid"].isin(riv_pids_b))], + ("1B", "all"): mp7_pls_b, + } + + +def load_mf6endpoints(p): + # read pathlines from CSV + pls = pd.read_csv(p) + + # select endpoints, convert to mp7 format and split by particle group + mp7_eps = to_mp7_endpoints(pls) + mp7_eps_a = mp7_eps[mp7_eps["particlegroup"] == 1] + mp7_eps_b = mp7_eps[mp7_eps["particlegroup"] == 2] + + # determine which particles ended up in which capture area + # (for now, use x coordinate of endpoint, todo: use izone) + wel_pids_a = mp7_eps_a[mp7_eps_a["x"] <= 9000]["particleid"].unique() + riv_pids_a = mp7_eps_a[mp7_eps_a["x"] > 9000]["particleid"].unique() + wel_pids_b = mp7_eps_b[mp7_eps_b["x"] <= 9000]["particleid"].unique() + riv_pids_b = mp7_eps_b[mp7_eps_b["x"] > 9000]["particleid"].unique() + + # return a dict keyed by subproblem (particle group) and capture area + return { + ("1A", "well"): mp7_eps_a[mp7_eps_a["particleid"].isin(wel_pids_a)], + ("1A", "river"): mp7_eps_a[mp7_eps_a["particleid"].isin(riv_pids_a)], + ("1A", "all"): mp7_eps_a, + ("1B", "well"): mp7_eps_b[mp7_eps_b["particleid"].isin(wel_pids_b)], + ("1B", "river"): mp7_eps_b[mp7_eps_b["particleid"].isin(riv_pids_b)], + ("1B", "all"): mp7_eps_b, + } + + +# - + +# Define a utility function for reading MODPATH 7 pathlines from output files. + + +def load_mp7pathlines(plf): + # Extract pathline data to a dictionary keyed on subproblem (1A or 1B) + # and capture destination (well or river) + mp7pathlines = {} + for dest in ["well", "river"]: + pdata = plf.get_destination_pathline_data(nodes[dest], to_recarray=True) + for pgidx, subprob in enumerate(["1A", "1B"]): + mp7pathlines[(subprob, dest)] = np.array( + [point for point in pdata if point["particlegroup"] == pgidx], + dtype=pdata.dtype, + ) + + return mp7pathlines + + +# Define a utility function for reading MODPATH 7 endpoint data from output files. + + +def load_mp7endpointdata(epf): + # Extract endpoint data to a dictionary keyed on subproblem (1A or 1B) + # and capture destination (well or river) + mp7endpointdata = {} + for dest in ["well", "river"]: + epd = epf.get_destination_endpoint_data(dest_cells=nodes[dest]) + for pgidx, subprob in enumerate(["1A", "1B"]): + mp7endpointdata[(subprob, dest)] = np.array( + [point for point in epd if point["particlegroup"] == pgidx], + dtype=epd.dtype, + ) + + # Merge endpoint data for particles that terminate in the well + # and particles that terminate in the river + for subprob in ["1A", "1B"]: + mp7endpointdata[(subprob, "all")] = np.concatenate( + (mp7endpointdata[(subprob, "well")], mp7endpointdata[(subprob, "river")]) + ) + + return mp7endpointdata + + +# Define a utility function for plotting pathlines, optionally colored by capture destination. + + +def plot_pathlines(ax, gwf, pathlines, subprob, plottitle, **kwargs): + ax.set_title(plottitle, fontsize=12) + ax.set_aspect("equal") + mm = flopy.plot.PlotMapView(model=gwf, ax=ax) + mm.plot_grid(lw=0.5) + + for dest in ["well", "river"]: + label = None + if "colordest" in kwargs: + # Use colordest to color pathlines by capture destination + color = kwargs["colordest"][dest] + label = "captured by " + dest + elif "color" in kwargs: + # Use the specified color for all pathlines + color = kwargs["color"] + else: + # Use a default color for all pathlines + color = "blue" + mm.plot_pathline( + pathlines[(subprob, dest)], layer="all", colors=[color], label=label + ) + if label != None: + ax.legend() + + +# Define a utility function for plotting particle starting points, colored by capture destination or travel time to capture. + + +def plot_endpoints(ax, gwf, pointdata, subprob, plottitle, starting, **kwargs): + ax.set_title(plottitle, fontsize=12) + ax.set_aspect("equal") + mm = flopy.plot.PlotMapView(model=gwf, ax=ax) + mm.plot_grid(lw=0.5) + startingpoint_markersize = 5 + + if "colordest" in kwargs: + # Use colordest to color starting points by capture destination + for dest in ["well", "river"]: + color = kwargs["colordest"][dest] + label = "captured by " + dest + if "PRT" in plottitle: + # plot points + ax.scatter( + pointdata[(subprob, dest)]["x0" if starting else "x"], + pointdata[(subprob, dest)]["y0" if starting else "y"], + s=startingpoint_markersize + ** 2, # todo: why does flopy plot_endpoint square s? + color=color, + label=label, + ) + else: + mm.plot_endpoint( + pointdata[(subprob, dest)], + direction="starting" if starting else "ending", + s=startingpoint_markersize, + color=color, + label=label, + ) + ax.legend() + else: + if "PRT" in plottitle: + # plot points + ax.scatter( + pointdata[(subprob, "all")]["x0" if starting else "x"], + pointdata[(subprob, "all")]["y0" if starting else "y"], + s=startingpoint_markersize + ** 2, # todo: why does flopy plot_endpoint square s? + c=pointdata[(subprob, "all")]["time"], + ) + else: + # Color starting points by travel time to capture + mm.plot_endpoint( + pointdata[(subprob, "all")], + direction="starting" if starting else "ending", + s=startingpoint_markersize, + colorbar=True, + shrink=0.25, + ) + + +# Define a wrapper function to plot all results. + + +def plot_results(sim, mp7, idx): + # Load MODFLOW 6 PRT pathlines and endpoints + mf6pathlines = load_mf6pathlines(mf6_ws / trackcsvfile_prt) + mf6endpoints = load_mf6endpoints(mf6_ws / trackcsvfile_prt) + + # Load MODPATH 7 pathline and endpoint data + mp7pathlines = load_mp7pathlines( + flopy.utils.PathlineFile(mp7_ws / f"{mp7_name}.mppth") + ) + mp7endpoints = load_mp7endpointdata( + flopy.utils.EndpointFile(mp7_ws / f"{mp7_name}.mpend") + ) + + # Get gwf model + gwf = sim.get_model(gwf_name) + + # ========== + # Example 1A + # ========== + + # -------------------------------------------------------- + # Pathlines colored by capture destination (well or river) + # -------------------------------------------------------- + + # initialize plot + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size) + plt.suptitle( + t="Example 1A - Pathlines colored by capture destination (well or river)", + fontsize=14, + y=1.05, + ) + axes = axes.flatten() + + # MODFLOW 6 PRT + ax = axes[0] + plot_pathlines(ax, gwf, mf6pathlines, "1A", "MODFLOW 6 PRT", colordest=colordest) + + # MODPATH 7 + ax = axes[1] + plot_pathlines(ax, gwf, mp7pathlines, "1A", "MODPATH 7", colordest=colordest) + # issue: the pathlines in the right-hand plot appear not to go + # go quite as far, despite their being the same as the ones + # on the left; axis scaling issue? + + # Save figure + fpth = os.path.join("..", "figures", "{}-conc{}".format(sim_name, figure_ext)) + if plotSave: + fig.savefig(fpth) + + # ========== + # Example 1B + # ========== + + # -------------------------------------------------------- + # Pathlines colored by capture destination (well or river) + # -------------------------------------------------------- + + # initialize plot + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size) + plt.suptitle( + t="Example 1B - Pathlines colored by capture destination (well or river)", + fontsize=14, + y=1.05, + ) + axes = axes.flatten() + + # MODFLOW 6 PRT + ax = axes[0] + plot_pathlines(ax, gwf, mf6pathlines, "1B", "MODFLOW 6 PRT", colordest=colordest) + + # MODPATH 7 + ax = axes[1] + plot_pathlines(ax, gwf, mp7pathlines, "1B", "MODPATH 7", colordest=colordest) + + # Save figure + fpth = os.path.join("..", "figures", "{}-conc{}".format(sim_name, figure_ext)) + if plotSave: + fig.savefig(fpth) + + # ----------------------------------------------------------------------- + # Particle starting points colored by capture destination (well or river) + # ----------------------------------------------------------------------- + + # initialize plot + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size) + plt.suptitle( + t="Example 1B - Particle starting points colored by capture destination (well or river)", + fontsize=14, + y=1.05, + ) + axes = axes.flatten() + + # MODFLOW 6 PRT + ax = axes[0] + plot_endpoints( + ax, gwf, mf6endpoints, "1B", "MODFLOW 6 PRT", colordest=colordest, starting=True + ) + + # MODPATH 7 + ax = axes[1] + plot_endpoints( + ax, gwf, mp7endpoints, "1B", "MODPATH 7", colordest=colordest, starting=True + ) + + # Save figure + fpth = os.path.join("..", "figures", "{}-conc{}".format(sim_name, figure_ext)) + if plotSave: + fig.savefig(fpth) + + # ---------------------------------------------------------- + # Particle starting points colored by travel time to capture + # ---------------------------------------------------------- + + # initialize plot + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size) + plt.suptitle( + t="Example 1B - Particle starting points colored by travel time to capture", + fontsize=14, + y=1.05, + ) + axes = axes.flatten() + + # MODFLOW 6 PRT + ax = axes[0] + plot_endpoints(ax, gwf, mf6endpoints, "1B", "MODFLOW 6 PRT", starting=True) + + # MODPATH 7 + ax = axes[1] + plot_endpoints(ax, gwf, mp7endpoints, "1B", "MODPATH 7", starting=True) + + # Save figure + fpth = os.path.join("..", "figures", "{}-conc{}".format(sim_name, figure_ext)) + if plotSave: + fig.savefig(fpth) + + # -------------------------------------------------------------------------- + # Particle terminating points colored by capture destination (well or river) + # -------------------------------------------------------------------------- + + # initialize plot + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size) + plt.suptitle( + t="Example 1B - Particle terminating points colored by capture destination (well or river)", + fontsize=14, + y=1.05, + ) + axes = axes.flatten() + + # MODFLOW 6 PRT + ax = axes[0] + plot_endpoints( + ax, + gwf, + mf6endpoints, + "1B", + "MODFLOW 6 PRT", + colordest=colordest, + starting=False, + ) + + # MODPATH 7 + ax = axes[1] + plot_endpoints( + ax, gwf, mp7endpoints, "1B", "MODPATH 7", colordest=colordest, starting=False + ) + + # Save figure + fpth = os.path.join("..", "figures", "{}-conc{}".format(sim_name, figure_ext)) + if plotSave: + fig.savefig(fpth) + + # ------------------------------------------------------------- + # Particle terminating points colored by travel time to capture + # ------------------------------------------------------------- + + # initialize plot + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size) + plt.suptitle( + t="Example 1B - Particle terminating points colored by travel time to capture", + fontsize=14, + y=1.05, + ) + axes = axes.flatten() + + # MODFLOW 6 PRT + ax = axes[0] + plot_endpoints(ax, gwf, mf6endpoints, "1B", "MODFLOW 6 PRT", starting=False) + + # MODPATH 7 + ax = axes[1] + plot_endpoints(ax, gwf, mp7endpoints, "1B", "MODPATH 7", starting=False) + + # Save figure + fpth = os.path.join("..", "figures", "{}-conc{}".format(sim_name, figure_ext)) + if plotSave: + fig.savefig(fpth) + + +# Define a function to wrap all of the steps for each scenario: +# +# 1. build model +# 2. write model +# 3. run model +# 4. plot results + + +def scenario(idx, silent=True): + sim, mp7 = build_model(example_name) + write_model(sim, mp7, silent=silent) + if runModel: + run_model(sim, mp7, silent=silent) + plot_results(sim, mp7, idx) + + +# We are now ready to run the example problem. Subproblems 1A and 1B are solved by a single MODFLOW 6 run and a single MODPATH 7 run, so they are included under one "scenario". Each of the two subproblems is represented by its own particle release package (for MODFLOW 6) or particle group (for MODPATH 7). + +scenario(0, silent=False) diff --git a/scripts/ex-prt-mp7-p02.py b/scripts/ex-prt-mp7-p02.py new file mode 100644 index 00000000..c84f3beb --- /dev/null +++ b/scripts/ex-prt-mp7-p02.py @@ -0,0 +1,1102 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.15.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# ## Backward particle tracking through a steady-state flow field on an unstructured (DISV quadtree) grid +# +# Application of a MODFLOW 6 particle-tracking (PRT) model to solve example 2 from the MODPATH 7 documentation. This example problem demonstrates a steady-state MODFLOW 6 simulation using a quadpatch DISV grid. +# +# In part A, 16 particles are evenly distributed around the 4 side faces of a cell containing a well in a locally-refined region in the center of the grid, then tracked backwards to recharge locations at the water table. +# +# In part B, 100 particles are evenly distributed around the 4 side faces of the well's cell, with 16 additional particles distributed over the cell's top face, then particles are again tracked backwards to recharge locations at the water table. +# +# ### Problem setup +# +# First import dependencies. + +# + +import os +import sys +from warnings import warn +import matplotlib as mpl +import matplotlib.pyplot as plt +from pathlib import Path +from shapely.geometry import MultiPoint, LineString +import flopy +from flopy.utils.gridintersect import GridIntersect +import numpy as np +import pandas as pd + + +try: + # append the common/ subdirectory to the system path + # (assumes running one level down from project root) + sys.path.append(os.path.join("..", "common")) + import config + + buildModel = config.buildModel + writeModel = config.writeModel + runModel = config.runModel + plotModel = config.plotModel + plotSave = config.plotSave + figure_ext = config.figure_ext + base_ws = config.base_ws + timeit = config.timeit +except: + warn(f"Failed to import config") + # default settings + buildModel = True + writeModel = True + runModel = True + plotModel = True + plotSave = False + figure_ext = ".png" + base_ws = Path("../examples") + + def timeit(func): + return func + + +try: + from figspecs import USGSFigure + + styles = True +except: + warn(f"Failed to import figure styles") + styles = False +# - + +# Define simulation and model name and workspace. + +# + +sim_name = "mp7-p02" +example_name = "ex-prt-" + sim_name +gwf_name = sim_name + "-gwf" +prt_name = sim_name + "-prt" +mp7_name = sim_name + "-mp7" + +sim_ws = base_ws / example_name +gwf_ws = sim_ws / "gwf" +prt_ws = sim_ws / "prt" +mp7_ws = sim_ws / "mp7" + +# We only need 1 working directory for the GWF model +# since it can be reused for both subproblems A & B. +# The PRT and MP7 models each need separate working +# directories for each subproblem. +gwf_ws.mkdir(exist_ok=True, parents=True) + +headfile = f"{gwf_name}.hds" +headfile_bkwd = f"{gwf_name}_bkwd.hds" +budgetfile = f"{gwf_name}.cbb" +budgetfile_bkwd = f"{gwf_name}_bkwd.cbb" +trackfile = f"{prt_name}.trk" +trackhdrfile = f"{prt_name}.trk.hdr" +trackcsvfile = f"{prt_name}.trk.csv" + +# - + +# Define model units. + +length_units = "feet" +time_units = "days" + +# Define time discretization parameters. + +tdis_rc = [(1000.0, 1, 1.0)] +nper = len(tdis_rc) + +# Define MODFLOW 6 flow model parameters. + +# + +# Cell types by layer +icelltype = [1, 0, 0] + +# Conductivities +k = [50.0, 0.01, 200.0] +k33 = [10.0, 0.01, 20.0] + +# Well +# issue(flopyex): in the original flopy example, the well +# coordinates were at the edge (not the center) of the cell, +# but it apparently worked out anyway +wel_coords = [(4718.45, 5281.25)] +wel_q = [-150000.0] + +# Recharge +rch = 0.005 +rch_iface = 6 +rch_iflowface = -1 + +# River +riv_h = 320.0 +riv_z = 318.0 +riv_c = 1.0e5 +riv_iface = 6 +riv_iflowface = -1 +# - + +# Initialize globally available lists of well and river cells, which will be filled when the MODFLOW 6 flow model is built. + +welcells = [] +rivcells = [] + +# ### Grid refinement +# +# [GRIDGEN](https://www.usgs.gov/software/gridgen-program-generating-unstructured-finite-volume-grids) can be used to create a quadpatch grid with a central refined region. +# +# The grid will have 3 refinement levels. First, create the top-level (base) grid discretization. + +# + +Lx = 10000.0 +Ly = 10500.0 +nlay = 3 +nrow = 21 +ncol = 20 +delr = Lx / ncol +delc = Ly / nrow +top = 400 +botm = [220, 200, 0] + +ms = flopy.modflow.Modflow() +dis = flopy.modflow.ModflowDis( + ms, + nlay=nlay, + nrow=nrow, + ncol=ncol, + delr=delr, + delc=delc, + top=top, + botm=botm, +) +# - + +# Refine the grid. + +# + +from flopy.utils.gridgen import Gridgen + +# create Gridgen workspace +gridgen_ws = sim_ws / "gridgen" +gridgen_ws.mkdir(parents=True, exist_ok=True) + +# create Gridgen object +g = Gridgen(ms.modelgrid, model_ws=gridgen_ws) + +# add polygon for each refinement level +outer_polygon = [[(3500, 4000), (3500, 6500), (6000, 6500), (6000, 4000), (3500, 4000)]] +g.add_refinement_features([outer_polygon], "polygon", 1, range(nlay)) +refshp0 = gridgen_ws / "rf0" + +middle_polygon = [ + [(4000, 4500), (4000, 6000), (5500, 6000), (5500, 4500), (4000, 4500)] +] +g.add_refinement_features([middle_polygon], "polygon", 2, range(nlay)) +refshp1 = gridgen_ws / "rf1" + +inner_polygon = [[(4500, 5000), (4500, 5500), (5000, 5500), (5000, 5000), (4500, 5000)]] +g.add_refinement_features([inner_polygon], "polygon", 3, range(nlay)) +refshp2 = gridgen_ws / "rf2" +# - + +# Build the grid and plot it with refinement levels superimposed. + +# + +g.build(verbose=False) +grid_props = g.get_gridprops_vertexgrid() +disv_props = g.get_gridprops_disv() +grid = flopy.discretization.VertexGrid(**grid_props) + +fig = plt.figure(figsize=(15, 15)) +ax = fig.add_subplot(1, 1, 1, aspect="equal") +pmv = flopy.plot.PlotMapView(model=ms) +grid.plot(ax=ax) + +flopy.plot.plot_shapefile(refshp0, ax=ax, facecolor="green", alpha=0.3) +flopy.plot.plot_shapefile(refshp1, ax=ax, facecolor="green", alpha=0.5) +flopy.plot.plot_shapefile(str(refshp2), ax=ax, facecolor="green", alpha=0.7) +# - + +# ### Model creation +# +# We are now ready to setup groundwater flow and particle tracking models. +# +# Define shared MODFLOW 6 PRT and MODPATH 7 particle-tracking model parameters. + +# Porosity +porosity = 0.1 + +# Define particle release points for the PRT model. Note that release points for part A and B are not identical, with more particles released in part B. + +# + +releasepts = [] + +# retrieve GRIDGEN-generated gridprops +ncpl = disv_props["ncpl"] +top = disv_props["top"] +botm = disv_props["botm"] +nvert = disv_props["nvert"] +vertices = disv_props["vertices"] +cell2d = disv_props["cell2d"] + + +def set_releasepts(part): + from math import sqrt + + global releasepts + + welcellnode = welcells[0] + xctr = cell2d[welcellnode][1] + yctr = cell2d[welcellnode][2] + vert0 = cell2d[welcellnode][4] + vert1 = cell2d[welcellnode][5] + x0 = vertices[vert0][1] + y0 = vertices[vert0][2] + x1 = vertices[vert1][1] + y1 = vertices[vert1][2] + dx = x1 - x0 + dy = y1 - y0 + delcell = sqrt(dx * dx + dy * dy) + delcellhalf = 0.5 * delcell + + # Reinitialize for the current scenario + releasepts = [] + + if part == "a": + # Example 2A + + zrpt = 0.5 * (botm[1][welcellnode] + botm[2][welcellnode]) + npart_on_side = 4 + delta = delcell / npart_on_side + baseoffset = 0.5 * (npart_on_side + 1) * delta + xbase = xctr - baseoffset + ybase = yctr - baseoffset + nrpt = -1 + for idiv in range(npart_on_side): + i = idiv + 1 + xrpt = xbase + i * delta + yrpt = yctr + delcellhalf + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + yrpt = yctr - delcellhalf + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + yrpt = ybase + i * delta + xrpt = xctr + delcellhalf + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + xrpt = xctr - delcellhalf + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + + else: + # Example 2B + + # 4x4 array of particles on top of well cell + zrpt = botm[1][welcellnode] + npart_on_side = 4 + delta = delcell / npart_on_side + baseoffset = 0.5 * (npart_on_side + 1) * delta + xbase = xctr - baseoffset + ybase = yctr - baseoffset + nrpt = -1 + for idivx in range(npart_on_side): + ix = idivx + 1 + xrpt = xbase + ix * delta + for idivy in range(npart_on_side): + iy = idivy + 1 + yrpt = ybase + iy * delta + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + # 10x10 arrays of particles on the four sides of well cell + zwel = 0.5 * (botm[1][welcellnode] + botm[2][welcellnode]) + npart_on_side = 10 + delcellz = botm[1][welcellnode] - botm[2][welcellnode] + delta = delcell / npart_on_side + deltaz = delcellz / npart_on_side + baseoffset = 0.5 * (npart_on_side + 1) * delta + baseoffsetz = 0.5 * (npart_on_side + 1) * deltaz + xbase = xctr - baseoffset + ybase = yctr - baseoffset + zbase = zwel - baseoffsetz + for idivz in range(npart_on_side): + iz = idivz + 1 + zrpt = zbase + iz * deltaz + for idiv in range(npart_on_side): + i = idiv + 1 + xrpt = xbase + i * delta + yrpt = yctr + delcellhalf + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + yrpt = yctr - delcellhalf + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + yrpt = ybase + i * delta + xrpt = xctr + delcellhalf + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + xrpt = xctr - delcellhalf + nrpt += 1 + rpt = [nrpt, (2, welcellnode), xrpt, yrpt, zrpt] + releasepts.append(rpt) + + +# - + +# Define particle release points for the MODPATH 7 model. + + +def get_particle_data(part): + nodew = ncpl * 2 + welcells[0] + + if part == "a": + # Example 2A + pcoord = np.array( + [ + [0.000, 0.125, 0.500], + [0.000, 0.375, 0.500], + [0.000, 0.625, 0.500], + [0.000, 0.875, 0.500], + [1.000, 0.125, 0.500], + [1.000, 0.375, 0.500], + [1.000, 0.625, 0.500], + [1.000, 0.875, 0.500], + [0.125, 0.000, 0.500], + [0.375, 0.000, 0.500], + [0.625, 0.000, 0.500], + [0.875, 0.000, 0.500], + [0.125, 1.000, 0.500], + [0.375, 1.000, 0.500], + [0.625, 1.000, 0.500], + [0.875, 1.000, 0.500], + ] + ) + plocs = [nodew for i in range(pcoord.shape[0])] + pgdata = flopy.modpath.ParticleData( + plocs, + structured=False, + localx=pcoord[:, 0], + localy=pcoord[:, 1], + localz=pcoord[:, 2], + drape=0, + ) + + else: + # Example 2B + facedata = flopy.modpath.FaceDataType( + drape=0, + verticaldivisions1=10, + horizontaldivisions1=10, + verticaldivisions2=10, + horizontaldivisions2=10, + verticaldivisions3=10, + horizontaldivisions3=10, + verticaldivisions4=10, + horizontaldivisions4=10, + rowdivisions5=0, + columndivisions5=0, + rowdivisions6=4, + columndivisions6=4, + ) + pgdata = flopy.modpath.NodeParticleData(subdivisiondata=facedata, nodes=nodew) + + return pgdata + + +# Define some variables used to plot model results. + +# + +# colormap for boundary locations +cmapbd = mpl.colors.ListedColormap( + [ + "r", + "g", + ] +) + +# time series point colors by layer +colors = ["green", "orange", "red"] + +# figure sizes +figure_size_solo = (8.0, 8.0) +figure_size_compare = (15, 6) + + +# - + +# Define functions to build, write, run, and plot models. +# +# Below we use three distinct simulations: +# +# 1. MODFLOW 6 GWF (groundwater flow) +# 2. MODFLOW 6 PRT (particle-tracking) +# 3. MODPATH 7 particle-tracking + + +def build_mf6gwf(): + global welcells, rivcells + + print("Building GWF model") + + # ==================================== + # Create the MODFLOW 6 flow simulation + # ==================================== + + # Instantiate the MODFLOW 6 simulation object + sim = flopy.mf6.MFSimulation( + sim_name=gwf_name, exe_name="mf6", version="mf6", sim_ws=gwf_ws + ) + + # Instantiate the MODFLOW 6 temporal discretization package + flopy.mf6.ModflowTdis( + sim, pname="tdis", time_units="DAYS", perioddata=tdis_rc, nper=len(tdis_rc) + ) + + # ------------------- + # Build the GWF model + # ------------------- + + # Instantiate the MODFLOW 6 gwf (groundwater-flow) model + gwf = flopy.mf6.ModflowGwf( + sim, modelname=gwf_name, model_nam_file="{}.nam".format(gwf_name) + ) + gwf.name_file.save_flows = True + + # Instantiate the MODFLOW 6 gwf discretization package + flopy.mf6.ModflowGwfdisv( + gwf, + length_units=length_units, + **disv_props, + ) + + # GridIntersect object for setting up boundary conditions + ix = GridIntersect(gwf.modelgrid, method="vertex", rtree=True) + + # Instantiate the MODFLOW 6 gwf initial conditions package + flopy.mf6.ModflowGwfic(gwf, pname="ic", strt=riv_h) + + # Instantiate the MODFLOW 6 gwf node property flow package + flopy.mf6.ModflowGwfnpf( + gwf, + xt3doptions=[("xt3d")], + icelltype=icelltype, + k=k, + k33=k33, + save_saturation=True, + save_specific_discharge=True, + ) + + # Instantiate the MODFLOW 6 gwf recharge package + flopy.mf6.ModflowGwfrcha( + gwf, + recharge=rch, + auxiliary=["iface", "iflowface"], + aux=[rch_iface, rch_iflowface], + ) + + # Instantiate the MODFLOW 6 gwf well package + welcells = ix.intersects(MultiPoint(wel_coords)) + welcells = [icpl for (icpl,) in welcells] + welspd = [[(2, icpl), wel_q[idx]] for idx, icpl in enumerate(welcells)] + flopy.mf6.ModflowGwfwel(gwf, print_input=True, stress_period_data=welspd) + + # Instantiate the MODFLOW 6 gwf river package + riverline = [(Lx - 1.0, Ly), (Lx - 1.0, 0.0)] + rivcells = ix.intersects(LineString(riverline)) + rivcells = [icpl for (icpl,) in rivcells] + rivspd = [ + [(0, icpl), riv_h, riv_c, riv_z, riv_iface, riv_iflowface] for icpl in rivcells + ] + flopy.mf6.ModflowGwfriv( + gwf, stress_period_data=rivspd, auxiliary=[("iface", "iflowface")] + ) + + # Instantiate the MODFLOW 6 gwf output control package + headfile = "{}.hds".format(gwf_name) + head_record = [headfile] + budgetfile = "{}.cbb".format(gwf_name) + budget_record = [budgetfile] + flopy.mf6.ModflowGwfoc( + gwf, + pname="oc", + budget_filerecord=budget_record, + head_filerecord=head_record, + headprintrecord=[("COLUMNS", 10, "WIDTH", 15, "DIGITS", 6, "GENERAL")], + saverecord=[("HEAD", "ALL"), ("BUDGET", "ALL")], + printrecord=[("HEAD", "ALL"), ("BUDGET", "ALL")], + ) + + # Create an iterative model solution (IMS) for the MODFLOW 6 gwf model + ims = flopy.mf6.ModflowIms( + sim, + pname="ims", + print_option="SUMMARY", + complexity="SIMPLE", + outer_dvclose=1.0e-5, + outer_maximum=100, + under_relaxation="NONE", + inner_maximum=100, + inner_dvclose=1.0e-6, + rcloserecord=0.1, + linear_acceleration="BICGSTAB", + scaling_method="NONE", + reordering_method="NONE", + relaxation_factor=0.99, + ) + sim.register_ims_package(ims, [gwf.name]) + + return sim + + +def build_mf6prt(part): + print(f"Building PRT model for {example_name}{part}") + + prpname = f"prp2{part}" + prpfilename = f"{prt_name}_2{part}.prp" + + prt_ws_scen = prt_ws.parent / (prt_ws.name + part) + prt_ws_scen.mkdir(exist_ok=True) + + # Instantiate the MODFLOW 6 simulation object + simprt = flopy.mf6.MFSimulation( + sim_name=prt_name, version="mf6", exe_name="mf6", sim_ws=prt_ws_scen + ) + + # Create a time discretization for backward tracking; + # since there is only a single period with a single time step, + # the time discretization is the same backward as forward + tdis_bkwd = tdis_rc + + # Instantiate the MODFLOW 6 temporal discretization package + flopy.mf6.ModflowTdis( + simprt, + pname="tdis", + time_units="DAYS", + perioddata=tdis_bkwd, + nper=len(tdis_bkwd), + ) + + # ------------------- + # Build the PRT model + # ------------------- + + # Instantiate the MODFLOW 6 prt model + prt = flopy.mf6.ModflowPrt( + simprt, modelname=prt_name, model_nam_file="{}.nam".format(prt_name) + ) + + # Instantiate the MODFLOW 6 prt discretization package + flopy.mf6.ModflowGwfdisv( + prt, + length_units=length_units, + **disv_props, + ) + + # Instantiate the MODFLOW 6 prt model input package + flopy.mf6.ModflowPrtmip(prt, pname="mip", porosity=porosity) + + # Set particle release point data according to the scenario + set_releasepts(part) + nreleasepts = len(releasepts) + particle_data = get_particle_data(part) + prp_pkg_data = list(particle_data.to_prp(grid)) + + # Instantiate the MODFLOW 6 prt particle release point (prp) package + pd = {0: ["FIRST"], 1: []} + flopy.mf6.ModflowPrtprp( + prt, + pname=prpname, + filename=prpfilename, + nreleasepts=len(prp_pkg_data), + packagedata=prp_pkg_data, + perioddata=pd, + ) + + # Instantiate the MODFLOW 6 prt output control package + budgetfile = "{}.bud".format(prt_name) + trackfile = "{}.trk".format(prt_name) + trackcsvfile = "{}.trk.csv".format(prt_name) + budget_record = [budgetfile] + track_record = [trackfile] + trackcsv_record = [trackcsvfile] + flopy.mf6.ModflowPrtoc( + prt, + pname="oc", + budget_filerecord=budget_record, + track_filerecord=track_record, + trackcsv_filerecord=trackcsv_record, + saverecord=[("BUDGET", "ALL")], + ) + + # Instantiate the MODFLOW 6 prt flow model interface + # using "time-reversed" budget and head files + pd = [ + ("GWFHEAD", headfile_bkwd), + ("GWFBUDGET", budgetfile_bkwd), + ] + flopy.mf6.ModflowPrtfmi(prt, packagedata=pd) + + # Create an explicit model solution (EMS) for the MODFLOW 6 prt model + ems = flopy.mf6.ModflowEms( + simprt, + pname="ems", + filename="{}.ems".format(prt_name), + ) + simprt.register_solution_package(ems, [prt.name]) + + return simprt + + +def build_mp7(part, gwf): + print(f"Building mp7 model for {example_name}{part}") + + # Set parameters according to the scenario + pgdata = get_particle_data(part) + nm_mp7 = f"{mp7_name}{part}" + pgname = f"BACKWARD2{part.upper()}" + fpth = nm_mp7 + ".sloc" + + if part == "a": + simtype = "combined" + timepointdata = [500, 1000.0] + pg = flopy.modpath.ParticleGroup( + particlegroupname=pgname, particledata=pgdata, filename=fpth + ) + else: + pg = flopy.modpath.ParticleGroupNodeTemplate( + particlegroupname=pgname, particledata=pgdata, filename=fpth + ) + simtype = "combined" + timepointdata = None + + # Instantiate the MODPATH 7 simulation object + bfile = gwf_ws / budgetfile + hfile = gwf_ws / headfile + + # create workspace for this scenario (part A or B) + mp7_ws_scen = mp7_ws.parent / (mp7_ws.name + part) + mp7_ws_scen.mkdir(exist_ok=True) + + mp7 = flopy.modpath.Modpath7( + modelname=nm_mp7, + flowmodel=gwf, + exe_name="mp7", + model_ws=mp7_ws_scen, + budgetfilename=budgetfile, + headfilename=headfile, + ) + + # Instantiate the MODPATH 7 basic data + flopy.modpath.Modpath7Bas(mp7, porosity=porosity) + + # Instantiate the MODPATH 7 simulation data + flopy.modpath.Modpath7Sim( + mp7, + simulationtype=simtype, + trackingdirection="backward", + weaksinkoption="pass_through", + weaksourceoption="pass_through", + referencetime=0.0, + stoptimeoption="extend", + timepointdata=timepointdata, + particlegroups=pg, + ) + + # Return the simulation + return mp7 + + +def build_models(part): + sim = build_mf6gwf() + gwf = sim.get_model(gwf_name) + simprt = build_mf6prt(part) + mp7 = build_mp7(part, gwf) + return sim, simprt, mp7 + + +# Function to write simulation/model input files. + + +def write_models(sim, simprt, mp7, silent=True): + sim.write_simulation(silent=silent) + simprt.write_simulation(silent=silent) + mp7.write_input() + + +# Because this problem tracks particles backwards, we need to reverse the head and budget files after running the groundwater flow model and before running the particle tracking model. Define functions to do this. + +# + +import flopy.utils.binaryfile as bf + + +def reverse_budgetfile(fpth, rev_fpth, tdis): + f = bf.CellBudgetFile(fpth, tdis=tdis) + f.reverse(rev_fpth) + + +def reverse_headfile(fpth, rev_fpth, tdis): + f = bf.HeadFile(fpth, tdis=tdis) + f.reverse(rev_fpth) + + +# - + +# Define a function to run all three simulations and their respective models. + + +@timeit +def run_models(part, sim, simprt, mp7, silent=True): + # Run MODFLOW 6 flow simulation + success, buff = sim.run_simulation(silent=silent, report=True) + for line in buff: + print(line) + assert success + + # Process budget and head files for backward tracking + prt_ws_scen = prt_ws.parent / (prt_ws.name + part) + reverse_budgetfile(gwf_ws / budgetfile, prt_ws_scen / budgetfile_bkwd, sim.tdis) + reverse_headfile(gwf_ws / headfile, prt_ws_scen / headfile_bkwd, sim.tdis) + + # Run MODFLOW 6 PRT particle-tracking simulation + success, buff = simprt.run_simulation(silent=silent, report=True) + for line in buff: + print(line) + assert success + + # Run MODPATH 7 simulation + success, buff = mp7.run_model(silent=silent, report=True) + for line in buff: + print(line) + assert success + + +# Define functions to load pathline and endpoint data from MODFLOW 6 PRT's particle track CSV files. Note that unlike MODPATH 7, MODFLOW 6 PRT does not make a distinction between pathline and endpoint output files — all pathline data is saved to track files, and endpoints are computed dynamically. + +# + +from flopy.plot.plotutil import to_mp7_pathlines, to_mp7_endpoints + + +def load_mf6pathlines(p): + pls = pd.read_csv(p) + pls = to_mp7_pathlines(pls) + return pls + + +def load_mf6endpoints(p): + pls = pd.read_csv(p) + eps = pls.sort_values("t").groupby(["imdl", "iprp", "irpt", "trelease"]).tail(1) + return eps + + +# - + +# Define a function for plotting flow model grid, boundary conditions, and head results. + + +# + +from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes +from mpl_toolkits.axes_grid1.inset_locator import mark_inset + + +def plot_nodes_and_vertices(gwf, mg, ibd, ax): + """ + Plot cell nodes and vertices (and IDs) on a zoomed inset + """ + + ax.set_aspect("equal") + + # set zoom area + xmin, xmax = 2050, 4800 + ymin, ymax = 5200, 7550 + ax.set_xlim([xmin, xmax]) + ax.set_ylim([ymin, ymax]) + + # create map view plot + pmv = flopy.plot.PlotMapView(gwf, ax=ax) + v = pmv.plot_grid(lw=0.5, edgecolor="black") + t = ax.set_title("Node and vertex indices (one-based)\n", fontsize=14) + ax.set_xlim([xmin, xmax]) + ax.set_ylim([ymin, ymax]) + + # plot vertices + verts = mg.verts + ax.plot(verts[:, 0], verts[:, 1], "bo") + for i in range(ncpl): + x, y = verts[i, 0], verts[i, 1] + if xmin <= x <= xmax and ymin <= y <= ymax: + ax.annotate(str(i + 1), verts[i, :], color="b") + + # plot nodes + xc, yc = mg.get_xcellcenters_for_layer(0), mg.get_ycellcenters_for_layer(0) + for i in range(ncpl): + x, y = xc[i], yc[i] + ax.plot(x, y, "o", color="grey") + if xmin <= x <= xmax and ymin <= y <= ymax: + ax.annotate(str(i + 1), (x, y), color="grey") + + # plot well + ax.plot(wel_coords[0][0], wel_coords[0][1], "ro") + + # create legend + ax.legend( + handles=[ + mpl.patches.Patch(color="blue", label="vertex"), + mpl.patches.Patch(color="grey", label="node"), + ], + loc="upper left", + ) + + +def plot_bc(sim, mg, ibd): + if styles: + fs = USGSFigure(figure_type="map", verbose=False) + fig = plt.figure(figsize=figure_size_solo) + fig.tight_layout() + ax = fig.add_subplot(1, 1, 1, aspect="equal") + gwf = sim.get_model(gwf_name) + pmv = flopy.plot.PlotMapView(gwf, ax=ax) + pmv.plot_bc("WEL", plotAll=True) + pmv.plot_grid(lw=0.5) + ax.set_xlim(0, Lx) + ax.set_ylim(0, Ly) + pc = pmv.plot_array(ibd, cmap=cmapbd, edgecolor="gray") + t = ax.set_title("Boundary conditions\n", fontsize=14) + + # create inset + axins = ax.inset_axes([-0.76, 0.25, 0.7, 0.9]) + plot_nodes_and_vertices(gwf, mg, ibd, axins) + ax.indicate_inset_zoom(axins) + + # create legend + ax.legend( + handles=[ + mpl.patches.Patch(color="red", label="Well"), + mpl.patches.Patch(color="green", label="River"), + ], + loc="upper left", + ) + + # set title + plt.suptitle(t="Example 2: model grid", fontsize=14, ha="center") + + plt.show() + + +def plot_head(mg): + # Import simulated head values from the binary head file + fname = gwf_ws / (gwf_name + ".hds") + hdobj = flopy.utils.HeadFile(fname) + head = hdobj.get_data() + + # Prepare a plot of heads in layer 3 + ilay = 2 + cint = 0.25 + if styles: + fs = USGSFigure(figure_type="map", verbose=False) + fig2 = plt.figure(figsize=figure_size_solo) + fig2.tight_layout() + ax = fig2.add_subplot(1, 1, 1, aspect="equal") + mm = flopy.plot.PlotMapView(modelgrid=mg, ax=ax, layer=ilay) + mm.plot_grid(lw=0.5) + ax.set_xlim(0, Lx) + ax.set_ylim(0, Ly) + pc = mm.plot_array(head[:, 0, :], cmap="jet", edgecolor="black") + hmin = head[ilay, 0, :].min() + hmax = head[ilay, 0, :].max() + levels = np.arange(np.floor(hmin), np.ceil(hmax) + cint, cint) + cs = mm.contour_array(head[:, 0, :], colors="white", levels=levels) + plt.clabel(cs, fmt="%.1f", colors="white", fontsize=11) + cb = plt.colorbar(pc, shrink=0.5) + t = ax.set_title( + "Example 2: Head in layer {}, hmin {:6.2f}, hmax {:6.2f}\n".format( + ilay + 1, hmin, hmax + ), + fontsize=14, + ) + plt.show() + + +def plot_gwf(sim, mg, ibd): + plot_bc(sim, mg, ibd) + plot_head(mg) + + +# - + + +# Define a function for plotting pathlines and time series points colored by layer. + + +def plot_pathlines_and_timeseries(ax, mg, ibd, pathlines, timeseries, plottitle): + ax.set_aspect("equal") + mm = flopy.plot.PlotMapView(modelgrid=mg, ax=ax) + mm.plot_grid(lw=0.5) + v = mm.plot_array(ibd, cmap=cmapbd, edgecolor="gray") + mm.plot_pathline(pathlines, layer="all", colors=["blue"], lw=0.75) + # issue(mf6): no way to output time series in prt + if timeseries != None: + for k in range(nlay - 1, -1, -1): + mm.plot_timeseries(timeseries, layer=k, marker="o", lw=0, color=colors[k]) + ax.set_title(plottitle, fontsize=12) + + +# Define a utility function for plotting particle endpoints, colored by travel time to capture. + + +def plot_endpoints(ax, mg, ibd, endpointdata, plottitle): + ax.set_xlim(0, Lx) + ax.set_ylim(0, Ly) + mm = flopy.plot.PlotMapView(modelgrid=mg, ax=ax) + mm.plot_grid(lw=0.5) + v = mm.plot_array(ibd, cmap=cmapbd, edgecolor="gray") + mm.plot_endpoint(endpointdata, direction="ending", colorbar=True, shrink=0.25) + ax.set_title(plottitle, fontsize=12) + + +def plot_results(part, sim, simprt, mp7): + # Get model grid + fname = gwf_ws / (gwf_name + ".disv.grb") + grd = flopy.mf6.utils.MfGrdFile(fname, verbose=False) + mg = grd.modelgrid + + # Workspaces + prt_ws_scen = prt_ws.parent / (prt_ws.name + part) + mp7_ws_scen = mp7_ws.parent / (mp7_ws.name + part) + + # identify the boundary locations + ibd = np.zeros((ncpl), dtype=int) + ibd[np.array(welcells)] = 1 + ibd[np.array(rivcells)] = 2 + ibd = np.ma.masked_equal(ibd, 0) + + if part == "a": + # ========================= + # MODFLOW 6 flow simulation + # ========================= + + plot_gwf(sim, mg, ibd) + + # ========== + # Example 2A + # ========== + + # Load MODFLOW 6 PRT pathlines + mf6pathlines = load_mf6pathlines(prt_ws_scen / trackcsvfile) + + # Load MODPATH 7 pathlines + plf = flopy.utils.PathlineFile(mp7_ws_scen / (mp7_name + f"{part}.mppth")) + mp7pathlines = plf.get_alldata() + + # ------------------------------------------------ + # Pathlines and time series point colored by layer + # ------------------------------------------------ + + # Initialize plot + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size_compare) + plt.suptitle( + t="Example 2A: Pathlines and time series points tracked backward from well", + fontsize=14, + ) + axes = axes.flatten() + + # MODFLOW 6 PRT + ax = axes[0] + # issue(mf6): no way to output time series + plot_pathlines_and_timeseries(ax, mg, ibd, mf6pathlines, None, "MODFLOW 6 PRT") + + # MODPATH 7 + ax = axes[1] + plot_pathlines_and_timeseries( + ax, mg, ibd, mp7pathlines, mp7pathlines, "MODPATH 7" + ) + + # Save figure + if plotSave: + fpth = os.path.join( + "..", "figures", "{}-paths{}".format(sim_name, figure_ext) + ) + fig.savefig(fpth) + + else: + # ========== + # Example 2B + # ========== + + # Load MODFLOW 6 PRT endpoint data + mf6endpoints = load_mf6endpoints(prt_ws_scen / trackcsvfile) + + # Load MODPATH 7 endpoint data + epf = flopy.utils.EndpointFile(mp7_ws_scen / (mp7_name + f"{part}.mpend")) + mp7endpoints = epf.get_alldata() + + # ------------------------------------------- + # Endpoints colored by travel time to capture + # ------------------------------------------- + + # Initialize plot + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=figure_size_compare) + plt.suptitle( + t="Example 2B: Endpoints of particle paths tracked backward from well", + fontsize=14, + ) + axes = axes.flatten() + + # MODFLOW 6 PRT + ax = axes[0] + plot_endpoints(ax, mg, ibd, mf6endpoints, "MODFLOW 6 PRT") + + # MODPATH 7 + ax = axes[1] + plot_endpoints(ax, mg, ibd, mp7endpoints, "MODPATH 7") + + # issue(flopy): the center "dot" looks to be in a slightly different location + # in the two plots -- true even if you plot the very same endpoint array in both plots + + # Save figure + if plotSave: + fpth = os.path.join( + "..", "figures", "{}-endpts{}".format(sim_name, figure_ext) + ) + fig.savefig(fpth) + + +# Define a function to wrap all of the steps for each scenario. +# +# 1. build model +# 2. write model +# 3. run model +# 4. plot results + + +def scenario(part, silent=True): + sim, simprt, mp7 = build_models(part) + write_models(sim, simprt, mp7, silent=silent) + if runModel: + run_models(part, sim, simprt, mp7, silent=silent) + plot_results(part, sim, simprt, mp7) + + +# Run the scenario for problem 2A. + +scenario("a", silent=False) + +# Scenario for example 2B +scenario("b") diff --git a/scripts/ex-prt-mp7-p04.py b/scripts/ex-prt-mp7-p04.py new file mode 100644 index 00000000..cb01217e --- /dev/null +++ b/scripts/ex-prt-mp7-p04.py @@ -0,0 +1,958 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.14.6 +# kernelspec: +# display_name: venv +# language: python +# name: python3 +# --- + +# ## Particle tracking through a steady-state system with lateral flow boundaries and a refined vertex grid. +# +# Application of a MODFLOW 6 particle-tracking (PRT) model to solve example 4 from the MODPATH 7 documentation. +# +# This notebook demonstrates a steady-state MODFLOW 6 simulation using a quadpatch DISV grid with an irregular domain and a large number of inactive cells. Particles are tracked backwards from terminating locations, including a pair of wells in a locally-refined region of the grid and constant-head cells along the grid's right side, to release locations along the left border of the grid's active region. Injection wells along the left-hand border are used to generate boundary flows. +# +# ### Problem setup +# +# First import dependencies. + +# + +import os +import sys +from warnings import warn +import matplotlib as mpl +import matplotlib.pyplot as plt +from shapely.geometry import MultiPoint, LineString +import flopy +from flopy.utils.gridintersect import GridIntersect +import numpy as np +import pandas as pd +from pathlib import Path + +try: + # append the common/ subdirectory to the system path + # (assumes running one level down from project root) + sys.path.append(os.path.join("..", "common")) + import config + + buildModel = config.buildModel + writeModel = config.writeModel + runModel = config.runModel + plotModel = config.plotModel + plotSave = config.plotSave + base_ws = config.base_ws + timeit = config.timeit +except: + warn(f"Failed to import config") + # default settings + buildModel = True + writeModel = True + runModel = True + plotModel = True + plotSave = False + base_ws = Path("../examples") + + def timeit(func): + return func + + +# - + +# Define workspace paths and model/file-names. + +# + +sim_name = "mp7-p04" +example_name = "ex-prt-" + sim_name +gwf_name = sim_name + "-gwf" +prt_name = sim_name + "-prt" +mp7_name = sim_name + "-mp7" + +sim_ws = base_ws / example_name +gwf_ws = sim_ws / "gwf" +prt_ws = sim_ws / "prt" +mp7_ws = sim_ws / "mp7" + +gwf_ws.mkdir(exist_ok=True, parents=True) +prt_ws.mkdir(exist_ok=True, parents=True) +mp7_ws.mkdir(exist_ok=True, parents=True) + +headfile = f"{gwf_name}.hds" +headfile_bkwd = f"{gwf_name}_bkwd.hds" +budgetfile = f"{gwf_name}.cbb" +budgetfile_bkwd = f"{gwf_name}_bkwd.bud" +trackfile_prt = f"{prt_name}.trk" +trackcsvfile_prt = f"{prt_name}.trk.csv" +budgetfile_prt = f"{prt_name}.cbb" +pathlinefile_mp7 = f"{mp7_name}.mppth" +# - + +# ### Grid refinement +# +# [GRIDGEN](https://www.usgs.gov/software/gridgen-program-generating-unstructured-finite-volume-grids) can be used to create a quadpatch grid with a refined region in the upper left quadrant. +# +# We will create a grid with 3 refinement levels, all nearly but not perfectly rectangular: a 500x500 area is carved out of the corners of the rectangle for each level. To form each region's polygon we combine 5 rectangles. +# +# Create the top-level grid discretization. + +nlay, nrow, ncol = 1, 21, 26 +delr = delc = 500.0 +top = 100.0 +botm = np.zeros((nlay, nrow, ncol), dtype=np.float32) +ms = flopy.modflow.Modflow() +dis = flopy.modflow.ModflowDis( + ms, + nlay=nlay, + nrow=nrow, + ncol=ncol, + delr=delr, + delc=delc, + top=top, + botm=botm, +) + +# Refine the grid. Create a Gridgen object from the base grid, then add refinement features. + +# + +from flopy.utils.gridgen import Gridgen + +# create Gridgen workspace +gridgen_ws = sim_ws / "gridgen" +gridgen_ws.mkdir(parents=True, exist_ok=True) + +# create Gridgen object +g = Gridgen(ms.modelgrid, model_ws=gridgen_ws, exe_name="gridgen") + +# add polygon for each refinement level +outer_polygon = [ + [ + (2500, 6000), + (2500, 9500), + (3000, 9500), + (3000, 10000), + (6000, 10000), + (6000, 9500), + (6500, 9500), + (6500, 6000), + (6000, 6000), + (6000, 5500), + (3000, 5500), + (3000, 6000), + (2500, 6000), + ] +] +g.add_refinement_features([outer_polygon], "polygon", 1, range(nlay)) +refshp0 = gridgen_ws / "rf0" + +middle_polygon = [ + [ + (3000, 6500), + (3000, 9000), + (3500, 9000), + (3500, 9500), + (5500, 9500), + (5500, 9000), + (6000, 9000), + (6000, 6500), + (5500, 6500), + (5500, 6000), + (3500, 6000), + (3500, 6500), + (3000, 6500), + ] +] +g.add_refinement_features([middle_polygon], "polygon", 2, range(nlay)) +refshp1 = gridgen_ws / "rf1" + +inner_polygon = [ + [ + (3500, 7000), + (3500, 8500), + (4000, 8500), + (4000, 9000), + (5000, 9000), + (5000, 8500), + (5500, 8500), + (5500, 7000), + (5000, 7000), + (5000, 6500), + (4000, 6500), + (4000, 7000), + (3500, 7000), + ] +] +g.add_refinement_features([inner_polygon], "polygon", 3, range(nlay)) +refshp2 = gridgen_ws / "rf2" +# - + +# Build the grid and plot it with refinement levels superimposed. + +# + +g.build(verbose=False) +grid = flopy.discretization.VertexGrid(**g.get_gridprops_vertexgrid()) + +fig = plt.figure(figsize=(15, 15)) +ax = fig.add_subplot(1, 1, 1, aspect="equal") +mm = flopy.plot.PlotMapView(model=ms) +grid.plot(ax=ax) + +flopy.plot.plot_shapefile(refshp0, ax=ax, facecolor="green", alpha=0.3) +flopy.plot.plot_shapefile(refshp1, ax=ax, facecolor="green", alpha=0.5) +flopy.plot.plot_shapefile(str(refshp2), ax=ax, facecolor="green", alpha=0.7) +# - + +# We are now ready to set up groundwater flow and particle tracking models. +# +# Define shared variables, including discretization parameters, idomain, and porosity. + +# + +porosity = 0.1 +nper = 1 +tdis_rc = [(10000, 1, 1.0)] +# fmt: off +idomain = [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, + 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0 +] +# fmt: on +disv_props = g.get_gridprops_disv() + +# from pprint import pprint +# pprint(idomain, compact=True) +# - + +# Define well locations and flows. + +# + +wells = [ + # negative q: discharge + (0, 861, -30000.0, 0, -1), + (0, 891, -30000.0, 0, -1), + # positive q: injection + (0, 1959, 10000.0, 1, 4), + (0, 1932, 10000.0, 3, 3), + (0, 1931, 10000.0, 3, 3), + (0, 1930, 5000.0, 1, 4), + (0, 1930, 5000.0, 3, 3), + (0, 1903, 5000.0, 1, 4), + (0, 1903, 5000.0, 3, 3), + (0, 1876, 10000.0, 3, 3), + (0, 1875, 10000.0, 3, 3), + (0, 1874, 5000.0, 1, 4), + (0, 1874, 5000.0, 3, 3), + (0, 1847, 10000.0, 3, 3), + (0, 1846, 10000.0, 3, 3), + (0, 1845, 5000.0, 1, 4), + (0, 1845, 5000.0, 3, 3), + (0, 1818, 5000.0, 1, 4), + (0, 1818, 5000.0, 3, 3), + (0, 1792, 10000.0, 1, 4), + (0, 1766, 10000.0, 1, 4), + (0, 1740, 5000.0, 1, 4), + (0, 1740, 5000.0, 4, 1), + (0, 1715, 5000.0, 1, 4), + (0, 1715, 5000.0, 4, 1), + (0, 1690, 10000.0, 1, 4), + (0, 1646, 5000.0, 1, 4), + (0, 1646, 5000.0, 4, 1), + (0, 1549, 5000.0, 1, 4), + (0, 1549, 5000.0, 4, 1), + (0, 1332, 5000.0, 4, 1), + (0, 1332, 5000.0, 1, 5), + (0, 1021, 2500.0, 1, 4), + (0, 1021, 2500.0, 4, 1), + (0, 1020, 5000.0, 1, 5), + (0, 708, 2500.0, 1, 5), + (0, 708, 2500.0, 4, 1), + (0, 711, 625.0, 1, 4), + (0, 711, 625.0, 4, 1), + (0, 710, 625.0, 1, 4), + (0, 710, 625.0, 4, 1), + (0, 409, 1250.0, 1, 4), + (0, 407, 625.0, 1, 4), + (0, 407, 625.0, 4, 1), + (0, 402, 625.0, 1, 5), + (0, 402, 625.0, 4, 1), + (0, 413, 1250.0, 1, 4), + (0, 411, 1250.0, 1, 4), + (0, 203, 1250.0, 1, 5), + (0, 203, 1250.0, 4, 1), + (0, 202, 1250.0, 1, 4), + (0, 202, 1250.0, 4, 1), + (0, 199, 2500.0, 1, 4), + (0, 197, 1250.0, 1, 4), + (0, 197, 1250.0, 4, 1), + (0, 96, 2500.0, 1, 4), + (0, 96, 2500.0, 4, 1), + (0, 98, 1250.0, 1, 4), + (0, 101, 1250.0, 1, 4), + (0, 101, 1250.0, 4, 1), + (0, 100, 1250.0, 1, 4), + (0, 100, 1250.0, 4, 1), + (0, 43, 2500.0, 1, 5), + (0, 43, 2500.0, 4, 1), + (0, 44, 2500.0, 1, 4), + (0, 44, 2500.0, 4, 1), + (0, 45, 5000.0, 4, 1), + (0, 10, 10000.0, 1, 5), +] + +assert len(wells) == 68 + + +# - + +# Define a function to build the groundwater flow model. + + +def build_gwf(): + print("Building GWF model") + + # simulation + sim = flopy.mf6.MFSimulation( + sim_name=sim_name, sim_ws=gwf_ws, exe_name="mf6", version="mf6" + ) + + # temporal discretization + tdis = flopy.mf6.ModflowTdis(sim, time_units="days", nper=nper, perioddata=tdis_rc) + + # iterative model solver + ims = flopy.mf6.ModflowIms( + sim, + pname="ims", + complexity="SIMPLE", + outer_dvclose=1e-4, + outer_maximum=100, + inner_dvclose=1e-5, + under_relaxation_theta=0, + under_relaxation_kappa=0, + under_relaxation_gamma=0, + under_relaxation_momentum=0, + linear_acceleration="BICGSTAB", + relaxation_factor=0.99, + number_orthogonalizations=2, + ) + + # groundwater flow model + gwf = flopy.mf6.ModflowGwf( + sim, modelname=gwf_name, model_nam_file=f"{sim_name}.nam", save_flows=True + ) + + # grid discretization + disv = flopy.mf6.ModflowGwfdisv( + gwf, length_units="feet", idomain=idomain, **disv_props + ) + + # initial conditions + ic = flopy.mf6.ModflowGwfic(gwf, strt=150.0) + + flopy.mf6.modflow.mfgwfwel.ModflowGwfwel( + gwf, + maxbound=len(wells), + auxiliary=["IFACE", "IFLOWFACE"], + save_flows=True, + stress_period_data={0: wells}, + ) + + # node property flow + npf = flopy.mf6.ModflowGwfnpf( + gwf, + xt3doptions=True, + save_flows=True, + save_specific_discharge=True, + save_saturation=True, + icelltype=[0], + k=[50], + ) + + # constant head boundary (period, node number, head) + chd_bound = [ + (0, 1327, 150.0), + (0, 1545, 150.0), + (0, 1643, 150.0), + (0, 1687, 150.0), + (0, 1713, 150.0), + ] + chd = flopy.mf6.ModflowGwfchd( + gwf, + pname="chd", + save_flows=True, + stress_period_data=chd_bound, + # auxiliary=["IFLOWFACE"] + ) + + # output control + budget_file = f"{gwf_name}.cbb" + head_file = f"{gwf_name}.hds" + oc = flopy.mf6.ModflowGwfoc( + gwf, + pname="oc", + budget_filerecord=[budget_file], + head_filerecord=[head_file], + saverecord=[("HEAD", "ALL"), ("BUDGET", "ALL")], + ) + + return sim + + +# Next we can define the particle-tracking model. This example problem uses a reverse-tracking model, in which termination and release zones are swapped: we "release" (terminate) particles in the constant head boundary on the grid's right edge and the two pumping wells, and track them backwards to "termination" (release) locations at the wells along the left boundary of the active domain. +# +# Define particle release locations, initially in the representation for MODPATH 7 particle input style 1. + +# define particles in MODPATH 7 input style 1 +# each particle is a tuple (node number, localx, localy, localz) +particles = [ + (1327, 0.000, 0.125, 0.500), + (1327, 0.000, 0.375, 0.500), + (1327, 0.000, 0.625, 0.500), + (1327, 0.000, 0.875, 0.500), + (1545, 0.000, 0.125, 0.500), + (1545, 0.000, 0.375, 0.500), + (1545, 0.000, 0.625, 0.500), + (1545, 0.000, 0.875, 0.500), + (1643, 0.000, 0.125, 0.500), + (1643, 0.000, 0.375, 0.500), + (1643, 0.000, 0.625, 0.500), + (1643, 0.000, 0.875, 0.500), + (1687, 0.000, 0.125, 0.500), + (1687, 0.000, 0.375, 0.500), + (1687, 0.000, 0.625, 0.500), + (1687, 0.000, 0.875, 0.500), + (1713, 0.000, 0.125, 0.500), + (1713, 0.000, 0.375, 0.500), + (1713, 0.000, 0.625, 0.500), + (1713, 0.000, 0.875, 0.500), + (861, 0.000, 0.125, 0.500), + (861, 0.000, 0.375, 0.500), + (861, 0.000, 0.625, 0.500), + (861, 0.000, 0.875, 0.500), + (861, 1.000, 0.125, 0.500), + (861, 1.000, 0.375, 0.500), + (861, 1.000, 0.625, 0.500), + (861, 1.000, 0.875, 0.500), + (861, 0.125, 0.000, 0.500), + (861, 0.375, 0.000, 0.500), + (861, 0.625, 0.000, 0.500), + (861, 0.875, 0.000, 0.500), + (861, 0.125, 1.000, 0.500), + (861, 0.375, 1.000, 0.500), + (861, 0.625, 1.000, 0.500), + (861, 0.875, 1.000, 0.500), + (891, 0.000, 0.125, 0.500), + (891, 0.000, 0.375, 0.500), + (891, 0.000, 0.625, 0.500), + (891, 0.000, 0.875, 0.500), + (891, 1.000, 0.125, 0.500), + (891, 1.000, 0.375, 0.500), + (891, 1.000, 0.625, 0.500), + (891, 1.000, 0.875, 0.500), + (891, 0.125, 0.000, 0.500), + (891, 0.375, 0.000, 0.500), + (891, 0.625, 0.000, 0.500), + (891, 0.875, 0.000, 0.500), + (891, 0.125, 1.000, 0.500), + (891, 0.375, 1.000, 0.500), + (891, 0.625, 1.000, 0.500), + (891, 0.875, 1.000, 0.500), +] + +# For vertex grids, the MODFLOW 6 PRT model's PRP (particle release point) package expects initial particle locations as tuples `(particle ID, (layer, cell ID), x coord, y coord, z coord)`. While MODPATH 7 input style 1 expects local coordinates, PRT expects global coordinates (the user must guarantee that each release point's coordinates fall within the cell with the given ID). FloPy's `ParticleData` class provides a `to_coords()` utility method to convert local coordinates to global coordinates for easier migration from MODPATH 7 to PRT. + +# + +partdata = flopy.modpath.ParticleData( + partlocs=[p[0] for p in particles], + structured=False, + localx=[p[1] for p in particles], + localy=[p[2] for p in particles], + localz=[p[3] for p in particles], + timeoffset=0, + drape=0, +) +coords = partdata.to_coords(grid) +particles_prt = [ + (i, (0, p[0]), c[0], c[1], c[2]) for i, (p, c) in enumerate(zip(particles, coords)) +] + + +# - + +# Define a function to build the PRT model/simulation. + + +def build_prt(): + print("Building PRT model") + + simprt = flopy.mf6.MFSimulation( + sim_name=prt_name, version="mf6", exe_name="mf6", sim_ws=prt_ws + ) + + flopy.mf6.ModflowTdis( + simprt, pname="tdis", time_units="DAYS", nper=1, perioddata=[(10000, 1, 1.0)] + ) + + # Instantiate the MODFLOW 6 prt model + prt = flopy.mf6.ModflowPrt( + simprt, modelname=prt_name, model_nam_file="{}.nam".format(prt_name) + ) + + # Instantiate the MODFLOW 6 DISV vertex grid discretization + disv = flopy.mf6.ModflowGwfdisv(prt, idomain=idomain, **disv_props) + + # Instantiate the MODFLOW 6 prt model input package + flopy.mf6.ModflowPrtmip(prt, pname="mip", porosity=porosity) + + # Instantiate the MODFLOW 6 prt particle release point (prp) package + flopy.mf6.ModflowPrtprp( + prt, + pname="prp", + filename="{}_4.prp".format(prt_name), + nreleasepts=len(particles_prt), + packagedata=particles_prt, + perioddata={ + 0: ["FIRST"], + }, + ) + + # Instantiate the MODFLOW 6 prt output control package + budgetfile_prt = "{}.cbb".format(prt_name) + budget_record = [budgetfile_prt] + trackfile_prt = "{}.trk".format(prt_name) + trackcsvfile_prt = "{}.trk.csv".format(prt_name) + flopy.mf6.ModflowPrtoc( + prt, + pname="oc", + budget_filerecord=budget_record, + track_filerecord=trackfile_prt, + trackcsv_filerecord=trackcsvfile_prt, + saverecord=[("BUDGET", "ALL")], + ) + + # Instantiate the MODFLOW 6 prt flow model interface + flopy.mf6.ModflowPrtfmi( + prt, + packagedata=[ + ("GWFHEAD", headfile_bkwd), + ("GWFBUDGET", budgetfile_bkwd), + ], + ) + + # Create an explicit model solution (EMS) for the MODFLOW 6 prt model + ems = flopy.mf6.ModflowEms( + simprt, + pname="ems", + filename="{}.ems".format(prt_name), + ) + simprt.register_solution_package(ems, [prt.name]) + + return simprt + + +# Define a function to create the MODPATH 7 model/simulation. + + +def build_mp7(gwf): + print("Building MP7 model") + + pg = flopy.modpath.ParticleGroup( + particlegroupname="G1", particledata=partdata, filename=f"{sim_name}.sloc" + ) + + mp = flopy.modpath.Modpath7( + modelname=mp7_name, + flowmodel=gwf, + exe_name="mp7", + model_ws=mp7_ws, + ) + mpbas = flopy.modpath.Modpath7Bas( + mp, + porosity=porosity, + ) + mpsim = flopy.modpath.Modpath7Sim( + mp, + simulationtype="pathline", + trackingdirection="backward", + budgetoutputoption="summary", + particlegroups=[pg], + ) + + return mp + + +# - + +# Define a function to build the three models: MODFLOW 6 GWF, MODFLOW6 PRT, and MODPATH 7. + + +def build_models(): + gwfsim = build_gwf() + gwf = gwfsim.get_model(gwf_name) + return gwfsim, build_prt(), build_mp7(gwf) + + +# Define a function to view the grid and boundary conditions. Also highlight the vertices of well-containing cells which lie on the boundary between coarser- and finer-grained refinement regions (there are 7 of these). Note the disagreement between values of `IFLOWFACE` and `IFACE` for these cells. This is because the cells have 5 polygonal faces. + +# + +from matplotlib.collections import LineCollection +from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes +from mpl_toolkits.axes_grid1.inset_locator import mark_inset + + +def sort_square_verts(verts): + """Sort 4 or more points on a square in clockwise order, starting with the top-left point""" + + # sort by y coordinate + verts.sort(key=lambda v: v[1], reverse=True) + + # separate top and bottom rows + y0 = verts[0][1] + t = [v for v in verts if v[1] == y0] + b = verts[len(t) :] + + # sort top and bottom rows by x coordinate + t.sort(key=lambda v: v[0]) + b.sort(key=lambda v: v[0]) + + # return vertices in clockwise order + return t + list(reversed(b)) + + +def plot_well_cell_ids(ax): + xc, yc = grid.get_xcellcenters_for_layer(0), grid.get_ycellcenters_for_layer(0) + for well in wells: + nn = well[1] + x, y = xc[nn], yc[nn] + ax.annotate(str(nn), (x - 50, y - 50), color="purple") + + +cells_on_refinement_boundary = [10, 43, 203, 402, 708, 1020, 1332] + + +def plot_well_vertices_on_refinement_boundary(ax): + for nn in cells_on_refinement_boundary: + verts = list(set([tuple(grid.verts[v]) for v in grid.iverts[nn]])) + verts = [ + v + for v in verts + if len([vv for vv in verts if vv[0] == v[0]]) > 2 + or len([vv for vv in verts if vv[1] == v[1]]) > 2 + ] + for v in verts: + ax.plot(v[0], v[1], "go") + + +def plot_well_ifaces(ax): + ifaces = [] + for well in wells: + nn = well[1] + iverts = grid.iverts[nn] + + # sort vertices of well cell in clockwise order + verts = [tuple(grid.verts[v]) for v in iverts] + sorted_verts = sort_square_verts(list(set(verts.copy()))) + + # reduce vertices to 4 corners of square + xmax, xmin = max([v[0] for v in sorted_verts]), min( + [v[0] for v in sorted_verts] + ) + ymax, ymin = max([v[1] for v in sorted_verts]), min( + [v[1] for v in sorted_verts] + ) + sorted_verts = [ + v for v in sorted_verts if v[0] in [xmax, xmin] and v[1] in [ymax, ymin] + ] + + # define the iface line segment + iface = well[3] + if iface == 1: + p0 = sorted_verts[0] + p1 = sorted_verts[-1] + elif iface == 2: + p0 = sorted_verts[1] + p1 = sorted_verts[2] + elif iface == 3: + p0 = sorted_verts[2] + p1 = sorted_verts[3] + elif iface == 4: + p0 = sorted_verts[0] + p1 = sorted_verts[1] + else: + continue + + ifaces.append([p0, p1]) + + lc = LineCollection(ifaces, color="red", lw=4) + ax.add_collection(lc) + + +def plot_map_view(ax, gwf): + # plot map view of grid + mv = flopy.plot.PlotMapView(model=gwf, ax=ax) + mv.plot_grid(alpha=0.3) + mv.plot_ibound() # inactive cells + mv.plot_bc("WEL", alpha=0.3) # wells (red) + ax.add_patch( # constant head boundary (blue) + mpl.patches.Rectangle( + ((ncol - 1) * delc, (nrow - 6) * delr), + 1000, + -2500, + linewidth=5, + facecolor="blue", + alpha=0.5, + ) + ) + + +def plot_inset(ax, gwf): + # create inset + axins = ax.inset_axes([-0.76, 0.25, 0.7, 0.9]) + + # plot grid features + plot_map_view(axins, gwf) + # plot_well_cell_ids(axins) + plot_well_ifaces(axins) + plot_well_vertices_on_refinement_boundary(axins) + + # zoom in on refined region of injection well boundary + axins.set_xlim(2000, 6000) + axins.set_ylim(6000, 10500) + + # add legend + legend_elements = [ + mpl.lines.Line2D([0], [0], color="red", lw=4, label="Well IFACEs"), + mpl.lines.Line2D( + [0], + [0], + marker="o", + color="grey", + label="Well cell vertices on refinement border", + markerfacecolor="g", + markersize=15, + ), + ] + axins.legend(handles=legend_elements) + + # add the inset + ax.indicate_inset_zoom(axins) + + +def plot_grid(gwf): + # setup the plot + fig = plt.figure(figsize=(13, 13)) + ax = fig.add_subplot(1, 1, 1, aspect="equal") + + # add plot features + plot_map_view(ax, gwf) + plot_well_ifaces(ax) + plot_inset(ax, gwf) + + # add legend + ax.legend( + handles=[ + mpl.patches.Patch(color="red", label="Wells"), + mpl.patches.Patch(color="blue", label="Constant head boundary"), + ] + ) + + +# - + +# Because this problem tracks particles backwards, we need to reverse the head and budget files after running the groundwater flow model and before running the particle tracking model. Define functions to do this (FloPy has a utility that can do most of the work). + + +# + +def reverse_budgetfile(fpth, rev_fpth, tdis): + import flopy.utils.binaryfile as bf + + f = bf.CellBudgetFile(fpth, tdis=tdis) + f.reverse(rev_fpth) + + +def reverse_headfile(fpth, rev_fpth, tdis): + import flopy.utils.binaryfile as bf + + f = bf.HeadFile(fpth, tdis=tdis) + f.reverse(rev_fpth) + + +# Define a function to run the models, reversing the GWF model's head and budget output files before running the PRT model. + + +@timeit +def run_models(gwfsim, prtsim, mp7sim): + success, buff = gwfsim.run_simulation(silent=True, report=True) + assert success, f"Failed to run MF6 GWF model." + for line in buff: + print(line) + + # tdis is required for output file reversal + tdis = gwfsim.tdis + + # reverse gwf head and output files (for backwards tracking) + reverse_headfile(gwf_ws / headfile, prt_ws / headfile_bkwd, tdis) + reverse_budgetfile(gwf_ws / budgetfile, prt_ws / budgetfile_bkwd, tdis) + + success, buff = prtsim.run_simulation(silent=True, report=True) + assert success, f"Failed to run MF6 PRT model." + for line in buff: + print(line) + + success, buff = mp7sim.run_model(silent=True, report=True) + assert success, "Failed to run MODPATH 7 model." + for line in buff: + print(line) + + +# Define functions to plot heads and particle paths over the grid, comparing the PRT and MODPATH results. + + +# + +def plot_pathlines(ax, grid, hd, pl, title): + ax.set_aspect("equal") + ax.set_title(title) + mm = flopy.plot.PlotMapView(modelgrid=grid, ax=ax) + mm.plot_grid(lw=0.5, alpha=0.5) + mm.plot_ibound() + mm.plot_array(hd, alpha=0.5) + mm.plot_pathline(pl, layer="all", lw=0.3, colors=["black"]) + + +def plot_results(grid, heads, prtpl, mp7pl): + fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(14, 14)) + plt.suptitle( + t="Example 4: Particles tracked backward on a locally refined vertex grid with lateral flow boundaries", + fontsize=14, + y=0.7, + ) + + plot_pathlines(axes[0], grid, heads, prtpl, "MODFLOW 6 PRT") + plot_pathlines(axes[1], grid, heads, mp7pl, "MODPATH 7") + + # for ax in axes: + # ax.set_xlim(2000, 6000) + # ax.set_ylim(6000, 10500) + + +# - + +# Define a function to wrap the entire problem scenario. + + +def scenario(): + # build models + gwfsim, prtsim, mp7sim = build_models() + + # plot grid with active domain and boundary conditions + gwf = gwfsim.get_model(gwf_name) + plot_grid(gwf) + + # write input files + gwfsim.write_simulation() + prtsim.write_simulation() + mp7sim.write_input() + + if runModel: + # run models + run_models(gwfsim, prtsim, mp7sim) + + # extract grid + grid = gwf.modelgrid + + # load mf6 gwf head results + hf = flopy.utils.HeadFile(gwf_ws / headfile) + hds = hf.get_data() + + # load mf6 prt pathline results + prt_pl = pd.read_csv(prt_ws / trackcsvfile_prt) + + # load mp7 pathline results + plf = flopy.utils.PathlineFile(mp7_ws / pathlinefile_mp7) + mp7_pl = pd.DataFrame( + plf.get_destination_pathline_data(range(grid.nnodes), to_recarray=True) + ) + + # plot results + plot_results(grid, hds, prt_pl, mp7_pl) + + +# Now run the scenario for example problem 4. + +scenario()