03_graph_statistics_part_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "revised-healing",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import networkx as nx\n",
    "import math\n",
    "import json\n",
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beginning-baltimore",
   "metadata": {},
   "source": [
    "## 1. Load nodes and edges from JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "analyzed-european",
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = json.load(open('./genshin_data/node_list.json', 'rb'))\n",
    "edges = json.load(open('./genshin_data/edge_list.json', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "compliant-discharge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'Keqing', 'nation': 'Liyue'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # example look of the node_list JSON file\n",
    "nodes[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dynamic-wrong",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'voice', 'from': 'Amber', 'to': 'Klee'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example look of the edge_list JSON file\n",
    "edges[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "blond-algorithm",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1e+03 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create directed graph\n",
    "directed_graph = nx.DiGraph()\n",
    "\n",
    "# add characters with a nation attribute\n",
    "for node in nodes:\n",
    "    directed_graph.add_node(node['id'], nation=node['nation'])\n",
    "\n",
    "# add character relations\n",
    "for edge in edges:\n",
    "    directed_graph.add_edge(edge['from'], edge['to'], type=edge['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "destroyed-raise",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 35\n",
      "Number of edges: 262\n"
     ]
    }
   ],
   "source": [
    "# basic node and edge stats from networkx\n",
    "print(\"Number of nodes:\", directed_graph.number_of_nodes())\n",
    "print(\"Number of edges:\", directed_graph.number_of_edges())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "earlier-italy",
   "metadata": {},
   "source": [
    "### In the Genshin Impact character network, we have 35 character nodes and 262 connections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "unable-choir",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting node colours for drawing\n",
    "nation_color_dict = {\n",
    "    'Mondstadt': '#aff8db',\n",
    "    'Liyue': '#ffcbc1',\n",
    "    'Inazuma': '#d5aaff',\n",
    "    'Snezhnaya': '#ace7ff'\n",
    "}\n",
    "\n",
    "# for each node, assign the node a color based on the\n",
    "# the color map dictionary if the nation is existed in \n",
    "# the color map dictionary\n",
    "node_colors = []\n",
    "for node, nation in list(directed_graph.nodes(data=\"nation\")):\n",
    "    if nation in nation_color_dict:\n",
    "        node_colors.append(nation_color_dict[nation])\n",
    "    else:\n",
    "        node_colors.append('#FFA500') # traveler doesn't belong to any nation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "located-companion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fffQRnTt3xtXVlVatWtkadQ4bNowvvvgCNze3h3bmAXBycmLjxo0UL16cBg0akDFjRipXrsytW7eoUqXKE3N+5513CAsLw8PDg6pVq9K4cePHjnd1dWXixIl06NCBzJkzs3DhQlq0aGE7X7lyZVtD1kyZMlG7dm3bm/63336bX3/9lcyZM/PWW2/h6urK+vXr+eWXX8iVKxc5cuRgyJAhtt1TnsTR0ZELFy5Qv359MmbMSOnSpXFycrI1lq1Xrx6ff/45bdu2JWfOnJw9e5ZffvnF9jg2bNjAihUryJEjB0WKFHlo1xuAbt26kS9fPjw9PSlZsqSt6JIYBQoUoGvXrrFm97z99tu0aNGChg0b4urqStWqVW0FiXTp0tl2BHJzc+Ovv/4CrIWRoKAg2641//0e4OOPP+all16ibNmylClThgoVKth2a3qSBg0aMGvWLFq0aGHbAUdERCQtMBkJnbMpIiIiT83IkSM5c+bMM1HAEREREXkWacaIiIiIiIiIiLywVBgRERERERERkReWltKIiIiIiIiIyAtLM0ZERERERERE5IWlwoiIiIiIiIiIvLBUGBERERERERGRF5YKIyIiIiIiIiLywlJhREREREREREReWCqMiIiIiIiIiMgLS4UREREREREREXlh2Sf0gqioKK5cuUJ4eHhK5CP/cHZ2Jnfu3Dg4OKR2KiIiIiIiIiLPLZNhGEZCLjh//jyurq5kyZIFk8mUUnm90AzD4Pbt2wQFBVGgQIHUTkdERERERETkuZXgpTTh4eEqiqQwk8lElixZNCtHREREREREJIUlqseIiiIpT8+xiIiIiIiISMp7Jpuvmkwmunbtavs+OjqarFmz0qxZs2SJP3LkSMaNG/fI81u3bmXXrl0Jjps/f35u3bpFQEAAU6ZMSUqKIiIiIiIiIpIMEtx89b/WRJwngpjkyAUAJ+xo4vT4vhrp06fn6NGjhIWF4eLiwoYNG/D09Ey2HJ5k69atZMiQgZdffjlR198vjAwYMCCZMxMRERERERGRhEjyjJHkLIokJF6TJk1YtWoVAD///DOvvvqq7dydO3do1aoVZcuWpWrVqhw+fBiwzgTp2bMn3t7eFCxYkIkTJ9quGTVqFMWKFaN+/fqcOnXKdnzixImULFmSsmXL0qlTJy5cuMAPP/zAhAkT8PLyYseOHaxYsYIqVapQvnx56tevj7+/PwC3b9+mYcOGlC9fnjfeeIP7fW6HDh3K2bNn8fLy4v3330/aEyYiIiIiIiIiiZbkGSOppVOnTnz22Wc0a9aMw4cP07NnT3bs2AHAiBEjKF++PMuWLWPz5s1069YNHx8fAE6ePMmWLVsICgqiWLFi9O/fn8OHD/PLL79w8OBBoqOjqVChAhUrVgTgq6++4vz58zg5OREQEICbmxv9+vUjQ4YMvPfeewDcvXuXv/76C5PJxIwZMxg7dizjx4/n008/pUaNGnzyySesWrWK6dOn22IePXrUlpOIiIiIiIiIpI5ntjBStmxZLly4wM8//0zTpk1jnfvzzz9ZunQpAHXr1uX27dsEBgYC8Morr+Dk5ISTkxPZsmXD39+fHTt20Lp1a9KlSwdAixYtYt3ntddeo1WrVrRq1SrOXK5cuULHjh25fv06kZGRti12t2/fzm+//Wa7b+bMmZP1ORARERERERGRpHkmm6/e16JFC957771Yy2gA25KVB93f5cXJycl2zM7Ojujo6Fjn/2vVqlUMHDiQ/fv3U7FiRdv4B7355psMGjSII0eOMG3atFjb7Gp3GREREREREZG065kujPTs2ZNPPvmEMmXKxDpeq1YtFixYAFgbpXp4eJAxY8ZHxqlVqxa///47YWFhBAUFsWLFCgAsFguXL1+mTp06jB07loCAAIKDg3F1dSUoKMh2fWBgoK3565w5c+LMY82aNdy9exfgoetFREREREREJHU804WR3Llz8/bbbz90fOTIkezbt4+yZcsydOjQWMWKuFSoUIGOHTvi5eVF27ZtqVmzJgAxMTF06dKFMmXKUL58eQYPHoybmxvNmzfn999/tzVfHTlyJO3bt6dmzZp4eHjY4o4YMYLt27dToUIF1q9fT968eQHIkiUL1atXp3Tp0mq+KiIiIiIiIpKKTEZc604e48SJE5QoUcL2fWps1/ui+O9zLSIiIiIiIiLJK8nNV1XEEBEREREREZFn1TO9lEZEREREREREJClUGBERERERERGRF5YKIyIiIiIiIiLywlJhREREREREREReWCqMiIiIiIiIiMgL65ksjGTIkOGhYz/88ANz585NhWxERERERERE5FmV5O168dkN0VHJkMo/7B3Aq1qCL+vXr1/y5SAiIiIiIiIiL4SkzxhJzqJIEuKNHDmScePGcfbsWSpUqGA7fvr0aSpWrAhA/vz5uXXrFgD79u3D29sbgJCQEHr27EmlSpUoX748y5cvT9pjEBEREREREZFnwjO5lOZxChUqRKZMmfDx8QFg1qxZ9OjR47HXjBo1irp16/L333+zZcsW3n//fUJCQlI+WRERERERERFJVc9dYQSgd+/ezJo1i5iYGBYtWkTnzp0fO379+vV89dVXeHl54e3tTXh4OJcuXXpK2YqIiIiIiIhIakl6j5E0qG3btnz66afUrVuXihUrkiVLFgDs7e2xWCwAhIeH28YbhsHSpUspVqxYquQrIiIiIiIiIqnjuZwx4uzsTKNGjejfvz+vv/667Xj+/PnZv38/AEuXLrUdb9SoEZMmTcIwDAAOHjz4dBMWERERERERkVTxTBZGQkNDyZ07t+3rm2++eWjMa6+9hslkomHDhrZjI0aM4O2336ZmzZrY2dnZjg8fPpyoqCjKli1L6dKlGT58+FN5HCIiIiIiIiKSupK+lMbeIfm3632C+8thHufPP/+kZ8+esQogNWvWxNfX96GxLi4uTJs2LWF5ioiIiIiIiMgzL+mFEa9qyZBG8mrdujVnz55l8+bNqZ2KiIiIiIiIiKRhz2Xz1d9//z21UxARERERERGRZ8Az2WNERERERERERCQ5qDAiIiIiIiIiIi8sFUZERERERERE5IWlwoiIiIiIiIiIvLCeycJIhgwZUjsFEREREREREXkOJHlXmlN/QExEcqRiZecExVokXzwRERERERERkUdJ8oyR5CyKJDTe1q1b8fb2pl27dhQvXpzXXnsNwzAA+Oyzz6hUqRKlS5emb9++GIbBtWvX8PLysn3Z2dlx8eJFevTowa+//mqLe39GytatW6lduzYdOnSgaNGiDB06lAULFlC5cmXKlCnD2bNnAVixYgVVqlShfPny1K9fH39//+R7QkREREREREQkxTyTS2kedPDgQb799luOHz/OuXPn2LlzJwCDBg3i77//5ujRo4SFhbFy5Upy5cqFj48PPj4+9OnTh7Zt25IvX77Hxj906BDfffcdR44cYd68efj6+rJ371569+7NpEmTAKhRowZ//fUXBw8epFOnTowdOzbFH7eIiIiIiIiIJF2Sl9KktsqVK5M7d24AvLy8uHDhAjVq1GDLli2MHTuW0NBQ7ty5Q6lSpWjevDkAO3fuZMaMGezYseOJ8StVqkTOnDkBKFSoEA0bNgSgTJkybNmyBYArV67QsWNHrl+/TmRkJAUKFEiJhyoiIiIiIiIiyeyZnzHi5ORk+7ednR3R0dGEh4czYMAAfv31V44cOUKfPn0IDw8H4Pr16/Tq1YtFixbZlszY29tjsVgAMAyDyMjIOOObzWbb92azmejoaADefPNNBg0axJEjR5g2bZrtXiIiIiIiIiKStj3zhZG43C9MeHh4EBwcbOsfEhUVRYcOHRgzZgxFixa1jc+fPz/79+8HYPny5URFRSXofoGBgXh6egIwZ86c5HgIIiIiIiIiIvIUPJeFETc3N/r06UOZMmVo1aoVlSpVAmDXrl38/fffjBgxwtaA9dq1a/Tp04dt27ZRuXJl9uzZQ/r06RN0v5EjR9K+fXtq1qyJh4dHSjwkEREREREREUkBJuP+Ni7xdOLECUqUKGH7Xtv1ppz/PtciIiIiIiIikryS3HxVRQwREREREREReVY9l0tpRERERERERETiQ4UREREREREREXlhqTAiIiIiIiIiIi8sFUZERERERERE5IWlwoiIiIiIiIiIvLCe2cLIqFGjKFWqFGXLlsXLy4s9e/YkS9ytW7fSrFmzZIklIiIiIiIiImlbkrfrXXkDIizJkYqVkxmaZXv8mN27d7Ny5UoOHDiAk5MTt27dIjIyMvmSEBEREREREZEXQpJnjCRnUSS+8a5fv46HhwdOTk4AeHh4kCtXLvLnz8+IESOoUKECZcqU4eTJkwCEhITQs2dPKlWqRPny5Vm+fDkAvXv3xsvLCy8vL7Jmzcqnn34KQHBwMO3ataN48eK89tprGIYB8Mj4e/fu5eWXX6Z8+fK8/PLLnDp1CoCaNWvi4+Njy7t69eocPnz4keNFRERERERE5Ol6JpfSNGzYkMuXL1O0aFEGDBjAtm3bbOc8PDw4cOAA/fv3Z9y4cYB12U3dunX5+++/2bJlC++//z4hISHMmDEDHx8fli9fTpYsWejRowcABw8e5Ntvv+X48eOcO3eOnTt3PjZ+8eLF2b59OwcPHuSzzz7jww8/BKyFl9mzZwPg6+tLREQEZcuWfeR4EREREREREXm6nsnCSIYMGdi/fz/Tp08na9asdOzY0VaAaNOmDQAVK1bkwoULAKxfv56vvvoKLy8vvL29CQ8P59KlSwCEh4fTvn17Jk+eTL58+QCoXLkyuXPnxmw24+XlZYvzqPiBgYG0b9+e0qVLM3jwYI4dOwZA+/btWblyJVFRUcycOdNWeHnUeBERERERERF5upLcYyS12NnZ4e3tjbe3N2XKlGHOnDkAtuU1dnZ2REdHA2AYBkuXLqVYsWIPxenXrx9t2rShfv36tmP3Y/w3zqPiDx8+nDp16vD7779z4cIFvL29AUiXLh0NGjRg+fLlLF68mH379j12vIiIiIiIiIg8Xc/kjJFTp05x+vRp2/c+Pj622R5xadSoEZMmTbL1Cjl48CAA33//PUFBQQwdOjRJ+QQGBuLp6Qlgm7lyX+/evXnrrbeoVKkS7u7uTxwvIiIiIiIiIk/PM1kYCQ4Opnv37pQsWZKyZcty/PhxRo4c+cjxw4cPJyoqirJly1K6dGmGDx8OwLhx4zhy5IitAesPP/yQqHw++OADhg0bRvXq1YmJiYl1rmLFimTMmJHXX389XuNFRERERERE5OkxGfenUcTTiRMnKFGihO371Niu91ly7do1vL29OXnyJGZzwupQ/32uRURERERERCR5JbnHyPNUxEhuc+fO5aOPPuKbb75JcFFERERERERERFJekmeMSMrRcy0iIiIiIiKSsjSNQUREREREREReWCqMiIiIiIiIiMgLS4UREREREREREXlhqTAiIiIiIiIiIi+sZ64wcvv2bby8vPDy8iJHjhx4enravo+MjEzWe23dupVmzZola0wRERERERERSTuSvF0vv+WAcP9kSOUfztmhjd8jT2fJkgUfHx8ARo4cSYYMGXjvvfds56Ojo7G3T/rDSozUvLeIiKSgyAgID4WYGDAMMJnAbAYnZ3BysX4vIiIiIs+kpL+LT86iSCLj9ejRA3d3dw4ePEiFChXo2LEj77zzDmFhYbi4uDBr1iyKFStGlSpVmDlzJqVKlQLA29ub8ePHU7x4cd58802OHDlCdHQ0I0eOpGXLlrHuERISEueY2bNns2rVKsLDwwkJCWHz5s3J8jSIiEgqCgmCu7cgOBDCQsBisRZCAGJtcm9YCyXO6cA1E2TMDJncVSgREREReYY8N9MbfH192bhxI3Z2dty7d4/t27djb2/Pxo0b+fDDD1m6dCmdOnVi8eLFfPrpp1y/fp1r165RsWJFPvzwQ+rWrcvMmTMJCAigcuXK1K9fP1b8UaNGPXLM7t27OXz4MO7u7qnx0EVEJDlYYuDOTfC7bJ0hYrHEPh8T8+hrw0KsX7f8wWyCbJ6QNSc4OKZsziIiIiKSZM9NYaR9+/bY2dkBEBgYSPfu3Tl9+jQmk4moqCgAOnToQIMGDfj0009ZvHgx7du3B2D9+vX88ccfjBs3DoDw8HAuXboUK/7jxjRo0EBFERGRZ5VhWIsh1//5//3/FkQSwhIDFqyxrl+CzFkhb2HQMksRERGRNOu5eaWWPn1627+HDx9OnTp1+P3337lw4QLe3t4AeHp6kiVLFg4fPsyiRYuYNm0aAIZhsHTpUooVKxYrpr//v8t6HjVmz549se4tIiLPkLBQOHscIsOTVhD5L+Of9TZ3b0LgHShY3LrERkRERETSnGduV5r4CAwMxNPTE4DZs2fHOtepUyfGjh1LYGAgZcqUAaBRo0ZMmjQJ458XsgcPHnwoZnzGiIjIM8IwrDM6jh+wNlVNzqLIf+8TE20tvpw9Yf23iIiIiKQpz2Vh5IMPPmDYsGFUr16dmP+sCW/Xrh2//PILHTp0sB0bPnw4UVFRlC1bltKlSzN8+PCHYsZnjIiIPAMMA86dsBZGjBQqiPyXxQIBt+DEQfhneaeIiIiIpA0m4/4UiHg6ceIEJUqU+PfAU96u90Xy0HMtIiJJYxhw5hjcC3h6RZFYTODoCCXKqzGriIiISBqR9B4jKmKIiMizwDDg3MlULIoAGBAZCScPWYsjasoqIiIikuqey6U0IiIiD7lxFQJvp2JR5D7D2uz1/MlUzkNEREREQIURERF5EYSHwdULKddkNaEMA4IC4M7N1M5ERERE5IWXqMJIAtuSSCLoORYRSSb3m62mlaLIfRYLXPSFqMjUzkRERETkhZbgwoizszO3b9/WG/cUZBgGt2/fxtnZObVTERF59t24Zt2SNy2yWOCCb2pnISIiIvJCS3DXt9y5c3PlyhVu3tT035Tk7OxM7ty5UzsNEZFnm2HAtYtpb7bIfYZhbQYbHgbOLqmdjYiIiMgLKcHb9YqIiDwz7t6C86fAEpPamTyGCbLmgHxFUjsRERERkReSmq+KiMjzy+9yGi+KABhw2/8ZyFNERETk+aTCiIiIPJ/CwyA0JLWziD/tUCMiIiKSKlQYERGR59O9u8AzslrUYrEu+xERERGRp06FEREReT4FBVqbmz4rQoJSOwMRERGRF5IKIyIi8nwKuZfgSyIiI3l3wvcUavUqxdt1xatzL5Zs3ALAH9t28v53U58YY9/xk7z28ecJvjcx0RAdnfDrRERERCRJtCuNiIg8fywxcGAXCV1K0+vzMQSHhjFn5DCcnZw4euYcjd56n7kjP6Re5Yopk+t9ZjsoXBIyZk7Z+4iIiIhILJoxIiIiz5+IcDAn7E/cxet+LNqwhalD/4ezkxMApQsX5OOeXfl0xmxmr1hDuyGf2MZ/PHUGhVt3pnbft3j/u6m81K0vAFv3H7T9O0EMw9owVkRERESeKhVGRETk+RMTA6aEXXLkzDkK5/bEPVPGWMerlinJkTPnYh1bsX0nf2zfic+CGWyeOoGTFy4mNWPAsDZhFREREZGnSoURERF5/iRilejjVpaaTLGrLFv2H6Rjg7pkSJcOOzs7erV8JcH3iyMBMFQYEREREXnaVBgREZHnj8mU4J16yxQuyJkrV7kTGLtp619HjvNy2dKxjqVMdy4TmPRnWURERORp0yswERF5/pjtEnxJ/lw5aV+vNv2/+obwiAgAjp45x4Sfl/BFv16xxtarVIHFG7cQEhZGTEwMs1asSXrOJhLcF0VEREREks4+tRMQERFJdk7OierXMXXo/xj2/Y+U7NAdk8nE1Zu3+GvmFLyKFcHH94xtXLOaL7P7yDG8XutFLg8P6rxUnqs3byUtZ5MZnF2SFkNEREREEkzb9YqIyPPp0B6Iikj05ZFRUfT7cjyX/W+w4psvbTvVxGXr/oO8991U9s2dnuj7YTJBuapg75D4GCIiIiKSYJoxIiIiz6f0rhCQ+MKIo4MDMz8ZmowJPYGdvYoiIiIiIqlAM0ZEROT55H8Vrpx/dnZ6yZgZipZJ7SxEREREXjjq8iYiIs+njJlTO4N4iwHCnNOndhoiIiIiLyQVRkRE5Pnkks769QyIiY6mUOVqvPrqq6xZs4bo6OjUTklERETkhaHCiIiIPL9y5EnU1r1Pm2OOPBw5dpyaNWvy2WefkTt3bv73v/9x8OBBtOJVREREJGWpx4iIiDy/LBY4tBtiYlI7k0czmaFUBXD+d3aLr68v8+fPZ968eWTIkIFu3brRuXNnPD09UzFRERERkeeTCiMiIvJ8u34Zrl+0FknSooxuULRsnKcsFgs7d+5k7ty5LF26lJdeeolu3brRunVr0qdXTxIRERGR5KDCiIiIPN8MA44fgLCQ1M7kYWY7KP0SODo9cWhYWBgrVqxg7ty5/Pnnn7Rs2ZKuXbtSp04d7OzS/nIhERERkbRKhREREXn+hYfCsQNpa+tesxnyFgGP7Am+1N/fn19++YW5c+fi7+/Pa6+9Rrdu3ShVqlQKJCoiIiLyfFNhREREXgzXL1m/0sKSGpMJXDNBkTLWfyfBsWPHmDdvHgsWLCBbtmx07dqVV199lezZE15wEREREXkRqTAiIiIvBsOAs8ch8G7qzhwxmcDBCUqWB3uHZAsbExPD1q1bmTdvHsuXL+fll1+mW7dutGjRAhcXl2S7j4iIiMjzRtv1iojIi8FkgoIlIENG604wqZRDjNmOMX+s41ZAYLKGtrOzo169esyePZsrV67QuXNnZs6ciaenJ71792b79u1Y0sJsGREREZE0RjNGRETkxWKxWGeO3At4ujNH/pkp4muXnmKlS2NnZ0fFihV5/fXXadOmDdmyZUuR2167do2FCxcyZ84cgoKC6Nq1K127dqVo0aIpcj8RERGRZ40KIyIi8uIxDLh2AfyuPpXiSGR0DHeiLeSoWgvsHShatCinT58GwNHREXd3d65du4Ypif1GHscwDA4dOsS8efNYuHAh+fLlo2vXrnTq1IksWbKk2H1FRERE0jotpRERkRePyQSeBaCEFzg5W3eISan72NlxOtpMzpr16dn3De7du0f//v1xcrJu0WsYBosWLUrRoog1FRNeXl6MHz+ey5cvM3LkSHbu3EmhQoVo3bo1v/32GxERESmag4iIiEhapMKIiIi8uNJlgFIvQXZPa9+R5CqQmEzWr0xZoHRlStWojZ2dHbNmzSJXrlzcuHEDi8WCi4sLHh4e7N+/P3nuG0/29vY0btyYhQsXcunSJVq0aMHkyZPx9PSkf//+7N69G00oFRERkReFltKIiIgAxETDbX/wuwLRUYnb1tdsh2GCCyGR5K9UDZOTs+1U2bJlOXLkCGAtTHh4ePDzzz9TqFAh6tSpQ//+/Xn33XeT69EkyqVLl1iwYAFz584lOjqarl270qVLFwoWLJiqeYmIiIikJM0YERERAbCzh2yeUKYyFCljnUWSLsO/M0nMdv98PfhvO+vMECcXFqzbxM0MmRmy8HcK1qxLnUaNiYqKsoWvUKGC7d9ubm5s2bIFb29v8uTJw9atW/nhhx8YN25cajxym7x58zJs2DCOHz/OwoULuXXrFlWrVqVmzZr8+OOPBAQEpGp+IiIiIilBhREREZEHmUzgmgnyFIKSFaBCdShZEQoWh/xFIG9hyFeEe1ly0nv8ZCxe1aBMJVYdO83y7buIsVgnYm7bto0qVapw7949AF566SUAypcvz927d3F2/nc2Se7cudm6dSvTpk1j7NixT/8x/4fJZKJSpUpMnDiRK1eu8P7777N+/Xry589Phw4dWLFiRayij4iIiMizTEtpREREEuH7779n0KBBTJgwgXfeeYfZs2ezevVqLBYLS5cutY3z9PRk//792Nvbc/z4cWrWrEm5cuW4ffs2ly5dwvxAX5OrV69Sp04devbsydChQ1PjYT3W3bt3WbJkCXPnzsXX15dOnTrRrVs3KlasmOLNY0VERERSigojIiIiiVCxYkUOHDiAi4sLx44dw9HRkbJly1KgQIFYzVTt7e3566+/qFixou1YQEAAOXPmpH379sydOzdW3GvXrlGnTh169OjBsGHDntrjSaizZ88yf/585s2bh6Ojo60fSZ48eVI7NREREZEEUWFEREQkgS5dukTRokWJiIjAbDZTqVIldu/eTenSpQkMDOTq1auYTCZcXV3x8/PDxcXloRgrV66kRYsWLF++nObNm8c6d/36derUqUPXrl356KOPntbDShTDMNi9ezfz5s1j8eLFlCtXjm7dutG2bVtcXV1TOz0RERGRJ1KPERERkQRasGAB0dHR2Nvb4+TkxJ49e9izZw8NGzakefPmHDlyhDFjxmBnZ8euXbvijNGsWTO6du1Khw4dHmpqmjNnTrZs2cL8+fP5/PPPn8IjSjyTycTLL7/M1KlTuXbtGgMHDmTZsmXkyZOH1157jXXr1hEdHZ3aaYqIiIg8kmaMiIiIJNCWLVvYs2cP27Zto3DhwgwZMoTcuXOzZs0axowZw9atW7lx4wb58uWjYcOGLF++PM44FouFfPny4e7uzqFDhx467+fnR926denYsSMjRoxI6YeVrG7dusWiRYuYO3culy9fpnPnznTr1o2yZcumdmoiIiIisagwIiIikkjffPMNly5d4ttvvwUgJCSEHDlycO3aNVxdXWnRogWbNm3i9OnT5MqVK84YFy9epFChQgwZMoRRo0Y9dN7f35+6devSvn17Ro4cmYKPJuWcOnWKefPmMW/ePNzc3OjWrRudO3cmZ86cqZ2aiIiIiJbSiIiIJFbevHm5dOmS7fv06dNTqVIltm3bBkC/fv1Inz49P/744yNj5MuXj++//56vvvqKffv2PXQ+e/bsbN68mV9//ZURI0bwLH6eUaxYMb744gvOnz/PxIkTOX78OKVKlaJx48YsWLCAkJCQ1E5RREREXmCaMSIiIpJIf//9N/369Yu1C81XX33F9evX+e6774iJibHNFLl69Sr29vaPjFW/fn3279+Pv78/jo6OD52/ceMG9erVo1WrVnz22WfP/Pa4oaGh/PHHH8ybN49du3bRsmVLunXrhre3d6wtjEVERERSml55iIiIJNJ/Z4wANGjQgPXr1wNgZ2dH3759sbe3Z8WKFY+NtXr1agzD4JVXXonzfLZs2di8eTPLly9n+PDhz+TMkQelS5eOTp06sWrVKk6cOEG5cuV49913yZcvH8OGDeP48eOpnaKIiIi8IFQYERERSaRs2bIRHBwcaylI+fLluXXrFpcvXwbg9ddf5969e0yePPmxsRwdHdmwYQObN29m2rRpcY7JmjUrmzZtYsWKFXz00UfPfHHkvhw5cjB48GAOHjzImjVrsFgsNGjQgJdeeomJEydy48aN1E5RREREnmMqjIiIiCSSyWQiT548tiIIgNlspn79+mzYsAGAggUL8tJLL7F//358fX0fG69SpUoMHTqUQYMGcfHixTjH3C+OrF69mmHDhj03xZH7SpcuzZgxY7h06ZKt70rRokVp3rw5ixcvJjw8PLVTFBERkeeMCiMiIiJJkC9fvoeKGA8upwHo27cvmTNn5ocffnhivFGjRlGiRAlq1KiBxWKJc4yHhwebNm1i3bp1DB069LkrjoB1GVL9+vWZO3cuV65coUOHDvz444/kypWLPn36sGPHjkc+PyIiIiIJoearIiIiSdCrVy+qVq1Knz59bMcuX75M+fLluXHjBmazmfDwcHLmzInJZOLq1au4uLg8NmZAQAA5c+akQ4cOzJkz55Hjbt++TYMGDahXrx5jx4595huyxseVK1dYuHAhc+fOJTQ0lC5dutC1a1eKFCmS2qmJiIjIM0ozRkRERJIgrgasefLkIVu2bBw8eBAAZ2dnunTpgoeHB4sWLXpiTDc3NxYtWsS8efNYuXLlI8dlyZKFjRs3snnzZt57773ncubIf+XOnZsPPviAI0eOsHTpUoKCgqhZsybVqlVj6tSp3LlzJ7VTFBERkWeMCiMiIiJJENdSGnh4OU3v3r25e/cu33//fbzitmjRgi5dutC+fXsCAgIeOc7d3Z2NGzeybds23n333ReiOALW/i7ly5dnwoQJXLlyheHDh7Nt2zYKFChAmzZtWLZsGZGRkamdpoiIiDwDVBgRERFJgrx588ZZGGnYsGGswki5cuVss0v2798fr9izZ88mS5YseHt7P3Zc5syZ2bBhAzt27GDw4MEvTHHkPnt7e5o2bcovv/zCpUuXeOWVV/j222/JlSsXAwcO5K+//nrhnhMRERGJPxVGREREkiBfvnwPLaUBqF27Nvv27Yu1lW/v3r3Jli0bU6dOjVdss9nMjh07OHr0KMOHD3/s2PvFkV27dvHOO++8sIWATJky0atXL7Zu3cq+ffvIlSsX3bt3p1ixYnz++eecP38+tVMUERGRNEbNV0VERJIgIiKCjBkzEhoaip2dXaxz3t7efPDBBzRt2hSwNlXNmzcvZrOZ8+fPkzlz5njdY+rUqQwaNIi9e/dSsWLFx44NDAykYcOGVK5cmYkTJ74QDVmfxDAM9u7dy7x581i0aBElSpSgW7dutG/fnkyZMqV2eikvKhKio+D+Lj5mM9jZg4Mj6PdDREREhREREZGkypkzJ/v27cPT0zPW8dGjR3Pz5k0mTJhgO9atWzeOHj1K9+7defvtt+N9j3r16nHgwAH8/f1xdHR87NjAwEAaNWpExYoVmTx5soojD4iMjGTNmjXMnTuXjRs30rhxY7p160bDhg1xcHBI7fSSzjDg3l0IvgdBgRAWApYYMJnh/q+B8c84kwlc0oGrG2TICBkzW4smIiIiLxgVRkRERJKoatWqfPPNN7z88suxju/bt4/u3btz7Ngx27Ft27bRo0cPnJycOHHiRLyLFpGRkWTLlo3KlSvH6l3yKPfu3aNRo0aUL1+eyZMnY9Yb3ofcuXOHxYsXM3fuXM6ePcurr75K165dqVChwrNXTIqKhBvXrF+GYS2GJIT5n9lOWXNANk9wck7+HEVERNIovUoSERFJori27AUoX748/v7+XLlyxXasVq1a2NnZERUVxZYtW+J9D0dHRzZs2MDGjRuZPn36E8dnzJiRdevW4ePjw8CBA7HcX0YhNu7u7vTr149du3bx559/kilTJtq3b0/p0qX56quvuHz5cmqn+GSR4XD6KBzeA35XICY64UURsF5jibEWVo7ug1OHICw0+fMVERFJg1QYERERSaJHbdlrZ2dHvXr12Lhxo+2YyWSid+/e5MiRI95NWO+rVKkSQ4YMYeDAgXHe77/uF0eOHDlC//79VRx5jCJFivDpp59y9uxZpk2bxvnz5/Hy8qJ+/frMmTOHoKCg1E4xNsOAm9etRYx7d63fG8nw870fJygQThyA65esx0RERJ5jWkojIiKSRJMmTeLkyZN8//33D5376aef2LRpEwsXLrQdu379OiVKlMBsNnP06FFy5cqVoPuVKVOGwMBALly4EK8lMkFBQTRt2pTixYszbdo0LauJp/DwcFasWMG8efPYvn07zZo1o1u3btSrV++hRrtPVWQEnDsBocH/NlRNKWazdVlNoZLgnC5l7yUiIpJK9MpIREQkiR61lAagQYMGbNy4MdZsjZw5c1K7dm3KlSvHjBkzEny/bdu2cfPmTXr16hWv8a6urqxevZpTp07Rt29fzRyJJ2dnZ9q3b88ff/zB6dOnqVKlCh999BF58uTh/fff5/Dhw08/qbAQOLYfQoJSvigC1nuEhcLxg9ZZJCIiIs8hFUZERESS6FFLacBaNHF3d+fQoUOxjvfq1Yu7d+/y448/Eh0dnaD7ubu7s2jRIubMmcPq1avjdc394sjp06fp3bu3iiMJlDVrVt58803+/vtvNm3ahIODA82aNcPLy4tvvvkGPz+/lE8iJAhO+Fj7iDztCb+WGDh9xLpsR0RE5DmjwoiIiEgSPW7GCEDDhg0f2kmmadOm+Pv7ky1bNlauXJnge7Zo0YLOnTvTtm1bAgIC4nVNhgwZWL16NefOnaNXr17ExCSiSadQokQJRo8ezYULF5gwYQJHjhyhRIkSNGnShIULFxIamgJNS8NCwfdw4hqrJheLBc4cs24FLCIi8hxRjxEREZEkMgwDV1dXrl69SqZMmR46v2LFCr799ls2bdoU6/iwYcM4fPgwUVFR8dqC978sFgt58+Yla9asHDx4MN7XhYSE0KxZM/LmzcvMmTNTt1/GcyI0NJRly5Yxb948/vrrL1q1akW3bt2oXbt20nu6xMTAkb0QHZU8ySaV2Q7KVAIHx9TOREREJFloxoiIiEgSmUwm8uXL98hZI97e3uzdu/ehmQQ9e/bk77//xsfHh9OnTyf4vmazmR07dnDkyBE++eSTeF+XPn16Vq1axZUrV3j99dc1cyQZpEuXjs6dO7NmzRqOHz9O6dKlGTx4MPnz5+fDDz/kxIkTiQ9++Yy1OJJWWCxw/pR2qxERkeeGCiMiIiLJ4HHLaVxdXalQoQLbt2+PdbxIkSKUKFGC6tWr88MPPyTqvgUKFGDixImMGjWKAwcOxPu6dOnSsWLFCq5du0b37t1VHElGOXPm5N1338XHx4eVK1cSFRVFvXr1qFSpEpMmTeLmzZsPXRMaGkpUVBwzQu4FwO2bybMVb7IxIDgQ7txI7URERESShQojIiIiyeBxDVjBujtNXMtlevfuzd27d5k7dy5hYWGJuveAAQOoXbs29erVIzIyMt7XpUuXjj/++AN/f3+6deuW4Caw8mRly5bl66+/5vLly4waNYo9e/ZQpEgRWrRowZIlSwgPDwegQ4cOVK9ePfbvQEyMdVveNFUU+YfFAhfPQFT8f99ERETSKhVGREREkkF8GrBu2LDhoeNt27bl8OHDlClThsWLFyf6/mvXrsUwDFq0aJGg6+4XR27evKniSAqys7OjYcOGzJ8/n8uXL9O2bVt++OEHPD096d69O+vXr+fw4cO88sor/xa3bvmlbrPVJzEs4HcltbMQERFJMhVGREREkiomhorFilDc3RWuXYIr5+HqBbh+yfrmNjSYihUqcO3aNa5duxbr0nTp0tGxY0dy5crFlClTEp2Co6Mj69evZ/369cyYMSNB17q4uLB8+XLu3LlDly5dVBxJYa6urnTv3p1Nmzbh4+PDvXv3iI6OJiIigh07dtCkSRNioqPB/4p1ZkZaZRhw83razlFERCQetCuNiIhIQhkG3LtrfVMYEgTRUVgAS0wM9v/dgcRsBkxgWLhy6zbB2FG8Vl1wSW8bsn//ftq2bQvAb7/9RoUKFRKd2tChQxk/fjxnz54lb968Cbo2PDyc1q1bkzFjRhYsWIC9vX2i85D4K1u2LEePHsXZ2Zno6GiioqL47otPeatJ3bRfdDDbQb4ikCVbamciIiKSaCqMiIiIxFd0FNz0++eT/JhEvWmNsViws7MHZxfImQfcPDBMJsqXL0/FihUxm838+OOPSUqzdOnSBAUFcf78+QRvFRseHk6bNm3IkCEDCxYswMHBIUm5yJONGTOGqKgo8ubNS548eciVKxdFiMAcFJDaqcWPS3ooVTG1sxAREUk0FUZERESexGKxLou5308huZphmu3AZIICxZg0byGbN29m69atnD9/Hjc3t0SHvXPnDrly5aJz587MnDkzwddHRETQpk0bXFxc+Pnnn1UcedoMAw78+exsh2syQbmqYK/fExEReTapx4iIiMjjhIbAsf3WWSKGJXl3CLHEQEw0nDtB3/o1OfD3XurWrcvcuXOTFNbd3Z1FixYxe/Zs1qxZk+DrnZyc+O233wgPD6dTp05xbyMrKSc81FpseFaYzBAanNpZiIiIJJpmjIiIiMTFMOD6ZetMkaexXarJRHBYGHN2/M3kufM5fvw4piS+OX7ttdf4/fff8fPzI2PGjAm+PiIigvbt22Nvb88vv/yCo6NjkvKReLrtb90K958dae4E3qNc5578NvZzKpUqAcComfM4cNKXBlVeIiwigsGdO6RiwibwzAc5E9bTRkREJK1QYUREROS/DAMu+MLdm0+9+WVYRCQfzZhD8249qVOnTpJiWSwW8uTJQ/bs2Tlw4ECiYkRGRtK+fXtMJhOLFy9WceRpuHja2tj3Acu3/cmw76dzcP4MfC9dpvFbH3Bw/gyyuWdOpST/I2NmKFomtbMQERFJFC2lEREReZBhwPmTqVIUAXBxcuSrN3pyaMvGJMcym81s376dw4cPM3LkyETFcHR0ZMmSJQC0b9+eyMjIJOclTxAS9NChlrVrUKFYUYZOnk73kV8yYfBAsrlnZuT0Wbz3rXWb59kr1tBw0Lt0HDaSUh26U73XQPxu3QagdMce/H3shC3eNwsW0XfU1wC89+0UKnXrS7nOPanXfzAXr/sBcOHadTzqt+CjKT9S/rVeFGvbhT99Dsedc1hIcj4DIiIiT5UKIyIiIg+6dBYCbqfqNqmO9na80cibO+fPJDlWoUKF+Pbbb/n888/x8fFJXD6OjixevBg7OzvatWtHREREkvOSx3jE796k99/mpz9WkS9ndjo0qBvnmL+Pn2Tc2wM4tngOJQvkY9Li3wAY1KE1U35dBoBhGExdupyB7VsDMLRHZ/6eO51DC2fyaqN6DJk0zRbvdmAg1cqU4uCCn/ikd/dY5+KTs4iIyLNAhREREZH77t6C235p4k2ei5MT6f0vQWTSixCDBg2iVq1a1K1bl+jo6ETFcHR0ZNGiRTg4ONC2bVsVR5LJ6NGj+fPPP4m1svkRPW027ztAxvTpOXXxMhGPmLlTvVwZ8uTIBkDVMqU4e+UaAN1eacS6v/7mTuA91u3eS3Z3d8oVLQzAml17qPp6f0p37MG4+Yvw8f23IJchnQvNar78T7yStngP0cpsERF5hqkwIiIiAhAdBRdOpYmiyH0mAyznTibLm841a9YQExND8+bNEx3DwcGBX375BWdnZ9q0aUN4eHiS83rRffrppzRs2JD8+fMzYcIE7t69i8HDTXdvBQTw9vhJrJrwFS+VKMaIabPijOf8QA8YO7OZ6BhrA9d0zs50blSfWSvW8P2S3xnYvhUAF6/7Mfib7/n5i084umg2M4cPIfyBoouTQ9zxHvIs7aIjIiLyHyqMiIiIgLXZahoqigA4OthjuRcAt28kOZazszNr165l3bp1/PTTT4mO4+DgwM8//0z69Olp3bq1iiPxEBISwsGDB1myZAljx47lzTffpF27dtSsWZOYmBjCwsK4dOkS//vf/3B3d+fwkSMPxRgwZgJ9WjWjXNHCfPfuWyxctzFWz5D4GNi+Nd/+vIT9J31pW7c2APdCQnB0sCdHFncsFgs//LY8cQ9ShREREXmG2ad2AiIiIqku8A7cu5smlwPYm01w6Qy4uYO9Q5JiVatWjffee49+/frRoEED8uZN3PaqDg4OLFy4kNdee41WrVqxbNkynJ2dk5TbsyQwMJBTp05x5swZLly4wOXLl7l+/To3b97k9u3bBAYGEhISQnh4OFFRUYC1Ea6DgwMuLi64urri5uZGlixZcHFxITg4GAcHB9zc3Bg3bhxlKle09rn5x+INm/G9eIUFnw8HIHNGV77/4B16fj6G5v8sc4mPAp45KZ4/L1VKl8TRwfq7VKZwIdrX86ZUx+7kzZGd2hXKsf3gIxqsPo7Ti/PzFxGR54+26xURETl+AEKDUzuLR7IAZs8CkDNPssQrVaoUISEhnDt3DrM58ZNHo6Oj6dq1K3fu3GHZsmW4uLgkS35P282bNzl16hRnz57l4sWLXLlyxVbouHPnDvfu3SM4OJiIiAhbjxaz2YyjoyPp0qWzFTo8PDzInj07np6e5MuXj4IFC1KkSBHy58+PvX3cn0XVqlWLPXv2MGLECN577z3rdsg3rsLlc8leqLsXHELx9l3ZO/sHcmfPlqyxyZ4b8hRM3pgiIiJPiQojIiLyYgsLtRZGHtHwMs2wd4ByVZNlycKdO3fIlSsXXbp0YcaMGUmKFR0dTbdu3bh16xbLly9P9eKIxWLBz88PX1/fWIUOPz8/bt68yd27d7l37x4hISFEREQQ80/PDDs7u1iFDnd3dzw8PMiRI0esQkfRokXJkydPkgpKDzp69CiZMmUiT54Hil7B98D3CFge0c8jEX5YupwvZs5j8KvtebdLx2SLC4DZDgoUg8weyRtXRETkKVFhREREXmwXTsOt66mdxRMZZjOmQiUhk3uyxPv9999p27Ytq1evpnHjxkmKFR0dTffu3blx4wbLly8nXbp0yZIjWAsdly9f5vTp05w9e5YLFy5w9epV/P39bYWOoKAgQkNDiYyMjFXocHJyIn369LZCR9asWcmRIwe5c+cmX758FCpUiKJFi5IjR45kK3Qki5gYOLgLeEZeopnMUPolLacREZFnlgojIiLy4rJYwGdXmmu6+kiublCsbLKF69y5M8uWLcPPz4+MGTMmKVZMTAw9evTg2rVrrFix4pHFkejoaC5cuGArdFy6dImrV6/i5+fH7du3uXv3LsHBwbZCh+Wfn429vb2t0JEpUyYyZ85M1qxZyZkzJ3ny5CF//vwUKlSIYsWK4eHxHMxcOLbPOpvpWZCMs5lERERSgwojIiLy4kqBJQspymyG8tWT7Q2oxWIhd+7c5MyZk/379ycqRmRkJGfOnLF9zZgxg9u3b1OsWDHu3r1LQEAAwcHBhIWFERUVhcViwWQyYW9vj7Ozs63Q4e7uTrZs2ciZMye5c+emQIECFC5cmKJFi+Lm5pYsj/eZctsfLp5J+7+bJjPkzAu5EtfIV0REJC3QrjQiIvLiCg1OkzvRPEqMxYJdZDg4/dvH4+TJkwAUL148wfHMZjM7duygWLFifPrpp4wYMYKwsDB8fX05c+YM58+f5/Lly1y7dg1/f3/bjitBQUG2HVcMw8BkMuHg4GArdERERHD48GFq165N3rx5bTM6ihYtStGiRZN1qc1zK3NWa2EkzTMga47UTkJERCRJNGNEREReKFFRUXz++ef88ssvOBgWLNFRNH25KsXz52XdX3/z65jPUjvFRwqLjMSleDlwz0pwcDAfffQRkydPpm3btixevDjOa4KCgjh58iRnz57l/PnzXLlyhWvXrnHjxg3u3LlDYGAgt2/fJjIy0naNyWTC0dERFxcXMmTIYNtaNlu2bHh6epInTx4KFChAkSJFKFy48ENb9cbExNC7d2/OnTvHqlWryJAhQ4o+L8+r6POnMN/2Jw11P3mYWxYoXCq1sxAREUkSFUZEROSF0qVLF8LCwpg9ezau548TFRrCrD9WExkdzdb9Pmm6MBJjsRCW0Z3Jy1bzxRdf2LaPdXd3p2jRorZCR0hICOHh4Q9tLevi4hLn1rJ58+Zl+vTpXL58GT8/v4cKHYlhsVjo3bs3Z86cYfXq1SqOJEBMTAzz589n2qTv2Dp5HI6P2Oo31ZnMULwcpHdN7UxERESSRIURERF5YZw+fRovLy+uXLlC5syZYf8O21Ka2SvWsHDdRjK7unL07HncXDOwdMxn5PDIQkxMDEMmTWPt7r0ANK5WmTFvvoGdnR09Rn6Js5Mjvpcuc9n/BtXKlGLOyA8xmUxcvXGTbiNG43f7DoVy58IwDBpVq0ybOrWo2LUP55f/grOTEwAt/jeMTg3rkiurB+99N5V9c6cDsHX/Qdv3W/cfpNfnYzh3NfYuOk5OTtSqVcu2tWzevHltO67kzZs3XjuuhIeHkz17dqpXr87q1auT5fm2WCz07duXU6dOsXr1alxd9Qb6cQzDYNWqVQwbNoyMGTMyZswYahTOB9cupr0GwSazdQlN3sKpnYmIiEiSpenZmSIiIsnp4MGDFClSxFoUgYf6i/x9/CTj3h7AscVzKFkgH5MW/wbA9N9X4ON7hgPzf+TA/B85eOo0039fYbvu6NnzrP52DMcWzWH/CV827t0HwFvjJlLnpfIcWzyHSe+/zbaDhwDIldWD2hW8WLRhCwAXr/ux78Qp2tXzfuJjuOh3g02bNvH111+TMWNGzGYzGTNmZP369cydO5cvv/yS/v3707BhQ/Lnzx/vbWidnZ1Zu3Yta9euZdasWfG65knMZjPTp0+nePHiNGnShKCgoGSJ+zzatWsXtWrVYsiQIYwaNYo///yTGjVqQPbc4JwGe7LYO0DugqmdhYiISLJQYURERF4YT5okWb1cGfLkyAZA1TKlOHvlGgAb9+6nR7PGODo44OjgwOvNm7Bx77+7uLSqXQNnJyccHRyoULyI7bot+w/yevMmAOTLmYN6lSrYrnmrY1um/LoMgKm/Lqdni6Y4Ojg88THkz5kDs9nMe++9x9KlSylevDgjR46M93PwONWqVePdd9+lb9++XLlyJVlims1mpk2bRqlSpWjcuDH37t1LlrjPi2PHjtGyZUs6depEr169OHz4MC1atMB0f+chk4mY/EWJiklDu9OYzFCohHWXJBERkeeA/qKlokgL+EfAyWDYeQc234KN/3xtuQ1/3YXTIXArEqLT2AxaEZFnUYUKFTh9+jR3797950jsbW+dHR1t/7Yzm4n+583o/Z1XHvTg985OD15nZ7vuv+Me9HK50sRYLOw8dIQ5q9byRusWANjb2WF5YNlEeERkrOvSpXNh6tSp1rH29ri4uDBgwIDHPu6E+PrrrylSpAg1atSIlUdSmM1mpk6dSpkyZVQc+cfly5fp2bMnderUoVatWvj6+tKjRw/s7OxijQsICKBZ2/aMXbwcI5m2aU4Ssxly5YMMGVM7ExERkWSjwshTFhYDR4Ng5Q3r118BcDwY/CLhbjQE/vN1JwquRljH7rwLf9yANTfhdLC1oCIiIglXpEgRWrRowRtvvGFd1mE2ERMTw3c//0pwWNgjr2tQ5SVmr1xDVHQ0UdHRzFm1lvqVKz7xft4VvJi9cg0Al/1usPnvg7HOv9mhDZ0+/JRqZUrZZqoUyJWTc1evc/deEIZh8PP6TbGuMZvtWL9+PX5+fgl9+PG2fft2/Pz86NevX7LFNJvNTJkyBS8vLxo1akRgYGCyxX6W3Llzh/fffx8vLy9y5syJr68v7777bpwNb319falSpQru7u743rrLuuOnrbM1UovJBFlzQc48qZeDiIhIClBh5CkwDLgRAX/egbU3rbNAIixgANGG9X8fxfLAmNAYOBYMq27A3gAIiHoq6YuIPFfmzJlDkSJFqFixIqU7dqfMq69z2f8GTo9ZxtK3dXPKFi5E+dd6U/613pQtXIg+rZo98V7fvfsWG/bso1znnrz33RSqlytNpgzpbec7NazL3aBgBrRrZTvmmS0r777WgYpd+1B/wP/ImSWL7VxMjEFwaCgdOnRgxowZiXsC4sHDw4MFCxYwY8YM1q1bl2xxzWYz33//PRUqVHjhiiOhoaF8+eWXFC1alKCgII4cOcKoUaNwc3OLc/yMGTMoV64cfn5+LF26lLlz57LT9zzkLZS6xZGb1+D8KQgNTr0cREREkpl2pUlh4THwdyDcjoKYZHymTVirWnlcoJwr2KvEJSKScBfPWN/opZCw8Agc7O2wt7fn+q3bVOr+Bpu+/4Zi+fMC8KfPYfp9+Q1Hfpn1yCU3D7KYTLQbOoIPPh9Nhw4dOH/+/ENLL5JTp06d+OOPP/D390/WHWUMw+Ctt95i7969rFu37pHFgedBdHQ0M2fO5NNPP+Xll1/miy++oFixYo8cbxgGEydO5KOPPiI0NNTWF8fR0RF/f3/rcxVwG86dBMPyUAPhp8ZkBmcXyJEH3LNaZ5OIiIg8o/R2OoUYBlwKhXW34GZk8hZFwDqDJAa4FAZrb8HNiOSNLyLyQsiQEcwpV1g4ffkKL3V7g3Kde1JvwGBG9O5uK4r0+nwMnT/+nMkfvB2voghY/2ib07ty7tw5PD09WbVqVYrlDrBw4UIyZcpEnTp1kjWuyWRi4sSJVK1alQYNGjzQ8+X5YRgGS5cupVSpUvzyyy8sW7aMJUuWPLYoEhkZSZ8+ffjpp584fPgwFSpUsP1u1KlT598CklsW1l2/yz3sUq8BqmGBsBC46AvH9kNYaOrkISIikgw0YyQFxBjW3iEpURB5FDugQDoo66oPbURE4i08FI4fgGRqMpri7B1Y7HuZadOm0aNHDxYsWMDatWtT9JZnzpyhePHifPLJJ3zyySfJGtswDAYPHsyff/7Jhg0b/t1G+Rm3ZcsWhg4dSmRkJF999RUNGzZ8YvHrxo0btG3bFg8PD+bNm8ePP/7Id999R8GCBdm6dSu//fYbrVq14vLlywwcOJAVK1bw7rvvMu7DIdbihGGk7u+xyQw581r7j+iFiIiIPGM0YySZRVlg2x3rDI6nVRQB6+yR86HWZTsWlbpEROLHyQXs7FM7i/hz86Bly5YcPnyYl156iQMHDnD27NkUvWXhwoUZP348n376KYcPH07W2CaTiQkTJlCzZk3q16/PnTt3kjX+0+bj40Pjxo3p3bs377zzDvv376dRo0ZPLIocOnSIypUr4+3tza+//sqECROYOnUqO3bsYN26dYwfPx5vb2+GDBlCsWLFWLlyJfb29jRs2NC6jKVcNchXFFzSp+4MEr9L1kJjtJqgiYjIs0UzRpJRjAHbblt3lUmtz2zsgJzOUDmTPrAREYkX/ytw9ULanzViMkOpCuCcjsGDB5M+fXoiIiIwmUyMHTs2xW9fu3Ztjh49ir+/P/b2yVtMMgyD9957jy1btrBx40bc3d2TNX5KO3fuHMOHD2fz5s189NFH9O3bF8cHtn5+nN9//52+ffsyefJkOnTowLBhw1i1ahUbNmwgR44ctnEbNmywFkL+kT59eg4ePEiRIkViBwwNhhvX4M5NsMTw9JnA0RGKe4GjUyrcX0REJOE0YySZGAbsupu6RRGwzhy5Hg6Hg1IxCRGRZ0mWHI/fHiyNiLCzB+d0APTq1YvZs2fTp08fZs2aRXh4eIrff926dURFRdGyZctkj20ymRg3bhz16tWjXr163L59O9nvkRJu3LjBm2++SaVKlShWrBi+vr4MGjQoXkURwzD4/PPPefvtt1m7di3t27fn7bffZuPGjWzdujVWUQSgQYMGrF27Fjs7OxwdHQkPDydv3rwPB3ZJb31RkmqfexkQGQknfSBKM0dEROTZoMJIMjkXat15Ji183nh/WY0asoqIxIO9PWT2SO0sHis0IoJOgz/AbDbj4uJC1apVuX79Ok2bNqVixYosWbIkxXNwdnZmzZo1rFmzhjlz5iR7/PszXxo0aEC9evW4detWst8juQQFBTFy5EhKlCiB2WzmxIkTfPLJJ/HeuSc0NJSOHTuyatUq9uzZg5eXF3369GH//v1s2rSJLA9s0fygX3/9la5du9KjRw/y58+Pk1McMzKuXoC7N61LW1LNP8WRU4fS/kwsERERVBhJFiHRcCTo6fYUeZIYYE8gROv1iIjIk3nmS73eDE9gANdu3mbFjl0YhkF4eDghISFYLBb8/f3p378/U6ZMeSq5VK9encGDB9O7d2+uXLmS7PFNJhNjxoyhcePGabI4EhkZyaRJkyhSpAhnzpxh3759fPfdd2TLli3eMS5fvkyNGjVwdnZm69ateHh40KVLFy5cuMC6devIlClTnNetWbOGDRs28N133zFt2jROnz798KDge+B/NY0UIwyICLcWakRERNK4tPkq8BliGLA3IG3MFPmvKAsc0pIaEZEnc3IBzwJpsjhiMpnJ492QjBkzPnQuODiYmzdvcvXqVXx8fJ5KPuPHj6dw4cLUqlULi8XC7NmzuXr1arLFN5lMfPnll7zyyivUrVuXmzdvJlvsxLJYLCxYsIDixYuzZs0a1q1bx/z58ylQoECC4uzevZuqVavy6quvMmfOHEwmE+3btycoKIiVK1eSIUOGOK+7e/cuffv25aeffrL9HjzU0NUSA2ePp/JMkf8wLNZ+JyF6MSIiImmbmq8m0aUwOHAvbc0WeZAd4J0F3BxSOxMRkTTOMODEQWvzyrTCbIYceSBXPi5evEjx4sXj7Cfi4uJCtWrV2LRp01NJ69atW+TKlQsPDw/8/PyYNGkSAwcOfPxFhgGREdbnNyLcOqvBsFgfo9kOXNJBOlfr0iasPTg+/vhj/vjjDzZt2pSgWRnJxTAM1q1bx9ChQ3FycmLMmDF4e3snKtacOXN4//33mT17Nk2bNiU0NJTWrVuTMWNGFixY8Ni+JD169CB9+vR8//33j77BpTNwyy+NzBb5D0cnKF0pTRYeRUREAJ6hPQrTppPBabcoAtaZLL4hUNkttTMREUnjTCYoVAKO7U8Tby4tgNnRGXJaG2zmy5ePSZMmMWDAAKKiosiWLRs3b97EZDIRFhbG5s2bcXNz49tvv6VHjx4pmtvBgwdxdHTk+vXrAOzfvz/ugVGRcNMPAm9DWIh1XZDJZC2IPPi5jMn873E7e0iXAVOWbHzx2WeYzWbq1q3L5s2bn2pxZO/evQwZMoTr168zevRoWrdu/cRtd+MSExPDkCFDWL58Odu2baNEiRLcu3ePZs2akT9/fmbOnPnYXX5WrFjB9u3bH79Vcnio9XlOS7NFHhQVZd39KWcczWJFRETSAJXuk+BuFISmxk54CWAAV8MhMo2+VhIRSVOcXKBQSesb9VQUY7EQFhkFRcvE2nu9V69eNGnShEmTJuHv78+PP/4IgIODA+nSpSMwMJDXX38dNzc3pk2blmL5jRkzhqgHdhzZt2/fvycNA4IC4fRROLwHrl+0LqW4P0PEEvPwjikPHo+Ognt34eJpTIf+4rM+PejV5TXq1KmDv79/ij2m+06dOkW7du1o06YNnTt35ujRo7Rp0yZRRZHAwECaN2+Oj48Pe/bsoUSJEty5c4cGDRpQqlQpZs+e/diiyO3bt+nXrx+zZs165DIbAPyupt2iCFhz87+aijvliIiIPJ4KI0lwOiRt9hb5LxNwMTS1sxAReUZkcocCxVK1OBJlwJvfz7AuQXiAyWRi+fLlDBo0CLAWSkaPHk3RokUJDw/Hzs6OokWLEhgYSL9+/ciYMSPfffddsue3fv165syZg6enJwDHjh2znggLgeP74fQRCLyTtG1jLdZiienmNQY3qMnPo0bQvGkT/Pz8kulRxHb16lX69u1LjRo1qFSpEr6+vvTp0+exhYvHOX36NFWrVqVQoUKsWbMGd3d3bty4Qd26dalRowZTpkzB/ISlJW+99Rbt27endu3ajx4UEwN3Ur5glGQWCwTeTe0sRERE4qTCSCLFGNaZGM/CZx8xwBkVRkRE4s89KxQu+fR7IphM4OBEeIHi/LpiFRERT953vUePHly9epWLFy/i4uKCr68vhQsXpkyZMgQFBfHOO++QIUMGxo4diyWZlgiZzWY6derEhQsXGDVqFGazmZNb1sPxgxAWmrxLkQwDDAtl8+Ri66SxfPzWQNsSnuQQEBDAsGHDKFu2LG5ubpw6dYohQ4aQLl26RMfcuHEjNWrUYPDgwUyaNAkHBweuXr1K7dq1adGiBePGjXviDJTffvuNvXv3Mnr06Mff7M6NROf5VFliwO9SamchIiISJzVfTaQ7UbDjDkQ/I8+eCWieDRxUChMRib/QEOtOH5ERKb9UwWy2zlbJVwTsHahatSpffvklderUeeKlbdq0oUmTJoSHh7Nu3TquXbuGj48P1apVIzQ0FB8fH0wmE87OzgwdOpSPP/74ibMV4i0ijJBDf+NoxOBgZ5c8MR8jMiaGtbv/plKH18iZyzPRccLCwpg8eTJjx46lRYsWjBw5kjx58iQpN8MwmDRpEqNHj2bRokW2mR4XLlygXr169O3blyFDhjwxzs2bNylbtiy//vor1atXf/zgY/utM3WeBSYTlK0CDo9uNCsiIpIaVBhJhCpVqhAUFkFQeCTXzvmSt3hpAAqULs/Ab2c98foti2ZT7KWXyVWoaJLymPx2DwqVe4kmPQexbs4PRIaH0fyNwXGOtTdB9czgodciIiIJY7HA9UvgdyVliiMmk3VXlgLFwC2L7fDw4cOJjo7myy+/fGKIVatW8fnnn7Nu3Try58/PiRMnOHbsGN27d8fPz482bdpw6tQpDh8+jMlkwsnJicGDB/PFF18krUASFgInD0FMdOJjJEJUTAzHzl0ke8365MydO0HXRkdHM3fuXEaOHEnFihUZNWoUJUuWTHJOkZGRDBo0iN27d/PHH3/YtvL19fWlQYMGvP/++7YlUE/SoUMH8ubNy7hx4x4/0DDgwJ/PTu8Osx0ULB7r91xERCQtUGEkkfYGwP7TFxjS6CVmHb8V7+tiYmL4tH09WvR/j5caNEtSDg8WRp7EDJR2hSLpk3RLEZEXV2iwdUvUkOB/3ogm8c+n2WwN4Z4V8hQE+9j7qu/YsYN33nnn0Tu+PCA6Opr8+fOzdu1aJk6cSL58+fjoo48AmDp1Ku+//z6GYTBgwADWrFnDsWPHMJlMODo6MnDgQMaMGZPwXhphIXDCx7pEIhVEx1g4c+UqGSvXIlc8ZnoYhsHy5cv58MMP8fDwYMyYMVSrVi1Zcrl58yZt27bF3d2defPm4erqCsCRI0do1KgRX3zxBT179oxXrMWLFzNixAgOHDiAi4vL4weHhsBJnzh/BlHR0YyaOY+f123C3s4OB3t7Cufx5LM3elKyYP4EPsJklDMveKbi/UVEROKgwkgirb0J5y9YCyMzDvsxussrBN29TWR4GEXKV6bv2Gk4ODqyZdFsdi77hYxZsnLF9zg123Zh0djhZPTIRjrXjHT7ZByZsmbnx6EDiAgNISoinPpd+tKs7zsA3L5+lUlvdSPghh858hfCMAy8vBvRpOegWIWRReNGEh4STPcR47h44kic8XI5QbXMqfu8iYg888LD4MY1uHWd8IgInBwciPd+JSYTEVFRWExmXPIXgSzZ4REFiaioKDw8PDhz5gxZs2Z9YuiPP/6YkJAQunXrRqtWrTh37hx2/yxtsVgsvPPOO0yZMoWsWbMyZMgQpk2bxsmTJzGbzdjb29O3b1/Gjx+Po+O/UwsNw4i7F0ZEmLWfyFOeKfJfUTEWDp85S47ajfB8YOZIUFAQr7zyCtOnT6d48eLs2LGDIUOGEBwczJdffknTpk0TtctMXA4fPkzLli157bXX+Oyf7YXBulNPs2bN+Pbbb+nUqVO8Yvn7+1OuXDmWL19OlSpVnnzBLT9rsS6Oni5dhn9BaHg4Mz8ZgpurK4Zh8OumrZjNZtrWfUwz15SWIRMUL5d69xcREYmDOk4k0oPb35rt7HhnykLGrtvHhK1HiYmJYfPPM23nT+79kw7vjWTs+v00f2MwBcu9RM8vJjJuow9la9UnW578jFi8ka83HODLNXvZOH86V3xPADDz47co/XIdvt12jF5fTOL47m1PzO1R8cLS+NbCIiLPBGcXyFuIy1ly02vUOIzsuSFDRusygfvLYh78MpmsPRUyuYNnATZe9Oe1MRMhu+cjiyJg3YK3du3abNq0KV5p9ezZk/nz51OyZEly5szJ6tWrbefMZjMTJ07k1q1blC9fnv/97384Ojoyf/58ChYsSGRkJFOnTiVDhgz07duX8PBw7t69i6enJ9u3b499I8OA08dSvSgC4GBnpmzhgqycPpkrV67Yjvft25ddu3bRq1cvmjVrRteuXenfvz8HDx7klVdeSbaiyO+//069evUYPXp0rGVJO3fupGnTpkybNi3eRRHDMOjfvz+vv/56/IoiAMH34iyKnL50hd+37uCn4daiCFh3NGpfvw5t69Zm0979VOvZn/Kv9aJMpx78sv7f3zHvN97m/e+mUqP3IAq27MTQSdZtn/8+doLSHXvEuk+5zj3Zdegos1esod2QT2zH//t9LM9KPxQREXmhJG4POsHywDwbi8XCH1PHcXDLGiwxMQQH3sXJ5d9u9sUr1yBH/kKPjBURFsr0If25ePwQJrOZO/7XuHD8ELmLluDYri30/GIiAFnz5KNMjXpPzO1R8UqWLJH4BywiIrGsXrsOk1sWzHkKWg8YBkRHWQsGFou1IGIyg4MD2P3757Zag0Z07tuP4OBgMmTI8Nh7NGzYkPXr18frzXXBggUpW7Ysy5cvZ8CAAUyZMoXmzZvHGuPm5sbq1as5efIkHTp0oGvXrjRt2pTx48fzzjvvcP78eWbOnMmsWbMoUaIEN27coFWrVpw4cYLs2bNbg1y/DJHhCXuyUpCDnR09X2lEu9e7M3nmbPbs2cPy5cuJiYlh165dvP322yxduhQnJ6cnB4snwzAYNWoU06ZNY/Xq1VSqVMl2btOmTXTq1IkFCxbQsGHDeMdcuHAhvr6+/Pzzz/FPJCruXYsOnvKlSJ7cZM7oGuf5CsWL8uePk7Gzs8P/9h0qdutLo6qVbeMv+fmzffpEgkJDKdSqM71avkKlUiXIkM6Fbft9qF3Rix0HD2E2mXi5XGl8L12Of84x+pRGRETSHs0YSQZ//raQE3v/5PNlO/hmyxEadR9AVMS/Lxqd0z/+he/CLz/ELVsOvt5wkPGbDlHYq3Ks6xP6ydaj4mnRlIhI8lm1ahWvvPLKvwfuzwxxTgfpMoBLeuvsErvYn0G4u7vz8ssvx5rR8SgNGzZkw4YNxHfVa69evfjpp5/o0KED+/bt49y5c3GOK168OIcPH2bVqlXs37+ftm3b0rx5c1atWkXu3LmJjo7myJEjxMTEEBAQQLNmzYiJibF+2n/9UvJux5sMHOzsmPPJEOrUrk2nTp0ICwuzndu5c2eyFkVCQ0N59dVXWbFiBXv37o1VFFm1ahWvvvoqS5cuTVBR5Nq1awwePJg5c+YkLNd4/hyOn7uAV+deFG37Gm+Pm8jNuwG0G/oJpTv2oNGb73Mn8B6nLv67lW77+t6YzWYyZchAiQJ5OXvlKgBvdWzDlF+XAfD9kmUMbN86/rnaGM9Os1gREXlhqDCSSOYHahUh9wLI6O6BSwZXQu4F8ufvCx97bTrXjITeC/z3+sAAPHLlwc7enksnj3Jy7w7buZLVvNmyaDYAt65e5ujOzU/M7VHx7JJn5rCIyAsvPDycrVu3JujN74PatWvHr7/++sRxRYoUwWw2c/LkyXjFbdOmDfv378ff35/u3bszbdq0x45v0qQJ169fZ+zYsfz444907NiRoUOH0rlzZ9sYwzDYt28fFStWxHLmeMpvW5xIbunT8eXbAzAMg9KlS+Pt7U2TJk1iFS6S6sqVK9SqVQsHBwe2bdtGzpw5beeWLFlCz549WblyJbVq1Yp3TMMweOONN+jXrx8VK1ZMWEKPqC+UL1aU05evEBAUBEDJgvnxWfgTb3VsS2BwCP2/+gbvCuU58sssfBb+RO7sWQmPjLRd7/xAnxk7sx3R/8zyaF+/Dn8dPc7BU75s2X+Qzo3rA2BvZ4flgSLNg7FERESeBSqMJJL9A0WG2u27ERYSxDu1SzG+T3tKVKn52Gvrd+nLrxM+570G5Tm8fSNt3/mYjQt+ZEjjSvz67ReUqPLvC6qen3/H4e0beLdeOeZ+9h7FKlUnnWumx8Z/VDxH/bRFRJLFtm3bKFOmDFmyJG7b0ZYtW7Ju3TpCQ0MfO85kMtmW08SHs7Mzr776KrNmzaJfv37MmjWL8PAnL3sZPHgw9+7do3PnzgwaNIh169bRqlUrmjZtSvr01u3Msjg5EBl0L155pAqLhfY1q/HtN98QEhLCrFmzWL16NVOmTEmW8H/99RdVqlShQ4cOzJ07F2dnZ9u5uXPn8tZbb7F+/XoqV66coLhz587l0qVLfPzxxwlP6hFbLRfJm5uWtavTZ9TXBAYH246HhFl/FwKCg8mfKwcmk4kNe/7mzOWr8bqdg709PVs0pcX/PuS1xvVJ989zUCh3Lg6fOUdEZCSRUVH8uukJ/dCSqceLiIhIctGuNIm06y5cj3tpb7KKCAvD3sEBO3t77vpfZ0iTSoxYvAnPwsUSFMcElMhg/RIRkaR5++23yZ49Ox9++GGiYzRo0IB+/frRtm3bx45bsmQJc+bMYeXKlfGK6+PjQ8uWLTl37hxNmzala9eudOnSJd553bp1i1dffZVNmzZRvnx5lixZwq5du8h85zqNq1TCzi4NV9nNZshXhIkLFjFhwgS2bNlC/vz5kxx23rx5vPvuu8ycOZNmzZrFOjd16lRGjx7N+vXrKVEiYb28rly5QoUKFVi/fj1eXl4JT+zCKbjlH+epyKgoPp8xl0UbN+NgZ0/mjK7kypqFod1f43ZgIAPGTMDDLRNlCxdiz9ETfPvuILwrlsf7jbd5r0tHmtV8GeCh76/euEmeZu059et8iuT9dyegfl+OZ9Pf+ymQKyfF8uXh+q07/Drms4cTs7OD8tUT/lhFRERSkAojieQbAseCIKUnFF84fpjJb3XDMAxioqJ4pe9gGnTpk+A49iao4gY5km+ZtYjIC6tIkSIsWbIkcW9m/zF9+nS2bNnyxGabt2/fpkCBAty8eTPe/ScqVqzIl19+SWhoKF9//TU7d+5McH5HjhyhY8eOnDx5km6vdmLW//pjehZeMjing9IvMXnyZMaPH8/mzZspUKBAokLFxMQwbNgwfvvtN5YvX06pUqVinR8/fjyTJ09m06ZNFCxYMEGxDcOgSZMmVK9eneHDhycqP25eh8tnn2rPl/mr1/Pz+k2s+nZM4gJkyAjFvZI1JxERkaTSrjSJlNnB2mfEksKvEfOXLMu4jT5JjmMxrDmLiEjS+Pr6EhoaSrly5ZIUp1WrVnzwwQeEhYXh4uLyyHFZsmShePHi7N69G29v73jF7t27NzNmzGDhwoW8+eabHDp0KMH5lilThuPHj7N8+XKOrF9NWFg46Zyfgep6ZDiEBjNo0CBMJhN16tRh8+bNCS5cBAYG0rlzZ8LCwtizZ0+sZVOGYfD555+zYMECtm/fTp48eRKc5k8//cTNmzcZOnRogq+1Se+KdU7o09Hozfc4e+Uaf4wfnfggGR6/HFhERCQ1pOH5sGmbmz3EPAMfnN1nbwIn/bRFRJJs9erVNG3aNME7hv1XtmzZqFChAuvWrXvi2Pu708TXq6++yvr16wkICKBv375MnTo10Xm2bNmSjwf1ezaKImBtSBpkbXA+cOBAPvjgA+rUqcPZs2fjHeLMmTNUq1aN/Pnzs27duoeKIkOHDmXJkiVs27YtUUWRixcvMmzYMObMmYODQxI+tXBO91Sb4a6bNI4zvy+kZMH8iQtgtvunmCMiIpK26K1yIjmYIavjk8elBWYgf7rUzkJE5Pnw0Da9SRDf3WkaNGgQ7wasAG5ubjRv3pz58+fTu3dvFi1axL17iWycahgQ9vgmsWmKYYHgANu3AwYMYNiwYdStWzdexZFNmzZRvXp13nrrLb7//vtYhQuLxcKbb77Jpk2b2Lp1Kzly5Eh4eoZBr169+N///kfp0qUTfH0sZjM4OT95XFphWFQYERGRNEk9RpLAPwL+CoDoNP4MWqKjuLf1Vwp65uDevXsEBgYSGBjIK6+8kuCpxSIiL7Lg4GBy5szJtWvXcHVN+hs8Pz8/SpQogZ+f32P7h0RGRuLh4cH58+fjvRPOtm3bGDhwoK1XSO3atRk4cGDCk4wMhyP7HpqZkL9FR1Z+8yWlC//7d+Slbn0Z93Z/vCuWj1foI2fO0vUT67KMO/fucS8klPw5rcWGPq2aMbBD64TnC+DgBOWqxDo0ffp0vvjiCzZv3kzhwoUfusQwDL7//nu++OILfvnll4eWLcXExNCnTx98fX1ZtWoVmTIlbknI1KlTmT17Njt37sTePhlWNPtfhavnn2qfkURL7wol4ve7ISIi8jSpx0gSZHO0LlFJ64WRmLs36NPtNQzDwN7eHkdHRyIiIsibN68KIyIiCbBx40aqVq2aLEURgBw5clC2bFk2bNjw0G4nD3J0dKR27dps2rSJDh06xCt2rVq1iIiIYO/evfTv358333yTAQMGJHwJUEiwdXvVFPhbV6ZwIXwW/gTA7BVrWPnn7rh3MkmoqEiIibHugPKPvn37xuo5UqRIEdu5yMhI3nzzTXbu3MmuXbse+tsYFRVF165duXXrFuvWrbNtYZxQ586dY/jw4ezYsSN5iiIAHtnhyvnkiZWCQsMjSFeoZGqnISIiEictpUkCkwmKpgO7Jw9NNXYm8C7qyZ49e7C3tyc6OprQUOuU6Ph+6igiIlb3+4skp3bt2rFkyZInjkvochqTyUTPnj356aef8Pb2JiYmhh07diQ8wYjwRPWx8H7jbVbu2PXI7x/nyJmz1OwziApdelOyQze+Xfjv8+N/+w6t3/+Isq++TplOPZi7au3DAcxmiIx46HCfPn0YOXIkdevWxdfXF7BuT9ygQQOuX78eZ1EkPDycdu3aERwczMqVKxNdFLFYLPTs2ZOhQ4cmeFvfB124cIF9+/Zx8OBBDh06xM6/9nD4ynUsaXwCcHhEBOv27EvtNEREROKkwkgSFUwPjmn0WTQB7vaQ3REqVarEr7/+iouLC/b29mTNmpVatWqRO3duJk6ciOVZmIIrIpKKDMNg9erVydZf5L42bdqwYsUKIiMjHzuuYcOGrF+/noSsgO3evTtLliwhJCSE/v37J64Jq2Gx9hmJQ7uhI/Dq3Mv2dfzchYTHj0P+nDnZ+P03HJg/g72zf2D6shWcOG+N/da4iZQuWJDDP89i/eTxDJk0jaNnzsWddxx69erFp59+St26dfnjjz+oXLky1atXZ9myZWTMmDHW2NDQUFq0aIGTkxO//fYbzs6J7+fx/fffExUVxeDBgxMdA+CVV16hZs2a1KpVi4oVK1KjRg3e/XqC9dOatMpkZsPh43Ts1Ino6OjUzkZEROQhafQt/bPDzgRV3dLmrBGzCSq5/ftaqWXLlowYMQKz2cyRI0e4cuUKL7/8Mu+//z7p06ene/fu3Lp1K1VzFhFJqw4fPoyzs3OsJRjJwdPTk5IlS7Jp06bHjitWrBiAbaZDfOTKlYuaNWuyZMkSunXrxtq1a/H3909Ygo8pxPz61af4LPzJ9pXo3Ur+IzQ8nF6fj6VMpx5U7z2Iazdvc+i0tXHqxr/380ab5gDk9MjCKzWqsWX/wdgBTDy250bPnj1p06YNrVu3ZsCAAYwePRqzOfZLonv37tG4cWNy5szJwoULcXRMfMf106dP8+mnnzJr1izs7JL2iqF79+5ERUURHBxMTEwMuXPnZtKMmYS7uKbd4oidHa37v0l0dDQ9e/ZM7WxEREQeosJIMnB3hAJpbEmNHeDlCi7/SeqDDz7gwoULeHh4kCtXLhYvXkxISAiffPIJa9euJVu2bFSpUoWdO3emSt4iImnV/d1okrpNb1ziszuNyWRK8HIasM6Q+Omnn3Bzc6Nt27b89NNPCUvOlLiXCvZ2dlgs/xZVwp8wI+ZBH075kRxZ3Dk4fwaHFs6kcqnihEf8e/1/fwYP/UwMrMtp4mAYBqNGjeK3337j448/ZsKECZw4cSLWmDt37lC/fn1KlSrFrFmzktQPJCYmhtdff53hw4dTtGjRRMc5e/YstWrVYujQocTExNiOX7t2jRIlStD6nffBLg22jjOZoVAJHJ1dmDNnDvPnz+fQoUOpnZWIiEgsKowkk9Ku4Gxn/ZAqtZkBD0fI5/LwOZPJRM6cOWMds7e3Z9iwYfj7+7Nx40aio6OpWbMmuXLl4ptvvtEyGxERUqa/yH1t2rRh+fLlREVFPXbc/eU0CdG0aVPOnj3LiRMnGDBgANOmTYv1xvqJ7OwSVRwplDsXfx8/CcDxcxfw8T0T72sDgoLJkz0b9vb2HD1zjh0+h23n6leqyPTfVwDgd+s2q3f+RZ2HdsExwPzwxxVhYWF07tyZ5cuXs3fvXj799FO+/PJL6tevz/HjxwG4ceMGderUoVatWkyZMuWhmSQJ9d1332FnZ8ebb76ZqOuPHTtG1apVKVKkCDdu3GDjxo2xGvBaLBacnZ2ZM28+FCz+yIJQqjCZIUs2cHUDoG3btlSrVu2xjYZFRERSQxr66/lsszNBbXdwSuVn1AxktIeqmRM3o7Zu3brs37+fa9euUbt2bYYNG0a6dOno0qULN27cSPZ8RUSeBbdv3+bw4cPUrl07ReLnzZuXwoULs2XLlseOq1evHtu3b39iP5IHOTg40L17d2bOnEmFChXIkSMHa9asiX9yzumsazMTaEi3zqze9Zd1C9/5v1C+2MNb5D7Kx7268uOylVTq1pcvZs6jVvlytnMT33uLQ6fPUvbV12kw6F2+GtSXUoUKxA5gGOAUux/I1atXqVmzJmazmW3btpErVy4AunXrxpgxY6hfvz6bN2+mVq1atG7dmq+//jrJs4NOnjzJl19+yaxZsxJcYPHx8aFixYqUKVOGkJAQdu7cycmTJ/H29qZWrVq2eOnSpeObb74hR44ckDEzZMmedoojDg6Qp1CsQytXrsTf35+PP/44lZISERF5mMlISBc3eaKQaNh6ByIsKbKz4WOZAVd7a4HGIZleE1ksFsaNG8eECRPw9/enQoUKfPPNN9SqVSt5biAi8gz4+eef+fnnn/njjz9S7B7jxo3D19eX6dOnP3bcSy+9lOD/H/b19aVmzZpcvnyZhQsXsnjxYlavXh2/i6Oj4dDux/YaSXOc00Hpl2zf7tmzh7Zt2/Lmm2/ywQcfxFnwmDBhAu+//z5vvfUW33zzTZJTiI6OpkaNGnTr1o0BAwbE+7o9e/bQp08fjh49Srly5ZgxYwYVK1YE4NChQ/Tv3x+At99+m9dee42SJUvi4+Pzb+HFMLiybT3ZXRxxSGI/kySxd4CSFcDR6aFT95/rCxcukDt37lRITkREJLY08pHC8yO9PdTNAunsnm7PETvA3QG8syRfUQTAbDbzwQcfcP36dbZs2YLJZMLb25ucOXMyduxYLbMRkRfC/f4iKaldu3YsW7bsibt2JGY5TdGiRSlevDgrV66kY8eO/P3335w/fz5+F9vbp83eFY+T4d/dZebPn0+zZs2YOnUqQ4YMibMocurUKSZMmECXLl34+eefOXLkSJJTGD9+POnTp6dfv37xGr9161ZKlChBtWrVcHFx4fDhwxw8eJCKFSsSFBTE//73Pxo0aMDrr7/On3/+SceOHZk2bRpLlizBbDZz/vx5xo0bh3uWLBRs0Iw7URZCwx/esjjlmaxFkRJecRZFAAYPHkyhQoVSbGmaiIhIQqkwkgJc7KCBB+RP93SeYDuglCvUcgf7FGxyUrt2bf7++2/8/PyoV68en3zyCS4uLrz66qv4+fml3I1FRFJRTEwMa9eupUmTJil6n/z585MvXz62bdv22HENGzZkw4YNCY5/vwmri4sL3bp1Y9q0afG/OL1rgu+XasxmyJCRmJgYhgwZwogRI9i6dSvNmzePc/jhw4epU6cOn376KbNnz2bChAk0bNiQw4cPxzk+Po4dO8a4ceP46aefnriEZt26dRQuXJi6devi4eHBqVOn2LNnD6VLl8YwDJYuXUrJkiW5e/cux44do0+fPraYvXr1Aqw7FpUsWZKhQ4dy9+5d6tWvzxmTE1sPHX2qy2piDMNaDClZAZziaHT2gDVr1nDs2LEnzpASERF5GlQYSSF2JvDKaC1WuJhTZvaIHeBmD/U9oEj6p7dLX7Zs2Zg/fz6hoaF8+eWXtrXaFSpUeOJ2kyIiz5q///6bXLlykTdv3hS/V3x2p6lWrRonTpzgzp07CY69e/durly5Qr9+/Zg5cyYREfGcUeCeLc5mpmmSYRBkdqBly5bs3buXvXv3UqpUqTiH7tu3j4YNGzJhwgRef/11ADp16sS3335Lo0aNErV7SlRUFN27d2f06NHkz5//keOWL19O/vz5adKkCXnz5uX8+fPs2LHDth30uXPneOWVV/jkk09YsGABs2bNImvWrA/F8fDwICIigoiICGJiYnB2duaTTz7hxo2bTN+wDQoUBzs7jBRsD2+xWDBMZqYvW0n3rycRE48ZRgULFuTNN9/krbfeIjg4OMVyExERiQ8VRlJYFkdolBVKZgBnc/LM6LAzgasdlM9kXbaTIZVmOJvNZv73v/9x7do1duzYgYODAw0aNCB79ux8+eWXT5wOLiLyLFi1atVTm/Lfrl07fv/998fuGuPk5ETNmjUTXIhOly4dHTt2ZPbs2RQpUgQvL68nFmFsMnsk6F6pKdjOkSo1apI3b17Wr19PlixZ4hz3559/0rRpU6ZPn07Hjh1jnevYsSMTJ06kUaNG+Pj4JOj+Y8aMIUuWLPTu3TvO84sWLSJ37ty0adOGEiVKcPnyZTZv3ky+fPkAiIiIYNSoUVSuXJlatWpx8ODBx/aTyZIlC2XLlsUwDEwmExkyZKBq1arcuHGDbNmyQWYPjNKV2H38JNEpsPw1Brjsf4O72fKw3/8uc+fPx9PTk19//fWJy22/+eYbMmXKRMuWLZM9LxERkYRQYeQpsDNB0QzQNCtUdYOsjtZtfe1N8fsBmB8Y6+lknYXSMKt1O96nNUvkSapXr86ePXu4ceMGjRs35rPPPiN9+vS0b9+ea9eupXZ6IiKJtnr16hTvL3JfoUKFyJUrF3/++edjxyVlOc3MmTOxWCz079+fqVOnxu9Csxmy5kw7f3QeIdow6Py/D3jzzTeZMmUKDg4OcY7buHEjrVu3ZsGCBbRo0SLOMe3bt2fy5Mk0btyYgwcPxuv+hw4d4rvvvmPGjBkP9TKZPXs2OXLkoHPnzrz00ktcv36dNWvW4OnpaRuzZcsWvLy8+Ouvv9i3bx9Dhw7F0dHxsfds3bo1a9asYenSpeTMmZOuXbtiMplshRE/Pz+KlChJjR5vYBQqCRkyWbfRTeLPMjI6hnAL2OUpRI1+g/lw1Jf07dsXOzs7/P396d69O8WKFXvsrBuz2czy5cvZsmVL/JsBi4iIpAAVRp4ikwmy/1PYaJ4NXnaDUhkgh9O/s0nM/FsISWeG3M5Q1hVqZIYW2a3b8LrH/TovTfDw8GDOnDmEhITw9ddfs3v3bnLnzo2Xl1eiXsSLiKSm69evc/78eapVq/bU7hmf5TQNGjRg/fr1JHRjuYoVK+Lq6mrruXHx4sX499LIlitB90oNl6/74ZDZA09PT1avXs369evZuXNnrOdp5cqVdO7cmd9++40GDRo8Nl67du34/vvvadKkCQcOHHjs2MjISHr06MHYsWPJkyeP7fiUKVPw8PCgd+/e1KpVi9u3b7Ns2TLrbI5/+Pv707VrV3r06MGXX37JH3/88dhlOPe98sorrFy5ku3bt9OmTRtOnTrF6NGjAbhx4wbnz5+nSJEinD17lkyZMuGQJRsULwelK0K2XIRGRBADWEymJzdTN5mty6lMJsiUmZk79uBRtykBTulp064dS5YsoWTJkrbhoaGh+Pv7Ex4e/tiwVatWpU2bNrz66quaaSoiIqlGhZFU4mCGrE7WmSTVM8Mr2aBldmidw/rVMjs0yQZV3KBQeuuSHLu0/UFdLGazmbfeeosrV66we/du0qVLR6NGjciWLRufffaZXvyIyDNhzZo1NGzYEHv7p7dmsV27dixduvSxb1RLlChBdHQ0p0+fTlBsk8lE7969mTFjBvb29vTp0yf+s0acnCFz1jQ7ayQkLIwRM+by22+/0bJlS9q1a0ebNm2oXbs2gYGBACxZsoRevXqxcuVKatasGa+4bdu2ZerUqTRp0oT9+/c/ctzo0aPx9PSkR48eWCwWxo8fT+bMmXn77bdp2rQpd+/eZfHixbi5udmusVgs/PDDD5QpU4acOXNy7NgxWrVqFefOOf91f9bQrl27bIW7DBky4OzsDMCKFStYvHixrX9HrEKLkwt30mcmZ9N2RBUuA/mKMHnJ71ic04ODo20nojtBwYRZAPeskKcgFCsL5atDkTKky56LkJAQihUrRq9evWzNYTNnzmyb5TJ48GCqVKnyxMeycOFCYmJi6NGjxxPHioiIpASTkdCPm0QS6c6dO7z33nv88ssvREdH06xZM7777rtYn6yJiKQlbdu2pWXLlnTr1u2p3rds2bJMmTKFGjVqPHJMz549qVixIgMHDkxQ7Dt37lCwYEHOnz9PWFgYpUqV4uLFi2TMmPHJF8dEw5G9kMaK25FR0azb8zctBg996Jy3tzdbtmxhzpw5DBs2jLVr11K2bNkE32PZsmW88cYbrFq1ipdeeinWuQMHDtC4cWMOHDjAnDlzGDNmDOHh4XTv3p3vvvuOdOnSPRTv4MGD9OvXDwcHB6ZOnUqZMmXilYdhGNStW5ddu3axe/duKlSoEOe4cuXKceHCBe7duwdAnTp12Lx5s+38zz//zMKFC1mxYgUAefLk4c8//7T1OgFwdnamTp06rFmz5qH4P/30E3369MEwDDJlykTmzJkpVqwYhQoVImvWrDg4OPDJJ59w9OhRSpQo8cTHtXTpUtq3b8+BAwfw8vKK13MhIiKSXDRjRJ4ad3d3Zs6cSXBwMN9++y379u0jX758lC1bNs4XXSIiqSkyMpJNmzbRuHHjp37v9u3bx3s5TUK5u7vTpEkTFixYQK5cuahfvz7z58+P38V29tZdTp7iFrDx4eDkxJ3/s3ffcTmv/wPHX/ddlAaVJBlJSEYpyt6yR9kRiY5N9uZwbNkcHHvvPbJlREYptJQQIaGlPe7790c/9/d0KsqKc67n4+HxOH0+1+f6vD93nLrf93W93xrFc1xpcfXqVXR1dRk/fjyXL1/+oqQIgK2tLRs2bKB9+/bcvXtXcTwlJYV+/fpRr149qlSpwpw5c3B0dCQ+Pp6NGzdmS4rExcUxevRo2rRpw+DBg7l27VqekyIymYxGjRrh6emJt7d3rkkRyGwzraWlhYGBAYULF86ydQcytxR16NBB8XXp0qV5+fKl4uvAwEBSUlK4cOECkZGR2eaPjo5WtA2OjY3l5cuXXL58mdWrVzNr1iymTZuGlZUVTZs2/fw2HTKTkPXq1csSkyAIgiD8KD/XbzbCf4JUKmXYsGE8f/6c27dvU7RoUdq3b4+uri4zZ84U22wEQfgpeHh4YGJiku0N5Y+Ql+00LVu25MqVK6SlpeV7/o/baeRyuaIIa54XkBbTAS3dn2ZLTWJyMr6xiTg6O+Pu7q54sw6ZP2/kcjnR0dG8f/+eLl26ZElq5Ffnzp3ZtGkTHTp04M6dO6Snp9OkSRMCAwM5f/48Q4YMIT4+ntWrV2crmiqXyxV1OD58+IC/vz8DBgzIEu+nyGQy6tevj7e3Nz4+PlSvXv2T48PCwhTbWZ8/f87SpUsV59LT0zl79my2xEh4eLji6xUrVijuO3r06Gzzv3v3TvHfRkZGeHp6AplFZj+6fPkyCQkJdO3aNU/PePr0aSIjI5k6dWqexguCIAjCtyISI0KBsrKywsPDg6ioKOzs7Fi6dClFihTB1taWsLCwgg5PEIT/MDc3tx/WpvefTE1NKVq0KLdv3851TIkSJahYseInx+SmWbNmxMXFce/ePZo1a0ZaWtpnO+FkUb4yqGkUeHJELpFw5OZdarVojbm5Ofr6+kyYMAElJSUkEoki2VO8eHHkcjkvX76kTp06WFpa4u/v/0X37NixI+vWraNp06YUKVKE27dvM2rUKBISEliyZEmO9WhCQ0Np164ds2fPZt++fWzevBld3by3QJbJZFhZWfHgwQMePHjw2a0paWlpxMfH06dPH8qVK0fJkiWzdL/x9PTE0NAwy7G/rxiJj49n586dQGZC59ChQ9n+njVr1oz58+czZcoUIiIisLCwoFGjRixbtkwxRk1NjdOnT3P8+PE8rUrS0tJi8eLFLF68OEuSRhAEQRC+N5EYEX4KWlpabNy4kQ8fPvDnn3/i4+ODkZER1atX58SJEwUdniAI/0GnT5/+YW16c5Kf7jT5JZVKcXJyYvPmzUgkkvy17s2cgNeaJQgJf0lGQZUqk0qR6BngMGEq/v7+yOVyTE1NuX//PlWqVKFjx45oampSpEgR3r59i5mZGUWLFkUul/P48WNq1KhBgwYNePbsWZ5vmZiYiLOzM7169QIyt6vMnj2b5cuX57jyIyUlhTlz5lCnTh2aN2+Oj4/PJ+vG5EQmk2FhYUFQUBD+/v5UqlTps9cMHjwYgE2bNuV4/uTJk9m2rPw9MXL9+nWSkpKAzIK9JUqUwM/PL8v41q1bM3HiREVB9U2bNjF79mwCAgIUdU0AmjRpwqhRoxgwYACvXr36bOyjR4+mYsWKtG3b9rNjBUEQBOFbEYkR4acilUoZNGgQYWFheHl5Ubx4cezs7NDR0WHatGmkpqYWdIiCIPwHPHnyhOjo6E/WcPjePtYZ+dQWl1atWn1RYgSgf//+7Nu3j8TERBwdHTlz5kyOtST+7uMWjI4dO2JQtiytRk5EqlEss5XrjySVgn5ZKFMBJBJMTU158OABp06dwsfHh4CAAG7dusXjx4/Zv38/enp6hISEEBERgaOjo6LQ7P3796lQoQKtWrUiIiIi19vFx8fj4OBAsWLFOHz4MAsXLmTYsGHUr1+fNWvWKLaR/N3Heibe3t54e3szYcIEChUqlK/HTE9Pp0aNGoSGhhIQEICRkdFnr4mMjGT79u3o6+tn287z0alTp+jYsWOWY39PjLRp04a4uDjGjRuHvr4+L1++ZODAgTnOpaysTNu2bVmwYAENGzakaNGizJkzJ8uYFStWUKFCBRo0aJCneiNnzpwhICCAv/7667NjBUEQBOFbEIkR4adlaWnJ1atXiY6OpmfPnqxcuRJ1dXU6duzI06dPCzo8QRD+xdzc3Gjbtm2e6z98D9WqVaNIkSJ4eXnlOqZBgwYEBAQQHR2d7/nLli1L3bp1OXz4MFpaWnTp0oXNmzfnOv7w4cMUL16c7t27c+rUKSQSCSvXrEFiYgYlS/+Q5IgcCXGJiURrlwQDw2xbeVq3bk3btm0xNDQkLi6OChUq8OrVK4KDg+nbty9FihRh586dFC5cmEWLFqGpqQnAjRs3MDAwwNbWlpiYGMV8MTEx9OjRAy0tLc6ePcuqVauIjo6mbt267N69m6NHj7Jjxw46d+7MzZs3AYiIiKBPnz4MGDAAV1dXjh07lqXTS16lp6dTvXp1Xrx4QVBQUJ7n6Nq1KyVKlKBKlSo5ng8NDSUqKopatWplOV6mTBlFYkQikaCpqUnp0qVJTEz87D1XrFhBWFgYfn5+dOnSRbEN5+88PDx49eqVYjXLpxgZGeHi4oKLi0uW1SeCIAiC8L2IxIjw0ytatCjr1q0jPj6e9evX4+fnh7GxMaamphw9erSgwxME4V+oIOuLfCSRSOjWrRsHDx7MdYyKigoNGjTI0oY1PwYOHKhIhgwbNoy//vqLjIyMHMdWrlwZuVxOfHw8AIULF6Z169aZqzfKGIFpTSiU8wqFb0Wirslq99v0HjYq28qDtLQ0+vTpQ3h4OH5+fnz48AF7e3uGDx9O9erV6dmzJ8ePH6dcuXLExsYyceJEmjVrxqZNm1BXVwfg3LlzFC9enK5du9K+fXuKFy/OtWvX2LhxI+/evWPo0KEkJibi5OTEmjVrKFGiBG3atGHnzp3Y2toybtw4atSoQdmyZfH396dTp05f9JypqalUqVKFiIgIgoODKVOmTJ6u8/Dw4MaNG4wYMSLXosEft4j9M+n3z640kJksSUlJ+ex9jYyMqFy5MqNHj2bu3Lm8efOGgICALGN0dXXZu3cvmzdvzlMnuiVLlqClpYWtre1nxwqCIAjC1xKJEeGXMnDgQJ4+fYqvry/6+vp069YNbW1tJk2aJLbZCILwTSQmJuLh4UGrVq0KOhRFnZHvtZ2mY8eOBAYGEhISQq1atdDT0+Ps2bM5jjU1NUVbW1vRErd58+aoqKhknszIgMhX8AUdcvIlMZ7J/XqR+P+dXz5KTk6ma9euJCQkcPLkSdTV1VFWVmbDhg1ERERgYmJCy5YtmTBhAqdPn8bJyQktLS2OHj3KyJEjWb16NRs2bEBVVRWZTMaRI0dwc3OjWbNmPH/+HCcnJ8W9pk2bRq1atbJ0WilRogTa2tqsXr2apUuXsnDhQkWyJb+Sk5MxMTEhKiqK4OBg9PX183xtr169aNasGcWKFaNEiRI5jjl16lSOtXM+Jkb+/nfN0NAwzz9bZ8+ezZUrV9DR0aF8+fJMnz4925guXbpgb29P165dP7sSRCqVcvz4ca5cucKpU6fyFIMgCIIgfCmRGBF+SWZmZri7uxMbG0ufPn1Yu3YtampqtGvXjsePHxd0eIIg/MLc3d2xtLSkWLFiBR0K5ubmKCkp4ePjk+uYj4mRPLfb/ZvChQvTt29ftmzZAsDQoUNZu3ZttnEymQxLS0uio6O5efMm+vr6DBgwIPNkYgL43YX3kcB3LsQql6EU9ZZLKxZwYMc27t+/T0JCAp06dUJVVZUjR46gqqqa5RJdXV0uXryIr68vCQkJVK9enSdPnnD06FGMjY3R0dGhV69ejBkzhpiYGLS1tVFTU0MqlXLt2jU0NTWZMGECMpmM69evc+DAAUVSJjY2llGjRtGuXTumTJnC6dOnGT9+PNevX/+ix0tMTKRy5cp8+PCBkJCQfLWKXrZsGRERERw8eJDIyMgcr/3w4QO3bt3KMemnpqZGkSJFeP/+veKYkZFRnmqCAPTs2RM1NTVmzpzJ4MGDc02w7dy5Ex0dHRo3bvzZOevUqUPXrl3p06cP6enpeYpDEARBEL6ESIwIvzQNDQ3WrFnDhw8f2Lx5M48ePaJy5cqYmJh8cvm5IAhCbtzc3Aq0G83ffdxO86nuNFWrViU1NZXQ0NAvusfAgQPZvn076enp9OzZk9u3b2ep4ySTyahXrx6hoaH4+flRt25dXrx4kbliIj4OgnwhLRXkeXsD/dXkMpTlMtzXLmfO5InY2NhgYGDAnj17Plnc1MzMjMDAQA4fPsz169dp1aoVVlZWivOJiYlIJBJ+++03Pnz4gKurKyoqKqSnp7N8+XLU1dXp3Lkzf/75Jzo6Ouzfv5+qVauSlJSEv78//fv3x8bGhr1799K1a1euXr2ar8eKj4+nUqVKJCcn8/jxY4oXL57na5OTk5k2bRouLi7o6Ojkmhg5f/489evXR0NDI8d5/rmd5mMMUVFReYrD0dGRjRs3MnbsWFJSUjhy5Ei2MVKplOvXr+Pn58e0adM+O+fu3buRyWQ4OjrmKQZBEARB+BIiMSL8azg6OhIaGsrDhw8pW7YsvXr1QktLiwkTJpCcnFzQ4QmC8AuQy+WcPn26wOuL/N3HOiO5rQiRSCRf3LYXMrfIGBkZ4ebmhpqaGv369WPDhg2K8y1btuT+/fv4+vpSrlw5ILMTiSQxHoIfgCznmiTfW2ElKdumjqVUMU22bNmCsrJynq6zs7PD3d0dPT09tm3bxsuXLxkwYAC1atWiYsWKLF++nFKlSmFlZUVsbCzz5s1DRUWFlJQUoqOjcXBwwNTUlHnz5nHgwAE2btyYJYnRokUL9u3bR/fu3bly5UqeYoqLi6NSpUpkZGTw+PFjtLS08vVaODk5oaKigqurK0CuiZFTp05la9P7d/9MjEilUqRSKU+ePMlTHAsWLCAuLo5z585hbW3NwoULcxxnZGTE2rVrWbBgAXfv3v3knIULF2bHjh3s3buXe/fu5SkOQRAEQcgvkRgR/nWqVavGxYsX+fDhA46Ojvz1119oaGjQpk0bHj16VNDhCYLwEwsMDAQyV2H8LCwtLUlPT+fBgwe5jmnVqhUXLlz44nv8vQjrkCFD2LJlCykpKXTs2BEPDw+8vLyoVKnS/y5IToRHDyCP2yy+F/UiRdg6eTSX3E5nOyeXy7N16/Hy8sLCwgIzMzN0dHS4evUq/fr1Y9u2bbx584ZWrVqhqamJtrY2jRs3pl27dowYMYLjx4+joaGBkpISCQkJBAcHEx4enuvWzebNm3PgwAF69OiBu7v7J58hJiaGihUrIpVKefz4saKVcF6Fhoayf/9+NmzYoCio+vfEiEwm48mTJ2RkZHx2NdTfO9N8VKhQIcLCwvIUi4aGBg0aNGDatGlMmzYNb2/vXD+YGDRoEDY2NrRs2fKzH17Y2dnRoEGDbC2GBUEQBOFbEYkR4V9LTU2NlStXEhcXx/bt2wkNDcXU1JTKlSuzd+/egg5PEISf0MfVIpJ/tIEtSHnZTtOyZUvc3d1J+8Lipz169ODatWu8fv2aypUrY2ZmRtOmTTl79iw3btygevXq/xssl8PjgAJbKfJ3EkBDTZ2SCe95GR6e5dycOXOoUKECCQkJimewtrZGKpXi7e3NgwcPaNy4sWLVSPny5Vm7di1ly5ZFW1sbU1NT7ty5Q/HixbGzs0NTU5OOHTsyfPhwChcuTFxcHE5OTpQqVYrDhw9ni61p06YcPHiQnj175to1KCoqiooVK6KiokJISEiuW1w+pUuXLpiamtKjRw/Fsb8nRh4/fqyopZKamkpgYGCuiYicOtOoqKgQ/o/X9lOWLVuGn58fNWvWpEiRIopVLDk5ffo0ysrKtGnT5rPznjx5knfv3jFlypQ8xyIIgiAIeSUSI8J/Qp8+fQgJCcHf35/y5cvTt29fihUrxpgxY0hMTCzo8ARB+En8TPVF/q579+6f3E6jp6eHkZERd+7c+aL5NTQ06NatG9u3bwcyV1vcunWLCxcuZKnDAcDrF5D682xPlEqgiqEhxzeuUxQKdXd3Z+HChSQmJlK+fHkaNWqEpqYmDx8+xNvbGwsLiyxz6Ovrc/XqVe7cuUNiYiK3b99WdJWRSCTEx8cjl8tZuHAha9asIT4+HhcXF5SVlXn79i3dunXD0NAw26qdJk2acOjQIXr16sWlS5eynHv37h0VK1ZEQ0ODkJAQ1NTU8v3sx44d4+HDh9lqefw9MWJoaIiysjJxcXHExMTQsWNHFi9enON8pUuXzpYEUVNTy5Ys+ZTatWtjYGDA2LFjadeuHZs2bcp1rLKyMpcuXeL69eusXLnyk/NqaWmxZMkSXF1def78eZ7jEQRBEIS8EIkR4T/F1NSU8+fPEx8fj7OzM1u2bEFTUxMbGxsCAgIKOjxBEApQbGws3t7eNGvWrKBDycbKyorExET8/f1zHfMtttNs2bIFFxcXLl++TPHixbMXAE1KhNfPC3wLzT8VVlZiQOtmbP5zDa9evaJjx44kJSWRmppKdHQ0Dx8+xNPTk2rVqn1yntq1axMcHMyuXbsICAggOjqa1NRUjI2N0dTUxNTUVNG6d+nSpcTHxzNs2DCUlZV5+fIlrVq1wsTEBE9PT8WcjRs35vDhw9jb23Px4kUAIiIiqFixIjo6OgQHB2frpJMXMpmMgQMHYmdnh4mJieJ4cnIySUlJiq5KKioqipa/hQoVombNmowfPz7HOXNaMaKpqcmbN2/yFdv48eM5fvw4c+bM4fnz559MZNSsWZPZs2czbty4z253HTlyJJUqVfqpagAJgiAI/w4iMSL8J6mqqrJ06VJiY2PZs2cPz58/p1q1alSsWJFdu3YVdHiCIBSACxcu0LBhwy/65P57y8t2mq8pwAqZrVGjo6NZvXo1Bw4cYOTIkaxbty7roKdBP677TD6pFC6MWVEVxfYZJSUlihQpAsDNmzfzPI+XlxfLli3DzMwMdXV1pFIpr1+/5s2bN7Rr144DBw5QvHhxjh07RuHChVm1ahUJCQk4OzujpKREaGgo9evXx9zcXFEXplGjRhw5coTevXuzZ88eTExMKFmyJIGBgRQuXPiLnnfatGkkJCSwc+fOLMffvn2Lnp5elu1glStXBjKLnl6+fDnXv+M5JUaKFSvG27dv8xXbqFGjkEgknDp1CgMDA6ZPn/7J8dOnT8fCwoImTZp8tj3w2bNnCQwMzP53UxAEQRC+gkiMCP95PXv25NGjRwQHB1OpUiX69++PpqYmo0aNEttsBOE/5GfrRvNPn0uMNGzYkIcPHxITE/NF8y9atIh3795Rt25dunXrhrOzM/v27ePDhw+ZAxLjM1eM/KQkgHmlilSrYMSiRYvYuHEjq1atYu3atTRp0uSz18fGxjJixAg6dOjAqFGjMDU1xcHBgfj4eDp37kxcXBxXr15FW1ubWrVq0aVLF+rWrUtkZCSFCxdm/fr1xMfH07dvX6RSKf7+/pibm1O3bl1CQ0Np2LAh69evp0+fPmhpaeHv7//J9sKfEhMTw/bNm9ixegVqMW8hNBBC/CD4AZpvnrN+0hh48xI+xEJGBnp6eqirq+Ph4aFYSZKTnIqv6ujo5Lld70dSqRQ7OzuWLVuGo6Mjx48f/+w17u7uxMfH071790+OMzQ0ZPTo0YwePZq4uLh8xSUIgiAIuRGJEUH4f5UqVeLMmTPEx8czdOhQdu7ciaamJs2bN8fPz6+gwxME4TuSyWScOXPmp06M1K1bl5iYGEXnnH9SVVWlQYMGn+2CkpM1a9YwdepUFixYQEBAALGxsZQuXZpmzZr9bxXdm5c/7WqRj1QLF2LJxDE8evQIJycnnJ2dGTRokGLFRE7kcjl79+6latWqpKWlERAQgK6uLu7u7ri6uqKqqqpYWVijRg1evnzJjRs3aNeuHa9fv8bAwICZM2dm3l9Vla1btxIbG0uvXr2QSqV4eXlRsWJF6tWrh4ODAxUqVCAxMVGxrSbP5HKIj4XHAUgf3ObpsX30qFUDXoVB9FuIjYK4GLTIoEM9Kwh/mpks8bnJ7kmjiLx3ixJFNT95C11dXRISEkhKSlIcK1GiBLGxsfmLlcwirK9fv8bGxoYPHz7kWoD2Iw0NDU6dOsXRo0c/WyDd1dUVHR0dOnfunO+4BEEQBCEnIjEiCP+gqqrK4sWLiY6O5sCBA7x+/RozMzMqVKjA1q1bCzo8QRC+g3v37qGtrU2FChUKOpRcSaVSunbtmmMHlI++ZDvN1q1bGTVqFPPnz2fy5MnY2Ngo3pgOGzaMdevWIU9Pg6j8bacoKE1rmHL39i0OHDjw2bHBwcHY2NiwcOFCDh06xF9//YVEImHQoEGKGlQflSlThps3b3Lz5k1KlSrF6dOneffuHb1792bhwoUYGBhw+/ZtIPNN/q5du4iOjqZLly4A3Lp1i6SkJKpUqcKuXbvo168fbm5un3+gjAyIfAUP70LwQ+Qx7yiqVgSVwoU+3RlILvv/83Kk6WmofYgGPy8Iug8xUZmJln+QSCSUKlUqy6qRkiVL/m/VUD6UKlWKGjVqMGPGDMzMzJg9e/Znr2natCkjRozA0dGRV69e5TpOKpVy4sQJrl69ysmTJ/MdmyAIgiD8k0iMCMIndO3alcDAQEJCQqhatSq//fYbGhoaDBs2jPj4+IIOTxCEb+Rn7UbzT926dePgwYO5nm/VqlW+EiOHDh1i4MCBTJs2jcmTJwPg7Oys6CTSvHlzUlJSeHz3VuZelV+ARCLh2NZNjBgxItein8nJyfz+++/Ur1+f9u3b4+3tTb169QBwcXHBzs4u1yK89erV49mzZ2zfvh2JRMLOnTupXLkyRkZG1KtXj06dOilWXBQtWpTZs2ejqqqKtrY2kFkjo23btpiZmeHo6Mjp06dzf5iY9/DgNoQ/yewEJJN9+bdBLs9MlsTHwpMACLgHydm3Rv2zzkipUqVISEj4olsuWLAAT09Phg0bxs2bN0lPT//sNatWrcLIyIhGjRp9cpyVlRXdunWjT58+eZpXEARBED5FJEYEIQ+MjY05deoUiYmJjBo1in379lGsWDGaNm2Kr69vQYcnCMJX+tnri3xUv359IiMjCQ4OzvF89erVSUxMJDQ09LNzubm50bNnT1xcXJgzZ47ieMuWLYmMjOT+/ftIJBKGDBnC+yePf7pONLmSyahQXJtx48bh4OBARkbWVRXnz5+nRo0a+Pv74+vry5gxY1BWVgbg+PHj3Lx5k4ULF372Nv369SMuLo5x48YRGBiIp6cntra2eHh4oKOjw8aNG/Hz88PCwoJatWrx7t073rx5Q9u2bZHL5bi7uxMVFUW3bt2ytdslPQ1CAzJrh2Skf/vXXiaDpITM5Mjr51lWj/wzMVK2bFmSk7+sPXO7du3Q0tLCx8cHJSWlPBdMvX79OuHh4QwZMuST4/bs2YNcLqdv375fFJ8gCIIgfCQSI4KQD4ULF2b+/PlERUVx5MgR3r59i6WlJeXLl2fTpk2fraYvCMLPJzIykkePHtGwYcOCDuWzlJSUPrmdRiKRYGNj89m2vVeuXKFTp04MGDCA5cuXZ7uHk5MTmzdvBqB///4YaH26NsVPJyGOCRMmoKyszIIFCwB49eoVPXv2ZMiQIaxcuZJDhw5RpkwZxSXv379n6NChbN26FXV19TzdRiqVsmTJEj58+EDTpk05evQoqamptGrViiFDhlCjRg1q1qzJ9evXkUql6OnpcerUKV6+fEnLli2RyWSkpKTQtWtXOnfunLny4UNs5raZmPffv6aLTJaZGAm4B2mpQPbESLly5UhLS/viWwwaNIhdu3bRokUL1qxZk6dr9PT02L17Nxs2bODcuXO5jlNWVmbXrl3s378fLy+vL45REARBEERiRBC+UOfOnfH39yc0NBRzc3OGDh2KpqYmgwYNEpXyBeEXcvbsWVq0aPHFbVN/tM91p/ncdprbt29jY2NDt27d2LhxY45jnJyc2LNnD8nJyWgXLYqBrm62MeU79cTv8RMAEpOTaT1yPE6zF2RboZEXJ67eYMLKb9h+NTUFKbBjxw5Wr17NmDFjMDc3p1KlSvj5+eW4OmjEiBH06tXrs1s4cqKmpsbly5d59OgRRYsW5cSJE8hkMooUKcLdu3cZOnSoInEeEhJCdHQ0586dIywsjMaNGwNw4sQJbJs1IdX/XuYqkRxqgHwXMlnmlpqAe5CSnK0zjZGR0Rd9Tz/6/fffSUpKokGDBoSEhPDu3bs8XdetWzd69OiBnZ3dJ3+mdu7cmYYNG9KpU6cvjlEQBEEQRGJEEL6SkZERx48fJyEhgbFjx3L48GG0tbVp1KiR+ARLEH4Bbm5uv8Q2mo8aNWpEeHg4T548yfF8y5YtcXd3z7Hugp+fH40aNaJNmzbs27cv13uUL18eS0tLjh49mrnlQin3Xxdi4+NpNWI8VQzLsWXmZJSUlPL9TJ2aNMDVZWi+r8uVRApJCbx69Qp1dXXWr1+Pm5sbc+fORU1NLdvwQ4cOce/ePebOnftVt61cuTKHDx9GKpUikUhISkrC0tKSHTt2ULx4cQ4dOkTjxo1p2bIlSUlJlCtXjitXrvDkyROG9O7JgQW/U1g5/6/fV5PLM1eMBPpQsVzZLIkRPT09gC9uA62qqkqLFi3Yvn07Ojo6/P7773m+ds+ePWhpadG0adNPjjtx4gTv379n0qRJXxSjIAiCIIjEiCB8I4ULF2bOnDm8f/+e48ePExMTg7W1NeXKlWPdunVim40g/ITS09M5f/48bdu2LehQ8kxJSQk7O7tcV43o6+tTrlw57t69m+V4SEgItWvXpkGDBnnq5DFw4MDM7TRpqShLc36zHhkdQ9PBLjSvbcHK8aOQSCRsP3WWOv2HUKvvbzQfOppHzzILoKampTF4/hJMujrQfOhohi1cRrdJmW1ut508o/jvK94+1Ow9kMHzl2Bm74R57wEEPn2muOe0tRupaNebOv2HMGn1emr3G5QtLhly1q5aSefOnZk9ezb9+vVj1apVOT9DZCQjR45k27ZtOSZN8sPDw4PGjRvTsWNHUlNT6datG15eXqSmplK6dGm6d+9OREQEUVFRzJo1S3GdUYnirBs3AjVV1a+6/1dLT6OVYUli379XHJJKpUilUp4+ffrF0y5fvpyQkBDatm3L/v3783ydVCrlxo0bPHjwgBkzZuQ6TktLi6VLl7JkyRLCwsK+OE5BEAThv0skRgThO+jQoQMPHz7k2bNn1K5dm1GjRqGhoYGzs/MXf+omCMK35+npSfny5TEwMCjoUPKle/fu+dpO8/z5c2rWrIm5uTmXLl3K0z1sbW25f/8+byIich3TY8osOjaqzx9DBgJw3ec+By66c23DKrx3bmSCQy8GzFkEwF9HTvD01Wv89m/j1PKF3AkIynVe/ydPGdK1Ew/2bqVHy2bM3bwTgJPXbnDKw5P7ezbjuWUtIc/Dc7w+4UM8ylIpAQEB9O3bl+XLl+Pl5cWuXbuyjJPL5QwbNox+/foputJ8qcuXL9O0aVNsbW05duwYysrKHDx4kEePHlGqVCn8/f0VY1NTU1m2bBlBQUGZ7XhDA36a4raFJeBimzVRqKys/FUJh6pVq1K+fHkiIyOJiorK12pKIyMjVq9ezfz58z953YgRI6hcufIvtfpLEARB+HmIxIggfEflypXjyJEjJCUlMWnSJI4fP46Ojg7169fn9u3bBR2eIPzn/Sptev+pSZMmPH36NNc3q61atVIUYI2MjKR69eoYGxvj6emJVJq3H/0qKir06dOH69euAjnXu2jXoA77L7jz+l3mCoOT129yPySUOv2HUrP3QCb/uYEXbyIBcPfywbF9GwopK6OmqopDG5tc721iWA4Lk8oA1K1eldCXrzLn8PahR8tmqBcpglQqxbFDmxyvL6KmxqDfnBUtctXU1Ni7dy9jxozJsgVp//79BAQEMHv27Dy9Jrk5f/48rVq1omfPntnaKVeuXJmwsDDKli2b5Xh6ejqWlpYkPHoIP1G7WSnQ1MIcWdRbxTFVVVVevHjxVfNOmzaNy5cvU7FiRWbOnJmva4cOHUrz5s1p2bIlqampuY47e/YsQUFB/Pnnn18VqyAIgvDfIxIjgvADKCsr8/vvv/P27VtOnz5NQkIC9erVo0yZMqxZs0ZssxGEAvKrtOn9J2VlZWxtbXNdNdKwYUPu379PWFgYVapUQV9fn3v37uU5KfLRwIEDuXDxEnIkOZ6f2Nee/h3a0HSwC6/fvUcuhwEd2+K7ZzO+ezZzf88Wnp/KTBTIc0mu5ERV5X+FcJWUpKT/f/FPuRwkOYeShbKSEvxj+0/NmjWZOnUqvXv3Ji0tjYiICFxcXNi+fTuqX7GFxc3NjbZt29K3b192796d45j09HQiIyMpVKhQlu9B6/p1UY6N+v7dZ/JJvYgqPH2U2TaYzMTSq1evvmpOJycnChcuTMWKFbl8+XK+f+6dPn0aiURCmzY5J8MADA0NGTt2LGPHjhVF0AVBEIR8EYkRQfjB2rZty/3793n+/Dl169Zl7NixqKur079/f6Kiogo6PEH4z3jx4gWvX7/G2tq6oEP5Ip/qTlOkSBGsra0xNzdHU1MTPz8/lJWV832PGjVqUFhNLcdCrh9NcXLAsUNrmg52oUPDeuxwO0/4/68SycjIwDvwEQAtrGqx0+086enpJCWnsOfcxXzH06yWBQcvXiExORmZTMZOt9y775BDEVgXFxe0tLSYPXs2Q4YMwdnZGSsrq3zH8dGxY8fo2LEjAwcOZOvWrbmOK1y4MO/fvycsLIygoCDmzZtHsWLFWDfRBZVC+f++/BByGbwIBUBDQ4OIT2ypygupVEqvXr24e/cuGRkZuSaRclO4cGEuXbrE1atXP9n2d9GiRejo6NCxY8evilcQBEH4bxGJEUEoIGXKlOHQoUMkJiYybdo03Nzc0NXVpW7dunh6ehZ0eILwr+fm5kbr1q2/qIvKz6B58+aEhITkuMUhOTkZLy8v0tLSCAwM/KpWxNZNmoHs0+1apzr1pV/71gyav4R5Q53pNG4q5r0HUL1Xf45f9QBgkF1HyunrUa1nfzqMmUwTy5r5jqVTkwa0rmuNee8BNB86hoplSlNMXT3buPS0VNrYdWXAgAEsXryYEydO8OjRIzIyMti2bRtr1qzh4cOH+d7S8XcHDx6ka9euDBkyhA0bNnx2vLq6OqVKlaJSpUpMnTqVZ/fvUTSH2H8WUoCod5CeTrFixXj79u3nLvksV1dX3r9/T9WqVVmyZEm+r7e0tGTmzJmMHj2akJCQHMdIpVJOnjzJ9evXOXHixNeGLAiCIPxHSORyed7XtgqC8F1dunSJCRMm4OvrS6lSpZgwYQKjRo3K9/J3QRA+r3PnzvTq1Qt7e/uCDuWLDRgwAHNzc1xcXBTH0tPTMTEx4f379xQvXpzQ0NCvukdcXBwZXtfRLqr5teFmse3kGU55eHJo0R/5uu5DQiKa6mrIZDKc5y7GoIQuc4c6ZxmTjoQbCTIePXrEo0ePCAoKIigoiJcvX1KmTBnCwsJQUVFh0aJF1KpVCxMTE0U9krzYs2cPffv2ZdSoUSxfvjxf8SsE3Yf42C+79keRSsGgPDZ9nUhISODmzZtfPWWdOnWIiYkhJCSEuLg4NDQ08j1H7dq1efnyJS9fvsz152OvXr1wc3MjKirqi1ZLCYIgCP8t4t2WIPxEWrRowb179wgPD6dRo0ZMmjQJdXV1HBwcePfuXUGHJwj/GsnJyVy5coXWrVsXdChfpVu3blmKfcpkMszNzXn//j3BwcEkJCR8VZtVyNzCEPj86wpvfkv9Zs3Dos9AqvZwJCU1jYl9sye2rnrdw8HBAXd3d4yNjVm4cCEhISFER0djYGCAnZ0dNWrUYPHixYwYMYJy5cpRsmRJmjRpwuDBg1m2bBlubm48efKEjIysq2W2b9+Og4MDY8eO/fKkSEoSJHz4smt/JJkM3oSjq6v7zTqqubq6EhISgoaGBvPmzSM5OZno6Oh8zXHlyhXi4uLo2bNnrmM+diBycHD4qngFQRCE/waxYkQQfmIymYzFixezYsUKIiMjqVWrFsuXL6dhw4YFHZog/NLOnz/P7NmzuXHjRkGH8lVSUlIoVaoUDx8+pFSpUlhbWxMUFERwcDAGBgb07duXRo0aMWjQoC+a//Xr19ja2jKkczscmjei0K/wybtEgtzAkNCEVK5fv6748/79e8qWLcvbt2/Zu3cvFhYWNGjQgDFjxuDk5MTr168VK0uCgoIUK00iIyOpWLEiVapUISkpCTc3NxwdHVm1ahWamp9fRbNjxw46d+5MsWLF/ncw/Cm8Cc+sJvuzkyqx+pw7i/5cT3h4zu2R86tkyZLIZDKioqKQSqW0adOGkydP5muOS5cuYWNjw969e3NNkJw4cQJbW1tu3779VbVkBEEQhH8/kRgRhF/ElStXGD9+PPfu3aNkyZKMHTuWcePGiW02gvAFXFxcKFmyJFOnTi3oUL5av379sLa25sCBA9y9e5eAgACMjIyAzDflJ06cyLVI66d4eXlhZ2fH4MGDmTp2DKm+nqh+Ra2SH0Yiheq1QSVrpxlvb2+aNWtGx44dCQwMJDg4mCpVqhAYGMiqVavo3r07RYsWzTZdQkICwcHBrFmzhi1btlCtWjUKFSpEcHAw2tramJiYUKVKFcUfExMTypQpg1QqJSYmBh0dHUqUKMGRI0do0KBB5qQB9yAx/ke8Gt+AhKuhYdgNHfVNCoTv2rWL/v37Z1mJ4+TkxJYtW/I91/Dhw9m0aRNhYWHo6+vnOKZJkyYEBwfz6tUrJHlpaSQIgiD8J4nEiCD8YiIiIhg7dixHjhwBoEuXLqxYsQI9Pb0CjkwQfh2VKlXi0KFDmJubF3QoX+3kyZM4OjqSkJDAvXv3qFatmuLc69evqVatGm/fvs1Xkdl9+/YxcuRINmzYgJ2dHQAvL52mdLFvW2fku9AoBlWyfl/lcjlt2rShcePGTJs2DcisneLp6cny5cu5ceMGMpmMKlWq0LBhQxo1akSjRo0oWbIkAKtWrWL06NHMmzePKVOmAJkr+l68eJFthUlQUBBxcXFUrlwZXV1drl27RkpKCioqKgwfPpyFCxZQ6OGdn65F76e8SkiiYls7EhMTv2qetLQ0DA0NiYiI4O+/fs6dO1fxfcmvypUrI5fLcy3GGhcXR4kSJRg1ahSurq5fdA9BEATh30981CwIvxh9fX327NlDYmIi8+bNw93dHX19fWrXrs2VK1cKOjxB+OkFBweTlJSEmZlZQYfyTWzZsoXo6GhOnjyZJSkCUKpUKcqUKYOXl1ee5pLJZEybNo0pU6Zw6dIlRVIEQK2CCR++8o3x95acls7646cJCwvLcnzjxo28f/+eSZMmKY4VLVqU1q1bc+bMGZo1a8bQoUNZvXo1BgYGbNu2jSpVqlC5cmWsra0ZPXo0EydOZPLkyYrrpVIphoaGtG7dGhcXF9auXcvly5d59eoVL1++5K+//qJ8+fLIZJkJkJSUFJYtW4Zjr57w/wsXomLjKNu+G3f9AxXzztuyk64TZ7D+8HGW7znw3V6rE1dvMGHlujyN1VUrQmpq6lffs1ChQty7dw9TU9MsqzfKlSuXfbBcntkNKT0N0tMz653k8Fmeh4cHz58/Z9iwYTnes2jRoixfvpxly5Zl+3shCIIgCB+JFSOC8C/g4eHB2LFj8fLyokSJEowZM4aJEyeKbTaCkIMVK1YQEBCQpxarPzsnJyd27txJ8+bNsbOzY+jQodnGjBs3Di0tLWbMmPHJuT58+EDfvn2Jiori8OHDlChRIusAuZy46+cpqlbkWz7CN5UhVWLq/uNs2rSZ9u3bM3HiRDQ0NLCysuLKlSvZEkcfvX37lpo1a7Jjxw5atGgBZCaJxowZw6pVq7CwsCAyMpKMjAwaNWqkWFViZmb2yZU4EydOZNmyZRQuXJjWrVvTu3dv2te3Ru1NuKIF8vGrHkz5cwM+uzYR/PwFbUZNxGfXJvR08t4l53uTA7otO/I+5tt00UlMTKRt27Zcu3YNgKVLlzJ2yKDMgrTxcZl/UpIzB3/Mn8jlmdukiqiBZjFQLwrqmqCiyv79+7G3t+fChQuK798/Va1aFYCAgIBv8gyCIAjCv4tIjAjCv8i7d+8YO3YsBw8eRCaT0blzZ1asWIGBgUFBhyYIPw0bGxuGDx+Ora1tQYfyVUaOHMnatWtxc3MjMTGRNWvWcOnSpWzjzp07x9y5c7l+/Xqucz19+pROnTpRt25d/vzzTwrnUkvk3sWzmBZRooiKyjd7jm9GKgWjKqCd2UFl3bp1rFixgoyMDHr06MHatWs/efmFCxdwcnLC19cXXV1dZs+ezezZs1mzZg3Dhg1DLpfz7NkzPDw8FAVdX79+Tb169RTJEmtra1RV/1fb5OHDh7x8+ZLmzZv/7zV9E55ZfPVvv345zJhLCW0trt7zZbJjb3rYNGfWhq3EJyaxZPQwtp08w55zF9HW1MQv9ClamhocXvQH+rrFSU1LY8TiFVy554uetjY1K1ck4n0Uhxb9QWpaGiNdV3LF25fSerpUMSxHZHQMhxb9kaVd8j9bJ//za7lUierdHbjl+yBPBWfzQiaTYWFWg65NGzGqZxe01NU+nsjbBFKlzNdQTR30y9JryHBOnjrFmzdvcmwB/Pz5c4yMjFixYgUjR478Js8gCIIg/HuIj5MF4V9EV1eXHTt2kJCQwKJFi7hx4wZlypTBwsIixzdMgvBfEx8fz61bt2jZsmVBh/JVJk+ezNq1azl06BCtW7emTZs2eHt78/bt22xjGzVqhK+vL3FxcTnOdfXqVerXr8+gQYPYsGFDrkkRALMmLbjlF4jsZ/tMRSIBTW3Q1gVAS0uLKVOmMHnyZIoWLcrZs2dp1KgRp0+fJrfPg2xsbLC3t8fZ2Zlp06Yxe/ZsNmzYoNiiIZFIMDIyom/fvmzYsIHAwEBCQkIYPHgw79+/Z/z48ejq6tKwYUOmTJnC6dOnKVu2LG3atMn6muawJWT1BBc2nziNYamS9LBpnmN8dwOCWOIyDP8D26lqZMjqA5l1pv46coLnEZEE7N/OxT+X4hX4SHHNX0dO8PTVa/z2b+PU8oXcCQj64pdXTVX1q1s/K6SmIA0LwWfbOqb07YlWEdXM1yWvSRHIXHEjl2WuMnn6iL2TXZjh7IhNy5xXjJQrV45x48Yxfvx4YmO/zcoXQRAE4d9DJEYE4V9IKpUyevRoXr58yfXr11FRUcHGxgY9PT3mzp1Lenp6QYcoCAXi4sWL1K1bN8dPlH8V8+bNY/HixWzfvl1RA6RIkSK0adOGY8eOZRuvpqZGnTp1cqxBtGHDBnr06MHOnTsZOXJkjl075HI5z58/5+zZs0yaPBmnPxaSkJT8rR/r60ilUL5ylkOhoaHMmzePM2fOEBwczPDhw5k2bRpmZmbs2rWLtLS0bNPMmzePW7duMX/+fLZu3Yqzs/Mnb1uiRAlsbW1ZunQpd+7cISIiglmzZqGiosKyZcsoW7Ys5ubmjBgxgn379vHy5csc62Rc9rpHUXV1HoW9ICWXWh4NzGtQVj+zyHbdGtUIDX8FgLuXD33btUJZWRlVFRXsW/0vMeDu5YNj+zYUUlZGTVUVhzY2n34dcyMHlcKFefbs2Zddr5hHDu8iwM8L3r9BChTKR1HgXMkykGSkM7FvL/ZOG8fGFctyHLZ48WKKFy9Ox44dv/6egiAIwr+KSIwIwr9cgwYNuHXrFu/evaNdu3bMmzcPNTU1unXrRnh4eEGHJwg/lJubG+3bty/oML7YihUrmDFjBuvWrcPBwSHLuW7dunHw4MEcr2vVqhXnz59XfJ2WlsbIkSNZvnw5Hh4eua6gcXNzo0iRIpiammJra8uyZcuI+hDP6OV/Ipf8JL9CSKRQ3gQKFVIckslkODk5MXXqVExMTFBWVqZXr174+Pjg6urK5s2bqVSpEqtXr87SaWXSpElERkYq6pLkl4aGBi1btmTWrFlcunSJqKgoNmzYgJGREfv27cPc3Jz5ixaR9rdWte9iYnBZuprTyxdS29SE3//amuPcf2+VrCSVkv7/c8iRk1sXWjl5W9mjrKSkKBILkJxDckYm5+t+ZqSmQPBDeP5YUV/lW5MChvol6VO7Om+8PHNcgXLixAk8PDw4fvz4d4lBEARB+DX9JL/VCILwveno6LBt2zYSEhJYtmwZt27doly5cpiZmXHmzJmCDk8Qvju5XI6bmxvt2rUr6FC+yKZNmxg7diyurq4MHjw42/m2bdty+/Zt3r9/n+1cq1atuHDhAgBRUVG0adOGx48fc+vWLSpVqpTrPevWrYu6ujqJiYmkpKRQqFAhrly5wtPYeB6+i81cqVGA5FIplC6v2ELz0apVq5DJZLi4uGQ5LpFIaNOmDe7u7uzbt4/Lly9jZGTEnDlzcHZ2ZtWqVezbt4/ly5djb29PcvLXrYwpVKgQderUYdy4cRw7dozIyEj6D3TmfxVFYdii5fxm2wHzyhVZOW4Ue85dzNKl5nOa1bJg15kLpKenk5ySwv4LlxXnWljVYqfbedLT00lKTmHPuYs5zmFcxoAHj5+QkppKaloahy5dzXI+IyODdLksc8XLl0iMB38v+BCTv+0yX0AikaCmqopG8gdkAd6ZXW3+pnbt2vTs2ZO+ffuK1ZOCIAiCgkiMCMJ/jFQqZcSIEYSHh+Pp6Ymmpibt27dXFBsUvygK/1YPHjxAVVX1k4mAn9W+ffsYNGgQv//+O+PGjctxjLq6Oq1atcrxk3AzMzOio6O5ePEi1tbWWFhYcOrUKYoVK/bJ+3p4ePDhwwcg8/8dNjY2WFpaMnDgQCYuXw0lyxRYciQ1PYNtZy8Ro5p1W1RwcDBz585l27Ztn+wYU7duXY4ePcqVK1fYtWsXmzdvpl27dtSvX5+BAwdSqVIlpkyZ8k1jlkqlGFSoSKH/X91y4MJlgsPCmeqUufpHu6gmf04czYA5i0jNYatPToZ07Uwp3eJU69mfDmOmUMu0MsU01AEYZNeRcvp6/39uMk0sa+Y4Rz2z6rS0rkX1XpnjTI2yts9NSUnBJyDoyzq6xMdBkC9kfJ9VIrlRV1UlI/4DBPhAWtYVMDt37gSgT58+PzQmQRAE4eclutIIgkBMTAzjx49nz549pKen0759e1auXEm5cuU+f7Eg/CIWLFhAREQEK1euLOhQ8uXEiRPY2dkpVot8yv79+9m+fTtubm7ZzjVr1gwvLy/WrFmDo6PjJ+eRyWT06dOH/fv34+joiJ6eHq6urgQEBFClShWSkpIoU6YM9+7dw1BFivzlMyQ/8tcJqZTUEqVRM6pERkYGdnZ2DBkyhKZNm9K0aVPs7e3z3HnEycmJHTt2sGXLFh48eMDWrVuxtbXlt99+o2fPnvz111+0bdv228Uuy4B7NyGP21zy4kNCIprqaqSkptJp3FS6t2iKs22HbOP+2W0mr55ERGLcsTsARkZGXLp0CSMjo//d/8OHnLvVJMZnJkW+8yqRT8mQyVAqog6mFqCsrDh+4sQJbG1tuX379hdtmxIEQRD+XcSKEUEQ0NLSYtOmTcTHx7Ny5Uq8vb0pX748NWrU4NSpUwUdniB8E6dPn/7lttFcunQJOzs7Bg0a9NmkCEC7du24ceMG0dHRimNyuRxXV1d8fX2xtrb+bFIkJCSEMmXKcPz4cU6fPs3WrVuZN28eN2/epEqVKkBmsVd7e3u2bdsG+mWRVDYjNjGJtPTvvCpAKoVChaGyGYXLGjFkyBAAjh49SteuXdHW1iY9PZ3hw4fnabo+ffqwc+dOzpw5g6OjI0uXLuXx48cYGRlha2uLoaEh/fr1482bN9/wGZRI/MarJ1oOH0vN3gMx7z2QSmXL0L9Dm286fwWzmjRt2pRixYrx9OlTKlSoQNWqVXn16hUrV66kWLFi3L59O+tFaanw6EGBJkUgsx6LLCUJQvyyFL7t1KkTjRs3plOnTlnqqwiCIAj/TWLFiCAIOfLy8mLMmDHcuHEDbW1thg0bxowZMz7ZylMQflZRUVGUL1+eyMhIVFVVCzqcPLl58yaNGzemV69e7Nq1K8/X2dnZYWtri6OjI8nJyQwaNAg/Pz/++usv2rRpQ2RkZK5bTJYvX86ECROoWbMmly9fpmjRornex8fHB1tbW548eYKSkhKPAgO5uX8nvVu3QOVvhVC/GYkUSuhDGSOQZsZ/9+5dmjZtSmJiIhKJBCUlJe7fv0/VqlU/O12PHj04cuQI58+fp3nz7C1yExMT2bJlC9OmTUNZWZldu3bRpk2bHDv35Ndh13nYNW2I9BvM9b3JpVIk5SvTa9goHj58yO3bt+nYsaOiy5FEIkEul6Ompsbbt29RU1PLvDDED2Kj+ZYrY76GXCpFUqYC6BkojsXFxVGiRAlGjhzJkiVLCjA6QRAEoaCJFSOCIOSodu3aXL9+nZiYGLp3786yZctQV1enc+fOhIWFFXR4gpAv586do2nTpr9MUsTX15emTZvSsWPHfCVFALp3786hQ4d4/fo1TZs2JTk5mevXr2NlZUWpUqXw9vbOdk1iYiINGjRg/PjxzJo1Cy8vr08mRQAsLCwoXrw4ly5dIi0tjZOnTzNonit/7D9GhromSCTk2i4lrz7OoakFJmZQrqIiKQJQq1YtChUqROHChZHL5VStWjVPSRFbW1uOHj2Ku7t7jkkRyGxzPGLECF6/fo2WlhYDBw7EwsKCvXv3flUtpokTJ7Lp6MksnWl+ZmmpqdzwC6JEiRLExcWhoaGBu7s70dHRlC5dmo+fryUmJv6vu1HU28xCqz9JUgRAIpNB+BNI+V9B3aJFi7J8+XKWL1/+9a2IBUEQhF+aSIwIgvBJRYsWZf369Xz48IF169Zx//59jIyMqFq1qmh3KPwyfqU2vYGBgdStW5cmTZpw9OjRfF/foUMH3N3dsbKyon379uzfvx919cxinH/vTvPRlStXKFmyJMHBwdy/f5/p06fn+V7Ozs4sWrQIS0tLLly4wLJly/DweYCSqQVUtwK90pmJDKlS5oqPvJBIM8crKUHJ0lDDKjMpopE9USOVSnFwcMDExARLS0v8/f0ZOHDgJ6dv3749p0+f5tq1azRq1Oiz4aipqXH+/HnS0tJwdnZm7dq1VK5cmXXr1pGUlJSnR0pKSuLAgQO0bdsWV1dXXsYnIlFS/vyFBSwtPYN7z8IZMnw4u3bt4t27d4SEhACZWzBfvXqVZbynpydtWrZA/iy4wLfQ5ESWngFPArNsqRk2bBhVqlT5tnVkBEEQhF+OSIwIgpAnUqkUZ2dnnj17xr1799DT06NLly7o6OgwdepUUlNTPz+JIBSAjIwMzp49+0u88QkLC6NWrVrUqlUrWwIjr86cOUNaWhpdu3ZlxowZWbZ+2NjYcP78ecXXw4cPp3nz5rRo0YLXr19TvXr1PN/n/fv3eHp64u7ujouLC2fPnmXIkCGEhITg5+cHKqpQtgLUrJeZ2ChbAXT0kKuokpKWhlwu/996Aokkc7yOHpSrkDnevB6UqQCFP73KZ/Dgwbx+/ZojR45w8uRJtm3blmvnHhsbGy5cuICnpyf16tXL87MaGxuzbNky1q1bx7lz5xR1SYyMjJg/fz4xMTHZrpHJZFy9ehVnZ2d0dXWZOHEivr6+qKmp0apVK0ITUvKeLCogGTIZH9SK8uDBA8aNG0d6ejqNGjWiXr16zJw5E4lEgkQioUyZMlSvXh0VFRXamFclI+3n/HkglUqQJcZDXHSW42fOnCE4OPiXK8wsCIIgfDs/909kQRB+SjVr1uTKlSvExsbSq1cvVq1ahbq6Ou3btyc0NLSgwxOELO7evUupUqV++i5LHxMTJiYmXL9+Pd/Xy2Qypk+fzqRJk5gxY0aOW94aN27MvXv3CA4OxtjYmE2bNrF7926OHTuGsnLeVjDIZDK2bt1K1apV0dHRoXv37sTHxyORSChUqBDOzs6sX7/+fxdIpaCumVnboUIVUiubo1q/JVLrptxX0oRajTL/1LCGClWghEHm+Dy0AU5LS8PR0ZEFCxZgaGhI27Zt2bt3L8uXL2f27NlZYm7WrBnXrl3jzp071K5dO0/P+ncODg6Ym5szfvx4GjRowIkTJ7h48SJBQUEYGxszceJEXr16RWBgIFOnTsXIyIiRI0diYmKChYUFYWFhREREkJiYyNKlSzl0xSPfMfxobz/EE/jsORKJhMaNGyOXywkPD2fGjBkcOHAAmUyGXC4nMjKSYcOGkZyYgEufnih/ok1yQZPK5RDxIsuxcuXKMX78eCZMmJBjkksQBEH49xPFVwVB+Ca2bt3KnDlzePbsGSYmJsydO5euXbsWdFiCwIwZM0hLS2PhwoUFHUquoqKiMDY2pmTJkgQEBCDNQ1Lg7+Lj4+nbty/v3r3j8OHDFC5cmHLlyvHy5ctsbVSrVq1KcHAwFStW5Nq1a+jp6eX5Pn5+fgwdOpSUlBTWr1+PpaUlV65cYeTIkTx48ACJREJ4eDhmZmY8f/4cDQ2NbHNERkZSsmRJIPOT+jZtvryDyh9//IGnpydubm5ZVsZs3LiRwYMHs2zZMkaNGkXjxo3x8vLCy8srX6ti/ik2NpaaNWuycuVKOnXqpDju7e3N2LFjuXHjBioqKvTq1YtRo0Zhbm5Oeno68+fP5/fffwegUKFC9OzZk507d8LLMHjz4qfcdpKUksKgJWuIiE9kyZIlFCtWDCMjI+RyOenp6aioqCCTyRSvu1wux/u8G5bFNX/K5/m7tPQMClnUy1yl9DcGBgYYGxt/UWJSEARB+LWJFSOCIHwTTk5OPHnyhAcPHmBgYECPHj3Q1tZm4sSJJCcnf34CQfhOfvb6InFxcZiYmKClpcWDBw/ynRR59uwZ9evXVxRC1dPTQ0tLi0aNGnH69GnFuPT0dNq1a0dgYCA1atQgKCgoz0mRhIQEJk2aRLNmzejduzeenp5YWloC0KRJE5KSkrh79y4AZcqUoUmTJuzevTvbPE+fPqVXr16Kr+fPn8/bt2/z9bwf+fr6snr1ajZu3JitU8xvv/2Gq6srY8aMoWLFinh7e+Pj4/NVSRGAYsWKsXv3bgYNGkRoaCh79+6lffv2tGjRAkNDQ/bu3cu4ceM4ceIEs2fPZvLkyZiYmGQpoGtqasqmTZsyvzAoB4VVviqm7yFdJuNm8BMKaRfn6dOn9OjRAzMzMwBmz56dmdQBlJSUqFWrFlu3buXIkSPU1C/+0ydFADJkGYR53cp2/NSpU9y4ceOLavsIgiAIvzaRGBEE4ZuqXr06ly5dIjY2lr59+7J+/Xo0NDRo27atomifIPwor1+/5unTp/mqJ/EjJScnU6VKFQoXLkxgYGC+22Ffu3aNevXq4ezszMaNG7Nc361bNw4ePAigqAt048YNtm7dmueioQAnTpygatWqvHz5kocPHzJ06NAs7X4lEgkDBgz435t9Mgtarlu3jn8uSr137x7u7u6Kr69fv87WrVvz9cwAqampODo6smTJEsqUKZPjmDFjxqCvr8/Tp09xdXXF1NQ03/f5J5lMRnJyMqVKlaJKlSps3boVe3t7wsPD2bFjB927d2fMmDEMGzaMixcvsnLlSooVK0Z4eDjNmzenRIkSnDlzBhWV/0+GSCRQoepPV2tEUqgwO91vcP78eTQ1NXn06BGPHz9GIpHw+PFjZs2ahUwmIyMjA19fXxYvXkwDq9pIf5FaU6qFC6MSH0N8fHyW45aWlvTq1Yt+/fqJulmCIAj/MT/XT2JBEP41NDQ0WLVqFXFxcWzbto2QkBBMTEyoXLky+/fvL+jwhP+IM2fO0KpVqzzXz/iRUlNTqVKlCmlpaQQFBeW7lfDGjRvp3r0727dvZ9SoUdlWTXTu3JkLFy4wefJkrKysMDc3582bN/Tr14/379/z/PnzT84fFhZG586dmTBhAlu3bmXXrl3o6+vnOLZ///4cOnSIhIQEAFq0aEFCQgK3bmX9VD42NjbbtVpaWvl46kxz5syhXLly9OvXL8fzMpkMCwsLPnz4gKOjIy4uLl9czBbA39+fyZMnY2hoyLhx4+jduzcWFhbY2Njg4OCAhoYG4eHhjBs3DmNjY8LCwvD09CQ2NhYdHR2Sk5OJiopi1apVim1ECmrqUKpsnmqq/AjJqWn0n7OIEaNGMXHiRB4+fMj48ePR1NREWVmZXr16YWVlBYCysjJSqZTAwEBOH9j39e2ZfyCdokXpkENB5h07diCVSunTp08BRCUIgiAUlJ/jp7AgCP9qDg4OPH78mIcPH2JoaEjv3r0pVqwYY8eOFdtshO/q9OnTtGvXrqDDyEYmk2FmZkZsbCyPHj3KVgfkU9LT0xk1ahRLly7l+vXrtGrVKsdxEokEuVyOq6srq1evxt3dHVVVVaRSKS1btsw1UZCWlsbixYupVasWVlZWPHjwgObNm38yJgMDAxo0aKBYoSKVShkyZAhr167NMm7AgAFMnjxZ8fXcuXPp27dvnp8dwMvLiw0bNrBhw4ZsySDIfH1q1KjBkydPCAgIYNu2bTg4ONC2bVs8PT3zfJ+IiAiWLVuGhYUFrVu3Ri6Xc+bMGXx8fJgwYQIHDx7E1dWVQ4cO4eTkhJmZGXK5nPv377Nt2zaqVatGTEwMV65cYdWqVcyaNYuVK1dSpUoVNmzYkPX/faXKgXaJgk+OSKSoVK5O6y7dad++PU+fPkVZWZkXL15gYWGh+G8VFRUaN27M5MmT0dLS4vr16zj16AayDOZt2UnXiTNYf/g4y/cc+G6h9p+1gDUHjgB80b2UChVCOSWJP/74I8txZWVldu/ezeHDh7l9+/Y3i1cQBEH4uYniq4Ig/HCJiYlMmzaNLVu2kJCQQPPmzVm5cuU3WeouCB+lpqaip6dHcHBwvgqMfm8ymQxLS0tCQ0MJDg6mVKlSeb42KiqKnj17oqyszN69e3NdbXHixAl69OiBmpoa9erVy1JrBDKLJZ89ezbb6q3r168zdOhQypYty5o1azA2Ns5zbMeOHWPJkiV4eGR2W3n//j0VK1YkJCQEXV1dxbhr167RpEkTgCzFO/MiJSUFS0tLpk2bRu/evbOdT09Pp1q1arx+/ZqAgIAs22xsbW1xc3PD29ubGjVq5Dh/QkICx44dY+fOndy+fRtbW1scHBxo2rRplu1DADdv3mTUqFH4+voyZcoUxowZg46OTpYxjRo14vnz54oOQXK5nOvXr7Nw4UJ8fX0ZPXo0Q4YMoWjRoiCXw5NAiI0qmDodEimUM4YSmX8f37x5w6hRozh48CAnT54kISEBe3t76taty6VLlxQrnI4fP86UKVPw2buV4MAA2oyaiM+uTejpaH/XcPvPWkDtqiaM6NHlyyaQSPB4+pImPfvg7e1NzZo1s5xu1qwZgYGBvHr1Kt91fwRBEIRfj0iMCIJQoPbu3cvvv/9OSEgIFStWZNasWWIJs/BNXL58mSlTpvx0n/rWr18fX19fAgMDMTQ0zPN1gYGBdOrUiU6dOrF48eJsb9QhM9HQp08f9u/fj6OjI4sWLaJy5cq8evUKNTU1xbjw8HBq1qzJmzdvUFJS4t27d0ycOJHz58+zYsUKunbtmq+EBWSuNClXrhzu7u5UqVIFyNxiU7VqVSaOHQNJiSDLwN/Pj7Hjx5OSmsYVz1v5Kj46efJkgoODOXz4cLb4UlNTMTU15f379wQFBeW47ad58+Z4enri5+enSPpkZGRw+fJldu7cyYkTJ6hfvz59+/alc+fOWV4zyExsuLm5sXDhQsLDwxk/fjy3bt1CRUUlS40VAA8PDxo3boynpyd16tTJFsv9+/dZvHgx586d47fffsPFxQX9kiUh7DFEvflhyRE5IJFIwagy6GRPIJYtW5bU1FS6du3KyZMnUVZWRk1NjW3btim21Dg4OFBCnsZVbx8mO/amh01zZm3YSnxiEktGD+Ph41CGLVpOQlIyyampDLLtyOje3YHMBIeqSmGCn7/gxZtI6tWoxvZZU5FIJLyMfEu/3+cT8T4K4zIGyOVyWtezZkSPLlkSI3+/17aTZ9hz7iLampr4hT5FS1ODw4v+QF+3OKlpaYxYvIIr93zR09amZo3q7D97gYyMDCIiIrLU6ImLi6NEiRIMHz6cZcuW/ZDvhSAIglBwRApcEIQCZW9vT3BwMEFBQRgbG+Po6EjRokVxcXEhMTGxoMMTfmFubm4/3TYaGxsb7t27h4+PT76SImfOnKFJkyZMnTqVpUuX5pgUCQkJoUyZMhw/fpzTp0+zdetW9PT0qF27NmfPns0ytkyZMujp6eHt7c3mzZupVq0axYoVIyAggG7duuU7KQKZbWj79evHli1bIDEeXj1nxfCBDKxdFfn9W/DYD0IDMVWVsH/+LI4vnQ8P74DPDQjyhZfP4ENs5sqJHNy6dYtt27axbt26bPElJydTuXJloqOjCQ4OzrUWysWLF6levTrm5uZcvHiRCRMmUK5cOaZMmYKlpSWPHj3Czc0Ne3v7LEmRtLQ0du7ciZmZGdOmTWPYsGGEhIQwfPhw1q5dy9WrVxXbiD7q1asXTZs2zTEpAmBubs7u3bu5e/cuHz58wNTUlKHDhhGaIYXyJiBV+v41O6RSnr2KYOmZyzkmRQCqVKnCn3/+SWpqKm/evKFkyZJMnz6dDh06MH36dFJSUli9ejWbj5/CsFRJethk33ZVvlQpLv65jHu7NnFn23o2HDtJ4NNnivN+oU9xW7EI//3b8Q4M5uIdLwBGLVlFs9oW+B/YzuoJLlz1uZ+nx7obEMQSl2H4H9hOVSNDVv//lpu/jpzgeUQkAfu3c/HPpXg98KNRo0bIZDI6dOiQZY6iRYuyYsUKVq5cydOnT/N0X0EQBOHXJRIjgiD8FExMTDh79izx8fEMHjyY7du3o6mpSYsWLfDz8yvo8IRf0OnTp3+qNr12dnZcvXqVW7duYWJikqdr5HI5S5cuZeDAgRw7dgwnJ6ccxy1fvhxTU1MMDAyIiIig7d+KSnbr1o1Dhw5lu8bS0pLu3buzceNGzp49y/LlyzO3dHwpmYzRfXszsG7NzETHqzC0JHKKFy2KRC6HjAyQZSCVy9HS1KCYhnpmEiQjA+Lj4PVzCHmYmSyJfAkZ6Yqpk5KS6N+/P6tXr1YUL921axd+fn4kJiZSuXJl4uPjefz48Se3Tb1+/Zru3buTnp5Oq1atSE9P58KFC3h5eTF69OhshVETEhJYuXIlFStWZOvWrSxZsgQfHx/s7e0VBX01NTXZs2cPw4cPVxS0XbZsGRERETm+7v9kZGTEmjVrePToEcWLF6dOnTr0GjaSh6hCUe3vV3dEIoWSZdh9/xHjZ/xOjRo1uHPnTrZhpUuXJjY2lk2bNlG1alV8fX05c+YMly9f5sGDB1hZWbFp0yaKqqvzKOwFKTl0c0lMTmbgnMXU6NWfBs4jePX2PfdDQhXnbZs0RFVFhcKFCmFZpRKh4a8AcPf2walj5t9lw1L6tLCyzNOjNTCvQVn9zL8HdWtU+998Xj70bZdZjFlVRQX7dq2RSqVcuHCBS5cu8ddff2WZZ+jQoZiammb59yQIgiD8O4nEiCAIPxVVVVVcXV2JiYlh//79vHz5EjMzM4yNjdm+fXtBhyf8Ip48eUJ0dDSWlnl7I/W9OTg4cPLkSa5evZqtlkFukpOTcXJyYteuXdy6dYv69etnG5OYmEiDBg0YP348s2bNwsvLK1tyw87ODjc3N0Wxz/j4eCZMmMDJkydRVVXl5s2bWFhYfPnDyeWZSQ1fT0qlxmNSrsz/bwP5gp26MhmkpkD4U/C9BS9CQZbBjBkzMDc3p3v3zO0XKSkpDB48mAYNGlC+fHmSk5N5/PhxthofH593x44d2NjYUKNGDR49esTJkycpW7Ysu3fvply5ctmueffuHb///jtGRkZcv36dQ4cOcfnyZVq3bp3jahorKyvGjh1L3759SUhIYNq0abi4uOQYT2709PSYO3cuT58+xcrKijYdO9F6xDgeJqQhL6L2bRIkEknmn2I6YGoBpcsz0NkZiUSCn58fTZs2pWXLlgQHBysuKV26NC9fvgSgUqVKGBkZoaOjQ8uWLenTpw+DBw9m8uTJnFg6n9qmJvz+V/b2y1PXbkS/uA4+uzZxf88WrKtVITnlfwkUVZX/bWFRkiqRnpHxt5Dzv2pGtfDf55Mq5pMjz7oI5/+/sLKyYurUqQwfPjzb6hA3NzceP37MypUr8x2HIAiC8OsQiRFBEH5a3bp1IygoiEePHmFiYsLAgQPR1NRk+PDhxMfHF3R4wk/Mzc2Ntm3b/hRFE4cOHcrevXs5f/489erVy9M1ERERNGvWjISEBDw8PHJ8837lyhVKlixJcHAw9+/fZ/r06TnOVbJkSSwsLDh37hxHjx6latWqvHnzBl9fX16+fPl1W9aSEsHfOzMxIstcEfJNyGQgl8Hb16R43yDQ6w5//vmn4vT58+dRUlIiLi6Od+/e4ebmlqUQbXp6OufOncPBwYEyZcpw8OBBfvvtN16+fMnmzZuxsbEhMDAQJSUlTE1NSf3/VQ7Pnj1j5MiRVK5cmdevX+Ph4cGhQ4cUtTQ+ZcKECSgpKVGvXj1UVFRwdXX9okfX1NRk3LhxPHnyhB49etDNeQh1Bwzn0sv3yHX0Mld65GebjURKUmoqciVl0C8HZnWgUvXMNsGAvr4+RYoUATJX5ri7u2cp1vv3xIi+vj6JiYmsWLGCI0eOMHv2bObPn4+zszPVjI1YOW4Ue85d5K5/YJYQYj7EU7akHsrKyvg9fsJ13wd5Cr2pZU22nToDwIuISC7f9cnbM+eiWS0Ldp25QHp6OskpKew/c15xbs6cOVSvXp2GDRsi+1t9l3LlyjFhwgQmTJhATEzMV91fEARB+HkV/G+MgiAIn1GpUiXc3NxISEhg+PDh7Nmzh2LFitGsWTMePMjbL9jCf8vPUl9kwoQJbNiwgaNHj3625e1H9+7dw9ramjZt2rB//37U1dWzjRk+fDjNmzenRYsWvH79murVq39yzmbNmjF8+HCmTp3Kjh072LFjB0ZGRlhZWXH16tX8P9jHVSIB9yA58fsVCpXJUJHACde56CbGKO6zfv16Pnz48P+hyGnXrh3p6en4+PgwduxYypYty4wZM6hTpw4hISGcPHmSHj16KBIAAGpqagQGBpKYmEilSpWwt7enVq1aqKur4+/vz4YNG6hcuXKeQ1VSUuKPP/7g4cOHTJgw4auTcioqKgwcOJCAgAAmTZrElDnzMG1ny46HIaSVrwyly2eu/ChUKIerJaCimlk3pJwx7cdMYez2A1DaEAoVzjpSIsHY2FixMqNfv36MGTNGcb5MmTJZEiMfk9L16tVj2rRpZGRkcOTIEdLkoF1Ukz8njmbAnEWkpqUp5pg+sC8bj53Cqt8g5m7ZSWML8zy9BivHjeLCbS/Mew9g/Mq1NDCvnrkF6wsN6dqZUrrFqdazPx3GTKGWWQ2KFSumOH/t2jWio6NxcHDIct2CBQsoUaJEtjokgiAIwr+H6EojCMIv6ejRo0ybNo2goCAMDQ2ZPn06AwcOLOiwhJ9AYmIi+vr6vHjxIsubnh9t9uzZzJ49m71799KzZ888XXPgwAGGDx/O+vXr6dq1a7bzr169olGjRoSHh7Nt2zbs7e0/OV9qaipLly7F1dWVpKQkIiMj0dTUVJxfsGABb968YcWKFXl/MJkMngb9+LayEimoqROppU9JAwMkEglFihShWbNm6OnpcefOHRISEnBwcMDBweGzdVzkcjlXr15lzpw5uLu7o6+vj7+/P9raX95m1tzcnOjoaAoVKoSPj8/X1WzJIV53d3cWLVqEv78/Y8aMYdCgQf/7fsrlmd8PqTTbahJVVVVSU1O5ceNGjquWhg4dyu3bt2nRogXLly/H399f8fp5e3vj7OyMj48P27dvZ9iwYSQkJGS5/v79+7y9dZWWtWp+s+cFSEpOoZCyEsrKyrx+9x4rx8Fc+nMZJuWzr6DKqw8JiWiqq5GSnk6nKXPo3qcPzs7OivPnzp2jbdu2HDx4MMu/wXv37lG7du1sxwVBEIR/B7FiRBCEX5KdnR0BAQGEhoZSvXp1hgwZgoaGBkOGDCEuLq6gwxMKkLu7O5aWlgWaFFm6dCmzZ89m48aNeUqKyGQyZs6cycSJE7lw4UKOb7y2b99O+fLlUVZW5sWLF59NinysZ+Lh4YG3tzdWVlZcu3YtyxgbGxvOnz+fyww5BgqP/X98UgQyt9YkxvPh7nUMy5Rh5MiRWFtb4+npibKyMuvWrSM0NJQ5c+Z8Mikik8k4evQodevWZdCgQdjb2xMUFERMTAydOnX64vCOHz/Ow4cPOX/+PC1btmTEiBFfPFdOJBIJzZs359y5c5w4cYI7d+5gZGTEjBkzePv2bWYyRCn7FpukpCTS0tIyW922bo2/v3+2uf/880/u3buHq6srFhYWNGnSRLGdpHTp0oSHhwP/a90rk8l49OgRO3fuZOTIkQwaNIgzHp6kpKVnm/trhLwIp3a/wZj3HkCLYWP43dnxq5IiAC2Hj6Vm74GY93Kikkll+vfvn+V869atcXZ2pnfv3kRGRiqOW1paYm9vj6Ojo2LrlSAIgvDvIVaMCILwr5Camsrs2bNZv349MTExNGjQgBUrVvw0xTeFH2fYsGEYGRkxYcKEArn/X3/9xdChQ1m+fDkuLi6fHR8fH0+/fv2IjIzkyJEj2bqqpKen06lTJ86ePcvo0aNZtmzZJ+eLjIxkwoQJuLu7s2LFCuzs7JBIJKxatQofHx+2bv1fccyMjAxKliyJr68vZcqU+XSgcjk8CSyYpMjfpKSlcT8kFNfj5+jVuzft27dHVVX189elpLBr1y5cXV0pWrQokyZNwtbWVtH6ODAwkJo1a9KyZcssNTbyQiaTUaJECZo0acKRI0dISEigdu3aTJ8+nT59+nzRc+bF48ePWbJkCQcOHKB3796MGzcOIyOjLGO8vLxo3ry5YuuRjo4O/v7+ubY0jo+Pp2TJkrRr146DBw8ik8lQUVFh3759XL58mbVr16KlpYWWlhbW1tbUqVMHa2traplUosiLx9+uzsz3JpGAZcNca7UYGxtTqFAhgoKCFMfS09MpXrw4NjY2eeo4JAiCIPw6xIoRQRD+FQoXLsy8efN4//49R48eJSoqitq1a2NoaMhff/2VpZie8O8ll8sLtL7Irl27GDp0KHPmzMlTUiQsLIwGDRqgra3NpUuXsiVF7t27h56eHjdu3MDDw+OTSRGZTMaGDRuoXr06urq6+Pv706VLF0XtiC5dunDixIksn3YrKSnRsmVLLly48PmHi3hR4EkRAJVChbCqasrBpfPp2rXrZ5MicXFxuLq6UqFCBQ4ePMi6deu4ffs2Xbt2VSRFAExNTfHw8OD8+fP07t07XzFNnz6d+Ph4du3aBYC6ujp79uxh9OjRPHnyJP8PmUcVK1Zk/fr1+Pv7o6GhQe3atXFwcMhSe8nX15ekpCQkEglSqRQrKyvS/lb/Iyd//PEHhw4dok6dOpQvXx6ZTMbq1asVXXZ8fX15+vQp+/fvZ+zYsTRs2JAiOrqZybNfRRH1TxawvX79Ok+ePMny71hZWZm9e/dy5MgRbt++/SOiFARBEH4QkRgRBOFfp1OnTvj5+fH06VMsLCwYMWIEmpqa/Pbbb6KrwL9cQEAAAFWrVv3h9z569CiOjo5MnjyZadOmfXa8h4cHdevWxcnJiU2bNqGiopLl/MyZM7GyssLc3Jw3b97k2K73o/v379OwYUO2bdvGhQsXWLp0aZZaIpBZRNPExITLly9nOZ6n7TRJifDqeYEnRT6SIIeotxAXk+uYiIgIpkyZQoUKFfDx8eH06dOcPXuWZs2a5doC1srKivPnz3PgwAGGDh2ap1hiYmJwdXVl1qxZqKmpKY5bWFgwdepU+vTpQ3r6t91i8k+lSpVi4cKFPHnyhBo1atC6dWvat2/P9evXqVSpEqNHj8bW1hZdXV3Onj1L2bJlAUhLS8PHx4e//vqLgQMHUqNGDUqWLMnhw4epUaMG9+7d48CBA1hZWTF37lzmzJmDRCIhKSkpexBKSqCt+12f85uRKoH+p1dIGRgYsHXrVlavXs2VK1cUx9u1a0fTpk3p1KmTSLgLgiD8i4jEiCAI/1qGhoYcO3aMpKQkJkyYwNGjR9HR0aFhw4bcvXu3oMMTvoOPq0Vye+P7vZw7d45u3boxbNgw5s+f/9nxmzZtomvXrmzbto3Ro0dniTcmJgZzc3Pmz5/P6tWrcXd3z3VVxIcPHxg7diw2NjY4OTnh4eGBuXnuHT+6deuWbQuAjY0NFy9ezP1NnlwOTwIya3z8TGSyzK09GVm3bjx+/JjBgwdjamrKhw8fuHv3Lnv27KFmzZp5mrZZs2YcOXKEDRs2MGnSpM+O79WrF7q6ukyZMiXbORcXF4oWLcoff/yRp3t/rWLFijFp0iSePn1K586dcXJyYsqUKTRq1Ij58+cTGRnJli1bFKs8tLW1cXBw4NatW9SuXZvt27cTHR3NzZs3efDgAeXKlcPR0ZGyZcsqOtMoKyvz7NmznAPQL5NZ/PVnJwG0Pp/E6dOnD7a2tnTo0CFLwdkTJ04QGxvLuHHjvmOQgiAIwo/0C/z0EgRB+DrKysrMmjWLd+/ecfr0aeLi4qhTpw5ly5Zl7dq14lO/fxE3Nzfat2//Q+/p4eFB+/bt6devH6tXr/7k2PT0dFxcXHB1deXatWu0bt06y/kTJ05QqlQpoqKiCAkJYdiwYTnOI5fLOXz4MFWrViUqKgp/f39+++23z7aI7datG8eOHcuylaJcuXLo6uri6+ub80URLyAl+ZPzFpiMDHgRCmTW0ujevTv16tVDT0+PR48esWbNmmw1N/KiU6dObN++HVdXVxYsWJDruHv37nH+/HnFFpp/kkqlbN++nY0bN3L9+vV8x/GlVFVV6dKlCytWrKB06dL07dtX0dJ56dKllCxZkjlz5vDq1Sv8/f3ZunUrQ4cOxdLSksKF/9fO9/r16zx9+pTHjx8rEiOFCxfmxYsXOd9YTQMKf77eS4GSSKBEqTwncA4dOoS6unqWdtsaGhqsXLmSVatW8fTp0+8VqSAIgvADicSIIAj/KW3btuXBgwc8f/4ca2trRo8ejYaGBgMGDCAqKqqgwxO+QmxsLN7e3jRr1uyH3dPLy4tmzZphZ2eXpahpTqKjo2nXrh1BQUHcunUrS+cUmUxG7969sbW1pVevXoSFheX6hv7Jkyd06NCBmTNnsnv3brZt20aJEiXyFG+5cuUwNjbOsjUAPrGdRiaD1y9+mi002chlyN5F0M22M3Z2dtSvX5+nT58yZ86cbPVa8svBwYE1a9Ywbdo01q5dm+OY7t27Y21tTYsWLXKdR19fn02bNuHg4EB0dPRXxZSbpKQkbt68yfLly7G3t8fY2BhjY2NWrFhBpUqV2LZtGzt37kRLS4uQkBBUVVWxtrb+bDthAwMDtmzZgq+vL7du3QJATU1N0aUm54sMf+5VIxIJ6BnkebhUKuXatWt4e3szb948xfHBgwdTtWpV2rRp8z2iFARBEH6wn/gnlyAIwvdTpkwZDh8+TGJiIlOmTOHUqVPo6upSr149PD09Czo84QtcuHCBhg0bZqnz8D35+/vToEEDWrZsycGDBz85NigoiDp16lC9enVOnz6Ntra24lxISAhlypTh2LFjnD59mq1bt+a48iMlJYV58+ZhbW1No0aN8PHxoXHjxvmOO6ftNK1atco5MRL9Fvi5C2rKZHKmDXIiNDSUMWPGoKGh8c3m/rg1asSIEezevTvLuc2bN/Ps2TOOHDny2Xnat29P586dGTJkCF/bDDAjIwM/Pz+2bNnCkCFDsLS0RFdXFxcXFx4/fkybNm04deoU0dHRXLx4kfnz52NnZ4e9vT0rVqwAMleCGBkZMXv2bN6/f//J+zk4OGBpacnRo0dJTExEXV2diIiI3C/Q1gV1zU8WNi0wUimUKpfvVS0mJiYsWbKEmTNncv/+fcXxM2fOEBoaqnhdBUEQhF+XaNcrCILw/86fP8+kSZO4f/8+BgYGTJgwgZEjR352e4Lwc3BycqJWrVqMGDHiu98rNDSU6tWrY2VlxbVr1z459uzZs/Tr14+FCxcyYMCALOdWrFjB+PHjMTc3x93dPddP8N3d3Rk2bBgVK1Zk9erVlC9f/otjf/r0KXXq1OHVq1coKysDmS1aS5UqRUREBOrq6v8b7O+VWXj1Z6ekDDXrfbc345MmTWLJkiUcPXqUTp06kZ6ejpaWFvb29mzcuDFPcyQnJ2NlZcXYsWNxcnLK0zVyuZzw8HDu3Lmj+OPt7Y2+vj7W1taKdrnm5uZ5almcnp5O4cKFuXbtGiVKlMDV1ZUjR47Qr18/xo4dS7ly5XK87tKlS7Rp0wYrKysSExMxMjLi6NGjud8oNQX87v58K42KqENVyy/+e9KkSRP8/Px48+aN4t/O1KlTWbJkCREREYquPYIgCMKvRyRGBEEQ/uHVq1eMHj2a48ePI5VK6dGjB0uXLkVX9xfpuPAfJJPJMDAw4ObNm1SoUOG73is8PJwqVapQpUoV7ty5k2viTC6Xs3z5cpYsWcLBgwdp0KCB4lxiYiI2NjbcunWL2bNnM3369BznePPmDePHj+fatWusXLmSzp07f5PCsrVr18bV1TXLtqMmTZowefJk2rZt+/9BxkOg789XdDUnUiUwMvmuXVGGDh3Khg0buHTpEnv27GHPnj3ExMQo3iDnhZ+fH82aNePmzZtUqlQp2/mYmBi8vLy4c+cOt2/f5s6dO8hkMkUCxNramtq1a3/VG3BjY2Osra3Zu3cvAC9fvmTFihVs3ryZjh07MnHiRKpVq5blmsePH9OkSRMiIiIoX748pUuX/mxCkLcR8OLxz5MckUozkyKqX76iLDk5mZIlS1KvXj3Onj2rOF6mTBkMDQ25cePGt4hUEARBKADiY1BBEIR/MDAw4MCBAyQkJDBz5kzOnj2Lnp4ederUwcPDo6DDE3Jw7949tLW1v3tS5O3bt1SrVg1DQ8NPJkVSUlIYMGAAO3bs4NatW1mSIleuXKFkyZIEBwdz//79HJMiMpmM9evXU6NGDUqVKoW/vz+2trbfrNtOnrbTxEZldqT5FcgyIObdd73FunXr6NGjBy1atGDz5s2sXLkyX0kRgOrVqzNr1ix69+6t6JizZs0a+vXrR5UqVShbtixz5swhOjpa0S0mIiKCkydPMn36dFq1avXVqxLatWuHu7u74uvSpUvj6upKaGgolStXpkWLFnTu3JmbN29mGfPu3Tvmzp3LkydPPr2V5iPdklBU+6eoN5KSlk5gfOpXJUUgs6jt+fPnOX/+PBs2bFAcP3XqFJ6entn+TQmCIAi/DrFiRBAEIQ8uX77MhAkT8PHxQV9fn/HjxzN69GixzeYn8ccffxAbG8vSpUu/2z1iY2MxNjamWLFiPHr0KNc3xW/evKFLly6UKlWK7du3Z9maMnz4cNatW0enTp04dOhQjnP4+PgwZMgQChUqxLp166hRo8Y3f5aQkBAaNWrEy5cvUVJSAuDu3bs4OTnh5+eXOSj4IcR9n2Kh34VKEahh9d1vo62tTUxMDAEBAZiamubpGplMRkhIiGIlyO7du4mPj6datWqKLTHW1tZUrVo138mW/AoJCaFy5crExsbmuHUrKSmJrVu34urqStmyZRWriHR1dQkMDKRGjRq8e/eOtLS0z///TyaDED+Ijyu4lUdSKf5R8VS3aUvRokXp27cvHTp0oHHjxl9cj2jq1KksXryY0NBQDA0NgcxaLMeOHSMqKipLZx9BEATh1yB+oxcEQciD5s2b4+3tzatXr2jSpAlTpkxBTU2NPn36EBkZWdDh/eedPn36u7bpTUxMxMTEhCJFiuDv75/rm1cfHx+sra1p1aoVBw4cUCRFXr16hbGxMZs2bWL37t0cO3Ys2xwfPnxgzJgxtGnThkGDBnHt2rXvkhQBqFSpEvr6+lmW/ltaWvL69WtFW1YS43O8dsAfC5m0en2WYy2HjWXdoWP5juPYleuYdu+LRZ+BPHr2PNdxr96+o9kQl09Plpr83bdtXLp0idjYWMzNzalVqxZhYWE5jouIiODEiROKVR7FixdXFEU1MjJix44dlChRgmXLlrFhwwacnZ0xMzP77kkRyPzea2hosH79+hzPFylShGHDhhESEsLQoUOZMmUK5ubmqKmpERYWxqBBg5DJZHTt2vXzN5NKoVJ10CwGkgL4lVOSWWy1Wss2mJiYEBcXx59//km3bt3Q1tbm9evXXzTt/PnzqVatGg0aNFC0e9+2bRtKSkrY29t/yycQBEEQfhCRGBEEQcgHfX199u7dS1JSEnPmzOHy5cvo6+tTu3btz++5F76LyMhIHj16RMOGDb/L/KmpqVSpUgW5XE5gYGCuRS4PHjxIq1atWLp0Kb///rvi0/Tt27dTvnx5lJWVefHiRbY3TnK5nIMHD2JqakpcXBz+/v4MHDjwu69G6t69e5al/0pKSrRo0YILFy5AehpkpOd43YqxIzlw0Z3bfgEA/HXkBBIJDOnaOd8x/HXkBH8MHoDP7s2YlM+58CeAQQld3Nev/PRkEikkf99CsQ4ODrRq1Yp79+5RuXJlqlevTmhoKFevXsXV1ZVu3bpRrlw5qlatytq1a1FSUsLFxYVHjx7x9OlT9u/fz7hx4+jYsSPbt2/H0dHxs11hvgdra2v279//yTHKysrY29vj6+vLokWLSEhIoEOHDjx9+hQVFRWOHz+erVNPjj4mR4rr/dhtNVIpGFbM7EIDuLq6UqRIESBzVYyTkxP6+vpfPP3Vq1d5//49/fr1AzJfr71793L06FHR2UwQBOEXJLbSCIIgfKWrV68yfvx4vL29KVmyJGPGjGH8+PFim80PsmPHDo4fP87hw4e/+dzp6emYmpry/v17njx5gpaWVrYxMpmMP/74g61bt3Ls2DEsLCwU13bq1ImzZ8/i4uLC8uXLs10bGhrKiBEjePHiBevXr/9uyZ2cPHr0iObNm/PixQvF39VNmzbh7u7O7r/WIg/2QyLLyPHaC7fv4rJ0Ncdc59Fi2BicbTtw6vpN0jMyUFUpzLpJY6lpkllcVGLVhHnDnDnqfp33sXG4ugyla/MmjFm2ho3HTqGnrYVhqZK4r1/Jbb8AJq/5i7iEzATHH4MH0L5hPZ69ek3tfoN5d/FE7g8kVQLDSplvwL+DhQsXMmPGDC5evEhQUBC3bt1iz549pKamYmVlRf369RVbYoyNjfNUD2b8+PGEhoZy5MiRb1Y/Ji927NjBb7/9RkpKSp6vcXZ2pnjx4ly/fh1PT0/q1KnDvXv3eP78ed4TDHHR8CQoM+n2vX79lEoza4kYm2Zur/p/6enp6OnpER8fT0ZGBhUqVOD+/ftf1d77zJkztG/fnsOHD2NnZwdAixYt8PPz4/Xr1+JngCAIwi9E/B9bEAThKzVp0oS7d+8SERFBixYtmDlzJkWKFMHe3j5vRQqFr+Lm5ka7du2++bwymQwLCwvevHlDUFBQjkmRhIQEevTowYULF7hz544iKXLv3j309PS4ceMGHh4e2ZIiKSkpzJkzhzp16tC8eXN8fHx+aFIEwMTEBB0dnSyfbtvY2HD27Fm2btlC3Ie4XK+1qWNFE0tzrBwHM3uwE8O62XJ3xwZ8dm9mzuCBDFm4LMv4ourq3N2xgZ1/TGPUklUALB87gtqmJqwaPwr39SuJ+fCBIQuWsmfuTLx3buTU8oUMXrCUmA8f8vZAcnlmEdZvRC6X8+TJE/bt28eIESOYOnUqAMOGDePWrVtYW1tz5coVSpcuzYsXL5g/fz69e/emYsWKeU5yzJs3j7CwsCyFPH+E3r17k5aWlq9i0mXKlEFFRYWdO3cCYGpqSkZGBqampoSHh+dtkqLamXVgdPS+/dYaiTTzT2kjMLXIkhSBzBUdLi4uWFpa8uTJE2JjYzE0NPyq/0e3bduWAQMGYG9vz7t3mcV/jx8/TmxsLGPGjPmqxxEEQRB+LJEYEQRB+Eb09PTYtWsXiYmJLFy4kKtXr2JgYIClpSWXLl0q6PD+ldLT0zl//vz/Wsx+IzKZjHr16vHkyRP8/PzQ08u+CiEsLIwGDRpQtGhRLl++TMmSJQGYOXMmVlZWmJub8+bNG+rXr5/lusuXL2NmZoa3tzfe3t5MmDCBQoUKfdP48+pjd5p3796xZs0aevToQWxsLO8jI7MUjc3JeIdeKClJGdCpPd6Bj2g8aCTVe/Zn7Io/8Q1+nGVsr1bNAahbvSqv3r4jOYeVCjcf+PP0VQRtR02kZu+BtB01EQnw+MXLvD/QV6xCePfuHWfOnGH27Nm0b98ePT09GjduzIEDB7hy5QoaGhq8ffsWf39/tm7dytChQ6lXrx5BQUFkZGRQtWpVUlNT83VPFRUV9u7dy/Tp0wkICPji2PNLWVkZIyMj/vzzzzxfU7p0aV6+fEnZsmUB+Ouvv7hz5w5xcXFUrFiRgQMHEhQU9PmJlJQzWytXtQTdUpkrPL5iZUVqRgZy5UJgYAhmdaBkacglMTVz5kw8PT0xNDQkLCwMbW1tjI2N8ff3/+L7b9iwgVKlStGoUSMANDQ0WLVqFWvWrCE0NPSL5xUEQRB+LJEYEQRB+MakUiljxozh1atXXL9+nUKFCmFjY0PJkiVZsGAB6ek5124Q8s/T05Py5ctjYGDwTedt0aIF9+/fx9fXl3Llste+uHHjBvXq1cPR0ZHNmzejoqJCTEwM5ubmzJ8/n9WrV+Pu7p6lHklERAR9+vRhwIABuLq6cuzYMUVHi4KQnJxM0aJFWb9+PRUrVsTT05PZs2czePBgyhqWR/n/u9XkRkkqRSqRkpqWRrfJv7Ni7Ej89m/j7CpXUv6RIFD9/y4dHzvgpGdkX9khl8sxq1QB3z2bFX9enD5E7apV8v5QeVypkZSUxM2bN1m+fDn29vYYGxtjbGzMkiVLSElJ4bfffsPX15fw8HAWLFhAQEAAO3bsyHHVkIaGBkFBQYrvvyyfBWBNTEwUq03ys7Xla/2zbe/nlC5dmvDwcAoXLoxEIiEsLIxatWqxa9cuUlNTycjIoHHjxnTp0oU7d+58fsIialC+EpjXgzIVSJYqk5KW9v+Jktz/7smlSqTLZMTEx3PjYQA9p8yi57xlvFVWhc8kGCUSiWI1T5EiRQgKCqJ27drUrFnzi5PXUqmUGzduEBoaqlglMmjQIKpVq/bNE7aCIAjC9yMSI4IgCN9RgwYNuH37NpGRkbRp04Y//vgDdXV1unfvzqtXrwo6vF/e9+hG07FjR27cuIGXlxeVKlXKdn7Lli3Y2dmxZcsWxowZg0Qi4cSJE5QqVYqoqChCQkIYNmyYYnxGRgZr166lRo0alC1bFn9/fzp16vRNY84rmUzG1atXcXZ2xsDAADc3N7S1tTly5Ai7d++mTZs2tG7dGs9bt/I8Z3JKKukZGZQtmbmqZu0XdKcBqG9WnZDn4bh73VMcu+sfSJ5LoUkkOa48yMjIwM/Pjy1btjBkyBAsLS3R1dXFxcWFx48fK7rFREdHc+nSJebPn4+trS2lS5cGoEuXLtSoUQNbW9tcb62jo0NAQAAvXrzA2to638kRZ2dnjI2NmTJlSr6u+xqjRo3izZs3xMXlvmXq7z6uGIHMBNezZ88AsLe3x9bWlkOHDvHw4UOaNWtG9+7dad68OefOnfv8909JCfQMqN1vEEZd+2auJDGslLnyo7geaBUHbV0oXhJKlUNSoQrKNeuRXMWSAQuXc8z9Gs9fvMDMzCzfdYakUilXr16lV69etGrViq1bt+br+o8MDAzYvHkzK1euVBThdnNz48mTJyxbtuwzVwuCIAg/BbkgCILww2RkZMhXrVolL126tFwikcjNzc3l58+fL+iwflk1atSQ37x585vN17NnT7mysrL8zp072c6lpaXJR48eLa9UqZI8KChILpdnfj/t7e3lEolE7ujoKM/IyMhyjZeXl9zKykreqFEj+cOHD79ZnPkVGBgonzp1qtzQ0FBeo0YN+eLFi+Xh4eFyuVwunzFjhnzcuHGKsbGxsfIKZcvKZV7X5PK7V3P98/T4PnnxYsXk8rtX5YtGDpYbltKXN7Iwk88f9pscUIwD5B+unsnx6yaWNeUnly1QnLuzbb28iWVNuVklY3mV8uXk7RrUlWfcds9yr1z/eHvIZR9i5M+fP5cfOnRIPnHiRHnTpk3lmpqa8kqVKsn79OkjX7lypdzT01OelJSUp9dt//79colEIg8NDc3T+MePH8tVVVXlzZo1y++3SP7+/Xt52bJl5WfOnMn3tV9KQ0NDvnDhwjyNffv2rVxLS0sul8vl6urq8k2bNinOZWRkyPX09OTW1tZyuVwuT01Nle/YsUNerVo1ec2aNeX79u2Tp6Wl5Tr3mjVr5IBcU1MzX/HPmDFDDsgBuZWVlbxixYryXr16yd+9e5eveeRyuXzatGlyiUQi//333/N97UedO3eWq6uryxMSEuRyuVw+depUeaFCheTv37//4jkFQRCEH0N0pREEQSggt2/fZsyYMdy6dQtdXV1FgUdlZeWCDu2X8Pz5c2rVqkVERIRii8bXcHZ2Ztu2bVy+fJnGjRtnORcdHU2vXr2Qy+Xs378fbW1tQkJCaNKkCTExMRw+fDjLsvnY2FhmzJjB/v37WbRoEY6Ojj+06whktjHeu3cvO3fu5NWrV/Tu3Zu+fftibm6eZdyDBw/o1KkTT58+VcTYuHFjLi2ZQyHpj435a6RnZFCphyMJScnUqVMHa2tr6tSpQ+3atdHR0cn3fDKZDB0dHdq0acO+ffvyfN2DBw+oXbs27dq149ixY/m655UrV+jduzc+Pj6KmjXfU4sWLYiJicHb2/uzY+VyOUWKFCEqKgpDQ0NGjhzJzJkzFecDAwOpXr06c+fOVax8kclkuLm5sXDhQl6/fs2ECRPo379/li1mbm5udOvWjaSkJJSVlYmOjkZDQyNP8Xfu3JkTJzI7FSkpKVGsWDGaNm2Kp6cn69ato3Pn/LWQ3rRpE4MHD6Zv375s27YtX9dC5vPq6+tToUIFbv3/qqsyZcpQrlw5bt68me/5BEEQhB9HbKURBEEoIHXq1OHmzZu8f/+ejh07snDhQtTU1OjSpQsvXrwo6PB+emfOnKF169bfJCkyevRotm7dysmTJ7MlRR49ekTdunUxNTVVbD1ZsWIFpqamlCpVioiICEVS5GPipGrVqiQlJREQEED//v1/WFIkMTGRffv20b59eypXroy3tzcLFizgxYsXLFmyJFtSBKBGjRoAnDx5UrHtwcbGhmdv3/2QmL+VZBlcuXadN2/ecPLkSWbMmEGrVq2+KCkCMGHCBFJSUvL9BtnMzIyrV69y6tQpHB0d83Vt06ZN6d+/PwMGDMj7FqKv4OjoiJ+fX57GSiQSxXYadXV1Xr9+neW8qakpixcvZvr06Yo5pVIpHTp0wMPDg+3bt3P69GmMjIxYuHAhsbGxPH36FDs7O5KSkgBQU1PDy8srz/H/vdirkpISlpaWeHt7Y25uzpgxY+jbty9RUVF5ns/Z2ZnTp0+ze/dumjdvnu8tUR+35ty9e5cFCxYAcOrUKW7dusWhQ4fyNZcgCILwY4nEiCAIQgHT1tZm8+bNxMfHs2LFCry8vDA0NMTMzAw3N7eCDu+n8/EN47eqLzJjxgxWr17N/v37sxVLPHfuHI0aNWLixImsWLGC1NRUGjRowLhx45g1axbe3t4ULVoUgJCQENq0acO8efM4cOAAGzdupHjx4l8d3+fIZDIuX76Mk5MTpUuXZtu2bdjb2/Py5Ut27NiBjY3NJ5NHEomEuLg4bG1tUVdXx9TUlG3btnHA7RyyX2hRqYaePoaGht8kCRUVFcXKlSuZN29eltUNeVWvXj3OnDnDrl27cHFxyde1s2fP5u3bt6xZsybf982v/Lbt/ZgYKVq0KJGRkdnOjxs3jrp169KsWbNsRaYbNmzIyZMnOX/+PP7+/lSoUIHly5czcOBANDU1gczE3u3bt/Mcv7KyMnXr1kUqlbJ06VIuXLiAn58flSpVIikpiffv32NmZsapU6fyPGebNm3w9vbm1q1bVKtWjeTk5DxfC5kJIldXV6ZPn87Dhw+pWbMmDg4O9O/fP99diwRBEIQfR2ylEQRB+AndvXuXMWPGcPPmTXR0dBg2bBgzZ878z2+zkcvlFCtWjDJlyhAaGsq5c+do0qTJF78ZXrRoEVOmTGHr1q1ZPt2Xy+WsWLECV1dXDhw4QMOGDbl69SodOnRAVVUVd3d3qlevDmR2d1m0aBGrV69mypQpjBo16oe03/X392fnzp3s3r0bXV1d+vbti729PaVKlcrXPOnp6TRo0CBbJ5HGljW5/NdKvn49zg8glYJh5cxind9AixYtePToEeHh4V81z+HDh+nRowfTpk3jjz/+yPN1jx8/pl69ely6dAkzM7OviuFzjI2Nsba2Zu/evZ8da29vT/v27dm4cSMSiYQrV65kG5OYmEjJkiVp2rQpJ0+ezHWuZ8+esWzZMnbt2kVycjKdO3emX79+VK5cGWNj43w9Q4MGDcjIyFBsX4HMzlEDBw5ET0+PZ8+e0aJFC5YvX55jZ6GcvHr1CjMzM5SVlXn48CElSpTIV0yNGjUiMDCQiIgIAHR1dWnevDlHjhzJ1zyCIAjCjyFWjAiCIPyErKys8PDwICoqii5durB06VKKFCmCra0tYWFhBR1egZFIJKirqxMYGEhaWhqtW7emW7duXzTXn3/+yZQpU1izZk2WpEhKSgoDBw5k+/bteHp60rBhQ4YPH06zZs1o0aIFr1+/ViRFLly4gJmZGffv38fHx4dx48Z916RIREQEy5Ytw8LCgtatWyOXyzlz5gw+Pj6MHTs2z0mRZ8+eMWbMGCpVqoSKikqWGhMaGhp4enpSwsiYxNS07/Uo35gks3PJN3D79m3c3d3zlCj4nK5du7Jp0ybmzp3L0qVL83xdxYoVWbp0Kfb29optJt9Lftr2flwxUrx4caKjo3Mco6amxpkzZzh9+jQ7duzIda7y5cuzatUqHj58SFJSEmfPnmXr1q3ExMTk+xlGjBiBt7d3lq0vDRo0wNfXl0aNGpGUlERYWBjVq1fn7NmzeZrTwMCAsLAw1NXVMTIyIjAwMF8xXbhwgdTUVDp16oSysjJ79+7l2LFj3LhxI1/zCIIgCD+GSIwIgiD8xLS0tNiwYQMfPnzgzz//xNfXFyMjI6pXr64oOvhf8/ETdLlcTqFChZg+fXq+59i2bRsjR45k4cKFWVrrvnnzhubNmxMTE4OHhweFChXC2NiYTZs2sXv3bo4dO4aysjKvX7/G3t6eQYMGsWzZMo4cOULZsmW/2TP+XUJCgqKVrqmpKQ8fPmTp0qWEhYWxaNEiRZLmU2QyGfv376d169YUK1YMIyMjdu/ejYWFBRcvXiQhIQElJSWKFCnC7du3qVu3Lq1ateLg9Vs5tsD9qUgkUKLUN4uzZ8+eNGjQgEaNGn2T+ZycnFi2bBkTJkxg06ZNeb7uY6Hc8ePHf5M4cuPi4sKbN2/ylJD4mBgpWbLkJ9v8NmzYEBcXF5ydnT/blvzKlSsULlyYFy9eUK9ePWxtbbGxseHSpUt5rrPSs2dPAPbv35/luKqqKvPmzeP8+fNER0ejr6+Ps7Mzv/32W57aFKurqxMSEoK5uTnm5uY5rpDJjaqqKufOnePs2bNs3ryZtm3b0rx5c+zs7PJdu0QQBEH4/n7y33YEQRAEyCzqN2jQIJ49e4aXlxfFixfHzs4OHR0dpk2b9p/au16/fn0g843HmTNnsLCwyNf1hw4dYuDAgUyfPp2JEycqjvv6+lKnTh1atmzJoUOHOHz4MOXLl0dZWZkXL15gb29PRkYGa9aswczMDCMjI/z9/enQocM3fT6AjIwMLly4QL9+/ShdujS7d+/G0dGRly9fsnXrVpo3b/7ZorPPnz9nwoQJmJiYUKhQIfr27UtERATjx4/n/fv3REZGcuDAAZo1a4aKigq///47+vr6mJqaNt2njgABAABJREFUApkFWOeu28DPv+FWAnoG32SmtWvXEh4ezuHDh7/JfB+NHj2aWbNmMWjQoGxv3nMjkUhYt24dbm5u3zUJWrFiRTQ0NFi/fv1nx35MjOjr/x97dx0WVfoFcPw7QyOCoGBgEYqICqJiY3esrh3Y3d3d3Z1rt2t3YouFgSAiKqBYiHTNzO+PWdnlJ5IDA/p+nsfHZe697z1DuMyZ856Tj7CwsCTPXbp0KZaWlskmmPbu3UuxYsUwMDBg+PDh+Pj40KlTJwYNGoSTkxMHDx5EJpMluYZUKqVcuXKsXLky0eNly5bl7t27tGrVKr4pcqlSpbhw4ULST/iftW/cuEHLli2pU6cOu3btSvaa7ypXrsyYMWPo168fb9684ciRI4SEhDBs2LAUryEIgiBkDpEYEQRByGYcHR25evUqX79+pV27dixfvpwcOXLQrFkzfH191R1e2snlEB4KnwPhQwAE+kGgP3x8B8FBEKtM/pibmwPKd4dT+67+qVOnaNeuHUOHDk3Q8+HQoUPUq1ePhQsXMnnyZJo2bUr37t0ZOHAgXl5emJmZce/ePSpWrMiBAwe4evUqc+bMQV9fX3XPH+Wo19GjR1O4cGHGjx+Po6MjXl5enDp1ig4dOiR5P7lczqFDh2jcuDG5cuWiSJEibN26FTs7O86cOUNMTAzu7u5Mnjw50UktkyZNQi6Xx08UsbCwQENLm09ogiRr/rogUyhwf+PHlVu3efv2Ld++fUvzu/ExMTGMGjWKvn37Ymamml4l/zVlyhSGDRtGx44dOX36dIquMTIyYufOnfTp0yfZyov0cHJy4sCBA8meV7BgQQICAjA3N0/RFh9XV1f8/f3p16/fT8+5c+cODRo0iP9YW1ubbt268ezZMyZOnMiiRYuwtbVl06ZNREdH/3SdAQMGcO/evZ9+/bW0tBg/fjw3btxAIpGQM2dOXFxc6N+/P6Ghock+l3379jFq1ChcXFyYM2dOsud/N2/ePEqUKEG1atXQ19dn1apVrF69Gh8fnxSvIQiCIGQ80XxVEAThF7BlyxZmzpzJmzdvsLGxYc6cObRs2VLdYSVNoYCQr/DlI4SHQEz0Py/AFcpjCoVym4REAkhAIQepBjIdPZ75v6dMrXqQTD+P2NhYPDw8sLe35+rVq9SpU4fu3buzceNGQJlMmDFjBlu2bOHIkSNIJBLq1q1LbGwsp0+fpmrVqgQHBzNp0iQOHjzIggULcHFxUen43YCAAHbv3s3OnTv5+vUrnTt3pnPnzpQsWTLZa9+9e8eKFSs4evQo3t7eSCQSbG1t+fPPPxk0aBB58qSu78aoUaPQ19ePTxoNHDgQK0sLhtWthjQ261UlySVSynbpw2OP52hqaiKRSIiLi6Nu3bqcO3cuVWt169aNw4cPExwcjDQDtw99719z5coVqlWrlqJrpk+fzvXr1zl79myGxLZ9+3Z69+6dZOIB4M2bN1StWpXNmzfTrFmzFFWq7d+/n/bt23P27Fnq1auX4FhUVBR6eno8f/6cEiVKJHq9QqHA1dWVefPm8fjxY4YNG0bfvn3jp0F9J5fL0dHRYffu3bRp0ybJmORyOWvWrGHq1KlYWFjw+fNntm7dSq1atZJ9PmvXrmXgwIH06tWLDRs2JHs+QHBwMPnz56dNmzZs376dMmXKEBUVxYsXL1J0vSAIgpDxsuZbQIIgCEKq9OjRA19fXx49ekS+fPlo3bo1xsbGjB07Nutts4mNhfd+4H4bfDwg6CNERykTIXKZsnLke85eoVB+LJcp/1sWh0ZEKGVyG8Lj2/DSA8JC+Nl+j3379lG2bFmmTJlC3bp1adOmTXxSJDw8nLZt23Lu3Dnu3r3LkSNHKF++PGXKlOHjx49UqVKFPXv2ULJkyfgES5cuXVSSFAkLC4sfpVu6dGk8PT1Zvnw5r1+/Zs6cOT9Nisjlco4dO0bTpk0xNjbG3NycjRs3YmNjw/Hjx4mOjubx48dMmzYt1UkRgNatW3Pw4EGePn3Kli1b8PHxYdr0GdTq0Z+orPZ9JJUitbTh5l03DA0NiYuLIzY2Fi0tLVq1apWqpd6+fcuOHTtYs2ZNhiZFADZv3kyLFi2oVasWjx49StE1EydOJDIykiVLlmRITN/H9l67di3J8/Lnz8/Hjx8pVKjQD+N4f6Zt27a0atWKFi1a/LD9Zvfu3ejo6Pw0KQLKLUU1atSIb+j68OFDLC0tmTBhAh8+fIg/TyqV4uDgwIoVK5KNSSqVMmjQIB48eEDu3LnR0NCgXbt2DB48mPDw8CSv7d+/P8eOHWPLli00aNAgRRVKuXLl4sCBA+zcuZOjR49y5swZXr16laqGvIIgCELGEhUjgiAIv6CwsDDGjx/PX3/9RWRkJPXr12fFihVYW1urLyi5HAJew8cA4itAVEEqBR09sCwBejkSHGrYsCFnz54FwM7OLn6byNu3b/njjz+wt7dn/vz51K9fn2fPnrFixQoGDBjAixcvGDBgAJ8/f2bt2rVUrlw53WHGxcVx4cIFdu7cyYkTJ6hevTouLi40a9YMPT29n14XGBjIypUrOXLkCF5eXgDY2NjQsmVLBg0aRL58+dIck0KhwN/fn7t373L37l3u3LmDq6srhQoVonr16pQuXZoZM2bg7++PcegX+BSouq9bekgkYGgMxZSNZ69cuULjxo3jt3dYWFhw8OBBHB0dU7Rc+fLlCQ0Njf/8ZoZ69epx7do1Hj9+TPHixZM9//Xr1zg5OXHmzJkUP6/UsLKyokKFCuzduzfJ8/Lmzcvt27extLQkNjY2RSPE5XI55ubmmJubc+/evfjHGzVqxPv371OcIPrue1Jhz549tG/fnlGjRmFpacnmzZsZMGAAkZGRKU5wKRQKtm/fzqhRo8iXLx/h4eFs27Yt2W16Dx48oGrVqlhbW3P//n20tbWTvVePHj3YvXs37969Y+nSpcyfP5/AwMBEt7YJgiAImUtUjAiCIPyCDAwMWLlyJaGhoWzZsgUvLy+KFy+OjY1NinoJqFx4KDx1U/YLUShU++JaLofIcPB4CO/exFePREVFJZgi8ezZM1xcXLhx4waVKlXCxcWFli1bUrRoUYKCgvD29qZHjx5MnTqVKlWq0KRJE+7du5eupIhCoYgfpVuoUCGmTJlCxYoV8fb25vjx47Rt2/aHpIhcLufUqVP88ccf5M6dm/z587N27VosLS05fPgwMTExPHv2jFmzZqU6KRIcHMyFCxeYM2cOLVq0oECBApQrV46//voLAwMDxo8fT9++fenZsyc7d+5k7NixlC1bFjc3NyhoCTkM/tnapGbaOmDxb5VBzZo1ad++PaDsF5M3b17Kly9P/fr1k522cvr0aR48eKDyhqvJOXv2LA4ODpQtWxZ/f/9kz/8+3rZDhw7JVjWkRUrH9pqbm/P582ckEglv375N0dpSqRRXV1cePXrEzJkz4x93c3OjYcOGqY7V0tKS1atX8/z5c4yNjXFycqJDhw6ULVsWmUyWqma1EomErl278uTJE2xsbIiOjqZly5YMHz6ciIiIn173vf9PQEBA/L8hydm0aRP58uWjevXqzJw5k7x589KkSZMUxyoIgiBkHFExIgiC8Jt49uwZQ4cO5fLly+TMmZPevXszc+ZMdHV1M+6mCgX4+/6TEMmESgOpFLR1wdqOv/buo3v37kilUqRSKTKZDIVCgb6+Pvv372fXrl3s3buXLl26sGXLFs6fP8/AgQNxcHBg2bJlFCxYMM1h+Pn5sXv3bnbs2EF4eHh83xAbG5tEz//8+TOrVq3i0KFDeHp6olAoKFasGC1atGDw4MEUKJD6qSvft9XcuXMnviLE398fR0dHnJyccHJyomLFihQuXDjB9qAbN27Qr18/njx5AsCMGTMIDQ1l4cKFIJOB5yOIivjp9qUMp6UDJcuCVsJ36MPCwjh69CidOnUClFUkHTt25NOnTwwbNoz58+cnWkVgZmZGxYoVOX78eKaE/19yuZwyZcrg5+eHj49PirY/devWDS0trfgtYary8uVLihUrxtevX8mVK9dPz2vevDk9evSgTZs2nDlzhjp16qT4HsuWLWPkyJE8fPgQKysrDAwM8PHxwdLSMl2xh4SEsGHDBpYuXUpoaCiWlpY8fPgwTdveDh06xMCBAzEyMkImk7F9+/b4SViJCQsLo1SpUgQFBfHgwYNkq/L8/f2xsLBgyJAhdO3aFQcHB/bu3Uvbtm1THasgCIKgOiIxIgiC8JuJiIhgwoQJbN26lfDwcOrWrcvy5ct/+qI9zeRyZQ+RkOBM336hkGpQZ8Bwbrk/YcGCBcydO5f379/HH8+dOzcREREcPHgQBwcHhg8fjpubG6tWraJx48ZpumdISAiHDh1ix44duLu706pVK1xcXKhatWqiL8jPnz/P2rVrcXV15cuXLxgZGVGlShV69epFixYtUtXrQi6X4+3tHZ8AuXv3Lk+fPsXa2pqKFSvGJ0JKliyZ7NYHuVxOoUKFuHjxIiVKlODWrVv069cPd3d35QkyGbx4DBHhmft1lUiUlSIlHH5IiiTl+6QhPT091q9fn+AF6PTp05k9ezZBQUEYGBhkQNDJi4uLw8bGhm/fvvHq1asfmor+v9DQUMqWLcu8efNo3bq1SmPJmTMnEydOZNy4cT89p3///pQqVYoxY8awatUqunfvnqp7ODs78+zZM6ZPn86YMWOSrMpIrejoaHr27Mnu3btxcnJi3LhxNG/ePNV9Y4KCghg5ciQnT54kLi6Onj17MmPGjJ9ueYuLi6NKlSq4u7tz6dIlqlatmuT627Zto3v37ri6urJx40YOHTpEUFBQirbjCIIgCBlDJEYEQRB+Y7t372bq1Kn4+PhgbW3N9OnT6dChQ/oXlsvh5VMIDVFLTwoFEBMbh1bp8hw6fZZ27drx3//daWpq8u7dO/bs2cPMmTPp27cvEyZMSPX43djYWM6dO8fOnTs5ffo0NWvWxMXFhSZNmvxQiRMUFBRfFeLh4YFcLsfa2prmzZszePBgChcunOL7BgYGJkiCuLm5kStXrvgEiJOTE46OjuTIkSP5xRIxZMgQ8ubNy8SJE4mLi8PU1JTnz5//u3VHLoe3L5UThTLh6xsnl6NpbAoWNpCCnhb/Lyoqiu7du7Nv3z5sbW05ePAghQoVwsTEhAkTJjBt2jTVB53K+CwtLZFIJPj4+CRbxXX37l2aNWvGvXv3KFSokMriqFOnDsHBwdy/f/+n58yaNYvw8HA2btzIsGHDmDRpUqruERUVhZmZGdra2hQpUiTJe6VFXFwcOjo6jBkzhgsXLhAWFsaYMWPo1KlTqhMPZ8+epXfv3kilUrS1tdmxYwcVK1b86fl//vknR48eZffu3bRr1y7JtZs1a8aVK1d4//49BQsWpFatWvz999+pik8QBEFQHdFjRBAE4TfWsWNHvL298fDwwMLCAhcXF4yMjJLdX58khQJ8Pf+ZFqOeRp0SQEdLE6n3E47u24uOjk6C4zKZjLJly/L3339z7do1Zs2aleKkiEKh4N69ewwdOpSCBQsya9Ysqlevjo+PD0eOHKFVq1bxL2wvXbpEmzZtMDMzI3fu3CxevJj8+fOza9cuYmNj8fLyYuHChUkmRcLCwrh69SoLFy6kdevWFC5cmJIlS8ZPUBkyZAienp74+vqyb98+Ro4cSfXq1dOcFIF/p9OAMolUu3ZtLly48O8JUikULQ7FS4Gm1j9jllVPIZEQI5Mzc+cBKGaXpqQIgK6uLnv27MHb2xstLS3s7OywtrbGyMhI7UmR7/F5enoSHR2NnZ1dshNfnJycGDZsGC4uLshkMpXF0bVr1/gGxT9jbm5OQEAAOXLkSFCFlVK6urqcPn2aL1++YG5untZQf0pTU5PSpUvHJw1XrVrFnj17sLKyYunSpT9MxklKgwYNePbsGc2aNePz5880aNCA8ePH/3Ss8eHDhxk6dCgdOnRQbj1Lwt9//42uri7169dn3759HD16lBs3bqTquQqCIAiqIypGBEEQhHhRUVFMnDiRzZs3ExoaSu3atVm+fPlPx8cm6kMABPgqqwqygFipJibO9QkLD8fIyAg9PT1CQkIYM2YMU6ZMSXEfgjdv3rBr1y527NhBTEwMLi4udO7cOUFPgeDgYNauXcv+/ft59uwZMpkMKysrmjZtytChQylSpEjSscbG8uzZswR9QV69ekWZMmUS9AWxsrJSydjgn5HJZJibm3P9+nWsra1Zt24dN2/eZPv27YmdDH4+8OWf0amq+LVCIiFOJuPxG39KNWlJYUsrLl++jK2tbfrXBlauXMmQIUPQ1NRk8uTJTJkyRSXrptenT5+wtramcOHCuLu7J7kFRCaTUbduXerVq8eECRNUcv+4uDi0tbW5cuUKzs7OiZ5z7tw55s+fz8ePHylRokSamjmHhIRgZGSEpqYmvr6+6ernk5g1a9YwcuTI+ClFAPfv32fBggVcunSJ/v37M3jwYExNTVO85vXr1+nevTsREREYGBiwe/duypUrl+i5y5cvZ/jw4fTv35/Vq1f/dM1nz55RpkwZ5syZw4ULF3B3dycwMDDDR0YLgiAIPxKJEUEQBCFR+/fvZ/LkyXh7e2Npacm0adPo3LkzAB4eHmzduvXHhpbRkfDsfpZJigDEyuTc9vXDMzSKKVOm0KJFC+bMmYOxsXGy1wYHB3Pw4EF27NjBs2fPaNOmDS4uLlSuXDk+MXH9+nVWrVrF5cuX+fjxIzlz5qRixYrxDSp/1tNDoVDg6+ubYEvMo0ePKFy4cHwCxMnJidKlS6ul98CAAQMoXLgw48aN49WrV1StWpV37979PCETGwOf3ivHMcvlafsekGoo/zbNT6BcSv6iFnTo0IHChQsTGRnJ8uXL0/6E/sPGxgYDAwOaNGnCvHnzyJUrF9u2baNRo0YqWT893r59i62tLfb29ty8eTPJc/39/SlXrhzHjh1LcotHaiQ3tvfZs2e0atUKMzMzNDQ0UjTJ5v+tWLGC8ePHU6RIESIiInj16pVKkwHft9OcPHnyh6k33t7eLFq0iAMHDtCpUydGjhxJ0aJFU7RuVFQU06dPZ82aNSgUCoYOHcrkyZMT/fn8+++/adOmDQ0aNOD48eM/fX4LFy5k3Lhx3Lp1C2dnZ3r37s2CBQu4fPlymvsdCYIgCKknEiOCIAhCkry9vRk6dCjnzp1DT0+Pbt264efnx/Hjxxk9ejTz5s1TnqhQwPOHEJHyUvXMEhMno8ucxQyfODnBC0hPT0/y58+PkZHRv+fGxHDmzBl27NjBuXPnqFOnDi4uLjRu3BgdHR1CQkJYv349+/bt48mTJ8TGxmJhYUGTJk0YOnQoVlZWicbw+fNn3NzcEiRCtLW1EzRHLVeuXIJY1OnSpUuMGTOGe/fuAWBtbc3ff/9N6dKlk75QoYBvQfD1s3I7VUxU/FYbmVxGdFQUmlraaGtp/XO+XDlpJkdOyJUbjPOAVEpsbCza2tpIJBKKFy/Ohw8f8Pf3T9cWIYCdO3fStWtXXr16RZEiRQgLC6NDhw6cPHkSR0dHDh06lGxlT0bz8vLC3t6eGjVqcPbs2STPPXz4MKNHj+bhw4fJNm5NiSFDhrBv3z4+fPiQ6PFv375hbm5O3bp1efPmDQ8fPkz1PWrWrElkZCQnT56kYMGCdO7cmU2bNqU39ATKlClDvnz5OHfuXKLH379/z/Lly9m4cSONGzdmzJgxyX9v/+PBgwd06dKFjx8/kjt3bvbs2YODg8MP57m5uVG9enVsbW25e/cuWt+/5/9P1apV8fLyYvbs2fTr1w9jY2O+fv1KWFhYur/fBUEQhJQRiRFBEAQhRaKiopg6dSrr1q0jJCQEUPYLWL9+PV26dFGO5PV/laWqRb5TAGjrIildQTndBLhz5w41atRg8ODBLFiwgLt377Jjxw727duHjY0NLi4utGnTBhMTE27dusWqVau4dOkSgYGBGBgYUKFCBbp160bHjh1/qAqJjIzk4cOHCbbEfP78mfLlyydokJoRPRZUJS4ujgIFCnDnzh0sLCzo378/1tbWjBw5MnULyeXK8b5REXh7ebF0yWI6dXaharXqoKML+gbKniX/R6FQxL/L/v3vIUOGsHTp0jQ/J7lcTq5cuWjRosUP24KePn1K69at8fb2pkOHDmzZskWtU0IePnyIk5MTLVq0SHa7Sp8+fYiKikp8q1MqfW/E/LOxvQqFgpw5c9K6dWtcXV159epVqu+RK1cuRo4cyeTJk/n7779p1aoVJ0+eVGnFzvLlyxk/fnyyvZKCg4NZt24dy5cvp1y5cowbN45q1aolu35sbCwLFy5k7ty5AIwcOZKJEyf+kPx48+YN9vb25MyZkydPniT6OY2MjMTU1BS5XB6//cfAwIAHDx5QrFixxANQKJRVWhFhEBkBcpnyZ00qUVZe6ehBDgPl+PIM3HYnCILwqxCJEUEQBCFVpk2bxuzZsxM0iJw1axYTm9ZR/qKeVUk1wMoWjEx4/Pgx1apVIzQ0FC0tLQoXLoxEIonvG2JmZsaGDRvYu3cvjx8/JiYmhiJFitC4cWOGDBmSYLSxTCbj+fPnCSpBPD09KVmyZIK+IDY2Ntmud0Dfvn0pVqwYo0aN4u+//2bdunXJVjAk5cyZMzRu3Jg9e/YkO7UDQENDA7lcjkQiIU+ePJiYmPD8+fM091cZPHgwmzdvJjg4+KdJjz179tCvXz9iYmKYN28eQ4cOTdO9VOH69evUrFmTbt26JVlRER4eTrly5ZgyZQodO3ZM932TG9trY2ND7dq1OXToEB8/fkzV2kFBQeTOnZt3796RP39+ADp06MDRo0cJDAxUSdULKCu/9PT04qu+khMVFcW2bdtYsGAB+fPnZ+zYsTRp0iTZn1lPT09cXFzw8fEhX7587Nu374fKk5CQEEqVKkVISAgPHz7EwsIiwfFnz55Rs2ZNPn/+HP+Yrq4uZ86coUaNGv+eKIuDzx/g66d/xmUrlEkPeSINeKUa/JMSBl19ZTWWaf5UjboWBEH4nWSv39AEQRAEtXNzc8PAwICCBQtibW2NsbExdy+cJfo/jQ6zJLkMAv1wc3OjUqVKhIaGAsp3fhs1asTu3bvx8fGhWrVq5MyZkylTpqCvr8/q1auJiorC19eXVatWoa+vz6FDhxg7diy1atXC2NiYli1bcvHixfhpMUFBQdy7d481a9bQrVs3bG1ts11SBJTTab5XK9SqVYubN28SFRWV5vXCw8ORSCTo6eml6HwtLS3y5MmDQqFg/vz5xMTE4ObmlqZ7f/z4kbVr17Jw4cIkK0E6dOjA169f6devHyNHjqRgwYJcv349TfdMr2rVqnHs2DG2bt3KqFGjfnpejhw52LNnD8OGDcPX1zfd93VyckqySsXc3Bxtbe0EzU1TavPmzRgYGMQnRQB27dpFrly5qFmzZlrCTZS2tjYlSpRg0aJFKTpfV1eXvn374uXlxeDBg5k6dSplypRhx44dxMbG/vS6EiVKcOfOHaZNm4afnx+VKlVi5syZCRLHhoaGvHr1CktLS2xtbblz506CNRYsWEBwcHCCx6KiovDy8lJ+EBEOvl7w6LaysXVYiPLfM4U88aQI/FtBIpcrq0re+8HjO/DyGYR9U02TZEEQhF+IqBgRBEEQ0k3h6Q5h38jyBdsSCW2mL+DQiZP8///+JBIJhQsXpmHDhgwePBg7OzuCg4O5d+9efCXInTt3kMlkCfqClC9fnty5c6vpCWWs2NhY8ufPz/379ylSpAhVqlRhxowZ1K1bN03rbd++ne7du3PmzBnq1auX7PkPHjygVKlSdO3alfPnzzNmzBg8PDz466+/Un1vZ2dn3rx5w5s3b1J8TVBQEG3atOHy5ctUqVKFgwcPki9fvlTfO7327dtHhw4dmDZtWpITdJYsWcLBgwdxdXX9adPflNi5cyc9e/b86VhaFxcXTE1NWbVqFTExqasSq1atGgqF4ofRtD4+PtjY2DBp0iSVjVBevHgxU6ZMITw8PNXXKhQKzp8/z7x58/Dx8WHkyJH07NkzyZ4fr1+/pkuXLjx69IhChQpx4MCBHyZ6NWvWjNOnT7Nv3z4MDAyYOHEit2/f5sSJE4wYMYLXr1+jUCiQSCTUq1GDs+uX/5MIUeEWRalUWUViWUL5tyAIgiAqRgRBEIR0iolGEh6S9ZMiKN8kHd2tc6LHvn37xoEDByhVqhTz58+nRIkSFCxYkBkzZhAUFETHjh25desWHz584Pjx40yePJkGDRr8skkRUFZstGjRgsOHDwNQv379nzazTInvFQYprRhxdHREW1ubjRs38u3bN2JjYzly5AhBQUGpuu/169e5fv06+/fvT9V1JiYmXLx4kVu3buHv74+5uTn9+/dPUA2QGdq1a8e6deuYNm1akpN5hg0bRs6cOZk5c2a67te+fXtiY2NxdXVN9HjBggWRy+Vp+jw8fvyYP/7444fHraysWLZsGTNnzuTBgwepXjcxAwcOJDIy8qfPIykSiYT69etz6dIlDhw4wNWrV7GwsGDGjBl8+fIl0WuKFi3K1atXWb58OX5+fpQvX57Zs2cjk/1b1XH8+HH69+9PmzZtaNasGc+ePePcuXO0aNGCly9fsmHDBqRSKbtXLePwzPEQEqz6vk3fq0iePVBWkoj3SAVBEERiRBAEQUin0G/ZprmfBAUF9HUoV64c+voJ3yk1NTWlV69euLu7U716dfbt20dwcDCurq4sWrSItm3bUrRo0TT3t8iuWrduzcGDBwFlYuT8+fNpXuv7O/cpTYx8Z2BgQM+ePZk/fz5NmjRJdcVI+/btqVmzZppH2lasWJHXr1+zZs0atm/fjrGxMVu3bk3TWmnVp08f5s+fz/Dhw3/6/KVSKX/99RcbNmxI1/YfTU1NLCwsWLNmTaLHzc3NiYiIQKFQpCo5EhgYSGhoKD169Ej0+KBBg3B2dqZu3bqprkRJjK6uLjY2NixYsCBd6zg5OXHo0CGuXbvG27dvKVasGMOHD8fPz++HcyUSCd27d+fFixfUqFGDOXPmYG9v/++2GKB///7o6OgQGxtLVFQUS5YsAZRfv17duvHN7TotHUuRQ1c3XXEnSyGH92/A44FyepQgCMJvTCRGBEEQhPRRdZl3BitomptPHz8mmFahq6vL3r17cXd3Z+PGjfTu3Rt7e/t0bUf4VdSuXRtPT0/8/f1xcnLC19f3p6NckxMREYFEIkE3DS/4VqxYQUxMDLq6uqxduxZ5Cr/nli1bRmBgYLKTXVKib9++fPv2jXbt2tGrVy+srKxUVt2QEqNHj2bixIn07Nkzvorn/+XPn5+NGzfSuXPnH/pWpEaTJk24fPlyosfMzc0JDAwEICAgIMVrbt68GUNDQ/LkyfPTc86cOYNMJqNZs2apC/gnunbtypUrV1Sylo2NDZs2beLJkydoaGhgb29Pt27d8PDw+OHcfPnycfr0af766y/8/f1xcHBg7ty5fP36lTJlyiTo1XPp0iXl5zE2Bp4/wEARh05m/dsjl0NkOHg8VE6PEgRB+E2JxIggCIKQPmHfADh29QYOHXsm+JO/YUvyNWjJtA1bGbUs8XefM0LNvkM5ce0mAFPWbWbfuUv/HpRI8Xhwn3PnzjFixAiKFy9OdHS0SppW/oq0tbVp1qwZhw8fRlNTk1q1anHhwoU0rRUZGYlCoUh1xcj3OIYNG8auXbvQ09Pj4sWLyV4TFRXF+PHjGTJkiMq2PGlqarJp0yb8/PwwMzOjfPnyNGjQIF1JiNSYOXMmAwYMoE2bNj+t3mnatCnNmjWjb9++P/TSSamhQ4fy8ePHRJ+Xubk5AQEBaGpqpurn5vjx4zg4OCR5jo6ODufOneP8+fNs3LgxlVH/aMiQIURERPzQ0yQ9zM3NWbRoET4+PhQrVoxatWrRokULbt269cO5bdq04dWrVzRu3Jjp06dToEABZDIZEomEbt26UaBAARQKBe1a/YnC4yFER6lna0tcLDx/BFFZvIm2IAhCBhGJEUEQBCHtFIr4dxmb16jKo92b4/9cWLMYbS0tVowarNYQZ/TrSbv6tf/ziAJ9RRz16tVj8eLFeHl5ERQUxIABA9QWY1bXpk0blWynSW2Pkf83Z84cNDQ0MDU1Ze3atcme36NHD3R0dFI8mSQ1ChQowK1bt7hw4QJPnjzB1NSUMWPGpLiSJT1WrlxJp06daNSoEbdv3070nAULFuDh4cG2bdvSdA8rKysMDAxYt27dD8e+J0a0tbV5+/Ztitd8+vQpLVu2TPa8ihUrMnbsWAYMGJCqZrmJ0dfXp1ixYuneTpMYY2NjJk6ciK+vL/Xq1aNjx47UqFGD06dPJ0hImZiYcOjQIQYPHhxfKaJQKHjy5AkBAQE8fviQ3VPGoohJvNltppHFgecjUHccgiAIaiASI4IgCELaxSU+xlImk9F+wgza1KlJ23rKpETAp080HjqGEq1daDJsLBH/vEAIi4ig+/S5lGrXjVLtujF/2+74dWr2Hcro5Wup1msQln+0Z9zK9fHHPF69pmK3fpRq143Ok2dRqXv/+CqR/+o2bS6r9v9n24Fc/sO7okZGRml+Z/13ULduXZ48ecL79++pV68e586dS9PnKz0VI6DswTBlyhRcXV25cuUK/v7+Pz3X19eXvXv3sn79+gwdlVy7dm3evXvHnDlzWLFiBblz545PImWk7du306RJE5ydnXn69OkPx/X09Ni9ezejR4/m5cuXabpHxYoVE21YmzdvXr58+YKurm6Kt9L4+/sTHh5Ot27dUnT+3LlzKVGiBNWrV093ssnFxYVLly4lf2Ia6evrM3DgQLy9venbty9jx47FwcGB3bt3x/dgiYuLY9myZQmuu3//Pnv27KG0oQ6F85oizQrti+LiwMdDNGQVBOG3IxIjgiAIQtrJ5Yk2Xh2/egMyuYz5g/vGP3bvuRe7Z03m+YHtxMbFseu0supg5ubtyBUKnuzdys3Nq9l+8iynb/z7LvjbwA+4bljBw12b2HT0JN5vlS+GXabOZnDbP3m67y+GdWiNm4dnKuJWTomIiYlh69atWFhY0Lhx47R8Bn4LOjo6NG3alMOHD2NlZYWuri7Pnj1L9TpRUVHpSoyAss+GgYEBZmZmSW61aNmyJba2trRr1y7N90ptXMHBwTRo0IC2bdtiZ2eXoOFmRjh69ChVq1alQoUK+Pj4/HC8dOnSTJ06lQ4dOqSpmWm3bt0S/TpramqSN29edHV1ef/+fYrW2rRpE7ly5SJXrlwpvv/Vq1f59OnTT5u1ptSIESMIDw/n7t276VonOZqamnTs2BF3d3fmzp3L+vXrKVasGGvWrGHPnj3ExcWRK1cuChQogLa2NgCPr1+Fr1+yUCJCoew58illX1dBEIRfhUiMCIIgCGmXyC/zhy5dZe+5S+ybMw0NDY34xxtUciJXzpxIJBIq2pXEx/8dABfu3qd3i6ZIJBIMDXLQoUEdLty9H39dm7o1kUqlGBkYYGtRGB//AELCwnnq40vHhnUBKF+yBGWsLVMcdkxMNL179yZPnjwMGjSIN2/eEBISwpcvX4iIiMiU7RDZzffpNN/HmKZlO833rTTfXxSmhVQqZf78+Xh7e7NhwwZiY3+sWjp27BiPHz/m0KFDab5PWnxv4uvt7Y2mpia2trb8+eefCRr9qtrFixexs7PD3t6ed+/e/XB84MCB5M2bl6lTp6Z67aTG9pqbm6OjoxPfhDU5J06cwNHRMVX3NzExYd++fWzfvp0TJ06k6tr/0tfXx8rKinnz5qV5jdSQSCQ0btyYq1evsmvXLs6cOUP37t0BZTJ26NChREdHI4+NYW6vLsrpMFmJXA7+r5T9TgRBEH4Tot2+IAiCkHb/V/v93Pc1/eYu5uSy+ZiZGCc4pvufF8MaGlIio5VVGwqF4ocRuP/9OMF1Ug3iZDIUKK9J6+jcd4Ef2Lp1KzKZLP6xJ0+eUKxYMSIjI4mOjkZbWxs9PT309fXR09P74U9ij6fm3P8+rqurm+XHADdo0ICuXbvy4cMH6tWrx6ZNmxg+fHiq1vg+lSa9z7VPnz5MmTKF2NhYjh49SuvWreOPyeVyunfvTosWLShRokS67pNWVlZWuLu7c+zYMXr06IGxsTGTJ09m0qRJKr+XVCrl7t272NnZYWdnh4+PDyYmJvHHJRIJW7duxcHBgXr16lG7du0kVkvov2N7nZ2dExwzNzfn27dvfPnyJUVreXh4pKnXS/PmzenYsSNt2rTh/fv3qao4+a9OnTqxdOnSNF2bHlWqVGHIkCFcuHCByMhIIiIimDBhAhYWFrQpXybrTvSSy8HXE0o4qDsSQRCETCESI4IgCELaSTXiq0ZCwyNoOXoys/v3wsnONsVL1KtYnk1HTlClTCnCIiLZe+4Si4b2T/IaIwMDSloUYc/ZC3RsWI8Hni944pPy6RhFLSx59+4d/fr149ixY8hkMvLkyRPfL0EulxMdHU1ERASRkZE//Ens8e+PffnyJdlz///xmJgYdHR0MiTpktjjOjo6qU5O6Orq0rhxY44cOULbtm3p1q0bUVFRqRq9GxERobJ+H6tWraJt27YsX748QWJk8uTJhIWFsXPnTpXcJz2aN2/Ox48fmTZtGjNmzGDlypVs376dBg0aqPQ+UqkUd3d3ihcvjq2tLa9evSJHjhzxx01NTdm6dStdu3bl0aNHqZrQ06RJE/bt2/fD4+bm5rx48YKvX78mu8arV6+IjIykS5cuKb7vf23fvp0rV65Qo0YN3N3d07TGiBEjmDFjBg8ePEh15Up6nTx5kpiYGLS1tdHQ0EAmk7FiyRLarFqY9apF/is8TLmtRi9H8ucKgiBkcyIxIgiCIKSdhiZIpKCQsfrA37z0D2DNwaOsOXg0wWl1Kjj+9IX45J5dGLRgOaXbK0vNXRrXp2GVisneevu0CfSYOZ/Fu/ZTrkRx7ItZYWSQ/C/w0XFxDBwylC1/H0cikcRvm8mbNy9yuRypVIpUKo1PJGQGuVxOVFRUihMp/33s06dPKTr3v4/Hxsaiq6ub6uSKXC5n0aJFxMbGYmZmxsyZMylfvnyy63xPxERGRibYXpUerVu3plChQty9exdPT09KlChBSEgICxYsYMaMGejr66vkPukllUqZMWMGo0aNomPHjjRq1AhHR0cOHTpEkSJFVHYfbW1tPDw8sLS0xNbWlpcvXybYslS/fn3atm1L7969OXToUIoTY0OHDmXlypUEBwcnqNYwNzdHKpXy7du3ZNfYuHEjuXPnxsDAINXPC5Sfw2vXrlGsWDEmTZrErFmzUr2GoaEhFhYWzJ07lwMHDqQpjrRaunTpj9UqgX4QkL6JOxlOIYcPAVC0uLojEQRByHAShWjDLwiCIKTH80cQHpLptw2PjET/ny0oHq9eU7PfULwO7sTYMGeS1ykkUk74vqfXwEF8/vz5h34ienp65MmThyJFimBnZ0f58uWpUaMGxYoVy8ink6lkMll8Iia5Cpj//gkJCWHJkiV06tSJBw8eEBMTQ/HixZNNxsTFxaGnp0d0dDQymYxixYqppALmyZMnDBo0iD///JNVq1bh4uLC06dPef/+fZbdmvT06VNat26Nt7c3nTp1YtOmTenqufL/goODsbS0JE+ePHh4eKCp+e97YNHR0VSqVIn+/fvTp0+fFK9paGjI+PHjGT9+fPxjO3bsYP78+Xz69IkPHz4keX3ZsmXJmzcvZ86cSf0T+o9169YxcOBA7ty5Q/ny5VN9/cSJE1m9ejXBwcHpiiPdFApwv/3TqV5ZikQKDpVBRQlNQRCErEokRgRBEIT08fdVvvuZyc7ddmP0irXxY2Nn9uvJHzWqJX+hVAplqyJXKFi0aBHTpk1DJpNx7tw5zM3NuXr1Km5ubnh4ePDmzRs+f/4c3xtDX18fU1NTihYtSsmSJXFycqJmzZoqfec/q2vbti3169enRIkSDBkyhAcPHiR7jUwmIzIykgoVKvD27Vvu37+f4qqY5B5/8uQJsbGxGBsb8/Xr1/itOqradpSSx7W0tFKdiNm1axcDBgwgNjaW+fPnM3jw4DR9PRITGBhIsWLFsLa25v79+wm2L3l6elK9enVcXV2xtU3Zlre6desSFBSU4Gt9+fJl+vTpw4cPHwgJSToxqqenx6pVq+jZs2fantD/xXLv3j0+fvyY6oRScHAwxsbGuLu7U6ZMmXTHkmYhX+GlR/x0rCxNKoVCVmCaX92RCIIgZCiRGBEEQRDS5+tn8PXKHr/kA+gbQMl/eww8efKEsWPHsmvXLoyNjRO9RC6X8/z5c1xdXbl//z7Pnz/n7du38f1EJBIJOXLkwMzMDAsLC0qVKkXFihWpUaMGBQoUyKxnlikOHDjA5s2bOX78OKampnh7e2Nqapqia4sXL86nT59S1Jcipdzc3HBycsLY2JjixYtz+/Zt4uLiVJJ0Senjcrk8TckVXV1dzp07x40bN8iVKxdDhgzB0dExRYmY5Lx58wZbW1vKly//w1SZDRs2sGbNGu7cuYOOjk6ya+3cuZOePXsSHR0NKBMvGzduZN68eURGRtKiRQuaNm2aYKyuQqEgMjKSt2/fYmtrS0REhEq2psXExJA3b17KlSvHhQsXUn190aJFqVy5Mnv27El3LGmmpmRymuXKDdZ26o5CEAQhQ4nEiCAIgpA+cXHKsvCs3ETwO6kUChSFfAVVtqRcLufx48fxSRNPT0/8/PwICgoiOjoaqVSKgYEBefPmxdLSktKlS1OxYkVq1qxJnjx5VBZHZgkLC8Pc3BxfX1+6d+9O+/bt6dChQ4quLVq0KBEREXz8+FGlMZmZmfHp0ycCAgLUkoiKjY1NV3Ll69evnDlzhsDAQIyNjSlVqlR8lU1i5wMpqmiJjo7m+PHjFClShA4dOiSYgrR582by589Pnz59km32++XLF/Lly4ezszPe3t4EBwfHx/FdzZo1uXz5cvzHFy9epF69euTKlYuoqCgOHz5MrVq1UpSISc69e/eoWLEiq1aton//pBs1/7/x48ezbt06lSbnUu35QwgPVd/9U0tLG+wrqTsKQRCEDCUSI4IgCEL6vfKEINW+2M0IsTIZu576kM+8IEFBQXz58oWgoCC6du1K0aJFVX6/uLg47t+/z/Xr13nw4AFeXl4EBAQQFBRETEwMUqkUQ0ND8uXLh5WVFaVLl6Zy5co4OzuneSxpZmjVqhVNmzYlIiKCe/fusXXr1hRdZ25uDhA//UcV4uLiMDQ0JDIyknXr1tG3b1+VrZ3Z7ty5Q7t27fD396dPnz6sWrUq0Sk+sbGxKU66eHp6smjRIuzs7GjatGmCZMzx48cpXbo0BgYGP10nIiKC//9V0dTUlE+fPsV/LJVKqVatGpaWlvHJlNjYWNasWZNgJHbv3r1p1qxZspOXUtKgd9KkScybNw9vb28sLCxS/DkOCgoid+7cPH36FDs7NVRBKBTw8OYPFXZFm7dDV1sbXW1tomJiqO5QmjXjRqCl+fM5CVPWbcbO0oJ29X8cwTxtw1bCIiJZNGxA+mOWSP7pMyJmNgiC8OsSiRFBEAQh/SLCwPMRyLNu1YgCeB74GbtmrQDQ1NREQ0ODmJgYbt++jZOTU6bGExMTw927d7l+/ToPHz7kxYsXBAQEEBwcTGxsLBoaGhgZGZE/f36sra0pU6YMlStXpnr16mme7qEqe/bsYefOnSxbtoxatWrh5+eXoh4bZmZm6Ovr8/r1a5XF0qdPH3bv3k2ePHkICgpKtt9FdrB27VpGjRqFVCplxYoVdO/ePV3rXbp0ifr169O7d2/Wrl0b//jly5fp1KkTjx49wszMLNFrX758Sfny5eOnz5iZmXHu3DmaN2/O27dvATAwMGDdunVER0cnSK7Mnj2b6OhoJBIJZmZmVKtWjaioqGSTOpqaminajnTu3DliYmLo06cPOXLkSHFvmNq1a1OlShW2bt2a4kSMykRHwdN7P1TYFW3ejhNL5lLK2hKZTEb13oMZ2r51okmPlFBpYkSqAcVKQU6j9K8lCIKQRYnEiCAIgqAaz+5BZIS6o/g5qRRs7HG9/5A6deoQFxcHKMecHjx4kGbNmqk5wH9FRUVx48YNbty4gbu7O97e3rx7945v374RFxeHpqYmRkZGmJubU6xYMezt7alSpQpVq1ZFV1c3w+MLDQ2lYMGCvH79mrJly3Lq1ClKliyZ7HUmJiaYmpri5eWlkjjevXtHoUKFWL9+PeXLl6ds2bLs3LmTTp06qWR9dYqLi6NPnz5s27YNCwsLDh48iIODQ5rXO3bsGC1btmTMmDHMnTs3/vHx48fz+PFjTpw4gaurK4ULF/6hAuPRo0dUrVqViIgI6tWrx7lz53j06BFly5YFoG/fvqxbt+6He1auXJnbt29jZWXFw4cPyZkz6YlRoOxNEhMTk6LtSF++fGHkyJGUKlWKFi1apHj70vv37wkPD0dPT4/IyEi0tLQyrVGvniwGqY8HyH6sGPmeGAmPjKR678HMG9SXmuUcmLhmE1cfPCImNo7S1hasHTcCA319uk2bS/mSNgxq+yffwsLoOXMBHr6vKZw3L6bGRuQ1MWHRsAGERUQweOFy3DyUP3cujesztmvHH+6b2MeA8t/OwsUgT94UfKcJgiBkT6ImThAEQVCNgpbg45F1q0ZyGEKOnDg7O7N79266du2KXC7H3NycFi1akDNnTrp27crs2bPVXpGhq6tLnTp1qFOnzg/HwsLCuHbtGrdu3eLx48c8f/6cy5cvM336dGQyGVpaWhgbG2Nubk7x4sVxcHCgWrVqODk5qWwsbM6cOalVqxYnTpygfv36nDt3LkWJEZlMppIeE9+1atWKokWL0qtXLwAKFCjAgAEDfonEiKamJlu2bGHGjBm0bt0aR0dH6tWrx759+9K0zap58+Zs27aNLl26YGRkxLhx4wCYMWNGfCXSjRs3GDt2LPPmzUtwrYODA6dPn6ZGjRrxW3sc7O2ZPHokT+/fo2/bVsqtdBKpcqyrXg7Q0kZbWxuJRMLVq1dTlBQBkEgk6OjooKOjk6LnaWVlRdOmTZk6dSp//PFHiu7x+fNnTE1NefDgAcWLF/+h0iWl25S+fftGYGBgqhr1VrcvxZHFc9HT/rGBbutxU9HV1sYnIID6FStQv1IFZm3ejpFBDu5uWw/A2JXrmPvXLmYP6J3g2hkbt2GYQx+P/dv5HByMY+fetK1bC4CZm7cjVyh4sncroeERVO4xgDLWljSqmsK+IQqyRw8pQRCEdBCJEUEQBEE1jEyU0wu+fsl6v0RLpWBhE/9hmzZteP36NdOnT8fd3R2AyZMn89dff7Fq1SoqVarEggULqFq1qroi/ikDAwMaNWpEo0aNfjgWHBzM1atXuX37No8fP8bd3Z2zZ88yceJE5HI52tramJiYULBgQWxsbChbtizVq1fH0dERzSR6GSSmdevW7N+/ny5durB161aGDRuW7DUymUxlFS2XLl3izp073Lt3L/6xjRs30qRJE7Zu3Zru7SdZRcGCBbl9+zYXL16kc+fOmJqaMmLECObOnZto/5GkdO7cmW/fvjF48GAMDQ0ZMGAAV65c4cOHD/j7+wNw//79RK91rlaN+eNH42htqawOi4pkRvsWRP/ZWDkl57X3vycr5CCVsmHkQPx6dsHcOOO2YDRu3JiuXbvSrl073r17h4mJSbLX5MmTh4IFCzJnzhy2bduGrq4uurq6P51KpUqK4C/KnkyJTPE6OG86pawtiYqOptXYKSzbfYBjrjcICY/g4MWrAETHxmBfzPqHay/ff8jK0UMByJMrF3/Wco4/duHufZaPHIxEIsHQIAcdGtThwt37KU+MgLI3iiAIwi9MJEYEQRAE1SlcDL59BVkWSoxIpVDYGrQTViqMGjWKPn36xFeHLF26lKVLl3LixAkmTZpE9erVMTU1ZcCAAYwfP15l1RYZKVeuXPzxxx+JvnP+8eNHXF1duXPnDk+ePOHu3bscP36csLAw5HI5urq6mJiYUKhQIUqUKEG5cuWoXr06ZcqUSfQFeLNmzRgwYAArVqygR48eREdHJ1sNIpPJ0NfXV8lz7dSpE/Xq1cPR8d/Ry40aNcLIyIihQ4f+MomR7+rUqcP79+9ZsGABU6ZMYePGjWzcuJFWrVqlap2BAwcSEhLCoEGD+PDhA7NmzUpw3MPDI+EFMdHw8R18eseoVs2QyOX/bplTKNDR1FS+aFb83wt9mQybAvkoXiCfsv+Qji7kKwTGpsqfSRXavHkzFy9exNnZmadPn6bomtatW7Njxw6VxpESEqkUkmnHo6ujQ9NqlTlx/RYKhYI1Y4dTu4JjktcktTNeoVD80APo+8eaGhrI/3NtVHRMIkGjbMAqCILwC1Pt/5kEQRCE35umJliWUPkLnzSTSCBHTsj94954iUSCkdGP72Q3bdqUR48eERgYSKNGjViwYAE5cuSgYcOGPHv2LDOizhBmZma0bt2ahQsXcubMGV68eMG3b9+QyWT4+/uzefNmWrdujYGBAdeuXWPChAk4OjqioaGBvr4+hQoVomrVqvTq1Yt169YREBCAs7MzN2/exNbWlps3byYbg1wuR09PL93PZf78+Xz+/Jl9+/YleFwikTB9+nTCwsJYtmxZuu+TFY0ZM4bg4GDq1q1LmzZtKFWqVKp7towfP542bdowY8YM5HI5xsbG5M2bFw0NDd6/f09sbCzExsBLD3hyFz74g0yGNJEX2MmRgHJ7XWQEvPGGR7eU66mwAkEqlXL9+nW8vLwYO3Zsiq4ZO3YsX758wcfHR2Vx/L+nT5/Sr18/zpw5Q3R09D/BJt/oVS6Xc/WBO8ULF6K5c1WW7N5PZJTy+tDwCJ77vv7hmjoVyrH1+GkAvgR/4+8r1+KP1atYnk1HTqBQKAgNj2DvuUvUdSoHgFXBArh5eAJw8e59PgQFJRKRRLlFShAE4Rcmmq8KgiAIqvf+rfKPuvuN6OiCraMyYZMO27ZtY/bs2Xh7e1OkSBFGjRrFgAEDUr2VITt68+YNV65c4e7du3h4ePD69Ws+ffoUP8ZVQ0MDAwMDDA0NadKkCeXLl6dmzZpYWVn9sJaWlhbNmzfn0KFDaY4nMjISY2Njhg8fnqCJ6HehoaGYmpqiqalJSEjIL/018vHx4c8//+TJkye0aNGCnTt3pqgi5+rVq9SpUyd+lK62tjZPnz7lyJEjTJkyBf8nj8gd+ln585sRvyZKpaCrD5a2oJv+RNl3mzZtok+fPty6dYuKFSsme765uTkNGzZk8+bNKovhv86fP0+TJk3Q09MjOjqaYsWKUc7Bns2De6MhTZhg+u+43pi4WEpZWbB+/CgM9PWYtmErR6/eQCqVIJFImNqrG3/Wdv6h+WqPGfN5/voNRfPnw9w0D0YGBvHNVwctWM695z82X3V79pyu0+dimCMHVcuU4uClq5xcOu//mq9qgK2DsneMIAjCL0okRgRBEISM4e8LHwPUlByRgLY22JYFLdVtgfH19WXEiBGcOnUKiURC8+bNWbJkCQULFlTZPbILhULBvXv3cHZ2platWly7dg0TExM+ffpEZGQkEomEHDlyYGpqStGiRbGzs2PNmjW0bNmSgwcPpvm+rVu35vLly3z69OmnSY++ffuyefNmJk2axLRp09J8r+zi6NGj9OjRg7CwMKZMmcLEiROTPP/FixeMHj2as2fPIpPJiIuLY9SoUSycOwd8vSA0OHN+bqVSMC8KZuYq26rRoEEDbt++zcePH5Pd2jV48GD27dvHx48fVXJvUP4bcebMGW7dusXdu3d/qOYxMjLC78QBcuqqrglxhpNIwLGa2E4jCMIvTSRGBEEQhIyjjsoRiURZKWJjr9KkyH/J5fL4niTv3r3DxsaGadOm0a5duwy5X1bWqFEjOnfuzIABA/Dx8SFPnjzI5XI8PDxwdXXl3r17eHp68vbtWwICAgDllhcDAwPy5s2LhYUFpUqVomLFitSoUYN8+fL99F5eXl7Y2tpy6NAhWrZs+dPznjx5QpUqVYiNjSUsLCzVjWWzI7lczuTJk1m4cCHGxsZs376dBg0aJHlNSEgIBw8eZMSIEZgY5MD7+EE05HGZ22hTKgUTUyhSXCUvvGNjY8mbNy/29vZcvnw5yXPfvXuHubk5r1+/pkiRIqm6z4sXLzh79iy3bt3i2bNn+Pn58e3bN+RyOTly5CB//vwUK1aM06dP/3Dt2dVLqP/PVpZsQVcfSpVXdxSCIAgZSiRGBEEQhIz19TO89sq4svz/iJPL0TTND4WtQCNzXgw/evSIkSNHcvXqVfT09OjQoQMLFixI00jV7GjLli2cPn2a6OhoOnXqlGRySCqVMnr0aNq2bcu1a9d48OABXl5e+Pn5ERQURHR0NFKplJw5c5I3b16srKwoXbo0FStWpGbNmjg7OyOVSnn8+HGycVWvXh03Nzf69u3L8uXLVfmUs7SQkBA6dOjA6dOncXR05PDhwxQuXBiFQsHx48dp2rTpj5U2MdGEuV1DX1sLqTqqAqRS5VQrS1uVJEcePHhAhQoVWLZsGYMHD07y3Pz589OsWTM2bNjww7HvCb7z589z69Ytnj9/jr+/P9++fUOhUGBgYECBAgUoUaIETk5O1K1bFw0NDfbs2cPly5d58eIF4eHh8etpamoyY8YMxnd3gQDf7DPpJU8+KFpc3VEIgiBkKJEYEQRBEDJeXCy8fgEhXzOmekQiQS6R0nHSTPqOHkutWrVUf49kREVFMWPGDDZs2EBQUBCOjo7Mnz+fOnXqZHosmenLly9YWloyefJknj9/nmS/BolEwuzZs5kwYUKix+Pi4nBzc+P69es8fPiQFy9e4O/vz9evX4mJUU7LyJkzJwULFsTKyooyZcpQuXJlnJ2dMTQ0TLDW7t27mTp1Km/evOHbt28qafqanTx9+pRWrVrx8uVLOnfuTNOmTWnbti1Llixh+PDh/54YGwvPHyinz6iTVAomZlCkmEqSI9OmTWPWrFl4eXkl2u/mu/79+3P48GHOnj3LuXPn4nvpBAQEEBoaCii/5woUKICtrS0VK1akXr16ODg4EBcXx99//82hQ4e4e/cu/v7+yOVyzMzMcHBwoEmTJjx58oRNmzZhYmLC2bNnKVeuHERFwrP7WW+seWKkUrC2A8OMH2UsCIKgTiIxIgiCIGSer5/Bz0eZKFFFgkQqBQWQ2wwKWXLq7DkGDhzI48ePyZkzZ/rXT6NLly4xbtw47t27h7GxMb169WL69Ono6uqqLaaMVL9+fZo0acKCBQvYvHkzvr6+9OvXL36CSZ48efjy5Uv8+RoaGri7u2NnZ5ei9eVyOSYmJjg6OlK3bl0ePXqEt7c3AQEBBAcHExsbi6amJoaGhhQoUABra2vs7OxYvXo1MpmMli1bsm3btgx57lndzp07GTBgQPyLfH19fR4/fqxMFigU4OUOYaEof5DUTCqFQtZg+vPtVKnh6OhIYGAg/v7+SKVS5HI59+/f58KFC9y5cwdPT0/8/f3jqzqMjIwoWLAgtra2VKpUifr162NnZxdfYfP27Vt27NjBuXPnePr0KUFBQWhra1O0aFGqVKlC27ZtadCgQYKKHDc3N8aOHcvu3bsTbhN7/gjCQ1TyPDOUljaUqSj6iwiC8MsTiRFBEAQhcykUEPYNAv2VFSQSSeqTJFIN5VaZfAWVo3j/00OiZ8+eaGpqsn79ehUHnnrBwcGMHz+eXbt2ER4ejrOzM4sXL8bR0VHdoalMWFgYrVq14vLly8TGxqKhoYFEIiEiIgItLS0AWrVqxeHDh+Ov0dLSIiQkJMWJotGjR7Nq1Sq+fv2a6DURERHcuHGDmzdv4u7uzsuXL3n37h1BQUF8/zUnd+7cFCxYkGLFiuHg4EDVqlWpUqUK2toZ04cmK5k1axbTpk2Ln0JTokQJnj17hvTLB2WiUt3To/5LKoVSFUA77c1JZTIZt2/f5tSpU8ybNy9+Uk9YWBhSqTQ+AWJnZ0elSpWYMWMG7dq1Y82aNfFryOVyXF1d2bt3L9euXePVq1dERUVhZGREyZIlqV+/Pp06daJYsWJpCzL4C7zyBLkszc8zw31vjpv392suLQjC70ckRgRBEAT1iYmGb0EQ+k357mlMNEj+ebf1+xuUCpQl51IN0DeAnEaQMxcYGCb6LmZISAhlypRh/fr1yTafzEwHDx5k6tSpPH/+nPz58zN06FBGjBiRrRuDRkVFkSdPHqKiouJfdAPY2Njg6ekZ//H79+8xNzePT1LMmTOH8ePHJ7n2169f0dDQIC4ujrx58zJ//nxGjBiRqvhev35NuXLlkMlk5M2bFxsbG3x8fHj//j0hISHIZDK0tbUxNjbG3Nyc4sWLU7ZsWapVq4aTk1O2/tr8l5WVFX5+fmhpaREdHa2somnSmEPTxyPJats5JBLIYQg2ZZKtUoiLi+P69etcunSJe/fu8eLFC96/f09ERARSqZRcuXKRK1cuXr16Ra9evRgzZkyiiYzevXtz/PhxZsyYwfHjx7l//z4fPnwAoECBAlSoUIE//viDNm3apGgccoooFPDoFsjiVLNeRpBIwb5SusedC4IgZAciMSIIgiBkHXI5REeCTKb8b4lE+a6ltk6qJsxcvHiR7t278/jx4yzXBPXdu3eMHDmSI0eOIJPJaNSoEUuWLEmyD0JWtmrVKkaPHk1UVFT8Y927d2fLli0JzqtatSo3b95EV1eXsLAwNDQ0kly3Z8+e7N+/H3Nzc0JDQ+Mn2qRW06ZNKVKkCOvWrcPPz48CBQrEHwsKCuLq1avcvn2bJ0+e8OrVKwIDAwkNDUUul6Ojo4OJiQkFCxakRIkSODo6Uq1aNRwdHX86KjgriouLIyQkhODgYL59+8adO3eoZmpIycLm6mm2mhypFApbK5t+AjExMbi6unLp0iXu37+Pt7c3gYGBREZGoqGhgbGxMUWKFKFUqVJUqVKFBg0aJJgy06tXL3bu3Im/vz958uQBwMPDg507d3Lx4kWePXtGeHg4urq6FCtWjBo1atC+fXsqV66csV/nT4Hg9zJrVex8J5UqxygXtFB3JIIgCJlCJEYEQRCEX9LAgQMJCwvLsr0l5HI5GzZsYP78+bx+/RorKyvGjx9P9+7ds9WLboAVK1YwcuRI4uKU736vX7+ePn36JDjnwYMHlCtXjqVLlzJs2LBk12zUqBFnzpwBlO/a79+/n6pVq6Y6tpMnTzJt2jQ+ffqEtbU1Fy5cSNF1Hz9+5MqVK9y5c4enT5/y6tUrPn78SFhYGHK5HF1dXXLnzk3hwoUpUaIE5cqVw9nZOUFPiiwrIgw8H2XNF+T/+PwthPI9B/HhwweioqLQ1NTE2NiYokWLUqZMGapWrUq9evUoWDD5bR4xMTEULFiQ6OhoTE1Nefv2LbGxseTJk4cyZcrQsGFD5s2bR6dOnVixYkUmPLt/KBTg9VhZLZfVfh3X0QW78soEiSAIwm9AJEYEQRCEX1JYWBgODg4sWbKE5s2bqzucJD1//pyRI0dy/vx5tLS0aN26NYsWLcLMzEzdoaXYzJkzmTJlCnnz5uXM0SM4FLNSvuALCwFZHHKZjLdv31LUwlJZ/ZPTCHLkBP2c8E8vkv+qVKkSd+7cAZQ9SRo0aMDx48dTHZdMJsPKyorRo0czePBgXrx4gbW1dbqeq7+/P1euXOHu3bs8e/aM169f8/HjR8LDw1EoFOjp6ZEnTx6KFCmCra0t5cuXp0aNGhQrVixrJE1eeULQR3VHkaTwqGg2XLqOqWUx6tevH/+zoFAo4pv6/szHjx/ZsWMHp0+f5vHjx3z+/BmpVIpMJqNEiRLMmTOHZs2aJdgq1aNHD86ePZvmyqQ0i4mGp25ZK0klkYKtg3LroiAIwm9CJEYEQRCEX9a1a9do164dT548IXfu3OoOJ1lxcXHMmzeP1atX8+HDB0qVKsWsWbOyfGIHgMhwPj9zJ4/0+ws8SfKNJTU0lC8INbXANL/yzz9bpvLmzcvHjx/R1dVl+vTpjBw5MtntNz8zb948Xrx4we3btzE0NOT27dtpWiclfH19uXLlCm5ubnh4ePD69Ws+f/5MREQEoJwKkydPHooWLYqdnR0VKlSgZs2aFC1aNMNiSiAuDtxvZ49RsYa5oHiZ+A+vXLlC69atuXz5MqVLl45//M6dO+zevZsrV67w8uVLIiIiyJkzJzY2NtSpU4fOnTtTqlQptm7dSs+ePbl27doP1Uc+Pj5YW1sTGBhI3rx5M+sZKmWlLTUSKeQVW2gEQfj9iMSIIAiC8EsbOXIkAQEB7N27V92hpMrt27cZPXo0N2/eJGfOnHTp0oU5c+ZgYJCF3sVVKJQjmN+/hajI9L3YlkgBBRiZIM9bEE0jY3Lnzs3NmzfTPvnjHx8/fqR48eIcPnyYunXrcv/+fcqWLZuuNVNLLpfj5eWFq6sr9+7d4/nz57x9+5bPnz8TGRmJRCIhR44cmJmZYWFhgZ2dHU5OTtSqVStBX5R0++APAa+zxovw5EgkUNoJtHVYtWoVY8aMITY2lnbt2hEZGYmbmxvv3r1DLpeTL18+ypYtS/PmzWnXrt1Pews1adKEa9euxSfd/itPnjx06dKFJUuWZMKT+z9vfeDze/V+XaRSMDQGq5JiPK8gCL8dkRgRBEEQfmmRkZE4Ojoyffp02rZtq+5wUi0iIoJJkyaxdetWQkJCqFixIgsWLKBatWrqDSwqAnyeK5vlqvjFXJxcwenbbjTqOxhNnbSPbf2vjh074uTkxM6dO4mOjubJkycqWVcV5HI5T58+xdXVlfv37+Pp6Ymfnx9fvnwhKioKqVSKgYEBefPmxcLCgtKlS1OxYkVq1qyJqalpomuGhIRQq1Yt5s6dS/369f898PyRcotTdiCVEmxkSq3W7Xn8+DHyf77PpFIpxYsXp3r16rRr145atWqleItSXFwc+fLlw9bWlmvXriU41rVrVy5duoSfn5/Kn0qyFAp48wKCPqknOSKRQk5DsC4l+ooIgvBbEokRQRAE4Zd3584d/vjjD9zd3TO/TF6FTp06xcSJE3F3d8fU1JT+/fszYcIEtLVTPrEn3RQKCPSHd28ydDuGQiJBoqEJliWU72Kn0/Xr1+nZsydHjx6lZMmSXLp0iZo1a6Y/0Awml8t58OABrq6uPHjwAC8vL/z9/fn69SvR0dFIpVIMDQ3Jly8flpaWlClThkqVKqGvr0/z5s2RSqV06tSJZcuWoa+nBw9vJr/FKYtQKGDzsZP0mb0QUPaaiYmJwcTEhC9fvqR53cePH1O2bFkWL16coBGwl5cXJUqU4NOnT/HTazKVQgH+vvDpXaYmR2QKBRompmBpKypFBEH4bYnEiCAIgvBbmDBhAh4eHvz999/JNm/M6j5//szo0aPZv38/MTEx1K5dmyVLlmBnZ5exN46LhRdPlNUimfXCTSIF03xQyCpdL9oUCgVlypRh2bJlzJw5Ez8/P3x8fFQYaOaLiYnBzc2NGzdu8PDhQ168eIG/vz8fPyZsrCqRSFAoFIwaOoSg1z50a9KA6mXt1RR16ih09ZGUKs+bN2+4du0ap0+fxsvLizt37qS55wzA7NmzmTp1Kh4eHhQvXjz+cRMTE3r27MnChQtVEX7aBH2C1y/++RnL2F/T5RIJY5avIdLAmBUrV6brcyoIgpCdicSIIAiC8FuIjo6mQoUKjB49GhcXF3WHozLbt29n1qxZvHz5kkKFCjFq1CgGDhyo+uknsTHKEa8x0Zk/WlQqBaPcyuqRdCRH1q5dy8WLF1m2bBmFCxfm77//5o8//lBhoFlD0aJF+fr1KyEhIfFJQIVCQYMqldg/ZyqGOfTVHGEqSCTgWC1DKhkqVKiAn58f7969i/956dSpE9evX+fNmzcqv1+qxMaArxeEfcuYJKRUCrr6YGmLc/0GXLt2DTMzM+bPn0+nTp3QSmRSlCAIwq9MJEYEQRCE38ajR4+oX78+Dx48oGDBguoOR6V8fX0ZOXIkJ0+eRCKR0KxZMxYvXkzhwoXTv3hcLHg8hJio9K+VVlIp5MoNFmlPjoSGhlK4cGGePn1Kv379ePDgQeaPZ81AXl5enDt3jvHjxwMQHh4ef0wikTCscwfuP33GaJf2NK1ehW7T5qKro82Lt374ffhI5dJ2bJs2AYlEQsDHT3SZOofAL0FYFSygTKxUdmJQ2z+TPFaz71BGdW5H0+pVABJ8vHjnPvaeu0icTIaujjZrx47AwUbZWFdSoQazB/Ti78vX+PIthIVD+9Oqdg2QSjn0wo+J06ajp6dHmzZtmDhxIqGhoeluRBwWFkbevHlp3LgxBw4cAODZs2eUKlWKL1++YGJikq71002h4NGVC5QxNkAql6kmQSLVUP4sFSgMpgVAIuHo0aO0atUKmUxGjhw5yJEjB2fPnsXBwSH99xMEQcgmRHclQRAE4bfh4ODAoEGD6NWrF7/a+wIWFhYcPnyYyMhI5syZw+3btylatCi2trbs2bMn7QvL5eD1GGKjVRdsWuMI/gL+r9K8RM6cOWnfvj2bNm1i27ZtBAYGsm3bNhUGmfG+N2pdsmQJbdu2pXTp0uTKlQupVEqJEiWYOHFifHWUnZ0dI0aMQCaTUbhwYXp07fLDNrKnPr6cWjafZ/u2cf/5Cy7cvQfAkEUrqFW+LM/2b2Pl6KFcfegef01Sx5LSpUkD3LZv4OGuzczs25N+8xJOfzHMkQO37RvYMWMiQxatAOBjUDB9Bg7i+PHjPHz4ED09vfR8+hIwMDDg6NGjHDp0iP379wNgZ2dHrly51LuVBmWT2NFjxlC2dn22P/KCYqXByESZFExtNZhUqtySZmCk7CNiXwnMzOMTjI6OjvEVIhERERgbG2frXkyCIAhpIRIjgiAIwm9l/PjxfP78mU2bNqk7lAwhlUoZMWIEfn5+PHz4EHNzc1xcXDAwMKB3794EBQWlbsF3b5STZ7JCIkkuh4/vIfRbmpfo378/GzdujE+SjBw5UoUBqs73pqsLFiygVatWlCxZEiMjIzQ1NSlTpgzTp0/n6dOnFC9enAkTJnD//n1kMhkhISGYm5uzcuVKDh06xJ49e4iIiFAumkiz3BY1qqGro4O2lhaOJYrh4/8OgMv3H9K9WSMAiuTPR50KjvHXJHUsKfefe+HcZzCl2nVjxLLVPHrxMsHx9vVrA1CpVEneffpMVHQ0t588w9HePn5kc48ePVLxWUxe3bp16dOnDy4uLvG9WRo0aKDW8d5+fn44OTmxfPlyNDQ0lNVtOY2gWCkoUxGKFFdWe+jl+DdRItX4zx+p8nFdPciTFwpbQ6nyUMIecpn8UHFVsGBBNDU10dHRQaFQ0Lp1a/Lnz6+mZy8IgqAemuoOQBAEQRAyk5aWFtu2baNmzZrUq1ePokWLqjukDGNvb8+FCxeIjo5mxowZbNiwgc2bN+Po6MjcuXOpV68eoJzSsXPnTubPn5+woiAiDD4EZOj0mVRTyOHVcyhVAdLQKLJMmTIULVqU48ePs3HjRoyMjFi6dCnDhw/PgGCTJ5fLuXfvHufPn8fNzQ1PT0/evXtHWFgYAIaGhhQqVIhSpUrRq1cv6tevT8mSJVPUQ8bGxobGjRuzZMk/lRmSH6/R1fl3opGGVIM42b8Ta5JqUvyzY5oaGsjl/ybRomJiAIiJjaX1uKm4bliBY4nivPv0GfPGrRLG8s90pe8NQONkMhQKBRJpxjZLXrduHefPn6d69ep4eXkxYcIEHBwcCAkJwdDQMEPv/f9evHiBo6MjkZGRyOVycubMiabmf35d19KG3GbKP6BMWMZGK5OGcrnyayyVgrZOirecSSQSevfuTenSpYmMjGTQoEE0bNhQ/SPBBUEQMpGoGBEEQRB+O3Z2dowePZoePXogz8SxmOqio6PD7Nmz+fTpE5cuXUJDQ4MGDRqQO3duxo4dy4QJE1i8eHF8bwpA+SLLxyNrJUW+i4sDv7Rvqenfvz9r165FX1+f3r17M2XKlAz/PoiLi+P69etMnz6dpk2bUrx4cQwMDNDQ0KBy5cosXrwYX19fHB0dmT17Nh4eHsjlcoKDg3ny5An79+9nxIgRlCpVKlWNdadNm8bq1asJDQ1VVhOkUE1HB/46cRoAv8CPXHJ7mKJjVgUL4ObhCYDHq9fxVSFR0THEyWQUyqt8Qb/m4JEUxVGpdEnuP3zEy5fKdf76668UP4fUuHbtGr6+vgwdOpQyZcpgaGjIokWLMuReSTE3N6dbt25IpdL4aUJJToqRSEBbV9lIVd8A9PRBRzfVfXiWLFlC9+7dGTBgAM2bN6dBgwYEBwen78kIgiBkIyIxIgiCIPyWRo4cSVRUFKtXr1Z3KJmqZs2a3Llzh+DgYNq1a8eaNWs4efIkcrmc5cuXs3HjRuWJH98pJ2NkRQo5fPkAkeHJn5uI1q1b8/jxY168eMGyZcuIjY1l2rRpKgktLi6OK1euMGXKFBo1aoSVlRU5cuRAS0uLGjVqsGLFCgICAqhUqRILFy7k5cuXyGQygoKCcHd3Z/fu3QwePJgSJUqoJJ6CBQvi4uKi3EKlrQMpfL28fOQQzt+5h33HHoxavoaq9qUwMsiR7LGxXTpy6uZtynfpw6KdeylrYw2AoUEOZvTtToWufXHuM5gcuropiiOviTHr1q6lSZMmVK1alcjISLS0tNDXV+1knQIFCrBlyxZWrlyJq6srdevWZdeuXSq9R0rkyJGDGTNmYGBgwJ9//klkZCQ5cuTI1BgOHz6MsbExVapUydT7CoIgqJOYSiMIgiD8tl68eEGVKlW4detWfA+D383s2bOZNm0acXFx8Y8NHzaMxd3aIcmqiZHv8uSFojZpunTcuHHExMSwZMkSJkyYwNKlSwkNDU24bSEJMTExXL16lcuXL3P//n1evHjBhw8fiIyMRENDA2NjY4oUKULp0qWpWrUq9evXV82EoPSIiYIn91JUBRQZFY2Wpgaampq8//yFCl37cnH1EmyKFk7ymMrp6hNaxIacOXMCsHXrVjZv3sz169dVfy+gefPmXL58mdOnT+Ps7My3b9/i751Zxo8fT1BQEOvXr+fbt28YGhomua0pI/j7+2NpaUmPHj1Yt25dpt5bEARBHURiRBAEQfitrVixgn379uHq6pp0yfovqlmzZri6umJkZISBgQFBQUFUty/FzmkT0NHK4q3IJFJwqAQaqY/T19eXChUq4Ofnh46ODoaGhvTo0YMVK1YkOC8iIoIrV65w5coV7t+/z8uXL/n48SNRUVFoampiYmJCkSJFKFOmDNWqVaN+/foUKFBAVc9QtRQKeHgT5LJkT33s7UOXqXNQoCA2Lo7hHdrQu2WzZI+pXJ58zN51gAMHDhAXF4eJiQnr16/H1tY2Q24XFxdH/vz5KV68OE+ePGHkyJFMnTo1Q+6VmA8fPlCyZEkePXpEoUKFMu2+iTl06BBt2rThwIEDtGrVKvkLBEEQsjGRGBEEQRB+a3K5nDp16tCkSRNGjRql7nCyBIXXYwgNTumuC/WRSqGgJZilLRHRpEkT2rRpQ7du3Zg1axbTpk2jf//+PH36lJcvX/Lp0yeio6PR0tIid+7cFC1aFHt7e6pXr069evUwMzNT8RPKBM8fQniouqNIGalUOVElT75Mve3Tp0+xt7fH1taWmJgYXrx4kWn3HjZsGADLli3LtHsmpX///mzevBkfHx+1J2oEQRAykkiMCIIgCL89X19fnJycuHr1KiVLllR3OOoVFwvut7PGeN6U0NVTTqj5x927d+nfvz/Hjh3D3Nz8h9O/ffvG+fPncXV15eLFi3h7eyORSIj5Z3qKnp4ejo6O2Nvb4+zsTL169TAxMcm0p5PhAv0h4HXWbKr7/yQS5ddWJ2X9SFRp3rx5TJgwAYCwsDCV9zRJjJ+fHw4ODjx79ox8+TI3GZQUOzs7goOD8fPzS1XjX0EQhOxEJEYEQRAEAVi/fj2bNm3i1q1bKe4z8Uv6FgQ+z1O03SJLkEigbFXkwJw5c5gzZw5yuZzNmzejoaGBq6sr7u7u+Pr68vnzZ2JjY9HW1sbU1BQLCwvc3d2ZPHkyffv25cCBA/Tt25fPnz+TK1cudT+zjBEXC+53skdiJGcusCmjtttXqlSJO3fuMHPmTCZNmpTh9+vXrx+5cuVi3rx5GX6v1AgJCSF//vzUrVuXo0ePqjscQRCEDCESI4IgCIIAKBQKGjZsSPXq1TPlRVCW9e4tvHsDJP/rQdC3EOw79uDwgplUsFP2fJi9ZQcPPF9Qr2J5IqOjGd6xbYaGq5BKufL+K+179OLz588Jxu7q6OhgZmaGlZUVjo6O1KhRg9q1a2NgYBB/zty5c/Hx8WHTpk0A5M+fn4oVK3LkyJEMjVutXj2HoE/qjiJpUg2wsgUj9VXrhIeHY2RkhK6uLmFhYRl6r1evXuHk5MSLFy+yZIXS9evXcXZ2ZuXKlQwcOFDd4QiCIKicSIwIgiAIwj/8/f1xdHTk3LlzODg4qDsc9XjxBEK+pvj0o1evM371Bh7u3MSLt340HDKGhzs3YWZinIFB/itWJmfC2s0s2bEbiUSCQqFALpfTtGlTjh8/nuz1Hz9+xMbGhlevXmFsbBzfcNLf3z/rNlFNr/BQ8HIHeRauGtHShjIVlRVBarRy5UqGDBnCli1b6N69e4bdp2vXrlhaWmZqo9fUmjFjBtOnT+fRo0eULl1a3eEIgiColEiMCIIgCMJ/bNu2jSVLluDm5oa2tra6w8l8j24pt1ukQufJszA1zsXVB48Y17UjbevVZtqGrYRFRLJo2ACevPRhwPylhEdGERUTQ58WzRjWsQ0AH74E0W/eYnz836FQKBjt0p4uTRqmLubcZiiK2vDgwQMOHTrEnj17KFq0KJcvX07R5R06dKBSpUoMHToUAAsLCywtLbl48WLq4shOnj+C8BB1R5E4qRQKWYFpfnVHAoCWlhZyuZyAgIAM6f3x/PlzatSogbe3N0ZGRipfX5WcnZ158uQJ79+/R1c383u/CIIgZBSRGBEEQRCE/1AoFPzxxx+UKVOGWbNmqTuczHf/Wqobr34NCaVI87bUqeDI3wtnAyRIjISGR6CtpYmOtjZhERE4devHofkzsLUoSrvx0yheuBAz+/fk/ecvOHbuxflViyllbZnyAAxzQfGEvSgUCgWSFFYbXLt2jd69e/P8+XMkEgkXL16kXr16eHl5UaxYsZTHkZ1ER8Gze1mwakQCOXJCCXu1V4t816RJE86fP0+RIkXw9vZW+frt2rXD0dGRsWPHqnxtVYuJiYkfZ3zr1i11hyMIgqAyorW0IAiCIPyHRCJhw4YNbNq0CTc3N3WHk/nS8H7JpXsPMMyRA683fkT/M93lvyKioug5cwGl23ejaq9BvPv0BXdvHwAuuN2n75/NAMifJzdNqlXm8v2HqQsgkRf3KU2KAFSrVg1NTc34CpM6depga2tL586dUxdHdqKjqxx1nNWmjEglYFkiyyRFAEaPHk1cXByvX79m0KBBKl370aNHXLt2TeXrZhRtbW2uX7+Om5sbEydOVHc4giAIKpPF/m8oCIIgCOqXL18+li9fTteuXYmMjFR3OFna5+Bghi5eycml8yhva8PU9Vt/OGfCmo3ky23Cw52bcN+9BSe7EkRF/5tA+f8kRmqSGkBK+sQmSSKRMGDAANauXRv/2LZt23Bzc+PBgwfpWzwrM80PejnUHcW/pFJlskYN43mTUrNmTXR1dWnXrh1r1qxJ8RatlJgyZQrjxo0jR44s9HVIhq2tLevWrWPu3LlcunRJ3eEIgiCohEiMCIIgCEIi2rVrR6lSpZg8ebK6Q8lcqUxKDJi/lN4tmmJf3JrlI4ew++wF3J49T3BOcGgYhfKaoampydOXr7j26HH8sboVyrHhb2WT1MDPXzh14za1ypVNXcwqqHro3LkzFy5c4N27dwCUL1+ecuXK0aVLl3SvnWVJJGBtp2x0qm5SKRjnyTJ9Rf6fs7Mzjx494o8//qBZs2aEh4ene807d+7w6NEj+vTpo4IIM1evXr34888/adq0KUFBQeoORxAEId1EjxFBEARB+InPnz9TpkwZ9u/fT7Vq1dQdTuZ4eBNkcSk6df/5S8zZugu37evR0tQE4LjrDSas2Uiz6lWIiY1j0bABPPR6gcuUOejpaGNV0JyvoaF0qF+Hbs0a8eFLEH3nLuZVQDqar5qYKbdfpFO/fv0oUKAAU6ZMAcDLywtbW1suXbpEzZo1071+lhUdBc8fprrprspIpWBoDFYls9QWmv86f/48DRs2JDw8nCJFimBhYcHt27fTtWb9+vVp06YNvXv3VlGUmUsul2NhYYGuri5eXl7qDkcQBCFdRGJEEARBEJJw5MgRRo8ezaNHj7JVuXuaeblD6Dd1R5FyEikUsgSz9I/WdXd3p0mTJrx+/RrNfxI9tWrV4u3bt/j4+KR7/SwtOgo8H0FsLOnem5QaUinkyg0WWauvSGL09PSYP38+9erVo1SpUsyaNYvx48enaa2rV6/So0cPPD090dLSUnGkmScwMJAiRYrQsWNHtm79cRudIAhCdiG20giCIAhCElq0aEHlypUZN26cukPJHDlzqTuC1JFIQN9AJUvZ29tTpEgRjh8/Hv/Yjh078PX15ejRoyq5R5alowslHZUTfjKrIatECvkLZ4ukCCib9G7atAlbW1vmz5/PpEmTePr0aarXUSgUTJo0iWnTpmXrpAgo+zEdPHiQbdu2sW/fPnWHIwiCkGaiYkQQBEEQkvH161fKlCnDtm3bqF27trrDyVjBQfDqOchl6o4khSTgWFVlL+Z37tzJ9u3bOXfuXPxjzZo14/79+/H9R35pCgUEfYQ3LzPseyBWJiNKISGnvRPo6WfIPTLC6dOnadq0KdHR0WhqalK1alVevHjB+/fv4yuMUuLs2bMMHz6cJ0+eoKGhkYERZ54hQ4awdu1aXr58SZEiRdQdjiAIQqqJihFBEARBSIaxsTEbN26kR48ehISEqDucjJXDABQ/jr/NsnR0VFrh0Lp1ax49eoS3t3f8Y9u2bePDhw9s27ZNZffJsiQSyJ0XSlcAIxPlx6qq5pBKQSrlTSzYd+xORDZ7a65Ro0Zoa2uzYcMGQNl3JCoqij///DPFa3yvFpkxY8YvkxQBWLFiBba2tlSqVAmZLLskVQVBEP4lEiOCIAiCkAINGzakXr16jBw5Ut2hZCwtbciRU91RpIxUCqbp7y3yX7q6unTv3p1169bFP2ZiYkKHDh0YMWKESu+VpWlpQ7FSUKoCmJmDVEP5Jy2kUtDRg8LWYF8Z62q1cHKqyNy5c1UbcyaoXLkyGzduBEBfX59Tp05x4sQJduzYkaLrjx49SlxcXKqSKdnFzZs3CQ0NpVmzZuoORRAEIdXEVhpBEARBSKGQkBDKlCnD2rVradSokbrDyTjBX+CVZ9bfTiORgn1F0FRtn4ZXr17h5OSEn58fenp6AERERGBkZMS8efN+/eRYYuRyCP4M375CWAjERCk///EUgAQkgFyh/FsvBxgYKcfw5siZoPIkICAAe3t7bt++jbW1dSY/mbQ7duwYf/75J1FRUfHbZ4YNG8aaNWt4/fo1BQr8PFEnl8uxt7dn7ty5NG3aNLNCzlR37tyhSpUqLF68mGHDhqk7HEEQhBQTiRFBEARBSIXLly/TpUsXHj9+jLGxsbrDyRgKBbjfgriUje1VGxNTsLTNkKUbN25Mu3bt6Nq1a/xjAwcOZPv27Xz79g1pZjUozarkcoiK4Ln7I16+eEGzJk3+2Sqjoewboq2T7BacRYsWcenSJU6ePIkkGzRf/U5XV5eVK1cmGLNrY2NDXFxcktOL9uzZw4oVK7h582a2er6pNW/ePCZOnMj9+/dxcHBQdziCIAgpIhIjgiAIgpBKgwcPJjg4OMXl89nSuzcQ6Kd8AZwVSaVgY59h236OHz/OrFmzuHPnTvxjcXFx5MyZk1GjRjFz5swMuW92olAosLa2xs/Pj9DQUHR0dFJ1fWxsLPb29syZM4cWLVpkTJAZoEaNGoSHh3Pv3r34xz5+/EihQoV+2Ib1XVxcHCVLlmTt2rXUqVMnM8NVi9q1a3P//n3ev3+Pvn72abArCMLv6zd/u0MQBEEQUm/evHncvn2bI0eOqDuUjJO3IGikfNJGppJIIVfuDO2F0rhxYwIDA3nw4EH8Y5qamowYMYJFixYRl9WraTLByZMn8ff3R6FQsHfv3lRfr6WlxapVqxg2bBgREREZEGHGGDp0KI8ePUL+n6ShmZkZO3bsYMOGDVy4cOGHa3bs2IG5ufmvP9XqH+fOnUNHR4caNWqoOxRBEIQUERUjgiAIgpAGN27coE2bNjx+/Jg8efKoO5yMEfoNXjzJelNqNDShtBOkYkRqWsyZMwdfX9/4Zpug7BNhaGhI9+7dWblyZYbePyuLjY3F0tISf39/AKytrXnx4kWatoh06NABa2vrbFOFI5fL0dPTY926dXTv3j3BsTZt2nDq1Ck+fPiAgYEBADExMRQvXpxdu3ZRtWpVdYSsFt7e3tja2jJixAgWLFig7nAEQRCSJBIjgiAIgpBGo0eP5vXr1+zfv//X7Rnwxhs+f8g6yRGJFKxLKkfJZrAPHz5QokQJfH19yZUrV/zjixcvZvz48QQHB/+22wQ2b95Mnz59kEgkyGQypFIply9fxtnZOdVrfW/EeuvWLYoVK5YB0apetWrViImJ4e7duwkel8vlFChQgEKFCuHm5gbAmjVrOHHiBKdOnVJHqGq1bds2unfvztmzZ6lXr566wxEEQfgpkRgRBEEQhDSKiorC0dGRKVOm0L59e3WHkzFkMnh2H2KiUU4eUSOJVNlw1cIm027Zvn17qlSpwpAhQxI8njt3bho3bvxr95lJwps3b7h48SJbtmzByMgIGxsbevfuja1t2prhLlq0iIsXL3Lq1KlskWTcv38/nTp1Ijo6+odGvC9evKBkyZJMmzaNkSNHYm1tzbFjxyhXrpyaolWvDh06cOTIEfz8/H7d6jpBELI9kRgRBEEQhHRwc3OjadOmuLu7ky9fPnWHkzFiosHjAcTFqi8GiRRyGkGxUslOO1ElV1dX+vbti4eHR4IX7Fu3bqV37958+vTp151OlAIuLi7UrVs3wfSetIiNjcXBwYHZs2dni0ascrkcXV1dtmzZQufOnX84vmzZMkaOHMnw4cN59eoVhw8fVkOUWYNcLsfa2hqJRIK3t7eY6CQIQpYk/mUSBEEQhHSoUKECffr0oU+fPvyy7zVo64BtWdDSztSkRLzvSRFru0y/f/Xq1dHQ0ODKlSsJHu/evTtmZmZ069YtU+PJakJDQ8mZM/1NcLNbI1apVEq5cuVYvXp1oseHDRuGk5MTS5YsYcqUKZkcXdYilUq5ffs2AQEBdOnSRd3hCIIgJEokRgRBEAQhnSZPnszbt2/Zvn27ukPJODq6yuSIto4yUZFZpP9MoClWSvnfmUwikdC/f3/Wrl37w7HVq1dz/Pjx+AakvyNVJUYAatWqReXKlZkzZ45K1stogwYN4v79+wmm0/xXw4YN0dDQYOLEiZkcWdZjZmbG33//ze7du9m5c6e6wxEEQfiB2EojCIIgCCrg7u5O3bp1efDgAYUKFVJ3OBlHLoeA1/DxXQY3ZJUoEyFFiyv7iqhRSEgIRYoUwcPDg/z58yc4ZmlpSdGiRbl06ZKaolOvihUrsnz5cipVqqSS9bJTI1a5XI6Ojg47d+6kXbt2CY59/fqV4sWLs3LlSjp27MjmzZt/mGDzOxo5ciQrVqzA09MTKysrdYcjCIIQT1SMCIIgCIIK2NvbM3ToUHr16vXrbqkBZbKikCWUsFdWj2REFYdUCoa5oHQFtSdFAAwNDWnbti2bNm364dimTZu4cuUK3t7eaohM/VRZMQJgbm7OuHHjGDx4cJb/OZJKpTg6OrJixYofji1ZsoQ//viD9u3bM2LECPr06fNbVxZ9t3jxYsqUKUPlypWJi4tTdziCIAjxRMWIIAiCIKhIXFwclStXplevXvTt21fd4WQ8uRw+vYcP/srGrD/ZUpCipRQKpFINyGEA+Qtnyjje1Hj06BHNmjXD19cXTU3NBMfs7OzIkSPHD6NbfweFChXixo0bFC5cWGVrfm/EOmvWLFq2bKmydTPCtm3b6N27N1FRUfFNRT99+kSJEiV48OABRYoUAaBkyZJERETw6tWr3775aEREBPny5aNixYqcP39e3eEIgiAAomJEEARBEFRGU1OTbdu2MWnSJHx9fdUdTsaTSiGvOZR2UvYAMTJRNkeVaqSsD4lUQ7mGVMq20+d5rqEPJRyyXFIEwMHBgUKFCnHy5Mkfju3YsYN79+7x4MEDNUSmXqquGIF/G7EOHz48yzdidXFxQS6X8/fff8c/Nn/+fDp06BCfFAHldKPAwED69OmjjjCzFH19fS5dusSlS5dYsGCBusMRBEEARMWIIAiCIKjcokWLOHHiBJcuXfr93h2WyyEyHMJDISxE+bcsDr7/uiGVgqYWGBiBgaGyQkRHj4mTJhEdHc2iRYvUG38SduzYwc6dOzl79uwPx5ycnAgPD+fZs2dqiEw9FAoFmpqaREVFoaWlpfL1O3TogJWVFbNmzVL52qpUvnx59PX1cXV15d27d5QuXZonT55QoECBBOcdPnyY1q1bc+rUKRo2bKimaLOOxYsXM2bMGG7fvk2FChXUHY4gCL85kRgRBEEQBBWTyWTUqFGDNm3aMHToUHWHky14enpSu3Zt/Pz80NDQUHc4iYqKiqJw4cLcvHkTa2vrBMe8vb2xsbHhwoUL1K5dW00RZq6IiAhMTEyIiorKkPWzSyPWTZs2MXDgQKKjoxk4cCD6+vosXLgw0XPbt2/PsWPHCAwMxNDQMJMjzXoaNGjAzZs3ef/+PQYGBuoORxCE35hIjAiCIAhCBvD29qZy5crcvHmT4sWLqzucbKFChQrMnj2b+vXrqzuUnxozZgwKhSLRF761a9fm9evXvHr1Sg2RZb4PHz5QqlQpPn36lGH3WLx4MefPn+f06dNIJJIMu096yGQydHR0WLt2LePGjcPT0xNT08SbBsvlcgoWLEi+fPl+y61X/y8uLg5zc3MKFCjAw4cP1R2OIAi/sd+svlcQBEEQMkexYsWYNm0a3bp1QyaTqTucbMHFxYUdO3aoO4wk9e3bl7/++ovIyMgfju3cuZPXr18n6DfxK8uI/iL/b8iQIfj5+XHkyJEMvU96aGhoUKZMGaZMmcKAAQN+mhQB5SSba9eu8fjxY6ZNm5Z5QWZRmpqa3L59m6dPnzJ8+HB1hyMIwm9MJEYEQRAEIYMMGDAAXV1dFi9erO5QsoX27dtz/PhxwsLC1B3KT1lZWVG+fHkOHDjww7ECBQrQrFkzBg4cqIbIMl9mJEa+N2IdNmxYlm7E2rx5cwIDAxk5cmSy51pZWbF06VJmzpwpqiQACwsL/vrrL5YvX86pU6fUHY4gCL8pkRgRBEEQhAwilUrZsmULCxcu5OnTp+oOJ8szMzOjWrVqWb7ion///qxduzbRY1u3buXjx49s3bo1k6PKfJmRGAGoVasWVatWZc6cORl+r7Ty9PREIpFw8+bNFJ0/ePBgqlevTp06dYiNjc3g6LK+Tp064eLiwp9//klgYKC6wxEE4TckEiOCIAiCkIGKFi3KnDlz6Nq1q3gBlALZYTtNkyZNCAgISPTdfhMTEzp06MCoUaPUEFnmyqzECCgnPa1bt44XL15kyv1S48mTJ1y5cgU7OzuWLFmS4uvOnDmDTCajWbNmGRhd9rF161YKFSpEpUqVkMvl6g5HEITfjEiMCIIgCEIG69WrF2ZmZsydO1fdoWR5zZs35969e7x7907dofyUhoYGffv2/WnVyPr16wkNDf3lt1BlZmKkQIECjB8/niFDhpDV5gZMnTqVMWPG0KdPH27cuJHi63R1dTl37hznzp1j06ZNGRhh9iCVSrl16xYfPnygY8eO6g5HEITfjEiMCIIgCEIGk0gkbNq0iVWrVomeAsnQ09OjZcuW7N69W92hJKlnz54cOHCAb9++/XBMX1+fvn37MnXq1F/6ne+wsLBMS4xA1mzEeu/ePe7evUv//v3p27cvMTExnDt3LsXXV6xYkTFjxtC/f3/evHmTgZFmD3ny5OHEiRPs37+fLVu2qDscQRB+IyIxIgiCIAiZwNzcnMWLF9OlSxeio6PVHU6Wlh220+TLl4/69euzffv2RI8vXboUmUzG5MmTMzmyzJOZFSOgbMS6evVqhg0bRnh4eKbdNymTJ09m4sSJ6Onpoa2tTcmSJVNdKTRv3jxsbGyoXr36L51IS6k6deowduxY+vTpg5eXl7rDEQThNyESI4IgCIKQSTp37oyVlRXTp09XdyhZmrOzM8HBwTx+/FjdoSRpwIABrF27NtGtHZqamowaNYrFixcTFxenhugyXmYnRgBq1qyZZRqxXr9+HU9PT3r27Bn/WI8ePbh27Vqq13J1deXjx48J1vqdzZ07F0dHR6pWrUpMTIy6wxEE4TcgEiOCIAiCkEkkEgnr169ny5Yt3LlzR93hZFlSqZROnTpl+aoRZ2dnJBIJV69eTfT49OnT0dLSYujQoZkcWeZQR2IElI1Y169fr9ZGrAqFgkmTJjF16lS0tbXjHx84cCBRUVFcvnw5VeuZmJiwf/9+tm3bxsmTJ1Udbrbk6upKXFwcDRo0UHcogiD8BkRiRBAEQRAyUd68eVm5ciVdu3YlMjJS3eFkWZ07d2b37t3IZDJ1h/JTEokkydG9UqmU6dOns2HDBiIiIjI5uowXGhqKgYFBpt+3QIECTJgwgcGDB6utEevFixd5//49nTt3TvC4trY2JUqUYOHChales3nz5nTs2JHWrVsTHBysokizL11dXa5cuYKrqyuzZ89WdziCIPziRGJEEARBEDJZmzZtcHBwYOLEieoOJcsqWbIk+fLlS/U775nNxcWFc+fOERgYmOjxESNGYGhoSJ8+fTI5soynrooRgMGDBxMQEMDff/+d6ff+Xi0yffp0NDU1fzjevXv3n1YRJWf79u3kzp2bmjVrpjPKX4ODgwPLli1jypQp3Lp1S93hCILwCxOJEUEQBEFQg9WrV7N379409SP4XWSHJqxGRka0adMmyXGrS5YsYe/evQQFBWViZBlPnYkRLS0tVq1axfDhwzO9EevJkyeJiIigbdu2iR4fPHgwkZGRXL9+PdVrS6VSrl27xtOnT3/pxr2pMXjwYBo2bEi9evUICQlRdziCIPyiRGJEEARBENQgd+7crFu3jm7duhEWFqbucLKkDh06cOzYsSwzgeRnBgwYwIYNG3667adr166YmZnRtWvXTI4sY6kzMQLKRqzVqlXL1EascrmcSZMmMXPmTKTSxH+N1tXVpXjx4syfPz9N97CwsGDlypXMmTOHe/fupSfcX8bx48cxNDSkWrVq6g5FEIRflEiMCIIgCIKaNG/enOrVqzN27Fh1h5Il5c2bl8qVK3PkyBF1h5IkBwcHzM3Nk2yauXbtWk6ePIm/v38mRpax1J0YAVi4cGGmNmI9dOgQWlpaNG/ePMnzunbtypUrV9J8n/79+1OzZk3q1asnprKgrKS5desWnp6eDBw4UN3hCILwCxKJEUEQBEFQo2XLlnH8+HEuXLig7lCypOywnQaUL2TXrFnz0+N//PEHFhYWuLi4ZGJUGSsrJEYysxGrTCZjypQpzJo1C4lEkuS5Q4cOJTw8nNu3b6f5fqdPn0ahUNC4ceM0r/ErKVKkCDt37mTt2rUcPXpU3eEIgvCLEYkRQRAEQVCjXLlysXHjRnr27Mm3b9/UHU6W88cff3Dnzh3ev3+v7lCS1LZtW+7fv4+Pj89Pz9m8eTNXr17Fy8srEyPLOFkhMQKZ14h1165dmJqaUr9+/WTP1dfXx9ramnnz5qX5ftra2pw/f57Lly+zbt26NK/zK2nbti09evSgbdu2vHv3Tt3hCILwC5Eo1DXnTBAEQRCEeH379iUuLo7NmzerO5Qsp3v37pQuXZoRI0aoO5QkjR49GolEwoIFC356TqlSpdDT08PNzS0TI8sYhoaG+Pn5YWRkpO5QuHr1Kl26dMHDw4McOXKofP3Y2FhsbGz466+/cHZ2TtE1M2bMYNGiReluGDpx4kTmz5/Py5cvKVq0aLrW+lWUKFGC8PBw3rx589NeL4IgCKkhEiOCIAiCkAWEhoZib2/PypUradKkibrDyVIuXbrEyJEjefjwobpDSdLLly+pXLkyfn5+6OrqJnrOw4cPKVeuHHfv3qV8+fKZHKHqKBQKNDU1iYmJQUNDQ93hANCpUyeKFi3K7NmzVb72hg0bOHjwIOfOnUvxNWFhYRgaGqrka21vb8+XL194+/atSAQAwcHBFChQgIYNG3L48GF1hyMIwi9AJEYEQRAEIYu4cuUKnTt35vHjx5iYmKg7nCxDLpdTpEgRTp8+TalSpdQdTpIaNmxIp06dkuwlUrFiRcLCwnj27FkmRqZaYWFh5M2bN0tNDHr37h329vbcuHGD4sWLq2zdqKgoihUrxqFDh3ByckrVtdbW1jg4OHDw4MF0xRAcHEz+/Plp3bp1tui5kxlcXV2pWbMma9eupW/fvuoORxCEbE6knAVBEAQhi6hZsyatWrVi8ODB6g4lS5FKpXTq1ClbvCDs378/a9euTfKcnTt38vz5cy5dupRJUaleVukv8l8FChRg/PjxKm/Eun79ehwdHVOdFAHo2LEj58+fT3cMuXLl4uDBg+zatYtjx46le71fgbOzM5MmTWLgwIHZOskoCELWICpGBEEQBCELiYiIwMHBgXnz5vHnn3+qO5ws49mzZzRo0IA3b95kma0biYmLi8PCwoLjx4/j4ODw0/Pq1KnDq1ev8PX1zbzgVOjFixc0btyYly9fqjuUBGJjYylbtizTp0+nVatW6V4vPDwca2trzpw5g729faqvDwkJIVeuXDx48CDJ74eU6tatG/v27SMgIEBUlf2jatWqPH/+nMDAQLS1tdUdjiAI2ZSoGBEEQRCELERfX5+//vqLgQMH8unTJ3WHk2XY2dlhZmbGlStX1B1KkjQ1Nenbt2+yVSM7duzgzZs3HDp0KJMiU62sWDECoKWlxerVqxkxYoRKtvmsWrUKZ2fnNCVFQNmgtmjRosyZMyfdsQBs2bIFU1NTatSooZL1fgXfK69q166t5kgEQcjORGJEEARBELKYKlWq0KVLF/r166fSLQHZnYuLS7bYTtOrVy/279+f5PjlAgUK0Lx5cwYNGpSJkalOVk2MANSoUYNq1aqluwnrt2/fWLx4MdOnT0/XOu3bt1fJdhpQbiu7du0anp6ejB8/XiVrZnc6Ojpcu3aN27dvM23aNHWHIwhCNiUSI4IgCIKQBU2fPh1PT0/27Nmj7lCyjPbt23P06FEiIiLUHUqS8uXLR7169ZJN4vz11198+vQpW45ozsqJEYCFCxeyYcMGvLy80rzG0qVLady4MSVKlEhXLKNHjyY4OJinT5+ma53vihQpwurVq5k/fz537txRyZrZnZ2dHStXrmTGjBlcu3ZN3eEIgpANiR4jgiAIgpBF3b9/n8aNG/Pw4UMKFCig7nCyhIYNG9KlSxc6duyo7lCSdOXKFQYOHMjTp0+RSCQ/Pa9Lly6cOHGCoKCgTIwu/Xbv3s2xY8fYu3evukP5qaVLl3LmzBnOnDmT5NcgMV++fMHGxoa7d+9iaWmZ7liKFi1KlSpV2L17d7rX+q5Bgwbcvn2bT58+id4a/2jRogXnz58nICCAXLlyqTscQRCyEVExIgiCIAhZVLly5ejXrx99+vQRW2r+4eLiws6dO9UdRrJq1KiBQqFI9t3rdevWERYWxsKFCzMpMtXI6hUjAIMGDSIgIIDDhw+n+toFCxbQunVrlSRFANq2bcuZM2dUstZ3x48fR0NDgwYNGqh03ezs8OHDmJiYUKVKFXWHIghCNiMSI4IgCIKQhU2cOJGAgAD++usvdYeSJbRo0YJbt27x4cMHdYeSJIlEQv/+/VmzZk2S5+nr69OvXz+mT5+OXC7PpOjSLzskRr43Yh0+fHiqGrEGBgayadMmJk2apLJYxo0bx9evX3n+/LnK1tTW1ubChQu4urqyatUqla2bnUmlUm7dusXLly/p06ePusMRBCEbEYkRQRAEQcjCtLW12b59O2PGjOHt27fqDkftcuTIQfPmzbNF75UuXbpw9uzZZJM4S5YsQSaTqfSFeEYLCwvL8okRUFbuODs7p6oR69y5c+nSpQsFCxZUWRwmJiYUKlSIuXPnqmxNAEdHRyZPnsywYcPw8fFR6drZVcGCBdm3bx+bNm3KtlOfBEHIfCIxIgiCIAhZXOnSpRk+fDg9e/YUW2rIPtNpjIyMaN26dbLNVTU1NRk9ejRLliwhJiYmk6JLn+xQMfJdahqx+vn5sXPnTsaNG6fyOFq3bs2pU6dUvu60adMoXbo01atXz1ZVRxmpZcuW9O3blw4dOoiEsiAIKSISI4IgCIKQDYwZM4aQkBDWrVun7lDUrlatWnz48AEPDw91h5Ks/v37s379emQyWZLnTZs2DS0tLYYNG5Y5gaVTdkqM5M+fn4kTJzJ48OBkE4szZ86kb9++5M2bV+VxjBs3ji9fvuDt7a3yta9evUpwcDCdOnVS+drZ1dq1aylevDiVK1cWCSNBEJIlEiOCIAiCkA1oamqybds2pkyZwqtXr9QdjlppaGjQsWPHbFE14ujoSP78+ZOtFJBKpcycOZONGzdm+XHEkL0SI6BsxPr+/fskG7G+fPmSw4cPM2rUqAyJwczMDHNzc+bMmaPytQ0NDTl8+DD79u1LU7PZX9XNmzf59u0bf/zxh7pDEQQhixOJEUEQBEHIJkqUKMH48ePp1q3bb/8OqIuLC7t27coWn4f+/fuzdu3aZM8bNmwYRkZG9O7dOxOiSp/slhhJSSPWGTNmMGTIEExMTDIsjlatWnHixIkMWbthw4b06NGDjh078vnz5wy5R3ZjaGjI2bNnOXXqlGhQKwhCkkRiRBAEQRCykaFDh6JQKFi+fLm6Q1Gr0qVLY2JiwtWrV9UdSrLatm2Lm5tbiip9Fi9ezN69ewkKCsqEyNIuNDQUAwMDdYeRKs7Ozjg7OzNr1qwfjnl4eHD27NkM38o0duxYPn/+jK+vb4asv2HDBvLly0eNGjUyZP3sqGrVqkybNo2hQ4fy+PFjdYcjCEIWJRIjgiAIgpCNaGho8NdffzF79uwUNZP8lWWXJqx6enp07dqV9evXJ3tu165dyZcvH127ds2EyNIuu1WMfLdw4UI2bdr0w8/O1KlTGTVqFIaGhhl6/wIFCpA/f/4M2U4Dyi1Z169f58WLF4wePTpD7pEdTZ48mWrVquHs7ExUVJS6wxEEIQuSKER7e0EQBEHIdtasWcP27du5fv06mpqa6g5HLd6/f0/JkiUJ+B97dx0W5dbucfw7M6SCLXYH28IuLOxu97ZA7MDuwO7u7sLYduu2223HNrG7UUFEYGbOH7xyRIkBZuYBvT/Xtc/RmedZ6zcIvs49a93r2TMSJEigdJxIeXt7U6pUKR4/foyNjU2k127fvp169erx+PFjox4Za0w5cuRg586dODo6Kh0l2qZNm8aePXvYt28fKpWKS5cuUbNmTe7evWuW76POnTuzcePGKI9xjo2lS5fStm1bTpw4gbOzs8nmiU8CAwNJmzYt2bNn58yZM0rHEULEMbJiRAghhIiHOnbsiJ2dHZMnT1Y6imLSpElDsWLF2L59u9JRopQjRw4KFizIxo0bo7y2Tp06ZMmSBVdXVzMki5n4umIE/r8R66ZNm4CQ1QQDBw40W3Ft4MCBvH792qTHyLZu3Zpq1apRrVo1WSHxP1ZWVpw8eZLz58/j6empdBwhRBwjhREhhBAiHlKr1SxdupQpU6Zw7do1peMoJr5spwHDm7ACLFu2jGPHjsXZ7VLxuTDyrRFrr169OHToENeuXaN9+/Zmmz99+vSkSpXKZNtpvtm+fTuWlpZUqVLFpPPEJ46OjixYsIBx48Zx6NAhpeMIIeIQ2UojhBBCxGNLlixhzpw5/Pvvv1haWiodx+z8/PxInz49t2/fJlWqVErHiVRwcDBZsmRh586d5M+fP8rr8+XLh7W1NefPnzdDOsNptVosLS0JDg5GrY6/n7G5urpy4sQJBg8eTNu2bc06d8eOHdm2bRsvXrww6TxXrlyhUKFCTJkyxeSNZeOTP//8k507d/Ls2TOTnkIkhIg/4u//mgkhhBCC1q1bkyZNGsaMGaN0FEXY2dlRu3Zt1q1bp3SUKFlYWNC+fXuDV42sWrWKixcvcu7cORMni57Pnz+TMGHCeF0UAahXrx5PnjyhRIkSZp974MCBvHz5kqdPn5p0nvz58zN8+HD69OmDt7e3SeeKT9avX4+Dg4Mif/ZCiLhJVowIIYQQ8dzz588pWLAgu3fvpnDhwkrHMbt//vmHQYMGxbmVFeH51jD20aNHBp2AUqJECT59+sSNGzfMkM4wz549o2jRojx//lzpKDGm1+spXbo0WbJk4fXr16GNWM3JwcGBv/76i9mzZ5t8riJFivDs2TOePXsW7wtaxvLy5UsyZcpEs2bNWLZsmdJxhBAKk78ZhRBCiHgubdq0TJ06FXd3d75+/ap0HLOrWLEiz58/5+bNm0pHiVKaNGmoVKmSwX1RVq9eza1btzh48KCJkxkuPvcX+Wbv3r18+PCBJUuWhGnEak516tRh8+bNZpnryJEjfPr0iSZNmphlvvggderUbNq0iRUrVsSLFWdCCNOSwogQQgjxC2jWrBk5c+Zk2LBhSkcxO41GQ7NmzVi9erXSUQzi4eHBvHnzMGTRbvbs2alQoQJt2rQxQzLDxPfCiF6vZ/DgwYwcORJra+vQRqx+fn5mzTFw4EBevHjBy5cvTT6XnZ0dW7duZePGjfz9998mny++qFWrFl27dsXNzY1Hjx4pHUcIoSApjAghhBC/AJVKxfz581mxYgVnzpxROo7Zubm5sXr1anQ6ndJRouTi4oJWq+XEiRMGXb9y5UqePHli0FG/5hDfCyNbtmxBr9dTv359AMqWLUu5cuXM3qcnW7ZspEiRgnHjxpllvsqVK9O+fXvc3Nx4/fq1WeaMD2bMmEHu3LkpXrw4wcHBSscRQihECiNCCCHEL8LBwYHZs2fj7u6Ov7+/0nHMKn/+/CROnJjjx48rHSVKKpWKTp06MXfuXIOuT5s2LXXq1KFLly4mTmYYX19f7OzslI4RI1qtlqFDhzJ69OgwvTYmTZrE4sWLuXXrllnz1KxZ06zbeObPn0+6dOkoW7as2eaMD06ePMnnz5+pXbu20lGEEAqRwogQQgjxC2nYsCGFCxdm0KBBSkcxOzc3N4N7dyitRYsW7N27l1evXhl0/bJly3j79i2LFy82cbKoxecVI+vXrydRokRUr149zOOpU6fG09OTrl27GrTFyVgGDhzIs2fPzLqC48SJE9y/f5+ePXuabc64zs7OjoMHD/LPP/8wbdo0peMIIRQghREhhBDiFzN79mw2bNjA0aNHlY5iVs2aNWPz5s18+fJF6ShRSpIkCQ0bNmTp0qUGX9+8eXP69etn4mRRi6+FkeDgYIYNG8bo0aPDPYGmS5cuvHr1yqwrOBwdHUmWLBkTJkww25xp06Zl8eLFzJgxw+DtXL+DYsWKMWbMGPr06cPFixeVjiOEMDMpjAghhBC/mGTJkrFgwQJatWpl9oaSSkqXLh2FCxdmx44dSkcxiIeHB/Pnz0er1Rp0/fz58/Hz82PSpEkmTha5+FoYWbFiBRkzZqRChQrhPm9hYaFII9bq1auzYcMGs80HISuWatasSfXq1X+7bXeRGTBgAC4uLpQvX16+LkL8ZqQwIoQQQvyCatWqhYuLC3379lU6ilnFp+00hQoVInXq1OzZs8eg621tbenUqRMjRoxQtMlsfCyMfP36lZEjRzJq1KhIrytTpgzlypVj9OjRZkoGgwYN4smTJ7x//95sc0JIE1pra2sqV65s1nnjun379mFtbS19WIT4zUhhRAghhPhFTZs2jV27dvHPP/8oHcVsGjRowPHjx3nz5o3SUQzSqVMn5s2bZ/D1U6ZMQavV4unpacJUkYuPhZHFixeTN29enJ2do7x20qRJLFmyxGyNWHPnzk3SpEnNup0GQlbIHDlyhDNnzjBlyhSzzh2XWVhYcOrUKS5fvvzbFZaF+J1JYUQIIYT4RSVOnJglS5bQtm1bPnz4oHQcs7Czs6NWrVqsW7dO6SgGady4MWfPnuXBgwcGXW9hYUH//v2ZNm0agYGBJk4XPj8/v3hVGPH392fs2LFRrhb5JnXq1AwePNisjVirVq3K+vXrzTLX9/Lmzcvo0aPp16+f2U/kicuyZ8/OkiVLmDJlCvv27VM6jhDCDKQwIoQQQvzCKleuTM2aNX+rEyhcXV3jzXYaW1tbWrRowYIFCwy+Z+jQoVhZWdG9e3cTJotYfFsxMnfuXEqWLEmhQoUMvqdz5868evWKjRs3mjDZ//P09OTRo0eKFDAHDhxIkSJFKFeunKJbtOIad3d3mjZtSt26dePNCjQhRMxJYUQIIYT4xU2aNIljx46xc+dOpaOYRaVKlXjy5Am3b99WOopBOnbsyLJly/j69atB16vVakaNGsXixYsVaa4bnwojvr6+TJo0iREjRkTrvm+NWHv37m2Wr3HevHlJnDixYo11Dx48yOfPn2nUqJEi88dVq1atIm3atJQoUUKKRkL84qQwIoQQQvzi7OzsWLZsGR06dODdu3dKxzE5CwsLmjZtGm9WjeTIkYP8+fNHa3VC9+7dSZw4Me3btzdhsvDFp8LI9OnTqVy5Mnny5In2vWXKlMHFxcVsjVgrV67M2rVrzTLXj+zs7Ni+fTtbt25VLENcpFar+ffff3n27BktWrRQOo4QwoRUenNtnhRCCCGEonr27MnLly9/izc+ly5don79+ty/fx+1Ou5/DrRlyxamTJnCiRMnDL5n1apVtGzZkjdv3pAsWTITpgurQIECLF26NFpbU5Tw/v17cubMyZkzZ8iePXuMxnj58iX58uXj2LFj5MqVy8gJw7p48SJFihThw4cPJEqUyKRzRaRz584sWrSIx48fkzp1akUyxEX79u2jevXqLF++XAokQvyi4v6/FIQQQghhFGPHjuXixYtm65ugpAIFCmBnZxetQoOSateuzcOHD7l69arB97i5uZE6dWqzv1GLLytGpkyZQv369WNcFAHzNmItVKgQ9vb2TJ061aTzRGbOnDlkzJhRjqr9QdWqVenVqxetW7fm7t27SscRQpiAFEaEEEKI34StrS0rVqygS5cuvH79Wuk4JqVSqXBzc4s322ksLCxo3759tI7uBZg3bx67d+/m6dOnJkr2s/hQGHn9+jXz589nyJAhsR6rc+fOvH792iwFxYoVK7J69WqTzxOZEydO8PDhQ7p27apojrhm8uTJ5M+fH2dnZ4KDg5WOI4QwMtlKI4QQQvxmBg4cyK1bt9i8eTMqlUrpOCbz9OlTnJyceP78OTY2NkrHidLz58/Jmzcvjx49ilbhIXv27KRLl46jR4+aMN3/s7W15e3btyRMmNAs88VEr169CA4OZubMmUYZ7/jx4zRr1oybN29iZ2dnlDHDc+7cOYoXL86nT59MOk9UvLy8cHNz49ChQ7i4uCiWI67x9/cnTZo0FC1alAMHDigdRwhhRLJiRAghhPjNDB8+nLt37+Ll5aV0FJNKnz49BQsWjDen8aRNm5YKFSpEe8XA0qVLOX78ODdv3jRRsv8XHBxMYGAgCRIkMPlcMfXs2TOWL1/OwIEDjTZmmTJlKF++PKNGjTLamOEpWrQoCRMmZPr06SadJyrNmzenbt261KpVC39/f0WzxCUJEiTg8OHDHD58mAkTJigdRwhhRLJiRAghhPgNXbx4kWrVqnHp0iXSpUundByTWb58OVu2bGHbtm1KRzHIoUOH6N69O1evXo3Wah4nJyesrKw4f/68CdOBj48PmTNn5uPHjyadJzY8PDyws7Nj4sSJRh3XXI1Y69Wrx40bN7hz547J5jCETqcjderUZMuWjdOnTyuaJa6ZOnUqffv25fTp0xQrVkzpOEIII5DCiBBCCPGbGjFiBP/++y+7du36ZbfU+Pr6kiFDBu7evUuKFCmUjhMlvV5Prly5WLx4MaVLlzb4vsuXL1OoUCH+/fdfihYtarJ8jx8/xtnZ2aw9TaLjwYMHFClShNu3b5vkz3vGjBns2LGD/fv3m+xn5vTp05QqVQo/Pz/FV+bcvHmTvHnzMnbsWPr3769olrimWrVqnDx5khcvXii67UkIYRyylUYIIYT4TQ0aNIhXr16xdOlSpaOYjL29PTVq1GD9+vVKRzGISqWiU6dOzJ07N1r3FShQgOLFi5v8hBo/P7843Xh15MiRdOnSxWRFsG+NWDds2GCS8QFKlixJggQJjNYfJTZy5crF+PHjGTRoENevX1c6Tpyyc+dOEiZMSJkyZZSOIoQwAlkxIoQQQvzG/vvvP8qXL8/58+fJlCmT0nFMYs+ePYwYMYIzZ84oHcUgHz58IEuWLNy+fRsHBweD77t37x45cuRg//79VKxY0STZ/v33X7p27crZs2dNMn5s3L59m9KlS+Pt7U2SJElMNo85GrHWqlWLe/fumaVvjCGcnZ3x9vbmxYsXWFhYKB0nznjw4AE5c+bEw8ODGTNmKB1HCBELsmJECCGE+I3lzZuX3r1707p1a3Q6ndJxTKJy5co8fPhQ8Z4NhkqSJAkNGjSI9kqebNmyUbFiRdq0aWOiZHH7qN7hw4fTq1cvkxZFwDyNWPv168ft27cJCAgw2RzRceDAAQICAmjYsKHSUeKULFmysHLlSmbNmsWuXbuUjiOEiAUpjAghhBC/uT59+uDv78+8efOUjmISFhYWNG3aNNqnvSjJw8OD+fPno9Vqo3XfqlWrePLkCRs3bjRJrrhaGLl69SpHjhyhW7duZplv4sSJLF261GQrOsqWLYutrS2zZs0yyfjRlSBBAnbt2sWOHTvi1c+ROTRt2hQ3NzcaNGjAy5cvlY4jhIghKYwIIYQQvzkLCwuWL18eeozvr8jV1ZXVq1cTX3YQFy5cGAcHB/bu3Rut+1KnTk3dunXp0qWLSXL5+vrGyUaTQ4cOpX///iRMmNAs86VOnZohQ4bQtWtXk31PlS1bluXLl5tk7JgoW7YsXbt2pXXr1jx//lzpOHHK8uXLyZQpEyVKlPhlV94J8auTwogQQgghcHR0xNPTk5YtW0Z7lUJ8UKhQIWxsbDh58qTSUQzWqVOnGK3iWbp0KW/fvmXhwoVGzxQXV4ycO3eO8+fP07FjR7PO6+HhwZs3b0zWiLVv377cunWLwMBAk4wfEzNmzCBLlizScPQHKpWKM2fO8OrVK5o0aaJ0HCFEDEhhRAghhBAAdOvWDY1Gw/Tp05WOYnQqlQo3NzdWrVqldBSDNW7cmDNnzvDgwYNo3ZckSRJcXV3p37+/0T+9jouFkcGDBzN48GBsbGzMOq+FhQWzZ8+md+/e+Pn5GX38ChUqYG1tHe0Tikzt+PHjPH36lE6dOikdJU5JliwZO3fuZOPGjSxZskTpOEKIaJLCiBBCCCEAUKvVLFu2jPHjx8eZ0zCMqXnz5mzcuDHONLSMSoIECWjRokWMVn7Mnz+fz58/M3nyZKNmimuFkWPHjuHt7U3r1q0Vmb9MmTJUqFDBZI1YS5cuHeeO03ZwcGDlypUsWLCAgwcPKh0nTqlYsSL9+/enQ4cO3L59W+k4QohokON6hRBCCBHG/PnzWbp0KadOnfrljuYsX748Xbp0iTena9y5c4cyZcrw+PFjrK2to3Vvjx49WLx4MZ8+fUKtNs5nYd26dSNr1qz06NHDKOPFhl6vp1y5crRp0wZ3d3fFcrx69Yq8efNy7NgxcuXKZdSx9+3bR40aNQgICMDS0tKoY8dWo0aN2LNnD69evYqTfWeUVLx4ce7du8fz58+xsrJSOo4QwgCyYkQIIYQQYXTo0IEkSZIwceJEpaMYXXzbTpMzZ06cnJzYtGlTtO+dPHkyOp0OT09Po+WJSytGDhw4wOvXr2nevLmiOVKlSsWQIUPo0qWL0RuxVq1aFSsrKxYsWGDUcY3h77//xt7envLlyysdJc45evQowcHBVKlSRekoQggDSWFECCGEEGGoVCqWLFnCtGnTuHLlitJxjKpRo0YcPnyYd+/eKR3FYDFtwmphYUH//v2ZNm2a0Rp4+vn5xYnCiF6vx9PTk5EjR8aJVU0eHh68ffvWJI1YnZ2dWbx4sdHHjS21Ws3Ro0e5dOkSY8aMUTpOnGJjY8OxY8c4fvw4o0ePVjqOEMIAUhgRQgghxE8yZMjAxIkTcXd3j1OnYsRWokSJqF69OuvXr1c6isHq1KnD/fv3uXbtWrTvHTJkCFZWVnTr1s0oWeLKipEdO3bw9etXGjVqpHQUIKQINWfOHHr37o2vr69Rx+7VqxfXrl0jODjYqOMag6OjIxMnTmTo0KEx+v78lTk5OTF9+nSGDRvGqVOnlI4jhIiCFEaEEEIIEa6WLVuSIUOGX+4TTzc3N1avXq10DINZWFjQvn37GK0aUavVjBkzhiVLlhjl5JS4UBjR6XQMGTKEUaNGGa13ijGULl3aJI1Ya9asiaWlZZw96aRXr144OztTvnz5OFm8UVLXrl2pXr06VapU4dOnT0rHEUJEQpqvCiGEECJCL168oECBAuzatYsiRYooHccogoKCSJ8+PSdPniR79uxKxzHIs2fPyJcvH48ePYpRYSJFihRUrlyZtWvXxiqHk5MTq1atIn/+/LEaJzbWr1/P1KlTOXPmDCqVSrEc4TFVI1YXFxd8fX25cOGC0cY0poCAABwcHChbtiw7d+5UOk6cotPpyJAhA0mTJuW///5TOo4QIgJxp8wuhBBCiDgnTZo0TJ8+nRYtWsSbY26jYmlpSZMmTeLVqpF06dJRvnx5vLy8YnT/9OnT+fvvv3n79m2scii9YiQ4OJhhw4YxevToOFcUAdM1Yu3ZsydXrlyJsysybGxs2L17N7t372bFihVKx4lT1Go1p0+f5s6dO3Tu3FnpOEKICEhhRAghhBCRatKkCblz52bo0KFKRzGab9tp4tPCWQ8PD+bOnRujzK6urqRJkybWx9r6+voqejSrl5cXqVKlolKlSopliMq3Rqx///230casXbs2Go0mThcdSpcuTc+ePWnbti1Pnz5VOk6ckjFjRry8vJg3bx7btm1TOo4QIhyylUYIIYQQUXrz5k3osbHOzs5Kx4k1vV5P7ty5WbJkSbx5PXq9nj/++IOlS5dSqlSpaN+/c+dO6tSpw8OHD8mYMWOMMlhbW/PhwwdsbW1jdH9sBAYG4ujoyMqVKylTpozZ54+OEydO0LRpU27cuGG0FTZlypQhICCAc+fOGWU8U8mVKxcBAQHcu3cvTvWAiQvatWvHihUrePDgAenSpVM6jhDiO/K3lRBCCCGilDJlSubOnUvLli35/Pmz0nFiTaVS4ebmxqpVq5SOYjCVShXjo3sBatWqRbZs2XB1dY3R/YGBgWi1WmxsbGJ0f2wtXboUR0fHOF8UgZDVExUrVjRqI9bu3btz6dIldDqd0cY0hePHj/P8+XM6duyodJQ4Z9GiRWTLlo0SJUrE+T9HIX43smJECCGEEAZzdXUlWbJkzJw5U+kosfbw4UOKFCnCs2fPsLa2VjqOQXx8fMiaNSt37twhZcqU0b7/xIkTlC1blmvXrpEnT55o3fvu3TuyZ8+Oj49PtOeNrS9fvpAjRw62bNlC0aJFzT5/THxrxHr06FFy584d6/F0Oh02NjYsXryYFi1aGCGh6WzatIk///yTPXv2ULVqVaXjxCkfPnwgbdq0VK1alS1btigdRwjxP7JiRAghhBAGmzVrFps3b+bw4cNKR4m1zJkzkydPHnbv3q10FIMlTZqU+vXrs3Tp0hjdX7p0afLmzRujN9ZKNl5dsGABRYoUiTdFEQhpxDp06FC6du1qlF42arWaokWLMmfOHCOkM62GDRvy119/Ub9+fTmm9gdJkiRh7969bNu2jfnz5ysdRwjxP1IYEUIIIYTBkiZNysKFC2ndujW+vr5Kx4m1+LadBkKaey5YsACtVhuj+1evXs2lS5f4999/o3WfUoURPz8/xo8fb9RtKebSqVMn3r17Z7RGrF26dOHixYvxYhvGmjVrSJIkCeXLl1c6SpxTtmzZ0NOLrl+/rnQcIQRSGBFCCCFENNWoUYOKFSvSp08fpaPEWqNGjTh48CDv379XOorBihQpQvLkydm3b1+M7ndycqJ48eK0bNkyWvf5+fkpUhiZNWsW5cuXJ1++fGafO7YsLCyYM2cOvXv3NkohsXHjxgCsX78+1mOZmlqt5vjx41y5coURI0YoHSfOGTFiBMWLF6dMmTIEBgYqHUeI354URoQQQggRbVOnTmXfvn3s3btX6SixkiRJEqpWrWrUo1XNITZNWCHk0/zbt2+zf/9+g+9RYsXIhw8fmDp1KsOHDzfrvMZUqlQpKlWqZJQVL2q1msKFCzNr1iwjJDO9bNmyMW3aNEaOHMnly5eVjhPnfNuSKKtqhFCeFEaEEEIIEW2JEiViyZIltGvXjg8fPigdJ1bi43aaJk2acPr0aR4+fBij+7NkyUKlSpVo166dwfcoURiZOnUqtWvXxtHR0azzGtuECRNYtmwZN27ciPVYHh4enD9/Pl5spwHo2rUrZcqUoUKFCgQHBysdJ06xsrLi+PHj/PvvvwwbNkzpOEL81qQwIoQQQogYqVixInXq1KF79+5KR4mVatWq4e3tzb1795SOYrAECRLg5ubGwoULYzzGypUrefLkicGrZcxdGHn79i1z5sxh6NChZpvTVL41Yu3SpUusG7G6urqi1+vZtGmTkdKZ3t69e9FqtdSuXVvpKHFOnjx5mDNnDqNGjeLYsWNKxxHityWFESGEEELE2MSJEzl58iTbt29XOkqMWVpa0rhxY1avXq10lGjp2LEjS5cu5evXrzG6P3Xq1NSrV4+uXbsadL25CyMTJ06kcePGZM6c2WxzmlKnTp14//59rLdtqdVqChQoEK+OzLaxsWHv3r3s27ePJUuWKB0nzunQoQN169alWrVq8X4FnhDxlRRGhBBCCBFjCRMmZPny5XTs2JG3b98qHSfGvm2nMcaxqubi6OhI3rx52bx5c4zHWLZsGe/evWPBggVRXuvr64udnV2M54qOFy9esGTJEjw9Pc0ynzkYsxFrx44dOXv2bLzZTgNQsmRJ+vbtS8eOHXn8+LHSceKcTZs2kTx5ckqWLKl0FCF+S1IYEUIIIUSslC5dmqZNm9K5c2elo8RY0aJF0Wg00T7CVmmxbcKaKFEi3Nzc6N+/f5Rvss25YmTs2LG0bNmSdOnSmWU+c/nWiHXkyJGxGqdVq1ZotVq2bdtmpGTmMWHCBHLmzEmZMmXiVVHHHNRqNWfOnOH+/fvR6v0jhDAOKYwIIYQQItZGjx7N1atX493pLt+oVKp42YS1Tp063Lt3j//++y/GY8ybNw9/f38mTpwY6XXmKow8evSINWvW0L9/f5PPpYQJEyawfPnyWDViVavV5M+fnxkzZhgxmXkcP36cV69e0bZtW6WjxDnp0qXj77//ZsmSJWzYsEHpOEL8VqQwIoQQQohYs7W1ZcWKFXTr1o1Xr14pHSdGXF1d+fvvvwkMDFQ6isEsLS1p165drFaN2NjY0LlzZ0aNGhXpp/jmKoyMHj2ajh074uDgYPK5lJAqVSqGDRsW60as7du358yZM0ZMZh7JkiVj/fr1LF++nN27dysdJ86pW7cunTp1onnz5rLlSAgzksKIEEIIIYyiWLFitGnThvbt28erXh3fZM6cmVy5crFnzx6lo0RLu3btWLt2LX5+fjEeY9KkSej1egYOHBjhNeYojHh7e7N161b69Olj0nmU1rFjR96/f8/69etjPEabNm0ICgpix44dRkxmHnXr1qVp06Y0bNhQmo2GY86cOeTMmZMSJUrIliMhzEQKI0IIIYQwmqFDh/LgwYN4tyXlG1dX13iXPV26dLi4uODl5RXjMSwsLBgwYAAzZsyIcMWMOQojI0aMoHv37iRNmtSk8yjtWyPWPn36xLgRq4WFBfny5WP69OnGDWcmq1atInny5Li4uAAQGBhIQECAsqHikFOnTvHp0yfq1KmjdBQhfgtSGBFCCCGE0VhbW7Ny5Ur69OnD06dPlY4TbX/++Sf79+/Hx8dH6SjR4uHhwdy5c2O1Umfw4MFYW1vTpUuXcJ/38/MzaWHk+vXr7N+/n+7du5tsjrikVKlSVK5cOVaNWNu2bcupU6eMmMp81Go1x48f57///qNdu3Zky5Yt2iuFgvQ6AvTB+OuD+KIPJlCvjZer1cKTKFEi/vnnH/bs2cOsWbOUjiPEL0+l/1X+9hBCCCFEnDFq1ChOnjzJnj17UKlUSseJlkaNGlGlShXat2+vdBSD6XQ6/vjjD5YvX46zs3OMx5k9ezY9e/bEx8fnp6N58+bNy9q1a8mXL19s44arYcOGODs707t3b5OMHxe9fv2aPHnycOTIEfLkyRPt+4ODg7G2tmbXrl1Uq1bNBAlNS6/X06hRo9AjpwsXLsz58+fDvTZIr+Od7gsf9AG81QXwUf+VYHSoUPHtbxgdetSosFdZkUJlS1K1DcnVNtioLMz0ioxvzJgxDB06lIsXL5I/f36l4wjxy5LCiBBCCCGMLigoiJIlS9KhQ4d4d/Tktm3bmDx5MsePH1c6SrRMmzaNixcvxnorUMqUKalYsSLr1q0L83imTJk4evQomTNnjtX44blw4QJ16tTB29ubBAkSGH38uGz27Nls2rSJQ4cOxaiI6OTkROrUqfnnn39MkM60Bg0axIQJE0L7aNjb2/Pp06cw13zSfeWu9gPPdH6oAC16DH3zYoEKHZBSbUt2TRJSqGzjXaEWoFy5cly5coWXL19iY2OjdBwhfklSGBFCCCGESVy/fh0XFxfOnTtnkjfTphIYGEi6dOk4e/YsWbJkUTqOwd6/f0+2bNm4c+cOKVOmjPE4q1evxt3dnVevXpEiRYrQx5MlS4a3tzfJkyc3RtwwatasSY0aNejcubPRx47rgoODKVq0KP3796dJkybRvn/69OkMGjQIf39/E6QzrcePH9O3b1+2b98e2l/k7du3JE+enDc6f/4LfouvPgidwaWQiGlQYYWGPzRJyahJFK8KJIGBgaRNm5bs2bPHy5OIhIgPpMeIEEIIIUwiT5489O3bl1atWsWrkxWsrKz466+/WL16tdJRoiVZsmTUq1ePZcuWxWocV1dX0qRJQ4sWLUIf0+v1Jmu+eurUKa5fv07btm2NPnZ8ENtGrB4eHgQEBHDw4EETpDOtjBkzsn79ei5dukTp0qUBWLR0CReCXnEm6AUf9YFGKYpAyEqTLwRzVfuW40HP8NcHGWVcc7CysuLkyZOcP38+0pOjhBAxJytGhBBCCGEyWq2WMmXK0LRpU7p27ap0HIOdOXOGFi1acPv27Xj1yfK5c+do3Lgxd+/eRa2O+edfu3btonbt2jx8+JCMGTMSEBBAokSJIjyxJjYqVKiAq6srrVu3NvrY8UmrVq1Injw5kydPjva9efLkIWPGjPHuqOkfzV63nKQu+bBPmsRoBZHwqAA1KvJpUpApHq0eWbp0KW3btmX//v1UrFhR6ThC/FKkMCKEEEIIk/L29sbZ2ZlTp06RI0cOpeMYRK/X4+joyKpVqyhevLjScaKlSJEijBo1iurVq8dqnJw5c5I6dWqOHTvGmzdv+OOPP3j37p2RUoY4dOgQHTp04ObNm1hYxN8Gmcbw+vVr8ubNy+HDh6PdiHXKlCkMHTqUz58/myid6d0N9uGm9j1aExZEfqRBRRp1QgpbpIo3xZG//vqLHTt28OzZM5IlS6Z0HCF+GbKVRgghhBAmlSNHDoYMGYK7uztarVbpOAZRqVS4urrGupGpEjp16sS8efNiPc6yZcs4ceIE169fN8k2Gr1ez+DBgxkxYsRvXxQBcHBwYOjQoXTp0iXaR8527tyZL1++cOzYMROlM62bwe/MXhSBkO01L3SfORP0Al08+ax43bp1pEqVihIlSvwyRxMLERdIYUQIIYQQJtelSxesra2ZOnWq0lEM5urqyvr1602yfcSUmjZtyqlTp3j06FGsxilVqhT58uXDzc3NJIWR3bt34+vrG6OGo7+qjh078uHDh59OBIqKjY0Njo6OTJw40UTJTMc72Ie72g9mL4p8o0XPW/0Xzge/jBeFBrVazZkzZ3j06BEtW7ZUOo4QvwwpjAghhBDC5NRqNUuXLmXixIncuHFD6TgGyZo1K46Ojuzdu1fpKNGSIEECXF1dWbhwYazHWrVqFZcvX+b06dNGLYzodDqGDBnCyJEjY9UL5VfzrRFr3759o92I1d3dnSNHjpgmmIm80H7mlgIrRX6kRc8rnT83te8VzWGo1KlTs3nzZlatWsWaNWuUjiPEL0H+l0gIIYQQZpElSxbGjBmDu7s7QUHx40QINze3eLmdpmPHjixZsiTWq12cnJwoUaIEo0ePNmphZMuWLajVaurVq2e0MX8Vzs7OVK5cmREjRkTrvm7duuHv78/JkydNlMy4AvVaLga/Urwo8o0WPXe1H/ig+6p0FIPUrFmTbt264e7uzoMHD5SOI0S8J81XhRBCCGE2er2e6tWrU6pUKYYMGaJ0nCj5+PiQOXNmHj16RJIkSZSOEy0VK1akffv2NG7cOFbjPHjwgKxZs1K6dGmOHz8e61xarRYnJyemTJlCtWrVYj3er+j169fkyZOHI0eORKsRa86cOcmVKxfbtm0zYTrj+DfoBa90n4lrB3knxJKKVhlRx5NmrAULFuTFixc8ffpUevUIEQuyYkQIIYQQZqNSqVi8eDGzZs3i8uXLSseJUtKkSalUqRIbN25UOkq0derUiblz58Z6nCxZspAnTx4uXbpkhFSwdu1akiZNStWqVY0y3q/IwcGB4cOHR7sRa4sWLTh06JAJkxnHC+1nXuv841xRBCCAYG4GG/f0JVM6fvw4nz9/platWkpHESJek8KIEEIIIcwqffr0TJo0CXd393jR2DS+nk5Tt25dvL29uX79eqzHatKkCZ8/f+bvv/+O1ThBQUEMHz6c0aNHx5vjUZUSk0asPXr04PPnz5w9e9aEyWJHr9dzNfhNnNlC8yMteu7pPhKojx8naNnZ2XHo0CH2798fr5pbCxHXSGFECCGEEGbXokULMmfOzMiRI5WOEqUaNWpw/fp1Hj58qHSUaLG0tKRdu3bMnz/fKOM5OjrStWvXWI2xYsUKMmfOjIuLi1Ey/co0Gg1z5syhT58+fPr0yaB77OzsyJYtG+PHjzdxuph7pw8gkLhddFABj7WGfc3jgqJFizJu3Dj69u3LhQsXlI4jRLwkhREhhBBCmJ1KpWLBggUsWrQoTn+6DWBtbc2ff/6Jl5eX0lGirV27dnh5eeHn5xercXx9fWncuDHv3r1j3rx5MRrj69evjBw5ktGjR8cqy+/E2dmZqlWrRquA2Lx5cw4ePGjCVLHjrfWJs6tFvtGix1v7IV4c3/tNv379KF++POXLl8ff31/pOELEO1IYEUIIIYQiUqdOzcyZM3F3d+fLly9Kx4nUt9Np4tMbJQjZtlSuXLlYH+np6+tLypQpadGiBQMHDkSni353iIULF5I/f35KlCgRqyy/m/Hjx7NixQr+++8/g67v1asXvr6+XLx40cTJoi9AH8wbXdz+Wf8mGB1v9PEj6zd79+7FxsaGMmXKKB1FiHhHCiNCCCGEUEzjxo3Jly9fnD+hpmTJkgQHB3P+/Hmlo0Sbh4cH8+bNi1VRx9fXF3t7e+bOncuXL1+YMGFCtO739/dn3LhxjBo1KsYZflfRbcSaKFEismTJwrhx48yQLnpe6j4TXzrLaNHzROurdIxosbCw4PTp01y9epU+ffooHUeIeEUKI0IIIYRQ1Ny5c1mzZg0nTpxQOkqEVCpVvG3CWrFiRfz8/Dhz5kyMx/hWGLGxsaFz586MHj06WqtG5syZQ+nSpSlQoECMM/zOOnbsyKdPnwxuxNqkSRP2799v4lTR9073Jc5vo/nee32A0hGiLVu2bCxZsoSpU6eyb98+peMIEW9IYUQIIYQQikqRIgXz5s2jZcuWfP78Wek4EXJ1dWXdunUEBQUpHSVa1Go1HTt2jHFvEPj/wgjAxIkT0ev1DBgwwKB7P336xKRJkxgxYkSM5//dRbcRa9++ffn48SNXr141QzrDvdd9DfdxP5+P/JU4D0v6/H//mXWjZrJ8QEgT2UMrNzGxaZdYz//9mIbw1wehi2fb5yCkuXWzZs2oW7cur1+/VjqOEPGCFEaEEEIIobi6devi7OxM//79lY4SoezZs5M9e/Z4+Slsy5Yt2bFjB2/fvo3R/d8XRiwsLBg4cCAzZsww6Ljl6dOnU61aNXLlyhWjuUWIkiVLUrVqVYMKTEmSJCFTpkxxajuNTq/Hn/CLisfWbcexeEGO/72ToDh0hLcGFZ/04Rdz4rqVK1eSLl06SpQoEaOeQEL8bqQwIoQQQog4YcaMGWzbti1On6jxrQlrfJM8eXLq1q3LsmXLYnS/n59faGEEwNPTExsbG7p0ifxT/Pfv3zNz5kyGDRsWo3lFWOPHj2fVqlUGNWJt3Lgxe/fuNUMqw/jpA9FE0GHk4IqN/DnAg0x5HDm3M/yff/+Pvkxo0pmeRWszpKob7569DH1uy5SF9C3dkN4l6jKmQXt8Xr4B4PNHXyY27ULXAtUYWbs1L+8/Dr3ni99nZrXrT7dCNehWqAabJy/4aU498Ekfdwo10aFWq/n33395/vw5bm5uSscRIs6TwogQQggh4oSkSZOyaNEi2rRpY9B2ASX89ddf7N27l48fPyodJdo8PDyYP39+jD49/n7FCIS86RozZgzLli3D1zfiBpWTJk2iYcOGZMuWLUaZRVjRacTav39/Pnz4wPXr182ULnLBhP999+DqTXzffyRf+ZJUaNGAgys2hXvdzVMXcB3Zm2nndpC3TLHQbTdH1mzjxb3HTDi2gSlntlG4WjmW9w9ZKfP32NnY2tsx6/JeeiybzPXj/380+IZxc9Dp9My4sIvxR9ZzxGsrF/YdDTOnHn2EueODFClSsG3bNtauXcvKlSuVjiNEnCaFESGEEELEGdWqVaNKlSr07t1b6SjhSp48ORUqVGDjxo1KR4m2okWLkiRJEv75559o3/tjYQSgS5cuJE2alDZt2oR7z6tXr1i4cCGDBw+OUV4Rvg4dOvDp0yfWrl0b6XXJkiUjQ4YMcWY7TURNVw8u34BL83qoVCpK1KvKnbOXw6wG+SaXc2HS5cwKQKVWf3LtSEgz4XO7DnL10El6l6hLz2K12T3fi9ePngHw39EzVGr5JwCJUiSjRL0qoeNdOXSKyq3/QqVSkSCRPWX+qsXVQ6fCzKkHtPGwx8j3qlatSp8+fWjdujXe3t5KxxEizrJQOoAQQgghxPemTJmCk5MTu3fvpkaNGkrH+YmbmxszZ86MsCAQV6lUKjp16sS8efOoVq2awffp9fqfttJ8M23aNFq0aMHr169xcHAI89z48eNxdXUlQ4YMsc4u/t+3RqyNGjWiVq1aJEqUKMJr//zzT5YvX26+cJFQoQJU8F2BJCgwkGPrd2JlY8URr60ABAcFc3j1lkjH0uv1oFKF/rrRAI/QAshP10U8CCpV2K09P/0eUMebA4YjNnHiRA4dOkSpUqV4/vw5FhbyFlCIH8mKESGEEELEKfb29ixdupT27dvj4+OjdJyf1KxZk2vXrvH48eOoL45jmjZtyokTJ6KV/cuXL1haWob7Zqp58+akTZsWd3f3MI8/ffqUlStXMnDgwFhnFj8rWbIk1apVi7IR68CBA3n//j23b982U7KIaX4oigCc3X6AdDmzsvjeCRbeOcLCO0cYtnMZh1b+vJ3m5umLPL/7EIDDqzaTr1xxAIrVrMjehWvw8wnZ3hb09SsPrt4EwKm8c+hYn9758O+2/z/COH/FUhxY9jd6vZ4vvn4c37ALpwrOYeZUoUKtiv+FEYBjx47x9etXqlatqnQUIeIkKYwIIYQQIs4pX7489evXp1u3bkpH+Ym1tTV//vknXl5eSkeJtoQJE+Lq6srChQsNvsfX1xc7O7sIn1+4cCH79u3j0aNHoY+NHj2adu3akTp16ljlFREzpBFrihQpSJcuHWPHjjVjsvDZqix+6tZxcOUmyjWtHeaxP0oURKfTcf3EuTCP5ylTjHUjZ9CzaG2uHjlDm8khW7RcmtejbJM6DK7cnB5FatG7ZH1unb4IwJ8DO+P34SNdC1RjRuu+5K9UOnS8Pwd2Rq/X071wTfqX+wuXZnUpVKVsmDlVqLD9RRbYJ0iQgMOHD3PkyBHGjzf8yGIhfhcqfVSdm4QQQgghFPD582cKFCjAxIkTqV+/vtJxwjh58iTt2rXj+vXrPy2/j+tu3rxJhQoVePToEVZWVlFef/fuXapUqcL9+/cjvCZnzpykSpWK48ePc//+fYoVK8bt27dJnjy5MaOLH8ydO5e///6bw4cPR/h92L17d7y8vGJ8VLMx7fp6n6B41MxUDVSxyoyN6tcojkDI8dm9e/fm1KlTFC9eXOk4QsQZsmJECCGEEHFSwoQJWb58OR4eHrx580bpOGE4OzsTEBDAxYsXlY4Sbbly5SJXrlxs3brVoOvDa7z6o2XLlnHy5En+++8/Ro4cSdeuXaUoYgaGNGIdOHAg79694969e2ZMFr7EqqgLcXGJBvUvVRQB6NGjB1WqVKFSpUr4+fkpHUeIOEMKI0IIIYSIs0qVKoWrqyseHh5RHk9qTiqVCldXV1atWqV0lBjp1KkTc+fONehaQwojpUqVwsnJiT///JPdu3fTo0cPI6QUUfnWiLVv374RHnGdOnVq0qRJEye206RQ28arVqaJVdZKRzCJnTt3YmdnR+nSpaO+WIjfhBRGhBBCCBGnjRo1iuvXr7N+/Xqlo4Th5ubG2rVrCQ4OVjpKtNWrV487d+5w48aNsE/o9eD/DJ7ugHtL4M4ckr1dQ8OCH+HJZvB7EHJNOFavXs2tW7do1KgRiRMnNsOrEGBYI9aGDRuyY8cOM6YKX3K1bbw55UWDilTqBErHMAmNRsOZM2e4fv16nOzjJIQSpMeIEEIIIeK8c+fOUatWLS5fvkyaNGmUjhOqRIkSDB06NE4eKxyVoUOH8uHDB2aO7QX3l8Org/DhKmgDQWMFOi3otej0enR6sLC0Bl0woILEuSFVecjqHvJr4MqVKxQtWpRMmTLh7e2t6Gv73bx584Y8efJw6NAh8ubN+9PzT58+JUOGDDx8+JBMmTIpkDCEXq9nX+BDAtAqlsFQalRUtcqMtUqjdBSTWbduHc2aNWPbtm3Url076huE+IVJYUQIIYQQ8cKQIUO4fPky27dvjzMNT+fMmcOJEyci7fEQJ+l1vL3mxa0tbSjlqEGFDnSB0RtDZQFqy5DCSO7+1O+6AqcChRk1ahS7du2ievXqpskuwjV37lzWr1/PkSNHwv35SJMmDXXq1GHBggUKpPt/d4N9uKl9j5a4/RYkrTohxSzjThHWVFq1asWaNWt49OiRnCIlfmtSGBFCCCFEvBAYGEixYsXo0aMHLVu2VDoOAG/fviVbtmw8efKERIkSKR3HMC8PwOkWEOQHwb5GGVKvseOTnz+25VZQp8tqbty4wePHj40ytjCMVqulaNGi9O7dm+bNm//0fKdOndiyZQsvX75UIN3/C9Rr2Rv4EF0cLoxoUFHKMh3J1DZKRzGLnDlz8vXrVx48eIBaLZ0WxO9JvvOFEEIIES9YWVmxYsUK+vbty5MnT5SOA0CKFClwcXFh06ZNSkeJWtAnOOUOR+vClxdGK4oAqLR+JLbVYXWhI9t6BhPo+zTO9YT51Wk0GubOnUu/fv3CbcQ6cOBAXr16pfjPjpVKQ3q1XZx+E5JAZUnSX7TxanjOnDnDmzdvaNy4sdJRhFBMXP47SQghhBAijPz589OjRw/atGkTZ06pcXNzi/un07w6Ctuzw+O/Qetvunm0n7F+f5z70zXsWdTBdPOIcJUoUYJq1aoxfPjwn57LmDEjDg4OjB8/3vzBfpDXIgWaOPo2RI2Kohap4sx2PXNIliwZO3fuZNOmTSxevFjpOEIoQrbSCCGEECJeCQ4OxtnZmdatW9OxY0el4xAQEEC6dOm4fPkyGTJkUDrOzx5tgDPuoP1i1mn9v8JF3Z+UbvO3Wef93X1rxHrw4EHy5csX5rl27dqxa9cunj9/rkg2rVbLkydPuHz5Mp8TqEhULm+c6jWiQUUOTRL+sEiudBRFeHp6MmHCBP777z/++OMPpeMIYVZSGBFCCCFEvHPz5k3KlCnD2bNnyZo1q9JxaN++PVmzZmXAgAFKRwnr4Vr4t43ZiyLf+H8Fm2LjUefpr8j8v6t58+axbt26nxqxPnjwgKxZs/Ls2TPSpk1rtjwrV67E09OTly9fYmFhQUBAALly5WLFlUO80PmhM1uSyNmpLKlgmRH1b7Ra5EclSpTg7t27PH/+HCsrK6XjCGE2cXMNmxBCCCFEJHLlysWAAQNo3bo1Op3yb6u+baeJU583PdulaFEEIIE1aC8NAe9FimX4HbVv3x4/Pz/WrFkT5vEsWbKQMmVKRo0axebNm7lw4YJZ8mTPnp1Xr14RHBxMQEAAVlZWbN26lQIWKbHFkrhQhrBATXGLNL91UQTgyJEjaLVaKleurHQUIcxKCiNCCCGEiJd69uxJcHAws2bNUjoKpUqVwt/fn8uXLysdJcTnJ3CysaJFkW8s1UHoL3YHnytKR/ltaDQa5syZE6YRq16vZ9OmTVhZWTF//nwaN25stga59vb2JE6cGLVajZWVFa6uruTIkYNrl66wtscYrPRqRYsjGlSUtkyLvVpWSNjY2HD06FFOnDjBqFGjlI4jhNlIYUQIIYQQ8ZJGo2H58uWMGjWK27dvK5pFrVbj6uoaN5qw6vVwqhlovyqdJJQ++Asc/xN0QUpH+W2UKFGC6tWrhzZiffv2La6urjx79gwAS0tLcuXKZdIMWq2WyZMnU6FCBcaOHUu2bNnQ6/UEBQXh4OBA0aJFWb5gMYW/JsEWC0WKIxaoKW2ZjiS/ydG8hnBycmLGjBkMHz6ckydPKh1HCLOQHiNCCCGEiNdmz56Nl5cXJ06cQKPRKJbjzp07lCtXjidPnmBhYaFYDu4uggs9QftZuQzh0GsSoPqjJ+QfrXSU38aPjVhPnDhBtWrV+Pz5M1ZWVhw8eJDSpUubZO5Hjx7h7u6OTqdjxYoVZMmShRs3blC4cGG+fv0auu2sVq1a7Nixg0C9ljNBL/io/2qWhqwaVFijoaSsFIlQ7dq1OXToEC9evCBRokRKxxHCpGTFiBBCCCHiNQ8PD2xtbZk8ebKiOXLmzEnGjBk5cOCAciG+vICLca8oAqDS+sOtqfDhP6Wj/DZSpkzJiBEj6Ny5M3q9ntKlS7Nnzx40Gg2BgYHkzJkzzPXBeh0B+mD89UF80QcTqNdGu2+OXq9n5cqVFClShBo1anD48GGyZMkCQO7cudm7dy9qdchbEHt7ezw8PACwUmkoY5mOVO/1BH4JQGXC2ogaFVnUialolUmKIpHYtm0bSZIkwdnZWekoQpicrBgRQgghRLz38OFDihYtyuHDh8mbN69iOWbPns3p06fx8vJSJsAVT7g5BXRxZxvN9/QqDaqMf0GpNVFfLIxCq9VSrFgxevXqRfPmzQFYtWoVrVq35sHHVwRYqXir/8IH3VcC0aJCFbqlRf+//5tQZUkylS3J1TYkU9tgpwq/mPD27Vs6dOjAnTt3WL16Nfnz5w/z/JcvX2jQoAF+fn5cuHABCwsL3r9/H7rCatu2bTRo0ID6Tf/CpYcb6XNnB43xPse1QIUVGopapiapbJ0xyOPHj8mePTtt2rRh3rx5SscRwmRkxYgQQggh4r3MmTMzbtw43N3dCQpSro9F48aN2bVrF76+vuafXBcEd+bG2aIIgEqvhSdbINBH6Si/jW+NWPv27cvHjx/5og+mcJPqrHl1kYu846b2Ha90/nzbwKJDj/Z//+nQowN89UE80n3iSvAbDgU+4UjgE55p/dB99/nq7t27yZ8/P1mzZuXcuXM/FUX8/PyoWbMmSZMm5dChQ2zfvp3JkydjYWGBt7c3Li4uNGrUCJ1Oxx+Zs7Gw9QBKalJj56dFjYqYtmdVEbJCJGGgilPz/ibo4A0pikRDxowZWbt2LQsWLGDLli1KxxHCZGTFiBBCCCF+CXq9npo1a1K8eHGGDRumWI7atWvTqFEj3N3dzTvx441wpjUEK1CUiQad2hZ1/tGQq5fSUX4rPTz749S4CikcM8P/Ch6xofnf2pIMWlvm9x3Nnl27WLFiBeXKlfvp2g8fPlCjRg1y5crFwoULw/QCOnDgANWqVUOn06HX67GxsSFVqlRMnToVT09PXrx4wSufdzzSfuTK64doEifE8n8rTMLrRaL9Goi1tTVa9CRQWZJalYCsFkmoVaEKR44cQaVSMW/ePDp06BDLr8DvpX379ixfvpz79++TPn16peMIYXRSGBFCCCHEL+PZs2cULFiQvXv3UqhQIUUy/P333yxcuND8vUb2FoP358w7Z0zZpoV6T0Gl5CGtvwedXo+31ofbwe8J1mlRG7lBceCXAII+B1DWPjMZEqX46fm3b99StWpVSpUqxfTp00P7i3zj4+ND69at2bZtG3q9Ho1GQ+HChXn16hWPHj0iadKkvH//Hj8/P5IlS8ZfjRszb8USPui/8kn3lWD0PHnxjHSp03Dn5i2WzJzL6rmLSWFlh/q776+GDRuyefNmAKysrGjYsCFLlizB1tbWqF+PX1nu3Ln59OkTjx8//unPUYj4Tr6jhRBCCPHLSJcuHVOmTMHd3Z2vX5XZUlK7dm0uXrzI06dPzTep9iv4XDbffLGk+/oOvjxTOsYv75MukMNBj7mj9UGnwuhFEQArWxsSpkjCJeuP/Bf0Fu13n7m+fPkSFxcXKleuzIwZM8J9M500aVIcHR1RqVQkTZoUrVbLjRs3ePz4MRDSI0Wr1VKnTh2CgoIICgzEXm1FBo09eSxT4PA2mNqZCnJ07lo2jJrF/qV/07VF2zBFEQB/f//QXwcGBrJu3TpatWpl9K/Hr+zUqVP4+PjQoEEDpaMIYXRSGBFCCCHEL8XV1ZXs2bMzYsQIRea3tbWlQYMGrFljxgajH66BRfz55Nv/SzC8v6B0jF/aK91njgY9wVcfZJbjb3XoeaD7yLGgJwTptTx58oSyZcvSpEkTxo0bhyqC1UGPHj1i0qRJTJ8+nQ4dOpA7d250Ol1oEeXLly906tSJkydPAnDt2rUw948aNQqACRMmcOjQISBk1daP1338+DHM73PmzKn4SVbxTZIkSdi7dy/bt2+XRqzilyNbaYQQQgjxy3n16hX58+dn69atlChRwuzzHz16lC5dunD16tUI3xAalfcCilf34GuQjsBguPMS8v6vDUDBzLDMRO0UHr6BIoPh7YLo3Reshfep2+FQeaFpgv3mnml9uRj82iwFkR+pAcsg6FO6AW1atKRXr8h7yeTKlQu1Ws3BgwfJkycP58+f5+vXrxQqVIikSZPy4sWLMEcGW1pa4uvri7W1NS9evCBr1qwEBARgbW0dZpVYypQpef78eeiJN8WLF+fixYvY2NiQJ08eypcvz7hx40zyNfjVDR8+nNGjR3PlyhXy5MmjdBwhjEJWjAghhBDil5MqVSpmzZpFy5Yt+fLli9nnL1OmDJ8+feLKlSvmmfD1cf4dqePyONjdD5IkgMvjQv77VhQJ1poniqGeXVLoSONf3CvdZ8WKIgA6IECtY8S+VXTt2SPSa6dPn463tzf79u1jxIgRtGzZkixZsrBixQratWvHkydP2Lt3L926dUOtVqNWqwkODg5dDTJ69OjQU6h+3Dr35s0bZs2aFfr7rVu34ufnx+rVq/ny5QsLFy7Ex0dOR4qJ4cOHU6JECUqXLq3YlkUhjE0KI0IIIYT4Jf35558UKFAAT09Ps8+tVqtxdXVl9erV5pnw081wH87cHUZtgfKjocMSePkh5NeFPSFPP+j3v90+/l8hRQd4+92BNr1Xw4hNIb/+9+7/31fYE3ZdCj9GRNc9fBMy/vBNUHoELD4Mqe38Q7dHCOPw0wVyNuilYkWRb1QaNVZJEnI2KOxqj++9f/+efv36MXDgQPz8/Ni4cSOenp58+fKFxYsX07VrV9RqNVWqVGHChAnodDrOnDnD3r17yZ07NwAJEiSgWLFi2NjYkCxZstDVIZaWlvTr1w83N7fQ+dKkSYO1tTW1atXC19eXEiVKMHv2bNN/MX5Rhw4dQq1WU6FCBaWjCGEUspVGCCGEEL+sd+/e4eTkxNq1aylbtqxZ57516xYVKlTgyZMnYY4nNYmduUOLI99vb8ncHWoUgLn/6zEZEAjBOrCzgaBgqDoB+tWCavmhzULInxG6VQtZXZKxG5waHrL6pPwY2N0X0iSFFz5QdAj8NwE++P//XB8+R35dlh6wrgs0LhmS5VOABUXGZ+HOnTum/dr8JvR6PYeDnvBJH6h0lFAaVOTXpCSjRaKfnitevDgvXrzg8ePH1K9fn5IlS9KvXz8WL17Mtm3b2LFjR+i1S5YsoUuXLhGu/nJ0dGT79u04OjqSMmVKPn78SGBgxF+H6dOn888//3D+/Hnu37+PnZ1d7F/sb+jGjRs4OTkxaNAgRo4cqXQcIWJFVowIIYQQ4peVPHly5s+fT6tWrfDz8zPr3H/88Qfp0qXj4MGDpp9MF/GbwBal///XWh30XQP5B0LhwfDfU7j8KOS5lmVh+fGQX++5ArnSQeaUcMobHryB6hOhwMCQ/69Swd1XYeeJ6jobS/jru3YvdgmtuXv3Lnv27DHCF0Dc1X7gsz5I6RhhaNFzVfuGAH1wmMe9vLw4d+4ce/bs4eTJk1y4cIGuXbui1+uZMWMG3bp1C3P9xo0bcXR0jHSub5/1pk+fPnR7TURatWrFmTNnKF68OAsWRLNBjgiVO3du5s6dy+jRozly5IjScYSIFSmMCCGEEOKXVrt2bcqUKUO/fv3MPrebmxurVq0y/URq6wifsrP5/19P3QM+n+HfEXB1PNQrDAH/ew9Z5g/w/QLXHsPyY9CyTMjjej04Zfj/niWXx8GTWVAka9h5orouoXVIoSQ0ssaaqlWr0qGDiTrD/kb8dIHc0r5XfAtNeLToOR/0KrRw4e/vT7t27Wjbti25c+emb9++jB49GltbWw4fPoxOp6NSpUphxjh37hzVqlWLcI7vGxw7OTkBRLpiJHHixLi6upIyZUqmTJlCQEBAbF7ib619+/bUq1ePGjVq8OHDB6XjCBFjUhgRQgghxC9v+vTp7Ny5kwMHDph13iZNmrBjxw7Tr1axSGjQZR8+Q5okYGMFz97Dth9OzG1RBqbshmO3oGGxkMecc4L3Kzh8/f+vO3cvpBDyPUOvC6WxYcWKFTx79oy1a9calF+E77r2XZwsigDoAR99AD76kOJDgwYNSJAgAfPnz2fLli34+/vTvHlzAGbOnEm3bt3CFDo+f/7Mu3fvaN++feTz/O8bLX/+/ABcuhRBI5z/6dq1Kzt27CB//vwsW7Yspi9PELKiJ0WKFIqcACaEsUhhRAghhBC/vCRJkrB48WLatGnDx48fzTavg4MDpUuXZsuWLaadKGkBgy7rVhVO3oGCg6DTMqj4w0mb7mVg1QmoWxgS/G8RStKEsL0XjNgSsgUnV18Yvvnngoeh14VKlAsHBwcaNmxI9+7do/Vyxf/7qtfySuevdIxIadHjrf3Avn37+Oeff9i6dStarZYBAwYwceJENBoN9+/f58SJE7i6uoa5d9WqVdja2pI1a9YIRg+7YuRbYeTs2bORZsqRIwclSpQgf/78TJgwIcrtNyJiarWaM2fO8ODBA9q2bat0HCFiRJqvCiGEEOK30bFjRwIDA1m6dKnZ5ly/fj1Llizhn3/+Md0k95fD+a4QbN4+KjGmsoC8QyHfEHx9fUmaNCkzZ87Ew8ND6WTxzu3g99zR+sTZFSPfqIHOuStRNH8hNm/ezNy5c9myZQv79+8HoFevXlhYWDBx4sQw91WuXJn3799z4cKFcEYNkStXLjZt2kTu3Ln58OEDSZMmpW3btixatCjSTAcOHKBHjx44ODjg7u6Ou7t7rF/n72zbtm3Ur1+fdevW8ddffykdR4hokRUjQgghhPhtTJo0iSNHjrBz506zzVmnTh3OnTvH8+fPTTdJssKAKsrL4gyLBJC8KAD29va0bNmSgQMHotPpFA4Wv+j1eu5pP8T5oghA4NdASjety7p16/D19WXkyJGhRRA/Pz9WrFhB586df7rvwoUL1KhRI9Kxv18xkiRJEgAuX74cZaaKFSsCUK1aNcaNG4dWqzXw1Yjw1K1bl06dOuHq6sqjR4+UjiNEtEhhRAghhBC/DXt7e5YuXUqHDh14//69Wea0tbWlfv36rFmzxnSTJMoFuq+mG9/YtAH/K+aEmDNnDgEBAYwdO1bBUPHPB/1XdPGgKAJgYW1F457tsbKyYvLkyVSuXJmCBQsCsGLFCsqXL0+mTJnC3PPp0yd8fHxo165dtObSaDQGvTFXqVR0796dY8eOkThxYjZv3hytecTP5syZg6OjIyVLlpRCp4hXpDAihBBCiN+Ki4sLjRo1omvXrmab0+Sn06gtIGVZ041vbAmzgE3K0N9aW1vTrVs3xo4dS3BwcCQ3iu990H+NJ2WRECo7a569eM7s2bMZPXo0ADqdjpkzZ4bbZ2bFihUkSJCAjBkzRjn2990BEiZMaHAvIVdXV86ePUurVq0YM2YM0mUg9k6ePMmnT5+oXbu20lGEMJgURoQQQgjx2xk3bhznzp1j06ZNZpmvXLly+Pj4cPXqVdNNkrsfWNiZbnxjsbCD3P1/enjChAmoVCr69//5ORG+d7ovkW6jaZ/ThS5OVelZtDadcldkbKOO3Dp9EYC9i9awfaZ5T2PRoGLaonm0bt06dHXIvn37SJAgAaVLl/7p+s2bN5MnT56fHv/R91tpAFKmTGlwM1VbW1vatm3L9evX0el07N6926D7RMQSJUrE/v372bt3L7NmzVI6jhAGkcKIEEIIIX47CRIkCO1p8Pr1a5PPp1arad68uWlXjaSuCJb2phvfSAIDA3lh/fPqFrVajaenJ7NnzyYgIECBZPHPe13UX6e+a2cx7dwO5t04SHnX+oyq15Y7Zy9TrV0z6nRrZYaU/0+r03HvzTMGDhwY+ti31SI/Fjcg5MjdWrVqGTT29ys9cuTIgV6vN/j7yMPDAy8vL3r06CGrRoykZMmSjBw5kh49ehjU70UIpUlhRAghhBC/pZIlS+Lu7k6nTp3M8kbIzc2NNWvWmK7Bo0oNf/QGja1pxjcCvcqSM6//II9TUTp06MDdu3fDPD9gwABsbW3p0qWLQgnjD71ejz/R23ZUsl5VqrZrytbpS1g3aibLB4wHQKvVsnzAeLoVqkG3QjVYPmB86PfpzLb9mNdlCEOquuGRpxIzWvcN/Xk5tm47/co0pFfxOvQqXoerh05Fnlmtop57U5IlSwbArVu3uHjxIk2aNPnp2vfv3/Px40eDjn/9sajyxx9/AHD+/Pko7wVInz49VatWxcfHh7dv33L06FGD7hOR8/T0pEyZMri4uEixU8R5UhgRQgghxG9rxIgR3Lp1i7Vr15p8rty5c5M6dWoOHz5sukmytY3ThZHAYD056izi9u3bpEqVipIlS9K4cWMuXgzZ3qFWqxk7dizLly/n06dPCqeN23TE7ByinEXz8+SGd5jH9i9Zx4MrN5lyZitTzmzl/uUb7F+yLvT5x9e9GbJtMTMu7ebepf+4cvAkAAUql2HCsY1M/Xc7vVdNZ2a7flHO75g3d+ivZ82aRfv27bGxsfnpumXLlmFnZ0fatGkNel3fFzdz5coFGF4YAejevTtz5syhX79+jBkzxuD7ROT279+PhYUFZcvGox5I4rckhREhhBBC/LZsbGxYuXIlPXr0MO1xuv9j8iasVomh5CrQJDDdHDGk1yRk7+vK5C1encWLF+Pp6cn9+/cpXrw4derUoWrVqhw+fJhOnTqRLFkyWrdurXTkOE1HzE78CG911JVDpyjv1gBLKyssrayo0KIhV75b/VG8TiWsbKyxtLIia4E8vHzwGICX9x8zolYruhWszmTX7vi8fIvPyzeRz68OKed8+PCBtWvX0qlTp3Cv27p1K/ny5TPoNf24YqRQoUIAXLt2zaD7AUqUKEHKlClJkiQJd+7c4ezZswbfKyJmaWnJyZMnuXjxIgMGDFA6jhARksKIEEIIIX5rhQsXplOnTrRr187kW2qaNm3K9u3b+fz5s+kmSVcD0tUGtbXp5ogulQWqpAWo22sXZ8+e5fTp0+TNm5djx47Rq1cv7t27R+PGjenYsSMlSpTAzc2NzZs3m6X/S3ylitF6Ebh7/hoZ8+QM85her+fHFh/fFxusbP7/e0mtUaMLDtlmM7VFT6p3aM7MS3uYcmYbGgsNQQGRHxut/t+4S5cupXr16hGuCLl69Wq0TjX5/mfXyckJCNmqEx09evRgzpw59O3bV1aNGJGjoyOLFi1i4sSJHDhwQOk4QoRLCiNCCCGE+O15enry/Plzli0z7Skd37aPbN261aTzUGx+3DqhRmMNpdaASkW2bNnYvn07M2fOpGfPntSuXZsnT57QunVrbty4Qf/+/Tl27BgajYYKFSoQGBiodPo4SYMq2kf1/rvjAHsXrfmp6WqBiqU4vGozwUFBBAcFcWT1ZpwqOEc53uePvjhkTg/AgWUbCPoa9Z+VBhVarZZZs2aFe0QvwJs3b/j06RNt2rQx4FX9vGLEysoKlUrFw4cPDbr/m0aNGnHnzh2KFSvG2bNno7XiRESuVatW/PXXX9SuXZu3b98qHUeIn0hhRAghhBC/PSsrK1auXEn//v15/PixSecy+XYaAKskUOGfuFEc0dhCmS2QMGOYh6tXr861a9coVaoUxYsXZ/DgwQQEBNCgQQPOnj3LqFGjuH79OpkzZ2b69On4+fkp9ALiJpVKhcaAysikpl1Dj+s9uHwDQ7YuxrF4wTDXVG7ThEz5/qBX8br0Kl6XTPn+oHLrxlGO3WaSJ+P/9GBg+Sa8fvQM++RJI88M2Kms2LFjB6lSpaJYsWLhXrdkyRLs7e1xcHCI+gX+z4+rvaysrHj//r3B90PItg8PDw8WLFhAz549GTt2bLTuF5Fbs2ZNaHFYp4vZVjAhTEWll/OohBBCCCEAGDduHIcOHWLfvn2o1ab5/Mjf35906dJx48YN0qRJY5I5Qr0+AUeqQbAJt+5EIiBYzQWrHpRqMiXS654+fUrfvn05efIkU6ZMoVGjRqhUKhwdHbGxsSFnzpwcOXKEzp0706VLF1KkSGGmVxB37dy5kxv2X8hZomDUF8cRFqgpapmKppVq065dO5o2bRrudc7Ozmg0Go4fP27QuPnz52flypXkz58/9LE0adLw6tWraL8Bf/PmDTlz5uTixYsUK1aMU6dOkSNHjmiNISL2+vVrMmbMSOPGjVmxYoXScYQIJStGhBBCCCH+p2/fvnz69In58+ebbI4ECRJQr149s5yEg0NpqHAQLBKhN/M/+/wDVbRaYkf9bqvYtm1bpNemT5+etWvXsmrVKkaNGkXFihW5fv06K1as4Nq1awwbNoyTJ0/y7NkzcubMSY8ePUy+sieu8vPzo3379nTt2hWn9NmUjhMtOnQ8v3mP27dv06hRowivu3btGvXq1YvW2D9+1pshQwb0en20+/mkTJmShg0b4uXlRefOnRk/fny07heRc3BwYPPmzaxatQovLy+l4wgRSgojQgghhBD/Y2FhwYoVKxg6dCj37t0z2Txm2U7zTYriBFc+w713tmhVPx+LanSaBJAwMw+yLWfbuSASJUpE27Zt2bNnT5S3litXjosXL1K/fn1cXFxYv349efPmxdXVlZw5c7Jo0SKuXbuGpaUlBQsWpFWrVty8edP0rymOOHXqFAUKFCAoKIgrV65QILMjFjFswqoEC9TMmTaTTp06YWlpGe41z58/x8/Pj1atWoX7fHh+7DECkDlzZoAYnS7TvXt35s2bR8eOHdmyZctvW4QzlRo1atC9e3datmzJgwcPlI4jBCCFESGEEEKIMP744w8GDRpEq1atTLYP3sXFhbdv3/Lff/+ZZPwfDZ/iRbfdpVA5jQjp+WGifwJ+DdYQkLE11LpFnjItWLVqFS9evCBLliy4u7tz8ODBKMewsLCga9euXL9+HV9fX168eMGVK1dCt1WkS5eOSZMmcffuXbJnz46Liwv169fnzJkzJnlNcUFgYCCenp40aNCASZMmsWzZMhIlSkRStU0MD+1Vhl2Qhk2bNtGhQ4cIr1m8eDGJEycmWbJk0Rr7xxUjjo6OqFQqLly4EO2c+fLl448//uDgwYO0adOGSZMmRXsMEblp06aRN29eSpQoQXBwsNJxhJDCiBBCCCHEj7p3745er2fGjBkmGV+tVtO8eXOzrBo5dOgQS5cuZdnylajz9IPqlyBlKdDYgCr8T+2jRaUJWSWSJD9TrtSh/zp1yCk0QMOGDRk8eDA3b96kSJEiNG3a1OC+EQ4ODixevJhdu3ZhZ2dHlSpVOH/+fOjzSZMmxdPTkwcPHlCpUiWaNGlC+fLl2bdvn8mPXTanGzduUKJECa5evcrly5epX79+6HPWKg0p1bYKpjOcBhUXN/9D/fr1SZkyZYTX7dy5k4IFo9c3JbwVI3nz5gXg+vXr0Qv6P927d2fGjBn07NmT1atX8+rVqxiNIyJ2/Phx/P39qVmzptJRhJDCiBBCCCHEjzQaDcuXL2fMmDHcunXLJHO4ubnh5eWFVqs1yfgQ0kiyRYsWLF++nFSpUoU8mMgRKh+DGtcgRwfQJAQL+2iPrVUlIFCngczNocopqHGZDoMWsXbtWq5evRp63YABA2jYsCEnT56kevXqNGzYMFqrO4oVK8bVq1cJCAigcuXKtG/fPsxxnwkSJKBz5854e3vTtm1b+vTpQ6FChVi/fr1Jv7amptPpmD59OmXLlqVTp05s376d1KlT/3RdDk1SNPFgO40VaqZ4jqRbt26RXnf9+nUaNGgQ6/mKFCmCXq/n7t27Mbq/Zs2avH//nkePHtGsWTOmTp0a60wiLDs7Ow4dOsSBAweYPHmy0nHEb04KI0IIIYQQ4ciWLRsjR47E3d3dJEu98+TJQ8qUKTly5IjRx4aQN9YtW7akefPmVKlS5ecL7LNDkVnQ8A0UXwQ5OkOS/KC2JghrgkiATpOIAJ0NOo09AVpLUFtColyQtQ0BeSeRrXcCfPPOhqQhp4EkT56ckSNH0qVLl9BVGyqVikWLFlGgQAG2bt1K8+bNqVu3LhcvXjT4tWTJkoXq1auTIEECbG1tyZ07N7Nnzw7z52JpaUnz5s25evUqo0aNYubMmTg6OrJgwQICAgJi98U0sydPnlC5cmX+/vtvzpw5Q7t27VCpVGzduhVbW1vs7OxIkiQJ9vb25EyRDis0SkeOlAYVn648IGvWrBQoUCDC6x4/foy/vz/u7u7RnuPHVUJZsmQB4NGjR9EeC0KKo127dmXGjBn069ePRYsW4ePjE6OxRMSKFi3K+PHj6d+/f5gVYUKYmxRGhBBCCCEi0LFjR+zt7U3WY8CUTVhnzJjBu3fvGD16dOQXWthCpsZQdDbUuAyN/Rl+phZ7vnTmTqox9N6SgReOi6gzOyX85Q+1bkCJxSTM50GBYuXYsWNHmOHatWuHn59fmFN3LC0t2bZtGylTpmTJkiW0b9+eGjVqcO3aNYNfz4oVK3j58iXPnj3Dz8+PhQsXUrhw4Z+25qhUKmrVqsXJkydZvnw5O3bsIEuWLEyYMIGPHz8aPJ8S9Ho9a9asoXDhwlSsWJFjx46RPXv20OdLlCiBTqfj8+fPfPz4EX9/f+rVq4djHF81ogLmeo6he/fukV63ePFikiZNSqJEiaI3fjhbadRqNRqNJszqouhq1aoV//zzD2q1mrp16zJr1qwYjyUi1rdvXypUqECFChXw9/dXOo74TUlhRAghhBAiAmq1mqVLlzJ16tRovYk3VNOmTdm2bZvR3wxcuHCBcePGsXbt2ghP/4iQSs2V+wGoHMrw2b4kZx7akzRnbY5deYdeFXZlwl9//cX69evDPKbRaJg9ezZ9+/bF19c39PEkSZKwf/9+rKysmDlzJt26daNq1aoGnSqj0+nw8vJCrVazadMmIGSLzqBBg2jWrBnNmzfn+fPnP91XunRpdu7cyb59+7h27RpZs2Zl4MCBvHz5MnpfEzN4//49TZo0YfTo0ezdu5dBgwZhYWER+vzTp0+ZOHFi6O81Gg25c+cmd+7c1MzvjPel/yAOtlbRoCLRYz8eeN+jTp06kV67e/duChcuHKN5wusrkzBhwlitFkqUKBEtWrRg7ty5DBgwgNmzZ+Pn5xfj8UTE9uzZg62tLWXKlFE6ivhNSWFECCGEECISGTNmZMKECbRo0YLAwECjjp0mTRqKFy/Otm3bjDamr68vTZo0YdasWaHbCaLr5cuXpE6dGrVajVarDd3C8uNWgjp16nDkyJGfVmI4OztTuXJlRo0aFebxLFmyhK4wmTZtGr169aJy5cpR9oF49+4dQ4cODe0ZotVqCQ4OpnHjxty8eZNMmTLh5OTExIkTCQwMRK/X80UfjK8ukA+6r2TK68j8lUv598I5Pn36RK5cufDw8OD+/fsx+voY2759+3ByciJNmjRcuHCBQoUKhT538+ZNWrVqhZOTEwB///03EFIsunPnDn379uXGjRsUUqdEE87KCSWpgBQqWxaNnEznzp3DFHrCc+PGjRj1FwlvxQiENPDV6/V8+vQp2mN+07VrVxYvXkyGDBkoX7488+fPj/FYImIWFhacPn2aq1ev0rt3b6XjiN+QFEaEEEIIIaLQqlUr0qZNy5gxY4w+trG303Tu3Jly5crRuHHjGI/xrTCi0WhCixFp0qT5aVVG4sSJcXFxYfv27T+NMX78eJYtW/ZT89qSJUuycOFCtFotc+bMoVevXlSsWJGHDx9GmCdlypTcvHmTYsWKodFoCAwM5OvXr0BIA8eRY0az7/Jp/NPbM+/yPrb5e7M/8BFHg55wIugZx4OecjjoCdfT6Kg9tQ9bH1/kj2qlaNy+Jc2aNePKlSsx/lrFhr+/P126dKFdu3YsX76c6dOnY2sbcsrMmTNnqF+/Pi4uLmTNmpW7d+8ydepU6tatS548edDr9QQGBmJhYcGCBQtwKeZMbk3yOLWlRoOKdO9DTppp27ZtpNfeu3ePL1++4ObmFqO5wlsxkj59egDOnj0bozEhpNdQyZIlWb16NYMGDWLq1KnxrmdNfJE1a1aWLVvGtGnT2LNnj9JxxG9GCiNCCCGEEFH41kB03rx5XLhwwahj16tXj9OnTxvlONCVK1dy/vz5WB0zrNPpeP36NQ4ODmg0GnQ6HQBp06YNd7tK48aNQ1cxfC916tR4enrStWvXn960Nm7cmN69exMQEMCaNWvo1q0bFStW5OnTpxHmSpcuHSdPnmTw4MFAyLYLX10gl4NesyfwAS9SqinQsDKZ8ucCCzU69ASjJxgdwejRokcPBKDFx0pL5qrFGbpzGfWn92fsqnnUbVCfY8eOme2o33PnzlGwYEF8fHy4cuUKlSpVQq/Xs2fPHlxcXGjSpAkVK1bkwYMHDBkyhGTJkhEYGEjTpk25ceMGanXIP+OdnJxo164dAFk1iUmhskUdB4ojalQUtUzN0vkLadKkCUmTJo30+sWLF5M8eXLs7OyiPVdEK0YyZ86MWq2OdVPPHj16MHPmTJycnChcuDBLly6N1XgiYq6urjRr1oz69evz+vVrpeOI34gURoQQQgghDJA2bVqmTZuGu7u7UT8xTpgwIXXq1AnTrDQm7ty5Q+/evVm/fj0JEyaM8Tjv37/H3t4ea2vr0K00EHFhpHbt2hw7dizcEzu6dOnCy5cv2bx580/PDRo0iKpVq/L69WsOHTpEhw4dqFChAi9evIgwm0ajYfjw4YyZPZWKQ9pzOOgJj3Sf0P6vCBIdOkCr0mOZOCGuo/vSctV4dj64SPnKFdm+fXtoQcjYgoODGTlyJLVq1WLkyJF4eXlhb2/PmjVrKFCgAP3796ddu3Z4e3vTpUsXEiRIAMDq1atJliwZu3fvZsOGDSxZsgSNRsPq1atDCwMqlYpilmlIorJWtDiiQUUhjQNJgiyYP38+Xbt2jfKePXv2UKRIkRjPGV5B648//gBCjgCOjfLly6NSqTh48CCenp5MnDiRoKCgWI0pIrZy5UrSp08f2mxYCHOQwogQQgghhIGaNWuGo6Mjw4YNM+q4sd1O8/XrV5o0acKIESPIly9frLJ820YDhFkxkiZNmnCLFvb29lSsWDHcPikWFhbMmjWLXr16/dRgVqVSsXDhQrJmzcqNGze4e/cubm5uVKpUiTdv3oSbTavX81/QW/K2rUP63NnR/W8VSGxpVXpUFhpKN6lF981zWbl7M/ny5WPlypVGfQN8584dSpUqxYkTJ7h48SK1a9dmzpw55MiRg/nz5zNu3DiuXLlC8+bNQ5vmPnr0CCcnJ9zd3WnSpAnv3r2jYcOGuLu7c/fuXXLlyhVmDo1KRSnLtCRT2ShSHNGgoqDGgfQW9qxfvx4nJydy584d5X23b9/mzz//jNGcEa0YcXJyQq/Xx7qXjEqlonv37kyfPp0SJUqQLVs2vLy8YjWmiJharebMmTO8ePGC5s2bKx1H/CakMCKEEEIIYSCVSsW8efNYuXIlp0+fNtq45cuX59WrV9y4cSNG9w8YMIDMmTPTqVOnWGd5+fIlqVKlAjBoxQiEnE4T3nYaABcXF5ydnRk3btxPz1lZWbF582YsLCzYt28fer2e+vXrU7lyZd6/fx/mWh9dAAcDH/FA9xEdejBBo1EdoLK2wH3GEMbvWc2av9eTPXt2Zs2aFauTg/R6PfPmzcPZ2Rk3NzfWrl3LsmXLyJo1K//88w9eXl4cO3aMGjVqhL7J1+l0eHh4kDVrVoKCgrh16xaLFy8ObWCqUqnInDlzuPNpVGqcLdOSXm1ntp4jeq0OC9QUt0xDegt79Ho9M2fOjPKIXghpMBsQEBCrN8HhrRgpWrQoer2eJ0+exHjcb5o3b87Zs2fx9vbG09OTcePGhf5sCONLkSIF27dvZ/369axYsULpOOI3IIURIYQQQohocHBwYPbs2bi7uxvtmF2NRkOzZs1itGpk586dbN68mcWLF0f4yXl0/LhixJDCSK1atTh58uRPxYxvJk2axLx588I9fSZZsmTs2bOHL1++MG/ePDJnzkylSpWoWrVq6Gk3z7S+nAh6hj/BaM1wJq0WPfpUiei9aQ7rtmzkyJEjZMmShVGjRkX4GiPy/PlzatSowdKlS9m4cSMPHjwgR44c3L17l0OHDrFt2zacnZ3D3LNr1y6SJ0/O8uXLWbBgATdv3iRHjhzRmletUlHIMhUlLNNghdqkq0eCvwZy5cAJin9JgoM6ZOvPqVOn+PjxI9WrV4/y/sWLF5MyZUpsbGxiNH9E3/cpUqQAQk41ii1bW1vatWvHrFmzKF++PMmSJQs9OlqYRuXKlenTpw9t2rTB29tb6TjiFyeFESGEEEKIaGrYsCFFihRh0KBBRhvTzc0NLy+vaO2pf/bsGW3btsXLy4tkyZIZJUd0t9JAyMkwVapUYcuWLeE+nz59evr27UvPnj3DfT579uxs3ryZwMBABgwYgIuLC8WLF6d69erc+fyaC8GvzVIQ+Z4OPf4E8zFPClZvXM/Ro0d58OAB2bNnp3fv3pE2iv1m48aNFCxYkKxZs5InTx4aNGiATqfjypUrLF++/KctJm/fvqVUqVLUrl2bChUq8P79+yhPc4lKSnUCKltlJsHHIIK/Bhq1PBL0JQDt1yBOz9/A9uGzGTV0eOhzM2bMoGvXrqFNYiPzzz//UKxYsVhliahprrW1tdEKmB4eHqxevZpPnz4xaNAgxo4da7Zmvb+riRMnUrBgQZydnQkODlY6jviFSWFECCGEECIGZs+ezYYNGzhy5IhRxsuXLx/JkiXj6NGjBl2v1WpxdXWlc+fOlC5d2igZIGxhxNCtNBD5dhoIOdnjzp077Ny5M9znS5cuzcyZM7GyssLd3R1XV1cqNK3HleA3IVtnFKAHvqLlWOBTsjjmYOnSpVy5cgW9Xo+TkxNt2rTh9u3bP9338eNHWrRoQc+ePfnjjz/YuHEj2bJl4+7du0ybNo0MGTL8dM+QIUNIkyYNT58+5cKFC2zatCnGKyh+ZKlSM6tdf15sPEFmdeKQ7TXBMd8GokGF2j+IjSNnUd06M71d2/H48WO8vLy4cuUKT5484cCBA7Rs2dKg8by9vWnSpEmM80S2UipJkiQA0V7pE5506dJRrVo1li5dSq1atdDr9ezatSvW44rIHTt2jMDAQKpUqaJ0FPELk8KIEEIIIUQMJEuWjIULF9KqVSt8fX2NMmZ0mrB+69lhzFUrEPWKkYg+Ia9Rowb//vsvb9++Dfd5a2trZs6cSY8ePSI81ad58+a0b9+eFClS0KlvT4q2rY+ljbURXlXMhRzxG8zpoOfo9XoyZMjA1KlT8fb2JlOmTJQpU4aGDRty7tw5AA4dOkTOnDk5cOAAKpWKP//8M8yRuz86efIkadKkYcKECYwZM4ZHjx5RsGBBo76G06dPc+nSJdo1a0F+y5RUVqVnaZ8x2AWpsECFGhUWEbwt+PolAIJ1qICPr95i8z4AZ00aRlVpQb2iLiSwtiVt2rQMGTKElClT4uHhwZw5c2jRogWJEiWKMtt///1HYGAgf/31V6xeY0Tflw4ODqhUKv79999Yjf9N9+7dmTVrFjqdjkGDBjFmzBhZNWJitra2HD16lKNHj4bbq0gIY5DCiBBCCCFEDNWsWZPy5cvTt29fo4zXrFkztmzZEuXS/5MnTzJ79mxWr16NRqMxytzfRLRixNbWloQJE0b4yXvChAmpVq1ahNtpAKpWrYqTkxOTJ0+O8Jphw4ZRrHhxPBaNQxtH3nDqAV99IPe0H0MfS548OUOHDuXBgweUK1eOBg0akCJFCqpUqYKNjQ2TJk3i3r17YY7c/Z6fnx/VqlWjTJky5MmTh7dv39KvXz/jZ9frGTRoEEOHDg1dgXLk0GHeXPamkl02alplpZJVRgpZOOCoSUpWdWJuHTzD51tP2T5jKaeXb6GMbQZqWmWlX9G6uOZz4Z8N29Dr9TRu3Dh0ns6dO2Nra8vz58+ZM2cOXbp0MSjfokWLcHBwwMrKKsavMbIVI+nTp0etVnPx4sUYj/+94sWLkypVKnbs2EGjRo14//690VaNiYgVKFCAKVOmMHjwYKMVuYT4nhRGhBBCCCFiYdq0aezevZt9+/bFeqw0adJQrFgxtm/fHuE179+/p1mzZixatIh06dLFes4fRdR89Vu+qLbTrF+/PtLxp06dyrRp03j06FG4z6tUKvovmkzi1ClQa+LOP1W16Lmpfcdnfdjje9VqNS9evODVq1d8/fqVVKlSkTJlSqytrSPsrzF9+nSSJ0/OxYsXOXToEAcOHDBodUVMHDhwgGfPnoXZ1rJhw4bQo3FVKhUJVJak1diRyyI5edTJGFynJR5l67Ks/zhe/XuD5GpbLFRqgoKCePfuHR06dGDixIlhXp+FhQXz58/nzZs3BAYGhjY+jcr+/fspUaKEUV/z97Jly4ZKpeL69etGG7N79+7MmDEDjUbDgAEDGDNmjNHGFhHr0aMHVatWpWLFivj5+SkdR/xi4s7/2gghhBBCxEOJEydmyZIltG3blg8fPsR6PFdXV1avXh3uc3q9nrZt21K/fn1q164d67nC8+rVq3C30kDUfUaqV6/O+fPnef36dYTXZM6cme7du9O7d+9wn/+k+8oDfLGyNU5/DWPSouds0Ev0ej0+Pj6MGjUKBwcHpk6dyoABA/j06RNPnjxhyJAhTJ48mVy5crF48WK+fv0KhGwbyZo1K3369KFHjx68fPkSFxcXk+X9tlpk1KhRocf8BgUFsW3bNho1ahTuPX///TdarZZPnz4BsGnTJp49e0ZAQEDoY35+fhw/fvyne4sVK4a1tTWpU6dm6NChBmW8e/cuTZs2jcnLCyOi7Sy5cuVCr9dz//79WM/xTcOGDfH29ubKlSu4urri7e0tqxjMZOfOndjb21OqVCmlo4hfjBRGhBBCCCFiqXLlytSuXZsePXrEeqz69etz4sSJcIsL8+fP5+HDh0yYMCHW84QnKCiIDx8+kDx5ciDsVhoIKYxEdDINhGy3qVGjBps3b450nr59+3Lx4kX279//03M3tO/MfgJNdPjqvjJ6/nQyZ87M7NmzyZUrF3fu3GHkyJGoVCrUajV169bl9OnTLFq0iE2bNpE5c2YKFCiAk5MTKVKk4OnTp0yYMMGgE1tiY8uWLQQHB4euDoGQHijZs2cnY8aMP12v0+kYMGBAmMeCg4Pp1KkT165dC90SpNfrGT58+E+Ngg8fPkzKlCkJCAhg9erVXL58OdJ8Fy9eJDg4mIYNG8bwFYaIbCtNwYIF0el0kRb0osvS0pLOnTszY8YMLC0t6devn6waMRO1Ws2ZM2e4ceMG3bp1UzqO+IVIYUQIIYQQwggmTpzI8ePHI90GYwg7Oztq167NunXrwjx+7do1hg4dyrp167C2Nk1D0tevX5MyZcrQviXR3UoD0Lhx40hPp4GQAsr06dPp1q0bgYGBoY8H6IN5rfsSi1dgejr0JCmYHQsLC/r378+ZM2fIlCnTT9epVCrKlStH06ZN+fDhAzdu3CBhwoRUqVLF5AURCDm1aPDgwYwZMybMfBs3bgxTKPnehg0bfjqGWK/Xs2PHDjZv3oyvry8qlYrEiROzcuXKn47YnTFjBj179mTy5MnY29vTuXPnSI+fXrx4MalTpw5dzRIbEa0YKViwIHq9nnfv3sV6ju+1b9+eLVu28Pr1a1q3bs25c+e4evWqUecQ4cuUKROrV69m9uzZsf77VohvpDAihBBCCGEEdnZ2LFu2jI4dO8b6TdiPp9P4+/vTuHFjJk+eTM6cOWMbNULf9xeB6G+lgZAGq5cuXeLly5eRXle7dm2yZMnCzJkzQx97qP0Uw+RmpFKROnc2Dp48Sq9evSIscjx69Ih8+fLRqlUr3NzcCAgI4PLly7x7944//viDLl268PDhQ5PFXL16NcmTJ6d69eqhjwUFBbF169YIt9E8efIEnU6HWq1Go9Gg0WhIkSIFgwcPpnz58iROnJjJkycTEBCAm5sbtra2offeu3ePU6dO4ebmhqurK1mzZuXp06eRnrJ08OBBnJ2dY/1aI1sxYmNjg1qtjrKhcXQlT56cRo0asXDhQmxtbenVqxdjx4416hwiYo0bN6Zly5b8+eefRl0NJH5fUhgRQgghhDCSsmXL0rhxY4NP5IhIxYoVefbsGbdu3QJCmg4WKlSIFi1aGCNmhH4sjER3Kw2EvBGtVasWmzZtivQ6lUrFjBkzGD9+PM+fhxyFe1/7AV0c3kbzjV6v5+yr++EeO6zT6ejUqRNZs2ZFp9Nx584dFi5ciFqtJlu2bMybN48bN25gb29P4cKFcXV15dq1a0bN9/XrV4YNG8bYsWPDFA2OHDlClixZwl3hArBkyRJy585N5syZadmyJXXq1CFt2rSMGjWKdevW4eHhQZcuXQgMDOT8+fNh7p0zZw6tW7cmQYIEqFQq5s6dy6dPn+jbt2+EvXfu379P8+bNjfKaIzsy99sWoDdv3hhlrm+6d+/O3LlzCQwMpGPHjhw8eJA7d+4YdQ4RsaVLl5I5c2ZKlCgR6cokIQwhhREhhBBCCCMaO3Ysly5dYsOGDTEeQ6PR0KxZM1atWsX69es5fPgw8+bNi/STcWOIasWIIVtpIOR0mqi20wDkyJGDDh060LdvXz7qv8aLogiAlY01CbKlJnfu3Gzfvj30Tfn27dtJliwZK1asYOHChVy/fp1s2bL9dH/q1KkZN24c9+/fJ2/evFSpUoXatWtz8uRJo+RbtGgRuXPnpkyZMmEe//40mh9Nnz4db29v9u3bF/p9liZNGj58+MC1a9fYtWsX/fv3x8rKinTp0jF//vzQe319fVmxYgWdO3cOfSxXrlx4eHiQKFGicBuxnj59Gq1WS926dWP9eqP6uUiaNCkqlYrTp0/Heq7v5c2bl9y5c7Nhwwbs7e3p0qUL48ePN+ocInKnT5/m7du3/PXXX0pHEfGcFEaEEEIIIYzI1taWFStW0LVrV169ehXjcdzc3Fi+fDldunRh3bp12NvbGzFl+AxZMWJIYaRKlSpcu3bNoGsHDRrE8ePHOXv7v3hSFglhnzIZ8xctpH///lSsWJGCBQtSr149KleujI+PD23atIlyjMSJEzNgwADu379PzZo1adGiBWXKlGHXrl2RroCIzOfPnxkzZsxPzUCDg4PZsmVLuNto3r9/T79+/Rg4cCDp06cHQla+pE+fHj8/P/r378+gQYNInDgxAOXLl+fAgQOh969YsYLy5cv/1NDV09OT4OBgVq1a9VMj1mXLlpEuXTqj9VuJ7OuVKlUqLCwsuHTpklHm+l737t2ZPn06er2erl27sm3btgiPohbGlyxZMnbv3s3mzZtZvHix0nFEPCaFESGEEEIIIytevDitWrWiY8eOMX6Dmzt3bnx8fPjrr78oXLiwkROGL7zCCBC6aiRNmjS8fPkyytdkbW1NnTp12LhxY5RzJkyYkClTpnDq5uVonUbTPqcLXZyq0rNY7dD/Xj98GuH1rx8+pUW6YhE+H10aVBQpX5o6depw9OhRrl69iru7O8uWLYt2c1xbW1s6duzI7du36dy5M56enuTPnx8vLy+Cg4OjNdaMGTMoW7YsBQsWDPP4kSNHyJw5M1myZPnpnurVq5M6dWpGjRoFhKzA0Ov1ZMqUCT8/P27fvk2nTp1Cr2/fvj2PHz8mODgYnU7HrFmz6N69+0/jJkiQgDlz5mBlZUWnTp3CrD46fPgwpUuXjtZri0hUK0YyZsyIWq3mxo0bRpnvezVr1sTHx4fTp0+TLFky2rZty6RJk4w+j4iYi4sLAwcOpGPHjty8eVPpOCKeksKIEEIIIYQJDB8+nLt37+Ll5RWj+4cOHUrGjBnD7WNhKj8WRiDsdhobGxsSJkxoUHNZQ7fTADRq1IjMBXJFO2/ftbOYdnZH6H8OmdNHe4yYCtZq8RjYi+nTpzN+/HiePn2KVqslV65crF27NkYFMQsLC5o0acKlS5eYOHEiixYtIkeOHMyZM4cvX6I+rcfHx4epU6eGFji+F9FpNF5eXpw7d449e/aEPvat0JA5c2YCAwMZO3YsVlZWoc+XLl0aCwsLNm3axL59+0iYMGGERY6aNWvi7OzMkydPwjRiffDgAa6urlG+JkNF9vXOkSNHSA+b+/eNNt83arWabt26MWPGDAB69uyJl5dXlM2HhXGNGTOGokWLUrp06TAnXQlhKCmMCCGEEEKYgLW1NStWrKBXr148e/YsWvceOHAgtL/Ili1bDHpTbAzhFUZiup2mUqVK3Lx586fjX8OjUqlIktYh+oF/8OOqkMhWiUxz70Uf5/p0L1yT8X954OfzMXqTadTkL1uCN2/e0LdvX9KkScPKlStZt24dEydOxMXFJcbHt6pUKqpVq8aRI0dYs2YN+/fvJ0uWLIwdOzbCRqYQcmR0vXr1fjq5KKJtNP7+/rRr1462bduSJ0+eMM/p9Xr+++8/ABo2bPjTXI6OjqxcuZIZM2bQvXv3SFdtzJw5Ez8/P/r06cOHDx84duwYer0+zIk5sRHVipG8efOi1WqjbBwcUy1btmT//v08efKE1KlT07x5c6ZOnWqSuUTEDh8+jE6no1KlSkpHEfGQFEaEEEIIIUykUKFCdO7cmbZt2xq8guDVq1e4u7uzcuVK8ufPT+HChdmxY4eJk4aIasUIGHYyDYCVlRX16tUzaDuNTq+HGDSWndS0a+g2mj7O9aN1b5spg5l8agszLuwiQ67sbJ6yMNrzV6hSmUSJEoV5rFSpUpw/f56mTZtSqVIlunXrho+PT7TH/qZkyZJs3bqVQ4cOcfv2bbJly0a/fv1+Kk69fPmShQsXMmzYsJ/GOHbsGOnTpydr1qxhHm/QoAEJEiQI00j1m+DgYMaNGwfA27dvf3q+Vq1anDx5ksuXL9O4ceNIX0OGDBkYPHgwNjY2DB48mGXLlpE+fXqj9ReByFeMFCpUCK1WG6s/h8gkSpQId3d35syZA0C/fv1YvHgx79+/N8l8Inw2NjYcP36ckydPMnLkSKXjiHhGCiNCCCGEECY0aNAgXr9+zZIlS6K8VqfT4e7uTsuWLalQoQIQ0oT1+y0IpmTMFSMQsp1m/fr1UV6nQ09Mztv5fivN5FNbonXvYa+t9C5Zj+6Fa3J8/Q4eXol+bwJtBG/GNRpNaL+DoKAgcuXKxeLFi2N1pGju3LlZsWIFFy9e5OvXr+TNm5f27dvj7e0NwOjRo3F3dydDhgw/3RveaTT79u3jn3/+YevWrT8VKFQqFbdv3yZPnjxoNJrQOb7XqVMnPn78iKurKzY2NlHm7969O4kSJWLVqlX8888/P52YExtRrRj5448/gJAVMqbStWtXlixZgr+/PxkzZqRevXrMmjXLZPOJ8OXNm5dZs2YxfPhwTpw4oXQcEY9IYUQIIYQQwoQsLS1ZsWIFAwcO5OHDh5FeO3XqVD59+sTw4cNDH2vQoAHHjx/nzZs3Js3p5+eHVqv96fQbjUYTpjBi6JG9ABUqVMDb2zvKUzpUqIxyIo3aQoP+u+JD4Nev4V5348Q59i1cw9DtS5hxYRfNhvckMCD8ayOdL4pqTvLkyZk3bx67d+9m2bJllChRgrNnz0Z7nu9lypSJGTNmcOfOHdKkSYOzszM1a9Zk9erVDBw48KfrtVotmzdvDlMYCQ4OpnHjxtSrVy/c3iBarZZr164xfvx4rK2tw+3N8W2lzPe9RyJjaWnJwoULUavVPH/+HDc3N0NfskEiWzGiVquxtLQEMPh7N7qyZs2Ks7NzaBFzwIABzJ49G19fX5PMJyLm4eFB7dq1qVq1Kp8+fVI6jognpDAihBBCCGFiefPmpU+fPrRu3TrCVQPnzp1j4sSJrFmzJvRNHICdnR21atUyaOVFbLx69YrUqVP/9Ol7TLfSQMib4fr160e5ncZY/yBNmjolwUHBvLgXUog5ti78LUifP34iQWJ77JMnJejrVw6u2BSj+TQGJjhXZoMAAQAASURBVC9UqBAnTpyga9eu1KtXjzZt2vD69esYzflNihQpGDFiBA8ePOD169fodDqaN2/OoUOHwhQJjh8/Trp06ciWLVvoY+7u7gQHB7Nu3bpwx/bx8SF9+vTkzZsXW1vbcAtbS5YswcHBIcyxvVEpVaoUxYsXB4h2353IRLViBEJ+jtRqNWfOnDHavD/q0aMHM2fORK/XkzNnTipWrBjuNiVhelu2bCFp0qSULFlS6SginpDCiBBCCCGEGfTp04cvX74wd+7cn5779OkTTZs2Ze7cuWTOnPmn582xnSa8bTQQu600YNjpNCqVCis0hof9n+97jPQsVpsHV27SZrInw2u0ZHDl5mg04Y9ZqGo5UmfNSBenqoys05asBXNHe24VYK8ybLUEhLxGNzc3bt26RdKkScmTJw8zZ86M9nG8P3r06BGPHj3i3r17NG3aFA8PD4oXL87mzZvR6XQ/baM5f/48a9euxcvLK9zVHo8fP+bDhw/ky5cPAHt7+5+KGFqtltmzZ9OqVSuuXbsWrbz29vao1Wr69u1rsp4f4UmWLBmWlpZcunTJZHO4uLhgYWERWiwaNGgQU6dONVvzZPH/vhXBvL296dixo9JxRDyg0sfkLDEhhBBCCBFtt2/fplSpUpw5c4bs2bMDIVsAmjdvTqJEiSL8dDk4OJgMGTJw5MgRHB0dTZJt06ZNeHl5sXnz5jCPOzg4cO3aNVKlSgXAqVOn6NWrl8GfvAcHB5M2bVr+/fdfsmTJEuF1pwKf81pvuh4QxmaBmqIWqUilSRij+2/evEm3bt14+fIls2bNwsXFJUbjNGjQAGdnZ/r06QOEFC22bdvG+PHj+fjxIy9fvuTUqVPkyZMHnU5HunTpcHR05MiRI+GO17JlS3bu3EnVqlXx8vKiYMGCZMyYkW3btoVes3XrViZMmMDhw4extbXl2rVr5M2b16C8mTJlIkOGDNy5c4c///wztGFpbJQrV44RI0ZE+jUsU6YMFy9epHr16gY1BI6pJUuWsHnzZnbt2gVAnTp1qFq1Kp07dzbZnCJimzZt4s8//2TDhg3hnq4kxDeyYkQIIYQQwkwcHR0ZPHgwLVu2DF2FsXz5cq5evcq0adMivM/CwoKmTZuyevVqk2WLaMVIbLbSQEj2Bg0asGHDhkivS6G2iVEDVqXo0JFEbR3j+3PlysU///zD8OHDadmyJU2aNDHoaOPvnT17lrNnz4Z5063RaGjQoAH//vsvHTt2RKfTUbVqVaZOnUrXrl3x8fFh+/bt4Y535coV9u7dS/LkyUO346RIkeKn/jbfjui1sbEhderUzJs3z6C8Op2Op0+fMnr0aLJmzcrKlSuNsoLDkK0034pyDx48iPV8kWnWrBnnz5/nzp07AHh6ejJx4kSCgoJMOq8IX8OGDWnfvj1NmzaN9s+X+L1IYUQIIYQQwoy6deuGRqNh2rRp3Lp1i379+rF+/XpsbW0jvc/NzY3Vq1fH6mSTyBi6lSZNmjS8fPkyWjkM2U6TRG2DJh6VRjSosVZZhP7+69evvHr1KlpjqFQqGjZsyI0bN3B0dKRAgQKMHTuWrxE0jf2Rp6cnQ4YMCfd7R6VSce/ePQYMGMC2bdvYv38/c+fOpVKlSgQGBoY73oABA/D09ESj0YQWRhwcHMJsebl69Sp37twJ/fS9XLly7Nu3z6C8e/bsQaVSUa5cORYtWoRKpaJdu3ZG+Z6OahG8o6MjOp0uWkW9mLC1taVdu3ahJ9IUL16cHDlymLSoKSI3f/58smfPTokSJUz296eI/6QwIoQQQghhRmq1mmXLljFu3Djq1avH6NGjyZMnT5T3FShQgIQJE3Ly5EmT5DJ0xYi1tTV2dna8e/fO4LHLli3L06dPuXfvXoTXJFXZEG/esuj1XDsash3KyckJBwcHbG1tyZIlS5Rv0MOTIEECRowYEboCJG/evKFbMSJy6NAhHjx4QOvWrcN9XqvVhm4jKFy4MN7e3vzxxx+kTZuWnDlz0r17dx4/fhx6/YEDB/D29qZDhw5hVmCkTZuWjx8/hv5+5syZeHh4hDYIbtOmDQ8ePDDoDefq1avJnDkzKpWKfPny0aFDBx4+fMjKlSujvDcyhqwYyZcvH0FBQXz48CFWcxnCw8MDLy+v0Lk8PT0ZN25cmAKjMK9Tp07h4+NDgwYNlI4i4igpjAghhBBCmFnWrFlxcnLi9evXEb6x/dG35p2masL68uXL0D4i3/txxQhEvwGrhYUFDRs2jHTViKVKTRp1zPp1mJtGpcbmhR/379/n2rVrvHnzBr1eT968eQ16kx6RrFmzsnXrVmbNmkXPnj2pXbt2uMUkvV7PoEGDGDlyZJgTjL536tQpHBwcyJkzJ+PGjePhw4ccPnyYhQsX8t9//2FtbU3BggVxd3fn2rVr9OvXj3HjxoU2ZP1W4MmYMSOfP38G4O3bt2zatIn27duHzlOxYkVUKhU7d+6M8vWdOHGCChUqhP5+2LBhWFpa0qtXr1g3Yo2qIFWkSBG0Wq1ZGqGmTZuW6tWrs3TpUiCkKWuKFClM2ttERC5JkiTs27eP7du3h9sAWwgpjAghhBBCmNn27dt5+PAh+fPnZ+LEiQbf17x5czZt2kRAQIDRM0W2YiS8wkh0tyQYsp0mhyZJvNhO4/f+I3ZBahYtWhRmG8u5c+dIlCgR9evXj9WxsNWqVePatWuULl2a4sWLM3jw4NDiBIR8//j7+9OkSZMIx/h2Gs3Lly8ZOnQoI0aMCP3zTZs2LRMnTuTu3bs4OjpSpkwZHjx4QLp06YCQIty3FSCZMmUK/X5buHAh9evXJ2XKlKHzqNVqcuTIwbJlyyJ9TTqdjmfPnoUpBNrZ2TFv3rzQQk9MGVKMSps2beivwzt+2Ni6d+/OrFmz0Gq1qFQqPD09GTt2bIxWFAnjKF26NMOGDaNr1678999/SscRcYwURoQQQgghzOjp06e0a9eONWvWsHLlSqZPn86VK1cMujd9+vQUKFDAoE/no8vQrTQQ0mckOitGIORNycuXL0ObUoYnidoG2+/6dsRFGr2KGzuP0q5dO9q2bRumaPTff//Rp08frl+/jrOzM3Z2dtSqVYtjx45Fex5ra2v69+/PlStXePDgAbly5WLDhg0EBwfj6enJmDFjUKvD/6e8Tqdj06ZNNGrUiBo1apAhQwY8PT1/ui5p0qT06tWLRIkS0bJlS5o3b46Liwt+fn6hb+CzZ89OUFAQQUFBzJ07l+7du/80TvXq1aPc4rVt2zY0Gg0lSpQI83jdunUpXrw4q1evjlUjVkMKDjY2NlhYWPDvv//GeB5DFStWjNSpU4c2uq1Ro4bBK2uE6QwbNgxnZ2fKlCljkgKziL+kMCKEEEIIYSZarZbmzZvTvXt3SpYsSYYMGZg0aRItWrSIsCHmj0yxnUan0/Hq1SuTbaWBkAJLo0aNojydJpcmeZxeNaJWqRjWtjtDhgxBo9GE/rk5ODiQLVs2hg4dyp07d/Dz82Pw4MHcu3cPFxcXEiZMSLVq1Th48GC05kuXLh1eXl6sXr2a0aNH4+TkhIWFBbVq1YrwnlOnTpEsWTJOnjwZetJMRObOnUuBAgWYNm0a3t7etGvXjpcvX7J//37WrVtHxowZ0ev1/P3332TPnp38+fP/NIaHhwdv3rzB19c3wnm8vLzImjXrT4+rVKrQY6rbtGkTo+aYhm5fsre3x8rKyign4RiiR48ezJgxAwjJOGjQIMaMGSOrRhR28OBB1Gp1mG1dQkhhRAghhBDCTEaPHo2FhQX9+/cPfczd3Z2MGTMyatQog8Zo2LAhR48e5e3bt0bL5ePjQ8KECbGxsfnpufBWjMRkKw1A48aNWb9+faTXpFUnRPvWF3Rx782jBhWFLBywUKkZMWIEDRo0CF2F4Ofnh729PfXr1+f58+ckSJCAAQMGcPPmTfz9/Rk5ciRPnz6lSpUq2NraUrlyZfbs2WPw3GXLluX06dO8fv2ahw8f0qtXrzBNUb+3YcMG6tatS+fOnfHw8CBnzpzhXufj48P48eMZP348ENILpnnz5uTIkYO8efMyZ86c0ELIxIkTw10tAiGrShImTMiSJUsizH/q1CkqVqwY7nOZM2dm0KBB3L9/n+XLl0c4RmQMKTakTJkSS0tLbt26FaM5oqtBgwbcu3ePy5cvAyE/ux8+fODQoUNmmV+Ez8rKihMnTnD27NlwV1KJ35MURoQQQgghzODYsWPMnz+fVatWodFoQh9XqVQsXLiQhQsXcu7cuSjHsbe3p0aNGlEWGKIjom00YLwVIwDOzs68e/eOmzdvhvv8q1ev+OuvvxjTuBOq/2PvPKOquLow/MwtNOmigmLBjmLDLtbYeyzYe+9GE1uMRmONJsbYey+x924sqNh7wa6gCAoC0m893w/iTQigoCiYb561WIGZM+fsmXsxnPfu/e5MposogOwKK1yU1kDC67ZmzRoKFixI5cqViYmJYdGiRVy6dAlXV1cqVqxoej0tLCz49ttvuXXrFnFxccyYMYNXr17RpEkTLCwsqFWrFrt27XpvtsTq1atNHWZiYmJwd3dn1apVia4zGo1s3bqVI0eOYGdnZ8pYSI63nZGKFSuW6LgkSeTIkYNTp06xevVqJEni5s2b+Pn5pSjGlCpVKsVsIL1eT3BwML169Uoxlm+//ZasWbN+kBFrajNGcuXKhSRJPH36NE3zfyhqtZoBAwaYXgOlUsno0aOZOnXqZ1lfJmXc3d1ZsGAB06ZN48SJExkdjkwmQBZGZGRkZGRkZGQ+Ma9fv6ZTp06sWLEikQnkW1xcXJg9ezZdu3ZNVd17epfTvEsYSc589UM8RiBBZPH29k6ygRZCsG7dOkqWLEmBAgVYs3QFZ5dtQ69JXXnR50CJgjKq7ImOWVhY4Ovry86dO4GEUpBnz55x/PhxNBoNFStWJH/+/IlELDMzMwYPHsz169fRaDT8/vvvvHnzhlatWmFpaUm1atXYvHlzEpEkNjaWyZMnM2XKFLJly8aSJUvYtWsXixYtwsvLi0uXLgFw7tw5VCoVFy9eZPfu3Sn6kPj7+7N8+XImTJiQ5Nw/hYaqVauiVCqpVasWfn5+5M+fn9GjRxMcHJzoGm9v7xS9crZt24ZKpcLT0zP5h/vXc1mxYgUGgyFRRlVqSU3GSIECBTAYDEli/5T06dOHnTt38urVKyDBQPnRo0cfZc4rkz706dOHFi1a0KhRo8/SxlkmcyMLIzIyMjIyMjIynxAhBD169MDb25uGDRumOK5du3YUL16ccePGvXfOunXr8vTpUx48eJAuMb58+fKdwkh6ldJA0u40z549o3bt2nz33Xe4urqyYMECihUrxq/fjsMZq0zhN6JEooLaGTNJmeScjY0Njo6OiY7VqFGDa9eu8ejRI4oUKUKHDh1wdHTkp59+SiQyqVQq+vbty5UrV9BoNCxcuJD4+Hg6duyIhYUFlStXZt26dRiNRubNm0flypUpV66c6fry5cvj6+tLnz59aNq0KX369GHVqlUEBwfTpk0bKlasmOI9jRs3joEDByYr1MHfQkNQUBAGg4ESJUqwdu1aLl++THR0NMWKFaN///6mdsI9e/YkJiaGhw8fJplr48aNFCxY8B1P+O/n1rhx4zQbsaY2Y6Ro0aLodLrPugnOmjUr3t7eJh8VtVrNyJEjmTJlymeLQSZltmzZgpOTUxJTYJn/P2RhREZGRkZGRkbmEzJ//nwCAwOZNm3aO8dJksSCBQtYt27dezt8qFQq2rdvz7p169IlxrSW0jg7OxMcHPxBRpmVKlUiMjKSmzdvsnjxYjw9PXF1deXly5dcuXKFqKgoFAoFy5Ytw8smL9kkS4Te8P6JPxEGnZ5yqhxkU1il+Vo3NzcOHDjAmzdvaNOmDdOmTcPKyopu3bol2ZwrlUp69OjBxYsX0Wg0rFixAoDu3btjZmbG2LFjKVOmTJJnrlAo6N69O35+flhZWbF06VKAd3p1XLt2jcOHDzNixIhkz0uSZBJGFi1ahK2tLWFhYUCCH8i8efO4e/cuWbNmpWLFirRv355Hjx6RLVs2FixYkGS+c+fOUbdu3VQ9szlz5qBSqejWrVua3l+pyRgpU6YMOp2O+Pj4D3rvfihDhgxh4cKFJqPeHj16cPny5VR3o5L5dCgUCi5cuMCTJ08StZKW+f9DFkZkZGRkZGRkZD4R169fZ+LEifzxxx+YmZm9d/zbjWW3bt2IiYl559jOnTuzbt26dOlwkdZSGnNzc2xtbT/IAFahUFC3bl2aNm3KihUrOH78OGvWrMHDw8M0Jnv27HTs2JFXr14xrc0Abh8/h5QBZqzauHimefenba1GaDSaD57H2tqaRYsWERMTw6RJk9i3bx9Zs2aldu3a3Lt3L8l4hUJBp06dOHv2LBqNhmbNmpkyTszMzChbtiyLFy9Gr9ebrrG3tzd5hRQtWpSKFSty6tSpZOMZNWoU48aNw8bGJtnzb4URjUbD4sWLyZcvHy9fvkw0Jnv27EyePJnHjx9TtmxZGjdujCRJbN26NdF7UqvV8urVq3f6i/x73hkzZvD48WNWrlyZqmtSi6enp+m97O/vn65zvwsPDw88PDxMmVIWFhYMHz5c9hrJJDg7O7NlyxZWrVqVrt5NMl8WsjAiIyMjIyMjI/MJiImJoW3btsyePTtVZQRvadGiBRUrVmTMmDHvHFemTBmTx8XH8j5hJLlP1z+knMZgMPDbb7+xdetW4uLiOHPmDPnz58fT0xM/Pz8KFCiAUqlk/Pjx7N27l9KlS1O8WDFG1G1HQZUDCiTE5xBIDEYMcVrOzN7AzWO+nDp1ChsbGwYOHMjNmzc/eFqFQsHIkSMJCQlh+/btPHv2DHd3d4oXL86hQ4eSvSYkJIQTJ05w4cIF4uLi2Lp1KzY2NgwdOhRzc3NKlSrF3LlziY+PZ+jQobi5uXHt2jXGjh1Lx44d6dixYyI/mMOHD/PkyRP69Onz3ng3bdpEqVKlyJ07d4oimK2tLd999x2PHz/G29ubZ8+eUblyZZOZ7KZNm1Cr1ZQoUSLVz6lPnz64ubkxbNiwVBmxpraUxtraGoVCgZmZGWfPnk11POnB0KFDmT17tkk06tevH8ePH09WGJP5/DRr1oyBAwfSqVOnzyqayWQeZGFERkZGRkZGRuYTMGTIECpVqkTHjh3TfO3cuXPZvn37O9t6SpKUbiasaS2lgbR3prlz5w5Vq1Zl165dXLp0CUtLS3bv3k3OnDl5/vw5Dx48wNfXlxYtWnD+/Hm+/fZbtm7dytSpUzE3NyfkzC1+atSNVwHPMer071/wAzHq9GTVqhjm2ZjWtRtSunRpAHQ6HQsXLqRkyZLp0m61efPm3L9/n2vXruHo6EjDhg1xcXFh7ty5iYSoqVOn0qlTJ/LmzYtCoeDrr7/mxIkTxMfHs2vXLrJly8bIkSOxtLQkPj6eZs2aodFoaNOmDX5+fuTLl4+SJUsyY8YM4uPjGTlyJNOmTUOtVqcYmyRJGI1Gfv/9d4YOHYqzs/N7fTnMzc2ZM2eOKSPop59+wsPDg19//ZVChQql6dkoFArWrFmDXq/nu+++S9U1qc2csrS0xNzc3NRC93PRqFEj3rx5YxIyra2tGTRokKlVskzGM3fuXNzd3alUqdJnLbWSyRzIwoiMjIyMjIyMTDqzceNGTp8+zbx58z7oegcHB5YuXUqPHj2IjIxMcVzHjh3ZsmXLR5V5wIdljKS2M41Op2PKlCnUqFGDrl27cuzYMQoVKkTRokVp3bo1pUqV4sWLF7i5uXHv3j0uX76MWq3m2rVreHl5ERkZSZ06dfjqq6/IZmZN+1zlKGLuhAIJKR33LnHRMaDVM6/HSIKPXGbOrN/o0qULa9asMWUkCCGoUqUKNWvWTLd1S5YsyalTpwgODqZGjRp8++232NraMnToUO7du8fatWsZO3Zsstc2adKEo0ePcuXKFSBBnFiyZAlWVla4u7szd+5cxo4dy7lz5/Dx8cHNzQ2NRkPLli3fGZMkSbx+/ZqoqCgaNGhArly5iIqKeu+9KBQK3NzcuH//PpcuXWLOnDncuXOHgIAAfv/99/eWh/2T0qVL07VrVzZs2GC6v3fFm1rs7e0xNzf/7JkaCoWCIUOGJGqfPHjwYHbv3i1nKGQifH19iYqKokmTJhkdisxnRhZGZGRkZGRkZGTSkUePHjFkyBD++OMPrK2tP3iehg0bUqdOnXd+Yp47d25KlizJvn37Pngd+PCMkfeV0ly9epUKFSpw+vRpLl++TL9+/Uz+GYcPH8bBwYETJ05gMBgYPXo0bdq0Yfbs2SxduhQbGxtmzJiBk5MTt2/f5tSpUxw4cABbaxuKq52ob5aXompH9LEatLHvb3GcHBKgQMJBMmfj6Jm0z+HJxMEjGDhwIA4ODjRq1Ihx48ZRtGhRJElCpVLh6+v7TmPTDyV79uz88ccfREdHM3jwYFatWoW7uztOTk7JPv9/0rBhQ3LkyMGIESOIjY3l+PHjFChQgMmTJ2NtbU2jRo3w9PREr9cTFRVFixYtePLkyTvnfPLkCYMHD0ahUJAnTx5iY2NTdR/169fHx8cHSZKoWrUqOp2OFStWmISZiRMn8vr161TNNWPGDCwtLencufN7P8FPbcZIjhw5UCqVPH36NFXj05Nu3brx559/EhAQACQIoL1792bGjBmfPRaZ5LG2tubIkSMcOnQokYgl899HFkZkZGRkZGRkZNIJrVZL+/btGTduHGXKlPno+WbNmsXhw4c5ePBgimPeltPcvn2bjRs3pnkNnU5HeHg4Tk5OyZ5PznwV3l1KEx8fz9ixY6lfvz7ffPMN+/fvJ0+ePERERFC0aFG2bt3K/v37sbOzY9u2bVSsWJG7d+9y/fp1mjVrxtWrV8mbNy/ff/89o0aNIigoCC8vr0RrmEsqiqgcaWJVgHUjpiO9isLir+a+KiQU/2zz+9emWYmEEgkFYCOZ4aawo5Y6NzXMclO1UEni4+Jo0KABP//8M23atKFHjx7cvXuXqlWrMm/ePKpUqYKZmRm9e/dm69ataX7WqcHMzIxp06Zx/vx5smTJQkxMDK6urpQvX57z588nGT9hwgSeP3+OmZkZ3t7eQELb27179xIdHc3p06cpVqwYM2bMIDQ0FDMzMyIjIylXrhw//vhjsoKHTqcjNDSUbt26AZgyTVLDgAEDCA4OJi4ujvXr12NhYUHr1q3Ztm0bPj4+BAQEUKhQIYYPH87z58/fOZeNjQ2LFi3iyZMnLF++PMVxackYyZ07N0ajkeDg4FRfk17Y2NjQpUsX5s+fbzo2bNgwNm7c+MHtr2XSn8qVKzN58mSGDx/+2UuuZDIQISMjIyMjIyMjky6MGDFCNG3aVBiNxnSb8+jRo8LV1VWEhYUlOffy5UsxefJkIUmSMDMzEzY2NmmePzAwUDg7O6d4vn79+uLAgQNJjm/btk00b948yXFfX19RtGhR0bJlSxEUFGQ6fu7cOZElSxbh6uoqXrx4IQwGg6hVq5awtLQUy5cvF0ajUcTFxYkWLVoISZJE5cqVRUhISKruYf/+/aJgwYIiPj5eaI0GEWKIFQ/14eKu7rW4FPlMdJ08QtzThQl//RvxxhCf7Ouzdu1aIUmSAIRarRYDBgwQefLkEceOHRNZs2YVt2/fFtHR0aJixYpCqVQKpVKZ7HNJL1q3bi2mTZsmhBDCx8dHlC5dWkiSJPLlyyc2bNgghBDi+fPnQqlUikGDBonChQun+L57/fq1cHJyElu2bBGtWrUSdnZ2AhBWVlbC1tZWrFq1KtG1OXLkEPny5TP9/OzZM5GWbYOFhYVYuHChqF+/vihVqlSS88+ePRPDhw8XDg4Oonv37sLPzy/FuYxGo6hSpYqwtrZO9ndAiIT36P79+1MV23fffSfs7e2Fubl5qsanN48ePRJOTk4iOjradGzw4MHiu+++y5B4ZFKmVq1awtbWVsTExGR0KDKfAVkYkZGRkZGRkZFJBw4ePChcXV1TvZlPCwMHDhSdO3dOctzb21solUoBCEAULFgwzXNfvnxZlC5dOsXzDRs2FHv37k1y/OzZs6JChQqmn6Ojo8XQoUOFi4uL2LJlS6Kxv/76q1AoFKJRo0bCYDCIp0+fipo1a4rSpUuL3LlzC6PRKJYtWyYsLS2Fvb292LNnT5rvo1mzZmLq1KlJjkdHRwtLS8v3Xr969WphaWlpepYKhUKMHz9eeHh4iDlz5ggPDw8RGxsr3rx5I0qWLCkUCoVQqVTixIkTaY71fVy6dEm4uLgk2ZA9ffpUNGzYUCgUCmFvby+yZcsmChYsKEaMGCHGjh2b4nzffvut6Nu3b6Jj165dE23bthVZsmQRgDA3NxfdunUTT548ESqVStSsWdM01mAwCECEh4enKv7y5cuLGjVqiKxZs4rRo0enOO7169fip59+EtmyZRMtWrQQ58+fT3bcw4cPhYWFRbK/A0II0aBBg1QLIytWrBAWFhYCEAaDIVXXpDfNmzcXCxcuNP3s7+8vHBwcRGhoaIbEI5M8Op1OODk5iXLlymV0KDKfAVkYkZGRkZGRkZH5SIKCgoSLi4s4fvz4J5k/OjpaFChQQGzbtk1MmjRJDB06VAghRFhYmChRooRQq9UCELVq1Urz3Pv27RMNGjRI8XyTJk3Erl27khz39/cXrq6uQoiErBY3NzfRqVOnRJs7g8EgmjZtKhQKhfj555+F0WgUq1evFk5OTmL69OlCp9OJPHnyiHz58gmFQiEGDBjwwZvVR48eCUdHRxEQEJDouE6nE5IkvTeLZ+XKlUKlUglJkoSNjY1wdHQUV69eFUOGDBE1a9YUrVu3Fv379xdCJDz3IkWKCECYmZmluKH/UOrXry/mz5+f4vmoqChRtWpVU3ZLlixZxMmTJ5Md++TJE+Ho6Jgoe+ffXL16VZQtW9aUMQOIvHnzihcvXpjGKBQKceHChVTFP23aNGFtbS0A8fjx4/eOj46OFnPmzBF58uQRtWrVEocOHUryeo0ZM0aYm5uLy5cvJ7m+QYMGYt++famK7erVq0KSJCFJkrh7926qrklvjh07JooWLZrovd6jRw8xfvz4DIlHJmXu378vlEqlGDlyZEaHIvOJkYURGRkZGRkZGZmPwGAwiDp16ohx48Z90nXWr18vVCqVMDc3F7ly5TIdj4qKEhUqVBCAaNeuXaJr9EaDCDPEiWB9tAjUR4lAfZQI1keLcEOc0P+18Vy+fLno1q1bius2a9ZM7NixI8lxjUYj1Gq16NWrl3B1dU2SVRIcHCxy584trKysxOnTp0VISIho1aqV8PDwEFevXhV6vV706NFDSJIksmXLJp4+ffoRTyeB8ePHizZt2iQ5rlAohFarfee1V65cEYMGDRL79u0TkiSJmTNnity5c4uAgADRunVr0bJlS5EvXz6xbds2IYQQISEhIl++fKaSlGvXrn10/EIIceLECeHm5iY0Gk2KY8LDw4WZmZkYPny4GDp0qFAoFEKhUIhatWqJO3fuJBrbqVMn8eOPP6Zq7aCgIGFjYyMkSTJlIrm4uIj+/fsLc3Nz8ccff6RqntevX5uyUNKCVqsVa9asEcWKFROenp5i8+bNQq/XCyGEiI+PFzly5BBFihRJIp41bNgw1cKITqcTgLCwsBCrV69OU3zphdFoFCVLlhQHDx40Hbt//75wcnISkZGRGRKTTMqsWLFCSJIkDh8+nNGhyHxCZPNVGRkZGRkZGZmPYObMmcTHxzN+/PhPtsbOnTvp1q0ber0ejUbDy5cvTV06rK2tOXnyJIUKFSJn3tw8Nrzhgi6YQ5qn7NU+5owukIv6YK7oX3JF/5KL+mBO6wLZq33EYc1TFJ55KdGoBlqRfOeTlMxXDx8+jMFgQKPRcOvWLRo3bmw6d/ToUfLmzYu5uTnPnj0jMjKSUqVKkS9fPi5evMjjx49xdHRk06ZNTJo0iSxZspAnT56Pfk6jR4/mwoUL/Pnnn4mOm5ubv9c8tEyZMsydO5dGjRrh4eHBli1b6NevH19//TWLFi0iJCSEypUr079/f/z9/XFycuLcuXO4uLgQGxtL9erVuXv37kfFL4Tg+++/Z+LEiZiZmaU4rkmTJmTNmpVff/0Vc3NzRo0axY4dOwgMDKR48eIUK1aMAwcOcPXqVY4ePcq3336bqvVv375Nnjx5cHd3J0uWLJQtW5ZKlSqxfft2NBoNPXv2pHfv3u/taOPo6IhKpcLe3j4tt49araZz587cvHmTH3/8kVmzZlG0aFGWLl0KwNq1a3ny5AmLFy9Ocq1IZVcalUqFSqXCysqK69evpym+9EKSJIYOHZqo60mhQoWoU6cOCxcuzJCYZFKme/futG3blmbNmhEaGprR4ch8KjJamZGRkZGRkZGR+VI5d+6cyJ49e5LyjfTmxo0bomjRoiY/CLVaLQIDA4UQCZ8+vzbEinPaF2JX/EOxO/6h2BH/IE1f2yL9xK74h+KiNkiEG+ITrd26dWuxefNm088hISGiY8eOIn/+/CJ//vzi6tWricaPGzdOSJIk2rVrJyIjI0W/fv1E3rx5xfHjx0VQUJAoV66ckCRJtG/fXmg0GmE0GkWRIkXSrRxlx44dwt3dPVGGiL29fZr8G86dOyckSRJ+fn6iW7duolmzZiIkJEQUK1ZMNG7cWHh5eQmdTieESDBAdXJyEoBwcHBIVelISuzZs0cUL17clCWRHFu2bBGSJInLly8Lo9Eo8ufPL65cuWI6f/PmTVGtWjWTIa+3t3eqy5OaNWsmFi9eLCpWrCjq1asnli5dKnLkyCH69+8vnJ2dhYeHh3BxcRGAcHJyEt26dRP37t1Ldi6VSiWyZcuWtgfwL4xGozh58qRo2LChcHFxETNmzBB16tQRVlZWiYxYGzVqlKwPTkrY2dmJXLlyiUaNGn1UfB9DXFycyJ49e6Jynhs3bghnZ2cRGxubYXHJJI/BYBD58uUTBQoUyDBvGplPi5wxIiMjIyMjIyPzAbx584b27duzaNEicufO/UnXKlGiBLdv32bBggXY2dmh0+m4cuUKr41xHNUGcEb3giBjDEYEBlL3yfk/UZipMCIINEZzSvecY9oAIowJGRYKhQKDwYAQgs2bN1OiRAmyZ8/OjRs3KFy4sKllr16vp2bNmkyZMoWFCxfyzTffULZsWWJjY7l69Sr79u3D1dWVsLAwbty4wYYNGzAzM0OSJNq0acPmzZvT5Vk1b96cPHnyMHfuXNOx1GSM/JOKFStStGhRevbsyeLFi4mMjGTatGkcOHCA69evExkZycSJEwHIlSsXly5dws7OjvDwcKpUqUJgYGCa4zYajYwdO5bJkyejVCqTHaPVaunWrRudO3fG09OTq1evIkkSpUuXNo3x8PDAx8eHP/74A3Nzc3bu3ImNjQ1DhgwhPj4+xfUfPXqEr68vnTp1Mh3r1asXfn5+KJVKQkJCUKvVPHv2jMDAQDp06MCRI0coUqQIWbNmpVOnTty+fRuAyMhI9Ho94eHhaX4O/0SSJKpXr87+/ftNGTCXL19Gq9XSvXv3RGNFKjNGICGjRa1WExAQ8FHxfQwWFhb06dOHOXPmmI6VKFGCChUqvLM1sUzGoFAoOH/+PM+fP6dLly6MGDGCGjVqpPp6IUATCVEv4M0ziPBP+G/UC9BEmTqKy2QgkkjLvyIyMjIyMjIyMjIIIWjXrh1OTk7Mnz//s64dGxvLwCGD6DjpO+IczT9ICEkNCiQKKOyY1mMYlStV4ujRo9y7d4/ly5dTuXJlQkNDad68OT169KBOnTpUqFCB+Ph4jh07xq5du1i8eDHz588na9astG3blsjISH7++WeGDh2aZK1bt27RqFEj/P39kSTpo2O/d+8eXl5e3Lx5ExcXF/LmzcuJEydwc3NL9RynT5+mevXqPHz4EHt7e6pUqcI333xDlSpV+OqrrxBCsHXrVmrVqgUkCAslS5YkNjaWvHnzcvHiRbJly5bq9TZu3Mjs2bM5d+5cis+gefPm+Pj48Pr1axQKBWPGjEEIwfTp0xONMxgMlC1blh9//JGmTZsyfvx45s+fT0xMDI0bN2b+/Pm4uromumbYsGGYmZnx888/U7lyZWxtbTl06JDpfLly5Xjw4AEFChRg3rx5VKlSBYBXr14xY8YMNm/ezLNnz7C3t8fNzY07d+6g1Wrx8fGhatWqqX4O7+Px48d07NiRc+fO0bp1a2bMmMHgwYPp168fTZo0SdUcXl5eBAYGEhsby6tXr9IttrTy4sULihcvzpMnT0xlRxcuXMDb25sHDx68s5xKJmPYuHEjHTp0wMzMDCEE0dHRyb5OQkB0MEQHQWwoaKMACd7+aguR+HsEmNuClRNY54Qs2f8+L/N5kDNGZGRkZGRkZGTSyPLly/Hz8+OXX3757GvHW0i0nT/uk4oiAEYEj41vqD2xHwvWraB48eJcuXKFypUrAzB58mR8fX1ZtGgRhQoVwsXFhePHj9O3b18uXbqEj48PCxcupHbt2pQrV47Q0NBkRRGA4sWLY21tzfnz59Ml9iJFitCrVy9GjRoFJHw6n5aMEYCqVatSsGBBevTogaOjI/v27WPChAkEBQWxZcsWDAYD7dq1M3kOFChQgCtXrmBubk5AQADVq1dPdcaETqdj3LhxTJ06NUVR5MSJE+zZs4etW7eiUCgQQrBlyxa8vb2TjF2/fj1ZsmTh66+/RqVSMXXqVN68ecPy5cu5evUqefLkoVy5cqbnHRUVxZo1axg4cCCQkKnx1sPmLW5ubuTOnZsRI0bQpk0bunbtSnBwMNmzZ+eXX34hICCA169f069fP+7cuWN63h06dODixYupe+ipIH/+/Jw+fZrcuXNz9OhRypUrx9WrV3n8+HGq58ibNy86nY43b96kW1wfQs6cOWnUqFGiDJEKFSpQuHBh1q1bl4GRySTHw4cPGThwIAqFAq1Wi5mZGXfu3Ek0Rq+BUD+4vween4PwR6B5A8IIwgBGfcLXv78XRoiPgLCH8NwXHuyF0Htg0GbMvf4/IgsjMjIyMjIyMjJp4M6dO4wZM4ZNmzZhaWn5Wdd+YYjmjO4F8Rg+qSjyFgMCO5ds/HR4HUN/+h4LCwsAIiIiTIaYly5donHjxvTq1Yt69erRq1cvqlevTokSJfDz8+PUqVPs378fa2vrFNeRJIm2bdumWzkNwA8//MCxY8c4ffp0mktp3rJw4UJ8fHwICAigQIECbN26lc6dO5MtWzYWLVpEXFwc7dq1M5VxFClShEuXLqFSqbh//z516tQhKirqveusXLmSvHnzUrt27WTPG41GWrVqRcOGDU1jrl27htFoxNPTM9HYuLg4fvjhB2bOnJlEZOnatSsBAQH4+PgghKBy5crky5ePgQMH8tVXX5kMcJMTZ3LmzGkqH7t79y4uLi54eHgwa9YsdDodkFCiMm3aNCwsLBg7diylSpXixYsXVKxYEVtbW5o3b87Zs2ff+zzeh1KpZOvWrcTExDBy5EhsbW2ZMGECjRs35tSpU++9vnDhwsTFxaHVapMIQJ+boUOHMnfuXPR6venY2LFjmT59erKmxzIZR5YsWShXrhzm5uYAxMTEmMRFox5eXEoQNELugEEDQv+u2VLGqAd9PITcThBYgq4mHJP5tMjCiIyMjIyMjIxMKnm7EZ4+fTru7u6fde3n+igu619+FkHknygUCpRmas7pgnhpjAFg1qxZxMXFmcbs3LmTZcuWsXTpUqZOncq4ceMYPXo0L168wMvLK1XreHt7s3nz5nTbqFpbW/PLL78waNAgzMzMPkgYqV27Nm5ubvTo0QNIyCKZPXs2TZo0oWbNmvzwww/4+vomKmXx8PDg7NmzSJLEjRs3aNSoUaJn9W/i4uL46aefmDp1aopjevXqRXx8PNu2bTMd27p1K97e3klEjLlz51KuXDlTqUtyVK1alcuXL/P06VM8PDxYu3YtBw8eZPz48aYN+r+r7XPlykV0dDSQ8GynT5/OmTNnOHLkCKVKleLo0aMAhIWF8ebNGwYOHMjSpUsxGo2EhoYyYsQI7t69i5eXF9bW1jRu3JgTJ06kGOP7qFChAi1atGDixInkyZOHJUuW0KxZM7p160bVqlXZu3dviu+lEiVKEBMTgyRJ+Pn5fXAM6UGFChXImTMnu3fvNh2rUaMG2bJlY8uWLRkYmcy/cXFx4fDhw5w/f56GDRsCMHXqVGJewYP98Cbgr8yQdNLa3maSRDyBhwcSSnJkPh2yx4iMjIyMjIyMTCoZOHAgr1+/ZuPGjenihZFagg0xXNAHY/zMosi/USJRIExJ8ZxJvTpcXV0JDAykSpUq7Ny5EycnpzTPX6JECRYtWpRqMeV9CCH46quvCAgIYNWqVVSrVi3Ncxw4cIDGjRvz/PlzcubMCcDEiRPZt28fJ06cYPDgwaxZs4ZTp05RqVIl03VnzpyhevXqqFQqatWqxe7du5P1Ivj11185ffo0O3bsSHb9GzduULp0aTZu3EibNm24dOkSpUuXpnjx4mzYsIFy5cqZxr5+/ZqiRYty+vRpihQpkqr7279/P99//z2VK1dm9erVGAwG7OzsKFq0KD4+PqZxmzdvpmvXrklEHiEEe/bs4ZtvvsHT05MiRYowZ84cU6aMhYUFixcvpmvXrkCCR87cuXNZvXo1d+/excLCgmrVqvHdd99Rt27dVMX8ljdv3pArVy5sbGxYvHgxzZo1Q6/Xs23bNqZPn45er2fUqFG0bdsWtVoNwPbt2zlx4gRz585FkiSKFCmS4eLIpk2bWLBgASdPnjQd279/P6NHj+batWsoFPJn2ZmRvbv3cevoa5pW74T0GXqaSEqwzwc5SoEieX9mmY9A/i2TkZGRkZGRkUkFO3bs4MCBAyxevPiziiIxQsfFTCCKQEJpzQ2zSCpW86JWrVomw0iA58+fM3v2bE6fPv1BogiQrt1pIKEkZO7cuTx79oyXL19+0BwNGzYkd+7c9OrVy3Rs/PjxFClShC5durB48WLKlStH3bp1iYyMNI3x8vLiwIED6HQ6Tp48Sfv27ROVSwAmQ9pJkyaluH6jRo2oVKkSbdu2JTAwkAoVKmBnZ0dgYCChoaGmMhZI+PTa29s71aIIwJw5cxg2bBgLFy4kOjqaadOmERERwalTp6hZs6bJQyF//vxotUkNDyRJolmzZty+fZtSpUoxY8YM7O3tTQKKu7t7Ir8MKysrRo0axZ07d4iNjWXy5Mm8ePGCBg0aYGlpSe3atdm3b1+qYrezs2P27Nm8fPmSe/fuAaBSqWjbti1Xrlzhl19+Yfny5RQqVIh58+YRGxvL999/b+pYJIRI9JplFC1btuTx48dcvXrVdKxhw4aoVCr27t2bgZHJpIRBB8WsGtPsM4kikJBBEvEU/E/KpTWfAjljREZGRkZGRkbmPQQEBFC+fHl2795NxYoVP9u6Qgh8dM+JEJpMIIv8hYDbJ85xecn2JFkOZcqU4cqVKx889b1796hVqxbPnz9P10/J3dzcKFy4cKIuK2lh165dtGzZkqCgILJnzw6ARqOhTp06eHl5MXHiRPLly4eDgwO3b99OJJxt2bKFNm3akCVLFlq2bMmqVatM9zZhwgQeP37MmjVrkl13zJgx/Prrr7x69Qp7e3v0ej2WlpYmgUWhUNC5c2eKFy9O06ZNqVq1Krdv3yZHjhypuq+7d+9Ss2ZN/P39Tb4JANWqVePNmzfodDru3btHkSJFmDRpEt7e3hgMhne+NtbW1ri5uRETE8Ps2bO5cuUKs2fPJiIi4p2xaLValixZwrJly7h58yZqtZpKlSoxdOhQmjdvnuKaQghsbW2xtLQkODg42XHnzp3j559/xtfXlxYtWrB69WpT6+JSpUpx7dq19z+sT8z06dO5e/cuq1atMh3bunUrM2fOfGenIpnPj0EHT46BLjr9ymbSgqRI6GCTrxYoVJ9//f8qcsaIjIyMjIyMjMw70Ov1dOjQgW+//faziiIATwxviBTazCOKAEhQrFp5IiwFlpaWqFQqOnTowIQJE/j+++8/auoiRYqQPXt2Tp8+nU7BJlC8eHEuXLjAhQsXPuj65s2b4+LiQu/evU3HzM3N2bFjB9u2bWPt2rVcunSJR48e0aFDh0TXent7s3TpUmJiYti1axeDBg1CCEFISAhz585lwoQJya7p7+/PjBkz+PXXX02ZOSqVyiTMqNVqSpUqhaOjI2PGjKF06dJUq1YtTS2C58yZQ9++fROJIpCQBZI1a1b8/Py4efMm2bNnp02bNkBCGVFK3h2vXr0iJiaG48ePs2TJEkaNGsWpU6d48+YNwcHB74zFzMyMQYMGce3aNTQajakcx9vbG0tLS6pWrcqmTZuSrC1JEuXLlycsLIxx48ZRokQJvv7660RjKlWqxI4dOzh+/DgajSaRf8qHeM98Cnr37s2uXbtMmU1CCOq3aEreSiXZ//gKJ7TPOKB5wj7NY/ZqHrNf85hDmqec073goT6cUGMcuozYpf+fYTQkZGxklCgCCetqIsHfJyEemfRBzhiRkZGRkZGRkXkH48eP5/z58xw4cOCz1vrHCh1/agM+u9lqalEYBPUt81OxTFlWrlxJmTJl0mXeqVOn8uLFC+bNm5cu8wG0a9cOBwcHLl++zLlz5z7oddy8eTPt27cnJCQER0dH0/H79+9TrVo1NmzYgF6vp1GjRvz444+cOXOG5s2bM2DAAAB++eUXRowYgb29Pb169UKv16PVapk/f36y67m7u6NQKLh9+3ai42XLluXKlSuULl0aHx8fRo8ezYIFC4AEP49ixYpx6dKl92YYhIeHkz9/fu7cuYOLi0uic9WrV0epVHL8+HHTsbCwMJycnFAqlajVarp3787MmTOxsrIyjZk2bRrTp083tcHVarXMnTuXESNGUKVKFQ4ePPjO7kTJYTQaWbNmDQsWLDCVmpQtW5YBAwbQqVMnFAoFTZs25erVqwQGBgIJ3XFev36d4pz37t3D3d0dIQTm5ubcuHGDwoULpymuT0Hfvn3JU7gALYb25InhDUYEep0OoVS88z2rABQoMGDEXjKnkNIBZ0UWFHKWSbrz4uLfJqsZjaQA+/zgkj7/9P7fIwsjMjIyMjIyMjIpcOLECTp06MCVK1dwdnb+rGtf14Xw1Pgmk8oioECisNKB4c06MXDgQBo3bpwu8z548IBq1aoRGBiIUpk+DoNdu3alRo0aLFu2jJ49e9KzZ88PmsfFxQUvLy+2bt2a6PjJkyfx9vbm5MmT9OvXDx8fHyRJomHDhon8MsaOHcvUqVOxs7NDq9Xi9/gBZtnsCTfGEyriiBY6jAi0Oi3x8fHYZLHGWmlOVskCR4UF9gpzmtdpyK1bt3j48CE2NjZ06dKFtWvXIkkSVlZWzJs3j27dur33Xn799VeuXr2ayP/jLTVq1ECSpCRdY6ytrZk1axbPnj1j7ty5REdH07BhQ+bPn0+ePHmoUqUKKpUqkWnr2/lu3ryJlZUVM2bMoH379h9UGmI0Gtm4cSPz58/n0qVLGI1GypQpw/Xr1xN5rVhYWKTYCUgvjEQKDQvWr0SysyIqLhYrG2sc7OwoXKAQ+ZyccVBYYC+ZYyZ9PofL18Y4Lr95RpTagFqt5mP23SokJCQKKO3Jr7T7rPfxXybmFQScTvD6yCxISshbA6yyZnQkXz6yMCIjIyMjIyMjkwyhoaGUKVOG5cuXU69evc+6tl4YOaB9kmmzRd5ihoJtA6dSuVKlROakH4unpyezZs2iZs2a6TJfnz59KFu2LBUqVKBhw4bcuXMnUdZHalm3bh1du3YlLCwMOzu7ROdWr15N//79E23InZ2dCQoKSjRu4JDBPIh5Rcvv+pI9Ty7MVCr0qXidVUgIQCEk8mJNIXNHzCUVXl5e+Pr6Urp0abZv346bW9KOQf/GYDBQsGBBNm/eTPny5ZOcr1mzJkKIRF1SALJnz07//v2ZOHEiAGvWrGHcuHE8e/YMT09Pbt++zZQpUxg+fHiSZ9O3b1+OHTvGoEGDsLGxYe7cuZQsWfK9saaE0Whk+/btzJkzh1OnTiU6J0lSopKbWKHjsf4NgcZo4tGjRMJgFCTnmWnQ6VFKCiSVAjUKsimsKKC0x0Ey/yQ+Hzph5KY+hEBjdLr/viuQUCJRVpUDZ2WWdJ37/w2jPqElryFzVF4lQmUJBRvKnWo+FtljREZGRkZGRkbmXwgh6N69O+3bt//soghAoDH6s6/5IRgQuFevwIsXL9J13rZt27Jp06Z0m8/c3ByNRkOZMmVo2bIl48eP/6B5OnXqRNasWenXr1+i4waDgTNnziTp2hIcHMzDhw+BhM35Nd0r6v08hO4zvsc5fx4UKmWqRBEAPQIDAp1k5LEUxSGtP+d1QRStUIZu3bpx6dKlVIkiALt378bZ2TlZUQQShIXkPju1sbExlasAdOnSBX9/f06fPo1Wm5Dl8ttvv7F27dpE17Vv3x6tVotarebixYt06NCBunXrMnjwYMLDw1MV879RKBS0bt0aHx8fvv76awYPHmwq0xFCUKpUKRZvW4+P5hlHtQE8NkYQ99fT1pO8KAKgVKtApUAAWowEGqM5owvkqDaAp4ZI9OlYQxFijOWI1p/nn0AUATAi0GHkoj6YC7ogtJkp1eELI/gaGHXvHZYhGLTw6mZGR/HlIwsjMjIyMjIyMjL/Ys6cObx69YrJkydnyPoPDOGZPlsEEoSRPDU9010Y8fb2Ztu2bUna234ob4URgMmTJ7Nly5YP7kQydepUtmzZQnT03+JVQEAA69evx8zMDEgQFt5+X69ePW5GvuBPbQD+xkiEQsLS+uM+vTciMCIIMsbQfPowhi+ZAYrUZzPMmTOHoUOHpnldBweHZE1Uq1SpQuvWrbGxsaF06dJ0794de3t7xo0bh16vx8zMjFy5crF48WKUSiV9+/blzp076PV63N3dWbZsWYqGrqmlZs2aREVFsXTpUop4lmTQxtnY1vQgDM1fz+vDMSCIQcdNfQiHtE95aYj5qFiFENzWhXJOF4QWwydvxW1AEGyM4ajWn0hjJkx5yOToNfDGP3P4iiSHMED4o4RuOTIfjiyMyMjIyMjIyMj8g6tXrzJlyhQ2btxo2tx+TrTCQIz4cv7CNctmx8uQV+k6Z/78+cmbN2+SUo4P5Z/CiKOjI5MmTTJ1h0krvXr1wt7enoEDB5qOubm5ERwczMyZM8mdOzdCCLJnz86PP09h/MHV+BnDMCA+yfbXiOCJMZKj2gDCjfHvHX/jxg0ePHhAq1atUhyTUsaIk5MToaGhyV6zd+9eypYty549e4iMjKRz587MmjWLLFmy0LFjR6pUqcLRo0dN47NmzcrChQs5cOAAK1eupGLFipw/fz4Vd5x8vJAgONTo1oqZvtvJViD3RwtQ/8bwVwbGBX0wl3TBH5SBIYTgmj6Ex8Y3n1X8NJKQAeOje56q94nM34Q/BjK7j60EEU8zOogvG1kYkZGRkZGRkZH5i+joaNq1a8ecOXPInz9/hsQQITQoM/1f4X8jCYHeUpXu87Zp04bNmzeny1z/FEYAevbsSXx8POvXr/+g+X766Sc2bNhAbGys6ZiNjQ0DBw7E39+fbdu20WX4AMoObYtDXhfMLC0++h7ehRFBHHpO6wJ5pI9459jff/+dAQMGoFarUxyTkjCSI0eOFEtfbt++TcuWLQGwsrJi7ty5REVF8fPPP3P06FG2bNmCv78/N27cSHRdmTJlOH36NEOGDKFly5b06NHD1LI2LQi1kuO6Z9w1hH1ywcGAINAQzRGtP2HG5E1ek0MIwQ19CM+NURmWEaZHcEYXyBs5cyRVCAFhDzKX4WpyCAO8vpcQr8yHIQsjMjIyMjIyMjJ/MWjQIKpWrUq7du0yLIZwY/x7N00/Ne3BoaUbEx0TQtC3SC1un7qQ5jVvnTzPtSN/G1iGvXjJuHqdUnexpMAyR9qNTN9HmzZt2L59e6JuIx/Kv4URpVLJvHnzGDVqFJGRkWmeb8CAAVhbWydbjiJJEhWb16XSgDafLEskJQwI7hhec1effKvakJAQtm/fTu/evT9o/pw5cyb7vAICAoiNjaVr166JjisUCr755htevnzJ3r17AShVqhRFixZN1K1HkiQ6d+6Mn58fWbNmxcPDg99//z1RKZUQIsUMH+tsDiiqFSJKaD+b4CAk0GHktCaQV8bYxOdSiPOBIZyADBRF3qJHcFoXSJxIn1K1/zLRwZlfFHmLUQexIRkdxZeLLIzIyMjIyMjIyJDQceT8+fPMmTMnQ+MINca9d9tUu2trjq3dlujYrZPnUaqUFK9WIc1r3vI5z7U/z5h+dsyZg0mHk7ZxTQ6hgFwehTAY0nf3kDdvXgoUKMDx48c/eq5/CyMAlSpVon79+vz0008fNOf48eNZvXp1knmDDTFc1r/KsM2vAcEDQwT39UkzO5YuXUrLli3Jli3bO+dIKWMkd+7cxMQk9ddYunQpDg4O2Nrapjhno0aN8PDwoFq1ari4uNC0aVNy5MjBrFmzTP4itra2zJw5Ex8fH/bu3UuZMmVMLYP79u2bbOejOKGn4U+DwFyVIU/cqACf6KdceHQHSCgpKly4MBEREYnGRRo13M1E3kF6jFzWBX9QOdn/E1EvEjrSfAkY9RAd9P5xMskjCyMyMjIyMjIy//c8ePCAYcOG8ccff5AlS8a2tYxOhb9IhWZ1CHoUwDO/B6Zjx9Zs46surdjx6xJGVG3Ft5WaM6VlH8KDEz5C/GPSHH7t/A2TmvdiUMn6TP66F5rYOPxv3ePQso0cX7+DYRWasm3mYl49fU6XXKkXWPKXLkZISPp/VJle5TTJCSMA06dPZ/Xq1dy5cyfNcw4dOhRLS8tErWkjjPFc1Ad/cjPN92FAcM8QxjP939kdOp2OBQsWMGTIkPden5Iw4ubmRnx8Un+K/fv3U7Zs2ffO26RJE27fvs3x48cJDQ2lXr16jBkzBmtrawYOHGgqTXJ3d+fw4cNMnDiRbt26UbduXdauXcvGjRtNQgkk+PH4aJ9jbm2FpMi4bY3K3Iwnjga6fzOAQYMG8eTJE1q1amUSfIxCcCETvC/+iQDChYYAQ1RGh5KpiUveUifTEvuFxZuZkIURGRkZGRkZmf9rtFot7du3Z8KECZQqVSqjw8GQiv4ZajMzqrdtwrG12wGIi4rm/J4jOObMQdCjAH722cKv53ZRtkENVo2aZrru0ZVbDF89i7nXD6LX6Tm5cTd5PYpQv1d7anVswW8X9tBqRN80x2yZJUu6d6aBhO40O3fu/OhympSEkezZszN+/HgGDx6c5k/OFQoFY8aMYdmyZWi1WgxCcEEXnGkyAgwIrhlCTOUS27Zto2DBgql6j781M/03BQsWTLZTkJ+fH61bt37vvP379ycsLIywsDAcHR1Zu3YtMTExjBgxgg0bNmBra0uTJk0ICAhAkiRatmzJ9evXuXTpEvHx8cTFxdGhQwfi4hJ8Pa7pX6FBj0KlfO/anxpzK0safN+H4JBXpvbNP/74IwD3DWGZsmzFgODGP94jXwL58uXj1q1bAMTGxlK/fn26d++ebhlrq1atMr2XhQBtCp3T52+ewMw136XLmqml24SanLicUJJmMBr4fn43+kxpQJzm71Ku+Dcf7zMyYcIEvvvu895bZkAWRmRkZGRkZGT+rxkzZgy5c+dmwIABGR0KQKq31bW7eXNywy4Mej2nt+zHvUo5Lh88wY1jZ/i2UnOGVWjK/kXreeUfaLqmdN1qZLG3RZIkCpcvRfCTgHSJ2dzC/JMII7lz56ZIkSL8+eefHzVPSsIIJGzWQ0JC2Lp1a5rnHTlyJGq1mlGjRuGnf42GzGVGYERwWfcSIQS///57mlr0JicU5cuXDyFEoqyRR48eERcXR+fOnd87Z548ebCxsWHx4sWmYyqViokTJxIeHs7q1au5desW+fLlo2zZspw5c4bx48cnKksJCgqiQ4cOBBlieGmM/ag2vOmOWkWXqSMB0Gg0TJ48mcUrl3PfEJFpBLN/Y0RwS//lpRm8efOGevXqUbRoUVasWIFSmf7imDaKT7pb1hs+TJDS6rUMn9WG2Pho5o3ajaW5VaLzutgULpR5J+lvIS4jIyMjIyMj84Wwf/9+tmzZwrVr11L8lPxzk9oo3Eq64+CcjSuHfTi2ZhtNh3Tj9Nb9tB49gDrdvJO9xszC3PS9QqnEGJ8+nSm0Gg0rVqzg2bNnGAwGLl++zJMnT1i0aBEODg7Y29tjbm7+/omSoU2bNmzatIkGDRp8cHzvEkZUKhXz5s2jU6dONGrUKE2lVAqFghEjRrDp4G5qGQdlqlIJeFsuEc+Jh9cJDg6mWbNmqboupVIatVqNJEk8evSI4sWLA7BkyRKyZs2KlZVVkvHJ4enpyY4dOxgzZkyScx07dqRjx46cO3eOwYMHU61aNRR/lcioVCq++uorXr16xYuQl1zRv8x0YoO5lQW1u7Qi6LIfHrny8/z5c5zLu2d0WO9EAEHGGLTCgJmU8Zk3qeHVq1d07tyZpk2bJvII6tixI/fu3UOj0VCwYEFWrFiBg4MDAKtXr2bBggXo9Xrs7OxYuHAhRYoUQavVMnjwYE6cOEGuXLkoWrSoaT5NFCzfMYNDvlsxGPVkd8jFxH5LyWbvDMDLsED6TWvE85ePyZ2jALOGb8HS3IpzN/9kzh8/oNHFYzDo6dNyLI28Egy9u02oSenCVbjx8DzmagvmjtxF/+mNeRP1mnhtHCUKVuDHPosxUyXfKj5OE8PA6U3I4ejKxH5LUSoSXrOZa77jot9J9HotzrmdWL1uBXnz5mXZsmXMmzcP+Ksr0o0bXLt2jR07dhAdHc0vv/wCJGSJ/PPnwMBAGjVqxOPHjylQoABbtmzBysqKP//8kx9++IH4+Hj0ej1jx47NULPy9EQWRmRkZGRkZGT+L3nx4gU9e/Zk8+bNODqmf1eVD0WJAlL5OXjtrq3ZNHkuIQGBlG9SG22chr3zV1OpeT2sHezQaTQ8v/cYt5Lv3pxZ2lrz+kXaW6S+RRev5cyZMxw/fpzIyEiEECgUCpo3b05ERATh4eEolUqTSGJvb5/q72vXrs2ECRNYvHgxZmbJbxbex7uEEYDq1atTrVo1pk6dypQpU9I097hx47BrXBaD0YikyBzi2j8xIAjNpmLQkCGp/lT9XSKhWq1OJIwcOnSI8uXLpzqe9u3b880337xzTKVKlbh48SLbt2+nTZs2AOj1enx8fLh//z6vnM0INGZObwxzK0uajR/IiQlLWLtmDQd1T9FmrryWJEiAvyGSQiqHjA4lVbRp04YBAwYkMU7+/fffcXJyAuCHH37g559/Zvr06Zw6dYrNmzfj4+ODubk5Bw4coEePHpw5c4bFixfz5MkTbt26hU6no3r16uTLlw+AP7asIyD4IRunnEOhUPDH4YXMXPMtM4YktPm+/fgSm6ZdxMbKjj5T6rP31Hq86/TG3c2TtZNOo1QoCY14SZvRZfEqVR8764Tn++DZLZaMPYRKqUIIwcwhG7C3yYoQgu/nd2XHsRW0rdcv2XufvHwg1cs0ZlL/5Yl+T3t9PZoRXX5BoYKjD5cxatQo/vjjD3r16mUyLR47diwFCxakRIkS7Nix453P+NKlS1y8eBE7Ozvq16/P+vXr6d27N56enpw+fRqlUsnLly8pW7Ys9evXNwlQXzKyMCIjIyMjIyPzf4fBYKBz587079+fatWqZXQ4icgiqVNd81+9XTNWj/mZer3aoTYzo2bHr4l8Hc4PdTsCYDQaadi343uFkUrN6vLzhp0Mq9CUqt5NqObdOE0xv3wUwKtXrxIdMxqNxMXFkSdPHry8vMifPz/58uXDzc0NW1tbIiMjTaJJREQEwcHB+Pn5ERERkej42y8HBwecnJzSJKq8/d7MzOydwgjAzJkzKVmyJN26daNQoUKpvvcodOQuUjBTiiJv0RsMNO7ZPk3XpOS5YmFhwZMnT0w/3717N1WGrm/p2rUr/fr149atW3h4eLxz7KZNm1AoFFhZWREfH098fDylynmy4skZUGZeR4AsdrbceelP014d6TNvYqaOFRLEs4eGCAoq7TNN5ty7aNSoEZs2baJ///64uLiYjq9Zs4b169ej1WqJiYmhcOHCAOzZs4fr169TsWJFIOG9HR6e0LXp+PHjdO3aFbVajVqtplOnTpw+fRqAA0d2c+n6JVqP8gTAYNRjbWVnWs+rVH1ss9gDUKJgRZ69fARAeGQI4xb2wD/4AUqFijfRYTx9cY9ShSsB0LhqB1TKhG24URhZuecXTl09gNFoIDImHAuzlLOvqpZqwLmbR3kU6EdB12Km46euHWDjofnExkcjmelR/GuXv3z5co4dO8axY8dMWVjvon79+tjbJ9xbxYoVefQo4d5CQkLo0aMHDx48QKVSERYWxr1796hUqdJ758zsyMKIjIyMjIyMzP8dP//8MwaDgbFjx2Z0KElwUljy2vD+lr0A1g52bIq4lehYsyHdaTake5Kx7cYNSfHnHG65mXV+d6LzawIvpCpeJRKl8hSkfv36GI1Gjh8/jl6vx9HRkWbNmvHo0SMePHiAr68vb968QaPRIIRArVZjY2ND1qxZyZkzJ25ubri7u1OqVCnKly+fKItnzpw5nD17lunTpycSTP75/aNHj5I9Hh4eTnx8PJIkUbBgwXcKKfXq1aNDhw7MmTPHdM7BweGdZUAPDRGozDKmVWxqsbTOwgtJS9H3DwVSLqUByJIlC8+fPwcSTFc1Gg0dOnRIdSwWFha4uLiwcOFC5s+f/86xM2bMYNiwYVhZWZElSxYsLCy4/iYQbapXyxisbK35ad1CtPEaRCYXRd5iwEi4iMdRsszoUN7LyJEj2bNnDzVr1uTEiRO4uLhw6tQpFi5ciK+vL9myZWPDhg0sWbIESBBCevTokWxr7neZLgsE/bx/oEWNHsmeN1dbmL5XKpRotAmmwD8t60+tcs34/bvtSJJEo6GF0ej+9uWxsrA2fb/v9Aau3D3N2p9OkcXShiXbp/I06H6KMTX0akfV0g3oPakuS8cdoaBrMV6E+PPz6mFsmnaRPDndeK7wpdfQv38nDx8+zMyZMzl16hSWlgmvr0qlMnVNApJ0m7Kw+Me9KZUmw+P+/fvTrFkztm9PuLfChQsn26nqS0QWRmRkZGRkZGT+r/D19WXOnDlcunTpkxj2fSzWBgW6eC0qiw8rG/ncSMCuNX9w6NAhzM3NTZ84582b11Tb/m+CgoK4dOkSN27c4N69ezx58oRTp06xc+dOoqOj0ev1SJKEhYWFSZy4f/8+Tk5OlCpVijJlyuDl5YVarU5VjKdPn2bIkCFs2rQpRfHkrT+Kn58f3bp1Q5IkwsPDTWVAyYkp2VxyUGNiXxTqzP8ndYzQEWHUYK94v9fLu4QROzs7k9HusmXLyJYtW6JNVGqoXr06hw4deu+4vHnzkjdvXtPPQghuOmkhk5ncJoewt8QCS3SZWjL7GyMQbtTgqMj8wggkmGYLIUziSEREBHZ2dmTNmhXNX55Hb2natCldunShT58+uLq6YjAYuHbtGmXLlqV27dqsXbuWtm3botPp2LBhA3ny5AGgUb1mzJn7O1+VbYGdtQNanYbHgXcpmu/dnZ2iYiLIlS0fkiThe+MIAcEP3znWwcaJLJY2RMW+Yd+ZDRTPX+6d8zet3gmBoNekOiwbdxSj0YBaZYaTvTNGo5EVGxaZxt68eZO+ffty6NAhsmXLZjpeoEABjhw5gtFoJCYmhr1796bKxykiIoJ8+RLu7ciRIzx8mPK9fWlk/n/FZWRkZGRkZGTSifDwcDp06MCSJUtwdXXN6HCScP78eQZ/O4xRB1e8f3AmwYCgcfXarJ6zCJ1OZ/oUUgjBlClT8PLyonz58olMTV1cXGjatClNmzZNdk69Xs/du3e5fPkyt27d4sGDBwQEBLBhwwZWrlxJXFwcRqMRlUpFlixZcHBwIGfOnOTNm5ciRYpQokQJypcvT+7cuYGELAej0ZiqEpkjR47Qt29fbt++jaWlpakLS3KZKhonS4Txy9j4GhAEGCKxV2R779h3lVM4Ojry8mWCH82hQ4dM5QlpoVevXmzZsgWj0ZiqtP63vBbx6DO5X8dbBIKUmkxHh7+hR74q1O/dnp6//JDmuX+o25Hmw3pSvtFXHxfkPzAiCBVxFMA+3eb81Hz//fcmceTIkSMUKFCAokWL4urqSrly5bhwISHrrXr16kyZMoVmzZphMBjQarV4e3tTtmxZ+vTpw40bNyhevDiurq7UqFHDVCrWtXtnHl8PpduEGkBC2Uu7egPeK4wM6zidScsGsGzndArnLUnhPCVTHNusRheOXdpFs+HFyeGYi7JFqxH/V+bJu2hWvTNC/C2O1K/kTfPhxXHJlod6zWrge9EHgFmzZhEdHW3y6oGEErVWrVqxefNmihcvTsGCBSlbtux71wSYPn06AwYMYPr06ZQsWZKSJVO+ty8NSaS1abuMjIyMjIyMzBeIEAJvb29y5szJnDlzMjqcRMTGxjJ+/HjWrVvH7NmzcWxRiVg+rJXj58ZeMsdLciZLlix4enpy8+ZNdDodS5cu5ebNm/j6+nL9+nXc3d3x8vKiSpUqeHl5pVmYWrBgAb6+vqxbtw5IaNd56dIlrl+/zt27d3n06BEvXrwgJCSEqKgotNqEggtzc3OsrKyIiYmhRo0aFChQgGLFilG6dGk8PT2T7ULTunVrSpYsyfjx44GE907v3r3p3bt3IiHgmu4VT42RH/roPjt2khm1zPK8d1zjxo15+fIlly5dSnLu66+/xt/fn6tXr2Jubs6KFSvo2LFjmuIwGo2Ym5uzbdu2VHfKAbinD+OuIewLycFImf0L13J2xyGe3X3I0oc+qNNoKpwewohBr0epSvwZuSUq6pvn++A5/2sIAXe3g/gytDgAJBUU/Rq+AKuYTIcsjMjIyMjIyMj8X7B48WIWLVrE2bNn05z6/yk5efIkvXr1oly5csyZM4ds2bLxRB/BLcPrTNeO9N8okfBU5SCX0hpnZ2cuXrzIpEmT8PHx4e7du6Zx8fHxXL58GV9fX86cOYOvry8WFhYmkaRKlSqUKlUKlSrlZOaXL19StGhRXrx4YaqTfxdGoxF/f38uXryIj48Pq1atwsPDg+DgYMLDw4mJicFgMJjMPe3t7cmRIwd58+YlR44crF27lh07dvDVV18RGRmJk5MTZmZm9OrVi+nTpye0rtT6EyV09ClcEzMLc9QWZujitbh7laPvnAmoUlnqA/Dq6XO+82qZam+XD0Gv0bK83Xc4/FUKlJLXyogRIwgPD+fSpUtJXpN+/fpx6NAhdu3aRenSpYmPj/+gbkHu7u4ULVr0vd0x/skZbSAh4v2fpmd2vq3UnK5TR7Ft5mLq925HlZYN+WPSHALvPyY2MpoXD55QwNODlt/1YeWo6YQEBFLp63p0mzYaSBBG3Eq58+S6H68Dg/Fq1YhOk74FICzoFcuG/0TIsyC0cfFUa9OE1qP6A9CncE3qdGvNzRPnyOGWm0GLpyWKSwKamOVHKX0Zviifg0eHQfMmo6NIPZaO4FY7o6P4MpFLaWRkZGRkZGT+89y6dYsffviB06dPZxpRJDIyktGjR7N7924WLFiQ6JPz3EpbbhleZ2B0qUOBhIsiIePCxcWFkJAQlixZgsGQ2APCwsICLy8vvLy8GDFiBEIIHj16ZBJJlixZwtOnTylfvjxVqlShSpUqVK5cOVELyBw5cuDp6cnBgwdp0aLF+2NTKHBzc8PNzY0qVaqwY8cOzp07l2hMfHw8169f5+rVq9y5c4eHDx/y4MEDzpw5Q3x8PHXr1gUSjAoNBgNxcXHMnz+fZcuW8c2wYVQc1z1hNwmM2DiXvMULJ5j61m7PuZ2HqZrK7j4GffplBxkMhhS9c1QqFYNGDONNUIipHCgkJIT79+8nKhO6e/cuGo0GCwsLLC0tE4knr169IigoiC5dumBlZcWCBQtSFFlsbGxSLJVp1KgRa9euTfbckydPKFWqFGPHjmXYsGEm4eWNeHdnoX/Tp3BNxu5YQt7ihdN03afkyQ0/osLeUKJWZcJfhvDn6m1UadkQgEdXbjHTdwcW1lZ8V+lr1v7wC+N3L8OgN9C3aC3q9WxHzoL5AHjm95CJB1ajjdcwukYbilQuQ/lGXzGn5wi8xwykeLUK6LRafmzQlYJlS1C6TlUAwoNDmHR4XbKxSUjoMP7VMlwGwCrrlyWMWL2/Uk4mBWRhREZGRkZGRuY/TWxsLO3atWPmzJkUKVIko8MB4ODBg/Tt25c6depw69YtU1vEt6gkBa4KGwKMkZk2Z0QBuClsUfyVs50zZ05evHiBp6fne01t33aIKViwIF27dgUSTP3OnTvHmTNn+PXXX7lw4QJ58uRJlFXi7e3N5s2bUyWM/BNzc/Nk2/VaWFhQsWLFZH0y4uPj8fDwYPLkyfj5+fHzzz+b5oiLi2Pnkf14juyAyiKxmakuXoMuXoO1vS03jvmyfuJv6OI1GPQGWo/qT7U2TYCET/2LVvLk/sXrmFmY0Wf2BABWjZ7O7dMX0cbF0/f3CRSrWh6DXs/kr3sTFRaBNi6eQuVK0m/+JNRmZhxbs43TW/Zh6+TIs7sPGbRoKjeOn+X0ln0Y9HrUFub0mzMRt1LFUCmVlK5eiVxKGyChROjo0aNkzZoVT09P0z00bdqUFy9ecPHiRaKjoxP5quzYsYOFCxcSFBSEq6srT548SdHQNjY2Fjs7u2SzUiCh9eevv/6Ks7NzojGXL19Gq9Xy008/MW/ePJYsWULtBvW+GH+Rd/Hnqi3U7Pg1kiRR6ev6LBs+ideBwQCUrluNLHYJr01ejyLkK1kUtbk5anPIVSg/wY8DTMJIrU4tUKpUWFqrqOrdmJsnzlGiRiVu+VzgTWiYab24qBie33tkEkZqdvw6xdgkyPRZap8b65wQEQCp7KCeoUgqsHZ5/ziZ5JGFERkZGRkZGZn/NMOHD6dUqVKmDXhGEhYWxrBhw/Dx8WHZsmWmjASDwcD9+/eJjIwkMjKS0NBQ9vx5iBa/jkBt8f4uIhmBhER+lb3p57fCyIdib29PgwYNTJ0R9Ho9N27cwNfXl8OHD/Pjjz8SHR1NZGQkxYoVo1atWpQtWzZVZTUpCSPvwsLCgt9//53hw4czfPhwNBoNnp6eDBgwgNatW6O3MeOs7gX6vzaSM9sPRm1hRvDjZ5Su40XputWIDn/D1GN/oFQqiXgZyndVvqZM3WpYO9gBEHDnPj/uXYFSpeLV0+dEvQ4nr0cRuk0fzS2f88zqOoyFd/5EZWbGsNWzsM3qgBCCOT1H8ufqrTTondCS08/3MrMu7MalQEIHF8ecOWj+TU8Arv95hkWDx/Ozz1aMgBYjQggOHjzIiBEj8PPzo2fPnixevBiDwYDBYMBoNKLX6wkPD8dgMKBSqXBwcMDW1paKFSsyb948wsLC+Pbbb/nqq6/Q6/UYDAb0er3py2AwoNFoTO/pt19RUVFER0cTHR2NQqFg3rx5ODg4EBMTQ1xcHHFxcURERKD/K4smNjaWRo0aUf6raozYthC1Zdp/H95VXrJq9HRun7qAXqvDJqsDgxZPI3veXKbSpno923Hl0Ak0sfEMXDSVYl7v7hjyLnRaLT6b9mJmYcaJ9TsB0Ov0HF+XUE5k9o/fdYVSkeTnFDOLhEjoJGQ0IkkSM89sT7GMy8I6qafOP5GtKRJj7QwKJRi+AGFEaQZWThkdxZeLLIzIyMjIyMjI/GfZsmULR48e5cqVK+/stPE52L59O4MGDaJ169bcvHkTa2tr07l9+/bRvHlzbG1t0Wg0aDQaFAoF/X4cyRsLKdN9iqtEoqTSCQvp7z8lXVxcCAoKSrc1VCoVnp6eeHp6MmjQIACeP39Oo0aNOHfuHHv27OH27duUKFHClFFSpUoVXFySfmT6IcIIJJiQLlq0iFevXvH8+XNy5cplOhdijCVhG5nw2rwtpdHGa5jRbiB75q7Es0FN5vUdw4uHT1GqlESFvSHw/mOKVCwDQLW2TRMZYKrM1NTo0BwAj+oVMbOwIPD+E3IXK8Su35Zz5fBJjAYjMRFvMLP6uyTMvUpZkygC8Ojqbbb9vJCo8DcoFApePEjosqHX6xk+5jt2/rYs0X0uXbqUpUuXAolb9To7OyNJUpKvt6VSkydPZsqUKUiShEKhSPTf5I79878KhQIzMzOCg4OxsLBAqVRia2uLvb09RqORsLC/sx6USiXZXXJ8sKHku8pLWn7Xh27TE7w7jqzYzNofZvLt2tkARL0Op0il0nT6aTgnN+5i7diZTDux6cOCAC7sPkquwvmZdvwP07G7564yp+cIqrdLvQktwMkNu6jq3RidRovv9oN0mDgMSxtr3L3KsX3mYtp8n/A7E/osCKVahYPz+2ssBAnlcTJ/I0mQtRCE3MncJqySErIWkU1XPwZZGJGRkZGRkZH5T/L06VMGDhzIvn37sLW1zbA4Xr58yaBBg7hx4wabN2+matWqScY0bNgQJycnQkNDgQRR4OjRo1Rzdee47hmRQvu5w04RCXCQzMmjTPxMc+bMydWrVz/p2q6urgwZMoTDhw+zb98+YmNjuXjxIr6+vqxcuZI+ffpgZ2dnEkm8vLzw8PDAzMzM1Eo4Le1hAWbPnk3FihXp3r17qsabWZhTrlEtLu0/zsV9xynf5CtGbZqPJEkM8KiLLv7v19LC2uqdc4m/MgFO/bEHP9/LTP1zI5Y21mz9eaFJ7Pj3PDqtlpntBzP56AYKlClO2IuX9Myf8J5TKBSU8PDgsJUV8fHxptbKzs7OFCxY0DTHnTt30Gg0placQohEXxcvXkSlUlG48N/eHf88/8+f336f3DE7OztevXpl+lmv1yOEMMX1FqPRiE6vR6F4d4lWchgNhneWl1w55MOBxeuIi47FqP+XN451FlPnl8IVSrNq9PQ0r/9P/lyzjRrtE7eoLlqpDEajkdunL1KgTPFUz5W/dHF+bNiVsBcvqdKyoSnOYat+ZcWIKQwtm+BvY2mThUGLp6VSGBGoSfsz/q9jnz9BGMns2Od9/xiZlJGFERkZGRkZGZn/HDqdjg4dOjBq1CjKly+fITEIIVi/fj3ffvst3bt3Z82aNcmWffj5+eHt7U1oaChKpRK1Wk337t2pUaMGABVUzhzXPcs0WSMKJMqqnZNk4OTMmZN9+/Z98vVbtGjBt99+S0xMDFmyZKFGjRqmZ2U0Grl//77J1HXOnDkEBQVRsWJFFAoF+/fvp3r16mkSygoUKED//v0ZMWIEGzduNB1PyaDSaDRy+9RFchZy4/bpi2TP64okSVw7eprgR/7vXEuv1eHzxx5qdmjOndMX0Wk05Czsxi2f89g6OWBpY03Mmyh8Nu2hoKdHsnMk+JnocXJ1BuDA4vV/x6xQ0KNrNyZ2H8KRI0cYPnw4fn5+tGjRggULFpjGtWjRgidPnuDr65vsGpIkUa1aNY4dO/bO+3kf0dHR2NjYsHv3bgoVKmQ6vmHDBrp27Yq5uTlDhgxhxIgRSHZWnNYFptln5G2WSnLlJa/8A1kxciozT28jh1tu7p69wqyuw03n1eZ/d9tRKpUY/iWcpJXxu5cne3yRX9LnOGTZjEQ/Tz6yPtnv/42DczZTxsu/WXL/xDvjs0SFUk45SILKHOzzQcTTzJk1IinBoQAoU98ESyYZZGFERkZGRkZG5j/HhAkTsLOzY9iwYRmy/rNnz+jXrx/Pnj1j3759lCuX1JcgOjqa9u3bs2/fPkqWLMnDhw/p1q0bT5484ZdffjGNs1aYUUaVnSv6VxgzWBzRa7QU1dpi6ZT0T0gXF5eP8hhJLVmzZqVy5crs27ePNm3aJDqnUCgoWrQoRYsWpWfPBI+N0NBQzp07x8mTJ5k2bRrt2rWjQIECiUxd3dzc3llqNWbMGNzd3Tlx4gQ1a9YEwFxSIv7xerz1GNFrdeQpXpg23w/i4eWbLBk6ge2/LCafRxHylni3+a9NVgeCHj5lZLVWaGLjGb76N9RmZtTs2IILe/5kSJmGOObMQTGvcmjj4pOdw8rWhvbjhzLCqxXZcrvgWb+G6ZyEhBlKJEmiXr163Lx5kyNHjuDklNiY4J/lNClRqlSpd55PDdbW1mTLlo0FCxbw22+/mY5XqFCBSZMm0a9fP5MxcYzQJXreqUaSUiwviYuKRmWmxt45G0ajkUNLN75nsv82DorM0bErM5KjFES+AEPyv3YZitIcsievk8qkAUm87189GRkZGRkZGZkviGPHjtG5c2euXr1K9uzZP+vaRqORpUuX8sMPPzB48GBGjx5tajX6zzFjx47ll19+wdHRkZUrV9KoUSMgoUtHbGwsefMmzYl+qn/DTUNohmWOKJB4stOHuWOncOTIEdzc3BKdDwwMpHz58p9FHFmxYgX79+9n69atqb4mW7Zs3Lp1CwcHB65du4avry++vr6cOXMGo9FoKr+pUqUKnp6emJv/bXwZHBxMrVq1CA0NZePGjVStWhVzc3P2aB9nuFiVFhRI1DHLg5X07o+WW7VqxcOHD7l+/XqSc76+vnh5eTF58mTGjh370TG1bNmSO3fucPfu3XeOE0Kk+Xn3KlCVSYfXY5HFihUjphBw5wHwd3mJa5ECLPt2Epf2HcMpd06KV6vA8XU7WHL/hMl8dU3gBYAkP6cGYTQipbF0K6NQIuGhcsJNaZfRoWRaYkIg4BSIj0scSlckJeSrCZaOGR3Jl48sjMjIyMjIyMj8Z3j16hWenp6sWrWKOnXqfNa1Hz16RO/evYmOjmbFihV4eCT9CG/Tpk3069ePuLg4JkyYwOjRo9O0RoA+kmuGkM++GddptFTNkhcXZRbmz5/PtGnTOHDgACVKlDCN0ev1WFlZERsbi0r1aZOSw8PDyZcvH4GBgYlMbN+Fq6srvr6+5MmTJ9FxIQQBAQEmkcTX15f79+9TunRpU0aJh4cHhQoVQgiBWq3GaDQmZDpcO4Rk/26fkMyEConGZvlTzI45evQobdu2JTIyEqPRiI2NDVOnTmXAgAFcunSJs2fPcvz4cXbt2kXfvn0Tld98KLt376Zly5Zotdr3+r8c0wak2m8nLOgVg0vVZ4X/WcwtMyYTQgiRYGX6BZSnKJGops6FvZw18k5eXII3/pmjpEZSgkN+cC6d0ZH8N/gyJEwZGRkZGRkZmfdgNBrp1q0bnTt3/qyiiMFg4LfffqNixYo0atQIX1/fJKLIjRs3KFKkCB06dKBhw4ZERESkWRQByKOypZLaBTMUn+WPOIUANHp2j/0dF2VCm8+BAwcyc+ZM6tSpw9mzZ01jVSoVjo6OJjPNT4mDgwNVq1Zlz549qb4mpc40kiSRN29e2rdvz7x587hy5QpBQUGMHz+emJgYxo4di7u7u6m0RKfTAeDh4cG5A8cQxi/nM0YrY4Kx5osXLzh06BAzZ85k3LhxpvMlS5YkJiYGvV6P0WgkLi6OypUrAwmZIsOHD2fnzp0YjUa2b9/Os2fPPjqmJk2aIITg6NGj7x2bVXp/a2aAvfNXM75+Z7pOG51hoghAVGgYUibYQKcGc5TYSZmzNXhmwrkMWNiDlMG7aEmRkCWSo2TGxvFfQvYYkZGRkZGRkflPMHv2bMLDw/npp58+25p+fn706NEDtVrN2bNnExlIAkRERNC2bVuOHDlC2bJlefz4cbJlMmkhu8KKumZ5ua4PIcgY88lKaySj4MSGXXha5STw9oNE59q3b4+9vT3NmjVj3bp11K9fH0gwYA0KCiJnzpyfJKZ/0qZNGzZv3kz79u1TNf59LXsDAwM5f/4858+f58KFC1y+fBlnZ2cqVqxI37592bBhA+fOnUOSJFQqFb6+vhiyZaFCy/oozTO/66HQG1j560Jq/zgLhUKBhYUFGo0GV1dXJk2aBED27Nnp378/c+bMwWg0Uq1aNcqUSWgtXLlyZSwtLYmKigISvFvSIzNIoVCQP39+li1bRr169UzH165dy44dO/D09KRo0aIULlyYGFsVZFOB6t270iYDu9JkYNePju1jMBqN3DxxDnevcjjmzJGhsbwPJRIFlfYZ3tL8S0ChhDzV4elx0EZlTOaIpABzO8hTNeMFmv8S8qOUkZGRkZGR+eK5fPky06dPZ+PGjajVn36TqtPpmDJlCtWqVaNz586cOHEikShiNBr55ptvcHJyMhlcXrx48aNFkbeoJSXl1M5UUDtjgRIV6behUSJhhQovc1eWDJ1Al46dCAwMTNJCtWHDhuzcuZMuXbqwadMmIEEY+RweIwDNmzfn2LFjREZGpmr8P4WRqKgojh8/zvTp02nZsiW5cuWidOnSrFixAmtra0aPHs3Tp0+5f/8+a9euZfDgwfTp04fs2bNz8uRJsmbNiqOjI1ULl3qvSWlmQafX06N+S/LkyYMQgtjYWAwGA/Hx8fz+++/Exye4So4dOxZJkpAkiRkz/u6MUqpUKeLi4oCEDi158uTBxcUlXWKrV68ep06dSnTszZs37N69mx9//JF27dpRqlQpGpT1+mI8XTQxcRxZtQX/E1dQpuPv56citzLjWpp/aSjVkK9WxmSOSEqwzJrgK6KQUxzSFdljREZGRkZGRuaLJioqCk9PT6ZOnYq3t/cnX+/q1av06NGDHDlysHjx4iRix+rVqxk8eDB6vZ6pU6fyzTfffNJ4hBC8ErE80EcQJuIBkcaGpgmmnADZFJYUUjqQVbJAkiRat27Ntm3bAChevDgrV65M0v74xo0bNGrUiLFjx3LlyhXKlStH37590+HO3k+zZs1o27YtHTt2THGMXq/n1q1btGjRAg8PD548ecLTp08pVaoUFStWpEKFClSsWJF8+fK98xNzo9GIwWBArVbTt29f1q9fT0REBH4igofa10gq5ae4xXTD78wlZrcfysmTJ/nuu+84fvw4BoMBd3d37t+/T1xcHHny5KFp06YcPHiQ4OBgU3bIW+zt7Xnz5g3NmjXj8uXLPH/+PF1iu337Nh4eHsTExGBhYcHGjRv57bffuHz5cqK1r127RmhOC54bozK9PBL24iUhm08zbOR3HNI+zTTttv+NAsitsKWM+vMaVf8XMBrg+SUd4U8EaqXZ+y/4SN56iuQoKWeKfApkYURGRkZGRkbmi0UIQefOnbGysmLJkiWfdK34+HgmTZrE0qVLmTlzJl26dEm0kb548SLt2rXj6dOndOnShcWLFyfpSPOpiRU6nhmiCDXG8UZo0GE0fVr99g+++Lh4lEoFSjM1Zn/5CmRTWJJbaYOFlPgjyOnTpzNmzBjTzxYWFoSFhWFpmdjr4fHjx9StW5c8efJQvXp1Jk6c+Env8y3r1q1j8+bN7N69G/jbSPXChQumspirV6+SJ08ewsLCaNeuHV26dKFEiRIfnFmk0WgoUqQIdnZ29O/fn+btvTlFMGYWmdefIT4mljndR3Dr+FlsbW05c+YMY8aMYf/+/bwMDSFaoeeG/wNuv3iKTgWSUoFCocDS3ILsDlkpkt0Ve8mCepWq4ZjFlmbNmvHTTz8RERGRLvG9Ne592/JZkiQ8PT159uwZQUFB5M6dm/Pnz+Ps7EyEUcMp3fNMKzRAwvN+dPAsYzskCISP9RHcNrzOlDGrUVDXLC9mUuYW9jIrgwYNwgonetWfgEH7aUprJCUozcC1MlhlTf/5ZRKQhREZGRkZGRmZL5bVq1czY8YMLl68iJXVp+sOcvbsWXr06IG7uzvz589PVEIQEhJCmzZtOHnyJJUrV2bLli2fxWMjNWiEgTdCg1YYMJDQIWPWL79So7IXjavVfu9maMOGDfTo0QONRoOlpSUXLlxIttsOQFBQEOXLl8fBwYHr16+/t8NIehAQEIC7uzvDhg3jxo0bXLhwAUmSEmWClCtXDjs7O+rXr8+wYcNo0KDBR605d+5cDh48yLRp06hTpw758+en+ZRvKFa1XCbc9iZgjpL+Rb8iIjwco9FIrrx52OiznyBzHVqlQIkCIyLFMhW9Todeo0VlZoaZUoUhIJRvWnTB3+9BsuNTg1arZcGCBaxYsYLbt28jhMDGxoZly5bRqlUrFAoF33//PTNmzMDJyQmAuLg4oqOj+e3iHvIUL/zBa39qdBotTawKYqlKEEaFEJzUPeeN0GSq94gSiXIqZ5Oxskza2L9/P/379+f69evYWtvz8gZEPAGk9GnpKykBAQ4FIHuJBH8TmU+HLIzIyMjIyMjIfJHcv38fLy8vjh07lqhtbHoSExPDDz/8wB9//MGcOXNo3bq1KUtEr9czaNAgli1bRq5cudiwYQNeXl6fJI70pFKlSvz222+mbiPv4sKFC3h5eeHo6EhISAhr1659Z9nKhg0bGDRoEE2bNmXZsmXp6vei1Wq5efNmIoPU58+fY25uToUKFejRowcVKlQgd+7cyZbENGvWjJ49e9K8efMPjiE6OppChQqxf/9+ypQpQ6NGjThw4ADnb1zhZWHbTJkRoESiotoFq1gjVerVon6/jpRrVgeEwNwqdV1e/o1Oq0UYjNgrLCiRxQVnhVWqjDtjY2P5/fffWbNmDffu3cPCwoIqVaowbNgwbt++zeTJkxN5xjx+/JgCBQokmadxJ2/6LJ2KMRNadygE5Jds8TBPXJoSI3Qc0wZkmveIAnBWZKGCOn18Yv7fePXqFaVLl2bjxo3UqFHDdNyghYin8PoeGPUJX2lFoQKFGrIWAft8CZ4mMp8euTpJRkZGRkZG5otDo9HQrl07Jk2a9MlEkWPHjlGyZElCQkK4efMm3t7eps3fokWLsLe3Z926dcyfPx9/f/8vQhQBCA4OxtnZOVVjK1SowLFjx8iVKxc9e/akd+/exMbGpji+cOHC5M2bl5CQEFq1amUy60wrQggeP37Mxo0b+eabb6hSpQoODg5069aNK1euULVqVf744w/Cw8OZM2cOCoWC1q1bkydPnhQ36O/rSpMa5syZQ40aNShTpgxarZaTJ09iaWmJQmOgmDJrpjPZVCCRU2FNVsmCx+ZxTD62kYot62NuafHBogiA2swMM0sLYs3hZMQjlt76kz1HDyUx6IWEzkzjxo2jYMGCWFtbM2XKFPLnz8+xY8eIjY3l6NGjNG7cmD59+hAVFUVAQIDp2tevXyd5Pd3c3NizZhN5lLYmb5xMg4BXAYH8MXkOfn5+iX5XskhqSiqdMs17RI2S0irZV+RDEELQs2dPunTpkkgUgYSSl6yFoVATcK0CjoX+MmlVJnwpVEm/3p6zcADHwpDbCwo1hqyFZFHkcyJnjMjIyMjIyMh8cXzzzTc8f/6cLVu2pHuLyTdv3jBixAgOHDjAokWLaNy4senc6dOn6dChAy9evKBPnz7MmTMnXVqWfi6EEFhaWhIWFpbq0qNr166ZBIkcOXJQpkwZDh8+nOzYFy9eULZsWQICAujWrRvPnz9n9+7d2NnZvXONsLAwky/IhQsXuHDhAubm5lSsWNFUFlO2bFlsbGySXBsVFYWrqytPnz7FwcEhxTU6depE/fr16dy5c6ruO7kYCxcujK+vL4ULF6Z58+b4+Pjwyy+/sHTpUs6cOcMpw4tMVS5hhoJyKmeu6l+hxfDJshUMej06jY51P8zkxfnbNG3ShIiICHbu3ElAQAC2trbUqVOHkSNHUrFixRTnyZo1K3369KFt27a0aNGCp0+fJjqvUql4/vw5t2/fZu2GddQc3we7HE6f5J4+BAUSNxbtYNw3I0ydfaysrChcuDBnzpzBwsKCe/ow7hvCMzRzRI2Cmma5ySLJu+4PYdGiRSxdupSzZ8+m2kdKCNDFgOavFr/C8JdQogQzG1BbgdwtOWP5cv5PLiMjIyMjIyMD7Nmzh507d3L16tV0F0X27dtHv379aNiwIbdu3TJt6F+8eIG3tzdnz56lZs2aXLlyxeR78CXx5s0bzM3N0+THolAoMBgMKBQKdu7cSbVq1di7dy9NmjRJMjZ79uyEhoYiSRJr165lyJAh1KxZk4MHD5IjRw4gIdvn2rVrJhHk/PnzvHz5knLlylGxYkV69erFkiVLyJUrV6ris7GxoU6dOuzcuZPu3bunOO5jM0ZmzpxJixYtKFy4MCdOnGDPnj0cOXKEWrVqsXTpUtasWUObbp04rglAjyCjEwM0sXG4ZsnGOX3QJ29xq1SpUKpUdJ06ikfXbvFTs17ERUVjZWVF69atmTp1aqJ21inh4eHBr7/+yvTp04GE1+zXX39lxowZBAQE4OTkRK5cuRBCkCdPHhzz5aLa8M6gzPgkeCUShZQONBv6HSf2HOTPP/9ECEFMTAzW1taYmyeY8xZROaLRanlgCEdp9nmFCYNej4VSTXUzV1kU+UDu3r3LDz/8wOnTp9Nkri1JYGad8CWTOcn4f0VkZGRkZGRkZFJJYGAgvXv3Zv369e/MDkgrr1+/plOnTgwePJjVq1ezZMkS7Ozs0Gq1dO/endy5cxMcHMyFCxc4duzYFymKQNrKaN6iVCoxGBKcBL28vGjdujXt27dHq9UmGatSqXBycuLVq1coFArmzJlD1apVKVWqFF27dqVChQo4OjrSr18/bt++zVdffcXOnTsJDw/n2LFjTJs2jRYtWqRaFHlLmzZt2Lx5c7LnIiMj8fX1JTQ0lKtXr7J7927Cw8PTNH9QUBBLlixh/PjxGI1GWrVqRcOGDalduzYKhYJ58+YxevRoVs5fzI8Nu6JP5tl8TjRx8fhf8+NpfNgnF0X+idrSnELlSrL1xRWu3LpOmzZtOHPmDIULF8bOzo6GDRuye/fuJCU39+/fp3jx4vj4+KDT6VAoFJQvX578+fMzbNgwnj17hqOjIzVq1GDZsmUsWbKE4sWLM2/SDKLO3kOv1X22e0wO5V8lS0WUCf8mrVq1ytS5SQhBeHg4r1+/Jj4+nuXLl1PeIS8+SzZ/1rIaJRLx4VHcWbYHG8Xn7Zb1X0Gr1dKxY0cmTZpE0aJFMzocmXRGLqWRkZGRkZGR+SIwGAzUrl2bunXrMnbs2HSZUwjB1q1bGTJkCG3btmXKlClkyZLQoeG3335j7NixqFQq5s6dS9euXdNlzYzkxIkT/Pjjj5w8eTLV19y9e5evv/6au3fvAgmms1mzZqV27dps37490diQkBAqV65MzZo1efbsGRcvXsTW1hYHBweePHnC3LlzadmypekZpxfR0dHkypXLlIHSuHFjsmZN6Gs5a9YsRo4ciVKpRKFQoNFo2LVrF02bNk31/AMHDsTc3JxZs2bRo0cPNm3axOvXrxFCsGrVKjZu3MiZM2cwGo2oVCqKVizDuD3LMbO0+Oz58QokHDAjzBiPUGRM2ooEZEFNDTNX1JKSiIgI5s2bx6ZNm/Dz8wMSskPq1avHwYMHuXnzZpI5smfPTqVKlejQoQOtWrVCpVJRtmxZbt++jUKhIC4uDqVSyevXr5m9bTUebeqhVH/+ZHglEjkUVpRXOSfKYBs3bhyTJ0/mp59+YvHixQQFBaFSqdDr9RiNRm7evEnuYoW4oAsmHv0nLa1RIOGmsIMHL6n7VW0eP378Sbt4/VcZM2YMt27dYvfu3emerSiT8cjCiIyMjIyMjMwXwaRJkzh+/DhHjhxBqfz4voVBQUEMHDgQPz8/li9fTpUqVQD4888/6dy5MyEhIQwePJhffvnls7Se/Rz88ccf7Nixg02bNqX6mvv379O4cWMePPi7NeuhQ4do0KAB8+bNQ6vVmspiwsLCUKvV1KpVi86dO1O+fHlThsr69ev59ttv2bVr1zt9JtJKTEwMGzZsYPTo0URGRiKE4OjRo9SsWRNIMP/MnTs30dHRAOTIkYPnz5+n2hvm8ePHlC9fnrt37xIUFGTqRNG2bVuuX7+Op6dnsqajHpXK8vPxzeillNvgpicSCRtgF0UWgowxGd79RAHYSObUULui+McmcseOHbRu3RqlUolOlzjTo0KFCjx69Ii6deuycePGJHMuWLCA4cOHo9FokCSJ+vXrI0kSR44coVLLBgxeMh21udln27QqkXBV2FBalS3JmrGxsaxevZp+/frRrl27RBlNdnZ2REREAGAUgnuGMB4YIhCIdH3VlEiYoaSC2hkHhQUALVu2pGbNmgwZMiQdV/rvc/LkSdq3b8+1a9fInl02rf0v8t/4v7yMjIyMjIzMf5rTp0+zYMEC1q1b99GiyNtP+UuVKoW7uztXr16lSpUq+Pv7U758eerWrWvqRjNr1qz/jCgCH1dK4+fnx6pVq+jfvz/ff/89SqWSoUOH8uDBAxo1asT+/fsJCwujRYsW1K5dm6ZNmyZaq2PHjixfvpwmTZpw5MiRdLunI0eO0KdPH8LCwtDr9Zibmydq8Wpvb8/YsWNRq9Wo1WrGjx+fKlGkcuXK1KhRgyFDhjB48GCyZctGo0aNqFSpEm3btgWgVKlSdOjQIdnrO33tTV3zfLgqrD95yYQSiaySBeVVzrzIBKIIgBGIFlruGcKIjIxk7ty5lC1blpYtW2I0Gk2iiLOzM82bNydnzpxcuHCByMhIduzYwY4dO5IITjY2NgghUCgUCCE4dOgQT58+pUyZMrSv0ZB6NgWxlsw++fOWABUSnqrslFFnT1aIsbKyon///kiSxMqVK8mdO7fpXGRkJPPmzQNAIUm4q7LylTo3eRW2KJE+On4lEllQU1KVjTpmeU2iCMDYsWOZOXNmsqVwMskTERFBly5dWLZsmSyK/IeRM0ZkZGRkZGRkMiVCCCRJIiwsjDJlyrBgwYJEHWI+hICAAPr06UNwcDArV66kTJkyxMfH0717dzZv3kyhQoXYvHkzJUuWTKe7yFyMHj0aOzs7xowZ885xwcHBnD9/nvPnz3Py5EnOnj2Lm5tboi4x7u7u5MqVC29vb1atWmW6dsKECQghmDhxYrJznzp1itatWzNv3jy8vb3T5b5+/PFHZs6cSVxcHCqVCo1Gk0jQio+PJ2vWrOj1eiIiIkz+D+8iX758+Pv7A+Dk5ESDBg3YtGkTL1++NPnb/PHHH3Ts2BFbW1tTBgAkGNY+e/aMnDlzAhBijOWi7iUGjOkqWiiQkICSSidyKWw4pgsgFn26zZ8eaOLiGVWtNQG37/PvbcfmzZsTvQciIyMZPXo0CxcuRKFQoFAoKFasGGXLluX48eOm1yNLliw4ODjw8OFDzMzM6NWrF5UqVaJXr14YhcBP/5qH+oTOL+ktbCaIUJaUVWfHXEpd1tGCBQsYNGgQX331FX/++SdNmzZl3759lChRgsOHDyfabOuFkWeGKB4aI4gVOpRIGBEkzUn6GxUSAhCAs8KKQkoH7CXzFDNnGjRoQOvWrenVq1fqb/z/mA4dOuDo6GgSs2T+m/x3PgKRkZGRkZGRyTQIITAKgU4Y0QkjxjR+DhMfH4+DgwOTJ0+mR48etGzZ8qNEEaPRyMKFC/H09KRatWpcvHiRMmXKMHXqVOzt7Tl48CAbNmzg7t27/1lRBJLPGImJicHHx4eZM2fi7e1Nnjx5KF68OIsWLcLMzIzevXvj7OzMo0eP2LBhA0OHDqVy5crY29uzcuVK1qxZw5UrV0zzubi48OLFixRjqFatGocPH+abb75hyZIl6XJfEydOZNSoUUiShI2NTZLNsMJczY/TJzPsh9EYLVTEC/1735P/LPMIDQ1l3bp1tGjRAnt7eyBhs9uhQwf69Olj8hb5J/9sLZxNYUU9s7wUV2bFEtVHZwQYNFrUKCisdKCeWV7yquy4awhDg+Gj5v0UqM3NGLFhLop/ZHqZmZmhVqtZv359orG2trYsWLAAc3NzBg8ejLu7O7du3WLlypU8ffoUa2trunbtiq+vL3fu3EnUFUQIgdFoZNHChTQuVI5vKjXn9olzaOM1aOPiP+oeJBIEERvJjLKqHFRWu6RaFDlw4ACDBw9mypQpHD16lF27drFx40bu379PZGQkuXLl4vfffzeNV0kK8iltmfhVR3YP/plK6pwUUzrhosiCrWSGNWqsUGEjqXGQzMmvsKO0Kju11LlpapafCmoXHBQW7ywnGjt2LNOnT0evz1wiWmZk/fr1XLt2jZkzZ2Z0KDKfGDljREZGRkZGRuajEEIQJXREiHjCjPG8FvHECC1G/u5YKvh7Y+EkWeCgsMRBYY5VCi0jr1+/TuXKlTEYDKjVam7fvk3evHk/KL4HDx7Qq1cvNBoNK1asoFixYuzfv59u3boRERHBd999x+TJk/9TJTMpUb9+fZo3b465ubkpI+Thw4eUKFGCChUqmDJCChQoYNpYBQYGUqFCBQIDA5Ods3r16jx48IDAwEAUCgV79uxh0aJF7Nu3752xPHz4kHr16tG7d29Gjx6dLr4Q3bt35+ad2+zxPU64MZ5QEUek0GLEiPRXdoUwfQmySGqyShY4KixxUlgmamHq6OiYpHuNUqmkb9++ODs78+OPPzJ27FgWLFiAhYUFQUFBVKpUiQsXLpA/f36+/vprZsyYkSRGIQRhIp4HhnBeGeMwGozExsRgZZMlRaNW5V+x6wx6wv2DOLNmJ43KV6NVy1YAxAgdf2oDPmsHmrSgiY1j/+xVxNz2x8vLC3Nzc8LDw1m2bBknT54kT548AOzdu5dZs2Zx4sQJhBAUL14crVaLu7s748ePZ9GiRRw4cIDAwECsra2pXLkyffv2ZfPmzTx+/JibN29iMBhwcXHh5cuXSJJEx57d6D/1e56KKFRqFQKQVO8vxzPo9Zir1BiBXIosFFA6YK8wT9N93759m9KlS9OpUydWrlyZ7JgffviBadOmUbx4cQ4fPoyzszOjR4/m559/pkSJEty4cSNNa6aW6tWr069fvxRLwWTg6dOnVKhQgcOHD1O6dOmMDkfmEyMLIzIyMjIyMjIfhCnl2xBO/F+fVKe2TECFhBGwk8wopHTAWZElkUHjhg0b6N27N7GxsSiVSqytrbl37x45cuRIdXwGg4HffvuN6dOnM3bsWIYMGcLjx49p3bo1N2/epGnTpqxfvx5ra+s03feXxPPnz03GqOfPn+fUqVPkzp2batWqmUSQkiVLYm6e8oYvKCiIMmXKEBwcnOz5yMhIsmfPTt++ffn999+5fPkyvXv3TpRFkhIvXrygfv361K9fn5kzZ36UOBJp1PLIEMFzYxQSoE+DSKD8qxThn+9HSwsLLC0tiY6OxmAwYGlpSbly5bC0tOTw4cOMGzeOJUuWIEkSoaGhtGjRgs2bN3Pv3j3i4uKoW7cup06demdbTyEEMUJHz+GDKFyhFHVbN8eAESMC6S+vCRvJjKwKS6wNSkoXcmffnr1cvXqVLVu2sHfvXgCu60J4anyTSWWRBCxQUt8sH5IkcffuXapUqUJUVBQdOnTA39+fc+fOodPpKFmyJDlz5sTHxwdXV1caNmzIzJkzE3kLRUZGsmDBAhYtWmQqr5EkCVtbWyIjI8mWLRvDhg1j5MiRKBSKhHbAHh5MXzCbi/dv8d2U8YQZ401lR28FM4C4iCie33nA8xv3+WHwcOwlC9RS2kXT0NBQ8uXLh6enJz4+Pu8c+/jxY+rVq4e/vz9NmjTh0KFDxMXFYW9vn+bW0qnl0KFDfPvtt9y4ceP/QhROKwaDgVq1atG0aVNGjBiR0eHIfAZkYURGRkZGRkYmTWiEAT/9a54Zo4DUiyEpofrrs/wCSnsKKe1RSgqGDx/Ob7/9hlqtxszMjO+++45x48al2nj11q1b9OjRA2tra5YuXYqzszOdOnVi165dFC9enM2bN+Pu7v5RcWc2IiMjuXTpkkkEuXDhAlqt1iSAVKxYkU6dOnH9+nVcXFxSPe+rV68oXrw4ISEhKY5ZtmwZffv25c6dO9jY2ODp6ZmikPJvwsLCaNKkCUWKFGHp0qWp7hbzllBjHLf0oUQKbbp09Xj7fnx17jZVXIpQtEgRrKysWL9+Pdu3b2ft2rVAgo+InZ0dOp0Od3d3Lly4kGie2bNns2/fPg4fPvxewefkyZPUqlWLR48e4ebmluyYjRs3smTJEo4fP05MTAyurq7cvn2bHC7O7Nc+yRSGq+9CF68h9pQfjsKcdu3aERsbazpXpkwZ+vbtS8+ePVGpVBw4cIBGjRoxderUJH44b31I1q1bR0xMDAUKFODp06fodDoUCgVGoxEXFxcaNGjA8OHDsbOzo3jx4kRFRWFhYYHBYDAZjwqR8H4xIFAgoQCyZcuGTqfD3t7eJLqkFa1WS968ebGysuLBgwepFh66dOlien8BmJub4+/vnyZBOLUIIShfvjw//PADX3/9dbrP/6UzdepUjh49ytGjR2Xh6P8EWRiRkZGRkZGRSTWBhmiu6l9h+ARJ+wokzFFSXu1M2QLuBAQEMGLECEaPHm3ydXgfWq2W6dOnM3fuXKZMmUKPHj346aefmD59Ora2tixfvpzmzZunc+SfH71ez82bNxOJIE+ePKF06dKJDFLz5ctn2pQbDAYsLCxMBqWpJTQ0lCJFivD69et3jitbtixhYWE8ePAAS0vLNK0TExNDq1atsLCw4I8//sDCwuK91+iFkVv6UAKMUZ+khESJRMzrCBb2+Z6DW3bi7e3NwYMHMTMzIy4uzjTO2dmZZ8+eJblXvV5PmTJlmDBhAq1atXrveoUKFcLV1ZXjx48ne75SpUqMHj3atInt1asXhQoVot13/bmhD8n0wogQgofnrjGyVptExyVJws/PjyJFigCwbds2+vXrR1xcHN999x0TJkwA4MSJE4waNYqLFy9iZ2eHm5sb9+/fR6PRkCtXLvr27cuYMWO4d+8ev/76KwcOHOD58+dIkpTI9FWSJCIiIrC1tU0So0ajwcrKCqPRaPpd+ZD7LFOmDP7+/jx79ixNGWn+/v6MGzeOdevWmcyn9+/fT4MGDdIcR2rYsWMHU6dO5cKFC5+txfGXwKVLl2jcuDGXLl1K1E1I5r+NLIzIyMjIyMjIvBeNMHBF/5JQY9wn34ApkBD+ryllkYN8eVLvK3L58mV69OiBq6srixYt4sKFC/Tp04fo6GjGjh3L+PHjP2HUnw4hBP7+/olEkKtXr5InTx6TAFKxYkVKlCiBWp28ZwskGK+WKlWKly9fpmn98PBw3NzcEnVdSY7Q0FBcXFz4/vvvWbp0KRcvXiRXrlypXker1dKlSxeCg4PZvXt3shtX01rGOC7qgtH9VXbyyRAJmQQ+q3ewYPgE5vz+O3369DFtWoUQ/P777wwZMiTZy0+cOEHXrl3x8/PDysrqnUsdOXKE+vXrExAQgKura6Jz58+fp3379jx48MCUNXXmzBl69uzJ3CsHiFFmPtPV5DDqDbxYdwx9nAYfHx+uXbtGUFAQAB4eHlhYWHD79m3Wr1/Pb7/9xqtXr2jatCmLFy8mNDSUYsWKoVaruXHjBra2tgwYMIAff/yRwYMH4+npSd++fU1rCSEoXLgwDx8+TBSDJEl07tyZ33//PYngunfvXlq1amXKKAkLCzN1IEotX3/9NQcPHuTevXsf5IskhCBv3rx89dVXrFmzhkKFCnH8+HFTh6P0xGg0UrJkSWbNmkW9evXSff4vkZiYGDw9PZk0aRJt2rR5/wUy/xlkYURGRkZGRkbmnUQatZzSPUf/Ga0dFUhkkVRUU7tiJiUtn7l06RL58+fH0dGRuLg4Jk6cyMqVK/nll18oU6YMbdq04e7du7Rq1YrVq1e/d1OamYiIiODixYsmc9QLFy6gUCgSZYKUK1cOOzu7NM177do1unbtyvXr19N0XWRkJK6urkRGRr537KxZsxg5ciTFihVj+fLllC9fPk1rGQwGBg0axIULFzhw4ECiNqZveap/w01D6GfNkIiPicVBYcE3VVtw9/YdHBwciIqK4urVq3h4eLzz2vbt21OwYEEmTZr03nXc3NwoVKgQhw8fTnS8Y8eOlC1bluHDhwMJItfPP//MgoULWffqCmpzs+Smy3SoUFBenYMciiyJjkdFRdG5c2fOnz9PiRIluHTpElFRUej1eszMzChZsiSvX7/m6dOnFCpUiClTptC6dWvT9X379qVMmTL069cv0by7d+9m/PjxSd7zZmZmaLVaXFxcqFevHsOHD6dkyZK0bt2abdu2mcY1adKEPXv2pPr+RowYwaxZszh9+jSVK1dOy6Mx4efnR/369fH39ycgIIB69erx6NEjpk6dysiRIz9oznexfv16lixZwsmTJ9N97i+Rvn37Eh8fz+rVqzM6FJnPjFwwJSMjIyMjI5MiEcZ4fHTP0H3mfhdGBNFCx3HtM+JF4paSoaGh1KhRg3bt2v2PvfMMq+Lq2vA9p9GLiAgoFqzYO1GjURF7j8au0aixY++9d2OJLfYe7CX22LF3jYoKKKIg0junzHw/iOeVgAo2SL65r8vrDXP23rPOMJz37GfWehbnzp2jXLly+Pn5cf78ebZt20aZMmUwNTXFz8+PHTt2ZGtRRKvVcu3aNX799Ve6du1K8eLFcXFxYdq0acTExNC1a1euXbvGy5cv2bt3L6NHj8bDwyPTogik36o3IygUCgyGjGUkDBkyhCJFihAQEPDelr3vQqlUsmzZMho1akSNGjXSeDw80Ud+dVEEwNTCnEQTgf6b59O9Vw+io6M5ffr0B0URgLlz57J8+fI0mQvpsWTJEk6cOJHKn+Xly5ccPnwYT09P+vTpg7OzM05OTmzatIlaTeqDKGb4ffQqWotnfz0CUjrFTG7SjSU9RzKmTnuuHjqZ4XU+FgMiUWJyqmOxsbG0bduWxMREBg8ezMOHD4mMjDT64IiiyLVr1wgMDKRKlSq0adMGCwuLVKak7yoDadasGbly5aJkyZKUK1cOc3NzKlasyIABA/D19aV58+acPHmScuXKYWlpyb59+1LNz4y3xKpVq5g/fz6bN2/+aFEEUkxR69evjyAI5M+fH19fXyZPnsyYMWNwc3MjKCjoo9dOj7Zt2/LixQvOnz9vPCZKElFiMk8NMVzXveK49hkHkv3Yl/yEvclP2Jf8hAPJfpzUBnJbF8pzQyyxopZ/+/P2vXv3cvz4cZYsWZLVochkAbIwIiMjIyMjI5MuMaKW87qXmeru8TmRgCT0nNUGoZX+tzEfPnw4Op3OWHowffp0ChQoQIkSJbhx4waHDx/mxo0b7zSxzCokScLPz4+tW7cyaNAgqlatSo4cOejevTs3b97k22+/xdvbm8jISM6cOcOcOXNo3bo1Li4un6X+/2OFEaVSmWFhBFI2dvHx8alMJDODIAhMnTqVPn36UKNGDR48eACkZIo8MERknZeGQsCpSEGKtPdk4+ZNVK9ePUPT8ubNy/Dhwxk8ePAHxzZp0oS8efPy008/ASmtphs2bEhiYiJlypRh9+7dNGrUiCdPnhAWFsacFYvRZ+J384b46FgmNe5GnmKu9F81C4Xy62wJJFLKoN4QFBSEu7s7Dx8+5MyZM4wfP54SJUrg4eFBcHAwgiBQpEgR4uPjef36NRMnTgRSxKZ8+fJRqlQpevXqha+vL69evUp3Y37hwgW6detGTEwMLVu25Nq1a8ybN4+iRYuyfPlyAgMDiY2NZfLkyRQrVsxYqiQIAjExMURERHzwfR0/fpw+ffowadIk2rdv/0nX6I0w8jZjx47F398fSZIoUKAAM2fO/KRzvI1KpWLkyJFMnz6dBEnHPV0Yh7QBnNcFcVf/mudiLPGSDsNbxsZvDGtjJC0BYgy39aGc1j3niPYpj/SRqT6v/y0EBwfTu3dvNm3a9N4yPpn/LnIpjYyMjIyMjEwatJKBE9pnaMn40+gvhQDYCCZ8p87L7du3cXd3N3oAAFhZWaHT6Zg8efIXSTX/WMLDw1P5gly5cgUzMzOjJ4i7uzsVK1b8au2CZ82aRWRkJLNnz87UPK1Wi6WlZapr/iFq1arFuXPnePXqFfb29pkN1cjGjRsZMWIEO/48RFRhm2xhMCrq9OQzsaWSOuMik1arpXTp0syfP58mTZq8d+z8+fMZNmwYtra2REVFoVAoaN++PbNmzUrlPSKKIvv9r4GLXYbj6FW0Fv1XzmT9qJlUalSHDhMHATDOsyPNB/9E5UZ1SIiJZd2ImTy954suKZlS37nTbc4YlEolEcGhrB4yhdfPg9EmJlHjhya0Htknw+cH0KCgkYkrs2fPZty4cej1emNb3osXL/LXX3+RL18+xo8fz9mzZzlz5gxPnz5Ns45er+f27dtcuHCBxYsXEx4ejlKppFq1alSvXp1q1aohiiK1atUiISEBKysr9uzZ88HrL4oitra2RuNVvV6Po6Mjnp6eDBkyhHLlyqUa//DhQ8qUKcMPP/zA5s2bM3Ut/klSUhK5cuUiMDDwnd4mM2bMYMKECRQuXJhjx46RL1++TzonQEhyDBsuHKZ41YoIAp/0qa8gRcR1VJjjpsyJlSL7l3mJokijRo2oUqUKU6ZMyepwZLIIOWNERkZGRkZGJg239KHos4EoAilPJ2MlLX6GKBo1apRmg25paUl0dHSWiiJJSUlcunSJRYsW0bFjRwoXLkzBggWZO3cuycnJ9OrVizt37vD8+XN27drFiBEj+O67776aKAJfp5TmDR06dMDc3PyTu2l06dKFVWtX42elzRaiCIBCrSJYjOeVGJ/hORqNhiVLljBo0CCSkpLSvH758mVatWqFjY0Nw4YNQ6lUkjNnThYtWkS9evXYvHkzTk5ORjPXAgUKoFaruX7/bqbjn9fJK5Uo8k/WjZhJyRpVmHt+Fwuu7Cc6NJw/1+8EYPFPw2nctwtzz+9i3sU93Dh6llsnzqe7zrtI0uswMzNj1KhRFC1alH79+pGcnMzq1auxsbHhypUrPH36lJ9++olevXoRGBiIXq9Ps45KpTKWxdStW5dp06Zx48YNOnTowIsXLxg8eDB16tRBo9HQvXt3DAYDFSpU+GB8CoWCfPnyYWdnR/ny5Xny5AktW7bkzJkzVKhQAQsLC2rXrs2WLVsICwujcuXKVKpU6ZNFEYBz585RunTp9xq+jhkzhqdPnyIIAq6urkybNu2jz6eXRG7qQrnKa4pVrYj0iaIIpJRBiki8FOM5rXvOQ30EYjZ/Dr906VIiIyMZP358Vocik4VkrlG8jIyMjIyMzH+eEEM8r8SEbCKLpGBA4oEhgq59e7Fg6sxU4khoaCihoaFpOnl8KURR5PHjx8ZMkMuXL/PXX39RrFgx3N3d8fDwYMyYMRQvXtyYlp8dCAkJwd3dPdPzlEolYiZ8LACcnZ2pUKEC58+fZ82aNcbSkI8hn2cVRF3UR8//EhiQuK57hacmP+p0zIHTo169epQpU4Z58+Yxbtw4Tp06xbx58zhz5gwJCQkUKlQILy8vhgwZwtGjR+nQoQOLFi2iePHiFC1alMePHwPg4OBAw4YN6d69O8I3roRLaYWW91Gx/nf47DxEg14dsHNKa2575Y8/eXztDvsWrQEgOSGJnHkdSYpP4N7ZK0SH/a+0JDE2niBfP8rV/TbD5xeUChQKBbVr18bHxwd/f3/atWvH/PnzsbNLnf1SvXp1VCoVu3btom3btu9e8+9SMxcXF9q2bWscmyNHDho2bEhAQAAajYZSpUpha2trzCipVq0apUqVSvN3WqhQIV69ekVwcDCFChVi2bJlACQkJLBy5Uq2bNlCt27d0Ol0qFQq6tSpQ0RERJr4M0t6ZTTpkTdvXh48eMCcOXMYM2YMGzdu5Pjx45nqgpOms9MX6NZrQOKxIZIXYiyVVU5YZ8PskXv37jF16lQuXrz43q5eMv99ZGFERkZGRkZGxohWMnBd/yrbPJ1/G4Mk4dSwCupZaipXrkzRokXJmTMnOXPm/KKZF6GhoalEkKtXr2JjY2PsENOuXTsqVKiQrU1e4eMzRt5sOkVRzLAZpbOzMzExMfTr149+/frRpk2bj6rbDxMTeS7GwlfywMgMekRu61+nKakxGAzExsamaQUL0LhxY3r37s20adPQarUUL16ccePGMXDgQMzNzfH19WXq1KkcPXoUURTx9/cnKSmJatWq8fr1a6KiooiJieHAgQMkJibSvsgYsLNIc5730WJoT679cZLx9Tox9djmtOKIJDHKexmOrqlLNBJj4xAEgbk+u1F9wgZSEASSkpJ4+PAhs2fPZuDAge+9r4oXL86GDRveK4ykhJ36M8vPz4+oqCgWL15MrVq1qF+/Pnv27OHhw4dcuHCBCxcu8MsvvxgFwzclOO7u7pQqVYqTJ08ay2neYG5uzuDBgxk8eDAVK1bk4cOH1KxZk6VLlzJ9+nQcHByMJTcZyU75J0ePHuW3337L8PgRI0bQqVMnPD09KVSoEBMmTDC2Jd+5cyeNGjVK93PJVx/BI0PkV/mcNyARK+k4o3tOBZUDeZRWX/ycGSU5OZmOHTsya9YsChcunNXhyGQxsseIjIyMjIyMjJH7ujCeiFHZKlvkbRQiVNY44qT8MkJIQkICN27cSOUNEhkZSZUqVYzeIFWqVCF37txf5PxfkuLFi7Nnzx7c3NwyPVelUpGYmJjhJ6rBwcGUK1eO4OBg8uTJQ9GiRT+qHegpbSDRUsa9Tb42CgQ8NPmwEFKui1arpVmzZjx79oy9e/fy6tUr9u7dy4EDBwgICEAURUxNTdFoNFSpUoWIiAieP39OVFSUMQtKEAQEQfhglo4gCCw8v4f8FUtmON5eRWsxds8q8pcsys7Zyzm1eQ9Tj21mQZfBRo+RpT+PRqFU8vOSySiVSmLCIkiMjSd3QRcmNOhCqZpV+GFMfwDCngejVKvI4ZgrwzEY9Absr4Xw3Xff8erVK7y9vdm3bx87d+5MV0waM2YMK1euJDw8/J1r9uvXjxIlStCvX7//vddevTh48CAvX77E1NSUlStX0rVr1zRzw8LCuHjxIj4+Ply4cIEbN25gb29PYGCg0TC5YMGCqQyQW7duzYEDB3j48KHR5DkgIIAFCxZw4MABAgMDMTU1pUqVKvTo0YN27dqhUr3/efSLFy8oU6YMoaGhH5VpNm/ePEaPHk2BAgUYNWoUPXr0YNSoUamMWiVJ4i99OAFidJaI30oESivtKaDKfFetL8HQoUMJCAhg165dn8XgWubfjSyMyMjIyMjIyAApLRoPawPQZVtZJAU7wZSamk8vmxFFkQcPHqQSQR4+fEjJkiVTGaQWKVIkU207syu2trYEBAS817/gXZiYmBATE4OJiUmGxhsMBszMzIiPj+fu3btUqlQJb29vWrduneFzxojJnNEFZcvspTfotVpOrNvB+hEz0el07xQzFAoFpqamWFpaotPpiIqKAlI2qmq1Gnt7e0qUKEHVqlXJnTs3kiQxduxYFAoFdevWxdvbGysrKxISElAqlfzwww/8+uuv+Fsm81yMy3C8bwsjADtmLeP0lr1IkkS3OaOp3KgOibFxbBgzh/s+1xAEAbWJhu5zx1KieiUiQ16zdvh0Au+nlPWYWVnQf+VM8hYrlOEYVCg4P3EVBw8e5MmTJygUChITE4mKikq3DXVQUBAuLi6Eh4e/s1QlPWHE2dmZJk2aMHLkSAoXLkxiYiKmpqYfjE+r1XLmzBnq1asHQK5cuVKZut66dYvNmzdz5swZatSoke4aSUlJrFy5ks2bN3Pnzh30ej1FixaldevWeHl5pWtIvG7dOg4fPoy3t/cHY3wXwcHBeHh4GDs5mZmZ8fDhQ6NB6319OH6GqCz9m1IgUF6ZCxdV1nZ+OXHiBD/++CO3b98mZ86cWRqLTPZAFkZkZGRkZGRkAHhhiONGNi2jeRsFAnU0LlgKmatXf/nyZSoR5Nq1a+TKlSuVCFKuXLkMbZ7+bSQmJmJra0tSUtJHPRk1MzMjPDw8U+VCefLk4fLly+TNm5euXbuyc+dOwsPDM3x9b+heESjGZjrWr42k05Nw4AajRozkxYsXxpIOW1tb5s2bR3x8PHv27OHmzZtER0djZWWFi4sLYWFh3L17FweHtD4fI0aMQK/XU7x4cfr27UtERATFixcnODgYSBFaRFGk+YBudJ05EkGVfbxsPkQONDTPXYbo6GjjMYVCQatWrXB1dU31L1++fKjVaqytrRk9ejSjR49Od81+/frh5uZG//5/Z7KEhZErVy4eP37Mhg0bWLZs2XszTtJDpVIhCAI7duygXLly+Pj4sGbNGk6dOoWJiQmVK1c2+pRUq1aNXLnenTVz/Phxli5dytmzZ4mKisLBwQEPDw8GDx5M5cqVAWjXrh2enp6f5MfzZp2dO3caDZM9PDw4ceIEz/TR3DGEZYvPdyUC1dTO5FSYZcn5w8PDKVeuHGvXrsXT0zNLYpDJfsjCiIyMjIyMjAwAp7XPiZKSszqMDyIABRQ2lFW/eyMSFxfH9evXuXz5slEISUhISCWCVK5c+ZNayf6bePr0Kd999x3Pnj37qPmWlpYEBwdjZZVxf4BKlSqxdOlS44be3d2dwoUL8/PPPxMWFkZUVBSRkZFER0cTExNDbGws8fHxJCQkICpgxuU9mJj9C0QqvYGVXpM4smZ7ui+bm5tTqlQpmjVrxk8//YSjoyOSJNG4cWPq1KnDsGHDjGOjoqI4dOgQP/30E+XLlyc4ODjdVrVKpZJixYrx244tRBSyQp8NNrsZpagiB0WwZuLEifzyyy8kJiaSL18+Zs+ejb+/PwEBAfj7++Pv78/Lly9xdnYmKioKlUrFkCFDcHV1pWDBgri6upIzZ04EQaB///4UL17cKIyMGTOG5cuXG8vgLC0tOXnyZKbitLOzIzk5maFDhzJlyhROnTpF3bp1GTt2LMOGDePKlStcuHABHx8fLl26RO7cuVO1CnZzc0s30+zZs2csWLCA/fv38+zZM0xNTalYsSK3bt3i7t27FChQ4KOvrSiKlCxZkidPnqBUKo1ZTBNnTqPi4HbZQhR5gwlKPDX5UQlfNxtPkiTatGmDi4sLCxcu/KrnlsneyMKIjIyMjIyMDMmSgaPagGxeRPM/1ChobOIKgF6v5/79+6kMUv38/ChdurRRBKlSpQqFChX6f1tHfvnyZQYOHMjly5ffO85gMPD69WtCQkJ4/fo1oaGhREREMGzYMH788UeSkpKIjo4mNjbWKGQkJiaSmJhIcnIyWq0WvV6PXq9P1eL3bd8MU1NTzM3NMTExwczMDHNzcywsLLCyssLKygobGxsKVSuPW9u6KE2yXxeL9Ih5Gkz3krXTtDW+fv16GhNOURTx9fVl27ZtzJkzh6JFixISEkJkZCR6vR6lUolKpaJ8+fKUKlWKhIQEfv/9d7Zs2ULnzp3RaDSsWLGCTp06YZBEDmr9s9F29/2oUFBR5WD0CLp+/TqtW7emfPny7N69O814nU5HYGAgS5YsYdmyZQwePNgomgQEBKDX63F1dSU2NpYCBQrw/fff4+rqSq9evahYsSJ79uzBysqKyZMnM2TIkEzFWqJECYKCgqhfvz4zZsygZMmStGrViu3b0wpgBoOB+/fvG31KfHx8iIiIoGrVqkaxpEqVKlhYpDbKTUpK4rfffmP58uU8fPgQQRAoXLgw33//PYMGDUo3mygjiKJIaGgoT58+Zd26dRTv1oiCZUuAIvt8/ikQyKewopz6497jx7Ju3ToWLlzIlStX/pPZgTIfjyyMyMjIyMjIyPBKjOeq7hX6f4s0IkpcnbOJ8yfPcOPGDZydnY0CiLu7O2XLlkWj+Xdsqj+FpKQkXr16RUhICKGhoYSFhREWFkZkZKQxGyM6OpqnT5/y4sULHB0dSUxMJCkpKZWQYTAYUvljKBQpLVXVajVqtZrY2FicnJywsLAw/nsjZNja2pIjRw5y5MiBvb09OXPmxMHBgaVLl1KlShUGDx5sXLdVq1b8+eefhIeHv9eM8r4unEdi5Be9dp8TXUISv/ecRHx8PGfOnDF2Mxk+fDgmJibcuHGDx48fExISQlxciieIhYUFarUaU1NTunXrRs2aNfn222+pUqUKS5YsoU6dOsb1bW1tady4MQkJCSxcuDBVVsHx5GfEo/uq7/djUSDgqcmPmfC/371Wq0Wr1b63s1RSUhJmZmZMmzaNJ0+eMHv2bBwcHIiMjCQgIICxY8ei0WhwcnLi8ePHnDx5EkEQMDMzIyEhgWbNmlGpUqVUZToODg7vFUqbNGnCuXPncHV1xd/fn2LFinHlypUMv9eQkJBUpq63b9+mePHiqVoFv/H+mDp1KpGRkTRu3JilS5dy5swZIiMjyZUrFx4eHgwaNOijWm0DPNVHczeblND8EyUCVdXO2H+lkho/Pz+++eYbTp48SenSpb/KOWX+PcjCiIyMjIyMjAwP9RH4GiLe+dW5V9FaaExNUJto0Gm1NPf6Cc/uP3zVGN9G0up5uu88JR3yUbly5Y8yFP3aiKJITEyMUcQIDQ0lPDzcKGRERUURFRWVKhsjISHBKGRotVp0Op1RyHj7K5xCoUClUqFSqdBoNJiYmGBqaoqZmRkWFhbExsaSlJREjRo1sLGxwc7ODjs7O3LmzEmuXLnIlSsXjo6OODg4pCso2dvb8/Dhw0yVHk2ePBm9Xs/UqVONx7RaLTlz5qRx48bpPnl/w1ltEBFSUobPBWmNRQGmNu9BjwXjcSqUP1NrZRZBgj/HL2PZwsWpskYUCgU5c+bExcUFNzc33N3dqVOnjrHMIj4+Hjc3NzZv3kzNmjU5fvw4Q4YM4c6dO6k27fPnz2f06NHExcWl+f081kfywBCBmA03vv/ERjChtsYlU3O8vb0ZPnw4gYGBqFQqDAYDL168wMnJyThmwIABFClShIEDB7Jw4ULGjh2LUqk0ilBKpZLixYtTunRpY8ZJQkJCGk+TNyU6BQsWZMqUKSxatIjk5GTy5MmDv7//BzvLvI+kpCSuX79ubBXs4+ODiYkJ1apV4+rVqwwePJjevXsbOz8FBgaycOFC9u3bx9OnTzExMaFSpUp0796dzp07p4nlzJkzVKhQIVW5m0ESOawNyNalVpaCmrqaL/v3CSmZhTVq1KBdu3Z4eXl98fPJ/PuQhREZGRkZGRkZzmtfECYlvvP1tzedz/56xLBvWrDS9xR2zl+uba3BYHhn20oBKKa0o7gqpUvFgwcP6NOnD0qlkj///POznF+v1xMWFpaqrCQ8PJyIiAgiIiKM2RjvKyvR6XTGbIw3X7kEQTAKGWq1Go1Gg6mpKaamplhYWGBubo6VlRXW1tZYW1uTI0cObG1tjdkYuXLlInfu3OTOnRs7O7sMdcyZPHkyoigyefLkj7oWDg4O3L17N1Ntin/77TcuX77M6tWrUx0/ePAgzZo14/z581SrVi3NPEmS+EPrn+nNXHrCyNdCTNaxdeQcDm34Ha1Wi0KhQKfTMXbsWKZNm/beuTt27GDq1KncuHGDli1b0rx5c3r06JF6fVHE1taWrl27smTJklTH9/5xAMN3RVBn87IjfVIyVS3zkyeTrbZPnjxJgwYN0OlSsmJsbW2JjEydTTRw4EAKFy7MwIEDKVeuHPb29kiSZPQVMTMz48SJE6nut5iYGKOfydu+Jv7+/jx9+hQzMzNj96CRI0dSokQJo4Di6Oj4yZ2q3rQCPn78OF5eXhQpUoTAwEAqVapkzCipWrUqdnZ2aLVaVq9ezcaNG7l16xY6nY5ChQrx/fff4+Xlha2tLba2thQqVIizZ88au6wEGmK4rX+dLbNF3qBE4Ft1HnIovmxZy6RJk7h48SKHDx/+T3QZk/n8fLzsKSMjIyMjI/OfIUbSZnhs/pJFschhTfjLV9w7e5mDv25Ar03ZtPw4cxRl6lQj4PZ9FvccaZwT4hdIh8mDadr/R05t3sPehasRBAFH13z0XjoVW4ecnNy4i/M7/sDa3o7nD5/Qf8WMlLr4dJCACDGRsLAwRo0axebNm0lOTsbKyorLly+nW1YSFRWVyuTzjZCRkbKSN74ParU6VTaGubk5lpaW5MyZE1dXV2xtbbGxsSFnzpzY2dmRK1cuHBwccHBwIHfu3O8tF/iShISEUKZMmY+er1Qq0/hnfAhnZ2devnyZ5niTJk3w8PCgefPmnDlzhvHjxzN69GgqVaoEgBbDZyvoelss+X3aEs55H0RtqkEQBKYe3YyFrTULuw7hxeMAdMlanArlp//KmVjmSNs29n0oTdTMWLKAncvX8fTpU3bt2sXGjRszdM1at27NihUrmDJlCpcvX063XatCoWDkyJFMmTKFsWPHEhcXx8mTJ5k5cybBwcH0+20W1b5v+E4hMTuQnJTMwmmTmTd3bqbm1alThzVr1tCzZ0+Sk5PfeR9LkoQoity7d4+dO3caS2pUKhUrV65MI8JZW1tTtmxZypYtm2YtURRp1KgRR48eBUCj0XD8+HGjcBIdHU2BAgWMGSb/zDrJyN/5Gz+RO3fuULt2bY4ePUpUVBSXLl3iwoULLFiwgCtXruDi4mL0KdmwYQNFixbl9OnTLFmyhFWrVjFr1iysra0xGAw8evSIypUr4+Pjk1JWZIjM1qIIgAGJJ4YoKiscv9g5Ll68yIoVK7h586Ysisi8E1kYkZGRkZGRkcnUVvTBhetY58xBgTLFyV3QhRptmyIIAi8e+TOxYRdW+52nYNkSLLxyAIAbx86ydvh0vmvXjGd/PWLT+HnMu7AHOycHtk5ayOohUxi2eZFx7QVX9meo9OH63dtUr9Q41bHY2FiqV6+OUqk0+mO8ycZ4U1ZiaWlJ3rx5sbKyMnpj2NnZGTMycufOjaOjI/b29v8Zn5KQkBDq1av30fOVSmUqsSgjODk5pSuMAOzbt4+cOXMyZMgQTp06xeHDh3F3d2fu3Lm4VSzL57aIjIuMZu8va1j//BImZqYkxsah+bvjzU/zx2Ftn5J5tGXiAnbPX0WXacMztb4E6KWU65M/f34qV67M/v37ef78+QfnCoLAkiVLqFixIj179sTMLH2/hdGjRzNz5kzy5ctnzJ54w8El66nZunG23QKLegN6/1B+WbgQSwsLJk2alKn5nTt3JjAwkHHjxqUqoXnDm7Kjbdu2oVAoaNasGefOnQOgW7dudO7cOVPnmzhxIsePHze2Re7Vqxd58+Y1vh4fH8/Tp09TZZmcPHnSmH1iZWWVpjznzb88efKkErCOHj1K/fr1gZRsmAYNGtCgQQMgJWvt7t27+Pj4cPz4cSZPnkxcXJwxo2TQoEE4OzvTsmVL7t27B0BAQAD58+dn3e7tWHumFX2yI8FiPFrJgEb4/MJebGwsnTp1YsWKFeneOzIyb5CFERkZGRkZGRmkDGyp5rYfgCRJhPgHMsr7V9QaDQH+D1jQZTARL1+hVKuIDAkjMuQ1ORxTWukG3L7PygETmfjHOqzt7TjnfZCK9b/DzimlE0G9Hu0YXKWZ8Rxu1Spm2A/CxMwMQRD4Z1Wwra1tynv6+/gbc8eYmJgMrfv23MzyKRXKX/Kc8fHxHDt2zOhLkNlzxcXFUbx48Ux19ZEkifj4+Hc+PZckyfhEHuD06dNUrlyZ4pXKMvXEVlSmn0+UMrO2JE/RgvzSbSjlPWtSqVFtzKxS4jq1ZS9nt+9Hr9WRHJ+Ac5GCH3UOnWRg/fr1zJo1i+fPn5OQkEDt2rUzNDdv3rxIkkRoaGi6r7+5Vubm5sTHx6d5ffzAYTipLAkR47OlfXJsZBTmIfFMnTqV8ePHY2lpmapNcUYYM2YM23Z6U6ByaR7qIwgXE4mWktEjUWtWSpteg17PumcXuagPRutiQ/Xm9Vm8/NcPri1JEpMnT+bnn3/m+PHjTJ8+nd9++41hw4YRGxvLpUuXaN26tXG8hYUFJUuWpGTJkumuFRISkqp7ztmzZ1m/fj3+/v6EhYWRL18+o2iyY8cOJkyYwK1btyhYsCA2Nv/LVnrTnah8+fLGVsRBQUFGU9dhw4bx119/odPpUCqVSJJkFISvvnjEd2JphGzUieZdCMBLMZ4CSuvPvvbAgQOpU6cOLVq0+Oxry/y3kIURGRkZGRkZGVK+mr5/szx82xLylyyKz67D/NJtGL/ePc6CLoPpNns07s08EUWRdjlKo0tKBiAsKJg57QcwaN08nAsXAP7ekP9jc/32ZtvU0jzDETs6ONCqVSsOHz6MTqdDp9MhCIKxC8qbFrHvfdfveP1Dcz/02sfO/VCa97vmvm+eIAhMnDiRAQMGpDJP/dCctxk/fjxeXl7vbB+aXlyiKNK/f3/mzZuXrmmlIAiMGzeOkJAQY7lSmTJlGDByGBqN5rNu8JVKJbPP7uDBhevcPX2JYdVaMmH/GhKiYzm6aiszT/+OTa6cnN2+n2Nrfv+oczx59Ihu3bqlOnb69Gmsra2N5Vfv+t+goCDs7e05cOAALVu2JH/+/JiYmGBiYsLz5885duwYr1+/TpMpAtCiRQuKFi2K2vc1uJqBMntthJUIPNvvw+QBwzl+/DijR49m1KhRRsFs0qRJPHv2DBMTk3TnS5LEKzGBx4ZIpl3YiajXpzGKfrP5V2nUqOxseC0lgrMlQzcv4pj+GTkNZhRR5SCXYJbuvern58e0adNYtGgR0dHRjBo1ip9++om5c+eSmJjIzZs3Uwkj70MQBJycnHBycqJ69eppXk9KSjJmm1y8eBGdTsepU6dYu3Yt/v7+mJiYpCnPefPPxcWFvHnz0qZNG9q0aQNAQkICixYtIiAggMDAQK5evYq1tTXl6lT/V4gikFJOEy4mfnZhZOfOnfj4+HDjxo3Puq7MfxNZGJGRkZGRkZFBgZDhrhbVv2+Iz65D7J63kvjoWBwKpKSYn1i3A11yildJYmwc01v1otOUobhVq2icW6Z2NfbM/82YVXJ8rTdl66Q14MwIttY27Ny5E61Wy9q1axk/fjwqlYqxY8d+1Hr/VSRJYvjw4YwYMQJz84wLT28zb9482rdvT+HChTM1b+rUqTRu3BgXl/Q7kdjZ2dGqVSsKFy7MwYMHKVKkCAmSjj+1gXxIqMsMibFxJMYlUKqmO6VquuN7+SaBfz3CzMoCcxsrrHLmQJeczJ8bdn30OUoWL8H9+/fx8vLCx8eHhIQEWrRowYYNG4w+NklJSan+Ozk5mYSEBHr27Env3r158eIFR48e5dtvv+Xhw4f88ccfvH79Gr1ebzyPmZmZsR2wmZkZr1+/pn///iQlJVHQvQwd54xCY/5ljSwziqg3EPP4BUO7/oyvz3Xq16/P1atXiY+Pp0+fPsZx+/fvN27032CQRPwMUfgZotEjpnhlKAQUGnXG7gwBBLUSEXgtJRKhS0KFgiJKW1yVtijeEkhOnDiBRqMhKioKlUqFp6cnAK6urjx79owHDx58hquRgqmpKcWLF6d48eI8efKEH374gTVr1gApf6uvX79OZQZ76dIltm7dir+/PyEhIeTNmzdNeY6npyeurq7kyJEDSZLwffSIh3n+J3xmVVexwVWaMuvMDkzMPnw/ZrYL1YcICgqiX79+HDhwIMu8nWT+XcjCiIyMjIyMjAwmKNBn4hl956nDGFa1BT0XTmRWm77YOeemZI0qWOVMaZt7ce8xXj4KYOec5eycsxyAFoN78F375nSaMpRJjX9EEARyF3Shz9Kp7zvVOzEXUr7GaDQaevfuTffu3Xn69OlHrfVfJiYmBo1G89GiCHyc+Sr8z4D1XcJI06ZNmTlzJmPHjjVmsJii+ujWs5MadUWp+p9PQWJsStlJfHQsc9r3R5uYjCiKFCpXkm9a1EepUnJm2376l6lPzjyOFK5YisdX72T6vEoEzAUV+dzc2LdvH8uXL2fmzJmIooiVlVWqFqr/5ODBg+TJk4cJEyYAULZsWZYtW0ZISAgJCQlAShZC9erVWbVqFUWKFMHCwgJHR0cuXbqUxjfhctILgrQxKDXqTL+Pz4lBbyDmdTj9ajana2yc8Xj58uXTZId069aNRYsWGbNo8pYqSk2vjphYmaP6TD4/BiQMGHhgiMDfEE0VtSO2f3dC2b17t1FsEgSB1q1b8/r1a0qVKsXJkye/2OfK0aNH6dq1q/FnQRCMZs3u7u5pxicnJxMYGJjK2+Tq1avG/xYEAVdXV8pWq0Kz2YNTzX2T8femq1jFBt99sa5iBr0epUpl9JnKCAmSDlGSUglWH4soivz4448MGDCAKlWqfPJ6Mv8/kNv1ysjIyMjIyHBdF8JzMe7DA7MJSgRKqewpqMxc95D/j/j6+tKsWTN8fX0/eg03Nzd2796Nm5tbpuY1b96cbt26fbC+v2zZssTHx/PkyRMAjmufES+lLRvJrohaHVv6T+Xi8dOEhYVhampKQkICc+fONZZ2vYt69erRuXNnChQowNixY7ly5QrJySnlaG8EkRUrVqTysxgyZAirVq0iJibGKChdu3aNYcOGceHiRSb9sY5i7uWzThyRICEmltx+cbwKeE6nTp3e62ujUCg4deoUSVotCfmskfLaIii/XPcQURTRJ2v5c+0Oji/bTICfP5Di6ZE3b17y5cuHtbU1r1+/5vLly5iZmdGtW7dU5U/vK43KyGsGgwEHBweePn2KnZ3dJ78nSZKIjIxM8TVJikBdvgCoUq7hP1tZ/5jvG8buXgXA6qFTSY5PxMTCjB7zx1OkUhmiQsNZ2HUIUaFhAJStU43uc8fy7J4vKwdOIjkhAW1SMvV+akvTASnlY4t7jMDMypLgJ0+JDotg/sW9tDQtwtawW5hZWrB+1Cz+OncFvVaHVc4c9F85E4f8eYzxqxCooc6LjSL9kqrMMH/+fPbs2cOZM2eydacmmeyFnDEiIyMjIyMjQ06FGS/F+Gzf2vENAmArfPoX6P8PhISEkDv3pz0ZVigUH5Ux8r7ONG9z9OhRXFxcmD59OmPHjsVOMP1XCSMKlYoXjwMIDg4GMHqB/PLLL1y5coU6derQrFmzNL+H+/fvc+PGDXQ6HVeuXDFmiEDKtTty5Ei67WnnzJnDr7/+yowZM9BoNCxatIjg4GBKlCjBtq1baeHRkhPRT4hKTMpQGcPnRiMo2DtlBRt+XUXevHmxtbUlMjIy1RiVSoWFhQXR0dGIokhEXDQWHmX+zl37sp9DCoUCjZkpjft2pkarhgyu1oIRg4fy7bffotPpjCVPoaGhXL58Ga1Wi5ubW6pyqLi4uDRlUemVSr3rtTddngoXLvzZxJY3/2tS2AmFUpFuDuDbXcX6lqhL/5UzKetRnTsnLzCnXX+W3T/B2e37yZXPmcmHNwApXZ0AHPLnYfLh9ahNTEiMi2fEt99TzrMGLsVTSux8L99k2vEtmFqkzU5rNawXP84aBcDxtd5sGjeXoZt+eWuEkKmsxXdx+/ZtZs+ezeXLl2VRRCZTyMKIjIyMjIyMDLaCyWdvkfolMUgS1oIJycnJPHr0iL/++osnT57Qt2/fz/L09b9ESEgIjo6On7TGp5bSfAhHR0emTJnC+PHj6datGzlzW/BSjPvXCHVWSg2XLlzk/PnzNG/enJiYGCRJoly5cty4cYP9+/fTq1cv1Go1jo6OuLm5kT9/fvbs2UNUVBSnT58GUjbs1apVY9q0abRt2zaVt8jbPH/+HGdnZ8aPH4+JiQktW7Zk/vz5ODs74+3tTbEiRXkWGMiwLYso61E93Y3ql0CBgAYFNTR5qTVnIZuWryYoKCjdDaper8fGxgYfHx+GjB2FupYbCei+6m9cFMDSwY6NfheobZoPUyHt1ujnn3/GYDAYu8J8LoYNG4ZarWb48OGZElT++b/R0dFpjrnWrsw3bs3hraybf3YVe/koAJVGTVmPFIPYMnWqodKoefkogKJVyrF/0VrWj55FyRpVKO9ZA4DkhCRWDJjI07sPUSgURASH8vTOQ6MwUrVlg3feazeOnuXwys0kxiUg6tP/LPnUv/fExEQ6dOjA/PnzKVjw47pLyfz/RRZGZGRkZGRkZLAWTP4lW9AUAh88Rl0xJS3c1NQUhUJBfHw8Xbp0kYWRf/C5hJE3T7gzg7OzMxcvXszQ2NGjR7Nu3ToaNGiAz61r/5r7UQAcBAsAo2lqy5Yt8fPzY9++fcZxCQkJHD16lM2bN3Ps2DGOHTuWah0LCwsaNWpEly5dqFq1KtOnT6d///6cP3/eWC6zYcMGpk2bxpMnT8iXLx9KpZKpU6fi5eXFhAkTWLZsGXFxKSVxkiShv+LP+Scv8RjYCYVK+UWvqQIBF4UVpVX2qAQFmKtp2bIlu3btSiWqvS2yhYSE0LNfbyYc3UhyVslgSgVaRM5qg6ilcUEjpBZxrKysiI6O5tmzZ+TPn7FW4hnh+PHjLF++/It8XvkborinD0uVf/HPrmKjd65Iv8uVIFD8m/IsuLKf23/6cGbrPnbPXcXMU9vZPGE+ORxzMXD1bJQqFZMa/2jsQgZg9o6uYqHPXrB2xAzmnt9F7oIuPLx4gwVdh6Q99Se+75EjR1K6dGk6der0iSvJ/H/kyxXvycjIyMjIyPxrUAgCeRVW/4qsEcEgcXXHEdRqNZIkkZiYSHx8PBqNht27d5OU9Hm7G/zb+RzCyJcupXnD0aNH+euvv9iwbBVWQtaah2YUUW8g/OYjbt68ydGjRzly5AgtW7Zkz549qcY9fPiQNWvWcOTIEaN4AVCqVCm6d+/ON998w4ULF2jZsiUmJiYMHDiQmzdvUrlyZapVq4aZmRk9evSgSJEi3L9/n2fPntGmTRvGjBmDhYUFCxcuJC4uDkEQkCQJpVLJiBEjqJbfjc09J2AnmKL8An/hCgQiX70mb4ie8mqHFFHkb3r16oWZmZmxXXPfvn2ZOHEiHh4eqNVqREmiw6JxxOuTs1QIk4BE9PjoXqTxQnFwcEChUHDhwoXPdr7g4GACAwO/mDGoAgXCO37X1b9vSNm633LlwAl0yVrunr4EwN3TlzDo9DgXKcCrgOeYW1tS44cmdJs9Gv+b9xBFkfjoGHLmdUSpUvHsr0c88LmWoXgSY+NQadTYOuZCFEWO/rbtHXF//P15+PBho/Hxh9q0y8ikhyyMyMjIyMjIyABQSGn7zi/T2QmFUsHa6Qu5ceMGNWrUMB43MzNj9OjRmJubU6hQIYYOHZqpTfl/lazOGMnM76BgwYIMHz6cIUOG4JyoRvUvuB/jX4ZR2706FSpUoGnTpvTp04eRI0cSHh4OwK1bt2jRogXffvstf/zxBwkJCSgUCtRqNWvWrOHu3busWbOGEydOEBQUhE6n4/nz5/z4448olUpu3LjBxYsXSUpKQhAE7t69S8eOHcmdOzfbt29Hr9fTpk0bbt++jb29vfH35OTkRK5cuWjZsiWnj54g/yuRCqrc2AiaT9qAvkH5d9lMMWUOBpZtSMhffmnG1KlTBycnJ9avX8+MGTNYvnw5JiYmnDhxAq1Wy557Pji55kOpyvokdgmIk3Q8NqT2QnF1dUWhUHDr1q3Pdq5jx47h4eFhFIw+hf3791O3bl1WrVpl/Fv7kATWeeow/tywk8Hr57N54nwGVWrC5onzGb5tCWqNhntnLzPEvTmDqzRlavMe/LxkCgqFgjaj+nJ8rTfDq7di56xllPi2coZizF+qGNVaNcCrfEMm1O9sbPH+NhJSuqVMGSE0NJSffvqJDRs2kCNHjo9aQ0ZG7kojIyMjIyMjY+S09jlRUvKHB2YRAlBAYUNZdS7jsTlz5jBy5Eg6duzI6dOnMTMzw9zcnMDAQKKiosiVKxeenp4MHTqUChUqZF3wWUTDhg0ZMGAAjRo1+ug1vvnmGxYuXEjVqlUzNe/Vq1eULl2a0NDQTM1zcXHBJX8+xvy56bMYMn4pVAhUUjly9+QFGjVqZPQEUSqVLFy4kNOnT3P48GFjK1ilUknFihVp0qQJR44cwcfHJ9V6CQkJjB8/nrVr1xITE8M333yDnZ0dhQsXZvr06fTq1Ytdu3aRlJSUqiRFEAQqVKjAvXspT/YNBgNdu3Zl7dq1APTu3RsXFxfGjh0LQIyYjJ8hiudiHApSRIEPFrJIoBIUGBCxE0wposxBboU5giBgbW3NjBkz0vXhkCTJ+AR/yZIleHl5MW7cOIZMGstZXdAXN1rNLAoEaqtdsFKktAkeOnQoixcvpnHjxuzdu/eznKNDhw7Url2bnj17fvJaW7ZsoXv37iiVSrRaLWZmZpSsUJYRf6xGoc56wSmjKBBoqnHNdLaHJEk0b94cNzc3Zs+e/YWik/n/gJwxIiMjIyMjI2OkmDLHF0m3/1woECikSt2id8SIEfj6+rJ582YCAwNZs2YN1apVQ6VSUb58eUqUKMHZs2epVKkSlpaW1K1bl127dn1UBsS/kaw0X82VKxdRUVFotdpMzTt8+DCXLlwk/t6zbH0/qlGSW2GOp6cn3t7emJmZGbNBBg4cyO7du0lMTDQKIsePH+fy5cscP34cLy8v4zpXr16lZs2aWFlZsXr1arp06UJ0dDQ+Pj7Mnj2b5cuXY2try65du2jXrh3h4eHo9XoMBgNnzpwB4O7du2i1WiRJQhRFtm3bhpubG+3atcPW1pY1a9YYy0SsFSaUV+emicaVb9V5sH+t48qeYySFR6NGgRIBBQIqFJiiRPcigjs7juGudqShpiA1NHlxVFoYN7EWFhY8f/483Wv09kZ3wIABrFmzhlmzZ3M4+G62E0UARCSu6kOM16pKlSqIosjTp08/z/qiyPHjx6lfv/5HzU9ISGDPnj0MGDCAatWqMXDgQLRaLYmJiRgMBuLi4lCLAkrFv6sji7Wg+agSmFWrVhEUFMTUqVO/QFQy/5+QhREZGRkZGRkZI44KC+wE02y5FVUiUFhpi6WgSfNa0aIpRqwKhYKaNWuyfPlyXr58yfTp08mXLx9xcXHUrFmThg0b8urVK9q2bYuJiQnly5dnwYIF/2lfkqwspVEoFDg4OBASEpKpeaVKlaJXr178VLsZalHIliKWEoFK6tzGzZybmxtubm6Iomi8nwRBMPqzGAwGYmNjuXnzJgEBATRt2pSZM2fi7OyMu7s7UVFR7Nq1i+joaBYtWsTt27epXLkypUqVwsTExHgfr1u3zmjY+eZ+z5s3L1qtloCAAHQ6HS9evGD+/PmULl2aW7dusWTJEgICAtBoNLi4uFCvXj0mT57MrZs3uX7KhxYVv6NokjntnCvS2MSVpiaFaGZSiCYmrjQwKYhrpJJd81aSS2GexpwUwNra2tiq+EN069aNrWcPI5hmXw+ZeElHkJjiA1OzZk1EUfxsZXk3btzA3t6efPnyvXecKIpcvXqVKVOm0LBhQwoWLIiZmRkWFha0bduWPXv2YGpqSrdu3VLaD2s0ODo6cvXqVc6dO4edyuyzxPu1yClkvq20r68vY8eOZcuWLWg0af9/QUYmM8jCiIyMjIyMjIwRQRCoqM6dLZ/Sm6KimDLjHRzUajUNGzZk48aNvHz5kgEDUtpVBgYG0rBhQ/r27Yu1tTXjx4/H3NycggUL/ud8SQwGA2FhYeTKlevDg9/Dx5ivXrp0ienTp5OcnMz3339PxYoVCQwMzPD8ZcuWYaoxwatmS/TJmcs4+dIoEcinsCanwoxHjx7RqVMnypcvz40bN1JeVyopXbo0hw8fRq/Xc+rUKczNzWnZsiVVqlQhNjYWKysrpkyZQp06dXj58iV37tyhWbNmLF26lLx581KjRg1EUeTUqVNERERgZWXFzp0708QyefJknj9/jkKhMLb9dXZ2pm/fvnh7e/Pw4UPi4+OZMWMGNWvWpGnTpkRGRrJ48WIqVqxI3bp1SUxMZNmyZfTr14/9+/enEQpLlCjBo0eP0Ol06V4POzs7Xr16laFrJ0kSNuVcMbW0yMQV/7oYkIxeI05OTgiCQHR09GdZ++jRo2myRYKCgli5ciUdO3akTJky2NjYoFKpcHd3Z9GiRYSGhlK7dm1+++03wsPD0Wq1BAUFcfLkSebNm0exYsWoV68eDx8+pFKlSgA4KMz/NRs9FQI5FZkTcrRaLR07dmTKlCm4ubl9ochk/j8he4zIyMjIyMjIpOG5PoZbhtdZ1UAzDQoEvlPnxUZh8slrxcTEsHfvXrZv386FCxdo0KABFStW5OrVq5w8eZLw8HDs7e2pW7cuQ4cONW40/o18rMfHP/Hw8GD06NHUrVs3w3MGDx7MkiVLjIKKqakp4eHhmJun39Lzn4SGhlKkSBFiYmLYcfssmmLO2ab0whQlrkEGpk+Zyu+//05ycoovj0qlomjRosydO5eGDRsas0lEUWTdunWpRAxRFClXrhyTJk2idu3aDB06lC1btqDX62nevDmLFi3C2dnZeE4fHx/atWvHgwcPsLS0BGDnzp388MMPrFixgmPHjuHj4/POzI2QkBDc3Nx4/vw5FhYWTJo0iQ0bNjBnzhzu37+Pj48PDx48IDQ0FJ1Oh4WFBS4uLpQtW5batWszd+5c9u7dS6lSpdKs3bRpU16+fMn169c/eO0ixER8dC+zzWfLu1AiUPPvzxwrKyvi4uIwGAzG1skfQ1JSEhUrVqRw4cKEhYXh5+dnLIsyNzcnT548lCxZkmrVqtGoUSNKliyZoXUTExMxNTVNVYqSKOk5rn2Wbf5m3ocKBY00BVFkopRmzJgx3LlzhwMHDshdaGQ+C7IwIiMjIyMjI5MGSZK4pn9FiBif5RsYJQIllDkppLL97GuHhYWxc+dOtm3bxr1792jRogUeHh5cvnyZ/fv38+zZM8zMzPjmm2/o06cPrVq1+qSN0dfm9u3bdO7cmTt37nzSOvXq1WPYsGHUq1cvw3NiYmIoWLAgERERCIJA586d2bBhQ4bmBgQEULZsWXLnzk358uU5fPQIkw5voFD5UqDI2k2QIEqcnLGalXMXGQURpVJJ4cKFmTNnDk2bNjVu1EJDQxk2bBg7d+5Ep9Ph6upKmTJl2LFjB+fPn2fAgAHGbicmJiZ4eXkxffr0d3Yr6dKlC87OzsyaNYvbt29TsWJF+vTpw5IlS4iIiCBXrlxs3bqVtm3bpju/WbNmNGnShHPnzuHr68uBAwfInTt3mnEvX75k3759nD59mjt37vD8+XPi4+NRKpU4OTlRvHhxqlevTtOmTSlfvjw///wzJ0+exM/vf51poqOjmTFjBmPHjsXa2tp4/LIumGAx/qOu/ddEAPIqrKiozk2RIkV48uQJjx494uXLl1SrVg21+t2lQKIocuPGDY4cOcLFixd5+PAhL1++NGbiODo6UqxYMSpXroynpye1atX6IqUgF7QvCZUSPvu6nxMFUEhhS0m1fYbnnD17lrZt23Lr1q10718ZmY9BFkZkZGRkZGRk0kWUJC7pXhImJWXZU0clAq4Km0x9af5YgoKC8Pb2Ztu2bTx//pw2bdrQsmVLbt68yebNm7l37x6Q4n/RqVMn+vTpk+Hsh6zi6NGjzJ8/n2PHjn3SOg0aNMDLy4uGDRtmat7evXv54YcfADh37hzu7u4fnHPv3j0qV66Mm5sbV65c4dChQ7Ro0YJvvq3GtBNbiZO0WdanRpuYxKQm3Xjgcw1IyRApUKAAc+bMoXnz5kbR7ODBg4wdO5a7d++SO3du+vfvz+DBgylSpAhHjhzh1q1bTJw4kadPn1KsWDFKlizJ2bNnCQsLo3Tp0kyYMIHvv/8+zfmDg4MpXbo0Bw8exMPDg2+++YY///zT+HqLFi24du0aQUFB6ca/adMm+vfvj4eHB5s3b87U/TthwgTu379P7ty5uXr1Kn5+fkRGppSbmJqaotPp6NWrF/Xr16devXr8+eefNG/enNy5c3Po0CHKli2LKEkc0Pr9C3IYUnjTKaV+/fqcOHGCypUrc+XKFY4dO4anpyeQkolz8OBBzp49y61btwgMDCQmJgYAW1tb8ufPT7ly5ahVqxaSJLFlyxaOHz/+VeJ/LSZwSRec5eL2+1AgUFeTD3MhY54zUVFRlCtXjqVLl9KkSZMvHJ3M/yf+PY88ZGRkZGRkZL4qCkHgG7UzuRRmWeI5okSgkNKWEqqcX+V8efPmZciQIUbzQgcHB/r378+SJUuoV68ely9fZtu2beTMmZMJEyZgaWlJwYIFGTx48Ds3olnN5zBehY83X23RogVFixZFo9FQpUqVD4738fGhQoUKVK1alWvXrrFmzRp69erFwoULuXT+AsLlAKwFExRZcD8mxsWnEkUAKlWqxKxZs2jatCkJCQkMHjyYHDly0Lx5c6ysrIylLWPHjsXb2xulUkn16tXp1q0bxYsX5/Hjxzx48ICdO3cSGhrKuXPnsLGx4YcffsDKyooePXoQFhZmPJ+TkxMjR46kVq1aODg4pNlgr169muDgYPbs2ZMmfn9/f6ZNm4Zer2fGjBmZFvUqVKhAYmIiv/76K1euXDGWgFy7dg1PT09EUWT//v20bt0aMzMzWrRogcFg4OXLl1SuXJm5c+cSIyZnS/+idyGQYsTq4OCQkkV37RoKhYJevXrh5OSEWq3GyckJLy8vLl68SMGCBRk1ahR37txBFEUiIiK4efMm69ato2vXrly+fPmju9F8iLCwMEaPHs2AAQPo2bMn33//PZ4VqqHKfDOpr4YA2AtmGRZFAPr160ejRo1kUUTmsyNnjMjIyMjIyMi8F0mSuK1/zXMx9qs9eVQgUPILlc9kBkmSuHv3Ltu2bWP79u2YmprSvn172rVrR0xMDAsWLOD48eOEhYWRM2dOoy9J5cqVszTuN8yePZvw8HDmzJnzSes0bdqUnj170qxZswzPESWJGCmZJ69f8iI6DJeC+TEgofy7Day1QoOtYJIidAgCBw8epEWLFjRv3pwdO3YwZswYdu/ezaFDhyhcuDBNmjRJ8cF45EugrYEX2hgE1ZdvSZqckEhcZDTTW/1MwO372NraEhUVlWpMmTJluHfvHlZWVnTr1o2pU6cafUD8/Pzo378/R44cwczMjD59+jB9+nRMTd/dhSMpKYmpU6eyevVqXr9+TalSpZgwYQKtW7fG3d2da9eusWnTJjp06JBmbqNGjbh3714qo9tLly7RsmVLxo0bh5+fH2ZmZkyfPj1T18Hf359atWqla6B7+vRpPD09jeasL1++xNPTk/v376ca12JAN7rOGgXKtM9mexWtxdg9q8hfsijJCYnM+qEvdk4O9F0xA6Uya1rPCgaJAzNXsGbaglTH7e3t6dKlC3Xr1sXDwyNDZTCSJFGoUCH27dtH6dKlP3usL1++JF++fKlMkm1tbfnrZQDXFOHZ0mtEiYBHJrJFtm7dytSpU7l+/Xq2z9aT+fchZ4zIyMjIyMjIvBdBECinduAbtRMalF/0ab0SAWtBQ221S5aLIpDy3suUKcPMmTPx9/dn7dq1hIeH89133/Hzzz9ToUIFbty4wYsXL+jatStXrlzB3d0dCwsL6tSpg7e3d5a2mv2cGSMZ6UoTL+m4pwvjePIzDmj9OK97QbCthJTfjkAxlhdiHIFiLP5iNHf1YZzXveCA1o/dYXfZcfccfYcOYsuWLXTo0IFz585x4cIFChcuDKQYjSYkJODinIeLy7yZ1b4/+qTkL/plNjkhkT837GRA2QYkv46iZs2aFCpUKF0PkD179hAVFcXChQuxtLRk3759uLm5UaRIEe7evYu9vT2xsbHMnz//vaIIpJSmTJ8+nVevXuHj44OdnR3t2rVDrVZz9epVfvnlF8aOHUtiYmKauWvWrCEoKIhDhw4BKdetadOmrF69mn79+tGtWzc2bNiQ6S5DBQoUIDIy0lg+8zaFCxdGr9cbf3Z2diYxMRGlUkm5cuWYPn06V69epf/4kemKIm8THx3LpMbdyFPMlf6rZmWZKAIgKeDbJvVo2bKl8ZipqSm2trbMnz+fhg0bZtgb5MmTJyQnJ6drXvs5sLGxSWUUbWpqyvnz53E2s0UKDEfSZ6/UESUCpZX2GRZFnj17xqBBg9i6dassish8EWRhREZGRkZGRiZD5FKYU0+Tn7wKSxQIn1UeUSCgQKCY0o7aahesFJ/fiPBTEQSBqlWrsmjRIoKCgpgzZw4PHz6kXLlytGvXjkKFCnH58mXi4+OZPn06UVFRdOzYEY1GQ7ly5Zg7dy4JCV/XCPFrlNJIksQrQzzntUH8qQ3ET4wiHh0SoEd6Z5aRAQk9EhKgsDKj+cBueEzuzapbx8hdshDHTxzH3v5/3jKmpqa4urqi1+sZNGgQZjE6mlkXx1Fh8dnvx4SYOMKCgpnStDsHZq9i+dJfefbsGatWrSIyMtIoAlhaWqJSqVi5ciXNmjVDr9czYcIEcubMSatWrcidOze3b9+mZs2ajBkz5qM2+VWrVuX06dPGMhgbGxu8vLyIiIigc+fOacY7OTnh4eFB//79mTt3LoMGDeLYsWM0btwYgNKlS+Pk5MSJEycyFYdCoaBUqVJGr523edM95+1MmkOHDhEeHs7NmzcZM2YMlSpVQrJ5vyAUHRrO+HodKV3rG3rMH48gCCzsOoRh1VriVbExs37oS1zk/9rmnty0mxE1vmdo1RaMr9+ZF4/8ARhYoRGPr/3PcHjforUs6zsOgPWjZjG8eisGV27KhAZdCH324t0BCQIFypdg9+7dQEoGxvbt2xk5cuT7L1Y6HD16lHr16n32DiparZZly5ZRpEgRnJycMDMzw8zMjFmzZlGwYEH69OnD8KadUYrZp4RJAGwEE/IrrT84FlLajnfu3Jlhw4ZRvnz5LxuczP9bZGFERkZGRkZGJsOoBAUV1LmprXYhv8IaJcIneQaoEFCjoIgyB/U0+SmqyvGvaL2oVCrx8PAwejoMHz6c8+fPU6RIEVq2bEmOHDk4deoUycnJeHt7kytXLiZPnoylpSUFChRg0KBBX8WX5HMJIwqFIt0Mg2gxmT91gVzRhxhNej82YV+hUiEJkK98CeqO6M4F5WtixGTj61qtFn9/f+PP586dw3vLNqqonVLdj6JW91Hn1yVrSU5M4sGF6yz+aThjq7dhaPc+PH36FK1WS7FixShevDiCIFCrVi1y5MjBjRs3aNSoEdWrV+e7777D3Nyc+fPn07p1ayIjIzl9+jR2dnYcOXKEbt26feSVgcOHDzNmzBjmzZtHZGQkly9fpmTJkuzatQtzc3O6du1KSEiIcfyqVasICAhg2bJlXLx4Mc1mslu3bqxbty7TcZQuXTrdDkcKhQKFQsHjx4+Nx4oXL46NjU2qcXrp/dlT8zp5UalRHTpMHGQ89tP8ccy7sIdF1//Axa0wu+evAuD++atc2HWI6Se2Mv/iXloM6cHSXqMBaNSnE0dWbgFShLujq7bSsHdHAFoN68Vcn90svHqAGj80YdO4ue+NSfd3zKampsTGxtK8eXN69Ojx3jnpcfTo0c/qLyKKIlu2bMHNzY39+/dz8OBB9uzZw9ixY/n222+pUaMGFStWJDY2lquXr1DDIl+28XdRoaCSOneGP+vnzJmDUqlk6NChXzgymf/PpN8LTEZGRkZGRkbmPVgpNJRTOFBKsifIEEuAGE2spDN+7X5XloAKBSISApBDMMFVaZvyxP9fIIa8C41GQ9OmTWnatCnx8fEcOHCA7du3M3DgQDw8PGjfvj379+/HzMyM69evs2DBArZu3cqiRYvImTMnHh4eDBkyJEMdWzLLl8oYESUJX0MEjw1RX8S7wIBEnKTjtC6Iokpbiirt8PHxQavVGsc4ODhQtGhR4H/3475pK7kXEcSAWeOJkZIRSXkKqE8nRoUESYlJKFQKgv0DuXXsHEdWbSUpIoaZM2fSYOkaxowZQ79+/TAYDNSvX58jR45QpEgRAH777TeqVq2Kvb09oihy9uxZ+vbty5IlS1K1dF6+fDkdOnTA1tb2o67Fo0ePaN68OV26dDFuDCtXrsyFCxeYNm0amzdv5tixY2zatInixYszZMgQdu3aRY4cOVAqlbi4uKRZs3379owZM4aIiAjs7OwyHEuZMmW4e/duuq9pNBr8/f3f66/zoXulYv3v8Nl5iAa9OmDn5ADAqS17Obt9P3qtjuT4BJyLFATg6qGTPL37kBE1WqdMljBmk9Tu2BLvGb8SGxHF42t3sHGwp2AZNwBuHD3L4ZWbSYxLQMxAecmbmHPnzs2zZ88QRTHTLbu1Wi1nzpxh/fr1mZqXHpIkcejQIcaMGYO5uTlr1qyhVq1axtdHjx6NRqOhfv36/PLLL3Ts2NH4mrvaiUu64Cz1G1EhUEOdN8MlNNeuXWPhwoVcv349S8uqZP77yMKIjIyMjIyMzEejEhQUUNlQABskKWUzGyUlEykmkYwBAyLC31klZqjIoTDFVmGCGap/RWZIZrGwsKBdu3a0a9eOyMhI9uzZw6pVq+jVqxdNmjShffv2rF+/HrVaTXBwMAsWLGD37t1UrVoVU1NT3N3d6d27N23atMn05is9vkTGSLyk46LuJYmS/otvsEQkHhuiCDLEMXbyeCClK0W+fPkYPXo0VlZWxrHnz59n8oSJ2NrasmPBSiRJIgkDUWIyMVIyeklEj4Q2KQmfs+c5tGsPj67eIeihH5IoYmVlxcyZM3FwcGDy5Mn07t0bR0dHJkyYwPDhw42+IqIosnDhQubNm0d4eDjR0dEsW7aMa9eusWLFCjw8PGjVqhWQYqL622+/cfbs2Y96/zExMVSuXJny5cunu6kePnw4GzZsYM2aNTg4ODBo0CB69uyJSqWiUaNG7N+/n7Nnz1KzZs1U83LkyEHDhg3Ztm0b/fr1y3A8pUuXZtu2bem+ZmZmxrNnzzL1/v5Ji6E9ufbHScbX68TUY5sJ8XvG0VVbmXn6d2xy5eTs9v0cW/M7kCIQ1OnSOlV2yRtMzM2o2bYpJzfu4t7ZyzT6O1sk9NkL1o6Ywdzzu8hd0IWHF2+woOuQDMVWuHBhnj17hq+vL25ubpl6Xz4+PhQvXpycOT+tw9b58+cZPXo0ERERzJgxg2bNmqX6HH3x4gVdunQhOTmZq1evUqBAgVTzHRTmuKscuaIPyZIWvmoUfKvOg3UGSyXj4+Pp2LEjS5YsSVfgk5H5nMilNDIyMjIyMjKfBUEQsFJocFFaUUadi8pqR75RO+OudqKS2pGSanuclZaYC+r/pCjyT3LkyEH37t05fvw4Dx48wN3dnenTp+Ps7MzPP/+Mr68vs2bNws/Pj4SEBGbOnElMTAydOnVCo9FQtmxZ5syZ89G+JMnJySQkJJAjR45Pfi9vzFdjRC2ntc+Jk3RfbWP1JntkkPcSAl6/ZOnSpYwYMYIKFSrQoEEDAM6ePUv9+vWRJMn4VFkQBMwEFU5KC4qp7HCOU7Jh5Ey+sS3A4CYdOL5uB8/u+WLQ62nSpAkdO3Zk9OjR/PDDD9ja2nLp0iVevnzJ6NGjUalURERE0LVrVywsLBgzZgx16tTh1atXHD58mIkTJ9KyZUv69u1LmzZt2LIlpYxj27ZtVKxYkWLFimX6fYuiSPny5bG0tMTHxyfdMSYmJixevBgvLy9EUeTZs2fMmDGDCRMmcPXqVQDq1q3Lxo0b08z9mHKa0qVLc/fuXdJramlpacnz58/fO1+Rga3H9yN6U7tTS8bX60TosxeY21hhlTMHuuRk/tywyziucqM6nN6yl7CgYCDFh8Lvxv/8Txr+3JEDS9bjd/MvvmmZUsKSGBuHSqPG1jEXoihy9Lf0RZ63Uf79WVWiRAkgpcNPZvnUMpo7d+7QtGlTOnXqRI8ePbhz5w7NmzdP9Tm6a9cuKlSoQO3atTl9+nQaUeQNuZUWVFU7o87Qb+PzoPhbGP9OnRcbhUmG5w0dOhR3d3fatm37BaOTkUlBFkZkZGRkZGRkZL4wjo6O9O/fHx8fH65evYqrqyuDBg0iX758DBkyhDt37jBw4ECuX79OcnIyu3btInfu3EyZMsXoSzJw4MB0W6W+i1evXpE7d8br+N+HUqkECxPO6oLQkQVddgQws7HiL6tE4sSUcprDhw8THBxMt27d8PT0NApIUVFRqcp+oqOjGTJkCPb29ixcuBBRFBEEAQsLCywsLADYv38/mzZt4qeffiI2NpazZ89SpUoVAK5evUr16tWxt7fnyJEjTJw4kcTERLZs2YKDgwN169bl4MGD9OjRA3d3d4YNG0bnzp1ZtWoVixYtYuDAgR/1luvXr09ISAi3b99OtwvOGxo2bIi9vT21a9dmwYIFjB49mvHjx/Py5Uv27NmDTqeje/fumJub07FjR6O3jYeHB69evUrXM+Rd2NnZYW1tnW5miK2tLcHBwe+dbypkrBSi9cg+1OrYgu3TFmNpa03/MvWZ0qwHruVLGMeUrFGFjpMHM7N1bwZXbopXhUZcPvA/Q9ncBV3IW8wVjy7fo/67c0z+UsWo1qoBXuUbMqF+ZxwK5P1gLCakxPym40tmrtcbPlYY8ff3p1OnTtSrV4+6devi6+tL165dU5WUxMXF0aNHD0aOHMn+/fsZN27ce+8XAHuFGZ6a/ORWWHxx3xEFAgUU1tTV5MMyE6ba+/fv59ixYyxduvQLRicj8z8EKT3JV0ZGRkZGRkZG5ovz4MEDtm/fzrZt2xBFkXbt2tG+fXtKlixpHHPz5k3mz5/PsWPHeP36NXZ2dnh4eDB48GCqVq36zrWvXLlC//79uXLlyifH2XeIF3Un/ozCNOu7BWlQUFuTDzNBxaJFixg8eDCurq74+/sjCAIKhQJ/f39sbGyYOHEiS5cuNXaREQQBU1NTqlevzs2bNwkPDzeua29vz8OHD43lDqtXr2bq1Kk8f/6c0qVLM3fuXOrVq/fOuO7fv0+DBg0YNmwYUVFRTJo0CXt7e0JCQjJdFuXl5cWvv/7KtWvXKFeu3HvHLlu2jIkTJ6LT6bh7926akgN3d3fi4uLo0KEDy5YtIzg4mKJFizJ69Gh8fX1JTExk4cKFGY6tUaNG9O7dm2bNmqU6XqtWLSRJ4syZMwB06NCB4OBgWrRogYeHByVLluSePgw/MTq9ZT87CTGx9C/bgDnndmKf1+mj1ymmzIGbKifPnj2jQIECRq+ZjPLq1SuKFSvG69evUasz5qsREhLCtGnTjF5FgwcPTlU29oarV6/SoUMHatSowaJFi9Id8yFeGuK4oQ9Fek8HqY9BiYAGJZXVjtgp3t+N6J+EhIRQvnx5du7cSfXq1T9bTDIy70POGJGRkZGRkZGRySLc3NyYPHkyvr6+/P777yQlJdGgQQNKly7NjBkz8Pf3p3z58mzevJnQ0FCCg4P56aefuH79OtWrV8fc3JxatWoZhZW3+Vz+IpIkUalHSwR19rCm0yFyVReCJEl4eXnh5uaGIAhIkoRGo6FkyZJMmjSJnDlz8ssvv2AwGBAEAY1GQ+HChdFqtVy4cAE3NzeUSiUKhcKYVXP9+nX69euHpaUlffr0oXz58jx9+pTbt2+/VxSBlFKLc+fOGYWYEiVKEBYWxuzZszP1/lavXs2SJUvYunXre0URURQZNmwYixcv5tKlSwwaNCjdrh1r167lwYMH1K9fnxcvXnDjxg3y589Pjx49WLBgAcuXL8fPzy/D8b0pp/knDg4OREREGH+Oj4/n9OnTDB8+nHLlyqFUKhnyU5+P7hqUGY78tpWB5RvS3Kv7J4kiKgRyCCmb+vz58wOk6ryTEY4dO0adOnUyJIpER0czbtw4SpYsiUaj4cGDB0yYMCGN4GEwGJg+fTpNmjRhxowZrF279qNEEQBnpSX1NfkpocyJKcpPyiARSBFErAQN5VQO1NXkz7QoIkkS3bp1o0ePHrIoIvNVkTNGZGRkZGRkZGSyEaIo4uPjw7Zt29i5cyeurq60a9eOtm3b4uT0v03eG2PPDRs2cPv2bSRJokSJEnTo0IH+/fuzdetWrl27xqpVqz4pngB9NNcTXqAyyfpskTcoESittKeAyoagoCBjloRCoTAKRG/+W6VSYWFhQXR0NIULF2bcuHF07dqVW7duceTIEU6ePMm9e/coUaIEp06dwtramj59+jBp0iQ0msy/59DQUOrUqcOTJ0+YMWMGw4YNY+zYsUydOvWDc8+fP893333H+PHjmTRp0jvHJSQk0LlzZ8LCwtizZw92dnYkJiZSokQJVq9ejYeHR6rxFStWRBRFbt68aTym1+uZN28eEydORKvVUqRIEUaOHEm3bt3em+GyefNmDhw4wO+//57qeL9+/di+fTtNmjTh8uXLPHnyxGjYKwgCdnZ2bNmzA22VfFli/PkxKBCop8mPqZAiCpqYmKBWq4mLi8vwGp06daJGjRr8/PPP7xyTmJjI0qVLmTt3Lk2aNGHSpEnky5cv3bHPnj2jc+fOqFQqNm7cSN68Hy4HyiiSJBEmJfLEEEWYmAikiB3pdXV6gwoBkZS/ydwKcworc2CbCR+Rf7J06VI2bdrE+fPnM5xhIyPzOZCFERkZGRkZGRmZbIpOp+PkyZNs27aNffv2Ub58edq3b8/333+fqs2qKIocPHiQX3/9FR8fHxISErC2tqZIkSLs3LnT+LQ7syRIOv7UBmbLjawSAQ9NPsKDQtLdRAqCYCytady4MfPnz6dQoUKpxnh7e+Pl5UVISAhFixZl+vTptG7d+pNjGzhwILt376ZGjRrUrVuXnj17MmjQIBYsWPDOOYGBgRQtWpTGjRuza9eud4579eoVzZo1o0iRIqxZswYTk/9tQvfu3cuYMWO4fft2qk3lrVu3qFChArdu3aJMmTKp1lu/fj3r16/HzMyMEydOoFKpaNq0KXPnzk33vrlz5w5t27ZlzZo17Nq1i7Nnz/L48WOio1NKZFxdXY2Gs/Pnz0cQBNq3b8+yZctQq9X8ofV/70Y7O6FBQSMTV+PPjo6OhIaGpsnOeheiKOLo6MiVK1fSNUPV6/WsW7eOyZMn4+7uzrRp097b8Wbbtm14eXkxbNgwhg4d+kXb10qSRAJ6osSULmMxkhYDIiISCgRUKLBVmJBDSOk09kY8+hTu37/Pd999x4ULF4xtsWVkvhayMCIjIyMjIyMj8y8gKSmJQ4cOsW3bNo4dO0aNGjVo3749zZs3x9LSMtXY27dv88MPP/Dy5Uvi4uKws7Ojdu3aDB48ON309NDQUGxsbFJtsgHOa18QLiVmy22sAMQ+DaFz8Rrpvu7o6MjgwYMZMmRIKjNKrVbLhAkTWLFiBbGxsXz33XecOnWK0NBQcuXK9clxxcfHkz9/fs6dO8eoUaPQarW0b9+ebt260atXL5YvX55mTlJSEnnz5sXJyYnbt2+/M2PjwYMHNGrUiC5dujBp0qQ0xrqSJNGwYUPq1avHkCGp29CWLVsWjUZj7Fbzhri4OFxcXHjw4IHRoHbx4sUEBQVRuHBhhg8fTt26dfH29ubEiRPcvXuX0NBQFAoFDg4OlCpVirp162JpacmwYcNITEw0vicrKytsbGwoXrw4+fLlQxAEOk8fQbKTRba8p95GgUBRZQ6Kq/4nQH777bf4+PhgMBgy5Btz48YN2rdvj6+vb6rjoiiya9cuxo0bR548eZg5cybu7u7vXCc6Opr+/ftz9epVtmzZQsWKFT/+jWVTkpOTcXd3p3///vTo0SOrw5H5f4jsMSIjIyMjIyMj8y/A1NSUVq1asWPHDoKCgmjXrh1bt24lT548tG3blr1795KUlASkbIJLlizJhg0bCA4OpkePHty6dYsaNWpgbm7Od999x5YtW4xPvj08PKhRowaxsbHG88WJWiKkpGy7gZUAE8ccOBVKm9VQsGBBgoODGTFihFEUefbsGc2bN8fCwoKlS5fSuXNnYmNjOXnyJObm5uzZs+ezxLVp0yZq1KiBm5sbu3btwsHBgZUrV7Jp0yZWrVpFly5dUo0XRZFKlSohCAJXr15954b71KlT1KpVi0mTJjF58uR0uw0JgsDixYuZMWNGmg4xq1ev5vr16zx48CDVcUtLS1q1asWmTZtQqVT8/PPPTJkyhXr16vHixQt+/vlnChUqxPjx43n9+jWdOnXC1dWVK1euEBwczPHjxxk5ciRVq1ZFq9Ua1zU1NcXT05Pw8HBjadjWrVsJvfYA4Qt3QvlcFFBap/q5bNmyQIrg8eLFiw/O/2c3GkmSOHbsGFWqVGH27NksXbqUP//8872iiI+PD+XKlcPCwoLr16//J0URgHHjxlGwYEF++umnrA5F5v8psjAiIyMjIyMjI/Mvw8rKik6dOvHHH3/g5+dHnTp1WLRoEc7OznTr1o1jx44RHByMo6Mjjo6OzJ49mydPnpCQkMCcOXOIj4+nW7duaDQaihcvzoMHD7h9+zbVq1cnMjISAD9DNFK2lUX+RhBo2KczQKrSkYCAAMLCwoCUzWmZMmUoWLAgt2/fZsWKFcTFxbFkyRLMzc0ByJcvH3/++ecnhyNJEosXL8bLywsAlUrFunXrqFKlCjNnzjSKA2+X67Ru3ZonT55w8+ZNTE3TN6rcsGED7dq1Y9u2bXTt2vW9MRQtWpSePXsycuTIVMcrV66Mm5sb3bt3Nx7T6/UcPXqUsLAwxo8fj6WlJTY2NvTt25fAwEDatGnDjh07mDVrFs7Ozty5c4e9e/diZ2eXpm1t4cKFEUURURR5/fo1ixYtwtfX1yjgmJiYsG3bNrq0bkcO4eM9KL4WDgozY3lIUlISderUYc2aNUDKtcybNy9Pnz597xpvCyOXL1/Gw8ODAQMGMGrUKK5evYqnp+c722nr9XomTpzI999/zy+//MKKFSuM7aX/a7wpF/ztt98+S3txGZmPQS6lkZGRkZGRkZH5j/DixQu8vb3Ztm0b169fp2PHjvTq1Ytq1aqlyUQQRZFDhw4xcuRI7t+/bzxuY2PD6XNneV7UIlt6i/yTxLh4KkVZUKRQYQwGA3ny5MHFxYWWLVvyyy+/EBYWRrVq1fjll1+oVKlSumt06NCB69evpyl5yCzHjh1j+PDh3Lp1K9UGT5IkY/eQmTNn0rFjRzw9PalYsSIzZszg7Nmz6ZY4SZLEpEmT2LRpE3/88cd7/SfeJi4uDjc3N7Zt28a3335rPL5x40a6du1KxYoVCQgIIDIyEoVCgZOTExEREfTp04cRI0bg4OCQ7roPHjxgxIgRHD58GIBmzZoxd+5cChUqhFarxcTEBE9PT65cuULTpk3p3Lkzbdq0QavVsnDhQnr37g1AqJjAZV1wtr2/lAhUUzuTU2EGpJR5ODg4EBMTYxxjaWlJTEzMOzfysbGxODs7c+rUKWbMmMHVq1eZOHEiP/74Y6rSrvTw9/enY8eOWFtbs379+lSmy/81IiIiKFu2LGvWrPlg5ycZmS+JnDEiIyMjIyMjI/MfIU+ePAwePJjLly+jVqspWLAgvXv3pmDBgowYMYKbN2/y5pmYQqGgSZMm5MyZE6VSiaWlJUqlkujoaIbMnoTuK7RV/RwIgGmB3ACEh4dTrlw5rl27xqRJk2jcuDFhYWGcP3/+naIIgKenJ4GBgZw5c4aDBw9+dCyLFy9m4MCBaTbLgiAwduxYhg8fzuDBg9m4cSPHjx9n2rRprFixIl1RJDk5mS5dunDkyBEuXryYYVEEUjbt48ePp23btjRu3BgXFxdUKhU//vgjCoWCx48f069fP+7cuYNer+f58+eMHTuW2NjYd4oikNJe+sCBA+zZs4dChQpx48YNChcujI2NjdEMuFy5cjx//pxNmzZRr149xo4dy/Tp042iCICDwhx7hVm2LKhRAE4KC6MoAinZLocPH071e+3atet7sxu8vb2xsrKiUaNGVKtWjUePHtGjR4/3iiKSJLFhwwbc3d1p27Ythw8f/k+LIpIk8fPPP/P999/LoohMliNnjMjIyMjIyMjI/EcwGAyUL18eURR5+PAhPXr0oFq1apQvX55t27axfft2NBoN7dq1o3379hQrVozly5eTkJBAqVKlKFGiBHnz5uVK0guCFUlZ/XYyjElYAhNb9uDatWvkzp0bFxcX7t69S2Rk5DvLU97Qo0cPDhw4QGhoKKamptjb2/P8+fNMx/D48WOqV6/Os2fPMDMze+c4b29v+vTpQ0REBEqlkooVK3Lx4sVUGT0RERG0bNmSnDlzsnnzZmPJz7tISkri4MGD7N+/nytXrvDs2TOSkpKM2SBNmzalZcuW1K1bl9OnT1O3bl0CAgJSdZ0JCgqiTJkyBAUFffB8165do06dOuTPn5/IyEgsLS3x9/dHp9NRrlw5vL29P9hVJEnUczjhCYL607uZfE40KPDU5EctpO340rNnT1avXg3Aw4cPKVasWJoxr1+/ZsaMGSxfvpzq1auza9cubG1tP3jeyMhIevfuzV9//cXWrVvTdA/6L7J+/Xrmz5/P1atXP/h3KiPzpZEzRmRkZGRkZGRk/iMolUr0ej1//fUXBoOBVatWsWXLFkqXLs2MGTPw8/Njw4YNREVFUatWLSpUqEBcXBxt2rShfv36uLi4IAgCMQpDVr+VTOEf+QqDwcDJkycJDg7m0qVLWFhY0LJlyw/O1Wq1REVFASkCw9tmmZlhyZIl9OjR472iCECtWrWIj49HrVazfPly7t27R/ny5dHr9QD4+flRrVo1KleuzI4dO9KIFKIo4uPjw5AhQ6hcuTI2NjaYmZnRsWNHfHx8KFOmDCtWrCA6Oprbt2+j1WqNZqoKhYI6derg6uqaymsEIG/evLi7u7/ThDYpKQlvb28aN26Mp6cnycnJTJ8+ncDAQB4+fEhSUhKWlpY8ffqUokWLUrBgQZYsWWI0+A0ICGDnzp1MmjSJWrVqYakx5bcBk1Bko0e0SgQqqnKnK4oA/Prrr8YskX+KIrGxsUyePJnixYuj1+txdHRkwYIFGRJFTp8+TdmyZXF0dOTq1av/L0QRPz8/hg8fzpYtW2RRRCZbIGeMyMjIyMjIyMj8h1i1ahVeXl4kJSVhbm7O/fv3U2UGvMFgMHDmzBm2bdvG7t27cXNzo3379nzfpjWXbWKRgF5Fa6ExNUFtqkGXpMWteiV+XjwJ1VtGp1+Dkxt3UaxqBfIUKQhAwO37vHz8lOqtGwEgSNDUpBCKt0obzp8/T82aNfnjjz9o2LAh4eHh5MyZM83aSUlJlC1blkePHqFWq9m1axdNmzbNVHzR0dEULFiQO3fukDdv3neO0+v15MuXD3NzczZu3EjLli0ZO3Ys48aNw9nZmZUrV9KuXTvGjx9P3759U95rQADbt2/nzz//5N69e7x+/RrA2CrXw8OD9u3bp/s7BhgyZAixsbH89ttvxmNHjhyhUaNGBAYGporX29ubVatWceLECSCl1OHy5cts2LABb29vypcvz48//kjLli1p2LAhkyZNok6dOsb5hQoVonbt2owcOZIRI0Zw6NAhABo0aMDNmzcJDQ1Fq9UiSRKCIODr60t0ASuCxfgs9xtRIuCisKKcOm0pkUESiZaSiZKSOXrVB7W9NbldnJH+tic26PTEx8aRGBpJKeeCmGmhbYNm+N75673lNlqtlokTJ7JhwwbWrFlDw4YNv+A7zD7o9Xpq1qxJmzZtGDx4cFaHIyMDyMKIjIyMjIyMjMx/ipiYGOzt7TEYDEyfPp1Ro0Z9cI5Wq+XYsWNs27aNe4FPGLt/NRpzM3oVrcXYPavIX7IoBoOBsR7tadKvK9+2afwV3sn/GOfZkeaDf6Jyo5RN+MmNu7h2+BQjti0FQIXAt+q82CpSdztp3bo1R48epXr16pw7d464uLh0N6r+/v4ULZryHmNjY7G0tMxUfIsWLeLixYts3779veOqVKnCo0ePeP78OVZWVty7d48GDRrQr18/pk2bRmJiIn369CE0NJTr168TFBSETqfDxsaGokWLUqNGDVq3bo27u/s72/r+k+joaNzc3Ni7dy9VqlQxHi9QoADFixfnyJEjxmNJSUnkzZuXAwcOcPr0aTZs2IAoivz444906tSJfPnyGcf269ePIkWKMGjQIOOxSpUq4ejoaPRpEUWRFStWMH/+fPz9/Y3jVCoVXl5ezJs3D1GSuKQLJkxKRMwicUQQwUllQWWVY6r7I0pMxs8QxQsxDgUgwgdjFAAMIgZJIofanKLKHDgpLFKJdgC+vr507NgRJycn1qxZ815vl/8akydP5vz58xw9ejTD97GMzJcmexX1ycjIyMjIyMjIfBLW1taULl0aPz8/hg4dmqE5Go2GJk2a0KRJE54mhHNTDEszRpeUjC4pGUtba3RaLVsmLuCvc1fRa7XkL1mMn5dMxszSgsU9RqA2NeHl46eEBwVTzL08A9fMQRCE976WEBPLuhEzeXrPF11SMqW+c6fbnDGc3rwHvxv3WDNkKlsnLeT74b3ZNmURCbFxDK7SlJLfVqb3gkkkSnps+Z8wIkkSjRo1YteuXRw7dgyNRkN4eDj29vZp3purqysDBw5kxYoVSOYaXhhi0SEiIiEgoEDAXFBhI5ig+UeZhcFgYMmSJWzatCnNupGRkSQlJeHk5ETXrl25desWf/31F1ZWVkbT05o1azJ+/HgMhpTypWXLllGkSBFq1KhBs2bNaNy48SeVGtjY2DBr1iz69+/PpUuXjBvRxYsX06JFC0JCQnB0dCQhIYE9e/Zgbm6Oh4cHXbp0Yd26dXzzzTfpikllypThypUrqY7Z29sbM1ogxeC3b9++lCpVioYNG5KUlIQoiuj1eu7evcuDBw9wc3PjG7UTl/8WR7525khSfALKiEQqFSqEIAhIksQLMQ5fQyQJkg7x76wQMYPrSQBKBQogWkrmhv4VAgKuCmsKq3KgRsHq1asZM2YMU6ZMoXfv3v+vWtReunSJZcuWcfPmTVkUkclWyBkjMjIyMjIyMjL/QkRJIlbSEiUlEykmocWAQZIQgMT4BEjQUswxH7YKE8xQZXjzFWSI5ZY+FD1SqlKaEP/nlKtbnZHbf2XHzF8BaDO6HwAbx85BoVTRacoQFvcYQbBfIJMPb0BQCAx1b073uWMpV/fb9772a+8xlKxRhVodWyCKIgu7DqF0rarU+6lthjJGyqtyk0f5v0yPEydO4OnpafzZ3Nyc8+fPU758eeMxSZKIkJIIMsTx2hBPlC4RE40GgZQN7psvycLf/wxIqFFiK5iQW2GOi9KKowcPMWXKFK5cuZLmGnt5ebFmzRq6dOnC8uXLad++PYGBgTx48MDYKtfU1BRBELC0tMTT05Njx44hSRK+vr4Z8qfICKIoUqNGDbp160aPHj2Mx11cXHBxcaFEiRLs3r2bb775hpo1a7JixQr8/f3fu3G9cOECgwYNSiWOdO3alYsXL/Lo0SPjsf79+7Ns2TKaNm3K9u3bqVq1KgUKFODevXv4+fnh4uKCl5cXAwcO5B4RBIlxX00cSU5I5Pi6Hfj/4cPJP/8kQdJxTfeKaCn5s8egABSiwJE5qzm/5zBbt26lRIkSn/Uc2Z3Y2FjKly/PnDlzaNWqVVaHIyOTCjljREZGRkZGRkbmX4JOMhBoiOWpGE2cpEPxd8PTNJs4c8Bcww196N9ZD5BDMKWQ0hZHhfl7RZK3BQGA4duWkL9kUbRJycxp148DS9Zx5Y+TJMbEcWFPShmGLllLwTL/ayfr3qwuGtOU7A3XciUJCQj84GtX/viTx9fusG/RGgCSE5LImdcxw9dG+sc18PDwYOfOnfTt25ewsDASEhJ4+vRpitGpJPLcEMtjQyTJGFKunwAqjfqDG2ItBkKlBMINifxlCOeRWQKDJ41JdU1DQkLYsWMHq1evJiEhgeXLlwNw7Ngx3Nzc6NevH40aNWLy5MkA/P7774iiSNOmTalVqxY+Pj4UKlQIX1/fdDNcMotCoWDp0qU0aNCAVq1aERMTw8aNG9FqtVy8eBFPT0/u3buHs7MzkiSxbds2Tp8+nco/5J+UKlXKaPKrVKZk0Tg7OxMbGwvAy5cvqVmzJoGBgWzatImOHTsCcP36dRQKBYIgEBAQwIgRIxg7diyjR4+mbt26TPp1HmHOpiRqk1FqvoyXTXJCIgkxcczvMoi/zqYIOz2njKDxsB4ISsUXkWVEQFRI1B7ShU6j+lNYk/F7+7+Cl5cXtWrVkkURmWyJLIzIyMjIyMjIyGRzosVknvztdfAmcwHSEUT+gf6tAoAwKZEofRIKBFyVthRQWmMqpP0qqEAgPdlEY2pCpUa1uXboFEgSvRZNokztqume943wAaBQKhD1hg+/JkmM8l6Go+v/fCwyg+IfUQuCwPfff0+jRo2YMmUKs2bN4tdlv1KyaS18DRHAh6/f+3gzt3D1CkiiyPrH51jrNYWrp84bjW8TEhJSzZk7dy7dunUjKCiIxo0bU7VqVZYuXYpKlfJ7OHr0KD/88AOlSpXC19eXwoULc//+fZydnT86zjcUKVKEkiVLUrp0abRaLe3ateOPP/6gWbNm3L1713gOQRDo1q0b69ate68wYm1tjYODA/7+/sbWvPny5SMuLo6NGzfSo0cPChYsSGBgII6O/xMB3ogoAAULFmTHjh2IosjatWuZM2cO7q4lKVy8GM3H9KZqi/qfVRzR6/QYdHrObNvPuhEzSE5IRGNiwrAtiyhdqyp8IVHkbZQaNaEkckL7jBrqvFgpNF/4jNmDXbt2ce7cOW7evJnVocjIpItc2CUjIyMjIyMjk03RSgau6II5qwsiSIxFRPqkzbweCS0ijwyRHNc+44k+kn9WVasFBaIhraOCKIr8de4qzkUKUrlxHfYvXktyYhIAibFxPH/45KPjAqjc2IPd81YZvTZiwiJ4FfAcAHNrSxKiY41jzf7xMwiohPS/1pqZmTFz5kx2HTlI8zlD8TVEYPjE65gKhYCgUmKdJzcDty9i7dmDREZHsXHjxpTIBAEzMzO6du2Kh4cHN2/epGrVqnTq1Inly5cbRRFIKffZs2cP9vb2ODs74+joSNGiRXn27NlHhSaKIidPnqRr1664uLhgYmJCfHw8f/zxB0uWLKFSpUrMnTuXffv2GVsWA3Ts2JEDBw4QHR393vVLly7NnTt3jD+7uLgQHx/Pjz/+SL9+/fD19U0lirwLhUJBjx49ePToEU+fPqV44SLM7zKYUbXb8uj0VQw6PYLh439fyr99YvIIFkgXnhBw4DxJ8QlY57Bl8aX9lKlTDVNL8w8v9JkQAS0iZ3RBRInJX+28WcWLFy/o27cvmzdvzrSxsYzM10IWRmRkZGRkZGRksiEhhniOa58R8ncr08/5JPuNwPLAEMFpXRBxktb4mrWgQS/9L8NjbvsBDK7SFK8KjRBFkR/G9KfV8J8pUNqN4dVbMahSE8bUaU/QQ79PiumneWNRKBUMrtwUr4qNmdLsJ8JfvgLA86e27Ji5jCHuzbj9pw9lalcjKSGRwZWbsnrIFEQkrIX0n7xLksRjfSTqWsVxcSvyxfwrFEoFKhMNlmUKcN0killLFqJQKJg5cyYvX75k/fr13Lt3j3r16rFw4UKGDx+ebkmTWq1mw4YNVKxYERMTEwoUKICbmxu+vr4ZjuXJkyeMHz+eggULMnToUMqXL4+vry+HDx9m7ty5DBkyxCiIdezYEXt7e3r37m2cnytXLjw8PPD29n7vecqUKcPdu3cBuHXrFp06dUKSJM6dO8fChQszHO/b5M+fn0KFCmFvb8+YfoP5tdcourq44z19KdrIWARSPGWU6eY1paBEQPV35pOloKaEMicNNQWpauFCu8YtOHz4MNGxMcw5+TsORfJjYm72UbF+KnpEzuteECtqPzz4X4ooinTt2pX+/fvj7u6e1eHIyLwT2XxVRkZGRkZGRiYbIUoSN/SvCP5bEPkaKBEorbSngMoGg8HAtte3sMxh81XO/TlQo6CxiWua45IkcUsf+lUNPVNOnOJjUdPEBScLWyCl28zUqVPZvXs9D5eCAABst0lEQVQ3VaumX4KUaglJYtq0aWzYsAFra2vu37/PlStXKFOmTLrjo6Oj8fb2ZsOGDTx+/JiOHTvStWtXypYtm2qcwWDgm2++wcvLi06dOgGwbt06evbsSUREBNbW1gAcPHiQ6dOnc/HixXfG+Pvvv/P7779TqlQppk+fzrfffsvZs2dJSEjAzOzjxYb8+fNTs2ZNBg0aRLVq1dBqtbRp0yalDbBC4PsfO/LziEGYOtmRJBkwIBq7B5kKSnIqzLAVTLAWTFC+w0/nui6EF2J8lrUIfhsNCupq8qfpePRfYMGCBezatYszZ86kyo6SkcluyMKIjIyMjIyMjEw2QS+JXNS9JOoLdMX4EEoEiipzsG/BapLLOVP6u2++6vk/BXvBjG81eVIdkySJ619ZYPonSgSqK52ZMWo8Bw8e5NChQ7i6phVw3sevv/7KzJkzyZs3Lzdu3MDHx4fKlSsDKSLHiRMn2LBhA4cOHaJu3bp07dqVBg0aoFa/25vj8uXLtGrVigcPHhiFEHt7exo0aMDmzZsB0Ov1uLi4cPLkSdzc3N65Ts2aNTEYDPzyyy/0798fhULBnTt3KFWqVKbe5xtiYmKwtbVl2bJlDBs2jPj4eNRqNY8ePSJfvnxs2rSJmTNn8ujRI5ycnOjbty8jR47M1KY7xBDPVX1Ilt0X/0QB5FZY4K52yupQPiu3b9+mbt26XLlyhYIFC2Z1ODIy70UupZGRkZGRkZGRyQYYJIkLupdEZoEoAilmor76CG7HvuSvc1fR63RfPYaPQQDsFaapjkmSxB396ywVRSDlmp6M88fv1QsuXLiQaVEEoF+/fsydO5eAgACqVKlCtWrV2LhxI6NGjSJ//vyMGzeO6tWr4+fnx86dO2natOl7RREAd3d3GjRoYOyKAzBt2jR+//13o2GsSqWiS5curF+/Pt019u/fT61atdBqtdy6dYv+/fsDKaVAfn4fX1a1ePFizMzM6N+/P/Hx8UCKT0xISAgKhYKuXbvy8OFDnj9/Tu3atZk+fTqmpqZ4enpy48YN4zqxsbFMnDgRrTZ1mYpWMnBd/yrbiCKQ4jkSKiYQbIjL6lA+G4mJiXTs2JH58+fLoojMvwI5Y0RGRkZGRkZGJouRJIlLumBeS4lZn9pvEBGehiO45kJ8t41DtkGBQG21i7G7x6tXrzhw7Sw235ZA/VYHnCxDkjAVVHhq8qN8h0FsRvD29uann34CIC4ujjZt2jBp0iRKlCjxUeuFhoZSsmRJTp8+TcmSJQGws7OjRYsWrF27FoCHDx9Sp04dAgMDjRkZoijSuXNntm3bRufOnblz5w4rV66kSpUqANjY2DBlyhS8vLw+Kq4SJUpQoEAB6tevz4gRI9Dr9SiVSry9vWnRokW6czZv3sz06dONZq+9e/fGzs6OgQMH8v333+Pt7Y0gCEiSxP7Am2hzmKI2yX7dYNQo8PyPlNR4eXkREhLC9u3b39seXEYmuyBnjMjIyMjIyMjIZDEBhmjCsoMoAqBUoCjkgLnw+dqkfkn0UXH0+7EHxYsXx8zMjGJlSmJepUj2EEUABAEtIvf1EZmeqtPpOHjwIG3atKFXr1588803qFQqatSowa5du3j8+PFHh+Xg4MCECRMYOHCg0Yh14sSJbN68maSklG5DxYsXJ3/+/Bw5cgQAPz8/8ubNy549ezh48CAbNmxIZcAKYGlpyfPnzz8qJr1ej6+vL0OHDqVnz57odDrmz59P9+7dKVCgwDvnderUiQcPHhAUFETdunWZNWsWAwYMQJIkDh48yNChQzl//jzfd+tIso0mW4oiAAZEHn7EfZLdOHr0KHv27GHFihWyKCLzr0EWRmRkZGRkZGRkspB4ScdfhvBsldr/pp1tdn9unRATxy8DxuHj40OZMmVYsWIF+wNuYGGdvVqCikgEiNFEikkZGn/nzh2GDh2Ki4sLM2bMwNPTk6dPn3L8+HHOnDmDv78/NWrUoFWrVmzbtu2j4+rTpw9hYWHs3LkTSHnKb2ZmxuDBg41junfvzrp16/jll18oVqwYjo6OhISE0KhRIyClM83bLXutra15+fLlR8Wzdu1a1Go1Hh4eTJ06FSsrKwYNGsSKFSsoV67cB+c7OzuzceNGzp49i0aTIn4kJSWxcOFC6tWrR9tR/TAxM/3AKlmHCDwTYzBIadtl/1t4/fo13bt3Z8OGDeTIkSOrw5GRyTCyMCIjIyMjIyMjk0VIksQ1XUj2yBT5B1oMGAzZe4NmZWnJ6d/34+fnh7e3N3U7fU+0UpcNr2aKOHJFF4L4jir2169fs2jRIsqXL0+TJk0wMzPj7NmzXLhwgV69emFrawukCBFnz57l+fPnVKtWjQ4dOrBmzZqPikmlUrF06VKGDh1q9PMYO3Ysa9euNXpzNG3alH379jF06FAmTJjAjRs3jIatAKVLl06VMWJnZ0doaOhHxbN69WqjsezatWv54YcfPmqdQ4cOodVqUSgUCIKAqakplrY2KJ1zZMt745+8EP+dXiOSJNGzZ086duxI7dq1szocGZlMIQsjMjIyMjIyMjJZxHMxlhhJmy03awYkdAY9SfGJWR1Kuhi0OnKFG1C8lar/IJtl3vwTHQZCxHjjz1qtlj179tC8eXOKFCnCtWvXmDdvHk+fPmXatGkULVo03XVcXV05f/480dHRuLu706NHD5YsWfJRMdWoUYOaNWsyY8YMAIYNG4ZGo2H48OGcPXuWIkWKoFKpGDZsGBMmTEgz/03GyJtynFy5chEeHp7pOERR5NatW/Tu3Ztr167x+vVrpk+fnul1QkJCePXqFXZ2dkycOJHo6GgSExO5EHgfjUk2Ka96DwYkHhsiszqMj2L16tU8e/aMqVOnZnUoMjKZRhZGZGRkZGRkZGSyAEmS8NVHZuuNvEJQIGm12a9DjSgR+yKUVlVqU65cOWbPns39FwEkSvqsjuy96P/e9F6/fp0BAwaQJ08efvnlF1q0aMHz58/ZtGkTHh4eKBQf/oru5OTEmTNnUKlUVKxYkYEDBzJnzpyPimvOnDmsXLmSx48fo1AoGDFiBEuXLqVWrVrUqVOH/fv3c+TIEdLr2ZA7d24EQSAkJMQYV2Rk5jf2Bw4cQBRF2rdvz5gxYyhevDgODg4Znh8dHc24ceMoWbIkJiYmPHz4kAkTJmBlZQXAUzEmW2ZmpUe8pCdeymZ/cx/g0aNHjBkzhq1bt2LyLxCgZGT+iSyMyMjIyMjIyMhkAZFSMklk7428Uq1CaWKCqDdkdSipUCoUtC1WnefPn/PLL7/g7+/PZp8j6AzZ+3oChCXH0Xf4YHLmzMnly5c5c+YM3bp1M27gM0OOHDk4duwYDg4OlCpVipEjRzJx4sRMr+Ps7MyoUaMYOHAgL168YP369YiiSMOGDdm3bx9169YlJiaGmzdvppkrCAKlS5c2+ozkyZOHuLjMl4IsXbqUUqVKIYoip06dYuTIkRmal5iYyLx58yhSpAgvX77k5s2bLFiwgFy5chnHiJL0rxIaFJBhP5rsgE6no1OnTkyaNAk3N7esDkdG5qOQ2/XKyMjIyMjIyGQBV3TBvHyrrCK7okRA9/Q1Bkdr1KZZ381DiUAZpT35VTbGY1rJwBFtANnbESUFQYL8CmvKaTKeDfEhdDodP/74I9euXePRo0cMGzaMuXPnZmoNrVZLgQIFCA0NxdXVlcaNG7NixQri4+NRKBRMnjyZsLCwdEt2Bg0aRN68eRk2bBibNm3i559/JiEhIVPnt7CwYOrUqUiSxLhx44znfRd6vZ7169czefJkKleuzLRp097ZujhKTOa8Lgg9Ej67DrNrznIkSUKXpMW1XAmGbFyYqVi/Bq4KG8qoc314YDZg3Lhx3Lx5k4MHD8pdaGT+taiyOgAZGRkZGRkZmf9v6CSREDFzG8eswoCEwsace0fPU65ZHcQs3PcoESiosEkligBEiEkoEP4VpRKSAKHS5/3dq9VqNm3axMCBA9FqtcyfP5/4+HiWLVuWofl6vZ6WLVsSEhKClZUVt2/fxsTEhJUrVzJ+/HimT59O165dqVSpEnPnzsXUNHVnl9KlS3PmzBkgxf/kjXFrRrl8+f/au+/wJuu2jePnnXRRVtl7770RZIOCFgEBcVC2G0FARQFZMsSFgEwVAWWICjhYyt6yKUtGkSWzrALdTXK/f1T6yAulaWmbtvl+jsPjeZrc40pZzZnrd/12KCIiQr1791b58uXl7+8fbyjicDi0ePFiDR06VIUKFdKiRYv0yCOPPPD6IWakTEnXLwbrq34jNP7PX5W7SAGZpqnTB44kqtb42G02WT2S763VNTN9dIxs2bJF33zzjQIDAwlFkK6xlAYAACCVhZixb+TTjWw+WvrZTK35dpEiw1wT6FhlqKglqyp55LrnuRtmpGzpIBS5I0K2ZN+S1WKxaPLkyerevbsKFCigGTNmqEePHgmeFxgYqHz58mnz5s3atGmTWrZsqU8//VQWi0V9+vTRxIkT5XA4VLx4cVWrVk2//fbbPdeoWrVq3M40ZcqUkd2euKVXn376qUqWLKmLFy/qzJkz952VYpqmVq9erbp16+qjjz7SlClTtHbt2gRDEUm64YiSXaZCLl+R1dNTWXP5SYpdBlSiWkUFnz6nboXqxh3//7/etWKd3nm0vQbUaaO3Hmmr0wePSpLa+5TRz59/raGPB+iHMZMVcvmqPnq2t/rXfkr9arXW+nk/S4oNc758c4T6VG2lAXXaaHDT5+Kuvef3DRrc9Dm9Xf9pvdekk47tiF2uFGomLlxyhZs3b6pr1676+uuvlS9fPleXAzwUOkYAAABSWYgjSo50sfAjVkx0tC7duq7Jrw1WyJVr8u/dVT6ZfVPt/lYZKmnJrooeue77qfRVR8I757xStqls0dH6+u/NslqtkqS13y7SlFcH6+UJw+X/etdkrzs+Vhm6aUYrp+GT8MGJYBiGRo4cqZw5c2r06NGaO3euwsPD9eOPP973+BEjRmjMmDFq2LCh/vjjD/n4+Kho0aKqWbOmunXrpg8//FBffPGFxowZo+HDh6tXr16aPXv2PdvoVqpUSUePHlVMTIxy584tSbp69Wrc/0/IunXr9Oqrr+rdd99VsWLFVKpUqbue37lzpwYPHqxz585pzJgx6tixo1MDau+IUmxQU7xqBZWpXVUvl2miyo3rqsKjtdSk89MPPPd80ClNe/19jV37vQqWLq6YqCjZov83r8R0ODRm9XxJ0mdd+qloxTIa9OM0Xb8YrHfqP62SNSrJHhOj/eu2acqBP2SxWBR646Yk6eLfZ/TjuKkasXSWfLNl1dm/gjS67Yv6+sQm2WXKNM003YXRp08fPfHEE3rqqadcXQrw0OgYAQAASGXXzIj7xiKvlG2qN2v6y+Fw3PXYmcPHk3Sf///Jd3ufMooITfxcE09PT7Xt+pxCQ0MVUOcxnVi0XuE3b8thS9lhp9GRUTKjbartkV+VPHPH+ybxlpOfrufIn0eBqzfHfb1+3s8qVbNystSaGA5JIWZUil3/zTff1MSJE+Xn56clS5bc88Y1JCRE1apV09ixYzVp0iRt3LgxbnlM0aJFNWDAAA0YMEAeHh569dVX9cknn8jhcKh9+/basWOHzp8/f9f1fH19VaRIkbhdbaxWq44fd+73bFBQkG7cuKEBAwZo2bJl6tOnT9xzf/31l55++mm1b99e7du319atW9WsWTMFBwfr3LlzOn36tE6cOKGjR4/q8OHD2r9/v/bs2aMdO3Zo69at2rhxo9auXavga1clxXbVDP5pukavmqfKTeppz8oNGlCnjW7/G1Tcz/61W1WzVRMVLF1ckuTp7a1MWbPEPd+sS4f/Hbtum1q+9LwkKWeBvKr1RFMd2rhd+UoUlcNu19RXB2vD/F/ijg9cs0WXTp7V+4911oC6bTShx1uy2+0KuRxbb1qOTr///nvt2rVL48ePd3UpQLKgYwQAACCVhTjif1McGRquDfN/UfOuHeI9JtVZLcpXrrgyZ86sVq1a6TH7Y5r3w/eKrlxQucoVS5Eth02bXbtXrNeoZ1+Vj9Uz3uPspkM2J99CNuvaQevmLlGtJ5rq8ql/FBURqaIVy0iSIkLDNHPAKAXtiV0S0rRzO3V451VJ0tDHA1S6dhUd2x6oGxcv69Fn/NVtzEBJ0q8Tv9GWn5bLbrPJ08dbr33xgUpUu/8Q0DscMnXbESVZ7/O6TVMOh0M2my3uP7vdftfXCT1us9mUL18+9evXT5988olWrlypypUra8iQIdqzZ48mT56szJkz64MPPpBhGJo0adJd14uMjNTGjRv13HPPqXDhwoqIiFCjRo1UvXp15cuXT+3atVOlSpXuqiM0NFRdunRR3rx5ZZqmXnzxReXOnTve+u7c69q1azIMQyVLllRkZKSGDx+uIUOGKObfLaItFos8PT01aNAgDR06VB4eHvf8Z7VaH/h4+7H9VSTH/35NilUqq2KVysr/tS7qW/0JnT18XOZ/wsjoqKj//oI88NfSJ8vdnVP/P7wzDEOZs2fVF/tW6tCmHTqwfpu+G/qpxv/5i0zTVM3HG6vfrPsPyk2rvSJnzpxRv379tHLlSvn6pl7nGJCSCEYAAABSWbTin8Hw/NC++mHMZDV67il5ev1vF5iLf5/R9DeG6dbV67J6WBUw6m3VbNlYknR8Z6DmDv1M4bdjt0l9YXg/1X6y2QNrOH/8pL55Z6xuX7shW3SMnurTXS26P3P/gw1DWfPllsPh0KJFi/T222/rypUrKlSokLYeP6C/7NcUZsbIIfOhIhJDkmEaymRa9NXAD/XP3r90o2nHBwYAMXLIrJRDsiT8NrJKk3r6/csFCr1xU+vmLlGzgKd14t8g5KdxU+VwmJq0Z7kibodqUJNnVaxKedVq1USSdPWfixq7doEibofp9Yot9FiPTipYuriaBjytdv1flBTbXTCj73B9vGlRgrUs+PEHtej3wX1DDovF8sA3/Il5rmrVqtq9e7cOHz6snj17Kjo6WiVLllTjxo119uxZXbhw4b7X8/f31++//6633npLjzzyiPbs2aMXXnhBWbJk0Zw5c9SnT5977hsVFaXKlStr48aNKlCggGrXrq0WLVooZ86c8dZXp04ddejQQbt27VKuXLlUs2ZNLViwQK+++qoGDhyoHDlyJP031L92xFzURUeYrp2/pCv/XFT5ejVif03PXdStq9dVuHwp2WJsuvj3GRUoVUybFi6NO7f6443000fTdOHE6buW0vy3a+SOas0f1apvftALw/vpxqUr2vPHRrV5s6duXrkmq4eHarZsrGotGmj3ivW6fOofVX+soX4YO1ln/wqKC+iCdh9QmdpVJUmWNLiMxm63q1u3bnrrrbdUq1YtV5cDJBuCEQAAgFRkmuYD+xtK1aqiUrUq6/evFqhNnx5xj0/o8ZZa9npej/XspH+OBOn9xwI0OXClPDw9NaPvcA39ZaZyFsir6xeD9W7DDpq0Z0W897DbbPq8+1saMGe8CpcrpYjboXrn0Q4qV6+GCpcrdd9zrJ4eypEjh0JDQ+OW+ly4cEFdWrWTzWZTjmIFVbPD4yrf7BHZo2PkmcnbqV06bNExioqIkIeXl/5cslIrp8/Tub9OKDw8XFmzZlWNGjUe+OY/e95cev2HifL0dmIrYcPQox2f1JaflmnrohX6cP3CuGBk/7ptevGzoTIMQ77ZsqrRs0/pwLptccHIox2ekMViUebsWVW4XCldOnlWBUsX19/7Dmvxx9N1+8ZNWSwWXQg6lXAdklq3eUoDW70Q91qsVqs8PT1ltVoTNT/DGUuXLtXTTz+t6OholShRIm7JS0Latm0rq9WqdevWKVu2bLLZbProo4+0dOlSlS5dWqVKldK1a9c0YsQIbd68WVeuXNEvv/wiKXa3kvXr16tr166qUqXKXde9ffu23njjDdWpU0fBwcHq1auX5s2bp+zZs6tGjRo6fPiw8ufPn2yvP9O/b3nsNrsWjp6kK2cvyCuTt0yHqc4jBqhM7ap68bP3NdK/h/IULagqTerFnVuwdHH1njZG47v0k8PukMVq0ZszP1GxyuXuuc9L44dpep9h6l/7KZmmqa6j31HRimX0977Dmtb7fTlsdtltNtVs1URlH6kui8WiAbM+09TXBis6Ikox0dGqUL+WytSuKs80OvHgzjbQAwcOdHElQPIiGAEAAEhjAkYO0LBWXfVYj06xD5imTu0/oubdO0qSilQooxJVK+j4zkBZrFZdPn1Oo9u9+J8rGLr49xlly3X/T9svBJ3SuaN/a3zX/nGPxURF69zRv+MNRixWi/z9/fXLL7/I4XAoOjpamTNn1qBBg+4KLCwnwmXL6qWYKFNHr5xUruKF5OHjHdsNIsn89z8PGcoqT+X2yqacmXyV2+qr53pV1sReAzV9+nQNGDBAt27dSvB7FW3a9Xv0KafnMTTv2kHvNX5GlRrVvfv7c59Bl//92tPH+67vhd1mU0x0tD59oa/GrFmgUjUq6fqFy3qxZEOn6sicyVe5szk3nPRhTJo0SW+//bbKly+vmzdv6vz586pYsaIOHTokjwSCqwkTJuiRRx5RQECAunXrpg8++EAvvfSS8ubNq2eeeUbBwcHq3r27NmzYoOvXr991bkxMjMqXL39PKCLFdh3MnTtXCxYskCQ1b95cFotF27dvV/ny5ZPvxf8rp8VHZx23lLdYIY1cPue+xzzWo9P//rxJem5o37j/X6d1C9Vp3eKec36ODLrra798uTX4p+n3HFeqRiWN//OX+963+uONVP3xRvc8ns1wIuhLZXv27NHnn3+u3bt3xw0wBjIKghEAAIBUZBhGXEAQn0JlS6pWqyb6bdIsSbFdJvFcTKZpqnjlchq79vt7ng4+fe6+p5mmlC1XDk3YufS+z99PTFS0vv/+e4WGhmr69On68MMPlTlzZj322GP3PT40NFTNi1bWq6++qi+mTpFDphwyZZEhqwxZjfg/EZ8xY4YaNGjgVF1WWRI1pDJ/yaIKGPmWytStdtfj1Vo00JrZP6p8/ZqKDA3T5p+Wq8dHgx54rZjIKNltNuUuHNvdsPLL+U7XkdIdAREREWrZsqW2bdumESNGaPjw4bpw4YJatGihkydPqmzZsvrrr7/ihq7eT6lSpdS7d28NHDhQ48eP1zfffKOsWbPGPW8YhkqUKKFff/1VLVu2VERExF2dKO+88859r5s1a1ZlypRJERF37yb0yCOPaPv27apQocJDvvq7ZbFbFRUdLatX/LNq0prclkyuLuEu4eHhCggI0BdffKGiRYu6uhwg2aXNHi0AAIAMzOrEWMXnhvbVyi/nKyI0TIbFohLVKmj93CWSpHPH/tbpg0dUtk41la9XUxf/PqODG7bHnRu0+0D8YYqkQmVLyNs30107ZJw79rfCb92O95yIW7HzS7JkyaKBAwfq4sWLWr16dbzHv/LKK3I4HDp69Kg8DIu8DKt8DA95GdYHhiLR0dE6dOiQ3n333XiP+S+rYcjrflNMH6DlS8+rRNW733x3GvyGTNNUv1qt9V6TZ9W0c7u4GS7x8c2WVS8M76eBDTrq/RYvOL2FsVWGslm8Ez4wiTZv3qy8efPqyJEj2rdvn4YPHy5JKliwoLZu3apq1arp/PnzKl26tMLDwx94rffee0+///67ChcufM9znp6eKlSokBo2bKhBg2JDpHLlysnb21umaer555+/63jTNPXLL7+oatWq9yzl8fLyUp06dVS8ePGHeOX3CgoKUstGzR745yGt8ZChHMm8lfPDevvtt1WnTp17fk2BjMIw09PfEgAAABnAhuh/7rtd6ytlm+r9n79SsUplJUlzBn2kXyd+o4l7lsvLxzve4atBuw/o28EfKyzklmKio5W/RBENWfKVrp69oHcadNB353dKit2ud8HVQGXKklkXTpzWrHfG6Oq5S3LY7cqeN7cGzp+kbLlz3luww9QfX3yn6e+Ndur1LVy4UD179lRkZKR8fX0VEhIiT0/nPq2fOHGihgwZkuAb9v/aFn1ewWZEwgemER4y1NCzsPxSIBzp27evpk6dqjZt2mjx4sX3XS4TFhamdu3aacuWLcqePbuOHTsmPz+/e467deuW2rRpox07dijqPzu1eHh4yDAM2Ww2LV++XE8++aQcDodKliyptm3byjt7Fm3dtV3LViyXqdiunr3bd2rIWwMVERGhcePGaeTIkdq1a5ek2M6T559/Xt99912Cy3ucZZqmZs+erffee08jRoxQlVfa6UYKbpGcnCwy1MqrmLyNtNHcv3TpUr355psKDAxU9uzZXV0OkCIIRgAAAFLZoZirOuEIcXUZTjPspmb1Hq5f59y7XOf/u3DhgkqVKqXIyEhJUrZs2bRkyRK1aHHvjIb7qVSpkooUKaLff//d6fqO2a7rqP16CmwanDIMSW28SiXrriMXL15Uo0aNdPbsWc2ePVsBAQEPPD46OlqdO3fWsmXL5Ovrq+PHjyt37rtnnty4cUNNmjTR33//rfDwcFksFpmmKdM0NXHiRPXv319btm1VybpVdc0RqWtmhG6Z0XI4HIqJjpGPj7fsdociIyNlWAx5enoqh4evclsy6fPhY3Rg3Tbt2blLHTp00KJFi+6Z8ZJU169f1yuvvKLjx49rwYIFqly5ss7ZbyvQFixbOvhdktvIpIZehVxdhiTp8uXLql69un766Sc1bOjc/BwgPWIpDQAAQCrLYfGRhxPLadIK0yJdOHrSqWMdDofatGmjbNmyyTAM3b59W6tWrXLq3MjISB05ckSDBw9OVH05LD5OLU9KK6xR9ruqjYyM1I0bN5J8vblz56pYsWKyWCw6e/ZsgqGIFLt05YcfflC3bt0UFhamkiVL6sKFC5Jil+I4HA7lyJFD+/bt09ixY+Xj4yOHw6GOHWMHABcoWUzbrp/UjZr5tMd2WSccIbphRskuU6bFkIePl2wyZVoNeWfOJK9MPjI8rApRlE44QtR68Ct6f+13GvjNeH23aGGyhSLr1q1TtWrVVLRoUe3cuVOVK1fW1atXNfezqQoLDUuWe6QkDxkq4+Hn6jIkxXbd9OrVSy+++CKhCDI8ghEAAIBU5mfxTtTAUFfzsJmKcPJNZeHChfXjjz+qTJkyatmypf755x8NGTLEqXMnTJggX19fNWnSJFH15TR80kEfQCxbdIx+/GKmvLy8lDt3bvn5+cnX11c1a9ZM/LVsNj311FPq3r27evfurePHjydqm1ur1aovv/xS77zzjqKiolSmTBnVr19fjRs31rp16+KO6d+/v44dO6aSJUuqUJkSWnhyuzyalVewr102mUnqwrB6e8nw9FCDzm21NvqstkSfV6gjOtHXuSMqKkrvvvuuunbtqpkzZ+rzzz/X4cOH1aNHD5UpU0ZBx44rT6Q1zb/5scqivIZzs2pS2rRp03TlyhWNGDHC1aUAKS6t/90AAACQ4WQ2POWbRuYHJMQiKdtthxyOxEU5Z8+eVa1atVSoUCGn5xLMnj3b6SU3/+VhWFTEkjVd9Ix4eXqqsMNXNptN165d082bN2WaprJnz67Tp087fZ3AwEDly5dPmzZt0qZNmzRx4sQk1WMYhsaOHauPPvpI4eHh2r49dojvF198cddxRYoU0R/HdqvFiFflXTCXLJ4eyRJGmYbkkKmrZoTWx/yjINuNRA9KPXLkiOrXr69jx45p586dunbtmurXr68OHTqoQoUKCgoK0qxZs1SvYFkpDf8uscpQGatfsnXPPIy//vpLI0eO1Lx585yeDwSkZwQjAAAALlDGmiOdLP8wlCNUstvtiTrr+vXratq0qdPHh4aG6sSJE053l/x/pax+MtLB9zO3xVcfDv9AmzZtkq/v/zoD9u/fr5IlSyp//vwaNWqULl26FO81Ro4cqVq1aqly5coKDg5OlmUOR44cuevrFStWxG2nG2pGa0PMPzpivy57Cvbm2GXqqP26NsT8E2/3yK1bt7RlyxZJsUs9ZsyYoUaNGum5555TtWrVVLt2bc2aNUvvvfeeTp48qffeey9udoqv4amyVr80++fORx4qafVzdRmKiopSQECAPvzwQ5UtW9bV5QCpgmAEAADABQpbsri6BKfksWSSj6yJCkauXLkiu92uRo0aOX3OZ599pixZsuiRRx5JSpnKavFSNsMrSeemFut/5kc0atRIK1euVKZMmZQzZ06dOXNGL730kqKiojRixAgVLFhQRYoU0YQJE+Lmj4SEhKh69eoaM2aMJk2apI0bN8rH5+G3dTVN857hp3a7XRMnTtR1R6Q2RP+jm2Z0ioYicfeVqZv/BjHXHZH3PD948GA1bdpU69atU9u2bfX555+rbt26+vjjj3Xt2jWtXbtWa9as0dNPPy2r9d5tnMtacypTGuzWsshQXc/8yTqQN6mGDRumYsWK6aWXXnJ1KUCqYVcaAAAAF9kfc0WnHTfT7HwMqwzV8yygW6cu6vHHH9fJk84NYF2wYIF69eoVtzONM0qVKqWaNWvqp59+Smq5uuII1/aYi6nyBj4pshleauZZ5K4AYsuWLTp79qw6d+4c99iZM2c0YsQILVmyRLdv35ZhGMqXL5+uXr2qPHnyaOvWrSpRokSy1hYVFaUtW7Zo4cKFWrhwoUJDQ1Wt2aMateJbOVz0Xt0qQ/U9Cyq3JZOk2OVZ5cuXV0REhAzDUO7cuZU9e3b17dtX3bt3d3rJ1i1HlDbEnJMjjfw+scpQaaufKnjkcnUpWr9+vbp06aL9+/ffs0sRkJERjAAAALhIlGnT6ugzaXILUUOxQ00behbSmTNn1KRJE505c8apc9944w0tXbpUZ8+eder4kJAQ5cyZU4GBgapatepDVC3tjbmsc47QNPOm9w6rDDXzLKIslsR1tRw7dkytWrWK+94bhqHy5ctr6NCh6tixo7y9vVOiXAWePKpTeQ0Znvd2XaQmqww19CykHBYf+fv7a+XKlZJivw8tW7bUihUrZLEkvgn+hC1ER+zXXB6iWSRlNbzVxLOwy7tFbty4oWrVqunrr79Wq1atXFoLkNpYSgMAAOAi3oaHanjkTZMzDywyVNszvwzDkNVqTdTw1TvzMpw1btw4Zc+e/aFDEUmq6pFHnmnsR1yrDJW35kx0KPL333+refPmunz5spYtW6ZDhw7p6aef1unTpxUQECBfX1/VqFFDS5cuTfQMmAeJNu06X8jL5aGIFLu0ZmP4GT3m30orV66UYRjy9fWN25o4qYNKS3v4qaQlu0v/7BmSfOWphp4FXR6KmKap1157Te3btycUgVtKW/9qAAAAuJlC1qzKbWRKU9GIVYaqWnPHzWKwWCyJeuN96tQpVatWzenjv//+e/n7+ye6zvvxMCyq7ZkvzYRNhmJ3ISqdyKGaX3zxhcqVK6d8+fLp0qVLat26tSpVqqQlS5YoPDxc+/btU6tWrXTkyBG1bdtW3t7eql+/vtatW5foXV3+v322YNnT0IbSNoddnT8cqOeff16LFy/W7t27FR4err/++uuhdnCp6JHLZeGIRVIWw1ONvArL03B9ADV37lwdPnxYH330katLAVyCpTQAAAAuFmXatCb6rGLSwJtRiwzlNHzUwLNg3JvOS5cuqVq1arp8+bJT1/D29tb8+fP1zDPPJHhscHCw8uXLp6NHj6pcuXIPVft/nbDdSPFdVJzhLauaeRWRj5MDPyMjI/X4449r27ZtGjFihIYPH57gObt27dLgwYO1adMmxcTEyMPDQw0bNtTHH3+sOnXqJCo8uGgP087oizLT2MenVhmq5ZFPBa3JP7Q46N/fK6m1/MoqQ36Gt+p5FpSn4fpv9KlTp1S3bl2tXbs2Wbq2gPTI9X8SAQAA3Jy34aGGnoXk4eIuhzvdDfU8C9z1ZjoxS2nCw8MVHR2txx57zKnjx40bp1y5ciVrKCJJpT1yqLQLt2Y1JHnJosZehZ0ORTZv3qy8efPqyJEj2rdvn1OhiCTVqVNHa9asUXR0tDZt2qR69eppy5YteuSRR5QpUyb5+/vr8OHDd53TtGlTLV++/K7Hzgdf0pZbp9JcKCLFLqnZawtWtJl8S4buKOORQ008CyuzPFP094uh2FCksjW3GnoWShOhiM1mU5cuXTR48GBCEbg11/9pBAAAgLJbvPWoZ0GXhSMWxYYijTwLyeP/vWFLzFKa9evXy9PTU35+fk4d/8MPP6ht27aJrNY5FTxyqbw1Z6qHI4ZiO0WaehVRZsPTqXPefPNNNWnSRE2bNtWlS5eS/Ca1UaNG2rx5s2JiYrR69WpVq1ZNq1atUuXKleXr66uOHTtqyZIl2rhxo9q0aaM//vhDN2/e1NChQzV4xqeyeLh+WUd8HDJ10n4zRa6d3eKtFl5F48K05P4dY5WhHIaPWngVVQmP7A+1BCg5jRs3Tr6+vurfv7+rSwFciqU0AAAAaUiII0pbY87Llor7qlhlKKvhpQbxfIodEhKi4sWLKyQkJMFrvffee/r222916dKlBI89d+6cihQpopMnTyb79rP/tXLvVtkrFZA9Fb6nVhnKbcmkmh755O3E7IiLFy+qcePGOnPmjGbPnq2AgIAUqWvZsmUaOnSoDh48eE/3j5+fn9p36KD2UwbLnsY/NvWSRU96lUjRYCHMjNEJW4jOOm5JUpKXY8V+K2OXppXx8FNewzfNBCKStGPHDrVt21Z79+5VoUKFXF0O4FJp/K8+AAAA9+L37yfXuY1MqdLpYJGh0lY/NfYsHG9rf2I6Rvbu3atixYo5dezYsWOVN2/eFAtFNm7cqMqVK8u/VkMVPhGm/JbMKfY9NSR5yKKaHvlU37OgU6HIvHnzVKxYMRmGobNnz6ZYKCJJTz31lAIDA2W322W13l1bSEiI6rdtKcOSdt60x8cuU5cc4Sl6j8yGp6p55pG/VwlV9cijbIbXv7++xgN//9w55k7HUEmLnx7zKqqGXoWUz5I5TYUioaGh6tKli6ZNm0YoAkhybsEjAAAAUo2P4aFHPQvqnP229tuvyC4z2TsdrDLkIw/V9cyv7BbvBx9rtTodjJw4cUItWrRw6tglS5aoQ4cOTh2bGDt37tQbb7yhv/76S+Hh4fLw8FDVCpVksVh0wR6qA7YripEjWQazWv59o5zf4qtqHnmdCkRsNpuefvpprVixQm+++aYmTpz40HU4wzRNffXVV3G/loZhyMfHR3a7XbdyeymPiwfVOsMuU8ftN1TAmjnF72U1LCpmzaZi1mxymKZumdEKMSN1zRGpcDMm7s+lVYY8DYtyGT7KYfFRdsNbXmlgp5kH6d+/vxo1aqSOHTu6uhQgTSAYAQAASIMMw1ARj2zKY/XVEds1nXOESkp6W/8dVhnykEWlrX4qZfWTxYlPsRMzfDU4OFj169dP8LhTp04pODhY77//vlPXTYwZM2YoMDBQNptNklSmTBlZLLHdMAWtWVTAkllXzQgdt93QNTNShhL/fY3tDDBUyuqn4tZsTg9Y3b9/v1q0aBE3KLVhw4aJum9SmKapNWvWaPDgwTJNU7Nnz1bHjh2VNWtWSZLddGhZ9Ml0EIvECjEjZTcdsqbi8FKLEbuTjJ+8VdyaPdXumxKWLFmiDRs2aN++fa4uBUgzCEYAAADSMB/DQzU886mKmUf/2G8pyB6iKMV+4u/0m3mbQ6ZM5fHMkqRZB84upbHZbAoPD9fjjz+e4LGjRo1SwYIFVbhwYafrcNbMmTN148YN/frrrzJNU7Vr177recMwlMfwVR4vX0WYNl10hOqqI1I3HJGKlE3WuNXmpvRvR8idnp1shpdyGZmU25JJeS2+TgVLd4wcOVKjR49Ww4YN9ccff8jHxyc5Xu4D7dy5U4MHD9Y///yjsWPHqmPHjnEh0R03zWhZZciWTqIRqwzdNKOV00j5719Gc/78eb3++uv69ddf44IxAAQjAAAA6YKHYVEJDz8Vt2bXbTNGIWakrjsidc2MVJgZLYcUN/3AVGxHQzbDW7mNTLryz1m992pfbd+4JUn3dnYpzY4dO2SxWFS0aNEEj126dKm6du2apHoScvDgQW3ZskXz5s1Tv3791Lhx43iPzWR4qKTVTyX/XflgMx0KNaMVI4ccMmX8O1fCx/CQrzySNCfi5s2batq0qQ4ePKiJEyeqb9++SX1pTjty5IiGDh2qHTt2aMSIEerRo4c8Pe+/Q06IGeVUJPJK2aby8vGWp7eXIsPCVaRiGXV4+xWVr19Tv3+9QNERUWr7Zk+t+26xdq9cr3e/n5K8L+pfDsV2jeQUwUhiOBwO9ejRQ71791a9evVcXQ6QphCMAAAApCOGYSib4aVs8lJRa7a4xx2m+e8b+di5F/99A+8om0MnjxzX6dOnVbx48UTf02KxyDRNmab5wGBg3bp1yp494WUGR44c0bVr1zR48OBE15KQ27dv69lnn9XEiRPVuXNnPfPMM/cMG30QD8Miv2TsRFi2bJk6deqkXLlyKSgoKEV335Gks2fPauTIkVq2bJneffddzZs3T5kyZXrgOdccEU53Hw38frKKVSorSfrzlz80+umXNGLpLD3xcueHrt1ZDpm66oiMC7PgnEmTJiksLCxFlq8B6R270gAAAGQAFsOQh2GR1bDcE15YLBY98cQTWrlyZZKubRixQUtCc0Z2796tIkWKJHi9MWPGqEiRIsqbN2+S6omPaZp69dVX1bhx47gdXry8vBIVjCQXh8Ohrl27qm3bturUqZPOnj2boqHIlStX9NZbb6lGjRoqWLCgjh8/rnfeeSfBUESSbppRSbpn/adbqdXLL+iXid9o4egvNGfQR/ccs+67xfrkhT7xfr34kxnqV6u1BtRpo0FNn3V6lk1Sa3ZXBw4c0Icffqh58+bJw4PPxoH/jz8VAAAAbsDf31/z58/X66+/nqTz7wxgfVDIcOzYMVWvXj3Ba61YsUKvvPJKkup4kJkzZ+rQoUPasWNHsl87MU6ePKlGjRrp+vXrWrp0qVq3bp1i97p9+7YmTJigL774Qs8//7wOHz6s/PnzJ+oadjPps0XK1qmmXcvWqmiFMok+d93cJdq5fK3GrV8o32xZdevajXvmn8TH8RA1u5vIyEgFBATos88+U8mSJV1dDpAm0TECAADgBlq1aqWNGzcqMjIySec7M4D14sWLCc4uCAwM1M2bN5N9Gc3+/fs1ZMgQ/fjjj051SaSUyZMnq2zZssqbN68uXbqUYqFIVFSUJk2apDJlyigoKEg7d+7UlClTEh2KSLFLU5LKfIiAYvfK9Xri5c7yzRY7BDRbrhxOn/swNbubQYMGqUKFCurWrZurSwHSLIIRAAAAN5AjRw5Vq1ZNGzZsSNL5CQ1gdTgcun37th577LEHXmfMmDEqUaKE/Pz8klTH/dyZKzJhwgSVL18+2a6bGJGRkWrUqJH69++vYcOGad++fU7NW0ksu92ub7/9VuXKldOaNWu0atUqzZ0796E6ASxK/EDZO07sPqii/84cue+1PTzuWh4THfWfJTAPEaokvWL3smrVKi1ZskQzZsxI0uBgwF0QjAAAALgJf39/rVixIknn3llKE58jR45IkipWrPjA66xatSpZd6MxTVOvvfaaGjVqpC5duiTbdRNjy5Ytyps3r44cOaJ9+/ZpxIgRyX4P0zT1yy+/qGrVqpo5c6bmzZunpUuXqmrVqg997aQGIzuWrtHvXy9Q2zd7xntM/pJFdebgMcVERSkmOlp/Lvkj7rna/s31+9cLFHE7VJJ069qNFK/ZnVy9elW9evXSnDlzlDNnTleXA6RpzBgBAABwE/7+/urQoYMmTZqU6E+PE1pKs2bNGmXJkuWBMyJ27Nih0NBQvfPOO4m694N88803OnDggMvmivTr10+TJ09W69at9fPPP6fIYMsNGzZo0KBBioiI0Keffqonn3wy2T79t9vtCgu+LuXN6tTxn77Q93/b9VYorWG/zFS5R2po36rN9z2+fL0aqtr8UfWr2Vp5ixdW4fKldONSsCSpWZf2un7hst5r3ElWDw/5ZMmssWsXODVnJItx/62HEcs0Tb388st64YUX1Lx5c1eXA6R5hvkwCwMBAACQbpimqcKFC2v9+vUqWzb+5Q/3kzNnTgUFBSlXrlz3ff6FF17Q3r17dezYsXiv0a5dOx05ckTHjx9P1L3jc+DAAbVo0UKbN29O9SU0ly5dUqNGjXTmzBnNmjUrRbpV9u7dqyFDhigoKEijR4/W888/7/Rw0oRcu3ZNM2fO1LRp09T+7VfU5KVOMqzpo5nckFTemlPlPOiCiM/MmTM1depUbd++Xd7e3q4uB0jz0sfffgAAAHhohmEkeTmNM0tpEgpb1q5dq5494192kRiunCsyf/58FS1aVIZh6OzZs8keigQFBen555/XU089pbZt2+rIkSPq3LlzsoQi+/btU69evVS6dGkdPXpUS5Ys0ZA+A+Tpgi2Nk8oqQzksPq4uI80KCgrS4MGDNX/+fEIRwEkEIwAAAG4kqcFIQktp/vnnH9WpUyfe5zds2KDw8HANGDAg0ff+/0zT1Ouvv66GDRum6lwRm82mNm3aqGvXrnr99dd1/PjxJO0CE58LFy7otddeU/369VW1alUFBQWpd+/e8vLyeqjrRkdHa+HChWrQoIHatWunsmXL6vjx45o9e7Zq1aolP8Nb9nS0y4tdpvwM3vDfT0xMjAICAjRixIgE5/0A+B9mjAAAALiRFi1aqFu3bgoNDVWWLFmcPi+hjpGbN28+cJbBRx99pAoVKsjH5+E/6Z81a5b279+fqnNF9u/frxYtWig6OlqbNm1Sw4YNk+3a169f18cff6yZM2fqpZde0vHjx5NlWObFixf11Vdf6csvv1T58uX1zjvvqE2bNvfMQfEyrPKVh8Jke+h7poZM8pCXkX46XFLTqFGjlDt3br3xxhuuLgVIV+gYAQAAcCPZsmVT3bp1tW7dukSd96COkfPnz8tut6tevXrxnr9p0ya9/PLLibrn/Rw8eFCDBg3Sjz/+KF9f34e+njM++OAD1axZU5UqVVJwcHCyhSJhYWEaN26cypUrp5CQEB04cEAff/zxQ4Uipmlq27Zt6ty5sypWrKhLly5p9erVWrdundq3bx/vcNjSVj9Z08FOL1YZKm31c3UZadKWLVs0c+ZMzZo1i615gUQiGAEAAHAzrVu3TvRyGqvVGm8wsnr1amXKlCneN92///67oqKi1Lt370TX+l+hoaHq1KmTxo8frwoVKjzUtZxx8+ZN1ahRQ6NHj9bEiRO1cePGZOl4iY6O1rRp01SmTBkFBgZq69at+vLLL1WoUKEkXzMiIkKzZ89W7dq11b17d9WtW1enTp3S9OnTValSpQTPL2LNluR7p7b0VGtquXnzprp27aqvvvoqWZd3Ae6CpTQAAABuxt/fXxMnTpRpmk5/svygpTRbt25V3rx54z33k08+UZUqVR5qVsaduSINGjRQt27dknwdZy1btkydOnVSzpw5dfz4cZUsWfKhr+lwOLRw4UINGzZMZcqU0bJly1SzZs2HuuaZM2c0ffp0zZo1S3Xq1NGYMWPUqlWrRA9q9TAsKmzJqrOOW2l22oghqZAlizwNPtv9//r27atWrVqpTZs2ri4FSJcIRgAAANxMuXLl5OHhoUOHDqlKlSpOnfOgpTQHDx5U6dKl4z1327ZtmjRpUpJqvWPWrFnat2+fdu7c+VDXSYjD4VD37t01f/58denSRXPmzHno3WBM09TKlSs1ZMgQ+fj4aObMmWrWrNlDXW/9+vWaPHmyNm3apO7du2vbtm0P/DVwRhmrn845bqfZQawWGSpjzeHqMtKcH374QTt27NDevXtdXQqQbhGMAAAAuJn/btvrbDDyoI6R06dPq2vXrvd97ueff5bNZtOLL76Y5HrvzBXZtGlTis4VOXnypBo3bqxr167pt99+01NPPfXQ19y6dasGDx6sq1ev6sMPP1S7du2SPP8hNDRUc+fO1ZQpU2QYhvr27au5c+cmaojug2SxeKmMNYeC7DfSXDhilaEyVj9ltTzcDj0ZzT///KO+fftq5cqVypw5s6vLAdIt+tAAAADckL+/v5YvX+708Q/qGLl+/boaNWp03+c+//xzVa9ePd75IwkJDQ3Vs88+m+JzRSZPnqyyZcsqT548unjx4kOHIgcOHFCbNm3UuXNn9erVSwcPHtTTTz+dpFDk+PHj6t+/v4oVK6Y1a9Zo6tSpOnjwoF599dVkC0XuKGvNIV8j7X12msnwUFnrw+/Uk5HY7XZ169ZNAwYMUK1atVxdDpCuEYwAAAC4oWbNmmnfvn0KCQlx6vj4hq/evHlTMTEx992q1+FwaMeOHerbt2+SarwzV6RevXopNlckMjJSjRs3Vv/+/TVs2DDt27dPfn5+Sb7eyZMn1bVrV7Vs2VKPPfaYjh8/rh49eshqTdz2sg6HQ8uXL9eTTz6phg0bytfXV/v27dPixYvVtGnTFNt1xGIYquNRIE3tUGOVoboe+WVhp5W7jB8/Xg6HQ++++66rSwHSvbQXBwMAACDFZcqUSY0aNdLq1avVqVOnBI+PbynN2rVr5eXldd/OhYULF8o0zXiX2SRk9uzZKTpXZMuWLfL395enp6f27dunqlWrJvlaly9f1pgxY7RgwQK9+eabmjZtmrJmzZro69y4cUOzZ8/W1KlTlSNHDvXt21c///xzsuyG46xsFi9VtebWAftVly+pscpQFWtuZbN4u7SOtGbv3r367LPPtGvXrkSHbgDuRccIAACAm7ozZ8QZ8S2l2bx5s3LlynXfcyZNmqQ6deokaXjpoUOH9N577+nHH39Mkbki/fr1U+PGjdW4cWNdvnw5yaHIzZs3NXToUFWsWFGenp46evSoRowYkehQ5M7SmJIlS2rv3r2aP3++du3ape7du6dqKHJHMY/sqmDN6dLOEasMlbfmVHGP7C6rIS0KDw9XQECAJk6cqGLFirm6HCBDoGMEAADATfn7+2vMmDFyOBwJhhfxLaUJDAxUiRIl7nnc4XDEvcFPrNDQUHXq1Enjx49XxYoVE33+g1y6dEmNGjXSmTNn9N1336lLly5Juk5ERISmTp2qTz75RE899ZT27t2b6DepNptNv/76qyZPnqygoCC99tprOnLkiPLnz5+kmpJbaY8cMmToL/u1VO8cscpQRWsulfLwS9X7pgcDBw5UzZo11blzZ1eXAmQYBCMAAABuqmTJksqRI4f27duX4PDG+JbS/P3332rduvU9j8+ePVsWi0XPPPNMomoyTVO9e/dOkbki8+fPV8+ePVW8eHGdPXs2SQGEzWbTnDlz9MEHH6hOnTrasGFDosOb4OBgzZw5U9OnT1fx4sXVt29ftW/fXp6enomuJ6WV8vCTj2HVPluw7DJTPB4xFBuK1PDIq0LWxC9FyuiWL1+u5cuXKzAw0NWlABkKwQgAAIAbu7OcJqFgJL6lNFeuXFGDBg3ueXzatGmqV69eopfRzJkzR3v27EnWuSI2m03t27fX8uXL1adPH33xxReJvoZpmlq8eLHef/99FSpUSIsWLdIjjzySqGvs2rVLU6ZM0W+//aaOHTtq6dKlql69eqJrSW2FrFmVy5JJe2OCdc2MSLHuEasM5TJ8VNMzn3zS4M44rnb58mW9/PLL+uGHHx5qQDCAe/E3DgAAgBvz9/fX+++/r2HDhj3wuPt1jERHRysyMlItW7a863GbzabAwED9/PPPiarl0KFDevfdd7Vx40Zlzpw5UefG58CBA2revLmioqK0adMmNWzYMFHnm6apNWvWaPDgwTJNU1OmTNFjjz3m9K4wUVFR+umnnzRlyhRdvnxZvXv31oQJE5QzZ/raetbH8FB9zwI67whVoO2KJFO2ZApIrDJkyFA1a24VtmZNsR130jPTNNWrVy/17Nkz3q2xASQdwQgAAIAba9iwoY4cOaIrV64oT5488R53v46RzZs3y2q1Km/evHc9PmPGDHl6eqpt27ZO13Fnrshnn32WbHNFRo0apQ8++EANGjTQqlWrEj3EdOfOnRo8eLD++ecfjR07Vh07dnS6A+b8+fOaMWOGvv76a1WtWlVDhgxR69at0/UOIoZhqLA1qwpYsuiCI1RB9hsKM2OS3EFilaHMhqfKWHOooCWLrAQi8Zo+fbqCg4M1cuRIV5cCZEgEIwAAAG7M29tbzZs31x9//PHAQaT3G766cePG+7b0f/XVV4n+VPuNN95QvXr11L1790Sddz+3bt1SkyZNdPDgQU2cOFF9+/ZN1PlHjhzR0KFDtWPHDo0YMUI9evRwav6HaZrasmWLJk+erDVr1iggIEAbNmxQ+fLlk/pS0iSrYaiINauKWLPqpiNKp+23dM2MUKgZ/W/vh+SQqTv9RRZJFhkyJZkylcXwUi4jk4pZs8mPbXgTdOTIEY0YMUJbt25Nk3NogIyAYAQAAMDN3ZkzklAw8v+X0uzZs0dFixa967Ho6GgdPnxYEyZMcPr+c+bM0e7du5NlrsiyZcvUqVMn5cyZU8eOHVOpUqWcPvfs2bMaOXKkli1bpnfffVfz5s1TpkyZEjwvPDxc8+fP15QpUxQVFaU+ffpo5syZypYt28O8lHQhu8Vb1SyxnUamaSrUjFGIGaVo0x7XSWKVIS/DquyGt7IaniyVSYTo6GgFBARo7NixKlu2rKvLATIsghEAAAA39+STT2rQoEGy2+3xLvW431Ka48ePq169enc9NnnyZHl7e6tFixZO3fvw4cMaOHCgNmzY8FBzRRwOh3r06KF58+apS5cumjNnjtPLXq5cuaJx48bp22+/1euvv67jx487Ndzy5MmTmjZtmubMmaNHH31Un332mVq0aJHogbMZhWEYymp4Kau8XF1KhjFs2DAVLVpUL7/8sqtLATI0ghEAAAA3V7hwYRUuXFg7duzQo48+et9j7reU5uLFi6pfv/5dj82cOVPNmjVz6r5hYWHq1KmTPv30U1WqVClpxUs6deqUGjVqpGvXrum3337TU0895dR5t2/f1oQJE/TFF1/o+eef1+HDhxPcwtfhcGjNmjWaPHmy/vzzT/Xs2VO7du1SiRIlklw/cD8bNmzQvHnzFBgYSJcNkMIIRgAAAKDWrVtrxYoVevTRR2WaZtwbsWXLlmn16tU6cuSIJk+erKVLlyp//vwqUqSIwsLC7gpGwsPDdezYMX399ddO3fONN95Q3bp11aNHjyTXPWXKFPXv319VqlTRoUOHnOr0iIqK0owZMzRu3Dg9/vjj2rlzp0qWLPnAc27duqVvv/1WU6ZMkY+Pj/r27asffvhBvr6+Sa4diM+NGzfUrVs3ffPNNw8cigwgeRCMAAAAuDm73a4iRYpo5MiRWrhwoXLkyKFdu3ZJknbv3q2pU6fKbrfrzJkzslqtypIliyIiIiRJdevWVcWKFVWjRg0ZhiFfX1+ntsSdM2eOdu7cGXefxLqzTfDWrVs1fPhwjRgxwqnXOW/ePI0YMUJVqlTRqlWrVLVq1Qeec+TIEU2dOlULFizQ448/rpkzZ6phw4Z8go8UY5qmXn/9dT399NN64oknXF0O4BYIRgAAANxcpUqVdPbsWUVERCg4OPiu+SBvvfWWxo8fr9DQUFmtVnXp0kUeHh765ptvJMXualOrVi3NmzdPNptNFotFvXr10qeffqpcuXLd934PO1dky5Yt8vf3l6enp/bs2aPq1as/8HjTNPXrr7/q/fffV86cOTVv3rwHhjd2u13Lli3TlClTdPDgQb3yyis6ePCgChUqlOhagcSaN2+eDh06lOTQEEDiuedkKAAAAMQZPXp03P83DEMNGjSI+zpbtmwaNGiQLBaLLBaLRo4cqeeff14eHrGfrzVp0kQvvviifHx8ZJqm7Ha7fvrpJ4WFhd33XmFhYXr22Wf1ySefJGmuSP/+/dW4cWM1btxYly9fTjAU2bBhg+rXr68RI0bo008/1aZNm+INRa5du6ZPPvlEpUqV0rhx49SjRw+dOXNGo0aNIhRBqjh16pTeeustzZ8/36kdkQAkD8M0TdPVRQAAAMC1ZsyYoX79+slut2vRokV6+umn454LCwuTn5+f6tatq61btyomJkbe3t7y9PTUpUuXFB0drUKFCslutyt79uzasmWLKleufN/79OzZUw6HQ3PmzEnUcpRLly6pcePGOn36tGbNmvXArYUlae/evRoyZIiCgoI0evRoPf/88/HuFhMYGKjJkydryZIlatu2rfr06aM6deo4XRuQHGw2m5o2bar27dvr7bffdnU5gFthKQ0AAAD02muv6dSpU/rkk09UtWpVmaapSNkV4ohSlI9NczetUNFiRXXaflOeFqsq1Kqmto89oRw5csR1inh6emrbtm2qWLHife8xZ84c7dixQ7t27UpUKDJ//nz17NlTxYsX19mzZx+4c0xQUJCGDRumTZs2aejQoXrppZfk5XXv9rExMTFasmSJJk+erDNnzsRt08ugS7jKRx99JB8fHw0YMMDVpQBuh44RAAAASJLCHNH6bc9mFapWTrfMKDkUu+7alGQq9kdGQ4YMSZFRUfLy8lJWi7f8HB56pl4LTf7wM/n7+9/32ocPH1bTpk21fv36eLtJ/j+bzaYOHTpo2bJl6tOnj7744ot4jz1//rxGjRqlJUuWaMCAAerXr99955dcunRJX331lb788kuVLVtWffr0Ubt27eKWBgGusHPnTrVp00Z79uxR4cKFXV0O4Hb4FwAAAMCNmaapYDNcQbYQXTcj5Vu1mK6bkXHPO+49Q5Lk4e0lh6SbZpRuKkqfbF4iH29fnbPfVkFLFln+0xHy37kizoYiBw4cUPPmzRUVFaWNGzeqUaNG9z3u+vXr+vjjjzVz5ky99NJLOnbsmHLmzHnPa9y+fbumTJmiFStW6LnnntPvv/+uKlWqOFULkJJCQ0MVEBCgqVOnEooALkLHCAAAgJu64gjXnpjLipFDdiXPj4TWf3tKqlpzq4g1qwzDUK9evWS3252eKzJq1Ch98MEHatCggVatWiUfH597jgkLC9MXX3yhzz//XB06dNDw4cPvGZAaGRmphQsXasqUKQoJCdEbb7yhHj16KEeOHMnyWoHk8PLLL8tms2n27NmuLgVwW3SMAAAAuBmb6dAB2xWdd4QmWyByh/3fhTf77Vf0j+O2/v55o/7880+n5orcunVLTZs21YEDBzRx4kT17dv3nmOio6M1c+ZMjRkzRo0bN9bWrVtVtmzZu445e/aspk+frm+++Ua1atXSqFGj9MQTT8Q7fBVwlZ9//lnr1q1TYGCgq0sB3Br/OgAAALiRa44IrY4+o3MpEIr8l12mrpoR8n68imatXKwsWbLcc8zWrVvVv39/maap5cuXK1++fLp8+bKOHTt2TyjicDi0YMECVahQQb/99puWLVumhQsXxoUipmlq/fr16tChg2rUqKGIiAht3bpVK1eulL+/P6EI0pwLFy7o9ddf17x585Q1a1ZXlwO4NZbSAAAAuImL9lDttl1O0UDkfqwyVM6aU2U9/reExTRNVaxYUUFBQapbt662b9+ugIAAffvtt3eFGKZpauXKlRoyZIh8fHw0btw4NWvWLO750NBQzZ07V1OmTJEk9enTR127dr1vEAOkFQ6HQ08++aTq16+vkSNHurocwO2xlAYAAMANnLff1h5bsBypHIpIsd0jx+zX5ZBD5T1ySYpdQnD27FnZ7Xb9+eefmjFjhl599dW7ztu6dasGDx6sq1ev6sMPP1S7du3iluMEBQVp6tSpmjt3rpo0aaLJkyerWbNmidoGGHCVyZMn69atWxo6dKirSwEgOkYAAAAyvMuOMO2IueSSUOS/rDJU0ZpLxY2sypMnj27cuBH3XKVKlXTo0CFJsTvSvP/++zpw4IA++OADde3aVVarVQ6HQ7///rsmT56sPXv26MUXX9Rrr72mYsWKueolAYl28OBBNW/eXNu3b1epUqVcXQ4A0TECAACQoUWaNu1MA6GIFNs5cth+TR+8855u3Lghq9WqUqVKqWrVqmratKlOnjypESNGaPXq1Ro8eLAWLVokb29vhYSEaPbs2Zo6daqyZ8+uvn37asmSJcqUKZOrXxKQKJGRkQoICNAnn3xCKAKkIUyhAgAAyKBM09TumMtpIhS5w2Gaavnei9q4eZNiYmJ07NgxTZkyRX/99ZcqV66sIkWKKCgoSP369VNQUJBee+01lShRQrt27dLcuXO1e/du9ejRg1AE6dKQIUNUrlw59ejRw9WlAPgPOkYAAAAyqH/st3XDjExDsYgkQ8qeJ5dy5suuW7du6dNPP9X06dOVN29eRUREKG/evFq1apWmTJmiY8eO6dVXX9Vff/2lAgUKuLpy4KGsWrVKP/30k/bv388sHCCNYcYIAABABhRl2rUq+nSq70DjLNPu0JjWvVStRFnlzJlT06ZNU0REhCwWi+rVq6e+ffuqQ4cO8vLycnWpwEO7evWqqlevrm+//VYtWrRwdTkA/h+CEQAAgAzoqO26jttvpKllNHcxTWUOc+jw3JV66623dOdH0kyZMum3337TY4895uICgeRhmqY6duyokiVL6rPPPnN1OQDugxkjAAAAGYxpmvrbHpJ2QxFJMgxFZPHQ7+tWK0eOHMqVK5cyZcqkyMhILV261NXVAclm1qxZOnnypMaOHevqUgDEg44RAACADOaiPVS7bZfT7DKaOywyVM6aQ+U8csY95nA4Yp+z8Pkd0r+goCA9+uij2rBhgypVquTqcgDEg39xAAAAMpgge0iaD0UkyaHYzpb/fk5nsVgIRZAhxMTEqEuXLho+fDihCJDG8a8OAABABuIwTd0wI11dhtPsMhUum6vLAJLd6NGjlTNnTvXp08fVpQBIANv1AgAAZCC3zWhZZKSLjhFJMiTdcEQqs9XT1aUAyWbr1q36+uuvtW/fPrbmBdIBghEAAIAMJMSMSvCYV8o2lZePtzx9YrfCNQxDVZs/qh7jBiX6fuu+W6zdK9fr3e+nJOq5O2wydd0RqcLWrIm+N5AW3bp1S127dtWXX36p/Pnzu7ocAE4gGAEAAMhArjsinOoWGfj9ZBWrVDYVKkrY9XS09AdISN++ffX444+rbdu2ri4FgJMIRgAAADKQW2Z0os/5b2fH+eMn9cXL7yk6PFJ2u13Nu3bQ0wNeUkx0tOYP/1x7V22S1WpVvhJFNOjHaZKk8Fuh+qxLP509fFyZs2fTuwunKEf+PE7fP9yMSXTNQFr0448/avv27dq7d6+rSwGQCAQjAAAAGYizs0U+faFv3FKaJ17uHPf4yi/nq1arJnp2SOzAyNAbNyVJSz75UpdOndX47b/I08tLt65ejzvnxJ6DmrhrmXIXKaCpr7+v5dPmqsuot5yu2ZFO5qEAD/LPP/+ob9++Wr58uTJnzuzqcgAkAsEIAABABmI6GTL8dynNuu8Wxz1eqWEdzRn0sWzRMarcpJ6qNK0nSdq9cr16fDRInl6xYUq23Dnjzilfv6ZyFykgSSpXt7r2r9uaqJodiToaSHscDoe6d++ufv36qXbt2q4uB0AisV0vAABABmLRw+2AUb/9Exq3fqHylyyqJZ99qYk935EkmWb8gYuXj/f/7m+1yG5L3Pa7/ECK9G78+PGy2Wx67733XF0KgCTg3yEAAIAMxPqQP95d/PuM/PLnUfNuHfXc+30VtPuAJKmOf3MtnTJHMdGxM0z+u5TmYVn4kRTp2L59+/Tpp59q7ty5slqtri4HQBKwlAYAACAD8TO8H2qXl62LVmjTwt/k4eUpwzD04vihkqQOA1/R3GHj9VbdtvLw8lSBUsUeuA1vYmQxPJPlOkBqCw8PV0BAgCZMmKBixYq5uhwASWSYD+qLBAAAQLpyzn5bgbZg2dLRQNOylhyq6JnL1WUAidanTx9dv35dCxYscHUpAB4CHSMAAAAZiJ/hnY4iEclDhnJYfFxdBpBoK1as0NKlS7V//35XlwLgIRGMAAAAZCCZDc90FYw4JPlZvBM8DkhLgoOD9dJLL+n777+Xn5+fq8sB8JCYdAUAAJCBGIahPJZMri7DaT6yykcMrET6YZqmXnzxRXXv3l1NmjRxdTkAkgEdIwAAABlMGWsOXXVEyJ7Ge0esMlTG6ifDeLgthoHUNGPGDF24cEGLFy92dSkAkgnBCAAAQAaTy/CRl6yKkM3VpSSoiDWbq0sAnHb06FENGzZMW7ZskZeXl6vLAZBMWEoDAACQwRhGbCeGVWm3E8OQVNiSVR4GP44ifYiOjlZAQIDGjh2r8uXLu7ocAMmIf4kAAAAyoKLWbGk6GLHIUFmPHK4uA3Da8OHDVbhwYb3yyiuuLgVAMmMpDQAAQAbkYVhU2zO/dsRcTHOzRqwyVNGaS5kNT1eXAjhlw4YN+u6777R//35m4gAZEB0jAAAAGVRei68KWrLIkoY6RwxJWQ0vlbRmd3UpgFNu3Lih7t2765tvvlGePHlcXQ6AFGCYppm2PkIAAABAsokxHVoTfUZRsru6FEmx3SLNvYrSLYJ0wTRNde7cWblz59bkyZNdXQ6AFMJSGgAAgAzM07DoUc+C2hxzTjYXL6mxyFBdj/yEIkg35s+frwMHDmj37t2uLgVACqJjBAAAwA1cd0Rqa8x5l80bschQTWteFfbI6pL7A4l1+vRp1alTR6tXr1b16tVdXQ6AFEQwAgAA4CZCHJHaEnNBdjlSNR6xylBtj/wqYM2cincFks5ut6tp06Zq166d3nnnHVeXAyCFMXwVAADATfhZfNTMq4iyG96pspWvRYYyyUMNPQsRiiBd+eijj+Tl5aW33nrL1aUASAV0jAAAALgZ0zR10n5Tf9mvySEzRbpHLDJUwpJNFT1yyWrwWRzSj127dumpp57Snj17VLhwYVeXAyAVMHwVAADAzRiGoVIefspvzax9McG6bkbKTIaAxFBsIJLZ8FR1j7zKafFJjnKBVBMaGqqAgABNmTKFUARwI3SMAAAAuLkwM0YnbSE647glSYnevcYqQ6akQpYsKmX1k5/FOwWqBFLeK6+8oujoaM2ZM8fVpQBIRXSMAAAAuLnMhqeqeOZRRTOXLjnCdc0RoWtmhG6bMTIkGffMIzFll6kshqdyGj7KZcmk/JbM8jKsrigfSBa//PKL1q5dq3379rm6FACpjI4RAAAA3JdpmgozYxT17z42pmK7QzxlURbDSxYj5Qe4Aqnh4sWLqlGjhpYsWaJHH33U1eUASGUEIwAAAADclsPhkL+/vx555BF98MEHri4HgAswIhwAAACA25oyZYpCQkI0bNgwV5cCwEXoGAEAAADglg4dOqRmzZrpzz//VOnSpV1dDgAXoWMEAAAAgNuJjIxUQECAPv74Y0IRwM3RMQIAAADA7bz11ls6c+aMFi1aJINBwoBbY7teAAAAAG5l9erV+vHHH7V//35CEQAspQEAAADgPq5du6aePXtq9uzZypUrl6vLAZAGsJQGAAAAgFswTVPPPPOMihUrps8//9zV5QBII1hKAwAAAMAtzJ49WydOnND8+fNdXQqANISOEQAAAAAZ3okTJ1S/fn1t2LBBlSpVcnU5ANIQZowAAAAAyNBiYmLUpUsXDRs2jFAEwD0IRgAAAABkaGPGjJGfn5/69u3r6lIApEHMGAEAAACQYW3btk1ffvml9u3bx9a8AO6LjhEAAAAAGdKtW7fUpUsXffnllypQoICrywGQRjF8FQAAAECG1KNHD3l5eemrr75ydSkA0jCW0gAAAADIcH766Sdt27ZN+/btc3UpANI4OkYAAAAAZCjnzp1TrVq1tGzZMtWpU8fV5QBI45gxAgAAACDDcDgc6t69u958801CEQBOIRgBAAAAkGF8/vnnio6O1qBBg1xdCoB0gqU0AAAAADKEwMBAtWzZUjt37lTx4sVdXQ6AdIKOEQAAAADpXkREhDp37qzPP/+cUARAotAxAgAAACDd69u3r65evaoFCxbIMAxXlwMgHWG7XgAAAADp2sqVK/Xbb79p//79hCIAEo2OEQAAAADpVnBwsKpXr67vv/9eTZo0cXU5ANIhghEAAAAA6ZJpmmrbtq0qVaqkjz76yNXlAEinWEoDAAAAIF368ssvdeHCBS1evNjVpQBIx+gYAQAAAJDuHD16VA0bNtSWLVtUvnx5V5cDIB1ju14AAAAA6Up0dLQCAgI0evRoQhEAD42OEQAAAADpyuDBg3Xo0CH99ttv7EID4KExYwQAAABAurFx40Z9++23CgwMJBQBkCxYSgMAAAAgXQgJCVG3bt00c+ZM5c2b19XlAMggWEoDAAAAIF3o3LmzcubMqSlTpri6FAAZCEtpAAAAAKR58+fPV2BgoPbs2ePqUgBkMHSMAAAAAEjTTp8+rbp162rVqlWqXr26q8sBkMEwYwQAAABAmmW329WtWzcNHDiQUARAiiAYAQAAAJBmffzxx/Lw8NDbb7/t6lIAZFAspQEAAACQJu3evVutW7fW7t27VaRIEVeXAyCDomMEAAAAQJoTFhamgIAATZ48mVAEQIqiYwQAAABAmvPqq68qMjJS3377ratLAZDBsV0vAAAAgDTl119/1erVqxUYGOjqUgC4ATpGAAAAAKQZly5dUvXq1bV48WI1aNDA1eUAcAMEIwAAAADSBNM05e/vrzp16mjUqFGuLgeAm2D4KgAAAIA0YcqUKbp+/bqGDRvm6lIAuBE6RgAAAAC43OHDh9W0aVP9+eefKl26tKvLAeBG6BgBAAAA4FJRUVHq3LmzPvroI0IRAKmOjhEAAAAALvX222/r1KlTWrx4sQzDcHU5ANwM2/UCAAAAcJk1a9bohx9+0P79+wlFALgES2kAAAAAuMS1a9fUs2dPzZ49W7ly5XJ1OQDcFEtpAAAAAKQ60zTVqVMnFSlSRBMmTHB1OQDcGEtpAAAAAKS6OXPm6Pjx45o3b56rSwHg5ugYAQAAAJCq/v77b9WrV0/r1q1TlSpVXF0OADfHjBEAAAAAqcZms6lLly56//33CUUApAkEIwAAAABSzZgxY5QtWza9+eabri4FACQxYwQAAABAKvnzzz81Y8YM7du3TxYLn9ECSBv42wgAAABAirt9+7a6dOmiGTNmqECBAq4uBwDiMHwVAAAAQIrr2bOnPDw89PXXX7u6FAC4C0tpAAAAAKSoRYsWaevWrdq7d6+rSwGAe9AxAgAAACDFnDt3TrVq1dLSpUtVt25dV5cDAPdgxggAAACAFOFwONSjRw/17duXUARAmkUwAgAAACBFTJgwQZGRkRo8eLCrSwGAeLGUBgAAAECy279/vx5//HHt2LFDJUqUcHU5ABAvOkYAAAAAJKuIiAh17txZ48ePJxQBkObRMQIAAAAgWb355psKDg7W999/L8MwXF0OADwQ2/UCAAAASDYrV67Ur7/+qsDAQEIRAOkCHSMAAAAAkkVwcLCqV6+uBQsWqGnTpq4uBwCcQjACAAAA4KGZpql27dqpQoUK+vjjj11dDgA4jaU0AAAAAB7aV199pXPnzmnRokWuLgUAEoWOEQAAAAAP5dixY2rQoIE2b96sChUquLocAEgUtusFAAAAkGTR0dEKCAjQqFGjCEUApEt0jAAAAABIsiFDhujAgQNaunQpu9AASJeYMQIAAAAgSTZt2qTZs2ezNS+AdI2lNAAAAAASLSQkRN26ddPXX3+tfPnyubocAEgyltIAAAAASLSAgABlz55d06ZNc3UpAPBQWEoDAAAAIFEWLFigvXv3as+ePa4uBQAeGh0jAAAAAJx25swZ1alTR7///rtq1qzp6nIA4KExYwQAAACAU+x2u7p27ap33nmHUARAhkEwAgAAAMApn3zyiaxWq95++21XlwIAyYalNAAAAAAStHv3bvn7+2vPnj0qUqSIq8sBgGRDxwgAAACABwoLC1NAQIAmT55MKAIgw6FjBAAAAMADvfbaawoPD9d3333n6lIAINmxXS8AAACAeP32229atWqVAgMDXV0KAKQIOkYAAAAA3NelS5dUo0YNLVq0SA0aNHB1OQCQIghGAAAAANzDNE35+/urdu3aGj16tKvLAYAUw/BVAAAAAPeYOnWqrl+/ruHDh7u6FABIUXSMAAAAALjL4cOH1bRpU23btk1lypRxdTkAkKLoGAEAAAAQJyoqSp07d9a4ceMIRQC4BTpGAAAAAMR555139Pfff2vJkiUyDMPV5QBAimO7XgAAAACSpLVr1+r777/X/v37CUUAuA2W0gAAAADQ9evX1aNHD82ePVu5c+d2dTkAkGpYSgMAAAC4OdM09eyzz6pQoUKaOHGiq8sBgFTFUhoAAADAzX377bc6evSo5s6d6+pSACDVsZQGAAAAcEPz5s1TxYoVtXLlSg0cOFDz58+Xj4+Pq8sCgFRHxwgAAACQTtgipYgbUsR1Kfyq5IiWHA7JkGRYJU9fyTePlCmH5OMnWR7w0/7mzZt15MgRPfXUU2rbtq2qVKmSWi8DANIUZowAAAAAaVh0qHQtSLp5VjJtkmGRHHZJ8fwUb1j+d4ynr5SzjORXXLJ63n3cI488op07d0qSPD091alTJ82fPz9FXwsApEV0jAAAAABpjGlKoRelq0elyBuxX98JQkxHAuc6/ndMTJgUfFAKPiBlKyLlKif5ZI997ujRo5IkX19f5cuXTy+++GLKvBgASOPoGAEAAADSkOgw6fx2KfKmZNqT8cJGbCeJX3Epa5lwZc2WWdmyZdPkyZMVEBAgq9WajDcDgPSDYAQAAABIA0xTuvG3dPnAvx0fKfRTumGVrJ6m1p2Yq569n5O3t3fK3AgA0gmCEQAAAMDF7DHS2S3/LptJzi6RBzCsUq6yUp5KkmGkzj0BIC0iGAEAAABcyBYlnV4fOw8kofkhyc2wStmLSgVqEY4AcF8MXwUAAABcxB4TG4pEhyrFls48iGmP3e1GIhwB4L4sri4AAAAAcEemQzqzMXbYqitCkbg6/g1Hrhx2XQ0A4EoEIwAAAIALXDkiRd2SlMrLZ+7HtEvXjksR111dCQCkPoIRAAAAIJVF3pSuHUu9QavOMO3SuT8lRxqqCQBSA8EIAAAAkIpMR2wAkZZCkTtsUVLwIVdXAQCpi2AEAAAASEXXT0ox4a6u4v5Mu3TjbynqtqsrAYDUQzACAAAApBLTlK4dTZvdIneYDul6kKurAIDUQzACAAAApJLwK7Fb9KZpphRyWnLYXF0IAKQOghEAAAAglVw9KpnpJHC4+Y+rKwCA1EEwAgAAAKQCe3Rsx0h6YNql68ddXQUApA6CEQAAACAVRNyQjHT003fU7dh5IwCQ0aWjv5oBAACA1FO8eHEdOhS7d214eLhatWqlnj17ym5P2uTUiOuS4z+n/rBqhr5dNiHJ9X23fKKu3QxO8vmb9q5QpWcNrd35y32ft1ilqFsJX2fDhg2qXbt2kusAAFcjGAEAAAAe4ObNm2rZsqXKly+vWbNmyWq1Juk64Vckmf/7+rmWr6n7UwOSXNfcFRN1/SGCkSXrZ+mRys21ZP2s+z5vmrFdLgCQ0RGMAAAAAPEIDg5W06ZN1bx5c02aNEmGYUiSAgICVLt2bVWpUkXt27fXjRuxCcKGDRtUrVo1vfzyy6pSpYpq1qypw4cP69lnn1WLgIp6eWwrhUeGSZKm/jhSn373jiRp37Fteua9muowsLravlVJy7d8L0latmWBnh/yiDq+W0Md362h7QfXSpK+XDJWwdcvaMDnz6jDwOo6ce4vhUWG6v1pPdXu7cpq93Zlzfzl43hfV8jta9pxaK0+eXOB9h3bqishlxQZHaEmrxTQlRsXJcXOGXl70Jv68MMPH/ia77puSIiaN2+uiRMnymazqVWrVqpdu7YqVaqknj17Kjo6Ojl+WQAgWRGMAAAAAPF49tln1aZNG40aNequxydNmqTdu3fr4MGDqlSpkj7++H8hxF9//aU33nhDBw8eVP369dWqVSt9/vnnWjrxL1ktVq3Y+v099/nm14/VrfUALfk0UL+OP6RGNZ6UJDWs1krfj92uxZ/s02f9F2rI1O6SpFc7vK+8OQtqwluLtOTTQJUuXFEzFo2Ww+HQL58d1PzR2/Tbpu+0ed/K+76u3zbNVdNabZTbL58eq9tBv238Tj5emdSuSXf9tOYrSVJ4ZJh+W7VQL730UoKvWZLOnDmjFi1aqHfv3urfv7+sVqsWLFig3bt369ChQ7Lb7Zo16/7dKQDgSh6uLgAAAABIq/z9/fXDDz/o9ddfV4ECBeIe/+677zR//nxFR0crLCxMZcuWjXuuXLlyql69uiSpZs2aOnPmjAoXLqy//pQqlqils5dO3HOfupWa6eufx+nClTN6tOrjqlrmEUnS2ct/a/KkF3T5+nl5WD11NeSSroRcUh6//PdcY/vBNRrUM7arJYtvNvk3eEF/HlwTF7L8188bZuu97rHzTZ5u2kPDZ7yoF9u9qxdavaGuwxvqlQ7va+mmuWpYu6Xy5s2b4Gu+ePGimjVrpu+++04NGzaUJDkcDn322WdauXKl7Ha7bty4IV9f38T+EgBAiqNjBAAAAIjHu+++qx49eqhp06a6eDF2icnmzZs1ffp0/f777zp48KDGjBmjyMjIuHN8fHzi/r/Var37a4tVdrvtnvt0a91fU99bqpzZ8mjsrL6atHBo7P0nvaDnW/bWb58f1qKP98pq9VB0dOQ950uSKVOGjLse+/9fS9Khv3fr5Lm/NGx6Lz3+RnG9NzlA/1w+qX3HtqlA7iKqXKqO1u36VQtXTVPXtm849Zpz5MihcuXKacWKFXGPLViwQFu2bNHmzZt18OBB9e7d+65zACCtIBgBAAAAHmDw4MHq3r17XDgSEhKi7NmzK1euXIqKinJ+eci9GUWc0xeOq2j+Unr28VfV1b+fDp3YKUm6FRaiQnlLSJIWr/tG0TFRcedkyZRNt8Nvxn1dv8rjWrxupkzTVFjEba3ctlD1qj52z72WrJ+lXu3e0+qpp+P+6/PsB/r53yGsAU/21cff9pfV6qHaVetLUoKv2cfHR7/++quOHDmifv36yTRNhYSEKHfu3MqaNatu3rypBQsWOPd9AoBURjACAAAAJGDIkCHq1q2bmjZtqmrVqqlUqVIqX768nnzySdWsWdOpa1gesIh93sov1PatSur4bg3N/32y3nx+rCRpUI+JevPTp9VlWENduHJafllzxZ0T8OSbGjq9Z9zw1deeGSbTNPX0O1XUeWh9tWnUVY2qP3HXfaKiI7Vy60I91TDgrsdbN+ysP/78SeGRYapTsYm8PH30fMvesnrHPv/kk08m+Jq9vLz0008/6fLly3rllVfUrVs33b59W5UqVVKnTp3UqFEjp75PAJDaDNM0zYQPAwAAAPAwTq+Xwq+6uoqEnQs+pS7DGuj3qSdUvI6vcpZ2dUUAkLIYvgoAAACkAt88aT8YmfzDcP28fpYGdh0vXx9f+eRwdUUAkPLoGAEAAABSwe0L0vkdkuPe2atpkyGVby9ZrK4uBABSFjNGAAAAgFTgk0MyHa6uwnmevoQiANwDwQgAAACQCjwzSd7ZXV2FcwyrlKOkq6sAgNRBMAIAAACkktzlH7w7TZphSn4lXF0EAKQOghEAAAAglWQtKMlwdRUJy1JQ8vB2dRUAkDoIRgAAAIBUYliknKVj/zetMqxS7nKurgIAUk8a/isZAAAAyHhylZMsnq6uIh4WKUt+KVNOVxcCAKmHYAQAAABIRVZPqXC92M6MtMZilQrWdnUVAJC6CEYAAACAVJY5r5StSNpaUmNYpUJ1JauXqysBgNSVhv4qBgAAANxH/uqS1VtpYhirYY0dDJu1oKsrAYDURzACAAAAuIDVUyreLPZ/XcmwxM4UKVTXtXUAgKsQjAAAAAAu4pVZKt783+UrLugcMayxoUjRRmlrWQ8ApCbDNE3T1UUAAAAA7iw6TDq9XrJHSaYjde5pWGNnnRR5lFAEgHsjGAEAAADSAIdNurRfunlGMu0peCMjNhTJX13yKy4ZaWDGCQC4EsEIAAAAkIaEXZHOb5fs0cnfPXJn6UyhRyTPTMl7bQBIrwhGAAAAgDTGYZOuB0nXgiSHXTJtD3c9wyp5Z5VyV5CyFqJLBAD+i2AEAAAASKNMUwq9JF07JoVfkywWyeGQlEAniWGNDT9Mh5S9qJSzrOSTPVVKBoB0h2AEAAAASAfsMVLkjdj/wq5IkSGxnSWmQ7FzQwzJw1vKlEvyzS355IgNQxisCgAPRjACAAAAAADcFvkxAAAAAABwWwQjAAAAAADAbRGMAAAAAAAAt0UwAgAAAAAA3BbBCAAAAAAAcFsEIwAAAAAAwG0RjAAAAAAAALdFMAIAAAAAANwWwQgAAAAAAHBbBCMAAAAAAMBtEYwAAAAAAAC3RTACAAAAAADcFsEIAAAAAABwWwQjAAAAAADAbRGMAAAAAAAAt0UwAgAAAAAA3BbBCAAAAAAAcFsEIwAAAAAAwG0RjAAAAAAAALdFMAIAAAAAANwWwQgAAAAAAHBbBCMAAAAAAMBtEYwAAAAAAAC3RTACAAAAAADcFsEIAAAAAABwWwQjAAAAAADAbRGMAAAAAAAAt0UwAgAAAAAA3BbBCAAAAAAAcFsEIwAAAAAAwG0RjAAAAAAAALdFMAIAAAAAANwWwQgAAAAAAHBbBCMAAAAAAMBtEYwAAAAAAAC3RTACAAAAAADcFsEIAAAAAABwWwQjAAAAAADAbRGMAAAAAAAAt0UwAgAAAAAA3BbBCAAAAAAAcFsEIwAAAAAAwG0RjAAAAAAAALdFMAIAAAAAANwWwQgAAAAAAHBbBCMAAAAAAMBtEYwAAAAAAAC3RTACAAAAAADcFsEIAAAAAABwWwQjAAAAAADAbRGMAAAAAAAAt0UwAgAAAAAA3BbBCAAAAAAAcFsEIwAAAAAAwG0RjAAAAAAAALdFMAIAAAAAANwWwQgAAAAAAHBbBCMAAAAAAMBtEYwAAAAAAAC3RTACAAAAAADcFsEIAAAAAABwWwQjAAAAAADAbRGMAAAAAAAAt0UwAgAAAAAA3BbBCAAAAAAAcFsEIwAAAAAAwG0RjAAAAAAAALdFMAIAAAAAANwWwQgAAAAAAHBbBCMAAAAAAMBtEYwAAAAAAAC3RTACAAAAAADcFsEIAAAAAABwWwQjAAAAAADAbRGMAAAAAAAAt0UwAgAAAAAA3BbBCAAAAAAAcFv/B4uw1yDOr+/3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.04 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create spring layout for 25 iterations to separate the nodes\n",
    "np.random.seed(2021)\n",
    "pos = nx.spring_layout(directed_graph, k=0.3, iterations=25)\n",
    "\n",
    "plt.figure(1,figsize=(15,15)) \n",
    "nx.draw(directed_graph,\n",
    "        pos = pos,\n",
    "        node_size=2000,\n",
    "        node_color=node_colors,\n",
    "        with_labels=True,\n",
    "        font_size = 11,\n",
    "        font_color='black')\n",
    "\n",
    "# create customized legend tiles\n",
    "legend_tiles = [mpatches.Patch(color=\"#aff8db\", label=\"Mondstadt\"),\n",
    "                mpatches.Patch(color=\"#ffcbc1\", label=\"Liyue\"),\n",
    "                mpatches.Patch(color=\"#d5aaff\", label=\"Inazuma\"),\n",
    "                mpatches.Patch(color=\"#ace7ff\", label=\"Snezhnaya\"),\n",
    "                mpatches.Patch(color=\"#FFA500\", label=\"Traveler\"),\n",
    "               ]\n",
    "plt.title(\"Genshin Impact Character Social Network\")\n",
    "plt.legend(handles=legend_tiles, loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "combined-charles",
   "metadata": {},
   "source": [
    "## Description Statistics:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "delayed-bangladesh",
   "metadata": {},
   "source": [
    "## 2. Centrality / Node Importance\n",
    "What characterizes an important node?"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "valued-plasma",
   "metadata": {},
   "source": [
    "### 2.1 Degree centrality (local importance)\n",
    "\n",
    "1. Assumption: important nodes have many connections.\n",
    "2. Definition: measure node n’s importance by the degree of node n over total number of nodes\n",
    "![image.png](attachment:image.png)\n",
    "3. Interpretation: which node is the most important one with the most number of connections/friends/followers?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charming-jesus",
   "metadata": {},
   "source": [
    "### Since the graph is directed, we will have in-degree and out-degree centrality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "chronic-police",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>in_degree_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jean</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>0.441176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kaeya</td>\n",
       "      <td>0.382353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Klee</td>\n",
       "      <td>0.382353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Diluc</td>\n",
       "      <td>0.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Barbara</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Zhongli</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albedo</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Beidou</td>\n",
       "      <td>0.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ningguang</td>\n",
       "      <td>0.294118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         node  in_degree_centrality\n",
       "34       Jean              0.500000\n",
       "24       Lisa              0.441176\n",
       "5       Kaeya              0.382353\n",
       "6        Klee              0.382353\n",
       "10      Diluc              0.352941\n",
       "19    Barbara              0.323529\n",
       "4     Zhongli              0.323529\n",
       "16     Albedo              0.323529\n",
       "15     Beidou              0.294118\n",
       "12  Ningguang              0.294118"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_degree_centrality_df = pd.DataFrame(nx.in_degree_centrality(directed_graph).items(), \n",
    "                                       columns=[\"node\", \"in_degree_centrality\"])\n",
    "in_degree_centrality_df.sort_values(\"in_degree_centrality\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "different-procedure",
   "metadata": {},
   "source": [
    "### Jean has the highest in-degree centrality. This implies Jean is mostly mentioned by other character in their storyline and voice memo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "unnecessary-daughter",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for a given graph and an important node,\n",
    "# return suitable node color list for visualization\n",
    "def get_centrality_node_colors(graph, important_node):\n",
    "    node_colors = [\"#EFD1BB\"] * len(graph.nodes())\n",
    "    node_colors[list(graph.nodes()).index(important_node)] = \"#F47315\"\n",
    "    return node_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bright-search",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>out_degree_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Traveler</td>\n",
       "      <td>0.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>0.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jean</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kaeya</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Klee</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Eula</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Keqing</td>\n",
       "      <td>0.264706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Diona</td>\n",
       "      <td>0.264706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yanfei</td>\n",
       "      <td>0.264706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Hu Tao</td>\n",
       "      <td>0.264706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        node  out_degree_centrality\n",
       "25  Traveler               0.411765\n",
       "24      Lisa               0.352941\n",
       "34      Jean               0.323529\n",
       "5      Kaeya               0.323529\n",
       "6       Klee               0.323529\n",
       "14      Eula               0.323529\n",
       "1     Keqing               0.264706\n",
       "29     Diona               0.264706\n",
       "8     Yanfei               0.264706\n",
       "22    Hu Tao               0.264706"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_degree_centrality_df = pd.DataFrame(nx.out_degree_centrality(directed_graph).items(), \n",
    "                                       columns=[\"node\", \"out_degree_centrality\"])\n",
    "out_degree_centrality_df.sort_values(\"out_degree_centrality\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "official-orange",
   "metadata": {},
   "source": [
    "### If we look at out-degree centrality, Traveler is the most important character to connect to the rest of the intermediate characters. It makes sence because Traveler is the main character visiting different regions to connect the storylines. "
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "geological-istanbul",
   "metadata": {},
   "source": [
    "### 2.2 Closeness centrality (path spread importance)\n",
    "\n",
    "1. Assumption: important nodes are close to other nodes\n",
    "2. Definition: normalize the total shortest path lengths between the node n and all other nodes in the graph by the N-1 number of nodes in the network.\n",
    "![image.png](attachment:image.png)\n",
    "3. Interpretation: \n",
    "    * smaller total shortest path lengths of a node n to all other nodes will result in larger centrality value; hence, the closer it is to all other nodes, the more central a node is.\n",
    "    * In social networks, we wanted to spread an innovation or a new product among the actors in the fastest amount of time, we would want to give it to the node with the highest closeness centrality first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "challenging-teens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>closeness_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jean</td>\n",
       "      <td>0.604329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Venti</td>\n",
       "      <td>0.542871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albedo</td>\n",
       "      <td>0.525072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Diona</td>\n",
       "      <td>0.508403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>0.492760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Barbara</td>\n",
       "      <td>0.492760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Diluc</td>\n",
       "      <td>0.492760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Xingqiu</td>\n",
       "      <td>0.478051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kaeya</td>\n",
       "      <td>0.471021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Klee</td>\n",
       "      <td>0.471021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       node  closeness_centrality\n",
       "34     Jean              0.604329\n",
       "28    Venti              0.542871\n",
       "16   Albedo              0.525072\n",
       "29    Diona              0.508403\n",
       "24     Lisa              0.492760\n",
       "19  Barbara              0.492760\n",
       "10    Diluc              0.492760\n",
       "13  Xingqiu              0.478051\n",
       "5     Kaeya              0.471021\n",
       "6      Klee              0.471021"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closeness_centrality_df = pd.DataFrame(nx.closeness_centrality(directed_graph).items(), \n",
    "                                       columns=[\"node\", \"closeness_centrality\"])\n",
    "closeness_centrality_df.sort_values(\"closeness_centrality\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mexican-silence",
   "metadata": {},
   "source": [
    "### Based on the closeness centrality results, Jean is also the the most important character with closeness centrality of 0.604. It implies that Jean is closer to all the other nodes with relative shorter shorest path lengths connecting all the other character. If the characters want to find someone to help spread the news in the fastest amount of time, Jean would be a good person to do."
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "moral-spray",
   "metadata": {},
   "source": [
    "### 2.3 Betweenness centrality (In-between/broker/bridge/middleman importance)\n",
    "\n",
    "1. Assumption: : important nodes will be the broker on the way to connect two other nodes without being directly connected to every node.\n",
    "\n",
    "2. Definition: quantifies the sum of (the number of times a node acts as a bridge/middleman along the shortest path between two other nodes) / (the total number of existing shortest paths between two other nodes)\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "3. Interpretation: \n",
    "    * Higher sum of fractions means more shortest paths between any two nodes in the\n",
    "network will have the node n involved, which implies higher importance as a bridge or\n",
    "a broker in the network.\n",
    "    * When interpreting normalized betweenness centrality, the number stands for how\n",
    "many percent of the shortest paths between pairs of nodes in the graph will pass\n",
    "through a given node n.\n",
    "    * The node with higher betweenness centrality is able to pass messages from a node a\n",
    "to another node b by traversing relatively small distances.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "forbidden-pencil",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>betweenness_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albedo</td>\n",
       "      <td>0.134878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Xingqiu</td>\n",
       "      <td>0.132108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jean</td>\n",
       "      <td>0.096456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Beidou</td>\n",
       "      <td>0.094075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Zhongli</td>\n",
       "      <td>0.092939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Venti</td>\n",
       "      <td>0.092471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Diona</td>\n",
       "      <td>0.085169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Eula</td>\n",
       "      <td>0.084201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xiangling</td>\n",
       "      <td>0.081891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yanfei</td>\n",
       "      <td>0.069288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         node  betweenness_centrality\n",
       "16     Albedo                0.134878\n",
       "13    Xingqiu                0.132108\n",
       "34       Jean                0.096456\n",
       "15     Beidou                0.094075\n",
       "4     Zhongli                0.092939\n",
       "28      Venti                0.092471\n",
       "29      Diona                0.085169\n",
       "14       Eula                0.084201\n",
       "3   Xiangling                0.081891\n",
       "8      Yanfei                0.069288"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "betweenness_centrality_df = pd.DataFrame(nx.betweenness_centrality(directed_graph).items(), \n",
    "                                       columns=[\"node\", \"betweenness_centrality\"])\n",
    "betweenness_centrality_df.sort_values(\"betweenness_centrality\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bound-secretariat",
   "metadata": {},
   "source": [
    "### Measuring the importance of node being the broker to connect the paths between any two other nodes, Albedo is the most import node with betweenness_centrality of 0.135. This implies that 13.5% of the shortest paths between any pair of nodes in the graph will pass through Albedo."
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "virtual-threat",
   "metadata": {},
   "source": [
    "### Edge betweenness centrality: \n",
    "\n",
    "### We can apply the same framework to find an important edge that has node s and node t pass through the edge instead of a node.\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "removable-property",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edge</th>\n",
       "      <th>betweenness_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>(Albedo, Xingqiu)</td>\n",
       "      <td>0.118807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>(Eula, Yanfei)</td>\n",
       "      <td>0.063184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>(Ningguang, Jean)</td>\n",
       "      <td>0.060183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>(Diona, Xiangling)</td>\n",
       "      <td>0.060026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>(Beidou, Kaedehara Kazuha)</td>\n",
       "      <td>0.055462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(Zhongli, Venti)</td>\n",
       "      <td>0.043013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>(Xiao, Venti)</td>\n",
       "      <td>0.038645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(Xiangling, Diona)</td>\n",
       "      <td>0.036625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>(Xingqiu, Beidou)</td>\n",
       "      <td>0.036291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>(Kaedehara Kazuha, Kamisato Ayaka)</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   edge  betweenness_centrality\n",
       "127                   (Albedo, Xingqiu)                0.118807\n",
       "108                      (Eula, Yanfei)                0.063184\n",
       "96                    (Ningguang, Jean)                0.060183\n",
       "223                  (Diona, Xiangling)                0.060026\n",
       "117          (Beidou, Kaedehara Kazuha)                0.055462\n",
       "37                     (Zhongli, Venti)                0.043013\n",
       "211                       (Xiao, Venti)                0.038645\n",
       "28                   (Xiangling, Diona)                0.036625\n",
       "104                   (Xingqiu, Beidou)                0.036291\n",
       "180  (Kaedehara Kazuha, Kamisato Ayaka)                0.028571"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_betweenness_centrality_df = pd.DataFrame(nx.edge_betweenness_centrality(directed_graph).items(), \n",
    "                                       columns=[\"edge\", \"betweenness_centrality\"])\n",
    "edge_betweenness_centrality_df.sort_values(\"betweenness_centrality\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "departmental-drawing",
   "metadata": {},
   "source": [
    "### Measuring the importance of edge being the bridge to connect the shortest paths between any two other nodes, the edge of `Albedo->Xingqiu` is the most import edge with betweenness_centrality of 0.119. This implies that 11.9% of the shortest paths between any pair of nodes in the graph will pass through this edge."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "earlier-interim",
   "metadata": {},
   "source": [
    "### 2.4 PageRank centrality (global inward importance)\n",
    "\n",
    "1. Assumption: a node’s importance is dependent on other incoming node’s importance\n",
    "\n",
    "2. Definition: PageRank computes a ranking of the nodes in the graph G based on the structure of the incoming links. It was originally designed as an algorithm to rank web pages.\n",
    "\n",
    "3. How to compute:\n",
    "    * Assign all nodes a PageRank of 1/n, where n is the number of nodes.\n",
    "    * Perform the following for k times:\n",
    "        * Apply the Basic PageRank Update Rule: each node gives an equal share of its PageRank to all the nodes it links to.\n",
    "        * Update the PageRank for each node to the sum of all the source nodes’ PageRank a node\n",
    "\n",
    "\n",
    "3. Interpretation: \n",
    "    * The PageRank of a node at step 𝑘 is the probability that a random walker lands on the node after taking 𝑘 steps\n",
    "    * A node with higher PageRank value will more likely be visited in the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "colored-termination",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>pagerank_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jean</td>\n",
       "      <td>0.061477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>0.055283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Klee</td>\n",
       "      <td>0.046893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kaeya</td>\n",
       "      <td>0.046560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Zhongli</td>\n",
       "      <td>0.042101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Diluc</td>\n",
       "      <td>0.040697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albedo</td>\n",
       "      <td>0.040236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Barbara</td>\n",
       "      <td>0.039043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Amber</td>\n",
       "      <td>0.036527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Beidou</td>\n",
       "      <td>0.033857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       node  pagerank_centrality\n",
       "34     Jean             0.061477\n",
       "24     Lisa             0.055283\n",
       "6      Klee             0.046893\n",
       "5     Kaeya             0.046560\n",
       "4   Zhongli             0.042101\n",
       "10    Diluc             0.040697\n",
       "16   Albedo             0.040236\n",
       "19  Barbara             0.039043\n",
       "17    Amber             0.036527\n",
       "15   Beidou             0.033857"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pagerank_centrality_df = pd.DataFrame(nx.pagerank(directed_graph).items(), \n",
    "                                       columns=[\"node\", \"pagerank_centrality\"])\n",
    "pagerank_centrality_df.sort_values(\"pagerank_centrality\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "individual-anniversary",
   "metadata": {},
   "source": [
    "### From the above sorted result table, we can observe Jean is the most important character in the network by PageRank score of 0.06. This implies Jean received more important in-ward mentions in the entire network. The PageRank of Jean at step 100 is the probability that a random walker passing informtion on the network stopped at Jean after taking 100 steps. And the probability is 6%."
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "broad-rebate",
   "metadata": {},
   "source": [
    "### 2.5 Authority and Hub scores (inward and outward importance):\n",
    "\n",
    "1. Definition:  \n",
    "For each node/web page:\n",
    "* Authority score (inward importance) estimates the value of the content of the page/inward link contributions.\n",
    "* Hub score (outward importance) estimates the value of a node's links to other pages.\n",
    "\n",
    "2. How to compute:\n",
    "    * **Hyperlink-Induced Topic Search** (HITS; also known as hubs and authorities) is a link analysis algorithm that rates Web pages:\n",
    "![image-3.png](attachment:image-3.png)\n",
    "\n",
    "3. Interpretation: \n",
    "    * A good **hub** (points out) represents a page that pointed to many good authorities.\n",
    "    * A good **authority** (takes in) represents a page that is linked by many good hubs\n",
    "    * For most networks, as 𝑘 gets larger, authority and hub scores converge to a unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "improved-cornwall",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute hub and auth centrality scores\n",
    "hub_scores, auth_scores = nx.hits(directed_graph)\n",
    "hub_centrality_df = pd.DataFrame(hub_scores.items(), \n",
    "                           columns=[\"node\", \"hub_centrality\"])\n",
    "auth_centrality_df = pd.DataFrame(auth_scores.items(), \n",
    "                           columns=[\"node\", \"auth_centrality\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "opposite-nancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>hub_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Traveler</td>\n",
       "      <td>0.067400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>0.063647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jean</td>\n",
       "      <td>0.058049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kaeya</td>\n",
       "      <td>0.057443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Klee</td>\n",
       "      <td>0.056526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Eula</td>\n",
       "      <td>0.055123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Diona</td>\n",
       "      <td>0.052835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sucrose</td>\n",
       "      <td>0.052102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rosaria</td>\n",
       "      <td>0.051441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Amber</td>\n",
       "      <td>0.046236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        node  hub_centrality\n",
       "25  Traveler        0.067400\n",
       "24      Lisa        0.063647\n",
       "34      Jean        0.058049\n",
       "5      Kaeya        0.057443\n",
       "6       Klee        0.056526\n",
       "14      Eula        0.055123\n",
       "29     Diona        0.052835\n",
       "21   Sucrose        0.052102\n",
       "20   Rosaria        0.051441\n",
       "17     Amber        0.046236"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hub_centrality_df.sort_values(\"hub_centrality\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adult-minority",
   "metadata": {},
   "source": [
    "### From the above hub scores, we can observe `Traveler` is the most important node because it has connected towards many good authority nodes. This finding is consistent with the out-degree centrality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "accessory-kentucky",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>auth_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jean</td>\n",
       "      <td>0.087187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>0.078032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kaeya</td>\n",
       "      <td>0.071204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Klee</td>\n",
       "      <td>0.071027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Diluc</td>\n",
       "      <td>0.068840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albedo</td>\n",
       "      <td>0.059420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Barbara</td>\n",
       "      <td>0.058982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Amber</td>\n",
       "      <td>0.058861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Noelle</td>\n",
       "      <td>0.047104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sucrose</td>\n",
       "      <td>0.035925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       node  auth_centrality\n",
       "34     Jean         0.087187\n",
       "24     Lisa         0.078032\n",
       "5     Kaeya         0.071204\n",
       "6      Klee         0.071027\n",
       "10    Diluc         0.068840\n",
       "16   Albedo         0.059420\n",
       "19  Barbara         0.058982\n",
       "17    Amber         0.058861\n",
       "31   Noelle         0.047104\n",
       "21  Sucrose         0.035925"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auth_centrality_df.sort_values(\"auth_centrality\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "computational-toronto",
   "metadata": {},
   "source": [
    "### From the above auth scores, we can observe `Jean` is the most important node because it recevied many links from good hub nodes. This finding is consistent with the in-degree centrality."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "entitled-pharmacology",
   "metadata": {},
   "source": [
    "## 3. Centrality Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "seeing-cherry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>in_degree_centrality</th>\n",
       "      <th>out_degree_centrality</th>\n",
       "      <th>pagerank_centrality</th>\n",
       "      <th>closeness_centrality</th>\n",
       "      <th>betweenness_centrality</th>\n",
       "      <th>hub_centrality</th>\n",
       "      <th>auth_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Xinyan</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>0.400368</td>\n",
       "      <td>0.009983</td>\n",
       "      <td>0.006785</td>\n",
       "      <td>0.006380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Keqing</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.020603</td>\n",
       "      <td>0.355882</td>\n",
       "      <td>0.006429</td>\n",
       "      <td>0.009574</td>\n",
       "      <td>0.005691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bennett</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.026852</td>\n",
       "      <td>0.376817</td>\n",
       "      <td>0.005751</td>\n",
       "      <td>0.038416</td>\n",
       "      <td>0.034150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xiangling</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.030609</td>\n",
       "      <td>0.464194</td>\n",
       "      <td>0.081891</td>\n",
       "      <td>0.009399</td>\n",
       "      <td>0.014293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Zhongli</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.042101</td>\n",
       "      <td>0.427059</td>\n",
       "      <td>0.092939</td>\n",
       "      <td>0.008452</td>\n",
       "      <td>0.010641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kaeya</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.046560</td>\n",
       "      <td>0.471021</td>\n",
       "      <td>0.033551</td>\n",
       "      <td>0.057443</td>\n",
       "      <td>0.071204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Klee</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.046893</td>\n",
       "      <td>0.471021</td>\n",
       "      <td>0.038863</td>\n",
       "      <td>0.056526</td>\n",
       "      <td>0.071027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mona</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.024750</td>\n",
       "      <td>0.427059</td>\n",
       "      <td>0.007703</td>\n",
       "      <td>0.045690</td>\n",
       "      <td>0.023333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yanfei</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.019851</td>\n",
       "      <td>0.421440</td>\n",
       "      <td>0.069288</td>\n",
       "      <td>0.009935</td>\n",
       "      <td>0.010727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chongyun</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.016459</td>\n",
       "      <td>0.372435</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>0.006443</td>\n",
       "      <td>0.004421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Diluc</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.040697</td>\n",
       "      <td>0.492760</td>\n",
       "      <td>0.019629</td>\n",
       "      <td>0.030534</td>\n",
       "      <td>0.068840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kamisato Ayaka</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008136</td>\n",
       "      <td>0.236111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ningguang</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.032566</td>\n",
       "      <td>0.421440</td>\n",
       "      <td>0.068590</td>\n",
       "      <td>0.015865</td>\n",
       "      <td>0.016515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Xingqiu</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.025774</td>\n",
       "      <td>0.478051</td>\n",
       "      <td>0.132108</td>\n",
       "      <td>0.014364</td>\n",
       "      <td>0.010220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Eula</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.026643</td>\n",
       "      <td>0.451118</td>\n",
       "      <td>0.084201</td>\n",
       "      <td>0.055123</td>\n",
       "      <td>0.026000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Beidou</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.033857</td>\n",
       "      <td>0.410633</td>\n",
       "      <td>0.094075</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>0.016559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albedo</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.040236</td>\n",
       "      <td>0.525072</td>\n",
       "      <td>0.134878</td>\n",
       "      <td>0.033290</td>\n",
       "      <td>0.059420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Amber</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.036527</td>\n",
       "      <td>0.438759</td>\n",
       "      <td>0.018928</td>\n",
       "      <td>0.046236</td>\n",
       "      <td>0.058861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ganyu</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.028239</td>\n",
       "      <td>0.385897</td>\n",
       "      <td>0.011198</td>\n",
       "      <td>0.007751</td>\n",
       "      <td>0.007369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Barbara</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.039043</td>\n",
       "      <td>0.492760</td>\n",
       "      <td>0.025373</td>\n",
       "      <td>0.041626</td>\n",
       "      <td>0.058982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rosaria</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.020710</td>\n",
       "      <td>0.400368</td>\n",
       "      <td>0.006914</td>\n",
       "      <td>0.051441</td>\n",
       "      <td>0.019867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sucrose</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.028129</td>\n",
       "      <td>0.427059</td>\n",
       "      <td>0.009059</td>\n",
       "      <td>0.052102</td>\n",
       "      <td>0.035925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Hu Tao</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.030513</td>\n",
       "      <td>0.405436</td>\n",
       "      <td>0.027174</td>\n",
       "      <td>0.009335</td>\n",
       "      <td>0.007150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Kaedehara Kazuha</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.008595</td>\n",
       "      <td>0.291176</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.001632</td>\n",
       "      <td>0.000663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>0.441176</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.055283</td>\n",
       "      <td>0.492760</td>\n",
       "      <td>0.036866</td>\n",
       "      <td>0.063647</td>\n",
       "      <td>0.078032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Traveler</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.004483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067400</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Xiao</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.027275</td>\n",
       "      <td>0.330200</td>\n",
       "      <td>0.030859</td>\n",
       "      <td>0.007268</td>\n",
       "      <td>0.005475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Qiqi</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.013945</td>\n",
       "      <td>0.372435</td>\n",
       "      <td>0.005079</td>\n",
       "      <td>0.001853</td>\n",
       "      <td>0.011914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Venti</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.030416</td>\n",
       "      <td>0.542871</td>\n",
       "      <td>0.092471</td>\n",
       "      <td>0.037686</td>\n",
       "      <td>0.032809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Diona</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.026012</td>\n",
       "      <td>0.508403</td>\n",
       "      <td>0.085169</td>\n",
       "      <td>0.052835</td>\n",
       "      <td>0.027349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Fischl</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.021946</td>\n",
       "      <td>0.355882</td>\n",
       "      <td>0.004055</td>\n",
       "      <td>0.022097</td>\n",
       "      <td>0.035435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Noelle</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.030965</td>\n",
       "      <td>0.421440</td>\n",
       "      <td>0.004782</td>\n",
       "      <td>0.040651</td>\n",
       "      <td>0.047104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Razor</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.020846</td>\n",
       "      <td>0.438759</td>\n",
       "      <td>0.008021</td>\n",
       "      <td>0.033531</td>\n",
       "      <td>0.035340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Tartaglia</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.008957</td>\n",
       "      <td>0.299340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>0.000937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jean</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.061477</td>\n",
       "      <td>0.604329</td>\n",
       "      <td>0.096456</td>\n",
       "      <td>0.058049</td>\n",
       "      <td>0.087187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                node  in_degree_centrality  out_degree_centrality  \\\n",
       "0             Xinyan              0.205882               0.176471   \n",
       "1             Keqing              0.147059               0.264706   \n",
       "2            Bennett              0.205882               0.205882   \n",
       "3          Xiangling              0.264706               0.235294   \n",
       "4            Zhongli              0.323529               0.235294   \n",
       "5              Kaeya              0.382353               0.323529   \n",
       "6               Klee              0.382353               0.323529   \n",
       "7               Mona              0.147059               0.205882   \n",
       "8             Yanfei              0.147059               0.264706   \n",
       "9           Chongyun              0.117647               0.205882   \n",
       "10             Diluc              0.352941               0.176471   \n",
       "11    Kamisato Ayaka              0.029412               0.000000   \n",
       "12         Ningguang              0.294118               0.235294   \n",
       "13           Xingqiu              0.205882               0.264706   \n",
       "14              Eula              0.176471               0.323529   \n",
       "15            Beidou              0.294118               0.205882   \n",
       "16            Albedo              0.323529               0.176471   \n",
       "17             Amber              0.294118               0.235294   \n",
       "18             Ganyu              0.205882               0.205882   \n",
       "19           Barbara              0.323529               0.235294   \n",
       "20           Rosaria              0.117647               0.235294   \n",
       "21           Sucrose              0.176471               0.264706   \n",
       "22            Hu Tao              0.235294               0.264706   \n",
       "23  Kaedehara Kazuha              0.029412               0.058824   \n",
       "24              Lisa              0.441176               0.352941   \n",
       "25          Traveler              0.000000               0.411765   \n",
       "26              Xiao              0.205882               0.147059   \n",
       "27              Qiqi              0.147059               0.088235   \n",
       "28             Venti              0.235294               0.235294   \n",
       "29             Diona              0.176471               0.264706   \n",
       "30            Fischl              0.176471               0.147059   \n",
       "31            Noelle              0.235294               0.205882   \n",
       "32             Razor              0.176471               0.176471   \n",
       "33         Tartaglia              0.029412               0.029412   \n",
       "34              Jean              0.500000               0.323529   \n",
       "\n",
       "    pagerank_centrality  closeness_centrality  betweenness_centrality  \\\n",
       "0              0.024052              0.400368                0.009983   \n",
       "1              0.020603              0.355882                0.006429   \n",
       "2              0.026852              0.376817                0.005751   \n",
       "3              0.030609              0.464194                0.081891   \n",
       "4              0.042101              0.427059                0.092939   \n",
       "5              0.046560              0.471021                0.033551   \n",
       "6              0.046893              0.471021                0.038863   \n",
       "7              0.024750              0.427059                0.007703   \n",
       "8              0.019851              0.421440                0.069288   \n",
       "9              0.016459              0.372435                0.005310   \n",
       "10             0.040697              0.492760                0.019629   \n",
       "11             0.008136              0.236111                0.000000   \n",
       "12             0.032566              0.421440                0.068590   \n",
       "13             0.025774              0.478051                0.132108   \n",
       "14             0.026643              0.451118                0.084201   \n",
       "15             0.033857              0.410633                0.094075   \n",
       "16             0.040236              0.525072                0.134878   \n",
       "17             0.036527              0.438759                0.018928   \n",
       "18             0.028239              0.385897                0.011198   \n",
       "19             0.039043              0.492760                0.025373   \n",
       "20             0.020710              0.400368                0.006914   \n",
       "21             0.028129              0.427059                0.009059   \n",
       "22             0.030513              0.405436                0.027174   \n",
       "23             0.008595              0.291176                0.029412   \n",
       "24             0.055283              0.492760                0.036866   \n",
       "25             0.004483              0.000000                0.000000   \n",
       "26             0.027275              0.330200                0.030859   \n",
       "27             0.013945              0.372435                0.005079   \n",
       "28             0.030416              0.542871                0.092471   \n",
       "29             0.026012              0.508403                0.085169   \n",
       "30             0.021946              0.355882                0.004055   \n",
       "31             0.030965              0.421440                0.004782   \n",
       "32             0.020846              0.438759                0.008021   \n",
       "33             0.008957              0.299340                0.000000   \n",
       "34             0.061477              0.604329                0.096456   \n",
       "\n",
       "    hub_centrality  auth_centrality  \n",
       "0         0.006785         0.006380  \n",
       "1         0.009574         0.005691  \n",
       "2         0.038416         0.034150  \n",
       "3         0.009399         0.014293  \n",
       "4         0.008452         0.010641  \n",
       "5         0.057443         0.071204  \n",
       "6         0.056526         0.071027  \n",
       "7         0.045690         0.023333  \n",
       "8         0.009935         0.010727  \n",
       "9         0.006443         0.004421  \n",
       "10        0.030534         0.068840  \n",
       "11        0.000000         0.000181  \n",
       "12        0.015865         0.016515  \n",
       "13        0.014364         0.010220  \n",
       "14        0.055123         0.026000  \n",
       "15        0.005981         0.016559  \n",
       "16        0.033290         0.059420  \n",
       "17        0.046236         0.058861  \n",
       "18        0.007751         0.007369  \n",
       "19        0.041626         0.058982  \n",
       "20        0.051441         0.019867  \n",
       "21        0.052102         0.035925  \n",
       "22        0.009335         0.007150  \n",
       "23        0.001632         0.000663  \n",
       "24        0.063647         0.078032  \n",
       "25        0.067400         0.000000  \n",
       "26        0.007268         0.005475  \n",
       "27        0.001853         0.011914  \n",
       "28        0.037686         0.032809  \n",
       "29        0.052835         0.027349  \n",
       "30        0.022097         0.035435  \n",
       "31        0.040651         0.047104  \n",
       "32        0.033531         0.035340  \n",
       "33        0.001038         0.000937  \n",
       "34        0.058049         0.087187  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine centrality measurement results\n",
    "centrality_summary = in_degree_centrality_df\\\n",
    "    .merge(out_degree_centrality_df, on=\"node\")\\\n",
    "    .merge(pagerank_centrality_df, on=\"node\")\\\n",
    "    .merge(closeness_centrality_df, on=\"node\")\\\n",
    "    .merge(betweenness_centrality_df, on=\"node\")\\\n",
    "    .merge(hub_centrality_df, on=\"node\")\\\n",
    "    .merge(auth_centrality_df, on=\"node\")\n",
    "centrality_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optical-moisture",
   "metadata": {},
   "source": [
    "### Rank node importance by centrality measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "demonstrated-surveillance",
   "metadata": {},
   "outputs": [
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       "        }#T_6ff42_row32_col4{\n",
       "            background-color:  #045b8e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_6ff42_row32_col5,#T_6ff42_row33_col2,#T_6ff42_row33_col5,#T_6ff42_row33_col6,#T_6ff42_row34_col1,#T_6ff42_row34_col3,#T_6ff42_row34_col4,#T_6ff42_row34_col5,#T_6ff42_row34_col7{\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_6ff42_row33_col4{\n",
       "            background-color:  #03446a;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_6ff42_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >node</th>        <th class=\"col_heading level0 col1\" >in_degree_centrality</th>        <th class=\"col_heading level0 col2\" >out_degree_centrality</th>        <th class=\"col_heading level0 col3\" >pagerank_centrality</th>        <th class=\"col_heading level0 col4\" >closeness_centrality</th>        <th class=\"col_heading level0 col5\" >betweenness_centrality</th>        <th class=\"col_heading level0 col6\" >hub_centrality</th>        <th class=\"col_heading level0 col7\" >auth_centrality</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_6ff42_level0_row0\" class=\"row_heading level0 row0\" >34</th>\n",
       "                        <td id=\"T_6ff42_row0_col0\" class=\"data row0 col0\" >Jean</td>\n",
       "                        <td id=\"T_6ff42_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "                        <td id=\"T_6ff42_row0_col2\" class=\"data row0 col2\" >3</td>\n",
       "                        <td id=\"T_6ff42_row0_col3\" class=\"data row0 col3\" >1</td>\n",
       "                        <td id=\"T_6ff42_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "                        <td id=\"T_6ff42_row0_col5\" class=\"data row0 col5\" >3</td>\n",
       "                        <td id=\"T_6ff42_row0_col6\" class=\"data row0 col6\" >3</td>\n",
       "                        <td id=\"T_6ff42_row0_col7\" class=\"data row0 col7\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row1\" class=\"row_heading level0 row1\" >24</th>\n",
       "                        <td id=\"T_6ff42_row1_col0\" class=\"data row1 col0\" >Lisa</td>\n",
       "                        <td id=\"T_6ff42_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "                        <td id=\"T_6ff42_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "                        <td id=\"T_6ff42_row1_col3\" class=\"data row1 col3\" >2</td>\n",
       "                        <td id=\"T_6ff42_row1_col4\" class=\"data row1 col4\" >5</td>\n",
       "                        <td id=\"T_6ff42_row1_col5\" class=\"data row1 col5\" >13</td>\n",
       "                        <td id=\"T_6ff42_row1_col6\" class=\"data row1 col6\" >2</td>\n",
       "                        <td id=\"T_6ff42_row1_col7\" class=\"data row1 col7\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
       "                        <td id=\"T_6ff42_row2_col0\" class=\"data row2 col0\" >Kaeya</td>\n",
       "                        <td id=\"T_6ff42_row2_col1\" class=\"data row2 col1\" >3</td>\n",
       "                        <td id=\"T_6ff42_row2_col2\" class=\"data row2 col2\" >3</td>\n",
       "                        <td id=\"T_6ff42_row2_col3\" class=\"data row2 col3\" >4</td>\n",
       "                        <td id=\"T_6ff42_row2_col4\" class=\"data row2 col4\" >7</td>\n",
       "                        <td id=\"T_6ff42_row2_col5\" class=\"data row2 col5\" >14</td>\n",
       "                        <td id=\"T_6ff42_row2_col6\" class=\"data row2 col6\" >4</td>\n",
       "                        <td id=\"T_6ff42_row2_col7\" class=\"data row2 col7\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
       "                        <td id=\"T_6ff42_row3_col0\" class=\"data row3 col0\" >Klee</td>\n",
       "                        <td id=\"T_6ff42_row3_col1\" class=\"data row3 col1\" >3</td>\n",
       "                        <td id=\"T_6ff42_row3_col2\" class=\"data row3 col2\" >3</td>\n",
       "                        <td id=\"T_6ff42_row3_col3\" class=\"data row3 col3\" >3</td>\n",
       "                        <td id=\"T_6ff42_row3_col4\" class=\"data row3 col4\" >7</td>\n",
       "                        <td id=\"T_6ff42_row3_col5\" class=\"data row3 col5\" >12</td>\n",
       "                        <td id=\"T_6ff42_row3_col6\" class=\"data row3 col6\" >5</td>\n",
       "                        <td id=\"T_6ff42_row3_col7\" class=\"data row3 col7\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row4\" class=\"row_heading level0 row4\" >10</th>\n",
       "                        <td id=\"T_6ff42_row4_col0\" class=\"data row4 col0\" >Diluc</td>\n",
       "                        <td id=\"T_6ff42_row4_col1\" class=\"data row4 col1\" >4</td>\n",
       "                        <td id=\"T_6ff42_row4_col2\" class=\"data row4 col2\" >7</td>\n",
       "                        <td id=\"T_6ff42_row4_col3\" class=\"data row4 col3\" >6</td>\n",
       "                        <td id=\"T_6ff42_row4_col4\" class=\"data row4 col4\" >5</td>\n",
       "                        <td id=\"T_6ff42_row4_col5\" class=\"data row4 col5\" >19</td>\n",
       "                        <td id=\"T_6ff42_row4_col6\" class=\"data row4 col6\" >18</td>\n",
       "                        <td id=\"T_6ff42_row4_col7\" class=\"data row4 col7\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row5\" class=\"row_heading level0 row5\" >19</th>\n",
       "                        <td id=\"T_6ff42_row5_col0\" class=\"data row5 col0\" >Barbara</td>\n",
       "                        <td id=\"T_6ff42_row5_col1\" class=\"data row5 col1\" >5</td>\n",
       "                        <td id=\"T_6ff42_row5_col2\" class=\"data row5 col2\" >5</td>\n",
       "                        <td id=\"T_6ff42_row5_col3\" class=\"data row5 col3\" >8</td>\n",
       "                        <td id=\"T_6ff42_row5_col4\" class=\"data row5 col4\" >5</td>\n",
       "                        <td id=\"T_6ff42_row5_col5\" class=\"data row5 col5\" >18</td>\n",
       "                        <td id=\"T_6ff42_row5_col6\" class=\"data row5 col6\" >12</td>\n",
       "                        <td id=\"T_6ff42_row5_col7\" class=\"data row5 col7\" >7</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row6\" class=\"row_heading level0 row6\" >16</th>\n",
       "                        <td id=\"T_6ff42_row6_col0\" class=\"data row6 col0\" >Albedo</td>\n",
       "                        <td id=\"T_6ff42_row6_col1\" class=\"data row6 col1\" >5</td>\n",
       "                        <td id=\"T_6ff42_row6_col2\" class=\"data row6 col2\" >7</td>\n",
       "                        <td id=\"T_6ff42_row6_col3\" class=\"data row6 col3\" >7</td>\n",
       "                        <td id=\"T_6ff42_row6_col4\" class=\"data row6 col4\" >3</td>\n",
       "                        <td id=\"T_6ff42_row6_col5\" class=\"data row6 col5\" >1</td>\n",
       "                        <td id=\"T_6ff42_row6_col6\" class=\"data row6 col6\" >17</td>\n",
       "                        <td id=\"T_6ff42_row6_col7\" class=\"data row6 col7\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row7\" class=\"row_heading level0 row7\" >4</th>\n",
       "                        <td id=\"T_6ff42_row7_col0\" class=\"data row7 col0\" >Zhongli</td>\n",
       "                        <td id=\"T_6ff42_row7_col1\" class=\"data row7 col1\" >5</td>\n",
       "                        <td id=\"T_6ff42_row7_col2\" class=\"data row7 col2\" >5</td>\n",
       "                        <td id=\"T_6ff42_row7_col3\" class=\"data row7 col3\" >5</td>\n",
       "                        <td id=\"T_6ff42_row7_col4\" class=\"data row7 col4\" >11</td>\n",
       "                        <td id=\"T_6ff42_row7_col5\" class=\"data row7 col5\" >5</td>\n",
       "                        <td id=\"T_6ff42_row7_col6\" class=\"data row7 col6\" >26</td>\n",
       "                        <td id=\"T_6ff42_row7_col7\" class=\"data row7 col7\" >24</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row8\" class=\"row_heading level0 row8\" >15</th>\n",
       "                        <td id=\"T_6ff42_row8_col0\" class=\"data row8 col0\" >Beidou</td>\n",
       "                        <td id=\"T_6ff42_row8_col1\" class=\"data row8 col1\" >6</td>\n",
       "                        <td id=\"T_6ff42_row8_col2\" class=\"data row8 col2\" >6</td>\n",
       "                        <td id=\"T_6ff42_row8_col3\" class=\"data row8 col3\" >10</td>\n",
       "                        <td id=\"T_6ff42_row8_col4\" class=\"data row8 col4\" >13</td>\n",
       "                        <td id=\"T_6ff42_row8_col5\" class=\"data row8 col5\" >4</td>\n",
       "                        <td id=\"T_6ff42_row8_col6\" class=\"data row8 col6\" >31</td>\n",
       "                        <td id=\"T_6ff42_row8_col7\" class=\"data row8 col7\" >19</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row9\" class=\"row_heading level0 row9\" >12</th>\n",
       "                        <td id=\"T_6ff42_row9_col0\" class=\"data row9 col0\" >Ningguang</td>\n",
       "                        <td id=\"T_6ff42_row9_col1\" class=\"data row9 col1\" >6</td>\n",
       "                        <td id=\"T_6ff42_row9_col2\" class=\"data row9 col2\" >5</td>\n",
       "                        <td id=\"T_6ff42_row9_col3\" class=\"data row9 col3\" >11</td>\n",
       "                        <td id=\"T_6ff42_row9_col4\" class=\"data row9 col4\" >12</td>\n",
       "                        <td id=\"T_6ff42_row9_col5\" class=\"data row9 col5\" >11</td>\n",
       "                        <td id=\"T_6ff42_row9_col6\" class=\"data row9 col6\" >20</td>\n",
       "                        <td id=\"T_6ff42_row9_col7\" class=\"data row9 col7\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row10\" class=\"row_heading level0 row10\" >17</th>\n",
       "                        <td id=\"T_6ff42_row10_col0\" class=\"data row10 col0\" >Amber</td>\n",
       "                        <td id=\"T_6ff42_row10_col1\" class=\"data row10 col1\" >6</td>\n",
       "                        <td id=\"T_6ff42_row10_col2\" class=\"data row10 col2\" >5</td>\n",
       "                        <td id=\"T_6ff42_row10_col3\" class=\"data row10 col3\" >9</td>\n",
       "                        <td id=\"T_6ff42_row10_col4\" class=\"data row10 col4\" >10</td>\n",
       "                        <td id=\"T_6ff42_row10_col5\" class=\"data row10 col5\" >20</td>\n",
       "                        <td id=\"T_6ff42_row10_col6\" class=\"data row10 col6\" >10</td>\n",
       "                        <td id=\"T_6ff42_row10_col7\" class=\"data row10 col7\" >8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row11\" class=\"row_heading level0 row11\" >3</th>\n",
       "                        <td id=\"T_6ff42_row11_col0\" class=\"data row11 col0\" >Xiangling</td>\n",
       "                        <td id=\"T_6ff42_row11_col1\" class=\"data row11 col1\" >7</td>\n",
       "                        <td id=\"T_6ff42_row11_col2\" class=\"data row11 col2\" >5</td>\n",
       "                        <td id=\"T_6ff42_row11_col3\" class=\"data row11 col3\" >13</td>\n",
       "                        <td id=\"T_6ff42_row11_col4\" class=\"data row11 col4\" >8</td>\n",
       "                        <td id=\"T_6ff42_row11_col5\" class=\"data row11 col5\" >9</td>\n",
       "                        <td id=\"T_6ff42_row11_col6\" class=\"data row11 col6\" >24</td>\n",
       "                        <td id=\"T_6ff42_row11_col7\" class=\"data row11 col7\" >21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row12\" class=\"row_heading level0 row12\" >22</th>\n",
       "                        <td id=\"T_6ff42_row12_col0\" class=\"data row12 col0\" >Hu Tao</td>\n",
       "                        <td id=\"T_6ff42_row12_col1\" class=\"data row12 col1\" >8</td>\n",
       "                        <td id=\"T_6ff42_row12_col2\" class=\"data row12 col2\" >4</td>\n",
       "                        <td id=\"T_6ff42_row12_col3\" class=\"data row12 col3\" >14</td>\n",
       "                        <td id=\"T_6ff42_row12_col4\" class=\"data row12 col4\" >14</td>\n",
       "                        <td id=\"T_6ff42_row12_col5\" class=\"data row12 col5\" >17</td>\n",
       "                        <td id=\"T_6ff42_row12_col6\" class=\"data row12 col6\" >25</td>\n",
       "                        <td id=\"T_6ff42_row12_col7\" class=\"data row12 col7\" >27</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row13\" class=\"row_heading level0 row13\" >31</th>\n",
       "                        <td id=\"T_6ff42_row13_col0\" class=\"data row13 col0\" >Noelle</td>\n",
       "                        <td id=\"T_6ff42_row13_col1\" class=\"data row13 col1\" >8</td>\n",
       "                        <td id=\"T_6ff42_row13_col2\" class=\"data row13 col2\" >6</td>\n",
       "                        <td id=\"T_6ff42_row13_col3\" class=\"data row13 col3\" >12</td>\n",
       "                        <td id=\"T_6ff42_row13_col4\" class=\"data row13 col4\" >12</td>\n",
       "                        <td id=\"T_6ff42_row13_col5\" class=\"data row13 col5\" >31</td>\n",
       "                        <td id=\"T_6ff42_row13_col6\" class=\"data row13 col6\" >13</td>\n",
       "                        <td id=\"T_6ff42_row13_col7\" class=\"data row13 col7\" >9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row14\" class=\"row_heading level0 row14\" >28</th>\n",
       "                        <td id=\"T_6ff42_row14_col0\" class=\"data row14 col0\" >Venti</td>\n",
       "                        <td id=\"T_6ff42_row14_col1\" class=\"data row14 col1\" >8</td>\n",
       "                        <td id=\"T_6ff42_row14_col2\" class=\"data row14 col2\" >5</td>\n",
       "                        <td id=\"T_6ff42_row14_col3\" class=\"data row14 col3\" >15</td>\n",
       "                        <td id=\"T_6ff42_row14_col4\" class=\"data row14 col4\" >2</td>\n",
       "                        <td id=\"T_6ff42_row14_col5\" class=\"data row14 col5\" >6</td>\n",
       "                        <td id=\"T_6ff42_row14_col6\" class=\"data row14 col6\" >15</td>\n",
       "                        <td id=\"T_6ff42_row14_col7\" class=\"data row14 col7\" >14</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row15\" class=\"row_heading level0 row15\" >26</th>\n",
       "                        <td id=\"T_6ff42_row15_col0\" class=\"data row15 col0\" >Xiao</td>\n",
       "                        <td id=\"T_6ff42_row15_col1\" class=\"data row15 col1\" >9</td>\n",
       "                        <td id=\"T_6ff42_row15_col2\" class=\"data row15 col2\" >8</td>\n",
       "                        <td id=\"T_6ff42_row15_col3\" class=\"data row15 col3\" >18</td>\n",
       "                        <td id=\"T_6ff42_row15_col4\" class=\"data row15 col4\" >20</td>\n",
       "                        <td id=\"T_6ff42_row15_col5\" class=\"data row15 col5\" >15</td>\n",
       "                        <td id=\"T_6ff42_row15_col6\" class=\"data row15 col6\" >28</td>\n",
       "                        <td id=\"T_6ff42_row15_col7\" class=\"data row15 col7\" >30</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row16\" class=\"row_heading level0 row16\" >2</th>\n",
       "                        <td id=\"T_6ff42_row16_col0\" class=\"data row16 col0\" >Bennett</td>\n",
       "                        <td id=\"T_6ff42_row16_col1\" class=\"data row16 col1\" >9</td>\n",
       "                        <td id=\"T_6ff42_row16_col2\" class=\"data row16 col2\" >6</td>\n",
       "                        <td id=\"T_6ff42_row16_col3\" class=\"data row16 col3\" >19</td>\n",
       "                        <td id=\"T_6ff42_row16_col4\" class=\"data row16 col4\" >17</td>\n",
       "                        <td id=\"T_6ff42_row16_col5\" class=\"data row16 col5\" >28</td>\n",
       "                        <td id=\"T_6ff42_row16_col6\" class=\"data row16 col6\" >14</td>\n",
       "                        <td id=\"T_6ff42_row16_col7\" class=\"data row16 col7\" >13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
       "                        <td id=\"T_6ff42_row17_col0\" class=\"data row17 col0\" >Ganyu</td>\n",
       "                        <td id=\"T_6ff42_row17_col1\" class=\"data row17 col1\" >9</td>\n",
       "                        <td id=\"T_6ff42_row17_col2\" class=\"data row17 col2\" >6</td>\n",
       "                        <td id=\"T_6ff42_row17_col3\" class=\"data row17 col3\" >16</td>\n",
       "                        <td id=\"T_6ff42_row17_col4\" class=\"data row17 col4\" >16</td>\n",
       "                        <td id=\"T_6ff42_row17_col5\" class=\"data row17 col5\" >21</td>\n",
       "                        <td id=\"T_6ff42_row17_col6\" class=\"data row17 col6\" >27</td>\n",
       "                        <td id=\"T_6ff42_row17_col7\" class=\"data row17 col7\" >26</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row18\" class=\"row_heading level0 row18\" >0</th>\n",
       "                        <td id=\"T_6ff42_row18_col0\" class=\"data row18 col0\" >Xinyan</td>\n",
       "                        <td id=\"T_6ff42_row18_col1\" class=\"data row18 col1\" >9</td>\n",
       "                        <td id=\"T_6ff42_row18_col2\" class=\"data row18 col2\" >7</td>\n",
       "                        <td id=\"T_6ff42_row18_col3\" class=\"data row18 col3\" >24</td>\n",
       "                        <td id=\"T_6ff42_row18_col4\" class=\"data row18 col4\" >15</td>\n",
       "                        <td id=\"T_6ff42_row18_col5\" class=\"data row18 col5\" >22</td>\n",
       "                        <td id=\"T_6ff42_row18_col6\" class=\"data row18 col6\" >29</td>\n",
       "                        <td id=\"T_6ff42_row18_col7\" class=\"data row18 col7\" >28</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row19\" class=\"row_heading level0 row19\" >13</th>\n",
       "                        <td id=\"T_6ff42_row19_col0\" class=\"data row19 col0\" >Xingqiu</td>\n",
       "                        <td id=\"T_6ff42_row19_col1\" class=\"data row19 col1\" >9</td>\n",
       "                        <td id=\"T_6ff42_row19_col2\" class=\"data row19 col2\" >4</td>\n",
       "                        <td id=\"T_6ff42_row19_col3\" class=\"data row19 col3\" >22</td>\n",
       "                        <td id=\"T_6ff42_row19_col4\" class=\"data row19 col4\" >6</td>\n",
       "                        <td id=\"T_6ff42_row19_col5\" class=\"data row19 col5\" >2</td>\n",
       "                        <td id=\"T_6ff42_row19_col6\" class=\"data row19 col6\" >21</td>\n",
       "                        <td id=\"T_6ff42_row19_col7\" class=\"data row19 col7\" >25</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row20\" class=\"row_heading level0 row20\" >21</th>\n",
       "                        <td id=\"T_6ff42_row20_col0\" class=\"data row20 col0\" >Sucrose</td>\n",
       "                        <td id=\"T_6ff42_row20_col1\" class=\"data row20 col1\" >10</td>\n",
       "                        <td id=\"T_6ff42_row20_col2\" class=\"data row20 col2\" >4</td>\n",
       "                        <td id=\"T_6ff42_row20_col3\" class=\"data row20 col3\" >17</td>\n",
       "                        <td id=\"T_6ff42_row20_col4\" class=\"data row20 col4\" >11</td>\n",
       "                        <td id=\"T_6ff42_row20_col5\" class=\"data row20 col5\" >23</td>\n",
       "                        <td id=\"T_6ff42_row20_col6\" class=\"data row20 col6\" >8</td>\n",
       "                        <td id=\"T_6ff42_row20_col7\" class=\"data row20 col7\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row21\" class=\"row_heading level0 row21\" >14</th>\n",
       "                        <td id=\"T_6ff42_row21_col0\" class=\"data row21 col0\" >Eula</td>\n",
       "                        <td id=\"T_6ff42_row21_col1\" class=\"data row21 col1\" >10</td>\n",
       "                        <td id=\"T_6ff42_row21_col2\" class=\"data row21 col2\" >3</td>\n",
       "                        <td id=\"T_6ff42_row21_col3\" class=\"data row21 col3\" >20</td>\n",
       "                        <td id=\"T_6ff42_row21_col4\" class=\"data row21 col4\" >9</td>\n",
       "                        <td id=\"T_6ff42_row21_col5\" class=\"data row21 col5\" >8</td>\n",
       "                        <td id=\"T_6ff42_row21_col6\" class=\"data row21 col6\" >6</td>\n",
       "                        <td id=\"T_6ff42_row21_col7\" class=\"data row21 col7\" >16</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row22\" class=\"row_heading level0 row22\" >29</th>\n",
       "                        <td id=\"T_6ff42_row22_col0\" class=\"data row22 col0\" >Diona</td>\n",
       "                        <td id=\"T_6ff42_row22_col1\" class=\"data row22 col1\" >10</td>\n",
       "                        <td id=\"T_6ff42_row22_col2\" class=\"data row22 col2\" >4</td>\n",
       "                        <td id=\"T_6ff42_row22_col3\" class=\"data row22 col3\" >21</td>\n",
       "                        <td id=\"T_6ff42_row22_col4\" class=\"data row22 col4\" >4</td>\n",
       "                        <td id=\"T_6ff42_row22_col5\" class=\"data row22 col5\" >7</td>\n",
       "                        <td id=\"T_6ff42_row22_col6\" class=\"data row22 col6\" >7</td>\n",
       "                        <td id=\"T_6ff42_row22_col7\" class=\"data row22 col7\" >15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row23\" class=\"row_heading level0 row23\" >30</th>\n",
       "                        <td id=\"T_6ff42_row23_col0\" class=\"data row23 col0\" >Fischl</td>\n",
       "                        <td id=\"T_6ff42_row23_col1\" class=\"data row23 col1\" >10</td>\n",
       "                        <td id=\"T_6ff42_row23_col2\" class=\"data row23 col2\" >8</td>\n",
       "                        <td id=\"T_6ff42_row23_col3\" class=\"data row23 col3\" >25</td>\n",
       "                        <td id=\"T_6ff42_row23_col4\" class=\"data row23 col4\" >19</td>\n",
       "                        <td id=\"T_6ff42_row23_col5\" class=\"data row23 col5\" >32</td>\n",
       "                        <td id=\"T_6ff42_row23_col6\" class=\"data row23 col6\" >19</td>\n",
       "                        <td id=\"T_6ff42_row23_col7\" class=\"data row23 col7\" >11</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row24\" class=\"row_heading level0 row24\" >32</th>\n",
       "                        <td id=\"T_6ff42_row24_col0\" class=\"data row24 col0\" >Razor</td>\n",
       "                        <td id=\"T_6ff42_row24_col1\" class=\"data row24 col1\" >10</td>\n",
       "                        <td id=\"T_6ff42_row24_col2\" class=\"data row24 col2\" >7</td>\n",
       "                        <td id=\"T_6ff42_row24_col3\" class=\"data row24 col3\" >26</td>\n",
       "                        <td id=\"T_6ff42_row24_col4\" class=\"data row24 col4\" >10</td>\n",
       "                        <td id=\"T_6ff42_row24_col5\" class=\"data row24 col5\" >24</td>\n",
       "                        <td id=\"T_6ff42_row24_col6\" class=\"data row24 col6\" >16</td>\n",
       "                        <td id=\"T_6ff42_row24_col7\" class=\"data row24 col7\" >12</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row25\" class=\"row_heading level0 row25\" >7</th>\n",
       "                        <td id=\"T_6ff42_row25_col0\" class=\"data row25 col0\" >Mona</td>\n",
       "                        <td id=\"T_6ff42_row25_col1\" class=\"data row25 col1\" >11</td>\n",
       "                        <td id=\"T_6ff42_row25_col2\" class=\"data row25 col2\" >6</td>\n",
       "                        <td id=\"T_6ff42_row25_col3\" class=\"data row25 col3\" >23</td>\n",
       "                        <td id=\"T_6ff42_row25_col4\" class=\"data row25 col4\" >11</td>\n",
       "                        <td id=\"T_6ff42_row25_col5\" class=\"data row25 col5\" >25</td>\n",
       "                        <td id=\"T_6ff42_row25_col6\" class=\"data row25 col6\" >11</td>\n",
       "                        <td id=\"T_6ff42_row25_col7\" class=\"data row25 col7\" >17</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row26\" class=\"row_heading level0 row26\" >1</th>\n",
       "                        <td id=\"T_6ff42_row26_col0\" class=\"data row26 col0\" >Keqing</td>\n",
       "                        <td id=\"T_6ff42_row26_col1\" class=\"data row26 col1\" >11</td>\n",
       "                        <td id=\"T_6ff42_row26_col2\" class=\"data row26 col2\" >4</td>\n",
       "                        <td id=\"T_6ff42_row26_col3\" class=\"data row26 col3\" >28</td>\n",
       "                        <td id=\"T_6ff42_row26_col4\" class=\"data row26 col4\" >19</td>\n",
       "                        <td id=\"T_6ff42_row26_col5\" class=\"data row26 col5\" >27</td>\n",
       "                        <td id=\"T_6ff42_row26_col6\" class=\"data row26 col6\" >23</td>\n",
       "                        <td id=\"T_6ff42_row26_col7\" class=\"data row26 col7\" >29</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_6ff42_row27_col0\" class=\"data row27 col0\" >Qiqi</td>\n",
       "                        <td id=\"T_6ff42_row27_col1\" class=\"data row27 col1\" >11</td>\n",
       "                        <td id=\"T_6ff42_row27_col2\" class=\"data row27 col2\" >9</td>\n",
       "                        <td id=\"T_6ff42_row27_col3\" class=\"data row27 col3\" >31</td>\n",
       "                        <td id=\"T_6ff42_row27_col4\" class=\"data row27 col4\" >18</td>\n",
       "                        <td id=\"T_6ff42_row27_col5\" class=\"data row27 col5\" >30</td>\n",
       "                        <td id=\"T_6ff42_row27_col6\" class=\"data row27 col6\" >32</td>\n",
       "                        <td id=\"T_6ff42_row27_col7\" class=\"data row27 col7\" >22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row28\" class=\"row_heading level0 row28\" >8</th>\n",
       "                        <td id=\"T_6ff42_row28_col0\" class=\"data row28 col0\" >Yanfei</td>\n",
       "                        <td id=\"T_6ff42_row28_col1\" class=\"data row28 col1\" >11</td>\n",
       "                        <td id=\"T_6ff42_row28_col2\" class=\"data row28 col2\" >4</td>\n",
       "                        <td id=\"T_6ff42_row28_col3\" class=\"data row28 col3\" >29</td>\n",
       "                        <td id=\"T_6ff42_row28_col4\" class=\"data row28 col4\" >12</td>\n",
       "                        <td id=\"T_6ff42_row28_col5\" class=\"data row28 col5\" >10</td>\n",
       "                        <td id=\"T_6ff42_row28_col6\" class=\"data row28 col6\" >22</td>\n",
       "                        <td id=\"T_6ff42_row28_col7\" class=\"data row28 col7\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row29\" class=\"row_heading level0 row29\" >20</th>\n",
       "                        <td id=\"T_6ff42_row29_col0\" class=\"data row29 col0\" >Rosaria</td>\n",
       "                        <td id=\"T_6ff42_row29_col1\" class=\"data row29 col1\" >12</td>\n",
       "                        <td id=\"T_6ff42_row29_col2\" class=\"data row29 col2\" >5</td>\n",
       "                        <td id=\"T_6ff42_row29_col3\" class=\"data row29 col3\" >27</td>\n",
       "                        <td id=\"T_6ff42_row29_col4\" class=\"data row29 col4\" >15</td>\n",
       "                        <td id=\"T_6ff42_row29_col5\" class=\"data row29 col5\" >26</td>\n",
       "                        <td id=\"T_6ff42_row29_col6\" class=\"data row29 col6\" >9</td>\n",
       "                        <td id=\"T_6ff42_row29_col7\" class=\"data row29 col7\" >18</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row30\" class=\"row_heading level0 row30\" >9</th>\n",
       "                        <td id=\"T_6ff42_row30_col0\" class=\"data row30 col0\" >Chongyun</td>\n",
       "                        <td id=\"T_6ff42_row30_col1\" class=\"data row30 col1\" >12</td>\n",
       "                        <td id=\"T_6ff42_row30_col2\" class=\"data row30 col2\" >6</td>\n",
       "                        <td id=\"T_6ff42_row30_col3\" class=\"data row30 col3\" >30</td>\n",
       "                        <td id=\"T_6ff42_row30_col4\" class=\"data row30 col4\" >18</td>\n",
       "                        <td id=\"T_6ff42_row30_col5\" class=\"data row30 col5\" >29</td>\n",
       "                        <td id=\"T_6ff42_row30_col6\" class=\"data row30 col6\" >30</td>\n",
       "                        <td id=\"T_6ff42_row30_col7\" class=\"data row30 col7\" >31</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row31\" class=\"row_heading level0 row31\" >23</th>\n",
       "                        <td id=\"T_6ff42_row31_col0\" class=\"data row31 col0\" >Kaedehara Kazuha</td>\n",
       "                        <td id=\"T_6ff42_row31_col1\" class=\"data row31 col1\" >13</td>\n",
       "                        <td id=\"T_6ff42_row31_col2\" class=\"data row31 col2\" >10</td>\n",
       "                        <td id=\"T_6ff42_row31_col3\" class=\"data row31 col3\" >33</td>\n",
       "                        <td id=\"T_6ff42_row31_col4\" class=\"data row31 col4\" >22</td>\n",
       "                        <td id=\"T_6ff42_row31_col5\" class=\"data row31 col5\" >16</td>\n",
       "                        <td id=\"T_6ff42_row31_col6\" class=\"data row31 col6\" >33</td>\n",
       "                        <td id=\"T_6ff42_row31_col7\" class=\"data row31 col7\" >33</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row32\" class=\"row_heading level0 row32\" >33</th>\n",
       "                        <td id=\"T_6ff42_row32_col0\" class=\"data row32 col0\" >Tartaglia</td>\n",
       "                        <td id=\"T_6ff42_row32_col1\" class=\"data row32 col1\" >13</td>\n",
       "                        <td id=\"T_6ff42_row32_col2\" class=\"data row32 col2\" >11</td>\n",
       "                        <td id=\"T_6ff42_row32_col3\" class=\"data row32 col3\" >32</td>\n",
       "                        <td id=\"T_6ff42_row32_col4\" class=\"data row32 col4\" >21</td>\n",
       "                        <td id=\"T_6ff42_row32_col5\" class=\"data row32 col5\" >33</td>\n",
       "                        <td id=\"T_6ff42_row32_col6\" class=\"data row32 col6\" >34</td>\n",
       "                        <td id=\"T_6ff42_row32_col7\" class=\"data row32 col7\" >32</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row33\" class=\"row_heading level0 row33\" >11</th>\n",
       "                        <td id=\"T_6ff42_row33_col0\" class=\"data row33 col0\" >Kamisato Ayaka</td>\n",
       "                        <td id=\"T_6ff42_row33_col1\" class=\"data row33 col1\" >13</td>\n",
       "                        <td id=\"T_6ff42_row33_col2\" class=\"data row33 col2\" >12</td>\n",
       "                        <td id=\"T_6ff42_row33_col3\" class=\"data row33 col3\" >34</td>\n",
       "                        <td id=\"T_6ff42_row33_col4\" class=\"data row33 col4\" >23</td>\n",
       "                        <td id=\"T_6ff42_row33_col5\" class=\"data row33 col5\" >33</td>\n",
       "                        <td id=\"T_6ff42_row33_col6\" class=\"data row33 col6\" >35</td>\n",
       "                        <td id=\"T_6ff42_row33_col7\" class=\"data row33 col7\" >34</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6ff42_level0_row34\" class=\"row_heading level0 row34\" >25</th>\n",
       "                        <td id=\"T_6ff42_row34_col0\" class=\"data row34 col0\" >Traveler</td>\n",
       "                        <td id=\"T_6ff42_row34_col1\" class=\"data row34 col1\" >14</td>\n",
       "                        <td id=\"T_6ff42_row34_col2\" class=\"data row34 col2\" >1</td>\n",
       "                        <td id=\"T_6ff42_row34_col3\" class=\"data row34 col3\" >35</td>\n",
       "                        <td id=\"T_6ff42_row34_col4\" class=\"data row34 col4\" >24</td>\n",
       "                        <td id=\"T_6ff42_row34_col5\" class=\"data row34 col5\" >33</td>\n",
       "                        <td id=\"T_6ff42_row34_col6\" class=\"data row34 col6\" >1</td>\n",
       "                        <td id=\"T_6ff42_row34_col7\" class=\"data row34 col7\" >35</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1fcc41470c8>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centrality_ranks = centrality_summary.rank(ascending=False, \n",
    "                                           numeric_only=True, \n",
    "                                           method=\"dense\").astype(int)\n",
    "centrality_ranks.insert(loc=0, column='node', value=centrality_summary[\"node\"])\n",
    "centrality_ranks.sort_values(\"in_degree_centrality\").style.background_gradient(subset=list(centrality_ranks.columns[1:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "french-surrey",
   "metadata": {},
   "source": [
    "### With so many measures, it's  hard to study which one is important. We use ensemble approach by taking the average centrality to study which one is the most important node."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "removed-lighter",
   "metadata": {},
   "source": [
    "### Average Ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "intellectual-platform",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_9bfa9_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >node</th>        <th class=\"col_heading level0 col1\" >average_rank</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_9bfa9_row0_col0\" class=\"data row0 col0\" >Jean</td>\n",
       "                        <td id=\"T_9bfa9_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_9bfa9_row1_col0\" class=\"data row1 col0\" >Lisa</td>\n",
       "                        <td id=\"T_9bfa9_row1_col1\" class=\"data row1 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_9bfa9_row2_col0\" class=\"data row2 col0\" >Kaeya</td>\n",
       "                        <td id=\"T_9bfa9_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_9bfa9_row3_col0\" class=\"data row3 col0\" >Klee</td>\n",
       "                        <td id=\"T_9bfa9_row3_col1\" class=\"data row3 col1\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_9bfa9_row4_col0\" class=\"data row4 col0\" >Albedo</td>\n",
       "                        <td id=\"T_9bfa9_row4_col1\" class=\"data row4 col1\" >7</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_9bfa9_row5_col0\" class=\"data row5 col0\" >Venti</td>\n",
       "                        <td id=\"T_9bfa9_row5_col1\" class=\"data row5 col1\" >9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_9bfa9_row6_col0\" class=\"data row6 col0\" >Barbara</td>\n",
       "                        <td id=\"T_9bfa9_row6_col1\" class=\"data row6 col1\" >9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_9bfa9_row7_col0\" class=\"data row7 col0\" >Diluc</td>\n",
       "                        <td id=\"T_9bfa9_row7_col1\" class=\"data row7 col1\" >9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_9bfa9_row8_col0\" class=\"data row8 col0\" >Diona</td>\n",
       "                        <td id=\"T_9bfa9_row8_col1\" class=\"data row8 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_9bfa9_row9_col0\" class=\"data row9 col0\" >Eula</td>\n",
       "                        <td id=\"T_9bfa9_row9_col1\" class=\"data row9 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_9bfa9_row10_col0\" class=\"data row10 col0\" >Amber</td>\n",
       "                        <td id=\"T_9bfa9_row10_col1\" class=\"data row10 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_9bfa9_row11_col0\" class=\"data row11 col0\" >Ningguang</td>\n",
       "                        <td id=\"T_9bfa9_row11_col1\" class=\"data row11 col1\" >12</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_9bfa9_row12_col0\" class=\"data row12 col0\" >Zhongli</td>\n",
       "                        <td id=\"T_9bfa9_row12_col1\" class=\"data row12 col1\" >12</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_9bfa9_row13_col0\" class=\"data row13 col0\" >Sucrose</td>\n",
       "                        <td id=\"T_9bfa9_row13_col1\" class=\"data row13 col1\" >12</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_9bfa9_row14_col0\" class=\"data row14 col0\" >Xiangling</td>\n",
       "                        <td id=\"T_9bfa9_row14_col1\" class=\"data row14 col1\" >12</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_9bfa9_row15_col0\" class=\"data row15 col0\" >Xingqiu</td>\n",
       "                        <td id=\"T_9bfa9_row15_col1\" class=\"data row15 col1\" >13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_9bfa9_row16_col0\" class=\"data row16 col0\" >Beidou</td>\n",
       "                        <td id=\"T_9bfa9_row16_col1\" class=\"data row16 col1\" >13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_9bfa9_row17_col0\" class=\"data row17 col0\" >Noelle</td>\n",
       "                        <td id=\"T_9bfa9_row17_col1\" class=\"data row17 col1\" >13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_9bfa9_row18_col0\" class=\"data row18 col0\" >Mona</td>\n",
       "                        <td id=\"T_9bfa9_row18_col1\" class=\"data row18 col1\" >15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_9bfa9_row19_col0\" class=\"data row19 col0\" >Razor</td>\n",
       "                        <td id=\"T_9bfa9_row19_col1\" class=\"data row19 col1\" >15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_9bfa9_row20_col0\" class=\"data row20 col0\" >Bennett</td>\n",
       "                        <td id=\"T_9bfa9_row20_col1\" class=\"data row20 col1\" >15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_9bfa9_row21_col0\" class=\"data row21 col0\" >Hu Tao</td>\n",
       "                        <td id=\"T_9bfa9_row21_col1\" class=\"data row21 col1\" >16</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_9bfa9_row22_col0\" class=\"data row22 col0\" >Yanfei</td>\n",
       "                        <td id=\"T_9bfa9_row22_col1\" class=\"data row22 col1\" >16</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_9bfa9_row23_col0\" class=\"data row23 col0\" >Rosaria</td>\n",
       "                        <td id=\"T_9bfa9_row23_col1\" class=\"data row23 col1\" >16</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_9bfa9_row24_col0\" class=\"data row24 col0\" >Ganyu</td>\n",
       "                        <td id=\"T_9bfa9_row24_col1\" class=\"data row24 col1\" >17</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_9bfa9_row25_col0\" class=\"data row25 col0\" >Fischl</td>\n",
       "                        <td id=\"T_9bfa9_row25_col1\" class=\"data row25 col1\" >18</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_9bfa9_row26_col0\" class=\"data row26 col0\" >Xiao</td>\n",
       "                        <td id=\"T_9bfa9_row26_col1\" class=\"data row26 col1\" >18</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_9bfa9_row27_col0\" class=\"data row27 col0\" >Xinyan</td>\n",
       "                        <td id=\"T_9bfa9_row27_col1\" class=\"data row27 col1\" >19</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_9bfa9_row28_col0\" class=\"data row28 col0\" >Traveler</td>\n",
       "                        <td id=\"T_9bfa9_row28_col1\" class=\"data row28 col1\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_9bfa9_row29_col0\" class=\"data row29 col0\" >Keqing</td>\n",
       "                        <td id=\"T_9bfa9_row29_col1\" class=\"data row29 col1\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_9bfa9_row30_col0\" class=\"data row30 col0\" >Chongyun</td>\n",
       "                        <td id=\"T_9bfa9_row30_col1\" class=\"data row30 col1\" >22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_9bfa9_row31_col0\" class=\"data row31 col0\" >Qiqi</td>\n",
       "                        <td id=\"T_9bfa9_row31_col1\" class=\"data row31 col1\" >22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_9bfa9_row32_col0\" class=\"data row32 col0\" >Kaedehara Kazuha</td>\n",
       "                        <td id=\"T_9bfa9_row32_col1\" class=\"data row32 col1\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_9bfa9_row33_col0\" class=\"data row33 col0\" >Tartaglia</td>\n",
       "                        <td id=\"T_9bfa9_row33_col1\" class=\"data row33 col1\" >25</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9bfa9_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_9bfa9_row34_col0\" class=\"data row34 col0\" >Kamisato Ayaka</td>\n",
       "                        <td id=\"T_9bfa9_row34_col1\" class=\"data row34 col1\" >26</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1fcc4ea1b08>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute the average ranking using the above ranks\n",
    "average_ranks = pd.DataFrame(round(centrality_ranks.mean(axis=1)).astype(int), columns=[\"average_rank\"])\n",
    "average_ranks.insert(loc=0, column='node', value=centrality_summary[\"node\"])\n",
    "average_ranks.sort_values(\"average_rank\").reset_index(drop=True)\\\n",
    "    .style.background_gradient(subset=[\"average_rank\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "synthetic-denver",
   "metadata": {},
   "source": [
    "### After computing the average ranking, Jean is the most important character in the Genshin Impact character story and voice mention network with average rank of 2. Ayaka is the least important character based on the average ranking. "
   ]
  }
 ],
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