SECOM_tanmay_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install numpy\n",
    "%pip install pandas\n",
    "%pip install requests\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Required Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import zipfile\n",
    "from io import BytesIO\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "secom_zip_url = \"https://archive.ics.uci.edu/static/public/179/secom.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "secom_zip_file = requests.get(secom_zip_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['secom.data', 'secom.names', 'secom_labels.data']\n"
     ]
    }
   ],
   "source": [
    "secom_files =  zipfile.ZipFile(BytesIO(secom_zip_file.content), 'r')\n",
    "\n",
    "secom_files_names =  (zipfile.ZipFile(BytesIO(secom_zip_file.content))).namelist()\n",
    "print(secom_files_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_file = secom_files.open('secom.data')\n",
    "l_file = secom_files.open('secom_labels.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of Labels data:  (1567, 2)\n",
      "   classifications            date_time\n",
      "0               -1  19/07/2008 11:55:00\n",
      "1               -1  19/07/2008 12:32:00\n",
      "2                1  19/07/2008 13:17:00\n",
      "3               -1  19/07/2008 14:43:00\n",
      "4               -1  19/07/2008 15:22:00\n"
     ]
    }
   ],
   "source": [
    "#Create  column names and pandas dataframes for labels data\n",
    "\n",
    "l_column_names=['classifications', 'date_time'] # create column labels\n",
    "\n",
    "secom_labels_df = pd.read_csv(l_file, sep=r'\\s+', header=None, names= l_column_names)\n",
    "\n",
    "print(f\"Dimensions of Labels data: \", secom_labels_df.shape)\n",
    "\n",
    "print(secom_labels_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "convert time stamps column to datetime format and Check if the timestamps column is ordinal (from latest to ealatest time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The timestamps column is in ascending order.\n"
     ]
    }
   ],
   "source": [
    "#convert time stamps column to datetime format and Check if the timestamps column is ordinal (from latest to ealatest time)\n",
    "\n",
    "secom_labels_df['date_time'] = pd.to_datetime(secom_labels_df['date_time'], format='%d/%m/%Y %H:%M:%S')\n",
    "\n",
    "is_ascending = secom_labels_df['date_time'].is_monotonic_increasing\n",
    "\n",
    "if is_ascending:\n",
    "    print(\"The timestamps column is in ascending order.\")\n",
    "else:\n",
    "    print(\"The timestamps column is not in ascending order.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date columns separation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>classifications</th>\n",
       "      <th>date_time</th>\n",
       "      <th>time</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-07-19 11:55:00</td>\n",
       "      <td>11:55:00</td>\n",
       "      <td>2008-07-19</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-07-19 12:32:00</td>\n",
       "      <td>12:32:00</td>\n",
       "      <td>2008-07-19</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2008-07-19 13:17:00</td>\n",
       "      <td>13:17:00</td>\n",
       "      <td>2008-07-19</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-07-19 14:43:00</td>\n",
       "      <td>14:43:00</td>\n",
       "      <td>2008-07-19</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-07-19 15:22:00</td>\n",
       "      <td>15:22:00</td>\n",
       "      <td>2008-07-19</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-10-16 15:13:00</td>\n",
       "      <td>15:13:00</td>\n",
       "      <td>2008-10-16</td>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-10-16 20:49:00</td>\n",
       "      <td>20:49:00</td>\n",
       "      <td>2008-10-16</td>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-10-17 05:26:00</td>\n",
       "      <td>05:26:00</td>\n",
       "      <td>2008-10-17</td>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-10-17 06:01:00</td>\n",
       "      <td>06:01:00</td>\n",
       "      <td>2008-10-17</td>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>-1</td>\n",
       "      <td>2008-10-17 06:07:00</td>\n",
       "      <td>06:07:00</td>\n",
       "      <td>2008-10-17</td>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1567 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      classifications           date_time      time       date  year  month  \\\n",
       "0                  -1 2008-07-19 11:55:00  11:55:00 2008-07-19  2008      7   \n",
       "1                  -1 2008-07-19 12:32:00  12:32:00 2008-07-19  2008      7   \n",
       "2                   1 2008-07-19 13:17:00  13:17:00 2008-07-19  2008      7   \n",
       "3                  -1 2008-07-19 14:43:00  14:43:00 2008-07-19  2008      7   \n",
       "4                  -1 2008-07-19 15:22:00  15:22:00 2008-07-19  2008      7   \n",
       "...               ...                 ...       ...        ...   ...    ...   \n",
       "1562               -1 2008-10-16 15:13:00  15:13:00 2008-10-16  2008     10   \n",
       "1563               -1 2008-10-16 20:49:00  20:49:00 2008-10-16  2008     10   \n",
       "1564               -1 2008-10-17 05:26:00  05:26:00 2008-10-17  2008     10   \n",
       "1565               -1 2008-10-17 06:01:00  06:01:00 2008-10-17  2008     10   \n",
       "1566               -1 2008-10-17 06:07:00  06:07:00 2008-10-17  2008     10   \n",
       "\n",
       "      day  \n",
       "0      19  \n",
       "1      19  \n",
       "2      19  \n",
       "3      19  \n",
       "4      19  \n",
       "...   ...  \n",
       "1562   16  \n",
       "1563   16  \n",
       "1564   17  \n",
       "1565   17  \n",
       "1566   17  \n",
       "\n",
       "[1567 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separating date columns \n",
    "t_df = secom_labels_df.copy()\n",
    "t_df[\"time\"] = t_df[\"date_time\"].dt.time\n",
    "t_df[\"date\"] = pd.to_datetime(t_df[\"date_time\"].dt.date)\n",
    "t_df[\"year\"] = t_df[\"date\"].dt.year\n",
    "t_df[\"month\"] = t_df[\"date\"].dt.month\n",
    "t_df[\"day\"] = t_df[\"date\"].dt.day\n",
    "\n",
    "t_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of failure: 104 \n",
      "The number of pass:    1463\n"
     ]
    }
   ],
   "source": [
    "result_fail = secom_labels_df[\"classifications\"].value_counts().get(1, 0)\n",
    "result_pass = secom_labels_df[\"classifications\"].value_counts().get(-1, 0)\n",
    "\n",
    "print(f\"The number of failure: {result_fail} \\nThe number of pass:    {result_pass}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of Features' data:  (1567, 591)\n",
      "---------------------------------------------------------\n",
      "   feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  \\\n",
      "0    3030.93    2564.00  2187.7333  1411.1265     1.3602      100.0   \n",
      "1    3095.78    2465.14  2230.4222  1463.6606     0.8294      100.0   \n",
      "2    2932.61    2559.94  2186.4111  1698.0172     1.5102      100.0   \n",
      "3    2988.72    2479.90  2199.0333   909.7926     1.3204      100.0   \n",
      "4    3032.24    2502.87  2233.3667  1326.5200     1.5334      100.0   \n",
      "\n",
      "   feature_7  feature_8  feature_9  feature_10  ...  feature_582  feature_583  \\\n",
      "0    97.6133     0.1242     1.5005      0.0162  ...          NaN       0.5005   \n",
      "1   102.3433     0.1247     1.4966     -0.0005  ...     208.2045       0.5019   \n",
      "2    95.4878     0.1241     1.4436      0.0041  ...      82.8602       0.4958   \n",
      "3   104.2367     0.1217     1.4882     -0.0124  ...      73.8432       0.4990   \n",
      "4   100.3967     0.1235     1.5031     -0.0031  ...          NaN       0.4800   \n",
      "\n",
      "   feature_584  feature_585  feature_586  feature_587  feature_588  \\\n",
      "0       0.0118       0.0035       2.3630          NaN          NaN   \n",
      "1       0.0223       0.0055       4.4447       0.0096       0.0201   \n",
      "2       0.0157       0.0039       3.1745       0.0584       0.0484   \n",
      "3       0.0103       0.0025       2.0544       0.0202       0.0149   \n",
      "4       0.4766       0.1045      99.3032       0.0202       0.0149   \n",
      "\n",
      "   feature_589  feature_590  feature_591  \n",
      "0          NaN          NaN          NaN  \n",
      "1       0.0060     208.2045          NaN  \n",
      "2       0.0148      82.8602          NaN  \n",
      "3       0.0044      73.8432          NaN  \n",
      "4       0.0044      73.8432          NaN  \n",
      "\n",
      "[5 rows x 591 columns]\n"
     ]
    }
   ],
   "source": [
    "#Create column names pandas dataframes features data\n",
    "f_column_names = [f\"feature_{i}\" for i in range(1, 592)]\n",
    "\n",
    "secom_features_df = pd.read_csv(f_file, sep=r'\\s+', header=None, names= f_column_names)\n",
    "print(f\"Dimensions of Features' data: \", secom_features_df.shape)\n",
    "print(\"---------------------------------------------------------\")\n",
    "print(secom_features_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.0 DATA PREPARATION\n",
    "\n",
    "Merge the Features and Labels data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of merged data:  (1567, 593)\n",
      "   classifications           date_time  feature_1  feature_2  feature_3  \\\n",
      "0               -1 2008-07-19 11:55:00    3030.93    2564.00  2187.7333   \n",
      "1               -1 2008-07-19 12:32:00    3095.78    2465.14  2230.4222   \n",
      "2                1 2008-07-19 13:17:00    2932.61    2559.94  2186.4111   \n",
      "3               -1 2008-07-19 14:43:00    2988.72    2479.90  2199.0333   \n",
      "4               -1 2008-07-19 15:22:00    3032.24    2502.87  2233.3667   \n",
      "\n",
      "   feature_4  feature_5  feature_6  feature_7  feature_8  ...  feature_582  \\\n",
      "0  1411.1265     1.3602      100.0    97.6133     0.1242  ...          NaN   \n",
      "1  1463.6606     0.8294      100.0   102.3433     0.1247  ...     208.2045   \n",
      "2  1698.0172     1.5102      100.0    95.4878     0.1241  ...      82.8602   \n",
      "3   909.7926     1.3204      100.0   104.2367     0.1217  ...      73.8432   \n",
      "4  1326.5200     1.5334      100.0   100.3967     0.1235  ...          NaN   \n",
      "\n",
      "   feature_583  feature_584  feature_585  feature_586  feature_587  \\\n",
      "0       0.5005       0.0118       0.0035       2.3630          NaN   \n",
      "1       0.5019       0.0223       0.0055       4.4447       0.0096   \n",
      "2       0.4958       0.0157       0.0039       3.1745       0.0584   \n",
      "3       0.4990       0.0103       0.0025       2.0544       0.0202   \n",
      "4       0.4800       0.4766       0.1045      99.3032       0.0202   \n",
      "\n",
      "   feature_588  feature_589  feature_590  feature_591  \n",
      "0          NaN          NaN          NaN          NaN  \n",
      "1       0.0201       0.0060     208.2045          NaN  \n",
      "2       0.0484       0.0148      82.8602          NaN  \n",
      "3       0.0149       0.0044      73.8432          NaN  \n",
      "4       0.0149       0.0044      73.8432          NaN  \n",
      "\n",
      "[5 rows x 593 columns]\n"
     ]
    }
   ],
   "source": [
    "secom_merged_df = pd.merge(secom_labels_df, secom_features_df, left_index=True, right_index=True)\n",
    "\n",
    "print(f\"Dimensions of merged data: \", secom_merged_df.shape)\n",
    "\n",
    "print(secom_merged_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Histogram of Volatilities of Features\n",
    "# Select numeric columns for calculating standard deviation\n",
    "numeric_columns = secom_merged_df.select_dtypes(include=['number'])\n",
    "\n",
    "# Plot histogram of volatilities of features\n",
    "plt.figure(figsize=(10, 6))\n",
    "numeric_columns.std().hist(bins=20, color='skyblue', edgecolor='black')\n",
    "plt.title('Histogram of Volatilities of Features')\n",
    "plt.xlabel('Volatility')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Histogram of Percentage of Missing Values of Features\n",
    "plt.figure(figsize=(10, 6))\n",
    "(secom_merged_df.isnull().sum() / len(secom_merged_df)).hist(bins=20, color='salmon', edgecolor='black')\n",
    "plt.title('Histogram of Percentage of Missing Values of Features')\n",
    "plt.xlabel('Percentage of Missing Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Frequency Distribution of Target Values\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='classifications', data=secom_merged_df, palette='viridis')\n",
    "plt.title('Frequency Distribution of Target Values')\n",
    "plt.xlabel('Target Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Or3o8nq3wbp54PB6Ki4tJT0/nt7/9LaWlpZx99tmMHDmSUaNGEQqF6NSpE1u3bqVNmzZ8//33AHTo0IHvvvuOyspKm9sUIT09nbfffpvf/va3vPPOOxx66KHceeedjBw5klAoRH5+Plu3bsXj8USu3bS0NHJzc/nuu++aePRGQ6nPtZ2fn8/27dvJzMyMLCZOT0+Pu4DYMBqB+4PB4B+S2cGLXY9M+A/WKd+uSbk70MYyoN8HRqnqpw2sNxS4RlVHN7BemqrWO6BNRAbgxHB/WN2AFpF2nTp1erNDhw4909LSMvfFyxBeXLh582bGjh1bdwXDMAzDMIyGUQKcFwwG/5fMTsyAdki631xEZgA/AOaIyAQReUgc5cK3wsqBrlJgwFUUfFNEBrrVbwZ8rqLgla5a4D1RbT/nGtmIyC4R+ZuILAdOEJFfy17Vw/vEkXKMiaouU9Wv45y+KTs7+yWcPNMN+qcJJ+fv168fb7/9NgUFBUyaNKkhTRgpQPRj3tdff52ePXtGHvOeccYZFBcXc+KJJzb1MI0EEB3CcfTRR9eY59NPP52MjIymHqaRIOpzbZ955pm1CmWlp6fHFFwyjCSQC9zn9XrzktlJMtLYpSKN5YH+DEd58CrgPVV9TEQ6AG8Ax+AYpiFVLXUzdzyhqv2re6BF5HxipJpT1YUiosBZqvpfETkMuBX4maqWi8i9wDJV/Xcd49wV7YEWkWOAiX369HmlsrLyTo/Hk1FfD3Q43nnGjBm8+OKLlJSUVPFE//KXv6S0tLR+H6BhGIZhGEbtVAKfB4PBQ5LZyUvdf5Rww/Hk9atTzopu7Mjt4cC1IvI2sBBH9KQnThqSB9zUcjNx8ko3lErgaff1MKAfsMLtaxiOF7zeiIgHuBO4GiAUClVQiwdaVavEsQYCAUaNGsXs2bMpKSkBnIWFAwcOZM2aNRQVFdGtW7eGDMlIYa677jq6dq1LJNNIdfx+PwMGDGjqYRiNiM250YxIAzp7vd6fJrMTT7okfEtFGtsDXYyTvDpY7fwkHBnwv+AY9aWqmh7DA/1rnEWFF7v7LwFTXA90tADLZUA3Vb2ugeOMbqM98DGwC6Bt27bdDjzwwAz3XIM/g+oUFRWRnZ3NtGnT2LBhvwVxDMMwDMMwKoLBYFLjyF499OiEG44nffhOylnRje2BLgYuE9cCdUMkANoDX6tqCEcwJRyv/B17hVnAEXDpKyIeEekBHBenn5eBn4tIF7efTiJyUEMGqqo7VLVAVXv16dNnSyKNZ3C8FldddRUPPPAAa9euZejQoQlp10g9JkyYwIoVK7jvvvvo2bMnaWlxw/WNFCZ6ng866CCys7PjlrWUaKlHv379yM3NRURqzHVOTlKF4QwjGgu4byQa2wP9PXAXMBBHofAzVR3txj0/jbOC9FXgMlVtKyIZwHygAHjYrfsY0BdYA3QFJlX3QLt9ngVch3OTUA5coqrL4ozvVuBXQDccffQHVXVS+LzX670kFArdLSJpyfhhM2+0YRiGYRgJQIPBYFKdo4sO65tww3HIurdTzmtgSoT1wOv1XoKzKLGm8kmCCCsXFhUVxZUANwzDMAzDqIVQMBhM6mNMM6AdUlP+pfEZjLPgMWn4/X7ef/99brvtNtasWcMdd9xBYWFhzMf5ImJpkVKY8KIjj8e5/MKPey+77LLIMSP1iTfP9913H126dLEwjRbGM888w9ChQ0lPT2fixImsWLEiEp5n17XRiHi8Xu/RyexA0iThWyrSqq5qEVnu5oWO3n5UV71gMHgW8F8amAe6oTz88MOUlZWRnp5OmzZtIkp148aNo7CwMJJrVFUJhULJHIqRRIqKili2bFlkDqdOncqzzz5L7969ycrKwufzMW/ePAYMGBDXyBKRWmNojaYn3jwfc8wxHH744fWa54yMDNq1a1drnmGjebBixQpKS0trZGQKM2jQoCo3VB6Pp8q8ezwe2rZta3NtJIL1TT2A1kCrl/IWkVyc1HmH4KTCm6uq17rnegKPdOvWrW+bNm3aO4eSf6dUXbmwX79+rFu3LpIOD5wvW4/HY5KxhmEYhmFEUxgMBpO2oGrJ0T9OuOE46J03U84N3Vge6IuBUxtiPLv0wlnc1yBqUx2MwzRV/SGOqMuJIjLKPT4R+G/btm0nquq2ho5jXwnni27Xrh3r1q2jffv27N69u0qZUCgUMZ4ta4NhGIZhGC7HJ7Nx8UjCt1Sk1Ut5q2qJqr7qvt4DvAl0D58G8oDBIpIUaczq4Rl+v5/TTz+dqVOn4vf7eeaZZxg9ejSdOnWK24YtBG15VP+/MFom9Z1nEbE42hZAx44d65zr8NNFw9hHFHi3qQfRGjAp76rj7IBjQJ+sqp+IyIHAggMPPPCQNm3a5IhI3FjF6M8x0WEeRUVFnHLKKeTk5LBkyRKKiooYPHgw8+fPZ+vWrVXiodPT0y2swzAMwzBaL+ODweB9yWp8af9jE244Dly5IuXc0I2dymE4MFZErnH3w1LeG4B7RKQvThxyn31oO56UN0AOsLG2yiKSDjwB/J+qfuIePht4uF27dqWhUOgmoKOqxjSQkxkbPW/ePEKhEG3btmXQoEE8//zzBAIBFixYwIknnsiIESO4+eabKSkpiXiqqi8yTEtLQ0TMuDYMwzAMw9hPGtuAFuDMOFLe3wJH40p5x6lfQdWwk+g0BKWqWhnVzyMNlPK+H/hQVe+KOnYBMBIY6/F4spoqVCIQCBAIBPD5fAwaNIi0tLRIJg6PxxNZ9R0eX2ZmJqWlez9CEaGysjJe84ZhGIZhtBzeTGbjnhRNO5doTMrbOT/FHcOfqp36AsebPVhVmzwYNRAI0L9//0gqrEsvvZRFixZx/fXX8/3336OqlJeXVzGewWKkDcMwDKOVECLJKXdtEaFDYxvQNwEZwLsissbdB7gXOE9EluGEb3zvHn8XqBCRd0TkSuA14FNgNTCNOHdZqvoeTgaNBSLyLvAicGCssiLSHZgAHA686S46vNA9fTXw+127dg1XVY9bfp/ffKLw+/0sXLiQ4cOHM2nSpKYejmEYhmEYzQMlyQa04WBS3vXAlfKeRpLVCOvC5/MxZcoU8vPzWbx4MaWlpQwYMIAFCxZEDOnohY4mtmIYhmEYrYoy4OBgMPh1sjpYMWhAwg3HY5csa3rvZAMxPej6MRjIbOpBBAIBhgwZUuN4UVERS5cujQivxLopEhFycnKorKykrKysMYZrGIZhGEbjkoWTB/rZJh5Hi6dRQjhE5HIRWScijzewXi8RabCQSi3t1SrlLSJz3NCS8P5VIvLerl27RoRCoWbrrZ81axYXXnghq1atinne4/HQoUMHSkpK2LNnD5mZse8FmkN4imEYhmEY+8yuYDD4bDI7kDRJ+JaKtColQlU9XlX7VttWu3V+BuyqVuUtoH/btm2L3TzTzZKVK1fy3nvvkZWVxWuvvcZpp51W5XwoFOLII49kypQpFBQUUFFRgc/nY/78+dx999306NGDrKwsBg0aFAkRiUc849swDMMwjCYn2+v1JkX4LYx4PAnfUpFWr0To1m2LI/IyJfq4qr6qqiXAYlXdmrhPJTn4/X5OPPFETjzxRFasWMG7777L2LFjqxi9mZmZeDweAoEAo0aN4uqrr+bLL7+krKyMQCBAcXExL7zwAkOHDsXj8UTirgsLC8nMzOT4449nypQptG/fPu44zJNtGIZhGE3C7mAwuLOpB9EaSLoBrarjcYRSTgLaAK+o6rHu/m0i0gZH5OQUVf0xcBbwf271a4GA6ym+s46u2gBrVPV4YIvbzomq2hdHZKU27/dNwO1ASZzzg0UkvpZ2M6Nr1660adMGgI8++og9e/ZEzpWVlVXJCR1t7Pp8PoYPH87gwYNZuHBhzEWI4fLVlRej22muoS6GYRiG0cIp93q9+yJGV28sjZ1Dq1cidPvsrapXikivWGXKysoOysjIkNo8q8mU8m4oF1xwAUVFRQwdOpR//OMflJeXs2rVKhYvXsyjjz7K9OnTmTdvHuXl5ZSXl0fqBQIBli5dWsXADou4hFm8eDGLFy+u0p8ZzM0Pn8/HhAkT8Hg8zJw5kwceeKCph2QkEZtvwzBcPsURpfugqQfS0jElQjgB6Ccin+F8Hl1EZKGqDnXHdjLQvlevXn/IzMy8P+4bayZhCz6fD7/fz/Tp0znggAMYMGAAlZWV3HXXXRQXF1NcXByR+45GRFBVUyxsIQQCAUaOHNnUwzAaCZtvwzBcfgSsS2YHpkTo0OqVCFV1uqp2U9VewCDggyjj+RjgvkMOOeSTzMzM6fvyhhuTsPH8yiuvcM0117Bnzx6GDBnC3LlzueGGG5g0aRIZGRmISEQG/Prrr+f0008nNzcXn8/HTTfdRFZWVpU2p0yZQo8ePcjJyeEnP/kJU6ZMIS9v7xqF6iEchmEYhmE0Cf8JBoNr6i5m7C+NkprN9e72x1EYvAsYiOMl/kxVR4vIoTjhFyXAq8BlqtpWRDKA+UAB8LBb9zGgL7AG6ApMUtWFIrJLVdtG9XkWcB3OTUI5cImqLqtjnL2A51T1SHf/JeBHHTt2LO/YsWPXtLS09FQyFH0+HyNGjGDGjBlcdtll+Hy+SK5oo2Vjj/RbFzbfhmG4bAUuSGYqu3dGDk644Xj0/MWpY1y5NNvcxs0Jr9f7FPBTmoGYyv5SVFTEKaecQlZWFhdddBHLly+vNYY5HNphGIZhGEaz5xfBYPB/yezg3VOHJtwoOOqFhSlnQKdm8r3GZzFOjHbKM2/ePESEtLQ00tPT6zSOzXg2DMMwjJThh009gNaCKRGK/EhEFopIMOpYl6g6v9y0adPEioqKA1LBmBw3bhyFhYXk5OTUOOf3+ykvL+e4447jiCOO4NRTT+WOO+6oIpxy4403csYZZ5CWFjdtNl27dq0SJ20YhmEYRrPgT8nuwNLYOTRWDPT7wChV/bSB9YYC16jq6AbWS4vKyFGf8gvdflZWO34o8N/evXt/6vF4xqpqWirFQNdFONXdggULmDRpUlMPxzAMwzCM/ad9MsVUVo8+KeGG44+eezXljCtTIqyd3wP/9Hg8LwMlLcl4BscjvXDhQoYPH16rAe3xeGqkvTMMwzAMo9mR9Fy0njRJ+JaKmBLhXv6fa2xfH6WY0gfo88EHH/yqrKys2S+49Pl8zJs3jwEDBjB48GDmzZsXkfIOp6Pr3LlzRKJ7/vz5dOjQgbPPPpucnByWLl3KjTfeWKPN559/njFjxpCTk8PQoUOZMmVKlbAPoNaQD8MwDMMwGoW3ki3lbSEcDo2dxm4+jvhJhXuqEzACV4kQJz1dJdBHVXOrh3CIyPlAf1W91N1/DpjmprGrALJUtVJELgX87FUfzAGeUNVJccZXqKpfiUg7nHR6j6nqv932y4FfHnLIIUs9Hk9/t3yNNpqLEmE4nVWnTp24+uqree2116pIcns8HlQ15uLA6JCOadOmUVJSUkPOOzMzk7y8PDZv3pz092IYhmEYRr3ZAWQBPYLBYNJ+pN87Y1jCDcfDn3k55azoxn4uH1Yi7OtuPVV1HXAle5UI+xM/XVxDlQjD/XjjGc8AqvqV+/c74D/sFWhZD8zu06fPY2lpacfEqw97xUSaOswjEAgwdepUFixYwCeffFLDMxwKheJm1giHdHTu3Jn8/HyysrIinuvCwkIyMzM5/vjjufLKK6sIqRgtF5/Px29+85tanzA09f+8UTvha/ivf/0ra9eu5YEHHqBLly415s3n8zF16lS7tg0jdWmPYxe1ravg/iAeT8K3VKSxPdBXAXk4QikqIseo6lsiciewXlVvF5HfAg85p6UfcIeqDnHbGQTciqMYWAisBcZWF1IRkcOB2TghHBtFpBPQTlU/jzG2dKCDqm52hVueAF5S1RkiMhI4u0+fPtmqeiauQmJrMBgami/aMAzDMIwmZzPQJRgMJu0He92ZpyS87cOefjHlDKvGNvtvAjKAd0VkjbsPcC9wnogsw4k7/t49/i5QISLviMiVwGvAp8BqYBrwZqxOVPU9YCKwQETeBV4EDowzpiyg2C33NvAVEJbxKga2bNq0abCqhuXH9+V9pxwNzRdtGIZhGEaTMyOZxjNYDHSYZr8wrjng9XpXAcfghIY0e8KPbDt16sTkyZOZM2cOxx9/PCNGjOCuu+5iy5YtqGrEQK6ocELShw4dyqRJk0hLS2PatGm88MILTJ48mezsbKZNm8aGDRsYMmQII0eO5Oabb2bHjh1N/E4NwzAMw4jCk2wDOnjWiIS3732qOCXsq2jMgK4nXq93AXBKU4+jKQgvLtyyZQtjxoxp6uEYhmEYhlETBb4BfpjMTBxmQDuYEqHI8dX2N4vIXW75LBF5qlOnTptCodCw1nqzMWvWLC688MK456svnuzUqVNjDMtIIq0lVMmoSXp6elMPwTCMfUOAYy2NXePQWN+UF7MPSoRAL+BXOJkx6k08JUI3R3Qs+kbVXQXMcncvALZ17tz5FVX9eUPG0JJYudIRaBwzZkzEG11cXMzkyZMBasRHb926tdHHaCSW1nqzaBAJ6TIMIyU5CGctl5FkTImw6lgPBboAAffQ6cAjwGLcnNKt3bAIp7obMWIEr7/+OmPHjiUnJ6eph2UYRgJ55plnGDp0qAkkGUZqocAhye7E0tg5mBJhVc4GntK9VnIh8CUwWEQK6vWGWwFhI/rNN99k2bJllJWVNfWQDMNIEOPHj+ebb77h008/NQPaMOpg3LhxFBQU4GkeRqAAPZt6EK2Fxg52Gw6MFZFr3P1snMneANwjIn1xlQj3oe1KHBVBgGFAP2CFG8uZw15VwtoYB5wbtR8dmBOiFpqLEmFj4ff7AWeBYXZ2NlOnTmXLli1NPCrDMPaXGTNmNPUQDCNlePLJJ5t6CNEojh0zNZmdeNKaxsZxtTnuxtHkeFBVb652/s/sdZamA4cBnVV1q6tH8h2OrVihqv33dzymRBiuIHI0kK6qq6IOr8eRxDwrFAqdX0f9ZqFE2Nj4/X5KS0uZM2cOxx13XN0VDMMwDMNIBgpker3eeLoXCaEpFhG6Ybj/BEYBhwNnu6J5EVT1trDdB1wHLFLV6EVZJ7nn99t4hsY3oIuBy8S1MkUkLI/dHvhaVUM4HuDwc8PvgHZR9T8D+oqIR0R6sFdyuzovAz8XkS5uP51E5KA6xnY2jgphNHOA87xe7xci8h+3rTqaaX34/X4WLVrEnXfeyWmnndbUwzEMwzCM1ogHKMEJP21pHAd8pKqfqOoe4EmcdWrxiGXTJRRTItzLL6n5Yf8LyN+0aVNOKBSqkdXD2EvYiJ4wYQJz5syJW85uQAzDMAwjaRyO44lOGslYRCgiF4nIyqjtomrdhtekhVlPnBsFEckFRrI3rBecz2SBiKyK0fa+fQ6tPatEffB6vZcARUBeU4+luRNOc7d582bGjh3b1MMxDMMwjNbGQcFg8ItkNf7p78Ym3HA8+KE5tXrXROQXwAhVvdDdPxc4TlUvi1H2LODXqjom6lg3Vd3gRia8CFymqov3Z8zNYtloCjAYJ8uHUQfhDB0FBQWcc07tiU9shb9hGIZhJJRyHO9s0mgiIZX1QI+o/e44CShiMY5qEQWqusH9uxF4hvghwPWmVRnQcZQIf1SPqouB3ckeX0vB7/czcOBAjjjiCF577bW4iwsrKy0qxjAMoznj9/sZMGBAvcLvYpXJzIyXE8BIEjuDwWCtWcP2lyYyoFcAh4rIwSKSiWMk14gXFZH2wBBgdtSxNiLSLvwaJyPcmv3+HBojhENELgf+CLypqvXJxxyu1wsYqKoNUiJsKCJyNuDHiZHZgOP63xx1/ueFhYUzc3NzLYa3gYTT3E2bNo0NG+LdLBqGYRiGkQAqgd7BYPCzZHXw+UU/TbjheND9z9ZpXInIqcBdOIkmHlLVqSIyHkBVZ7hlzgdGquq4qHo/wPE6g5Pe7j+qut+p/hrLgH6ffZDydlUGr1HV0Q2sF1PKO07ZdByj+XBV3SwitwIl4bR3ItKusLDw49zc3AJn1wzofcEMacMwDMNIOpXAwGAw+EayOvhi/M8Sbjj2nDEr5Ywrk/J2ckYL0Ma1jvOoGldzk6o+XVlZaXJ7+0E4X/TMmTPrzBft8XjIzs6utYzRvAk/9q1LnauwsDASC2+PelOb+s55586dI3OekZHRGEMzEoRd1ymBAJ809SBaA43lgf4MRyDlKuA9VX1MRDoAbwDH4IROhFS1VEQOBZ5Q1f7VPdCua76/ql7q7j8HTFPVhSKiwFmq+l8ROQy4FfiZqpaLyL3AMlX9d5zx/Rx4CCd93oc4ybYr3TzVE1X1zE6dOgULCgp6i0irihtPJOEMHQsWLGDSpElxy2VlZZGdnc2OHTsab3CGYRiG0QIIBoNJ9eZ+efGZCTcce9z7tHmg62A4cK2IvA0sZK+UdwbwgIisBmbi5DFsKPGkvN92938Qq5KIZODEZx8DdMPJPX2dayjfCVzt9Xqfys/PP5RaPi9VjWxGbMKLCzMzM1m6dGncfNFlZWW0adOGrKysBvfh8/mYMmUKhYWFpKc3tlK9YRiGYTQpIa/XW5DMDpKRBzoVMSlv6Augqh+rY/3+FxiIo4B4JLBw06ZNJ4dCzqLWeAZya5Xy3heiU93FM6I3bNjAnj17AEhPT2fcuHHk5+fX2XYgEGDixIl89dVXVFRUJHTchmEYhtHMUWBLUw+iNWBS3vAVcLiIdHb3TwHWqeoOVS1Q1V6dO3d+KRzPZQZyYog2om+88caYZcI3KxUVFTz55JNs2bKl3jF4hmEYhtEKESCevZOgHiTxWwrS6qW83eTak4HFbtm+OKqD0SwOhV3QRsIIh3RkZWXx2muvMXr0aCZMmMDixYuZPn06BQUFDB48mClTptC9e3eysrIoKipi2bJl2HQYhmEYRhUUJ5z186YeSGvApLzrgSvlfSuQ29RjaakUFRUxZMgQ/v73v/Pcc8819XAMwzAMI9UIAVuDwWDnOkvuB19dcVbCDcfCu59KOTe0PQevH4OBhq9oM+rNrl27+PTTT5k4cSLPP/88fr+fsWPHkpubGwnbiIWFchiGYRgG4Nh08VL2JgxbROjQKKMWkctFZJ2IPN7Aer1E5FcJHEdMKW8ROUtE3hWRta6QSrh8TxF59YMPPuizY8eO7eatTw4+n4/c3FyuvfZaiouLOeCAA+jZsyerVq2iX79++Hw+DjjggJhZNSyUwzAMwzAAJ4Tj0KYeRGvBlAhF8oG3gH6quklEHgH+raovi8j9wFt9+vQZqqo/Bzy2iDD51JYvum3btpSUlKCqpKWlWaYNwzAMw3BQXGXlYDC4M1mdfH31rxJuOB54+39SzrgyJUJnbB+o6iZ3/yXgTPe14igTGkkknLu5e/fuTJ48mUGDBvHOO+/QoUMHli5dynPPPUfbtm0REXbt2kUoFEJVzXg2DMMwjL0ocH8yjWdjL61eiVBEOuJk9RgErAeeAjJVdYyIHAgs6NChQ/eOHTvmpaenmwc6wYwbN44XXniBnTtjX+/1US/0eDwWymEYhmEY8F4wGDwimR188+dfJ9xwPOC2x1LOuGr1SoSqug1HifApIICTazrs2jwbeLhLly4T09LSdrvlY3ZuSoT7xpNPPhnXeIa9+aKHDx/O7NmzY5YJhUK2mNAwDKOZEv2UMS/PHuomGa/X67UPuRFobK3jsBJhsMpBkUnsVSL0AKVx6jdUifC6+gxKVecCc92xXIRjjANcAIwEpnk8nuzajGPzTO87Z511Fs8++yxlZWUxz/v9fsDxRt9xxx1MmzaNDRs2VCljHmjDMIzmSSAQIBAINPUwWgtbkh3CIR6zd8CUCHHPh8t1BC4GHnRPfYHjvV4cCoUspihJPPXUUxx55JFkZdWeKdDv91NaWsrMmTM57bTTGml0hmEYhpEytE92B+KRhG+pSKtXInS5W0Tec9u/WVU/cI9fDfx+165dU9yYbfM0J4lVq1bF9UBH4/f7WbRoEcOGDSM/P78RRmYYhmEYKcN2r9db0NSDaA2YEmE98Hq9T+Fk5kh6gnKj/hQVFZGdnc3UqVPZsmVLUw/HSCITJkzgpz/9KW+++SbXX389mzdvjhm2IyKISCQm3uPxsGfPnsYerpEAwnOem5vLJZdcwqJFi2KuMUlPT0dEyMzMjKS4NFKD6nO8ePHiuOF46enpdO/enc8//zwyx2lpzk9yZWW9sta2Fr4BugWDwaRdCBsnnJ/wtrtMfTjlvJNmQNcDV8p7ArV7sY1GJpyhY/PmzYwdOzZmGY/Hg4jYF6xhGIbRGrgjGAxencwOzIB2SGgIR3NRHKyln69EZI+IVFZTIhzs5p+uEJGfR5XvKyKvb9q0aUJFRUUXu9loXoQzdBQUFMRNcRcKhcx4NgzDMFoLVya7g/CTvkRuqUiiY6AvBk5V1XMaWK8X0GADuhZxlHicCRwE7FbVvu62Gmex4PnAf6qVLwF+07lz50BaWloI4qexM5oGv9/PwIEDyczMZOnSpXFT3RmGYRhGKyCU7Bho8XgSvqUiCRt1CigOoqrLVPXrGMc/U9V3gVC14x+o6oduv6l5i9RKiPZG15ahw/JFpz4+n49FixYxY8YMunfvXsN7kZGRwW9+8xtWrFjBr371K3r27El6etWMnSJCTk5OYw7b2A98Ph+33norBx10EG3atKlxHWdkZHDuuedy1lln0aNHDw4++GAyMjKqlMnOzqZNmzaNOWyjAURf1z169IjEN4cJz3FxcTFnn302PXr0iHkN2xyTBtjCj0YgoTHQzVlxsNo4d6lq2xjHHwaeU9X/RR/3er2rVPXHUeUa+tEY1fD5fPj9fqZPn87zzz9fI8wiMzMTVaW8vLxB7YYXFsbKFW0YhmEYLZzSYDCYVO/AlkkXJvxRfP6kB1POsEqWO67ZKQ7uDx988MHoPe5S/njGsykRNoxAIMCoUaOYM2dOzBjlPXv2NNh4BscTfdVVV3HppZdyxx130K1bt0QM12im+Hw+5s2bx4ABA2Jem3l5edxzzz3Mnj2bIUOG1Djv8XjsqUSKkYg5N5o/gwYNYsCAAXHnKy8vj3/84x/85Cc/qfG0AVr1PJdaGrvGIVke6GLgV3EUB9sCf8FVHFTV9Bge6F8DA1X1Ynf/JWCK64GOeI9F5DKgW30VB6PGUW8PtIjkdevW7bM2bdq0F5FWe0WmIpbmzjAMo2UiIuawik/7ZKoRbvnbRYn3QN9wv3mgXZql4mBDEZFM4JnKyspXRGRroto1GoewcuHcuXPjZukwDMMwUg8znuPyPZCZzA5MidAhWQZ0s1QcFJFbRWQ9kCsi612POCJyrHv8F8B9IrLWrfJLYPCePXsGlJeXt7MQjdQjvLhw+PDhzJkzJ7JQZfr06TEXm6SlpTF06NDII+JYiAhZWVlxw3la8aNDwzAMo2nJAOyRayNgQir1wBVSuQI4tKnHYuwb0aIrt912G36/n1deeYWTTz6Zf/7zn8yfP5+//OUvrFmzhhdffJHvv/8+blsejyeuWpZhGIZhNCEVQJtgMJi0TBzbpv4x4YZjxwnTU84Nba6y+vE7oHdTD8LYd+bNm8eOHTsAuO+++/j000+56667GDFiBHPmzGHPnj1MmTKFuXPnctxxx9XqgQ6FQhGFw1iYB9owDMNoItKB+LlcjYSRXneR1MPNEZ1V7fC5rmjKvvARcDR7Y7aNFCMQCFBUVMSIESP45JNPOPbYY7n88ss5+eSTmT59OvPnz+fPf/4za9euZd68eYwaNSpuW2lpabWqG5p32jAMw2hCkhtakKIxy4km0Vk4Lgf+CLzZEDVCEemFk3WjuhJgQhGRqcBvgI7RWThE5CrgQpxHH5uA36nq5+65noWFhW/k5uZ2AcKLIpM5TKMRKCoqYsiQIVx55ZW88cYbTT0cwzAMw0gU3YPB4FfJanzb3y9OfAjHdfemnGHV2qS85xI7o8dbOMItRwH/wxFnCfNvYKaI7Gjo+Izmi9/v58QTT+SnP/0pd9xxB/n5+U09JMMwDMNIBAnLRhYLk/J2MClv5/irqlri7i4Durt9HQ6kt2nT5n0gU0TM+9zCCKe6mzBhgomuGIZhGC2B7GQ2bmnsHBJmQKvqeGADcBLQBnhFVY91928TkTbARuAUVxb7LOD/3OrXAgFV7auqd9bRVRtgjaoej5Oq5SzgRFXti6NS2FDvd3UuAOa5r/sA27///vvrVDWntjR2pkSYGowbN46CggI8Hg+dOnXi/PPP584776SsrIxrrrnGPNGGYRhGKlOB81TfSDIm5R2Fq4DYH7jNPZQO+DIyMu4HdtdRF/NQN3+efPJJNm/eTCgUYuvWrTz88MNs2rSJ6667rl6iK2lpabVm4DAMwzCMJiQdWFtnqf1BPInfUpBkjVqAM12Pcl9V7amq64ArgW9xMlr0J75aTkW1sUU/jihV1XAKBAEeierHq6qT9mnAIicDE4CxqlrmHl4PvJWZmblFROKnXTCaPT6fj3nz5vHnP/+Z2267jS5dukSM4TDRoitz586N2U44+0ZmZqYZ0YZhGEZzpGNTD6A1YFLee8d3H47xvDHq1AqcjB0XVRufkWIEAgFGjRrFbbfdxp///Gc2btxIZWVljZRzfr+fgQMHsnr1al577TVOO61mOs1QKERZWZmF67QQwjdXY8aMqfOmyG6aUp+ioiKWLl3K7Nmz6yxrOd2NFCWpP04WA+1gUt4OtwFtcbJtvC0ic9z2K4FrNm/eXBCy5L6tCr/fz6JFi5g0aRLvvvsuAwYMiGk82Q9s6hMIBJg9ezZTp05lyJAhtc6pzXfqE37S1K1bt1qvbbCc7kbK8m1SW/d4Er+lIAkVUlHVXlG7f4hx/kPgqKhD17nHy3Hil6OJuRgwOn+zu/8U8FQ9x/cX4C8xjp9cS50XgUKv17sEOLE+/Rgtg3nz5jF8+HBef/11Pvroo5geZ/uBbTm89tprvPfee7WWqU1Ax0gd6nNtG0YK80lTD6A1kFAhlZaM1+tdDxQ29TiMpqGoqIjs7Gxuv/12vvoqafnpjSbG5/MxYsQIZsyYwdatWykpKam7kpHS2JwbLZCLgsHgA8lq/Lu7r0644djuittTLo4joX5zEblcRNaJyOMNrNdLRBospFJLe8vdUIzo7UciMlVEvhSRXXHq/VxEVET6Rx07r1OnThsrKysPrH6zEY6li15w5vf7Of7446uU83g8ZGVVVxY3UolwvuiZM2fy8ssvM2/ePMaOHUt6es2HON26dSM3N7cJRmnsL4FAAI/Hw3//+1+ee+455s2bF/cRf0ZGBtnZSU23ajQCgUCA4uJiHnzwQYqLi1m6dGncOT/ggAPIyclpglEaRoN4p6kH0BpokUqEqnp8VGaO8Laa+EqEiEg74HJgedSxTsCNBQUFGzweJ8o92oieNWsWF154YWR/8ODB7Ny5k+XLl5OVlRUR5ggvOisoKKjRb1paQ8UUjaZi1qxZXHTRRXTp0oVf//rXzJkzB4/HU+UH9dhjj2XDhg0UFBTENK7BFqI1d8Lz3LlzZy644AKWLVtGfn4+eXl5gDN/J598MuXl5ZSWlkbioqvHR1u8dGrg8Xi44YYbuOiiiygrK2PBggUsX76ciy++OHKtDh06lOzsbL755hs6d+4cc27tujaaCRUkO4TDYqCBBIZwuEqEvwOCwJPAIcCPcOKsJ6nqbBHpBTyKI4YCcKmqLnUXFR6Gs3DwEWAbjrT2pW7bzwHTVHWh6z2+AxgBXI1jfF+OkxJvOXBxVJq7eGPdVT2WWkTuAl4CrgGuUdWVInI2MFRV/+D1eleoan+3bPX2EBGLh20lFBUVMXToUDZv3szYsWNjlsnIyKC8vLyRR2YkEo/Hw+DBg/nrX//K5MmTWb58ucXKtmDS0tK4+eabOfHEE+3aNlKZ+4PBYI01aIlk1z//kvAvwraX3Jpyd6CmREgkjV0PVX2u2qlC4Ev39Tfx6quqGc+tiPAq/oKCgriiK+Xl5eaBTHFCoRALFy6kqKiI0aNH061bt7hPFYzUp7Kykj//+c8MHDiQ2267jSlTpsRUJjXj2WjmxE2KYCSWVq9EKCIe4E4cb3aN0wBer/cpYGRt7ZhnqnURNqJPO+00pk+fHpEHjyZ8U2WPdlObcIzsf/7zH/74xz/WakRnZGQ04siMZBEIBJg4cSJbtmxp6qEYRkPp4vV6f5rUHkyJEDAlQnAEUo4EForIZ8AAYI67kHA90MMtV6uFHMtI8vl8zJ8/n+LiYg4//PCIYMPYsWNrjX22uOjUwO/3c+yxx7Jt2zbmzZvHO++8U2Wup0yZwhlnnMGUKVPixr/bXKcGgUCA1157jd/85je89dZbMed56tSpdO3atYYRbfOcuhQVFbFixQpWr17N1KlTycvLqzHnsa7taOwG2mhkKoLB4LNNPYjWQKtXIlTVHapaoKq93DzWy3AUCVe672P4Bx98MP7TTz89cF/FVFauXMn69esBWLVqFatWrarVY225ZlOLefPmISKkpaXRpk2bmLmEY813LCVEo/ni9/t59913SUtL48ADD2TXrqrJfPr370+PHj1qPHmorKy0azpFib62hwwZws6dO6uc79+/Pz/84Q9rDddSVTOijcYkz+v15iW1B48kfqsHIjJSRIIi8pGIXBvj/FAR2RGVfe2G+tbdFxKaB9r14PbHURi8CxiI4yX+TFVHi8ihOOEXJcCrwGWq2lZEMoD5QAHwsFv3MaAvsAboirMQcWH1BYAichaOIIsHKAcuUdVlccZ3K062j2448doPVvdYi8hC3EWE7v7vDj744PvS3ee2+/JFGM4zOn36dL799lsqKioa3IaRGoTzRU+bNo0NGzY09XCMJGHXdOujPnMuIhbOZzQlIUCDwWBSF2vsmnFd4hcRjv97rcaVm3XtA+AUnOiAFcDZriJ1uMxQHPttdEPr7gsmpFIPvF7vJTjZOTqzN4OIYcSkqKiIIUOGUFRUxPPPP9/UwzGShM/nw+/3M336dObNm2eLy1oB0XP+wgsv1Hrj5PF47AmT0dhUAt8Fg8GOyezk+/smJNxwbPOHqXUZ0CfgOFJHuPthJeu/R5UZSmwDus66+0JqRm43PoNxvNYNyqAfjoGuHjtXWFhoGRpaED6fj0WLFrFmzRpef/11OnTogIhw7LHHRuLejzjiiEh5e5ybuowfP57Vq1ezevVq0tPTeeuttxg6dCinnXYa8+bN44QTTog5vzbnqUn1a3vLli0UFRVx3HHHMWbMGIqLixk0aFDM73Mzno0mIA24Kem9JCGEQ0QuEpGVUdtF1XqNzooGjic5ljr0CSLyjojME5HwD2996zaIhLr5ReRy4I/Amw0RU3HzQw9U1f8kaBzLgerSf+cC44DfAB2rhYGcD9wGhDWa71HVB8PnVbUP7oLHhvwQBgIBRo6sNXmH0cJYtmwZN954I0cffTQTJkxg5MiRLFiwgE8//ZT09HQqKioij3jDP7r2Q5t6vPbaa7z11ltUVFQwYsQIRIQ333yTL774gtzcXEpLS6vEPduTvtRn2bJlrF+/nvz8fPr378/JJ5/MggULWLFihV3DRnOhFJiKo5WRUqjq/cD9tRSJZXxV/2J9EzhIVXeJyKnAs8Ch9azbYBIdA/0+MEpVP21gvaHEcLvXo15aXaIp1coPAD4HPoxhQEeEW6rTpUuXV9u2bds3PT29QyI9SWeddRZz586lpKQkYW0azYuw6EpxcTGTJ09u6uEYSST68f7zzz9vCwdbOHZtG82QChwvdJdgMLg5WZ2U/OuGhHsEci/4236HcMSo8xnOurxDG1q3PiQsjsBVIvwBTgq4CSLykIisEJG3ROR0t0wvEQmIyJvuNtCtfjPgc1dNXiki54vIPVFtP+ca2YjILhH5m+tlPkFEfi0ib7h175M48t4AqrpMVb9u4PvqFwqF2nk8nngp9/aZp556yoznFk44X/Tw4cM57rh4yWRM9rklEAgEKCoq4thjjyUrq/oDMKOlEb62R4wYwezZs+ssb9e40Qik43hb9zT1QJLACuBQETlYRDJxIgrmRBcQkQOisr8dh2PjbqlP3X3BlAj3cqaIvCsi/3NT54VFVm7v0KHDBo/Hk11HfaMWxo0bF1NspDXg9/s58cQT+elPf8odd9xBt27dapRpqY+Ao9cB5Obm1jifl5fHPffcw7333kvv3r2rnMvKymL8+PFxw6B8Ph833XRTzHYB2rZty7333stpp51Gu3btqpzr2LEjmZkJvycmEAiwYMEC/H4/hYWFrVJYJXrOY91ItG3blunTp3PllVdy4IEHVjknIpx66qn86le/itluOI9+LDGbzMxMpk+fzuzZs7noootqfNccccQRdOvWjezsxH2V+/1+Bg4cyOmnn15n2VS+xn0+Hy+88AK9evWK7Ic1Dnr27ImIkJGRwamnnsrUqVPp2LEj99xzD3PmzGHmzJl06dIl0paI0KtXLy6++OKY+dHD83z88cfHHEv4/+fZZ5+tsrYEnOu6Y8ekrp9LBSpJdrIDkcRvdaCqFcClOOmF1wH/VdW1IjJeRMa7xX4OrBGRd3Dsy3HqELPufn8MSUpjNx9H/CS8RLkTMALHwL4HJz1dJdBHVXOrh3BUD6kQkeeAaW4auwogS1UrReRSwI9jmIOzyO+JusRUYqTCywd2qWqZOxG/VNWfuO3n9unT5/tQKHSHiGTGC+GI/hzrG+YRfuQ7efJkli9fHmnD7/ezZs0aXnzxRXbv3l2vtozUoKioiFNOOYWVK1cyZcoU+vTpw6RJkyJiDH/4wx9YsmRJlTppaWmEQiGLo00hwguG161bx9SpU+ndu3eVeZ40aRKzZ89mz56W6ChqXUTP9fXXX89hhx3GTTfdFJnr8ePH89prr6W0AW2kDAqUAaODweDLyeqk5OHJiQ/hOP/GlFtpnaxcgWElwmCVgyKT2KtE6MEJeI9FQ5UIr9ufwapqtF7rA8At7usTAN+uXbs6tWnTJtMtm7BV9oFAgFGjRtU4XlRU1OC2jNRg3rx5DB8+HI/Hg4hUSYMVCAR4++23a9SxWNrUpE2bNpG8wNHzvGTJEhYuXEhFRYXlDW7hLFmyhM8++4ycnBx2795tRrSRbMTdtjfxOFoFyTKgw0qEl6mqisgxqvoWjhLhelUNich51K5EeLEbQlFI7UqEs0XkTlXdKCKdgHaq+nlDBisiB0bFRo/FcfETziTi9XovCYVCt4hIG0tHZewPgUCA/v37U1RUxH//+182b97MkCFDYpa1PLKpS3ieYe+Tpttvv93yRbdAAoFAlWt406ZNca9pw2gEduM87U8eZgcBycsDfROQAbwrImvYm5fwXuA8EVkG9MFRLAR4F6hwc/ddCbwGfAqsBqbhpCapgasiMxFYICLvAi8CB8YqC44SoYisB3JFZL3rEQe4XETWunEzlwPnR9cLBoP/rKioaFBmkfoQjiO7++676dy5c8Qr6ff7GTt2LDk5DUo7baQA4byynTp1YsWKFRQUFPDyyy+zaNEiVq9ezVlnnRWJCzTjOXUJz/OMGTP47LPPuPXWW7n66qt5++23Wbt2LePGjbPru4UQPdedO3dm8ODBLFq0iLVr17J27Vp+8Ytf2FwbjUn7YDDYoGQJxr6RUA+0qvaK2v1DjPMfAkdFHQqnEikHhlUrHnMxYHTssrv/FPBUPcf3F+AvMY5fFx5LLLxe71OZmZmH1aeP/UFEmDBhAmPHjuWcc86hTZs2PP300xYn2UKZNm0akydPZsCAAYRCIXbu3Bkz5t1ioFOfc889l86dO1eZZ5tPwzCSwJa6i+wf0gqTAcTCpLzrgSvlPRnIb+qxGC2ToqIisrOzmTp1Klu2JP37z2hCfD4fI0aM4NZbb2Xnzp1xy1kIT+oTnuu77rqLzZuTlpbXMKIZGAwGX09mB7sfK0q44Zjza3/KxYUk9DZCRC4XkXUi8ngD6/USkZp5i/Z9HMvdvNDR249EZKqIfCkiu2LU+aWIvOeGcvwn6vitmzZtmlBRUdFBVc1rZCQFv99PaWkp8+fPZ/r06RQWFsZM1WWkPoFAgOLiYiZOnMhBBx0UN62aGc+pT3iun376aaZPn15nCj0RMdl3Y3+oBB5r6kG0FhLth78YOLUhMt4uvYAGG9DxRFNU9Xg3p3T0thqYS4wFiSJyKE4Ix4mqegTwJ/f4QODEgoKCQKx8lYkmnA7pjDPOYOrUqZE8suGY6OicqonKBGI0H/x+P8ceeyzbtm3jv//9Lz/+8Y/jlk1LS2uVObVbCoFAgLlz5zJjxgz69u0b99pNS0ujXbt2rTKfdEshEAjw2muvcdRRR3HUUUfFLZeWlhbZDGMfSQN6eb3egqT24pHEbylIwkI4XCXC3wFB4EngEOBHOHHWk1R1toj0Ah5lb5LvS1V1qbuo8DCchYOPANuInwd6F47O+wjgahzj+3IgE1gOXFyXvHeMPNC3Ah+o6oPVyp0A3NO7d+9HRaQIJ8+0GapG0ikqKmLIkCFceeWVvPHGGzHLZGdn4/F4TM0yhYmXCz6a3Nxc2rdvz+bNmy2DRwpjc200EhuBA4LBYNIel+/+z98TH8Lxq+tSzrAyJUKHPkAfEXlNRJaJyEj3Pb0OvLp58+aiUCiUBWY8G42D3+9n0aJFjBs3jvz82KH3paWllJbGS6VupAJh+e/Ro0eTn58f8/ulpKSEsrIy++5JcWyujUbi5mQazwAinoRvqUiyRj0cuFZE3gYW4gih9MRJbfeAiKwGZgKH70PblcDT7uthQD9ghdvXMOAH+9BmOnAoMBQ4G3hQRDqISG/gsPz8/Akej6cCiBsDHY6PthhpI1GE46Lnzp3LpEmTYpYJhUL2yDfFCQQCTJw4MaJgF0sCfOvWrezZs8cMqxSnIXNtayCMfWRaUw+gtZAsAzqsRBiOP+6pquuAK9mrRNgfJ+wiFg1VIgz3461LxjsO64HZqlquqp/ihKEcCpwBLPvoo4/uFJFPamsgvPjDfuCMRFNSUsLIkSPjGtF1Yf+TzZ/o9Q833HADHTp0qDJvIsLxxx9Pbm5u3DZExG6mUoCioiLOOOMM2rZty9VXX01eXl6NMj179qyiXmkYDcDj9Xp/ntweLAYakmdAh5UIBUBEjnGPtwe+VtUQcC61KxH2FRGPiPSgdiXCn4tIF7efTiJy0D6M91mcUBNEpAAnpOMT4AtgSJ8+fS5T1cJ9aNcw9gu/38/JJ5/MK6+8wvDhw2Ma0XVJfasqmZnx7lWN5kBYze6II47gscce44orrqBbt26ReVNVli9fzvfffx+3DVWlsrLSbpiaOX6/n6uuuoqSkhIGDBjAZZddVqPMF198Ua+2bCGxEYMQcEJSexBP4rcUxJQIHYqBLSLyHvAq8GdV3QL8D/h48+bNE0KhUK7bRsM/DcPYT/x+PwMHDiQzM5OlS5cyd+7cuGVjPQ2JFuOJzuZiND8CgQAej4eZM2fy9NNPxy0XVi4N/w0TDiOzjA7Nm4Zc0/HmOjrVoc21EUWgqQfQGjAhlXrgCqlMo2ooiWE0Ov3796ekpIRbbrmFMWPGxCxTWFjIV199hYhw8MEH88knTvTRddddxy233GL5hVOAuuZZRKoYyrGeQoRvomy+mzcNmetwXHQ4vCNsVNf1FMpoVVQARwSDwQ+S1UHpzNsTbjhm/+LqlPNOpqbfvGmwdAdGk7Ny5Up27NgR93x0WjtVrSIN3qFDhyrGVG3xtEbTUtc8p6WlRTyRlZWVpKen4/F46NChQ6SMqprxnAI0ZK4rKipqeKHNeDaq4QEubOpBtAZapAdaRJYD1Z9Tn+uKqewTXq/3ehw575S7SzJaLmEJ8GnTprFhw4aYZcKr/PPz89m2bRtlZWWNOUQjQYRloadPn863334bc5FZVlYWoVCI/Pz8SDYHI/Wo73Xdtm1b8vLy2LBhg+WNNsKEgCuDweD/1VlyHyl9+s7Ee6DPvDLlbKsWKeUdT4kwnpS3iPQUkVdF5C0ReVdETnWP9xWR10Vk7Zdffnmeu/jRMJoN4VR311xzTdx80eXl5fz5z3+mW7du9iObwoRloR988EH69+8fcz1GWVkZo0aN4le/+pU9YUhh/H4/zzzzDBdffDEFBQUx57q8vJxBgwbRuXNn80Ib0VTiJEBIHraIEEiwB1pE3gdGuangGlJvKHCNqo5uYL20ulQHq5UfAHwOfFhNifB+4C1VnS4ihwMvqGovEekDaJ8+fV5W1R5R5RsyTMNIOkVFRQwdOpTdu3dTXl7O6tWr+cc//kFFRQWFhYWsWLGCCRMmMGbMGLKzsznzzDP5+OOPm3rYxj4STnuXn59PaWkpixcv5o477mDnzp3s3LmTvLw8Hn/8cXr06MEVV1zB4sWLa+SoFxHS09PtpqqZU5+5Lioqok+fPjz00EM888wzcZ8yRcdTGy0SBV4PBoMnJrOT0ll3J94D/bMrUs6wSpjZ70p5/wCYIyITROQhEVnhenVPd8v0EpGAiLzpbgPd6jcDPhF5W0SuFJHzReSeqLafc41sRGSXiPzNDdM4QUR+LSJvuHXvE5G4S5FVdZmqfh3rFBBOxtkeR1ERVf1AVT8MBoM9RcTygBnNFr/fz7p16zjwwAMpLCxkz5493HfffRx77LEEg0EA1qxZg6ri8Xjo0aOHpcBqQQwYMIB+/fpF9k866SRUlfLy8lrz05vx3Pw5//zz6dy5M6pKcXFxZI1DmF/84hf069ePAw44IO6i0fC1bsZzi0eAutSc9x/LAw2YlHeYScCv3RR3LwA1EnOWlJRMUvv2MZopHo+HQw89lEsuuYTPP/+csWPHctVVVzFnzhz+/e9/U1hYyCuvvMKaNWs46qijWLJkCQUFBZH64RRYIkJ2tiWbac54PB5uuOEGQqEQp59+OllZWbzyyiu89NJLkbmePXs2r7/+OjfeeCNLliypEt4TDu1QVbKysuxGqhlT/bo+7bTTmDJlCt9++y0PPfQQmZmZvP3227z33nscffTRLFmypEr98I1TKBRi4MCBsbowWhYaDAb/19SDaC0kOoTjMxyFwfk4Kd/Cq1w6ASNwDOx7gL44xm4fVc2tHsIhIucD/VX1Unf/OWCaqi4UkQogS1UrReRSwI9jmAPkAE/UpUYoIruqhXBchfNZ3C4iJwD/Ao4Mxzz36dPnXeBHbtmYbUZ/js0lxCO86GjGjBls3bqVfv364ff7mT59Oi+88EJcpSuPx2Or91OccEjHli1b4qa7s8e5LQefz8eECRMi+aMfeOCBph6SYRhNw7nBYPCxZHZQOvuexIdwnH5p8zCcGkB6ktoNS3kHqxx0hEvCUt4e4qeGa6iU93X7Od4LgJEAqvq6iGQDBcBGEcnr2LFjh/z8/B0ej6d9vAaai9EcxuPxMGHCBE477TQAunTpwo033shZZ53Fli1byMjIiHieqifjtwUpqc+sWbN47LHHuOWWWyLHom+MMjMzycnJiaTPMmM6dQl7pH/729+yceNGZs6cySuvvFIlxr1Nmza1qhgahtEi2Ag8BCTVgKaZ2TtNhUl5O3wBDHPbOAzHYN/kxj0/k5mZ+YbH48nYh3abjKOOOopFixZRWVlJZWUlP/rRj9i2bRtbtmwBIDs7mx49elQx/HNycmp9nNvcbhKM+MTKLZuTkxN53blz5ypPIMLnaouXNZonRx11FJ9//nkkldlLL73EsGHDqpRp165dlf1wakMwOehUxubOqMY7AF6vt6Cugsb+Y1LeDlcDvxeRd4AngPPdeOdfAoPT0tKGhUKhHFVNGS9d165d+fTTvclQOnfuTGnpXoe/x+MhLy8vomzVrl27iBcy3pdyqrx3IzbRHsi2bdvWeNJgP8apSdeuXfnmm28IhUKoKl988QVdu3atUqa8vDwi4e7xeKrIPodCocj3gJFaWKidUY0hwFac9WHJw+NJ/JaCJPRbU1V7Re3+Icb5D4Gjog5d5x4vx/UARxFzMWB07LK7/xTwVD3H9xfgLzGOvwfUSPuiqo/hPgrxer23A+cBsZPtNjOqexHjPaIPeyF37dpFbm4uHo8nbmy0YRjNj/C1np6eHrl2q1/rW7duJSsrK3LjFG142TWfesT6PrcwLAPHpvsyGAzaP0IjkJpmfyPj9XqfwsnMkY+j8tPs+eabbzj44IMj+zNnzuSCCy6I7IdCIb777ruIF1JV+f777025rJWwa9euKl7I3bt3R4wq+xFOLb755hsOOOAAKisrERG6du3Kxo0bq5RRVUpLS9m1a1eVuQbzYqYisa5Ru24NYBFQkPQQDpHEbylIorNwXA78EXhTVeudTk5EegEDVfU/CRpHTClvYBzwG6BjtSwcB+EE3nfGefzxa1Vd757rCTwI9Dj00EN/CKi08CDRcPaOu+66iy1btjBo0CD8fj8zZszgxRdfpKSkhIyMjBo5ZKM9YEbzJVp0JTc3l44dOwLOD3D4+0BEWLJkCcXFxbRt25ZHH32UyspKPB4PqRTKZOwV4ujcuXOVfMDRc/jaa69RXFzMj3/8Y3bu3EmvXr34+9//zubNm9mzZ4/li04hwvO9bt06brjhBg4//HD+9re/8d5777FgwQL69evHqlWr6N+/P/feey/bt2+noqKiSogfWDamFGURcCjQPZle6NLnZyQ+C8dp41POrkq0B/pi4NSGGM8uvYAGS3nHE02JJ+UNzCX2gsRpwL9V9Sjgb8Dfo879G7itT58+R+F4n1Nukg0jmmjRlby8PEKhEIFAgIkTJ7J582YzkFs41ee6hfsDWhVFRUXccsstVFRUsHPnzhrnPR5PjflWVXN8tByGAJckPYTDpLyBBMZAV1MifBI4BCd3cjowSVVnu57mR3HEUAAuVdWlOEqEh4nI28AjwDbi54HeBdyBk1f6arfNy4FMYDlwcTx5b1Vd5rZX/dThwJXu61eBZ91yhwPpqvqi1+u9qLX80AQCAQKBQK1lqnukbAFaavHwww/Tu3dvunTpErke6mM0h0IhM7hSHJ/Px6BBgyLzXdu8V1ZWmnGVQvj9/ogHOi8vr8b58ELTuo4ZKcufg8Hgs0nvxX7vAVMiDPMOcKb7+gygnYjk42QK2S4is7777rubVdXT2r9oouNmoxER+6FNQVavXs33339fI/55yZIlTJgwIeKlrE70DVNaWpoZ1c2YsMf55ptv5vDDD+f3v/89GzdujMxrIBDA7/ezadMmQqEQ9957L+PHj+fzzz9nz549ZlylIIFAgH//+9+EQiEWL14cuZYXL14cefoQHZ5RfY4tfCNlOaGpB9CaSNZtxHDgWtejvBAnr3JPnNR2D4jIamAmjue3oVQCT7uvhwH9gBVuX8NwvOAN5RpgiIi8hfMI5CscMZd0wAdc07Zt2y9rayD82Lsl/tgEAgFGjRrF7Nmz+e677yLHw56OgoIC+7JNQQKBABdeeCH9+vVjy5YtXHfddeTn5xMIBJgwYQKHHXYYJ598Mjt27OC5557j9NNPJyMjA1Wt9cfXaH5EG1TVj0+cOJHDDjuMU045henTp7Nr1y58Ph/z5s1j7NixVXJGG82f8Jzu2LGDQYMGsWjRIu677z46d+6MiODz+Rg+fDjTp09n48aNNdIawt6FpfZkMeU4vVF6sUWEgCkRAqCqG4CfuWNs6459h5sz+q0+ffqMwpEdj9tGS/bA+Xy+GosIjdTH5/Pxs5/9rMrCMoAhQ4awaNGiyPEf/vCHpKens2nTpkimh7BiZdiYDi8uNJoftS0i9Pl8LF68OPL91bVrV6655ppI3bBXuvqch7HUac2P6osIt2/fzrRp0yLn/vSnP1FQUEDXrl256aab2Lp1a405DN8o21PFlKPlGiLNkGQZ0GElwstUVUXkGFV9C0eJcL2qhkTkPGpXIrxYRDxAIbUrEc4WkTtVdaOIdALaqernDRmsiBQAW12FxOtwMnIArAA6VlRUtE1PT7db8WpEx0qHvRj2hZuaBAIB5s+fz/XXX092djaqyquvvsrLL79cIwsHUGOe7QlE6hE2flU1knHlxz/+MX/84x/p1asX48eP59tvv43MbUVFRRVHQdigNpo/JSUlVUJ2wlk4YsU/p6WlmfGcujSOAZ2ii/4SjSkROgwFgiLyAdAVmOq2XwlcU1paep2qtprnmNGhGS3Zs244hEM2nnnmGS677DJ2796Nx+OJqU5nC45Sn0AgwJAhQ/jDH/7Apk2bap3P6gsNo8ua8ZwaRMdAxxJfCYVCVYzl8MJRC99IScTr9a73er01V5AmtBcL4QBTIgwf/x/wvzh1XvR6vRNwMn9k1qefVCb68V8s4zlsWImI5YZtIfh8PqZOncr8+fO54ooryMzMJBQK0adPH4466ij++c9/VomJbKmx/q0Jn8/HFVdcQX5+fq03yeFz4RCOaEPLPNCpweDBgyN5oEWEfv36MWzYMF588UXWr19PWlpajRz+GRkZ9lQpdTkrGAzWzGFoJJyECqm0ZLxe7ztUNf4No8UTFl0pLi5m8uTJMct4PB6Tg24h+Hw+Dj74YB5//HEzjls4Pp+PCRMmsHLlSiZNmmTXb8vh18Fg8PFkdlD68r8TL6Qy7Dcp54ZOqAe6mSsR/h6YhJOfuhKYq6rXuuUHA3fhGMjjXI90uK3zunXrdk+bNm3auvuJGGKLoKioiOzsbKZOncqWLVtilrFFRqlBdNjOkiVL+P777xkwYAALFixg4cKFtG/fnvz8fLZs2UJ6ejqhUCjioQq/trlODWqb60mTJrFkyRKmTp1a5doOP843r2RqEl4Ivn79eo444ojIXI8cOTJm+XBqSjOqU5JrgaQa0IZDoqW83wdGqeqnDaw3FLhGVUc3sF5aPNGUGGVzgeNV9VURycRZgFikqvNcAz4PJ53dnLAB7S5KXPmDH/xgelpamh/o4B5vyDANo0VQnxsmo2Xh8/kYMWIEd911F1u2bLEbJMNo/nwM+ILB4NfJ6mD3K48m/Isg5yfnppxhlbBVAtWUCCeIyEMiskJE3hKR090yvUQkICJvuttAt/rNgE9E3haRK0XkfBG5J6rt51wjGxHZJSJ/c73MJ4jIr0XkDbfufbXIe5eo6qvu6z04CxO7u/ufqeq7OFLd0YwAXkxPTy8RkXgp9wyjVeD3+yktLWXChAnk5+c39XCMRiAQCFBcXMyf/vSnOuOlDcNoFpTgZC9LHiblDbRSJUIR6QCMwfFC10Yh8CXwO5zsHIbRqok2ort169bUwzEagVGjRvGTn/yESy+91DzQhtG80Wp/jSTS6pQIRSQdeAL4P1X9pI6+wu6Wj6jpna5CS1YiNIxowkb0//73P+bMmVNnefNapjZ+v5+BAwcyadKkKgqF1dXrohERsrKqL0MxDCPJCI6zb0NyezEPNCTPgA4rEfZ1t56qug64kr1KhP2JnxauoUqE4X68qjqpjrHdD3yoqnfV432sB3rUoxwiEtkMo6Xj9/tZuHAhBQUFTJo0qc7ysXJKG6lHIBCgqKiIP/7xj5x66qm1ft9lZGTQoUOHxhucYRgAFcmMfzb2kiwDOqxEKAAicox7vD3wtav4dy61KxH2FRGPiPSgdiXCn4tIF7efTiJyULxBicgUdwx/asD7GB4KhTJU1Sxjw4gi7JnMzMxk6dKlzJ49O2Y5VaWyspKsrCzS0tJMoCHFCQQCjBo1im3btnH88cfHnE9VZdeuXezatasJRmgkiugnDvFugrt160Zubm7cJxIiQqdOnZI5TKORUZGEb6lIq1EiFJHuwAScsJE33UWHF7rnjnUVCn8B3Ccia932twI3bdmyxVdZWalu2X39TAyjRRLtjT7uuNj3uuHwpqysLDOgWwgiwo033shxxx0X93vR4/HYd2YKM2rUKNq3b88xxxwTN6Xd5s2bAeJe16rK1q1bkzZGowYHeL3e3zb1IFoDJqRSD7xe7yXAZMBSDxhGHMJp7qZNm8aGDbFD8MJKhjk5OZSXl9dQs8zMzGTPnj2NMVwjAYTT3E2fPp2NGzfWmM+w8Vzb70xGRoapmjZzjjjiCLZt28b27dspLS2NmQ887KHu0KFDjZSHHo+n0XOIh0Wgdu/ezapVq5g2bVpkXNHKqi0wh30ZMCgYDK5MVgcli/+b8A8rd/AvU+5O2wzoeuD1elcBx7B3UaGxj/j9fl555RXeeOMNE2VoodQnX/SRRx7Jrl272Lp1K999911L+vFqldQnX3Q4fCcjI4OSkpImGKWxP9Tnuj7kkEOorKyscV23MAO1ufM1MCYYDK5KVgclgZmJN6B9v6jTvhKRkcDdOOG/D6rqzdXOnwP81d3dBfxRVd9xz32GEy5cCVSoav/9HXOLfJYqIsvdEI3o7Uf72l4wGOwHJO2fsTVRVFTEsmXLzHhuwYSzdMyZMyduSMeaNWv4yU9+wsyZMznppJOqPOavLbuD0TwJ54t+9NFH48ZFV1ZWMnDgQJ544gmGDBnSBKM09of6XNcff/wx55xzTo3rurrxbGE9SaUtyc7C0QS4Gh//BEbhhOKeLSLVM7l9CgxR1aNwQofvr3b+JDfhxH4bz5BgA1pELheRdSLSIBlJV2DlV4kah6oeH5WZo6+bI/pjEXleRN4XkbUiErlzEZGrROQ9EXlXRF6uvhCxT58+b6lqf7uDTgx1LUwREbp06WKxsimM3+9n0aJFjBs3jsLC2Dn9H3roIRYtWkS7du3o2rVrJO1ZZeVecVERITs7O2Z9o3kRjpcdOXJk3BvkJUuWsHTpUtq1a0dGRkYjj9DYX/x+P9deey1jx46le/fu5Obm1ihzxx138PHHH5OTk0PXrl3JzNybbCvacDYjOmmUJT0Lh8eT+K1ujgM+UtVPXDG8J4HTowuo6lJV3ebuLsMVy0sWibZQLgZOVdU6xUyq0QtosAEdT3WwFqap6g9xwjFOFJFR7vG3gP7uXcv/gFujK23fvn27O2FGAgiv4p8zZ07MhSmqysaNG1uclzp84zBgwIBW8+MxYMAAZsyYEfe8iHDJJZdw33330aZNmxrns7KyOOWUU2LW9fl83HTTTbXmGz722GM59dRTycnJqdFvsqjvPLc0T3s4K8vLL7/M/PnzmTp1ag0Dq7KykunTpzNy5Ej+97//MXLkyBrtZGRkcP3119fI3FCfz/WII47gmWeeqfFUo127dnZDniDCTxsefPBBnnnmGXr27Fnl/O7du7n22msZNWoU999/P4899ljk+lNVRIRzzjmHOXPmxLzmw/P8wx/+MGb/p59+Os8++yyzZs2ie/eq9pHNMQAVXq+3T1MPIgmEhe3CrKd2xcULgHlR+4qTcGKViFyUiAElLAbalfL+HRDEuTM4BPgRkA5MUtXZItILeBRHTRDgUlVd6mblOAzH/f4IsA3HoL3Ubfs5HON3oYjsAu7Akdm+Gsf4vhwnp/Ry4OKoPNG1jfduHEXDB6odPwa4R1VPdPf7de/efVZOTk53wNNaDB/DSBThxTwLFiyImzN6woQJdOjQgYkTJ1JWVhY5npuby80338x//vMfli1b1kgjNhKBz+fj1ltvpWPHjgCUlpbyz3/+kwcecL5y8/LymDVrFp9++ikTJkxg69attGnThjFjxnDmmWfywAMPsHDhQnbv3m3xs4ZRfx4F5gaDwZnJ6uD7pbMSfkG2PfHMPwDRhu39qhoJwRCRXwAjVDWcPe1c4DhVvax6WyJyEk7Wt0GqusU91k1VN7hpj18ELlPVxfszZpPyrknkrkVEPMDtWVlZ79TVpikRGkZswmnuhg8fzty5c2OWmTp1Ktu3b+eGG26gW7duEU9SSUkJd999Nx9++KF5l1KMQCDAI488Ejf92c6dO/nZz37Gxx9/XOX4W2+9xfjx41m2bBmlpaWNMVTDaCkocCywLqm9JEGJUFXvd0Nlw1v1+OXqwnbdiRHrLSJHAQ8Cp4eNZwBV3eD+3Qg8Q3x9kXpjUt5Vz/0aRyHxNvfQxcALaWlpu+salCkRGkZ8Zs2axYUXXhj3/ODBg+nZsycTJkzA4/Hwox85a34LCgr4/PPP2bZtG6FQyIzoFCI9PZ1LL72UrVu3Mnr0aESEcePGccghh0TKXHrppTz66KOUlpbSu3dvOnTowM6dO/n222/ZunUrBQUF9r1qGPVHgPuCweCaph5IElgBHCoiB4tIJjAOmBNdQER6ArOAc1X1g6jjbUSkXfg1jo2635+RSXmHGxI5GUdoZayqhp8hnwBcumnTppPD8bjmYTaMhrNy5Up27NgR9/ywYcN45JFHAFi/fj0jR46kbdu2kUWm4esvLS3NZMFThDPOOIPdu3fzwQcf8NFHH7FixQrKy8sZNmxYpEzHjh3Jzc2lc+fOfPHFF4wePToSF5ufn09BQUEk/V14MwyjVv7i9XrzktmBiifhW519qlYAl+IoRK8D/quqa0VkvIiMd4vdgKPXca+bfS2cC7srsERE3gHeAJ5X1fn7+zmYlPfe8d2HYzxvDB9X1XNUtWfnzp1vEJHdbtmWtbLNMJoBXbt25Ztvvonsp6Wl0b59e0KhkBnMKcoPfvADdu/eHZnXr776iuzsbLp27Rop8/XXX5OTk8ORRx7JihUr6NmzZ6T8gQceSEVFBRkZGS1uQbFhJIlyoDAYDO5s6oEkA1V9QVX7qOohqjrVPTZDVWe4ry9U1Y5RTtX+7vFPVPVodzsiXHd/MSlvh9twcifOdI/PidFEWCrLXCCGkWDiPaKPNpzM+5haxJrT6utE1q9fT2ZmJqFQiBdffJGKigo6depEVlYWFRUVVFRU2A2UYdSfTcFgMPmPyUUSv6UgCf1mUtVeUbt/iHH+Q+CoqEPXucfLceKXo4m5GFBV21bbfwp4qh5jW08cJUFVPbmu+h6PZzOO95p47RiGsW/MmTMn4nkUESorK9m+fTudOnVi+/btgGNMmycydQjnAj7ggAMAKCwspKysjI0bIw/5KC0t5bvvvuODDz5g7ty5XHrppaSnp5OVlcX27dvZvn07ZWVltkDbMGKzh6qhsF29Xm9hMBj8Kpmd1ifkojVgn0I9CAaD/8RJs/etuxmGkUB27drFHXfcATiG1vz58/n+++/58ksn7Wc4Z7J5oVOH2bNnk5ubi9fr5ZBDDuHYY48lIyODV155JVLm888/Z8eOHYRCIU477TTmz5/P7t27IwsJ8/KSGsppGKlOtPGswB+SbTwbe0lYHmhwlAiBPwJvNkRMxc0PPVBV/5OgcSwHqqsr/B6YhJOfuhKYq6rXuuXHA5e4x3cBF7nhIYjIrcBpgKdr167b8/Ly+omISWgZRoKpT75oo+Xh8/kYMWIEM2bMYOfOnezc2SLDNw2jMfgWGBEMButMvbs/fLfihYQ/Dmp37Kkp92Q/0Qb0+8AoVf20gfWGAteo6ugG1kurj2iKWzYXOF5VX3VToLwMFKnqPBHJU9WdbrmxOGIsI0VkIE589OD8/HxPXl7ed2VlZXe3bdv2Cmoa6IZh7CdhI3rLli2MGTOmqYdjNBI+ny+SwnDmzJkRsRXDMOrFTiD8uOajYDB4aDI7MwPaIWHPQ10lwh8Ac0Rkgog8JCIrROQtETndLdNLRAIi8qa7DXSr3wz43AV8V4rI+SJyT1Tbz7lGNiKyS0T+5nqZTxCRX4vIG27d++LJe6tqiaq+6r7eg7Mwsbu7H+3yaIPzKAT3bzaQmZeXd2JFRUVZbm7uqTjG88aoOuUYhrHf1JYvOi0tjZycnCqLyuKFdFioR+rg8Xi44YYbuPDCCznttNMYPXp0JG46TDiVXfR+GFtkaLRSFAgBW4FV7muAzknvOQlCKqmIKRHuPXaJiHwM3IojDY6qvg68Cny9adOm51X1E4/H8wOgDMh1+wMnkN8wmj3NXZAiXr7osJF04IEH1mshoS02TB2OOuooPv/8c9avX095eTkvvPACHTp0iJwP53+Ofloa/X9cWVmvh5CG0dIQHKM5HceIDgu+WQx0I5GsW/fhwFgRucbdDysRbgDuEZG+OMZnn31oO54SIUAOVT3DNYinRKiq/wT+KSK/wkmNd56I9MZZPNi9S5cuZ+zevXuaqma6OaEzcf5504C0eF/uhtGcEJGUyWYQPdZ4ctBhwgZWuHwqvc/WTvUc4N98800VozicCzw9PZ3y8nJEBI/HE5nj8NyHs7cYRisiHUdD4zScJ+MKLEl2p2o2DmBKhLF4Evip+/oMYFmfPn3OS09Pn9a2bdvO7P2HzcLJdQ2QbVLeTYPf72fs2LHk5OQ09VBSglTwzH711VeMGTMmYgAPHjyYlStX8t577zFnzhxGjx5NRoZz6YXfTygUipRv27YtP/vZz8jNzbXrMQUoLi5m4sSJkf3Zs2dz7bXX8t5777FmzRqWLl3KyJEjI+dVlT179kTmu7KykpycHE477TSb8xTC5/NFrus1a9Zw+umnR67rWOTk5PCzn/2MNm3aVAnhMRAcG0nc7YPaiyeiRwvhAFMiDB+PDrg/DfjQff0FMERVh6hqHpb/2TAaHVWNxMC+9tprLFy4kPLy+MsOysrKmDNnDiUlJbX+IBvNl4cffpiysjLS09NZvXp1nXO+Z88e5s2bR0lJicVEpxDff/89IsLSpUtZtWpVrXNcXl7OggUL2L17N6FQyG6U4nOD1+v9aVMPojWQ6Cwcn+F4lr8H7gIG4hidn6nqaNdQfRoowYktvkxV27pp4eYDBcDDbt3HgL7AGhwd80mqulBEdkWLqYjIWTiCLB6cxXyXqOqyGGPrDnwJvI8Twwxwj6o+KCJ3Aye79bcBl7oa62k46omDAT3ooIM6ZGVlxVQ6NJoHgwYNomPHjrz88suUlJTUOG+P9lObcJaO3bt38/rrr3Pvvffy9ddfV5nTnJwcfvvb39K9e3fuv/9+VJVt27ZVSY/WqVMnPB4Pmzdvboq3YTSA8JwvWrSIf/zjH2zfvp3S0tIqT1NycnIYOHAgX3zxRSTMY9OmTezatQtwrvuOHTuyffv2lHgK09qoTwrLrKwszjnnHDp06MA//vGPWo3tVowCnwaDwUOS2cmON19K+I9o+x+fnHJ3RAk1oFsyXq83A8fwTrlJbk3UZUAbqc+//vUvTjjhBBYtWsRf/vIXvvvuuyrnMzIy6NmzJ1OnTmXOnDkEAoGIIEsYu5FKLf71r39RUVFBUVERGzZsqGE8ZWRkcMEFF7Bz504CgQDp6el8+mnVbKrZ2U4kYGlpaaON26g/Pp+PKVOmsG7dOqZOncqGDRuqxLRnZGTQrVs3/vznP/Paa6+xZMkSNm/ezO7duyNl7LpGgV8Gg8H/JbMTM6AdUjPwpGn4LWY8N3uWLFnC3Llzm6Xx7PP5mDdvHmPHjm01oQU+n4/58+czderUuKpyxx13HM888wxjxoyhc+eqGZiys7Pp379/lcfyF1xwAc8++yxHH300V111VY32ysvL+fjjj1mwYAHnnXceRx11VJUUaOEFaMmiNc5zNNFznpubG7NMeM5Hjx4dMWzDiEiVLBzgzPljjz3GjBkz6NevX435Ky8v57777qNHjx7cc8899OjRo8r5Aw88kD179pjxnCDCc1xcXMzhhx8es8xxxx3Hs88+y3PPPcfvf//7KucOOuggnn76aY455pjIsUAgwJAhQ3j88ce5//77OfTQqqmMy8vL+fzzz/nrX//KiSeeyD333EOnTp0S/+ZSmxDwWrI7UfEkfEtFTInQKX8+jmBKOP1LOLSjLzC9W7duh7Vp0ybPKWo2dCoQVjebPn06GzdutMd9LZSioiKys7OZOnUqW7ZsiVnG7/ezZs0aXnrppWZ5Y2U0jKKiIoYMGcKVV17JG2+8EbPMhAkTGDx4MLfccguvvvpqFa9k+GbKQjmaL3Zd7xcPB4PB3yazg+1vL0y4B7pD36EpZ1yZEqGjRHg+0F9VL61Wpw+gffr0Ga6qd+BmDTEj2jCaD2GDqqioiOeffz5mmQkTJjBkyBA+++wzLr30UvbsqZq63R79phY+nw+/38/06dOZP39+jfnMy8ujqKiInj178sUXX3DVVVfVKGM0bxJxXXs8ntZ4o2QGdCNhSoS1oKofqOqHwGA3f7SRgowbN47CwkKyskx9vSXi9/tZtGgRw4YNo7CwMGaZqVOn8vTTT3PnnXcSCoVqhIqEM30YqUEgEKCoqIjjjjuOLl261AgP2rlzJ36/nzfeeIN//OMfhEIh8vPzm2i0xr6QiOu6FRrPsFegLmmoSMK3VCRZWTiuAt5T1cdc1b83gGNwpSdVtdTNyPGEqvav7oGu7hEWkeeAaW4WDgXOUtX/ishhOMqBP1PVchG5F1imqv+uY5wdcAzok1X1E7e/vwObcHIoXqmqkVVHXq/3klAoNFVE2rv19/ejMgwjwYRX8m/ZsoUxY8bELBP2WO3evZtzzjknkqUBHHno6HzSRvPH5/MxYcIEPB4PM2fO5IEHHqhyPtoTXVpayvnnn1/lcb89eWj+JOK6bk0CO8FgMOkGyrZ3FiX8oul49JCUM6ySFbk9HLhWRN4GFrJXiTADeEBEVgMzgdirD2onnhLh2+7+D2qrHEeJcC7QS1WPAl4CHomu88EHH8zatm1bOaDxjOewCpp9GRtG0zBr1iwuvPDCuOcHDx5Mr169GD58OFdccQVlZWWRc+EfWVWlS5cujTFcYz/xeDzccMMNXHjhhZx22mmMHj2aQw6pmr1r/PjxrFu3jrFjx3LZZZdF1kKEnzaEv6/bt2/fuIM36s2sWbNqLEKMJvq6/tOf/hS5rsMLj1uT8Qzg9Xq9ye7DFhE6mBIhoKpbVDX8a/oAjlHudCCSd9BBB33UqVOngtpWEJoSoWE0LStXrmTHjh1xz5988sk8++yzAHz22Wfk5+eTnp5Ou3btqly3paWlpmyZAhx11FF8/vnnrF+/nvLycp5//nmGDRtWpcwhhxzCsmWOLMC3335LQUEBaWlpNbJ8mOOj+bJy5coqHuXqDBs2LHJdf/rpp+Tn55ORkUGHDh2qXNet6Lc5/oeVKEQSv6UgpkToHI8WRxkLrHOPZwLPlJaWzhORD3EMe8MwUpAuXbrwzTffRPazsrLIysoiLy+vSpq7srIy80imAF27dq0yn99++y1du3atUub999/nlFNOAZyUiB07diQrK4s9e/bUMK4sBr75UpsXufr/QVZWFm3btq3h0PJ4PDbHRkJJlgF9E064xrsissbdB0fV7zwRWQb0wVEsBHgXqBCRd0TkSpw8hp8Cq4FpOPHKNVDV94CJwAIReRd4EYipFOgqEU7ACRt50110GH7ee7mIrBWRd4DLgfPd478EBu/Zs2fAnj17eqhqmnkqDKP5UpuXKdpIDqOqrckz1aKoPm+x4pnvv/9+8vLyePbZZ+nUqVMkD/R3332HqpKdnU3btm1b3WP+VKO2391Y128r/51+w+v1xk66nyAshMMhoZklVLVX1O4fYpz/EDgq6tB17vFynPjlaGLmkY6W8Xb3nwKeqsfY1hNHCEVVrwuPpdrxx4DHvF7vU8AZdfVhGEbTUtsP57PPPsuHH34Y2d+6dSslJSU1csiWl5dX8WgZzZNvvvmGAw44ILLfpUsXNm7cWKXM999/j9/vj+xnZ2dXEVMJv26l6c5aBLNnz65yXX/55ZeEQqEaN1StYIFwOXBRMBh8uKkH0lpITbO/kQkGg2cFg8HMYDDoAR7HySZiGEYzJjMzs4rXuaSkhNtvvx2AXr16VTGkoh/tWrqz1GD16tX06tWL7t27k5GRwWmnncarr75apUynTp0iapAnnngiFRWxo/AsZCe1yMzcu3yqrKysynVdfYFomBZuPIMTEtujzlIJQJGEb6lIQj3QqapE6Nb5pXtegXdU9Vfu8Z7Agzj/mOrxeE7t3bv30TihIB63fHhLc/+m5n+DYTQzMjMz6y2Acfvtt3PcccfRsWNHFi1axD/+8Y/ISvwnn3yShQsXMnjwYF588UVKSkr43e9+F1E5q6ysjGTi2LRpU6RNS3PW+NTXG1xZWcnf/vY3HnzwQdLS0nj66af58MMPGTduHODMec+ePbnlllsIhUJ8+OGHLF++vEob4fndtm1blX2j+VDXdf3yyy9z4oknRq7rc889t4rybCtKY/c9sKqpB9GaMCVCR4nwUOC/wE9UdZuIdFHVjW69hcBUVX1RRNr+4Ac/GJaenv4wkMdeD34I2AYEgSNwFioahtEA4v3QpaWlkZGRwZ49exLymD06r+zjjz9OIBBg69at7N69u0q5jIwM2rRpw3fffddafoCbFVlZWZSXl9c65w0xjsI5o4PBINOmTWPTpk1VnkKAzXljEl7kl8jQmfC1vXz5cqZOncrWrVtbamhOJY7DLvzmvsdJxPAtcGowGIy5bixRbFq7POF3mZ2POD7lHI+mROjwe+CfqrrNPR82ng8H0lX1RYA+ffoMTk9Pf5qqxjPu63xgIGY8G8Y+Ec9gqayspLS0NGE/hH6/n4ULF9KjRw/8fj+FhYU1jGdwYqF37txpHskmoqysrM45b4iRGwgE+Pjjj/nJT37CIYccUsN4BmfOwwsMbXFpclHVpBi3ubm5DBs2jOOPP74lX7thO8fjbu1wjOh84NGk925p7IAEGtCqOh7YAJwEtAFeUdVj3f3bRKQNsBE4RVV/DJzFXsnJa4GAm8v5zjq6agOsUdXjgS1uOyeqal+cu7I6Q0dcJcIxOF5ocDKC9BGR10RkmYiMjDq+XURmichblZWV/1NVwWLHDSOl8fv9vPDCC+zatYtTTz01brnMzExLfdWCuOSSS7j44os5+eST6datW0wjOSyo04KNrxaL3++nb9++XHLJJZxwwgnk5+fHvRFqgdd1Dk746GFerzdmNjIjsZgSoUM6cCgwFDgbeNA1stMBH3BN7969r/B4PFnE+cxMibDl4fP5WLRoETNmzKBz584xv4jNS5W6+P1+Bg4cSGZmJitWrGD69OkUFBTUEFWJjqc0Up9AIEBxcTFPPPEE06dPj3ltq2rMtIdGahAIBJg4cSKbN2+O+5vcAkN0PDgGdBlQmMyOFE/Ct1QkoYsIowgrEQarHBSZxF4lQg9Q8xmaQ0OVCGukoKuFGkqEwHpgmZtO71MRCeIY1OuBt1T1E6/Xe7brfY6JGVKtE7thSn38fj9du3bF4/HENZoszVnrw+bbSEHCRrT9MDUCpkTo8CxOqAkiUoATuvEJsALoeMghh/wV8GPZNVoVgUCAIUOG8Pjjj3PFFVeQn59vXqkWygUXXMCWLVu49tpr6dSpU43zZky1LOzaNlowWTjhtElDRRK+pSKJzsLxGdAfJ5j9LpxFdQJ8pqqj3WwXTwMlwKvAZaraVkQygPlAAfCwW/cxoC+wBugKTFLVhSKyK1pMRUTOwhFB8eAkEr9EVZfFGFt34EvgfZxHHAD3qOqDrqF/OzASJ0Rkqqo+6dY7pWPHjg936tSpq8fjSXOPAVVX848ZMwZwvFkvv/xylXRJHo+HjIwMysrKMFKX8Hzv3r2bPXv2MH36dF544YUauWW7devG9u3bawh0GKlB9XmePHkyy5cvr/G0ISMjg7S0tJiL0YzUIpyhIzc3l/T0dK666qqYc37AAQewY8eOmItODaMZMSAYDC6vu9i+8c37byXcw33AD49JOSs6oQZ0S8br9a4Dfhh9rH///pSUlHDLLbcwZswYBg8ezFFHHcU999xDVlYW+fn5bNiw90awoKCAzZs3V2m3FeWoTHn69+9PaWkpTzzxROTGKbzILPyDeuyxx7JixQp69uzJhg0bYgo3WK7Z5k3//v0JhUI88sgjDBkyhK1bt1JQUMCePXvYuXMnIsKwYcN46aWXgL3hHRbmkZp4PB6Ki4u54IILeOihhwgEAvztb3/j4osv5t5770VVGTp0KMuWLaO0tJSePXuyfv36GnNt17XRTAgB44LB4MxkdWAGtIM9s6oHrpR3n+rHV65cSX5+PhkZGRQXF5Oenk6HDh04/vjjKSsrixjPHo+HnJwcduzYUaNtM55Th5ycHKZNm8a2bduYO3cuY8eORVWreKNWrFjBQQcdxMaNG+OqntmPbPMmJyeHoqIiysvLeemllzjttNPYvHkzO3fuBJz5e+mll2jfvj3Z2dkRQ8qM59QkFArxt7/9jfvvv5/s7GxGjBjBmDFjuO+++yLX6sKFCyktLSU3N5ctW7bEDPew69poRnydzMZNidChRXqg4ygRnquqq/e1Ta/XOxdokNCL0bLx+XyMGDGCe++9l6+//tp+QFsg4XCOBQsWMGnSpJhlzPPc8ghf23feeWdErdIwUoRdQF4wGEzaD9LX77+d8LYP/GHflLOiEx0D3SykvOP0kYuTOq+GlLcr1/0I0AFnYeO1qvqCe+68jh073tapU6dOHo8nzbJtGNEcccQRfPPNN2zbts3SGLZAPB4PU6ZMqdOINgzDaCZ8EwwGk5oHekPw3YT/0HXzHpVyxpVJeTtS3vfjpKub7qoPvqCqvUSkE7Dy0EMP3QkchZuFo75GdHhhiohw5ZVXkp+fj9/vZ/r06Tz//PNxwzcsLjq1KCoq4pRTTiE7O5sNGzZE5nrEiBGsWrWK/v37c+edd8aMfwcL40kV6jPP06dP59tvv62SO9rmOXWJnvM5c+Zwyy23cPTRR9d5bUdjsdFGI1MJ9A4Gg58lq4OvPlid8H/owj4/SjkD2qS83dM48tzgpLkLr/wbAbz4wQcf9BWRDLdeg1m5ciXr168HYNWqVaxatarWL1T7oU0t5s2bh4iQlpZGmzZteO+992qUiTXflZWV9ug/hfD7/bz77rukpaVx4IE1HTz9+/enR48ekTkN32hXVlbaNZ2iRF/bQ4YMicTBV8fj8cR1rJjxbDQyCnze1INoDZiUt8Mk4Ncish54AbjMPV4IfOkuIixxPdf1wuPxcMMNN3DBBRcwYcIEzjvvPCZOnMiNN97IV199RSgUiqTBihoXOTk59e3CaAaE5/n6669n3LhxvPPOO2RnZzNhwgQmT57MH/7wB/7v//6Pbdu28YMf7BXJzM3NxePxoKq0a7c3BXpWVvXQfaO54PF46NOnD5dccglPP/005eXlTJw4kcmTJ3PxxRezePFili1bht/vj3gdBw4cGDGs0tLSyMzMjLSVkZHRlG/HqIPwtX3DDTdEru2cnJyY1/aZZ56JqpKenh65ni2vtNFESDLjn8EWEYZJVh7o+TjqgeE0BJ1wvLkbgHtw8jtXAn1UNbd6CIeInA/0V9VL3f3ngGluHugKIEtVK0XkUhyBk41uPznAE6o6qZYxpgNzgeKwGqGIXIXzWdwuIicA/wKOBK4Gsvr06XMGcExYidDioI3a8Pl8kVCdWHmijZZBUVERQ4YMoaioiOeff76ph2M0Ag25tm1xqdEEVAB7gsFgm2R2sv6DNQk30Lv3OTLlDKtk3SKHpbz7ultPVV0HXMleKe/+QDyPbkOlvMP9eGsznl1iSXlfAPwXQFVfd/srwJHy7gF8hCPS0qB/Gp/Px/z585k6dSp5eXn4fD6mTJlCYWGheSdaED6fj0WLFrFmzRpef/11zj77bNq3b8+Pf/xjTjjhBObNm8cRRxwRKW83YKnL+PHjWb16NatXr+bFF19k0aJFDBs2jJ/+9KfMmzePE044Ieb82pynJtWv7S1btlBUVMRxxx3HmDFjKC4uZtCgQTG/z814NpqANCCQ7E5UPAnfUpH0JLUblvK+TFVVRI5R1bdw4ovXq2pIRM6jdinvi0XEgxNGUZuU92wRuVNVN7qL/tqpasz4nygp7wurnfoCGAY8LCKH4RjQm9z3UaSqO4AMGijlHQgEGDlyZJX9QCDp/9tGIxOWBY6mqKiI4cOHAzBq1Kgq56Kf+tiC0dRixowZzJgxI7J/yimnMGDAAHbu3FljnqPxeDw2zylIrGs7zIgRIzjnnHNqXUBoGI1MKc5atKSSqiEXicakvB0p78OBB4C2OF7mv6jqArfe7zp27HhLXl5eXmZmZmYiPUlnnXUWc+fONcnnFo7P52P48OH8/e9/t7luoYTzRW/evJmxY8c29XCMRiJ8bU+ZMoWysrK6KxhGcgkBXweDwe51ltwPvvhwXcJDOHoeeljKWeUtUkglGXi93hNwFhh2aOKhGCnIoEGDqKio4I033rBHuy2UsBFdXFzM5MmTY5axuNiWh8/n47rrruO3v/0t3377bdxyNvdGI6HBYDCpMRGff5T4RYoH9famnAGdmoEnjYzX610FvIYZz/tEOPa7oKCg1caCigg33ngjo0ePJj09WZFTRlPi9/tZuHAhI0aMYPbs2THLmAHV8ggEApx66qm1Gs9gc280GuL1epMqpGI4JNSAFpHLRWSdiDzewHq9RORXCRzHcjcvdPR2rIg8LyLvi8haEbk5qvxBIvKyiLwrIgvdcI8IlZWV/6moqNBEe+t9Ph/z5s1jwIABVQxLv9/P2LFjW0xKu0AgwMSJE9m8eXOrzYkaCAQYNWoU27Zt4+WXX2bGjBn06NGDk046iUWLFrF27VrWrl2Lz+erUTctLa3V3nikGn6/n4EDB7J27VpWrFjB9OnTKSwsrDHPP//5zyMp7YzUJrzQcMaMGXTu3JnBgwfXuKaj05UaRhIpwclwdngyO7E0dg6JdoVdzD4oEQK9gF8BDZLyjqdE6OaIrl42FycVXkSJUERGqeo8YBrwb1V9RER+AvwdODdcd+fOnaPz8vLKcNLkJYywUVWdoqKiRHZjNDPatGkTyRMcnQYrEAjw1ltv1Shvi89Sj3nz5jF8+PCIwEb1dGcbN26sNQWaqde1DJYsWcJnn31GWloaqmpeaCPZ5AJ7gO1NPI5WQcJioF0lwt8BQeBJ4BDgRzhG+iRVnS0ivYBHccRQAC5V1aUisgw4DPgUeATYRvw80LuAO3DySl+NY3xfjpMSbzlwcX3kvUXkbhxBlgdEZC0wQlXXi+Pq26GqeW65fr169VqQkZHR0dlNzTslo/kRnVN23rx5VeSfjZaD5Ys2DKMR2QYcEQwGv05WB59+/FHC7+4PPqR3ncaViIwE7sbJ4Pagqt5c7by450/F8cafr6pv1qfuvmBKhA7vAGe6r88A2olIvptG7/bPPvusb2VlZcL/GcN5ou+++246d+4c8Va1tBAOYy/Rj3s/++wzbr31Vq6++mrefvtt1q5dy1lnnVXjcW9tMsFG8yR6nqdPn04gEGDSpEmsWbOG1atX84tf/ILs7Oy6GzKaPXWFcPziF7+w73KjMdmSTOMZmiaEQ0TSgH8Co3BCVM52M6hFMwo41N0uAqY3oG6DSdZqpuHAWBG5xt3PBnriKhGKSF9cJcJ9aLsSJxUeOLmb+wErXAMjh72qhDFxlQifAP5PVT9xD1/jjut8YDHwFY6Yy8XAC3369JmmqgfU1m60J39fjR0RYcKECYwdO5ZzzjmHNm3a8PTTT7Nnz559as9o/px77rl07tyZUCjEzp072b17d40y9jg/9enSpQtt2rQhFArx3Xff2TVtGEayaKl35scBH4XtNhF5EjgdeC+qzOk44bgKLBORDiJyIE6kQl11G0yy8kAXA79S1WC185Nwci3/Bcf7Xaqq6TGkvH8NDFTVi939l4Ap1fNAi8hlQDdVva4BY3wI2KWql8c53xZ4X1W7u4shfR07dmyTn5/fMRy/Yd5AIxkcccQRfPPNN2zbts1iJVso4VR3CxYsYNKkSbWWtbRnqY/P52PEiBHcddddJrhiNBa/CwaD/y+ZHXz8yScJ9+j0PuSQP+B4jcPcr6r3h3dE5OfASFW90N0/Fzg+HOrrHnsOuFlVl7j7LwN/xTGga627LyQrjV1YiTBscB7jHm8PfK2qIZxFerUpEfYVEY+I9KB2JcKfi0gXt59OInJQvEFFKRH+qdrxAjdcAxxRlocAVPUcVe3ZuXPnG3DEYcx4NpJGp06dmDVrFvfeey89evSoM92d/S+mHuFUd2FhnW7dupGVlRWzbNh4thCe1CUQCFBcXMzTTz/N9OnTaw3bSUtLs7k2EsGIph7AvqCq96tq/6jt/mpFYl0Y1Q35eGXqU7fBJMuAvglH+vpdEVnj7gPcC5znLhrsg2uUAu8CFSLyjohciZNz+VNgNU6GjDdjdaKq7wETgQUi8i7wIhAz/6Gbmm4CTvzLm25qu7Ck91AgKCIf4KgeTq1WfbCbxcMwmgUZGRmWBi1F8fv9rFu3jk6dOpGRkVFrlpWMjAxyc3MthCeFiU7jWRsiQm5uLhkZGY00MqOF8jOv1/vTZHagKgnf6sF6oEfUfnecsOD6lKlP3QZjSoT1wOv1XgLcipMixjCSTjhDx+TJk1m+fHlMAyonJwcRMXnwFCacoePKK6/kjTfeiFkmJyeHjh07snnzZoudTmHqc03n5ubSvn17Nm/ebFl5jH3lk2AweEgyO/jw488TbjgeeshBtVrR7vq1D3DWvn0FrMAJFV4bVeY04FKcLBzH46x1O64+dfcFUyKsP/ZczWg0AoEARUVFjB49mvz8/JiPdXfv3k1paWkTjM5IFH6/n0WLFjFu3Djy8/Njltm9ezfff/99zHNG6lCfa7qkpISysjIL4zD2h6VNPYBkoKoVOMZxMbAO+K+qrhWR8SIy3i32AvAJ8BHwAE4iiLh193dMiV5EeDnwR+BNVa0znVxUvV44iwYbJKRSS3vLgeqBhecCt+GEeKQDAeASVa0UkSzg3zgZPbYAZ6nqZ25bt3br1u2iNm3atI9qPxHDNIw6CS9CWrVqFf379+fOO+9ky5YtVbxX6enptYpyGM2foqIijjvuONq2bcv8+fNjLjDs2bMn33zzTa1eaMvY0vyp65oWEQ455BA++uijJh6pkaJoMBhMqnP0g4+/SPiXTJ9DeqacYZXoD/li4NSGGM8uvXCUCBuEm9uvBqp6vJtTOnpbDfxSVY8GjgQ6A79wq1wAbFPV3sCdwC1u+wOBEzds2JAPvN/Q8RmGYRiGYTQi4vV6T23qQbQGWqUSoYhkALOAx1T1KREpdsf4uhsr8w2OgT0AuAcY1Lt37+dcmW/zQBtNQthzNX36dDZt2mTxsC2QhqS5i0dGRobFz6YI+3tN29MnIw7TgsHgn5PVePDjLxPugfYe0iPlDKtWp0ToGssbcVLn/c89XAh86b6PCmAHkK+qrwOvdujQYYuqDnXr1+PTMIzEEwgE8Hg8zJw5k6effjpuORGJbLGIlzbNaHr8fj8DBw4kMzOTpUuXMnfu3LhlwynPqqc+CxvPaWlpNVQtjeZFfa/peHMdbTzbXBtRJPWfoSmUCJsjyYqTGQ5cKyJvAwvZq0SYATwgIquBmTgp5RpKPCXCt939H9RWWVVH4MRBZwE/cQ/HzBEoIr2Bw/Lz8yd4PJ49bv147UY2w0gWs2bN4sILL6y1TLdu3SL/iz/4wd7L4brrrsPj8VBWVpbsYRr7SV3zLCKEQiFUNW7cc/i80bxpyFx7PJ4qhrKIRPZrS4dotDpipv41EkuyDGgBzoyKP+6pquuAK4FvgaNxFAvjJbKtqDa26OzzpVEhGgI8EtWPV1Un1TU4VS0F5uBIOUJUjkA3hKM9sBU4A1j20Ucf3Skiz9b6huvw+hlGIli5ciU7duyIe97j8VRJaxctDd6hQ4cqynaWb7b5Utc8p6WlRb5rKisrSU9Px+Px0KFDh0gZVTUlwxSgIXNdUVFRwwtthrMRA28yGzcPtEOrUSIUkbauJnrYSD6VvQsD5wDnua9/jhN+osAXwJA+ffpcFg7hMIym5quvvmLMmDH4fD6mTJlCQUFB5Ac1FAqxbdu2SNnt27eTnZ1Neno6f/3rX6u0Y3GyzZvwPIMTG33HHXdEUt1VVFRU8S5XVFSQkZFBWVlZnQqWRvMjeq6rX9fV53rPnj14PB6ys7Np27ZtUw3ZaJ5sxFHY69nUA2kNtBolQpzY6TluuXdw/tFmuOf+BeSLyEfAVTgx2eDESH/84YcfXrx+/fpSoNI8zEZzISwT/Kc//Ylu3brFNJx2794deczfoUMHe0KSovj9fkpLS5kwYQLdunWLWaasrIyKiorIXBupSX2u68rKSlSVtLQ0evbsaaqkRphynCfzLyWzkyZSImx2mBJhPfB6vV9QVQbSMJoNdSmcpaWlcffdd/PJJ5/w6KOPsmnTpiYaqbG/1Ee58KSTTuK4447j//2//8fGjRsbeYRGoqiPcuHYsWM58sgjuffee9m+fXvjD9JojpQCucFgMGnG3XsfbUh424f37pZyVrQZ0A3A6/W+CRxTZ0HDaALCj37z8/N55ZVXmDZtGhUVFRQWFrJixQry8vJ4/PHH6dGjB+PGjeP992umNjchjuZPONXd7t27KS8vJzc3l5UrV3LHHXewc+dOdu7cyYQJExgzZgzZ2dmMHz8+pgEWzupgMbTNm+jrurS0lMWLF1eZ6+jr+uabb+bpp5+OG57l8XgsLr7l83YwGEyqnbL2o68T/iNxRO8DU86AbpFS3iKyXETerrb9aH/a9Hq9GUDfxIzQMJLLwIEDefLJJzn22GMJBoOA45lUVcrLyznggAPweGpe/mY8N3/8fj/r1q3jwAMPpLCwkEAgwGGHHUa/fv0iZdasWRPJ2pCdnR03dMeM59TnF7/4ReS6ritEy4znVsGIZHdgiwgdEmpAi8jlIrJORB5vYL1eItJgJcJ4xFMiFJH5bpz1WhGZEVYyFJGrROQ9EXlXRF4OL0QUkb4i8nqnTp02VFRU7NZUDdQxWjwej4cbbriBUCjE6aefTlZWFq+88govvfQS//73vyksLGT27Nm89tpr3HjjjSxZsoROnTpFfnDDqbAsRrr54/F4OPTQQ7nkkkv4/PPPGTt2LFdddRVz5syJzPUrr7zCmjVrOOqoo1iyZAkFBQWR+m3aODpWqsrRRx/dVG/DqAfVr+v09HRuu+02srOzeeihh8jMzORf//oXy5cvZ9KkSSxcuBBVjbm4sF27djF6MFoglhC8kUhoCIeIvA+MUtVPG1hvKHCNqo5uYL20ulQHq5XPU9WdbnaQ/wEzVfVJETkJWK6qJSLyR2Coqv7/9s48TIrqetjv6dnZ92F3FGXEBTckig6gRhBRXGKMiWuiSYxxQ/2S2CgSkTEmKibRAC75mWiMW2RxYVEUHMEFcGOzxV2QZRiQfdY+3x9V3fT0dA8z0E1N95z3ee7TXVWn7rlVt6v61K1zz/mJiPQFtG/fvsNU9U9AK7eexjTTM0JZriZPnsymTZs47rjj8Pv9TJo0iVdeecUyWKUxRUVFjBkzJpyk4ZFHHqm1PRRyMdaIlLlxpBYhl46ysrJwJIdo6utvwzDSiscCgUD9yQL2kaWfrU/4H8SRB+enhmEVQcJGoN1U3gfhRLoYIyL/FJFFIvKBiJzjyhSISImIvO+WQe7ufwKKXFeL0SJyhYg8GFH3S66RjYhsF5E7ReRd4EQRuURE3nP3nRIaVY6Fqm51v2bixKBWd/0bqhoKnvsO0NNd/6mqrnL17iSF8Pl8jBkzhjvuuIO1a9fStm1b7rjjDi655BJmzJgR9n+M9RrfSG1Co1ZXXXUVI0eO5KyzzqJPnz61ZFq2bEleXl54OfKh0Izn1GJPiThycnL46U9/GjaeQyPQULvfLUNl08bu1UYDubywsLCv141oDlgq77pcCcyMXhkMBtvUV29Ty0TYv39/5s+fT01NDTU1NRx55JFs3ryZsrIyAHJzc+nVq1etP9Ds7GxLB5sG9O/fn6+//po1a9ZQVVXFyy+/zGmnnVZLplOnTrV+q3l5eeHfgv1RpxbRiTii35D17NmTuXPnhpfjPTiZP3TTJtabT7tWjRiU4ySrSxoWxs7BUnlHICKX4GRI/Evk+mAwOMyVj+u+0dQyEebn5/Pll7s9aTp37kx5eXl42efz0aZNm3CM0ZB/XEZGRtybclM5NqN+8vPzWbduXdhAXr9+Pfn5+bX6taamJuwX6/P5yMzMDD88BYNBfD6fjUimKNEP8V27dmX79u3h5ZqampjXshnQTZtY/RPPBcto1mwAViZTQRBJeElFkpWyKpTKO1Brpcg4dqfy9uE8KcWisam8b21M41S1XERCqbxfddv2Q2AMMERVKyLa3Abo1atXr5fz8vJOBDo2RpdXRN9E4/m1hvygt2/fTnZ2dr1+kk1ldN2on1Df5+TkUFlZCdRN67xmzRoKCgrIzc0lGAyGEzOE9g8Gg1RUVNSt3Giy1Gc4xbt27ZpOTTIzM+POYbE+bdbsAKoDgcAyrxvSHEj0JMKvcEZwbwLaANepqorIMar6gYhMBFar6n0i8nPgn85mOQ64X1WHuPWcDPwZOBnoASwHRqnqPBHZrqqhyXyHAdNxXDg2iEgHoLWqfh2jba3cbWvFSeX9Hxy3kQfFSTX+PHBGyOfZ3ScbmNm7d+8Oubm5h+OMoBtGShNK0DBp0iRefvnluCOPGRkZNiqZwoQmF27cuJFRo0bVK2t9nfpEXtczZ86MGwva+jqt+QhoCxwfCAQ2JkvJB6s2Jvwp7ZhDOqXcMHSyDOgdwAPAIJxR4q9U9SwROQTH/WIn8AaOgd1KRLKAWUAn4HF33ydx4i4vA/KBcdEGtKvzJ8CtOCPWVcBvVfWdGG3LB17CccXIAF4HRqtqtYi8BhwJrHXFv1HVUa5Lx//hGPAccsghRwEqafyOLBS0f+XKldx+++1s3LiRk08+Gb/fz+TJk5k7d27YFSR6BKS+URGjaRHq586dO7NgwQLGjBlDv379uPPOO+nUqRMiwoIFC5g9ezatWrXiiSeeoKamBp/P16R8/Y36iUy60qJFC9q3b09JSQmzZs3ihhtuoGPHjvh8Pt566y1mz57Nsccey9atWykoKODuu+9m48aNVFZWxjXGjKZH5D187NixHHbYYdx5552sWLGCOXPmcNxxx7FkyRIGDBjAP/7xD8rKyti1a1edeizpSsqhQAlwMNAzmZkIzYB2SKgLh6oWRCz+Osb2VUD/iFW3uuurcPyXI4k5GTDSeHaXnwGeaUDb1gPHx9n2wzjrn8Qx5CksLDwJeJM0TT4ToqSkhNtuu43hw4fHNJKCwSDV1dVkZdlgfLpQVFTEhAkTGDNmDLfffjvjx4+vFTc4EvtDTS38fj+PPfYYJ554Yi03npKSEkpLSxk/fjydO3eOu7/1d+rz5ptvMmbMGIYPr5tfI43HgpojAhQBg5NpPAMpO+kv0aS1MZhghnrdgP1BaPQiNAoZTWjCmY1Apg8lJSXhEeiQ8Rzvj9Xn89mfborx+OOPU1paWits5cknnxy+zuvDojykPoMHD2bChAkx+9ru42nHfwOBwFvJVmKZCB3SMhOhxEnlXU8mwqtFZKkr95brWx2qq7eIzNm2bdvvVNVnNxwjXQm9fdi4cWPcP9ZgMGh/uilKZL+99dZbjBkzpt6+BhuBbg6EBkWMtOACrxvQnLBMhE4mwjahJCsiMgq4RlXPcJfnARP69u17l6oOdNc1pplpSyjT4aRJk9iwYYP5SaYQob57++23+d3vfldn1LmkpITZs2czYMAA3nvvPa6++urw5KRQZI9ILHth6lBcXMzpp59OXl4eb731Frfffjv9+vVj2LBhTJo0ibVr13LSSSfh9/uZMmUKL7/8sl3bKcjhhx/OIYccws0330zHjh3DD8ihN00dO3ZkwoQJzJ8/n9LS0jp9LCJkZGTYvJbUQoFjAoHAR8lUsjiwOeE3+wGF7VPOsErYY2dUJsKngT44E/MycSYATheRAuAJnGQoANeq6kKcTIT93FjO/wI2AwNU9Vq37peAe0OTCIH7geHAzW6d1+NkFnwXx/iNaVTXk4lwa4RYy9B6dyQ6s2/fvn2BI5ur4Rya3T158mReffVVdu6snZSxoqLCZnWnIEVFRfTt2zdm8pzBgwczZMgQgLBMaWkp1dXV4T/W6NB3ZkA3TUJuWevXr6d79+60b98+HLN+yJAhzJ8/H3BGIrt06cL/+3//L7xvaWkpwWAwZp/bhNKmSejh+JVXXuGtt95i8+bNjB8/HlWlqKiIG2+8kY4dO7JgwQIWLFjA1q1b6/RhaI6LPTilHIozzyypBrThYJkId6//rYh8jhM+73p3dV/g+7Kysl8Gg8Ec+7OoTeQrf8BeA6YgRxxxBJ06dWLhwoXhqCuh33mof1966SXOOOMMFi5cGN6vurq61rVgr/qbPtF9XVpaChCOuBK6ln/zm99w8cUXc/XVV/Phhx+GDeVYfW73w6ZJUVERl112GT6fLzzHIXRtl5SU1LrWa2pqal2/IkJVVZUZz6mJD/hrYWFhvZmT9xXzgXawTIS71z+kqn2A3wO3uaszgaJ27dqtFpF6/ymaWirvfWFPEwmN9CL0Bzt16tQ9+sWa0ZQehB6OysvL673Gq6ur7RV+mhB5X4+eHBppQEe+YTBSkh8HAoGtexYz9hXLROhmIozgaWCS+3018EFGRsZM4FRVzYtXZ7oYmpExROuLwgF140AbqUkojF1kbOD6onCky4Nic6aoqIgbbriB7OxsgsEgffv2pX///rz66qu1MlBG/g4sLnBqMnjw4DpxoEP9qKrhe7rdz9OGLslWYGHsHCwToZOJ8JBQBkIRORu4Q1UHuFE63j/ooIOeyszMvFNVs12ZhJ0zw2jqFBUVMWzYMO6666646b0tu1nqE0q68tBDD/Gf/zQqkJKRghQVFTFmzBgWL17MuHHjzIBOH8YGAoHxyVTwzidbEj6CcsKhbVPOsErWO5rxOO4aH4vIMncZ4B/A5SLyDo5/8Q53/cdAtRtibjSwAPgSWArcC7wfS4mqrsBxt5gjIh/jjCZ3i9OmljgTHD/GcbDfAEx2t13rhrb7EMf4v9ytvwa4pby8/FZVbbaZQ4qKipg5cybnnHMOLVq0CK/761//SpcuXcKv+qJ9oO1Bo+lSVFTE/PnzefbZZykuLq7lrhPaNmXKFC644AL69OnDuHHj4hrPgBnPTZhQf65YsYJPPvmERx55hC5duiAitfr6/fffZ968eZx55plMmTKFAw44wBImpSCht4ht2tR1gw1tO//88xkxYgRXXXUVEyZMMOM5vbDXRPuJhI5ApyuFhYW/Be4C2nnclCZFcXExubm5TJgwgbKyspgyFp0hNYj0j3zrrbfYsWMHJ5xwAnPmzCE7O7tWP2dmZhIMBuu8zre+Tg3q6+tx48YhIkyYMKFWn4ceks2FIzUJRVJavXo1hx9+eLiv45GRkYGImGGdmhyd7DB2b6/cmvAb/Yn92qTciJsZ0A3ANaBvA7p63RbD8IJQaKwHHniAsrIyM5SbAQ15QDYMo2kRCASSboiaAe2QaB/o64HfAO+rar3h5KL2KwAGqepTCWrHuzhRNiK5FPgLjotHJlAC/FZVa0TkCnfbGlf2QVV91K3r8gMOOGBKdnZ2jruciCYaRkpihnTzwwxpw0gp7ggEAncmU8HCldsSfuMf1K91yhlXifaBvgY4szHGs0sB0OhU3qFU3NGo6g/cmNKRZSlwoaoeBRwBdAZ+HLHbMxGyIeO5A3BHVlbWTJzIIGYwGM2akpISfD4ffr+fbt3iTTcw0o0TTjiB6667zutmGIaxZ15MtgJVSXhJRRJmQEdlIhwjIv8UkUUi8oGInOPKFIhIiYi875ZB7u5/AopE5EMRGS0iV4jIgxF1v+Sm+0ZEtovIne4o84kicomIvOfuOyWeUQ3xMxHWw3DgVZ/P17u+eg2jOeH3+5k6dSrXXHMNPXr0sAQ6aY7f72fevHkMGzasXr9ZwzCaBBd63YDmgmUi3M2PRORjEXleRHq563oA3wKfYTNbDSNMKBHHQQcdxIsvvsioUaPqNaTz8vLIz8/fjy00Eonf72fQoEFkZ2ezcOFCZsyYUa+8iNCqVauYaeINw0gq3ydbgWUidLBMhA4vAgWq2h94DfiXuz7Uq28Cu/ZQryWYMJodJSUlFBcXM3DgQNq2bRtXLhgMmv9sGhAaje7UqRMXX1y/p15NTY2FNzSM/c/1XjeguZAsAzqUiTDkU9xbVVcCo9mdiXAAjhtFLBqbiTCkp1BVx+2pcapaDoQyEaKqZaoaCnL7CI5RDk4mwl7A4Kg21D1gkXAxjOZESUkJs2fP5oknnog7ylxRUUEwGMTn89UZlezevbuNVKYQISP6mmuuYeDAgTFlVJXy8vK4caStv1ODUA6AE044Ia5Mbm4uubm5df77cnJyzL3LIwoLCzsls/6gJr6kIskyoGcD14l7RYnIMe76tsBaVQ3iRMUI3UW3Aa0j9v8KOFpEfK47Rey7NMwFLhCRLq6eDiJyQCxBEWklIt3c75nAmcAn7nLkbKhRwMqI4xhWXV29SFXXN+jIDaMZUlJSwt133811111Hjx49YhpOoRjCwWCQFi1akJPjBMr57rvvAMKxhlOdyDjL8R6oU/1B2+/3c9JJJ3Huuedy//3307179zoyqkpVVVXM/WtqasjKyiI7O94YitEUKCkpYcSIEWzbto3u3bvTokWLOtdpeXk55eXlZGRkkJmZSZs2bcjJyaGiosLzONKPPfYYK1asYPLkyXTu3BmfzxfzPpPq12MUrXDcW5OGuXA4WCZCh+vdTIQf4bz+uMKtfxMwfvPmzb+vqanpCml3oRlGwgiNRP/mN7+hbdu2Ma+VYDBIXl4evXr1qrW9pqYGn8+XFpnvQv7hGzdujOvSlS6uXn6/n/Lycp577rm4o9EhcnJyOOig3R52VVVVVFVV2T01BejQoQOPPfYY/fv3r/c3PXz4cI4++uhaWUtbtWpF375991dTa3HllVdy2GGHcfXVV1NaWhozARSkz/XokoljfxlJxhKpNIDCwsIlwDGQoo9JhrGfCWU+++Mf/8h7770XM2vh7373OwYNGsT999/P/Pnzw9syMzM9H7kyGk9xcTFDhgxh9OjRvPfeezFljjvuOIqLi7nnnnt444030s1wSXv2dF1nZWUxceJEevXqVeu6Dj0khfrb5/PZnKHksRHIDwQCSQt8MG/ZroR33NAj8lLOvkqPd6bJ55/sHi039pGQX128yA0iQu/evcOv+I3Uo6SkhA8++IALL7wwZrxoVeWee+7h3XffpV27dnTt2jXc35HGs4iQm1vv9AOjCSEinHnmmXG3f/jhh7z++uu0aNHC/GNTkNCk4bPOOivs0hFJVVUV/+///T/WrFlDq1at6Nq1K9nZ2WFjOdKQNuM5aVyRTOPZ2E1CDWgRuV5EVorIfxq5X4GINDqRSj31vevGhY4sR4rILNdNZLmITI6M7SwiF4rICndbrYyIwWDw1GAw2NIu+MQQ8qubMWNGzJFGVeWbb76p9RowHWiIb2w6EYoX/dhjjzFhwgTatGlTR+bBBx/k9NNPZ8qUKZx66ql1jKqsrCyKi4sZOXJknX2LiooYP358vQ9axx9/PGeeeSZ5eXm11ocmsSWjH5pbP4eIjBf94ouxcznU1NSQmZnJ9ddfz/PPP0+7du3qyHTu3Dlmn0ZOaIt1Xtu0acN9993H1KlTOeWUU2r5uubm5trExQQRctV69NFHmTp1Kr179661fdeuXfzhD39gxIgRPPzwwzz55JPh6y/0HzpixAheeeUVevbsWaf+UD8feuihcdswcOBAXnjhhTr7h34Xzem6i8GDhYWFdW+2CUQ18SUVSXQq70+AEar6ZSP3GwrcoqpnNXK/jIiIHA2Rb6OqW93Jjc8Dz6nq0yJyCPAscKqqbhaRLqq6IbTfQQcdtD4zMzM0UbExTTQMg90pwCdPnszWrVvZunVrre1t2rThiiuuYMqUKVRXV4fDn2VlZXH33XczceJE1qxZ40XTjb2guLiYoUOHEgwGadeuHZmZmZSXl/PQQw/xyCOPADBmzBjOPfdcPvzwQ+666y7KysrIyMigffv23HTTTcydO5fXXnuNnTt32milYTScFcDPA4FAbD+qBPDG0sS7cJxyZDN24UjxTIS/BB5S1c2uXNh4FpHjtm7d+q2qxp5ObhhGgygqKmLWrFnMmTOHX/7yl7W2HXrooVx22WX85S9/oUePHrRs2RKfz0dVVRXFxcXs2rULn89nD7Apgt/vZ+XKlXTo0IHMzEyCwSDl5eW1ZF599VVyc3MJBoPs2rUrvP28887jv//9L2+//Ta7dtUbft8wjLp0Ar5IpoIgkvCSilgmQoe+QF8RWSAi74jIGa6sD7ivffv22SJis1oNI0kceeSRrF69mm3btgHOm56Qf+WmTZvYtGmT+cSnGI8//njYDStW6LAjjzySzz//nJqaGvLy8sK+7i+//DIff/wxmZmZdVxvDMPYIw8GAoGNyVSgKgkvqYhlInTIBA4BhgI/BR4VkXbANcArPp9viqpaJkLDSBKPPfYYEydOpHXr3eHgKysrAejTpw+dOnVKO5/45kDkqHN2dnbYfQOcPi8vL6/j975mzRpGjBhBdXW1jUAbRuO5Ptk+0IaDZSJ0WA1MV9Uq1387gGNQnwhcW1paeqeq5rj7xj5gy0RoGDHx+XyMHTuWYDDI2Wefzfr16zn//PPp06dPWKZ169bcdddd3HHHHWzatImamhp69eqFz+fj66+/rnNt2XXWtAn1eZs2bbj77rv56quvyMvLY+jQoWGZ1q1b06JFC2688UaGDh1K586d6dy5M7/85S/53//+h6rSuXPn8D3X+twwGsQPA4HA1j2L7T1NcRKhm0jvVRFZ5X62jyHTS0TecINdLBeRGyK2jRORNRGBJ+KHE3KxTIQO03BcTRCRTjguHV+o6sWq2rtz585jRWSHu73hZ8EwDPr378+mTZv4/PPP+fLLL3n55ZdZs2YNp512Wljm7LPPZs6cOXTo0IHKykp69OjB4MGDad/euQe2aNGCmpoaMjIyyMnJsQx2TZz+/ftTXl7Otm3b+Ne//sXcuXOprq7mkksuCcucffbZPPnkk3Tr1o033niDiy++mL59+7Jq1aqwC0+nTp3w+XxkZ2enTaZKw0gyXb1ugEf8AZirqofg2IZ/iCFTDdysqv2AE4DfikikJ8TEiAHZV/ak0DIROswGykRkBfAG8P9UNToVZoOjfRiGsZv8/Hx27tzJunXrAFi/fj0+n4/8/PywTEFBAZ9++ilHHHEEJ5xwArfddhvvvPMOGRkZZGdnU1VVFU6+kJWVFTObmNF0yM/PR1XZvHkzAKWlpagqXbp0CcsUFBQQDAY5/PDD6dq1K/369eP999/n22+/xefzkZeXF44XnZGRUSuOsGEYMVEcd9kkK2mSqbzPAf7lfv8XcG6ddquuVdX33e/bgJVAj71VmNBI9qpaELH46xjbVwH9I1bd6q6vwvFfjiTmZEBVbRW1/AzwTAPath44Ps42BW5ySywG+3y+FnG2GYZRD7Fcm6LnC2RkZNCnTx9eeeUVli1bxvnnn895553Ho48+GpYJBoOWfCNFiGfoRvd5z549Wbx4MTt27CAYDFJdXR1Ot7x9+3batm1LRkZGOKyhYRj1UgN8k2wlwSRM9RKRXwG/ilj1sKo+3Igq8lV1LTiGcsgzoR59BTgZpt+NWH2tiFwGLMYZqd5cXx32b9Qw3sQ50QU458yGQQyjgaxbt468vDy6dnXeLIZGJzdsCEeLZO7cuXz11VesW7eOYDDIwoULyc3NpU2bNmzatCmcBKO6uprt27d7chxGw1m3bh0iEnbBCSWVKS0tDcvMnTuX5cuXs2vXLkaMGMGyZcvo0KEDvXv3ZtmyZezYsYNvvvmG8vJym5xtGHtG3fKx1w3ZG1xjuV6DWUReI7aLypjG6BKRVjjBKG6MCG88CcdbQt3P+4Bf1FtPOt6Y3BjR0TGvLlXVpXtbZ2FhYSbwLY7xnL8HccMw9pKioiLGjBmDz+fjueeeqxW5wUg/QklXysrKOPvss71ujmGkOsWBQKBRBmVjmflBVcINxxHHZO3TwKSIBICh7uhzN2CeqhbGkMsCXgJmq+r9ceoqAF5S1SPq05mWqbxV9QcRjuChslTipPIWkYkRMy8/FZHvI9r2ZxFZ/umnny6tqalpo6otE9VOwzBqE4recNVVVzFy5EjOOuus8Mh1iIyMjFoTyiJTNNtEs9TjhRde4Kqrroq5TUTIy8ur13Un5C5ifW8YANzjdQM8YgZwufv9cmB6tIAb2OIxYGW08RwKMuFyHrBsTwoTfce5BjhTVetNZhKDAqDRBnR9WQfjcKGqHgUcAXQGfgygqqNDhjbwd+AFt/5BwElA/4MPPviqysrKUDbC7cAuIBSY1mY0GcY+kpGRQf/+/fn6669ZvXo1VVVVvPzyy7Rr1y4sk5ubG55MGCLS39YmF6YeixcvZsuWLTG35eXl0a1bt1r9Gm0oh34L1vdGMyHyh17tLu/ESeENTsCEpNIUw9jhZLQ+XURWAae7y4hIdxEJRdQ4CScC3KkxwtX9WUSWuoEmTsEJu1wvCfOBltqpvJ8G+gBHujrGqep0d1j8CXZ38LWquhDnQPu5yVD+BWwGBqjqtW7dLwH3quo8EdkO3A8MB25267weJ6b0u8A1EXGia1FPKu9IfgrcEdoFJwZ1tqoeUF1dXQW0AKpwXDlCBnzQXRZ3H/ORNoxGUlNTQ35+fjhaBzjRGyInkFVUVJCVlUVmZiZVVc7zbCjFdyhKQ6jYxLPUJ1YilaysLCorKxGRWkZz6DdgGGlO5BNkJo790QInMV0lUAQ8m8wGNMXU227ktOhgFKjqdzhhi1HVt4hjn6nqpY3Vaam8a287ADgQeN09prdxwtqt3bBhwyMZGRmr3dGuFjiGdegBJHJiYdP7ZaUxfr+fUaNGWcrfNGH27Nncdttt4eWpU6fy85//nEWLFrFs2TImTZpUK/wdOBkLQ4aTqpKRkREOf5aVlbVf22/sHWvWrKnl/1xcXMyiRYtYunQpv/vd7+jRo0e4LysqKlDVWsZzVlYWrVq1sj5PIUJ9vGzZMiZPnkyvXr3q7bvMzExat25NVlYWOTk5FtJwN9EG9b1eNaS5Yam8a3MR8HxoBFtEDgb6HXLIIV927dq1RV5e3tGqGvMKt1TehpEcZs6cSWZmJpmZmYgIW7ZsCY8+x6O8vJzq6mr7k01R9qXPjdRg5syZ7Ny5s9achj31MThvqiorK5PdvFTFB2QVFhZ2SqaSJurCsd+xVN61uQj4b8TyecA7n3766TEikhUMBnfGq9NSeXtDcXExM2bMiPmq10gPSkpKOOaYYzj00EN58skn+d3vfkenTvH/H3w+H1lZWWRkZNgfbYoS6vMXXniBI488kptuihei37n3ZmRkhCcahowwuxc3bUpKShgyZAj9+vXjiSee4Ne//nU43GEsQkZ2KKmODVbFpQrn7byRZCyV9+7thUB74O2I3b4BhohI5rp16zJEJNKQN5ogJ598MmeffTYtWsTOe2N/qqnNiBEjOPXUU3nmmWe466676NixYx0Zn89Ht27duPrqqznggANo1apVrX7Pzc2lS5cu9ltIAfx+P/PmzWPYsGHcfffddO/enZyc2hFKQ77vgwYN4pBDDuGAAw6gdevWdeoK+cobTY/QdX3ttdfGNYwzMjLo1KkTF154obnp1E8ete2phKMqCS+piKXy3s1Pgae19tX7PPA5sLSqquoTcWjoOTA84K233uLFF19k587YLwu8HLUoKirirrvuqneUJd0oKipi1qxZTJgwgTZt2sSUGThwIFOnTuXss8+mc+fOtbbl5ORw4IEHkpvrPLv6/X4GDRrEaaedxuzZs7nxxhvrGFS7du3i888/5/HHH+eWW25hyJAhtWTKy8vZuHFj0n4LzbGfI4ns81gPsldeeSXTpk1j6tSpjBw5so5MdnY2ffr0CZ+7UJ+rKs899xxHHXVUnTp37drF3LlzKSoq4uGHH2bAgAG1+jc3N9cmGSaQUB/Pnj2bww6L7Yk5cOBApk2bxksvvcQvf/nLWtsOOOAA/ve//3HMMc7YWqiPx40bR1FRETNnzuTQQw+ttc+uXbv48ssvmTZtGhMnTmTatGn06LHXWZjTlkAg0DkQCGzds6Sxr6RlIpVEU1hY+FugGIhtARiGsd8pLi4mNzeXCRMmUFYW+43lOeecw0cffURZWRnbt2+vZUD5fD4LfZZi7KnPRYRRo0ZZn6cwocQ6Dz30EP/5T+yUEmPHjqW8vJxJkyaxbdu2Wtusj/l7IBC4PpkKpi2qSbjheO7xGSk32mCR5xvGYKCV140wGk5oFDBy9r6RXvj9fsrLyxkzZkxMVw6A6dOn87Of/YxTTjmlTqSWZv4nm5KE+nzGjBkMHFjXs09VmT59OhdffDHPP/88p5xySp044T6fz5KuNGFCbjvHHXdc3Ov6zjvvJDMzk1NOOaXOGwy7rvky2QpsEqFDQkegReR64DfA+41JpuLGch6kqk8lqB0xU3kDf8Fx8cgESoDfqmqN6zf9T5zkKpuAS1R1tYgcDUxq3779AR06dMj3+Xy+5vhKNtW56KKLeO2119i0aZPdXNOUoqIihg8fzuTJk9m6dStbt9Z+g9mmTRuuv/56nnvuOT7//HPatm1bZwQzIyPDYkenEKHR6Pvuu481a9bElPn1r3/Nm2++yapVq2jfvj2lpaX7uZXGvpCI67oZMjYQCIzfs9jeM/W9xI9Anzcw9UagE5ZIxeUaYISqNvYJqAAnE2GjDGgRyYiVNMWNER1L/kJV3eo6Mj+Pk4nwaRw/63+r6r9E5FTgbhyDeydwWefOnYep6gSgbWjCipE6PP300143wUgyJSUlADz66KP4fD6ee+45HnnkkfD2rVu38re//Y3i4mJ69+5NeXk5V1xxRR1fefOTTS1OOOEEJk+eXCuGdCSdOnXir3/9K+Xl5fz+97+vY0Bbfzdt9vW6FhF8Pl9zezBO+iiRWroLIIEuHFGZCMeIyD9FZJGIfCAi57gyBSJSIiLvu2WQu/ufgCI3reJoEblCRB6MqPslERnqft8uIne6o8wnisglIvKeu++U+tJ715OJ8DCciB7gJE45x5X/VFVXAYPdRDCGYTRBfD4fY8eO5aqrrmLkyJGcddZZ9OnTp5bM1VdfzcqVKxk1ahTXXXddONxZKA5tTU0Nqhr3tbHRtHjhhRe46qqr4m4fPHgwBQUFDBs2jNtvv72WkR1KD6+qtGnTxlw6mij7cl23bt0aVW1uxjPAS143oLlgmQgdPgJ+5H4/D2gtIpH/om/WFwPaMAxvOeqoo/j6669ZvXo1VVVVvPzyy5x2Wu2srocccgjvvPMOAOvXr6dTp05kZGTQoUOHWgZUVVWVZbZMARYvXsyWLVvibj/ttNOYNm0aAB999BFvvvkmPp+PFi1a1IreUF1dTcuWNj7SFOnfv/8er+s+ffrUua6zsrLC/u4hmtFDUtJ9oIOa+JKKWCZCh1tw4j1/AAwB1uAkcwEgGAwOw42rGM99wzIRGoZ35Ofns27duvDy+vXr66T8XrFiBaeffjrghDVr3749OTk5lJeXh5NwgHONt23bdv803Ega0b+JtWvXkp2dTW5ubjgsIjgPTDY3omnSkOv6k08+qXNdt2zZkh07doTXZWdn17rG05ykxoAGm0QYwjIROsvfqer5qnoMMMZdtwVARNrs3LmzSERqRCRuN1smQsPwjljXXfTD7MMPP0ybNm2YNm0aHTp0oLy8HIBt27ZRVVWFz+cjJyeH6upqexBOIeLdc6Pvx9anqcfeXteqGk4DX15eTmVlZdgfuhmwbc8iRiJI1iNZKBPhdaqqInKMqn6Ak4lwtaoGReRy6s9EeI2I+IAe1J+JcLqITFTVDSLSAWitql9HC4pIK3fb2ohMhCXutk7AJjdD4q04ETkQkWxgamZm5tcicjiY57xhNEXWr19P165dw8v5+fls2LChlsyOHTvw+/3h5bZt24YnG4X8JCsqKqiqqgqPXhmpy7p169i1a1d4uVu3bnzzzTeUl5ezadOm8Pqqqqrm6CebEmzYsKHR13UoDnR0POiKiormMsC1orCw8LBkJlOxZ1EHy0ToMBQIiMinQD4wwV1/ITD4m2++kU8//XTFd99990UwGNzeuFNhGEay+eijjygoKKBnz55kZWUxcuRIXn/9dTIyds8p7tWrVzgm+EknnRTXSO7UqdN+abORGKJHJLOznRebr7/+OjfddBPg+MgPGTIkLBOaRAjEzZBpeE+86zrUxwCdO3eudV2HRpk7dOhQp75m8BZiSiAQ6GmZCPcPlomwkRQWFgqOwX8YzgOIRpQM97NZPOYaRlNi8ODB+P1+MjIy+N///sfkyZO56KKLACeU4dFHH80999xDMBhk1apVjB49utbIY6w40BbmrOly3333MXDgQNq3b09ZWRl///vfw36uodCVY8eOpaioiF27dnHrrbeyfPnyWnVE96/1d9NjX67rZhTGrhp4OhAIXLo/lD29MPEXyUWDUu/1gBnQycFOqmEYhmEYiaRJGJn/XZB4w/GnJ6WeAZ1QH+imnolQVZe622cAB6nqEe5yDvBvnIgeZcBPVPUrd1tv4FGgF6C9evW6Jy8v7wGgDbtdYILAZiAAHJ6IYzCM5kZmZibV1dV11mdkZJCVlUVlZeVeRUuINapYVFTEmDFj6NChA3/5y1946aWXavnLAmRlZdGyZUvKy8vDEw6N/UdOTg6VlZX1jgjHyx4Z3efFxcUMHTqUsrIy/vOf/1BSUkJZWVmdfs3JySEvL49t27Y1h1HL/Ua0PzI40TEyMzPZvn3vvCJ9Pl+dyFeR1/XNN9/MggUL0i3CiuKMNlfjBFcI4tgh1Tj23O2BQOAu75rXvEh0Ku9P2ItMhG6SlFtU9axG7hczE+Ee9jkfuADoH2FAX+MuXy0iFwHnqepP3G3zgAmq+uohhxxyp4jc3kwmIhhG2hNKFTxp0iTWr18f04CPZ9gbqUfIkJ4zZw7jxo2LKZOdnY2qhhNyGKlH6Lp+4IEHKCsra05uOQq8HwgEBiRTyVNvJf6E/uzk1DOsmlUmQjcSx01A9BPaOcC/3O/PA6eJw2FApqq+WlhYmOXz+fwiklaPs0ZsiouLWbBgASNHjvS6KUYSKSkpYfbs2Tz66KOMGDEi5iz9SOPZHp5TG7/fz7x58xg2bBjTp0+PKVNZWRk2nptJ2LO0o6SkhNtuu42NGzc2J+MZnDwZdpPaTzS3TITjgfuA6KyCPYBv3eOoBrYAHXEihXwvIi9s2LDhs2AwiKraHbUZ4Pf7+cMf/sAf//hHpkyZYpEZ0piSkhKmT5/OmWeeSefOnes1mprZn3Fa4vf7+f3vf0+PHj32eG2n2et/I/3JBHomW4llInRoNpkIReRo4GBVnRprc4x1ivNjLAJu6dy58wv1jW5bJsL0o6SkhAEDBlBWVobf76djx4573slISSZPnsxTTz3F9ddfXye1t5F+2LVtpDF5XjegudCcMhGeCBwnIl8BbwF9Xf9mgNU4kwRxk6y0BTa56z/o27fvCBG5ut4DtkyEaYvf72fq1KmMHj2anj170qJFC6+bZCSBkDvHjTfeSPfu3a2fmwF+v5/y8nLGjBlDjx49vG6OYSSCloWFhX2TqUBVEl5SkWQZ0KFMhAIgIse469sCa92Mf5dSfybCo0XEJyK9qD8T4QUi0sXV00FEDoglqKqTVLW7qhYAJwOfqupQd/MM4HL3+wU47icKLALaV1dXtyLOuSoqKmLWrFnMnj2bww8/HL/fz6GHHlpLxufzkZeXFw72bqQekb6ys2bNYuHChYwaNSpmnx5wwAHk5EQHgTFSgch+nj17dr393LZtW+vnNCBkRD/33HPMnTuXmTNnxu3zFi1aWJ8bTRkFKnEGKZOnRBNfUpHmlImwPh4DOorIZziTDP/g1l8D3FJeXn5rMBjco/XbpUsXcnJyaNu2ba315kfX/MjMzLS3Ec0A6+f0YObMmWRnZ9OtWzdat27NjBkzYkbhEBEyMzPDCVsMo4khOK6nK71uSHPAEqk0kMLCwrVA1/pkLItV8yAUCmvjxo2MGjUqpkxWVpaFwUpxfD4fJ598Mrfeeit//OMfeffdd+36TmNEhAkTJti1baQ6OwOBQMtkKnh8XuKTxV0xNPWih5gB3QAKCwt/CxTjJE8xDMMw0pjQQ/Ls2bP54x//GFMmVnIQw2gCbAYKAoHA1mQpMAPaIS2nmovIu25c6Mhy5N7WFwgEHgJG4mT9MQzDMNKYyHjRAwfGnoJjxrPRRPkqmcYzmA90iERnImwSqbwboC86lfdNwFU40T9KgV+o6tfutsu7d+/+YIsWLVq6yVUarCeUVlREGD16NB07dsTv9zNp0iRefvnluKli46WnNZomxcXFnH766eTm5vLdd9+F+3r48OEsWbKEAQMGMHHiRDZu3Fhrv4wMZw6t9XVq0JB+DmU0jHzFb/2cukT2+YwZM7jnnns46qij9nhtR2KufcZ+pgw4KJlG9D9fT/wI9C9OtRHoa4AzG2M8uxQAP2ussvriMtezz/nA9qjVHwADVLU/TibCP7uyHYA7WrRo8ZqIVMHeJVJYvHgxq1evBmDJkiUsWbKk3nrsjza1mDlzJiJCRkYGy5YtY8WKFXVkYvW3qtooVgrh9/v5+OOPycjI4IsvvuCrr76qtX3AgAH06tWrTp/W1NRYP6cokdf2kCFD2Lq1rk1SWVkZ/h4rfrgZz8Z+pmWyR6ANh4SNQLupvH8BBICngT7AkTgzQsep6nR3pPkJnGyCANeq6kI3Kkc/nMgb/8Lx4Rmgqte6db8E3Kuq80RkO3A/MBy4Gcf4vh4npvS7wDURcaKj29gKmAX8Cng2NAIdJXMM8KCqniQiPwWG9u3btx1OXOsMV2avz5OR3hQVFTF8+PDwSGRkGmgjfbB+bn4UFxeTm5vLvffey3fffed1cwwjFuVAViAQSGqYmEfnJn4E+qrTmvEIdIqn8o7kSmCm+z2U4vtg0tRf3EgskXGEzzzzTIv9naZYPzc/IuNFjxw5co/yls3S8IBcnDjQxn7AUnnXlrkEJ0PiX0KrAAKBwHE4sakNY4+UlJTwwQcf8Ic//IExY8bUKxvyjzVSj5KSEkaMGMHmzZu544476NSpU9y3U2ZMpQd+v5/58+czZswYxo0bV6+sue0YHvF6shXYJEIHS+Udqkjkh8AYYJSqVrirVwO93DB2BzfkwOujqKiIu+66ix49etgfahpRVFTE/PnzWbZsGRMnTqRFixbMnDmTrVu3csYZZ3DnnXfSokWLOskXfD6f+UemGFdffTVLly5l8uTJtGvXLuzKsWTJEkaPHk337t1p2bJlnQcj6+fUpCHXtmUmNJoYh+5ZZN8IBhNfUpFER+H4CscwvgknZvJ1qqoicoyqfiAiE4HVqnqfiPwc+KezWY4D7lfVIW49J+NM5DsZx41iOY5hO09EtqtqK1fuMGA6jgvHBnfSX+tQBI162lkAvBQRheMYnMmDZ6jqqgi5DsCSgw8++AMRORvHn9t8oI1GUVxczJAhQxg9ejTvvfdeTBmLKZseFBUVMWzYMO6++2527qzPU8xIB0LxoktLSznnnHPqlbVr3NgPKCCBQCCpRsqUOYn3gf71sGbsAx1FqqXy/gvQCnjOjRk9w61/EzD+s88+619ZWRmExBrPRUVFzJo1i7/+9a/06tWLvLw8RAS/38+oUaPIy8tLmC7DO/x+PyeddBLvvfdeeERr8uTJ9OrVi1NOOYX58+ezdOlSli9fzgUXXFBn9NLn89lDW4pQUlLC7bffznHHHRezn5cvX87y5cv58Y9/TG5u7p4rNJo0oXjR3bt3Z9KkSXTq1InBgwfX6usLLriA3NxcM56N/YEAFBYWdkqmEnPhcEjoTE1VLYhY/HWM7auA/hGrbnXXV+H4L0cSczJgaPQ5YvkZ4JlGtvMr4IiI5R/WI/vPwsLC4cCBjdHRiLbw3//+l9WrV6OqjBkzhnPPPZeLL76YrVu3Mn/+fHv924xQVevvZkBGRoY9FKUJM2fOZNiwYWG3vOjrV0Ssr439jc1q3g9YKu8GUFhY+AxwARaJwzAMw4hDKNTdhAkTKCsr87o5RvPkE+DSQCCwOFkKJs1KvAvHb85o5i4cInK9iKwUkf80cr8CEWl0IpV66qs3lbeIzHBdS0LLV4vIUlfuLde3OkxNTc17NTU1CR8ZjHTh6Ny5MxkZGdx+++0sWrSI5cuXM3ToUBu5SDPiuXCEXvf+5Cc/iTnZ0H4HqcWe+vmiiy4yF600IbKvJ06cSHl5ObNmzWLZsmUsXbqUH//4x9bXxv5Cge7up5FkEh1s+xpghKp+2cj9CnAyETYqlbeIZMRKmuLGiI63T6xMhE+p6mR3+yicRC1nhDZu3br1rLZt2wbZzyPQ3bp1Iysrq1amK8MwDKPpkp+fT6tWrRARNm7cyLRp0yxcpbG/EJwADknN9BM08xywTISxMhH+FLhMVUe4y8d17NhxSvv27Y8UkWwbCTSSxeGHH866devYvHkzYHFk05VQ6LuJEyfaa/40JhShY86cOXuMGW0YCaQm2ZkIH3wl8b6/156ZesaVZSJ0EZHfisjnOOHzrnfX+YD7WrduHTf5imEkig4dOjB69Gg6dOhgEwnTmFAWw5EjRzZoZNIe2lOTUISOYcOGMXfuXMaPH19v5JWMjAxz1zISwRdeN6C5YJkIXVT1IVXtA/weJzQeOC4pr2RnZ3/PHs5VKHqCGT7GvhBKttOzZ886vtDR2B9tanPEEUfQs2fPPYazsygOqYvf72flypV069aNjh07Ul1dvcd97D/E2Ef6FBYWdkimAgtj52CZCOvyNHBuxD7XlpaW3qmqPoh/cwv9ydkfnbG3lJSUMGTIEK6++moKCgp48cUXOeGEE+L+pnJzc2nRosV+bqWRCEpKSvjd735H7969mTp16h77uWvXrmRlWWSqVOTKK6/ksMMO4/vvv2f+/PkMHDgwplxNTQ05OTl0796d7Ox4f42GsUe2BwKBTV43ojmQLAN6NnCduP8IbqY/gLbAWlUNApcCofeX24DWEft/BRwtIj4R6QXEvuPAXOACEeni6ukgIgfEElTVSara3Y1VfTLwqaoOdfc7JEJ0JLDK3ediVe3duXPnsSJS6co28BQYxt5TUlJCcXExZ511Fh07doz5u9u1axfl5eUetM5IFA3p5507d1JRUWH3nhTH7/czf/58LrroIjp27BhTZteuXezYsSPmNsNoIK0KCwvbJFOBpfJ2sEyEDteKyHLXDeQm4PKo7YNFxIYEjP1KSUkJt912G/369WP8+PH06NGjziikTTRMfRrSz5s2baKysnKPbj1G08bv9zN16lRmz57NlClT6NmzZx2ZnTt30r59e3vjYOwtNUDXZCpoii4c7gDqqyKyyv1sH0fuq4iwxYsbu3+tuszfqmEUFhaOBf7odTuM5kVxcTEDBw6kVatWzJo1K+Zs/oyMDGpqYgaeAZy3JnadN21Cvu+dOnXirbfe4rbbbmPjxo3hfhMRBg4cyLJly+odobS+bvqEorC0atWKYDBYJ+mKiNCnTx8+++wzD1tppDAbgWsCgcBzyVLwwIzE32RuHLVvr9hE5M/AJlX9k4j8AWivqr+PIfcVTpS3jXuzf6197Ga7ZwoLC38LFOPEVzSM/c6+hsTKzs62eOIpQsjAmjRpEqWlpY3qt9A8DHsz0fRJRJg7n89nfW1EEwRGBQKBl5Ol4P7piTccbzpnnw3oADBUVdeKSDdgnqoWxpD7itgGdIP2j8QyEdbe7wIRUREZELHuz9u3b58QDAZbW5QNwyv8fj+DBg0iOzubhQsX8uKLL8aVjTWhNdIIy8nJSWpbjX2jpKQEn8/Hc889x//+97+4cqGQZ5Ghz1Q1bFBlZGRYAo8mTGOu6Vh9DbVduKyvDZcKoDnOLs9X1bUA7meXOHKK4/a7RER+tRf7h0m0D/Q1wJmqWl8s5lgU4GQibBQiEvOOoao/iIjMESpL3X1iZSJERFrjxH9+N2LdIOCkli1bzhYRe8w3POeFF17gqquuqleme/fu4Qe9gw7aHdXx1ltvxefzUVFRkdQ2GvvOnvo5NMqsqnHdNkLbjaZNY/ra5/PVMpRFJLxcnxuX0azIIUaui0SSDB9oEfmViCyOKL+K1isir4nIshjlnEY0/yQ3F8kI4LciMnhvz0PCDGhxMhEeBMwQkTEi8k8RWSQiH4QOzh1pLhGR990yyN39T0CRO1I8WkSuEJEHI+p+SUSGut+3i8idIvIucKKIXCIi77n7TolnVLv7tsKZJHhXjM3jcZKoRIY1UCBXVd/GiRRiGJ6yePFitmzZEne7z+dj507n3qmq7Nq1K7ytXbt2tUasLARe02VP/ZyRkREeiaypqSEzMxOfz0e7du3CMpGj0UbTpTF9XV1dXWcU2gxnIwZDklm5BjXxRfVhVR0QUR6uo1f1h6p6RIwyHVjvul7gfm6I2XbV79zPDcBUdkd5a9D+kVgmQsJh9nqp6ktRx/Q28MZnn302bseOHa1c2T00zzCSy5o1azj77LNrTTwL/S6DwWA4FThAWVlZeNvvf197PkTI0DaaJqF+Bur0dXV1da3R5erqajIzM+s1xIymS2RfFxcXc//994dD3UX3dcgdK9pNyzAiWO11AzxgBrsjqF0OTI8WEJGWrrcBrk06DFjW0P2jafaZCMVJ1z0RuDnGPgcD/YCeOTk5IwCN90rUMhEa+5uQr6zf76dbt9jRG0Ohz7KysmjXrp394aYoofTfN954I927d48Zzq6yspLs7Gzr6xTH7/dTXl7OmDFj6N69e0wZVSUzM5O2bdtywAEH2LwGI5Ia4JtkKghq4ksC+BNwuoisAk53lxGR7iLyiiuTD7wlIh8B7wEvq+qs+vavD8tE6CRwOQKY5247AccNZQBwHvBO3759V2RlZb3q6ot9wJaJ0PCAUFzZa665ptZIdCTBYJD777+fgoIC+32mMCEj+tFHH2XAgAEx+7KiooIRI0bwi1/8grZt23rQSiMRhIzoW265JW7SlaqqKo466ijy8/Opqqrazy00mii7gL8FAoFpXjdkf6OqZap6mqoe4n5uctd/p6pnut+/UNWj3HK4qk7Y0/710ewzEarqFlXtpKoF7rZ3gFGquhjnKW7Ip59+etCnn36aXVFRYX7QRpMjMhHHvHnzWLZsGX/729/o3bs33bt359hjj+W6667jiy++YPr06Xz44YcMGRLfRc7nS9ZtwdhXSkpKGDFiBFlZWeG+Xrx4Mffffz89e/akTZs2zJgxg0cffZQnnngi3Nfx+tQiNzRdQkb0iy++yNy5c5k1axZvvvlmrb6eN28ep59+OgsXLuTDDz/k7LPPtgQszZs8oG52ngTTFBOpeIFlIqyf54HP3XZ8lJOT09pG8IymyhVXXEHnzp1RVbZt28aUKVM4/vjjCQQCAGzdupWvvvqqVkisWNjEs9TjhBNO4Ljjjgsvn3LKKagqVVVVcd86iIhNQGvi+P1+Vq5cSbdu3ejRowclJSV15i6sW7cuHKGjVatWMeuxh+JmQzAQCFyUdCVBTXhJRRKaF9YdwQ3x6xjbVwH9I1bd6q6vwvFfjiTmZEBVbRW1/AzwTCPb+RWO20asbUMjvtcAv3YTqYyPJW8YTYGioiIOPvjgsG9k586d+dWvfsW3334LwMCBA/nHP/7B5s2befvtt1mxYkUdQzkUEqu6utqLQzD2gXfeeYclS5aEl6dPn87333/Pj370I1asWBHTiLa+bvpEX9fnn38+r7zyCqtXO3PEevbsyW9+8xs2b97Mxx9/zNy5c61Pmze+wsLCkwOBwFteN6Q5YJkIG4BrQP8JiP14bxiGYRhJJpS9sKysLBy1I5rs7GyCwaAZ0s2TcmBtIBCIGUwhURQ/U5Nww9H/k4yUe71vmQh3r7tQRFaIyHIRecpdd7SIvF1aWjqmuro6zx42DMMwDK8IJV2JjMIS6Z5xyCGHkJ+fT3V1NVlZWbW2RX433/e0JZf94ANtOCTUhQMnE+EIVf2ykfsV4GQifKoxO4lIRkREjjBujOh4+9TJRCgih+C4k5ykqptDkxJx4kVf1rlz52Gqei+QEcr8ZRiG0ZQIRQGK5ZpjD//pweLFi+nRo0ctYzgvL48dO5zpRG3btmXTJid4QFVVVVgu+j/LfN/Tmu8LCws7BQKBjclSYLcTB8tE6PBL4CFV3QzhDDWo6qeu3/ZgEUn0w4Zh7HdsMlH6Est4Bsx4TkMi+zlkPIMTxrC8vLyWbOjByn4HzQIF2uAkmTOSjGUidOiLExd6gYi8IyJnRG1/MxgMfr+HdhlGk8cibKQvZiA1HyKv48gkKvYbaPasBzYBsYOHJ4igasJLKtLsMxG6ZAKHAEOBnwKPiki70MZPP/30hTVr1uwEqkQkpgVimQiNVKC4uJiFCxcyY8YMr5tiJJhY956ioiJmzpzJqFGjzO81TVizZg0jR44ML1dUVISv6+Li4loj0sFgEFUNfxppTyXOKHRSR6A1mPiSiiTLLSGUiTBQa6XIOHZnIvThzBiNRWMzEd7agDZFZiLMBLqIyDw3bN1q4B03nN6XIhLAMagXiUgb4OWKiorfi8iTxHnoML9oIxXw+/1eN8HYj4QSrxjpjd/vD0foGDduHOPGjYsp5/P58Pl8FqEjfekNjA4EAva0tB9IlgEdykR4naqqiByjqh/gZCJcrapBEbmc+jMRXiMiPqAH9WcinC4iE1V1g4h0AFqr6tfRgqo6CZgEji828FJEzOdpOCPPj4tIJxyXji9EJBuYCvz74IMP/hpYSzOY4VpUVMTw4cN54IEHKCsr4+STT8bv9zN58mReffVVdu7cSVZWVp30sZmZmXZjThEee+wxTjzxREKTYhcsWMCsWbO44YYb6NixY3jd7NmzadWqFU888QQ1NTX4fD5705JiFBUVcdddd9G5c2d8Ph8lJSVx+/rYY49l69atFBQUcPfdd7Nx40YqKystVXQKEDKihw0bRlFREUuWLOHee+/lsMMO484772TFihXMmTOHAQMGsHjxYgYMGBCODV9RUVHn3u3z+czlK/UIApcADyRTid3/HZJlQI/H6cCP3XTeXwFn4WQi/J+I/Bh4gxiZCIHH3X1DmQiXUU8mQhEJZSL0AVXAb4E6BvQemA0ME5EVOC4i/09Vy0TkEmAw0HHdunU3dOnSpUdoQkY6U1JSQklJSb0ysf5QzXhOHR5//HEOPvjgsFFVVFTEySefbDfGZkB0X0ffzyZNmsSWLVsAwg9MITn7fTRt/H4/RUVF/O1vf6N9+/YAddw3oh+Ag8FgnagczeF/Lk0ZB1ztdSOaC5ZIpYEUFhbeDtzpdTsMwzAMY08UFxeTm5vLhAkTKCuzoAzNhM3AvEAgcH4yldzx76qEG45/vCwr5Z7YLKZVwznG6wYYRqIoKipi/vz5PPvssxQXF3Peeefx5ptvsnz5clasWMGUKVPo0qULQ4cO5a677qJly5Zx67LQeE2bkAvHyJEjmT9/friPQ/3cuXNnRISioiJmzZrF4YcfHt433iik9XnTJdTfL730EiUlJcyZM4eVK1cyefJkOnXqhIgwePBgZs2axY9+9CO6detGVlZWnXpsBDolEeCAZCuJDJqQqJKKpOVdcE+ZCBuLm8p75B4F05TQTP5zzjmHFi1a1Fp/11130alTJ/tDTVGOOOIIzj//fO6+++6wO0foD/bNN99k8uTJHHrooUybNo1BgwaFt2dmZob/YIPBoPV/E6eoqIi//OUv5Ofnk5GREe670IPUihUreOSRR/j2229ZvXp1+JofNGhQWD5Wn5uR1TQpKirisssuY8GCBdx8883s2LEjfF3PmzePyZMn8+2337Jo0SK2bdtWx4DJysqqle3QSBl2AV29bkRzIaEuHCJyPfAb4H1VrS8ec/R+BcAgVW1UJsK9RURmAAep6hHu8kSceNUALYAuqtrO3da7Q4cOszt06NDX9bO2Pw3DMNKGoqIi/H4/f/zjH3n33XfrHQ2yiWWpS8il49577+W7776jqKiIMWPGsHjxYsaNG2dzWNKHciA/EAhsTZaC2x6vTPiQ8V1XZKecYZWWqbz3sE+dVN6qOjpi+3XUdtf4d9u2bdf7fL6+qfqaIVk0xMfOJh6lBpFvE9566y127NjBCSecwJw5c8jOzq7Vz5mZmQSDQUsZnaLE6uu2bdtyxhln8M477yAiTJgwoVafh94wmPGcmhQVFXHMMcewevVqnn/+eebMmcO4ceM444zonGEOobcOZlSnJJJM49nYTcJGoN1U3r8AAsDTQB/gSBwjfZyqTndHmp/AySYIcK2qLhSRd4B+OJE3/oXjCD9AVa91634JuFdV54nIduB+YDhwM47xfT2QDbwLXBPPqHZTec8CfgU8GxqBjpJZCNyhqq+KyGHAw6p6cmFh4WqckHppz96EsbP4oqlFQ0KblZaWkpuby0MPPcQTTzyBqloYuxQkuq+hbiSGeGHs1q9fT0VFBeAYVZHXd0ZGRp3oDYb3hPp75cqVjB07tk4Yu1/96le0a9eOV199lbFjx4YT7ET2bVZWVszoHEZK8EkgEOiXTAVj/lmR8D+ACb/ISbkR6ES7cHwFDABuAlao6pNuRr/3cEZ1FQiqarmIHAL8V1UHiMhQ4BZVPcut5wriG9AK/ERVnxWRfsCfgfNVtUpE/oGTEOXfcdo3EXgT+AAnDvQRUdsPAN4BeqpqjYicC1wFVB500EFnZ2RkZLpyiThdhpESFBUVMWzYMO66666wMRWNvdpPD4qKijjwwAP5z3/+Y8ZTmhEaGJk0aRLXXnstQ4YMoaysjLPPPtvrphmJ5bJAIPBEMhX4H0u8AV18ZeoZ0MmaJTAMGCUit7jLoVTe3wEPumm1a3ASljSWeKm8AfKADbF2ikjlPdodCY/FRcDzESPYmUBRQUHBfRkZGSOo53xFPoiYgW2kEw2JC27Gc3rQkL42UpPIvr31Vid5b3FxMQsWLGD06NG89957Mfcz952U43GcN/1GkknW1PlQKu+j3dJbVVcCo9mdynsAjttFLBqbyjukp1BVx8WpMzKV91tAXxGZFyVzEfDfiOXVwAfZ2dllIlLv3SMUeN6MZ8MwDCMV8Pv9zJ8/n4kTJzJyZOxAU7HmOxhNm8LCwk7JrD8Y1ISXVCRZBnQolbcAiEhoUl5bYK2qBoFLqT+V99Ei4hORXtSfyvsCEeni6ungumHUQVUnqWp3VS0ATgY+jUjljYgUAu2BtyN2WwS0V9Vf4YxuG4ZhGEbaEDKi/X4/AwfG+6u1N6sphA8npbeRZJJlQI8HsnBSeS9zl8FJ5X25O2mwLzFSeYvIaGABu1N530s9qbyBUCrvj4FXgW572eafAk9rhC+GO9J9y8aNGzvVmEOgYRiGkYb4/X5OOukkzj33XO6//346duxYR8YmDqcMnwYCgU3JVGCJVBwslXcDKCwsfAY4D+ehwDAMwzDSEksBnvKsCQQCPZOp4PcP70q44XjPr/JS7hWHpQ9rOOoWwzAMw0hbTjjhBK677rq4230+X8z035FEZq019iu5exbZNzSY+JKKJNSAFpHrRWSliPynkfsViMjPEtiOelN5i8gM17UktHyAiMwVkY9FZJ6I9IzYdnlpaekp1dXVGTZav3e0b9+eFi1ahOONGulHUVERr7zyCgUFBXFlsrOzycjIIC/PphOkAyeffDInnHBCvWncLR106uH3+xk0aBDZ2dksXLiQGTNm1JEJBoNUVVUhIhQUFMQ0lnfu3Lk/mmvUJemGSlA14SUVSfQI9DXAmY1J4+1SgJOJsFGISEyLTFV/EBGZI1SWuvvUyUSI42f9b1XtD9wJ3O3KdgDu6Nix44KMjAzFXF72is2bN7Nz506LK5vGjBgxgrZt29KlS5d65XJycmol4AHswSpFERHuuOMOBg4cGHeCmc/ni7nNRiebPn6/n3nz5tGpUyfuuOOOmDKqynfffQewxxFpY78RLCwsbON1I5oDCTOg3UyEBwEzRGSMiPxTRBaJyAcico4rUyAiJSLyvlsGubv/CShyR4pHi8gVIvJgRN0vuclWEJHtInKniLwLnCgil4jIe+6+U+IZ1e6+rXCSvNwVtekwnIgeAG8A57jfhwOv+ny+3iJiQymGEYeGTEKqrKxk586dZGZm0q9fv/BIdOjByufz2YhlClFSUsKIESPIysrilVde4YQTTqhjLFdWVoYnCGVnZ4ffUIRGJ0Mpo42mSWg0OicnhwULFsSM0hG6roPBIKNGjeKUU06ptT0nJ4e+ffcm5YOxl3QieTk+AJtEGCJhBrSqXo2TKOUUnFTdr6vq8e7yX0SkJU6Sk9NV9VjgJ8Df3N3/AJS4I8UT96CqJbBMVX8AlLn1nKSqR+MkWalv9Hs8cB8Q/W7pI+BH7vfzgNYi0hEndfe3wD8Byy2fAPx+P6NGjSInJ8frphhJwO/3U15ezpgxY2Ia0QDl5eVUVlaSn59PmzZtwgaUpQ5OTUpKSvjggw+48MIL6dChQ1y56upq1qxZQ8uWLcN9XlNTk7J/ns2JUKi7iy66KO51XVNTw/Lly/nyyy9rXdcVFRWsWrVqfza3uTM/2VE4DIdkTSIcBvxBRD4E5rE7E2EW8IiILAWewxn5bSzxMhF+6C4fFGuniEyEU2NsvgUYIiIfAEOANTjJXCKHRur9Z0/1J6n9RXFxMTNmzIibEtpIfUJG9IwZM+LGlf3888859dRTee655zjllFPCf7aqai4dKYjf72fq1Kk8+eSTcf2ig8EggwYN4qmnnmLw4MEetNLYFxp6XV988cUxr2tjv1GYbAWWSMXBMhECqvqdqp6vqscAY9x1W3AyEfYCfgG0q/eALROhsQeKioqYOXNmzFfdIeL5jKYaIZeOnJwcZs2axYQJE2jTprZb3j//+U9+/OMfc/755zNt2jRGjBhBZmZmrVHo3Nxc7r333pivgIuKihg/fny9bzOOP/54zjzzzDoTF5N5jhvSz5B+vt/RLh0TJkyo4+s8f/58Lr30Un784x8zffp0zjjjjDr1ZGVl0bp16zrnriHn9fDDD2fq1Km1jDeA1q1bp9359oJIV614EwwnTJjA8OHDOfHEE5k9ezbTp0+v9TsQES677DJmz55Nq1at6uwf6udDDz00ZhvOOeccpk2bxgsvvEDPnrWjtdU3obUZkZtsH2jVxJdUxDIROvt1EpHQubgVx2UjdBzDqqur/+sa1IaxV/h8PsaOHcvFF1/MO++8w9VXX11HJi8vL63eYoSO+corr2TMmDFcdtlldQzZq6++mpUrV3LOOecwb948rr322vC2P//5z5SXl3PLLbdQWlpap+4xY8YwduxYKioq+M1vfhNT/8iRI5kzZw67du2qZWjn5u5+Jk+kMd2QfhYR8vLywg8K6TT5KrrPYxmtF1xwQbjPX3/99VoTT9u1a0dVVRXbtm2rdR1En9eOHTvStm3bWvWKCFVVVfziF7/gjTfeqNWvu3btMvegBPLCCy9w1VVXsWjRopjbBw8eTEFBAcOGDeOGG25g165dwO7f+pNPPsmZZ55Jq1atahm9oX7+2c9+xieffMKll15aZ17E9OnTOffcczn//PNZs2YNv/71r2O2IR0GIvaSaq8b0FxIaFQJd3R3AE6GwQeAQTijxF+p6lkicgiO+8VOnMl616lqKxHJAmbhOL8/7u77JHA0sAzIB8ap6jwR2a6qrSJ0/gTH6PUBVcBvVfWdPbSzAHhJVY9wly/AibyhwJtuHRXutl8ccMAB/8jOzs5xl/f+BBmGYRhGGlBcXMzQoUOZM2cO48aNiykzZswYBg8ezO233857770XXl9QUMBVV13F+PHjzZ0v8awH+gYCgaTN27rhr9sSPsrz1xtap5xxZWHZGkBhYeFvcVKGd/W6LYZhGIbRFAgZ0WVlZZx99tkxZeIZ0Ycffjjr1q1j8+bNBIMpmkmjaXJcIBB4P5kKzIB2MIehhjEYZ3TcMAzDMAx2u3LEI+TKMXz4cBYtWsTf/va38Lb169eHjWfzT08obyfbB9oSqTg0q0yEbpbBQMS6kO90jog8IyKfufsWRNYXDAYzg8Fgpo3WG4ZhGIbD4sWL2bIl/vSg0047jWnTpgFOJI6+ffvSuXNnfD4fVVVV4ZHnli1bWgz4xLELJ9xv0tCgJrykIon+xV4DjFDVLxu5XwFOJsKnGrOTiGREROQI48aIjiUPcLGqLo7adCWwWVUPFpGLgHtw4ksD8Nlnn61u167dgk6dOg10/bUNwzAMw6iH/Px81q1bF15et24d+fn5bN26lerq3XPdsrKyzI0jcSzByWGx1uuGpDvNKhNhPZwD/Mv9/jxwWkQEkeN69ux5bufOnU8049kwDMMwGkasSff2JjepfOh+JvUk2wi0Q3PLRAjwf66xfbvsvrpDGQdR1WpgC9DRDW13X2Zm5sPBYLDUHe0OTRkOkuQfqWEYhmGkKnfccUetLIRdu3Zlw4YNVFVV1XLZKCsr26sR6Og4480IZbf9Ue1+Lw0EAscAPXFsMSPJNJtMhC4Xq+qRQJFbLnXXx5r9qTguKa98+eWXE7744gt/MBjcAVzrbvNF7Be0TISGYRiGsZvCwkLuu+8+wBmN/uyzzygtLSUYDJKZmRmOAb23kwh37tyZsLamGMJu+yPT/d65sLCwBtgZCASS6r4R1MSXVKQ5ZSJEVde4n9tw/K1DCVpCGQcRkUychC+bcLIXXisiX6nqn7///vscVZ1CXYPbZ5kIk0tRURGvvPIK+fn5XjfFMAzDcFmzZk3cEHbz58+ntLSUhQsXcscdd9RKlBQ56mxJbhKGD/B73YjmQrPJRCgimSLSyf2eBZyFk6QFYAZwufv9Ahz3E1XVi13jv0BE/l/79u1rRKS0bu1GsikpKeHMM89k/fr1XjfFMAzDaCB+v5958+YxbNgwXnzxxXplMzMz6x2NtlTdYWKN2SpwYiAQmJl05eYDDSTPgB6P467xsYgsc5cB/gFcLiLvAH1xMhYCfAxUi8hHIjIaWAB8CSwF7gViBgVX1RU4CU7miMjHwKtAtzhtygFmu3IfAmuAR9xtj+H4PH8G3ITjk12Ltm3b9vH5fNnufqHe3hBqCnF8ou2CN4zUxN4mGUZiqC9etM/nQ0Ro3bo11dXV1NTU0KpVq5iyImLh7hzWAZuj1m1iP83LinRZTVRJRRL6S1TVgojFOgnqVXUV0D9i1a3u+ioc/+VIYk4GjEzj7S4/AzzTgLbtwPGXjrWtHPhxfft36dJlJ477yUFAJY7B3NbdHHr/VOd8ZmZmUllZuafmGYbRxEjVm7pRl+zsbLsPe8jixYvp0aNHzG0iQrt27Wr5M7dp04bt27cDtftOVc3dw6ErdY3ltwKBwLteNKa5YsOjDacjsB3IwxnNDn2CYzjHfBixm3Zy8fv9jBo1iry8PK+bYiSJ4uJiFi1axLJly5g8eTK9evUiKyt+RMmsrCxat25NZmZmvXJG0yXRfW73Ye+J9pUO9fFHH31EcXExnTt3Dvfdd9/tDiIR6rvMzExatmxJVlYWOTk5zf0NkVDXfjunsLDw//aH8mBQE15SkbQ0oONlItzb+goLC38L/BRn9DnexEfDMJLAzJkzERF8Ph8nn3wyxx57bL3yOTk5DBs2DBGhqqpqP7XSSCT5+fm0bOkkU+vfvz/HHHNMvfLZ2dmccsop1ucpRGOv66ysLE4//XREhIqKCntDFJsfet2A5kRapvJW1R9EROYIlaX1pPIe7CZ2qRaRC6La9ufS0tIxVVVVnVQ1S1Wb9WOvYXhBRkYGPp+PBQsW8MYbb9RrJFVUVDBjxgyqqqrIzrbn3VTk8ccfp6KigszMTJYuXcq8efPq7fPKykpmzpxJVVWVvXVIIXbs2IGIsHDhQpYsWVJvH1dVVTFnzhyqq6st4lV82u0PJU3RB9oNIvGqiKxyP9vHkCmMGljdKiI3utvGiciaiG1n7lFnIp/iROQT9iKVt5tl8BZVPauR+8VM5V2P/DxXz+Ko9QVAG+AWYIaqPu+uHwT8BRjcuXPnjLZt2+5yI4M0ppnGfuTkk0+mffv2zJ07N2aMUBGxkYsUpri4mKFDh7Jr1y7efvtt/vGPf7B27dpafZqXl8fPf/5zevbsycMPP4yqsnnzZrZu3RqW6dChAz6fj40bN3pxGEYjCPX5/Pnz+fvf/873339PeXl5rcQbeXl5DBo0iG+++YaqqipEhNLS0rAfrYjQvn17vv/+e0sZ3QQJ9fGcOXMYN25cTJmcnBy6d+/OoEGDePbZZ+1NQ2y+DwQCdQzHRHPl+NKE/4k+dnvnfTKsROTPwCZV/ZOI/AFor6q/r0c+AycoxA9U9WsRGQdsV9V7G6ozYZMIpXYq76eBPsCRro5xqjrdNVSfwMkmCHCtqi7ESeXdz02G8i+c2aUDVPVat+6XgHtVdZ6IbAfuB4YDN7t1Xo/jWvEucE1jjGoAVf3K1RN9Z1WcGNTZFRUVWW5mQqMJs3nzZr744gvAmd0d/WdpxnNq4/c7IU6LiooYPnx4TJldu3bxj3/8g+OPP57DDz+cJUuW1PGB3bp1q0XISRFCfX744YfHldm1axdz584lPz8/7BYQOdlMVfn+++/JyMgwA7oJ4vf7KS4uZtiwYQAxjeiKigoKCgq47LLL2LJlC7NmzaK6urqWTKy+b2bEDl/SPDgHGOp+/xdOEr+4BjRO4IrPVfXrvVVoqbzrP6a3gTfatWtX1qlTp+8hfmirVA/Hki4sX76c7777jp07d9ofZRpTUlKCz+fjlltuoUOHDjFlFi1aRGZmJrm5uWRlZdW6dqurq+v8+RpNh1gPN8uXL+faa6/lrrvuon372INsW7Zs4Qc/+AHZ2dlkZmbW6vNgMNicDasmR5cuXWot+/1+Bg0aRHZ2NgsXLuTii+v+lb/xxhuMHDmSAQMGcNNNN9G6deta2+0/mMzCwsK/7Vls30hGHGgR+ZWILI4ov2pks/JVdS2A+9llD/IXAf+NWnetiHwsIv+M5QISjaXyrgcRORjo17FjxzEZGRm7IP4IpmUibFoUFRVx11130aNHD/OJTFP8fj/l5eWMGTOG7t27x5SZPn06P/vZzzj11FPJz8+v5RNtD1hNl3h9E9nnHTt2rLO9vLw83OennHJKneg8wWDQ7tFNhA0bNsRcH0q8ctxxx8W8rqurqxk3bhyZmZmceuqpdO3a1a7r2vzb6wbsDar6sKoOiCgPR8uIyGsisixGOacxukQkGxiFY4eGmITjOXE0sBa4b0/1WCrv+jkPeCcjI6NSnBTfRopQUlLCbbfdxpo1a8xXLo0JGVTPPfccI0eOjClTXFzMEUccwb///W8efPDBmBMLzahKHfx+P1OnTuXJJ59k1KhRMfvzwQcfZNiwYTz99NN1+ryZj1CmBIm6rpupm9Zvk60gqJrw0hBU9YeqekSMMh1YLyLdANzP2E9oDiOA91U1nNpYVderao06mbIfYc/2oaXy3sNxfAMMUdUhqmoGtGE0Qfx+PyeddBInnXQSCxcujJkueMKECQwbNoyvv/6al156ienTp9fKdqaqZGRkmCGdIpSUlDBixAg2b97MjBkzmDNnDr/85S/D27du3cq1117LJZdcAsDzzz/Ps88+S4sWLWrVY/3ddEnEdR0MButNDZ6mpKQLRwKYAVzufr8cmF6P7E+Jct8IGd8u57Fn+9BSeQOIyPEishonG+EUEVnu7vM88PmqVauO3Lp16xZXtrHnwjCM/UB96YIBBg8eTEFBAcOGDeOGG26goqIivC0jI4OamhpUtY5vptE08fl8jB07lquuuoqRI0dy1lln0adPn1oyV199NStXrmTUqFFcd9114bdRIXe70Gh0tC+t0XR44YUXaj0cRVPfdQ00N793DQQCH3jdCI/4E3C6iKwCTneXEZHuIvJKSEhEWrjbX4ja/88istS1EU/B8ZioF0vl7WxbBPSMsb4G+LWbSKV4TzoMw/CO+tIFA/zwhz9k2rRpAHz11Vd07dqV0tJSWrRowa5du8Jy5eXl5OXl1VpnND369+/P119/zerVqwF4+eWXOe200/j888/DMn369OHhhx1XyvXr19OtWzc2bNhARkZGLdeunJwctm3btn8PwGgQixcv5oADYr5YBuC0006rc12XlZXRpk0bNm3aFH5IaiYhTKWwsLAwEAgEkqmkKZ5HVS2jrh2Jqn4HnBmxvBMns3S0XL1z4mLRLB2E9oLBQIs9ShmG0WTp0qUL69atCy/n5OSQm5tLmzZtavlKVlRU0LZtWy+aaDSC/Pz8Wv25fv168vPza8l88sknnH766QDk5ubSvn17cnJyqKysRFXJy8ujbdu2dUYtjaZFfaPI0b+DnJwcWrVqhc/nq/XG2OfzNQd3jo+SbTwbu0nLTIQSJ5W3xM9EeLU7dP+hiLwlIoe5648WkbdLS0uLqqqqgo2NL20Yxv6lPhereBOKmsGfaloS3dexRhgffvhh2rZty7Rp0+jQoQPl5eUA4bCGu3btYsuWLeH4wUbTpL4Rz1jXfGZms52ydFRhYWGbZCsJBjXhJRVJ9K/sGvYiEyFQAPwMJzJGg4mXidCNER1LHpxQdoujNj2lqpNdmVE4iVrOAHYCl3Xu3PlpVe2Km7nR/KANo2lS3x/ttGnTWLVqVXh506ZN7Nixgx07dtSSq6qqqjWiZTRN1q1bR9euXcPL+fn5dUKj7dixg1tvvTW8nJubGzaiI9m2bVuTfC1t7Jnp06fXuq6//fbbcLjCyD4NBoPNpY9XFBYWHhYIBLbuWdTYFxL2yB2ViXCMG4h6kYh8EIrR5440l4jI+24Z5O7+J6DIHQEeLSJXiMiDEXW/JE66b0Rku4jcKSLvAieKyCUi8p677xRx0jM2ClWN/KG1xMlAiKp+qqqrAoHAcZ9++qlv7dq1XwLNPsikYaQiO3fu5L77nNCeBx54YC1DKnIUOlZ8YaPpsXTpUgoKCujZsydZWVmceeaZvP7667VkWrduHY4Df9JJJ4WT50S/dWjZsiVG06W+Qavy8vLwdV1QUBDu2+h9moHxrMBJgUCgZ7KN5yYahWO/k7ARaFW9WkTOwJm9eBNOJsJfiEg74D0ReY3dmQjLReQQnDAiA3AyEd6iqmcBiMgV9agKZSIcKyL9cFI1nqSqVSLyD5zJh/UFEv8/EQklY7lL3atKRH7rtjsbODV6JxEZCFTgBN+egfmPG0aT4r777mPgwIG0b9+e+fPn8/e//z38Kvfpp59m3rx5DB48mFdffZWdO3dy8cUXhyeS1dTUhCNxlJaWenkYRgOpqanhzjvv5NFHHyUjI4P//e9/fPbZZ1x00UWA0+d9+vThnnvuIRgMsmrVKt55553wvrDb7WP79u2eHYdRP3u6rl9//XVOPvnkOtd1KKFK6LpuJlwILEy2kmbwMNIgJJEnQkS+wjGIZ+EkPwnlyu0ADMdJ9f0gTqaXGqCvqrZwR5ejDegBqnqtu/wScK+qzhORaiBHVWtE5FrAz+6A2XnAf+MlUxGRHqq6RkRa4xjQT6rqv6NkfgYMV9XLI9Z1w8moeLmqvhOn7l8BvwKYMmXKcb/6VWOzUBqGYRiGYcSlSfiP/uwPqxNuQT/1p55N4tgaQ7I87UOZCGvNBhWRcezOROgD6jqjOTQ2E+GtNIDITIQiEspEGD1a/TROSsdQm9sALwO3qeo7hYWFZ+CMnLeJaGOwb9++m4EAcPh9990XfqVkGKlIUwr5lJGRQVZWFpWVlQlJ01tcXMzQoUMpKyvj7bffZvny5cyZM6dO2Lrs7GxatGhBeXl5TL9ZI7lERsuIR2NGF4uKihgzZgyBQIB7772X0tLSOv2ak5NDXl4e27Zta06jlkkn1v2kZcuWiEhCR/9D17aIMHr0aN577710S+2tOPZRNY5dFMSxQ6px7LnbA4HAXUlvRHqd073GMhE6y4dEiI4EVrnrs4GpwL9V9bnCwsIzgZeobTzjfu8IDHKP0TBSmqZiPIPzur28vDxhf4R+v5958+bRq1cvLrroItauXRsz5nNlZSVbt26lsrIyIXqNxlFRUbHH32FjjNySkhI+//xzTj31VPr06RPzoaiioiI8odAmiyeOWP24Y8eOpLjOtGjRgpYtW9KxY8cmdR9LIJnsHlT0RawLAncUFhae7EmrmiHJcuHYATyAY1AK8JWqnuUaqv/DiW7xBnCdqrZyDdpZQCfgcXffJ3FcPZYB+cA414Vje2QyFRH5CU5CFh9QBfw2lpuFiLQE3sTJkJgBvAbc5LqC/BX4obv/ZuBaVV0uIpcA/wcsB+jTp8/hPp/PJyLm/9wIiouLyc3NZcKECZSVlXndHMMwDCONCY1Ez5kzh3HjxnndHC/oGAgENiWr8p/c8nXCn0yeufeAlHtiTagBnc4UFhaeBLyOM8nQMIwoiouLGTJkCMXFxbz88ssxZXJycqipqaFDhw51Qo5169aNK6+8krvuSvobSCNBFBUV4ff7mTRpEq+88ko4ykUIESE7OztmohIRIT8/n5///Oc88MADlvmxCVNUVMStt97Kz3/+c9avXx9TpkWLFvzoRz/ik08+YdGiReH1mZmZZGRk7PdkNSEjeteuXWRkZDBlyhRmzJhRJ2xlmpJUA/rCm79KuOH47H0FKWdA20hqwxmChbAzjLjMnDmTrVu3UlZWFvf1d3V1NXl5ebRq1aqOzPr16ykuLt4fTTUSxIgRI2jTpg3r1q2rYzyD8+o+ZDhFJypRVdavX8/dd99txnMKsGTJknrTnYsI7733Hl9+WTsNRHV1da206fsLv9/PypUr6datG5988gmvvfZa3N9ZmrnrrEum8WzsJtEuHNcDvwHeV9WLG7FfATBIVRuVSKWe+t4FcqJWXwr8HegGhK6iYaq6IWK/C4DngONDyVZEpHeHDh1mt2/fvq/P/QdIs4vNMAzDMNKWSDfCww47jOHDh/PAAw9w2GGHMWzYMB544AHKysrCPtPRkx6b0qTqhhAIBJJqpPx49JcJPxnPTTww5QyrRBvQn7AXmQijw9g1Yr+YmQjrkZ/n6onORIgb2u5lHBeNayMM6Hk9evRY2rJly4tUNTQJsTHNNAzDMAzDQ5rZXJzOgUBgY7IqNwPawTIR7mY88GciQuuJyGFAZsuWLT8BWomIGc+GYRiGkWL4/X7Ky8sZM2ZMumcbTXr8RctE6JAwA1pVr8ZJlHIKTrbA11X1eHf5L24UjFAmwmOBnwB/c3f/A1Ciqker6sQ9qAplIvwBUObWc5KqHo3zw9mT68j/ucb27VFh9nqp6ktRsn2B73fs2HFrMBjMUdWUeo1jGIZhGIaD3+/npptu4uabb2bRokVMmjSJ8847jzfffJN58+axePFiJk2aRI8ePcjJyYk5YBadBr4J4sOxjZJGUIMJL6lIsiYRDgP+ICIf4mTwywV644SQe0REluL4Gh+2F3WH0nADnAYcByxydZ2GMwoej4tV9UigyC2XuiHpJgI3x5DPBIqysrIeFpF6nfJDxrUZ2IZhGIbRdMnPz6dly5YAnHvuuXTq1In169cjIvh8PiorK6mqqqrjBw2Niz3uEUL83BlGArFMhDAdOAKY514gXXHcUEYBq4EPsrOzzwE61Gccm2uHYRiGYTQ9ioqKwhMHb7rpJrZs2cLMmTM58cQTmTNnDldddRWFhYWsXbuWzZs34/P56iRuSrHBsd7Au8mqPFVdLhJNs89EqKpbVLWTqhaoagHwDjDKnUS4CGj/6aefngF83OizYBiGYRiGp5SUlHDbbbexceNG/H4/bdu2ZcSIEXz00UcceOCBfPTRR9x+++3MmTOHSZMm0b59+1phF1Nw/lOp1w1oDiRrBHo8TjbBj10j+iscg/UfwP9E5Mc4mQhDEc0/BqpF5CN2ZyL8EliKk4nw/VhKVHWFiNwGzHFdMaqA3wJfxxDPAWa7xnMoE+Ej9R2Em6Xwlvbt239YU1PTNTqOqWEYhmEYqUNRURFr165l8ODB4TB2GzduRES45pprKCsrqzMCnWKjzzVA4nOkR2Aj0A6WibABFBYW/hbHTzrL67YYhmEYhrH3PPbYY5x44onMnz+fFi1acPzxx7N06VIOPvhgFi9ezG233UZZWVktIzrFYkHfEggE7ktW5ede82nCT8S0f/RNqSF+SN4IdDpSjRnQhmEYhpHSXHnllXG3FRcXc+utt/Lmm28yYMAAJk2axPr162Nm2myiKJCQpHRxFaTOg0RSSahPgohcLyIrReQ/jdyvQER+lsB2vOuGqossR4rIPBEJRKzrErHPhSKyQkSWuxMMQ+sv//TTT2+srq7Osh+NYRiGYaQvkfGizz33XGbNmsWJJ56YSj7QQRx31uQpCAYTXlKRRI9AX8NeZCIECoCf0cinpniZCN0Y0bHkwQlltzhq/SHArTjxpDdHTkoE7jj44IM/EpGD3LpT6UIyDMMwDKMRzJw5kxEjRpCZmUlNTQ1ff/11Ko26KkmOA204WCZCh18CD6nqZgBV3eCuHw68umrVqvNEZO1e1GsYhmEYRgpRUlLCMcccwwsvvMCWLVt48MEH48o2weACAiQ11aJlInSwTIQOfYG+IrJARN4RkTPc9T2AbwsLC5/BiQ9tGIZhGEYzwO/3M2/ePDp16sS4ceNiyjRB94MMoL3XjWgOJGsS4TBglIjc4i6HMhF+BzwoIkfjGLt996LueJkIAfJwjPR4XKyqa0SktVvHpcC/cc7DIcBQoCdQIiJH4DzJEQgEflJYWPg59WQ5jJWxyDAMwzCM1MXv9wPO5MKFCxeydOlSSktLWbJkCVdffTUPPfQQr7zySlObZNgbWJWsyjVFU28nGstE6BjQq4F3VLUK+FJEAjgG9Wocoxpgc311m9FsGIZhGOnJzJkzOfbYY9m5cyeLFy9mwIABXHzxxZSVlTVF/+ikTiJMVZeLRNPsMxG6m6fhuJrgyvQFvnCPY5iItFfVzg0/fMMwDMMw0oWSkhImTJhAv379GDJkCIsXL+aJJ57g7LPPJjOzSUUEVmCL141oDlgmQoeQobwCx0Xk/6lqGYCIjO/evfs3qtoyBdN5GoZhGIaRIL799lt27drF0KFDadu2LTU1NU3NfQOgUzIrtxFoB8tE2ADcSYQ/YveIuQFcdNFFlJSUsGnTJnbt2uV1cwzDMAxjv1FcXMyQIUMoLi7m5Zdf9ro5IRQ4PhAILEmWghFXfJxww3Hm4/1TbnTSDOgGUlhY+D2OC4phGIZhGAbFxcUMHTqUXbt2kZmZSadOnQi9rR4/fjwvvPAC5eW1p3uF4kvXZ3/l5eVRXl6+N/7VQaBnIBBIWujd4Zd/mHDDcfa/jk45A7rJBTBMBPEyEe5jtRaY3DAMwzCMMH6/n5UrV9KtWzc6duyYsLexezKw60GTaTyDxYEOkZapvFX1B25M6ciyNF4qbxGZGLHuUxH5Pqp9bcrLy3vaaL1hGIZhGJE8/vjjVFRUkJmZSYsWLaioqOCtt97itddeo7KyEoCMjD17gEbOsQrttxek3EhuqpLoEehrgDNVdU/JTKIpwEnl3Sj2MuvgxRFG9QYAVR0dWgf8HXghap/xIrJ9L3QZhmEYhpHmbN26lZqaGkSEvLw8Tj75ZAYPHhw2nGtqavZQQ+18Ejk5OXvbFF9hYWGbvd25IWgwmPCSilgq77r8FPhvhO7jevbseW52drb5PxuGYRiGERdVDRvC2dnZe13PvkT8CgQCW/d6Z6PBWCrvCNwY0gcCr7vLPuC+3NzcLVgEDsMwDMMwoigpKWHIkCEcfvjhHHbYYfTr14+pU6cyYMAA2rSpOxh85513smDBAi655JK4dd58882cddZZZGVl1TKmu3XrRm5uLj6fD5/Pm2ls5gPtkKyzPwz4g4h8CMxjdyrvLOAREVkKPAccthd1x0vl/aG7HDfdNo77xpFAkVsujdp+EfB8RKbDa4BXfD7fSldvXEJPneYnbRiGYRjNG7/fz9SpU5k9ezZTpkyhZ8+etbYNGjSIU045hSlTplBQUEC7du1o37592CieMGECJSUl3HbbbRx88MH07duXNm3asHbtWsrLyxk3bhw333wzffr0oXfv3rV8rAsLCw/Z7wfcDEmWAR1K5R3yNe6tqiuB0exO5T0AiPd+o7GpvEN6ClV1XLxGRabyBkKpvCO5iAj3DeBE4NrS0tIfVlVVheqIfcBu2BpLtGIYhmEYRklJCQMGDKCsrIybbrqJjh071tp+5ZVXUlZWxlNPPcXTTz9NUVERubm7zZ0tW7Zwxx13cOGFF/Lggw/y5JNPcthhzrjj2LFjeeedd3jooYd47LHHuP766yOr/jyZx6UaTHhJRRIaB1pEvsIxjG8C2gDXqaqKyDGq+oGITARWq+p9IvJz4J/OZjkOuF9Vh7j1nAz8GTgZ6AEsB0ap6jwR2a6qrVy5w4DpOC4cG0SkA9BaVetkIhSRTKCdqm50sxH+F3hNVSe72wtxMhIeqFEnpbCw8BlVvQDwmYFsGIZhGEZjaEjSlX79+vHXv/6V559/nkceeSTmgF1GRkbMCYk+n49OnTqxYcOGhwKBwLUJP4AITrnw3YS/an/j2R+knHGVrBHo8TjuGh+LyDJ3GZxU3peLyDtAX2Kk8haR0cACdqfyvpd6UnkDoVTeHwOvAt3itCmUyvtj4ENgDbtTeYMzefDpaOPZ5U1VbXK5Og3DMAzDaPr4/X4++eQT7r33Xj755BPuu+++OjKffPIJn332GTfeeCPLly/nvPPOIysrC6gdBu+oo46ibdvacQ2CwSAbNmwA+HWyo3AYDpmJrExVCyIWfx1j+yqgf8SqW931VTj+y5HEnAwYGn2OWH4GeKYBbduB4y8db/u4+vb3+Xyp+Y7BMAzDMAxPKS4uZsCAAYAT0m7btm11ZFq1akXr1q3DMkE3vFsomkdo5DkYDJKVlYXP5wvLRJAJHAq8l4zjAFI27FyiSctMhElgMM6IumEYhmEYRr0UFxdz//33h/2e/X4/Rx11FP369WP69OkMGzasThSObdu2cemll3LYYYfx9NNPM2DAADIyMqisrAwnVunSpQuffvopLVu2JDMz7hhoXhIPzXBJtA/09cBvgPcbk0xFRAqAQar6VILa8S6Oy0Ykl+IkSekGhHJtDnN9p3sD/wLa4YSr+4OqvuLW9efu3bv/qkWLFm1CYe/MD9owDMMwjPooKipi4sSJLFmyhPHjx7N69epa2x977DGqq6u5++67+f777/H5fGzfvj1sLLdp04abb76ZJ598EhFh3bp1bN26lQMPPJCHH36YV199lRdeeAFV5aCDDuLVV18NVX1dIBB4kCQx5PyFCfeBnv/CoJQzrNIyE2G8VN7u5jqZCHH8qJ9V1WNwInH8w61/EHDSd99919ENvWcYhmEYhrFHoqNwdO/evdb2WFE4OnToEHbZ2Lp1a8woHF9++SWnn346b7/9Ng8++CCTJ0+msLAwsuoeyTwui8LhYJkIHRQnaghAW5yEMKH1uUC2qnbZi3oNwzAMw2jG/O9//+P3v/89F1xwQZ1tL7zwAtdccw1VVVVMnz6dLl260Ldv3/D2K664ggMPPJBhw4Zx++23M27cuLD/89ixY7n22ms566yzGD58OH369AntZpkI9wOWidBhHHCJiKwGXgGuc4/pbeANYG15eXknMPcNwzAMwzAazpIlS/D5fAQCgTrbFi9eHHbZAPj444/5+msnEm9GRgbnn38+U6dOBeCjjz6ibdu25Ofnc/TRR/P1119TVlZGTU0NL774IqedFo7FkLQJhGCZCENYJkKHnwKPq2pP4EzgCRHxicjBQD+gZ25u7lKIn0jFMhEahmEYhhGLjIwMysrKYm5r1apWcDFycpwpXFlZWWzevJl169aFt61fv56uXbvSuXNn1q1bx/bt26murmb9+vXk5+eDk4huUZIOw4ggoWHsIghlIqz1uCUi49ididAHlMfZv7GZCG9tSKMiMxGKSCgT4b+BK4Ez3G1vi0gu0Ak4D3inb9++jwFH1Gcc28i0YRiGYRiJIhgMxgpTh6qGU4OHsiSHdgHmBAKBpLpwWBg7h2QZ0LOB60SkViZCHP/i1aoaFJHLcSJeAGwDWkfs/xVwjYj4cJzho1Nuh5gLTBeRiXuRifAs4DV38zc4o9ePi0g/HIO91F3/S1WdjjMSfWRDDl5VG2xQN0a2MTS03mS1NV1lvdafarJe6081Wa/1p5qs1/rTWdZr/Y2VbQxenK+dO3eyZMmSmNu2b99ea7miogKA6upqVq9eTdeuXcPbunTpwjfffMMll1xCx44dadWqFeXl5eTn51NZWfkFUDs2XhJ468UhNmKIZSIMZSK8GfiliHyEk+L7Cjcj4fPA56tWrbpm1apVmdu2bVvo5UizuYd4j71pMPaGZPxu0vm3mM7HZiSHpv7/mJGRwbXXxs6wXV6++2V8//79OeCAAwBnBPqrr77i/PPPB5wMhNu2baO0tJSxY8fSvXt32rdvj4hw3nnnlf/iF784NxAIbE7+0RiQ4DjQzYHCwsIzgWdxJjMqjk92pvtdgR2q2trrp/F0HWXwWtZr/fsgqzguTwkhGSND9dRZRVQio/14buOeN/t9JW+EMBgMBn0+3x4HeJrCcaWrrNf665GNeU163d765O677z5qamp48MEHKS0tpby8PGzw33fffQwcOJD27duzefNm/vrXv4YTpDz99NMAjB07lqKiIsrLy7n99tvXfvjhhy2BNoMHD8bv95OTk1PVsmXLe9q2bXt7gw7KSAyRk9+sJKYAi1NF1mv9qSbrtf5Uk/Vaf6rJeq0/1WS91p/OVPespQAAGZJJREFUsl7rTzVZr/Vb2f8lWT7QniJxMhHq7mQqyebhFJL1Wn+qyXqtP9VkvdafarJe6081Wa/1p7Os1/pTTdZr/cZ+xlw4DMMwDMMwDKMRJGsSoWEYhmEYhmGkJWZAG4ZhGIZhGEYjMAPaMAzDMAzDMBqBGdCGYRiGYRiG0QjMgE4QInKDiLQRh8dE5H0RGZbA+o9toFwbETlORNo3UL5TA2Q6NLS+vSERxyYig0Wk0P1+sojcIiIj49TTVkR+IiI3icho93u7enT2ibG+fwPaWxxnfW9x0sXj/l5+LiJ/F5HfiJMxM+nHlq7HlYxjS9fjSuaxpetxeX1s6XpcyTy2dD0uw3ssCkeCEJGPVPUoERkO/Ba4Hfg/VT02QqYX8Bec9OQzgb+oapW7bZqqnut+jzYoBZgOnI3TZ+9H1PkkcKM6KcqHA48CAeAQ4BZVfS5CdgRONsg1wHXAkzhpy3OAy1V1boRsb+DPOCnOv3fb0AZ4HfiDqn7V2ONK1rGJyAM46d4zcdLIn+a2Ywjwgar+v4g6LwPuAOa45wGgJ3A68EdV/XeE7IXAA8AGnEQeV6jqInfb+1F9+7cYx3Up8G8AVb0+QnYZMFBVd4rIPUAfYBpwqiv7iwjZhB9buh5Xso4tXY8rWceWrsfVFI4tXY8rWceWrsdlNBG8DkSdLgX42P38K3Ce+/2DKJlXgauBo4G/AwuBjtGyQNDd9kZE2eV+vh5V59KI7wuBAvd7J+CjKNkPgX7AiUAZcIK7vh/wfpTs28BPgIyIdRnARcA7e3NcyTo2YDnODawFsBlo4a7PApZF1RkA2sXov/bApzHOVzf3+0DgE+D8OMe1GueB5DLgcreUhr5Hya6I+L4E8EUsR/dZwo8tXY8rWceWrseVrGNL1+NqCseWrsdlv8XG95kV74u5cCSOJSIyBzgTmC0irXGMxUg6q+pkVf1QVa/DGQ1+031lpBFyF+KkL/6Lqp6iqqcA69zvp0bV6RORNu73IPANgKpuhDqJcoKqulJV3wZ2quo7ruxK6rrzdFLVZ1S1JrRCVWtU9Wmg414eV7KOTdW5y4TOt0bsE31cEqNNIdnoPKwZqrrWVfAecAowRkSuj1FHP2AjcAbwmqr+C9imqv9yv0fyrYiEjvUroBeAiESf12QdW7oeV7KOLV2PK1nHlq7H1RSOLV2PK1nHlq7HZTQB0jIToUdciTMC+4U6r2o6Aj+PkskSkVxVLQdQ1SdFZB3Oq5qWISFVfV5EZgHjReTnwM3EvgkA/BF4Q0QeAhYAz4nIdJzXQ7OiZL8XkV/juGJsFpHRwLPAD4HtUbJLROQfwL+Ab911vXCesD/Ym+NK4rG9LCIlOO4ojwLPisg7OK+93oyqcwLwvvuwEzqu3jiv9MZHyW4TkT6q+rnb9rUiMhTn9dvhUce1DbhRRI4DnhSRl4l/w7sK+LeIjAO2AB+KyAc4oyI3Rckm49jS9biSdWzpelzJOrZ0Pa6mcGzpelzJOrZ0PS6jCWA+0AlERHoABxDxYKKqb0ZsH43jKjE/ar9jgD+r6ukx6jwamAgcrqpd4ug9GPgl0NfVvRqYpqqzo+R6AbfhPM3+EfgpjuH/NY5P8coI2Wx32zk4vs3i1jsDeExVK/bluKKO7QhV7byPx3YiztP7O+7I93k4I9bPq2owSrY9MDzquGar6uYouaNwRupXRa3PAi5U1f/EabMA1wAnquolsWRcuX5Rx7Uouq17cWwdgGH1HVuKHldj+myHqn6W6GNL1+NKxrGl+TXm+bF5de9I9nEl49jS9RozvMcM6AQhzuSAnwArgJDbg6rqqATULUBrVd26r3U1NRJ9bO6fgEbfHL1ARI7ViEmRCaqzDc4kyi8acowi0kkdl5d429sD1e6Iyp7qsnO7d/XW2weuTJM5t42hIf2QrPPq1t2Qc2u/cRJ/73BlUu7cptK9w617j/1geIQ2AUfsdCg4kxVy9mH/sVHLw3FGgAui1v8iallw/Ip/7H4/DfgbzhOxrwF6X4+z/jygg/u9M44rx1LgGaBnlOz9wEkNPM4OwFic11oC+IGXcKJ4tI8hfwrwIE6kjv8BfwIOjpLpDTyNM3t6FfCZ+/3pGOevl7u+xNWdFbFtWpTsoTgzoF/GmTn9OE5EkveAflGyx8Yoq4FjgGPj9SHOqMhcnAkjC4G+UbJP4vijh34T3wKv4bw1+HGU7AjgS+AtV+9y4HO3HadFyHXHmS2+Bedh7xu3jIs8H1HntnRP53YP/b40arkx/bC357ane26/35dzC2zCeZ16Gu6gQz3H2aA+aOzvNonndl9+48fF6odG/mb39dx+Fufc7s1vvMnfP/D43pFq94+Gntdk3TuSef+w4n3xvAHpUtwbZat92P+biO/FOP5OD7gXz3UR26KjZfwDeB7HteJJ4DmcmcFPA3+Nkv04qiwFKkLLUbKRM4efAUa7N5UrgFejZEuBxe7N48/AMfUc5yvAPcAkYB5O1I4i4E5gepTsn4D/Ay5xj/EvOO4cH1DbwElWxJA3ccLr/dQ9totwjP6zgblRso2JLvJ+xPdngV/j+M+dF6PehEdZwQlFONT9fj6OG01L4C7g4ag6G3Nuz49TfgSU7kM/eHpucR6Or8Xxw1+DE2nnhDi/7wb1QRM6twn/jTf0vCb53CbrN+71ufX03pFq94+Gntckn9uk/MateF88b0C6FJzR0c+AKTgjwH8D/hYlszVO2YbzGiwktxTIdL+3wzE6J7rLH0TVudT9zHIvuGx3OZO6T+0hI/tQHF/tApwn5wOAA6JkAxHfl0Rt+zBq+QP38xCc+NfLcUIA3UHdJ/cP3U8B1uyh3sibVCawwP3entqhilbV0y+r9qDjEre9faJvUNS+EX8WtS1a9gJgPnBmxLov47Tp/XraE92/y4E27ve3qB0CaXk99X4b77ipe4NfEvH9k/rO3x7ObRXOKNv/xSjb9qEfPD23UXX2Bn4HvA98ARTvTR80oXOb8N/4PvxmE3luk/Ub9/rcenrvSPK5TfhvvKHndT+e24T9xq14XywKR+KY4Zb6+B44XlXXR28QkW8jFjNVtRpAVb8XkbOBh0XkOSA7ateQXJWILFLVSne5WkRqIgVVdZSInAc8DNyrqjNEpEpVv47R1nkicidwt/v9XFWdJiKn4Ly6q1W1W/8qnJnN48XJ3PRTHOP/4AhZn+s31xpoJSIFqvqVOFFLoo8tKCIdVHUTzmvDDFfPZtd3OkRSIoaE9LncH7WtVlu1cdFFeooTiF+AziKSpW7iGZwHoUgaGokEGh5lpVRELsEZSfoRTmilkD969Ezyxpzbj3F+V8uiD1hEfhi1KlmRW5JxbsO/NVX9Bucty5/FyRZ2UVSdyYp0k5RzS3J+4435zSbr3CbrN+71ufX63gEpdP9oAvcOSN5v3PAary34dCo4N8Uj3JIVY/tdOBmJYu17T8T3l4AhcfYPRq2L6ToCdAXei6OrJc4NfQawOo5MFo5PW8i/LYgzUv4U0DtK9oNGnKOfAuvd8iMcv7FXcV5t/SpK9ic4rz7nuG0Y6a7vDDwVdd5/g3PjWgosc8/LNUT5peO4osQ6t8dQ1zXl13HO7cHAA/Uc49E4rwg3xNl+eVRpH9FnxXH03QNMBV7EcX8ZHkOuF84bkEluXaPdc/EyET6XOKMgz7rbnmR38oCOwI9i/KYbem6Lon8bEdsG7G0/eH1ugfsb8ftuUB/Uc25n7c9zm4DfeGmc7Q39zSbr3O7rbzxeP3h6bpPx+25C5zap948G/GaTdW6T8hu34n2xKBwJQkSG4jxlf4XzxNkLJyPRm3tR17HASlXdFWNbD1Vd434/XFWXx6mjJdBSVTfEk3XD9pyoqpOj1teSFZG2OKPiZTH0HA58rap7fDoO1SsiGTiTKapFJBPnxrZG3SD2UbIdgINwXoF+X1+9e9K/P0nnyCleY+e2aWD9kDzs3CYHO69GQvHagk+XgpOiszBiuS9RvsONqKtBkwUaKtecZYmKbrKHepq9bCw5dkeEOSBq/S/qkS1IMdl6jy0Z+mlEBJ1UkvVA/2/2pd4410HM6ERNUdZr/fXJ4kaqiFi+xO2HXxEVkSIZsl7rT6DsL6NlrXhfbAQ6QYjIx6raf0/rGljXB6p6TKLkmrOsiHyjqr0bWE+zl42WE5Fi4GScSS9n47x6/ru77X1VPXYvZe8GTkqFepPY1n8AXXBec28FcnBeBZ8JrFfVG1JR1mv9eyH7MbURnAGQAEDkPdxrWa/174Vs5HV0G46bxlPAWTjug6OTKeu1/mTKGk0Ary34dCnAP4HHgKFueQT4v72sy0agGydbQwOim7iyDYqEks6yjayzURFh0lE2mfrdz4ZE0EkZWa/174XsDBoenchTWa/174Vs5HX0Po5bYahfovsh4bJe60+mrBXvS/SMWWPv+Q1OaJvrgRtwMhJe7WmLmg81wCGq2iaqtAbWRsl+b7KNqrNWRBicUdU2EjsiTLrKJkt/OIIOTlrfcAQddmczTUVZr/U3SladbLH/w4lOdJSqfgVUqerXGhWhyGtZr/U3VhbIE5FjROQ4nFjQO9w6qqjbZ8mQ9Vp/MmUNjzEDOkGoaoWq3q+q56vqeao6UVUr9rK6ygTLpbvsBpzRj1g8FbX8b5NtVJ2fi8iQ0IKq1qjqlTiva/s1E9lk6V8nIq1cuTNCK0WkK3V//6kk67X+xsqiqlNxssANFZEZ1H3YaTKyXutvpOxanIhP9wKbRKQbgDhhS6v3g6zX+pMpa3iNNoFh8FQuwLPu51LqZvr7OM4+gjM5YKy73JsY4e0aKmeyDe6rw0224bLA4UAekBdne49mIntsMvTXc95bAl3STdZr/Q2VBY4Cro6xvsnJeq2/sbIR2zKAFl7Jeq0/mbJW9l/xvAGpXtgdA/OAWCXOPpOAh3BC1YGTWW/R3sqZbIP7qin4a6eMrNf6U03Wa/2pJuu1/nSW9Vp/qsl6rT+ZslaSV8yFYx/R3bGLr1HXB0x3+4JdE2e3H6jqb4FQFqXNxH4F1lA5k20YsmcRk21C+lNN1mv9qSbrtf50lvVaf6rJeq0/mbJGkjADOnGcHmPdiDiyVeIkE1EAEemMk+lvb+VMtmGoyTZK1mv9qSbrtf5Uk/VafzrLeq0/1WS91p9MWSNJmAG9j4jIb0RkKVAoIh9HlC9x/KBj8Tec9J9dRGQC8BZQvA9yJmsYhmEYhrGfyPS6AWnAU8BM4G7gDxHrt6nqpmhhEfEBXwK/w8mOJcC5qrpyb+RMtlE0hYghqSTrtf5Uk/Vaf6rJeq0/nWW91p9qsl7rT6askSy8dsJOt4KT/ap3qMSRebuBdTVIzmTDcp5HAUklWa/1p5qs1/pTTdZr/eks67X+VJP1Wn8yZa14VzxvQLoUnIQJq4AdOCOmQWB5HNk/Aj+C+nPbN1TOZMNynkcBSSVZr/WnmqzX+lNN1mv96Szrtf5Uk/VafzJlrXhXPG9AuhTgI6AjbipO4BTg4Tiy23AM7Ep2p1DeurdyJhuWe9/9/CCyX+LU2exlvdafarJe6081Wa/1p7Os1/pTTdZr/cmUteJdMR/oxFGlqmUi4hMRn6q+ISL3xBJUJ2XyHmmonMmGaQpRQFJJ1mv9qSbrtf5Uk/VafzrLeq0/1WS91p9MWcMrvLbg06UArwGtgL8D/wX+CiyMIzs4VtlbOZMNy10MzABWAxNw0if/OE6dzV7Wa/2pJuu1/lST9Vp/Ost6rT/VZL3Wn0xZK94VcTvL2EdEpCWwCyc04MVAW+A/qloWQ/bFiMVcYCCwRFVP3Rs5kw1H6zgB2MTuaB1zNX5kj2Yt67X+VJP1Wn+qyXqtP51lvdafarJe60+mrOExXlvw6VKAA4HciOU8oKCB+/YC/psoueYqSwpFDGkKsl7rTzVZr/WnmqzX+tNZ1mv9qSbrtf5kylrxrlgilcTxHLV9lGrcdQ1hNXBEAuWaq+wcEfmRiEgD6jBZ7/WnmqzX+lNN1mv96Szrtf5Uk/VafzJlDY8wF44EISIfqurRUes+UtWjYsj+nd2pOH3A0cBXqnrJ3siZbFhuG9ASqAbKcV59qaq2iVFns5f1Wn+qyXqtP9VkvdafzrJe6081Wa/1J1PW8A4zoBOEiLwK/F1VZ7jL5wDXq+ppMWQvj1isxjEGF+ytnMkahmEYhmHsPyyMXeK4GviPiDyI87T4LXBZHNl2qvrXyBUickP0ukbImayzbnCMfVHVN6PXmaz3+lNN1mv9qSbrtf50lvVaf6rJeq0/mbKGd9gIdIIRkVY453VbPTLvq+qxUes+UNVj9kbOZMPrUiJiSFOR9Vp/qsl6rT/VZL3Wn86yXutPNVmv9SdT1vAQbQIzGVO5AJe4nzfFKlGyPwVeBDbjxHgMlTeA1xorZ7J77JsmGzGkKcp6rT/VZL3Wn2qyXutPZ1mv9aearNf6kylrZf8Vc+HYd1q4nw3JlrcQWAt0Au6LWL8N+Hgv5Ey2fppyxJCmKOu1/lST9Vp/qsl6rT+dZb3Wn2qyXutPpqyxnzADet/p436uUNV6w9ap6tfA18CJiZAz2dpI7GgdH5lsbFmv9aearNf6U03Wa/3pLOu1/lST9Vp/MmUN7zAf6H1ERJYCxwLvapSfbj37nICT8rsfkA1kADu0buidBsmZbFjO8yggqSTrtf5Uk/Vaf6rJeq0/nWW91p9qsl7rT6as4SFe+5CkegH+AmzB+ZFvjSjbgK1x9lkMHAx8gGMM/hyYsLdyJhuWu6Eh60y2aehPNVmv9aearNf601nWa/2pJuu1/mTKWvGueN6AdCnA9EbILnY/P45Yt3Bv5Uw2vO79GOs+iFNns5f1Wn+qyXqtP9VkvdafzrJe6081Wa/1J1PWinfFfKAThKqe0wjxnSKSDXwoIn/GmSjXch/kmrWsiPwU+BlwoIjMiNivNVAWWZHJeq8/1WS91p9qsl7rT2dZr/WnmqzX+pMpa3iPGdD7iIi8paoni5N6UwGJ2KwaO/XmpTgTA64FRuOEqPnRPsg1d9mmEAUklWS91p9qsl7rTzVZr/Wns6zX+lNN1mv9yZQ1PMYmEXqEiOQBvVU1kAg5kzUMwzAMw9g/+LxuQLogIn1EJMf9PlRErheRdnFkzwY+BGa5y0dHva5plJzJhuVOEJFFIrJdRCpFpEZEtsaps9nLeq0/1WS91p9qsl7rT2dZr/WnmqzX+pMpa3iI107Y6VJwDLxMnGgRnwMTgVfiyC4B2hIxKYCICXKNlTPZ8LqUiRjSFGS91p9qsl7rTzVZr/Wns6zX+lNN1mv9yZS14l0xH+jEEVTVahE5D3hAVf8uIh/Eka1W1S0iEmdzo+VM1kVVPxORDFWtAf5PRBaabHxZr/WnmqzX+lNN1mv96Szrtf5Uk/VafzJlDW8wAzpxVIkzg/Zy4Gx3XVYc2WUi8jMgQ0QOAa7HmTywt3Im69DkI4Y0MVmv9aearNf6U03Wa/3pLOu1/lST9Vp/MmUNjzAf6MTxc5yU0xNU9UsRORB4MlJARJ5wv34OHA5UAP/FSbxyY2PlTLYOkdE6dtDwyB7NVdZr/akm67X+VJP1Wn86y3qtP9VkvdafTFnDK7z2IUnHArQH+sdYvwI4ACenfYfo0lg5k4157vOAwgb2U7OX9Vp/qsl6rT/VZL3Wn86yXutPNVmv9SdT1oo3xfMGpEsB5gFtcIy7b3Amvt0fJXM9sBJnJPWLiPIl8EVj5Uy2Th+cDQSAL93lo4EZcfqr2ct6rT/VZL3Wn2qyXutPZ1mv9aearNf6kylrxbvieQPSpeBGiACuAv7ofo8XVWJSA+tskJzJhuWWkCIRQ5qCrNf6U03Wa/2pJuu1/nSW9Vp/qsl6rT+Zsla8K+YDnTgyRaQbcCHwUn2CqvqbhlTYUDmTDVOtqltMtsGyXutPNVmv9aearNf601nWa/2pJuu1/mTKGh5hUTgSx53AbOAtVV0kIgcBqzxuU3PD6yggqSbrtf5Uk/Vaf6rJeq0/nWW91p9qsl7rT6as4RE2Ap0gVPU5Ve2vqte4y1+oqs2a3Q80hSggqSTrtf5Uk/Vaf6rJeq0/nWW91p9qsl7rT6as0QTw2ockXQqQC/wW+Afwz1Dxul3NodAEooCkkqzX+lNN1mv9qSbrtf50lvVaf6rJeq0/mbJWvC/mwpE4ngA+AYbjuHNcjBNBwkg+k4FZwEE4KVBDCKDuepPdLeu1/lST9Vp/qsl6rT+dZb3Wn2qyXutPpqzhNV5b8OlS2B2F42P3Mwt43et2NadCCkUMaQqyXutPNVmv9aearNf601nWa/2pJuu1/mTKWvGuiNtZxj4iIu+p6kAReRO4BlgHvKeq9sRoGIZhGIaRRpgLR+J4WETaA7cDM4BWwFhvm2QYhmEYhmEkGhuBNgzDMAzDMIxGYCPQ+4iI3FTfdlW9f3+1xTAMwzAMw0g+ZkDvO63dT8WZKRuJDe8bhmEYhmGkGebCkSBE5F/ADar6vbvcHrhPVX/hacMMwzAMwzCMhOLzugFpRP+Q8QygqpuBY7xrjmEYhmEYhpEMzIBOHD531BkAEemAucgYhmEYhmGkHWbgJY77gIUi8jyO7/OFwARvm2QYhmEYhmEkGvOBTiAichhwKs5kwrmqusLjJhmGYRiGYRgJxgxowzAMwzAMw2gE5gNtGIZhGIZhGI3ADGjDMAzDMAzDaARmQBuGYRiGYRhGIzAD2jAMwzAMwzAagRnQhmEYhmEYhtEI/j/o0DxW7wzZnAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Correlation Heatmap\n",
    "plt.figure(figsize=(12, 8))\n",
    "correlation_matrix = secom_merged_df.corr()\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}