{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b10e4154",
   "metadata": {},
   "source": [
    "### **Hypothetical Business Case**:\n",
    "\n",
    "The real estate agency, N_Move-in Realtors, gathered data from the previous year to analyze a newly developed area in Wellinborough, Northampton. With the objective of predicting house prices, this data was collected to provide valuable insights to landlords seeking to determine the market value of their properties for potential buyers. As the newly appointed data scientist, my task was to develop a model capable of accurately predicting house prices based on key features such as house age, distance to convenience stores, and distance to the nearest MRT station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5d575822",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5be27580",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Teni\\Desktop\\Datasets May-April\\Real estate.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a3c8b1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>X1 transaction date</th>\n",
       "      <th>X2 house age</th>\n",
       "      <th>X3 distance to the nearest MRT station</th>\n",
       "      <th>X4 number of convenience stores</th>\n",
       "      <th>X5 latitude</th>\n",
       "      <th>X6 longitude</th>\n",
       "      <th>Y house price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2012.917</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012.917</td>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013.583</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013.500</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2012.833</td>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  X1 transaction date  X2 house age  \\\n",
       "0   1             2012.917          32.0   \n",
       "1   2             2012.917          19.5   \n",
       "2   3             2013.583          13.3   \n",
       "3   4             2013.500          13.3   \n",
       "4   5             2012.833           5.0   \n",
       "\n",
       "   X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n",
       "0                                84.87882                               10   \n",
       "1                               306.59470                                9   \n",
       "2                               561.98450                                5   \n",
       "3                               561.98450                                5   \n",
       "4                               390.56840                                5   \n",
       "\n",
       "   X5 latitude  X6 longitude  Y house price of unit area  \n",
       "0     24.98298     121.54024                        37.9  \n",
       "1     24.98034     121.53951                        42.2  \n",
       "2     24.98746     121.54391                        47.3  \n",
       "3     24.98746     121.54391                        54.8  \n",
       "4     24.97937     121.54245                        43.1  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8bf39d",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9485e033",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['No','X1 transaction date','X5 latitude', 'X6 longitude' ], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d92d3b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'X2 house age':'house_age','X3 distance to the nearest MRT station': 'dist_MRT_sta','X4 number of convenience stores':'no_con_store', 'Y house price of unit area':'house_price'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a90c3204",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_age</th>\n",
       "      <th>dist_MRT_sta</th>\n",
       "      <th>no_con_store</th>\n",
       "      <th>house_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   house_age  dist_MRT_sta  no_con_store  house_price\n",
       "0       32.0      84.87882            10         37.9\n",
       "1       19.5     306.59470             9         42.2\n",
       "2       13.3     561.98450             5         47.3\n",
       "3       13.3     561.98450             5         54.8\n",
       "4        5.0     390.56840             5         43.1"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db17da6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "67528449",
   "metadata": {},
   "outputs": [
    {
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1AgBgohNrMlbiHAAAYCCxJgAAsDv3Eia2iny/4Sc+8YmIiJg/f348/PDD8Sd/8iex9957x5FHHhl1dXWRSCSiubk5Nm7cGK+88kpERCxcuDDuueeeuOeee4Y8ZyKRiCuvvDJv/wbyo6v7zcGKivK854Xzhktvboh1j23p91xrZ9eQZc/90X1RU9m/WVkxvy4uf//hOasfAEA26HuWPrEmmch3nNPV3dPXzlSUl2lnAACgRIg1AYDxwn0QRmOif2/kTDFQIplMJvP5hmVlZZFIJPp+73373Z8b7vmBkslkJBKJ6O7uznJNJ5bGxsaor6+PiIhNmzbF7NmzC1qfHR1dcezX1kVrZ3fUVJbH/ZesiNqqvOeGj0uZXAjbOrvjqK+ujbado//72qOyPB689OSYXFk+6nMAAOSSvmd6ii1mGEisWbyK7buTrzinN/ba0dEVb//7O+P1zl3vt8eksnjg0pO1MwAA8IZiixl2J9YsXsX8vQGAYuM+CKMx0b83cqZKXy5ihrz/BRxwwAEjBpqwZuPmaH3jRmRrZ3fc+vDmOOeYAwpcq9KX6YXwyeaWaO8a24BP287ueLK5JRbVTx3TeQAAckXfc3wQa5KufMQ5u8deleWJ6Ox+c57z6zt7tDMAAFAixJoAwHjwk/Wb+t0H+en6TfHxpXMKXCuK3US/fyZniqHkPdn2j3/8Y77fkhJ0zQPP9f/9/ucnVIOdK5leCBfVT40rz1sSn79uY7S0D70M+nCmVFfEdz50pIsGAFDU9D3HB7Em6cpHnLN77LV7om0v7QwAAJQGsSYAMB78y91/GPD7s5JtGdFEv38mZ4qhDL+HPBRAQ+O2eKRpe//nmrZFQ+O2AtVo/BjqQjiSd86dEWtWLYtD6mozeq9D6mpjzaplsXxuXUbHAQClr6u7p++n2Ol7wsSU6zhnYOw10HhsZ0qp7QcAAAAoZcZhyERD47bY/Fp7v+eaXmsbd+OTZJf7Z7vImWKgvK9sm21PP/10nHLKKZFIJOKZZ54pdHXI0KU3N8S6x7b0e661c+jZAOf+6L6oqez/lV0xvy4uf//hOavfeDLchfDw2XsNe+yc6TVx0wVL4ws3PBy3P/riiO+1csHM+MbZC6O2quSbGAAgQ7tvnV5TWR73X7KiaPoE+p5kQqw5/mU7zrn05oZYvaEp2nd2R8/gxWwHOeN798Tuu9FWVZTHnyzevyTbmWJu+wEAoJiINQEYK+MwDGeo+yAt7TuHLPuhK+6NKdWT+j3nPsjE5P7Z8ORMsbuS/1/t7OyMP/7xj5HY/Q4VJaGtsztufLAp2nZ2p1W+pb1r0LLcqzc0xSWnzY/JleW5qGLJysWFsLaqIs5cPDutC8dZS2a7aADABLX71umtnd1x68Obi2JLGX1PMiXWnBjGGuf0xl7JZDKaWzoyeu9kRCR3S8pt29kdNz7YWJLtTLG2/QAAUGzEmgCMlXEYUmnr7I5rH9gU3emsBBC7vj+936Ve1z2wqSTHJxk998/SI2eKXmWFrgAT15PNLdHelV5jnUrbzu54srklSzUaH3ovhC9ub+/3M/Bi16ulvWtQ2dUbmqKtc/D/zc8bXkirDrelWQ4AGH8Gbp1+zf3PF6gm/el7AqmMNs7ZPfbKNNE2lbadPSXZzhRr2w8AAAAw3hiHIZX/bnwt7UTbVLp6kvHfja9lp0KUBPfP0idnigjJthTQovqpceV5S2JK9eiy+adUV8RV5x0di+qnZrdiJS5XF8KOru5Y+1hzWsevfbQ5OsZYBwCg9DQ0botHmrb3f65pWzQ0bitQjd6k7wkMZSxxTjZir91NnlQW//rx0mtnirntBwAAABhPjMMwnKpJ2VlVNFvnoTS4f5YeOVP0smYxBfXOuTNizaplcf7V6+PpLTvSPu6Qutq44mNLYs70mhzWrjT1Xgg/f93GlKvZDmdKdUV850NHDroQ3v3U1mjp6BpU9pMnvDX+5e4/9Huvlo6uuOfprfGueTNG9W8AAIpf79bpu2vtHLrvce6P7ouayv6hx4r5dXH5+w/PWf2Gou8JDDSWOGessdfu3jJtj/jxJ44p+namFNt+AAAAgFJkHIZMLaqfGld9fElceM1D0TrETsYjqaksj++de9S4T5pkMPfPRiZnil5WtqXg5kyviZsuWBorF8xMq/zKBTPjpguWTojGOiKiq7un7yddvRfCQ+pqM3qvQ+pqY82qZbF8bt2g1wYuh55IRLS0d8U/3/VMXHf+cYPey7LoADB+7b51+u4/qZLNWtq7BpVdvaEp2kYx2DNW+p7A7gbGOQfX1UZXd098a+1T0dXdEwft2/9vf2CcM9rYa3cr5tbFbZ8/oejbmVJu+wEAAABKiXEYRuudc2fEzz53QhyU4VjjQdNr4mefO2HIXJGJYDR5OeON+2fDG+u9BMYPybYUhdqqijhz8ey0yp61ZHbUVk2MRZl3dHTFwsvuiIMv+UUsvOyO2NGR/mpJ2b4Qnr5oVuxTUxkREQtm7RnJ5K7nWzu7478bt8VNFyyNUxbsmpWxT01lnL5wVtp1BQBKSza2Tm/b2R1PNrdkqUaZ0fcEep284M2Z5eWJiDOP2j/adu4aUG3b2RMfPe4tI8Y5mcZeA51z7AEl0c6UetsPAAAAUCqMwzAWc6bXxC0XLoujDpiaVvmjDpgat1y4bMIkTQ40lryc8cb9s9R2z5lauWBmfPiYAzK+l8D4INmWojFwFkAq4y37f7gZMms2bu5b3r+1sztufXhzRufO5oVw+WF1sfbik+LrZx4REcl+r11z//NRW1URP/jw4vj6mUfE2otPiuWHTcwZTwAwEfRunT6lenRB9JTqirjqvKMLuhXRRO17Av292rqz73F3MuLq+57r9/qNG5rSinMyib0G2r2dKeYVFMZD2w8AAABQCozDMFa1VRVRVVGeVtnqSeUTKmlyoLHm5Yw37p8NbfecqR985Ki48aHGfq+ney9hoijmex1jJdmWotDR1R1rH2tOq+zaR5ujY4yzuIrFSDNkrnmg/43ea+5/PuP3yOaFcFpNZcybuWc8urn/DLiGpm3R0LgtysoScfaS+pj2xmwOAGD8Gu3W6YfU1caaVcsKuhXRRO17AoMNjLk2v9be7/eGpm3x6ObtacU56cZeA93xyIvR0dVdEisolHLbDwAAAFBKjMMwFh1d3fG7P76SVtkHnn1lQt8HyUZeznjh/tnwptVUxtlL6uORpu3xSNP2fq9lci9hvCuFex1jMXGnJlBU7n5qa7QM+OOaUl0Rnz7poPjhXc9ES/ubr7V0dMU9T2+Nd82bMfA0JWeoGTLnHHNAREQ0NG4bsnFuaNwWh8/eK63zj+ZCuPvspktvboh1j23pV661c+hG8Nwf3Rc1lf2blBXz6+Ly9x+e1vsDAKWnd+v0L9zwcNz+6Isjll+5YGZ84+yFBZ8hPVH7nsCbLr25IX7R8GK83No5Ytl0Yp1MYq+BdnR2xz1Pb43m7R0p48NiUqptPwAAAECpMQ7DaN35xJbo6kmOXDAiunqS8esntsTKt+2X41oVn2zk5Ywn7p8NJm8qc8Plwo0HrrAUhYErAB06ozau+OiSOHB6TZx2+H5x/tXr4+ktO/pev63hhXHRYA+cIfPXNz8S/7ju6YjITuM8lgthW2d33PhgU7TtTG8mSkt7V7/zRUSs3tAUl5w2PyZXprc9AQAwtN232KgoL67NKXq3Tk9noO+sJbOLYpBvovY9YSLbfUAsGclo3t6R9rHpxDpDxV6ZuK3hhXiquf8OItfc/3xeBqBGc40pxbYfAAAAKC3FPC6eT8ZhGI2r/+u5Qc/VVJbHZ5cfHN//9e/7EuH6yt/73LhPtpU0OTL3z/qTNzU6Q60WPZ6SbSduj4SicvqiWbHPG8tor1wwM1Z/dmkcOL0mIt6crXXKgl0N9D41lXH6wlkFq2u2DDVDpqsnGS9ub48Xt7cPaoB7tbR39ZXp/Vm9oSnaOgc37kNdCG9dtSwuWH7wkFtO3LZb+SebW6J9jEu+t+3sjicH3DAGADJTClttpLt1+m2j3GI92yZi3xMmst4Bsd74KZNE25TnHBDrDGwHyxJvPj7+oGlx0BttTK+Ze1X1PZ5SVRGH779XyhUUcmks15hSa/sBAACA0lEK4+L5ZByGTCUG/H7wvjVx2+dOiAuWHxw/+9wJg8Yrx7uBY8TZzMsZT9w/60/eVOaGWy16vDClhaKw/LC6WHvxSbHu8eY4a/HsSCT6X/prqyriBx9eHD/d0Bgr5s2IaW807qUikxkyo9HbOC+qn9rv+dMXzYq7nnopXm7tHLRlRO+F8C9u2Bi/fLR50IVwUf3UuPK8JfH56zam7GAMZ0p1RXznQ0cOqhMAkJli32ojk63T1z7aHB1d3VFVUdjZm+O97wn0l40Bsd0NFesMjL0uec+8WPd4c+xRWR5nL6mP1s7uvtirqqIs2ju7Y8/qiuhJJmPypPL41tqnhnyvXK+gMNprTCm2/QAAAEDpKPZx8XwyDsNofPKkt8bjL26PV1/fGafMnxHf/OCifrkit1y4LC6+/qG447Etsfcek+KTJ761wDXOrWwmTY7nHBj3z/qTNzW8ibpatJVtKRrTairj7CX1gxrrXmVliTh7SX3JNdaZzpDJ1JTqirjqvKOHbJx7L4RfP/OI+MFHjhq0ZUTvhfDrZx4Ray8+KZYfVtfv9XfOnTHkCrgjOaSuNtasWhbL59aNXBgAGNZQW20Uk6G2Tp9SXRFfPOWwmFLdv+/R0tEV9zy9NZ/VS2m89j2BwXoHxAa2SaORKtYZGHvVT9sj/nTpnPjg0QdEIpHoi70uf//bIpGIeK2tK7a3d8WOju7YsqOjYCsojPYaU6ptPwAAAFAain1cPJ+MwzAayw+ri1/9xTvi62ceET/86OIhc0V++JEl8fUzj4hf/cU7BuWKjDdjHSMeLi9nvHH/rD95U0ObyKtFl3yybV1dXXzlK1+JL3/5y4WuCgwp26so7S6dxnmsF8LeFXBXLpiZVp1WLpgZN12wNOZMsG0HACAXSmGrjYHbVx06ozZuXbUsLlh+8JDBp22sKBVizfFltANiuxsp1kkn9nrb/ntFR1fPqOsQkb1tp8ZyjdH2AwDA6Ig1AUZWCuPi+WQchtGSNNmfpElGS97UYNlcLbrUJJLJZLLQlWhqaooXX3wxXn/99ViyZElMnjy50FWacBobG6O+vj4iIjZt2hSzZ88ucI3GlzufaB71suKprFwwM75x9sJBM5ByZd1jzfE/r14/Yrkrz1sS75o3Iw81AoDxJdVWG0P1H6ZUVxTNVhu/fnJLfOGGh/u2Th/YP9nR0dW3dfo+NZXxjbMXjvsZ0uNVKcYMYs3iUEzfnR0dXfGFGx6O2x99MeNjsxXrjCU+7N12KtOB3WxfY7T9AABkUzHFDOkQaxaHUvveAEMr1XHxfDIOA9mVyRhxvvNyKG7ypvorxL2OTOUiZihYa9DS0hLf+MY34qqrrorNmzf3Pd/Q0BDz58/v+/26666L1atXx1577RX/8i//Uoiqwpj1zpA5/+r18fSWHVk551lLZuf1gj5wxlwqtzW8MCEuGgCQTb1bbbTtTG8GYEv74MHG1Rua4pLT5sfkyvJcVDGl3q3T1z3eHGctnj1ohnTv1uk/3dAYK+bNmDAzpCkcsSbDqa2qiDMXzx5Vsm22Yp3RxoeH1NXGFR9bkvFs+FxcY7T9AABMNGJNgOwr5XHxfDIOA9mVyRhxvvNyKG7ypvrL972OYlFWiDf9/e9/H0cddVRcfvnl0dTUFMlkMlItsHv88cfHTTfdFFdddVXcc889ea4pZE+my4qPJJ/bP3R0dcfax5rTKrv20eboGONS4QAw0ZT6VhulvBVRV3dP3w+lT6xJOtIdEBsom7FOPredytU1ppTbfgAAyIRYEyA3Sn1cPJ+Mw6TPmH9qPps3ZZI0CRHyplLJ572OYpH3ZNuOjo54z3veE88880zsscce8aUvfSl+9rOfpSz/lre8JZYvXx4REWvWrMlXNSEnemfIZEM+G+e7n9oaLR39ZwlOqa6IL55yWEyp7j+Lp6WjK+55emte6gUA48Wi+qlx5XlLBl1X0zWluiKuOu/oWFQ/NbsVG+d2dHTFwsvuiIMv+UUsvOyO2NGR+TYnFA+xJunIZEBsoGzHOpnEh2NZQcE1BgAARk+sCZA7xizINmP+qfls3iRpktGQN5Vavu51FIu8J9v+8Ic/jKeffjpqamri7rvvjr//+7+P0047bdhjTj311Egmk3HvvffmqZaQO5muolQMjfPAOh86ozZuXbUsLlh+cKxZtSwOqavt97rZPQCQud6tNgZeV0dySF1trFm1LJbPrctRzcavNRs3R2vnrkGS1s7uuPXhzSMcQTETa5KOoQbEMpHtWCdfKyi4xgAAwOiINQFyy5gF2WTMPzWfzZskTTIa8qaGN5FWi857su3q1asjkUjE5z//+Vi0aFFaxxxxxBEREfH000/nsGaQe5muolRVUVYUjfPpi2bFPm9sObFywcxY/dmlceAbS3r3Lgl+yoIZERGxT01lnL5wVl7qBQDjzUTcaqOQrnnguf6/3/98gWpCNog1ScfAAZ/9p06OqZMnRUTECQdPj4P2Td2elpclshrr5HsFBdcYAADInFgTIPeMWZAtxvxT89m8SdIkoyFvKrWJtlp03pNtH3vssYiIePe73532Mfvss09ERLz22mu5qBLkzXAzZPaoLB9UvqOrJ1rad5UvZOO8/LC6WHvxSfH1M4+IH3zkqEFLetdWVcQPPrw4vn7mEbH24pNi+WFmEALAaE20rTYKpaFxWzzStL3/c03boqFxW4FqxFiJNUnHwAGxX/75iXHnF94RXz/ziLj6z46JW1Yt64u5BuruScb0mqqs1SWTVXaztYKCawwAAGRGrAmQH8YsGCtj/qn5bPqTNMloyJtKbaKtFp33HkhLS0tEROy1115pH9Pe3h4REZMmTcpJnbJp69atcdVVV8Utt9wSzzzzTLz66quxzz77RH19fZx44onxgQ98II4//vhhz3H77bfHFVdcEQ888EC89NJLse+++8YxxxwT559/fqxcuTJP/xJyYeAMmYqyRFRVlMW/3ftcJBJDH3Puj+6Lmsr+f6rHv3Wf+KcPHxXT3ugA5MO0mso4e0l9ytfLyhLDvg4ApC+TrTbeNW/opDDedOnNDbHusS39nmvtHDrBbai+14r5dXH5+w/PWf3IDrGmWDMdvQNi6x5vjrMWz45EIhGX/mJXG/GtO56KiIhkMhnVFWXR3tUz6PhstRGX3twQP32wsd9zZYmInmTqYy64ZkNMnVw56vfs5RoDAADpE2uKNYH8MWZBuoz5p+azGdlQY8S7602a/OmGxlgxb0Ze83IobvKmhjbUatFXfHRJHDi9Jk47fL84/+r18fSWHX2vl/p1PO/Jtvvss0+8+OKL0dyc3vLBERENDQ0RETFjRnF/0D/5yU/iM5/5TLz88sv9nn/hhRfihRdeiAceeCCefvrpuPnmm4c8PplMxqc//em44oor+j3f1NQUN910U9x0001x/vnnxw9/+MNBjT2l4fRFs+I3T26JV17fGRERXT3J2Lqjc9hjWtq7+la37bW9fWdMnjR4JVwAoPSNZquNqgr9glTaOrvjxgebom1neluSDNX3Wr2hKS45bX5MHmInAoqHWFOsma7dB8QK0Ub0vmf7zv7JvMMl2kZEtO/siRd3to/qPXu5xgAAQGbEmmJNID+MWZAuY/6p+WzSJ2kSsuf0RbPirqdeipdbO2PlgpnxjbMX9q3827ta9F/csDF++WjzuFgtuizfb7ho0aKIiPjVr36V9jFXXXVVJBKJOPbYY3NUq7G7+uqr40Mf+lC8/PLLUVdXF1/5yldi7dq18eCDD8Ztt90W3/nOd+Lkk08edhbrpZde2heQHnnkkXHttdfGAw88ENdee20ceeSRERFxxRVXxF//9V/n5d9E9i0/rC7+74eOjLEOKbTt7I4nm1uyUicAoLhMtK02cu3J5pZo70pvYCkVfa/SINYUa45GIdqIQrZLrjEAAJAZsaZYE8gPYxaky5h/aj4boBB6V4v++plHxA8+clRfom2v3tWiv37mEbH24pNi+WF1BappdiSSyeQIa6dk15VXXhmf/OQnY88994yHH3443vKWt0RERFlZWSQSiWhoaIj58+f3lb/sssvisssui0QiETfddFOcccYZ+axuWh5//PE48sgjo6OjI0444YS49dZbU24n09nZGZWVg5cY//3vfx/z5s2Lrq6uWLJkSfz2t7+NyZMn973++uuvx0knnRTr16+PioqKeOKJJ+Kggw7K2r+hsbEx6ut3zcrYtGlTzJ49O2vnZrA7n2iOz1+3cdAsoXRMqa6I73zoyFg+t7QbHwBgaBdfvzFWP9TU9/vuW208u7V10FYbHzhq//jW2YsKUNPSoe+VHcUeM4g1izPWjCj+704h2ohCtUuuMQAAFKNijhnEmmJNID+MWZAJY/6p+WwA3pSLmCHvK9t+/OMfj3nz5kVLS0ucdNJJcdttt8Xu+b6JRCJ6enri7rvvjtNPPz3+9m//NhKJRBx99NFFGZBGRFx44YXR0dER06dPj9WrV6cMSCNiyIA0IuLb3/52dHXtuth997vf7ReQRkTsscce8d3vfjciIrq6uuL//t//m53KUxDvnDsj1qxaFofU1WZ03CF1tbFm1TKdGxhCV3dP3w9AKTt90azYp2ZXn3Hlgpmx+rNL48DpNRHx5lYbpyzYtQ3heNhqIx/0vSYGsaZYc7Ty0UYM7KsWql1yjQEAgMyINcWaQH4YsyATxvxT89kA5FbeV7aNiHjmmWdi6dKlsWXLlkgkErHHHntEa2trJBKJmDNnTjQ3N8frr78eERHJZDJmzZoV9957b1+mcTF54oknYt68eRER8Td/8zfxla98JeNzJJPJqK+vj6amppg7d248/vjjKcvOnTs3nnzyyZg9e3Y8//zzkUgkRl333Zn9WRg7OrriCzc8HLc/+uKIZVcumBnfOHvhoOW2C2X3hMaK8rzn7UM/Ozq64tivrYvWzu6oqSyP+y9ZUTR/KwCj8UprZ6x7vDnOWjx7yP5eT08yfrqhMVbMmxHTaoa+6cFgpdz3KgalEDOINfsrhlgzojS+OxG5ayOG66sWol1yjQEAoNgUe8wg1uxPrAnkijELMmXMPzWfDeSevKniNy5Wto2IOOigg2Ljxo3xnve8J5LJZLS2tkbEruDsD3/4Q7S2tkYymYxkMhnvfve743e/+11RBqQRET/5yU/6Hp911ll9j1999dV4+umn4+WXXx7xHM8++2w0Ne3aEuGkk04atmzv642NjfHHP/5xFDWmmNRWVcSZi9P7Qz5ryeyi6dzs6OiKhZfdEQdf8otYeNkdsaMj8y0IIJvWbNwcrZ3dERHR2tkdtz68ucA1AhibaTWVcfaS+pQ3IMrKEnH2knoDihkq1b4X6RNr9ifWzExtVUW8d+F+aZXNpI0Yrq9aiHbJNQYAADIj1uxPrAnkijELMmXMPzWfDeSWvKmJq2Bp1TNnzoxbb701Ghoa4vLLL4/3v//98fa3vz2OPfbYOO200+KSSy6JBx54IG6//fbYb7/0bnYVwn333RcREXvttVfMmzcv/uM//iMWLlwY06ZNi0MPPTSmT58eb33rW+Oyyy6LHTt2DHmO3Wd8zp07d9j32/314WaKUjp+3vBCWuVuS7NcPkhspNhc88Bz/X+///kC1QSAYleKfS8yI9Z8k1gzc1fe/Wxa5TJpI0bqq2qXAACg+Ik13yTWBKCYGFtLzWcDuSNvauIq+NSEBQsWxIIFCwpdjVF77LHHIiLiwAMPjAsvvDD+6Z/+aVCZZ599Nv7mb/4mfvrTn8Yvf/nLmDVrVr/XN23a1Pd4pOWKd58Ju/txI2lsbBz29RdecPEshI6u7lj7WHNaZdc+2hwdXd1RVVGe41qNbKibxeccc0CBasNE19C4LR5p2t7/uaZt0dC4LQ6fvVeBagVAMSrVvhejI9bMT6wZMX7izY6u7ni48bW0yqbbRozUV9UuAQBAaRFrijUBKB7G1lLz2UBuyZuauAqebFvqXnnllYiIeOKJJ+Lhhx+OqVOnxt///d/HBz7wgdhzzz2joaEhvvzlL8cvfvGLeOSRR+Kss86Ku+++O8rK3lxUuKWlpe9xbW3tsO9XU1PT9zjVjNKhFOt2NRPd3U9tjZYBS4lPqa6IT590UPzwrmeipf3N11o6uuKep7fGu+bNyHc1+0l1s3jj86/G2/bfKyrKC7ZgNhPApTc3xLrHtvR7rrVz6OX4z/3RfVFT2f8yt2J+XVz+/sNzVj8Ailsp9r2YuEol1owYP/Hmv9/7XPQkBz//0ePeEjdvbBqxjRhNX7V9Z7d2CQAAyBuxJgDjQe843FBja6m0dHTFsV/7VVRPKp8Q94zdD4HcsSDcxFa0ybbPPPNMbN26NQ488MCYMaN4G/TW1taIiOjo6Ijy8vL4xS9+Eccdd1zf60uWLImf/exn8d73vjd+8YtfxH/913/F6tWr48wzz+wr097e3ve4srJy2Perqqrqe9zW1patfwYFMnDZ/kNn1MYVH10SB06vidMO3y/Ov3p9PL3lzcGH2xpeyGsHJ5Obxe///n9FIiLq9qyKRCQiQmIj2dXW2R03PtgUbTu70yrf0t7VL0iIiFi9oSkuOW1+TK40Kw9gIir2vhf5IdYc2kSLNYeKdbbu6Biy7M0bm6KqvCzayhLRtVs27u5tRDb6qhERB9fVxo8+pl0CAIBSI9Yc2kSLNQHIvUzH4Xb3WtvOiLadE+KesfshkB0WhGOgvC9B+dJLL8X3v//9+P73vx/btm0b9Prvf//7WLx4cRx66KHx9re/Pfbff/8488wz47XXXst3VdNSXV3d9/iss87qF5D2Kisri3/4h3/o+/3aa69NeY7Ozs5h36+j482bf5MnT067nps2bRr254EHHkj7XGTP6YtmxT41uwYiTlkwI1Z/dmkcOH3XLN8502vipguWxikLdnVo9qmpjNMXzkp5rmzr7aS+uL29389QN4R7JSOieXtHX9nVG5qirTPzTi4M5cnmlmjvGtv3qW1ndzzZ3DJyQQDGpd37XisXzCyqvhdjJ9YsTKwZUXrxZqpYp2uoZW1jV2Ls1tbOQa+fMn9m3+Ns9FUjIr76vgUxe+/J0dXdE/V7T9YuAQBAgYk1xZoAFBf3jNPjfgiMXaZ5Uy3tXYPKypsaf/K+su2NN94Yq1atisMOOyw++9nP9nuto6MjTj311PjDH/4QyeSum1jJZDJuuumm2Lp1a/zmN7/Jd3VHNGXKlL5ZoKeeemrKcgsWLIj9998/mpqa4ne/+92gc/QaaQuV3veKGHlrlt3Nnj077bLkz9EHTou2N2Y83P3US4Ner62qiB98eHH8dENjrJg3I6bVDD9DOJuy2UldVD81O5ViQltUPzWuPG9JfP66jcMmfacypboivvOhI30fASaw5YfVxdqLT4p1jzfHWYtnRyKR6Pd6IftejJ1YszCxZkTpxZvZSoydsdebN5jH2letriiL73/4qDh89tRYeNkd0drZHTWV5XH/JSu0SwAAUEBiTbEmAMXFPeP0uB8CYydviqHkfWXbO+64IxKJRPzJn/zJoNd+/OMfxzPPPBMREWeccUb84z/+Y5x++umRTCbj7rvvjhtuuCHf1R1RfX193+ORgr7eslu29F9eevfjGhsbhz3Hpk2bhnxvStOajZvj9Z09ERHx+s6euPXhzYPKlJUl4uwl9Xnv3PR2UqdUjy4nf0p1RVx13tEuGGTVO+fOiDWrlsUhdZkNyh1SVxtrVi2L5XPrclQzAErFtJrKOHtJ/aCBpV6F6nsxdmJNsWa6shHr/OvHB8c6Y+mr/uKiE+Od82bEmo2bo/WNWe6tnd1x68ObtUsAAFBAYk2xJgDFxz3j9LgfAmMjb4qh5D3Z9sknn4yIiGOOOWbQa73bkLzzne+Mm2++OS688MK45ZZbYsWKFZFMJgdtU1IMFixY0Pe4u3v4bPbe1ysq+v8Rzp8/v+/xE088Mew5dn993rx5adeT4nTNA8/1//3+5wtUk6GNtpNaloioKi+Lv1rdEMf93a/iuL/7VVx6c0OOaslE07utxcoFM0cuHLu2xbjpgqUx541tMQCA8UmsKdbMxJ1PbImq8rKoKBt6oDmVirJEVJWXxa+eaB7y9bH2VYs9RgQAgIlGrCnWBKA4uWcM5EOu7iVQuvKebPvSSy9FRMSsWbP6Pd/W1hb33ntvJBKJOP/88/u99olPfCIiIjZs2JCfSmbgxBNP7HvcO3s1lT/84Q8REbH//vv3e37OnDl9n8ddd9017Dl++9vf9p3jwAMPzLS6FJGGxm3xSNP2/s81bYuGxm0FqtHQ5kyviWs/eVxkct3oSUZsbe2MF7e39/2s3tAUbZ1j36oVInZta3Hm4vS2kDpryeyorRrdTCMAoHSINcWa6Wrr7I4bH2yKra2d0dWTzOjYrp5kbG3tHDa+GW1ftVRiRAAAmEjEmmJNAIqXe8ZALuX6XgKlKe9Xktdeey0iIsrK+uf53nfffbFz584oKyuLFStW9Httzpw5ETF4m5JicMYZZ8SkSZNi586dsXr16vj0pz89ZLm77rorXn755YiIOOGEE/q9lkgk4n3ve1/84Ac/iCeeeCLuu+++OO644wad47777uubAfq+970v5VLvFJ9Lb26IdY/1//62dnYNWfbcH90XNZX9/zRXzK+Ly99/eM7qN5LG19oiw+vGIG07u+PJ5hbLo5M1P294Ia1ytzW8EO+aNyPHtQEACk2sKdZM15PNLdHeNbbBrZHim3T7qhdcsyGmTt61TVspxYgAADBRiDXFmgAUn93zL15r60zrmN3H4YytAenIx70ESk/eV7atrd21Hf2LL77Y7/nf/OY3EbFr65G9996732uTJk2KiMHblBSDffbZJ/7n//yfERGxdu3auO666waVaWlpiYsuuqjv90996lODylx00UV9/74LL7ww2tra+r3e1tYWF154YUTs+hx2Px/FrXemw+6rvL64vT1a2oe+kdrS3jWobKFnOiyqnxrHH7TPqI+fUl0RV513tIsHWdPR1R1rH0tvuf21jzZHxxg7QABA8RNrijXTtah+alx53pKYUj26//eR4ptM+qrtO3tKMkYEAICJQqwp1gSguAzMv2jf2ZPWcbuPwxlbA9KR63sJlKa8J9vOnTs3IiJuv/32fs/feOONkUgk4qSTThp0TG8AO2NGca5MeNlll8UBBxwQEREf/ehH48ILL4xf//rX8eCDD8aPf/zjOOaYY2Ljxo0REfGZz3wmjj766EHnOPTQQ+MLX/hCRESsX78+li5dGtdff32sX78+rr/++li6dGmsX78+IiK++MUvxiGHHJKffxxjls2ZDoXS0dUdDZteG9Wxh9TVxppVy2L53LrsVooJ7e6ntkZLR/9khCnVFfHFUw4b1NFp6eiKe57ems/qAQAFINYUa2binXNnxJpVy+KgfWsyOu6gfWtGjG8y6auOVqFjRAAAmCjEmmJNAIrLeMi/AEpHLu8lUJryPqXyPe95T9x3331xxRVXxLx58+KEE06IH//4x/HYY49FIpGID3zgA4OO2bBhQ0REzJ49O9/VTcu+++4bt99+e5xxxhnx+9//Pr73ve/F9773vUHlPvGJT8Q//uM/pjzP1772tdiyZUtcddVV8dBDD8WHPvShQWX+7M/+LC6//PKs1p/c6p3p8PnrNqZcqWg4U6or4jsfOrKgMx3WPtocO0Yxs2vlgpnxjbMXRm1V8c3eprQN3Jb30Bm1ccVHl8SB02vitMP3i/OvXh9Pb9nR9/ptDS/Eu+YV58AmAJAdYk2xZqbmTK+Jz73rkPj8dRvTPubPTz405kwfflAt075qpoohRgQAgIlCrCnWBKC4jIf8CyglXd1vrh5dUZ73NT2LwpzpNfHnJx8aq655KO1j0rmXQGnK+1/BqlWrYr/99ovOzs5YtWpVLFy4ML797W9HRMTxxx8fy5cvH3TMrbfeGolEIk444YR8Vzdt8+bNi40bN8Y//MM/xLHHHhvTpk2LysrKmD17dnzwgx+MO++8M6688sq+rWOGUlZWFldeeWXcdttt8b73vS9mzZoVlZWVMWvWrHjf+94XP//5z+NHP/pRlJVNzMarlPXOdDikrjaj44plVdgr73l2VMedtWS2RFty4vRFs2KfmsqI2JXUvfqzS+PANzoqc6bXxE0XLI1TFuxKrt2npjJOXzirYHUFAPJDrCnWzNSOjq74ixsezuiYO5/YMmKZTPqq5YlERu9fLDEiAABMFGJNsSYAxac3/2LW1OqMjps1tdrYGmRgR0dXLLzsjjj4kl/EwsvuiB0dmSe4jxd3Pj7yvYF+5dO4l0BpSiSTyWS+3/Txxx+Pj370o30zOyMiTjjhhLj22mtj1qz+CVEPP/xwHHnkkZFIJOLXv/51nHjiifmu7oTQ2NgY9fX1ERGxadOmop1tW+p2dHTFF254OG5/9MURyxbTqrAn/cOv47mXX8/4uA8ctX986+xF2a8QRMQrrZ2x7vHmOGvx7EgMkaTQ05OMn25ojBXzZsS0N5IdAIDRK4WYQaxZnIr1u3PN/c/H/7qpIe3yVRVl8cOPLo7lh408GJ9uX/X4t+4TX7vt8ZKLEQEAIJuKNWboJdYsTsX+vQEg93Z0dMXnrn0oraS2d86ti++cc6SxNcjAwDH0//2Bw+OcYw4oYI0K59dPbolP/9uD0dHVM2LZTO4lkFu5iBkKchWZN29erF+/Pp599tl48cUXY7/99osDDzwwZfl//dd/jYiIt7/97XmqIeRGbVVFnLl4dlo3Ugu5KuylNzfEusd2dUh3dvfEy62dozrPTRua4j9/vzUSkYgV8+vi8vcfns1qMsFNq6mMs5fUp3y9rCwx7OsAwPgj1iRd7/72XfFU846Mjuno6ok/+/HvIiLi4LrauOPPT0pZNpO+ainEiAAAMJGJNQGgONVWVcS5xxyQVrLth489wNgaZOiaB57r//v9z0+4ZNt3f/uu+P2WXfcSetJczjSTewmUnoJeSebMmRNz5swZtszChQtj4cKFeaoR5N7PG15Iq9xtDS/Eu+bNyHFt+rv05oZY+1hzNG/vyMr5khF951q9oSkuOW1+TK4sz8q5c62r+83ZKBXltjgCACglYk2G88qOzowTbXv1DqY91bwjXtnRGdNqR95BYaTYophjRAAA4E1iTQAoPsbWyAb5IYM1NG6LR5q293+uaVs0NG6Lw2fvVaBa5Ve+7yVQGkzbgBzbfZXYZDIZW1rSS2S9aUNT/OfTW+PkBTPysiJsW2d33PhgU7Tt7M7N+Xd2x5PNLbGofmpOzp9NOzq64tivrYvWzu6oqSyP+y9ZYZYbAACUqN1jsoiI1o6dWTnvzx95IT5y3FuGLTNSbNHR1R1rH2tO6/3WPtocHV3dUVVRGhMYAQAAACCXjK2RDfJDBo+hR0S0dnYNWfbcH90XNZX9P5/xutP1zx9JL5k/nfOMdC+B0iEdH3KoN4H1xe3t8eL29mhu6Yg0VxXftSpsS0es3tAUbZ25SYDd3ZPNLdHelZv3mVJdEf/y0cXxtll75uT82bZm4+ZofeMzb+3sjlsf3lzgGgEAAKMxMCZ7cXt7tHRkJ+552/4jz94fKba4+6mt0dLRf9BySnVFfPGUw2JKdf8By5aOrrjryZeiq7un30oLAAAAADARpRpbu/jkQ4ccW7vn6a35rB4lYqLnhww1hv7i9vZoaR862balvWtQ2XzlNeVbOvcA8nkeikPeU/E/8YlPjPrYRCIRV155ZRZrA7mVjQTWfK0Iu6h+alx53pL4/HUbU140R+OgfWti82tt8T+vfrBkZgFd88Bz/X+///k455gDClQbAADSIdZkKLmYVLhHZXn807lHpRWjjRRbDNzm7tAZtXHFR5fEgdNr4rTD94vzr14fT295c5uqz/7HhujqSZZMbAUAAKVOrAkAxWvg2NrBdbXR9Orr8a21T8XkSWVx0L418cxLrX2v39bwQrxr3ox8V5MiN9HzQ0oprynfFtVPjas+viRWXfNQvD6KZOJM7iVQOvJ+V+bHP/5xJBKJjI9LJpOCUkrOWBNYJ08qi+9/eHHeGt53zp0Ra1YtG3RDd7RWLpgZx86ZFpf97LGIeHMWUDF3TBoat8UjTdv7P9e0LRoat8Xhs802AQAoVmJNhpLtSYVzptfEVR8/OuZMrxmxbDqxxemLZsVdT70UL7d2xsoFM+MbZy/sS6CdM70mbrpgafzFDRvjl482R01VebR29F9hoZhjKwAAGA/EmgBQvAaOre2em9C2syc+etxb4t4/vBy/fLQ59qmpjNMXzipwjSk28kPGPoY+pboivvOhI8dtQuk7586I2z53Qnzix7+LZ7e2jnzAGzK5l0BpSSSTyXR3tc+KAw88cMSgtLW1NbZu3bV8eyKRiOnTp8cee+wRERHPPvtszus4ETU2NkZ9fX1ERGzatClmz55d4BqNL89ubc04gXXOPnvEv5y3JA6um5LDmg1tR0dXfOGGh+P2R18c03muPG9JfHvdU/06J4fvv1fceuGysVaxz+7bp1aUl2V07KU3N8S6x7b0e661s2vIDsSU6oqoqew/P2HF/Lq4/P2HZ/SeAAClqthjBrFm8SqG785oYrKBDtq3Jm5ZtWzI1WTHElv09CTjoLqauOaTxw35He7pScZPNzTGVff8IZ548c36Zzu2AgCAQimGmCEVsWbxKubvDQD580prZ6x7vDnOWjw7Tv/ePYNyE265YGn8dENjrJg3I6bVVBawphSa/JDhjWYM/ZC62rjiY0smRELpjo6uOON798QfXho54Xa4ewnkVy5ihrz/r/7xj39Mq9wrr7wS11xzTXzlK1+JqVOnxi233BJz587NbeUgR3pXJEo3gbW8LBHPvvx6vO97/1mQrUFrqyrizMWzx5xs+x/3PZ/TWUA7Orri2K+ti9bO7oy3UW3r7I4bH2yKtp3pLfXe0j64k7V6Q1Ncctr8mFxZnnHdAQDILrEmw8k0JhvK9radQ8Yb2YgtdnR2RfvOniFji7KyRMybuWe/RNuIibfCAgAAFIJYEwCK27Sayjh7SX3KFUof3bw9zl5SX6DaUSzkh4ws0zH0gTu1jXe1VRWx/fWdaZVNdS+B8aFo/2enTZsWq1atine9611x/PHHx6mnnhobNmyIvffeu9BVg1HJJIG1u2fXgtOF3Br05w0vjPkcdz65Zcjnz/3RfVmZBbRm4+Zo7RzdNqpPNrdEe1d6HalU2nZ2x5PNLeN2OXwAgPFIrDlxjXVS4Us7OqOlfWdMqZ7U7/lsxxapVlgYSrZiKwAAYGzEmgCQP8bPyJT8kPRkMoZ+1pLZEyqhtKV9Z2xt7UyrbKp7CYwPme25XgDz5s2Lz33uc/Hcc8/FN7/5zUJXB8ZkNAms19z/fA5qMryOru5Y+1hzzs7f0t4VL25v7/ezekNTtHVm1rm55oHn+v+ewWe1qH5qXHnekphSPbqL/5TqirjqvKPHdUcKAGA8E2tOTGOdVPiD3zwz6Llsxha9KywMjJeG2sosInuxFQAAkB1iTQDILeNnjIb8kPSlO4Z+WxYW8Csl3//14HsDwxnqXgLjQ9En20ZEnHzyyRERsXr16gLXBEZvtAmsvVuD5tPdT22Nlo7BndF9aiujoiyRk/fsnQWUrlTbQGTyWb1z7oxYs2pZHFJXm/YxERGH1NXGmlXLYvncuoyOAwCguIg1J5ZsTCr8yfpNQz6frdgimyssAAAAhSHWBIDcMX7GaMkPGVkmY+hrH22OjjH+LZaSoe4NTJ5UFn9x8qExedLg9MtU9xIofSWxnnNt7a6G7vnn87/CJ4xVV3dPRET85oktgxJYExFRU1URrR1dkRzmHPne2uCWjU2Dnjvh4Onxg48ujlsf3hyX3vRIdCf713hKVUV0JZOjmgE2pboivvOhI1POAsrlNhBzptfETRcsjS/c8HBaS+GvXDAzvnH2wgm1HD4AwHgl1pwYeuOJjq7uIScVZuLV13fGcX/3q4gYHGdkI7ZYVD81rvjI4rj4Jw+nXI1jOCPFVgAAQO6JNQEgdxbVT413HLpv/ObJl4bNsUglERHvOHRf42cTlPyQoY1mDL2loyuO+7tfRVVFeU7zl4rFvntWxdbWzr7fD5y2RzS3dMQ31z4VkyeVx1um7RHPvfJ63+vTp1QVoprkQUmsbPvQQw9FRMSkSZMKXBPIzI6Orlh42R1x8CW/iAuueajfa4mISL5RZqROYL63NphWW9nv9zMW7hdX/9kxUVtVEeccc0D85ovviLfN2jMiIvapqYxPLD0w7vrS8vj5507I+iygfGwDUVtVEWcunp1Wfc9aMnvcd6QAACYKseb4t3s88errO8d8vq6e5LBxxlhjix0dXXHR9Rujpb0rEhluKjKRVlgAAIBiJtYEgNxp6+yO+/7wyqgSbSN25Wjc/+wrOcu1oPjJD+lvLGPor76+M+f5S8XiSyvnxtTJu/r3pyyYEectPTDadu76N7ft7I4/XXpgvHv+jIiImDp5Unxp5dyC1ZXcKvpk22effTb+5m/+JhKJRCxatKjQ1YGMrNm4OVrfuKB09SSjpqo8IiKOmzNtV7btGORya4MHn3u13+/Pbn09Ervd6a2ftkesWbUsvn7mEbH24pPiy6cviGk1lX2zgFYumJnW+6xcMDNuumBpzJlek7JMvraB+HnDC2md67Y0y2Wqq7un7wcAgNwTa04M2YgnUumNMwb25ccSW+weQyaTEQvemOQ4knRiKwAAIPfEmgCQW/nKH2B8K3R+SDHxN5We5YfVxZ1feEd8/cwj4ocfWRw3bmjs9/qNG5rihx9ZHF8/84i48wvviOWHWRRjvMp7+v3VV189Ypmenp549dVXY/369XHLLbfE66/vSvT79Kc/nYcaQvZc88Bz/X4/YO894k+XzYmzFs+OXz+5JT5/3cai2xq0oXFbPNK0vf9zTduioXFbHD57r77nysoScfaS+kHH984CSmfJ/XRmAS2qnxpXnrckp59VR1d3rH2sOa3zrX20OTq6uqOqojzjuqSyo6Mrjv3aumjt7I6ayvK4/5IV4352FABAtok1GcpY44lUeuOMg+tqY+Fld/T15X/7l8vHFFsMjCF3pFnnibDCAgAAFIJYEwCKSz7yBxjfCp0fUmz8TaVvWk1lnL2kPmVe1aObtw+ZR8X4kvc7MR//+Mf7rZA5kmRy1+Lvn/vc5+KDH/xgrqoFWTdU4/r4iy0xb+aekUgk4p1zZ8SaVcvi/KvXx9NbdqR93kPqauOKjy3JyopFl97cEOse29LvudbOoS+e5/7ovqip7N9krJhfF5e///BBZTOZBfSueTNGLJfrz+rup7ZGS0f/f/eU6or49EkHxQ/veqZfh6KloyvueXprWvVO1+6rV7V2dsetD2+Oc445IGvnBwCYCMSapDLaeCKVirJELD+sLpbPrYtr7n++X1/+H9c+PerYYqgY8rlXXk+rTunGVgAAQGbEmgBQfIoh14LSVej8kGLkbyq1XOVVUboKsuxJb6A5kqlTp8aJJ54Yn/3sZ+Pd7353jmsFozeWxrUnmYz6aZNj0yttI77PygUz4xtnL8zKikVtnd1x44NN0bYzveXgW9q7Bs1iWb2hKS45bX5MrnxzFk+uZgHNmV4TN12wNL5ww8NprZqbyWc1MDn40Bm1ccVHl8SB02vitMP3G9ShyPaN7IGrV11z//OSbQEARkGsSSqZxhPD6epJxrrHm6Ots3tQX37Nw5v7/T5SbHHBNRti6uTKiEgdQ6ZjIqywAAAAhSLWBIDi0puf0d3Tk9Fxr73eGedccZ/ktwmu0PkhxSqXOTmlKld5VZS2vH/jn3322RHLlJWVxZQpU2Lq1Km5rxCMUTYa18qKsrSOzebWoE82t0R7V3p1TqVtZ3c82dzSbzn4XM4Cqq2qiDMXz07rwp7JZ3X6ollx11MvxcutnYM6BL0dir+4YWP88tHm2KemMk5fOCut86Yj1fLyDY3b4vDZe2XtfQAAxjuxJiPJJJ4YSdvO7vh5wwuD+vKvte2MvaorYlt7V8rY4qLrNsa6x3dNUGzf2RMv7mwfc30mygoLAACQb2JNACgumeZn7O6lHZ0RIfltoitkfkixy1VOTqnKVV4VpS3v3/q3vOUt+X5LSlRX95uzkCrK00tGLYRsNK6dXenNuMrmjJlF9VPjyvOWxOev2zgo+TcdU6or4jsfOnLQBSHXs4AGnj+VTM67/LC6WHvxSbHu8eY4a/HsQVtC1VZVxA8+vDh+uqExVsybEdNqKtOu7+4sLw+MB6VyfQYmHrEm6Ug3nhhOIiKmVk+Kr6x5ZMjXu5MRe02eFBs3vRYrvnlXRLzZl6+tqojPLj+oL9l2LOr3nhybXn1zh5RcrrDg+g8AwEQl1iQTYifILn9TDEXyG2OVr/yQUrJ7LstrbZ1pHbP7rm3jNZclV3lVlDY9EorSjo6uWHjZHXHwJb+IhZfdETs6Rr+dZq71Nq5TqkeXu15bVR6TJ6U3Y6p3a9BseefcGbFm1bI4pK42o+MOqauNNauWxfK5dYNeO33RrNjnjc7GygUzY/Vnl8aB02si4s1ZQKcs2HUDONNZQB1d3bH2sfRuSmf6WU2rqYyzl9QP6kj1KitLxNlL6kfdkeqdYffi9vZ+P6kuyC3tXYPKrt7QFG2d2fv/B8hUKV2fAWCgTOKJ4SQj4tW2nbGjY+i++Y6OrtjWtjNlX/6oA/aOqz4++hhySnVF/OvHj45fXHTiqGOrTLj+AwAAjEzsBNnlb4pUxpqfMaW6Iq4672jJbxNcrvNDSsnAXJb2nektFti+s2dC5LLkIq+K0ibZlqL0k/WbovWNhri1szt+un5TgWs0vLE0rn912rxBWxxMqa6IL55y2KAOYu/WoNnUmwC7csHMtMqvXDAzbrpgacx5I4F2oN5ZQF8/84j4wUeOGrRsfO8soK+feUSsvfikWH5Y+heWu5/aGi1pBlK5+KzGIpsz7MaTru6evh+g+K3ZuLnf9fnWhzcXuEYAkL6h4okp1RUxa6/qnL/3wL58NgboxhJbZcL1HwAAYGSldm8Tip3xCIbTO7a2X4bjevvtVS35jSFN5LwFuSwjy3ZeFaWtoMm2r7zySnzzm9+MU089Nerr66OmpiZqamqivr4+Tj311PjmN78Zr7zySiGrSIH8y91/GPD7swWqych6L7j1e08eVeP64B9f7ff8IXW1cdNn3x6fOvGtQ958vS0L254OVFtVEWcunp1W2bOWzB6UQDtQtmcBXXpzQxz3d7+KVdduSKt8rwuu2RDH/d2v4tKbGzI6LhfMsBtsNDNSJ3InF4rBNQ881//3+58vUE0AhifWZCg/HxBL7TV5UlRVlMVLOzpy+r5TqiviXz66JN42a89+/dhsDNANFVtlu8/s+g8AALuINRlOKd3bhFJgPGJ4E/2e6aU3N8Q5V9wXr76e3nb3vV59vTPOueK+osgfoHhM9JW05bKkJ9t5VZSugiXb/vM//3O85S1viS996Utxxx13RFNTU7S1tUVbW1s0NTXFHXfcEV/60pfiLW95S1xxxRWFqiYF0NC4LTa/1t7vuabX2qKhcVuBapTawItuRGTcuJ6+aFbs88aN0RXz6qLp1ddjxbd+GwsvuyP2nVIVN12wNC9bgw688ZxKLpJ9h7P7kvXpLlffq3fZ+mJZsr5YlpcvluAr0xmpE72TC4XW0LgtHmna3v+5pm1FeX0GJjaxJqnsHnudPH9GdOzsjq07OmNndzJn73lIXW1cd/5x8fnrHhqyH5vtAbqBfebXXu8cU9/f9R8AAHYRazKcUrq3CaXAeMTwJvo90/GUP0BxsJL2m7ksme4CN2uCrRZdrHlV5FdB0qj//u//Pi655JJIJnfd0Nprr73iyCOPjJkzZ0YymYzm5uZ46KGHYtu2bdHa2hqf+cxn4rXXXosvfelLhaguOXTpzQ2x7rEt/Z5rad85ZNkPXXFvTKme1O+5FfPr4vL3H56z+o1kqIvu755Nb9bybQ0vxLvmzYjlh9XF2otPinWPN8fOrp5Y9/iWfuc755gD4gcfXhw/3dAYK+bNSHs12Ex0dHXH2sea0yq79tHm6OjqjqqK8qzXYyjZXLK+GGbS9K5e9YUbHo7bH31xxPIrF8yMb5y9MGuzXnZ0dMWxX1sXrZ3dUVNZHvdfsqJgM2qGmpF6zjEHpCw/1N/bcOWB0Rvq+tzaOfRgzbk/ui9qKvu3I4W+PgMTl1iT4eweex1SVxvrHk8vBhqtqoqyOOote8fDm7YN24/NZIDuXfNmDFtmYJ/5mK+ti87uZFp9f9d/AAAYmliT3ZX6vU0oNsYjMjfR75mOt/wBCi/TvIXxas70mrjj4pPic9c8FHc+uWXE8u88rC6+c+6RE2YF12LOqyK/8r6y7SOPPBJ//dd/HclkMvbbb7/493//99iyZUvceeedcc0118S1114bd955Z7z00kvxH//xHzFr1qxIJpNx6aWXxqOPPprv6pJDbZ3dce0Dm+LF7e39flpTzCBq7eweVPa6BzYVdMbRwIvuv9/3XMaNa0TEtJrKOHtJfVz7u/7bX/RuhzHU1qDZdPdTW6NlwIy3KdUV8cVTDhu0VHxLR1fc8/TWnNRjKONxyfpCLi9fLLOyRjMj1XYxkB+7zwje/aelfejBrZb2rkFlzQgGCkGsSTp6Y68jD9g7rjxvSUyelLthkY6unrjxwcb49/v+2O/53fuxoxmgG87APnPnG6v2jtT3d/0HAIChiTXZ3Xi4twnFxHjE6Ez0e6bjMX+AwrGSdn+1VRVx7rHpJRp/+LgDJkyibURx51WRX3n/1n/ve9+L7u7u2HfffePee++NAw4Y+o+0oqIizjnnnFi2bFkcffTR8dJLL8X3vve9+MEPfpDnGpMr/934WnT3jG27zq6eZPx342tx7Fv3yVKt0jfURffRzdsHlZtSXRGfPumg+OFdz/QLDHob196ViYa7iB8+e68c/AveNHAlpUNn1MYVH10SB06vidMO3y/Ov3p9PL1lR9/r6ayolE29S9YPrMdIDqmrjSs+tiTmTK/JYe1GJ5urV2WiELOysjEj9YNLDijY3wdMNGYEA6VKrEmm3jl3Riw7ZN+0k11Ho6snGY+90NLvud37sUMN0KUyMIYcaKiYcnfD9f1d/wEAYGhiTXZX6vc2odgYj8hcIXMKiklv/sCH/+W+2LytPe3jZu1VHf/xyeOKMn+A3LOSdnoKlctS7Io9r4r8yXuy7Z133hmJRCL+6q/+KmVAurv6+vr4y7/8y/iLv/iL+NWvfpWHGpIvVZOys1z2SOfp6u7pe1xRPrpVizK56O6uoiwRVRVl8cK2tiGTRT/7Hw/G3ntUDXu+fFzET180K+566qV4ubUzVi6YGd84e2HfDJQ502vipguWxl/csDF++Whz7FNTGacvnJXyXNn4vIfSW48v3PBw3P7oiyOWH/jvKCaFWl6+EMFX74zUtp3pBcot7V2DZqv+x33Pxy0bh16FayJ3ciFXemcEf/66jSlnjw9nSnVFfOdDR06YgS2geIg1GY1zjz0g1v/xlXj19aG3/MyVD/zgP6O7JxmZ3qO94JoNMXVyZew5uSK2t/W/To8UozY0bYvFX10bk8rLBvWZXf8BAGBoYk12l697mzBRGI8YnsTA1N797bvi91t2ZDy2tnlbe7zrm7+Jg+tq444/Pyk3lStiucrlKAXZyFtYvaEpLjltfkyuHL/X8ULlspSCbOZVTQTjub3JexZaU1NTRES8/e1vT/uYpUuXRkTE5s2F2e6c3FhUPzWu+viSuPCah1JurzKcmsry+N65Rw3bed7R0RXHfm1dtHZ2R01ledx/yYqMky8zvejurqsnGVt3dPZddG+6YGms+o8H4zdP7VouvKMrGS9uH36mVT4u4ssPq4u1F58U6x5vjrMWz45EItHv9dqqivjBhxfHTzc0xop5M2JaTeWQ58nG5z2c2qqKOHPx7LSSbc9aMrsoE20jUi8vn84KyOkqluArGzNSkxHDbhczETu5kGvjcUVxYPwTazIaRx84LTq6ekYumGU7u0e3ElL7zp54cWd7vJh6AdthvdzaGRG7JrStfbQ5EolEX9/f9R8AAAYTa7K7fNzbhInGeMTQJAam9sqOzniqOf3vykA9yYinmnfEKzs6Y1rt0HkP41GuczmKnZW005OPXJZSla28qolgvLc3eU8dLi/fdSHv6kp/ZlJv2bKy8ZXpzK7O888+d0IclGEn+KDpNfGzz50Qy+fWDVtuzcbNfcFua2d33Ppw5gMb2bzo1lZVxLz99hzTuXY/XzZNq6mMs5fUD7og9CorS8TZS+qHvSBk4/MeSSZL1heroZaXv3XVsrhg+cGxZtWyOKSutt/rmf5beoOvF7e39/sZLmF1YNnVG5qibRQDRQP1zkidUp2/C2cu/j5gIuqdgbdywcy0yq9cMDNuumDpuB3YAoqfWJPRWLNxc7yehX5vqUlGRHNLx6C+v+s/AAD0J9ZkoFzf24SJyHjEYNnMURhvfvFIdvIAsnWeUpGPXI5iNta8hSnVFXHVeUeP60TbiNznspS6bORVTQTjvb3Je5TXu8VKJlun9JZNZ3sWSs+c6TVxy4XL4qgDpqZV/qgDpsYtFy5Lq/N8zQPP9f/9/uczrl+2L7p3/37rqM6T6nzFJBuf93BGs2R9MTp90azY542L68oFM2P1Z5fGgW98n3uDyVMW7Jr9M5rl5Yst+OqdkTqw45ULxfz3AaWod0XxdBTziuLAxCDWZDQGxjClYEp1RVy04uCsTWgb2Pd3/QcAgDeJNRlKLu9twkRlPKI/iYGpLdh/r6I6T6nIdS5HKRht3sIhdbWxZtWyCTFhJte5LEwM4729yXuy7cknnxzJZDK+8Y1vRENDw4jl//u//zv+4R/+IRKJRLz73e/OQw0phNqqiqiqSG/7gupJ5Wl1nhsat8UjTf331mxo2hYNjdsyrl+2LrpD1Wks5ysm2fy8U0m1ZP0XTzlsUKDRu2R9MepdXv7rZx4RP/jIUYO+z73Ly3/9zCNi7cUnxfLDMvv/LsbgK9MZqSccPD0O2jezQadi/vuAUjYeVhQHJgaxJpkaLj5LFWcUWm+f96IVh2VlQluqvr/rPwAA7CLWJJVc3NuEic54RH8SA4e2qH5qXPXxJTF50ujSnSZPKot//fj4TEROJR+5HKXCStrDy3UuC+PfRGhvEslkMpnPN3zuuedi7ty50dnZGbW1tXHppZfGn/7pn8b06dP7ldu6dWtcddVV8Xd/93exffv2qK6ujieeeMIs0BxpbGyM+vr6iIjYtGlTzJ6d3qyxbOno6o4FX/5ldPWM/HWsKEvEo397Sr8A9tKbG2LdY1v6lWvt7IqW9sHb+kyproiayv4XhBXz6+Ly9x8+4nvv6OiKL9zwcNz+6Isjlq3fe3J0dvdEIt5cPjxVndKxcsHM+MbZCwddzLq6e/oeV5TnJ38+X5/3QBdfvzFWP9TU9/uhM2rjio8uiQOn18SzW1vj/KvXx9NbdvS9/oGj9o9vnb0o4/cZL4b6TEZySF1tXPGxJTnrLK57rDn+59XrRyx35XlL4ti37pP231uqvw9gbDq6umPJV9cNmugwlClVFbH+r1ekPcAMlJ5CxwwjEWsWr2L47mQSw5Qldm0HVVFWFl09PfHyjs7I1cBJVUVZJJPJ6Owe+R2qKspir8mT4t0LZvTFU5nEqAOl6vu7/gMAkG/FEDOkItYsXoX+3oz13ibQn/GI1DIZ/5lI90yf3doa7/3O3X1blaejprI8fva5E8Z14mShcjlKwe6fTUdXd7z6+s4Rj9l7j0l9bc14/mxgNEqhvclFzJD3lW3f8pa3xD//8z9HRMSOHTvi//v//r+YOXNmHHzwwbF06dJYtmxZHHzwwTFz5sz4q7/6q9i+fXskEon453/+ZwHpOHbnE1vSCkYjIrp6kvHrJ978Y23r7I4bH2yKF7e39/tJldTa0t41qOzqDU3RlkYnLJPtK5q3d0Tz9o606pSOobbD2NHRFQsvuyMOvuQXsfCyO2JHGsHHWOXz8x7IkvWZKcZZWZnMSLVdDBTeeFlRHJgYxJqkkmkM05OM2LqjM17c3h5bc5hoGxHR0dWTVqJtb9ktLR394qlM+sy7G67v7/oPAABvEmuSyljubQKDGY9IzT3Toc2ZXhPfOHthlCVGLhuxa4L9N85eOK4TbQuZy1HsBn426STaRkS8+vrOcf/ZwGhM5PYm78m2EREf+9jHYs2aNTFr1qxIJpPR09MTf/jDH+K+++6Le++9N/7whz9ET09PJJPJmDVrVtx6663x0Y9+tBBVJU+u/q/nBj1XU1keXzzlsKipHDwj7ep73yz/ZHNLtHeN7Y+vbWd3PNncklbZdJMFO3dbcTYbhtoOY83GzX0ztVo7u+PWhzeP+vxd3T19P8PJ9+e9O0vWZ66Ygq+Oru5Y+1hzWmXXPtocHV3dtouBAhv4N3jojNq4ddWyuGD5wUNuXeRvESg0sSZDyUYMU0wGxlPp9pl3N1zf3/UfAAD6E2sylLHc2wQGMx4xPPdMh3bq2/aLvfeoTKvs3ntUxqlv2y/HNSqsQuZyFDufDbmQbp7VeDSR/6YKkmwbEfGe97wnnn322bj++uvjk5/8ZBx//PFx6KGHxqGHHhrHH398fPKTn4zrr78+/vjHP8Zpp51WqGqSJwMnGx28b03c9rkT4oLlB8fPPndCHDTM7KJF9VPjyvOWDJrRlq4p1RVx1XlHx6L6qSOWzSRZsKqibNR1Gkpv8uHurnmgf2B+zf3Pj+rcmayQm8/PeyjTairj7CX1kUgMPUWtrCwRZy+pj2k16XWqJ4JiCb4ynZH66ye2ZJycC2SXFcWBUiTWZKCxxjCTJ5UPilkLpbaqvF88lUmMurvh+v6u/wAAMJhYk4HGcm8TGMx4RGqjWdBoomhp3xkvt3amVfbl1s5oaU9vNdNSVehcjmLmsyHbCrETeTGZyH9TiWQymcsdESkRjY2NUV9fHxERmzZtitmzM9+Gcix+/eSWuPj6jfHq6zvjlPkz4psfXNRvlZ0dHV1x8fUPxR2PbYm995gU3/rgokGrlj67tTXOv3p9PL1lR9rve0hdbVzxsSVpbxXw8X+9P37zZPpbUkypqoi2nd1pbyMTsWv7gk8snRPXr980aHntK89bEu+atyuIaGjcFqd/755Bx9+6alkcPnuvtN8vYleS7v+6qaHv9//9gcPjnGOG394oH583Y9fR1R1LvrpuUJLrUKZUVcT6v14RVRWDZ1zvPhOnonx08zQuvn5jrH6oqe/3Q2fUxhUfXRIHTq8Z8vt0/Funxb1/eKV/Hasr4tMnHRQ/vOuZYf8+gOx5pbUz1j3eHGctnj3kRIeenmT8dENjrJg3I6bVVGalvQCKU6FjBkpXsXx3RhPD1E2pius/dXzc+8zWuPSmR2Is89M/fOwBccvGptjRMfobHn/3P94W5x77lrj05oZY99iWaN/ZHa+1ZX6TYFJZIh7521OG7PtHZH79BwCAsSiWmIHSUujvTTbubQL9GY/ob7TjP1MnT4rqSeWxYn5dXP7+w3NYw8L7P7c/Hj/4zR/SLv/ZdxwUX1o5N4c1Kg5yOVJ7dmtrnPG9e1JudT+UKdUVsWbVsnH/2aTi3u/QRpNnNR4Ve3uTi5hBsi0RUfiANCI7necdHV3xhRsejtsffXHE91u5YGZ84+yFKbfOHKitszve9pVfRncO/2Rqq8rjhk8dH/Nn7TVkg1Q9qSymTt71b2/t7BqyAzCluiJqKvv/m0bqSL/3u3fHI03b+34/fP+94tYLl41Y31x+3mTHusea439evb7fc5kmrO7o6Ipjv7YuWju7o6ayPO6/ZMWo/h9//eSW+MIND8fLrZ1Dfh92dHTFX9ywMX75aHPsU1MZh82cEv/1zMt9r4+UnPuBo/aPb529KON6AdmTrfYCKE7FEDNQmorpu5NJDBMRMX/WnnHDp47vu74lImIsEWEidk2w7B7lST5w1P7xtfcfHkd9dW207RzbKiXf//CRcdrhE2cVGAAAilcxxQyUjmL43kgMBHKlrbN7zOM/e1SWx4OXnhyTK4eebD0eLLl8bWzdkd7KthER/z97dx4fVX3vj/81SzKBSQAhJBATBFkCRCBCFBQ0oli5tLTWIlXUar9t1Su4UW3v/UFvr7dqW67d7IKXou21FS1VQNCqbIrYa0CMQNhdUEiACVv22Wd+f4QTZjkzc7aZOXPO6/l45PEgkzMzhzNnzvks7/f7M7DQgQ8Wz0jjHmWfEKQdRhgtXX54A6nLBzjsVvTrnQcLLKYI0v7HnuN46MWP4JMwSJtvs+DXt16KWZcMzsCe6Q/nfhNTGmdlRHqOHUtHnyHr34D29nYcPnwY7e3tCAZTNxSuvvrqDOwVZUN/Zz7m1lQk/LvVakn6dwAodNgxZ1K5pC/wzTXlsr68B13taQ20BYD7rhmBsWXdVWmF5TAeeukjbNzfDADw+EM44fckfY12T3wQ7qr6JiyaNVa0Id3Q2Bp1AwCAhqZWNDS2pqyQm87jTdr4R8zysJEBq7PGDY4LWH294XhcsO3ancfQ6eu+Pnf6gli365iijJzplSXYsLA24cBTocOOpbdN6hl42tXYgoMn2nG604cbqkrxi7nns8KF78fDf/sIG/Y1o3/vPFMtF0OkV1pdL4iItMC+JomR04cBgH3H2rD07U977m9qe4RhKA+0FZZIPOhqh0eD5QBX7mhksC0RERERkUzsa1IkLeY2iYjEaDH+4/YHcdDVnpNLdEs1sNARFWxb7MzHqc7zvw/onY/TXRF/LzR24oPbF8QrHzbJDtL2BkJwtXkBJI8tMYpZlwyGd04ID/9tZ8ptl8yZYNpAW4Bzv4moibMyIrPFjmVt7//4xz/iD3/4A3bv3i35ORaLBYGA9FLeZE6xwYWJiAUVJlNd0Q/P3VWD+1/8CJ0qlv1M5pcbDuH597+IemxCRR/V1ZOEhvTLHx7Fxn3NUX/r9Il/p+Ytr5NUITddx5u0Mbu6DFsOnRStJisErEZWkxULWF2xPfqcXLHtiOIGlJyBp8uG9of73Pm59dBJ0e3/75PuyreeQAiXDe2vaJ+ISDtaXi+IiJRiX5PECBUdAKDFLb3aBQD8/p1P0rFLsky8qB+Wf+uynkpIz95Zgwdf2ilrubNIRQ477rxyqIZ7SERERERkbOxrEhFRJlVX9FM1/lNUYMfTt1xq6EBbAPjBv4zGQy99hFZ3AF8aU4LDp7uig22L8jHxor7YsP8k+vay4wf/MjqLe5t+DNJOTskY8b+t2o2fvXEAQOoVpY2Ic7/R541AbZyVEZkpdizjwbbBYBDf+MY3sG7dOgBAOM2VQslcvIEgNuxzSdp2w14XvIEgHHbpGTnXji7Fa/dfFVcNVCuBUBgn2qIr17Z97McfbpuIH7yyW1VDurK0SFYWk5QKuek+3qSe3GqyscsYZTMjZ+3OY+jydy9r0eUPxWVJRWZRdTGLiijrmMFHRNnGviYlorSiQ7oVFdgRCITglrCU28cnOuB0nO9LXTu6FGsXTJPdNx1ZUogbL70Qt14+hEuYEhERERFJwL4mERFli5rxn2XfqsGwYmca904fpleW4O1HpmPjfhdGDyrCV3/3z6i/H3J14L/nTMD1VYNF58KNhkHaiSkdI45cfdoMVX8jce5X/nkjdyVyozBb7FjGg22feeYZrF27FgBQWlqKb3/725g0aRL69+8Pq9Wa6d0hg9l66BTavdEXrqICO+6tHY5ntnwadVFr9wbw3senZEfMC9VAH1m5S/LSo2q4/UEM7tdLdUN659EWzbOYMnG8ST2p1WT1lpGTKkuKWVRE2aO36wUREcC+JiWmRUWHRCwAeuXZ0CVzkNZq6X5ubKCtnP6U3L5p7EoXRERERESUGvuaRESUTRz/ESdnnur2Z7fBmW/HL9cf6nnMyPNUQpD2bX+sw7FWT+onnFPWtwAvfG+KYYO0WfU3Oc79iuN5I43ZYscyfod9/vnnAQBjx47F1q1bccEFF2R6F8jAYstSjyotxLI7ajC02IlZ4wbHBasqLU9d6LBjzqRySQ3a0YOKcOBEu+z3AOIzh9Q0pNORxZSp403pp7eMnFRZUsyiIsoevV0viIgE7GtSImr7QsmEAdmBtgAQCgNtMftisQB/v+cKjB7cR3J/Sk7f9OaacsNPtBARERERaY19TSIiyjaO/0TjPFVqw4qdmHTRBTi2W9qy7gAwaegFhg20BVj1Nxl+pxLjeSON2WLHMp5yuX//flgsFvzoRz9ih5Q0N7u6DAPOlf6fWTUIq+6biqHnGgRC1tcNVd1f2AHOfMyeUKb4vWIvFon0d+ajX+882a8/sqQQaxdMw/TRJT2PCQ1pKcQa0kIW08iSQln7UlLkiNsXIHPHOxAM9fxQemiZkSPX4jUNmPLkpqifecvrRLf96u/ew8X//jq++rv3RP8+b3ld3GstXtMge5+IKLFsXi+IiJJhX5OSEfpCZX0Lsr0rCYXDgOdcpVs5/SmpfdPXJW5HRERERETnsa9JRER6wPGf8w662iUHBSbS5TP+PNVNk8pRJDHwushhx00TpcWh5DKlY8RlfQtE42WMgnO/ySmNsxKL+TKqTMbq6UHWUloqKyuz9dZkYNMrS7BhYS027nfh5knlsFgsUX8vdNix9LZJeLm+ETPGlKL/uS+7XN5AEBv2uSRt29DYivULr8bG/c1479BJvCXhecmWd5DTkBbLBJC71AQAFBc6RLOYMnG8O7wBTH5iIzp9QTjzbdi2aIbhs/GyIVsZOXKzpMLoDkJIxExZVETZwgw+ItI79jUpkWHFTqxfWIsHVnyEzQebUz9BBme+FZ0+9cmBQy7o3fNvKf0pOX3TDXtd8AaCcNjZLiYiIiIikot9TSIiyhaO/0SLHD/Tw+vo1fTKElw9aqCkAOzayoGYXmn8gEBA/hjxtZUleHrepYaOUeHcb2py46ySxXwZUaZi9fQi45VtR44cCQA4c+ZMpt+acozHF+j5kaO/Mx9zayrivrwCq9WCuTUVqr68Ww+dQrs3er+KCux49IZKFBVEXyzbvQHsO9aGO6ZchJtrKiS9fqLlHZQ0pAWR1WHlVMgFgH3H29DQ2Cr6t3Qf77U7j6HT1/3/6PQFsW7XMUWvQ6llIyNHiyypVIycRUWULczgIyI9Yl+TpCh02DFv8hBNX3PEQCfuqR2uyWttPNDd3xP6bqFwOGl/Sm7f9L2PT2myn0REREREZsG+JsnBlRoT47EhUo7jP9GE8TO9vI5eeQNBvHvopKRttxw8GRVbYnRyxohvmzLEFAGTnPtNTe1K5EaXiVg9vch4sO0tt9yCcDiM1157LdNvTTnE1ebB6P94q+fH1ebJ9i5Fia0uO6q0EOsWTMP86SNEb0BCtpDa5R2UNqQ7vAFMeGw9Rix6AxMeW48Ob0DyvgjmLa/DlCc3Rf0sXtMg6zWUWLH9i+jftx1J+3uamZCRM7NqkKTtZ1YNwur5U0UrH0shZEnFnr9aKSqw47k7LzN0FhVRtmT6ekFElAr7miSV3L5QKsdaPbh4oLxBSDEPzRiJuTUVov23RJT2TYmIiIiISBr2NUkqOX05s+GxIVKH4z/R5tZU4MHrRqh6DWEczsgYpJ2c2tgdI+Lcr7jFaxp6YqQWvFgv6TnzV9RnNK6KMivjwbYPPPAAxo8fj6VLl2Lr1q2ZfnvKET/9x4Go33/2xoEEW2bH7OoyDDgXbT+zahBW3TcVQ8/dQIQb0A1VpQCAAc58zJ5QpqoqrUBpQzq2Ouzq+kasl7gvgnZPACfaPFE/q+qb4PalL8OpobEVe5raoh9rak1YZZe0kemMHKVZUqmYKYuKKFuYwUdEesK+JknhDQSxXsIyT3J0+YJ49r3Dip+fb7PiL9+5HA/NGAVA3uoeSvqmREREREQkHfuaJBVXakyMx4ZIHY7/xHv4+kr85TuXI98mL+QpdhzOyBiknZgWsTtGxbnfaG5fEK982NQTI+XxS6vQ7/GHMhZXRZmX8WBbh8OB9evXY9KkSbj++uvxgx/8ADt37oTHo6/KpZRdb+6NvpG/uUf5ZGg6liWZXlmCDQtrsWTOeCy9fWLcDaTQYcfS2yZhyZzx2LCwFtMrSzTJHFLakI6tDrt862FNskbd/iAOutpVvw4QnQ0i/MxbXie6bbaq7JpJpjO55GZJpWKWLCoiPWDmJxHpBfuaJMXWQ6fQkYaBrbOdvp6+2jWVA+GwSxtuKbBb8fYjtbhq5MCex+Ss7qGkb0pERERERNKxr0lScaXGxHhsiNTh+E+8xWsa8Ojfd6Nv7zxZz+vbOw+P/n23KWILGKSdGKv+Jse53/MOutrhURlsrWVcFelD2kLMbTZbym3C4TB+8Ytf4Be/+IWk17RYLAgEuKyE0a2ub4rLBnD7g1hd34SvT7xQ1mt1eAOY/MRGdPqCcObbsG3RDM0yK/o785MuLWC1WqL+HdbTFgABAABJREFULpY5tOyOGgwtdmLWuMG4+/kd+Li5o+fvrzccx3VjSqOChIWG9Mb9Ltw8qRwWiyXqNYWG9Mv1jZgxphT9nfmi1WG/ONOl6P8cqajAjqdvuRTVFf1Uv5aQDeL2S7tJtXsCaPdEXwtW1Tdh0ayx6JWf+tpDySnJ5HLY1R93IUvqTQ0qjZkhi4pID7J1vSAic2Nfk9SQOlAo1+enu/DX70zGsVY3vjphMMb/5wZJz/MEQrj+l1uwffH1KHTYk67uMa68r+hryO2bEhERERFRPPY1SQ0lfTmz4LEh0gbHf86TG1sQ6WS7F4A5YguUxJaYhdLYHTPg3G+06op+ePbOGjz40s64GCUptIyrIv1IWyRSOBzWdDsypi/9ags+ibhJAUAowSnx8Mqd+P7fd0Y9NqKkEOsfrk34+mLLktx6+RBV+6zU7OoybDl0Eqc7fZhZNQhPzZ3QEwwoZA59f+VOvLXX1ZM5JBYsnKwhvXhNAzbuawYA/HL9IQBAp0/8gm8BEAaQb7MgGAojKOOrOLKkEMu+VaNZ1VAts0EyfZOKDIa2y1ymQq8SZXLdWzscz2z5NKoRIWRyadW41Cr4wUwNXqJsyub1gojMi31NUiOyX6a1O57dBosF+OHLuyHn7Ovyh3Dpf63HAKcjYf9t3vI6OPOjh3BmjC3B4zeOU7HHREREREQkYF+TpIqcixOwL9eNx4aIMuGgq11RoG2kLl92YgsyjUHa4sRidwrsVgSCIVRc0Es0dscsOPcb79rRpVi7YBpu+2MdjrVKX92irG8BXvjeFK7GbEBpC7b98Y9/nK6XJoM40+HDIVdH6g0jxAbiHnJ14EyHD/0LxbNsxJYlyVawrZLMoRXbjkgOFpabwSUcSp+cKFsgLlBYC7maDZLOysnZlK1MLjlZUqmYIYuKSA+Y+UlE2cC+JqkR2S8b4MzHQ39T1gcREwagdN7dHwzjRFvigTqu7kFERERElF7sa5IUXKkxMR4bIsqUytIi5Nus8AVDqTdOwGG3orK0SMO9olwSG7vT6QtiwmPro+I+WPW3G+d+uw0rdmL9wlo8sOIjbD7YnHL7aytL8PS8Sw0RP0TxGGxLWbPxgDZBdRsPuESzbfS4LInczCE5wcJaVIeV4uaa8rTcEIRskNibcypaV9mVQ0+Vk7WkpAqzFpJlSf1i/UHRqtdmz6IiyrZsXS+IyNzY1yS1IvtlSjLS9SJbq3sQERERERkR+5okRS6v1JhuPDZElCkHXe3wh5QH2gKALxji9cbkosaIE8R9sOov534jFTrsmDd5iKRg29umDGGgrYHxk6WsmVtTgaazXfjNpk8Uv8ZDM0Zibk2FIZclkRssrLY6rN1qQUAsmjFGOjJTIj+/UDgMh90KbyB1A9lht6LV7cez732Wlc9PT5WTtaSkCrMWEmVJtXsC+O+3DsZt3zvfhnULppk+i4oom7J1vSAiItKC0A852+XN9q7Ilq3VPYiIiIiIiMwsV1dqzAQeGyLKlOqKfhjctwDHWpQnzw/uW8DrDfUwatyHEpz7TS42piURxqoYG4NtKasevr4SNUP74zt/3iGrzH++zYpn76rBVSMHGmJZEq2ChZVWhx0+0IkTrR4EfKmP4Ya9LngDQTjs2hwruZ9fJG8ghOZ2b1Y+Pz1WTtaS3CrMWphdXYbXG47DGwjBYbeipcuPW5bVJfwueP1B3LKsruf3UDiMigt64ehZt+myqIiyKRvXCyIiIrXU9EOyLZurexAREREREZldLq7UmCk8NkSUCWc6fKoCbQHgWIsHZzp86F9orkBBimf0uA8lOPcrzhsIYsM+aSu4ax1XRfrCYFvKuqtGDsTWH07HV3/7HlztqSsKlRY5sPb+aSjtUwAg95cl0TpYWCjd/sjKXXhz74mUrzezahC+MmEwFqz4KOrxogI77q0djme2fBr1fu3eAN77+JRmWRhafH5dviAeXrkTz9w+SZN9imXEysl6NGXYAAh5UUIgdTLBMHCiLbojVWC34L++WoWvTCgzXRYVEREREUl30NWek4G2sUt3ERERERERUeYpmYszS1+Ox4aI0u3VnU2avc63pw3T5LVyRSCiAJ7dZs3inmQH4z5Ija2HTqHdG32+ZCquivTFfFdP0qXSPgW4WWLmwzcvq+gJtAXOL0tSVKCsE1ZUYMdzd16WtWUCtAwWFhQ67JgzqVzSc2+uKcfm/dENilGlhVi3YBrmTx+BtQumYWRJYdTfX5dYGl2K6op++MNtl6p+nXcPnYRbQmVeuYRg6BNtnqifREvgtHsCcduuqm9Ky74ZzUFXO7wyKlyL8QTCePKN/ci38/ZGRERERIlVlhblZJvx5ppyTkASERERERHpgNy5ODP15XhsiCidtBrTy8WxQTU6vAFMeGw9Rix6AxMeW48Or3i8g1Ex7oPU+kdMnFQm46pIX8x19yBdWysxA2mNyHbCsiSxF69URpYUYu2CaZg+ukTW87SUrmDh2At9Iq83HMfs6jIMOFcFdGbVIKy6byqGnluqRchAvaGqO+NigDMfsyeUKdrXRAb37d1T0VSp2IBjraQjGJrECd+FPJu6s8HjD2HdrmMa7RURERERGdFBVzv8KhO9soEDdERERERERPohZy7ObHhsiChdqi7sq6vXyRVrdx5D57lA0U5f0HTz6Yz7ILXE4qrKL+iFQDCEigt6pT2uivSDwbakC+0eP46cdUva9sgZN9o9/rjHhaDQmVWDJL3OzKpBWD1/KoadCyrNJq2Dhb2BIDbsc0l6jQ17Xbhy+ABsWFiLJXPGY+ntE+MySAsddiy9bRKWzBmPDQtrMb1S2+Dk6op+ePYufVYnzvXKybnmiouLYbOoDb0G/lr3hQZ7Q0RERERGpbadny0b9rrgVTkoTEREREREROrJnYszU1+Ox4aI0kmLFascdisqS4s02qPcsGJ79Pz5im1HsrQn2cG4D1JremVJVFwVgKhq0QDSGldF+sFgW9KF/9nymazt//iu+Pa5vCyJlsHCWw+dQntM2f+iAjsevaEyrvHQ7g3gvY9Pob8zH3NrKmBJEOhotVowt6YC/c9lamhNCDju3ztP1vPK+hakvTpxLldOzjUHXe3walBhbO+xNjQ0tmqwR0RE2RMIhnp+iIhIe0I7v6xvgaLnFxXYVa/KkOy1k/Xf5OI9hYiIiIiISFtK5uLMgsdGHvbZieTRYsUqXzBkqgqlDY2t2NPUFv1YU6vp5tMZ90FqRcZViVWLTndcFemDfiINydTW7myK+t0CIJzk9zU7m7DwS5WiryVnWZLrxnSX8I7svNht2YtBF4KF39x7IuW2yYKFY4/BqNJCLLujBkOLnZg1bjDufn4HPm7u6Pl75LHIpmHFTkwdUYx1u6V9htdWluDpeZdmJGhaCIZ+ZOUuSZ/PzKpBeGruBF0FdOcCIaPswZd2ot0TSP2EJL76u/dgsQAFeTb0KegO4p4xtgSP3zhOi10lIkqrDm8Ak5/YiE5fEM58G7YtmsF7ChFRGgwrdmL9wlpc99Q7cLV7JT9vVGkhZo8vwy82HNJ8nyzorq5xvNWNtQumqe6/8Z5CRERERESkvVydi8sEHhvp2Gcnkq+6oh9qRw3EloMno2JIpLIAuGbUQMNWKF28pgEb9zVHPdbpE593n7e8Ds786GuO0efTGfdBWhGrFn3r5UOytDeUSaxsS7rwjZpyCPWA+vfOj2sUhc89DnQ3fr6RoHqtkmVJOryBqNLeHV51AX5qyQkWTmR2dRkGnMuUmFk1CKvum4qh5yrgCo2HG6q6O60DnPmYPaFM5V5rwxsI4p2DJyVvP/eyzFYnzuXKyblEyChTu6RvGEAoDHT5gjjR5sGJNg9W1TfB7eNyRESkf2LZkERElB55Ngva3H7J239pbClW3TcVb+1LPRirRBjAqQ4fVtU3YVCfAtX9N95TiIiIiIiItJerc3GZwGMjHfvsRPK5fUFs++yMokBboHvsbdvhM4acM3b7gnjlw6aeuXHhJ1GRq3ZPIG5bM8ynM+6D1GK1aHPjFYGyKjKrZoAzHx3eALwB8Ru9NxBAgd2KQocdL247ihe3HY3LqhFblkTgzLf1dFaA88uSuNq8cZ2YbGUbKAkWdthtcX+bXlmCDQtrsXG/CzdPKofFEr20aaHDjqW3TcLL9Y2YMaZUNyXMk31+Yp5//wvMvGRwGvconpLKyUaVzorQZf0KEA4p7SIl5vYHcdDVbthMRSIyDmZDEhFlztZDp+AOSF967hsTL8Qnro64wTStdfmCmPrzzZg1bpCq/hvvKURERERERNrL1bm4TBA7NpFzSmY+NrHYZyeS76CrHZ6AumBQo84Z89hIx7gPkorVoikWg20pa4SsGrdf2s2+09fdCfMEfD2PvbT9KDbsdfV0YFvcPtHnCuxWCwIRAXxPvL4fvR3RwarZ7MSIBZsWFdhxb+1wPLPl06iMIyFYONGNvb8zH3NrKhK+l9Vq6fl7OoMm5ZDaoBFsP3wmYcBxOmgVDG0E6V7WZuuhU+jQOGOuqMCOp2+51PAdAyLKfcmyIceV983SXhERGZfcqjH3/LU+TXsS70xnd4XbRbPGJu3fJcJ7ChERERERUfrImYszm8hjk2hOyazHRsA+O5Ey1RX98OydNXjwpZ0JK7YmY+Q54+qKfqgdNRBbDp5UVPnXAuCaUQMNeWwiMe6DpJIb19buCcRdl4Tx/V75PIeMgsG2lDVaZNUEQmG42r2Stu0UCdw7erYL/mB0MyObnZjYYNNRpYVYdkcNhhY7MWvcYNz9/A583NzR83ctsmjSHTQpx+zqMmzY55IccBwIhZMGHGtNy2DoXCe2rI2WQepyA69TGVlSiGXfqsGwc8sUERHpBbMhiYiyb2iCNmKezQKH3YoOb3aXTZNaTYL3FCIiIiIiItKjdM8p5QL22Ym0de3oUqxdMA3XPvWOrKBSC4C1C6YZds7Y7Qti22dnFAXaAkAYwLbDZ+D2BQ0dGMi4D5KK1aJJTPZKWJLpCRlHRQXKAjt7a3Bzjw20FcxbXocpT26K+lm8pkH1+6Uyu7oMA84tlTKzahBW3Te1Z+J3WLETq+dPxQ1V3TfxAc58zJ5Qpvo9xTq4WgsEQz0/yUyvLMHVowZGPTaqtBDrFkzD/OkjsHbBNIwsKYz6++saB2UmIxYMrZd9yzSxZW20NLu6DP1752nyWjOrBmH1/KmG7TQRUe4SsiFPtHmifhJlYrd7AnHbrqpvglvjSuBERGZz9EyX6OP+YBhfrb4ww3sTLc9mwXN3XpZyII73FCIiIiIiItKrdM8p6R377ETaW7ymAbcuq4NVZsST1QrcuqwuI7Ef2aBlYKCRMe6DpFIb11ZUYJc0vm9EUuPEchGDbSmrhIyj2JtVKiNLCvH6A1fhubuUX9SSyVYnZnplCTYsrMWSOeOx9PaJcRVmCx12LL1tEpbMGY8NC2sxvbJE9Xumu4Pb4Q1gwmPrMWLRG5jw2Hp0eJMv5TCnpjzjAcdSZSMYWo+SLWujlemVJfjR7Kq4x3vlyQ+yv7mmPGvVmomIkuGgBxFRdsQO8kS28/vE9C8bGlvx+9suhd1qyfh+AoDNasGUiwek3I73FCIiIiIiItKjTMwp6R377ETaigxglxvDFQzB0AHsDAyURizuo/yCXggEQ6i4oJdp4j5IGjVxbWsXTMP00erjunKN3DixXGMJh8NKK4iTgTQ2NqKiogIAcPToUZSXl2f0/Tu8ATyychfe3Hsi5bYzqwbhqbkTeoLnDp/qxN3P78DHzR1p3UeLBVh931RDNSwaGlsx+3fvxT2+bsE0jCvvq8l7rNh2BP/f6vOZYT+9aVzKpWHOdPqwcb8LN08qh8USP6kdCoXxcn0jZowpRf9zjaBM0fO+pUOiZW3Esm2LCuyaLmuz8G87seqjpp7fR5UWYugAJ9bvc8l6nZsmXohfzq1WtA9EROm2+YALD760M2EVg2SKCux4+pZLTdlJo+zIdp+Bcpeezp0ObwCTn9iITl8Qznwbti2agUKHHWc6ffjz/x3G05s+iXvOAGc+LBbgdIdP8RJsSsnph/KeQkRERES5Sk99BsodPG/0J5tzSnrHPjuRdnYebcHX//BPqIl0MmLsRyQlMTQjSwqx7Fs1plktNjLuo9MXjBsz7p1nM1TcB6mnJq7NbJTEiaVLOvoMDLYlAProkG7c58J3n9+Rcrtn76zBdWNKox6Tc1FTwgidmMgObvjcFHGXL5j2Du5Xfrs1KmN13IV9se7+abJfhzLP7Qti4k82wO1XntXXO9+GDxdfj1758ivSvn2wGY+s3IXTnT7MrBqEJ266BNcseQftMrNeihx27PjRDDjs8veBiCgTOOhBuUIPfQbKTXo6d2IHefoU2NH7XN8n0QRgtlgA/PFbNZgxtjTltgLeU4iIiIgoF+mpz0C5g+eNvmR7TikXsM9OpB0GsKd2ptOHy5/YiEAodUiY3WrB9kUzTBtUqqfAQNKfyFgrbyCIs13+lM+5oHdeT3yKkZOJEtFTnFg6+gxW1a9ApJF/NByXtN3rItsVOuyYM0naF6Ksb4Gs/TJCae/IpRROtHngavPC1eZN2Phs9wR6thV+lCylkGtLw8QuJ2t22V7WZnplCTYsrMWSOeOx9PaJ+OiLFtmBtgDQ7g3gvY9PKdoHIqJMGFbsxOr5UzGzapCk7WdWDcLq+VM5wEpEpMCK7V9E/d4W0ffRU6AtAIQB3P/iR2jp8knup/CeQkRERERERNmQ7TmlXMA+O5F2hGXd5cZ+lPUtyPnYD6nqvzgrKdAWAAKhMD46cjbNe6RfsWPGK7YdydKekN7ExlpJCbQFgLNdflWxVrks1+LElDBnvWLSHW8giA0Sl4bfsNcFbyAYV6VSarDu8BInjrV6JG1rlNLeWnZwEy2lkGhpGDHzltfpbmmYRMvJmll1RT88e2eN6qxANctv9HfmY25Nd5aJ1O+4mNcbjsdVxCYi0hMhcUhKlf6ba8pNf48iIlJCbJAnE4oK7IoDed3+IKr/awOA7kq3JX0cuH5sadK+E+8pRERERERElGl6mFPKBeyzE2lnWLETky66AMd2S59DnjT0AtMEsMudWzfrfHqywMBx5X2ztFekF5mItcpluR4nphRbZ6QLWw+diqtYWVRgx721w/HMlk+jOmVClcrIG72cYN2tH5+WvF+eQNAQnZh0d3CFbA6pS8O0e+KXZ11V34RFs8ZmbWmYtTuPofNcNkmnL4h1u45xaQCczwrUw7I2s6vLsOXQSZzu9GHSkAtw+HQnznT6orapKuuDvcfa0L93HoYWO1F/pAUDnPmYPaFMs/0gIkoXOVX+zTjgQUQkh5xBnnSwoLsy7cyqQfjapWVYvHoPTse0XeUKA3C1eSX1nXhPISIiIiIiokzT05ySnrHPTqSdmyaV452DJyWtjlrksOOmieqXD88VM8cNwuqPmiCltq0FwMxLpFXdzmVmDQwk5ZhMlJgR4sSUsmZ7B4iA+E7FqNJCrFswDfOnj8DaBdMwsqQw6u+vx2wvFqxb6LDBbrWo2q+tH58yTDnva0eX4qk5E2Q/b2RJYcqlFIywNAyXBkhML8vaTK8swYaFtVgyZzxe/tcrsHFhLS7sF700iAXAkjnjsfH71+Dle6/EkjnjsWFhLaZXGn8pECLKbUqq/BMRkbjYpZ2EH6XVZZUIA/j2lUOx9PaJ+JdLBmPDwlr0752nyWun6jvxnkJERERERETZopc5Jb1in51IW9MrS3D1qIGStq2tHGiqOWMrLJICbYHusUybRV1sjd7JHTNu9wTitl1V32SY+CGSTkgmKiqQV6ixqMCeMtYqlxkhTkwpBtuSLsyuLsMAZz6A7k7VqvumYui5TpXQKbuhqjtzT6xKpViw7n/PmYBgWGrzQVwwFMaeY62qXiNbFq9pwJQnN0X9PPLyLlmvIbWDK2RzyL25CIoK7Hjuzsuyls2RbGkA6iYsayNFOpe16e/Mx9yaClgsFjSddaOpxRP19z3H2jBmUB/0d+bDarVgbk0F+p+7thAR6VmiKv+P3lAZd38VqvwTEZE4LQZ5lBjUxxH1e6vHD8u5QWqnwwa3P6TJ+xQ67Lj3Lx9i8ZoG0b/znkJERERERETZpJc5JT1in51IG0IsxOQnN0qvFr37OCY/uRFTntyUcFzNSKQeF0FswTujMXNgIKk3rNiJn88Zj3ybtKD0fJsFP58z3tDJRLkeJ6aGeVqupGtCxcqN+124eVJ5z4SkoNBhx9LbJuHl+kbMGFMaFzwXubz8zKpBeGruBBQ67LhjykV4/v3oiqVytbn9qp6fDXLLdSfy1eoyyR3cXFkahksDKJfNZW34uRGR0YklDi27owZDi52YNW5w3P2VS4gRESWmdmknpUr7FGBCRT+8tdeFAc58nO30YcqTmwAAHn9Qdf9MICy39ELdEWzY64LFYolq7/KeQkRERERERNmWzTklPWOfnUg9ty+IF7cfRTAkr/BaGICrzQsAeGn70ZxculwKYV7d6w/CagFCYcButSAgcryEx60W4O39zZjy5CbDzqurHTMuKrDj6VsuzcnAQNLGrEsGwzsnhIf/tjPltkvmTMCsSwanf6eyLFfixLTGyrakG5EVK8Ukq1IZubz80tsn9gSI1h85q3q/cjGDR6tKTn/fcVTW9npfGoZLAyiXzWVt+LkRkRmorfJPRETRhEGekSWFGXvPhqZW/Pqb1VgyZzzWLZiGus/O9LRHW9KQxBkG4Gr3xrV3eU8hIiIiIiKibMrmnJLesc9OpN7uxhbZgbaxAqEwdje2aLNDOhI5r37W7YdwmMQCbSMfD4WBs26/4efVlY4ZjywpxNoF0zB9dEma9oxyxdZDJyVt9+7H0rYzAr3HiaUDg23JMGKDdRsaW7GnqU3268wYXYLrxnTfJHO1E6O2XLdgx+dnZXdw9bw0DJcGUC6by9rwcyPKLYFgqOeHpEuUOCQcywK7FUtvm4Qlc8Zjw8JaTK9kh56IKBW5gzxSFRXYcdvkIXGPh8LAim1HMbemAs0dXk0SIKWKbO8muqcIhJVjeE8hIiIiIiKidMjmnJLesc9OpJ4jT5tqtFq9jp5wXj01MwYGkjaYTJSYnuPE0oHBtmQIi9c0YMqTm6J+5i2vU/Rat04egj/eUZPznRgtKjkp7eDKWRomk9QGIRcV2PHcnZeZcmkAsWVt1i2YhvnTR4ieZ1p+tvzciHJHhzeACY+tx4hFb2DCY+vR4c3c0t1GEJs4FHs8u/zBhFX+iYhInJxBHkGiCUAAsFq6V1J4YdsR0ef+9I39mPLkJtz7lw/Rr1cexNdt0ZZYe1fNyjFEREREREREamRzTikXsM9OpE51RT88d1cNnPnKgmWd+Tb86S5jzh1XV/TDNaMGKh6TtAC4ZtRAQx6bSGYLDCRtMJkoOb3GiaUDg20p58ldYj6V1xuOG6YTIzcr56oRxRg+MDojR+6FTu/ZHFwaQJlsL2vDz40oN6zdeQyd55aW6fQFsW7XsSzvUWYq7abrPfR4PImIcpHUQR4gfgJwcN+CqL+nWqEuEAr39EnPdvkRBtIacMv2LhEREREREelNtueUiMj4rh1ditceuArDZVYbHV7sxGsPXGXYsTS3L4i6z84gxRBmQmEA2w6fgdtn/IqcZgoMVMvjC/T8mBmTiRLTe5yY1hhsSzlPi1L4kYzwxY4kJyvnrqlD8eqCaao6uLmQzWHmpQGUBoTpYVkbo3xumQj8I8qWFdu/iP49QdW/TMlEpd10vofejicRUS6SM8hjt1rw4t1ToiYAn7jxEvU7YQGmDOuv/nVi6LW9S0REREREROamhzklIjK+YcVOvHr/NEwc0k/S9hOH9MOr908z9FiaFrEzbn8QB13tGu2RPpktMFANV5sHo//jrZ4fV5sn27uUNUwmSkwsTiwRI1T9ZbAt5bzqin6oVVEKP5YRvtix5GTlqO3g6iWbI1VAoxmXBkgWECYlAFQPy9rk+ueWicA/omxpaGzFnqa26MeaWtHQ2Jq290x17cpEZdh0vUc2jicRkRHJSQYMhMLYeaTl/O/BEJpa1A0eCkuvffeqi1W9jhihvctkLn3h50FEREREZF7sD5ynhzklIjK+QocdDrtN0rYFeTbdzR1rTW3sjDCWWV3RT8O90h+zBQaq8ZN1+6J+f/y1fQm2ND4mEyUmZ3VBIPer/hr7TkKm4PYFsU1FKXwAsFiAcMQLvN5wHNeNKVW9b3qgJCvHYbdhbk2FovebXV2GLYdO4nSnDzOrBuGpuRN6bjJCNsf3V+7EW3tdacvm6PAGMPmJjej0BeHMt2HbohmiDWc5Qci1owb2/G635WaeglhA2K2XD5F8vPRCzuemt+9xos+AKNcsXtOAjfuaox7rTLB0yLzldXDmR19TZowtweM3jlO1D1KuXWKVYbX+zmnxHno4nkRERiWWDLjsjhoMLXZi1rjBuPv5Hfi4uaPn70Ib8oev7MLKDxpV9TOB80uvOR3SBv3lmL+iHn175aG5zYswuivz7vzxl3Tdlje6XOtbERERERGRdtgfICLKPG8giA8+PyNp2+2Hz/TEQhiV2tgZYSzT7QuiV77xjpMwH9fi9sl63vwV9ejXK9+U83Fv7TsR9fube08k2NIchGSiRIRkIrMQvlNefxBWCxAKd89TBELxVyHhcasFeHt/M6Y8uSlnv1O5GTFGFEGLUvjhMDDl4u5lPY1WzltOJSctsnL0kM0hpcqgnCDk9XtdGP+fb+V8NdJEy5FnovKjVnJ9SQcuCU9G4PYF8cqHTTjR5on6afeIXxvbPYG4bVfVN8HtU/f9THXtykRlWC3eQy/Hk4jIqJQs7eT2BbGqvkl1oK2gyxfEa7u1H4T0+ENwnQu0Bbor877yYaPm70PS5VLfioiIiIiItMX+ABFR5m0+0Cwa1CUmEArj7QPNqTfMYVrEzrj9QRx0tWu0R/oROR/n8curQO/xh0w5H7e6vgn+YPT3yx8MY3V9U5b2iPQk8jt11u2HcClOdE0WHg+FgbNuf05/p5hORzmvuqIfnr2zBg++tDNhYEoyRQV2PH3LpagdNRAv1zdixphSTZcsiVwqJhsVUZVWclIj29kcUqoMJgpCvrd2OJ7Z8mnUuRQZXJur1UiTBYRlovKjVuR8bkLwuF6q2yb7DMaV983SXhHJp2VHXc0yNLHXrh+t2YPfbPy453etK8Omq/qsXo4nEZFRCcmAG/e7cPOkclgslqg+mpAMeNvyOnza3Il/f6UB/mAobhBRa4nasGo9//7nuPPKoXGPZ7tfaha51LciIiIiIiJtsT9ARJR5z//fF6k3itz+/S8w85LBadqb7NMqdsaI802cj0vuS7/agk8i4oYAIFEc+8Mrd+L7f98Z9diIkkKsf7g2TXtHemTm7xSDbckQrh1dirULpsUFjkoRCIZw2bD+aQkA1cOSMbOry7Dl0Emc7vRhZtUgPDV3Qs8+CJWcvr9yJ97a6zJEVV+pAY1yg5Aj6X2ARE5A2DeXvY+umEwRPQeAZiN4XAkuCU9GpoeOuti1PhAK40SbJ+Vz2z2BuP1eVd+ERbPGJlwSR8jMc/uldRjkvIcejicRkdFFJgOK9dFsFgt2Hm2VfJ1Xa1RpIcYM6oO/vP8FHHYrOi2JBy7l+vRkZ1xbXg/9UjNgch0RERERkXmxP0BElB2WbO+ADgmxM1/93Xuy5p2KCuxYu2Aahp1bFcxoqiv6oXbUQGw5eFLRimYWANeMGmjI+bgzHT4ccsmLs4odzz7k6sCZDh/6F2pX2JD0zcxz3JxdIcMYVuzE3+65Apc/sVHyUgEA4PaH0lapVGzJmEwHaYpVcookVHJKR1XfdFMT0Oj1B+GwW+ENhFIGIcfS8wCJ3ICw2EBbgV4DQHMheDydQXlEeqE0yWVkSSGWfatGVkddzrVeqVRZc+nOzMvk8SQiMrvYPtp3/vwBPmnuyFigLQAs/vIY3POX+rS9Z2xbvssXyHq/1GiYXEdEREREZF7sDxARZZ9wLfb6g7AACAOwWQCxxaqExy0A9h9rw5QnNxn+Wjys2InHv34JHnxxp+TnPP71Sww93+T2BbHtszOKAm2B7nNs2+EzcPuChpvDf2PP8dQbSXyd26ZcpMlrUW7YfKAZDrsVbqtFVpye3WqBw27FpgMuTB9dksY9TA8G25Kh1H9xVtYXWJCuSqV6WTImspKTmHRU9dVKouVOtQho7JVnxRM3XoJ5k4fAYrHEDZCEw2EU2K3wBEKxL6fbARItAsIA/QaA5kLwuJnL5ZO5CAHuj6zchTf3nki5fWyAvBRyr/VKSMmay0RmXiaOJxERxffRtn9+BmGNqspKdc9fPoTbH9/H0IpYWz6S3lfq0Dsm1xERERERmRf7A0RE2ZfoWiwWaBv5eBjAWbcfcPtNcS125smbPyrMN/Z800FXu+r5xi6fMefwqy7UpsicVq+TSxLFM5mBmnn8QCiMUx2+nL0Wm+uTJsOLXWJeKqFSqZaSLRlD0nR4A5jw2HqMWPQGJjy2Hh3e8wMSWgQ0egIhVF3YFxaLpedGcKLN0/PjaveKBtoC3QMkkdueaPNgVX0T3AkqxWaKEBBWVKB9Y1gIAM02IXg8NtBWIASPZ6tKs9rPoKjAjufuvMxwjXQypkKHHXMmlUva9uaactmBoVolECQysqQQaxdMk5QxJ1SfHVlSmLb3SPfxJCIyO7E+WqYDbQGkNdBWCvZL1dEyuY6IiIiIiHIL+wNERNnHa7E0cmNnXlcYa5MrKkuLkK8yGNJht6KytEijPdKP6op+eO6uGvTKU3Z8euVZ8ae7zBffkCyeyQzMfC3mDD0Zysxxg7D6oyZFpd/VVCrlkjHaicz8iF3iNXK5U62rDBqpGqnS5ciTkVKVMVdkIruIS8KTmUjtrL/ecBzXjSmV9dpqr/XJKKkMm4nqs+k8nkREZiKnj2ZG7Jcql4mK90REREREpE/sDxCRXpi5miKvxckJ46Iev7xjs3m/C1Oe3GTYMcKDrnb4guqKIHgDIV3Eg6TDtaNL8Y8Hr8b/+9N2HD7dJfl5wwb0xnPfvtyU8Q3J4pnMwMzXYnPddcnwrLAoCrQFlFcqFauIeqLNk/BioteKqHoQm/nx17roJV5XbDsS9buWVQaNVo1UCAibWTVI9WvJqcqod5nMLpL7GcysGoTV86easiFKucsbCGLDPpekbTfsdcGrIKlB6bU+FaWVYdNZfTYTx5OIyAzk9tHMiP1SdTJR8Z6IiIiIiPSJ/QEiyjazV1MEeC1OJHJctMUt77xocQcMPUbIyrapDSt2Yt0DV+FiifEKwwd2b2/W+IYV25PHM5mBcC0eIfNaPCLHr8UMtiVDkVsKPxUpJavNXBpba7GZH/uORy/xKrbcqZYBjZlulAeCoZ6fdJATEJaIw25Fm8ePW5fVYfGaBo32LHvEsovSiUvCk9FtPXQK7TGDOEUFdjx6Q2Vc8kK7N4D3Pj6l6H20TCAQqFkOR071WTkydTyJiIxOiz6aHt106YWKkwOlYL9UHibXERERERGZF/sDRJRNmZ7v1Ctei+MddLXD7Vc3LtrlM+YY4UFXO/whdXEZvmDIkMcmUqHDjjaPX9K2bW6/aeMbGhpbsacpdTyTGQwrduKh60bKes5DM0bm9LXYnGc9Gdbs6jJsOXQSpzt9mFk1CLOrB+PfXmlIa8lqM5fG1oqwlMGpDm/KbRMtdzpnUrmkJb1TBTRmYolwoDvjcPITG9HpC8KZb8O2RTM0bYgIx7TF7VP1Ot5ACK627s9lVX0TFs0ai175Ni12MSvEsovSXco/XUvCm3l5GNKP2PN7VGkhlt1Rg6HFTswaNxh3P78DHzd39Pxd7nkeSQhel3JtlkKoDOuwy7umKak+K/U9Mnk8iYiMTG0fTbcswNoF03DbH+twrNWj6UuzX6qMnPYJk+uIiIiIiIyF/QEiypZszHfqjTAXDgAeicGldZ+dxoxfbAHQHV/w+I3j0rZ/2VJZWoR8uxW+gPKgUqNWb2VcjzTtHj9OdUiLMTnZ4UO7x4+igrw071V2RV5vBJ0+8XMoUTyTEa83kTYfaE69Ucz2Xxlflqa9ST9G55ChTK8swYaFtVgyZzyW3j4RXx5XlpFKpVymQLnIpQwCoXDK7RMtd7p2V5Ok95NSZTAT1UilZBwqrXwbeUw9fu2q5uZ6pSuts4ukfD7pWhKey8OQXsyuLsMAZz6A7uSDVfdNxdBzWWhC8sINVd3BoAOc+Zg9QV2jWcsK9mKVYaV8r9NZfTbTx5OIyMiU9tH0bMNeF8r6FWDSRRdo+roWC7DynitM3S9VI10V74lyWbpX8iEiIiLSC/YHiCjTWE0xei78RJsHLW5pVThb3P6o+AK3z3grYx10tcOvsi9u5OqtwpjxoD4OWc8b1MdhmrieP7z9iaztl77zaZr2RB9irzfCT6KA7UTxTEa83kSaXV2GfLu0EFSH3Zrzc9xMoSPD6e/Mx9yaip7fM1WpNFPvYwSRmR++YEj1UgZufzAukyQRqVUG01WNVJAq41BN5dt0LJuba5la6c4uSvX5CO/v8QfjgvISafcGMPmJTSjIs6V8f7Fg7VsvH8Jqt5RxQpLLxv0u3DypHBaLJervhQ47lt42CS/XN2LGmFL0PxdIqoSc4HWB1QIky+OYv6Ie/Xp171Nt5UC8tutYyutuOqvPZvJ4EhGZgdw+mhZS3XvUaPcG8PaBZrxz8KSmrxsOd69oIQXbm9HSWfGeKFeleyUfIiIiIr1gf4C0wr42JcJqiuIOutpVxxd0+bqLTOXK3LdUrN6a2rBiJxZ/ZSwWrPhI8nMWf2VsTi95L4VwvXG1y1tRbemWT7GqvsnQ1xu1sTdCUTsjfq+E8yYcDsMvcY7BFwjh317eDYvFkrPnDVtrZAqZqFSayffJZbGZH2c6pZWgT6SowI77p49AV0yDWk2VwXRVIxVIyTiUUvk2EaERbbdaUm8sQVnfgpzK1MpEdlGyzyfy/aVmUgqEjMpU7y8WrM1qt5QtQpJLbGCowGq1YG5NherAUDkVZQWpgp08/lDP9351fZOk6266q89m6ngSEZmFnD6aFtIVaCv43//7QnIyl1RSl4ZjezNeOiveE+UqNeMZRERERLlErD+QCPsDlAj72pQIqykmVllaJLmCYiJSx8NyEVdlTk1ucZ+NMrfPNZHXm7DM8e1wGIa+3gixN4nmolMpKrDjuTsvM2SgbeR542r3QuqpEwbgavfm9HnDYFsyjUwt5cIlY5LTsuqq0OBrPOuOenxUaSHWLZiG+dNHiDYkUx17LSdMF69pwJQnN0X9zFteJ7rtvOV1Pdv8x6t7ov62YtuRpPsc69rRpXj8xktg0yDedtLQC3STqSVlKUots4sSEQt2zdT7JwrWXvr2p5zQJEMTqygrXOt//o3xql/fF3NdSXTdFarPLpkzHktvnxiXOCNUn10yZzw2LKzF9ErjD0oQEemd1D6aHiRrv1stwI4vzkQ9NqR/b9XvKXVpOAbQxUvWPlHSFyUygmT9ZSIiIiIjie0POGICv2J/Z3+AxLCvTYlkYr4zVx10tcMnsYJiIt6AtPGwXCUUhynrWyBp+7J+BVg9f6puYgLSbegAef9PudvnGl5vkmMAuzgznzcMtiVTSHel0ky/Ty5Tm/khmFk1qKfBp3WVQa0mTNVkHAZiSmLFVr6V4pbLh+CdR6fjkrI+sp4Xqchhx00TM1MJLFUgrdTs3nRnF6WqTKzl+8sJ1l665ZOo33+0Zg8Wr2lQtA9EepTsWj9r3GD8/rZLFVf0dubHL9+W7LrL6rNERLlDTh9ND4JJ0r9DYcAfsYHNYsEr/3olnrtLXf/qf26fJCmzngF08dJd8Z4o10hZyYeIiIjIKCL7A1dcPADemMAvbyCEKRf3B8D+ACXGvjYlwmqKiVWWFiHfxsq2iQjzy9c99Q6OtXokPedYiwfXPfUOpjy5yRTzy5+f7kzr9rmG15vUhLHemVWDJG0fGc9kVGY+b8y3hj2ZUqJKpffWDsczWz6NCn4UKpVeN6ZUt++T64TMj7uf34GPmzsUvcbNNeU91QSFKoMb97tw86TyuOAnocrgy/WNmDGmNGXw0+zqMmw5dBKnO32YWTUIT82d0PNewk30+yt34q29rqQDJFpW8QW6K98686Mv2zPGluDxG8clfE5F/954aMYofPf5HYres7ZyYEaqMnZ4A5j8xEZ0+oJw5tuwbdGMuGqRYtm9t14+RPT1lJ5jI0sKsexbNT2NnsVrGrBxX3PUNp0+8SDf2M/HYbfC2suOVrf0JX8i318I1nb7pZ1DsUsWB0JhvLyjEYtmjUUvkUBColyT6lr/5XFlGD2oD25dVofmdq8m7yn3uhuZLGBXOdBDRETaEOuj5dksyLNZ0ZWDyxNFCiGMphY3rh1diumVJfhHw/G4pD0pfvjKbrz78cmk/YpkAXTjyvvKfk+j0LovSpRL1PSXgdTjGURERER6FdkOslkt6NsrD3uOiScX7T3Whr698mCzWvDvr3QHL7EdRAL2tSkVreY7jeagqx3+kLrKtsJKT7kY5JWM3PnlSK5zc2ur6psMP78sVqk2UVxPou2NZvOBZjjsVritFlljzHarBQ67FZsOuAxbwVVQ6LBjzqRyvLn3RMptI+OZjMys543xP1kiiFcqXXZHDYYWOzFr3OC4BurrDccVBcFm6n2MQAhafWTlLkk3o1ixx06oMpiIUGVQCq0mTIVMjgdf2pmwmq0c7Z5A3OtIaeyqWTZ3y8GT8AaCcNjT25iWEkgrlt2bKNgWkH+OxQZWy+2MiH0+vfJsuH5MKTbsT11NLfb9tQjW9gRCmPbzzciLCPrjQB7lslTX+uEDC7H5kWtk31s6EwRbybnuSkkaICKizIttC48ocaLxjDvnA22LCux4+pZLUV3RD25fd/VeJYG2AHC2yx91f2MAnTxa9kWJcoUW/WUzTN4RERGR8ShpB8ViO8ic2NcmpdTOdxqR2jn4yHE1o9FyWXcjHh/B0TNdUb+nius5crYr9iUMRU2QdiAUxqkOn2naN1Jjb8wQC2bm84Ylt0i3Ui0pL0emlnbkEpLyCJkfSmzY64JXw6qxsbRaIlzIOBxZUpiO3exp7CbiDQSxXmLAWb7NGlfiXajAnG6plslRuhSlnHMsNrtIm2DXIC4bdoGi91dbdl9wutOHE22enp9V9U1w53hwCVEyau4tUiS67oolDRARUfbF9tF+8rVL4NWgj5lNI0sKsXbBtJ6Mby0H0YUBssj244k2T8KJi3ZPIG5btjeJjE/L6w4RERFRLmE7iJRgX5vUUjPfaVTCHPwImXPwI2LG1YzGzMu6y8G4nmhs30jjDXQXvZAi3fFMemDm84bBtqRLHd4AJjy2HiMWvYEJj61Hh1ddVVChUumSOeOx9PaJcQ1MoVLpkjnjsWFhLaZXKmtcZep9jERq5keeLTroNVNBoFoQGmQzqwZp+rpSGrtbD51CR0zn2yoePwxfMIR//5fRcYHBr6uojCtFokDaST/ZgClPbsKUJzdh3vI60efOW17Xs43ws3hNQ9Q2crKLImnVGTlwXFrjQOw4pyNYO1cbLERyqKnonUyy626qpAEiIsqO2D7aFcOLNUloypaZVYOwev7UqGUAhXZrrzxl2d+R9zczD5ARkXScvCMiIiKzYjuIlGBfm7SgdL7TqBavacCty+pwqt0j63mn2j24dVld3HyykQjzy3LvVUUFdkMHIkdiXE80tm+k2XroFNpjYteKCux49IbKrBW1yyYznzcMtiVdSkd1OKWVSuVW2NWqIqoZyMn8yLda4zLTcqmzoHWlxdhqVomIdbwG9+3Vk6nldERPyO/44mxaM7UWr2mIC45NFEgbWZFVaXav2uwipcGuwudz5YgBqrObtAzWzuUGC5FUcr73ciS77iqtvk1ERJkR20dTWvlCD26aeCEK7Na4/um1o0tx5fABsl8v9v5m5gEyIpJHbX/ZDJN3REREZExsB5Fc7GuTWqymGM3tC+LF7Udxos2DFre8om0t7u655Ze2HzV0tehhxU4smTMe+TZpIWH5NiuWzBkfleBvdIzricZq0anFxt6MKi3EugXTMH/6CNG2YS7FMym1+UAzHDYrbIkq/iVgs1rgsFmx6YD2c/qZwGBb0qW/1H0e/fv7X4hvmGaxFXZbunyyAm8pObHMD+ESbI+5GHf6g3hoxsicLtevVaVFsWpWiYwr7xv3WFOLG7+59VIsmTMedf9+XdwxTVemltxlchS/T0R2rxbZRXKDXSM/H62ym7QI1uZAHpmF2PdOrcjvtZykASnVt4mIKDuGFTvx0HUjs70bst33Qn3CFWAuuTC+7Z/M8IFO0X4FJ46JSCo1/WUiIiKiXMZ2kHxyixsZDfvapAarKUbb3diCYCis6jUCoTB2N7Zos0M6I8xjPbZ2H3rlS1sJq1e+DY+t3cd5LJNTMmb+0IyRpmnfzK4u6ylqN7NqEFbdNxVDz/3fhbZhLsczySXE/5zq9Mm+JgdDYZzq9EUV0sslubluIhlaQ2Mr9scsu77veBsaGltFAwfTKbbC7uVPboIvEIIz34Zti2bElZPPhMhOqF1iJpJeiWV+LPnGeBxq7kDNRRfgnr98iI+bO3r+vvlAM5beNgkv1zdixpjSrGURKfkMtKy0eHNNuei5t3hNAzbua456rNMnHnD2r3/9EM58O365/hAAoG+vPFw7uqQnsFbI1NKSFsvkpFJUYMfTt1zak90rdo4tu6MGQ4udmDVuMO5+fkfUOfZ6w3FcN6Y07nWFYNc3955IuQ+Rn49W7y/2WnLMrBqEp+ZOyMo1iyjTtEpsiCR8r4VOg9sv7VrW7gnEJRSsqm/ColljJQ9wEBFR+mw+0Jx6I50JnBu0ElaAufXyIT39gBa3T9ZrHTnThRm/2IIZY0vw+I3jov4mDA4+snKXpDYw25tE5qW0v0xERESU69gOkq7DG8Dlj29Alz+E3nlWbF98vSmPB/vapJSW841G4MjTZn5Fq9fRE7nzWIJWtx+tbj8A885jGSkORw25Y+abDzTjK+ONHVQqmF5Zgg0La7Fxvws3TyqPq4osFLXLdjxTpmgR/yMU0su16v3mvUKQLmSiOpyaTMkV26Mr6voC3a8hTGxmWmyl3dhKRrlGLPOjesgFmFtTgYsHFopmfmS7XL/Sz0BOxmEqYuXm5VaNbfcEorZrdfvxesPxtGaNqF0mJxWx7F4ts4ukBvBFfj5avb/aYG2zD+SRuUR+7yZd1A+FDvWDAcL3WstOAxERZd/s6jLk2/U9LNI7yaD2im1HovoBHr+8Pq8/GMaJNk/C7HE5qyuwvUlkbkr6y2Zh9gpuRERERsd2kDR//+Aous71Wbv8Iby842iW9yh72NcmJVhNMVplaRHyVQZDOuxWVJYWabRH+nHQ1S470DZWl89881hGi8NRQ86YucNuNfz1JlZ/Zz7m1lTEBdoKsh3PlElq43+KCux47s7Lci7QFmBlW8qiTFSH6/AGMPmJjej0BWVXo21obMWepraEf1+x7QhuvXyIpNfSSmylXaGSUa7KxcwPpZ9BqozD7/7vB/j0ZKekfdiw1wVvIAiH/fx5nytZI8IyObEZlmrZrBb89buTUdqnIOpxrc4xOcGukZ+PVu+fKFi7on9v7DuW+DolMHoGK1Gk2O/d56e7VF9zhO+10Gl48KWdCZMZkomtvk1ERNl15fAByLdZe5Iq9cTpsOF3t07E0GInvvk/76O53Ru3TUNTK/7RcDyt/QA5E8dsbxKZk9L+shmoGZckIiIi/WM7SLo/vvdZ9O9bD+OuqcOytDfZx742yZWLc+rpdNDVDn9I3XieLxjKyWqKqQiByD4VCZ9GDUROxmhxOGrIGTPPs1lx5fABGdgr0ish/kdOvBMADB/oxPI7L8Owc4kjuUbfJVzI0DKRVSN2UxQjp8KuoKGpFZN+skFyhV0txFbaXbHtSEbeN51yLfND6WeQKuPw4etHxT0nUeXbdm8A7318KuqxXMoaETIsZ1YN0uw1g6FwwiUNtDjH5FQmjv18tHh/sWDtV+69EkdPdyV8TiRhII/ILCK/d3KvOSNLCpN+r4VOw8iSQln7JFZ9m4iIsmvroVNxVQrsVvE2Wyb1KbCjl92Gf1/VgFuX1eFMpy/htv+5bi/6FuRB6V73yrMm7AcomTgmIvNR0182OqnjkkRERJRbhDnFyU9simsHJdLuDWDyE9JX7TSShsZWHGvxRD3W1OJGQ2NrlvYou9jXJqVybU49naor+qF21EDF42EWANeMGmi4QFtA20BkMzFiHI5SYmPmicZ5Okw2ziOGKxoljndK5uHrR+VsoC3AYFvKosrSIqidx7RakDSrRspNMXLZzcgfKRXrTnf6cKLNgxfqjmDykxvT2kkWq7Tb0NTa0xnlRTz9Un0GyQgZh0vmjMfS2yfGVTLZvD86UHRUaSHWLZiG+dNHiAZ1iS05lEsBYHKWyZEqnY1esWBXuZ+PGmLB2kfOdIkO5PXKs8VV+zbbhCZRLDnXnH/7l9Epv9dyA3hnVg3C6vlTc7rTQERkRGJtvMe+WpWlvel2/ZgSeP1BnDrX1zzR5kEgFE64fbsngBa3H4m3SO6qkQMT9gMYQEdEUmS7v6xnnKwjIiIynsg5xRa3X9ZzW9x+nGjzYFV9E9w+YwZQihU3+uay90W3/eay9+O2NUMgMvvaROq5fUFs++yM4vGwMIBth88Y8lpcXdEP1zAQWRY1MSBGxHEe6Tq8AUx4bD1GLHoDEx5bHxekbCax8U4pt09QSC9XcN0qyprdjS1IMmcoSSjc/TqTL44vTZ7spjiuvG/PYwdd7aqX3QwDcLV1L+u5qr4Ji2aNjQt2k2PxmgZs3Bd9cen0iV+Y5y2vQ688G062exFGdwNozqRy/PSmcQAAu40x9UrI/Qyc+dGX0xljS/D4jeOiHhMyDsXMri7DlkMncbrTh5lVg/DU3Ak9AblCUNf3V+7EW3tdGODMx+wJZaKvI2z7yMpdeHPviZT/z9j3yiSpy+RIJfb91opWn49SYsvDxDV0Swpx48QLcctlQ9Dq9uPu53fg4+aOnr9zuSEyOzlLc/1ybnXK77UQwCvlWntzTTmXiyUi0iGxNt5/rNmT1X16/9PT8AZVdpQl6u/Mx62TEy+HJjawuuyOGgwtdmLWuMFsbxIRgOz3l/VK6rgkERER5RYt5hTd/qAhly53+4J4cftRBCVO/nb5guiKCXR7aftR1XOsese+NpF6vBYn5vYFUadRILIRr8XpiAExGo7zSPf3HUejVjR6ecdR3DV1WJb3KjtmV5fh9Ybj8AZSF4d02K05f95w1p+yxpGnzc3ZkWdTfVO8ZtRA7PjirKRqtqmobZgJWbFuv7QGYrsnELXfYQB//7ARf/+wEc58G7YtmsEAH5nUfgaA/KBrsWDKSIUOO5beNgkv1zdixpjSpEuAaBUAFlklWeugbTnL5ESyAHA67AmzgtLV6NXy81EqNlg7WUO3vzOfDV2iCEqW5pLyvZYTwMsBUSIi/Ylt4/mCIUVtVC11qKyoUWC3wiNhQOvbVw7F/deNTNpu5cAqEUmhh/5ytnGyjoiIyDyEioHvHDypKJDJyBUDdze2SA60TSQQCicssmQU7GsTqVdd0Q/P3lmDB1/aqSi+o6jAjqdvudSQ1+KDrnbJMQ6JdPmMG4ic6RiQXMRxHumWvftZ1O//8+5npg22vXL4ANitFnglbGu3WnDl8Nxu6zECj7KmuqIfnrurBvev+Kgn2l8OZ74Nv5s3EZWlRapvim0eP1751yvxwIsfRWULyqVFw0yLTCxBpy+IdbuO4dbLE1cronjZyoZLVvkWAKxWS9K/R1IbANbhDWDyExvR6QumJWg70TI599YOxzNbPk3YMQqf27dE0tno1fLz0QIbukTSybnmCEtzXTemNOn3WkkAr8Nu3M43EVGuimzjid0vpAav6sX4in7YfvhMyu1aPf6U7UO2N4lIKr31lzOJk3VERETmwoqBiTnybLAAio8N0B2MrFWxJr1iX5tIG9eOLsXaBdMw6zfvwu2XPnbXK8+KtQumYVixM417lz2VpUXIt1vhUzGe6bBbUVlapOFe6QMrIktn5nEeqRoaW3G81RP12PFWj2lXNNp66FRc3F+iufhOX7BnLj5XcX15yqrNB5rRK98Gm9WSeuMINqsFvfJt2HTApdlN0RsIYfX8qZhZNUjRa4wsKcTaBdMwfXSJqn0RMrGKCrQJbFyx7Ygmr2Mmaj+DogI7nrvzsqw1spQEgMVau/NYVMn7dbuOabqPYsvkrFswDfOnj8DaBdMwsqRQs/cSGr1GJDR0YwdjBEJDl4MxZHZyrzmvS0hYSBTA++gNlXH3j3ZvAFsOnlS49yQmEAz1/BARaUXsfvHUzeMhr7eaHXarBb+5pRr7j7Wl3hiJ+wGx2N4kIkpOy8k6IiIi0j/e+xOrruiHZ++qgdOhLFjW6bDhubuyN7eWSexrE2ljWLETl1woL6jtkgv7GjbQFui+T/lVzpv4giHj3qdyOAYkmzy+QM+PGS1e04ApT26K+rll2fui296y7P24bRevacjwHmdeOubi9YzBtpQ1QuWHUx0+2cuKBENhnOrwYVV9EypLizS7KRY67JgzqVz2a8ysGoTV86dq1jATMrG0CDhsaGrFpJ9sMM1FXCtKPwOtgq7VkBsA9t7Hp+JeY8X2L6J/1zhoe3Z1GQacGySYWTUIq+6biqHnvj/CMjk3VHVnshTkWdErT9ntysyNXjUYxEZGI+eaI3VpLrmdhvteqE9amZuk6/AGMOGx9Rix6A1MeGw9jysRaSb2fvGX70zGD19pUFWVJ1P+62tVKLBbVfcDiIhIHk7WERERmQvv/cldO7oUr91/FYYPlDdfOnygE6/df1VW59aIcg3n8rpdObxY1vZTZW6fa3ifSi6XY0CyxdXmwej/eKvnx9XmSf0kA3H7gnhx+1GcaPNE/SRavb3TF4zb9qXtR+FWsNp7LknHXLyeMdiWskbL7E8tb4qxwTNS3FxTjkKHNpVoBcIFR2ml3UinO3040ebBC3VHMPmJjQy8lUjuZ6B10LVSarNGGhpbsacpuiJWQ1MrGhpbNdtHYZmcJXPGY+ntE+O+P8IyOUvmjMf//dt1+MeDV7PRmyEMYiMjknPN2bCwFtMrU183pHQaxg7u07N9IBTWvEq4WaW7+joRmVfs/WLT/uaoQbM8m35r3NZ9dgbzV3wU9ZjRs8eJiPSCk3VERETmwnt/csOKnXh1wTRMHNJP0vYTh/TDqwZe0p0oHTiXd97RM12ytj9yVt72uUi4T8mtjt3fmW+a+1QuxoBky0//cSDq95+9cSDBlsa0u7FFdvHIWIFQGLsbW7TZIZ1Kx1y8njHYlrJG66waLW6K3kAQG/a5ZO9LuiYplVbaTSQMwNXuxYk2D1bVNxk+e0ILcj6DdARdKyEna8Rht+LtA81RZeznLa8Tfd15y+s0LXkvZ5kcrRu9zPZMTEoQG48f5SKtl+aS0mmwWKI7X1pXCTerdFdfJyJzi7xfxF5v/EH91rh9a+8JBCIG/arK+hg+e5yISE84WUdERGQuw4qdeOmeKbBbpSVl2q0WvHTPFNPc+wsddjjs0sIQHHlWXcytEeUSFqQ4v6z75oPNsp63+dy8uNELkw0rdmLq8AGynjN1xABT3adyLQYkW97cGx0L9eaeE1naE9K72Ln42JgSuXPxesZgW8oqIavmgt55sp7X35knmlWj9qa49dCpuGU3pdiw1wWvyiq9iSiptCuFUBWYUpP6GeilMpTUrJHHb7wEFgtwtssfVca+3SP+HWj3BOJK3mcyaFurRi+zPZNLFcTG40d0XrIA3obGVuw9Fn2f1bpKuBllovo6EREgfr3RM28gOgnKAhg+e5yISG84WUdERGQuH33REpX0mEwgFMbOIy3p3SEd8QaC+ODzs5K2/eDw2bTNsRIZldkLUrh9QbzyYRNOtHnQ0uWX9dyWc/PiZihMNrtaXqK92RLzcy0GJBtW1zfB448ed3b7g1hd35SlPco8R55NV6+TK4weU8IRPcq6YcVOjCwpwvbPz0h+zsiSooRZNXJuiteNKZX1XKsFEOs3t3sDeO/jU3Gvp5bSSrupFBXY8fQtl/ZUBabE5HwGQtC1w579G6UQAJaI1WrBJRf2jZuUl0sI2pZyLkVWQbXblOV6qPl+C8SyPW+9fIii/TGaZEFs48r7AuDxIxKzeE0DNu6Lzp7u9Il3GuYtr4MzP7oJPmNsCR6/cVza9i9X8bgSUabIud7kij3H2qLacAIhe5yIiNJDi3ELIiIiyg1yi+WY6f6/eX+zrEDktw80Y+Ylg9O8V0TGIGUuz+gOutrhURmkL2eOO1dZIa36usCWYGVII8rVGJB0+tKvtuCT5o6oxxLdyh9euRPf//vOqMdGlBRi/cO1adq77Kmu6Ifn7qrB/Ss+6omRkMOZb8Pv5k009LVGjNFjShhsS7ogdZkVgS3B9mpvirOry7Dl0Emc7vShrG8BjrV6ev42qrQQv/5mNX6z6WO8tdcFm8WCYPj83SUdnWSxSrtFBXb8v6nD8Pu3P5HcUY00sqQQy75VY5olANRK9BncWzscz2z5NKoKbLqCrtOluqIfnr2zBve9UB+XkSSFnKDtDm8Ak5/YiE5fEM58G7YtmiG7gotWjV6xbE8j3dilUhrEdqrDG/U3sx4/IoGQQe32S+tgtXsCcRXEV9U3YdGsseiVb+yOuhw8rkSUKXKvN7mEiQhERJnFyToiIiJzmTluEFZ/1AQpM3UWADMvGZTuXdKN59//XNb2//v+5wy2JRLBghTihDnuB1/amXDF1mTMUphs3a5jsrZfu+tYzsQ5qCVnxetciwFR4kyHD4dcHak3jBAbqnTI1YEzHT70L8zXcM/04drRpXjtgavw3T9/gE9PdUp+3vBiJ5bfdZkpY7OMHpOjrLQgkca+V3sx8m3SAm7zbRZ87+qLRf+WKDDy0RsqUVQQ3bgUboqRpleWYMPCWiyZMx5PfP0SDHB23whmVg3CqvumYmxZ355lN9959BrcUNV9Qx3gzE9LWf3YrNhRpYVYt2AaHr5+FB77WpXs15tZNQir50815cVcqdd2RzdChc9g/vQRWLtgGkaWFEb9PdeWEbh2dCkq+veS/byRJYVYu2Aapo+WtvSsWOaKXGq/3wCXHxdELq8S+ZOoQ9ruCfRsExvkb8bjRxRJywxqOo/HlYgyRYvrjV5FtuGEH70ukRcIhnp+iIhylRbjFkRERJQ7rLDEBdoWFdix8PpRcff+MMxVMdAi8/9qkVl9kcgM1Mzl6X0cSAvXji4VnatPRe4cdy4bKjMmRO72uSw2DqekyJH091yLAZFr4wFtVtvW6nX0aFixE6/ePw39e+dJ2r6/Mw+v3j/NlLFZZojJYWVbyiohGyscDsMflFal1R8M499e3g2LxRKXjSUWnLrsjhoMLXZi1rjBuPv5Hfg4ovS5WDXa/s78nmU1Nyysxcb9Ltw8qbynYxi57ObS2ybh5fpGzBhTiv5O7TM0IivtzqwahKfmTuipBvrh52dlv97NNeWyq4maWYc3gDf2nOj5fcaYEvz6lkt7juGwYidWz5+K76/cibf2utIWdJ1ODY2t+NglPfsGQNy5KIUWmStyv9+Pv74fi1bviXoOsz27aR1UYrbjpweRgSh2G3OnsokZ1OnB40pEmaL2epNr9LhEnharYBAR6YEW45JERESUO2Lv/SNKCtF0tgu/3HAIvfKsGD7QiU9Pnp9/McO9X5j39fqDsFiAcLh7dVOxlTqFxy0WYP+xNkx5chPnFYgiaFmQQk/jQFoS5uofWbkLb+49kXJ7JXPcuUi4Fre4fbKe9z9bPsXfth81xbU4Ng7nizOdaG4/v7prcaEDlw7pl7MxIHLNralA09ku/GbTJ4pf46EZI3viqIwqz2ZBq1vaHEKrO4A8iQUnc5lZK7AzOoOyJjIby9XulbTMCtCd/elq94pmY82uLourRitk4AiNLTnVaIXA20QZmELgrZRAWyWVeiIr7S69fWJPw0/OsnSRjJ5xk4jSKklrdx6DN3D+OdeOLolrfBc67D3VjjcsrMX0Sv1mwS1e04ApT26K+pm3vE7268gN2tYqc0XO97t/7zw0tbiZ7ZmAEFQSm12vlNmOX7Z1eAOY8Nh6jFj0BiY8th4dEpc5ofRhBnV68LgSUaYovd7kmqICO5678zLdTbBosQqGnrBKL5F5aT0uSURERPoWe++/7fIhcPu7+wFufwh3TLnIVPf+yHnfs24/wucmfsUCbSMfD4eBs24/5xWIYqidy9PrOJBWhHnvGb/YgrrPTkt6Tt1npzHjF1sw5clNWLymIc17mB2R12KPX97YlMcfMs21ODIO575rhmP/8ehVEvcdb8N914zIiRgQrTx8fSX+8p3LkS+zyFO+zYq/fOdyPDRjVJr2TD82H2hGMCwtsi0YCuPtA82pN8xhZq7AbuyUDdK1dGRjCTfF2Gq0AiEwMp3VaMWoqdQTWWlXILYsnRQb9rrgDQThsNtkPzdXqTn2sdVYX9x+FPMmXxS3XWS1Y70SbnRuv/obVbLs63Rmrsj5fg/qU4A7/7Rd6n9JVKJsT6NUFBWCSmIr62jF6Nmy2SQWkCK3UjRpjxnU6cHjSkSZIvd6k2ssAFbecwXGDO6T7V2Jo8UqGHrBKr1E5qbXcUkiIiJKj9h7/5effi/q7ys/bMRrC6aZ5t7PKpxE2lM6lzeypBDLvlVj2OXLlc57t7j9gNsPAFhV34RFs8aiV76x4iZ4LU5OTizD7c9ugzPfjl+uP9TzWK5W4ZTqqpEDsfWH0/HV374HV0Sl30RKixxYe/80lPYpyMDeZd+f//m5vO3/73PMvGRwenZGB8x8vcndKCHKeenKxtKyGq1WtK7UE7s0TSxrgmrk7d4A3vv4lKr3zjVKj71W1Vj1QosbnUAI2o6VicwVqd/vq0cNTMv1xWgVRYWgkplVgzR9XaNny2abWEAK6UOhw445k8olbSu3SriZ8bgSUabIud7kHAuiVu3QC6P1u4xWpZeI5NPjuCQRERGlj3Dv39PUhn3Ho/s2+461Ye+xNtPc+1mFkyg95M7lzawahNXzpxo20BbQNsDLaHgtTszMVTjlKO1TgJslFpr75mUVpgm0BYDWc8H6kZz5Njx6QyWcIoH7rV3x2xuJma83nImmrNp8oBkOmxVuqyXhMiJi7FYLHDYrNh1w5cQSwVpX6pldXYYth07idKcPZX0LcKzV0/O3UaWF+PU3q/HI33fHdeyB5FVJjUjKsU9nNVa9EG50D760M2GDUSohaDv2PNJb5ko6sj2NWFFUCCrRqoKb0bNlsy1ZQMq48r5Z2ivjUVPBOlVCjMBs92O1eFyJKFOkXm9ySa88G/5w28SsD1qZod9lpCq9REREREQkzgx9G6XMMu9LlGly5vLMUJBC7bx3UYEdT99yadbHytKFFZHF6S2WQc/W7myStN2anU1Y+KXKNO+NfvTvHZ08NWKgE8vvvAxDi52YNW4wvvvnD/Dpqc6ev19ggmQrs15vjH2XJV1Ts6x9IBTGqU5fTpT3T0dgVOTSNMWF+Xj077txutMXtXSyxRLfiR3gzMfsCWWK3lMv5ARASTn2cs/Ddk8grtGeC+choPxGd1H/3oAF+OJ0V89jYsFMeuzYaL38uFEnz7UKKuHy7drioG12qFn+2RsIYsM+l6RthSrhDru+7x16wOOqLgCciFITvmNyrje55KqRxVmfrDRDv4tJUURERERExmeGvo1SZpn3JcoWFqSIJsx7f/V378maly4qsGPtgmk5G+AlldZz5Eagx1gGPRHmpUPhEJrbfZKec+SMG5c/sQFWi9UU89Lfq70Y+0+04WyXHzeMLcUvvlnd850ZVuzEq/dPw8K/fYT1+5pxQe88fO/qi7O8x5lhxutN7u455TwjZo5kMjBKWJoGQE/g7c2TymGxWNDQ2Iq9x+KXPfjNLZdi2shiSa+vR8kCoJQee18wpKjjH0lv52EySm50P/tG9zn5g5d3Y/0+V9KgbT1mrmiV7WnUyXMtg0rMkC2bKRy0zR41Fay3HjqFdm/051BUYMe9tcPxzJZPoz6jRFXCKZ7Zj6uaAHAiSi3yO+awW+ENhOK2efC6kXjun4dVrxCRLW8faM56IoLR+v9MiiIiIiIiMqeDrnbVc0pdPv30bbRktH4fkZ6wIIW4YcVOLJkzHg++uBO+YPyYXqx8mxVL5ow3fKCtgBWR4+kxlkEP3L4gXtx+FEEZVekFQmDuS9uPGn5eenplCTZ9/5qo2KxIhQ47nrm9Bi/XN2LGmFL0N0FlW4HZrje5vfeU04yWOZKtwKjISb5frj8EIPEk37++8GFOT/IlCoDS4tgrpZfzUKziXaIqeHJudF+ZMBhTf7YZnb4geudZ8ZMbL8GXxw1O2jDQY+aK3GxPo0+eR54bcoLYUjFLtmwmcGAye9RUsI691owqLcSyO2p6lg+J7bzzOyON2Y+rmgBwIkot8jsmFmgLAIP6FiiqlKEX/lAYm/a7MGtc9lY5MVL/n0lRRERERETmVVlahHybVVJQVyIOuxWVpUUa7pU+VFf0wzWjBuKdgychP1QHsAC4ZtRAXfT7iPRGbC6v0GHDPVcPx/+8+yk6vOfHKIxYkEJM5FxuQZ6063JBnhWPrd2Hx9bu0/1crlZYETmeHmMZsm13Y4uiQNtIgVAYuxtbMPniARrtlT5FFkUUY7Vakv7dyMx0veEapJRVQubI8IHyMkCGD3Ri7YJpWV8KM5KWgVGStz83yXeizRP1k2jyst0TiNt2VX0T3D51+50pYgFQgDbHHgAqLugla/uRJYW6OA87vAFMeGw9Rix6AxMeW48Ob0D0MUEgGMJru49Jeu1ntx7uCT7o8odgt1okZeAIAb1SpDtzRW62Z0uXz9Dfq9hz49WdTVF/H1VaiHULpmH+9BGyr89CtiypJwSkFBUo/278ft5EDkzKlKyCtRSzq8sw4Nw1cmbVIKy6byqGnstyFTrvN1R1dx6SVQmnaGY/ronaP0SkjdjvmM1qid9m25GeShn5tvi/54KVOxqzvQs9/f+RJYWynqeXfpcgG31/IiIiIiLSh4OudvhDygNtge4VF43YH3D7gqj77IyiQFsACAPYdviMbudWiLIpNoBpxEAngiHgFxsOIRgChsdU23xdYsBTroqNkWiTmNjdFjGnq+e5XK0oqYhsFnqKZdADR542BQG0eh3KPWa73jDYlrJuWLETD18/StZzHr5+lO5KtKsNjCoqsOO5Oy+TFRhlpkm+ZAFQWhz7P911Gd546GrMrBok6TkzqwZh9fypujgPxSreiT0GAC1dPoz78ZtYs1NasO2uxpao3+UE+MjJXEmXQDCEdw40i1ZuffSGyrhzpt0bwN93HDX09yr23BhQ6EgaxCZ2fU52/N77+FSa/wfmoTQgRdDq9mu8R8ayeE0Dpjy5Kepn3vI60W3nLa+L23bxmoa47aZXlmDDwlosmTMeS2+fGNf5LnTYsfS2SVgyZzw2LKzF9MoSBIKhnh8Sp+S4GoXaAHAiSk7sOyaWwS987/7lksH4v3+7DlNHiGfnO+xW6DEUt8hhx51XDo16LFv3HyFJIhf7XYJs9P2JiIiIiEgfhOqtSvt+Rq7eaqY5S6JMiy1IcduUi3pW3HH7g7jjiosMXZAiFq830ohVRE7EjHO8eohl0Ivqin547q4aOBWuwuXMt+FPd3G808wSraZs1JgSY4ffU87YvL859UaR2x9oxlfG66+RKARGxS5nnMrIkkIs+1aN7AlEIy3FGSly2QdBp0/8/zdveR2c+d2XMofdCrfVgoCMEvexx37OpHJJywXoKYNJrOJdOCZ3eMW2I5g9oQyXP7kJvkD88SkqsOPe2uF4ZsunUedS7KEUAg3GlfdNuk9KMlcc9vjGW+Tkv90mLz+kwxvA5Cc29gSWClItP77/RHvGv1dq/p9yxZ4vOz4/gw0La7Fxvws3TyqHxRI9TBh7fTbb8u3ZJncpk0grth3hUvMJpHP5ZznLh0Rep5z5NmxbNEM39xa9McOyLErbPwKzLHtFpJTYd+x0p1fy829a+k8McDoAAC1un+g23oD+EidurC7Df8yuilqdItv3H6FyRK71uyJluu9PRERERET6oGX11thxxVxn1DlLIj0QClIIc3mzf/de1N9fqW/Cq/On4uX6RswYUyppldJcJiQ+vHPwpKLrsZETHyLFBpM67Nao8cvY380wxyuMEYcRRnObtLHh1fVN+Ocnp2CBxdDzMNeOLsVrD1yF7/75A3x6qlPy84YXO7H8rstMO96ZyTgPPYu93hg9psS8nzTpyuzqMjjs0k5Hh92q62ysTFfqMcpSnILYZR+En0Qd8/aI5R5OdfgQCIVhs0jLKRY79rmWwZSo4p3YY0vf/hQ+kQCAUaWFuHb0QPzl/S/gsFlhF1k6N5KUCo9aZK50eAOY8Nh6jFj0BiY8th4dEjPvBJEVXAVSlx/P5PdK7f9TDrHzZc+xNnzc3I65NRU9gbaRlc7Mvny7HshZyiQSq1/GE87rfcdadZH1nKgKOZmPmvaPmZa9IlIq0XfMH5Q+HO8Phnue5/HrL6g2kdd2H4fVEj3op4f7T671u8QYoUovERERERHJw2qKyW0+0AyHzYoU00xxrBbAYbNi0wFpRVyIzEgoSLGnqU10HnjvsTbMrakwfKAtoG3ig5FFzvFecfGAuEIB3kAIUy7uD8Acc7yRY8SuNq/k8ycMwNXmNcU8zLBiJ169fxomDuknafuJQ/rh1funmXa8s8MbwLgfv4kRi97AuB+/mdY4D70zW0yJ/sqDkCldOXwA7FYLpOSO2K0WXDlcfNlOvch0pR65lQ9nVg3CU3Mn6LJCkBYDFaGwtKZR7LHXqhpruqitxvX7dz4Rf91ZY3HPXz/UtMKjFpkrYkEAcqp0xlZwvbBfLyy9fWJc5VZh+fHYbM9Mfa/U/j8TkVMh8c7ntqNfr+7/t5DJFwZ6Kp0lq3yb6PiRtqQGpMRi9cvzYqv4/W7eRPzbK7uzWmVBrDI5qxGbk5YTNUbPxidSQovvWK4KhMKo/q8NAIDSPg5cP7YUO4+2RG2T6fuP3vtdchihSi8REREREUnH6q2JyV1NLFIoDJzq9CVcTYzIrLgamriDrnZF15pIXT7jjqdHnjc2qwV9e+VhzzHx4jx7j7Whb6882KwW/Psr3cW1eN4kZuTzRlDosEseiy3Is5l6vPOv738O97lVpd2BMF6o+xz31I7I8l5lR2wFdqPHlLCyLenC1kOn4ipQJqqC2ekLilbB1JtMV+qRU/lQz5N8wkBF7OcuVVGBHVdcLC0YO/bYa1GNNV20qMaVyD1/3aG6YRmbia1F5opYEJpUYhVcm1rccY8JhOXHY2/qmfheqfl/JiK3QqLHH+rZJjKTTwj+FbJlYxtFgkTHj7QhJyAlFqtfnhcb2N7m9mPtgmmSK+sLtKoMn6gyOasRm5MW7Z/n7rzM0AM8RGqo/Y4ZhavNi5d3NGb9/qPnfpcSRqjSS0RERERE0gkr45X1LZD1vLK+BbpccVIrrPpLpC2uhpZYZWmR7ArasayW7tcxmtjzprndi1a3P+l50+r2o7nda4rzJl/mnGAsh91qyPMmkjcQxAefn5G07fbDZ+A1aZELAFi65dPo39/5LEt7og9miikx90wT6YYWVTD1JFuVeuRM8un5+AkDFbGfeyp2qwX5Ngve/+y0pO1X1zfhn5+cggUWzBhbgi5vdENAT+dhOqtxuVUugyuWia02cyVZENq48r5Rj6c7qzOd3ys5/085tDxfWGkz+8QCUtQwa/VLscD2S8r6xi2bk4zSCtbMPicplLZ/RpYUYtm3aky7TA+RVEq/Y0bjSXDfy+T9xwj9f+HeLqwKIUVs/5P3diIiIiKi3CT0B852SV95EOheqfDWZXWG7Q+w6i+RtrgaWmK7G1sQUlmPKhTufp3JEot45QqeN4kddLXDH1QXF+ELhgx5bADgS7/agk+aOxAOA1K/XoFQGKMXvwmLBRhRUoj1D9emdR/1pKGxFa3u6PZOi9uvOs6DcgODbUkXZleXYcuhkzjd6YsLZBGqYH5/5U68tdeVsAqmniSq1HNv7XA8s+XTqE6mUKmndtTAnsfsNvkZNUZaihM4/7k/snKXpCU5rZbum/npTr/k9wiju7oTAKyqb8IvvzlBt+eh2kGKdEkW4CNkriQiZK6oCUILI4xTHT4EJfao2j2BuOOXbFkiLb9XmQy2E86Xe/7yoerqx1oE/5I6sQEpZf0K0Or2o9Mrv7Ns1gHLRIHt31z2vqzXuWnihbIDbeUunyb3OkXGIrf9ozQAnMis5H7HzCST959c7/8rXRo1tv/JezsRERERUe5R2h8AAG8g3FMx0Kj9ASHRdd6y93FcYmIiAAzu48CKu69gMjlRhOqKfqgdNRBbDp6UHPgWyQLgmlEDTTcfZHY8bxJjUkhiZzp8OORSVqAiDCAcBg65OnCmw4f+hblftTSWWJxHS5dPdNubn/k/9OsdfQyMmmhlZpyVJV1QWwVTb+RW6lmzswkPvPgROn1BOPNt2LZohuygCSUBvnqrDhSr0GHHnEnlkibC1Wavuf1BDOrTS7fnoXADd9iscFstCKj9D2tAiwAfLYLQ1EqWnafV9yobwXZXXFwMq8UC6blnibHSZnaJBaScbPey+mUCcgLbu2Qug/OvL9Rj14+/JOu6t+9YK7OISRY57Z+ba8oZaEskk5zvmNml6/6T6/1/VgghIiIiIjIv9gdSG1bsxKKvjMWCFR9Jfs6ir4w1/Lg1kVxuXxDbPjujeJYvDGDb4TNw+4KGDO4ncTxvktt8oBkOu/x4C7vVAofdik0HXJg+uiSNe5gdGw9IKz4m5XWSFWPLRW5fEC/UHZH8nfIEQjjR5ol67IW6I4ZNtDIr+eUzidJEqIIZO9EmEKpg6m2iTczs6jIMOLefM6sGYdV9UzH0XCdRqNRzQ1V3QN4AZz76O/PReS7gp9MXxLpdx2S/p1iA77oF0zB/+gisXTANI0sKo/7+esz2ehX7/0rkyuEDUFSgLNikqMCO5+68DNUV/XR5HgqBmifaPDjV6dNFoC2gTYCPFgNTFgC9FTZMIj97MVp9r7QcgJPqoKsdPpVLYQjaPQGcaPNE/ayqb4JbZqAiKSMEpCyZMx5Lb5+IQoe9514ys2qQpNeYWTUIq+dPNfyAZeT1MvJHqyD9YCiMK57chClPbsLiNQ0pt+/wBvCt57YjrOKyneo6RcYktf2TK+05Ir2R+h0zs3Tff/TY75JKqIKhRf+TiIiIiIhyC/sD0mze35x6o8jtD8jbnsgMsjG3SLmP501iwhziqQ758RaBUPdqu0adH59bU4FxF/ZR9RrjL+xjuEBbAPjg89OqS5uFz70OGQeDbYnSQCwwKpJQqWfJnPHYsLAWH35xNurvK7Ydkf2ecgN89bYUpxhvIIgN+6Rl0TQ0tuLle6+MC35MZWRJIdYumKbrDCQtGsXpoEWAj9qBqUKHDX/8Vg3WLpials9eq+9VNgbg1L5nKkbtaOmVWECKUJlPCrNUv8zE9bLdG0gZcB4IhhAIhrC6vqknmQYASoocst4rF+5RpD057Z8Ne13w6rCNQKRncr5jZsX7T2rC0qhG7H8SJSO0cwMaJXYSERER5SKhPzC4b4Gs5w3uW2Ca/sDs6jI47NLCEBx2a07MWRJlGoP7E3PkaVMdUqvX0ROeN4kxEDkxty+IT5o7Vb3GJyc7DRmIfKLNq6vXIX1gsC1Rmkit1NN01o09TW1Rf2toakVDY6us95Mb4Du9Uv+d+a2HTqHdG12NsKjAjkdvqIxrILZ7A2g822XISo/pDppUSqsAH6UT1cOLnQiGgO8+vwNf+90/8dfvTtb8s9fye5WNCXnhPbU+d4zc0co1rH4ZLZPXy0Qd6g5vABMeW48Ri97Aj9fuifpbcaHDcPco0p7c9s97H5/K5O4R5Tyx71ivvMRDI/kSJweNgvcf6bjSAJlNZDt3wmPr0eHVZvUIIiIiolw0rNiJmqH9ZT3nsqH9TdMfuHL4ANit4vOjsexWC64cPiDNe0SUm5jsK666oh+eu6sGToUrnzrzbfjTXcad5+R5I46ByIkxEDmxuTUVePC6Eape46EZIw1Z9dfMzDVrRJRli9c0YMq55aeFn3nL60S3nbe8Lm7bVEtWZ3opznRXNIkNIhtVWoh1C6Zh/vQRog3E1xuOG7bSo9JGsRYyEeCjZKL69isugtvf3ejr9AWx+UBzWj57Lb9X2ZiQL+tXoGoJ+1hG72hlk9xrKqtfisvU9bJfrzzc+5cP4+7Ra3ce66lmG7sKzb7jbbhs6AWSXj+X7lGR9FztTM/7FklJ+4eIpBP7jl01cmDC7S0AbBInCI0gV+8/2WLU/ieRmMh2bqcviHW7jmV5j4iIiIiy66aJF0oO2CkqsOPrEy9M8x7px9ZDp6JW/Eqm0xdkMjlREsOKnfjbPVfICmD/2z1XGD64/9rRpXjtgatkB9w682147YGrDD3PuXhNA25dVodWt0/W81rdPty6rC5lTEou23ygGQ67FXJHey3orsS+6YAxV0xjIHJyD19fib9853Lk2eSdOXk2C/7yncvx0IxRadozyhYG2xJliNsXxCsfNuFEmyfqp90jXgmk3ROI2zbZktWZpkVFk1RBL7OryzDgXADjzKpBWHXfVAw91zEQghZvqCoFAAxw5vcsM2PUSo/C/3nikH4Ze0+rpbvh+Jf3v4DDbo3ryGl5DOVOVL9S3xj12IptR3Lis8/0hPzWQ6c0qzjEalzpI+WaGnvNZPXLxOQGtitxtssfdY9+oe4IJj+xEf/x6p6kz/vpGwckvX6u3aMAfVc70/O+xVLa/iEiacS+Y7dOHtLz2PTKgYhs8noDIQRjsycM7NVdTdnehZyTC30QIi2s2P5F9O/bjmRpT4iIiIj0YXplCa4eWSxp26tHFefEipNqCUWHFrxYL+t581fUSyo6ROaTKwUU0q3+i7MISByfCoTC+OjI2TTvkT4MK3bitilDZD3ntilDDD3PGRmT0twuL9i2ud2nu5gULbl9Qby4/ShOdfggd7Q3DOBUhw8vbT9qyGMDsCJyKleNHIj3fngt+vXKk7R9v155eO+H1yYt9EG5i8G2RBlitNLraiuaSAl6mV5Zgg0La7FkzngsvX1iXNBhocOOpbdNwpI547FhYS2mV5YYvtJjocMOh13ZkhhKhMLdDccTbR6c6vAhEArDdq7CazoCfKROVP912xfY09QW9VhDUyve3HNC0vOz/dlnckJe6ntJwWpc6ZPqmip2zWT1y+TkBLZrIQzA1e5NOeAldUBsfQ7eo/Rc7UzP+xZLSfuHiKQT+45FPvbAdSNlD7Yayfo9uXf/ySaj9z+JBA2NraJ98IbG1iztEREREVH2eQNBvHtIWoGDdw+eMnx/IDLAy+OXFxjp8YcMHeBFyuRSAYV0kzvXZ6b5oF1H5fVLdxu8H2u0mBQt7W5sUV1UIRAKY3djizY7pEPDip14/juXy3rO89+53NAB7JFK+xSgtI9D0raD+jpQ2qcgzXtE2cJgW6IMMVrpdbUVTaQGvfR35mNuTQUsFvGS7FarBXNrKtD/XCUqM1R6zPYitiGEseDaEZoH+MiZqH77wEnRx7v80Z0HC7q/e9n47BNl22Z6Qn52dVnP90MtM3XOMy3VNVXsmsnql+Iiv3taBpunW74tulnekYP3KD1XO9PzvomR2/4hInnEvmPCY5cOuUBVvy3XeQKhhPcfVpOJZ4b+J5mPUI0s8mfe8jrRbectr4vbltXIiIiIyOgi20ux/YFE2r0Bw7eXGOBFWsulAgrpNnPcIMnzwxYAMy9J36p/eiFciz/8Ql4V3x2fnzX0tdhoMSlacuRpU9BMq9fRqxfq5M2fvajz+TYteQNBHHJ1SNr24IkOwydamRmDbYlEpGsS0Sil17WoaJKuoBejV3r0BoJoaMpexp3QwH7kS5WaB/iITVQXOmywW5WHF4cBHHK1Y+U9V2T0s0+WbZvpCfnplSX40ZfHxD2e6D2TYTWu9JByTRW7ZrL6ZbzI7974x9Zj/V5p1a6zyZlvw5/uugwXFfeK+1su3aP0XO1Mz/tGRPqktN9mFGL3H1aTEWf0/ieZT2Q1ssifdo/4d77dE4jbltXIiIiIyMgi20tnu/yynnu2y2/o9hIDvEhruVZAIZ2ssMStxJRoni8M9KxSalRuXxAvbj+KE20eyasJCgKhME60efDS9qOGvBYD58c2y/rKq6pZ1rdAVzEpWquu6Ifn7qqBM19ZsKwwn2f0+9TanU2ytl8jc/tc9taeE5JXxQsDOTFPTcqYs1QLURId3gAmP7ERnb4gnPk2bFs0Q9Nl04Vqg4+s3IU3JVxcZ1YNwlNzJ2Rt6fbFaxqwcV9z1GOdPvFJlnnL6+DMj97PGWNL8PiN46IeSxb0Mq68r6r9nV1dhi2HTuJ0py/u2AnH/vsrd+KtvS7dVnqMDPK2x1Q6TBSoeW/tcDyz5dO4CbA8mwX+YDhu+7k1FXjuvcOylsgdWVKIZd+qSdsyAGIT1Q/PGIX7VtSrel23P4gubyCjn71Ytu2tlw8BIP7/XHZHDYYWOzFr3GDc/fwOfNx8PiPq9YbjuG5Mqar92RoTsBv5nv165WHRmj1Rf7dbLXj4+lFx55QQ/Kt2f8xMyTXVHwzhdKcv6m+R18y5NRUJ30+ofhkp2TUm10V+97pEBkmKCuyouKA39h1vi/tbthTkWbFyx1F87OqM+9u4C9XdE9MlHW0DM+wbEeWWZ9/7DEfPdGV7N7JigEhSXbL2rdZyqa1ihP4nUSQtq5EZffKJiIiIzIntpeSEAK/YeY5U0j3/RLknnXPJuUhsbvHX36zGkAt6YfqogXjwbzs1n1vUs92NLQjKDLKNFQiFsbuxBZMvHqDRXumHME9ytssr63mnO724dVmdoedJfvbGAbj9yu7jbn8QP31jv2GDkYXzpt0rL5nodKcPU57cZOjzRvD7tz8RffyqkcVx8RjC9rMnXJju3aIsYLAtUYxMTCIWOuyYM6lcUrDtzTXlWQu0FTJ0pTY42j2BuGDPl7YfxYZ9LlgiFrdIZ9CLUOlx434Xbp5UHrf8slDp8eX6RswYU6q75ZdTBXunCtSc+et34Q2cn5yO7WiU9nHgb3dfgaHFTtw08ULMfeb9nvM9mUwEfSeaqH72zho8+NLOhJV0UgmHgW89tx3bFs3I2Gcvlm0rXEeyMSGf7D1jl1exWYAnbrwE37x8SNqCf81Ki2tqJCXXzNhrzD//7dqec0HvwSzJCEE5L2z7IuE2wvXy8OlOPPzSTrS4E3cWrRZA5TiNZKc7/Xhjj3h74JcbDuF/tnwW9Vi2O6tanMer6puwaNZY9FKYPZyL+0ZEucXtC+LlHY3wBLRd6SRX/HHrYTz73uGox/Ji2gmR7VstpTv5VWu53v8kiiVUI1PaBy8qsOPpWy41ZOAIEREREcD2khS5VnSIso8FFFKLnedbcO1wzHr6vZ6/v3b/VPx28ydM9qWeqr9KgpG9gfNVf404T3Kmw4dDLumJILFCYeCQqwNnOnzoX2isMT6582uROr1BdHqDpphfOxNTHCvfZsVzd9Vg2siB2PrxSfy/P38QVQjvdIe8wGXKHbkbVZEDfvCDH8BisfT8vPPOOymf8+abb+Kmm25CeXk5HA4HysvLcdNNN+HNN99M/w4TgMwtSREbNJlINpeZ1CJDNxAKw9Xmzejyg/2d+ZhbUxE30SkQKj3qcaJTLNg70uzqsp5KUzOrBmHVfVMx9Fymb4cnEBVoC8QHiw1wOnq2ryrri59/Y7yk/cpE0LcwUb1kzngsvX1iz/spXca3pMjR82/hWFqtFtx06YXoU2CPqpilpVTLlSf6fwqECfklc8Zjw8JaTK9Unx2X7D0jz6lLyvrgnUen45vnAieEQbEbqrqDa9lBV0eLa2okJdfM2GvM5U9szPnloCOXtd57TLxibeT1cnplCTY/cg3+62tVPed2pFGlhZIDyi8d0g/pXI1Jj8vyalm9Q2t63jcirbGvmV7d1xNzBtoKQuHon9h+RmT7Vkup+kN6lMv9TyIxSvvgI0sKDb3cJBERkVmwv5ka20upCUWHpMhm0SHKPiHAK3YcPN1zybkmdp5v8eq9UX9fvGav5nOLlJu0rPprNBsPuHT1OnrC+TVpbr/iop4Sg6VFDmz94XRMGzkQAHDVyIF474fX9sSoWADcfkV6Voaj7LOEw+EM1e0yl127dqGmpgaBwPmG4Ntvv41rrrlGdPtwOIx7770Xy5YtS/iad999N5555pmEEzhqNDY2oqKie5npo0ePorxcWgfIaBoaWzH7d+/FPb5uwTRNl6TwBoKo+clGtEsIbCpy2LHjRzPgsGcnA2TzAZfiDN3e+Ta4fUGouchYLMDq+6YaOss30ld+uzUqUHPchX2x7v5pUduc6fRh/gv1OHwqesnxTl/yKpiCAc58/Mu4QXj8xnFY+LedWPVRU8rn3DTxQvxybrW0/4TGhIzWUDiMVrc/bqJfjMNuRSAYQkTiEC4p64O/fncyrvzpJnT5Q5pUykqUbSv2ORQV2HWbbXum05ewGhcAhEJhVuPSiJprqhSprpmx15hIP71pXNqWg9Za5HevyxdAW5LjWZBnRb9e0eet8N0LhcJ4/B/7sLq+CWe7/JhZNQhP3jQOtUvelnyP/st3Lsctf6yDx5+ZoCw93BfVnMdC9Y50TSroed+MgH0Gfci1viaQm+fO5gMu3PfXD+EJcMgkkXS0b6X0h4goMzq8AVYjIyKijMnFPoMR5Vp/M1vnjdI5i7698mC1WHQzL5BuuTD/RNm382gLvv6Hf0JNxIoexswzbdi/vR41/26xAId/+uWs7U+m7Tzagq///p/qYhAArJ5vvPNm59EW3Pj7f6p+nTUGPDYAMPEn63GmU3m10QHOPHz4oy9puEf6wfk1aT492YFntnyKn980DlZrfH3TQCCEf1/TgHtrh2P4QHmJWZQe6egzsLJtGoRCIXzve99DIBBASYm0i8nixYt7OqOXXnopXnzxRWzfvh0vvvgiLr30UgDAsmXL8KMf/Sht+202i9c0YMqTm6J+5i2vE9123vK6uG0Xr2lQ/N5bD52KC+IpKrDj0RsqUVQQPTHR7g3gvY9PKX4vtdRk6L7+wFV49q6auP+TVEUFdjx352WGbMiJSVURVdArz4adR1skZ3nGOt3pwwt1R3D5ExuxWsJABwBs2OuCV8OKnFJFZrQ2t3slDVoB3ZW3gjE9rD3H2lD9XxvQdS4gTm2lLCNl27IaV+YovaZKkeqaKXaNiZSuSu5ai/3uJQu0BQCPP5Twu2e1WvAfX6nCpu9f05MRXv/FWdF79MyqQXHBA+3eAE53+vDAtSM1/3+K0ct9Uc/VO/S8b0RaYF8zc64dXYofza7K9m7omtbtW6n9ISLKDFYjIyIiMhf2N6VRM2fR3O7V1bxAOnkDQWzYJ63yX7bmn0gfqiv64dk7OZcsx9MbP44LMg2Hux83i+qKfrimciCUpnFYAFxTOdCQ501laRHy7erCwBx2KypLizTaI/040+FTFWgLAKc7/TjT4dNoj/SF82vSDB9YiP+eM0E00BYA7HYr/nvOBAbaGhxHQdPg6aefxgcffIAxY8bgxhtvxE9/+tOk23/yySdYsmQJAKCmpgbvvvsuevXqBQC47LLL8NWvfhW1tbXYsWMHfv7zn+Pb3/42hg8fnvb/h5EJnWG3X1rnrd0TX6lyVX0TFs0ai1758ivO/qPheNTvo0oLseyOGgwtdmLWuMG4+/kd+Li5o+fvrzccl7ykdToIS8krqWgyrNiJtQumxf2fUhlZUohl36rBsGKnml3XrUQVUcXMW14XVTHKFwxJPncTCQNobveK/q20jwNdvmDUOS8EfWf6PNRiyYJkfrRmD/Yea1WURa7lcgpG7MxRYnKvqVLEXjPlXGMEDU2tmPSTDcizWTWvrhAInh90ttvUdfLT8d0TAs6B5Pfow6c6cePv/4lW9/nO+PwV9QjERvenQTbvi2Kfn5q2Qbrped+I1GJfM7M+/Pxstnch50ht36rpDwH6WSGCyOhi28aJZHvcioiIiNRjf1MazgtII1Z0qNBhwz1XD8f/vPspOrznj2G25p9IP4QAL84lx5vw2FtodUsrtvTLjYfwy42Hoh7r28uOXT++IR27llVuXxB1n51RXNk2DGDb4TNw+4KKYj307KCrHf6gupUYfcGQIe9Tb+yRNsYh5XVum3KRJq+lN5xfk0/LOXDKHfykNXb06NGeDM2lS5ciPz91FcBf/epXPUuy/Pa3v+3pjAp69+6N3/72twCAQCCAX//619rutAlp2RlWYnZ1GQacqxA5s2oQVt03FUPPdQSEG9gNVd2dygHOfMyeUKZqX7WgpqKJ8H+aWTVI0vNnVg3C6vlTDds5UlsR9UynDw67VXGWZyquNi+emjMhLmvpdYmTbFpSm9GaSiAUxss7GhVlkTPbltSQc01NJfaaKfcaE+l0pw8n2jx4oe4IJj+xUXUld6B7+dkJj63HiEVvYMJj69Hhlb/8CHC+Iv29f/kQfQvyFGcsp/ruJbtHD+pTAG9MsoPHH0IgpGxIp6xfgaTtsnlfTPb5ZaraWSAY6vmRipXYyIjY18y8yHsCpSa1fWukFSKIjIzVyIiIiMyD/U3pOC8gTWzS1oiBTgRDwC82HEIwBAyPGefMxvyTnigZ/zQaziXHazrrlhxom0irO4Cms26N9kg/sh3roWe8TyU2QqNVR7V6Hb350q+24OJ/fx3j//MtvLVPWrGot/adwPj/fAsX//vr+NKvtqR5D/VHqzlwyj0MttXYfffdh46ODtx5552ora1NuX04HMarr74KABg9ejSmTJkiut2UKVNQWVkJAFizZg3C4fRXUDOybDcypleWYMPC2p4lq2MDPAoddiy9bRKWzBmPDQtrMb1SHyXX5VQ0icWgl/O06AD4giH8/Bvj07IUPQA88vIutLr9cJxbZsJqAc52ZmdJBCGjdXBfaUFpcnkCIUz9+WZMeXJTz09scGGigQ6lyykIr3nZsP6q9p1ym9Rraiqx10wtrjFhAC6NljVbu/MYOs89v9MXxLpdx2S/RmxQTovbryhjefjA1EuZJLtHH3S1w6vRgKfDbsXNkyokbZvN+2Kqz09N20AKNR3VdO8bUaaxr5l5wj1h4pB+2d4V3ZOzXBgnRIhyg1g1sqICOx69oTJuPE2oRkZERES5if1NeZTOWQzuW2CaZZZjCxrcNuWinhUb3f4g7rjiIt0VHcoWBuqcx7nkaC9+cERXr6Mn1RX9VM8bD+5bYMiAUkD5/LWc8b1c5MjTpoqxVq+jJ2c6fDjk6kAoDITCgNQmW/jc9qEwcMjVgTMd2YknyRYt5sApNzHYVkMrV67Ea6+9hv79++O///u/JT3n8OHDaGpqAoCUHVjh742Njfj8889V7Stlv5EhLFltsYjX5rNaLZhbU4H+OqmkpEVFEwa9dNMq2HvWuMGysjzlaPcE0NzuhTfQHVQWCp9fTiMbhhU7UTM0fYGpZ85V8xSrlJVqoENutq3A7Q+xwWVicq6pqcReM7WuCK02mGXF9i+if98mf2BHi6AcAHj8xqq4THexYPpE92gtj22ezYrPT3VK2jab98Vkn18mqp0p7aiyEhsZDfua2eN02HDoBIM6k5FbTaa6oh9qRw1UXKXeAuCaUQMNOyFCpBexYzijSguxbsE0zJ8+QnQ8zehjOUREREbF/qYySuYsLhva39BVOCPFFjR4pb4x6u+v1DfpsuhQNjBQJxrnks975EuVuGhAr9QbJjF0QC888qVKjfZIP850+HCsxaPqNY61eAwdGDis2ImX7pkiefzNAuCle6YY+j5VXdEPz91VA2e+smBZZ74Nf7rLmFV/Nx7QZs5aq9fJFVrMgVNuYrCtRlpaWvDggw8CAH7+859j4MCBkp63f//+nn+PHj066baRf498HinHJSmkU1vRhEEv0bQK9tZyKfpUsl096qaJF2oWPJhK5P9VykCH0s+BDS7zknNNTUXsmqmm6nLsPqmp5N7Q2Io9TW3RjzW1oqGxVdbraBHk+uB1I3HF8OKox5RUDdDq2HZ4A1i/V9oyLNm6L6b6/DJR7UxpR5WV2MhI2NfMrq2HTqEjSwlnuUJuNRm3L4htn51RVKUe6K7An81EQCKziK1Gtuq+qRh6bjxMGE9jNTIiIqLcxv6mOnLmLIoK7Pj6xAvTvEf6IhQ02NPUJjrGuPdYm66KDmULA3XO41xyNLcviOY2dcGgze0+Q46fMDBQmu0yxt/CAD44fCadu6ML144uxWsPXIV+veTNN/brZcdrD1xl2Kq/c2sq8OB1I1S9xkMzRmJujbQVPY1Aqzlwyk3Grq2fQT/4wQ9w4sQJXHnllfjOd74j+XlHjx7t+Xd5efJArYqK8xemyOdJ0djYmPTvx48bP/srESFI7k0JAS9mWJIiEbGKJsvuqMHQYidmjRuMu5/fgY+bO3r+/nrDcVw3prTn90RBL/fWDsczWz5Fu+f834Sgl8jnG5EwOfXNZ97H3uNtKbefWTUIT82dEHcOarUUfTJFBXY8fculWc3Uml5ZgqtHFuP1BmnBaUr1zrfh9/Mm9vxfxQY6br18SNzzlHwOQoNrXHlfRftKuSvVNfW2P9bhWGt0Vq7ca6ZwjXlk5S5J97hYI0sKsexbNZITTBavacDGfc1Rj3X6xINX5y2vgzM/+lo2Y2wJHr9xXMLXF4JcY+83Uh092xX3mFgwvdj3WyBUv624oJeqYytwB0JRv+fZLHhoxqis3BeVfH4t7uiBPrltg1SSdVRTXTfVtluI9ETvfU3AeP1N4ZrY5vGjK40TEjaLBUEDLKMq9xqqRcV6ITnOiJUkiPRCqEa2cb8LN08qj1v1odBhx9LbJuHl+kbMGFNq+iAJIiKiXKT3/qbe+5py5iyuHlVsiuqt6R4jNho1459GJDaXnIgZ5pI5fpLY3JoKNJ3twm82faL4NcwQGPj8+5/L2v5/3/8cMy8ZnJ6d0ZFhxU4UFuShxS3tegMAhQV5hi/Id7rThwt65eGs2y/7uRf0ysOpDm8a9kof2L6hWOaMGtTYe++9h+XLl8Nut+OZZ56JG3xOpr39fJXIwsLkFdKczvMX744OeUEmkZ1ZiidnSQojN9qTmV1dhi2HTuJ0py8u6FMI6Pr+yp14a69LtKIJg17EFTrskjuOYsHeWi5Fn4jcgLt08QaCePdQeisPlvUtwAvfO79EhtSBDjWfAxtc5pTqmjrpogtwbPf56+aF/QrwwnenyL5mykkoiZQouD8Rty+IVz5sgtsvbeCn3ROICiYFgFX1TVg0ayx6JVm+RU0AsZDp7rCff32pwfRAdyXayU9sRKcvCGe+DdsWzVB0bJOxWS34f1OHZfy+qMXnZ7NY8MJ3p2BgkQOAtLZBJK07qmrbLUR6kQt9TcBY/U2510SlZlYNwtkuH7YZoGKE2D02GaFi/YMv7Yy7n0ihh0RAIrMQqpElYrVaDD85SUREZFS50N/Ue19TzpzFuwdPyeo35aJMjRHnKgbqJCYcm9jiDqnMX1GPfr3yDXtsOH6S3MPXV6JmaH9869ntslZPsgB4/juX46qR0qq55zK5Kf4GqAkg2Very/CHtz+Vtb2RqR0TP+v2G/YezvYNiWGwrUo+nw933303wuEwHn74YYwbJ68h5/Gcr1qXn5+8AoTD4ej5t9vtlrejlJCSJSmM3BlORG1FEwa9iGtobMWRM/HVFsWIBVrJyfJUQm7AXTrJqY6sxKC+DgRCYdy6rK7nMakDHR5/UPHnwAaXOaW6pt40qRz//OQUznT5cW1lCZ6ed6nia6aSqstyK7lnMsNaaQBxbKa73KoBYlVwtV5SxxsI9RyDTN4Xtfj8QgijqcXdE2wLSK92lo6OKiuxkRGwr5kdWlwTpaj77LRhRrCVVJNRWrFeL4mARERERES5jP1NbbAKZzRW4UyMgTqJqQnw8vhDOOH3GPbYABw/SeWqkQOxZM44PPpyg+TnLJkzzhSBtgAwedgA1H0WPY+VbG598sUDMrl7WRUOyRyXNcYwbkIHXe2qi090+Yx5D2f7hsRkP3oqxz355JPYv38/hgwZgh//+Meyn19QUNDzb58vebaW13u+7HavXr1kvU+qpVmOHz+Oyy+/XNZrGoWcAD4zdIaTUVPRhEEv8rJWxazbeQw/vWlcVLB3bBBdWb8CHGvxxD5VMbkBd+mUqjryd//3A3x6slPx65/p8MMXDKXeEOIDHVpig8sckl1Tp1eWYOP3r1F9zVRadVluFdVMZ1hLDSAuKrBH7Y+Q6Q7Irxqw82hL1GN/rfsCR05LS5aQuq+RxyCT98V0fn5Sqp2lq6PKSmyU63KlrwkYq7+p9pooVYuC5biyqaTIgeZ2b8LflVRgH1bsxN/uuQKXP7ERAQkD3HarBX+75wpD9hWJiIiIiDIpV/qbeu9rxo5RpqPflEtYhTMxBuokxmOT2rBiJ5bfWYPa/35H8nOW31mDiwYYO9BWmHf3yAwQfOL1A/jF+o8NWxE50tGYgl+pVh6O3d6IhPPG1S4vnmLplk+xqr7JsOdNZWkR8m1WybESYhx2KypLizTcK31g+4bEWLO9A7nswIED+OlPfwoA+O1vfxu1FIpURUXnLzaplk/p7DwfRJZqWZZY5eXlSX8GDx4sb8cNRCyAb92CaZg/fQTWLpiGkSXRx/p1BRUCqZsQ9JJoOSIh6MWIk6dCZuaJNk/Uj5wbsj8Uxqb90UFzZ7t8sEYcTi0DbQF9ne+zq8sw4Ny5MbNqEFbdNxVDz2VkDit24uHrR6l6fTWNx0gzqwbh97ddiqICZUHKRQV2PHfnZWxwkSbXTKXVr4VK7nIIGdax981URpYUYu2CaZg+ukTS9nICiMOhMK6NeF2PP5Ty+tvuCcRdq1/e0RhXBXfvsTbRZJ3vThsWdV2WItExyOR9MVOfnxiho8rrJtF5udTXBIzX31R6TTSqmVWDMKAw+l5UXOjADVXdk8RqKrDXf3FWUqAtAARCYXx05Kyi9yEiIiIiom651N/Ue18zds4iXf2mXJLNMUY94/hnYjw20ix586Cs7Z96S972ucbtC+LF7Udxos0jO6G9xe3HiTYPXtp+FG5f+le3yqZUc+ur50811X0q8ryRu+BYOAxDnzcHXe3wh9TFSviC3at3GhHbNxRLH+UKc9SvfvUr+Hw+XHzxxejq6sJLL70Ut82ePXt6/r1582acONG95PHs2bPhdDpRXl7e8/fGxsak7xeZwVlRwSpcWpldXYYth07idKcPM6sG4am5ExQv1U36E4gIoLTbspdfoNVytCt3NGLWuO5z0O0Lou6zM5C7yoEcQsBdZDXdbElVHXnz/uiqwX0K7GiTEczcK88GmxXo8Mr/nIoK7HjixkvgCYR69m3s4L5c1oWyLlUFWKsFotcQpZXchfvmIyt34c29J1JuH3vflUIsgLjAbsWCa0fgf979LCqItsMXxOVDL8DmA82xLyOLJ5C6g2m3WuCwW+EJBLHu/mmY+z/vo1PC9UTJMUiXTHx+iXA5LKJo7Gtml1DhIBQOI99mgS9o8HXCRDgdNrzwnck41NyB0aVF+Orv/xn1933H2/DTm8bhujGlqiqwS61WL8iFSlB66YMSEREREYlhf1M7kXMW6ew35ZpsjjHqGcc/E+OxSe3tg/LmONTOiejd7sYWBFVOkAdCYexubMHkiwdotFf6w5WHo/G8SYzVW1Nj+4Yi8VNVQVj65LPPPsOtt96acvuf/OQnPf8+fPgwnE4nxo4d2/PYgQMHkj4/8u9jxoyRu7uUABsZ+qLlxGSHN4DJT2xEpy8IZ74N2xbNyNrNTIvlaIscdtx55dCe37UK4BXcNnkI1u46FrV/SgPu0iXZkuCRgfNfGluK//vklKzXdvuDePLrl+BP//xck848G1ykB5Hfi7K+BTjWer769ajSQvz6m9X4zaaP8dZeF2wWIDKWSGkwS6HDjjmTyiWd9zfXlMs+58Uq0gvL3nx5fFncgNxT6w/Jen2lAqEwTnX4sKq+CYtmjcXPvzEeC1Z8lPJ5So5BOin5/LS6dwvXzSt/uklSsgSvm2Rk7Gtmj7AihVvmEnRGk2e14N6/1gMAOn3i1+Tbn90GZ74dv4y418pdSm3muEFY/VETpAxzWwDMvGSQ5NfOBj31QYmIiIiIxLC/qZ6QoBkpnf2mXJTuMeJcxXmjxHhskrtuTAnW7ZKesHztGGNXUXTkaVMkSqvX0bNkc+vA+RUWiYTEh+t/uUXySmRAdzGitQummSLxge0bErDERpYNGzYMZWXdVSq3bNmSdNt3330XAHDhhRdi6NCh6d41U9FiqW5Sr8MbwITH1mPEojcw4bH16FCw9HmktTuPofNcGf9OXxDrdh3TYjcVU1Ne/tEbKrHlB9MxvfJ850gI4C10aNMR2LjPBYfNCnvM+uevy6w2lS1C4PySOeNx86RydIgs4dDfmYd8e+L13Xd8cRar50/FzCppk/gzqwZh9fypCRuPQoNLCja4KB0ivxePf/2SuOVixpb1xdLbJmHJnPF459Hpmi0XI7VKnZLri5xlb5z5tqgOYUmRQ/b7yeX2B3HQ1R5XbTuRdFxjA8FQz48Scj4/re/dh092Sq5KzusmUXLsaypz0NWekUBbC4D504fjqhHFaX8vJVrcAZxo8+BEmydhsmC75/w2ws+q+iZZS6lZYYkLtC0qsOPRGyrjlpAMA7Al6LPrhd76oERERERE6WDm/qaQoBnbF0pnvylXpXOMOJdx3igxHpvEvjbhQlnb31gtb/tcU13RD9MrB6p6jemVAw1dhZPiMUg7tWHFTtxTe7Gs59xTe7EpAm0FbN8QwGBbVf785z8jHA4n/fnxj3/cs/3bb7/d87jQobRYLPja174GoDu7s66uTvS96urqerI/v/a1ryUMCiXKZVpPTK7Y/kX079uOqHo9LQiBYHKDOedPHyEa7H3t6FIsmTNBk31ztXtxqtOHQCgM27lLjNqAu0wTAuff2BOfTXTViGK8+4Nr8dZDtaIBzwOc+fjyJYM178yzwUXZJnwvrh1d2hN4u/T2iT3nrpBQUtG/d0/g7YaFtVHB/XJ4A0Fs2OeStO2GvS54ZVbojgwgjvx/CISK9EvmjEdF/95Rfysucki+/ipRVGDHc3dehjGDi9J6DJJRG/wq9/NbVd+o+N69eE0Dpjy5Kepn3nLxtrCYJ17fL3lbolzDvmb2VFf0w7euuCit71Hax4HV912JR28YHbVyhREISSdSiVWsX33flbjn6oux+l+vjGu3673NrMc+KBERERFRJPY31dFixUG5/aZclO4x4lzHeaPEeGyiCWPoC16sl/W8+SvqMeXJTVi8piFNe5Zdbl8QdZ+dUfUa2w6fMUXiA51XXdEPz91VA2e+smBZZ74Nf7rrMsMHaT9w3Ug4bNLabA6bBQ9cNzLNe6QfbN+QgMG2OvDQQw/Bbu8OFLn//vvhdruj/u52u3H//fcDAOx2Ox566KFM7yJRRmg5MdnQ2Io9TW3RjzW1oqGxVfFrakXrYM5Z4wbj97dNjKtIq0YwDNx/7QhVAXdKqK3GKIisfHnt6BL819eq8Px3Lkehwx5X+bJ/73zYrcDpTh8eeOkjdHgDmnXm2eAyB63O20zIRCX3rYdOoT0mwDNRhbp2bwDvfXxK9ntI+X+MGdQHB05ED1rvO9aGmqEXSHqPsr4FsvZpZEkh1i6YhumjSzJyDBJRm7gid9+f3Xo46jGp9265VUDEfHaqE61dfsnbE5kR+5rK1B85m7bXnlk1CJu+fw2qh3Tfj17bbZzKp0UFdvzxjhpcUtZHcrsotmL9X74zGV/73T8xYtEb+Nrv/4m/fneyZpX3003PfVAiIiIiIq2Ztb8prDgYO04mlZCsb/RAnWyOj+od540S47GJFjmG7vHLm3/y+EOGrqTNxAflcmlOMx2uHV2K1x64CsNlVmIdXuzEaw9chemjMxc3kS1bD52CNxi7Fpk4bzBsqnu4WPsmEbO1b8zGPLX1dWzUqFF45JFH8LOf/Qw7duzA1KlT8cMf/hDDhw/Hp59+ip///Of46KOPAACPPvooRo40R2ZA5A3ebmNcuNElm5gcV9436XMXr2nAxn3Ry3V3+sRvcvOW18GZH33pmzG2BI/fOE7BXisnJ5jzujGlKbf78rjBaOn0YdGaPWp3DUUFdjx9y6UZbyx2eAOY/MRGdPqCcObbsG3RDMVLwAiVLzfud+HmSeVxAXlC5cuX6xvR2uXHE//oro7Y6QtidX2j7M68wy6eAZdoQOne2uF4ZsunUUFlQoNLyudN+qHleWsUYhXqlt1Rg6HFTswaNxh3P78DHzd39Pxd6nUOSNw2kHMf+PmbByS916ShF2B8IIw398ZXyo41s2oQnpo7oeezT+cxSOUvdZ9H//7+F7j18iGSny9337840xW1vdR7txaDYQCw8sOj+N5V8pa0ITIT9jXl23nkbFy/REs315TjZ2/sx8Z9zQiFw2hu96btvVKxAEg0bDq4bwGOt3okv9bIkkL8+pZqzH3mfVntoth2+4vbj0YljWw+0NzTbp8xplRVQpCWcrEPSkRERESkJTP3NzcfaIbDZoXbakEgJC0YBQDsVgscNis2HXAZPlgnm+Ojesd5o8R4bKJpGVBqtAB/IfHhwZd2yirgIRDmwo12XFLhnGa3YcVOvHr/NFz98804I6GgS39nHl69f5ppjtU6mcUh1u06ZuhrMXB+LLjF7ZP1vPkr6tGvV75px4KNHPNnjqtBDnjiiSfQ3NyM5557Dh999BFuueWWuG2+853v4PHHH8/C3mUeb/TGpuXEpJDV5/ZL62y0ewJxje5V9U1YNGsseilcMkAuJZmZiYI5I334hfoKXCNLCrHsWzUYJjObSwti1RjlBIjFEipfJiJU8PzKb7dGPb5862HNOvMcUDI+rc9bI5hdXYYth07idKcvLghVqCz9/ZU78dZel6wKdYnaBnLvA36J2ZjvHDiJJTePlxRsG1uFPF3HIJWGxlbsPx5Tzfd4m6Tg11T7Lty7wwjDYbfCGwglDNKSElSkdjBMsP94+gLiiIyCfc3EYvslYYThaktv8Ouru5qwYW+z5PuWWlYLIDb3O3RAb/zhton46RsHsFUkw/6yof3hC4RkJZ0obRdFttvFVju59fIhSdv1mZaLfVAiIiIionQwY3/T7Qvixe1HEZQRZCsIhMI41enDS9uPGr4/kK3x0VwgNm/0h9smYugAJ24YW4p/faHetPNGnFOLxoDS5DYfaIbDbkWX1SLrmmyzWuCwmyPxIRbnNM/Ls1nQ6pa2cmJrlx95Nu1WGNa7oQPkxYnI3T7XyB0LjuTxh3DC7zHlWLDRY/6M8z/JcVarFc8++yy+8Y1vYNmyZfjggw9w6tQpFBcX47LLLsM999yDf/mXf8n2bmYMb/TGpfXEZC5m9aUjM1NOAG8isQMemZZoYl2NVNkyYhWVY6s0qunMc0DJ+NJx3uY6OZWl5VSoS9Q20KpCau98G7oillNq9wbw/P99keQZ58VeB9J1DCKlq6Ke2L4nuncnGr6SGlR07ehSrF0wLe66morTYUOnN8jrJpFE7GuKUzNIpsbGvc2a3LekuqB3HgYWFaDxbBc6vN3vO2NMCX59y6UodNjxv9++HC9sP4L1e49j68enAXT3Tb4+8UIEg9IqvAtJJ2rbRWpWO8mkXOyDEhERERGlgxn7m7sbWxQF2kYKhMLY3diCyRcP0Giv9CcT46O5Knbe6LGvVeHap97pCUbZ/Mg1+I9X95hy3ohzavGUjqFns7hSJqgZ1wuGwjjV4TNl8BvnNM/bvL8ZEmvzIBgG3j7QjJmXDE7vTmWZ0uqtz7z7KV764Khhq7dyLFgZo8f8WcLhsLoeARlCY2MjKiq6K8UcPXoU5eXlWd2fr/x2a9Qk27gL+2Ld/dOyuEeklZ1HW/D1P/wTaq48Fguw+r6pPTejzQdcqrP6Mpm5tvBvO7Hqo6ae3yODOQ+f6ozrMN008UL8cm510tfcuM+F7z6/Q9V+PXtnTdYyQBsaWzH7d+/FPb5uwTTFE+ux2TJfmTAYWw5GV+3q9MUHhAHnl9R12K3o2ysPVoulp4HY4Q1EdeafmjsB0ysTnz9nOn0JB5QAIBQKm3JAyQjScd5SYsnaBmruA06HDb+7dSKGFjvjrr92iUvBFTns2PGjGZKqkGvB7Qti4k82qAoQ651vw4eLr5c0kJSOe3ekDm8Aj6zcJbl64pI53RWHed3MPL31GSh36PHc0eLalsrYsj44eqYr7v70wHUj8OzWwz2DTenWO9+GjQ/X4q19J+DMt+Hmmoq4dmkoFMbKD4/C7Qvia9UXor8zP67fkshNEy/Et68cJqtdlChpROxeXlRgl5w0kim51gclIiIi0js99hlI/7Jx3uw82oKv//6fCZPQpbAAWD1ffJyMzCFy3ujF7Ufx/61u6PnbT28ah2/WVJh23ohzatEiV5xr6fLDGwilfI7DbkW/3nmwwJL18ZN0SfechRFxTjPaN595H9s+PyN5+8nD+uNv91yRxj3KrkzPO+YajgXLp6eYv3T0GRhsSwD0NZDBG73xpeNmJBakmkq2svrePtiMR1buEs3MBCA7mBOID+BVQkpQrxYyNbG+YtuRqAGKPJtF8vLxYiIbiJnozKeqykuZJfe87Z1vgwXnB4JyaUBDj+eelLaBkvvA8IFOLL/zsp77QOT1t8hhl1yFHMhswsLOoy248ff/VP06a2QM6qe7Iyk1aSSbiSGkrz4D5Ra9njtqrm2p9OuVh1/dUo2hA5y48ffvodV9/j0K8qywWSwZC7YFkl/zxe793kAQNT/ZGHcvFGMBUFhgl9yev6ZyIF7deSznB29zqQ9KREREpHd67TOQvmXrvNl8wIX7X/wInV75fRoh8d9sAReUmJ6CUUhf3L4gLvnPt1RV07ZbLWj4zxsY/BbD6MFvuZ7kngkzf/0uDpxoj3rMmW/DfdNH4A9vfxI3bjtmcBHeePDqTO5iRmVj3jHXcCxYOr3F/KWjz5CdtcKJzknXEsikb+lY8kJYPkROZbzYINdMScfyOZFLqyi1Ya8L3kAwrZUZ5S7rIXUpcjGxS2GoCbQFosv7W60WzK2pUPV6ycRW5d22aEZWzlXqlsnzNtuBrno499S0DULhMCr698LRM+6U7zOzqhRPza2O+v9FXn+3HDyJ1xuO9/wtsgr5rHGD4+5hrzccz1gQaGVpEfLtVvgkZLEn4rBbUVlaJPmcS/dyVf+IONbJZPI4E5FxRd5rHDYr3BIrmUvhsFvhsFuRb7fi319pwDWVA+Oqjnj8yq/fSuQnueYnuvdvPXQqLtDWmW8TDRAOAwknNsTaRas/apJUiSUZPSy9lUt9UCIiIiIi0s7mA83olWeD2xeEnK6k1QL0yrNh0wGXYQO8kvFEjPEW5LNfBHQHo0QG2gJAQ1MrGhpbWYCKsLuxRVWgLQAEQmHsbmzB5IsHaLRX+iHMWcz7Yx2Ot3okP29w3wKs+N4Uwwa/ZXJOM5f17x0dfzHiXHEeYQ7wu3/+AJ+e6uz5+wW9jV1JW8t5R6PiWLA4s8b8GftTJV3jjd7c0nEzKnTYMWdSuaTXu7mmPKs3tv7O/KTBmnKDOadXlmDN/Kmo/e+3owZ3rBZIHuxp9wYw5clN+PL4wWm7oR10tcMTUFfBS8rEutgABdBdAatLQQUxIcMxU5P5a3ce6wlk6PQFsW7XMdx6+ZCMvHe6ZDuIVI1Mnbd6CHTN9rmnRdsg3y7t/Lq5pkL0+ArX34FFDtR9dlq0CrlwD4usQj57Qpmk99XCQVc7/EF1QUq+YAg7j57Fd/93h+RzLl0dSW8giA37XJL2OxOJIURkbHLvNXJ5AyF4AyG0nbs/aRFYqpY/kPian+je/8Q/9kW9ht0av3SjUr5gCIu/PBq/2fSJquojeqiSkEt9UCIiIiIiUk9NnzIUBk51+Ew5t+lq82Dyk5t6ft/2/12H0j4FWdyjzDNrMAop58izwYLuJGelLOdex6iGFTuxaNYYLHjxI8nPWfTlMYYNtAUyN6eZ675XezH2n2jD2S4/bhhbil98szpqDvDV+6dh4d8+wvp9zbigdx6+d/XFWd7j9NJq3tHo5w3HgqOZOeYvt6JdyFC0vNFTbhJuRlJIvRnJqYxnNFs/PhUXWCs34fFslx+r6pvgTtOSttUV/fDsnTUoKlDWsCgqsOO5Oy+LaqQtXtOAKU9uivqZt7xO9PkWi/xAgZElhVi7YFpGM81jq/Ku2HYkY++dDh3eACY8th4jFr2BCY+tR4eE5Yj1JB3nrRixYJdMy/a5p0XbQGrWZar7gFCFfMmc8Vh6+8S4e5BQBXfJnPHYsLAW0yszd43Q6pw8fKpL9jmXjnu3WPXEogI7Hr2hMu7/2O4N4L2PT0l6fyIiMVrca+QQAkuVXrO1MGRA74TXfLF7v9sXRNPZ6IoggVBYtKqtXMI96PPTXXDYrbLb5narBQ67FZsOSEvSyAQz90GJiIiIiMyGc5vK/PQfB6J+/9kbBxJsaUxCMMqJNk/UT7JVYmK3TefcHelTdUU/XFM5EErTny0ArqkcaOjAN6C72ng6t881mZrTzHXTK0uw6fvXYMmc8Xjmjkmic4DP3F6DJXPGY9P3r8noHGA28LyRjmPB55m5XcxgW8oaXrAJ0PZmpKQynpHETpQrle4bmrCsx8iSQlnPEwt6lTtA0ekNyloiuE+BHavnT81ohmOyZYNylR6CSNVSet5W9O8tOVg724Guejj31LYNCh029JKYpS3lPiBUIbdYxIezhCq4/Z2ZXz5Gi2tpqnMuEAz1/ETSuiMZ+3qjSguxbsE0zJ8+QvT/aIYOKhGlj9p7jRxCn/W7Vw1XdM3Wyhenu/Dse59FPbZi25GE9/5/NByHP6R9NV7hHjTl4gF45cMmnOrwyWqbA91Bv0IlKD1MMpq9D0pEREREZDac21Tmzb3R43lv7kldEc5IzByMQsq5fUHUfXZGcWXbMIBth8/oYvwknWZXl0le8dBht2Z0lcJs0XIu3sj0PAeYDTxvUuNYcDQzt4uNXbOYdE+4YN/9/A583Nwh+XkjSwqx7Fs1WS3xn8tLouuF1stGJ6qMd2/tcDyz5dOoAEyhMt51Y0qV7bzOiE2UK5HuJVkjl8nxyViKwGG3os3jx7PvfRa1TE66K5O1eQI4fLIT48r7puX1zbJskFhA362XD8nS3ig3rNiJ1fOn4l9+8y6OnnFLes7ZTi9uXRZdaVnsc0sW6JqO80/P556atsFdU4di0eo9UY8b+T4gnJNf+uUWHGv1pNy+rF8BVs+fikKHPeU51+ENYPITG+OWG9f63g10D4ZtOXQSpzt9mFk1CE/NnRC1XM/q+VPx/ZU78dZeFwY4800xGEZE6aX0XiNHbJ9VuJ49snKXpGWm7FaL7EDUZD492Rn1e0NTK77+h/dEt/3PdXuRZ7XKaq+nEnl933m0xTDL2Zm5D0pEREREZFZCn3LeH+twXMKYnGBw3wKs+N4UQy9fLmZ1fRM8/uj+pdsfxOr6Jnx94oVZ2qvMqq7oh2tGDcQ7B08qCpy0ALhmlPErlFI0LYO0jXjufOlXW/BJcwfCYUj+XnkDIfy/P30AiwUYUVKI9Q/XpnUfs0nuWGTs3AyZjzB3HAqHYbVIW0HZagFa3X7cuqwuZ+IW1OBYcLxcjvlTg1dKyrpcvNEnCkCRy+wBu1rfjMQq4y27owZDi52YNW5w3AX+9YbjOXlzkxMkJ0c6b2iL1zRgw14XXO1eRc/3BkJwtXmxqr4Ji2aNRa/87sAtIVvmwZd2Jqxmq1a6Ag2Fqrxuv7SOcrsnEPd/jD0eepTpINJ0s1kscLVKP487vEF0eKM/45e2H8WGfS5YIhb/yWSgaybPPaX3OaVtg/9YEx1oa6T7QKJjmWez4GynT9JrHGvx4LpfvAMLLCnPuS5fIK4i9a2XD0lLR3J6ZQk2LKzFxv0u3DypPC6LuNBhx9LbJuHl+kbMGFNqmixiIkovufcaORx2KyZedEFUu1pow3sk3n/zbEBA++KyURK9fjra1XWfncaMX2wB0N2WUdOGl5MgmO4+t1n6oEREREREFG1YsRMVF/SSFWxb0b9XzgYUSCUEv0VKFKzz8Mqd+P7fd0Y9ZtTgNy0rlOp5PigdPBHj2AX55gptqa7oh9pRA7GFQdpxznT4cMilLIE+DCAcBg65OnCmw4f+hcadbyh02DFnUrmksc+ba8oZaGtibl8QL24/iqDM4g+hMNB8Lv7jpe1HdR+3oBbHgsXlYsyfWrm752QouXajF1sSXW6VRq0CdnOZ1jcjM1TGkxskJ1U6b2ha7rNYBma6K5OlK8jViBmpkYHg4XNd/64Ey9PkaqXeg6521VXeAqEwXG3SAnbTcf5l6txTe59T0jYw6n0g2bHceugU3DKisVKde2LnHAD89f0vcOvlQ9LWkRSW60lEWK6HiEhLcu41cngDIazbdQz/ObsKvfJtitrDbr92VW31oMXtB9x+AOfbMmsXTMP3nt8RNxGbzIiSQvxRYoJgJvrcRm17EBERERFRanIT+uxWYxfdURL8FhvTY9TgNyPOB2WCq82DyU9u6vl92/93HUr7FGRxjzLL7QtiG4O0Rf1jz/HUG0l8ndunXKTJa+lJ5Hxti1tasZb5K+rRr1f3tTcX5mtJW7sbW2QH2sYKhMLY3diCyRcP0Giv9IdjwYnlWsyfWsZu1VNOiQ3eSOR1idulk9iS6HKJBeyazezqMgw4V51uZtUgrLpvKobGLLN6Q1V3gI6Um5FQGW/JnPFYevvEuAu0UBlvyZzx2LCwFtMrS9Lwv0ovLTrkYtJ5Q9Nqn3vn2/DcnZeJDiQI58vMqkGq30cKYVBDDaEqb1GBsuNeVGBPeDyyQQgiOdHmwYk2D1xtXrjavAmrlbV7Aj3bCj+r6pvgThCcqxdjBhehV57yQYne+TZYUm+WlNrzT4tz74931OCSsj5R1eJiaXGfk9s2MOp9INmxzFT7Ye/xNjQ0tmp+7yYiyjap9xq5Iu/X6WrD5yrh2AwrduKh60bKeu5DM0ZKrgSViT63UdseRERERESU2veuvhj5Nmmjvfk2C7539cVp3qPs2njApavX0ROjzQdlyk//cSDq95+9cSDBlsakZZC20VxyoTYrZ2r1OnoSO1/r8Usr1uLxh3Jqvpa05chTP39tOfc6Rsax4ORyKeZPLQbbki54A0Fs2Cet87RhrwveLE5UJlsSXQ4tAnZzXTpuRkJlvNglqAVCZbxcXYJabYc8kcdf36/p60XSap8Xf3kMpo9OfA4I2TLppuWghlCVd2RJoaznjSwpxNoF05Iej0wzS6d/66FTiqs0jywpxOsPXIVn76pBoUNZZ0Or80/NuffS3VPw4EsfYcSiNzDhsfXo8P7/7N17fFT1nT/+11wyE5wEIoQEIkEQQpAIRIiCEsQgKmVL67ZIlaq4a2ut4F2/u/uDvbCttnWtu23d1VK0W23RUgUEWZWbUmwFRQxGkIuKQoIkBMiVydx/f4QT5nLOzLnOnDPzej4eeTwgOTNz5syZcz6X9+f9Fg+o1nqfU9s2yMb7QLJjOUIk4Kgw34lHrq/U/V6xYMUO/NMrDXDYbOif78SHR07jp6/H3j9yvSNJRNai5F6j1K9vmdx3vxbaw/3y1A3BFLgdmDJyoOYBTzOIb8ts3d+i6PFKtk9Xnzsb2x5ERERERCRt6doGTH1sC/7x5Y8QCMnLAhcIRfCPL3+EqY9twdK1DQbvYWbMrynHfdeM1vQc98+qyNrqVlv3t8DttMNpV9a7d9ptcDvt2JKFQcipvBGXvfR1nbKZWgWDtKVVlxfhudtrcJ7KjL3nuRz47e3ZeWxyZb6W9FVdXoRnb6+BR+X8tcftwHNZ+p2Kx7FgcVaK+dMDg23JFLYfbEVnXLCOVKBIpy+Idw61pmW/hA5z9M+CFTtEt12wYkfCtlIdZr0CdrMBb0bKqQ2SS+ZwazfazsgrI6GGHvu868vTKbeRu1rG7ey9/SltMBoR5Ko0K+/sqiFYs2ia7Ixe6ZIrnf74c0xuRujoz23m2FLcedUoxa+t9/mn9tzbc7Q9ZZY4Nfe5YCjc9wOoaxvEP0c2SHUsj546E/O3MaUFWL+4FovqRut+rxAyUrd0+dDRE0Rzp090hTPv3URkFWL3Gr047LaY+9LMsaWorRis+HnKz++Hl+6cio8a21WXDTSLwnxnQltmbnUZXE55Q1Nup112xnT2uYmIiIiIyAjRGQObO32y+2kRAM2dvqzPGPjAtZV44Y7L4XIoC0FwOex44Y7Lcf+sMQbtWWYJ501rlx9BhWW6g+EIWrv8WX3eiFmzuwk9wdhx/p5AGGt2N2VojzJDCNJWI9uDtGeOLcWGe6crni8dWezBhnunmyqhkZ5yZb6W9DdzbCleu2e64mrIhW4nXrsne79TqWTj3LQaZo35M4oxNcOJFIoPXhpTWoDlt9ZgRLEHc8YPxZ3P78Khlq6+v29o+ArXXFxq6D4JHR+5GQw7e4IJ5dJX725CMBTB2wdOxPy+2y8+obtgxQ54XLFfy1njSvDjG8Yr2HPKBUKQ3MOr9uCNvcd1ec6//9/3sfruabo8lxgt+yynFLmS1TJ5Djv+de44zJ1Yhkf+9JGs/ZldNQRPzJ+ouIEph5CVV85+3FgzzJB90MPMsaV4Yt5E/OD3Hyh6XEVJAZbfVmO6AGIxc6vLsO3gCZzs9mN21RB8fcJQLH7xw5SPi//cNu5T9h0w6vxTc+7FZ4n7jzcP4L82H+z7vw02xfe5f/zaxZjy6GZ0+0PwuBzYuWSW4rbB2vom3PvihzHPYdbvipSlaxuweV9sxr5Ux9IXCMFuA8IRoHxgP6y+e1rf+xauuw+tqsebe5sx8Lw8jBxcgA9kLF6QS1jhzIEXIrKi+HvNiEHnIRwBjsQtZFBj9e7E+9KCKcPxwZencapb/iK302cC+Pqv/qJ5fzLN5bDj8XkTEtp7V44aBJfDDn8w9WBknsOOK0cNSvi9mvtnNPa5iYiIiIhILj0zBmbreNr0isHY/g91+Mav3kFzpy/l9qWFbqy7pxal/fPTsHeZwfMmuev+cxs+jRrrB3rHu8U8sKoeD/2pPuZ3o0sKsPGBGQbtXeZ4/SG8+N5RhBQGaAtau/x46b2jWDJnHPqpzABrdiOLPVh/Ty2+8dQ7+PxEd8rtRw324NXFtZabO1Jq6/4WuB12dNukv0ti7DbA7egN0s7VwMlcN7LYg8dvnIB7V34oa3GI027Dz25MHPPNFV2+YML8drZfX6SYMebPSMxsS6Ywt7oMg85mP5tdNQSr757WVxZZCBS5vqr3iyYn6E4PenR8zvhDWPNh7wrX6J/4oFyBkDEu+ifXViqSfEKQnF72NLYbfq6p2efJF54vqxS5ktUyXb4gSvvnozA/T/b+GB3kKjcr7waZ26WDWPbvh1/eo+g5zJqpV0pdZQk2PTgDj8+bgKdvmSS7lHH05yaWaS0VI88/Jeee2L6f6vajucPX96PmPvfKB40J2XKVtg0GelwpM+6aWXRWDCVthtPeQN9gSWunD464TPEFbiee/u5kPD5vAjY/dDV+OEN5VmUpXOFMRFYXf6957d7p+Nm39Qm6jCCScF+qqyzBa4trE67VyXQZlHk33R6fNx5fu2Rowu+3H2xNeI/J2vDxK97V3j/Z5yYiIiIiIjWYMVCe0v75uLGmXNa237msPKsDbYHe82bGmMGQPxoQywbg6jGDs/K8OdXlx8HmLoQjiPlJJn7bg81dONVlXPXOTPmosU11oK0gGI7go8Y2fXbIpArcTnScCcjatsMbyPpAuL5M2t1+RYG2QO/3qbU79zJpU6w5lwxFdfkAWdtWDx+AOSJjvrliXf0xS89N68mMMX9GYrAtmUJ88FJ8Iyc6UERO0J0e9Ogw//PfXAy/xnThwkpFIjFyg+TkCIUjaTnXlO7zweOd8LhTr7gUWy2TrJS6EPxohiBXJVl5N+1thk/jQgA9KA1skPKN6jLLdWwHelyYX1MOfygs+3Nbs7sJUx7bjKmPbcGCFTsUv6ZR55+Sc2/N7iZV+56KNxDC7979IuZ3K3ceUdw2iM/WunLnEd331Uh6LPLpCYZFr+N2uw3za8ox0OPS7b5RUVKQUAqciMhqxO41V4wqxr3XjFb9nPlOO355U3VCdtx/Xvsxpj62BXOfegehiLaJEiv6s0RZKLVteIGe2YGIiIiIiIjkmDm2VLS/kkqujaetq2+Std1amdtZmdcfws7PT0HtaEAEwM7Dp7Iy+G3zfnnzE+l6HjNx5+mTjVav5zGrzp4AWmVWkTrR5Udnj7zAXKs60Nwpu3KzlDN+jpXlMl8whPqj7bK2rT/Sboq4hUyJrwZrtblpPZkx5s9IDLYl0xCCl2wSWX6iA0XSRWuH+Y7pF3GFKxlGSZBcKh63A7+93fhzTc0+d4pksBKjZrWMWYJclWTllXs8jKZHYAMA/GnXUR32JjOUfG4RIGXW12SMOv+k3sP9syoSto0AqvY9mcJ8J/7lb8YllPdpaGpHQ2O77LZB02lvQsZd4TmsIh1ZMfS6b1gtIzURUTJi95rGU15Vz2W3Aa8ursXI4oKE+1IwHMHxjh6clDn4n22k2jJaV7wzqxQREREREWWC0F+ZXTVE1va5Np7W2RPAkdPy+tZHTnlzIviNC0XFzZ1QBqddbc7fXk67DXMnWDtDnpjq8iI8d3sNPC51wbIeV3rmfjPtf976TNH2T7+tbHurqSwthMupLQzM7bSjsrRQpz0iq9n6SQuCMtMiB8MRvCWzCmy2EasGa7W5ab2ZMebPKNZKJUeUAUKH+eFVe/DG3uMpt59dNQRPzJ/YF6kvBOze+fwuHGrpkv26FSUFWH5bTc50vEk5sSA5j9sBO2wJv08mneea2D4DQHGBC15/qC/NfrwNDV/hmotLkz63sFpm8yfNuHHysISbuLBa5uXdjZh1cSkGelzYvK9ZNNDwrhmj8My2z2ICC4Ug11T7oYZYRq/lt9ZgRLEHc8YPTbh+yDkeRhMCG+57qV5TAOauL07DFwzB7bTeytpUn9ttz+7EUZkDiqkYdf5JvYc2bwC/2HxI9Up7OZx2G9xOO57cfFD07wtW7IDHFdtUnTWud5Xb5n2xHbduv/g5KPUcP75BnxLhejO6zSAVXC12zUvmW5MuQL7GwRoiIrPSsjAhHAG+8+t3czJzbbRBHhd6ArFte6m2jJo2fDz2uYmIiIiIKBMK3E7MmzxM1tzhjTXDLFfhTYtfb/tc9PfTK4qxXSSZyG/+/DkevK7S6N3KGK3zKYX5TvzypkuzMmjyQHOn5nGUUKS3cmc2Hp+ZY0vx2r3TcfPyd3G8wyf7cUP6u/HinVfkxJiHWFKf81wOLKobjf9+61OciZt7/tOuo/h/s8ema/fS7kBzJwIaKy/7Q+Gs/U6RtKVrG7B5XwtOdsu/1gDAPS9+iEGefaaef9VKODbRsmVumtThLDmRDEKHWQ6xDnOmV7gGQ+G+H8oeYkFyG+6ZjqsVlCBK92pqsfLlV1UU4+1H6vDavdNFs0gLGazknMdKV8toLVurF60ZvTJFbfbvaGbJ1KtGqs/t9fuvwqThRaqfP34xtxHnn9R7qC4vwrO3q88SJ0cwHEFrl19ycLGzJ4jjHT0xP6980ISXdzUm/F7Jc6ze3WTqcltGthlSXfPkft53vvABJi7biC4FCzuIiKxCanGYXG3egO6Z4PWgLUeMfFVl/bHloRl4ddE02W1pPVa8Z7rPna04lkBERERElJzYnIcYo+YWzGpdfVPM/10OO35/x+V44Y4peOGOy+FyxIYorI3bPhsJ8yllA/IVPa5sQD7WLa5FnYK5PythxZrURhZ7cNmIgYoec9nIgTkz5jG4vzvm/xcVe/B/907HorrR2HDv9ITjUFwYu322qS4vwowxg1WPBdoAXD1mcFZ/pyiR1x/CKx804XhHDwIhZQsgAqGIJeZf1Yo+Ntk6N03KMdiWSCatHWatAbtqdfmCmLhsI0YveZ2BMVlGLEhuaFE+3laQqv+GS8vSupo6ep9nVpbgRzdcgt/9/eUocDsTgkoHnpeHv68dgU0PzsBlIwYach6bJchVyOj1+LwJePqWSQmfiZDR6/F5E7DpwRmoqzTPoIrSwIbpo4sxanBsx9aqA42pPrd8px0vfX8qHrp2DC5SOKhRUVKA9ffUGn7+JXsPegRT660nEEJPUFuwhxXKbRnVZkh2zSsrykdEZlkYAOj2h7B+zzHZ2xMRWUV8v69fnvWy78dzOWy486qLZG2rZaEQABxq7kT1v2/CN//7L/j996akdcFYpvrc2YpjCUREREREySmpjLJpbzN8wdwJsvh2zbC+QK/SQje2/0MdaisGAwCmVwzG9n+oQ8nZgDcbgG/L7MtZ3chiD167dzoc8Zk2JDjsNrwmEiyYbdTORVSUFGR1IHK0b00eBqfM88Zpt+Fbk3LjOwUA/2/2WBT1ywMAXF9VinX31GLY+f0QDIVRfn4/rL+nFteN6x2fKuqXl9VZbYHewMCdn59SXbkyAmDn4VMMDMwxB5o70aOxnWKF+Vc1eGxIDGcWiGRQ02EWK4muJGBXr1Lh6+qP9ZXvFAJjbr58uC7PTZklVnJ1875mRZm4fvfuF5h9yVAD9zKW2jKxK3ceMeQ81qNsrV6EjF5ShIxeZqSkXNbt00ZgykWD8NCqery5t9lUmXrVkPrcunxBTHl0M7r9IXhcDmx9+Gr866t7ZR2j2VVD8MT8iWk7/5Kde0Iw9fd+tws7Pj9pyOvLJZTKiiCSE+W2jGgzJLvmbT/Yiq64wZPCfCfumjEKz2z7TPR4r9x5hG2KFKIzATodXOdIZAVzq8uw7eAJnOz247pxpfjrp9bMwC+YOGwAnrv9Mjy64RNZ27ud2q5V/rOZD7r9IWzd35K2trQgE33ubMWxBCIiIiKi5JRURhEqvOVKP+S+a8bg6xPK8My2z/Czb42H3R7b1yztn4+//sNM/NPaBtw1YxRGDTZPwgej7f7yNEIykx6EwhF8eOR0Tpw3wlzEZT/eBG8gdcKNfnl2rFk0LWcW0l45apCh21tZXWUJtj58dd+8R7c/hInLNvbNz+1cMgvP3JLe8alM0jMw0ApzaaQPIct4Lsy/KsVjQ2I440skg1iHuTDfiUeur0woayFVEj1TK1xXvvdl7P93HtHleckc4kuuyp1cFrx/+HTaV1OrKRNr5HmsR9laUhbYYOZMvXqJD07Yur9FVaY1M5x/BW4nvlc7UtfnlLkAu0/0CvVcWOVuZJtB6poX/x0eU1qA9YtrsahuNNYtrkX5+f0SnquhqR0Nje2yXzvXMCMgkTVFZ32/cfIw0YUID183Bvkag1LTZVRJAQrynbLvK+9/cVq3116580ha2zLMKqUvjiUQERERESWndD7GqhXe1Bo1uAD/MW9iQqCtwOm04z/mTcypQFuA500ykUhEVqAtAHgDYUQianN3Ws/W/S0IygzSDoYjeEtBFdRsED3vIbZ42AxzbekiBAbGx7DIVZjvxHMLL2NgYA7KhflXtXhsKJ41ZoeIMixVAEr8RVWs46NHwK5SDY3t+LipI/Z3DIzJanOry0Qbz8K5VuCOzbgcDEd0OdeMxPPY/NQENmR7x1YsOEFJQLLZKB0AjDbI44In7toTjsgvUz27agjWLJoWUypLWOU+u2qI6ucws0y0GeZWl2HQ2e9j+fn90O4N4KblOzD1sS24efkOtHkDoo9bsKJ3m+ifpWsbNO9POgVD4b4fPYkN6hGRNQgD9K9/HJuRXugHLp5ZgdfvvyqhH1hWlJ/O3UxJqCCg5L4id+IGAB6YVZHQv4iW7jZ7Ju6f2Yp9MCIiIiIiaUvXNmDqY1vw1oGWvqQCUuXdhd/bbcBb+1ssOXZG+po9fgjk5qKwAZh9ibwx8Gzw622fK9r+N39Wtr2VPf/XL1NvFL39u8q2zyZcPHwuMHDoAGVjlUMH5DMwMMdl+/yrFjw2FC038uoTaRRdSjS6vDdw7qKaqiS6WMDu8ltrMKLYgznjh+LO53fhUEtX39+VlrVcurYBm/fFrlLr9otnUFuwYgc8rtiv/6xxJfjxDeNlvx6ZU11lCa6qKMaGhnOBAfHn2vd/9z4+PdHd93czlVDleWxNUoENYuXnc6FcllRwwmdR1/hkhIBkt1M6eCWdlARTx5s+uhhP3zoZJzp9Cfc5uWWqozP9RitwOzFv8jC8sfe4yKPkPYdZGd1mECNkcvy/hq/w6IZP4A3Iy/bX2RNMKJuyencTlswZh34uc5zDyXT5gpjy6OaYklJ6nStig3osv01kLUr7gTdOLscvthzK8F73cjlsWHdPLS4o6ocH/1gf87dU95VohflODB2Qj4PNiX//7V++QL7TAa8/hJBEjG462+yZuH9mA/bBiIiIiIjk8/pDeOWDpoSxM6mFi8LvwxHg9JkAgIClxs5IP0LfyxcMQe4y1wiAf3j5I/yLc29O9L3W1jcp2n7Nh0148LpKg/bGHITz5mS3T9Hj3v/iFKY+tiUnzptoyRYPjx82IEN7lRkjiz24dPj5+EpBMp1Jw89nYCBl9fyrVjw2JOAnSySDEICy+ZNm3Dh5WELpZaEk+su7GzHr4lLRTI16BOxKkercS7F6YAwlN6+mHDs+PyV5rq1dXKv6XDMSz2PryuXABiXBCWfizm0bgInlRfjsRJepA5LFgqn75TlkfVdvnzYCBW4nCtzOhPtc/L1USrLzRUm2YLMcTzmMbDMkM9DjwiUXDECPxrLa3kAIB5o7LVFmSCz7rB4BsRzUI8oOSvuBP35tn+LXsKF38sxptynKKpuKPxTBN371DvIcdlxcVohBHpes+0o0oU13+GQ3HvhjPdrOxGY5l8p6Hi2dbfZM3T+tjH0wIiIiIiJlDjR35tTYGelDad8rWi4FaU8efj4aT3tjfieV2AUAJl14fjp3L+20nDeBUATHO3qy+rzh4uHUlJa8H12qbHvKXtk6/6oHHhsCGGxLJJtQSlSKUBJdih4Bu1LYuadoRp5rRuJ5bF25GtigZaAD6A2sOdjciVd+eCXuffFD0wYkiwVTjxjkwUYZ2W6j30f0teeqisG49sltsl5fKtOvkoy7ZssWnEqq63i+045f3XQpVn/YhOuqhuh6Ha8uL8KzC2tw30v1CYOXchTmO/HLmy61zHVYj+yzHNQjym5y+4FqMsGPKS3A4/Mm4mBzJ2ouPB8/eOEDyQyzapzs9gMAOnoC2PTgDPzl09ak/YMfb9iHNR824fSZQEybbkSxB1sfuhobGr7Ca3uasPPwaU37ZVSb3ar9oExiH4yIiIiISJlcGzsjfbDvJY/DHtuPT5XYJX77bMPzRhoXDycnzFm0ef2KHvfrbZ/hj+8d5ZxFjsvm+VeteGxIIK9+LxHpQpiolcrmJ0zUKp30Ezr3hfnq4ucL8514buFlWdfQzmVGnWtaBUPhvp94PI+tSwhseHzeBDx9y6SEkghCYMPj8yZg04MzUFdZkqE91ZdeAx2+YBhrFk3D9VW9QalKA5KTfa/0MLe6DIPOXitmVw3Bi3dOxbufnZT1WKEjIRCuPR83tSdkyy3Md+KR6ysTrgFCpt94Yhl3lT6HmUldx7t8QUxcthFj/vkN/Oi1fXA59W/OzxxbinWLaxWveq4oKcC6xbWoG2uN73iy7LNyCYN6xzt6Yn6kJls6e4IJ267e3QSvX9u1hIgyT8l9CQAmDS/C6runobq8CPNrynHR4AKs/P5UGDFP5A2EcKLTF3NfiW8/2O02/MvcKmx56GrRNt1Ajwu3Tr0QL37/CvzdtBHwuNUNEhrdZjdrP8is2AcjIiIiIlIuV8bOSD/se8kTPxex+u5pGHZ+PwRDYZSf30/TPIoV8byRpmcgcraJnrPoCSibN+wJhDlnQVk//6oFjw0JmNmWKEsInfv4VX2pVJQUYPltNRhZ7DFw74h6A8SmPLoZ3f4QPC4Hdi6ZlRCUyfPYurRm/7YivbMYqMm0Jud7pVV8lrgtn7QkdCQK3A7UVgzG9kMn0O071wEXOhLxWXrFsuUmW6EululXj+ewonX1x9B9dpCj2x/C+j3HFGdilUPISv3wqj14Y+/xlNvHZ7U2G6OyzzK7ABEJlN6XRgzyoMDtjLk++UNhhCP67pdY1qRk7Qc5bbp/nVuF264YwTZ7lmAfjIiIiIhIuWwbOyPjse+VWvxcRLc/hInLNsaMX+RaxZqt+1vgdtrhtdsQVDBo5LTb4HbasWV/c1YG+DPLuDTOWZBWuTr/KgePDQmY2ZYoiwid+9lVQ2RtP7tqCNYsmpYTHTTKHCFj1prdTQkBYmJ4HpOV6JnFQE2mNbHASyNEZ4kT60i8ds90PHPLZLx2z/SEY7EhbntAfIX6iLPfYeEakGqFuh7PYUUr3/sy9v87jxj2WgVuJ+ZNHiZr2xtrhpl2ssDI7LPMLkBEAsX3peqyhOvTqW5lpd1ScTvtolmT9Gg/sM2eXfh5EhEREREply1jZ5Q+7HulFj0XITZ+kUsVa4Rxo9Yuv6JAWwAIhiNo7fJndYZSZhkXxzkL9YyuJGoVuTr/KgePDQnYqifKMkLnXs5KWnbuc09049DpMH69RXTWrPiSuCt3HpHMxsjzmKwkk1kMxAIvjchyGm1udRm2HTyBk93+hPciHIuHVtXjzb3Nkh2J+BXq8aWeC9zOlCvU9XgOq2lobMfHTR2xv2tqR0NjO8YPG2DIa8YHV0sRVmem+z4jh9EruZmVgogAdfel+qNtmq9PyfiCYXSJLCxI1n5Qch23WpvdjPcoM7Ha50lEREREZAZKx85yGftkvdj3ki8T8x9mwgylqTHLuDhhzuK7K3bgWFuP7MeVFeXjD9+bmpNzFumoJGoVuTj/KhePDQly8+pAlOXYuc8tcgcoMtFIjF51Gr/oMlWAGM9jspJMDJBlIvAS0K8jEV2mWuw6JqxQT0ZOqetUz6EHIwaKo8uKC7r94plYF6zYAY8r9pyaNa4EP75hvKZ98AVD2LSvWda2m/Y241S3H9N/ttV0gxHpKCnFQT0iApTfl7Ren+SIv0f4gyGcOhOI2UZoP4wc7FHcXzB7m124R3f5gpj2U/Pdo8zG7J8nEREREZGZKB078wVDcDsdBu+VOTGIKRb7Xqllav7DTNIxrm1l0XMoPQF5Qck7Pj+JWT/fBkCfORQzG1nswcYHZuDeFz/E1v0tKbefObYEv7z50py9Notl0s6l4P54Zpl/NSMeGwIYbEuUddi5zy1KBijS1UiM7ty0dvmSbisVIPbPXx+XdecxV21nPyMHyMwQeBlNz46E1Qdajdh/oTyUV+YAUWdPMGGwbfXuJiyZMw79XOqvjdsPtqLTl3ieXTeuFO9+fjLmNTt9QfzXpoOmHYxIR/ZZZqUgIjXUXp/kErtHiFmwYgdsQMx1/I7/fR9//MEVko8xe98z+h7tctrhD/a2x812jzILs3+eRERERERmIzZ2VpjvxF0zRuGZbZ8ljJ29c6g1ZwMnGcR0Dvteicw2/2EmwrjR95/fhU8VjBuNLinAb7K4qprXH8KL7x1FKD7TUwpt3gDg7V2E/tJ7RzXPoZhdgduJBZcPlxVs+90pw3N6ziLXM2kTkTKM9iHKMlKd+0eur0RhfmwDSejck3WJDVBIEWsk6k0IEDve0YPjHT0IpujkdPYE+7YVflbvbsLmfc1ZdR53+YKYuGwjRi95HROXbUSXSPAaWZuaATK54r9Xwo9U4IzU98rrN65MtRZKrmNmZMT+61keSgupAPKv2nuwbnEtKkoKYn6/Lu69G3Gf0ULIPju7aois7WdXDcGaRdMUDUgqCbonIhII16crLhqUsX3o7AmiI65t8f4Xp5K2H8ze94y+RwuBtgKz3aPMwOyfJxERERGR2cSPA40pLcD6xbVYVDdadOwsl8eD0jE/ZRVSCQ7E5ELfK9vnP/QwstiD+6+pUPSY+2dVZG2gLQB81NimONA2XjAcwUeNbfrskIlxziK1ZJm0iYjEMNiWKMuwc59b5A5QGN1IXLq2AVMf24Lan22VnYlRijcQwp92Ncb8zurnsdWDCSk1I4MTzBJ4aRSrD7Qasf9Ceaj4c0euwnwnnlt4mebyUHOryzCgX17C7xua2tHVE8SaRdNwfVVvNo4B/fJ6V4XHbWe2wQgh+6wcSrPPGhl0T0TZR2g/Cz+zfr4N9UfbMr1bMcKR5O1ss/c94+/R0cx4j8o0s3+eRERERERmM7e6DIM8LgC9i7ZX3z0NI84GtwmLKoWxs0EeF+ZOLMvYvmYSg5hixfe93E570v9ne98r2+c/9CInM6mW7a3GneeATeNz2M4+TzbjnEWi+DHZqY9twYIVO0S3XbBiR8K2S9c2pHmPiciMcjcPOFGWmltdhm0HT+Bktx+zq4bgifkT+wJFhM79Q6vq8ebe5pzu3GsVDJ3LjOR0ZGbdgtQAxQ9//wE+PNIW83sjy60oLXeeTGG+E7+86VLA1vtesuU8ZumJ7CcWnLD81hqMKPZgzvihCaWhNzR8JbtkmBB4ed9L9bLKQMcTvldaAy+NkGygdfywARnaK/mM3H+1ZcUrSgqwXGV5KLFyXcFwWHTbbzz1Dmw2IBLpHZRqjwu0FZixrJeSldxKSvuxdCARSYnvO+jZfjbasvV78cSbB2J+J1zHzdj3FO5lgVAYJ7v9Sbc14z0qk8z4eRIRERERmVldZQk2PTgDmz9pxo2Th8Fmiw39KnA78fR3J+Pl3Y2YdXEpBp4NzM1mYuOLRs5PWVF03+uKiwbh3c9PxvzdFwz3/T4X+l7V5UW4esxgvH3gBNTkKbUBuHrMYFPOf+hB+E75FI4hvfVJS+/i7iz9TlWXF+HZ22twz4sfotunfHzN43bgqZsnZe15I1CTSTub5yyUjsl29gQT5mVX727Ckjnj0M+V3YHaRJScLRKJaMuvTlmhsbER5eXlAICjR49i2DB5Gb/InE51+yU79wAQDkdyqnOvty5fEFMe3YxufwgelwM7l8xSlPlODakBCjWBd6mc53Lgg6XXym4k1h9tw9/+z1+g9W5Sfn4/PH/HlL4AsWw5jxsa2zH3qXcSfr9+ca0lgglJnrcOtODhVXtEgxOA3utGdHDCE/Mnoq6yRNFrHG7tTmvgpd6UXMcK852mG2jN1P53+YJ4eNUevLH3eMptxc49ubz+ECb9aFNaAr+U3mf05AuGUPOjzbIGmArdTuz651lwO+Xt54N/rMfqD5v6/h8ddC/2/f3WpAvw5Pxqxe8hk9hnILVy+dwR6zt82tKlS/s5U6Kv42Zoswv36AgiaO7waXquTN6jzMAMnycRERHlplzuM5B6PG/MRY/xxWzuk0WPL4fDEfQEQwhHIugSCRQscDtgt9mQ73TAbu/tm2V6fNwoPG+kef0hXPJvbyIUVj+A5LTb0PBv12fdsREcbu3GzcvfxXEF40FD+rvx4p1XmGLezCjC9abN60dPQDyhipj8PDuK+rmy9nqjR0yDzQasuXta1gdqE2UTI/oMzGxLJMEMmUvV7sNAjwvza8ol/26325L+nZJbV38M3f7eTl+3P4T1e44ZmqU03ZmvhHIrchuJWrNuAsDk4efjd3dcHhMgZsXzmKu2c1c6shgIGcXSEXipN6uvFs3k/he4nZg3eZisz/zGmmGqP289ynXJpfQ+oycjs88yIyARiZHqO2htP2dS9HU80212vftKmbxHmUE6P89U4x1mGJMhIiIiIiL59BhfzNY+mdK+qxCA24FzYwbZmk2R5420jxrbNAXaAkAwHMFHjW2YctEgnfbKPM4tvgacdiAoI6bUaQciAG5eviNr52S1jJX1BMI4HujJ2utNNlcSJaL04mg1kYguXxATl23E6CWvY+KyjeiSmV4/2/aBxK1878vY/+88YujrpTMAqjDfiecWXqa4kSiUO68oKVD1unfXjTJFQKAWQufleEdPzI9UY72zJ5iw7erdTfD6zV9OmMQJwQliWcCAc8EJWrKACYGXcmgJvNSbngNmmZDp/f+/hq9kbbdB5nZihEGGwnxjzxm19xm9xB/LMaUFWL+4FovqRovex5QcUyHo/vF5E/D0LZMSvn9C0P3j8yZg04MzFGe3JiJrkuo7aG0/Z0qmr+Px9Owrme29ZbNU4x0cDyEiIiIish6t44vZ3CfL9PiymfG8kebOc0B8tkk+29nnyTbRc7LNHT5ZgbZAb0Buc4cvq+dkeb1JTu2YbEVJAdYtrkXdWM7rEBGDbYlEiWUfysV9oEQNje34uKkj9ndN7WhobDfsNdMVAKW1kShk7ZtdNUTxY7UEiJkFOy+ULukIvNSb1QfMMrn/vmAIm/Y1y9p2095m+DRch4wO/DLDYMTc6jIMOhvwPrtqCFbfPQ0jzpaLEu5j11f1ZrJVk302HUH3RGQdqfoOWtrPmWCG63g8vfpKZnxv2SzVeAfHQ4iIiIiIrIlBTOKsPj5uNJ434qrLi/Ds7TXwuNUFy3rcDjx3e3aeN5yTlcbrTWpKx2RnVw3BmkXTMPLsXBIRkS0SiWjLPU9ZobGxEeXlvaUAjx49imHD5GXNy1Zf/9X2mEnR8RcMwPp7anNuH3KdUH4iWrc/sTQ40Nvw9LhiG616l5843NqNO5/fhUMtXbIf43E5+iYpk9Gz3Pzmfc343vO7FD2m0O3Ern+eBbfT2qsrt+5v1lx6IlsHBUgfvmAINT/ajE4Z2b3M+L1Scx2rKCnA8ttqTNGJNWL/U5VIFrumFuY7cdeMUXhm22cJ15tnF9bgmotLZe+fmC5fEA+v2oM39h5PuW1poRvNnb6U2+l5n9HqVLcfmz9pxo2Th4kGxYbDEby8uxGzLi5lUGwc9hlIrWw9d6Kv4f+2fq/qvkNPIIQ2b8C4HdWBma7jYtTcowVmf2/ZKNV4B8dDiIiIck+29hnIWDxvzEvJ+GIu9cmsPj5utJPdPkx5dAuC4dThK067DTuXXINBHnca9iyzDrd243u/ex+fneiW/ZhRgz1YsfCyrD5vbv/te9h24ATUBDvZAFxdORi//bvL9d4t0+D1JjW5MQ16zLsRUeYY0WdgZluiOJnIXGrGfch10eUnon+kgig7e4IJ2+pdfkLpKiuH3SYr0BbQt9y83Kyb0Tp9QbxzqFWX188krr4lo20/2JoQaFuY78Qj11cmrFI14/fK6qtF9d5/OSWS46+pY0oLsH5xLRbVjRa93uiR0bjA7cS8yfI6GvMvK5e1nZ73Ga2YfZaItAqGwmg74++7hk9YthEv72pU3Xcwe6AtYK7ruBgtWYLN/t6yTarxDo6HEBERERFZn5LxxVzqk1l9fNxoH37ZJivQFgCC4Qjqj7QZu0MmMbLYg1cX12LS8CJZ208aXoRXF9dm9Xnj9Yew8/NTqgJtASACYOfhU7rO45sNrzepWbGSKBGZQ260XIkkSGUuFbNgxQ5DMpeaYR8okZ7lJ/QssyAMUMhZDRyS2SEFehuJeqzIUlLu3G4DondRr33INKHzwlXbZASxwMvlt9ZgRLEHc8YPTVilasbvlZLrmBkHWvXcf7ESyTdfPjxmm7nVZdh28AROdvsTrhfC9eahVfV4c28zBnlcmDuxTMO7O0fuIMOr9U2ytjPjuUhEpEaXL4gpj26OWdR2JosH5gVWuI4ruUdHs8J7syol4x3feOod2GyAVP0tjocQEREREVmLkiCmXOqTWX183AjX/ec2fNrSBQXTmgCAO363C3YbMLqkABsfmGHMzplEgdsJt1NeHj13nj3rzxuzzuObDa830pTENGza2wxfMGSqSqJElFnMbEs5ywyZS82wDySuurwIzy6sScgUKVdhvhPPLbzMkAa6msyxqQiNRK3Esm4CvZlb449lOAJcenYVpp4BYmbAVdtklLnVZRh0Ntvm7KohWH33NIw4u8pUCLy8vqp3YNLM3yurrxbVa/9Xvvdl7P93HknYpq6yBJsenIHH503A07dMSrheFLidePq7k/H4vAnY9OAM1FVqz5CtZJDhyCmvrO30us8QEWVa9EKJaBcOOk913yGeVNb6TLLKdVxNX8kq781qlI53RNDbR5SaW+V4CBERERGRdagJYsolVh8f19OpLj8ONisPtBWEI8DB5i6c6vLru2Mm4wuG8P4Xp2Vt+/7h01n/nTLzPL7Z8HojTiqmQYwZK4kSUWYx2JZylp4rnqy8DyRt5thS0RLdqVSUFGDd4lrUjdUe8BRPyQCFGKPLzcc32EcN9uD/za7ExgeuEj2WIwd5dA0QMxN2XsgImQi81JvVB1r12n8lJZIHelyYX1MOm80m+lx2uw3za8ox8GwgtlZigwxyA7+Mvs8QEWVa/EIJwZcnz+A/5k1U3HeIv16OKS3A+sW1WFQ3WlVfRKsCt3Wv42r7SlZ4b1akx3hHKhwPISIiIiIyJwYxSbP6+LjeNu9XP+dpxPOY1dZPWhCUGZEcDEfw1v6W1BtanBnn8c2G1xtpShfscz6fiKIx2JZylhlWPJlhHyg5IVPk7KohsrafXTUEaxZNw8izmSb1JjZAYQNwnit12QK7DXA77Xjh3S/hdtrhtMcGbenRSIzPuvnq4lrcffVo2Gw28ayb1WW6BoiZhZLOyxsNx3Oq80LapTvwUm9SgZwPXTsGBe7Ya5kZB1qVBKIK+790bQOmPrYl5mfBih2iz79gxY6EbZeubTDs/YiJH2SQG/iVajul95lgKNz3Q0SUKdHX8Mk/2pSwUCLaIy/vQUdPQHZZv+vGleInfzs+adb6P3zvcjjs4vd8vdltQH7euf7CgH6x9zWzDyonm8xNdQjN/t6sSOt4RyocDyEiIiIiMh+hD734xd2KHrdo5e6MjINmgprx5Ww2v6Yc910zWtNz3D+rAvNrynXaI3N6/t0vEn7ncTvw4LVj4HEnzhH/TmT7bDSy2IM//uCKhDlvKU67DX/8wRWGzeObDa83iYT71FsHWvrGC6XOH+H3dhvw1v6WnLlPkTKcy8xN5qlJSJQBwoqnO5/fhUMtXbIfV1FSgOW31ejSEDPDPlByBW4n5k0ehjf2Hk+57Y01wxIyTepJbJVVBMAZGaUzwxGgNa6MisNmQygS0a3cvJB1c/Mnzbhx8rCEYEAh6+bLuxsx6+JS0wYDavWD5z+QvWr7TCCEqY9tgdt5rjM8a1wJfnzDeKN2jyijxAI5/+s71bjxmXfR7Q/BZgMiUQu0NzR8hWsuLk3zXkoT2//lt9ZgRLEHc8YPTbifv7qnCZv2tsAbkBdU39kTTCivvHp3E5bMGYd+MhZW6GFudRm2HTyBk91+zK4agifmT+y7twkLJx5aVY839zbDht77UKrtlN5nunxBTHl0M7r9IXhcDuxcMsvQ+ysRUbylaxuwaW8zmjt9sh8jdg1PZtvBE9i4rxnn5dnxoxsuwS1Thve1n5eubcCmfc1o7pD/+gCQ57AhEFJXezG+v9Avz4FZF5dg8yctuvUXjBR/jxb6OrOrhuCea0bj/pfqY+7RZQPycay9xxLvzarUjnekwvEQIiIiIiLz8fpDePG9owjJzL4ZrScQxvFAD15672hax0EzQen4stnGx43wwLWVqBkxEH/32/dlZ28FegPhfvt3l2F6xWAD984c4udbLyr24Kt2L57cdBD98uwYWezB4dbuc9sjPQu3M2np2gZs3tcCXzCkKOvvNT9/G26nIyfmYnm9ieX1h/DKB00J83VS54/w+3AEOH0mACCQ9vk6MjfOZeYuZralnGeGzKVm2AdKTm4pAaMzIl0v8xyRK4wIFs8crWu5eatn3dTK6w9h+6fKVv6dPhPA8Y6evp/Vu5vglRFATWRF8RmwV989DfVH29F99pyPRIBxQwsBwJTBL2L7H52BMD6D98RhRZpLKKe7RLKwcOLxeRPw9C2TEjqGwsKJx+dNwOaH5G2n9D6zrv5Y3znR7Q9h/Z5j2t8YEZFMwsCrkkBbNXzB3tXuZwJhOO22vvZz3+srDLQF0Bdo63Jon1TpCYaw6OrRqq7jmRB/j377kav77lFVZQPwp7uuwLXjet/DII8LP77hEsu8NytTOt6RCsdDiIiIiIjM6aPGNlWBttGC4Qg+amzTZ4dMSun4stnGx40yvWIw/vKPMzG4wC1r+8EFbvzlH2fmRKAtAHz/qotw/nl5AIDZVaW4deqF8AZ6x5W8gTAWXnEhrhvXe96cf14evn/VRRnb13QQxs6Od/ScDYKUT5iTzYW5WF5vYh1o7rTcfB2ZG+cycxdDqolgjsylZtgHo0SnTHc6rBfj7wuGsGlfs6xtN+1thi8YislSqqfSAfm6PVdhvhO/vOlS1I3lxLKeDjR3IqxxQEloqLMkKmUjsQzYK9/7MmYbu92Gx+dNMGUGbDUZvEcWe3DfS/WKsh0KhGt1uq8HwsIJKcLCCQAYNbhA1nZKxJ8TK3cewc2XD1f8PEREaugx8KpU9HVOj9cPhCNYMmcsnth4sC+oVwnh/nPphefj0gvP17Qv6SJ2jy4feB6A3iwD0366Fd3+EFxOO9Yumtb3NzKekvGOVKw2HkJERERElCvcefrMi+n1PGbFCpHSSvvn4zuXleOptz5Nue3Nl5ejtL9+c6ZmV1dZgi0PXd133sz55faYv6/a1YjX7qnNmfNGz6DJbJ6L5fUmVnV5EZ5dWGO5+ToyL85l5i7rRb0RGcQMmUvNsA966/IFMXHZRoxe8jomLtuILp/yhkumbT/Yis64/S7Md+KR6ytRmB87ydfpC+KdQ8qymipRXV6E526vSXhdpSpKCrBucS0DbQ1QXV6EZ2+vUT0BXJjvxHMLL2NDnbJadAbshsZ2fNzUEfP3j5s6cPGQ/qbt2CvN4C2UUK4okQ5KFZOr12qxc6KhqR0Nje0Z2iMiyjXCwKvWNrdAqu8QLfo6V11ehH/+m3GaXu+5hZfh+1eNwhv3X5VT9x+pe3R0lgF/MIx3FFaiIO3kjnek8mo9M0QQEREREZmRMH/lUVla2+Ny4Le358bcSK5XiExmXX2TrO3WytwumwjnzcdNHfjkq9jMmvu+6sDeYx05c95oHbvLpblYXm9icb6O9MK5zNzGVBBEMEfmUjPsgxHEUqdbbTVH/KTgmNICLL+1BiOKPZgzfijufH4XDrV09f19Q8NXuObiUsP2R2gExr+uXP3znVizaBqzARlo5thSrL9H+WdUUVKA5bfVsCQqZa2laxuweV9LzO+6/eKLMBas2AGPK/Y6NWtcCX58w3jD9s9IQkmeh1ftkZXVbXbVEDwxf6Ih12ozZZzP5XOCiMxNa5tbMKCfE3kOO15490u4nXZ47TYEJaogfOOpdyCMe0dUFkqIb0+a6f4jJl33JGYZyCwl4x2pvLn3uGXGQ4iIiIiIcs3MsaV47d7p+N7/vo/PWrtlP25UsQcrbr+McyM5rrMngCOnvbK2PXLKi86eAArz8wzeq8zi+Lk0YezuG0+9oyhLaWG+E+sW1/J6k8PMPl5K5sNrMcVjZlsimCNzqdg+FLgdGcmeqiexSU2rmVtdhkFnV3PNrhqC1XdPw4i4yevrq3qDawd5XJg7sczwfRJed3bVEMWP7egJ4vAJ+YMcpI7Sz2h21RCsWTSNnTvKWl5/CK980ITjHT0xP1KDIJ09wYRtV+9ugtef3rLeehJKKMthVIlkM2Wc5zlBRGants1dePb6fe3FpfAFw2jt8uN4Rw9au/ySgbYAEAEQjvT+qIm1lWpPmuH+IyZd9yRmGcg8JWMuggK3Aw9dOwbxeVd8wbBlxkOIiIiIiHLRyGIPXr2nFpOGF8naftLwIrx6DwPfCPj1ts8Vbf+bPyvb3mo4fp7ayGIPfjZvAlwO8ayt8VwOG342bwKvN2Ta8VIyH16LSQyvCEQwR+ZSsZKKoTCw8MoRGcmeqodkk5rjhw3I0F4pV1dZgk0PzsDmT5px4+RhCWUWCtxOPP3dyXh5dyNmXVyatjILQiNQzoqreFxRo56S7FtKPiM21CnbHWjuRE9QW0fCGwjhQHOnpUv7yC2hbNR93kwZ53lOEJEVqGlzz6gcjKvGDEZFSQE279cnm6ccydqTmb7/iDHinsQsA+aUaswlPgtNoduBYBj4+aaDos/3hx1HTD8eQkRERESUywrcTtnVKPLzHJwbIQDAuvomRduvrW/Cg9dVGrQ3mXeguRPegLbx8zP+7B8/n3PJUPjmhfHAH+tTbvv4vImYc8lQ43eKLMGM46VmZaaKmenGuUwSk1vfAiIJZshcGr0PAm+gd8IxU9lTlVi6tgFTH9sS87NgxQ7RbRes2JGw7dK1DWneY2UGelyYX1OeEGgrsNttmF9TnrZAW4HcRmA8rqhRR032LSUNdaJsVl1ehGcX1khmL0ulMN+J5xZeZumOiJISypv2NsOnsfMmxoiM88FQuO9HCZ4TRGQVStvc2w6cwDery3Dp8PM1XeeUkmpPmuH+I0bvexKzDJhXqjGXn82bAHtUVzsYjiSdUHz381bLjSkQEREREeUSXzCE9784JWvb9w6fSls/lMxJmGM+1e3v+51UntLo35/q8md1f7CytBAup7ZwHrfTjsrSQp32yLy2Hzwha7s/H5K3HWU/s46XmpGZKmZmAucySQyDbYlwLnPp4/Mm4OlbJiWsoBQylz4+bwI2PTgDdZUlhu3DBUX9Yn4vTDimYx/U4qRmZihpBMohrKghaWLZt5JhQ50o1syxpVi3uBYVJQWKHldRUoB1i2tRN9Y89z41lJRQ7vQFdS+RbEQZba2d7Fw/J4jI/NS0uaOv4Wqvc2pItSczff8RY8Q9Sc8sA6SvVGMudWNKkBcVbesNJF/A4w2EOaZARERERGRiW/e3IBiOyNo2GI7grf0tqTekrBQ9x9wV1aeTOnuif9/lD2V1f/BAcycCChNcxPOHwlk/zsG5WFLDjOOlZqU0PiIbcS6T4rEmA9FZQuZSKULmUiM1nfaiqc0b87uGpnbUH2nDJRf0h9NhN3wfpCRLDc/U6Znx9v4TCY1AtQrznfjlTZfy+Kcgln0rWalbqYb6XTNG4Zltn8UEpAsN9VwvQ0HZT8jW/vCqPbJKcs+uGoIn5k/MilJiqUoo3/n8Lhxq6er7u5bSNOkqo61HCfBcPieIyPzE2nMAUFzggtcf6rsGxou+hiu9zslltwHRc5dS7clk959QOIInNx2U3Hc9pOueJGQZuO+lesmFn8lkW58oHeXdlLxGsjGXA82d8MuciJfCMQUiIiIioswT+n/RGUrluPfFegz07JPd/8s2LM/NOWYxHOeQR2ou9vvTL8Jvtn/OuVgSlc75OqtTGh+RrTiXSdH4qRJppLYDpGTC8Yb/+QtsAAYXumG39WZ7SWeHs8sXxJRHN6PbH4LH5cDOJbNibgps7Kdfly+IRSt3x/zOYQNCkd4syEoyC1aUFGD5bTUYebaMZ65K9V1Oln1r/LABos/JhjqRuAK3E/MmD5PVGbmxZljWdETmVpdh28ETONntT+hkCZ20h1bV4829zRjkcWHuxDJVryNkA0hWfjlaZ08w4f69encTlswZh34uR9LH6tXJztVzgojML749BwBXVRTjf26ZjBOdvoT2HADRa7iS65xc4Qgw7Px+aDx9bsHmq/XHEtqTye4/G/fF7o/DblN9/xGTznsScC7LgNjnkoyZ+kR6TLKm6sPr8XpKXkNK9LhIUb88tJ0JSGYvSoZjCkREREREmae0/xfNHwr3ZSiV2//LFnr0raysurwIV48ZjLcPnFDVH7QBuHrM4KztD2bDOIfR4sfuRpcUoOn0GTy56SD65dkxarAHn53o7vs752IJSN98ndWpiY/IZpzLJAE/WSIN1HaA1HQ4IwBaOn19/09nh1NO1jo29tNrXf2xmBI8VUP745lbJ+Pdz0/ia5cMwSN/+kjWTb6qrD/++IMrcv5GH/9d/vrEodh2ILYchJrsW2yoE0kTC14Sk00DH0IJ5c2fNOPGycNgs9li/l7gduLp707Gy7sbMeviUgz0uFS9TrqyAejdyc7Fc4KIzC+6PTezsgR1F5fglinDYbPZUOB2xrTnBp6XhxsmXYDFdRWi13C51zklTnT2xPx/077jWLq2IWZhptT9R+w6HgpHUOxx67Z/mchQY+UsA3pNssrNPK/l9bRmt9cyER9t1GAPViy8jGMKREREREQZxgyl6vxp19GYvtXLu47i9mkjM7xX6eP1h7Dj81OqAm2B3vnznYdPwesPZW2QtjDOcd2T23CsvSfl9mVF+VizaJopxjnSIX4udsrIgVj22j4AgDcQxq1TL8S7n5/kXCzFSNd8nZWkqzqZ1XEukwAG2xJponZyyWodTrlZ66w8qWk18Z+J3Q6UDzwP5QPPAwDZK2q6eoI8/kj8Lq/e3YRASF7XPln2LTbUicT5giFs2tcsa9tNe5vhC4bgdmbHQFmyEsoAYLfbkv5dDiMyzhvdyc7lc4KIzE2v9pyS65wSvmBsm9UbCIsuzHxy0wFs3teCJzceRCTS+5h0DJZmqgqKVbMMaA1gFcjtw2t5Pa3Z7fUYFwGAH99wCQNtiYiIiIhMgFUw1fnN9s/j/n84p4JtDzR3al6Eecaf/UHaeQ4bTnf7ZW17usuPPIct9YZZIn7sbu5T78T8/ZXdTXh10TTOxVKCdMzXWUW6q5NZFecySaCuHh0RARCfXJJD6HAW5qub0CvMd+K5hZelpdOQLGudGGFSUw4zTWpaifhn0hHzmchdUfPlqTOSn2Uuif8uyw20lSIEwwPnGurxgRkCoaHOzh3lku0HW9Hpi+2EFbgdeOT6yoR7Y6cviHcOxWaaptSEjPMVJQWKHldRUoB1i2tRN7ak73dCJ/t4R0/Mj9SgeWdPMGHb1bub4PVLd9LFzonCfCfPCSIyBT3ac0quc1p5/efaosL/o6/jzZ0+NHf60OUTvy6ruY4no+c9SQklWQbMQu0YQzQlfXi1r6d0nECM1nERALjvmtG4YlSx6scTEREREZG+MtX/s6qGxnYca4vNVNrU5s2pebvK0kK4HNpCVtxOOypLC3XaI3PafrAV3mBY1rbeYDjnxs+FsbuPmzpExyv2HuvgXCxREnomC8xmnMskAaPciFTSWjpZ6HDe+fwuHGrpkv26dltvp+GfVjf0/U6vlOx6Za1j6nT9qPlMIpEImjt9sl/j5t+8iwJ3HgD0Zbm6ZlwJHvvbCSr32lyCoXOdT6dIh13suwwA57kcOKMioCBXV19bUapzg4wjdp+4a8YoLKobjTnjhybcG3m/UEevjPPpyMgff06MKS3A8ltrMKLYw3OCiLKC0uucFvl5dtz1wgd9//eHwpqzxGitrJLuKihKswx0+wJ9WQbS0S4Ua4dKjTHUHzmNSy4YILpvWvrwgVAYJ+Oy4oiNaRiZ3X7r/ha4nXZ47TYEw8oXPK7b8xUeuLZS8eOIiIiIiMgYQv8hHInA7bTDJyM40O20o90bwM3Ld2R1CWqxvlVnT0B025uWv4vC/LyY32XrsTnQ3Al/SF4QqRRfMJz1mW3X7zmmaPt1e45l/fg5S94T6YfZ6eXhXCYJGGxLJINRjbWRxR6s/P5UTHlsM+TOK4UjQGtX7ISYHinZ9UoN//B1lUydrhM9PhM5unyhhKxWK3cexYOzKlFc6Fb0XGYLXuzyBTHl0c3o9ofgcTnw9YlDse1A7Aoiqe+yzQY4FU76VpQUYPltNSxjagHx58bOJbOYaTuN5laX4dU9xxCK+n69ubcZi2dW9AXjPLSqHm/ubcYgjwtzJ5Ypfg2zXY8yRY8y2unoZM+tLsO2gydwstufEGCl1zlBRJRJcq5ztz27E7uPtAEAxg0tRE8wjM9PdCt+LW8gDG8gNjOO22mHy2nP6GCpHvckuaSyDNw1YxSe2fZZzHHo9AUx6Ueb4QuG09Iu7PIFUb1sI4LhCGwASvq7YYNNsl9yw//8FQBitp01rgRL5owzpL8YPaahdCGnkhJySvu7Yg63dqPtjB9F5zEzDRERERFRpqlt4/uCYbSc7Xdkawlqrz+EF987GjMenky3P4TuuGQwL713NCuPTWVpIew2yJ4nF2O3Iesz245QOO+odHurYcl79Th3RlLUJgvMpfgIzmWSgFElRCkY3VhravMioq1iveYsQ4B+WetW7TqqaFLznUOtXM0hQY/PRIvn/nIY/2/2WNnbmzF4cV39sb4BiW5/CKt3NyEQkjmYIVFWV4rW7FuUXvHnxvo9x3Dz5cMzvFe5o9jjThhYjM6kVuB24unvTsbLuxsx6+JSxaV9zHg9yiQ9Ms4b3cmuqyzBpgdnYPMnzbhx8rCEUu1azwkiokyTc517+a4r8bt3v4AvGMYvNx/EmUAYDhsgs/malD8UxgOzxuCnb+xX9Di9B0vTVQVFaZYBIdtSOtqFL+9q7FvQFwHQ3CEvmDV629W7mzB3Qpkh/UW1izilSI1X6NXf/dOuo/j+VaM0Pw8REREREWmTjupYVvVRY5vsQFspwXAEHzW2YcpFg3TaK3P4qLFNU6At0Buom43HJtrRU2di/l9S6O4LUhf7f/z22YbXG3U4d0appLs6mdVwLpMEXKpAlIKejTUx1eVFmKqh8V+Y78RzCy/T3BAUstYV5qu7EQr7sf+r2Pc5prQA6xfXYlHdaKxbXIuKkoKYv2+QOdmai7R+JlptP9SaeqMoYsGLmbbyvS9j/i830FYNrdm3KL3iz42VO49kaE+y39K1DZj62JaYnwUrdohuu2DFjr5trvzpVjy58SCe3HRA8Wua8XqUKUrLaPuStHmETvbsqiGynm921RCsWTRNdoDWQI8L82vKEzqnArvdhvk15eycEpFlybnO/d20keifn4czgd7gTz2arzYARf3y8N9vf6rocUqv46noeU9KZW51GQadvV/MrhqC1XdP68vqItzPrq/qDeZ12GM/D6Pbhc/v+ELzc3gDIbjzHBntL8qRbLxCr/7uJ8fFx1qIiIiIiCi99JpnzMbAN3eePpkz9XoeM+GxkSd+nGNQQewYeXGBu2+cIxeyKVaXF+HqMYMhPsKWmg3A1WMGZ+X1JhnOnZEcQnUyOXIxPoJzmQQw2JYoJaM7h75gCB83tat67oqSAqxbXIu6sSWqHh9PyFoXHxCrZD+UTGrmQmNfK7WficMGnOdywKG2l4FzWSblMlvwYkNjOz5u6kj4/XkGlQNh4Lh1iJ0bSs93kkfIDn+8oyfmRypbWmdPMGHb1bub4PUrC7Yx2/Uok6TKaD9yfWVC20bIOJ8MO9lERMaLv49pFQFw+kxAcbbSb1QP1fU6rvc9KRkhy8Dj8ybg6VsmJbwPIcvAfTMrJLPtG6GhsR2fn+jW9Bwel6NvjGHr/ha4nXY47Ro6fikU5jvxk2+N1zROIEXo745W+Nyes306jikQEREREZmL0MYfOiBf0eOGDsjXdb7TbKrLi1BXqS0wsK4yOwMDq8uL8NztNX39PKU8Lgd+e3t2BmlHix7nuPvqUfgkLvnVvq86cPfVo/H4vAnY9OAM1FVm53dJ4PWHsOPzU1C7Rj0CYOfhU4rnnqyOc2ckl5LqZES5iLPfRDIYWTr5By/sSphwlMPttKPdG8Cz73yOH98wXvHjpWhNDc/U6fpT+pnYz5aaPaNDB2HBih3wuGJvFbPGlSScc8mCF8cPG6B5P1JZurYBm/e1xPyu2y/+vbLZAKfd1le6VS9C9i23M7tXz1qNknND7vlO8mWilE+mr0dmo7SMtpyy3XqVAA+Gwn3/djq4BpCICJBeMJYJq3Y1Ys54/QIZjbgnJSNkGRAY0S5Mdi9T8npK/dPqBkQiETRHlYg0QvSYxtyJZbqXkBOOkVfhcXHabRjQLw8zx5Zk/QQiEREREZGVCG38tjN+RY873e3Hzct3ZO14vJ6Bgf0MSiiTST99fb/qOc0z/hB+8vonWRuorWRs4ZZnd8LjcuLJjQf7fpet36lMzD1ZHefOUuOcVS811ckYH0G5hsG2RDJpDUIV4/WHsP3gSVX74wuG0dLpw+rdTVgyZ5yunSsha52c9ymWtS5+UjOekDqd5CtwO/H1CUNlfSYRHWNIO3uCCRmwXnrvKDbta4Ytag1uJoMXhcyZ3oC8TlW3T1vnqzDfibtmjMIz2z6LOTZC9i0tAQGkL6Xnhtj5bsQ1NpcI2eHve6lecTY9oPf79subLpUc7GAwdWpzq8uw7eAJnOz2J7RNhLbNQ6vq8ebeZlnZ4fTqZHf5gpjy6GZ0+0PwuBzYuWQWs+ASUc5RG4yZ77SjJxhOuV2/PIfsdpCYD744retgqd73JCWMaBcmu5cpfT0luv2hvpJ/Ror/jLSOE8TTcozaz342Gxq+wr9/8xK21YmIiIiITEBLG78nGO6rcpaN4/EHmjs19w/P+LMzMPBUlx8Hm+UnuooXAXCwuQunuvwYWJBdCZ44xyXN6Lknq+PcmXKcszpHqjoZ4yOIzsndcHwiFfQunXyguRMhjZGRwqorvTE1vPk8+85hWdtNHTUooQyrnoLhCJo7fGktCZ+MHqsX5RpTWoD1i2uxqG401i2uTSilyu+Duei5spXUE7LD6116WBhoir/GZPJ6ZEZyy2jLLS+lVwnwdfXH+gKFuv0hrN9zTOlbIyKyNKX3sWhyAm0BoHZ0MUYrvP9GS3YdV0Pve5ISRrQLk93L0tlHMYrYmIae4wRsqxMRERERZRe28aVVlhbC5dQWluF22lFZWqjTHpnH5v3yEjuk63nMhN+p5Iyae7I6zp2pwzmrc8SqkzE+gigWg22JFNJzcqm6vAjP3V4Dj1vdarICtwPPLbxM91VXarLWkbF8wRD2NLbJ2vbjxna8fNcVijsXcpznckTls1VH746dsHrRqABj99kBkOvGlWLVD67AsPP7ATiXfev6qt6VWnpn3yLttJ4bhflOQ66xuUj4vsyuGiJr+9lVQ7Bm0TSMLPZIbsOBJvmEjPM2m/gVXMg4P9CTeuW/WDsoGApj4ZUjFHWyV773Zez/dx5J+dpERNkkHcGYmz5pxqctXXDIbMBPryjGqLh7r5bB0mAo3Pcj0POepIQR7cJk9zKj+yjpEP/Z6z1OwLY6EREREWUjsX5QrmAbX9qB5k4ENJ4T/lA4K8ey59eU475rRmt6jvtnVWRlRVV+p1IbWezBSz+YCqdd3uCX027DSz+YmnTuyeo4d6YO56zOmVtdhkFnx2ZnVw3B6runYcTZ7wzjI4h6MdiWSAEjglBnji3Fa/dMVxUY+f/NudiQVVd6Za0j/bzw7pcIiyRBvnXqhaKfSeNpL9YsmoZJw4t024eKkgJsuHc6nr3dfB07tasX5chz2PGvX78Yf/m0FdX/vgkTl21E19nvh5HZt0gfXNlqHnpnh+dAU2ZEd7IF3kAY6/cck93Jbmhsx8dNHbG/a2pHQ2O7cTtORGQyegZj5jvtWFQ3SvK5QjKLqdx+5Qi8ek+tLoOlXb4gJi7biNFLXo9pP2eSnu1COfcyI/sogrIB+aoX76YSP6ZhxDiBcIyGDshXtG9DB+SzrU5EREREpmPGflC6sY0vjmPZyT1wbSVeuONyxYErdgAv3HE57p81xojdMgXOcaX24ZdtCIpNoosIhiOoP9Jm7A5lGK83ynHOKlYmq5MRWYV1U2wQZYDU5NJdM0bhmW2fxaTfFyaXrrm4NOXzCsEp1z25Dcfae2Tvz64vT2PBlAvlvwGZxFLDL7+1BiOKPZgzfijufH4XDrV09f19Q8NXst4nybN0bQM272uJ+d3Jbp/otmvrm+B22uG122I6EsJn4tZYlkYwu2oInpg/EQVuJ0YWe7BucW3CeZBKRUkBlt9WY9hqQeF79PCqPXhj73HFj5f6Lnf5gjh88kxC6YibLx8O4Fz2LSNFr4J3OrhORiml50b0+U76UpIdXs59RRhoMtv1KJsJney5v9qOprZzbZaVO4/g5suH93WyX97diFkXl+LJTQfwT680xDxHt198omHBih3wuGK/d7PGleDHN4zX/40QEZmA2vtYNKfdhoJ8J9q9Ac3PJdx/o6/jajPMipVeE9rPmaSmXTjgvDzcvHxHzO+V3MtmVA7GqMEFsvsozri+nZTrxpXiye9U40SnD9946h3JcoCpyB3TMGqcYGSxBzUjBioqz3fZiIEJ7Tj2mYiIiIgo08zaD0onYX6rzetX9LjTZ/y4efmOrB4LFMYA5vxiO7wB+Vkn++U5sG5xbdaPZU+vGIxLLyzCB1+2yX7MpAvPx/SKwcbtlElwjis5ufNOglyIa+DcmTSxOAzOWSUSqpNJSUd8BJGZcfSZSAGxyaX1i2uxqG60otLJYgrcTtyo4IY0yOPC31wy1JByNEwNnzlefwivfNCE4x09MT8BiXRUnT1BtHb5EyZjrzv7+UiVZ1UqPsuk2pLw5ef30+WclSrFpCRzZrRU3+V19bETv+ksHcHV8PrQO6sqKWdEdnhA/fUomwcLjNZ02hsTaAvErvIVOtn98hyi9zSpYKDOnmDCtqt3N8HrN7bMOhGRUnqWBR1Z7MHK70+FzGp3ifsSjqC1y4/Vu5swpH8+1iyahisuGqTquYT7r3AdVxtoC5i79JqSduE3qodiXf0xTfey9XuOYe7EobJer6woH8u+USVr220HTwDoPYd+Nm8C1HT9lIxpGDlO8K1JF8jOuFKY78TfTrog5nfxfaa2M/6cLNtL8qSztHMul5EmIiLKRWbuB6VD9PxWT0BZ+6cnEM6JscCRxR5MuGCAosdMGDYgZ8ay3Q5llVtcOiUcsgLOcUmbPX4I5A6J2ADMvkTeXJLVce4skVQcBuesiEip3LnLEulgbnUZth08gZPd/oRVYUKD5aFV9Xhzb7OqINSjp87I2m7ShUX4xXcuxez/+jO6/SF4XA7sXDJLt4azkLVu8yfNuHHysISAzfisdVomYSnWgeZO9MgMMJNiAzCkfz/4giHdyhuIrfITOnZyVlHeWNPbAZy4bKPmc7bLF8SURzdLPo/SFYxVQ/vjj3ddIfldHpDvRJs3EPMYIahs/DBlgyJqcDW8fvTOqkrKGJUdHlB+PcqlgSattKzy9YfCirI0iPEGQjjQ3JlTJYuIyNxStUXVaGrzIiKv2p2kM/4Qpv1sK1wOO7p8gaTb2gB43E50+4KIflml918pyUqvpaP9LIfcduGqXY2a+2feQAh/2tUoa9vJF56PD748LWtbXzDc1ze4ZmwJ3A47eoLyJ7SVjmkYOU5QV1mCqyqKsaEhdVvuqjHFCeXp4vtM1f++SfdxEsoORlzDzfBaRERElHlW6AcZTY/5rWweCxTGWXskxlalHPiqA1Mf25IT2RS/P+MivPv5ScgZIrEB+P5VFxm9S6bCOS5xdtgSzhmpuacIAIdOiaqsgHNnsXifIiK9ZPfVkkhnRk4uKcn4d+h4F97a32JoAB5Tw2dGdXkRnl1Yg/teqldVBrQw34lf3nQpqsuLsHlfc0Jgm1pCliu3M3ZVqZKOXXOHT5dzNlnwqZLvkcBuR1/nIT6obEC/PIQkSrimq3SE2Gp4Btsqpyaravz5TtoYVXpY6vml5NpAkxbCKl+5AbOdPcGEe5fbaYfLadd8TyMiMgsjFkJVlxdh6kWD8O7nJzU9z6lueWU6I0BftQSbDTGBvkrvk1YsvaakXfjBF6fxzC2T8PCfPlJ9L/v5jRPx0Ko9srZ/a/8JKIm8/v2OL3Hz5cOx/WCrokDb268cgX+dO07xmIZR4wS+YAh/Ptgqa9s/H2hNaKvH95kALlQkcelczMqFs0RERNnLiv2gdNBzfivbKB1njdbWEwR6gli9uwlL5oxDP1f2zlvUXHi+rEBboHdso+bC843cHVPhHJc0o+eerI5zZ+fwPkVEesmd3PpEOhEml+InpQRqS25KZfx75PrKhHKKnb4gVrzzeczvcq0cTTabObZUtIRnKhUlBVi3uBZ1Y3uz/MQ3nt1x5VTyHInncLJz7p1DsZOfSjt2v9/5Rczv1J6zyUoxKfkeCRqaOtDQ2C5aOqLdG+gLRIiXjtIRyVbDkzJKr7Hx5ztpZ2TpYTUDTZSaHqt8/aEwfvbtCZrvaUREZmFEWVBfMISPmzLTvotEgKkXDQSg/P5r1dJrStuFTrtdU//MbrPJfr0uXxBdccdD2PY8kQnVvcd6+zLr9xxTtG+nz/h1H9PQQktbXazPJOA4CcVLZ2nnXC8jTURElK2s2g9KF2F+q2xAvqLHlQ3Iz+qxQD2zKWazX2/7PPVGUX7zZ2XbW5lYv1lKrs1xGTn3ZHWcO0ukVxwGEeU2ZrYlMgmlq66OnPLGbJ+sHE0wdC7DjdPBGPt0Unvsn33nc7R7A3A77fDJyFDkdtrR7g3g5uU7APSujJ5bXYZtB0/gZLcfV4hkygqEYteHXlCUjz98b6rslX5KS8LvOxY7CKCmhFKqUkzx3yOn3Qa3044X3v0Sbqcd3TZALFHtghU74HLYM1runKvhjcWVrZlnZHZ4pdcjPUpk5wK9VvnWjS3BVWMG4+FVe2SVK4ova01EZBZGlQVVMmESbXbVEMytHop/fKVB03V6xpjBqu6/Vi29prRd+OiGT3DGH0I4ElHVP+vniu0Hpnq9aNH9GakqhwtW7EBYpJNTmO/E96dfhN9s/zzh/BAmncxC6WeyaOVuFPXrPVel+kxA7/dz8o82Ie9sX5x9ptyWztLOLCNNRESUvazaD0oXYZ7j9Bmfosed7Pbh5uU7srbNzmyKyQnnzYnOHkWP+++3PsWqXY1Ze94A545Nm1deNSOB0G/O5mMjMHLuyeo4dyZOCMLmnBURqWWLRBTUp6Os1djYiPLy3lJ/R48exbBhwzK8R7nnrQMteHjVHpzs9sfcsIVGdDgSQbs3AF8wDBsgWkajMN+ZEIA3o7IYr+35Ct3+EDwuB3YumcWGQJp0+YKY8uhmxcfe6w9h0o82aQr8tAEo6e9GJAz0BHsnhrt80s/ncthwvscFG2x9Ha8uXxAPrarHm3ubMcjjwhPzJ6Ku8txqrQf/WI/VHzb1/V+YEF3xzud4o+E42rwBBMUiW6OInbPC60sFn4oNRAjP4wuG0O4NiAbUGi06qEwpPT7z81wOfLD02qwuIaSF1DVWkOp8J3OTuh6NKPbgcGt3QoDGtyZdgCfnV2dgT61J7BimUlFSgOW31WBkVDDP5n3N+N7zu1I+9tmFNTkxoGNF7DOQWlY8d9S0RaMpmcyIv4+VFeXjWFvqCSbheqnXdVqNrfubdVmUkU5K2oUDPS509QThD6UOsJXidtrhcTtxSsbrFeY7EQ6H0e1X/3pAb3/Q7bShJxhBvzw7yor64bMT3X1/N1tbSM5ncv9LH2LzJy1JniU1G4CSQjeurSrN+snGXJfOa3g6X4uIKJtZsc9AmZep8+bKn26R1WeTUlaUj7/+4zU67pE5eP0hXPJvbyKkYYLGabeh4d+uz9p5jsOt3fjGU+8o6kMX5juxbnGt5v67WfG8kca5Q9KKc2fJcc6KKDcY0WdgiksikxBWXT0+bwKevmUSCtxOdHoDeHlXI4539KCl09eXQUequyFWjmbN7iZ0ny1H0+0PKS4vSeqtqz+m6tjrsTI6AqC5w4eWLh86eoJJA20BwB+KoLnDF1PCSFjp9/i8Cdj04IyEwEOxshyl/fPxygdNaO32pwy0BaRLKJ3q8qsqxXT6TGYCbbWWjmAJIeOJXWOjpTrfydxYJshYwjGcXTVE1vazq4ZgzaJpCQPA8VnrpGyQuR0RkVHSXRY0/j42+cKBsh4nXC/1uk6rYcXSa0rahf/1nWoEwtoCX/2hMP7rO9WyXm/bI3W48bLhml4P6O0P9gR7O0beQBi3Tr3Q1G2hVJ9JvtOOH1x1ESSS+8oWAdDc6cvqsr2U3ms4y0gTERHlnlNdfk2BtgBwrK0Hp7qUZam0go8a2zQFTAJAMBzBR41t+uyQCY0s9uDxeRPgklkJ0+Ww4/F5E7I20BbgeZMM5w5JK86dJcc5KyJSi8G2RCYy0OPC/Jpy2Gw2dPmCuOyxzeiRUaIyGX8otoOycucRTc9H8q1878vY/8s89kI5Ga0TiWpFd7zsdhvm15SLltQQmxDVq+O3eX+z5uexoXfFptH0CFYQPvPCfHVZpwvznXhu4WVZW0JIL9HXWDHJzncyNysEUwdD4b4fKypwOzFvsryVfjfWDEv4DHzBEDbta5b1+E17m+HTeA8gItIi3ZMZ0fex/7ppIt7eLy97Z/T1Uut1WotMBvuqJbddeNWYwbq0068aM1h2O3TXF6dUvZagwO3AhYP6xfzuld1NGW8LpSL1mXT5gpi4bCNu/PUOuJx2Xfp4nGzMbum8hnPym4iIKPds3i9vfCtdz2Mm7jyH5nkt29nnyWZfu2QofvZteZUNHp83Hl+7ZKjBe5RZen3e2XjecO6QtLLC3FmmcM6KiLRgsC2RSa2rP4aegLaAHLFJqIamdjQ0tmt6XkqtobEdHzd1xP5OwbEfXJAvmcE4GYm5W9mUdrziJ0T16vjNrynX/jy3X4YN905XnGVLKb2CFayYFYzITMwcTC0EiYxe8jomLtuILp/yUttmoGWV7/aDreiMe9+F+U48cn1lwrW+0xfEO4da1e8oEZFGmZjMEO5j7xw6qfp6mclsDJkM9jWa0E4fOiBf0eOGDshX3E4X60cqUVFSgJ/fWI0vT3pjn7epHXuPdVhqYZmwSGnt7sa+ijG+YBg/vHoUyoqUfRbRONmY/dJ5DefkNxERUe6ZX1OO+64Zrek57p9Vgfk15TrtkXlUlxfh2dtr4HGrC3r0uB147vbcaBttlzn2+eccGCOtLi/Cc7fXwKNyYaXH5cBvs/i84dwhaWXmubNM4pwVEWlhndkNohzz+x2xWVHdTjt8CrLcOu02ycDLBSt2wOOK/frPGleCH98gbyUlxVq6tgGb98Vmn+r2iwdSiR37/v2c6PDGbi/1+GQG9HOiPep5SgrdaOn0yX58RUkBlt9WoznDlNDxu/P5XTjU0qX69fV6njWLpuHhVXvwxt7jyt6ITBsavsI1F5fq8lxCVjC5+zu7agiemD/RUsEKRLloXf2xviCRbn8I6/ccw82Xay8RnU5qVvm6necGSOMDwMaUFmD5rTUYUezBnPFDE671el5biYjU0KstqpTa66XW67Ra0X2hNq+8MqiLVu5GUb/eAXwr9EOF9yj3/QlOn/Hj5uU7JN+jkn6kHG6nHe3eAB5+eY/o3600DtDlC2LKo5vR7Q/BHjeusXFvMzY+MAM//P0HsieoBXr1ecn80nkNz9T9goiIiDLnZLcf55+Xh9NnAoofe/55eWjtkj9vYzVb97egn9OBM76QooQyNgD9nA5s2d+ctcGBQh8wgghaOuSdA2t2N+Evn7bCBptp+2962Lq/Bf1cDvQEQggpOHEcNqCfK7vPG8ASpNEAAPFUSURBVIBzh0RG4JwVEWnBzLZEJtTQ2I59X8Vms/EFw7jiokGynyMYjqDbJ57OvrMniOMdPTE/q3c3wetn+nulvP4QXvmgKeF4dvaIT5KKHfuDzV2yH59M/GR5caE7Y6Vc9Solq8fzKMmypcamvc3o9gV0Kw+fzVnBiHLVyvdiF9Cs3HkkQ3uintZVvnOryzDo7Mro2VVDsPruaRhx9lotXOuvr+odqBjkcWHuxDKj3goRkWx6tWmVUHu9lLpOl/Z3J7yGXtkY4vtCciuz9ATClumHRr9HpZVnhPcp9h6V9iPl8AXDaOn0KeqLmvX4Ry9UCsdNtDY0tePwiW4svGKEoufUu89L5pfOa3gm7hdERESUGUJbXk2gLQCcPhMwbTtcK+HYtHb7FVdujABo7fZn/bE53tGD5g6f7OMTAdDc4TN1/02rvvOmy68o0BYAQhGgtSt7z5tonDsk0hfnrIhIC95liTJMSTab+qNthu2HNxDCgebOrC2zYZQDzZ3oCZqjAxefxXbfsQ5869ILZK1yNKLjJXT8tL6+Hs8jt6SuGp2+ICb9aDN8wTA8Lgd2Lpml+VgqKQHMVXRE5iZWDrqhqR0Nje0YP2xAhvZKOa2rfOsqS7DpwRnY/Ekzbpw8LKFkUYHbiae/Oxkv727ErItLc65kERGZl5K26GUjz9fcDlR7vRS7Tj90bSV+8PsPRF9Hj3akHn0hs/dDjXqPZulHmun4R4+NpMr0tWDFDgQULnTkZGNu0mtcwmyvRURERJmTC/0gtXhspPHYSDvQ3AlvQNuxOePPzmMTj3OHRPrhnBURacFRPaIMElbrye1EaO1sSCnMd+KXN12a9Z0QI1SXF+HZhTW476V6VVmICvOduKN2BJ595wtNWYyk/OyN/bK2M6rjpVfHT8vzKCmpK4fH7cCA/Dwca++Jeo3eiV49ysNnqgQwEWmnZAGNlUo5A72rfLcdPIGT3f6EMlTCKt+HVtXjzb3Nkqt8B3pcmF9TLvkadrst6d+JiDJh6doGvPxBo6xtf/r6fvzmz4djfqfm2q7menna64fd1psF1O20o90bwMMv7xF9vA3AW/tbMPWxLZr2U4++kNn7oUa9R63PqwczHX+lYyNqjhknG3NXOiekOflNRESU/XKhH6RWdXkRrh4zGG8fOKE4sy3Q21e9eszgrD02PG/EVZYWwuW0wx9UXznS7bSjsrRQx70yD2HOIRKJJCRdkrJmdxP+cqgVNpvN1HMORJnGOSsiUsue6R0gymV6rNbTqqKkAOsW16JubElG98PKZo4txbrFtagoKVD0uIqSAtRVDsZL7zXC7bDDabelfpBCAZk1V4SgTT2pCRo14nmkSureUTsSSg95RUkBXrtnOjY+OKOvdIQj7km0lofXWqqdiDJDaTloK5VyBs6t8n183gQ8fcukhExcwirfx+dNwKYHZ6Cuku0KIrK+3mt7I3oC8iZ8AqFIRq7tXn8IOz47hfDZpr8vGEZzh0/yHhRBb+lSPfZT6AuVFeUrelxZUb5l+qFb97fA7bAr7jvYbYDbYceW/eJ9GbX9SLsNuKN2ZELfQAmzjAMsXduAqY9twbSfbTV8bMSIPi+Zn17jEmZ7LSIiIsosoS0/pL9b0eOG9Heboh1uFK8/hD8falUVaAv09lW3H2o17fioVlrmErP5vDnQ3Kkp0BboHQc50Nyp0x6ZR/ScQ3OnT/Z3KwKgudNn+jkHIiIiq2KwLVEGVZYWwuXQ9jV02IECt7qslrOrhmDNomkYWezRtA9aBUPhvh+rErL6za4aImv72VVD8OL3p2LTvhYc7+hBa7cfwbDaIQjlznPFnjNGBG3qFTSq9XniM8tUlBRgzd1X4p++Nhbr76mVXbox+vsiBJXde81ohOI+N6E8vFpiJYDXL67ForrRogMxG2RmzomWDd85IrPRsxSYWQmrfOPL6QiEVb4sp0NE2eJAc6fsQFsp6bi2Z/oeNLLYg40PzMBMmRN/M8eWYOMDMzLeD5VDmNhq7fZDaXctHAFau/1JJ7aU9iML3E68dk8t/vnr41RN0gLmGQeInjQ81e3X7Xk9LgcGibRFuFAxN6VzMSsXzhIREeWWkcUefLP6AkWP+ealF2S8HW6kjxrbEuZLlAqGI/iosU2fHTIhNXOJZui/GUmPufJszWyb6fEeIiIiEsdgW6IMOtDciUBY2+RtOAI8Pm+iqkm2G2uGyQ40NEqXL4iJyzZi9JLXMXHZRnT5MlNCUw8FbifmTR4ma9sba4ahsc2ruZOklMftwG9vvwwb7p2uS9BmMnoFjWp9nrnVZX2TrbMuLkHT6TOY9eSfMXHZRlw4yIOffkte+ZSeYAizfr4NUx/bgqmPbcGVP92K3/7lC9FtF6zY0bed8LN0bYOs14ne39lVQ7D67mkYcXYgRRiIEbLqSpVqTyabvnNEZiKUAlObZa4w34nnFl6WlaXAiIisqrq8CFMvGqT68em6tpvhHlTgdmLB5cNlbfvdKcMz3g+VKx0TW0r6kT/99niMKxsAILFvIPfzN8M4AKDPsY03prQAG+6djm3/rw7TK4oT/q53n5fMLx2LWTPxWkRERGQOryu8nyvd3mrceeoSAxn1PGaldC7RDP03Ix1o7oRfY2KYbM1sa4bxHiIiIkrEYFuiDNKrkTxn/NCYSTaXU95X2wwD++vqj6H7bJafbn8I6/ccy/AeaRM/uSJlQ8NXmj9/pWVMRw324LV7pqNubIkuQZup6BU0qvV5okuf11WW4MzZ7GTC+bZ1f4us97P9UGtaysMbXao9275zRGbCUmBERNnnzhkXoahfnuLHpfvaboZ7kJK+kFWka2JL7rGL77tE9w2uEgkuFWOW46/12MZz2Gz4w/emYsTZSiS/+7vL8aMbLkFd5WAAxvR5yfyMXsyaqdciIiIic/h2jbyAyb7tZQZYWlV1eRGeu70GHpe6YFmPqzdZTC4EBmZj/1mtytJC2fPaUrI1sy1gjvEeIiIiimWLRCLpq1tOptXY2Ijy8nIAwNGjRzFsWHZ3+MzmcGs37nx+Fw61dMl+TEVJAZbfVhNTOiQcjuCl94/gsf/bLytbZaHbiV3/PAtuZ+ZWiX79V9vxcVNH3//HXzAA6++pzdj+yBWMWmXpPFvexBcMoeZHmxPKBoqJPvZqPn+7DXjlh1fi19s+xxt7j6fcfnZVKZ6YX50QuBkOR/Dy7kbMurg0oey32HtU6lS3H5s/acaNk4eJlh2Pfv3+UZOs8a+n5HmSlS+PP9+qyvrjyMkzsj4zrWw2YM3d0zI+UGTV7xyRlXT5gnh41R6Z1+cheGL+xKzPUEDZgX0GUsvq586pbj9e++gY/vppK97Y25xy+0xe2zN1D1LbF7KKw63d+N7v3sdnJ7plP2bUYA9WLLwsZblPPY6dkucocDmwc8k18LiVB5Eb4XBrN257dieOnvZqeh6bDXj5B1dg8oiBMb+X21ek7KXXeILZXksJPcZ3iIiMZvU+A2VGps8bXzCESf++qS+5RTIelwO7/+VaS/WD1Drc2o2//+17OHzyjOzHjBx0Hp77u8tT9p+yQbb3n5WqP9qGv/2fv0BLxIpZ5t+MxDkHIiIidYzoM3B0jcgEhAwXZQPyZW1fNiAfaxZNS+h02u02lBTmJwTaFuY78cj1lQkZYzp9QbxzqFXbzmvQ0NgeE/QHAA1N7WhobM/QHsnT5Qti4rKNGL3kdUxctrHveG8/2JrQOZZz7IXPf9LwItn7EI70TuLILzVTLtqpstttmF9TnjDJI/UelRrocWF+TbnoJFP067uc9qSvJ/d5kk1WiZ1ve491yP7MAKCfyvJFZinVYtXvHJHVsBQYEVF2Gehx4bYrRmDe5HJZ22fy2p6pe5DavpBVjCz24IFrxyh6zAPXjpE1UazHsVPyHF3+ECb9aLPqPp7eRhZ78Pr9VynqD4uJRIDbnnsv4X3J6StSdtNjPMGMryWXXuM7RERElGj7wVZZgbZAb5U5q/WD1BpZ7MFD11cqesxD11fmRKAtIN5/k2LF/rNS6aooY3WccyAiIjIP3mWJomQy00Oew4bTZwKytj19JoA8h/jAfXzpkTGlBVh+aw1GFHswZ/zQhAyqGxq+wjUXl6rfcZmWrm3A5n2xJS+7/eKdyQUrdsDjir08zRpXgh/fMN6w/VNiXf2xvgGUbn8I6/ccw82XD9d07AvcTsUrUzc0fAXIXOmp9HOWeo9G0fv1lJxv0Zx2G9xOO75q92Ld4tqEz2x6RTEOt3ZrzkKdDtn0nSPjWDHDkVX2WUkpsHTch4mISDurXNszsZ9m7YfqaesnLak3it5+fwu+PqGsr+0SDIVF2y56HDulz+ELhg3v4ylR4HbieEeP5udJR9+VyGrSPb5DRESUS+Lb4SWFbrR0+iT/b8V+kFpq+0+5gOdNogf+WI/OHnWLwjp7grj/jx9iz79er/NemY9VxqWIiIiynXkjFIjSLNOZHrYfbIU3IG8FrDcgvQJ2bnUZBp3NkDG7aghW3z0NI84G+QkZVK+v6m1gD/K4MHei8Z1Xrz+EVz5owvGOnpgfqY5TZ08wYdvVu5vglblC2Ggr3/sy9v87jwDQfuzFw6fFDfK4MPuSIdi0L3UZWwDYtLcZvqD84yf1Ho2i5+spPd+iBcMRtHb5sXp3E4b0z0/4zG6eMhxrFk3D7KohsvZldtUQ0SzURsu27xwZI9P3PTWsss/dvgA2yijnBCi/PhMRUWb4giHVbe9gKNz3YzQt+6mFGfuheptbXQa3U94wmttpx9yJZTFtl7H/8iZGL3kdE5ZtxIR/e7OvPXNtVanmYyfn+PePyxJkdB9PiVNdfhxr0x5sCwC/3/Fl6o2Icki6x3eIiIhySXw7fFBBbPb64gK35ftBas0eL28OpW/7S5Rtb2Xx5835cVUPBnpcOXXeNJ32ot2rbZy/3RtE02mvTntkTpka77GqdI7FERFR7mFmW6KzMp3pYf1Hx5Rtv+eY6Kq0usoSbHpwBjZ/0owbJw9LKF1X4Hbi6e9Oxsu7GzHr4tK0lK470NyJHo2Nem8ghAPNnRkvA9LQ2I6Pmzpif9fUjobGdk3H3hcMoaGpXdY+5DvteO3eWuxt6hAtFXrXjFF4ZttnMYGVQqkZOSsZk73H8cMGyNpHJfR+Pb3PN7HPbN7kYXhDRiBdpkq1ZNN3joyT6fueGlbY5y5fEJN/tBm+YOwgjh7XZyIiyhyxMo9yru1dviCmPLoZ3f4QPC4Hdi6ZZWj7UO1+amXGfqjerhw1CE67Db7Um8Jpt+HKUYPwygdNCWVlz0T9v9sfQtuZgOZjl+r4Hz7RjY64hXdG9vGUemOvvOw8cuw91mGa90WUaeke3yEiIso10e3wsaWF+MZ//yXm7/u+6sBPvjUe11xcatl+kFqhsMyyjGeFFW5vZTHnzZBCfOOp2PNm//FO/OzbE3LmvHnxfX0Wg734/hE8fF2lLs9lRmLjPVJyfc4h3WNxRESUe3hXITpLLNNDOgN4RgxKzHwpNSkqtb1goMeF+TXlkn+3221J/6636vIiPLuwBve9pK4MSGG+E7+86dK0B/0tXduAzftiS910+8X3f8GKHfC4zl1Sn9x4ELPGleDHN4yP2U7q2CuZFO8JhrHvWIcu5Ua1vEcAou8xmXS8nt7nm9hnZvZSLVb9zlF6Zfq+p4YV9nld/bGEQNtsLKVNRJRr1La9071QRI8+glpm64fqbfvB1oTAWak+W7e/txpNfNtFjNCe0XrshOOf7j6eHkoK83V9PrO8L6J0suJ3n4iIyKqU3HdveXYnPC4nntx4sO93uXDfff6vyipOPP/ul5h9yVCD9sYceN6Ie/i6Shw52Y11e9QvwvxmdVnWBtoK502b16/ocYtW7kZRP1fWnjfJWCFpCxERWZu8+ndEWS5Zpod0OXrqTMz/x5QWYP3iWiyqG411i2tRUVIQ8/cjp2O3N7uZY0tF30cqFSUFWLe4FnVjSwzaM3FefwivfNCE4x09MT9SgYudPcGEbVfvboLXLy+7qNikeLLP/7WPvtJcqjXd7zGdr6f3+RZdbsQqpVqs9p2j9DLDfU8pq+xzfFBN/3xnVpbSJiLKNWrb3uku3621j0DSxPpsa+6+Ej+46iKs+eGVCe3uF979MqHtIkbP9ky6+3h6kbuYUS6zvC+idLHqd5+IiMiKeN+Vx5Z6k5zC8ya5X948Cf8xb4Kqx/7HvAn4xU2X6rxH5hB93vQEwqkfEKUnEM7680ZKusfiiIgo9zCzLeUcs2Z6mFtdhm0HT+Bktx+zq4bgifkT+0oaCJOiD62qx5t7my07KSq8j4dX7cEbe4+n3D7+OKTTgeZO9GgMkvQGQjjQ3CkrO6icz/++lz7Elk96z93/a/gKP7rhEk3lRvV+j8HQuY6e05G4liPdx1Tt+ZbvtPe9F6fDnlBu5GfzJmSkNK8aVvrOkXHMet9Lxor7DIgHBHf0BHH4RHdMmdZsKKVNRJRross8ym17yynfnaoNHS/V9mr2k+SJ77Mt+2YVZj7xNrr9ITjtNhSdlwe7DRCqn7598IT8537qHdhtgNvpwIB+eQDUtWfS3efSS/SxvaSsEB8f69T9NTLxvojSxarffSIiIivifTc5YVzXFwjBBiACwGEDQpHEbYXf2wB8cqwDUx/bkrVZOA80d8Ib0HbenPFn/3nTz2mDNyhyskjo57Th5xsPYk9jW9aeN7zeKCNnLI6IlFM6hk2U7WyRSER+i4WyVmNjI8rLe0sSHj16FMOGDcvwHhnD6w9h0o82aerQnOdy4IOl16Kfy6HjnvU61e2XnBQFgHA4khWTopv3NeN7z+9Kud2zC2syWlZ76/5m3PdSveTK0mQK85345U2XKsoOmurz//2OL7F07cd9///Jt8ZrLnuh13uMD0jduWSWaMBmuo8poOx8m3LRoIT38addR7Fs/b6+7SYMG4CPojJPRZfmPdzanVCa91uTLsCT86sV7bPerPKdI/2Z/b4nxir7LBUQLHZ9K8x3miYgmEiLXOkzkP5y4dxRc1+IIIKWDh8iAJx2G+r/9bqki57ktrnJONF9thffO4r/b02DYa+ltj2TiT6XHoRj++5nJ7HmwyZdnzuT74soXaz63SciEuRCn4H0l6nzhvddcVYZ180Erz+Eics2wh9Slp00mttpR/2/XJeVx4bnjTReb6RxjoYoPTgmTVZnRJ+BIeeUU/RcAWaEgR4X5teUiwZaAoDdbsP8mnJLB9oC8ktEbtC5lKRSM8eWYt3i2oRyoKlUlBRg3eJaWZ2XYCjc95Pq83/p/dgyF3qUvdDrPa6rP4bus2VIuv0hrN9zzNDXU0LJ+Sb2Pn6z/fOY7ZpOey1Xmtcq3znSn9nve2KssM8s+0VERNHU3heazwbaAkAwHMErHzQmfR25bW4yTnSfLb4sod7Utmcy0efSg3Bsv1FdBruONWcz/b6I0sWq330iIiIr4n1XnBXGdTPlQHMnAmH1gbYA4A+Fs/bY8LyRxuuNOM7REKUPx6SJEjHYlnJKdXkRnl1Yg8J8dSstCvOdeG7hZTlTasEIvmAIm/Y1y9p2095m+DR2sLQSgidnVw2Rtf3sqiFYs2gaRp4NvkymyxfExGUbMXrJ65i4bCO6fNKrEpOVvdBKj/cYP9GcLBDYyGMaT+n59oedse/j2e2HcaytJ+Z3J7v9+MVNl+LxeRPw9C2TElZuCaV5H583AZsenIG6ysx2ZK32nSN9WfG+Z4V95gAgERFF0+O+AADPv/tF0r8raXOTscT6Z3rS2p5JZ59LbzUXno+wTjW4zPS+iNLByt99IiIiq+F9N5EVxnUzhcdGGo9NarzeJOIcDVH6cEyaKBGDbSnncAVYZm0/2IrOuKDSwnwnHrm+MqEj1ekL4p1DrencPVEFbifmTZaXSvzGmmGy0+ZLrQJaurYBUx/bEvOzYMUO0edYsGJHwrZL1yovZarlPaoJBDbqmMZTer7tPRb7Pj490SX6vD94YRee3HgQV/xkq+hxN1MWait+50hfVrzvmX2fOQBIRETRtN4XBJ+d6JZsQxu5+I6SU9I/04Ne7Zl09bn09uttn4v+3qEi3a2Z3hdRulj1u09ERGRFvO8m+unr+9GdJLFMMt2+IH7y+ic675F5mH3MO5OEY+NxORQ9zuN2ZP2xEfB6E4tzNETpwTFpInHZfZclkiCsAHt41R68sfd4yu1nVw3BE/MnZn3DNB3iy9mPKS3A8ltrMKLYgznjh+LO53fhUMu5AMcNDV/hmotL072bCeL3W4qS/RVbBXRD9QV45YMmeAPyVuN19gQTSmKs3t2EJXPGoZ/CTqnc97ho5W4U9TsXRNrtFx84WbBiBzyu2O/MrHEl+PEN4xW9npZzQOp8W/HO53A77fDabQiqSJvU7Q/1BUoLXnrvqKrjbjSrfudIX0bf94KhcyWwnA591nKZ/V4tDADGf4dSqSgpwPLbarJ6VTkRUS5Se1+It2DFDgRDkZhqA/l5DslAw1RtbtJGKEsot3+mld7tmXT0ufS2rr4p4XcXFOXjD9+biqOnz+Dv//d9BELy+nBmel9E6WTF7z4REZFV8b57zqkuPw42q+8PhyPAweYunOryY2BB5hOZGEEY877uyW041t6TcvuyAflYs2haTsxPjyz2QOlsXSSCnBpn5/UmFudoiPS1dG0DNu9rifmd2jgQomzHzLaUs7gCLDPmVpdh0Nlsn7OrhmD13dMw4mxjVuhkXl/V2wEY5HFh7sSyjO2rwBcMYdO+ZlnbbtrbHDMxLkVqFdCGhq8yUvZCyXvsCYRxvKOn7yc+2FfQ2ROM2e54Rw9W726C1x8y5JiKETvfSvvn45UPmtDa5VcVaCslGI7go8Y23Z5PL38zfqjlvnNkDKPue12+ICYu24jRS17HxGUb0aUyc4EYs9+rWb6JiIiiKb0viOnsCcIbCCEcQd/PGX9IVZubtNOjLKESerZnfMEQNqahz6W3b9cMQ3Ro+dVjivHmAzMw7Px+uOKiQdh4/1Wyn8tM70tMMBTu+yHSS7rGW4iIiIj33Xiviiycy+TzmFWew4bTZwKytj19JoA8h/IqH1Z1XZWyAFGl21sZrzfiOEdDpA8h4UD8ODPHpInEMdiWcpqSFWCkj7rKEmx6cAYenzcBT98yKWEiscDtxNPfnYzH503ApgdnoK4y86U/th9sRWdc8FhhvhOPXF+ZUJ6i0xfEO4daY36npOzosvV7UdQvD2q7zmrLXih5j1oIgcBqj6nSyUix883ICXN3Xmay2kodly5fEPe+9CFOdvvhctjwHzdOsMR3joxjxH1vXf2xvkzP3f4Q1u85pmrfpJj9Xm32gGAiIkovJfcFo6hZfEfiqsuLcPWYwar7Z0rp2Z7ZfrA1YRFUfp5ddj82naL7M/ddMwabH5qBb0wsw8++dQl++3eXA0Df4q7Zv9ie8Hgl/XOzMHLBGuU2rWNYREREJB/vu7E8Os0l6fU8ZrX9YKvs6ineQCjrz5to//WdSzGjoljWtjPGFOO/vnOpwXtkHrzeSOMcDZF2esRPcEyacgmDbSlnKVkBtvHj4zmzAiwdBnpcmF9TDptNfMrSbrdhfk05BnrMUSYmPtBrTGkB1i+uxaK60Vi3uBYVJQUxf4+eIFWzCuj0mYDiUilAb9mLdYtrUTdWebCk0veoRnQgsJpjqnYyMv58qy4vwrMLa3QNIgaA268coTjIWQ/Jjkt0EKQ/FMFrH4lP3pvtO0fGMGrl88r3voz9/84jivdNilVWa5s9IJiIiNJL7n3BCGoX35lVpjN/ev0h7Pj8lKr+mRp6tmfEzsPhA8+T1Y9NJ7H+zKjBBfjlzZfiO5dfCJvNhj/tOtrXr/EFY88FJf1zMzF6wRrlLi1jWERERKSM2H13zd1X4gdXXYQ1d1+Zc/fduRPK4LRrW6rotNswd0J2V99b/5Gytn8u9RV8wRB2H2mTte3uL9tyau6e7fzkOEdDpI3W+IlsG5MmSoXBtpSzkq0Acztjvxpd/txaOUix5laXYdDZIMTZVUOw+u5pGHG2vIRQnuL6s6VKBnlcmDvx3EBAusqOai17oeQ9FvXLQ1lRvqLnjw8EVnNM9ZyMnDm2FC/dORUS8d6qfPDlaf2eTIFkx8XIIEiyHiNWPjc0tuPjpo7Y3zW1o6GxXfsOwxqrta0SEExEROmh5L6QisuhbMhGy+I7MzJD5s909ecEerZnxg8bkPC7g81daGhsT9mPTSc5/bzfbP885v/C/L3S/rmZsK9GRtEyhkVERETKxN93X7hjCr751F8wesnr+OZTf8Hvvzclp+67B5o7EYpoW6oYikSyPiveiEHK5vGUbm9lYvMBUnIteyvb+dI4R0Okj5ljS1UlYcu2MWkiOZgjnXKW2Aqw5bfWYESxB2vrm3CouSvm7xsavsI1F5emcxfJJOoqS7DpwRnY/Ekzbpw8LCEjb4Hbiae/Oxkv727ErItLY7KDCquA7nupXjKbbTL98hyyysloLXuh9D26nHY8vGoP3th7POVzz64agifmT4zZPzXH9Ou/ii0XunLnEdx8+XDV73nP0XZoHPeJIQQYik1qG0lskvbmy4cnDYJM9z4CiMlG5lQYOEL6SHbfmzN+KO58fhcOtZy798Xf95aubcDmfS0xz9HtF7+uLVixAx5X7DVp1rgS/PiG8Wnd53SQCgi+a8YoPLPts5hrvzAAyPYEEVH2UnJfSGZ21RAs+2YV/vXVvarb3FYnFoSppf2vhtb+nBpq2jNq22mRSAT9852oGzsYdZWZGRCX6s8IGhrbcaytJ2abcAS4b2YF7r+2QlH/3CzM1lcja5Dbp9YyhkVERETKxN93X3zvaEwfZuv+lpy672rtPxXmO/HLmy7N2qx4Qr+tzetX9Lhn/vwZXnr/qKrxdatRWikol+bu2c6XxjkaIv0Iwfta4kCIcgHPeMpZc6vLsO3gCZzs9sfcBBoa2xMCbQFg/AWc8MhlAz0uzK8pl/y73W6T/LuwCig+KCyVUYM9+KK1W9a2enQolb7HeZOHyWpkSQUCK3k9IyYj4yd19aBXgKFcUsdl8o82wS9Rajfd+wj0ZiWb8uhmdPtD8Lgc2LlkFhvdGSB13wPOdZ4eWlWPN/c2J6x89vpDeOWDJlnB/wDQ2RNMGFBdvbsJS+aMQz+XIy37nC5WCAgmIqL0SXVf+O5vduBYe4/Eo8+5sWYYSvvna25zW9nvd8S211/Y8WXag20B9f25VJx2IBju7ReNGHQedh9pU9We0aOd9n8Nx/Gjb4YUtdP0kKw/k3c2mLCzJyD62BXvfI4/7joa8zuhX5Osf55umViwRtlHaZ9ayxgWERERKRN93/3du1/E/O13f/0CN18+PKfuu0L/6bsrdiQsmkumrCgff/jeVNXVG81Oab8tWk8gjOOBHlXj61Zx3X9uw6ctXVCaH2fNh01Y+2ETRpcUYOMDMwzZNzNhO18c52iI9FXgdub0mDSRHDzrKWfVVZZg5tgSbNzXjPqjbZj1820ApCc9ntx0EL/eFlu6kJMeJJeaVUCTLyzCo/+3X9bzC2Uv3M70dbLlrjBV2mlJx2Sk2KSuHvQKMBSj5Lic7JZeGW3kPkoxQ1Yykr/y+Y+7juKai0sw8LxzK5/1KKHsDYRwoLlTUWYCK6zWtkJAMBERpU+q+8LkC8/HsY9St6OFNrRRbW49GFm5oKGxHfu+im2v7zvWkbHMnyOLPXjpB1Mx5dEtCIb1KY+Rn+eEw26D027DsbYeDOiXh5ljSxRnmM1UO00pvfoz5x4b6utjCF5676ipJp8ztWCNsg/71ERERObX0NiOA8c7Y363/3hnzlUvENr9IYVlBQOhCG5eviNr512t0m/LhFNdfhwUSYIlRyQCRAAcbO7CqS4/BhbkTjZXOodzNKQXVmk9x8xj0kRmwGBbyllefwivffQVvIEQ2r3imVKicdKDtFK6CmjZ+r0JvzdL2QtfMIRN+5plbaskENiIyUglk7pG0GMARMuqZ1nPb/AgTarSsJQ+qVY+nwmE8OPX9uGfVjfEZEzKZAkws6/WtkJAMBERpU+y+4IvGMLbB07Iep5Ne5vR2RMwpM2tBz0rF1gl8+eHX7bpFmgL9B7DeBsavsK/f/MSRWMMVijVanR/RhAMR/BRYxumXDTI0NeRixPqpBf2qYmIiMzFKn2YdNPS7j/R6QOQvfOuVui3Zcqr9U26Pc/f1Y7U5bnIWjhHQ3pgldZzjIoDIcomuR2OTzlNz0kPIrnkrgL6w44jOHLKm/D7J+ZNxKK60Vi3uBYVJQUxf9sg87m1CIbCCIbCeHv/CXTGTQ4X5jvxyPWVKMyPbXgKgcBy6P29FAZ3jnf0xPyoGcxQozDfiecWXqZ5AESP4yJFr32UIlUatqGx3ZDXI23EMiYJhBJg8deeVCpKCrBucS3qxirL0mYlQkBw/CCOQAgI5iAOEVFukLovbD/YqqgNvfzPnxvS5tZDsjaDEkrb6509wYRtV+9ugtdvbBAnIL8vp4XaMQYt7bQ1d1+J6RXFMdk79GZkfyaeO888g/vChHr891Uuo/tqZA3sUxMREZmLlfow6cZ51+Q4vi7Oo7K/ZNTzkDVxjoa00musMxsoHcNO55g0kVmw1UE5i6sIz2FKfGXUHi8lq4C2HmgR/f3DL+/Bv67rzXgbjkTgdtrhC4bTUvYiekWX0x7bWRlTWoDlt9ZgRLEHc8YPxZ3P78KhlnNlX+SWEND7e5nOSd14FSUFWH5bDUYWezQ/l9bjIkXPfQS4oj8bpMqYJJTceXjVHllZuuNL9qQD72lERGRW8cGaqdrQ8dld9Gpz60GvLIsHmjs1Zzs94zc286fQxu0xuDqG0jGG+DaPmnbasm9WYeYTbxuetcOo/kw0j8uBpxZMUn0eGNWGFCbU47+vqejdV9ML29rGSnefmp8nERGRclbow2RKdXkRZowZjG0HTkBNTRAbgKvHDM664xLNCuPr6Ta/phxNp8/gF1s+Vf0c98+qyGj1OyKyPlaUOUfpGHY6x6SJzIKjaJTTuIqwN4By4rKNGL3kdUxctlG0lCWdo+V4Sa0Cig9cTSZ6FXRLpw++YBguhw3rF9eirtLY8zF6RVcwHIHnbBmf2VVDsPruaRhR7EEwFEb5+f3wp7uuwPVVvY0qpYHAar+XowZ7Er6XWjMJxbtU5iDP7KohWLNomq4To2qPixS995Er+q1PbsakArcT8yYPk/WcN9YMS+tAIO9pRERkZnOryzDobAaN6DY0cG7CTWhDDzwvD3976QUYeF6erO3TsfhOoGeWxcrSQrg0Bni5nXZUlhZqeg4p0W3cNgOrYygdY5Bq8yhtp235pCVtWTv07s9EG1XswWv3Tlc9RmN0G1L4vs6uGiJreyP6k3pgW9tY6e5T8/MkIiJSZ/j555nqeczE6w9h5+enVAXaAkAEwM7Dp7J+jsDM4+uZ8sC1lXjhjsuR55A/XwoAeQ4bXrjjctw/a4xBe0ZWJVRqNbKKD2UPVpSJpWQMO51j0kRmkv2tM6IUcn0VoVhK/FxdpSOHluMltgrogVljcPfK3Zr2KRCOoKXLh7Lz+2l6nlTiV3QNH3Qe/m7aSNw4eRhsNltM5luPy4F3/+kaXHPxccy6uFRxWQ6l30sAuHXqhaKTkTPHluJn356Au/+g7TgDwNfGD8WHR9tSbmfUAIia4yJF733Us0RUNq9cNwutGZP6ueQFxaR7NSPvaUREZGZ1lSXY9OAMbP6kua8NHa3A7cTT352M3+/8Ej99fT9+seVTnOdy4N+/WYVbp14ouf3LuxtVtbnlMDrL4oHmTgTC2iY+/KGwYW3IdFTKUDPGkKzNE9/vlLKh4SscjCvPanTWDj37M4JJw4vw/B1TYo6f0myd6WhDChPqct63WSfU2dY2Vrr71Pw8iYiI1Nm8X171QjnPk22ZODlHIJ+SfluuZAucXjEY7/zDTNQ98TbOyAi49rgc2Prw1Sjtn5+GvSMriZ+vNqqKD1kTq7SmJncM28gxaSKzY2ZbIuT2KkKxlPgkTcvxElsF9LXxQzVlXi3Md+K5hZcZPvAgtqLrk686cfGQ/n0NrPiJmg0NX2F+TbnqBpaS7yUAvLK7SfJvc8YPxX9/91JFWYSjFeY78dvbL8P+rzpSb4zeARCjKD0uUvTeR61ZhNN1LpM+GZMOt56R9Vqb9jbDZ3CASjTe04iIyOwGelyYX1OeMEgpsNttcNrtfRNLZ/wh5DnsSbfX0uZOJh1ZFs3ehtS7UoYYNWMMUm0eXzCETfvkTf6/0XA8I1k79OrPCPLzHDHHT022znS1IZVMqJsR29rGSvf1kJ8nERGROvNrynHfNaM1PUe2lrw3e//OLJT029I9vp5ppf3z4ZA5j+ew2xhoS6LEFhYSAazSqoScMWyjxqSJrIDBtkRnWX3SQw2mxFdG6/ESVgE9Pm8Cnr5lUt+EoNpymkpLjcq1dG0Dpj62JeZnwYodotsuWLGjb5t/efXjmL/pMVEj93sJpP4s/mZ8GZZ9s0rxPgjH+crRg0wzAKLkuEgxYh/Ndi6TOCMytBXmO/HI9ZUJA6mdviDeOdSq62tJ4T2NiIiyhVkCoPTMSpSM2duQavdP7gSz0jGGZG2e7Qdb0RkXXFqY70R1eRHih8bPBMQ/2+g+nvCzdG2Don1MRY/+jOC9w6di+jVKJ9XS1Ya0+oQ629rpka7rIT9PIiIibYSS9y4ZVRSiuRz2rC95v3V/C9xOu+LEJ067DW6nHVt0yhxsZmL9NinpHF83g86egGTQW7yOniA6ewIG7xFZkVnG1ch80jXWSUTZL3vScxJpoGbSw+10GLxX+mJKfGWMOl7CKqB4Sstpqik1KoewossrMfEar7MnKNnxFSZqxg8boGpflHwvBak+iw++OK3o+aKP8+Z9zaIT13fNGIVntn0WcxyEARAjyvsoPS7p3keznMskTcgwcN9L9bIHrqI57TYEw5G+/48pLcDyW2swotiDOeOH4s7nd+FQS1ff340odcV7GhERZatkAVBq29VqaW0zFOY78cubLpWVlUhoQ1735DYca+9JuX3ZgHysWTQtbW1IpW3c6y4uxV8/PynruZONMSht8wRC4ZjfDejnxB/vvAJ/+z9/RUT0UYnE+nirdzdhyZxx6OfSPg6ipp+XTDAcwVv7WzD7kqEAxCfVbr58OIDMtiGlAqHT3Z+Ug23tzNK7T83Pk4iISH/X/ec2fBo1/ilXMBzGwufew+iSAmx8YIYBe5ZZXn8IL753FKGw3N7HOcFwBK1dfrz03lHd+h5mI7TL2rx+RY9btHI3ivq5cqJd9quthxRt/9TWT/FPcy42aG/Iisw0rkbmk86xTiLKboxsIYK1Jj3U0COAUs/JNSWCUZOFToWrhNXK1PESymnKmUxRU2pUDr0zXmqZqFGyuleQ7LP499f2Ys2HTYqeb+fhk5j1820AgH6u2PMvEwGGQPLr1X9uOhgTBAkA//S1sVgw5cK07qMZzmVKTsiYFH9OpFJRUoA7akfiP948gJPd/oSJXWFi+KFV9XhzbzMGeVyYO7FM13238j2NiIgomhUCoLS0GZbfVoORxR7Zj8lz2HD6jLysNKfPBJDnUJYpSa3oz6lHZvvjL5+19mVXFSgdY1DT5onnD0bQ7g3olrVDj8kEJeMv0TwuB+6uG43/eevThGP7/LtfYvYlQ5NOqo0uKchoGzI+m2+m+pOpsK1tDnr1qfl5EhER6e9Ulx8Hm5UH2gKAMHR/sLkLp7r8GFiQXaWXP2psUxVoGy0YjuCjxjZMuWiQTntlDkrbZdF6AmEcD/TkRLvsxfeOKtz+SE4H22ZiDt1MrDCuRuaTzrFOIspeuXfXJRIhNumxfnEtFtWNFi3fprTMY6ZZNSV+ly+Iics2YvSS1zFx2UZ0KQy8VCuTx0tuOU2jzkFhRZfckqepdPYEcbyjJ+Zn9e4meP3Sx3fp2gZMfWwLFr+4W5d98AZC+KixDa980CQ7m5Pg9JlA334fa+vBQE/v4NfsqiFYffc0jDjboBYCDK+v6p0MNSLAUBB/jlSUFGDN3VfiB1ddhB/fcAniYw52fXla8T4GQ+G+H732U4rVrqfZRDgnZlcNkbX97KohWLNoGm66fDg2PTgDj8+bgKdvmZQwsVvgduLp707G4/MmYNODM1BXqW9pZ6ve04iIiKIJE23xbWWpQEM17Wq9qG0zKB183n6wVfbEozcQUlRKU237Nv5zavPKCwaODwYdXaJ8jEGPNk9PMAR3nkNTH68w34nnFl6mW9YOpeMvADB6sAcb7p2ORXWjsequK2CL7/N8cRpTH9uCBSt2iL7mghU7MO1nW1VNbEfT0oacW12GQSboT6bCtrZ56NGn5udJRESkv8379anSoNfzkDWwXZacMC/YpTDTZGdPEFMf24KlaxsM2jPzytQcullYaVyNzCddY51ElL0YbEsE60x6qKU1gFLvyTW51tUf65uk7PaHsH7PsbS8bqaOl5JymkKpUSMIK7rEJjn1kGxAILpz1BNQH+gpED4Ld54Dfg2BowDgD4XxX9+pzliAoSD6ejXr4hI0nT6DWU/+GROXbcTXJ5bh7UfqcElZfwCJ1ys5+6hHB90s5zKlJmRMkiM6Y9JAjwvza8phi490OMtut2F+TXlfgLqerHpPIyIiima1iTa1bQYl1n+krL8nt3+opX2rV+WPH32zSvEYg55tHrV9vIqSAqxbXIu6sfr1beSMv0waXtS3/aTyIqxdXNu3zZ6j7YjEraL0h8IpJ9VOdSsr1SrG5bBjtMp+cl1lSUYXrMnFtrY56NWn5udJRESkv/k15bjvmtGanuP+WRWYX1Ou0x6ZhztPn4yrej2PmbBdJi16XlBpwpwIkLNBk3/adTRmDv3lXcqyAlud1cbVyHzSMdZJRNmLwbZEsM6khxZmmlyTa+V7X8b+f+eRtL12Jo6XVDnNR66vTOiAC6VG9SCW5Unpii65Ug0I6DWZDcR+FtXlRbh/VgXUFpoV9vuqMYMzFmAoiL5e1VWW4MzZoGQhIL184HlYt7hW8nqVah/1CHLP1LlM6lgxC7EV72lERETRrDjRZnSbYcSgxOwQUm1Iqe3FaGnfav2c8p12PLewBleMKo75fYHbiV/ddCl+8q3xeP3+6ZJjDHq2ecyStUPO+MvLd12Jf/76xVj6Nxfjjz+YGrNN/DhBOvmCYU2LgDO5YE0JtrUzT88+NT9PIiIi/T1wbSVeuONy2BVOONhtwAt3XI77Z40xZscyrLq8CM/dXgOPS12wrMflwG9vz86AUoDtMikMmlTnN9s/j/v/4QztSWZYcVyNzMeK86NEZA4MtiU6yyqTHlqYZXJNjobGdnzc1BH7u6Z2NDS2A9CnzH0q6T5eSstp6tGwS5blScmKLjnkDAho7RwJxD6L+2eNwZq7r0R+nrJbX36e3XQDGcL16sX3YwPQhYB0LdcrPYLcM3EukzpWzkJsxDU6HfcWIiIigZUm2tLRZjh66kzM/1O1IY+cjt1eitb2rfA5Ke2jFOY78fr9V2HmxaUJf+vyBXHpjzbhn1Y3oO4/3k6abVfPNo9RWTuE9lOPPyirLZVq/OVMIIQnNx7Ejzd8gup/39R3fMTGCdQocDsw+cLzZW3bP+5zT+ci4Eyy0vhRNtK7T83Pk4iISH/TKwZjSH+3oscMGeDG9IrBBu2ROcwcW4rX7p2OAreygNsCtwOv3TvdVPMwRmC7LBGDJpVraGzHsbaemN81tXn75tBzhZXG1ch8rDw/SkSZx2BbohxjxpT4S9c2YOpjW2J+FqzYIbrtghU7MOWxzahY8jpGL3kdFUtexz+88pFh+5bO4yWnnGaqUqNKpcryJHdFVypKBgSEzlFJobKBqmhSn0X18PPx5Heq4XLIX3JeUug25UBGqoD0TD5nJs5lUsfqWYj1vEZrKTFNRESkllUm2tLRZjCiDalX+3ZksQePz5sAl0PeMJrLYcfj8yZIfk5Ks+3q2ebRO2tHdBtq7L+8qUtbKv74XPmT5OMESlSUFGD9PdPxwxmjZG3f0RP7PrT2uazEjONHucKI6yE/TyIiIv195/Lhyra/TNn2VjWy2IP/uHGi7EqDNgD/ceNEU87DGIHtskQMmpQmNod+0/J3Rbe9afm7CdsuXduQ5j1OL6uMq5H5WH1+lIgyK/tbZ0SUQMnk2jUiWXj05PWH8MoHTfAG5K0G6uwJojNqsisCYM3uJvzb3Cr0U1maJpV0HS+hnObmT5px4+RhCVl+CtxOPP3dyXh5dyNmXVyqS5ZlsSxPN58dIFKyoisVpQMCI4s9KC5wo6XTp+r1kn0Wcy4ZCt+8MB74Y72s5zpyqnc16PhhA1Ttix6Wrm3A5n0tMb/r9otPXi9YsQMeV+yxnjWuBD++YbzhzynIxLlM6ohlTFp+aw1GFHswZ/xQ3Pn8Lhxq6er7ezruC0rpdY0WC3q5WeGAORERkRrCRNsbe4+n3DZTE23paDNobUMa2b4FgK9dMhQ93w7hgVV7Ur6Xx+eNx9cuGSr592T9MCl6tHl8wZCs8ww4l7XD7Uzez45uQwm0tqV+v+OLmP939AQTgl7VmF01BE/Mn4gCtxO/2nJI9fOoOX+sykzjR7nEqD41P08iIiJ9RPc97DYgHEn9GLsNeHHnUby482jWth2jzRxbApsNiMg4NjZb7/a5hO2yRELQ5MOr9sjqt0b377KV1x/Ci+8dRUjORQa9ffH4/vlL7x3FkjnjDJtDNwMrjKuR+WTD/CgRZQ4z2xLlGLOlxD/Q3Ikeja/hD4VxoLlTpz3qJZS+7PYF0nq8UpXTtNttmF9TrktwYqosT0pWdKUiNytS9L7t+0p9edBUn8X2gycUPd+CFTsythpUCEg/3tET89MpMdnc2RNM2Hb17iZ4ozrYRjxnvHSey6Se1bMQ63lP01pimoiISAu9s43qLV1tBiVtSKHPFgyF09K+BYDtMrNY/DnJdmqy7apt88RnwJny6BacSfEeBZ2+IH7wwgcpt4tvQ/X9XkFbKvqz7O0L6tu/FwiTaloXlqo9f6zGbONHuUbvPjU/TyIiIn3E9z1kxsAhHEFWtx3jbf2kRdGxeWt/S+oNswTbZYmEvuusn2/DzsMnZT1m5+GTmPXzbVmdvfWjxjbZgbZSguEIPmps02eHTMzs42pkPlafHyWizOKyDaIcIxVAedeMUXhm22cxk5FCSnwjV+lUlxfh2YU1uO+lesmJUDkcEpMPanT5gpjy6GZ0+0NwO+3wBcMxf8/k8VJLTZanNq8/5vfxK7q+//wufBq1oiuZZFmRlOybXMk+C18whI0KJ1bjMyoDwOrdTWlZDapHQLo3EMKB5k5UlxcZ9pxkTVbPQqzXPS1Z0Esms1oTEVFuUDPRlirbqN7UtBmCoXP9KKdD37Xe0X02j8uBFQtrDG/fqvmcfvTaPl2y7app81w+YiBe3tWInrj+rBLbD7XC6w9J9nnE2lB9f5PRllq6tgGb9jWjpcOHCHrLxxYoXNiphJCFROx4apWN/SOzjR+RNvw8iYiI9MGxdXn+969fKNv+L19gdpLqINlESX8kF9plXn8If9hxBEpDSk+fCQAIAAD+sONIVmZvdefp8370eh6zssK4GpmP1edHiSizGGxLlGPMmBJ/5thSrFtcm/DaSuhZxjG6DGZ8oK0ZjpdSwkprb0BmBiORwFKHDVj5vakoLnQD6F3Rdf81FVj84ofynlNiQEDpvikh9VlsP9iKLh0mVtM1IKY1IL0w34lf3nRpzH4a8ZxkXULGJClCxiQzUnNPe+tAi6ElpomIiJSySgCUkjZDfDDsziWzdC3TF91n6/aH8MXJM4a3b5V+Tls+adbcDxMW+Clt86ytb8Kilbs1BdoCQCgc6evzCIsk272BvsxKqUrCfuOpdyDMVeTnOdA/P6+vLSXWF4wAmhbhpiJMqokdz7+bNgKP/d9+9o+imHH8iNTj50lERKQPjq0nJ/QbWrt8ih6368vTvZlNs3jsVTg28YluUlm0cjeK+rmy9ti8/8VJxYG28SJnn+eqMSV67JJpVJcX4bnba3DPyg/7xkCU8LgceGrBpKy93gisMq5G5mPl+VEiyix9U4sQkemZNSW+8Nqzq4aoeryeZRzjy2A67L2zg2Y6XkrosdI6FAEa27wxv9saV9ZncIEr6f/FSnPosW9ikn0WckuJJFOY78RzCy9LWwdVCEivKClQ9LiKkgKsW1yLurGJAwxGPCdlt+jyvmah9J52/bghaSkxTUREpIRYANT6xbVYVDdatL1mhZJ38cGw6/cc0/R88e2Q+D7byp1HDG/fKv2cVu1q1C3jldI2z0CPCz0BbW02j9uB397e2+eJLpXrDYQQjvSWek01IRoB+rY94w/FtKWM6gsmI0yqiR3Pmy+/kP2jOGYdPyJ1+HkSERHpR+h7KM1yN9Djytq2I4CYfkMwrCx8MhiOZPXYa/SxUdpX6wmEs/rYHO9QFpht9POYzcyxpXjt3ukYdbbtLteoYg9eu3d61l5voomN16y5+0r84KqLsObuKy05rkZEROZmi0RS5aGgXNDY2Ijy8t5VGUePHsWwYcMyvEdkpFPdfsmU+AAQDkcylhJ/875mfO/5Xbo8l80GrLl7mqKAyIbGdsx96p2E3983swL3X1thuuMl19b9zZpXWsd3yN460IKHV+3ByW4/ZlcNwZ7GNnzV3tP396ED8jFh2AC8ubcZgzwuPDF/IuoqEzt1WvZNTL7Tjv75ebDbz31W0St+o/d7ekUxjp324rPWbtnPX1FSgOW31WCkwo6tHpo7enDlT7ciJGOganbVEDwxf2LKDGJdviAeXrUHb+w9rttzUvaRyk5nZHlouZTc046cOoO//Z+/pMzCloyaewtRtmCfgdTiuZNcfLs6vr3V5QvioVX1KdvVZvL1X23Hx00dff8ff8EArL+nVtVzxbdDnrv9Mnxn+Y6E7dYvrsX4YQMMa9+q+ZwikYhu/TAlbZ7bntsZc/zdTntC1ZZk4vs89UfbNLehBNFtqefeOYx/f22f9idVID/PjqJ+LtRWDMLlIwclHE/2j2KZefyIlLPi52mGPicRpQf7DKRGps+bLl8QP/z9B9h+qDXlttMrivH0LZOzuu2oR78hW8deeWySm/ur7WiI6sMqNeGC/lh3z3Qd98h8unxB3PbsTuw+0pZy20nDi/D8HVOy+noTLX68Ztk3qzDzibf7xpG2Pnw1/uXVjy01rkZERPoxos/AYFsCkPkOKZHgwT/WY/WHTZqfRypANJpQsiVatz+xdKfwfFYvJX64tTuhLGAqqQJLhYmasUMK8Y2n/pLw91cXTcOB5s6UEzVq9k2J81wOfLD0WvRzOWL2+8bJw/DeF6fwnV8nTtaLyfRk6sqdR/D/rWmQte2zC2uSlkGJPv97AiG0eQMpn7OoXx7y83qPodXOf9Im/tz7ybfGY+7EMkPLQxvFiMUHRLmCfQZSi+dOalYMgJIitYBRCIZVYunaBqyrP4aOqPu2VOBodJ9Nbvs2VZs5nprPyYh+WDJSx/+Kiwbh3c9Ppny8VJ9Hj0WSNvT2Kdxn+xRS/e90iO8jRpO7CFjp+UNEykgt+iSi7MQ+A6mRqfOGY+vSOPYqjcdGnNcfwiX/9qasJDNSnHYbGv7tetH+XTa5efkOWf36K0cNwsrvT03DHplH9HjNi+8dTZjP+k5NuWXG1YiISF9G9Bk4OkVEpuELhrBpX7Pm55EzMSmUbPEG5JVc6exJnARcvbsJS+aMs0znTSgLqDVLj1iQcmeP+GDSgt/sQGF+Hp7ceLDvd2IDSUr3TSmh/OrLHxyN2fcnNx5Et1/+wEZPMJTRiZ34crnJbGj4SnLiV+n5L2jzBoCzA4dWO/9JG7FSzZEIEspD33z58EzsniJCmbd0Br0QERGlMtDjwvyacsm/2+22pH/PFKkFjGIWrNihaAGjVJtVKkOrWJ8tlWRtZjFqPie9+mFilBz/+qNtKZ8PAG6sGSb62kIb6rbn3sPRU2dkPVc0G4AIgNNRfYpMEvqIYlmh4ktQSlF6/hCRMuvqj1myz0lERNnN6w/hxfeOKg4MjB5bf+m9o1k7ts6xV2lb97fA7bTDa7chqOD8cdptcDvt2LK/OSuDbT9qbNMUaAsAwXAEHzW2YcpFg3TaK/PxBUN4/4tTsrZ97/Ap+IIhuJ3Zd42REj1eIzafdfPlw005rkZERNbE2ktEOSIYCvf9mNX2g63o9MVODBbmO3HvNRVw2hOzBomZXTUEaxZNS9khP9DciZ6gskDDeMLknJUUuJ2YN1neSg2xSVZhIOl4R0/MjzD5Ea/bH0rY9qX3jsIrsr2SfVOiMN+J5xZehsrSQrzyQVPC/iiZkN9+qFV031PR4/vX0NgeUwo2lU17m+GTOMdz9fwndcTOvYamdjz7zucxv1u580g6d0sTIehldtUQWdvLvbcQERHlEiEYVm77urMnmLDt6t1Nku3rA82diheHKZWszawnrf0wMUqPv9xjuSEq0HTp2gZMfWxL38/Ny3fgdLdP1vNEm3rRIOhdVqtu7GDVjxX6iNGBtkJ/rdsXkL0IOF3nD1GuEpskJyIiyjQ9AwOzFcdeEwlza61dfkWBtkDv+dLa5ZecW7M6ofKJWZ7HrLbub5F97gTDEby1vyX1hllIaj6robE9Q3tERETZiJltiXKAVcquxWePGVNagOW31mBEsQelhW4sWftxyueQOzFZXV6EZxfWaC7ZIpYFx+y0ZOkxeoWp3H2TK3o1dP3RNs0BpqFwBB8fa8NlI+SvjlXz/VOSoQronbzv9gVjJrA7fUG8c6hVNNNSLp//akQHSTsd2b1OScm599mJ7pj/NzS144e//wBP3zLZsP3TkxD0IifDnNx7CxERUS7RcwFXdXmRaDukX55Dl4Dbwnwn7poxCs9s+yym/Zuszaw3vbOl6nH8xQgBpOEwVFXDEPP96SNx51UjVfc/xOz64jTOPy8Pp88oy5IrljErus/mdtoTsieb4fwhyjXJJsnHDxuQob0iIiLqDegTqjaoZUP2BwZy7DUWs7dKqy4vQl3lYLx94ISq75UNwNWVg7N+vur5vyZWvvS4HLi7bjT+561PE5IiPf/ul5h9ydB07V5GGFltiYiIKJnsbrkSEQDrlF2bW12GbQdP4GS3P6F05gdfnpb1HMLEpJzguFwsZ+MLhhRn6YkuM2LkClMl+yZH/DmkNcBU0OFV9lil3z+pcrnJdPnE9ynZRL1w/n/jqXcUHY/CfCfWLa615PmvhlUWK+hBzbkX7/WPj2PKo5ths53LRm7mAQuWCCYiIlJPzwVcerRDpEQv4pwzfmhC/y8d93mt/TAxevVv4gkBpIMK3LoF825o+ApPzq9W1f+WouY9x/cRBdF9tvhAWzOcP2aSSwsRKX04SU5ERFZSXV6EZ2+vwT0vfohun/L2ssftwFM3T8rawMDo+3qb1y/rMYtW7kZRPxeA7L2vM3urNK8/hB2fn1IdwB4BsPPwKXj9IfRzZd/xEcTXfx092IMVCy/r66t+73/fx2et3aKPzUZKx5E6e4IJ4wirdzdhyZxxWX3eEBGRMTgqSpQDrFJ2ra6yBJsenIHH503A07dM6psAUzoxebLbh4nLNmL0ktcxcdlGyUBEIPfK2Ww/2IrOuONRmO/EI9dXojA/drJCmGSNVl1ehOdur4FHZcfD43Lgt7dfJjqQpGTf5BBbDS0EmFaUFCh+PsEGhdl3lX7/9MpQ1T/fibkTy5JuM7LYg8fnTYBL5iSpy2HH4/MmWPb8V0MsWDpb6XXuNXf6ZJeHziQ1QS9EREQUS237uqKkAOsW16JubAkAfbO0Th5+PgZ6eidqZ1cNweq7p2HE2far0P+7vqo3OHKQx5WyzawHrf0wKWqPv8MeO01XUuiO+f+Ghq/6gnnV9MXiCW0ppf1vvUllzIrvswnHxyznj1l0+YKyx1qI5BImyaP7kMc7eiQD6jt7ggnbmrXPSURE2Wvm2FK8ds90DOnvTr1xlCH93Xjtnul9/aBsE39f7wmEUz8IQE8gnPX3dSPn1qxOz6o52ez7My7C+eflAQCuH1eKtYtrY/qqr95Ti+vG9V5bzj8vD9+/6qKM7Ws68LwhIqJMYrAtUZZLVnbNjAZ6XJhfUx6TFVHpxOQvNh9SFBwnlLORw+rlbOKzOI4pLcD6xbVYVDdadJJWCCwNhsJ9PzPHluK1e6djlMKAy1HFHrx2r/RAktJ9S0UqKFbLBO8gjwtzxg/tOxapqPn+aZ3ULsx3YuGVF+LtR+pQV5l60O5rlwzFz74tb6X44/PG42tZXnYmnlUWK+hBz4CKaGYdsDAq6IWIiCjX6LGAUc92yN11o7BZZBGnoMDtxNPfnYzH503ApgdnyGoza6W2HyaH0uNfWujGqMGxfbniArdoAKkeixWB2LaUkv633sSOq1ifLRSO4N5rRvedP9H9YTXnT/TjrSyXFiJS+nCSnIiIrGpksQebH7oaEy7oL2v7CRf0x+aHrs7qRBa8rycnzK2VKgzSLu3vTjq3ZnV6zIk9tzA7A5Gj1VWWYMtDV+PxeRPwzK2TRfuqz9xSg8fnTcCWh65Oy1hHJvG8ISKiTLJuxBgRJVi6tiEmS58NtqwouyY2MZmsjOO6+tgJn5U7j+Dmy4creg0pVi8RObe6DNsOnsDJbn9CCU1hkvahVfV4c29z3yRrly+IKY9uRrc/BI/LgZ1LZvWtkrzt2Z3YfaQt5etOGl6E5++YkjRQWc6+PfDHel3KrwoTvG/sPS7ruQQ//dYE3PfihzHHQthHPcseCpPaSkus2my9WV5e3tWIR64fK/tx22UGEf75UCtuuDQzE+OZkCxYevywARnaK2OpPfekRJeHNhul9xarX/+JiIiMpKR9LbWAcev+FrgddnjtNgTDagtInrtnz68pl9zGbrcl/bvelPbD/uaSoX2BmU4ZVSiUHP87r7oIP9rwSczv9n3VgZ98azyuubgUsy4u7csMHL1/D6/aI+v5C9xO0Yyn0W0puf1vuew2QM4ps77+GP7yaStsUQU4pfpsv/3LF1j1fiMiiKClw4cIAKfdhvp/vQ4Fbqfs80esP23VBbxiCxFTjbUQpSJMkt/3Ur1kNttkzNznJCKi7HXdf27Dp2fHDeV2XT5q6sCEf3sTADC6pAAbH5hh1O5lDO/rqY0s9uDykYMULVybMnJQVgdpA+fmJb799F9xqtsv+3EDPS688sMrs/74CISEVYB0XzOdYx2ZpnY+q6KkAMtvq8mZ84aIiPTHzLZEWcLrD+HlDxrR3OHr+8mWsmtzq8swSGYZ0AH5TrR5AzGPT5VJNJdKiddVlmCTwixPUtlrCtxO0UBWMfl5jpQTinL27UaRDEhqM1GqmeBd/OLumGNx5U+2YOnaBkPKHirNUFVV1h+RswN7SrIM5dL5n8zStQ2Y+tiWmJ8FK3aIbrtgxY6EbZeubUjzHhtHr/K+8eWhk8lEti8l95ZcKxFMRESkhpIFjPGE9nRrt19ToC1gzjbr9NHFeP2+6fjpt8an7IetXTQN9770IUYveR0Tl20UDVwVI/f4/+T1/aK/v+XZnXhy40HM+cX2hDaukmy0P/32+L42lMvRG9Qa3ZZS0v+QY9LwooQAB6k+YiAc6Rsrkdtnaz4baAsAwXAEr3zQqGj/siUbrNWqJpG1zBxbirrKwXDabak3juK02+B22PFPqxuyqk9ORETmdqrLj4PNXQhH5AfaCoTHHGzuwqku+QGFViIEv5UNyFf0uLIB+bLHkq3uW5MukN3ucdpt+NtJFxi8R5m3dG0Dbl6+A6GQsr58KBTCzct35GRbMFv6mlrpUW2JiIhIKWumUiCiBAeaO+ELaAtSEsqzmG3VqBCEufmTZtw4eRhstt5OaHwm0QH98hCSGN1IlklUqpT4XTNG4Zltn8VMvgkBnGbObhgdrCaWBSl65aOY+CxPUtlrfMEQ3v/ilKx9eu/wKckss0r27Y2PYzMpqc1EqXaC1xeM/Y519ASxencT5k4o0608UvT3T0mGqs6e2CBzuVmGsu38V0MI7vAG5H2GnT3BhEn51bubsGTOOPRzyQtANzu12Z8F8RnbkslUti+pe4tACHp5eXdjQoY3IiIiiqVmAVd030CPcqMCs7VZu3xBXP7jTTgTCOO8PDu+PrFMtK0j9MNW7jySMGGWql2v5PhLBTOnauPKDebdur+lrw01c2wJtu5viWlLKel/yOF2xvZ5U/URtXr+3S+w8MoRsre3YjZYPau2EMnh9YewaV+L4sUWwXAErWczn2Vbn5yIiMzr9Y/1qdLw+sdf4btTL9TlucxmZLEHGx+cgXtXfoitB1pSbj+zsgS/XHCpZStAKHXlqEGGbm81SudnorX3hNDeE8rJtqAV+5pG0aPaEhERkRLMbEuUJarLizB8UD/Vjy/Md+K5hZeZLtBWIARhCsFQYplE270Byaw/yTKJipUSX7+4FovqRmPd4lpUlBTE/F0sE5OUdGdr7PIFMXHZRsVZkKQky16zdb/8iZBgOIK39qceVElFr0yUYhO8ap3xh+DOc+DZhTUJmZPkSvb9kzupfeSUN+b/crMMGXn+W4UewR1CsHQ20VLeV8mARSZXYMffW+IJQS8MtCUiIkpOKoBSbgUKodyo2vZ0fFIgM7VZ//T+UZw5uzD2TCCMl3cdTbq92IRZKmLH/zwdJhmFNq7SYOpAOIz5NeUoLnAntKWU9j9SsdlssvuIhW6n5snXz050y87masVssEZUbSFKhX1yIiKyktEK26tGP49ZFbidWDBFXuDfd6cOz6ngt0zMrZkZ24LKWbGvaTQt1ZaIiIiUyp2WK1GWa2hsx5cnvak3FFFRUoDlt9VYqmSCnp2vudVl2HbwBE52+xOyMQqTcw+tqsebe5sVlRLPRLZGsaA1uSsZlWav8YmsMvW4HLi7bjT+561P+/ZD8Py7X2L2JUNl7YsUvTJRxne67DblJZ8E/fIcuOuFDwD0ZlXy2m2KsrEk+/5pLbEqJ8uQUee/lQjBHfe9VK84kxbQG0jyy5suNe1iBTW0nntSWaXFcAU2ERGR9YkFUCqtQCGUG/3e797HZye6Zb/2qMEe/PLmS/HLLYdM2Wb9zTufx/5/+2HcPm2k6LbJJszGDxsg+RpSx//z1i7cs/LDhL6ZHNFt3M37mmUvVkyVWViq/yH0RyOIwOWwwR+S16d697OTKPa4cMVFA/H0LZNS9hHbvQHN2W7F+ln9+znR4Y09RlbMBqvnWEs29Y/IWOyTExGRlbjz9MmcqdfzmJmS4DezVCZJh+f/+mXqjaK312Fuzcyqy4tw9ZjBePvACaiZprMBuHrM4KxtC7LySGpaqy0REREpxWBbIgtS0rBORUmpbzPReyDeiFLiWgJf1VIbtKa0TIvUMX/lh1di7ND+mDN+KL73v+/js1b5k+RyCZkopQiZKJOJn+C955rRuP+lelUTrt5AKOG4OWw2hCKphwVSff+0ZuBNVQoW0C+A2eqE4A6lE+9WXKwgh1R2Ol8gDL+MTN1yByzUBpQQERGRuei1gGtksQcPXDsGi1d+KPu1H7h2DKrKBpiyzdrQ2I5jbT0xv2tq86KhsR1/3HVEtwkzqeM/otiD1+6drrhvFt/GjZ8kLyl0o6XTJ/n/ZJPlYv0PLWVDIwBOdPvR3diOnkBYNHNtdB9xoMeFNYum4eFVe2SVmBQj1s863iGxsczHm6XsKYMeKVNmji3Fy3ddia/94s+KFkNna5+ciIjMq7K0EC6HXdYYqRS3047K0kId98p8GPwmLb7GWnGBC61dfsn/ZzuvP4Q/H2pVFWgL9PYJtx9qhdcfynh/Sm9q5m7N2tc0ktR81l0zRuGZbZ/FHJNUC4SJiIjksFZ0HRFpmoQSo6TUt9noGRynRwBnvHRna9QStKZH9hqbDegJ9g4wjSz24NV7avHgHz/Exn0tKOrnxN/XimdvygSxCV6tE67RwjICbYHU37/4Se3hA8/DkVNnNO2bWJYhI85/KxICQeSeB1ZdrCCHWHa0hVeMwJK1H4tu74zL6Cw2YMEV2ERERNlLzwVcWz9RViJz6/4WfH1CWcbbrGJtnY6egOi283/9V3gD8ifnU02YJTv+Qt/soT/W400Zk93XjSvFk9+pjmnjxgfzHjl1Jia4trjQjUuHF8nOLBzf/0h3NtUCtxPzJg/Tpe+nFzNlg+VCRMqUo6fOKAq0nTS8CM/fMSUr++RERGReB5o7EQirD7QFAH8obJq2n1EY/Cbt+zMuwifHO3D6TADXjyvF0dPemODakv75mDS8CBv3teD88/Lw/asuyuDeGu+jxjaE1JaePCsYjuCjxjZMuWiQTntlDqw8Io8e1ZaIiIiUsGd6B4hIGT0a1tE2yCzjYlZCcNzsqiGytp9dNQRrFk0zfPInWeCrHpaubcDUx7bE/CxYsUN02wUrdiRsu3RtQ8w2Qvaawnx1ExSF+U48t/CymM5agduJJ26shstpR5s3iPte/BBdGrK06k2Y4BUmooUJV60K8524QmaHPtX3b251GQadDUSYXfX/s3fn4VFV9x/HP5MMSSCJRAgJRqKsYd8kohYQUBS0pYgC7kKlahWs1draFlu1Lq3aaqu2Wn+iFC1YqkBBKrKoiLKJiIZFNlFIgEAEQhKSTGZyf3/gjJlkJpnlTmZ7v54nD2Hm3Dtn5t7cOd9zvvec9vrfXcP08hRzjxPc+XMeRPPNCk2pf+7Nv2OIPvn6mFuZtORTd0K3TU3SI1f0UbesNLfn657fzhtFDp2ocvvxNltWWZW9Qdn5m4pUGcASyAAAoHnUb1/X50yGbWrW2bEDcpRs9a27Ktma0GRiZ3OotDk0d8P+Bu2Xk17aLv4k2np9zW8HzJwa+/zTkq2a6GMi8qqdRxo85kzmfWJCP90xsou2HXSPdbcdOKE7RnTVExP6afk9wzWye5Zf7yXYeLRliwS/4xxfl7T1RXqKVT+7uFtMxWmR2teC2Obv32VKi8SYjckBAJErFGMpschT8tvi6UM1bWRXLZo+tNG+5Fg3snuWVv58hJ6Y0E/TLurqMb6aNrKbnpjQTyt/PsLv+Aqxg+uNbzyNZ3X8NjZ1xraje59KrvXlBmEAAJpCbxQQZYJd0q++WFiexZ9ZaUKRHNfcszWGatmQUMxe89bnB2X7drbbCptDiz87ENLZfeuz11nKyZrY9IB9sAOu3bLS9Nx1AzXh+bU+lW/q78/TDFXMMhR6vp4HsXz3q6dzr/6MZk9M6KelWw+5Zqf7Qf8cr8tDcwc2AADw1fe6tJU1waLqpovKmmDR97qEf+YaM2bi8Ud6ilXPXDMwJMml1fZaV9zmT6x7w8z1Sk2y6qllO12P+RPrBhrndM5M1cwp5/oV5/izpG1T6sZZ4waeGVNxWrj7WhA8f/tEwq1uzAkAQCRztl2nztqgL0t8X4muc2YrzZwyOCLbfmar35f8p0n9lWJNkN1Rq9zTW2rBtCFe+5JjVXPGV4gdjAk2zczVlgAA8EXk97IBaMDZsK5/52dT2qYmKTXZPanPuTxLtPMnOc5M4Zit0cyktfrMnr1mzoav3f+/fp//lQ1QebVd/R9apq4z3lb/h5Y1OatusAOuzs9i/9HKBssjSaeWY61/96kvf3+eZqhilqHQ8ec8cCZLx6r6517dGc2ev+EcndayhdvsdM4OC08zmnEHNgAA8NXqnSWq8DE+qrA5IiKeTW6RKM/z+ZqvW1aaFk0fqpE9fJ/dqNru0DIfEiadXlv3dVhi3UDinEV3DvU7zvG2pO0vRnf3q71aP86KxTgtXH0tCJ6/fSKRYGT3LL3106FK8XF284LC0piOyQEAka1TZqpfbXJJuqhndkS3/cxUvy9ZklvbRJLXvuRYxMpv3pkRT1u+3U+sisVY02xmrbYEAIAvSLYFopS/DethXTO16pcj9dadw2JueZZwJseFMvHVm1AnrTlnr/FFY7PXFBSWakuR+/I3BUWlKigs9au+ntgdta4fbxZtPuAapHfOqtsYbwOuI7q386lOzs/C22DkwdIqU5dHMus4wZ0/A++xcrOCP4LpsAj0RpFAEkoAAED0qt+eTq6XdFX//5EQzw7IzdDMKfkNbm71la8TTgY6YLZ6Z4nK/RiU3XrghJYUHGz2WFdqnjinqSVtc1qnBPz6sRSncSNidPO3TyRSbC06oSq7e18PMTkAIFL9d7N/368LPy0KUU0iU92+ZE9tk3hKfgvHWGK0CDaeTk1O1MtTYn+ijliKNQEAiHYk2wJRzJ+G9ZQhHZWWbHUl6Y7ufWrp81hYniWcyXHhmq0x1Elr/s5ec//CAp3/2Eq3n+teWudxm+teWteg7P0LC3x+D77OzuLvrLr137M1waJka4LP58u0OZt0/8ICjR2Qo9YtWzR4vqCoVOVVdlP//phlyHxNDbzH2s0KzY07sAEAQFPGDshR228HWy/o3FbV9ZKuqu21uqBzW0mRFc9e1CNbb905TF3a+ddu6dIuVb8f18ensv4OmDnjtOlzN/lVJ0n6xX8+U1JiQsAzDAWzMkGo45y659iY3u01/44h6lhndtpBZ58e1OvHSpzGjYjR7dV1X7n/f+3XngtGGGJyAEA0SWnh3zC7v+VjSThXQYwEA3IzNCKvXcDxlUXSiLx2MZtQ+u4Xh9UygBluLZJatkjUyi8CX7UymsRKrAkAQLSL31Y9ECMCaVg3ttR3NAp3R3y4ZmsMVdKav7PXHD9pa9blb3yZnSWQWXUv7ZPt9n97raGScpvstYZP9aqqqdVr6/bpV298Lnut5xl3r3tpnUb9eZU27zuu01KsSrRYtHJ7YJ0AzDIUGk0NvMfazQrhkJZs1bkdfUtg4A5sAADihzMx9NdvFijRYtFpKVYVFB33WLag6Lv29K/fLPD7Br5Q6ZSZqv9OH6pzzsrwqfw5Z2Xov9OH6pOvjvlU3p9Ytu4ypVU13lcE8cbQqcRm36Ihd8HEus0R59Rf0rZue7Pa7tD7O44E/PqxFKeFu68FgSsoLNX2g+6znm07eMKUlYZCjZgcABANnLFL8Ylqv7YrPlEdMbFLcwrlKojRotLm0LovjwYUX0mn4rP1e4/6PI4WTZyxa0m5ze/Px5BUUm7za4wxWsVSrAkAQLQje8EEmzZt0tKlS7V69Wpt2bJFhw8fVosWLZSTk6Pvfe97mjp1qoYNG+bz/pYuXaoXX3xRGzZs0JEjR9SuXTsNHjxYt956q8aMGRPCd4JoE0jDOtl6ahkO5/IssWDsgByt2nlE31TYNKZ3e/1pUn/XYJmzI/7n8zbrna3FIeuId77OvfM+09Kth5osX7+egXLObuzLa/qatOZt9pqfDO+iF1btcUuiLau26z8b95u2/I0vd+XWvwP6yXd26K8rdrk9VmHznOh73UvrlJrk/hmM6pWlR67oq/antZRFCrizw6m4zHsHW1mV3e3zOyG75m8q0ozLe6llkn9L5Ph7nD7cVaKLe2bX3w3qcQ68r9herImDOshicb+X2nmzwhubCjWqZ3ZcLHEVrPsXFmjFtsNuj5WU+9YRPW3OJk0Y1EGPXNE3FFUDgIhHrIl44Rxcq6zxLa4orz5V7oS+a/MG2q42W1qyVclW3+5tT26RoBaJloDjek+cbS+bo9bnz9NMwca6zRXnOJe0Nfv1YylOi4S+FjTNU7wVSJ9IpCAmB4DmQawZuEqbQ3M37JfDx0k66qpxGDp0okqvb9gfEbFLKMRa28QsO4rLmnUcLZrw2fgmlmJNAACiHTPbBmn48OEaNGiQZsyYoaVLl6qwsFA2m00VFRXatWuX/vnPf+rCCy/UTTfdJJvN1ui+DMPQbbfdpssuu0wLFixQUVGRbDabioqKtGDBAl122WW67bbbZBjBpoIhVrCk3ymNzUojNd9Mvs7EV1+YOVuj2cuG+Dt7zfZDZZo5Ob/BOecrf5YX9XQH9NEKmymz6g7IzdDMKYG/j0A5OwH8xSxDoeMceK8/qOfkvFmBQb2m1Z1Rre6PPzNGv/lJYczflQ4AnhBrIp6YObgWbtV2hz72cabaj/ce07tfHDYtrq/b9jpa0fh1wV8tW/iWCBBsrBvuOCfY1w93/c0UKX0t8M5bvBWqlYaaCzE5AIQWsWZwPi88HlCibV32WkOfFx43p0IRJFbbJmYYkJvRbONo0YbPxjexFGsCABDtSLYNUlFRkSQpJydHd911l9544w1t2LBBa9eu1VNPPaUzzzxTkvTqq69qypQpje7r/vvv14svvihJGjhwoObOnasNGzZo7ty5GjhwoCTpxRdf1G9/+9vQvSFEFRrW34mUjnizE1+bEoplQy7p/d2djokW6dWp5zW5ZN9FPbI9nnNNaWx5UedSTHV/rntpnV/790XdQflA30eggukEYGlFRANzEmdqIyJxBgCaG7Em4kksDa69u/2wzzcW2WsNzfroK7fHgonrzWh7edItK03Dumb6VDbYWDfccU6wrx/u+pstUvpa4NmO4rKgZ7A+aYuMGxUAAM2HWDM4yT7ehNZc+4kksXQTZSiEYhwtVjg/m6REz3GHN0mJlpj/bJxiLdYEACCaWYxYup0wDH7wgx/opptu0lVXXaXExIaBUUlJiYYMGaKdO3dKkj744AOPS6/s3r1bPXv2lN1uV35+vj744AO1bNnS9fzJkyc1fPhwbdy4UVarVV988YW6dOli2vsoLCxUbu6p5fP279+vDh18m50T4fXejsO6d95nHpf0k6Tyarvbkn5/mtSfmUZCqNruUP7DKxrMSuRJerJVG387qtHlP32xYluxfjx7o/u+vSwbIkkzJ+c3uWzInPX79JsFBa7//+HKvrp28FluZWprDY9L9pVX23XvvM+0dOuhJuve2PKilTaHznl4eciXPU1PseqZawY2CMT9eR+je2WrZ85pWrT5gL4sqfD5tbtlpenFm/LV6dtgOBBHK2xel1aUvB8noDm9+0Wx7np9s9cZDJryvS5tNeeW802uFYBAEDM0r1iJNSXOHfhub0mFbp29UbsOl/u8jRntajNd++Jarf3yqNtjqcmJumNEV/39/d2qqHaPcXq0T9ORMptpcX2wba/6xvRur0ev7KMRT7zfbLFuuOOcYF8/3PVH/Ki0OdT/98tks9cGvI9ka4I2/+7SmFzGGkB0IWZoPsSaweH7t3HBxCPexmtizTcV1Trv0ZU+3aRpTbBo/YyL1TY1uRlqFn6XPrVKO/3oD8jLTtOyu4eHsEaRhVgTAAD/hSJmYGbbIL311luaNGmSx4BUkjIzM/XnP//Z9f833njDY7mnn35advupwOPZZ591C0glqVWrVnr22WclSXa7XX/5y19MqD2iHUv6RZbVO0tMW/7TV6GY3XjOhq/d/79+X4My3mavSUu2asIg376cGlteNFQzMtXV2N3A/ryPSefm6mej8rTozqEa07u9T9uM6d1eC6YNCTohgFmGEA2CmTE6o1UL3XJh5xDUCgAiH7Em4pFzNpbmblebqX7bvGtWmpbcOUzTRnbVW3cOU5d27nVtk5psalxv9modE/M76NOvjzdrrBvuOCfY1w93/RE/dhSXqcYReKKPJNkcrCQCAPGGWDM4fP82jtlbm/bp18f9Wg1l877joa1QBMlM9y+pODMtPpKQnYg1AQCIDCTbNoMRI0a4ft+zZ0+D5w3D0H//+19JUo8ePXT++Z5nbzv//PPVvXt3SdLChQvFpMSQaFhHklAkvjbF7GVDCgpLtaXohPtjRaUqKCz1uU71PwdvGnv/wS4j2xRfBuX9fR9mJRoDscjfxJnRvbL18BV99O7PR3CjCAA0glgTsSja29W3XNhZp7dqIUka0ztbC6e5x2j/nT5Ul/Y6FaOd/u2NRWbH9f62vRqzpOBgWGJdAE0Ltu8kPcWqlyefqwG5GeZWDAAQ9Yg1veP7t2mxcBNlKPk69uQUT/HVLRd2VlKi57i4vqRECxN1AACAsIisEYkYZbPZXL8nJDTMb967d6+KiookScOHN77UwfDhw7Vjxw4VFhbqq6++UqdOncytLICAjR2Qo1U7j3hc/tPZuVB3+c+mEl994Zzd2NuyIc5ZkDwtG3L/wgKt2HbYrXyFzfPSPte9tE6pSe5fGaN6ZemRK/q6PVZtd2j5tmKf6r58a7Gq7Q6vy4s674D2dxlZXzQ1KB/o+/AnQffintk+lQVihTNxZunWQ02WnXRuLn8jAOADYk3EqmhuV4/snqWVPx/RaIz2wg2eYzQz+dP2aszyrcX689X9mz3WBeAbZ9/JFX/7SKWVNT5vZ02wKDkxQb+eXyDJcx8PACB+EWs27t0vDis5MUGVCRafZyiVvvv+XflFcczP4OpPPBKJN1GG0pi+7bXg0yL5cuZYJI3pE/xNlNHie13aKsmaKJvD81hlXUnWRH2vS9tmqBUAAIA7ZrZtBqtWrXL93qNHjwbPb9++vdHn66r7fN3tAISfM/HVrOU/fRXILEiVNofe/KRIh05Uuf2UVXkOYMuq7A3Kzt9UpEqbw63c6p0lpi4v6u8d0DmtU3wq19SdwIG8j0ASdIF4Y8bM1wCA7xBrIhbFQrs6Ulag8XfGJE/Kqu1KtFjCEusC8E3701JUXePftdBea6ikwtZoHw8AIH4Ra3rnHFspqbD5lWgrfff9Gy/fu/QFe5YgS4NEW2/jT4akRC9xZSxavbNE5T6OzZX7MMYIAAAQCvFzm1iY1NbW6o9//KPr/5MmTWpQZv/+/a7fO3RofKnE3Nxcj9s1pbCwsNHnDx6Mr0AGCBXnoKo3zkHVcNtRXKaqIAelK2sc2lFc5rbckaflRV+8MV8dM1N1ed8zGsxS68tMVP7cAX2swtZkGanpWXUDeR+GIY8Juj8Z3kUvrNrjlsjsTNCNtFm4AH/ZHbWu362Jjd/DZebM1wCAyIk1JeJNmMvbjW/N2a72p40Tqfxpe9WXnmJ1+5ydcVs0xLpAPDrVx1PbdMFG1O/jiYbrYDTUEQCiEbFm40I1thJr6Av2LhTjaLGCzwYAAEQDeqFC7Omnn9aGDRskSePHj1d+fn6DMmVlZa7f09LSGt1famqq6/fyct+XVc/NzW30Z/DgwT7vC0D0G5CboZmT8xvcCeqr9BSrXp58boPOoLEDctT229mZxvRur/l3DFHHzFPXLecstaN7nwp8/Vle1Nc7oCvrDS4FOqtuIO/DUyfA4ulDNW1kVy2aPlTdstyv7/F2tzZiT3m1Xf0fWqauM95W/4eWNbjjvL73vzhi6szXABDvIiXWlIg3Ya5QtKvtjlrXT1P8beNEKk9Jy5LULStNKdaG3YHnnJUh6VR88/iEfgHFbQDCY0Buhn56cdeAt6/fxxMN18FoqCMARCtizcaFamwl1pi9CmIsCdU4WizgswEAANGAmW1DaNWqVfrVr34lScrKytLzzz/vsVxVVZXr96SkxpcQTE5Odv1eWVlpQi0BxKuLemRr0fShuvofa3W4rNrn7bplpenFm/LVKTO1wXMju2dp+T3DtWJ7sSYO6tBg2VTn8qJvbCrUqJ7ZPi2bGuiMTMHc8Rro+1i184i+qbBpTO/2+tOk/q7lVZ2dAD+ft1nvbC2mEwAxYdHmA6r4drmzCptDiz87oGsHn+WxbHm1XdPmbHJ7jLvSASBwxJqIZWMH5Jjari6vtuu8R1eowuZQalKi1s8Y5dqfJ/60cSJZ/aTlLu1SNeGcDvrJiC766puTmjrrY31ZUuF6/uy2rXTN4LNc8c2YXu39itsAhNfXJScD2s5TH080XAejoY4AEI2INX3jHFu57C+rVGU3fN4uxWrRoulDPY6txBpmKPUuFONosYLPBgAARAOSbUNk69atGj9+vOx2u5KTkzVv3jxlZ3sOElJSUly/22yNL4FeXf1dQlzLli19rk9TS7McPHiQ2YaAONQpM1WZack+J9vWH+z2pE1qkqnLi/qzjKynetodtco9vaX+85MLdN+bn/s8KO/v+6ATAPFmzoav3f+/fp/Xwc1Fmw/IXvtdx3PvM07Tv39yQaOJM9/vc4Zr9jmWBAWA70RarCkRb8JcZrer/U3I8qeNE8maSlpedOdQt7bXDwecqZHds1zb+xu3AQivjgEk7Xjr44mG62A01DGe1Z1JnngeiB7Emv7plJmqHw3tpOff/9LnbW4e2jkuEm0lz/FIijXBNV4T75OTmD2OFkv4bAAAQKQj2TYE9u7dq0svvVTHjh1TYmKi5s6dq+HDh3stn56e7vq9qSVUKiq+m3WkqaVZ6urQoYPPZQHEj4LCUm07eMLn8hPzOzSaaBsK/t4BPejs0/X8DefIYrE0mMVq7a8v1sU9D4Us2ZVOAMSLgsJSbSlyv3YUFJWqoLBUfTu0blC+/kBoQoIaXEvqJs5c0LmtxvzlA59noAOAeBGJsaZEvAnzmdmu9ichy982TiTjZkAgvuw/6v/Mtp76eKLhOhgNdYxn/s4oDyAyEGsGprjU9xUDJenQiaqmC8WI+vFIhc2h/g8tc/t+IB4BAABANKKXw2QHDhzQqFGjdODAAVksFr388ssaP358o9vUDRYLCwsbLVv3Ls7cXBK2APju/oUFWrHtsNtj31T41xk0bc4mZbT8rtNjVK8sPXJFX1Pq542/y8hOv6irayC5/ixWSwoOMtMJ4CdP144KW8MZpSXpupfWKTXJvXk58KwMDwOhJzwOhDoTZ+as38eSoABQD7Em4L/GErL+vXFfUG2c5oiFgsXNgED8qNt3ktM6RQdKm07meWTJds1YsMXtsUi7DgYbj0bDtTrW+DujPIDwI9b0X/+H3lFppefvo8bM31Sk+ZuK1LqlVZ89MDoENYssdeMRb98PxCMAAACINiTbmqikpESXXHKJvvzy1JIhzz77rG666aYmt+vVq5fr9y+++KLRsnWf79mzZ4A1BRBvKm0OvflJkSprHEHtp6qmVodqvhuwmb+pSDMu76WWSYnBVtGrYGZkYllBIDj+XjvKquwqq3LvaH57yyGPZRsbCOVvFwDcEWsCTfMnIeva/1ur8mrfYyNPbZzmiIUAwFfOvpOlWw7qsSXbfdpmb0lF04W+FY7roBnxKNfq5kc8D0QXYk3/FR2rDCjRtq7SSruKjlXqzNNbmlSryMf3AwAAAGJFQrgrECtKS0s1evRobdu2TZL0xz/+UdOmTfNp206dOiknJ0eStGrVqkbLfvDBB5KkM888Ux07dgy8wgDiyo7iMlXZg0u09aSyxqEdxWWm77c+5x3Q9RNtnZwzMtVNtG1sFisAvgnVtUM6NRB66ESV28/8TUX6eO9R/nYBoA5iTaBpzoSs+m2L+klXTv4k2np9zWaKhQDAV21Sk5SVnqJyW2hiuPpCfR00Ix7lWt286IsDoguxZmBeXfdVRO0nGvD9AAAAgFjCzLYmOHnypL7//e9r06ZNkqQZM2bovvvu83l7i8WicePG6fnnn9cXX3yhdevW6fzzz29Qbt26da47QMeNG+c16QwA6huQm6GZk/N11+ubvQ44+ys9xapnrhmoAbkZpuwvGPVnsTJkqMLLADrLCgK+C8W1ozEnbQ5NfmWDx+f42wUQj4g1Ad+E8gYhTyIpFgIA6bt+keOVtmZ5vea4DgYbj3KtDi1/ZpQnngciD7Fm4Mb0OUMvrPrSlP3EIr4fAAAAEOuY2TZINptN48eP10cffSRJuuuuu/TII4/4vZ+f/exnslpPBRR33nmnKisr3Z6vrKzUnXfeKUmyWq362c9+FlzFAcSdi3pka9H0oeqWlRb0vrplpWnR9KEa2SPLhJoFx9MsVsUnqlVe7bkDx9tsmpXNNPMLEG3MvHb44qSXv0X+dgHEG2JNwHfOhKz0lNDfUx5JsRAASO79IlU1tSF/vea8DgYaj3KtDi1/Z5QnngciC7FmcLpnpyspMbjh9WRrgrpnp5tUo8jB9wMAAADiATPbBunaa6/VsmXLJEkXXXSRpk6dqi1btngtn5SUpLy8vAaP5+Xl6d5779Uf//hHbdy4UUOGDNF9992nLl26aM+ePXr88cf16aefSpJ+8YtfqFu3bqF5QwBiUt27iWsNQ8nWBFXbAxuAGdO7vf40qb/SkiPjK8TMZQWZ7QTwrFNmqhZMG6J7532mpVsPNVk+wSLVGqGvF3+7AGIZsSbgH2dC1q2zN2rX4XKft+uSmaqc01tq9a6SJstGWiwEAFLzzu6dbE1QaWWNrn1xXbPNPOdvPMq1OvToiwOiG7FmcHYUl8nmCO7mlmp7bUxeA/l+AAAAQDywGIbRDKkQscvfJU/OPvtsffXVVx6fq62t1S233KKXX37Z6/ZTp07Viy++qIQEcyclLiwsVG5uriRp//796tChg6n7BxA+lTaHznl4uSprzBl4mTk5Xxf3zDZlX2Z594vioJcVZLYToGkrthXrx7M3mrpPa4JF9gAyc/nbBZofMUPzipVYU+LcQfMqr7b7nZC1bs83PrVxIjEWAgApuH6RQLVKStQn91+ilkmJzfJ6vsajXKubB31xMBsxQ/Mh1gxOpc2h/r9fJluAk5lIp25e2fy7S5vtO7Q58f0AAACASBKKmMH8yAYBS0hI0MyZM7VkyRKNGzdOOTk5SkpKUk5OjsaNG6f//e9/eumll0ISkAKIXWbPcLKk4KBp+zLD/QsL9Jv5W5ScmKBEPzsKrQkWjezejs6bOGV31Lp+4Jv/heDv//fjerMkKACEGLEm4klaslUTBvnWYbbuy2806s+rNH3uJp/KT5uzSec/tlLnP7ZS9y8sCKaaQAPEJwjE/QsLdP5jK139ItYE//pFguGcea65+BqPRlq/VaxyzihPPA/Et3iMNXcUlwWVaCt9N7NtLOL7AWYjTgIAAJGGtZSCFIqJgS+//HJdfvnlpu8XQHwakJuhmZPzTZvhZPnWYlXbHUq2hveu6/sXFmj51mIVl1UHvA97raEV2w+r0uaIybvI4V15tV3nPbpCFTaHUpMStX7GKJaYbEK13aHl24pN3+/6vUdZEhQAPCDWBALna0LW8coaqbLG5/1W1dTqUE2VJGn+piLNuLwXcQRMQXyCQFTaHJq7Yb8cAawUEiznzHPNtcSzP/FopPRbxYNOmanE80AUItYMzlmnt4qo/UQivh9gFuIkAAAQiWLnVkIAgFeB3k0snVrSqK6yars+3FViVtUCUmlz6M1PioJKtHXtq5lnYkFkWLT5gCpsp2Z8rrA5tPizA2GuUeRbvbNEZdXmL0m6fFuxWiRafJ6BbmJ+BzrUAACAV6G6Qag+4giYifgEgfi88LgpibZtU5P8Kn9G65Rmn3nOUzyanmLVL0Z3V3qKe3wYCf1W8cSfGeWJ5wHEgre3mDODuln7iVR8P8AMxEkAACASkWwLAHHCeTdxTusUv7YbnteuQZJuuJfk21Fcpiq7I+j9tEpK1MuTz222mVgQOeZs+Nr9/+v3NcvrRvOSR/VniMvLTtMfruzTYGDTXydtDn24q0Rvfe5bR1m4rz8AACCy+ZOQFaj0FCtxBEwVrvgE0S25hTkzt/Y64zS/yp/bsY06ZaYG/HqBxMWe4tHF04dq2siuHm8uJ25sXr7OKM9xARALep/ZOqL2E8n4fkCwiJMAAEAkItkWAOJIWrJVE/NzfS7fNjVJ1553lhZMG6LRvbNdj43tnxOqKvpkQG6GZk7OD2qw3CJpwR1DmnUmFkSGgsJSbSk64f5YUakKCktD+rrl1Xb1f2iZus54W/0fWqbyEMwSG0pjB+S4Zjwa07u95t8xRNcOPjugWbOTEt2boPM3FWrhZt+SbZ1LggIAAHjib0JWyxb+dY3lhGFGR8S2cMUniH4DcjP08pR8pSYFnnT74o2DdPOwTj73r6SnWDX+nDMDfr1A42JP8WjHbxN+nTeXR1K/VTzxZ0Z54nkAscD5/etvHOHUskWCXpkS+zfu8f2AYBEnAQCASMWaDAAQZ/YfPelTuXPOztBLN52rNt8OZjx//SC9salQo3pmux4Lp4t6ZGvR9KG6dfZG7Tpc7vf2hqTCYyfVvX26+ZVDxLh/YYFWbDvs9liFzfNg3nUvrVNqknvTaFSvLD1yRV9T6uJpyaNrB59lyr6bw8juWVp+z3Ct2F6siYM6yGKxSPpuYPPeeZ9p6dZDTe5nTO/2emhcb/3uv1v0ztZitU1NUmVNwxmN0lOs+snwLnph1R6VVX13zJxLgl7cM9u8NwcAAGLG2AE5WrXziL6psGlM7/b606T+riVJne2Wn8/b7GqHPDK+j/7zcaHe3XG4iT1LF3XP0jPXDWSJUwQskuITxIaLemTrrZ8O049nfaw9JRV+b5+YYNHI7lm6sFumlhQ0Hc9dmJepkd0Dv9kg0LjYWzzqlJZsjbh+q3jhaUZ5b4jnAcSKi3pk6393Xajxz32o41W+T6iQkWLVgulDg5ohPlrw/QB/ECcBAIBowsy2ABBH/LmbeNehcqUmfzc7SkKCRZPycyNqwMI5WD6md/uAtmd5othWaXPozU+KdOhEldtPmZcO0LIqe4Oy8zcVqdJmzl31sbDkUZvUJE3Kz/U4sDlhUAef9jExv4OyT0vR89cP0hMT+mn5PcO16etjbmWSrQksCQoAAALiTMh6YkI/PX/DOQ0SY50JWc52yGV9ztB15/l2A9T1559Foi0CFmnxCWJHp8xU/ffOoTrnrAy/t11ScFDVdoc+2FniU/kPdpQENfNcMHGxt3jUKRL7reJB/Rnlk60Jjf6feB5ArOiUmarvdWvn1zZDurWLi0RbqeH3Q/1v7/r/5/shfhEnAQCAaEOyLQDEEU93E6enWPWL0d0bLBnovJs40vmT5FcXywrGvh3FZaoKcvmpyhqHdhSXBV2XeFjyqH4HqjfOjlPnQGjRsUodr6xxK1Ntr3V1prEkKAAA8Je/CVn+tmOAQERSfILYk5ZsVbI1semCkuva54ytmquvKB7i4ng0dkCO2n57Tl3Qua2q7e4r11Tba3V+5zaSiOcBxJ76EwQ0pWu2f+WjWd3vh75ntpZR73lDUp8zT5PE90O8I04CAADRhuk4ACCO1B9EzstO04s35qtjZqou73uGbp29UbsOl7ueX1JwMCqW7vF1cNwpxZqgt346VGe0bhmiGiESDMjN0MzJ+brr9c1e74JuTHqKVc9cM1ADcjP82i4elzzyZ9bsBZuK9NHuElm+nb/An8/mgs5t9bfrz2GmIgAAYBp/2jHLtxar2u7wOaENqCtc8QniQ7XdoY+/OupT2dKTNj02vo/G9DlDbVKTdM+/N7s9b0ZfUTzGxfGm7jFOTLCodcsW2nLAc+L01gMn1LplCyUmWPTrNwskcYwBxIYvj5Q3XaiOvX6Wj0aXPr1Ku+u0Gyw6dXONJ1uKTsgi6dhJm6bO+liS1DUrTcvuHt4MNUWkIE4CAADRhpltASCO1L2beEzv9pp/xxB1/HbZomidPdKfwXGnKnutth040XRBRL2LemRr0fShfs8y0C0rTYumD9XIHll+bRevSx75MxOSIan4RHVAn81nhcfVsgXJLQAAwDyxuPoHIldzxyeIH+9+cVj22vpzxnnmME7Nbuu8idHsvqJ4jYvjSf1jfLisWqWVNY0e49LKGh0uq+YYA4gpDsO3715XeR+/q6PV0XKbdhaXq9aQ66epd2xIbuV3FpfraLmtOaqLCEKcBAAAognJtgAQR0Z2z9Lye4briQn99PwN5ygt2X3wOC3ZquevH6QnJvTT8nuGa2T3yA9QPQ2OS6fumG7VwvvXHEvAxg/n4OCY3u19Kj+md3stmDZEnb4dXPRHvC555GnW7MXTh2rayK4BdZJ5E42fDQAAiGz+tmOIIxCs5oxPED9mr/m6wWOpSYm66+Junsuv/a682X1F8RoXxxOOMQCcsmGvb7PKf1f+WIhqEhlWfOHfpCih3g+iC3ESAACIFiTbAkCcaZOapEn5ubJYLB6fT0iwaFJ+btQs015/cNzJkHT7yK4ek/yiZdZemCct2aoJgzr4VHZifocGg4u+ci55VH8WNF+lp1j18uRzo27JI39mQvJ85WlatH42AAAgssXi6h+IfM0VnyB+1I+zurZL1ZKfDlP2aSk+bW9mX1G8xsXxhGMMAKe0S0t2+39mve/Jtq3c/5+ZFh1jLoGalJ+ruy7uGtQ+fjaqmybl55pUI0Qb4iQAABANSLYFAES1uoPj6fUC62Vbi90Gx9u0aqGbh3aMmll7YS5vidn1BTtbWTwueeTPTEgL7vheXH02AAAgssXi6h+IDs0VnyA+3DK8s05v1UKSNLpXthZOH6qOmamas6HhjLent2qhWy7sHNL6xGNcHG84xgAg/fKyHmrd8lT8cGnPLGXVu8klOyNFl/RsJ0lq3dKqX17Wo9nr2NzuvqS7Xp06WEmJ/qUgJCUm6NWpg/WzUXkhqhmiBXESAACIdBbDMIxwVwLhV1hYqNzcU3cK7t+/Xx06+HbXGABEgqMVNs1as1fPrNzd4LnF04eqd85pemNToUb1zI6aGXthrmq7Q/kPr1BZtb3JsunJVm387SglWxODes3yarvunfeZlm491GTZMb3b60+T+sfNndh8NkB0ImZAoDh3AMBdOOITxL6jFTat2F6siYM6yGKxqKCwVGOf+7BBudd+fJ6Gds1sljoR+8U+jjHMQsyAQETCeeP8/u3RPl0/fO6jBs//d9oQ7Sgui7uxieITVfrhsx+quKy6ybLZ6cladOdQn2fkR+wiTgIAAGYLRcxArwYAIOrcv7BAK7YddnuswuY5+L7upXVKTTr1dffUsp2SpFG9svTIFX1DW0lElNU7Sxp00KSnWPWT4V30wqo9Kqv67rmyars+3FWii3tmB/WaziWPfBlwirclj/z9bFKsCbI7aiVJVj9nRQAAAPHN2YaQaEcgcoQjPkFsq9tP4uz78NZPcvtrn7j6SZxC1U9CXBz7OMYA4pE/4xM3zFyv1CSr6/tZio/xiezTUjQxP1fPvddwgpT6rj43l0RbSCJOAgAA0YFRBgCA7I5a10+kq7Q59OYnRTp0osrtp26QXVdZlb1B2fmbilRpczRzzRFO9ZceystO0+LpQzVtZFePyx6atQQRSx555+tn89/NB9T/oWXqOuNt9X9omcp9uKsdAABAOjXbHu0IRKJwxSeITZHeT0JcHPs4xgDiSaR/70aSRZuLfCq30MdyiH3ESQAAIBqQbAsAcS7aBqB3FJepyh5cR1RljUM7istMqhGiwdgBOWr77TJdY3q31/w7hqhjZqokqVNmqhZMG6LRvU/dAd02NUlj++cE/ZrVdoeWbyv2qezyrcWqDvK8jib+fDbvbD2kim87nytsDi3+7EAoqwYAAGLIos0HaEcgIoUjPkHsiuR+EuLi2McxBhBvIvl7N5KUVdVo37FKn8ruO1qpsqqaENcI0YA4CQAARAOSbQEgzkXbAPSA3AzNnJyv9JTAlp1LT7Hq5cnnakBuhrkVQ0Qb2T1Ly+8Zricm9NPzN5zTYNnCtGSrnr9+kJ6Y0E/L7xmukd2zgn5Nb0se/WJ09wbnr3PJo3jhz2dTbXefcXvO+n0hrx8AAIgNczZ87f5/2hGIEOGITxC7IrmfhLg49nGMAcSbSP7ejST/WPWlx8eHdcv0+Pj/feC5POILcRIAAIgGJNsCQJzzNABtd9S6fiLRRT2yPS4Z05RuWWlaNH2oRvYgAI9HbVKTNCk/VxaLxePzCQkWXTnwTJ2WYjXl3GfJI+/8/WzqKigqVUFhaairCAAAolxBYam2FJ1wf4x2BCKIL/HJpPxctfl2ZiegMZHaT2JGXBzpfVTxjr4PAPEoUr93I8mizUVu/09KTNBrUwfr1ann6dWpg5WU6J6isLBeecQv4iQAABDpArvtDgAQE7wNQPd54B1V2WuVmpSo9TNGNbh7NBI4l4y5d95nWrr1UJPlx/Rurz9N6h+R7wWRobzarvMeXaEKm8OUc3/sgByt2nlE31TYNLp3th6/qp8yWp3qAHKevz+ft1nvbC2OuyWP6n42zr/NP769XSu2HZYk1RqGkq0JDWa1dbrupXVKTXI/NqN6ZemRK/qGvO4AACDy3L+wwNWOcKqw2T2WpR0BIFZFYj+Jp9jP+XrO+t7z781ats1zXGx2nA7z+XKM47XvA0DscsYfTfVh1pVsTVBpZY2ufXFdXMQfV+V30F+W75IhKTs9WYvuHKrs01IkScO6tdPq+0Zq7LMf6nBZtSySrhrUIaz1BQAAAHxlMQzDCHclEH6FhYXKzc2VJO3fv18dOhDUALHG2wB0WZXnQWin01KsapVkjdgOoBXbivXj2RubLDdzcr4u7pndDDVCtJqzfp9+s6DA9f8/XNlX1w4+K6h97j96Uhf/+X3ZHIbHgcHaWkNvbCrUqJ7ZcXcn9tEKm1ZsL9bEQR1UVVOrcx5ersoaR8D7a5WUqE/uv0QtkxJNrCWAuogZECjOHYRSpc1BOwJA3Kvb51NV49Dxypomt8lo2UIpLU5d90LZ51M39qs/Q1l5tV2DH1mukzW1atUiQRvuv8QtZg5FnA7zNXaMpfju+4DviBkQiHCcN5U2h/o8+I4ctYEPr1sTLCp4cHTMxx97jpTrhVV79PiVfZWQ0HCxXbu9Vr9eWKCfDO+iLu38myUYAAAA8EUoYoaGLVsAQMyptDn05idFOnSiyu2nqURbSTpRZdehE1Wav6lIlbbAB7BDpf5ydd6wTB2aMmfD1+7/X78v6H2u3lUim+NUx2uFzaHFnx1wez6elzyquxzUjuIyVdmDu75U1ji0o7jMpNoBAIBoQTsCQLyr3+fjS6KtJB2vrHFtE8o+n8aWAl60+YBO1pyaDfBkTW2DmDkUcTrMx3LPAOLJ54XHg0q0lSR7raHPC4+bU6EI1qVdmp6c0N9joq0kWa0JenJCfxJtAQAAEFVItgWAOBCrA9DVdoeWbyv2qezyrcWqDvIzQOwqKCzVlqIT7o8VlaqgsDSo/TIw6JsBuRmaOTlf6SmBLQeanmLVy5PP1YDcDHMrBgAAIh7tCADxLpr7fBqLmUMVpwMAEIzkFubMRmvWfgAAAAA0r8BGIgAAUcU5AH3X65t9ms3Wk4yWLfSTVz9x/T+USwz6avXOEpVVu7+f9BSrfjK8i15YtcftvZZV2/XhrhJd3DPb79exO2pdv1sTuU8l2tVdXtOpwub57+K6l9YpNcm9ueTrud/YwGDfDq39rHXsu6hHthZNH6pbZ2/UrsPlPm/XLStNL96Ur06ZqSGsHQAAiGS0IwDEswG5GRqR107v7ziiQObZs0gakdeu2W868BYzD3p4uVokJoQkTgeiHX2UQPgNyM1QTkaKDhyvCngfORkpcXmzH9cwAAAAxAKSbQEgTjgHoK/424cqrfQ/4fbYyRpJ3y1FOH9TkWZc3kstk8J3B/b/Cg66/T8vO00v3pivjpmpurzvGQ0G25cUHPQ72ba82q7zHl2hCptDqUmJWj9jlNKS+fqMVs7lNStrfJv1p6zK3iBB3dO531wJvLGuU2aqFkwbonvnfaalWw81WX5M7/b606T+/E0CAADaEQDiVqXNoQ92lQSUaCtJhqTVu0pUaXOErI/Hn5j5mwpbo/vyNU4HYg19lEBkOFpuCyrRVpIOHK/S0XKb2qQlmVSryMc1DAAAALGC28YAII60Py1F1fbapgv6IBxLDNodta4fSRo7IEdtU091SI3p3V7z7xiijt/OSuUcbB/d+1RybdvUJI3tn+P3ay7afEAVtlOJmRU2hxZ/dsCMt4IwCcXyms4E3kMnqtx+vM0iXVZlb1B2/qYiVdqCq1esSEu2asKgDj6VnZjfgU5ZAADgQjsCQDz6vPC4HLWBptqeYq819HnhcXMqVI+/MXNArxGGPiqgudFHCUSG/24uiqj9RAuuYQAAAIgVJNsCQBzZUVxmSrJtq6REvTz53GZd6qi82q7+Dy1T1xlvq/9Dy1RebdfI7llafs9wPTGhn56/4ZwGg+VpyVY9f/0gPTGhn5bfM1wju2f5/bpzNnzt/v/1+4J6HwivAbkZmjk5X+kpgSVWpKdYG5z7oUjgjXf1Z632ZomP5QAAQPygHQEg3iS3SJQlyH1Yvt1PKJgRMzfGU5wOxCL6KIHIkBpgv3Ko9hMtuIYBAAAgVpBsCwBxxJlo2CIx8GGYnNYpWvLTYRrZw//E1WB4u/O5TWqSJuXnymLx/J4SEiyalJ+rNqn+L8lUUFiqLUUn3B8rKlVBYanf+0LkuKhHthZNH6puWWl+bdctK02Lpg9tcO6HIoE3nlXbHVq+rdinssu3Fqs6hIO2AAAgutCOABCPBuRmaOaUfKUmB5Ysm5qcqJenhC4mDTZmboy3OB2INfRRApFjUn6u7rq4a1D7+NmobpqUn2tSjSIf1zAAAADEkvi6bQ4AoCFdM5WUkKAah/8Dy8O6Zur5GweFZblVT3c+Xzv4LNP2f//CAq3YdtjtsQqb5yUNr3tpnVKT3D+DUb2y9MgVfU2rD9zZHd/NyGxNDP5eoU6ZqVowbYjunfeZlm491GT5Mb3b60+T+ns9950JvLfO3qhdh8t9rke3rDS9eFO+OmWm+rxNrFu9s0Rl1e5/e+kpVv1keBe9sGqP21KjZdV2fbirRBf3zG7uagIAgAhEOwJAvLqoR7beunOYps76WF+WVPi8XefMVM2ccm7IY9JAY+bGNBWnA9GKPkog8t19SXfld2yjKa98LEet4fN2iQkWzfrRuRrWrV0IaxdeXMMAAAAQ65jZFgDizOqdJaqoCWwGpylDOoZlECPUdz5X2hx685MiHTpR5fZTdzC+rrIqe4Oy8zcVqdLGzFihUF5tV/+HlqnrjLfV/6FlKq/2fFz8lZZs1YRBHXwqOzG/Q5PnvjOBd0zv9j7tc0zv9lowbQiJtvXUX/o5LztNi6cP1bSRXT3OSMwS0AAAwIl2BIB41ikzVfdckufXNvdcmtdsMam/MXNTfInTgWhDHyUQPYZ1a6cBHTL82mZgbkZMJ9pyDQMAAEA8oDcKAOJM/QFofywpOBjymZ/CcefzjuIyVQW5hGxljUM7istCtuxiPFu0+YAqvu1gq7A5tPizA6bNauzr34Ov574zgdeX2XIZGPRs7IAcrdp5RN9U2BrMVOQcnP35vM16Z2ux2qYmaWz/nDDXGAAARAraEQDiUd1+lOOVNr+2vfc/n2ndl9802wxy/sTMTWmOPiqgudFHCUQH53fv0Qr/vnc/LyzV+Y+tjNnZW7mGAQAAIB4wsy0AxJmxA3LUNjVJkpTTOsWvbZdvLVZ1kJ0ljQnXnc8DcjM0c3K+0lMCS3xMT7Hq5cnn0gEUInM2fO3+//X7TNlvtd2h5duKfSrrz7nvTwIvGhrZPUvL7xmuJyb00/M3nNMgITkt2arnrx+kJyb00/J7hmtk96ww1RQAAEQa2hEA4k39fpSqmlq/tq+qqW32GeSCuQm8rlD3UQHhQB8lEPnqfvfaHP5979octTE9eyvXMAAAAMQDkm0BIM44B6AfG9/HaxKrN2XVdn24qyRENTP3zmd/XdQj2+PSsk3plpWmRdOHamQPBupDoaCwVFuKTrg/VlSqgsLSoPe9emeJyqrd/wbSU6z6xejuDToEfT33Q5XAG2/apCZpUn6uLBaLx+cTEiyalJ+rNt/eOAAAAOBEOwJAPAlnP0og/ImZmxLqPiogXOijBCJbtH33NjeuYQAAAIh1rF0MAHGoTWqSstJTGiQaSlLbtCRV1ThUUe25wyiUy/Q573y+6/XNficCS6cSJZ+5ZmDAdz47l5a9d95nPi1pWH9pWgSn7tKXThU2z+fBdS+tU2qS++fu7/Jb9WfTyctO04s35qtjZqou73uGbp29UbsOl7ue9+Xc95bA+5PhXfTCqj1u57VzYJBlLz2z15kZwprI/WEAACBwtCsAxKpg+1ESE6SXbmq+GeQai5n/smKnahyG23MWSe6PuJs2Z5MyWp66eSJWl+RGfKKPEohcA3IzNDyvnVbtONLod5Q3Fkkj8trF9OytXMMAAAAQyxhhAIA45WnZvmFdM7XqFyP11p3DPN553DY1SWP754S0XuG+8zkt2aoJgzr4VHZifgc6gExSf+lL54+3wcKyKnuDsv4uvzV2QI7afjuj2Zje7TX/jiHqmJkq6bsOwdG9TyXC+nrue0rgXTx9qKaN7OrxvF5i0vKZsaa82q7+Dy1T1xlvq/9Dy1Tu4cYAAAAAX9CuABDrAu1HkaR+HTKadQY5bzHzzUM6KcHDjORNJTFV1dQG3CcARDr6KIHIVGlzaP2XRwNKtJVOfbet33s05r+zuIYBAAAgVpFsCwBxqm6i4UU9svT7cb01e+pgpSVbPSYa3jyko5bfM1wju4d+EMb5+mN6t/ep/Jje7bVg2hB1+jZRMlieEpE9IVHSPOFYfmtk9ywtv2e4npjQT8/fcE6DDr20ZKuev36QnpjQz+dzPxQJvPHoPxv3q+LbDucKm0NvbNwf5hoBAIBoRbsCQDxwxpuDzjrd520yWrXQTy/uFsJaNeQtZt5RXCZbnVnIAxHLS3IjftFHCUSecPRjRyuuYQAAAIhF3CYGAHHKmWi4YnuxJg7qIEu9GUSciYZvbCrUqJ7ZavPtYEhzcd757MsyQ2be+Vxtd2j5tmKfyi7fWqxqu0PJ1kRTXjueBbv0ZXqKVc9cM9Dv5bfapCZpUn6u1+cTEiyNPl9fpP9dRYv/W/1lvf/v1ZQhncJUGwAAEM1oVwCIF2nJVt0+oot+PHtjk2V/9L2OuvPibs0ek3qLmc3oE3h6Un/1yTnN7CoDYUMfJRCZwtWPHW24hgEAACBWMbMtAMQxZ6Jh/YRAJ2eiYbgSAsNx5/PqnSUqq7e0bHqKVb8Y3V3pKe4JvWXVdn24q8S01453gS592S0rTYumD23WpS8bE+l/V5GuoLBUB45XuT1WdLxSBYWlYaoRAACIVrQrAMQbX/tRSqtqwhaTeouZA+0T6NIuVXZHrX48+xP1f2iZyqv9T3wCIhF9lEDkipV+7FDiGgYAAIBYxcy2AICIFK47n+sPTOVlp+nFG/PVMTNVl/c9Q7fO3qhdh8tdzy8pOKiLe2YH/bo4xbn05b3zPvNpVuPeOafp37ddYNrMxvHIXmepTmti896Hdf/CAq3YdtjtsbKqGo9lr3lxrdJTWrg9NqpXlh65om/I6gcAAKIH7QoA8S4WZpDzt09gTO/2Oq9TGz301jZJUoXNocWfHdC1g88KdVWBkKOPEohcztij1jD82q60skbXvrguLmIPT9ewv19/jjq2TdXoXtm6/V+buIYBAAAgKjGzLQBAdket6ydShOvO57EDctT229ldxvRur/l3DFHHzFRJ3w36jO59qtOnbWqSxvbPMeV18Z20ZKsmDOrgU9nyKjuJtkEor7ar/0PL1HXG280+A1ClzaG5G/br0Ikqt58Km8Nj+Qqbo0HZ1zfsV6WX8gAAIH7QrgAAc/tRwtlP5E+fwMT8Dnrz00K3x+as3xeKagHNjj5KIDLVjT0Ol1X7te3hsuq4iT3qX8NenXqexj33kbrOeFvj/vaRXvvxeVzDAAAAEJXITgGAOFdebdd5j65Qhc2h1KRErZ8xKiKSF+vf+dwtK1XP3zBIHduGdvaGkd2ztPye4VqxvVgTB3VosKxhWrJVz18/SG9sKtSontlhW3Yx1vm69OXXR0+qoLBUfTu0DnGNYtOizQdcSSjNPQPQ54XH5aj1b/aH+uy1hj4vPK7zOrc1qVYAACAa0a4AAPP6USKhn8jXPoF/rdunLUUn3B4rKCqlnwAxgT5KIDIRe/im/jVs7ob9bv3Q735xmGsYAAAAolL4s6kAAGEVzmS7xowdkKNVO4/omwqbRvXI0povv9Gopz5wDfQsmDZEP5+3We9sLVbb1CR9v88Zpr12m9QkTcrP9fp8QoKl0ecRnGq7Q4s/P+Bz+Wv/b63SklkGOBBzNnzt/v/1+5rt7z+5hTnLlZq1HwAAEL1oVwBA4/0oH/3qIv3nJxfovjc/d/WjeJtBLtz9RNV2h5ZvK/ap7Ls7Dnt8/LqX1ik1yX3og34CRCP6KIHIQ+zhu7rXMG/90FzDAAAAEG0Swl0BAEB4eerkiATOO5+fmNBPI3tm6WS9gZ60ZKuenNBfSYkWfVNh009f/1Tl9ZZLRHRaue2wahy+zw5QXt1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      "text/plain": [
       "<Figure size 2800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(14, 6), dpi=200)\n",
    "\n",
    "ax[0].plot(df['house_age'], df['house_price'], '*')\n",
    "ax[0].set_xlabel('house_age')\n",
    "ax[0].set_ylabel('house_price')\n",
    "ax[0].set_title('house_age vs house_age')\n",
    "\n",
    "\n",
    "ax[1].plot(df['dist_MRT_sta'], df['house_price'], '*')\n",
    "ax[1].set_xlabel('dist_MRT_sta')\n",
    "ax[1].set_ylabel('house_price')\n",
    "ax[1].set_title('dist_MRT_sta vs house_age')\n",
    "\n",
    "\n",
    "ax[2].plot(df['no_con_store'], df['house_price'], '*')\n",
    "ax[2].set_xlabel('no_con_store')\n",
    "ax[2].set_ylabel('house_price')\n",
    "ax[2].set_title('no_con_store vs house_age')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e599521a",
   "metadata": {},
   "source": [
    "**Notes:**\n",
    "\n",
    "- From the above, distance to MRT station has a strong inverse relationship to the price; the lower the distance the higher the price.\n",
    "- From the above, the age of the house seem to have no strong effect on the house price\n",
    "- The number of conveninece stores have a positive relationship with the house price; the more the conveniences stores, the higher the price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c2149621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4200x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6), dpi=300)\n",
    "\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238a0908",
   "metadata": {},
   "source": [
    "**Notes**:\n",
    "\n",
    "From the heatmap correlation diagram:\n",
    "- the higher the distance to an MRT store, the lower the price. There's an inverse relationship. Hence, it's negative\n",
    "- The age of the hosue seems to follow in an inverse relationship pattern with the price of the house; the higher the age, the lower the price. This could be because new houses come with modernized house equipment anmd designs than the older houses.\n",
    "- The number of convenience stores have a positive relationship; the more the convenience stores- the higher the price."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cface09",
   "metadata": {},
   "source": [
    "### Define & Split the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "14c72844",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop('house_price', axis=1)\n",
    "y= df['house_price']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ac55af",
   "metadata": {},
   "source": [
    "#### Train|Test|Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c6a850c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34433c14",
   "metadata": {},
   "source": [
    "#### Data Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5e6c5c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.shape\n",
    "\n",
    "# # 80%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d1d4d392",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.shape\n",
    "\n",
    "# # 20%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c926722",
   "metadata": {},
   "source": [
    "### Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "c29fb8d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Linear Regression:\n",
      "\n",
      "the MAE is: 7.134280616424499 \n",
      "the RMSE is: 9.983424849795908 \n",
      "the R2 score is: 0.4534255433018419 \n",
      "the Coef is: [-0.27060981 -0.00550487  1.17473128]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_model = LinearRegression()\n",
    "\n",
    "lin_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lin_model.predict(X_test)\n",
    "\n",
    "# Metrics\n",
    "\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "coef = lin_model.coef_\n",
    "\n",
    "print('Using Linear Regression:\\n\\nthe MAE is:', mae, '\\nthe RMSE is:', rmse, '\\nthe R2 score is:', r2, '\\nthe Coef is:', coef)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264c8838",
   "metadata": {},
   "source": [
    "### Ridge Regression (L2 Regularization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c1ce6147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['explained_variance', 'r2', 'max_error', 'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_absolute_percentage_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_root_mean_squared_error', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance', 'accuracy', 'top_k_accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted', 'balanced_accuracy', 'average_precision', 'neg_log_loss', 'neg_brier_score', 'adjusted_rand_score', 'rand_score', 'homogeneity_score', 'completeness_score', 'v_measure_score', 'mutual_info_score', 'adjusted_mutual_info_score', 'normalized_mutual_info_score', 'fowlkes_mallows_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'jaccard', 'jaccard_macro', 'jaccard_micro', 'jaccard_samples', 'jaccard_weighted'])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import SCORERS\n",
    "SCORERS.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "d0cbca93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Ridge Regression at Alpha 0.5:\n",
      "\n",
      "the MAE is: 7.134318591690922 \n",
      "the RMSE is: 9.983536627340133 \n",
      "the R2 score is: 0.4534133039965046 \n",
      "the Coef is: [-0.27060287 -0.00550532  1.17439856]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge_model = Ridge(alpha=0.5)\n",
    "\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "\n",
    "# Metrics\n",
    "\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "coef = ridge_model.coef_\n",
    "\n",
    "print('Using Ridge Regression at Alpha 0.5:\\n\\nthe MAE is:', mae, '\\nthe RMSE is:', rmse, '\\nthe R2 score is:', r2, '\\nthe Coef is:', coef)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae36b0b4",
   "metadata": {},
   "source": [
    "#### Ridge Regression (Alpha-.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "0c770e85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Ridge Regression at Alpha .001:\n",
      "\n",
      "the MAE is: 7.134280624021592 \n",
      "the RMSE is: 9.983424872151428 \n",
      "the R2 score is: 0.4534255408539932 \n",
      "the Coef is: [-0.27060981 -0.00550487  1.17473122]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge_model = Ridge(alpha=0.0001)\n",
    "# Can't use solcing for Ridge Regression- only for Ridge CV\n",
    "\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "\n",
    "# Metrics\n",
    "\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "coef = ridge_model.coef_\n",
    "\n",
    "print('Using Ridge Regression at Alpha .001:\\n\\nthe MAE is:', mae, '\\nthe RMSE is:', rmse, '\\nthe R2 score is:', r2, '\\nthe Coef is:', coef)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba73213a",
   "metadata": {},
   "source": [
    "#### Ridge Regression (Alpha-0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "a0ec348b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Ridge Regression at Alpha 0.0:\n",
      "\n",
      "the MAE is: 7.134280616424499 \n",
      "the RMSE is: 9.983424849795906 \n",
      "the R2 score is: 0.45342554330184204 \n",
      "the Coef is: [-0.27060981 -0.00550487  1.17473128]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge_model = Ridge(alpha=0.0)\n",
    "\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "\n",
    "# Metrics\n",
    "\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "coef = ridge_model.coef_\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print('Using Ridge Regression at Alpha 0.0:\\n\\nthe MAE is:', mae, '\\nthe RMSE is:', rmse, '\\nthe R2 score is:', r2, '\\nthe Coef is:', coef)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7803c2",
   "metadata": {},
   "source": [
    "### Ridge CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "a1365529",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Ridge Regression CV at an optimal alpha .005:\n",
      "\n",
      "the MAE is: 5.507843305934233 \n",
      "the RMSE is: 7.073327044109009 \n",
      "the R2 score is: 0.563817550889042 \n",
      "the Coef is: [-0.27137426 -0.00561047  1.41767531]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)\n",
    "\n",
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "ridgecv_model = RidgeCV(alphas=(0.005, 0.00005, 0.001), scoring = 'neg_mean_absolute_error')\n",
    "\n",
    "ridgecv_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridgecv_model.predict(X_test)\n",
    "\n",
    "# Metrics\n",
    "\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "residual= y_test-y_pred\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "coef = ridgecv_model.coef_\n",
    "\n",
    "print('Using Ridge Regression CV at an optimal alpha .005:\\n\\nthe MAE is:', mae, '\\nthe RMSE is:', rmse, '\\nthe R2 score is:', r2, '\\nthe Coef is:', coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "9f981a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgecv_model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "8d7052c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x19532c32b80>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = ridgecv_model.predict(X_test)\n",
    "residual= y_test - y_pred\n",
    "\n",
    "sns.scatterplot(x=y_test,y= residual)\n",
    "plt.axhline(y=0, color='red')\n",
    "\n",
    "# The residual are heteroskedastic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4853dc6",
   "metadata": {},
   "source": [
    "### To Deploy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "fca1b983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.25285576, -0.00537984,  1.29743961])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalcv_model = RidgeCV(alphas=(0.005, 0.00005, 0.001), scoring = 'neg_mean_absolute_error')\n",
    "\n",
    "finalcv_model.fit(X, y)\n",
    "\n",
    "y_pred = finalcv_model.predict(X)\n",
    "\n",
    "finalcv_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "fb1f24d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "00549be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['house_model.joblib']"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(finalcv_model, 'house_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "40b82eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load('house_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
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    "#### **Conclusion with Model Caveats:**\n",
    "\n",
    "After evaluating different regression models, including Linear Regression and Ridge Regression with various alpha values, Ridge Regression CV with an optimal alpha of 0.005 was selected as the preferred model. This decision was based on its superior performance, as evidenced by the lowest Mean Absolute Error (MAE) among the tested models. However, it's important to acknowledge certain caveats associated with the model.\n",
    "\n",
    "The model's ability to accurately predict house prices relies on the assumption that the selected features—house age, distance to convenience stores, and distance to the nearest MRT station—retain their significance in today's real estate context. Given the dynamic nature of the housing market, changes in economic conditions, urban development, and societal preferences may have altered the relevance of these features over time.\n",
    "\n",
    "Furthermore, while the model identifies significant relationships between the chosen features and house prices, it's essential to recognize the potential existence of other unexplored variables that could exert a stronger influence on property values. These unaccounted-for factors may possess considerable predictive power and could significantly impact house prices in ways not captured by the model.\n",
    "\n",
    "Continued monitoring of market trends and consideration of additional influential factors will be used as the market evolves."
   ]
  }
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