day3_2.ipynb

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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ebaf11ac-e1c3-4826-81a0-696bbc7c43e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9da5430a-9ef1-4862-b5b3-7a6442ebf7a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/fenago/DSBook/main/Chapter%2010%20-%20Analyzing%20a%20Dataset/Datasets/bikes.csv'\n",
    "\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32cbe2a8-d5a3-462b-9e26-e3ec68d66da7",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81        0.0       3          13     16  \n",
       "1        80        0.0       8          32     40  \n",
       "2        80        0.0       5          27     32  \n",
       "3        75        0.0       3          10     13  \n",
       "4        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "id": "c68e6ec6-68f4-432e-815d-617c4e334068",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>2.506614</td>\n",
       "      <td>1.116174</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>0.028569</td>\n",
       "      <td>0.166599</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>0.680875</td>\n",
       "      <td>0.466159</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>1.418427</td>\n",
       "      <td>0.633839</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>20.230860</td>\n",
       "      <td>7.791590</td>\n",
       "      <td>0.82</td>\n",
       "      <td>13.9400</td>\n",
       "      <td>20.500</td>\n",
       "      <td>26.2400</td>\n",
       "      <td>41.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>23.655084</td>\n",
       "      <td>8.474601</td>\n",
       "      <td>0.76</td>\n",
       "      <td>16.6650</td>\n",
       "      <td>24.240</td>\n",
       "      <td>31.0600</td>\n",
       "      <td>45.4550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>61.886460</td>\n",
       "      <td>19.245033</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>77.0000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>12.799395</td>\n",
       "      <td>8.164537</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>12.998</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>56.9969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>36.021955</td>\n",
       "      <td>49.960477</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>17.000</td>\n",
       "      <td>49.0000</td>\n",
       "      <td>367.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>155.552177</td>\n",
       "      <td>151.039033</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>118.000</td>\n",
       "      <td>222.0000</td>\n",
       "      <td>886.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10886.0</td>\n",
       "      <td>191.574132</td>\n",
       "      <td>181.144454</td>\n",
       "      <td>1.00</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>145.000</td>\n",
       "      <td>284.0000</td>\n",
       "      <td>977.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count        mean         std   min      25%      50%       75%  \\\n",
       "season      10886.0    2.506614    1.116174  1.00   2.0000    3.000    4.0000   \n",
       "holiday     10886.0    0.028569    0.166599  0.00   0.0000    0.000    0.0000   \n",
       "workingday  10886.0    0.680875    0.466159  0.00   0.0000    1.000    1.0000   \n",
       "weather     10886.0    1.418427    0.633839  1.00   1.0000    1.000    2.0000   \n",
       "temp        10886.0   20.230860    7.791590  0.82  13.9400   20.500   26.2400   \n",
       "atemp       10886.0   23.655084    8.474601  0.76  16.6650   24.240   31.0600   \n",
       "humidity    10886.0   61.886460   19.245033  0.00  47.0000   62.000   77.0000   \n",
       "windspeed   10886.0   12.799395    8.164537  0.00   7.0015   12.998   16.9979   \n",
       "casual      10886.0   36.021955   49.960477  0.00   4.0000   17.000   49.0000   \n",
       "registered  10886.0  155.552177  151.039033  0.00  36.0000  118.000  222.0000   \n",
       "count       10886.0  191.574132  181.144454  1.00  42.0000  145.000  284.0000   \n",
       "\n",
       "                 max  \n",
       "season        4.0000  \n",
       "holiday       1.0000  \n",
       "workingday    1.0000  \n",
       "weather       4.0000  \n",
       "temp         41.0000  \n",
       "atemp        45.4550  \n",
       "humidity    100.0000  \n",
       "windspeed    56.9969  \n",
       "casual      367.0000  \n",
       "registered  886.0000  \n",
       "count       977.0000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35f616f4-3868-4baf-aebf-0ae448c01125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      0\n",
       "season        0\n",
       "holiday       0\n",
       "workingday    0\n",
       "weather       0\n",
       "temp          0\n",
       "atemp         0\n",
       "humidity      0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "count         0\n",
       "dtype: int64"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d17777ba-6436-408a-9a1f-105969e196db",
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8976</th>\n",
       "      <td>2012-08-16 09:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.52</td>\n",
       "      <td>33.335</td>\n",
       "      <td>54</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>41</td>\n",
       "      <td>296</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5543</th>\n",
       "      <td>2012-01-06 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>11.365</td>\n",
       "      <td>69</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>2011-05-06 19:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.14</td>\n",
       "      <td>25.760</td>\n",
       "      <td>52</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>39</td>\n",
       "      <td>253</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>2011-03-07 11:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.605</td>\n",
       "      <td>48</td>\n",
       "      <td>30.0026</td>\n",
       "      <td>19</td>\n",
       "      <td>41</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4862</th>\n",
       "      <td>2011-11-15 16:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21.32</td>\n",
       "      <td>25.000</td>\n",
       "      <td>68</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>31</td>\n",
       "      <td>240</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2471</th>\n",
       "      <td>2011-06-10 21:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.16</td>\n",
       "      <td>36.365</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>55</td>\n",
       "      <td>163</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>2011-08-10 23:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.34</td>\n",
       "      <td>32.575</td>\n",
       "      <td>37</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>14</td>\n",
       "      <td>94</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8141</th>\n",
       "      <td>2012-06-19 14:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.16</td>\n",
       "      <td>35.605</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>61</td>\n",
       "      <td>166</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6146</th>\n",
       "      <td>2012-02-12 08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.790</td>\n",
       "      <td>53</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3818</th>\n",
       "      <td>2011-09-10 02:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24.60</td>\n",
       "      <td>25.000</td>\n",
       "      <td>97</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime  season  holiday  workingday  weather   temp  \\\n",
       "8976  2012-08-16 09:00:00       3        0           1        1  29.52   \n",
       "5543  2012-01-06 02:00:00       1        0           1        1   9.02   \n",
       "1917  2011-05-06 19:00:00       2        0           1        3  22.14   \n",
       "1030  2011-03-07 11:00:00       1        0           1        1  10.66   \n",
       "4862  2011-11-15 16:00:00       4        0           1        2  21.32   \n",
       "2471  2011-06-10 21:00:00       2        0           1        1  31.16   \n",
       "3385  2011-08-10 23:00:00       3        0           1        1  30.34   \n",
       "8141  2012-06-19 14:00:00       2        0           1        1  31.16   \n",
       "6146  2012-02-12 08:00:00       1        0           0        1   3.28   \n",
       "3818  2011-09-10 02:00:00       3        0           0        2  24.60   \n",
       "\n",
       "       atemp  humidity  windspeed  casual  registered  count  \n",
       "8976  33.335        54     7.0015      41         296    337  \n",
       "5543  11.365        69    11.0014       2           3      5  \n",
       "1917  25.760        52    23.9994      39         253    292  \n",
       "1030  10.605        48    30.0026      19          41     60  \n",
       "4862  25.000        68     8.9981      31         240    271  \n",
       "2471  36.365        66     8.9981      55         163    218  \n",
       "3385  32.575        37     6.0032      14          94    108  \n",
       "8141  35.605        58     7.0015      61         166    227  \n",
       "6146   3.790        53    16.9979       0          26     26  \n",
       "3818  25.000        97     7.0015      18          46     64  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "04d46d46-c19c-4e41-8e6c-f7fb11c24658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='weather', y='count',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f9e61b5-2b2e-4798-8a8b-a3bfe830c608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cNhVjNBZnU4vhCquNznhnkJUU9qwQgmylhJWofFnKYrI02SD0N54fx9MeRa9YeR0vjLFzeudhK9BGHF+iFUXESUFt1hEQ6DN5XjD5O06wkwiGsOvbHHQ2G3y/VKroH6FUsH0BzCf30Uz/KYhDfD6ISUw9H6wkLvtw0/hEfyHLqECypriugE+/58H6Hzbsv5BCvsH2dpyJUZIYEEXSBFlI29qXcMPLP1oXD9l2YBu21E8TFhYlv8TGtfX1Hbumn2tQoDUGPO01rD4imfEXF5GhiDgpcPfuRbW3128M4wZ1lI3AHCQWw+Tz9ftpXScIeDDS69bB9Z9aeMHdWVcuKMNp/WyRTS2AX6ypmbBZP1s85LHzMaTztLX1Q3a0kjWVaO1lSOcZ2H4vA7+6C4qzEE9x2bIecsbD6BqpDhFanJYGo3TB4AXNL2ioFNcdStsq4sQkMhQRJwXNlF5xbCi5ELqe0Bq0JrZ6dcPxVjqJV2soarYvlKMh9zGg0mwc28VgW4ohx6bf9Vg/M81AauWCzzE3a6rVaaWgXZI91ftQ8PL0F3Kw5XOAgHKglGPdzBS3p6sNjUxQPMG6/sV9zrmrj0gU8MVFZCgiTmgWKovRzPWj0m3guphSCeN5iG1jtbXR+6cfbTheijOoJOgCgftEQCWC7ccVEQYKxUCwLxQAxPiQXljWUK0gXzlraqYwU8k6qnvC37cTELDK04LNqG3T7vtkbAdtNEoUaSfNaH604VoxgizfZtsjmfEXN5GhiDhhWYwsRjPXz2LkwJ1WH1EWqmYC1q7BTi5QPwmCQrp7vxSkv3asmDfusCiKM9B+ap2biNZlMDsSVHQf4lpNs6aSHTiJDjoSHfVP+I+9JVhJ1DDk2PiGuhoIX/sMzQ41rFRSTooJN9Mwhp54VyQz/iInMhQRJyzNAtQ63N5ssp/P9bMQd1DXq/oYvnMYjUJsMB4YX9P1qr5DHgscfrU186yeyqm03TXusuxoYEAyw4e81tDkdi5+dIxX/dLQOW2YbPf5+csL/PzcHB2JjvoBxFNsVV4oP27R7/lMA7NWfXLkrDcLBRpWKtNejrQ2ZJWgCVIqu7ShaLyGfedrchQZlBOTKD024oRlvlakTWUxDpP0tRvpG1DYLaCLBrsF+gYUnPuWhRXs1VZbiwSvKhZsXwDN0nuHb/g0mfgVjam0uXFIdC3oWq96fJrfvluTzhryCUhnDVffrVn11EyDJtRXXzLApq52RpXQ5mtGlQRGoknwf9abrXTeq/T9NoaSUpxtbM41NmcbmzSKUqnJvk16hDctLmymVRVxzDkuhkJE/kREHheRx0TkmyKSEJEuEblLRJ4NXztr9v+kiGwXkadF5KrjMeaIY4+zfPmCe1MfNmddSfp9/4sV165k9e9brLh2Jaz5zwzffHvj5N3MWCyi2hpoEAWc+PLnm4sK3vEwj1zw33gsk2Tf8H4eyyQpWq2Q6l3Qta65t4RrQ9ERjAhFRyja8Jb7TcPEfWvhOZxkF8lQCzaJNDUSZebWRvRogwfk/SLGLZD3i7iAo82C6ihq274ezKBEHHuOuetJRPqBPwbONcbkReTbwLuAc4F7jDGfFZFPAJ8A/lxEzg2/fx6wDLhbRM4yxizCeRxxotPM7bKo2oQjwZyU1Ynr1pNXHmPuJG7BxbEcelS6uetrEdXWzdxU7q5Z1JLlUNNJWxIJZp7bzYe29eDEbyCZssi7Pp/L/jfOzs7Smkof8lqtsxYS8xg34Ao4Boo2LJmu3y9hJci5ORKdZ0F7jSEefyIYy5y+E0BDbYSDcEapSIc2dRlagz1LGF1AHUWztq9RYd6JwfFyPdlAUkRsoAXYB7wFKJfB3gL8TvjvtwD/zxhTNMY8B2wHLj22w404mszndgHou/5T2L296Olp7N5e+o5Af4cFj2v3dva7QbWxUhae9tjvjpPZvb1x50VUWzdzUzltFmZquG43UyiwJ96BYwktsaBivCVm8y+xtzKbyy7oWs5pp9PqwkotnKktVmqh1YXxrvqgdcEv0OK0NGhFtdpBaqwxpvIF8PK+lzdUZrsCH5mc5uYDI9yxZ4ibD4wwkMuz3nUa9/Vd1vStYcOdG7j6u1ez4c4NQdruQrSqIo45x9xQGGOGgM8DzwP7gWljzI+APmPM/nCf/UBZGKYf2FNzir3htgZE5I9EZJuIbBsdbUzfizgxOVgvh/S6day4ZZDV99zNilsGj5mRANibdol5oEQhBK8xL9jewFlXwhs+3yCr0TSQPbUbfBfGt8PIEzC+na4LBVNy0flgMtX5PKZU4vtnv46kY9Ud/ljLpXxO/ecFXavrQx/DJLrR2sJoD60tOq00/zHQ0TBxX3vOtQ0TelIUas7a3fbhPZ0XNcqFTOcZKM65N0oxMD3JNWdcUycWeGHvhWzesbkuHjGeGydTzDSMKyrMO/4cD9dTJ8Eq4XRgCvgXEfn/DnZIk22NSmWAMeZrwNcA1qxZ03SfiBOPZlXVRytovRjueGWc3/thHlXSuI7guAbLh399ZZyrm+yfORBn4sc9uHsLOMt76FoVJ31Wkx1jaRh7OmgeJAp8l3THPnjt6UwM99a532aeipHPFGiJVf9U867Pvt4BWP/xQ36G9Lp1cMOmOrfeig3v5W2rpGl20fk959dtL07tRqlgohCCPzyt4ItPfoPvrv3j+oykX50W7OXUxCN8j622ZvOOzfQke0ikgrqNu56/i854J23xwNWUtJOQAEecxrTdKOvpuHM80mOvAJ4zxowCiMj3gMuAYRE5xRizX0ROAUbC/fcCp9Ycv5zAVRVxktCsqvpIBa0X08d67r7dF/Vw2zU2r906Q+dkicnOGD8eaCPzstObHjtfzQfMqeVY6pJOaqD2UV1Inx4n/bnBuvO+b8kI129+nFzJI+kEMQrXN7z6glE23LlhQRPqg0vO5qZX/Rf2TOY4tbOF9y1ZxeXLlzTdf25h3CXfuAAlVddD+altV00P7QpWHMgFFfDl4kBgMJ2oBKkhMAq+9smUMnQnuyuHJ6wEM6UZvnvVd5t+jojjx/EwFM8DrxCRFiAPvB7YBmSB64DPhq/fD/ffDPyziHyBIJh9JvDAsR50xNHjaAWtF1Ow12zft26e4eYrFTf94bK6CuaNTVwh89V8jP7tF9DZbP0YnsjAJQnSS/NUy8BVEAyfw+VnL2Hwsf/NksdvJmFyFKSFf3/JG/n6vj0Lqkv4yVOBoXEsoSPpMJIpcP3mx7khPPchkcblu2GeZKjel8DETihMV4sDE+0MObGGhkoxFaPk19dxR/GIE5fjEaO4H/gO8BDwaDiGrxEYiCtF5FngyvA9xpjHgW8DTwB3AB+MMp5OLtLr1h2VoPVi+lg32zeZbGP9rzsPKdsN89d8lJ57rnEMSjPxRDKcbaUqXqibCGD85K9Z9fiXSZlZbDQpM8sdUz/GKcwsKI30pi07G4LhjiXctGXngu7hytZT0EC5KWr53ytbT2nc+bIPg+VAehn0nhO8Wg797ac3BKnb4m1YyjpkgDuqoTgxEDNXXfMkYc2aNWbbtm3HexgRx5Htr78C1d5e103NGIOenmb1PXe/4H2bsfu69UHWVo37TOfzuHv3Elu9uv68+3+NLhpWv7k24cKwta2bwfNeW+9OuuWd4GapDdVdvfwU2gzIKS+tG+tMaYY73n5H3bhe/df/QUfSafhc03mXrX/+ukN+rq17t3L9lj8nU8rgAxaQjqW5Yd1fAzTGOXKFBvn0rS2JSmV27crsmjOuYdvwtsrxa/rW8O2nv03GzeBrH0tZpJ00N7zqhqbG+UVWxf2ibukXSXhEnLQsJvZxuHGSrg3vZfdffIzJ6VnyliHpC50qRXLlSkyhUH9eP9CWqnXqbE0m2dSewJlTlbzR9hlwqZtm+j2XUcuitrxvPrfNqZ0tjDQJhi/vbGnYtxkDywe4Yd1fN0zI0Cjhsen+TcGKa06fjAFgI4dWj3375rczWZzEUha2stFoJouTfPGhLzaV+phPFgSaGLAT14C8KIgMRcRJy2JiH4cbJ3k4/jAPnJXh8m3QWYJizHDnmgyvWruOnm8/Un9eX9h/YYkblvYyZDv0ey5TysIxpi7gCzDY3sZAvt5ts346w6buLvDyh+zv8L51q5oGw9+3btWC72Mz5dcNd25oCFADfPGhLzadpBeiHrtrehdKFCr0iCsURgy7pnc17FtbxT33+jk3t6D4TcTCibSeIk5aFhP7ONw4yU9++HXWPgbZFAwvCV7XPgZ37bm74bxjr/b4nxemGbVs2rRm1LLZEXPw5kSIE1aCoViCzL44u+/pYvvmXnbf08VFOwwbC/aCYieXn72EG645jyXpBNN5lyXpBDdcc97CAtkHYWh2qEGWw/M9dkzteOFaTVJfAR5skqZOm2bXT1gJdk3vimRAjgLRiiLipGG+VNiFTvaH03jowvsDTaW8EziUik4gxnHh/SXSn64/7w1/fy6O0STD9KGkAccYRpSQHtteyRgqJDt41chShh8qIUqjHI1XsBh+qBPnJRfA/kdAFyEzBh2/gnmemC8/e8lhG4a59Kf6Gc2P1slyjORHcFTjU/7g44MLeppfmV7JzukgyB6oTRm00axqa1z9NLt+wS+ANGpQRTIgh0+0oog4KZhXfXU+tdcjTPe0IWdXow4GyNnB9rkMOTESyg6znQCRQFBPhLwuYZQir0u42RGuuV8jHaegWloRJ45qaSXfkmLkzocZ1UXaRDGqi2x6/O/Zev/fHZXPtnXv1oZMpPXnrW+o4va0R0+yp+7YxUzSH7nkI7TH2lGi0ARNktpj7Xzkko807Nvs+q7vsjK9MpIBOQpEhiLipGAxqbBHgrmT53inQyysQSt7SmIeTHbFG47tbz+dAhKkkjoJsBwcYzjDM/RiMwP0YrOx4NC6bwRp6w76USw5F7pXMyZ5uqcNSbEQhKRYOMDgk/94VD5nM+lvoEHC44z2M3CsRg2phU7SA8sH+PSrP81Le1/KkpYlvLT3pXz61Z+etzCwQUJk7UY+cslHmhqQSAbk8IhcTxEnBcdSBqRZxs131xo23KkQNEUb4h7EtcXtA228ac7x61/+UTb95M+gMEVCawpK4YrwcT/NgF9jWJRhd+sY3pysKVzDVJtQ9DTGhLqCShjSxabj/clTI9y0ZWe1MnvdqgW7ouYLGg8+PsjNV91cN4mX7wtwyCB7ef/5At8LYb59F5JhFbE4IkMR8aKjWSzCWb6c4u5dmMwsplRCYjEknSK+YuXhXeubX2TiG7fgThZwOhN0vec6BrsebZg8nzgzwSBF3n4fdE5rJtsVdwy0k7nkzIZzDuQKbJyeZTARY8iBfg3rJycYwEC6ZgJ380HnvftKdVlTcV/Y/HKDY0qBJ98IWa1YopIN1/rJUyP86suf4S8e+w+cWRc35XDXI6+j8+2v5sLnbzlkK9XFSH8PLB9Y8CR9sPTWw53Uo/7cR57IUES8qJhPliP+spfhb9sWPF5bFqZYhEKB5O+944Vf65tfZPgLNyGWoBIW3kyR4S/cRPrN7WReVt/7ocdq4aHTC4ytUCTEpmA0LjNs7Liw8cT3fokBnWDAq6llUG7QuS6eCpoQuXnQJdLXboQr43WG8fFzDE+ueg7HQMIIBQEthpfMNioQ7vj7z/GGX9wJlqBjgp33+O1f3ME+63Y2XNjKULui39/D+rs+ygBfaDAW8wWN53MnLXSSHnx8EFe7TBQmcLWLoxzSsfSCA98Rx5bIUES8qJhPUyn3059i9fRgZmfRpRIqHkdSKfIPPAAf/MALu9Y3bgmMRCyQ+ZaYhS75XP2zDDe+dE7TnuIMZ3iaDt9nyBb6PcP6gmLgyR/B2j+uP/HUbjIHWpjYNok74+O0WXStSZPudgPJ8JqqZs66Eg7UB+TXZEdZOu7x3XbFfltxiqd5+7TPsmJjh7u12wIjYewgHGlsKIlP5kmH0ZdBGzCqYFMSNt77V9CSqFsRrOlbw+Ydm4GFuZMWyvbJ7WTcDIKgUHjaC4yG30TCPeK4ExmKiBcV88UidDZLfPlypKeadWOMOawYhTtZQCXqe0GIo1g+6VcmtOrkWeLjk1MMFNyqdhMGio36TZnxHobvOYAoH2VpvIxi+B4Xrl5O+uP1Vc3NVlAtIxlWmyR/sbwaONZa06kae7A4sy7GAceUUBg0woGYRdd02OqUsKeeKL7oj5Kb4w7avGNzg9TGkfD5u8bFYLAkNMIInvFwTWQoTkQiQxFxzFmM9Pdc5pPaUK2tjVIZi5QqnxuPkJjCuBqJVY2FcTXpzgQb187xxU8/BsawYUknQ7ZFv+ezfjrDQBOhv4lHQMRDqUBqT5SPNoaJRyA9d98mKyhxHGafdvnCy2DEMSxxhbdPe7w+fXpDgPgD7Yr2jIvYJtSpNeAL0+0GvCLlaHhCWeywhP4mgettw9u4+aqbF3QPF6q/FFMxsjpLkWoAXhBiKrag60QcW6L02IhjyuHWO3RteC+mVGroBNd53XVNty9UgqMcj/BmipV4hJ/z8QoaXfKDc5Z8jG/oes91DCwf4OarbuaOt98RTKKWw6audkaVCqqtlWJTVztbk40BZvfACGLV11eIZXAPjDTu20SVttDWQTEP05am1Q9e/6FH8f9e8tKGVNZbXinkEbQngUCtJyQ8wx2Xwi5L8axjs8tSTBgfRB1Wsdp8qbTNKrO7k92VKuxyD25B6vpTRJw4RCuKiKPK3NWDNznZNMZQbnt6KNLr1sH1n5p3RTJ5yy3obBbV2krnddcteKXSLB4BIJZgtcbqsp7S7/5Iw/GD7WnOezbHax8wdE7DZDv8+FJh8Mw0c5+nnWQBL6cQh8oTvXEFJ1mAwTfVZSI5y5czs/cAI55Q8jQxW2HrDDNdMdKOHVZxx8knO7h15Of0JHvqVgTbV8K/vN5i/b0uflZhtWqeu6TE1jNbEAMWhpIIY7ZFnwkC1Q2Ba5VsGFezDKmDpdI2rCoMiAiOOHVV2LPF2QU3ZIo4dkSGIuKo0cy/Xtq9G3vZsjo57sXWOzST2shs2cLMbbdh9/Qgy5djCgVmbruN5AXnL8hYzBeP0AWfVT//1aHH9LzNW+82+Bbkk0I6C2+92/Av8cY/sa5zcgz/MoV2g26oxguawXWdnQmaFyU6g9d//xjTl7yD/ONPsFRlsS0fL28xJnDHK7vY5ZRwLYOjbLrtOLnsOIlU/YrAwube1T7XtgYZVj5wV+8MPb5PxrJxEWJA2ggprcnNjb0UZlg/Pg46UTcuaOzRvZhU2qyXZWnr0rqsp6SVZDg/TNyJR4J+JxiR6yniqNG0Wtpx8Efrg65Hou3p4VZmO50JjKvrthlX43Qm5jminqt/aXAdCzcW/Em5MYXrWFz9y0YJj3S/T98l09hJjXYVdlLTd8k0ALu/X2T718fZ/f0imT1C28y/sPSSqWDfUrDvD16n2XraNJ7xsMTCMx77svuIWTEmChPsmt7Fs5PPsmt6FzPxFpZ6GkfnwRgcnWefbdNpFCuNxZnGYqWx6DKGrGU1VjsXFAM6AbGWsLKvBVQs6Dkxh/5U/4LlM/pT/TiWw8r2lZzZeSYr21eS9bLYyo4E/U5AohVFxBGhWYC6WYaStaQXb2gfOp9/wW1PF3qteVcqz9wVNtepulK63nMdw1+4CV3yEScIYpfjEQthecZhn6MQpRBRGKPxRbM84zTubDSpZS6pZdVA7uy+OMMPtiN2oZoJ9VOLrpdNkaUFx2h8FJbRgIPCEPxf9e8nVZKx/BgigoVFSZcoYliePJ+P9T3DlF2kw4shVgtFnSFpqr2tCyaQFmmog/jiBcFKohYnGaTwzmH9eevZ9LNPQWEPCd+jYNm4iXbWr/l4833nVnFrl2Wty+r2iwT9TgyiFUXEYTNfgFqlUphC/ROmsh3iq1e/YDnvxVyr6UrlmbsC18kcF0/6kvPo++j7sNvi6IKP3Ran76Pvg/6L2X3dera//gp2X7d+3qB7esVqTnG6g4Y72sdWNqc43aRXrG7Y10fhYaGDHCQ0wvhTKUQZlGWCFZFlEPGYeKSFwoMKPy+Io/Hzwlv+A175rIdnPIq6iGc82mPtzPqz9CR7iKkYGk1MxUioFL/Wz7Ld6WFCVrDd6WHIGCbtOHntYtw8ee3ixlOsf/lHGz9Yx4qg+K8WNx/UecxhIFdg4/g4vb5mRil6fc3G8fGg693cfZtoNZ3RcQa2Vf/sGgn6nRhEK4qIw2a+IjhjTJCJRH0zoCWf+PMXLOe92Gs1rFTu/VLgOomFVdGxFigF29Prf1gXqJ6vCpzrP0V6abFuVdJ19RW4Nw9xWqwPSR98pbRHltHP83jYmDCU62YtlKOpbb4glsGdtrFbfZQdurBsgy+G1/9SeODsoA+2wTBdClxXc9NLs14WxMdRw3gYHISEdugqFugxmiFL0e9r1o9PNJ3QuezDgWEtUVcxzmUfbty3WcW5zgX3qUnwe+7qZbFaURHHjmhFEXHYNEvhlEQCk80eVjOgo3Ktqd3BhFfLPK6UeeMeX/58w6okPfIN+ja8sfn1n7kryBr64gUw+CZ+ZNYyQxqNwgoEtXFafXzfCuIAhJlQJnBbKcsgUPnybOidDt6IBI19DAYlir2ze8l5OVztkvNygAdofAwKwcdQUiWmLLh5ZJI7Dkxy88gkA9NjcM//aLzhZ10Jb/h8UDFemApe39AYyF7svW3GfIqwUSD7+BOtKCIOm4P1mz6cZkBH5VodK4IJPlbz1Ft2pcyJXbjPTaJ6ltYdLokE7vPPgEo0rErSxbtJ31JfWV1xdalYxai8RZ7l+85vc4l5jD49zLDqI3HOBOZBjdYWYpczoTSxNh/jC2JXg+KWJ0y3gS12JWOor7WPvbN7KzGLuahwpSIIPpqMwGX9veSU0KIN185kef/Ys83v2VlXNjcMi7m3CyQS9DsxiQxFxGHTteG97N/4F7j79mE8D7FtVCrFkk/8+cJP0iTA3GxyWmxv64bA99VXkJ7+RqMrZeUAmZv+hImHwc0YnPQuxPMxMw7SXiMLUijgtPoLf3Ju4upKtfhcmH2Uj7b+ZaWP9ctWbOOT8a8z+3j5+kLXRQoyGXb/qp1JhLyjSLqamG/Y8nJY2b6ycpm8l5/HSAT12GWnliFYY6AUxhhsY8gL3NSRAoH3z/PjWRCXfZitd32UQbvEkApVcT3NQDM3VcSLishQRADNM4mABUttGBNOUmHf58r7hdDkqbucq585EG8YQ99BCu7mfqaGGMPNt8OG95Au3l0nvpe5dRPDWzViKVRc8HLgF8EURrFjrfVG6VV94M4u7Ml5andD1lBra4pz9ThL0gn2TuZY3tnCm974B+zdtwJr6Zfpcvcz4ZzC1Cs+xMy2D/O9XsXrHgi65Y23C3dcKjy+Smjz8nW+/DK1fafLPwUbcAEH8CQIots13/OM4da29GEZiq0tCTZ1d+MUpmnzPUYtm03dnWxsSTQUHR5NFiojErFwZFF/0C8i1qxZY7Zt23a8h/GioHZCLU+I/sxM8MTZ3l43STbz++++bv28vSBW3DJ46AEMvqnRZVHKkRlJMXyfXTeu+cbQjN3XrQ+yo2rcVDqfx+7tbRjX7tedi5dXKKc6yWrXIOJjnbm23igtLc5ZfQhdF0H6ff+rcRU0z2ebtLr4gP0/K82EXrmqiyc338lVj95Nz+w4Y6lu7rzgCorn/m/GLIL+2iF5CRJkO067rG4y/OhPPhrUMdT+SYfLiNM1JERRMJrnVLCSqH1KNIAniofXP9pwHxc68W64c0ODJHney9Ob7F2wVtThUtvnotaIngCxDjn0LicuUTA7omnQVs/OYrLZBRWwFbdvxx8bx7hu0AvCdfHHxilu376wAcwTBJ34+fDiiujmBI3d555tGvhuVlvhZi3Eqi+4E0ujPcWK146x+s3DrHjtWGAkDsTZ/UALzxc0OxOa5wua3Q+0kDnQ2PaUyz4cuLZKuUCuo5QjX8jzmakrGckU6Eg6jGQK3PvNH/Kue79FR36GfLyVjvwM777v26T2KOIGgqk8+IobGFMCz22Byd3B68/+jveecjkN3icDL48tYUwJz4hmTAkxQ1A/UZm7BF8ULabxHm69/+8WrN80NDt0WFpRR4JaGZGoaO/IEbmefgMZ+cqNdZpIWmvip9W7TYznVdxIZeabZI0buj2Uqr5qXd0+hwY312k9pJc0unLcrIWxXLzh4aDHRCyG6uqav4hujvvKsbN4zWIMTarAndNOx9vzLOJUi9CMq3FaTUPNxc6f9DBh8niJGCp8Sh8t5vFv/AIXzl3pnHUl7Pt9+MVXoDgL8RSb7WuwlfCF/KfoywbB7P3PWLRIlk41i/INWgkzJs1V9wtfWWnRanSoiCRMWEJWCaOWok37jFqKTbOPcs3Yr0m32MwqCSMTkDCGnaVRejpWVp6wx6aeI0vggrIQfAL7cq3nNNzDwUf/Hie9hGQ8kOao1W8qv5ZXGq1Oa3OtqGNYB7EYGZGIhRMZit8wRr5yI+M33hgYAdtG53LgeZSGhojXTKBiN/5qzCu1YdtBOqfW1V4M4fnn0jRusNuFS/KkV1IXYJaOXtyh/YHhUQrtuuj9+4n19zaK1DUJGndd7DL8s3H03BhDk8B314c+xvD1G9HuDCIuxjgYA12XOA3ZTbl9+/FaHZQEhlFE4cUMued3Nd6bZ+6CR/4ZWvugYyW4eV4/vpkrRTErrWRI0aknSOZcLMdHTDDJK2Nolxn0VJxZJQyLwhfBMgYthm7fD91REr4abm2xaDEa3yhcBAeDC2SNT3ZS4/qzOJaiy+kmXRonI5ATaDFwbRHerxNBp72azztkFZD8GMPebCXDqivRxY6pHQ2tTGcKM4guQSl/yMrso8ViO/JFLIzI9fQbxuQtt4AIYgfFWmLbgbtoZqZOolulUkhr64JkuxNnnonV1YVyHNAa5ThYXV0kzmzsF920NqGlnYnnVzXk6kuiDUxYQyBhiNZoZHZ/w1M+I082uK/Sq9toW+3hjY1RfPppvLEx2t761qbxjfS6dfTdsAl79cXoeD/26ovpe3VwjjqcJKPtECvpoJ+DmwevSKykGe1ocsNrDViolZSiQMpM06/3sVrvoF/vI9bqYXyhXDFhEIwvqJTLjElAWJgXuIkED9hl2zzrOOyy7cAgKMW4UngEqrAe4IpQFAGzG7GeB7ObiUKRnBXnHNdlqedxjuty/mmvhVKm4R62GsUBNJ72Kp3oDmQPkHWzDS6eNrHoymeaVmZv3buVDXdu4OrvXs2GOzc0dV0dCdaftx7Xd4MsMGPIe/moaO8IEK0ofsPQ2Wzjk75tg+ti9/ZW3EHl1NZm2UVzXUfJSy/FHRpC+voO+eQ+rybTxDSsv7t+rLN/jd2/DD0+UQmSWy0+uiSNldXaDSbtGvdVZvsMM9vtBSvKNtRhNAtEu3keudTitXd5iDa4Njiuj/I1j6xbyjVzT9ok6ylGKXQjVZNXu8+eZfjBdoxnEMsERkMLP3wF2DrOUt8lJh4lYzHkeIzbNjFjUBg8gQOOExh0QEuQDlt+CjSAL2HBnRh8O0NJwyMxBx8YA64f/jE3JBPgzzKYMAyJpt8osmjChUtFDtwYg6e9xnhEfpIZBd8tddT8EHNsvfev+DMnT87LodHsn93PE2NP8LnXfO6IB5gHlg+wkY1R1tMRJjIUv2Go1tbA3VRrLHwflUo1zVBqJuc913U0c9tttL31reQfeOCQKavO8uUUd+2q9raOxZBUivjKlU339UZHsWu+p4cex26fI7TnJMGKh0FjKu6riYdKSGrJC+59MZ98xeVL4btXwOsekErK6n9cCm9LHWh0iTUpQlOYSgyhTCAQOM3EUyncrIXT6tN19iw/X93NqXoKI4JnFLb4qJoDy7UR5exFXRNXmhOar7xqTFjNHUwAGpgU+EysiBiDA7ShGMXjgIJOJ00Ov664bzg33CQe4dIvc6YUJ8lf6lFmveomjWbWm+Uv7/tL7vy9Ow/9c1gkUdHekScyFL9hdF53HeM33hgEqy0LfB+MofO6hamkzqe1lH/ggQWlwiYvvZTctm2BG8ay0MUiFAqoSy9l93Xr6wxN0+I67KAQrRY3D70v4ZHTrsP6RbUOQRVtEn31HdMW1fvirCuBz4eFgNWai4Fv/yfoiDH4u631bU/HsmDm9G248PeDGEWtsaF5rmR6WZF0jaIsQL/nc8ASWo2PSBDM9sUmZsq1EUEsok8l2UsRSxuMUDFEZWPhoGviFsHVy3dREey/TwxL4u1MlGZw0TgiKBEypsQZHWdUxpT38qxsX0nOzQE1ukyWxZqiZoMzU1mRrC8I+8ML1dd3GPbn9i/s5xBx3IkMxUlMsyK6JR/8ANDYCa68/VAsSs67CfkHHsDq7anWXMTjGMcm+6Mf4fT314nv9V3/qcbiumuvJD3yjSDdtOYp/5HTruND23pw4jeQTAXVzv819r9YPZ0l1ZGqXP+gvS/mqw6fWxthYCBfZKBU85js5oEmLrFdWwNtpFpjM7GdzJDTsHpILyuGnYz84LWlh/dMz/CZ7k6KGBLGUBSwjKHFQCmeClxuyqEUS2MVxgEfS2sUBo3gBlJQrHA9yuuPJ2NOg6Eqr0wm/BxiOSgED4M2Pq5fZMfUDnztYymLtJPmhlfdANRnPa1Jv4TNu+/AQVdWJJsSYOapIJhPbiTixCMyFCcpB1M+XfLBDyzYMMzlYFpLC8Hduxe7qxvprqasFp97DuN5TV1EK24ZbHR/fTNoXepOjobtSd/DZ7efimMVaIkFv9ItMZt7LrySJfd9m5a4dWi5j/mqw/f9fjDZ1xqPnjPJPLKTiSdbcGctnJRP10s06dPmzIhlWY85xibzwdPZd7+D8YI4hFdQ7Lu/g2Vrp0hfck71+FKOgfER/mJ8gsH2NEO2Q7/ncm6xxB2pVkSXKn0nxvJj9CX7yBanyJgcGkEBSQ2eaPIixA0URbANaKFO1kMDSgRtdKDEG+raVn5uvotGo31NQQKV2bkung13bsBpXUIyPwV+iaQVg2QHqjiGNvWOMKDObRVxYhMZipOU+VxEC/bPz8NitZbm0tTQhCuLWuZbpWS2bGH45tuR2DLUqQm8QoHhm2+n7RyLzJkX1e27a9WF3OobPjnz4KFlSJrJj2ezZL71ZSae6ajoP3U9+idw+msZfnAcER8V03h5xfBD7bgxxZRkK72t+xKa1u5GscGRXyXQJQMSfBkDpqQYeaSN9AX1KyUwDOQLDOSrEuAblvbS43lkULh4xBDSsTbELzDr5VAEUh0+UFSGi4uGpxyrIgB4VUnY4hhmVdVFlbISeErIe2XXmKBrohy2sivB7IJf4IsPfrEhDjA0O0RbSze0Vh8CEsbQ4uUqgewyCsV7zntP488h4oQkMhQnKYfrIpqP9Lp1TL3sZcz+278F8Q3LIvXbv71g49PM0IhlodrSdfvNt0qZzwC+5an/4G9XXlBZUQDkXZ/pC17Oij/60KEH1iQ7KbN9luEHkkiMiv7T8FaNemAL0tKB8iaC9F1HKNHC2K/zxHpzWJJAeTmmZj2GV1zMqjkrFXdKVyXCy58XgztrQ3a4UpjHKz4IP9lEZl+8zk2VGnDoWuHTrQldVRqTn2aHBT0aMirQdYoBMV/zq5hgi0IBrhK2xKAkEnTiC9ceRtkELZUEHa4oghscvFRqRsINuzK7Gm7hfDUM5/acy5q+Ndz65K3k3BwtTgvXnnMt77/osCQII44hkaE4STlcF9F8jHzlxsBIiEA8Dr7P7L/9GyMrVy7InZVetw7mxB3Sb3wjM7fdtqD2qPMZwFNnJzhr96MNWknv/MA7F/bBOlbAxE4oTINfAivGxGNxxJaK/pM4oFGUJorEOkugnEoVN16BbKGFvJUKpMOtPr5p/w7/3zPfh/SclQqZ6qN85R9BoWKhUAgm60IBue9rFPYlGH2wLeh+5wSrl3fdZfjuFcKe091KcWNBWWAMXWLTXRMU2G6Bh0EZjSJwMWVEQITYnFWCZ7zgfLUYQISSX6q4o8qtVufStL1pWMMwsHwgMgwvYiJDcZJyuC6i+agt2APAtjGex+QttzQ1FM0C6umlRdKvHYOpYehIwGVnAm9tCLDPl17bzAC2drbzgUduY9KFjNNCVzHDBx65jZUjF8LZSw79wVYOwK6fU9FU8l3cbAsqPkfGJNSDMi6I7QZGQhR4Qizl84n2v6qOyxi6Jv8POKfUnSPW7lOcsgiXFeX8Vpw2L3T4KASNKk4y+VRHYCTCfhRiG1LaZ90vLf7hdEgIFADX+Kx0NQVbkZTqJF4KS/jKGU61n2buKoHGMEL4QUBL+LnDXt1LW5c2FQvcuDaqYTgZidRjTxIOVyZ8oTx53vkQVnWXMcaA53HO4481jGmuKq3JTdN3yRTplcmKLz6zK8/wgx1Iy6GVapues1RCWlqgJiAO8yvFNuXGy6oB6tDF4xcVRgvKgUppnG+BaIwHokwlSUn70HqJx/suuLVyylzJ40vF6zk/PacQ8IkR9v9Uo11VsTPK8Vly6QyJZdXZWvDZ84MucnEYtxUlUcSMptvTeK7iv77frmk8NMv5nmFTVzsO9UqxyhhqG6QWRLhoh+Z3HlD0TGnGOhQ/eKXiwZU6vG6VyuwwR/crZaXoSHaciCqtJyqRemzE8aU8eXqjo/UZTsCKWwZZfc/dTbOHXgiqtTWITdTi+8H2OTSV63BnmHiYOkmLiYdB3JkFqcSm161r2vLUZLMLVoptRubR3Qw/2IZXsFAx8AoWbkHhu4IOPTLaCxYQSy7O03fJDHbCR5fATvj0XZIhtaxIruRhjCFX8nB9g/+KDzWox6ZXxTjlP/8Wyf4ETgqS/QmWXDrDQ2co3r+0g7cs7+b9Szv4eTJONm0YxwoF/AyeCGPYPN+h6PE1Z7kePb5mc6oF7AQbz/tDelWcGaPpVXGWuYG4Y7Hm66IdmvfeZWib1WST0DarWX+Hy0U7dLWAj0Yh2lpm/UD7aTg7zPap7Qxnh3G1yxcf+uIxkeqIOLZErqeTgKOV4dSM+Qr2Wl7zmoaCuabxBHFxM/XPJ27GoGL1SrMHm+SbtTydmC8m0+rDZ0+rDxBf3th5b+KJROjiCa9vg200KIOdbsWd8XHaLLouUqTbRsjsrf/TETQJx6lrRvS+dau48OwlsLyjoWgvfdaVpP9r9fjvfP6l/EOHi2MMbdowZik+193Oha/UvPE/wEcoOhB3Qfmw+RWQBDCQFEAsBjtauXntHzOw9o8r5/3q/zqV/9PZXjfpX3O/wbOgGK6Uio6gULz1fs3DZ7BgxvPjKKWwxMIzHqO5UUZzo6xoX1EnSb6Rha8yoqZDJyaRoTgJOFoZTs1oVrDX8prXUPzlvcGqQFy87SMMX78daesOsppqJ2/j4KTrneFOWvAK9UVgiw28N43JzIzQdf5eKAVFaZRysOVzwQFzjIWbT6CsIrUeArEM2rVY8brxSoCb1l4yj9sMP9iOWAQB5oLF8IPt9MU8vvkXr2gcXJOivbmuwl+d0YPVfoCkCZ7lk0bQCHee5TBsa950v6F3Gkbb4ftrhV+foThbq0owPWEMQ4nGVd221jS9nkdGKVwRHGNYOg0ziaBuIoiFC64jdE/Pd3fn8ZpIkOYKwatGI0gl6+lgkuTNDEBt06EXamgijg6RoTgJOFoZTvMxt2Bv9+9dgxTGUY4CsRHjowvjSCKOmyli9u3D+D5iWUgizpKL8nWV1V0XwfCDbQvKepqP9Lp1PPqeDzE1+A1SI8PMdvZy2UtHSS8JO75prxo4/sVXGgyFs+IMvOefQaxydwbBaIXT6oHvBmmovgvTe5h4KjQSNoBCVBAHHn0wxsScVdV8LVr3/dmfYmazGG3whvfx5scMxWu6eWbZLLZx8cRBtfSi3QM8cobi8VVUxlUUCGZpp2LACskO+jtWNlxryLHpcgt066pxHmm3aMuCSlZrV6yix0jHPNHsee1EUKBXzpwKRmjYNb2rTpJ8++T2BRmA2qZDUG9oIkNxfIkMxUnA0cpwmo+5T8OFHTtwWiTsmgaIQmyDO3IAaekKphBTztpJwJr/BDU9q9Pv+zA06Y09n9usWeD+wSVn88Pdz/Cf1o1yihlhv0Danwym1rLfJRxDZmehYULv+tDHGN74p2h3GlEGowVjFF3nzpYPrlzfzdqohAX4lawnbSy8KYhtf6huVcUNmxo+x8hn/gd6ZrYmK9aQysMb/mOKPR8/DwgK5vJeHmvG4qXPurz5fkPvtGG0Xdi8VjBieMv9BXqnNKMdmh+/Ose73rO+4V71ux6jokjWVEZvXiu890cGyeVxbYPjCZZWbL40TAOuOb7SXK9mo0IhCF2JLjKlTMUoeNpDN5EkdyyH9kT7IQ1A1HToxCUyFCcBzWoTjkSGUzOaSYOYosYTwanxfBhfgedjtbXh9PVVtut8nok7HiZ9yw/rP8NZjUq1C73+8A2fZseFF/LRts144pAhRbeZrM5tNRk7maFY4Dbqqw/89214I22nZ5l8xEK7FsqBzpdkSJ+RBjdbdT21LMNpy+PlAjnw4MMa/FygyOrNuGgflOUizjgTX/58w+dy9x6oZMZWhgj0jPnkD/yahNYUlMJNdHDVgSX89l17KVqGTALas4b33x4I/+USMJuAjlnNO2+fwTnvUZjz5L1+JsumjiSgSBhDQYTtq4TvXKG5+gHonIbJdsOPX+7zxKogZbdsF0zN2JJ2S8UgpGNp2uPt5Nwcfa19laynPTN7ECMNkuSudhfUIjVqOnTiEhmKk4RmAd6jQbPAuWqx0DkfHTOIDcYD42vEVoeViTTf9bXrYiYn62TK1267E+/1DkUJrlckEZaHmbr0nYmnUkER3ZzA/+iN30AXDHbSR1o1RitmdiZJ9uRIX7S6eoJSjq6X2+y/axa3FKTPijIYP7hMUQIdJaXBLoDZsaPJpzCNT+mhvHevpxlybPpdj/X7dmP9Rx9Zy1Bygt1LDiTCWEI+JSQEsEEbzdQ/3Axvq69lGchl2egXGGxvY8i26fc83jCbZfPprdx0ulSMhwv85+kM/9TeTqZGfTZhwBGhaHyMMfjGx9c+H7n4I0B93GGmOEOL3cJ4YbxekjzfTJK80QAcrGAv4vgSGYqIRdEscG4vOQVv317sFkJNJKHrIsXE86sojo5XlWJjMSSdIr5i5Qu+fuHZZ9GZTPA4HrZHZWICS7lkh2LIUwXMrEFSwsRLUnQtm8USVVFkdXMOxnbwdu2qjEl1d+FPFHFafZQFIEEg28DE4zHS587RX5IYvhdaBEPgpgrFMMIH6qBZkAHtNfr9Y10ximMl6h7dDSTaXG4ez1Z39D3um8ijE1LZTQCrfMpqiwk8G1LjucYbplQQX6nh/FKJ88dLdcZj/UyWgXye8512BhOKIQX9Gta4mm+lWykZL+wyKJXeF81EAXfP7K67VkmXWJleSc6bI0nexAAstulQlCF17IgMRcSiaBo4NzHiq89kxRt1XQpo/q5nyd34ldCPDybvQiFP8vfe8cIH4AVFDWKFk59IkKrrK9jmYiwgJpi8ZuLBVmLK0H7esspEr+KzFKcMYrlBC1jXxRvaB5rQlVSf9eRm7aA1a83nGln/x4HiXm2Wrx84bcSExiJcNBSa/IX1fuA97PurmwLNvxDlaHovygbtVcs9x8VitF1ozxq8ml5Nfm3nohDbh9k2gZ/8dRCsD9OBt6ba2ZSyw7Rbn1HLYlN3JxvHJ7n5wGjNGQw4rQxc+QUGalJ5N3Q5tIlPX81qIO/lmwaY1/St4cHhBysSH2VV27ef+XbO7zl/QZP6QpsORRlSx5bjYihEpAP4B+B8gr+n9wJPA98CVgK7gHcYYybD/T8JbCD48/xjY8yRb4v1G05TqY0mrqz5AufJN76V3T9+AHdvAWd5D12r4uR/cgdWXGNcQr89iKPJ/+QOeIEy5+KEM6bWwdNyOZvHKGzbR1ugTZCRpEQxte8s2l/RWpn4TCoN06N1AfZgdQLGF2obtBlfcNoE1tfHU9zpwBLUxhjKpWq+CiZtz4JCHPZ1Wrx87ofovxhaUogOsp5ESdg9SAdf5bEZzSOXeLz2xzZC2HbVg0IMELBcg2eD7YHlQcdFEqb/SiUdeLAjjmN0mHZLGNQWBtvTdYq0ALR0N6TyDn33amaKM2TcTGVb2klT8kvMZdvwNnqSPZUAd0zFSMfSbBvexvsvev8RncCjDKljy/FaUXwJuMMY87siEgNagI3APcaYz4rIJ4BPAH8uIucC7wLOA5YBd4vIWcYYf76TRyyOg/WumGss0uvWwYY3Bv0g9hRwOhMk172Wmdtuazjen9iH0yKIqro+jPZxn3/uBY81vno1xd276txZKp1CT0xi9ZyCnR0NA89xTGsvbtbUTfTm1itQHR3oiYmKsVFdXUhugjzCpDHkHSHpGjoRVrx6aeMglAW+Vxdj0AKWEVSYJqRM0PDh9kuX8fY5h0/c/HXsriWo/hq5kb2PsvOZNr50MZW+E++ZznB5MsM/X9HJ638p9IR1FPe8FtblC3Q8miQ1Y5htEzouEl7RMQm+gFX+s7YZsm3atE/tYBPGMGQ71DVJSvaASIM7Z7o4zaw7WxfnybgZHOU07Ltjagctdk1/cSCmYkclaynKkDq2HHNDISJtwDpgPYAxpgSUROQtwOXhbrcAPwH+HHgL8P+MMUXgORHZDlwK3HdMB34Ss6jK7mfuIj3yDdJvjoHTC26e3bfdgaglqGR73fF4GuNblaxZCLKhnNZGGz/fimbu9uSllzKz63km421k4zateHQaaFm5EuN5SHc18GzyeWa72nn3137Bnskcp3a28EkngXPgQCBqGK5I9OQk7rJOvrF2ktc9YMI+2IrvXSq87U1vZe7zaWzVGRSffQbjVWdPIQxZGMCEhWzA6flGQUJ3716MpfB2DVeMXUkZMjmbMcvQrn3GLIvPdHfylky2LgOp7Bzrv3QNAy/N17nE+OY7CYWpKvR7Lp3PW7z2lxJmOMGPXw6Tp7mBO66czaUstnb0NLhz5hqJ8iAmihMN+04Xp5kqTGFZVqVae192X9BGdb7ugS+QKEPq2HI8VhSrgFHgGyJyIfAg8GGgzxizH8AYs19Eyn9h/cAvao7fG25rQET+CPgjgNNOO+3ojP5FxELdSYuq7G7S4MfNZFEtk0BP3fFiK4wfqqHWZEN1vbwVBt9UmTQy8SvCZkT1K5L8W9/auFL59nf44bKLOefAsxU58W9ecAXXrl3Bkm98uc4llsvm+fHpq/mz4Y8FtRXDS5iZ0nSVB1njeprwcuy4YCn7Vk9VJs98soPBqUcaDEXqt36L4rPPUjuDKsCPwXi74IrBMUJX0fDWBx9puIUqlaK4fXsQZwnjJLiKYg/EjWAIutEZ4FfjKf7gboO2wI3Dkll4993wY55g4DOP1584ngpiE9pU4hx/+LiN+0AK3wrSaVuz8JZ7hKHLPDa8pJUhpz3IsJqZZjDRj6tdJgoTlayleQWfDA2uH0Hw8LCMVSnAEwQKmebdA/n8CzYWUYbUseV4GAobuBj4kDHmfhH5EoGbaT6a1YU2/fU1xnwN+BoE6rGHO9AXM4txJy2qsrtJgx+n3cabLTVIcMTPOIOu03Yy8XBNNtR5HumeKchQmTQm/vFrTVckk7fcgt2WROVGIVNErDj5YpJLdv2KYks6yABSgqWEL2eX8KE5ldnPXXgW7+y+Hc9UayuSrk+mpY02rSvptaqrC3t2H+fvdnjVT4XOCWGyS/j5axx+rp+vW5G8b90qTn/gAazeXszsbOUcOjeL7QrLJkzYV9RAHNSs2/A0bQq5IAgPdcZKjEFV5PiEhIErHxB8i0ow23WCP6DzH9BsuHNDfXD4zKvh0W/V/BAMpzwSYwafMVuhRWFsTdI3xB9L8uuXKnyE8bjDp3q6KBaGKVoOglQK5g7G3NqI8tKnVBOlTztpsrmRoKo9O1ZdwSTa+eq9/4NbH/zvL6iZ0WIzpCIOj+NhKPYCe40x94fvv0NgKIZF5JRwNXEKMFKz/6k1xy8H9h2z0b5IWYw7aVGV3R0rgifCGtnsrosU+39s4e7YEbh/bBuVSrHkE58Jek/UCuLlJsjszDHxcBF3JofTZlGcNNhtjSsSPTuLNjN4hXIwvEhaCrS604waTS7eSjo7zbvu/RZfL3h8YtWFOFd/mKRjkXd9/nL6k5SwcWtqKySVI5nPEltZ7U2t83m8RIzf/tchtG2Rb7FIZzyuvm2IocuXsrenQEfSYSRT4PrNj/OV53bT2tON9FTHW3jq8SDVAkI/FJAD1Wk1PE2biVnsnm50plAxNBOJErES1DqZCqLoC2smujNg+eBbMN0idM3A6P6HaPM9RjPDbJrcwcaCYiDeBaXpajpw1qLV0aTcaprujrhD1wzosOudRpiWYPiW1lhGw5ye2U1/xwoTdZXZvm7iUnQztPkaZkeCyv1QCuWrKstNKMRzsMUm7+W56dc3ASzKWESG4dhwzGXGjTEHgD0i8pJw0+uBJ4DNwHXhtuuA74f/3gy8S0TiInI6cCbwwDEc8osSd+/eBRe7zSfd3bSA77IPN8hmYzyME65GwlSgSp+Ts64Mgskf+TWs/yGZ7dMMb3HxsjpoL5rV6BJ4s/VZNEGLVIOXDRObJHwtCUoMvWqMFXo3vWoMS1ze8MQ9OJbQEgt6ZbTEbE6VUTK+Q9HTFFyfoqfRL3FwtI/O5zHGBK+lEl3JLlwxFFUJ4xUoqhKuaN7ygKk7p2MJe+IdmMKcjKG6QEz1n0qouupCWXWnzUK5M8RWriRx1lnEVq4kJcJ4u5AVC03wWhCLhOXTmQkK+MqFfF0ZKDqGpPYRZZHUPk52nMHSAbZ297Hh1BVcvWIlG05dQTZtMH79hG98YaQdPKBE8Fpex2jjU8RQAIoHERzvclKM5UYpuTmU71Jy63ti1zJSvjVKBUZUKW5ta0UM2BLc2/LrrU/e2vQcEceX45X19CHg/4YZTzuB9xAYrW+LyAbgeeD3AIwxj4vItwmMiQd8MMp4OjSLFQpccGX3WVcCn6+TzZ54XmF3+Q1Ng5qtXiYei6H9AiYvGN8glgSVxQUaRAGteGAoJNREqhZZayw8fBQWHp1qCpX1uWjfE1z+8I/omhljoq2HkdUdpPuyFEw80AM0BmepixroxprorYvdDP+3TwIu41oClVWt6UTTNj1TN/6kY7H57NfxsSc3163AMILqSEEui/YMyhZUVxcmOx4EjWvoenkr++6YwezYURFLbDEadalNq6cYdQy9rvD7M0KPX6BgFJ4QihoGd8AOu/CVYxEJZbHdhk1kcUTRBoyKZvBVNht+pEl6GrECo+GoQO+pKuRXbm4ndYFzwm0NCJQ8lx5f1/Xn9kzz3d2w9SpaVz5DTinsOfbHwiLnNikaXCRRId6R57gYCmPMw8CaJt96/Tz7fwb4zNEc08nGURUKnJNr7956xYKD4cUZBz9XQJQJahe0wXiA4+CNjdW1Qp35x6+iEh66QDXdRzQYISw8ABSW1qRied6y5Ztoy664pIrbhI5LfJKnlMgbh6S4JMTn5/0v450X7qq2Yl1aZMKepDXvk66ZvbQn+C312tt512f6gpcz9lqLqW/cQmp/ltmeVpadtpREzsO4BkwJnBjG2MQ6E0FFd42rDq+IWBbGpzLxK8viwmKe1w9ncXBxcZihhVnPwmnxsYrlbniGsRQ4JWGXpXCV4GhDWvu4onAMJDEQCgHuWGn4pyuEy39pkZpRzLZrvvVKePgM1TCnl1WenPDuaoIVR+OKQpg1RTppQhNjIUiQfluaqcQoWgzkReomIB+fmBVrjL0sYpKPCvGODlEr1JOYhWY9zcdPnhrhpi076wK5lzfpP737uvUUd+2qC+5KKkV85cqGNqRPX7oWnc0iEvjBQSoTpiQSdTEOxMcfn6yfePzgCTrWYdVkUhmwNM/rPlwnjkgoM1EooFoVbZcXWcYI+1jCo/YFvMH/Mf3d7XWyHJnH94c9Jkzlydv4wuRZJXaP9dA7U2C0LcEdF7yOl/3eOn64+29xCtMkfI+CZfOaXyje8JNcw+Tb/ftv4Onuu+pkMf74n12ShTZMwaveL9sQi0/S/7opPBQ2Gktgzz0deHlV6ZkNMK4tRmMW2SSVHhU/WCs8c7pwavpUpFJLEiPjZdln25zmuhVdp12O02TqD8erhYlQNj0wFvPPDxaCxVyjQoOh6LJa+el4PnDBhff8q06Bm5IKUQoLCx8frTUtdgu9rb0vuL3qhjs3NKTN5r08vclebr7q5gWd4yhx8IDPCU4k4XESczhCgT95aoRv3fgt3vno3ZU01G89dgV84J0NxiJ56aXktm0L3AqWhS4WoVAg+Y53NBibvxCFBaFxIHQphZORW0TEgOvjT5YQyw6+pQm1SAOtITsl2K2qruvc8Fafnu52RrMlSp4mZitKsTipyUl2/cdS8llhuLWLC8/ehneqU5feSwnSy4rANBNPpSo9s0f7fQo7k3RbOUpx6M7m+IN7/41/6/4FztIpkiIQxgjWPjQnZhEyfM/9bHp/N05hOgg8WzaFSYNTmEZ06EhzSyBCXuLs/F4v2hWUY+g8X9F17gy7H2xlEiHvKJKuJuYKHS44OlSPzcKGHxn+6UrDgTUxkj3VWpKR0cdwjKmpzDZYJmipOhdba6aUhV3+sVAz+TehfIZDPWr2tPXDpf+lzl35/ss+zO4D93DHrjsomiKWWHTEOnBsp9JWtaxUu5hq66gQ7+gQGYqIptx9y7/y7vu+jbZs8vFWOvIzvPu+b/Oj1jiX/9Uf1e2bf+ABJJ3CzGQCLSalkLY0+37yM771tF9nbCZ8ix6tmz9elRtHiAFt0KUSvgS908qOES2ClAwr3hKvWxFMnHY6rVmPdE9V63zfc3tp9Qp05mfIxJJ05mdIP5TFKAP29rqudSCklxVJL6tOjU/+rI+EBdoRlIB2QFzNmp+Os/NdFrX9NzpnQrtXOwEbgxqZwEmcSTIVSK0ngbj766DWoRYTBGC0G8iDaFcY/xVkL3L4+lUWr38AeqZhpF3RYkFcB0qyELxaxvCG+4WvvSxoKVt+IvdEOMWtbzPbjPK9LXfMa4xVNLJUw4SAK+CY4NXM+cEqFGO5Mba2JBg8pY+htEd/qo81ue08MvoIp7WdVhnrruldKDdorVpOz50oTOD6zcffLBYRFeIdHSJDEdGUi++7Hd+y8ZygC5rrxLGBS+67nbCmsULh2Wcx2RzYdiVYabI59NNP8e5de+qMTUd2Eo1gOdV9KTV5bq0oowqeFatsVtqn5Au7f+DhTo7idCboes976PrQxQ0xmVQxRyaZouTEgss4sSAD6MkSLK/vWkdrL+TGqlpLQGpGkUuAF4Z9heAj9k4HqavJJsNljitXTGO9geWXj2icikWFVxITxCSeSPDzqyx+trq6APu7r/rMJij3PcIAOUdYMm245oxruPXJWyu1CX06iDnU4ofGrBzlEUCMwRehx25lws1UXE8Hw2ldwsr8VMXgPiml8F5UrYVGM+vNcv3PryfjZvC1z1h+jIdGHqLbaqWtlAe/RNKKgYAfyplX1o8iuKbRUGzdu7XhnNf//Hre8ZJ3sHnHZiAqxDuSHPP02IgXB0tzE5Ss+immZDn05SYady4ruioVSFErBcbguEV8y8Z14iASxA8AD0E5DmgdvIbzijHVLwyVXhISTt5iNGI0yvPx7GWoU8/Fs5cxfPPtALS99a14Y2MUn34ab2wM33bIJtLEbYukYxG3LbDAzZa1p2om6tZeeM0nINEeGJBEO1PthkQO+ibh1JHgNZGDqTZDSftkvSLazZP1ihSdOecr3zNHKOTGYWw7DD8BY9sxYvDlYM/r1ZVVvCTo6hYMQUwi7tV/gpgHY+3C5h2b6Un2cFbnWfQkeygJzFg2eQlK+cqvFhAzhrgxxIxBEfwYJtxMQ+OipojgGp98GMXI10QzTM1/ACW/xGRxEoPBVjYGg6c9porToN1AO0u7iCnntOlQtl3jlxt9zOGLD32x4ZyTxUnufv5uNq7dSG+yl5nSDL3J3kXFOCKaE60oIprSsuJUcrv3kbeciup10ndpWXFqw74STvqmWCuRLSgMJcupexpxLQfH94itXFndtvtJ/KwOFhg6rMuKgZewmSmlaSnlsX0Pz7KD8yvBGx6uC5yP/u0X0Nksdk8Psnx5UBm+Zy+thQzZZFslwK18g9XmgOXUda2jNAuX/zm1vbR3Pv0SXrs3kA3XEii3drrw8AWGT4yNc0t7in22zTLPwzu/ROJX9WmwAN4aGzcb1I4mlKKgS4x0wrJxCBUEA3RlCUXlOd9APlbdWmbzWuG9dxnEQNGBuAtx3/DPr7QbFVWN4HgeHdpUGiKVxOOAZdUErIOv+WIXTTFwzdQ4t8YhZ0GL8YJzzXO4EoUKfxMUCgxh7++q+87gN82a8kxjhfiu6V0N5zQS9OuOCvGOPJGhiGhK7nfehf2/PkfcGArKIe672MYn9zvvatjX6u7Gn5ysul1CY2FiMRyvVJeJlIsl6HTzdTUTKtVJxzkT5PepmsZH8NyKd2N9YzOidbBi0RpL+0i5YVFN4yJ/bIzYihV1lehOVycd4xPkVZy8sklqD4NN7yU21IgHUsoFPSfmSG2s3KuYaYVkKZQOt4OJe8VexTkrknwln6mkssZeUmTENWSeTqI9QdmG1PmKc1YdYGOmh8GEZsgW+j1D6mWzWFtaMJ5UO+QpDTpIga19nL+9QaM8SG39Opp3/UJXhP7uuCzBr8+0OGNuy1FgRim+u29/ZdvWZJzre7rIKIWHYGHo1JpJpRYUmwh/yGyOCT0oEgYKQAbddKKHoAOfr/2KSynsOM5O8YOK8INct+SXGuIRflhKVXtOEcGqUSqOOHJEhiKiKV/OLqH71e/iqkfvpmtmjMm2HjZfcAXj2SVcNmdfnS2XT1ONO2hNLJViie/iT04i2sMoGyudonfD+8g/8EBN2u6fk15arMuK4bIPc/qBOLvlNkR7gQ6SNmEphQ7cXeWeFFBJr62l0NpObipLNtVeCab/+7kX8l/67gmMQ23XupUDDVIb3dOGqVZhKlVzUgM904ZJaWdUt2OMQURYLXs45bxpus7LoQlWU5ZofN8wMDPKwIxU7013iczaUl2GVfrsLN6EzeTTrWhPoWxN50uy/PrSFBYW1WTi4OuxVcJNK6TaX1tcVsaWNLYcNT79Xv0T+UC+wA1jEwy2pyuS5uunM3xoSc8CjURARiAjekHxDL+mRrYqGFit+z7YdbXRDbURTc9poC/eN99pIg6DyFBENGXPZI7Mqgv52hkXVbYZY5iebKyc9UdHq02EyqsKpfBnZnDa2rAdFciNWwqxhOQF57OkWeOiOUqiIx97C1apCI5TEc2rBL7Lhqn8qlQg+1FTHT41lWGiaxk3v+3jlW25kkfRWc0n0nfWS3Tf+6UG4brJ9jbiOSr9qg1BLGCiXejZW0CeVqisxrQqcmfbPLDKCtqLhi6eP5jOMFAo4GuDh6CNQQk4Ne6WIHCtsDFYXS6JTq9iPGJdLh+ZnOJbM1289pfVmokfvxzO75llWzJRbWU6PQteK5vaW6Cwp1Lf4WJYUyix4ZQ+hmyLfs9n/dQUA/lCQ+OimIG81C8IDjaBT9fsWCfeUXuQECY1SyVuIQhGDJaGM0w10+pJZcJWslJzqsAQz3WpKVH4xq+ct3xMKl5r1SOOFJGhiGjKqZ0tjGQKtMSqvyJ512d5Z0vDvkbrqjxDmXCb3d6OWlpt/jOftEcz3F27glTb8qqhrLjaBLFtTKlUL6vhumx95VV1+yUdi9vz5/GJP/5Q/Qm+/wHIjVeznnyXuy9t4+13BxNn0Q4CyHFf8YvTbM58qIhSGuOAymuGHmrje70WY9066CdhKz7b04mMTXBZoRjWJhiMEWb2xRh7sB2xQMU0Xl7Yf39H0GjP0YijcfOKsQfbOXsix/t3+0zaQj6u6Mto3v8jw5IVmt8e8XGz4LT6dJ3tk+7fzkavPyzuU/T7mjUzGb7VliarBF+EcSV8qqeLd87Msi0Zr1lRzNIClCQwaBUPUo1sSP0NlzDhoIqp/HRqU4SDXZfFOpgoTuICDlAQQSvDLgMuPo4ErqOqrEjZRWVhjGnIHDPGYIlF3IpXai66E91k3SwRR57IUEQ05X3rVnH95sfJlbyKGqvrG963blXjzo5TyXyay0KFCedlPuWAOfUKRmv6rv9UXSX6jy58Cw/3nEWtacu7Pq+Z2s7u6/6xvmK9lA2rAKvnzZzq8v0rHF77cCudkyUmO2PcMdDG2nummbSTpNVsEKOwHaYwvP4Bw/4VAELSAGhuaW/jVYUxqs/chqmnUqBAhfdGHDDZEgawk6FbxjZoDyafbiWZ0LRiAlElwPUUE0+kcFJ+YGgKiuEH24FpBroSDHjVT/z2VJFpS6EMKGPQCFOWxf/paMchSIkdsxTX98To1uC3tDNTmsEYjYjCmOABQJlqtTYiQY5TQ0Cj1plUJa7ByU2wMuwDjtE8KwZXgWc5KCRIQdZuw+SfjqXJebkGl5qlLARhZfvK6s82rMCOOPJEhiKiKZefvYQbgJu27GTvZI7lNRIeDdXWsQRWOeOpJuupmTvoYMKEc4mdfjrF7dsr56q4maDegFgWolRDJfoVT40wfuO3uKqm4O/JpWfypn0P4bUm6/t0rDag43Vxgz+8sMD/vNDhmZcvq8vJ/53v2cw4bcyqjsq1tOyme7o8SQYTZsLAkGNjEHwcDApB42WtoEKtBhOmC9U+oSvL4HoKsetVWXVJYYygrNCoWAZtYOKpFOmL6zOvdtkOymhU6OYSoBRO8sYIdmg8Ji1Bo5gpzlRUYHVNTYmR4DOZsFeFEtPoTppHq9ND44oCERIEMiKGIGvL1dUaifJqolxL4RsfX/tce861fOupb7Hf249vfCyxiKkYru/y5PiTleunnBQfX/PxpmOIODwiQxExL5efvaRBruMnT41w/ebHcSyp9Gh4tqWX1bZNrJCrtPaUdAq7vQOdzS5ImHDkKzcyecstdaKAvX/6UfZv/Av07GxFA4qyjlMtvo9d494qc8nIU3Q/chuTLmScFrqKGa557C6c7s6GPh2jD7ei3SADSTkaL6/ouTfJfyfL37+pt676N7Xiq5gdO2kpFXC0h6ts3LhhtAtq01vzIvR7LsP00sV0JUPKatX4+frwr6jGlZPxBWVrjC+IXeO314JYcwr7LHCzdqMAoYCPqisaLLuNVPCmUng3IUHjJKlxPZmKbHxgNAwGI4ZLlrych0YeQtVqNfnNDYUrwka3lUG7yJBo+o1iTGlyEAbpy+Isgm98HJygHofqz7qkSxT9YlCDgYevfTzjUeuqyrpZHht77EWTGisircC3CXrsWMCnge3AF4AUMAasD3v0/CFBpWss3OdaY0xORH4P+O8ESWTTxph1IpIAvkogvOoBHzXG/FhE1gPXAC3AGcBtxpg/W8hYI0MRASxcAPCmLTsrfR8AWmI291x4JUvu+zZL+/rqDELvn36U/KOPNRiAB5eczU01XeP+ZP8W0t/+x2ACs210Lsf4jTfS/YEPcMqmz9S5kzK/fhSVbwyoFzKzDdsmbv46cdH0FWcrBkz7HnomA931TZJKMzZOq4fYVdcPHpzy6xg3f7leTO6B8++j47FHgpWCCI7vkcgJP7sI8hKsJAoSTJC/P13Akzb2yDJU6P9Pnj2N96CgSz7iKIyrg+tpKM7YlZRZ5Rg6X5JlZlcr2jOB18YPYr/KDgtOwindaAun/xTQE4FAU5jR1WtrhqxyrlToFKpagcqboEjSx9a6LoOpGO7Rok0gv24CpVqZep73vfR9dVXgmXmkNgAGZqYZqFGPveyUTkSEuBWv/gz9AgrFqo6qezPv5fn6Y1/H0x6OciqrmHIXvdrYhWc8bn3y1gU3PjoBuBrYZ4x5I4CItAP/DrzFGDMqIu8kUM1+L/A9Y8zfh/v9JbAB+DJwPXCVMWZIRDrC834QwBhzgYicDfxIRM4Kv3cR8DKCH+3TIvJlY8yeQw00MhQRTVcJ129+nBugwVjsmczRkayv2N616kLuGdrJm575KbFSgVIsgfe2d9IHzNx2W10R3PC3v8O3nvYZWXFB5Vrqu/8vcGnY4a+jbWM8j8lbbmHJA/fXuZMeP/vchlR9A6iZ+r4RAMXt2/GnJoMKbzGYggfaNDQdMoVC8OTtCzpvVSfqmEZnG/Pyx352H0sdm4RbwtEGI4Jnw2897nH+bkVqxmK2TdN+TobJ7pfR1/oc44USs75DynLpPjNJxulncusutKtRjqHllAK5A4m69CFjINnlkjzFZ+IRU02lPcMwsyOOFgsRF2McTLKNro/9T5iTZmz0bpomoBpBW9WJV5sgY23uuqbyvmprcIxhKLuPmy96f92kfNHgBfhN0g0shK3+FINdrQzZbfR7bhADUQ7a6MoYmlF2+cVULHSf1WdF1V/nyPSzOIY8CnxeRP4a+CEwCZwP3BVqhllAuQDm/NBAdBCsNu4Mt/8cGAx79nwv3PZqAiOCMeYpEdkNlA3FPcaYaQAReQJYAUSGIqKRuauHqVypYZWQK3nctGVng6Folg219OmHePXOB5hOpHHTPThuEeuHP+C5+7bQOqcd6+RUnqsevZu9Z76scq2EW8RVFvHaC1lWUJ8xh/KUYuaYimb5UKaQC7KYyo/IZdkMoxuaJPlxweSqRsFowfcsdEejoVgy8hxJr1iJxQjgeAYzZXHKbNCFLp0TrLEWel+xi+S7rmX5L74CehacFBn3UmYeeAw7oZHWQNI8uyeJimlibVX3jfaEiadSPP/GWQbPS4epsIb10xkuuuAiJn6yA3fSxem06HrP71YNak2a8dg3zscygTErB6ONCdJQyymmlliknTSJ2QMM2TbN1gW50AXlipBzbM4qNe51dc8l3D7WKO1/sQubujtwjKFNa0Ytm5IILb7Gte1K4FobjTaapyaeQodxlbSTPqghqcXHp8VpzMo7UTHGPCMilwC/DfwVcBfwuDHmlU12HwR+xxjzSOhCujw8x38RkbXAG4GHReQimpY8VijW/NtngTYgMhS/YfzkqRE+/p1HyBQ8PK0ZyxQp+ZpTO+uDoEnHYm+Tmon3rVvFD7/3j/yn3L9yihlhvyxh5Nc2vmVTVA7a05SUEwjm7Xke3b8Ub9dw1fUjSXrc8bpzFp04jlcieIAK8X1Ua2tDT42SsnF0uXlnNe2mYMW49/9uZmrwG6QnR8l09nJ6KQ/MSZxSgA12b32Hu8f/5E9pJ0OoMFVx1BRcBwbfVKnW5rIP02JKgdumtvtpud7QCFoMYgRTssg9XIBz/hla+6BjJbh5Jr7zqyAkbANI0FfDCNorC2qE57IMmZzNpu7OcJL1GbUsNnV3snH8Zwy8+VRweoO4xMg34JnzGmpRyh/ZqrkJfvjlWA6u65KwE7zz7Hdy1y//ruHY+dhnNxrQN/ZezN0jv6SoqvNUXBumjYtDKKIoQhLo1JopBcvcUljzYTighHy5upsgmD5dmiblpMh7eUSCQLqmugrxjFeJkRhjuPacaxf8GY43IrIMmDDG/JOIzBLEIHpF5JXGmPtExAHOMsY8DqSB/eG2/wQMhec4wxhzP3C/iLwZOBXYEu7zH6HL6TTgaeDiFzrWyFD8hvHXdzzFZM7FUoJtqUqi0v7pAm3JqkrrfDUTl6tHWOsMMu7DlG6l15oimfPYb3dRwCnHScli06s13tC+SiGe8Tx6yLO/85S6c959wet500O317uElKLlNa9h70f/FHI50Bp3//7qhAwESZrBxP6L5Rdw8ec/w7JSHkv7pPMzoYWQYFYsY0NLjzQ0VErmC3gorPCMBkEjtOQLkBmuVGvz7x/DUmFekAGRoDaijJFqcBUEMla1ZzZArAV3dgrl6DpvkCjVtLf13g5p6CcBMNieYkDqe2pw75caDMVKX7PdVtTWZpvgNtCT7CGRClw7m3dsZm/Mnj8deQ6zVqOhGHzyH+lAkYFKvUQaYZdjk/YNu+2qvlSn55PShl50peZjqHb1V0PJL9EZ7yTjZvC0h6UsuhPdrD1lLVuGtlRiJNeec23gCpsjxcJlH25qQE8ALgD+RoIuXi7wfoLg89+F8Qob+CLwOPAp4H5gN4HLKh2e429E5EyCP4l7gEeAp4D/IyKPhudbb4wp1kngL5IFGQoRuccY8/pDbYs4siw0wLwYdo5lUQIq/KURCbJfSr5ZWM3EvV8imUiyvK1qRJ5qHaEnP82sqvaCiPtukNHi13itTZBVEy/m6661q/s0TLIVCrmKLIe0tpL7+c9htiZIrTVxqs/85XpcsQwvG32K1kIeLQpPWShjgljD3PF7oDoaxfsMhF0vyn2jAxkOkIYmR4n2LMUZQbuqqtWkVaN7pJwqPKdnttPq4+VVJXAOQeGdLiq0J9UOe0Zxx1pFYk7aacIYhmyn3gA6ySAuMWeSvMIkeKZJ+6EUql48sMzcArvy5NJs2xy26yIZFcxYimCGmlDgGhi3qxZAA+O2xTLX42bdUdl+QXwiNL71ldklXeIdL3lHEDjXOWJWjHe85B3Ng9bP3NUgxcK/fwz4/AlnLIwxd1KNNdTSUI1qjPkqQSbT3O1va3J8AVjfZN9BAhdW+f2bFjrWgxqKMM2qBegRkU6qvq82YNlCLxKxeBYTYC7v/0KNiqUCeYwl6URDzUQDU7vJkGZ0LFvpJOefbWM/4LI8N4ylfXxlkbMT4WQRNP1RxqBF8A10FTP119p/H7F0K0ZMVRG2pQV/eDi4Zs3EIaZsJAIMIL7QnstSEquSzqkrxzSGvjPbXXZft77O9TQRT7M0P1m9Triy0Lbw1D+PYWYNkhK6LknSdU6W4V+msFp0JRMpNytIEDMPZCjCerSJLoI+F4XpSsZP19kuww+2oz0qRkEpyK4skRtKECtBKQb64hiZU7MUxKqsJCCoam7Vmg3OTCXddH1BGIinydz0J0w8TCiuuIvdr/B56zC8+ZeQKEEhBj94Odw+YAXS5+GYEq01hWpNDcGhn0ZdFazFymsNITAW1eysekZsmw2l4YpYIg2dM6qU5dNrV0AA24a31ffXvvdLDSu4+VZbEQvnUCuK9wEfITAKD1L9bZkBvnL0hhXRLA11vgDzYozK6d0tbB/NItpU9fsMrO5N8c0/esUhxzUZW8b06B5cSWApwfU1Nh5exf0SvKpyfrtS+MqqPvxqH22ou9bTg3+Kn8lUCuvKirAV5rhDmmXmCAZLDBZuxXUUfE/AorLN1wqr5OKNjtYV3LXUOGfK+xoEVQIzFriJTMEwfk+WZa9U9F2eYOLBAm5GcNKG4hlFeCJBohh8do1QiEP8FQpmDwT3RSzwiqSXuTRru5rfmcRPBSq1tgfWEyWu7PD4P+c77FeqorLqGMN5Ow1XP1CgZxrG2uGbl4LjaXq2asRSqLjg5eBtP4AWN5D/9lQgSf579wKUuPPVGtcChxLpzFBdiOhQNzxtN67KYrFWcqXZSkpteWUxH57AqII2XzOqpPIwMXdl5iinQesp5+a4+bGbWZZaVhEK3HT/JjbO7mXA6ay/UHm1FfGCOaihMMZ8CfiSiHzIGPPlYzSmCJqnoc4XYF6MUfnEG87hY995hNmih68NlhI64g6feMM5CxrXTd6b+AO+jKJI0cRJUqTwlGYi1kEu3VGRE3fcInG3iIPGGF2u3UWA/eleXlp70nLjo7LfWwQzR/G0zNz1QRXBDiU4qhJxwYRdqnlSjRk3SKfNDUGmiFhxtN1BWyHT4NIqRxp8gq5wFgarBPt+1cbyAQvsZNCuzrY4zZtkRDS+HbijtIKEBStmp6BvCZRrCOw4lNyw7Wo1AeXJny0lbkHeCT5jyYGkgdRjCeT8coJrMImufs7w+3eDZ0EmAW1ZeMfdkHOK5BWMOV5Q8+AYet1gpeOHt9YXsHSwwrjt1cE2F8jVtrube8ObUPTyDdLf8VgbvjtbOcaE1wveS11mWvlfyTDolDSGhNHkpNFaWVi4vlvXS9v1XQymwX02mIozkJlTdOjmA/HHiBfMgmIUxpgvi8hlwMraY4wx/3iUxvUbz2JE+RZjVC4/ewmf/90Lm0pzLIQf5s9j3PlDfrd4G8sYYR9LkKzLTKyNhKUq7qietnYKpRxFA0mvWHFJ5Z04W177Tn6r5pzihGMvy4bXSHU0n6eabRWICcYDNBgFogTRYGkdTPQmlCmP+VDKBufxPcQtIEG+aKXns4EwBhBmMWGCBj9KsGZg+KdFxBJUXOHNekzsb8W2BacsTyGCiGHiiQTps+M0CRPU0TqjmK55SDdA1oH0TFDhXQzdQR7Cm+8PjEQxvG3l1yUTsL+bMDsoaEKkTOPd8iVwQy2GuXUrvqFB+nt/dn/jzuUDpEmCq9Y85ThoCVcT5Z1rG28LFHSB/dn9aBN0vvN0UJEdU7G60yWsBEOJVpieqis6RJeCgHbEC2ahwexbCUq+H6YaQjNAZCiOEosR5Tu1s4XnxmbJFDxKviZmKdIJm9N7mksuN5PmWCjpuM1tI+exWZ1fcV39z5Yb6XMzrOxpr+yn83n06tXcdMor67SW7rzgCt553e/UnTO+ejXF3bswmWoFtUqn8A4ML2JkBs60kKd1Rb3OP0twMh7+HkVc+/iikBg48fKUFa4bXD8wUKa6kijHQeZOb2KC1Fdp70N5U+CXkEQMM10MMtQtAwqMNugcFHUMJstFbwRS5k0Y6YAlk9BaDJskWZCNw4EuIavqHTi90zBbr7VI0Q6uEPMMvlP+dIErZ27XVcsEsYrFMHeS10KDO2i+WgcRiId9M6onNIGbsXK+eWIg5cS1MKBfW1PhzwnyF/wC/Z2r4eJ3NfQ2ieITh8dC02PXAOeaBpGdiKPFwUT55vLKVV08sGsizGaCkq8ZnS3x+5d2HfFxVX4FaqLJ3zvzcj706L82FLGd8Yn/wjuXnM1NW15e9xkuGXmK3X/1Z5VgcvLSS3GHhpA5EiDzU19eF0gWGazdGj8pwUzoC9YOg2csDqR6yCuHpHZZ4e7HLwVP++VAtPGFrnNnmXiqNXDkh+ZCI5XodDVAbdAKtIrhZWxMSSMxG0zoRgrF8yqiiL5PZp9TF4/oOnu2zu0E8Ohp8Lt7qjWBlg8xF+660DRkIo22Q0e2upKAQAJ9fye0uYF2k2sbHE/IOUGMwtKh2ykMsv+gSee8pj49IXALUvVMBfdbNUh/z4cBWglUY30CF16hnHU3Z7/5TmCpQG7cYCrtT33jk/fydYKN689bD8sHIsNwhFmooXgMWEq1nDziGLDQJ//7dk6wJB1jJl9dUbQlbe7bOcEfL/Bacwvbuja8t2nPiNmST39HgrHZUuVaw2e/jP/bGuOTMw82HH859QH1zJYtdUJ/3vg4xR076Hj3u+d0vXsvz//R+xrmrtpAdR0GLAscJwwa25pC1uBjUWiJgYGCHQuC3pbBjoM7q3BSmq5zcoHEtQ2+McHjsjIUbfARHD+caBVkE4K2DPbQviCmYlkYt0YBVTnVid0YjHHZd39Hpe2pV1Dsu7+DZWun6ozF2qeCz1CejE34mV75VDmWUJ2qN69VvPeu4Fm83CfD9uEfr1T0acNrHzCVFql//3rh9P2mIevpuVOE6//Zp3faMNoubH6F8PAZ84eek8bgIjgY0hpy8USD9Pd8CEGaZF9NrOiJMHmg1gAd7CnUEgtVsyLRRuMbn7H8WF0dxYtFEPB4ISJfB94EjBhjzl/ocQs1FD3AEyLyADUl4MaYaxY1yoijwp7JHN2tcXpS1Sc8Y0zTGEUzMlu2MHzDpwOXT6309vWfajAW5djJqt6qWytX8uhJxYNcuEMw+rdfCFqjWlagBqs1/uQksz/6Eau+/691+xpRSI3UNVATaKZhu050401MoD0PZUsgoIdmWWYUx/dwLRsvZuNonxVvnKqZ0H123d2BdhSxpF8xRiNikbMgm6x2l/vBWuEPfmwgq8OAe81ztlIox6mm+KZS+KP7MZ6inDtrDJiSYv+2NiZSurLK6B8P3E117nkDp0zCRTt8rrnfVMawea3w9SulbtsP1gqjZyYZK2V5ZmVZmNDwnGPx8BnVwDXARTs0773L4FmBC6sja3jvjwxf/y0ajUUYiPbDplE+gq/gWs9hc+hGKz/Rl6um5yIiJExYqRJ2s1MSeAhjc9J+58M3Pq52qVWaVaIaUmbP7zk/MhYHZxD43ywybLBQQ/E/FjmYiKNEs3qJxQS+mzFx89cDIzFHertZJ7pmsZOzdj/KOx+5rbHHQxNDU3ruuSAeUH46VAqMCbbPJeZAsdi4vUltBAju6BQeVtCz2jfYeCgMjvExSuEYHz8vWG0EE5b2QNmQaKeUdzDaUMrY1Q49rZD2DP/jPVZFObXL94mXBDvhBgVyWlDKgKPRrmDPcZ/5YSC3vs+Swc/ZeJZXkTS3DXg++DV/keVajPpJPXi/5fxycWAwcaYR/nBsAqwEgwlTqU1ocle55n5DIg/pEkFDIoFMLNj+8BlNDgAkFO8LuvQpzs/OcP4Vn63LehrNjTLrNqr4KmVRSHWRzE9V6jbSYpg2JbzQFeUfpE7DFoU/54FBo0k76casp8cHj5qhmCnNEFMxEvbCXG6Hy8pP3H418HHgdOA54G92ffaNdxzOOY0xW0Rk5WKPW2jW008XPaKII8589RK/e3E/33loaGGV1U1w9+7FKIU3PFx5GlZdXU070TWNney/DzszTWm4xjPZ0jJ/y9O5T47zPUk2kYmAxtoIgwXaBMlSYbZTgxKqlLcphqWHs5afVRfstO7+K/zn94OEZkdDd8YwlRKu/6amc9ow2S78+FLFbJvPkrG5H8GgHI/S7t3g+2BZpH77t/H27wlkR2ojyuGywctbFUPjhfEDqyYrWAu4dlCGlq3JcOooaH73PphoV+QT0DdreP+PYMVFOdIvW84AQrks5ALqdbUAVhyAVKmcchsMrb0I1v75nD+GEoIW8I0QNz6DyVhD6W8zIwHgaQ/XjkFHf2X1kZ56nle7whZbyImhxQhZY9CNvkY0sMRpI1OaqUiD5KW+6RGEWU+zQ/N8hheOMYbxwjizpVmWtjb2PTkahEbiKwQenAngFOArKz9x+wcP11i8EBaa9ZSh6kKMEf7uGmPajtbAIhqZr17ivp0T3HDNeS845VVaW3F37qw86WvXRe/fT2xVc0NzychTfPbn1XhG7slHg1qIWnI5cg8/3HCss3IlpR07Ar9+WeJCKZwzGh9lPddnvuQcT1s1Ka+GkpPAi9mkC7OIdjFhRo0RhStWpcHQdEuKdMll9497cPcWcJb30LUqTlZaUKFsR0W/SQtts0EBXC4BrVl4y92GVHcJvxgnCFobtE/gNwJQflC4Znxm/+12VNxC5zUuYCSogA/6ZwfqsABaCxaNfnohCEAnFRS14IrBMUJLQVAalosPbuDO0VhMPJ0mff6cGoImJN3m8YDk/O0kKhO4Fpi2FI8YvyE9tkJDpgFc2Hshd+y6o6JWe3XR543SyqgpMkRQXb5NhXGLORIeGk1XfprumlaqzwmUvHq5+IJfoD/V3/wDGBP0RG/taf79efC1z2h+lMKcax0DPk5gJMr+41zN9hPTUBhj0rXvReR3gEuPxoAi5udg9RKHk/Iq4dN2kM0SPnkbQzMRsWbxjAYjUaaJ2yh91VWMf7mmdtMY8H3SV13VsK8VnjeYd4JptBr8NJWU15/0X8iZxX2cXjwAbUFLTwuDTAdPwntTVXmKjkKGZGmW/COPYHwfb3ycfZ/cSDHvkkt101bIYPsenmVj42Ibn44MWL7gWwbbAWePgyR8TFnryQr6Wwe3L3xOl6A1qOdZzLZAvChhQLxayld7d8ufy0i9O8gmCP34YrAx+BIYFpSmNC1obaOUQcU8XNUa1AzU1BAknDlpqTRPbpq/kLF2dNX3xSbpsZWfZ8OJhX9/7t8r8Qvf+PxbHLaYLD0o2oBR0aHWVjUFthyH0NqnIJAM+1EgijbjM2U0+bFnQvVZGzfRzvpmrVDdAswOB6nJizAUru8ynBvG09Xfb20aYzBHidMJVhK15MLtx5wXpB5rjPlXEfnEkR5MxME53FjEfOjZWez+ZejxiUodg7W0Dz3b6EpoFs84GHOlv08tTjX9pZv+3vcasp6MUvgaFDpozxlqRSkMrgoaDHlK8bKRZ0muLqF3CmQJ3FAKUIKlfBJeiaLlEPddOgoZDAYTFvcZrTHT09hG0OkWxjqqroX+0d0IQjx80ra1gAsaRbzVRRLVSaMw6WAAV4LvKwyWAeNq7r0sxbr7ZrH9oEJae6aptIUArqpO2q0eFCxoKUBi2qA0aBUaS18oWYTFamDlLeKJIltf8X4Gn/xHhvQk/Yk4F+RL/DJZ71PPx4IMKAWVi2lqaiuaeqAazUhDeuz8niu06Lo0BCOGWeBUbUAUSaNxJLh/5ROZMA3sFE3QcxtDgkDxzgE2TE2zLd1ZUZ9dPz7OQK5QrRjP7KW/pY/1p7+Jgd7FKWzn3Bxj+bE6w/DjPT/m9p23M3j1IKlY8xqlI8hzBO6m2oyUlnD7MWehrqdahUJFUFcR1VQcYxZThLcYnOXL8UZHsVeurGzT+Tz2Kb0N+7p796La2xu2N8OI4H3xb0gqi3yileTMJNb0cNOnV29oCH98vJIyu3/jX0BXF2psJChyCycNi+AXL2FcRAy2EWKuh7XLD8qxqZ7cKHCW+nTty5DwihTsOAqDrxRO+Sk7zOaxPZ9kMUciM16pIpeDPGcHfayrfwKm5qtcEqYJ3Fav+mUW2w9WC7Yfup3CIG7jPaueDw22CWLulg4MggRNpjESTqRhJpUvMI7Ppue+h4NPm4FRfPYm4g3X+MHLA70nnyDlt662YqGlUiJN0mMPYimY0+tcgs+wS4GLjyNC0kri6nxd4Z4g/I5q4/xcPgjSl0UQJyZ4zLF5UhlyYphRwmO2gnv/ik3pOI6yabOSjOZG2PTY19h43h8ysOSSBX20mdIME/nqw7yrXW5+9GZuf+52AP7uV3/HxrUbF3afXjh/Q1VPL0dgJOLh9heMiHwTuJxA6HUv8N+NMTcf/KiFryjeXPNvD9gFvGWRY4w4TA5WhLcY9di5+37ot97Gkm98OVj612TsdG14b8OxZaNyqJUEgGcEX1l4sQQCeLFDZItoXZcym0i3BHUNAFSrpgVAVzWZLDQmp3DawbdUxW1BTuPvsZhsTVO0uoj7Lv1uHqPnukcMltRMZFJ3pQaXe+CIcdDaR5TGaEUhDrFiIOZX60Yq2JDKBx3ldKVob27JIJXrlN1OKvy24wUX9FUgoicGHD/4I9QqKMzzLZhpAXwLJztKUvtgDEnt4jrBn3iiZpK+7dUW4DfUVgTbF06mmGHf7D587WMp6+C1EHO/Edpgz3awUHhocn4eSyziVryi6ZSOpdlmp3n/9E4G/FjFpfZVW7ipPYUQxHzyYrgpbujTo8RlBclQMyppJ8ArMLjzXxloOxP2/BJObVZtGDCeHydTylTej+ZG+etf/jVPTz4NwGXLLuODF31wUffphbDrs2+8Y+Unbv8gRz7r6d0v5Dg5WYut16xZY7Zta2zL+GJioZN/bTZU7UrjhmvOa6o0+60bv9Ugq3Ht2hX0/+h7hyy4q41RlI1KaffuIBBeU3hGIoFfLLGve3ldrKN/dHfTz9rw3D5H86nKfL+vgkqBLlBNb9UGzyiea++vlEysmNmPo31ULFa5hvF9JBYj09bFcEmhjUGJsGpyT7mMoCIfrgFLGXoubWHyoQK6ZFAx4b5Vhgt2GBKlqqEoxMBxg5VAraupHJ+o/cw6NB21++VikCwFxeKm5htO6DI/0Fe9F7YLIynhX343dPGEg3gyZh/kjh36rs6HhdCR6CbrZSsB6iDgu5CrVYlZsUpMoqRLxFSM1Z2rq+MyhpnSDHdc8Kd1shyXmd3kpf5J1yNYJS1vWcp4aQpXezjKpttp59Uz42ycnA7G8V9/CS31qgXaaEZzo+S9fGXbI6OP8Llffo6Z0gyC8O6z382HL/7wC223On8I6EXAQl1Pywmadb+K4DfhZ8CHjTGN+ZMRR4TFSIcvRj327lv+lXfd+y1KYjGhErTOTvGue7/Fv7X+AX85p+tbM9Lr1pF/61uZvOUWdDaLam1F2tsxMzMQi1WL2LwgIOy4xUOvJGgSTG1qJA5+BjerUKGAn9HBI7xRFkqkMvm7nT3EJkdAlzBukERjtaUpaYtstsiyYqZSnOcqcHQ4UZdXAwamW4XY4wXsFoW0gfHg4mc8ZpMw1VkdkeVC70RjiWDFSFQj83hicHT9OqOlFNo8YxBf6o0V4Lhh+qwXJFvddSkURJEsn0WoZpY1oebyqNB9tVCU9mlLtNFn91W2PTH+xKItjq2qPbONMQ3B4kom01lX1sly5G65EHvOvhaBEOK+wiiWKCxR9JYK/PnQHgbyoQGIpWD/I3DGayvHudplNDdKyQ9kY4wxfOfZ7/BPT/wTGk3q/2fvzePlqMr8//epqt7vvmYnC4FEdggiq0FBUBFBQcX58jXKjA7jd3Sc0VH5jY6jXxlncZvvqCMzKuqoI4IIgrIJgbBDgAAhQBJIQra7771WnfP741RXV3VX39udHdMfXqFv163lVN3u85zneT7P54k08ekVn+aU3lMwRFh26Y8ftYaefgT8HLjcff+/3G0NQZX9hHom/3rUY098+DaymOTNKEJAVkSRdp4TH74N3bJ3ekw88ADjN9+M1dWFmDdPexSvvQaOgzCMAGvK7OzAzNiQz1KIxIgUwornSgibY+pZhikMbCG8im4THQc6Zk6JxS3HBkEprITpNvcRdJyY5vlHWukcH0EZAikMLOlguPpIytAFfNIUZOMGUwnoyTkYlk7ECktiSUjkYCpVmoAdq3qYKXDDCiKq9LZ4fHEaNBVe3F4oMBCMxGGgSdA1piuz//BGOKlzkl8azbp3hUsdVp6RqPQfpO+dMcOD9hsV3POGaj0Ve+GWH1gFC1sXej8PZYYYzY2G6zeVIRlNkcmnXWOh+VKOMDW9Gd0j/IqxUf58eLjU9GnRm+GS70LrPO88WTvLQHrAExicKkzxrae+xaO7HgXgyLYj+dypn6M31cvhjFoNRbdS6ke+99cLIf5qP4ynARf1TP71sKG6J4cYsxL454+cGaFrsrIwKwyhrCcpteaRbXtS4UZHB4mIycTHPsWkj/WUnRohauenbWgDfg9jeuKmHoReao80d9CcGfforelokpbMONn160v7mjDv7TGal/oS8vk0zQ+NARLDKRXySQHDzYLJNlEquDu7lff+fgrRORumBrxKY2E4xArQPeyn0krEjHfquw1KCfHie+FSYg3lM0ACJpvhH68wPJG9ZgnHTUYC5wjOz9Mv9WNSkjGqj9VvKDqkZNwwq2s9lf+5qhgLQ4iAUYgYEc5bcF5FH+ywSusrl1/Jf6z7D7Ki9PkwgKSR4LiCzad2b2O5Kyw5aFp8b/YRfOEdXw8Yicn8JEPZIS83tWVsC9c+fq0nl/62I97Gx47/GFGzTqndP0LUaigGhRD/C/iF+/4KCCn5bGCfoZ7Jvxob6i9T/RUtPweaOmmeGiPv0/KPOQUGmzprGlco68k0IZ8PhJ7k6CjW4sWc8ScXw5+UJMG+8+HPc86jt+IIvXI3lMSUjl4TugVpSggdMfFVG5SY9S4M38zjFh84wgjQW7tHdlYylxzF6Is5mpf6xh9JYGUnKCgjWNugIG4rvv4Bk7iwyCpJgXGu7O1B5aKIzlIsnaHnEQ7EbB2fsmxcJdpaEVize1tAQMqhkDM8AzSRVDTlYIkvJ5QRgp+2pOiSkh7b8UJOL7jJ7LB8hP+Kns5Sec9sF9L3OmgYzJWSQpnW03Qohv/87w1h0p3o9iRAVvSuCG15WqHf5Ngcm5xDRFjkVElluEUZ/NXIMJcM92O6Y72ppYV/62inp3lOIAQ3nB1mPFcSJ1v92mr+3zP/j7yTJ2JEuPqEqzn/iGDARAjRCD3NgI+ghaS+if5sPQx8eH8NqoH6qLArl/Vw2fZR/uvBV5nKO6SiJp9tH6bnRz/ELhP661u8gu7n1mDYeU96O4rDmjMu4rIaxhXOenLDIjBjwd4xn/0Ut/1DgfPX30u8kCMbifFS82yOH96CEgJpaOOhNYVKRWn+1qaCYhtXVy48IkkeG0e8ZKOyGbJGhLgsELULhHkjk5vLJsJCRmeLDYEQxbQyGBJSGcWXvl0gmhfkowp5vGDxsZJd9wxQ2DahhQdNQAqMqIOSwivCMyIOTias5lq/s63S/Vi2nrLLRyuFIikkRlOp98KIiJJOBePzcaVpohPxFrYWJr18fvFqYf6E8r/6/1ZBYarKA4Ugg+IDSy7mpxt+6q3+K04M3uMXQteWeKJ+QmAYBj+4oMTMvOrOqyjIAsPZ4QDr6fr11wO4tRGvMTfexfap3Tg4RI0IAsGZU5N8bnAHs239nDZFIvxjTw/PxOM4StLjjqM8aV1Ofe1J9vD5N36eI9t8iwB0cWF3ovuw9S5qNRRfAT6klBoBEEJ0AP+KNiAN1IFamUz19KNY/WI/Nz61g+7mGAtco5L8zf+QMUyayoT+zpzcyvfPeH+A9fTrkGZCEC493nHVR+j78lcCVFocidHdDen0jAV7K5f1wN//LV9/4DLvvkamcvzZrd9g2a6XMXFQwIuzj2LZrperPsdEl1OSCT/B4JVLvsAPf7mdd6z/A73pEfqS7cyf6CM0bKWAgZdKooCxZkQkheVVkpeouCaQyGsvIZEHnoTRwW3IyYjupgfeqxFRmFHprdIV4GSKFwxevvSDf31fOczRJkVcCqQNwlQoRxAXil+/0WBLxPBJf0sMqRi0J71TSQClOHGzrFCfDZcUrz0jNGwa3PDSDeSdPALhJYKr0WD9TYaKhXS98WDcf9PIJiYKEwgEBga2tBnODjNVmOLax75KBEPXRmSHdcIak24p+ZvBAc53P2s5Ab/omsd/tiTIKIeIMOmNdTJlZyhIm/6pXZ4nNJgZ5GuPf82jvp7cczKfXvFpmqMBIQqao810xDtCFz6vNwgh5qOVY2ehPyLXuS2vp0WthuL4opEAUEoNCyFO2qORHsaoh8mEu60WWY6wxHfXxBC74knMwSmvPWl3KkrTcD/LP3YBX+w4yvM+/vSsRRXXqSY93vvFL9D7xS8EDIiIRFCFAkZP6RzVCvYAlvzgX/ni737niefl5x2BsXsL0jC9cNRRuzdVnbYUAmLNkM5CLAWnf4jPPdvLxs4Uj61c5kVPbr7xr2uc+gRWrEB+SlVMdAqwTVFa+UuYfDWiKbPFHkcKlAQ7bSJzmnElDIWICYyIRFiqJPdhKCYsQSyne1z4mUzFttWlUSmUYdByxCQjL6WQtoFhSYaPzzNmxPn0L2RAZnxocfG40l2fsNkJVZ/9IbJ6/4nAM6iWjRYMZ4c9X8VxnJnq7WZMcheUlhE33RoIgcBWNhk7Q8qIMmynPcqrUIpLJ8b465FRml2G3BPxOP/Q1UG2qRvbnvLOn5cF5qVmsVvYOK6RWDewjn954l8Yy4951Nf3H/3+QGjJFCadic49pcMeqrCBv1FKPSWEaAbWCiHuVkq9MN1BtRoKQwjRXuZR7JH8x+GEcu9hZCpXM5OpHoQlvgebO0lOjGLH45iGwHYkg0NjRHt62HDrnXzZX0cxfB6r57UFxjCd9PgRP74+UGNRNCphBXvlz+DTa39O8v67SwN1HKJbX/EW1yalled0yPSDciLY/bDz32+kdZnkFBTvfXk1PVPD9Kc6KADRsGS4AXQfXXqfT4M9Air4kQ4mhfWUWBBaPsKjt3qvesaXDiAUygHS0LQgS24whkg4nkdgOxFERGE5uLIcuojOQbOPPLkSBG0TipGNKU33BZQU9D4X5c9fVEzFg5P/D86H55daXm9pge5ZEdZfW0uKV+ZFaq7MhtDeE+FwrWHZpt3pXVx151VejgIFUkpy5EohKneEw/lxL0cwP5fh8/39nOJ6gKOGwbc6O/lNU4rWSDOjuWGk+1crODYZJ8dbF56PE20Kpb7+zYq/YUXvisDwElaCrkQXplFfEeI+xZdaK2TG+dLY3hbc7cJtQKeUmhBCbADmAvvEUHwdeFgIcSP67/Y+4Kt7Ptw/foR5D1uGppjXFmSJVGMy1YOwxPcNi9/MR5/5NcLOU7CimmkkHe6KzeWKR25AmhaZWIq2zDhXPHIDd6VirPzHEj02LGkt4vFQ6fHmc86BMi+j46qPsLZnWcUziD7wB3fy9SdtQ3vWTcvTUbmczoXYNqpQ4KNP3UjCKNBkpIlEbTqzo64PULpGMRluJAQMbfIYSyS7KUyFr67Lx+Xl0MsbUReZN6YW7TNMhbAcZMai55Qxhl9swp4ysVIOWUeSVYJCxPDGNacvRP9JaK9D5g2XAqVVbRN5QcSG0WZt8wq+yf/5oyDqIyp0j+VD+2t3j1W7wwMHqRQDmQFPfdZf7Kah0Hq+2vAllODDI8N8aGTY65V3e1MzX+9oZzwSpd1KEhWRioyMQvHwzke5+MhLAtTXJa1L+NwbPxeQDhdC0B5vpyV6kIWxtZGokBnnS60f31tjUYTbl+Ik4LGZ9q1VPfYnQogngbegP1nvmclVOdwRFg6KGAZ9EzlaEqUv8r4Q9QtLfL/QezRtZ76fd6z/A+3jg4y0dLH6xLdx6uO/xzEt7IjWACpEYljAKY/cjr+OIixprbJZIvPm0f+d7wYK7to/9CF6Pv4XFZXc37/u0YpnYKpiDL8ybj8dq7K8BWqpd7cCx6E3M4KRUJATSGlgGVoUyVWJQrmBHYFAZSX5fE5TS50cVuE1lBOmKqpHIKTwZDWEUozHBa25kBSxoYg2l5RGlYLClEnznFyg7enkbb1sTxrYPrfEEWApHfpSaPZXUYMJIcpcGLDKFvN5C3rG4PhNNu96NE/3qGKgTZCJ6lap5f21B1qLTz3sae8h9uAUfvVZIQRSSaJGBAOBROEoiaEMTslk+LvBIRa6TK/XLIt/6e5lKhonqvKcYCtWzXsrn3jtN6FD2DaxjU+t/pRHfT3/iPP58+P/PJCcPsQS1vtVZlwI0QTcBPyVUmrG3pQ1h49cw9AwDjUiLBzU2xJj+2h2n4v6hSW+I4bg5c7j2L60lEpK520uTv+MbDwVWL3mzQi96aCicVjSWuXzGD09DH33u3rysixkOs3Qd79LbssWZH9/wKN4bcTmqC3P8rbn7qE3PUxfsqOMjRNEeCRcBX728avK9gOyQkc4hM4ZFEMXQuDSRd3ryCJpVp/RUSGhEXdEetKGqFt89+BywZZlsOq3ElXw3YlQGJbukOflKCKSWLPD5M4Ywy82eW1PZUQRscH2zUfKgGK+NyQgFAq/rYjZYMctPnlfkhFnksm4on1SEM+USEz+/tq3nlZ+9r00EkWE5iJq9FqUwsDAEqaXi1hitfLBnRu5ZELrLxWAn7e188vOXmZnJ/nBmANWCuwsPH0DTmd4qEgi2TW1i4gR4c+P/3PetvBtgd8fggnrRewnmXEhRARtJH6mlPp1LccctDyDEMIEngR2KKUucvMevwQWokUH3+fLiXweuAodxv2EUurOgzLoOhAWDrJMg6N6mmhLRveowdB0KG8mtONt7+FzO5orjJI5Zy6R/n4KkRjCFcKL2HmSR8wPnK9aOGnnpz+jJ11XpqPYynTyttuILloUSHy/o/N43vrCfaQKGZ0WSI/UFeiovm/YNKpX4xURIUCYPk/EUQhL4WARoUCBCAO0IiIKVQies9gfYXdHSSpj2U7ItymdyDZLiWullBsiUqUchWNi9OTYvbZV94xw257mbJMmocXsCpYiYuteFaNJQbKgsBydV5hIQtcErnouXuZbIZAGxArByT8ST9Ia66I9Ufpbbtq9HisPbZNBAcBqiexqDKlgHcYMf8UaC+4Q8OLwi0glMYSBQBAxLBY2zQWlOHO0n//96nO0uQnolxJN/Of8ZbwcjaAmd7HCNriqGXYYGeZKg1W5GIayq0qR9CR6+PxpQerrIZywfpX9IDMutCX8AbBBKfWNmo87WKKAQoi/RsuVt7iG4p+BYaXU19xeF+1Kqc8KId6ALvR7IzAHuAc4Sik1bdbzYIsC1iPUt7eYeOABdn7+GtTUlBa4M01EKsXQX/wt/2+qJ2CUTul/kS1f+BIjBZjCIoVNewQWfuVL4W1Ly7Bh+RuqJjyNVMqjx4rmJtL9Q0R9TV/qRbjCqn+9XV6gVu2zHJzmWo9NM3xMp1fr0WTk4OEMma1BSW6FIBuF3R2lDEeiYNAy6dAWszF8y6zcmIGSvhyFoRCW9OTIpY/1NBQ3yMUlmYSgfQxGWsHMCEyFlgVxYRZAGIrZkyALeMePxA3uOBmO3Sa8ENNvTzP407sUbd3zGMoOUXAKRMwIcnyKnnFt7PxG5YfnV1JkT9wsPYZU+b7rfPsKtIhh3emNkPqKciTNOMcbKT6+awsnTmiipW3FefjIs7jG3s6kzJI0E5wzOc66qEVEQRxBVkAOha0Ug6ZRkWhPmAn+64L/CuQe4lacrkQXlnFA1sv1Pa1gjsIvM75XOQohxFnAGuA5Sk7pNUqp30133EHxKFyRwXeiE+J/7W5+N7DS/fnHwGrgs+72/1FK5YBXhRCb0EbjkQM45LpRTx3E3qL/X7+OHBvzWpkWm/H0/vIH/OLWW4I7L+th4Ve+RIvnKSyoqhQbiiqVu4Bub2qaqEIBObh3RqIofBdylSqvNSICyY4caXJkRZQ4OQxZQGYEIiJRdqlzkFIKISGmBEUNI9t0iOVBJINGqshKijaXqqWVgtxoRBMSi6eQgpYphZ0VtE5ALA+xHDyxBE7ZDMlcyQRmo/DTdxlc89IEIy8lPUNxx0nw6zMtbj7Ld30Ufa0SZ2QndszEMExsadM1xTSsp+CjmY4htWGx8vSjmqUiGm1mV1nviIo/EQSnx5CpspTOF1gKPjg8zJ+PbiLmUl4H557I5pOu4B9fvZEOq505Zoysk+OewhRtUtEiTBSKCFBQiqRhkTBjTDlT3jViRoxPn/ppz0gIIWiLtdEaa60c0KGCL43dwZdaK2TG9wHr6UH2gMFwsEJP3wL+FvBXtvS61C2UUruEEMUZdS7wqG+/7e62CgghPoqbkV2wYME+HnL92Jv2pPWgsGULGIYW5QOKzXjymzdXSHg0n3OO92+PME0M16/1tCcoWKWgvZAOpnSobghKHkVYctyPeE/pY57L2ux8sZPxWW3MFf3sUD1837mIa6auJ5pyEKLkqKYnLCKOoGNYU1ltU/fOFlHIT1moAj7nRiHKLJtyhBsLE26LVP1qSoFpC2xXmTZSgLNDsn+xApyx3qF/awpHCp3glQYXPA2vzHZYt8R3QaHDRB+7S2JKm4KliNoCyykmrksIsp5K6B6jKkPKRqD/GoLTCoojTIvvhsX5yjfVkmwBjsnl+OLgMMtcfSZS3XDO39K15C189tEvEBGW7i0BJCIpHGEwLiQdysEWBlJJIgr6TStgJASCN815E2+cpTs3H2IJ6+mhjcIB748dhgNuKIQQFwH9Sqm1QoiVtRwSsi10VlBKXQdcBzr0tKdjPFQQVhldbYJXtq21dFyNn2Jc2x4YCOQN+OIXyDz3fAVr6YW3XlZTxbgwTX2dMhlwAaVtdUuE4x2nW3sqr5ZAlHkvpXB3KfxUTFqH5i3AzWwb+tVQ5KZinr0TArpSEcyUgz1uoGzDCx0ZUtNRowW3f7NUxApgtQqcMV/gXelrK0eSG/clsy3lSr9a6EhpcNaUrj2VosRiKvi+kYaEY140kK4orzQVQkFzBj64WvHMEl8ITum8w+/OzXHmk4L2McFIq2TKNCoYUiXWUxADrbomI4wh5YgSO+v2uEmTHK90+ZTvmQe2Vw9TJaTk/4yMcsX4hKfPZBz3Pjj941oSHNiR7qcl0qT/hoYFQhC14mTtDAVloZwCjmmx2xBkZUlzysJCCcWa7WuY2zSXPzv+z+iMdx5KCevXDQ6GR3EmcLEQ4h1AHGgRQvw30CeEmO16E7OBfnf/7YA/0zoP2HlAR3wQUK0ymi9+ocJYFNo6MPt3lza4k6syzYqCud3/8GXs3bsDrKXB736XRx58hf4VF81YMZ6bPQ9j21akaVGcpCKOradAN7Gt6ZxCV167KKe3hkEK4bUhTUdTxC0DIzNB1Ammo4KKSML3WiVjaka8molMPk4qNUmnWSBLC7MZ41OF/0T1FMgNJClmxLUzo5O4+gql0TtjxQk6mPsAw2MuKVfyNZpycKwO1MS47pVtCZy8rguIOF5+OnA3xSpwX+F3qUWqG/mbMwyGKtJ2BUmpEMLh+cWCzYuU9yyirwk+crf+2Z93eG4BfPHnTiBpfetp4ftWMqRg0us66w9/VVsgFGN5vp8FvDmd5v8bHGG2+/d9ORLhm73zyccm2fHg3zA32cOqxZcwN9nLQH6chE/WvCnSREEWyCa1DMjuqd3YquCNyRKWZxAcHG7dfCufP+3zVcbXwEw44FKISqnPK6XmKaUWAh8A7lVK/S/gVuBD7m4fAorB9VuBDwghYkKIRcBS4PEDPOwDDn9ltBBCv0ajDP/ghxX7DkoLx524/Ctru2xiFvE49s6d2uOw9BdJWBaOgvPX30syqrcloxYRU/D9B16puNavTn43k7EkypXaUMKg2BHAy10UPRu0gShNftOHiKRp6cZ0psUDx7yZ/kScWNJBtAtUu4FoL95PHc6iYSCTc1Hdy5HJuaRUDmO5QYYYWtwuRh6LQr+FGXcQpkur9Ud1TB1WEl5hQ21QBQNZEBj5MawkxDpNrGTpWbiOgO92BFGliClJVCkMVZpUlfsklS+JPNuGxXn9mlKCK8cmKAhBxg09ZoROWP/wfMFoCpqyMJqC+46Fc5/X3oO/shuo2LeU9FbBf/UsyqV0Pw/6Gt22zdf7Bvj3vkFmOw5ZIfhWeyvvnzuLhyMOA9kRWiJNDGRHuPaFH7Bi3tmM5yd5ZfQVNo5sZPPIZvJ2nsuP0u1xtk9ux1Y2pjDxdKKUTV7mKcgCKEKK+RqoB4eSDMfXgBuEEFcB23CbJCml1gshbkDXcNjAx2diPP0xoJ7KaCubYaSlK9CLwZASw7HJb9kSYCKhlJYF98FBEC9rKpSImGzsG+eK6x4NhKNWtx1J5q2rWPnMXV4hX9v4IG0TLuXb71F4mJFQCcBEspVhK0rEznPmxkdJzBtHbdWCeJgKx9HJz6qICchTivBEASWxBwdLYbajHBLL2xmYynsaWJ2pNnZPDWDGFGa89NHKj+ivh/L4ltMzfUrmq+Td2BmTee+MMfyMpDDuEGmxSOdypDKVSrGgfNdyn0lCkswZ3vkFuvBvd4ei3RH0RxQ9BcHl4w6Xpsc5Np/n+tZmdlgR5toFXjNNnlliBBLXX/y5UzVp/eUPmjy3UOGYtVmCcF3aMrg5K6EUl01M8amRUZrdhcQj8Thf6WpnRySKdD8nCSsOwiBhtYCT5Z5t91BQhYCUuWmYbBzZyPZJ/X0oUl8/+8BnycuS9LhC4eAQN2busthAdRxUQ6GUWo1mN6GUGgLeWmW/r3KYSYZMVxldjon2bhLjI4FeDK3jAzQ5BWQup5lIuRxks4hoFOU4YJX+9CaKgmnx0d/8Kx3jgwy3dHHLsnPZ2H4U/RPZQDiqKWqSc3SIwVvfy6KkeHk+IXwSCQtDSaBrrA9DKaQQpCMJmgazFBIGxmBpP3/6OgilDUXK5yRnJORVoBvf+KY0ia4RFveMg5HXmWnZykAKChkTy5Kl8fkIXsVwkF5MVwujVYbElAKsGFBa0SbsyhxvtZzvhoWCpVt1W1RTgmPAeAxuOwf+qd8hR4wYOSxVwMbi7EyWszOlCfW4hfMrzlk1aT0KrZOKqA0DbSGD8cPPUA7cc/jffEm+wN8PDXNSTk/iw4bBv3S0cVtTyl1U+I4zIzofge51sXF0I1JKIq6kuFSStJ3mib4nAK36+jcr/oaWaAtJK0k+ny+/PKlIaoYbamA6HJ5dOF4H6LjqI6h8HpnJ6GKuTMYT2itH26oPY0oHK59FKYWVzxKz88imFoxYDKTEiMUwu7owurrwNJLcV1MpDMdh0Y6XaZ8YYtGOl/no/ddzzsjLFeGo5a+t509W/4T5u1+hZWqU+btfoWNqGKOsf3E94nKgP4hC6fWpUIqmfBq738AcDO5XDGOVQyEwHIlZyBOROcxCHnIKGTWC4btYiuGn82Dn9FXtHEz2Yb55OUoK8o5Fjgh5x3JVXYMhPYkgHzUCVy6NrBIK6Lt3AntKYsQE9pTELAjvPor/yjMeRRy/SXDdO2HTHBhu1q/XvRPWLI0wR+7kSLmZOXIn61jG4BHvrOlZD7Tq/IN/kIk8jKXglE2ST9wq+X/fc/jizx1O3Fw979DqSAw3FFV8LT0L/S8qFX85Msavdu72jMQtqSTvnjvLZyT8Z1WekQDdM9t2ada21OEkW5UG35Po4bWJ1/ja41/jyb4nmSpMUQ6BYCw/xlV3XsWFN13IVXdexZrta2p6Vn9sEELEhRCPCyHWCSHWCyH+oabjDlbB3f7GwS642xeoh/X08M9uZdTXcnTB+G6Sc+cEGB5KKeTYGC3veU+A9SSlRE1OVtDfh5s6+KcP/VPg+E/8/EvMm+jDRiCVwBAKy3GzIRXJ7Op1FNUm+9L1le/V70OET8wK6Dh2nDGfHDcIxpqa6Z6zsLTf4EacqRxz3zWqV+EighFrJT5rKc+On475619iTuRxmqNkh/JEHYHhmyulodlI/nT2TAxQpx0Go0Irzyro7ZOBO/HfTaWfJPhfnzUCBCMHOOEVyd/fnyM/ZRFN2XQsT9OyMAq5scDxxy2cX1Ft/dwCuPBp3ePbdPQ9ZaOw5g1w2ssuDTg2fXEewPJ8QZs49yG84KkQ6A2nZnJ8cWiYhbb+HGy1LL7c2c7jCZ87E8JAipkxHOlgGiZNVhPDuZKceTnmNc0jakbJyzxSSvrSfTjKKUuy62MXtS4K9OK+5rRrQtus7iccElQrtzI7pZSadKU8HgQ+qZR6dLrjDqUcRQNlqKfeobzl6NYPraoauur5+F/Q8/G/8LZvWP4GoPKT3D45EnifKTjMmhhAmCZRX62EKhoEKV0epzuzmgKkqohGVMtYGGU7KkohnzCzUoLeZ6xMjls50JwNrjCdbB6VLHXPU0oxkFV0DbzKonnnMRw1KRiCRNQkV2Tq+K1BWR63lmXW9qhAujUHBeFLZgfOG34mhUL6WFASOO4VyUfuUrxmRMmkdLX45FMWR6hRmucEj3/Hi3kuXG0G+lFc8BS6VavvkkrBia+AY+rNiTxk3EL1sOI8lGLYMJkwBQUhiPjG3+rY/M3IGJdO6mdfAH7U2sx1rS3kaqixKXbAQ4EjHQwMnCry8zErhiEMms1msk4WqYpGuPJ5+gUIAb619lu6a54rc77qmFUH0nDMiON+fFyFzPhzH3pubwvuFFDsKBZx/834MW4Yij9SVBP1CwtdVfMqBapSK8oQqHwh9IuoilXbQiCEoWsgykNS9TCWmM6jqDynzPmNlzudZ3TYrvgMbAdmLxvDwsHBwMJhFoPs2tRF/obvI0yBETexx3PECmVRd1WtWrw6FGDZiryvNsE2IOI2LSoNP5zRpWKSMzc5nPsEnkfQnIGCKXAiOuSTjcKAMHFebmF8yUQgmX3pTQLThpa01qoqmNpIKAP62kvXidrQM6oly5N5PcZt3dWL807N5ngqEUcoMJUi7yZ03jmV5jPDo3S6i4V1sShf6mxnU7T2ArdFrYtwlIOjHCZzk4wWRkP30wKCltczIu7SZ0tNV4vLDC026EfBKbBtahsL1AJP5vzax67lGg6ol1EVrpGokBk/7sfHfXxvjYWrs7cWOBL4jlJq38iMN3BwUGvb1DBUE/VrPuecivP+HeF+sQJ6muMBCRL1yyiiUJksBErFcUohlePVAfgnv9r4T+753P/759P6/XdFbvuriIJERQyiRzlkh036HmnxQlStR09h9zsI08KIut3VoiYK2zcODQd89zUzcqaeoAVaCjxqgy0gErJvSVm3ZJ5ipuR/3y0YjxhMxKF1CmYPw1CLIhvRarlCgbQUk2mLL3R1MGUYOMCQGeNPhySprJ4yJa54oAqUuGA60JTR26PFjqZCN1MyVGVx3qmZLEJAk5RMGAY2ggUFm/9vaJgzspo9NyEE32pv41fNKb2ACEX4dlvaSCQT+QkGMgPedkvo6coyLGyp6bD+xkJZJ0tTtImklWQiP+H13S44BYwyT2YwM4hlWBVexvXrrz8kDAX7UWbcZY2eKIRoA24WQhyrlHp+umMahuIQRb1tU8MQFroKO+90+MVH3xR4//xUZR/sMPjW9vgnhOkK78p9hVLcfm/yaIJIrICRVEhH4LwkGCk0U1R5lbZgZH0zwpTk2yRDSC+U0k0lhbVYPayoZII46MiboXS19VQUtvXC706Dix6DrjEYbIWuEaoiFyGgHts+aTGeEOQjrrGJaK+gZUow4UYVlYCILdjZBkM+6nOBYohJoIygzyLQBqI1Dalc6XeOgIkEDDVBzAkvuFubiBOREtswMJVi1fgEV4+OE3cXCnclE3yto50Bawb/q4r9cJTDYGaQ8bxuk2AKk9ZoK8lIkqgZxZY2U3kd1srYmUDe4crlV3Lr5lvpTfV628ezujteYF9ZYE4qGKeLm3F2TO6YfswHDovYTzLjRSilRoUQq4ELgYaheD0irPHRvmibGnbeqUiCRCGL6ZuQJYJ8LFFxfLH0qpoHUslLKt+zXp+gViNRnZrpZAzsohy47fophvAOUxKkYzAkdSLXQGGLOmvK0OGc0VZJJmKQKEjabcWTp8AzS6I8u7ike/uTf5KhRtEAdnWUvIREQecn8lbwzoZT0DNeKTNePqEncsqTCSlWgAtV8lzmjAQNxJo3wMtz4c1uMZ5fZrz8XnOGwXG5PH8/OMzRbjOh3abJtZ3t3Jes/NwURRUDN1wF2ye2e7UQUSPKJ0/+JMlIkps33sxAZoD5zfNZ9cZVAKE5hmO7jg1s/8yKz1TsGzEiuhjPh6yT1S1ZDw28yv6RGe8GCq6RSADnAf80w2ENQ3GoIqzx0b5om/raSJrT+l9k5TN3eTUTT899A2dsfZoChqe1ZKDYft4lnFx2/HSNh4oaU0oIbAwiypVN9aGYcagnBBUKV//IK/gNxPn9V9OJ7aJya/nvcA9TSpDMQny81Me62gjD7t8AhCFZECl4DYo6jpvkojbBY04pHFRcY1c7d8TWIaqIDcIR7OzQ4Sq//pK0YHsnTCap6BsBEC0onZNwYHc7dI27dRi2fmYmQQMx2Ay3vAnWL9THr1sMA23V/z4pKfnEyBgfmJj0Euy/aGnm39paSFdJVgsEKkxEMARFIxExIrxn6Xs4Z945JKwElx55aSDUVI3ieva8s0PDR/5ta7av4YsPfZGdkzs9hlVzpNkzKocA/gWdo4CgzPi/7OV5ZwM/dvMUBnCDUuq2mQ5qGIpDFGGNj/ZF29SVo5t42wO/QJoW6ViK5qkxluYHeLZjIccPbyEiHRxhsHruCdw+7xwuLj9BWzuMhsdOhFFsPOrWRKjK0E25vzE9pplYpO+3Bro2RBgueUibI49FJSvPVF4BnbdKBXWBkoBpxlbuQRkOLHjLUGCvszLwlcFhftTazE4rwhy7QDphkspUTqiTMS2bUZz8f3uafpph+ks/CaGsmo42EHHfQvneE+CyNbpQL6pKPb/TUbjjFMFDy8G2ajfab5lKc83wKL1ukuPFaIR/6OrghVhci0WGPjjB3Ka5DGeHvbxB2q6+4BEIepO9WIbFfa/dx6mzTq3oSLdm+xqufexaImZkj5PRmvmmGVYCUZXUcTDw3Ieeu+O4Hx9XITO+D1hPz6L7ZNeFhqE4RBHWB3tftE29bNNqhg2TghVFCEHOihLPZjhmZBu7mrrImRFiToFjhrfy0PNrgTcHjo+nkmQLBQx/rsIyEbbjtoTTrUMtJCSjkMnh9XxWaLpsjYbCo5GWba2A1Pv2pzppyU0QcWwKpkWiMF3+JciksqOSdFww4RO+n9033chCWFhKsO3ezpJHsWySpjk5Ttys+PsXJYUpm0hKsiUSIZEJeiYSGGqGL3+wMq7/Q8K7znl3IhXNmVIviyKSWcWS3dCa1d4FaDbT40vh12cKCnUYiB7b4fPDI5yX1hXmGSH4fnsbv2hrxxEGFoq8k6cyBCiIKC3vvbB1obd108imgNSGHwrF7vRuBIKWaAu/fOmXFYbi+vXXEzEjNSej12xfEwg9jWZHaYm30Gv1evtk7MyhlMzGNQqHp8x4A7VhfzU+ahrux+hsDWgdNRUymEqSc/tB5KwoKLj05fu44rozAqyrRfPm4aTTyIzh9Z4wIgoRASeL1z+0aalB2wkdvHbbCGRyvnDTnuQoajsmIgtlhWvV8xYIVWwVQc6ySGZthppE1TaatcLOGF7L0761rWSG04y/mkT4WqF2TMJoUkt5RGwtLT6a0InlckXXai1LAVCKVE4zlgzfbSZyipXPKVY+V/IupmJw7/GCB46FfCT8JqWAqbjetwhDKd43McknR8ZoclfcDyXi/N/ODjKpTqSdQSqJaZgeFbVskMSFwcDUAGk7jXSrQswaiMYKxVh+jA1DGyom+k0jm+hN9Qb2r5aMDvM+to1vY3Zqtmdgpju+gYahOKSxPxofRebNIzUwQHNXSftmst8mX9YOMmtG6JkartB6+ma8hcSIL/QkJTIDWBBtMxEWKBtyuxWZlt0Iil9EMd20XRXKm/JVFQ8D7/etmQmUEEhhYDllFWXl+7cJHAQmiriycSZ0LiAbLTdN5YZqmsCZIbEzZqkfRUQy8lIKM6G08iwgLM1oSuVhR1fp0JZJrdh65E6t6dQ6BVf/TnHHSQ7nPk+gYO4jdyv+u+Dw6qxgr4lYXnHBs4rTn9c5CdAhptXHCVYfB7loFTqqoQ1EOkagUnppPs/fD41wgiu9MWQY/HNnO79LJUEIvnvCX3L9q79lR2aAuc3zeHr34xRCHnlaKJRrJPQTVNjU3v1wojBRMdFP2pNEshE6E53eftWS0WHeh2VYDGYGaYm1zHh8Aw1DcdghrBDPsCzS7peoqMARcwrkojH+6nff8pLedx53HsZD91ecUwHCVhhGHhyJMAykNBl6CpTcN/LOteQ1xpItJPMZT0HXmqZDnoVNxDU+tmMw0KYNhZXXK/yIDUGRDv9YwChrm4oCJQ1yApThht+ypi5IS9luXbVGGGupLa0ZSYbS+QRD6cK6Sx6DoZZSMjtvaVrrxY/Bd96lt0ULirPXw1vXaQ9D76d45hj49YkGmVjls1u+TXLus9A+Cf1tQe8lJiUfGxtn1diEV+9xc1OKf21vZdw0QQiiIsLZR76Ls9/wfu+cJ/7o2FDnz1G6UFOoEI9D6UnIAPLT/InLJ/r2WDvD2WFGc6MVyehy72Pz6GZ6ksEFV0+ih51TOyvotauOWVV9EIcxGobiMENYIV73O9+JvOFGVMFhSliksLEKaeKOQ0z2Y1k2bROj/O+HdhK181WDQfkxgZKWDrPEbJQ8sB+vqJMlZucQSmGq6kYCAFvp7nMOmFJy80qLpJSc9SRudzhF13D1mcuMKqRSnvfguLRb3WBIoYRmFJmAPWUEjEoqphVgZw27QyimcFQplwC6LiMutTFBaU/DULqOomMCIrbizBfgvHU6RwHaQDy9HNYdqxiLCzIhYaYlOyWXPAK2pWsmil7KD5HE5uT54tAIC1x9pi2uPtMTfn0mpShQ4Hsv/Jifbvgp6UKaZCQ5bdjOCesM4P55PG7cNFHGuBmUu40aUW0gTDOQjH5+8Hlu3Xxr0PsoTGJlrYD3YZkWS9qW0BZrO2QlPA4lNEQBGwCg/zvfDQgFjitFU3ZclxEXv8CWQhXCv8mCYs9olwArRb0Cst55pvMeZi6+m0nuQyPZkw8knT95UguDlknCN+i//0ZYHF2fvXwc5dXjCh3zlwKi5cK6IXdR7Y4VmuJq+4YSLWiDEbN1XgMgb8LDy+GEBWPc1J2iz7TodWyei0W982SiOsR0zQ2SWSO60K7YCzzX5DD7xHEW9miLUwB+2NrCda0t5I3w0RnC9MJJ+iLT/G3Kq7On+zOGXG5R66JAPmHz6GYtPW5GPCZVc7SZtJ2mK9EV2HcoM8RIboS5TXMPe1HAPUXDo2DvpDL+GDDxwAOM33xzoG9D0yuvEPhsK7SiXQiCk3upkE3Lhk8/6ftzEK658SZ5OU1OojrCrlilfsKHVeMT/H1XBzuFgSMEpirK8YWdK5wCWr5dKLBUySgE77e2fI1Ch8EMBXkDWjM6l2EWV+MGPLwM7jlJ0BZ1+HBfllMGSmyvd86fTTqmDYR0J/x5g5DK4no9iq55GeacMI4V05P+U7Eo/9DZwSvRMKGREmTZX0gV3aKKR1P2OfJ+KG2f6XkUHJ2VL070eSePIQxsaeuOdtJmODuMLW3aom1smdriGZDOeCdNVhPdie6G9+DCraN4EtihlLpopv0Pe0OxL6QyXu/wt10FAoqz5RAhE3hxcjcMgXQUhikw4orCRHXDUm4USv8vrdnDjvYaCO0lytlJ0ViGZeNw3hPSk9ooGJXeQHXoMdlmqbLaUApDloyD30jUCgHcfLrOScwdKRkIR8Cjy+CukwSjTQIDuGAsXzq3AWZU0t9GhdaS5UaBrCabBSeP0tyrk9V2XnDt7DZubJpOn2kazPRnqfi9IomggNa+qlZZIRBcc9o1gbzD7qnd5J08trQ9moMQ+t+OyR1eLsSWNjsmd3Bk+5H84IIfBM5bnss4zIzHJ4ENQMtMO0LDUOw3qYwDjXp6V5QjrO1qNRTF5XR1r99jULpEApBKIcom2DCvY7qpqGhKyv2DPTMSlccUfOwkIyLJPp3iqnGI53VitXOMKsLWxZGHexWOWQrnDCUFs0ZLZXl7AgVc+ih0a9kjHAFPLIU7TxEMN5fOGpWSDXELI2NgRCWGKRGGCJ3wbUsx78hJet8wQbHQefS1OBtfaOVXH6tV5dU9b8CD8AfeiruF7adhKMVCZYAwQEleMFTog4qZsYpq6zN+fgYZlXFHInQITBWDguVhQcVkLqhRtmb7Gj635nNM5CdQKHZO7uTF4Rf52tlfO2SMxYZlyytkxpe/uGGv6yqEEPOAd6K7hv51Lccc9oZif0llHEhMPPAAfV/+ivYKWluxBwbo+/JX4ItfqMlYhLVdnQ6W0NQo5RbX4a6ahaP0UtoRFNLBrm1B72HmaVMQHkQqnkeV7Vs38VaCcIWPnKxJV1ZgqFJRtkCvckuSJXprNgrxvH8qqh40idm4AoPhQ6jFeJhoIyEFPHkk3HWyYKC18sjRmMGTzRYR24QyqrO/cZEzN8+x547S3KST1fkpk23rWtk5HKevo4YBeVCgwsx2mZGYJm8hEGQMk7hjkzUtTCFxkN7vik/Zn4T2HYwpTN2lES0jLoTAUQ4mJjpwKPV2BIO5YKvErz72VU90sHit8fw4X33sq9wx7+DXuLlGokJmfMOy5R/fB8biW8DfAs0z7OfhsDcU+0sq40AiLHQk3e21GIqOqz7Cli98iZHRDFNo1lMX4ROZViEVXpuJ4qQqIg4od5VugmEpyAZj0L6zBIyAqLLPdCneYgBqj4xE+UWVwvAmvaAJMtDeQTGcZNn+g8vHqYjYelKP2DrpnLYEkaD2XF3DA3hqsZbb6GuvfEKZmK6vcEyBqQxQDmDpu1DaSHzkbgUxycLjxjlibtqbv7duS3HD+GkcldtKq5WpEBWcCTEJubB6wGm8iMD4BXTPPtkL/YyPbkYgGM4NI5XEEAYdsXDrFRERDGGUGE/uf45yMAwDU5Sy/45yKj4iOyd36qF63q3wPItDBPtFZlwIcRHQr5RaK4RYWetxh72h2F9SGQcSYaEjEY9T2L69puPX9izjlydcygXP3UPX5BCDTZ1k2wVzCpOY6UmvAls0N2MURlDpkNlBleUYjOJKO3zF7f+tPqaU1J6pgru8651nNgTgF56T05zDz+UM7FY5MlMK11JQ8TvfHQPaw4pIfel0tFTXUCvKTZAN/Pi8MvVWoYvjJuO6ZqMIRwjWxKP8qKWJHRGTuQWHi38t6Zid5ehlY8TdZPXYRISf9Z3Bj7ouZaC3hwtTv2fqiHunrwIPQcyAPGV/3QojUd3jkhDIG1x151UMZAboSZVCvhk7Q3eiu+LYI9uPZOv41kDfieZoM+P5cbJ2FptS7gJgafvSwPHVWqtW234QsIj9IzN+JnCxEOIdQBxoEUL8t1Lqf0130GFvKPaXVMaBRFjoqNj2tBZ8/4FX6D/iOLYvPcnbdsTmZ7jikRuYtbAr0CEvt2vMkxoPwPZNMhKUPX0+IZiD8DFnao7mC9/0rK9jJWxk3kRKgWEoryNr+PV9Ho0KjjWYMBd4y3Ml3Hi4Pkgov1lxpyXXYGqZ8PBq9LA7LDcQEm0QlO8Xjq+COiz3IBD839kLiOXGaHX0zZ+7ZISuXs2Csh3BbcMn8Q+xqxiZ1+4dNxTtJA4zhorKrzZesaloJKrdZeU5zvjFGV4dxjlzz/FW9DMVwa06ZhVfePALXutTqSS2Y/OW+W/h96/+PlAFbmBw3oLzAscXz19uGMrrNYo4CInvV9kPMuNKqc8DnwdwPYpPz2QkoGEoAPaLVMaBRFjoqD0CCz9X2fY0DGHS46tPfBs/fePlfH58bSBBvu2jH6My9FOJ6dLV5SygPUE5Qwp0HUe0uSQNkR2pTu8U/sWzckNnebe7nS8IJVFa01CBEgqpdBMhKQTxAl6fh6JD44fpGqrpqJ9hBqK4zVCwq01TWCcTgnQMhKhufpUAy4iREFEuSI9yxdgYiV6993PpefyV+ks2N893x+Zw9o6nee+m1cyb2k0mBt97h6ziVZRdMWAQfDcS8CLK9g/P/ZOxM1jCImNnuGPLHVy48EIGMgM1Tcj6Wej+50oohBBsHN1Id7K7wtN4si9YU/WRYz/C99Z9z72NkufxkWMrvzP7Qql2D7C/ZMb3CA1D8UeAsNDRL447j/f3LGNlDceHSY+/+4Ff8NyxZ1XsK4WBUMWEY8kjKIaCaskblHSbwnMVRVQLDoQaIAFKChwbhKkqZMRDT1M8VbQ0mgrvpugieJdWOEJQsLQhMKVe6ceqSBcVJ//y6bfcQBRh+H4vgUeOBtmiyJrlTVLDLqZYML6Tj4+McGxeJ0aGVYIv5T/MrcaZgCDm5Lhgy2NcsukBOvOjSFGSC/ng6mqGomzEJVeubFsVA+INvJINJaXExvZ6Wt/72r0c13VctTv0cP3662mONVeEqbaMbaEl2kLWySKVxFEOSZmsEPu7+sSrAQKV5Vcuv9LbXn6tepRq9wWWv7jhjg3LllfIjO8L1lMRSqnVwOpa9m1UZv8R4IrrHmXL0CTjGZu8I4maBi0Ji4WdTRWtTKGywPDjv/0Gmd19ZMyoNwe0ZSfodDLE588LhJ7Gdu4mIasLuhUn/+LkWH39G0xG1+ddhMkDKmafM8zoi03YUyZWyiHTHyHUqOAOzhuB4cqfl9MqKyHR6qvpGCTyJfXXRIhidvGMkwlI5MCSQYNRfn6/NwHaQ3itE/7nXHjXY6U+FbeEqMrGpeTPR8f50PiEt/r7hb2Sf7Q/yDhNpAqTXPDaGk6YeJiTtmRQAq/7HehmTQj4X58pXztW8RKqbKqKKh7FTIgbcb5x7jcqJuQLb7oQA4Oh7FCgsG7n1E5PLsTPnJrbNJc73rtnc+yFN11IS7QF4Qv3KaVZUnWcc8/d50MADY/idYawKvKN/ROMpQsYhsA0BLZUDE7kKTgTocd/+sZ1TOZsHKkYnMxh79hBJpIILJxThQxKORVMKi34V73LXXHCnyktWhk6qkQ1OQ9/Ktn/2+Y5eVrmDHmr7pdu7NUSJGGQ/iVxpZEIG682KYJoQU/6yj2N5QT3849TApMpXVFdRM7U+k0Rtye1bWmBQHCjX+6whIK5w/Dhu7W+02Rcq8oWdZmKxuL0TJYvDA0z39YD2Sxn8/nCn/K4Wo6IDLHEuYurnn+cOaM2A636/KrssZS/D6JOI1ERkppmvxmmz6zM8ncP/h33f+D+wPZUJMXmkc1IJAqFLW3drc5X/eLPP/Sn+7nqzqsCIS0Ib6VajrlNcxnIDARkQQ43pdmGoTiEUW4UTl/cwY1P7aioIp/K2SDAcFc8QoAUiqmczRXXPRowKl/7/QbetvZ2Ltn0AAk7R8aKMWHFiToFCpFSsVVU2uQMi77BKa9vRXcqSkQ6KGGSF6VMRVRWL02rhnJPw80yBLZVr8Iu1Xb7q50NSmEiTYDauxpu/xxXSksrDARSgTKK46w+51nAXJfCv6sT1p1oc+qjFlNxvDy5NOCIfte4+k4ilA5t2UZJPbb4+sH7FJc/lefUuRPMnqPznTll8T3nYr5rvxs71ke882es6F/HVffKgEy5Qhs6v/itqWB7ByETe9kGf3gp7IbLIxTe2z1fUA/nysk/MJmfrDAKxfcWFtL9rxjSKsgCA5kBL8fwxYe+iFKKlnjLjHmHVces4trHrgVmTrL/saJhKA5RhEmLfGf1ZtqTEVpdJc9iFXnB0QlXKZVHXHGkwpGqop/EWY/cwhUv3o1EYAuDmJ0nWciStmKMihavw50jDCYjCWxHai/FkQwOjdFtGPRFm2nLT3md5KoZij2prC4PXYWh1slfTUOPDfTNkdVP6s9Y+H8SUtNgy3+rKIbcSjCiDtKG2UMGc+4XDMa0XlMiX/IoHKOkDutJgKC3Z8ty8oatWNaVp/ekCZKWjnk9JpdxTeEqtiTSRGdfTzS5CSHg3bdpI+E3NGNJ7ZkoUcqxjMfg5+dW8Rw8a1k0Eqr0sx/7MYpdzjraOTVNvYMBUVFa9GSdLAIRyDHsnNyJQHgd7qbLO5w972yu4ZrDWe6jYSgOVYRJi9hSMpG16fbVUyYiJqYhSEQMxrM2UoHheheWUSlNcsmm+5EIpKGnYSkESDCVZDTeopsVpTq4d+6JvG3bk8TsPAUrStTOY0iHbckuZk8MEFc6TxGxq3sTYVXU08Hrt43CpDorym9spjU80124fifId1rNsAk7v99IOMDWHljc7xKKhULZgnY7GHCz8tpWFVz5DwP9PhuDgWadKC9O9DRFWLgix6Im3Zt7TCX5WuEKdrzWzvsGbuDfP7A7MJ7uMT2O2RPBbnpTCd04qaKbXvk9FT2E8vqIUI+iTktRzY6Xha4Egi88+AWm7Ckc5TCUGapyYHGIujGSiel5GZ2xYHW3I51AzgGm73BXLiFyuKFhKA5RhEmLxEyDnB0sDsgUHLqbouwaz2EagojQedmCo+hIVkqTJOw8tgiu1aUQxJxCYFW8sX0+/bMXcenLq2kfH2TEpcy++f4bPCNRG2am0lZD+FHVA956Dqug4lTdv/J9bSPUuQeFNEphHMM38fszHkf0u+/cRy4MECFGygAsR+FY2hOShiIuJI8sMzn3eRiKt2Ee28zFbU8REzqhcbt9Gve/upyVGx9n7uQQCPj3shajmQjMG9YaUY7QTY96JnSYKaw/d+jzDa2vqPMvWr5iqOPPkjKjjOXHMISBgeHVTlTDx47/WIDN1BxpJmbFAvsUW7f6cbjlHepBw1AcogiTFmlNRhiczLOxfwJH6q5hTTGL7qYYjlIB1pNAMp616W0tnTNTcMhYUWJ2QXsSLiy3Mq0tM85YJEFbZpy/fPY3/OxNl/O5M/88wKT6yGRfXfcRVnFdOWdMsxItLq9ngDBhfFec0RdTXp8JKyWxpyonQ78/Unwn0KqvSpXYMuUMUP9RhqEQTul+9HlVxVzoHS+L/K7pbzXVXNL7kLagY2A+//2WpXzWeoCjjBcA2CE7uWPLyRy9bisfyt2l5cKpkpAWpZdiSMu/fVrUIsUxXT6jfBAzISTZE5OQJYdhRjFEkSI8/fmuPvHqAM21WAcBpRxDc6QZpVSjw12NaBiKQxRh0iK2o0hYBnlHFxnpSmHon8jSFAv+KVsTFoOTBZ7fMeZ991oSFg8cfz4XPHW77nUthJbCRjIeSUI8TkIIlDIp5HNc8vhvOC/eRO/UMH2pDm5curJur8DBoFRjIXTCuR4vwx/0V0xrNHY90ub1zNAGonLncAKsNgyOt/BXIfv7jQoYTjlryo9Sar6Uaq8N+QkLKQXruo/ktuNO5+09a/mB9QsAHCW4PnY0829Pc/rQ+tLolRYw7EtCUkrSRsljTORhoEW3Wi2p2obTeYO3UK+RCN1Q/VfV/vgC3qBKxj0jFFuQM+a2iji199SKbWE5hs+s+AxQG+upgYahOGQRJi0SMQQFqQJeRjpvs2ssy1gmi+mnx04WKr6L4xmbtedcwtBUjks3P0DCzpOxouSUxXBTB1GPNSUw7AILMqPsVNLzMj6+7ua6Ag6ami99RWT6y17uZUzPzNQS1BVFBmVQjvCK7Ern1bkBw8KT9XBC6bLuXRkqoEQSsf2GICxuUsncmtlv8B8fhCNMVs86lpuOXMnRHTv5RuQ6esQoAM+brfxDd4IX41P8s+Hg6OH6joVsvNI0DrRqptNOn7ZerABDqemGFxZq2htU+dTU8GGKK4kptESHEEKHnpCYwsQUJnlZsnhLW5fywwt/WFVuI8wINAxDbWgU3L2OcNY/3UtbIlJR+PPS7gkUYArhfcdzrtZPIlJandlSYjvKjbGX8I8Pfo+O7ASFSNRLhs8Z70MA25t7vcV01M4zb7J/L4iO1VGNBluJ6p/XMIaVAvIRMB2BYyqsQtGrCUIiUEKryFa7uj/EVPszUBgxhcyXuKiqLEaUMyzuWXAqNy19M0YKvhz5EeeZTwOQd0z+oyPF9a3NFAwDQym+/R8SKYJewmhSG46/vDoYaiuqx9pu7UbM1sfcdywct00ns3e3wfffYTDYVuT71k4/KL/X2vetfoo3SOEtEDJKEWmezbAqMFGY0H2yDZOYGSMiIrTEWwKho4uXXOz1zD6IbU/DsD++NgcMDY/iEECtrVirSaIbhmBWS4zBybyXT8iFJUxFOOf/xqUr+fi6mxE25MwIUbtARDqkzSiLxnZgKIUUgtFoU+C4mfpb14pp1IsIn65rMyrFvaIF/c4sFskRpN7mDZAmRKu2eg2er2SCguMRoOVD3IZIwlJYcQekwEiW/iCZEU3dnIgkuG3RGdy65GwmYkk+ZN7Jp60bSAktOTvUH+PV51v4xaooTjGcJERdXsIzSwx+SKkfxUArPLcAzn1eG5id7fA/55oMttUQagp9KvsYgkCPikK8lc+c8QWeH3xeJ6hlmqgZpclqIhaJVchq/HTDTwM9sw+E3MbhgIahOMiopxVrNUn0RZ1JClKxuLvJ23f9zjFO2r2B92+636O83nTUSh7rXgYEp9q1vcv5zglw+cbV9KZH6Eu2k8yn6fB1BTOUoiOnK73DWpnOZDCmMyrVwk/1hLnqmd7Ky/gsBZOmImILHHSJhb/Z2vTnDuYzkp0FL5nesUw/v761rUifBlVfopVblpzDHQvfRNaK8Qaxha9Z/8nx5qsA5HIGL29sYXhnnKEmg7RheB6gRFNZr/6domusVAeRicFP3hL+tJ5ZYvDMktL7L/7cwTZhwzz4xUqTyaQ+7uznJGuOrfbE6/PuakKVSw3GEi5jKcGVy94PwK2bb6Ur0UW8SXsJ28a30e600+f0BSQ80oU0E2KCrfmtpZ4W0Q7yzkxJmQamQ8NQHGTU04q1miQ6wKdvXMeO0YzHhnpT/4v86bqbyRsWY5EELZlxPvb0r7FPuJS1vcsrxrG2dzlP9S73Juff/OYz04y6NNEGKwKq7RluVMpbqdZObQ27Tti0Fba1csRKQTKrKakRKWo0EOFY8JYwjv8Ywy82sZG53HjkudzdtQLHMEmQ5YvGj/lQ5C5Ml4409EqS3euakY5BPAq3vFUb6fIGpRUF0HUMtmsM7lghuGOFgTQE8ZzifQ9ITt2oWHNsmNbTPjASlVS3KrsJ8k7ee73hpRu4Z9s9FaJ8hjAYzA0SNaOYwsRWtleE5+9mJ5VkMDfIHGtOfeNtIICGoTjIqCbx/XjPstCQVBFhPBsUHhvq0o2rsU2LvKmnmLwVxXTgso2rQw2F/5wKiFSZCMI8A1Vle+kI/37BUFO1BHcYjZayYytHXxPn09u7eIyBS6ySJVPm37f264SEvySstZby/WXv4pGOkirqRfbD/N/Ij2iLTgGQG7PY8UQrY6MxTIlXo6FzJ4Ks75wXPyZJJ2CkpbQtVoCLH1MBz6EavvdOgw1H6FDW7CHFn9znMGcYTwdq3+coROBlOigUOUeH3mzHJufkGM2PsrQt2HjIM+eq9PcSiIAgoP+cI7mRmS/eQFU0DMVBRjWJb+esD/CZGx0msja2lAxO5PjkL58mYhq0JiKBMFUqanqMJ0fp1+7JIU159SFvRZmVDv/CfODFu3iPjwlVDdV6VoeL99Wew/CnkPd0RR8u+xE+2fs7evuT1OEobdfXCN9PoXj65tlE87q39uOnLOXO9reyruVIb5/j0pv4F/l9lnXpCmDpwOALzQxtaEJJgYrqyylDV2q/+zFtLvw5hrmDkI3CnIlgMrt7bLqnU0LRSJz6kuRdj0qacvo8t55Wje1Uj+EI2Xcv0xm2tMk62YAon6McIiKCrexAiGkwN4jlFuUVTbopjEboaS/RMBQHGZdtWs2wYVKwogghyFlRIkrx5qfv4rYzFmEaAss0UArGMjaWIcjknUAR3Mb+tEsdFJhCYDuKXckO2rMT5NxJXwFmIc/uZDun9G3gso2rmZUeZneyg4F4C2/Z/rT3fU4VslXHi3u2yp7VYcnkeiQ89jYxrsX6yrdVJetDmUEK8yIqzaEReB9yVsfgziNO4eYlKzVjzMUbxl/h79L/zWlzX8KM6PNO9UfZ9UQb+YnS19B0ShXUrVMQz+Gxloqifk1ZaEmDY5b27R6H7Z3MDJcWN29AceGTkvapMgmPmlGjV+W+NTFRKE+oT9ZSRelDwdGFiEUmkxACW9pEzIh3vpH8iP4MKBkI1dlKkjQT4SduoCY0DMVBRtNwP7nmFANTBXdlJGhrTmEMD3JK3wbe+/JqLxn9yyPfzNre5ZhuHqIoJ66ZsMG64CKTCZfJFHMKRKXNuq4l/PVTN5C0s5jSoS03yfFOoY4JPTxMNP3eeybhMR1EMdusXLppgNZanvsIHFnmQQR5XOX7lu42mLQuR9qK8buFb+KWJecwlGj1tq/YvYE/G7yVs494geRCPdk5OUHfMy2MvpoAr7GsvkpRx1AKregaL8BomahfsX23cgfjjUdVJuq935YV0G3vFnwlVMJjL1Hlj1yUAy/+XC+uOS1YMJcr5OjP9FeEnlqVYgzda9zErVgHrrSrdztsYGY0DMVBxmRHD+NbdyINnRWQSjE+PkXeivGxp39NwbAYjyRodQve7lrwGicObqY3PUxfsoOblq7kiSrJ6e+cgOs5jLA72c6NS1fy4fW/ozk/hRQGjmEiVGlKrXcir8UDKJqJmfaslzWllPCK8IyIROaNitxHqedecVtlfmJvmTvDsWZuWXI2ty86nXREr1oN6fDmHU/zvs1/4JS5r9B10gSGOyePbY3T91QLTs6sGA1Q6rmtB6x7b2dh9rBmYsmicRSuxpS7bTQBiUKVu6qlyrpuKC2poZQX8ptOrLd6WK82lBfMXXjThcxKzWI4O+yxnjriHaix7ZwuLe6wbHJoY3GhbXH1ZGVvlgZqR8NQ7EfUUh/xH7NO532b/4eYobyVvyltsGLkDcsLHWWtKPFsjg+8fC+7kx2MR5K0ZSe4et3NyBOomqDWKH1J500OeAlSwHutB7WmMPW+yvf/mfae7vgybpUvQy7z0wuS+/lWpfMGx1LdUIWn1Lenuvj10pXcM38Ftqm/RjE7z4VbH+WSTWtY1LSb2aePEmvVydX8pMnuJ1uY2h2veq8SHU4qqryOR6ElAx1uDwmFNhzFfHPBLBmG1ixsT5bfJfvJSBTHq7wPQ/0+Qu2IGpU5s2IzoYWtC71tGTtDxLBYh80CZRAHssA6w2ZNWzeNKoo9R8NQ7CfUWh9xZ2oxAydcGqhh+NXSlfyfdb9mIpL0pikBNBUyGEp6xiNnRcEOZzKd0reBj7v02PFIkvbsBB9fd7NbPLdn91RcOe7JVI/vPurHnmUvwgzE3uKltvn8aulbeHjOsShXpK4lN8nFrzzERa88RBuT9J44TvsS3UxISRh6KUX/+iawTZRQ3mTvR9qEuHRbkqJfraJ1CL0312C4Xoe30f+Q3XzEol2SK+9TlZLie4U9WWAEmUjuDzOeMmbGKmQ5VvSu4NbNtwLBZkKRVDeR8V0kUCAMEkpXWX4raXF9WYe7RgFe7WhIeOwnXHHdoxVV1Om8TcQQtKdinpfxyCuad+9f2CulZTV6C5OkjYgnq3HE6E5yhsXO5h7fvoqWQoYPv+2awPX/8cHvBZLZoFe83ekRIspBiuIqfc861NWCehsX1dvkaLorF8+oAu/DrlLcWr5f8DdP9izjxqXn8mx3icE0a2qI92y6n/O2PUHcydMyP8usk8ewEnq2Tw9F2PVEG1MTVkDZ1bLdid4n1uQow+sHVAwnpSOQLOAVAQpVMgwCvY8Xekrq3tyfu8ooVXErxfKtkqt/Xynh8cPzqxmLOsJxe+CNBqAgCRTQooYZqFDANTCIWTE6450VshwXL7mYJ/ueDEz+X33sq7RIEFMD4OTBjDIea2JXYYwFLQsOpqzHvkzRHXA0PIr9hLB+ErYj2TyQwRCTSAU7R0uNlMvt9U1LV/KJ52+hSQgKVpSInddd56wgeyPuFNidbK+4/qz0MDaCuZNjXie6kWiKrBUjAySdHKaUOIaBwkFRFGndN7IcUC/raV8gaBDCJ//a79AWBg/MPYEbl57LltZSwdaS0e1ctnE1Z+18FlNJIkmbWSvGaJ6j+f9OQTDwXDPDG1NIJTyDUFzsF59JQRjeZG8V+5C7D8tQkHQZncKAgm9Oj7rtQBxDi+UaCiaT8C+XmThGMNR0+UOqosMdTFdzcYD+Wm4mfmHBoWghX4iYpUy9C4kkZ+cqCu4Anux7kh9c8IPAaeeud/tbd5UM+uDoK1iG1ZD12As0DMV+Qpgu0/YRbRik+12U03wn51/wFn7RHOOC5+6ha3KIwaZO7pp9POdtfZK4nSdnRYjZBSxpc+PSlRXHT5kxFkz2I4VACoElbXozo2xr6uFHx74zkOQ+emQrCVUgkAuQ+8pg1Md62jNDNR2ltdr+1R9+1oxy5xFv5NdHvpkBnxE+sf9lLt94HycObHSTzYqOo6foOW4Cw9Lnm9gRY9faNuy0Tyo7DrG8DidJQxfRTcUglYeI6xGEjbTYikPgSnW4TCjwdW9V8OjRgv98u0EuKgIrDoGurZgsS4vkrNprLvYI/lVPVa9D77PZMnCEwPSOqfykSCS2Y7NlaktAriOsG92qY1bxhQe/wC57F45yMIVJzsnRGetky9iWQOK7Wje7BipxwA2FEGI+8BNgFvrzfp1S6ttCiA7gl8BCYAvwPqXUiHvM54Gr0F74J5RSdx7ocdeLj52zmM/cuI4dIxlsKbEMA6eMqQil75Qh8EJMqajJu0+cBye+n+8/cKon13H64g7++6bfccHzf/Aos3ce+1bWti6tHIAQCCWxVKkNj0Kv3J6d8wZemHest+svV38GJgTKBqQu9hIhiYw9yTHUIyleJKPWnvSuZiBqgF+jWwlQMBZN8tvFZ/HbxWcyEU25u0nO3Pksl798H0vHtntXircXmH3qGIkOTXktZAx2r21lYns8MC4DuOFCxblPQPuYYKRVkRwXdE0EPQKjSvTPAP7nDHjXExDP60I7gPE4NOXgxrMM7jhVuxttk4qxpPI+YIKSzHjO59zGbLcKe38gTFtkmhCV8P2bDjundmIaZkCuY0lbeBm6kHmUnXUJAAWEEIzkRzCFiYGBLW12T+1mcevi0OMbqMTB8Chs4G+UUk8JIZqBtUKIu4FVwB+UUl8TQnwO+BzwWSHEG4APAMcAc4B7hBBHKaX2T2B9HyJb0IVxUjFj+8Zj5pS+uUWtp1989E0VLKl/A77au5ypvEMqavKnZy3iwXs3kS9zT9qz43i9HADc5F57doKTd73ApS+v9ii2id4chXGzRK3xKJoK6fsK741/UZuXUH8d9x6HSlzjgID+1jZ+teAt3L3gVC+nE3EKnL/tCd6z6X7mTpW0g4Ql6Tl2ko6jpih2lB3emKT/2RZkISzmr2jrM+jp1xN9JCfImzrPYNVIFXp1tmBLbykZ3ZSBdAy+9R6TLbP0szhqu66y/vp7Te/pKHTi+iN362fkz1Hcetp+CJlXy3e6xiIsmb244HgO3gvR6tORQFTUTIT96a9/4huY2Qndw8L1wApK4RiO1/5U4Tb+el1nDQ4sDrihUErtAna5P08IITYAc4F3Ayvd3X4MrAY+627/H6VUDnhVCLEJeCPwyIEdeX342u83kClIIoZR0SOi/ANe/nlNREy2j6Qrzrn6xX423HonX/aFo+4cPo9TjjmFR14dCewbUQ4SgTRLS0lDSuJ2jr988pcknRyWlLTmJsmNmlqCuzhxOdrGSARK6v4NEsGkGaXJyR3E71e9BqKaD6Qt4ebWOdx45Lk8MPcEpFvo0JRP885XH+biVx6kPTcZuE5qdpbZK8aJpPQaJTtmseuJVjKDsZBrlPDuR/RZbENrMqVytdNJFZWV2Tva4T/fYTKe0vd23tOSix6TXH+eXpv7WV5hMuMl1lM9ROe9R2WjWOUxs0peR/inK6xmYsqeqthv09hmJgx9FgOwhfusFVjC8o7vTfUyVag8voFwHNQchRBiIXAS8BjQ6xoRlFK7hBDFpfRc4FHfYdvdbWHn+yjwUYAFCxbsp1HXhleH0hgCDDe5KIQOL3ixZR8SEYNXBiY9WY7muMWirqaKc97z499wxSM3IE2LTCxFW2acKx65gf/M2pgdR+F3sQqGRYw8QimUEAilXYWotInmbYpFcIbjeO0/A7kEqZiKxMA0PP2nm5ecw9u2PVFVLwr2bTLcf9YSap/M9FQUNBYKxbNdR/KrpW/hqd6jve1d6VEu3Xw/F2x9nKSdC5zHjDv0njRO6xFa2kQ6MLi+icEXm0oVcNPlQ6QOKZqyxOox0Cv8ImJ2+NEFU3sArWkt1XH7GwW/PEervsbyig/dI5k1orj+PHfy91bKpTxMucx4CQeR8ejlW8o4wFUeY8SMVNRMdCe6K/YrKIUSmiXmP52Amo5vIBwHzVAIIZqAm4C/UkqNi+pxzOqcxfKNSl0HXAeaHrsvxrkvYRggZTAfYQrI2hLTEBgC8o5kYDLPB9/YUXH8yY/cjmNa2BG9gi1EYljAO174A2vOOiqw77bmXhaO7aLZzmBI3XhowkrQUgh6KqUwRTCXAIJUIU/OjCARWNLholce5umepfSkRyrE96ZThPWjZI5qwd6HmIr5DgfBw3OO41dLz2Vje2kRccT4Lt67cTVnb3+GmLLLrilpW5Kh94RxzKi+/lRflF1PtJKfrF0SwvQNX/huI2KXpnN/p9fitqylGyq1TulQ03UXGTxxtH7y8wYU27vgureX/SU8I+E/U62oY3+vkK+O05c5FA6QEYK4UmRnoNqWaz0VnAIreldwVVltRFQYZFyRkCIZoPiEMnYmcPyqY1bVMfjDGwfFUAghImgj8TOl1K/dzX1CiNmuNzEb6He3bwfm+w6fB+zc32OstetcNSzuSrGxf1JLZLjetS0hagqOnlXSh97YP4GQiohpBIT+HnllmE+UnXNWepiJSCLYnc2MhK7w13Ut4dihV5EYFAzNKmmy/WJ/4ZOwf3IX6GSuFAJDSdrsDGfsWs9QrJWUk/Vot+Ur8L3HPshBuMgZFn9YsIKbjlzJzqbSCvLYwc1ctvE+Tu17ESOEShttKTB7xRipHs1RtXOC/mdaGHs1sc88plIv8eC6uvjeMXUeY2cHfPNSk51d+rpnPS/5k3slV/9lmFZTvcbBjzqO87yBPTy1m88YNCOkLUVSCSylsKFiaZiwEhVaT8WCu4gZoSXawkBmgGsfu5bOVC/WxC4mjFJ9RruE1ubZtCW6GwV3e4iDwXoSwA+ADUqpb/h+dSvwIeBr7ustvu0/F0J8A53MXgo8vj/HWE/XuWr47IXL+OQvn2Y8Ywf487Nbg1xFRyqkVPhaWxM1DbaPpCuM1cd7ZxPp76cQiSGEQClFxM6H1lGcMLiZ4VgzTXaWiNQT+qQVpytb5EUWRzX9lz2iZCmGLARxJ8/2pm5GRbO3z1Gjr4Ueu3dU170zEBORBLcvOoNbFp/NaLw01tN3Pc9lL9/L8pGtnncTWOgaiq43TNC1fBLh/k1GtyToe7oFJ7e31czVUbxr2ywxoRIFePQowXXvMMjGBKaj+NDdkrc9rciaulNdZd7hQDjSe2OMgsgL/QnJC0U1dkp7rL1C6+mqO68Kra3AShJJ5unNjpXaqSZb+avTv9AwDHuBg+FRnAlcCTwnhHjG3XYN2kDcIIS4CtgGXA6glFovhLgBeAHNmPr4/mY81dN1bjpETINYxPC6ztmOJGcH05gCcBTYPkXYHaNZZrXEKozVzxadzZ8O38SULDCFRQqb9rjBdxavrLj2rPQwo7EmxuLNnkkwULRlJzBcsmxxkpyWslpksigFSu/flpssGSDDqi9iUXXH6QxE7VpRA/FWfnPkOfz+iDeRiWijbEmbt7y2lss2rmbeZL93luL9K3diTnbnmH3qGLEWnTDIT5rseqKVqb5gsloiQvpeFFE+vtoNpaAkrJc34YZzStTXjnHFp252OHKXS8914MidOu/ROgVX/07xvXfIfSDNUQv2nTEq+qLFFI0BIIxAj4mwsPSOyR20RFsC2+JmnPH8OP/fWV8JeB8N72HvcTBYTw9S/dvz1irHfBX46n4bVBnCqqqrMZGq4fsPvEJrIsLs1lIl9eBkluGpAqmY5fW8lq56a96WAc9jeKrA7LZ4wFi9fMRx/Ldlcv5zd9M0MsBkezfWqg+z9rnKEMRutx+FHYl6+ZCEtHmtpZe27ESgMjvmyozXAgfozI55E6XlyX+UtQ2ypKb5zIjpPYhy9dlqOY6tzb3cdORK7pt/Mrahn1mikOUdWx7hks0P0JUd964iAccqFcAVUpIlR0/Qvtinz/RiEwPrm1BO+T2oaYyE/56KY5zJFAcRcbQcx7cuNXlpvj7u2C2Sv/itpCmray8spxgW1O8NBc0Z+ODq2jrcHcqQApJS6bCRUpiqwNymynqHoiigv5lR1skyt2luhfcBVGhFNYxHfWhUZocgrKo6U3CY156c5qggwoxNZypGwVH0NMe9Irq+8QyZgkT6HA1hQNZ2sB0ZYEOloia3xI7g92/6qOelNL1qAZXdu7x+FAXdj8KyCwhp86MTLgWC8uPHD26u+b5MwkiVonKNbRuBBHklagsxlavPlgr39HHrOxbyq6Vv4bHZx3jHtGfHeffmNbxzy8M0lTVhkggQirwpSDiKpiMyzDp5nJTp6jMNan2m3Fi1ZPV0k35tvSumw4tz4duXmow26TNd/LDkbU9LUnnNErJNbUwUwd4VKJgzXOfFDhSECHkQwrWnqmw/RdrduYAimxvlvVYlsWPVMav43JrPMZGf8D4RzdFmPrOistf7mu1ruPaxayvyGddwQLWeXtdoGIoQfOycxXzx1vWk87a38i84KtCzeiZUMzZLe5r5xUff5G07/kt3YghFNFKaaG0pcZQOQRVbnNpS0T+pDYKSYAqBkjCaLoRev1o/irW9yzmlb4O7V/0hhLBpspoh2BsDEUQw0yERPD7rDdy49Fxe6FzkbZ87OcB7Nq7mvNeeJCpLfFNh+iZuCaYSJGM28980Rsvskj5T/7MtjGxKUqFMFxhJvXVate2tgNtPFfzsLZr6msgq/uK3kiMGlBYRdHnVQrqJ7jK7PM2QDw2Uhye9976BhxTsSeCerXdxNf8S2P784PNM5IM9JibyEzw/+HzF5H/9+utD8xkNrafa0TAUIVi5rIcvo8NHxZV/vaynasbm9MUdXHHdo16CGgClGxYV2VHFL5RSirxdmUfw12aoaQSj1vYur0l+fG/nmJmkOTT2PkldECb3zT+ZG49cyWsts7ztR41s4/KN93L6zucxw87vy9sbKDqWTdFzbEmfaWx7nL61rdiZmTu+TX+fe5bkzUTh+283eOQNevaf36/4y986xPK6w125EZC4qrGqdGumgu2VC+/9i1BPIQT+mKp/W43YZDgVoaPnBp/DEAZF9WuBQAjBTzf8lKtPvDpwfLV8RkPrqXY0DEUVrFzWU5dhCDu+3NicvriDnzy6lcmcjSMVg5M5bEfSEo+Qs2WJHpuKMDiRp+AzAvsqfXjZxtWBhkh+GfK9Q7k585M9/e/rx5QV4/cLT+c35W1G+zZw+cb7OG5w8/QBITdGE+/IM+vUMRLt2tsopA12rW1lYkeiLmNZjBLui7Tx9k74+ntK1Ncz1kv+9E6JqWAqpvMRo00wXCJu0TIJ7ZPBDnfpKPz83H3hVtRp7PYdk7kqJFSEjjJ2xr18MdinkEoylZ+Cl++Gh78No1uh7QjmtiYYcLKh+YwGakPDULD3NRPVUG5sLvzm/YymC5hCeKEjpWA0UyBq6tWRIxW2Mz2p1O99TKdAG4ZZ6WHGI7XnWmpFpfjf3huI4Vgzty4+i9sWn8mUr83oOTvWcfnGe1k8vqum8xiWpPu4CdqXpnG7dzK8MUX/s83ImhLuQYOdt0TVSupySMBNi3goPplHlgn+w0d9XXW35Nx1ytMoarMhYwUruEFXawv31ZSuuOA+a3+9H+m1e3xqEU6FBfBrqAn3U/f7T4MRhXg7TPSxairLtZ2dEKdRcLeHOOwNxb6omagVYbIeQoHjluUKoRN8CrCnaSgl0L0tLMOgPRlhYLIymQ06zKRzFMPsTnZw49KVHhvK70mU8ZU8lBeAqSr7lY+uuP+eNh7akeripiNXcs+CFRRcraqYneeCrY/xns3301tVQiR4RYGiaU6WWaeMEUnpCSU7arHriTbSQ2GeVDWvCIpPQaD7QYQZ8rD8hYxKJiyDZL40qRsSfnGuwe1v1E+zc1zxVzc7LN2p1WGLrVCnorqWImYH1V87pvTvd3SVtsUK0/WY2B8QGBhuDbS3qRq7OWR7ucc57aWIF/IwusNrRqQXSmW9C5XSHe2MOETdxVA0ydl5uCZrcH17o+BuT3HYG4p9VTOxpyiWVSztKcUW0nmbwSqTP8BJC9oDuZNV1z9RsU+1Vqh3LVjBZS/dS0LZMwYZRNnP09ZbuK9+dlK9eLltHr9a+hYemnOcr83oFBe/8iAXvfoQrfkqIm7uDOy/ohV3mHXyGC0LXH0mGwbWNzP0YgpU0NzprnF+OmvYvZW2VzOWYc8na8JP3w5vfUL3gHilF35/qsmLC0rU10/+RtLi9rDa5c8zKIhM6PATBNVfy2XC93uPiQooJDLgKanih6SCyRR2fMjTCpMFcTdlx3eQEAIME2QBQ0ickPqKVqcAkfbgxkiCs0cHOXvVvTPfVgOhOOwNxb6omagVi7tSvNw3QUE6geWnZRCgwXY1TZ838LOmIHzBFpqLsOGizQ+SVHbg2CKC8h21TfRh6299a7XFuhXwVM/R/GrpuazrLvXV6EkP896Nq3mb22Z0WrgUoGIBXfuSND0+fabJ3VF2PdlKYbL4cddCicX4vmNJzELxjqs9kT1DKmPw6JEGjx/peopuhTvAJQ9L3v+ADLTG8CNmw/YuXXXtV3+1jUqJ8v3aY6IaFBUreg0R2Gdarlg4ja7iOgWBVgZA60KFGQmAIcOAyQHIj3veB9EW6HqdF5gcZBz2hmJf1EzUircfO4uX+iYoX7zaEuy8XjYWHIfMcCb8BHUgLBeRMyPML6QrJkRB2YBqQNCDqJTqmIlG6vjajL7SWkoqLh7bwWUbV3POjmcwZ+jhUT6iaEue2aeOkezWlGE7J+h7uoWxLcmK0dgmmFLhGIJsxCBSCBvx3mvhCtwCv+LEJjT19eO3SU7dWPm8Y4XKvhHl6q8nbpbT9JjYM+bVoY5r7BTXWzl2CMlcZbBV6Baq0mcwDKUoGAakd6FjuSbYObD7YeGqgzb2PwYc9oZiX9RM1IpHXhmmpznGRNb2vId03qn4Wk83PVaLjZcjLBdRrMCulj8ohoymmxrDUtRhvbGrnSFrRrlrwan8+sg305fq9LafMLCRyzbexyn9L01zdJUxGYquYyboXObTZ3rV1WfKV8nyFmMmQiGN6UZcOfFW79VX+UwzUVA+LwKluPZ6hzkhaRYFtE2WOtn99lRCJTmeWWJw3y4n0PWutO+BMxImpdxV0SDWhVo/NMDZOYezVYniempkgKwRfDZSCOJSQlMvZMe0R2HFIN4KW9ag29s0sCc47A3FvqiZqBWvjaTpaorR3VwSBnxuR32BZYUu0vN3uAuDV5lta08i5hSISrtq4rpUAe0nHPq9jtL1q40s2Bs7uOd4RLcZvXXxWYzHdJtRoSRn7nyOyzbex9EBYcHa1/HJnhyzVowRa9EeWX7CZNeTraT7YtOew5A67GRIaK4zylicFKsTAPQMuKMDvv5eM2AkABJVxHaV0FTYopdw7vPw6uxK/aYTN0vOfb62ffcniq2PivN99QChmKbgzodqbqgAZF5fIJKAQoZOU7LDqLzXTikh2QMpX68JpWB0Wy231EAVHPaGAva+ZqJWhIW59gTjWdt7/dYfNobuU60y+2+f+BnNdjbkiGAAqmguaiW5hvWzECj6ku3cvOQc7jjiNHKWFteLOAXO2/Yk7920OtBmNHw04TCjkp4Tx2lbrMN0SsLQhhSDLzSjnJmNjCm1U6FE/SthgS7cq8zQlN4/sszwqK/lFcfjKd2IyCg72jFK7KbiaxiT6eLHdMe7WvYtDdqfN9gTr6PymSYlTBjVjWbFodUIZZRtD/vzvf1f3dqIbdC2gKwzFWp7ssKA9GDJozCj2qPoaPTH3hs0DMUBxMfOWcxf/Gwt6ULdTnpV1FZHUdopJu2K72K1cNOeRLuL53m1ZTY3Ll3J/XNP9NqMpgoZLnrlIS5+5UE6chPTnWbaK7QckaH3pAmseEmfafcTrSH6TNUzJcItdxaq2A2t2r6VT6AUbqmMm9gG/Hylwe2nlaivxWK5Ir04kYfRFLSlSwVzoL2bOcM632CbWhwwjMnUPabbovpRH+tpz/6ywVMIJkyBdI2OdLdV7Fob+3Xa0ViGBUedr/+5yP/wGIqBthLTDvIC1tijXN/RxI5IK3MLNqvGRzl7YYMKuzdoGIoDiFue2b5PjcR0qEaPNV2F9uAX01/7UMKeTCXPdS3hV0vPZW3vMm97Z2aUSzc/wNu3PLpXTY4iKZtZp47RNEsHOZy8YODZZq3PVHVGKr+LveUxFc9SeZ7RFHzzkiD19RO3SP78Lw0sSh3XMlGYN+EWygltLIqKsKYDjtCv3eO6crscA626d7a/tmJG1tNMYZ89gnDZr2WdPep4xH5no8SZKpVtGsJgccviCgmPgmnh+LoNFI8tCJNrO9uJSIcWx2bANLi2s51rttzJ2Y0cxR6jYSgOIG59dndN+xUnlL1BNXqscGXNq9VFlTATb6kEB8Ejc47lxqXn8rKvzej88d1cvmk1K197isjetBARis6jp+g6dgJXQZzx1+L0PdUS0Gcq3VPYHRa3VZshp3sqMxubF+fBty4xGWnWv3u3S301FXRIyYRhYCO0FpXyjVf5J8jKbWFDuvU0MQ3rqRpU6I97DAWWaWGIUtAp64SFNKtgmscdM2M4ysEUJikrxXlHnFch4ZHzApy+0JfSirMRKUkoQBj6VUqud3bT8Cn2HA1DcQDh1Ki3UTQSYZXV5SJ/1RBGj827ISCHYCKyiCDVdWYjkTcs/jD/FG5aGmwzunzoVS7feB+n7X4BYy9npXiHprzGffpMu9e2MrkjGHsp5yVVx0zL6fInMv34FfC7UwU/O9fAMSupr46Aa5+ZYGxDiqZxg8kWSUvaoL8Z2jKlKmzT9Sj8+k2jCV2ZXY5nlhj8EBmorSh1uDtQUCjpgCrgLSpm+siUezXl6RsBESPC8d3HByqoQ9Vflb6mhW8JIMBGEVeyFAYTEFeSHeY+0zg5LNEwFIcoqoWOvnMCNRkLjx5rRjBQGFISs3PYGER1VwYP5VTXaij6GJOROL9beDq3LDmbkXiJsnjarvVctvE+jhneAsyckJ4OwlL0HD+u9ZncCWVkY5KBmvWZvDOF/BxsgOoPdVQeW/0eshH4j3eUVF8X9Cv++tcOs13qqwIGW2D+wwl6DAdh2qgxwUTOwDaCVdjz+7WRKBil59yahe1VynnKayvC77c4in2LZinJCIGNg40vXzOdE1qF4SRE8Nn/2XF/VqH++tXHvlqp/qogJ/QEVuyN3awgrSRZIVyPQl8nKwRzD0zE948WDUNxAFFPaLha6Oiyjat5atZy74tmoBPaJ5d5H+s6F3P5xtUkZGlJKouhDxe1jqX4VR6Kt3DLknP43cJSm1FTOpy7/Sneu3E1R0z01XjGSvgNVNPcDLNPGSOSdPWZRix2PdFKdni6ivXaQkT+3xXvq9j4p57sRZH6usNVfT3recmf3SGJ+zwAga5zkIDKmSgpEIYiF1G0pgXN2aD+k0I3JSpC1ubYld/WfimliPpClhnDoMW2GbUMz/v1h8r8rUtVII5WBgVNsSbShTTJSJIrl19ZYSQgvJtdizAZVQ69GMSBLFBQkiunCtyaioF0iEtF1hAUDJNVhQRcf5GnKMsZnwwkxxuYHg1DcQARtQyv5Wk5ytmL1SqrZ6VHAnHZVNRk6bbnS96HlaAzM8blL91LwteuXoBnJOoxECDY2tTLTUsr24y+feujXLJpDV3Zmuk2017LTEitzzS/qM8kGHi+ieGXUszcmae+2TEXU0TzgnxUYb8hS9PTiSrnqJx5Hzta8N13llRfP3SP5G1PhftiLRlwhIkQCoRCSUhmdT/AvM8xqlYq11ZF3mpaBNzFei1H9f29EI+CjGESV5AOuenyZLSjnFAqa0TBw1c8POOIVh2zimsfuxYoqb9G4m1cNTrEkxHYYcBcCauycPZxf8qx6/+b6+NRdkTc7RNZzs7nIY6nKMvvPw38a8NY1IiGoagT9UiSl+/b3RRl93gOUwgvnJJz9JqsnNperbJ6d7KdqGlgS60eG4uY2vsQJrZhElEOtmESd43E3rCYXuhYyK+Wnsujs4/1trVlJ3j3K2t456uP0FTIuOffS6ELoWhfkqb7hAnMiB7l5K4Yu55sxZ6qJ7Zc+6R4wiW7Sx4Fgpefjle5A6U1v9H5hp+dY3LbaXpMHeOKT93scNTO6UeEUiifoSsaBb/668Ld7uhFqb4DVRIE3GPU/EimT/ZbQEEIIkrR6zhst6yqp7VMy1OWdaRDlNLCpngVCSwUldNPtd7W13BNcPuKz3B2OhuoreB87SWcPedkzvZvj46AkQ8oypJHH9swFDWhYSjqQD2S5Ktf7OfTN64LNCmKGIKoKcjaEqnAENW/x9Uqq29cupK8o4+XSiIKBeZMDSIVdGdHiThaFba8mKv4GozYV07wEsETvcu4cem5rO8qFSnNmRzgPZvu5/xtTwTajJajXnWkWGuBWaeOkexy9ZmyBrufbmF8a33NhPRzrOAPVcWmX/cgbQPDkrQdPaWNQbVCPSkYbVJ882KLDQv0ky1SX1trqOouddLWa+1pWWfuL4RyDcq+CCN5rRP9VwpPGhioCsadQLDQdkBJEAaZZAfKHq8I1yn31AKBLW1Mw6Q91s77ulbwy613MCVc6q/S+YS/OubDgeus2b6Gax/8ApHsmKa2TvRx7chmrjnrK5w97+xwWfCwib6s5oJvHac9CT8iiUa1dh1oGIo6UI8k+dd+v6GiSVHadnB838/pSFDT9bxG6f4MzYU0kYkCWSPCnKlBTKb3IPypXAjKdeSFxf3zTuKmpSvZ6mszeuTIa1y+8T7O2Pkc1jQrzrBzTgdhFvWZpigyLEdeSdD/TCuOG5Op10/xR8aLx1cropO2XrpLWzC8vplIwiafNzDs0rpXWgozKnmxx+SbF0VCqa8zQ+EIPQELpapHgty53O9RSIIhyZqfQIVNCDMK4aiW8810Lw00/WFyvNLeuEbihO4TKjyCY3+X4/rtd7HDEMyVilXz3sbZp30icI3rn/gGkakhT048IR2YGuL6J76xd70j2o7Q4aaoL5RbyGhvo4Ga0DAUdaAeSfKwJkVOnWGEip7XSpEsZGgqZIhIfbLeqSF6MqMBIzHz/FXiI01ZMe464o38esmbGUy2eXuc3PcSl2+8lxMGN7tH1HLm2ib2ZG+O2SvGiDbre8iNa32mqf6Y7zwznKk4SQkwIhKZ9xNx/TNYubFwx6hE4HZkziQel5CwcSyQjtANhs4T3HK0hTQEsbzi/a+lecf9sfoNmG84xZW3XykWdPJaGq4GlU5pYNfF6izGrPyb6nFJwg2rpaA7EWz689k1nyVdSKPc/3Q+QpCMJPnBBT8InuDluzl74/2cbTR7Wk1svF+3LPWt/HeMvUqLAG/lIAziOOwYe7WOewjBGZ/UOQmfVhQyr7c3UBMahqIO1CtJ7kiFLR2UqndlGIShJCnXQBjuF787PcK7XnmIN/lqFZTvX/XmOvprPRJr4tbFZ3H7ojOYdFdahnQ4e8c6Ltu0miVjOyuOKaYp9xRmVNJz0jhti1x9JgcGNzRpfSap57hiDcFEQtGSni6EpLxgvywUn0B4nL2WMStH0LpijOEXm5GTJrk2h2+eH+Gp2XphML9f8YlbbFrTUZyQfhDVIF1XoeTN6dENNOtKbn+PibZJaM7qJK8UMBHX/SjqQ72J65mPLQgqJv8rl1/J95/9PoZhYGLi4KCU4srlV1ae4OFv69akM+QI5toOA6aBr9EpWWEw197LRM1R5wNBragG66k+NAxFHahHkrynOcZrI5nSKn8P5teIY9NUSJOwc955OjNjXPTqw5yx6zlMVTIQDpUFdGHYmezipqVv5u4FpwbajL5t2+NcuukBZqWHK47x+wlqj4yFonVhhp6TxrFi+tj0QIRdT7SSH494Z7PdwjPHxJX+rn4dwwQpBYapEJaDnTWr7F8+2upez+R8h+j8UbZbFv/Y1c5rEf18zlwv+fBdDomCXuWLKkai2B/bM3ZuK9OoDI6saMT/+YqSOX/7Q5LLH9bHFQwdx2/KwXMHNDpS5cmEbL76xKvZOr6VO7bcQU7lMIXJhQsvDKW3Mrq1phzBKquba+WA60mUKK+rrG72GuV5iwbqQsNQ1IF6JMlTURND1CraF0TMztNUyAQ6u7Vnx3nnq49w1s5nsdyGPkPxFn676AzevulBegqTgVRuOTa2zuXGpefy0Nzjka5r35yf4l2vPMS7XnmQ1vy+7+gHEGmymb1ijJRPn6l/XTOjmyv1mUxHT5SmAy0z0EKjzaWEulJgZy1qq6OYPt3+UCLOv3W0kTEMTEdx5R8kFzylu9Ap4YaFqhxrADmzVBuhTDAKbiW8L/dgK933OutzM4/bBiNJSOWDPbOP2wavbj5AVdhhsUt321V3XhUIPQGsG1jHgpYFXu5i3cA61mxfU5lPqDFHcPYZn+eau/+a6+NllNfzP79Pb7OB+tEwFHUiTJI8jDI7MJmr20hEnQKtuUmPVXTM4GYuevVh5k4OEnfy3gQ1HGvi9kVn8OCc43EMk3NfW4ssTFZMYAp4uvsobly6kmd6jvK2d6dHeM+m+7lg62PEnRCNiH0BoehcNkXXMT59pm2uPlM2GHgvhmMcE0xH4JgKMyIR6eqTYXY04oXUhSXZ21oBR8B/Fjr47XxdSNgxofjkzQ7LdqhSMKvElK3jMgrDcA1HscJAVY62e0zLj483+TYqmDsIH7lby4pPxnVdxUfuVvyQfdx7QlT5GR1wHMgMeDpL1z52LUkrWSmrAVy//vpKQ1FrjuCo8zmbbwSprec3QkSHAhqGYi9RjTI7ka1OIa2GhJ3zjMSKXetZteEOYtL2vrcSuH/uCfzyqPOwTQtHGIxHUzQXgq1THWHw4JzjuXHpSja3zfO2LxzbyWUug8kAlBDkDWtaums5agk7JTrzzDp1jHibq8805eoz7YxXPUYACaWpmVGlMJDY03U58CeHC8X99iwRVFR93bBAh5qWb5V84laHjsn6z6rcvIlyjYO0IFLQldfFQduiUhW2miJsxIGpentP7C1CPIq+qT4KskDEiNAcbWbLxBZ6Ej2B7R3xDnZM7qg831Hns2bkKq7f8BN2yBHmxmOsWn4VZ9dCbW3gkEDDUNSJcu9hZCoXSpnNO9UnVMOlQoqQ0FQqn+GCbY9z4ZZHvWmyuKKVwJLRHeTNCBPRJJORBAhBMpfGALJmhHsWnMqvjzyH3alSFvS4wc1c9vJ9rOh/EQEMxZppz09iyGDP4b2FYUm6j58o6TNJGN6YYuC5JlQN+kzS1mORUiDtA/PRfHGu4JuXllRfL3rM4fI1YdTXmWszICgT3joFjll5nFDwyLLgcdUUYW1Tv/ejvt4TNaJIeAph0iqhsKWNgYEtbYazw9jSZvfUbgxheNt3T+1mcWtlvm7N9jVcu/33RNrm0GLGGXCyXLv991wz96S9o702cMDQMBR1IMx72DI0xby2RGC/RGR6TmPMKv0+U9CMjqZ8mve9fC/v2PKol5soX9iZwOypYfpSHV6eASAbiXPT4jPcNqM6diGU5PRdz3P5y+VtRqE9N+EZIWOfVHRB89wsvX59pmGLXU+0kR0pbya0P1FLjqK05+9XGPz0LSXV14/9zmHF5srnUU4Nnu6J+bWaHDS11Ew4qILhaT0NJgTHbYObfcdVU4S9+DFVf++JPYWqUnei8OTEBQJb2Z6YX7F2QqFQSoUeHqr+SpUwVQOHJBqGgtplOcIK7iKGQd9EjpZESWqjOPlXg1QqUCwrlOSb9/8b89y2oGkrRtzHdPLDRAaMBMCqC/7Ok/qwHJvzXnuS92xc7Z2vHHsT2S5nPVkJh1mnjNE8TzckkrZg4Lkmhl+uRZ9pX6K2FT9o1dfvv8PkYVf1db6r+to7ply2VfC80rfc9pVvzAgT7TFaMQnxElVqLBoN9QjCFWHlHvSe2PfIOTmvXqJY4zKnaQ5D2SEv9NSb6mWqUMlC2DG5o1L91YyHh6kaOCRx2BuKemQ5wgrueltibB/NVlBmp4MAbEdrNQEoYXDHwjfxJy/exT0LTuHuBW/kW/d/u+Z7yFlRkoUM73z1Ed69eU3NbUbrldsojl3/oGg/Mk338T59pp0xdq9toTB1MD5WypOHCPtdETs64Bvvsdjere/krPWSj/7eIVZwGU2yVPCmjQRaDVUBQmgGk6rdWAgUtmNgWdIzMvV4BIdG74kghBBEzSiWabGwdaG3PWNn6E5UUlnD1F+zTpa5TXMPxHAb2Ac47A1FPbIcYQV3lmlwVE8TbclogDL7if95mvGQhHbMMohaBoW8ImoZZG290rxt0Rk8Nms5yvUWbASRsnDH852LuWHpuYHzRZwCl2y8n/dvuq/mNqNhchvVDIY/dK3QCrSx1gKz3zhGorOkz9T3VAvj2+LsaUJ5X6BIP/W/+qFVX82S6usfJBesLU3gTy6DFS9CxDUWr3bBgmGdG3Lc2oZilXWtdynQdRfSFghToRyBZYZ7BCdWocFW7z0RdkER4ljVGF4MPZaKm5VK0h5r11IeEJD2WNG7ooJKG6b+WnAKHs22gUMfB29ZcojgtZF0RU6hmizHx85ZTMFRpPM2SunXgqN4+7FaG8n/HTtmTisdyQiuggeGgKaYvk53c4zls5rpbo55++esKBPRFFIIRqNNvNbUgwMUEDw4+1j++py/5LNn/wVrZ2lJj96pIa584ff8+73fwESC4wREKySVuj2l9yKw7/TTnvD2URZ0Hz/OogsGPSMxujnB5t91M74tMcN59h2U9094PwtcdVLXExCqtKcj4L/PNfnGeyyyMUH7hOIffuZw4dpSAyeB4tgtioIpsA1BwRS0pWHNMp0fsKR+/dUZ9Y5U0LoojZM1yI9FcLIG9x1DhUdw4mYdYmqbCtJgT9y8Bx13hO+ft6F8h8pNKTPl0xtx/4ninSjvVSDI2lmuOe0auhPdjOfH6U50c/GSi7l1860VVFqgYt9rTrumkZ94HeGw9yjqkeVYuayHy7aP8l8PvspU3iEVNXnrsm5ufGpHRejqspPnsmM0Q3sq6oWkto9kaE9GAt6LHxkrxng0iRIGPzjuXZzc9zK/X3x6oM3okSOv8eYdz3Ds4GaP4nrWjmfJRROYdhZTOjiGieHYAY8EijLPld6DqrK99FtIzcowZ8UY0aaSPtPuJ1pJD8RCjtl/KK+y9v8kQraPpODb77Z44Qj922O2SD55i0Nr2p9x0B5ELGvoymt0CKrFgUX98JFPBRcSH3hwuhxUJW2of2MKRxkYQiCVwQVPw6tzgnUQFz+mayX2jgY7vcGveF+2acqZKglR+W+n3KNAknEyFYquV915FQVZYDg7HKDSXr/+en5wwQ8ahuF1jMPeUNQjy7H6xX5ufGoH3c0xFrj7/u75PjpSEVoTukagGLp65JVhvnzxMYEq7tF0nq6m6hNr3izlP57uXcbTvSUO5dHDW3jfy/fSmRkLdBDLmRGOmOhjd7KDwUQp8H3k6PbQaxTDTeWMqmqhJyvm0HvSOG0Lg/pMQy80oeS+9SCEL8GgFG6Ltz1HOfX14kckH7hfYqqiWSwW0+nltz+/UVxYzxuCL/7cCYSDZriLsvcK8gaW0sa4aIA+eF/QAHSPaU/CjyINtlpIasZLF7eFMdvCaNGuFyYqNlUalbxPNaCIzaObGcuNaf0nYWIrm6HMEHYddToNHJo47A1FPbIcYfkMRyrG0gW6mkrf8mLoqryK+4rrHuWpbcPk7PopqS91LCRp54g7hYpmRgCpfJp5UwMYStdGTD+dlbhLJepn5QTXuihD74ljnj7TVH+U3U+2kB/fP5RX5VRMUSGoxm4q9cFWCO5YIQLU14/fJjl1Y+m4QqSkK5WOKdomw5+YqagIB9ULoYIEW0PB3DJJrWoFd5lIHZXZtWbYq0L5/j/9iaWqDInlZR6FwnbsEkNKCPKy0qg08PrCYW8oIFyWIwxhrKeYZZAtU7cshq7+7Z6XA2GqiMEeGYkiqjUzmrRidOYnvf1mqo0orhn9qrBFCBSRZofZK0ZJ9Zb0mfqeaWH0leR+y0IUo+Il/6aasat2b3p7NgLXvd3goWN81NebHeZUah16cGaQ8u6cKBW/TdURadPTaxUDVDbP3nqa4M9vV3SNlbSi0lEt61F7SCrEUlT7LOylpLERkt5UUuGo0neh2C9b7YngWQOHFBqGog6E5TOa4xZ2WlWErma1RPn2vZswBFiGNh7jM9BmZ8La3uXcteA13rP5ARJ2nowV5ddLzuGKF+/Z21vT/oXh02dyJ8+xrXH6nm7FyRr1GQmvn+fMKD4V/7o7uK027OyAr/uor2eul3z095J4iJxVpFDKRbTPUOUccxfEpqOPqxWiys8KbQgq9heV79umYKg5uH3Gyuz9MC/7+2ADLGlfUtG21MHxDIhEej8LY38tLxo4UGgYijoQls+IWibvOLaDP7w44HkOf3rWIv7rwVeRUrG3LY/9OKVvA2/b9iTDsRZySe1RvG3bk0Sq9iULh/BNw8WfE115Zp86Rqy1qM9ksvvJFiZ3VddnmhZ7WWxX7ehS4Ck49T52lMH3LjLIuNTX//0HyQVrq1eJCN9rPSOtSxQQQms7BDCUCm67+DGFLbS8uin1qy0gYesQVM2V2XtlJKqH9aJmFEc6mIZJc6SZ8xacx7WPXUvEjHgMp5xLz/b3zJZSEjWiIeds4PWEhqGoA2H5jNMXd/DTR7eStyUCRd6W/PTRraE1FNVwct8GLt+4mlnpYXYnO7yWp6f0bXBboertzbkp8obl5ShyVhT2Kk+oMCKSHlefCVx9ppdTDDxfmz7TvkH1CWp6npOeiH+x0uC3b9IuUPuEDjUdtaOWgH2161bu5b9qPYZFUqLu+rdly+zv3EHdf0JS0opqy0DW1GEvOACV2YJwAy8gakZJyzRRM8r7jn4fT/Y9WcFwMoUJAixhlVhPiWaOaDli34+1gQOKhqGoE+X5jLd/6wGGp/IUxUEdJclP1p68O6VvAx9fdzN5w2I8kqQ9O8HH193MXQte412vPkLSpby25SaJODbD0RRd2TEijk3BtBiJpuqavPwVFM3zsvSeMk4koT2SzLDF7gOuz6THUt92jdEkfPsSkxeO0NPwMVskn7jFoa2G1hrB9HLtC/F6p+eI0hO/P1Wg3JBSYD9HF+ZFioMRrpGx4IfniwNXmR1GiQOm8lNIJFP5KX6y/icgtKyHQHiigEUPYl7zvEZh3R8ZGoaC2rWewrCpfxJH+b5fqj7v/7KNq0O9hMs3riYibaQwcAwToTSdsys/Sd4wkUJgSZvezCgZTJJ1BLmspKvPNNfVZyq4+kwbD7Q+055AP+mXQqmvjhvmqc1TCGJPsiI1nllA3pcwN2XJS/DD9H92lNaKgmoaUFWvVsfAfD/PcNvSDW9KJJP2pDYQwkCpUs9sIQRxK17RX7tRP/H6x2FvKOrRegqD7WtHuieYlR5mPBIs7suZEeJOHgcDSzkVxXCWlPgrAeJoTaLa1piK+WcPE2/XYFGgogAADvpJREFUMauJHVqfyU4fvI9CcFoPZiHKtysUd5xi8NO3utTXnOIvbnN448v7IoO7n4ykcHP7xSu4Hoa/PsOUpTBV8b6LmadLH3R41xMQz0M2Cr89FW4+awaqVr0oDq6yXjD0sSjCGU6WsCr6azfw+sdhbyjq0XqCkvexbXiKOW2JvWaY7E520J6dCK2NMMtENyorjxUIAyOE014dgv5nWpjzplF2P9XCxGsz6TOV11vsH4RVVZdnJzT11fSor/MGFH/zazuE+jrTOMur0EvU2jCG1N5iNBFscZoRkCrAkTu1gWid0r/zG/qiwbDycPnDeoS2AbGCfg/OfjAWVa1CzTY059SmN9bA6wuHvaEIq41IREw29k9wxXWPBsJRAF+45XlMQ5CMmGwZnNrrqbNabUQeg1gVNpO/n1tY4dNMmOqLsem2nrICt2ooTanlEuMHBvrqmvpqzkB9rT1TU/5eoXMJ+wOmLHa4069Nth6CoTRN1lDVvcGom+Mo0mkdoc/3rifg5rP2dmT7/m/ZKK7748TrxlAIIS4Evo0O3f6XUupr++K889uTPLdjlMlcyY2OWwYK6J/IeuGov7vleWKmIJMvMJadWUq8VqztXc53TsBlN42wO9nOjUtX8neP/Kim4/c0pVmbkQjiYBmJx48SfLeM+uoX9IPqEiT1XCm1n+a48voIE7BVSaHEr1RSnjYQVBowR+gwVAMNHCi8LgyFEMIEvgOcD2wHnhBC3KqUemGvT65kwEgAZG1J1NCehVRgGQZZ5fDqUKaidem+wNre5aztXR7YFq2zNuKPESXqqzaH7ROKT93scPSOfZtNcCgljvcHpuIw7CuaW7RbG/hQ+kExTyB0HYUpdZLbbyxMpXMVBwx1PGxDHChKdQMHEq8LQwG8EdiklHoFQAjxP8C7gb02FI9vHQ3dnpeQdySOoxjNFBhN5wNGwhBgGmKfeRblONS5R/sbo0n49rsN1i/UE88btko++RtZE/W1FhTMUoMiw5853g8o73ldMCHqBBPcxdeCUfrZVDAah5a8K+shSj0xfnvqfhpsFXpsrUhZqZl3auB1h9eLoZgL+Bs/bwdOK99JCPFR4KMACxYsqOnEjjv7+1uTFtE/nmMsWwhsF4ApwDDE/szt4ggDcw/yD38MeGmu4Buhqq/77hpSlDSVpuLQMlV/lXatKK+snkjqBLbyjyEGKN00qbhtPAb/8U7Bol1q/7Oe/KjhIRgYtMRaGM+PI5XEEAYt0RaOaj9q/42rgYOG14uhCPvoVkwbSqnrgOsAVqxYUdO0YhoCR6pQ7bTRTMG7eDJq0pmKkM47TOUdCo4kZpnk8/tSpKOE++aewHnbn65pcVc7NXZfYP9dTQF3nGLwEx/19erbJKdVUF8r/1h+HaKZIIFNc/CoqXe9Ed71rM2RL1s1Pe96ChwdKiurbRNuPAOO20aFfHl4h7t9kbiuAVJUjcGVaz29fdHbWTewjvZ4e6O47jDA68VQbAfm+97PA3buixNffPwsbn5mV+jvLEMQjxgs6mziT05bQDxi8vW7X6I1WWpGNDiZYyxTqaPRmbQYSu+5vsY3VvwJAOfuWIepJI4weLZjIW8Y2hJgQ+Uw+OWy87jixbvwc7eqTedZDOI1xlkcBKZv6hzobGa4p42jN7w2zVElROfniBoOk9sSFHuTxrry5EKaHTlNNv/+llhJ9XVI8onXssx/OUK5iEbYJL3xpGaOfHqyQirDqJjWFRPnT/L7N8TZGYkwp1DgyvE0yy49jw03PUT35qiXExhYksdJm8zZaXoRmR1zHLYsOoIzH9peMY7yK0lg2+kGE21TRNcn6ByDoVbYfkKem98Q5+azKu+kvLDunYsv4vZXbw+6u8XseNm2vzjhL/ju098l0ANWCf1BKNvXchxso/S0YlLy5Fnf5iOv/IIn+p7wtp/aeyqnzjqVn274KelCmmQkyZXLr+TqE6+uEAVsFNf98UKoGSSpDwUIISzgZeCtwA7gCeCDSqn11Y5ZsWKFevLJJ2s6/+Xfe4gnfLmKZNTk6jcv4fw39BK1DJpiFm3JKFHL8Ooo/L0rni3revenZy3iE+cdxYXfXM2LfSWthmW9KQYmcgED0pm0yNmSyXxp8m6KGjz/5bez8HO3V4z1jX0buNTHkLp56Upu+NGnee+H/7WCOfWel+/jxKFXvEnumc7F/MlDt3Pvijcxa3LMWyPubmolWijQkUt724ZjSf7vqq9XjP+OT63kd5dfyOIXX0XZAmEpXlm2iO7xIZq2jXsGYXJBC6fe+QQvfel4jlJbvXO8LI5g584uFr24BXvKxEo5vLpsIStPT/KpzAb+kEpyzlSav0wsIf6e63jhC6tYvO41xKRBNOWw+zhNU5793Cve8buOW8zp37mHb/3zV5h71810j+cYaImx422XcsHW20k81+/tmzmuh+Wf/Ao8/G0Y3QZtC+CMT8JR5/PKjV+kZ/0PiKs0WZGk/5ir+H/2Jdz67G4cqTANwcXHz+KbHziZ2//m3fTe87IXDuo77yjIjdO7Zndp29mzeOe/3wc3fRTW3wjSAcOEYy5jTSLO9dvvYochmCsVq+a9jdubktyx5Q4c5WAKkwsXXsjXzgkn933vme+FTt5hCJ3Q09nQZ9DAfsPrOu34ujAUAEKIdwDfQjvHP1RKfXW6/esxFNmCw5lfu5eelhgfOHUBZx7ZiSFEwEA0sP8xmZ/kN5t+w5lzzgx08WuONtOZ6DyII2uggb1Gw1AciqjHUADsGs2QztsIIUjFLNqSEWLW/iRNNhCGocwQE/kJAIQQdMQ7aI42z3BUAw0c8nhdG4rXS45iv2NWa5y+8RztqYaBOBRgCpOuZBcJK3Gwh9JAA4c9GobChRCCWa172KSngX2KiBmhJ9FDxDzQcucNNNBAGBqGooFDCnErTnu8vVHh20ADhxAahqKBQwqpSKOyt4EGDjU0lm0NNNBAAw1Mi4ahaKCBBhpoYFo0DEUDDTTQQAPTomEoGmiggQYamBYNQ9FAAw000MC0aBiKBhpooIEGpkXDUDTQQAMNNDAtGoaigQYaaKCBadEwFA000EADDUyLhqFooIEGGmhgWjQMRQMNNNBAA9OiYSgaaKCBBhqYFg1D0UADDTTQwLT4o+1wJ4QYALZOs0sXMHiAhtMYQ2MMjTEc3mMYVEpduA/Pd0DxR2soZoIQ4kml1IrGGBpjaIyhMYZDcQyHEhqhpwYaaKCBBqZFw1A00EADDTQwLQ5nQ3HdwR4AjTEU0RiDRmMMGo0xHGI4bHMUDTTQQAMN1IbD2aNooIEGGmigBjQMRQMNNNBAA9PisDMUQogLhRAvCSE2CSE+d5DGsEUI8ZwQ4hkhxJMH6Jo/FEL0CyGe923rEELcLYTY6L62H4QxfEkIscN9Fs8IId6xn8cwXwhxnxBigxBivRDik+72A/YsphnDAXsWQoi4EOJxIcQ6dwz/4G4/kM+h2hgO6GfCvaYphHhaCHGb+/6AfjcOdRxWOQohhAm8DJwPbAeeAK5QSr1wgMexBVihlDpgRUVCiHOASeAnSqlj3W3/DAwrpb7mGs12pdRnD/AYvgRMKqX+dX9dt2wMs4HZSqmnhBDNwFrgEmAVB+hZTDOG93GAnoUQQgAppdSkECICPAh8EngPB+45VBvDhRzAz4Q7lr8GVgAtSqmLDvR341DH4eZRvBHYpJR6RSmVB/4HePdBHtMBgVLqAWC4bPO7gR+7P/8YPVkd6DEcUCildimlnnJ/ngA2AHM5gM9imjEcMCiNSfdtxP2nOLDPodoYDiiEEPOAdwL/5dt8QL8bhzoON0MxF3jN9347B/gL6kIBdwkh1gohPnoQrl9Er1JqF+jJC+g5SOP4P0KIZ93Q1AFz8YUQC4GTgMc4SM+ibAxwAJ+FG255BugH7lZKHfDnUGUMcGA/E98C/haQvm2HynfjkMDhZihEyLaDEXs7Uyl1MvB24ONuSOZwxfeAJcCJwC7g6wfiokKIJuAm4K+UUuMH4po1jOGAPgullKOUOhGYB7xRCHHs/rxeHWM4YM9BCHER0K+UWru/rvHHgMPNUGwH5vvezwN2HuhBKKV2uq/9wM3okNjBQJ8bLy/GzfsP9ACUUn3uZCGB/+QAPAs3Hn4T8DOl1K/dzQf0WYSN4WA8C/e6o8BqdG7goHwm/GM4wM/hTOBiN2/4P8BbhBD/zSHw3TiUcLgZiieApUKIRUKIKPAB4NYDOQAhRMpNYCKE+P/bu5+XKsIojOPfB9pEBJK06AchldBKgiAIWrQQF26ibQhC0aZ1QT9AhBYFRdAyqJVUa6OICG0VQUKWXkkI6S9olRAReVq8rzBGDtxoZtR5PvByx7k699yXOx7eufeeswMYAjrlf1WZp8Bo3h4FJusOYPVkzM5Q8VzkN1AfAp8i4m7hrtrmYr0Y6pwLSbsl9eTt7cAgsEi98/DXGOqch4i4GhH7I6KP9P9gOiJG2ADnxoYSEa0awDDpk09LwPUGHv8g8DGPhbpiAJ6QlvE/SSur80AvMAV8zre7GohhApgH5kgn556KYzhJutw4B3zIY7jOuSiJoba5AAaA2fxYHWAs769zHtaLodbXRCGeU8CzuudhM4xWfTzWzMy617ZLT2Zm1iUnCjMzK+VEYWZmpZwozMyslBOFmZmVcqKw1pDUI+li03GYbTZOFNYmPYAThVmXnCisTW4Bh3KPg9uSLkuaycXnVnsh9ElalPRAUkfSI0mDkt7k3gTH8++NS5qQNJ33X2j0mZlVyInC2uQKsBSpCN0roJ9UR+gocKxQnPEwcI/0zeEjwFnSt6kvAdcKxxsglac+AYxJ2lv9UzCrnxOFtdVQHrPAe1JC6M/3fYmI+UhF6RaAqUglDOaBvsIxJiPie6QGVK9prrijWaW2NR2AWUME3IyI+2t2pv4QPwq7Vgo/r7D2nPmz/o3r4diW5BWFtck3YGfefgmcyz0hkLRPUrfNaU7nvs+9pIJyM/8tUrMNxCsKa42I+JrflO4AL4DHwNtU9ZtlYAT41cUh3wHPgQPAjch9Rsy2GlePNfsHksaB5Yi403QsZlXzpSczMyvlFYWZmZXyisLMzEo5UZiZWSknCjMzK+VEYWZmpZwozMys1G9iGaOI29wkyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 403.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lmplot(x='temp',y='count',data=df,hue='season')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ad093a6-acd1-4340-9150-8418467b295e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029368</td>\n",
       "      <td>-0.008126</td>\n",
       "      <td>0.008879</td>\n",
       "      <td>0.258689</td>\n",
       "      <td>0.264744</td>\n",
       "      <td>0.190610</td>\n",
       "      <td>-0.147121</td>\n",
       "      <td>0.096758</td>\n",
       "      <td>0.164011</td>\n",
       "      <td>0.163439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>0.029368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.250491</td>\n",
       "      <td>-0.007074</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>-0.005215</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>0.008409</td>\n",
       "      <td>0.043799</td>\n",
       "      <td>-0.020956</td>\n",
       "      <td>-0.005393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>-0.008126</td>\n",
       "      <td>-0.250491</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033772</td>\n",
       "      <td>0.029966</td>\n",
       "      <td>0.024660</td>\n",
       "      <td>-0.010880</td>\n",
       "      <td>0.013373</td>\n",
       "      <td>-0.319111</td>\n",
       "      <td>0.119460</td>\n",
       "      <td>0.011594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather</th>\n",
       "      <td>0.008879</td>\n",
       "      <td>-0.007074</td>\n",
       "      <td>0.033772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055035</td>\n",
       "      <td>-0.055376</td>\n",
       "      <td>0.406244</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>-0.135918</td>\n",
       "      <td>-0.109340</td>\n",
       "      <td>-0.128655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.258689</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.029966</td>\n",
       "      <td>-0.055035</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984948</td>\n",
       "      <td>-0.064949</td>\n",
       "      <td>-0.017852</td>\n",
       "      <td>0.467097</td>\n",
       "      <td>0.318571</td>\n",
       "      <td>0.394454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.264744</td>\n",
       "      <td>-0.005215</td>\n",
       "      <td>0.024660</td>\n",
       "      <td>-0.055376</td>\n",
       "      <td>0.984948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043536</td>\n",
       "      <td>-0.057473</td>\n",
       "      <td>0.462067</td>\n",
       "      <td>0.314635</td>\n",
       "      <td>0.389784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>0.190610</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>-0.010880</td>\n",
       "      <td>0.406244</td>\n",
       "      <td>-0.064949</td>\n",
       "      <td>-0.043536</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.318607</td>\n",
       "      <td>-0.348187</td>\n",
       "      <td>-0.265458</td>\n",
       "      <td>-0.317371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>-0.147121</td>\n",
       "      <td>0.008409</td>\n",
       "      <td>0.013373</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>-0.017852</td>\n",
       "      <td>-0.057473</td>\n",
       "      <td>-0.318607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092276</td>\n",
       "      <td>0.091052</td>\n",
       "      <td>0.101369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.096758</td>\n",
       "      <td>0.043799</td>\n",
       "      <td>-0.319111</td>\n",
       "      <td>-0.135918</td>\n",
       "      <td>0.467097</td>\n",
       "      <td>0.462067</td>\n",
       "      <td>-0.348187</td>\n",
       "      <td>0.092276</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.497250</td>\n",
       "      <td>0.690414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.164011</td>\n",
       "      <td>-0.020956</td>\n",
       "      <td>0.119460</td>\n",
       "      <td>-0.109340</td>\n",
       "      <td>0.318571</td>\n",
       "      <td>0.314635</td>\n",
       "      <td>-0.265458</td>\n",
       "      <td>0.091052</td>\n",
       "      <td>0.497250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.163439</td>\n",
       "      <td>-0.005393</td>\n",
       "      <td>0.011594</td>\n",
       "      <td>-0.128655</td>\n",
       "      <td>0.394454</td>\n",
       "      <td>0.389784</td>\n",
       "      <td>-0.317371</td>\n",
       "      <td>0.101369</td>\n",
       "      <td>0.690414</td>\n",
       "      <td>0.970948</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              season   holiday  workingday   weather      temp     atemp  \\\n",
       "season      1.000000  0.029368   -0.008126  0.008879  0.258689  0.264744   \n",
       "holiday     0.029368  1.000000   -0.250491 -0.007074  0.000295 -0.005215   \n",
       "workingday -0.008126 -0.250491    1.000000  0.033772  0.029966  0.024660   \n",
       "weather     0.008879 -0.007074    0.033772  1.000000 -0.055035 -0.055376   \n",
       "temp        0.258689  0.000295    0.029966 -0.055035  1.000000  0.984948   \n",
       "atemp       0.264744 -0.005215    0.024660 -0.055376  0.984948  1.000000   \n",
       "humidity    0.190610  0.001929   -0.010880  0.406244 -0.064949 -0.043536   \n",
       "windspeed  -0.147121  0.008409    0.013373  0.007261 -0.017852 -0.057473   \n",
       "casual      0.096758  0.043799   -0.319111 -0.135918  0.467097  0.462067   \n",
       "registered  0.164011 -0.020956    0.119460 -0.109340  0.318571  0.314635   \n",
       "count       0.163439 -0.005393    0.011594 -0.128655  0.394454  0.389784   \n",
       "\n",
       "            humidity  windspeed    casual  registered     count  \n",
       "season      0.190610  -0.147121  0.096758    0.164011  0.163439  \n",
       "holiday     0.001929   0.008409  0.043799   -0.020956 -0.005393  \n",
       "workingday -0.010880   0.013373 -0.319111    0.119460  0.011594  \n",
       "weather     0.406244   0.007261 -0.135918   -0.109340 -0.128655  \n",
       "temp       -0.064949  -0.017852  0.467097    0.318571  0.394454  \n",
       "atemp      -0.043536  -0.057473  0.462067    0.314635  0.389784  \n",
       "humidity    1.000000  -0.318607 -0.348187   -0.265458 -0.317371  \n",
       "windspeed  -0.318607   1.000000  0.092276    0.091052  0.101369  \n",
       "casual     -0.348187   0.092276  1.000000    0.497250  0.690414  \n",
       "registered -0.265458   0.091052  0.497250    1.000000  0.970948  \n",
       "count      -0.317371   0.101369  0.690414    0.970948  1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "27309f5d-4ecb-41ad-971b-aafda99e14f2",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'(slice(None, None, None), slice(1, None, None))' is an invalid key",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/hn/3wwh9yy571jbqjltnqglztg80000gn/T/ipykernel_12064/1779166483.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3359\u001b[0m             \u001b[0mcasted_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3363\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: '(slice(None, None, None), slice(1, None, None))' is an invalid key"
     ]
    }
   ],
   "source": [
    "#sns.heatmap(df[:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "24cd08de-a90e-454e-a97f-a23c3e470661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['casual'] + df['registered'] == df['count']).unique().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f78a9ef8-eb5a-4f8a-a4b9-a70e1288e206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fae43910370>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='workingday', y='count', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1c7274df-b784-4e84-ac7b-51bd3ef33fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime       object\n",
       "season          int64\n",
       "holiday         int64\n",
       "workingday      int64\n",
       "weather         int64\n",
       "temp          float64\n",
       "atemp         float64\n",
       "humidity        int64\n",
       "windspeed     float64\n",
       "casual          int64\n",
       "registered      int64\n",
       "count           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4c2c255-964b-4922-8492-6ac879d9f46f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}