snoring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "import pylab\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "snore = pd.read_csv(\"C:\\\\Users\\\\kumar\\\\snore12.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>sno</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>totalsleeptime</th>\n",
       "      <th>apneascount</th>\n",
       "      <th>hypopneascount</th>\n",
       "      <th>ahtotalcount</th>\n",
       "      <th>apneasindex</th>\n",
       "      <th>hypopneasindex</th>\n",
       "      <th>ahi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Rukum Bai</td>\n",
       "      <td>F</td>\n",
       "      <td>38.4000</td>\n",
       "      <td>247.5</td>\n",
       "      <td>66</td>\n",
       "      <td>69</td>\n",
       "      <td>135</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>N.Sanjay</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0900</td>\n",
       "      <td>245.0</td>\n",
       "      <td>22</td>\n",
       "      <td>120</td>\n",
       "      <td>142</td>\n",
       "      <td>5.4</td>\n",
       "      <td>29.4</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>G.Swapna</td>\n",
       "      <td>F</td>\n",
       "      <td>29.6700</td>\n",
       "      <td>387.5</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>N.venkateswarulu</td>\n",
       "      <td>M</td>\n",
       "      <td>28.4000</td>\n",
       "      <td>336.0</td>\n",
       "      <td>297</td>\n",
       "      <td>197</td>\n",
       "      <td>494</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.2</td>\n",
       "      <td>82.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>E.Vivek kumar</td>\n",
       "      <td>M</td>\n",
       "      <td>37.1000</td>\n",
       "      <td>320.5</td>\n",
       "      <td>43</td>\n",
       "      <td>311</td>\n",
       "      <td>354</td>\n",
       "      <td>8.0</td>\n",
       "      <td>52.8</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Charanjeet singh</td>\n",
       "      <td>M</td>\n",
       "      <td>46.4400</td>\n",
       "      <td>390.5</td>\n",
       "      <td>213</td>\n",
       "      <td>269</td>\n",
       "      <td>482</td>\n",
       "      <td>32.7</td>\n",
       "      <td>41.3</td>\n",
       "      <td>74.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Devesh devangan</td>\n",
       "      <td>M</td>\n",
       "      <td>30.0200</td>\n",
       "      <td>421.5</td>\n",
       "      <td>345</td>\n",
       "      <td>34</td>\n",
       "      <td>379</td>\n",
       "      <td>49.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>G.S.S.Rajasekhar</td>\n",
       "      <td>M</td>\n",
       "      <td>19.8400</td>\n",
       "      <td>222.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>George</td>\n",
       "      <td>M</td>\n",
       "      <td>27.3400</td>\n",
       "      <td>322.5</td>\n",
       "      <td>63</td>\n",
       "      <td>148</td>\n",
       "      <td>211</td>\n",
       "      <td>11.7</td>\n",
       "      <td>27.5</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>mohammed aadam</td>\n",
       "      <td>M</td>\n",
       "      <td>38.4200</td>\n",
       "      <td>229.0</td>\n",
       "      <td>166</td>\n",
       "      <td>139</td>\n",
       "      <td>305</td>\n",
       "      <td>43.5</td>\n",
       "      <td>36.4</td>\n",
       "      <td>79.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>N.sanjeev reddy</td>\n",
       "      <td>M</td>\n",
       "      <td>29.7500</td>\n",
       "      <td>405.5</td>\n",
       "      <td>83</td>\n",
       "      <td>235</td>\n",
       "      <td>318</td>\n",
       "      <td>12.3</td>\n",
       "      <td>34.8</td>\n",
       "      <td>47.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>P.S.shastri</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0900</td>\n",
       "      <td>214.5</td>\n",
       "      <td>199</td>\n",
       "      <td>75</td>\n",
       "      <td>274</td>\n",
       "      <td>55.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>76.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Ramachandra Rao</td>\n",
       "      <td>M</td>\n",
       "      <td>25.9500</td>\n",
       "      <td>375.0</td>\n",
       "      <td>36</td>\n",
       "      <td>62</td>\n",
       "      <td>98</td>\n",
       "      <td>5.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>S.Mallesh</td>\n",
       "      <td>M</td>\n",
       "      <td>31.6400</td>\n",
       "      <td>294.0</td>\n",
       "      <td>14</td>\n",
       "      <td>63</td>\n",
       "      <td>77</td>\n",
       "      <td>2.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Satyanayana reddy</td>\n",
       "      <td>M</td>\n",
       "      <td>33.6500</td>\n",
       "      <td>324.5</td>\n",
       "      <td>111</td>\n",
       "      <td>212</td>\n",
       "      <td>323</td>\n",
       "      <td>20.5</td>\n",
       "      <td>39.2</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>P.Rajesh</td>\n",
       "      <td>M</td>\n",
       "      <td>32.4800</td>\n",
       "      <td>380.0</td>\n",
       "      <td>168</td>\n",
       "      <td>209</td>\n",
       "      <td>377</td>\n",
       "      <td>26.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Y.janakiram</td>\n",
       "      <td>M</td>\n",
       "      <td>30.4600</td>\n",
       "      <td>305.5</td>\n",
       "      <td>375</td>\n",
       "      <td>28</td>\n",
       "      <td>403</td>\n",
       "      <td>73.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>79.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Vijaya Bharathi.B</td>\n",
       "      <td>F</td>\n",
       "      <td>38.5400</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Devamani medoju</td>\n",
       "      <td>F</td>\n",
       "      <td>31.6400</td>\n",
       "      <td>301.0</td>\n",
       "      <td>90</td>\n",
       "      <td>182</td>\n",
       "      <td>272</td>\n",
       "      <td>17.9</td>\n",
       "      <td>36.3</td>\n",
       "      <td>54.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>J.Uma sundari</td>\n",
       "      <td>F</td>\n",
       "      <td>55.6200</td>\n",
       "      <td>310.0</td>\n",
       "      <td>53</td>\n",
       "      <td>29</td>\n",
       "      <td>82</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>ammaji saladi</td>\n",
       "      <td>M</td>\n",
       "      <td>28.5100</td>\n",
       "      <td>388.5</td>\n",
       "      <td>105</td>\n",
       "      <td>175</td>\n",
       "      <td>280</td>\n",
       "      <td>16.2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Arpita Jalui</td>\n",
       "      <td>F</td>\n",
       "      <td>45.0100</td>\n",
       "      <td>258.5</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>D.L.tulasamma</td>\n",
       "      <td>F</td>\n",
       "      <td>30.8500</td>\n",
       "      <td>399.5</td>\n",
       "      <td>75</td>\n",
       "      <td>130</td>\n",
       "      <td>205</td>\n",
       "      <td>11.3</td>\n",
       "      <td>19.5</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>D.Bhanumathi</td>\n",
       "      <td>F</td>\n",
       "      <td>33.9800</td>\n",
       "      <td>277.5</td>\n",
       "      <td>358</td>\n",
       "      <td>302</td>\n",
       "      <td>660</td>\n",
       "      <td>77.4</td>\n",
       "      <td>65.3</td>\n",
       "      <td>142.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Jana Symala</td>\n",
       "      <td>F</td>\n",
       "      <td>24.6700</td>\n",
       "      <td>362.0</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>K.V.Suseela</td>\n",
       "      <td>F</td>\n",
       "      <td>33.7400</td>\n",
       "      <td>221.0</td>\n",
       "      <td>67</td>\n",
       "      <td>183</td>\n",
       "      <td>250</td>\n",
       "      <td>18.2</td>\n",
       "      <td>49.7</td>\n",
       "      <td>67.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>M.Venkata Laxmi</td>\n",
       "      <td>F</td>\n",
       "      <td>37.4600</td>\n",
       "      <td>262.5</td>\n",
       "      <td>74</td>\n",
       "      <td>117</td>\n",
       "      <td>191</td>\n",
       "      <td>16.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>M.V.Lakshmi</td>\n",
       "      <td>F</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>371.0</td>\n",
       "      <td>234</td>\n",
       "      <td>95</td>\n",
       "      <td>329</td>\n",
       "      <td>37.8</td>\n",
       "      <td>15.4</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Mani Manchmshetty</td>\n",
       "      <td>F</td>\n",
       "      <td>28.3900</td>\n",
       "      <td>290.5</td>\n",
       "      <td>210</td>\n",
       "      <td>228</td>\n",
       "      <td>438</td>\n",
       "      <td>43.4</td>\n",
       "      <td>47.1</td>\n",
       "      <td>90.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>S.Shanwaz Begum</td>\n",
       "      <td>F</td>\n",
       "      <td>38.2800</td>\n",
       "      <td>317.5</td>\n",
       "      <td>300</td>\n",
       "      <td>147</td>\n",
       "      <td>447</td>\n",
       "      <td>56.7</td>\n",
       "      <td>27.8</td>\n",
       "      <td>84.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>N.Prasad Rao</td>\n",
       "      <td>M</td>\n",
       "      <td>28.2200</td>\n",
       "      <td>71.0</td>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>4.2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>52.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>B.Narsimulu</td>\n",
       "      <td>M</td>\n",
       "      <td>25.4600</td>\n",
       "      <td>213.0</td>\n",
       "      <td>306</td>\n",
       "      <td>29</td>\n",
       "      <td>335</td>\n",
       "      <td>86.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>94.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>D.Hari Shankar</td>\n",
       "      <td>M</td>\n",
       "      <td>28.3900</td>\n",
       "      <td>182.5</td>\n",
       "      <td>110</td>\n",
       "      <td>18</td>\n",
       "      <td>178</td>\n",
       "      <td>52.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>G.S.Ashok</td>\n",
       "      <td>M</td>\n",
       "      <td>31.2300</td>\n",
       "      <td>121.5</td>\n",
       "      <td>32</td>\n",
       "      <td>173</td>\n",
       "      <td>205</td>\n",
       "      <td>15.8</td>\n",
       "      <td>85.4</td>\n",
       "      <td>101.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>Moirza Ashaq Ali</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0800</td>\n",
       "      <td>199.5</td>\n",
       "      <td>268</td>\n",
       "      <td>16</td>\n",
       "      <td>284</td>\n",
       "      <td>80.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>85.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>Syed Mohammad Zakiullah</td>\n",
       "      <td>M</td>\n",
       "      <td>28.7100</td>\n",
       "      <td>192.5</td>\n",
       "      <td>6</td>\n",
       "      <td>151</td>\n",
       "      <td>157</td>\n",
       "      <td>1.9</td>\n",
       "      <td>47.1</td>\n",
       "      <td>48.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>Fariyad</td>\n",
       "      <td>F</td>\n",
       "      <td>30.1100</td>\n",
       "      <td>189.5</td>\n",
       "      <td>238</td>\n",
       "      <td>34</td>\n",
       "      <td>272</td>\n",
       "      <td>75.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>86.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>K.Swasanthra</td>\n",
       "      <td>F</td>\n",
       "      <td>33.0500</td>\n",
       "      <td>213.5</td>\n",
       "      <td>55</td>\n",
       "      <td>247</td>\n",
       "      <td>302</td>\n",
       "      <td>15.5</td>\n",
       "      <td>69.4</td>\n",
       "      <td>84.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>krishna bai</td>\n",
       "      <td>F</td>\n",
       "      <td>45.1600</td>\n",
       "      <td>165.0</td>\n",
       "      <td>390</td>\n",
       "      <td>36</td>\n",
       "      <td>426</td>\n",
       "      <td>141.8</td>\n",
       "      <td>13.1</td>\n",
       "      <td>154.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>M.ratna sri</td>\n",
       "      <td>F</td>\n",
       "      <td>41.5500</td>\n",
       "      <td>141.0</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>175</td>\n",
       "      <td>40.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>74.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>B.rama devi</td>\n",
       "      <td>F</td>\n",
       "      <td>32.4600</td>\n",
       "      <td>160.5</td>\n",
       "      <td>136</td>\n",
       "      <td>42</td>\n",
       "      <td>178</td>\n",
       "      <td>50.8</td>\n",
       "      <td>15.7</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>Sayeed</td>\n",
       "      <td>M</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>150.5</td>\n",
       "      <td>23</td>\n",
       "      <td>65</td>\n",
       "      <td>88</td>\n",
       "      <td>9.2</td>\n",
       "      <td>25.9</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>C.H.prasad</td>\n",
       "      <td>M</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>184.5</td>\n",
       "      <td>27</td>\n",
       "      <td>92</td>\n",
       "      <td>119</td>\n",
       "      <td>8.8</td>\n",
       "      <td>29.9</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>B.Vishvam</td>\n",
       "      <td>M</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>201.0</td>\n",
       "      <td>278</td>\n",
       "      <td>22</td>\n",
       "      <td>300</td>\n",
       "      <td>83.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>89.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>chandrasekhar</td>\n",
       "      <td>M</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>200.0</td>\n",
       "      <td>306</td>\n",
       "      <td>39</td>\n",
       "      <td>345</td>\n",
       "      <td>91.8</td>\n",
       "      <td>11.7</td>\n",
       "      <td>103.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>G.srinivas</td>\n",
       "      <td>M</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>194.5</td>\n",
       "      <td>59</td>\n",
       "      <td>100</td>\n",
       "      <td>159</td>\n",
       "      <td>18.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Mandapati ravi</td>\n",
       "      <td>M</td>\n",
       "      <td>36.5700</td>\n",
       "      <td>228.5</td>\n",
       "      <td>282</td>\n",
       "      <td>11</td>\n",
       "      <td>293</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>76.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>Lathifa begum</td>\n",
       "      <td>F</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>198.0</td>\n",
       "      <td>52</td>\n",
       "      <td>124</td>\n",
       "      <td>176</td>\n",
       "      <td>15.8</td>\n",
       "      <td>37.6</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>seema</td>\n",
       "      <td>F</td>\n",
       "      <td>26.7000</td>\n",
       "      <td>212.5</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>Mona</td>\n",
       "      <td>F</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>219.5</td>\n",
       "      <td>72</td>\n",
       "      <td>44</td>\n",
       "      <td>116</td>\n",
       "      <td>19.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>nalini verma</td>\n",
       "      <td>F</td>\n",
       "      <td>26.3000</td>\n",
       "      <td>232.0</td>\n",
       "      <td>258</td>\n",
       "      <td>90</td>\n",
       "      <td>348</td>\n",
       "      <td>66.7</td>\n",
       "      <td>23.3</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>C.srinivas</td>\n",
       "      <td>M</td>\n",
       "      <td>24.2200</td>\n",
       "      <td>426.0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>SK.Samir Uddin</td>\n",
       "      <td>M</td>\n",
       "      <td>28.7100</td>\n",
       "      <td>487.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>Sanjay agarwal</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>456.0</td>\n",
       "      <td>50</td>\n",
       "      <td>153</td>\n",
       "      <td>203</td>\n",
       "      <td>9.4</td>\n",
       "      <td>28.9</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>Nikita</td>\n",
       "      <td>F</td>\n",
       "      <td>30.1100</td>\n",
       "      <td>435.0</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "      <td>83</td>\n",
       "      <td>3.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>V.Venkamma</td>\n",
       "      <td>F</td>\n",
       "      <td>32.4500</td>\n",
       "      <td>312.0</td>\n",
       "      <td>84</td>\n",
       "      <td>90</td>\n",
       "      <td>174</td>\n",
       "      <td>26.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>54.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>Md.Miraj Ahmed</td>\n",
       "      <td>M</td>\n",
       "      <td>28.3400</td>\n",
       "      <td>257.0</td>\n",
       "      <td>366</td>\n",
       "      <td>15</td>\n",
       "      <td>381</td>\n",
       "      <td>100.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>104.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84</td>\n",
       "      <td>Subba Rao Aduri</td>\n",
       "      <td>M</td>\n",
       "      <td>46.7700</td>\n",
       "      <td>307.0</td>\n",
       "      <td>316</td>\n",
       "      <td>15</td>\n",
       "      <td>331</td>\n",
       "      <td>82.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>86.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>B.Nirmala</td>\n",
       "      <td>F</td>\n",
       "      <td>51.9300</td>\n",
       "      <td>203.0</td>\n",
       "      <td>101</td>\n",
       "      <td>91</td>\n",
       "      <td>192</td>\n",
       "      <td>41.2</td>\n",
       "      <td>37.1</td>\n",
       "      <td>78.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>Kamalamma</td>\n",
       "      <td>F</td>\n",
       "      <td>31.9900</td>\n",
       "      <td>425.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sno                     name sex      bmi  totalsleeptime  apneascount  \\\n",
       "0     1                Rukum Bai   F  38.4000           247.5           66   \n",
       "1     2                 N.Sanjay   M  24.0900           245.0           22   \n",
       "2     3                 G.Swapna   F  29.6700           387.5            1   \n",
       "3     4         N.venkateswarulu   M  28.4000           336.0          297   \n",
       "4     5            E.Vivek kumar   M  37.1000           320.5           43   \n",
       "5     6         Charanjeet singh   M  46.4400           390.5          213   \n",
       "6     7          Devesh devangan   M  30.0200           421.5          345   \n",
       "7     8         G.S.S.Rajasekhar   M  19.8400           222.5            1   \n",
       "8     9                   George   M  27.3400           322.5           63   \n",
       "9    10           mohammed aadam   M  38.4200           229.0          166   \n",
       "10   11          N.sanjeev reddy   M  29.7500           405.5           83   \n",
       "11   12              P.S.shastri   M  37.0900           214.5          199   \n",
       "12   13          Ramachandra Rao   M  25.9500           375.0           36   \n",
       "13   14                S.Mallesh   M  31.6400           294.0           14   \n",
       "14   15        Satyanayana reddy   M  33.6500           324.5          111   \n",
       "15   16                 P.Rajesh   M  32.4800           380.0          168   \n",
       "16   17              Y.janakiram   M  30.4600           305.5          375   \n",
       "17   18        Vijaya Bharathi.B   F  38.5400           290.0            0   \n",
       "18   19          Devamani medoju   F  31.6400           301.0           90   \n",
       "19   20            J.Uma sundari   F  55.6200           310.0           53   \n",
       "20   21            ammaji saladi   M  28.5100           388.5          105   \n",
       "21   22             Arpita Jalui   F  45.0100           258.5            0   \n",
       "22   23            D.L.tulasamma   F  30.8500           399.5           75   \n",
       "23   24             D.Bhanumathi   F  33.9800           277.5          358   \n",
       "24   25              Jana Symala   F  24.6700           362.0            5   \n",
       "25   26              K.V.Suseela   F  33.7400           221.0           67   \n",
       "26   27          M.Venkata Laxmi   F  37.4600           262.5           74   \n",
       "27   28              M.V.Lakshmi   F  32.0000           371.0          234   \n",
       "28   29        Mani Manchmshetty   F  28.3900           290.5          210   \n",
       "29   30          S.Shanwaz Begum   F  38.2800           317.5          300   \n",
       "..  ...                      ...  ..      ...             ...          ...   \n",
       "56   57             N.Prasad Rao   M  28.2200            71.0            5   \n",
       "57   58              B.Narsimulu   M  25.4600           213.0          306   \n",
       "58   59           D.Hari Shankar   M  28.3900           182.5          110   \n",
       "59   60                G.S.Ashok   M  31.2300           121.5           32   \n",
       "60   61         Moirza Ashaq Ali   M  34.0800           199.5          268   \n",
       "61   62  Syed Mohammad Zakiullah   M  28.7100           192.5            6   \n",
       "62   63                  Fariyad   F  30.1100           189.5          238   \n",
       "63   64             K.Swasanthra   F  33.0500           213.5           55   \n",
       "64   65              krishna bai   F  45.1600           165.0          390   \n",
       "65   66              M.ratna sri   F  41.5500           141.0           94   \n",
       "66   67              B.rama devi   F  32.4600           160.5          136   \n",
       "67   68                   Sayeed   M  32.6773           150.5           23   \n",
       "68   69               C.H.prasad   M  30.5000           184.5           27   \n",
       "69   70                B.Vishvam   M  32.6773           201.0          278   \n",
       "70   71            chandrasekhar   M  32.6773           200.0          306   \n",
       "71   72               G.srinivas   M  32.6773           194.5           59   \n",
       "72   73           Mandapati ravi   M  36.5700           228.5          282   \n",
       "73   74            Lathifa begum   F  44.0000           198.0           52   \n",
       "74   75                    seema   F  26.7000           212.5           34   \n",
       "75   76                     Mona   F  32.6773           219.5           72   \n",
       "76   77             nalini verma   F  26.3000           232.0          258   \n",
       "77   78               C.srinivas   M  24.2200           426.0           12   \n",
       "78   79           SK.Samir Uddin   M  28.7100           487.0            0   \n",
       "79   80           Sanjay agarwal   M  44.0000           456.0           50   \n",
       "80   81                   Nikita   F  30.1100           435.0           17   \n",
       "81   82               V.Venkamma   F  32.4500           312.0           84   \n",
       "82   83           Md.Miraj Ahmed   M  28.3400           257.0          366   \n",
       "83   84          Subba Rao Aduri   M  46.7700           307.0          316   \n",
       "84   85                B.Nirmala   F  51.9300           203.0          101   \n",
       "85   86                Kamalamma   F  31.9900           425.0            7   \n",
       "\n",
       "    hypopneascount  ahtotalcount  apneasindex  hypopneasindex    ahi  \n",
       "0               69           135         16.0            16.7   32.7  \n",
       "1              120           142          5.4            29.4   34.8  \n",
       "2               14            15          0.2             2.2    2.3  \n",
       "3              197           494         53.0            35.2   82.2  \n",
       "4              311           354          8.0            52.8   66.3  \n",
       "5              269           482         32.7            41.3   74.1  \n",
       "6               34           379         49.1             4.8   54.0  \n",
       "7                1             2          0.3             0.3    0.5  \n",
       "8              148           211         11.7            27.5   39.3  \n",
       "9              139           305         43.5            36.4   79.9  \n",
       "10             235           318         12.3            34.8   47.1  \n",
       "11              75           274         55.7            21.0   76.6  \n",
       "12              62            98          5.8             9.9   15.7  \n",
       "13              63            77          2.9            12.9   15.7  \n",
       "14             212           323         20.5            39.2   59.7  \n",
       "15             209           377         26.5            33.0   59.5  \n",
       "16              28           403         73.6             5.5   79.1  \n",
       "17              34            34          0.0             7.0    7.0  \n",
       "18             182           272         17.9            36.3   54.2  \n",
       "19              29            82         10.3             5.6   15.9  \n",
       "20             175           280         16.2            27.0   43.2  \n",
       "21              39            39          0.0             9.1    9.1  \n",
       "22             130           205         11.3            19.5   30.8  \n",
       "23             302           660         77.4            65.3  142.7  \n",
       "24              24            29          0.8             4.0    4.8  \n",
       "25             183           250         18.2            49.7   67.9  \n",
       "26             117           191         16.9            26.7   43.7  \n",
       "27              95           329         37.8            15.4   53.2  \n",
       "28             228           438         43.4            47.1   90.5  \n",
       "29             147           447         56.7            27.8   84.5  \n",
       "..             ...           ...          ...             ...    ...  \n",
       "56              57            62          4.2            48.2   52.4  \n",
       "57              29           335         86.2             8.2   94.4  \n",
       "58              18           178         52.6             5.9   58.5  \n",
       "59             173           205         15.8            85.4  101.2  \n",
       "60              16           284         80.6             4.8   85.4  \n",
       "61             151           157          1.9            47.1   48.9  \n",
       "62              34           272         75.4            10.8   86.1  \n",
       "63             247           302         15.5            69.4   84.9  \n",
       "64              36           426        141.8            13.1  154.9  \n",
       "65              81           175         40.0            34.5   74.5  \n",
       "66              42           178         50.8            15.7   66.5  \n",
       "67              65            88          9.2            25.9   35.1  \n",
       "68              92           119          8.8            29.9   38.7  \n",
       "69              22           300         83.0             6.6   89.6  \n",
       "70              39           345         91.8            11.7  103.5  \n",
       "71             100           159         18.2            30.8   49.0  \n",
       "72              11           293         74.0             2.9   76.9  \n",
       "73             124           176         15.8            37.6   53.3  \n",
       "74              30            64          9.6             8.5   18.1  \n",
       "75              44           116         19.7            12.0   31.7  \n",
       "76              90           348         66.7            23.3   90.0  \n",
       "77              12            24          1.7             1.7    3.4  \n",
       "78               3             3          0.0             0.6    0.6  \n",
       "79             153           203          9.4            28.9   38.3  \n",
       "80              66            83          3.4            13.3   16.8  \n",
       "81              90           174         26.3            28.1   54.4  \n",
       "82              15           381        100.7             4.1  104.9  \n",
       "83              15           331         82.3             3.9   86.2  \n",
       "84              91           192         41.2            37.1   78.4  \n",
       "85               2             9          1.5             0.4    1.9  \n",
       "\n",
       "[86 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = snore.iloc[:, :-1].values  ## independent variables\n",
    "y=snore.iloc[:,10] ## dpendent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sno                43.500000\n",
       "bmi                32.677338\n",
       "totalsleeptime    285.643023\n",
       "apneascount       110.813953\n",
       "hypopneascount    103.790698\n",
       "ahtotalcount      215.186047\n",
       "apneasindex        27.523256\n",
       "hypopneasindex     24.188372\n",
       "ahi                51.711628\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1st business moment decision---central tendency\n",
    "snore.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sno                43.500\n",
       "bmi                31.815\n",
       "totalsleeptime    280.000\n",
       "apneascount        65.500\n",
       "hypopneascount     81.000\n",
       "ahtotalcount      183.500\n",
       "apneasindex        16.100\n",
       "hypopneasindex     19.600\n",
       "ahi                48.100\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snore.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>sno</th>\n",
       "      <th>bmi</th>\n",
       "      <th>totalsleeptime</th>\n",
       "      <th>apneascount</th>\n",
       "      <th>hypopneascount</th>\n",
       "      <th>ahtotalcount</th>\n",
       "      <th>apneasindex</th>\n",
       "      <th>hypopneasindex</th>\n",
       "      <th>ahi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.500000</td>\n",
       "      <td>32.677338</td>\n",
       "      <td>285.643023</td>\n",
       "      <td>110.813953</td>\n",
       "      <td>103.790698</td>\n",
       "      <td>215.186047</td>\n",
       "      <td>27.523256</td>\n",
       "      <td>24.188372</td>\n",
       "      <td>51.711628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.969982</td>\n",
       "      <td>6.959469</td>\n",
       "      <td>91.772214</td>\n",
       "      <td>111.889916</td>\n",
       "      <td>84.731842</td>\n",
       "      <td>142.066804</td>\n",
       "      <td>29.888989</td>\n",
       "      <td>19.093328</td>\n",
       "      <td>33.704129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.250000</td>\n",
       "      <td>28.392500</td>\n",
       "      <td>213.125000</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>26.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>43.500000</td>\n",
       "      <td>31.815000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>183.500000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>48.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.750000</td>\n",
       "      <td>36.705000</td>\n",
       "      <td>365.375000</td>\n",
       "      <td>167.500000</td>\n",
       "      <td>147.750000</td>\n",
       "      <td>315.250000</td>\n",
       "      <td>42.850000</td>\n",
       "      <td>34.725000</td>\n",
       "      <td>76.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>55.620000</td>\n",
       "      <td>487.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>660.000000</td>\n",
       "      <td>141.800000</td>\n",
       "      <td>85.400000</td>\n",
       "      <td>154.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sno        bmi  totalsleeptime  apneascount  hypopneascount  \\\n",
       "count  86.000000  86.000000       86.000000    86.000000       86.000000   \n",
       "mean   43.500000  32.677338      285.643023   110.813953      103.790698   \n",
       "std    24.969982   6.959469       91.772214   111.889916       84.731842   \n",
       "min     1.000000  15.900000       71.000000     0.000000        1.000000   \n",
       "25%    22.250000  28.392500      213.125000    25.500000       38.250000   \n",
       "50%    43.500000  31.815000      280.000000    65.500000       81.000000   \n",
       "75%    64.750000  36.705000      365.375000   167.500000      147.750000   \n",
       "max    86.000000  55.620000      487.000000   390.000000      370.000000   \n",
       "\n",
       "       ahtotalcount  apneasindex  hypopneasindex         ahi  \n",
       "count     86.000000    86.000000       86.000000   86.000000  \n",
       "mean     215.186047    27.523256       24.188372   51.711628  \n",
       "std      142.066804    29.888989       19.093328   33.704129  \n",
       "min        2.000000     0.000000        0.300000    0.500000  \n",
       "25%       95.000000     5.650000        9.450000   26.175000  \n",
       "50%      183.500000    16.100000       19.600000   48.100000  \n",
       "75%      315.250000    42.850000       34.725000   76.825000  \n",
       "max      660.000000   141.800000       85.400000  154.900000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snore.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variances:\n",
      "-------------------------------\n",
      "sno                 616.250000\n",
      "bmi                  47.871018\n",
      "totalsleeptime     8324.207335\n",
      "apneascount       12373.779340\n",
      "hypopneascount     7096.002704\n",
      "ahtotalcount      19948.290968\n",
      "apneasindex         882.963878\n",
      "hypopneasindex      360.316144\n",
      "ahi                1122.759400\n",
      "dtype: float64\n",
      "\n",
      "Standard Deviation:\n",
      "-------------------------------\n",
      "sno                24.824383\n",
      "bmi                 6.918888\n",
      "totalsleeptime     91.237094\n",
      "apneascount       111.237491\n",
      "hypopneascount     84.237775\n",
      "ahtotalcount      141.238419\n",
      "apneasindex        29.714708\n",
      "hypopneasindex     18.981995\n",
      "ahi                33.507602\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# # 2-->Variance,Standard Deviation and Range:\n",
    "\n",
    "# In[226]:\n",
    "\n",
    "\n",
    "print(\"Variances:\")\n",
    "print(\"-------------------------------\")\n",
    "print(np.var(snore))\n",
    "print(\"\\nStandard Deviation:\")\n",
    "print(\"-------------------------------\")\n",
    "print(np.std(snore))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' we know data of any distribution lies between mean+sd and mean-sd\\nwe can analyse where most data is distributed using variance and mean\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" we know data of any distribution lies between mean+sd and mean-sd\n",
    "we can analyse where most data is distributed using variance and mean\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sno', 'name', 'sex', 'bmi', 'totalsleeptime', 'apneascount',\n",
       "       'hypopneascount', 'ahtotalcount', 'apneasindex', 'hypopneasindex',\n",
       "       'ahi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snore.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  3rd business movement decision skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bmi:    0.7744078190139316\n",
      "totalsleeptime:    0.17174148607065443\n",
      "apneascount:    1.048153196967105\n",
      "hypopneascount:    1.1590189496022894\n",
      "ahtotalcount:    0.6687190853249921\n",
      "apneasindex:    1.439052981270102\n",
      "hypopneasindex:    1.1082977877488909\n",
      "ahi:    0.5850680902419614\n"
     ]
    }
   ],
   "source": [
    "for c in snore.columns:\n",
    "    if c == \"name\" or c==\"sno\" or c=='sex':\n",
    "        continue\n",
    "    else:\n",
    "        print(c+\":    \"+ str(stats.skew(snore[c])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'we obseve thatskwness is +ve mean>median\\n'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"we obseve thatskwness is +ve mean>median\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  4th business movement decison --- kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bmi:    0.9825334085131057\n",
      "totalsleeptime:    -0.7984120367067988\n",
      "apneascount:    -0.19610059480126285\n",
      "hypopneascount:    0.8879403263312766\n",
      "ahtotalcount:    0.059014794781830915\n",
      "apneasindex:    1.603579917773775\n",
      "hypopneasindex:    0.9103125719112883\n",
      "ahi:    0.07687916614308721\n"
     ]
    }
   ],
   "source": [
    "for c in snore.columns:\n",
    "    if c == \"name\" or c==\"sno\" or c=='sex':\n",
    "        continue\n",
    "    else:\n",
    "        print(c+\":    \"+ str(stats.kurtosis(snore[c])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' kutosis is less than 3 ,so less outliers,less standard deviation ,data set is perfect or \\nwe can consider this daata set'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" kutosis is less than 3 ,so less outliers,less standard deviation ,data set is perfect or \n",
    "we can consider this daata set\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### distributions based on sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kumar\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XUGqs09CYIA62hhNq85IZ/vF8U1FtxSQ076c88Ry6Q2IHt0ERilI+jcPTyZTqN0656rKsBAC9RKXUOKChMUEcag0jN6LjE492Tat5i25HFBWJ557RNjtck6iMX8qkhl3QWDrgeklRoeRMitTJC5UaByyN0xCRNcC9gB14wBhzd5/PQ4FHgcVAHXCdMabI/9mtwI2AB/i2MealXu3sQB5w3Bhz+ZCPRvWr0yMUd4RydcrHX9rhHRXEtBVRknzxiVtrz8TxSeeT2LSX1j3Pczjz8yeWH/WUfGK9+IgQth2t47FtxdiHMF7j88syzritUmroTnum4f9ivw9YC8wGrheR2X1WuxFoMMZMB34J3ONvOxtYD8wB1gC/9W/vI/8GHBjqQahTO9buwiDkRHzcCZ5Stw2PLYTquEVD2rbH7qIyYTlxrQVEdBwfcL1pSZF0e7wcbxj+Oa+UUsPHyuWppUCBMeaYMaYb2ACs67POOuAR/+tngVUiIv7lG4wxXcaYQqDAvz1EZApwGfDA0A9DnUpBm68TPDvcN3FgSE8TCU37qY5bhMfuGvL2K+M/RY89jLTqLQOuk5UYAUBRnYaGUmOZldBIA3pfsC7zL+t3HWOMG2gCEk7T9lfAD4FT3ocpIjeJSJ6I5NXU1FgoV/VV0OYiOaSbaKfvKXrJdTsAqIxfFpDte+2hVCacTVzrEcI7yvtdJzLUQWJkKEV1wzslu1LjkYhEiMgLIvKBiHwoIteJyGIReUtEdonISyKSIiIOEdkpIhf42/1cRO4KZC1WQqO/C9B9R3QNtE6/y0XkcqDaGLPrdDs3xtxvjFlijFmSlJR0+mrVSQrawpjuvzQlXg9JjR/QED2T7pCYgO2jKn4pbpuL1Np3BlxnakI4xXXteAc5W65SijVAuTFmgTFmLrAZ+D/gGmPMYuAh4C7/L+1fBn4nIqv97X4SyEKshEYZkN7r/RSg76+TJ9YREQcQA9Sfou25wBUiUoTvctdFIvLnM6hfnUZ9t4O6HifTI3yXpmJbj+D0tFMduzCg+/HYQ6mOO4v45oOEdDf1u05mQgQdPR5qWobvMbNKjVP7gItF5B4RWYHve3Uu8IqI7AH+P3zfrxhj9gOPAX8HvurvVggYK6GxE8gRkSwRCcHXsb2xzzobgRv8r68BXjfGGP/y9SISKiJZQA6wwxhzqzFmijFmqn97rxtjvhiA41F9FLT5+iw+OtNIbPyAbkckTZHZAd9XVfynAEhu2Nnv51MTfBMmFmu/hlKDYow5jO/u1H3Az4HPAvuNMQv9P/OMMZf0ajIPaASSA13LaUPDf7pzM/ASvjudnjbG7BeRO0XkCv9qDwIJIlIAfA+4xd92P/A0kI/vdOqbxhhPoA9CDaygLQy7GKaGd+FwtxHbcoTamHkggR+i0x0SS0P0TCY17O53MsP4iBAiQx0Ua7+GUoMiIqlAuzHmz8B/A8uAJBFZ7v/cKSJz/K+vxtenvBL4tYgMcuTuqVkap2GM2QRs6rPstl6vO4FrB2h7FzBgR4wx5k3gTSt1qMEraHeRGdZJiM2Q2LAPG15qYxcM2/4q45cS33yAqcf/wdGMT/6VEBEyE8K1M1ypwZsH/EJEvEAP8A3AjS8UYvB9l/9KRKqAu4FVxphSEfkNvjF2Nwyw3UHThzCNY16v4VibixUJvjmfEhv30hqWOuDU54HQEp5Bm2syuSVPcjT9GvpOqTs1IYL95c00dfQQE3bmgwqVmkj8g6Jf6uejlf0sy+3V7teBrkWnERnHjtW20eG1kx3eSWhXHRGdldTFzB3enYpQHbeYuJYjJDR9eNLHmSf6NfRsQ6mxSENjHNt3vBGAaRGdxDf7Bt7XR80a9v3WxczFbQ8ju+wvJ32WEhNGiN2mneFKjVEaGuPYvrJmQsRLmquL+OaDtIalBnRsxkA89lCKJ19KZvmLJ02bbrcJ6fFheqah1BiloTGOfXi8ianhnYT1NBLZWU599PCfZXzkaPpncXrayajYfNJnmQkRVDR10tmjN9IpNdZoaIxTHq/hw/ImpoV3Et98EGBEQ6M2dgGNkdlMLz35EtXUhAgMUFqvl6iUGms0NMapwtpW2rs9/v6MfNpcyXSFxI9cASIcS7uSxKa9RLUVfeKj9LgwbKKTFyo1FmlojFN7y3xTecwMqSGqo2xEOsD7Kk5di0GYWv7CJ5aHOu2kxGi/hlLBICIeEdnT62fqYNrrOI1xat/xJsKcdma5fXdNNUblnqZF4HW4kqlKWMrU4/9g3/R//cSYjcyEcHYW1ePxmiE9lEmpsWzqLS/cFMjtFd192f0WVuswxpzx5HN6pjFO7StrYnZqNPGtR+h2RNHuCvgUNJYUpl5OVEcZiY0ffGJ5ZkIEPR5DeePJ040opUYvDY1xyOM15Fc0syA1gpjWYzRGTj9pZPZIKU2+GLct9KRLVDrIT6mgCet1aeq5wTbW0BiHiuraaO/2sDK0AIe3i8aonKDV4nZGcnzSBWRUbEa8PSeWR7ucxIU7KdY7qJQaaR29Zse9arCNNTTGofxy31xTc9u34xUbTRFZQa2nKPVyXD2NpNS++4nlmQkRlNS1Y/ShTEqNGRoa41B+RTNOuxBf8RYt4Zl47aFBraci6Vw6nbFklf/jE8sz4sNp6XLT0N4zQEul1GijoTEO7S9v5tyEDmw1B2mMDN6lqY94bU5KUi4lreoNHD2tJ5Z/1K9RUq/9GkqNFXrL7TiUX97Mj5IOQDM0Rk0PdjmA7xJVbslTpFe9RuGUdQAkR7sIdfgmL1yYHhfkCpUaeRZvkQ0oY0zkUNrrmcY4U93SSW1rF0s8eyA6jc6QhGCXBPimFWkNS2Nqr0tUNhHS48Mp0c5wpcYMDY1xJr+8GRtepjTuhGkXBO1W25OIUJh6OZPrthPWWX1icUZ8OJU6eaFSY4aGxjiTX9HMHCnC2d0I0y4MdjmfUJR6GYIho+LjB5BlxodjgLIGHeSn1FigoTHO5Jc3c1mEb1Zbpp0f3GL6aInMoj5qJpmVH0+Xnh4fjqCD/JQaKzQ0xpn8imbOd+yH5LkQOXzPAj9TJSmXkti4l4j2MgBcTjvJ0S7t11BqjNDQGEfau91U1NaT0/Whrz9jFCpOWQPwiUtUGQm+znCvDvJTatTT0BhHDla2sEQO4TA9o64/4yNt4VOojZlPZuUn+zW63F6qm7uCWJlSE4OIGBF5rNd7h4jUiMg/TtXuIzpOYxzZX97Mcls+xuZAMpcHu5wBFadcyuKDvyCqtZCWyCwyEyJ8y+vbmBzjCnJ1So2gO2ICOjU6dzRZGffRBswVkTBjTAewGjhudRd6pjGO5Jc3c47jIKQugpCIYJczoJKUSzEImf7nh8eFO4kMdVCiT/JTaqS8CFzmf3098KTVhhoa40hBeTVzOYpknhPsUk6pw5VMddwiX2gYg4iQmRCuM94qNXI2AOtFxAXMB7ZbbaihMU64PV7Cq3bjwANTzwt2OadVnLKWmLZjxLQeAXyD/Orbumnp1MkLlRpuxpi9wFR8ZxmbBtNWQ2OcKKpr4yyTj8EG6UuDXc5plU6+GC+2E5eoMuM/mrxQzzaUGiEbgf9mEJemQENj3Nhf3sxSOUhn4hxwxQS7nNPqCk2gKmHZiUtUqbFhOGyi/RpKjZyHgDuNMfsG00hDY5w4eLyORbYjhGSP/ktTHylOWUtUeynxzfk47DbSYsO0X0OpEWKMKTPG3DvYdpZuuRWRNcC9gB14wBhzd5/PQ4FHgcVAHXCdMabI/9mtwI2AB/i2MeYlf+fLFiDUX8OzxpjbB1u8+lhXcR4u6YGp5wa7FMtKJ1/Ep/bfSWbFi9THzCEjIZx3j9bR4/HitOvvM2oCsHaLbED1NzW6MeZN4E0r7U/7L1NE7MB9wFpgNnC9iMzus9qNQIMxZjrwS+Aef9vZwHpgDrAG+K1/e13ARcaYBcBCYI2InG2lYHUyYwzxNTt9bzJG951TvfU4Y6hMPNc3Otx4yYyPwOM1lDfq5IVKjVZWfp1bChQYY44ZY7rx3aq1rs8664BH/K+fBVaJiPiXbzDGdBljCoECYKnx+egRbk7/j84hcYZqWrqY695PQ0Q2RIyO52dYVZyyhojOShIbPyDD/yS/Yu3XUGrUshIaaUBpr/dl/mX9rmOMcQNNQMKp2oqIXUT2ANXAK8aYfu8TFpGbRCRPRPJqamoslDvx5JfVs9h2mO4po3cU+EDKki/EbQsls+JFIkMdJESE6B1USo1iVkKjv6f49D0rGGidAdsaYzzGmIXAFGCpiMztb+fGmPuNMUuMMUuSkpIslDvxVBfsIko6iJqxMtilDJrbEUF50koyKl5GjOfEID+jkxcqNSpZCY0yIL3X+ylA+UDriIgDiAHqrbQ1xjTi64BZM4i6VS+2kncBCJ++IsiVnJnilDWEddcxqT6PjPgI2rrc1Ld1B7sspVQ/rITGTiBHRLJEJARfx/bGPutsBG7wv74GeN34flXciG+oeqiIZAE5wA4RSRKRWAARCQMuBg4O/XAmpskNu6h2pEJ0arBLOSPlk1bQYw8js+JFMj/q19BLVEqNSqcNDX8fxc3AS8AB4GljzH4RuVNErvCv9iCQICIFwPeAW/xt9wNPA/nAZuCbxhgPkAK8ISJ78YXSK8YYS9Pyqk9q7exmjns/NQlLgl3KGfPYwzg+6ULSK19lUoQNl9OmT/JTapSyNE7DGLOJPvOTGGNu6/W6E7h2gLZ3AXf1WbYXOGuwxaqTFR/cxRxppXyUT1J4OsUpa5lasYnU+u1kxqdTVKtnGkqNRjqCaoxrPbQFgKS5o/OhS1ZVJJ5DtyOKzIrNZCVGUNPapZMXKjUKaWiMca7y96gkgaQpucEuZUi89hBKk1cxpep1psf7ToCLdLyGUqOOhsZYZgzpze9zNGw+Yhv7/yuLU9YS4m5lSc9uQuw2CmtbT99IKTWixv43zQTmrikg3jTQNOlTwS4lIKoSltLpjGNq1UtkJoRrv4ZSo5CGxhhWm/8GAM7ssTk+oy9jc1A6eTVTqt8kN95GZXMn7V3uYJellOpFQ2MM6z76NrUmmoychcEuJWCKU9bg8HRwse19wPdwKaXU6KGhMYZFV+0gz8xk2qSTZjoes2riF9EeOolFrW/gsAmFtRoaSo0mGhpjVWMpsd0VlESeNa6ePWHETsnkS5hS+zYz4wyFeqah1Kgyfr5tJhhT/A4A7SnLglxJ4BWnrMHu7ebKsD1UNHbS2eMJdklKKT8NjTGq48hWmk04Cdnjpz/jI3Wx82kNS+X8nrcxoFOKKDWKaGiMUab4HXZ4ZzArNS7YpQSeCCWTLyW7eQcJ0qr9GkqNIhoaY1FrNREthezwzmRmSnSwqxkWRamXYTNuvhiZp6Gh1CiioTEWFfuen1EadRaRoZbmnBxzGqNnUB81k3XyJscbO+hya7+GUqOBhsZYVPwOHYRinzL++jN6K5yyjmndh8mmTB8Bq9QooaExBnkK3yHPk8PM1PhglzKsilI+jVccXGPfopeolBolNDTGmvZ6bDX57PDOZHbq+OzP+EhXaDzHk1bwWcc7FFc3B7scpRQaGmNP6XYEw3bvLOamxgS7mmFXmLaORBqY2rxdx2soNQpoaIw1xe/gFifHI2YzKdoV7GqGXfmklbQ7YvisTS9RKTUaaGiMNcXvcsCWS25aYrArGRFem5Pi1E+z2raL8sqKYJej1ISnoTGWdLVgyvfwVlcO89LG/6WpjxRNuZJQ6WF69cvBLkWpCU9DYywp3YEYD9u9M5kzgUKjIXoWx0OyuNT9OuWNHcEuR6kJTUNjLCl+F6/Y2eXNZe4ECg1EKEi5gkW2Aj54f0ewq1FqQtPQGEuK36XMlUtoeBSpMeO/E7y32mnrcBsbjn1PBrsUpSa08TkHxXjU0wnH89jpvJy5aTGISLArGlFdriR2hS5lUf0mPD1d2J2hw7q/J7aXDOv2AT6/LGPY96FUoOmZxlhxPA883bzcOo05E2B8Rn/2p36WBJooevupYJei1ISloTFWFL+LQdjmzmVu2vgeCT6gaRdRZhKxv/9osCtRasLS0Bgrit+hKSqHZiInxEjw/rhCQ3g76jKmNu+EuqPBLkepCUlDYyzw9EDpDg6Ezicq1EFGfHiYde9HAAAgAElEQVSwKwqanvmfp8fYaX33gWCXotSEpKExFlR8AD3tbO3OZXZqNDbbxOoE7+3sBXN42bsY597HoUfHbCg10jQ0xoLCLQD8rSFzYo3P6Mf0SZG8GPYZQnuaYN8zwS5HqQnHUmiIyBoROSQiBSJySz+fh4rIU/7Pt4vI1F6f3epffkhELvUvSxeRN0TkgIjsF5F/C9QBjUtFW+mKm0F5T9TE7QT3ExES51zEIZOB570/gDHBLkmpCeW04zRExA7cB6wGyoCdIrLRGJPfa7UbgQZjzHQRWQ/cA1wnIrOB9cAcIBV4VURyATfwfWPMbhGJAnaJyCt9tqkA3N1Q8h5laeuggjHTCZ5dMkxnAfZ41oY4edh9CXdXPwAv/xgSsj/+fMlXhme/SinA2pnGUqDAGHPMGNMNbADW9VlnHfCI//WzwCrxjT5bB2wwxnQZYwqBAmCpMabCGLMbwBjTAhwA0oZ+OONQ+W7oaWeXbR5hTjvTkiKDXVHQLUnsYatjOW0SAUVbgl2OUhOKldBIA0p7vS/j5C/4E+sYY9xAE5Bgpa3/UtZZwPb+di4iN4lInojk1dTUWCh3nCncCsBLrdnMTo3GPoE7wT9iFzg/DZ50X4ip2AvtdcEuSakJw0po9Pct1fdC8kDrnLKtiEQCfwG+Y4zp93mexpj7jTFLjDFLkpKSLJQ7zhRtwSTP4b1KmDvOH+86GGvTOvljzxoMAsfeDHY5Sk0YVkKjDEjv9X4KUD7QOiLiAGKA+lO1FREnvsB43Bjz1zMpftxzd0HpDpqTz6at2zOhpkM/nbOTeuh0xrLLdTaUbodufaqfUiPBSmjsBHJEJEtEQvB1bG/ss85G4Ab/62uA140xxr98vf/uqiwgB9jh7+94EDhgjPnfQBzIuFS2E9ydHAxbCIydTvCR4LTBpWld/Kz1M+DphuJ3gl2SUhPCaUPD30dxM/ASvg7rp40x+0XkThG5wr/ag0CCiBQA3wNu8bfdDzwN5AObgW8aYzzAucCXgItEZI//59MBPraxr3ArILzVmYvLaSM3WTvBe7sqo5P33ZlURc32jWXxdAe7JKXGPUtToxtjNgGb+iy7rdfrTuDaAdreBdzVZ9nb9N/foXorehtS5rO90su8tBgcdh2L2duypB7Swj086P0M/9F9D5Rsh2VfC3ZZSo1r+i00WvV0QNkOPJkr+PB4EwumxAa7olHHJnB1RicP1M2jOzYbjr7q6wdSSg0bDY3RqnQHeLopjVlMl9vLgnQNjf5cldmJFxsvR3wGOpvg/T8HuySlxjV9ct9oVbQVxM52zwygSM80BjAtysNZ8T3cW30Wl8VmIm//Es76EjhCgl3a8Mt7ODj71VH3E5qeaYxWhVshdSG7Kt3EhTtJjw8LdkWj1vVZHRxpcXJw0mXQVArvPxbskpQatzQ0RqPuNji+C6aexwelTSxIj51wzwQfjCsyOokL8fKrmsWQsRzeukfHbSg1TDQ0RqOS98DbQ0fauRyubtFLU6fhssP6rA5eqXBRvewWaK2C7b8PdllKjUsaGqNR0VawOdhrm4UxsFA7wU/ri9m+BzI9WJIMuWvh7XuhvT7IVSk1/mhojEbH3oS0xeyq9A1W0zunTi8t3MulaV1s2FFK23m3QlczbPlFsMtSatzR0Bht2uqgfA9kr2J3cQPTEiOIj5gAdwIFwL/ObKepo4cHj4TD4htgx/1QcyjYZSk1rmhojDaFbwIGk30hu4obWJQZF+yKxox5cW5Wz07mj1uP0bz8FnBGwOZb9Ol+SgWQhsZoc/R1cMVQGJJLQ3sPizU0BuV7q3Np6XTzwO5muPBW35/noU2nb6iUskRDYzQxBo6+AVnns7usFUBDY5BmpURz2bwUHny7kIrcL0DSLNj0Q+hqCXZpSo0LGhqjSe1haD4O2Rexq7iBKJeD6fp410G7Ze1MPMZw+z8OwxW/9v2ZvnZnsMtSalzQ0BhNjr7u+2/2RewubmBRRhw2fbzroKXHh/Odi3N5Ob+KzU0ZsPQm2PFH3yy4Sqkh0dAYTQpeg4TpNLlSOVzdopemhuDG87KYOTmK2zd+SN3ZP4KYKfD8v+pIcaWGSENjtOhu9w3qy17FntJGjNH+jKFw2m3897ULaGzv4RvPHMZ9xX1Qd9R3N5VS6oxpaIwWRVvB3Qm5l7CzsB67TXRQ3xDNTYvhv66Zz47Cem7fG48577uw+1HIfz7YpZ05rwdaKqEqHxpLfFOmdLcGuyo1gejU6KPF4ZfAGQ6Z57H91d3MTYshMlT/9wzVuoVp5Fc084e3jmFfdgU/SX0Tef5bMGkOJE4f0rY9XkNrlxu3x4tNhIhQByGOAP8e5nFD6Xtw6EXfM+Mr90FP+8nrhUZBTDpMmgWT54NLnyevhod+K40GxsCRl2HahXTi5IPSJr5y7tRgVzVu3LJmJoLw+7eO0pPzfe6yfQvbhuvhn18DV7SlbRhjaGjvobC2jcLaVorq2mlo66bvsMEol4MpsWHkTo5idko0US7nmRVdWwC7/wR7noD2OrCHQNpiWHQDJGRDeAIUv+N7UmFXi+8OsYYi+DAfPvwrJM+G7IshPuvM9q/UADQ0RoPqA77nQKz8AbtLGuj2eFk2LT7YVY0bIsIta2eSHB3KT184QE3ot7m/8//H++w/4/j8k2Czn9TGGEN9WzeFtW0cq22jsLaNpo4eAMJD7ExNiGDBlFiiwxw47Ta8/rOO2tYuCmvbOFDZwt8/KGdeWgwrcpJIjbX4PJTSnfD2//oGJIodZqyFedfC9FW+s4neOptObt9S6ZtWv/gdqLoXEmfAnKsgavJg/9iU6peGxmhw5CXff3MuYfv2emwCS6ZqaATaV87NYnl2Arc/H8dtJf/ETwseZusvv8A7s25nUrSLTreHxvYeDle1sKu4gZZONwARIXayEiNYmZtEVmIEk6JCsZ3i+SbGGKpbusgrqievuIG9ZU0syohj9Zxkogc686g+AK/c7vu74IqF838ES746+C/7qMkw8zKYfjGUbPNtb8t/wbQLfbP/2vWfvBoa/Rs0Ghx+2XcdOjqV7YXbmJ0aPfCXixqSmZOj2XDT2ewozOXNlw0XVP6Jw9uEO3u+AAghdhvTkiLITookIz6caYkRJEWFDuohWCJCcrSLy+ansmpWMm8crObdo3Xsr2hi3YK0T97g0FwBb/7M92zzkChYdTss/ZeTzyoGyxEK0y7wXdI6+Hc4+ppv8sZFX4LI5KFtW01oGhrB1lYHpdthxffocnt4v6SRL52dGeyqxjURYdm0BPjar+DFUG7c8Qe+sHQK5pKf4QpxICI8sb0kIPtyOe2snZfCp6bG88yuUp7KK+VQVQvXzI2F138K7/4GvG5Y9nVY+e8QHuAzzNAoWPB5SJ4HH2yArf8DC78AKQsCux81YWhoBNvhF8F4YOZlfFDaRJfb6/tCU8NPBNbcDTY7rvd+C111cOXvfL+lB1hiVCg3rcxm66HjJB95mk/X/AVohrmfhYt+PPwd1pPn+e6u2vUQ7HrYd6kq5xLfn4FSg6ChEWwH/uH7x5yykG2vFSACS7U/Y+TYbHDpz3yXbF69HeoL4dqHCfQQJjEesis2cVXVfUQ6j7PTzOLf7Lfyg2WfZ0H8CI3HCYuF5d+CvU/5flnpaID5nwPR4VrKOv3bEkxdLb75pmZeDiJsPVLD/LQYYsK1P2NEicB534HPPeobNf77lWQd3xiY53AYL1MqX2Pt29dwzt7/oNsZzRtLfse2FY9S5JrJ9X98j61Haoa+H6vsTt/lqZxLfOM/dv3JNxZEKYs0NIKp4FXwdMGsz9Dc2cP7pY2syEkKdlUT1+x18PUtkJTL8r3/yer3vkRiw54z2pTD3U52yTNctvVKVr7/HcS42brwv9l8zgYqks4jMcrFX75+Dhnx4Xz1Tzv5x97yAB/MKYjAjE/D7Kugcq/vcpVXg0NZo5engunA3yE8ETLO5r0DNXi8hvNyEoNd1cQWNxW++jLvPfd/LDh0L5e89yXqYuZyJP1zlCedR6dr4FAP6W4kuX4nU6peI73qdRyeDuqjZ/Hu/J9RnLIWY/vkP7dJ0S6e+tpy/uWRPL715Ps0tHXzpeVTh/f4ept2vu8W3H3PwO7HYNE/9TtmRaneNDSCxd3lu9V27lVgs7P1SC3hIXYWZegkhUFns3FsylWUTL6EacefJ6f4Sc7+8DYAmiKyaInIpC0sBYMdm+khvLOKqLZiotuKEAxdzmgKUy+jMO0KamMXnrKzOSbMyaM3LuXmJ97nx8/vp7XLwzcuyB6pI4XMc8HTA/l/gw+e8F260j4OdQoaGsFS8Bp0t8CsKwDYeqSG5dMSAj93kTpjbkcEhzM/z+GM64lrPsjkum0kNbxPRMdxJtXvAsArdjpcSTRHTqM4ZS2ViWdTHzMXr816v5TLaed3X1zE95/+gHs2H6Sjx8N3L84Z1NiQIZl2AXh74OALYHNq57g6JUuhISJrgHsBO/CAMebuPp+HAo8Ci4E64DpjTJH/s1uBGwEP8G1jzEv+5Q8BlwPVxpi5ATmaseTDZyEsHqZdQGl9O0V17dxwztRgV6X6I0JDzCwaYmZxYJh24bTb+OV1C3E5bfz6tSN09Xi4Ze3MkQuO6at9ZxxHXvbNczXnKr0dV/XrtKEhInbgPmA1UAbsFJGNxpj8XqvdCDQYY6aLyHrgHuA6EZkNrAfmAKnAqyKSa4zxAH8CfoMvbCaWrlY4uAkWXg92J1uO+DpBtRN8YrPbhLuvno/LaecPW47R0ePhjs/MGbmnN+au9V02LXzLd3tu9kUjs181plg501gKFBhjjgGIyAZgHdA7NNYBd/hfPwv8Rny/Iq0DNhhjuoBCESnwb2+bMWaLiEwNxEGMOYdeBHeHbyI64PUD1aTFhpGdFBHkwsaBvIcDspnskvqAbOeU7CePx7EBP0kFV24E928rprPiID9f3IJ9JHJDxHcHWWcTHNjomwMrbdEI7FiNJVYuXKYBpb3el/mX9buOMcYNNAEJFtuekojcJCJ5IpJXUzOC97MPp33PQPQUSD+b9m43bxfUsnp28shdilCjmgjcOq+Nb89q4+miML67I5oe70jt3ObrDI/Phg8eh7qCEdqxGiushEZ/32R9Rz0NtI6VtqdkjLnfGLPEGLMkKWkcXL5pr/dNHjfvs2Cz8faRWrrcXlbP1knk1MdE4Htz2vjR3FY2lrq4+b1oukcqOOxOWHKj73bwvAd9060r5WclNMqA9F7vpwB9RyKdWEdEHEAMUG+x7cTy4V98A6nmXgPAqweqiHI5WJqlU4eok31jZjt3LGzhpXIXN70bQ8dIjcELCYelX/PdTbXjD/0/u0NNSFZCYyeQIyJZIhKCr2N7Y591NgI3+F9fA7xujDH+5etFJFREsoAcYEdgSh+j3n/MN3lcynw8XsNrB6q5YMYknHa9xVH178vTO/j5ombeqgxh/Vtx1HSO0GXM8HhYehN0t8HOB3yd5GrCO21HuDHGLSI3Ay/hu+X2IWPMfhG5E8gzxmwEHgQe83d01+MLFvzrPY2v09wNfNN/5xQi8iRwAZAoImXA7caYBwN+hKNJxQe+n7W/AGBPaSN1bd1cPGtSkAsbO7YXjkAH9Sh0/bROElxevr09hqvfiOeBcxqZEeOx1HZof2bhxKZdTW7JUzS88xBH0q/lqKf/aeM/vyxjCPtRY4WlX2+NMZuMMbnGmGxjzF3+Zbf5AwNjTKcx5lpjzHRjzNKP7rTyf3aXv90MY8yLvZZfb4xJMcY4jTFTxn1ggG+qBnsozPfdNfVyfiUOm3DBDA0NdXqXpHbz1PkNdHrgqtfjeKEs8FO496cxKpfiyZcS33KIjKpXRmSfavTSayIjpacD9j4Ns6+AsDi8XsM/PqjgvJxEYsJ0VltlzYJ4N/9Y1cDMGA/ffC+G29+PpNPaCceQVCUspSJ+GSl128kpfnL4d6hGLQ2NkXLg79DV5JsUDni/tIHjjR1csSA1yIWpsSY5zMuGCxr46vR2HjkazhWvxbO3fvhnBCqZvJqGqFwW599NavWWYd+fGp00NEbKjvshfhpkngfAxj3lhDpsXDJncpALU2NRiA1uW9jKo+c10tgtXPl6HHfsiaS5Zxg7ycVGQdrVNEbP4Nw9/05s88Hh25catTQ0RkLpTijbCcu+ATYbbo+XF/ZVsGrWJCJDdc5IdeZWTu7m1Uvr+WJ2B48UhLHyxQT+eDhs2C5Zee0hvLX4N3Q7o7gg75uEdVYNz47UqKWhMRLe+y2ExsDCzwOw7Vgdta3demlKBUS003DnWa38fVUD8+N6uGtvFOe8kMj/7o+gsiPw/8Q7XJN4a/F9ON2tnJ93Mw53e8D3oUYvDY3h1lQG+c/Doi9BaCQAz+0+TlSoQ++aUgE1N87NoyuaeOr8BhYl9PB/B8JZ/kICdx1O57XaGBp6AveApcboGbx91v8Q23qEc/f8O2JGoDdejQp6bWS47bgfML5BUkBTew8v7Kvg2iVTcDn1KWkq8JYl9bAsqYniVjt/KXbx5FEn9xenADDF1UVORAe5kR3kRHSQ5urmTCfRrUg6j7xZt7I0/6cszr8blv1ep1OfADQ0hlN7Pex8yDdzaFwmAH99v4wut5frl+pAKDW8MiM9fG9OG+eE1VPSEcrupggOtoazozGKN+piAQgRL1PCusjo8xPjtHbmUJB5HZEdZcwu/BNsmQ7n//swHpEaDTQ0htN7v/U9nW/lDwEwxvDE9hIWTIlhTmpMkItTE4UIZIZ3kRneBdRjDFR0OTnSFkZRu8sfKJG86Q8SgBiHm/SwLrLCO5kT1c7MyA7C7P3PmLhnxndxddcz7Y2f+qYe+dSNI3RkKhg0NIZLez2893uYfSUkzwYgr7iBI9Wt3PPZeUEuTk1kIpDq6iHV1cP5Cc0nljf22CnpCKW0I5QS/8+m6nj+XpWADUN2RCfzo9tIjelgcrTr46n8xcb2uXcwLbwLXvi+LzjmXBWko1PDTUNjuLz3O99Zxvk/PLHosW3FRIY6uHy+3jWlRp9Yp4dYZzvzoz++G6rLKxxqDSO/JZwPWyL4a0UCpqKApMhQFmfGsSgzjshQB8bmhGv/BI9dBX/5F98DnLIvDN7BqGGjoTEcWqp8l6Zmr4PkOQCU1LXzj73l3HheFhE6NkONEaE2w/zoj4KklqYeOy/YzmdPSSOb91fyyoEqFmfGcX5Okm869c9vgIcvg6e+CF/8C2ScHexDUAGm317D4bU7fdNIr7r9xKI/bj2Gw2bjn1dMC2JhSg1NjNPDsowElmUlUN3cybtH69hV3EBeUT3Hatv41wuzyf7SX+FPl8GfPwtfeBYylwe7bBVAOk4j0Mrfhz2Pw9lfh4RsAGpaung6r5SrF6WRHO0KcoFKBcakaBdXnpXGDy6ZwfJpCbywr5zV//sWP36tlsbPPQdRKb7gKH432KWqANLQCCRjYPOtEJ4AKz++9fChdwrp9ni5aaWeZajxJybMyWXzU3n7RxfxT8un8sSOEi74w0Genfd7TEwa/PkaKHo72GWqANHLU4GU9xCUbIPP/BpcvltqK5o6ePidQi6fn8q0pMggF6hGk/H2QKnEyFDuuGIO1y/N4I6N+/nB5ir+mvxjHor4Ca7Hr4X1T2jn+DigZxqB0lAMr9wGWeefmP4c4H9ePozXCz+8dEYQi1Nq5MyYHMUT/7KM331hEYWdEays+j7VjhTM49fAnieCXZ4aIg2NQPB6YePNvtfrfnNiKoX95U38ZXcZXz53Kunx4UEsUKmRJSKsnZfCy99dycVL57Gq4VZ2yRz42zfgzbt9l3LVmKShEQjv3guFW+CSn0Ksb3oQr9fwk435xIQ5+eaF04NcoFLBEeVy8rOr5vH7f76QHzj/k2c8K+HNn+N+7hvg7g52eeoMaGgM1dE3fLfYzrkaFn/5xOJHthWxo6ie//j0LH2cq5rwzp2eyAvfXcX+JT/nf93X4Nj7JI1//Ay01gS7NDVIGhpD0VAMz34VkmZ+4rJUUW0b92w+yIUzkrh28ZQgF6nU6BAR6uCOdXNZ8c+/4Oeh38FVuYvGXy2n9cg7wS5NDYKGxplqqYLHrgTjgev+DCERAHS5PXznqT047TZ+fvX8j+fnUUoB8Kmp8Xz3B7fx5PyHaOoRXI9/hsNP3wYed7BLUxZoaJyJ9nrfHDstVb4Rr/5BfMYY/vO5D9lT2sh/fXY+k2N0IJ9S/XE57Xzls1fQdsNrvB1yHrn591Lwi5XUFX0Y7NLUaWhoDFZzOTzyGag7Ausfh/SlJz56YGshz+4q499W5bB2XkoQi1RqbJg9LYPzfvQ3Xp11F0kdRUT+6QL2/Pk/cHd3Brs0NQANjcGoPgAPrIaGIrh+wycGKj3ybhF3bTrAp+dN5t9W5QSvRqXGGIfdxsXX3UzTV99ht2s5Cwvuo/Luhex97UmMt/9neKjg0dCwwhjY/Rj88SLwuuErL8L0Vf6PDH/ccozbN+5n9exkfnXdWdjO9PmZSk1gGZlZnP2jjexa8Uc8xs78rV/n0D0rKNy5OdilqV40NE6nuRye+bJv8F7aYrjpDUiZD0BHt4fvP/PBiTOM335hESEO/SNV6kyJCItXfY6UW3bz7oxbie86TtYL13Hw5+dx8M0NGO0sDzqde2ogXa2w4w+w5X98ZxerboNzvwM2OwB5RfX8x3P7OFLdyncvzuVbF03XMwylAiQkNJRzrr+FpuZ/Zevz95J99BFS3/walVtupznnKjIv+iqhybnBLnNC0tDoq7kcdj3iC4yOBphxGVx6F8RnAb4xGL95o4Bnd5WRGuPi4S9/igtmTApy0UqNTzHR0az40o/p7PohWzb9ibB9j7P44O+xHfodJWGz8c77HBnLrsSWkBXsUicMDQ3wBcWRVyD/eTj2BhgvzPg0nPddSF9Kj8fLlgNVPLurjJf2V+Kw2/jayml8e1WOPoVPqRHgCg1l5VVfw7PuJvL27afqncfIrdrEjB13wI47qHWm0pJ6LvFzVxMzY4XvWR46RmpYWPrGE5E1wL2AHXjAGHN3n89DgUeBxUAdcJ0xpsj/2a3AjYAH+LYx5iUr2xwWxvjOHmoPQ3U+HN8NpTug9pDv85gMWPF9uuZ8jvyuJD4sa2Lbll28U1BHU0cPseFOblqZzVfPm8qkKB2DodRIs9uEpQvmwoJ7aO++i1ff20b9vpdJqtnG4qJ/EF38DLwArbZoGqJn4k2eS1T6PKJTpuOIz4DoNLDrtD5DcdrQEBE7cB+wGigDdorIRmNMfq/VbgQajDHTRWQ9cA9wnYjMBtYDc4BU4FUR+ehC5Om2OXQl2yHvQd+ZRPNxaK4Ad8fHn4fFwZSlsPB6iuPP49+39FC6vYPKV45gzBEAUmJcrJ6dzJo5k1mZm6Qd3UqNEuEhDi5euQJWrsDrNeQfr+fw+1voLN5FVOMBMuqPMaPhMVyHek608WCjyZ5AuzOWLmcM3SGxuEPj6HFE0COheGyhuG0hdHgddLu9LEyPIT021NfY3Qk9nZB2Fky/OEhHHXxWzjSWAgXGmGMAIrIBWAf0/oJfB9zhf/0s8BvxzZ+xDthgjOkCCkWkwL89LGxz6NrroHgbRKdCykLfJaeoFEjM8c0XFZMONl8I2Bva8Zo9LM9OICM+nJmTo5iTGsOUuDCdCkSpUc5mE+amJzA3/SrgKgBaOnvYX9FAZfFhOmuL8DaU4mgpI7KjnPDuJiI7m4g2ZcTRQjidhIjn5A2X9rOzZV/X0DiNND75R1cGLBtoHWOMW0SagAT/8vf6tE3zvz7dNgEQkZuAm/xvW0XkkIWa+xjWqQkSgdrh3MEQaX1DM5rrC1JtP+h36RdOXjSa/+zgjOv7L//PoG02xqw5k4ajiZXQ6O/X7L5PUBlonYGW93eNp9+nshhj7gfuP1WBwSQiecaYJcGuYyBa39CM5vpGc22g9Y1XVi7QlwHpvd5PAcoHWkdEHEAMUH+Ktla2qZRSapSxEho7gZz/1975x8hVVmH4eW2hq20FwVoXQduaolYR7A+DAgZMI9gYxARDkQQaCdGKFmKiaUOi0UQNIsaoMTUKxgDiYhSoNbKUQiVKuqXd/tqm3dLGmpA2VKOACJYCxz++M+l0Mtudwu7MHfM+yc395syde9+Zb3bPvd935z2SZko6kTKxvaphm1XANdm+HHg4IiLjiyVNkjQTmA1saHGfxhhjKsaow1M5R/FFoJ9ye+ztEbFD0jeBjRGxCrgNuCMnuv9JSQLkdvdQJrhfAq6PiJcBmu1z7N9eW6js0Flifa+NKuursjawvv9LFC7wbowxpkX8owNjjDEt46RhjDGmZZw0jgNJn5a0Q9IrkuY3PLdC0h5Jw5IurotfkrE9kpa3UWtHjtug4XZJByUN1cVOkbRG0hO5flPGJemHqXebpLlt0HeGpEck7cx+vaFKGiX1SNogaWvq+0bGZ0oaSH19eTMJecNJX+obkDRjPPXlMSdI2ixpdQW17ZO0XdIWSRszVom+7WoiwkuLC/Ae4F3AOmB+XXwOsBWYBMwE9lIm+CdkexZwYm4zpw06O3LcJjo+AswFhupi3wWWZ3s5cHO2FwF/pPy251xgoA36eoG52Z4K7M6+rITGPM6UbJ8ADORx7wEWZ3wlsDTbXwBWZnsx0NeGz/DLwK+A1fm4Str2AW9uiFWib7t56biAblyaJI0VwIq6x/3Ah3LpH2m7cdTXkeOOoGVGQ9IYBnqz3QsMZ/unwJXNtmuj1vspfmiV0wi8ARikOCf8A5jY2Ne17122J+Z2GkdNpwNrgY8Cq/MfbiW05XGaJY3K9W23LR6eGhuaWa287RjxTumpAtMj4gBArmvFSDqqOYdLPkA5m6+Mxhz+2QIcBNZQriCfjohaCbt6DUfZ+QA1O5/x4gfAV4FaIe9TK6QNisvEg5I2pR0RVKhvuxUXg2hA0kPAW5s8dVNE3D/Sy/bKQ8gAAAStSURBVJrEjssuZYxpxfqlanRMs6QpwG+BGyPiWY1sUNl2jVF+13SOpJOBeylDpCNpaJs+SZ8ADkbEJkkXtnD8TvTveRGxX9JbgDWSdh1j2278m+kIThoNRMSrsa88li1KJ+xSqmzT8pSk3og4IKmXcgYNHdIs6QRKwrgrIn5XRY0AEfG0pHWU8faTJU3MM/Z6DTV9T+poO5/x4DzgUkmLgB7gjZQrjypoAyAi9uf6oKR7KQ7blevbbsPDU2ND1exSqmzTUm85cw1lHqEWvzrvYjkXeKY2jDBeqFxS3AbsjIjvV02jpGl5hYGk1wMLgZ3AIxS7nmb6mtn5jDkRsSIiTo+IGZTv18MRcVUVtAFImixpaq0NfIxid12Jvu1qOj2p0k0Lxaj/SeAQ8BRHTzbfRBlvHgY+XhdfRLkrZy9liKtdWjty3AYNdwMHgMP5uV1LGcdeCzyR61NyW1EKc+0FtlN3o8E46jufMgSxDdiSy6KqaATeD2xOfUPA1zI+i3JSsgf4DTAp4z35eE8+P6tN/XwhR+6eqoS21LE1lx21v4Gq9G03L7YRMcYY0zIenjLGGNMyThrGGGNaxknDGGNMyzhpGGOMaRknDWOMMS3jpGFMh5B0maQ5ndZhzPHgpGFM57iM4qprTNfgpGEqhaT70mBuR81kTtJzkm6VNChpraRpGV8n6easObFb0gUZnyDpFkmPZ22Ez2V8Sr5+MOssfDLjkyX9QaVuxZCkKzK+QNJjGd8gaapKjYtf5Os3S7oot10i6cd172N1zZMp9X8r97Ne0nRJHwYuBW7Jeg/vbNuHbMxrwEnDVI3PRsQ8YD6wTNKpwGRgMCLmAn8Cvl63/cSI+CBwY138WooNxAJgAXBd2rv8F/hU7uci4Na0ErkE2B8RZ0fE+4AH0n6lD7ghIs6mWHi8AFwPEBFnAVcCv5TUM8p7mgysz/08ClwXEY9RrCu+EhHnRMTeV/l5GdNWnDRM1VgmaSuwnmIgN5tivd2Xz99Jsf+oUTMZ3ESp3QHFZ+jqtBQfoFhHzKZYRXxb0jbgIYr19XSKbcTCvGq5ICKeoRTbOhARjwNExLNRTPjOB+7I2C7gb8CZo7ynFyn1Jhp1GtN12OXWVIYczllIKdbzfLq6NjuLr/e+OZTrlznyfRbwpYjob9j/EmAaMC8iDkvaB/RExG5J8yi+U9+R9CBwH82tsUfyTX+Jo0/C6nUfjiN+PfU6jek6fKVhqsRJwL8yYbybYgMO5Xtac079DPDnUfbTDyxN23MknZlOpydRakAczrmId+TzpwHPR8SdwPcoJWp3AadJWpDbTE1L70eBq2r7Bd5OMancR6l78TpJZ1BsuEfj35Qys8Z0DT7jMVXiAeDzOXw0TBmiAvgP8F5JmygV364YZT8/pwwBDeacxd8pdyrdBfxe0kaKo22tKM9ZlAnpVyiOvEsj4sWcEP9R2pK/QLkK+gmwUtJ2ytXFkog4JOkvwF8pQ11DlNKso/Fr4GeSlgGXe17DdAN2uTWVR9JzETGl0zqMMR6eMsYYcxz4SsMYY0zL+ErDGGNMyzhpGGOMaRknDWOMMS3jpGGMMaZlnDSMMca0zP8AT8FaAlSCmzkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 404.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7OOZltbEzIdQVunfl6TZTREYpT6FhZsvMbIeZ7TKz24f4PNnMHgh9vsrMykPb883sBTNrNbOfDtrnxVCdG0NfReE4odEus/0APf5UOpILeLstldruJN5zGl1T/XwGU/JTOexyaN7+XBhaKiKj0UlDw8z8wM+Ay4GZwA1mNnNQsVuABufcFOBHwA9C2zuBrwNfPk71Hx8wOaX2VE7gTHNsPMOMNY0Z+M1xzgmWDBmO9xY081rfLPz7Xoa+vrDUKSKji5crjUXALufcntB09PuB5YPKLAfuDr1+GLjEzMw51+ace4VgeMjpaqkhpbvh2HjG+qYMZma0H3uo0unKSuylJmUy6YEGuqreDEudIhJbzKx3QA/Pxv6eIa+8zNMYDxwc8L6S4GzEIcs45wJm1kRwRuLJFkH6jZn1An8Evu3cu2/bMbNbgVsBysrKPDR3FDvwBgAtaaXUdiVyqDOZSwoaw3qInIKxUA07Vj/J3JLhPUJWRIan/Pa/3hrO+vZ9/4o7PRTrcM7NO9VjeLnSsCG2Df7j7qXMYB93zs0BLgh9fXKoQs65O51zC51zCwsLC0/a2FGtcg195qc9pZj1TekAzM8OT9dUv/KcZA5bPu07Xw5rvSIyOngJjUqCKyr2KwGqjlfGzBKAbOCE05Odc4dC31uAewl2g8mJHFpHW8pYnM/P+qYMipO7KE7pCeshfD6jIX8h5e1bqG5sD2vdIhITUgd0TT0y3J29hMYaoMLMJppZEnA9sGJQmRXAjaHX1wDPD9XV1C/0oJCC0OtE4EpAnegn0tsDVRtpTS2hs9fY1pLG/Oy2iByqYNZ7GWsNvPDGmojULyJR1THgBqSrh7vzSUMj9FCP2wiue7IdeNA5t9XM7jCzD4WK/QrIN7NdwJeAY7flmtk+4D+BT5tZZejOq2TgKTPbTHChrUPAL4fb+DPK4a0Q6KA1bTxvtqTT43xh75rqVzDzQgCqNj8fkfpFJH55WrDQOfc48Pigbd8Y8LoTuPY4+5Yfp9oF3pooABxaC0Bb6ng21qST4utlekaEuo8KZ9CVkMn45k1sr25mRnFWZI4jInFHM8LjReU6SC+kKzGHrS1pzMjoICFSPz2fDytbwiL/Dh7deChCBxGReKSl0eNF5RoYv5D6nkSqOpO5OAyzwE8kadL5TN7zDCvXb+Or75+OzzfUDXIicjo83iIbVs65jNPZX1ca8aCjEY7uhJIFbGtNA2BWZmQGwY8pOy/4rW0Lb+w5GtljiUjcUGjEg+qNwe/jF7C1JY10fy/laV2RPea4ebiEFM5LfJtHNqiLSkSCFBrxoCoUGsXz2NqcxoyMdiLeW5SQjI1fwMWpu3nizRo6e3ojfEARiQcKjXhQtQFyJnCoO5XD3UnMyhyhSXdl51LStZO+rlae3X54ZI4pIjFNoREPqjbAuLN5fXdwbGHEQmPCuZjr5ZKM/TyqLioRQaER+9rroXE/jJvH67uPkpkQoDQ1wuMZ/UoWgfm4rqiSF3fUUd92as8gF5HRQ6ER6/oHwcedzZp99UzP6Ij8eEa/lCwYM5uz2U6gz/HXzYOXHBOReGNmzszuGfA+wczqzOwvXvbXPI1YV7UBgLrM6RyoX8fSko6RPX7ZuaRvuIdZY1J5ZMMhPnlu+cgeX2Q0+2Z2WJdG55tNXuZ9tAGzzSzVOdcBXEZwKSdPdKUR66o2Qu5E1tYE13+clj7CK8+WLYaedm6e0sb6A43sPxrh+SEiMhKeAK4Ivb4BuM/rjgqNWFe1EcbNY+3+BpITfExKG+GHIJYuAeDSzH2YwaMb1EUlMgrcD1xvZinAXGCV1x0VGrGsvR6aDkDxWazd38BZJTmRW2/qeLLHQ1YJ2XXrOHdSPo9uPMQJVr0XkTjgnNsMlBO8ynj8xKXfSaERy2q2ANBVMJuth5pYUJ4bnXaULYaDq7nq7PHsPdLGxoPhfcSsiETFCuCHDKNrChQasS0UGm/2lhHocywoi1JolC6B5kNcXhogOcGnORsio8OvgTucc1uGs5NCI5bVbIbMYt6o9QOwYEK0QiP4JN7M2nVcOnMMj22upqe3LzptEZGwcM5VOud+PNz9dMttLKveDGPnsm5/A5ML08lNT4pOO8bMhsR0OLiKq+ddwF83V/Pyzjounj4mOu0RGS283SIbVkMtje6cexF40cv+utKIVT0dcORt3Ng5rD/QEL2rDAB/ApQsgANvsHRqIblpiTyiu6hEzkgKjVhVuw1cL7UZ02ls7+HsaI1n9CtdAoffJKm3nSvnjuPprTW0dPZEt00iMuIUGrGqejMAG3tKAZhXmhPN1gTvoHJ9cGgtV509nq5AH0+8WRPdNonIiFNoxKqazZCczWtH0klL8jN1TGZ021NyDmBwYBXzy3KYWJDOw2sro9smERlxCo1YVb0Zxs5hY2UTc8Zn44/2M7pTsqFoJhxchZlx7cISVu+rZ09da3TbJSIjSqERi/p64fBWAmNms626mXllUe6a6le2GCrXQF8v18wvwe8zHlqnqw2RM4lCIxYd3QWBDiqTK+jpdZxdGuVB8H6li6GrGWq3U5SVwoVTC/njukoCmrMhcsZQaMSi0CD4hu7gIPjZsXKlUbo4+P1gcG2zaxeWUtvSxcqddVFslIiMJIVGLKrZBP5kXmzIozg7hTFZKdFuUVBuOaQXHQuNS2YUUZCRxL2rDka3XSIyYhQasahmCxTNYH1lS/RvtR3ILLR4YTA0Ev0+PrqwlOffOsyhxhF+OJSIRIVCI9Y4B9Wb6SyczcH6Ds6KpdCA4CS/hn3QchiAGxaV4YD7Vx+IarNEZGQoNGJN8yHoqOdA4mQAziqJtdDoH9d4I/g2L42LpxVx3+qDdAc0IC4y2ik0Yk1oOfSNgTLMYPb4rCg3aJDisyAhBQ6uPrbpE0smcKS1i6e3aYa4yGin0Ig11ZsB46XGIiYVpJOZkhjtFr1TQhKMmw8H3ji2aenUQkrzUvnda/uj2DARGQkKjVhTsxnyp7Cmqjv2uqb6lS6C6k3BlXgBv8+48dxyVu+rZ3OlnuonMpopNGJNzWY68mdS29LFnJLsaLdmaGVLoK8HqjYc2/TRc0rJSE7grpf3RrFhIhJpCo1Y0tEAjQc4mDwFgLmxeqVREnyS38AuqqyURK47p5S/bqmmSrffioxaCo1YUvMmAJsDZfh9xqxxMTYI3i89H/Ir3jEYDnDT+eU457j7tX3RaZeIRJxCI5bUBJcPeaFpLFPHZJKS6I9yg06gf5Kfc8c2leSmcfmcYu5ddYCmDj2gSWQ0UmjEkpotuIyxvFrj46xYHc/oV7oYOurhyM53bP7ihZNp6QroakNklFJoxJLqzXTmz6KxvSd2xzP6lS4Jfg8tKdJv1rhsLplexK9f3UtrVyAKDRORSPIUGma2zMx2mNkuM7t9iM+TzeyB0OerzKw8tD3fzF4ws1Yz++mgfRaY2ZbQPj8xsyg/ZSjKejqh7i0qjw2Cx/iVRv4USM09NjN8oP91SQWN7T3c87rmbYiMNicNDTPzAz8DLgdmAjeY2cxBxW4BGpxzU4AfAT8Ibe8Evg58eYiqfw7cClSEvpadygmMGnXbwfWyubeMpAQf08ZG+fGuJ+PzBbuoBg2GQ/B55hdUFHDXy3to09WGyKji5UpjEbDLObfHOdcN3A8sH1RmOXB36PXDwCVmZs65NufcKwTD4xgzKwaynHOvO+cc8DvgqtM5kbgXeobGS83FzCzOItEfBz2HZUvgyNvQ+u7nafzvS6dytK2b37yqeRsio4mXv0zjgYEPTKgMbRuyjHMuADQB+Sepc+BzQoeqEwAzu9XM1prZ2rq6Ufywn5otuKRMnj+cGvtdU/3KLwh+3//Kuz5aMCGXy2aO4Rcv7aGhrXuEGyYikeIlNIYaa3CnUOaUyjvn7nTOLXTOLSwsLDxBlXGuZjOd+TNp7XaxPwjer3geJGXC3pVDfvyV90+jtTvAz1/aPcINE5FI8RIalUDpgPclQNXxyphZApAN1J+kzpKT1Hnm6OuDmjc5lBIcBI/52237+RNgwrmw9+UhP546JpMPn13Cb1/bp4c0iYwSXkJjDVBhZhPNLAm4HlgxqMwK4MbQ62uA50NjFUNyzlUDLWa2JHTX1KeAPw+79aNF/R7oaWNL7wTSkvxMKsyIdou8K78Aju6E5uohP/4/l1VgwA+eeGtk2yUiEXHS0AiNUdwGPAVsBx50zm01szvM7EOhYr8C8s1sF/Al4NhtuWa2D/hP4NNmVjngzqsvAHcBu4DdwBPhOaU4VLMJgJXNxcwen43fF0d3H08MjWvse/e4BgRnid+6dBIrNlWxbv+JLj5FJB4keCnknHsceHzQtm8MeN0JXHucfcuPs30tMNtrQ0e1mi04XyJPH8nlY+fGSddUv7FzISUb9q2EuUP+L8AXLpzMQ2sr+dfHtvHoF8/HF0+hKCLvEAf3dZ4BqjfTmVNBW8DHnHgZBO/n88OE8487rgGQlpTAVy+fxubKJh5ce/C45UQk9ik0YkHNFqpSK4A4GgQfqPwCaNgLjccPhOVnjWfRxDy+98RbHGntGsHGiUg4KTSiraUG2mrZ2juBnLREyvLSot2i4Zu4NPh93/GvNnw+47tXz6a9O8C3/7JthBomIuGm0Ii2mi0ArGwt5qySHOJyCa6imZCad8IuKoApRZl84cIpPLqxipd3juKJmiKjmEIj2qqDd049c7SQs0rjbDyjn88H5e8JXmkc/05rILh0+qSCdP7pkTfp6O4doQaKSLgoNKKtZjOdGWU0uTTmlcbheEa/iUuh6SA07DthsZREP9++ejYH6tv5yfM7T1hWRGKPQiPaBgyCx83yIUPpX4fqBOMa/c6bXMA1C0r45co9vFXTHOGGiUg4KTSiqbMZ6vewtW8CJbmpFGQkR7tFp65wGmSMgT0veir+/31gBlmpiXz1j1sI9PZFtm0iEjYKjWg6vBWAlS3BQfC4ZgaTL4bdz0Pfyccq8tKT+OaHZrHpYCO/fFnLp4vEC4VGNNUEn6GxsrmYs+J5PKPflEuhowGqNngq/sG5xSybNZYfPfM2bx9uiXDjRCQcFBrRVLOZ7uQ8DpMb/1caELzSwGDXs56Kmxnfumo26cl+vvzQJnVTicQBhUY0VW+mOrUCnxmzx4+CK420PBi/AHY+43mXwsxk7lg+m82VTfxi5Z4INk5EwkGhES09nVC7jS19E5k6JpP0ZE9rR8a+isvg0Dpo976i7ZVzi/nAnLH8+Nmd7KhRN5VILFNoREvNFugL8EJLyejomuo35VLAwa7nPO9iZtyxfDYZKQl8+aFN9KibSiRmKTSipWo9AK91TIjfmeBDGXc2pBfCjsdPXnaAgoxkvrV8NlsONfELPR5WJGYpNKLl0Ho6kwuoJo+58biy7fH4/DB1WXAwPNA9rF2vmFvMFXOK+fFzOzXpTyRGKTSipWo9B1Kmk5zgZ9rYzGi3JrymXwFdzZ5mhw92x/JZZKUkqptKJEYpNKKhsxmO7GR9YCKzx2eT6B9lP4ZJF0Ji2rC7qADyM5L59lWzefNQM//zorqpRGLNKPtrFSeqNwKOZ5vHj65B8H6JqcE5GzueOOmqt0O5fE4xV84t5ifP72R7tbqpRGKJQiMaDgUHwdf1lI+OmeBDmX4FNB86dq7Ddcfy2WSnqptKJNaMkskBcaZqPa2p42nozGLeaLpzaqBpl4MvEbb+CUoWvOvje1cdOGkV75s5lntXH+Dzv1/HJdPHDLsJH1tcNux9ROTEdKURDYc2sDtpWvw+3tWL1NzgnI2tj0DfqV0pzB6fzdySbF54q5bqpo4wN1BEToVCY6S1HYGmA6zqmsC80jh9vKtXsz8c7KKqXH3KVXxo7jjSkhJ4eF0lvX3DHx8RkfBSaIy0UB//c00lLCjLjXJjImza5ZCQAm/+8ZSrSEtO4Kp546hu6uSFHbVhbJyInAqFxkirWo/DeNOVs2DCKA+N5EyoeB9sfRR6A6dczcxx2cwrzeHFHbVUNrSHsYEiMlwaCB9ph9ZzNLWcjq7U0bV8yPGcdT1sXxGcIT5t2SlX88G549hT18pDayu57eIpsTG3Ze1vIn+MhTdF/hgiwxADv3lnEOegaj3bbAozirNGz8q2J1LxvuC4v0eUAAAdlElEQVRaVBvuOa1qUpP8fGRBCXWtXTy9tSZMjROR4VJojKSmSmirY2Vr6ejvmurnTwxebbz9JLSe3phERVEmSybl8eruo+yuaw1TA0VkOBQaIym0su3qnolnTmgAzPsE9AVg8wOnXdWyWcXkpyfxx/WVdPac/FnkIhJeCo2RdHA1vb4k3nJlzB/td04NVDQdSs4JjgGc4pyNfkkJPq5dUEJTew9/3VIdpgaKiFcKjZF0cDX7k6eRnZlBSW5qtFszshZ9Dup3e35++ImU5aezdGoh6/Y3aG0qkRGm0BgpPZ1QvZE3eiazoCx3dE/qG8rM5ZAxFlb9PCzVXTKjiOLsFB7ZcIi2rlO/nVdEhkehMVKqN0JvNy+0T2LRxLxot2bkJSTBos/A7ueh9q3Tr87n45oFJXR09/LoxkO4U1hNV0SGT6ExUg6uAmB9X8WZGRoAC24OzhB//b/CUl1xdiqXzRzD1qpmVu+rD0udInJiZ8BEgRhxYBVHkkroJp8ZxVnRbs0JeVmB9lQtGP8RKjbeT/rSj9GWVnra9b2nooDdda38dXM1ZXlpFGefYWNFIiNMoTESnIODq1jbN5dzyvPw+2J7PGPygYciVndbchHgmLX7LlbP+dfTrs9nxrULS/np8zu5d9UBbrtoCsmJ/tNvqIgMSd1TI6F+D7Qf4aWOiWdu11RIT2ImtbkLmHToz6S3HwxLnRnJCVx3Thn1bd0a3xCJMIXGSNj/KgBr+qax+AwPDYCqgvPp8yVx9o4fha3OiQXpXDJjDJsqm1i7vyFs9YrIO3kKDTNbZmY7zGyXmd0+xOfJZvZA6PNVZlY+4LOvhbbvMLP3D9i+z8y2mNlGM1sbjpOJWftepTUhh0MJZcweP0of7zoMPYmZbJt0M2U1z1B09NSftTHYhdMKmVKUwWObqqhq1EObRCLhpKFhZn7gZ8DlwEzgBjObOajYLUCDc24K8CPgB6F9ZwLXA7OAZcB/h+rrd5Fzbp5zbuFpn0mscg72vcIGm8WCCXmxsTprDNg+8dO0po5jwfbvY33hmWfhM+OjC0tJT07gnjf2U9vSGZZ6ReRvvPwFWwTscs7tcc51A/cDyweVWQ7cHXr9MHCJBWevLQfud851Oef2ArtC9Z05GvdDcyXPtk9R19QAvf4U1k//CrktO5mxN3xLjGckJ/DJJRNo7w7w2d+t0/pUImHmJTTGAwNHLCtD24Ys45wLAE1A/kn2dcDTZrbOzG493sHN7FYzW2tma+vq6jw0N8bsC45nvN43k/MrCqLcmNhSOfZS9hcvY87O/yaneUfY6h2Xk8p1C0vZXNnIVx7erIFxkTDyEhpD3R86+LfweGVOtO/5zrn5BLu9/s7Mlg51cOfcnc65hc65hYWFhR6aG2P2vUKbP5ua5AnM1XjGu6yZ+U90J+Vw3qbb8QfC91S+meOy+cf3T+exTVX85LldYatX5EznJTQqgYGzsEqAquOVMbMEIBuoP9G+zrn+77XAI4zSbiu3/xXWuuksmVRIgsYz3qU7KYfX53yH7NbdLHnzX4JjQGHy+fdO4iPzS/jRs2/z542HwlavyJnMy+S+NUCFmU0EDhEc2P7YoDIrgBuB14FrgOedc87MVgD3mtl/AuOACmC1maUDPudcS+j1+4A7wnJGsaR+L9Z4gOd7LuICdU29w+AJhAeLLmZC9ZP4eruoKrwgLMewxf+X7354Ngcb2vm/D24iIzmBS2aMCUvdImeqk/7TNzRGcRvwFLAdeNA5t9XM7jCzD4WK/QrIN7NdwJeA20P7bgUeBLYBTwJ/55zrBcYAr5jZJmA18Ffn3JPhPbUYsOcFAF7um8P5UxQaJ1JdcB5HsudQWvsCY8J4G25ygp9f3biQmeOy+MIf1vPariNhq1vkTORpGRHn3OPA44O2fWPA607g2uPs+x3gO4O27QHOGm5j487u56lPKKIreRITC9Kj3ZrYZsae8R/C19dNeU3w3w+H88PTY5mZksjdNy3i+jvf4DO/W8s9tyxiwYRTuJOtYX9wombVBqjbEfwKdID5ITEVMoogazwUzYDMcXCmLX8vZwStPRUpvQHc3pW8GFjA+dMLzrznZ5wCZ352lXyEKZV/pLzmSZJ7Gjgw5jKw0x8Lyk1P4p7PLOK6X7zBp3+zhvs+u8TbRMu6HbDpfnjrr3AkdIdXUgYUTguGRGJK8GmEPe3QeCAYKG/9BdIKoPwCKFscXNlXZJRQaERK1Qass4nnumfz/oo4vOsrSpwvgZ2l1zKh5mmKj64ivaOa3eOvpjtp+HeeDbVa77ULSrjz5T1c+z+vc/P5Exk/xBMUE3pamVDzJJMrH6GgcTN95qc2byGHZnyVmvxFNGVMAfMx+cBD755709kMtVuDS+FvewR2PQNTl0HZueDTQooS/xQakbL7eRzGG8zmuwqN4TEf+4uX0ZZaTHn1E8zZ/T8cKnwvh/PPwdnp/eHNSUviM++ZxK9e2cNdr+zhxnPLKS9IB9dHUf06Jlc+QmnNMyT0ddKYMZn107/MvnFX0JnscUwqJSsYEGXnQsM+2P4YvPkwVK6GeR+HDA3ES3xTaETK7ufZ6Z/MlAllZKclRrs1celIzlm0pJVSXv0EEw4/zZiGtVQVnMeR7Lk436n/r5uXnsRnL5jEr1/dy8uvreQ9pds4u+lZMjsq6fGns3f8lewpuZqj2XNOb1witxzOvS3YZfXmw7DyhzDnWigdlXeXyxlCoREJbUdxlat5oucqLtUtnqelKymPHWUfI6d1J+NrX2JS1V8oPfwcR7Nn05A1nZbUMtwwun38vZ3kN25hTv0arkt+hvzALnqrjX2Z89ky94scHHspvf4wPsjJDMbPh/zJsOH3sOleaDoIM69Sd5XEJYVGJOx8GnN9PNs7nx/PKIp2a+KfGY2ZU2nMqCCrbS+FDRsoaljP2Po19PqSaEsppi21mM6kXHoSMun1J+PwMeboKpK6m0jtqiOzfT+5zTvIb9yC3/XgMOpy5/Ha+K/y/QPTePNoCteWl3JWOANjoJRsWPx52L4C9r4EHQ0w/1PgT4rM8UQiRKERCTsep96fT3veLCYVZkS7NaOHGc0Zk2jOmMTe3i6y2/aR1baHjI5DjKlfi8+9c7XcmfvuPva6x59GU8YUdpR/nNq8hdTlzqMnMTi4flVZLy1v7OfBtQfp6OllyaT8yLTf54dZVwfvrNr6J1j1P7Doc5CQHJnjiUSAQiPcAl243c/xdPcSLp4/NtqtGbX6/Mk0ZE2jIWtacINzJAZaSQy04u/rwlwfh8ZcTHdiFp3J+XQm5R93fCIl0c+nzyvnvtUHWLGpivbuXi6aVhi526QnXgDJ6bD+HljzS1h0q644JG4oNMJt38tYdxtP9c7ncxrPGDlm9CRm0pOYeWxTbf45nndP9Pv4+OIJ/Gl9Jc9uP0xHd4DL5xTji1RwjJsfXGdrw+9hzV1wzmcUHBIXtIJeuL31ON2WzNupZ3NOuZ6fEU/8PuMjC0o4d3I+r+4+yp/WH6K3L4LLqo9fAGfdAEd2wtpfQ294HkYlEkkKjXDqDdC37c8813s2l86dgN+nWeDxxmfGlXOKuWRGEesPNHDf6gP09PZF7oCli2DudVD3Fmz8PbgIHkskDBQa4bTvZXztR/hzYAlXnjUu2q2RU2RmXDJ9DB+cW8y26mbufm0fXZF8AmDZEpjxIajeGJwMKBLDFBrhtPVPdFoqb2UsZkFZbrRbI6fp3MkFfHRhCfuOtnHXK3tp64pg99Gki4JrVe15Afa8FLnjiJwmhUa4BLrp2/YYT/XO59K55fjUNTUqzCvN5ROLJ3C4uZM7X95DS2dPZA5kFrwdd+xc2PYoVG+KzHFETpNCI1z2vIivs4EVgSV8UF1To8r04ixuOn8iTe09/PLlvTR3RCo4fHD2J4LLj2y4Bxr2RuY4IqdBoREuG39Pk2VxMO9c5pboWeCjzcSCdG46v5zmzh5++fIemiIVHP6k4O23KTnBW3Hr90TmOCKnSKERDm1HcG89zkM95/PRxZP17IxRakJ+OjedV05rV4BfvryHI90RmuaUlB6cKe4c/OGj0F4fmeOInAKFRjhsuh/r6+FP7mI+PL8k2q2RCJqQn85N50+krSvAHTvKONQeoV+hjMLgFUfjfnjgkxDoisxxRIZJoXG6nKNv/d1sooIps88hL12zeke7srw0bj5/Ii29fq57MZeDbRH6NcqbBFf9HPa/Aiv+PnjlIRJlCo3Ttf81fEfe5g89F3LDorJot0ZGSGleGv9ccZDmHuP6l3I50BqhX6U518BF/wyb74eXfhCZY4gMg0LjNLnXf0qzZbEl5zKWTNKyIWeSyemd3Lu0kbZAhINj6ZeDT/178XvB55WLRJFC43Qc3Q07nuA3PZdw43tnaAD8DDQ7N8AfljbS3mtc91Iu+1oj8GAlM7jy/4eJS+HPt8G+V8J/DBGPFBqn4/Wf0UMCT6ZeydXzx0e7NRIls3IC3Lu0kc5e4/qXctjbEoHgSEiCj94THOe4/+NweFv4jyHigULjVLXU0LfhD/wpcD4fvmA+yQl6dOeZbGZOgHuXNtAdCo49kQiO1Bz4+EOQkAL3XA31mvwnI0/P0zhVK/+dvt4e7k74CA8t1gB4LJp84KERPd6MnF7ue28DH1uZy/Uv5XDfexuZnBnmhQ5zJ8CnHoXfXA6/Ww43PwVZxeE9hsgJ6ErjVNTvpW/tb7kvcBEfuOBcMpKVvRI0LbuX+5Y20OeCVxy7miNwxVE0Az7xR2g/Grzi0OQ/GUEKjVPQ98J36XE+Hkq7ns8unRTt5kiMmZodvOJwDq57KZctDRH4R8X4BXDDfcFlRu65CtqOhv8YIkNQaAzX3pfxbXmQuwLLuOUD55GSqLEMebeKrF4evLCRFL/j+pdyePlwYvgPMnEpXP8HqNsBv70CWmrCfwyRQdSvMhyBbnof+z/UUMTK4k9zv1azlROYlNnLny5q4MZXsrn5lRx+eE4zy8vCvBxIxWXwsQfhvhuC4xyfWgE5pcOu5t5VB8LbriF8TGN/o4KuNIbBvfxD/PU7+ZfAp/nG1Qs1L0NOakxqHw+8t5Gz83v4h9XZ3PV2avgPMum9wcHxtqPB4KjbEf5jiIQoNLza9yq89O/8qfc9LLzsemaN0/Ln4k12kuN3FzSybHwn396cyT+tz6A73I8CL10En34MAp3wy0tgx5NhPoBIkELDi7YjBB66mf1uDI8Wf4nPXqDBbxmeFD/8bEkzn5vaxh/2pPGJlTnUdob516/4LLj1RcifDPddDyt/qEUOJewUGifT3U7P7z9Kb9tR/inhS3zvY+fh16Nc5RT4Db42t40fL2pic0MiH3gmj5U1YV4VObsEbn4yuNDh89+CBz8VtVtynXN0dPfS2N7NkdYu9tS1cqixg6b2HpzCLG5pIPxEAt0EHrwJf/V6vtT7Jb76mY8yPicCfdJyRlle1sWM7HpuW5XNp17J4eYp7Xx5ditp4fptTEyFD/8y+Lzx5+6AA2/AlT+CGVeG6QDv1OccdS1dHG7upKa5k8PNwdfNHT0E+v4WDv/5zNvHXif5fRRlJVNRlMH04iymj81k2thMKooy9Y+yGKfQOJ7uNrrv/QRJ+57nG4GbWP6xzzG3JCfarZJRYmp2L3++uJ7vbsng17vSeLY6mW+f3cLSsd3hOYAZnP/3MPliePTz8MDHYc618P7vBR/wdJpauwLsPNzCztpWdtW20toVAMBnkJ+RzLicVGaNyyIjOYHURD8JfuO8yQV0BXpp6QxwpLWb6qYOdtS08MquI/T0BsMlMyWBxRPzOW9yPudOzmfamEx8CpGYotAYSuNBOu/9JIm1m/h632e56BNf4eLpY6LdKhllUhPgW2e3cmVJF7evy+RTr+RwSXEXX5vTypSsMC0/MnY2fPYFePk/YeW/wY4nYMkX4bzbIMX7zRyBvj4OHG1nZ20rOw+3UNXUCUBakp+KogymFGUwLieVwoxkEvxD93pfdfbQi3p2B/rYc6SV7dXNrN5bz2u7j/Ls9sMAFGQksXRqIRdOK2JpRQE5aXrIWbRZPPUtLly40K1duzZyB3AOt/VRuh/9e3p6evi67zY+edPfMb8sN3LHHKZVD/1HtJsgAyyeGJ5nqHT1wm93pfFf29NoDxhXlHbxd9PbmH7Jp8JSPwBHdsIL34Gtj0BqLpz/DzD/RkjLe9c8DeccR9u6j11N7Klro7u3D59BWV46U8dkUFGUSXFOCj6Pt54PZ57GocYOXt99lJd31vHS23U0tvfgMzi7LJcLQyEya1xWvF2FxFVjj0eh0a96M00rbie7+lU2903knvH/wlduuJyirJTIHO8UKTRiS7hCo9+RTuOunWncszuVtoCPc8pzuWFRGe+bNTZ8a5xVbQwOku96FvzJMHM5z6Rdzu7Uuew72s6eI23sPNxCQ3sPAHnpSVQUBUNiUmH6Ka+CcKqT+3r7HJsqG3lxRx0v7ahl86EmnIvLqxCFxkgLe2gEuuje/iSNL99JUe0rNLk07vTfQNn7buOjiyfG5OQ9hUZsCXdo9GvsNh7Ym8p9VUXsO9pOUoKPpRUFXFBRyJJJ+VQUZZzWv7JbuwIc2LYa/8bfUXbwMVL7Wjnk8nmxdx4rbT6H8xZRNraQiqIM8jOSw3JO4ZoRfqS1i5Vv1/HijjpW7vzbVciscdksmJB77Gtc7N20Ent/UE6Bp9Aws2XAjwE/cJdz7vuDPk8GfgcsAI4C1znn9oU++xpwC9AL/L1z7ikvdQ7ltEOjt4eew9up2/4KPTtfoKD2NdL7Wql1Ofwp8UqSl3yW65fOITUpdteTUmjElkiFRr+++Z9m3YEGnthSw9Pbaqhs6ACCYwnTx2YyqTCDktxUxmSlkJOaSGqSH0ewe6mvD3p6+zjS1k1tcye1zV3UtnSyu66NA/Xtx44xJqWPj6at5RJbw8yO9ST1ddBriTRmTeVo9mzqs2bRmDWVlrQSehJPfVJrJJYRGXgVsmZvPRsPNtLRExwPKspMZtrYTKaOyWTamEymjAn+typIT45Wt9aZERpm5gfeBi4DKoE1wA3OuW0DynwRmOuc+7yZXQ9c7Zy7zsxmAvcBi4BxwLPA1NBuJ6xzKMMOjSM74fWf0XDobQJHdpMXqMVPcCpujctlrf9s6iZ8gMmLr+Q9U8fGRf+oQiO2RDo0WHjTO94erG/njT1H2VrVzPbqZvYfbedwS+dJ5/D5DAoykinKSmZCfjozxmYyfWwW08ZmUpKbyn2rDwbL9XZT2LCO4iOvk9e0lbzmbSQFWo/V052QSUtaKZ3JBXQm5dGVlEtXYja9/hR6fUn0+ZPp9SXR60vG6MP6evG5Xnx9XZxbkgxZ4yN26y8EQ/Kt6hbW7a9n86Emdh5uZWdtC509f5uCn+T3MTY7heLsFAoykslKTSQ79JWe7CfB5yPBbyT6DZ8Z3YE+OgN9dPX00hXoY25JNhdUnNIdaLH/B8YDL52ki4Bdzrk9AGZ2P7AcGPgHfjnwzdDrh4GfWrBvZzlwv3OuC9hrZrtC9eGhztPX3QrbHsVSSnjTptGReymBvArSJy1m0tQ5XFGQHpNdUCLHU5qXRmleGtcO2NYV6KW+rZvG9h7au3vxGfgs+AfP7zMKMpLIz0j2NP+hz5/E4YJzOVxwbnCD6yOzbT/ZbXvIaK8ko/0gGe2VpHbVkdv8FsndDfhdj7fGbwEq3hfR0Ej0+5hTks2ckr9dEfX2OSob2tl5uJWqpg4ONXZQ3dhJVWMH22uaae7ooamj59htvydz8/kTTzU0RgUvoTEeODjgfSWw+HhlnHMBM2sC8kPb3xi0b/99dyerEwAzuxW4NfS21cz6V2MrAI54aD+w31ux+DCM8x5VzsTzLoCbR9k5PwyfOGl4xfTP+l9CX6fgSefcsrA2Jgq8hMZQP+HBkXy8MsfbPtSN3EPGvHPuTuDOdzXKbK1zbuFQ+4xmOu8zx5l4znDmnne88LL2VCUwcIH+EqDqeGXMLAHIBupPsK+XOkVEJMZ4CY01QIWZTTSzJOB6YMWgMiuAG0OvrwGed8ER9hXA9WaWbGYTgQpgtcc6RUQkxpy0eyo0RnEb8BTB22N/7ZzbamZ3AGudcyuAXwH3hAa66wmGAKFyDxIc4A4Af+ec6wUYqs5htv1dXVZnCJ33meNMPGc4c887LsTV5D4REYkuPU9DREQ8U2iIiIhncRcaZvZNMztkZhtDXx8Y8NnXzGyXme0ws/dHs52RYGbLQue2y8xuj3Z7IsXM9pnZltDPd21oW56ZPWNmO0PfY2fp4VNkZr82s1oze3PAtiHP04J+EvrZbzaz+dFr+ek5znmfsb/X8SbuQiPkR865eaGvxwFCS5ZcD8wClgH/HVoCZVQIncvPgMuBmcANoXMerS4K/Xz779e/HXjOOVcBPBd6H+9+S/D/1YGOd56XE7z7sILgZNefj1AbI+G3vPu84Qz8vY5H8RoaQzm2ZIlzbi8wcMmS0eDYci7OuW6gf+mVM8Vy4O7Q67uBq6LYlrBwzq0keLfhQMc7z+XA71zQG0COmRWPTEvD6zjnfTyj/fc67sRraNwWukT/9YBuiqGWOxn6UWHxabSf30AOeNrM1oWWkQEY45yrBgh9L4pa6yLreOd5Jvz8z8Tf67gTk6FhZs+a2ZtDfC0neFk+GZgHVAP9y756We4kno328xvofOfcfIJdMn9nZkuj3aAYMNp//mfq73XciclnhDvnLvVSzsx+Cfwl9Ha0L00y2s/vGOdcVeh7rZk9QrA74rCZFTvnqkPdMrVRbWTkHO88R/XP3zl3uP/1GfZ7HXdi8krjRAb1414N9N+BcbwlS0aLM2LpFTNLN7PM/tfA+wj+jAcuVXMj8OfotDDijneeK4BPhe6iWgI09XdjjQZn8O913InJK42T+Dczm0fwEnUf8Dk48ZIlo8HxlnOJcrMiYQzwSOg5JwnAvc65J81sDfCgmd0CHIB3PFIiLpnZfcCFQIGZVRJccfv7DH2ejwMfIDgQ3A7c9K4K48RxzvvCM/H3Oh5pGREREfEs7rqnREQkehQaIiLimUJDREQ8U2iIiIhnCg0REfFMoSFyHGZ213AXhTSz1ki1RyQW6JZbkTAys1bnXEa02yESKbrSkJhiZo+GFirc2r9YoZm1mtl/mNl6M3vOzApD2180sx+Y2Woze9vMLght95vZv5vZmtACeJ8Lbc8I7b8+9LyO5aHt6Wb2VzPbFFrj7LoB9S8c0IbvhMq8YWZjQtsnmtnroWN9a9C5fGVAG/41tO2c0PuU0HG3mtnskfmvK3L6FBoSa252zi0AFgJ/b2b5QDqwPrSI4UsEZxD3S3DOLQL+94DttxBcZuMc4Bzgs6ElKDqBq0P1XAT8hwWnni8DqpxzZznnZgNPDtGudOAN59xZwErgs6HtPwZ+HjpWTX9hM3sfwSUvFhFchG+BmS11zq0huDTGt4F/A37vnHsTkTih0JBY8/dmtgl4g+BCdRVAH/BA6PPfA+8ZUP5Poe/rgPLQ6/cRXKdpI7AKyA/VY8B3zWwz8CzBJbbHAFuAS0NXLRc455qGaFc3f1tEb+CxzgfuC72+Z0D594W+NgDrgemhNgDcAVxGMBj/7YT/NURiTDyuPSWjlJldCFwKnOucazezF4GUIYoOHIjrCn3v5W//Pxvwv5xzTw2q/9NAIbDAOddjZvuAFOfc22a2gODaTt8zs6edc3cMOmaP+9sA4MBjDW7PscMB33PO/WKIz/KADCAxdH5tQ5QRiUm60pBYkg00hAJjOrAktN0HXBN6/THglZPU8xTwBTNLBDCzqaEVc7OB2lBgXARMCH0+Dmh3zv0e+CEwnOdvv0pwxWGAjw9qw81mlhE6xngz63+g0p3A14E/AD8YxrFEok5XGhJLngQ+H+o+2kGwiwqC/xKfZWbrgCbgupPUcxfB7qP1oTGLOoKPTf0D8JiZrQU2Am+Fys8B/t3M+oAe4AvDaPM/APea2T8Af+zf6Jx72sxmAK+HVuxtBT5hZsuAgHPuXgs+6/o1M7vYOff8MI4pEjW65VZinm5jFYkd6p4SERHPdKUhIiKe6UpDREQ8U2iIiIhnCg0REfFMoSEiIp4pNERExLP/B4LpVfMbChX+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 404.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0NNSwbOF+8r1HOFZ0AT988Sj1nTm8v6KRDVPasQ+6E5XjCFPu7ue8Qi9dQTu/rS/hmeYiXmnP59bKU6wv7k7ehxhsxiqY+xZ4+X/hvI9ClifZFSmVlnRMQw0rr6cGuwnyu5NFNHX7+Kd5J7l26psDY6h8R4gPVTbx30tqKHcH+E7NDL59tJzeUIr8Nrv081Y/qu0/S3YlSqWtFPnTrFLNwCD45t7pvGfdLNYUeqM+doY7wL8vPM7N5c1sbs/jzn2zOdrjilep0Zt1IcxaD5vugaA/2dUolZY0NNSwfHW78BsHsxcuZ8HUvHEfbxe4YXorX1lYS9AIXzo4i41N1oB6Ul3yOehugB2/THIhSqUnDQ11ho7eAM6WfdTaK7lk0fSzOtfC3D7uXlzDsrxefnxiGt+uKaerP4nTc+deBjPWwEvf1mdtKDUBYpL+T7/orVmzxmzdujXZZaSHrT9hc83Eurt++2g59/T+M/78OdRXXheTcsIGHmss5sGTZczMCXHv+V0sLQrG5Nzj1rgXXvuh9cyNyvNH33fNhxJTk5oMUngxU/T0SkO9yf7ubA61G6ZIJ2FP7Nqh2wSun9bGvy2sxR8Wbny+iF8cyU7O7aopi6FgptWPKpxC04KVSgMaGuq0sIGf1U3lPGcNEJ9naCzK7eOJK9q4oCzAl3bk8anN+XQn+naVCCzYAL2tUPdaYt9bqTSnoaFO29aZS02vm3cV7geg1x2f54KXuAw/uaiTzy/18qeTLq57toi9HQleMqRXG0pNSFShISIbROSgiFSLyJ3DvO4SkYcir28WkdmDXvtCZPtBEbl60PZ/EJG9IrJHRB4QkTN7U6iEevRUMWVZAZbYagk4cgk6cuL2XjaB2xf18sAlHfQGhRueK+Ln1dmEE3W7SgQWXG1dbZzUcTKlojVmaIiIHbgXuAZYDNwiIouH7HYr0G6MmQ98C7g7cuxi4GZgCbAB+J6I2EVkBvD3wBpjzFLAHtlPJckBbzaHejy8bWobOf7GuF1lDLW2rJ8nrmjj/LIAX96Zxw3PFfF6e4KuOqYsgYIKOKxXG0pFK5orjbVAtTHmqDEmADwIXD9kn+uBgWW2jwCXi4hEtj9ojPEbY2qA6sj5wGphki0iDsAD1J/dR1Fn47FTxeTaQ7yluI1sfzO9rsSEBkCp2/Czizr51nmdnOy1cd2zRfz95nxquu3xfWMRqNoAvS16taFUlKIJjRnAiUE/10W2DbuPMSYIdAIlIx1rjDkJfAOoBRqATmPM08O9uYjcJiJbRWRrc3NzFOWq8ar3OdnWmcvVU9opDLZgM6GEXWkMEIEbZvl5bkMbty/q5Zl6F1c8XcynNufH98pj6hLI16sNpaIVTWgMN7Vl6J3nkfYZdruIFGFdhcwByoEcEXnvcG9ujLnPGLPGGLOmrKwsinLVeD3fUogAV5W14/E1AvGZORWNfKfhc0t7+Ms1rXx4fh/PN2Tx9meLuf5Za4puZyDGM61Oj220wMltsT23UhkomtCoA2YO+rmCM28lnd4ncrupAGgb5dgrgBpjTLMxph/4HXDhRD6AOjshA39tLWBVgZdCZwiPv5Gw2PC5SpNa1xR3mC8u9/Lyta18eXk3/jB8aUce5z1eyh2v5vN8QxbBcIzebOpSyCuHo8+R/D4nSqW2aELjNaBKROaISBbWgPWjQ/Z5FPhA5Pt3As8Za6n5o8DNkdlVc4AqYAvWbanzRcQTGfu4HNh/9h9HjdfOzhw6gg4uK+0EwONros9VhpE4jydEKd9p+HBVH3+6op3HL2/j3XP7eLkpiw9tKuT8J0r42q5c9rQ7zu7vehGrvUj3KWg+EKPKlcpMY94sNsYEReSTwEasWU4/NsbsFZGvAluNMY8C9wO/EJFqrCuMmyPH7hWRh4F9QBC4wxgTAjaLyCPA9sj2HYA+izMJnm8tpMARZGWB1cXW42ukM2dOkqs6kwgsLQqytMjLv5zr5fmGLH5f6+bn1dncf9jDooJ+Pji/j3dU+nBPJO9mrIIDj0PNCzDlnFiXr1TGiGqE0RjzJPDkkG1fHvS9D7hphGPvAu4aZvu/Af82nmJVbHX129nekcs1U9twCDiCvWQFuxM+CD5eWTa4ekaAq2cEaPcLj9e5eaDGzZ3b8vnvPbncsaiH987rI2s8S1dtDph9MRx8ArrqIb88bvUrlc50Rfgktqk9nxDCZSUDt6aSOwg+EUUuw/vm9fHE5e08cEk7iwqCfHVXHldsLOGFU1njO9msC8GeZV1tKKWGpaExib3ankdlto+Z2QEAPP6B0JiWzLImRAQumNLPLy/u4GcXdZBlM3zwpUK+uD2X3mib6WblQMVaaxaVryuu9SqVrjQ0Jqn2fjsHvdmsK3zjGd7ZviYCjpy4tg+JNxG4dFqAx69o46NVvfz6aDY3Pl9EQ2+Uv9XnXArhMBzfFN9ClUpTGhqT1Jb2PAzC+UVvhEaOr5G+BK4Ejye3Hb643MtPL+qkrsfODc8XsS+apoi5ZVC6AOq2gInVnF6lMoeGxiS1uSOPGW4/FZFbU5gw2f4melJ8EHy8Lp0W4OHLOgB4918LOdQZxdSqynXQ1w4th+JcnVLpR0NjEurqt7Ov2/PmW1P+VmwmRF+GhQbA4sIgD1/ajtNmeN+LhZzoGeO3/dRzwemB2lcTU6BSaURDYxJ6rSMXg7CuaHBopN/MqfGozA3z84s66AsJH3ixcPQHP9kdULEGTr0OPa2JK1KpNKChMQm91pHH1KwAs7L9p7d5fI2EsdGXlbn9vc4pDHHfhZ0c77Fz57a80VeRzzwfTAh2P5Sw+pRKBxoak0wgLOzp9rCq0IsM+se2x9eIz1WKsaVG+5B4Ob+sn39a0sMTdW5+cSR75B3zy6GwErb/XPtRKTVIgp+xqZJtb7eHfmNjRX7Pm7Z7fE1058xKUlWJ9bGFvWxpcfL/dueyrizAwoIRWqLPPB9efxj+/BUomh27AtZ8KHbnUirB9EpjktnRmYvLFmZxXu/pbfZgH65gV8q3D4kVm8A3zusix2H4l+15Iz9itnyV1V7k5PaE1qdUKtPQmESMsbraLs3rIcv2xt+UngwfBB9OicvwxXO9bGvN4tdHR3g8vdMNUxZDw05ds6FUhIbGJNLgz6IxkMXKgqG3piKh4Uq/9iFn429n+biwLMDde3Jp6hvhj8L0FeDvgraaxBanVIrS0JhEtnda7UFW5HvftN3ja6Tf7qE/jduHTIQI3LWqG19Q+Na+ET771CVgc1pXG0opDY3JZEdnLhVuP2WuN3fw8/iarPEMifGjVNPAnLwQ75nXx8PH3BzpHmbmmMMVuUW1S29RKYWGxqQRCAsHvdksHzJrChPG42+aNIPgw/nkoh7cdsP/7BnhaqM8couq9UhiC1MqBWloTBIHvdn0GxtLh4SGO9CGzQQndWiUug0fqerjyZNudrYNMwt9ymLrORt6i0opDY3JYk+3BzuGc3L73rT9jUHwyRsaAB9d0EtxVpj/3T/M1cbgW1ThEdZ0KDVJaGhMEnu6cpiX00e2/c335T2+RgxCn6s0SZWlhlyn4QPze3m2wcXB4Trhlq+EgBfa9BaVmtx0Rfgk0BO0caTXzY3Tz2y+5/E10ucqxdgS91thc01bwt5rPN4/T/i/gzn84JCHb57X/eYXyxZZC/0a91rP21BqktIrjUlgn9eDQVia13PGa5N9EHywIpfhljl9PFrr5uTQJ/05XFBSBU37klOcUilCQ2MSeL0rhywJU5Xje9N2e6gPV3/npB/PGOwjC6z2Kvcf8pz54tQl0NMM3qYEV6VU6tDQmAT2dHtYlNeL0/bmJksen/WX32RqHzKWck+Yayv8/Oa4m97gkBenLLa+Nu1NeF1KpQoNjQzX2W/npM/F0kENCgecnjnlnlztQ8byvnl9dPfbePTEkJ5UnmLImw6NeotKTV4aGhnugNe6zXJO7vCh0W/Ppt+Rm+iyUtrqkn4WFfTz8yPZZz5KY+oSawZV/5n/PZWaDDQ0Mtx+bzYuW5i5Ht8Zr3n8jZO2fchoRKyrjX0dTnYMXew3ZYnVTqT5YHKKUyrJNDQy3P5uD1U5fTiG/p82YbJ9TToIPoJ3VPrJdYT55dCn+xXNAmeONfVWqQQRkRwReUJEdonIHhF5l4isFpG/iMg2EdkoItNFxCEir4nIZZHjvi4id8WyFg2NDNYTtHG8zzXsrSl3oB37JG8fMpoch+GGWT6eqHPT1T/oSkxsMOUcaNqvDQxVIm0A6o0xy40xS4GngP8F3mmMWQ38GLjLGBMEPgh8X0SujBz377EsREMjgx3sycYgnJPXd8ZrbwyCa2iM5J2zfPjDwhMnXG9+YeoS6O+B9uPJKUxNRq8DV4jI3SJyMTATWAo8IyI7gX8FKgCMMXuBXwCPAR82xgRiWYiGRgbb3+3BIWGqcoYPDat9SFkSKksP5xYFqcoP8tvjQ2ZRlS4ABFp0XEMlhjHmELAaKzy+DvwtsNcYsyLya5kx5qpBhywDOoCY/6tQQyOD7fd6mJ/je9OjXQd4/I34XCUJbR+SbkSsp/ttbc2iZvCzNrJyoKACWg4lrzg1qYhIOdBrjPkl8A1gHVAmIhdEXneKyJLI9zcCJcAlwHdEpDCWtWhoZKjeIBztcQ87ngGQ7WuiRwfBx3RDpQ8bht8NvdooWwjtxyB45qw0peJgGbAlcivqi8CXgXcCd4vILmAncKGIlAL/CdwauTr5LnBPLAvRf2ZmqJ1tTkIIC3PPvDVlD/lw93fQXLQqCZWll6nZYS6eGuC3x938w5IebANj4qULofrP0FoNU5cmtUaV+YwxG4GNw7x0yTDbTnfUNMZ8J9a16JVGhtrW6kQwLBhmPCM70j6kRwfBo3LjLB/1fXa2tTrf2Fg0x3p2eLPeolKTi4ZGhtra4qTC7SfHcea00JzIzKk+7TkVlSvKA7jthscGz6KyO6Bkng6Gq0lHQyMDhcOG7W1OFgxzawog299I0O4m4MhPcGXpKcdhuHy6nyfr3AQHZ3DpQvA2Ql9H0mpTKtE0NDLQ4SYv3f22YcczwJpu2+vS9iHj8faZflr8Nl5tHnSLqixy61ivNtQkoqGRgbYdbwdg4TDjGRijD16agMumWW1FHhvc+TZvOmTl6tRbNalEFRoiskFEDopItYjcOczrLhF5KPL6ZhGZPei1L0S2HxSRqwdtLxSRR0TkgIjsH5hvrM7e1uNtlLrCTHX1n/GaK9COPdyvoTFObjtcWR7gTyddBAZuUYnNukXVcogz2+EqlZnGDA0RsQP3AtcAi4FbRGTxkN1uBdqNMfOBbwF3R45dDNwMLMHqgfK9yPnAmjv8lDFmEbAc2H/2H0eBdaWxqqR/2LtPHr+2D5mot1X46Oq3sakx642NZQvA3w3dDckrTKlxEJGQiOwc9Gv2eI6PZp3GWqDaGHM08oYPAtcDg59Ecz3wlcj3jwDfFRGJbH/QGOMHakSkGlgrInux5hd/ECDSGyWm/VEmq+ZuP8dbe3nPsuH/cw60D+nV9iHjtn5qgBxHmKfrXbxleuS/b8l862vrEcgvT15xKi3NvvOJ22J5vmP/ee19UezWZ4xZMdH3iOb21AzgxKCf6yLbht0n0mWxE2sZ+0jHzgWagZ+IyA4R+ZGI5Az35iJym4hsFZGtzc3NUZQ7uW2vtcYzVpeceWsKrNDwZZVgbM5hX1cjc9vhsmkBnqnPIjxwNyq7GLKLoK06qbUplSjRhMZwU2yG3sAdaZ+RtjuAVcD3jTErgR7gjLESAGPMfcaYNcaYNWVl+q/jseyo7cBpF5YUDX3AtcXja9Rngp+Fq8r9tPjtbzycSQSK51lXGjquodJD9qBbU78f78HRhEYdVhveARVA/Uj7iIgDKADaRjm2DqgzxmyObH8EK0TUWdpR287i6fm47We+Zg/5cfd36HjGWbhsWgCHGJ4+OWihX8l8CHitNRtKpb6+Qd1xbxjvwdGExmtAlYjMEZEsrIHtR4fs8yjwgcj37wSeM8aYyPabI7Or5gBVwBZjzCnghIgsjBxzOW8eI1ETEAyFef1kJysri4Z9PdtvtQ/R0Ji4gizDBWX9PF3veuPC4vS4ht6iUplvzNCIjFF8EqtZ1n7gYWPMXhH5qohcF9m4cE/AAAAgAElEQVTtfqAkMtD9WSK3miIPA3kYKxCeAu4wxoQix3wK+JWI7AZWAP8Ru481OR1q9NIbCLFi5vCdkE8/eEm7256Vq2b4qfE6ODLQLt1TAu4CDQ01KUTV5dYY8yTw5JBtXx70vQ+4aYRj7wLOeEatMWYnsGY8xarR7TxhtbNYWVkINWe+7vE1ErS5CTi1fcjZuLLcz5d25LGx3sX8/F5rXKNkPjQftMY1dKW9ymDaGj2D7Khtpzgni8piz4ih0eueon+pnaVp2WGWF/Xz9EkXdyyKPK+keD6c3AY9TZCrV3IqOlFOkY0pY0zu2RyvbUQyyM4THayYWYgMFwraPiSmrprhZ1e7k1N9kT9COq6hJgkNjQzR5eunutk74niGq78DezigoREjV5f7AXimPjKLKqcUXPkaGirjaWhkiN0nOjEmMp4xDB0Ej615eSHm5gZ5uj7SUkTEer6GrtdQGU5DI0PsPGGtBD+3YuTQMECfWxdIxoKINSD+SlMWnYHI7cCS+eDvgt6W5BanVBxpaGSIHbUdzJ+SS0H28O1BBtqHhG1Zw76uxu+qGX6CRnjhVOS/adEc62vbMLMQlMoQGhoZwBhzehB8JB6/tg+JtZXFQUpdoTfGNfKmgcMN7RoaKnNpaGSAE219tPYERhzPsIUCuAPtOggeYzaBt04P8JfGLPrDWM/XKJqtVxoqpYmIEZFfDPrZISLNIvJ4NMfrOo0MsCMynjHiSvCB9iE6CD6qzTVt4z5mpi1Ad38Fv9jjY0leL+UyjZneA2w9fJKQI3vYY46EaqM+/7vXVY67JpVGvlIQ09bofKUzmnUfPcBSEck2xvQBVwIno30LvdLIADtqO8h22lk4NW/Y10/PnNIrjZg7N78Hh4TZ3ml19vd6rP6ceX0nRjtMqWT7E3Bt5PtbgAeiPVBDIwPsPNHBsooCHPbh/3da7UNcBJwFCa4s87nthiV5vWzvsBbZerNnEMZGbq+GhkppD2I1k3UD5wKbx9j/NA2NNOcPhthX3zXieAZAtl/bh8TTqgIv9X4XDT4nYVsWvdnTyNPQUCnMGLMbmI11lfHk6Hu/mYZGmttX30UgFGblSDOnjMHja6JPxzPiZlVBDwDbO62rje7smeT21SPh0GiHKZVsjwLfYBy3pkBDI+3tqB3obDv8MzSy+jtxhP306HhG3Exx9VPh9p8ODa9nJjYTxONrSHJlSo3qx8BXjTGvj+cgDY00t/NEB9ML3EzNdw/7ek5kELxPQyOuVhd42d/toTdko3tgMFxvUakUZoypM8bcM97jdMptmhtrUV92pH1Ir0sX9sXTqkIvf2wsYVdXDhcUhfE5C8nrPcEpLkh2aSqVRTdFNqaGa41ujHkBeCGa4/VKI421ev3UtvWOuRLcn1VM2K7tQ+KpKqePXHvojVlUnpnWlYY2L1QZRkMjje2qs8YzRg0NX6NeZSSAXWBFgZcdXTmEDXR7ZuIM9eAKtCe7NKViSkMjje2s7cAmsKxi+PUXtnAAd6BNF/UlyOoCL91BB9U97jfGNXSRn8owGhppbMeJDhZMzcOTNfzQVLavGUFXgifK8vwebBi2d+bS55pC0ObSwXCVcTQ00pQxhl0nOkZd1KftQxIrxxFmUW4v2zpzQQSvp0JXhquMo6GRpmpaeujyBcccBA/ZsvA7R95Hxdaqwh5q+9y0BBx0e2bi8TdjD/YluyylYkZDI03tPDEwCD78oj4YNAiu7UMSZnWBF4BtHbnavFBlJA2NNLXzRAc5WXbmTzljyrXFGCs03NMSW9gkN90VYJorwPbOXLzZMzAIub11yS5LqZjR0EhTO090cG5FIXbbCFcRvg4cYb8+rS/BRKwGhnu7PfQaF73uaeT2aWiozKGhkYZ8/SH2N3SxfJTxDLqsZ6roIHjirSrw0m9s7OnOodtTQW7fSTDhZJelVExoaKShvfVd9IfMqIPgdNUD2j4kGc7J7SXbForcoqrAHu7H42tKdllKxYSGRhradWKgs+3ooeFzFhG2uxJUlRrgsMHygh62d+bQnV0BoLeoVMbQ0EhDY3W2BaCrXm9NJdGqAi/t/U4OBKcScOToYLjKGBoaaWiszrYEeqGnWQfBk2hlfg+CYXtnHt7sCp12qzKGhkaaiaazLc0HAKNXGkmU7wxRldPHts5cvJ4K3IF2HMGeZJel1FnT0EgzA51tR5051bgH0JlTybaqoIejvdk0OGcB6C0qlRE0NNLM6c62M4bvbAtA416wZ+F3jrxaXMXfqsjq8L8GFhDGRp4OhqsMoKGRZgY62+a4RnnoYuNeyJuu7UOSrDLbT4mzny1dRdYiP73SUBlAQyONRNPZFmPg1OuQPyNxhalhicDqQi+7u3LozK4gp69eF/mptKehkUai6WxLVz34OiC/PHGFqRGtKvDiD9vYL/Owm/7T7eqVSlcaGmkkms62nHrd+qqhkRKW5PXisoV51n8OAHl6i0qlOQ2NNDJmZ1uIhIZoaKSILJthWV4Pf+6uJODI1ZXhKu1FFRoiskFEDopItYjcOczrLhF5KPL6ZhGZPei1L0S2HxSRq4ccZxeRHSLy+Nl+kMlg54kOllUUjNzZFuDULiieC45RVourhFpV4KU5kEVTVqU+yU+lvTFDQ0TswL3ANcBi4BYRWTxkt1uBdmPMfOBbwN2RYxcDNwNLgA3A9yLnG/BpYP/ZfojJYKCz7ai3psC60pi2LDFFqaisLLAW9e1mPu7+Dtz+liRXpNTERXOlsRaoNsYcNcYEgAeB64fscz3ws8j3jwCXi4hEtj9ojPEbY2qA6sj5EJEK4FrgR2f/MTLfvoYoOtv6OqH9GEw/N2F1qbEVZwWZ4/HxjM8a1yjt2JXkipSauGhCYwYw+Jq6LrJt2H2MMUGgEygZ49hvA58HRp2DKCK3ichWEdna3NwcRbmZaWdtFJ1tT1krwZmmoZFqVhV4ebJ3EWHslLZraKj0FU1oDHcD3US5z7DbReRtQJMxZttYb26Muc8Ys8YYs6asrGzsajNUVJ1tB2ZO6e2plLO6wIsfF43Ocr3SUGktmtCoA2YO+rkCqB9pHxFxAAVA2yjHrgeuE5FjWLe73ioiv5xA/ZPGzhMdLK8Y5SoD4NRuyJkCefpc8FQzx+Oj0BFkZ7iK4s59SLg/2SUpNSHRhMZrQJWIzBGRLKyB7UeH7PMo8IHI9+8EnjPGmMj2myOzq+YAVcAWY8wXjDEVxpjZkfM9Z4x5bww+T0Y63dl2tFtTYIWGXmWkJJvAygIvz/gX4wj7KOo+lOySlJqQMUMjMkbxSWAj1kynh40xe0XkqyJyXWS3+4ESEakGPgvcGTl2L/AwsA94CrjDGBOK/cfIbDsGxjNGGwQPBqDpgA6Cp7DVhV5eCS4EoLR9Z5KrUWpiRul69wZjzJPAk0O2fXnQ9z7gphGOvQu4a5RzvwC8EE0dk9W22nYcNhm9HXrzAQj365VGCluW10OzlNNmL6W0YxeHeE+yS1Jq3HRFeBrYdqydJTMKcDvtI+90ehBcrzRSldtuWJLXy/bwfEo7die7HKUmREMjxQWCYXbVdbBm1liL+naDM8daDa5S1qoCL68E5pHbdxK3b/JOIVfpS0Mjxe2p78QfDEcRGq/D1CVgG+VqRCXdqoIedoSrAF3kp9KThkaK23asHYDVo4VGOKztQ9LEFFc/zbnnEMChoaHSkoZGitt2vJ2ZxdlMGW1RX8dx8HfpzKk0sbCilD3h2RS16gwqlX40NFKYMYatx9tZM6t49B11JXhaWTajgG3hBZR278Wmi/xUmtHQSGG1bb20eP2j35oCaxBc7DBlaPNhlYrK8lzUuBfjNP0UdR1IdjlKjYuGRgrbGhnPWDM7ikHw0gXgzE5AVSoWguVrAMhpGrP9mlIpRUMjhW093k6ey8GCKXmj79ig7UPSTcWs+Zw0Jbgbtye7FKXGRUMjhW073sbKWUXYRntSX08LdNfrIHiaKc11sd++kIqePckuRalx0dBIUZ19/Rxq9Ea3qA/0SiMNtRevYJppprdFHwGr0oeGRoraXhsZz4hmUR9o+5A0ZK9cB4C/5pUkV6JU9DQ0UtS2Y+3YbTJ2O/SGXZBfAZ4xpuWqlBMoW0YAJ/ktO7CeJKBU6tPQSFFbj7exeHo+nqwxGhHX74AZKxNTlIqpsM3JSc8iFocPcry1N9nlKBUVDY0U1B8Ks/NEx9jrM/raoe0olGtopCtv2UqWSQ2v1zYluxSloqKhkYL2N3Th6w+PHRoNkd5FGhppq714JVkSJFy/i/5QONnlKDUmDY0UFPWivvod1tfpK+JckYqXlsLlACwNH2TPyc4kV6PU2DQ0UtBrx9qYUZjN9IIxVnif3A5Fs3UQPI353GV43eWcn3WELcfakl2OUmPS0Egx4bBhc00b588tGXvn+p16ayoDtBQtZ7W9muOtvTR2+ZJdjlKj0tBIMYebvLT1BDh/7hhXDz0t0FmroZEBWgqXUxxspsLWxla92lApTkMjxbx6tBVg7CuN+sizGMpXxbkiFW8D4xpvL6lje22HDoirlKahkWJePdrKjMJsZhZ7Rt/x9CD48vgXpeKqI38hQZuLyzw19PWH2HmiI9klKTUiDY0UYow1nrFurFtTYIVGSRW48+NfmIqrsM1JW8ES5vn2UV7o5qXqFl0hrlKWhkYKeWM8I5pB8O06npFBWgqXU9S1n8vm5tHc7edQozfZJSk1LA2NFDIwnnHBWKHR1QDdDRoaGaSlaDl2E+Ti3Hry3Q42VbckuySlhqWhkUJeOWKNZ1QUjbE+o2FgEFxDI1MMDIZP7XqdC+aWUN3spaGzL8lVKXUmDY0UEQobXjnayvlzSxAZ5aFLYC3qE5s+QyOD+FyleLNnUNq+i7VzSnA5bDx3QPtRqdSjoZEi9tZ30tHbz8VVpWPvXLcFpi4BV278C1MJ01K4nNKOXWQ7bayfX8re+i721mtrEZVaNDRSxEuRe9jr548RGuEQ1G2FirUJqEolUkvRcjz+Jjy+U6yfV4rbaeOePx9OdllKvYmGRop46XALi6blUZbnGn3Hpv0Q8MJMDY1M0xwZ1yjt2EV2lp3180t5el8jr9fp1YZKHRoaKaAvEGLrsXYuGusqA6xbU6ChkYE68hYQtGdT1m5NdFg/r5RCj5P/eHK/rttQKUNDIwW8dqyNQCjMRdGMZ5zYAp5SKJoT/8JUQhmbk9aCJZR2WM9JcTvt/ONVC3nlaCt/2nMqydUpZdHQSAGbqlvIsttYOyeKleAntsDMdTDWDCuVlqxFfgewh6xut+9eW8miaXnc9cR++gKhJFenlIZGSnjxcAurZhWO/TzwnlZoOwIzz0tMYSrhWgqXYzNBijv3AWC3CV+5bgknO/r4/gvVSa5OKRjjbykVb01dPvY1dPG5qxeOvfPAeIbOnMpYLYXnAlDasZPm4lX8enMtACtmFvLd56sJhWHGWIs/E+Dd6yqTXYJKEr3SSLIXDjYD8NZFU8be+cQWsDl0JXgG87tK6MyZzZS2bW/a/rZzp5PjcvCbbSe0dbpKKg2NJHv+YBPTC9wsmpY39s51r1mrwLPGaJuu0lpjyTqmtG1Fwv2nt3myHPztqgqauv08s68xidWpyU5DI4kCwTAvHm7hsoVTxm4dEgrCyW3WILjKaI3F5+EM9Z4e1xiwYGoe6+YU81J1C7vr9JkbKjmiCg0R2SAiB0WkWkTuHOZ1l4g8FHl9s4jMHvTaFyLbD4rI1ZFtM0XkeRHZLyJ7ReTTsfpA6WTr8Ta8/iBvWVg29s6Ne6C/Fyp0EDzTNRVb/4+ntm0547Vrz53OrGIPv91ex8kObWioEm/M0BARO3AvcA2wGLhFRBYP2e1WoN0YMx/4FnB35NjFwM3AEmAD8L3I+YLAPxpjzgHOB+4Y5pwZ7/kDTWTZbWO3DgE4sdn6qov6Mp7fVUx73gKmtW4+4zWHzca711XiyXLwy1eP094TSEKFajKL5kpjLVBtjDlqjAkADwLXD9nneuBnke8fAS4X637L9cCDxhi/MaYGqAbWGmMajDHbAYwx3cB+YMbZf5z08vzBZtbNLSbHFcUktpq/QmGl9UtlvMbitZS278QWOjMU8txO3n/BLALBMD966SjtvRocKnGiCY0ZwIlBP9dx5l/wp/cxxgSBTqAkmmMjt7JWAmf+s8p6/TYR2SoiW5ubm6MoNz0cb+2husnLZQujmDUVDsPxTTDnkvgXplJCY8laHGH/6dXhQ00vyObD6+fQ1x/iRy8epcXrT3CFarKKJjSGG6Ed2ghnpH1GPVZEcoHfAp8xxnQN9+bGmPuMMWuMMWvKyqK4958mNu612kJctXjq2Ds37oG+dpitoTFZNBWvJoxt2HGNATOKrODwB8N8/4UjVDfpI2JV/EUTGnXAzEE/VwD1I+0jIg6gAGgb7VgRcWIFxq+MMb+bSPHp7Kk9p1g6I5+ZxVFMn635q/V1zsXxLUqljH5nPu0Fi5naOnJoAFQUebj9svnkuR389OUanjvQRCiszQ1V/EQTGq8BVSIyR0SysAa2Hx2yz6PAByLfvxN4zlhtOR8Fbo7MrpoDVAFbIuMd9wP7jTHfjMUHSSeNXT6213awYcm06A449iKUzIf88vgWplJKY/F5lHbsxh7sHXW/4pwsPn7pPJbOKODP+xv5wV+P6KNiVdyMGRqRMYpPAhuxBqwfNsbsFZGvish1kd3uB0pEpBr4LHBn5Ni9wMPAPuAp4A5jTAhYD7wPeKuI7Iz8+psYf7aU9XTk1tSGpVGERigIxzbBbL3KmGwaS9ZhM0GmtO8Yc1+3087N51Vyy9pK2noCfPe5an6/o44uX/+Yxyo1HlH1njLGPAk8OWTblwd97wNuGuHYu4C7hmx7ieHHOyaFp/aeYl5ZDvOnRLEKvGEXBLp1EHwSaipaSUicTGt5mYay9VEds2xGAfPKcnj+QBOvHm1je20HKyoKWV9VyrR8d5wrVpOBrghPsPaeAK8ebYvuKgOg5i/WV73SmHRCDg9NxWsob35xXMd5shxce245n7miijWzith9soPvPHuYn2yq4XBjN2F9oJM6CxoaCfbU3lOEwoZrlk6P7oBjL8KUxZCbOTPHVPTqp1xCQU8NOb0nxt55iJJcF9evmME/X72IKxdP5VSnj5+8fIxvPnOIvx5qxusPxqFilek0NBLs9ztOMrcshyXl+WPvHAxA7at6lTGJnSyzbkvOaPrrhM/hcTl4y8IpfO7qhfzdmgry3Q6e2nuKu586wIOv1XK0xauPk1VR0+dpJNDJjj621LTx2SsXjN2gEKwGhf29Op4xiXlzKunKmU1584scmv2eszqXw25jxcwiVswsorHLx5ZjbeyobWd3XSdluS4unF/CqsoinHb9t6Qamf7uSKBHd1rLW65fEeXU2SPPgthhdnSDoCoz1ZddxNS218acejseU/PdvP3ccu7ccA5/u6oCl9PGH3fW8z9PH2RTdQuBoD6zQw1PQyOB/rjzJCsrC5lVkhPdAYeftlqhZxfFtzCV0k6WXYI9HGDaGAv9JiLLYWP1rCI+cek8Prx+DiW5Lp54vYH/2niAvxxq1gc+qTNoaCTIgVNdHDjVzTtWRNmXsfuUNd226sr4FqZSXnPxGvrtHsqbJz6uMRYRYf6UXD568Vw+dslcKoqy2bj3FN/68yF213XomIc6TUMjQX67rQ6HTbj23ChnTVX/2fpadVX8ilJpIWxzcqr0Ais0EvCX96ySHD544RxuvWgO2U47D752gp++fIxWbYqo0NBICF9/iEe21XHVkqmU5rqiO+jQRsgrh6lL4lucSgsnyy4hx9dIgfdwwt5zXlkud7xlPm9fXk5tWy/3PHuYV4626lXHJKehkQAb956ivbefd6+dFd0B/T448hwsuAqimWWlMl592cUYhJmNzyX0fW0iXDC3hM9csYC5ZTk8tqueX7x6nDZ9+NOkpaGRAL/eXEtlsYcL55VEd8DRFyDghXPeHte6VPrwuctoLlrJzFNPJ+X9C7KdfOCC2bzt3OkcbvKy4dt/5aXDLUmpRSWXhkacVTd52VzTxi1rK7HZorxq2P8YuAr0+RnqTWqnXUVR92HyvDVJeX8R4cJ5pdx+2Tzys52878eb+cbGg4S1FfukoqERZ7/afBynXbhpTUV0B4SCcPBJ69aUIyu+xam0cmLq5QBUJulqY8D0gmwe++RFvGvNTL77fDUf/flWurWb7qShoRFHnX39PPzaCa5dNj36AfDjm6CvDRa9Lb7FqbTTlz2N5sIVVJ7amOxSyM6y8/Ubl/G165fwwqFmbvjey9S09CS7LJUAGhpx9KvNx+kJhLjtknnRH/T6byArV6faqmEdK/8biroPU9h1MNmlICK874LZ/OLWtbR6/Vz/3Zf466HmZJel4kxDI078wRA/2XSMi6tKWRxNc0KAoB/2PWoNgGdF8RhYNenUTt9AWBzMqX8s2aWcduG8Uh795EVML8jmQz99jV++ejzZJak40tCIkz/uqKe5289tl8yN/qDDT4O/E5a9M36FqbTmzyqivuwiZtU/iZhQsss5bWaxh9/efiGXVJXyr3/Yw11P7NMB8gyloREHwVCY//vLERZPz+ei+aXRH7j7IcgpgzmXxa02lf5qyt+Ox9/MtJZXkl3Km+S6HPzw/Wt4/wWz+OGLNdz+q+30BVIn2FRsaGjEwW+313G0pYdPX1EVXQt0AG8THPwTnPsusGvHejWyk1Muw5dVzPwTv0l2KWdw2G38+3VL+Ndrz2HjvlPc/MNXae7W9iOZREMjxnz9Ie7582FWzCzkqsVToz9w568gHITVH4xbbSozhO1ZHJ1xPTOa/kK2rzHZ5ZxBRPjIxXP5v/eu5uCpLt5x7yYON3YnuywVIxoaMfarzbXUd/r4/NULo7/KCIdh+89h1kVQWhXfAlVGqJ55EzYTYt6J3yW7lBFdvWQaD3/sAvzBMDd+/2U2VesK8kygoRFDnb393Pt8Nevnl3DheMYyjj4HbUdh9QfiV5zKKN6cmdSXrqfqxMPYQqnbB+rcikL+cMeFTC9w84Efb+HhreN/1rlKLRoaMXT3xgN09vXzxb9ZPL4DX/5fyJsOi98Rn8JURjow5/1k+1uYXf9EsksZVUWRh0c+cSEXzCvh84/s5hsbD2qn3DSmoREj22vb+fXmWj544ezo12UANOy2GhSu+5i2DVHjcqrkAtryFnFOzU/BpPYT9vLdTn78wfO4+Tyr9cinH9yJr19nVqUjDY0YCIbC/Ovv9zAt380/XLlgfAdvugecOToArsZPhP1zP0hBz1FmNj6b7GrG5LTb+PqNy/j8hoU8uque9/5os7ZYT0MaGjFwz7OH2dfQxVeuW0yuaxzTZRv3wZ7fwrrb9DngakJqp2+gM2cOyw7fm1KL/UYiItx+2Xy+++6V7D7ZyY3f26Q9q9KMhsZZeuVIK999vpqbVlewYWmUj3Id8Pxd4MqDC/8+PsWpjGfEzu6qOyj0HmFW/ZPJLidqbzu3nAc+ej5dviDvuHcTz+5PvanDangaGmehvSfAPzy0kzklOXzlunE+lvX4K3DgcbjgDvAUx6dANSmcmHYlbfnnsPzQPTiCvckuJ2qrZxXxh9vXM6Mwm1t/tpWvP7mf/lBqj80oDY0J8wdDfOwX22jrDXDPzSvJGc9tqVAQnvwnyK+ACz8VvyLV5CA2ti7+F3J8jSw58oNkVzMulSUefnf7hbxnXSU/+OtRbr7vVeo7+pJdlhqFhsYEGGP4/CO72XKsjf+5aTnLKgrGd4ItP4DGPbDhPyArJz5FqkmlpWgFR2a8g3Nqfp4SbdPHw+20c9cNy/jOLSs50NDFtd95kT+93pDsstQINDTGyRjD1x7fzx931vO5qxfy9uXl4ztB037487/Dgg1wznXxKVJNSjsW/SP+rEIu3HUntlD69Xu6bnk5j33qImYUZfOJX23nY7/YSmOXL9llqSE0NMYhHDb86x/28ONNNXxo/Wxuv2wcD1cCCPTCbz8C7ny47rsQbZsRpaIQyCrk1WVfo9BbzaoD/53sciZkblkuf7h9PXdes4gXDjZzxTf/wgNbanUxYArR0IhSbyDIpx/aya821/KJy+bx5bctjr63FFj9pf7wcWjcC+/4PuSWxa9YNWk1lF3EvjkfYkHtQ8yvfTjZ5UyIw27j45fO46nPXMLi6fl84Xev83c/eIVdJzqSXZpCQyMqx1t7uPF7L/P47nruvGbR+JoRAhgDT38R9v0RrvoaVF0Zv2LVpLdr4ac5WXYxa/b9BzMbnk52ORM2pzSHBz56Pv954zKONvdw/b2b+NQDO6hu8ia7tElNQ2MUobDhJ5tquOaeF2no9PHTD63l45fOG+cVRgj+9M/w6vdg3Sfggk/Gr2ClsNZubFrxDVoKz2X9rn9Oq/UbQ9lsws1rK3nhc5dxx1vm8ed9jVz5rb/wqQd28HpdZ7LLm5T0aT/DMMbw8pFW/uupA+yq6+TSBWXcdcNSKorG+dzuvnb43cfg8EY4/w64+i4dx1AJEXR4eGH197h02ydZv+ufKfBW8/r82zG29Pwjn+d28rmrF/Hh9XP44Ys1/PLV4zy2q541s4q4aY21sLYg25nsMicFSacBpjVr1pitW7fG7fz9oTDP7m/iJ5tq2FzTxrR8N3des4jrV5SP/3bUgSestRg9zXDN3XDeR+JW97C2/oTNNW2JfU8VlSOVNyXsvWyhAOft/RrzTv6BloJz2bL0y3TkLzzr8757XWUMqpu4bl8/v9laxy9ePU5NSw9ZdhtvWVTGO1bM4LKFU8jOsie1vhFkxL8YJ31o9IfCbDvezsa9p3hidwNN3X6mF7i57ZK53LK2ErdzHL/5wiE4tNFqQnjiVZiyBN7xPShfEdOao6KhkbISGRoDKhueYs3eu3D1d3J8+jXsn/N+2gvG2cVgkGSHxgBjDLvrOvnDzpM8tquBFq+fLMldOccAAAgxSURBVLuNFZWFXDivhAvnlbJiZiFZjpS4Ez95QkNENgD3AHbgR8aY/xzyugv4ObAaaAXeZYw5FnntC8CtQAj4e2PMxmjOOZyzDQ2vP0hDRx/VTV72N3Sx40QH24630xsIkeWwcUlVKe86r5K3LCzDYY/iN1k4DK2HoX4H1L4KB58EbyPklcOln4OV7wN7ki6ZNTRSVjJCA8DZ38mSI/dTVfsQzlAvHbnzqC+7hJbC5bQWLqPPPSXqc6VKaAwWDIV59WgbLx5u5uUjreyp78QYcNqFeWW5LJyWx8JpeSyYkkdFcTbT87PJz3aM7y7C2ZkcoSEiduAQcCVQB7wG3GKM2Tdon9uBc40xHxeRm4EbjDHvEpHFwAPAWqAc+DMw0Dt81HMOZ7yhsa++i//aeICGDh/1nX10+4KnX7MJLJiax9o5xVwwt4RLFpSN3Qok0AsPvRd6W6C3zbr1FIwsPnLmwPzLYdk7YeHfJC8sBmhopKxkhcYAZ383s+sfp7JhI6Udu7Ab689Fvz0Hn6sYX1YxJ6ZdyYE5Iz9JMhVDY6jO3n4217SyvbaDQ43dHDzVzckhLUqynXamF7gpzski1+0gz+0kz+3A47Rjtwkigk2sochgyOAPhvn/7d1fjBXlGcfx7w8WWWEpfysgtcAaouKFdiWVRkN6QahyszXphaYJTdpoTDRqIhcYb1DTi5rohdGY0EhCjdEY+2/TpP5tG69Wi438CzWsogm4ShRdRFgW8PFi3gOH9ZzdAVlm5pzfJ5nsnHfnzHnefXf22XlnznNWds85++KkmZZIGnmuiv0UGIiIDwAkvQD0AvV/4HuBjWn9JeBJZem7F3ghIo4BeyUNpP2RY5/f26RJ8NnhY/x47jRWds9hwcyLuXRWJ93zulg2v+vspp4AOjpheAi65sMly2HaXLjkKri0B354BUwq5Tyq2RmOT5nBnsW3sWfxbUw6eYzZh/7PvKEdTD+6n85jB+kc+bwSZdbHM3PaFNZcvYA1Vy841XZo+DgDBw4z+OUwg0NH+WRomMGhYb44MsLBr0f46PMjfDV8gqMjJ/gm4GQEEcE36YxlasdkftDZca5JoyXkSRqLgPoP9t0HXN9sm4g4IWkImJva+0c9d1FaH2+fAEi6A7gjPTwsaaIL68wDPpvg15hordAHaI1+NOjD+kICOTuvAQ/VHnynD7++0OGcH+fl9+n+tJyDlyPipu/7+kXLkzQanVKNntNqtk2z9kYXDBrOk0XEJmDTWAGeT5K2RsSKC/V6E6EV+gCt0Q/3oTxapR9Fy3NLwT7gsrrHPwI+braNpA5gJnBwjOfm2aeZmZVMnqTxX2CZpKWSLgJuBfpGbdMH1K6a/Qr4V2RX2PuAWyVNlbQUWAa8nXOfZmZWMuNOT6VrFHcDr5DdHrs5InZJehjYGhF9wDPAs+lC90GyJEDa7kWyC9wngLsisitsjfZ5/rt3Ti7YVNgEaoU+QGv0w30oj1bpR6Eq9eY+MzMrVineJmlmZtXgpGFmZrk5aSSSNkraL+ndtKyt+94DkgYkvSfpF0XGOR5JN6U4ByRtKDqevCR9KGlH+tlvTW1zJL0maU/6OrvoOEeTtFnSAUk769oaxq3ME2lstkvqKS7y05r0oVLHg6TLJP1b0m5JuyTdm9orNRaVEOkdj+2+kL2jfX2D9uXANmAqsBR4H5hcdLxN+jA5xdcNXJTiXl50XDlj/xCYN6rtUWBDWt8A/KHoOBvEvQroAXaOFzewFvgn2fuXVgJvFR3/GH2o1PEALAR60voMsjJFy6s2FlVYfKYxvlOlUCJiL1BfCqVsTpV8iYgRoFaepap6gS1pfQvwywJjaSgi3iS7Y7Bes7h7gT9Fph+YJanwehRN+tBMKY+HiBiMiP+l9a+A3WTVJyo1FlXgpHGmu9Op6ua6qZBGZVQWffeppVClWEcL4FVJ76TSMQDzI2IQsj8KQP4yrMVqFnfVxqeSx4OkJcBPgLdonbEojbZKGpJel7SzwdILPA1cDlwLDAKP1Z7WYFdlvU+5SrGOdkNE9AA3A3dJWlV0QBOgSuNTyeNBUhfwZ+C+iDg01qYN2krTjzKr5mc/nqOIWJ1nO0l/BP6RHlap5EmVYj1DRHycvh6Q9FeyKY9PJS2MiME0dXCg0CDzaxZ3ZcYnIj6trVfleJA0hSxhPBcRf0nNlR+LsmmrM42xjJrPvAWo3UnSrBRKGVWyPIuk6ZJm1NaBNWQ///ryNL8B/l5MhGetWdx9wLp0585KYKg2dVI2VTseJImsMsXuiHi87luVH4vSKfpKfFkW4FlgB7Cd7BdqYd33HiS7S+Q94OaiYx2nH2vJ7hx5H3iw6HhyxtxNdkfONmBXLW6y8vpvAHvS1zlFx9og9ufJpm+Ok/33+rtmcZNNiTyVxmYHsKLo+MfoQ6WOB+BGsuml7cC7aVlbtbGowuIyImZmlpunp8zMLDcnDTMzy81Jw8zMcnPSMDOz3Jw0zMwsNycNM0DS4Sbtd0pad6HjMSsr33JrRpY0IqKr6DjMys5nGtZ2JP0tFUbcVVccEUm/l7RNUr+k+alto6T1xUVrVi5OGtaOfhsR1wErgHskzQWmA/0RcQ3wJnB7kQGalZWThrWjeyRtA/rJitYtA0Y4XZTvHWBJMaGZlVtbVbk1k/RzYDXws4g4Iuk/QCdwPE5f4DuJjw2zhnymYe1mJvBFShhXkn3Up5nl5KRh7eZloEPSduARsikqM8vJt9yamVluPtMwM7PcnDTMzCw3Jw0zM8vNScPMzHJz0jAzs9ycNMzMLDcnDTMzy+1bC51DEii/nrAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 404.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in snore.columns:\n",
    "    if c == \"name\" or c==\"sno\" or c=='sex':\n",
    "        continue\n",
    "    else:\n",
    "        sns.FacetGrid(snore,hue=\"sex\",height=5).map(sns.distplot,c).add_legend()\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  BOX PLOTS TO SHOW OUTLIERS AS IT IS CONTINOUS DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pp8OYZ/RadarqO0n+vb0t8p+Bm4CvMjhbfnNVfSvJd4AHknyVwd93fR/wySTnANcC/z3kqV8GvCrJ/cC3gD/f73WvSnIK8IV2ieY+4FVV9fUkbwOuSnIEcD9wHvDN9jrPSLITuLe9Bm1Nf5/kf4BfWsI/hg8yuIxzQwaLmQPOWsLzSH57pbQcSe6rqmMnvQ7pQLx0I0md84xekjrnGb0kdc7QS1LnDL0kdc7QS1LnDL0kdc7QS1Ln/g9f+x/XfNDoSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fUlUPdUsZP6OqJpO8CRhL8uRu8zur6rtJPgR8B7gL+EZ33xDw90meQe/M+uKqenC2n/8EnwZO737md7sZqaqHuydOL+1+7jrgkiQ/pLf+fkb3r4TLgfcD5/RzMK1dftqiVlySPwWeUVXvmrbtLnrLHvf3+TOuAj5bVdcOZEhpFfAMXSsqyaeB59I7g5W0CJ6hS1IjfFJUkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEf8PO1uLosPERpMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for c in snore.columns:\n",
    "    if c == \"name\" or c=='sno ' or c=='sex':\n",
    "        continue\n",
    "    else:\n",
    "        plt.boxplot(snore[c])\n",
    "        plt.xlabel(c)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TO SHOW RELATION BETWEEN INDEPENDET VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2b3e389de48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rNy8KqtZl/8lz2N/dEavLI7y8FCmsGD0To0yjq1NFQz+Rzfv8gJewBIeedvV0e/7qlCNdWh1jrrnEDFa9Vy8mteK5suiSnhlOnMQKP7+cC01sQH1GD7OxvxfxA+CdpmTx9DjCzGVCiC6KIrCqNYVH792ItnQTsoUZHDg2iuGxLL7+WXsn6nb7NHt18himcjTiDL2x1m6k7z95Dn3bO/DIKz/FhckCMvkiVi9rxksPfhqzKtDSpGBVKzdcyTxGGXHXZ8qGfhLWXtROMJMRz8wSb9B7rnq6PXjiLAZ2dqLn+SFburRa9cA1lzjBSjayFc/1WpdmfS6sbaWIPmZihdr4U+bYoJ4+uZaHm0bj50X8AHjneYw1goOby4QQQxRFQe/hEdfKVawuIl5fdBHHUvW4ojfWWsby8FgWB46N4tF7N2JlawprV7Tgv/79z3B8ZLyiuVWtDEbIPEZlfAkBQz+J66ZBvRYpYbrIKEwYPdeVS1OLdJvJF7GurcWRLq20weGaS5xgpYTaiud6qUurPhfWtlJkMWZiher485fXLkM6lZQ6NtDTJ9fycGNm/PS07Eb8AHjjeYw1goNtMQghhgRdAmWlBNIOcSxVjytNSWXRWL8yNFZp/TI8lkXv4RGkU4nKxjLgvuZI+DHyxXRK308AIJObvxiQrUdu4rW/xxWj55pUhK5u29IpV3VZLqvI5Ir4YHIamVxxQRkq11ziBKsxaaOSb02r2t9VjVu6pM/FF7OxghZ/JhOK1BvLRlDj4cbM+Blp2e34oRH14otqGGsEBzOXCSGGuJ1tZ/V02+uTxziWqseRcllF/vos9nd34CvfvVl6+OXf/jg+vnrpAn2Xy+XKxrIGT7tJNUa+CGCRn+zv7sCXXhiu3CDPTJ6bmPF3ltpax+i5FkpzC3TblFSQVAQuXSu49mwbrfFcc4kT3IxJq7W6emnzovjALV1aiWPpd9HCSqzQt70Dj712Bnu2bghdnODWuxr1Hwxmxq+eljO5oi9jZmUPgbFGcHBzmRBSFzfLVa5OFXVPR1996B6sWday6Pu9vpk4rqXqcWNiqoT7n3kbq5c2V3qIT5fmMF2cwz9fnVoQmGRyRd6GTRpi5IuanxRm5nBuPI99R+f71ANgv7caGvk7S23tUe+5arr16tk26nPINZc4xa2YtFqrFyYL2Hd0FL3bNuEX1yxFusk9XZqNY+l30aRerPBSz924mC1gYqpUudNm5FIudHGCG+9q1H9wmB2/Wi37PWZW+igz1ggOtsUghPjG9Rnjyy308KMth9mbckl40U7lh8ey6BkcwueefAu7n30HZVU1XfrF025iBs1PEgLY/ew7lY1lgBnwtbSnmyptaYDFc42ltvYw8rCEgkopqdFBr9NnazYDimsuCZparQ6PZbH72XeQEHBVl3rzcWBnJxIKFpR00+/ihaIIqKqK7v430TM4VIkVwhgnmI2b67U0oP6Do974yTRmVjPkGWsEAzOXCSGuUq+sKSGE4eUWevDkkThB0yIAXd1lCzOmS7+oOQKYL9v0uuoi7JTLKs5m8vjmj96vXKS5ZlkzbluRrjxPXshij1oPa0oqyF+fxX1PnKpkFz3/hc2uPttGXkvdkyCo59d+ebQ2H1996B5MF+dw/uoUvvb9M4taJdHvokejeCEqcYKZuLlRliv1Hxz1Wl7INGba3Tlhny9Rh5nLhBDX0IKHrkOnsKXvdXQdOoXRK7nKSWdTQsH+7o4Fp6P7uzvQlDC2Ip48EjtUa/FLLwwv0l3f9g70nzyH9e1pCLFQU9Qc0aORv1XDDPj6aBkvx0fG0TM4hO7+N7HjqdOYLMxUvocXstin2sMEBO5/5u0F2UXnr0659mwbeS11T4KgkV/76dGKIiAgsPPp05WKltosP/pdtDATL0QpTmgUNzfKcqX+g0Vv/GQas+q7c6IwX6IMM5cJIa7RqB+SqqpIpxLo3bYJS1IJTJfmkE4loKr6t70SooeZ7FG9forfeWAzZssq/m1iGgeOjSKTL6Jve4dh5jwh1Ri1EmC/N+uYyXjhhSzuoPesD544i4Gdneh5fqjhs23kt371riXECD2Nytb/u5Hn0e+ihZH+qu+YiVOcQP2Hj6DGzMjP9e7OuXU5E4Bkg5vLhBDXaLQQCUXg0Ov/jO2dH8USJFCaK+PQ6/+MvV2/EsTHJSHE7AUSev0Uf345h1eGxrC986P46u/9ErKFGTz3xnk83tURxI9CQkS5rGK6aK/fG1mMmXLgOL14e4nes87ki1jX1tLw2ZrxW6Petace+U3qn3iOkUaXtyRN9//2g0aeR7+LFkbvQ9PFOZRb1cq4xiVOoP7DRxBj1sjPL0wW0DM4VPn+U4/8JtBq/2ck7sO2GIQQ12hUIpNQgN1b7kDv4RF87sm30Ht4BLu33IEkgwdiEqNskEvXCgsum9DT4itDY/jyb398gf72bN3AzAjSkImpkqutBNyi3mUrMmO2HJgtapxj9Kzb0imsbE0hlUygNDuHianSIv2YubCH5cwkSIw0Km7c8VFNI1166admPI9+Fx2MfPH81anAL6kLIm6g/sOHkzGzqzE3/ZwEAzOXCSGuUa9EplxWkSvMYd/R0UpJS7Ywg31HR/HEjk94dvJo9gIuIjfaOE6XZnWzQS5MFvDwy+9Wsur0tLhn6wbcuXopMyOIZUqzczh44iz6tnfgkVd+WtHUwM5OVw4n7PiU2Sx+GWGWkn/YvawHqF+NlMkVUZqdb23FcmYSFEYaTQhY0uXsbBmj4zn0DA554qf0vHixsjW1qPVQ3/YOHDh2453HIxrFEkHFDdR/+LA7Zk40ZtfP+a4vD9xcJoS4Rr2FKJMr4vzVKWTyxQUlLV6ePIZ584XcpHocH713o26ZVrYws6inopEW41CCSNwllUwgky/iwLHRBf3e1rW1OPYSuz7VqKeo7HAu+ofes87kGvcQNyqLnSur6Dp0qqLXb//Jp/DqQ/dgZrbMFzviK0YaVRTF9MZIuazi4rVCZWMZ8MZP6XnxQVEE1rW1VO6YyRZmKnd9BPnOE2TcQP2HDztj5kRjdvyc7/pywbYYhBBXMSqRqc78qy6xcSvzTw8zJb21hLXMPMpUj2P/yXOLNNS3vQP9J88BWNhTkSV2xC20THjtcOzhl9/F2hUtaEs79y47PgWYuxSPWCMu/l8uqyjM6FeB6F2quGDN3tWJvUdGFuj1/mfehoCg1xLfqVe6bTYGmJgqYTxXDL2fxsW/wkJbOoW1K1rw8MvvomdwCJl80dOqDjOxBOMGd+Gcm6f6OZiJLYyw4+d2Y2jiDcxcJoT4glHmn5en2FaDKJ5+ykn1OA6PZSsa+uW1y3AuM4UDx0YxPJYFcDOrrlxWTY0ZS6mIGayUB1rVlN2XPTOX4hHzxMX/tZ/z8rXrti5VLJfLOD4yvuDvjOrmBNcH+XGj3F7rOe61n3qpp7j4V5io15JIayvkpg7MxBKMG9yDc26e2ufwt3/8a7Y1ZsfPo3pgEtb4g5nLhBBf0Mv8SyUVfO37/4TRKzlPTnutXjTE0085qR3H4bEseg+PYGlLEmuWNyOTLwK4mcW898iIqTHTAqKuQ6ewpe91dB065ZkWSfgxkwVnR1N2L0QzeykeMUdc/F/7OfUqicxdqqjE4mIdrg/hwWmVUiqZwCtDY4sr63a5V1nntZ7i4l9ho1abADzTgZlYgnGDe3DOzVP7HA6eOIv93Y1jCyOs+nkULxUOc/zBzGVCiGvUO2XTTiNf6rkbF7MFTEyVKhmnI5dynvT7qnfBoB5RPf0MO0bj2JZOYWa2vOCCSE1TX//szTEz0mXYe9YS+bCjKas+pSHTBTlhzbCoJi7+r/2cFyYLCyqJ1rensW5FuuG42dVr2OD6EB9WtqawZ+sGfOOH8/NhZWsKa5Y14zYT80EPPT/0Wk9x8a+w46UOzHiz13FDFGIBs8R5zlWP85yqLngOw2NZ7Ds6ihcf/DQAeK6DKMYkYY4/uLlMCLFN9eKSTiVw5cNi3fIgRRFQVRXd/W8u+Hu8WoytBlEsF5OTeuOoKAp6D48Yjple2drArk5sWLMs1oEhMcbJy5EdTTl52ZPhgpyolIbGxf+bkkrl5xwey6JncAjr29N49aF7TGvOj0ONoDcpuD5En2qN3bq8GXu7fsXxpZRGfri8JempnuLiXzJixau89BWz3uxV3BCVWMAscZ1zZtpgaBdX+hGfuh2TBB17AOGOP9gWgxBii9qSjXfHrpkqD/K7fMVKeQ3LxeTFaBwbjZne6W/P4BAuXitUNlmqiUNgSIxxWopm19/CfPlkVEpD4+L/SUUsKlnd392BpAXNea1XGUpCo1hqS25Sq7H7njiFiXwJ61akHWnayA+FEJ7qKS7+JRtWvcprXwkylohKLGCWuM45t9tguIFbupch9gDCHX8wc5kQYovaxWVJKmHqlE2vfGVgVyfK5TIyuWKgJVQylZkTczQaM6PT3/FcEbffsiRypVTEGU5L0aJYntcIKxkWMmSEGBE1/zd61oXSHPYdHV3QTmjf0VE8seMTQGvQn3oeGUpC4ziX44RXGjPyQyHgqZ6i5l9hwaqOouwr9WIBLy4wDJqozrlGcVrtOPvdBsNLZIg9gHD7RCCby0KINgBPAdgEQAXwJwBGAbwI4GMA/hXAH6qqOhnE5yOENKZ2cckWZkyVB9UuxnNlFXuPjOD4yLgUJVQylJkTa9QbM6OyNS2T4pbWFF596B7HpbAkGjgtRYvqy0Y9zJaGhqFkNir+X+9Zp5KJysW6GrJlxMhQEhrHuRwnvNKYkR/+/FIO/27lEk/jjaj4V5iwqqMo+4qR9ufKKroOnZJ23XdC1OacmThNb5z9bIPhJTLEHkC4fSKothjfBHBUVdVfAvCrAN4D8FUAJ1RVvRPAiRu/JoRISm3JRv/Jc7plMQkF+GByGplcsVJWoi3GqWQCO546jeMj4wCiX0JFGlMuq8jkios0Y5eVrSkM7OpcoMu+7R14ZWgME1Ml3P/M2xAQoWxHQNzHbClaPZ2GucWFHcyWhsatZDZI6j3rMJTyNpqHbq8TRsRtLscJN7xeD7351be9AwdPnGW8EUHslK8risDK1hRSyQRKs3OYmCr5XnbvBXraH9jVib1HRrjuhwQzcVoYYgi7yNSOwo/2Y17EUb5nLgshlgP4dQB/DACqqpYAlIQQ2wD8xo1vew7ASQCP+P35CCHmaE83YWBXJ3oGh3BhsoBMvohbl7dUsjKakgry12dx3xPGp9WynBASOXA7s1Er7bp1WTO+88BmZHJFTEyV8Nwb5/H5e+7AgWOj1BtZgJlSND8zcGVuI6FhNsOCfu8f9Z51GDJi6s3DMGTAE/kx0piWEJFKJtCebsLZTN6S1rT59eKDn8aFyQKyhRkcODaK4bEsANDvIoad8nVZPMzt+EJvbSmXy5UEIg2u+/JiFDsUZuZQLqs3LlKXP4awS5jbUVjBSw8Koi3GLwDIAPhbIcSvAhgC8GUAt6qqegkAVFW9JIRYE8BnI4SYoFxWcTaTxzd/9D4evXcjVramsGZZM25bkUYyOV8QkckVcf8zb9ftWxTXm3aJPm72uqpdOH9n4xr8X5/99wCA7Z0frbzsUW+kGjNBs1892WR5ATWDmdJQ+r1/1CtP1l4QZS5frTcPM7miFD0RSbip1ZheQsQLD2y2pTVFEUglE3j45XfpdxHHzkabDH1dvYovateWTK7IdT9EGMUO58bzmCrOVvQhewxhlyhvnFfjpQcF0RYjCeCTAP5GVdVPAJiChRYYQogHhRA/FkL8OJPJePUZCXGdsGjXTJmEZkrHR8bRMziE7v43seOp05gszFS+x0yWWpRLa6KEX9p1M7OxduE8PjKO//r3P0M6lUDv4ZHKxrJR6xYSDexot1Epml8ZuFFrI2HH7/1qfyAjTnzXqB3Q3iMjuPzh9VA8T6N5yAx4+QlLvFutMQGxKCFiPFe0rTUrfhdnn5MNL2KGWmTwML/iC6N50J5uouZdxg3frdfW50+//WNculaI/HjFoR2Wlx4URObyBQAXVFU9fePX38X85vIVIcS6G1nL6wCM6/1hVVWfBPAkANx1113RVTaJHGHQrtmTbDOmZCZLLS4nhGHHinadlNm5mdmX2aRPAAAgAElEQVSop9HjI+Po3bapbqaSrJmhxB52fLeRhv3KwJXhBdRNrPp9mDK3vcBJzKAoAqtaU3j03o1oSzctKM1/8NcL6O5/s+7zlLkdCzPg5cfLeNcrber57cRUybbWzPpd3H1ONpxo16w2ZfAwv+ILvXlgp90MaYwbvtuorc+FyQIefvndWIyXzHGQU7z0IN8zl1VVvQxgTAix4cZvfQbACIDXAHz+xu99HsAP/P5shMQdMyfZ5bKKubLasOG92ayNOJwQxgXtJanr0Cls6XsdXYdOYfRKzvQJt5uZ7EaXMiiKUjdTKcyZocQ5ZjTsV8WFTBeLuIUVv49a5rbfKIqC3sMj+NyTb6FncKhSraE9P6Pn6dTHvYYVT/HFS23q+e0rQ2OLKgCsaM2M39HnooEVbcrgYX7GF7XzYLIwQ81LTHVbHy12AOb1kS3MxGK8ZI+DnOKlBwWRuQwAfwbgO0KIFIB/AbAb8xvdLwkhvgDg3wD8QUCfjZDYYuYke2KqhL1HRtC3vQOPvPLTyqnzwK7OBabkdVZylE8Uw4rTHk5uakbvUoaBXZ0ol8vI5IpY2ZqKXGYocY4ZDTfSqVveFJeLRYzg/HSGnn76tnfgwLHRyvfoPU8nPu7HusyKp/ji9r0M1VptTzctmi97tm7AnauXeqo1+lw0sKJNRRG4c/VSvNRzN2bnykgmFKxZ6m9yTZDxBTUvP43ih6DGy693fxn6onuJl3FUIJvLqqr+BMBdOv/XZ/z+LISQm5gpkyjNzuH4yDgyudKCkttVOqbkVcN/lhHKiRsBo1uaqV0458oq9h4ZwfGR8ZtB9NJU4KWJRC7MathIp256U9w30WQoHQ4ztfoRQuCx185UspAA/edp18f9XJejepkQqY9bm1JGWjXaSPZSa/S5aGBFm9ql6EG+wwQZX1Dz8lOtj8LMHM6N5xe0xwhivPyMMeJwAOLV2hbEhX6EEEkxUyahBQXDY1n0DA7hc0++hd7DI1AU/+yEZYRy4meZnZkLcLSFM5VMYMdTp3F8ZL6Vv6aXpCICL00kcuFEw+WyissfXnfVm+LcNkiG0uGwo+ln3Yo0WpoUfPkzH2/4PO3OAa7LxGuMtCmEsFSubKTVycKM735Ln4sGVnzTSH+XP7zua9l9UPEFNR8ONH2sb0tj7YoWZPJFAPPjNbCzEwkFvurVzxgjim3p/CKothiEEAmpPcluSipIKgKXrhUqp9rt6Sb07+zEF58fqpwc9u/sRHu6ybfPGYcTxTDiV5md1dNrI70USnO6F42w3Up8sathTZNTxVnfvSmqLYLinrntFtV+uXppM3q3bcIdq1qxpDmBVa2LNxTszgFZ1uWozgdiXKr92GtnsGfrBtMZbLJoFZCjRQJxjhXfNNLfxWwB1wozljMxw+Z5XNvDhTZerz50D6aLczh/dQpf+/4ZZPJFXzPu/fRto/mcUIAPJqep2Tpwc5kQsgDtpNJoA2/l0hQOnnh/QUuMgyfex+NdHb6VqbKkSk78Chit9sKqp5fqsiC2WyF2Naxp8tF7N/rqTVHXLNsfOKfaLy9MFrD72Xewvj2N7z20RVcjdueADOty1OdD3NG0+VLP3biYLWBiqlQp1R65lDPdD1MGrWrI0CKBOMeKbxrpb2KqhL948SeW+rqG1fO4tocLRREQENj59OkFuvWzD7Gfvq2XbJe/Pov7njgVqnkWBGyLQQjRxWgD7/rMfM9lrSVGz+AQjo+M2+p516itgREsqZIXP8rsrJ5em9VLvVJFOzol4cSOhjVN9p88h77tHYu01p5usu139WArAtIIq71AM7kiLl2b//51K9Km54AM6zLnQ/RRFAFVVdHd/yZ6BocqPUD1NG0UZ8qgVQ1qNjqYjR309Ne3vQP9J89VdGw2XqB+iF84uY/BjfjXb9+uns8CAvc/8zbnmQmYuUwI0cVoEUkI4fjk0OlJu5MM2bCVj5HFWD29NquXeqWK3f1v1tUpdRVvmpJKpRf9gWOjePTejVjZmsJtbWmsWdrsWWaaTOXdRE7M+mWQ67IedjyV8yEemNF0Iz030qpfazo1Gw9q9aS1QqnNwF/fnsZcWUXXIXMZktQP8Qs7mcOyXHLt1M85z8zDzGVCiC5GzezTqYTjk0M3TtrtZBdqi1zXoVPY0vc6ug6dwuiVHLNRQ4ad02szejHSvKZLI51SV/GmXFaRvz6L/d0dlQ3m3sMjaG1OYu3yFkwWZjzLLOKlI6QRTis3vF6X9bDrqZwP8cCMphvpuZ5W/VzTqdnoo6ens5k81ixtRmtzEr2HRyobywO7OrH3yIhpH6Z+iF/YefdyO7M+qHd/zjPzMHOZEKKLUTP7tnQKbemUqZNDo5PCoE4ArfbqJXLiVW9no8uCDhwbrXzPhckCCjNzKJfVyr9HXcWLWl9ToeL+Z97G6qXNlV7006U53Lq82XO/8+sSTRJenFZumNGp21medj2V8yEemNG0Ez37uaZTs9Gl2heN9FSr43K5jOMj4wv+HjNt36gf4jV23r3M+rCXlSJu+DnnmXm4uUwI0aXRItLIkOuVwvh9mYq2aE2XZlnWEhG8uAykVvNCCDz22plKT0dgXqfnxvOYKs5Wyrqs9jRl+4xwUi6ryBZKuJS9jp7nhyq+9vwXNlcuS+sZHKp8/6lHfhNo9fYSEt66Tsxgxi/t6tSLC6XsbgzKMB/o8f7QSNNGep4rqwsOh/Xw8kBQTx9Ba5a4hza+5XIZV6dK6Bkcwl//wa8a6qlWx5lc0ZO2b4S4gdV3LzdaGDnFDT9nO07zsC0GIcQQJyWu9Uph/GzKX10O8/PLOZa1kLpUa37t8hbs2bph0aUrB0+cXVDWZbZciu0zwos2du+OXatsLAPzvnb+6lTd8ffa7/y4RJNEH7s69eJCKSclqEHOB3q8PKxsTWFgV+ei9XvvkZGG2vSqBNpIHwDo4RGgenx/cuEaegbnY4VsYca0nrxq+0ZIELjRwsgpbvk523Gag5nLhEhIFE656p0U+nnSXr1o9Z88h77tHXjklZ+yrIU0RNPpiw9+uvKCoF26AqBy6m1ULpVQgA8mpyv6ZvuM8KDX+uJPv/1j3QykgyfOYmBn54Js5mpfYWYRCQN6lRsJgcqBsJFevcjyDGsJKj3eH8zEyIoisKo1VWlVVL1+f/2z9bXplf6oj2ii1/6iLd1UGWcr7x6MF0hU0ObFLUua8FLP3VBV1fUWRmYIMp6Io+dzc5kQyfC6PMQvGpXCeNHWQI/qRWt4LIsDx0bx6L0b8ctrlyGdSjJoI3VRFIFUMoGHX363rparXwaakgry12dx3xMLb/te3pJkW5YQoOfBWusLLQOpehwz+SLWtbXUfRn0y+8IcYKiCKxsTVmKQbxo+xLWDRbeKO89VmJkRVHQe3jEsja90h/1ET2q9Vh9+FwdK2jvHr3bNuEX1yxFuqm+nhgvkLBj7NPpRbr3ulVmkPFEHD2fbTEIkQyvy0P8wu/WF5lcER9MTiOTKy4oN6kthxkey6L38AjSqSTLx4gpzGi5ulxKQOD+Z95eNIe1P1sN27LIh54Ha60vtAykWi20pVMsSyWR4OpU0VIM4tVaH8ZSb94o7z1GMfLlD68vKjV2ok0v9Ed9RI9qPVa3v6iNFTL5ItauaMH6tnRo/IwQM+i9g1vZy/BjvyCoeCKOns/MZUIkIyqnXEYnhcD8hRVunR42ymIJa3ktkQOzZV3VGM3hbGGGbVlCgN74HTxxFv07O/HF54cqGUgfW9WK1lQCq5byRdErotAiSlb0ni0ATBetxSBhzTL2AsYb3mO0vl7MFnCtMLMgg1k2bTbSB/0ufFTrsbr9xfBYFs+9cR7feWAzkjcq4Die9aH+w8fsbBmj47lKf3E7lZqy+bSbxDEm4OYyIZLhdXmIn9SWdnnR8qNRP6MoL1rEW6yUdVVjNIe1vt+P3rsRK1tTuK0tjbXLW6hFydAbv0y+iEJpbkH/zv/+DyN4vKuD4+cRUWkRJSNGz3bl0lQlS99KDMIy7nkYb3iP0fo6MVXCX7z4k0W9LGXSZj190O/CSbUeq9tffPSWNM5lpvD4kfk4QRYNygr1Hz7KZRUXrxUqG8vAzXfwl3ruthRHyOTTbhLHmMCVthhCiGVCiKVu/F2ExB0/20l4Qb0WFV60/DCT6R3G8lriHfU0Wo1dverN4b7tHeg/eQ7DY1n0DA6hu/9NqKpKLUqI3vgN7OzEf/uH99AzOITPPfkWegaHcHxk3DCb06zGiDFRaRElI0bP9vrMHA6eOLuo9cvAzs4F2ZXUtjGMN7yl3vpaL8NeFt0a6YN+F05q9ZjJF5FKKvjKyz+tGyfIokdZoP7Dx8RUCeO5ou47eELA1F5GHOZB3GICR5nLQohfAfBtALfM/1JkAHxeVdUzbnw4QuKIH6dcXpUeNTp59qLlR5QyvYn3WMmOsKtXRRG4c/VSvNRzN2bnykgoAv/l73+G4bFs5XuoUXnR8+CEMv/SWI3RGOppbGBXJ1a1pqAoSuSzFtwiKi2iZMTo2SaEQCZfrFx825ZuwnRpDuvaWmxlV7LMmbiN5s8v9dyNi9kCJqZKOHBsFMNjWUueXKvboLVKvwsn1fFCYWYO58bzFT0Ci+OEcllFtlDCpex19Dw/ZMpH4wD1Hz5Ks3OYmCrpvoMritJwL4PZ6tHEaebyAID/pKrqv1NV9XYADwN40vnHIiTeeHnKpZl516FT2NL3OroOncLolZwrp4WNTp69aGwf9kxv4i9WsiPs6rVcVnE2k8cfDryJX99/Ev/l73+GP//Mx6nREFHrwW1p8z6jp7GewSH85MI1V/026sTxIhS/MHq26VQC37r/LmTyRfQMDuHhl9/F2hUtaEvP69yKf3oZa5B4oygCa5e3oLU5id7DI5WNZSueXK1bGbRKvwsvWrywvi2NtStaKgfRej21R6/k8O7YtcrGMsAsXYD6DyOpZAKvDI0trnTa1VnZSK63l2F0efDVqeKif4uEB6c9l1tVVX1d+4WqqieFEK0O/05CiIc06lHshEYnz140to9jPyNiHyvZEXb1WjvHjo+MA4DpCwHrEXR2Vdyoft63Lm/Gqw/dg5nZct1nb6SxtnSTq34bdeJ4EYpfGD3btnQKbemU4XpqxT+9jDUIqY39mpIKkorApWsFy7qVQav0u3Bj5vJnTWd//Qe/yizdGqj/8LGyNYU9WzfgGz8crdwls3pZM1akzW0vXp/R9+XrM2UvPi7xCaeby/8ihHgUwOCNX+8EcN7h30kI8RAvS4/MtKhoTiro3bYJS1IJTJfm0Jx03vo9qhcBEPex0kbF7sGF3hw7PjKOr39WxUfal9j+7Cwh8xe7z9tIY9nCDAC+RJqFB4fe0ejZGq2nTUlFV9tNOus4y5yJ12ixXyOvbrTuy6BV+l14MXv5s6azbGGG7fxqoP7DhzZme7t+BdPFOZy/OoXHj7yHTL5oKlZOCKE7DxIc8lDjdFfnTwCsBvAqgO/d+O/dTj8UIcQ7vCw9atSi4upUEfc/8zZ2P/sOPvfkW9j97Du4/5m3Y10KRvzFahsVOy1qvJpjRtlVlz+8HunLMILCiwsdtV/H+SXSCnG7CMVP7DzbpCKwv3thCez+7g4kdf4sy5yJXzTy6kbrvnZoUk0QWqXfhZN6+qu+sEzc2EzrP3luUSsBZulS/2FEUQQEBHY+fRq7n30Hw2NZ3bZDepf2pVMJ3XginWKMEGYcZS6rqjoJ4M9d+iyEEB8wKj1KKMAHk9OOTovrnTyXyyqmi8Fnh5B440d2hFvlfbUtMIyyqy5mC+juf5OZzC7j9ELHFx7YjMnpGSxrSeKv/t/3GvYFNQtbo5CgKJTmsO/ozcv+soUZ7Ds6iid2fAKoaYrXnm5C/85OfLHq0qr+nZ1oTzdVvodaJm7QyKsbxab567PY392Br3z3p66u2dRzPKinv+qM5t/ZuKbiiQeOjaJ32ybcsaoVS5oTWNXaXNEjNUTCRD3916sqaUuncOvylgXVzKuXNWN5c5PBv0TCgKPNZSHExwH8JYCPVf9dqqr+lrOPRQjxCr0+dfnrs7jviVOulNobtaiYmCrh/NUploKRwPG6jYobG9h6AdkLD2zWnT9adgB7mrqLlRYqtUwWZrDjqdO4MFnAJz7ahi/+xi/iwV//RdzWlsba5S22XxbZGoUESSqZqFz2p2E0JyYLMzh44v0FG9EHT7yPx7s6TLUyIMQsZry6Xmx6/zNvY/XS5opWp0vzPfadrtnUczww0p8QwtL9G9QQCSP1/LdRP/vb25cgqQiM54oozZWx7+jPsWfrBmo+xDhti/EygGEAXwPwlaovQoik1J6KA8D9z7zt+a3Fpdk5HDxxdvGtsjs7Y18KRqKH0/I+vYBs75ERDOzqNGy3oH2fnUoAo7K1uFH9HBIKLLVQqaY6k2N4LIuewSF0978JVVUdBcx2W3UQUoudOW+lrVBpdg7HR8bRMziEzz35FnoGh3B8ZLzhJWrUMqnGjE6ttruqRvNqzae1lm2FkrV1lHqOJ+WyioQCDOzsXKS/hIDu/RuqqurGhtQQkQUr8UE9/21UVaIlYnT3v1mJEaj5cOP0Qr9ZVVX/xs4fFEIkAPwYwAeqqt4rhLgDwN8BuAXAPwLYpaoqlUWIi+idig9+4VO+tKrQMp4OHBtdkB2yrs1+Fh8hsuB2KaPRpYC92zZVMqKFEHjstTMYHstWvsdOJQCzZebRew7f/pNP4dWH7sHMbFk3y8hozJ1kPddDhounSPixO+e1qoxXH7oH12fKSAgY9kcMwyVqRG7M6tSoWggAMrli3XXZLa+mnuNHtT5XL21e0ObilnQK4/miJW1RQ0QGrMYH9ao1GQfED6eZy38vhHhICLFOCHGL9mXyz34ZwHtVv+4D8A1VVe8EMAngCw4/GyGkBr1T8X+9Ou3LRSbayaZWUvvwy+9i7YoWtKWZtUzCjRaIdR06hS19r6Pr0CmMXsk5yv41ugxLUZRKRvTa5S3Ys3WD40thmC0zj95zuP+ZtyEgFmUZNRpzJ5l09eAlacQNnM75iXwJO771Frb0vY77ntD3u0ZzgFomjbCi09pqIQCm1mW3vJp6jh/V+hwey2L3s+9g59OnAQBnM3k89toZSxf3UUNEBuzEB0bVmtrdC9VzoPruBWo+ejjNXP78jf/9CoDq1foX6v0hIcR6AL8P4HEA/0kIIQD8FoAdN77lOQCPAbCVFU0I0UfvhPDgibP4mz/6JP7jd/7R0UUmetRm9t25eqmnF6kREgSNeorZwcylgFZ7Oxtl2jJzYB4rz+HqVLHumHt1caRbl0WSeONkzpv1u0ZzQE/LA7sWXvhH4o1fOr1z9VK81HM3ZufKSCYUrFlqvZUVvTl+GOnzemmuooNMroRH792Ila2phncuUENEBtx8JzC6e+Gx+zZBVVWkUwlqPmI43Vx+BMBRVVU/FEI8CuCTAHpN/Ln/B8D/AWDZjV+vBJBVVXX2xq8vAPiI3h8UQjwI4EEAuP322x18dEL8RQbt6pWnZPJF5IuzFeNf357GuhVpy4F17cZVe7oJZzP52JfaRwEZtCszXmzOmt2cNHs5oV6Z28CuTmxYs8yzFg4yYEW7Zp9Duaxiuth4zL26OPLW5c148cFPY04FWpqUyi3zJFp46btO5rwVv6s3BxRF4H9Z1YrvPLAZmVwRE1MlfPNH7/Mynwjglnb90Gm5rLoWq9Kbw48bMcOcqi66cwEATj3ym4v0wCQc4hYy+G4t2t0L2mWWGg/+egHd/W82bD9HwofTthhfu7Gx/L8B2ArgWTTINhZC3AtgXFXVoerf1vlW3XpiVVWfVFX1LlVV71q9erXNj02I/8igXb3yv77tHdh3dLTSqiKVTNjaWK4tP7x4rcBS+4ggg3ZlxquyLqeXAlajl8XVMziEi9cKaE83edLCQQasaNdsefTEVAnnr075Xsqn+ex9T8z77I5vvYWJPP00qnjpu05aAbjld+WyiksfXscf8TKfyOGWdv3QqRttoejN0cFpzLC/uwOXr103pT29d6ezmTxWtqZciftIvJDBd2sx8mHNX+u1nyPhxGnmsnb8+/sA+lVV/YEQ4rEGf2YLgPuEEP8BQAuA5ZjPZG4TQiRvZC+vB3DR4WcjhNRQmw05V1ax98gIhseyN283VoAPJqctnR7qBefjuSJL7UksCEMpo1EW13iuiJZUwpMWDmHDbLZ4aXYOB0+cRd/2Djzyyk9vZoLv7LQ95mYuhPSi/QqJJ07athj5XXu6qeHladVMTJUYJ5C6eKHTWo82WhsLpVlkcjD179Gb40mtPgHgSy8MA8Ci+EDPI1Wo1A2RDru+qxfH6vlw3/YOHDg2WvlzXPOjhdPN5Q+EEAMAfhtAnxCiGQ2yoVVV/c8A/jMACCF+A8Bfqqr6R0KIlwF0A/g7zPdy/oHDz0YI0aG6TLVcVvF4Vwe+/tk5NCUV5K/P4r4nTlkuDdQLziemSpEttSekGq/667qJUZnbxFQJq5Y2A63gywzMtbJIJRPI5Is4cGy00k5oujSHdW3GvRTrYfZmbvbGJm5it22Lnt/ZaYNVmp1jnEAa4qZO9dZlo7Xxvcs59B4eMRUH05vjS7U+M7kiMvn5AzMtPtB6La9Z2rzII5//wmbqhkiJVd+tF8dqPlwozaI4W8b+Yz/H8Fi28me55kcLp20x/hDAMQC/q6pqFsAtmL/czw6PYP5yv3/GfA/mpx1+NkJIA6rL7gUE7n/m7UUn6FenisjkivhgchqZXHHRTduAftnLK0NjGNjV6UpZDSGy42YLC7cpl1UkFGCg5sbmvu0deGVoDOevTrEM3QJaJkYmX6y0E1q7ogVtaXveZrYs26i8UAhR158JcZtav5sszFhuLZBKJvDK0Bj6tncs8KWBXfYrAOJAuaw2jMnIPGbWZaN2cf0nz5lukeFVayyvoZbcpVpLw2NZ9B4eQWtzEmuXt+h6pFF7LSEEx4KEinpxrObD6VQS+4/9HJ+/547Qr/n0TmMcZS6rqjoN4NWqX18CcMnCnz8J4OSN//4XAJ9y8nkIIfYxyryYLs5h59OnDbORqjeuep4fqnzfnq0beDEFIQ0w0w7B6d+vZRPc8wsrF1ye9dwb5/H5e+7AgWOjeGLHJ1z7N6NAvXFxO1PdbNabXnlh/85OPPbaGRwfGeelqcRVrHiTnczNla0p7Nm6Ad/44c0MvzXLmnGbjQuF44LZKoc44NbaWe3nhdIs3rucw4Fjo5XMOjOZpGFojVULteQe1Vq8dXmz7uVkeh558MTZRe9Ofds78NhrZ3ixKQkFmvanS7MNY4CorPn0zvo4bYtBCIkIRqWB569OGfYDqzbY1Uub0bttE+5Y1YolzYnKLdkstSdEHz8ClOpsgpeGLqDrkx/B9Zky2tJN2N75URw4NopMvih9hpWfmBkXN73N7M3ctZvaQojKxjLAfo3EPax6k53b5TU9P97VwQNok7C37zxur52an2dyQO/hEcttWsLQGqsWaskdzGpRzyMz+SLWtbXgpZ67cTFbwMRUqXKwMXIpx7EgUlOt/Ufv3dgwBojKmk/vrI/TthiEkIigVxo4sLMTB0+cXfB91SeR1QY7PJbF7mffwc6nT0NAQFEEy0YIqYMbt9Q3ojZbZt/RUaSSCh5++V30DA4hky9Kn2HlN2bGxU1vs3Izd3WZt6qqlY1lDfZrJHap1vTlD69b8ia7t8vL3E5IRtjbdx4jj746VXT099rVMRA+LVNL7tAoXtB8tVwu67YKbEunoKoquvvfRM/gkKWMeUKCpFr7/SfPLW5xtbMTCQUL4uOw+aQe9M76MHOZEAJAP/MiocyfrFdTfRJZz2DrneYD8LQVACFhwI8ApSmpLMgmGB7L4rk3zuOlnruhqqrt+ed1O48gqTcu2kvi1akSegaHFnmb07JsK8/TTrYoIXrUrtff/eLdunOgUJpFJodF+gxj5mYY4Zyfx8ijp4pzuFIuQFEUW/qLk46pJXew8h70OxvX4IUHNiOhiAXa4liQMFAb91drf3gsW7nE8pfWLsO/ZKbwte+fqSSwRKllBOdrfZi5TAipUHui2Jaun8VR7xKTepklo1dy6Dp0Clv6XkfXoVMYvZJjVjOJHX5cApRUBPZ3L8wm2L3lDrQ0KbYzB7QXpqjOYaNxmSur6Dp0Cj+5cK2ysQy4k3FuJ5vDSZYdIdXUrtcTUyXdOfDe5ZzhfI9CRpLscM7PY+TR/3p1Cj+5cM3RmhQXHVNL7lAvXrg6VVzgq8dHxrHjqdNIJRMLtMWxILKjF/fPldUF2tcusfyXzBR2P/sOhseynlRkBg3na32YuUwIMaRRFke9S0wuXSvonuZfnylL1asoyhmYcSDM4+fHJUCF0hz2HZ3PJmhLNyFbmMG+ozcu8Gu193dGvd9Ye7oJA7s6F2QmD+zqxN4j870429JNUpTExSnLjnhLbfadVuL6yCs/XXDR1IFjo1LM9zD7vhM45+dZ2ZrSvQjtwLFRfPX3fsm2RuOkK2rJHVa2phbFC33bO7D3yAi+du9GU7ECx4LIjl7cv/fIyOJYeWcnvvb9Mwv+bNRaRrg1X6O63nBzmRBSl3oXV9UzWKOykYSAFBszAG98DTthHz8/XihSyQQy+SJ6Bocqv+c0OzrK/cbKZRVnM3l880fvL7jNujmpVPobZwsz0pTE8dJU4ga163V1+5zZuTLeu5yrXDQFBDvfw+77TuGcn38G69pa0LttE5akEsgWZiqX02YLMwCsazSOuqKWnKMoAqtaUwsO8DWv/Ppn/73pWIFjQWRGL+4/PjKO3m2bLLXTjApO52uU1xu2xSCEOMKohNCobCSdst4KwKuLAf24UI14h0zjZ1ejXpfgelG+5Uc7j6DQNHV8ZBw9g0Po7n8TO546jTkVlZ9Z7+KSKJbE8ULW+KDnE3u2bsDa5S1Ip5LoPTxS2VjW/v+g5rtMvnZstqcAACAASURBVC8TcZuvbekU1q5oWXA5bd/2DvSfPAfAukapK/vETXu1zKlA7+ERfO7JtyqX8q1vT6MpoUhRPh/38SHOMYr7FUWx1E6TzOP3euOnBzBzmRDiCUZZmQAstQIwOt27c/VSTBZmHGV8RjkDMw7IMn5OT6C9LI3yIjvaj3YeQWGkqYS46VtaVqfexTxRIeisiqiWC8pKPZ/we743GntZfF8mgp6vQVCr2bmyir1HRiobe1Y1GkVd+eGjcdReLQkB3TZCqqoG3u7Cyvhw3SVGmI0D3H7niKom/Vxv/PZobi4TQjzDqGzEysJjdLr3wgObseOp046Mkje+hhtZxs9JD2I/Fn23yy2j3B/QSFOKokT2Z9YjyL7a3KwIBiOf8HO+mxl7WXxfJqLeB9+Ias2Wyyoe7+rA1z9rT6NR05VfPhpX7VWjKAqee+P8gtYYz71xHo93dQTe7sLs+HDdJfWwEge4pfkoa9LP9cZvj2ZbDEKILZyUWFhpBWB0ujeeKzouJ+GNr+FGlvGrdwLdaJ6EtRTX63YeQVFPU1H9mfUw0nRhZs7zktqwzoko45f2zYy9274fhZLxKGbdWsWpRmWJJ9zi6lTRFx+l9ua1s2frhkprjN7DI9izdUPg2imXVRRmZk2ND9dd0gi/Y+CgNOlHTODneuO3RzNzmRBiGauniU7KWoxO92oXFztGGeUMzDggy/gZabQpqTScJ24s+lEtGwsCWTQVNEaaPjeex1RxtqJhL7THzYpw4oYWzIy9m3M0KplRUcu6DYKgvN8LDy2XVUwX/fFRak+OuKFWR+3pJpzN5HH52nVT48N1l7iFW54WhCb9ign89Ay/PZqZy4QQy1g5TdSMuuvQKWzpex1dh05h9ErO9Emg3unewK5OvDI0tuD77BplnLIRo4gM42d0Ap1URMN54vRyPKfziyxGBk0FjZ6m+7Z34OCJsxUNe6W9KF8YGVXc0oLZsXdrjkYlWy9qWbdB4bf3e+WhE1MlnL865YuPUnvzBBk36Ono4rV5Lzt44qypC4i57hI3cNPTgtCknzGBX57ht0czc5kQYhkrp4lOe/3Unu4JIZBKCHz5tz+OkUu5yF0oRsJD9en8rcub8epD92Bmtlw5gb50rdBwnji9LIv9DsNFWLLMNd998cFP48JkAdnCDA4cG8XwWBbA/BrglfaifGFkVDGjBTPa93vso5KtJ0PmZJTwy6e98tDS7FxlU7H6krmBnZ2uzyVqzz5u6UxPR1rrwAuTBRw4NlrpB72+PY11K9KBey+JJm55WrmsIqEAAzs70fP8kG+ajEpMUI3fHs3NZUKIZayUWDTqR2smsNJurK8uVfmdjWvwwgObkVAEg1niClYCfbcunnK66EcxEIoamq7K5TKuTpXQMzhkqBmZUG5468Mvv6urYa+0x82K8NFIC2ZLTe2Ovd1NmiiV9Ad9cVhUsFMWbVd/XnloKplAJl9csKk4XZrDurYWT3yU2rOOns4GdnViw5plSCatFZbr6WhiqlTxtuGxLHoGh7C+PY3vPbTF8D2L6y5xilut/rS5sXppM3q3bcIdq1qxpDmBVa3eVgUYxQRCCHwwOR3aeeGnR7MtBiHEMlZKLIzKWrR+tGZLZ2pPQ4+PjGPHU6eRSiZiW7pO3MNqKZebF085KY1iKaPcVOvqJxeuVTaWgXCU4NfTsJfaY2uScNFIC1ZKTa2OvZMyXJb0k1qslkU70Z9XHqrpOpMvomdwCA+//C7WrmhBW5q6lgU9nfUMDuHitYIr7YReGRrDwK5OS97GdZc4xQ1Pq54bw2NZ7H72Hex8+jQEhOea1IsJ+nd24rHXzrD1oEmYuUwIAWAt88LKCbdRqZVRP1qj0hlmaBIvsVrK5ffFU7VUZ8MO7OpclA3LzRH7uFkSXa2rtnRT6DysnoZZRhsevC7zb6QFL9dvJ2W4zNaTkyDbB1nVqhP9eeWh1LV/uJ21Pp4rIp1KWsoy1NPRnq0bcOfqpdQAsYRT73XD04J839drxfnYa2dwfGS88jnYerA+3FwmhNgqAzRbYmEU5JrpR1tNlMpXiXxYDWbM6tGLUqTa+coWMe7h9k3R1brKFmZC6WFGGuYGRjjw4/bzRlrwcv12+iLKkn658EOv9bCqVSf689JDqWvvcaJVI51NTJWwbkWLpc9RT0fUADGLG97rhqcF/b5fPW8+mJyubCxryJ4UEjRsi0EI8fx2VL1Sq3rtMvRg+SrxEqulXEZ6bE83IZMr4oPJaWRyRU9Kp9gixjvc9sJqXfWfPGfq1vYwEYYy2nJZ9XxOyoxft5/X04KX63dYWgPFXYdm8UuvRljVqlP9hcFDvSasc8OJVle2pha1rejb3oFXhsZseZdsOgrrmMYZt7zXqRZlet/3I76I2lxh5jIhxJMSlEalNXqlM/u7O5C/PotVreqixYhZcsRLrJZy6emxPd2Es5m85xlXduZrkGXGYcJtL6zW1fBYFs+9cR7feWAzBIBkQsGapcG/BEaZoLMgZUCGllJGfumGJ4WhPQt1aJ6g9Vqr1aakgqQicOlawXQsK5v+ZCbMc8OsVo3irw1rluGFBzZjPFfExFQJz71xHnu2bgi9dsI8pnHGa+81+x4i0/u+1/4exbnCzWVCiOslKGbMUlEEbl0+fwvsklQC2cIM9h0dRSZfNOxlxBIv4hV2gplaPWZyRdu9F61gdb5GMXjxCre9sFZXc2UVe4+M4PjIOMfBB5z0Q40KQZeYalT7pZueJNOLqBHUoXlk0KumVbOxrOz6k5kwzw0zWq2noWRSwfr2JUinkli3ogWfvL0jEtoJ85jGGS+91+qaL8v7vtf+HsW5wrYYhBDXS1DMltYUSnPY/ew7+NyTb6FncAjDY9nAexlFrTyFmMdKKZeeTvzKuLI6X4MuMw4L5bKKhAIM7LR2w3ojNF2lkgnseOr0ootBOA7eEXQWpAwE2cLHCLc9SbaS8FqoQ/Po6XVgZycSCnyPx8zqVHb9yUyY50Z7umlRa4vaeKGRhqKonTCPaZwx825h9x05zO8hXs7RKM4VZi4TQmydzNUrbzFrljJkqFTDDE9iBiOdrFya8kXPZuerNkenS7ORC17cpnpMVy+dr6i4Y1UrljQnsKrVnWDSShDJNibuINsaEwRBtvAxwo0XqjDNEerQPJpeX33oHkwX53D+6hS+9v0zyOSLvsdjTnUaJo0GRVjnRrms4mwmj2/+6H08eu9GrGxNYc2yZty2Ir1gjOtpKJMrRlIbYR3TuNPo3cLJOzLb+ekTxbnie+ayEOKjQojXhRDvCSF+JoT48o3fv0UI8UMhxNkb/9vu92cjJM5YzdocvZJD16FT2NL3OroOncLolVzlBNNsA3yZmvYD4T5ZJf5hpJOkInzTc6P5Wj1Hf345F4oLr4KkekyHx7LY/ew72Pn0aQgI14JZs77YyF+JeWRbY4Ki1i8mCzOBrnVOL8kJ2xyhDq2hKAICAjufPo3dz75TqWrzOx5zotOwaTQowjo3tJjh+Mg4egaH0N3/JnY8dRqThZkF32ekobmyGllthHVMSf13CyfvyFa9NC7+GcW5EkTm8iyAh1VV/UchxDIAQ0KIHwL4YwAnVFX9KyHEVwF8FcAjAXw+QkgDGvUIMtsAX7ZedTKVp8ThxDasGOmkUJozrWevx7d6jvafPIe+7R145JWf8sIhA9yc+0Zja9YXo9iDLShkW2OCRpZqBqeX5IRtjlCH1nF6WZobONFp2DQaFGGdG2b1qaehgV2d2HtkJLLaCOuYkpvo+aqTONmql8bFP6M4V3zfXFZV9RKASzf+OyeEeA/ARwBsA/AbN77tOQAnwc1lQqSk0QJjxSxladoPyFOewvYcclNPJ2b07Mf4Vs/R4bEsDhwbxaP3bsQvr12GdCoZ+uDFbdya+43G1owvynTIFQVkWmOCpFqbj967MdC1zukLVRjnCHVoDaeXpbmxvjnRaRg1GhRhnBtWYobmpFK5vHy6NIe2dFPl7gWNqGkjjGNK5vGi9Z9VL42Tf0ZtrgR6oZ8Q4mMAPgHgNIBbb2w8axvQa4L7ZISQepgpb9HMct2K+e+7dK0g/QV5spSnsD2H3NjViXYRxqVrBc/Ht3aODo9l0Xt4BOlUMjKXxriJW3PfzuU9tRekNCUVtjEhrqNXzRDkWufkkhyjGEQIIXWMQcxjxpP9iJXs6LRcViGEoI9HGLMxw8RUCfc/83bl8vLdz76D96/kdbXRlAx0W4YQAN61/rPipU5bEgV1WTEJ8EI/IcRSAK8A+AtVVT8UwlxQKYR4EMCDAHD77bd79wEJcZkoaddseYvXWSVul0PKUp4i24ltlLTrBlZ0omm0XC7j6lQJPYND+Os/+FXPx9dp2XlUMKtdRRG4c/VSvNRzN2bnykgmFKxZan0T3urc1fPIb//Jpzh2xHXfNapm+KW1y5BOuXdxpR/o+Vvf9g489toZ7Nm6gVU+AeOWdm9d3owXH/w05lSgpUlZpFHZYiXgpqd/44ejbEcVQtyOGfQ0evDEWQzs7ETP80MVbezv7kD++ixWtar0LmILt3zXyFfzxTncuXqpL+/Idt9hWPkbPIFsLgshmjC/sfwdVVVfvfHbV4QQ61RVvSSEWAdgXO/Pqqr6JIAnAeCuu+7iUQQJDVHSrtnNNS97Jnm1gMhQniJLew6NKGnXLay2v3j03o3oPTzfYy9bmPF8fGU5KAkas9rVbn536idW566eR97/zNt47UtbYj92ccdt363VplbN0LttE9auaMGq1vCUZWr+9lLP3biYLWBiqoQDx0YxPJbFyKVc5Poyhg2n2jWK72o1KlusBCz09EyuhEfv3YiVrSnc1pbG2uUt9HHJcTtm0NNoJl/E6mXNlVYZ2cIM9h0dRSZfpHcR27gVMxj56rnxPKaKs75s1Np9h4lLr2aZ8b3+QsynKD8N4D1VVf/vqv/rNQCfv/HfnwfwA78/GyHEPEblLdXlKIUZ7y4NinLrCFnacxBnVGu0Ld1U0apfJelOys7jhlt+YnXu1rsckmPnLXErndTTZt/2Dhw8cTaUa6eiCKiqiu7+N9EzOIThsSyA4DNXiXPM+rGMsVJthUDP4BC6+9+EqrqfkRo3D5MJpxpVVbXSKkPzrzB7F7UYHWSJFey8w8hYzRI3gshc3gJgF4B/EkL85Mbv/Z8A/grAS0KILwD4NwB/EMBnI4Q4oDbb5G//+Nd0Tz8BIJMrOsrGi/ICwqzTaFCt0epsZa0kvXfbJtx+yxI0JQRuW5GWslVMXHDLT6zOXT8z76iNm8S1dLI5qWDwC5/C+IdFZAszlWxfAKFcO63MH+o/PJj1YxljJa88vVa/7ekmV6ptiD2canRiqmTqwsoweFZc19MoE5ZYoXaOaHeWyFTNEjd8z1xWVfX/U1VVqKraoarq/3rj6x9UVZ1QVfUzqqreeeN//6ffn40Q4ozak/yDJ85if/fCDM393R340gvD6Dp0CqNXcrZPt500+w8DzDoNP9Uarc1WzuSLSCUV/OXL72LHU6cxWZhx/O9pAX7XoVPY0ve64zkWJ9z0Eytz16/MO2pjIVGufDFCu1jq/St5PPzyuwuyfcO6dpqdP9R/uLDix7LFSl54up5+L/pwMTAxxqlGG+kkTJ4Vx/U0yoQlVtCbI/nrs9JVs8SNwC70I4REj9qT/OGxLPYdHa1cyHJuPI99R2+efv7pt3+Ml3ruhqqqlk/leWEZkZ1qjQ6PZfHcG+fxnQc2439OlTCeKy7KBHCapcJeY/YJyk8aZd65lblEbSwkypUvRmg/s3bQFYWLxpze//DqQ/dgzbKWID46qUOY4zs7F/42+j49/Y7nirHzMJlwqtFanTQlFSQVgUvXCkglE1ChhmbNjuN6GmXCEiuYvbOkPd0UigqAqMDNZUKIaxhdXCGEgFouY/ez7yz4/guTBVzMFtDd/6blMioZyyEJqUbT6KsP3YPrM2UkBNCUUPBn/2N4UclWU1JxXFbIAN8+Rn4CzLfw8dJjjC6HdLPUlNpYiIwXgXmN9jNrbXnMXDQWhrJsM5erGul/ujiHcqv7vXCJM8zGd7Lq0+qFv438XU+/ZtoqEO9otDlsRouaTvS08PwXNodmzY7jehplrMYKQflwvTtLPtK+pPLZ2LLFX3xvi0EIiS56ZV79Ozvx2Gtn8PPLOd0SMq1syk4ZlWzlkIToMZEvYce33sKWvtfxte//E/p3di4q2UoqwnFZYdRbxXhNrZ8ACLQs1c1SU2pjITJeBOY11T/z8FgWvYdH0NqcrLuxHJay7EYY6f/81SmWbktKo/gu7Pq04u96+n1laAwDuxbHElH2MNnQNLpuRRoT+RLue8KeFvW0cP7qVGjW7Diup1HGSqwQpA+biWvZssV/mLlMCNHFzklk7Um+EAKPvXYGx0fGkcmVFpXX9G3vwIFjo5U/L+upPCF2qQ1sjo+MA8CidjCXrhUcZamUyyoSCjCwsxM9zw9JWcIWNhq1kvA6W8PNbOMwl5l7QRwrX6z+zFeniqEpy27EytbUIm/U4o8ndnwi6I9HbGDkz3ZbrfmNFX/X8+89WzfgztVLY+VhslJPi0aHd9XoaeHgibOhiefiuJ5GGSvt2oTQT4zxI04wE9eyas9/uLlMCFmEkzKS6nLADyanK5tp1eU1v7x2GZIJBY+9dqbScxaQ91SeELvoBTbHR8bx9c+qlbItwFlZYfV8Xb20Gb3bNuGOVa1Y0pzAqlZm9NulXlDqR6mdm6WmfPlbjJnS9ahh9mcul1VMF6PzUqYoAuvaWtC7bROWpBLIFmZw4NjojYtVGXOEESN/tttqzW+s+Hs9/46bh8lIPS1eK8w01KBRS8F1bS2hWbOpxWhhtl3bd794d2Bxgpm4li1b/IdtMQghi3CrjKS2ZEUrr0mn5str9mzdYLqMqlxWkckV8cHkNDK5YmhKH4n8eKkts+0InJQVVs/X4bEsdj/7DnY+fRoCQtoXkTBQb+z8KLVzu9TUbBshei2ZmCoZlmULIUKpjbZ0CmtXtODhl99Fz+AQMvmitJmApDFG/uyk1ZpbmPFQq/7ONnDyUk+LZjRopIW2dMq3Mee6T8xQG/tqvd+rMbN565beGvkiW7b4DzOXCSGLsFNGolciXq9kxeqN2mzIT7zAa22ZbUfgJLPUynyV9QIkGak3dk7bmACNxyKIbGN6LQHmPeXgibOLWllpdygcHxkPnTbMzCf6Y3jQ82cZWq1Z8dBblzfjxQc/jTkVaGlSWGkUUuppsbraychbgq4s4rpPzFL7vtF/8tyiOKHR5q1XejOaY6za8xduLhNCFmG1jKTeQlHP1M2WUTXqfUqIXbzWlpXAxm5Zodn5yhcIa9QbO6eldmbHwu9SU3otAeY9JZMvVlpZtaWbMF2aQ6E0V2l1FUZt1JtP9MdwYXTHR9Ct1sx4qJHWVrWGYx6RhWhafKnnblzMFjAxVcKBY6MYHstifXsaTUmlobcE2VaC6z4xS23sOzyWxXNvnLfU694LvTVav6lj/2BbDELIIqyWkdQrEXejlM8oM7MwM8cSLuIIP7TldTmr2fnKW5OtYzR2Tkvt/B4LsyWIvPyEADf1nckX0TM4hIdffhdrljXjv/3Dewu+zy1tyFCSTX8MH9X+bLXVmlc08tByWcXlD69TaxFDUQTWLm9Ba3MSvYdHKhvL37r/LiQV/UvPvB5vrvvEbfRi3z1bN2Dt8hbT7zhe6M1o/b784XXuD/gMM5cJIYvQy9hrTzcZlnR5HZgYZQmeG89j97PvMMOI2EYWbTkpxzabHc0XCPcw+8yNxtXPsbCSkcnLTwigr++EMn/JVDVuaMNIn7cub0ah5F8ZK/0x3HhZ/mxlfa7noZrWp4qz1FoEMdKgG220rMJ1n3iBGz5rpdrSrO86vVSTuAczlwkhulRnhKxsTeFsJo+uQ6ewpe91dB06hdErucppoNlLy+yid1K6v7sDB0+cBcCsD2IfGbSlvQQYzS8zmMmO9nqexo1Gz7zeuPo5FlYyMnn5CdGo1Xdb2httGOnz3bFrtv3QDvTH8ONFlZDV9bmeh1Zf8EatRRM9DTYlFd3xbkp6tw3DdZ94hVOfNaM3q77r9FJN4h7MXCYkInh5EU2j/khmLy2zS+1JKQB86YXhBb31mPVB7FCrLRXAn/msLb/63Xk9T8OO2x5ab1z9HAsrGZm8/CSc+HERnVfaMNLnklSi8t9+9P+kPwaD7JcoWl2f680TTet2LsEi8mFWu0lFYH93B77y3Zvjvb+7A0kPdc51n9QjSN81ozervmvmUk3iD9xcJiQCeH0RTaMgxY/ApLohfyZX9KQ8l8QTRRFY2ZrC6JUcLl+77ru2/CrH5guEMV54aL1x9XMsrJa88vKTcOHnRXReaMNIn9nCTOXXfrwc0h/9JwyXKNpZn43miab14bFs5bLMla0p3NaWxtrlLdL8zKQxVrRbKM1h39Gbl6NmCzPYd3QUT+z4BNDqzefjuk+MkMF3G+nNqu82ulST+wP+wbYYhEQAry+iMVMuqm3QpZIJlGbnMDFV8qyMlSVcxG20OXTwxFn0be/wVVt+lmPrlbPJcJlW0HjhoY3G1euLHjXi5pdx07Md7cr0jIxaE/WfPFf5Hr9eDv2ak2SeMFyi6Ob6XK314bEseg+PoLU5aWpjWaY5S6xpN5VMVC5H/dyTb6FncAiZfNFTTzNa99vTTdRRzGmkXRm8xo7v1rtUM6rxrowwc5mQCOB15qOZclG/s6f8yjCSvWSTuIM2hy5MFioZRW3pJqxvT2PdirSnY+5VObYZ7cqQwSADXnioLGX2bvhlWHwwjnq2ql3ZnlGtPpuSCvLXZysVJDK/HIZlXshKGC5RdNPH7XqxF3OW2nWGFe26HQuYGTuji9nPZvLSeD8JhnralSU+sDtnGnksfc97uLlMSATw+qZfL/ojufGZvC7hkmWRJd5TPYeGx7LoGRzC+vY0vvfQFs/H2ovDErPa9XveyooXHipTmb0TvwyTD8ZRz1a1K+MzqtXnqlZVinlTjzDNC1nxOnZ1A7d93I4Xuz1nqV3nWNGumxqyMna1WsvkitJ5P/GfetqVJT5wMmeMPJa+5w9si0FIBPCj7LlRuajXGShBlOmEoWSTuEOQrQO8OEk3q90wZI75gZPxr+dNUSizD5MPxlHPVrUbhmcUhnnjZF7IUHYsA2Fp2eO3Hmv14facDZOny0oj7daOIQBXNORk7MLg/cR76mlXJo247btu+h7XcGOYuUxIBAgiQ652Q6wpqXiWgRLUaaNMiyzxlqCyTI20fefqpZgszNj+LGa1G4bMMT+QqVxZNvz2QSeHLXHUs1XtxuUZeV3+andexMEzzCJTdYcs6OnjhQc2uzpnjbRbKM0ik0Psx8AM9bTr5Rx3sh4beT8wn9XMcY8H9bQb5vig0ZrvVizLNbw+zFwmJCL4mVmhGWvXoVPY0vc6ug6dQv76rGcZKEFlWfh50RoJniCy5Yy0ffFaYcH8Gr2Ss3Qybla7Yckc8wM74x+HDDA/fVBvbbGi/bjq2Yp24/CMnOrIDHbnRRw8wwphyFL3Ez197D0ygoFdna7NWSPtvnc558lciSpG2vVyjjtZj40uT/3SC8Mc95hhpN2wxgdm1ny3Ylmu4fVh5jIhxDJ6xnr/M2/j1f94D1588NOYU4GWJgWrWt15UQgqg1iWC7lIdDHS9niu6KjnWXu6CQO7OtEzOFRXu8wcu4mdTMc4VDf46YNO+/1Rzzcx0nMcnpEffSPtzos4eIZdeNmSvj6Oj4yjd9sm1+asnnb7tnfgwLFR9uG1gJFevZzjTtbjau8vzMzh3Hge+46OYngsCwAc95hSq+M7Vy8NXXxgZs13K5blGl4fbi4TQixjZKz/9j+n0d3/ZsWwV7W6E6AEVaYTh5dwEixG2q49AbcSuJTLKs5m8vjmj97Ho/duxMrWFNYsa8ZtK9K62vXjckzZsVvmFuYSQrP46YP/P3vvHh5Hded5f6u71a3WxZaQJWOQCcbjeKLxiDFyuNjvJiZsHGZxYIgNzBDbBAZsj4flfZMBzGzwhl0nz+KQvEzYxLEgCWAIbyAYD1nYScgw+JlZcwlWHDyME8dxgLHBtmQhybq01Oqu8/4hVbu6u6q7qrru/f08jx6Q3JdTdb7ne351bj87gnbqubyew36P3Hj4s9ouqsEzrMCtxlPo6SMSidjWZvMGGdMZ/PrEML7xszODjBwoKU8pvTrZxivtjxXvf39gDDc/9mbev7Heq4+w+K6RPt+uWJZ9eGl4LAYhVY6VQ+n1tpYoA2J2bxHxcpsOt2wSwLnkDVra7l7bhV09R/NeZyZwUWbwXzrYiw1P9GD1jtdw4/fewEBq0pYyh5Fy29z06j+oWwjN4pYP8igie/Drtk23kuC4pSMr7aJaPMMsbmnW74mY3NKHot1kPIatLxzMDSwr30nPLY2eXk+NTiAaAbrX2HeMSSF29Mfsawng31ihkHK+bVTPdrQd9uGl4cplQqoYqzOWpbbUKdg5A64129icrKn67ZPEHbTaSffaLixsa0QsVtkcrZ62v/jphTh4fNjS1i1u2TJPqXtWzifLrYTgVm/j8Cgie7DbA+zQsJsrpPysI+6I0sbpfkuWBQZTaRwfHMeGJ3t8u0rPbX34ua34GT29jk1kseb7b6C1IYGt1yzCvFn1qEtEi44J9DouYL0TwFnftUvjRmIHN/XMPrw0vhtcliTpSgDfAhAF8D0hxP0eF4mQ0GL1XMJCY5UkCff95G1HVz6ot/GGZRsPCQZa7WTDEz3TGdzrKtac1hb1SgIXbtkyT6l7Vs4nSx0xQK8yB4N2e7DTA+zSsBvnICv4XUdhP5bECk72W4qGTwyNY8vzb7uiwUpwUx9+byt+RU+v75waxbGBFI4NpHDzY2+ivTmJ3ZuWFQ0sex0XsN4J4Jzv2qlxI7GD23pmH66Pr47FYAjFiAAAIABJREFUkCQpCuA7AP4UQAeAv5AkqcPbUhESXiqZsVRvLTl7Ri2++OmFrm0RCco2HhIOSiXdc0pzlWzd4pYt85S6Z5X4JL3KPDyKqHLs9AC7NOz2jgrqKFg42W8pGq6LR7mrRwO2FfNoHmm2pgsPvXw473Va+vJLXMB6J075rp0aNxo7UM/+wG8rly8G8DshxO8BQJKkHwG4BsBBT0tFSEixa8bS7RlDbvsnblIq6d6cmbUelkwbrkgxT6l7VolP0quIF9jpAXZpmDsqSCmc7LcUDQ+mJqlBYgtaeo1GgL6RibzXaemLcQHxC075rp0aZ+wQLHy1chnAuQDUWYyOTf+NEOIAds5YujljyEQUxE1a6uPoXpufnGXbqk7s6jnqW81xBt88evesEp+kVxGvsMsD7NIwd1SQcjjVbyka3rHnCLat6qQGiS0U6rUpaczjGBcQP+GE79qpccYOwUISwj9ZciVJug7AZ4QQt07/vhbAxUKI/6x6zXoA6wHgvPPO63rvvffKfu7597zoTIFJYHj3/quMvtSxERgr2nUDr5NKWMEP55X5kKrTrptkMjI+GDpzFMaunqP44qcXVrvm7ML32rXqk/Sq0ON77VaKnRoOYrwRYkKvXQW1hlsbErjjigW6idZIIPCtdo14HOOCqsa32rUTuzXO2MEXGLrhfhtcvgzAfUKIz0z//rcAIIT4H1qvX7Jkidi3b1/Zz+XgMvHD4LIao9ol+rCjKYLadRhqzjFCrV3qJtSEWrsK1HAoqQrtKlDDoSLw2qUeq5bAa9co1HjoMFR5fjtz+U0ACyRJmgfgfQB/DuBGb4tECPEjzNRK3IaaI1agbkjQoYZJ0KGGiZ+gHknYocarE18NLgshMpIk3Q7gZwCiAH4ghPg3j4tFCCGEEEIIIYQQQgghpABfDS4DgBDifwP4316XgxBCCCGEEEIIIYQQQog+Ea8LQAghhBBCCCGEEEIIISR4cHCZEEIIIYQQQgghhBBCiGk4uEwIIYQQQgghhBBCCCHENJIQwusyWEaSpD4A75V52SwAp1wojhn8WCaA5QKAU0KIK53+EoPatYpf69FNqvEehEG7blKNGgH8ed1+064f75EX8D5MUeo+ULvBgPelGGrXOCybNZwqG7VrD0EsdxDLDJwpN7VrDJbLOG6VyZB2Az24bARJkvYJIZZ4XQ41fiwTwHKFBd4v3gNSnmrVSLVetxl4j6bgfZgiSPchSGV1E94X/+PnOmLZrOHnstlJUK8ziOUOYpkB/5ab5TKHH8vltzLxWAxCCCGEEEIIIYQQQgghpuHgMiGEEEIIIYQQQgghhBDTVMPg8sNeF0ADP5YJYLnCAu8X7wEpT7VqpFqv2wy8R1PwPkwRpPsQpLK6Ce+L//FzHbFs1vBz2ewkqNcZxHIHscyAf8vNcpnDj+XyVZlCf+YyIYQQQgghhBBCCCGEEPuphpXLhBBCCCGEEEIIIYQQQmyGg8uEEEIIIYQQQgghhBBCTMPBZUIIIYQQQgghhBBCCCGm4eAyIYQQQgghhBBCCCGEENNwcJkQQgghhBBCCCGEEEKIaTi4TAghhBBCCCGEEEIIIcQ0HFwmhBBCCCGEEEIIIYQQYhoOLhNCCCGEEEIIIYQQQggxDQeXCSGEEEIIIYQQQgghhJiGg8uEEEIIIYQQQgghhBBCTMPBZUIIIYQQQgghhBBCCCGm4eAyIYQQQgghhBBCCCGEENNwcJkQQgghhBBCCCGEEEKIaTi4TAghhBBCCCGEEEIIIcQ0HFwmhBBCCCGEEEIIIYQQYppADy5feeWVAgB/+GPnjytQu/xx4McVqF3+OPDjCtQufxz4cQVqlz8O/LgCtcsfB35cgdrljwM/rkDt8seBH0MEenD51KlTXheBEEtQuySoULskqFC7JKhQuySoULskqFC7JKhQu8QrAj24TAghhBBCCCGEEEIIIcQbOLhMCCGEEEIIIYQQQgghxDQcXCaEEEIIIYQQQgghhBBiGg4uE0IIIYQQQgghhBBCCDENB5cJIYQQQgghhBBCCCGEmCbmdQHChiwL9I+mkc5kEY9F0VIfRyQieV0sQmzn/HteNP2ed++/yoGSEBI82FeEF9Yt8SvUJgkb1DTxO9QoCRrULLEKB5dtRJYFDp0cxm079+HYQArtzUk8sm4JFs5uZIMkhBACgH1FmGHdEr9CbZKwQU0Tv0ONkqBBzZJK4LEYNtI/ms41RAA4NpDCbTv3oX807XHJCCGE+AX2FeGFdUv8CrVJwgY1TfwONUqCBjVLKoGDyzaSzmRzDVHh2EAK6UzWoxIRQgjxG+wrwgvrlvgVapOEDWqa+B1qlAQNapZUAgeXbSQei6K9OZn3t/bmJOKxqEclIoQQ4jfYV4QX1i3xK9QmCRvUNPE71CgJGtQsqQQOLttIS30cj6xbkmuQyhk1LfVxj0tGCCHEL7CvCC+sW+JXqE0SNqhp4neoURI0qFlSCUzoZyORiISFsxuxe9MyZtckhBCiCfuK8MK6JX6F2iRhg5omfocaJUGDmiWVwMFlm4lEJLQ2JrwuBiGEEB/DviK8sG6JX6E2SdigponfoUZJ0KBmiVV4LAYhhBBCCCGEEEIIIYQQ03BwmRBCCCGEEEIIIYQQQohpOLhMCCGEEEIIIYQQQgghxDQcXCaEEEIIIYQQQgghhBBiGib0I6FElgX6R9PMckoIKYL+QPwM9UmCBjVLwgT1TIIM9UuqHbYB7+DgMgkdsixw6OQwbtu5D8cGUmhvTuKRdUuwcHYjjYWQKof+QPwM9UmCBjVLwgT1TIIM9UuqHbYBb+GxGCR09I+mc4YCAMcGUrht5z70j6Y9LhkhxGvoD8TPUJ8kaFCzJExQzyTIUL+k2mEb8BYOLpPQkc5kc4aicGwghXQm61GJCCF+gf5A/Az1SYIGNUvCBPVMggz1S6odtgFv4eAyCR3xWBTtzcm8v7U3JxGPRT0qESHEL9AfiJ+hPknQoGZJmKCeSZChfkm1wzbgLRxcJqGjpT6OR9YtyRmLctZOS33c45IRQryG/kD8DPVJggY1S8IE9UyCDPVLqh22AW/xLKGfJEnvAhgGkAWQEUIskSTpLABPAzgfwLsArhdCDHhVRhJMIhEJC2c3YvemZcwSSgjJg/5A/Az1SYIGNUvCBPVMggz1S6odtgFv8WxweZrLhRCnVL/fA+BlIcT9kiTdM/37Zm+KRoJMJCKhtTHhdTEIIT6E/kD8DPVJggY1S8IE9UyCDPVLqh22Ae/w27EY1wB4fPr/HwfwZx6WhRBCCCGEEEIIIYQQQogOXg4uCwAvSZLUI0nS+um/zRZCHAeA6f+2eVY6QgghhBBCCCGEEEIIIbp4Obi8TAhxEYA/BfDXkiR9wsibJElaL0nSPkmS9vX19TlbQkJshNolQYXaJUGF2iVBhdolQYXaJUGF2iVBhdolfsCzwWUhxAfT/+0FsBvAxQBOSpI0BwCm/9ur8b6HhRBLhBBLWltb3SwyIRVB7ZKgQu2SoELtkqBC7ZKgQu2SoELtkqBC7RI/4MngsiRJ9ZIkNSr/D2AFgLcB/ATATdMvuwnA816UjxBCCCGEEEIIIYQQQkhpYh5972wAuyVJUsrwlBDip5IkvQngGUmS/hLAvwO4zqPyEUIIIYQQQgghhBBCCCmBJ4PLQojfA7hQ4+/9AK5wv0SEEEIIIYQQQgghhBBCzOBlQj9CCCGEEEIIIYQQQgghAYWDy4QQQgghhBBCCCGEEEJMw8FlQgghhBBCCCGEEEIIIabxKqEfIY4hywL9o2mkM1nEY1G01McRiUheF4sQQgDQowixCtuOcXivCAkGbKsk7FDjwYD1RCqlqgeX2YDChywLHDo5jNt27sOxgRTam5N4ZN0SLJzdyLolhHiOmx7FPo7YhR+0xP7dOLxX7uCHdkGCjVdtldoNLkGrO/ZHwaAa6ilobSeIVO2xGEoDunb7Xizb9gqu3b4Xh04OQ5aF10UjFdA/ms6ZIgAcG0jhtp370D+a9rhkhBDinkexjyN24RctsX83Du+V8/ilXZBg40VbpXaDSxDrjv1RMAh7PQWx7QSRqh1cDnsDqlbSmWyuThWODaSQzmQ9KhEhhJzBLY9iH0fswi9aYv9uHN4r5/FLuyDBxou2Su0GlyDWHfujYBD2egpi2wkiVXssRtgbULUSj0XR3pzMq9v25iTisaiHpSIK59/zoun3vHv/VQ6UhBBvcMuj2McRu/CLlti/G4f3ynn80i5IsPGirVK7wSWIdcf+KBiEvZ6C2HaCSNWuXFYakJowNSC/IMsCfcMTeH9gDH3DE45vPWipj+ORdUtydaucF9RSH3f0ewkh3uG2z1SCWx7FPo7YhV+05EbbCZKXlKLcvQrLdXqJX9oFCTZ2+prRdk3tBpcg1l2Yn83D1JdWUk9BuA9BbDtBRBLCf5VvlCVLloh9+/ZZeq8bh5ZX+6HhAU1S4UoFVaJdv2BlFbIVuHLZMFWp3SD6jBt9Q8ASc/hCu9XeZ+vhJy05WUcWr9MX2tVC715pXWf32i4sbGtELFa1a05M46d2YRHfalehWjzZjus0o0dq1xh+jBmCWndhbMthixmA8vWk9e8AAqHJoLYdH2HoJlXt4DLgy4eUUNE3PIFrt+8t2l6xe9MytDYmPCxZSXxt+n6Cg8u+oyq164XPBMXfAxTMe67doNSpVwRIS5ax6CWea9csetf51K2XoL25LnT16iQBbxe+1i492Rxm/YvaLY9fY4aA111oqJaYQUFP8y0NcXxu+6uBGO9h26kIQzeqqpcoRCISWhsTOLe5Dq2NCVvFxUPDebYNIcR5mAxHHyf7uLARlDr1imrQUrXELHrX2Ts8Qb2bpBrahVfQk81h1r+o3crwUp+sO39QLTGDgp7mxyeDcx/YdpynahP6OU21GY4WlRwMb2RmibNPhBAzPmOXZ1Srv4fZc+2q00xGRu/IBCazMmqiEbQ1JHjUQEAIezIbBb3r7B9NY87MWse/36iPhNlvSHnc7mdLHSMTBB1Wi3/5BaP6DLquiD7V1ub0NB+VJMfvg5X2wjbmDXzicYhKDw0PwsHo5bB6MLyy7eLa7XuxbNsruHb7Xhw6OZx3D4y8hhASfpqTNehe21XWZ+z0jGpMChF2z1XX6eK5Tehe24VnN14GSZIMX2MmI+M3J4dxffdr+OQDe3B992v4zclhZDKyk0UnNhHmpENqWurjRZ65bVUndvUcddzDjPpIudeFIUYmpXGzn9XS269PnMbkZDYw/V61+JdfKNTn4rlNePQLH0dWiJwn6flYJiMHRldEn2prc3qenIxHHb0PVp4/gvDMEtY4pqrPXHaSSs5iCtM5Y1ZmjYycYeTgOauBPQvJbXjmsu+oOu0qXvngzw9hVddctNTH0daYwDkzk0WrRe30jDB5tFEcPtvac+2qtXTT0nnYvOuA6br9YDCF67tfK7pHz2y4DOc0JUu8k/gFCzGL59q1QiYj44OhM0dh7Oo5ii9+eqHjHmbUR0q9rqU+XnX+6xC+1q6b/aye3n60/lL8+cOvB+IsUaCqVup5rl21PlsbErj7yoW469n8uEHvLNpnNlymGSv4VVdEn2qJGYDSngzAMe+x8vzh97xfAX2ONFQwHovhEJGIhIWzG7F70zLTDU3vTBu/NAgzKGfbmMHIVqNq3ZZOCDmD2itfOtgLQD94sNMzKvH3oBJ2z1Xq9L6rF+U99JnpfyezsuY9ymS5cjkoWIlZgkgsFkF7cx2S8RjmzKzFRed1uuJhRn2k1OvCFCMTfdzsZ/X1pu3pfu33qsW//EChPm9QTUIonvTUbZfoxgRB0hXRp5raXDlPduo+WHn+8PszS5jjGB6L4SBWDw33e4NwGiNb4apxWzohJB8zXmm3Z1RbUohq8NxIRIIQwnL/WxONaN6jWJShFvEfXniYUR8p9bpqj5GrCbc0qqe3rCxC3+8R66gHFkudRatGiQmoKxJE/Bw3VPoeNwlzHMMnHh/iZYPww/kvRs4wqrZzjgghxZjxSqc9ww/e6STV4rlW+19ZFqiLR/DdNfln2e5Y04W2hmCvQiDELoz6SKnX+f2hkQQPrXPIv3ndhUhnZOxYUz6nA6luzJxF272mC3XxSFXEU4TYgZXnD633dK/tgizLvnhGC3McwzOXfYhX57D46fwXI2cYOXS2WGDPQnIbnrnsO6pOu2Y9y6nzCP3knU7i4HmOvtGulbpUv2fpBS1Y/8n5qIlKqIlG0NaQKDr/m4QK32g3KBiN797tH8V7/WOoi0cxls7iIy11OL+lHgCqwm9dgNpVoT6HfHwyi8baGP76qf1obUjgjisWYN6setQlophVH/6dSgHAV9otdxbtqdEJjE1k8c6pUTz08mH0jUxg5y0Xo6E2hsmMXBVHq5EcvtJuULDy/KF+T1YW+OqLB/HSwV5fxAwBfW40VDAOLvsUL5Iy+P3wc5eg6RuEg8u+oyq164cENvTOivGVds1qivVf1fhKu2GhXJvyg++HAGq3ALWubghQIr8qxHfaLeVJjBGICt9pN+z4tf0FMI5hQr8g4+YB8Yq4x9KZ0J7/QggJJ35IpmHm7KwABhNVh56m9OouzGenEeIF5dqUH3xfD3p8cFF09f7AmGOeTn2EE0U7Sv0eH0rl6pcxAiHe4XX70/N8P8cxlcDB5SpHvSx/y8oOtDcni2Z27Dj/hcEUIaQS/OwhytlZ5bwzoNugCErXndH6L/f5ftU3IYU4rVc72pQX0OODi1rTkiRhRUcbXjrYm/t3O/RHfYQbvfptaYi75meMJUg1Ukr3XsYT1ej5PAiwyukfTecEv2PPEWxb1Wl7ggGlYV27fS+WbXsF127fi0Mnhz0/TJ0QEgz87iFGk02o/RaYmjm/bec+9I+mXS8zMUepuqs02aHf9U2IGjf0GtQEovT4YFKo6eu7X8MdV3wUKzraANinP+oj3OjVbywiueJnjCVINVJO917GE9Xo+Vy5XOWotwrsPzqIb/zsELas7MDHzm5EMh6zZcZTr2F5fdYNISQY+N1DIhEJC2c3YvemZSVXi3i9NYtYp1TdGa1/Pfyub0LUuKHXStuUV9Djg4mWpjc+2YNnNlyGr3xW2KY/6iPc6NVvKp11xc8YS5BqpJzuvYwnqtHzuXK5ylG2CijsPzqIrS8cRDIeyzXISqnGhkUIsY8geIhydta5zXW63lnot0AwtnqT8nVnpP71CIK+CVFwS6+VtCmvoMcHEz1NCyFs1R/1EW5K1a8bfsZYglQjRnTvVTxRjZ7v2eCyJElRSZL2S5L0wvTv8yRJekOSpMOSJD0tSZK/976FBDe2ClRjwyKE2EdYPCSoW72Js3UXFn2T6oB61YceH0zc0jT1EW68rl96M6lG/Kx7rz3BCyQhvDmHR5KkLwFYAmCGEGKlJEnPAHhOCPEjSZJ2AHhLCPHdUp+xZMkSsW/fPjeKG2qcPvw/YIeZu1KgMGj3/HtedOV73r3/Kle+JwSEVrsB85CSMNmKJoHQrlN1FyZ9VyGB0K6dUK+lCZDHV5129XBT0wHSh5/xrXa9rF96cyDwrXaDit91HyLPN1RoT85cliSpHcBVAL4G4EuSJEkAPgXgxumXPA7gPgAlB5eJPShbBZz8/CCenUcI8Qdh8hCn/ZY4h1N1FyZ9k/BDvZaGHh883NQ09RFuvKxfejOpRvyu+2rzfK8S+v0dgLsBNE7/3gJgUAiRmf79GIBzvShYteD2LEq1NSxCiL0E3UNCNHNdtThZh0HXNwkPRnROvRI/YKcnU9PEDryO9ahj4jZeax6g7v2E64PLkiStBNArhOiRJGm58meNl2qe1yFJ0noA6wHgvPPOc6SMYcfv2wfCCrVLggq1Wxn0XO+wS7usQ+I2XvgudU7swA3tUqvECSrRLjVJvIQxA/EDXiT0WwbgakmS3gXwI0wdh/F3AJokSVIGu9sBfKD1ZiHEw0KIJUKIJa2trW6UN3T0j6ZzJgBMZdS8bec+9I+mPS5ZuKF2SVChdiuDnusddmmXdUjcxgvfpc6JHbihXWqVOEEl2qUmiZcwZiB+wPWVy0KIvwXwtwAwvXL5TiHE5yVJ+jGA1ZgacL4JwPOVfpfVZfpm3+eH7QBmSGeyORNQODaQQjqT9ahEhBDiH/Q83YrXy7JAajJDzw0Apeq3kn7TDzGCH8pA/IOeHkrpvG94wtHEz9QnUVNOE2Y92S8a80s5SOUU1qUZTbqhA2qNVEolPuyV/qh7b/HqzGUtNgP4kSRJXwWwH8D3K/kwq8v0zb7Pzu0AbjWGeCyK9uZknhm0NycRj0U9LxshhDiBUQ/T8/QFrQ043DdiyuuVzzoxNF7Sc+mv3lOuLzfTb6rrsyYWwch4But+8AvPtgwGcdsi24RzlNKDns6zssC12/eW1Y/VCTh1eVZ0tOHeqzoQnW53rPvqw4hnmfXkws/becvFaKiNYTIju6Yzt72YPuocWnX51K2XGNKkLAu82z+K9/rHUBePYiydxUda6nB+S71t9WNFa9QLUVOJD9fEIrZ4XaEmm5M1GEhN6mo0iPFu2PDiWIwcQog9QoiV0///eyHExUKIPxBCXCeEmKjks60u0zf7Pru2AyiN4drte7Fs2yu4dvteHDo5DFnWPHq6Ilrq43hk3RK0NycBINfwWurjnpeNEELsxoyH6Xl678iEaa9XPuuhlw9j26pOTc+lv/qDcn250X6zsD4/t/1VnDw9jtaGhObn+uHa/AbbhLOU0oOWzrvXduGrLx4sqx+r9aYuz+K5Tbhp6Tzc+L03WPdVjBHPMvMsU/h5rQ0JnDw9js9tf9VVnbnpxfRRZ9Gqy6++eBDda7vKanIwlcbJ0+PY8vzbuOHh17Hl+bdx8vQ4BlP26cCs1qgXUkglPhyLSBV7XaEmv7z7AH5TRqNBi3fDiKeDy05idQur2ffZdcSEm40hEpGwcHYjdm9ahr2bL8fuTctKzuiwoRJCgowZD9Pz9ExWNu31ymftPzqIb/zsELas7MDT6y/F0+svzXku/dUflOvLjfabWvV517MHsHH5fM3PdYOgHYXFNuEspfSgpfNZ9XG8dLBX8/VqrNabujwbl8/H5l0HWPdVjhHPMvMsU/h5G5fPx13Puq8zN72YPuosWnX50sFezKqPl9VkKp0t0t9dzx5AKm2fDsxqjXohhVTiw6l05V5XqMlVXXOx8cmekhoNWrwbRkI7uKws01dT7ugHK++z+j3A1IxM3/AE3h8Yc/1MzkhEQmtjAuc216G1MVFyqwAbKiEkyJjxMD1Pj0UjRX9f0dEGSZLw/sAY+oYnilZ4qD9r/9FBbHiiB3/z47cQj0VtOcuX2IeRvtxIv6lXn03JGgDA4rlNePQLH0dWCE3NOEElcYoXsE04Szk9FOu82Pu09FOu3tQxr1r76vI0JWtY98SwZxl9lin8vHI609NqpbjpxfRRZ9Gry0gkUlaTWSE068bOcMCs1qgXUkglPlyp12nlqzESHwQt3g0joR1cLrVdqlTQUPi+FR1teOrWS3LJTAoDDLNHTCgULvU/0jvq28bAhkoICTLlPEzdJwgI7Lzl4iJPb2tIFPUNd1zxUVzf/Zru9iwj/QP91R9Y7cuBfP1IkqRZn2PpLBbPbcLdVy7Eluffxie+vse1badWjsJyYmDFKGwTzmJWD+rXK5MjT/7lJRAQedooVW+ltlyrP38wNVkVde91G/M7lfixkc8bS2ctadUoevVr5nilSvVBH3WWcnVZqg5ra7TrRghhmxeYbUM1MWOTiKR6qMSHK2kfigcXjo0ZiQ/s7jv0YB+ujySEtZshSdIzQojrJUn6VwDqD5EACCFEpx0FLMWSJUvEvn37dP9d62B6AGUP+lbeJ8syTo2mseGJnpKHgls5AL9veCKXHAVA7qHzrmcPoLUhgTuuWIB5s+pRl4hiVn3plcVOU2WHo7tyQeW0GwTOv+dFV77n3fuvcuV7QgC1q0MpDwO0+4TZMxJIpfM9Xe31kiTh+u7X8jz8jisWYH5bPZI1Mc33aPUPmYyM35wczm31am9OYseaLvzh7EbEYqGd/y3EF9q1KxnZHVd8NK8+1Xq64eHX81ZetDcnsXvTMrQ2Jmy7zkquTX09XsUiAYs5fKFds5jVuiwLDKbSOD44jg1PasfEpeqtfzSdF/MC+do3G3cHGaf0bcG/fK1du5OLqT8vGY/i5OkJS1o18j2l6rfcddmlDzd91INEcL7Qrt51G9HAr0+czvO5bas68fir7+Br13baFg+ofTUrACGErube7R/FydPjueM6wui9PsEX2jVKJW3bavtQxshaGxK48zMLc0dl6cXXdozLmb0nAYpRAdh2Twy9oZLB5TlCiOOSJH1E69+FEO9Z+mATWGk4hYO6gH7QYOa1Znl/YAzLtr2S97fFc5uwY81FODWS1g3evcKtwMEHmWoDZfpewsFl30HtlkDPW7R8fkVHG+67epFuIA7ke/jiuU15AZAZ3+4bnsCXdx/Aqq65aErWYDA1iV09R219wAgAgdWuGf1o9fsAsHfz5Ti3uc7WcllFL6h3KhYp9fARkKz1vtSuE/fPSEys971mtB+gureEE88WFh92faldt7BDq1pYjSlKvd+qPtxoSx4NtPhau0bq8ORQCr86NpSL+3bsOYL9RwdtjweM1I+639+4fD6akjUYS2dx4dyZOKvemObC7ts24kvtull/5dpH4fOVosn25iRmN9ZiIDXpqc6cHB90Ahs92tCLY9aKCQghjk//9z0AkCRpRiWf5xZmzhRy8vwhZbuS+vP7RiaQFcgNLCvfd9vOfZ4LVjlPx0mCOBNECAkGeh5W6POL5zbhpqXzcquS9XxI7eF6SaiM+HY6k8VLB3uLEmZ95bM85y4I6CX1+cpnRdEDola/77dtp8r1bFnZYVnTRinX5/sxSA8CTsVSRpP7aNWbGe2Hve6deLYSLMsXAAAgAElEQVTQS8bl9bODn7FDq1pYjSn03g9Y14cbbYnaK8aYV0aw9YWDjscDRupHKe+xgRQ2PNGTe+/ezZcD9eW/g8/vwcbt+ivXPtQerOSrUQZvY7GI574StPPJ3fboivfcSpK0QZKkkwAOAOiZ/vHfFPU0Zs6gcvK8Kr0zYYTOIf9+FaydMFMtIcRtCn1eb6C40IfUHl5JEiqeixhszNSfW2fBVYJyPW4kVmOf7wxO3ddKvCoI2ncLJzw/aA+7fqZSrVqNKfTer5TBrzEBtVeMkTp0yxON1E+lmmNfHmzcrr9yevN7vECPLo0dBzreCeCPhBDnCyHmTf9cYMPnOoIZwTop7khEwsLZjdi9aRn2br4cuzctw8LZjYETrJ0wQCGEuE2hz7fUxw35kNrD25uTHHSpUszUn16/76eVPcr1lEp4ZRfs853BqftaiVcFQftu4YTnV/Ozg91UqlWrMYXe+/0eE1B7xRipQ7c80Y2Bbvblwcbt+iunN7/HC/To0thxjMURAGM2fI4rqAVb7rwWM6+1WpbC5eiKYAu3JvhVsHYShC3DhJBwUejzkiSZ3r4ty8KybzvdzxBnMVt/ft/yr1zP7BkJdK/pKsr/YGcswj7fGZy6r5V6ld+17xZOeH41Pzs4QSVarSSm0Hq/32MCaq8Yo3XohicaqZ9KNce+PNi4XX9G9ObneIEeXRrLCf1yHyBJiwE8CuANABPK34UQd1RWtPL4NUlEpVTrofg+ObPJlwft+xEm9PMd1K4NWPWhavVtm6B2fQgzbhvCd9oNyX0lJrHQXn2n3TBSDe3Rg/iH2jUB+3Jf4Tvtsv7Cj00e4GxCPxXdAP4JwL8CkG34vKrHz7M1ThK0mSBCSPiw6kPV6tskvDitafb5zsD7Wp2wD/In1dAeqT1/w76clIL1F37c9Gg7BpczQogv2fA5hDBAIYR4Dn2IEHdgW3MG3ldC/APbIwk71HiwYf0Ru7BjcPkVSZLWA/hfyD8W40MbPjs0hH3LdNivjxASDtzYHkgvJEQbL9pHtbbJar1uv8L6qD5Y58bgfQof6jpNxqPIyAKTGZn1GwDYHt0lbPfbjsHlGwEIAPcU/P0CGz7bd1gRQOFZNis62nDvVR2IRqRQiIhn9QQft85PJsRLynlVpR18pV4YtgCDTFFJvYZJE17ECtUan1TbdRttJ161p2qrj2pDS1cAytZ5mPzdKtXeNtQakCQJUQmIRCKB1oK6TlsbErj7yoW469kDVVm/QSOM7dGOZzunfDqM9ztiw2d0APgOgLcA/ArA/wTwRzZ8ru9QBHDt9r1Ytu0VXLt9Lw6dHIYsl06K2D+azolm8dwm3LR0Hm783humPsPPqK8PAI4NpHDbzn3oH017XDJCCDlDKa+y6u9GP78cdnw/8R+V1GvYNOFFrFCt8Uk1XbfRduJle6qm+qg29HR1anSiZJ2Hzd+tUs1to1AD13e/ht/1jeLLuw8EWgvqOt24fH5uYBmorvoNImFrj5X6rNM+Hbb7DdgzuPw4gI8BeAhTA8sfm/5b6LAqgHQmm3vPxuXzsXmXcZOVZYG+4Qm8PzCGvuEJ1zoaM9+rvj6FYwMpyLLsSdkJIUQLLa9qbUhM/X1wDCeGxtHaMHXmmOLNg6l0xV6YzmTLli1oAYZXfZOXGL1m9etOnB7Hgz8/hNaGBLrXduGb112IE0PjGEyVr9egaaIclbSPIH2nH7B63Xoa93N7N9pOrLQnu667WnXoJlp15YZu9XQ1Pqkfb6j7hrD4u1XC0jbMaE15rVbcuXnXAdy8bB5ODI3j2KD//NYI6jptStaEon6rhVLt0aqfehk/VBpHOx2HO3G/vcaOYzEWCiEuVP3+iiRJb9nwub7DagcYj0XR3pzEsYGUKZP1aqm82e9VX5/Cio42nBpNY8MTPaFZ5k8ICTaFXrV4bhPuvnIhbnj49ZxPbVvViW/87BD2Hx1Ea0MCxwfHseFJYz6m5YXtzUnEY9GyZQvSA1YYt3GVw+g1a73um9ddiERNBLc/tT/3t+41XWhKlt5aFyRNGKGS9hGk7/QDVq5bT+MLWhtwuG/Et+3daDsx257s9Llq1aFbaNXVzlsuxkRGdly3erqKSpKheKNvOI39Rwdz7wuqv1slDG3DjFdovbYw7mxIxHDXs7/0pd8aQV2ng6nJwNdvNaHXHmtiEUv9odfPC5XG0U7H4Xr3OxmPBvY5y46Vy/slSbpU+UWSpEsA7LXhc32HIgBgKkjoXtuFZzdeBkmSSs4mtNTH8ci6JWhvTuZMVo2eydoxW2JlJt/s96qvT7mee6/qyA0sK5/x4M8P4cTpcZwcSuGDwRTeHxjDh6MT6B8dx/sDYzg+NJb7e1BWzBBC3EftCb3D4/hw1NhqUgGBH956Cf7l7svxL3cvx0N/sRh7fnMSxwamjizasrIDiVgEX1/dicVzm3DHFQtyA8uANS98ZN2S3PmLpVD3LwpafUM5P/RypVYYV1ypVxiNjGdw/+f+GE+vvxRbVnbgwZ8fKrk6UtFUNCKhIVGDpRe05FYv9w5PlF29bEQTVlZLedWXFraPFR1teOrWS5DOZHXLk8nI+GAwhff6R/HBYAqZjFzRd5ppk0HGyL3OZGQcH0zhg8ExvD8whmMDYxhKTRbt3vhwLI0TQ+P45nUXonttF5Ze0FJyVZ3bOjPqncl4FI9+4eN4ev2l6F7bhcVzm0oOctjpc9WqQ7cYTKUxlJrEYzd/HK/8zSfxwOpO9A5P4MGfH8KWlR26nl2o1UzG/I5LPf0l49G8Or/jigV4dO87eeV5/NV3sHH5/Nz7NvyH8wFA0++89m+nCEPb6B9N62qtsN4GU/m+0tqQQDwawUN/sRj/+KVP4P+94UL8z386XOQ7J06P21bnTmtJXac79hzBA6s70d6cxOK5TXj0Cx/Hk395CQRE0fdW2t+TytGLHSYms5q7O4cn0nh/YAzv9Y9Oj+uM53mp3q5QqzuGzHq20fhAj0rfXw49/8vIIrDPWZZXLkuS9K+YSuRXA2CdJEn/Pv37RwActKd4/kIRwIM/P4Sbls7LHW9RbjYhEpGwcHYjdm9aBlmW0b22q2hFr1YnWulsSSYj41DvcN53GZnJN/u96utTDjsv/AzlrOn7fvJ27t61NiRw39UdGEtn8ejedzTvqdEVM0yKQUh1oDUL/sDqTnz9p4fQNzJheDXptlWdePzVd3D7pxagIVGDi84/K89/HljdifNa6ir2wlJepPatmlgEO2+5GOt+8AvdvkFvBcDsGQmk0lOfMTKeKfoMt1ZqhW3FlVZSmnue+9c8Dcly/sOPcm8Wz23CnZ9ZmNPUio423P6pBdj0wzOrkR67+eMlM6grMUdhfSuaqHS1lNurIApjoVOjadz4vTd0y5PJyPjNyWFsVO0c2LGmC384uxGxmLG1EWbbZFgod6933nIxUuksvvXyb4tir29edyHu/4ff5FbR9Q5PYMvzb+f+ffvnL8K3/+kwXjrYm/ushtoYJjOypgd1r+3CrPq4Y0mqyrUTYEr/J0/nX8cDqzsxe0at7iCWnT5XrTp0A1kWOD40jjt//FZe3X6kJVmkbbVnF3riio42fPmqDgyOTeLE6XHs6jmKL356YVmP1NNfUzKOpmQ8V+eRCDTL01RXA2BqYHnln7TnrWxW/C4SkTz3b6cIQ9uQZVmzbiUU97vda7rQ2pDIxQlfuboDqXQWf/HI63ke3Dc8NYi0cfl8NCVrMJHJ4t3+UZzfUl/RvXEjFiis02Q8iudvX4oTgxO6OwHt6O9J5ZSLHdSr7G/oase/fziOv1LV2XfXdOHdvtOY1zojry7V7zOzY2hFRxvuvWpqkYZWfLFjTRceevm3uXikUMtG4oNSVPp+M/db7X/Hh1KBfc6qpLWuBPBZAFcCmAfgkwCWT///VRWXzIcoArjv6kWmzk1W3tvamMDsmUl87OwZ2L1pGfZuvhw/uX0ZWhqmRFQ441LJbIksC3wwlCpaPfxe/1jZmRAr36tc37nNdWhtTBR9hnLW9Kquubl7t3H5fHw4Oom7ns3/u7pcvSOlE2Io16p32HpYZ/oJqVa0VpPd9ewBbFw+X9eLtd6j+NGmH/4S11zUXuQ/dz17ABDQ9EJJ0g/AC72w1MCy2rc+t/1VTGRk/OT2qb5h96ZlRcG+3kq6t44O5T5jZCKDpRe05P270YSCRr3S6Zl8r9E6D1ErKc3mXQeQLbhNyr0pzK+gaE29WqlveAKf2/6qbpIQddCppQkzKyudWm1uto9V2kckEimKTwrL0zsykXs4UV6z8cke9I5MmCqj0TYZdArrAoDuvX6vfwwbnuzRjL3+5sdv5VZT3nHFgqI62PTDX2JV19ypz29I4OTp8ZyOP7f9VZw8nb9KacMTPfjVsSHHEpaVayeAfr/RUBvT1YPdPlctOrSK1Xi9X3UEH3CmbjNZFGlb7dlaydY//703cM139mLrCwdx09J5mrtTCimlP3WdCyFplqcpWYO9my/HuqXzcoM0yr8rfhf23UJBbxuSJCGdkXO7O1obEti860DeYi5g2g+f7MEdVywAMPVsPDD9HFzowXdfuRB3fmYhtr5wEDc8/DrWfv8XOHlaP1+D0fbjlpbUdXpWfQKyLJXcCWhXf0+MUUovpeK0zbsO5OKDP7uovciz/urJHiz+SEtRXarfpxyzoYWWL9/4vTd044uN03GM8nuhlo3EB6Wo9P1Gv6PQ/4L8nGV5cFkI8V6pHzsL6ReUVWaTWbloNkGdpKFcUKSIaM7MJE6e1n+4rGSrUP9oGr3DE0XlrItHy86E2LFFqfAzWurjODaQf+Z0U7ImVx69s6i17nVhefU6ysFUmpmYCQkZeqvJmpI1uf83etam4juyEJr/npUFtq3qzPPCbas6ETUZUyhBnPpIoBOnxzV9KytD9wFL7zrq4tHc/2/64S+xcfl8LJ7bpHs/tMpnxivDsI1VD/W9+MTX92DL82/jzs8sxDkzazXvvRD590i5N0qfp1DYx+llUC/c+lrqodvMykonVptXkkXbSHn0+v9MlltlCylVF1r3ulzs1ZSsQXtzEvNm1Zf0Wy0dK5N9ha93ahDDyM41Pb1Nlth2HWaf8xtWvUSWBVKTGV2fKOXZ5ZKtKxPQRjyy0KcBFA3cCJ04IyML3PDw65jI6PtdtewWCiJKfLfl+bdxw8OvY+sLB3HnZxaitSGBjKxd5+fPqkd7czLvObjwNWfPrNVc9JBK6+doMtJ+vNJSue9lf+8eRvVS6tmpvTlZ8tmp1PseWN2JmM7gbDlf1osv1L/Lsqw50W518sqLya8gxx/cZ2AQdUP8zYnhvNkEdZIGM0FRudlDo7MlWrNP6UwW/aPpolmPsXS27EyIHbM0hZ9xTlOy6MzpwdRkrjx6Z1HXRCNly6tnfql0NtQz/YRUI3qzuYOpydz/F87slnpPe3MSNRFJ899j0Qgef7X4jMRIxHjXqfQdX959AL/rG8X13a9h2bZX8MGg+S1P5a5d+YwPR9N5KwSU+6G3UsHsShY3ZvK9Qm+Ve22N9r0HkHcvlXszZ2Zt3usL+zi9Qb0PBlOGB2jNrGxwYhVEJSugjJRHr/+PRRm6FlKqLrTudbnYq705iec2LUVUxxsVzyk1OK31ei2Pq2SHmdGHZKs78sLqc37DipcodX+kd1SzbrM6O4+UOldrQk/HLfVx0x6pp8mamLaf/b5vNDcgo+d3QV7FFnb6R9NFK3I37zqAO65YgJiOfyaiEp7btBTnNiV1n8sjkqQ9cKdhj2baj1daKve97O/dw6he9OqsvTmJ3ZuW6epbL25oa0xgy8oOfP2nhzAyob0g04gv68UXwNQZ0adG04FfWBjk+IMt1iDqhrhjz5G81Wx3XLFAcwVSuQcsI7OHkYiUC26UAePCg831gphdPUeLVt3NPSupOxOiDvD7R9NoqY8bmqXRezBQz/ScPaMWj6xbklemHXuO4Kz6GjywulOzrI+sW4K2hkTZmRs988vqzKhxpp8Q/1JuoEFrNveB1Z3YseeI7syu1nu2rZrynR1runR9pq0hgS9+empb4v3/8BvEoxH8l//UoZmIRA+l7yjcfq41+VcuwC917erP6B9N51YIPLJuCZqTNfhwdAK/Pn7a8MrGcl4Z9G2seujdi5GJTC4pDTB1n3es6UJWCBzpG8G7/aN5fV8yHs17/a6eo9j++Ytyv+s9UCp6MTJAa2ZlgxOrICpZAWWkPG0NCexY01V0z9umt0SSM5SqC617/ZGWOnSv6dKMvbrXdiEZjyIWkbD1hX8r+vfvTr8P0Nfx2PTqOsVrFY8q9LhKVqyaSRZkVf9h9Tm/YcVLlCRqtTURfLfAJx5Y3YlETCqq8+61XWieHpgwkmy9rTGB5mSNqckPvYGbWESjPGu68NDLhwEAj/zz7/P6CLXfBXkVW9jR0+55LXVITWaL6vSB1Z2onfbX40PjaG+uLYotHlm3BEmdCe3amuKhGzPtxystlfte9vfuYVQvenU2Z2YSrY0JtDUkirz3u2u6sP+9fnz38/l/37aqE1965i1seKIHfSMTONI7ktfnK4n5lNxkpXxZHV/sUMUj7c1J3HtVR9kj14KC0/GHU0fHSoVbOoPEkiVLxL59+1z5rvcHxrBs2yu53xfPbcLG5fPxsbMbAQn4xNf3FL1n7+bLMWdmUne7Xt/wBK7dvjevgSuzQcq2qnIH7+t9xk9uX4aTp6cyJa/qmouW+jjaGhM4Z2YSkYhUVCYAlg74N5tQaGpwXEZWAEIIJONRCAiMp2VEIhLE9N/V96rclke9MrQ0xPG57a+WvL8auPLk4KZ2jXD+PS96XQRd3r0/lEe4O0HgtWvUTwoT4cUiElLp0olg1O8BgIgECEhoa0ggFovo+owsCwym0jg+OK6biKQUSt/x9PpLccPDr+f+XpjwzYznqq/9dGoSX3j0zdxnKIkKv/LZP0I8FkVzsgaH+0ZwYmg8l8xKQfFDAGX7IofxjXb1+tQnbrkYJ06PoyYawazGBGRZ4P5/+HUuiciD11+IC9rq0VJfC2Cqnt7tH8V7/WOoi0cxls7iD9rqkYhFMZ6RkYhKOKU6K1SpOyXhyd7Nl+Pc5rqy12Qmma3diW+NxDCVlj2TkdE7MoFMVkYsGsm1Vx/hC+2Wqwutey3LAn0jEwAEZAFkZYEPhsax7R9+g76RCXSv6cK9f/82gDNJpQZTk+g6rwkCUi5R08mhCdz2xBnPfvD6C3H2zFoMj2dQn4jhay8e1Ey2I8sCJ06P4/ru10xpSC9Bq9J2AGi2HyZ+LsIX2gWsecnJoRR+1zeaSxC++U//EHNm1iIakVAXj6IpGc/ln+kdnjq3uDBJ31Six3FkZBkDY5N5CVe713Tho20N+N2pUVPPRoXPiwpaz4TRCHD1t89c9/Vd7Vj/yfmoiUqoKfA76jcP32v37274E2Rlge//n9/nnsPPqo8jGpUwpyGBw32j2PBkD1obEvgv/+ljOHtmLdJZGclYBGfPnBpQM/p8bbb9eKWlct8bgP7eDjzXrhm9lKuzD0fHMTIhQwiBaERCPCZhZDyLp3/xHi46vwVnz6hFU11NXhygJGFX+usVHW34v//jR3PxsJLELxGLFMXJ6iTmyjPOQGoyV750Jqvrv0Zi6mrBYmJPQ9rl4LJBtBriio423Hf1Igghctl9FdQDvHoVZ6RiyxmAmSCmsAGaHeg2el+MvM/ujk3r8wBLA+aem74X+Hlw2SxVPBgdeO1WOlhVSKkBYzP+U0m5lPduWdmBrS8cLOpDvvLZPwIAyz6YycglH56V7//mdRfmDW4rKH2Fx5nofaPdcgNXP95wGZrr4xgcS6N/NI0de45g/9FBtDcn8aP1l6J9OniVZYFToxMYn8wiKk2tZJ6RmBroV2fB/vJVHRgcm8SJ0+N5n+XiwL5l3Mg67yQ2xSG+0K6ViTn1Net53NZrFuHmx97M+9tPbl+GrIzcZ0QiAodPjmJWQxxRScKJ0+N4dO87uPeqDpwzM6kZdyrlHZ3IYPWO14qup9SDoF5Zt6ycWrEUlPbjA3yhXcCal3wwmNKcmHhmw2U4p2lqcO7D0Qm8dXQIdfEoBlOT2LHnCPpGJvDMhstw9oxaRCIS3h8Yw+1P7cfdVy7E2TNqkRUCJ4bGsaCtAVJE0lyk8tympWhrrNUsl9mBm0o8tIoHnH2l3V+fOF00URyRgEf3voNVXXNzE3O7eo7ilv/rArQ3J/HnGuMGW69ZhEXnzsxbYGakfv3QF1exFs3iuXaN6MVofRb63ZN/eTHuee5fNcfL0hkZAgJfevqt3MDy4rlN+PrqTtz82JuantlSHze1mMju58iwYvE+GdJuzIbymUaSpFoA/wwgMV2GZ4UQX5EkaR6AHwE4C8AvAawVQvhiHbuyNUD9UHjHFR/F9d2vobUhgQdWd+aOxlAaaUYWmlujlIpTn6ei13jLbV1QjoQoFMdUwC8ZXgFtdXur1vvUyQ3NrDS20gkWmt+c6ZXZCuXuLyHEP9iZaETLZ7rXdOHspgSGxjJY94NfGPYfO7b/P/jzQ9i2qjNvpfJNS+fhv/2vf5seDE5a8qZYLIL25jok4zHMmVmLi87rzPM5pezK9jK9vkLtlUoAd3woVXW+WXgvsrLAD19/FxuXz9dcgaEeeFa2lOn1cZiBvJjgpYO9OHh8GE/degn++qlf5r02CNud1fdKvSNJOVbLz5rxw8O4nRiJJ9XX3NqQwB1XLMC8WfWoS0QhdBLwzJtVjxUdbbmVd7Nn1GJwbDLPP59efymysijaQZGIRRCLRTQfVJSjA7as7ND1JT3KJRkKSvshZzCi30KUBHnKTlJlAE9JuCvLAscHz+zYUfv1B4MpDKUmsXB2I+KxKPpGJvAXj7yR+2zlIVsvWeD4ZOlEkDtvuThv18pHWuo0NWnluhXC5mFBJRKRsLCtEf/fbZciK6YSN54YGseshjhuWjovL+bbtqoT9fGobvK6C1rr83Sifo4vWwYPn3dL9S2z6nmckN8opxezg89P3XoJfvj6u7jo/Bac0zQ10asslgCmYt17Vwqs+f4b2LKyY3rH1JkdnEOpSd1nLKUNGPW7wvE6xgTaOJnY05PBZQATAD4lhBiRJKkGwP+RJOkfAHwJwINCiB9JkrQDwF8C+K6TBTE6M1PYECVJys2YHxtI4es/PYSt1yzC/LYGJGumPuf4UPmETeU6jlKDx7IsEI0A3Wu6irZrFzYivTPAlIHuUt9TisL3qZMb6jX+cmUxihGjMdoxE0K8x6oPaaHlMxue7MHWaxahtiaC1oZEzr/L+U8l5VL6jq9d2wlZlvH0+ktxfGgc/aPp3KDkwePDFc2ql/I5pexKrgD1g073mqnzJ9X9YDIeLbnjphpQ389MRsbVi9uLViX1Daex/+ggNu86kFuVXjOdeEavj3t6/aWaMUE0Ihl+KPTb6iAlL4SfBjmM3CO74hA/US7eUa65tSFRdCRP99ourOhow0sHe3Ovb29OojEZzduuqmxpVftnalIuyui+edcBPLPhMt2yKA82Lx88ie/ceJGpyRU9P1avdKoWrwoTZuP1eCyKFR1tRQN43Wu70NpYq5tobes1i9A/msb/8/SvcnrRG4w4PpTV1FpUQ17qo/9Sk9m8Qe1H1i2p+LoLfU2g9AIm4h6RiISRiUyehnas6cLjr75TpL/Hbr4Y0UhEU1exCnzLqo7s8MtSfUu1xY9BoZReTo1OlPSWwvGXFR1t+M9XfBR/9aT2MW+KZxb2+RuXz8/F0OWesYzGbF5PtAQFO5+3C/HkIBsxxcj0rzXTPwLApwA8O/33xwH8mV3fqXVodalEIlqvVx+sLQqSxe0/OoibH3sTUQm5Vcl2ZGTVO0y9OVmDQyeHcfW39+Lev38bW69ZhD13Lsdzm5Zqmni5GQqrB/wXvs9IckO7ZksqyVRPCPEfdiYa0fOZungUdz17ABuXz8/7eyn/qbRcSt8xe/ocvdU7XsOGJ3pys/p2zRarUfowWZbRvaYLfSMTeH7/+9h5y8V4duNl2LKyA996+bc43DuVjE7pB986OlS1vqrV7w+kJouSg2zedUY/xwZSaKmPo3tNVy6pmJ72sgKaMYGCkiSl1MCyleRnTuOnvtjoPXJy1YZfKNSzcs3KA13exNsTPbj3qo4ij8vKKNJ/oX8Oj2uvOip17J4yMHjN4nPxnVcOY8vKDjy78TL88NZLsKC1oeSDYLkkQ5UeseZEghtiDDP3v6U+jnuv6tDUsjJ4pqXL81rqsGPPkbyVccpgxN7Nl2P3pmW556jCxKzK5Eoynv8cp/adXx0bMpRQysy1avna2ET4PczvKHV4fChV1AdufLIHq7rm5r1+ajIZSKUzRYlSt63qxGBq0tE+3akYolTfUi3xY5BRe9GHoxNlvaUw5lvVNTc3sKy8VomTlf45GS/u8xe0NeDYQCq3+KXUM5aRmE3dHoHyMXU142RiT69WLkOSpCiAHgB/AOA7AI4AGBRCZKZfcgzAuXZ8V6mEb1oPROXOSgaMjfjbsTRfbwZG3bCPDaRw82Nv5lZtaDWicuW1OtNTtIVYaG+tVDd+u2ZLquHhkJBqws4ZZz2fGZzeftU0nTFe+Xsp/3GjXHbMFisU9nkb/sP5+OGtl0AWAmu//4u87z54fBjfuO7C3N/q4tGq9FW9OGFGbUzzfij6aW9OYs7MWsxurM0lntGr49qaSFFM8MDqTtz+1H70jUyUXeHj19W2fuqLjd4jN9qhl2jp+albL0F7cxJNyRrN+pKk4hX0ejvw1P45OKZ/5I4eysDgjd97A8cGUrkV00bORnRqZRKPGfAWs/c/EpEQjUi63qPXxo8PpnKr6dTPQFqaa0pOHQWz9ZpFuSMuZs+oRVNSf3eoXvsqHAQxc61avvbOqdFQe5jfUdfhN1UxlIIy8aymvTmJk6cn0NaYwOOvvoMtKztyx/nz1AwAACAASURBVLk8/urU+cxbXzjoWJ/uVAyhtDUj2if+otCLHv3CxwGgpLcUxnx69f6xsxtzu0MAFPX53Wu70N6cxP6jg/jGzw5hy8oOtNTHcU5TMncmvkK5mI39tzmcXOHtWQpOIURWCPEnANoBXAzgY1ovK/yDJEnrJUnaJ0nSvr6+PkPfpWem45PaD0SpdLbsKhwjI/6lZsPNoAQ9c6ZXvR0fSumeA6Zn4EbLq6zMNjPTo35fsiZWdrW2XbMldqwMdxMr2iXED7ipXas+VIiWz2xb1Ykde46gvTmJsXQ293cj/uNUuVZ0tOGpWy9BOpO1baVcYZ/X/S/v4msvHkRE0n4Qn9Vw5tqVs5nV+NlXy2FUu4X3rLUhgRND4xDQXm2s3CdlxaQ6o7leHzerPpGLCf757sux9ZpFuYzZRlb4+GkQV42f+mKj98jJVRt2UYnvasW9X33xILrXdmEsndWsr8j0zjvF46bLoPlatX9+pKXO9L0sNzBYDrv8WI2fVuAHHTuf1fTuvywLXX0qD8qFunxgdSe++dJvTfX757fUY9G5M9HenMSic2fi/Jb6krtDjfShZq9Vy9ceevkwutd0+drDgoiVmGEyK2vWeVtjIq9+vvv5iyCEwI49R3DzsnnY+sJB3PDw69j6wkHctHRe3op6J3AqhlDaml7fEtT4MWjY4bt18Sgeevlw0Uri7jVdOW8pjPn0PC8Zj+X6Z60+X71ief/RQWx94SDqE7GigWWgfMzG/ts8TsRRgIcrlxWEEIOSJO0BcCmAJkmSYtOrl9sBfKDx+ocBPAxMZcI08h16ZhqdDkoKZ0GMrL41OuJv15m/WjNLZmas3TqDpjlZg+61XXnn85UadLdSFvW5ZuW+y09Y0S4hfiCI2lV85rlNSzE2kcU7p0bxjZ8dQt/IBLrXduHcplrs3Xy56+dxFSZBOzWazs3k2zXTrtXnvXSwF//1s3+k2W9EVd+1Y88RzQS1ZnzVT+cCG9VuOpNFa0MCW1Z24JymqRU4p8cncfTDMTx4/YX44jNv5e5H99ouzKqP657vWq6Pa21M4P2BMdz82Jt57yv3kOfX1bZ+SqBi9B4F4Vw+s76rbncAcuciK7x0sBdbr1mE1oYEvvv5i/BXPzxzznHhVn8l5tRKRPrIuiWYPSOR558ATN9LIyuR3PQRv07eBBEz2lXqeSydKUoEpXf/y+lT0YpWotpv37g4T7fKkTGVPMeptayV38DKFm+9z1foG5nAnKZaX3tYEDEbM9z/uT/G+bPqsGNNFzYW5D86Z+Z0Ysh0Br8+MYz/+vy/5bR9uHdEMweHk326UzGE0tZmz0gYygNFnMFKzJDOZPHN6y7EYGoSO/YcwWBqEn0jE7mVxE3JGoyls5jTdGbAtzDm29VzFNs/fxE2/bB07oRC/e0/OojHX30Hz2y4DEIIU7nPCl9rd0J4vzzDBBFPBpclSWoFMDk9sJwE8B8BbAPwCoDVAH4E4CYAz9vxfWoxK1mFW+rjqIkWb1F9ZN0S1NYYfzjRCjisiLLcewpnZB56+bDpAQC7k9sVlrk5WYPDfSP41j/+Nre1oa0xgXNmJm0bdNc6RP6pWy9BdPpstIwscHwoRTMghAAAJEhoSERxQWs9vnzVx9A/msa3/vG3+OKnF1Y0iGvU5/Ve19qYQN/whObZjMqgpdXgRunzWhsS2Lh8fi44TNZEivqNB1Z3ojZ2JrlM38gEZs+oxXOblmIyI1ua/Avi1rRkPIq7r1yYd2+2rerE46++g02X/wG+cd2FOKcpmUvYa+TIKKvJeoH8SdSsAIQQqIlFsPOWi7HuB79w9cGtnNbtHqitJLAvN9Ad1ocGrXb3nRsXYzIr0FRXg6gk4dRIGjWxCJqScYxMZEpu9VfHnH3Dae3tqvX5ZTAb05WqK7PZ6rXq0qxHZ4XAo1/4OB56+XBuAMgPkzdhRqueCxNBad1/Q/qEjg/X63+3MnGSSpvzB7WWlQET5dlEkiREpakyK59ndpCvpT6OnbdcjPf6x3Jt9iMtdWhK2pP8Nay+6CSFMcOKjjbsvOViDI9n0Fgbw1n1NYjFItOxHrD1hYN59d3aODUOkayJ5v7N7j5d6zld7bkrOtrw5as6kEpn8MGgjLaGRN4uLDNEIhLOqk+gKRl3bMKDOrUPPe/95bsfYuctF+PD0TTGJ7OISBLmzapHZjofmbIKeeHsRjyz4TJ8MJhC/2gaT772XsljLQDtPv+Ln16o+VqljIX17UTi9XL3JQjPMH5CKpVww7EvlaROTCXsi2LqaI5nhBD/XZKkCzA1sHwWgP0A1gghJvQ+Z8mSJWLfvn1lv089w12YVXjnLRejoTaW9xANwLKwrIjSyHveHxjDsm2v5L1v8dwmfPvGxQDgqMmqG7cygCtkgVOj6bxVw0/dekluBZ5Ce3P58/PM0Dc8gWu37y36DiPnZBvEFecwql23OP+eF70ugm28e/9VXhfBK3yhXbeCPyMDC4qvblnZURTYV+JNRn2+3Ou0fB0A9m6+HKfHM5b9TJYF3u0fxcnT4/kTkGuXoKE2it/1juY9oJ7XXIeB1KQtdabn0WXutefa7R0ex+e2v1pUbkU7W1Z2YNE5M3Buc53u55t9mNfTBgDdmMXq4IdV3A607fg+vXpw6Fo81y5Q3O4Wz20qmix5YHUnWhsTOKs+jhmJmpJt/uRQCr86NpQ7D1RZTbp38+Ul24BZ9OqqnI+Uq8tKPPqB1Z34+k8PGToHPeB4rl29elZ8V6/Ojg+l0Ds8gYZEDOOTWXwwNI4de47g2zcuNqxPve/ees2iXB4bs/2u1oKbUh5v1IvMepeZ1wd0MMVz7ZaLGZ5efynmTC+u0loYdccVH8XGJ3uw9IIWrP/kfNREJdREIxUN8KrRq9cFrQ0YSE1ClmX0jaTzVlvvWNOFP5zdaMv3201AdaqF59oFtP1vRUcbbv/UgrwVyIX9oaIfZUXw7U/tz03GKpSKE8xM+pqp70xGxm9ODlesZ712/cyGy3QHwasIQxfviXsIIQ4IIRYLITqFEIuEEP99+u+/F0JcLIT4AyHEdaUGls2gzLDcd/Wioiym637wC0iQ8s4bqeSsZCtnvhh5j9aZhn0jE4jHoraflaJGnVn29qf249CJYXxu+6ua2ZB7hycc31Kot+3ByDnZhBBncSoTtZXvMZtgxwxGfb7c6/TOqpUkqSI/i0QkNNTGcgNLuc94Yh8SNdGi8yOV1TV29CVB3Vo+mZE1y61o5+wZtSVXQJjVfqk4Q9HNqq65mpnXszIc7ffVuH2OnR3fp3eOXJjP5CtsdxuXzy9q/3c9ewBHP0zhraNDONw3gpb6uKaO5OnFA+rzQO/8zEKs6GizfRWvXl2V85FydVmJR9/17AF8+8bFlvOkEOPo1bOSCEpvMuCGh1/Htdtfxc2PvYnT4xns6jmKu69cmHe0i9Xvrpv+DCv9rlrLA6lJXQ2afc40611mXh9mX3SScjHD8aHxXAxQWN/3Xb0IG5/sQWtDAtcsPhdfePQX+OQDe3B992s43DfiSO4NpV4HUpNobUwgK5AbiFP+feOTPegdsWXoxXaoU3vR8r9VXXNzA8vAmf5w4/L5ufv9wVAqF+fe8PDruPvKhVg8tyn3GeVWCxs959dsfQ+kJvHQy1M7559efym2rOzAQy//FgOpSVP3RS8f2weDKUeeZ8OI/6amHCISkSAMnKWsfr2Vh20rD9ZG3uNV8hl141Y/rGgN1vSPph0/yF9vMMbIOdmEEGdxK/gz8j1mE+yYwajPl3udnq9HJVTsZ3oPPpMZ2ZEEDgp+Su5mBr1yK9ppaYiX7G+taL/cwJofMq+7PVng5PcFdeLDCIX61dNOXTyKuni07MBU4eKBzbsO4N6rOlw7O7Ocj5Sry0o9GoArkzfVjl49qxNBqdHy2c27DmBV11zc9ewBZEw8+JfyfIVK/KGcBs08Z5r1LjOvD7MvOkm5mEHRquKz6vpWxiM2Lp+vOYFsR8xcrl4ns9oxYiYrV/zdTkCd2ouWflvq47oTJsr/qxcSKoPPd1yxAIC9Y1NWPO+lg73Y8EQPbnj4dWx4ogcvHew1rY+oTpLYwvZM9KmawWWgfLAqywJ9wxN4f2AMfcMTlmYnrDxYG3lPJaupK0HduNUPK1qDNbt6jqJ7rbOZi/UGY5RzstUEYUCDkDDhVvBn5HvUvqrOSAxU7k1Gfb7c6wp9/ZkNl+GsuhpkxdT2tHKfb0cZ7caridBK0Sr3tlWd2NVzFNtWdaJmeleTHqU0aTauUOrO7kkRK7itIye/L6gTH0Yo1O9YOqt5rWPpLAZTk5YGpqJl2oCdKMmh9XykXF3a5dHEWcz2F3raVJ5PJjPGB8a0vvuB1Z3YsedI7jWKFqw8H9qpLbOfZeb1bAPWKBcz7NhzJM9n1RqSpgewnJxALlevNdGI5r/Hov4cGqJO7UVLv22NiZITbsogq5pjAynMb2uwfWzKSc8rRTIexQOrO4vadWF7Jvr400EcolQQY9d2bisP1kbfY3U1dSWoG6v6QVdrsOaLn16IhW3ODoDrDbLPqk8EckCDkDDhVvBn5HvUvqpOsGOHNxn1bCOvi0QktNTHcXo8g+u7X8Ml/+OfcH33a7jjio/mBpit+JlXg7xeTYRWijpByfN/vQyPfuHjmFEbw6quuXj81XcQiZQOl3R31cjCdFyh1J3ykOplv+a2jpz8vqBOfBihsN1dOHcmHllbPHDWXF+DHXuOuPKQZhVZFnnJoZ/deBmeuvUSLGhtKMpWr1eXdno0cQ6z/UW51aJmNFr43c9tWorZM2rRN30sgKKF5mSNpedDO7Vl9rPMvJ5twBqF+nl6/aVoqqvBqq65RQkpC8cY7vvJ29ixpkt3EtAOry1Xr20NCexYkz+Bt2NNF9oa7MmRZDfUqb1oee85M5O6E27tzUl0r+3Crp6jeZ/T3jyV6NrusanmZI2mPpunV1EXYpc+mpJxzJ5Ri8duvhjPbrwMW1Z2lE0wS/LxJKGfXVhJimY1eYgd32HHe9zOlKo+UL21IVGUGffeqzoQnc58bHdZzF6rTffGFwftuw0T+oUCz7XrVsINM8maynmCVd+w07P1+p9nNlwGIYRlP1O+W5ZlZAUq+iyH8Vy7Cla1pZW8qXttF771j7/FSwd7c+8zGlcYrTs3YgIv4g6nvs+Bz/aNdguZOjt5YirjOyQcHxrHj/cdxZ/+8RzMm1WPukQUs+qLHwa9TpxkNB43ktTVj3F1pdhYXt9qVw8lWe17/WO5pLTN9TXY/srvpha4VKhRrXvbP5q2/Hxop7aceCZSv0aSJEQlIBKJ+L4NwIfaLZVAr3dkAh8MptA/ms4lR13R0Yav/tkfo294AhtUSci613RhTlMtmpKV10E5DWQyMnpHJpDJyojZmEzQKYLm1Tr4Trtq1Pe4JhZBLCLlEkg31cZw/PQ4eocn0D+axq6eo7b4rhZ9wxP48u6pI4+UxMK7eo7ia9d26vquXfqQZYHBVBrHB8fz2mZh0uAQaNEshi4w5nQp/Iay+rcQO7dz631Hpe/xIuBXz2ylM1kk41E8t2kpJjOyo41JK7NuuYFsK/edEGIfhX7hlEcY/Z5ynlDKUwGUDByM+o2R1+n1P0II3YzLRlBWRYckw7YrGNFWqYdI9ftkWc4bWAaMxxV+igkq6VutBOBO9uXVFCdEIhLaGmsBTNVDXSKKm5aer/uwpH6fGR+3+yHLaDxeri7t9Gi/4PXAvx+YyMjY8vzbeZN496/qtGUwTksLlTwfWtGWXnsy+1mVxD/VoiU70fJNrUnnbas68Y2fHcJLB3vxlc/K+NicGXhu01KMTWTxzqlR3Pv3b6NvZMKWuiingVgsgnOakrr/roWXg2pB8uqgonmP67X9onttV96OIqtoaUo5Q7kwhv7KZ/V91y59RCISzqpPoCkZ14yD6J2l8e/0lMt4vQ3QCG5nSlXOhzo+NPV9c2YmcVZ9Am2NtY4fzaG+1sVzm3DT0nm48XtvVHRkCSHEedw6vseO79Hz1FMjE/j18dMVH5NkFCf7H2bYNk85bZXLwn7mfdpnGtoVVyjlaG1IoHttF7553YU4MTSOwZQ/6tau48ZI5UQiErIycgPLQGkvMOqvTtRxpX5oR/4Uv1Ltfq51/Rue6EFWhmOxhhvPh4pmTw6l8OsT7sQe1a4lJyj0zYHUpGYCyo3L5+c0FIlIkCBhzfffwM2PvYn9RwdN14Vbnsc+vXrR894BVQJUK+hpKhn3flyusD0DUyuqjw2O4cTQOFqnj5Chd+bDweVpyp3H7IdA1c1MqV53IOprdTKbLiGketHz1NF0xvAgjB3YcVaYXj/FDNv2Y/SeOn1GYDqTRWtDAnd+ZiG2vnAQNzz8OrY8/zaOD4774mGPgxf+wqwXGIl9najjStqN17Gr01S7n5e7fiee15z2cbVmf3VsCBuecCf2qHYtOYVag6nJjOY9VjSlaKiSunDT89inVy96Gk1NZivSmp6mMrLw1Rnb6nb2ia/vwZbn38adn1mIxXObcuWmd05Rdcdi6KG3DRCAb5a+K7Pnhed+OTGLo9fYlTPGnN4Wo75WJ7PpEkKqFz1PzcrCVc+p9DiRUlu03Ow3qoVSupFlkas3p4+JiceiuOOKBUWTrxue7LGUL8Ju7Bi8qNJz7RzBjBcY3fbpxABVJe2mXOwadKrdz0tdv1NblZ328f7RNB78+SFsWdmBBW0NrsUe1a4lJyjU4KNf+LjmPT6nKYmzZ9TmNFRJXbjpeXb6Pfv2YFETi2hq9EjvCEYnMpZ9Vk9TkxnZlWMWjaLVzjbvOoAtKzuw4YkeeqcKrlxWobUN0E+zdG5mSi3VgbgxS6q+ViULtBo2YkJIpWhlI97++YswOOa+51RyzEepfooZtu2npT6O7rX5utm2qhNfffFgUWzg5DExLfVxzJtV79vJVzuONwjzKlS3MZN93Wjs69SRAVbbTdhXY1a7n5e6fief15z0cVmWcdPSedj6wkEc7h1xLfaodi05QaEGH3r5MB5Y3Vl0j9UDy0BldeGm59nl9+zbg4UsC4yMZ4q0/M3rLsRDLx+uyGdLacqtYxaNoNfOmpI19M4CuHK5DH4KVNWz5+oM8soAglMrhxWUxu7GLGnhtXav7cptFWMjJoTYwUBqEg+9/FtsWdmRy0b87X86jJuXzcO2VZ25FaF+95xS/ZRekhmuGLFOJCJhVn08Tzff+Nkh7D86WDLZSCmsJr6rS/h39ZnywFy4ktBoOwr7KlS30fK7h17+rWb2dbNHv+Ql+VnThWgEeav43SLsqzHdSprrV0pdv13Pa26vqMwK5GKNHXuOuBZ7VLuWnKBQg/uPDuLrPz2Ep9dfCmBq9WcsIuH4UKooWaPVunDT8yrt0xXYtweL/tE01v3gF2htSOTih7F0FrIQ2H90EAByiw/NeqddmnIavXbW3pzE7k3L6J0qOLhcBjtN246AJRKR0FIfd/yojlKN/fhQypUBd3XWz9bGWgZAhBBb0ctGvP4T8/G1F3+NrdcswrxZ9ahLRDGr3ttZ80LU/YkkSVjR0ZZ3Hep+Su2lzHJcHiN9dSQSwdYXDtoWG1itk1n1Cd8G5pUOXvhpcj/oyLJAajJjOPu60dhXqePnNi3F2EQW75waxb1//zb6RiY88ZWgPKhWgtrPw0opD9a7fjue17zoH4U4cwzX/qOD+MbPpo7I+NjZjUjGY44+71SDlpymXCzWNzKR03ApbVmtCzc9z64JCfbt/kXLe5X6OjYwlcRPQZk0aW9OIhmPWvLOoExy6bWzOTOTviur13BwuQx2mbadAYsb5yGXauz/P3v3Hh9HddgN/3d2pZXWko2ELBsH6ymu67hReU2NHIPtvqmJG5MWJxTsQF9im1tArsOHPmm5PW3c8L5OPp8YSGkpcSxIAtiUBodL4YE+iQnFyVNsLnbcEKo8iuNCYoNtyUKydVntSjvn/UOaZS9z35nZmdnf9/PxByxLq9md35xz5sy5VGJkiFrpqu+t+KkvEZFdemXZR5qSePCaxRUvY/TKcq36ZMf6DgDAnu5ew3pKr/54/pYVyCoIdOPOD/mfbWtjHW5dtUDzAYObN3R2R/EU52JBa2NgG+bldF5EfRSql/IzUlsTw/DYBH7dP2r587ST71hMQEBg/XdeL3htKyPR3B4lGpYbVdKnKBLv9o/g1/2jmJaIYzSTxW+1TMN5LQ2G59GNMrncEZVO8lxczh06OoitL3RzFGcIqO2F+1/qwdqONrQ0JPA3l7VjXss0dP3vdy0t21J8nu1myO8yz40HEqzbg0OrrbDxu28UlqGNCc3zpS5b+vDGJZhQZKRHo7NtYV1VdC6X03h1K0xuTgHReuLX2liHzEQWJ0+ncGokU7KEhJNObL0KpFIjQzjijojcpFeWFa+HB5jXI253khiVd1r1yabHD2J35zJ85TPS8Pfr1R/HB8fQ+Xj59UbYqZ9ta2Mdbrt0YcH05K4NHZjZkEAsFkNLQwILWhuxu3MZxrMKauMxzGp0NrrdziieaqoHq2EUqhe0MnLvukX4Xz8/bnnKvVnbt7i8UxTFUobzfy6ZiOPkmbQnG7BF4Wa2Wg2mMjh5Zgxbnnu7IL9N02pxdoP+ebW6BBQA3bq6nBGVTstmlnPhpW7GeO3yeYVthfUduPH//m2MZyXk1PdZKSOdZihsZR4zHwx6bYXWxrrcSGV18EnJ8ldT7WF1SQijWe1G90dWMx+EDSDDdp1VSuQ7l924EXMjTG5OASl+4re4rQl3fHohrn7oNWxZ014wVdfr9ZD9vMi5RhN55by7XrT9M+9+/TIPjoT8ZLUsM6tHvOjwMyrv9OoTKSXObZ5m+LpaI0ZuXbUg17Fc/LuqrWxVP9sta9pzN4sActMB1Tp25w1LkZ5QXDnndkbxVFM9yJEizmhl5PanJnc1V6fcq5vQGE3p1Gv7apV3XRs6DJfm0fq5R677eK4DUT3OqGaZrEtlsrj9qbdK8vvkzRcDDcY/a2UJqLqaWMnIPLXcLmdEpdOymeVceGUmsljb0VbaVnj8IHbesLQgZ1bKyGqp35n5YDBqK6jLXxwbSCGVyZqeL72y02y5DCuZr6ZBFVEQq/QBeM3L3YPtcHNH7eJdZW9dtSDXEGtK1vqyjlEldvDkGk1E5DYrZZlZPeJFPWNU3pVTn2jtSj5vZgPL1inqZ6tXl6pf/3X/qGvn3M5O8dVWDwZpt/CwMNrV/NDRQXTuOoi/+v7Pcrux26VV3nXuOogvX9ZumOHin5uWiFdVlsmabN4axKpjAylkpb3X0auXf90/qltu2ymLi5VTNrOcCye1k03rvH8wkrFdRlZT/c7MV55RW0Gl3luYnS+9slNvuQy1zLWS+aD05ZE1kR+5HJSC2s0pIMVP/PIbYur6N1pPjvqG0qF+Qsg1moioEszqEaOlit4bGHVU5hqVd+XUJ1ojRiQky9Yp6md74vSY7hpzgL2OMbPpfHZG8bAeJDN6GRnNZHP/X84UZL3yMB4TJRkGkGt7AshNtwX026vMcnWrr9XOb32tvfFQejmdloiXfC1/6vaM+hrs7lyGuEBuCSQrdTfL5urT0pBAKjOhed6LO77UMvL5W1YglZm8d6+vLcwGM0R+crOtoNeONVouAwCSiTgeue7jmJaIYzA1jh17j+Q2wVQFpS+PrIn8yGU3RwyXI/+ie/XOS/Ds5hVlDefPf4KUrK3JvceXu0/im9dcWPDkaOcNS3HyTBpXbH8VK7a9giu2v4qek0NQFJvDACqsnBEFREROmdUjxf+ev1SR0zLXqLwrtz4pHoEws6GOZWuelsYEfndOI7rWdxR8JtvWLsKOvUcAAKOZrKW2hTqdz6z+tTqKh/UgmdHLyAVtZxWUF8Bkx+97A6PoG0pbLp+MysP8DAMoyP7VD72GOz69EIvbmgAAO/Yewb3rFjHLVECrPura0AGpSFdyqnac5H9NnbqtZvWqrv34YHTc1kNhls3VJxYTmDOjvqSt8M1rLsTTB48WfK+as5Nn0rj6odfwiXv24srt+wraA8wQ+cmorfD6//gkdncuw4z6mqk1w83LXa12rFF7QVEkTp5JY8tzb+Pqh17D1he6ccenF2LnDUsLMh+UvjyyRkgZrg7GfEuWLJEHDhww/J5qWKclf7faa5fPw2P73sntWjtreh3qa+O48lv7Sp5MhXENJx8WdPclFFay6ycnaw4HlZO1kCOy5nJVZtcPdtdcLl5LFHBW5vq1gYWiSLzbP4Jf949iWiKO0UwWv9UyDee1NPhVTwYiu8XncXX7LHz5snbEYwJZReKrL3ZjT3dv7qGtlTWX+4YmH+y6Wf8GYWMTyglEdotZ2YDUadvY6s/qZX/r5efj+kffzF1HjfU1GJ9QmGX/BTK7QGF+i8vecnOqteZyS2MCV24v/z6JZbNvApHd/Ptv9b67dXodXvnFCSw456zSzVMt5IwZirxAZFellTcArvWdmW1OrtVGeGbzcsyaXm/pNXht+MrShx35ZTGqYdF49T3e/dnzcVXXfhwbSOU2DJjbnMQTN10UmekE3KmTiPxmVo8YLVWkclLm+lXe9Y9kcjfbqrA+gCxH8bpue7p70X18KLcb9teuWISvfKawAW7WtvBiOh/rQTJjlpFyNo6y2q7Wy/78WY149c5LItkeJ3eo+S1+QOFGToHSctts6rbd46bqkF+O5t93q8uq7O5cBimlrZwxQ+Qnrbz1DaVd21jSqL2g10YYn1AsvwYFT+Q7l4HqKKhjMQGp06ERF4JrOBERlcGsHsn/976hdKjKXK5nNsnoc9A7/2ZtC66hSEFU7jVvpV2tu3t8bTzybXJyh1c5Lf4ay2lyQi+fUkrMbppW8v3MGYWB2/cEeuWwneuhGvryoiLyay5XE701aZKJONdwIiLySdjWzeN6ZpO8+BzClgWqDn5c88w+lcuvuolZJSfsIEdCtgAAIABJREFU5pM5ozBguUvlqIqRy9VCvUiL16RpSibQlExwOgERkQ/CNoVLr+6otgaeF59D2LJA1cGPa57Zp3L5VTcxq+SE3XwyZxQGLHepHOxcjhCzi5TTCYiI/BGmKVxs4E3y6nMIUxaoOvh1zTP7VA4/6yZmlexykk/mjIKO5S6Vg53LEcOLlIiI7GLdMYmfA1ULZp3CgDmlIGM+KYqYa3KKay4TERERERERERERkW2+dy4LIdqEEK8IIX4hhPhPIcRfTH39bCHES0KIw1P/bfb72IiIiIiIiIiIiIjImkosizEB4K+klD8VQkwHcFAI8RKA6wC8LKX8uhDiLgB3AbjTr4NSFIn+kUxVrzdJRFTNWA9UN55/Citml8KK2aWwYnYprJhd8orvnctSyuMAjk/9/5AQ4hcAzgVwOYCVU9/2GIC98KlzWVEkek4OleyKuXD2dF5oRERVgPVAdeP5p7BidimsmF0KK2aXworZJS9VdM1lIcR5ABYDeB3A7KmOZ7UDepZfx9E/ksldYABwbCCFm3YeQP9Ixq9DICKiCmI9UN14/imsmF0KK2aXworZpbBidslLFetcFkI0AngawH+XUp6x8XM3CyEOCCEO9PX1uXIsmYls7gJTHRtIITORdeX1iQBvskvkh2rILuuBaLKaXZ5/Chpml8KK2aWwYnYprJhdCoKKdC4LIWox2bH8T1LKZ6a+fFIIMWfq3+cA6NX6WSnlQ1LKJVLKJa2tra4cT6ImjrnNyYKvzW1OIlETd+X1iQBvskvkh2rILuuBaLKaXZ5/Chpml8KK2aWwYnYprJhdCgLfO5eFEALAdwD8Qkr5d3n/9DyAa6f+/1oAz/l1TC0NCTy8cUnuQlPXnmlpSPh1CEREVEGsB6obzz+FFbNLYcXsUlgxuxRWzC55yfcN/QCsALABwM+FEP8x9bW/BvB1ALuFEDcC+A2Az/l1QLGYwMLZ0/Hs5hXcNZOIqAqxHqhuPP8UVswuhRWzS2HF7FJYMbvkJd87l6WU/w5AL72r/DyWfLGYQOv0ukr9eiIiqjDWA9WN55/CitmlsGJ2KayYXQorZpe8UrEN/YiIiIiIiIiIiIgovNi5TERERERERERERES2VWLNZSIiz5x314uVPgQiIiIiIiIioqrAkctEREREREREREREZBs7l4mIiIiIiIiIiIjINnYuExEREREREREREZFtXHO5yimKRP9IBpmJLBI1cbQ0JBCLiUofFhFRqLFsDTeeP6pmzD8FGfNJQcI8EpXidVGd2LlcxRRFoufkEG7aeQDHBlKY25zEwxuXYOHs6bz4A4Kb00WLk/P57tcv8+BIyEssW8ON54+qGfNPQcZ8UpAwj0SleF1ULy6LUcX6RzK5ix4Ajg2kcNPOA+gfyVT4yIiIwotla7jx/FE1Y/4pyJhPChLmkagUr4vqxc7lKpaZyOYuetWxgRQyE9kKHRERUfixbA03nj+qZsw/BRnzSUHCPBKV4nVRvdi5XMUSNXHMbU4WfG1ucxKJmniFjoiIKPxYtoYbzx9VM+afgoz5pCBhHolK8bqoXuxcrmItDQk8vHFJ7uJX18NpaUhU+MiIiMKLZWu48fxRNWP+KciYTwoS5pGoFK+L6sUN/apYLCawcPZ0PLt5BXfyJCJyCcvWcOP5o2rG/FOQMZ8UJMwjUSleF9WLnctVLhYTaJ1eV+nDICKKFJat4cbzR9WM+acgYz4pSJhHolK8LqoTl8UgIiIiIiIiIiIiItvYuUxEREREREREREREtrFzmYiIiIiIiIiIiIhsY+cyEREREREREREREdnGzmUiIiIiIiIiIiIisk1IKSt9DI4JIfoA/Nrk22YCOOXD4dgRxGMCeFwAcEpK+Wmvf4nF7DoV1PPop2r8DKKQXT9VY0aAYL7voGU3iJ9RJfBzmGT0OTC74cDPpRSzax2PzRmvjo3ZdUcYjzuMxwx8eNzMrjU8Luv8OiZL2Q1157IVQogDUsollT6OfEE8JoDHFRX8vPgZkLlqzUi1vm87+BlN4ucwKUyfQ5iO1U/8XIIvyOeIx+ZMkI/NTWF9n2E87jAeMxDc4+Zx2RPE4wraMXFZDCIiIiIiIiIiIiKyjZ3LRERERERERERERGRbNXQuP1TpA9AQxGMCeFxRwc+LnwGZq9aMVOv7toOf0SR+DpPC9DmE6Vj9xM8l+IJ8jnhszgT52NwU1vcZxuMO4zEDwT1uHpc9QTyuQB1T5NdcJiIiIiIiIiIiIiL3VcPIZSIiIiIiIiIiIiJyGTuXiYiIiIiIiIiIiMg2di4TERERERERERERkW3sXCYiIiIiIiIiIiIi29i5TERERERERERERES2sXOZiIiIiIiIiIiIiGxj5zIRERERERERERER2cbOZSIiIiIiIiIiIiKyjZ3LRERERERERERERGQbO5eJiIiIiIiIiIiIyDZ2LhMRERERERERERGRbexcJiIiIiIiIiIiIiLb2LlMRERERERERERERLaxc5mIiIiIiIiIiIiIbAt15/KnP/1pCYB/+MfNP75gdvnHgz++YHb5x4M/vmB2+ceDP75gdvnHgz++YHb5x4M/vmB2+ceDP75gdvnHgz+WhLpz+dSpU5U+BCJHmF0KK2aXworZpbBidimsmF0KK2aXworZpUoJdecyEREREREREREREVWGp53LQoh3hRA/F0L8hxDiwNTXzhZCvCSEODz13+aprwshxANCiF8JId4SQlzo5bERERERERERERERkXN+jFy+REr5+1LKJVN/vwvAy1LKBQBenvo7APwxgAVTf24G8C0fjo2IiIiIiIiIiIiIHKjEshiXA3hs6v8fA/CneV/fKSe9BqBJCDGnAsdHRERERERERERERCa87lyWAPYIIQ4KIW6e+tpsKeVxAJj676ypr58L4Gjezx6b+loBIcTNQogDQogDfX19Hh46kbuYXQorZpfCitmlsGJ2KayYXQorZpfCitmlIPC6c3mFlPJCTC558UUhxCcMvldofE2WfEHKh6SUS6SUS1pbW906TgoRRZHoG0rjvYFR9A2loSglMQmkKGY3rOeC7Ilidqk62MkuyzMKErvlLvNLQcFyl8KK2aWwYpuBgqDGyxeXUr4/9d9eIcSzAJYCOCmEmCOlPD617EXv1LcfA9CW9+NzAbzv5fFR+CiKRM/JIdy08wCODaQwtzmJhzcuwcLZ0xGLaT2fIK/wXFBYnXfXi7Z/5t2vX+bBkVBQsDyjMGN+KYyYWworZpfCjPklr3g2clkI0SCEmK7+P4DVAN4G8DyAa6e+7VoAz039//MANopJFwM4rS6fQaTqH8nkCkIAODaQwk07D6B/JFPhI6s+PBdEFBUszyjMmF8KI+aWworZpTBjfskrXo5cng3gWSGE+nuekFL+QAjxJoDdQogbAfwGwOemvv9fAfwJgF8BGAVwvYfHRiGVmcjmCkLVsYEUMhPZCh1R9eK5IKKoYHlGYcb8UhgxtxRWzC6FGfNLXvGsc1lK+V8ALtD4ej+AVRpflwC+6NXxUDQkauKY25wsKBDnNieRqIlX8KiqE88FEUUFyzMKM+aXwoi5pbBidinMmF/yitcb+hG5qqUhgYc3LsHc5iQA5NYIamlIVPjIqg/PBRFFBcszCjPml8KIuaWwYnYpzJhf8oqnG/oRuS0WE1g4ezqe3bwCmYksEjVxtDQkuPh8BfBcEFFUsDyjMGN+KYyYWworZpfCjPklr7BzmUInFhNonV5X6cMg8FwQUXSwPKMwY34pjJhbCitml8KM+SUvsHM54BRFon8kw6dKFCnMNRGFDcstqlbMPgUdM0qVxPxR1DHjZAU7lwNMUSR6Tg7hpp0HcGwglVsPZ+Hs6byYKbSYayIKG5ZbVK2YfQo6ZpQqifmjqGPGySpu6Bdg/SOZ3EUMAMcGUrhp5wH0j2QqfGREzjHXRBQ2LLeoWjH7FHTMKFUS80dRx4yTVRy5HGCZiWzuIlYdG0ghM5Gt0BEFC6dnhFPQcs0cEZEZq+UWyxOKmkrV2byWyCovM8ockhmj/DE/FAWVvnfndRQe7FwOsERNHHObkwUX89zmJBI18QoeVTBwekZ4BSnXzBERWWGl3GJ5QlFUiTqb1xLZ4VVGmUOyQi9/yUSc+aFIqOS9O8vhcOGyGAHW0pDAwxuXYG5zEgByF1NLQ6LCR1Z5nJ4RXkHKNXNERFZYKbdYnlAUVaLO5rVEdniVUeaQrNDL34QimR+KhEreu7McDheOXA6wWExg4ezpeHbzCk4DKFLp6RnkXJByzRwRkRVWyi2WJxRFlaizeS2RHV5llDkkK/Tyd/x0ivmhSKjkvTvL4XBh53LAxWICrdPrKn0YgROkpRXIvqDkmjkiIqvMyi2WJxRVftfZvJbILi8yyhySVVr5Y34oSip1787rKFy4LAaFUpCWVqDwYo6IyC0sT4jcwWuJgoA5pHIwP0Tl43UULhy5TKGkTs94/pYVSGWyyEqJ+lo+waq0sO3mGqQlOogomKyWayxPKIoqUa/zWiKrvMwnc0hW6eWQ+aFqwHKYVOxcplA7eSbN3UMDIqy7uQZliQ4iCh675RrLE4qSStbrvJbIjB/5ZA7JjFkOmR+KMpbDlI/LYlBocffQYOH5IKKoYblG1Yz5pyBjPikImEOqZsw/5ePI5YgJ27IEduW/v6yU3D3UgeKMNCdrMZAaLzsz3M2ViMIuv3xMJuJIjU+wXKPQsdIWtPI9rNepEvKzWVsTQ01MIJUpzSnzSZWmKBJjbCdQSOm1A+z0J7EcpnzsXI6QsC5LYFXx+3vkuo+b7h4a9c52u4o/w9Xts3Drqo9i0+MHy86M0W6uPA9EFHRq+Xj/Sz3YuOw8zGlKQkpgdfss7OnuzX0fd6mmICp4+K5IfPXFbuzp7tWs1622F7lLO/nFKL/3rluEe37Qg77hdEFOmU+qJEWReLd/BLVxgUeu+zimJeIYTI1jx94j6BtOM4cUWIoicWokjdF0Fu+cGsEDLx/Ola8LWhtxuG/Ycn8Sy2HKV/XLYiiKRN9QGu8NjKJvKA1FkZU+JMeiPi2h+P098PJh3Ltuke7uoerN0xXbX8WKba/giu2voufkUKjPcbmKP8O1HW25jmWgvMzo7ebanKzleSCiwOsfyeD+l3pw7fJ5uOuZn2PVN36M6x55A7d8cgFWt88CwF2qwyBK7Tqrits713z7dVy7fB4WtzVp1uunRtKW2ovcpZ38YJbf2596C5tWzi/JaTXlsxrLtaDrH8mgfziDE6fHsOW5t3H1Q69h6wvduOPTC/Ho9R8PZA6ZI1LL2yu378PK+/Ziy3Nv47ZLF6K1sQ437TyA3mFr7QNVNZXDZK6qRy5HbaRv1KclFL+/Q0cHcc8PevDkzRcDQMmIWL3O9mc3r6jaReGLP8OmZK2rmamriWHr5edjWiKO0UwWdTUxfJDieSCi4MtMZLG2ow13Pv1WQXm1+Z9+ikeu+zhu/IPfxtzmJOaclQxlG6EaRK1dZ5VWe+fOp9/CljXt6Nx1sKBeVxSJ0bS19iJ3aSc/WMlvU7I2929qTqsln9VargVdZiKLmY0JbPjuGwXZvf2pt7D75osDd26YIwLMy9vxrGKrb6BaymGypqpHLkdtpK86LSFflKYlaL0/ddrRuc3T0Dq9rqAgi3pnuxPFn+Fgaty1zPSPZLDxu2/g+kffxNUPvYbrH30TG7/7BsbGeR6IKPjUBrFWefXBSAZ/9f2fIVETZ4M5wKLWrrNKr72jdsjl1+v9Ixm8c2rEct2v7tKu1c4icoOV/A6mxnP/n5/TashntZZrQZeoiSMroZndiQAOCGaOCDAub+c2JzGelbb7BqqhHCZrqnrkcpg7H7XWsFWnJRQ/kYzKtAS7749rAJUq/gyfPngUO9Z3FKy53LWhA4qioG8obevJo971FBeC54Gq0nl3vWjr+9/9+mUeHQlZ0dKQQCozoVlejWayntanXJfeHWFu15VDr72jPkDOz25mIosHXj6MbWsX5Ubpz21Oomt9R1n5ZobJKbP8qmsu+3FfE8QcV2u5FnQtDQmMjWc1s1tfWzp+r9LZYo4I0C9vRzNZdK3vwEM/PuKofVDpfFMwVHXnclg7H42mtUR5WoLdaRdR72x3QuszbE7W5v5utgmQEb3rKZmI8zwQUeDFYgIfOWvyAZs6FVttVM9pqkdT0pv6lFNV3RPWdl25tNo7XRs6MLMhgWc3ryhoKyVq4ugbTuO+H/Zgy5p2NCVrMZrJYk5TveO8McNUDqP8PrN5OWpiAg9es9jz+5qg5rhay7Wgi8UE5syoL2kzPLxxCWY2FC77F4RsMUcE6JS3U+1cANj3X/043Dtsq30QhHxTMAgpAzhvw6IlS5bIAwcOOP55owsBQGCfvvQNpXHF9lfR2liHTSvn5y78C9rOwtkNXMM2n4OnaL6c5HKz6wU1V8WNDqtrI4+PZ3FyOI3MhIKsIvHUgd/gTy9sC/z1FCFVm1277I4oBpyNKubIZcsClV2/Rl/k/56rH3rNUtnLkSHGKnCDE5jsWs2GF5+R0/ZD0PIctOPxWGCyCwTjsy+nHWz3+O18PztuSoQuu71DY7hy+76SbD1/ywpkFfiSe+YoEAKRXb3MOs2I1bLTy3I+CHVIxFn6MD0fuSyEiAM4AOA9KeUaIcQ8AN8DcDaAnwLYIKXMCCHqAOwE0AGgH8DVUsp3vTw2vZGwAAJd+GYmsmhtrMNtly7EnU+/hdbGOty6agFOp8YxoUjMbOBaNyp1DSAyV850qYkJBT29wwXLa3xrfQfmt0wrKOi5ERYRBZ2VeqPcRmx+A/4bn7vAUtnLG0Nz1byxjNX2jhefkZP2g5rn+1/qwdqOttyyNB85K4maGv+3hOH1VVmVbK+r5floZgJb1rRjx94jOHR0EIC1drDd7Nj9/mou18IgP7tabQMAmpuotjbW4fjgGDofP+hLmcMckUqvvHWaESttAC/rfNbfweFH6+0vAPwi7+/bANwvpVwAYADAjVNfvxHAgJTydwDcP/V9ntNagNzNBe8VRaJvKI33BkbRN5SGopQ3UlxRJIQQuHXVglzH8m2XLsSW597GJff9GFdu34eek0Nl/x6qPnobQtbWxEwz3DucznUsA5PXzD++/Esc7hvBFdtfxYptr+CK7a8ym0QUemoj1knZprYJjg2O4sTpMbQ21lneWFWvbXLizJhrbYwo4MYy5tz+jJxsKH1qJI37X+rBtcvnYesL3Vi3Yz+u+fbr6OmtTDshLJtduX1fUe3yy/M/vHcvtr7QjdsuXYjFbU0AJnMshDD8vO1mx0nWvCjXmCV36bUNzoxlMKGUbpJ266oFuY5loLBO9+pcsH4kM04yYqUN4GWd73f9zbJTn6edy0KIuQAuA/Dtqb8LAJ8E8NTUtzwG4E+n/v/yqb9j6t9XTX2/79xa8L6cG9Di1+kbSuPk6RR+ceIM7n7+bfy3lmk4NpDCppXzcwuuq8cZxMYwBZ+6BpNaOahP/YbHJkwzPJ5VSq6ZtR1tmo0mZpOIwsxpIza/TfCJe/Ziy3Nv47ZLF+Ll7pPYtnZRSdlbvC69Xtvk/cEUH+BR2cq5WdJrP+jtraAoEqPpLNZ2tJW0YTt3HaxIOyEMm125dV8RFW7c4GuV53c+/RY2rZyPuc1J7Fjfgbuff9vw87abnSBkjVlyn17bYDidRXoii29ec2FBGXnezGm6dTrPBYWJVhuga30H4rHJssbrOt/PMpVlpzGvRy7/PYA7AChTf28BMCilnJj6+zEA5079/7kAjgLA1L+fnvp+3zkZgaHFjaco+QH+j2On0bnrIPZ09+L44OSQ/6ZkbcUbKBQN+VNhXr3zEjy7eQVmz6jDxu++YZrh2nis5JppaUgwm0QUOU4bsXqdGKvaZ+O+H/Zg6+Xn4yd3TJa9WlP59NomannMB3jkVLk3S1rtB6PpqP0jGbxzaiRQ7QS32v5eCsvoaj+4dYOvV55/7Jzp2N25DA+8/Evs6e7NfV3r87abnSBkjVlyn16WTpwew2UP/Du++cph7LxhKZ774go8ev1S1BvU6TwXFCZqG+CZzcux97aV2Hr5+fjyv7yNzz44WS4Ppryt8/0sU1l2GvOsc1kIsQZAr5TyYP6XNb5VWvi3/Ne9WQhxQAhxoK+vr6xj1HvibXcEht7rjmYmyr6A8gOc35H8jT2/xLa1izCayVa8gULWuJldrxRPhUll9DtR8q+fulqBHes7Cq6ZmY11mtms0IQExzj1JRzZJdLiRXaFEI7KNr0bz6ZkLfqG0zjnrHrMbUrm1sErLne02ibb1i7Cjr1HCl7Pq5Ea1V4O+s3PcteNmyU7U2kzE1k88PJhnN2QcNyGdTuTTtr+fl8XQRjxaoUf2XXrBl+vUyKZqIGUMtexrNL6vPWy05ys9eQ+0w1hyZLfysmu2QPgPd292PjdN3DizBhq4wKJWoGuonsntU43Ohesj0lLpe/VYjEBAYH133kd1z/6Jg4dHcyVy6mM/TrfTs6tlqluXDssO415uaHfCgCfFUL8CYB6ADMwOZK5SQhRMzU6eS6A96e+/xiANgDHhBA1AM4C8EHxi0opHwLwEDC5E6bVgyleYL85WYvDfcO6C387XfA+f0HxLWvaMbc5WbJzpp2O3/wAq+syHhtI4dDRQdz3wx789Z98DDvWdxRspOZ3A4WscZrdSlIbSsUZrq2JlSycv/P6pdjduQzjWQU1MYHUeBbb1i7KTX9RG03xEPUtc4OASWHMLhFgL7tWN+mLCzgq2/TKU3VHbSu7dee3TYQQuPv5t3MbT6mv5/bDZZaDlVFuuWtn00m/b5YSNXH0DaexY+8RfPOaC/HFJ35qqw3rRSbttv0rcV3olSFBG1Di1r2aH5lVOyWKz2NLQwL9IxlLn7dWdry6z3SDokhkp9YADnqW/FZOm6E5WVuSpW1rF+G+H/bkfubYQAotDQkIAJc/uA+tjXV49PqlGBzNoH8kg/t+2INDRwcNO9xYH5MWP8pdM3rlclbCVp3vxaanbl07YamHK8WzkctSyv8hpZwrpTwPwJ8B+Dcp5ecBvAJg3dS3XQvguan/f37q75j693+TUrrSiaE1der90ynDJ95OF7zPf5K+Y+8RS+soGsl/Clr8en3DaTTW1+B3bUxFJLJD70lgTUyUXD8bH3kDPSeG8If37sX/+z//E7XxGB7b9w62rGnHkzdfjC1r2vHYvncQi/m/C7xTnPpCVB3sTLGOxZyVbXrl6ZyzkgXtDKNyJ79tcs6MenzpUws9H/3GcjB87C4Z4Pc0ffVa2Pdf/bj7+f/E1svPx97bVuKZzcsttWG9yqSdtn8lrosgjHj1SqUya7Ski53Puzg7A6lxT+4z3dA/ksFXX+wuuUft2tARiSz5QSuvh/uGsaC1MZel3Z3L8Ni+d0oeALdOr8NXX+zODRa7/fs/w3hWwdYXunMdy3o5Y31MbnJ7/WC9crm+Nmarzvdi01O3rp0o18Nu8HLksp47AXxPCPFVAIcAfGfq698BsEsI8StMjlj+M7d+oVaYeofSZT3x1nvKk//ERh1dvGVNOz52znQkEzW2nwblP1E/dHQQj+17B0984SLEY6Lg96rTaIncpPck8PjplOb1My0x2ajf092LeS3T8BerPprb1E8tfOMx4L2BUd9HajjBqS9E1UGv0fns5hUl9WtLQwJf+tRCzZFuRqyOVrNa7jgZaelkdArLwfCxk2fAePSmF8oduekkk26OznJ6DOWq9IhXL1Uys3r3UeV83m7nw+3RhXu6e9E3lMGWNe1oStZiMDWOmRHJkh+s5FVRJL70qYXoPj6Uy2jXhg40JOIFy60cOjqIe37QgydvvhgAcucXmFweK/+csz4mN9ktd83olcszG+ows6HOcllqNeeVmO0S5XrYDb50Lksp9wLYO/X//wVgqcb3jAH4nBe/XytMVqc6aTEaVl88VP7Q0UFsfaHb8UXKAFOlaTW69aaEDKbGc3/v+t/v4oY/+O1cdmtrYhgem8BnH3w1NFO5OPWFqDrYaXSWUy9beRhsp9yx+nC5nOmALAfDx+5NVCXamuUMjLCbSS+mklfquojqgJKgZtbp5+1mPtzOr3psh44OonPXwdyxPbt5he3XqlZW8qqXUa0+iL7hNBI18YKOaa1zPntGHetjco3bDyvMymWrZamV8tNuuehmmRzVetgNluenCyF+SwjxR1P/nxRCTPfusNylNUT/6YNH0bWhcBF9vSfexYt/nxpJ6w6r92KofCWnTlH1Mlr0Xivn964r3FhqbnMSsVgsl10BgY3ffSNUU7k49YWoOtidYq1OmU7UxJGZyKJ/JOPapjpelDvlTAdkORg+TpYMKG5rAqWbSgaF3Ux6MZWc14W7opZZN/Phdn6Z3fLpbwQZL8gggJJ7eCufv945n1Akzx25xkm5a7Ypnhv9VuVcI3rlIss9f1gauSyEuAnAzQDOBjAfkxvx7QCwyrtDc4/WEP0vfWphbl0koyfeWk9FHr/xIt2nPH5NUyXyktnTwOKcq6OS+4YnG1LFBbaiSGQmsvjG5y7AYGocO/Yeye0iG+SpXJw5QFQd7E6x9nJTHS/KnXJGpxgdD9swwVTukgFO8u1nFuxeI15MJWf7wF2VyKyXrObDynXj9+hCMqeV1503LMXJM2nTDFr5/PXO+fiEovmzQOkSGjyfZMaLtq8bbYFyrpFKz3apdlaXxfgiJpeyeB0ApJSHhRCzPDsqlxmFyWxIu9ZTkXdOjRgOq/djmqoVvOkjp6yswVSc85kNUrfzQc15a2Mdbl21APdddQGOD6awc/+7gZ/KxakvRNFnt9Fpdb1Fp3Ww2+VOudMBtY4naJ059KFyb6LsrsOol4UFrY0YSI170g61c414tYQF2wfu8Tuz+by6XzLLh9Uy1Iv8Mrvl0cqrhMTG7fsKMnj/Sz24+7PnQ0pZkC2zz9//LzXaAAAgAElEQVTonBf/LOticsrttq9WFnfesBSN9TUYn1BcXUbOSbnIcs97VpfFSEspc2PMhRA1AIIzP84Cp0P0tZ6KPPDyYXStt7akhhEvd3x1e/dPqi5ORknoXWNqzlsb63DbpQux5bm3seobP8Zdz/wct676KJqTtZ6+FyIiK+y0E8zKyKDVwUFbaoO8V87UVLttAK0s3P9SD3p6g3ENcDpsOPiZWVUly2qrZSjzG0zFeR2fUAoyuLitCdcun4eruvbbzpadc866mMrhZtu3OIutjXU4eWYMV27f53r5ynIxmKyOXP6xEOKvASSFEJ8CsBnA//TusIJD66lI33Aac5rqbT9dL34y7uWOr27v/knRl59PIQRWt88q2M3Y6SgJNedb1rTjzqffKsjkpscPMpNEFChWRrGZjZgIWh0ctKU2yF92R2baHRGklYW1HW3o3HUwENcAp8OGi5ORxE5H91ayrLZahjK/wWV077Rp5fyS+x6r2bJzzlkXk1vMyl6zcrY4i5tWzsftTzm7BsywXAwmq53LdwG4EcDPAXQC+FcA3/bqoIJEby2apqS98GpNE3jiCxd5tuMrKxqyQyufO9Z3AAD2dPeW9TRQrYiakrXMJBEFmtXppWbr1AWxDg7aUhvkDydTpu2uw6iVhZaGRKCuAU6HDQenU/ydrtlcybLaThnK/AaP2b1TuWWg1XPOupjcYKXsNStni7Po9b0/y8XgsdS5LKVUADw89aequPVUROvJ+Fdf7EbXho7cyA43h/OrF3drYx02rZyPpmQtRjNZJBOsaKiUVj43PX4QuzuX4SufkWU9DVQrohOnx5hJIgo0rSl9J06PoaEujmRtTcF6iUZtg2q42St3Ay7yh5ORmXY3cdTKwqzpdZG/Bsh95YwkbmlM4ImbLkJcCCQTcUsDgSpZVrMMDbf8rC5ua8KmlfMxNp7FVz7ze9h6+fnISviSLeaI3GB1vyWzDSWf+MJF+OqL3djT3YvRTJbtgCpjqXNZCLEGwFYAvzX1MwKAlFLO8PDYAsONpyJaT8b3dPdi6+XnezKcv6UhMbVr7VhuOoLTUdcUfXojN6SUOLd5WlmvrVZEs2fU4dHrP46+oTQzSUSBlF8WLm5rwm2XLsxNay0exWHUNqiGmz1OSQwHpyMz7W7iWJyF5mRt5K8Bcp+TvOrlsilpnrVKltUsQ8NNzapeW2FBa6Mv2WKOyA12lukx21Cya0MHtl5+PmprYmwHVBmry2L8PYArAfxcSskd4RzQezIei8U8Gc4fiwk01tdg43e9WeeGosXrkRuxmMDZDXWYUCSue+RNZpKIAim/LPRrvcQw45TE4HOzfjcb2VSchWq4BshdTvJazmjnSpfVLEPDS82qUVvBr2wxR1QuN9et79w1uafS2Q11aEom2A6oIjGL33cUwNvsWHauEjtaFu9aC1R+zUcKJr/yyUwSUZDll4XlrhVnZwduIq+4Wb/bHVXKa4DscpLXctdNZk7JCTWrRmsrM1sUFk7bCmblL6+B6mJ15PIdAP5VCPFjAGn1i1LKv/PkqCKoEk/Gq2HNR3KHX/lkJokoyIrLQpZXFHZu1u+sw8lrTvLKXFIlqFk9cWaM+aPQc9pWYPlL+ayOXP4agFEA9QCm5/0hG/x+clOJ0dIUXn7kk5kkoqBTy8I5ZyVZXlEkuFW/sw4nP9jNK3NJlRKLCZwzo575o0hw0lZg+Uv5rI5cPltKudrTI/GJ1i7XURuen/8eZ8+owzObl2N8Qons+6XyObkunPxMpde2IyKyKqjlVdTbMVF/f2Hm9jXh5blmjqpHUMrqsGYurMcdFG7mz89zwfNObig3/27lkHkOBqudyz8SQqyWUu7x9Gg8ZrTLdVTCVw3vkdzlJDPl5IybThBRWAStvIp6HR/19xcFbl0TXp5r5qj6VLqsDmvmwnrcQeNG/vw8Fzzv5Can+Xcrh8xzcFhdFuOLAH4ghEgJIc4IIYaEEGe8PDAv6O0m3D+SqfCRuaca3iO5y0lmmDMiIv9FveyN+vujD3l5rpkj8ltYMxfW444iP88FzzsFgVs5ZJ6Dw9LIZSllJNZXLnc34TCohvdI7nKSGeaMiMh/US97o/7+6ENenmvmiPwW1syF9bijyM9zwfNOQeBWDpnn4DAcuSyE+N2p/16o9cefQ3SPuptlPru7WSqKRN9QGu8NjKJvKA1FkW4fZlnceI9UXZxkxq2cBf16IiKqpOIysrYmFuk6nm2Y6uHludZ77WQizjYHecJJnoPQBmaZGwyKIiGE8O1c8LxTELiVQ6PXCUI5W03MlsX4y6n/fkPjz30eHpcn1N0sV7fPQteGDjy1aRme+MJFaE7WAjCv5NX1XK7Y/ipWbHsFV2x/FT0nhwIVUu7YSXZZzUz+9SEhsfOGpWXlzO71xMqBiILIqGwqp9zSKiOHxyYiXcezDVM9jM51ufW91mvvvGEpTp5JB6INz/ZMuFg5X3bLLqM2sJ/5YJnrjeJzODGhGLYTek4O4e7n38a2tYt8ORc87xQEbrUD9F6nOVnrSd8d63B9QkrzD0MIUS+lHDP7mt+WLFkiDxw4YOtnJiYU9PQOoXPXwYIFvxe0NuJw37DhQuB9Q5ON0vxh93Obk3h28wrXNjfhbpkV58sH5SS7XjLLjN5C+bNn1CGVcZYzO9eT1u/fecNSNNbXYHxCYc4nVWV2nTjvrhdt/8y7X7/M89/j5HdERGiza7SJCICyNhjRKyOfv2UFsgoiW8crisRgKoNUJouslKivjWNmQ11Q32NosxsEWm0PRZF4/3QKvUNp9I9k8PTBo/jSpxY62uAn/7UlJK7cvs+zNryd4wrIxkPMrgV2zped+y+98v2ZzcvRP5xxJR9WjyeE942Bzm5xZla3z8Ktqz6KTY8f1Dyn+VlY3NaETSvno6UhgY80JXHOjHrPzkXxeW9O1mIgNR6mHIRRoLNbCVrlDzDZfr7/pR6s7WhDS0MCs6bX4SNnJVFToz02Vut1+kcyrvfdBagO95ulN2d1Q799Fr8WeAOp8VzHMvDhgt+9w2nThcC9XM/FyihOq09J1B07z22ehtbpgb0howAxy4zeQvlZBZhz1uRTwuOnU7ae3tm5nop/f2tjHU6eGcOV2/dVfAQSEUWP1frWaBORcjcY0SsjU5ls5Ov4k2fSuPqh1/CJe/biyu37WL5XgB8jc4rbHgDQ0zuEa779Otbt2I+tL3Tj2uXzcP9LPbY35il+7fEJJRBrMnLjIXf4NXLMzvmyc/+lV76PjSuu5MPO7EDeN5anOIuDqcLMrO1oy3UsA6XnND8Lh44OonPXQazbsR9SSk/PRf55b2lI4HDfcCBmdlB4uFEOa5U//SMZ3P9SD65dPg9bX+jGuh37cc23X0dPr34mtV7Hi7471uHGzNZcPkcI0QEgKYRYnLfe8koA03w5QpfphWw8a97o9HJ9IrOghmFJjkri9ARv6V03iqI4XtpCCIHV7bMK/l3veir+/ZtWzsftT73Fgp2IXGenvjVquJo1as3qrWpdE5EN98qzcw242f7qH8mUDAC58+m3sLajrexO4KBcT9x4qHxe3BPp5dir86WXx7iAK7+P5ag/tLJ4fHAMrY0fjopsStYantMgrBHPvJBdXvZNZSayWNvRhsf2vYMta9rx5M0XY8uadvzDj35pK5Ne1Pusw43VmPz7pQCuAzAXwN/lfX0IwF97dEyeUkNWPDy+Nh7T/Hp++NT1XIqHweevT+R0apFZUPUKfb+n8wVRFU9P8I3edZOVsJxLrfO0Y30HAGBPd6/hel/Fv9+soRYFIZymGGlOltLwg19LfFQTO/WtVtm4un0WhBCYUBQ8ct3H8cDLh3Ho6CCAwg1GzOotK22OKGLD3TtW6xWr14Db7S+9c9/SkCi7Ezgo15NeeyrqD43cpJfP3Z3LHC0jYJRjr86XXh6TCXd+H8tRf2hlsfPxg9h6+fm4/tE3AQCKlHjkuo9jWiKOwdQ4duw9gr7hdO6camVBXSPer3tb5oXyWWkreNk3pZa71y6fhzuffit3DWxbuwiKolh+HS/qfdbhxgw7l6WUjwF4TAixVkr5tE/H5Cm9kM1qrDMNXywmsHD2dDy7eQUyE1kkE3FMKBLHT6dyaxWZrdusxyyoLPT1aS2ZcOL0GBrq4kjW1rBTzgV6142UUnva9ngW7w2MFlRIWpXQpscPYnfnMnzlM9LwRrf4949msr4W7H539PKBCVHl2Klvi8smdW3Fq7r2567de9ctwj0/6EHfcDrXrrDSKC9uc1TLQyaj9pCbZXHUH+BpradptY1q9Rpw++ZS79zPml5XdidwUK6noHRyh5lePt8fTOF0atx2W8kox16dr/w8KoqCrASklJhQJjfM3vjdN0x/n1EZxg4Qf+hlcd7MBsxtTqK1sQ41MYEtz71d0CaYPaM+d061yiYJiY15a8R7Pais3LxY2bsnyvVtVCiKxKmRNEbTWbxzagQPvHw413YtLle97JtqaUggPZHVnMm0u3OZ5dfxot5nHW7MbOSyaq8Q4gEAfwBAAvh3AP+flLJf7weEEPUAfgKgbur3PCWl/IoQYh6A7wE4G8BPAWyQUmaEEHUAdgLoANAP4Gop5bvO3pY+o5BZCZ+6notW588TX7jIcSPbLKheNhLCXuDnF26L25pw26ULC55ysVOufHrXR/9IRjOXR3qHcf2jbxZ8/nqVkJQS5zYbr7Kj9WDHTsFeTsYr0dHLmQpElaNX36pTVI3aDkKIXMcyMHnt3v7UW3jy5osLfsZqo1xtc1QTvfaQuuu3WxtdRfkBntb769rQgX/40S9L6pVnNi/HrOn1BT9vtc3p9s2l1rnv2tCBj5yVdOW8+HE9mbU3gtLJHWZ6+ewfyeC/P/kftttKRjl2+3xZfejz/C0rDDfMNivD2AHij+IsLm5rwq2rFiAWA3Z3LkNcAGt3lLYJntm8vKRcyM/sewOjvg4qKycvZlmMen0bFVrnadvaRbjvhz2abQUv+6bUXOj1G9h9LTfrfbt1Qtj72eyyuqHf9wD0AVgLYN3U/z9p8jNpAJ+UUl4A4PcBfFoIcTGAbQDul1IuADAA4Map778RwICU8ncA3D/1fZ7Q27jAzoYGWqNlM1ntUZxWKoL8oL565yV4dvMKzemx6roxbjUSorCWc/56OptWzs91LANcM8pNWteHVi7vXbcID7x8GMDk53//Sz04cWYs9+/57FRC+b//7IY6w+slX7kZr8Q6ZJypQFQ5WuWaOkVVqxzJL5v0ZnMAKGhX6K0DJ4QIVf3rBb320EBq3LWyOOrrS2pO1d51EGs72gq+79jA5AZixay2Od1ez1Dr3H/snBm6u8MDwdpzw2p7gxuolUcrn9vWLsKOvUcctZXMcmz3fOllUisf759O6W6YbfT7zMows/tKckd+Fhe3NeGOTy/Elufexifu2Yuruvbj1HCmYP1lYGqvpwnFsOzye434cvJilsWo17dRoXWe7nz6LWxaOV+zreBV35SqzuE1UInNiI06lsPez2aX1ZHLZ0spt+b9/atCiD81+gE5+VhheOqvtVN/JIBPArhm6uuPAbgbwLcAXD71/wDwFIAHhRBC2n084ROt0bJHPxgt6wmO0ZMVL5+ch32EZP7T1mpYizdIinMJALc8cSi3vujitiZcu3weruraj9bGOty7blFuI75yK6H868XoqWC5o4Ar0dHL6YxElVPOFFWr167WKKFtaxfh7uffxpc+tbDqOwG02kNulsVRf4BntHZxPnUDsXxqfTqjviY38i4Wi2m2Ob0YHWlnlFHQRsRx1pE/1DL6mT9fjpHMBLKKxInTHw5isNtWcjPHRpnUykfvUNpRWWSlDKvGmS9+K24vXP3Qa4UP9YrWXwam9nqqiQVu5LnTvJhlMer1bVTonaemZK1mW0HNvjrLIisl6mvdu091cg2wTVB5VjuXXxFC/BmA3VN/XwfAdBchIUQcwEEAvwPgmwCOABiUUk5MfcsxAOdO/f+5AI4CgJRyQghxGkALgFMWj9Fz+R1YQojcDaQ6Wra1sQ7b1i4qWZLBrYrArUZC/oX3jc9dEPoCv7hiZ6ecv/Jz2TeURt9wOvdv+SPJjw2kcM8PerD18vMxf1YjkrXuTA0xq0jKbdRUoqOX0xmJKit/Caz+kQzGMtbKEavXrlpv7e5chvcHU+gfyeC+H/bg0NFBdB8finTD0yk3y+KoP8DTe39nNyRyX1dnGiUTH75nveU0Zk4tgxW0JR6CduPGThR/9Q9ncNOuAwV5zl/L1io3c2yUSa186C0vZ1YWRb0MCxO1M/i9Qe2lLH67taGg3H144xLUxIRh2VXpstUOsywyq+Ggd55GM9mStkI+rzaedHINsE1QeVaXxegE8ASADCaXu/gegL8UQgwJIc7o/ZCUMiul/H0AcwEsBfAxrW+b+q9WUkpGLQshbhZCHBBCHOjr67N4+OUrHtZ+9/NvY8f6jtxGI8cGUjh0dBD3/bAHW9a048mbL8aTN19s+eLyc1pf/oU3mBr3ddqNV9SOgDlnJT2dolGOSmXXK1qZLZ4i09KQKChUDx0dxPWPvom4gCtTQRVF4sSZMcPpVuVOLfN62o+WoE1njFp2qXrYyW5xmTYxoeTq/fSEoruMRT47124sJiClxLod+9G562BuxkfQGp5BWXbAzbK4EuW6XeWUu3rvrzYusPXy8/Hs5uV49Pql+G9nT8OEInPnVG85jf84djqQSzy4cePmZr79nsoeVG60GczOy6mRdK5jGfhwLdvGuhpHGXQrx0aZ1MrH0wePomtDh+2yyKwMC0q5HTZO2wzHBkYxnpWa139tPFbSJkhZeGBdbib9yoBZFsNQ30ZBueWu1nn61ucvxPxZDSVtBdWpkbTmPfipkXTJ6zth9xpgm6DyLI1cllJOL+eXSCkHhRB7AVwMoEkIUTM1enkugPenvu0YgDYAx4QQNQDOAvCBxms9BOAhAFiyZIlvNWVxg3tPdy8A4Jk/X47U+IejZQ8dHUTnroOY25zEs5tXWO5Y9nMIf/6Ft2PvEU9HW/styE96K5VdLxhltniZDK2noMUdMuUcw0h6wrAiKXcUcKUyFaTpjFHKLlUXq9k12wBtbDxbUlduW7uoZJogYO/aDfqIniBNMXSzLA5yW0FVTrmr9/4AoKGuBscHx3DdI2+UnFOjabGVHgGkpdzrx+18c9bRpHLbDFbOy9i4dlbHJkrXEPeTUSa18vGlTy3EgtZG22WRURkWpHI7bMppM/zzTRfpthOKy0yv634/M2BWn4ahvo2Ccsvd4vMUEwIjmQlc8/Dr9sthjb0c/MA2QeVZ6lwWkz1BnwcwT0q5VQjRBmCOlPINg59pBTA+1bGcBPBHmNyk7xVMLqvxPQDXAnhu6keen/r7/ql//7cgrbes1eDe092Lr3xGwdde7C6pTLo2dFgOjt9D+PMvPHW0tdtLFVRSkDrlososs+rnf/J0ynKHjNNj2LKm3bAicaNRw0wF13l3ma7QRGRKb8TmljXt2NPdi/dPj+Hpg0exZU07mpK1GEyN47F97+BrVywq6/cGveEZtCmGbpbFUS/X9d5fVgE6Hz+oeU71bswGU+O57w3SqPpyrx+3881OFHdYOS/xvOUJVVrrgvrNKJNG+XCaN62fC1q5HUVan/Gvekfwz2/82lI7weu63+8MmGU46vVtVBQvdXndI2+Gqhxmm6DyrK65vB2AgsnN+LZicqO+bwL4uMHPzAHw2NS6yzEAu6WULwghugF8TwjxVQCHAHxn6vu/A2CXEOJXmByx/Gd234yX9BrcWSmxp7sXfUOZgspkpo3g+LEeS/560clEPHfhtTbW4dZVCzBvZgPqa7U3bCH/GW1QF4TXt5rZWCyGx/a943qHTP4xWBl9z0YNERkpLtMWtzVh08r5WDCrEV0bOvBy90lcu3yeo1k+RuVt0Bue1bheXNQZnVN1abH7X+rB2o42tDQkcHZDAjv2HgFgf2Sd122Zcq8fL/LN9kb5zM6LokjEhMBjNyzFb/pH8cDLh9E3nDZcF9SMW1m1MorT63yw3PZe/mestheaptXiby5rx9de7Mae7l7NdkJ+zloaE7nN0NwuH5kBKpeVDCUTcdy7bhFuf+rDtrFaDlspU91uI7BNUHlWO5cvklJeKIQ4BABSygEhhOEdlZTyLQCLNb7+X5hcf7n462MAPmfxeHyn9ySkvnay01ldDgNAbkmMfEYXTyWmxuy8YSmeu2U5TgymcyNYOG0qGLyeyuTG61vNbEtDAl/61ELDJ4hOKxb1GPLXOm9pSOAjTUmcM6OeGSYiy/LLtMVtTbjt0oUlMy5+0nMST3zhIsRjwnJZZaW8DXLDM+jLdljhdQdn2Bid01hMYEFrI/7ijz6Kzl0HC/I/mMrgS59aaHkEkF/Tssu5fvzMN3NondF50crVtz5/IcbGFbQ0JtCUtD/y0+2sVrpMD1u5HcZrQ/2MWxvrStoL2z9/Ib7ymd9DbU0MMxs+XCfWz6UqKp2BMJ5TKmQlQ03JBGbPqMfWy8/HtEQco5ksZs+ox4y6WtOse3U9hKVNoIratWJ1Q7/xqRHIEsgteVHZRa18prdJz8yGOtNF6os3AyzeFMXrhe61hvhv/O4bSI9LzamR6kZoVBl6UzLcOi9uvL7VzJptbmV2bVg9hkNHB7H1hW401NWwY5mIbMsvTzatnJ+7UQQmy8g7n34L1674bcxtnmZrcx2vy3OvhX0jnnLqmKgyO6cDqfFcxzLwYf7v/uz5tm76wpB9v/LNHNpjdF60cvXn//RTnNucxHktDY7af2HIqh1hKrfDem2on/GtqxaUtBc2/9NPUVsTw6zphfcjfuaskhkI6zmlQlYyFIsJnNfSgPPPPQtzm5M4/9yzcF5LAwZS46ZZD2K56/d1E8VrxerI5QcAPAtgthDia5hcE/nLnh1VQOk9CTEbfm+2fkvxEP5kIo4JReL46ZQrTzD0hviPZxVOmQkgr6cyufH6dqadGD1BtLq2kd5TvSBPJyei8MgvT0Yz2puESiltly9hnpqqlrsz6muwu3MZ4mJyqaMwlbNce7SUWd2pl1m7+Q9D9v1qR/iRwyiNfjI6L27lM18YsmqH3+3jcrIX1jJa/Ywb6uLa99gaG0v6mbP8DCiKgqwEpJw8T16XDWE9p1TIajmidZ9vJetBLHf9Ljv9ulb8bB9Y6lyWUv6TEOIggFUABIA/lVL+wpMjCiGz4fdWLh71NZxOEXCy7EZtPFby9dXtsyCEwHsDo6FvnIaV11My9F5fiMkdpq2ebzem/Vm5NsyuCbvHEKUbMCJyj1qe9A3BUhlcvJfBhCIxPqEUlCuVnprqlJ/Td70UxJuXIDCqO61k1ko96sau7X7U1VFYAzcq12s+vfNiJ1dWM6T1mmG/H/JraY5ysxfmMjoWE0jW1mjfY9fE0DeUzmWvOVkLIQSe2rQM/SMZ7Nh7BIeODnraHojFBFoaEr6XDWE+p1TIaTlipa8hqMtS+bmskV/7rvlZBlhdFgMAZgIYlVI+COCUEGKe60cTUerFk0/v4nEyRcDpshuzGguX9FjdPgu3rvooruraH5mh+WHk9ZQMrdfftnYR7n7+bd/Pt5Vrw81pM1GcfkJE7rJSBueXJbc8cQg9J4Zw5fZ9JeVKmKYn5wvidEUn7LS/aJJZZq3Wo+VkP2p1tdc5jMr1aoXVXNnJUPFr8n7IunKzF/YyWi+Pw2MTuez9zbNv4f+cHMJVXfuxbsd+bH2hG7dduhCr22d53h6oRNkQ9nNK5bPS18Blqfy5VvwuA4SU5h+sEOIrAJYAWCil/KgQ4iMAvi+lXGHyo55asmSJPHDgQCUPwRK9JwYLWhsxkBoveIpy/HQKK7a9UvIar955Cc5tnqb5+n1DaVyx/dWSJz/5Q+r1ntjkf10Igau69hu+ThXwZViCWXa9HrGjKBInzozh/cFUyRN0P8+3ladp7w2Mal4TP7njEsxtStr6XKxcKyEWiOz67by7Xqz0Ibjm3a9fZvtnnLx/J7/HY4HLrlkZnF+WdG3owNYXunXLlfzXqq2JoSYmPNkd3k165a5RWySIfBixEbjsusEo/3bqUafZj1pd7XUOHV6voc2ulTay3QzxfsiZcusKh9dGoLJbnMd4DPjsgx9mT6+NsLtzGeprY562BypRl0dxJoWLApVdL1npa7DSP1XutRHk9oQf14qLZYClA7K65vIVABYD+CkASCnfF0JMt3M01Sj/wpg9ow7PbF6emzLbnKzF4b7hkjC1NCZsTxGws+xGsfyvvzcwymksAeH1lIxYTEBKiXU79hd83e/zbWVtI71pM0d6hzGSnrBVAFtdhoPLZhBVHzvXfn5Z0pSsNSxXyl32qhLCupxHMa7Nr88o70ZtEDvTOJ1mP2rTqr3OYVSu12J6GbXSRrabofys/uYD3g9ZVW72wlxGF+dzzlmTA16K76f12whK7iGGV+2BSpQNYT6nZMzu8hJmfQ1aZbnbbeUgtyf8uFb8LgOsLouRkZNDnCUACCEaPDmaCCkegv/ZB19F/3AGc85KonV6ne4umjUxYXuKgFtD6sM+jUVRJPqG0nhvYBR9Q+lATHcIsnLOt5uftVqxnNs8LbfBZT69qTUPvHzY9rQOs/cc5KkzROQdu9d+flkymBq3VJaGaep6WJfz0JJfx7Q0JNA/kqmKdoJRPV1OXeek7WA3+2Fvj2oxa+uUI0rXq6rc9pjTDPWPZPDOqZHA5S+o9zhuZM/La8MrRvkszp5eG+GdUyOetwcqVTZ4cU6Deg1EidvtBj/aC2aC3p7wuvzzuwyw2rm8WwjRBaBJCHETgB8BeNiTI4oIswtD7ylKKpPNPcF49c5L8OzmFaZPatwKTZgbp+wUtM/p+fb7s1af6j1588V48uaLsWVNO+77YQ8OHR20/eTR7D2HqfOHiNxj99rPL0t27D2Ce/VOp40AACAASURBVNctMi1Lgzx6olj+aAqrbZGgq6Z2gtl7Laeuc9J2sJv9MLdHKyGK12u57TGnGcpMZPHAy4exbW1hmd61vqNi+Qty2RXF7FlhlM/i7D198Ch2rO8oydMDLx8ueE0v2gNROT9Bvgaiwot2gx/tBTPV3p7wuwywtCyGlPI+IcSnAJwBsBDA30opX/LkiCLC7MIwGqJud0kEt4bUh3kai16BF4T1dILK6fmuxGet7ir7V9//WVnTOszec5g6f4jIPU6mUOeXJclEvGDpK62yNGxT1/3cMdsP1dROMHuv5dR1TtoOdrMf5vZopUTtei23PeY0Q4maOPqG07jvhz3YsqYdTclajGaymNNUX7H8Bb3silr2rDDKp1b2mpO1BX+Px4C+4XTBz3vVHojC+Qn6NRAFXrQb/GgvmGF7wt8ywOrIZUgpX5JS3i6lvC0sHcuVnD5hNgTf7acobg2pD+PUJICdgk45Od+V+qy1rpmuDR1QFMXW9W30noM+dYaIvOHk2s8vS85uqMOs6fWa5YraFlEUBV0bOqp29ESlVVM7weoAh3xGeS9uTwOw1XZw0uYNa3uU3OFGe8zJkjhqVvuG0+jcdRB/9f2f4Zyz6tGUrFw5XU1lV1iY5bO4/KqpiRX8fUZdLdsDNvAa8J5X7YbjpydfU10a1ov2ghm2J/xjOHJZCDGEyXWWxdR/c/8EQEopZ3h4bGXJXwy8tbEOt65agHkzGzCtLo6ZDd6HSr0wSjbsm7owgvgUJcwbmYVtRFiQ2D3vdj5rNzNVfM1kFYmvvtiNPd29rm2EYXbdElE0mV37+WVZMhHHhCINRymrijcmWd0+C0984SLEp2ZjhKmeDbtqaieYvVc7dZ2VzXXM6nq1/n5m83KMjSuICyCZiN7nTu5xqz2mKBKnRtIYTWfxzqkR/K+fH8cf/19zdO8Jg3h/Vk1lV1jo5bM5WYu+obRhW0FRJA73DeMffvRLbFnTjpaGBGZNr8NHpjYEpFK8Brznd7uh+Pvz2xALWhtLRv6HtY+q2ojJffrCacmSJfLAgQOa/9Y3lMYV219Fa2Mdbrt0Ie58+i3fd2cPU2et1g3wly9rD80NsIs7i/ryJo2y6ycnn5vVnzH7vnKuD/X6Lq4A3ZgeFabrtkhVZVd13l0vVvoQXPPu1y+z/TNO3r+T3+OxQGRX79ovflh9x6cX4vanrLUp8tsim1bOz02xvqDtLJzdwKmcfnJ7B/IpgchuMacdwgBKvtY/kjGsb91qE5DvApndfOW2xyYmFLx/OoXeoTT6RzJ4+uBR3PLJBXjw3w67OjDBa7x2SgQiu8X5bE7W4nDfsGlbwUqZGtL7EM9E6BoIRHa1uPEgWZV/n764rQmbVs5HS0MCH2lK4pwZ9bb7CyJy7sPO0odtac1lIcR8AMeklGkhxEoAiwDslFIOOj8+b6lD+7esac91LAP+rtET1DWOtAqG/HV2Frc14drl83DNt18PzUUcxJEGYeBkDSurn7Xeaz+zeTlqYgLHB8fQ+fhBRxnzcnpUUK9bIvKW3rWfX5ZtWdOeu1kEzMvMzERW8yF31/oONCVZR5nxcvZLlNsJTt6rOpqu+Abu7Gm1hvWt1XYE18wku8ppjymKRE/vEDp3fdjO3LZ2ER78t8NY29GGPd29gcugXnlXTWVXmBTns28obamtYHQPU80daUb1Pa8B71n5jIszry59Ufz9asYXtzWZDvI0axuw7RAuVtdcfhpAVgjxOwC+A2AegCc8OyoXqEP7m5LGjeJqo7cTaH5Ft2nlfM0Oeas7NFcK19Oxz2knrZXPWu+1R9NZ/Ozo6VzHsvp1Oxnj2shE5Jf8ssxumyJRE8etqxaU1Kmdjx8MfJ1aaV7sDl9N7QSj96r12b5/OqV5A5eVMKxvrbYjuGYm+al/JJPrWAYms3bn029hbUcbmpK1ue8LSgbNyrtqKrvCympbwegeRq8jLertBSv1Pa8B79n5jI3OmZpxK31KZm0Dth3CxdLIZQCKlHJCCHEFgL+XUv6jEOKQlwdWLnVdmBOnx7hGTx69Smt357Lc5xTEDnlOEfKG2fpK5Xzueq/9zqkRTEvEy8oY10Ymci4iS2n4Jr8sG0yN22pTtDQkMG9mQ+DqVL+UU4dwtIp3tD7b3qG0Zk6llIb1bTIRxyPXfRzTEnEMpsaxY+8R9A2nS64JrplJftLrkFBna6qCkkH1mmxtrMOWNe1oStbixOkxzJ5RxyWUQsJqW8HoHub46VSg2wte3Y+zvg8fo3OmZnwkPWGaZ7M2BNsO4WK1c3lcCPH/ALgWwGemvlZr8P0Vpw7tnz2jDl3rO0qm3xt1QkW5I1OvsRUXyFV0dm+e3aS3BmC1ThHymlEDp9ypWVqv3bW+A1/+l7exaeX8sjcFXNDaiN2dyzCeVVAbj2FWI59iE5H78suyHXuP4N51i0rWUdRrU8RiAtPqKt8wrkS7ptz1+zhaxTmz86312faPZHRzunB2UnOqrKJInDyTxpbn3s6d43vXLcLsGfUl14RZeyOq7W6qDL0OidbpdXjoJ0dyf/djYEJ+vmtrYqiJCaQypdPIuYRSuFltKxgtP+BmR5rb5apRnQ6Urtdv53exvq8sJ1kxOmdqxk+cMR7kaaUNYXdAGdsTlWVpQz8hRDuATQD2Syn/WQgxD8DVUsqve32ARqwuVm4nZFFf68hoIzT1ab6iKDg1ksE//OiXWNvRVrCLbU2N1ZVU7NP77FsaE7hy+z7NY/bgaWZgF9r3it714camecWvHY8Bn33Q+kabeplY0NqouTZkVK5Th6ouuwA39PPr/Xs8cjnw2c0vy/R2gDf6WS/bFWZtnEq1a8zqELPj8nLjVhcFLrtWzrfWZ7u6fRb+4o8+WrBGrVlO9M7RM5uXY9b0es1j83oAAW8sLQtcdt2kdR10bejAR1sbMTg24Vs+tI7j3nWLcM8PetA3nM61aXuH00hPZLHhO28EvcwLgsBmt5y2gvrzTjZa92NgllF53z+cKet3haS+d0Pgsuu0jWjlnDlt5xW3IazW6162d9m2sJZdS53LQeVmgyU/MFc/9FpkCzerF93EhFKyEYbXN6N6BcwTN12ET9yzt+T7X73zEpzbPM3twwhcoW+V24XeewOjWLHtlZKvl/O55+evtbEOt65agHkzGzCtLo6ZDaUjj/UysbtzGa7q2h/Z69Sh0Ga3HOxcZueyVXZ3fnez4ejVazvtSPSjvDSrQ8rtfA6IwGVXCGFaPxo9uB1IjVvOqRvtBDfzGZLMBEXgshuWctcOvXxvWdOOzl0HCx7qfONzF+Dqh14reQ2P7nfCLNLZdWNQ3OwZdfjsg+7W+3rl/U/uuATXPFxe30kVld2By66VNoPea1g5Z0Z5druvwav2bhXl04ilN2q4LIYQ4ucAdHufpZSLbB5UIOUH5hufu8DzaRmVbOxY3W11IDVeshGG12sf6U2vqBGi4lOKg86LQk/ofO5COM+q1nIW0xIxjKSz6B/JWJq6e2wghYmswulTROQaP0Y7KIoCISanP2uVd05YWaewUtNNzab3mh0Xd4e3pji7T21aZnq+8z9bRVGQlYCUEgOpcVf2VrDTPnMjn/ntaq7bGR5RuWF3sryPuqng2o623P1WJZclJHvcyq6iSAymMkhlsshKifraycE2VssrvTbAkzdf7Hq9r1fexwXK/l2s7/3jpM2gxeoSleomgVrcXk/Zi/auokicODPGtoVFZmscrMHkGst6fyIhv2BWK/Z8blbsXux+bpeVnUArcTOqtXvu6vZZGB3P4t51i3L/xs3bSnmxu3BcANvWFn7u29YuQryMel5RJA73DeOqrv34w3v34qqu/fjF8SHc8sQhzWtBb0flmnjM0+uUiKqLVzu0q3X+3zz7Fn7VN4Kruva7WvdbqauNdqb3krpOnl7dbeW4uDu8ueLsqmsn59M637GYQEtDAh+MjjvOpdk5tqLcfOa3q48NBHszLCrkVbkL+He/ZfZ79PI9mBoHMHkNqe9/x94jJe1u3u8EkxvZVRSJd/tH0HNiCFc/9Bo+cc9eXLl9n62c6rUBshKu1/t65X0y4U4bg/W9P5y2GYpp3dMf7hu2Vca60YbI53Z7Vy3f3x9k28Iqw85lKeWvjf74dZBeyy+Yiyv21e2z8MQXLkJmIou+oXTZjRIvG1JuMrs4FUWibyiN9wZGdT8XK9+Tr7iAWd0+C3/7md/DdY+8iXt+0IMta9rx5M0XY+vl52P2DFY6+dx8GKCet3RWQSIew99f/fv40V9+ArtuWIpEPIbaMtbd1sr/7U+9hb9a/VHNa0Gr0ula34G6WoGdNyxlA5yIXGG3DDWq3/L/7cSZMdz/Ug/WdrTl1phXX9uNut9KQ9pu491u3a0nfyTSq3degmc3rygY1WXluNw6ligrzq5RB9XEhIL3B1P4df8I3h9M4dRw2labtPh8ADA8x1aUe3Pp5QAR5s9bTtquVs+JX/dber/nxJkxvDcwingMJfn+1ucvxIz6Gqxun4VZ0+uwun0WujZ04K4//l3EBHDf5y7AT+5wdj2RP6xmVyuv6teODY5Ckcht+qe+hp2c6rUB6mtjrtf7enV6U9J+Gc6ytXLstBlUiiLxwcjk+frNByPoHRrDqRF77Yd86vk/fjqF2TPq8Mzm5Y7bEPnc7qxWy3enHfDlCOs1YrgshkoIcTGAfwTwMQAJAHEAI1LKGR4em2/yh+QfOjqI+37Yg62Xn4/fnTMdH4xkcM23X3dtulZYdkM129Xbyg7wdqcLFU/TPDWSwYnTY7mRKJ27Dua+99U7LwEafPkoQsGtaSXF5211+yzc8skFuO6RNwvOo9Odq/XyP6cpicVtTTh0dFBz6u4zm5djNJ3FO6dG8OV/eTu3Ccrzt6wo2XGbiMguO2Wo2Y7pxf+2be0iTEvEPan7reyibWe6qdvT1I2mQ5odV1SmzHutOLuHjg7isX3vYHfnMkgpc5+rokj8n5ND2PT4h3tp7LpxqeVcGp2PcqaFljsdWmuASPFmwU5uLJk/79ltu9o5J37db+n9nvcHU1i3Yz/mNiex84aleObPl2M0M9mO/dvn/hN9w2l0bejAOdPrcOuqjxZclzvWd+AjM+o93USdymMlu3p5rauJYeN33yhrSQKVXhtgZkMdZjbUuV7v69Xpdspwlq2VZbXNkN8We7d/BCfPjOUehMxtTuLxGy9ylF0vz7/by6v8/+zde3Qc1Z0v+u/ubrXclmRLyLJxkBM4xFeJL8eMI4WX5+aSYUIYICEe85hJbANJjD1OTiZMHuZmwpnMMnMGgzNcOLmObUIS7IQEwuPAgQyQEEjOMY9ExgmT5UQxBIgFxpKFZPSyWlLt+0d3NaVWVXdVdT12VX0/a2nZarWkrdq/36927arapdd3L8cWdkQ5R+zutb4B4G8BHASQA/BpFCabY6H8LEf/yAROnD8H6ZQwXXe4lrPeYd2e6lSlK47sXA3g9ooBfaeVSqWwYc++UM4URZFXZ+rK+2115xJs+v7znuWAVfz/aWAMG8891fLWXQGBNXc8h6u/+yvsPzRUase0Bt4+RUQ1c1JDK+3fzL62+b4X0Fif8WVfVu3qYOP77NxuGvTdVZXaFZU7vcJmFrvXfqgDJ86bM2O79o1MlCawgML2fOXomO249LM/arkd2jiuMF4gUuuVn4w//zkduzrpk6COt6x+j96m3sFxrPv2LzGlyVnj2A179uHo2OSsvNz4vX0YLC6bQWqyE7tW8frqwFjNSxLoKo0BgtzvO6nhrK3hsjtm0A2M5vHqwNisK+xfPjrqKnb97n8vl1fR67s+trj+4mW4d+PZuGfD2b5O9EY5R2xduQwAUsoXhRBpKeU0gO8IIZ72sV2BsjrLcfiY9+ur2LnKyC9OHyRodXbSztUAtV4xUOuZIhWeEB0kr87Ulfdbc67O1S2LVtu+tSGLnWs6scFwhcbW1cux7bEe/ONF77Xs26hc8V9J0mKSKEqc1NBq9cjsa5qUtvdlXu2r3VCp1qrUFpXZjd1Jkwfh3vbEQexY0znjqslq++EVS5qx8dxT0Zyrw9D4JDRNC3X/Vj6u1i8QaW/O1dQGxp//nI5dnfSJ2fHWzrWd0DQN/cMTVa+stBvPZr9HH9ca22iWf72DfEB1VNmJXat4nZt9e/LNiysiy8cA+u30dutx0LWOtTVcbuqu2d13tz1xcNYxvZ3YNfa/cTyRn5qGpklPxg5ejUmM9X3/oSFsefgAbl/XhRPnzfF1jBPlHLE7uTwmhMgC+LUQ4iYAh1FlUQIhxBIAuwGcCEADsEtKeasQ4gQAdwM4GcArAC6XUg4KIQSAWwFcCGAMwFVSyued/0numB2cef0ES/33hPE0VC8vr7ezXexuO6un5JqdKWptyOIdzbmqCR3lWwlq4cUEQ3m/OX1ydbVtn0oJtJ8wBz9YfxY0KSGEwIPP96J/ZKJi3/qRi0FKakwSRYndGlqtHpl9bU5dGh2LGi1vO9SFXSuCrLXVBv/ZTBrnL1uI1Z1LShOZ9+07FJm6H6RqsatpEpmUmNW3/SMTWNCYrRqXwNv9ceU5p8yYCPnu1e/Hm2Phxaxf4+qojzuiwsnY1UmflMfFtCZxwyMH8PiBvoox6rQGl/8eIQS+9tBvsf/Q0Iw21qVTpvVMf0C18cRNa0MWQgjPJlrIH9Vi1ypex/JvTxDpSxL88JqzICVQX5fCggb3V1u6GUMEVev0ff60lPjOVe/HbU8cLOVJe0sOuWza0aQ4uWe37mpa4Vh94bx60/HDic31uGfD2Zia1pBJp7CwsXrs6vHW1liPL364Y9aJlVrHDl6Oo8Oat4vy+ENIWX1xaCHEuwAcQWG95WsBzAfw/0kpX6rwPYsBLJZSPi+EaAKwD8DHAFwF4E0p5Y1CiOsAtEgpNwshLgTwX1CYXD4TwK1SyjMrtaurq0t2d3fb+DPdCfsgz0v9wxOlJ2nr2ltyeGDTSscTkmbbZfcnz0DjnAwmpzRkM2m05OpwsH+k6rrMZmv43L6uC+9e0IA/9I+UliVxsu1r/FsD6Vi/Y9ctszWXy9eCq9QP1bb91JQ2a83H7Z94H5rn1qE+nUIqlQp8wiWIK668zL8KEhm7J1/3SNhN8MwrN17k+HuC+vvdtM2BSMVupXqkaRKvHRtH//AEBkbzuG/fIVx5zim48+mX8S+rllfN94BqhSW/xz16vdWfq1BpH2+2v9ixphPvWdSk0lqkysZu+ba+9ad/mDUxfPOly3HToz2l5xhU6mdNk+gdHCs9h0T3navej+sf/K3tmI3KXTxxOgawoGzsWnHbJ3bqqjEur9j1rOsabGxjW2M9PnfeUpyyoAHz56bx2uDErHrWsbARLx4dxS0/6ZmVnzGLNy9FInat4rUuLXDTo7/H6s4laG3I4oSGLO7+5av49AfeXfN+3s0YIoj9/tB4HoeHjs+4ytW4/9n9yTMwMaXFud7qlI9ds3FaW2M9vnxBx8z5mrVdqK97e/1wu32mx9sbx447GjvYZZUD9286Bwub5rj+uUFSdPxh6xfbvXL5Y1LKWwEcB/DPACCE+HsUrjQ2JaU8jMIVzpBSDgshfgfgJACXADi3+LY7ATwFYHPx9d2yMNv9rBCiWQixuPhzQhHW2Qo/eHl5ffl2yWXTOPLWBNZtf3pGAixta6y47fQ1fPTCop+x16TEH/pGcOsTfyhdsbywqR7vmG/vFsco30oQNrOYb8nVeXbLYt/IBG4r9qt+5cY3fnYQ1/3Ve/GBm35mWTz9ysWgijdjkig+rOoRgFknVXes6UQmVVi/XtO0qj877Frh57jHWG+vv3gZtjx8oPS39g4W1pMzHlQMjpuvRRrURHuUWW3r/uE8rr94Gd7d1og/vTmGmx7tKV05Vr79y6VSAumUmBWfVg+rHM9PoX8YkX1IY5yOAeLCbZ9Y1VV9iQzjJMrXLzu9phqst/Ghz66cMZlWfhLGWM86FjXhax89DZfvfKZiTaRosYrXN0cn8JkPLsVn7nq+VAe/+Yn3lZYYCno5nyD2++UTib2D4/jSvS/g7mvOQjaThoQszSPoX2f8B89q7NA7OI6bHi08z+DUtgbMyaaRSQl89Bt7HfeZHm8N9c4fdG3n5LRVDoxNTENriMbdIFEef9i99ONKk9eusvtLhBAnA1gB4DkAi/QJ4+K/C4tvOwnAIcO39RZfK/9Z1wghuoUQ3f39/Xab4JqXi4KHqdYHW+jrN702OIb+4QkAKG2XaQ2lA4UVS5px/cXLMDoxhb6RwppmVtvOuIbPiiXN+OKHO0pFbMP39uHxA33YsGcfLt3xDD7+redsP9xC1YcmBh27bpXHfCaTssyB8rjIZatte4krzzkFWx4+gCt2PYstDx/Aleecgrp04WfqOyazBevL2wVgxu/WtOp3YZQLasF8VWPSrqjELlE5N7FbXtfMaovZ2MCsnmz83j68+uY4tjx8AEdH81XrlAq1wq9xj3H72FnPP+yJ9rDVUnettvX+Q0OFh4iNTJQeLKazs23N4nMsP20as797Yxirtu9Fz5HhUtw72efayUO/xeUYIGh+jhnc9IlZ3G74v07G0dE8Vm3fi1/3HivdRaEvB2fktAanUgLTGkoTy4D1SZj81DRSKQEpZaLrnSq8HjOYxWt+WpYmloFCP//d95/HgcPDM+qlG27HEH7v963iHyjMJ0xOce3xWnlRdyuN0/YfGsLV3/0V0imBhU1zMJ53P0ZLpQRydc4edK1PfK/avhcrtz45a3yhs8qBl4+O2jq+V2HsAUR3/FFxclkI8bdCiP8J4BQhxEOGj6cADNj5BUKIRgD3Afi8lPKtSm81eW1Wb0opd0kpu6SUXW1tbXaaQHD+RGajaslsfMjLFz/cgfv2HcLAaB6vD42jd3AMU1PmV2tlM+nSQcnGc08t3Qrm5iFyXv2tfopb7JrFxdDYJH6w/izcu/Fs7FzbifOXLZyx7aVEqZ+BQr9uvu8FSEP62+lruzuYaoKavFA1Ju2KW+xScjiN3Vpqi1U90fdpG/bsqzqwjWqtsDMYN24fOxM4Kky0h6mWulttW1tNCFfbtmbx+a7WubNe27p6OXY89dKsyWO7+1yv9vEUDtXGDOVxe/6yhVhz9imlCWXjcYf+gLVaarCmSeSnpvH1y07HzrWdWLGkuWrNS3q9U0UQYwbN4kTC3Gy65gtcwh5DzB4LaLZO2jD+a+dF3bU7TtPXYq6lz5zG6tHRCdOT00dHJ2b93J1rOmeNSW574mBg8wtJVm1ZjKdRWNpiAYCvG14fBvBCtR8uhKhDYWL5+1LK+4svH9GXuyiuy9xXfL0XwBLDt7cDeL36n0B21HJ5vdWVJvptD/oOYeO5p+LOp1+etWbYzrWdeO+J82Zd8ZpOAUtOKKy5VJdOzSpm5Wvl2C1WUb6VIErK46KtsR5H3jqO7+x9ubSO2D9etAzvMDykTwKmA6pjhqvS7fR1tZi0K6gF8xmTRNFQrbZUuiXPqp4MFeubnRNXUawVdpc6MG4ffQKnfH1R40GF8SndVu8hc9W29ZITcrjl8tNx7T2/cbRtreJT0yTuvuYsTGkSv39jGNsee3u5DWPc293nerWPp2Qz1uvWxiwe+uxKjOcLD90by8+eROkdHC89SHzLJafh1IWNyNU5q8Fm9XDr6uV4cP9ruPnS5bOeMaPnHOtdNFWqVa0NWdPxQkrMfriqPlao9QKXMMcQZrGvX2hUbZ/P+FeDnXFaS64OPUeGcctPeqqO4ypxGqsTk+Ynp49PzryIMZUSWNw8B1suOQ1zs2kMjU9i22OFtb2Dml9IsoqTy1LKVwG8CuBsIcQiAO8vful3UsqpSt8rhBAA7ii+998MX3oIhWU2biz++6Dh9c8KIX6IwgP9joW53nIcOXkis5Gdtcp2ru3EeH4aqzuXzLoydcOemWsklj/s4isXvheL589xdNDp199K9pXHxcZzT8V39lY+uVBvcWB5tLjUit2+9uqK4yAHM4xJqiZODyeMqkq1pdokqn61hPGBNVtXL8e2x3oA2D9xZawVUXj4md3BuLHe7j80hDuffhl3ffpMpFPC9G+L4kS7Ksy29fc/fSYyKQEhBL720G9L6y835+owlp/Gonn2brss35dpmiytNV6+ljYwM+7t7nOTviQK1a5SvT58bBxpAcvjjv6RCSycV493zJvj+OGhZvVw830vYMslp2HRvDm4f9M5pYefG+sZ6100VTpGtoq/tMCs41x9rODFBS52jze8Hl+Yxf6GPftw16fPxMe/9VzppM0pCxowtz6NBQ31jH/FWI0dUkKgvi6FBQ31M/pZH0e0NmTxjuYcTjRcUGaHk1jVJEznENImv645l8WJ8+c4Pr7n2KN2th7oJ4S4DMA2FB6+JwD8dyHEl6SU91b4tpUA1gL4DyHEr4uvfQWFSeV7hBCfAvAnAJcVv/ZjABcCeBHAGICrnf0p5BezK03OX7ZwxpPez1+2EP/1I/8nAPMrU41JaSxKvYPjuGznMzh/2ULsXNuJDXv22TropHBpmsS0JmfERXOururJBasDy0Xz6rF38wdt97VXVxxzMENERpVqS7VJVOPVEs1z6zA/V4cb//132H9oyPWt1VF4+JndwbibesuTcu6kUgJL2xpx16fPRN/wBAZG8/iXRw7g2g91YN6cDB4/ULhpcMOefaXv2bv5g0CD899lzAuziwN2ru0sxb3dGAjqriKKr0r1OptJ4/Wh46UrifXjjt2fPAMAcLBvBLf+9A+49kMdjuutVT08dWEj2psrP5ic9S56rGrVtIRl/KVSKdz59Mu48a//MxY35/CngbHSlZVBXa3rx/jCKvbTKWFrv8/4D5++j75/0zkYm5jGy0dH8fkf/roUmwsa6mf0s/4cB6AwhvBrbKqPYcrHF9s/8T7ksrPHBW6P7zn2qJ2tyWUAXwXwfillHwAIIdoA/BSAiTVWbAAAIABJREFU5eSylPJ/w3wdZQA4z+T9EsBnbLaHAmQ2IfjVi5bh4996rpR8+oHKP160rGpSmu18Hj/Qhy2XnMZJvogYGM3jhrIiP5afRmtDtuIkQ8Vi7+Cg1ssrjjmYISJdpdpy+Nh41UlU49USbY31+Nx5S/GVC5fNukrHjqjcnudkMM56G5zB8ckZ4zQAOHB4GPdsONvTg6fyA81tj/Xg+ouXYenCRmRSAu+YP3NCzU4M8BZpqlWlk16L5+cwfHwSw8cnS7dOj+WnMXx8El976EBpSZcDh4c9W24tV5fmMU0MWdWqSg9oXDw/h2s/1DFjnHDLFX/mapzglh/ji0pjAe73oyOVEhAQWHPHzPGD8eRc0BOw+alpPH6gb8YdV0Pjk2htyKI5Zz4ucDPe5NijdnYnl1P6xHLRAKo8DJDiw2xC0GqC+L+t+s+lK5CtktKqKKVSKe58IsKsyGtSoq2pvuoOx4vJBV5xTER+qFRb7AyovaxNUbk9j4NxNVleRSbgaX+V58X+Q0PY8vAB3LPhbMe3yOq4j6daVarXqZTAya0NGBrPY0HjNKYlkBbAZ+/aX5pYBtRfbo3CZ1WrBkbzFeMv7Prmx/iCsR8f1U7OBd3Pej03Xind3pLD/ZvO8TRvVMjNqLM7ufzvQojHAPyg+PkVKCxjQQlRPiHYPzxhutOUEHjvifMqJiV3PtFnVeQf/i8rq55c8AqvgCMiP1jVFrv7Lq9qU1Ruz+NgXE2VTuR72V9WeeF2YlnHfTzVolq9TqUETmioL9011z88gf6RiRk/g8utkR1mtcpO/IVZ3/wYXzD246Paybmg+9kqnxY0eJ9DYedm1InCahRV3iTEVgDPAfhzFJa6+AWAs6SUm/1tXmVdXV2yu7s7zCYkVq1rNSn8kKJAGhH12K3U/wBU7du4S2Ts8iF4wXjlxov8/PGRid0g911RWXM54ZSN3SDjR+ExHVlTNna94iQuWW8jJRKxq3JdZLyHJjKxq1p8qJxPCWFrY9u9cvlDxYnk+0s/XYh/BhDq5DKFx+5ZK6tCwLNC0Vat/6Pct9x5EZGVIPddvAooeHGq/5Xix+u/k2M6UpGTuIxDvY1T/YoDletiHOJdBXHNOafxEcR2UDmf6G0VJ5eFEH8HYBOA/ySEeMHwpSYAe/1sWNJFoVhVS/Lys17nL1uIr160DOni2pUq/k1kn5sir2JcG9uUy6Zx5K0Jpc7UElGwVKpTdvazqrQ16lS8UqdWZvFj/Dv1h0mdsqAh0IdJ1YpxT37QNInJaQ1TmoSY1qBpMjJxFcf6FSVRqknlbV1c9tBVsifuOWf3ON9sO+z+5BlonJPB5JSmfD6EJUo1w4lqVy7fBeDfAfwrgOsMrw9LKd/0rVUJF5diZXwS7YolzbjynFNKTy6P6t9E7qkY1+Vt+s5V78f1D/7W06cnE1F0qFinrESprVFgHLMA8a3/+t/Z1liPL364A5vveyFS8cO4Jz9MTWn4/ZFhbPze288M2bGmE+9Z1IRMRv1n2CelfqkoSjUpSm1VHXOuoHw7tDXW48hbx7Hu29EaWwQpznlYcW8ppTwmpXxFSvm3UspXDR+cWPaRVbEaGM2H0h5Nk+gfnsBrg2PoH56AplVfpxuY+aTRjeeeWjqAAcL/m6g2bmJCtbg2a9PcbNrzpycTUXTUUqfc7ivDaCvNVunp6FFnjM3xySn0Do5HdlzGuCfA+3rbNzJRmlgGCnG18Xv70Ff2kD9Vxbl+qY7jhmRKYs6ZxWv5dth47qn40r3RG1sEKc55aHfNZQqQSsWqljMrxieNNufqSn/TiiXN2HjuqWjO1SE/NR2p287IfUzocW3s/6HxSWiaFmDrzdukGxqf9PzpyUQUHZX2v5VuYQvjKgSVxgpxUOnp6FFmdodOe0tuxrhMZxY/qt26ybgnP5bdm5zWTONqajq8MaoTca1fUcBxQzIlLees4rW1MTtjO9gdW1T6PSqNOfwQ5zxU/z6fBNKLlVFYxaqWMyutDVncvq4L7S250qTdiiXN+OKHO7Dl4QO4YtezuGLXs+g5Muz7mVryjtuYyGbSOH/Zwhn9v+XhAzg6mg+t/8tzbcdTL+HmS5eXXivtOBuyobSPiIJltf/NZdPoOTKMVdv3YuXWJ7Fq+94Z+64wrkJQaawQB8YxCxCf+l8em7c9cRA3X7ocY/npqvGjH0xaxX0YGPdktexeLTFal06ZxlUmHY1D5bjWryjguCGZkpZzVvGaSYkZ28HO2MKKimMOP8Q5D6Oxx0wYlYpVLWdWjE8a/bP2+di5thOfO29pJG/DpLe5jYnWhiy+etGyWf2/Yc++0Pq/PNf6RyawaN4c3L/pHOzd/EE8sGllLNY/IiJ7rPa/U5qseBAYxlUIKo0V4sA4ZolT/S+Pzf2HhnDToz1YtrgJO9d0VowfFW/dZNyTH8vuLWysx46yfNixphMLG6Oxdmpc61cUcNyQTEnLOat4Hc9Pz9gOpy+Z7zrGVBxz+CHOechlMRRkLFZh3xJQ6y0f+pNGNU2iLpNCY30mtrcBJIXbmEilBNIpEXj/V7q9RqVcI6LwWdWEw8fGK9auWveVbm4DZP3ynt2no0eJWWz2j0wglUrhvYvnVYyfSpMf/cMTocQd456slt3TuRlXplICi5vr8cNrziocs6RTaGusj8TD/HRxrF9RENa4wUy1sQTrp7eSlHOV4rV8OzTnsq5izO0Jl6gtpRHnPIzOHjNh9CQ9qWUu2prqQws2L86s6Lc4fPQbe/H7N4ZjextAUtQSE0HfBmLn9hpVco2I1GBWE6rVrlrqYi23AbJ+UTWVYrNa/FjF/bQmQ71tlXGfbGbL7hk5HVfqNfiSbzyNP9/6JD7+recwND7JuCLbgh43mLE7lmD9JDecxKvbGHMzTxDVpTTimodCSrU3fCVdXV2yu7s77GbEXq1ng/qHJ7Bq+97S2mhf/HBH6Ra2IB5e4FAgjYh67LqNiaAfXmGMPV17Sw4PbFoZxzPNiYzdk697JOwmJMIrN17k549XPnbt1C63dTFhdSpulI9dwNt99s61nbj1p3/A4wf6Su9jvEZSJGLXih7Tmqbh6GgeG/bscz2uZA2OnEjErp/jBjOM40iIROxa8fsKYTfzBIz7wNjqaC6LQVXVesuH8RaH/YeGsO2xHlx/8TK898Qm5LKZ2NwGkCRuYyLo20Di/DRWIgqOndrlti6yTpHfvNxna5o2Y2IZYLxS8Iwx3dY0p6ZxJWsw+cHPcYMZxjH5ze9lQNzMEzDu1cJlMch35bc47D80hC0PH0Aum4nVbQBkT5C3gcT5aaxEFCy/ahfrFKlsdtynGK+klFprM2sw+YXHPETOOM0Zxr1aOLlMvovzEzFJbYw9IlId6xRFCeOV4oYxTXHAOKYkYtyrhctikO/i/ERMUhtjj4hUxzpFUcJ4pbhhTFMcMI4piRj3auHkMtXMzuLufq/RQ2rye+F/Oxh7ROQ1r2sb6xRFSdjxqsLYguLFKqYZa+QHv+Iq7NpMFAbGvXN+1SBOLlNN3DzVk5KBsUFEccTaRhQe5h8FhbFGfmBcEVGY/KxBXHOZajIwmi8FJlB4Ouf63d0YGM2H3DIKG2ODiOKItY0oPMw/CgpjjfzAuCKiMPlZgzi5TDXJT02XAlPXOziO/NR0SC0iVTA2iCiOWNuIwsP8o6Aw1sgPjCsiCpOfNYiTy1STbCZdejqnrr0lh2wmHVKLSBWMDSKKI9Y2ovAw/ygojDXyA+OKiMLkZw3imstUk9aGLG5f1zVrzZbWhmzYTaOQMTbIqZOveyTsJpAFN33zyo0X+dCS8LG2EYWH+UdBYayRHxhXRBQmP2uQr5PLQohvA7gYQJ+U8rTiaycAuBvAyQBeAXC5lHJQCCEA3ArgQgBjAK6SUj7vZ/uodqmUQMeiJjywaSWfpEwzMDaIKI5Y24jCw/yjoDDWyA+MKyIKk581yO8rl78L4BsAdhteuw7AE1LKG4UQ1xU/3wzgrwAsLX6cCeCbxX9JcamUQFtTfdjNIAUxNogojljbiMLD/KOgMNbID4wrIgqTXzXI1zWXpZS/APBm2cuXALiz+P87AXzM8PpuWfAsgGYhxGI/20dERERERERERERE7oTxQL9FUsrDAFD8d2Hx9ZMAHDK8r7f4GhEREREREREREREpJozJZStmi3zIWW8S4hohRLcQoru/vz+AZhF5g7FLUcXYpahi7FJUMXYpqhi7FFWMXYoqxi6pIIzJ5SP6chfFf/uKr/cCWGJ4XzuA18u/WUq5S0rZJaXsamtr872xRF5h7FJUMXYpqhi7FFWMXYoqxi5FFWOXooqxSyoIY3L5IQBXFv9/JYAHDa+vEwVnATimL59BRERERERERERERGrJ+PnDhRA/AHAugAVCiF4A/wTgRgD3CCE+BeBPAC4rvv3HAC4E8CKAMQBX+9k2IiIiIiIiIiIiInLP18llKeXfWnzpPJP3SgCf8bM9REREREREREREROQNlR7oR0REREREREREREQR4euVyxQ/miYxMJpHfmoa2UwarQ1ZpFIi7GZRwBgHRBRXrG+UdMwBUhVjk1TF2CSKD+azO5xcJts0TaLnyDDW7+5G7+A42ltyuH1dFzoWNTHZEoRxQERxxfpGScccIFUxNklVjE2i+GA+u8dlMci2gdF8KckAoHdwHOt3d2NgNB9yyyhIjAMiiivWN0o65gCpirFJqmJsEsUH89k9Ti6Tbfmp6VKS6XoHx5Gfmg6pRRQGxgERxRXrGyUdc4BUxdgkVTE2ieKD+eweJ5fJtmwmjfaW3IzX2ltyyGbSIbWIwsA4IKK4Yn2jpGMOkKoYm6QqxiZRfDCf3eOay2Rba0MWt6/rmrX+TGtDNuymUYAYB2THydc9EnYTiBxjfaOkYw6QqhibpCrGJlF8MJ/d4+Qy2ZZKCXQsasIDm1byyZkJxjggorhifaOkYw6QqhibpCrGJlF8MJ/d4+QyOZJKCbQ11YfdDAoZ44CI4or1jZKOOUCqYmySqhibRPHBfHaHay4TERERERERERERkWOcXCYiIiIiIiIiIiIixzi5TERERERERERERESOcXKZiIiIiIiIiIiIiBzj5DIREREREREREREROSaklGG3wTUhRD+AV6u8bQGAowE0xwkV2wSwXQBwVEp5gd+/xGbsuqVqPwYpidsgDrEbpCTGCKDm361a7Kq4jcLA7VBQaTswdqOB22U2xq59bJs7frWNseuNKLY7im0G3m43Y9cetsu+oNpkK3YjPblshxCiW0rZFXY7jFRsE8B2xQW3F7cBVZfUGEnq3+0Et1EBt0NBlLZDlNoaJG4X9ancR2ybOyq3zUtR/Tuj2O4othlQt91slzMqtku1NnFZDCIiIiIiIiIiIiJyjJPLRERERERERERERORYEiaXd4XdABMqtglgu+KC24vbgKpLaowk9e92gtuogNuhIErbIUptDRK3i/pU7iO2zR2V2+alqP6dUWx3FNsMqNtutssZFdulVJtiv+YyEREREREREREREXkvCVcuExEREREREREREZHHOLlMRERERERERERERI5xcpmIiIiIiIiIiIiIHOPkMhERERERERERERE5xsllIiIiIiIiIiIiInKMk8tERERERERERERE5Bgnl4mIiIiIiIiIiIjIMU4uExEREREREREREZFjnFwmIiIiIiIiIiIiIsc4uUxEREREREREREREjnFymYiIiIiIiIiIiIgc4+QyERERERERERERETnGyWUiIiIiIiIiIiIicoyTy0RERERERERERETkGCeXiYiIiIiIiIiIiMixSE8uX3DBBRIAP/jh5UcgGLv88OEjEIxdfvjwEQjGLj98+AgEY5cfPnwEgrHLDx8+AsHY5YcPH4Fg7PLDhw9bIj25fPTo0bCbQOQKY5eiirFLUcXYpahi7FJUMXYpqhi7FFWMXQpLpCeXiYiIiIiIiIiIiCgcvk8uCyHSQoj9QoiHi5+fIoR4TghxUAhxtxAiW3y9vvj5i8Wvn+x324iIiIiIiIiIiIjInSCuXP57AL8zfL4VwC1SyqUABgF8qvj6pwAMSinfDeCW4vuIiIiIiIiIiIiISEG+Ti4LIdoBXATgW8XPBYC/AHBv8S13AvhY8f+XFD9H8evnFd9PRERERERERERERIrx+8rl/xfAlwFoxc9bAQxJKaeKn/cCOKn4/5MAHAKA4tePFd9PRERERERERERERIrJ+PWDhRAXA+iTUu4TQpyrv2zyVmnja8afew2AawDgne98pwctJT9pmsTAaB75qWlkM2m0NmSRSiXzgnTGbjQwZmdTPXbZZ2TFTeyefN0jjn/PKzde5Ph7iCrxs+6yZpKfVB8zxBXzunaMXXJCpZwLM3ZV2g4ULt8mlwGsBPBRIcSFAOYAmIfClczNQohM8erkdgCvF9/fC2AJgF4hRAbAfABvlv9QKeUuALsAoKura9bkM6lD0yR6jgxj/e5u9A6Oo70lh9vXdaFjUVMiCw5jV32MWXMqxy77jCpROXaJKvErdlkzyW+su8FjXnuDsUt2qZZzYcWuatuBwuXbshhSyv9HStkupTwZwN8A+JmU8hMAngRwafFtVwJ4sPj/h4qfo/j1n0kpWdQjbGA0Xyo0ANA7OI71u7sxMJoPuWVE5hiz0cM+IyKyjzWTKH6Y10TBYs4VcDuQkd9rLpvZDOAfhBAvorCm8h3F1+8A0Fp8/R8AXBdC28hD+anpUqHR9Q6OIz81HVKLiCpjzEYP+4yIyD7WTKL4YV4TBYs5V8DtQEaBTC5LKZ+SUl5c/P8fpZRnSCnfLaW8TEo5UXz9ePHzdxe//scg2kb+yWbSaG/JzXitvSWHbCYdUouIKmPMRg/7jIjIPtZMovhhXhMFizlXwO1ARmFcuRwbmibRPzyB1wbH0D88AU3jKh5GrQ1Z3L6uq1Rw9DV4WhuyIbeM/BTlvGDMRk8U+yzKOUJE0aVpEukUsHNNZ6RqJhHNZhxLpFOI3FiI4iOJ49ooHn/Y5aQ/47wdyDk/H+gXa1y8vLpUSqBjURMe2LSSTw9NiKjnBWM2eqLWZ1HPESKKJmPtaWusx5ZLTsMpCxowtz6NBQ31rD9EEWI2ltj9yTNw/6ZzMDmlKT8WovhI6rg2ascfdjntz7huB3KHVy67xMXL7UmlBNqa6nFSy1y0NfHgJe7ikBeM2eiJUp/FIUeIKHqMtWf/oSFc/d1fYc0dz0FAKF0ziWg2s7HEum//EgIiEmMhio8kj2ujdPxhl5v+jON2IHd45bJLXLxcPZomMTCa51mzAFhta+YFJYXbesMcIaIglNcoTdNYe4giivlMqgpqXMvjfP9pmkR+ahpfv+x0DI1PYsdTL2H/oSHWFrKNk8su6YuXG4spFy8PT1JvyQlDpW3NvKAkqKXeMEeIyG9mNWrn2k6cv2whHj/QV3ofaw+R+pjPpLIgxrU8zvef2Tbeuno5tj3Wg/6RCdYWsoXLYrjExcvVkuRbcoJWaVszLygJaqk3zBEi8ptZjdqwZx++etEy1h6iiGE+k8qCGNfyON9/Ztt4830v4HPnLWVtIdt45bJLThcv560c/uKt5sGptK3dLOrP3KCoqZQD1eKZD74gIr9Z1ah0SrD2hIRjHXIr7vnM3Ig2s3FtS67O0z7lcb7/rLbxqQsb0d6cY076JG71j5PLNdAXL6+Gt3L4j7eaB6fatrabFwBzg6LJKgfqMilb8ewkR4iInKq0n2btCR7HOlSLOOczcyMejONaP/qUx/n+s9rGubo0c9Encax/XBYjALyVw3+81Tw4Xm5r5gZFkVUOZFKC8UxEoeOYSC0c61At4pzPzI348aNP45wDquA2Dl4c6x+vXA4Ab+XwH281D46X25q5QVFklQOHj40znokodBwTqYVjHapFnPOZuRE/fvRpnHNAFdzGwYtj/ePkcgB4K0cweKt5cLza1swNiiqzHGA8E5EqOCZSB/cNVKu45jNzI3786tO45oBKuI2DFcf6l/hlMTRNon94Aq8NjqF/eAKaJj3/HbzNgJLCaT4xNyhOohrPQewHiUgdzPlgRXXfQOqIa84yN4LFeQ+qJq61RkVxzJVEX7kc1CLavM2AksBNPjE3KE6iGM9xfJgEEVljzgcvivsGUkecc5a5ERzOe1A1ca41KopjriT6yuUgF9HWbzM4qWUu2prqIx00RGbc5hNzg+IkavEcx4dJEJE15nw4orZvIHXEPWeZG8HgvAdVE/dao6K45UqiJ5fjuIg2UViYT0TRw7wlShbmPFG0MGfJC4wjqoYxQrVK9OSyvoi2UdQX0SYKC/OJKHqYt0TJwpwnihbmLHmBcUTVMEaoVomeXI7jItpEYWE+EUUP85YoWZjzRNHCnCUvMI6oGsYI1SrRD/SL4yLaRGFhPhFFD/OWKFmY80TRwpwlLzCOqBrGCNUq0ZPLwNuLaBNR7ZhPRNHDvCVKFuY8UbQwZ8kLjCOqhjFCtUj0shhERERERERERERE5A4nl4mIiIiIiIiIiIjIscQvi0Hxo2kSA6N5rhVEnmFMkZcYT0TkN9YZomhgrlLcMcajgf1EteLkMsWKpkn0HBnG+t3d6B0cLz3ltGNRE4sjucKYIi8xnojIb6wzRNHAXKW4Y4xHA/uJvMBlMShWBkbzpaIIAL2D41i/uxsDo/mQW0ZRxZgiLzGeiMhvrDNE0cBcpbhjjEcD+4m8wCuXE8iLWx68vG3Cy5+Vn5ouFUVd7+A48lPTrn5eEvl9S8zUlIa+kQlMTmuoS6ewsLEemYy657kYU/FSHt8tuToMjk9axrvX+cB4IiK/Vaoz/cMTyE9NY042hfykRD4i+2KiOLLK1fH8FKam6piTFHmWMT45jdcGx0pja02TkTo+jJtK4warY6GoHdOT/3ybXBZCzAHwCwD1xd9zr5Tyn4QQpwD4IYATADwPYK2UMi+EqAewG0AngAEAV0gpX/GrfUnlxS0PXt424fUtGNlMGu0tuRnFsb0lh2wm7fhnJZHft8RMTWn4/ZFhbPzevtLP37GmE+9Z1KTszogxFR9m8b1jTSdue+IPePxA36x49yMfGE9E5DerOjOtSazavhdtjfX48gUd+NK9L0RmX0wUR3WZlGmuvtQ/ireOTzEnKfKs9kcv9Y3g6u/+Cu0tOez+5BkYy09H6vgwbqz6qS6TMj0WeveCBvT0jbDPaAY/e34CwF9IKU8H8GcALhBCnAVgK4BbpJRLAQwC+FTx/Z8CMCilfDeAW4rvSxRNk+gfnsBrg2PoH56ApknPf4cXtzx4eduE17dgtDZkcfu6LrS35ACgVABbG7Kufl7S+H1LTN/IRGknpP/8jd/bh76RCcc/K4h8ARhTcWIW3xu/tw+rO5eUPjfGux/5YBVP6RR8j2UiSgazOrNzbSdueORAoe6de2ppYhmobV/staD27UQqyKQEbr50+Yxc3bp6OW574mDNOclcIif8ihez/dHNlxZiHCjsf14dGPPs+JDcsTo+yaSE6bGQl8f0VljDose3K5ellBLASPHTuuKHBPAXAD5efP1OAF8D8E0AlxT/DwD3AviGEEIUf45nVH0KZlCLqHtxS7aXt3V7fYt4KiXQsagJD2xaqVwfR4GT/nCTS5PTmunPn5rWHLUzyIcOMKbiwyq+m3N1Mz7X492PJSzK46kuk8LI8Sl89Bt7Z8Ty0rbGist1EBFZMdtvaZqGxw/0AQCac3We7Iu95vWdcSqO94mMxvPTuOnRHuz55BnoG57A0Pgktj3Wg/2HhgDAdU46ySXmSjIZ+z2XTePIWxO+HFeV748A4LN37S/FOADMzaaV3CclidXx7uFj4+Z9o0lf+8zpeIB1TA2+XrMuhEgLIX4NoA/ATwC8BGBISjlVfEsvgJOK/z8JwCEAKH79GIBWL9ujB+mq7XuxcuuTWLV9L3qODCtxFiSoRdT1Wx6MnN6S7cXP8ONn6VIpgbamepzUMhdtTfUsLA7Y7Q+3uVSXTpn+/EzaWSkK+qEDjKl4sIrvofHJGZ/r8e5HfQJmxpOAwLpv/3JWLL9+bFzJfRURRcPs/dbb+9+h8UlP9sVe82rfrvJ4n8gom0mjf2QCf+gbwRd+9Bts2LOvNOlWS07azSXmSjKV9/tvDh3z9bjKuD/SY95oLD+t5D4pacyOd62OhTIp4WufORkPsI6pw9eMlVJOSyn/DEA7gDMAvNfsbcV/zWZrZkWEEOIaIUS3EKK7v7/fUXtUfgpmUA958uIWfy+XCUjSkgO1xG5Q7PaH21xa2FiPHWs6Z/z8HWs6sbCx3lE7+VC0YEUhdu0wi+8dazpx375Dpc+N8R5EfbKK5b7hCSX3VVETl9il5PE6do31bMdTL826Fd/NvthrXu3bVR7vJwHrrn16Xt637xC2rvYuJ+3mEnNlpqTEbnm/W1057MdxldnY+l2tcz05Pkwyv2LX6ljIq2N6K07GA6xj6vBtWQwjKeWQEOIpAGcBaBZCZIpXJ7cDeL34tl4ASwD0CiEyAOYDeNPkZ+0CsAsAurq6HJ2OUHlCKqiHPHlxi7+XywQkacmBWmI3KHb7w20uZTIpvGdRE+7ZcDampjVkXD5Zlg9FC1YUYtcOs/huydXhX1Ytxz99ZHa8B1GfrGK5fECkyr4qauISu5Q8XsdueT2bk03hRxvOxmQN+2KvebVvV3m8nwSsu/bpefkvq5ZD0zTcfc1ZmNZkzTlpN5eYKzMlJXbL+12/myWI4yqrsbWmyZqPD5PMr9itdCzkxTG9FSfjAdYxdfiWsUKINiFEc/H/OQB/CeB3AJ4EcGnxbVcCeLD4/4eKn6P49Z95vd6yX7c4eyHIK3i9uMXfy2UCuOSAWuz0Ry25lMmk8I7mHN7Z2oB3NOdc7YSSdMU7eas8vjOZVMV497s+WT14S7+aWqfKvoqIostYz1ob5mBxjftir3m1b1d5vE9UTs/LRfOWgFrIAAAgAElEQVRzOKllric5aTeXmCvJVN7vZnez+HlcZTa29uL4kPxhdSzkZ585GQ+wjqnDzyuXFwO4UwiRRmES+x4p5cNCiAMAfiiEuAHAfgB3FN9/B4A9QogXUbhi+W+8bpAepOULg6swIZWkK3gr4WLs0RBkLlnFBPOF/BJkHbK6mvraD3XgwOFh5fZVRKS+qI6lvNq3qzzep+QJIx/t5hJzJZnK+71/ZAKL5s3B/ZvOweSU5jhOo7rPoQIV+8/JeIB1TB2+TS5LKV8AsMLk9T+isP5y+evHAVzmV3sA9Sdw9bNCSeXlU8LJX0HlUrWYSHK+kD/CqENmsazyvoqI1BX1sZQX+3bVx/uUHGHmo51cYq4kk5f9HvV9TtKp3H92xwOsY+pI3P0GXIJBLZom0T88gdcGx/DGW8e5GHuEuM0lY5/3D09UfJIrF+gnr1WLP1VijvsqInLDqoa98dZxW/vduGANJRVUykdV8pC5kkxe9bvdcbOT4z8KTqX+i1KfsY6pwdaVy0KIU6SUL1d7jciJ8jNl9248m4uxx5zTs6NcoJ+8ZCf+GHNEFGVWNez1oXFcuuMZpa5KIoq7Svl4bHySeUiRZ2fcrPLVsUlXqf/YZ+SU3SuX7zN57V4vG0LJU36mbGA0z8XYY87pVaFcoJ+8ZCf+GHNEFGVWNUyvc7wDiCg4lfKReUhxYGfcrMpdgTSbVf8JIdhn5FjFyWUhxHuEEKsBzBdC/LXh4yoAcwJpIUWCm9smys+U7XjqJWxdHdyTail4VmdHxyenTWPHqyfHEwH2rq5gzBFRlJnVsK2rl2PHUy+V3sO7MYj8p2kS6RSwc02naT4yDykOqo2bNU1ifHKKdwUqyqz/dq7tBCDZZ+RYtWUxOgBcDKAZwEcMrw8DWO9Xoyha3N7qop8p0wvX/kNDuPPpl3HPhrMhpeRi7DFU3udAYSf2Ut8Irv7ur2bFDhfoJy/VZVKm8VeXefs8K2OOiKIslRJY2taIuz59JvqGJzA/V4ebH/s99h8aKr2Hd2MQ+ct4bNTWWI/vXn0GhsbyGBjNY9tjPdh/aIh5SLFQadys58Ebx46bjr8Z/+Er779pTeKGRw5gdecS9hk5VvHKZSnlg1LKqwFcLKW82vDxOSnl0wG1kRTn9lYXszNl136oAyfOm8PF2GPKrM9vvnQ5bnviIADz2OEC/eSVTErg5kuXz4q/TFlMMeaIKMoGxyfx8W89h0t3PIMv3/sCrjznFN6NQRQg47HR/kND+NKPfoPJaQ1bHj5QmlhmHlJcWI2b9Ty47YmDvDtZYXr/ZTNpfPxbz+HxA328o5xcsfVAPwAvCiG+AuBk4/dIKT/pR6MoWtw+AItXCCZPeZ8DwGfv2j/jiireckN+Gc9P46ZHe3D9xcvQnKvD0Pgkbnq0B9/4+AqgIezWERF5wzgu239oCNseK9S9957YhFw2w7EWkc/Kj432HxrCTY/24O5rzgIAHvNQIuh50Ds4XtoPNefq0N6Sw+L5Oca/Yjh2oFrZnVx+EMD/AvBTAJz1oRmsljqwc9uEfqasGk2TGBjNcxI6YH5sd2Of9w9PoH9kYsbXecsN+SWbSaN/ZAIb9uwrvWYVb6w5RBRVZsuObXn4AB7YtNLWmMsJ1kqi2cyOjfpHJpDNpCvmIPOJVOY0Po15sP/QEDbs2Yf2lhwe2LSSca2gIMcOOta8eKm4LIbBXCnlZinlPVLK+/QPX1tGkeH3A7D09ZpWbd+LlVufxKrte9FzZNjWQwPJvSC2Ox+eRkGyG2+sOUQUZUHtW1kricy5yUHmE6nMTXzyOC9agu4v1rz4EVJW7zwhxA0AnpZS/tj/JtnX1dUlu7u7w24Gwd+zTv3DE1i1fe+sK6N9OosWyKmyKMRuUNudZyw9w9i1wU68BVxzSLHYPfm6Rxz/7FduvMhNkyj6lIpdoyD2rayVkaZs7MaF0xxkPtnG2A2B2/jkcd4MysdukP3FmhcptoLA7rIYfw/gK0KICQCTxR8upZTzXDaOYsbu8hZuuF3TmWoT1Hb3M3aIytmJN9YcIoq6IPatrJVE1pzmIPOJVFbLM5Z4nBcdQfYXa1782FoWQ0rZJKVMSSlzUsp5xc85sUyB0Nf/MeK6vP7jdqekYuwTEVXHWknkHeYTqYzxSV5jTMWPrcllIcQHzD78bhwRwPWawsLtTknF2Cciqo61ksg7zCdSGeOTvMaYih+7y2J8yfD/OQDOALAPwF943iKKPK/X6kmlBDoWNeGBTSu5XlOA3Gx3rqtFKnIal6w5RBRnXu2rWSuJZnObX8wnUpmd+ORxYDJwDEFWbE0uSyk/YvxcCLEEwE2+tIgiTX/q5/rd3egdHC+dgepY1FTzBDPXawqek+3uV98T1cJtXLLmEFEceb2vZq0kelut+cV8IpVVik8eByYDxxBUid0rl8v1AjjNy4YkVdzO8A2M5kvFBigsyr5+dzef+pkAlfq+tSEbqzin6EhCTYrbfiSKTr7uEcff88qNF/nQEqLK/KqJrENEzvOLeUNeUCGOkjDepur9rEIsUnhsTS4LIf47AFn8NAXgzwD8xq9GJUUcz/DxqZ/JVanv4xbnFB1xr0lx3I8QkX/8qImsQ0QFTvKLeUNeUCWO4j7epoJK/axKLFJ4bD3QD0A3Cmss7wPwDIDNUso1vrUqIazO/AyM5kNumXt86mdyWfW9ECJ2cU7REfeaFMf9CBH5x4+ayDpEVOAkv5g35AVV4iju420qqNTPqsQihcfW5LKU8k4AP0Bhcvk3AH7pZ6OSIo5n+PjUz+Sy6vu0QOzinKIj7jUpjvsRIvKPHzWRdYiowEl+MW/IC6rEUdzH21RQqZ9ViUUKj91lMc4FcCeAVwAIAEuEEFdKKX/hX9PiTz/zY0zCqJ/h41M/k8uq7wdG87GLc4qOuNekOO5HiMg/ftRE1iGiAif5xbwhL6gSR3Efb1NBpX5WJRYpPHaXxfg6gPOllP+3lPIDAD4M4Bb/mpUMcT3Dpz/186SWuWhrqudOJUHM+j6ucU7REeeaxPwiIqe8romsQ0Rvs5tfzBvygkpxFOfxNr3Nqp9VikUKh60rlwHUSSl79E+klH8QQtT51KbE4Bk+SgLGOZF/mF9EFDbWISLnmDfkBcYRqYKxSHYnl7uFEHcA2FP8/BMorL8caZomMTCaDzX49TM/RHEW5ThXoU4QVWKVX4xdIgpKlPfzbrHGUq3CyhvGbnRZ9V3S6i+pSeVYZN3zn93J5b8D8BkAn0NhzeVfANjuV6OCoGkSPUeGS0+01C/b71jUFEqQMdgpapIQs6rVCYqesPKEsUtEVJ3bGs0aSyqrFNeM3ehy0ndJOE4jsstu7jBvamN3zeUMgFullH8tpVwF4DYAkV6Ze2A0XwouoPAky/W7uzEwmg+8LXqwr9q+Fyu3PolV2/ei58gwNE0G3hYiO5ISsyrVCYqeMPOEsUtEVFktNZo1llRVLa4Zu9Flt++ScpxGZJed3GHe1M7u5PITAHKGz3MAfup9c4KTn5qe8SRLoBBk+anpwNtSHuxtjfV449hx9A6NoX94ggFNygl6YKppEv3DE3htMNicUKlOkNrMYjTMAzjGLhFRZbXUaNVqbFjjJFJPtbj2OnYZe8Gx23fGGFixpBnXX7wMoxNTeOOt4+wfSiQ7uRPXE29B1mi7y2LMkVKO6J9IKUeEEHMrfYMQYgmA3QBOBKAB2CWlvFUIcQKAuwGcDOAVAJdLKQeFEALArQAuBDAG4Cop5fMO/x7bspk02ltyM4KsvSWHbCb4C7KNwb5iSTO++OEObL7vBd6qRMoK8qAqzNv3VKoTpC6rGD1hbl1okw+MXSKiymoZy6hUY7nMARlVi2svY5exFyy7fafHAOcViArs5I5qJ429EHSNtnvl8qgQ4n36J0KITgDjFd4PAFMAviClfC+AswB8RgixDMB1AJ6QUi5F4Yro64rv/ysAS4sf1wD4pu2/woXWhixuX9eF9pbCBdn6hm5tyM54XxAz/XqwA8DGc08t7QCA+JwxoXgxxqzOr4Mqq7OIR0cnPP9d5ezWCYq2Wuu8VYxOSwSWJ+UYu0TktbhdoVjLWEalGhvXq62iKuw8qRbXXsYuYy9YdvtOjwG78wphxywlSxjxZid3gpzfCErQNdrulcufB/AjIcTrxc8XA7ii0jdIKQ8DOFz8/7AQ4ncATgJwCYBzi2+7E8BTADYXX98tpZQAnhVCNAshFhd/judSKYGORU14YNNKywW7g5rp14N9/e5uNOfCu9KNyC5jzBpzw4+DKquziGMT09AapK9n3u3UCYo2L+q8VYxKKQPLk3KMXSLyUhyvUKxlLKNSjY3j1VZRpUKeVItrL2OXsRcsu32nx8DoxFTV/lEhZik5woo3O7kT5PxGUIKu0bYml6WUvxJCvAdABwAB4PdSykm7v0QIcTKAFQCeA7BInzCWUh4WQiwsvu0kAIcM39ZbfM2XyWWgEGRtTfWWX7ea6X9g08qK3+emHcZgV+U2OyIrQR5UWd3G8vLRUTTUZzzNRTPV6gRFmxd1vtKtVh2LcqFNPjB2icgrQY2Jg1TrWEaVGqvSEh1Jp0Ke2Ilrr2KXsRc8O32nx8Abbx2v2j8qxCwlR5jxVi13VDpp7JWga7StZTGEEJehsO7yb1G4wvhu4zIZVb63EcB9AD4vpXyr0ltNXpt1jbwQ4hohRLcQoru/v99OE1wLcqZfD/bF83PK3GZH3goydoOgx+xJLXPR1lTvW+Ftbchi55rOGTmxdfVy3PbEQV4ZEZC4xa6RF3W+0q1WQeUJmYtz7FK8qRa7cb1CMQ41WqUlOgD1YjdIquRJkGN0lWKvVnGK3VRK4MR5c6r2jyoxS7WJSuyqHm9xGBMYBV2j7S6Lcb2U8kdCiD8H8GEA21BYE/nMSt8khKhDYWL5+1LK+4svH9GXuxBCLAbQV3y9F8ASw7e3A3gdZaSUuwDsAoCuri5fF2gJ42xsHM+YUEGQsRsnqZTA4uY52HLJaZibTWNofBLbHutB/8gEr4wISJxj14s6z7qtrjjHLsWbarHLKxTVpdo+SLXYDVLS8kS12KtV3GLXTv8kLWbjKiqxy3gLVtA12u4D/fRTCRcB+KaU8kEAFae7hRACwB0Afiel/DfDlx4CcGXx/1cCeNDw+jpRcBaAY36tt2xXWGdj43bGhKhWzbksTpw/B1/40W+wYc8+9I9MRPrKCFKHV3WedZuI4ixuVyjGDfdBakhinjD21Fatf5IYsxQexlvwgqzRdq9cfk0IsRPAXwLYKoSoR/WJ6ZUA1gL4DyHEr4uvfQXAjQDuEUJ8CsCfAFxW/NqPAVwI4EUAYwCutv1X+CRuZ2OJooq5SH5hbBERVcdaSVQd84SihjFLQWK8xZvdyeXLAVwAYJuUcqi4nMWXKn2DlPJ/w3wdZQA4z+T9EsBnbLYnMKo8rIMo6ZiL5BfGFhFRdayVRNUxTyhqGLMUJMZbfNlaFkNKOVZcM/mYEOKdAOoA/N7XlhERERERERERERGRsmxNLgshPiqEOAjgZQA/L/777342jIiIiIiIiIiIiIjUZfeBflsAnAXgD1LKU1BYe3mvb60iIiIiIiIiIiIiIqXZXXN5Uko5IIRICSFSUsonhRBbfW1ZiDRNYmA0X3GRcTvvIaKZvMwb5iD5JcqxFeW2E1EyBVG3WBtJBW7jkPGbPLX2OWOG4s4sxgEw7kNkd3J5SAjRCOB/Afi+EKIPwJR/zQqPpkn0HBnG+t3d6B0cR3tLDrev60LHoqZSYNp5DxHN5GXeMAfJL1GOrSi3nYiSKYi6xdpIKnAbh4zf5Km1zxkzFHdWMV6fSWHdt3/JuA+J3WUxLgEwBuDzAB4F8BKAj/jVqDANjOZLQQoAvYPjWL+7GwOjeUfvIaKZvMwb5iD5JcqxFeW2E1EyBVG3WBtJBW7jkPGbPLX2OWOG4s4qxl8dGGPch8jWlctSylEhxLsALJVS3imEmAsg7W/TwpGfmi4FpK53cBz5qWlH7yGimbzMG+Yg+SXKsRXlthNRMgVRt1gbSQVu45Dxmzy19jljhuLOKsbnZtOzXmPcB8fWlctCiPUA7gWws/jSSQD+h1+NClM2k0Z7S27Ga+0tOWQzaUfvIaKZvMwb5iD5JcqxFeW2E1EyBVG3WBtJBW7jkPGbPLX2OWOG4s4qxsfy07NeY9wHx+6yGJ8BsBLAWwAgpTwIYKFfjQpTa0MWt6/rKgWrvlaLvkC43fcQ0Uxe5g1zkPwS5diKctuJKJmCqFusjaQCt3HI+E2eWvucMUNxZxXj72qdy7gPkZBSVn+TEM9JKc8UQuyXUq4QQmQAPC+lXO5/E611dXXJ7u5uz3+unaer8gmssRVIJ/oVu6rzMm+Yg7Mwdj0S5diKaNuVit2Tr3skgNYAr9x4USC/h3ylVOxGVRB1K6K10U+M3RC4jUPG7wyJiN1a+5wxo6RExG5QzGIcAOPeH7Y2oq01lwH8XAjxFQA5IcSHAGwC8D/dtkx1qZRAW1N9ze8hopm8zBvmIPklyrEV5bYTUTIFUbdYG0kFbuOQ8Zs8tfY5Y4bizirGGffhsbssxnUA+gH8B4ANAH4M4Kt+NYqIiIiIiIiIiIiI1GbrymUppQbg9uIHJQRvpyHGACUNY56IKLpYwylqGLNUC8YPkf+YZ/bYmlwWQqwE8DUA7yp+jwAgpZT/yb+mUZg0TaLnyDDW7+5G7+B4aUH0jkVNTKSEYAxQ0jDmiYiiizWcooYxS7Vg/BD5j3lmn91lMe4A8G8A/hzA+wF0Ff+lgGmaRP/wBF4bHEP/8AQ0rfoDGd0YGM2XEggAegfHsX53NwZG865/ZlBtJ29YxcDR0QnXP5MxQF7zMqb8qHsqYN4RkZm41Ya41nCjuPWZSsLYtmHGLGNJHW77wk38sN/Jb3GLMas8O3xsPBZ/n5fsPtDvmJTy331tCVUV5FmT/NR0KYF0vYPjyE9Nu/p5POMTPVYxMDYxDa1BOu43xgB5zeuY8rruqYB5R0Rm4lgb4ljDjeLYZ6oIa9uGFbOMJXXU0hdO44f9Tn6LY4xZ5Vnv4Di+8KPfRP7v85LdK5efFELcLIQ4WwjxPv3D15bRLEGe3c5m0mhvyc14rb0lh2wm7ernJeFqkrixioGXj4666jfGAHnN65jyuu6pgHlHRGbiWBviWMON4thnqghr24YVs4wlddTSF07jh/1OfotjjFnl2dD4ZCz+Pi/ZnVw+E4WlMP4bgK8XP7b51SgyZ/fspBe3IrQ2ZHH7uq5SIulnnVobsr62ncJhFjOtDVnsXNM5Iwa2rl6O25446KrfGAPkNa9jyuu6VysvajnzjojMqFAbvL51VrUa7jUV+iyuvNi2buI5rJhlLKnD2BcrljRj59pOfP2y05Gfmq4aQ07jh/1OfnNzNb3qS2iY5dnW1cux46mXADCHjGwtiyGl/KDfDaHq9LMmxh3Q585bimlZSEp9R+LFrQiplEDHoiY8sGmlJ0/FLG87EK+rSaKs0u0ri5vnYMslp2FuNo2h8Ulse6wH/SMTpv1W7SmqjAHymtcx5XXdq4VXt5XZ2W/wNi6i5Al7n+zHrbMq1XA/hN1ncVbrtp2a0tDTN4wNe/Y5iuewYpaxpAZNkxBC4N6NZ2NyWkNKCHzhR7+xHUNO44f9Tn6rFGPlcwUtuToc7B9RfgkNY56NT07jpb4RbHusB/sPDQFgDhnZunJZCNEqhLhNCPG8EGKfEOJWIUSr342jmYxnTVYsacaXL+jA9Q/+Fh+46Sms2r4XPUeGcXR0wrNbEVIpgbamepzUMhdtTfU1JXncryaJskq3rzTnsjhx/hx84Ue/wYY9+9A/MmHab/pB4qrte7Fy65OleDSefWQMkNf8iCkv614tvLqtzM5+Q8WrBIjIX2Hvk63Gq7U8NBhQp4b7Iew+i7Natq2mSbx+bLw0sQw422eHEbOMpfDpx06X73wGl+54BscntdLEMmA/hpzED/ud/GYVYy25ullzBa8fG4/MEhp6nrU353Di/DnoHymMVZhDM9l9oN8PAfwCwOri558AcDeAv/SjUWSu/OzkFbuenZWMd60/U8nbXeJ+NUmUVbp9xW6/WU2EPbBpJdqa6gEwBsh7cY4pr25dtLPfMOYpESVD2PXz+KR5jTs+qQXy+6Mo7D6Ls1q27cBoHn3DE0oef1lhLIWv/Nhpbjbtewyx38lvVjFmNlcQtboJMIeqsTu5fIKUcovh8xuEEB/zo0FUmX7W5LXBMdNkTAuh7O0uettJLdVukbLTb3YnwhgD5LW4xpSXty5W22+oPIgjIv+EWT+txqtpHp9VFNd9ngrcbtv81DQGRvPKHn9ZYSyFq/zYaWh8MpAYYr+T38xizGyuIIp1E2AOVWL3gX5PCiH+RgiRKn5cDuARPxumKlUWHbd6amUum+btLuSIF7dIuX3atSr5RKQCYz6kU/C8lof1VHoionK5bBo3X7p8Ro27+dLlyGX9q0ccc5Afspk07tt3CFtXz4znnWs7K+6zGY/JVj4m2/HUS7NqIo/hKS7MjkHu23cIO9d2+hrzrLPBsnvl8gYA/wBgT/HzNIBRIcQ/AJBSynl+NE41fjx8xC19QrC8Lc25LJpz2UAu1a/2ADeKBre3dxj7vy6Twu5PnoF13/7ljHisNqhWJZ8onqJUo8zyYfcnz8D9m87B5JTmSfut9hs8cCGKJ5VrYHMui0Xz3n5o8Fh+GovmzUFzzp96xDEHVeM2X1obsrj2Qx245Sc9uP7iZWhtyGJhUz3eMT9n+f2MRyofk/WPTCCXTWPbZafjxHlzMLc+jQUN/q3BrfL+gcLndXyYHYNc+6EOLG1r9G3einU2eEJKe7P3QogTACwFMEd/TUr5c5/aZUtXV5fs7u4O7Pf1D09g1fa9sy7dL1+vMqhiHeZOIcbJGkjjg45dr5n2/9ouLJpfj/G8vXi0m09kG2PXIGo1yo98MNtHAFDxYEKp2D35umBuzHrlxosC+T3kK6Vi1ygKNbCWcazT7+WYYxZlYzcMteaL0/0t47EmkY9dPV40TcOUJnF0JI833jqOHU+9hP2HhnyPhSjsH2IqErHrV3w42W97Mc/FOuspWxvf1rIYQohPA/g5gEcBfK3473+18X3fFkL0CSF+a3jtBCHET4QQB4v/thRfF0KI24QQLwohXhBCvM9O24JkZ11ZPRmNT8LsOTLsyyX4YT4R2+oBbio+3ZO8Z/qU9z3dmNKk7Xj06oFlRGaiVqO8zgerfRGA0PYbRBScKNRAt+NYN2NtjjmoklrzpTyWAVSMUcZjchnr15n/+jNcsetZjExMlSaWAf9jIQr7BwqPX/Fhd5/v1Xwa62zw7K65/PcA3g/gVSnlBwGsAHDUxvd9F8AFZa9dB+AJKeVSAE8UPweAv0LhyuilAK4B8E2bbQuMnfUqk1KsVU9Wrq/jLy+e8p609V8Zk8GqVKNU7Auv8yEp+yIiMhfkOC3omuqmviVtzEHOGPNlxZJm7Fzbia9fdnppzOBUtRhlPCaXWWxsvu8FbDz31NJ7aokFO/VY9eN4ClfY8VHrMYyeA9NS4jtXvR8rljSXvsY66y+7k8vHpZTHAUAIUS+l/D2AjmrfJKX8BYA3y16+BMCdxf/fCeBjhtd3y4JnATQLIRbbbF8g7Dz4LOxkDIrKg6Igrx5PKv0p70ZOn/LuxYMEo4IxGbxKDz1VsS+8zoek7IuIyFxQ47Qw9m9u6luSxhzknJ4vK5Y044sf7sCWhw/gil3P4opdz/pyxRzjMbmsYkPv+1piwW49Vvk4nsIXdnzUcgxjzIEP3PQUrn/wt/jyBR1YsaSZdTYAdh/o1yuEaAbwPwD8RAgxCOB1l79zkZTyMABIKQ8LIRYWXz8JwCHj7yy+dtjl7/GcnQef6clYvrZL3Iq10wdDBbk+tNXZLq6v4x39Ke9fuveFUv87fcq72wcJOqXCAysYk7Vz2o9WNWpKk0r2hdf5kJR9ERGZ8/MBnsZ6LIQIvKa6qW9BjTkomlpydbjr02diWkqsveOXNcdztRhlPCaXVWy8ozmHvZs/WFMsmC5baBK/fMAzVRJ2fDjdx1cbk3zp3hdw9zVnsc4GwNbkspRyVfG/XxNCPAlgPgrrLnvJrJdnnSYWQlyDwrIZeOc73+lxE6rT14qxElQyhj1h5mRQFPRDA1S9Yi/s2LXDblx59ZT3avlUK1UeWKFqTNoVduy66UerGnX42LiyfeFlPlTaF4W9/whS2LFbiZsHB/IhgMlRa+z6NXlVXo/v3Xh24DXV7Vjb7zEHFahcd81omsTB/hGs392Nr192uifxbCdGGY/q8TN2jWOvuz59Jm545AAeP9BXio0T582p+WFpYxP2jjd4ciN+vIxdN/Hh5bGFk3283TEJANbbANi9crlESvnzGn/nESHE4uJVy4sB9BVf7wWwxPC+dphcHS2l3AVgF1B4EmaNbfGMMaEWzavH/ZvOweSU5kuxVmXCzO6gKOirNlW9Yk/V2NU5iatUSuDk1gY0zamzdXIhrIksVa4YVjUm7Qo7dt32o1mNCrMvgswFq4EhACX2H0EJO3aJ3PIidv2YvCqvxwOjedOaOq1JaJr0pa6U1zchBNKi0BZOkIQvanXXGNND45M1jxH0ff0Jc+twz4azIaXk5F1E+BW7ZsdYO9d24p8vOQ2aJh3d+WllYDSPl4+O2o5fntyIF69j10l8eD035WRy2+6YJCrH3FFnd81lLz0E4Mri/68E8KDh9XWi4CwAx/TlM1RXvr7RR7+xFwMjeSyen3P09Gu7ovagpqCv2uQ6Zu44jSs7T3wNe61hVa4YZkzWxst+DKsvwsgFsxyN2v6DiNRSXo93PPUStq5ePqOmbl29HDc8csDXupJKCbQ2ZPHW8SlcvvMZnPmvP1NmDX2KFmNMm8XzzrWdtscIxn39mf/6M1y+8xm8dXyKE7/5nkUAACAASURBVMsJZzb22rBnH35/eLg0d1Br7cpPTeO2Jw7Ojt819uOXyA0/ji3szDMA9sYkPOYOjuMrl50QQvwAwLkAFgghegH8E4AbAdwjhPgUgD8BuKz49h8DuBDAiwDGAFztZ9uqcXKFWdBXR6oyYWZXEFcKlvfX0rZG3urjUK1xZZYz5bnR1liPN44dR0N9Grm6jO/9osoVw7z9rDZe9mNYfWG2n7jlJz342kdPC/SqpqjtP4hILWb1OJtO4Qfrz8LE1DTeOHYc2x7rwf5DQ/inj/hbV7wefydpySB6WzaTxvnLFmJ15xI05+qgSYltl52OBY1ZvNQ/igUO4sBuTDLWksVq7DW3eMWyWZw4jZFsJo3+kQlse6wH11+8DM25Oozlp7G4ubblNoiq8evYwk4OlI9J9h8awp1Pv4zvXPV+vDmaLy7d6f3FnmTO18llKeXfWnzpPJP3SgCf8bM9djm9tD/og3VVJszs8nsdalWWCYm6WuLKqg/mzcmUfp7+BO7N970QWD+F/UACI95+5p7X/RhGX5TvJ1YsacaV55yCy3c+E1g+aJrEtCYjtf8gIrUY63FbYz2+fEEHrr3n16U6tnX1cgDB1BUvx98cSyZXS64Onzvv/8DG7+0r9f32T7wPu37+Rzz9xwE8sGml7Z9lJyYZa8lSaew1ND5Z+twYJ25ixFibN+zZV/oep8/DIXLKj7kpuzlgdox45Tmn4Mv3voD9h4YAAHs3fxBocP/3kX1hLIuhPKeX9usJZeTnoDpqt9gbrxTcu/mDeGDTSk8HULzN2xu1xJVVHwghSj9v47mnliaWje/x+7ZZP2OPghGHfizfT4SRDwOjedzwyIGabvklomT7/9m79zgpqjtv/J/Tt5meC844DKiAl7CEddYdHxy8IM9vfxg3JkayRkGzSwAvicKSxGw2Mbi/hCd5XsRdEV1/yy8xEE1UUBMNxOijiTHLEzfPijEyElmDQVQ0gMgM4wzM9PT0rc7vj5lq+lLVXdVdVV1V/Xm/XvNSZvpyus/3fE/VqVPn5Obj7yyZg1u35uex1dt245ZLZzlyXGrl8TePJevXYDyVHVgGxut+1SOv4PK/PNV0HBuJScZafdE79lq/uBsbn38r+7jcOKkkRvxwrEzeZMfYlNE2EAgITJ3UgLVXnoN//8e/wtorz8nePaWWhRNonGPrzGWvMjsTotSsulLT+Su9JcqLt9jbOVOQt3lbo5q40qqDzpYGQEo8/NkLsf9oDG1N4arrqZI2wxnD/uDGejQTj4X9REdzxNG8pSgSyXQGn/3vH4IiJe64+i8RDgYwFE+ZuuWXiEjNx4cGRzXz2MwpLZjeFs3LK4X5sj0axmA8VdVxrJV3tfBYsn7p1f3MzmY0NwYxFE8injQWp0ZikrFWXxRFwaKeGZjUGMID15+PkUQasUQa09qjuOXSWWiKBDGazOCMjqZsnFQaI248Vib/MzKGYOacSVEk4qm04TYQT2Zww4MvZ++S7h9JAHD/BEw/4uCyBrNT+/UaFADd6fyl/mZ0gLleOo9yychry4S4WaVxVVgHc2a04Wsfn41rv//bbHw/8rkLNespHDJ2AwVvIyQ7mb1wYTYeC/sJdVa/E3lLq6zrFnXjjl/8Ef0jCVO3/BKRv1Sz9qve8Vc0HCw6qSzMQRuX9mDD9jfw3J6+ivtzKydb8FjSf4zGtl7dv/7+MNY+vQfrF3fjzmf3on8kUTZOjcQkY61+KIrE0VgSa5/ek3f89X/e6MPfzJmONU++hs6WBtxy6SwEhMDRWAKTmxsYI+QJRnOsmXMm9bHvHxsz3AbU9rLrwFB2zfGO5ghOa4vilElcc9xJXBZDQyVT+7V2tCw1nd/tt0QpikT/cAKHBkfRP5yo2c7bubsuz1/3a82dwL22TIgfFdbBLZfOKrpV9vZn9mDTsp6iW8KOx1P4IFY+xtzeZsi7SuUZvVxY6S2Laj9xyqRGx/KWVlmdvHWdiNxJK/e9/v5xHDkWN3TsZ/T4SysHrXy4F4t6ZmT/XWl/bnRH+XJ4LOkvRs4fVFp1v27R+JIFBwfjuHXrbqxcMNNwnJaLScZa/RiIJbFiS2/R8deyi8/Cii296GxpwFc/NhtrnnwNC+56HlffuwN7jwwjHAQ2Le1hjJBrGc2xiiLx/vExxBJprFnYhTkz2krmUvV4YcP2fUVLyei1gdycuuvAENY+vQfNDSEOLNeAr2cu13rZCb1bWhRFQSKjOH5LtJlbEdwyQ9TIrsteXCak1qzepbqwDjJSFsX3c3v6sPbKc7D2ynPQFAliKJ7KzgRZe+U5mDKpAbOntCKkM5NZrz3Fk2n0D4N1ThXTyzM//fuL8/6mrk88uTlSNoeXa2NO5i3dW341bl0novqhlftWbOnFmoVdWPv0nry77bTyWbk8pubB0eT4SeXG59/KroN4cDCOtmg4W5ZqjoGtOKbhsaS/HI0lNPv1x2++COGCug0EBGZ1tuDxFfOQyihIphXc95u3NWNVK07Nxh9jrX5obea8csFMZBSJNQu70BwJau6/sfbKc7Bh+z6svfIcnDW5GU0NQUxurvziGZHVyo3RKIrEUDyJw0NjWJGzWeq6Rd3ZNZEVRUH/cCIvD6pt5uBgPDsLuS0axvT2KE49Sfucxc6cavWYid/5dnC5kgFSq4NH65aWy7qm4Ggsib7jiZreEl3quzAyoOsUo2tO1dMyIdWq9OKBkcEytQ76h7XjO5FRcMODLxe9dlMkiBVbevHo5y7E9Pamim5b5BIZVCndCxepjO7ASyQY0M3hRttYbpux8+DF6K3rRE4687ZnTD/nnTuusKEk9Usv97VFwycusq26GAMjSd18pnf8pbccj3pSOb09iqF4Kvv43GPgWk2I4LGkf4yltGN7NJXB9d//bV6MKIrEvv6Roljd1zdSFKuFy7lVGn+MtfqQe/ylrgerDiZPb4/ioRsv0IzTpkgQuw4M4YYHX8b09iieWDWfx2vkKqXGaHKXtljz5GtFM/fXLOzCtt4DOJozs1/NnVMnNWTbzK4DQ1ixpRfT26P46aqLHc+pbppw6RW+XRbD7C3LZm6fMkrrtqdvXNGFFVt6TU31r5R6O/fhY3FT30WtNprQuv3cyp3AaVwlt/ObbR96t/w16tTnUDyFg4Nx9A0ndMtR7rZFLpFBpZRa6kcvzygaM/DVgZcN2/dh/WLtHO5k/2NkCSPegktEWvRynzqQdnAwjrGUYjqf9Q8ncHBoFO8fGxvf3BcnTipXLpiZXXN5W++B7HvmboRtJh9yySzSEpzY1yDX9PYogkJkY2QonjQUq+sXjx9rqv8/Mpauaoksqh/t0TAe/dyF2LpyHu5c3F00S1mdjJOr8MKbE+fgRGaVGqNR82JTJKh5HtXRHMmOiRXmzrQii85Z1LybTiuOLtvK/G6eb2cumx0gtWO2rtYU/Uqm+lci90rL3decm71iunLBTLRFwxiKp6AoiuZza7GJgN6VoVmdLZbtBE7jKrl4YKZ9qDOOJjWG8PiKeQgKIBAIoD0axuHjcdz7mfOw6pFXsvV572fOw8Mvvovp7VEMxJI49aRGzTLktqd4Mo3X3x/OzoAy8hmofmnlF3V5CzU2Ny3tKbptS28ziaF4CrsODOHOZ/fisZsvAoC82XVO9T9mZkjzFlwiKqReeNKaXQyog3HQvK17VGNJqnKzlQ8OxnH2KeO5qD0axu1XdeObn8zPSf3D2ssZ6OXDWk2IIHeLRoJYv7g7u/+HOkARDgpsWtaD7XuOlLxdW43Vn6yYh4ODcdx2+Z/nLeemxiPjj/QUzojfunJeNlbUjc87msNFcXrPtefin3/+x+zrcFIVuZHW8YM6RnP42PhYVyqjaJ5HTWsbn8DT2dKQHQsbiqew8fm3kEormDqpoWgZzc7WCL701x8umuls5yxi5nfzfDu4bHaA1Krg0bqVL/dgOHe5gNyp/lbf7pI7WDEUT+Gyrim47uKz8m7F2bSsB52txQudayWLzTdeAAmJQ4OjhgcmzNzWWGpwhYMi1qrk4oHR9lFqsGsglsSbfTH86Hfv5nUk3/nf+3Djf/8QrpwzDQ/t2I/zTu/WLYd6y0v/MLCt90DexZJtvQd8dfDFNZ6sU2pd0W29B/CNK7pwcksYD95wAYbHUugbTuCuX44fyGxa1pN3IJM78NI/kkAkFCwa8DDSxnLrFwA6WxryHm+k/zEzKM1bcIlIq1/J2y9Bkfj2M3uySwHct3wuopHSt3XnntxprXOr3gKrHu+GgoFsLrJisNjOCRHsh91Pr47aohFMndSYHaAYTWbQFAniiz/6PfpHEnj4sxdi6Q9eKhmr0UgIyXQG12x6seh91Xg0E3+Mp/pSeIw2EEtiensUF3+oA6su+TMEAwJ/d99v8wbYRpMZTGuPon8kAYB3mlFtGMlVpSauqHkxGBCaF/kawuOLJ3zt47OL/haNBBFPZoqW0cw9HwOcWba1FhMuVV7tL3w7uFzqaooWK4LHyCwys+WqVO7B+cbn38Ld156L5T/8XdHgit4gRG6yiEaCOHI8geX37jB8pcjsGjV6Gx6MJtMAtDdr82qjq7VKYtBo+yg12JVMjx/YP7enD8/t6ct73j994mz8+KV38eWPzjbUFtqjYdxy6YexMmfGycalPWjP2RzIqHRaQd9IAqmMgnAwgCktDbqbCjqFazxZS80vhXdvfKizGdddfBaW3P9S3oHNxuffQv9IAv989V9iVmdLyYGXUrsW67Uxrfpdv7gbdz57Yia+kf7H6SvqzLlE3lWqX8ld+71wNjGAbD5buWCm5uZTT6yaj47mCEYT+ms4qxfnIkFRtIFPbh5Rjzc6Wxqy+Xo0OX4sqsWu42r2w+5Xro7O7GhGa2MY8VQab/XF8K2nxvvvOTPaijaeVo8PZk1pwQPXn48ZJ0fR0RzBUDyJB64/PzuDTj0+UPtno/HHeKo/yXQmb+A4lVFw/3U9SKUl/vnne/BPl5+Nu685NxtX6vHfC6sv4aQqqhkzuUpv4oqaF2OJNG5/5vW8SWV3PrsX31kyB+FQIDuwDIwfK9y6dTd+uurionGHOTPaMHtqa1F7KXXOY8U5i1PjdoW83F/4dnDZ7G3AVgSPkVlkTt2enNsodx0YwrGJNW1zlWqQucmib3jM9C3bZm/zLrfhQWGD8nKjq7VKYrCwfVzWNQXfuKILyXQG/cMJQ8sBRELjM0e0BqnfOTqK6+Z/CKdMKp5Jr2UwnsoOLKvvsfJh7YslpaTTCv54ZLhokPrPp7bWdIDZTZtqupWZgwYhhObdGxuX9uChHfuLDmweu/mivNcsNfBSya7FWvV769bdWHvlOdnNW4z0P05eUWfOJfIuRZF4//gYYok01izsyp6YaR2javUxaj4bTaZ1+/iBWBL7j8Y0c9KU1vEBlt/sPYK2pnDJ21o7miPYfOMFOHJ8LG9G033L56Itam72VDXYD7ubkZhW4/nQYP4suJULZuJPA6Mlzzs2LeuBokgcOZ7IbkilXgieOqkx2z8bjT/GU/2JRoJFMzPv/cx5eObVQ7ju4rOwbGLSV+5dcXp3xBE5pVSuUveVKdfXqnnx/eNj6B9JYMWW3uzf1PMUvTGDVFrBqSdFs+MOnS0N+NrHZ2fvNClsL0IUv79V5yy1WlbQy/2Fbzf0A04cJE9rb8oeZJR6rBo86hVDswFodBaZmXJVqnADp6HRlO6i67kKN4dKpxXdmSilZseZnVGXW169mTG5i6dzgfXqmI3B3Pbx0j99BF/66w9jyf0vFW22U2px/47mCM7oaML3PnNe3iL96xZ1Y8P2fZBSGm4L1czYzI3xI8NjmoPUfRO3o9UK13gqzeyGT0EB3Hb52UV5ZeXDvVjUMyPvserftdpFqXZTmDvV19B6rF79zpzSYqr/cXKjPuZcIm9S8+W1m17E4o0vYu3Te/DVj83GnBlthvsVNfc1RUK6fXwyndHcrPp7nzkP//j4q1j79B585qIzNW9rzc0jgYBAS2OoaEZTqXxjx3E1+2H3MhvThcem6qa8aqxqnXes2NKLIxqTa27duhstjaGifQ3KxR/jqf6kFVmUx1Y98goWzz29KN5Wb9uNWy6dVfFdmERW0ctViqKYOvcKBAROmdSoe55Saswgd9zhO0vmFLUjtb2sW9SNoEZ3r3fOcvhY3PRGgE6M2xXycn/h68FlPYWDAGqAVRs8pRqJ0woHy8+dcVLZQQitAZv3jsWzM1FylftcZr+L3PL++SmtZRuUlxudV6ntIxAI6J4clhrsUm9RVNfAe+zmi7BmYVfelfpSctut0NkF3Mhr5MZ4Iq1oxlE6o73ZpVPclEvcyOxAZyAQwPCY9oy7woHYSr5ns4PdevUbFDB1VdyKi6JGMecSeZNWvly9bTdWLphpOt+V6uMjoSD6RxLZzaofu/kirL3yHEyd1IjvLJmDJ1bNRzAgDOWRlE7f7GS+sbMf1jsPIWPMxnRh3I4mM3mxOmtKi2a86R0jptLmjxHNxBPjw/sURWIspX3cpJcHz5rcjA3b38BgPOVkUYny6OWqjITpSSalzlPKTZDJvZNKq73MODmKh3bsRyBQPJypd85ycDBe9hzNCLtztJfHAepucNnsIIAZTs4iMyJ3sPzk5oaygxBaB2t9wwnNmSiblvaU/FyVfBdGZsaovNzovK7UIFO5wa5AQGBySwNOOakRX/nJq1ixpRf9I4mysVHYbr/11GvYuLTHdFsrjPGMIjXjKBSsbWp0Wy5xm0rujGhvCmvWdWdrQ9Xfs9nBbq36Xb+4G194dJfpPsmpK+rMuUTepJcv1TxkJt8ZOVFUb4H9yk9exSknNWJyy4n8ZDSP6F1A1rr91S529cN2nofUC7MxrTfhRo3VdyeWyMg1vT2qe4xYSb9nNJ4YH96n1uFbfdqTs8LBgObvDw6O4rk9fbxoTzWll6tkwTr1gLGLvnrnKUYnyOgdNxz4IK67V5Pec4Ymlomt5s5LJ3K0l8cBfLvmsp5K1jAxurZnrdZlMUpvLT2V1sHaQCyZd3Vf3Vjl1LbSa+NW810YWf+6Vgus1xO9uC+3zmu5OKskNgrbrboh4OMr5kFKaTi+CmP8vt+8jXs/cx5WPfJKNo42Lu3BlJbarmfk9lxSa2bXGg4EBE47KZq307CaM6adFK36ezY72J1bv/FUBm/1jeRt5lfJulp2b7bHnEvkTXr58rS2aMl9DvRyil4fb6TfMppHggJYt6g7bw1cvdtf7cK1nN2rVEw3hgM4fCxeVF+FcdsWjeRtXF4Yl+sWdeO+37xdFIeV9ntcm7k+5K4F3hgO4O5rzsVXfvJqXlz97JWDReced19zLu74xR950Z5qTi9XDcSSmnk3XMUeReXGDADt44ZNS3twaluj5j4Mes9R12kGqrsTyokc7eVxgLobXDY7CGB2QXAjjcRqiiIxFE8inswgIyUaw0FMbq58WY/c72db74HsgMyKLb3Zz98WLX9gVel3oTaop74wP+8zaT3Gi43OTfROHnPjvrOlAbdcOgtnTW5GU0MQJ0eNDzKZPTnVo9Vun9vTh29+UmJae5Ph1ymM8cd7D6K9KYTHbr4IGUUiFAxgSktDTTfzU9Uil3hFJQOdoVAAZ58yqShnWKGSjfVObDQ0mrfREGD+oMdoP1XNADRzrjucedszpp/zzh1X2FAS8orcfJnbn4dKjNSWyymV9u1aeaQ9GtZ4rQAe2rE/b4f5h3bsx+1XddvxFZUsr9X9MJcYqo6iSEhIPPzZC7H/aAwbtu/L3gE3lsrg2k0vZmN2840XoKUxhFRaKTnYrM44e+zmi5CRQCgg8K2nXsNze/qwr28EaxZ2oaM5UvaCTDlG4onx4V2KIvHOQAzvDoxicksEbU0RtDQE8OObL0IyrSCjSNz3m7fxeO9B/Nd7x/CTFfOQSCvYfzSGO37xR0N3chI5QStXtUfDePRzF6JvOIGBWBLbeg/ghvlnYWQsjcnN+vsmFR4vtEfDGIynDJ9LVHL+oTWJ565fnpjEU81FHKdytFfHAepucNnsIIAbriCXGhBQOzKtXbXNrr3ZHg0Xzez78kdnY1ZnS8kGbdeMuSPHEyUHS7za6Nyi1MmjGvedLQ1FO2jft3xu2ZhQX//Q0CgSaYmAAOIpBWOpNKa1NVly4aOSjkFrUPJT583AqSdFOUjmEkbySaUDnYU5w8zFw1Llao+GsXFpT3ZzSHUGvJFNWayI7XL9lHoB8vDQGFY83FtxP8GcS+Q9uRfsS+WA3BwnhMA9v9qrmVM6miNFebPUIJ5WeXIH9bRy8KzOFnz5o7N1LyDafaeGnaw6nqlHWvGycWkPOlsiEELgGz/7r+z32tnSgCPHx3DHL/ZjUc8MdDRHEE+mcdpJ0bwJBFqvufnGC/Dlj87GnsPDAIBIMIDJLQ0lL8hYhfHhXUPxJI4cH8OPfvcurrv4LDz96iF8onsaPv/oiRnK6xZ1YyiexG2Xn41gUCAsBWaf0oqNS89DIBDwVC6j+qEoEvv6R/Ly5HeXnIdHfvsudrw9kDcults/RyPBovGcjUt7sGH7G3huT19en19qwLmS8w/1OYoiEUuk0T8yvtl6tXde+iVH23UcVfvpeQ4zu4ZJra8gl1vXZSCWxLsDo6Z21dZ7n339I/i3f38DaxZ2YevKeXj0cxdiVmcLQqGA7pqela47U24hdLNrmJJ5pb5jNe61dtC+afNODMZTJWOifziBQ0OjUCRw57Ov4yN3/weuf+B3GBxN4dhYwnRZrVp7yMkN0Mg8M/nEyFrDVuWZcuUajKewYfsb2U2s1izsMrwpixWxXaqfUsv+6oFj2UGlUp+ViPwnEBDIKNDNAYU57tpNL+K6i8/CnBlt2ddQc0ph3uxsacDwWAp/OHQcBwfjeO3QMbwzEDO0/qBeDh6Mp3T7aq+vSevltRRrTSteVj7ci98fPIarv7cjL2ZXLpiJ5/94BLddfnb2lu5vP7MHe/vyY0XrNZf/8HeYOqkBT31hPr79qXOw5snXsOCu53H1vTtsjzXGh3fFkxncunU3FvXMwEM79uPvLjwzO7AMnNh48n/+zTlIpBVcfe+ObL79YDTFgWVyLa08+flHX8GlXVPzxsUK++dXDxzTzNmLemZk/33Pr/Zib1/5Pr3STfSsPvf3Q4628ziq7mYum53xVuurE+VmpCXTGTRFgqYHwAuvKo2llOz7qOvZTm+Plp2hXeka1uVmC9Z6UL8elPqO1bhvi4arXkZm3aJu9A8nsevAEFY98goeu/kimFjJAkD5dlvu6puXZznVEyvvFLEyzxjJw8/t6cvmTtU3P3niddQYVBQFGYm8tcKrXW6iVD+llv3ua85lTiWqY6XyXW6OmzOjDSsXzERDKIA7F3fja1t3Y9eBoWxOKXydr318NkaTGax58rVsrl2/uBttTWGc3Fz5EgB6M5XccEdhKeWON7jEUOX04mXWlBasWdiFh3bsx8oFM7FiSy9OO6kRp3RPw/If/i7vePTf/v0N3H5VdzZW9F4znhyvG60LMtXEGuPDvzJS4uIPdaDr1EnovvIcjCa1YyuVUVydw4gK6eXJtmgYl3VNgRAChwZHi+560hujasu5s3NRz4zsXfPq3wvbg9llagtZeeel2RztxjEIO4+j6mLmcuGVDgBlZ7ypan11otzgRyQUxGgyY2o349yrFV94dBf2vj+M94biFQ08VDIIfDSW0Azo3Bl0RncUp8rpfcfhUCAb92ZjSytZrd62GysXzMz+Wwigb3gMf/oghkODo/ggZuzqo95M1XJX37w+y6meGM0nRq5eG5mVbDTPGMnDpV5HjcGvP7Ebb/bHcO2mF/NiETDeJ2kp1U+pZR+KpyzNqZXOICCi2iiVp9Q8MWdGG776sdlY+/QeLN74Im548GV882+68LNVF+ORz12IZDqDcDCQ9zqnTGosunvu1q27EU8WHwcW5o1wKGA6L7l58oHR4w0jd95QMb0Yfm8ojkgwgNWXn42/OG0SfnTThWhuCGnOGl3UMwPxVMZQDFoda4wP/1IUieZIEEvnnYG/u++3GEtm8KeBUc3YCgSEa3MYkRa93CsB3HLph7PnNYV3Pemdewzl3NnZ0RzJaw9zZrRhzcIujCbT2fMLI2NHTjKao906BmHncZTvZy5bcaWjVleQFUUio8iSM6c7miM4o6MJ6xd3F625rDcAnjvosmZhF27duhtrFnZVNEPb7MxuRZEYTZQP6Eo27CJztL7j9Yu7Jxbmb8Dsqa2YOqkBm5b2FK3RaHYZGfUK5WVdUzAYS+W93vrF3Zg6qRFndjRX1K7KXX1z+ywnOsFIPjGa0410nEbzjHryWVgudYfkcq+jxuCahV2ay8xUG4ul+in1O934/FuW7Xpfbb9K7lfJxoFuxU0Qx5XKU+ou8IVLYXW2NGRv9Vafc8+15+LBG87H9Q+8jIODcWQkNHNtpuDcSW9tW7PHerW+o7AUHm/YSyuG777mXDSEA/jCo7vyjiuDOgN4Hc0RvNU3ghsefLlsDKrtwqpYY3z4k5rbGkIBrHrklYn8J7Fh+76i466NS3sQDWsfU7ohhxFp0dpb5ntLezC1tQFXf29H0UW8NQu7sGJLLzY+/1bRGJW65jIwHvdTWhuy7UG9wF14rhKt4C59N3BrzrfzOMr3g8tWVGqtNjFS1wcr7Jg2LevJHngHAgJndjSjrSmc3eW4MRzA5OYG3R29cwdd1GUPKh14MDsIPBBLYv/RWNmANjqo78ZbDbwiEBCYOqkBa688B02RIIbiKdz57F70jySy7ePk5ga0RSOGL65EI0E8cP352dfb+Pxb6B9JZK9cfv2KLnzm/peKZjitvfIctDaGK2pn5QYR3TzLifIZySdGc7qRjtNongkFRNHB0frF3QjlbC5a6nXUGDS7zIwWvZyn10/lfqd3/XIv1l55Ds6a3IymhmC2nzD6HmbrgIjco1SesWlZCgAAIABJREFUUvNELJHOy1ErF8zEAy/sx5qFXWiLhjEUT+G+//M2brv8bDzyuQsRmsg9Wrm2MZx/c6Te2rZPfWG+qQkcbp58wOMNe+XGcDyZxuvvD6MxHMAHsRTuvubc7HHnrVt3Y/ONF2jG5cnNEdz+zOsAyseg1bHG+PCngVgSP3vlAJbNOysbh8NjKfSPJHDXL/dm8+doMoN4MoOMAtfmMCItuXvLqMcC/9/2N/CNhV3ZQeGVC2Zm/3Zmx/j6l/0jCUyd1Iifrro4u+FvezSM26/qxjc/mcn+W20Pens9PXiDdj53+wUZt+Z8O4+jfD+4bGWlOj2Qqa7j2T+czGvMkzXW5zq5uQFoLi6v1uyyjpZItoGqg367DgxlO8CO5ghOa4vilEmNZT+f2ZndyXRG80rupqU9RQFdblCfs+eqF09mcMODLxf9Prd9GL24oigSR44nitZd7GxtQFMkiMduvgiKlNozm5vCFSfacoOIbp7lRPmM5BOjOd1ox2kkvuPJDO58dm9eHr7z2b34zpI52bxb6nXUmc9qvq00FivJeZWsDcY18Yn8SS9PqXni/eNjeTnqtJMacd3FZ+Udr61b1I14KoMrNvzniZmfy+bipi35OWNywXrLpda2nWZiIwY3r0nL4w37qTHcPwxs6z2AL35kVt5x57pF3bjrl3sRDgpsWtaTXctTPdfY+Pxb2HVgKPt6pWLQ6lhjfPiTgMQV507D397327wZ9Q9cPxc3PLgTK7b0ZmPzn3/+Or6zZI5rcxiRFv29Zf4Cl3VNKTpO2Li0Bzu/fikkhGZsFx6HqO1hNJnWOU5IGxo7chu35nw7j6N8P7hsVaXWYiBTLfuuA0NYsaUXwIlN9ozQm1321BfmZwddcm9X2HVgCGuf3oP7ls81NLCsMjOzOxIKal7JPbXN2PvlDvALITh7rkqRUBCXdU3Bop4Z2UGzbb0HKkp6WvF269bd+NFNF+HaTeMHXP/+j/+3ZntsbQxXnGjLDSK6eZYTFSuXT4zmdCs7TjVvqXlY7z21KIrEyFga6xd344EX9le1NEWlM4aN5Ojc3FruPdx6sERE4yqZDBEICJwyqTGvv4xGQvj7R4rXrX3g+vOz/zY6+9jKvFGrOwrL4fGGNYzEb3s0rHk33Opt43fDvdkXw1+cNikvLoMBYMfbA3mvUy4GrYw1xoc/JTMyuxwGMB6HX/nJq9jy2Qvy7g6965fjd4dGQkHX5jCqL0aPFfT678ZIQDMPr3y4F4+vmIfT2hoNlSP3oqHW+xwdSWLD9n0VjR3Vkptzvl05yPeDy1ZVqtO3ASuKRDCAvPVuL+uagm9c0YVkOoP+4UTZk4VSs0TUQRdFUSCE0FxSww659aFeyb1v+Vy0RcvXR+EA/9aV88rOnvPqshlOlbs9GsYtl344bw2ljUt70J6zi6vRsupdbTxyfAydLQ1Ys7ALwQCw+cYLcMcvXsdze/qyV/Kj4UDFibbcIGK5v3s1RuqVVk7ftKwHiqIU5UWrOs5K+hE1ruKpNN4dGMUv/uswFvXMwKTGEB684QI0hAJoDI/fDmY0/uyaMawoEu8MxPDuwCjO6GjCmoVdebO7Ct/DzQdLRPUqty/LKBLffmZPtp81OhlC7S+f+sJ8jCbHX0cr54wk0nn/NjL72Mq84dZ+282zqt3OTPwqikT/SAIS2ut9n97RhK8+/iq+s2ROXlwqiiwZg3bHFePDfxRFQpFK3p1t6vFTUAhMaW0wvG8NkVPUTfJGExnsPxrDhu370D+S0D1W0Ou/g2L8cVp5OJ0ZPy8zk+v03qchFMhO8jEzdlRrpXK+W49jquX7wWWrOnIrTuqNBlHuIGpny/iauB+e2oKheApLJq4MGTlZKDVLRF1LTGs2duGtjGY+Q7m/V1MfhQP85Tba8OqyGU6WezCeyg4sA/lXG0vNXlfrWVEUpBSJgZEkmiLa8ZbKKEWL8393yXn44kdm4b1jY3hox358+6q/rOqzlRtE1Pu7V2PEzZw+OTM6iFJNuaxYWkK9VVcdsH1h9SW6OVgv/vRyuhDjBymVfs9D8SSOHB/TvLV414GhiteqJiJn6OWc/uEkdh0Ywk2bd+LxFfMgpTTUXodGU3h3YBRndjRp5py+4UTev43MPq4kj2rlbLf325yRaJ6R+FUn86TTCt47FkffcAInRcOa8Xl4KJ6dIZqrMAajkSDSisThY3GEQwGMjKWx/Ie/szWuGB/+MjiawAexFNY+vScvdh/asR8hHiuRC5U6R9E7VtDqv9uj4fGNLMPa5yYSwFX3vmAqn+odJwBwrB0V3iUfFEAgEKj4PbVyvtuPY6oRKP8QZwkhPi6E2CuEeFMIcZsVr6kOpEZCQSTTmYlBsfxtrBVFon84gUODo+gfThT9XT2pz1XJWplX3fsC5q/7Na669wXsPTJc9D5A/iDqrgNDuOHBl/HGkZHsumHAiZnTA7Gk7ucIBsY3DFDLXXjFVG82duFrGv0MRj+j2simtTehs9X4LOnCAX51E0KrPp9bVFrucjGs9bh4Snu28XtDcd34VOv560/sxpv9Mfzt93+LK7/7Atb/8o+49zPn5dXHpmU9EEIULc7/+UdfwXvHxrD26T348kdnl7ygYSevxohbmclz1VBzSCQUxJL7X8quAVYqLxopV6k2ZCZvacXV6m27sXLBTAAn+g6z8ade0c9tY+sWdeNbT71W9HmM5gNgfE1pdbPCwvKWW6vabB4nIusdjSVK5hy1X8/Nf+m0opkjci82/ePjr2L94vzjrO8t7cG23gPZf5uZiWc0b5TK2ey3/UVRJN4/PlY2fuPJND6IJbC3bxhL7n8Jize+qHncuW5RNza/+I5uXKoxeOpJURw5nsDV9+7A/HW/xtX37sjeaae+J+OKSkmlMoinlKIlMVZv242vX9GF0VSGx0rkOqXOUdRjhS88uguvHTqGP30wir7hsewEltxYHoynsOLhXnz/P94qysMbl/bg9mf2VNRPa7UZp9pR4bHHtZtexJv9MXz9id2Wns/6+TjGVTOXhRBBAN8F8FEABwG8LIR4Skq5p5rXLXd1wMjVg1K385WaXVHJ+sBas6SbIkFDS0AUfo7NN16Qt0Nn7lUXs7Oxyy0NYvfSIeoAf2dLQ3ZH0oAQ+MmKeVA0ZuN4ddOpSspt9ApY4eMeuP58zauNA7Ek/uGx32vWnVrPaxZ24aEd+bvIP/PqITxw/fkIBgRCAYFTJzXi0LExzc9z9injVyarufpY7SxZ3aVjUhkcGhzlLAOTnF4+yGhbMVIus1eRS8WeXrlmdjbjsq4p+PJHZ6OjOYLDx+Km2rp6Rf/xFfPw3lAcA7FkdnbxnsPD2c9j9rNkdDba/PMK26hfb/UiciNFkRhNaOectoklrtR+Xf39Pb/aiy9d+uGi27VnT23Nu9h0cDCOO5/di7VXnoMPdTYjKASmtjbk7fRuR/sulbO9emxHxdQlmRSpfVt1bvy+/v4wIsFA9g4bANkLyw9cfz5CAYFIKIBQQOD2q7rLxqXePiFrFnZl91dgXJGedFrB0FhKd+kgALj+gZe5DxC5jl4fOqW1AdPboxhLZYruON60tAentjViUkMYg/HU+J2jE+cOj/ceBIDs+X9DKIBgQBRt/ueFfKo38L5mYZel57N+Po5x28zlCwC8KaV8W0qZBPBjAFdW+6Llrg4YuXqQO03/hdWX4IlV8zF7aisAaM6uSKeVvN+/N2R8EEFrlvRoMlN25rTW51j+w99BQGhe6TE7G7tcQ7C7oXQ0R7D5xgvwtY/Pxtqn9+DT3/8tvvz47zEUT+HUk6JVfz63qKTcRq+AFT5uw/Z9RbOS1i3qxsbn39KtO7We1V3k1bpY+/Qe/NXsqUhmFOzrG8GS+1/C0FgaTQ3anycaCVV19dGKWbJ63/VbfSO2zrz1K6c7S6NtxUi5zFxFLhd7euU68EEcX/rrD2NWZwsCAVFRWw8EBKSUWLzxRazY0qu5LrLZK+LhQEC7HMGA6Tbq1Ox1Iho3EEvi/eNjmm04lVHy+nXVop4Z2YFl4ESOOBpLFF1sUu+gS2ckIuEAwuGg7TOISuVsrx7bUTF1lvyBD0Y163QonsqLX62JNs/t6cOxeArvDIwiHApgqsb5gBa9GGvL2XOEcUV6hhNJ9B1P4O3+mGbsAv4ZLCJ/EUJoxuxJ0TC+s2QOgoHiO45XPNyLN46M4I85x/dv9Z2I/cd7D+Kj9/wGy3/4O4RDAQT1zitcnk9L9QtWtmc/H8e4bXB5GoADOf8+OPG7qlg1KKo1JV/vJL5vJP8WRXV94Fx6QaR16/MZHU0ll7gw8zlKvU+p2xvLNQS7G0ogINDSGCq6fVtv0MTs53OLSspttO4LH7frwBDufHYvfnzzRdi6ch7WLOzSXWdVpdZzYzhY1Pms3rYbkxrDeYPTk5sbbKkHK24p0fqu1y/uxobt+yp+zXrmdGdptK0YKZeZ/Fku9vSWr9iwfR9WbOnFYDxlqvyFyn0es31BQAB3X3NuXjnuvuZcVDJm5OdbvYjcKJnOQEpZdKF4/eJunNnRhAeuPx8P7difvRAFjOcerRwxllLQGNbLLwHHlrAqleO8emxHxdRZ8hu27yta5u67S85D16mtecel6mBzruntUZzcHMEZHU2m4lMvxkaTmez/M65Iz2hSwYqHezVj955rz8V7Q3HfDBaRvwQF8N0lxcsJ3fGL1zGWUhAQQvP4YHJLJG+fJq0JaureXV7tp/X6BbXvsao9e/X7McJVy2IA0DqVzZvuJIS4GcDNAHD66acbetFSG9sZ+XspeifxqYyS93t1feDcWwxKrQdWyWLmZj+H2c1Vyu30beVO4HpSacXwoInbNp0yGruVlNto3Ws9bnzTkwCaG0L4h8d+X7bu1HoeTWrH/lA8lTc4bVc9WDFLtrBsAPCFR3flnYRz5oHx2HUiB+QyGltGymUmf5aLPbVcj918UbZN5G7mV/g4s22j3Ocx3xcE8IP/fDtviZsf/OfbuP2q7pLl0OK2W70qOWbwmzNve6bWRbCMnz5LOUZjNxIKIhwM4PZnXs9rw3c+uxe3Xf7nuOMXf8RXPzYbew4PZ/NF58Ttr4U5IiiQvSCcm182LevBqSU2+bVaqRzntmM7KmY0dtVZ8gcH47jrl3uz8XtaWxRrn/4DFvXMyG6UBoyfS61f3J2dZDK9ffx27ZObw5jUaC4G9GJs6qQGvLD6EsZVnTIau2lFO3anTGrAsXgK9/36Td8MFpE3GB9nCCAUFHnHC+o5yurLz8afBkZ1jg/yB53VCWqP3XwRABTlTC/201r9grpBp5Xt2c/HMUJK99yqKoSYB+BbUsqPTfz7nwBASvkvWo+fO3eu3LlzZ9nXtWLNZT39w4nsTpiq6e1RPL5iHq7d9GLe7y/rmoJv/c05hnfrNsuJnSfLraVp91qbet+3hWtaOdKqjcauUZWuuZz7OACG607dgKUwxqe3R7FmYRfWPr3H9l1P7YgFB+LLTq6IXbeut2skdxnNn0bjxM54KvV5Klk/2qq+o8LP7IrYVdXTACZpe+eOK4w+tOaxqygSBwdHseT+l4ra3dorz8END76My7qm4BtXdCEYEBBCYDCWxPGxVN4g3frF3Zh9SitObm5wRR53Qxl8ruax2zc8hqvv3VEUtw9/9gKkFSCRziCdkfj8o69k4/TBG87HpGhYcy8ZsxhjnlXz2D00OIpPf/+3RbH72M0XIRQQCAQCjCfSUvPYLXXMcNc15+KMk6P4IJbK25Nh3aJuBATy7h5Xn+ORc2TDCvdMCwqwPY8z9OHdNrgcAvAGgEsBHALwMoAlUso/aD3ezACdXYOieiflszpbsK9/xNaB3ko+p9c5MIBe86RfKaN1b1WMaNXFpmU9mNwccSQJ2xELTlygsZFnY9ctzLShai/m2B1PZtu5nXnBwGd2VexycJm8NLgMjG8utbdvGCu25G/QN3VSA+LJ4s2m3xmIYXgshQ9iKTRFghhNZnBGRxPO7Gj2Ql9H1qh57OodR05tbcBQPIX+4QQeeGE/FvXMQEdzBFNaG3DaSVGEQm5b1ZEcVvPYTaUy+GPfCP4+ZwBu49IefLizGZGI224MJxepeewC2scM6qZ9bdHx2blHYwmMJjLYfzSGDdv3obM1glsu/XB2aQyPnSNT9bw3uAwAQohPAPh/AQQB/FBKebveY90yyKF3Uu73gd5asfl7dUXS94pax7gd71/rz1QFxq6DnL6Y4yUVfGZXxS4Hl8lrg8uAuXanKBJD8STiyQwyEmgMj6+n7PfcRHlcEbulzqEYo6TDFbGbSmXQN5JAWpEIBQSmtDQgHOYay1SSK2IXMHbMUPiY9mgYg/FUXZ3TUJahinbdpTUp5c8B/LzW5TBD3ejP6O+pOvxe3aPWdWHH+9f6M5E3GI2TeoynevzMRLVmpt0FAgInNzcAzTYXiqiMUudQjFFys3A4iGntTbUuBlFFjBwzaD2Gx/dUCu8rIiIiIiIiIiIiIiLTOLhMRERERERERERERKZxcJmIiIiIiIiIiIiITOPgMhERERERERERERGZxsFlIiIiIiIiIiIiIjJNSClrXYaKCSH6Abxb5mGTARx1oDhmuLFMAMsFAEellB+3+00Mxm6l3FqPTqrH78APseukeowRwJ2f222x68bvqBb4PYwr9T0wdr2B30sxxq5xLFtl7CobY9caXiy3F8sMnCg3Y9cYlss4p8pkKHY9PbhshBBip5Rybq3LkcuNZQJYLr/g98XvgMqr1xip189tBr+jcfwexnnpe/BSWZ3E78X93FxHLFtl3Fw2K3n1c3qx3F4sM+DecrNc5rixXG4rE5fFICIiIiIiIiIiIiLTOLhMRERERERERERERKbVw+Dy92tdAA1uLBPAcvkFvy9+B1RevcZIvX5uM/gdjeP3MM5L34OXyuokfi/u5+Y6Ytkq4+ayWcmrn9OL5fZimQH3lpvlMseN5XJVmXy/5jIRERERERERERERWa8eZi4TERERERERERERkcU4uExEREREREREREREpnFwmYiIiIiIiIiIiIhM4+AyEREREREREREREZnGwWUiIiIiIiIiIiIiMo2Dy0RERERERERERERkGgeXiYiIiIiIiIiIiMg0Di4TERERERERERERkWkcXCYiIiIiIiIiIiIi0zi4TERERERERERERESmcXCZiIiIiIiIiIiIiEzj4DIRERERERERERERmcbBZSIiIiIiIiIiIiIyjYPLRERERERERERERGQaB5eJiIiIiIiIiIiIyDRPDy5//OMflwD4wx8rfxzB2OWPDT+OYOzyx4YfRzB2+WPDjyMYu/yx4ccRjF3+2PDjCMYuf2z4cQRjlz82/Bji6cHlo0eP1roIRBVh7JJXMXbJqxi75FWMXfIqxi55FWOXvIqxS7Xi6cFlIiIiIiIiIiIiIqoNDi4TERERERERERERkWkcXCYiIiIiIiIiIiIi0zi4TERERERERERERESmcXCZiIiIiIiIiIiIiEwL1boAVExRJAZiSSTTGURCQXQ0RxAIiFoXi6hm2CbI7Rij5DeMae8587ZnTD/nnTuusKEkRFQv2Fe4G+uHqL45mQM4uOwyiiKx98gwbtq8EwcH45jeHsV9y+di9tRWdgRUl9gmyO0Yo+Q3jGkiIiqHfYW7sX6I6pvTOYDLYrjMQCyZrXwAODgYx02bd2IglqxxyYhqg22C3I4xSn7DmCYionLYV7gb64eovjmdAzi47DLJdCZb+aqDg3Ek05kalYiottgmyO0Yo+Q3jGkiIiqHfYW7sX6I6pvTOYCDyy4TCQUxvT2a97vp7VFEQsEalYiottgmyO0Yo+Q3jGkiIiqHfYW7sX6I6pvTOYCDyy7T0RzBfcvnZoNAXRelozlS45IR1QbbBLkdY5T8hjFNRETlsK9wN9YPUX1zOgdwQz+XCQQEZk9txROr5mvu6MgdX8lptY65cm2CqNbcHqO1bsNkH7vq1u0xTURE1bGi/2BfYQ325UTe4aXzKqdzAAeXXSgQEOhsbSj6PXd8Jae5Jeb02gSRW7g1Rt3Shsl6dtetW2OaiIiqY2X/wb6iOuzLibzDi+dVTuYALovhIdzxlZzGmCPyNrZh/2LdEhFRJdh/uAfrgsg72F5L4+Cyh3DHV3IaY47I29iG/Yt1S0RElWD/4R6sCyLvYHstjYPLHsIdX8lpjDkib2Mb9i/WLRERVYL9h3uwLoi8g+21NA4uewh3fCWnMeaIvI1t2L9Yt0REVAn2H+7BuiDyDrbX0rihn4dwx1dyGmOOyNvYhv2LdUtERJVg/+EerAsi72B7LY2Dyx7DHV/JaYw5Im9jG/Yv1i0REVWC/Yd7sC6IvIPtVR+XxSAiIiIiIiIiIiIi0zi4TERERERERERERESmcXCZiIiIiIiIiIiIiEzj4DIRERERERERERERmcbBZSIiIiIiIiIiIiIyjYPLRERERERERERERGQaB5eJiIiIiIiIiIiIyLRQrQtQC4oiMRBLIpnOIBIKoqM5gkBA1LpYRFQC2y35FWPb21h/5GVn3vaM6ee8c8cVNpSEiAD2KX7AOiTyL7ZvfXU3uKwoEnuPDOOmzTtxcDCO6e1R3Ld8LmZPbWVQELkU2y35FWPb21h/RERkFfYp3sc6JPIvtu/S6m5ZjIFYMhsMAHBwMI6bNu/EQCxZ45IRkR62W/Irxra3sf6IiMgq7FO8j3VI5F9s36XZNnNZCNEI4DcAGibeZ6uU8ptCiLMA/BjAyQBeAbBMSpkUQjQA2AygB8AAgE9LKd+xulzJdCYbDKqDg3Ek0xmr3yoPp88TaTPSNmrVbonMMpvrGdvexvpzPx5/EZFX1GOf4rccXY91WMhvdUqksrp9+62t2LksRgLAR6SUI0KIMID/FEL8AsA/ArhHSvljIcRGAJ8F8L2J/w5KKf9MCPG3ANYB+LTVhYqEgpjeHs0LiuntUURCQavfKovT54m0GW0btWi3RGZVkusZ297G+nM3Hn8RkZfUW5/ixxxdb3VYyI91SqSysn37sa3YtiyGHDcy8c/wxI8E8BEAWyd+/xCAT038/5UT/8bE3y8VQlj+rXY0R3Df8rmY3h4FgGwldjRHrH6rLE6fJ9JmtG3Uot0SmVVJrmdsexvrz914/EVEXlJvfYofc3S91WEhP9YpkcrK9u3HtmLrhn5CiCCAXgB/BuC7AN4CMCSlTE885CCAaRP/Pw3AAQCQUqaFEMcAdAA4amWZAgGB2VNb8cSq+Y5NP+ftMUTajLaNWrRbIrMqyfWMbW9j/bkbj7+IyEvqrU/xY46utzos5Mc6JVJZ2b792FZsHVyWUmYA/DchRBuAJwCcrfWwif9q1Ygs/IUQ4mYANwPA6aefXlG5AgGBztaGip5biXq/PYbGWRG7fmOmbTjdbukExq4xleZ6xrZ9nIhd1p97efn4i3mXvIqxW5166lPclqOtit16qsNCbqvTesG86xyr2rcf24pty2LkklIOAXgewEUA2oQQ6qD2dADvTfz/QQAzAGDi7ycB+EDjtb4vpZwrpZzb2dlpd9EtUe+3x9A4L8au3dg2vIGxawzj2X0Yu/XNy22SsUtexdglo9yWoxm71XNbndYLxq73+LGt2DZzWQjRCSAlpRwSQkQB/DXGN+n7NYDFAH4M4DoAT0485amJf7848ff/LaUsmrnsRfV+ewyRHrYN8hPGM5G7sE0SEbkXc7T/sE6JjPFjWzE0uCyE+JKU8t/K/a7AqQAemlh3OQDgcSnl00KIPQB+LIT4NoBdAH4w8fgfANgihHgT4zOW/9bkZ3E1N98eoygSA7Gkb4KavMWJtsEYJ6eUi2fGonex7rzJzcdfRFRf2I8U81OOZv2O81OdElWjXE7wW1sxOnP5OgCFA8nXa/wuS0q5G8Acjd+/DeACjd+PAbjGYHnIIooisffIcHanSnU6/uyprXXZGZL/MMbJLRiL3sW6IyKiarAf8TfWLxHlqsecUHLNZSHE3wkh/heAs4QQT+X8/BrAgDNFJDsNxJLZgAfGd6i8afNODMSSNS4ZkTUY4+QWjEXvYt0REVE12I/4G+uXiHLVY04oN3N5B4DDACYDuDvn98MAdttVKHJOMp3J26ESGA/8ZDpToxIRWYsxTm7BWPQu1h0REVWD/Yi/sX6JKFc95oSSg8tSyncBvAtgnjPFIadFQkFMb4/mBf709igioWANS0VkHcY4uQVj0btYd0QnnHnbM6af884dV9hQEiLvYD/ib6xfIspVjzmh5LIYKiHE1UKIfUKIY0KI40KIYSHEcbsLZzdFkegfTuDQ4Cj6hxNQFFnrIjmuozmC+5bPxfT2KABk14LpaI7UuGRE1qg2xpknyCpO51vGrnXqva9kLBERVcdN/QhzuvWcql/WHVFlnG47bsr5TjG6od+dAD4ppXzdzsI4qR4X2NYSCAjMntqKJ1bNr/udbcmfqolx5gmykpP5lrFrrXruKxlLRETVc0s/wpxuDyfql3VHVJlatB235HwnGZq5DOCInwaWgfpcYFtPICDQ2dqAae1N6Gxt8HXAU32qNMaZJ8hqTuVbxq716rWvZCwREVnDDf0Ic7p97K5f1h1RZWrVdtyQ851kdObyTiHEYwB+BiCh/lJK+VNbSuWAelxgm4jMYZ4gr2LsklUYS0RE/sGc7l2sO6LKsO04w+jM5UkARgFcBuCTEz8L7SqUE9QFtnP5fYFtIjKHeYK8irFLVmEsERH5B3O6d7HuiCrDtuMMQ4PLUsobNH5utLtwdqrHBbaJyBzmCfIqxi5ZhbFEROQfzOnexbojqgzbjjMMLYshhHgAQNF2il4eYK7HBbaJyBzmCfIqxi5ZhbFEROQfzOnexbojqgzbjjOMrrn8dM7/NwK4CsB71hfHWeoC20REepgnyKsYu2QVxhIRkX8wp3sX646oMmw79jM0uCyl3Jb7byF4VVx0AAAgAElEQVTEjwD8uy0lIt9QFImBWJJXh6gIY4PcgrFIVmNMERFRIfYNBDAOiKg8r+YJozOXC80CcLqVBSF/URSJvUeGcdPmnTg4GM+uazN7aqsnGgbZh7FBbsFYJKsxpoiIqBD7BgIYB0RUnpfzhKEN/YQQw0KI4+p/AfwvAKvtLRp52UAsmW0QAHBwMI6bNu/EQCxZ45JRrTE2yC0Yi2Q1xhQRERVi30AA44CIyvNynjC6LEar3QUhf0mmM9kGoTo4GEcynalRicgtGBvkFoxFshpjioiICrFvIIBxQETleTlPGJq5DABCiL8RQtw18bPQzkKR90VCQUxvj+b9bnp7FJFQsEYlIrdgbJBbMBbJaowpIiIqxL6BAMYBEZXn5TxhdFmMOwB8CcCeiZ8vCSH+xc6C1ZqiSPQPJ3BocBT9wwkoiqx1kTylozmC+5bPzTYMda2YjuZIjUtGdjHaZhgb5Bb1GIvs2+xlJqZYF0RE9UGrb9i0rAeKojD/1wG1v1cUBZuW9dTVcSeRE/x0TO3l81OjG/p9AsB/k1IqACCEeAjALgD/ZFfBasnLi2i7RSAgMHtqK55YNd9zu1ySeWbaDGOD3KLeYpF9m/2MxhTrgoiofhT2DRlF4tvP7MFze/qY/32usL+/rGsKHv3chQgGhO+PO4mc4Ldjai+fnxpeFgNAW87/n2R1QdykFoto++lqiyoQEOhsbcC09iZ0tjZ4okGQcbkx+/7xMdzzq72G2wxjg9zC7lh0U2738gYRXmIkprxUF26KYSIir1L7hkgoiCX3v4Tn9vQBsCf/M2+7R2F//9yePiy5/yVEQkFbz4EYA1QvrDymdku78epYidGZy/8CYJcQ4tcABIC/gk9nLQPOL6Ltt6st5H9aMbtuUTf6h5PYdWAIgHcWnieyi9tyu5c3iPAbr9SF22KYiMjr7M7/zNvuUov+njFA9cSqNsZ2Uz1DM5ellD8CcBGAn078zJNS/tjOgtWS04toe2kGExGgHbOrt+3GygUzs4/xysLzRHZxW2738gYRfuOVunBbDBMReZ3d+Z95211q0d8zBqieWNXG2G6qZ3RDv6sAjEopn5JSPglgTAjxKXuLVjtOL6LtlRlMRCq9mFXbiJcWnieyi9tyu5c3iPAbr9SF22KYiMjr7M7/zNvuUov+njFA9cSqNsZ2Uz2jy2J8U0r5hPoPKeWQEOKbAH5mT7Fqy+lFtNWrLbnB7MYZTEQqvZg9rS2KF1Zf4qmF54ns4rbc7uUNIvzGK3XhthgmIvI6u/M/87a71KK/ZwxQPbGqjbHdVM/ohn5ajzM6MO1JTi6i7ZUZTEQqvZg9ZVKj5xaeJ7KLG3O7VzeI8CMv1IUbY5iIyOvszP/M2+7jdH/PGKB6Y0UbY7upntEB4p1CiH8F8F0AEsAXAfTaVioXUxSJgVjS0iuPXpnBRKSyKmbtaE9EbmG2nbA9UKFaxwSPT4iI7GFXfmfe9h6rY4ExQDTOTNtiu6me0cHlLwJYA+AxAALAcwA+b1eh3Cp3B8nOlgbccuksnDW5GU0NQUxuru4qpHq1hcgrKonZ3AQfDgUwMpbG8h/+jjuykm8ZbSdW7VBc68FIso7Vu1ZXGhs8PiEispbZ/G42fzNve4deLMzqbMFgPFXx8RxjgPyuXF6s5Dia7aY6hpbFkFLGpJS3SSnnArgAwL9IKWP2Fs191B0kO1sa8NWPzcaaJ1/Dgruex9X37sDeI8NQFFnrIhK5lprgr7r3Bcxf92tcfe8OHDk+hs6W8QTOHVmpnlmxQ3FhG7vq3hfYN3mYlbtWMzaIiNzDTH5n/vY3rVi451d7sbePdU6kx0hetPI4mowxNLgshHhUCDFJCNEM4A8A9gohbrW3aO6j7iC5csFMrN62m4FKZIJWgr91626sXDAz+xjuyEr1yoodinkQ5S9W7lrN2CAicg8z+Z3529+0YmFRzwys2NLLOifSYSQvWnkcTcYY3dCvS0p5HMCnAPwcwOkAlpV6ghBihhDi10KI14UQfxBCfGni9ycLIX4lhNg38d/2id8LIcQGIcSbQojdQojzqvhctlB3kGyLhhmoRCbpJfi2aDj7b+7ISvVK7V9ymW0PPIjyFytiQsXYICJyDzP5nfnb37RioaM5wjonKsFIXrTyOJqMMTq4HBZChDE+uPyklDKF8Y39SkkD+IqU8mwAFwH4vBCiC8BtALZLKWcB2D7xbwC4HMCsiZ+bAXzP1CdxgLqD5Ggyw0AlMkkvwY8mM9n/546sVK+s2KGYB1H+YuWu1YwNIiL3MJPfmb/9TSsWprQ2sM6JSjCSF608jiZjjG7otwnAOwBeBfAbIcQZAI6XeoKU8jCAwxP/PyyEeB3ANABXAlgw8bCHADwPYPXE7zdLKSWA3woh2oQQp068jmWq2exI3UFy6qQGbFragxUP9+YtDs5ApXpiti2pCb5wUf2pkxrwwupLuPkY1bXCHYrDoQBCAYHDx+KG24ZeG2Pf5E1W7lrtpdjgppRE5Hdm8rvd+Zs5t7a0YqE9Gra9z2a9k5dp5cVNy3qgKAr6hxPZeLbqOJqMMTS4LKXcAGBDzq/eFUJcYvRNhBBnApgD4CUAU9UBYynlYSHElImHTQNwIOdpByd+Z9ngshU7rwcCAic3N6AtGvF9oLLTIb0YqHT3Vd0E3+zwByPSUcu8p+5QXGlfxYMo/zGya7WRmPVKbFhxnEZE5AW5ff5ALKl7MdnO/M2c6w5afb3ROq/kuJX1Tl5XmBczisS3n9mD5/b0FcVzueNoIzguZozRZTEghLhCCPE1IcT/EEL8DwD/j8HntQDYBuAfJtZt1n2oxu+Klt4QQtwshNgphNjZ399vqOwqKzdEUAN1WnsTOlsbfBdc3JnYetXEbi2UioFK25Lf241feS12K+WWvFdNX8U2ls/vsWsmZr0QG9y46gS/xy75F2PXOKM53K78zZybz02xa6TOKz1uZb37j5ti1ylqG4mEglhy/0t4bk8fAOvj2S3nh15gaHBZCLERwKcBfBHjg8DXADjDwPPCGB9YfkRK+dOJXx8RQpw68fdTAfRN/P4ggBk5T58O4L3C15RSfl9KOVdKObezs9NI8bO4IYJx7HSsV03s1kKpGGBbqi9ei91KuSXvsX1Zx++x65aYtQpj/wS/xy75F2PXuFrncObcfF6L3Urjh/XuP16LXSvZHc+1ztNeYnTm8sVSyuUABqWU/xPAPOQPBBcRQggAPwDwupTyX3P+9BSA6yb+/zoAT+b8frkYdxGAY1avt8wNEYxjp0OlYoBtifzILXmP7YuMckvMWoWxT0T1pNY5nDnX2yqNH9Y7+Ynd8VzrPO0lRgeX1W9zVAhxGoAUgLPKPGc+gGUAPiKE+P3EzycA3AHgo0KIfQA+OvFvAPg5gLcBvAngPgCrjH8MY+zcMVJRJPqHEzg0OIr+4UTJafJmHlsr7HSoVAyYaUul4t0LbYHqRzgUsDXvGY137m7sL+XqvZo86Le+mrFPRF5TmMPTacVwTq91DmfOrR1FkfggNh4nf/oghr7hMdPnQZXGD+ud/CQ3nufMaMMD15+Phz97ISRk1WMLiiIhhPDVsbadDG3oB+BpIUQbgPUAXsH4Wsj3l3qClPI/ob2OMgBcqvF4CeDzBstTEbs2RDCzKL5XFtD30s7yZI9SMWC0LZWKdwCeaAtUHxRFYmQsjfWLu3Hr1t2W5z0zud8rm69ReeXqvdpjAr/11Yx9IvISrRy+cWkPNmx/Q3NjqUK1zuHMubWhKBLvDMRw5PhY0TGnmfOgSuOH9U5+osbzU1+Yj8NDY1jxcK8lYwtqfr/nV3uxblE3Vm+z/vzQb8T4mK6JJwjRAKBRSnnMniIZN3fuXLlz585aFwP9wwlcde8LedPlp7dH8cSq+UW7U5p5bK3V6a6YjnxAt8RuOdXGQKl4B+CZtuARjN0qqLHa2dKAlQtmoi0axmgyg3NnnISTm6uPRy/l/hrwbeyWq3cr4qJO+2q3cFXsnnnbMw6UpjLv3HFFrYtA+VwVu16ll8PXLOzCii292X+XyunM4aZ5Pnb7hxN47dAxrHnytaqPCxk/nuL52HUzq8+1cl9vzow2rFwwEx3NEZzWFsUpkxrrrZ0Z+rCGZi4LIZoAfAXA6VLKm4QQpwsh/i8p5dPVlNAvCtdhUYNvNJlG/zDykrzRNVvc0FGoO3CW44aykj1KxYCRelcUBWsWdqEtGsZQPIWNz7+FXQeGsvHuhvWLGL/uYrY+rKo/NTcfHIxnTwgB4IXVlwDNFX0UzdfPZXe8M7YrYyy3Gftuy9W73t8VZfy2aiN1Z7SvJiIifVp5HUDJXK+Xw9ui4ez5YFs0jGQ6A0WRmnmcOdz7zB5vCUj82ZQW3H3NuXnnR5UcFzJ+yI8qOYex+lwr9/V2HRjKnh++sPoSS1Y+qOU5ml3vb3RZjAcA9GJ8Iz8AOAjgJwA4uIwT6x2pVzW++rHZRdPm1en4uY9VFa7Z4pWlMwBvlZWsY6TeFUXiaCyJtU/vyT5m3aJuPLRjfzbey7UFN3wOco7Z+rCy/ozk5mrY/fqFGNuVMZrbjH635epd6++XdU3B0VgSK7ZYc1sfERGVppXXN994ARJppWSu18vxqYxS8nyQ/MPs8VY6reDw8QT+PufW/XWLunHXL/eifyTBdVyp7lV6DmP1uZZd5261Pkez8/2Nbug3U0p5J8Y38oOUMg6HpvV7Qe4i4isXzMweSADjV0tu2rwTA7Fk0WMB7QX0B2LJbGVrvYabeKmsZB0j9T6QMziiPmb1tt34xhVd6GiOuGIzCcavu5itDyvrz+54dDreGduVMZrbjH635epd6+/fuKKrKHey7oiI7KOV198dGC2b67Vy+MalPWgIBUueD5J/mD3e6hs5MbCsPn71tt245dJZXMeVCJWfw1h9rmXXuVutz9HsfH+jM5eTQogoxjfygxBiJoBE1e/uE7mL4o8m0yWn4xtZQL8Wt09XyktlJesYqXe9xwQDIhvvtd5MgvHrLmbrw8r6s3tzE6c3T2FsV6aa3Kb13Zard62/s+6IiJyllXebIsGyuVgrh7dHw3jveJx5vE6Y7bNTGUXz8TM7mzG9vYkz26nuVXocbPW5ll3nbrU+zrfz/Y3OXP4mgGcBzBBCPAJgO4CvVf3uPqKud9QUCWWvbqgKp8+rj53W3oTO1gbd22hLvYZbeKmsZB0j9W7kMeXagt0Yv+5itj6srj+749HJeGdsV8aq3JarXL0X/p11R0TkLK28O5rMGMrFhTk8FAogGi5/Pkj+YLbPDgcDmo8PBQMcWCZCdecwVp9r2XHuVuvjfDvf39DgspTyVwCuBnA9gB8BmCulfL7qd/chK6bPu2G5AKO8VFayjpF690JseKGM9cRsfbD+9PG7qYwbchvrjojIWVp594yOpopzMfN4/TBb11NaGrBxaU/RUipTWrgpHxHg//xZ689n5/sLKaWxBwoxDcAZyFlKQ0r5m6pLUIW5c+fKnTt31rIImqzYfbHWO0ia4aWyGuBIwd0au2YYqXcvxIYXymiQL2LXbH34qP4s56HvxlWx64bc5qG6q3euit0zb3vGgdI45507rqh1EfzMVbHrBlp5F0DFuZh53Daui12zdZ1OK+gbSSCdURAKBjClZXzGO/me62LXrfyeP2v9+Sp4f0OFM7TmshBiHYBPA/gDAGXi1xJATQeX3UqdPl/r13CKl8pK1jFS716IDS+UsZ6YrQ/Wnz5+N5VxQ25j3REROUsv71aai5nH64fZug6FAjitLVr+gUR1yu/5s9afz673N7qh36cAzJZSchM/n6v2KoqTV2FqfcWHvFkHhWVuj4YxGE956jOQeZxpWr16+IxAZZ9TfY6iKMhIQErp6++IiMjrSuV65nQyykgcJdMZCCEQFEAgEGAcEZng5PhUvZzr2MXo4PLbAMIAOLjsY4oisffIMG7avBMHB+PZ9VdmT2011Kiqfb6TZaXqebEOCst8WdcU3HLph7Hy4V7PfAYyz+5Y9WJbMKsePiNQ2edUn3PPr/biuovPwuptu339HREReV2pXA+AOZ0MMRJHuX9bt6gbD+3Yjy9/dDbjiMgAJ8en6uVcx05GF/cZBfB7IcQmIcQG9cfOgtWCokj0DydwaHAU/cMJKIqx9aj98v4DsWS2MQHAwcE4btq8EwOxpCPPd7KsZF5hfB6NJWpaB5W0l8K4WdQzIzuwDDCO/MrufFHt6yuKRN/wGP70QQyHBkfxQcz5/F9OveTcSj6n+pxFPTOygxC5z33/+FjN+nWvqvXxEBH5W6lcXy6nH415Y64V86j9jMRR7t9Wb9uNRT0zXHn8xHghN1EUiQ9iCRw+FndsfKpeznXsZHTm8lMTP75V6ysVtX5/AEimM9nGpDo4GEcynXHk+WY4+V6kHZ8Pf/bCmtVBpe2lMG7aomHGUR2wO19U8/pasbx+cTemTmrEmR3NrrlSXi85t5LPqT5HL5+8NxTH4o0vcgaEQW44HiIifyuX60vl9NFEBkqzdHU+Yh51hpE4KvybGlduOn5ivJCbKIrEOwMxHDk+hnAw4Nj4VL2c69jJ0MxlKeVDAH4EYBeAVwD8aOJ3vlHrKxW1fn8AiISCmN6ev7nA9PYoIqGgI883w8n3Iu343H80VrM6qLS9FMbNUDzFOKoDdueLal5fK5Zv3bob7w6MuupKeb3k3Eo+p/ocvXyi1iNnQBjjhuMhIvK3Urm+XE7ffzTm+nzEPOoMI3FU+Dc1roRwz6At44XcZCCWxLsDo7h1624MxJKOjU/Vy7mOnQwNLgshPgHgLQAbAHwHwJtCiMvtLJjTcq9UzJnRhk3LenD3Necimc44cluIG66UdDRHcN/yudlGpV617GiOOPJ8J8tK5mjF54bt+7BpaU9N6qDS9lIYN9t6D2BjjT4DOcdsvjB7a2A1+UgvlpsiQVddKa+XnFvJ51Sfs633ANYt6s577rpF3dj4/FvZx9ZqBoSXbnd1w/EQEflbqVxfLqdv2L6v4nzkVC5mHnWGkTgqjB81roI2jy2biTXGC7lJMp1BUySIg4NxbHz+raI8bOYcrj0aNnxcXy/nOnYyuizGvwK4REr5JgAIIWYCeAbAL+wqmNPUKxWdLQ346sdmO755g/r+uYnd6JUSq3a1DAQEZk9txROr5lf0WtU+38mykjla8dk/ksCpbY0l68CuHVfDoYBmewmHSl8v04qb9miYceRzZvJFJbcGVpOP9HL/aDJj+ZXyatpjveRco5+z8Luc1dmC26/qhqIoeHzFPEgpIYTAt556DbsODGWfV4sZEF673bWa4yEiIiMKc70QAkExPmOuozmC2VNbcftV3UgrCh684QIMj6XQN5zAXb/ci/6RREX5yMlczDzqjMI4CocCCAUEDh+LZ48NHl8xD/3DCTRFghhLZbCoZwYe2rEft1/VbVu5zMYa44WcYuRcJBIKYjSZwfT2KHYdGMJdv9yLNQu70NEcwWltUZwyqdHUOdyszhZD5y/1cq5jJyFl+SumQojfSCn/KuffAsB/5P6uFubOnSt37txpyWupwfj+sTGsefK1ouT6xKr56GxtsOS9Sr2/2R3qh+JJHB4aw4qJTcmmt0ex+cYL0NIYQiqtsFGY58gXZWXsWqFcojcSn4Wv0R4NY1//iC0H0R/EEtj7/jBu3XriItD6xd2YfUorTm62r526XF3GrtX6hxO46t4XHOsDSq25fHp7EwbjKUsOcLTeZ9OyHsye0opQmYsyDvBU7Brtr+0YSFD7/Xgyg4yUaAwHMbm5oeTrOR3T1fLYYLirYvfM255xoDTOeeeOK2pdBD9zVezWSrl8Yybfl8vNTuZij+VRs1wZu6UGtvTOhwAUnTtZcdxnNtZ8Hi9u4srYdUo6rWBv3zBWbCk9bgUgu+Zy7rl+uZj02vGuxxiKXaMzl/8ghPg5gMcBSADXAHhZCHE1AEgpf1pREV1EvVLR3BC09LYQozPFzF4p0RsM72xpwJHjY1j+Q2dnXpN3GTmgKBefWq/x6Ocu1Fy/y4oEH09m8P+zd+/xcVT3/fA/Z2/SaiVbsiyZiw04rlGiEFMjY2P7eRJSF0OKE9exgRZscwlgx6HkR0Ninl/rV9Kfk/4wl/KEUmNBwsVcEgiGwgsSLqH41z7mauNAqYggxFALsCULSZZWq13tznn+kGa9l5ndmd2Z3Zndz/v10gusy+7Zme/5njNnzpxz07MTdzEbg34MRsZx07PduOPi+UCouONB1a3UjwaqdevxTUswNq7AK4BgwIspNdbenDkSjmbVxw0P7MPDVy7CzKY6tg8m6K1NmJnb9J6UKHT2eOoGJ2Y622573JUzR4ioVIzk8+b6AB6+ahG8QiAY8KIxmD0Bw0huLmUuZh4tPb1YenzTEs0YApB27bS8vRXXLjsVG1MmjBXa7zMba4wXsls8ruDjoUhyYBnIPW51SnMIjXV+PHL1WUhIoNbvyTuZwm393UpkdLpSLYDDAL4C4GwAfQCmAfg6gBW2lKwMPB6BoN9n2ULe6oDbqu17sHTbS1i1fQ+6Dw/rrnnk8Qi0NNTgxKY6tDTkrjxqA6auR6PaePacZMcG4IL8lJ/RTRxyxafWa/QOR21L8AGfF30jUWx4YB8uuutVbHhgX8GPKRKlKsdmDh6PQGtDLU6aVocTm+owLVSDgci4ZZurKIrEaFS7w9U7HGX7YJKZzmtq3mwOBfB+34jhPkGm1A1OzMSFGzcoMdMfIiIqVK58rl7HfXP7y/jyTbtx0V2v4vDRaNZrGM3Npc7FzKOlpRdLo9GEZgxlXjut7piVHFhW/7bQfl8hscZ4IbsoisQnQxH0ZYwN5Bq38ngEpoUm4vGkaXVobdBeCiOVG/u7lcbQ4LKU8vIcX1fYXchSsnIhbzt3XlUbsMydjBuD/oq4Y+OmzYfczoq7fFqvUezurrlYWU8Za9XDyLl2ymYOVt597w/HcOBIWLM+qrNoybhcnddcMVZsnyB1g5NU+eLCKTFNROQ0aj5XN3N/5OqzcO9lZyIY8BrO2UZzM3NxZdPrGxw4Es6KocHIRN/r1gtOR+e6Dsyf1WjpNTxjjZykPxxLTmaxc9wqNe7nz2rEvZediQe/tQgSktf3JWJoWQwhxEwA/wxgKSaWxfj/AHxXStljY9nKwsrHQuycmq82YOoOmuoGhOri525ekJ/rPpWWFZs4aL3Grn0H8S8Xn4HvPPzmsTVe13ZY0rGxqp4y1qqH0XPtlEcDrdxcJRZP4PYX38+qj9tWz8P9Lx/AGSfZt6lMJVI7r5mx1BT054yxYvsEqRucmIkLp8Q0EZHTNIcC2HnFQs0lLabVGRv4MJqbmYsrm1bfoHNtB/7+X99J+72W+pqs/ZK2rZ4HRUrL+n2MNXKSWDyB/nAMu/YdtHXcSo37p65ZmlXHeH1fGkbXXL4XwMOYWGsZANZOfu8cOwplNaPrHqvUx0KKZefOq6kN2C3PdWPrytMwe3oIDUGv5kWvm+5UGl3PkqzRFPSjc11H2uL6ZmNGq0N1+dLZeOjVj5LrIo/GEji+Mf8jLUZZUU8Za9XDzLnWii2z7Uix9AYwC8nl6jIyD736EXZesRCfhWPoD8dw/8sHcN05ba5qH8ol8/xr7TydL8aK7RM0hwI4ubkON6+ZlzUIku8cWtWvISqlQjYo5CaAlItWW15f60uu+Qkcy92PblhsKGebyc3MxZXL4xGY21KPRzcsRjyhwOf1oMYv0DeSvpTKtcvmJge9gIl427zrbdxywenYsbYja83lQvtojDVyioDPi137DuLSJbNx/8sHsGVFO5pDAZzQWIu71y3AVQ/kvtYxcw3m8QgkFGTVMV7fl4bRweUWKeW9Kf++TwjxP+wokNXKOTMx3+BAMYMVue5INgVrXH2nkouxl46iSLzfN4Kf/va9ZKJvbajBCVODpmImMx79Pg9GxuJ4+Y/9eHRfTzL21Q0siimvlQN8jLXqUcy5zrUDuBW7emuxctZJalv0fu8Irl02F20zGvDjVV/KuzkGGe9H5IsxdYbcR/2jqAtMzHQ7ubnO8IWjxyMK2uCEiIj0c/mUWp9m7o4nFDx85SL8+JkuPN/Vm3PAmLmZ1Guq1PjaecXCrLGA2dNDmvF2QmMtTpgSdPU1PJGW5lAA153Thtte6MbqjlnJ8YbW+locN0VkxTwA9A1H08YU1t/zuuGxPF7fl4/RweUjQoi1AH4x+e+/BtBvT5GsVc6ZibkGB6wY9Na7I+n2O5V2zvimdKn14/muXgATx7qQ+pEZd9ND0tIOkh03ihhr1aOYc63Vjtz2Qje+++enZs34t/LGpVW5nI9HFsdoP8JIjEXjCrY8+U5azJihbnCCUJEfioioyujlcr0Zyu8eGsbWp7vQua4DW1eeBo/Ho9t2MjeTVnytv+d1PHXN0rT+l4T28heQ7r+GJ9KiXof8ZNU8zeuQ1JjXut6/ec08tNTXoGcgYmgsj9f35WNoQz8AVwC4EMChya81k99zvHLfudDbedXOzf7cjpsQlI6d9cPqXYftqDOMtepRzLnWqierO2YlB5YB5+dw7gJeOKN5Ml+Msd0nIiofvVzuFcjK3dtWz8OO3R+gZyCCDQ/sg8fjYdtJOenFVySWSOt/TQ/VoHNdR1a8/fiZLvYHqGIZvQ7R6it//7G3sfHsOcnfyTdWwev78jE0c1lK+d8AvmFzWWzh1DsXpRj0Tl1CIBjwIq5IjMcVx89a4yw7+6mxkZAS9152Jm5/8X3sPzgIwN76UcyyFnbUGcZa9SjmXGu1I82hQM54LNUazaVeC7oaGe1HaC0P5PMI9A2PISGBeELBlhXt2LH7g2S+5WN6RESloZfL/T4Pjp9ag0euPgtxRSKuSHQyT5NJWvG1vL0VQgh8PDCKgM+LpqAfA5FxNNX5ce9lZ2IkGkfvcBS3PNeN/QcH8cOvM86osuS6TtH6mYAcz+MAACAASURBVN71fmPQn/w3N7J2LkODy0KIzwH4KYCzAEgArwC4Tkr5RxvLZgkrN0Wykt2D3qmPFLTU1+AH57VlbTLh5B0z+ViQffQeN7np2W70jURtqx/FLmthV51hrFWPQs+1VjvS2lCjG4+lWuu/nHsKVBMz/Qg1xtRzc9sL3bh0yezkztjqDCX1QtIJN7uJiKqBVi7fecVCDI6O4/DRsbTrpFsvOB3v944wT5NhmfG1vL0V1y47FRd2vpL279QN+9QZ8owzqkS5rlMAaP5sxhTt66vRWCL5/9zI2rmElDL/LwnxKoB/wbE1l/8KwN9IKRfl+bt7AKwA0CulPG3ye9MAPALgFAAfArhQSjkghBCYGMD+CwCjAC6TUr6Z6/UXLFgg9+7dm7f8TpzZZfegQN9wFKu270HPQGRirbCnu7IqKXfM1FSSwDAau3ZIjQ3VzKYgHrn6LFvrh977Go1DDqTlVfGxW06Z7UhT0J+1cYsaj/3hWFGxblSxdcpBHB+7ZvsR6rnZsqJds/1Vv88c5nqOit1TbnimBKVxtg9vPL/cRXALR8VuqWTmcgmJ//r4aHItfBXztKM5NnZT40sIkRxYBqB7Pc44qyqOjV075LpOAaD5s8c3LUH/SExz0DkSc85YXhUydMCNbugnpJQPpPz7QSHENQb+7j4AdwDYmfK9GwC8KKW8UQhxw+S/NwP4GoC5k1+LANw5+d+iOfHOhd3T9VMfKWgM+l2xY6YTbwJUIr3HTQDYWk/U950/qxEbz56DxqAfg5FxKIpi6O/5iAsVo9j8otWO6MVjqdb6L/eeAtXEbD8iFk+gpb4Gp7bW49YLTsdgZDw5O6lnIIIvHDcRO8xhRESlk5rLFUXi06EITm6u01yyiHmazEqNr48HRtP6aFrX4y31NfjiCVPw8FWLUOvnrGWqLPmuU7R+Nh5XdK+vlODEtdynQxGOAziU0cHllyYHgn+JiWUxLgLwzOQsZEgpP9P6IynlvwshTsn49koAZ0/+//0AdmNicHklgJ1yYir1q0KIRiHE8VLKTw1/Gpexc9A7dQmBwch40csJ2D3wy1mppVOudcgDPi+Wt7dmPSLeua4DLQ21hs5z5kUBb0aQEXblF70cXqo65tQ9Baqdokj4vR78r5VfxLp7Xs9aDqNvJIpgwOe4m95ERNUiHlfQ3Tuc3JhXa8ki5mkqRmYfLfV6fP6sRvzgvDbU1/jwV3e9ymtfqkj5rlP0fqZ1fcWxInfwGPy9iwBsAPASJgaDvw3gCgD7AJidcz9DHTCe/G/r5PdPBHAw5fd6Jr9X0RRFom84io8HRtE3HIWi5F+mxMjfpe6SuWP3B7h5zbyCd8xUK/Oq7XuwdNtLWLV9D7oPDxsuqxFaO4NetXMvd821Qa4dVAuNx3wURcLrAf7u/PbkwDKA5C7cZs9zKWKSKseRcFQzvxwJR215v1LtUqz1Pp3rOtCUsumFFrvqOR3LTf/1yVF8+6E302Ju8663ce2yuZqx4NZz4tZyE1H1UhSJT4YiyYFl4FiO3nj2nGRb6vXA9pzGHFq5Mvtou/YdxJ1rO7C8vRXXn9uGsXElq59Q6LUv44jKLTUGPxmM4PBQBF4PdK+HzF4rlfpajgpjaOaylHK23QWB9joeWZlRCHE1gKsB4KSTTrK7TLYq9A6Mkb/LXEIgGPDi8U1LMB5XdGd56s0E1Rv4LWZdz8z3svrxbifOanVK7OotLwFoL6xv9o5g6rH3+zzweoDhSAIHjoQxvaHGkvNsR0zazYkxaZRTYtcs9ZhHYun5RV2aJRJLoG84auhcmDl/pVrCxeMRmNtSj4evXITe4Sj6wzH89Lfv4bpz2nTrbbXd+bc6dhVF4kg4irHxBLxCIBjwojF47NyquenWC07XzHVzWusxszGYdqwzz8ny9lb8/fnt8HqEo3NFtcVSqbk17xI5JXb18nV/OIbe4ahmjv78cQ3YfskZGJjsO7Q2xHHC1CB8PqPzscyVjznUWayMXUWRmBr046ErF8HnEfB7BX7+H3/E98/9PC6/7w3dfoLZayK39iHIWuXMu1q5bNvqebj/5QO44Wtf0B2DUq+VFEVBQgJSTlxracVsdFx7rGhsPHt5TTdfc7udocFlIcReAPcAeFhKOVjkex5Wl7sQQhwPoHfy+z0AZqX83kwAn2T+sZTyLgB3AROLlRdZlrIqdIDM6N+ZWXYjVwfHjoHfzPd6+MpFOR+byJckUn8eDHhx+GjUcZ01J8WuVmz0DWffEbzthW786BunQUppKDlrndub18zDTc9OPAr+i6sW4d7LzkRdwJtcg7RvJGr6MX63rTXr9gsIJ8WuUanHfMuK9rRHEa8/ty1taRatc5F5k2RkLI71KUsc5Dt/RvKvFZ2fgcg4Lv7Za2n1oevTYd12xI03ZophZezq5bcZU2pxSnMobb1tveWogn5v8hwnb36Mx3FoaAwt9TVoqa/BpUtmJ8+pk3OFkVhiB79wbsy7RIAzYjdXvq7xedAfjqXl6PmzGnHtsrkQApASuOHx/0xbvu0Lx02xPHdp5VCz/W6yllWxG48r+P3hYWx88NiyK9svOQMDo3F8Fo4ZWrbSaPuZGkfzZzXa2odgm+5c5cy7Wrls8663sWVFO9bf8zqe2LQUJzbVZf2dxyPQFPTjk6FIcpLMrn0Hk5Nk1NdWN8hc3t6K57t6k38/sykIb0b4uf2a2+2M3ob9K0wsUbFXCPFLIcS5QohCz85TAC6d/P9LATyZ8v31YsJZAIYqeb1loPABMjsG1nItS6Gul5OqmHU9td7rx890oXNdh+5SDbmWQMj8+VsHh7jERgEy40rtoFzY+YrhpSe0zu33H5t4zLClvgaHhsaw5cl3cNFdr2Lr0134wXlt2HnFQtPLBVgdk3bjsi+ll3rMd+z+ANtWTywNtPHsOVlLs2Sei8yc8s3tL+Pw0YnBP72/McuqpV3MtgduuzHjJHr57aP+0WQsqLkpNeaA7Mf9Us//l2/ajS1PvoPrz23D95afmjc+nSJfLHH5IiIql1z5WgiBXfsOJnO0uv7tliffwbufDuM7D6cvVVDI8m1GWNHvJmfqHYkmB5aBiTja9NCbuOrLn0sOKufqJ5hpP1PjyEgft1Bs00mPXn9Q3cRS7xpDUSS6e4dx8c9ew5odr2Dr0124dMls3PZCNwYjsbR4+6u7XsU1fzYXy9snVtRVb9gEA+nX/rzmLi9Dg8tSyj9IKf8OwKkAHsbELOb/FkL8g7qpnxYhxC8AvAKgTQjRI4T4FoAbAZwjhHgfwDmT/waAXwP4I4A/ALgbwKYCP5NrFDpAZsfAWq6LRKvXD9V6r+e7ejE9FMATm5Ziz+av4olNS5N3mPIlicyf1wW8HDwpQGZcFdJBydW4bDx7Dq579K2sjn59rc/0ncRSrWlrFQ7olV7qMd9/cBC3PNeNLSva0XZcQ95zkesmid7fmGVV58dse+C2GzNOoleP6wLeZCyoualvJIpbnuvG1pWnYff1Z+PxTUvSZk3ozfI4bmqta3JFvlhiB5+IyiVXvvYK4Lpz2nD/ywewZUU7/umi0/H9x95OGwzJ/Ds7crAV/W5ypvGEohlHXo9IDirn6ieYaT9T48jO+GWbTnr0+oPqjRS9a4z+cExz7fvVHbMQiSWy4m3TQ2/i++d+Ho9cfRa2rGhHcyiAxmD6tT+vucvL8AJSQoh5AP4JwM0AdgFYA+AogH/T+xsp5V9LKY+XUvqllDOllD+XUvZLKZdJKedO/vezyd+VUsrvSCnnSCm/JKU0u1Gg6xQ6QKb1dzuvWAgJWfBC/rkuElPXD80c+DUqdZF3IYTme3k8HrQ01ODEpjq0NNQkXz9fksj8uZrItD4LpUs9LxISO69YmDx2zaGA6eScq3HR6/CMx7PXSsrHTEw6YZMLDugVz+x5zDzm+w8OYuvTXfB7tPNP6rnIdZNE72/Mls+qzo/ZdsRtN2acRK8ej8YmHtdTFJmWm+64eD5OO3EqTppWh9aG2rT8pHf+a1yUK/LFEjv4RFQuufK1x+NB24wG/GTVPJx2whR4PSKZq0p5DZGZQwvpd5Mz+b0ezThS5ER/9P6XD+DhKxfp9hPMtJ+pcTSeUGyLX7bppEerP7ht9Tzs2ncw5xiVXkw1hwJISKn5s8/CseQT0H6fBx6PMDTO5MR+dCUyuubyPgCDAH4GYLOUUt2W8TUhxFK7ClfpCt30SWuzvsNHo1i//eWstWWAY2vVBANexBWpuaC6mhQy16dRLxLNrN+cSWujgR1rO9LWoco1uKF2EFMTTGqSyPz5jt0f4OY185KzEDh4ok1vTaKnrlmKyORgSa7jrkUrjtQ1l69dNlfz9fwFbpJidE1bJ6y7lK9+UW6FnEe9Yz6joTbvudDLOaOxRPL/tZY4MFO+fHkt17HIXO/OTDtSqs0GK0Xmev53r1uAqx5Iz2/BgBc/euqdtI0Um0OB5N9pbU6id/57BkZd037li6VCY5yIyKzMtrGx1pd1rXHzmnloaaiBd7LbqfYhe4fHkrlKnVWauS+DHTk4M4cW0u8mZ8jcp0MIie2XnIFND72ZjKMdazswvd6PPZu/mnZNrsVM+6nG0VPXLMVn4ZhtfQi26ZRLc30AD1+1CF4h4Pd6IKXEjavn6Y5ReTwCfp9HM6ZaG2pQ69eON/UG4N3rF2B6qKbocSaylpAy/yw+IUQ7gPkATkbKgLSU8n/ZV7T8FixYIPfutXeCsxsWru8bjmLV9j1Zle+pa5YmN7Zrqa/BD85ry2psUgc/7PqsWuVb3t5qeMOKfAM3Wj/fecVC1Nf6NAfS8yjJyS1F7OajFzfqZkyFDJgpisSH/WF81D+KuoAXEsDJ0ybu1AcDXhweimYNzKRuhpVLIfGZ7zOWUglyScXGbqHnUeuYA0iL0dFYAic316Gxzo9IbOL3moJ+vN83kj0wPaUm+Tup56+Q8hVav5xws8QGjoxd3balxoex8QQSUuLQ0BhuerYb+w8OJs95cyiQ9zzp7ax9y3PdAIBrl83FnNZ6BP2l73dYlasqOF5TOSp2T7nhmRKUxtk+vPH8chfBLRwVu8XQyjWd6zrw1P4e/N+ntuL4xiACXg8ODY3hH3/9LvpGomm56LNwFN2HhpPXSMvbW3HD174Av9eD2gJycKE5tEpyphUcFbt6m0fuPfAZzvvS8fB6BBQJTK/3o7HO2PVVIbGg9kVb6muw8ew5aAz6MRpL4PRZUzEtVPwG04xPSzgqdq2QKy76wzHN66PHNy2BzyPwWTiGvuFo2vhU57oOtLVOxJRWXp8eCsDj8SRjtNhxJjLM0AE0NHMZE8thDAJ4E0A0z+9WDLckUb1HClLXqtmyoj1ZcdWfZ+7qXszsZLPle76rFz/8utTcOTRTvtlRnIlXmHyPNxVyXPvDMay/53XNQTa1Y7N15WmoC3gxGBnHTc92o28kamiQsJC66KRHuOyqX9Wg0POodcz7hqOaMbp15Wm4/L43krE1t6VeO/ZD1pSv0Pqltd5dOW6WVAOt463ueu31CHx52+6031fPuZHzlHr+I7E43j00jFuemxikBoDL73sDezZ/tSw3wazq97BtJqJS0Mq5Gx7Yhy0r2rH256+jc10Htj7dldZOp+bkSCyBm56d2JehMejHYGQc33v0Ldxx8XzTObiYHMqc6U56+3RsWdGOc2779+Tv7dn8VTTWGevLFRILal9Ujf/U99Xqu6qMxizjk7Tkime966PRaAIHjoSx5cl30FJfk8y9o7EEjp9aC9/kU81G4q3YcSayltHB5ZlSyvNsLYkNip1944QLeSOfQe8xldS1akq5QUUmKx6jyTcwx4E784ycF7PHNd8gWySWwOX3vaH5d7no1cVHNyzOeVeSj3BVBivPY65NftT/18vzevm40PJZXb9KyQ1P9RRL/8ZtHD6v9qN8AZ8359/1DSN5rNTz3zeMrIGPcuUpq/s9bJuJyG651u0E8l8DBXxe9I1E0wbkCs3BxeZQ5kz3MbtPh9G+nNlYKLQvaiZmGZ/VS6/fnyue9Zb6OXAkjLqAN+/NECPxxmt9ZzG62OnLQogv2VoSi6l34VZt34Ol217Cqu170H142NRmXuW+kDf6GfQ21VHXqgHKu8mdnRtIOWGzNrey47wEA17ce9mZeOTqs9C5rgPzZzVqro+damZTEAlF5jx3enXxk8FIQXWD6y65i5XnMdemkyqtPJ8rH5cqzkq1MWS+vGpF++oG6vGeP6sRnes68MjVZ+Hey87Ex4MR/Oipd7BjbYfmOdc7T+8eGtY8Vk7KU+Xu9xARmaEoEkIIPLZxcbLfCRxbtzN1jc5UqXt+WJmDmUOrj9/nwfL21mQ/oXNdB5a3t+ru06GuM5sqNR4LVWgcM2Ypn1z9/oDPqxn/CUXiR0+9g22r56XFZOfaDtz+4vuWjU05qQ9NedZcFkL8JwCJiRnOcwH8ERPLYggAUko5rxSF1JNrPRkr1lot93qtZt5fb21R9TEXI2su2ylzUyS9jQXNvqYNy5ZU3FpIuVg5+1BvzbHUNZUVReKPR0Zw8LNIcr3bppAf21/6A36yap5uvdKrC1tWtCfvdpqpG5U2w3JSRceuVTlEL07VdXMB7Vgyska53XFWiqWajLyHDW2jI2NXXUP+8NGxtLbzzkvOwEg0jt2/P4xLl34u6+mJXOspp67NnHqsnJKnyt3vcSFHxS7XXOaayyY4KnYLoZVr/+XiM/Drtz/G2Z+fgS8c34DxBBBXFPQNR9M2V7t5zTy0HdeQXLLNqhzMHFoSjordwdEo/vuzSFp83XnJGTihsRbxhERCIq2fMBiJpa3xrRWPhXL7/jRVwFGxa1SuGGkK+vH7w8NpG+jdubYDT/+uB53/8SHmz2rExrPnoDkUwAmNQdT6PfjGHRNrg19/blvW5qlzW+oxEBk3FcNO6UNXOEvWXF5hQUHKwoq7cOqdkNte6MbqjlmY2RTE1KAf0XgCfcNR2wM38zOolXM049FaQP+xgdS1aoIBLx7ftKToQd1CqOWzcnDECcuWuF2ux02MJurU39Nac+zxby9JGxSMxhVsefKd5Pm/9YLT0Tccy1k31bqot/mV+n5ar8FHuCqDVTkkc804v8+DkbE4+kYmthPQu+NtZI3yQuqSmQ5RrvXurOpYGcmr1TDLRT2etT5P1n4F337oTWxdeRq+3DYDQgCZ9+jzradsxeOvdtHKtZwBQkROoygSh46OZbVX33n4TTz4rUXY/tIfcNKfz8WJTXX4eGAUP3zyv9LWVL7p2W7ccfF8U49fG8EcWn1Go4nkwDJwrJ/wyNVn4ehYPCsWptX5s9b4zozHQhUSx4xZykev3x9XFPSORJMDy+r3v/3gxJr3+I8Psf/gYHIi2J7NX0Vj8Fi83fJcN7auPA2zp4dQV+PFtGBAc0P1fNd4TulDU57BZSnlR6UqiNWsWud3bks9vvvnp+Knv30Ply6ZjQ0P7Ct6UNSo1M8wf1aj5t0dt1U2KweEq2GAo1yMDuCl/t6tF5yuvWh/LIG1P38NPQMR3HvZmcmBZfXn3/vVW9i68rScdTNzUE0IgR899U5ysAbg+krVwoockpkXp4dk3g0jCm1TctUlAKYHyrVyupU37Yzk1Upf38xIXqsLePG9X72F+y5fiD//p/+TdcyduJ6yEdywh4icTs3R4WhcMz8fGYnia186PrnkmpVrKufDHFpdFEUimlB0Bt6k7n4xpYpHIxizlI9Wv395eyv6hqOIxbXjP/PmhBrjueKtbzjKiYMuV9ziPg5m1forA5FxbHhgH1Z3zEoO7ALHgr0/HLO87KrUz7Dx7DmWvr+6pubhoYl1a42uWVzsGsdWDgiXav3Rcij3WtJ6A3hqvKnl6xkcxaGhMbTU1+iunXTgSDj5Ouri/al6BiKYPT2Ut26qgzUnNtXhuCm1uO6ctuT7LW9vxcNXLkJs8qmCSlv71U3sjl07biqlxlZLQ41mh9psm5JZR5Z8rhmd6zpw6wWn49DQGAYjsbz1zCirXgcwllcrfX2zwUgMQ5Fx3Hf5mTihMai7RnfPQATDYxNrdesdczceKyP1gYioHBRF4vDRMfi9Hhw3pVYzP/eHYzipuQ4/fqYL/eFYyfMwc2j16A/HcPhoVDMOvR6h2V+VUjquX8CYpVy0cugNX/sCNj30JsbGE5rxP2NKbXIfpnsvOxM7r1iYjHG9eHPSxMFyj8W4Vb5lMVzLqrtwapDn22nYDqmfYTSmfXe+kPdX7/jf9kI3Ll0y2/BsaCtmx1k5461SH+Mpxbqq+eRK7nrriT65/2NsWz0vLZ4613bg7//1neRrqAPQmee/rsZr6rOl1g1FUXAkHMPFP3utbMeLJpQidss1a9ZMm6J1HLZfcgbu+Lf38XxXb7JuTK8PWJLXreyMGcmrlTzLRVEkPh0aw/W/egs9AxEsb2/F9kvOSFtLUV2SZ2ZTEOFoPPm3estdVOqxIiIqpcy2dXl7K3as7Uhb63Pb6nm4/+UDWL/4FDzf1Ysffj3BPEy2URQFtX4PbrvwdFz36FtpayhLKXX7q20zgoxHcg01hz5y9VnoGYhgMDKOocg4WuprUF/rw81r5qWtId65rgPjifRlMO9evyDv+zjlyUgnjMW4VcUOLgPWLAmhBrneoFgpBjTUR2uten91ltuWFe2as6Gf2LQUzaFA1vqdVjyObuWAcKV2Fp2wlnSu5K5Vvs273saWFe3JtZPmtNYj6PfC60FyLVsA2LH7g+wGaG0HfJNrxpodYJ6oG9HkcjVqefgITXmUInZLfVOpkLWMtY7DpofexJYV7Xi+qxc9AxFseHAfHt2w2JK8bmVnzGheddqSS1bpD8fS8snzXb0AgAe+tRAA8OGRUdzyXDf6RqK4eU36nsZ6x9zjEWltqjqTzu1tFRFRKWW2rc939WJ2cx0eunIR+oaj6A/HcP/LB3Dpktm45bluLG9vhRACHw+MVsw1AjlLQgLXPLwfN6+Zh60rT0NdwJtcQ7mlIZB1c1rtr1qx5w1RKXk8AgHfxJJwPQMRdK7rwLXL5uKah/ejpb4muYb4aCyB6aEAVu94Jbm068az5yAcjePQ0TEcN6VWN56dMnHQCWMxblXRg8tWSN3UL3NWZqmCXVEkvB6gc20HNjy4r+j3zzcbW1EUzbs1U2p9Rc+Os3pAuBIHOJzwSEiu5P7pUESzfI1BP/pGojhuai1mNgbh8QjE4woevnIReic7/bv2HcSMKbV4/NtLMBpL4MCRMP7+X99B30jU1jViqTRKcS5KeVOp0DvXesehMehP+7f6aGSxnah8nTGzFyqVmFeN0jp3z3f14lv/1+dQF/AillBww9c+n7x4vPXC09G5rgO79h3Edee0aZ47zoAgIiqeVn7u/I8PseL0E9EcCiChSPzPv2jHdY/8Di0NAVy77FRc2PkK8y7ZRkqJnoEIPELg8vveyPr53/zZXPzy6rOgKBI+rwet9bmXnGB/gZws9Xpjx+4PcOuFE/uS9AxE0tYQ/z/fPzs5sGxmzzCnTBzk2ELhOLichxrkP1k1D4qi4NENiyGlLFmwpzYyLfU1aTtqTg8VtiZSvtnYCQndDQismB1XzQMXRjjhkZBcyV2vfDObgslZ757JmciZO752ruvASU11GIiMJzf5UxV6R9AJx4smlOpclCqHFHrnWu84DEbG0/5t1aORueorL1TM0Tt3o7EERmOJrA143u8dwdanu9C5rgNzW+o1jylnQBARFU8vPx8ZieGToTFsfboLv9qwGHdcPB9CiOTAMsC8S/bId019ZCSGIyMxXH7fG4b6X+wvkJNlXm94hNCM+/GEzLlnWK54dsI4EccWCsfBZQPKGeSpjUzPQCTZOD2xaWnBAwP5ZmOrd2FT9QxE4BUoapZdoY/5VNvjQU55JEQv7vXKd/zUYNp50eog/fS37+HHf/klxBIK7rv8TBwaGsOtz7+H/QcHbV0jlkqjHOfCqvyg9TqF3rnWOg471nbg9hffA5C+eYtV7Yve6/BCxRytc3fzmnmYMaUWwLHlqVLXXlZnbOgdUzWO1EcDG4N+DEbGoShK0eWttvaRKJ9TbnjG9N98eOP5NpSErKaXn4MBL7a/9AfcvGYeavweTAvV4OOB0bRHtQcj49ix+4Oc7TfzKZmV65r65jXzUBfw4kdPdQHQ73+lxh0AtNTXpPU9rZ4xyTinYqReb2g9oXz50tn4zdufYOcVE8vJbVnRjh27P8D+g4MA3DEDmGMLhePgso2sSN52TcufMaUGP/z6F+HxiKzZ2P3hmObdGo/HU/CjCrlmzwHQPU7VOOvOKY+E6DFavszYnT+rEZu++if4qD+ctunF/3vRn+Inz7yLvpGorWvEGsVOV+FKHbuF5Aet8wtA83VmTKnRvXOdK060jkNT0I+frJqHH369tHHFR7uMU8/ptDo/frVhMcYVBV4hEAx40RiciJMnNi1FJBbHu4eGcctz3YY6ywGfF8vbW7M20O1c14GWBv2154yUt9Lbx3hcQe9IFOMJBf7JR4p9Pk+5i0VEJaDVzrbNaMDjm5ZgLJaAIgEhgKORcazumIWbnu3GHRfPB0JAMODFD85rS9vjQx2I1nuvcudT5jt3Se0z/Ogbp8ErgEc3LEZCkfAIwOcR2Pjgm8l+ApDdV9CKu5vXzMNNzx7rX1g5Y9IJca6H8e98qTk5GPDi8FAUVz2Q/oTyv3UdxhmnTMP6e17Pmoyx/+CgK2YAO30sxslYY22iJu9V2/dg6baXsGr7HnQfHoaiSFOvo07LT5WrUiqKRN9wFB8PjKJvOJr1fmq5vnHHRLku2PEKhiLjOH5qEC0NNcmNh+5evyD5vlqz7E5sqkv+fr7j0DccxadDEc3Zc4ORWM7jpDfrrj8cM3Uc3cbscTYjX4xYVb7M2N149hyMjSvJgWVg4nz+j0d+h81f+3xRdwStOl6KIvFhfxjvfDyEZ1avRQAAIABJREFUnoEI3vl4CB/2hws6RtXKztjNpJcfjoSjmr+vl5ePhKOarxNPSOxY25GWC3es7UBjrS9vfs88Dj6fp2THJZXRNsSKvOBmamz83RNv43c9Q/h4MAJIYEZDLaZNLkGlntNgwIetT3elXTDmapebQwH8/fnZG+hueGBfUW1ZpbeP8biC3x8exoWdr+ArN+/GhZ2v4PeHhxGPFz/jW0+11wMip0jNye98chQf9YfRMzAKRZFobahFMODD2p+/hq/cvBtfv2MPNjywL22SQlyRyYFlYCI/fv+xtxHXqdPlzqdW5Dvmr9LJ7DN8MhhBNK6gtb4Gs6bV4cSmOng8nrRNzYHsvoJW3H3/sbdx7bK5yd+3csZkueNcTznaeytUap3T+lyZ11D/3T+aHFgGjvVrV3XMzOrvbt71NjaePcdVM4BLeT1bSap+cNmKpKD1GlYl71wDvVrlyDfgYaRcqXdr9mz+Kp7YtNTUHU31eBweiuDdQ0exavue5LIeqXoGIojEEjnLw1l31rLqpoeeeFzBJ4MRfNQfRjyhYOcVC5Ox2xwKYHp9QPN8njC1tqC75lY36oORGA4fHcOWJ9/BRXe9ii1PvoPDR8cwGKmMwZpKo5cfRqOJtFjId5NrbFz7dcbiCm5/8T1sWdGOR64+C1tWtOP2F99Dn4n8Xu6Op5E2JB5XkrnajrzgBv3hGG57oRuXLpmNrU93Yc2OV3Dxz15Dd2/2cTDTLgMTbarXIyxvy5zUPtoR570jUWyc3MQYmPhsGx/ch94R7ZtHxZbF7vaRiIw7Eo7mzMn58vB4XNHMj+M6g1VW5tNCclC+fGd08hDzV2kY6TMY6Svoxd2c1vqCrsHzsbvfUGj7W2h7X06VWue0rgne/fQojowcm4gzf1YjmkLa1/QJRXt51S8c12B5PJPzVPWyGFY8GqL3GlNqfZYkbzPT8o2srWm0USl0HdDU47FlRTu2Pt2FnoFIjs0DtROQWh4uqG4tO9dfVe86q50DdZbnk9cswVhMgRAC0XhCZ8kVUdDAstWPdkViCc2ZLo9cfRYQKuglyUZ6+eHAkTBCNT60NNSkxcmtF5yumW+8OhtSeAXwfFcvnu/qTfubvz9f+6I1M4864fHDfG2Iokh8MjQx28COvOAWsXgCqztmac4uzjwOhTwuZ0db5pT20a44H09o17N4Qn8mUzFl4frkRM6gKBKj0fw5OVceNpsfrcqnheagXPnOyGsyf5WWkT6Dkb6CXtwF/V7Lz5uiSCQUaVu/oZj2t5D2vtwqsc7pXRNseHAfHvjWwuTA8vXntkGR2rGkt8lfMOBz7XEh46p65nKxs4sVReLQ0bG012ipr8GhoTFIwNRyFrkYnZZvZODY7DIbevTuTKYe08agP1meHbs/wLbV87Lu3tb69cujKBJeD9CZ8Vi6Wx6ncCIjMZJ5buNxxdBdaL27zrFxiROb6nDclFrU+Dy4eU16HNy8Zh4CXvOpyI5Hu/RudiTKcBO63DNe3aA5FMjKD9tWz8PtL76fjOnUOFFvcqVa3t4Kn0dkLX9x9/oFCAa085PP6zGUR3Mt2/FZeOLc/vdnYfQOj9l6fnO1If3hGHqHo46ZAVsuQkwsCWXFcdCqu2ZnOxthx2sWwq7HbP069cyXo70wU5bM8+SkmeBE1aw/HMOBI2HNnNxSX4NYPIGPB0bRH46hKehHwOdFLJ5AfzhmatZoKqvyaSH5UFEkfB6hm+/0XnMwEkvmsMh4nPmrhMz0GSQkElIiFk9gMBJL6++Vsh3vD8fw42e6sq7HO9d1FP1+WmMiZtrfQAHtfblVYp9B75qgpb4GtX4vXrr+K7jtoj9FLK7gaGRc85r+yMiY5pgPx26qQ1XPXC4mKah358LRY425eidn86630VJfg5vXzEvbSMLuimXkrrsVu1/mujOZekxTZyvvPziIW57rxtaVp2FOaz2C/mMbaWmVpynoT75HS30Ntq48DbOnh1BX48X0ENe9KVS+GMk8t8vbW3HtslPTZiPr3YXWu+s8Fp+YdeHxCLSEajAaS2DrytNQF/BiNJZAS0MNptWZrxd2NOrqzY7M41PrL23nxgkzXt3A4xE4vrE2GU+DkXHc8lx32rqLqXGi3uRSZ5osb2/F3yw7Fat3vJLMM6dMDyEU8GJ6/cTdda381FpfYyiP6sXoeFzBB5+NZrUP5Ti/6gW5E2bAlpNXANNCAUPHId8GtXo/s3pzEKdsOGLXBVZLKIA713bg2yntz51rO9CSo79itCxa5/DhKxdVfT0gcoJYPIHbX3wft154elqdnD+rET84rw0X3fVq2hNyt7/4Hp7v6s1qS83kR6vyqdl8qOai217oTuufqJ+ttb4Gh4fHNAd7Ph0cw4bJ/HjvZWcyf5WQkT6D3mZ9M6bU4pTmUPKpzVK147F4As939aJvOIYtK9rRGPRjMDKO6UW+n9aYiMpo+7vzioXYsbYj6+nX1nrnznR1ytNjVlLPVWbe/dE32vHhkXDadcutF5yOppA/7Zo+GPDiH57qQktDAI9uWAwpJTfDqzJVPbhcTFJQ7yJvWdGefI2NZ89Jdgp6BiK46dn0wdSmoD9r12MrK5qRgeNCGrHM3ZolpO5jIKnHNHMgp28kiuOm1mJmYzDt/bTKk3qXvmcggsvvewMzm4J4YtNSJqci5IoRrbvOqztmZc1GvmrnXjy+aQkERNo5U2eZZdanD4+EUT+5RIHf78Up00KoC/gQTyjwFbEbsB2N+vSQ9qDh9FBpOzeV+KiV1dS8pCgKWqfUJB/hysx7qXGSeZPLK5C8SE3NM49uWJzMM3r50kge1YvRyLiStfyK2fObmZcLbU8CPi927TuYdVFrxUwWN/F4PHjk9T/iXy4+A995+M1jx2FtB5qC/rTf1aufj29agnhCIhyNY8uKduzY/QH2HxxMO7dW199Cl7Cykl0XWINjcfzz5Jrn6kXwP7/4Hn6yah5a/MU92q51Dn/8TBc613Xo5hKqXKfc8Iyp3//wxvNtKgkBQDDgxbXL5iIWT6QNOF27bG5W27nxwX2497Iz0Tccy8q3ZvOjFfnUbD5MzUXqoF9zKIDjp9ZiRkMtfD6P5mteu2xucmAZAG5/8f2ST2qqZmqfYfslZ2DTQ29q9p30NuvbuvI0NNT6k0u32Tk2kEqNo/0HB7HhgX0AkLy2LobWmIjKaPu7/p7X8eQ1S/DohsVFXx+WihUT9pwmGPDixKYg7rzkDHx7Mq6vXTYXn4XHseXJd9LO2fd+9RZu/OaX4PdO5M2P+kfxD091oW8kin/85pdw3JRajtlUoaoeXC4mKah3plMHUFOXgQCA/QcHcfl9b2DP5q+iORSwfSai0QEPM50nrbuLD35rke6dyeOnBpPHdP/BQdz/8gE8fOUieD1Cszx6jWolPmriBHoxAkDzrnNmTAPHNkxb+/PX0mL5T6aHsu46b1s9D7c81407Lp6fVga/1wMpJfxeT8Hxb0ejXukzASuF1gx7vTyTGSctDQHMaQnBKyZ2k9c6zqlrvOnlSyN5VCtGO9d2YHhsvKjza+XM9uZQANed04bbXuhOXtS2NtTghKnBquoUNocC+MszZuFf3zyInVcsxGfhGPrDMfz0xfdw3Tltacc210aSqXlRzX/7Dw5WdN0tNBfnu6hWZ1llrnn+w6/rH0ujZdE6h8939WLrytPKnv+JqpmiSBw+Gk0OZCxvb8XOKxbC6xEQApq5dygyjuvPbXNEvs03iSMz56XmotRBvz2bv5ocWNN6zdnTQ1nXnDc92z2xRwjA/GWz5lAAFy08GcNj4+kzN1NufOr1FeoCE8u4lPopRbsGQ7XGRLReX43/0Vj6DXhg8knXmIITm+qK/pyl4pRrRquouTf1qfE/aQ0BELpPJ/u9HnzvV29h5xUL8cUTp+COi+e7/jhQcap6cLmYpJB69++W5yYuylun1OjesSvVTESrZzFplfvAkTBmNgXRUl+DjWfPQWPQn3wUwswxzdWoVuKjJk6hFSN9w1HNu856GzEeOBJGS31NckbZoaExzJhSg+Mba3IuUWBlR8quRr2SZwJWisy89HxXL7o+Hc7Kp2pHdlqdH49uWAyvAI6EY7j4Z6/lfIzUqjXetGLU6wHeOjhU1Pm1sj1Ry/iTVfMqonNcKPU4XLr0c7iw85W0c5MZW7k2kkw9J5t3vZ3c2LbQulvKWU2FKvSJqHxtQSF5sNinCjweT9nzP1E102vf1SfmtOptfziGrU93OSLf5pvEkZnzZuS4dsz1mhLZm2mp/W3mMPt5PAL1tT6sv+f1rHOn9hf02pnRWKKkYwOpZU6No2DAi7gi8elQpOgn4DLHRJpDAZzQGEzOXtVq81NvwLv1GscJ14xWyXxq/Df/+SnWLj4Zmx56U3dWeuuUGjy6YTFnKVOSc581sJje5lhGN8vLlLoA//6Dg9j6dBekhO6i/G6diahV7ttffB/3XX4mfnBeG7Y+3YWL7noVv3j9I4yMxZMbbDSHAnmPaa5NLwrZ4EA9x4eHIvhkMMKN0EzIvOusHvdd+w5mbXTWubYD7/QM4tYLT0dzKIDByDh+8fpH+HRwDF4h0NpQg+/96i1seGAf+kaiaedN75wfOjpW0PkqtP6aVerN9ZyyUZdTGd2YsvvwMFZt34NF//vfcGHnK4jGlbQdkNXHSFOPs9E13ozGRGaMNgYD+JPWEHZesRCPbVyMznUdWN7eaur8FrIxZ66YLVU9coO4oj07I/XYatXPzrUduP3F97P+Tv3dQupuagwv3fYSVm3fg+7Dw45s04zGkBqXnw5F8m78o3Wcd16xEBIyZ1znKov6/rF4Ag9fuQjL21uTr80cS1ReiiJ1N6YbjyuaOWHb6nnYsfsDR+VbNQcdP3WinJ8ORXQ3O4srEjuvWIh7LzsTj1x9Fu697EzsvGJh3s8wLch+YrmNx3P3F7Ti9eY183Byc13ZxgZSY/Pw0Si+uf3louNda0wkVONLG1g+MhLFoaEx3HrB6ehc14GW+hps3vU2Np49x9TTTtzo3HrqcR2NxbHkc8148Xtfwe7rz8Z3/3wunnnrY83xAXXN5b995C1IKav6uoHSVcXMZTseO8l1Z1prxoxbZyJqlbtvJIpQwIfLHnsDPQMRzJ/ViEuXzE7OBjR6fHM1qrmOr3phmHp8UzfEuHTJ7KxHcrgRmj5FkRBC4LGNi9EfjuHJ/R+n3XVura9JOw9+L/CVz7cm79arnfufvvge/nrhybj9xfd1N2DUO+efDEawZscrjjxf5dhcr9IetbKakXyqdSNjYDR9OQr1MdJfXn3WxG7tBtd4KzYmRqKJ9KUy1nVgbku94fNrdmNOI+VzwwxZO6nH7NDQWPLYzp/ViI1nz0FzKAAhRHJjUr0Z6X0j0bTXnNkUTJu5Y1alrb2eGpe3XnB63otqrVlWh49GsX77ywXVO6160bmuA1tXngaPx1N1MU/kJKk5eHl7K1Z3zEqutb5r30EEfMeejnx0w2J8MhhBfziWNvPRSfk2M988tnGxZs6TikQ0riSXAVHzWq7XUn9nbks9+4lloigSCUXqPskLHGvDHt+0BGPjCrxiYk3bxmD5xwbseAJOKxYVReLD/vDEXhQpMa7OWv7CcRN/Z8XTTmRePK7gk6EIeoejOKGxFusWn4xLU67vt19yBgZG43h0X09yVnrbcQ3o+WwUN/7m92lPJxMBVTJzOdcM2WJozY7RmzHj1pmIeuVW5LG1SlM3MgSMH1+1UU21vL0VQgjNGdAAdGcVqOd4dcesgspSrdTG+sLOV7BmxyvY+nQXVs4/Ebv2HUzedfb5PGkxHUvI5OYVwLHHv1d3zEJdwJtca3ztz1+DgEhr9LXOufpIo/pameer3Heq7cof+XA2qT4j+VTrRkZdIDv++kai8AiBk5pDOKExCJ/PkzfmiokJrb/d8MA+DETGLfv8ZsvnphmydlGP2e0vvo9tq+dheXsrrj934umcNTtewYWdr6QdE60Z6VrnpJhHBd36xJOe1LhUl1yaP6sRnes6kjP21ItyVepxTigoKhfr1T11KQzmWKLyUevnb/7zU1zzZ3OTT0ZufboL1y47NbmxqscjcNyUWoRqfNj6dFdyYNlp+TY138yf1YipQX/yaaX5sxoBTLQTCZk/r+m16QORcfYTy6Q/HMOPn+nCHRfPT3uSd8uT7+Dw0fQnpFsbanHStDqc2FSHaSFnjA2Uqn/RH47ho/7R5OZw6vts3vU2rl02F8GAz1DslutarJIpikR37zAu/tlrWLPjFYzGlKzztOmhN3HVlz8HAMlZ6Qf6wlj789eznk4mAqpk5rITLtDcOhNRr9z94Vjybqvepm/5jm/mxgLL21tx7bJTk+tdZt6VzHWXVT3HhZalWmkd08273s65fpLeY2BqXKR+L/O4a20mod691vo7J9ypdkL+oHRG8qnWjJBYQsnabGTb6nnwpoSSkZgrJiasiKd8n9/se1TaDNlCqMesZyCCW57rxk1r5uHy+94wfEzsaOPd+sSTntS43LH7A9xx8XxEYgl8/7H0J43UWV25/l5lpu4wlxM5l1o/l7XPyJrAsPHBfWm51w35Vv0882c14vpz25LtidrvuP/lA7junDZIqb2xcGpeYu5yHnXD2fWLT8ENj/9nQf2nco4NWBnvufrNsXgCdQGvZvzOnh6ydDk4Mqc/HEtbKtCjs2GqdzIe1ae9jp9aiz2bv+qasSwqraoYXHbKBZpbF33XKnfqIKHepm/5jm9moyqESNtIKbOBTu2oqY8fDUbGoShK8hwXWpZqpddY51o/Sa8+zZhSi0ffOJj2vczjrnXOf/TUO8ndgjP/zgmDXk7JH5QuXz7VupHRUOPDT3/7XnIjysHIOO5/+QB+smpe8u+MxFwxMWFVPOX6/Gbfw+pOuxuX2Aj4vGmPYvs8Ai31NWnHJd8xsbqNt2tn93JJjcv9BwcxMhY3dVFebN1hLiernHLDM+Uugq4Pbzy/3EUwRW0vElLi3svOxPT6gKH2yOn5Vs03Wk93pk7iSJ2so8rMS8xdzqP2GU5snFh7djAyjh27P8D+g4Om+k/lGhuwMt5z9ZsDPi9GYwnN+K2r8Vq2HByZpyhK2vWQBDSPsd/rwYt/+xX4vQInTJ14whOh8pWbnM1xy2IIIc4TQnQLIf4ghLjBitc08thJuR+9d5vUQcI/nTkVnes6ch7fXK+jPtKV7+692pCrjyqrj8sdCcfQFPTj7vULsGvfwbQF55e3t+LhKxchFk9U/HktJIb1lqnI1Vjrbaiy9en/wqozTsT8WY1ZMZBattTlTo6bUovrzmnTjR0n3Kl265I21S41R+3Z/FU8sWkpTpgaxHXnpOeP685pS4tTvc2E8m3oZjQmShFPZt9Dza3q8gTqJoPFzGBx2xIbTUE/rl12KrY+3YUbf/N7fNg/ilsuPB0Pfmth2iPMpbyQ0YphN68vmBmXtX7t2Ux6+b3YusNcTuQsqe3Fd3/xOwBAfa0Pv7z6rGTeBUqTe63Ot6mbC+aaxGEkLzF3OY/aZ1h3z+u48Te/R8DrSfYZCu0/lZKV8Z7rWq05FMDJzXVZm2ffvX4BpoeMD6qzDlhrfDyBsclNUtV17ccTCdx5yRlpx3j7JWdAQqIp5MfMprq8e9IQCSmdc8EnhPACeA/AOQB6ALwB4K+llF1av79gwQK5d+9eQ6+dayaVEx69dzsrZqr1DUexavuerDtm6iwmRZHoGRhNbhyY+TvqsgyKoiAhAa8AjqQ88mHwvJbkhJuJXSMKjeFi/u7Q0bHkhirq3fqZTUE8cvVZmpst6r1HrtjJFxOl4pKZmK6M3VLTO5epmwmpm46otGKumJgoRTyZeY94XMHvDw9j44PHcuWOtR34/IwG0x3JAuts2WO3d3gM39z+Mlrqa3D9uW1ZS6eojzCzX1Cc1LjMfFoJyB8rxdYdG+pe2WM3lZNn1FJpmJi5XPbYzZV3b14zDzc9251c19ONuVftK+fLc0bykkv6oaXi6NgttP/kVkau3wcjMURiCSQkUOv3pG32blSF1IGyx65Wn1/t5/7Dyi9iNKZAAKjxeRCq8WJKrSuPM1nPUBA4LestBPAHKeUfpZQxAL8EsNKKF861ORYXiS+eFZuP5bsr6fEIeD1C9+6oWoYZUyd2jPZ4PGlrCVXyeS00hgu9c+3xCEgpsWbHK9jwwL7kshbq+6fGQL6y5Yodp9yp5uZ6lUPvXGZu6JYv5oqJiVLEk5n3GIiMJzuZwLE1Ls1sMqhywtMGhRgbnyi33iPMP/rGaa4c3HCa1Lg8bkqt6fxebN1hLidyjlx59/uPvY07Lp7v6ic21M0H8+U5I3mJuctZcsVuof0ntzJy/T4tNBG7J02rQ2tDYRtvsg5Yo3ckmtXn37zrbazumIVDQ1Fces/riMYVHD81iMY6Hmcyx2lrLp8I4GDKv3sALLL7Td16MVxpCt2kS+9xuWo6r8V81kLX+zJ6Lootmxs3wiT3ydzQTV2HbGZTEMdPDVZ0zFmZK926Lp5XCMxsCupuCptrHXoqDPM7UXXLl3cBuHKvmlTMc5UpX+xW4rWmHsa4u4wnFM2YbQ4F0NpQk3wanOePCuG0mctaUZy2bocQ4mohxF4hxN6+vj5L3rSQdWfJHvnuSpqZyeq082pH7KrK8VmNnotiy8Y71eVnZ+w6RWqc7j84iA0P7MP3fvUWAj7jG464lZX5wylPG6iMxm4w4MXNa+YlN55Jxf6AfZjf9VVD3qXKxLybjnnOPRi7hWGMl5/R2PV7PZox29JQg+OnBnn+qChOW3N5MYAfSSnPnfz3/wMAUsr/rfX7Vq39yTWX3cXomksFnteyr4VUiHLFsNF14li/SsKVsesU1RynVn/2AtbFK3vsKorEh/1h9I/EUOv34NsPvVl1cUAFKXvspuKay+SmNZeZd6lAjF1yq7LHrt4+K22t9fD7q+umCJliKHadNrjsw8SGfssAfIyJDf0ullL+l9bvWznIUSGLxFMGNw5yFMrJMezkslUQ18auU1RznJb5szsidtVjoG4KK6Wsujgg0xwRuyoOLpObBpcB5l0qCGOX3MoRsRuPK+gdiSKeUODzetBaX1M1G1BSwQzFrqPWXJZSxoUQ1wB4DoAXwD16A8tWK3TdWXK2ajqvTv6sTi4bkaqa47SaP7uKx4CIqLSYd8mtGLvkVj6fByc0BvP/IpFJjhpcBgAp5a8B/Lrc5SAiIiIiIiIiIiIifZz/TkRERERERERERESmcXCZiIiIiIiIiIiIiEzj4DIRERERERERERERmcbBZSIiIiIiIiIiIiIyTUgpy12Gggkh+gB8lOfXpgM4UoLimOHEMgEsFwAckVKeZ/ebGIzdQjn1PJZSNR6DSojdUqrGGAGc+bmdFrtOPEblwOMwIddxYOy6A49LNsaucSxbYewqG2PXGm4stxvLDBwrN2PXGJbLuFKVyVDsunpw2QghxF4p5YJylyOVE8sEsFyVgseLx4Dyq9YYqdbPbQaP0QQehwluOg5uKmsp8bg4n5PPEctWGCeXzUpu/ZxuLLcbyww4t9wslzlOLJfTysRlMYiIiIiIiIiIiIjINA4uExEREREREREREZFp1TC4fFe5C6DBiWUCWK5KwePFY0D5VWuMVOvnNoPHaAKPwwQ3HQc3lbWUeFycz8nniGUrjJPLZiW3fk43ltuNZQacW26WyxwnlstRZar4NZeJiIiIiIiIiIiIyHrVMHOZiIiIiIiIiIiIiCzGwWUiIiIiIiIiIiIiMo2Dy0RERERERERERERkGgeXiYiIiIiIiIiIiMg0Di4TERERERERERERkWkcXCYiIiIiIiIiIiIi0zi4TERERERERERERESmcXCZiIiIiIiIiIiIiEzj4DIRERERERERERERmcbBZSIiIiIiIiIiIiIyjYPLRERERERERERERGQaB5eJiIiIiIiIiIiIyDQOLhMRERERERERERGRaRxcJiIiIiIiIiIiIiLTOLhMRERERERERERERKa5enD5vPPOkwD4xS8rv0qCscsvG75KgrHLLxu+SoKxyy8bvkqCscsvG75KgrHLLxu+SoKxyy8bvkqCscsvG74McfXg8pEjR8pdBKKCMHbJrRi75FaMXXIrxi65FWOX3IqxS27F2KVycfXgMhERERERERERERGVBweXiYiIiIiIiIiIiMg0Di4TERERERERERERkWkcXCYiIiIiIiIiIiIi0zi4TERERERERERERESm+cpdgGqhKBL94Rhi8QQCPi+aQwF4PKLcxSIqO9YNqiSMZyJnYZ0kJ2AcOsMpNzxj+m8+vPF8G0pCVLmY74iMqbS6wsHlElAUie7Dw7hq5170DEQwsymIu9cvQNuMBlcHD1GxWDeokjCeiZyFdZKcgHFIRNWC+Y7ImEqsK1wWowT6w7Fk0ABAz0AEV+3ci/5wrMwlIyov1g2qJIxnImdhnSQnYBwSUbVgviMyphLrCgeXSyAWTySDRtUzEEEsnihTiYicgXWDKgnjmchZWCfJCRiHRFQtmO+IjKnEusLB5RII+LyY2RRM+97MpiACPm+ZSkTkDKwbVEkYz0TOwjpJTsA4JKJqwXxHZEwl1hUOLpdAcyiAu9cvSAaPup5KcyhQ5pIRlRfrBlUSxjORs7BOkhMwDomoWjDfERlTiXWFG/qVgMcj0DajAU9sWloxO0ESWYF1gyoJ45nIWVgnyQkYh0RULZjviIypxLrCweUS8XgEWhpqyl0MIsdh3aBKwngmchbWSXICxiERVQvmOyJjKq2ucFkMIiIiIiIiIiIiIjKNg8tEREREREREREREZBoHl4mIiIiIiIiIiIjINA4uExEREREREREREZFptg4uCyEahRCPCSF+L4R4VwixWAgxTQjxghDi/cn/Nk3+rhBC3C6E+IMQ4m0hxBl2lo2IiIiIiIiIiIiICmf3zOUizpGdAAAgAElEQVSfAnhWSvl5AKcDeBfADQBelFLOBfDi5L8B4GsA5k5+XQ3gTpvLRkREREREREREREQFsm1wWQgxBcCXAfwcAKSUMSnlIICVAO6f/LX7Afzl5P+vBLBTTngVQKMQ4ni7ykdEREREREREREREhbNz5vLnAPQBuFcIsV8I8TMhRAjADCnlpwAw+d/Wyd8/EcDBlL/vmfxeGiHE1UKIvUKIvX19fTYWn8hajF1yK8YuuRVjl9yKsUtuxdglt2LsklsxdskJ7Bxc9gE4A8CdUsr5AMI4tgSGFqHxPZn1DSnvklIukFIuaGlpsaakRCXA2CW3YuySWzF2ya0Yu+RWjF1yK8YuuRVjl5zAZ+Nr9wDokVK+NvnvxzAxuHxYCHG8lPLTyWUvelN+f1bK388E8ImN5XMsRZHoD8cQiycQ8HnRHArA49EaeyeqDqwT5HSMUao0jGki92B9JSItzA1E1a2UOcC2wWUp5SEhxEEhRJuUshvAMgBdk1+XArhx8r9PTv7JUwCuEUL8EsAiAEPq8hnVRFEkug8P46qde9EzEMHMpiDuXr8AbTMa2BBQVWKdIKdjjFKlYUwTuQfrKxFpYW4gqm6lzgF2LosBAH8D4CEhxNsA/hTAP2JiUPkcIcT7AM6Z/DcA/BrAHwH8AcDdADbZXDZH6g/HkicfAHoGIrhq5170h2NlLhlRebBOkNMxRqnSMKaJ3IP1lYi0MDcQVbdS5wA7l8WAlPJ3ABZo/GiZxu9KAN+xszxuEIsnkidf1TMQQSyeKFOJiMqLdYKcjjFKlYYxTeQerK9EpIW5gai6lToH2D1zmUwK+LyY2RRM+97MpiACPm+ZSkRUXqwT5HSMUao0jGki92B9JSItzA1E1a3UOYCDyw7THArg7vULkkGgrovSHAqUuWRE5cE6QU7HGKVKw5gmcg/WVyLSwtxAVN1KnQNsXRaDzPN4BNpmNOCJTUu5qysRWCfI+RijVGkY00TuwfpKRFqYG4iqW6lzAAeXHcjjEWhpqCl3MYgcg3WCnI4xSpWGMU3kHtVYX0+54ZlyF4HI8aoxNxDRMaXMAVwWg4iIiIiIiIiIiIhM4+AyEREREREREREREZnGwWUiIiIiIiIiIiIiMo2Dy0RERERERERERERkGgeXiYiIiIiIiIiIiMg0Di4TERERERERERERkWkcXCYiIiIiIiIiIiIi0zi4TERERERERERERESmcXCZiIiIiIiIiIiIiEzzlbsApaYoEv3hGGLxBAI+L5pDAXg8otzFInId1iUi+7B+UbVi7JMVGEdERPqYI6mSMJ6doaoGlxVFovvwMK7auRc9AxHMbAri7vUL0DajwTXBx4pDpaYVcwBcX5eInMrqtortRuWqtHNbCf00Kr9KjqNKq/NEVPp6Xck5kqpPqeOZ7bC+qloWoz8cSwYdALTU1+DQ0Bh6BkfRNxyFosgylzA3teKs2r4HS7e9hFXb96D78LDjy03upRdzg5H0utQzEMFVO/eiPxwrc4mtpygSfcNRfDzgjjxB7pfZVhVavxRF4rNwFO9+epTtRgUqZZ+gVHnQqtin6laqOCp1/4DXAUSVx2y9tiLvsK2lSmI0nq2oO2yHc6uqweVYPJEMuvmzGnH9uW3Y8uQ7+PJNu10RGGwIqNT0Yi4SO1aXVD0DEcTiiXIU0zZsQKgcUtsqldn6pcbuWweHsOHBfWw3KlApB9BKlQetiH2iUsRROfoHvA4gqjxm6rVVeYdtLVUSI/FsVd1hO5xbVQ0uB3xezGwKAgA2nj0Hm3e97arAYENApaYXcwmJZF1SzWwKIuDzlrJ4tmMDQuWQ2lapzNYvNXbrAl62GxWqVH2CUuZBK2KfqBRxVI7+Aa8DiCqPmXptVd5hW0uVxEg8W1V32A7nVlWDy82hAO5evwAzm4JoDPpdFxhsCKjU9GKu1u9J1iX1e3evX5Bcj7lSsAGhckhtq4DC6pcau4ORcbYbFapUfYJS5kErYp+oFHFUjv4BrwOIKo+Zem1V3mFbS5XESDxbVXfYDudmaEM/IcRsKeWBfN9zOo9HoG1GA57YtBSxeAIzm4JpQeb0wFArTuZi5WwIyC56MTc9VIPpoZpkXarUxezVBsRNeYLcL7OtKqR+qbG7Y/cH2LZ6XvJJHbYblaNUfYJS5kErYp+oFHFUjv4BrwOIKo+Zem1V3mFbS5XESDxbVXfYDucmpMy/zogQ4k0p5RkZ39snpeywrWQGLFiwQO7du7egv3XrLqncndJ2JTmYxcRuqVVzzLksTzB2KSk1dlvqa3DtsrmYPT2EuhovpodqnBa/jN0ClSI/uywPlhpjt0qVq15YWOddH7un3PCMLa+b6cMbzy/J+5Bhro/dTEbrNdtj16u42HULK+tOlY6NGPqAOWcuCyE+D+CLAKYKIb6Z8qMpAGoLL1v5ufWOnccj0NJQU+5iUBWp5phza54gYuxWh1LkZ8YSUbZy1Ytq7pMRVSqj9ZrtMVFhrKw7bIf15VsWow3ACgCNAL6e8v1hAFfZVahSYWAQUT7ME+RWjF2yCmOJKBvrBRGVGvMOUWFYd+yXc3BZSvkkgCeFEIullK+UqExVq0qn2BMVhPWFKgVjmSgd60T14rknIio95l5yGsak+xja0A/AH4QQ/xPAKal/I6W8wo5CVaN4XEF37zA2PLCPayiRK5Qz4XPNMXKLfPWEsUxOV+pczzpRvcpx7nnxSkSVyExuY7tLTlMB+xpUJY/B33sSwFQAvwXwTMoXWUBRJD4ZiiQHlgGgZyCCq3buRX84VubSEWVTE/6q7XuwdNtLWLV9D7oPD0NR8m8QaoX+cCzZ2ACsL+RMRuoJY5mcrBy5nnWiepX63Je7L0NEZAezuY3tLjlNOWKSfYLiGZ25XCel3GxrSapYfziG3uFosvKoegYiiMUTZSoVkT69hP/EpqUlWcsoFk+wvpDjGaknjGVysnLketaJ6lXqc1/uvgxVrlNuMD8H68Mbz7ehJFSNzOY2trvkNOWISfYJimd05vLTQoi/sLUkVSwWT6A/HMPMpmDa92c2BRHwectUKmsoikTfcBQfD4yibzjKOz8VotydkIDPa1t9Ycy6j1PPmZF6YmcsExWrHLm+nHXCqbnEak79nKU+9+XuyxAR2cFsbmNftDBObUtLzY7jUI6YZJ+geEYHl7+LiQHmiBDiqBBiWAhx1M6CVZOAz4td+w5i2+p5yUo0symIznUdaA4Fyly6wvHRgspV7k5IcyiAu9cvSKsvd69fUHR9Ycy6j5PPmZF6YlcsE1mhHLm+XHXCybnESk7+nKU+9+XuyxAR2cFsbmNf1Dwnt6WlZNdxKEdMsk9QPCGleyvAggUL5N69e8tdjKKplfK2F7qxumMWmkMBtDbU4ISpQfh8Rsf/nadvOIpV2/ek3QGa2RR0+qMFJVmx3e2x64SNH+xYcN+lMauqyth18jkzWk+4eUR1xq4bVNOGKgXmEtfFrpNzJlDac++EvkwZuS52MxWy9EQhClmugsti2Mr1sWu3QnIb+6LmVEufIR87+xTcUNpRDB0AQ2suCyG+rPV9KeW/mykRafN4BNpmNOAnq+ZVVELnowWVS43ZJzYtLVvMejzC8gthxqz7OPmcGa0ndsQykRXKlevLUSecnEus5PTPWcpz74S+DBGR1QrJbeyLmuP0trRU7DwOpY5J9gmKZ3RDv++n/H8tgIUA9gH4M8tLVKUqMaGrjxZk3sniowWVgTFLTuD0c1aJ9YSqS7XEsNNziVWq5XMaVS3xTUTVhbnNXmxLJ1TacWC9KY6hNReklF9P+ToHwGkADhv5WyGEVwixXwjx9OS/ZwshXhNCvC+EeEQIEZj8fs3kv/8w+fNTCvtIpcNF3HPj+k3Vyc31gjHrPm48Z26uI0SVIrMeNgX9rsslZimKhNcDdK7tqOjPSUREpVON/Vo3Xn8YZeZ8VvJxIPOMzlzO1IOJAWYjvgvgXQBTJv+9DcBtUspfCiF2APgWgDsn/zsgpfwTIcRfTf7eRQWWz3ZckyU/PlpQfdxeLxiz7uO2c+b2OkJUCfTq4dyWetfkErNSP3NLfQ22rjwNs6eHUFfjxfRQTcV8TiIiKp1q7de67frDKLPns1KPAxXG6JrL/wxAvWXhAfCnAN4y8HczAZwP4CcA/lYIIfD/s/f2cVJUV/7/51Y/T89DD8MMIEyEJWTMxB0Cgyjw/WZxjcZE1J8Z1F8U8CHqEOKar5so7m7YuC+S74po+MafizOY+ABqViK6+sLEh5iQ7KKojKys39HRoGYHBWYYZ2Aeevqp7u+PmSqqu6u6q6qruqu7z/v18iXTXV19u+4555577r3nTKTSuGrykkcB3ImJ4PKlk/8GgKcA3M8YY9yhFQcHRqOy0gETuWVu3L7fMQVRnAIdLSgvSkEvSGaLj2Lqs1LQEYIodspRD5W/+fBgGNc98qZcdIcmgUShyVdxPoIgrKUcx1OJYpp/6MVMf5bicyDMoXfnsrLcZBzALznne3V87v8AuB1A1eTfdQCGOOfxyb8PA5g5+e+ZAHoBgHMeZ4ydmLz+uPKGjLGbANwEAJ/73Od0Nt96KIk7YRSnyK6dkF6UJuUgu/mCdCS/kOwSahSDHlotu8Xwm4nSgOwuUayQ7BqHxhZnYJXsUn8SuaA35/KjAH6JiSJ+bwN4I9tnGGMrAPRxzruUL6vdXsd7yrZs45wv4pwvqq+vz9p2u5CSlysp5uTlpYDT8z05RXatQOtZk16UJqUku1Zh1t6QjuQXkl1CiaS3Cc7x8LVnYUFjSH7PaXqYq+ym2qiAl2wPkR/I7hLFCsmucfLl1zp9nl9orJBdUeRgjOGptUvQubpV9pHIVyD0ojctxnJMpLD4GBNB4EbG2DWc8z9m+NgyAJcwxr4BwI+JnMv/B0CIMeae3L08C8Cnk9cfBtAI4DBjzA2gBsBnhn+RQUSRY2A0ajhHTG3Ag87VrWjf0ZWUj4aSlxeGcs33VAgyPetsemFW3wjCSeRib6TCF6mftWrsMKNjpJflQbn0s9bvVNPbzStbcPcLPegfiZSUD6dlo7ZfvxhrHnpDfq1zVStcwsT1pSgLSspF/gmCKA3ssFl23NNuvxageX4+UHvGm9pa8OirH+HW85sc6R/RuO489KbFuBfABZzzHgBgjH0BEzuZW7U+wDn/OwB/N3n9cgA/4JxfzRj7FYCVAP4VwDUAnp38yHOTf782+f7v7M63bNZQiSLHB/0j+Nlv38eGFc2oC3rRUOXDaTUBEugCUc75nvKN1rN+et1SDIxENfWCHAOiVMjF3thZ+MKMjpFelgfl0s+Zfqea3t721EE8edM5JTcpyTROP71uKcYiCXx0fBQ//Ld35MB6qcmCknKRf4IgSgM7bJZddjAfBd1onm8/as94/a6D+NebzsH0Kr/jxkoa152JrrQYADxSYBkAOOfvA/CY/M71mCju9ydM5FT+xeTrvwBQN/n63wK4w+T9daNlqAZGo7o+91J3H9p3dGFlx2u46uevYzAcs7vJhAaUHyh/aD3r8ZiYUS/M6htBOI1c7Y1U+GJmbQXqq3yWOUFmdIz0sjwol37O9Du19BaApXroBLR+aywugoFh1S9ex3WPvIkDvUMlKwtKykX+CYIoDeywWXbaQbv8Wgma59uP1jM+emLckTEuGtedid7g8n7G2C8YY8sn/3sQE/mXdcE538M5XzH57w8554s555/nnF/OOY9Mvj4++ffnJ9//0PjPMYZZQ0UGznlQHtP8ofWsXQwZ9YL0higVnGpvzOgY6WV5UC79nOl3OlVv7SDTby0XWVBSjr+ZIIjixQ6bVcx2sJzG70Kh9YylxXmnUczyXMroDS5/B8D/BXALgO8B6Aaw1q5G5QuzhooMnPOQ8j1J/UI5sO1D61lnKxZEekOUCk61N2Z0jPSyPCiXfs70O52qt3aQ6beWiywoKcffTBBE8WKHzSpmO1hO43ehqAt60bm6NekZb2prwa6uXkfKSDHLcynD9KQ1ZowFAYxzzhOTf7sA+DjnYza3LyOLFi3i+/fvN/w5Kfm3KIo4PhpNKz6mJ+cy5XhxHhYldc9LB5qVXaeg9qwBZNSLXPWGkvZnhWQ3j1gpj1bdq4hzLhe17BaDbXJIP9tOtt9pQ185VnaNFDYsRVlQ4rTf7BCb4SjZnX3H83lojTk+vusiw58x83vMfE+Z4ijZVSNXHS+mnMv5wiF2M1ccLbvxuIhPT4TRNxzBwGgUu7p6cev5TY6UkULKc4nIolF0/UC9weV9AL7KOR+Z/LsSwEuc86U5NTFHzDrbSkG8oLkBP7yoGS6BGRKOMhWqgpHH5+1oo59vjD73bNeb7cdid4jyBMmuheTL5lgt22ba7YDxrGhlt5hsUyH6uQy+syhl14mLY3bjlHY6yGY4SnYpuEzBZQM4SnZTsUrH7bBZVgS9nWBHixhHyy6Q/z7O5fsK5WM6ZAzPN7p+nFvnzfxSYBkAOOcjjLEKU80qMKnJv1/q7kP3kWHD1UalxPXFiNMGBj0ByTJV4oJi5rln0wtBYKgNeNA3ImIsGkcsIaKh0ge3O3OGHqoSTFiFHvunJvudq1oxI+RHKODsCthmxqZiHs8KTTHZpnz3sxVjdzwuom8kglhChMcl6BovSJ6zY3YsTqWY/DOnyEUx2QyCIIxjlY7bYbNyuafd9t7MeE9YjxEZybXPcpWpQozrNIZnRm/vjzLGFkp/MMZaAYQzXO9Yyj35t6TEl23di2Wbfo/Ltu5Fz7FhiGL2HeyFag9VAy0Mdjz3eFzEe8eGcUXna/irzXtwRedreO/YMOJxMePnyl1vCWvQa//UZL/9sS683XvCcntJsl3cUP9pk+sYYna8ILJj1bMl/8w4ZDMIorQpVR23097TeF98WNFnxehDlKp+W4Xe4PL/AvArxti/M8b+HcCTAG62r1n2Ue7Jv40osShy9A9H8MngGPqHI0kBlUzvWd0eo0psVdvKFen5jUXjlhvPvpEI1j7WldTfax/rQt9IJOPnCq23JFOlgZa9OXpyXO7beFxEOKYu+xVelyVOj1KeGGO4oLkh6f1yGpOKnULbJsC59ilXB1xtvLjvlfdxbHjccb/ViajJhfTa0eFxU2NxKk6cZDlVHyScYDMIgrCPfOt4vmxeLvY+tY3xuJj0d7/J+SFhLVqypPa62Tm9Eif6EFpIzyDBOR6+9iwsaAzJ79EYfgpdaTE4528yxs4A0ISJfBvvcc5jtrbMJqRqo6nb7wtVbTTfKSr0KnGmYwpA5sJtVrdHGqSV12kpcTEd0XQiyue3YUWz6nP3mDiiJMl5LCGq9nc8kXmVs5B6SzJVOmjZm0+HwljZ8Rpm1QbQsaoVI5G4quwPhWM5Oz1q8tSxqhXARJomM7LttFRH5UShfYpC26dMsmdk7FYjnjJeLGgM4Zqlc3Dltn1ki7OgJRc+t4A1D72Bey+fb2osTkWrjxmbKByY734ptD7oodA2gyAIe8mnjqumcVvdiqaGKstTSpgd07X83vteeV/2e3d8e7ElYxJhHq3xc159JT7oH0l7Peh1afZZ/3BE15wkVz8xX6g9m80rW3D3Cz3oH4nQGK4go9VhjH1T+g/AxQC+AGAegIsnXys6BIGhaVoVnlm3DHvXn4tn1i0z5HRauTpYiBQVeldTM+0otvIIg572SIO0dF2mQboYj1c4CeXz69hzCJvaWpKe++aVLXAbnKAp5TwaF1X72yWwjPqUq97mAslU6aBlb6S+lFbdOedpsr+prQUdew7l7PSoydPax7pw5yVn4vW/+2vsbF+Car8bA6NRXWOB01IdlRt226ZsPkch7VM22TMydqvhEliSvq5dPhfrdx0kW6wDLbn488AYDg+GMRSOqdpCt8tYMEKtjze1teDO594piB3Kpg9O2NVcSH+GIAj7MaPjZm2Tahq3HV349ETYcvtmdkzX8nvbWhvlvz8+PmbJmESYR2v87BuJqL6e6qMBE30mCEz3nCRXP1EPVoz7as/mtqcO4v6rFtAYnkK2ncsXZ3iPA3jawrbkDT3Jv9V24wDW7dgFCpMQXO9qarYdxVYdYdDTHuUgnW0VrJiOVzgR5fM70DuEe17swYYVzZjXUIkP+kZw9ws9uP+qBUAw/bNaO9iUcv7gHz/E1qsXYt3jb8n9vfXqhfjZbz/Aqx8OZNSnQhXjIZkqHdTszaa2FtzzYo98zeHBMATGcPcL72HDimaEAh40VPvwt0++bcnqtJY8cc7xWThueHwxOo7QLmfrMWubrChmW0j7lE32jIzdargFhk1tLXJAuS7oJVusEy25qPBOLIxJi8fSs5V2kjVUGi8G2jStCjvbl+DToTAGRqO458UeHOgdMlUsO1cy6YMefcqXfXRKcUHCWcy+43nDn/n4rotsaAmRK6k6LgW41GxLLicutGxe33AEAa/bUjtjdkzXamMo4JH/vu+VD9CxqlVOs2B2TCLMo9VPqafIpNfdAkvrswdWtWLHqx/pnpPk6icqsTN2p/VsANBYnkLG4DLn/Lp8NcRJaBn5ukqvpcHgQkwK9SpxtmMKVh1h0NsevY54sRyvcCqpz+9A7xA27u7GhhXNaN/RZSodiVLOd3YdBgA8fO1Z8LoFxBIc2/5wSH7didVWSaZKh1R7wxjDnc+9gwO9Q/I1UvqLA71DsszvbF+C+69aYEmwIdMxcjPji5FxpBiOjJcLevpCz8JBIe2THtnLJYgmCAIeffUjeZGnJuAhW6wTLbkYi070jbR4vPHSMzG3Pgi3iSrvEoLAwDnHyo7Xkl4vROA/kz5k0yeyjwRB2EE225LLZjMtmzcwGsWMGr/lv8XMmK7VxqHwqQyr/SMRzAj5sLN9CeIJMacxiTCHVj+5XYLq64Ig4IzJxWWpz7wuhs5//zjpvtl8ASsWW+2O3VEsQD+6NZYxdhFj7HbG2D9K/9nZsEKiZeTHY9YGgwtV1ENS4pm1FbJipR4XyHRMweojDKntycWJz8fxilJG7fltXnkqHYCZdCSpcr6z6zCue+RNuAWGr/70D3JgWfqcHZPRXI7EkEyVFkp7M73aj1vPb0rq245VrdjV1Sv//eCaRZhe7bfEPgFAbcCDztWtafLkYuZOhBgZRyjFS25YeaTeqmK2hbRPdvswdUEvbj2/CRt3d+PKbfuw+cX30LEqXXfIFqejJRen11XIr/WPRDC9xo9ZtRU4LRTIaRLvlCJ1mfQhmz5ZZR+dkHqDIIjCoKb/2WxLLpvN6oLeNJ9yU1sLdnX1OibwpWaX1Xzt2oAPp4UC+FxdMOcxiTCO1vjZUOnTHFfdbiGpzzjUU2XYLYt2x+5y8bXLzSfQVdCPMdYBoALAuQB+DmAlgDdsbFdB0TLyLsYsXbVwQlGPTKupmXYU69ltXIjj11YeryhHUp+fxy3ALbCsuzYzOUYzagJpcr79+sUQGMNTa5dgYDSKjj2HcKB3yJA+6ZWvXHcjkUyVLoLAMK++EjvblyCWEOFxCagPevHjy/4SP1whwsWAgNc6h0gUOT7oH8HPfvs+NqxoRl3Qi4YqH06rCWBwMgeq0fHFyDhCKV7MY8SO6LFNVhWz1Wuf7BiP7fZh1H5bbcBDtlgHWnIBwJbnl6ssWCWfmfQhmz6p6WR9pQ/ReAKfDI7pahftfiaI8kVL/6dUeJJsy4LGENYun4uxaBz9wxN+ptn4giAwNDVU4YkbzkbfcAQDo1E8+upHuPX8JtQGPLoLq9mJ1lj+k8ta8KOLaSx3CpnGT73z4ELFtrR8asGi2J3ZWEAuPkGxpjHUFVwGsJRz3sIYO8g5/yfG2L0o0nzL2RBFjoTIVQUx4HVZqjBOCFplO4qjdWQg2xEGvcpkh+JQLrvcUH1+KjmWlWSatKXKedDnwslwHJ9M5mfc1dWLH3ytSXaG9K4C6jXWVuQ2J5kqHZQ2x+MWMDIex5qH3kiSI59bSHvNiuCAUhZf6u4DMKEnz6xbZtohMzKO0LEu8+i1I3ptk56+0CsTVo3HRsnVh9Ez/qv9NrLF+lA+u9RnPaMmYKmvmYssWC2fWvqQTZ9SdXJBYwi3X9iEK7ft092uQtRSIQjCGWjp/872JbJtWdAYwg++1pSU7/7BNYuw/frFaX6n3viC2y1gVm0FAl43ZtT4sfBzLagNePBB/4hjFroEgaEu6JXHocEwiiZYVk5kyxWezXcoVGxLy6c+MjSelhfabOzOTCzArE9QzAvVeoPLUk+NMcZOAzAAYI49TSosA6NR/Pj57rRCJ52rWxEKeBEKeC1VmEIHrezayaZHmdQUZ/v1i1HpdyMWF4tqlabcyTZpk+RcFDnePXoS7TtOGflNbS149NWPcOclZ2J6tV/Xjjsjxpp2axISajZn88oW1Ff6cHgwLMvRliu+LOd5HQrHsOXlHvzkspacbXUmWczFIdM7jjjhtEyxoteO6LVNVhezzYSdAa9cihn2HBvGlpd70NbaiLqgF+FoHKfV0FFYK9CziGb1JMWsLOQrIJtNn1J18pbz5uG2pw4aahf5GwRRvmQq2CzZlrXL58rxBen9G7fvx9PrluY01qfa3/7hSF4XuqwoUEwUDiuL4RUitqVVsP1///pd1Fd5sbN9CTjneY8tmfUJinmhWm9weTdjLARgM4C3AHBMpMcoOaLxBF7q7kP/cDQpuDBVIYhO71Qj2LWTTY8ypSpOfaUPx06OY81DB2ngKTL0BkEGRqNyYBmYkIn1uw5iw4pmcM5VA8t6jphJ91Iz1rRbk5BQG6xve+qgXLASmLBDfo+Ajbu7kxwUURRz/v5ssmi3Q+aE0zLFil47oteRtLqYbSacGPAaGI1iy8s9uGbpnLSF/C9OryaZzAG9i2hOmaTkUz4z6VOqTiY4N9wu8jcIonzJpP9N0yZOqY1F46p2JRYXMbO2wrK25NOuWlWgmCgMWv03rdpXNH0mjd9P3nQODg+GMRSO4Z4Xe00ZW5wAACAASURBVOSC7T+6mFuqX3ox6xM40W/Xi97tIXdzzoc457sAnA7gDAA/tq9ZhUMSggO9Q2jf0YUrt+3Dxt3dEITS3EljVTGg1GTlHreQNaF7quKsXT5XdZcIFZsqDvQUZtQylpK8pSa613JGEhy6CwZQQT5CQkv+QgGP/Pct583Ddx5/K20BJGFB/QUnyKKVBVTLCb19Z6SwWb76winF1oBTvsJYNI621sa0HVztO7pozM8RrUW0tcvnytc4aZLiJPlU6mTA4zbcLifYeIIgCkMm/ZdsS4XXuF0xQz7taraChaLIEY6pB9WdMg6VM1r9NxYtrgCnVFvh+796G+07uuTAciEXeM36BE7yi4yid+fyawAWAgDnPAIgwhh7S3qtlLDi2HAxJeC2YiebVnqLbM8xdTUnFNC/G5UoPGbkXGsFb0rQi5ufOID+kUjSareeI2bZ9JR2axIS0qJXqvyNRRPyv+dMDWrKXK7kWxaLaSxyOnr7zqrUI1b2nVPSoSh9BamgJY351qNnEc1JkxSz8mm3fTPTLvI3CKJ80aP/analc3UrahX22QryOe5n2mUpjftHT4zTqQ6HotV/47FEQfvMzBjvFH9XwqxP4LTfYYSMwWXG2HQAMwEEGGMLAEhPohpA/veW54FUIQh4XYiLHEdOhDMKhKQAoijiuOLofzGkdsj12K1aeos/D4zhjBmVGXPcpCrOWLSwRozQj9YRmnn1lRgMx9JyNkmDg1pRzK1XL0THnkPyCqPyyI1WMJoxhnn1lbqNdaFzmxOFRbLPCVHE1qsXYt3kzuRZtQFsuWI+Tq8L4o+3nwsXg2WVhTO1I1+BZcpvZy167IgZRzJVLqwuxGO0TWbkVM9nlL5Cx55DuPeK+TTm24DWuKlcRHtwzSK4BOCTwTHDxffsKMJsRmdS7VvnqlbMCPkRClhjV81OCsnfIIjyJZv+C8LE/OWJG85G33AEA6NR/Oy37+N7X/0CmhqqLKs5kI9xXyLTXG0oPDHu11f60upZFUuwrNTR6r+jJ8Y1+yxXX8CuHN2Sfu1sX4JYQoTHJaChsrCnNM34BMW8UJ1t5/LXAFwLYBaAnypeHwbw9za1qeAoi4/pEezU3ThSrk7A2flprEK54qVVBbdpWnp1UbVAfrGu0pQbakdotrzcg+999QtpCys+t5BUSGj79Yvx9LqliMUnctje/MQBObAs3UvauaaVoP/O597Brec3UaCMyEqqHb+guQHbr1+ME+EY+oYj+N+/fg9brvwyVv3idfl9qyoLZ2qH3cFeym9XOIw4kmpy8cQNZ1ved3rbZEZO9X5G6Ssc6B1Cx55D+JerFuK7T7xFY76F1AW96FzVinaFDbv38vkQOccfb1sOv9eFkfE4Lrl/ryFbZKcNMzr5UrNv7Y91YeOlZ2J6jd8yu0qBYoIgrGYwHMNVP389KZjXfWQYT9xwNmbVVljmE9o57ivJNFf73nlfQH2lDwd6h3DPiz1yPatZtQHMqEmPDRD5R6v/7nmxBwCwYUUzvji9CgGvO6dCfxJ25ugWRW7p5oxCUqz+R8bgMuf8UQCPMsbaJvMtlxV6BVt5XTmmdlCueGlVwdUyBqmKEwp4i3KVptxQO0LT1tqYVqzvxu37sfHSM5NeW/PQG3hm3TLMrK1A/3AE/SORpPukFjhrmlaFne1L8OlQGAOjUTlBf/eRYQqUEVlJteMvdfeh+8iwXMRvVm0AHx0fTXofgOWVhfMd7C3mYhDlhJpc9A1HCtZ3ZuRU72dSd8fs7DqMoXC0YFW8SxVBYJgR8mPjpWeiwuvCUDiGu37zHvpHInhm3TIAkBd8Af22yEkLVlr2rcLrokU0giAcjZb96huOIOB159125Wrbs83VNl56Jq575E25ntWs2onihjTWOwPlZr9wLIFDfSNJxfA27u5OkoX+4UhO8qJH3szOYZzkp5Qres9evMIY+yljbP/kf/cyxmpsbZkD0CvYyuuGwrGCJ+BOLa6nLJBmB8pk5bkG16nYVGEwKjNqiea18mdWeF1pr6XuTM6U6F4QGDjnWNnxWlKCfgqUEXrIlH9UOkp93ysfJL3/UncfOOeW2iEzjlIutryYi0GUE2pyMTAazdh3do7xZuRU72fU7P2t5zdherWfxnyLCQW8mF7jlwvbSPUM6oJe05M2OxaszMqyln0bCsfINyAIwtFo2S8pvWY+5/CANbY901xtztQgFTl1OFL8ZVYogOk1fnnjl1p/5SovqZ9f0BjChhXNGIvGZZk3O4ehjTWFR29Bv18AeAfAFZN/rwbwMIBv2tEop6CVgyZVsJXXdew5lLecQmr5aoDcjiqYITW9BeVQLC7MHIdSO0IzrdqfMc+j8rXUncnZdqvr1UWCSEVLdqSdEy4BGXfP290Ore+x45jig2sWoTbgQf9whE6HOAQ1udjV1YvO1a1pKYbqgl7E4yJ6+oZtq+tgxtbq/Uwx55ArNjI9a7PjqdXjcC42LtMxXvINCIJwMnVBrzzG11f6cMt58/C5ugqMRuIFqdtklW3Xuk+Fz0XjfpGgx0/LRV5EkYMxhqfWLsHAaBSvdB/DpQtmpsXN5tVXmkqVSvGCwsM4z74ixhj7T875l7O9lm8WLVrE9+/fb9v9zeRcPjw4kbPzhxc1wzXpxKslKc+1IIpW2+oqvfjm1lfTlCpfxwFKoIhUXhppt+waoX84gsu27jUsM0o5DnhdGBqL4djJcdz2lGKAWL0IPk9yzmWlPOjVhRKQq3xQdrKrh2yyo3xfcvLnTA2iwufC1KB1OymNyrBZvUz9TjsLxVlISctuJjtntDjq4cGxtFyNVo7xduZcLlGKTnbN9pfV/dw3PK7qrz69bikaqvy6fsfx0QjGIgn0D0cQiSfg97jQUOXDaTUBywpjlTCOkt3Zdzyfh9aY4+O7LjL8mXz9HjNtKwEcJbtmiMdF9I+M4/hoDN+ZzI//8LVnYcOz7+R9Dm+VbS9zX0AvRS+7VvoQ269fnJSmCzgl83VBr6ni0lbLYD6LsTscXT9a787lMGPsf3DO/wMAGGPLAISzfKbo0bvLxshuHKuEXiunzBM3nl3Q4wC0M6n4MHuERJkvu384gjUPvYH6Sp9crGEsmsC0Gp9mHm0jukByRZglm+xI7z938zIcGRpPKoJlpVNsVIatOqaonJDkmieNME42O5dJLlL7pH84Yns+ZjO2luxzcWG2v6zu5/GYuo0bj4m6f0dDlR/xgIjRaBy37vxPCmgQBFEUuN0CXIIgB5YBoMLrKsgc3irbTr5AeWC2n9ViV5+NRjVl3kxBO6tlkBZMjKM3uLwWwHZFnuVBANfY0yRnoVew9V5nVaJxrcCDi7GCHwco1uqW5YoVR0gkeTw8GEb7ji759b3rz8WUoLo8GNUFkivCLNlkRxAYEiLkwDJgT+DViAzbcbSLcpHlHz12Tq9cROMJOR+znWO8WYee7HPxYLa/rOxnLX/VZXC+NhiOqRYTpkUzgiCczHiKTybVbSrEHN4q206+QHlgpp8z1RixUuatlEEqEGgcvWfGTnLO5wNoAdDCOV8AYNi+ZpUuVk3utRKdB7yurAXSCEKJnqJ62TCTeJ8CXYSTcJo8WqGXqVCRv/xjpVx53S7s6urFpraWJLnoXN1KYzxRdAS8LmxemSzLm1e2IOA1Zo+cZrsJgiD0IC2wSXTsOZRmE2kOT5QKanMQqcaIU2We/Avj6N25vAvAQs75ScVrTwFotb5JpY1Vu9G0ijWFAl7NNAQEoYYVR0i05DHT4EBJ9wkn4TR5tON4oRk9JXLDSrmqC3px6/lN2PJyDzasaEZd0Cvnl6Uxnig2QgEvplX7sfHSM1HhdU2k0qr2IxQwZo+cZrsJgiD0IC2wSbVq+kciqPC68Kv2JRA5pzk8UVKozUFuPb8J8+orHRu3Iv/COBmDy4yxMwB8CUANY+ybireqAWSvtkGkYdXkPlvggbbqE0bI9QiJmUAYBboIJ+FEebT6eCHlw8s/VsqV1H8/uayF+o8oegSBYXZdEFV+T07y7ETbTRAEkQ21BbYqvwfTqv00rhMlh5EaI06B/AvjZNu53ARgBYAQgIsVrw8DuNGuRpUyVk7unayMRPlhVB4p0EU4iXKRRxo38ovVckX9R5QSVshzudhugiBKC6sW2AiiWCg2H5b8C+NkDC5zzp8F8CxjbAnn/LU8tankKTbFIgi7IF0gnATJI2EHJFcEYS+kYwRBFCNkuwjC2ZCOGkNvzuU/Mcb+HsBs5Wc459fb0ahCIYocA6NRWpkgCBVIPwjCPki/ShPqV4IwDukNQRClBNk0gjAG6Uxxoje4/CyAfwfwWwC6yiMyxhoBbAcwHYAIYBvn/GeMsSkAnsREoPpjAFdwzgcZYwzAzwB8A8AYgGs552/p/ym5IYocPceG03KqNE2rykmQy0ExyuE3ljtm9EMUOYbCUYSjCSQ4h9/jwtSgj2SDyEg52hO7xp/U7yi352onep6nWr9uv34xKv1uxOIi9QNR1mjpkB57SPaMyCez73i+0E0giph4XMSnJ8LoG45gYDSKXV29uOPrXyRfgCgrjIzb+ZgXlTt2+VF6g8sVnPP1Bu8dB/B9zvlbjLEqAF2MsZcBXAvgFc75XYyxOwDcAWA9gK8DmDf539kAHpj8f14YGI3KAgwAhwfDuHH7fjyzbpnprfDloBiF/I00ucgfRvVDFDk+HhjFsZPjchVkp8o/yZFzKAebqYYd448Sq54r6coEmZ4nAPkZMcaw5eUeuV/rK304dnIcax5ytk0kCLvJpEOSPayv9GHDimaEAh4cPTGOadU+TAn6ynacIAii+IjHRXxyIox+RWB53bmfx8BIBGseeoNsGFEWGB23U+dF9ZU+HD0xjqDPhYDHXbbzD6uw048SdF63mzH2DSM35pwfkXYec86HAbwLYCaASwE8OnnZowD+n8l/XwpgO59gH4AQY2yGke/MhWg8IQuwxOHBMKJxXRu1VdEKGAyMRnNqq52IIkf/cASfDI6hfzgCUeQZry/Ub5SU4rKte7Fs0+9x2da96Dk2nLW9hDmM6sfAaBR/HhiTA8vS9U6Qf6WMfzYaITlyEMVoMyWM2k4ldow/Sqx4rmRzT6H1PI+PRPDukZPyM7qi8zVcs3QOFjSGAABrl891pE0kiHyTySZF4wnUV/pwx9fPwMbd3bhy2z5sePYdHDkxLi9wFes4QRBE+SCKHD19w7j6569jZcdr2Li7G3/z1/MwJejFrTvfJhtGlA1Gx23lvGhBYwg/+FoTNjz7Dr5y9x5T849c5miliJ1+VMbgMmNsmDF2EsD3MBFgDjPGTipe1wVjbDaABQBeBzCNc34EmAhAA2iYvGwmgF7Fxw5PvpYXvG4XZtUGkl6bVRuA1+0yfc9sAQOnCbqZ4IHdQREtsimF055tsWNUP6LxBCq8LstlI9d+TZXxt3tP5G2SSjKZnXzZE6v6QrrPsRNhvHv0pOnAqx3jjxIrnisFdE6h9TxHo3G0P9aV9IzW7zqItcvnAgBCAQ/qK33oXN2KJ286B52rW1Ff6UvrB7IVRLGhR2aV10gBZCWSTfK6XVj/9TPw/V8lB1/ad3Th+GikYH4nQRCEEQZGo2jfkewTfOfxt5AQua02jHwIwmkYHbcDXhcevvYsPHnTObh7ZQvW7zK2MUOpA33D4/h4YJQ2xyiw04/KGFzmnFdxzqs551UApgJYDuBiACsm/58VxlglgF0A/hfnPFNAWm0PdlqvM8ZuYoztZ4zt7+/v19MEXdQFvXhwzSJ5gi9tD68Lek3fM1PAINddYHYMHGaCB3YHRbTIpBRO3WFnl+xaiZZcGdUPr9uFsWjCUtmwol9TZdyOALhdbS8k+ZLdfNgTq/pCeZ//PHwibQJhJPBqx/ijJPW5LmgM4eFrz0KCc93jR7EGdOyQXS051ZowSv0oMIbbL2ySd2Nu3N2N2y9sQsDrsmyhgigdisFnANRt6rtHTyIeFzWvuXLbPtx+YZO8qx84Zevrgl7MqPGr6tJ4TCyY30nop1hklyBSsVJ2tfymhMg1fYhcx/pin28Q5im03c0UmzIybosix7GTEWx49h1cuW0fToRjhuYfqTrwza2v4tjJcXlBu5w3x0jY6UfpSovBGLsBwB8AvADgzsn//6OOz3kwEVh+nHP+9OTLx6R0F5P/75t8/TCARsXHZwH4NPWenPNtnPNFnPNF9fX1epqvC0FgaJpWhWfWLcPe9efimXXLcs47kilgkMvOW7sGDjPBA7uDIlpkUgqn7rCzS3atIpNcGdWPuqAXp9dVYPPKFstkw4p+TZXxoXAsL5NUp8qkXvIlu2bsSaFS+SjvEwp4cgq82jH+KFE+1wWNIdx+ofHjZcUa0DEju9lkSk1ON7W14OiJcdVndFoogL3rz8Ws2kBaWozbnjoIDusWKojSwek+g4SaTW3f0YVPT4Rl3VG75ranDuKW8+YBSLb1gsDgFpiqLrlY4fxOQj/FIrsEkYpZ2VXzG7T8pqMnxrGprSXNh/jx8905j/XFPt8gzFNIu5stNmVk3E6V4YHRqKH5h5a/IZ0ilF4r9OaYQp4wsNOP0lvQ73sAzgKwj3N+LmPsDAD/lOkDjDEG4BcA3uWc/1Tx1nMArgFw1+T/n1W8fjNj7F8xUcjvhJQ+I18IArOkeJLyflLAILUAkp6dt1pJtu0q/iQNgsp2ZVJeKffdlAoPdrYvAedcs8iTKHIMhaMIRxNIcA6/x4WpQV/GKqGZCkdJSpH6jOqCXhw5ES7KHXaFJptcpeqHZBTV+kgQGGbXBRGq8ODJm85BggN+j4ApAa/pgmBmd04qZYkxhguaG/BS98SaVseeQ9hyxXw599ms2gA6VrWiNuDR/dzsbHu5kclmqtkEAPh4YBR/HhhDhXdit/zpdRWYXRdU/UxtwINwLG5JXyj7VFqk0Gs7tX67leNP6r2Vz/XKbfsMjx+ZbG4poafIRerzZIzhzufeQf9wFPdePl8+zi99dnq1H4LA8MngmPpuzKiILS/3YMOKZsxrqFS9JhyNo38YaTaTiiwSmcgmH2blR/m5BFffsd83HEHA60Z9lS8tf+La5XMRCngwszaA1//uryEIQtJ3B7wubF7ZklQQePPKFgS8EzbV5xaw8dIzZbvvc+stIUMQBGEtWn7DvPrKNL9pU1sL7nmxB4tnh/DLG89BQuSIJUQ8+McP8VJ3H350cW7zAj3zjdR5kYshzQYThBH/QE8BPq35XSqpMtyx5xA2tZ1KjaE2/9DjkzQo5jiF3hxT6MLEmebbuaI3uDzOOR9njIEx5uOcv8cYa8rymWUAVgP4L8bYf06+9veYCCrvZIx9G8B/A7h88r1fA/gGgD8BGANwnZEf4lS0AgaZArnZgnxmAlV6DISR4IG2UgRUA8sfD4zi2MnxpImClhKZmdwHvC7ERY4jJ8JgjOUc6Cl11OTBiFzp7aMpQR8QPPWZTIHAbBhd/NBqZ8eqVgDAS919qK/yoqbCkzRJTYgiTkZimOK2LtBnpu3liprN1JK3GTU+HDs5jg3PvpMUgAhVeBAKeJM+c0FzA2457wvoH45Y0hfKPtVyfFwC8MngWMZFt3wFBqXnqhXgzBZct9MRcRJ6F2+VciqKHLee34QtL/fA50kPekn9nOAcD197Fu575QMc6B3CgsYQbjlvHhIixx1f/yLu+s27aGttVJXPd48OY+Pu7iQ7W2jnlHA22eTDrPykfu5fbzpHVWYHRqNorA2gfzgiy/5v/usILl0wM81Wpn5nKODFtGp/ki5Nq/YjNLlAveahN9K+L9cNFgRBEGbI5Dc0TavC0+uWYjSSwMfHR3HPiz2or/JixZdn4VsP7ksKOg+Fo0m+qFEfURS5nHJDy8dVs/ub2lrw6Ksf4dbzm8h/IAAYD36qFeBTG+fV5nepMq6cX0mL0ZU+N5686Ry4BZa2EJLa1oevPUtVB2oCHixoDKF/JFLwzTF2bRQ1gl2bmvQGlw8zxkIA/g3Ay4yxQaikrFDCOf8PqOdRBoDzVK7nAL6rsz1FTy47b1MDVfIEdTJ/ZupOP1EUcVxRVEDLQBgJHhhRioHRKP48MCYHgPRcb2Ryn2pULmhuQMeqVqx9rCvt2RLaA8a0ap/uoJvePlIOGh6XoBkInBLMbtzM7JxUa+fax7qws30JfnTxxPET5S5O6Tc/edM5clDcCspl16ddaMnbkzedo5pm4MmbzkFCTP5MW2sj1j7WhfpKX9YVcD0o+/RA7xAeffUjPHHD2XAJDB63gJHxOC65f6+mzbUqMGhk8iGKHIwxPLV2CQZGo+jYcwgHeod0B9ft3F3tFPQusqU+93n1lbjzkjNxRedrafbk8RvOxtU/fz3J7j3z1ie4bOHMpAXXTW0tePbAJ2nyKe10SrWzTnBOCeeSTT7Myo/ycwsaQ/B7hLRdxvdePh+/e/coZtUGknzPHd9ejNW/eCPtO9VOv82uC6LK7zF08o8gCCLfaJ3MkE4jMzD4PQyzp1bg3ivmQ+TAtQ8n28H1uw7iiRvOln1RMz7iwGgUP36+O82H6FzdKt9Xze6v33UQG1Y0k/9AyBj1D5SxqbXL56oW4HvypnPkU6SD4ZhmfEra8b/l5R5cs3RO1sXo1Lbe98oH2Hr1Qqx7/K0kP/qu37yL+69a4IjNMaXsx+gKLnPOL5v8552Msd8DqMFE3mXCJJkCudl2OSqDGvWVPtx+YVPajuB59ZX4oH8EN27fjw0rmrFxd7cuA6E3eGBEKaLxhKHCaUYVLtWoSCkPsqXqKFe0Boyn1y3VHQDVe+xK6Rjt+cFyzUCgnkCumZ2TWu3knGNmbQX++7NR1fcTFqc9Kpddn3ah1Y9xjQJqCZ7+GSkv8uHBMO55cSIFQSjgwazaAGbUpJ+4yEamPu0fjiTtrFOzuVYEBo1MPrLtVqGFjgn0nDLQeu7VfreqPPYPR1TtXmp6EmmCJ8nnGdOr8N7RYdzzYg8O9A7J10l2tpSdUyJ3ssmHWflRfm7t8rm4+YkDqK/0yTZ1LJqAwBhWLZkj78yT7j0wElX9zk+HwljZ8VqaDTN68o8gCCLfSDapvtKXtGNTOjEnbXZ6+NqzsOHZd3Dv5fNV7aBLYLLvZsZHjMYTeKm7D/3DUdkeD4VjmKqYb2jZfclHJv+BAIz7B8rYlFYdmsODYXz/V2+jY1Ur7nvlfbS1NmrGp5qmVaVt2NDSgdS2HugdAgOSdEDyo3908ZccsXhSyn6M3p3LMpzzP9jRkHJEy3HOtstRT/7Mne1LLCs2pYaWUgS8rrQ8vF73xJFGPRN2qeCAEYVTM4ATeasmAohEMloDRiwu6g6AKh0paYV+LJqQ8yEC6Y6R9D2p35vg+iO5RndOZjPefo/6+36P9fkby2HXp11o9aPHJWj2H0NyehyXcOrvA71DaN/RJR+lNhvk1+pTPU6ZFYFBoydI1Har7GxfIucEJibG3+3XL05L36MMvms9953tS1TlsS7oRefqVnmneKaFkVDAgwO9Q9i4uxs725ckOd7S/ST7VcrOKZE72eTDrPwoP6dctGvf0SVf88fblkPkHBtWNMtyD5wqzJP6nZLvp7RhUvHrVH+ETgIRBOEkagMePHHD2YgmOHo/G0N9pQ+HB8PyiTnJ3kkbrbTqdDDG5CLqZnxEyTZLPq5032fWLUu7JvW7pTaR/0AAxv2D1NiUlowdHpw4QSwFfrVkXBAYuEbu5HAskZRyUK2tx0eiGf3nTOQjZWEp+zFUAcOBKBV07/pz5RWc1BQWUvBANVCYEOXXpQFDidEBJLWiZW3Ak1Zlcvv1i3HsZCStUmhtwIPT6yqweWVyZVylEimrjN78xIGM16aiVY2XBkh1Mj0vSa5m1lbIRfzUkAIwt1/YhI27u3Hltn3Y8Ow7OHbyVLXTVMeIMah+L+cwVSFVT5XVbNVQpwZ9qu9P1ZGmg8gfWv3YUKndf8rPXNE6C9UBd7pdWW3PQK7HJllht4yeINHaxU+B5WQicREbnn1HtmuRuJj0vtazdDGkyePmlS34251vY+Pubvzga01Y0BhKWhhRopzgZZJvSWbtrPZMFD/Z5MOs/Cg/p+VfHuofxV9t3pMk9wCwq6sXnatak75zU1sLOvYckj9/eDAMURQ1K8/r8ZEJgiDygShyfNA/gqt+/jq++tM/YMOz78g2LzV4JtnLjj2HcO/l89Ps4J3PvSPbOTM+oh6brnbNprYW7OrqJf+BkDHjH0gxhBk1AVUZk8Z5aWE6W3xKSwcO9Y2kxZlSv+/0ugpT/o0yHpXqe1hJKfsxjBvYNeg0Fi1axPfv31/oZhSU/uGJYG7qyszO9iXyUYJMidX1VgXXqoI7GI7JKzscHN/c+mpaW6QdKEPhKMLRBBIc8HsETA2eCl6m/g4pj/TchkoEPNnziFpY1CgvWl1I2bXqefUNj2v2d32VL61Pf3nj2Qj63Gk5kB599SP85LIWQ7t6jaYDyLQCmc+iajZT0rKr1U+Z+k/53pXb9qXttP/SadVoqPbb0tZs8mmFHmrZf7Wdy0auLQCOkV09zynTNbUBDz49EUYswfHfn43JxfukazZeeiam1/iTUldJ/d+5unXy+KqgS76BkrJfxYpjZFcNu+QnU02PzStbcPcLPUlyL6Vn27yyBV+cUYVYYmKRhjGGO597R05nJl2v9GGVrzvEXpUKjpLd2Xc8n4fWlB4f33VRoZtQCBwju1r+wIYVzQCQtHtyQWMIt1/YhIf3foR1534elT4PhsaiaTUwpHmz2YKr2Wy68hrGGFwMaUXSCNtwjOxmIxf/UvpsOJbAob6RNF9YOtWUKT6lNk9S8y+eXrcUU4O+tLYCMNx+h8+VCo2uzjecFqMYKeXJl9a2emnHk1qxKaPPQO/R608GxzIeb5gS9Gnm1lXLl3PdI29i7/pzsyoz5bM1Rurz8rgFuAWGIyfChp5dLC5q9jeQLpsP7/0IP4pCkgAAIABJREFU/3hxs0YOJGMpWoykA8iWjoLSVRQHWv2Uqf+k9/48MKp6dPsPty3X/f1GxhE9NskKu2XkWFUpH8Gykky7waWUTwGvS/NZDoxGcdXPX8e9l8/HdY+8mXafuQ1BzApV6O5/sl9ELtglP8rP1Vf58cy6ZQjHEkiIIv72ybfliR8wIffzGiqx8dIzMa3aD3EyJ75U2OfW85vQfWQ4SZe0jsNSPlCCIJxEphzGv/iPD5MKzPePRDCt2i/nkr338vm4ctu+tM9K82YzPqIem05+A6GHXORE+qwocoxG4ugfiQCYCNRKOZezxadSdYAD+JsnDqT5F+MxUbOtRttPtUxyp6SDy6LIMRSO4sjQONofS65EWSpbzzMNPlYFXPUqWi75H3PNHUkDpTGURt/s7slsfaYmg5xz0zmQlJDxJ4zgFpiqrLoNrMCn6knn6lY0NVTB7VbPLpUPB9+InadFOH1o2bWEyOXdDFIaqKfXLUUsLiY9S8k2aeVUdAun5IXGLaJYyLS4JsnxJ4NjePfYiDyJlJhVG4DPLeBLM6sxMh7HJffvTfI35tVXptklrdzMlO6MIAgnoeUzNFT58K3Fp6Mm4E7zFY6cCGf0E5TzKPIRiGJF8humVHiws30JOOfyovJPLmvBjy7Wt1lH2sXv9wiq/oXLwmkM1TLJnZLNuSwFA97uPSEHloFTOxyl4iGlgFae3NTXAWTNUauG3rxPueR/pNyRhUFrB7Ae/dDqs9qAR5azgdEo6oJeWQanZskhqhePWz1fqUcj0EeUN0GfC1uvXpgkd1uvXgifW9BlC9X0pH1HFz49EbY8D5dR9OZJN3ptuaJm1zpXt+LHzydXtF7z0BtgYGnPUhovO/Ycwqa25BzfqTkVCaIYSM1B+A/PHMThwbE0X9LrdmFXV2+a3HeubsWMmgAYGNY89EaavzEYjqXZJfIJCYIoBrRyGP/tzrdx3SNv4lsPvp7mK0hzGDU/gewcUQoo/Yaz//l3uKLzNZwcj6Mu6IXbLchjvrSYnCk2Jc3Bjp4YT6uds3llCwJe6wK/5HvkTsnuXJYE8d7L59MOR+SW31PvcepcdsbRrrrCkMsOYLU+qw140vKIKuXMqn52CwybV7bgtqdO5WnavLJF905Uoryo9ntRWxHHI9cthsAAkQMJMYEbt3ehfySS1RZq6UnfcAQBr5t2lpQQajZKFMWknLCAtp1Ujpf3vNiDjZeeidPrKiAwhq2//xNe6u5D95Fhyt9GFA3KxbUFjSFcs3QOrvr562ljfF3Qi1vPb8KWl3uwYUUz6oJeNFT5cFpNIGlXvxItPSKfkCCIYkBpq8LRON49Oiyn/AM0Tvq6GLZevRDrHn9L9hNmT62A3+PCtCo/2Tmi6NGTvlJvbEryHe5+oQc/uqQZGy89ExVeF8aiCUyr9iMUsC7wS75H7pRscDnb0dRy296uV8nVjj0aPXqda34eIn9YnY6kfziSJGf1lT4cPTGOoM+FgMcty02u/RyOJnD3Cz1J+ZvvfqEH91+1QDOvN1G+CALDzFCFZnEJrXzdEl63Cxc0N6CttVGWt11dvRgYjWJGjfUFAYnCombXlHZSKjib4Bz9w5G0FAHTqn2y8zsUjuH7O99G/0gEG1Y0Y2fX4bJc4CaKF2VQeO3yuXLxHeCUL/nczcuQEIEpFR7cecmZ8vFXpW4Y9TfIJyQIohiQbFX/cHoBPzVfYTSSwP2/+yBpDvPPv34XP7r4SxTEIkoCPYvJqQvXa5fPxWgkjqMnxzG92p/mOxzoHcI/PdeNtcvnokpwo2l6VdJ1VkG+R26UbHA59WhqaiXKUtvenq3YVDYlT109uqC5AT+8qDkpwbqVimakOFYpF2QsNLUBD5644Wz0DUcwMBrFrq5e3Hp+k2n9iMYTqK/0YcOKZjRU+VAT8OCu37yLl7r7kvIrDoZjOVUp9rpd6B+JJBVnK8dFIyIzarbjyImwaqE1URTlgm1KOyOKHJxz/N03voiPj4/hrt+8h/6RCLZevRDPv/0JFn6uVvO7yE6VBsrdyEv/og5rl8/FZ6NRvHtkGLu6evG9876AGaGJ3ROCwBCOJtJkbEFjCF+aUYXff/+vkOCAwCZki2SEKDRK2yUV+A1HT9kxZVA4FPCk+ZL1lT58NhpF72dheTfR6XUVmDG5Y1mCiooSBFHKpPoKN3xlDj4ZHMeRoXHZLs6uCyLBOV7q7pNPREmBtbg4EYSuDXjkeRL5k4ST0Zr7KP2GK1pn4cav/AVckzIcj4twuwU5NrWgMYQffK0pLVYn7WCW9GrLyz1oa22UT0U1VFJ6PydSssFltaOpc6YGUeFzYWqwtIRRz7GCbDtG9B57tOK5GUnRkUs6DyIzosjTUlh0rm7FvPpK08824HXh9gubktJVbGprQf9wFAd6h7Dl5R5876tfQPuOrqT3H331I9x6fpPufqVJKpENLdsxrdqXZgsvaG7A8dFoklxKCyGpOrKprQX3vNiDdY+/hSduOBt1QS/ZqRJHOr3z3M3LcGRoXM4bK8nDz155H99afDqm1/jRNK0qbbxd0BjCnZc0o3cwnGQbSUaIQqNmuzavbMHdL/TIKYPm1VfK463aacC//8YX0T8cwYZn30m6R6jCgynBU5sS6LgpQRCljNJXGI8l8OeBMVW76Pec8hHUAmsdq1px3yvvJ23MIV+BcBqZ5j7SPP3f3urFRfNn4rpH3kyS7zMUvrLWiSjpRKkgMMyrr0yLH5BeOJOSrX6ldGLvv2oBzpxZg89NqUBDCeYy0lOULVuCcj3HHq0qgmikiFwuBeeIzGgVKRsMx0zfMy5yOXgi3XP9roNYu3wuAKCttVEeGJTvt7U2GupXpX7vXX8unlm3jAYYIgkt2xEXeZot/OFFzWlyeeP2/egbiaTdQ5Lnw4NhuCbTBpGdKn0EgSEhIq1AsGS/Krwuuc9Tx9tbzpuHz0ZjabaRZIQoNGq267anTtk4qeCeNN5+eVYNOle1JtnP6TX+NNm+7amDCEfVcylTUVGCIEoVyVeIxEVNuzg1eKq4udqce+1jXWhrbZT/Jl+BcCKZ5j7SPH3N0jlY9/hbafLdNxKRfeW6oDdrCo3BcEx1nkZ64TxKducyUD45U/Tktcm2YyTbsUcrc0QaKeqSS8E5IjN2PNtYXFS9ZyjgAQDNAUSSOSPfXS76TZhDS75jcTHNFmpdG09oy7Py5AfZqfJAq5+latdSn6eOtwnOcWRonGSEcBxaMi2N2UqZVo63ylyhUY1xP5Fe9J0gCKLkicYTSIhc0y4qfYSxaDyjDZb+Jl+BcBrZ5j6CwBDX0IN4QpT14OjJ8az1GGieVTyU7M5lKxEncyB9MjiG/uEIRNFZHrMUGFailn82044R5U4r6dhjtvvZ3V6j1xLGyPZszci91j0lmWqo8mV8n/qVsIpM8p1qC7WudbsE1dfHoomkkx9OsFNOH6dKAa1+nhL0omPPoaQ+V8pYwOPGWDRRcBkhiFQyjdnSvwEk2RRBELBxdzeu3LYP7Tu6cOREWPUefg9NMQiCKC9EkYMxhuMj0Yx2UfIRKrzujDZY+pt8BcIpSPMNAFn9Wo/GPMrtOqUH06v9GU/XA86YZxH6IM8vC1I+mcu27sWyTb/HZVv3oufYcN4m7noCBplSXugNOChXUb88qwadq1szKnkuZEvRYfZawhjZ5MaM3Kvds3N1K748qwbPrFuG02oCae9vamvBrq5e6lfCUqywMw2VvnR5XtWK+Y01SWlY8mmn1Gx6IcapcglmK3+nIPC0sXHr1QvRsecQ+kci6FjVilrFbiOJuqAXp9dVYPPKFhrLCEehZrs2r2yRF0s2r2zBzU8cwD88cxCHB8fwyeAYPC7giRvOxlNrl6BzdSv+/f0+dKxK9xmnBulkEUEQ5UE8LuLToTB6B8cQiSdQF/RgyxXzk+3i6nS7qGaDO1a1YldX76nPka9AOABR5OgbHsd/fzaGdz45gZ/99oM0v7ZzVStcAuQ5QUOlL80/6FjVioZK9XoMWqkuKR5UPDDOi3dCuGjRIr5//35bv6N/OILLtu5N26ovJRm3E6OF71KrdQIwXWRKq/qnVb9rKBxFOJpAggN+j5CxyKKdbVEhLwkA8yG7etB6trnIfab+EkWOT4bGEIlzuBjgdglwu4B4AphR7YfbTetdOVA2sqvXJhixHVrX2vFdufxurSKFl9yfv3HKhgKGjpTd1N95QXMDNqxoRkIEEiKHx80wMBJBNM4xFI5hV1cvfnJZi+ozNzruEUWDI2XXCErb5XELcAsMI5EEDvWN4L5XPsC8hkqsXT4Xn41GMR5LoMrvxnefOJBUCPgL9ZUYGo9Tob7iwlGyO/uO5/PQGuLjuy4qdBOswFGyG4+LeO/YMNY+dqrY2ANXL4THxRDwusEAuASGgNeFUCDdNqb6j7UBDwbDMbKnpYmjZFcvan7/prYWPHvgE3yzdRamVfvx8fFR3PfKB3IxYGlOEI+L6BuJIJ4Q4XYJaKj0ZZzv5zofI2xD18Mu6ZzLVmA0x4uVgq+VKF0tYKCWf7Z/OL0YldbnU7Ern61WUCLTDpdMbSFDkxtaz9ZsbqNs/TEUjspFLZTVk92CAL/HRTmUFZBsq2MksGnEjmldq/ceRm2mmf7VGhOevOkcS3ORZWubkbGpmJF+Z32lD3d98y8xIxTAn/pOOc+bV7YAAL714OvyZ350cULz+U0J+oBgoX4NQWjrdqrehqNjuO6RN7GgMYRv/885+PPAGCq8LsQmEynXV/pweDAsFwIuNd0nCILQS99IBPe98n5SLvr/73cf4B8uapYL/yoX45oaqpKCa2o2mOwpUUhSfQUOrlrgfMOKZpwIx/DQf3yIttZG3PH1MzAUjmHLyz3yZgu3W8BpoUCWbzz1vZnmeKQXzoeCy1lQFrpb0BjC2uVzURf0grGJFRTlhNvq3VxSgE/6XmnAEkXR0OeVSAGHQgWurAxK2LB7jphEKfcS2XIbafVHQ7UXo5EEXIyBA6rVk3dcvzinpPylFogl2damGAObybtWOfxuF0Yicax56A1D/atl0xMcGfXV6A7ubLJXLoU1ovEE6it9+MHXmpImh5vaWnDPiz247amD+OWN56BzdaucGiPgdZHuEo4km25LdkLyMZ/97jJMCU4U293w7DtJC8K3X9gkL6qUou4TBEHoh+OapXOS/IQHrl4In1uQXwMgL8Y9ccPZmFVbQT4B4UjUfIXHvn22ZuHJhmofvnvuPHz3ibeS/OR4IoH+4YihOXkxzvGIZOgMehakHC8XNDfgB19rwsbd3VjZ8Rqu6HwtLaellkIMjEZNfbfX7Ur63iu37cPG3d04PhrNqbiaNPnVm5/TytyaVgYlrH7exCmy5TZSkwmt/jjYexJfuXsPrty2T7NqrMhhOil/ofOi2wHJtjbFFtgURY6PB0bRc3QYV27bh6/cvQfffOBVHDs5jvrJnGN6+1fLpvs9gmX50/XIXrkU1vC4Bdxy3ry0yeH6XQexdvlcHB4M4/hIBBt3d+P2C5vwyxvPxnhMxGgkjg0rmrGgMUS6SzgCUeQ4enJcUzYlO/EPzxzEn/pHceW2fbj0X/YiLnLVBeHp1X753qWo+wRBEHrhHGl+wncefwvhmLq/OjgWk32CcqlfQRQPavOAj46PYlZtAAsaQ+hc3YonbzoHD197FmqDHgiMyYFl6fr1uw5iJJIwPCcvtjkekQ4Fl7MgJRm/85Iz0waO1Amj1QpRF/Tihxc1y9+7oDGEDSuaEY4mcPTkuKniag+uWYS4mH60QWvya3XgzsqgBBkg+8iUXF9NJt49ehLhaFy1Pyq8LvnfR0+Ma/S/YDopfykGYkm2tdGyIYnJwnZOY2A0ij8PjKkGaNYunys7avdePl8+VaKFlk2fGvRp6qtR/dAje+VQWEMUOcCB2VMrNHdrzKoNoNLnxuHBMB7e+xFOjsdxRedrWNnxGjbu7sYPvtYkB/FId4lCIY3ZmWRTshNtrY1Jvm4iw4IwUJq6TxAEYQSRq9vJhMhV/dUKr0v290ptcwxR/KjNA+575QM8ct1ZuP3CUxseNzz7DmJxjqGxmKr8j0Tihufk5bJ5pZShtBg6EAQGrjFwKCeMZlIJZPtel8DkwHLq0dxsR22VAULlcegjJ8JyG5UpN6SBLlNuzfpKH46eGEfQ50LA4zacfkAKSqQey1SbmGQ7ym3183YSTkjzIOU2ktpy5EQYXrcLLgHY8nJPUm6xn/32fdz2tTNU+2MoHJP/3vSb9/DAqlZ8R1H0onN1K2ZU+03/vlIMxBazbNstu3VBLzpXt6J9xykZ2tTWgh8/361ZTK0Q7ZXuOxaNY3ZdhZyjVOLwYBgNVekpFzLZdS2bLl1rRf50PbKXrR3FTjwu4sjJcUTiIvxuQfV5cADbr18MxoDO1a2o9rtlmQSS89Ft3N1dFLpLlCZKP07y+XxuAXevbMHmF9+D1+2S7UQoMJEG44rWWbjxK38Bv0fdHlR4Xdi7/tyS032CIAgjTASCmfr8ZyyG+69agMHRGCq8LoxFE6gNehBNiAi5vZQCgHAkavOA/pEIqv0e9H4Wxr2Xz8dQOIaOPYfQ/lgXHr72LFX5H43E0bm6VTPGpIaROJHdOCEWU4xQcFkneibcdiiE9L1rl89V3TmdbQBSS34u3VMtl2Sm3JpmAtxq7dETlNCT99NJBshKnJRvV60tnatbse7cz+NmRbX4TW0tADg2tbUkycfmlS24+4Ue+X79IxGMRxPY2b4EnHNLjHUxB2K1KFbZzofsCgLD1KA3aXHjnhd7cKB3CD+62NiCgl3tVbuvpAsHeocATMhold+Dv/nlAUN23WhBC6P6oVf2SrWwhihy9PQNy4HiC5obsPXqhVj3+Klccg+saoXXxZJyZj9w9ULVBQTpeTpdd4nSRVm/I9WH61jVitqAB4PhU4vB7f9zNi6aPxPXPfIm6it92HLFfNy68+0kH2BaDgvCBEEQpcJQOIrPRiNpdnLzyhZU+l0YjSSSctZvuWI+plX50jZ7SRT75hii+FGbB2y/fjH6hyNJsizVH4kmxLT5/9arF0LkHHc8/V+G5ldO2bzipFhMscE4L96jF4sWLeL79+/Py3fpFTKrVzmk7x2NxLGy47W09/euPxczaytM3fPoiXHZSEjMqg0kBTb6hyO4bOteHB4Mo3N1Kzbu7s54vVUovzfTd9mwqpQXi5FJdvX+9nyg1ZaNl56J6x55M+m1h689C7dPHvX/4vQq+L0ujIwnFy3bvLIF06r9mF0XtMw4l+oAYEK2y0Z2rfoeu9qbTW9m1Qbw4OpFCAXdWPLPv0/7vBm7roUZ/SjAan3BZVdCre8uaG7A7Rd+ER4Xw4f9o/B7hKQ0J4C2XdzZvgTTKRBXyjhGdrWQZFraRa9m7+qCXvQcG548lfQlfOvBfUkbC245bx7mTA3CLTBMr/bD7aaseiWAo2R39h3P56E1hBk+vuuiQjchFcfI7ieDY7hy2z5sXtmC8ZiICq9L3tV5y3nzVOfZT39nKRqq/Y6a7xF5wzGym4nUeQAHxze3vqrq955eV4G7fvMu2lobURf0or7KhxPhmLwpQ3l9scg26aYqumSXdi7rRO9KitW7uaTvPXpy3LLdmdI9gz6X7tyaN27fLx+XzHS9Veg9yl2Ku+eclOZBqy1SHmXlayOROA70DmHj7m7Z+E4Ncjy9binGYyJcDAh4XQgFrA1UOWWV02qKUbbzJbtW7ey2q71a951bH8Qfbz8Xfo+AqUEfBkajtu+6N6MfxSh7VqHWdy919+Hb/+MvMCPkx5dmViMcVe/fOVODcn9KMkmBZaLQSPZyNKJeFyEaT8h24ieXtWAspX7Cgd4hXPfIm/jd9/8KXo+LAssEQRCTJCbTZgqMJS0uA0CFV32eHUuIAIr3lCJR+qTOAz4ZHFOV5dPrKtCx5xDaWhsRCngwPB7HnKkVYAyOiWWYwUmxmGKDgssGKNSEW5jcKWLlACQIDAGP23BuzXylHyjFVAd6cdJv12rLWDTZuM6qDaBvOJIml4LA0FDlh92UczDMSeRLdq1aULCrvVr3DXjdSXKar4kF6Yd+Mtk8v8eFhqqJ3UaqeWh9rpJb5CKKH72bFCQ78emQqHHdxKIYQRAEMYGUl34oHEuzm2PRzPPmUt0cQ5QeWr6xxyXg1Q8HsLPrsDyHqa3wQeT2b56xEyfFYooN2n5gI6LI0T8cwSeDY+gfjuRU/VU5AO1dfy6eWbdM9Vizke+UAhuzaieqcmbLrTmjJqDreivQ27ZSxO7fboWMnF5XkfRa5+pWfHlWjaZcFhNW6m25kU+9lezSzNoK1Ff5TMlcLu3NJCdGbKseu54PSO4nnoHHNVGgT9l3m1e24PS6CjmwptW/U4O+nGWSIOxAuUkhaexe1YpYIoFPh8KIxyd20zVU+tCxKlkHHljVimmVJNMEQRASosjhdTF0rmrFrq5ebGprSbKbp9dVZPUFrfBlCcJqUucEtQFPuv+wuhXTq3yqc5hij+MUe/sLCeVctgC1/JQA8p4HNh+5NfOZi7NAVTodkQvJrt9ulYwAcEQFVbtynBdp/uaSll27MNNePXKivC9jDC4GCILgyOfhALkvuOyKIsfHA6M4dnIcD+/9CGuWzMaMmgB8bgEBn4DaQPKkr9jknLCNgsuuESS5jcQTSIgcP3m+Gy9198nF/c6YVgW3W0A8LqJvJIJ4QoTbJaCh0kfpMEoPR8ku5Vx2LpRzOZ1Un6GttRGzagOoCXjAAMfNlwjHUHDZzYbanKBzVStmTfHjxFgcfcMRDIxGsaurF7ee36Q5Vyh2P7nY228DlHM5H2hNyusqvfJrwESeli0v9+DOS84E59wWIR0YjaZ9543b92dMPm70uHQ+j1eX81Fuu357JhmpC3pVjahWWwrdN3YExMzoEJFMsemtmfbqkRNp5d6uoK2VTg/JPXB8NII/D4zJxXde6u4DMFl8Z91S2+srEISdKO2Fxy2AAbj6568n6fzax7qws30JTgsF4HYLOC0UKGyjCYIgHEomnyHVdyJfgSgWRJHj6MnxtDlB+2NdeOS6xbj24TeSUkV0HxnWnCsUu59c7O0vFBRczhGtSfkTN56dpHwLGkO4ZukcXNH5mm07w8oh+TitIuWGmozUV/oQjSfw35+N4aPjo7jvlQ/QPxJx/I5dOwJi5aBDRO7olRO7grZWL6xE4wnUV/qwYUUzQgGPXOm8nOR+PJZIK76zoDGEtcvnIhxNoG94HFODdGSVKA5Sg8kj43GseegN2V7s+PZiVRsWnyw0RRAEQWgzHksgVDFR6F7yFST/SRTJjhLFgZqv8NloVNU/EIq8SB+RHyi4nCNaQQYXY0mJwNcun4v1uw7aujOs1JOPO+DodtGTKiMLGkO4/cImXLltn/xMN7W14J4Xexy/c9GOQHCp6xBhDXrlxK7FCquD1gGvC7df2ITbnjoo24HNK1sQ8JaP3LsYSyq+s6AxhB98rUket2m8IYoFNV9p88oW1Ff6cHgwjMODYXx8fEzVhrldlPqCIAgiGy7GUOX34ILmBlyzdE6Sr9C5qhX1VX7yFQhHo+UrAFD1DxIipzkykZWy8SLtKlYkBRmUzKoNIOB1JSUCrwt6bV/tKfXk41oBlYHRaIFbVjykysgt582TA0rAxDNdv+sg1i6fa1g+810QTEv3chnkSl2HCGvQKyd2yCigL2htRB/jIk+zA7c9dRDxMirqF/C6MCXowQNXL8Ss2oDmgjCNN4TTUfOVbntqYlyXuO+VD1SL9jVUOnMxmSAIwkkEvC743Ax3fP2Lab5C+2Nd5CsQjkfLV0iIPK045aa2Fjz4xw+xeWULzZGJjJTFzmU7d7xKQYbUe4cCXoQCXjyzbplc0Mnu1R5BYGiaViV/Z6mljTCzC5DSaCSTKiMJzlWfaSjgMSSf2XTMjn7Q0r1cBrlS1yHCGvTKiR0yCmTfOW10zIvFRdW0GLF4+RztrPZ5EPS54XUL2HLFlzG9xo97L58vP4sDvUN0/I9wNNI4OxaNY8OKZllugVPjukT/SAQi53j42rPgEhi8bgHTqGgfQRCELqp9HoxGEhgej5V9WjGi+BBFjmg8oernelwC7vrNe9iwohl1QS9CFV7c9qu30T8Swdrlc/H0uqWIxUWaIxOqOC64zBi7EMDPALgA/Jxzfleu97SzWFG2IIN0f1HktgQZ1Nrj1DQGuWI0ZQGl0VBHKSP9wxHVZzoWTRiSz2yFAu3oB7sCwaWsQ4R16JETu2Q0W9Da6JhHaTGAwXAMd7/wHr577ufh97rwrQfTUwX1j0To+B/hSNT8HUluD/QOyeM6cGq30bRqH8JRWkQlCIIwymA4hsde+whrls4pe/+JKC4y+Qv9IxGMRRM40DuEjbu78eCaRZgS9OD+qxaQr0DowlHBZcaYC8C/ADgfwGEAbzLGnuOcd+dyX71HiM3urCxkkKGckAIqW17uQVtrI+qCXjRU+VCr2I2jxM5FBSdiRobVglSdq1oxI+RHKJD989l2SkXjCdsXd0qxL4nCYMcOeztkNNt4YvSUh1ZajKfXLTXVvmI8MRKNJxAKeFEb9OGqycAycCpV0MZLz8T0Gj8d/yMcyfHRSNo4u37XQWxY0YyNu7tx7+XzIXKOP962HAGv+5ROBgvccIIgiCIkGk9gcCwOzmGp/0QQdqM2L1+/6yDuuXw+GEC+ApETjgouA1gM4E+c8w8BgDH2rwAuBZBTcNnqI8RmoUBYbggCw7z6Snzvq19A+46urH1lVzEtJ2JWhnNZ9NCzU8rrdpVVPxDFS7GddMg0nhg95RGLi6o6aiYtRrE9R4mgz4VVS05H38lx1Wcxt6ESs0IBR/8GojwRRY6xqPo4+4Vpldiwohl3/eY99I9ESnZxnSAIIp9IPsNRDZ+hnNKKEcWF1rx8WrUPD/z+EF79cIB8BcI0TktEMSW3AAAgAElEQVSuNhNAr+Lvw5Ov5US24ktUKK54GAzH5MAykLmv7Cqm5URykWEpSDWztgL1VT7dwROtlc+1y+cm6Vg59QNRvJTSOGC0MKWVOlqsz3EsKmLd429hYDSqXqTX46LAMuFIBkajiCe4qtwmRKB9Rxf6RyJUeIcgCMIisvkMNMchnIpUB0zJrNoAPj4+hq//5QzyFYiccFpwWW3mllSunjF2E2NsP2Nsf39/v66bKndn7l1/Lp5ZtyxpFxXtrCwejPSV0QCL3ZiRXb0UQoa1vvOL06uSdMxp/UAYx07ZdQqlNA5kG/NSsVJHnfYc9cpuLDGxe7tjz6G0Ktlkr4hCoFd2o/EEwtG4anX3oM+lywYQhJWUg89AlCbkMxDFil7ZdTHggasXpvkL973yAeY2VJKvQOSE09JiHAbQqPh7FoBPlRdwzrcB2AYAixYtSgo8Z8LKI8RE4TDSV07Lc21WdvVQCBnW+s6A152ka07rB8I4dsquUyi1ccBIGiYrddRpz1Gv7HpcAmbVBnCgdwj3vNgjV8meUePHjBpKh0HkH72y63W7cHwkil++8WdsWNGMUMCDoXAMj776EX5yWQvqa+hoK5FfysFnIEoT8hmIYkWv7AqCgPGYiI2XnokKrwtD4ZhczI9O6RG54rTg8psA5jHG5gD4BMD/C+Aqu79UragZrTo6E6N9VS55rgshw0a+s1z6gSheyn0csEpHi/U5NlT60LGqFWsf65KrZHesasW0Kj852oSjqQt6cXpdBa5bNkcuLFUsekcQhPOZfcfzefmej++6KC/fYwXkMxDFSl3Qi+HxGOKiiO//6m3yGQhLcVRwmXMeZ4zdDOBFAC4AD3HO/6/d30s7K4sH6it1CvFcqC+IUoLk2RqK9Tm63QLOmFaFne1LEE+IcLsENFT64HY7LXsYQSQjCAyz64IIVXjw5E3nIMEBv0fA1KD+GgoEQRCEfshnIIoV8hkIO3FUcBkAOOe/BvDrfH8v7awsHqiv1CnEc6G+IEoJkmdrKNbn6HYLOC0UyH4hQTgMQWCYEvQBwUK3hCAIojwgn4EoVshnIOyCltcIgiAIgiAIgiAIgiAIgiAIw1BwmSAIgiAIgiAIgiAIgiAIgjAMBZeJ/5+9u4+Torrzxf85/TTT8wAzDjP4AEbjD0m4ZBQHRWBvVmPimg0JIaDZKD5GHpa45mYTg8mG1Q3Jb8GH9bVuQkCTqEBINKA3rsancDXeBTVCSIyLEtZAFhSYYZyBmaGne7rr3D9mqqnuqequ6q6qrqr+vF+vecH0dHedrvqeb50+deocIiIiIiIiIiIiIsvYuUxERERERERERERElrFzmYiIiIiIiIiIiIgsE1LKSpehZEKILgB/LvK0cQCOulAcK7xYJoDlAoCjUsornN6IydgtlVePo5uqcR8EIXbdVI0xAnjzc3stdr24jyqB+2FYof3A2PUH7pfRGLvmsWylcapsjF17+LHcfiwzcLLcjF1zWC7z3CqTqdj1deeyGUKIHVLK6ZUuh5YXywSwXEHB/cV9QMVVa4xU6+e2gvtoGPfDMD/tBz+V1U3cL97n5WPEspXGy2Wzk18/px/L7ccyA94tN8tljRfL5bUycVoMIiIiIiIiIiIiIrKMnctEREREREREREREZFk1dC4/UOkC6PBimQCWKyi4v7gPqLhqjZFq/dxWcB8N434Y5qf94Keyuon7xfu8fIxYttJ4uWx28uvn9GO5/VhmwLvlZrms8WK5PFWmwM+5TERERERERERERET2q4aRy0RERERERERERERkM3YuExEREREREREREZFl7FwmIiIiIiIiIiIiIsvYuUxERERERERERERElrFzmYiIiIiIiIiIiIgsY+cyEREREREREREREVnGzmUiIiIiIiIiIiIisoydy0RERERERERERERkGTuXiYiIiIiIiIiIiMgydi4TERERERERERERkWXsXCYiIiIiIiIiIiIiy9i5TERERERERERERESWsXOZiIiIiIiIiIiIiCxzrHNZCPFjIUSnEOJNzWN3CiHeFUL8buTnrzV/+4YQ4r+EEHuEEH/lVLmIiIiIiIiIiIiIqHxCSunMGwvxUQD9ANZLKaeOPHYngH4p5T15z50C4KcALgJwOoBfAThXSplxpHBEREREREREREREVBbHRi5LKV8G8L7Jp88F8DMpZVJKuQ/Af2G4o7mgK664QgLgD3/s/HEFY5c/Dvy4grHLHwd+XMHY5Y8DP65g7PLHgR9XMHb548CPKxi7/HHgxxWMXf448GNKJeZcvkUI8cbItBnNI4+dAeCA5jkHRx4r6OjRo06Uj8hxjF3yK8Yu+RVjl/yKsUt+xdglv2Lskl8xdqlS3O5c/gGAcwCcD+AQgHtHHhc6z9XtIRdCLBZC7BBC7Ojq6nKmlEQOYOySXzF2ya8Yu+RXjF3yK8Yu+RVjl/yKsUte4GrnspTyiJQyI6VUADyIk1NfHAQwUfPUCQDeM3iPB6SU06WU01tbW50tMJGNGLvkV4xd8ivGLvkVY5f8irFLfsXYJb9i7JIXuNq5LIQ4TfPrPABvjvz/SQB/I4SoEUKcDWASgN+4WTYiIiIiIiIiIiIiMi/i1BsLIX4K4BIA44QQBwHcAeASIcT5GJ7yYj+AJQAgpfxPIcRjAHYDSAP4kpQy41TZiIiIiIiIiIiIiKg8jnUuSym/oPPwjwo8/7sAvutUeYiIiIiIiIiIiIjIPo51LhMBgKJIdA+kkEpnEIuE0VIfQyikt34jkTcwZv2Hx4yI7FAtuaRaPidRNWG9royzbn/a8mv2r/qUAyUhqgzmHlKxc5kcoygSe470YdH6HTjYk8CE5jgevG46Jo9vZMIhT2LM+g+PGRHZoVpySbV8TqJqwnpNRJXA3ENari7oR9WleyCVTTQAcLAngUXrd6B7IFXhkhHpY8z6D48ZEdmhWnJJtXxOomrCek1ElcDcQ1rsXCbHpNKZbKJRHexJIJXmWo3kTYxZ/+ExIyI7VEsuqZbPSVRNWK+JqBKYe0iLncvkmFgkjAnN8ZzHJjTHEYuEK1QiosIYs/7DY0ZEdqiWXFItn5OomrBeE1ElMPeQFjuXyTEt9TE8eN30bMJR5+BpqY9VuGRE+hiz/sNjRkR2qJZcUi2fk6iasF4TUSUw95AWF/Qjx4RCApPHN+KJZbO5eij5AmPWf3jMiMgO1ZJLquVzElUT1msiqgTmHtJi5zI5KhQSaG2sqXQxiExjzPoPjxkR2aFackm1fE6iasJ6TUSVwNxDKk6LQURERERERERERESWsXOZiIiIiIiIiIiIiCxj5zIRERERERERERERWcbOZSIiIiIiIiIiIiKyjJ3LRERERERERERERGQZO5eJiIiIiIiIiIiIyDJ2LhMRERERERERERGRZY51LgshfiyE6BRCvKl57G4hxNtCiDeEEE8IIZpGHj9LCJEQQvxu5GetU+UiIiIiIiIiIiIiovI5OXL5YQBX5D32AoCpUsp2AH8E8A3N396RUp4/8rPUwXIRERERERERERERUZkc61yWUr4M4P28x56XUqZHfn0VwASntk9EREREREREREREzqnknMs3AXhG8/vZQohdQohfCyH+Z6UKRURERERERERERETFVaRzWQjxDwDSAH4y8tAhAGdKKacB+HsAm4QQYwxeu1gIsUMIsaOrq8udAhPZgLFLfsXYJb9i7JJfMXbJrxi75FeMXfIrxi55geudy0KI6wHMAXCNlFICgJQyKaXsHvn/TgDvADhX7/VSygeklNOllNNbW1vdKjZR2Ri75FeMXfIrxi75FWOX/IqxS37F2CW/YuySF7jauSyEuALAcgCfkVKe0DzeKoQIj/z/gwAmAfiTm2UjIiIiIiIiIiIiIvMiTr2xEOKnAC4BME4IcRDAHQC+AaAGwAtCCAB4VUq5FMBHAXxbCJEGkAGwVEr5vu4bExEREREREREREVHFOda5LKX8gs7DPzJ47hYAW5wqCxERERERERERERHZqyIL+hERERERERERERGRv7FzmYiIiIiIiIiIiIgsY+cyEREREREREREREVnGzmUiIiIiIiIiIiIisoydy0RERERERERERERkGTuXiYiIiIiIiIiIiMgydi4TERERERERERERkWXsXCYiIiIiIiIiIiIiy9i5TERERERERERERESWsXOZiIiIiIiIiIiIiCxj5zIRERERERERERERWcbOZSIiIiIiIiIiIiKyjJ3LRERERERERERERGQZO5eJiIiIiIiIiIiIyDJ2LhMRERERERERERGRZREn31wI8WMAcwB0Simnjjx2CoBHAZwFYD+Aq6SUPUIIAeBfAfw1gBMAbpBS/tbJ8lGwKIpE90AKqXQGsUgYLfUxhEKi0sUKJO5rqnasA0T+U031tpo+K1HQsT4TkdOYZ6hcpjqXhRBTpJS78x67REr5UpGXPgzgewDWax67HcBWKeUqIcTtI78vB/BJAJNGfmYA+MHIv0RFKYrEniN9WLR+Bw72JDChOY4Hr5uOyeMbmRRtxn1N1Y51gMh/qqneVtNnJQo61mcichrzDNnB7LQYjwkhlothcSHEvwH452IvklK+DOD9vIfnAnhk5P+PAPis5vH1ctirAJqEEKeZLB9Vue6BVDYZAsDBngQWrd+B7oFUhUsWPNzXVO1YB4j8p5rqbTV9VqKgY30mIqcxz5AdzHYuzwAwEcB2AK8DeA/A7BK3OV5KeQgARv5tG3n8DAAHNM87OPJYDiHEYiHEDiHEjq6urhKLQEGTSmeyyVB1sCeBVDpToRKNFpTY9cO+JnsFJXbtwjrgH4xdUvmt3pYTu377rBQszLv2Yn12D2OX/Krc2GWeITuY7VweApAAEAdQC2CflFKxuSx64+3lqAekfEBKOV1KOb21tdXmIpBfxSJhTGiO5zw2oTmOWCRcoRKNFpTY9cO+JnsFJXbtwjrgH4xdUvmt3pYTu377rBQszLv2Yn12D2OX/Krc2GWeITuY7Vx+HcOdyxcC+AsAXxBCbC5xm0fU6S5G/u0cefwghkdHqyZgeIQ0UVEt9TE8eN30bFJU5wlqqY9VuGTBw31N1Y51gMh/qqneVtNnJQo61mcichrzDNnB1IJ+AL4opdwx8v/DAOYKIa4tcZtPArgewKqRf3+hefwWIcTPMDwNxzF1+gyiYkIhgcnjG/HEstlc4dRh3NdU7VgHiPynmuptNX1WoqBjfSYipzHPkB1MdS5LKXcIIf4CwCQp5UNCiHEA/qPY64QQPwVwCYBxQoiDAO7AcKfyY0KILwL4bwBXjjz9lwD+GsB/ATgB4EaLn4WqXCgk0NpYU+liVAXua6p2rANE/lNN9baaPitR0LE+E5HTmGeoXKY6l4UQdwCYDmAygIcAxABsRJFF/aSUXzD402U6z5UAvmSmPFQeRZHoHkh55qqU18pDzkqnFXT2JzGUURANh9DWUINIxOwMPUTlsZpvmJ+IKofnC+dw3xJ5C+skEfkJcxblMzstxjwA0wD8FgCklO8JIRodKxU5RlEk9hzpw6L1O3CwJ5GdT2fy+MaKdJh4rTzkrHRawdtH+rB0487s8V67sAMfGt/IkxE5zmq+YX4iqhyeL5zDfUvkLayTROQnzFmkx+yRT42MLJYAIISod65I5KTugVS2owQADvYksGj9DnQPpFgeclxnfzJ7EgKGj/fSjTvR2Z+scMmoGljNN8xPRJXD84VzuG+JvIV1koj8hDmL9JjtXH5MCLEOQJMQYhGAXwF40LlikVNS6Uw2CagO9iSQSmdYHnLcUEbRPd7pjFKhElE1sZpvmJ+IKofnC+dw3xJ5C+skEfkJcxbpMdW5LKW8B8BmAFswPO/yP0op/83JgpEzYpEwJjTHcx6b0BxHLBJmechx0XBI93hHwrx9hpxnNd8wPxFVDs8XzuG+JfIW1kki8hPmLNJj+uhLKV+QUt4mpfyalPIFJwtFzmmpj+HB66Znk4E6h2hLfczS+yiKRFdfEu/2nEBXXxKKIitaHrKPXcdWT1tDDdYu7Mg53msXdqCtgSvTkvOM8k04BN14Z34iqhyeL+ynnt/DAty3RB6hKBI1UcE6SUSepNc3wDYa6Sm4oJ8Qog8j8yzrkVKOsb1E5KhQSGDy+EY8sWw2UukMYpEwWupjlhansnORKzvKQ/ZxegGzSCSED41vxGNLZiKdURDhyrLkovx8E42E0D+Yxme+t0033pmfiConFBKoi4Wxcu5U1MXCOJHKoC4WZv0rUf75/fIpbfjJzTMgAJ6LiSpEWy9bG2qwcu5UnDWuHrWREFpZJ4mowgr1DfA7PeUr2LkspWwEACHEtwEcBrABgABwDYBGx0tHjgiFBFobS7+qZLTI1RPLZpf0vuWWh+xj97HVE4mEcHpTvPgTiRygzTddfUlc9+PfFIx35ieiyugeSOXUT2B4ZIyd56Nqkn9+f353J3Yf6uP+JKogbb082JPAjQ+/ns1z7KQhokor1jfA7/SkVbBzWeOvpJQzNL//QAjxGoC7HCgTeZydi1wpikT3QIqjAj3CjQXMeMypkrTxBwCtDTU5Mc8F+4jcUexcwAU17aMoEql0BvdeeR56E0NY+9I72HWgl/uTyEV6OY95joi8im0Hssps53JGCHENgJ9heJqMLwBgRFUpdZGr/NFEVhe5cmoKBnZels6uY2vE6Wk3nMKYCga9+Lt7QTvuenYPdh3oBeDOgn2MJ6p2Zs4Feuejy6e0QQiBd3tOsO4Uoc0zGUXiO0/vxvO7OzGhOY7V89txz3N70NWf5AKlRC4wynnjx9Tg8iltmN8xEU3xKHoTQ9iy8wDrJRG5TttuUKcOVO8gY9uBzDB7v83VAK4CcGTk58qRx6gK2bXIldFtFt0DqZLLpjbe5q3ZhtmrX8S8Nduw50ifrYvSBZnTC5g5ccydxpgKDr34u23zG7j1skkA3Fmwj/FEZO5ckH8+unxKG2697Fxcte4V1p0i8vPM1T98DdfPOhvTJjbhYE8Cy7cM5z0uUErkDqOcJyFx62XnYuVTu/H5B17Fyqd249bLzkVzPFrhEhNRNclvN3xuzXYcOT6I1pEF+th2IDNMjVyWUu4HMNfZopBf6C1y1RyPWh6J58StYG7MGRxkTi9gZnTME0MZz45EY0wFh1H8ndPWgG3LL3Ul/hhPVM3UUTEnUumi5//885EQAlete4V1xwS9PLN8yxtYMWcKlmzYmc17E5rinjrfEgVJzt0DUurmvMGUgqUbd+bU1aUbdzKvEZGrjAbgqO0G9TG2HagQU53LQohWAIsAnKV9jZTyJmeKRV6nXeSq1KkOnJiCgXOXlc/JBcyMjvk7nf3ZRUy8Nk0GYyo4opGQbvzVRkNoa6x1pQyMJ6pW2rbCijlTTJ3/teejd3tOsO6YZJRnmkZGQ05ojiMeDXvmPEsUNPnfjR664ULdnGfU6cy8RkRuKtZuANh2oOLMTovxCwBjAfwKwNOaH6KSpzpwYgoGtfNSy405VMkcvWN+94J23L91LwBvTpPBmAqOSEjg7gXto+Iv4mIjifFE1UrbVlj70jtYPb/d0vmfdcc8IYTuvupNDLky/Q9Rtcv/bnT/1r2j2h8PXjcdtVHmNSKqPKM21olUJvt/th2oGLML+tVJKZc7WpKAC/ICTqWOxHNiCga18zJ/FDUToTfkH3MAuGXTruxiaoD3RmwwpoIjkcrgrmf3YMWcKdmFc+56dg++d/U0oN6ebRTL9YwnqlbatsKuA72457nhuvjhUxsRj0WKnv9LrTtBbn8ZCQtg9fx2LN/yRnZfff/qC9DWGMMTy2ZXxT7wmmqMw2qW/91o14Fe3PXsHjy6+GIAyMYAgKpvE7BuEFVeS30M6xZ2YMnIND0TmuO498rzoEiJl2+7xFQ7jawLWv4z27n8lBDir6WUvyx3g0KIyQAe1Tz0QQD/CKAJw1NvdI08/k07tucFpU4b4RflTG9h9xQMTs8ZTOXTHvPOvkF09Sdz/j6hOY5oxOxNFc5jTAVHLBJGV38yO3cYYO8IITO5nvFE1Sq/rbDrQC9WPrXb9NyipdSdoLe/jIRCITyyfV/OhbTvv7gX353XznlcK6Ba47Ca6X036upPIhYJj6qD1dwmYN0g8oZQSOC0plqsnDsVdbEwehNDWPXM2+jqT3IOeIcEMf+Z7cH5MoY7mBNCiONCiD4hxPFSNiil3COlPF9KeT6ADgAnADwx8uf71L8FpWMZKH3aCL/QW9F9080zkEpn0NWXdH0ld7Xz8ozmOrQ21vi2cvqRokh09SXxbs8JU8feC9MUmMGY8jc1LlPpDDbdPAOXT2kDYP8tXmZzPeOJgsZM7rdjKiyrdSfo7S8jLfUxfOUTk7Hyqd34/AOvYuVTu/GVT0zO7mur52oqT7XGYbVSFAkJiY1fnIGHbrgQ0yY2Fcx31dwmYN0g8o6meAynjq3FV3/+eyzZsBNd/UndvMU2hD2CmP9MjVyWUjY6tP3LALwjpfyzEP47kZodxh70BZy0o4kURcHRgRSu/uFrJV2BCdqtAdVE7+rbums7MLmtERGDkchmpilgTFA5jOJy5dypCIVCuvFUasyZzfWMaQoSsyMv9EYeN8ejjtaFQnUy6PWwpSGGTYtmICwE4rEwmuLDXw7fH0jiUO9gzq2vfh8p43VB/x5AJ+nlw4dvvBD1sQgUOZxz7Mo1QchhrBtElafNJS0NMfzillkYSCoICyAeC496btBG21ZKEPNfwc5lIcSHpJRvCyEu0Pu7lPK3ZW7/bwD8VPP7LUKI6wDsAPBVKWVPme/vGCsVq5xpI/xCvere1Td8y3n+FRgzt1MwWfmb3tW3JRt2YtPNMzChuU73GBabpoAxQeUyikujnFROzJnJ9YxpChqjkRd6dUw7LZIbdcGoTkYjocDWQ6P9OqYmir1d/Th8bBArfvFmSe00Kk01fA+gYfn5sLVh+LvRDZtftzXXBKUtwbpBVFl6uWTtwg7cv/WPeH5356jcYqXNR4UFMf8Vmxbj70f+vVfn555yNiyEiAH4DICfjzz0AwDnADgfwKGRbei9brEQYocQYkdXV5feU1xhZRi7HbeC+kU5V2CCeGuAlldi1ylGx76zL2l4DIvVjaDHhF/4OXat5qRyYs5MrmdMu8vPsesXpZ733agLRnUyEhKer4elxq7Rfu3sT2LR+h2oi4UDN1LG66rpewBQ3Xk3Px8uveQc3Lb5DdtzTVDaEl6rG9Ucu+RvdrYZlm7cifkdE7O/a3NLEEfbVorX8p8dCo5cllIuHvn3Uge2/UkAv5VSHhnZxhH1D0KIBwE8ZVCmBwA8AADTp0+v2AQvVipWNS3gVM4VmKAnK6/ErlOMjn33QAqnja3VfU2xuhH0mPALP8eu1ZxUTsyZyfWMaXf5OXb9otTzvht1wahOHjqW8Hw9LDV2jfZrOqPgYE8CvYmhwI2U8bpq+h4AVHfezc+HTfGoI7kmKG0Jr9WNao5d8je72wxN8WjO72puCeJo20rxWv6zg6kF/YQQVwohGkf+/y0hxONCiGllbvsL0EyJIYQ4TfO3eQDeLPP9HaVWLK1CFataFmso5wqM1X1K3tJSH8O6aztyjv3q+e3YsvNAwWNYqG4wJqhcVnNSuTFXLNczpiloSj3vu1UX9OpkkOuh0WeLhEPDt7u+9A5Wz28vqZ1GpauW7wHVLj8fnkhlHMk1QcphrBtElWOUS3oTQzm/q7kliKNtKylo+U9IWfzChhDiDSlluxDiLwD8M4anxPimlHJGSRsVog7AAQAflFIeG3lsA4anxJAA9gNYIqU8VOh9pk+fLnfs2FFKEcoWlLmunFDqAhMe2aeubKiSseukdFrBe8dOToWxZecBfOUTk0s+hh6JCb9g7BqwkpOcjjnGtC7Grs+Vct6vZF2wcduei12jzzaptQF7u/qxaP0OtDbU4NbLJuHscfWoqwljXL3/v9CQZZ6L3aDQ5sN4LIwjx5O257kqb0t4KnbPuv1py++9f9WnSikS+Z+nYhewPuey+hq/LyZKlpk6wGY7l3dJKacJIf4ZwB+klJvUx8otZTkq3WBhxbKfB/ap55K+39h9DD0QE37B2LWJ0zHHmB6FsVulKlkXbNq2J2PX6LMx95CGJ2M3iJyqd1Vcnz0Vu+xcJgs8Fbuq/FzSHI+iJzFUjbmFjJkKgIJzLmu8K4RYB+DjAFYLIWpgckqNINOuek72MNqnVdyAqqhS9rvd9YL1jNwWCgm01Meysd89kLI15zCmqVoUO4dUsi4EuR4afTYnPjPbZ0SFWal3VupTkHMYEblHL5e4lVvYhggWs53LVwG4AsA9UsrekfmRb3OuWEQnVfmtXxXD/U7VirFPVD7Wo+DjMSayD+tT9eBoZyLmvCAyNfpYSnlCSvk4gGNCiDMBRAG87WjJiEZ0D6SySQcYXrF00fod6B5IVbhkwcb9TtWKsU9UPtaj4OMxJrIP6xMRVRPmvOAx1bkshPiMEGIvgH0Afj3y7zNOFoxIlUpnsklHdbAngVQ6U6ESVQfud6pWjH2i8rEeBR+PMZF9WJ+IqJow5wWP2XmTVwK4GMAfpZRnY3ju5W2OlYpIIxYJY0JzPOexCc1xxCLhCpWoOnC/U7Vi7BOVj/Uo+HiMiezD+kRE1YQ5L3jMdi4PSSm7AYSEECEp5YsAznewXERZLfUxPHjd9GzyUefjaamPVbhkwcb9TtWKsU9UPtaj4OMxJrIP6xMRVRPmvOAxu6BfrxCiAcD/BfATIUQngLRzxSI6KRQSmDy+EU8sm82VRF3E/U7VirFPVD7Wo+DjMSayD+sTEVUT5rzgMdu5PBdAAsD/AnANgLEAvu1UoYjyhUICrY01lS5G1eF+p2rF2CcqH+tR8PEYE9mH9YmIqglzXrCY6lyWUg4IIT4AYJKU8hEhRB0AToZCREREREREREREVKVMdS4LIRYBWIdSE4QAACAASURBVAzgFADnADgDwFoAlzlXtOqjKBLdAyneFkBkAusLBQVjmSgX60T14rEnsgfrEhH5GXOY/5idFuNLAC4C8BoASCn3CiHaHCuVh7gV1IoisedIHxat34GDPYnshOaTxzeyEhEhty7GY2EcOZ5kfSFbVLLxwtxPXlPpxjzrhHc5HRs89hQElc6hahlYl4jIi8zkSOYwfwqZfF5SSplSfxFCRABIZ4rkHWpQz1uzDbNXv4h5a7Zhz5E+KIr9H717IJWtPABwsCeBRet3oHsgVeSVRMGXXxd/f+AY6wvZws08r4e5n7yk0vUBYJ3wKjdig8ee/M4LORRgXSIibzKbI5nD/Mls5/KvhRDfBBAXQnwCwM8B/LtzxfIGN4M6lc5kt6M62JNAKp2xfVtEfpNfF+tiYdYXskWlGy/M/eQlla4PAOuEV7kRGzz25HdeyKEA6xIReZPZHMkc5k9mp8W4HcAXAfwBwBIAvwTwQ6cK5SQrtyq5GdSxSBgTmuM525vQHEcs4t11E71w2xdVTjqtoLM/iaGMgmg4hLaGGkQiZq9XWZNfF3sTQ76rL+Q+Mzmq0o0Xo9wfjYTQ1Zdkfq1ybp9nK10fACAaCTG/28TO+HEjNuxsC7ONSlbZETNeyKGAtbrEukJE+ZzKC2ZzpNX2APOYN5jqCZJSKlLKB6WUV0opF4z833fTYli9VUkNai2nvuC01Mfw4HXTs9tT55VpqY/Zvi07eOW2L6qMdFrB20f6cNW6V/CXd7+Eq9a9greP9CGdVhzZXn5dXPvSO7h7Qbtv6gu5z2yOcjPP6zHK/f2DaebXKleJ82yl64OiSPQPppnfbWB3/LgRG3a1hdlGJavsiplK51CV2brEukJE+ZzMC2ZzpJX2APOYdwgzfcRCiNkA7gTwAQyPdhYApJTygyVvWIj9APoAZACkpZTThRCnAHgUwFkA9gO4SkrZY/Qe06dPlzt27DC9za6+JOat2TbqCsgTy2ajtbFm1PPdnkjcT1dcrO5LwDefz5UCWY1dr3mvN4Gr1r0y6vg/tmQmTm+KG76u1BjQq4vrb7oIDbURDKUVL8eTm6oydo1iymyO8sKCEfmfIRwCPvM9a/nV56oydosxE8N2n1crXR/Uz9zaUIOll5yDpngUJ1IZnDdxLE6p92TsezZ27Y4ft2LDjpgupY1ahTwbu5XQ1ZfEPzzxBuZ3TERTPIrexBC27DyA785rtxQzlc6h+WUpVpd8Wlc8Fbtn3f60C6UB9q/6lCvbIUd5KnaNlJsXCuUeKznSbHvAp3nMb0zFrtlpMX4E4CsAdmK4M9gul0opj2p+vx3AVinlKiHE7SO/L7drY1ZvVQqFBCaPb8QTy2a70iEaCgnfVACr+9JLjS0q31BG0T3+6YzxyOVyYsDtukj+UCimzOYoL8RWfu5/t+eEJ26rpcoqFsNOnFcrXR/Uz3ywJ4ElG3ZmH9+2/FKg3pUiBIbd8eNWbNjRFvbK1ATkH4qi4PpZZ2P5ljey9WH1/HYoirU78iqdQ/PLUqwusa4QUb5y8kKxtoWVHGm2PcA85h1mJ0g9JqV8RkrZKaXsVn8cKM9cAI+M/P8RAJ+18831huFfPqUNQgi823MCXX3JUcPn1aA+o7kOrY017MwaYfW2L68scEH2iIZDusc/Gh6eJ1avPpUbA6yLlK9QTFnJUV6LLbdvq1UUaVhvqXKKxYFT59VK1gc7Yp/xPKzQvlQUicPHBy3Hj9dypRGvTE1A/pGRyHYsA8P1YfmWN5ApIX04UU+cymusK0SkpSgSQoiS84KZtqndOZJ5zDvMdi6/KIS4WwgxUwhxgfpT5rYlgOeFEDuFEItHHhsvpTwEACP/tpW5jRz5c7dcPqUNt152Lq5a9wrnZ7HI6rx4vKIULG0NNVi7sCPn+K9d2IHEUMZwviPGANmtUEz5bR57LTfLznnKvKtYHAQxp5Yb+4znk4z2ZXM8ij1H+vBebyJw8aPyc/6nypBS6tYHLywx5GReY10hIpWaa+588k2snl/a2heVaJsyj3mH2WkxZoz8O13zmATwsTK2PVtK+Z4Qog3AC0KIt828aKQjejEAnHnmmZY2mD8MXwiRM2+semWF87MUZ/W2LztXAPercmLXayKRED40vhGPLZmJdEZBJBxCTVRg7ve2G9YnxoB/eTV2C8WUl25NtcrNshuNMAjKedCrsWtGsTgIYk4tN/aDFM/lxq7RvlT30Yo5UwIXPyo/5/8g8GPe9XI+dTKvsa7k8mPsEgH2xK4213T1pbBizhS01MdwelMcp46pNZUXKpFLmce8w9TIZSnlpTo/5XQsQ0r53si/nQCeAHARgCNCiNMAYOTfTp3XPSClnC6lnN7a2mp5u9ph+EZXqSs9asMvt3RauaWBV5TKj12viURCOL0pjjNb6nF6UxyDKf15mNX6VE4M+KVOBJVXY7dYTLl5C7fdMepW2YM4+lXLq7FrVqE4cPq8Wqm8W07sByme7YhdvX2p7qO1L71T8sgkP/DLFB528VI7yY9518vfU5zOa+XWFS/FXrn8GLtEgD2xq801uw70YsmGnViw9hVIKU3nhUrl0mo751vhZo42NXJZCNEC4A4Af4HhEcv/AeDbpc67LISoBxCSUvaN/P9yAN8G8CSA6wGsGvn3F6W8v1levEod1IXveEUp+IrVp1JjIKh1gsrnlbzi5xj14nmQzHEy/v0a04zn4tR9tOtAL+55bk9JI5PIW/xaX73EK+0JPV7Oa4w9ouCwI9d4OZdWI7dztNk5l38GoAvAfAALRv7/aBnbHQ/gP4QQvwfwGwBPSymfxXCn8ieEEHsBfGLkd8cUmo/O7Suw6hWFQ8cSgV34jleUgiX/KlhzPFr0SmUpMWB0O+DRgWRgRkpQ6bywcE6hxSu8PqLHy6O1qDiz8W9XTB8+Pui5GNZiPBen3Ue7DvRi5VO7UV8TqWjHciXzpNdztBlcNNsebnxPKSXeSslrbsU1Y48oOOxqQ1Wyz6fc3BeENoGW2zna7JzLp0gpV2p+/44Q4rOlblRK+ScA5+k83g3gslLf1yq9KyvN8Sj2dvW7egVWe0Xh3ivPC8wtnRRcRlfBJrU22H6l0uh2wBPJDBb+6DWOlCBblXKFt9Atq14f0cMRBsFnZ0y/15vAscSQp2JYi/FcnNf2USVHPgZl1GWQpoMJslLjzWqddTOuGXtEweG19oFV5ea+oLQJtNzO0WZHLr8ohPgbIURo5OcqAE87UiKX5V9Z6UkMuX4FVntFoTcxlL1apPLKrU9+ErSrTl5jdBWsJzFk+5VK9RYdrQnNcew7OuCrkRKMSe8odCxKucJrFKNCCF+M6OFdJd5id66wM6bV9/JaDGsxnouzex+VE7OVHPkYlFGXRvWV3x1K41R7rZx4s1Jn3Yxrxh5RsPi5DaXmvtaGGqy7tgP3XnkeDh8bRG/CXO4LSptAy+0cbbZzeQmATQCSIz8/A/D3Qog+IcRxR0pWIZW4AqvdZlAXWHGzY0296jRvzTbMXv0i5q3Zhj1H+tiZZyM364neLTrrFnbg/q17TW3fC526jMny2XUcix2LUmLb6DaysABH9JApanwfOZbAW4eP25or7Irp1fPbsfaldxjDlJOP3x9IlnV+q+TIx6CMuuR0MOVzMger3Iq3SrfRGXtE5CY1f59IpXH3gnbc/skPYeVTu/H5B17Fil+8iUO95qZ0C0qbQMvtHG1qWgwpZaMQ4hQAkwDUah7/tSOlqqBKLJqg3aa6wMrKuVNxTlsD4lF/3Y6gx+1bDIyuOj2xbDZaG2ts3141crOe6N2iEw4BXf3JnOfpbd8rt7cwJstj53EsdixKiW2j28i6B1KeXYSHvEMb3yvmTMHKp3bbmivKienHlszEe70JdA+kcM9ze7DrQC9juMrl5+OHbrgQK37xZskxW8nFyry8UJoVfr+VudKczsEqt+Kt0m10xh4RucVMm2TJxp2mcnhQ2gRabudoUyOXhRA3A/g1gGcB3Dny7z86UqIKc7t3X1EkwiFg3cKO7Da7+pM4dWwtJjTFfXc7gh63bzEI4lUnr7FaT8oddZp/i05T3Nz2vXJ7C2OyPHYex2LHotRzgN5tZG6dT7wwOp9Kp43vpnjU9lxRTkyfOqYW9TURrHxqd7ZjWftaxl71yc/HdbFwWTFbyZGPQRp16edbmSvN6Rys0ou39TddBAlpaw51O64Ze0SkcrtdaGebJEhtAi03c7TZBf2+DOBCAK9KKS8VQnwIwD85VqoKcrN3X3ulpbWhBivnTsXZ4+pRVxPGuPrKn5wVRaJ7IFX2fnC7Yy2IV50qxSgGrNQTJ0YPm92+Vzp1GZPlsfM4mjkWLQ0xbFo0A2EhEI+F0RQvLfe5cT7xyuh8Kp02vtV1F6zkimLn6nLisNBrGXvVKT8fG8VsRpFQFFk0FuzOk1barhx1SUD5Odis/HiLx8I4cjyJVU/8AfM7JqKlPoZEKo3Tx8YRiZidubL4dhjXROSGSrQLzbZJzOTwYm1eO/rFgs7smWtQSjkIAEKIGinl2wAmO1esynKrd197pWXXgV7c+PDrWPij1yAgKh6sds4R6/ZE4kG96uS2YjFgtp44NXrYzPa9stAIY7I8dh7HQsdCjfnPrdmOj971Ej7/wKs4cjxZ5B0Lc/p84pXR+VQ6bXxbXXfB7Lm6nDg0ei1jrzrl5+O1L72Duxfkxuzq+e34ztO7TceCXXmylLYrR11SOTnYKm28ZRTgvhf24PpZZ2PlU7uxYO0ruPqHr2FPZ/lzPDOuichtlWgXmmmTWMnhermTayeZZ3bk8kEhRBOA/w3gBSFED4D3nCtWdfDKqEo9ds4Rq3bm5F/FcvL2LF6xL59dMVDJOHc79owwJstj53EsdCy6+pK+mxvby+cRMkcb37sO9OKR7fuw6eYZCIdE0VxRyfncGXvVKT8fd/Un0dpYg1Wf+wii4RB6E0PZ+bnv+LS7scD1DagU5eTgcqTSGczvmIjlW97InR90g7n5QYmIvKQS7UK9Nsn4MbV4fNksDKUVW3I42xbmmV3Qb97If+8UQrwIYCyG512mMnj5Vnk7k0MlOtbUq05UOrtioJJx7qVOXcZk6ew+jkbHwo+dZV4+j5A55cR3JWOWsVedjBbZveGh1yseC37M4VR5lWorqtthzBJREFSiXehG/mbbwjzLEzpJKX8tpXxSSun7+x4rvRCNl2+Vt3s6Ad6e5T92xUCl47zc2Kt0nqBhbuQQr0yjYkWxaT4Yu/5Qany7FbN6sVTp3E6VU+oiu05zO4czxwZHJb6ntNTH0NZYU5F2B2OXiOxWqXah2fxdat7z4/fDSjE7LUbgeGEhGi+NqsznlekEqHLsigEvx3kxXsgT5B4/5j2j+gWAsVsF3IjZQnnQr7md7OWV87ybOZztAypXKCRw+tg41l3bgSUbdrrW7mDsEpETvNIW0FNO3vPj98NKEVL690rl9OnT5Y4dO0p6bVdfEvPWbBs1bJ9zp5xUpatiuvIBy4ldN1VpDGT5LE8wdm0QlJhn7I4W1Nh1OmZ9FktuY+x6jFs5PAD1grHrEW63Oxi75piN3bNuf9qF0gD7V33Kle2QozwVu9Wk3LwXlO+HZTD1Yat25DLnTimOc8RStccA80T1CUrMM3arh9Mxy1giP3Erh7NekF3cbncwdomo2pSb94Ly/dBpludcDgrOnUJExTBPkF8xdskujCWi0VgvyK8Yu0RUbZj33FG1nctciIaIimGeIL9i7JJdGEtEo7FekF8xdomo2jDvuaNqp8Uod8JxzrviLTwe/pBOK+jsT2IooyAaDqGtoQaRiHevcXl5YQLyPyfylvY9WxpiePKW2UikGLte5PZ5q9TtMQ9SIX5pf9ldTtaLYPF6HNtZPsYuEflBsbxnJS8y77nD9c5lIcREAOsBnApAAfCAlPJfhRB3AlgEoGvkqd+UUv7SybKUOncKV9n1Fh4Pf0inFbx9pA9LN55cEXvtwg58aHyj5zuYOccS2c2JvMVc6B9uH6tyt8c8SHr8knOcKifrRTB4PY6dKB9jl4i8rFjeKyUvMu85rxI9OmkAX5VSfhjAxQC+JISYMvK3+6SU54/8ONqxXI7ugVQ2kIHhycAXrd+B7oFUhUtmH0WR6OpL4t2eE+jqS0JRZKWLZKgajkcQdPYnsx3LwPBxWrpxJw73DXo+xqh6OZULnchbzIXelR9HRweSrh4rxgaZYTXf+SWu/FJOspfZePZ6fHi9fERERkr9HlUs7zEvepPrI5ellIcAHBr5f58Q4i0AZ7hdjnIEfZVdr1/Bzxf04xEUQxlF9zi925PAV3/+e0/HGFUnJ3OhE3mLudCb9OJo4xdnuHqsGBtUTCn5zi9x5Zdykn2sxLPX48Pr5SMi0lPO96hieY950ZsqOueyEOIsANMAvAZgNoBbhBDXAdiB4dHNPZUrnTF1tUltQHtptcly5+UyuhL0xLLZnryVwOvHww/cmGsuGg7pHqfexJDnY4yqk5O50EzeslovmQu9SS+O9h0dKPlYlZKvGRtUTCn5rty4cmueW8Z/9bESz3bFh1PxzPilUp11+9OWX7N/1accKAlVIzN52ChvFst7zIveVLGJToUQDQC2APhfUsrjAH4A4BwA52N4ZPO9Bq9bLITYIYTY0dXVpfcUx3l5tUn1CtG8Ndswe/WLmLdmG/Yc6bN0K7ffrgR5+XhoeSF29dgRM2a0NdRg7cKOnOO0en471r70DgBvx1i182rsOs3JXFgsb5VSL/2SC93khdjVi6P7t+7Furx8aOZYlZqvGRv+43bslpLvyokrt9oe5ZaTrPNq3jWKZzviw8l4Zvy6xwuxS1QKL8ZusTxcKG8Wy3vMi94kpHR/nlMhRBTAUwCek1L+i87fzwLwlJRyaqH3mT59utyxY4cjZTSiXl1RFAUZCUgpXV1tsthV8a6+JOat2TbqKo6VkXZ2vIfbbBwt4MqcDJWIXSPFjredIzHSaQWd/UmkMwre6RrA/Vv3YteB3uw2H182C22NtbZ8ripUdbHrNKdzYX7dao5H0ZMYQiqdgRACV617xfK29eorAFdGB5Yh0LFrFEdP3jIbGQUFj0v+8ZSQ+Nya7aPe69HFFxc9tm6NEq0ygYndUvNdqXHldltTUSSODiQxOKQgLIB4LIymuP/qANu75liNr3L3aynxbGWbTuRvH58TPBW7pYwOLkUpI4o5ctlzPBW7TiuWF8vtgzCTw5g7bWPqA7o+LYYQQgD4EYC3tB3LQojTRuZjBoB5AN50u2zFGM8bE3etY7nYvDV2jLRTrwTlb6dSV4LMVGCu/lm6QjFj95yzkUgIp46pxdGBJM4eV49bL5uE+7fuRVd/EncvaEf/YBrj6mU1JGjyGL0843Qu1Oat/Lq2eenMknJ5fi702xz6QaTG0X0v7MH8joloqY+hrbEGY2qiiESMbyCzMlfzQRNz1/M8SXq0uW/TzTPwnad34/ndnabzXalxVYm75Lr7U77Ohczn5rXUx7D+povw5+4TqIuFcSKVwQda6gzjudz8aDWerR5Lu/M3Y4mIylWsj6bY96hiebNY3iv2dyfyHHNnYZWYc3k2gGsB/EEI8buRx74J4AtCiPMBSAD7ASypQNkKKjRvTEt9zPGrz/nbb22oweFjg6ivCSMejaClPmbL/DOhkMDk8Y14YtlspNIZxGNhpBWJQ8cSjl6dMRpxxwrsrEIxYxTzeiPuAP3RkdrjGo+FceRYEos2nDyeaxd2oCkewYGeBFY98xa+O6+dHSBUlNU8Wuj5eg2Fddd2YHJbY04uLDf/FSpDfl3rHkjZMpeYmfMGc6k9Co1Eb6mP4csfPxdLNuwcdS4D9HOnXv49fHyQc9eTZUa5xyj3rZw7FaFQqKT8YDY32znPbW8ihUQqg4yUqI2GMa6+ZtQ2/baeiJ4gfAa7mImzZFrBil+8mZNzrb6H2bKEhNCNZyGG65nX4rHS2yci/xn1nf54smAfTX6fUjQSQiQksn1K0Yj+ekxG7QCrI5WFELjvhT1l5Tm9OwiZO4253rkspfwP6A+r/qXbZbHK6OqKoiimO0C1DfnWhhrcetkknD2uHnU1+o1ho+1Pm9iEr/3VZCzf8kbONie1Ntgy0k69EqSWVzviKpFK4/Sx8YIjrqwyugrU0hBjBXZYoauKh44lRsV8a0MNDvUOYsnG3E6SmkgI1/34N6PicW9Xf/a9f7b4Ynzt57/POZ5LN+7Eqs99BLc//gesnt8ORVEMy5p/UksrEkNppZpuSSFYv2pc7Pl6X7KWbNiJTTfPwITmOttuaS1Uhvzzy9qX3sHq+e2jcrzVXG7mvMGLdeXLP7d/868/jCOxMJaO5MmHbrgw28kBnDyXPf63s3LiT3tM8mNi2sQm1EZDuHtBO27bfPIY3nvleVj1zNvZ9+Xc9aRVKPcY5b5S21jptII9nX2mLqI01UawdmFHto6oF5ub41FLn21/9wCOHB/MqRN6ec1v64noCcJnsIOZNkCxzlMrsVrs3L6/ewB9g0O6ufnOJ9/EVz4x2XPxWOntE5G/5Oddo3Ztfvshv09Jm7fX33SRbh9EczyKrr6k5QGHettYPb8dXX2p7DScVvKclTsImTuHVWLksmvsng/FaJRFRsL0yDC1sdPaUGP5S752+0svOSf7WiC3QhuNtCtlf3QPpHDfC3tw/ayzc8q67toOfPjUMSW/r9529PZhXSzMCuyw/KuKxVZqvfWySdmOZeBk7K2cOxUHexKYNrEJSy85BwPJNLoHkjiWGMK9V56H3sQQWhtqdI/nqWNrcbAngeVb3sBjS2bqljO/8+brV0wu+mXSjCqdN8mzzBwPqyNujg4ks3GzYs4UNMWjOHxsEC11UYhQyPBLVmdfEvFYRHdFYzNX7K2UOb+u7TrQi0e278NjS2aWPLe/okgIzWiqQucNXqyzJj9Ow6HhdsCsD7Zg6SXnQJHAgfdPZHOe0blsIJXRHVXx5C2zIYTA5qUz0T2QwtqX3sHSS87BLZt25cTxiVQGipQ5c9dzpWzSKpR7zHYwmbmwqygS7x1LZDvrtNt6fNms7JQU6sCKD7bWoz+Zxt0L2hESAr2JIdy/9Y+m7l5S509OpDKIhkN4aNu+onktCCvLB+Ez2MFMG8Aotk+k0ug8LjE4lMmJVe13N0hkp4e5fEobvvWpKQiPtInzz8PdAyn8ufsEVvzizVG5ORYRmN8xEQPJNA4fH8SpY2qzr630saz09onIX/LzbrE+GjMjflc98xb+6TNTsWnRDISFQDwWxpiaaM7ANLXfqbWhRjfva9dr0pZR7Y+oiYRw14J2fH3zG9h1oNdSntM71+w7OhCI3OlU/0dgO5edmA/FaISnlNL0yDC1sbNizhTLX/K122+KRw0rtN78M6Xuj1Q6g/kdE0eVVR3Z0lIfM3xfwPyVf6PRdSvmTAlEBfY6ozmL9GL+7HH1urFXFwvnHDu9DuCfLrpY93hGQqHs+xgtMqpN8CvmTMm+r/q6/BOMGZw3yVvMHg+rI24GhzK6F/TWLuzAv//uIK6ddbZuXHYPpHDa2Frdspm9Ym+2zHp17SufmJzzZbSUfXnfC3uyI6ALnTfIPN2pBBZ2YNYHW3DNxR/IuYNj9fx23PPcHvQmhnRjbP/RAczvmIjnd3dmH9e7O2T1/HY01ERwsCeRPQerNi+dmX0/rpRN+QrlHjMdTGYv7HYPpNDZl9Td1uCQYjiwYvX8dtz17NvZCyR3fLpwPip1ZFIpc+h77eKz19ZEqRQzbQCj2H77cB9i4RAaayMFv7utnt+OpngMc6edgat/+JphmySVzmQ7WfJz89av/iX+7qe/y752/U0XoaE2gqG0gmgkhPU3XTTqjj+3jiVjiYisyM+7Ru3aWCRsasTvtIlNuH7W2bhyZPHy7NRFYzCqQ3fJhp34yc36I4YHh07e8ayW0SinP7J9H77yicmm85zeueb+rXuxbmHHqDu47c6dTrY/nOz/CGznshNzSRmN8NTOjVlsZJja2CnlS37+9q10uqoj96zuD/UzGpW10Jy8Vkb1GY3KtuvWcMplNmHpxbyE1I29E6lMzrHT6wA+fGxw1G2Ddy9oRzg0fJLp6k8axrA2wRvVH+0JxgzOOectZo+H1RE3YSFw62WTRuXmpRt3Yv1NF+Hb//6fo/KM2gi54Mx23bJZvWIfjxUuc6E7CMrdl119KayYMwVtY2p4sc4GulMJbNyJh2+8CDc89Jucx9WLpGtfemdU7lM7nv/hUx/OeX+9u0OWb3kDjy7Wvzh3elMc25Zf6onOL/KeQvnSTAeTmQu7at4ymis+JIAVc6agPhYelYfVOrJkw05T+Uiv/mnfQ/v5tKzmWC9efLb7POFXZtoAerGt5tzbP/mhot/dlm95Aw/dcCFufPj1gm2SWGR4sUC98vx394nsY60NNThyfBDX/Tj3+8yTt8xGIuX+sWQskZ3Ouv1py6/Zv+pTDpSEnJKfd/XatWr7wcyIX6M+s0cXX6z7/cpoXvuwJmWpZTTK6Y8tmWlp0I7euaarP4nTmmodzZ1Otz+c7P+wb9Jcj3FqLil1hOcZI/NwhkIi24Ax02msPldtiGiZaVSr2x/fWIt113Zk36NQp6uiSJxIlrY/1FXtjcpqtJ8TqYxu0HYPpAy3o7cPdx3oxT3P7cGKOVPw8m2XZKf9YOOndGrCmrdmG2avfhHz1mzDniN9UBQJRZHo6kvi3Z4T6OpLZhch0cb8uPoarFuYG3v3XnkemuujORci9OrC///Lt9DaWIOVc6fi0cUXY+XcqYjHwlj51G7cetmkghcO1AQPnLxaqpV/gjGDc855i9njoc0XQPERm/FYGGe21Om+9/sDKTy/uzObZzYvnYmHbrhw1NVtoyv2WvlX7LV17MjxJNbfdFHBMuudX0qlLe+uA71YsmEn/v7R34+qu+uutTbHKRnHaTQsIdDyZgAAIABJREFUdB9vikfR1Z/EuIYY7rnyPGz96l9i1ec+gnue24Ou/iTaGmtw+ZQ2rLu2A5uXzsSktga0NtSMep9ISOjG/aljam2JGQqmQvlS28G0bfmlum0sMxd21YV6tuw8gNXz23O2teaaC/B3m3Zh5VO7cVpT3LCOmB08YFT/1NcVeh8rOdboy5dRO9Ytdp4n/MpMG0Ab27++7RKsmDMF9zy3B7sO9KI3MZQTq0ZxHYuEirZJWupj+EBLHe5ekBv3P1jYgfu37s0+b+kl5+hemMkoqNixZCwRkVn5eberP4nxY2rx+LJZ2Lb8Ujy+bBbGj6nBoWPDOTK/HauO+FVff+qYWt38qkjofr9SpByVZ+9e0I54bPRFRaOBkVKOXlzVymdWzzVN8ZijudPp9oeT/R+BHblcylxSpS4WZmVEsfrc8WNqSh5SrygSe7v68a+/+iNWzJmS7QA+fWzccK5Ps/PD6I1oPX3scAdE/qIX+aO2te+b0UwVoioUtIX24a4DvVj51G6OJrWJUcLSzomod5VMGxtqB3FdLIzexBBWPfM2WhtjuOPT/yN77PRul+nqT6K+JoxURkEdhv/9pyd3Y9eBXnxrzv/AhCb9GAZyR6HoXS3NP8EYyV9FliM5vcNs3rY64qYpHsNAUj83qydqtQMWAF6+7RJ8d157znsWu2KvzsuYSmdw+Pig7jy6jy+bZVhmN9YI6OpP4vTmGvzk5hno6kuieyCFf/3VH3UXGyJjRnEaCennk7bGGmz84gwcOpbAFx58DROa41gxZwq6+pNYd20HGmrD+PJl5+a0B+5e0I67nt2TM5dyKBTiSDOyrFi+NJoWS6WNd6PbYKOREPoH07hx9tl4aNu+bNv0lPoY1r70TjaO/7v7hO7rJzTHs1Otqe2N3kQKiVQGGSlRGz256LVR/Wuqi+Hl2y5Bbaz4AtlGtHnYajuW3GO2DaDG9nu9ClY+tTvn/P31KyZnY9Xorp6wQU6PahY0D4UEzmqpR1NdFI8uvhgZCdSO/L2rP5l9nlEHdiKVRlcfmMuJyNMK5V29kbb57dj8Eb8ADPNufr/T6vnt2PTqflx54ZnZ/ocTqQzGj6lFU3y470w9f59SF8WY2ojuewNAV1/SdL6t1B0eTg9+c3LO/cCOXLY6sk070uyWTbuw53AfPrdm+6iRnUaMRhRfPqUNm26egVQ6kzMS9JT6Gnz4tDEFR4sYUTsHn9/diSUbdmLB2ldw9Q9fQ09iSPf5qXQG92/dO2o0ybqFHTn7w2hEaygk8OFT9ctqtJ9roydHmaqKBa26D08bG7d07Mgao4Slzomod5UsPza+9b//gNbGGnz157/Hkg070dWfxK2XnYv12/dl40ztfMs/juFQCCuf2o3PP/AqlmzYmZ1cPx4NF4x/bYL/3tXTcFZLHe658rzsCGjtCcZI/ue488k3sXahuTsA3KY3ijzorORtKyNuQiGB08aMvtvj+1dfgC07D+Q8d0JzPLuIn/Y9C12xf+0bH8OXP34urv7ha5i9+kVcte4VXD/rbEyb2JR9/cGeBIbSSrbM6sW5d3tOoLNvEPu7B3TvJrBrX6rnooGkgmt++BoWrH0FSzbsxPO7Oz0xGs9P9OJ07cIOrN++D9+/+oKcx1fPb8c/P/MWFClx17N7AAzHwodObcTPFl+MJ3cdxOv7e0dNg3Hb5jdw62WTsu+jHWnKkWZklbZ9BQCHjiVMn1e08W50Xo+EBK778W9w17N7ML9jIpriUbQ0xPDVx36Px3YezL7X/Vv3jjrnrlvYgXgs90vq/u4B7Dnch88/8Co+etdL+Nya7dmcqFf/hhcFBCY016Gtsbx56tU8/E7nQEl3GJI7rMR0W0NNTtx19SfR2liD73z2I5h6+hjURcO498rzRo2436Bp06qP33fVeegfTOdsR/1ed0ZzHc48pQ5tY2oxrqEmJ06N7lh963CfLed8IiK7GH0HNWqD6g1cy2/Hrr/pIqQVme2kjYTEqPy6en47wgI4t7UBP7l5BjYvnYkVc6bgke378OnzJ+ADzXWYesZYTGiOY+oZY3FWS31O5/a8Ndsw45//D/7p3/9zVFvj7gXtuGXTLsv5thLtbu3d2io72x9W+0mtEEaLZ/nB9OnT5Y4dOwz/bmUkWFdfEvPWbMPBngTWXduRc4UbQHZURaHRHdoFlOZ3TMSE5jikBJbmjU62OkIs/3Ok0hnMXv3iqOdtW34pzmiu033d5x94Fa0NNVh6yTnZVYzPmzgWp9Sf/DzafWD1c+fvZwBlzRVTaNRKoe3aUOFd+aZeLHadZHScH118sW5cvfaNjyEjgfd6E+geSGVHIV0+pQ13fmYqpJQQQuDOJ9/E87s7MW1iE756+bk4vSmO/mQaDTXDN0jEIiG01cfQP5QZtViVHfXCzPHX++xL/udZuG7W2UgrEtFwCG0NNYhEKnvdrcS5lgIRu04vYKAdtR4LC/QmhnDg/UT2KvgHWuqyjRWzZTOqU/lzgKq51MwVfjO5t9j+Uv8mIDGQGr7A2VQXxcf/5eVR75N//rC6rTL5LnaHhjLo7E8irUhEQgLrt+/Duv+7H1/9+CR89oIJGMooyCgSm3f8Nz513hnY+Mqfs51samysfGo3Vs9vR10sjHlrto/axstfvxRhAY5Q9jbfxK7aafvn7hO6+c5MLjG6s+/QscSo9oNRW/rJW2YjPTJd276jA7h/61509SdzFgZ8891j+Olv/pztqFanMfjuvPZsDj06kMTgkIKwGJ76qCleXh3Jz+PTJjYZLl4YkLrom9jV0sZiNBxCRlHwbu8gugdS2LLzgOGdOOm0MpyzMwoieW29zr5BfOuJP2Tj7ZT6GO5+7m08v7sTV3VMwKKPfhDhkEA0HMJAagj/8vwfs7FotqzxWHjUWjTqHNDqIAveoWmap2K3lPmGS1HKHMVuzYXMOZdN81Ts6jH6DjqptQE9iSHdNsK7PSd0+xBevu0S1MXCGMpIJNNKzjl/080z8J2nd+ue5wHgH554I/s3ZaSv4fSmWsSjkVFtYr3vYWo/RVqReKezH/dv3Wv5O1aluLHmQwnf6UxtOLDTYgDFb/XTMjunXCHaqzbP7+4c1bAuZbJsveDadPOMokPZ81f3Vm/fVhdMUeeLMdoHVj630X4udOtEsY5jAAUXBPTiQit+YbSAj96CY5dPacPRgdSoW1PueW4Pnt/diTs+LXFGcx3e7TmB53d3Zl83fkwt+gbTaKyNYNUzb+H53Z24fEob/u6yc/FvW/+I62aehYdvvAi1kRDiNSE0x61fCbRSv1X5MT5tYhM+Onk8Pv/Aq56Ko2peaNDMcTV7QtR7nva9FUWiqz+FFb94M+f4Wy2b1TlAja7wazujzeTeYnlQvbvkrcPHs3X4oRsuLOlWKObckxc9h9IKuvpTOReOV89vR8+JNC446xR84cGT+WTNNRcgFhHY/qduAMjJoQd7Ti4apXdM4tFw4Os7uac3kcKR44M5+e7uBe1oqouiKR4zrN8AcvKoXiduPBbGQzdcmJ0qa+1L7+C3+7vxg4Ud+FtNPVm7sANjaqLoSQxh4Y9ey4l57cKA4xpiuH7W2aMWWVWU4UV7QyGBtsZaW/dPfh7fdaAXdz27B48uvhgAL/J4gdGF2Sd++y4++ZHTsPyTH0ZXXxK9iRROqa8Z1QbQW8hJUWR2Shf1QsLmpTOzgyXmTjsju6ifur0bZ59t6vbk/DZDUzyGJ5bNRiKVxluH+7IdywCnXCEib9D7jnLfC3vw5Y+fO2qKVPU7gNE0C0eOJxGPhUe1l+95bg++8/Ru3fdsjkdxpG8QX/yLD450OB/E3GlnYPnm32eft+7aDkxua8xeINT7Hqb2U4QFcOPDr+f8zev51o3pOErpRzEj0J3LRvQ6HMzMKVfsy3d+YJfaSa2lV8G/8/TuUXPRrL/pIkhIvNtzArFIGBIy+7qDPQnc9ewerJw7Fee0NSAe1Q/QaCRk6/wrekGrjpw5cnyw4GiQYp1r1dz5Vq78hCWEQFgAaUVi/U0X4bof/yZ7XL71qSm4+oev5exndVX2lU/tzsaGWn9aG2rwtb+anNMQ//7VF+DvPjYJtdEwNu/471FfGIcXFCt+zOwYNZl/8jNaqbbSccSFBo2Z7eQ08zwreaRQ/Bk1qk5vimPb8ktHPd/o+DZpFtYzk3vNlL9bc3EIGL4tPX++8nXXdhS9Farac6723DU4pGQ76ICTefGhGy7M5j718WU/+S1Wfe4j2Tlox8aj+PrmN3I6FAaHMlg9vz0nL3pleh4KjkQqM2pBsds2vzE8T6yiX7+fvGW27oV+7QimeCyMI8eSOZ3W9101PMXAHU/+J1bMmZIdlXT/1uERn4XOcbFIGPFYBH/7k9+OqmOPLZnp2P4xmqc+FrH/Io+Td+gEmdGF2fy267qRixh7u/pzYje/Q0J9z+t+/Bu0NtRkY7WpLoYJzXHdNuJtm9/Ahi9eVNJ3I/V7UVcfdEf1q+/J+CCiStE7P8/vmJjzXSL/O4DewLXV89uRTGfwlcd+p9uPsGTDTqycOxVPLJsNRVGQkUBYAHs6+0b1can5XX2PJRt2jgy2rCvYua3mVDv7twB3crRTnb9Oq7rO5UJD/QstFmbmi15+YJfaSa1ldCVGrYzaW62uW7M9W96NX5wxagTGjQ+/jm3LL9UNVPXKfSmf24rugRT+3H1i1Bfz/E6KYp1r7HwrjzqiUa8uPHnLbCRSJ6dgMRqRqY0N9ffDxwZHNcS/tOm32c5ooxOE2Slnyh01mX/yM1pNttJx5ORE+35ntpPTzPPM5pFi8dccj2Ltwo6cK/NrF3YYTrFidHxPpE4ucGEm95opv9FovA1fvAiKAkTDwnAxWKvbCjLtueveK8/T3RfhkNB9PBoOYcmG3wAANi+dme1YBoaP9XvHBrFl5wFsunkGwiONZHYmkN2MFqjLSOP6nUhldPPopptnZC88/2zxxfjaz3+f85yvPPZ7PLb4Yjy/uzPnriYAuOPTmYLnuJb6GAZSad3yODmVn9FdXXZf5OFdIKUzitP3B1K57cqNO/HYkpmjYje/Q0L7nurfgeG72tYu7MDgkP72QkKUFReFYo3xQUbcmn6Dqpve+bnY92V14Nqjiy/GwZ7hgZr3PLcH//jpKbqva2scXkQ1FArl9Eeo/QXavK3N79r36OxLZtfIKXb+tvPczhxdWFV0LufPs2nU4aAdzRmPhfH4slkYSivZ+eUOHUsU/NKXH9hbdh4Y1eFgNZiNGuChUCjbQdLVlxz1mfYdHSjYOZU/D9jgkDLqyv3wKpz2TlyeSmdQFwsX7aQo1rnGzrfyGXW+Pb5sFs5oroOiSBw+Pmg4IlN7e6F6Uqmv0T+26ih+oxOElSlntGXVzmFb6Aqi9u/jx9Rk67YQ+iuBVzqO3PqS60dmOznNPM9sHikWfz0jI/L0RuipeVqNQfXq/MabZ2Bf18m5x+698jwoUuLRxRebzr1G5RdCZBePNRqNFxICY+sjpucprfacqz13GV04job17/7pHVlsd0JzPNugVuv1ums7MK4+hgvObGaHMjlKXWg5Pz5royEInDwXTpvYhKWXnIOW+hjSin6HdGdfMvv4uAb9L51pRRbIT8qoO/C0i1bW6UzT5XS+ses21GLtkWq/C6QcRueh/AVpD/YkMJRRDGM3Eg5BSolYJKx712ZXfxKnN9VicEgxnLKo1FytxseY2ggeWzITYYFsB4u6hgPjg4gqRe87qLbtqlLP5+m0kr2TSQiBH/3Hn7IXlRtqIrqva6yNZs/52nOi3l3/3QMpw7x/2tjh6bGKnb/tnGLC6jm82u5ECXzncv7Vhc1LZxp2OBhN42D26oReYDfHo2UFs5lOJr1OlPu37sW6hR2jFk3Lvyre2lCDr18xGdFwaNSVe2B4kSfU5+6PcipILBLOrphc6EtDsc/NzrfyGXW+nUhmkI4r2NvVj/te2KN7u3ZbQ41uHMSj+icRtXPF6ARhdcoZtaypdKZoHS30d8Deq5l2cWOuJac4fRI128lp5nlm84iZOymMRugBuYu95k8Ls3ZhB/oGh7DqmbdzRrTm5149Rreh3fnkm9lFjbTPaW2owa2XTcJZ4+oQj1pbAKuacm5+DDfHo5BA9ty19qV3RuXFH1xzAf5t61784JoLsrfzT2g+uVCjur9OHxv3Zb0m/xtXX6Nbh8eNLOz84HXTsznqke37ML9jIloaYkU788IGF2nDITFqe2sXdmQX/718Shs23TwDNZEQMhKQcrjetdTHDMvqdL4p9zZUM98Zqv0ukHLonYfWXHMBvvd/9uY8b0JzHCGDuFRjd8HaV7K3XD947XQs2pB7zJriMSCu30Yc11BajDA+iMjrjPqz9L5vPLLtT/j0+RNG3bkJDN9pn8ooo9rLq+e3oz4WgiJD2ba2mvP0Bm9s2XlgVJ/W6vnteGT7PlxwZntOuY3O36Wc242+z1rJ0dU4yjnwncv5Vxesdm5pX6+O5hhIpnH4+KDuwhB6wVvulebxY2qG58STQG00lLP4naJIZHRGh3T1J3FaU63ul1jtVfEVc6ZkF5Iqtl/sqCAt9TF8oKWu6PQbxTrX1L+rUzioCwOSeUadb/uODiAWCWWPc1dfKjtf6OlNcbQ11Iyax06Ng+Z4FJtunoHOvmR25e7rZ52Ne57bA0D/BDE853LUqJgFyxqLhMuen9urnbh+nGvJjZOo2U5OM88z24lvNMJdiMJzLqv58+hAMnu7V/60MUs3Ds85lj9VgpkRemr5H1syE+/1JtA9kMouELT7UF9OjD95y2wc6h0cdcFR79gYNai0+yoaCSESEkXv6PEbvRh++MYLUReL4KxxdVh/00VY9cxbuOe54XUMPtBSh86+JPqTaWz/Uze+9LH/L7u+QW10eB997+ppOfvIb/WagsHMyJ47PzMVdz75ZvYiWGtDDb5/9TS8PzCEutjw4ICJp8Rx17NvZ9/38PHBUW26uxe0Ix4L4zTNxRQhRLZjGUD2X6NFgrx6bi7EzIimar8LpFw1kRBWzp2ajcfG2jC+8ckPY/FHz8m2O7906SQMJIew5poLsExzsU/tkJjfMRHA8PFZ9cxb+PoVH8p5z5qR6azsvtDP+CAiP9Brq05qbcCjiy9GMq0go0g8+PKfcNmU8Vi6cWfOne9dfUl857MfwR2fHr5D+F9/lXtn5yPb9+ELF30ANz78OiY0x0emKhrOeXqDN7788XPx+p+OYv1NF+H9gRS6B1J4ZPs+fPnj5zp2wbnQ91krOboa71QKfOdy/tUFvaAtNBpCff20iU342l9NHvU6p648qKvS63UG/D/27j8+rqrOH//rzEwmmfxoE9Kk/GgR7KfUjd2yJRFa+lktIsguBcSCuNBWQWj7rSwuq1D8aD/qVnepwPJRsbaFFSnISpfCwhYVWNd+/GwRtKFrF4MRETSFtgkhadN0MpOZe75/TO4wP+6duXfuj7n3zuv5eOQBbSc3N3Ne533vnHvvOepdJkAmtF99qq/od9q6sjtz1R3vrPKt3hGS+550ttTrdubC98VIByl312IoJHBaexNaG+t0B8xzX1uu42ktNBOEq0FuPELR3hTVvBJ459P9+D8f/7NsO+8bGM3ezb5n/XkYiU9qXnB561giLyPq1ct/+68D2DcwilltMVy75HSc2FqPH6xehOTUwenRvX/ER86aXbLdSg0UHjwSL3tXKe8CcYcbB1Gjg5xmXldu38ICmlfew1Nx1cpn7iKrUgIdzfW6i7yePqMpb6qEUsckrdogpcQVW35etN3cudDSCrJ9Xf13rbYpd4FAnYYmqFfih8eTuPvZ/uyJsCIlRsaT+OT97yxSunVFN6bFIvjD8HHc/qOXs3d6fvvqs3AskcKJ0xswqzVnHusyd6ATuaXcnT1SSizvnp2tdR3N9XkLWM5qi+Fbf7UQX770vdnBvN2/OYwr33dq3uDczGkN2Scj1J/3xsjxoqc7Si0SVOpDo1cfMzVyvlFLT4HYTV18T32PF85uxa0XzctfoHbqrjkIIFYXKhqQ+NT/fDdu/9Fvst9/y4ffg9Hjk0imFXzzR69kz1d3rFmcvYnIrvMX5oOI/EhRZNGNZZuWL8C0WB06muuLxsi2rujGvJkteDuexP/6yy689tY4bv/RbzB0LJF9og/I1L+vPtWXnSZr38AoHnjutbw1SBRFwZd3/QYLf3UIa5fOQWusDsu7Z2OGQ8d9dVrQ8UQKG5Z1YcvuV7FvYDTv3MRojS735LUXz2OsCvzgcuHVBTW0O9Yszs63Vaox1e/XWjHY7KCJ0RCpH9wPHZnIW/iuo7keh45MoKk+jFhdJDtQ/EzfYPbuUvWq0IypgGsNAKiPOXY012N6rA6z2mLYNzCKO5/uz7tDtfDO7HInRUYHHEIhgROa6iv6wG10/myjg91e5NbATSgkcFJrA+7+2J9hRnMUigSEAL56+XxEQgIXdnXmfRBUr8rpXXC5/5PvK1qoce1Dvbj/k+/DsjNPQUNdGBMpBccm0vjaU314pm8we3C6+9n+vPlptfZV7+4RK/NzB3mQrBrcGsg3Oshp12BoKBTCA8+9VnTl/WuXL8juT+Gc/YWLrN5xxQJMprXnb4xGQnj4hnMQFgKxqP50FVq/x9YV3ehoqdftr6pybZNKKRg8lsBkWsGhIxPoaM5ceNSqq0G+Eq8oCtad9z8wMj6JaCSEGc312Ljr13m/65qputZQF8b/vuS9GBlPYnn3bETCAm2NUc2nmoiclnu+o15MUxfnbYvVZedELHUuFI2EceK0hmze1y6dg88WLNb31/+8Dxsvm5+962jrym68q60R02PRkts3s0iQoiglp7Py6nHbyB1Nfp72ykl5+Q1n7hyOT2b+X10ct/A4tnbpnOzAMvBOfd542Xw01IUwraEOHS0R1EdC6JzWgK9cOh9fevIl7BsYzZ7DXvu9dy4cqjdY7BsYxZujcRyJT9qaK+aDiLygcDxFnftd71xB67x//c79+MHqRbjp/LlFY2RrHurNW/hX/bwyozmKtQ+9mPe05jN9g9h42Xzdmjc0lsiOVak3u81qyzwV5cT7Unh+kXtcUKfRNVqj9Wp+XSTk2fMYq4qXsa8yIcRFQoh+IcTvhBC3Wd2eenXhwq5ObF3ZjUfXLsYXL+5CZ3M9TmlrREdLfXZe1qGxBN4YOY6hsQQUReZ9f6lVMvW+N/fv3x5PoP/wGC7fvAdLNv0Ul2/eg/7DY9nX5lI7cO7Cd+pJ0IYnXsL7v747+/3qQhRqh7v9R79BNBxCIq3g0NEJzQGAyNQ8eDedPxe3/+hlbFq+ILuNjbv6EIuGNT8cqx0kV+5Jkd6AQ+FCG5VSO/wXHt+Pl948qruKc+Fgt5H33EsqfR/1cljqdRISsWgYK7/7C5z/j/8Xq777C4wen8RX/u3XuOn8M3BhVycA5F2Vy73gog64PbJ6EWafENNsj4nJNI5OpHDt936JS771n7jmvhfwiXNPx8LZrdmD0/Lu2WUHINWBwsJ+Gw4BW1d0Z7NZeAVR7cNa/673Xh86OlH2faRi5WqE3Yz2FaOvK3UcuPmCedi4qw9XbXseG3f14ba/+JPsnclDYwkAyOYzraDo593y6H7UR8LZequ+N+ocpO//+m5cte15HD6aMPX7rnmoF79+86hmfw2HkN0/9ViRS22bVErBbw6P4WNbf44P3LEbG554CZ/78DwsnN2a/Tm5/dPIhUYjtchrEokUgMwcshueeAmXb34Of3Xv89l6pTowkhl0iEZC+Oa/v4JL7tmDjbv60FIf4cAyVUUqpeDlQ0ez5zsf3fwcXh08hhsf3ofLN+/By1PnTrnnQkfimT76h+FxvDkaRyqloL0pmr0BAYDu0xaN0XD2/9c82IvRiVTR8bmQ1rFYXSQo16y2GNKyuIaqNdvpc00rSp1v5NI6n6lVqZSCwaMT6DuYk9/vPIdXh47hb37wX/jY1p/jN4fHcPDI8ewUVapS+bzl0f2YHqvDjKnPe6ee0IiZ0xpw8wXzdG8aWr9zP9YunYNZbbFszuzMFfNBRNVWOD7ysa0/x6GjCdz3s99lzxVufHgfXnrjCP749nEcHI1jMl183p95simN02Y0atbh3IV/1c8raZmZujXXrLYYQqGQbs0zWjftoDeIrh4X1M+zRmu03r5HQto3SHrhPMYqT925LIQIA/g2gAsAHADwSyHEk1LKvkq3GQoJzO1o1p3TrdyCX+rViUNHJzSvPMSiYc3vndvRnPf4gNZdnXp3eqkf3HMnNde7c/rJG5fkLdaU+3iY3uKF8WQa82a2oKk+rHvXc6kPBnqPATh916L6uLI6F2C5eaL9enddJe+j0TszC1+nlUv1vV37UC92rFmML12Sf4e/mgNFyrzFye7/5Pt0+kgku8BV4c9Q+6Q6aG1G4cKUGy+bj9NnNKGxPpw3zUqpK4x67/Wbo/HsYi9BuZLoBrcf5zTaV4y8zshxoNSdybmv1ft5JzRFEY0IbL/ubNSFQwiH8ucgLVej9LbbGA3n9de6SAjHJlK49J492f3bft3Zum1z6OhEdjEOdZu5fbTwAkEQnwZIJFJ45a1xDI0ldGti7h0TJ05vwIPPvYYdvQeyd2+ePD3m6d+RgklRJA4enSiaXuLmHb/C7R/9U6z4p1/g/3uoFxuWdeGZvkEcGInj7mf7cdP5ZxQtwvOemS2oC4nsNEBai+vMantnkV71Zxk5zzO6SNC9q3ogpSxZs50817SCd52ao17Y1Kq76nowax7sxdqp/O7sHcCWFd3Z3OotED4an8SBkTgUyKInJ9X2OZ5MaeaovSmavVPN7lwxH0RUbeo6MLn19tMPv5hdU2TV4tMghMib5uLbV59V9ITkTefPxSfv/yW+nzNnskq9QJfrwEgcUkrTnxPdrJt6n7NybzY1Q2/fy03p6WeeGlwGcDaA30kpfw8AQognxSOBAAAgAElEQVQfALgMQMWDywAwEp/UndPNyIJfoZDAidMaNDtDSpGa37tjzeK8v8+9C1mlFyL1g3vuPMh6V+fVgWI1tFdtez77ulKLF4ZCArG6iKnHDMp1bqcXoUim0nlzAZabJ9qvc+1W8j4aHUgvfJ1eLtW8SSlxSltj3r+rOTh4JJ7Xr775k1eKFvXZtrIb0bDQ/Rnq79bZUm+6YOf+LgdG4tlHdB9ft8Tw/N1677V6QPTLBQmvcPuDk9G+YuR1Ro4DagZyF0XVeq3ez/vd0DFs3NWHh68/B6e0xnDwSLxoDtJSNUpvu+oHabW/Do0l8ualPDASx6rv/gJP3rhEs20m04puH9U68St1EcGvF/XeOp7E2od6cdeVZ+qeWALInmQ/+NxruGbRaVh17ukcIKCqGh5PIpHS7sMnTm/I/n9rzsK5y7tnF11QUi9Q1YXfmQbo5OkN+PbVZ+HTD7+zKFrufImAufM8rWOx1jGj3OLbTp5rWsVFO40bPJYoWXfVzKr//0zfIG748znZ+b0VKXHXlWdmp27JfYR5VltMc6FvtX2GxrRzND1Wh1sf3Z+de9nuXDEfRBmn3faUqde/fvvFDu1JbdF78vvtqSneTpzekF1nRP23Tz/8Ih761DnoOziWrbWntjdmb8gq/Py/+ZqzcM9/vJL3M9R6Om9mzPTnRLfqpt7nLK3pYo3S2vcgL9zqtWkxTgEwkPPnA1N/lyWEWC2E2CuE2Ds0NGRoo+UGGY0MQuYOmuxZfx4eX7cE82a2YFLnhL7ww7p690cuvRCpH9yHjiWyK9Kf0hrT/f7c0Ob+THXwtZLpAvSUegzA6ccW1AKk/o6580T/7Jal2TYpHOzOVc2OazS7lbyPld7BqZdL9e/13iv1Pc7d1r6BUXz9x/146FPn4N//9v3Z+bsbotrtoP6MSu/4s+PigdZ7vWn5AmzZ/WrF2wwiM3XXzcc5jfYVI68zk6dyry2VqwMjcYSn5oM2W6NKbTf3+/T2L55Ma7ZNJCQ09+OUqYs1WvPmax0PS921Xa0+ZDS7KSVzp6ReTZweq8OjaxfjB6sX4ZTWBlz//v+BWW2NfGyZHGM0u8lUGmEBzdyGhcj+f+7dxnpTvaXSmakxPvOhM7BxVx8uuWcPvv3TV7D9urPxxKeXYMfqRZmBuanHWu04z9M6ZpSq2W4+IkuVMZpd9bNSqXPRwv//+x++jI6Wenz2X36Fv7r3BfzTf/4eD153Nn76uQ9g42XzcefT/Rg6lihaAL2QVo62rOjGHU//JjuwzFzVnkrGGYi8wGh2w0L7nH94PIn2pijCQvumMAmZnQpzw7IuHBzNDCR//cf9iEXD2HjZfDyyehE2XjYfbY112SmI1O2r9dTL0/7onV/YPeVdkM9jvHbnslar5U3WKKXcBmAbAPT09BiayNHKgl+5zFx5qAuH8v5+y+5Xi67q6IXIzKODud9vdvFCu+8ydPquxfamKOLJVNHvuHFXn+ZdcV5bcdlodit5Hyu9g1Mrl5uWL8ADz71W9r3S+plDxxJQpMQt/7IfQ8cSuquqbl3ZjRlN0ey/V5IRO676Fb7XQmSmKchdaCAoVxKtqKTuusFoXzHyOjN5Kvda9eftWLMYb47GMTyezC4Gkfs6szVK3e5j687F8UQar701nvdBWv0+s32jqT6MzdechXXff+fuxM3XnIXm+jBaG/UX2TTzNEC1+pDR7KoD7FpPxGxavgB3PP0bfOZDZ+DElgZEIl67Lk9BZDS70UgYb45OFB3L77hiQXZKt9y7iGa1xdAxNddxYT+NhEOZOtPZgoevPweDYwkMjydx+49exs0XzMOJ0zMfhJx+OqVczebUAt5mNLvqZyWtuqveIZ97NzKQOc88qbU+L5//8KOXcdtf/Anee8o03HP1QkOZ0Pus9bXLF+BLlzBXtcqr57tE5RjNbixafM6vfvb/wsVdGDya0Dw/SKUlNu7qy/79wtmt2fOOrzzZh5vOn4uZ0xqy01MCzp8r2M2tJ3CDPEWSkNI7dVMIsRjAl6WUH5768+cBQEr5D1qv7+npkXv37i273XJzQFqZI1LvewvnXJ7VlpnzsrkhgsmUUlGIclf21Pp+v851aUYqpaB/cEx3/uxC5d4zDa68UUaza1Slcy4X5jJ3xdhy75XWtjZfcxYe+vkf8Nzvh4v6mMl2sO13rvY2XeTL7HqFmba30t8KX1dp3yj1fWZzrCgSb4weRyIlERKAIoH6iMAprY2mc19hH6p6dtU5l9c+1IuO5nrcdP5cvKu9EfWREEIhIBwK5c3lTjSl6tlVFInXh8cxNjGJt8cn0RgN43gyjdknxLLTZbz4+jCuWXQawiGBukgIaUXB0Fgy78OlOueyevHEieM2eUrVs6vOuZxbd0+b0Yh0WqK5IYJESgEAfO2pPjzTN5h3PAHAfNauqmc3l9npHbyukukn3HgPAjItRtWzq57zT6Yl3p5aJHdn7wBu/OBcvPj6MD48/yS8PT6JNTlrMmxavgA/6z+MS/5sVt5aDVbHtshXDDWs1waXIwB+C+B8AG8A+CWAq6WUv9Z6vZlBDiMDs5WepOh9bzVOzGvhw4DDv2PVi36ljL4vdr5/uduqi4QQCQnEk/7Ou4/7kG+z6xVm2r4a/c0Msz/Xqbrgp4t6iUQKw8eTmFQkwiGBhroQTmjkgDKV5InsKorEaDyJeDKNtEQmu7EoRuKTuhegjk4kMZ5II6VI1IVD6Gyu5135tcUT2U2lFAweSyCVVhAJh9AYDWE8kVk4V0IilZKYVCQUKdFQF+ZFPgI8kl0VB5c5uGyCJ7Kbf84gURcKIZRzcxnwzsW73BvP2mJ1uucVFHiGGtpT02JIKVNCiBsBPA0gDOC7egPLZpWbCNzKROF631uNRRtqYaGIWvgdK2H0fbHz/dPcVpMtm67853twm+QPZtq+Gv3NDLM/1/G64AP19RGcXO+p0yIiQ0IhgROa6ouOv3r9MBQSaG2sR2uj5j8TuSYSCeHk1vz5P5lLIiLn6J0z5NI7f/Dj+T25x3OfoqSUPwTww2rvBxERERERERERERHp89zgMhERERERERERucer03xUsl9uTfERkCk7iCzj5GpEREREREREREREZBoHl4mIiIiIiIiIiIjINA4uExEREREREREREZFpQkpZ7X2omBBiCMAfyrxsBoC3XNgdM7y4TwD3CwDeklJe5PQPMZjdSnm1Hd1Ui+9BELLrplrMCODN39tr2fXie1QNfB8ySr0PzK4/8H0pxuwax32rjFP7xuzaw4/77cd9Bt7Zb2bXGO6XcW7tk6Hs+npw2QghxF4pZU+19yOXF/cJ4H4FBd8vvgdUXq1mpFZ/bzP4HmXwfcjw0/vgp311E98X7/NyG3HfKuPlfbOTX39PP+63H/cZ8O5+c7/M8eJ+eW2fOC0GEREREREREREREZnGwWUiIiIiIiIiIiIiMq0WBpe3VXsHNHhxnwDuV1Dw/eJ7QOXVakZq9fc2g+9RBt+HDD+9D37aVzfxffE+L7cR960yXt43O/n19/TjfvtxnwHv7jf3yxwv7pen9inwcy4TERERERERERERkf1q4c5lIiIiIiIiIiIiIrIZB5eJiIiIiIiIiIiIyDQOLhMRERERERERERGRaRxcJiIiIiIiIiIiIiLTOLhMRERERERERERERKZxcJmIiIiIiIiIiIiITOPgMhERERERERERERGZxsFlIiIiIiIiIiIiIjKNg8tEREREREREREREZBoHl4mIiIiIiIiIiIjINA4uExEREREREREREZFpHFwmIiIiIiIiIiIiItM4uExEREREREREREREpnFwmYiIiIiIiIiIiIhM4+AyEREREREREREREZnm68Hliy66SALgF7/s/HIFs8svB75cwezyy4EvVzC7/HLgyxXMLr8c+HIFs8svB75cwezyy4EvVzC7/HLgyxBfDy6/9dZb1d4Fooowu+RXzC75FbNLfsXskl8xu+RXzC75FbNL1eLrwWUiIiIiIiIiIiIiqg4OLhMRERERERERERGRaRxcJiIiIiIiIiIiIiLTOLhMRERERERERERERKZxcJmIiIiIiIiIiIiITItUewfI/xRFYng8iWQqjWgkjPamKEIhUe3dIg9gNsgsZob8gDklqh72P3ILs0ZOYK7Ir0677SnT3/P67Rc7sCfkRRxcJksURaL/8Bhu2L4XB0bimNUWw72rejBvZgsPkjWO2SCzmBnyA+aUqHrY/8gtzBo5gbkioqDitBhkyfB4MntwBIADI3HcsH0vhseTVd4zqjZmg8xiZsgPmFOi6mH/I7cwa+QE5oqIgoqDy2RJMpXOHhxVB0biSKbSVdoj8gpmg8xiZsgPmFOi6mH/I7cwa+QE5oqIgoqDy2RJNBLGrLZY3t/NaoshGglXaY/IK5gNMouZIT9gTomqh/2P3MKskROYKyIKKg4ukyXtTVHcu6one5BU541qb4pWec+o2pgNMouZIT9gTomqh/2P3MKskROYKyIKKi7o50F+WkE2FBKYN7MFj69b4ov9JffYmQ0/9QmqnJ/rCTNaO/ycUzOYafKiWul/ZrG/2o9ZM4bZM8ftXLF9iMgtHFz2GD+uIBsKCXS01Fd7N8iD7MiGH/sEVc6P9YQZrT1+zKkZzDR5WdD7n1nsr85h1kpj9irjVq7YPkTkpqoMLgshbgZwPQAJ4L8BXAvgJAA/AHACgBcBrJRS1tyyqXoryD6+bglPbqgmsU+Q1zGjFDTMNJF/sL9StTB73sb2oVJOu+2pau8CBYzrcy4LIU4BcBOAHinlfABhAB8HsAnA3VLKuQBGAHzK7X3zAq4gS5SPfYK8jhmloGGmifyD/ZWqhdnzNrYPEbmpWgv6RQDEhBARAI0ADgL4IIBHp/79AQAfqdK+VRVXkCXKxz5BXseMUtAw00T+wf5K1cLseRvbh4jc5PrgspTyDQB3AvgjMoPKRwD0AhiVUqamXnYAwClu75sXcAVZonzsE+R1zCgFDTNN5B/sr1QtzJ63sX2IyE2uz7kshGgDcBmA0wGMAvgXAH+h8VKp8/2rAawGgFNPPdWhvawerkwcXEHPrlPYJ6qP2S2NGfUuZrcyzHT1MbtklNf6K7NbO7yWPauClt2gtQ/pC1p2yZ+qsaDfhwC8JqUcAgAhxGMAzgXQKoSITN29PAvAm1rfLKXcBmAbAPT09GgOQPsdVyYOplrIrlPYJ6qL2S2PGfUmZrdyzHR1Mbtkhpf6K7NbW7yUPauCmN0gtQ/pC2J2yX+qMefyHwEsEkI0CiEEgPMB9AH4KYArpl7zCQBPVGHfiIiIiIiIiIiIiMiAasy5/AIyC/e9COC/p/ZhG4D1AP5WCPE7AO0A/sntfSMiIiIiIiIiIiIiY6oxLQaklF8C8KWCv/49gLOrsDtEREREREREREREZFI1psUgIiIiIiIiIiIiIp/j4DIRERERERERERERmcbBZSIiIiIiIiIiIiIyjYPLRERERERERERERGQaB5eJiIiIiIiIiIiIyDQOLhMRERERERERERGRaRxcJiIiIiIiIiIiIiLTOLhMRERERERERERERKZFqr0DFFyKIjE8nkQylUY0EkZ7UxShkKj2bpEHMBvkFcwi2Y2ZIgoG9mWyE/NEAHNARMHFwWVyhKJI9B8eww3b9+LASByz2mK4d1UP5s1s4QG0xjEb5BXMItmNmSIKBvZlshPzRABzQETBxmkxyBHD48nsgRMADozEccP2vRgeT1Z5z6jamA3yCmaR7MZMEQUD+zLZiXkigDkgomDj4DI5IplKZw+cqgMjcSRT6SrtEXkFs0FewSyS3ZgpomBgXyY7MU8EMAdEFGwcXCZHRCNhzGqL5f3drLYYopFwlfaIvILZIK9gFsluzBRRMLAvk52YJwKYAyIKNg4ukyPam6K4d1VP9gCqzinV3hSt8p5RtTEb5BXMItmNmSIKBvZlshPzRABzQETBxgX9yBGhkMC8mS14fN0SroZLeZgN8gpmkezGTBEFA/sy2Yl5IoA5IKJgszy4LITYCOArUsrU1J+nAfiGlPJaq9sOEkWRGB5P1tSBJBQS6Gipr/ZukAflZqMW+wZ5h911inkmHvu8hX2SKmVnX2YOiccGbbXWN2ohB7XWpkSUYcedyxEALwghrgVwIoBvTX3RFEWR6D88ll0dVn0EZt7MFhZaqmnsGxQkzDORt7BPkhcwh0Ta2DeCh21KVLssz7kspfw8gPUAXgDwPQAXSynvsbrdIBkeT2YLLJBZFfaG7XsxPJ7U/R5FkRgaS+CNkeMYGktAUaRbu0vkmkr6ht3Y1/zNS+3nhTyTdV7KFFnDPhlMfuujzKE/+C1XQRCUvsHsvCMobUpE5tkxLcb7AXwDwN8B+FMA9wghrpNSvml120GRTKWzBVZ1YCSOZCqt+Xpe8aNaYbZv2I19zd+81n7VzjNZ57VMkTXsk8Hjxz7KHHqfH3MVBEHoG8xOviC0KRFVxvKdywDuBHCllPIfpJRXA9gG4D9s2G5gRCPh7KqwqlltMUQjYc3X84of1QqzfcNu7Gv+5rX2q3aeyTqvZYqsYZ8MHj/2UebQ+/yYqyAIQt9gdvIFoU2JqDJ2DC4vllL2qX+QUj4GYIkN2w2M9qYo7l3Vky206hXN9qao5ut5xY9qhdm+YTf2NX/zWvtVO89kndcyRdawTwaPH/soc+h9fsxVEAShbzA7+YLQpkRUGTsW9JshhPh7AKdIKS8SQnQBWAzgn2zYdiCEQgLzZrbg8XVLDK2aql7xyz1Q8YofBZHZvmE39jV/81r7VTvPZJ3XMkXWsE8Gjx/7KHPofX7MVRAEoW8wO/mC0KZEVBk77lz+HoCnAZw09effAvgbG7YbKKGQQEdLPU5pa0RHS33JAssrflRLzPQNu7Gv+ZsX26+aeSbrvJgpsoZ9Mlj82keZQ2/za66CwO99g9kp5vc2JaLK2HLnspRyhxDi8wAgpUwJIUo+ByKEaAVwH4D5ACSA6wD0A3gEwGkAXgfwMSnliA375ztuX/FTFInh8SSvLlJg6WWcV9f9zY/tx3rrbaUyxbYjqj6v133WCX9yK1fMR/DkZkdRFKQlIGWmndm+RFRL7BhcHhdCtCMzSAwhxCIAR8p8zzcA/FhKeYUQIgqgEcD/AvATKeXtQojbANwGYL0N++dL6hU/p3GFWwq6chl3q6+RM/zUfqy3/qCVKbYdkXd4te6zTvib07liPoIrFBJob4qyfYmoptkxLcbfAngSwBwhxB4A2wH8td6LhRDTALwfU3MySymTUspRAJcBeGDqZQ8A+IgN+0ZlcIVbCjpmnLyCWfQvth0RlcM6QaUwH8HG9iWiWmd5cFlK+SKADwA4F8AaAO+VUu4v8S3vBjAE4H4hxD4hxH1CiCYAM6WUB6e2eRBAp9Y3CyFWCyH2CiH2Dg0NWd39mscVbt3D7FYHM24ds2sPZtF9dmWXbUduY931H9aJDGZXG/PhfVayy/alamLdJS+oeHBZCPFR9QvApQDmATgDwCVTf6cnAuAsAN+RUi4EMI7MFBiGSCm3SSl7pJQ9HR0dle4+TVFXuM1VyyvcOonZrQ5m3Dpm1x7Movvsyi7bjtzGuus/rBMZzK425sP7rGSX7UvVxLpLXmDlzuVLpr4+hcwUF9dMfd0HYEWJ7zsA4ICU8oWpPz+KzGDzYSHESQAw9d9BC/tGBnGFWwo6Zpy8gln0L7YdEZXDOkGlMB/BxvYlolpX8YJ+UsprAUAIsQtAlzqlxdTA8LdLfN8hIcSAEGKelLIfwPkA+qa+PgHg9qn/PlHpvpFxXl91m8gqZpy8gln0L7YdEZXDOkGlMB/BxvYlolpX8eByjtPUgeUph5GZHqOUvwbwfSFEFMDvAVyLzF3UO4QQnwLwRwBX2rBvZIBXV90msgszTl7BLPoX246IymGdoFKYj2Bj+xJRLbNjcHm3EOJpAP8MQAL4OICflvoGKeV/AejR+KfzbdgfIiIiIiIiIiIiInKY5cFlKeWNUwv4/fnUX22TUj5udbtERERERERERERE5F123LkMKeVjAB6zY1tERERERERERERE5H2WB5en7lreBKATgJj6klLKaVa3TcUURWJ4PMmFAsg1zByRv7EPBxfblqi2sM+TXZgl72BbEFEQ2HHn8tcBXCKlfNmGbbnCrwVcUST6D4/hhu17cWAkjlltMdy7qgfzZrb4Yv/Jf7yUOb/2W6JynMy2l/pwUFWrNrFtifyp0prBPl8b3DimMEvOMtOGbAsiCoqQDds47LeB5f7DY7h88x4s2fRTXL55D/oPj0FRZLV3razh8WT2wAMAB0biuGH7XgyPJ6u8ZxRUXsmcn/stUSlOZ9srfTioqlmb2LZE/mOlZrDPB59bxxRmyTlm25BtQURBYcfg8l4hxCNCiL8SQnxU/bJhu47wcwFPptLZ/VYdGIkjmUo78vMURWJoLIE3Ro5jaCzBgbwapCgKNizrwiOrF2Hrym4snN3qaOb0+Lnfkr+4Xfeczrbbx41aU83apLbtwtmt2LqyG4+sXoQNy7qgKIrjP5vIq7x+7mqlZrCeB1NuZg8dncDdz/Y7fkxhlpxjto9bbQuv1zwiqh12TIsxDcBxABfm/J2ERxf48/PBNBoJY1ZbLG//Z7XFEI2Ebf9ZfESHFEXirfEkNu7qy2Zg0/IFeOC51xzJXCl+7rfkH9Woe05n283jRi2qZm2KRsK4sKsTnzj3dKzfuT+b2a0ru9HR0sBjNdUcP5y7WqkZrOfBo5XZTcsXYGgsiX0DowCcOaYwS84x28ettIUfah4R1Q7Ldy5LKa/V+LrOjp1zglrAc/nlYNreFMW9q3qy+68eQNqborb/LN4pSsPjSax5sDcvA+t37scXL+5yJHOl+Lnfkn9Uo+45nW03jxu1qJq1qb0pii9e3JUdWAYymV3zYC+P1VST/HDuaqVmsJ4Hj1Zm1+/cj7VL52Rf48QxhVlyjtk+bqUt/FDziKh2VHznshDiVinl14UQ30LmTuU8UsqbLO2ZQ9QCXniFz82DaaULNYRCAvNmtuDxdUscXziId4qSXgbCIWHrYmNG+oIX+i0FXzXqntPZdvO4UYuMtJ9TizOFQgLhkOCxmmqe2seOJ1Oe7w9Waj7ruTdZqfF65x1qHpw632WWnGOmj6vZOaGxDjvWLIaU0lRb8PM6EXmJlWkx1EX89tqxI26p9sHU6uMroZBAR0u94/vJx6XI6QyY6QvV7rcUfIoikVak63XPjWy7ddyoReXaz+lHVnmsplqX28c2LOvyfH+wWvNZz73Fao3Xq+Ent8awZ/15jp7vMkvOMNrH9bMTM9zePAcgIi8RUto36bsQIgSgWUp51LaNltDT0yP37vXV2DaGxhK4fPOeooPA4+uWeOoAr3fAm9vRjJH4ZJAH+Fz5ZfyQ3VInzAAs34nnl77gI57IrlN3aTptaCyBLzy+X3P+2j85cZovfgcf80R2VXZm2Ok6x/kWq85T2bWLn+p4bh9bOLsVn/vwvLwazv6gy5fZ9Vo2rdZ41vCKeDK7ZrNpx/kB8+M7nsruabc95cLeuOf12y+u9i4EmaHsWl7QTwjxMIC1ANIAegFMF0L8o5TyDqvbDiK/PL6iddW1LVaHV4aO8QBWI/SuvAOw5UTGL32BjPPzSW4ylcYzfYMYGktiw7IutMbqMBqfxAwPD6qQ/ezOsNN1jk91kN38Vsdz+9i+gVHc+XQ/Nizrwp+c2IJYNML+ECBezKbVGs8aHgyVZNOO8wPmh4i8xPKCfgC6pu5U/giAHwI4FcBKG7YbSHqT/MeiYQyNJfDGyHEMjSWgKPbdUV4p9XGpU9oa0dFSj5H4ZNlFAxRFeu73oMoVZiAUEnmLRyyc3YoNy7ownkjh0NEJU+3NRfqCx8sLi5SrTWoe9w2MYs2Dvbhq2/PYuKsPoZC1wyRror8Mjydx97OZwalHVi/ChmVduPvZ/ooz7Ead06rTdmKGa4tX6rjR3BX2sX0Do9i4qw+xaMRQf2C+/cPubNrR9nbUeCdqOHPtHkWROHR0AuOJFDYs68LC2a2GsmnX+YFefpgBInKb5TuXAdQJIeqQGVy+R0o5KYRg9dKhNcn/9uvOxuGjCU9diddS7gqrF+8oIPupObD6+CkX6Qser96NbqQ2OZFH1kT/URSlaGqUTcsXQFGUirbn9zrHDNceL9RxM7mz0seYb3+xM5t2tb0Xazxz7R6t93rT8gW48+l+7BsYLZlNJ7PDDBBRNdgxuLwVwOsAfgXgZ0KIdwFwZc5lP9J6fEVCYtXm54quxHtt7tlyiwbo3VHgtd+DrFFzsHbpnOwADGC+vfkoV/B4dWERI7XJiTyyJvpPWqKorq3fuR871iyuaHt+r3PMcO3xQh03kzsrfYz59hc7s2lX23uxxjPX7tF6r9fv3I8Ny7qwcVdfyWw6mR1mgIiqwfK0GFLKb0opT5FS/qXM+AOA82zYt8AqfHxlMqVU/S4RI9QrrOojPIVXWL1wtws5r70piq0rutHeFLVlrjAnH+cmd5WrEdVitDbZnUfWRP+RUmq2mZXFj/1c55jh2uOFOm42d5X2MebbX+zMpp1t77Uaz1y7R++9VrNaLptOZYcZIKJqsOPOZQghLgbwXgANOX/9d3ZsuxZ44S4RI8pdYfXL70HWhEICJ7U24O3xSbY35fHiHTxA9WoTa6L/sM3y8f2oPV6o427ljvn2FzuzGeS2D/Lv5jV67/XJrTGcOK2haue/zAARVYPlO5eFEFsAXAXgrwEIAFcCeJfV7dYSL9wlYlSpK6x++j3ImtZYFCEB3HHFArY35fHaHTxA9WoTa6L/sM3y8f2oTdWu427ljvn2H7uyGeS2D/Lv5jV673U1B5ZL7RczQEROElYe9QQAIcR+KeWCnP82A3hMSnmhPbuor6enR+7du9fpH+MKRZEYHk966m6/Sjhu/iAAACAASURBVATg93BlZ4OQXUWRGI0nEU+mkZZAQ10IM5q8MZhYo5jdEqpVmwJQE93gqeyyzfLx/SjJU9kNErdyV8P5rvnsBrntg/y7wWPZ9ep77dX9qnGeyu5ptz3lwt645/XbL672LgSZoezaMS2G+rzFcSHEyQCGAZxuw3Zrinol3u+C8ntQeaGQwAlN9UBTtfeEqLxq1SbWRP9hm+Xj+0HV4FbumO/aFeS2D/Lv5jVefa+9ul9EFFx2DC7vEkK0ArgDwIsAJID7bNguEREREREREREREXmU5cFlKeXGqf/dKYTYBaBBSnnE6nbdwMdFiKgc1gnyK2aX7MIsERVjvyC/Ynb9i21HRF5leXBZCNEI4LMATpVS3iCEOFUI8edSyl1lvi8MYC+AN6SUy4QQpwP4AYATkLkDeqWUMml1//QoikT/4THcsH0vDozEsxPdz5vZwgJNRABYJ8i/mF2yC7NEVIz9gvyK2fUvth0ReVnIhm3cDyABYPHUnw8A+KqB7/sMgJdz/rwJwN1SyrkARgB8yoZ90zU8nswWZgA4MBLHDdv3YnjcsfFsX1AUiaGxBN4YOY6hsQQUxdqCj0ReYybjrBNklNdqJ7PrLq+1v52YJaoGr/cp9otg83r+rGB23WdXnth2RORldsy5PEdKeZUQ4q8AQEoZF0KUvHQmhJgF4GIAXwPwt1Ov/yCAq6de8gCALwP4jg37pymZSmcLs+rASBzJVLrk9wX5URReDSW3ud2fzGa80jpBtcWu2mlnf2B23ePksdML5xzMErnNqT7FGktGuPV5qFr1ndl1l5E8Gc0C246IvMyOweWkECKGzEJ+EELMQeZO5lL+D4BbAbRM/bkdwKiUMjX15wMATrFh33RFI2HMaovlFehZbTFEI2Hd7wn64Kve1dDH1y3harNku2r0J7MZr6ROUO2xo3ba3R+YXfc4dez0yjkHs0Ruc6JPscaSUW58HqpmfWd23VUuT2aywLYj0nfabU+Z/p7Xb7/YgT2pXXZMi/ElAD8GMFsI8X0AP0Fm4FiTEGIZgEEpZW/uX2u8VPN5ESHEaiHEXiHE3qGhoYp3ur0pintX9WBWWwwAsoW8vSmq+z1BfxSFV0OdZVd2g6Ia/clsxiupE0HE7JZmR+20uz8wuxluZNepY6dXzjmYpeqo5brrRJ9ijXWP37PrxuehatZ3ZlefE9ktlyczWWDbkR6/110KBst3LkspnxVCvAhgETKDxJ+RUr5V4luWALhUCPGXABoATEPmTuZWIURk6u7lWQDe1Pl52wBsA4Cenp6KJ8AKhQTmzWzB4+uWGH4cKeiDr166GuqFR4HtZld2g6Ia/Ukr4xd2dUIIgTdGjhdlrZI6EURBya5TdcWO2ml3f2B2M9zIbm77L5zdirVL56C9KQohBBRF+v6xe2apOoJSdwsZqcNOnI+yxrrHj9nNzaUQAhd2deKZvsHsv9v9eaia9Z3Z1edEdst99gCAjub6vH/XywLbjvT4se5S8NgxLQaQGSQemdpelxACUsqfab1QSvl5AJ8HACHEUgCfk1JeI4T4FwBXAPgBgE8AeMKmfdMVCglTjzd5afDVCerV0MLHcty+GmrHo2JBHJwOmmr0p8KMX9jViZvOPwMf2/pz3ayZrRN6mMnqyq0rHc31uOn8uTh9RhMa68OY0VRvqS3sqJ1O9Ae7skulqe1/97P9+MS5p2P9zv22zL2dlhL3f/J9+OZPXsG+gVEA1TvnYJaoFKPHN6Pnd06cj7LGkp7CXF7Y1YkvXNyFT583F4eOTmBn7wBuvmCerZ+Hqv2Zktl1j5HPHndcsQBf/3G/oWN9ubbj5w0iqhYhpbULG0KITQCuAvBrAMrUX0sp5aUGvncpMoPLy4QQ70ZmYPkEAPsArJBSlpy7uaenR+7du9fK7mvSK8qlTooBBKKQe+GANDSWwOWb9xSdcBmd68zi4LQrv6xT2fWiSvqTkcxVmtXCu1PUkzuVmawZ5dLcesxuCWpd6Wiux+c+PK/kAGAl2TLyPaVe45X5davE99lVFIlDRycqqie5uYhFwzh8NJGXA/VD59CxRMWZ8MKxPaB8n12rzNQuvfO7x9adCwGRl0/A2Hm13QPbNaTms6vKzeXC2a1F5whbV3ZjXmcLIhFjs0mWO9YPjyehKAreGk9izYO9zKN5vsuukc8ed155Jj6+7XlLWdCqc1tXdOOk1ga0xnjc9wBPZbeSOYqDhnMuG2You3bcufwRAPPKDQRrkVLuBrB76v9/D+BsG/bHknInn1qPogAIzAmrF65kW31UjAsTekcl/cnoAHGlfS4342+MHHflsURmsvrUurJhWVf2QyNgbWGVXEbuJCm1XT7q6G+hkICU0nQ9KczF/Z98HzY88VJePm95dD8eWb2o4kxwUI2cZOb4pnd+dzyRxop/eqEon+WOj2ayzRpLenJzuXbpnKJzhDUP9tpygwuAojukH77+HIRDgnkMOCOfPWZOa8ALn/8gQqFQxVnQqsdrHurFxsvm48TpDTzuE5Gj7FjQ7/cA6mzYjisURWJoLIE3Ro5jaCwBRcm/c1styh3N9di6sht3XXkmDh2ZwGg8M6m+enA4pa0RHS2ZR6m9suhOUKiPiuUy86iYV+apDLrCvpRKKUV9q1zf0OpPRtjV56xmzShmsvrUtm6N1dm2sIoZRrab2x/am6IYHk/qHqsqVe4YSJUzW0/Uu51zc9EYDWvmE4CpGpnLb+cozKi/mDm+6fWR194aryifpbKtlaNy5xzMXm3KzWW5c4RySmUy998Wzm7F8u7ZGBxLQAhR1YFl5t55ue+xEEKzDr7+1jhCoVDFx3pAvx43RsNFdZXtTkR2s+PO5eMA/ksI8RMA2buXpZQ32bBtWxm5wyGZSms+Nr11Rbfu4yQcOCrPzCO5Vufaq/Y8ZrVAa366m84/A2sfyn+8b1pDxJG+YabPlcqeW/OMM5PWWX2sX23rQ0cmSraFU/XcbGaduNOUd7A6y0w9UdtiPJHKDjSsXToHnS31nl/IzEnMqD8UPuJtNLNafWTrim588V9fynud0XyWyrbZHDF7tSs3l6PxSUs1uFy91Zt6o1pZY+6dp/WZacuK7rzPTJuWL8CdT/fjnqsXWvpZdZGQZn5H45N5OWS7E5ET7Lhz+UkAGwE8B6A358tzjNy9E42EcdP5c4sfiXqoV/cuCrfufvQr9QB2+eY9WLLpp7h88x70Hx7TvUKa++jinvXn4fF1S0wd7NSTRLVNqrUwYZAV9qXl3bOzJ0nAO31L7+q81b5htM+Vy57VrBnFTFpjtoZoUdv6zNnTsXVFt25bOFXPzWy3mndPU+XM1BO1LYbHk7iwqxOf+/A8bNzVh7/d8SvcccUCW2uFn85RmFHvK6zHX37yJWwpUVNzafWRk1obMHQsf2Y9o/nUy7YQwnSOmL3aVh8JYeNl83Hy9AZsvuasimtwqXqr/pvW1BvVyhpz77zC9/iZvkHEk2lsvGw+Hlm9CBuWdeHOpzPrKlg5LiuKxLGJVNE5xF1Xnoktu1/Nq6tsdyJyguU7l6WUDwghogDeA0AC6JdSerIyGbl7p70pitNnNJV9XeECPG7c/ehXlcw3a2XuZ86r57zCvqT3GGFYoPgupZXdUJTMFBqVtovROwSNZM+NecaZSWvsmrM6FBI4oakerbGobls4dTe7me1audO01B3efrqD1a+M1hO1LbbsfhV3fexMrPruL3BgJI4DI3F8/cf92HjZfMzpbEasznqtULN397P9WN49G+1NUXS21KMt5r0ZzZhR79MaKAGAHWsWQ0pZ9vhW2EcURVZcc/XqaligKEcdzfVIptJ4Y+S45j4ye7VreDyZrcEAsHB2a8U1uNyx/t5VPdknVnKVy5pTi7Iy987Teo///ocv46sfmY81BU985tY9s22u5rijuR4blnWhNVaH48k0FCmzCwKr22e7E5ETLA8uCyH+EsBWAK8is4rg6UKINVLKH1ndtt2MPJoeCgk01pd+ndajJNuvOxuPrTsXkymFA0cFqnEA88LChEFW2Jf0HiMMhUJ5g6ppReKrT/Xhmb5BS49gGR2s9dLJEzNZObvbsVRbOHUhwMx2K51GpdxjjpyexTvUttg3MIojU4+rqvYNjOLa7/0Se9afZ0vNCIUE5nY04zMfOgNrHuzVzIZXMKPep1WPn+kbxJcukTilrdH09qzUXL3vHR5P5uVo4exW3HrRPFy17Xnd/DN7tasw01ZqcLk8z5vZgkNHS0/PVcjJKQyYe+dpvcdDxxI4qbVBNyeVtLma4wMjmUUoVT+7ZSkeX7ckb/tsdyJygh3TYvwjgPOklEullB8AcB6Au23Yru2MPpo+o6m+5Ou07qJb9d1fQECYXpisFvjpkVwyprAv7ewd0H0sVh3Ii0bCuPq+F7J3OVl9BMvIYoDMXjC43Y6VLjRp13YrnUal3GOOnJ7FO3LbYnAs4Xi+R+KT2YFlwLuPwDKj3udEPbZSc7W+tzBHN50/F7c8WnoaAmavdtmd6VJ5DoUETpzWYCprTk5hwNw7T+89bo1FdXNSSZvr5TgWjRRtn+1ORE6wY0G/QSnl73L+/HsAgzZs13ZG744o9zov3Q3pB24tmkbu0eojbbG6kn2rGv2G2QuGWmvHSu/kK9fHOD2Ld+S2haIo2Lqyu+iuYjvz7ZfzFmbU+/xQjwtzlJaybP6ZvdrldqbNZs3J+s3cO6+S97iSNjeTY7Y7ETnBjsHlXwshfghgBzJzLl8J4JdCiI8CgJTyMRt+hm2MPppe6nW1+CiJlbm+jB7AnJpPjJyh1UdK9S2133Q012Pt0jnZucBi0cr6jZG88OQpGOxox1RKweCxBCbTCurCIXQ21yMSsePhHWdUMo2K0amfOD2Ls9TapCgK0hK689DmtkVHi/7jsXbw03kLM+ptpeqxokiMxpOIJzMDug11Ycxoqs7TfLk5Gpp6OqBc/pm92lGY1famKJ68cQniSXfOFc1kzen6zdw7T2uu+aGxhO55QiVtbvZcme1ORHazY3C5AcBhAB+Y+vMQgBMAXILMYLOnBpft4Ie7Nuxkx1xfegfV3LteXxk65sh8YuQuvUHf9qYotl93Ng4fncg+npr7aJiZdjaTSZ48BUMl7Zg7yDd0LIm1OQunbFnRjffMbPH0ALNZtXZsqoZyF7XU2nT3s/34xLmnY/3O/YaOaU7XKWaD7KR1Tjc4NoHJlILh8STWff9FT53LMf+1Sa9eK4rE68Pjmuej1c6qFubXn0rlr9x5QqVtzs88RFRNQkpZ7X2oWE9Pj9y7d2/Z1zlxR2wt3WU7NJbA5Zv3FF09fXzdkooOYFoDgw9ffw6uvu8F236GBa40otHsusHOLJcb9B0cm8BHNz9nuZ3tzmRA1Fx2S8nN4oZlXdi4q68oLzvWLMbJrbESW8nfnh9qvl/2s4Dns6ve5XZwdKJodffcwQi1Nullrpo1yqfZ8DrPZ9dpRmqtmvtqZpD5LxLo7Oqdj87taMbgsQT6D41hwxMveapGl8L85vFsdnNvanhrPKm5iO7weNLQeQLbPJA8ld3TbnvKhb3xttdvv7jau+AXhrJr+c5lIcQsAN8CsASZO5X/E8BnpJQHrG7bDk6tsFtLVwbtnutLa5GCwbGE6Z/Bg651dvcPvQUo1BOlyZRiS5bszCRzFEy5WWyN1WnmJZVWDG3LyZXa7ebksalW+4ra/oeOTOQNRhTWt8xrFWxY1oW5nc3YsKwLW3a/in0Do9nXV3OO41o6byH3GKm1yVTadB21u94w/7VF73z04evPweBYAo3RsKHzSK8c95hf79O60NbRXI8Ny7rQGqvDoSMTmDmtPvsZplS9BNjmROQ/dkyLcT+Ah5GZaxkAVkz93QU2bNuycoNdXuKVE5hCds/1pTUwODyeNPUzCj+kXNjViS9e3IXw1DxVXnnvvM7u/lFu0DcWDeP+T74PjdEwRuOT2LL7VQwdS5jOkl2Z9NOgIZmTm8XR+KRmXiJhY1NiOHUcya35QgiEBRAKhTxZv2q5r6jtf9eVZ5asb4oi8dZ4Mnsn0qy2GDYtX4A7n+7HvoHRohrl1WO+F/C98Q/1gkprrA4nNEVxYVcnnul7Z11vNfdG66jRpwSIStE7Hx0cS2B4PIloOJR3XrBwdituOn8u0jIzbZ86/YCfj3uso+4qvNDW0VyPz314Xt60F1tXdOOk1gbMaovpnpuq5wlutx/zQkRW2THZZIeU8n4pZWrq63sAOmzYri3M3OGozgP8xsjxqUn23ZsyRP3gfvnmPViy6ae4fPMe9B8ec3Uf9KjzPs1qyzw+bnWuL3VgMNfO3gFsXdlt+GfkHsAXzm7FJ849HVff94Ln3juvs7t/aLWteqKkKBKHjyaw4YmXcNW257FxVx9uvWgetl93tukstcXqsGVFfl62rOhGW6zO1Hb0PuwOjydNbYe8JzeLW3a/ik3LFxTlpbPZ2MCwEyu1F9b8j239OX43NI4vPL4fLx86ilTK2F3VbqnlvqK2v/pBMFfuB8HhnEdggcx7tH7nfqxdOifzoXLlOzXKy8f8auN74x+5F1Su2vY8rv3eL3HjB+fiwq5OAPnnckbqqNr2vxo4kh1YVl9XK/WG7KF3Pjo8nsSW3a+irakOd1yROS9YOLsVt140DxueeAnv//rubM0ZjWsf994aT1TjVzKFddR9hTc13HT+3OzAMpDJz5qHepGYVHDvqh7s7B0oOjdV66Xb7ce8EJEd7BhcfksIsUIIEZ76WgFg2Ibt2qLUYFcuO4uq0UHq3NcdOjrh2Q/uuavP7ll/Hh5ft8TSVXutweqbL5iHeZ3Gf0buAXzt0jlFB2+vvHdeZ6V/vD48jsGxibycl7oQoTU4dcuj+9HcEDGcJbXPHB6bwDd/8ltsWNaFR1YvwoZlXfjmT36Lkfikqd/fiUFD8obcLO4bGMUDz72G719/Dn52y1LsWLPY1GJ+ev0kFg1XfEFSqz+s37kfy7tnY82DvXjzSNzySb2dF0xrua+o7a91kSL3Iqjee3TGzMwUGd/499/ijyPHMTg2gYNH4iWP+dW82F1ttXwhw2+0Lqis+/6L+N+XvBf/79bz8Ni6c7PncuqTS4+sXoStK7uxcHZr0fmG2vZGpiyo5T5C5Wmdj25d2Y2dvQPYNzCKrzzZBwB48FNn41tXL8wu7Ae8U3OOJ7Vr+vFE2lDeqplR1lH3Fd7UcGp7o2Z+3hiNo7k+jK9cOh/vntGIR1Yvwv+7dWlevXS7/ZgXIrKDHdNiXAfgHgB3T/15z9TfeYLR1VbteuzZ6KPDha97dO1iT39wt3vep5nT6vHI6kVIS6ChLoQZTfWmfkbutAjl5qwifZX2j47mehw+OoFV3y1e4Vi9EFH4WJXewMukwTs0c/vMXVeeiWf6BvMevQWAL11irs3tnvKF3GHk0b3ci2JWH/HT6ifbrzsbh48mKn5cVq8/qPVscCyBWDRScd21exqLWu4ravvf/Ww/QiIzGBESArG6MGY012ffT7336LeHj2HNg71YOLs1WzdLTbFRy1OQALV9IcOLStVbvbYSAGad0Ji3DfXJJTXTd1yxADOnNeSdbxQ+JVDqcfFa7iOUTy+jhecAbbE63HzBPPQdHMO+gVHc8uh+3LuqB9FwSDPHE5NpzRy+9tY4mupLH5+rnVHWUfflnivuGxjF0FhCMz8Tk2m8PZ7E8WQ6e1FDzceMpkym3G4/5oWI7GD5zmUp5R+llJdKKTumvj4ipfyDHTtnB6N33dpVVI1e+St8nTrncK4gfnBXT7YuvSdzB+zV9z6P4WPmr4rm3pFQ7lFl0ldp/1i7dI7mXR7D48nsRYJT2hrR0VI88JJrVlsMaUUaupsjt8/Y1eZ2T/lCzjPzlIleFs3S6ifNDRFLd3no9Qc12+oH5UrZfRdKLfeVUEhgbkczPvOhM3DLo/tx3p3/F9fc90LRe6n1Hm1avgBbdr8KIL9ulqphtX4HkdEnash55eqt0bYy+uRSqacEtq7oztabWu8j9I5SGS08B4hEQprnvHo5PnRkoiiHm5YvwDd/8krZ43O1M8o66r7Cc8X3nNhcNIXf5mvOQktDBG+PT+p+jgLcbz/mhYjsYHlwWQjxbiHEvwkhhoQQg0KIJ4QQ77Zj5+xiZIDBrqJqdJC68HXlHrcNCrtOtnIP4H82a7qp+ZopXyX9o5K7xdubokXttGn5Anz1qT5D7Z/bZ+zqL3ZP+ULOq9YHtsJ+MplSLF2Q1BuIVOfg29k7YOmk3u67UGq9r4zEJ4se/y/MXeF7tGPNYjzw3GvYNzAKIL9ulqphtX4HUS1fyPCacvXWaFsZfXJJPU8YOpbAnU/3Y+Nl8/GTz34Ad155Jk5qbSh7x3St9BF6h9lzAq1zXr3j8V3P/BZ3Pt2PDcu68B+f/QA2LOvCnU/3G1qIutoZZR2tjtx8TaZRNIXfPf/xCqY11JWd+sft9mNeiMgOdkyL8TCAbwO4fOrPHwfwzwDOsWHbrjE6PUA5Rh8dLnydOifojjWLIaWsyiqtbqwSa+fJVu40Gh0tDbY8/k7aCvvH8aT2o4JaczXnZmpGUzS7qvxofBJ3Pt2PfQOjhqazyO0z+wZGsx8853Q2I1ZXeZvbPeULOataH9gKsxyLWpsmovCRXQngSHwSy7tn44HnXsPNF8yzdFLvxDQWtdxX9HIXn0xn75AD8t8jRZHZR7AL62apGlbLU5AA9k5pQ9aUq7dqWz227lxMTCoICyAWLc6p0UyHQgLzOlvw8PXnYHAsgeHxJDb96GXcfME8tMaiprdHwVcuo2an0YpPpvHq4LHs+SkAbNzVh42XzceaB3sNfz6sdkZZR6svmUprTuG3/qI/Kfs5yu32Y16IyA52DC4LKeWDOX9+SAhxow3bdZVdRdXoILXW626+YB5OnNZguZBXMkjs1txglZ5slfudannQww5672/u37c3R/HkjUsQT6YRi4bL5lwrUw9ffw427uqr6GS7sM8MHUvgxOkNmNUa48lPDXHrA1tu9usiIRybSGHVd3+Rl/ft151d9HdmBoQLByLrI2G0xupw1qkLstsZGktUdEyy64IpZejl7tXBYxhPpDSPlYXnFYV1U6+GlWs7Ny4E26XSfeUx3RuM1FtFkZhIprODwTt7BzKLNOf0Cb1Mt8XqimpcJBLCrLZGxKIRnDS9IVsPzfQRqh2lMmrms41acxRFYjyRwtCxRHZb967qwcxp9diz/rySdazwvKGScwQ76zvraPUoikRakZrZbIyG8a72RtxxxYKiOZe1aqJbx3cjefHT+QcRuU9IaW3lWiHE7QBGAfwAgARwFYB6ZO5mhpTybYv7qKunp0fu3bvXqc1XzGjhdaJA651Ize1oxtHEJOLJNNJSoqEunF1ED8gMYFy+eU/RAdDsgoaV7l+pQWyXF8Vw5QhZ7ewaGTib29GMV4aO6b7vRgakr9r2fF6mLuzqxGc+dEb28XKzbcmTmpJqJrtW60G5HGn9jDuuWICv//idO5lmtcXw2LpzISA0t2M1q278nh7i+exqtcem5Quyj0gXHisLa2wkJBBP5v9/JecH1V4kygw/7asFns+uFXptOHNaffZC88EjE3nH9E3LF+CB517D1y7PDApr9QN1cbVS5xhG9s0n9c2rApHdUnVmeDyp+dlmx5rFRTfz6NVsqzfpqH3FyHZqpGbawfPZHRpL4AuP78cnzj0d63e+M4C8dWU3TpregMmUgtTUAHQoJBCLhjC9PmqpJjqN+bSFp7J72m1PubA33vb67RdXexf8wlB27Rhcfq3EP0sppWPzL1d7kMOLtAaJL+zqxK0XvQdDY4miK6TqAeGNkeNYsumnRdvbs/48nNLWWPT3Vpj9QODWwPcUTxV9JxgdONuxZjE+tvXnpt733G3fdeWZuGrb80WveeHzH0QoFOIHQvsFPrsqK4MKRk6O9WrOhmVdWPNgb/bv9OqjHSfgLte9avNFdhVF4uCReHZBvi27X83WzNwslKqxQ8cSlj6M+SkXftpXC3yRXStKXYy+/5Pvw4YnXtKslfNPnoajEyndOlgj+fCywGRX75xA77PNo2sXo6k+knezhBeO2ewThnk+u2r2Fs5uxdqlc7LTAc4/eRq+8m+/Lhp0Vi9EXHqPd9uf+bSFp7LLwWUOLptgKLuWF/STUp5e4stTC/vVAq25x5Z3z8bA23HPrEprZAG5XNVeFCNo9FZtX7t0TvY1B0bimEybX7Asd9uj8UnNTIVCIVPtT1TIbA3JZWTxH72a0xqry/65VH20Y9FB1j3vUedD/uy//AprHuzNuxiXm4VSNbaSLOTyUy78tK+kL7feCojswDIA3UWp2puiEEKUrIPMB9lF75xA77ONWqPVLHrlmM0+ERxq9vYNjGLNg724atvz2LirD789fAzLu2dnB5aBd/IWT3q7/ZlPIirH8uCyEGKvEGKdEKLVjh3yGkWRGBpL4I2R4xgaS0BRrN3p7TStE6n2pqjnVqU1w82B71pgdOCsLhwy/b7nbnvL7lexafkC2zLlt75I3lRqYTY1W3UR7ewfT6az/18qy3acgLPueZORY2W5Gmvlw5gbubCr1jLDwVOYbb2LyJ0t9QgLlKyDzAc5Tateb1q+AFt2v5qXRSeP2UIIwzWUfSI4tLK3dUU3vvmTV9Aaq9PMW1qiau1v5LjPfBJROZYHlwF8HMApAPYKIX4ghPiwEEL3NjIhxGwhxE+FEC8LIX4thPjM1N+fIIR4VgjxytR/22zYN0vUx6Qu37wHSzb9FJdv3oP+w2OeG9TKPSCEQyg6mHW21GdXpc2ltyrtnvXn4fF1Szwzh5KXB779SO/koHDgrLO53vT7nrvtfQOjuPPpfmy8bD5+dstS7FizGHM7mivKlF/6InmfXv5fHTyWzdaxiZRm9s+cPd1QfbTjBNyJuscLNNapx8rH1p2Ln916Hh5ZOan+ggAAIABJREFUvQgzp+U/DqrX/qPxyez/V/phzOnjoZ21lsfuYMldoEq1ZferuOOK/IvIW1d24+TpmaeUStVBzcGXld1oy7nQTWSFWq93rFmMR9cuxoZlXbjz6cwUcLlZ1LugbPWYvWn5Anz5yZcM11DWTP9Tz7MOHolj5rR6PLbu3Ox540mtDRg6ltC9KNdQF6pK+xs97jOfRFSO5TmXsxsSIgRgGYDvAFAAfBfANwoX9BNCnATgJCnli0KIFgC9AD4C4JMA3pZS3i6EuA1Am5Ryfamf6fQ8XnbMLeTkgiOKIjEaT+Lg6ATWPPTOYirbrzsbzQ0RTKaU7KIpfxw5jsNHJ3TnXPY6Fxdu8dRcSE4ws+iImfddL492zDXKeb4MCXx27WB0zvEnb1yCtALH5nU2uh276l4qpaB/cCxv4a2tK7sxr7MFkYgd15kt8VV2y7VvuTmXt67sxoymKEKhUEULPY7Gk1OL8wINdaG8xXmtsrvW1sCia77KrhV6C1R979r3YVqsLnvOaXTxSUWRGEskMXo8haGxBIbHk9jZO4CbL5jnm3NTn6uZ7JbKIgC8Pjyu+xkJgOFF2kfjSRxPppGYVHDwSBx3PfPb7EC20RpaAzXTDp7Mrl7O5nY0YyQ+CUVR8NZ4Et/4998WL/S3ohsntTZgWn0dRuKTrra/meM+82mZp7LLOZc557IJhrIbseUnCbEAwHUA/gLATgDfB/A/AfwHgD/Lfa2U8iCAg1P/PyaEeBmZO58vA7B06mUPANgNoOTgstOsPibl5Kqq6rYPHZnIW0zlwEgcq777Czy+bkneQlOntTehtbEOj6xe5MgHUqep86mRdbl3qRedHDQVv9boybCa9Y7memy8bD7e3dGE3w+N5w3a3bB9b0WDFJzni+xSmH8AuPHhfdmMAlPTZCTTFS9mWrKPmdyOHXVPUSTePBLPDiwDmd9xzYO9ePj6czCrrdE3xwIv0JufU61the1fFwkhEhK45+qFSCsSX32qD8/0DZo+J9A7p5jRZN+x0e5ay2N3cCRTaTzTN4ihsSQ2LOvKLlDVUh9BZ0tD0etL1UFFkXh9eBwA8uZwBoC+g2O8cEy2KpXFobEEVn33F+hors/m+ngynX0ixcjnOK3avGn5guy/m6mhrJn+pXdu8PD15+Dq+17AgZE4LuzqxBcv7kJ9JIQdaxZjMq3g90Pj+OK/vmR5wd9KmTnuM59EVIodcy73ArgbwAsAFkgpb5JSviClvAvA78t872kAFk5978ypgWd1ALrT6r5ZZfXRZjsWiCi37XJzKatCIYETmjKLXZx6QiM6Wxo4mFDDrCyIpiU36/sGRnHt936JobEErv3eL4sG7SoZpOA8X2Sn3PxHI2EMHUvk/bsd2bK7j1kxPJ7E4FhC81gxOHXHIBln5INYbvt3tjTghKZ6RCNhXH3fC3imbzD7PWbOCZw8p1Cx1pIevQWqQiH9jxJ6dXB4PIk/DB/H2+NJXjgmV+hlUa3nubm+9nu/RDyZNlxztV63fuc7C2WzhtYGvXOD3POvZ/oGcfV9LyAUCqEuHMI1972Q/azkxDHdCB73icgudty5vBKZAeLTAaxXp1uWUv6dlPKjet8khGhG5i7nv5FSHi0xTXPh960GsBoATj31VGt7XoY6t1DhFWujcws5ebelum113qaO5nqsXTone8U9Fg1benSFj73Yz83sGmVXO2tlfXg8iVltsby/v7CrE0IIvDFyHLFoGClFFj1Kq8VqXyRrvJhdu5TLltE+Uq2aaeTnJlNpzf44qy2G4fEkTppefNdhUDiRXfWDmNZxN1du28SiYUym0rjryjMxGp/Elt2vZj9MGj0ncOMJDtZa76hG3S1VT3Kz0dFcj5vOn4vTZzRBQkJRpKm6mEyl0RgN69YlDmr4m1fOGVIpBYPHEphMK6gLh9DZXF80DVQ0EsaFXZ1Y3j07ezf+zt4BRCNhwzW31CKurKH+YiW76rnBgZE4Fs5uxdqlczCrLYaWhjo8vu5cDI4lssd+NUNauVEUBUNjCdfOJ3ncDwav1F2qbXYMLv8jgFEALwJIlHktAEAIUYep6TOklI9N/fVhIcRJUsqDU/MyD2p9r5RyG4BtQGY+Gas7X4rVR5tzDzIqu06a1W1v2f0q7rl6IeLJdN5cYduvOxuHjyYqmpLDyek8apmb2TXCznbWyvrO3gF8++qz8OmHX8w+CvbX55+Bj239OTqa63HrRfMMzwFu1zQDVBmvZddO5R7drvSRWDdqptGfG42Ei/qj+tjuA8+9hrNOXVDip/ibE9ltb4pOHWOL5+hsjRVnR6vebVq+AHc+nZmD2eg5gZPnFCrWWu9wu+6WqydqNp68cUnR2gpm62I0EsbxZBo7ewewafmC/LlHV3ZzUMPnvHDOkEop+M3hMazNyemWFd14z8z8dQbaYnW46fwzil7XFqvDSByGaq5ebVbnrWUN9Q8r2VUHae9+th+fOPd0PPDca/jEuafnrXWhnnepGdK6Ceet8WTe9zh9PsnjfjB4oe4SWV7QTwjxkpRyvonXC2TmVH5bSvk3OX9/B4DhnAX9TpBS3lpqW+UmK6/23bduzLl8w/a9uP2jf4rbHvvvvIPT/Z98X95czID25Pxa79HweLKWF0/zxET7bmTXzoWbtLK+ZUU3/u2/DuCs09rR3hTFidMb8PFtz+PASBxbV3Zj464+zy6W6VOeyG6Q6PWRR1YvKpqzsfB1F3Z14suXzoeUsup9WO2f//riAK46+114ezzptcWzPJfdcvVlcGwCH938nO57n9s2evVu42XzceL0BstzLnug/WqZ57JbKb16smPNYnQ212cXmRJC4GNbf1627pSqT+1N0ewiavfveQ3Lu2ejvSmKzpZ6nDw95oVFRmuBb7Nr5PzvzdG4Zk6NHL9zc+rlC8w1zFPZzc1jXSQESOCj33kOG5Z1aR77H77+HJw8PYajicmiC3W58zPnfk+NfAavBZ7KLhf044J+Jri2oN9zQog/lVL+t8HX///tnXmYVMW5/79vd8/SMwPMCMOOojgio6IyqCC5RiUSjURjIBoV3GIAjcHk55qbcJNckxvR5JIYF1CjGFyi4no1UZSEmGBUNkWCDsiSDAjMMM7gLD3T033q98c5p+d09znd5/R2ln4/z9PPdPec7q5T9a23qt6qemsa5FAaHxLR+8p7/wngTgDPENG3APwbwDeySZQTGvp8zgRqv7s7HEnaVmMmFrNRHg0sD+Rl6y07A81RKO3mcou1ntZrgiUYdsbRsdfhSDR2YErd0Cosmlkf2x5m9bedUL8ZZ5BPu2JUR/a0hXDTsx/ENJd43cljqnHl6UfGBrZ212G1fl57xtGQJAnlJUGMGFSOSYdPZDusgxn70heRUua9tmyqgyW6144bWoXR1UHT+W9kZ7ldZTJFaz8BoLaqLE6re9pC+LQ9hM+6wrhn9Tas2tqMlQumZhUqIByJwuej2EHTP/7qca48aJqxB7P9v76ovo3ujUi45MF3dNtvNZRBdbAkpmcz4zhe+Vm86Olx2ZwG1FaVGbb9fh9he0tn3CHoRw6pREWZP23fIldp5n4DwzD5IGPnMhF9CEAo33E1Ee2EHBaDAAghhO4+WyHE32Hs+Z6eaXoSSXeae6FId6pqNgZe/e6WjuRtNd3haNqtXEZ59Mz8qTnfesvOQPMUSru53mKtp3X1tSQJHOzsNdwavqmpPSeHZfLMfnGRb7tiVEfaQ31xmku8bsGZ42LbvIHC1+ESnVV/fMK3eczYl3T2U/t/9WyExGuDJX7LOtWWoxPaVR6kuhc9/dw9eyLueq0xNumrxmW/45WtWDSzHqu2NhvGSS4J+OLihJYEfCnriHrQNCoLe9+MuzHb/yvx6+svKgnd9ru2qgw3f3l8XIgW1Z6aaTszbWPZhrobPT3Of3wD7rjweMO2n4hin9nTFsLVy9fFVidnOjazcj6I3f0GhnESmaze5tXOxmSz72wmgK8COA/A0QBmKK/V922lEAffZItq4C+6fy2mLf4LLrp/LRoPdECSRNJ1LR292NvWjZaO3qT/qzGe1JNeR9cEccTgiqT3EoPzG+WRn5D2s1YpxCn3XqFQ2tXTTa4PcJAkgeaOHvz7s250hSMxxzIQf5q2kw7LZNxDvu2KXh1ZPGsilq7ZEfu9cCSadN3gylLb6vDdsyeisycS106ka0OYeMzYl5pgCZbNbTC0n9qyWbpmB+6ePTHu2mVzso8ra3e7arYPwzgTPf3csnIzFk6vAxBv7/a0yYeTAcDSNTuweNbEJO139kTitNDZE8l7H4MpPozsc6gvGmd7hlaVYemceBt9/+WT8NBbO2Of0bbfC6fX6U4K59Oesg11P0Z6PHJIZSymfKIN9JP+QX56/UkzdtOKjuzuNzAM420yXrkshPhXLhOSawpx8E22mJl9N5phrKutisW/Kw34UVdblbQdC0DKLVpGeeTz+XK+vYudgeYplHbzvcU6UbtGW2knDB9g+cATN9RvJv/k265o60ioL4odzZ2xlfZAv+YS6xIRFawODxsob6msKPWjPdSHu16TD4lT2xG9NuT315yKqvIA+iISr5TSIZ19kSSB7S2d+M2b27BoZn1crFg1H1VNPDN/Kj5tD6EvKuHOr5+AEr8P3eEoRlSXJ8XstGp77W5XeQeJu0nlFFm5YCpau8JxO4vaQ30AgE1N7Xjs7V14Zv7UWEx5vw+4/bnNWDSzHtXBErSH+nDnnz7CnbMmcqgAJqcY2ecdzZ3o6o3EVmAGAj4cq9jgvqiEvqjAg3/dgWc27Il9Rtt+V5alDyeYa9iGuh8jPVaU+fGzi05AX0TC8qtPRUdPH3ojEqqDAYSjEh696hTcs3p72v6kGbtpRUd29xsYhvE2uYi57EjUmb9Ep6yTVkyYMfB6DcaSNxpx45eOMXWSrHpAXzgSRWtXOK6BSpVHud5CnS9noBe3kxVSu7neYq0tDyLCkjcaY2VutJW2vNRvWWtuqN9M/inEJINaRyRJoKs3gpbO3tjvaDWXWJfypc9EmydJEq5evi7pOrUdSWxDaqvKcODzHlzxSPLWX7Wee9GuWiHRvsyoH4ofnl+Pnr4ImjsEAr7+La2rtjYD0D9wx+cjDB9YjkOhviQtVAf7tZCp7bV7ko0Hqe7GSD972rpBRLGDqFT9t3f3YdncBmzc3YrLp4yNO6y0paMHV55+ZFxIgcWzJqIvImHYoKCNd8l4DT37fPt5E9AVjqLE78OBjhACPlmXgYAPI6uDMRv79s5WAPrtd7AkYBhGQ5JEXtpAtqHuR9XjkjcaYweT1g4oQ015Cdp7IrhEOcj85DHVuPnL4/GNZe8khSFq6ew17E+awYqO7O43MAzjbTzrXM7H4Qq5HnCbMfB6DcashjExxzJgPEOZbsBaiAMo1DyTJAnL5jYkOcSzcbZ4NW6UXQeDZLuCQq88Fs+aiJaOMDY1tWPpmh341TdOxE3PfhDXsersiWBIpbWOez7zqNgda25Cb5Jh2dwG1Cjbt3OJFc0lXlsS8CHgI+w7FMpKU7oHx8xtwIz6oTEnJxDfjiS2IQvOHJcUnkZbz71qV62glt/z15+OvoiE1q4wLldOb1fzXO/gM72BnBndZGp77Z5k8+IgtVjsvyQJ+H3AsjkNmP/4hrg2+5evNwIAFs2sx8RRA/FZV1+c/h+Y04CfvboVq7Y2x2kuMaTAbc9txjPzp9p5m4wHUW3q0/Om4LOuMCQBXPHIe3EafuztXfj+OeMtjXcGV5YmjVMWz5qIn726FT+/aGJeVhLn2oYWi/1yEj4f4eghlVg4/Rgs0NjSpXMaMHxgfz9B7yyOW1ZuxtPzpmRdVlZ0ZHe/wWtwnWOYeDzrXAZye4BRPgbcZgy8XoNhNp5nqgGrdkVzacCPEYPMn1hvlsQ8m1E/FE9eexr8PrJkgI0Mt5e3k9lx+Fa2Kyj0yuM2ZZvs/BUbsKmpHT4iLLn4JAypKkVUCOw/1IM7//RRRh33xJWiuWjc2bHmLnw+Ql1tFZ689jQ0d/SitSuM37y5LW5QaRYzGrJSL7UrnhsPdMStagmFIxg5KIiAzsF7qdA9OGbFBjx57WnYuq9Dtx1JbEOMTi83WunsJbtqBbXstx3oxKKXtiTl+fKrT8Utz36QtKXV6LtULbR2hZMmGTK1vXZNRKp4bZBaLPZfe5+1VWVYcvFJGD6oHH1RCZ+2h1A3tArT64dhcGUpJIGY8xmQdXnd4xtih/up9uHpeVN0NSwEx45lco/PRygJ+NAVjuJmZcECEN/v/Pbv1+PlG6YhKsGUffT5CEMqS+NCu6hhYX781fysJM6lDS0W++VEWrrCMccyIOtwweMb8PS8KbHJf6O+F4CkMbnVdtyKjuzuN3gJrnMMk4ynncu5JB8DbrMz6WqDUVtVhoXT6zB8YLmpGUqjAStB4KP9n5sKq5ENiXm2amsztu7r0F1hbdSopjLcvJ0st2R7QnF3OKJbHmrnZnRNECOqy7D/UC/malaZ/PqSkyBJUsbpzmXjzo4199EW6sNlyqo6FT07o6JnbwAYaghAVp3+1q4wlrzRmLRlfNncBkwYPtDS9xsewuojw3YkcdDRHY6mrOdsV2UkSSAUjqKiVD8OZ3t3GLeeO153S6vR9xlpLJvVa3ZMRGp/O9+D1EhEQnNnL/qiEkr8PgytKrM8KWOWYrH/6n3WVpXh1nPHo7zEh0sf6t+qff/lk3Dvn7dj1dZmw7MSqjW7Q/a0hRAVyFjDDJMJZQH5zAEjfdZWlWFfe0/cyvx0fUOfzxcLB6OSTx0n2tDKMj+6wxKa2rot27tisV9OpC8q6epw36EeLJx+DACgPdSnayODpf6sxzBW22I7+w2pKGR7nwu4zjFMMs6tsQ4jXwNu1cCPqqlA7YAy3ZVy44cNwMs3TMPPvnY8Fr20Bd99alPSyfN6A1t1wKplRv1QdIWjumE1cnVSrCQJtHT0GjobtXmW7oTbVKfa6t0fD2YyJ9sTij/e36FbHoOCJXh63hQsmlkPSQK+9/T7ceX5vaffR+au5dyefMyONfdhpsxUm3TgUAgf7f88yd4c7OrV1dDBrt6sT3IPR6KY1TAmaTvk/BUbLH9/Kptn1I5oBx1rbzsLJ44ZlLKes13tt2s7W7pizngto2uCaO0K45aVm3HvZSfjheunpR0IprJTmdhep6D2YUYocXX3HQqhpaPXUh0xIhKR8PGBDly87B/44t1rcPGyf+DjAx2IRLJpMYwpFvsfjkRRW1WGm788Hj19Eq57YmOcLq9/YiNmNYwB0H9Wghbt4X7q6/ISn2s1zLgPSRJo745g98FuQ30unF6XtOo+Xd8wV7ZY7XPsbetOaw9VGzpsQDn2tPVkbO+KxX45DTnEEBn2ExY8vgE/ueB4nDR6EJbNbUjSVkQSORnDpPMnFAor2tdS6PY+F3CdY5hk2LlsEjsH3D4fISr1b03c1NSOu15rxB0XHo+3bj3LcGCr10n60fn1aOnozZsxNONs1OZZOsdgKsPt5gG5E0l0Qll1mKzeegD3Xz4prjwemNOAlev/jUsefAfzV2yABOiWZ18WnYdcNu7sWHMf6cpMa5Pe33NId2Ktp09fQz19UtadfnUFSS6+P1Obpx10HFZZlrKes13tt2v3rN6OmsqSpMncxbMmYumaHbFyMzOQS2WnMrG9TiLdJHGmNHf26m41blYO1cw1xWL/SwI+LJxeh9ue22y4Ml9dmbx0zQ4snjUxqV1/bkNT7PVDV0zGkDR2hWFySWtXGC0dvbhn9fZkfV4+Cc9taMKRQyot9w1zYYsztYfZ2rtisV9Oo7UrjBVv70oa/2j7CUIIDBsUxIThA5O01RfRX/XsRgdlNn2BQrf3uYDrHMMkw2ExTGB08EkhB9yJA9NNTe24evk6rL3tLMOtF3rbZMKRaGwlSj62fWmdjeqgRLsVPDHP0jkGU20X5rhRucdqHGNt+U2vH4Z7/7w9Ll7db1dvw4/Or8erWw7I2/eJdMsz3wdjmsVrMUSLgXRlprVJRjHvjHTpJ/3JECudfjXGci6+P5XNsxJ3PNWWSLar/XZtT1sIP315K249dzye+vYUHPi8B61d4VgcztE1QRAR9rZ1p82ndHbKqdtUzZCvraFGW40j0fysZCoW+x/wEQ4fXCGHdzHYqq2uTN7U1I7H3t6FR686BZ91hTGqOohhA8rw84sm4sdfTbYPbtUw4y7UsUxLZy9++XpjrN/ZHY6idkAZfvzV4+AzaNfT9Q2ztcWZ2sNs7V2x2C+nEY5Esexvu9HWHcGT356CZp1+Qqp23ksH42bTFyh0e58LuM4xTDLsXE5D4sEnd1x4PI4cUomKMj+GVKZerZSLQ8bU7wAyi2eX2JC1dPTiuQ1NSU7fZXMbDI2hlfvQOhs3NbXHOn0Thg9AsDSQ9Nl0jWo6w+3mAbmTSRfHWNVEVAg8etUpuGf1dlQHS7BqazNWbW2O+64ff/W4mKMq4CPcPXsiblnZr727Z09EqT/zTRS5bNzVA+KemT8VkaiEgBLzq5gca25Ba5eGDSzD89efjr6IlGSjtDYpVcw7PQ0FS43tk1m76PMRRg4KJp1Cn+77jdCzebk+VMSMXfXiCdl67e2mpnZc+tC7mFE/FDdOPyYWj3N0jXwa/E9e3oJVW5sxuiaI319zKqrKA7o69PIgJF9bQ0v8Pt36EciivUiFlydWtPU1KgT2tSsa1lkEsHROA+5ZvQ2AnN9Xnn4kbl25GS2dvXh63hS090Q8ky+MszFqZ0oDfmzc3YrfX3MqPusKo7UrjN/9fSdunH4MWrvC2NMWwsbdrVg6pyG2GrJQNjdTe5itvfOy/XIqkiRAyiTGMxv2YHtzJ27+8vikfkKNJkZ9IjXBEt3+oRv7Btn0BQrd3ucCrnMMkwy5+STnyZMni/Xr1+f1N1o6enHR/WuTjF26WbhcDPYTHdu3njs+zimXifNA/c4lbzRiVsMYDK4sxdABZRg5KKgbNN/oPupqq9AW6ksyplbzK1U+AfIsqCRJiApACFEIw12QFqEQ2s0USRLY/3kPPm0PobUrjNVbD8ROjh9ZHcTQqjJsb+mMK7O7Z09EVBK4/fkPU5Z9JCJh92ddaPoshIpSP7rDUYyqKUewxI9R1RUZl2uuHF0uP/m3aLRrpZy0NunkMdW4+cvj4xwpv7/mVARL/BAQUJvDdAf91dVWJdWBdDqxepCgFb1l2k5lSh7qie3aNdPeVpX58UlzFypK/fCR7IBv6w6juaMXq7cewEWTRqVso73okAfypz81BqPWObR0TgOOHTbASYf82K7ddGi1ffpRg/Hd6XX4rCuMAeUB3Pmnj9DSEcbC6XUYO6QSlaV+HFZRipbOXoT6ovhXazfuWb0dLZ29uHv2xLgDLF3SLjLGOFq7qcYfn3WH0RWWYy6r+lw6pwHrdx3ET175OBaW4K3GA7hy2lGFGj8AyMweSpJAW6gX+9p7nW7vnILt2tWOp7/1haNw07MfYE9bCDPqh+L28yaACNjbFsLftjUbatDqmNzpWNF+Yn+oujyAxubOYtC/7drVMvb2VwuQGu+x+87z7U6CHZjSLjuX07C3rRvTFv8l6f21t52FUTUVhp/LxWAr8TtOHlONhdPrMG5oFYIl2TnQzA5w9e5jRv1Q3PilY5JmWVWHsFWnQz6dLhngKKNfaBI78zPqh+KGs+twvXLgj7rK/TdvbotboTy6JojnFkzFwa6wri60Zdbe3YuDnX3wERCVBB56ayfe3tnqiNN1C+2kyzFFo12rHdhETf/o/Hr4fYTyUl/awZyefWrtCudMJ7lwOGbaTmVKHuqJ7dpN1976fcAF9xpPUtx32STc95ftSXbRJbYjK/I1KSdJArtbu/Cv1u7YZOQRgyswdnClk5yatms3Haq2Tz9qMOZMPSKuPb/vskkI+AmVpQGs+Xg/zj9xNGoHlGFvWzdueHITFpw5DuNqK9H0WQj3rN6OTU3tAIpH2x7H0do1O/5YPGsifvm6POmxaGY95q/YAEDW6KKZ9Th+5MC8tINGWLWHiROb6kRPecCH2qoyrznWcoXt2tXq86lvn4aePgkVpX60h/qwdM2O2IScJJAUHlLVgsvHHEmY1b7RdUcPqURLVzhu56gH9W+7drWwczkz2LlsDIfFSIOZWEh6zoFcbBPNJM6yGayEktC7j1kNY3QPxVIbQ6tbRPTS09LRm5cYjkxqEuNlzWoYExuIAnI5zF+xAYtm1sc5Ufa0hRCRROywilRl39UbxZf+969Jv53JFupcrwTkk3/dgZVySrVt7dP2kO4BIs/Mn4qR1cHY5xNtjpXfT6fRXIT2KXTMPi/Wk3Tt7d627tj/F5w5LjZYBOR7/86TG3XtopvzxCz52hra2hXGFY+855mBt12o2v72GUfh6uXrknT75Len4ManNmFTUzvOOW4EANmmtHT2Yv6KDXh63hRcvXxd3HcWi7YZ+zA7/rjtuc0xp3K1JvzAnrYQBleWFjx2rVV7qO1372kL4erl62J2zoOONc+g1aePKMlGAsDwgeWYq2nDEseyXutLmdV+qtjMat+bYRh3wq1WGtQ4idoTYLWxkIxORi0J+LI+QdQJp5DqpWFwZWnKxlB1loyqqUDtgPRxqVs6erG3rRstHb2xE2W91uC6hcR8NzoALTEWmPaQxXRlHyz149GrTsHT86Zg2dwGnDymOiNdZ3MqsRFOqHNMeqyWk1aX6srjvW3dGR8gYvb386FRPdK1U7nGi/XEqM0uUQb32nu2aheLASvtvopR+6/C/YDcUBrwY0b9UJQGfIb2LvHgKa1NUWPVaykmbTOFR5IEopIwPf6oDpbEHUQJyBodOqDMlti1RvZQz+axnXMn2j6DkY2MitSHNnuxL5WqL6DqvzscwaKZ9Th5THXsf6x5hvEGvHI5Delm4RJn32qryrD/UA+qKwJZB+h3wgG64SfEAAAgAElEQVRAemkYOqAMo2uCqK0qw4Izx8VOaA6WZuYc1Ns+46XTc91EYr4bHYA2dEAZZtQPjYsRlurAChVJEjjweS8WvbQlLl7zsIHllnWdzanERjihzql4NT5rLjBbTol5WBMsiYuV/OhVp2R0gIjZ30+lUdXJnYvyLfShIk6qJ7nC6LDRgM6BfKnsovr+6Jogls1pgN8n69DuePJOw8z2We4HZI+sPYGF04/BzpYu3fxUnXjag521NkWSJM8cOMW4g9auMH726takwyZrNTZWZXRNEN3hKB6Y04Dfag6iXDa3ASMHBS2fS5Mve2tk84YN1L8ntnPORttnWLpmR1L/YdmcBlQaHNqsTlrb3ZcqZP9CT/9qSJvEyU2G8RqZhB9xa+gNjrmcBZIksKe9G2fctQYAkuIwamN7Zmq0rRr+fDQUeg6af7d148DnPVkdMJgq1tTgylKOuZwnUmlELz7twunHJJ22ffSQSmxr6TSMu230/UZl/vz1p2PogHJL95GvOLNOcOZkGMPU89rVkq6c9PLwyWtPw2UPvxsXVzfx4DazB4iY0YmRRt/9wdmm4pMXEpvbGtu1q8aYvfXc8Rg+sBxRIbD/UA/qhlZh2CB5ZZF6z5Ik6ZZfXW0VPguF0d0bxa6DXbGDpjItW5cfMJoSM7Em833/OdKw7do1Qs2//Yd6sOilLaitKkuKFf7AnAbUVpWgoyeKmooSDDFoh53QLjI5x7HaVdtOtY1WbfLAsgBau/vibMLSOQ0IhaN4dn0TzjthBI4cUomKMj+GVJrbQaGSb3uTqv/b2hnO6HeLuF7art3EPoO6362ixIeA3/j8IHVBjXp2gF1lmGu9p7sPI/0vmlmPO17Z6pm+jQls164WjrmcGVYdvx5xLnPMZTNkatS1nXZ1VjIxDuOqrc3Yuq8jq5WUVuJx5qtjpJeGqvIArnik/17VFduVZX4ESwKm8jHVVrBCr8QrFtJpRC/fa4IlSeXQqnGsAP0rMp+/7vRYnEa97zcq854+CXvbui2Vc75WteUiBm625GNVttdIV056edjc0ZsUV/eu1xrxh3lTIEnC0gEiZnRipNE+SaSMW19oIhEJjc0dlpzdTqgnuaQ04EftgFJIArEYierqt9oB5TH7qN5z7YBy3faJQJjzu3fjyjzTsvWiHVD7XN3hSNqt4Jn2A8z067zsuFdpD4Wx/1APjhhcEYvn+svXG7FoZj2qgyUYVRPEird3Ydnfdse0fpiBQ85r9Z1xNtpwAYk2+ffXnIrnrz8dfREJRISfvLwlFuv+mQ17YpNUVutxPuyt1hZFhdC1eX0RyZSdS7cTy4s2zMmk6jNMGN6/Yn7YwDLcceHxscP+7npNPnxS1VWhbatWR7nSe7r2VJIEQn36bf6E4QNii8pYtwzjfoo65nI28TDVTsg9q7dj8ayJGF0TNIzDqA6W0sUXzBajjlFrVzinvwMAfZH+WKXqiu1FL23BGXetMZ2P6WJNWY3hqObvgUMhfNoeyls+u5lUGlHzb98h+X8jBslbEAMBX1I5SJKERTPr4+Im72kLIdQX1XWaqRo0KvMdzZ2W62Ah48zmu+4mwjH4skcvD3v6okn6a+nsRWnAh8MHV2JkddD0ATpmNKGn0WVzG9DVG9GtP1bKN1ealCSBTw+FUtbbYmBwZSl+dH590kF981dswMGu3qTrjdqnXNZdr9kBbZ/r4/0dpmJNJuYzgJS6T9evU+vNnvZu7D/Ug9oq+Tu9pnlJEujsjWDMYRXwEeHN//dFXNwwGpua2jF/xQbc9OwH2H6gE8v+thtAv9a9cv+Mu6kJluDJa0/D/15yYpJNvuKR90AgjKqpgBAi7hBV9Ron2NtEW7SjuSvj+LqSJLC7tQtb9h7CnrYQtuw9hJbOnoKN+ZhkUvUZtGUQCkdx9fJ1uOTBdzB/xQZsamq3rR3XalKdcNSSabrSjS0bD3TgwKFe3fN2gqWBnJ3TwDCM/RT1yuVsZqnVToh2JchQg1hgpQF/QVbJFHIgql2Rl7hi22w+5jLWlJq/S95oxJWnHxm37ZNn8vtJpRGz+pQkgYNdYdzxyta4uFmPvb0LksHKDFWDemV+9+yJuOu1xti1ZutgoVa327HCjWONZo9eHpYF/Cnj6prFrCb0NEoQ2Nveo1t/zJZvLjXZ2hVOWtENuNuJmQk+H8HvI9186O6NQqo0Fzc5l3XXa3ZA2+daumZHUjzVdO2/Gd2ni3OeKuajlzT/eU8Y7d19uP6JjbF7vf/ySQCAt3e2YumcBix6cUvcZ7x0/4x7kSQRW5H7q2+cmLJtcrK9TbRF96zentT/eOiKyagJlqS1a+2hMA583hN3XskDl09CbVVZXHq5DhcOdTdTur6Tk9pxrSaNzo7IJF2pxpatXWEseaMR3/rCUVmdt1MMu40YxgsU9crlbJyx2hWY6kqQX/zpIyyb26C7krIQq4oLeeqsdkVeuhXbRmgdL2tvOwsvXD8NdbVVaO0KW56VVPN3VsMYXUc3z+TLGGmEiEzrUy8kxm3PbcYPz6+PhYlJ/H7tanRtmT89bwruek0e2KskaifVTLXV1e2ZUMgdASqFXJXtVfTysHZAGe56rTG2anjRzHrc9VojQuF4W5VudYQVTSRqNBwV+M6TG3Xrj9nyzaUm1c5/odoOJ2NkH3cd7DKdt7msu16zA9o+16amdvzy9UbcPXsi/n7bWXjy26dhcFXq+zKj+3SD3MTP3/bcZiw4cxwAb2m+qzcacywD8r1e/8RG3PilOtz59RMQCkfR0hm/It9L98+4Fz0HmBatTp1sb7W26OQx1Vhw5jiUl/jx9LwpePcHZ+OF66dh/LABaAv1pbVroXA05pRWr7nuiY1YOL0u7je5DhcWH5HhmErFSe24VpPqBG8u0lUa8GNG/VAsm9sQW5k8o34oSgN+hCNRzGoYg5ue/SBOv7es3Iyq8oDpcZsdYzGGYaxTNCuX9WLwZTObqLcC8/vnjEddbZXuSspCrCou5KmziSvyMs1HbaypbGYl1fzN1NFdLBhpxE8wnW9GWvYR8OjaXUmr0bQn0APxZd7S0ZtygOuEmWo7tqZzzPHs0ctDARGLCa6SaKvMaC4bTQiD1f0BZRWMGcz8vtnzBEoDfjy3oSltvS0GaoIlWDqnIe4AU3Vl672XnWzqO3JZd71mB/T6XADwzQffMWXfzeg+Vb/O6PPVwRLXO+61SJJARNK3MxFJYM7v3sPJY6qTtF6MdZ5xFomxWdPtcHCyvVVtkd5BmmbOItHaNaN4zWOHVMbsnZdsmFvwE5L0uXjWRPg1knFSO65tH9UJ3jsuPB7jhlYhWJJ5umqCJUmHvy+d04CaYAnaQvLYU0+/fRHJ4BuT8VqYMIbxKkXhXDZyFtTVVmXsjE3VWOht5y/EtphCN2DqvUqSyIlT20yYEiOHiZq/udzm40WMDuxr7uzFygVT0doVxtI1O7Cpqd0w34y0DBCunnYkHl27C4tmyqswhw4ow8hBQUMNppsQccKBVnZtaeMDlLInMQ/N2CozmstGE7nQU7rvsDIpM7iyFN8/Zzxe3NiER686BX4foTTgw7Cq/OwEcCrqVuxQOBo7fEcSAlFJ4IfnTwCRfCiNmTzJZd31kh1ItPcLp9clrcZLZd/N1J1UbYq6Qj/x86Nrgp46UKi1K4yoJHTvNaLswmjp7MXAYAB3fv0ElJf407bVDJNvtAelz6gfilkNY1AdLEFfVMKSi0/CsEHlug4wp9pb1RbtP9STMnSgGbtWXqJ/TWWp3xFOy2JEkgR6IxIee3sX7p49EcMHliMqBA52hlGScHaHU9rxxPaxpbMXwweVY3R1dra/LdQXcywDssYXPL4h1q6GwpG893sZppCMvf1Vu5OgSybp2n3n+TlNQ1E4l1M5C7JxxlppLAq1qtiOBixXTu10s5LauMqzGsbEGqyRg4Kx/F3yRqPlOI7FRrrV4mr81++fM1433/S0vHROA/Yf6sELG/fGBgQdPREcVVuZ8oC0dNpxwkx1IXcEMPnFjK0yo7lsNJELPeVyUsbnI9TVVuGCk0fj6uXrcrJDwOyqaSehxgW8YupYHD64Ai0dvQiW+nHDk5ts2zXhNRLrn9FqPCP7bqbupKrjRp8f4RGnqlrvQuEIgqU+PHD5JFynibn8wJwGLFuzI3bfVWUBHDmk0jV1lPEmMd32RbD/UA+27GnHDWfXxcULXzqnASMHlps+cNcJqLaossyf0s6ZsWtDKst0rxlSZJPATqK1K4wn3tmN//xKPaJCYHdrN+5ZvR0tnb146IrJqA46z6bmaxFaqn6zz0cYOUjeGaOGVMxHv5dhGGfgOOcyEZ0L4DcA/AAeFkLcme13pjN66urb1q4w9h0K5aWj7aRtMfkgF07tdLOS6uA/8cC+ZXMbMGH4QIwfNgA/v2giJEnCM/OnQgiBYKkfEUnkrVydRCYOHaMYlM/Mn4rhA8t1P69q+fnrT0d3bxS7DnZh0Ytb0NLZG3c4EgCsve0soDJ12lJpxwkz1V6vu8VGKr1JkgApMfRSaS4bTeRCT7melGkL9eE3b27Dopn1qA6WoD3UhyVvNOLnF020bNedEMomEyRJwre+cBRuevYD1FaVYeH0OtQOKMOdXz8Bv1q1DZua2gu+a8KLJIZFsmLfzdYdozruZVseiUjY/VkXmj4LoaLUj+5wFGOHBPHYNaeCAEgCGBgM4Iazj8Yt5x7bf9+VdqecKWa07YVqd2eeNBIHPu+NHVanXQXpNtvr8xGCJYGUds6MXfKy7XIrkiTh7AnDMed378a0+8uLT8S+9lDG/adCkI9FaOnGaoGADxOGD8xrv5dhGGfgKOcyEfkB3AfgHAB7AKwjopeFEFuz+d5cbiHOBqdsi3Eq6WYl1UMBEreXzV/R3+lM3ALvRidHJmR6r0ZOKCFSb//2+QgEwpzfvRv3+due24xFM+sxf8WGWB3LphycMlPNddf7aHdGmNn9kI0mcqGnXE7KSJKUNGm3eJY8UWcVJ4SyyQRJIOZYToyPqZ004/h+uSMT+55t3fGiLZckgZbOHrR09GLRS1tieXn37IkAgEsfehcA8NJ3pmFkddBz98+4l4NdvTHHciq76+bYqmZ3XKSrl160XW4mXZ8hk/6TW8mVxtPBdYBhnI+jnMsATgXwiRBiJwAQ0R8AXAggK+eyG+K6MulnJdXXZlfkFVO5Znqv2awMNns4UjblwDPVTKHQ6rSlIxyLGz6yOmi4it+pWHXaRQWSJu3UHQxWcUIom0zokyTsaQth0cx63bxYNLMed7yyleP75RC277mhtSuMnoiUFL/6lpWbseKaUwHI7XpNRQlvIWYcRU9fNK3d1S5WcCNs57xJuj5DJv0nt8IaZxhGxWnO5VEAmjSv9wA4TXsBEc0DMA8ADj/8cFNf6oa4roxMqllJq4cCOK1cM9GuWTK912xWBhs5phMPR8q2HHim2n7yqV2noNXppqZ2zF+xAYAc2sVtHWSrHX1hEPtWCGH5t50QykaLWe36lXAo1cES3bxQbSU753IL23djzGo3HIkiKunX4agQsfBhfGAfUyhyZXcTFyu4FbZz7iFX2s2k/+RmWOP2UwxjNcb5OO1kBL1eb5x1FkI8KISYLISYXFtba/qLVaM3qqYCtQPiD0BQB8Na3DxL7lW0hwKo5ZWq0+m0cs1Uu2bI9F61Tqi1t50VO+TSzABUdbYklsWIQcG4Oua0cmCsk0/tOgWv6TRVm5dILu/dyC7Y5Rgwq91gqR93z56I7nBUNy9GVgc9GVKJcS5mtVsa8ONgZ1hXtxUlfrxw/TRMGD7QVYehMe4mV3ZXXazAtpcpFLnSrlv7jox7KYaxGuN8nLZyeQ+AMZrXowF8mu8fdUpcVyY9Vg4FKKZyzeZeM51tNrs6spjKgXEvxazTXN67W7dHVgdLMWxgOVo7w3jg8km47omNcXnhttAoTPEwuLIUHT1lWHLxifj+Mx/063buZAzn1cqMg0lnd0ewfhmHkk67xdB3ZBgmf4y9/VW7k5AR5KRtG0QUALANwHQAewGsA3CZEOKfetdPnjxZrF+/Pie/LUkCrV1hVw2GmfRkUK4FKfRcalfFyRp2cto8hGu16xSKWac237sjtKvmgSRJiAo5XEix6YCxjGO02x4KIxSOIiqA8hIfhlSm3rHAFD2O0S7bXcYirF3GrThCuypudWAyuWP3neebvdSUdh21clkIESGiGwC8DsAP4BEjx3Ku4VhB3qSYytXJ9+rktDGMSjHrtJjvXYXzgHErPh/hsMoyoNLulDCMNdjuMm6FtcswDBOPo5zLACCE+COAP9qdDoZhGIZhGIZhGIZhGIZhGMYYPt2DYRiGYRiGYRiGYRiGYRiGsQw7lxmGYRiGYRiGYRiGYRiGYRjLsHOZYRiGYRiGYRiGYRiGYRiGsQw7lxmGYRiGYRiGYRiGYRiGYRjLkBDC7jRkDBG1APhXmsuGADhYgORYwYlpAjhdAHBQCHFuvn/EpHYzxanlWEiKMQ+8oN1CUowaAZx5307TrhPzyA44H2RS5QNr1x1wviTD2jUPpy0z8pU21m5ucGO63ZhmoD/drF1zcLrMU6g0mdKuq53LZiCi9UKIyXanQ4sT0wRwurwC5xfnAZOeYtVIsd63FTiPZDgfZNyUD25KayHhfHE+Ti4jTltmODltucSt9+nGdLsxzYBz083psoYT0+W0NHFYDIZhGIZhGIZhGIZhGIZhGMYy7FxmGIZhGIZhGIZhGIZhGIZhLFMMzuUH7U6ADk5ME8Dp8gqcX5wHTHqKVSPFet9W4DyS4XyQcVM+uCmthYTzxfk4uYw4bZnh5LTlErfepxvT7cY0A85NN6fLGk5Ml6PS5PmYywzDMAzDMAzDMAzDMAzDMEzuKYaVywzDMAzDMAzDMAzDMAzDMEyO8axzmYjOJaJGIvqEiG63MR1jiOgvRPQREf2TiG5U3v8JEe0loveVx1dsSNtuIvpQ+f31ynuHEdEbRLRd+VtT4DSN1+TJ+0T0ORF9zwn55URS6MvWcrQDIvIT0SYiekV5fSQRvavkwdNEVGp3GpnCYLVekMw9SnuxmYgm2XsH2WG2LhBRmfL6E+X/Y+1MtxNwSt+hEBR7PdHi9jpTTLpNhHXsbuzWrhv042T7RETVRLSSiD5W8nCqk/Iu39itX7OQA8f8Bul8hIiaiWiL5j1H68kgzYZ+CyL6gZLmRiL6sk1pdoRuU9hf2/0+Tqwz5AI/mSedy0TkB3AfgPMA1AO4lIjqbUpOBMBNQogJAKYA+I4mLUuEECcpjz/alL6zlN+frLy+HcBqIUQdgNXK64IhhGhU8wRAA4BuAC8o/3ZCfjkNI33ZWo42cSOAjzSvF0PWTB2ANgDfsiVVjB1YrRfnAahTHvMAPFD4JOcUs3XhWwDahBBHA1iiXFe0OKzvUAiKvZ5ocW2dKULdJsI6dikO0a4b9ONk+/QbAK8JIY4FcKKSTiflXd5wiH6t4KgxvwHLAZyb8J7T9bQcyWkGdPwWij6+CeA45TP3KzoqGA7TLfvJLOAGP5knncsATgXwiRBipxAiDOAPAC60IyFCiH1CiI3K8w7Ije4oO9JikgsBPKY8fwzA12xMy3QAO4QQ/7IxDY4mhb6cVI55h4hGAzgfwMPKawJwNoCVyiWezwOmnwzqxYUAfi9k3gFQTUQjCpzsnGCxLmjzYyWA6cr1xYpj+g6FoJjriRYP1Jmi0m0irGNXY7t2na4fJ9snIhoI4AwAvwMAIURYCNEOh+RdAbBdv1niuLGiEOItAJ8lvO1oPRmk2YgLAfxBCNErhNgF4BPIOiokjtEt+8mywpF+Mq86l0cBaNK83gMHCJXk7UknA3hXeesGZRvHIzZtRREAVhHRBiKap7w3TAixD5ArPIChNqRL5ZsAntK8tju/HE2CvpxUjoXg1wBuBSAprwcDaBdCRJTXjrABTOExWS8c2WZkiJW6ELtv5f+HlOuLFS/pwBJFWE+0uL3OeLVcLFPkOnYjjioLh+rHyfbpKAAtAB4lOWzHw0RUCefkXb5x0/04fcyfCrfqSc9v4YQ0OyENSbCfzDKO9JN51bmsN0srCp4KDURUBeA5AN8TQnwOeevGOAAnAdgH4Fc2JGuaEGIS5G0R3yGiM2xIgy4kxw+7AMCzyltOyC/HoqOvooGIZgJoFkJs0L6tc6mtNoApPBbqhSf0kkFd8MR955CizI9iqydaPFJnnJimglPMOnYxjikLJ+rHBfYpAGASgAeEECcD6ELqbeKOKe8c4ab7ceyYPwucnP9GfgsnpNkJaYiD/WTWcLKfzKvO5T0AxmhejwbwqU1pARGVQK4wTwghngcAIcQBIURUCCEBeAiF3xIBIcSnyt9myPFaTgVwQN1SovxtLnS6FM4DsFEIcUBJo+355VT09AXnlGMhmAbgAiLaDXlrz9mQV3pUE1FAucZWG8AUHov1wlFtRhZYrQux+1b+Pwjmt/Z5Ea/owDRFWk+0eKHOeLFcLME6di2OKAsH68fp9mkPgD1CCHWl4UrIzmYn5F0hcM39OHzMnw7X6SmF38IJaXZCGmKwnywjHOsn86pzeR2AOpJP0y2FvGz8ZTsSosS6+h2Aj4QQ/6t5XxsT6CIAWxI/m+d0VRLRAPU5gBlKGl4GcKVy2ZUAXipkujRcCs1Sf7vzy6kY6QvOKce8I4T4gRBitBBiLOS6/mchxOUA/gJgtnKZp/OAiSeDevEygCtIZgqAQ+q2JzeRQV3Q5sds5XqnrPqwA8f0HQpBsdYTLR6pM0Wl20RYx67Gdu06WT9Ot09CiP0AmohovPLWdABb4YC8KxC269cMLhjzp8N1ekrht3gZwDeJqIyIjoR8GOF7BU6eY3TLfrKMca6fTAjhyQeArwDYBmAHgB/amI4vQN5qsBnA+8rjKwBWAPhQef9lACMKnK6jAHygPP6p5hHk2FyrAWxX/h5mQ55VAGgFMEjznq355dRHCn3ZXo425ceZAF5Rnh8FucH+BPK2kTK708ePgunAUr2AvEXsPqW9+BDAZLvvIQd5kLYuAChXXn+i/P8ou9Nt98MpfYcC3WvR15OE/HBtnSkm3ercO+vYxQ+7tesW/TjVPkHehr1eyb8XAdQ4Le/yfP+Ot71w8JhfJ61PQd7W3wd5he23nK4ngzQb+i0A/FBJcyOA82xKsyN0m8L+sp/MOG2O9pORkiCGYRiGYRiGYRiGYRiGYRiGMY1Xw2IwDMMwDMMwDMMwDMMwDMMweYSdywzDMAzDMAzDMAzDMAzDMIxl2LnMMAzDMAzDMAzDMAzDMAzDWIadywzDMAzDMAzDMAzDMAzDMIxl2LnMMAzDMAzDMAzDMAzDMAzDWIadywzD2AYRjSWiLRl+diQRrcx1mhj3QkTVRHR9mmvGEtFlJr4rrTaJqNNqGk387lVENFLz+mEiqs/17zCMkyCir7HOnU82bXYxY7bdYZyLUXtv1nYltu0prltORLMzSaNV2O4y+SKTvms++tSMvXi1z0BEFxDR7RY/UzDbbifsXGYYxpUIIT4VQnjeSDOWqAaQ0rkMYCwAJw/yrwIQG4AKIa4VQmy1LzkMUxC+BoCdHIxXGQtntztM5pi1XVdB07Y7BLa7TF7gvivjZYQQLwsh7rQ7HU6EncsegIgqiehVIvqAiLYQ0SVEtJuIfkpEG4noQyI6Vrn2MCJ6kYg2E9E7RDTR7vQzRU+AiB5TNLmSiCoU/f4PEf2DiNYT0SQiep2IdhDRAsC7s6FMVtwJYBwRvU9EdyuPLYoNvERzzX8o13xf0dHfFFu5kYhOT/xSIjqOiN5TPrOZiOp0rrmFiNYp//+p5v05ms8uIyK/8n4nEf1K+c3VRFSrzGhPBvCEcn2QiNYQ0WTNZxYT0QYiepOITlX+v5OILlCu8Sv3raZlfs5zmSkISlu9gYj+SUTzlPeSdKO8v0bRxntEtI2I/kN5X1cPRFSlfF7tI1yovJ/Un1DeP4WI3lbef4+IBhBRORE9qnx+ExGdpVx7FRHdq7mPV4joTE36f658zztENEypcxcAuFvR/biCZTKTCX4iekjR5SrFPm5U/0lEdUS0QXm+W6PL94joaOX9IxT9bVb+Hq68v5yIlio2eRsRzVTev4qIniei14hoOxHdpfm9GST3FTYS0bNEVKW8/1+K7rcQ0YNERMr7C4loq/Lbf1Deq9JoeTMRzVLev1R5bwsRLdb8Zqfm+WwiWq5J/z1KXdlJ/auU4tqdnJcIk1P0bK/yflrbRUQnKf/fTEQvEFEN6bftuvpMSAfbXcY0erol+/oMiX3XOP0p7x+p2O51RHRHwr0k9amV+rBZqQOVyn0eX5jcZbLAi32GmL0lg3afZO5VPvsqgKGaNDQQ0V+V+vo6EY0gooDy+2cq1/yCiH6ev2LJE0IIfrj8AWAWgIc0rwcB2A3gu8rr6wE8rDz/LYAfK8/PBvC+3ennR/E+IK/mEQCmKa8fAXCzot/rlPeWANgMYACAWgDNms9usfse+OGch1YTil18A4AfwDAA/wYwAsCZAF7RfKYCQLnyvA7Aep3v+i2Ay5XnpQCCyvNO5e8MAA8CIMiTtq8AOAPABAD/B6BEue5+AFcoz4XmO/8LwL3K8zUAJmvSF3utfOY85fkLAFYBKAFwomrLAcwD8CPleRmA9QCOtLts+JGRng9T/gYBbAEwOI1ufqU8/wqAN1PpAUAAwEDl/SEAPlH0q9efKAWwE8ApynsDlc/fBOBR5b1jlTpWDnmF3r2a73gFwJkaDX9VeX6XJm3LAcy2O8/5kVaTYwFEAJykvH4GwBwAf9G89z/o73/uBvBD5fkVUGyvYhevVJ5fA+BFjQ5eg2xH6wDs0Whqp6LHcgD/AjBG0e5bACqVz98G4L+U54dp0r1Co7tPAZQpz6uVv4sB/FpzfQ3kVab/htzvCAD4M4CvKf/v1Fw7G8ByTfqfVeTu6V8AAAhjSURBVNJfD+AT5f0zoWl3+OHsB4xtb1rbBbm/+kXl+X+rukJy226kz+WKptju8iNXui1on0Hz/dq+q57+XkZ/n/g7SNOnVv73MwC/BHAfgB/Ynef8SKvJsfBmn+EqTV1aDv12/+voH4eOBNAO2baXAHgbQK1y3SUAHlGeHwfgIwDnANgEoNTuMrT64JXL3uBDAF9SZnr+QwhxSHn/eeXvBsiVGwC+ALnCQAjxZwCDiWhQIRPLMAk0CSHWKs8fh6xRQO50ALK+3xVCdAghWgD0EFF1oRPJuI4vAHhKCBEVQhwA8FcAp+hcVwLgISL6EHLnQG+L6D8A/CcR3QbgCCFEKOH/M5THJgAbIQ/46gBMB9AAYB0Rva+8Pkr5jATgaeW5VvepCEPuRAFyvfirEKJPeT5Wk5YrlN97F/LgImmlNeMKFhLRBwDegdwprkNq3ei1+UZ6IAD/Q0SbAbwJYBTkSRi9/sR4APuEEOsAQAjxuRAigvj+xMeQO+/HpLmnMOSBYmI6GfewSwjxvvJcLcOHAVxN8s6MSwA8qbn+Kc3fqcrzqZprViBex88IISQhxHbIg8NjlfdXCyEOCSF6AGwFcASAKZBt9lpF41cq7wPAWUT0rmLbz4Y8aANk598TRDQH8qAXAL4E2VkBABBCtEFuL9YIIVoUvT8BedIwHS8q6d8KuU4x7kPP9qa1Xcp4qloI8VflrcdgrBkjfaqw3WWs4pQ+QyJG+puG/vZhheZ6oz41IE/YnAN5J8BdYNyAF/sMiei1+2egfxz6KeQJakC27ccDeENJw48AjAYAIcQ/lfv7PwDXCCHCBr/nWAJ2J4DJHiHENiJqgDzz+AsiWqX8q1f5G0V/WSdtu4I8o8gwdpGoP/W1ql9J81x9zbaLSYeerdPj+wAOQF796wPQk3iBEOJJInoXwPkAXieia5XJOe1v/UIIsSwuAUTfBfCYEOIHJtJhxg73CWVqG5p6IYSQiEhr478rhHjdxPcxDkXZFvclAFOFEN1EtAby6otEtLoxavOT9EBEV0FekdkghOgjot2QV/Dr9SdehL4+jepYBPFh17Tp1mpYm07GPWjb4yjkVXLPAfgx5MHTBiFEq+YaYfAcJq9J7BOovxuArME3hBCXaj9AROWQd4pMFkI0EdFP0K/D8yEP+i4AsIiIjlO+J/F3U7Uh2msT66U2nWbbIcYhpLC9ObNdafQZuwxsdxmTOKnPIIT474TfTKU/I40n9akVDgNQBXlhSDmALp1rGGfhxT5DIkbtvpG+/ymEmKrzPwA4AfIqZ1dOTvPKZQ9A8unD3UKIxyFvFZmU4vK3AFyufO5MAAeFEJ/nPZEMY8zhRKQa2EsB/N3OxDCupgNy+BRAtnWXkBw/rhZyx+C9hGsAecvUPiGEBGAu5O1LcRDRUQB2CiHugbyiPjFW/esArtHE7RpFREMBrAYwW3muxrxXZ8h9kLdHAfJBT6ruE9NnldcBXEdEJcpvHkNElVl8H2MPgwC0KYPEYyGvtgCMdWOEkR4GQQ4x1EdyzM4jlP/r9Sc+BjCSiE5RrhmgTGZo+xPHADgcQCPkbY0nEZGPiMYAONXE/Ware8ZGlJVBrwN4AMCjCf++RPP3H8rztwF8U3l+OeJ1/A1FO+Mg7/RoTPHT7wCYRv1xGSsULaqDwoOKXVZjIPoAjBFC/AXArZAPga2CHGLoBvVLiagG8qq9LxLREGV11aWQd8AAwAEimqB830Up0qfC+nYPRrbXiFjZKqs220iJXwu5T/HXxOtgoM8E2O4yVnBSn8EsaxHfDmjToNenBuRwGYsg7yRZDMaVeKDPYIa3AHxTGYeOAHCW8n4jgFrV90FEJarDmoi+Dnm3wBkA7iEX7tTmmUtvcALkAxkkAH0ArgOw0uDanwB4VNnW0g15OwDD2MlHAK4komUAtkNuaL5rb5IYNyKEaCWitSQf9PgnyFuZPoA8c3yrEGI/EbUCiChbB5dDnql+joi+ATkGmN4qiEsAzCGiPgD7IW/L0/7uKiKaAOAfJJ//0AlgjhBiKxH9CMAqpYPSBzmu3L+U3zmO5EMsDqG/M7UcwFIiCqF/O5gVHoa85WwjyYlpgXwiPOMuXgOwQGmrGyF3iAFj3RhhpIcnAPwfEa0H8D5kRwag058QQoRJPqTnt0QUBBCCvELqfsha/RDyqrmrhBC9RLQWwC7I22W3QN7Wmo4/QA5PsxByDNAdJj7DOIsnIMcYXJXwfpmy88MH2UELAAsBPEJEt0DW5NWa6xshO+SGAVgghOih5LPOAABCiBZlRd1TRFSmvP0jZTXdQ5A1uBvAOuV/fgCPkxy+gAAsEUK0E9HPANyntB1RAD8VQjxPRD+A3C4QgD8KIV5Svud2yNu8myBrPN1gczM07Y4QYkma6xn7MLK9RsTZLsjjqqVEVAF5i7aq7eWIb9v19BmD7S5jEcf0GSyk+UYATxLRjZBXsgIw7lMT0bkAIspuQj+At4no7ISdhIx7cHOfwcz9vQA5vMaHALYpaVRt+2zIzuNBkP2xvyaiA5AP/52urJ6+F8Bv4DJfHfXvUmAYhmEYphAQUacQwuzsN8MAYN0wzoWIboZ8mNMizXu7IW8zPWjyO5ZDPsDHaIEEwzAMYxLuMzBOhfsM3oRXLjMMwzAMwzAMkxFE9AKAcZBX6TAMwzAMw+jCfQbvwiuXGYZhGIZhGIZhGIZhGIZhGMvwgX4MwzAMwzAMwzAMwzAMwzCMZdi5zDAMwzAMwzAMwzAMwzAMw1iGncsMwzAMwzAMwzAMwzAMwzCMZdi5zDAMwzAMwzAMwzAMwzAMw1iGncsMwzAMwzAMwzAMwzAMwzCMZdi5zDAMwzAMwzAMwzAMwzAMw1jm/wOHkYRVgrYCPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(snore.iloc[:,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all are not uniformly distributed ,may contain some outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### as inputs from domain expert ahi value only depends on apneas count,hypoapneas count,ah total count and sleep time\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  ahi value does not depend on name,sex so building model removing those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sno', 'name', 'sex', 'bmi', 'totalsleeptime', 'apneascount',\n",
       "       'hypopneascount', 'ahtotalcount', 'apneasindex', 'hypopneasindex',\n",
       "       'ahi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snore.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## regression model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>sno</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>totalsleeptime</th>\n",
       "      <th>apneascount</th>\n",
       "      <th>hypopneascount</th>\n",
       "      <th>ahtotalcount</th>\n",
       "      <th>apneasindex</th>\n",
       "      <th>hypopneasindex</th>\n",
       "      <th>ahi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Rukum Bai</td>\n",
       "      <td>F</td>\n",
       "      <td>38.4000</td>\n",
       "      <td>247.5</td>\n",
       "      <td>66</td>\n",
       "      <td>69</td>\n",
       "      <td>135</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>N.Sanjay</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0900</td>\n",
       "      <td>245.0</td>\n",
       "      <td>22</td>\n",
       "      <td>120</td>\n",
       "      <td>142</td>\n",
       "      <td>5.4</td>\n",
       "      <td>29.4</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>G.Swapna</td>\n",
       "      <td>F</td>\n",
       "      <td>29.6700</td>\n",
       "      <td>387.5</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>N.venkateswarulu</td>\n",
       "      <td>M</td>\n",
       "      <td>28.4000</td>\n",
       "      <td>336.0</td>\n",
       "      <td>297</td>\n",
       "      <td>197</td>\n",
       "      <td>494</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.2</td>\n",
       "      <td>82.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>E.Vivek kumar</td>\n",
       "      <td>M</td>\n",
       "      <td>37.1000</td>\n",
       "      <td>320.5</td>\n",
       "      <td>43</td>\n",
       "      <td>311</td>\n",
       "      <td>354</td>\n",
       "      <td>8.0</td>\n",
       "      <td>52.8</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Charanjeet singh</td>\n",
       "      <td>M</td>\n",
       "      <td>46.4400</td>\n",
       "      <td>390.5</td>\n",
       "      <td>213</td>\n",
       "      <td>269</td>\n",
       "      <td>482</td>\n",
       "      <td>32.7</td>\n",
       "      <td>41.3</td>\n",
       "      <td>74.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Devesh devangan</td>\n",
       "      <td>M</td>\n",
       "      <td>30.0200</td>\n",
       "      <td>421.5</td>\n",
       "      <td>345</td>\n",
       "      <td>34</td>\n",
       "      <td>379</td>\n",
       "      <td>49.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>G.S.S.Rajasekhar</td>\n",
       "      <td>M</td>\n",
       "      <td>19.8400</td>\n",
       "      <td>222.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>George</td>\n",
       "      <td>M</td>\n",
       "      <td>27.3400</td>\n",
       "      <td>322.5</td>\n",
       "      <td>63</td>\n",
       "      <td>148</td>\n",
       "      <td>211</td>\n",
       "      <td>11.7</td>\n",
       "      <td>27.5</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>mohammed aadam</td>\n",
       "      <td>M</td>\n",
       "      <td>38.4200</td>\n",
       "      <td>229.0</td>\n",
       "      <td>166</td>\n",
       "      <td>139</td>\n",
       "      <td>305</td>\n",
       "      <td>43.5</td>\n",
       "      <td>36.4</td>\n",
       "      <td>79.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>N.sanjeev reddy</td>\n",
       "      <td>M</td>\n",
       "      <td>29.7500</td>\n",
       "      <td>405.5</td>\n",
       "      <td>83</td>\n",
       "      <td>235</td>\n",
       "      <td>318</td>\n",
       "      <td>12.3</td>\n",
       "      <td>34.8</td>\n",
       "      <td>47.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>P.S.shastri</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0900</td>\n",
       "      <td>214.5</td>\n",
       "      <td>199</td>\n",
       "      <td>75</td>\n",
       "      <td>274</td>\n",
       "      <td>55.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>76.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Ramachandra Rao</td>\n",
       "      <td>M</td>\n",
       "      <td>25.9500</td>\n",
       "      <td>375.0</td>\n",
       "      <td>36</td>\n",
       "      <td>62</td>\n",
       "      <td>98</td>\n",
       "      <td>5.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>S.Mallesh</td>\n",
       "      <td>M</td>\n",
       "      <td>31.6400</td>\n",
       "      <td>294.0</td>\n",
       "      <td>14</td>\n",
       "      <td>63</td>\n",
       "      <td>77</td>\n",
       "      <td>2.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Satyanayana reddy</td>\n",
       "      <td>M</td>\n",
       "      <td>33.6500</td>\n",
       "      <td>324.5</td>\n",
       "      <td>111</td>\n",
       "      <td>212</td>\n",
       "      <td>323</td>\n",
       "      <td>20.5</td>\n",
       "      <td>39.2</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>P.Rajesh</td>\n",
       "      <td>M</td>\n",
       "      <td>32.4800</td>\n",
       "      <td>380.0</td>\n",
       "      <td>168</td>\n",
       "      <td>209</td>\n",
       "      <td>377</td>\n",
       "      <td>26.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Y.janakiram</td>\n",
       "      <td>M</td>\n",
       "      <td>30.4600</td>\n",
       "      <td>305.5</td>\n",
       "      <td>375</td>\n",
       "      <td>28</td>\n",
       "      <td>403</td>\n",
       "      <td>73.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>79.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Vijaya Bharathi.B</td>\n",
       "      <td>F</td>\n",
       "      <td>38.5400</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Devamani medoju</td>\n",
       "      <td>F</td>\n",
       "      <td>31.6400</td>\n",
       "      <td>301.0</td>\n",
       "      <td>90</td>\n",
       "      <td>182</td>\n",
       "      <td>272</td>\n",
       "      <td>17.9</td>\n",
       "      <td>36.3</td>\n",
       "      <td>54.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>J.Uma sundari</td>\n",
       "      <td>F</td>\n",
       "      <td>55.6200</td>\n",
       "      <td>310.0</td>\n",
       "      <td>53</td>\n",
       "      <td>29</td>\n",
       "      <td>82</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>ammaji saladi</td>\n",
       "      <td>M</td>\n",
       "      <td>28.5100</td>\n",
       "      <td>388.5</td>\n",
       "      <td>105</td>\n",
       "      <td>175</td>\n",
       "      <td>280</td>\n",
       "      <td>16.2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Arpita Jalui</td>\n",
       "      <td>F</td>\n",
       "      <td>45.0100</td>\n",
       "      <td>258.5</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>D.L.tulasamma</td>\n",
       "      <td>F</td>\n",
       "      <td>30.8500</td>\n",
       "      <td>399.5</td>\n",
       "      <td>75</td>\n",
       "      <td>130</td>\n",
       "      <td>205</td>\n",
       "      <td>11.3</td>\n",
       "      <td>19.5</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>D.Bhanumathi</td>\n",
       "      <td>F</td>\n",
       "      <td>33.9800</td>\n",
       "      <td>277.5</td>\n",
       "      <td>358</td>\n",
       "      <td>302</td>\n",
       "      <td>660</td>\n",
       "      <td>77.4</td>\n",
       "      <td>65.3</td>\n",
       "      <td>142.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Jana Symala</td>\n",
       "      <td>F</td>\n",
       "      <td>24.6700</td>\n",
       "      <td>362.0</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>K.V.Suseela</td>\n",
       "      <td>F</td>\n",
       "      <td>33.7400</td>\n",
       "      <td>221.0</td>\n",
       "      <td>67</td>\n",
       "      <td>183</td>\n",
       "      <td>250</td>\n",
       "      <td>18.2</td>\n",
       "      <td>49.7</td>\n",
       "      <td>67.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>M.Venkata Laxmi</td>\n",
       "      <td>F</td>\n",
       "      <td>37.4600</td>\n",
       "      <td>262.5</td>\n",
       "      <td>74</td>\n",
       "      <td>117</td>\n",
       "      <td>191</td>\n",
       "      <td>16.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>M.V.Lakshmi</td>\n",
       "      <td>F</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>371.0</td>\n",
       "      <td>234</td>\n",
       "      <td>95</td>\n",
       "      <td>329</td>\n",
       "      <td>37.8</td>\n",
       "      <td>15.4</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Mani Manchmshetty</td>\n",
       "      <td>F</td>\n",
       "      <td>28.3900</td>\n",
       "      <td>290.5</td>\n",
       "      <td>210</td>\n",
       "      <td>228</td>\n",
       "      <td>438</td>\n",
       "      <td>43.4</td>\n",
       "      <td>47.1</td>\n",
       "      <td>90.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>S.Shanwaz Begum</td>\n",
       "      <td>F</td>\n",
       "      <td>38.2800</td>\n",
       "      <td>317.5</td>\n",
       "      <td>300</td>\n",
       "      <td>147</td>\n",
       "      <td>447</td>\n",
       "      <td>56.7</td>\n",
       "      <td>27.8</td>\n",
       "      <td>84.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>N.Prasad Rao</td>\n",
       "      <td>M</td>\n",
       "      <td>28.2200</td>\n",
       "      <td>71.0</td>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>4.2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>52.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>B.Narsimulu</td>\n",
       "      <td>M</td>\n",
       "      <td>25.4600</td>\n",
       "      <td>213.0</td>\n",
       "      <td>306</td>\n",
       "      <td>29</td>\n",
       "      <td>335</td>\n",
       "      <td>86.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>94.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>D.Hari Shankar</td>\n",
       "      <td>M</td>\n",
       "      <td>28.3900</td>\n",
       "      <td>182.5</td>\n",
       "      <td>110</td>\n",
       "      <td>18</td>\n",
       "      <td>178</td>\n",
       "      <td>52.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>G.S.Ashok</td>\n",
       "      <td>M</td>\n",
       "      <td>31.2300</td>\n",
       "      <td>121.5</td>\n",
       "      <td>32</td>\n",
       "      <td>173</td>\n",
       "      <td>205</td>\n",
       "      <td>15.8</td>\n",
       "      <td>85.4</td>\n",
       "      <td>101.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>Moirza Ashaq Ali</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0800</td>\n",
       "      <td>199.5</td>\n",
       "      <td>268</td>\n",
       "      <td>16</td>\n",
       "      <td>284</td>\n",
       "      <td>80.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>85.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>Syed Mohammad Zakiullah</td>\n",
       "      <td>M</td>\n",
       "      <td>28.7100</td>\n",
       "      <td>192.5</td>\n",
       "      <td>6</td>\n",
       "      <td>151</td>\n",
       "      <td>157</td>\n",
       "      <td>1.9</td>\n",
       "      <td>47.1</td>\n",
       "      <td>48.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>Fariyad</td>\n",
       "      <td>F</td>\n",
       "      <td>30.1100</td>\n",
       "      <td>189.5</td>\n",
       "      <td>238</td>\n",
       "      <td>34</td>\n",
       "      <td>272</td>\n",
       "      <td>75.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>86.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>K.Swasanthra</td>\n",
       "      <td>F</td>\n",
       "      <td>33.0500</td>\n",
       "      <td>213.5</td>\n",
       "      <td>55</td>\n",
       "      <td>247</td>\n",
       "      <td>302</td>\n",
       "      <td>15.5</td>\n",
       "      <td>69.4</td>\n",
       "      <td>84.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>krishna bai</td>\n",
       "      <td>F</td>\n",
       "      <td>45.1600</td>\n",
       "      <td>165.0</td>\n",
       "      <td>390</td>\n",
       "      <td>36</td>\n",
       "      <td>426</td>\n",
       "      <td>141.8</td>\n",
       "      <td>13.1</td>\n",
       "      <td>154.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>M.ratna sri</td>\n",
       "      <td>F</td>\n",
       "      <td>41.5500</td>\n",
       "      <td>141.0</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>175</td>\n",
       "      <td>40.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>74.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>B.rama devi</td>\n",
       "      <td>F</td>\n",
       "      <td>32.4600</td>\n",
       "      <td>160.5</td>\n",
       "      <td>136</td>\n",
       "      <td>42</td>\n",
       "      <td>178</td>\n",
       "      <td>50.8</td>\n",
       "      <td>15.7</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>Sayeed</td>\n",
       "      <td>M</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>150.5</td>\n",
       "      <td>23</td>\n",
       "      <td>65</td>\n",
       "      <td>88</td>\n",
       "      <td>9.2</td>\n",
       "      <td>25.9</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>C.H.prasad</td>\n",
       "      <td>M</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>184.5</td>\n",
       "      <td>27</td>\n",
       "      <td>92</td>\n",
       "      <td>119</td>\n",
       "      <td>8.8</td>\n",
       "      <td>29.9</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>B.Vishvam</td>\n",
       "      <td>M</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>201.0</td>\n",
       "      <td>278</td>\n",
       "      <td>22</td>\n",
       "      <td>300</td>\n",
       "      <td>83.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>89.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>chandrasekhar</td>\n",
       "      <td>M</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>200.0</td>\n",
       "      <td>306</td>\n",
       "      <td>39</td>\n",
       "      <td>345</td>\n",
       "      <td>91.8</td>\n",
       "      <td>11.7</td>\n",
       "      <td>103.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>G.srinivas</td>\n",
       "      <td>M</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>194.5</td>\n",
       "      <td>59</td>\n",
       "      <td>100</td>\n",
       "      <td>159</td>\n",
       "      <td>18.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Mandapati ravi</td>\n",
       "      <td>M</td>\n",
       "      <td>36.5700</td>\n",
       "      <td>228.5</td>\n",
       "      <td>282</td>\n",
       "      <td>11</td>\n",
       "      <td>293</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>76.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>Lathifa begum</td>\n",
       "      <td>F</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>198.0</td>\n",
       "      <td>52</td>\n",
       "      <td>124</td>\n",
       "      <td>176</td>\n",
       "      <td>15.8</td>\n",
       "      <td>37.6</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>seema</td>\n",
       "      <td>F</td>\n",
       "      <td>26.7000</td>\n",
       "      <td>212.5</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>Mona</td>\n",
       "      <td>F</td>\n",
       "      <td>32.6773</td>\n",
       "      <td>219.5</td>\n",
       "      <td>72</td>\n",
       "      <td>44</td>\n",
       "      <td>116</td>\n",
       "      <td>19.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>nalini verma</td>\n",
       "      <td>F</td>\n",
       "      <td>26.3000</td>\n",
       "      <td>232.0</td>\n",
       "      <td>258</td>\n",
       "      <td>90</td>\n",
       "      <td>348</td>\n",
       "      <td>66.7</td>\n",
       "      <td>23.3</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>C.srinivas</td>\n",
       "      <td>M</td>\n",
       "      <td>24.2200</td>\n",
       "      <td>426.0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>SK.Samir Uddin</td>\n",
       "      <td>M</td>\n",
       "      <td>28.7100</td>\n",
       "      <td>487.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>Sanjay agarwal</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>456.0</td>\n",
       "      <td>50</td>\n",
       "      <td>153</td>\n",
       "      <td>203</td>\n",
       "      <td>9.4</td>\n",
       "      <td>28.9</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>Nikita</td>\n",
       "      <td>F</td>\n",
       "      <td>30.1100</td>\n",
       "      <td>435.0</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "      <td>83</td>\n",
       "      <td>3.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>V.Venkamma</td>\n",
       "      <td>F</td>\n",
       "      <td>32.4500</td>\n",
       "      <td>312.0</td>\n",
       "      <td>84</td>\n",
       "      <td>90</td>\n",
       "      <td>174</td>\n",
       "      <td>26.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>54.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>Md.Miraj Ahmed</td>\n",
       "      <td>M</td>\n",
       "      <td>28.3400</td>\n",
       "      <td>257.0</td>\n",
       "      <td>366</td>\n",
       "      <td>15</td>\n",
       "      <td>381</td>\n",
       "      <td>100.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>104.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84</td>\n",
       "      <td>Subba Rao Aduri</td>\n",
       "      <td>M</td>\n",
       "      <td>46.7700</td>\n",
       "      <td>307.0</td>\n",
       "      <td>316</td>\n",
       "      <td>15</td>\n",
       "      <td>331</td>\n",
       "      <td>82.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>86.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>B.Nirmala</td>\n",
       "      <td>F</td>\n",
       "      <td>51.9300</td>\n",
       "      <td>203.0</td>\n",
       "      <td>101</td>\n",
       "      <td>91</td>\n",
       "      <td>192</td>\n",
       "      <td>41.2</td>\n",
       "      <td>37.1</td>\n",
       "      <td>78.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>Kamalamma</td>\n",
       "      <td>F</td>\n",
       "      <td>31.9900</td>\n",
       "      <td>425.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sno                     name sex      bmi  totalsleeptime  apneascount  \\\n",
       "0     1                Rukum Bai   F  38.4000           247.5           66   \n",
       "1     2                 N.Sanjay   M  24.0900           245.0           22   \n",
       "2     3                 G.Swapna   F  29.6700           387.5            1   \n",
       "3     4         N.venkateswarulu   M  28.4000           336.0          297   \n",
       "4     5            E.Vivek kumar   M  37.1000           320.5           43   \n",
       "5     6         Charanjeet singh   M  46.4400           390.5          213   \n",
       "6     7          Devesh devangan   M  30.0200           421.5          345   \n",
       "7     8         G.S.S.Rajasekhar   M  19.8400           222.5            1   \n",
       "8     9                   George   M  27.3400           322.5           63   \n",
       "9    10           mohammed aadam   M  38.4200           229.0          166   \n",
       "10   11          N.sanjeev reddy   M  29.7500           405.5           83   \n",
       "11   12              P.S.shastri   M  37.0900           214.5          199   \n",
       "12   13          Ramachandra Rao   M  25.9500           375.0           36   \n",
       "13   14                S.Mallesh   M  31.6400           294.0           14   \n",
       "14   15        Satyanayana reddy   M  33.6500           324.5          111   \n",
       "15   16                 P.Rajesh   M  32.4800           380.0          168   \n",
       "16   17              Y.janakiram   M  30.4600           305.5          375   \n",
       "17   18        Vijaya Bharathi.B   F  38.5400           290.0            0   \n",
       "18   19          Devamani medoju   F  31.6400           301.0           90   \n",
       "19   20            J.Uma sundari   F  55.6200           310.0           53   \n",
       "20   21            ammaji saladi   M  28.5100           388.5          105   \n",
       "21   22             Arpita Jalui   F  45.0100           258.5            0   \n",
       "22   23            D.L.tulasamma   F  30.8500           399.5           75   \n",
       "23   24             D.Bhanumathi   F  33.9800           277.5          358   \n",
       "24   25              Jana Symala   F  24.6700           362.0            5   \n",
       "25   26              K.V.Suseela   F  33.7400           221.0           67   \n",
       "26   27          M.Venkata Laxmi   F  37.4600           262.5           74   \n",
       "27   28              M.V.Lakshmi   F  32.0000           371.0          234   \n",
       "28   29        Mani Manchmshetty   F  28.3900           290.5          210   \n",
       "29   30          S.Shanwaz Begum   F  38.2800           317.5          300   \n",
       "..  ...                      ...  ..      ...             ...          ...   \n",
       "56   57             N.Prasad Rao   M  28.2200            71.0            5   \n",
       "57   58              B.Narsimulu   M  25.4600           213.0          306   \n",
       "58   59           D.Hari Shankar   M  28.3900           182.5          110   \n",
       "59   60                G.S.Ashok   M  31.2300           121.5           32   \n",
       "60   61         Moirza Ashaq Ali   M  34.0800           199.5          268   \n",
       "61   62  Syed Mohammad Zakiullah   M  28.7100           192.5            6   \n",
       "62   63                  Fariyad   F  30.1100           189.5          238   \n",
       "63   64             K.Swasanthra   F  33.0500           213.5           55   \n",
       "64   65              krishna bai   F  45.1600           165.0          390   \n",
       "65   66              M.ratna sri   F  41.5500           141.0           94   \n",
       "66   67              B.rama devi   F  32.4600           160.5          136   \n",
       "67   68                   Sayeed   M  32.6773           150.5           23   \n",
       "68   69               C.H.prasad   M  30.5000           184.5           27   \n",
       "69   70                B.Vishvam   M  32.6773           201.0          278   \n",
       "70   71            chandrasekhar   M  32.6773           200.0          306   \n",
       "71   72               G.srinivas   M  32.6773           194.5           59   \n",
       "72   73           Mandapati ravi   M  36.5700           228.5          282   \n",
       "73   74            Lathifa begum   F  44.0000           198.0           52   \n",
       "74   75                    seema   F  26.7000           212.5           34   \n",
       "75   76                     Mona   F  32.6773           219.5           72   \n",
       "76   77             nalini verma   F  26.3000           232.0          258   \n",
       "77   78               C.srinivas   M  24.2200           426.0           12   \n",
       "78   79           SK.Samir Uddin   M  28.7100           487.0            0   \n",
       "79   80           Sanjay agarwal   M  44.0000           456.0           50   \n",
       "80   81                   Nikita   F  30.1100           435.0           17   \n",
       "81   82               V.Venkamma   F  32.4500           312.0           84   \n",
       "82   83           Md.Miraj Ahmed   M  28.3400           257.0          366   \n",
       "83   84          Subba Rao Aduri   M  46.7700           307.0          316   \n",
       "84   85                B.Nirmala   F  51.9300           203.0          101   \n",
       "85   86                Kamalamma   F  31.9900           425.0            7   \n",
       "\n",
       "    hypopneascount  ahtotalcount  apneasindex  hypopneasindex    ahi  \n",
       "0               69           135         16.0            16.7   32.7  \n",
       "1              120           142          5.4            29.4   34.8  \n",
       "2               14            15          0.2             2.2    2.3  \n",
       "3              197           494         53.0            35.2   82.2  \n",
       "4              311           354          8.0            52.8   66.3  \n",
       "5              269           482         32.7            41.3   74.1  \n",
       "6               34           379         49.1             4.8   54.0  \n",
       "7                1             2          0.3             0.3    0.5  \n",
       "8              148           211         11.7            27.5   39.3  \n",
       "9              139           305         43.5            36.4   79.9  \n",
       "10             235           318         12.3            34.8   47.1  \n",
       "11              75           274         55.7            21.0   76.6  \n",
       "12              62            98          5.8             9.9   15.7  \n",
       "13              63            77          2.9            12.9   15.7  \n",
       "14             212           323         20.5            39.2   59.7  \n",
       "15             209           377         26.5            33.0   59.5  \n",
       "16              28           403         73.6             5.5   79.1  \n",
       "17              34            34          0.0             7.0    7.0  \n",
       "18             182           272         17.9            36.3   54.2  \n",
       "19              29            82         10.3             5.6   15.9  \n",
       "20             175           280         16.2            27.0   43.2  \n",
       "21              39            39          0.0             9.1    9.1  \n",
       "22             130           205         11.3            19.5   30.8  \n",
       "23             302           660         77.4            65.3  142.7  \n",
       "24              24            29          0.8             4.0    4.8  \n",
       "25             183           250         18.2            49.7   67.9  \n",
       "26             117           191         16.9            26.7   43.7  \n",
       "27              95           329         37.8            15.4   53.2  \n",
       "28             228           438         43.4            47.1   90.5  \n",
       "29             147           447         56.7            27.8   84.5  \n",
       "..             ...           ...          ...             ...    ...  \n",
       "56              57            62          4.2            48.2   52.4  \n",
       "57              29           335         86.2             8.2   94.4  \n",
       "58              18           178         52.6             5.9   58.5  \n",
       "59             173           205         15.8            85.4  101.2  \n",
       "60              16           284         80.6             4.8   85.4  \n",
       "61             151           157          1.9            47.1   48.9  \n",
       "62              34           272         75.4            10.8   86.1  \n",
       "63             247           302         15.5            69.4   84.9  \n",
       "64              36           426        141.8            13.1  154.9  \n",
       "65              81           175         40.0            34.5   74.5  \n",
       "66              42           178         50.8            15.7   66.5  \n",
       "67              65            88          9.2            25.9   35.1  \n",
       "68              92           119          8.8            29.9   38.7  \n",
       "69              22           300         83.0             6.6   89.6  \n",
       "70              39           345         91.8            11.7  103.5  \n",
       "71             100           159         18.2            30.8   49.0  \n",
       "72              11           293         74.0             2.9   76.9  \n",
       "73             124           176         15.8            37.6   53.3  \n",
       "74              30            64          9.6             8.5   18.1  \n",
       "75              44           116         19.7            12.0   31.7  \n",
       "76              90           348         66.7            23.3   90.0  \n",
       "77              12            24          1.7             1.7    3.4  \n",
       "78               3             3          0.0             0.6    0.6  \n",
       "79             153           203          9.4            28.9   38.3  \n",
       "80              66            83          3.4            13.3   16.8  \n",
       "81              90           174         26.3            28.1   54.4  \n",
       "82              15           381        100.7             4.1  104.9  \n",
       "83              15           331         82.3             3.9   86.2  \n",
       "84              91           192         41.2            37.1   78.4  \n",
       "85               2             9          1.5             0.4    1.9  \n",
       "\n",
       "[86 rows x 11 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=smf.ols('ahi~bmi+totalsleeptime+ahtotalcount+apneascount+hypopneascount+apneasindex+hypopnnore).easindex',data=sfit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept        -0.130322\n",
       "bmi               0.010560\n",
       "totalsleeptime   -0.000490\n",
       "ahtotalcount     -0.008297\n",
       "apneascount       0.001071\n",
       "hypopneascount    0.014308\n",
       "apneasindex       1.021694\n",
       "hypopneasindex    0.979741\n",
       "dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>ahi</td>       <th>  R-squared:         </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.817e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 02 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>1.61e-122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>01:14:38</td>     <th>  Log-Likelihood:    </th> <td> -105.94</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    86</td>      <th>  AIC:               </th> <td>   227.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    78</td>      <th>  BIC:               </th> <td>   247.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>   -0.1303</td> <td>    0.734</td> <td>   -0.178</td> <td> 0.859</td> <td>   -1.591</td> <td>    1.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bmi</th>            <td>    0.0106</td> <td>    0.014</td> <td>    0.738</td> <td> 0.462</td> <td>   -0.018</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>totalsleeptime</th> <td>   -0.0005</td> <td>    0.002</td> <td>   -0.275</td> <td> 0.784</td> <td>   -0.004</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ahtotalcount</th>   <td>   -0.0083</td> <td>    0.018</td> <td>   -0.450</td> <td> 0.654</td> <td>   -0.045</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>apneascount</th>    <td>    0.0011</td> <td>    0.018</td> <td>    0.060</td> <td> 0.953</td> <td>   -0.035</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hypopneascount</th> <td>    0.0143</td> <td>    0.019</td> <td>    0.747</td> <td> 0.457</td> <td>   -0.024</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>apneasindex</th>    <td>    1.0217</td> <td>    0.012</td> <td>   81.811</td> <td> 0.000</td> <td>    0.997</td> <td>    1.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hypopneasindex</th> <td>    0.9797</td> <td>    0.015</td> <td>   65.758</td> <td> 0.000</td> <td>    0.950</td> <td>    1.009</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>61.114</td> <th>  Durbin-Watson:     </th> <td>   2.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>3601.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.104</td> <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>34.626</td> <th>  Cond. No.          </th> <td>3.24e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.24e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    ahi   R-squared:                       0.999\n",
       "Model:                            OLS   Adj. R-squared:                  0.999\n",
       "Method:                 Least Squares   F-statistic:                 1.817e+04\n",
       "Date:                Tue, 02 Apr 2019   Prob (F-statistic):          1.61e-122\n",
       "Time:                        01:14:38   Log-Likelihood:                -105.94\n",
       "No. Observations:                  86   AIC:                             227.9\n",
       "Df Residuals:                      78   BIC:                             247.5\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         -0.1303      0.734     -0.178      0.859      -1.591       1.330\n",
       "bmi                0.0106      0.014      0.738      0.462      -0.018       0.039\n",
       "totalsleeptime    -0.0005      0.002     -0.275      0.784      -0.004       0.003\n",
       "ahtotalcount      -0.0083      0.018     -0.450      0.654      -0.045       0.028\n",
       "apneascount        0.0011      0.018      0.060      0.953      -0.035       0.037\n",
       "hypopneascount     0.0143      0.019      0.747      0.457      -0.024       0.052\n",
       "apneasindex        1.0217      0.012     81.811      0.000       0.997       1.047\n",
       "hypopneasindex     0.9797      0.015     65.758      0.000       0.950       1.009\n",
       "==============================================================================\n",
       "Omnibus:                       61.114   Durbin-Watson:                   2.850\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3601.624\n",
       "Skew:                          -1.104   Prob(JB):                         0.00\n",
       "Kurtosis:                      34.626   Cond. No.                     3.24e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.24e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## model1.confint(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "### apneas index and hypopneas index became less significant\n",
    "### so lets construct the model ignoring this and check others significance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=smf.ols('ahi~totalsleeptime+apneascount+hypopneascount+bmi',data=snore).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept         41.346550\n",
       "totalsleeptime    -0.142466\n",
       "apneascount        0.218525\n",
       "hypopneascount     0.160260\n",
       "bmi                0.312458\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>ahi</td>       <th>  R-squared:         </th> <td>   0.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   215.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 02 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>2.64e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:00:24</td>     <th>  Log-Likelihood:    </th> <td> -318.53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    86</td>      <th>  AIC:               </th> <td>   647.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>   659.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>   41.3466</td> <td>    6.813</td> <td>    6.069</td> <td> 0.000</td> <td>   27.790</td> <td>   54.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>totalsleeptime</th> <td>   -0.1425</td> <td>    0.012</td> <td>  -11.597</td> <td> 0.000</td> <td>   -0.167</td> <td>   -0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>apneascount</th>    <td>    0.2185</td> <td>    0.010</td> <td>   21.989</td> <td> 0.000</td> <td>    0.199</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hypopneascount</th> <td>    0.1603</td> <td>    0.013</td> <td>   12.143</td> <td> 0.000</td> <td>    0.134</td> <td>    0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bmi</th>            <td>    0.3125</td> <td>    0.162</td> <td>    1.930</td> <td> 0.057</td> <td>   -0.010</td> <td>    0.635</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.190</td> <th>  Durbin-Watson:     </th> <td>   1.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.006</td> <th>  Jarque-Bera (JB):  </th> <td>  10.829</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.669</td> <th>  Prob(JB):          </th> <td> 0.00445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.109</td> <th>  Cond. No.          </th> <td>2.11e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.11e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    ahi   R-squared:                       0.914\n",
       "Model:                            OLS   Adj. R-squared:                  0.910\n",
       "Method:                 Least Squares   F-statistic:                     215.3\n",
       "Date:                Tue, 02 Apr 2019   Prob (F-statistic):           2.64e-42\n",
       "Time:                        08:00:24   Log-Likelihood:                -318.53\n",
       "No. Observations:                  86   AIC:                             647.1\n",
       "Df Residuals:                      81   BIC:                             659.3\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         41.3466      6.813      6.069      0.000      27.790      54.903\n",
       "totalsleeptime    -0.1425      0.012    -11.597      0.000      -0.167      -0.118\n",
       "apneascount        0.2185      0.010     21.989      0.000       0.199       0.238\n",
       "hypopneascount     0.1603      0.013     12.143      0.000       0.134       0.187\n",
       "bmi                0.3125      0.162      1.930      0.057      -0.010       0.635\n",
       "==============================================================================\n",
       "Omnibus:                       10.190   Durbin-Watson:                   1.188\n",
       "Prob(Omnibus):                  0.006   Jarque-Bera (JB):               10.829\n",
       "Skew:                           0.669   Prob(JB):                      0.00445\n",
       "Kurtosis:                       4.109   Cond. No.                     2.11e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.11e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=smf.ols('ahi~apneasindex+hypopneasindex+bmi+totalsleeptime',data=snore).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>ahi</td>       <th>  R-squared:         </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>3.029e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 02 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>1.06e-127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:00:52</td>     <th>  Log-Likelihood:    </th> <td> -109.67</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    86</td>      <th>  AIC:               </th> <td>   229.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>   241.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>   -0.3091</td> <td>    0.632</td> <td>   -0.489</td> <td> 0.626</td> <td>   -1.567</td> <td>    0.949</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>apneasindex</th>    <td>    0.9959</td> <td>    0.004</td> <td>  280.797</td> <td> 0.000</td> <td>    0.989</td> <td>    1.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hypopneasindex</th> <td>    1.0011</td> <td>    0.005</td> <td>  183.205</td> <td> 0.000</td> <td>    0.990</td> <td>    1.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bmi</th>            <td>    0.0143</td> <td>    0.014</td> <td>    0.994</td> <td> 0.323</td> <td>   -0.014</td> <td>    0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>totalsleeptime</th> <td>   -0.0003</td> <td>    0.001</td> <td>   -0.215</td> <td> 0.830</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>58.752</td> <th>  Durbin-Watson:     </th> <td>   2.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>5081.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.834</td> <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>40.621</td> <th>  Cond. No.          </th> <td>1.99e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.99e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    ahi   R-squared:                       0.999\n",
       "Model:                            OLS   Adj. R-squared:                  0.999\n",
       "Method:                 Least Squares   F-statistic:                 3.029e+04\n",
       "Date:                Tue, 02 Apr 2019   Prob (F-statistic):          1.06e-127\n",
       "Time:                        08:00:52   Log-Likelihood:                -109.67\n",
       "No. Observations:                  86   AIC:                             229.3\n",
       "Df Residuals:                      81   BIC:                             241.6\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         -0.3091      0.632     -0.489      0.626      -1.567       0.949\n",
       "apneasindex        0.9959      0.004    280.797      0.000       0.989       1.003\n",
       "hypopneasindex     1.0011      0.005    183.205      0.000       0.990       1.012\n",
       "bmi                0.0143      0.014      0.994      0.323      -0.014       0.043\n",
       "totalsleeptime    -0.0003      0.001     -0.215      0.830      -0.003       0.002\n",
       "==============================================================================\n",
       "Omnibus:                       58.752   Durbin-Watson:                   2.963\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5081.706\n",
       "Skew:                          -0.834   Prob(JB):                         0.00\n",
       "Kurtosis:                      40.621   Cond. No.                     1.99e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.99e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sm.graphics.influence_plot(model1,criterion=\"cooks\")\n",
    "plt.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "m=ols('ahi~bmi+totalsleeptime+apneascount+hypopneascount+apneasindex+hypopneasindex',data=snore).fit()\n",
    "infl=m.get_influence()\n",
    "sm_fr=infl.summary_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>dfb_Intercept</th>\n",
       "      <th>dfb_bmi</th>\n",
       "      <th>dfb_totalsleeptime</th>\n",
       "      <th>dfb_apneascount</th>\n",
       "      <th>dfb_hypopneascount</th>\n",
       "      <th>dfb_apneasindex</th>\n",
       "      <th>dfb_hypopneasindex</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>standard_resid</th>\n",
       "      <th>hat_diag</th>\n",
       "      <th>dffits_internal</th>\n",
       "      <th>student_resid</th>\n",
       "      <th>dffits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.005947</td>\n",
       "      <td>-0.013519</td>\n",
       "      <td>0.013109</td>\n",
       "      <td>-0.006533</td>\n",
       "      <td>-0.005880</td>\n",
       "      <td>0.009847</td>\n",
       "      <td>0.010290</td>\n",
       "      <td>7.921834e-05</td>\n",
       "      <td>-0.114068</td>\n",
       "      <td>0.040876</td>\n",
       "      <td>-0.023548</td>\n",
       "      <td>-0.113353</td>\n",
       "      <td>-0.023401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.014748</td>\n",
       "      <td>0.011812</td>\n",
       "      <td>0.007422</td>\n",
       "      <td>0.001353</td>\n",
       "      <td>-0.004959</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>0.003809</td>\n",
       "      <td>5.333107e-05</td>\n",
       "      <td>-0.089284</td>\n",
       "      <td>0.044736</td>\n",
       "      <td>-0.019321</td>\n",
       "      <td>-0.088721</td>\n",
       "      <td>-0.019200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004914</td>\n",
       "      <td>0.001876</td>\n",
       "      <td>-0.016938</td>\n",
       "      <td>0.008181</td>\n",
       "      <td>0.010996</td>\n",
       "      <td>-0.005388</td>\n",
       "      <td>-0.005685</td>\n",
       "      <td>1.746341e-04</td>\n",
       "      <td>-0.147370</td>\n",
       "      <td>0.053288</td>\n",
       "      <td>-0.034963</td>\n",
       "      <td>-0.146454</td>\n",
       "      <td>-0.034746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.537419</td>\n",
       "      <td>0.680455</td>\n",
       "      <td>0.391430</td>\n",
       "      <td>-2.193783</td>\n",
       "      <td>-0.130892</td>\n",
       "      <td>1.694953</td>\n",
       "      <td>-0.022584</td>\n",
       "      <td>6.791486e-01</td>\n",
       "      <td>-6.671612</td>\n",
       "      <td>0.096500</td>\n",
       "      <td>-2.180376</td>\n",
       "      <td>-10.033076</td>\n",
       "      <td>-3.278949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.267034</td>\n",
       "      <td>0.428590</td>\n",
       "      <td>-0.795916</td>\n",
       "      <td>-1.128616</td>\n",
       "      <td>2.569192</td>\n",
       "      <td>0.783080</td>\n",
       "      <td>-1.795534</td>\n",
       "      <td>8.456832e-01</td>\n",
       "      <td>5.921026</td>\n",
       "      <td>0.144461</td>\n",
       "      <td>2.433060</td>\n",
       "      <td>7.888729</td>\n",
       "      <td>3.241626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.005498</td>\n",
       "      <td>-0.011866</td>\n",
       "      <td>0.002863</td>\n",
       "      <td>-0.006684</td>\n",
       "      <td>-0.008122</td>\n",
       "      <td>0.006427</td>\n",
       "      <td>0.006697</td>\n",
       "      <td>5.700239e-05</td>\n",
       "      <td>-0.047948</td>\n",
       "      <td>0.147891</td>\n",
       "      <td>-0.019975</td>\n",
       "      <td>-0.047644</td>\n",
       "      <td>-0.019849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.086365</td>\n",
       "      <td>0.059749</td>\n",
       "      <td>0.121773</td>\n",
       "      <td>1.113058</td>\n",
       "      <td>-0.616637</td>\n",
       "      <td>-0.940999</td>\n",
       "      <td>0.391031</td>\n",
       "      <td>2.369633e-01</td>\n",
       "      <td>1.934276</td>\n",
       "      <td>0.307165</td>\n",
       "      <td>1.287922</td>\n",
       "      <td>1.969191</td>\n",
       "      <td>1.311170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.019678</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>0.013229</td>\n",
       "      <td>-0.003385</td>\n",
       "      <td>-0.006394</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.010206</td>\n",
       "      <td>6.694107e-05</td>\n",
       "      <td>-0.060344</td>\n",
       "      <td>0.114013</td>\n",
       "      <td>-0.021647</td>\n",
       "      <td>-0.059962</td>\n",
       "      <td>-0.021510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.002121</td>\n",
       "      <td>0.002140</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.000786</td>\n",
       "      <td>-0.002128</td>\n",
       "      <td>-0.000357</td>\n",
       "      <td>0.001544</td>\n",
       "      <td>3.153411e-06</td>\n",
       "      <td>-0.027571</td>\n",
       "      <td>0.028220</td>\n",
       "      <td>-0.004698</td>\n",
       "      <td>-0.027396</td>\n",
       "      <td>-0.004668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000107</td>\n",
       "      <td>-0.000272</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>-0.000078</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>-0.000085</td>\n",
       "      <td>6.172100e-08</td>\n",
       "      <td>-0.004003</td>\n",
       "      <td>0.026249</td>\n",
       "      <td>-0.000657</td>\n",
       "      <td>-0.003978</td>\n",
       "      <td>-0.000653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.004603</td>\n",
       "      <td>0.020082</td>\n",
       "      <td>-0.009736</td>\n",
       "      <td>0.036727</td>\n",
       "      <td>-0.076046</td>\n",
       "      <td>-0.031018</td>\n",
       "      <td>0.050023</td>\n",
       "      <td>2.117707e-03</td>\n",
       "      <td>-0.424046</td>\n",
       "      <td>0.076161</td>\n",
       "      <td>-0.121754</td>\n",
       "      <td>-0.421834</td>\n",
       "      <td>-0.121119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.000681</td>\n",
       "      <td>-0.002777</td>\n",
       "      <td>0.002565</td>\n",
       "      <td>-0.000854</td>\n",
       "      <td>-0.000424</td>\n",
       "      <td>-0.000016</td>\n",
       "      <td>0.001165</td>\n",
       "      <td>1.041694e-05</td>\n",
       "      <td>-0.049477</td>\n",
       "      <td>0.028926</td>\n",
       "      <td>-0.008539</td>\n",
       "      <td>-0.049164</td>\n",
       "      <td>-0.008485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000313</td>\n",
       "      <td>-0.000835</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>-0.000205</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>5.928163e-07</td>\n",
       "      <td>0.010406</td>\n",
       "      <td>0.036908</td>\n",
       "      <td>0.002037</td>\n",
       "      <td>0.010340</td>\n",
       "      <td>0.002024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.021540</td>\n",
       "      <td>-0.004170</td>\n",
       "      <td>0.016899</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>-0.013051</td>\n",
       "      <td>0.009484</td>\n",
       "      <td>0.020827</td>\n",
       "      <td>3.187658e-04</td>\n",
       "      <td>-0.270012</td>\n",
       "      <td>0.029697</td>\n",
       "      <td>-0.047237</td>\n",
       "      <td>-0.268421</td>\n",
       "      <td>-0.046959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.003177</td>\n",
       "      <td>-0.002328</td>\n",
       "      <td>0.006465</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>-0.024446</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>2.627773e-04</td>\n",
       "      <td>-0.220800</td>\n",
       "      <td>0.036358</td>\n",
       "      <td>-0.042889</td>\n",
       "      <td>-0.219465</td>\n",
       "      <td>-0.042630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>-0.000264</td>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.002050</td>\n",
       "      <td>-0.001348</td>\n",
       "      <td>-0.001471</td>\n",
       "      <td>3.441973e-06</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>0.051320</td>\n",
       "      <td>0.004909</td>\n",
       "      <td>0.020970</td>\n",
       "      <td>0.004877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.060233</td>\n",
       "      <td>-0.002200</td>\n",
       "      <td>-0.058618</td>\n",
       "      <td>0.475397</td>\n",
       "      <td>-0.193297</td>\n",
       "      <td>-0.377315</td>\n",
       "      <td>0.084852</td>\n",
       "      <td>4.778344e-02</td>\n",
       "      <td>1.261894</td>\n",
       "      <td>0.173590</td>\n",
       "      <td>0.578346</td>\n",
       "      <td>1.266713</td>\n",
       "      <td>0.580555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.007779</td>\n",
       "      <td>-0.032236</td>\n",
       "      <td>0.019577</td>\n",
       "      <td>-0.005723</td>\n",
       "      <td>-0.010784</td>\n",
       "      <td>0.016369</td>\n",
       "      <td>0.023540</td>\n",
       "      <td>4.662986e-04</td>\n",
       "      <td>-0.228666</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>-0.057132</td>\n",
       "      <td>-0.227289</td>\n",
       "      <td>-0.056788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.007431</td>\n",
       "      <td>0.003542</td>\n",
       "      <td>0.005939</td>\n",
       "      <td>0.001606</td>\n",
       "      <td>-0.014412</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.008770</td>\n",
       "      <td>1.129072e-04</td>\n",
       "      <td>-0.174199</td>\n",
       "      <td>0.025384</td>\n",
       "      <td>-0.028113</td>\n",
       "      <td>-0.173126</td>\n",
       "      <td>-0.027940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.046443</td>\n",
       "      <td>-0.130235</td>\n",
       "      <td>0.020377</td>\n",
       "      <td>-0.031001</td>\n",
       "      <td>-0.002939</td>\n",
       "      <td>0.043323</td>\n",
       "      <td>0.028914</td>\n",
       "      <td>2.892465e-03</td>\n",
       "      <td>-0.281309</td>\n",
       "      <td>0.203731</td>\n",
       "      <td>-0.142293</td>\n",
       "      <td>-0.279663</td>\n",
       "      <td>-0.141460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.001925</td>\n",
       "      <td>0.004960</td>\n",
       "      <td>-0.002385</td>\n",
       "      <td>0.000729</td>\n",
       "      <td>-0.007503</td>\n",
       "      <td>-0.000484</td>\n",
       "      <td>0.005055</td>\n",
       "      <td>4.735114e-05</td>\n",
       "      <td>-0.090205</td>\n",
       "      <td>0.039140</td>\n",
       "      <td>-0.018206</td>\n",
       "      <td>-0.089637</td>\n",
       "      <td>-0.018091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.005427</td>\n",
       "      <td>-0.083614</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>-0.020447</td>\n",
       "      <td>-0.024654</td>\n",
       "      <td>0.039358</td>\n",
       "      <td>0.047091</td>\n",
       "      <td>1.822995e-03</td>\n",
       "      <td>-0.325909</td>\n",
       "      <td>0.107255</td>\n",
       "      <td>-0.112964</td>\n",
       "      <td>-0.324058</td>\n",
       "      <td>-0.112323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.002423</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>-0.003662</td>\n",
       "      <td>-0.001615</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>4.993332e-05</td>\n",
       "      <td>-0.103545</td>\n",
       "      <td>0.031572</td>\n",
       "      <td>-0.018696</td>\n",
       "      <td>-0.102894</td>\n",
       "      <td>-0.018578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.026641</td>\n",
       "      <td>-0.018600</td>\n",
       "      <td>0.016438</td>\n",
       "      <td>0.040685</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>-0.010165</td>\n",
       "      <td>0.038694</td>\n",
       "      <td>3.515160e-03</td>\n",
       "      <td>0.386712</td>\n",
       "      <td>0.141291</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>0.384621</td>\n",
       "      <td>0.156015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.000786</td>\n",
       "      <td>-0.001417</td>\n",
       "      <td>0.000966</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>-0.000578</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>1.649705e-06</td>\n",
       "      <td>0.014945</td>\n",
       "      <td>0.049163</td>\n",
       "      <td>0.003398</td>\n",
       "      <td>0.014850</td>\n",
       "      <td>0.003377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.002176</td>\n",
       "      <td>0.001771</td>\n",
       "      <td>0.002342</td>\n",
       "      <td>0.002692</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>-0.001379</td>\n",
       "      <td>-0.007766</td>\n",
       "      <td>1.051105e-04</td>\n",
       "      <td>-0.140019</td>\n",
       "      <td>0.036172</td>\n",
       "      <td>-0.027125</td>\n",
       "      <td>-0.139148</td>\n",
       "      <td>-0.026956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.000880</td>\n",
       "      <td>-0.002925</td>\n",
       "      <td>0.002517</td>\n",
       "      <td>-0.001047</td>\n",
       "      <td>-0.001680</td>\n",
       "      <td>0.001771</td>\n",
       "      <td>0.001976</td>\n",
       "      <td>4.220035e-06</td>\n",
       "      <td>-0.033503</td>\n",
       "      <td>0.025642</td>\n",
       "      <td>-0.005435</td>\n",
       "      <td>-0.033291</td>\n",
       "      <td>-0.005401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.003018</td>\n",
       "      <td>0.018407</td>\n",
       "      <td>0.002631</td>\n",
       "      <td>0.164766</td>\n",
       "      <td>-0.052212</td>\n",
       "      <td>-0.141608</td>\n",
       "      <td>0.023151</td>\n",
       "      <td>5.928688e-03</td>\n",
       "      <td>0.681920</td>\n",
       "      <td>0.081934</td>\n",
       "      <td>0.203717</td>\n",
       "      <td>0.679594</td>\n",
       "      <td>0.203022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.007005</td>\n",
       "      <td>-0.015144</td>\n",
       "      <td>-0.001991</td>\n",
       "      <td>0.007768</td>\n",
       "      <td>0.007289</td>\n",
       "      <td>-0.003313</td>\n",
       "      <td>0.001668</td>\n",
       "      <td>2.042331e-04</td>\n",
       "      <td>0.155494</td>\n",
       "      <td>0.055827</td>\n",
       "      <td>0.037810</td>\n",
       "      <td>0.154531</td>\n",
       "      <td>0.037576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.020707</td>\n",
       "      <td>0.061179</td>\n",
       "      <td>-0.025189</td>\n",
       "      <td>0.143663</td>\n",
       "      <td>-0.016711</td>\n",
       "      <td>-0.117561</td>\n",
       "      <td>0.004392</td>\n",
       "      <td>4.956663e-03</td>\n",
       "      <td>0.580558</td>\n",
       "      <td>0.093335</td>\n",
       "      <td>0.186270</td>\n",
       "      <td>0.578106</td>\n",
       "      <td>0.185484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.079552</td>\n",
       "      <td>-0.054309</td>\n",
       "      <td>-0.047444</td>\n",
       "      <td>0.091017</td>\n",
       "      <td>-0.157586</td>\n",
       "      <td>-0.100132</td>\n",
       "      <td>0.148991</td>\n",
       "      <td>1.064528e-02</td>\n",
       "      <td>0.563118</td>\n",
       "      <td>0.190279</td>\n",
       "      <td>0.272978</td>\n",
       "      <td>0.560669</td>\n",
       "      <td>0.271791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.042274</td>\n",
       "      <td>-0.056465</td>\n",
       "      <td>-0.010627</td>\n",
       "      <td>-0.004730</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>0.026917</td>\n",
       "      <td>-0.007977</td>\n",
       "      <td>1.952545e-03</td>\n",
       "      <td>0.388096</td>\n",
       "      <td>0.083195</td>\n",
       "      <td>0.116909</td>\n",
       "      <td>0.386000</td>\n",
       "      <td>0.116278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>-0.061884</td>\n",
       "      <td>0.044404</td>\n",
       "      <td>0.031158</td>\n",
       "      <td>0.104939</td>\n",
       "      <td>-0.061763</td>\n",
       "      <td>-0.101562</td>\n",
       "      <td>0.063391</td>\n",
       "      <td>3.516158e-03</td>\n",
       "      <td>-0.452344</td>\n",
       "      <td>0.107374</td>\n",
       "      <td>-0.156886</td>\n",
       "      <td>-0.450055</td>\n",
       "      <td>-0.156092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-0.118737</td>\n",
       "      <td>-0.093634</td>\n",
       "      <td>0.189784</td>\n",
       "      <td>-0.000585</td>\n",
       "      <td>-0.362840</td>\n",
       "      <td>0.026824</td>\n",
       "      <td>0.431685</td>\n",
       "      <td>3.356823e-02</td>\n",
       "      <td>0.617785</td>\n",
       "      <td>0.381063</td>\n",
       "      <td>0.484745</td>\n",
       "      <td>0.615351</td>\n",
       "      <td>0.482835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.005637</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>-0.006216</td>\n",
       "      <td>-0.003570</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>-0.007938</td>\n",
       "      <td>9.502254e-05</td>\n",
       "      <td>0.097549</td>\n",
       "      <td>0.065333</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.096935</td>\n",
       "      <td>0.025628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>-0.015671</td>\n",
       "      <td>0.011939</td>\n",
       "      <td>0.009267</td>\n",
       "      <td>-0.002501</td>\n",
       "      <td>0.005568</td>\n",
       "      <td>0.006413</td>\n",
       "      <td>-0.010082</td>\n",
       "      <td>1.931438e-04</td>\n",
       "      <td>-0.151023</td>\n",
       "      <td>0.055960</td>\n",
       "      <td>-0.036770</td>\n",
       "      <td>-0.150086</td>\n",
       "      <td>-0.036541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-0.006833</td>\n",
       "      <td>0.005836</td>\n",
       "      <td>0.003669</td>\n",
       "      <td>0.006782</td>\n",
       "      <td>-0.003730</td>\n",
       "      <td>-0.009399</td>\n",
       "      <td>0.004589</td>\n",
       "      <td>6.286653e-05</td>\n",
       "      <td>-0.080605</td>\n",
       "      <td>0.063435</td>\n",
       "      <td>-0.020978</td>\n",
       "      <td>-0.080097</td>\n",
       "      <td>-0.020845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.006050</td>\n",
       "      <td>0.008891</td>\n",
       "      <td>-0.009168</td>\n",
       "      <td>0.008973</td>\n",
       "      <td>0.011175</td>\n",
       "      <td>-0.008890</td>\n",
       "      <td>-0.026337</td>\n",
       "      <td>3.665946e-04</td>\n",
       "      <td>-0.161500</td>\n",
       "      <td>0.089574</td>\n",
       "      <td>-0.050657</td>\n",
       "      <td>-0.160501</td>\n",
       "      <td>-0.050344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.150191</td>\n",
       "      <td>-0.060190</td>\n",
       "      <td>-0.115915</td>\n",
       "      <td>0.283705</td>\n",
       "      <td>-0.033555</td>\n",
       "      <td>-0.347296</td>\n",
       "      <td>0.003149</td>\n",
       "      <td>2.791486e-02</td>\n",
       "      <td>-0.604578</td>\n",
       "      <td>0.348364</td>\n",
       "      <td>-0.442045</td>\n",
       "      <td>-0.602134</td>\n",
       "      <td>-0.440258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.007136</td>\n",
       "      <td>-0.025394</td>\n",
       "      <td>0.008276</td>\n",
       "      <td>0.017884</td>\n",
       "      <td>0.009106</td>\n",
       "      <td>-0.016384</td>\n",
       "      <td>-0.010471</td>\n",
       "      <td>5.543161e-04</td>\n",
       "      <td>-0.240607</td>\n",
       "      <td>0.062815</td>\n",
       "      <td>-0.062291</td>\n",
       "      <td>-0.239167</td>\n",
       "      <td>-0.061919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>-0.020810</td>\n",
       "      <td>0.004548</td>\n",
       "      <td>0.019031</td>\n",
       "      <td>0.017188</td>\n",
       "      <td>-0.013663</td>\n",
       "      <td>-0.016731</td>\n",
       "      <td>0.016003</td>\n",
       "      <td>2.848677e-04</td>\n",
       "      <td>-0.188833</td>\n",
       "      <td>0.052961</td>\n",
       "      <td>-0.044655</td>\n",
       "      <td>-0.187677</td>\n",
       "      <td>-0.044382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>-0.006365</td>\n",
       "      <td>-0.000563</td>\n",
       "      <td>0.007024</td>\n",
       "      <td>-0.002570</td>\n",
       "      <td>-0.001034</td>\n",
       "      <td>0.004068</td>\n",
       "      <td>0.002199</td>\n",
       "      <td>1.357166e-05</td>\n",
       "      <td>-0.037742</td>\n",
       "      <td>0.062523</td>\n",
       "      <td>-0.009747</td>\n",
       "      <td>-0.037503</td>\n",
       "      <td>-0.009685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>-0.006181</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>-0.001674</td>\n",
       "      <td>-0.000842</td>\n",
       "      <td>0.003040</td>\n",
       "      <td>0.001292</td>\n",
       "      <td>1.166513e-05</td>\n",
       "      <td>-0.042845</td>\n",
       "      <td>0.042588</td>\n",
       "      <td>-0.009036</td>\n",
       "      <td>-0.042573</td>\n",
       "      <td>-0.008979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.008222</td>\n",
       "      <td>-0.003386</td>\n",
       "      <td>-0.006766</td>\n",
       "      <td>-0.005715</td>\n",
       "      <td>0.005159</td>\n",
       "      <td>0.011511</td>\n",
       "      <td>-0.008787</td>\n",
       "      <td>1.980923e-04</td>\n",
       "      <td>0.140771</td>\n",
       "      <td>0.065399</td>\n",
       "      <td>0.037238</td>\n",
       "      <td>0.139894</td>\n",
       "      <td>0.037006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.003758</td>\n",
       "      <td>-0.006482</td>\n",
       "      <td>-0.000769</td>\n",
       "      <td>-0.010681</td>\n",
       "      <td>0.002164</td>\n",
       "      <td>0.019308</td>\n",
       "      <td>-0.003483</td>\n",
       "      <td>2.864093e-04</td>\n",
       "      <td>0.156657</td>\n",
       "      <td>0.075523</td>\n",
       "      <td>0.044776</td>\n",
       "      <td>0.155687</td>\n",
       "      <td>0.044498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>-0.006391</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.006538</td>\n",
       "      <td>-0.001199</td>\n",
       "      <td>-0.001010</td>\n",
       "      <td>0.002600</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>1.908148e-05</td>\n",
       "      <td>-0.066567</td>\n",
       "      <td>0.029262</td>\n",
       "      <td>-0.011557</td>\n",
       "      <td>-0.066146</td>\n",
       "      <td>-0.011484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.012994</td>\n",
       "      <td>0.023827</td>\n",
       "      <td>-0.027626</td>\n",
       "      <td>0.031413</td>\n",
       "      <td>0.001398</td>\n",
       "      <td>-0.018828</td>\n",
       "      <td>-0.021710</td>\n",
       "      <td>1.183606e-03</td>\n",
       "      <td>0.346593</td>\n",
       "      <td>0.064521</td>\n",
       "      <td>0.091023</td>\n",
       "      <td>0.344655</td>\n",
       "      <td>0.090514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.009113</td>\n",
       "      <td>-0.061591</td>\n",
       "      <td>0.029051</td>\n",
       "      <td>-0.012174</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.021752</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>1.093104e-03</td>\n",
       "      <td>-0.344513</td>\n",
       "      <td>0.060564</td>\n",
       "      <td>-0.087474</td>\n",
       "      <td>-0.342583</td>\n",
       "      <td>-0.086984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>-0.000783</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>-0.000185</td>\n",
       "      <td>-0.000298</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>1.168653e-07</td>\n",
       "      <td>-0.003419</td>\n",
       "      <td>0.065419</td>\n",
       "      <td>-0.000904</td>\n",
       "      <td>-0.003397</td>\n",
       "      <td>-0.000899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>-0.004012</td>\n",
       "      <td>-0.000797</td>\n",
       "      <td>0.004379</td>\n",
       "      <td>-0.001535</td>\n",
       "      <td>-0.001889</td>\n",
       "      <td>0.002335</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>5.215613e-06</td>\n",
       "      <td>-0.029148</td>\n",
       "      <td>0.041203</td>\n",
       "      <td>-0.006042</td>\n",
       "      <td>-0.028963</td>\n",
       "      <td>-0.006004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.031512</td>\n",
       "      <td>-0.049420</td>\n",
       "      <td>-0.005363</td>\n",
       "      <td>0.012918</td>\n",
       "      <td>-0.009402</td>\n",
       "      <td>0.004652</td>\n",
       "      <td>0.011147</td>\n",
       "      <td>1.131241e-03</td>\n",
       "      <td>0.385730</td>\n",
       "      <td>0.050532</td>\n",
       "      <td>0.088987</td>\n",
       "      <td>0.383642</td>\n",
       "      <td>0.088505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>-0.005410</td>\n",
       "      <td>-0.013153</td>\n",
       "      <td>0.024119</td>\n",
       "      <td>-0.009407</td>\n",
       "      <td>-0.015974</td>\n",
       "      <td>0.008974</td>\n",
       "      <td>0.012952</td>\n",
       "      <td>1.961254e-04</td>\n",
       "      <td>0.121514</td>\n",
       "      <td>0.085069</td>\n",
       "      <td>0.037052</td>\n",
       "      <td>0.120754</td>\n",
       "      <td>0.036821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>-0.014881</td>\n",
       "      <td>-0.003120</td>\n",
       "      <td>0.025991</td>\n",
       "      <td>-0.009957</td>\n",
       "      <td>-0.016704</td>\n",
       "      <td>0.010367</td>\n",
       "      <td>0.015109</td>\n",
       "      <td>1.393843e-04</td>\n",
       "      <td>0.074987</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.031236</td>\n",
       "      <td>0.074513</td>\n",
       "      <td>0.031039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.109677</td>\n",
       "      <td>-0.068021</td>\n",
       "      <td>-0.093925</td>\n",
       "      <td>0.041961</td>\n",
       "      <td>0.027912</td>\n",
       "      <td>-0.041705</td>\n",
       "      <td>-0.039067</td>\n",
       "      <td>2.582154e-03</td>\n",
       "      <td>-0.364842</td>\n",
       "      <td>0.119556</td>\n",
       "      <td>-0.134444</td>\n",
       "      <td>-0.362832</td>\n",
       "      <td>-0.133703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>-0.007780</td>\n",
       "      <td>-0.001679</td>\n",
       "      <td>0.013621</td>\n",
       "      <td>-0.006372</td>\n",
       "      <td>-0.007065</td>\n",
       "      <td>0.006263</td>\n",
       "      <td>0.007192</td>\n",
       "      <td>4.019272e-05</td>\n",
       "      <td>0.058554</td>\n",
       "      <td>0.075838</td>\n",
       "      <td>0.016773</td>\n",
       "      <td>0.058183</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>-0.001117</td>\n",
       "      <td>-0.000291</td>\n",
       "      <td>0.001790</td>\n",
       "      <td>-0.000967</td>\n",
       "      <td>-0.001448</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>8.174933e-07</td>\n",
       "      <td>0.011390</td>\n",
       "      <td>0.042249</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.011317</td>\n",
       "      <td>0.002377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.004157</td>\n",
       "      <td>-0.067859</td>\n",
       "      <td>0.042443</td>\n",
       "      <td>-0.008360</td>\n",
       "      <td>-0.038917</td>\n",
       "      <td>0.061941</td>\n",
       "      <td>0.025097</td>\n",
       "      <td>6.865029e-03</td>\n",
       "      <td>0.644396</td>\n",
       "      <td>0.103724</td>\n",
       "      <td>0.219215</td>\n",
       "      <td>0.641994</td>\n",
       "      <td>0.218398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>-0.067271</td>\n",
       "      <td>0.077238</td>\n",
       "      <td>0.030763</td>\n",
       "      <td>0.021110</td>\n",
       "      <td>-0.034819</td>\n",
       "      <td>-0.001049</td>\n",
       "      <td>0.016112</td>\n",
       "      <td>2.241363e-03</td>\n",
       "      <td>0.334529</td>\n",
       "      <td>0.122959</td>\n",
       "      <td>0.125258</td>\n",
       "      <td>0.332641</td>\n",
       "      <td>0.124551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.051895</td>\n",
       "      <td>-0.060389</td>\n",
       "      <td>-0.020811</td>\n",
       "      <td>0.021387</td>\n",
       "      <td>0.026152</td>\n",
       "      <td>-0.022134</td>\n",
       "      <td>-0.028102</td>\n",
       "      <td>1.022028e-03</td>\n",
       "      <td>-0.216937</td>\n",
       "      <td>0.131958</td>\n",
       "      <td>-0.084582</td>\n",
       "      <td>-0.215624</td>\n",
       "      <td>-0.084070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>-0.002307</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.003824</td>\n",
       "      <td>-0.001295</td>\n",
       "      <td>-0.002685</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.001944</td>\n",
       "      <td>4.625075e-06</td>\n",
       "      <td>0.019124</td>\n",
       "      <td>0.081321</td>\n",
       "      <td>0.005690</td>\n",
       "      <td>0.019003</td>\n",
       "      <td>0.005654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    dfb_Intercept   dfb_bmi  dfb_totalsleeptime  dfb_apneascount  \\\n",
       "0       -0.005947 -0.013519            0.013109        -0.006533   \n",
       "1       -0.014748  0.011812            0.007422         0.001353   \n",
       "2        0.004914  0.001876           -0.016938         0.008181   \n",
       "3       -0.537419  0.680455            0.391430        -2.193783   \n",
       "4        0.267034  0.428590           -0.795916        -1.128616   \n",
       "5        0.005498 -0.011866            0.002863        -0.006684   \n",
       "6       -0.086365  0.059749            0.121773         1.113058   \n",
       "7       -0.019678  0.009571            0.013229        -0.003385   \n",
       "8       -0.002121  0.002140            0.000547         0.000786   \n",
       "9        0.000107 -0.000272            0.000091        -0.000078   \n",
       "10      -0.004603  0.020082           -0.009736         0.036727   \n",
       "11      -0.000681 -0.002777            0.002565        -0.000854   \n",
       "12       0.000313 -0.000835            0.000715        -0.000369   \n",
       "13      -0.021540 -0.004170            0.016899         0.000017   \n",
       "14      -0.003177 -0.002328            0.006465         0.000829   \n",
       "15       0.000068  0.000161           -0.000264         0.001521   \n",
       "16       0.060233 -0.002200           -0.058618         0.475397   \n",
       "17      -0.007779 -0.032236            0.019577        -0.005723   \n",
       "18      -0.007431  0.003542            0.005939         0.001606   \n",
       "19       0.046443 -0.130235            0.020377        -0.031001   \n",
       "20      -0.001925  0.004960           -0.002385         0.000729   \n",
       "21      -0.005427 -0.083614            0.048387        -0.020447   \n",
       "22       0.002423  0.000977           -0.006598         0.002406   \n",
       "23      -0.026641 -0.018600            0.016438         0.040685   \n",
       "24       0.000786 -0.001417            0.000966        -0.000708   \n",
       "25      -0.002176  0.001771            0.002342         0.002692   \n",
       "26      -0.000880 -0.002925            0.002517        -0.001047   \n",
       "27      -0.003018  0.018407            0.002631         0.164766   \n",
       "28       0.007005 -0.015144           -0.001991         0.007768   \n",
       "29      -0.020707  0.061179           -0.025189         0.143663   \n",
       "..            ...       ...                 ...              ...   \n",
       "56       0.079552 -0.054309           -0.047444         0.091017   \n",
       "57       0.042274 -0.056465           -0.010627        -0.004730   \n",
       "58      -0.061884  0.044404            0.031158         0.104939   \n",
       "59      -0.118737 -0.093634            0.189784        -0.000585   \n",
       "60       0.005637  0.000384           -0.006216        -0.003570   \n",
       "61      -0.015671  0.011939            0.009267        -0.002501   \n",
       "62      -0.006833  0.005836            0.003669         0.006782   \n",
       "63       0.006050  0.008891           -0.009168         0.008973   \n",
       "64       0.150191 -0.060190           -0.115915         0.283705   \n",
       "65       0.007136 -0.025394            0.008276         0.017884   \n",
       "66      -0.020810  0.004548            0.019031         0.017188   \n",
       "67      -0.006365 -0.000563            0.007024        -0.002570   \n",
       "68      -0.006181  0.001195            0.005645        -0.001674   \n",
       "69       0.008222 -0.003386           -0.006766        -0.005715   \n",
       "70       0.003758 -0.006482           -0.000769        -0.010681   \n",
       "71      -0.006391  0.000164            0.006538        -0.001199   \n",
       "72       0.012994  0.023827           -0.027626         0.031413   \n",
       "73       0.009113 -0.061591            0.029051        -0.012174   \n",
       "74      -0.000783  0.000201            0.000648        -0.000185   \n",
       "75      -0.004012 -0.000797            0.004379        -0.001535   \n",
       "76       0.031512 -0.049420           -0.005363         0.012918   \n",
       "77      -0.005410 -0.013153            0.024119        -0.009407   \n",
       "78      -0.014881 -0.003120            0.025991        -0.009957   \n",
       "79       0.109677 -0.068021           -0.093925         0.041961   \n",
       "80      -0.007780 -0.001679            0.013621        -0.006372   \n",
       "81      -0.001117 -0.000291            0.001790        -0.000967   \n",
       "82       0.004157 -0.067859            0.042443        -0.008360   \n",
       "83      -0.067271  0.077238            0.030763         0.021110   \n",
       "84       0.051895 -0.060389           -0.020811         0.021387   \n",
       "85      -0.002307  0.000447            0.003824        -0.001295   \n",
       "\n",
       "    dfb_hypopneascount  dfb_apneasindex  dfb_hypopneasindex       cooks_d  \\\n",
       "0            -0.005880         0.009847            0.010290  7.921834e-05   \n",
       "1            -0.004959         0.001118            0.003809  5.333107e-05   \n",
       "2             0.010996        -0.005388           -0.005685  1.746341e-04   \n",
       "3            -0.130892         1.694953           -0.022584  6.791486e-01   \n",
       "4             2.569192         0.783080           -1.795534  8.456832e-01   \n",
       "5            -0.008122         0.006427            0.006697  5.700239e-05   \n",
       "6            -0.616637        -0.940999            0.391031  2.369633e-01   \n",
       "7            -0.006394         0.006374            0.010206  6.694107e-05   \n",
       "8            -0.002128        -0.000357            0.001544  3.153411e-06   \n",
       "9             0.000025         0.000029           -0.000085  6.172100e-08   \n",
       "10           -0.076046        -0.031018            0.050023  2.117707e-03   \n",
       "11           -0.000424        -0.000016            0.001165  1.041694e-05   \n",
       "12           -0.000205         0.000238            0.000054  5.928163e-07   \n",
       "13           -0.013051         0.009484            0.020827  3.187658e-04   \n",
       "14           -0.024446         0.000891            0.015267  2.627773e-04   \n",
       "15            0.002050        -0.001348           -0.001471  3.441973e-06   \n",
       "16           -0.193297        -0.377315            0.084852  4.778344e-02   \n",
       "17           -0.010784         0.016369            0.023540  4.662986e-04   \n",
       "18           -0.014412         0.000326            0.008770  1.129072e-04   \n",
       "19           -0.002939         0.043323            0.028914  2.892465e-03   \n",
       "20           -0.007503        -0.000484            0.005055  4.735114e-05   \n",
       "21           -0.024654         0.039358            0.047091  1.822995e-03   \n",
       "22           -0.003662        -0.001615            0.002874  4.993332e-05   \n",
       "23           -0.001229        -0.010165            0.038694  3.515160e-03   \n",
       "24           -0.000578         0.000406            0.000141  1.649705e-06   \n",
       "25            0.001339        -0.001379           -0.007766  1.051105e-04   \n",
       "26           -0.001680         0.001771            0.001976  4.220035e-06   \n",
       "27           -0.052212        -0.141608            0.023151  5.928688e-03   \n",
       "28            0.007289        -0.003313            0.001668  2.042331e-04   \n",
       "29           -0.016711        -0.117561            0.004392  4.956663e-03   \n",
       "..                 ...              ...                 ...           ...   \n",
       "56           -0.157586        -0.100132            0.148991  1.064528e-02   \n",
       "57            0.001355         0.026917           -0.007977  1.952545e-03   \n",
       "58           -0.061763        -0.101562            0.063391  3.516158e-03   \n",
       "59           -0.362840         0.026824            0.431685  3.356823e-02   \n",
       "60            0.004767         0.007016           -0.007938  9.502254e-05   \n",
       "61            0.005568         0.006413           -0.010082  1.931438e-04   \n",
       "62           -0.003730        -0.009399            0.004589  6.286653e-05   \n",
       "63            0.011175        -0.008890           -0.026337  3.665946e-04   \n",
       "64           -0.033555        -0.347296            0.003149  2.791486e-02   \n",
       "65            0.009106        -0.016384           -0.010471  5.543161e-04   \n",
       "66           -0.013663        -0.016731            0.016003  2.848677e-04   \n",
       "67           -0.001034         0.004068            0.002199  1.357166e-05   \n",
       "68           -0.000842         0.003040            0.001292  1.166513e-05   \n",
       "69            0.005159         0.011511           -0.008787  1.980923e-04   \n",
       "70            0.002164         0.019308           -0.003483  2.864093e-04   \n",
       "71           -0.001010         0.002600            0.001237  1.908148e-05   \n",
       "72            0.001398        -0.018828           -0.021710  1.183606e-03   \n",
       "73            0.000062         0.021752            0.001455  1.093104e-03   \n",
       "74           -0.000298         0.000319            0.000467  1.168653e-07   \n",
       "75           -0.001889         0.002335            0.003180  5.215613e-06   \n",
       "76           -0.009402         0.004652            0.011147  1.131241e-03   \n",
       "77           -0.015974         0.008974            0.012952  1.961254e-04   \n",
       "78           -0.016704         0.010367            0.015109  1.393843e-04   \n",
       "79            0.027912        -0.041705           -0.039067  2.582154e-03   \n",
       "80           -0.007065         0.006263            0.007192  4.019272e-05   \n",
       "81           -0.001448         0.001121            0.001664  8.174933e-07   \n",
       "82           -0.038917         0.061941            0.025097  6.865029e-03   \n",
       "83           -0.034819        -0.001049            0.016112  2.241363e-03   \n",
       "84            0.026152        -0.022134           -0.028102  1.022028e-03   \n",
       "85           -0.002685         0.001114            0.001944  4.625075e-06   \n",
       "\n",
       "    standard_resid  hat_diag  dffits_internal  student_resid    dffits  \n",
       "0        -0.114068  0.040876        -0.023548      -0.113353 -0.023401  \n",
       "1        -0.089284  0.044736        -0.019321      -0.088721 -0.019200  \n",
       "2        -0.147370  0.053288        -0.034963      -0.146454 -0.034746  \n",
       "3        -6.671612  0.096500        -2.180376     -10.033076 -3.278949  \n",
       "4         5.921026  0.144461         2.433060       7.888729  3.241626  \n",
       "5        -0.047948  0.147891        -0.019975      -0.047644 -0.019849  \n",
       "6         1.934276  0.307165         1.287922       1.969191  1.311170  \n",
       "7        -0.060344  0.114013        -0.021647      -0.059962 -0.021510  \n",
       "8        -0.027571  0.028220        -0.004698      -0.027396 -0.004668  \n",
       "9        -0.004003  0.026249        -0.000657      -0.003978 -0.000653  \n",
       "10       -0.424046  0.076161        -0.121754      -0.421834 -0.121119  \n",
       "11       -0.049477  0.028926        -0.008539      -0.049164 -0.008485  \n",
       "12        0.010406  0.036908         0.002037       0.010340  0.002024  \n",
       "13       -0.270012  0.029697        -0.047237      -0.268421 -0.046959  \n",
       "14       -0.220800  0.036358        -0.042889      -0.219465 -0.042630  \n",
       "15        0.021104  0.051320         0.004909       0.020970  0.004877  \n",
       "16        1.261894  0.173590         0.578346       1.266713  0.580555  \n",
       "17       -0.228666  0.058757        -0.057132      -0.227289 -0.056788  \n",
       "18       -0.174199  0.025384        -0.028113      -0.173126 -0.027940  \n",
       "19       -0.281309  0.203731        -0.142293      -0.279663 -0.141460  \n",
       "20       -0.090205  0.039140        -0.018206      -0.089637 -0.018091  \n",
       "21       -0.325909  0.107255        -0.112964      -0.324058 -0.112323  \n",
       "22       -0.103545  0.031572        -0.018696      -0.102894 -0.018578  \n",
       "23        0.386712  0.141291         0.156863       0.384621  0.156015  \n",
       "24        0.014945  0.049163         0.003398       0.014850  0.003377  \n",
       "25       -0.140019  0.036172        -0.027125      -0.139148 -0.026956  \n",
       "26       -0.033503  0.025642        -0.005435      -0.033291 -0.005401  \n",
       "27        0.681920  0.081934         0.203717       0.679594  0.203022  \n",
       "28        0.155494  0.055827         0.037810       0.154531  0.037576  \n",
       "29        0.580558  0.093335         0.186270       0.578106  0.185484  \n",
       "..             ...       ...              ...            ...       ...  \n",
       "56        0.563118  0.190279         0.272978       0.560669  0.271791  \n",
       "57        0.388096  0.083195         0.116909       0.386000  0.116278  \n",
       "58       -0.452344  0.107374        -0.156886      -0.450055 -0.156092  \n",
       "59        0.617785  0.381063         0.484745       0.615351  0.482835  \n",
       "60        0.097549  0.065333         0.025791       0.096935  0.025628  \n",
       "61       -0.151023  0.055960        -0.036770      -0.150086 -0.036541  \n",
       "62       -0.080605  0.063435        -0.020978      -0.080097 -0.020845  \n",
       "63       -0.161500  0.089574        -0.050657      -0.160501 -0.050344  \n",
       "64       -0.604578  0.348364        -0.442045      -0.602134 -0.440258  \n",
       "65       -0.240607  0.062815        -0.062291      -0.239167 -0.061919  \n",
       "66       -0.188833  0.052961        -0.044655      -0.187677 -0.044382  \n",
       "67       -0.037742  0.062523        -0.009747      -0.037503 -0.009685  \n",
       "68       -0.042845  0.042588        -0.009036      -0.042573 -0.008979  \n",
       "69        0.140771  0.065399         0.037238       0.139894  0.037006  \n",
       "70        0.156657  0.075523         0.044776       0.155687  0.044498  \n",
       "71       -0.066567  0.029262        -0.011557      -0.066146 -0.011484  \n",
       "72        0.346593  0.064521         0.091023       0.344655  0.090514  \n",
       "73       -0.344513  0.060564        -0.087474      -0.342583 -0.086984  \n",
       "74       -0.003419  0.065419        -0.000904      -0.003397 -0.000899  \n",
       "75       -0.029148  0.041203        -0.006042      -0.028963 -0.006004  \n",
       "76        0.385730  0.050532         0.088987       0.383642  0.088505  \n",
       "77        0.121514  0.085069         0.037052       0.120754  0.036821  \n",
       "78        0.074987  0.147860         0.031236       0.074513  0.031039  \n",
       "79       -0.364842  0.119556        -0.134444      -0.362832 -0.133703  \n",
       "80        0.058554  0.075838         0.016773       0.058183  0.016667  \n",
       "81        0.011390  0.042249         0.002392       0.011317  0.002377  \n",
       "82        0.644396  0.103724         0.219215       0.641994  0.218398  \n",
       "83        0.334529  0.122959         0.125258       0.332641  0.124551  \n",
       "84       -0.216937  0.131958        -0.084582      -0.215624 -0.084070  \n",
       "85        0.019124  0.081321         0.005690       0.019003  0.005654  \n",
       "\n",
       "[86 rows x 13 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_fr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cooks_d values are less. .. there are no influential points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "### so apneas index and hypopneas index can be removed as they tend to overfit the model and have less significace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  vif values are also high and removing those attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "## so our final model contains only bmi,apneascount,hypopneascount,totalsleeptime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets remove bmi and check  adjusted r-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3=smf.ols('ahi~apneascount+hypopneascount+totalsleeptime',data=snore).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>ahi</td>       <th>  R-squared:         </th> <td>   0.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.907</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   276.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 02 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>8.93e-43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:12:50</td>     <th>  Log-Likelihood:    </th> <td> -320.46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    86</td>      <th>  AIC:               </th> <td>   648.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    82</td>      <th>  BIC:               </th> <td>   658.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>   52.0081</td> <td>    4.052</td> <td>   12.834</td> <td> 0.000</td> <td>   43.947</td> <td>   60.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>apneascount</th>    <td>    0.2204</td> <td>    0.010</td> <td>   21.916</td> <td> 0.000</td> <td>    0.200</td> <td>    0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hypopneascount</th> <td>    0.1639</td> <td>    0.013</td> <td>   12.337</td> <td> 0.000</td> <td>    0.137</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>totalsleeptime</th> <td>   -0.1461</td> <td>    0.012</td> <td>  -11.834</td> <td> 0.000</td> <td>   -0.171</td> <td>   -0.122</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.183</td> <th>  Durbin-Watson:     </th> <td>   1.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  15.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.787</td> <th>  Prob(JB):          </th> <td>0.000464</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.345</td> <th>  Cond. No.          </th> <td>1.23e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.23e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    ahi   R-squared:                       0.910\n",
       "Model:                            OLS   Adj. R-squared:                  0.907\n",
       "Method:                 Least Squares   F-statistic:                     276.6\n",
       "Date:                Tue, 02 Apr 2019   Prob (F-statistic):           8.93e-43\n",
       "Time:                        08:12:50   Log-Likelihood:                -320.46\n",
       "No. Observations:                  86   AIC:                             648.9\n",
       "Df Residuals:                      82   BIC:                             658.7\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         52.0081      4.052     12.834      0.000      43.947      60.069\n",
       "apneascount        0.2204      0.010     21.916      0.000       0.200       0.240\n",
       "hypopneascount     0.1639      0.013     12.337      0.000       0.137       0.190\n",
       "totalsleeptime    -0.1461      0.012    -11.834      0.000      -0.171      -0.122\n",
       "==============================================================================\n",
       "Omnibus:                       13.183   Durbin-Watson:                   1.135\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.349\n",
       "Skew:                           0.787   Prob(JB):                     0.000464\n",
       "Kurtosis:                       4.345   Cond. No.                     1.23e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.23e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "### adjusted r squared for the model cotaining bmi,apneascount and hypopneas count is 0.907"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# including bmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model4=smf.ols('ahi~bmi+apneascount+hypopneascount+totalsleeptime',data=snore).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>ahi</td>       <th>  R-squared:         </th> <td>   0.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   215.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 02 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>2.64e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:14:41</td>     <th>  Log-Likelihood:    </th> <td> -318.53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    86</td>      <th>  AIC:               </th> <td>   647.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>   659.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>   41.3466</td> <td>    6.813</td> <td>    6.069</td> <td> 0.000</td> <td>   27.790</td> <td>   54.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bmi</th>            <td>    0.3125</td> <td>    0.162</td> <td>    1.930</td> <td> 0.057</td> <td>   -0.010</td> <td>    0.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>apneascount</th>    <td>    0.2185</td> <td>    0.010</td> <td>   21.989</td> <td> 0.000</td> <td>    0.199</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hypopneascount</th> <td>    0.1603</td> <td>    0.013</td> <td>   12.143</td> <td> 0.000</td> <td>    0.134</td> <td>    0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>totalsleeptime</th> <td>   -0.1425</td> <td>    0.012</td> <td>  -11.597</td> <td> 0.000</td> <td>   -0.167</td> <td>   -0.118</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.190</td> <th>  Durbin-Watson:     </th> <td>   1.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.006</td> <th>  Jarque-Bera (JB):  </th> <td>  10.829</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.669</td> <th>  Prob(JB):          </th> <td> 0.00445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.109</td> <th>  Cond. No.          </th> <td>2.11e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.11e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    ahi   R-squared:                       0.914\n",
       "Model:                            OLS   Adj. R-squared:                  0.910\n",
       "Method:                 Least Squares   F-statistic:                     215.3\n",
       "Date:                Tue, 02 Apr 2019   Prob (F-statistic):           2.64e-42\n",
       "Time:                        08:14:41   Log-Likelihood:                -318.53\n",
       "No. Observations:                  86   AIC:                             647.1\n",
       "Df Residuals:                      81   BIC:                             659.3\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         41.3466      6.813      6.069      0.000      27.790      54.903\n",
       "bmi                0.3125      0.162      1.930      0.057      -0.010       0.635\n",
       "apneascount        0.2185      0.010     21.989      0.000       0.199       0.238\n",
       "hypopneascount     0.1603      0.013     12.143      0.000       0.134       0.187\n",
       "totalsleeptime    -0.1425      0.012    -11.597      0.000      -0.167      -0.118\n",
       "==============================================================================\n",
       "Omnibus:                       10.190   Durbin-Watson:                   1.188\n",
       "Prob(Omnibus):                  0.006   Jarque-Bera (JB):               10.829\n",
       "Skew:                           0.669   Prob(JB):                      0.00445\n",
       "Kurtosis:                       4.109   Cond. No.                     2.11e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.11e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmi_new=new_snore['bmi']\n",
    "apneascount_new=new_snore['apneascount']\n",
    "hypopneascount_new=new_snore['hypopneascount']\n",
    "totalsleeptime_new=new_snore['totalsleeptime']\n",
    "ahi_new=new_snore['ahi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model4=smf.ols('ahi_new~bmi_new+apneascount_new+hypopneascount_new+totalsleeptime_new',data=new_snore).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>ahi_new</td>     <th>  R-squared:         </th> <td>   0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   216.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 02 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>1.02e-41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:26:58</td>     <th>  Log-Likelihood:    </th> <td> -310.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    84</td>      <th>  AIC:               </th> <td>   631.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    79</td>      <th>  BIC:               </th> <td>   643.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>   38.1693</td> <td>    7.052</td> <td>    5.412</td> <td> 0.000</td> <td>   24.133</td> <td>   52.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bmi_new</th>            <td>    0.3711</td> <td>    0.165</td> <td>    2.248</td> <td> 0.027</td> <td>    0.043</td> <td>    0.700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>apneascount_new</th>    <td>    0.2199</td> <td>    0.010</td> <td>   22.087</td> <td> 0.000</td> <td>    0.200</td> <td>    0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hypopneascount_new</th> <td>    0.1646</td> <td>    0.014</td> <td>   11.898</td> <td> 0.000</td> <td>    0.137</td> <td>    0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>totalsleeptime_new</th> <td>   -0.1394</td> <td>    0.012</td> <td>  -11.274</td> <td> 0.000</td> <td>   -0.164</td> <td>   -0.115</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.330</td> <th>  Durbin-Watson:     </th> <td>   1.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.006</td> <th>  Jarque-Bera (JB):  </th> <td>  11.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.678</td> <th>  Prob(JB):          </th> <td> 0.00395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.149</td> <th>  Cond. No.          </th> <td>2.15e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.15e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                ahi_new   R-squared:                       0.916\n",
       "Model:                            OLS   Adj. R-squared:                  0.912\n",
       "Method:                 Least Squares   F-statistic:                     216.3\n",
       "Date:                Tue, 02 Apr 2019   Prob (F-statistic):           1.02e-41\n",
       "Time:                        08:26:58   Log-Likelihood:                -310.65\n",
       "No. Observations:                  84   AIC:                             631.3\n",
       "Df Residuals:                      79   BIC:                             643.4\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept             38.1693      7.052      5.412      0.000      24.133      52.206\n",
       "bmi_new                0.3711      0.165      2.248      0.027       0.043       0.700\n",
       "apneascount_new        0.2199      0.010     22.087      0.000       0.200       0.240\n",
       "hypopneascount_new     0.1646      0.014     11.898      0.000       0.137       0.192\n",
       "totalsleeptime_new    -0.1394      0.012    -11.274      0.000      -0.164      -0.115\n",
       "==============================================================================\n",
       "Omnibus:                       10.330   Durbin-Watson:                   1.149\n",
       "Prob(Omnibus):                  0.006   Jarque-Bera (JB):               11.067\n",
       "Skew:                           0.678   Prob(JB):                      0.00395\n",
       "Kurtosis:                       4.149   Cond. No.                     2.15e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.15e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "### after removing influential points bmi became signifiacnt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "### final dtaset ----new_snore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_final=smf.ols('ahi_new~bmi_new+apneascount_new+hypopneascount_new+totalsleeptime_new',data=new_snore).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>ahi_new</td>     <th>  R-squared:         </th> <td>   0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   216.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 02 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>1.02e-41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:28:45</td>     <th>  Log-Likelihood:    </th> <td> -310.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    84</td>      <th>  AIC:               </th> <td>   631.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    79</td>      <th>  BIC:               </th> <td>   643.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>   38.1693</td> <td>    7.052</td> <td>    5.412</td> <td> 0.000</td> <td>   24.133</td> <td>   52.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bmi_new</th>            <td>    0.3711</td> <td>    0.165</td> <td>    2.248</td> <td> 0.027</td> <td>    0.043</td> <td>    0.700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>apneascount_new</th>    <td>    0.2199</td> <td>    0.010</td> <td>   22.087</td> <td> 0.000</td> <td>    0.200</td> <td>    0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hypopneascount_new</th> <td>    0.1646</td> <td>    0.014</td> <td>   11.898</td> <td> 0.000</td> <td>    0.137</td> <td>    0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>totalsleeptime_new</th> <td>   -0.1394</td> <td>    0.012</td> <td>  -11.274</td> <td> 0.000</td> <td>   -0.164</td> <td>   -0.115</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.330</td> <th>  Durbin-Watson:     </th> <td>   1.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.006</td> <th>  Jarque-Bera (JB):  </th> <td>  11.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.678</td> <th>  Prob(JB):          </th> <td> 0.00395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.149</td> <th>  Cond. No.          </th> <td>2.15e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.15e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                ahi_new   R-squared:                       0.916\n",
       "Model:                            OLS   Adj. R-squared:                  0.912\n",
       "Method:                 Least Squares   F-statistic:                     216.3\n",
       "Date:                Tue, 02 Apr 2019   Prob (F-statistic):           1.02e-41\n",
       "Time:                        08:28:45   Log-Likelihood:                -310.65\n",
       "No. Observations:                  84   AIC:                             631.3\n",
       "Df Residuals:                      79   BIC:                             643.4\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept             38.1693      7.052      5.412      0.000      24.133      52.206\n",
       "bmi_new                0.3711      0.165      2.248      0.027       0.043       0.700\n",
       "apneascount_new        0.2199      0.010     22.087      0.000       0.200       0.240\n",
       "hypopneascount_new     0.1646      0.014     11.898      0.000       0.137       0.192\n",
       "totalsleeptime_new    -0.1394      0.012    -11.274      0.000      -0.164      -0.115\n",
       "==============================================================================\n",
       "Omnibus:                       10.330   Durbin-Watson:                   1.149\n",
       "Prob(Omnibus):                  0.006   Jarque-Bera (JB):               11.067\n",
       "Skew:                           0.678   Prob(JB):                      0.00395\n",
       "Kurtosis:                       4.149   Cond. No.                     2.15e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.15e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_final.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### multicollinearity is not exist in our final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_final_pred=model_final.predict(new_snore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      43.796475\n",
       "1      37.554185\n",
       "2      -2.306455\n",
       "3      99.626025\n",
       "6      72.031613\n",
       "7      14.903653\n",
       "8      41.584724\n",
       "9      79.899237\n",
       "10     49.631734\n",
       "11     78.147916\n",
       "12     13.655096\n",
       "13     22.382756\n",
       "14     64.740317\n",
       "15     68.612338\n",
       "16     93.973205\n",
       "17     17.647485\n",
       "18     57.712546\n",
       "19     32.030843\n",
       "20     46.501603\n",
       "21     25.262073\n",
       "22     31.830556\n",
       "23    140.552252\n",
       "24      1.918394\n",
       "25     64.748716\n",
       "26     51.018622\n",
       "27     65.435847\n",
       "28     91.934193\n",
       "29     98.299110\n",
       "30    120.252279\n",
       "31     61.896864\n",
       "         ...    \n",
       "56     49.228740\n",
       "57     90.000373\n",
       "58     50.422350\n",
       "59     68.342096\n",
       "60     84.583362\n",
       "61     48.170885\n",
       "62     80.869565\n",
       "63     73.435304\n",
       "64    123.627225\n",
       "65     67.943074\n",
       "66     64.668278\n",
       "67     45.077604\n",
       "68     44.855397\n",
       "69     87.040819\n",
       "70     96.136857\n",
       "71     52.624214\n",
       "72     83.721085\n",
       "73     58.749683\n",
       "74     30.874951\n",
       "75     42.779366\n",
       "76     87.149861\n",
       "77     -7.605135\n",
       "78    -18.562077\n",
       "79     27.123553\n",
       "80      3.315799\n",
       "81     40.014314\n",
       "82     95.827006\n",
       "83     84.700634\n",
       "84     66.339004\n",
       "85     -7.328389\n",
       "Length: 84, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_final_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=new_snore.iloc[:,3:9].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=new_snore.iloc[:,10].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sno', 'name', 'sex', 'bmi', 'totalsleeptime', 'apneascount',\n",
       "       'hypopneascount', 'ahtotalcount', 'apneasindex', 'hypopneasindex',\n",
       "       'ahi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_snore.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_snore\n",
    "type(new_snore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.2604947185579\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(x_train,y_train)\n",
    "accuracy=regressor.score(x_test,y_test)\n",
    "print((accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final model accuracy is 94.3%\n"
     ]
    }
   ],
   "source": [
    "print(\"final model accuracy is 94.3%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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