Human_Action_Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a91cf8b0"
   },
   "source": [
    "## Important Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "c214ebb2"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# import libraries for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "# Libraries for models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0c3cf764"
   },
   "source": [
    "## Reading both training and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "9b3293b3",
    "outputId": "2ded7115-508d-477b-9e14-cb5202453ea1"
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.288585</td>\n",
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       "      <td>-0.983111</td>\n",
       "      <td>-0.913526</td>\n",
       "      <td>-0.995112</td>\n",
       "      <td>-0.983185</td>\n",
       "      <td>-0.923527</td>\n",
       "      <td>-0.934724</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.710304</td>\n",
       "      <td>-0.112754</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>-0.464761</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.841247</td>\n",
       "      <td>0.179941</td>\n",
       "      <td>-0.058627</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.278419</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.123520</td>\n",
       "      <td>-0.998245</td>\n",
       "      <td>-0.975300</td>\n",
       "      <td>-0.960322</td>\n",
       "      <td>-0.998807</td>\n",
       "      <td>-0.974914</td>\n",
       "      <td>-0.957686</td>\n",
       "      <td>-0.943068</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.861499</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>-0.007435</td>\n",
       "      <td>-0.732626</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>-0.844788</td>\n",
       "      <td>0.180289</td>\n",
       "      <td>-0.054317</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279653</td>\n",
       "      <td>-0.019467</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-0.995380</td>\n",
       "      <td>-0.967187</td>\n",
       "      <td>-0.978944</td>\n",
       "      <td>-0.996520</td>\n",
       "      <td>-0.963668</td>\n",
       "      <td>-0.977469</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.760104</td>\n",
       "      <td>-0.118559</td>\n",
       "      <td>0.177899</td>\n",
       "      <td>0.100699</td>\n",
       "      <td>0.808529</td>\n",
       "      <td>-0.848933</td>\n",
       "      <td>0.180637</td>\n",
       "      <td>-0.049118</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279174</td>\n",
       "      <td>-0.026201</td>\n",
       "      <td>-0.123283</td>\n",
       "      <td>-0.996091</td>\n",
       "      <td>-0.983403</td>\n",
       "      <td>-0.990675</td>\n",
       "      <td>-0.997099</td>\n",
       "      <td>-0.982750</td>\n",
       "      <td>-0.989302</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.482845</td>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.012892</td>\n",
       "      <td>0.640011</td>\n",
       "      <td>-0.485366</td>\n",
       "      <td>-0.848649</td>\n",
       "      <td>0.181935</td>\n",
       "      <td>-0.047663</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.276629</td>\n",
       "      <td>-0.016570</td>\n",
       "      <td>-0.115362</td>\n",
       "      <td>-0.998139</td>\n",
       "      <td>-0.980817</td>\n",
       "      <td>-0.990482</td>\n",
       "      <td>-0.998321</td>\n",
       "      <td>-0.979672</td>\n",
       "      <td>-0.990441</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.699205</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.693578</td>\n",
       "      <td>-0.615971</td>\n",
       "      <td>-0.847865</td>\n",
       "      <td>0.185151</td>\n",
       "      <td>-0.043892</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
       "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
       "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
       "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
       "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
       "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
       "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
       "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
       "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0         -0.923527         -0.934724  ...                        -0.710304   \n",
       "1         -0.957686         -0.943068  ...                        -0.861499   \n",
       "2         -0.977469         -0.938692  ...                        -0.760104   \n",
       "3         -0.989302         -0.938692  ...                        -0.482845   \n",
       "4         -0.990441         -0.942469  ...                        -0.699205   \n",
       "\n",
       "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                    -0.112754                              0.030400   \n",
       "1                     0.053477                             -0.007435   \n",
       "2                    -0.118559                              0.177899   \n",
       "3                    -0.036788                             -0.012892   \n",
       "4                     0.123320                              0.122542   \n",
       "\n",
       "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "0                         -0.464761                             -0.018446   \n",
       "1                         -0.732626                              0.703511   \n",
       "2                          0.100699                              0.808529   \n",
       "3                          0.640011                             -0.485366   \n",
       "4                          0.693578                             -0.615971   \n",
       "\n",
       "   angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  subject  \\\n",
       "0             -0.841247              0.179941             -0.058627        1   \n",
       "1             -0.844788              0.180289             -0.054317        1   \n",
       "2             -0.848933              0.180637             -0.049118        1   \n",
       "3             -0.848649              0.181935             -0.047663        1   \n",
       "4             -0.847865              0.185151             -0.043892        1   \n",
       "\n",
       "   Activity  \n",
       "0  STANDING  \n",
       "1  STANDING  \n",
       "2  STANDING  \n",
       "3  STANDING  \n",
       "4  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading Train Data\n",
    "#Runing on local machine run uncomment bellow line\n",
    "#train = pd.read_csv('Dataset/train.csv') \n",
    "train = pd.read_csv('Dataset/train.csv') #For google drive path\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "80d342d9",
    "outputId": "ceaf12ce-dae8-4b10-90f0-687f276667b9"
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-0.023285</td>\n",
       "      <td>-0.014654</td>\n",
       "      <td>-0.938404</td>\n",
       "      <td>-0.920091</td>\n",
       "      <td>-0.667683</td>\n",
       "      <td>-0.952501</td>\n",
       "      <td>-0.925249</td>\n",
       "      <td>-0.674302</td>\n",
       "      <td>-0.894088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.705974</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>0.162920</td>\n",
       "      <td>-0.825886</td>\n",
       "      <td>0.271151</td>\n",
       "      <td>-0.720009</td>\n",
       "      <td>0.276801</td>\n",
       "      <td>-0.057978</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.286027</td>\n",
       "      <td>-0.013163</td>\n",
       "      <td>-0.119083</td>\n",
       "      <td>-0.975415</td>\n",
       "      <td>-0.967458</td>\n",
       "      <td>-0.944958</td>\n",
       "      <td>-0.986799</td>\n",
       "      <td>-0.968401</td>\n",
       "      <td>-0.945823</td>\n",
       "      <td>-0.894088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.594944</td>\n",
       "      <td>-0.083495</td>\n",
       "      <td>0.017500</td>\n",
       "      <td>-0.434375</td>\n",
       "      <td>0.920593</td>\n",
       "      <td>-0.698091</td>\n",
       "      <td>0.281343</td>\n",
       "      <td>-0.083898</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.275485</td>\n",
       "      <td>-0.026050</td>\n",
       "      <td>-0.118152</td>\n",
       "      <td>-0.993819</td>\n",
       "      <td>-0.969926</td>\n",
       "      <td>-0.962748</td>\n",
       "      <td>-0.994403</td>\n",
       "      <td>-0.970735</td>\n",
       "      <td>-0.963483</td>\n",
       "      <td>-0.939260</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.640736</td>\n",
       "      <td>-0.034956</td>\n",
       "      <td>0.202302</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>0.145068</td>\n",
       "      <td>-0.702771</td>\n",
       "      <td>0.280083</td>\n",
       "      <td>-0.079346</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.270298</td>\n",
       "      <td>-0.032614</td>\n",
       "      <td>-0.117520</td>\n",
       "      <td>-0.994743</td>\n",
       "      <td>-0.973268</td>\n",
       "      <td>-0.967091</td>\n",
       "      <td>-0.995274</td>\n",
       "      <td>-0.974471</td>\n",
       "      <td>-0.968897</td>\n",
       "      <td>-0.938610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.736124</td>\n",
       "      <td>-0.017067</td>\n",
       "      <td>0.154438</td>\n",
       "      <td>0.340134</td>\n",
       "      <td>0.296407</td>\n",
       "      <td>-0.698954</td>\n",
       "      <td>0.284114</td>\n",
       "      <td>-0.077108</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.274833</td>\n",
       "      <td>-0.027848</td>\n",
       "      <td>-0.129527</td>\n",
       "      <td>-0.993852</td>\n",
       "      <td>-0.967445</td>\n",
       "      <td>-0.978295</td>\n",
       "      <td>-0.994111</td>\n",
       "      <td>-0.965953</td>\n",
       "      <td>-0.977346</td>\n",
       "      <td>-0.938610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.846595</td>\n",
       "      <td>-0.002223</td>\n",
       "      <td>-0.040046</td>\n",
       "      <td>0.736715</td>\n",
       "      <td>-0.118545</td>\n",
       "      <td>-0.692245</td>\n",
       "      <td>0.290722</td>\n",
       "      <td>-0.073857</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 563 columns</p>\n",
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      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.257178          -0.023285          -0.014654         -0.938404   \n",
       "1           0.286027          -0.013163          -0.119083         -0.975415   \n",
       "2           0.275485          -0.026050          -0.118152         -0.993819   \n",
       "3           0.270298          -0.032614          -0.117520         -0.994743   \n",
       "4           0.274833          -0.027848          -0.129527         -0.993852   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.920091         -0.667683         -0.952501         -0.925249   \n",
       "1         -0.967458         -0.944958         -0.986799         -0.968401   \n",
       "2         -0.969926         -0.962748         -0.994403         -0.970735   \n",
       "3         -0.973268         -0.967091         -0.995274         -0.974471   \n",
       "4         -0.967445         -0.978295         -0.994111         -0.965953   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0         -0.674302         -0.894088  ...                        -0.705974   \n",
       "1         -0.945823         -0.894088  ...                        -0.594944   \n",
       "2         -0.963483         -0.939260  ...                        -0.640736   \n",
       "3         -0.968897         -0.938610  ...                        -0.736124   \n",
       "4         -0.977346         -0.938610  ...                        -0.846595   \n",
       "\n",
       "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                     0.006462                              0.162920   \n",
       "1                    -0.083495                              0.017500   \n",
       "2                    -0.034956                              0.202302   \n",
       "3                    -0.017067                              0.154438   \n",
       "4                    -0.002223                             -0.040046   \n",
       "\n",
       "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "0                         -0.825886                              0.271151   \n",
       "1                         -0.434375                              0.920593   \n",
       "2                          0.064103                              0.145068   \n",
       "3                          0.340134                              0.296407   \n",
       "4                          0.736715                             -0.118545   \n",
       "\n",
       "   angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  subject  \\\n",
       "0             -0.720009              0.276801             -0.057978        2   \n",
       "1             -0.698091              0.281343             -0.083898        2   \n",
       "2             -0.702771              0.280083             -0.079346        2   \n",
       "3             -0.698954              0.284114             -0.077108        2   \n",
       "4             -0.692245              0.290722             -0.073857        2   \n",
       "\n",
       "   Activity  \n",
       "0  STANDING  \n",
       "1  STANDING  \n",
       "2  STANDING  \n",
       "3  STANDING  \n",
       "4  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading Test Data\n",
    "#Runing on local machine run uncomment bellow line\n",
    "#test = pd.read_csv('Dataset/test.csv') \n",
    "test = pd.read_csv('Dataset/test.csv') #For google drive path\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cae35096"
   },
   "source": [
    "### We will check the shape, columns names, memory usage and null values inside our both datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "02495744",
    "outputId": "71414b40-8939-4357-c8a9-cbad19467d80"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7352, 563)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ef1d39f7",
    "outputId": "b8cc24cd-e10a-4a85-87e2-87ca18a5b1ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2947, 563)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "924b1a8f",
    "outputId": "2e45bb4c-afb0-4fac-ba24-92a9738fc7b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tBodyAcc-mean()-X', 'tBodyAcc-mean()-Y', 'tBodyAcc-mean()-Z',\n",
       "       'tBodyAcc-std()-X', 'tBodyAcc-std()-Y', 'tBodyAcc-std()-Z',\n",
       "       'tBodyAcc-mad()-X', 'tBodyAcc-mad()-Y', 'tBodyAcc-mad()-Z',\n",
       "       'tBodyAcc-max()-X',\n",
       "       ...\n",
       "       'fBodyBodyGyroJerkMag-kurtosis()', 'angle(tBodyAccMean,gravity)',\n",
       "       'angle(tBodyAccJerkMean),gravityMean)',\n",
       "       'angle(tBodyGyroMean,gravityMean)',\n",
       "       'angle(tBodyGyroJerkMean,gravityMean)', 'angle(X,gravityMean)',\n",
       "       'angle(Y,gravityMean)', 'angle(Z,gravityMean)', 'subject', 'Activity'],\n",
       "      dtype='object', length=563)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "22887a9e",
    "outputId": "5ca1ba81-edb8-4261-b48b-85b50b8459f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tBodyAcc-mean()-X', 'tBodyAcc-mean()-Y', 'tBodyAcc-mean()-Z',\n",
       "       'tBodyAcc-std()-X', 'tBodyAcc-std()-Y', 'tBodyAcc-std()-Z',\n",
       "       'tBodyAcc-mad()-X', 'tBodyAcc-mad()-Y', 'tBodyAcc-mad()-Z',\n",
       "       'tBodyAcc-max()-X',\n",
       "       ...\n",
       "       'fBodyBodyGyroJerkMag-kurtosis()', 'angle(tBodyAccMean,gravity)',\n",
       "       'angle(tBodyAccJerkMean),gravityMean)',\n",
       "       'angle(tBodyGyroMean,gravityMean)',\n",
       "       'angle(tBodyGyroJerkMean,gravityMean)', 'angle(X,gravityMean)',\n",
       "       'angle(Y,gravityMean)', 'angle(Z,gravityMean)', 'subject', 'Activity'],\n",
       "      dtype='object', length=563)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "f305c452",
    "outputId": "e63d8cd2-1960-4752-e4dd-3f28f85c056a"
   },
   "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>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-skewness()</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.274488</td>\n",
       "      <td>-0.017695</td>\n",
       "      <td>-0.109141</td>\n",
       "      <td>-0.605438</td>\n",
       "      <td>-0.510938</td>\n",
       "      <td>-0.604754</td>\n",
       "      <td>-0.630512</td>\n",
       "      <td>-0.526907</td>\n",
       "      <td>-0.606150</td>\n",
       "      <td>-0.468604</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.307009</td>\n",
       "      <td>-0.625294</td>\n",
       "      <td>0.008684</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.008726</td>\n",
       "      <td>-0.005981</td>\n",
       "      <td>-0.489547</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>-0.056515</td>\n",
       "      <td>17.413085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.070261</td>\n",
       "      <td>0.040811</td>\n",
       "      <td>0.056635</td>\n",
       "      <td>0.448734</td>\n",
       "      <td>0.502645</td>\n",
       "      <td>0.418687</td>\n",
       "      <td>0.424073</td>\n",
       "      <td>0.485942</td>\n",
       "      <td>0.414122</td>\n",
       "      <td>0.544547</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321011</td>\n",
       "      <td>0.307584</td>\n",
       "      <td>0.336787</td>\n",
       "      <td>0.448306</td>\n",
       "      <td>0.608303</td>\n",
       "      <td>0.477975</td>\n",
       "      <td>0.511807</td>\n",
       "      <td>0.297480</td>\n",
       "      <td>0.279122</td>\n",
       "      <td>8.975143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.999873</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.995357</td>\n",
       "      <td>-0.999765</td>\n",
       "      <td>-0.976580</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.262975</td>\n",
       "      <td>-0.024863</td>\n",
       "      <td>-0.120993</td>\n",
       "      <td>-0.992754</td>\n",
       "      <td>-0.978129</td>\n",
       "      <td>-0.980233</td>\n",
       "      <td>-0.993591</td>\n",
       "      <td>-0.978162</td>\n",
       "      <td>-0.980251</td>\n",
       "      <td>-0.936219</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.542602</td>\n",
       "      <td>-0.845573</td>\n",
       "      <td>-0.121527</td>\n",
       "      <td>-0.289549</td>\n",
       "      <td>-0.482273</td>\n",
       "      <td>-0.376341</td>\n",
       "      <td>-0.812065</td>\n",
       "      <td>-0.017885</td>\n",
       "      <td>-0.143414</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.277193</td>\n",
       "      <td>-0.017219</td>\n",
       "      <td>-0.108676</td>\n",
       "      <td>-0.946196</td>\n",
       "      <td>-0.851897</td>\n",
       "      <td>-0.859365</td>\n",
       "      <td>-0.950709</td>\n",
       "      <td>-0.857328</td>\n",
       "      <td>-0.857143</td>\n",
       "      <td>-0.881637</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.343685</td>\n",
       "      <td>-0.711692</td>\n",
       "      <td>0.009509</td>\n",
       "      <td>0.008943</td>\n",
       "      <td>0.008735</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>-0.709417</td>\n",
       "      <td>0.182071</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.288461</td>\n",
       "      <td>-0.010783</td>\n",
       "      <td>-0.097794</td>\n",
       "      <td>-0.242813</td>\n",
       "      <td>-0.034231</td>\n",
       "      <td>-0.262415</td>\n",
       "      <td>-0.292680</td>\n",
       "      <td>-0.066701</td>\n",
       "      <td>-0.265671</td>\n",
       "      <td>-0.017129</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.126979</td>\n",
       "      <td>-0.503878</td>\n",
       "      <td>0.150865</td>\n",
       "      <td>0.292861</td>\n",
       "      <td>0.506187</td>\n",
       "      <td>0.359368</td>\n",
       "      <td>-0.509079</td>\n",
       "      <td>0.248353</td>\n",
       "      <td>0.107659</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989538</td>\n",
       "      <td>0.956845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998702</td>\n",
       "      <td>0.996078</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 562 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  \\\n",
       "count        7352.000000        7352.000000        7352.000000   \n",
       "mean            0.274488          -0.017695          -0.109141   \n",
       "std             0.070261           0.040811           0.056635   \n",
       "min            -1.000000          -1.000000          -1.000000   \n",
       "25%             0.262975          -0.024863          -0.120993   \n",
       "50%             0.277193          -0.017219          -0.108676   \n",
       "75%             0.288461          -0.010783          -0.097794   \n",
       "max             1.000000           1.000000           1.000000   \n",
       "\n",
       "       tBodyAcc-std()-X  tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  \\\n",
       "count       7352.000000       7352.000000       7352.000000       7352.000000   \n",
       "mean          -0.605438         -0.510938         -0.604754         -0.630512   \n",
       "std            0.448734          0.502645          0.418687          0.424073   \n",
       "min           -1.000000         -0.999873         -1.000000         -1.000000   \n",
       "25%           -0.992754         -0.978129         -0.980233         -0.993591   \n",
       "50%           -0.946196         -0.851897         -0.859365         -0.950709   \n",
       "75%           -0.242813         -0.034231         -0.262415         -0.292680   \n",
       "max            1.000000          0.916238          1.000000          1.000000   \n",
       "\n",
       "       tBodyAcc-mad()-Y  tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  \\\n",
       "count       7352.000000       7352.000000       7352.000000  ...   \n",
       "mean          -0.526907         -0.606150         -0.468604  ...   \n",
       "std            0.485942          0.414122          0.544547  ...   \n",
       "min           -1.000000         -1.000000         -1.000000  ...   \n",
       "25%           -0.978162         -0.980251         -0.936219  ...   \n",
       "50%           -0.857328         -0.857143         -0.881637  ...   \n",
       "75%           -0.066701         -0.265671         -0.017129  ...   \n",
       "max            0.967664          1.000000          1.000000  ...   \n",
       "\n",
       "       fBodyBodyGyroJerkMag-skewness()  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "count                      7352.000000                      7352.000000   \n",
       "mean                         -0.307009                        -0.625294   \n",
       "std                           0.321011                         0.307584   \n",
       "min                          -0.995357                        -0.999765   \n",
       "25%                          -0.542602                        -0.845573   \n",
       "50%                          -0.343685                        -0.711692   \n",
       "75%                          -0.126979                        -0.503878   \n",
       "max                           0.989538                         0.956845   \n",
       "\n",
       "       angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "count                  7352.000000                           7352.000000   \n",
       "mean                      0.008684                              0.002186   \n",
       "std                       0.336787                              0.448306   \n",
       "min                      -0.976580                             -1.000000   \n",
       "25%                      -0.121527                             -0.289549   \n",
       "50%                       0.009509                              0.008943   \n",
       "75%                       0.150865                              0.292861   \n",
       "max                       1.000000                              1.000000   \n",
       "\n",
       "       angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "count                       7352.000000                           7352.000000   \n",
       "mean                           0.008726                             -0.005981   \n",
       "std                            0.608303                              0.477975   \n",
       "min                           -1.000000                             -1.000000   \n",
       "25%                           -0.482273                             -0.376341   \n",
       "50%                            0.008735                             -0.000368   \n",
       "75%                            0.506187                              0.359368   \n",
       "max                            0.998702                              0.996078   \n",
       "\n",
       "       angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  \\\n",
       "count           7352.000000           7352.000000           7352.000000   \n",
       "mean              -0.489547              0.058593             -0.056515   \n",
       "std                0.511807              0.297480              0.279122   \n",
       "min               -1.000000             -1.000000             -1.000000   \n",
       "25%               -0.812065             -0.017885             -0.143414   \n",
       "50%               -0.709417              0.182071              0.003181   \n",
       "75%               -0.509079              0.248353              0.107659   \n",
       "max                1.000000              0.478157              1.000000   \n",
       "\n",
       "           subject  \n",
       "count  7352.000000  \n",
       "mean     17.413085  \n",
       "std       8.975143  \n",
       "min       1.000000  \n",
       "25%       8.000000  \n",
       "50%      19.000000  \n",
       "75%      26.000000  \n",
       "max      30.000000  \n",
       "\n",
       "[8 rows x 562 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "557e756a",
    "outputId": "611dee01-34a0-4bb0-f12c-981067ac8c76"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-skewness()</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "      <td>2947.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.273996</td>\n",
       "      <td>-0.017863</td>\n",
       "      <td>-0.108386</td>\n",
       "      <td>-0.613635</td>\n",
       "      <td>-0.508330</td>\n",
       "      <td>-0.633797</td>\n",
       "      <td>-0.641278</td>\n",
       "      <td>-0.522676</td>\n",
       "      <td>-0.637038</td>\n",
       "      <td>-0.462063</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.277593</td>\n",
       "      <td>-0.598756</td>\n",
       "      <td>0.005264</td>\n",
       "      <td>0.003799</td>\n",
       "      <td>0.040029</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>-0.513923</td>\n",
       "      <td>0.074886</td>\n",
       "      <td>-0.048720</td>\n",
       "      <td>12.986427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.060570</td>\n",
       "      <td>0.025745</td>\n",
       "      <td>0.042747</td>\n",
       "      <td>0.412597</td>\n",
       "      <td>0.494269</td>\n",
       "      <td>0.362699</td>\n",
       "      <td>0.385199</td>\n",
       "      <td>0.479899</td>\n",
       "      <td>0.357753</td>\n",
       "      <td>0.523916</td>\n",
       "      <td>...</td>\n",
       "      <td>0.317245</td>\n",
       "      <td>0.311042</td>\n",
       "      <td>0.336147</td>\n",
       "      <td>0.445077</td>\n",
       "      <td>0.634989</td>\n",
       "      <td>0.501311</td>\n",
       "      <td>0.509205</td>\n",
       "      <td>0.324300</td>\n",
       "      <td>0.241467</td>\n",
       "      <td>6.950984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.592004</td>\n",
       "      <td>-0.362884</td>\n",
       "      <td>-0.576184</td>\n",
       "      <td>-0.999606</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998955</td>\n",
       "      <td>-0.999417</td>\n",
       "      <td>-0.999914</td>\n",
       "      <td>-0.998899</td>\n",
       "      <td>-0.952357</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.993402</td>\n",
       "      <td>-0.998898</td>\n",
       "      <td>-0.991096</td>\n",
       "      <td>-0.984195</td>\n",
       "      <td>-0.913704</td>\n",
       "      <td>-0.949228</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.262075</td>\n",
       "      <td>-0.024961</td>\n",
       "      <td>-0.121162</td>\n",
       "      <td>-0.990914</td>\n",
       "      <td>-0.973664</td>\n",
       "      <td>-0.976122</td>\n",
       "      <td>-0.992333</td>\n",
       "      <td>-0.974131</td>\n",
       "      <td>-0.975352</td>\n",
       "      <td>-0.934447</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.517494</td>\n",
       "      <td>-0.829593</td>\n",
       "      <td>-0.130541</td>\n",
       "      <td>-0.282600</td>\n",
       "      <td>-0.518924</td>\n",
       "      <td>-0.428375</td>\n",
       "      <td>-0.829722</td>\n",
       "      <td>0.022140</td>\n",
       "      <td>-0.098485</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.277113</td>\n",
       "      <td>-0.016967</td>\n",
       "      <td>-0.108458</td>\n",
       "      <td>-0.931214</td>\n",
       "      <td>-0.790972</td>\n",
       "      <td>-0.827534</td>\n",
       "      <td>-0.937664</td>\n",
       "      <td>-0.799907</td>\n",
       "      <td>-0.817005</td>\n",
       "      <td>-0.852659</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.311023</td>\n",
       "      <td>-0.683672</td>\n",
       "      <td>0.005188</td>\n",
       "      <td>0.006767</td>\n",
       "      <td>0.047113</td>\n",
       "      <td>-0.026726</td>\n",
       "      <td>-0.729648</td>\n",
       "      <td>0.181563</td>\n",
       "      <td>-0.010671</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.288097</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>-0.097123</td>\n",
       "      <td>-0.267395</td>\n",
       "      <td>-0.105919</td>\n",
       "      <td>-0.311432</td>\n",
       "      <td>-0.321719</td>\n",
       "      <td>-0.133488</td>\n",
       "      <td>-0.322771</td>\n",
       "      <td>-0.009965</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.083559</td>\n",
       "      <td>-0.458332</td>\n",
       "      <td>0.146200</td>\n",
       "      <td>0.288113</td>\n",
       "      <td>0.622151</td>\n",
       "      <td>0.394387</td>\n",
       "      <td>-0.545939</td>\n",
       "      <td>0.260252</td>\n",
       "      <td>0.092373</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.671887</td>\n",
       "      <td>0.246106</td>\n",
       "      <td>0.494114</td>\n",
       "      <td>0.465299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.489703</td>\n",
       "      <td>0.439657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427958</td>\n",
       "      <td>0.786436</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998898</td>\n",
       "      <td>0.986347</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973113</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 562 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  \\\n",
       "count        2947.000000        2947.000000        2947.000000   \n",
       "mean            0.273996          -0.017863          -0.108386   \n",
       "std             0.060570           0.025745           0.042747   \n",
       "min            -0.592004          -0.362884          -0.576184   \n",
       "25%             0.262075          -0.024961          -0.121162   \n",
       "50%             0.277113          -0.016967          -0.108458   \n",
       "75%             0.288097          -0.010143          -0.097123   \n",
       "max             0.671887           0.246106           0.494114   \n",
       "\n",
       "       tBodyAcc-std()-X  tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  \\\n",
       "count       2947.000000       2947.000000       2947.000000       2947.000000   \n",
       "mean          -0.613635         -0.508330         -0.633797         -0.641278   \n",
       "std            0.412597          0.494269          0.362699          0.385199   \n",
       "min           -0.999606         -1.000000         -0.998955         -0.999417   \n",
       "25%           -0.990914         -0.973664         -0.976122         -0.992333   \n",
       "50%           -0.931214         -0.790972         -0.827534         -0.937664   \n",
       "75%           -0.267395         -0.105919         -0.311432         -0.321719   \n",
       "max            0.465299          1.000000          0.489703          0.439657   \n",
       "\n",
       "       tBodyAcc-mad()-Y  tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  \\\n",
       "count       2947.000000       2947.000000       2947.000000  ...   \n",
       "mean          -0.522676         -0.637038         -0.462063  ...   \n",
       "std            0.479899          0.357753          0.523916  ...   \n",
       "min           -0.999914         -0.998899         -0.952357  ...   \n",
       "25%           -0.974131         -0.975352         -0.934447  ...   \n",
       "50%           -0.799907         -0.817005         -0.852659  ...   \n",
       "75%           -0.133488         -0.322771         -0.009965  ...   \n",
       "max            1.000000          0.427958          0.786436  ...   \n",
       "\n",
       "       fBodyBodyGyroJerkMag-skewness()  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "count                      2947.000000                      2947.000000   \n",
       "mean                         -0.277593                        -0.598756   \n",
       "std                           0.317245                         0.311042   \n",
       "min                          -1.000000                        -1.000000   \n",
       "25%                          -0.517494                        -0.829593   \n",
       "50%                          -0.311023                        -0.683672   \n",
       "75%                          -0.083559                        -0.458332   \n",
       "max                           1.000000                         1.000000   \n",
       "\n",
       "       angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "count                  2947.000000                           2947.000000   \n",
       "mean                      0.005264                              0.003799   \n",
       "std                       0.336147                              0.445077   \n",
       "min                      -1.000000                             -0.993402   \n",
       "25%                      -0.130541                             -0.282600   \n",
       "50%                       0.005188                              0.006767   \n",
       "75%                       0.146200                              0.288113   \n",
       "max                       0.998898                              0.986347   \n",
       "\n",
       "       angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "count                       2947.000000                           2947.000000   \n",
       "mean                           0.040029                             -0.017298   \n",
       "std                            0.634989                              0.501311   \n",
       "min                           -0.998898                             -0.991096   \n",
       "25%                           -0.518924                             -0.428375   \n",
       "50%                            0.047113                             -0.026726   \n",
       "75%                            0.622151                              0.394387   \n",
       "max                            1.000000                              1.000000   \n",
       "\n",
       "       angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  \\\n",
       "count           2947.000000           2947.000000           2947.000000   \n",
       "mean              -0.513923              0.074886             -0.048720   \n",
       "std                0.509205              0.324300              0.241467   \n",
       "min               -0.984195             -0.913704             -0.949228   \n",
       "25%               -0.829722              0.022140             -0.098485   \n",
       "50%               -0.729648              0.181563             -0.010671   \n",
       "75%               -0.545939              0.260252              0.092373   \n",
       "max                0.833180              1.000000              0.973113   \n",
       "\n",
       "           subject  \n",
       "count  2947.000000  \n",
       "mean     12.986427  \n",
       "std       6.950984  \n",
       "min       2.000000  \n",
       "25%       9.000000  \n",
       "50%      12.000000  \n",
       "75%      18.000000  \n",
       "max      24.000000  \n",
       "\n",
       "[8 rows x 562 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "22e4e8d8",
    "outputId": "fe890b4c-29af-4a4b-8f13-5138a35627bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7352 entries, 0 to 7351\n",
      "Columns: 563 entries, tBodyAcc-mean()-X to Activity\n",
      "dtypes: float64(561), int64(1), object(1)\n",
      "memory usage: 32.0 MB\n"
     ]
    }
   ],
   "source": [
    "train.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "20c56f5f",
    "outputId": "68426699-3e47-42e2-bffc-cec2fafa8f82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2947 entries, 0 to 2946\n",
      "Columns: 563 entries, tBodyAcc-mean()-X to Activity\n",
      "dtypes: float64(561), int64(1), object(1)\n",
      "memory usage: 12.8 MB\n"
     ]
    }
   ],
   "source": [
    "test.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "315b9737",
    "outputId": "ae12a11c-8c8a-44f7-8a92-8b4673265046"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tBodyAcc-mean()-X       0.0\n",
       "tBodyAcc-mean()-Y       0.0\n",
       "tBodyAcc-mean()-Z       0.0\n",
       "tBodyAcc-std()-X        0.0\n",
       "tBodyAcc-std()-Y        0.0\n",
       "                       ... \n",
       "angle(X,gravityMean)    0.0\n",
       "angle(Y,gravityMean)    0.0\n",
       "angle(Z,gravityMean)    0.0\n",
       "subject                 0.0\n",
       "Activity                0.0\n",
       "Length: 563, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "26cbcabd",
    "outputId": "ebd76a65-e0b0-4908-a47a-cb2213d063cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tBodyAcc-mean()-X       0.0\n",
       "tBodyAcc-mean()-Y       0.0\n",
       "tBodyAcc-mean()-Z       0.0\n",
       "tBodyAcc-std()-X        0.0\n",
       "tBodyAcc-std()-Y        0.0\n",
       "                       ... \n",
       "angle(X,gravityMean)    0.0\n",
       "angle(Y,gravityMean)    0.0\n",
       "angle(Z,gravityMean)    0.0\n",
       "subject                 0.0\n",
       "Activity                0.0\n",
       "Length: 563, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5dd55061"
   },
   "source": [
    "## Exploritory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 518
    },
    "id": "d1aae1b5",
    "outputId": "71137e17-b0cd-4488-da99-8c3e217f50af"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking data imbalance\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title('Data provided by each user', fontsize=20)\n",
    "sns.countplot(x='subject',hue='Activity', data = train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we can see We have got almost same number of reading from all the subjects means there are not sygnificant difference in reading then we should not worry about it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "id": "68f2f454",
    "outputId": "f66825bd-2268-4f08-c66c-9df143c0a50f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('No of Datapoints per Activity', fontsize=15)\n",
    "sns.countplot(train.Activity)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From above graph we can see that our data is well balanced (almost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing feautre names\n",
    "### we will remove the commas and brackets to out features so that we can apply directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "838058bf",
    "outputId": "fae3af53-0c54-4df9-8306-9686d883a3d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['tBodyAccmeanX', 'tBodyAccmeanY', 'tBodyAccmeanZ', 'tBodyAccstdX',\n",
      "       'tBodyAccstdY', 'tBodyAccstdZ', 'tBodyAccmadX', 'tBodyAccmadY',\n",
      "       'tBodyAccmadZ', 'tBodyAccmaxX',\n",
      "       ...\n",
      "       'fBodyBodyGyroJerkMagkurtosis', 'angletBodyAccMeangravity',\n",
      "       'angletBodyAccJerkMeangravityMean', 'angletBodyGyroMeangravityMean',\n",
      "       'angletBodyGyroJerkMeangravityMean', 'angleXgravityMean',\n",
      "       'angleYgravityMean', 'angleZgravityMean', 'subject', 'Activity'],\n",
      "      dtype='object', length=563)\n",
      "Index(['tBodyAccmeanX', 'tBodyAccmeanY', 'tBodyAccmeanZ', 'tBodyAccstdX',\n",
      "       'tBodyAccstdY', 'tBodyAccstdZ', 'tBodyAccmadX', 'tBodyAccmadY',\n",
      "       'tBodyAccmadZ', 'tBodyAccmaxX',\n",
      "       ...\n",
      "       'fBodyBodyGyroJerkMagkurtosis', 'angletBodyAccMeangravity',\n",
      "       'angletBodyAccJerkMeangravityMean', 'angletBodyGyroMeangravityMean',\n",
      "       'angletBodyGyroJerkMeangravityMean', 'angleXgravityMean',\n",
      "       'angleYgravityMean', 'angleZgravityMean', 'subject', 'Activity'],\n",
      "      dtype='object', length=563)\n"
     ]
    }
   ],
   "source": [
    "columns = train.columns\n",
    "\n",
    "# Removing '()' from column names\n",
    "columns = columns.str.replace('[()]','')\n",
    "columns = columns.str.replace('[-]', '')\n",
    "columns = columns.str.replace('[,]','')\n",
    "\n",
    "train.columns = columns\n",
    "test.columns = columns\n",
    "\n",
    "print(train.columns)\n",
    "print(test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Static and Dynamic Activities Diffeentation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 586
    },
    "id": "604a3ae5",
    "outputId": "1c754289-edfc-4571-ab53-caa15effbc8b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1019.88x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(\"Set1\", desat=0.80)\n",
    "facetgrid = sns.FacetGrid(train, hue='Activity', size=6,aspect=2)\n",
    "facetgrid.map(sns.distplot,'tBodyAccMagmean', hist=False).add_legend()\n",
    "plt.annotate(\"Stationary Activities\", xy=(-0.956,17), xytext=(-0.9, 23), size=20, va='center', ha='left', arrowprops=dict(arrowstyle=\"simple\",connectionstyle=\"arc3,rad=0.1\"))\n",
    "\n",
    "plt.annotate(\"Moving Activities\", xy=(0,3), xytext=(0.2, 9), size=20, va='center', ha='left', arrowprops=dict(arrowstyle=\"simple\",connectionstyle=\"arc3,rad=0.1\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we can clearly see the difference between stationary activities and Moving Activities as per above pdf distribution we can look closer by dividing these pdfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "6990a42d",
    "outputId": "43d3335e-1e94-4440-9d04-ba4642bb8196"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f773b7006a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for plotting purposes taking datapoints of each activity to a different dataframe\n",
    "df1 = train[train['Activity']==1]\n",
    "df2 = train[train['Activity']==2]\n",
    "df3 = train[train['Activity']==3]\n",
    "df4 = train[train['Activity']==4]\n",
    "df5 = train[train['Activity']==5]\n",
    "df6 = train[train['Activity']==6]\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Static Activities(closer view)\")\n",
    "sns.distplot(train[train[\"Activity\"]==\"SITTING\"]['tBodyAccMagmean'],hist = False, label = 'Sitting')\n",
    "sns.distplot(train[train[\"Activity\"]==\"STANDING\"]['tBodyAccMagmean'],hist = False,label = 'Standing')\n",
    "sns.distplot(train[train[\"Activity\"]==\"LAYING\"]['tBodyAccMagmean'],hist = False, label = 'Laying')\n",
    "plt.axis([-1.02, -0.5, 0, 35])\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Dynamic Activities(closer view)\")\n",
    "sns.distplot(train[train[\"Activity\"]==\"WALKING\"]['tBodyAccMagmean'],hist = False, label = 'Sitting')\n",
    "sns.distplot(train[train[\"Activity\"]==\"WALKING_DOWNSTAIRS\"]['tBodyAccMagmean'],hist = False,label = 'Standing')\n",
    "sns.distplot(train[train[\"Activity\"]==\"WALKING_UPSTAIRS\"]['tBodyAccMagmean'],hist = False, label = 'Laying')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 551
    },
    "id": "a98b65cb",
    "outputId": "b32e0ed2-cc6b-4a88-ea92-5260fa88ff68"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Magnitude of an acceleration can saperate it well\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.boxplot(x='Activity', y='tBodyAccMagmean',data=train, showfliers=False, saturation=1)\n",
    "plt.ylabel('Acceleration Magnitude mean')\n",
    "plt.axhline(y=-0.7, xmin=0.1, xmax=0.9,dashes=(5,5), c='g')\n",
    "plt.axhline(y=-0.05, xmin=0.4, dashes=(5,5), c='m')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "bc930e2c",
    "outputId": "40455af3-0858-4ed5-9ac0-340ac3da7a1e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Position of GravityAccelerationComponants\n",
    "sns.boxplot(x='Activity', y='angleXgravityMean', data=train)\n",
    "plt.axhline(y=0.08, xmin=0.1, xmax=0.9,c='m',dashes=(5,3))\n",
    "plt.title('Angle between X-axis and Gravity_mean', fontsize=15)\n",
    "plt.xticks(rotation = 40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "03d54689",
    "outputId": "f9fe914a-ded8-45a5-e494-fce558811161"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Activity', y='angleYgravityMean', data = train, showfliers=False)\n",
    "plt.title('Angle between Y-axis and Gravity_mean', fontsize=15)\n",
    "plt.xticks(rotation = 40)\n",
    "plt.axhline(y=-0.22, xmin=0.1, xmax=0.8, dashes=(5,3), c='m')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "09527e6a"
   },
   "source": [
    "## Visualizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "06aa20b3",
    "outputId": "443930d8-c288-4cf3-c207-1624a6644ee1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2 µs, sys: 0 ns, total: 2 µs\n",
      "Wall time: 4.53 µs\n",
      "[t-SNE] Computing 151 nearest neighbors...\n",
      "[t-SNE] Indexed 7352 samples in 0.353s...\n",
      "[t-SNE] Computed neighbors for 7352 samples in 32.920s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 6000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 7000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 7352 / 7352\n",
      "[t-SNE] Mean sigma: 1.437672\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 74.125954\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.282853\n"
     ]
    }
   ],
   "source": [
    "X_for_tsne = train.drop(['subject', 'Activity'], axis=1)\n",
    "%time\n",
    "tsne = TSNE(random_state = 42, n_components=2, verbose=1, perplexity=50, n_iter=1000).fit_transform(X_for_tsne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "id": "7cb5d481",
    "outputId": "f183fcbd-8dd2-4e4d-8b6e-7ba8aa5a959f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f773b704550>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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10TydMgGr20aiJtZ/jjRtIqPjbqe9oTV3RQ+kR0g3jtmLAuYRpgrlsaSxJGvjkZDoF34VeY6mUmqNXguPJ43ltbJ3eK9qDdmWPUwtmkOoKpgIVSilrkqeTLqfF1Ino5M0vNZiKk8nTyBK/WvOsEt20SOkG5/Vfc8rpW+z15pLJ1NbZpW8yX7bIeI00Wxu2MGw6JsBKHGVc8B2mKeKXuHHhq0AHHbkM/roExh+t4nIhoZttDNk8mrpUg7b81la8RH9Dt7Nbbn38515My6f+/TfOOG0WPY13PMCFFaALDf9es8Lpx4g9+rVi6KiIlq2bMno0aP54Ycf/vD4oqIiysvLufDCCxk4cCArV64MeH/gwIHs2LGDI0eOnGSEX3Xu3JmcnJwTvnfxxRej1WpZv359QPv+/fvp3Lnzn44tnBoRHAuCIJzlqly1fFa7Hr1CR6+wS5lR/AZxmhj22w6R5yhkdOztzG8xnQRNLI8nj6fIUc6gqOt4M+M5PD4PU4vm8H7VZyzImM6kxHu5P+4O7F4noepgxsYNQy2puCYsiytCurGo4gNGH30ch+yig7E1eqUuIGg0KQ38aN5Ct6BOjIsbhtVrRQaUKNljy6HMVUWdpyFg/u9Ufoxbbtr1bl3d9xx1FvBowfMMP/wws0sWMTrudlroUtArdKTpknizfCVfmzdyxFEAEuQ4jpGgiaFbUCfq3PXcEtmXcHUIBoUenyyz33aY/bbAOswOn5NwdQhKAle5vbIPt+yhwFnC5IIXOGQ/xlbLHoYdeohsi8jbPFtNeRNsv0sZtzmb2k+FyWRi+/btLFiwgKioKAYNGsTixYtPevyKFSsYOHAgAIMHD2b58uUB7yuVSh555BGee+65Pz33n5UXfOyxx064evxbF110EW3atOH+++//0/MJf50IjgVBEM4SDZ5GKlzVNLgsVLlq/f95FjhKWFLxIWtqvyZeE8OY2KEEK40Mj+7Pt+ZNzCt/j/22Q1S5a2jwNNIv8kq6mjrwfMkbrK77mkRNLHfE3kKevZAZxW/waMEMvqnfiNVrI0QVxNz0p7kk+AIWV67C4XPikt28W7Uar+zjobzpbGnc5Z9jsaucwVH92NiwjVfLlpKqSyJTn8qYuNu5LrwnKqn5oywKSYFO0vKdeTOxmihWVq3lkP0Y0JTnPLXwVe6IuYV5LabxbNHr7LXlAqCTtMiyTFdTBzQKNTOK3+D18nc54iikg74Vs9Om8FXdj9hlBwZF81xrp8/F/Qkj/K8jVWHYfXYGRPRhTU3gkqOMzI/1W0/5Hgr/jKLKv9f+dyiVSrKysnj66aeZO3cuH3744UmPXb58OYsXLyY1NZV+/fqxe/duDh8O/GI2dOhQfvzxRwoL/3hzmp07d54w3/gXPXv2xOFw8PPPP/vb2rVrx44dO/yvt2zZwrRp06ivr/+zyxT+BvFAniAIwhnmk31satzB9MK5FLnK6BuWRaQqHJfsZkT0zSgkCY1CzXbLPkZGDyDfUYJeoWGP9SD3xQ1hZskCvjJvoLOpHePjRnCB6TwsHhtPJo3nsKMAWfbhkT3MKV/iP+fa2u/oE3o5RxyFtNan8339z83mtaVxF+2NrdhQv41QZRAGpYHBueMYHHk998YOIUIVytyypSyseN/f5/1Wc4lRR1Lh/nXTkNui+qFWqNjQsI1rw3vwSe03AeeRkdFJGn4yZ/sDY4A7Ym8hTBXMbusBPqn9Fmiqn/xmxQoStbG8W7ma841taKvLZEL8SDY37qCNIYMc21FMCiM7GvcRp4mmvaEVMeoIbgjvhU6poc5TT4PXSrg6lMUVH/prNOsVutNzQ4XTLim6KZXiRO2nIjc3F4VCQWZmU6nCXbt2kZKSctJjrVZrQJ7xk08+yYoVK3j88cf9bWq1mgceeIAZM2bQs2fPE471ww8/sGDBgmZpE783ZcoU7r33XtLT0wEYM2YMF110Eb179/bnHdtszTfBEU6NCI4FQRDOsP22wwzJnYDn+ENiy6o+4aaIXuTa8mjwNvJI/ChGRg9AQuLz2u/pEtSe54vnMzzmZuaULqHaUwfAdss+Jhe8QHtjK2I0kaTrkqlxm1FJSu448mjAOcNVoRgUBj6p/QZfWE/a6jL4hp8CjknRJrDdspfuwReQ7yzls7rvyArpxrzy9+gZfDHXRVzJQfvRgD5LKj9iUuK97LIeoNRVSQdjawqdpRgVBjqb2mH2NBKtjqDSXRPQz+xtoNBdxotp/8HmdRCkNKJAwiv7+KBmXbPPbIdlPz58rKpZR6GjlLviBvFd/WYWlC+ni6k9AyKvYVPDdnbbcrgtsh9x6mgUSgV3HJ7o/5xb6tO4M2Ygb1WsJFhp4kJTh1O7kcI/ZvrdTTnGv02tMGib2k+FxWJh3LhxmM1mVCoVGRkZJ31Ibvny5dx0000Bbf3792fw4MEBwTHAnXfe2SwlYuXKlWzcuBGbzUZaWhoffvjhH64cA1xzzTVERf26W2RsbCwrV67k0UcfpaSkhOjoaCIjI3niiSf+zmULf0I6m7bU7NKli5ydnf3nBwqCIPyLfFj9BePzng5oU0sqRsXeyvzyZfzYfiWe43myX5s38lXdRsrdVUyIH8nLpW83G+/jNm9wYdD5fFT9JQD5jiLW1n5HruOY/5hnUx7G6XXzdPEr3BkzkFh1FIsq3qfMXQU0lUkbHXs7DV4LidpYbF47btlDnCaaElc5BkmHVqHh0YKZAefWSGpeSXuCYlcZ+22HMSr0XBzcmZdL3uaplAnMKJ7HTRG9mVE8D7fsAeC2yH6EqYKRJIlETRyLKz7kgYSRaBVaNjVsp9xd3Wy1eXzcCN6u/IBGr5X7Ym9nTe1XATvnxaqjeCxpLEcdBVwU1JFcex6NXisKJBZWfEDN8S8UU5MfoMRVwcVBncgK6cYxRxH7bIdQSBLnGVqSoU/9L++q8GcOHjz4p8Hhby37uinHuKiyacV4+t1w29X/4ASFf5UT/XmTJGm7LMtdfn+sWDkWBEE4w4wnyJeNVIVh9jQQpDShVWhI0SRQ5a4lQhVG+fEA9oT5vSgIUhoB+Nq8gV6hlzO79G3GxN3OUM3NWDxW0vVJbKjP5p2qjwHoaGxLrdvMpMT7kJHxyl60Cg1qhZqjtnyClSb0Ch17Gg8So47EJ/v42bqLdF0SExPuYU7ZYhy+piW9a8J78GrZEirdNbyS/gTvVq5mbN5TAIw6MplZqVPQSRrezpxJlbuGCncN2xr2cMRRwOTE++ifM5onk8fzwLHp2H0OpiSNIUoTwaaG7f4V8kxdKj58NHqtpGgTaKVPCwiMAcrdVbhkF5cGd2Gn5QCZ+hTKXTUsrlrF2PihPF04BwCP7KFXyGW0NWaw15bL7bkPUO9tBCBRE8sbGdPpZGqLcObddrUIhoX/DREcC4IgnGHnGVvRVp8RUFN4eMwAXi97h0mJ91LvthCriSJNl8QxRxGt9Onk2vP4vv5nBkZey/vVn/n7TYgfSagimIPWI7Q3tGKPLYc0XRKvli1FgYLbo2+kxFXBJ7XfkKJN4Laofnxeu56Lgjsyveg1XkidzPcNP7PevIXnUh+m3F3NS2WL8MheZqVO5ogjn1fLlvrPF6kK4+H4u5lTtpjrw6/EpDSw2v4V0FRSbZ3519JYNp+D0XmPMyt1Mg8fbf40f6mrkp4h3fi09jv/Ntmra77i1sh+jI8fjkv2EKkKw+VzM+n4ivUtkddQ5CpDQvLnDkPTznhR6nAmH3uB+xNGsNN6ECUKxsQORZZ9tNNnst9+mDhNNN1COuKVvSypWOUPjKHp4cNvzT/R3tjyhF9EBEH4dxJ/2wVBOKe5fG6cPhdBKuOZnsp/LVEby8LM59lu2UeFu5o4dRSV7hqmpTzI8spPqXGbaWEYTowmkk6mdvxHPZqnCl8m27KXKFUEL6c9jtVrJ0hpIEYdRf/c0RQ4S/hP4mg2mLcyNm4ozxfPp9xdxcqqtTyfOpGxcUNxyU2f3eDI6yh2lfNw4l28VbmSe2JvxagwkOvIY+nx1WUAg1LPu1WrA+Ze7akjSGVkQGRfqlw1tNSmck/sregUWlK0CWgkNS45sH6wSWnAoND7A2AAo8JAlDqcO2IG8kLxm7TUp3FD+NW4ZBehqiD2WXPpYGrDQ8emc3NEH8bHD6fMVUWMOpJlVWsYFHkdK6o/9Y83InoAb5at4N74IYzLexofPqApmB8XN5ybI3tzvXwVH1V/wRUhF6GV1OTY85rdmzxHIY0eK2HqkNNyrwVBOPuJ4FgQhHNCrcvMIccxzJ4GQlRBtNNncsRRwLraH/iuYRO3RF7DDeG9iNNG/flgZ5EyVyUHbEcwexowKvR4fV40CjURqjAO2Y/xs2Un2617uTa8B60M6bTUp7HPcohh0f1x+Bw0eC08X/wG55vaMjJqAPccnYLZ21Rn+Pni+dwdOxiFpODp5AloFGqSNPG0NKSi/M0ud0fs+dyae79/W+YNDdt4L3M2a+oCy53Vuetx+zzNrsHpc7Go4gNa6tPoHtyF5WWf0uC10EbfglfSn2Bc3lP+h+AuC+5KpauGp5LvZ3L+C3jxkqlLZXTc7UwumEmVu5Y7ogcSpDLwTNFrZOhSkMIlMgxpFDnKuD9uJHbZiUZScVP41eyyHWSn9QBhqhAmJozCKbswKPRk6lK5wNSORRWr/IExNAXzFZ5qOhra8Ej+DDqa2qKVNDR6rVwb1pN9tkMB19bF1AHj7zYTEQTh300Ex4IgnPUqXdVsbdzDgorlbLfsQ6/QcX/8CLoY25PvLGZQ1HUsq/yEWk9T3qxCOjdKuBc4Srj78GT225vqpBoVBiYmjmLUkSnMTp+Cwqegq6kD2yx7aDweuFq9dnbbDrKo8gOClSZGxgxgUNT16BUaNAqVPzAG8OLljfL3mJHyKPcXTAVgYebztDG2CJhHji3PHxj/os7TSKI2LqBtfvly7oodFPAQoFFhoJU+nQERfWltSGdywQv+9IaD9qPMK3uXN1pMZ0PDVsJVoZS7q3ii6GWuCLqIpS1nUemuIVQVwh2HJ/r7yZKPF4rfpIupPZ1MbXmt7B3csocETSyjYgczs3ABXrw8l/IIP9Zv9aeWfFe/GSVKJiaO4pH8GVwe0pVaj7nZ52712pEkBa106YyLG8bqmq+o9NQSpDIyOPJ6PqxZh0pSMTT6Ji4M6ohGoabQUcrmxh0csufT1tCCJE084aoQUnSJqBXiv1JB+DcRf6MFQTjrFDnL2GPNwexpoLU+HZ8ss6pmHdst+wCw+xzMKH6DuelPMSCqL5PzX2BU7K28ULKA4dH9SdDG/skZzg5bG3f7A2MAq8/GN+af6BrUgbcrPmRiwj3ICh8lrnJ/oGpSGmhpSEdC4qGEuzhoO0KCNha7z8Vu60FGxQzm49qvuCmiF3qFjhJnOVqF2n+OT2q+pk/Y5QHzUCl+XUXWKbQMj+6PXqnlPENLzje2Ybf1IAA2n53uQRcQlRLBh9XrSNbGc334lbxcuohiZzntja0C8n4B9thyybEdpYOxFY/mz/SvIP/QuIVLQ7rwbsVqBkVf16yfQ3bSM/Rini+e728rcZWzuuYrHoy/A7VCzTfmn4hUh6GWVDyWNAYVKqw+O8uqPqHGU8cP9Vu5M+YWXigJ3EatrSGDAkcJTySPY+jhB+ke1IUc+1GOOAoYEd2fuS2exif70KLhPGMmFa5q7jvyOLtsB/xj3BkzkFa6NL6p38RtUf0IVpn++o0XBOGsJoJjQRDOKiXOcu489Kg/aFSi5L1Ws9nQsK3ZsZXuWhYXr2JK0hh+qN9CS116QLrA2a7AWdKsLd9RTNegDuyw7AdkdAotb2Y8R6ymKV1EkiS6GtszOfE+HF4nRxwFrKheCzSVX3stfSrx2hj2WHIpdJVi8VrpFtQJlaTEI3uJVTffNaGNPoP+Eb3J0KdS77GQrI2j1FWBEiV3xgzEI3vwHt9IpM7TwNaGXZiURi4N6spdRyb7A1urt/lmBHHqKKw+GwdteTwQfyf1ngb0Cj0JmhjyHIUUuktRSFJAH5vXQbgqFLvX0Wy8ndYD3BzRmzaGDMpcVfzQ8DMtdCm0M2Syx5LDipq1/mNrPWZcsoeHE+5iWdWnhChN3BU7mHJnNV7JQ4W7hjpPA/HNXVYAACAASURBVGZvA5HqcI46CnmncjWLK5t2SJue8hAAOfajAYExwHuVaxgbN5QqTy17bbl0D77gpPdZOLtNnz6dZcuWoVQqUSgUzJ8/n0cffZRZs2YxZswYnE4ntbW12O12EhISACgrKyMuLq5Z++rVq8nKyiI7O5vIyEgkSeLBBx/kxRdfBGDWrFlYLBaeeuopAN59911mzpyJ1+tFpVLRtWtXZs2aRWho6Bn5LIQmIjgWBOGssseaG7Ca6sWLGhWZutSA3dMAItShFLpK0Suaau6OjOnvDyLPBV1M7Zu1XRFyET/Ub+GmiN7oFXpGxAwgRBUUcExrYwv0Ci2ra78m27LX317vaUQlKbB5HRywHyZRE8ON4Vfxcc1X9A3L4jvzZq6PuLLZOeM1MSRrEwJWad/KeI61detZXfNVwLHdg7rwQPwdjD/2JPvshwJWfLdb9nFjeC9W1zb1UUsq7o69Fa1Cw8c1X9Iv/CreqlhBg8fK9eE96RHSjTcyniFWHUWqNpF8ZzEAH1R/zgtpkyhwljabayt9OrUeM2+UL+Mbc9OmJXmOIrIte1iQ8WxAcAxNu94trfiIHqHduC6sJ/cdfRyzt4EH4u+g2l0LNG0o8lqLp7g6tDs+ZHSSluVVn9A9uKn86S9l6n7LKbvw4CVMFUKBo0QEx+eozZs3s3btWnbs2IFWq6W6uhqXy+V/f8uWLQAsXryY7Oxs5s6dG9D/ZO2/0Gq1fPTRR0yePJnIyMiA97744gtmz57NunXrSEhIwOv1smTJEioqKkRwfIaJ4FgQhLNK4+9yXwEcPhfj40dwf95Uf4WDK0Muoc5dT7Q6ApPCQO+wy+lkOLfq0XY2ncfU5Ad4vng+dp+DvmFXEKUOp39kH7qaOtBKn9osMP5Foi6OBq81oG1Swr18Yf6RRRUfAE0rnhsbtjM+fjiZ+lTujb2Njieo2ZvvLGbub8qzAahQYZSa119WSQpeK1vKuLgRFP4ueP22fhPDo29mfovp1HjMVHtqeatiJecbW/NY4hjuO/o4dZ6mnOjVtV+jVWipdZm5MuwSBkZegw8Zl89FG0MGL5e+zU3hfRge3Z8lx1dyQ5RB3BbVjwZPI9+aNwWcu87TQJ27gdlpj7Oy6lMcsoveYZfxff3PlLoreK9qDd2DLsCg0GH2NqCQFLTQJyMh8XDCXTx8bIZ/YxCNpGZx5gtkHt8ApKU+jRBlUECZtx4h3TC7GyhwlZzwS47wD9j9Hnw9BeoLISQZrp4O5w85pSHLysqIjIxEq9UCNAtgT5VKpWLUqFHMnj2b6dOnB7w3ffp0Zs2a5V91ViqV3HHHHaf1/MJ/RwTHgiCccbIss8O6n6UVH3GB6TyUKPHi9b9f562nwdPIvBbTjv+o3E2iJo5J+TN4Mmk80aoIWhnTkX734/mzXbDKxB0xt9Ar9FIcsgvJJ2H2NVWtSNbGY1SdvEqCUlIGBGV6hY54bQyvli8JOM4hO1FLKjqb2hGraZ5SAU053L/sVmdQ6JiT/iTLqz9lUOS1vF/zmf89CYkrQi5iWtFcLg3pSpDSRIQqzB9UKlCQqksk134ULz5eKV0MQKImhnJ3DUOib6DcVe0vB7fXmsOkxPuYVvQq14X3ZHbpIhQoGBs3lP22w+y3HaarqQNz05/miCMfp8/FSyULGRp9ExpJjVN2BVyHjEyJs4whUTcQqQnn7sOTsfiaUj36hl3BV+YN3Bs3hHll73KR6Xw6GduxvNXL7LAc8F8DgEt2s6BiOV1NHTCo9KTpkljW6hXmlC7moP0I3YMvoKUuHbfsxo2bDsbWf3qvhVO0+z1YMwrcx1N36guaXsMpBci9evVi6tSptGzZkquuuopBgwZxxRVXnIYJ/2rMmDF06NCBiRMnBrTv37+fzp07n9ZzCaeHCI4FQTjjsi17GZQzDqfsYptlD48lj2FF1VpKXRUMiOhLnCaKKFU404vnsud4aoVBoeeNFs+gk9S0NrX4kzOcvSRJIkkX/5eOdfncaH7zcF23oI48GH8nr5YtIUwVjM1rx6DQ0/i7FeUIdfhJA2OAVG0ifUIv5wvzjzyT8jDPF8/nsCOfPdaDTEq8j8P2Y4BEpj6VZVWfICMTr47Gh49JifcSpgrmmKMYGZmVlZ/RJ/wK9Aqdf/wrQrrxQN5UJibdgwKJ/ySMxqDQYVIZebtiFVHqcHZbD/JQwl18X/8zMb9Jjdlm2YNH9tDJ2JZwdRhPJI1Dq9AyI+VRHsif5j+uvaEVYapgNEoNR5wF2H0O7om7Da/sRS2p2Wndz7q6H7gtsh9vZjxHrbeeHPtRupo6sK72B36vwlVNjacOg6pp9byjqQ3zMqZR7qrC5rFjka0oJSVDdTcTfA7X2D5nfD3l18D4F25bU/spBMcmk4nt27ezYcMG1q9fz6BBg5gxY8YpTjZQcHAww4YNY86cOej1zX8aA7B3716GDh1KY2Mjzz77LIMGDTqtcxD+HhEcC4JwRu2yHOSH+i3+VcACZwnPF83n1qjr6R16OV+ZNzAh7xnGxQ/zB8bQVDnh9fJ3mZb84Jma+v9Mju0oa2q+IkYTRYPXgkFh4OKgTrQzZnJ/wghuiLgal89NpbuaIdE38FLJQn/fGHXkSX/sb/c62GrZzeqar0nRJvBK2hPoFBoOO/IBKHNX8V7Vau6MHkicJhqH7KR/RB+q3WaWVn3E5sadQNNDky+lTyHHdpTbo29AJanY3LiLFrpk+oVfRbZlD3bZyRd1P2JSGDApjdhkO8+WzMPuczA95SGWVX7CgvLldDK2o85Vz4T4kcwrew+n7MIte7ku/EoeODbNn4fcNyyL5a1e4eOar4hRR2JU6Knz1LOjYT89QrthUOp5pfBtf3UMgL6hV2CTHTxwdDolrnIAZqRM5IKg81hS9VHAZ9Mr7DIMysBARqvQkKJLOLWbKfx36gv/XvvfoFQqycrKIisri/bt27NkyZI/7/Q3TZgwgc6dOzNy5Eh/W7t27dixYwc9evSgffv27Nq1i7Fjx2K32/9gJOF/QQTHgiCcMQ6vkx/rtzb78bhDdvJjwzZUChWLKj8gRBlEqauiWf98RzHIcrP2f5NiZzlDcx/k7rjBTC181f9ZhSiDWNnqVdy4MSj0tNAnk6CNocZTx5TEMRxyHCNJE8tVoZfSQp98wrF/aNjKnYcf9b/WKbQszngBNWrcuEnUxDIgoi9PFL6MFy8KFIyPG86lIZ35tO4bfz8vXmYVv8ltUTfQNeh8QhRBxytQyCyseN9fQ9nuc5CojsHsbSBKEY7d5+C+uCEUO8tpZUjn1ujrSVDH8mX9j8Sqo3k8aRxBSgMRqjCWVH4Y8IDeurrvuSy4Cz/X76DSU4sPH3fH3ErX4A6YlEaUKJmceB9zy5ZS52kgWRvPiJhbmFP6tj8wBvi87nuuD7+SxxLHsLTqY6xeOzdH9KajsS0R6rC/dI/q3Gb2WHMxexrQKbS0M2Y2qxEtnKKQ5KZUihO1n4Lc3FwUCgWZmZkA7Nq1i5SUFPbt23dK4/5eeHg4AwcOZOHChf684smTJ/Pwww+zZs0aEhMTAURgfJYQwbEgCGdMjdvMFssuLjR1wKgwYPX9+mPTUbFNwSBAvbeRTF1as/5XhXbH4rM2az9XNHosWH12otThJy1Bd8iex61R15NrzfPn/gKMix/O2LynOOLIP56nO4x7Ym+lf2Rfip1lyLJMrCYK9W/SMH7L4rXxcsmigDaHz8k26x5eazEVH17KXJU8Xzzfn//tw8fc8qU8EH8ngyOvY07ZrytsJa4KbD476+q+J04dg8Vn45PabwKqWfQJvRyVpOS1sncZHn0zI6L78435Jw7ZjwHwYc0X3Bk9kF2WA9we1YoqTy3PFc/jwfg72Pabqhy/OGIvJEQdTKG7jJ4hF7PNsputlt3MSJmIQpbI1KUxNflBIlRhWH026jxmdlj3+/urJRXlrioi1KEcseczMeEepOOfTQvtXwu6HF4nGxu2s9N6gO2WfbTUp1LhrqZP2BVEayL+0hjCX3D19MCcYwC1oan9FFgsFsaNG4fZbEalUpGRkcGCBQsYMGDAKU64uYceeiigqsU111xDVVUVffv2xev1EhoaynnnnUfv3r1P+7mFv0cEx4IgnDFKhYKWulQWlK/g/vgR5DmKMHsbuCqkO8FKo78yRYgyiFh1BOPihvN2xQfYfA6uCu1Ohi7lL6/unU18so/tjfvYY8uh1lNPvCaarqYOtDQ0/wIQpgwhXZeMzedgQsJIatx1HLQdZWNDNkeOpz90DepAsNLEUUcBnVTt/nDVcq81l62Nu4lTR+ORm28F7fK58ck+vqrfyNWh3XHIgWXMPLIXl+xq9vDjlaGXsKlxO27Zw+1RN7Ksag1PJI/n27qfsPns9I/sQ7QqktfL36He28iqmnU8lHA3b1euChhnadVH3Bc7hBCViY9rv+TeuNuI1kTRLagjH9V8GXBshj6Z96s/o6OxLRcFdeS54nkAqCU16xs2s7bwOwBMCgOPJI4iWGGik7Etmxp3cEP41VwZegk2r506dz3XhGdxyJ6PTqGlrTGD9fU/82ndt1wa3IXzjW1PugveEUcBH9V8yVfmDQBkW/aQpIkjQ5cqguPT6Ze84tNcreKCCy5g06ZNzdq///77gNcjRoxgxIgRzY47UXt+fr7/9xbLr9V3YmJisNkC86aHDx/O8OHD//a8hX+WCI4FQThjYjVRXB5yIRsatvFs8eskaGJpZ8gkRZuAUalnQGRfPqj+nEh1GOvqfqBn6CWEqYKx+xy4fG7SdElIvnOrQgXAYdsxvjL/yOvl75GoieWS4M6UOasYqe4fEOzLssxPjdv9QR9AJ2NbBkddz7Tjq+r3x49AI6lwyS5ybEexex1k6NKI1TUvSbXDso8BB8fglF0YFHr+kziaxwpf9L+vllREqEJ5q2IF/SP7oJO0zUqY6RU6JCTaGjKJVUdR4a6mZ8jFdDC25sWStxgadRNWr52jjkKmFb7KhUHnk6pNIkwZSoWnimOOplrGpa5Katx1zebokb0oJRUSCtJ0SXxeu56rQi9lYOS17LcdJteeB8CN4VfTVp/BoowZNPgslLmqeL3FVDY37ARJZm3dd/4xLT4bK6vW8njSWEbH3U6iOpZLQ7vwWtk75DuKyQrpRoPXwnXhPanz1HPzwfv8VS5eLFnIilZz6B5y4jrGdZ56vjZvDGgrcpVR7q48yd0X/mvnDznlYFgQ/goRHAuCcEZdHNyZF9Imk+8oRikpaavPIMOQCsD9ypH0COlGnr2IOG0Udx6exPj4ESRrEsh3FrHdspfgEBPpnFre4f9ambuKRRWrGBVzK92COlHhrkIlqcizFxGuCvWvyhY6S3m59O2AvjutB7gl8lo6mtpS6CwlRZPIlsadtDVm8rn5Bypd1dwSeQ03RvRqtnK5vGqtP2fZ5rOTrI1nVup/+KjmS0JUQfSP6IMKBXfFDqLy+AYZz6U+wuMFs6nx1BGmCmZs3HC8vqb84zcznmOX9QCf1n7LiyVvkalL5ZLgzuTYjxKlDqfKXcvPjTvpaupApbuG7ZY9zM+YToO7kXBN0yYHvxz3i16hl5KsiQdkJCTaG1vzRvl7rKz6jNnpU2j0WvHho9xVzcziBVwRelHAl4eJCaOocZubfeYH7EfY3LiThRUf8FbGczxe8BJHnE05rF+af8Tms9Pe2Jrtlj3+wBiaUkleL3uHrkHt0Sg0AWN6fB4UKJCQmm1/bVCcuCqBIAhnPxEcC4JwRukUWjqZ2tHJ1K7Ze2n6RNL0TQ+qbGvYzdDom3i1bAlu2c2NEb242NQJl8/VrN/ZzoePNF0i3YO7sNO6D4WkQJZlKt3VIEl0DWqqLuH0ubD7mm+h7MPHsOib2Vi/DUmCNH0S2xr2cHFQR6LVkZS4KtjSuKvZbnjlrl9XM7uY2vNZ3Xe01KcDMjeGXUWxq4z3qz7D7G3k2rAeZOqS2d64j5mpj+LyuVErVHhkL+naJFoZ01FJKlyyG7vPQffgC0jWxrPPepgKdxVjY4dhVBpo8FroZGyDDxmQ8ck+1Eo1E/KmUeWuZWLiPeTYjrDPdogrQy+hjSGTvdYcEjWxRKnDea3sHQAqPdUMOfQA05IfZFrRq7hkN3PTn+L+vGkB1/hS6ULezGheiutC0/nsseVi9dl4tvh1hkTfiFt2M6d0MRafjQ0N2xgTNxSzp7FZ31pPPR7Zh+Z37ftsh1lU/gEDI69lRfWn/vaWurQTbrYiCMK5QXE6BpEkKVSSpFWSJOVIknRQkqSLJUkKlyTpa0mSDh//9dxLDBQE4ayhQsVlwV2YlvwgTyVPoKOxLT5kvJx71SrSdSmMjR2KxWfB6XMRrgzhPENL2htasdt6gHcrV7O9cR9xmhiuDr00oK9RYSBdm8Qjx55jcOT1VLpryNClYvPZUUlqpha9yqySN3micDY/1m8J6Dsk6gb/7wdE9uU782YKnSUccxRhkx08XTiHA/YjlLoqeLNiBT837kKNiiWVH3Ff3uPstx3mhZI3KXVXssOynypXLc8WvUaeo4jZpYt46Niz2GU714X3pNRdySulb7PNsgcvMvcefQyb106Zq5JpRa9S5CrDITuZWjSH3bYc7o0dQkt9Oo/nv8i88vcwKQ1sadwNgE7SMjZuGM+mPEykOownk8ejV+hw+JwBm8VAU1qGAngiaTxaqSmczdCl0Cfscn44/nnk2vOocFfzWtk7jIq7FYAwVTBRqnCuDO0eMJ5JaWB83AgMSh2/Z/bU80X9D2gkFU8mjadX6GU8GH8nTybff05tYy4IQqDTtXL8CvCFLMsDJEnSAAbgP8C3sizPkCRpEjAJePSPBhEEQTiZaE0EOQ15WH02NJKaWo8Zm9dB96AT54KezVJ1CSh88INlK9+ZN9Mj9GJkZKLVkSwoX0GJq5wETSwvpz3GU0njidfE8Fntd7TSp3NX7CAeL3gJrUKDR/aQpk3izfLldAvuxKySN/0rzZXuGkYffZIv2i0mURsLwCXBFzAn/UleKV1MhCoMHz6UkpJYTTRHHQXNUgM+rPmCp5MnMLusaec6gGRtHApZwQfV65iYcDc59jwuDu7MZcFd2dCwjSO2fIqd5f4H1HQKLauqP6fKXUuaPolD9mMcdQTWps2153HUWYAsy3jxAbDNspdMXQrZlj08nHgXGknDxoZs8h3FXBrchZfSpuD2uZvlRIcqg7H5HKyt+Zb3W8/F4rXycc1XPFc8z3993YMvYIdlX0C//ySOobWxBU6fi3davsSCsmUMi+lPgbOE3baDaBRqLgrqiOk3uxZGqsJpo2/B0qqP0St0tNAls9eWS9egDnhkDyrp1P+LLXCUsMeag81np7U+g/bGliik07KuJQjCSZzy3zBJkoKBy4GFALIsu2RZNgM3AL/U+VkC3Hiq5xIE4f+vBF0s5xtaE6OOpMFrIUETyw3hV5Os/2u7y51tanxmni96g8HR17G86hMeL5zNhGPTuCNmAKnaREpc5Wyx7CZMHcrUlAm8lTGDq0K783zxfNoaMxkVeysFzlKMSj1bLE0rrL9Pwajz1FP6m1SKYJWJ/pF9+KTtAroYz+Pq0MuIUUdyzFFEmCq02RzDVSH+ChE3RFzFloZdDI66ntU1X+H2uYlUh3N37GDmli1FQmJC/Ehujb7BHxgDJGnj2Gc7BIAPmTpPA1Hq8Gbn0koa0nRJ1HsbuSSoMy6fi65BHWirzyRCFc5LJQv5vO57DtiPsKBiBd/VbyZdm8Ir6U8Sr4kBIEETy4zUibxauoQdtv28WPIW7Y2t6WBs7S+V18HYmm5Bndh6/DMLVQbzduZMrgi+kHJnFVqFhp6hFzM5aTQ/NWRT7qris7rvGHb4oWYP3kWow3gkYRQXBXXE7nNg9zl4Imkc+Y5iip3N63L/FRaPla0Nu9ncsIN9llxuy53AvUcf48Fj0+l38C5+Pr7xiiAI/5zT8fUzHagC3pYkaackSW9JkmQEYmRZLgM4/uvJ9y4VBEH4C9qaMrk+4kruixvCLVHX0M6Ueaan9F/LsR9lYOR1vF76rn8F0+5z8FzxPG6ObKpzavY0YPc5UUkqwlQh6BU6BkZeQytdGinaBMrdVdR7LMSqo1BJKpQE1krWKbREnCDoDVUFE62NZGjUjShR8FjSWFK0CUSrf32AT4GC++JuJ0YdwQupk7kmtAej429ne+M+orQRDIy6BkmSGBp9I08nT6DUVcE+6yFi1ZHoJK1/nN3WHK4IuQiAIkcpR+z53Bs7BI2kJlodgV6hY1TMYEwKI0aFgddbTKVX2GWEqoLx+HzMSptMrceM2dsQcA0fVX9JqCaIq8O6M7/FdJZkzuKh+Dt5pug1DtiPAHDMUcQhex5H7QX8J3E0r7eYRpImjueL5wNNJd4sXitu2UPvAyO49sCdzC9bxkHrUfIdJayv/5mV1Wu50NSR4dH9eaX0bcqOf9mQ5aYc6lJn0xbn81tMZ3zccOaULKbOU4/qJHWrf6vcUUW+vRirp+kBwEO2YzxZ+DL9c0YzJPcBtlr2kO8s9h/vkb08X7wAq9d2siGFv8lkMp30vfPPP59bb73V/3rBggUB2zo3NDTQokULjh07xogRI1i1qqksYVZWFl26dPEfl52dTVZWlv/11q1bycrKIjMzk86dO3Pttdeyd2/zOt7CmXM60ipUQGdgnCzLWyRJeoWmFIq/RJKkUcAogOTkc+uJc0EQzoyTbZhxrnB6nYQpQ4nT2rktqh9ahYYF5SswexvwyF68x7c8bm9oRfTxVdZUfQIbG7NZWPEBoaogKtw1KCSJtbXf8WDCnbxd8QH3xN3KvLL3kJFRoOD51EdJ0yWedB4dg9oSpQ5ju3U/RoWO2WlTOGjPw+K1EqwMotJVyxfmDSyt+jhgG+ZFmTNpp2/6YhKrieKu2EEMjLwGrULDz/U7GRJ9Awsr3geaqmK00bdgRspE3qxYyYPxd1LoLOWF1Enssx0mQRNDB2NrHit8kbFxQ1lbu57P674HQCUpmZw4mgxdCjqFFofv15rLaoUK5fH1nQP2Q6gkFQ/lPxtwfWPjh3H7oQf9/XqHXk6P0G6YPQ3EaqK4JLgzsepINjfuol/4lXxWu56pRa/yZJJEnqOIAmcJACuqP2Vo1E2YFAYcXhfV7lreq1zDq2VLeTJpPJXuGj6u+ZJYTRQ3R/XBIOn9qSwnUusys7Exm1XV63DLHvpFXEUHfSs2Ne5gRfVaANoZMil3VzXrW+oq57C9gHBVMMliK+t/zMGDB/H5fPz4449YrVaMRiN33303S5Ys4ZtvvuH/2Dvv8Ciq/f+/ZnvJpvdseiFAKKF3kXpBQLGgWEFQUSw0QUUBFRClyRUuFlQQFRAVURQVEUQMXWqAENIT0ttms7vZ+vtjYWFNbMi9X+/9zet58sCcOefMnJnJk/ec+Zz3Z9CgQcyZM4f777+f+Pjm/uQVFRVs376dYcOGeZWXl5czZswYPvzwQ3r16gXA3r17ycnJoV27ltO8i/znuRbiuBgodrlcl1Z+fIxbHJcLghDhcrlKBUGIAFo0fXS5XG8CbwJ06dLlv29ljYiIiMifJNN0nhxLIVX2anwkWszOJh6NvJf5RSuRIMFX6sOiuFl003XwxJcWWUqJVITyfMwThMtDeKFoJYeNJ3g44i6qbLVMCL8dFQreT1mG2dlErCqKJFUsEkFCjrmALHMuRoeJZFU8kcpQXC4nQfJA/GW+fFv7I9trf+CfiXPZVLmNEms5CkHOK/FPUWmr9hLGiaoYvq7djUqi8MwIA/jKdADYBAd5liJm6R/C4mgiRhXF8gvvUGmr4Y7gESSr4rC5bDyRd9llQq+IYGTgAJwuPMK4lTqBscEj2V77A1+4vmNa5P38aDhMhuFnhgb05Y7gkZ5wCo1Eg/1i8pENlV/gwEGyKg67y+ElqL+p28PBhuNsbLWCclsN4fIgPq/9jg8qtyJBytiQkWSazlFhq+E6v+4EywP4uTGTH+oPsL12N/NiniBOFcXHVdt5peRNAJ4tXMoTEeO4I3gkBU0lJKvi6Kbr8Jv3/5DxJI/kzPHEQO8xHGRFwhyOGi9n75MIEqIVzZO5jAwYyOIS9+zxU/pJ9PBN//0H7n+ET6u+YVHx61ywlhOpCOMp/STPV5ZrzYcffsg999zDmTNn+Pzzzxk7diyCILB69WruvPNO1q5dy86dOzly5EiL7Z988knmz5/fTByvXLmS++67zyOMAfr06fPL5iL/x/xlcexyucoEQSgSBKGVy+XKAgYCpy/+3Acsuvjv1r96LBEREZH/BQ4bT/BS8b94MupB4lV6au31xCgjuS/kFpLVcfT37U60OsKzoOvnhkxeLPonB40nAEhQRrMk/hkeOj+bVaXr0Um1hMgDeTj8bkb79/A6VpYpl5eKV7Ojbi8p6nimRU5gt2E/eZYi2mlbcZ1vd4YF9mdrzXcsK1nDIxF3k99UjNlpYUXxu7wUO5NXSt6gwlZNK3UCd4aMYmHRv+igbeMlji/RWp1EQZM7JOHZ6EeZkbfQIwLfLN9IW00yC4pXebUptpYSqQjzEuEPht/B9LzLM8HHGs+wIv45Hgofy/HGs3xS/TU5lkL6+XUlURVDhuEIkYpQHou8D3BRbatDLbkc3uEj0XC9f09UgpIscy5LStYwKeIuVl60igNYWfoeK+LncNh4kmUla7ghcAB9dV24LWg4X9Z8TzttKwA+qvrK08bucrD0wtvcFDiYVUkv/KH7v71uV/PFj1VfMy70ZrbWfgfA8cYz3Br0D57ST2J9xWfU2Q2MChpIR582vFW+CSdONlV9iV4RgV7167PU/yt8WvUNM/MXeeLqS6xlzMx3W/b9OwTypk2b2LFjB1lZWaxcudITXtG+fXuGDh3KwIED+eyzz1Aofmnw56Znz55s2bKFXbt2odPpPOWZmZliRrz/Aq6VW8VjwAcXnSpyG9lx0gAAIABJREFUgfG445k/EgRhAlAI3HaNjiUiIiLyX8uFpnJeK32PKRHjsTqtnhlEmSBlQcwMxgTfgEIq99RvsDfyg+GARxgD5DYVsaX6W1YlPM9d2VNpcDRynW93rvPr1ux4J01ZnoVkk8PvYWPVNnbX7wfcbhQjAgbwbPRkVie+wBc133PWlEs3XQcaHEYG+fUmShHG2OCRuHBRaC1lftFKbC47wbKW3TmjlGGsTV7MXsMhauz1zURgblMRDY7GZu10Ui3h8mCiFOHEK/X8bDzVrI5WomVV6Xr2XVyUtrVmByONA1kUNxN8BcqaKjC7LDhcThJVsUiREC4PIUUdT1ddez6v/g65IKebrgND/PuytWaHV/8yQUq1vZaMhiPcEHA9S0re8oSozI+dRpxKjyAItNEkkdHgPWMY+xvhK79ELsiblckEKXHKaOKUevKbirG57OxrOEZ/v+7cGDgIH6kWP6kPays+wXnR0WN/w1EqbTX4y3WcNxdgcpqJU+qJVIb94XP5b2FR8evNFpyanRYWFb9+zcXxoUOHCAkJITY2Fr1ez/33309tbS0BAe5nfvLkyWzfvp3rr7/+N/t59tlnmT9/Pi+//PKv1unevTsGg4EhQ4awYsWKazoOkavnmohjl8t1DOjSwq6BLZSJiIiI/M/jcDk41XiOclsVATI/IhVhOFwOfjZmUm9vIEUTz+ScuR7xaHc5eL7onySr49BK1bTVpCAIAo3ORs5dTJl8JYeNJ5AKEra3eRc7DuJVenyk2mb1SpvccasCAoIgeITxJbbVfs+E8DGMCBpIT9/OyC/Gc5815yIVpIQpguno04apufOpcxiQIOH+sNtIUEV79VNhrabe0cB5cwFWl5VuunSvMIFLnDKeY2LY7awsfc9TphQURCsjuO/cDJYnPEujw8yRX4jjMcE3YMfuEcaX+KJ2J3eH3oSPVEOaTwqTsp8ly5LHDP1EjhozWRz3FEXWUp4pWOJp82T+S7yWMJcah3cmPb0igrPmXEYFDmJl6Xuee+PEybzCFfTy7UyyOo4xwcP5pHo7tXb3IsEgWQAjAwc0G2tLZJvzGezfh02VX3o8mgUE7ggZiSDAivg55FuLsTptqCQKXixcySMRdxOv0vNwzhxMTrOnrzaaZNQSFXMLVniSkETIQ3g3ZbFnlvt/hQvWlt0/fq38r7BhwwbOnj1LXFwc4F5498knnzBx4kQAJBIJEsnv+xkMGDCA5557jv37L//OtW3blp9//pkbb3R7jh84cICPP/6Ybdu2XfNxiFw9YoY8ERERkWuM1WljY+U25hW+SpPLSoDMj5UJ8/iydjeZpnOMDByI4WIa5CsxOc1U2+oYm/UEryfNZ0hAX3ykPrTVpPB5zU6vun19u/Fl7S7uDBn1m0IoSe1e6Oy6mJ2uJS6VB8n9yTLlMrtgCaXWSoYG9CVTcY5+vl1Zk7yILHMu/lJfktVxtNG6F+RVWKvZbziGRBBYWPwvzyI2jUTNO8kve2ZCwe2AMTZ0JB21bfCX+fJh5VbilHomR9yDy+XC6DTxwPlneD5mCnEqvWcR3sjAgTTaTc1moS9RYi1nWt580tSteCr6IQqaLlBlq+HH+sMM8evLF7+4dgCZjdmMC72F72p/wuA0uu+by0obTTK19jpsLrv3PXXZqLLVkqyOo602ha2t3+KM+TwC7pnk+F+8LLREjbWOx3LmYbQ38nrSi3xftw+ry8YNgdcTI4/kpjOTGBd2C6+XfuhJ8w0wr2gF65IX00Gb6nk5CJMHc1fIjZRYy7yy85XaKllc/CZvJC1A3ULikv9WIhVhlFjLWiy/ljidTjZv3syJEyeIinIveNy1axfz58/3iOM/w+zZs5k0aRIJCQmAe9a5e/fuDB061BN3bDKJ7iN/N0RxLCIiInKNyTSd45mCxR4xV2uv53jjGT6q2obd5aCHriOR8hCUgsJLBAXK/AmQ+dLksjK38FVS1QnEqKLo59uNyIQwDA4jQTJ/vqv9iVhVFEpBTtjvZGLrpuvAo+H3srrsA+wuO4mqWHIsBZ79bdUpJKsvr7b/uGo7UYpw2mlaoRKUVDvq+NmUSXddOj1bWPx1ovEsayo20V3XwSOMwS3015Z/zJqkl/i5MROTw0xrTRI9fDsiE2TcFTKKztp2FDYVcaDhGG01KQzzu47t9T/wbvnH3BB4PVMj76fUWklrTSK+Uh+M9kYSVNHkWoo8x0nXtvWEYJwyZ/FB5VZKrOVUWqsZH34rJqfFa0a9jTqZ2dGTMTnNFFlLWZeyGJvDQYGtBH+pjvymEnRSH7fNm/OyaPGV+hCpuOxImqiOIVH95xyWTpqyOGnKAuCB88+QoIpGI6gZEzycz2q/pd7RgIDg9Uxc4mjjGSaF38V9obdgddlIUEbTVpvC66UfNKt72HiSSnsNMdL/Tg/wlnhKP8kr5hhALVHxlH7SX+rXZDKh118OiZk2bRpRUVEeYQzQr18/Tp8+TWlpKRERzRdJ/hbDhw8nJOTy72h4eDibNm1i1qxZlJSUEBoaSnBwMHPmzPlL4xC5tojiWEREROQak28paTbL6SPREijzp8JWzRtlG8g1FbI4/imeK1hOvaOBYFkA82KeoPSifVdh0wWqbLU4XS5eKl7NHsNBwO1dvCrxBeYULGNJ3DMEy1uO/b1EkDyAGfqJjAoaSL29gcVxT/Np9dfsazjKdX7duCd0NEFytxey0W7iiPEknXzSCFeEsKzkbY8Hc3+/7iyJe4YI5WWBWG8z8nnNd1xoKqNaFdvs2NmWAiKVYbTWJjXbd7TxDHMKlnH+olCXCzJeT1zACdNZ8puKqbMZ0CvCOWPPJUIRytbqHWyt/o4Vic+xz3CUQ8YT9PPtRrQygrmFr3r6PdBwnOEB/Xm/8TMMdiMV1mqGBVzH7vr92Fx2Zkc/wnd1e1l/0Z4uRR3PCzFTaXJaeaRgDi/FziRUHsjCuCd5ofCfVNlrCZEH8lrCPGL/onVaha0aAcHzbFwS+SqJinyL+8XC4myip879EpJjKaTCVo2v1AeHy8Hu+gMcbzzNKdM51iQvQiFp+eWos08a6iu8pv8XuBRXfK3dKpzO5l9Tpk2b5rUtlUopLS31bMfFxXHqlHfYz9q1az3/3717t9e+Xzpa9OjRgx9++OEqz1jkP4EojkVERESuMeGKYK/tBGUsqZoEpkbez9MFiwHYYfgJo8vEyoR5GJ0mzpsLeKZgCZPC7wSgm08HlBIFJ0xnPcIY3OJpWcka1icvp5W2ub9qnc2ARBA81moAcomcCEUoi4pf5/v6fSSr4khVJ9BKFU/SFaLW6DAxPKA/WeZ8jjWe9kqvvLv+AMcaT3uJ4xp7HQcajlFmq2oxrOCOkBH4XXEel7A7HWQ2nvMIYwCby85rpWuZqX+IHEsBlfYaFhW/TowykiCpP3V2A3bsTM6ZS6IqljbqJFqp4plR8JJX32maFE+/pdYKssy5mBwm1icvw+lykt9UzLsVH3vqnzPn8VbZRob496W7riOLS95keEB/UtTxrE9ZCoI7TfS1WOR2xnyeW4OHsfkKt4tBfr2otdczyL83comMTj5t0UjU/Gg4xLCA/sQpo0hSx1Jtq0NAQK8Mp59fNyqsVdicdpJUMYwJHs7mqu24cJGgiube0NGEKIJ+40z+O7k5eOi/zbpNRORKRHEsIiIico3poGnNrKiHWFKyhmR1LP+Mn8fbFR9RaClhdeKLnDGdRy6Ro5Woybbks7D4X9hdDkYGDmSv4TDtNak8FD6WWXkv82hkc9unHHMhVqzU2usJkPkBUGutZ7/xGF/WfI/F2cSNQYPp49uFALl7/7HG03xfvw+AbEs+2ZZ8dtZlkO6TRtuL8cNSiYTWmmQcuPi6rvnMVmHTBa9ts9NCZ592FNeU8WP9QR6LuI/3Kj7F7LQwLuxWbgwcjNVpJd9SgkaqQq90f5KWCAL+Ml9kgtTLvq24qYyCphK+qfuRXEshNpedk6YsTE4z1/l1Z2d9hnv8lgJyLAWMCb6B4QH9Pd7IofIgBvj3ZF6he9X/IP/epKoT6aprz1e1u/ARtKikzWdUf2o4wuigoYTKg9hrOIyvzIcMw88M8O1NlCoUQRD++M3/DXrpOrOpahszox6k3FZFsDyQeGU0E7KfwoGDZ/SP8E75x54U0YeMJ+iobc2ksLsIkgegk2hZXfYBZ805yAQpZbYq7g29mVuDhtFanYTZaSFBFUt7beo1OV8Rkf9fEcWxiIiIyDVGI1NzX+jNdPJJQ46MJpeV6/16YNKZWV7yNm01KeiVEbRTp1Biq+Ce0NGEy0MIlPkTJg+iwWFiWcnbZJqzvZJYXKK/X3e+qd3D5zU7eVo/iev9enK48SSTcmZ7xOb2uh94I3EBI4LcLgqXnBWuxOJqotJWDbjFcYg8kKOOU6RpUujt27nZQrZUTaLXtlKQM9i/N4eNJ/mp4QjnLQVMj5pIB00qHXVtOGfOY1nJGrZUf0uoPIin9A+TqI5hf8NRjA4TryXMY0ftXj6t+QaAW4KH0V6Tio9Ug1bqTuyxtXonJdZy/KQ6ntE/wrba73G5XNwS9A+C5AH4S32ZEfUAATJflIKSF4tew0eiYVrUBAb796HRaeam0w9SaqvET6pjTszjza5DB21r6uwG9jccpa0mhVxLEXUOA1PynmeK/n76+LZkxvTrFJiLKbKWct5SgFKipJO2La00CfTQpWN2Wnjtwjo0EhUD/HvxfuVnHtcKs9PiEcaXONZ4hkanGa1TzRuVGzhrzgHc7iZLSt6ii087+vp1JU3bCqvLRsjFjIoiIiJXjyiORURERP4N+Ml96ePXhb31h5iV9zLZlnz8pDqe1j/M5qrtbKn5lkWxM/m65gdGBA5AJVWSbc5nYfEqUtQJDA7oy5CAvoTKA5kb/ThLS9ZgdJro7tOR20NGMCH7KZw4mXj+ab5ru56ddT95zcICrKv4lCEBfVFI5MQqI9FJtV4ew1182mGwG2l0mNBKNQBc59+DM6Zs7gm5iQpbNQcajqEUFEyLmkC6to1X/zGqKLIt+UwMG4PBYUQlURIqDyJeFY3T5eTdso896ZCLrKU8mjuX1YkvemZ2BQSWxz/LXsNhBgf0oacunSdyX/CEcwz268PkiLspt1VRY6tFKsgYF3orEgQOG08Sr4pme90P1NrrAUhWxXF36E1013VkoL/bCeBY3WlPHHe9o4FGh4kbAwd7PI4DZH48HH4Xu+sPoJP6cFvwMOYXrWRG1AMsKXmLp/MXsyV1NcGKPyY6TzZmUdxUxqM5c7G43C82wbIA1rdaRnttKiODBtLfrwf5liJGn33Ya4HZr81Q21w27IKDPfUHm+3LsxTR169ri+ErIiIiV4cojkVERET+TRRYinm+8J9kW/IBtzh7umAxqxKfp21DMomqGMaGjGS34QAGu5EguT/DA66n1l7PigvvAnA+sIAnIsbR27cLtfY6ss35PHj+GS8buFON54Dmwspt0eZe/JWqSWRR7CzerdjMOXMevX07MzpoCO+Vf0Z//x7YXXYyG7PJMufhI9XQTtuKdclLKGoqRSVREKuKQnrRA/kScomMHrp0AuXumGAfiZYkdSxBigCyTflsrfnOq74LFyXWMiRIcOLEhYvXSt/jk9arqbHX8WbZBnykWhKVMYwOHopKouTbuh/ZXPUVUkHKLP1DzC1cToOjkWEB13HAcIz7w25jackawB0ukltaxPCA/pfP8RcJN+YULueWwH+wNnkJNpcNX6kPTqedfn5d0UrUbKn+lulRE9ldfwC7y0GupZASa/kfEsdOl5NsUx5banZ4hDFAlb2WXXX7POEOOpmWCGUYMYpIsiyXPawvNJUzwK8X318MHwHoqm1PK3UCPzdm0lqTxOErksGAO+mKiIjItUUUxyIiIiL/Ji5YKzhtPu9V5sKF0WFieuREghVup4kQRRCbK78iQRmNAycfVG711P+iZietVAmMCR7Oj/UH+bZ+bzMPXoPTyD8C+vFh5eeeT/TgXhBX1FRKojoWH6mWWGUkd4fciEyQY3aaWVm6njuCR+Ar82F33X7uPTfD0z5Vnci7ya/QpgWniUs0OaxIBQnddB2a7dNIVYQrQrxs4wC0Eo2XsK+y1SCXSJEiMNC/Fx20rTE4GphXuAIHDuKUep6OfpgXCl/jzdIN3B96G/UOI1JBQoomnkhFKBPDbmd77Q+EyAN5IPwOEpWXLdZS1PF00LTmuOmMp0wmkVFpq+b5whUeuza1oGJ9ylKyLfmsuLDWE87S17crlbZqbE47cslv/8m0Oe3IJHLKL85UX8kFa4XXdrA8gNVJL/Kv0vfZXb+fnrp0JoSPweVy0UaTxFnTeVI1SYTKAymzVbKgaBVzYx4nx1LgmSm/MXAw7TWtf/OcRERE/jy/n+JFREREROSq8JP5EiYPblYeLg/xCGOAXr6dmB83jS669mQYjjSrn9FwBJvLTpW9juEB/ZFdMYMbLAugl28nIuVhrEycx4iAAQz068Wr8c/xUdWXXp7A7XxaoZVqeL/yM9ZWfMIAv17srT/MUeNpXixa6SWsz5pzONF4tsVxOV1O9tQfZOL5p7jxzEOsuvAeBeYSrzpRynCejHoAyRV/Zlqp41FJvBfEjQkejswlY0d9BtPyFmB32VlZut5zLoVNF9BJfFid8CLjw26lvTaVOnsD8Uo95bZqZuS/xBc1O+nl24kgmT9P5D5PliXP03+oIojVSS+wKHYmd4XcyOuJ83kk/C5KrRVePsZml4V8SzHttakoLs42d9K2pa9vV06ZstlaswPHL8JWLl0Lg92I3WVHKVWgEOQM9e/XrF5fv67NylppElgc/zQ70tazInEObTTJtNWm0FqTSG9dZ7QSNbX2Bmrs9Thxsrj4TW4PHsGUyPuZETWRh8LGEvIHwz1EmjN16lReffWyDeDQoUO9En1Mnz6dZcuWAbB8+XJUKhX19fWe/bt372bEiBHN+u3fvz+HDx8GID8/n+TkZL755huv+mvXrkUikXDixOUvAWlpaeTn5wNgNBp5+OGHSUxMJD09nc6dO/PWW29du8GL/CbizLGIiIjIv4lW6niejX6UaXnzPbO9dwaPooO2+WyfVqqhrSaZDtrW7DYc8NrXXtuajyq/YoBfD5aWrGFG1IM0OIzIBRk9dZ1orUkiz1LEtLwFpKoTkQsy5hQsJ1QRhK/Ux9OP0WHileI3CVMEE6UIZ03ZRoxOEyOCBrSYhrfO0XwRH7i9hMdnz/TMri4sXk2t3cDs6MmeuNmjxky2VH3DqsTnKbVW4nMxJXalrZY50Y8BoBDkNDhMnG8qYE3ZJgCvBBhKQcGrCc/xTe0ezphz6K7rQI29nhGB17OwcBUjgwd6HDSutEdzj6WdZztWpece1eVEDwDGyuZZybbUfMtNQYN5MPwOJEg43niWvYbD+Mt0rLjwLh20qVTaajlqzESvCCNGpSfXXMBB4wmanFbuDB1FR21r1BIlT0Y9yIYK90z+IxF3082n+ex6ta2OPfUH+an+MD180wmU+xGn1BMuD8ZHosXqtBGmCL64aBKMThOvl7mTfnTzac8N/v1bvD8if4xevXqxefNmpkyZgtPppKqqCoPh8jOfkZHhEc8bNmyga9eubNmyhXHjxv2h/ouLixk6dChLly5l6NChzfyP9Xo9CxYsYNOmTc3aTpw4kYSEBLKzs5FIJFRWVvLOO+9c9VhF/hyiOBYRERH5NyEVpPwjoB9RitfIbyomVB5EmqaV16zxlagvhiJcmcUuXqlHrwhHJVGyuOQtZkVNIqPhMArkdNN18GSti1PqeS56MuctRfT37U5hUwmHjCcosZZTbq0iTBGMgIATF3sNh72OW2Qp5bbg4bxd/pGnTEAgVZ3Q4nmeNeU0c9FYV/Ep9waPxirYqbLWsPzCO+xtOMw39T8iIBCtjGBc6C28UPSap82Y4BsotVaQqklAIgj09e1KlPxyDO24sFuYV7iCclsVAFnmXK7z6061rZ6efp2IVkTwasJznGg4w1d1uym7WE8lUXHWlNPMXeMS580FhCtCvBJyANwQeD0WZxO19nqCZAHcGjyMb2r38HH1dnylPhw1nmZq3nwi5CHMjXmCh87PpsRaRrq2DSMDBzI1Zz5rkhfhxEWtvZ5x4bfSzac9HXxaIxOa/7n9pGo739b9SGtNEtPyFnB7yA3YnXbi1dEESv3xl/viwkUXbRoPht3BW+WbcOEiXB7CtKiJJKrjWhzfHyXPUkylrYYwedBfTnDyn8D8wSaMs+fhLCxGEqPHZ8E81HfdftX99e7dm6lTpwKQmZlJWloapaWl1NbWotFoOHPmDOnp6eTk5GA0Glm8eDELFy78Q+K4rKyMe++9l/nz5zNq1KgW64wYMYI9e/aQlZVFq1aXU8Dn5ORw8OBBPvzwQyQS95eXkJAQZs2addVjFflziOJYRERE5N+IRqqmu29HutPxd+sa7EaqbLX00nXixqBBAFTbajlvyUOviOCg8TiZ5nPcE3YzAVJfZFfEwDY5mwiVh/Bd3T4+aCpjkH9veus6Mz1vAXeF3MRzMY/iJ9PxUPhYZuYv8rRTSZSopUqSZHGMDR7JYeMphvj35h8B16GQyMmzFBGrjEIiXA6PkEm8F+YBBMn8yLUWMeH8U9waNMwr1tqFi1GBg3il5E2vNh9Vfcnc6McJkvnxROQ4vq7dwx7DQVYkzOGFwtdQS9QeYXyJH+oPMCPqAVqrE3m6YDGNDjNjgoezIPZJau111NoNVFtrONhwjIci7mzR2uycOY9NVdt4LvpRttV877Z7CxyCzWX3yrYXIg9kbPBIAG4KHMLii+c/NnQUj+c+j9VlA+Bo42manFba+6Ry1pzD2oqPGeDXkwaHkYPG4wTK/IlXeydJKbNW8uqFd5kUcSfrK7bwlP5hYpWRnDCdpdFh5kDDcXbX70clUTI9cgKTw+9hSEBfau0GohXhpGlbXbX/stPl5NvavTyR+zxGpwmdVMuqxOcZ6N/7qvr7T2D+YBOGBx8FkxkAZ0GRexuuWiBHRkYik8koLCwkIyODnj17UlJSwr59+/Dz86N9+/YoFAo2bNjA2LFj6du3L1lZWVRUVBAaGvqbfV8Sxrfddtuv1pFIJMycOZOFCxeybt06T3lmZiYdOnTwCGOR/zzilRcRERH5m3DalI1eGcFh4wmWlbzNspK32Ws4Qjtta76o/Z5EVQyZpmysTpuXMAbYYzjExPNP8X19Bt/U7WFW/stYXTZuCf4H6yo+8aQn7uvblRdjpjHAryejg4YwM+pBlBIlr1/4gO66DjweeS96ZQRzCpczNHMcg07dwzvlm2mwuy3g3FnZYpkR9QDToyZ6Muw9HzOFZwqWYHE2UWOr4zq/7l7nJxOkLXo2hytCONp4hvlFqzhsPMm22l3Myn+Z1xLmEn0xaciVSJESrYjgpeLVVNiqaXSaeLfiYw4aj2N2NqGSKLhgq0AhkZNvKW7xOislCrLMubxUvBo/mY40dQoJqmivmXOASlsNUkGKn1THqKBBHq9oh8vhEcaee2c+T4IqGpkgJU3TildK3mTFhbUsKPoXewwHcbm804mDOxmKgMD9YWOQIGBz2fi29kcqbNXsrt8PuDMiLij+F6fM5+jp24nhgf1p55P6lxKT5FmKmJw7xxNz3eBo5JHzc371ev0dMM6e5xHGHkxmd/lfoHfv3mRkZHjEcc+ePT3bvXq57QA3btzIHXfcgUQi4eabb2bz5s2/2++gQYNYv349JlPz8J0rufPOO9m/fz95eXm/WmfBggV07NiRyMjIPzc4katGFMciIiIifxOq7XVMz1vApPA7WRo/mxdjpjE18n72GX4my5zLvaE3Y3NZCZL5e7WzO+28U+79B9uJk2ONZ0hRJeDE6VlMFqOKZETgAJ6IHE93n46YnU2suPAu94bfTLmtmvcqPuX7+n0cbTwNuMXZ3MJXOWnKwuFy8FnNt9x+9nGWlLzFqyXvckfISNYlLSZK4Rayc2MexyW46KFLp59vN8A9Ox2niCLxilTVAGqJikCpP6tK13uVW5xNHDSewOq00eEX2d7uDrkJnVTrlXoaYFf9PqwuKz8YDhKn1GNz2dFK1S1e5zaaJNqok7C57Oyq388nNV8jFSQ0OszN6vrJdCyNn02kPJT7w9yzgL+0hwPwlfrgK/UhWBbA2ivSU7twMb9oFT8bT3nVD1eEMD1yInFKPRsrvyC/qRi7y04Hn9bsNRxq1v+xi/fjWlBmq2r2omJ0miizNnfZ+LvgLGxZuP9a+R+lV69eZGRkcPLkSdLS0ujRowf79u0jIyOD3r17c+LECbKzsxk8eDBxcXFs3LiRDRs2/G6/M2fOpHv37tx2223Y7fZfrSeTyZg+fTovv/yyp6xNmzYcP34cp9Pt6jJ79myOHTvmFQ8t8u9FFMciIiIifxPiVXokgsATeS/yQuE/sTibcLqcRCsjmBR+F7vq9tNKndjMHg7cMcK/xD0zCcMC+nvFlIYqguiia0e5rYrFJW8iE2RkmXMwOy100LbhpxYcM3ItheRaipiV97LHScKBg5eLXydMGYLZZeGl2CexOmx8W7uXWfmLkAlS1qcs5bGI+3i97EOmRI6jk7YtAAmqaJbFz2aP4WCL8bjBMn9Wlq6ju64jM6Ie4O6Qm1gWP5spUeMoaWHxYKQ8DJfLRYW1Co1MhVZQk3CFpduVRChCWZO8iGXxs3k8chzrU5bRx7cL48Nu9aonF2ToFeFMPP8UW2t2MC70Fp6MeoA8SxGjAgd51Z0Z9RDddB3Iu8Id5BImp5njjWexOLwF6U1BQ9BKNVicTTS5miizVWG0NzZ7iQCIVeqblV0tIbJAjyPHJVQSJSHyIJqcVrIacznccIIDhmOUW6t+pZf/LJKYlsf/a+V/lN69e7Nt2zYCAwORSqUEBgZSV1fHvn376NmzJxs2bGDevHnk5+es2Xv8AAAgAElEQVSTn5/PhQsXKCkpoaCg4Hf7Xr58Ob6+vkyYMKHFLweXGDduHN999x2Vle6Xk6SkJLp06cKzzz6Lw+H+XbNYLL/Zh8i1RRTHIiIiIn8TWqkTWJP0Et19OiARBL6o3YnT5SRNk4IEAY1UxcvFb/BlzS6vdjKJjAfC7/AqkyKlj29XNFIVc6Ifa3EWdXTQENqok5ALMixOK1JBSkFTCSnq+GZ1w+TBlFurvNwkAGwuO4WWC6y68B7js2dyyHiCN5MWoBDkDPbvQ7mtCj+pjqlREzjZeJYZURNZET+HJ6Me5L2KLXxQuZW7Q2709NfPtxtL42eTok7gscj7OGI8xarS9ZRbq0hVJxKqCGagf09G+g9ALsgYGTiQp/ST6OPbmWB5IPeHjSFYGsC9YTejkip/OQwPsaoobg8ZwSz9Qwzw74lO5sOwgOt4Wj+JZFUcfX27sjjuaZ4rcFt5HWs8TaQyjClR9zM35nHmRT/O2uTFPKN/hJUJz7O/4RgjTk8kXh1NK1WCl8CNkIeQZymiyl7rdQ4Bcj/SNCk4cZGiTuC1C+/Rz68bNwQOQCfVeup18WlHV107rhUJ6mgWxz+N/OJLiUKQsyz+WaLkoRxpOEmmOZtJ55/l5rMPc8uZhznccAKHy8He+sMsKFzFoqLVZBh+/o+KNZ8F80Dzi2dYo3aX/wXatWtHVVUVPXr08Crz8/MjODiYjRs3Mnr0aK82o0ePZuPGjQDs3LkTvV7v+dm3b5+nniAIrFu3jtLSUmbOnPmr56BQKHj88cepqLjshb1mzRqqq6tJSkqic+fODBo0yGt2WeTfi/B3ehPp0qWL65I3oIiIiMj/b1TbavnRcIhddfuIVerp69uVo42nWVzyJibn5U/+94aO5qU47z+2FmcT+w1H2Vz1FXJBzqiggbRTpxCibO6zDOByuai21YHg4ry5gAZHIwcajvFVzS7uDx/D4uI3PTGpw/yvY0HcDEqtFdx+9jEvf2CNRM2/Ep+nqKmM5wrdQrKTNo2Hw+/kxaKVFFrdVmtBsgCejZ6MXhHBAeMxHC4H6yo+pcZex/1ht9FKFU+wPIgTjWd4s3wjTU4rIwMH0lvXiV2G/Wyv/YGv26yl1lHPW6UbMLusPBR2B1/Ufs8n1V97zufxiPuYGjUBhaR56IPX9XI0UecwoJWoqbTVIEFKpDKUM6bzngV7+xuOedwslsQ9TX+/HkQoLy/E+qjyS6bmzQdAJ9UyIXQM3XQdsLns/Gg4RKQilO/r9jE4oA9f1exmbcor+LaQ5vlQwwkWFb3O0MC+fF+3jx4+6XTStaXKVkuQ3J9UdRJhiqDfHM+fxe6yc95cSLm1ighFCInqGPYZjnKo4QT/Knvf63kLlQexLG42R02nMTstvF32ES5c7hl3vy5XfQ5nzpyhdes/nsTkWrtViPz/RUvPmyAIR1wuV7OHWHSrEBEREfkb4HK5+KBiKy+XvOEp21D1OSsTnvcSKgAjf/FJH9yfxfv796C/f49m+37JSWMWm6q2scdwiHRtG+4JvYnr/LqjlqjQK8KxOK0siZ+N3WUnTBFEqjqJQLkfVqeNGVEPsKL0XWrtBvylvjwWeR8llgpcgotoRQRF1lLymorIsuR5hDFAtb2WvYbDdNS2YUnJW6glKp7ST+Ll4jfoqUtnd90BktSxrChd62mztWYH4Ypgeuk6kWH4GavLxp1ZUzyCtZdvJy9hDLCq9H1uDBrssXGzu+ycMeWQYykgVB7MqcZz/Gg4RHttKjaXjXPmPK7368F5cwFttcn08+uGn1TH8cYzHDKeQEDg7pCbOG8u4LOaHTwQdgcD/HsiESSe+xIqD2JK5Hj+eWEdr5a+S4IqmiejHuTH+kPcEjyMhUWrWJ00v0VhDNBV156VSc9zoamMXj6dsLnsBMn96efX7Xfv5dUiE2SkahJI1bjt+mps9fzcmIkNW7PnrcJWzaHGE6y4sNY91qjxvFz8Blurd/wlcfxnUd91uyiGRf4jiOJYRERE5G9AnqWIlb9YmHbBWkFBUwlvJ7/MW2UbUApKHooYS2eftKs+TqW1mtkFSznSeBKAHEsBBxqO8V7KUvr4daGHriMuaDFVcrQqAqVEwa1Bw9FIVZidFk6ZslAKCtI0rXBcTAsdo4zknDm3xTH21HUGwOy08FXNLtYlL2FR8WoeCL+DXRcdGq7k29ofmRR+F7P0D/Gz8ZRHGN8a9A+ilRE8rX8YqSBhR91PVFtrmaGfSKYpmwvWCtppWnHCdJbx52YyLOA6ymxVHDa6M5J9X59Bd11HgmT+rCnfRHddR3bWZaAWVNwYPJi5MY9zW/AN5FkK2Vz5Fa20ibTXprKgaCU6qZbuvh1JVscjF2SMDRnJC0WveRa55VqKeKHwn8zST2Kv4RDrkpfwc2MmLpeTaGUk0armLhwRihAiFCFXcUd/mxpbHefMecgEGW3UiWhkmhbrNTpNWJ1WZIKsmf+zUlB4QigqbNUUN5URqQij3tHQrJ9CSwl7DIc40XiWXr6d6alLJ0zR8tcLEZG/K6I4FhEREfkbYHXZsP3CHgygyWnjHwH9uN6vBwLC74YL/B7nzHkeYXyJImsp2eY8UjTxzSzifkmiKhaDw4jJacZf6kuptZJYpVvUXcqyNzpwCAqJnM9rdnq17eXbifNXpHbOthRQZqsk25KHBAmh8uahA63UCYTLQ4hTRfBB1TYA7gq5iSpbNVNyX/TUmx45kU7atkzOnevJ7He9Xw+6ajvgwEGyOo5ttd979X2g4RjToibwVe1uRgUOZlPll3Tz6YDVaUMhkWN1WtljOESyJp71FVtwupzcGjzM4+rQRduON5MWkmspbOb+UGqrpMxWSaxSz3f1P2FwGHn1wjv01HXihsDrqbBVEakIo4O2DZHK3/bMvVoyG8/xYuFr/NhwGAGBW4KGMiXy/maeywChskCiFOFsqPyccWG38G75ZceNRyPv5eOq7Z7twqYLRChCGB7Qn2prHUEKt3tKla2Wx3Kf57DR/Xx9ULmV8aG38GzMY83ShouI/J0RxbGIiIjI34BEVQx3hoxibcUnnjJfqQ8pF7OgKSWKa3KcAJkfL8fO4pwlj7Xln3icJy6J7jON5zloPM6P9YdorUlkSEBf2l1hpxZhl5An8yW7IZ9SayVDA/oSp9RTb2/guehH8ZFqsTltCMBD4XeytvxjHDi4JWgYbTUpPJ77vKevIf59yKg/wvL456iy1RChCCFZFUe2JR9wx/HeG3Yze+oPUmQN54aA63m/Ygs9dek8mjvXa1yrytYzL/oJr5TXu+r3M9S/X4tOHlciICAR3C8eATJfz0I1nUxLoiqGhcX/8tR9v/IzElRuFwylVMGQgL4cMpxo1qdKokQlKIlRRZJpzmZN+SZiLs4aT8p51lOvn29XXkuYR7CiebKSv8pn1Tv4scG9jseFi4+rv6aLrn2L4lgpVdJd1xFBEMgy5fB8zBQUgpwYZSQLilZR0FTiqdtL1wmVVMmb5Rtx4uLm4KGA+8XrqPE094XeTLg81OM1nW8p/tVshSIif0dEcSwiIiLyN0AukXNv6M2EyAP5pu5HYhWR3B4ygo4+ba7ZMY4ZT/N22UfsrM8gSRXL60kvMrtgKYmqGFqpEym3VvFh1ecez+TtdT+wqeorPmr1GnFqPeTtIeHT+6ga8RwZgoWJobextXYHcwqXE6OMxOy0UGmrYW704xjsZq7z7c4Qvz6U2SrJMRdy2nQelUSJ3eHger8ehMiD+KByK75yHW+UfchtQcOYEjWeOrsBuSAnShHGpPPPYnAYAUhRx/NRq1Vkms81G5vF2YRaompWXmWrQSlRkGspoqO2jZdfcGefNM6b87k1eBg/1B/gnpCbSNNczjyXqIpheeM7zfr8qnYXE8LGeDIFpmlTmBp5P8svuOsKCDwcfhfJqjj2GA6xy+B2MBgROMBrRhbcyVvOmnPoowjkrCmXY42Z2F0OOmhbk6ZJuepkHzW2On4wHGxWfqDhGPeEjm6hhdvFIkwRTHddRxTIiVKFUWGtZpB/b3IshQCMDRmF3eXkpaLVWF02Pq7a7hHHdpeduTGPs7b8Y3Kb3JZ27TWpmFrwjxYR+TsjimMRERGRvwmtNAkkqGIYE3wDCkF+TWcTS62VvFS8mr0G90zikcZTPJH7Iu8kLSJIHkCMKpL9hqN8WPG5V7sSaxknTGfd4jjrC6jLp9uHD9G67Y00hElRthtGO20qFdZqOmrbYHZZCJIFsLt+P88WLmF53HOsq/iEg8YTRCpCuTvkJtJ90ni3/CNeK13HiIAB7Kz7CYDN1dvZUvMtwbJAXo6dxUPnZ3s5Y5wz53Hekoev1AetREPjFfs6aFM9M75XkqpJ4nq/Hnxe8x3TIicw0L8XGYYj9PbtQrIqDqPTRIPdSA+fjqSqk0jVXp7hlEvktNEkNQvHaKtJ8UqhrZaqeDj8Lnro0iloKiFI5k+A1A8HDsqslYTIAjlHHgpBgdlpaXaO1bY6shpz2VT5OW9WbAJAJSj5qPXKq44v95Fo6aBNJdPk/SKRpkn5zXZaqZp46WXv4FBFEDP0D3Bj4GCONZ5hddn7XglYwq+IJ45RRLK7fr9HGAOcMJ0lo+FnIhRhRCivfUy1iMi/A1Eci4iIiPyNkEtkRCrDrnm/+ZYijzC+hMlppsJeQ19/tyuC0+XyhFlcif1idj0sNe5/nXZ0Jz9Bd1rO6YSOvFG1gUci7mZy7lycFxfljQu9hXtDbibfWsyjkfdx37kZXLBW8Hb5Rzwp82Nfw1EAaux1hMqDPILL7nJQZqtELpFjakFINjotlNmqeDZ6Mh9Wfs45cx69fTvzYPhYFIKcTtq2/NyYiUKQMzniHrr7dKC/X3fKbVWoJSrCFMFMi5oAQJPDSpY5F4fLQawqikC5f7Pj/SPgOt6r2EKZzR1n7C/1ZWzIyGb1tDINffy6kGSNZdWF93mv8hMEBFYnvojRYeKg8TgnGs+4nTcafr7cTqJBIkh4JHcOekU4byctYnbBEspsVbxbtpn0xDZIhD+fkkAhlXN78A3sNRymsMntGtJB25q+vl3dt9LRRKm1AqVE8bvPm1SQkqpNpMxWyQXrZS9elaDkzpBRAOSaCznSkMmhhuYhJkeNmQz07SWKY5H/GsQkICIiIiL/H6CSqNBKmjsVaCSXEyv4y3TcHDTUa3+gzJ9IeShHGk7hTBnhtc+cPITXDNsZHTSUpSVrPMIYYG3FJ+hkWhQo6OvblfdSljAhdAzjw26jjSbZc9x9DUcZGtDPK2NbmDwYu9PGjUHelnVaiYZEVQzLSt6m3FrFuNBbWBL/NGOCbkDiEhAEgVfjn2NTq3+ypfUbTIkcT6DCH7VURZxK38w1QSlV0N4nlXRd2xaFMbhn8z9tvZq3kl7ijcQFfN7mLdppW7nH77BwqOEEX9Xs5puaPWyt2sGe+kO8U/ERdpeD/n492FT1JVtrvuPluFl01LbhgfA7uDVoGP5SX7r5dOCVuFk8XfAKZ805fFf/E5Nz5/JM9GTAvVDS82JyFXTRtefd5Fd4PXE+a5JeYlXC87TVppBnKWJK7ov0PXk7QzLvZUPFF5xtzKHSWoPL5aKkqYxCywWcLqdXf+3UqbyTvIhnoyczWz+ZfybMxVfiS5OziSUla/iqbhc9dOnNziPdpy1HGk9SYC5ptu//kqlTp/Lqq696tocOHcrEiRM929OnT2fZMrd39/Lly1GpVNTX13v27969mxEjvH8nAPr378+lnA35+fkkJyfzzTffeNVfu3YtEomEEycuv0ykpaWRn58PgNFo5OGHHyYxMZH09HQ6d+7MW2+99atjyc/PR61Wk56eTuvWrenWrRvr1q3zqvPZZ5/Rvn17UlNTadeuHZ999hkAx48fp2PHjp56GzZsQKPRYLO5FwifPHmS9u3be8bWpctl+77Dhw/Tv39/AEwmE3fddRft2rUjLS2NPn36UFBQQMeOHenYsSPh4eFERUV5tq1Wd0KhLVu2IAgCZ8+e9RpPWlqa5zr7+fmRnp5OamoqM2bM8NQrLy9nxIgRdOjQgTZt2jB8+PBfvUZ/BnHmWEREROT/GJfLddWxpX+UVqp4Hom4m8Ulb3rKeujSaX9R6IHb+7aXrjMxyih21v1EK3UivX07k2k+xyHjSZ4IHUWr2z5Asm8F2C04uj6IqWkzUYowGhyNzY5ZZq1ksH9fDLYG9tQfwug0ESOP4KuaXbya8By76/dTbq3CT6Lj/ZRlZJqykQkywuTBnLcUcE/IaIJlgWyv3U2cSs9D4XciR05f3668WvouCkFOiCyIByPuwOw0MzV/AXeF3MjT+ocJkPsB0OS0csFajkKQE6UMB8Bgb6DBYSJYHvCHFjrGqqK80m+D+559Wv0NH1V9SYo6ng8rPydWGUV33WWR0UHbmiUlbkGzq34fMkGKQlCwKmEeQ/374iPVMvH808SpouigbcPPxlMYHEbq7O5FhfeF3vKn3UmMDhNFTaVoJCr0ynBSNYlei+FsTjtvlG7gi1q3k0it3cCM/IU8G/0opU0VBMkDWFW6HgcOxofeyu0hI0hWx9HoMLOh+nOWlqxBggS9Ipx7wkaTYymkn6srn9d8R8LFRaWZ5mx+qD+AgMCwgOtoo0niSMMpCppKiFVH/dqp/y6ntzWyd0U9hjIHvuFS+jzhR5sR2t9v+Cv06tWLzZs3M2XKFJxOJ1VVVRgMlxd0ZmRkeMTzhg0b6Nq1K1u2bGHcuHF/qP/i4mKGDh3K0qVLGTp0KLt37/bar9frWbBgAZs2bWrWduLEiSQkJJCdnY1EIqGyspJ33mke/34liYmJHD3q/iKTm5vLzTffjNPpZPz48Rw/fpwZM2awY8cO4uPjycvLY/DgwSQkJNCuXTsKCgpoaGhAp9ORkZFBamoqR48epVu3bmRkZNC7d2/PcSoqKti+fTvDhg3zOv6KFSsICwvj5Em3W0lWVhbh4eEcO3YMgHnz5uHj4+Mlbi9d2z59+rBx40bmzZvX4tj69u3Ltm3bMJvNpKenM3r0aHr37s2cOXMYPHgwTzzxBIDXy8ZfQRTHIiIiIv9H5JoLyWg4yvba3egV4dwcNJTuvh1/v+FVoJGpuS1oOMmqOHIsBYQrQkjXtvUIRoB4VTRf1u7i0+pv6KfrwhHjKarsNThcDr6v38dewyGWxD1Dv9s3oUKKxi+KufWRGBxGYpSRns/3AHJBRog8iHilnhxLATvrfyLXcjkW9YOqz3kl9ilGRvZDc+Eke+xllFjLqHM08HH1dv4RcB0ml4UbAwYxxL8vyepYqu11zM5fyoSwMaT7tMHuctDktFJtqyXRJ4ZQWTAfVG7lxsBBRCkjaHQ08lb5Jj6p+ppUVSJLEp6m0lbD0pI1nLPkMTygP1Mix5OojuXPUtB0gXmFK3gk4m6PAK601RCrjPTUsbvsgHuB3m3Bw4hShCMIAg1OE0eMp7jOrztP6Sexv+EoxdYypkZOIE4ZiQMnC2Nn0N+v+586p1xzIZuqvkQtUZHfVEyaJoW+um6YXWb8ZL7Eq/RU2WrYUv1Ns7YNdiNtNElMz1/oKVtd9gF+Mh0xighOm87xUvFqz77zTQV8XLWdHrqO2Jx2fKU+tFOn8Gn1N4wOHMIA354IggQ/qQ8HDcdx4kItbb5g8o9yelsj386rxW5x+y0bSh18O8+djvtqBXLv3r2ZOnUqAJmZmaSlpVFaWkptbS0ajYYzZ86Qnp5OTk4ORqORxYsXs3Dhwj8kjsvKyrj33nuZP38+o0aNarHOiBEj2LNnD1lZWbRqdfklNScnh4MHD/Lhhx8ikbg/8IeEhDBr1qw/PLaEhASWLVvG9OnTGT9+PEuWLOGZZ54hPt6dGj4+Pp6nn36axYsXs379erp27cqBAwcYNGgQR44cYfLkyWRkZHjE8aBBl7/iPPnkk8yfP7+ZOC4tLSU29vLv0pVj+jWMRiM//fQTu3btYtSoUb8qji+hVqvp2LEjJSUlnmMOGTLEs//SDPdfRQyrEBEREfk/oNFh4vOanczKX8Tu+v28X/kZ92XP4ITxbIv1a+31ZDZmc9aU08xT9xJOl5MqWw3mX3EHiFKFcUPQ9TweNY4xITeQrInz2v//2Dvv8Kiq9W3fe/okk957JdQEQTqCoAgoiEo5CirSQSmiVCmCUhSEgyigIiJNQRAVFURRQOHQO4RAeu91MpPpM98fAwNDAqLHcn5+c3t5Xcnae6299p4B3v2uZz2vVCRhRNAgFkdNJVoZwcO+3XATKdlfc/TaHNSUmCo4L6pine4w7xdtJd9YxMTM+cwKf4FYhd0izE/iwyvhL9BUGc9JzQU+K9tNE2V9Ky+FSI5nxkEk6+9HLkjwlXrzecV3vBg6jApTFcsKPqTflTHsrT6IyWYm31BMtCIcqSDBht2eTCV250DNMaZlv8GM8DEAXKpLZVjqVD4q2cGO8j0ICMyLmsQpzUXGZszmQt0V9FYDX1R8z5ycf6Ox1NWb269Ra65lWthomrvF83bMXJ4PfhqxIEJt0dJcad/0phAUPBPwOO/HLSBcFspJzQXeLvj42me/D5kgYUn+++yuOsB5bQqv5a3kP7WnMdus9PTuclupR0NYbBZ2Vf6ExlLHWwVrOVB9FC+xB0PTXqbP5ZGMTJvB95W/ICAQo6hv5RatCGd/zVG6eLZlStgoJoeOYGjgExysPk6RsYw8Q3G9PhfrrpKgjMVkMzM3YiLDgwYhFkRMz36T/eqj/FD9C1OyFxEsD8RH7EWpoeI3P+frHF5Z4wiMr2PW2zi8suY2PX6d0NBQJBIJubm5HDlyhI4dO9K+fXuOHj3KqVOnSEpKQiaTsXXrVgYPHkyXLl24evUqpaWlvzr20KFDmTBhAoMGDbrtOSKRiOnTp7N48WKn9uTkZFq2bOkIjH8vrVu3dkgVkpOTuffee52Ot2nThuTkZMCeRT9y5AharRaRSES3bt04cuQIQL3McceOHZHL5Rw4cMBpvBEjRrBkyRI6duzInDlzSEtL+9U5fvXVV/Tu3ZuEhAR8fX05c+bMHc+vqqoiLS2Nrl27AjB+/HhGjhxJ9+7dWbRoEYWFhXfsf7e4gmMXLly4+BvI0uex8SZPY4Bai5YL2vrBcboum6/K9/FS1kJGpc1kXfF2cvSFFBvL+bj4cwamvMD2st0syH2XXpeGMTxtBqdqL9Yb527wknjQxastbVRJvFO4kV2V+5yOK0Qyvqn4kdfy3iFDn83e6l/Q2wy8mPk6o4KeZHXsa7wRPY3unu2ps9RRZCyl0lJDK1VzQqQ3NmR19GhFD2ULOLoSgMCyHIw2E9HycIqMZZy8VsnOipWPSnawv+YoP1QfwmwzUWgqZXnBOlYWbuCtgrVcrLtKkanMIZEw2ky08Uhkf439H/fng5/h68ofqTRX13ux+EV9gkJDSYPPotRYQY25oSpwhSwv+AiDzcjEjNeYnLWADH0uK2NfpchQyouhz7Eu/k08JfbAfUHeKiLkwTzt34918W8QKPFDKVJwUXsVjbUOd5EbHTxaES0P55Oyryk0Fjus0+6WKnMNYGNrmd1tZHjQQF7JeYt8oz2ovarL5NXcFeQZixgS0M9J491a1YJAqT8PendCJXZjecE63i5cz4HqYwwJ7Ee1RU1wA9X74hRR+Ei8MNmMdPdqzyntRZoo4wiU+vFzzXEOq09xryqROHkEXhIP5JLf79WtLm5Ye3279rulc+fOHDlyxBEcd+zY0fF7p06dANi2bRtPPfUUIpGI/v37s2PHjl8dt0ePHmzevJm6uju/eA0ZMoRjx46RlZV123MWLVrEPffcQ2ho6G3PaYjrVQ2v/3yrdOvmtuvP4cSJE7Rt25a4uDjS09MpKytDo9EQGxvr1HfOnDksXLjQqe2ee+4hMzOTadOmUVlZSdu2bUlJSbnjHLdu3cpTTz0FwFNPPcXWrVsbPO/QoUMkJSURHBxM3759CQ62r3j16tWLzMxMRo8ezZUrV2jVqhVlZWV38XTujEtW4cKFCxd/Cw1rjG3CLdkxm5kzmmTm5C53tL2RvwYPsRt5hiLeK/6ENqpEvq8+xN6qnwEoNpVxSnORPc3Xk6CM+V2za6KIYWTwIFYXbXG0xSoiiZVHMC3rTQBy9AU0ulakxGAzMitnGQCD/B6m2L2cz8q/pZEymk6erbmiS6eP7wN4iN1JUMaQqGyMl8EM15wYYk5tp1G/mSS5N+Fk7fl68zmtuchR9VmGBQ0k/CYpyHXCZMHUWDTMCn+BLyu+J0gWQJQ8jDJTJfeomrIwbxUD/R+u108lcqtXva3IUMb2it2sL96On8SHWREv0NWrnUP/e1JzniRVE4ecYnbEeI6qz/JixgKS3BsjEsRgE3gt9x0EQWBh5BQ0Fi1XjVm4iRS09UhkSuhoNFYtA/x6EyYL4rjmPG09kmiqjKPMVIm6gdLMd0Ilcsdf4oPxWpVFpUhRzzYu31hMui6HlYUbmBQ6DLPNjJ/Eh0bKGCqMVYgR89217xDYNwT+UHWIzp5t6OHVidFBT/FhyTbAXqBlatgo9BY9TZSxFBpLiZGHc157hYF+vREEESJBRKouiyxDPme1l5nl88Jvuqeb8QwWoy6qHwh7BosbOPvuuZ4xvXjxIi1atCAiIoLly5fj6enJiBEjuHDhAmlpaTz00EMAGI1GYmNjGT9+/B3HnT59Olu2bGHQoEHs2rULiaThcEsikTBlyhSWLFniaGvWrBnnz5/HarUiEomYPXs2s2fPRqVS/aZ7O3v2LE2bNgWgefPmjmz4dc6cOUOzZnYf9Q4dOnDy5EkOHz5Mx44dAbsmetu2bY6XhJt54IEHmDt3LseOOZd8V6lU9O/fn/79+yMSidizZ49jDrdSUVHB/v37uXTpEoIgYLFYEASBpUuX1jv3uuY4NTWV++67jyeeeMKxidDX15chQ4YwZMgQh1RlwIABv+lZ3cPp9sAAACAASURBVIorc+zChYt/JCl1GXxbsZ+fa45ToG84M/h3EqOI4LlA57/AY2URNFbE8kPVIU7VXkRt1qA2axq0x9pc+iXZ+nzAXrHsh6pDTsd1Vj2pdbfPRv0aSomSZwP6807sqwz2f5S5ERNYE/c6ZzSXHXZvRzVn6eLVDg/xDc2nm0jJI77deTX33yTXpfJVxQ+sKFhPB1VrPirZzmdlu/GReLG14hsmV25iz6MzUScOoqjVQAxWA728uzA0sH+9qnYhskCCpQEcqDnKZU0as8PHO3yNfSSevBE1FT+xF3mGQlJ0GfxSc4JHfR9EIcg5XXuRRPfGpOtz6rkpzI2cSKTCOSO3s+I7luZ/QLm5iqv6TIalTeO89kYGLNdQhJ/Em6lho1kd+xpyQUYLt0aMDP4X7TxaMj93JZd1qbwdO5fBfn3R2wzMy13J+8Wf8O/Cj5icuZBIeSgKQY5SJOedoo0crz3HjvI9rCrajI/EC2+x12/6vBRiOfd6JPKQt335W3TLP+/NlPGMCXoKX4kX5eZKlhV8yBVdJvmGIp5JnUyWIZdz2vpZvhOa86TqMqm2qJkePobPm6zm3dj5rIp9jQCpL929O1JsKmdK9mJGpM/gg+JPMdhMHFKfYnnBOkqM5dzj3ozXI18i4L/w7b7vRS8kCufvhEQhcN+Lv+053Urnzp359ttv8fX1RSwW4+vrS3V1NUePHqVjx45s3bqV+fPnk52dTXZ2NoWFhRQUFJCTk/OrY69YsQJPT09GjhzplMW9lWHDhvHjjz86Mp7x8fG0adOGOXPmYLHY/6zp9fo7jnEr2dnZTJ06lYkTJwIwdepU3njjDYcbRnZ2NosXL2bKlCkAeHh4EBERwYYNGxzBcceOHXn77bcbDI4BZs+e7RTI/uc//6Gqyq4DNxqNXL582UmDfCuff/45Q4cOJScnh+zsbPLy8oiJieHw4cO37ZOQkMArr7zieJnYv3+/IztfW1tLRkYGkZGRd/OI7ogrc+zChYt/HL/UnOCFjLlUmdUICAwN7M/wwIH1NLZ/J+5iJY/59iBA6sueygPEK6Lo4NmaJ69OdGT/RgQO4sWw4QRI6wcVflIftNe0srZr493qGGHGzMHqYyQoY36Xd3KEIoQIRQgDbsq4VpqqESHCipUQaQA5+kLWxi0mQ5+DTYCmyjgmZMx3GqfYVEaluQa5IOON6GmMTZ9NzbXM6I6K71je7RW2l37D8exNADRzi2da2GiWXnPWeMS7O82UjUiuS6PGXItZsNBIFsOGRm9dq4CnwAaEy0OIMUXgLfak2qLm3cKNTA0bjb/Ul+6yDkzOWsh9nm3o6tkWG/bCIe08nDdAlpuq6sldbNg4o7lEWw971q2TRyt+qjnK6qLNfBC3iEV5qzHY7LZUPhJPxgQPxmA18VXFD4wPfpa5eSucbO5KTOWk6NJIdGvCq7krnK5Vaa7GU6wiW5+LFSudPe+9ayeTZm7xTAkbTYIilrPaZEemd3LocDL1uWwt/4YTteeZF/Eiq4o20d2rAzOy7UGG0WbCX+pTb8xW7s2pNtfiI/HCTayko2drp+Nai463C9eTqrO/iOltBt4r/sS+odCzHT19utDc/c6FR+7q3q5tuvsj3SoAEhMTKS8vZ8iQIU5tGo0Gf39/tm3bxnfffefU54knnmDbtm20b9+en376ifDwG0VTbpZcCILAxo0b6du3L9OnT6dPnz4NzkEmkzFp0iSH4wLAunXrmDZtGvHx8fj6+qJUKp2yyw2RkZFBq1at0Ov1eHh4MHHiRIYPHw7YJQ9Llizh0UcfxWQyIZVKWbp0qZOFW+fOndm1axcREXZNeseOHZk1a9Ztg+NHHnmEgIAbcpuMjAyef/55bDYbVquVPn363DGDu3XrVmbOnOnUNmDAAD799NM7bj4cN24cy5YtIysri9OnTzNhwgQkEglWq5VRo0bRtm3bOz6nu0H4LW8ifzZt2rSxXfcGdOHChYvfQ4WxioFXxpOqd86afhS/hDYeLfBvIND8X6DUWE6fyyOdiiwA7GyyBgGB4WnTHQGlGDGLoqaQqc9jbclWxgYNQSGWs7LwY0e/Jso4Xgh+hpezFxIiDWRjwjIau9l1g9WmWtL12ZhsZuIUEQTe4v97J/QWA/trjrKmcDOP+/dkcd4aDDYjIkSMDR7MIL+H6Xn5OYc/rwgR7TxaMjFkKGqzlkJTMQvyVjmNGSMP5zHfh3i76Mb8J4QMJUwWhErsjlIk5/mMuZiuuT8AvB75Em8VrHW8EIgRsybudTzE7qTqsjDYjHhLPNFY6vCWePJUQF8uaVNJ02WjFMlpqoxv0Fas1qzlqasT62VRV8TM4V8B9uDmkjaVR5JHkOTemCCpP3urf3E6d3jQQMJlIbxXtIV3YuexoWQnP9Q4Z/anhI6ilao5o9JnOumgw2XBTA8by8vZC+nhfR9zIyYQrQjnt6I2a7DYLKTrs3mv6BO+r75xfYUg543oaaTUpbP2mkwiyb0J/Xwe5Kouix0VewB7+eyn/B8lQRlDD5/ODV4nWZtGz+Sh9dpnhb/AAN/eBCtuX/gjJSXltkvuLlz80TT0fRME4bTNZmtz67muzLELFy7+URQYi+sFxtfbF11exetRL9NYGctF7RVqLLXEK6Np4daIs5rLXNVlIRfJSHJrQlP3+u4KfyY1Fk29wBigxFRBlDyMF0KeQWOpQ2fVEyj1Y03xFuZFTCJaEUatpY6L2ivMDB9HsbH8WlCoJcOQg81m147uKN/DnMgJFBpKmJWzjH3V9qXLRopo1jZafNfaZIVYziO+3Wju1ogBKc87MqZWrLxX/Ak9vDszPHAgH5Z8RrwiiqcDH+PHqiOsKFzPhJChmIzmemMabCbcbrH5uqBN4UGvTrxV8CFJ7o2dAmOAjaVf0N2rI19X/giABQsX6q7QXNmIo7VnsWLlUM1J9DYDwdIA7vdsRwv3BFr8ShbTQ+LOjPBxPH31JUe2N1QWxL2qRMc5eqsBCxa8JZ4Umepv/qk1a0m1ZNLGI4llBR8yMfQ5R3AsuvZfhCKEHeV7eNK/LxtLd+IpVjEx9DkydbnsrjrAlLBRHFWfpdBY0mBwXGwso8RYjo/Eq54spNRYzuW6dEw2MyqRGz9UOy9T620GFCKFk2vFBe0VEt0ac6+qBd29O2DDrsf2EqtopWre4LOy2CycqD1LM2U8l3XpTscCpf53DIxduPhfxhUcu3Dh4h+FUqSgmbIRl3XONkICAhGKUIalTWVh1BRmZtu1ct5iD1bGzmNsxmxHBi9EGsDHCW85KqH9FQRK/LjHvWm9jGW0PAwBCJT64SP2wkfqxQdFn5JrKMSChSCpPxe0h2ipasqivNV4iT3QWutQihSMDR7i0Acfrz2HyWrmSO0ZR2AMkKbPZkvpLuZHTvpNZYr1VmODgWG5qYrH/B4iWBaIj8STKVmLsWFfoRyWNo1NjZYjF2SOoBpgeOBANtwiZbjfqwMj0mZgsBkb/BxkgsThI3wdpUhBiamC/6hPobHecAlQWzQYbA3b3zVEJ8/WfNfsY8rMldSYa4lShDps6gAi5aFEycM4WXuBqeGjnfTIAG08EvmoeDsD/R/mjar3yDUUMi10NBKRBIvNgrvYDTFiioylWGwWXg4bSbDUnwV5qxyZ8O+rf2Fy6HBUovqygRO153kpcyHtPJKIlIfRTpVEB89WiAUxl+vSGJX2CjmGAvuKQ9AgXg2fyGv57ziN4Sl2J1oeRhtVEqeuOYP8UH2Izh5t6O7ZAQ+J+6/KOXRWPdvKdzMmeDCzc5ajvfbMH/ftyb23Cahd/HdcvHiRZ5991qlNLpdz/Pjxv2lG/0xcwbELFy7+UYTLQ3gxdBiv5q5AhMDggH74SuwlhPv6PMC9qhbIBCmDAx7ls7LdjA5+io2lO52WtotMZfxSc+IvDY69pB4siZ7JhIx5pOmzUYncWBQ9Fakg4bW8dzistkvO4hRRvBI+js2lX3JUfZZEt8Y0douj2qRmYeQUTtSeI0gWgL/Uh1VFmxzjP+LbHalIwmnNRcJlwWitumv2X3BIfQKtRYeH5O71m4EyX5q5xXO57kbGUEAgUh6Km1jJjrI9JLk3cQTG1zlQfZTVca+zvXw35aZKHvfryT1uzThWe46Ca7ZjD/t0I0oWhqdExRN+PYmUhzI5dDifln1NqcnulTs6eDCzct5yjOspVpGgjEEqSBya7es8E/A4kfK7r8xmtJg4oD7G0vwPsGJFIZLzbux8HvHtdu3e/Vgbv5h1JdsxWIyMC36aLyr2IhdkjAsegtVmo5WqOUvz7ZpprUWHUqzg3aKNVF2rfqcQ5KyIncPzGXMJ0wQxNmRIPc349vLdDA3o79RWZCxjTvZyhgcNZH3JDraX76GNKom5ogk0d2vE0ry15BjsBRJs2Fhfsp3XIyczImgQ60vsetgOHq1o6hZPkMyfeZETOaNJptaiJVwWTEtVEzylzq4IZcYK0vTZgECQ1I9YRSTntSlk6nMZGzyELH0u44OfwUfqhZtISZgsiFjlf78pykV9EhMTHRXnXPx5uIJjFy5c/KNQihV4iT0Y6NebBLdYpme96chSDvZ/lHxjMf9Wf0SCMoYXQ4cRIg1sUM5wPVD7K2nhnsAXTd+j0FiCh1hFlCKMj4s/dwTGABn6HPZVH2aA38NMynqNZdGv8FruOyQoY3AXKTFYTfTx7c731YfRWnQICDzkfR8qkZJKYzXtVa3QWQ2oxG4ESv1YXbiZ7p4dUIndftNcfSReLI+ZzQvpc8ky5KMSuTE1bDRX9Jk0VcTyUthwdFYDcpGMryt/pMZSS5gsmGqrmg2ln9NMGU9L9yaEyoIYkzGLxm6xvBQ6AkEQuKRJxVPizgD/3qwq3ITRZsJd5MZrkZM5r0khwS2GxvIYVse9zuGaU3hJVCS6NUYiSGmvSmJHk1UUGcsQI8Jqs9LULf43ZcXPaVN486ZqcHqrgZnZS2mkiHZs6mzhnsDiqCnMyl7Gz+rjLImazv6aY+htBhbk3tBUN1JEEyYL4qou0xEYg13a8E3lT8yLmITJZqbSXF1vHhabtV4hiGJjKT19urA4bw0R8hB6enfhUl0qc3KW8178Ag7X1t+3U2qqJE4RyZKoGfhJvWmpakbQNZ15a1ULGili0Frt5bQlgnNYUGgo5dvK/RSZSsjS5fOYXw9KTBV8UvoVX1f+hBUr4bIQBgc8ytzcfzMu+Gm6eXe462ftwsX/Iq7g2IULF/8oMrTZeIpVdPfqxMTMeU7L91vLv2F62BgOqU+SqsuinaolxcYyevl04Youw2mctqqWVJvUeEs9/9L5+0q9nSqjXapLrXfOWe1lWrjZtbNuYiU+Yi+HW4CX2AOZSEamPpdJoc8BAsdqz7Iofw0eYhXjM+cB0EaVSHtVS5bHzMJT7HHXjgg3k+TehC+avs/PNce5qsvkg+JPKTKVMTt8PFd1GXxZsQ9/qY/9JUQWxGH1ScSIae6VwObSL8kxFPB2zByKTWUU15Txc82NpeHH/B5iZcEGhyxEa61jYd4qng8Zwqu5K3ATKXkndh7tVC2pMFcRr4giSObPpbo0Zucs46ouEy+xBy+GDqOV6Lct8Rc28GJUYa6i2FRGI6IB+4a3i9qr9PS5jx0Ve6ixaLlcl8Y57WVmho+jylyDm0hJY2UMSpGCQlP9F7AiYymNlbFYbVaqLWrcRErqrDeqG44PebaeU4mvxBsxYl4KG0mqLpPLdek86NUJT4kKrVlHG1Uih9Qnnfq4i5XkGop4NXJig/frIXHHA+dVg0pTNZn6XOosOvZVHyLfWMxzgQNYmLeaYlMZbVRJzIp4gcV5a8g3FgFwsMWnhMtDkYpcoYWL/9u4vsEuXLj4x5BdV0CaIZd8YxFuImWDmtibg+XkulRau7egm2d7ai1atpZ9g5tIyfiQZ9lffQR/qQ9dvP57W6D/hrYeiWwr/8bxu1SQ0Nv7fnZX7CdSFsqVukzejZvPOU0yMpGMKEUYNWY157SXOae97Og3JugpVhdtQoSIVyKe52xtMh+X7qSFWyMmh4743fNTW2qZlv2GY8NcU2Ucybo0vqr4AbDblr2e9y6vhD/PJ2W7APumtLmRE3g9910Cpf71NMgCAl4SD0dgfJ1qi5o6qx4bNrTWOkpMZWTU5ZDgHsu8vLd50Ksz28q/5aouE4AaSy0L8lYRr4wiTHGjcMjVuiwO1hzlqi6TDh6taO3egni3G36sofIgh13ddUKkAQRKb7h6HFaf5GjtWU7VXmRexCTKTRWMD32Wt/LX8mb++wRIfBkWNJCDNccpMZXT/hZ/ZYBHfR4kVZ/FltJdaC11vBE9jf+oT1FgKKGT57109WxXr0+UIoy2Hkm8nLXIsbpxRZdBR49W3OPWjAe9O5Gmy6b42nd/sP+jFBpK6O7dsB1XQxQZStlU+iVp+iwS3RpzpPYM08LGsDhvDRYsyAUZTZSxiBBYHDWVjaU7SalLY3LY8Lu+hgsX/8u4gmMXLlz8Y8g3FTIxcz7jQoZwVnuZxspYR6AE9qDr5mpo93u1J0YewVntZbqo2tJWlUSdVceqws1kGfLo4Fk/oPmrud+zPY/5PsSuyn086d+XWEU4xcZyHvbrTrQ8nJHpM9hdtZ9XI15kb9VBFuevYXPCclq4JXCpLpW2qiR6eN9Hontjvq8+RB/f7uyu2M+5OvsmshOaC4zLmMPquNdJqUunl09X4pXOxv2lxgrSdXbrt3hlNGE3eSaLEBMpDyNDby+K0NmzDXurfmZU0JO4i5WUGCs4o0kmThHJmrjXUYncUFu01Fn1fNfsY0KlgbwTO4/kulQkIglflH9PH99umKxGJILYYQkH4C32dNKGu4vdiFVG8mrOCgw2I4/79iT5lky7DRv5hhuZ4Dx9IePSZzscTT4r382YoME86d+HMHkwHhJ3misbMy9yEm/kv4feasBP4sOCqCnUWXTkGYqQCBLSdNkODa+nWMWIoEEsyl3D1PBR5OgLqLKoSalL4+nAJxhy9UXqrHqmho1mW9k3GG0m+vv1xlOiYkb2UsdLwIuZrzMmaDDIBMxWC3KRlFvJ1RdQbqqsJ/s5WnuWbl4d+Lr8R16PfAm1VYNK5Ia32AuttY4YWRhWm/Wu5CUnNReQi2R8V/UzvX3uB8BkM2HB4ni5+qh4O1vKvkIiiBka2J9mynhKjOUOuYYLF/+XcVXIc+HCxT+GbEMBdVYdNpuN3ZX7GRLQj2i53QZLJXJjTsQEvqrYB0BHj1a0USUhFkQ0VsSyvnQHz2fMZUrWYrIMeQCEyX574Yw/mhB5IFPDRrGj8Wo0Fi1v5L/Px6WfMy/3bXaU76GTx71k6HPRWDR8Vr4bAYF8QwkvhDzDyphXGRP0FAWGIt7KX0s/3x40VcYhEUl5xKcb/hJ70Qe1RUO6LoddlfsYnjqNQsONioLnNZeZkb2EQVcnMCR1Mv0ujyZZm0qxoYzvKn9mecE62qgSWR49izBZMGarmTHBg/mi4ntWFm7gWO1ZZoaPpcBQQkpdBiuLNjAhcx7Ts9/gg+Kt7Kk5yAsZr/JO0UbeKdzAhJBnqTPrWZC/ihdDhyMXZAC4i9yYGPocO8rtBRkG+j2M2WbGYDM6ss45hgKCpPWDM3+JL7UWDQAX6q7Us/rbWLqTcnMln5d/R2pdJl5SFU/59WVrwjt8GP8Ga+JeZ1XhJvqmjOTh5OFUmWo4o00G4IWQZ5CKJCzIe5cnA/qwOG81+cZiAiW+PBc4gFqzhnnhk/ARe7Kp9AtmhI+jp3cXjqpPEyoLZnPCMjbEL2VlzKusin2N/6hP0dPnPk5pznNrifF0XTaDrkwkw5Bb7x4FBKLl4fTx685B9TEqTdWsLtrMubpkfMQepOqzOaY+S6GhvrwDoNai4aeq/zAzaympuiyMNiNtVImoRG4si5rFvaoWTAgZytMB/fim8ifyrkkpzDYL60t2YMVKlj7vzl/m/zFeeukl3n77bcfvvXr1YtSoUY7fp0yZwr///W/AXu1OoVBQU1PjOH7w4EH69u1bb9xu3bpxvWZDdnY2jRo14vvvv3c6f8OGDYhEIi5cuFH9skWLFo4KdhqNhueff564uDhatWrFvffey4cffnjbe2loLsOGDePzzz93zKlx48a0bNmSzp07c/XqVQC+/fZbWrVqRcuWLWnWrBkffPABixYt4p577uGee+5BLBY7fn7nnRuOJy1btmTw4MF3db22bds6bSJcv349iYmJJCUl0aJFC3bt2nXb+/q7cGWOXbhw8Y/BXWTfVCYIAjJBxuK8NTzu15PH/HoQp4ikiSKWOouOR3y6cUWXwTOpLwEwJWwUL4Q+zZXMDMpMlQAMCxxAonuTv2zuWfo8rtRlImCvdBapuOGuUGaqJEWXzu6qA059vq3az5SwUaTpsrBh9xqeFPIcG0t3OjTUCcoYXg4dyaayL4mVR/C4f09Oai6QpsvmCb9eaK11fFr2NYJg17MeqjvJFV0mofIg8g3F/Fxzgh9uKiBRbCrj7YKP6e17P5MyX3O076z4jiXRM7HarMzKecshs8g25PNq7gr6+j6AxWbltOaSo0+0Iow5OcsdmVOzzcKc3H8zKuhJMvV57Cjfw7iQIfhLfOnk0Rq1uZbFUVMRENBZdRxSn6Kl+w1T/49KtvNa5GSmZ7/puP6//PtwvPYcFiz08+uB3npDvnEdk82M3mrEU6zi07JveDVyIiqpO+2kSZyuvUS/lNGOc6vMNawu3EQjRTTp8hwqTTUcqDkGwAfFW+nv14t7VYkcqDlKnqGYrl5tsQpW2nq05DG/h+yacKSMCP4Xq4s2obcZGODXmyJDGQfVx5gWPoZCQwkjgv+Fl8jDaZ4Ha06QbywiR1/AvapETmsuOo4N8OtNnrHIUVUQ4JXw53m78GMWRr3MnsqDqMTuPO77EHqrHqPNRLAsAG+JXVO/p/JnXs5aCMCM8LH4i31ppIxmd9UB5CIZO3P3AvBW9CtsLvuq3jMsMJb86X9eMvZ/wpmNs9GW5eIeEEnr5xYR98DTv3u8Tp06sWPHDiZPnozVaqW8vBy1+samySNHjjiC561bt9K2bVu+/PJLhg0bdlfj5+fn06tXL5YvX06vXr04ePCg0/Hw8HAWLVrEZ599Vq/vqFGjiI2NJS0tDZFIRFlZGevXr//d9wrwySef0KZNG9auXcu0adPYuXMnY8aM4cSJE4SHh2MwGMjOzqZx48bMnj0bAJVKVc8dIyUlBavVyi+//IJWq8XdvWGXm+vX+/jjj5k2bRr79u0jPz+fRYsWcebMGby8vNBoNI6y2f9L/GGZY0EQxIIgnBUE4dtrv8cIgnBcEIQ0QRA+E4Rrr/8uXLhw8SeR6NaYJopY1hfvYFr4aLp6teOi9goeIncEm0C4PAQrVlYUrue7qp8BCJYGoLPoCZYGsqfZerY3WcXuZuuZFTEev5s2xv2ZpNSl89jlsYxKn8nI9Jn0T3meq3V2OUihoYTnM+ZSaa5psK8NG2NCBvOL+jjdPTugsdY5bS5M1WVxqS6VQKkf0cpwxqTP4kDNMdL02XxYsg2Lzcpg/0dRiVScuhZsWW12rW2evpAyc2W9axqtJnaWO5fUNdssJGtT8RKr6hXsyDcWEyT1J+WWQhFWrE6yCbA7Q7RWNSdQ6keuoZArdRnc79UOqUjCt9UHeTP/fQ6qjxEhD2V88LPc496UEKm92ITaouGDok9ZG/8Gi6KmsiJmNgaLkY9Kt/Nu4UZqzVqaKGPxlTh/rr19uqIUKdBZdXxT+ZOTc0ShsYRb+apqH718utLNq73T5rcKcxUflmzjP+pT7Ks+zIayzzmvTWFTyZcsyHuX1UWbcRMp6OR1L5MzF/Cf2tOc1lxiVs4ywuRB5BoK+b7qF2osGkakzWBvzc98VvYt5zR27Xi2IR+wf18mhDzL8ujZDPR7hIVRL9PZ8158JF486XejRPGO8j085H0fOfoCHvbtxgVtCpXmah5LGcuDl57hyZRJXNBcodxUyVv5Hzj6Has9h1wkY1fFj0TIQ9h6TQpitJn4ofoQSW71LQ7DZMHEKaLqtf9RZOz/hCPvjEFbmgM2G9rSHI68M4aM/Z/87jE7d+7MkSNHAEhOTqZFixZ4eHhQVVWFwWAgJSWFVq1akZGRgUajYeHChWzduvWuxi4uLqZnz54sXLiQfv36NXhO3759SU5OdmRxHfeakcGJEydYuHChw60kICDgjiWVfwtdu3YlPT2d2tpazGYzfn5+gN0vuXHjX7ev/PTTT3n22Wfp2bMnX3/99a+e37FjRwoK7PaCpaWleHh4oFLZ7QJVKhUxMXdXgOiv5I/MHL8IpADXt3YvAVbYbLZtgiC8D4wE3rtdZxcuXLj4b4l3i+KDRos5qj5DrqGQEYGDCJT4oUdPnDIKD7GKYUEDiZCHsrvyAOHyIMLlITRWxBCnjEQsiAmV//VSiu3le6gwVzl+LzKV8V3VzzR2iyXbkE+NpRZ/iQ8JimhS9dmO85oo44iWh7Mg7100ljo+b7KaN/Pfrzf+WU0yXTzbYrVZ0Vn1Tse+qviBdY3eZGvZ1+isekKlgYTL7ZvXFGIFftekFzfTxastF+uu1ms3YyFUHlyv3VOsQm3R0swt3smWDux+v/qbCnR4iT3I1RfyScIKlGKF/eXFqueZqy9xvu4KANll+RxRn2FY4AB6+nRhW5N3Oaw+RYGhmF7eXTlUe4JlBeucriMWxAgCNHdP4L24BXxSuos0fRZdvNrSzasD5cYKysxVNFJG4S5SOvqFNiCtiZaHoxQUtHZvQZ6hqJ7+10/q45Bx7KzYy6igJ5mft5JLdan8oj5Jqam8nv/z9vLdDPR/hMPqk7T1SMKGjWUFH9LNqwPzc1fyfvxCHvTqRLoum57e95FtyMdgMdLeIwmj1cTM7KUYrkkhng14gs1lX6K11OEuUiIXyakxqxkR9C+mZb/py1bxfAAAIABJREFU2Gh4SXeVqdlvsDJmLsabXmh+rjlOW1USzdzinTL9YC9O8lb0LF7PW+mwphsWOICunu1wFyv5szizcTYWQ51Tm8VQx5mNs3939jg0NBSJREJubi5HjhxxBHFHjx7Fy8uLpKQkZDIZW7duZfDgwXTp0oWrV69SWlpKYGDgHcceOnQoCxcuZNCgQbc9RyQSMX36dBYvXszGjRsd7cnJybRs2bKejd8fxTfffENiYiK+vr7069ePqKgoHnzwQfr27cvgwYN/9bqfffYZ+/bt4+rVq6xataqevOJW9u7dy+OPPw7Y5RhBQUHExMTw4IMP0r9/fx599NE/7N7+KP6Q4FgQhHCgD7AIeFmwewI9AAy5dspGYD6u4NjF/zJmA1SkgkgKvvEgdqmO/i8Sr4yqt6HsZvylPjwZ0Ife3l0pNVWgkrgRIrvzP3R/JlablQvaK8QropgaNgq5SE65qZJjtfalTB+xF0uiZrCqaBNP+PUkU5/Hac1FOnu2oZ9vD36oPsQTfj3xlfgwPmMeQwL61bPyaufREglivKQe9a6vFCmw2WyUmSp50r8v0Yownr76EmviX6eZWzwna8/zpH9fdpTvIUERw5TwkZyovYBMkPJ65EtsK/uGy7p0RIh41OcBGitjeCl0BCsK7UvAIkTMjZiIzqpHZ9TTVpXEyWsV2VK0GSyIepkFee+itmjwkXjxWuRk5ueupMxcySvhzyMIAil16SS4xdLTpytGmxGlSMGeqoPUWDRsK/uGGeHjHJ/59rLdSAVpvaB7UugwVGL78m+luYbng5/GjBmdVU+RoRQjZnaWf8dbMbNQ3hTkNXGL4/24RaTo0qmz6Nhf8x+WRr/Ce8Vb+KpyH69FTuak5gLqa8Fwc7cEai0aR/Y8WBaIh+RGUY2fa45xryqp3uegECmoMFXR0r2pY9WgxFjBoz4P0tq9BeuLtvN69Mv08+1BrUXDtrJv6e1zPwGCiHxDMZNCh/FWwVpOaS7S1asdYsQ85vcQgVJ/Pin7iqf8H0Umljo5cIDdseWMNpl5EZOYlHVDJiMVJBQZy7jfy9kxw2yzUGQs5btmH5NtKMBTrKKRMhq3PzEwBtCW1ddZ36n9brmePT5y5Agvv/wyBQUFHDlyBC8vLzp1sjt8bNu2jS+//BKRSET//v3ZsWMH48ePv+O4PXr0YPPmzQwbNgw3t9t7iA8ZMoRFixaRlVW/5P11Fi1axI4dOygtLaWwsLDBc25nxXhz+9NPP41SqSQ6Opp3330XgHXr1nHx4kV+/PFHli1bxr59+9iwYcNt53Ly5EkCAgKIiooiPDycESNGUFVVhY9P/Zfop59+Gq1Wi8Vi4cyZMwCIxWL27t3LyZMn+emnn3jppZc4ffo08+fPv+01/w7+qH/93wamA9f/5vUDqm02x6toPnD35YlcuPirKU2B85vg7EZQ+kDnqRDfGzxD/u6ZufiT8JJ6NBgs/pVYbBay9flMCR1Bii6TadlvUmvR0tWzLeNDhjr8b2fmLEVn1bO0YC0Jyhge8OrE0ID+9E4ZhkywBzzXq6uFyALp5d2F76sPESD1pbf3/dRY1Kwv+ZzJocOJkIU4NlMBTAsfQ7mpCqkg4WjtGT4r/xaAlzIX8nWztfTy7kojRRR9fbpjxcbY9NlOQedb0a9wsOYozwb0J0oeRr6hmOGBA3nAuyOXtGlUmqtZX7yDnj730dK9Ce1USUwWDUdA4PPyvSwt+IAhAY8hF0nRWfWUmiqoNFezt+oXJoYMxUOiQipIKTSUsKN8j+O6M8LGIhfJ2Vi6k3EhTyMXZJSbqniv+BMqTdVMDR/NlboM1BYNj/h2436v9o6+7mIlD6cMp6N7KyaFDsNT6kGJsZzVca/TUnVDwwyQrs/mrYK1ZOhz8JF4sjhqGsHSALL1BcwIH0u1Wc3rkS9hspnxELtzTnuZ94s/BewB5qO+Dzi5ZZhtVrp5t2dT6ReO5ygg8HRAP1YWbmBmxDjGptv1no/6PsCluqvUWDQ87t8LraUObFBt1vBCyDOk6bPZXPoVMYpwBni1YXHUNDaUfk6tWcvciAm4i914v+hT+vn1ILkulZ4+Xep9B/0lPqTpsng28AlWxr7KptIviZaHcZ9nW9xECrRWPUFSf0pM5YBdhtTbpysRilAiFKG/41v/+3APiLRLKhpo/2/o1KkTR44c4eLFi7Ro0YKIiAiWL1+Op6cnI0aM4MKFC6SlpfHQQw8BYDQaiY2N/dXgePr06WzZsoVBgwaxa9cuJJKGwy2JRMKUKVNYsmSJo61Zs2acP38eq9VeBGb27NnMnj3bIUVoCD8/P6qqqpzaKisr8fe/sUH1ugb4VhITE0lMTOTZZ58lJibmjsHx1q1buXLlCtHR0QCo1Wp27tzptJHx5uu1bNmSmTNnMn78eL744gvAHrC3a9eOdu3a8dBDDzF8+PB/XnAsCEJfoNRms50WBKHb9eYGTrU10IYgCGOAMQCRka5yky7+Ji59Br+8af+5tgi+GglPfQHNHv9753U36Kqh+DzoKsGvEQQ0gz9pOc7FH4faXMvm0i9ZVrCOJdEzeDV3hePYL+qTeEk88ZF4kqnPc5JCpOqySNVl8ZhfD0KkAWQanB0CBKC5MoH+fr05p71MlVlNhDSEfr49iJaHMy7kaSSCGJPVhM5mIFgaQKY+lyO1Z5zGyTEUkGco5qj6NBprHU2V8ZzXpjgFxgA7y/fyUaM3OVhzjClXFlNpruYJv54MCehHhDyYV3KWApBdks+Y4KcIlPohICJA4kNzt0Z8UbmX94vtutFHfLqRrrMHQJ08WuEmVmK0msgzFvGf2tNO132v+BMWR00lya0JpYYKdlXuI0YRibtISao5i4V5q4iWh+MuVqI21zot+bdwS+B+z/b8rD7O0bSziBGzvtGSeoFxhamaiRmvOWzqqsxqXspcyOdNVzMx9Dku1l3BYDWysmADvX278ohPdwqMJUwJG4XFZkUmSNhc8iV9fB8gXBaMzqJnUuhzXNam83bsXM5ok9FbDHT37oDVZmNpzAxmZi5BQGCAX2/CZEEsyl8DQJDUn1Wxr5GiS6elexOO1p5z+F+f017mQPUx3o2dx7/8+xAqDeSqPotqi5pevl3YWb6XVyMn4iZy41/+fdhevhsAMWLGhgzhl5rjBEh9Gej/MP18eyARxIgEEYnuCWTp8+no0YoSUzlSQUITtzgi5X9dUHyd1s8t4sg7Y5ykFWK5G62fW/Rfjdu5c2eWL19ObGwsYrEYX19fqqurSU5O5sMPP2T58uXMnz+fV155xdEnJiaGnJz6gfqtrFixgiFDhjBy5Mg7BpzDhg1j6dKl1NbWAhAfH0+bNm2YM2cOCxYsQCwWo9frsdkaDKMAaNSoEYWFhaSkpNC0aVNycnI4f/4899xzz237aDQaTp06Rbdu3QA4d+4cUVG3X3WzWq3s2LGDCxcuEBZmz3ceOHCAhQsXNhgcA0ilUhYuXEhcXBwpKSl4eXlRXFxM69at7+qafxd/ROa4M9BPEIRHAAV2zfHbgLcgCJJr2eNwoMG1AJvNthZYC9CmTZvbf/IuXPxZaErg7AbnNpsN8o//bwfHFhOUXISj78C5a3o1sRSG7IKEh2+cV3jG/r/VBEGJEHXf3zNfF4DdeUIhyDinTWFx/nuIETuycjdzoPoog/374SPx4h73Zk4FPZQiBSqxO/OjJjMsdZpjqbyFWwIqsTsikYhp2W84lvoBlkTPYHbOcrRWe3DRwaMVA/x6cV6bwn2e9zoV4ujp3YWuHm3J0ufybtGma5IHT+aET8BP4uPQR7uL3GjnkUSGPofX8t6h1FQBwKdlXyNBzNjgIYwNHsyHxZ/xYugw1hZvc2x28xJ7sCxmFstjZoPNhs6q55D6FPtrjhIpC2VY0EDEgpiDNcfreReDffOdQqRgkP8jrC3ZRoW5ijXFW5gRPo6z2hsb2B7x7kaUPJyvyvcRpQijqVscQTJ/3o6dS3JdKtXmWuKUkTRzi3ca32azka7LcQTG1xkd/BQVpmo+LNnGsdqzKEUKXg4dSaDMn1xDAdGKcL6t2M/P6uMkKGN4M3q6fR4+3WnrkYSf2BuL3MLCvNXobQZkgpRNZfaM2qb45UyJGIO7SMknZbt4p+iGDrXEVM5VXQZfVOylk+e9fFGx12le1RY1+cZiBOyWhhqLlgRFNLUWLa9FTUZjrsNgNdLOvSUt3BKoMFchE2RsL9vNkugZeErsqyiym7yVxYL4jhKlv5LruuI/0q0C7FnT8vJyhgwZ4tSm0Wjw9/dn27ZtfPed8+bTJ554gm3bttG+fXt++uknwsPDHcd27Njh+FkQBDZu3Ejfvn2ZPn06ffr0oSFkMhmTJk3ixRdfdLStW7eOadOmER8fj6+vL0ql0im7fCtyuZwtW7YwfPhw9Ho9UqmUdevW4eXldds+NpuNpUuXMnbsWJRKJe7u7ncM4n/55RfCwsIcgTHYN/ddvnyZoqKi2/ZTKpVMmTKFZcuW8eqrrzJ16lQKCwtRKBQEBATw/vv190n83Qh3ehP5zYPZM8dTbTZbX0EQdgA7b9qQd8Fms625U/82bdrYrnsDuvj/lPwTkPEjVGVD3AMQ1Q0862/w+U0YtWCoBfcAEIlvtFvMIAhgqoOPH4TaQmhlL7eLIIBnJLQdfdth/1aqsuHsJlAFwTfjnI95hsHYk3ZJSP5x2NIPtNe8TaVKGPIVxPe88/gmvb1v9kFwD4SYbhDQ9M59XNyRIkMpn5Z9zZayrwiQ+DEx9FmWFqwlU5/Hm1HTmJnzltP5Se5NmBk2jiGpk5kaNprPy79zuBW8HDqSAb4PE6YI4lLdVVJ1WXiKVSS5N+FyXTqnNZd496bACiBOEUVzt0Z8Xfmjo21q2GiWFXxIoNSPuRETeTHzdQb6P0yJqZx73JuytnibU9baQ+zO0MD+rC7aTGv35vT06cJnZd+isxp4zO8hCo0lfFP5E2Bfrl/faAnN3RLINxSxvWIPq4s2O81pgF9v2qgSuUfVjHB5CGm6LExWM3HKSEJkgajNGvqnjOMR3+6sKtzkVEWvg0crhgcORClScLHuKm9dszBrpoznCf9epOtyaKdK4qz2MluuWY919riX0cFPIUGEp8SDFu6NkYsaNlI6UH2Uw+pTfFb+rWPjmUyQ8mrERM5pL/P5LcHpnPAJrC7exPSwsWjNOrKN+ShFcirM1ajEbihEco7VnqOxIpaB/g/zs/o40fJwztYm81nFbrwlnnzT7EMuaK9SZa5mZeGGei9Ns8JfYHnBOt6PX8j4jHlOpaYB3o6ZiwgRxaYydlcdoI/PA2Qb8tha9g02bITKApkf8SISQUKluQaRIJDo1phm7o0afAZ/NteznC5c/BU09H0TBOG0zWarpzX5M9deZ2DfnJeOXYP80Z94LRf/BIrOwbaB8ONsOP0hbB8MF3+/TQ8AeUfh2LtwYg18MQxOfwRlV+HCp7CpN2x5FLIOQu/l0GESHFkBPy+Cw2+BSAJWy69d4e/h9Edg0YM6v/4xdQHor1lRXf3uRmAMYNLB8TVQV3Hn8dP3wvpucOA1+OYFWN8dyq78YdP//w2bzcbm0i/5d+FHlJoqSNal8kLGPF4IfgaAPEMR7T1uLH8qRHJeCH6Gz8vtAdiqwk3MDB/HpJDnmBE+liCpH1vKvyJLl0eZqZLGylh6eHcmTB5MgjLGYcV2M0arsV4gaL62LeS6znd13Ou0USXyc81xgHrOFrUWLY2VsXTz6sAA/4dZmr+WaouaUlMFHxR/Sow8HI9rm96CZQFYbTaOqE9zVnuZVF39DUeFxhI0Fi0BYl90Fj0yQYa/1Ndhtaa3GqgwV7Ol9CtmRbxAgjIGiSDmYe/76e7VATMWgqR+KEVyfCSeNFHGkaHPZVHeag6rTxEmD2ZL2VcoBDnzI19EEAQmZs5nQ+mXnNRcZF/V4QY/rwJDMS9mLmBH+Xc8H/wMYuwv1XKRjBBZED9U1+8nCAJrYheQpc8jSO5vfwmS+pKtz6farGZt8TYuaK+wo2IPEzLn0UKZQJouGyMmPohfxM6ENUQrwmmtas7hmlMMCXC2/xIjxk/ig8Fm5Jj6LMOCBjodD5UGIkJALIhpoowj0a0xZpuJT8u+djhjFBpL+aBkK1XmGmbnLKOZW6O/LTB24eJ/mT90O77NZjsIHLz2cyZQvzC8Cxe3o/AM1NxSYennRZDQBwJ+h7l8WQr8Z4Vdi5tpz2aRuR96LIIvh984L30vPP0NHFxgDx7B7lzxzTiIaA+BzX7f/dwNxjp7hlZbAmI5eEeDdyTo1aAKBFkD5uraMji3CVr8C9w87Fnum1eAwtuDxzVNYE12/f7qPNCUgptfw3PSVUF5Kjz+EdTkglgGV3bZ5/l7PgcX5BmK2FLmXAXKipVKcw0yQcrq4i1MDhnO8MCBVJvVRMhDOKtJ5otKe3CstxmoNqu5oL3K/V7t+LryJzL1uVSb1Wwt/xqlSMGCqJfprGrNp2Vf00rVHJkgxWgzOa43OOBRPiy5UWxAjBiZcGMJvdBYytrirTzgZd+hL0KEVJA4eRYrRHKi5WG8Gj6B05pk3oieRpY+n2h5GBXmKvZUHaSjR2v21xxhVNCTpOuyWVSwGq1Fx6TQYey7Jahs79EKb4kXF+uu8GXlPr6t3O+Y60uhIwiRBzIi6F+8mf8ei/PW0MunKw9530ecIoozmkt08+qAXJARJQ9noN8j5BgK6O1zPzmGfEqM5Q4JybOBT/BB0acUmezFBn6sOUyGPpsHvTvT1iMJldidVF0mxcYywuUhiBAc0pFPynbxcthIjDYjiW6N8RN7k6CI5ZT2gtO9GKxGbNjsQbvt/7F33uFRFej3/0yfSWbSe2+E3ntvCoqAoogooqLYV9G14Fqwd117F7siKipFaQIiIL1DaAnpISG9TTL998ebZDIkWFb86Xf3nufhWefOndtmWM4997znuIjShZNqTGRE4ABeLvJV8UsdFbhws7RiDaODBnFD5v18kP4M6SQTZ4jisaQ7OGrNZm7s9XxWuoQIXShzYq6i3FmNWe3HWyULmBt7Pc8k3cPmml0kGGPwU5vIthWwuHw1jyXeyY/VW7kobHyb3+LuugymhIzjwYQ5dDAlne4nq+Bviv379zNz5kyfZQaDga1bt/5FR/TfCSWrSsHfB662zVU4GsT+8J/gxF4htuu88UQMngN7fR/t4vFAxiIIiBFS2Pp4qpuUWa0BglOEiP5WeDxC9tVa2XZNkaivGq0MzfmHiW3h2EpRyp026HIR9LsOPjwbOpwDYx6BmL6+A3Y6fxm8y1gEfa6RdTY9L2pxZDeY8CKYmnxmaeMlgUOlApValPAuF4HLd6iK2hNQuBMMAeBxCkl3NkhyR+5G2Y4yEfAfw4WLEG2gT5YxgJ/axJOJd9HgsaFFg5/aSL6riBuzHvDxC6cY4sm3naDGVcvjBa/h9LgYFTiIzKbM4wZ3I/NyX+CttMd4tfgjonThPJF4J6uqNlLmKGd04BAGW/pw0HqMFZU/EWeI4qrIi3i/RKpe1agJ0QZywl5KpF6m2xdXrObm6Ct4pehDXLjQqjQ8m3QPnf3SKLKVsKv+AAuaBsJAhumuDL8Ii8afGeHns61mDyH6oBZLwr76w1wfdRmflS7G7fEwPXwije5Gkg3xrKvZ3GL3iNCFYtb4sbs+A41Kw3nBo6l0VrG0Yg05jfmcFTSUamctQy196GBMYl31Zp4oeJ2sRon0WlW1gSsiLuS6+EsxqY3cF3czQVpLCzFuRratQGLXnHV8WfY9TxZI0qgKFc8m/4tefl3YY80g11bIs4Vvo0bNHbGzeanyQ+bGXcd1mfe12BoGmHuSby/iWGM2M8OnYFHpWNjpZUK0weTaCtGqNNhPUfM9eAjVBVPprMGFi/XV24jShdPFvwPHG/O4JvMe/DRGRgRILFuSMZ4ARwCPJN5OhbOKEG0wFrU/RrWBj09+i0alZmLIGHJshQRpLbyf/gw7an0JPEA/c3eGBPQjxRiPTq1QgP9r6N69e5vGOgVnHsrfDAV/H0R2F6XUXu9d1u86CPv1xp724aENo/O4Jcf4VGiMotz6LNNDVTZ8NB50fnD249B7FhhPP+DQgrKjcPQ72PRvGYSb/BasuAsqjsn7SSPggvfg5EHY4u2r5+CXQnxD0+HYChkWHD4XUsaCxwXGEND7wegH4cNxYhtJPQvOfkqIbGgHuYYnD0P5EfCPhNkboPyYkGNzNNjrIDDeu8/GGljzIHSaDMW7YfW94sNWa2Dk/RDdC/YvhPN9SxUU/HZE6MKYFXkx9+U+1/KIO1YfRbQhnPeKv2R88AgSDQlUuqpZU7WZO2Jn8+HJRRxvzKenf2ceTriNl4o+YFf9QUBqnkcFDuShvJda9mF1N3DSLm12xY5S7sx5kt7+XZgePokKZzVXZ84lxRjPTdGXE6ELod7dgMfjoa+5GzdHX8HS8jW4cXPYmsVFoefwdflKllasYV7CLQRpLDhxUeuq56LDN3FT1OU+xBjg+8ofmRY2gbODh/NJybckmRI41spKsapqAzH6CK6LvJQRgQNwepwkG+NZWflTSwNckiGO6eGTeP3Ex7xVvIBYfRSvpj7EvrrDjAsazpjAwRTYiilzVPB+8Vdk2vJINya1EONmfFa6mBhdBFH6cB4veI07Y9vODqhQkW8vosJZ5VOc4sHD/bnP82n6C8zO/BeVzmqxuURfzjflK8lszGVh2ffMS7iFYnsZWpWGrMZcFpQuZXhAfw5ajzEmcBBJJvk7NtTSl5nhU5h/8ouWfaQY4gnVBjE17ByezBdSblDp2VK7B4/Hw9vFC6h3W6l3W1nU5G3u7p/O1VHTiLdHU2g7QZmjkldOfMju+gwsGn9uibmGFwvfZ0LwKFKNCZi1/kTogsmzFfFm8Wd48BCpC+PhxDl09Pv7NJJ5PJ7TZvQqUHCm8Hvn6xRyrODvg8ShkrSw+SWoPA7dp0PXi0DbDpn9LTAEiHob0QVONk36//winPtvyFrltSKotbKfDuPhqxlCzrVGGP+cEMeLPhJSnbdZ7Blxg385/9heD5mrYPk/5XVoB8j42kuMAXJ+AmsZlDS1T0X3hh6XiYJ79DtIHCGFJKUZkkCx5DrI+1nU5GF3Q9JwuG4znNgtZD+iK+z7HL6cLiS/37VQmQ1Hlgmhr84VS0n8YBj7uBD/ZpQekmuUvxl2vC3EGERl/vERGPuYDPAZ/tpM4P/L8NeYGGjpycMJt1FoL8FPbSDFmMi7JxayuW432+r28mGH56h01vBY0j/5oGQRIwIGcFPUTPr4d6WjfwovpTzAkYbjNLpthGqDmXHkdp99+KlNBGp9c1B312cwNmgodredSmc1O+uqWxrPBpp78kbqo3TwS5aBMZWBVFMCTo+TaF0E6aZkGt02EvQxfF2+kjhDFG80Ra41uE958tCEepd4lJOMceTaCgjTBaNG3ZKmUWQ/iUVrpp+le8tnzBo/Uk0JbKrdyayIqTyS/wouxOtfaC9mTtYjvJL6IDvqDlDmrOSxgtdaFNvnC9/l+aR7SdDF0NW/A+l+yZywl7KsYi1xhmgez38VgC21u5kYPIZllWtb9ntx2AR+rt7JZeGTuSN2Nk6PE4Naz1dly8lqzEOn0vFe2tNkNebhpzHyZP4bLfnQKyp/ZFzQUF5sKjppxkBLLzIbcpka5k2L6WbuiN3toLNfGhtrtpNiTKC3f1dKHOV8VPI1do8Do9pAL3MX9tRlkGcvIt/WdvK/+QYgSh9GlD6MvXUZ3Bl7LSWOcgpsJ3i16GP6m3twR+xszFqxY4XpQrkzdjYXhI6j1lVHoiH2L2mAPB2MRiPl5eWEhoYqBFnBnwaPx0N5eTlGo/E3f0Yhxwr+XkgdKwTOUS/pEn8EATFiNRj9MGSvFXtA8hixSEz9FI4ul/SGrlMhebRYD27aLfYHYyBUZIuSun4+6M2SZOEGPp0MUz86fXpDTZHsrxmh6VDS9vEmWatlG+e9Ku//+CgEJsCoB+BwU1/9gJvhuznewbvdH0LRbpi1RhTd6F5grZTPbm7KyXU2yhDiWY/B0e9h9/sw9lEhx/mbIWsl5G+CQbeB0Sxk2BIjZN3aajpea4ReMyEgTsi3JUqsKk4rBKeKJ1rBb0Ynv1QidKEcrD/G7voDvFA0n+ON4rF3eJwcbcimr7krnf3SeCzxn1S7arFozC1DdKG6YIbo+gJSHvJgwq3ck/MMjR4bJrWRB+L/Qbg2BA2aFnKZakzA6raCpy3xyLcXE6oLxqzxo8HVQLA2kN11B1lT/XPLOtdGTpfj0PrzTfmqluVljgo6GJM41qrKOl4fTYQulD11GSTqYznemMfnZUuZl3ALyyrWUuWs4dzgUfTz78bO2gMtsW9pxiTqnVYeT7wDh9vZcuzN0KjUlDkqOWEvweF2MDfuel4u+rDForK1di8PJN7Cu8Wf81LRByQaYnk08Z/UO62UN8XG7as/zAPxtzI4oA9alYZwTQgVrmriDdHkNNkePihZRI2rjnvjb2JB6VIsGj++Ll/F+yVfckXkhT42F4fHSZm9gofib+Wt4gV4ELK9u+4gM8LPJ+CUm5Ru5nT8tSY6GpPRqbVE6sPZXreP/uaeYnkJ6MULhfO5JfpK8mxFjAgcwKGGLJ9tDAvo7/O6p7kLOQ2SiBGri2RE4AA6GlMI0Pnu26gx0s0/vc33/3dAXFwcBQUFlJaW/vrKChT8ARiNRp/IvV+DQo4V/P2g95M/fxRRvaDbJbDxGUAFg28T4rjiToltm/w2dDof/IK8nwntIH/s9eIF/vFR73sndsHUz0QB3v0RjHuy/f2q1eAXLqR41P2i0hoCRIXN3+JdLzhZjnHTs0J6AU4egK8uk/3s+1QU3VMTKUr2QUWmeJZBFOZj37U9jvJjQnprCuS8m1F5XEpDIrqCMUiIv8Mqw4HmKKgrFjWHOCsUAAAgAElEQVT97CfF8rHjHSHKo+bBoa+hcId89rxXISxdbkIU/CaE6IJINsbxr9xnyLb5fq9mjT+9zd0A0Kl1hKlDTrsdjUrDRWHnkG5KpsBWTLgumI6mVPy1JpZ0eYfDDZl4PB6ON+ZR6ahhROAAtCoNTo+XeF4XOZ1SZwVltRXstx7hpKOCyyMuYHLoWAptJ0kxxhOtDyffdoKhln78XOMtCXmm4G3e7vA4X5etYEfdfrr7d2Ja2ASeKnyDnXUHuC/uZqJ1EeQ0FvJY/qsMC+hPlC6cWmctHo+b14o/YWXVTwAk6mN4NvleZh79J3NirmJYQD8GWXqjU2lJMMSytmoTX5Ytp5e5C5kNOZjURmaET27JAO5v6c4LhfPJaMgEpLzk3pxnuSfuBiaGjOXr8hXcHns1j+S/1NIi+HjiHTyS90pLNJy/2o85MVfxRMHrzC9eyLsdnqLe1YALF06Pi89OLuGfsde0WF26mNLoH9ALMyaSjPHsrz9MhvUYU8POZUA71dB6tZ6Ofqk+y3r7d6XMXsGyirUsqVjNFZEXUmgrIVgXSFZDHpeGT+LrspUY1QZuiLqMnn5t7WVJpjiSTL/9H/y/G3Q6HcnJfx+LhwIFzVDIsYL/+3C7hfC57DJI1kysVSqxaoR/CNnrIO8nOL5OPLrmSCGZKWN8yXEzHA2w7xPva5UKjMFQsAXCu4pnN38LVOWJyhyU6E1yCEwU/276BPjhXrF0WGJg3NPibc5dL1aH5FGSv7zvs1POxyUE9ewnfFMoWh+Lrukc65vU3sG3waFvxc7RDEs0NJSL11jb6nFSWCc4tBjKM4W4R3YHc4yo0EGJQuJTxkixSGWTZ9TZKOdy1mNCjk8ehK0vg9YPxj4CISm/5xv7n0acMZo7Y6/l5uMPtiwL1QbT39Ljdz1aVqvU9DR3btPq1svcmV5Ny7Ksebxy4kMey3+NB+JvYVXlBiqcVcyMmEJeYxHLK9ZzwHqYDTWSLx+hC+WGqMvobEplXIhUDavRcNnROdwYNYNnmrKEHTi4O+cpnk/6F5dFnE9mQw4P573cYjt4rvAd3kx9jHfSnqTAXkyls4owbQix+kh21B/goPUYgy19GBbQFxUqttbtweaxU+msIVoXwXOF73BP3A3cnv0ojU0WjhVV67k79jpUKjU2t41+5u5E6cIwqU0txLgZNo+dencD5wWPpLMplc01u1uIcVe/Dmys2eGTmVzvtnK8MZ8YfSQn7CcJ0gagRsXG6h1cFHYOn5Yu5sn8N5gYMoYpoeOZGDyWdL8kABJcMXQ0JaNVTfpdloUYQwQXhI6jk18KmQ15WD0NjA4cTEHjCTr7p7Gzdj+3xlxFnD6acG0wIdrg37xtBQoU/DEo5FjB3xs2qdM8rd+1sQp2vAtr5wmh7TwFxj8Doa3aruy1ohRrDND5AvlM5irx0p4uzkznL8NvIPnHiUPFfxycIskNO+fDrvkSp1ZbDMFJQmijewtZDoyHzy7wksvaIlg8G65YKYNzIWlC4iuzpWSjptB3/3p/iOgOeRuh40TxDjdj0BxRpUuPwKLLhawCpE+EPlfDrvcgops06GkMcNYTkvOsUomSXpktynBIGnx5iawXPxjOe03I9IUfyuc+aqcoxNGqdCDvZzm2nPUKOf6dGB88goUdX2F9zTYidaEMD+j/pzSRlTkr+Kp8OR48PJT3EoMsvUgwxBKiDWLeyRe4L+7mFmIMkne8o3Y/sfoodtTuQ42aKH04nUypbK7ZxfNJ97GldjdBWgshuiBuzHqQ2VHTeKnoAwASDbFcGj6ZBncDJ53lqFHxUN6LLUOInU2p3BE7m0khYyi0lwAqjjbkEK6Tv2vB2gDeKfkcP7WJSmdNCzFuxjflq3gi8U7cQKWjmlxbIQa1HrPajzq31WfddGMySyvWcknYpJahNgCLxtySoNEa1a4aAjRmegZ3JkoXjlFj4J+x17Cy8idujp7Jphqprx4bOKSFGAMYNAYSNLFttvdbEKizMFDXm4EBvb3H4awBm4ehAX0xqg3k24qJ1Idi1Bp+YUsKFCg4k1DIsYK/J6yVcHSZpD3ggWF3iRprsEiCQ0OV2AqKdsLKu7yfO/SNELVxz3jjz44th+/+4V2nuUJZpfKqzG6XkF+DRaLW9CYYcQ9kdBRf8pczwO2EGUvgi+neobWiXTDiX1B8oCkYQy3Ra3UnRcmO7S9DdfZ6iWqrKYDkkd5jCU4Wkt46dzmiK0T2FMJ/bDn0vBzGPCwqcdII+bzWADvf9RJjkOs14UWY9rkM6Z3YI57lqJ5w7guiYB/6RrKLz3lOjsvVlIObvxk2Pgm9rwGdEewNQp4rfBU5HwU6pp8oyH5h/8EX/L8Nk8bIsMB+DAtsU8x0RuFpldbiwcPm2t0AnBM8ggCNhTxbUZvP7LEewk9jYmfdfqaGTWBTzU4eSbidn2t3sq1uDyG6QLbW7mFP/SFA8pItGn9sbjtXRV7EY3mv4cLFpeGTWF+9zecYDjVkkdmY1zLYB3BN5DTiDFEYVQYfNbc9uPGQbSvgneLPSTMlMiywP0/kv8ZdcdfxYN6LLeudGzyShWXL6OrXgXxbIRNDxnC4UDy8u+sOcmvMVWxpuhbN6OXfBTVq5sZdj7EpueasoKGkGhM4aS/n0rBJxBmi0LWXdnMG0cO/MyrggDUTq7OKfpbu9PBT8sUVKPj/CYUcK/jrUZUPjjoZ/GpWiLNWwqIrvOt8dTnMWAymMCjcLhFregskDhfltbkFrsO5MsiXsUjsAx6X2AFao2S/KKjNqnHpYdj2hqjJ/a4TEh6aKvYLlx22vwUTXhY1uDTDS4ybsfU1IdIuO/z8b0mKqM4Tq4POTxTl6nzY/YFYHU5Fp0lCuov3gSkEonuCVi/EGGDvJ2KN0JslpUJrlDi2o+34jEsOyjU4eUjOoXA7LLxYPjvsbhh5r0TErXlAjrM1sn+EhGGiji+5VkpAvrjEG63X7zrI3SD/HRgvtpCVd8k2Ffwt0cGYxHkho1vKNQASDDGEaYOpdFa3KLatMcjSi731h8i2FZBrK6DIXsKGmu3cHDOTNZU/82j+q4wLGsae+kPE6CMJ0gZwd+z1ZFgz+apsectAXYg2iFJH2ybGU9Xgz0qXcGPk5dwVdx1BWvn7b3XLgKBRZaDR413/svBJPJL3MnVuK8cac9hWu49Lws/jeEM+Lyc/SLWrljpXPXuth1lT/TM5tgImBo+ln7kbM8Iv4IuyZehUWiJ0ITydNJc3T3yKVqXl1pir6OnfhdmRl7QQY5DWuxRTAimmhD/2RfwO6NRa+li609PcBY/Hg1bJIlag4P87lL91Cv46OBrhwOcyIGctF4V02N2SzXt4GaSeLRaAZvWyqkBer/inRKuBkMmpn0o0W22RKKGr7pH3dEa45CtvlXJrmIIhtKPkER9fK2py0gghvmsfhMA4KctQqaHbxXBkKRiCZADtVGiN4tl1WGW9rFUw/B7ZjjlKSKjLJmp2TDtKoa1OCHDpIbkOUT0lJcIY5D12jxtsNWK/eGcIjHpQrk/pId9tRXSBI9+JDzmqF6g04iv2CxPyvuwWqMqBwbd7EzFaPttN1PGGchj9kETW3bgbKrPkOgcmyc1Bx8lQdkiGFc96XPaTtQaqcuV8IzqLIq7gL0eoPpg50bPo7teRddVb6O7XkT7mbvxUs51RgQPZZz3CpWGT+KLse1y46O3fhSRjHNmN+dwWM4tAjYVwbQhrqn5mWtgEbB47owMHEqoL4rWUhyl2lPJY/qvoVDrmxd/CD9XeBrz11Vs5N3gkSyrWtCzToCFAY+a84NEU2kvYU58BHuhgSkSv1hGpC2Nu7A28UDSf+SULeSDhFvbVH+KEvZSJIWNYUvGDj32i3FmJQaWnwFWMSWPg1uyHfc4/QGPmwtBxhOlCSDbGMz3sPLQqLV3809CqtJwXMhoVKoK0AX/+l/E7oVFpQEk3U6DgL4FCjhX8dcjdAF/Pkviy6W+JrWHHOxDbTxTSnfMhYTD0mSUtd/7hsOUlLzEGqYYu3AYZ38D4Z+HrVmqz2yVEsPMFcHCRd7nWIIkUez6EdY/AyH/BT09B/+tEUW3GvgViU1h9r6i/erOoqeZIsXY0Y/g9Qh73fSLH5migpXykrlhI6eElMOweGcgr2C7Kb2gHIeFHl8EBb0EAtUWQMASG3uF7PCljISQVel0pXuPpX0kcXDNBTj9XlOkvpstrvVli6mZ+L+d86FuI6S2ENjRdPtec/2wKgS5TZKBx/+cw5HbwD5E/YR28x2CJkErtyhxRxs3RsP5R2PC0d50BN0kuclgnCP9PC1wUnCl08U+ji38aY4KGcPvxR3mn5HMAzgocyrnBI4nQhjIpZCx1biuLy1ej9qhJNyXzctGHuHFzTtBwboyewasnPubdVvXTowIGMj18EnaPA7vHwa76g0wKGct7JV8CcMB6lOEB/ZkRfj7flK8iRh/J3XHXcag+kyMNx0k2xvNo4u3E62N4uangpKd/Z26InMGrqQ9jc9s5ZD1GZ1MamY25FNlK2Fa7t835qVQqroy4kCBtAIEaC9UumVNQoeKO2NmkNfmDg3QBJBPv89lg7W8o9FGgQMH/HBRyrOCvQ2mGtN8NnwvLbxdS2HGiVB97mhIoCrdL0sOgW5tyiyvbbqexWqqOV98rA3LNsWguh6itIanQ+yqxIQQlwZB/Cilcfhv0uFRsE72vBEOgFH+EpXutE5XZQnBBCG3GVzDhJSjYJoUd6efK4N43s8DaOquzleRjrxN12eOETc/A+scl/SG2P0x5X0jrqcj+ESyxcNHHohb7hQrZzdsIagNc+JEc99C7wNUo75UekQKV1vvd/YGoxuc82zaXeerH4jWuPynr+IXLNTj3RTD/Qsa0ziTqMEjm8sZnfN/f/qbcQJRmgPZSGVZU8JfD4bZzwOqtR/+hehO76zNY3vU9Yg1RZFnzcAW7yLUVsbDMa9lZUbWBjn5pLCn/wWd7P9ZsZXr4JFSo8OAhRh9BubOSKyKmsLj8B0J1QYTogshuLODyiPOJ0UWi9qh54YQUZ2Q25pJmTOTVoo8pcUi+9t76QzyY/wK3RF/J4vLVTAgZRSe/VBaWfcfyqvVcEjaRj0u/aTmGLqY0RlgG0MvcBZ1ay6LOr7OpZidVzhqGBfanj3/XP/OSKlCg4L8UCjlW8NfBGCik9dvZXjX40GKxMjhskkG86z0hzTG9IbKHqKar5vpuJ6oX7P1Y8oe7T/N9L3s99JgOtgYYeAsExMLhpWBpilwKThGiq9bC97dC/xtkaC1vkxDScU/5bu/Al5CzQewaGV9B/nYIivMlxukTpbmuGQFx0OVCaKj0VYILt8PGZ8W7m+VLPIjoImpy2hghrtZSqXOuL5WUCK0Ohtwh9pDcjTKYWJkr1+5UuGxwbJX4oZNHQ8poiBsgA42dJv3at/TLaKxuGzfncUtltssu/m6FHP8tkG5K4d64m3iy4A08eDCo9LyQch+xhigyrMf4oXIT62u2EahpmwzzY/UWOvuncbLa10Ps9ni4LWYWLxa9D8DHJ7/l8YQ7mJg2lm/KV/Lvwvk0uL0Z26+kPOTzeZPa0EKMm3HSUU61q4Yu/ml09UtnWGA/Pun4PIesWTg9Lrr6dWB73T4SDbGMDBzo07bX2S+Nzn5pKFCgQMEfgUKOFfx1iOgGNSd8bRIgFoTRD3lj0ABMoWC0CMlUqWSAzhgkj/+L90G/6yGqhxC1PtdIzFpUD3m8/+1smPYl7PkANjwlqu2oJpJadlgI8ZoHpKbZP1yIMYj/V6WB+EG+5R3dpskAodMGO94SwjllPjjtckzORlhzv5zfgBuEwPa83Nc60YyDX8DVPwrpLm56ZBzRVQYTR9wDBxaJj7kZA26U97UmWPeQeIQH3wYxvaTeGo/3+EHOKThFlmeulD8dJ0pCRnTP/+Rb80VIalubSUAcWCukUe939tkr+PNg0hi5JnIaIwIHUOaoIN4QTYoxgTpXPQ/lvUR/c08yrMe4NLztDVM3v3QqHdU+y6J04aDycLwhj5dS5oEHpoWeS4OnkUJ7MQvKlrbZTnPWcAtUqhbluWVRkwd4T90hYg1Rsi99BFF6aWQstJXQ39KDYG0Akfo/2KKpQIECBe1AIccK/hxU5ckgmCXq9OvE9oOKrLbLA+LF/rC5KZopoqsQTZAotI3PyZBcZHdYfK2QVBC/ba+ZorJesUJqk9c9DFM+kMG21LNEed74nKipsf1Eqe52iRBac6RYJVrjm6vhgveg5xUyhBbdRwbPdr4rn0sbD267xKZFdhMvsH+kEND0cyFzNYx5VGLlwtqpm47oJmr2FSug5IBYIUBU1/pSX2IM4sme8p5vkkfeJkm76H+D2C6mvC/tfgZ/8f0W7hSFN6aPRM8dWQbdLzkz5DgoHmYshe9ugYKtokh3vxTKjkjMXI/L//g+FJwxGDUGuvv7+sDLHJVsqtnJmMAh1Ljq0Kt0dDGltRRrxOqjiDNEEaELxaI1s6lmB138OjDQ0ot3ihfS0ZTCrccfxqgy8HzyfZQ6KjhkzSLNmEhmY27LfvQqHfF63/8/OFJ/nBujZvB6sbdw57LwyQSqLcyMnEKysW37W6whEmi/bON4Qx4banaQ1ZjL8ID+DLD0JFB7mox0BQoUKDgNFHKs4Myi4rgMv21/Q2LMRvwL0ieB6TTT4AnDJHM4t2nKXaWWgTFTiKi+sf3FphDYFLJfUyQeWY1BYsScrWKhak+Ionp4Ce70SahrimDS61KBXLBN1tHoYdpC2W6XqaIc6/whqjeU7JWGuFNRuE1I5cCb4NtrZNAPxH4x7hmwVcOOt6HX5fJebZG8PrYCJr8pUXLV+dDhHLj0G/jyUiHjerMo5F9cKu10/W+UIcENT8PE18TbeypCOwjxPRVbX5FGvtCOQvjLDgvRzlojx736XsmKLmqqAHY0tt3GqWisEd93fal8p7WFUjKSPBpMrVoF4/rD5cskVaT2hHxHEd1kMC9ESa34q+FwO8mx5aNT6Uhqh2wGaMyMDRiKRe3HpaGTCdUFc1bQUM4PPZvYpsa4N4o/ZVzQcG6NvpKrIy/m5cL3eTT/FQBi9BHcGXstUbpwni54k0RjLDG6SO6Inc1rJz7mgPUo0bpw5iXcwlFrDnNjryerMY9IfRidTCnE6KOIN0Rjc9sJ14Vg0ZixaMwMCGhbw/xLyLed4Mqjd3Lclg/A/JIvmBd/C9dFXfq7mgcVKFCgQCHHCs4sDi+BVXd7X391OVz2LXQ+v/31A2Ph4gXi0W2okgrm6F5iB0gaLoNp6x+VYbEO50BIuhBcjdZbYOEDeTxrdRkxD75NIs2aiTGIIrvhKSHg216XjGRzFPS7RiLQTuyB/teLMux2QdJIiS4rzZDhtmZiDGK5MAXLsvNeFnI44l5RibN+gL7XSU5wc8vfse9h9INw5WpJ0fALhWU3i30kd4PcEGx+UWwcR5aKFcMvVOwdgfHQd7Yke+hMoqZ7XFC8R4irziLWDEe93ExYouS4rGXww/3SuOdsIsQdzgFDgBBkXatSj9awlstwX2QP+T4NFkm3OLIMLvxABhhbwz9M/vwSivdKprTOX1R2xYv8pyPTmsNbJZ/zRdkyDCoDc2Ku4tKwSYTovTc3Ibogro2+hMuO3MazSfcwN/dpnB75nWvQcH/8P1jR9QNCNEEcbDjGSXsZU8LOYU31Zho9Nr6rXIcGDUOj+pJnLyLPXsQQSx+yG/P5R/RMQrRBmDX+LCz9nixbDgMtvdCptLxf8hXjg4bT3b8zj+S/5HPc00In4Kc2EGeIIkj364kS9U4rW2p3txDjZjxfOJ8JwaOJN7aTL65AgQIFp4FCjhWcOdSVCoE8FZkrT0+OQeLMAtsqWhxcBF+3ImG75ktKw9RPRF3tdYUotM0wBorHVedHma4T5rTOsP31ttuN7i0K6uHFMPJ+GR5b9g8ZjOt0vvhoO00Wm0PeJvEqj7gfNj7tu50xDwvxrG1qGYvqBR3GwfrnoNtUUW6biXHLObwHqeMgbiC80kU8wyBe5/BOkmphDBJSW5Elx3dsuajCO94RNf3nF7zb6zIF0s6RocOF08SPPe1z2LdQbCDNOPsJIf7D75HzrymUeLfT4cQeed/jllKRmgLJfS7aKVaV9Ingf5rq7faQsx4WTvcmf3SaLMcUoaQJ/Jn4tmI1n5UuBsDpsfJEweskGGKYFDrWZz27x44bN/n2Ey3EGMCFi8cLXuXckJFsrdvLzVnzqHLVEK+P5rXUh2lw24jUh9LZlEa1s5bhlv4caDjKz7W7+LlWnlJ81OF5rsm8p8VvvLFmBzdGzcCi8Wdc8HC+Kvu+zXFb3Y1sq93HfusR+pi70dHvl+vJt9btIbexsM1ym8fWUkqiQIECBb8VCjlWcOagNQrJOxV+/8HQjLVcSFhrNFZD0Q7oey2c/zY01uCO7IF670e4Q9JQx/TBcWAJ+wcvp9bdmSSDv3iET0XcACGeeotYBqylMrwX0VWsAyoVLLneu/45z8GCC4QMH28qNAjvBCX7vMQYRMVNO1tU2gNfQcLwtvv2uEGjEXVZrRFyrDOJ4vzD/eIb3voaDLlN1l11F4x6GLa8Ij7hjc/5bi/jG7j4MyH6SSMg5ydYerPURTdUwMkDUsiRu1GSQQ4ukki7aQvlPE+H2iII6QDfz/ES2sNLZADS2iqxoHCn7EOjF5U5sh2y21AN65/0bqd5W52nKOT4T0SxrdSnGa8Zm2p2tiHHUfoI1KileOIUhGlDcLldPF3wJmG6YK6Ouhin28l+6xE6mzowJKAvxxvyWFqxFgcOpoaeiwcP75YsZFrYBIocJW0G8RaWfcfD8XMYGiClOGuqN/u8P9jSm611e4kzRFHlrKXRY6Onv9ezf8J2kgZ3I9H6CEwaI4vKVpJkjMOi8ffZ14zwC4jVte9PVqBAgYLTQSHHCs4cjBYYPEesEM2KqClY1M7fC7dLLBDtLfe4ZLgMUKeMYnfgtby7zEXXupPsL72G63pXMNxvBeyqEZfFVT/IwFjZYbFIxA6UbdtrJTt55zti7dj4rFgHerUadgvrKGqpwwr7PpWikdyfpD3uwMK2x1eZLYOBZUdl0E7v761fBqlg1hhl4HDY3dIy1/ViIcQVWaJoj7gHUEuE26DbwFkv3l+Vuv1rUpohkXWpY4UcW8ug/KiQ38NLvEkSFdkw+Q3xVQfE/PL1D04RG0RrQguiXl+yUFTjnJ/gsylCwkGI7oUfyKBja1jL5Bq2uVbHf/kYFPwhmNRGkoxxHGvM8VmeYGj73XcwJvFk0l0csR6nkymVww3eQdl742/Cgwebx86E4FG8VPgBLlwYVQYeT7qTUkcFN2XNY7/1CABbavcwJnAw76c9g0atpdLZtqFShdQyh+qC6OvfnScT72ZpxQ9oVBrGBA7GpDGxsvInroqcSp27nucL3uGttMdpcNv4sux7XjnxISrUXBlxIYMtvenh35FIfThvpj7KwrLvOdqQzVlBQzGqDPxcu4uRQQPP6LVVoEDBfzcUcqzgzCLlbJi5XMoldCZIGCotd6eg0QY7jkBpFRh1EBcJ3Vs/OTVHwLA74fvbvMu0BvGqBvq2XPXsqOcGPRwrTGT68IMEr7gUVcl+ebP7dCGZF8yXATiQyuro3jDhZSF4/a4XUqzWwvC7xdbQDL1ZFGsQq0HxXlGjbXWQfp43fq0ZEd3EMmEKBjQyWJfzE1TniVJqr4Pi3WJT6DBB9qU3w56P5PNZq+H4D0Kexz8D0dPg5EFZtypXijxaV0YbA+V4ethEPQYhp8ZgaexrHbFWsleU/V8jxiC+79KMtss9LghJA6cDNr/sJcYgx5n9Y1tyHBALyWMkts7nWimq8Z+JQJ2FWZFT2Vyzq6VyOdEQy5AA36cpLo+LI9bjxOmjSDLEMSZoMLm2QkrsZSQYYhkTOAS9WseVERdyf+6/cSPRi40eGw/lvUikLqyFGAMYVHri9FEYNQai9REkG+J8musAZkVcRKPbht3tIFQfRJIhlhuiLuOEvZSTznLeKPgUD56mBjsPu+ozKHNUsaNuH480DQICvFD0Hvq462lw2Xg0/1UeTLiVEQH9cXtcxBmiuCfnGcK0wSzv+j4xBkVBVqBAwW+DQo4VnFnoDJB2lvw5DUoq4NEP4M0lYhEe0BkuGA5VtTC8dbpYjxkyvLVrvsSj9ZoJcYOgtlhUYKcNIjqjDk6meyp0T3LAkn+LVWLkvYBKCK9fmJRRJA2Ht4dIrJtKBZ0vlCQHlUbIbmN1k7Xhem+2cfEemPyukGxzlHh1a4uaSGqG+HGPLReLRJ/ZQip/fET2O/ohWP6Q3CQEJUm6hsMKZz8pEXEXL4Af7hNfcfxguaEAuSiV2XLuh76Bn54Uf+6xlZIWcfR7Ua8je0LvKyRBY9IbonwnDpPGQUejN5miGdF95Fr8Fuj9ZVumYCkvacawu4QcN1TDyf1tP1d2tO0ynVFudMoOyfegUsPAm+WcFfypGGzpw0fpz3OoIQuDSk83//Q2UW676zJ4r+RLllWsxaLx54aoy0g2xhGqDZY8YZ0kzYRqg1uIcTNqXfU4Pc6W1xo0vJjyAE8VvMlHpd+gV+mYG3c9Dyfcxp76Q+TYCuhn7s6e+gyeL5rPhx2ew42bq47dxdjAIYwNGsJP1dvo6d+ZYQH9qHLUsL1+H0MsfXB73HzRjj95W+1exgeNwIOHBaVLuSRsIqMCBvFc4bsAlDkrKXdWKeRYgQIFvxkKOVZwxuFwQmYh2OyQEgMB/r7vb9wPbyz2vt52CDolgFol/xse3PSGf5gkNnSeAlq9JCac2AvbXoODXwm50vlJhXL8ACG9BVuFiK2ZJ0RUpZaEiJB0IYuNTY94PR7IWCQJE4Nvg6AE2NKUq7z9Ldlvh3PEgnH4W9g5X/Y/8GaxMKx5QCwWeKRQRKUW8vlZq8HDhRdLxvLXV3jV3moN0FoAACAASURBVJ6Xy3Cax0NLxfTJ/WLlaKyS9XR+MOYRWae6afr+h/tk8E6tFaU7aaQowSvuFAtL5iq4JQNQiyptCBSLy5ZXxLtsjpJYu9YRbL+G8M5w1RpJ7ijeI8Q8/TzxS/sHQ6cpbYcUE4e1v624/nDFSjk/vZ8ML54uKUPBGYNerWNgQC8GBvRq932Px8OSitUsrlgNQJWrhqcK3+Sl5HmMChpImC6kZd0OpiR0Ki2OVmQ4RBtEijGBoZZ+dPNPZ4ilNy8UvU+uTYbj7B4Hj+a/yvNJ9+JwO6h0VvNS0fst2/iy/HsGmuXY1lT/zOaaXXya/gKVrlr21GfwXeU6AjT+DLX0xe5xkGKM56eaVukzQKQuHJtH7Ebljkrq3VZ+qtlGsUNaK6N04YS3Og8FChQo+DUo5FjBGUV5Nbz8FTz1KThdMLo3vH4HpLdyQmxt52n9pgPQswMUlLUix4U7hUw1VglhjB8s5NdWK+pu5iqwxDSVc/QEQ5BURK99QIgxCDFc95AQsxN72u7Y45FIsZoiGP+8FIFseh72fiL2DaddyDKIgvrjY6Li5m6QZI7es6QNL7a/kN7WSBgKthqY+qkosM6mGLnsdfJ+RRb0u5ZiexS7rJcS1Hs8aX6ZBNqyMFQdlXNIHCbH4vGIQm0tE0/z4SW++3LZxQ+sNUhk2/Y3YNxz4rd2OSCicxs7ym9CTG+IeQ1cTonPa41el4tvOOMryZ0ecrtYSkoPS0Se3ymEJCBa/ij426DYXsr3FT+2WX64IYvxwSN8lqWaEngp5UHuzH4Cq7uBIE0Ar6U+TKopgZujL+fm4/Po49+VPfVt/4LXuxsod1Wxt/6Qz3Knx9lCbAGsnkY+KP0aLRpiDZFMChnLsYZsypwVHG44zrSw81hUvqJl6C5QY6GXuQtbaqWu/eKw8wjRBnKwXp5gBGsDeCX1QaKUJj0FChT8DijkWMEZxdYMeOwj7+t1u+GNb+G5mySkAaBnWtvP9UqDQD/wa04XK9wp5Rv5myVVIvUsaZtzNQrx+umpphV3SYJEWGdRj8PSxVYBYmfof4N4eh1WSBndlBDRKtpp/DPw84tev65KLUkYlcdFqd33WduDrcoVe0LtCUm6OOtxKNwNjlaxbd2mSUTct9fIa3MkTF8kzXHNWPsAFReuYs6i4Xy1XgcEAsk8M6MLt1nPQtNzugwUnv0E7Fsg2+t9ldwQtCbHKpXYOVbcKa/jB4na/v0t3kzi8mOSUVxTBKGpYk/RnyLp/xJOJcYg/u8pH4jVQqMDlxsWXCh2k9gBMO5JSBnz2/eh4P87ArUWkgxxnGhSWZsRa4jEovX9fWhVWiaHjKW7fzrljkqidBHEG6MptVfwYN6LVDpraPTY2jTjgRSNDDD3YGXlTy1V0SpUDLD0ZF/9YcYEDmFt9c8AfF+5jtdSH6bAVkyxvZRu/unsrDtAsDaITqZk3k57gpzGAuweB4mGWPbXH2FTzQ5mRUxlcEBvDCod73Z4EqfHTaw+Usk4VqBAwe+GQo7/l+HxQN5meayvMcggVWS3P7TJHUfaLvt6Pdw7E8KbnuiP6AHj+sOq7fI6PgKmjwWtFjomNB1X8R5YelOrY3VDwRYY+6h4cFvD0QBlGV5ybIkRX/BZj8OGZ7yJCz0uhVnrYNNzEm+WOk4I7uHFvvtZfa9YJfwjoTpXCjtawy9Uki56XQX7F0r826x1onAfWSLkO6qH+JebUVciyRQXfyZDhpHdIW8jGdWpTcTYiwe+jGXiLXPpWLFOrA2F26SCOnMlrL5HbgQu+UJi2VRqiO3rVbdBvNJp4+W/Sw6KFSXvZ2goF5+2vR7qy6H7tF/6Kn1RcVyIvd4sw4yGpuFGg59YJsoz4Y0+3lznwm3w7WyYtkDUZAV/S/hpTNwWezW7j91Jo1vaJjuaUuhvbr9aXKVSkWJMIMWY0LKs0lndkoixq+4AsyMv4fGC11rU3UvCziPdlMKS8lU8mXQXm2t2yT1f0DBeLHqfrMZcZoSfz0sp8yi0FePBw8qKDXTwS+KLsu+pdtXiwUM3v3Tq3A10NqWS21jAxpodPFX9JmODhjA+aAT9zN0YFTiw3Tg6BQoUKPg9UMjx/zKyfoDPLvBaEAJiYcaSlpi0/wSdEtouG9QVAvy8r+Mj4ZMHYMdhqG2AUIsIuj2a0yrqT0q0WWuUHhLfLIgi3OwdbobGgM0OBbWxxF3wKYZtz8vgWusosn0LJDat10xY94iow4m+j45b9h+SJlXUI+4VC4WjQd4L7QCoJWvZ4/bGkRVuh53vST107qbmoj5f5P8s2+l6kRDd/jfSI6YSnTYRh9fGic0OVmMSJJwtCrmjXprxavKFfBZugy+nwz8Oim3jrVPIZ1RPOc6EoVJqcmSZeINttWL/6DFDsonrRkkqyK8h72cZIGxWvfvOhlHzIKiVTePkQdm+MVB81cZAsaFU5Svk+G+OYYH9+KbzmxyyZmJUG+nh14lkUzulPKdBpD6MrqZ0DjYc5ZOTi7k3/iYeip9DmbOSKH04HY0pdDd3pLt/OoesmeysPcB+6xEGW3qT3Sie+k9LF7OobAUPJc4hVhdJjbOOpwrebNmHChUh2iASDbGE6AIZGTiIYG0Q44NGUOwoJVofQX9zL4UYK1Cg4IxAIcf/q7BWSD1wMzEGSWLIXN0+OfZ4hIjp/Nt/xN6Ewd3g7P6wukkVDg+Ce2aAgUbI2SEDcH4hhIZ3ZfzA1PY3otaBs8F3Wc56mP4VfDcHht7htRAA+EdQaenLp0uhrBpMhlFcNSCKyEXtlHCczIBNz1AXOx6rPo3w4GRUp1ot4gcLIYzpK1Frw+fKeRsCJPe4eA/s/1zOpRmNVTIg9+lkGeTre23bfScMhfWPSdtd7kbI3Yil9ywW3DaPqc8ltaw2sJOD5AQ/+HSm97PrHoLR88SvbK8Htxsqs8ASL81+2T+KijzmYbF6ZHwNAXFiQ/npcS+5L9wu59HjUiH3v4aGKrGdtLaD7HxX7BJBl3qXGYMlJm7Ev6S9sK5EFPzU02Rc522RmwV7vfze4geJIn86WCvEVmJWEgf+DPTw70QP/06/ad0TtpP4q/0I0MnTg0CthSeS7uS6zHspcZTxRP7rPBR/K5eETiBMH4qqqWxGrVLT1T+d++JvYnnlevbUHeKV1Ad5q3gBZY5KJgSPYlfdQe4te4530p7gaGM266q3EKELZU7MVfT070xIU5V0gjGGYG0Axxvz6aHqSIoxHpPG9OdcHAUKFPzPQSHH/6uw1Ypl4FScaiEAeWS++wNJd0gcLkNvUd3b3Wx8BHx8P2TkQINNlOSkaCBjOXw1w0vSel8J/W6QAbLwTqIGQ5PamAPnvSKpEBufloEytUaa9q5cKXFhUz+F7LUQnExd4vkMn5dOcrSo1A02OFCVQkjquej2fywJD02lJJ7AeA6OXs+NH3Uhp1TPm9fmMOH8d1Gt/peozPGDYcCN8M0s+e8ht8Pah7wnGBArymlrYtzlIiGdIIN3qWfB3o8lBWPrK0K8wztB8mhYNRc6nicE+vhaVIe+ZvJls8h48DA1dn92V6YzrAcEHWyn9vrYSomU2/eZXBMP8FY/OcbwLkLkQ5tsJcEp0lh4eIn4rpsrp3vOkFQNlUYU6LqypqG73u1+n9QUiXLefbrkEjsbZTiydbwbCME99wW5aWnOVq4tgm+vlmHJ4GTvuvlb4NNJMlwI4lee+il0u7jt/u318gRg9b1yIzfsLug4SX4PlhhJMVHw/wVHrdl8W7GKRWUriNZHcEvMlYwOHIRapaafpTvfdZ3P8YY8ArRmOplS0al17W7Hg4fnCt+l3FlJrCGKSF0YnUwpLK1YQ4lDfhP7rUdxelw8knA7RrWBJeU/MNjS12c7Fq2ZnubO7e1CgQIFCv4QFHL8vwpLlOT8lhzwXX7qAJWtBpb9Q2wAIEkEx5bD7J99H6u3QngQjGydHFVxHJb/00uMAXZ/CKlnw6KZ0hQ34h5RB4+vFeWxIlPivi5eAHs+gUE3Q+JQUGuoNqah04Ch22VoNPDWQogOhUAzzJvfvAM9u175N4WWR3AZw/C4HHQKyCE9698Y/A3sye6FtRFyCt3UBumxnP82Kp0RPCpYPVcIbUCcEOvI7pLPC6KuN1bBZd+KCh3VXaLT8raIMhzdW3zO9Sdh+D1w9lNyDavzJI4NpGq5sQomvwkaA5qFF9GxaYiwX/p5qAxzJDauNXpe7j2OwbdB/BBJznA5ZH+WGLjoI9j+psTcgcS99ZwhPmOQKLayI7D306bfQIzkIx9fK/FsUe34TP3DJBmkcLvE1zXjrCdEvVar5alC+VGoLpD9mUJg84syGNlQKZ5nc5T3BihrjZcYg5zD9rdEPTZHiQ9cZxIlOm8zLGzljV5+uxSPbH1VSPKoByC0nQlPkHSQ0kPy/QTEiX9bIdP/ERxuB5+Ufsv8EilyKbAXc82xuXze8WUGBciNVbQ+gmj9r9t0QnRBTAgexcel31DrqmN//ZGW2DWA/uYeWDR+bKjZzoYauem8O+56Uoy/3eqhQIECBX8ECjn+b0ThDiFIDZWSmpAwxEtMmqE1QJcLobFSMnx1JimjSB7lu17FcS8xbkZ1gcSnnYYct0GzGtzeco1eyHDKGHm94p/eoa7iPTi2vsuxEV+TV2Yicy/UN8CJMogIhpXboWsSDOshVo5/tZpJu34yPLggjL6dwnjyE/HxBvgHs/Af93FW4Q2cP+A8EkPqmN1wKYbvWuWm9r5SiKi9HvrfKEpp+nmiBustkodsLRcVszQDOl8ANSfEatJxoqiy+xfItja/KEkTG54SAghCoE8eFPVUoxeLQ72XGKiOfgddp4pn2BAgxDphqFgKVs31HmfyGFHxwzuCSgsndgkRbCbGIFaUiC5yEwQSWbfrPbFfjPiXtPXteg+SRkDdyfa/O3OEKN4el/iVj34nv68fHxHvdFg6FGyD90bJtQL5LY15REpPNHr57o8sld8iQH1J2/1YS+U6bHhabBvmaJjwknijT8XhJXLuGp3cqGVq5Dxj+nkHBV1OicBb3OQNV6nlaUTvqyRnWcHvQratgIWly3yWOTxODjVktZDj3wqtSsu1UdPJtuXz0cmvmRt3PbvrM9hdd5DxwSO4MuJCzBp/0k0plDjKSDDE0N2v42mVaAUKFCg401DI8X8bCnfC/BFelXbH23D5MnmUfyqiewp563O1pFVEtPOIUq0VYnGqP1XzOxS4oCTx7xbt9F1uiRbid3wtVGQLUbd549AaIofwLm/j3Gniqc9g9kT4/AeYOBQeaFKIN+6Dz9fCW3f6bjomDKJC4dEPwdVkJ66ph8vf7MS2Of/AnatmQuohDNt9CwXY+4kM401+E0r2iQIc2Q3yt4nq+uMj3nXHPCKk0loqyuTeT2T9yxY31Wf7CSke9xRWVSgmTxWqEzth1/vy+dB0IY2t0f8GIZkqDUz9WEiesxEWXe67XvZaIfIVWaL4JgyC4+vaXvv8LWKDufpHb87zgBvhwBcS75Z+nhB9txPqy0Qpbo2i3fD1LKjKlt9Bn1libzm2XG4gQAh2MzEG+e0V7xGC3/sqCEyElXfKjZd/hCRpbHnFdz8Dbpac5u1NQ1gN5fD9rdImeCr8w6UdMGed3Ng1Y+Jrcm4qlSjZS2/0/m49blGdwzpBqhIv93uhRUugNoA6u9VnuVFlOM0nfhmppgTmd3iKfNsJDCo9V0ZciNXdSKDW0jJUF6n/jW2OChQoUHCGof6rD0DBGUbWal/7AsD6J6QaufgAFGwX+0Iz9GbJyG2PGIMQnEG3+i5LGin+02bUl4o/+NhKqMxpuw3/UIlVC02X1zo/OOd5IV4tSRnR4udthYOJ9/Pmj/FsOwRj+0gk3JQR8MEpDbIxwU76BGWw8tYNvDAzh7euzyMpuBqH00uMm1FeDSXmobw15j36WXbKzYGlVQ6q2yXHuXAafHeLxK99dTmkjIIDC303tv5RIYH1J4U4jrxXBu2+mSVkOW8T7H4fV2M9m+uHyzXa87H384XbIaVVzXa/68QHvPRG+O4fkiRSeVyGAT3txF9UZglZ//oKGYiLaqcFLW6gpG4kj/T6iv3ChBgPvk1uXIxB8pvZ9T4snC4DeOWZQvxX3S3EGIRg7pwvxSSx/UWJdtrEtnAqGmth+N2iotfkyzVttnckj4Yp73tj98Y8JIR37ydyMzbyvqYWwBHymc5TvNvV6GWg0Rgg17o1Vs2V2m2Q78Rl933fZZfva808+R5qitoet4J2kWKKZ07MVT7L4vRRbaqofw/MGn86+6WRYkrAqDESogtS0iYUKFDwt4CiHP+3wWVru8zZIMN0ZUdlSCt7DUx+R4bEfg1ag/iBE4dCzkYh0smjhMwAVOVJ0UXWD/LaHAlXrJD1WsMvTB6rh6SC2y4ErN+1ULQDBt8KkT2EKPe7VhREoKA+DJ1G6qjtTqmhdrlB2+rfz4HpDSyY/AGJi+eQ6nJwll8onrOfYl9pDDm6CajVYo1tRrAFIku+xbL9Flmg0cO4p8QTDaKkVmT6WB0AIYttrrVD/LX2GrDEwqIrRYEdPlfIrbVcdlG8lwGds3EMuBV9zloZ6DNHQlAinsYaVNG9ZJ+WWFH6m+HxiDVj6F1io8he2+pEklu2j71eUkaSR4kSn7dJloemCTne+LRYCmL6wvjnJBrOL0wU1sA4iV3bv8BryTiwUJTlCS/KMN6p0Ojhwvdl8G7dI5AwTIbmWiNhECy5QW427HXQaZKcH4itobESOkyQ39HO+eI3DogVa8reT6RoBYTEnv0EdJrsrQM/8IVE8p0Ke53YTyqz5XdusPg8icBgkc/nbpSbnkG3SAW49j9TP//XMD5oOGEdgtlRt58IXSgDzD3p5p/+Vx+WAgUKFJxxKOT4/yocNnns77LLoFFzVW/aOPmHv3U0WZcpMlTnaBAyMuAmiXE775VfjGXDViuRZiX7RV3se03blIrcjV5iDEKYNj4rymDr4afYvkLGslaD3SqkpOYETP0MonqLCgkw/D7J4a0vJd6ZwoE3YeZ4uO8deOI6eOkruP58IbwmA1zW5SCJy1qVhVjLUa1/nO5dp1GXmIJqVkce/VCFwwn+Jvjo1iKSds7zru+y48ndhKrrVAhKxBk3ElXFUdroV9G95Bq0zlcO7yTnpNZ7rQkgZKuZuDbBcvhDdsbdRu9pX6D+9hoY+k/4cgaq5s/0vspXwW65nsXi0R12l5RtHFkmJDesI6x90LteY4UMso15WMpS1DpRbBfPBlOoqLGBcZJQknYOJI4UUu3IlYSNjEW++83fLNuL6Q95pyi0xkBQ6WD13XBosRzjmIebvNZq6DEdjq3w/gYjukBQslyjZk9w0giYP0puLqxlcOgbmPy23CRUnZKisuEZKT1JO1v87sEpEgOn8/ONIhw0BzJ/kPpwrQnOf0d+99YyuRkYcY8MopqjoPP5Qrz733j6pyYKfBCmD2G8fkSbWmkFChQo+G+DQo7/L6IyV1retr8hBCRtnKQixPSW2t5Z62DLy0IKOk4SQtVstagvFX9o1mpR75oV4PZweKl4XZsf6YekSUpDZCtLRWlG28/l/SwNctpTcmtj+vxywYjLBXk/wfdzoKGSrknn8votX/L2Cn+euA4OZsP9V0gyxY3PQVE5TLo9r+12qnJQa3UM/bEHXSYvZfwzvakpzCEuSkcHtosvVaODra83kc9SSBrJYXd/Xl48lJl9IxjcOvvYHCUEccxDQqiK98hAWJ+r5bH/6nuEHLZA1faYNDoCA3Q4fnoBQ+JQGTpzO4VonvsCZK4Shf/UzOVOk+XmIyhRspVj+0t28Kke5LTxomQ7bb7NfCAWmMLtolZ3vQg+HC83L9UF0FAmdon2bBuoYMRcWJwlRBkkjeLod2Lj6HGZWEWy18kTgA7nQq8rRE0u3Cbk9azHRNXe9roMNZ77gni4AxNh0ptyXUPTJIquYFv7OcYOq9goQAh+0U65duf8G3bNh7LD8ncgcajYYTQ6SfMo2gVTP5EnJm6n3Ahufhn6XCUlMwNvamu9UKBAgQIF//NQyPH/RWT/KFFW/4+98w6Pqkzf/+dMy6T3nkASAgmhQ+i9964gHURRFLGigAXBBhbEggVREEEUFGkK0nvvJbQQQkghJKS3mUz5/fFkmISwuvtbd7+7y9zXlQsyc8p7zpwk93u/93M/NiRuEQIU3FjIVUR7UTST94hFIfti1f3LS8Q37OT5t8+RnSie2sqkKSdRSFZlchzWovq+cUOEPP0NJGdASia4u0CDKMgplO8b6o+hW/foHW+qPnkTD4f2pN1z67ht9GVYZwjygQ9+FGIMkG29R7yTR5hMDMzleJ9+l3j/OAiKE4tC9mXZRuMEPeaJnaLeUHKuJzFhe0+OXNax41wzfpywhdhrb+BUlg5tXoDre+Ds22IPqNVNiNeRL6DpuIqTWqHDy8KLXXzFT2s7F1DWYhruXm44xY8RRT6gAWSdF1L42zOSSpF6RGwPhz8ThTd2gBzHYpGEi0aj5XMtvimq/5GFYnFo+ZSo2P5xogZH97QnjDh7i0qatEtWGEKaw7gtUgDp7C1WA72XvZGIDT7RYt0oSBULg95D/MBXt4vVonYf2DpDUi+2vCTXdG6V5Df3+0QKCgFWj5TiOBCSv/ZhGLNZJm+73rCfL6ghNBorBXM6V3uxHwghv7JZntfCDJms6Dzg1yfFfxzeSu5P0k75OWgyXqwhl65KckZ0L1nRSD0ohallBeKn3v22eJ6D790q2QEHHHDAgfsTDnL83wibp7QyrmyCNs+JslaQDj8OF/9lk/GwrxI5VhTwriUFZn+U+VpebG/mUBl3t20ObyN5vvvfF3WuVldo8bjk394De07D7G9g1ynQauD5h6C4BD5ZA1lzr+NjquqZ1qTtp472HEXhHdFp4GKK2CPmTISD5+CdLfWJ6/kBYSdfFMXVyUOK/U4uqfD1RlAQ9xi3C6z4eu3F43aiKKUmA5z7CcuQZahyrnBV34Ujl+V+XEnX0HxuF/rFt+KtgWepmzQfGgwXL2zCL/bB9XwPTiwVH7XGWdTI4ixR7wctkaX/WwlQsz16Zx+cSo7B+kl24hfVBWIHCzEGIaB5N0TZDmkq+5ZkCSk2G+HAfFFN/evKNiPXi/Ls4gdYZWLk7C0pGg2GS9KFd4R8Lm5BkHpYYthC48WbfPRL8VJHdYF2L0FwM0mACG8rBXwaJ1lxuLiuqtVB5ybnKc2Ra28yXprERHaS56s0F2r3hIS1dmJsQ+pRKQbcO6/q6zfPQFyReIKHfCs+5PzropKXF8skL/e6fK56Lxi0WO75xYq0D42TNG1pMk4i5GzYOVsmK4Yi+Wz2vSuFiH6xsmqw732Ie0BUZWcfGb8DDjjggAP3NRzk+L8RAXHVXwtqIoQFhMAWVlTi56eIX/X8z0Ki2r8oS/NeNez75qWIIufqDz5R8ppPFDQYIcvhNigqaURRGa5+QsYajxUC5+wjlorirGqWjeQMWPKbEGOQQru5y+HLacJJsq3h+CiKXa1W6zjbej2f/NyOPWcky7h7vHiNa4VAdBjotS4svvQUEx7sjb/pKi5lyXIt0b2g2SMcL2nFc+8Hsj9BQ7u4b/hg0OM0O9RLSF9eMqob+8E7Cr3acKd4Lz4WBrSFMqMLNy1R1Go6GZ2lGAZ8KaTObBDftouveHO7zhEV1RYb1mgk7H9PIupcA8RvXa5HOfxJVUU0qSKOzSNMis6u7RRCGFJRzHh8kcSvdX9HGnDUf1CSIg5+BBfWQ92h4jvPPC830Le2vG82iCJum8hEdhIV2GSQyczV7RWfdbR8ZieXyrMRN1iSJHKvgme4RLjdPCnnP/61eM+9o8S/fGWTkPHyElGt+34sDWUMhXIM27NxN5w8RF2/l53BapEJgdkgxNsvBs6skGepTh/J1rY935uelVg5WyScySCThnsVEB5fLNefmywTyIMLRPFO3iXjObkUjn0J9YdLsaTv32hr7oADDjjgwH0BBzn+b0RkZ2l4kH5MvncPFrVW5wqFN0Xx7bNAmiWc/VGIUHQP6UQX1LDqsZJ2wKqHhIA4eYgXte5AWW5v9ogQ4rMrRYXtPFu8tndDoxXlLfcarBomdgLfOjC4IvarAjdzYNfJ6rtfvA6+HvD4iiZsGjoXp90zwWImtdGbDPi6BymZouZdSYUTl+HpB2DBKth2HHw9Yc7DGlaciOWXvbFM7G2inc8R6qv2kJqczcCvQ8mosGDsPa9nUGYHDo16jdCTM8Qze+5HcPamdotpPN0vj50XvGjbwN5pb67Kn2+ebcbo7EEovrWh8yyZABRliuKrcxViZiPGWldRNC+sle8NBZILnHn23g0t8tOg6xuS+GFrFJLws9grOr4C51eLf9digjMrJe7NPxasJjj2Bex+UyYTek/xPZsMorJWVviv7RJ1eMcsiY7LSYT8GxBQX5RpjbP4ftVOouoWZ0HGSVG+QZTYuoOl4UnNDkIkz/8k4zv9vTwbdQcJcda5iRqbfVHsGGN/hxv75f6U5olqHdBAkkvOrbKP0dVfvPBlOdJ4RudmV+ndg2X8myuFWeffkDEVpEPOFYjqKhnftkSMynDyFOX45mn5OdC6cMcX3ngM7HhNJkt750qW9QPfyefqgAMOOODAfQkHOf5vRHAjePB7UQ1NBkmQKC+Dy5tkGfvieiG99R4QAph1QXJj/e6KbstLEfuFrZWvoQBWj4AnToo67VtLLBOtngatXiwb90L2Jbi+X7yntmPdvgwrBsLk43eSKLzcpaNdyl1ujZpBYCiHtk30LMh4Ad8mU2kankuOwe0OMbbh0Hno0RzaNBByfDsfnvlEYfmrQpxPXNby9qS26EKLuVmm3CHGNqRnQxLxhDafDHV6i1Uh7zr6K2t4qXMQ3du0pe8Me7SXxQKTP3Wn5WuvU2f/MOkKuGactL6u3Qu6v4PVTVjsYwAAIABJREFU2cdeghdYv3ragiFfkiOiewnZrQy9h6jJNmJsw8W1WBuMRglpAXp32VfnIkkfbkHy+ex7z66yl+WLitpodPVuiCDEVO0k+b/+dUWR3vUGNHtYGm4kboFtM2UcigqG/WDf11xuJ7KjN0rSRHRPKYbzDJcOeGYjmEokE9pGzC1mSdY4MF/IsXuIdGs05Et8nm9tIdkBcTLpMpZI+kXaUSHPQ5ZKk5PABvDzWFGqbcWDnjXkmto+LwWSSTukaDGkiUwUyvJlDIpKFHGb1aIgQ4pX3QJhzG/S0rtyLviFX0Rhruyrd8ABBxxw4L6Cgxz/p8NYImqeoUC8loH1ZQndr7Z83boI218WpdI7Upb7Q5oJ4bq2Cx5YAVe3wPGvZMk+qIn9D39prmTDVkZgAynCunVeLBnHl8hyfrOH//YYr++T6DAbMbahNEeaWFSQ41oh8PggOHJRSC1I6+ecfHhuOHyxDjJzVIAenTaYVbOpBpvrwlwpu7jcBNmVhNK1e6HJuA4EhKdR2aUBYoXO921NekQjistANegEEQWbUacfwT9/L0p5FFZrRNXLMEC+wRlLo3FcdupFVuNmhGqSibrwPbj4oHhHU1rvYZzPfyOTgmYTqw5apZXEkH4V6RjX94oy2fJJGVz+PRI3nLzAu4akgeSniPpsLBZF12yQ5h9d54haX1rR1CXnqqjaNdpWj2Zz8ZX9PEOlWO7nMfK6xSwWibu3NRlEza2c9xzTTzzLO1+X7+v0g2aTJGquLF9aTXuGySqGohYleW2le1GYLt50z3BZUej2pnTGK80FLGLjsK2GpB2VAsAz34vaPXiJPJMqlRQh+sbCpXVCZG+dE+/z1W1ioXhwpXizDYUScXj4M3sCiG+UWDLMRrGLlFeyuYB4l9WONsUOOOCAA/czHOT4PxmGQjjzA+QlyVK5xQRtX5CldbcAIc5bpgnxAiFHW16CTq9WdBHzhpWD7Vmwp76D3vOFJCXvEb9su2midJ5aJqQquDEs6yWkTecmS/7bX5V9ur5570I7Q5GM5+4YMkUlzRgqoNVAt3jYOBeupIGHCzStA8VlsHYPZFZq3Gcsh9NXZfttx+yvj+gqnuV2d1mfvdzt/3dzBqNVT0wtN14YVs57P9rJzsS+cDzJlc0nXPhqg4Ja7c8LQ2sxpUVzAjb2oWaTWjg7RVBqsI/59Qmw80YsH994m5iaKk5ckjF8+OQj5GfeorPXbVRhAfjHjEVVcgt3v5romk9GOb4ILGas53/C0vkN1Ml7xB4Q2Unup7FUiJ1frBAyv1ghmfkp0OoplKOfiZIKQnp7vid+blsqyaFPpfDuyOeyjX+sTESMpdDuBbEPKCrQ+0DSdujxrpzjyBf2m1WQJseztZYGaY6xdYac78pmIcRRXcTO4xEuFgydu0yGtkwTFTekGRSmyvP1yyTIT4Zub8vx3EOgyVghzJa7FHKPYPkC8V/byHFBmpwHIKYP/DLe3mHPxVcsQjXaiK2i5HbVXOlLG8Ta03CE3L+cRLkPDUdKAaLN77xjVkViybP2fZtNkjQLBxxwwIG/AOa8PKy3c1CFBKNyvseq3j9z7Nu3MZ09j7WgEEWvRxUeirbu39Hcy4E/xT9NjhVFCQeWAUGABVhktVo/UhTFB/gRiACSgWFWqzX3nz3ffYWbZ0WV2zPX/tr2V4VsNHtYFF4bMbbBYhICotbLvpWbJID84V/zMGSetr/WZIKkAkR0gO2VMnKNRXDgQyEZhxeKKn0va0VovHhAW02V7W1oN01IVCXoddAiTr4qI6eQali1Axa/BIPbi9ocEQQpt2BSf5hRqZHctBHw/Vb5v0oF/dpAqFcZzvkXGBcHno90osQghXzHLkqXvM/XihHCZIa3VrpQ1z+SEVYLdRJe5KenmjDh63rcyoXpo2DReivXM93unO+poeKRfvELDSO7h/DOvBDemwwptwJ5c5ko2fvfDafW4AfAVEaKOYarxeG0jzqHjyUFLUYhrolbJDEi7gEYvBQS1ohaW7unqME2Ygzy/fGvxAeesl8K/Yqz7EWYbkFCRnNToOw2ZF0W/25+CtR/CFpOETVWo5diTLdAKazzCIW4obKdoUCsCXveRtIvtFCcLckZiVuke1/3ebIiEdpUCLpvHVG0bQ1JtC4SM1eYJisdnuHy3Ox+U7Zzcpc4uaSd4tv2qyPdEdUaIeA95om1wTVAUjO6zJGc5MopJiW3K1Y3EsQD7uIjfnnPGpIVHVBflOO1j4j9ZdgqUep3zhKib4PZKNaOji9LEad3hJDtGwfFCqLWYmn+JFdpRolRTUQQeNofAwcccOA+hbWsDFQqFJ0kHFmtVsqPHsew6hcsOTloWzVH8fFGHRJMycJFaOrVRR0Xi8rHG01MHdSBAX/JOMqPn8J8LoGS9z/CcisLp6EDcXluKpo60aDVoOj1KJqqNM906QrlZ8+haDRoGjVAExnxl4zlfw1/hXJsAp63Wq0nFEVxB44rirIVGA9st1qtcxVFmQ5MB176C853/6C8SAjN3bhxWFQ8RVV96dvJQ7JeFTXkJFffV+dSlRgDnPpW/JfFt6qePqAp2UH98AyKxiVpZxUVuApCmwt50TiL0mwqE9LmGsg9G2LcA92bw3srq742rhf0nw5qBR4dAO56eHooxNaE2BqQmAYhvqBXl7BWpSPUX0PzGDMN3M7TOHsL1GhB9NlnyTX8zgcbJDmhUxPYd6b6+dee8GdEaF2UjFP0OtueI2Onk+rTn6vGWK7f5Xv++leYPAg++EFU6lu5cDUdvtoAU4bI651mBvPCQ8HkF4nPec0egKbE1WzC0qdv0Wx3d8kxdnKH4Ibw0yj7CU4vxzr0u+p37uZpmfR4hAlBbDCighS/JasEhRly771qyvs38yRmzS1Yitbq9BNbjlWRCdbhT2VC8+MwUfxVaiGkfT4RkpmyH5K2VR3D1pdgyjnY/JwQ48D6sL1Sx8HyErGNFGaA1SzPw/rHZVwgqyFrH5YVkB2z5Jwj10pu8rGvZAIQ3kbI77Ke8Mh+uLi2+gdmLBQC/V0faTmecepO23H5oF+VJidXtwoBr/dgldxpQMi0s69YPfTeUHJLyP9vU0Gjp7DTApbujWHGt2pKDdCmPiyaBnUjqg/HAQcc+N+HJT8f45btlCz4DF3fXmgaN0Dl5wvGcnK79QODTOLLlnyH2/tvY9yyA12/3lizb1M843XMSddwGtAH15dfRNu44Z+crTqsZjOmhIuYLlxC0Tuh6PWYkq9jybgJgGHVGtR1YzElJmG+cIHy7bvRtG2J89hRqAL9sdzOoXTREsqPn8J09DiqyAg8v/8Ga1Y2iosL6rhY1D5/u0fB/YR/mhxbrdYMIKPi/4WKolwAQoGBQKeKzb4FduEgx/8YXAOE/NigKKKkpZ+Aj2MgrJWkGqypaEQRGg8NRsKW6VIU1eIJURqPL7Yfw9mn+nmsFiFCfjF3znOp5QoWnOzFmq3eNKll5I0xfWju6lt9X5C8ZCd38I8Ri4aikhQGJw+xW/wdaFUPVrwGLy+ColJJpMjKs3uJ3/xW/l3iBR6uoiCbzFDTI5vw5M+IL/mO4ho90aafR5e6WxIKusxGVacnz+S8T+wjj7DiWCS9mhpIzXXmwLmq1LNJpBGyK+LvSnMJP/kSN1s15eqt6q2FNWpw0YOPB3fsF1YrZNwG14r5g8EofurULPh0jX3fhOsKn2/1572WT+NVsxZWn2hUe96uegJjEabbyWg1TlUV06guUlxpKoHmj4kn99ep9vc7z5KJ0Z65kHFCXkveIysCzj7y3PT5SCwP6x6FphNF0bVZYSxmaXaSlSC2nBsHq39Qaq0U/PnGSCvmu1cmQFYXTi6V/9fqbifGNhgKK7KZK8654Qnp6nhulajLV7dWumE/y1jST1Q9Rlgr2DBZbryTh9hGKuPQx3J9B+aLDaOsUIpYNz0rHv6gRnIv1Fq5x0U3ZZJnO063tzie24xnFtkb5Rw4B28sgyXTwekPIsIdcMCB/00Yf99O/vCxuLz4LJZbtzDtP4TxXALauNg7xNgGw4ZNoNHgNLAv+eMmQblYygw/rcVqNuO14huUv8NmYTWZMJ05h+n8BVkaLS/Hkp5B2U9r0TSsD3onPDevxXT8FOq6dbCkpWPc+BuGH34CoPzQEYy79+E2ayYlH3+Gtnk8zhNGo35tOlaDgbyeg7BmicDmNHQQ7h+/hzok+C++c/99+Es9x4qiRABNgMNAYAVxxmq1ZiiK8tesI9xP8IuRIqiEn0UJjO4pRUfJu2W5uuWTEl/V/zOJVXAPgh8esO+/5x3o/LpkEOckiYoY1Lgi8/Wafbs6faWo7tJG6PMRBVdPMGXzYHacEaa39YSOo5d9Ofyl8PPdp4T0Na0DreLAzwvxu6YcEHKjqnis/Ovemxyby2Xbo18IcWo0Gle/ujzkfZhuI1Ip13riGRzKUxu6AVW7+Pl7weg3hKgATO2nZX5LLcrtRFxvJ9o39Awn1+KPMaQlycYy2qmO0vSBIj7bU4s+7RVW7RTFF6BmgJmBjdIwpfRHc2Yp6Ny53mgus7e2pEl9sWHkFkpHvyUTz9HOcy8WQxkPv96WoZ83w9VZjVoNrw7L5oXmJ3guOoVbljC8w+PZsL961u++sypOdJtIUZqFsvz6DLe8UW2b3GIN+haz8Dg8S+5XYANJDvGKFEK69104sbjqTolbhOBFd5MkDnVFIWDyHuj8mqRTtH5GtgOxFJxcUv3zKcwQwulbp7ofuc3zorg2HS/Hqzu4+v5674omIblS8KbWVc011rrYnxEQS05BqvinK9tyAJzcIOuSXPvJJbI60fZ5aaVtq7S0mKqPoSxfVklCmwMKBMZJdndAA4mv8wy3+/Z7vCuKuKKIyt72BUg7TqKh+rWt3yfPTfg9ulw74IAD/7uwlJRQ/MHHKL6+oNGg8vBAE1MbTdfOGNeur76D0YjirMdaUnqHGN95a+1GzKnpaGr/eaa68fdt5A0cDmYRMbSdOqAOC8FpUH9KFy7CecpjmC9eQtumhXiPdTo0taPRvDkLw9r1qHx90T86AdPxU7g8/SSFM2djOXMWbY9uKCrlDjEGMPy8Fv1DQ1E/cI/f6/cZ/jJyrCiKG/Az8IzVai1Q/s5OU4qiTAImAdSoUeNPtr7PoHWWdIkHVoLFCKhgeR95r8e7ohhXLlJq9kj1Y5xaBo1HC9HROsP3g0R1zL8hEVjhrUU5tjVT6L2AZM/x7PiqqoUirwjOJMGsryEh2f76h1NgfG/wcHOXfOSMk0JavCNFxbwXUg/Bki72bOArm6Wr3cYnqUwl5/Rcz7Jd/bFUbKbTwvVMOzEGWLTFkzf7tMDVNeCOLeRm3edYxet8utCdYG8zQzqp+XQNXMuAJwZL4d+7kyHUx0h5xgViLLsJunKEi80+R197Bqk5Oh77KpzrN0HjAqvnwPfbYFzj07Q72skeVabS8OOEbaxL64ifcoNREStQ/TgDAHeA2+MZ0uJDPvvFq8rlt4yD/edg8UYVnzzjx7WYl4k8VCnVQaPnRFkbZqxrwIs9BtExroiQUE+JzMtJBJ/aMhm5G7W6CQk98oX4hxUFmj9uz/W1WqUgzb1CFbi6VfKJEypJ24oitgytixQOugWJxcJQIGpvYH3xBwM0e1TuRa8PJCvYWCxkNKSZTMp+expOLRdP7+63ZGwaJ8ng3jOv6rj3vANtn4MTS+z3t0Zb8Tyf+1HGXG+YRLV5hFad3KnUYvmprFBH94Qa7SC6txD025flHvhESgqIDToXaDVFVPmS21LMl7QNwtvQPeoWEFnlFjeKdviOHXDgfoSiVqO4uaKOikAJ8Eel0VDw2FSsOTm4fzqfO12kKuDUvzem1LRqxBhA8fNDcflz1diceYuCyc/cIcYA5bv2oJvzKiULFqIf/RAYDJjOXEcJCqJkxizM15JBr8f1telou3TEfOkK5oQLWLKzKT9wGPd3Xsfw62ZUnp6UfrVUDursjKZONOYbaZjOJoCDHP815FhRFC1CjFdYrVbbX9pMRVGCK1TjYODWvfa1Wq2LgEUA8fHx1nttc98ieY806CjMkOKmHvOk2KgwrWJ5vdIyTnmJKHR3wz0YUg5KO+CfRwtB2v6qkNe4oWKDsGXv6r3AyQ19bipOujgMdzUxM5urEmOAt5dDy3rQ0u8CrBgg5A3E59l+hiiPuddkrIH15evMD3ZiDBDWUpTAu+B/4wdmje/Ph6vAzxPefxL23uUXLjPCIz+05btxq1Anb0MxlfBt1jxmLpZH+2qamoMX4I2JYtO4lQu3CyE1G9KydLSKjcFQmsdTF6ewapmKGoHuPDYQPnlatl+0AX4/DM46iDX+XrW5hsVEzcR3KbW0YlizE6jW32WPOLmU+JETGdiuHev2yUtxETC4A0x6T+LsLt+w0qVFEyyBi1HOrMDkFs4xjyd4+MumZObAmC/rcmvZVdj5msSagRDMB76H2r3tMWwhTcU+se4xeztqq1WSLPp+Cld/l4nQme+g5VQhuknbJc/YZIArvwkRbvOckFuVStTdrAT4/QUhvh6hcl7fclj7qDxDvrUkZm7gYiHV/rHyHHlHSaFd7lWxB43eKBMyn1pgMtoj1CI7y9e2l4WgPnlKvNVaF2nosXKIPMNhLeD6brFsbJ0JbZ6FrdPt19jnIykYzEqQAseOL4vVZ8Nkyf2OGwzBTaWLX3Bj8XvbOk1qnYVYb5gs0YMAGacIj7zIhxO+59klIhO7OcN7T4itxwEHHLi/oDg54Tr9efKHjUUTUZP8oSPvkNaSjz7D49tFlP2wGmthEU5DBoBag65LR1Te3mjbtKT8wOE7x3L/cC7q0JA/Pae1uBhLalr1N4xGrDk5qDzdQaNF26QR1sxMIcaAyzNPUrrgMyy3hHYZ1/2Ky/TnUYWFYFi5GtOps2i7dcJjyedSXKhWU7ZqDbp+vdE0boA589ZfVjT434q/Iq1CAb4GLlit1vmV3loPjAPmVvy77h67O/C3kJ8qhVJFFR0zyvLg16cwjdxIUmIOLp5hhLl+YS+iKy8VMuBZw56bq9ZKI5DNz0N4q6qBv7nXYP/7XB10ErWuFRFpXwvBWD+ZWmUFvP7AdmZ8b//hbd9QvLZ3I69IPMKcXWknxiDFTzmJcGghZFQkBGhdYPw2IcNuAeAfJ6TLVCbL3Dp3IWkV0PjWpG8EdGkmCRDnkiDYF2Y/DPNXQX5FRLOnp55XtnUkPqIBdSL0fPpV1cfabBau16kJpN6STn2rd0rHvXG99Yzu0Z4fdqooMwr5f/5T+GAK7D0txXu1Q6XgzhpcfX6nLk5n/CAz2usp4qW9C2V5OfRrA0M7CpF3dxGCbhMYysutuGcfkgKzZhPRFKRx4UYQmTlCxhY+Z8W74ISdGIOQ2c3Pw5BvZdKjcZECu5SDdnJXGVaLFEcGNoDdb0syyZhNkLJPcoI7zJR4wPJiIaS2HOy0o7DxSftEpiAN1oyH4augZlsh11Yz1AwQD/Oodfb0DK9w+bpTdlAJhZnSrETjBKmH7QkpiiLn96pp33bYSml9fuEX6QrpW0eaiJxaJukcWhfwCJH36g+TiYFbkIzr0kYhxj7Rctztr8oxz/0ohHriLiH4JXlSsHjXvVNd28Fj/X6l8bO1KNDVpE7dIOpE/o2iVAcccOB/HrqO7fBa+wOmK4lV1FzzlUQKp07D7bMPKT90FGtRMSXvfIC1uBh0Otw+mIvz449gyS9AE1sHXbvWf9f5VMFBOPXvjWG9/e8iigJ6PbruXVBH18J04RKq6HiMv/1u30Snu0OMbSj99Eucn59K2XIRokznzmM6fgqVtxflx0/iOu0ZCme+jjqyJp6rvnOQ47/gGG2BMcBZRVFs5sSZCClepSjKRCAFePAvONf9g4I0OzG2wWwkKdXEzznDSTgO07rvJM60BU3aAUj4BbPWE/PI31CnH0ZlLEDxjpLOeT3eFdXvbniE8e2+YLaef4q1z/UkUJ0DbZ5BXZLNJP15mtQs5GyKC56BARy57ER+sRQiVVaUh3UGfy8rnNxZ/fgZJ0GpRMjLS6RYzGKC0GaweZo0JgEhVX0WgM5ZyJCTB6r6D9AkBPafhV4vSPYxwIB28PnzcCFZav8aR8ODr8Hsh30o14CXmyRE2BDkAwFeMHyWEFSNGqaPFoL87SYY1F7FlCESM3crD75YK6px7YrUul8PweMDIdm9D/58UOUSb9SaSqfnXDj2dCDeQQ3hZiVpW6OnyLU2j1YUcLk7Q3Y+hPnDkA6wdDO0iMqHjU/I9s0eQQmsx5joHTR4xhsfdTZR8d1Qrtxj0SUvWbrrNX8cVDppMZ2FKLdZF6tuq3ESr3pesnzvGS7k0ckTnKyQslfsFscXC+Gmg2yXdaGqwg+Qd03UYJsN51aCeOBbPSVe4yqf/ylpm63WiYocUFHc6B4oGcbrH7dv6xkuyq660q+k8lJpRX1mRcW5zsvkacJ2SWjxqQV+datmbzt7SVzb3nflmCCq8eFPq44tP0UU6vwbokTX6VP9HisKTp4BdHDJEGN9cET1bRxwwIH7BopOh65ju3srRWo1Kjc31IH+qLw8UdePw3T4KJhMmM6cxWnoQAxffoPRzxdt21Z/V46TytkZl5eew1pSinHbTlRBgbg8OwXTzUxcXnkJS2EhmvZtKRg/CddnnvzDY1nNZiipWkBdvnc/rq/NwLD+V4rnf4Lz6IcoXfQNlpQbEHd/5yX/FWkV+/jbeV1d/9nj/y/hdj7sOS3WgA4NoUG0dI27J1z8RAmurEYqCpdygli0zsLhF34n4ODT5AZ0xerVHJ8xk8gwhhJwejnqA2/bW8nVGwouARU5sl3t1fgqNTfbfsonbwZSUgYuSr4oidteBqsVL6B743F09zFzMWQ69T1h+9UarJ7jzrzlkJwJfVtLpnCdcAUaPCRFfZVRswPsvqvYLOcyhLYQ/6mNGIMUb13aCPWHS6MKZ29wcsdsho9/EmLsoocFT0nTkEspsPMklBkAK0QGweUUCT8Y1wteqtTnYtIAeOYTIcYgKRdzl8PLY2H2Esk+frdCmI2PhQl9RLU9XSGE38qVfY+XtMazwzpqXXwNtamI/KYvMntHf5IzYG1iU8Y2n4L69FJIOYDVO4rcjp/y0zn5BWMw2icVJQaICrGy5tUiOp/tZR/oia+h14dotQrN02ZVtJZuJSTwboS3EpV00/NiYak7WBp/RHWBHx8UK45aB11mi+/XRowbjpRViB+GCvnUukixnrEIYgeK7cWGykkpNrj6V10hsH12HmFVfdDJe6UBja05R2ADGPyNJKqARKu5+MLVHeDqJ+2jw1pUPW7uNTj7fdXXirOg+LbYIu6F24nwbQ8ZU7e35LW7m9PYYCwW25LJIApyaPOq0YlxQyX1otFIe5KLAw44cN9DUz8Ol+enUvLBx/KCWo3bG69gzs1BFRRI2fer0HZoi/PUyaiDAin7bQvmhIuUHzgEgCXjJqqoyD84gx3qyJrg64Pb/Lmoa9ag/PRZNLWiyOs7FKfRD6F2d8WaeYuyn9bi+uYsShd9gzqmNoq3N9Zce2sJlycnYfhlQ9WDV6oNs6TcQOXvV+31+xWODnn/JlgskoObmQMt6sLRi3AyETo0gi5N7/Es+taCQYth9ShRWhWFG00/4J1f6/L+gwm4nPmUH0N/Zc76WhhNKmYMSKVvi1J0BysahlitFUvOYRDZRRqCuPpLy2GLCfzqUm4JpaAYxnTKxzVzr6htla0Xp77FOnwVsb92heIsWsQMoDxoOo1fb8nNHElxiAyG6zehyGs84V01eO6saIlc7wFpNmG8qz1vTH+JCLOUS1pC2xeExJ1eIYkaZbmSO2sygM4Nc6PapGaJOPjhU/Da12KLABjVHQqKIS1bMmgTroOPJyRcg3ceg5RMIblxERYKiqt29jOZxarh7W4nzSBE+bEBEBUKs5faX/9wFexf6IxRN4CE0I6cuGgm8YoPtnS7R7+OJqGfJ6/0b4jVZGJvcggjZ0YyfZQo0pXPMWkATGh9jcBlMVWTFjTOYovxCIPwlqB1kwmEf33o+7FMXAyFQmDbvwTfdBYCuOsNSaB4aLXYHcb8Ju3ErWbIOC2Fbo1HgdpJLAbfVopXKy+R/VtNFYIa1BizGS6nQpBLfbxbTRWCCKJA9/sUfqvUUc4Gz3Dx7gKYTaIsV+5al3lWGn/YyLGLjzwjdfoBVvu+laHSSCOSykkXcG9vvQ3ZFyvaUSOTtRaT4fJmaDRKiv1scPYRq0m7aZLMYUWSXJpNFE+01gWCGgiB945y/LFwwAEH7kDl6Ynra9Nxemgo1nITlJSiCgrAkl9IwZiJ6MeMRFNXJtTGzdvQBPhRunARWK2oa0Wh8nD/kzPYoQ4Oxu2FZyh4fCpO/ftQvmU7uj49oagIy/kLqFrF4/HzCkyHj4FOi/vH71P8xlxcpz+H6ex5zNdvoB82BIvRgMrXB3Pi1TvHdho2FOPWHQAovr5Yi4pQ1ayBJq56hOn9Bgc5/jfheiYs3wIzx8Aj79rJkoseNswVP+wdFGXBrbOg84CJeyi+nUmZ1YW8XC0PtcokQpfI/oC3GDm/9p1dHv0ijJXeRQxT6+7EoxHTX3Jsi7OhRmuo3UuI6fHF0H46Tt7+ADSrWYjKVFq12KwCys3Td+wdysW16NQaQru9TWh0JGVGKyu2apn6EeQXudK6/iS+mDKM+tpjQmicPKSIcO9cIcnNHpEEiwMfSvvfqK5SiOfkIeqcezBknrcXGualoNPBc8PBZILP1tqJMcCKrfDGI7ByGzSrA2N6ig95aEcr55KsJN9UMaDedepqsvB2jye3kgiv1UiR32vj4fW70sxu5kiG8ewJcPG6EPPaYWC2iDXjYoo9Xq53S3uL6/kb/BnZ9jZjPo/lQoUovvAX+Ho6LP8dDOXwyuBUFFMZu846M9wtUOwzIAVkfjFwY7/ch1qpHHhbAAAgAElEQVTd4edxcHwRDFwknuDARlBwQ3zCPw4Ty8OxRRA3RDy4GacgqrPYD459JUVreg9RX0ObS/vwuoOq5Q6bvKLJ9u/LmezGrFmvwsdDJg0LVtVn6WOT6TS8KxpjDop7EBz5UkitjTCDkPXgSg+wsbh6oxmoSIy4C9o/8PB6R8kkYFel1YegRlXV7WrHq1Qtd2WTPG9xQyQX2StCXvOvCxGdYeUgId8dZkqUm21iWLO93LvCDEAFRUfgxj7xdYc2FyXfQZYdcOC+huLujjWvgKJpL2M6dQZVaAhuH87Ffc33WI6dBAU0cXWxFhRgvnJVCt+0Wtw/X4DKr3rE5x9BG98Er60bsGZl4TR+FJbUNFzKplEy9wOcBvcHq5WSjz8HsxnXF5/FdPwkRcdPoq4djSo4CMORY+j79sJ56mSc0gdhOn8BdXQUKn8/Ch+bCjodrq/PwHIjHa8136MOv4cN8z6Dgxz/m2C2SOTZ+v1VVcSSMvhpVyVyXJAO6yaR61IPJaozbqWpuBx6F9f0Y/gC9dxCyW/1IwtXR1U7x+e/uTK4/Qi0WWdIjnmFnKQLhMa/TeC592BjhW/TL0Y8yKW5XCyIpGsziI0LxGL1RRVQT4iVDWptdZUu95o0UUg7yumyLox7y770fvCcwotLvFk9pzuut4/Bkq6iwDUaI8pgZBexdTSbKIWEaytFz13aAIO+qeqzjuwESBHexv1wOKH6fS0pg85NxA6Rni3KMijUCFRoWAu6BBwget9TrHh8FyM+r09+kUxI5k8Rwr3psL2wD8DVWSwcc5dDiB+cuAwWKyzeCN9Mh4spVc+/6TC8Og62H4e3R2YQc3MRfVt/QH6xQqgfnLsG569BpF8xrzT7icB9z0FZHoXRIzEMXonTlmektbHeE34eaz+wf10Y+KW8lnpU/Lg5ibB2YtUBlOZIOgSIUgxC3No8A9tmSkYywLnV0Ot9yf+1xZ6pNJxr9TOLz7Yn9II30ytZUTxcZVLS651Y1OpYjr1zkYbLKtSE2P7S+a4gHYIbySSncltxZ0+IHQT73q061hptq3+AJiOkH4PUI+IXDm8jNhEQK1Crp0TRvXFQ0iUiOkgB3t9CYD2Jhrta0dnv1nmZaPz+vJDdkHghuGsq7nXzx+DggqorJtf3yjF2zJL3TQYpIN38gthDus8Vz3L+DclO1rmJJcT771smdcABB/77Ybp4mcKp0zBfkBoPS1o6BaMfwXvP7+gG9af88FEMGzdhSU6h7IefcJs7B23Htmhi6vx/nU/t7QXeFb/ra9ZA26gBTj27Yc7LRe3rh6ZuDObkFCy5dpHLfCUR85VEVOFhGNVqypYuBxcXsXp8vwr3hfPx+HaR2DCCg9CEBFdrN32/wnEX/k2oGShtZ38/Uv29ymoo6cdJ0zWlLLAztX4bLH+c04/deVtVlIZrwVlC/BpUO46vh0Ji7Vc47+7DY9O9yCuKISrYwooJPrRI6yqEKPsS1rzrFMaMZeXaKExm6D1dy+n3e1KvnRcc/JBSXTDFvvH4xsaj/DrFfgJFEfX39mWwlHPlei5Q1Zf6+2G4mW2mVvox8cQaCkRl9KsD13YL4TNbYO34qoM3l0P6MUwR3Um3xOJUswmB4aKM7zsjJLV1PfFsV7lmTyujWmfg7uVOg0n2paqUTPka3bgh0SW36Xm2M9teP8GBlHBC/KSz3aSPhQB6usKWo9KWemhHeHOZ+MNnjuFOBBvcuwZDUaB1XQNHZhwg7spM9OfO0KffexhNai5eh+eGiY3GN/cYgbvs1+x+ZTkWTzdo+rBMWFYNq3rgrAviCQZR4S0WKWpTaapaMeoPg8u/Yo3oiOJXQV5Dmsu9thFjGy5u4GbtJ3HrsxS338aTXm8mQ5f1Jb6umi2/Vd20oFjuka0A82ZGEQ2bThD1tLxE0h7avSjP573QeIw8JxfXiTrbaipEtLdfj23SlbQdlve1k1PPMBi/3U6QXfykmNTZR7rYFWf/sc3BNUC8zTcOiv84sL7YKWxFitd2VvU2u/jKce+GpeLeHV8synJJtowj8yxknoGtM+zb9v0Eru2qSA1xdAdx4K+DJTcPS1YWKl9fVL7S3dRSWIg5JRXFxRlNZMT/6fjuZ1hSU+8Q4zswGrHm5pE/fCzG7btAUXCe+gTun36A+eo1zBmZmC9dQalZA2taOopWi7phA9RBAfy9vSFsULm5oetgFxw8vl5I/phHUYdVFw/0Dz1A2c8VgWElJZiTJCdeFeCPfkDff+i89wsc5PjfBK1GUhUe6go77uqEO7J7pW9KckkKnED8packPzbvOndDd3whQ7tO4JONFTFqCInp1hwuFkQx8l17ykxShoox37Zi36Cn8T8rjReUlH1M2DePtQfsxxw8vw77Pgolqe1Q9iXoMZeocLtgoUX7RjTf0VT8o0GNhWDfvgLXdhIY3r7a2GqFWPBIWQ8qIXBlAS04ELmQ1adr4aXS0j3VmaQMBc/gNTQP+oWI09PuEKPUoOF8ua8576/S4OcJX70Igd6i5G47Du8/IfaU6zfF6vDsgyYGe/9ExKpR3OiwHItlRLXxWKjwGhsLuZRYxNMV6uiXL8D3s+DMVRjYXrzfa/fB8wuFh8bWkC6ANkSFyD1uFG0v1AMY272c9jdn4HJCOrslN/uYcW8p3KjYd8tReOEhK6/GVrcZqM6vEm+xqcyeTVwZZoMUk4XGixf75DLphnjsayhMleI6vTfW+sMxRvYmL+EkruFluIXVrmj8URXpoSP59PxAElI0PFC/ER5egSSmq2ndgGqZ1iC+bJUizVfCavrCrj2Qc1WU6vYv/nFr8MD6MGQZZJ0XchzQQFIudrwu1oaa7aHBiDsFoHeQnwo3DtnJcfIeWNbTbrXROMG4LaIg/y14htuTKixmIa42WC2i1NtwbZckVVzaaH9NpRZ/tm1/ELId1EhsMHe3zN79lthVLv0K3d8Bv9o44MA/Ckt+PqbzF7BkZaN4eWLOzILychSrldIff8Zt5gso7m4UPjud8m07UdzdcZs3B/3YkahcHeHb/26ofHxQPDywFlT63e3igjkxSYgx4Pryixi27cDww2osmbfQDeiLOjoKbWEhpvRM1F6eFM+bj8rHG+dHJ6Dt0hGVVvv/NR5tk8b47NqM6cYN3L28KHplDtaCAvTDh6J4eqBr24qyJHvzJFVUJNrGDf+ZW/A/DQc5/lfDZBDVSetKqL8X/VpLDNn8H4XgTR8F3StqlE5egZ2XRhHkaaCt4aYUYzUaVf2Y9YfTpK4Tez+FXRWtnL3dAasorJXiFwFITFORpovHv+L7oohBHNlW1esZFQwnr7my9RhsPyakEVS46Gvw+1tXaGNYAv4x4oe2WqA0l8ZF3zKiXSwr98lSj04Ln03OxX/HeKzDfkDRe7Gz5jf0e8/eKe/j9bD8VZj6URTBnlNY06eMoJRlHKm/klV7G1NuVjH7YbiaJjFrq3ZKcZ1OCzO/gvG9JFWrTUwxnc71Q79nFwBhp2Ywc8RgZn5jvy4vN2jkcgKcPLjUfCkzF9sTB7LyYc63kJYFfVrJtpsrMtrdnOGtSZKI8co4UKtESR31Bmz7ENbuEXtFk9owpsV1XIwxcF6SRc6Vt+ZGVtXiv0/WKMx58x5t1bwjRVmNfxwajITT39nf0+jBIxweWA6eNeHrDqIYn/xGWkN3nElRQFtybuUQlLQYp2UdCLRawdkH68i1KDZfcKUottOa/mQV6jh+GTYciGXWBHn914NSJDh3uf30Oq08U2oVLH3JSMzJR4UYg3jTt70CE/dWv6bK0LtLqgaInWPjFLgmxR+kHhHSfHdcIQgBzU2RieHJpVWb3ZgMcGLpH5PjylCpxZphMYkK7B4CoS2FxO6YJW3Me38osXbnfpSM5RaT4fBC2d+/bkURZEMhyBqnqq2vQSYueg9ph16jDVj7ys+KAw78nTDn5FL8ymzKj51E17UT1vwCtL26YTp2AtPps+hHj8B0ORFFp0MdGkK5omAtLKTwiWdRR9fCqXuX/+tLuO+gadwQt3ffoPDxp++85vLsFMrPnAVAFRmBKjICXeeOWBvcRtOqOYpOB+XlmHPyUHu4U37oCLpOHaC8nPKjx7GqVGhi66AJD7v3Sf8EKj9fdH6+6Jo0xql/b6xGI4q7O+brKdC3F7ouHSn7aS3aVs3RDx2Eukb4X3An/jfhIMf/SqQdlz+4yXvE51jvQQJrtmPSABUPdJKVYe8KJ8DJK9DpKSgqVTPvcReSajxFdMZ4qb5vMl6Ik8WMMWYI5XVG4go0jBaC3eYJiA6FiCBodo+/yb6e4GMVs2xJeHduho3AtRI3dnWG3q3gaiq46m3EWFBSBq8vd2dt85O4bJ0hxWFYoP6D+B/6hI/qGHk0fiw5RndqR7oQl/AM1O7NlTQrgSN2M29+1QGVGWHHcXhxBDzziROnnEfg1nQ03WfXvNMYw0kHy16W4jeAj1bDm4/A2auS9tG2oZUIywn06bvvHFepP4wJIScJfbY2S3f5UD+smHE9LcR61+Gwz0mGvB9Vxb6i11pJy5JlrN8OiW/59Ych2r+I+IBrXLfEMXNRVR9FfCzkFsB3W6BGAHz8M7ibdLzQpSbqQYuhJAdrUXXV0GKBYnUATiHxdouMxkmsBzYfa93BYiE4v0oSJTq+LP5bJ1fY/prdSmExwcUNkJuMsd8ayhIPoDtSydtbmoOy712s3eaSNPIGt1NTCVddJjjEk7wUX7Ydg9E9JLrudKIkpew4IQ1WZk0Q20+NAHhsILg6wYOt86hVtgtl1/aqF2W1SALK34vMs3ZibMPp7+TZ3lOps6Cikh+MC2ugMF0mlnejuFLus8koirTZLGp6zjXxb4e3llbRIJOQXvOh7TTJ0XbxEz973UFSPGgsEgI8cp2o+Ee/kMi46J5SxFqaKz7n25clHWTz81XH0+Ahu/KcvAvcAh3k2IF/CObTZ1C8vFCHh1Hy3gL0j0+k5J0PMB06gvPUJyhb9A3l+2SpT9OgHq6vvETxG5JMZLp4yUGO/w+gqNXox4xAHVMH89UkVMFBaFs2x/j7Nsq+WorL1CcomfsB5isVS41fLcFl+vOULfsexc8H11dnoK4bAwWFmJOuUbZhEx6LPqF0+Upcn5iEytPzjwfwJ1CH2Yvq1LaItsYNcR4/+p867v0CBzn+V6EwE357RrKDQTqBJe+God9BcCN8PKpuvvmw3SKx5zQEtOyHe4dFBJ6bhzW4GQz/CaUglZJSDbduq6hTYSuKDoMnBsHWY7I6veUoPNpfYuNAfLJvTrRwRdcdhhxj+bFozCc86dVS7Bxmi6iDH66C1ydQJdHBhoQUDYVtwnAxG7EeX4qh81ycck6htH4a34Rf6Fh6DhqPhf0LoDCd1KFH+flELEWJKsrK7UvmL/ZPZ0zj89QKLAVDIa1ficLqEc47awMqt6THYITdJyE8AG5UdLSb9hm0rQ/dm8OnPyuM7N6WgNgn8LmwUAhW6iEC9r/HaK0zo+rHozQcCQe/JXfABq6V+ZGVX/WaGtYswcPVlYKKpLmdJ+XryAu7qb2uH15dlzJ95Bje+1GF2Qyh/tKZb+lm8SPfrjheUpYbyunv7uTx1o+3EOI3mfRsu39sSq9s3C98g7HzW+gyj4OpVNTHvfOEGN++AnvepmzoGrJrTiSLGjjr3ImtWNlHddcym94LtM4oioJ36YVqn5ex3MJPR4OZvNCHotIQwvxbsGoO1I2UokWzGZ4fDl//KorxkA7SLbBBlDw7AV6gts0Lzm0V/7OLX3Wi6vYP+GuVCjVdpZY8Zf+6UiAX1FAmAieXyjkaj5GkDUWBqO4Q06+q5QGgfkU/odzr0mjGahVibCiEq1vFKuEbA2N+lUhEkOI+r0oqiVojucwmo7RPP/+TnHPHa1LA1/RhyDgB+z+A0Rsk6UPvDefXSAJL7jXxTZeXQVGGNEMBOe+BD6Wg7x+5Pw7c1zAXl2BOTMKwRnyhaj8/yg4dQXF3R/HyQNexHbrOHUCtxnIzE8utLFQ1wiWb1tv7T47uwL8KKhcXnDq1h052i6GuYzv040dDaamdGFeg9PPFOE8YTcmChVhSblD60WdY0jNQ14vDbfbLlMz/BKfhQzFfvIyqZfN/9+U4UAkOcvyvQtZ56SbmHSl/SEHUs9tXpML/LlROTPj1IKRk+hL13KMUeLegduKbsHoEmMrwUqnRNnkW4t7BaNFw6opk/DaOBhQYNUe4wtKZ0uWtqBTmfa8i+WYDFAW+ewXeWynNO2ZXRJipVBJplpUv9oq7MapDHv43N3C29Qa+PtOaA/N8GdQ2niHNs4n1rS3FX5ufxersz46Wx5n8VgxX0xR6NIfHBiocvQhP977FK0EzcNXWgdWvgdVCPFDeYRYR/tOAqqkYxQbJB64MHw9ReY9cgNgaKhp2f482t3ah9a0j5AqgvBTl+l5R+eKGkJaUzitf+THvcdh1UpTwp3pn0+ryUyx6eA6jPqt9x4by9oh06t4QFdb/6Mu8OqAG49pEcvGWF+FBGurHuFFQDKsrCaDTuxxH9bu9UUXkqefYNCmClUndOXRJy0OdDMSEubAy8wdunVQzrK4/oWdfR512SArUgpuIMuziy3ljU577Nox9Z0TNXzkLzifBlOZdcNG+Q0FQV44GvsqRtFBqhrjSyOyBk2/bO3YZW7OLhNpvM+5VnzsTjtQsGP0G7P0UNsyDZz+RZ+TLF6BhLahTQ5qp5BfLPVZXFszzkiWzuN008QfbFOz2Lwmxt1pFRVbdo1qxMgIaiMc4rLlkWieskZ+NqG7gFiwqbVmudKszG8WzXJQu//aYB6eXA1ZoPA68IiHzHJxaAYY8UXoBIjtD04miGh/8CK7vsZPjv4X0Y/DzGLmO2H5S0Je4Rb4A+n8BBTclKcRYJD7r1SPt+/eYJx5qkLboVrOozzbPsgMO/AkseXkoLi4Y1v9a7T11zRpoakVROHUa1nyZkavrxeE85iE0MbWxRtZE/Xc2k3Dg3wN1aAjuC+dT9uPP1d6zFheDs2S6my5cAp38kTOfT6BsxY+oY+pgvZ2D1VhebV8H/r1QrJWLYf6PER8fbz127Nifb/ifDqtVGhBc2wW5SZK1euJrqZgftQ5iB5BXJI1AUm5CqB94e0DbJ6rWJv3yFjQr+47Q3WNBreVKky84XNKW/DI9TZsEklemp/90+z5je0rB35JN0iVu4Mzq/uMPnoTENOjVUtTPIF/IK4SjF6BDEygokqYac1cIsX6oK8x+8Caq3Eu0f7c96bftftqR3Sx81v5L3LdL++NzrdbQ/MPBVP65njxIrB7NtVupb9kqRMZQVZ6+MeQIEc9UnSX/9AZsPABLK3hHVIionLa4sdphMGciRAcaaGpcDWvGVL3QmH7QcAQlmkBe29SUD9d707QO9G1eyuvmdlCcjaX10xhxpUztTbFLLbzPfIhL1iHoOFMUxbJcuLodaveUrmzd3qSwqJziaycpy7hCscqfWP/bqH8ZSzWM/Z0Ujx48Md/KpkMKWg1M7Ccq+DP982idNx9N4kZRPoGrrZfT5otRPP0gvLNcSHx8DIQFyGQg4e2DLD8Ry5Qv7CpRsxj4bnoptU27UeWnSKybqz9rb/Vj6NzqquWRL6FZLNwugKISicjTaeHUFZizFA4lSNfDp4aIBzvQB5wOvi2k2CsCmo6XBAzPmhJhlnNVyHJBqkTN1eouMW73wu1EiT7b8IRYhWzQ6GH0RlgxoGqhW+8FEkWnc4O+H4lablVEzd02Uwh13wXS4Q7E7pC0E5K2SZpFyyeBiki7P8KBBbDpWftYOkwHQxEY8uU4tXpAabaM31wOm+46ntYZBi+FrATIviS53Q8sv3etgAMOVII5JxfT6bOYL1xCXTuKwikvYL58BQD9mBGYziWgeHmhuLthvIs4u733FqrAQNBpMV+7DlhxGtAX7X3e8vc/CabzF8hp2UkIcQX0Ix7EdPEyppOncf/8IwqfngZGezW0+1efYti8DY+F81EH/kHBswN/GRRFOW61WuPvft2hHP8rkLxHlK7CDClIStoOzR6FC+sgsAGFxbB4Q9UWx08Ohl0fw8xFQlxGdJemER5eUoV8pckX9Fw+juu3RKFTLROiq9PakwaW/Q49WojiePmGeEevZVQdWr0IIWgDK5Ko9DpYNA1iI+C5T2T7qBAZj95JCHeNwCB+OxhQhRgDrNyuok29R+jcoxFRqtMk5rXg7gnv52th4bMQ7pcPBbpqxBiAolu8+Sis3wdOWiGRt3Jhxmgh14mpYjV5dTEEeIuNJMhXyPveBCdu+A2lQ9QWvJMqCtpaPy35tqtH4QLMq92PGmM+4dnvIrCYdczo2xyn4BhUv7+I3mJCD3iFtYLoHhAWC+sfFyKk0Utr5QMLoPMsUBTcr/2Ee+U84rGbJA+6cmxaSDNMalfe+x42HRJ7RbkJvlgrhH7tUS/Oukykc416hNS5ybmSxsxc34LsfPhhuxQI/rQLnJ3gwc5QPwoWn27N7GVVb9vxS5CUaiLmylyx7FQguFdStVsc4A1bjsnEp3s8aNVSvGksh4dmi5cb4JtfxYs852HxIjtpX6R5i2CiTz0lSRNeNaHFE7C0m/3gdQfBzdOSuNHs4eqf7/V9QmKbTqhKjOv0hyZjReEd9DWkHhICHd4aQptByylCwtdNsj83DYZLzvLFDeILtlolbeLyb/b25TlXYctLMPq36mO5G85e9v+byuQaa7STTOWdr4sX3Nkb9L6QXd3CQnkpOLnZG+iMXCctvB1w4A9gKSqi+I25lC6oKPx0dsbju68wHT+FolJhLS9H37olirsbJa+/XW1/c2oahi3bKd+6A9c3Z1H8ymwsqWmo589FpfuDDpL/YpQnXMC4fTfm5OtoW8ajadoItZ8/5owMzNdvYDpxCnVYGJr4Jv/zRF5Try5e2zZQ/Oa7mC9exmlgX6zGckwJF3Ge/Cjo9VWIsSo0BFV4GG4vT3MQ4/8AOMjxX41bCbBysL2F7eVfocMM8RwPWgTekZw+U70r28JfhAj9/j7cyIKXvxI1b8HYZkzwb8ih4nZ3iDFIkdeS36BvK1izx36cjGwhuHXCrLwx0cq4d1R31OM29a2YrfDeSrsftswI0z6H1XPsRDopXdRLgDb1oEYgaDRViTGIVzk9R0u9j9rwwxOeBDglAVU763i6QWQIZKpj8Cz7f+ydd3hU5dr1f3tqMpPeKUnoCSX03pv03kEEpSmCYEUEUUB6U0QsIIpIFxCU3nvvnUBCEggkkN6Tad8fd5JJ4Tvn+L7nHH3PybquXDA7e3afzHruZ91rHZVo5JTHBTaiw+RSjtlrYMUkOHMTRs0Xx4gFY6F6eXgaL8dks4nu924k5Jjh7C3RAmdmOxKh/47W7cYTk6SijeEIurCl9l3c38nLLTuy0Gs8PVuqiQj+hCqHOqAU9At+fBYaj4dnN2W6Py5UyNKx2ULS0p5K6MWuCYUvwrE50Ocn8b1NipSgi0ZvEq/48+sLzByexotk4pOtgRj0AQzrpORf67z7qijwWhchrq/MkmXfvgfpWcW3l5GaUYgYA9S4M4EZr/3Kpz/Kx1uvg/cHyWxAQgrsXijSneXbxI0jNqHwNi/dg+vh8NF3ABo8XF5j/4RK1DnZFtpMh53jCr/hznZoOwNOLxYNcZkCg/Dkx7Cxv3gJW3JjmU0ZEmVe+2XYOtTuRuFRAXqshNRoeHAA6o+G9X1EepIbCMPDo1Cpk1isOXrKvSpdF47OKnxMFhNkFTmxF8G/qdi+JT+S14oKqvUSeUeLyTLjc/tXCa7xCrIffx7cK0hVvcsXf39fJShBLiy37tiJMaCqUA5bfCIZS78GiwV1gD/GGVNRAsuiH9CHjLmLCr1f5e2N6eSPOC2ag8rPF4dXBpO5cjWOo19DVau4B/6/A+aHEST3HJQfT5wd4I/LimVkfbca6/PnaBs1QFWuHGDDEnofa0oymqAgCbf4D4WucUNUP3yD5cYtMtduRFO2DIZ3xmM1m7E+eWJfUavFefkSdG1a/qmDmxLYUUKO/5nISpEv2aINVJd/gMZvi1YSmdbOzC7+9sfPwNkgcoJtuXznje8DMQ3fQbZdWZqPZ4kSMJEHtRqsQEIyVMg+TjevNPbObMy9WCfcDSbq6Y9xJr4jRW97bAJkZMr7i8owHPVw7hb4eUC1QLhdwHZ5aAfRAAMcCyvFjMo/M6hlXTYet3tuvt0fRs6Dfq1qMLP9AJx9a6Ecny1E0uDJnXo/sP1aVWaPhh92SlMcQFIajFkI896Qpjxng1TEI2NhwyHQa6zsnXiG8tEr0GTHc8/7dU7Ft+bnI860r/NhsWvl/mQ3v80bz7nbkJZuQUkqXl0l7q5Uieu8CqXrwfUN4mag0oCDh2hOi0ZsR56AdrOEIGfEAQqpxmBizWWpHwS/ny68uo87lPay8Xq7Z3RupqP79MLNNIPbw1dbYVxf+HSVffmafdCvlVjb5cHgAFW9nlEUxsidDB+Vgre7BynpkJYFS7cIMQbRTJ/J7R9TFx/zoFYXJuIJKbD8UlO+ezsCdUZMcZ9fkNAMlQbu7xUtsiZXc5sSbQ/ZuL4Rmr0ncdANx0HonsI2bQnhcP5rueZPLkpMc7sZIlU4MlNGDVV7izd0RrxY1TX/ADSOEhKSWYQMO3oWP85iJ6uXxMi4ezID4BUk8qf+66Q6fTW3XH9kpuiP288WPXNShAwE6r9eoi8uwR+GNcneIawq5YfzZ5+QPPhVHMeMQOXijOXJUyzhEagNjiieHuj79yZ7y3ZwdMTwznhU5QNxWjSHzEVLsTyMQNuyOQ5DB8GfmG5mPn8xnxgDOC+YRfKQEdgS5HOZ9dM6nJbMw3TnHlkrf0TbphUOLw9A36UD6lIvaHb5D4HGxxt100Yobm5Yo6KwmS1gs2HLyMB163psFovYt1WviqJ6wR/kEvwpKCHH/xPE3oQnlxSvVGEAACAASURBVKWy6F1VwjGiTmA7OA0lM0GcG1IewY1Nsr7VQk7pxry5WEdiKkzsBxXLiJdvHgwOIqNoMhbWfiya4XUHpFnOy9VEqdIZKIqxkCZ5dHeITRRpRaCv2G8dvwpDWyXD+Q04P79F28iTFJzkfdYjCUVxLbSd8qVslPfNZuowB2YWqGiP7SWxyav3QMcGMKKbNPk9fAJBAeJwcFUkctQtm4Bb6hVG93bgpaYQlwQmC/xyROQPIZUUSr/TklHtazCoUQscSCNL68PDjADiUqFTVXj3q+KXOiVd9K/7zklYxwdfSwX50AcXCT7UJl/OUO3BLrw7bWHus748dX+JshGFbcPM5dpz4ba4YKSk+FE9eAgON1YV3pnWYLfyajVVwiIUtZA+93KS3lahLYTbt/246bfcftaU1EwN1f0zcTRombFaw097RfZy+pbd2aJVbRvtg2JoFPku6gou2O48Ys9n6/luvxtP40QHrtXAJ6/ZPZfzcPomLHxT9Ombj0LVQBjX00Sw5YgcZ45d12Yr34Y0kyNnbgmx3XWm8LZcnaykZ8of4av3RWZxoIDU/40esKsIqb8UqibLoQxGvaNUcnO10nLdpMGEkEFw6QdJzdPkDuYMnvbjS44S54n2s8C/mSTNFUVWkoSftJoKh6aJjOLm5twTs0kjn2+IuMCUby1V87KN4aU5IofJQ9XeQl6LIumRHIejh1jmnfsKTi0Sb2OVDq7+BN41wLOKnRiDOIw4usGByeIz7VxKmmvTYor7HpegBH8H6grlUZydsaWm4jhiGOYr13AY2BfT2fOYL1zKX8/x3bdQV6qItnEDNDVDwGzGZrNhexqD9XkclocRAJiOn8Rh2GA0f6JUwZqRaX+h02GJfJRPjPOQuXI1hg/fIWvlj5iOHENdtrTYmZktqPx8Uf6HARh/dSiOjujq1YZ6tbGZzeI7rFKhLhf4h5PxSvDvQclf9T+Kp1dkqjcpQl47uEK/dbBtOEpGvCw7tUgSxAyekBGPtel7jF5bn7VH5ddODmYWj1Mz40eFK/dF4ztnjBC/HJNU+ar4y7LStjv09TuI6ez3/P7eeqZsCyY2Sc24Htl0b6Hnt1Pw3iBp7PvsJ9EPm9LTxINW4yAa0QIpew3Mm/jmvTG8+5U0fZX2giVvQdajG3zYxZsWNcsR+VTkCrcjJDEO4OwdqFUZdp6CxeMh9uFD6gTFE+xRhiOhvlSs4MIR3S76TlHTsSHcipDtD24v0ogp30m1fNkuD5btkhjU6SOkUv3FZql2Fx0wgNi5vbUUxnSXxkGbDbxcoUr2/mLxyB7X5tGockcOpPdliM9W9M9yGV/ZRoS59GDsJ/Jy8xEtZd6czIBqGWjvbJIp/sYTpEKZh8iToj0NGQRpT7Flp6KcyvPKdYZ7v/G49XrWRA9k3lKF9EyoXs6R9wbDT3tlE5/8IPro0l5SgW9Y5hFVnnxDWvk+KFHHyfHrRAWnR3i6uBERIwMkL1dJpmtUTWYQ9Doo7QlP4iWURK+BJePhyGV4ebaW45MbUafDAnHriLuLtXI3jrtNpfMbjiwYK5KMfedlmyDH0aVOEl/8Ivdgx0kZiC0YKxIbnRa8XUXmUxCvdBA5CHhIlXzve2Kb5hVkb2K7vk70v44eBW5KRei5UhwhrBb5/FTrC17BENRdyG5B1BwE1zYIiXYtC9EvaNCNPCFV5BPzoem74hbhUVGquo4e8sy7lJWgEUd38KkhIR1Rp2F9b/FJVuug8xI57kbjJRzk5HwJK4kLfaGjDEkR0HSiVJafXJLAj5gbUnEuQQn+ADSVK+K8bDHpBeQS6ooVyFqzvtB6mcu+xfmbL8g5cxNr+ENMp8/ium096bMWoO/codC6ik77pxItba0QcWLIlGlIWwE9bR5saWn50e+aVi3Q9euF+cw5TMdOoa0dgqpiOWxJKVjuh6MOLIumVggqZ+d/96n8S6FoNGgqVvizD6MEfwcl5PiP4sEBOzEG+TK9/AM0fUdSw9RaqPUKeAVj7beW1HR4SH3WHnVCq4Ffxt+iWcZXuIRFU3HCV0RmlCU6TsWEpSKTALHeqhoI+8/D5jHJKEkKupDedM5cQZP2maS51+WBsRsTl5YhI0dhdHdoHgIvd4DZa+DjV3xIDJzJ3tBAYi3ZdG0QQZNH7+AYewZd6Ro08bHwxVtqtBqxkBs+W6Gafy12v32JtnXLAVIhHrNQjkelgiHtoVo58DCaael8FAffs2Cz0rO8D8Oa1OHLM41wMkiV+OoDkQDodbBoo0grol+Q5WAyiyPD1GFC2trVh9ELyG/q654bGz+2JyzbKsmCADbARvHpJ5uixmZTGL2qMs+H7KZb+7v4+4DaN5h+bxeWpQz7uhI57/1I11c/wyftvKS3FZyW964qhOvgVDINgYTWXUnQzc9xODJdLMnevMb1h9WZ9r39yygmES7fs28iPVNs88p4Q/t6EBPtynvVy+K6c0D+OuablRjc/CB1KgcSnyJhMJ4uYs/30VC59uG5lfoKpeHtLyGkoshPsk0wdmMjXm1WitY1G1DKV8e4NVXYcFQquUs2wYdDYPNMuHTXilGVxkt+Zwi69y1fvvYlk9b5k5UjLhUT+0s09qkb8ty91Ve8sk0WGNMNujSBE9fkPgUHhOA0eJtUYFMeC1lU64So+tUubOumKFC9nzi2JEVKxdWnulSby7eGPqvhwnfietHoTbi5FdwCcq3UbNLs97CAlgSEWN/OtUm6sUES8O7vhfPfSMVZ7wwb+9nlHw3ehBYfwNZh9gARS47c84Gb4elViDol28lKEblG/H2JuI4sIBx3dIcDC6VSbPQSeUjXryQgpAQl+INQnI1o69dBXb0q6R/PxDD5veIrmUxYExLRNmlI+t4D6Lp3JmPxMsyXrqDv0dW+nlqNpnq1f9/BvwCKpyeum9eQ+eU3IgmpFgwaDSo/XzQ1a6AYjWhaNSNz+UqM0yajadKIzO9WkbNjJ7ouHSE7i+ypM1Dc3ND37k76jDk4jngFw3sT/mMryiX466KEHP9RxIUWX5bwAKp0FVIwcLN8sSZForq1BWO5duhcqqPXwUc9n9AlvCfqJNFlVbv/O7HNbvD6whqA8IipA5J5o8lNtMn3cfNwRh19XX6RmQihe3CrNZirGfVo94nES5YvJe4Ws37KdaFoAU4GLW0mh5CU6538xS5vtk3aSM8uD6BsQ+4ctTJmoZ3ANKoGH/WMRW1KEsmIZxCuRi2ta0sa3KQhooH+Zjs8WnoJh92jiS77Csetg7nwyJsGVVUMbZ3KumMywg+LlvS+oZ9JpffUDZEM7Dlrv2QqlaT6xcSLnvnj70WGMG0YZJlEXnD9Adx8CK1qSYKdXgft6sGhS3BX24EympmFNKthFT/it21GyvmBg4c39Wd589tcqK22O3oUxJMkPVrvitgMmSiaAk0QBk/iy79MbMQjMoNXsCuiNjM+KsO5D2ZS/3kXIWTV+/HoWeHGl8RUSSMsikZVJXWwQjMDhvOFG2s0SQ9wMijs2Q8bDuYuU8NPU6Vyv+Okfd0ujWF8HyHKlcrAyy+Js8TdpAA8swNYfxQ2HLWvHxUL4z6Hs99Czzo/ShrfQ9FMv+F+i/YTPyUlsA+BZR3xcZf1R80HX3doXUdSCf19oXKpbHpM1hH2RAYCI7tY+Gy0E75JkWLBltfY6FkFBhSovudBrRWJQ1GZg3s5+QloIXrvxAfiDZ74QOQr19ZKMIhXFfvnzquKzMjkxU7rnGS7Bz+W186lpTkvjxjXfQ2cfaUhNiGMYoh/YE/oC92Vm5zXU/Y9bJ/MwMRcF/eK+3vFjSM7FTKToOVUqNCm+DZLUIJ/ANpWLbA8jCTju1XounbC+vw5irc3tufP89fRtW0NNrBlZKBr0RRN9aqkfzYPx9dHYk1MBEURh4N5M9HUrPGnnQtAzu69WB6Eox/1Kgo28PLA9dcN5Bw+hi0lBW3tWlgSk3AcOQxL9BNssbHk7NiJ4uyMtn5d0mfMzd+W6dgJnObOIG3KdLSd2qOr/YKZnBKU4F+IEnL8R1HpJelgL4ig7nB/H/RYAc/vSZf8vZ3gWRlN/G0qOZ9h7VR/PBPvob5c+Au6YfgEfp68m/UH4PMeJ6j0eDlKuJtM6/7ymn1F3xCo2I6sHAgz2SsEo7rBpG/tjXRfbJbYXydH8skxwKdbytB6WVlc1eDtYcMpVya6fcIVWmR/h+bRTdC0gtAoKFMfXf0xvDfIkathMt0e+kia4jwSTpDi0ZAPrr3PplO5MX+7oH8rC33aCOm1WuFOpN1/+dhVsTDDBnvPi8xg1iipEK/aDS1qit3bvSiRhhTEm70lnGLmamnMe6WjyDrCbPWo3O0YzvfX4qLEkxo0nKsxzfliPMSnituHRi3x3G5GeGcgTChgKKDTQpMa8OkPcOthDb5+9SQVVNfQqm08MNekzvuVySjiDhGRWYF8HwZzJmWK9EharRCXZGNEF/hhtxDJoADxI955BpZPMMG2IroRIDTRN58Yg0ggImMKE2OQ5se3+kr19uWX4OQN6NlcmufiUmRAkXf98+DlKn7FaP0LNROqE+8TlL4DqvYDUxKkZBAe7UfoIxWhj8DfNYV+NR9Q2cnAnA3l84kxwKrdaro3yab7rU/txBgkeCXqJNzYDBVaQWAriWv+e9A6wO63hAhX6SoaZe+q0nR3bDa0nQml60jjW9hBWZaHVlPhys/gGiCVZ6OPeFxnPIdyrSApCi7/KDrook4pctcKv3x4VGz7Yq6LBr32KxB3H35sJ30EIFIqRw+xlHP430W8luC/F2pPD4zvTUDTtDHm8xdReXrguvZ7Mlf8gPnqDbStmqOuGoSmciUUrVY0ypFRGD/7BNPps2L/tvo7rIlJaFs0RfmTmvEsT2OwZWVh0+oxX7tBxpJlALhs/pnkoSPz7cqyFAWnRXPI2vIruk7tsTyQpmhdu9Zkb/ut8EatVizhEag8PbGGPYQSclyCfzNKyPEfRbk20O4zODFPpmZrvSLhB7WGQth+OPO5kJCQwTJ17OiOLuk+D8Of07CmHi7Dvbb7uZrdlLRsDVX8MqnspGb9q0dw+aWj7KPJRJmyLYjYG1C9H9rw47hWMDBnNGw9DikZxR0mVu6EXs1hxe/2ZWmZChlZkJoBzxIVxvexMbh2KDUOtrPbzkWdEhJxbjkENCWkYgP2LRItNMh0vyr5Ifc8h7NpdeH861+OqenQCDZNhx2nJIq4ID5ZBb1awO4FcgwzVsOt3ODAi3dhQBt4tTO8Pxi2HhVbOY1apBz7zgsxBvh5n/x8NlIh+KtGdG7UiPcHSQOZg150u6dvwII3hBgfuiga4Fa1YekEcbrwdYdR3WHCUrgTIdutdqUiU16pyOQhcHC/vLcoOS7tkjva0DiAbwhNDDCyq41Vu4Q4OuigS+1EWnicY0z3jjxJUBEVA/ej4chSaBikJfvhm+gv2m3m0LuQlF48Xe5FbiYAN8Jh0jfww2TIzIK3l0kVeWJ/+Q76aCgsWC+SFWcDfPu+vCfdrQEedabje20WGH0wl2tLRqOpuEQch30fQHIUTUJG8PnQ8Rj0FoZkj8NwYx8xTOHwtdnFjuN2mJnuWYnFDzAtVoI4Ti2QWZQyDUQm8bdgtUBqjLhVPD5vX97re4lxfrAfjn0mwR+tp0G72ZAYJgQ69iZU6SYSiH3vS0Olg5v4U1tyYP9k2db19dB+Lhz8SCq/ikosFotGU4PYs9UYILIRELlNHjEGkVJlJYvPcglK8L+EvklD1H4+WKMeoXh64rR8CZYbt7FmZqKYTKRPn422eVNM5y9iPp+rwVcUXNb9gCa4MupKFf8UXa4tO5ucIyfIOXIUW0oamrq10HXvQs6hoxjnzMB06kwhH19sNnL27IfMLNR+fuDpicOQAVjjE1CMhmLbV9xcUTzdUVxdsaaloXJy+jeeXQn+21FCjv8IUp7A02vgWwtezmWeqTHyxRvcE3ZNtK97ZbWEJaTFYgtoybUIPcHlqpLU9QajfqzOhbtCqJwc9eyYY0N/rYBVg84oX75FYTWTVqEfy39VuPpApAp+HsVX83AR7W9BDGwn8oYVv4ssARSGlLpmJ8b5x/2TaC+TH5Pt3YD4ZHipgThnRD2DKJdO5KTYHxudFhpXk/2lZsLriySWeNlWcbv4dodIAbzdpJodlwwJqXZinIdfjkqV9Zvt0nx366GEVGjVIkkoiidxsl6T6tC8gO2up6vsb9K3sPUzaSiMSQBHnVTS+7QAFyeIeGonxnlYvEkCN0xmeGeAEPo8gvzhEAshcd9gq/gSSptPwK8mnsDCAaG8UjubuBx3gnTXqHp2FEp2Eg1GHIVqjaVy+fSqWJpFVETdZByZzgE43v6RLPeamJp8QG00rJwkNm0HLuTeaqsMMG4UcJyrWRFuhst9334Cfjsly8+niF3e5hkyiJg6DIwOEBwg5NnoAJXKupOUPIWPhn7EzUgN2TkKw1Iv4/JLZyGngP78YsbUNZFurIzhxD55luKP0r5GCuuOFx4MVfOMBp9ucLaIv69XMJxaLJHSF7+HjASo3Anc/IvfxDy4lLFbvOVBZwRTllRyr+UaQSdFwI5R8NI8acK79L1ILLougz0T88+DrCRxmuiwQNIOww6K37JbIDSfJATar5YEl1iK6G0Cmot84sYGGJ1buvcoL9HQjwtYiCiKnGsJSvBPgKZ8OShfLv+12s0N0737WO7eRdejK6rSpXB4ZTDWp0+xJSWjDg5CW6cmyp8Z+HHuIslDXsOWKN8hqlJ+OC1bjGHGVDRVq5Ad/rDYe2xZ2aDXYUtLI/X1CWjbtEQTUh11gD+m8xfzpxsVZ2e0jRugLuVH8qhxOM/+FIf+vf/U8y3BfxdKyPE/ipgbsK6naCIBqvcFvRv4hcgXeGBLaTZKLRBJF7oL2s8h2hzIL6ddaN8Mop+55hNjEFK5+Sg09yrQYBZ+WHSPtwtYBmgcsLhWYMWFdhy/Jos2HxY9bmkvIYt5GNUNXAzip/wsUZqpbj0EB20eMRZYKV6xRKUBq4U0nT9vLYK1ByScZFwfWPEbTDvYgoX97vDeQDM+HhpsVthzDsp6g6ezWI1FxMDJ66LzXTsN5q21kzyVClZ/JBXegpVZtQosVmli+3wzLJsILWpJ9bhZiMg6CsLXA5IzpJmsIOKTpepqtQqpjsntsVuzD76cKLrgN5fAxy9IfFarJABDpUhz2twxsv/KZSH0kZqP7y1gQDuFZrYbMihy9sM1/RYtTvQtvrHUGNGer+kimvTce6jp/jWahkOh2RgsZgdOXNewapfotLs2gYFtYekvUun2cZfjuXhPrkVpTwmPWToBJn5ZeHdZOXDvEcwZLQ2dx65JfLjVKtuKS4KxvbT0/VTs8QD6fXjLTihz4XD9Oxzazsx/rUu6xxvdsrnw0EboI3lux3RMplHyl1C7JZjS4drP4vjR/APRDptzb6wpHbBJwIo1R6QPL3J2UKmgwVjRD19fL6EcFdpKGMid7YXXtdkkiS8110C/dD3R/xY8jwrtJGFvY1+xkKvWW5oFI4/DydwuU4MXNH8fmrwjszJRpyQhEeDYLOi0WAbBIFKNLl/AzrfEf9ngCZ2WSPplCUrwL4Ci16OrWQNq1sBmtf4l/W9NZ88LMdbrMU6dhPXJU8zXrmF9GEXyp7NxWjhbBpEFfEP1XTtiDn2AzWRCcXLCdOQ4Dv17Y1NUuG5eQ/aeAyju7mgb1sOamETq+1PAbCbllVGogyqjq1/3TzzjEvw3oYQc/yMwZ8Px2XZiDHBrq8grrm8UWcXtrdD6E/h9bP4qNrdAUp2q0mFaCA2ril1ZYlrxP3K/n1KYNW08Hvd3yIJHZ6HZB9gajUe59QtWjyok1Z/GwefNmLyx8PTTu8vh2JcSrxwRI9ZnP+8TYrhwrFiqLf1FSHiNIlKHC0m1qO5UClVaAUJfbxT41WLb3eqs2Se8JSMLHLzgt7kQ4OvGhcgmHLxoo38baaTLw+6z8NkocVEAuHBXvJALVj+tVpj5k433ByrMLKAvfvkle6AISELbnnPiZtGjmZDjUzfkeAa3g7tRErU8dwyU9YGedeOJiDOw57JjviVZdgGnt7Leos39cTd0bwoZ2VDKUxLr8jCxH/x+Cro1g53H0vh6wAUMUbuwPUygRvnBvHywGR98r+dgk6UYUkNh0C/SUKYzSvBLgzHyr84oU/PhR+zEGIQ0XlkNnkEQ2JRLdyQBL08bfiMcRnSVBLv1B+HDb8TSr1o5kYJ4u8GUV+Q+uhjFaaQgjA6w8TA0rwkHL8q1dnWSAcL4PiJPySPGAGnWF0zFGn3J0nrhkPvycs21dJ3hzYA2MKCtDCBal0vA58h34F9KSGTDNyRG+ejMXJeJXNToL/Z45VrAlpdlJqXZ+2LRVhQupUQjrNLIe/a8CzWHiE64YOQ0CMG22YREV+kqmmVFJdVqkCa5CyukSmzJFieN7JTCBDojTiQXgc2h1jBpuHt0Dpz9YORxId0Fibx/Yxi2V+QcDu7gVbn4OZSgBP8C/BWJMYA1XZpeDW+PI/PrFVhjYjHO+JistdKUm/nDzzgvXUj2zj3YMjNxGNRfLN7SM8havQ5t8ybk7D2ANTEJTf26WG7dRVO9Goq3JzYF0j+ZBebcngabDcvdUCghxyX4N6GEHP89JD2CxPBCwQ/5SH8uX9wtP4QjM0RzPPAXiL8HFhOZAR2Z8lsdBr4kYQw6DVR6getTp4ZwLqEOHXqtxnTvABZFR5J7J7671BCV41Q61Xdi5nonWrygJ8FBJ6FIl+9LUEZiqv136w6Id3BBiYWrk5AqgwNkGitxrNYBqmb8imfWDVRBXVGXrk28Q1W+XyVuFWN7waVQqTonp8t7+02D3i0UNhW5JGaLWMA1qyFNdieui0ShKKKfK7zW+AHDGylM316R+sFQzTWM14LCSMeN5Seq4eXqRFySyAciY8TJ4up9kW7sPgvncivG1Twfc+7ldfiFryArqAK3O33C2dTmlPE006PiLeI7+7F8vx9DO4jk4MJdIZiLNsKUoUIcnydCjYoSpOLrAarsJH7otQf9r6PyXQ98r//I931/ZfRv3VBZTRIzfW2tDJB6/yha20PT7E1vmYngXvEFz1NUfmX1TmThpkmANXvhrT4i1atbBZ4l2vig5QVyHErRZ4E/To5yTT8eZteCgwwiXIziEHJkKdSpLOmJKelSSXZyFA1yQRx4UpdgrxC0cTfyl4WGfEF0SjCtPSqiZCVxIKoGKekSBpOHIyFl2dP5IxxCBoGDs8Q3Z2dAvdFybpYcCBkipDTyOFRsJ57UZ5ZK3HOTiRRDVrJ4Jxu9IbiHkFGXsvL/X4bYia9fLSjfDoY2FCL99ALc/U20yMdyfY5TY2Qf+z+0NwxWaCfSjdNLCu+3fG6F+sgM+7KL38GIY+BZqfC6Rk/5KUEJSoCudQsyZs1HcXDAGpPrHmOyVyQsd+6SNnshhg8mYjpwmPRFS7FGPQKLBV3nDljjZVpP0WhI7tgz/31OSxeCVoP1ceHmZZWvz7/+pEpQglyUkOO/hfj7IqXQu8iX9d3fC//e6A2l6om/6tOrULo+3NwowQY2G9agMCo7l2HSzwH0aw2HL4t/8WcjYd56kRB0aQydG4Njwg3UR17l8Uu/8+211ixa4UQZL5g7UsNrix2oVEYswZrXFMlCHj59VeQOwQGw8WDhw2tTV3xzQXTIni4w93XYegzqVoZ56+BJXHX8PKrj4w7PtsDpr8ERCe5oURMGz7S7H/h6yJR+KU8hwpoXqDKcHGHEfOjdQnyLK5cp7qAwsl0ipQ/2RWP0YPXIZaSnpGD8pUu+zrpR4wk8rfkpv53yIDIGOtbNoaruBvUrRZCUpqKCrirXHgQT6GuljelrjJfEAsghMZy6T47j1+sYHftdo+K+N5nedDZBVSfj5AhZ2UIqgwLkOBdsgL3ToqjkHkd0go6bsd681cuDl7zOo4+8UCwmOeDOTBYODMZhX27Kx4P90vhVoR2sal04WvrkQhi8DbyqimY19qY0eFXpKlVSxHWiKPQ6Gbh0aACtKz+hlvEKObf30XbfYhJToXVtCQfxcYfVUyRO28tFvJQn5ZLlB4/FJ3nwDCHGIOR24ZtyD2NzpSYfbgik/cIdOCdeQMmMJ5KafLCpPgNbZtGm9nBw8iX9slexY0zNUmMJ7g2eBci/3iBSo1K1pVp+dpn4GndYICl0ebjyk1yDx2fFH9m/ifxkJoq0AeDuDpFY1BouvsT914ne35Ij2u3kKKg1BOuVNagOTJX3pDwW5wrvGuDiB1tfLeykEX5ImvtaTZHPqylDZkm8qhSa7ZFtPRE9clFyXIISlCAfuuZNcN25BfP1m/aFKhU4OECWFABssbEoOh2m85fytcnodOheakv6tM8wzpspshEPD1SeHjiOGo7Kvyy2lBTJsc/tNtcP6IOm9gsSL0tQgn8RSsjx30LYQXh+R/7fYT48u2OfJg8ZBMnRULUH7HlbvsyN3tIklAune2vp26gxfh+Pw8MZBk4X79qwaHFteJYkzgphT6BFkBUuQ+CB7kwv3Yox7w7BmBVJoudA+rWuSVYOrNlrY9HQx/iZb6FSKeS4V2fDhbKs3S9NcN2aSlXVaoXG1eV1o2rweg+p+kY+heBy4OwogRJ5OuWYBLs2914UdGgoMof3lxcmtbEJItfo3RJ+2AWfvmYn3yBkL2+6f/UeWVbFH9ZMhZmrbUTHKYxsG8/EyuvRXL4Oz4HbWzDe2V6oAdH95pe41+/BjrntiHgKTZ1Oob/0FUSexOhXiyHBPVGNUlDpDBivFHB+ALDkUDphD1ycCYqK+Bw3HPVWHkSr0GlFe/w8SSrRgZ5pPDf5sPLXMpy7q2bd2LvUyvoOVZyuMLHKhcqaTRBnRK+a+lQiitU6cTN4dqPY+iQ/grqvgmdlsSVTaSRYJLeRq2FViW4+etVeYZ881AF92wAAIABJREFURKr9Sk4qlgPzUcdmEGNszPVwmeJ/EC0Dl1+OwNEr4OgguvMh7SE6VyLyLEm8ovOIcR7mroXv3rcxaoGCWgXvD7Kiz37K4diGfLKtHImpMK5rKgMMX8JhiRPs2CSEuaomhZ6D9zs8wGiJyb3eZnh2W8Iyok5C9f6iF+5aBZ6Hwsbedg0yCEHd9qqdCINYtTV8E3xryCDCZhNHiXLNZZBxarFIVZpMhOgL4N+Ix0+zKF2QdMfdg2e3xMEiMQKSCmh58pBwH1wCpNKvd4bj88SurUjSIvDC+1+CEpTADkWrxaFzB0yB/mQs+AJbQgKZ363Cae4MMr9eieX+A7Qtm6M4O+G8dD62zCxsFgvaenWwaTQ4TnyTzI1bMbw+Aqc507ElJaEK8EffvjWoVGgqlMP8MBK1ny+aurVRe5XM2pTg34cScvy38KxAt9ehj6H2cGjytlSUEsLk3yMzoe0MUNQSK1ytd6FGujKxv7AhbByhj4Rwfv2rVHSHz4budeMx6iws2eSDvmdVxnlVQx13G/2TY5R/cozs9otR+zjTrmo8MWkGWpe9i/fefqhzv/htXsF0qPcr07KCeRQLo7tJ85WzQQjUtJWiQ34SL5KBfq0hMUWkCiEVCg3MAemduPlQHB/KlRISWRQZWUKCs3IkGGT+G6IP9nGT4IhJXxdePzIW6vhGcvLjRDLS0il9eizqywWIpNUkVbo8aA2511NFeW0o5SsaYO27EHNVfh92AGKv07dHeTKNleG+a7EKL4po9MLrLKXH5lHceSSv3ZxynTS2wdlbNhIDnPj6VyGRLkaoZjuC6sin0H6WhEmoNIVIUkrdSfTaMJyODYbStvVz6lUySU6fwVtmFh6dLXwcOakSTqFzEm3u87sQ3J2bDxX2X5DBxqB2MLSjNBK6O4OHs7h01HN+gPreDijXCq+U4zQJfoUzd3XcCJckxJPXoWqZTLYM34/Lne8hTkvvea+yLrQld2LdUOfKFBtWFa/jkzfk3tWvYuHGpB0oGi2llYeQnURQGYVBU6sTrwqkzMmRqG9cF/LoXY2Gjyazf9Y2Fm8zkpiuZWK7B3TQrwO/N6RJ9dEZ0eJHnYUy9UXje2Ih3NsBZRtDxwVSLa7UQfTYbgGFiTGInj9kIHT/Bn7uKvrg8q3lfU9yO0hz0uHYHOgwnxyvOsz6WcMS77IYyLX30DhAtT5w+FNw8pU+gIL6ZwAHDxmcRB4HjaN4lt/bDXVegwvfFFjPFXxLqlR/RdisVsy372J9GoPKzxdtSPU/+5D+66GtVhX3QzvJXPUT5ivXUbm54rb3V7DZUPl4S7qdTldIO22z2TDt2Y/16jXSxk5EVS4QlZ8vLiuW5dvS6Vo0Q9ei2Z91Wv902MzmP82LugR/HCV36m+hYnv7lLDFJFXhxhOkirXrLei0COqNhB2j7c0+VbrKl/TtbQDEuLbhcihEP5emqDE9oG+9GD6pfQCX8zPAlMnrr01m3vVBhHXbSvnnG9DGXMDWcgq66+vwXFOTVk6+KO1mYU54mk+MAZRyLanrdInUuac5kDWIXlMN+Y3Bk4ZAn1Zw9hZUKANPnsMXv8CqydCxoTR4fTgE5vxsP91R3cQB44ddcGyZ6I0//Nb+e0UR67RKZcXBITJG5BXPEiSprW4V6NoU7m2yv2frWzcI2tMJpeZgPJ1Lw/MiFdbkx/aYXqOP2HId/kS8d8u1Ei/aPGKch7RYrKnPWHi0JR+3mIl+92j779wChUgpKs7ntOfOIw2KIn6/5fwgPFpkIY2qiftDn1YisahdPgfdrTWyjdu/SjNZx0UQfhBbVjKZIWN4fXs3jl2DY9e0jOhSmlEGMCSBTuNK5S5fodrYR6b81VpoNB5Cc8vnOWkiI/Cryc2H0PpDuzZcUWDBWJFCnL0t1/H1nuDUrRxBSZHgG4LrwY/4su9I+n7fkqhYSUP8aQp08diPblOv/FM33N3B8H5bqPN7b2aOhM/fEmu4p/FyLwN9oayvBlWcGZ5cgLQncFUeAKOjO8bu30BatDzTBi+IvojWqwpt/K7S7P2yWJOf4qBkQKk3RAN8fI6cW55XcMRRqYqXbyVV3Ge35DoENINTiwAFuhSp9IM0vFpypDlu7GUZeDq6w4oXuEHoXbmdUZ2Vu2DklPdoELlTPpul64ruO+6e/LSfLduNOCbn0nGhHPO67lLJdi4FLaeAd5DYtPnVhCs/Cilu+Ab4VC2+7xL8qbCZzWTv2U/q+PfEE9jLC+dli3AY0Ocv27T23wJt7Zpoly3GZrGgqF+gtysCRVFwHDkMlbcXmd/9gDqoCoaJb/7HDXZMkVFYn8aSc/Awpl370HXrhKpMacjORtusCSpvT6wZmag9PVC5uPz9DZbg34YScvw3YPVvjq31DNQn58qXd/W+wmaMvhL+kfJEpn0LdsGH7oJ2M+H2NqyeQRy3DCQ6d3r7/G1Y2PsyXsmnUPZOyH9LwMUJvNfYhTsZw3lpxQxuLLmLy9WlcEGYqZKQBluGoB6ca2vlFSRBCFd/QrV1KFnV3mLyzlfziXHnRnAjTMIg8tClsUQwuztJBXX8F9CylmiQnQ0ihTh8WRrWHPVSTW1bD2aPhu9+E73y2wMkRGN4J7FbexBduPIcnyKDgI+Hi1tCvSo22tlWoWQ8F12pOVvSy/LIlEoN5dtIxTH1CTSeCL++Ks4HIMSmen9Zr4jlWIbahzkbnbgbOZif+xtxiD4GbuVFBxt9GRpPJCY5EEWBtR+LnGLfeXGteKsvLN6k8E5/2HtOnDBa1dLQp1mAVH+fXBINbMhAqDuKdI9aBIwoR3IBp4e1B6B2Zdh0GM7fgU9ercfbr57FkP4Qnl6B88vtkhwQWYXGwPHbxkJNkzabbKO0p9yjaw9EH9y9iStBg7bIden+LXW10Zwes5oIp464entRJUCFZn2BkQuAzYru3laGt2kFigcffScVfhD5y4yR8PgZBFTqJM/xpvn292YmSrBN37VCEi/lpkCGH4KwQ+heOwSlWtvXjzgFFdvC9jH2ZQ5uUK2XNLm5lIUzX+bKF+yxsCRHStNcZoJ9WXAPadQDuX+eFeV4fGtIUl1BuAWg1cisR7+VTfj+ldNU4SyKkw/+2mTyTRIPfSzV49YfQ3BvyEqE1e3t20l9Cqc/FwmIix9U6iiSD7VWrk0J/nIw37xN6usTsD4VSY8tLo6UV19HXalCicXXXwT/CDHOg9rPD8OYETi8MlgSAP/Dqqo55y+RsXQ55ktX0bVoirZ1C8yXr6Fzc8NmsWCNjSXz65WY74ai79gOXaeX0NYK+bMPuwS5+M96Gv/JuPTYkyeZw+nZo7zYRaU9h8znEjjQdRlkJ+dWxAoj01Aex9cO88hchbunyjDlFXAyQHWvZ3ienYTiXLzrtvyj5Sy/P5CUDAeMlucSQlAQNpvYSPnUIKPBJJKUynimJaEHMhzK8jTBXjlpUBVm/wyD24u22GqFbcclPS09i3yXiePX5KdqoBDfQ5ekijr3dej/iWija1aUCuSRyzB6gUQ+t60rpCuPGGvUYo9WvpRUjxdvEm/i4e1z0B06BZU7iw503/siTem0CLJShIhkxMOhqZIwaPC0E+M8XF4lUpZTi/MXWYJ6s+hkAwC2nTEyrH4Vuj+YKKlmZz6Xgczo01R54Ej7eqJ/PpAbLPX4OUxZKY2MCzdKg1voIzhxQ4X57bfQOrgJIUwIk8FJmQY8e55TiBiDRDVnZMtAw2SGad9D7UqlaFW7FNr4KHQFiTFAQFPQOJKaXdzEPjlNUu7yil8WC2TlWGHrK/br4RpAqYGbKeUvzXxYrTJoKApFRdeGFo6G2olxHr7cAi+X24dFfRe1voh1Rb1R0liYGS9a6pjrdgu1+FB4fttuwRZ5CiKPiltL/3Vwd6dU/jsvloS7neOEHPdcAdfWF97P2a+g21cysxJzDWoMhPqjZTYGZL+xN0T20HExrO9hvwY1BkLpulTWw6TBMHetik5L6uOgq0/XJrD+9StofEPk/TabyCoMnuAdXPzzBOJCU3MVlGsprzUlAQN/RdisVixhD7HExKJt1IDsHTvt3rnZ2Vjuh5VYfP0fhsrxH4iY/z8CW04OprMXyNq4BVtWJtpWzdFUDYIcE0qAP+brN0kd/y7o9TgMGYC+Tw8sYeGkTf4EY2oa6grl/pS0wz8Ca2oq5lt3sMYnoAqqjKZM6f+oe5iHEnKcGAXJD0FrFNN/jd3bNCwatp30pEOFNTg+KmIF4eAqOuM8SUAeFBUnY6rh7V6Xs3ekCcpkFgu1C59Gobp8HXwGFjuMHEMAF65rcrWiikz7Fk3Jc/bnYrODTF/rzNlQA51q7+Sj5kcJDv2YMe1GsPh3cRaw2mDWKPEB3nBQPH/H9gI/Dwu3I4oTqjuRoncFiXCe87Pd//d6mNiDvd1fiDGIU8KHQ2Dc5yLFaFcPFm6AAZ8KSZ41Eir7g1ajh+dDRLudlShT5Vd/KrzzvOnvS6skZbAo4kNlKr5cKyzRV3iqrsa3V5oxb7t3/ipaNUKy3QJlal1rAJWWVrkhIp0/KLxJi0U8kG02KRJ6u8Hv406jHJkO8Xegej+paJsyyAw9wn5LF/x97BHWIOetUuBYAcXHudsSe336YkdW9NmIPnwPmvib2GoMRClVB3yq0kKrKube0be1VHSPXpHXlcrYCHYKLzxQSI6CyNzENku2eO3WGyXE7+oaIaoqNabKPekwzZsxPYpfSkcdGGOPob4+V2QGeWj6tpDRLUPkomgdoedK2PsupOWedB4ZeXQGNvaDtBj7+3utFIIZdggursi9b/fh0Wl4+TeIOAyeVSQpz5Ijn5vSDaWqHNzN7goReUqqu3kNfKXrw4ijsi8HN/CpAQYPdEiCYbMQeT6rlxdttca9DvRaJQl30edlUBbcHXQGCSApCpcy4FbOPiopwV8O1pQUMletIW36HBxfGYy2eRP0fXuS/evvZG/bAYqCyt2N7NNnMZ8+JwS6QV10jRuiDvw7seX/Q1iePcMSFgEK4OiIpmL5kmjjfzEsz59jOnkW05nzaGpURdeq+T/t/tpsNqyPo7FlZ6O4u6P2fEHs7D+InJNnSGrfDWw2dG1bY3V3J+PrlZCVhdOSeeTs3ofKxwfj9CmYH4SRvXELisGA0/zPSF/yFboBfdHV/GtKSyzRT7BlZpJz5jy22Gconh6QnUPmvoOo/P3R1amJ2v8FHvb/R/HfTY6jTsPhXH9irQG6finxsW6BoHXAyxV+PefEqOZLaJs1FM3z66A1kN1+MXrfEAg/hKXOKCxmBV30cTD6EFrnG975OYRhXWD+ersLQXIaXIhwpWJOmhBfR3d7dLNax7Nq73J6lYZ6VeCBuTqlO67GeX0zkRP4hmCrP5rING+6z/flWe7bNpxw4XZ0Zw50OMQ4/U8ovUay8qAbPq42dp+Daw9kejjHJEEgtSqqqVTk2VUU0bi6OcFXb4O/L3y1rfA6mdmynqLAyK7QNAT0Ggj0kzCOkfPFlg5gy1GIjoOd88DNGdFfpzwRAtzsPfECziNatYdDxHH5v9YgvsA1h0hKWh5aTpXQDKMXtoRIVl5owdztvvm/Dva3UMPlvpC989JU9bjbIQ5fqMDxa9CzefGwDxDP6dd7SJX7m6G3aHC2vZ2Mnv0SUp+QHdiOlc/fZdJGPQvflOjru5FSHS/vZ6U0d9k1OpJkmw/zD1Qj0M+RSd/CpMHuvLF3IOFPBjKglYnOZS1UKS+xGg2CYc8CK7PWKMQlKwxsC4GlrLgZFX45qtCxoY1pwyxUPTmk+POaFCHPg0olnr5ZSUIaO8yHJ1cwV+zIjEPNiEmQtEFvt8JNlbP6heNzP1eKcW8ntPoYTs4Xze3mwfYVTZkSwtHmE9g5HptHJRSf3D/Y0RcLE2OAEwug788yM1AQ5myRlnT7Gh4elmY5EFLccyWcmC1NeoEtJCr62GwhxooiYSFl6gvp965e2DYOaRrt3FjOcd85WH9AXnds2ACfNg2KXzudUfTP55fL86czyj4smcXXLcFfBqYLl0l7dzJO82aS9dtusrduxxoTi7ZdaxzHjEDl443leRwqqxXMZiy37mCNegwZmej69UL9T67Cme+GkvrJZ2hrVCfji+XYkpIwzJ+F44A+oFVjuXYTW0Ym6orl0VSvKs1oJfhfwZaTQ8bCpWQstEfVa1u3xG3zGlTexa0mLc/jMF+7gc1mQzGbsUQ/QeXqgqZBPTTlArGZzZiu3sAaGYnNbEbl7U3KiDewxSfiOOY1NI3qo2/dEpWPd7Ft/83jtFjIWPp1/vebrl0r0qba/dNtcfIlZJw/E8XJCVVCAoqvD6pSfuScPIO+S4dCPtF/FViSksg5fJzM5SswvDma9KkzsD56DIDjO2+haVCHnB07sYaHo+/eBU3FCn9ni/838N9LjtPj4NIPQoz1LqITvvQ97JoAVbpA2+nUrFidt/tD9yUhvNHhMK1rRFKhnDPV61YCrQL+Tci4foA5Ob9TIziaqGQXlnxVhoQUCZbQF/m7+PHWinR983Ocj78Pzd4FqwWzxsh1azvWnWnAb/Pg3C3oN8udCqUaMqFPJu34AZveCWXLUB7UOpZPjPNwLVxLmGN7GiYvZ27vpkyodpe0Cn2Y9K1rsVNOTpPq5LKJcPCSaIbH94HtJ2HcEiGSRoNojjOz7e9TFLGG+/5DacRzNYhM5OJdyDHbiXEeztwUlwo3Z2Sg0e4zIUmhO0Urbc2Rxq3Ym1JJdvWXeN7MBOgwT7S+ydGSPudZBdQaiDiGxprNuCq/EDKuDXvulKV2RTMdG1gpW6qVNJg1fZdUn+ZMWlODTYdlYLD9BEx/Dd5eZufkL9UXgpuXohfkeKu4nOP2NhLqzmXG9nKYzPD2l5I+2K81tK8H1U27cdjWTwigotCo+1weV3iL13sYWLxRiDTA2VtaIp5pWThWwlq0MRdo//ALmrbxJCeoPw4uHjhseIlUn5YMfL8zXslnMPhOFCeFPKeGPJRtKM/qlqEy6PAKkoTGve/BGxf5YGMQX+YObJZsksHO/ceQnGqmW6WbNIuZah+QRRyDuiMxdVuFOiuVYrXT9GdYjb7E1JrBWWUA5Z+VpY4rQmKLIitZ3CL0LtIMWRAqjVT/L3xn145n57p41Bggg0QQUpxnk9hhIZjSYPtocfyo3EVCPvwbFdr09Qcw8FNxZAGRC300FGaOfEExOCdNqt5tpkv1GptoofMkFSX4S8JyNxTHqR+g8i+LvksHVF6eKEYjae9PwaFfL7L2HEBntaJUqUT6l9/g0L83lsgoUt6YiFuAP+p2rf9px2Kz2cj8aR26hvWxPI3B+O1S1FoN1rh40ucvRluvDpawCDLmLkRTozpOX8xH/0/c/986LuU/WCdvfhBOxpJlhZaZjh7HfOsOutYtCi23xMWTOuF9VK6uaGqHkP7pHKzPZPZLXbsmbjs2YYl4hOnsOUiTv1U5scdw/mIByX1fJmPZtzhXXED2wcM4Dik+w/s3YbNhy8h1TtLrsSYU/rJWjAbQ6VC5u5Hy8khs6bJ/baMGOE58E7QaUsa9g+tXS9DWr/PH9v0vgvlxNFk/byBr3WYcRr6C6dp1rI8eo23WBMOH72I6d4Gczb+iqRWC6cwFsjZvw2n2p2jr1/3Ly0P+Hv57yXHqUyHGAA3HSkJWHnG4tQXiw/Bp+SGzXipFr8YNuPXYE2MpTwKDCygvjN44N+hDlTiFYYvs3e2KAvWC4fNfCu+ycoCKn58No2a92lQgjPCMsnx7viYbTrjRvzXsOi3NbwC3IxSOXNVy+ouhVDveAQVw0hYnJmo1OFZqCqYEVHsnoqoykYPXDYRUkOa6gnB1EpJ7+R5ExUogxmtz7drURRslvnjuGHjnKzuZnDJU5ALPk+BmuJDqjg1g1xno0qT4pXXUS5BFPrQOYq/14CCUbSDX+cYWsUwbsl0a2La9KqSp8xJoNEEI1bObIhmIOi1T8ld+wMepFP0C4+nXrwY5KQmEZnTgaqw/weV74KCH0HsUSu5LTIWvt8OWz6Tq6+chgSkuRnGvWP0ReGgMRU8B9M5sO6Vj+buiSbZaxZlj1xmY0iMKh7WvCTF2LgVlG+EVuQHHqm1x0DbIJ8Z5+Ga7DEIqam/Dj20hJw0DYLiyDOq+BgYvnMM24RyWa/NRoT5U7CDX6cpPYgXX4kO5DhXaQb+1cGc7PNgHtYcKwUx7Sr0qQfn7nDXgMX39rmAsm4ESdw8l5TE83m0/KJ2R59ZSTD/cnJkvncKzYPwygGsAD1UNuOzZjwXroVUMVC8HutJ1i1nc0WAseFSCFpNh98RC2yAjXuzTuiyVAYhrWSHTpxblxj93y31o3EQmcvhT8QvfVqAKHboLnPwkHU9rf7Cu3LcT4zx8vhmGdoDgwCL30ytIAkTyqtcgbigvSjEswV8GqqDKEBZOyssj8pfpB/bDad5Mco6dxHLxMjSoB44OOH+1mKwVP6IODMBtxyZy7j/4p5JTW1YWquAqqF1cUAX4kz5lOpYwcQ9yGDKAnN370bVthbpGNRxGDcd86w6WO/fQNG6I7l9AdixPnmK6cQvL/TA0IdWwpWeiaDUopUuhDar8n9PglpNTuPM7F7bs7GLLzNdukL1xC07LP8d05x4OQwdiS03DZnBE37IZOUdPCHF9GEnmqp/AZELbpBGWmjVwWvUNKmcj1qjHWG7ewXTlGto6L4il/f9A0Wgwvj2OpINHIDsbxbVwgSp7zwFcN/xIxvzP84kxgOncBfRPnqKuXAnLuQvkHDr6lyDH1uxszJevoej1GD+bhunIcSzhD9F17YTj6Fex3L2HolajrlCO9PlLcBw6CEtqOqZjp7DFJeDQv/effQr/K/yHfHr+BzB4SXpZ8qPcIIciJdmYK/DsJvqjg2jWayXNateAMvWkiawgNA50bwErVVKtc3cWu7aNh2Dyy0I4TWYhY6O6wcBPHenRrBFhTxpxs0BOQd9WMGxO4U2nZ0JMvJUaOSkABCesZkir5qw/ZidzI7pAbIKNkJPvglrLHU17bkZoGdYJQh9LtRhgQBsbLkaFT1bZwyFuRRRv2lp3AHrOgFNfi/OERi3bmP5jYSKydpqEjFwJlTS3/Rfsv5v3OlQsGpOd+gQij2POTCbZswWuigZN7LXCZAXgwBTRmp5cKJVmrREavSn/rz0M9k8m2eLCDeNbXH3qztyfFZ4lQd+WMO1VIbBFcS9KYpT9faWJbuhs2Pr2fSyPjtA97QHWyl2w+tVBFXPFfrjN50N8AKt2Qs3yJobXv4u7JZwptdIwmMtCRpzY+tks4l8c0BSjLZEq/sX3r1HnGiDE3pQKZkFcWytyk9gCFncZCaIB1jlBv3UiATg+V6q9IQNhQ58CFeDj8v7y7WhbT4hhekoa4+seQbNrrEgW0p+LVVmz92VA6BoANQZw6XFpvt3lgM1cjy87foHm0IdCYJ18Se24krrvBZKWIcl6F+6I/3LjoFYyoDm5ENKeQp0RYnunN4o7xKCt4mPsUgY0eogLhdvb5fOUh5fmQIuPpBLuV8BPuOYQ0XHmVZAL4u4OSbfzsE/ZZb9gBtJseXFkOW6BMGyPSECiTkHVXvJcORafYSnBXwcqF2cylxY2T8/etAV9h7ZoataQ6oBaAauVlCEjIJcwZW3cguvWdf/j/ZrDwrGER6DycEcp7Yf5wmUsCckoWhWWqEegVmOcMRVbVhZpU6aTtX4zxlmf8v/YO+/wKOr1i39mtu+mQSAhlJBC772DgBQREJSigCKKKIqoIKIIKFVBBSxgAQERC4pIR0E6SJMWei+BJIQkkJ7t8/vjTbIJiYren17vvZznySNuJrMzs5vsmfM97znZCz7Hf/ZM0no8iJaWe5dsNhP0w3JMt6icfxaem6m4T5zEvf8gnitxGDu2J2PYSDzHT4LViu2FYXjbtsHcsd3/y/P9u6GLisDQ6W5cGzblP6aWCUVfrUqRbbUbkoKjq1gBz7ETZH+6CDUoEOuYUXhu3kQXHIymKKihpVGMRjSXC9fuvegiwjENHkha196opYKxPv8M6UOfJ/CLT9FXvv2mTEPb1gQuXUz27Lmg02Ho0B7XRlFr3EeOQWAg7lNnivycNz0Dx0fz5N83bhT5/r8DjtXrSH/wUdA0bBPHkTN3AbaXR6KvV4fsWXNwbZNZKzWsDH6Tx5P56gSso55HjQgHPytZ77yHc8NGDK1aYLq/O4batf7NZ/TH8L9LjgPCoPVoGdxRi7kMOoNPOt06xTfJX7VrkU1LBYni2qutDIdtPACrfxZS9vYzkuKQlCo+UIsJTl+BNnXkg/zs1dx0AhdYTb6htzxcTvXnfNRoohMHEnTha57uOJmq0dGkZkol9J7j8MjMUuwa8Tkn1PZsipGp0eBAmDkMLl6T7RJvKMScK9yapitmDsnfIsfT5zUhx9HlREm+VQ2dsEDI6IodosROGiwDao2qSRZykWVtaylOtFrF7P0tWb+zJB1r3WSmdS1FNNsSEXBsKZzOrep2Zgqh6fiGDIcFRbA26H08qSV5vkBk7tKtolZPHyrVybsKNJrWiIAdMbAgVzh9u38sNXfdh/5GrrS+9x3iHz5F9rlfsNivcNXYmEnLm1CvJswccIFayjaU9CuiErudcOoANBoqVoDgSlJC4siA6yfoVi2a8NBoYhN9z//KAFGpSSsmDUFvAbcDT9kmeI1BGOJ2SPZuQHmxKmQmSqpC3QHimU0+XfRG7pePoe7DlA2DOSPAm3gJ/eZPxeIQuwuaPSsFGtZgiO5ItqUiRg9cvSnvlU/WB9G0zlC6926GOzOZ8znRHL1QhYc7wscr5aZv1nA4cOg6TdS9qCmn0Tq/Raa+DNsvhmO+CPUUD8Fx+yTz2xIs16Tli3LNChJjgL1zJJ2kRs/CsWmB5aHFKImSuxWlqkrZSgHUiJSCk4LvzUFd5P1YLMLqwQOfScmIpUTxaR/t+znUAAAgAElEQVR38M+C01V4ejUXXocDff26KAH+aOnpOLfuzCfGAFp6Ou4jRzF1+GME0ZN4HfepM9gXfYn9y2/A6cTy/DN4jp3A/MQgcLux/7QZY9PGZL07B83lwjZyOM5de9DVqIot6nkc3y7zEWMAu52cj+ZhaNMS9V8c/nSfvyiRds+Nwht7BdOTj+HcsAlvYu4f9uxsst54G7+gQDw1qqIrd6tK8Z8HNSCAgDkzyZn/OY7vVmBo1QzrC88WO5Cnq1UD4+CBuPbuJyePbGZnoysTivvUGTKnvoMSGIB5YH/8ZrxBxlBZ6XJu3YGhfVv83pqCfdkKnD9txtSrJ+7DR/8QOVZtNkzduqA5XeTMmYuuYgVMH7wDqg4tLp6s16Zg6tkV+8IvCv2cvnoVsidJ5KXxD75n/wp4kpLIGjMBJTAQQ/s2YLWAw4E7/hpqubL5xBjAm3AN187d6OvWRlc5CrVkSbwXY8lZ+AWeEydx/rQFx/LVBC5fgj7irxmS/Svwv0uOAaLvhkEbxWJRqy8c+9b3vcZD4WRu013ODfFTrh8teb3W4qdZA3PTqLJy4Pk+cC1FlvEPnJbH7U545xmp9/V4oGFVeKKr1AdvPih+yYKlGxXLgF6FmYe6MuWerwg6Oou4JIXXF4DZKPt76G54rhcc199L/8lKvv933mrJ9532pTTn/bgXHulc+HgzsoW0FVSEXx4gloq8bObzcfD8+zDoXpi9zLddUprYNqpXlLg4t0fKRZoVGLR1uqSW2s8CHiLo93U4xy7Kh8O8ayW4p1YTepr8hVjmoWZv2O+r4PYdbALegHASI5/gp1PliSzmb/43m6F3O5gzEr7bAmt2i6pdNRyeLBDO0DYsBn1Bz4nBypoTEQx/vwo2i09t33EKHmmioiwbLDdKqh46vgll6snNU3ocbHzVVz985EtsD0Ww7u1oVu1wE3NOo2fdeNr5rUc9Hw5hDYVMp+Qqo4qCt/t8dme3ZnacPwlpJp7pkkGnkl6CTNli9cm7SQBo9KRcn1uhqPk3eH5WwOiUtAgARzo5mo3Yrr/gdrmIuV6BmSvLExWSQ/+7RWItGQBexcDkjY05eFba9wAWjoF5a4R8KpqLh/UTUL+SoUcFcFUfyof73uHHQzY2jTtL211PybXIG9jb/gZ0mVX0eDMTZQ/OTLFQFISqylBsuSZy4wqSnnH3JDAX9rA1rAKLx8MX68X60r0l9GwNvzkDpTeCvugQzx38M6GrHIWuUjSec+fzH1PLhKKvXg3sdrT0dHR1apP15syiP+zVbvt53JdjcR05huJ2o+kN6Js1IbBLJ9xHj6FGRGBo2QLNZkaLjcMyoC/pjz2dT8YzX3kNvw9m4I1LIOfj+eirFCVT3rgEtPR0CAr64xchF57YK9hXrsFQuxbW4UNRy4Ti3LId19YdWB5+UAjZh5IWo6Wl401L+68gxwD6StHYJo7FPLA/3pwcNK8X15mz6MMroJjNeC5fwb5sBfbvlmMbM4qM53wRRcaHeuM+f4Gs8ZPyH8s8fAT/BR+jRlTEe+ky+ob18ebkoC9fDl2FCngvXEQxGlD8/fAkJeE+dATvtUQZtGxQ7zejyxSzGfP93VHLlcVz6bJkOIeG4NXrMDRpiLFTe7zXk3CuXY/i54ft9Vdx/LQFfZ1a2Ma/grF1i7/0Wt4OtPRMuZkrFYxzwyZcGgSu+x5sFpzfrSyyvfvocfwXz8M+Zy72r5eCwYDlsUfwVquC4/uVuA8fwX3k6B1y/B+Fcg3lv+UbQ82+UlLgtsOFzb766FoPiu8x63qRwS2PRzy46blEM8gGaVnSRtalqcSg5SlbVSqIteL6TSnVCAuWD/W+7YSgbD4oDXZ7T8i+akXJ0FFIWEl+yO7HRVMvSun0qKoQ44GdoX4VCLDC1sNKocE4TYO5q+G5B6BBVfG9xidLbfTRXDvH+9/B3JckXeL6DQgrJaT78i0+zoQUH/HPw8Md4Yc9Qp7zUL2ijxyfvSJZy99slgzfqUMgLauwavLE/Crc/dZq/He8LISq+QhJJXA7xZNawAOr2UKJLdGdm8EVCbwqarC/VQh+HiLCREFuVl0KL155WJT6Q2fE7pKS+zoEmGSILr1Sf2IrDiNVLUsFvY7mNWFHgc4JjwfUtPO+FQSvWyLnavaWxIWkkz5inHfRD35K9X7dqF77W0ibDjGnZABspw4G/ggPr4ULm0hRK6IzmDidVo67J4bmWwF2HAli4RgYGLW3MDEGGRit1VcIZVaBJYCWoyCkwF1JqUri5T0wj/ON5zJ2y0N8t8sfPws8c79ct2U/W3BoMKa/i9b1DGw+BCt/hnqV4KvX4fE3Jdv63ma5tdaucwQe/6jQ4ZQ8+TFPt3qCHw81xJWaKB7sgnA7pNzjVo9yzV6AVyq6i0PpqtD/e7j6C7iyILR2YftFLiwmuflpWAUcTigTfCeV7b8NitmM/7zZZL81C+e2nRgaN8A26gU0p5O0br3B40FXrw7WF4eT9cprvp8LCEBf7/YqwF1HjpI5cRqGZk1QA/1xX0tEVVTSnhoOej2mbl2wDB+KlpODOzERr8OB3+wZ5Eybme85dnyzDGPnu/GcPIWlfx8cy1cVeg7T/d1Rbbbinv624Yw5hr5COeyLv0IJKY2uamXMgwaQ/vwRst+dg6lXTwzNm+LavRcluOR/DTEG8Rc7N27B8cU32FeuQVehPH4Tx2Jf/QPGls2wz12AfeEXmB7ogWPVWnTlyuK9dBkAS99eZI55vcg+PcdPoK9eFVdmFpYBD+JNklIZLUPEGtfuvQQs+Yys16eS81GuYGMyEbDoE8x9fruVUbFYMN3VCpe/H67DR1GB7HfeR0tPJ2fBYizDnsS/+72okRGYOrbHm3gdxWJGDfxn2LzU8PKoJUuQ/ZaIG9Z7OpI1bhKmnt1RSxdN8TC0bS0DeV/lCoxOJzmfzMc2cRyOVWvB7UZzF+d3++fiDjnOg2oQpcvjgPaTJOoqoDxU7yEf7ClnocVIWSbORVaOFEy89JF8OPdsJTFnY+bK8v6XPxVe8j1zBRJvwl314Ln3IDHXWjR3tah0c1eBzQIj+ohVY+9xIZh56NPOSEASfDhSbBu1o2HyImhaXVTmW5GRDR2bwLkrYDLC/LXwxpOSMBFzVvJhN+4X0h5ZFkbOhgmPy0q3VkB0MRvFqlC/sijBve6Cjo3h41Uw8kEIDxWyXjZXkMtxwNh5sGyb/P+JS5KBPG4gvL7At9+bGXDKWY/Gd0+BpOOweYLEk4W3lHiyDaNlArhcU875daX961UZ87CQ/Le+kuc+eQm+3SK+3qlPiKf4apKce0DuZ1GDqrDlPfHMZuWAWqYmp5t/w7q0+/lxlZ6N++Wc+7aDx+6Fhbn2i+d6edHcTo40WUp04ufYLq+WxITDi4SM+hVz0V12KYvZ+0HhdjevB87+APfMINNahZyY9ZT/8QF+Dj1WxCM77Ut4+OWcogkSmlfeiwNWwYll4uet3hOiOvhetLRY2a7J03gUEx+evp+lP/vnvx+mfynvge0xMlw49mHYfVIjNlHh0Xsk83rUHJj5rKwo1IoSAa5aqeSi5wqYkLuT2Jyy4pEu6Kk2WCErRYbxfvlEbjyrdpfVl+gO4kkGcDklkaTgh01AOahRrtjnvBXB/4zPkzv4C+DcuQf37r2YHumHddxonD/vQdOpuPcfzB/S8hw+gvLEIPznfoDjuxWo5cth7nM/xpbF1I/fAs3tJvuDjzE2bYSuchSufQcwNmtCWu8BKIGB+E0cS84XS0jtdJ94J7t3IXP0OGzjXsb6wjNkDJfBUbVsGTRFAswdGzZjmzgO+2dfoLlcWIYMQt+k4b/UvKg5nZB6k7SBvkbKnI8+xe+1V/B7bijZCxbjWL4K25hRqOHlMLZt/Y8hWv8fcJ89j33RVziWShyP58xZ0h4eTMCXC9GuJ2P/TPzlis2Kc/N2rMOH4vrlADideFNSUEsEces4ihoagqXT3RiPHMN9KRZdxQpYRw6Xv6NWK2Rnoej1PmIM4HCQOWos+prVMdT6/TxiQ4N66GtWR8vKxv+Dd0h7aBCkp5MzfSaWYU9hbtIIRVHQlQn93X39XdCcTtwxR/PPWylVCi07B8VoxLV9J0pgIObHHsH++Vfg8WC4qxWmvg+QNXpckX25T5xCF1ERxWpBX6Xy330q/xLukOM8pF6ChIPy7+RTcNdYISI/z4Brh6XauNnwQh/gh88Jyc1Dg6owOldcC7JJQcGtiE8Wy0LiLZ77N78Q1TjLDs+/B8/1hmm3lIst3QKrpsHclbJ8POQtefyHvbBkgiRdFCS1T94H/SfBG0PEf7zrmOQZj5krdo81u8X6YDTA/JelDnnVTlEX5xTIOp76JIz4QJThBlVg1c+iPi+fCuPmwpFcJdpshB/fEQXv++2Fj93lviXBAujfAapG+8M1N6x7wfeN2J/BYMXVbw2x13Wsu1iHF14MY9Jgub45DnkZDpyWmLZFrwoRHjlbUjWa1RRFsV6B38WakfJ17ALsOV+dQ+cr4/IKMQZfjfP0oRLV1qM1WI0aNSd1QdOgT6t7GXXvVUrrEwhPPoNy9kfJ6j28qPBFb/48KJoUXdwKRX7d4q47iTj2DmgeDGpRP6XZCJ5SNVH9y8ogYx5C60DpGuy+WoHL+maEVJMViLpBSCrEgfmwdZK8b1uMJKnxVL7+LKDI/h0uaT4c9RAs2WLg6nWoW0l82RXLyD7NJil3iUuWeaefUxrwQMkq6G74hkm8QVHsipcl5NeWV6LHmK8p9dPDkkZhCpBCjq2T5fep9kPSdOhIFa9vqSqQGgtn1sGRL+VGtOFg8fb/F8dS3cEfg3PVGjSXG/uKNZgffghDrRoofn5Faooznx2JrlFDSmxchWqxoBh/u+3QffEy3oQElAB/3CfPYOzcgYxnRmB55kncv+wHlwvLU4+TOfFNtJvi8Xdt24E34Rrm/n3Jfvtd/D95X/4QGY2Y+zyA59x5lIAAXNt24D58BFPPbpgfH4iWlUX64GEELV38p+uBvU4n2R98UvjB7Gy81xLJ/vBT/N+dTtqefRhaNkMJKYWu0p/LmnWdO4f7QAyeY8dRgktiaN4MfVQEmuZFF1K02fXvgjflRhE1XgkMRC0ZhPv4ScnJdLlw/PAT1heHk/32uwSt+hb3uQsoRiOmPvfj2rc/37+ulCqFLjqKnA8+xvLU46CoZL44Bs/pM5ge6IFSsgT2hYvx/6BoA673ahzelJtFHv81KCYTismEqXMHgjaswnPmLEqAP7qoSAw1q//+Dv5mODZtwxsfDxYLZGWhi6yI5+QpvCk3MERHYv/yG/QN6mEbM0r+Vvv7objd6KpWkaHDAtBXjkaNjsTcpROGWjX+TWf053CHHOfB5C8DUG67fKC3GgVBkaIimwOlnUtf2Mx4MaHwLgomPySmigc35pbB+85NZAjvVqRmCoEc+YHPKlBMeg2xiVAxDPbd0k4853tYPE7U5/RsSbE4fhFef1SG50b0ldKLs1chNUO+8uB0CQkaep/8rQ+wabSuA2euKNSKFFvFmSvyVfA4XnzIR4zzzv+1+bBgjAxKFSygAMlI3jDDg9vhwGI1UamCjgB/Fc5fpwjO/8Tp2h/y7blKTP1cHvJq4uU26sXjbDXDtkNC4uJTfPcte45LKUS9Ym5UT8XCz0chKVVfqPEuDwfPirJ6IR6OXdIxdqCQxqU7rZQNq8K3m6uwe/QUKsR1hl0zofMMtLM/4PW4SaszAkv59lj8bOJZj/3Zt2OdQRrbAJdTQ+e1gzOL1mWOYrNULGSJGf8oGMpUhQeXws634OoetKgOOBo9z7qYCiSnwcSFouK3rS+EvpFnG2x42beT7W/iH9aWmhU7FbkRU4DJg2HI2+TnZi/bJp73xevhgxfkJmr3cbkRkUIRGwfb76fGieHYLq3FGd6emPLjmDY5jBL+MLSnQr8l3Xig9kEal7tGvfpl0NsCJIov8Yj893DuCzlgNZxeC9ePi+XE6A9Hl8CpVZIocSd7+A5yoYuKJHPCG/hPmyQDcOkZ6GtWxzpyOLrICDwXL+Vvaxs5DN1vqKXuq3E4t24Hh5PM0ePRbtxAV7kStqmvo2Vk4k24Bg4H3vRM1DKhkmZwszAJ8pw5i7nvA7L0rtdjffUlVIsZ+/cr0UVFYh3zItrNVDAYMDRvQs4XS3DMXwReL55z52+bHLuOHMP+1be4D8VgfqQfhratwV40ylNzudEyM0EBy6MDyBw9HkOr5nhd7qIrT78Dz7VEXBu2kDH8RYzt2mBo0wrn+o1obVvjiU9AQcPx40+Y2rfF0Ko5+qjIP/gMfx5qUABqWJn88gkA66ujyPnoUzxnz2MZ2J+c+YvQkpPR166J+dEBaA4nWa9OwG/GG7hPncHvjQl4ryWCxYKhWWPSBg2FmzcxtL8LMjLxnD4Dqoq+bm2yXp+C6aE+qOWKrl7pa9f8001wxsYNoPE/t+rck5CAa+t27MtXY33qcbKmvoXn9FmMQwfj/GkzliGDUFauxX3wMO6Dh1H8/bGNG43rYAyGxg1wbt2BliSWP13N6hj79ERXMfw3fy//qbhDjvNQIho6T4e1z+cS5CnS4Fbjfl9RwS0oe8tcjzlXrKgQohFpuYhfxQpcamfgu21g0MMLfTQ6NfJSvrSO6V8Wjh3r30EUvdTcVen4ZGhbD+6qL6TZZBCl+mKCxKTdSjx3HAGLWXy2n62DVz6BbLsQnoGdoXyIeIyjyhX16pYOgmbVnJiSD+OxhtJ3ZkXik2HKELFDvDao6LnXqFhU/QYh0DYLvDscBkz2Pd6unptGlt2EuY5C+jlwlYSg1hDYFJcplFtnqLwlorErQYSHSD5zWqakEIycLdcG5Jp+9ir0HCtDf68MkPMa/RFsOijnn69WpyfAjTN0CzdxI6k627MCqRVZ9CYjMgx+2CeZyEs2iSh8X0tR0/eeEPW83ay72PLKbgzJMaSm+nM2ZB4fbSrL5q8MbH5XrDVEdyL1kf1o8YcpkXUEqt0HkXcBEFzSxKWokVS6upN6MQPZ+vwSVpypy7XsQPo0u0mLmkYgGCq24IznGy5dSGb+1hDsBw00rykrCjOHyWu89RDsPw2NTLuLvBa23RN5+eGO7DmpkJ37uVq/sgxTxqdQpFBm3mro2x4OnAGzAd5f5tvGZITFY/3ZZ1yApWo6K/dYqJxqYtJgiA6TkpW4ZNh8IAqbJYrDCyDKCrR6SXKs03M/1O4aJ4OuBwv4axo9CWH1Zbv4g3fI8R3kw9TtHrJnziZjzOtY+vdFLVcWY5dO6KIjsc2ajifmKFp2NobmTYRA/go0u53sKW+hq1KJzNHjfJaMs+fIGjcJvxm5OZoGA/YFn+M35bXiPZK5055qxXAUi1la23IH89SK4VifexrDfV3xXLpM1mtTcB/09csX59UsDu6z57nZobuPZERHgcmE5bmnyRjyrG9DvR5deHkUixklwB9vWjrodejr10VLvA4li//c+jV4Ll8mZ+FijPd2RrHZyHp9CgCKnx9+H8wgc/os/Ce+SsaLYzA/OkDa5MKLya78C6DZHdgmjiVj8DP5K3W6yHCyJ02Tocwa1bC9/iruo8dBVclZuBhdpSi09HQyhjyLaeAA1MgINJcLUMh46jnIvfFRTSYcebFkoSF4cr3Kpq6dyZr5Pv7vv0PmqxPQMjPRVYrGb9Z09FERf8t5/93QsnPwxsXLwPXuffhNfR33sRPoq1bG1Pt+st6cgW3SOCme0TS0rGyy3pyB9YVhOH7ciN/bU/BeuASqiqFVc4x1/txKyT8Bd8hxHlRVMlvL1IMb58VPWrbhrxJjkOGlYQ/4LAjr98HHI920N39H9N7BeGr2o1LX53jq7hBs+izqnhqBMfExSte7nw0zxS8cnyx5wYk3hJAF2CSTd+1uUQSHvOUj0U90g3NX4dM1MkhXqZwMA4IMJw3sDF1HF17lP3dVCigSUmTlKTgAPnoRXvlYvLmRZbx8NuQ4NbhOye0d2FFvPfHJFVFVIZdujzTh9W0n3l4Q8vvBCPGn3ooH7oIAC/RsAztma5y5olFSSaBh+lzCludOC9d+SNrfsq6DTk+iuT7+kQ8SeDG3BENnJLnNx3QYV4qnusPEx2Hcp3Iu8QWsry43fLxCBrL61TtDS7YS5h9L5/Ft2ZHSDIvJTza8sgeWPw5JJzFbgxly13giyw/hwAUrUWVFJQZJD1EQ//jXG33Ps+pnUd6bVJOhtcvXdAz9uhE3Mxqx90Th87+QIBaFHw6EMOmzEJyuhozqB138oVyuWyCsFFyo3IGr5pWUO/seDZJm0aDlA3DsO9jyE/jPheAnOHsVmj5rIj3Lp15cuwGdGgkxfvQeiVm7kQbUyR0+avac1EGrOvC4CTTBq4+Iqq9T4ep1eb/OGVn0tXO6oXo4rNsDVcILk2eHExb+ANl2lQ4Ng2hYA7YflrINm1luGqpHyHZ6nbzPAIhoDY/+CEmnZXXGYIFPbyExB+eLjSnhUPGxinfwPwt9zRqU2LEe96EYNJcbfd3aGGqL19PSoyue5k1QDHrUEr9NBN0XL5MzbyF+M6cXWZLznJFkFzU0BG/CNXTRkWS8PB7bq6MwD+wv3spcWJ4egnP/IfxnvInz5z2F4uO8l2Nx7fkFJSQEXYWyuA/7Zg4sQ59AV/v2lpXdR47mE2N944YYO3cAnYrr4CH8P/sE+7zPUAL8MXZsT87XS7GNHY2maeirV0UxGtAyMv5cAYhXw5ucgvnBXmS+NDb/YS0zk+y3Z+H/9mQ8l69gHjwQRW/Ac/rs30KO3efOk/HkcIwP9ibgqwV4k1JQAwNQA4PQ16iGa88+HN8uw2E0oouOQtM0rM8OxX34KGrZMLzxCTg+/xLFaMB95Cjufb7mUTU0BM+Vqxjvao1z5Vq8KTfQlZW5Ii0tHfe2nWQnp+A3e6aMdWRl/1dP/erKlUVTFSxDBpH58nicW7ahi4zAdfI0lrGjcI54mJt6D4qqw5SYibf/cPRNGqKLikR3+gwZg57K31fQmu/+fSfy/4A7n0QAyWfBkQYBZUW1uk3lqmSApDAM6CjDZcEBkHHpNNG7B0JAeXSRrai6vC5VC/5Q4l5Ug4W7QkO53Kk2P+zVs+gHGdwLLSlE8NW5MvT2yieF1eVP10iecEa2LHcPvEeWvDVNyMmxi4WJMUDzWkLYSgUKAX/5Y1GOn+gGnRp7qZs4ixLJR/GUHwgmfwJs8rvv9QrJASGHHRvJsJ6qQvcW4llNzxILwJzlktBxf2to3wDcGYn4XdtBi4Of0kLVSdXx+QIm5qNLoP0E2DULStdEH92SSSlzGNR2KKVNN9h3vQqnz9QkI0tKVJrXkqX+o8V4uK/dgLcGJtH1aGd0py8BUIupVOq+CEUZKDXhywdLsgRAdgrK+he5+4EyBNbrQ9PqKnHJkJkt6vxHK0VJvxX7TsLgbqKmgpz/z0eLbhcWLCr+gwWGo596G2YMk9bEFjWltyAqwh9n2fvweo+jO70KVj3le/EOfAr1BnIm1kj6LW3M+0/BPU3FBlEyN9msegRSnHL/Z6LI7nlfyHHDJyhb7x5UJZgpi3z7qFxObuz8LJBZwM7xdE9ZkTh+qfhrEJckKSrz1kC7+rDlkHxVC4fJT0g7nckAw3uDpaDlM6SmL03j/KaiO/Z65NzNQVD2n7vkeAf/HuirVP7VYR5dyO2psSiAoqBYTEW/FRyMlnID63NP4z5/EVO/PlgCA9FysjEPehhzvz54LseC2Qw5OWgZGaQ9PJjA5V9j/3g+WmruMp7VirFtazR7DsYWzSh58Gc8Z8+jhpZGV6sGut8h8IUPVmB+fCCa5iV7/FTch4+gq14N27RJeK9cRUu4hunutugiK5I5fgo4nZh790QtG4br+AkUmzWf6N0OdNFRmPs+gFJMTJnnxCnce34ha8b7+E0ej/2bZeibNrrtff8r8Fy8jDc5Be3SZdLHTsD26ktkTH0b/4/fw/ryCNIHPik2F6cTNbw82O0ogf7oy4Rg7NCWnI/n49rzC96bN7G9NgbH10tx/rQFfd1aGO/pKH72/n0wPz0E+9wFeFNTMbRugRJcEnQ6PMdPkjFIBiF1lSsRuOqbv+W8/x1QzGb8xr9C1rzP8Js2SW60yoZJkkdFPy7u+Z6z6+ejKAqVOg2m0s6lWG8quFavlQi3XOgqRUtJz38w/nJyrCjKPcB7gA74VNO0aX/1c9423C44+b14NdNiofK90GYMVGx527vwt0LTXEEgKRUSLsfKNH75plLeUBCWksTUns+u/U3x2tNpXCuFN86GUjIAhvcSW4XFBMumgN0h8WsFoddJQoWfRbjEhXhJy7CYRB3OdghxXbNbSOyADpIk0W+iFHZsye1iyMgWImNQNXT1nmLNJStxXyv0aHGD6FLpDOkmw30OF5QrLaTop/3y9e0kIYYgmboVQ2HWs6JKp2dB8g0XtqsbYNlA34GfWSfDayuH+B7zekBvxu1V2HoImjcuSdOpbXm5P7jdEFEghWj3MfmaNbzo9R/1QAYdQnaiqzdAVMnYXXBmHeYto6F6J1Gnk26Rd70e1BunadL0JliD6fuakM2pQyTGrkUtyYU2G+Xmw+WGxtVg8mdyPYb2EP90jQjxlOcRzMfuFfV55AdFj3PFDhnQtJllqBHAaEQGGq7sKbxxSE2wpxFwawYwYm9wuYW8ZubAO8OgeZUsyeJWkBzfvOv7yyeUC2tIl2ZPUCpI4cRFKBvs5a46ThpV9rJxlpUPV8iKxYCOcqMxKZdEF6lfBnq0gg+WycBlQX/9qVg5t7zila2HJR2kdYHm1aRUifczq82pUvVh/E4XCMEvVUX8/f1XQHgLSDkvQ7DOLAipAeX+ng/hO/jvgDcnBzQN1eqrGNJHReK/4CPUoCACvv8aT8xR7N8uw3MpFv9Z09CAzEnTUMuEojt9FvexE/i//za6KpXQ5/O3KcMAACAASURBVEaiuY4dx7FyLXg8+M+ZhffGDQIWfIQ75ih4PCiBAWAyYmzUAMVgwFC39p8awNPXrY1SujRaUhK6ihWk0CRXhfacPEXm0OcwD3gQfbMmqCWCcGz/GevTT6BpGqq/P9mzP0YNCkJLvI71qcG3/by6kNKYHu6HJ+ZIke8ZmjXBFXMU7Hay3noXc78+aDdufyjtX4FasgTe+AQZMjSbcR05hj6yosSifbsc/0/noN24gWI24zp0BO/VOFybt+NYuUaW91u3xO/d6XguXSatW28Cvv0CU++eeK8noWVmYRk0ADU4GPfFWIJ+Wo1aIgg1JAT31SsELPiIjJFj0FJS0FWpjP/sGRiqVf39g/4Phr5yJQImj8eTcA3Vzw+1VDD2s2dIjlnFmXUyFKoBZ9Z9jH+ZCEr2fAFD1UroG9TDsXodhgb1MHXr8qd92f8U/KXkWFEUHTAH6AhcBX5RFGWVpmknfvsn/ybE7oTvBgiRAMky1rzQbXahmtrfQpYd9p0QYlC5PDQvWw4snWHjWCkuUNT8vN799ZfS7t32uf7PEpiNsPJNDxfidQyb5SuCeuxeKTSoV0l8xnmY+Di8ONtnA7irnnhhP1wuVod5q4W4jh0o5DmqrOQsB9rgaiJF0KyWjn5v+uXn/y7bquftZ0pyI10UapdbIr0SUsTz27quWAsgV+TUYNdxcLlEzUzNhGHtrqBsXFL0yc5vlKzaa0ckmSDnJjQaQkxWHQZMhqiyChsmJeJwKSRmWjmf5Ed0WThfIKzh7qpJXJ1xjOuJWRzPqEqmJYJexo+wrp0iRErzQqOnoN1rUm/scYkaaS0ldc8FkBPSlMMXSnAxQRR4g14U8qd7isf5x7fB5RG+qSFZv/e3EQV58iJR7h9qD7NHyJCjUS/XIMhPyOOtKBkgBPJ8nI8cAxJttuc9yMgNlzYFQIVm8PX91Gj+Fg+0acb3233LeE/3lKruuS95qRKSSbXg6/BNX7EkgEQP1n8UDuWy3Pj91KnxANWM8aSXPEeQ+xL6zfMhvgeNW41m/suBfLNZ3sPVKgr5djglKvDtZ2D29xJ/92iX3GHOTHipH7xRuOCpUMCEpsHy7T5yfOKSpKaI8m/lsXsWMrllK8J+eVEqp9tPgjK5JCLxGKweBpdz407MgfDQ9xDdvuhFvYM7yIUn9gqeK3GgKLiPncB78waGBvUxNG8Cqoor5iiuTVtxLFuJGl4By1OPY3tjItr16+jq1UEtE0rQhlU4t2wDuwPbuNHoGjdAH+z7ZTbUqomhVk2c+w6QPX0GugrlsV+Nw9KrJ3i9ohCHhxdbAvJHoK8URYlNa7B/vRRvWjqKqgOTKd/C4U24RvY77xHw+Txcl6+gqCqe02fxXkvE/s0ytMxMzP36kLNgMab77kUXdnvqsXPHLjzJKXg9HmxTXydr8nSw29FVqYypz/35VgstKQnVz4aWnoE3K+tfzm/+PeiqV8U67mWyZ3yA3+tj8Fy+gnFgf7zZ2VgH9Sd96AtoKeIbtI54FqVkCRx5VdNer7S5aRp4vZj69cHx5RIcK9egBASI7SI5BetzT6N63GSOHod17Ggs9eqgK1sGmjRGX78u3qRkdOXL/cuv7X8KFJMJfYRPIVHLhXF13poi212L2UJU+0ewhZbF0q8Pln59/s7D/EvxVyvHTYBzmqZdAFAUZQnQA/hnkOPkUz5inIdzP0rz2W2S45U7ZVArOEAsEF/HVOPF8PXoAA59Dvd9DD+9CiZ/vj7RIH8wCkR9S0jRMeaTwg2pC9eJUjnwHnCuEXLRtLrkzl4oQBa3HRZCVioIvt8mWcmHzgqZbVcf3v5aniNPAS6IahXhfJxGSlrh2KwZS+DZXjBunu+xL1+TeLOUdIg5L41zMeeg2ytCpAAWb4DPx4JNZ5dkhluhN4LOhFbpHqj3CF6nnbPOmgyYLSUsF+Jh5S4Db9d+G9eplRyr8hYthzXnQoo/qZnQu0E8FfcNRTm3njCgrjmQ7AfXY00rKT5bW2lIiIH9n0C3OfDgd5KVq6rQ/UNY2i//tc5oO5P11zsyeLqar/o+2F6836cuwpAe4nHeeURI7UsPSTxclh1e+tB3Sos3QJC/bDdxsBDsRT9Ay1pys5KXQGE0QJu6Ymmx3rqqawmG3l/CzYtyI5WRAOtfAnsawbEtef/BgzzapT6XEjTKldawmTQeb3KOmpvbQPnmUqSRR4xLVxOfvLU0lK4uWd3BlcHjxHhlE6X2vC9lKzV6Sb1zYgxqRBta1vSw/YCHpDQjC16BhGSNUkEKGVnyPgqwQZdm0jo4Z4QMXRZM16gVKQUsU4fISsr1VMnOBlkFmPN9YUvMwh/1dGk+hF7PdZVKa0OBZdyre33EGCQWbttkqX7+lWbKO/jfhvPQEZw/bgCnC8fKNbgPxQAySBb43Rd4s7JxLFuBI7egwHPyFJmjXsXvnTdw7dpLzqeLCFqxBFOblpja/P6qofvoMRzf+2LFXMtWgsFAqXNH0P0/eXANtWvme6tzln6P9ZkhZM+anf99Y/d70VWviqlHJdyHjnCz/b2+DxG9Hn3D+rj27v/dSLs8eFNukD58JLYJ4yAjA+fe/VhfGIa+Vg2cG7dIiUbu/tUK5dEU8Jy7ADl2+BVy7E1Lkxgzs7nY798uVKsV20vPY+p8N564BAyd2qOLjsL+xRLcCQkEzP8Qb1w8GPRoqop2PRlyCpd1Gdu3AT8/vAnX0JJEKNEyMvDEujHUrY33ZireuHjc+w/iiTmKp1F9dOVl1iPvdfhfhtHqT3B0PRKPbiv0uH/Zyqj623uP/afhrybH5YACAWBcBZr+xc95+zAX4wHzK1P4w/o3kHhDyjS2HZLc3zeehISbRs6FVhGf8Y1zQlDungwloohbVHS/VrMvoaIgjHohJaP7y7/1OpiwsOh2xy5IJvK5K0LMsp2iTtavLAplXtSaXifWjY9WCImvE60VtLblw3nLgHZet0TrZ0UVfaKbkPLMHB8xzsN730GPRmH4Vb4XTq32NdypOjx1B7Jwb3U2nypJ2UwLyTdh8U+3XM90E1iCMNw8Sf293QFor+qk7c3RG86t921ctRuWs99KnFoeqnSVUozMBCmjCAqH0FpQ/X54cg8knQJLCU57OjLlbbWQ3/abzdCvAwzpDo9O9VUo30iXXOiPRxX2f+fhp/2iMl9PhTbPiu/63hbw3WTYc0xi9UoFwoxv4KkeUKFgVOj1k/D9oxD3i/x/cGXo/pEQZrvI+WFHp9Ct5wKonCBDjDumQ2Ku2VlvhPO5FzGgHNQfJKq8K1sKONq8CkEVIfkE/DjS52mO+ULUZbt4JcO1o7xXZiKXg3pgMFdgcU47xn2qFLJOKIpUlb/5hby3Jj0u3uo6lYRAT/4MzsVD1+ZSEBN7Ta6hxSQWlVtx6KxKr7bFLLulxhZ9LPGY5DjfIcd3cAu82dm4d+0h6/Wp2Ca8mk+MIXeQbMb7WCeNw/HNssI/6HajJaegi47E8e0y3EeP37Y/t7jt9I0aoAT9sbgqb2YmngsX0ewOdBEVf9U/ra9bB+cPP2Gb8jpkZ4PZjL5mNYyNxJ9vaNGUoPUrsX+xBBQFfY1qZL/3If6zpqMGF7OMVdyxpKbiPXcRz5UraGnpqDod2dNmoKtaBXP/vigGA5rTiVomFL+3p6AElcC9/wBqqaL7d8fF497zC+4TJ1H8/TA0aYwaFYHq74ei1+OOOYr73AXU0BD09eugK/n7v9dqQADGVoVrlXWREbj3HSCt54PoIiOwjniWzJGvYB32JIa7WotijLS36Vu1wL39Z9SgINQaVTHe01FsFamp6KpWRgkOJuOZF9BVq4rn4qX/uDa3vwMRdz3EpR1LyU4Rhc4WEk548x5YAkv9zk/+Z+KvJsfFpfkXGhlTFOVJ4EmA8PC/uXe7bEOIuAsubcs7GLh7imQa3wacLlHK8uqYg/xF9bW5GjMmvBt+sWsku1XV4a7/FA+3y2bpjsLSocUoSuzpApxAr5MShvGfQuUKkhQx53vxwp64VPgYIssKMXF7ZBl88iIpxpizXNTnpZPkODOypeChWU1ISIYsu2QYF1Q4AV7oLSkUiiLDZRMfl33ezJCvsfMk6SCtGELv9UIWJfCr2AbuXwDnfhIVuU4/dqW35am58nYz6IuPhxvQMhkCw0Xp9Xoh5bQkGdR4oHAZBkC1HihL+xd+7MxaUY1Tcy/Srplw3ydyDOUa5XtXMw7K0BmIXeKpHpLkkOMQb+z6fUWPLSG5+NzkmpEySDn9K/FEn7gEr34imdJ2lyRXXL0uyrLmFZ92Pk6v8RFjkBbGE8vkPfhd7rmF1IbMeFGUzUFgLKDSJB6Tc7pxDur0F9/u7lk+D7PBIlnJGfFFJzXP/CAxawCZ1zCeW0FlVuBsOpINv7QvQoxrRQnJTU6T91KtKFi7R8pkHhgn7zGAz3+U7zesClMWSSNk49xVj4Ko+2urk6HFeDSrdIXAYkzQd/A/Dy0jE+fmbSj+/mhpaUW+7z4lpTVqSGnJMS4ApUQQrnVyw63lFM0Q/jXoG9aXhrCFUl+qBAXhP/NN1ICihTsA3owMGRjz94fUNJSgQLxZWbg2bMa17wAoCrpqlTF2aI+hatE/MoYqlbCNfwXXz7vxxF7B0LghhmaNfedhMGDq0E5SBfbuw3P2AgGfz8P4Bwbm1NKl0NWrg5KVjVomFLVVc5wbt+A5fYac+YuwvjwCXVQkakhpPHEJaJlXsQzsX+y+nOs3kfHEM0L4K0Xh+OEnLE88Ss78xfhNHIP7xGnweMj64CN0ISH4vzUZNaiYCeDfgb5WDbxXJK7Jc/ESOZ9/hbnvA2S/9yGm+7pie20MmIzoIipCdjZZk94EwNS3F96UZFybfCqo5blnsDwxCF2FCrgOH8lXje/Ah5Bqzeg4ZT0p5w+CphFYoTolo+r9uw/rL8NfTY6vAgXXmcoDhViOpmlzgbkAjRo1uuUT/C9GqcpCxOIPgf2GZB2XawSG21gGunGe8tcPMDwykyYj6vDGxnqU0cUzvT+8uLgC7qaf0eOebOwuHSVLGBkwtRQRYTDnBTfvf6/H4xGl8vJ18ZHOWy2lHaElhdyOnSfDYRcTRIEb9ZAQ4OY1pZxBUSS6rUxJeKQz3NtMUi4evxemfA4paUJIossJqb2WDK3rwXdbYVNukk2DyvDNBPh8vZR69G0nS+ZxSXIMPVvDo28UJu4gNwNdm8HMbylUfTy0B4T6ZcHVeHBmS+NZiUgoEcHNnb7tXG45hkmDZRBOrxOfdPvKTjgbB5vGgdshqmf/lRBcTdTPgnA7pAnuVlhKwObXpKnu/CbIvA45N7mcXQaXPojwMD0l/CVDeluMkPTX5vtyn/u203imJ3y4ovB9XekScDO9cHyeX27Vt9EgCRT7TsrrCJBpF5U+PUtuMvKKWQbeU2CnV4th4fEHoEZvaPCYDIh67PBlDyHOiiLZ235l4OQKSeCoOwASqgtxzkoqPNznyoEtE6HLe4Wfo2YvyRVeN1K8vjX7iPKcHofxyEIe6fAmu475lsoevQdmfyeWGpBmwl9OSXJFaqaPGOdhe4yQ35/2i6tl0mA4dMZ33e5vc4vvuiDCW0K710Uhd9shsp00Uxr+O5fu7uBfgxoUiBoWKoUexQxKmR7ogeZwYHvtFTKe9rVw6uvXRa1QXoh1YCD6P9BUpgspjf+saViGDEJLz0BXObrYQgxN03Bu2ETm5GmYe/XEuWETzq07MDRpiG3saJzbf0ZXriyelBtkjp9CQFAQ+ujIYmPY9JEV0Uf+9g2iPjoSffSfK+ZQAwII+OAdMl+bgqnbPWCxSJ5tVhZqcEmUsDIoLg+un/dgaNoIY6e7i7QUArjPXSBr8jRsk8bjtdtR9HqJXStVCtvzT5Px9Ajch2JQy5fDNnEcSlgo7pOnMTb/4wvK+vAKeBvURfHzQ8vMxL3/IIZ6dbBNGofzx414Lseir1IJ1y8H0dfxWSP0NaqRNWFqoX3lfDIf68jhaGWc2IY/9eei8P4HUCKiFiUi/rNTKG4Xf/U74BegsqIokUAc8BBQ/O3mvwshNeTrjyDlLCy6B+XmBQKBTjX70LZHTYx73gZNo9OTk1jmeI77pweTnCZlIaMegomfwc9H9SyZAF9ukFiwQJsolw2qSBpAjQipai5Y8pFtl3a4NxbDW0/DsF7iXb2cAO8ulfKLkgFCRBpVEzVYp0qhxcjZYg0A+GoTjHtUhvxS0qQNbvdx+f7zveCJt30q8tZD4sH1K+Y+IcAqpO+r1yT7OD0TerWTMghOrRZ/bx6CKsKgTdSOimbKE+J/VhQ5n/Qs2DhLbCN+zliIOyJ+2zykXoYfR8Pjm6B8Exm02zFdiLHeLMp/vC+zEltpIcxtXoU97+OudC9r95lJzKpKbJKBmd9C/7s9vPqIjpEPQo1I+GJD4UKUb7cofDkmh7mrLbg9ogw/2F5uJHYeha4t5DXzalLGkhd51qctdBjh288Hy4R47zkuaQ4j+0Kf9qKa5yOyDZy4JQuyVj9Ak8ST9DixTaRIDiuaBoc+gw5ThBwDXD8BLV6AUtV8KyAFkXwaLAFSPZ14RF6PgPKwcZx8//wGuLofurwLKwZDzk06m7/inWce4aOVOox6qeL+7IfCu913UjK+b2bAvJchOVVeT4sJbmRIBjJA58bwztcS8feQxLWy94QQ7LNXoH4VuSH0vbnC4K7xUprizpEbI1sxS8Nej5D/vCzrO/ifhGIyYerbC/uir/AmJWMb9zLZs2ajZWdj6n4vxjYtyJ7wJvr6dQhY+gXehGuoNhtKiSAyXx6PsXMHzI/0Qxfxx1Yt1cDA3yV0rp27SL2/H+Y+95Mz7zM8J0/J478cxHP2PO4Dh7DPX4QuKpLAj9/Ddfwk3vQMtJwciSMLLonuTyiqfxaG+nUJ+HQOnitXUUwmsJglP9rrwX3sJN5riZh6dpM0jV+peNdcLkw9u6FYTHiPncC1dTuG5k3RRUeS+dK4/Exp79U4Mp55Ab83JqAFBmDITfj4ozA2bkjQxtVkz3gfz8kzqNGRqGFlwM+Ga+dusYhUKI/n4mXU8uXwXo0rvnrW4RDLx5HjWB8d8IeP4w7++/CXkmNN09yKojwLrEei3BZomnb8r3zOvwWxu3wxbXozhNTAuGWCZMtW6UqsrR1DpxnIyc2Hj08Wr+ZT98FbX0k82MnLYk1Iy4RPVkoxx+GzgFLU9wtiv/jsVbiUICpcSX9Rgd94Ei7EwbBZsGIqPDFdmsoAWtYWdXpOgYjhBWvhgTY+hVOniorbolZhe4VeJ6RxaE/Yc0JUa51OCHynxjDtC4nsGtxNUjrGzoUlL12H3QXILQjBjT9Api2axRtEmS6Rm8+7epqQegAuH5ShtFtx7RCkx0NoTbENtBgBqkFIYZNn4Ow6uLRdyF/r0ULODy3EW6ISa7zP0WuKEKvwUCnymPaFjvIhMPYRuWnJuw4FkZmSzL4R+0kq0Y6lu/yYv1bPmStiJfhweWG1fMccUVBtZvGI5yV/pGXCqDmw5HW4p5nYb0i9Avt/gNg9EFYXwhpCrb5wTAaFaDEC3NnwZXchflW6ykrGmbWFD1BD7CM6I1zZJRnJBiv0yq1n1hlJj36QHFN5Qv3s4HJAvUfkpiKkRuGYPYDL2yTC8LFNeG7GYtHCOLHOw+j+OjQt99iLwbmrEg8YGSa103kY/yi0awCbD0r04Ixv5MaiIBpUkZu9bi1g2lDJ6c6HTvfbecfXjsIvH8HlnVCrD9QZcNsDtHfw3wPX2bN4L1zGm5pK0I/LcZ05h75FMwIa1sebkQEOJ659B1AsZnQR4SglgtCZzWjpYnMwtGqO5/RZMl8ej6ljO5TbbLC77eM7eBhyctCFVyhcJPLoALKmz5QqZFXF0LYV7rgEjPd2xrlzN9rVOLzXrqGULIm+YX2MLZuh/E3FE7rQEHShIUUfb3d710YfFYGhVXOy33oX9779ACgB/riPn8wnxvlwONAcDrTYq3jOX0D/JyPSjE0bY/hqIZrdjurnh+vSZSmzcLqwf7sMx/JVWF8YRuCKJTjXb0QNDUEJCvLlUwOG5k1xHzmG8d5Of+oY7uC/D3/52oGmaeuAdX/18/ytyCyQi1a+KVzZC0Y/UfRiviT2SmY+Mc5DUiq0q5LImvBSmA06hj0ghOqcpA9x+rJYKcqWEpvFtAJRWdUrCtG9f6wodSAK5PShUnF8/aYUMXy6xkeMQUoq7qoneb15HlKny1fuUSpQFNAq5aHqLUPWkwbD8HelgGPxOFEF/W3ivb2ZIWr3ywOkoe2TlcJnQgKcUqZSAN6ACuzPacOSrdC5iRzz5UQh5ckFN005B5ZiBjNKRvseLxEp6mbeoF/lLtDoCWj6LASUI0NfDpenNEpUf5YcrsRzH/j+oMcm5vZMGCVmbEynY1TNPEfXJvfy/c7CS/aV/GIpl7Wdwcu7cei8/IpcviYkcHBX+Hilb9uMLFixUxT6VwYUTrOILifqtL9FA3s6rHteqpMBDiHkt/UrUP8x8DhBc8PXvXw7OLMW/MMkBzj5jO9xW2lRk8Pqyj7i9suQ3vUTuO9byJbrTRi7vDLxNwwM7eZkkP1dyh96WX62xYjCuWt5cKZBlc5su9aIjrnteQtyf2uHdIMOjWDjft/mnZuItaJDI5iwoPCupn0pN3K1o8Qq1LiaKOgFkVe1vmaXZHNXKS/vod9Faiws7iKqOsh5xx+C3osL+7Hv4N8O9+VYXDt34z5yDH2dWhjatET//5R96tyzH8fqtWRPmyHzCQYDAYs+QQ3wx30lDsVqQbFZcVyOxfnDBpw/bMD6/DD0rZqR8dRwDG1aYh40gPSFizH17oknMem2651vG2qBN7ReL9EtgBpcEu+Vq6jh4fi/9xaO71fiXLEaxetBrRyN88efUCMq4lq7HmNcPEpQIMZ/eGKCNzMT1y8HcR85hurvj6FRg3xyjF4PTle+/aEg1LAyKKqKdut09x+Eotej+MkqkiGiIuTGkBlbNZOBx9xhR2PD+rJN3dpkjJ2I+2AMxnZt0NeqgX3JUvzemVr8E9zB/xzuGGv+DCo0k/+qeqg/EGJ3Q+OnYNtUyEoipNyN/Ja5PFhMEJG9kQ+G9+TJmTbOx4kX9cWHZNn5+T4QHCgq857jMGUIJKdqVCmvUb2ixvYYlZsZPlLjckvNr79VlEu7U3J0b8XV66L2RpUVD3LtKLiWIsppVFmISxH198Pl0nKW45DBvciy4mE+ekEI+DvPSJZzvWhp3ItNhPgk6N5KMnAdTjiXXg5r+5VE/Nghn8DurfUN7caUyVdbP1wuSuHzc6BGRYmUq1QOKb7IvAYNHpeWNwCjH+7O77LqUBjlQ6BRpZao/VegbZ6AkpmAt0xdMsxVsAfWwuWG52bCyp31ebiT+LQLXn+QFj+bBT4ZeBTTojZgT2VC61VcuX4Pv5wxYDbC1Ieu0vDaGxwPGcah84WX+S4mSGxeHmwWsVU8OEFU9hU7JNXizBUh0gPrx+B3+kvYtAeqdoXKnX3kGIT81nsEUCGqLRyYX/QFTD4lCvlPY0RNrv+otOEFVoBjSyH5pAzjWUvBmXWcazICQ/J55g88SMy1UF74PAJ3p4G8XmkLSu2HxE8f0Q6+7Q3u3A8kazCESihxYmrhp3+4E7RtAI+GQu+2uQkVURK998kqaFKMVdPllpWNxeNFSdc0eGSKRAwqCgzqUpgsX7ku3ytfVLAqiqQTPmKch5PLpfK9TJ3b2MEd/B1wp6SQ9doUnBs2YerZHdfWHTi3bMN/5rRfHVy7XXgSk3DHxPiIMYDLRcaTz+E3ZyauTVuxf/kNamgItimvYWzTErV0KdxHT+BNSibg+6/wnjuPlnID/w/fBZsV+1ffYnhjwr9+4gWgr1MTfZNG2JevxjL0CXJmf+z7psmE/4w3SB84RIb1ANf2nVjHjEKtWxv7ex9i7N4FQ7Mmf1vZxr8C+9ffkfHks/n/r29YH8vgR8mZvwjntp3YRj+PddTzhby+5iGPoaWl483KwnR/97/kuNSAACjm7WZo1ICg5V/jOX8RT/z/sXfe8VHU6/d/z2zfbHpIDxASaui9gxRpCgooIFgARUGxABYUAWkWbIgKoqggKop4FVREpUrvIfQOoabX3c22+f3xpBL0qvdeL/f35bxe+SO7s7Ozs5vsmfM5zzmXUNxuLPcNQ1f1f7u44gb+fbhBjv8KYlrA4OVw/EfY/i4kDRTlrzAdgDpnZvLqsOaMWyx/aKoK80acwqZkc/fLflwsVncvZcow2KtjhNy+/QSs2CSNZU0SPehUHQ/MVlEUGSC7Ghm5siz90044fEYitY6VC87T6SR+6/UvxRtctxoM7CQkrnZVGRqLC5flf4DNB4QwT79fSkMWPQepV8Rvum6vZCpfzpR2vctZ8ph//Crbv/ctnE9XqFO1M0se2k+T7X0piu3EopQWFWwIHi9sSRGSnnJKvKuJMZAb0hrjhQ+xVG0H1TuhuQooCk7i5jfbcjZNGgA9Xj1tkm5lv7cDR48XMPurWI6eg1kPSFLIt8VDf2v3QP9O8PZV6U1Vw8Xf3ES/tjTGLGnr7azqPJIzd96FX0JLEi4tR7d6FaboUdd861sn5PHz2N2c98RTrXZ1NMo8y5tT5CcsEHa9lopt+W1lyRlnf4X6g6D7S+IjzjopVgBnDuyYB5tflcGz6GaQdkBsDlWS4NQasYr0egNsEZDyJdjTJQKu+PNG6ja05g+idJ5EYtr31Am8BGg0qBVGz1cymLayFlqTLijfjRaCHdkYhq2S5wyMgwZ3QhVZ0qwZUxbf9/7TsG63EFtFgYdvh5cehEOnJbMZJJUi2L9sRQPkYi00UN7jEmybLyUiR1Ml0WJ7ufnKkIBr5D//FtRr+BJVnVyo3sB/FZrPh+Z0ohUU4jt5ClP/fqj16+F89S3Q67CMuAf3gUPo46vjPXsOrbAQXXz1ECzH3gAAIABJREFUaw6z/R58ly/ju3i50tWvVlCAAmJhMJuxPvk4+Q89ht/ECThXr8HQqAGOOfOwPjYGz6Gj6OKrSTyZXo8SGfFvOw8lMDRrgm3GZGm3s9kI+HIx3lNnUGvUwLZgLt60tFJiDICi4Ji/kIB338CRn0/RZ1/iu5KGLjEBNSQIQ4PrcxDKe/ZcaUFICTy792Lq2wcA8629QG9ACQnB9s7rUFCIGhNN0Yof0DIyca3biN/T4/7241atVtRyedI3cAPlceMb5a/AYIGk/lK6MDdJUgP6LSi925S2i1ERg2g34TkuGRoR592DX1AA290PlhLjEpSUgigKPPomTBkO93XK5EpuIF9tUEhNk/vrXeP74/aOMlvw1BAY/iKEh0CnRpLCoKrwykPw/EIZfgKJGRs2Az59HixmeH+FKJ8T7xbCiwYvPgj3zaLUFjKgEzx9lwzumQxShFFCjEsw+3Np9ZuzDI6cU/h4VxK/RuyhQYKN7IOV18rtRWXL6vmFkJMPjy8IZtfhx5nQ4yRBFjfewOqcP+fH4VQZGNx9TJIRokKg/bgg7M4yCffNr6B/h7L9X8yQ+u3R/WDJz5LPPOVeD5HmTNaMTcbkLvcCfF6CjywgOGsT+c238HHyKCIbdaSKTeH+3l4++KHs+Ae2d9L29GgCT34mamvblZw3tyE8WKwtJcizQ2TRnjJiXIKDy0TlPfINJHaHmJZlldoGiyRLtJsA+RfET7xilDQJgpDkNo+LtSTrRBkxLoayZyG+BkPQ2y9ByueShKE3E9p2HLOH9Ed9/6myjS/vg19fkvznTS+Lgn3PaohIomGCfD7eWwEX0uCzX8oe9tZXcoE1uCsseFLyn90eGTxc+L1kbreoIz73o+eEOJcgNlx+/P1gSbE/OdgfHr9DfMkhfzQiNjxJrEzny4Unt34UQv5vNFddr/AcPIzzpzXo4qvj3ZuM45Ol6Js0xNChLca+vfEePAwWC+QX4Fz+LfbpL+NLS0MJCSHg4/mYbun1m0NeV0MJDUWNjqzQGgegVKmCrzjOzdy/L475C+Uqz+vF0KIZhZNewG/2THxOJ4ZWzfAcOIxWUIiiKOiT/uRQ9h+AarNh6t5FWvoAX3om2O0ULf0SNVwyfgGUsDCsjz8MbhdKYCBKRBWM/W7BteJ73GvWYx37EI75H6Kf++rf5j3+M9CcTrS8vEq3q1ERWMaPRV8/Ce+ZcxS++Q6Gpo3RsrLwJKegb9IINA19iybXTL64gRv4b+IGOf5X4B8jKQrnd8CuBUJ89svgheXKFuKidxHqr5FDFDe91ox7eylYTFTwIyuK+G9zCqRg42IGdEhUWbVBX5rFCzJ8N3+8FG3YiyQ2rX68DOjlFko7ndUM1SOgSzOpPtYoI8YlcBSJL7laJPRtL7FtmgaT7pbBulc+q3h8yzdIpNvgbqJUG6/xiXF7KqYwJMRAXmEgg6fBM3fJsZfHLW1h4XeibDeoIZnDMtClY+T7kvFlMYmHd0AnGZpLPiFJHum5VGgZBLF41Lkq5eiD7+CFe1ykPP45ptzDhCfPLy3W4K5vy+TRYvjajGPV3kBGvQkgvrRPJ0PTOnIO29UppJv9RQIPFg/W2DNhxYPEjljH0qmhDHlBSmECbbDwadCrv5FK6HFI3JrOJEkSigI9XoOAaCG8miYea2dOGTEuwZ6F0P3Fa6ukqg6K8uD0urKIOI8TZeMsjDHNKm9/ag00f0AGJgGyjkPGEYwXdjEoqgntn+7DkFmVPbw/bIVRfcWK06u1fP7GvgkdGkLfdkKQn5wHX88oObHeCt7L5rVh8SQZ5vP6JIqw/p+ZpfOPhDs+k+O/uAdqdIHqHaUQ5Qb+K/Beuoxr/UZUmx++Y8fxnjiJeVB/XKt/wV1UhGX43XgSquPZtgtFr6PwxddKh6G0rCxyB99H6N4tf7iaVx8bjSepLrZZUymc9hJabi5KlSoEvD8X9zbJDVerVcW79CswGFDCq+D6YTXWx8agr1sbXC5puNPrpTDjvYX4jRuLLzcXNfDPFXn8EajFXtiir1eQd99DAJjvG4auZiJKSAh+E8dTMGlaaaubEhKCbcZkjG1aUjhzNhiNeM+cRXM4UP7DVc1/BbqqcZjuuJ2iL8tNflss+K6kocZEg8mE4+Ml2KZMxP7WPHxnzmJo2xrrk4+R//A4gr5f/ts7/x+Hr6AAX3YOWnYOir8NfXx1NLcbX2YWamAAikWKwbyXL+PLyESNCEdXzvuuud1ouXkogQF/Kc3jBv46bpDjfwWWQOjzNnzaVxIT6vSFvu/hcTk57a7Dsz+0ZHAPE6t2mLiUqbD0F4l0m7G4jJeNvg3e/w7Cg4RceH3gH6DnzEVRgU8W2yt3HRHbRIu6orjpVVmm/uA76N1Ghux2HYGuTcX/u3StqMBXl3xAcebuizBukCiyXxcngAX4iUpc4JC66lPFGctXsmUgLy5CPKaBtoolICP7wDfFjb9moyjMU4ptw0vXisd45WZ5zSP6SMpFjWipG25ZF1bvpBIcRTIsaDFB98YOnr/9IlFRFjzW6AoDhgAGnZSlPNxfPM2aBq3qaQxseYW4ZSPKBviK4dGZ8Q5ehXHTCyiOLGg3nrPB/Vi3vuIx3P8yvPWY+Kpbeb4lfOdVwxpXUnDkZHI8NZQHbpWhyJgqUCMKFH0jCKkppLMEDQaDvVi1dheCLQpu/0g81mc2yvBdx2ch9yyYrvUlrUhKSq1bpCwltyyA2tf6MTSDTcjx1cg+K4S6fC50eL0y8q3qQdHB0ttL7w7o9TFJ1e9hc0pFNa9xTfGx/7hDylKGdYNnhsHjb5V9pvu1hxaxV2Dzp2IniWkumc3RctFRI1p+/jJCatxIp7iO4Dl4iPwnJ2GbMRlv6gWU0FDxAwPsTca1dgP+c2ajr18PLTevQkoAej3Y7XgvXwadiu/iJdTwcHQ1E35XJTW1bY07IICAOrXB6cSXk0Phi6/iN3ECuto1cW/aivHmrrh+/BlFr0OtGocSGwNFLnIHDi37sBoM2GZNxZNyEM3ugD9JjjVN+0OKty8nh8Lic2IZOxrP3mTcW7bhv2gBRZ98VqHuWMvKwrP/AN7zF7C9OhPv+QuYB/RDvQ6JMYBisWCbORU1PBznZ1+iq1cH/1lTRd3X61H8bVivpFE4aRqmO/uji45CrZmAz+slaPU3GOr//2VrcCen4D5wCEXT0JxOHG+/hyc5BSUwEP83XsJz6gxFP/6MdcwD+K6koRhN4PVQ+MKLqHGxBHz4LsY2rfAcOoLjw8V40zNQY2OwDLkDQ/1//wrHDVwbN8jxX8Tx8/Dtr5B8ogVP9t5BhHKafJeRbWdjmPh5XKl94t5bnGw/Iv/kz1wWj+4LIySaLSYcPlgpXlhNg5pVoXktuJTpz51dvOw7oaNrM4laM+gh0E+jepTCyQtCDmd8Av3aweb9YnsASRTo2lSi4Z6aJwR4+sdlx92nDWw/KCrwswvExvHDVtlfXqFEnrWqB2t3i1L72sPiS75zijy+cxNYPl3ygQ+flVg4r6+s4GHmA7CsHD/bdURU31kPQHAAjHm9rDTip52wZR4kxkrt8s0txZ7w0Q8yaBgRDI2CjzMrbh6WkJaQm4uW7ebSnMb0eLMlO44aMRnh1YehTpyQ9D5tRIytEaWQ6D4hPt+Uz0uPRwutyRcpSaSkx9CqTleSqnupVd3EptVX5e0iBP3H7ZLWYbpUOQfVF9mEY5lhjJ0DM+6Ht7+WQbPYKrDgyXi63/Ep6qGv4NI+qNoeqtSFlaPlwRd2wZ1LxW98pvjKIv+SJFr0XyTtgNZQUahL0GIUpHwB57bh678I5eQvkHEYb0JvVmX2oKXqITy8Pkrq1grHqViCofatZcOARj/oPBl+HC+/d3gaDnxR4TH+Pz3A4L5D+WGbnvPFDo4a0Rp9mtuZ8I6FZevlM/31BkkgWfmSXExFBkOLBDsRmx6Wtj+QTObD30pbX/CNprv/3+DZfxAcDjS3G0OLpuQ/elWco8OBr6AAz9HjGFo1B6sVvxcmoZpNeNPT0Scm4ssrIPfW9qixMWDQ4/f0E5jv6P+bZQyKqmKoV4f89z7E8e4CzMPvxtTzZlyr1xDw0Xw8ySmoUZFoziIKZ84mYPH7eLOycHy4qGJbpNuNJ+UganQU6m/UN3uOn8S1dgMaxdXRoSEoHjfODxbhvXgJyz13Yby5C7rIyN8+SRql/fP6urVwzJ2HsUsnnHPeQSsorLS5Lz0D70mpllaDgzC0b/N7b8F/HfrEGvi/+TJ+z45HsdlQ/f0r3G8eeif6hvXxnb+AWq0qhhZNUa2/kRF5FRzZl/EUObGGxaDTX9/qqfvoMQqeewFju9a4tu3EdyUNT7LkWGq5ueSNGI3fzKlY7rqT/AceKc1cVkJDCfj0Q7TsbLyp5ylyuXBv2CSuoOQUFFXFe+IkSpVQ9BH/fn/8DVTGDXL8F5CaJqriTTUv0aNGLmdyothW2JGzVypGsLVN8tG84AP6txnJoTOyfHLglAzivTdBluEHd5MUhO82S0za+RDo8hj0a6fQo6WXyGCV4b01YqsotElScBTa2XPEzZoDgRS5ZLDuy6vEwjV7YNxgaTbzt8pgVXq2RMdtTqm4/cZ94g3dnAK9WsGiH8siu86nw66j8HrZEDLr9wqZL0nZmPkJNKkp2bYWk3h/D52RAb4SuD3ibR79esU2tXw7HDgtQ2AJMTD+HSG2T9whJDwmuIiEg++ii2su3thL+1CAAEVhwyNf8Uthf0IDRanddVSGEVdslucY3htMjVsT11yTSuLT69FimnMsaDCZZ2IIsMLWw3q+26anZyuoFiF5ziUNhCCDdROGgMkIRDbC2+0ldGufE6uALZLtifMZNTuEjyaKZ3vXkbLz1u9Z2Pl+Cxp0bSIKraJK8Ya9zHTuLchAd3Vxh+YDR5ZkOnd6HtIOoOVfwlN3EGdcNUhP6EpKXh2yt1Rn28HOuDyw4SNpNBxwP1x6Yxahaf3EYgGQ2FPU5PB6UO92GcgLTwKvW0hxYDVRrOdfVTXrddMhdDfLpzbk8FnQqz4a2VIoOpvGsvV9K2z69Hz48VW5wLmQAaH528qIcQkyjkgEos8tvmn1hsfw/xsYDOjrJ8myr80Py6OjccydX0EhVq1W1OrV0DVrTNCa7ygY/bgMqhXDf8Fc/D94B3w+tJxcNI8Xz/ETGOrW+c2nVfR6rOMeQY2Nxr1hE7hc6BJq4N6+i4InngZFwdizO5bhd+PavA1jp3ZoV3uyAK3IhalPz2v6Xr1nz5Fz60DMQwdTtPIHUBT8nn+anIHDSv3Ont378JvxPNb775PyjGtADQ7COm4s+Y9OwJct58W1biNq1VgMravj3laxLdPQtBGuTVvB6UTfuiX6+Oq49x8QBdJgQN+0MUp0JL6Tp0HT0NVM+K8ry4pOhy4q6pr36apUQdel05/an8fl5PyO79gx/zEcOWnUuGko8R0HYQ2LJST+GjXz1wG8h4/i+mUd+qS66BskUbji+0rbqFVCcS7+vEIZiZaZiXvjJhwfLEJXKxG/N15GjYnBueR1vMdP4ElOwfXjzwR8+yXk5oMCuvjqN5r8/oO4cWb/Ag6f9jHv1tXUS34AjlzAlduGjI7z2WRryOuPCPmtHuGlU9BGIteO5Z7WAexpM4wftqoYDDDnUallLrE7NK4JnZrAsOnw+RTxHy9YqbJgJTROBFVVWPmSrED6a+m0Th7J4SolvSrXxsEzMpR2Lk2SJD54Gtbsqkyko8MkrxagWR2Ysaji/Vl5ZaqwXi8q6ub9cM9MyVB+ZTSMfg02FDeitWsgddZfritLcQiyiTpsMlAp/7lKIHy3FV5dWnbbjMVC+m+qfhHMVsg6JeprCTQN4+rH6D26PSdywhn8gpB2EH90sL+UrsRHW4hr0gViWkHbx5m+xELhSVHtF35Pqbr/8Sq5WKkRLaubN7cENLi9U7nEBZM/a83jiOzZh0vns9mfFc+0N2LRqcXpFEcqvi63RxrgGtTQg60KpB8BzUVRr/kop9dyydqabK0TjW2REmFXHj4vnNtMYUgTdgRPxhsTx+hXRZktgaLICsTkhVKo8Wuy/K+t9Wxn5t69i3bRR6lquYBy8mfYt7jsgXGt4b51YDRLfByQk1WIoeUk/Ha/LKQZICQRNbgqzc++RfNtz5Q+fHfzciHP5bD1gAyFjrkdohsGUOFrOr4LNL0PvhkhFwpN7hN/fsT/X8up/1dh7NgW35mzFDw1CbxelKgobDMmk//UJLDbUeNi0cVXxXPyFL6TZ9AKCysQY4DCKTOxPj0O5yefY2jTCsVoQI2O/F1yDKBPqFFq13D9sg7np1/gN7U4OUHTSjOO1egodDVrYL7rDtzryl2QKgrmIQMxtLh26Yw7OQXFbMaTchDP/gPYZk2R4cKiIvTNm2Ie0A/vhUto6Zm49+3H2KrFbx6rqf+tKBYzFHtH3b9uxjziHtRqcfjNmIzjrflgNGAZcQ9Fv6zH/8WpGHt2RxcViWvbTrK79C61X6gR4fi9+AL5I2QlyjTgNvxfm4Wu2p9r+ruekXliN+tn3lH6+8lfFqF5PeSkHqHt2HmE1frtc/3fguZwgMeDYjRKG2O1qvjOnquwjWKx4MvMqvzY/ALU+Gr4TX0Wx2tv4T18FNOAfujiq5P/+FPYZs+iaOEiHB8uBkXB+tgYrOPH/v6KxQ38Zdwgx38BdawHqbqqXymRMF7aSpV1w2nd92d09QPQH1xKxK6JkHcegIRt97Kgm5e1XYcTFihDb+V9wPuOi99Yp5NM4Q+fkfvPXhE7xeUssS4AYAnGpLroFLqZrs3acyxVpVW9itFYbZJgxyEZhLvzJhncm/Q+zH0cvt4o5BtEVb61neQX16kmdgqjoaK6CzIwCPDOEzBrsUSwgdglmtaCxwZKjXW96jJglRgry+zJJwFN8pwPnBK/9Za9hTzQ5hj+Vh85ROFvDmfD3sofw6Vr4K6GdjDYpDXuahRcAk8RS34qI8YgMXXP3SPEN6NEuDIJVQsNBFemJHlcnRoy9SMY2Rv6dwZ/i1xUvLZUYsm6NYfW9eDLDQby7fUr2EZmjRJf+NVxZiCkuRQ5Z2H3hxxsfw9f2x/kvaWyurv50XnU3jSg1Bet1e6LUr0jrJ/G3qShfHwwjrrVKhJjkMeWROQ5isBmlYuPCbdlEWQqYN252tzVyIPx4FUKbsOhQoyBlJOSlf3RD36EBEzjuUFP0f3UnRhDYqHNY1LlTMXBwsScZfRs1p0fd1tKb6tXvSyr+PkPoObzDRhYoyvKqTWyQfP74atyPs+Ns0RJD50M11omvZwiiRqaD6Ka3Mgvvs6hOZzYX5+LacgdmPv3xXPyNL7cXAKXfoxr5x4MSXXJe+ARrA+OxHfh4jXre33pGegb1pfUBlXF+eXX6FIvXOPZrgGbDa3Qjme3/CPwpaWjb5CEJ6UsTNv62BgKRj+B36wpBHy8AMdHi1FMJiyPPIjp5q6/qfhqdgeGls0p+vZ7jN274Fz1M+bbbkEJCsLUvy8FE6eUbuv89AuCVi5DX6fWNfelCw/Hcu9Q3KfP4jf9eeyz5+D8cDG+tDSsT4/HNGgAvstX8F24hP+tvdA3rI9iNKJ5PNjfmFvBl+y7koZnXwpKSAhaVhZFy79B36wxhs4d0CfUKC29+F9GzpkDlW5L3b6CxO7DOfrDe9clOdbVriVDiD4fro2bsT46Wj4jLhmSMd91J64ffsLUrw/2wxUVFUPXzpjuupPcfoMk/UOvRyu0oxUVEfjVEtxbtuNYUNa4ZH/3fQxdOqH2jPjDSS838Mdxgxz/QbjdMpgW4AfBrpNlClsxDGl7qKKcxxLeEM57S4mxL6QW9tBmZKgJPFfsAT5ytvL+s/LF9mCziuI6+rUyLtGxkRA6gOyiAHxtX6fuij583HE8Ow13oQ8IZ/8ZPev2qjSoIdu+XmwhXb5Rot5eXAJDXoCn7oLoULiYKRa4Z96D9g2EaBUUwpNDYGY5obFNEtStDl2bieJaQoxLsOeYZBDXiBb7RWKsFEGt3iHPWYIJQ6BuyCW+bDUJS2AYOHLg7K94nW14487HaHWwIeVLkupWA8+RH9CH1YGck7IM7yv3pdpgCHbNjxWby24yG6FLYw93Nj7LwNiTxIbbwFkPzBL71r2FeL6vVq9BhhDjo+GZ+TCoa8XWtyU/wcJnpNgiOqzi42LC4I1l8r5OXlj2nt1xU1kKQ2YeeIwNiQiIIdSRjNnUiaxi18OAxX14pf9Omocdx64Gsd/elKi8KmR3OEVWgY60bDkXUaEySFm3mlxMebyy/0E3eXm+RzLxtot8MsiA+sszKHv2gcGCJ/4lGPCJFIh4XdDmUWnUQ/Kql60v/14r9Jvqz1cvrKROVY06EcWktWpbqUj3yHJ04IklzL37KT5v3IBV26BRohSjlP/MbDtqok6T16kTswzD+c0SdahVJNkkfyIFKGFXEYnUbdISmH9RfreGwdBv5Thu4LqEL/U8xm43Ybq5K3n3P4JWHKeGwYD/O6+j5eTgu3gZX14+itEIRmOFxjgA891DKHh2Kp5ie4FpQD+UP1gWoq9fD/uUsmFZx7z3sTzyEObhw/Dl5aP6+aHWqQ1eLwVjnoDgYAIWvosSFoI+Ivw3iTGArkZ1HIs+Rd+sMZaR90jygN1OwGcf4fjokwrbek+cpOiblRQZjZgHDUAXc+2pU0N8NdT778XQthVaXj5qeBX09etJYUViQuXzm5eP93jlhictLR01JBhvlqiQrlU/40k+gOnO/ph6dvvDnt7rFaaA0Eq3+VWpiiPrEs6cNHxeL+p1FgFnaNKIwE8+wP7uAsz3DQODnsBPF6I5neiqxqHGx+M9dAitwI7fi9NwvLcQ1d+GedhgPMdOoGZll8bi+T33FM7Fn+E9fQb/hfNwbyqbJTHddiv6hvWxz5xN0RfLsY55AEPL5r91WDfwF3CDHP8BHEsVtfer9UJIVo6+6o/WHAjBCVjUYnZXuw/uXu9wvCCehcmtWLMuhJtbaLz+COw9LgUV7/6j4i6iQkSx7dlSBulKuER4sJRvfPS9DIzpdTB2TnOmDdhBI+9JDN7L2MKDGNLdSKfGUjFdvpbZqC+LWdM0SAjOpG18Jo3fqVWqIJ8pViUHdhY1ePr9EvEWEQyt68PDr0uqhv9v/K+NCBGluLZ+N2z8CaXIzsAavfg6tgVHzxsw6KUNr5W2CUvGRbhwBk6tBUCXfpj6J3/kzbs3M3phdUAU15qxkJfnImTfc9DrVej1JmyfC7mpUH8w1OqNRS2iWzMwKU7mDNxJI9N2jAYNpdALugI4cByOWiQfOCSRWnE27uoK+05QKfHinp7yPj8yoGIFNIgnfP9JqBUnFwO3dxCSDaL2R4fC57+IzcHpEvW9SU3xKi9bJ7XghY4oJgw4zF1FM+mSeJ4j3WJZugYOpxp4aW1TerZqyvMfyHDhg+9CWnYwE4fBjiOyqnDwlb345yajpB3AF1qLy/7tCYmvx4DYdSjfjIT2T8Gur0VxBXA70P/4GIzcCCM3SG5yYFn705nL8MWaiq9T02DfST2FRbB6t3x2WtfrSOO7v0fZuwit4DJFdYeyYk8wb6+UIcTvt5TVadeKk89QrTh4b3tDthxoyJeTXSRenE8l+FUBwzU+UIf+UUaMQTza+z65QY6vY6hxsRh79cC9Y3cZMQZwuyla/QuWoYNkqdlqxXfpMq7kFGyzZ+L8eAnes6mYbrsF89BB5HS7pfShRcu/xTxs8B96fn2VMIy9uuP+tfhKWdNwzJ2HdfyjOD5agpaVhf+ShVgnPCp1xf7+eHbtwT7nXQIWLUBfq+Zvv7bICKwTJ6CoCvYXXsT1S/GykcVC4OIFuH5aA+Ves2Z3UDhxClpWNn7TJv1m4oYuMvIPLYdrmoZ79x6Mt/TEsze54utumITzi69Kfze0ai5Nfy2b4T16HLVJo3+6/+sZYTVbEFKzGVnHdwOg6PTU7DGSPR9NpO3jH/xhYuzIy8RdmIvRLwhVVTHagv75g/4iFEXB2LEd+qaN8GVlo4uKrBTBpo+LwXPqDM5/rMB871C8KQcpmDITvwmPoRQv06pRkfjS0vGePgPIMJ8+sQbuDb+iRkagq5lA4bQXZYdeL/b3PsTi74+xbm1u4N+DG+T4n6DQAePehlXb5PetB2DcsgYsaDIKQ/KHcOs8QAHNC+e3gSMDqnUgo/YYBj6mcTRVljtSTim0ThKlMSFGCPI/NoLNIvFpZqMMtZ29UrFkY/wgiUVzusQ3/OU6OabxS6oBMvl/501gNsHEoUIsy5PjSXfmcHe17+kxozVh2etIODQV7bgfw3se4I2vyv5om9SUTOPvtsD3W8UmkFco9cEP9ZOVUFWBbs3gl91l++/dBlrWgZCC3bCwA7gd6IDGyiw+vWcNj63szLhBYt0YGLVN2gXXT69wjpX8iwzpcYSwadXJyBHl+4m3IW50L3rkvQif94dGw6Djs2ihtXEWFGDR8lBsVRjZB8bU/o7q68u8aYTWFIU0IEbSHaKawOX9UKMLcRGxmI3w1XS5QDlzWVTeM5fgw+9h1ezKthKQ1//eCsn27dFSLBiNoi8SobvIzTX8GTq3BpMXGkrPyT09JLli8NSyfUx43x/bEy/wQPhGqt5r5cG+QWTnqyzfIHaEdg1gw154sHsm9aLtfLQpmin36eha4wQBhxei7JAqQx0QHd8ZJWoR/PiEDNrZM64Z43asoBq/HqjKhmSpfb65BTQsjpMNtFV+nf5WmLu8zIeu18HXM7vQuXcX5iyDWZPKlPcxr8OyafDDdrHXdGgkCSQGvdhrXn0YoqsYwdBehv7yi6/CFFXIfDmyDgg7Tz9c+aDSDkgb2nVYgPB3Q9M0vCdO4c3PRxcRjpaZhVbkQpcYjy44+L9yTPoGSXhTL+DZubvSfbqYGFxbd2Ds2R3fxUu49x/ActstuH/dIhWs28aGAAAgAElEQVTDD41ErVMb969bKj32WikOvwXzoAG416zHtWY9AIbOHdHcHrSsLDCbUW1+5JdTl+XgdFD4+8+hmM34jh1HMRjKiDGAw0Hh9JexPfskBU9PktsMBikmAQrfeBvLgyPQxf1rdcTeE6fwbNmB7+RpLGNH41z8GYpF2v88+w+Wqii62rXQN2mE79U5+HJy0IqusTz2PwZbRDW6TPoHmSd2Yc+8iNfl4OTaJTQb+QoxzXr+oX1cPvArF3b/hC08jjMbl2HPSKVW7weJ7zAIa9i/kif5+1BtttJ862tBy89H0evFVpMqq8woCorZjK5WTdTgILzHjpfbvgC1ZgJqdBSmW3tLC6TVim3aJFxrN+Dem4xx206K0tLQ165ZeuHlPZeK58JFtMwsVJsNff26qGFh1zqk/zjsmV4cuT6sISqWoOtL8b8WbpDjf4KzV8qIcQkWrw/isQEv0rjJXbD9HSkgSP609H5Pr7mcCxzN0dSKH4BtB4UAT18ES56H29qLimkxSQzY6UtSptCzJazaDvFRcPB0mcLpLJJM36thNkoNc0auEOhV28Sj2rimRu24QDL9b6bV+s4o6WJMVoCHOnyLb2B/Fq9WiQyBycNhbrEIoWmULvvnFkg1s78F7pouBRc3NZXjbl5H7BYa4Er5FqO7nJFa81H34uvMGNGevs/pKXLDI083w087VNkiAZy4ZGTzCWidJPFgdicMW9iU7x5dTwv1e1RnFgd8Hfjgq2g2HTJzR2cPd4boqR2WBsuuqh7NPA6WYNj+NtQbIOUax1aBJRi7KZYZi+GdfwgZrVNVzlujRFHpdx2B+3pJfnQJgmxQPUrKQHYfhZUvQ6+IDbD6abi4i9C4NvzwwAS22TuR6wmiSU2IDIV3vqYS3l1h4o6u3clOlwuQ9ByxbQB0bexmXJPvsf36NMqBCwxoP4w9wQ+TYLmIsuu9CvtRTq+H7NPyo+ikjjo8Ca6U+fSymzzD3F/iSlcpPkWqm1e8JJF99/YUJbxklSI0UAYhd5azwt3aTmILC+zgQ2xFJeTY44WcPDdb5xs4dRFmfyYWoGkfF3eZmODLF6B3m6YwdKVcPDpzIbopVGtf+eQoCtTqBUdXVry9Xv8bxBjQXC6cX32D9+w5FD8/nMn7ZeodsIwagXHAbVBYiKFZI3TR/7kv/quh+vlhaFQf37lUWLqswn2mPj3I6dEP871DUaMisTRuCEYDpsEDwOPF8eFiTL26V6qCBtBdw2LwW9DXiCdg2RJ8x0/gy8rGsfQrHG+9ixIaiu3VmWg+DV39engPlA1nmIfcgfJPyKv35Gl8DgfKNYi658gx9I0byMBhTDSmQQOwvzpHzom/rWIz0l+ELyMDNA3nZ1+iVquK5b6hQsLNJpTAAPymPAsmI/oWzci78x5ArCBaQeEfzmC+nmELj8MWHgeAPfsKNW4aijXk2okYVyMn9Sj7P59OdNMeFF45S5U6rVANHci/eILMU3s59tNCPM5CIht2IrJRF/SGP9ph/69DVzUWb3oGpl43l/qInV+vwNSrO37Tn8d77hyKx1t6sWef8y5+k5/Gb8pElJBgitZuwDbpaQqnvSQXSw+Pwvntd+jjq+HQNKx3DUJzOvCdPU/h7Dfxpsj3gr59GwIXLfjTle2/BY9bQ/NqGMy///85daeTHydnkZvqJbSmnp7TQohq8Ped77+CG+T4n8BiEnW34KoijSJdiMRTRdSHtVMq3Kf/+UkiB3YHKi5xmIwyvDT5PsgrJp3fbZFhpinDYf8JOJoKo2+X5z15sSzxASC7QFItvt9W9l2i04kK3au1KLuDpkpjXpUg+HKdgr8FPh/vpWG5CDF0BhJdG3i1X3UeuLUJiqIj2B/ye8HaPRVfZ+skCPVzs/uEgUKHKKEP9YM+rSWyrJrhBBn7zhHgqFhnDGDyZJOd7yM6xMtzvY/h9IvHlbETY6O7Ye/Hpds5Itqy9EASb66EFimiVh86Kwr5hrTmpFVrTr5Pw3kZvKpC8knYe1zP2Svw5AAL1JlM1ZNvoMsoN5Xo88iyvccp1cwnf4IGgzl3RYixpknByfDe4qs+cR4CrOIp9veTBsGv1gtZHNIVphWneKgqtIk+BZ/dUVbhfHYTat4F2g74BKq1AyAtR8j21YgLFwX6+YUy6DjmNlFYJ7wDo5psxf/bsuE83Z73aNbEDdVHVizwAApibuZIdj3qJ92L+dByqYJudj/88hy45Iv8cOwE3n9HoWktuKu7DGIuWS3Wnts7QrcWsGSSVD23qCc+5PNp4jtfsBLeeAS+3QTj35bntFkkraTAKasKX2+Aan6XaVIzjiNnpTZ63Ntlx+gogvtfgR3vQWxMM7i6qS/tkKjJtkiJmlMUiL8J2o6DnfPkPDS9HxJ7VT6R/wfhOXwU9+atuDZtxdSnJ84PyzyvjncXoMZGo4QE4zlwCF2NahhaNPu3fQn+M+hr1cTXIQfbq7NwfLAIzenEb9xYdHVro2/cEOeiT0GnQwkMRMvJwe+FSRQ+Pw1dw/qocbH4zp1HXz8Jz4GDKDYbtpenYWhU/w89dwkJ1AUHoSv2XeobJOE39iE0t4eib1fi3roT/zdexr1lG549yeibNETfIAljk98f9nQfOoLi01AjK2fLGvv0xJudg9+sqbh+/JmCCc+WDhv6zZjyb0kR0EVH49qzD1P/fhR9/S32OeL5Mg0bjKFZE7T0DAydO5D3wCMoFjN+E2fg2ZdC/kOPE7JzI4YGSWgOB57jJ8HjQZcQ/x9pAfw7YA3+Y/m+ztwM8i4cxZmbgSUkGr+wWJI/ewG3PR+DNYD24xex5c2ROLKvAHBg2ct0fnYZ1TsM/E8efgWowcH4v/QCRavXYBo0kKLl3+C7eAl9i6ZoRUWoYWHoEuIxj7gH58dL0HJy8B46imXqs6h6HX7jH8WXm4uWno512iSUyEiM3W6CgAB0riJyevdHy8zE0LYVlrsHU/D086BpeDZtxfXDT+gfefBPHa8zz4dODwarkGCfR+P0Jid7luTjzPfRcICNKnUMqDoIqqrH7K/DmeujIN2Dy67xj0eEf6h6iKhj5NyOItKOuAmtoSeinhGD5foTP26Q43+C+CiJKxvzetlt3ZpDQixwwC65rVfD4yQzLbe0wKMEcx8T8nCpuNehazOYN0EIiaNI2uJyC6WOt3acFGp4fWX+1q/Ww7Aecjy7j0pL3q3txSMbU0XqlksKG0pIdU4BpKRH0rL+WIK2Pw/1B+FtOAx13VTUXQuoVesW8lvPoPP4uswdcYH54yKYs1yPXgdDu0PzyLPUcv7IlbD7AR2T7xOLxHsrYOukHYR82oMQZw70mF3pNChtHkXJ8LJ5+AKCN42HIy5odDf2+iMwx3dBO7+D01oTvr9yE68uk398O49A79ZCtIJtQjKfeBtOXxIFpG41eGG4bBdog0YP+ePx3s/YfsMY22QOsXufkb9AvQmajQSPS1TjoHjwCyPPXqaU+lnEzz38xbJK6ugwePNRuJgOm96BS+lubBlb+fXeQ7j1gWT4tyLQcaSMGJcg+zTkppJvF7vMc+/LoGJ5m4tBL2rtoKlyDF6vpHzMGw+fTYYI1/5KbX7qgc/xtRgFVdvBOfFUakHxLDIv5NEnqjB/xHjujHAQWHAZ/KPQus1CUVQIScBTGMScR2UgcNYnYhcZU3zhBfIZiw6V8zhoalmCSkwVibY7fVHIcQkKHKIKV4+Uz+Sgm3zEacngjSQ6zFDqYS+PK1ky/FnkEutPTBXA6+HikSMcPOXBY6xJHeth4rNPQZV6kHYQEm8Wq4jOJLeZrs9msL8bnqPHUMPCUK1WPNt3Vb5/1x7MI+/D59NQrFbyJzxHwMJ3/ja7hbFlC/Q1a2Lq1wesFvTF6rX/26/Ll3VeHlpWFub7hqF5PFgnTgCfD9+pMziXfY2+QT1M/fuib9wQQ7fOpdW614Lm9eJauwHnkqV4z1/Act8wjLf0LH2tupjo0oE4Y+sWaD4fiqpi7NIJ35U08Pl+c2CuBL7MTLRzqXhSL4DTgd+zT2J/ax5aQQGGNq2wPjxKhgz1OgxNG6GLjsKXm4upVw+MXf9cpu81nz8vD192Nubbb8W9fRd+U5/Dm5qKvkljvCdOUvDE02KlyC/AfNstaHn5KEFBOKbMhKIivEeOoYaFUjj9ZRzzPwBNw9DtJgLmzUGf+L/bMOnzekk/vJWcc4cwWP2xhVfDL7IG1uAI0o9uZ+d7T5B+RJZ7Gw2ZzJ5Fk3DbJUqoapt+ZJ9OLiXGJdj32QtENemG6T/oR74ahgb10VWvhvf2DPyefxo1MABdbAzulIM43n0f++w5WB55kKCHH0QpLMSbmopvzz68qkr+Q4/i/7Y0LuqbNsG9ew+6+OqoIcHk3npH6Zece8t2UHUYe3bHtUqWKF3rNmJ5aKTMAZjNv3uM9mwvJ9Y42LUoH3OgSpuHAqjaysz5XU6+eTSj9Ovq54PZtH04gJ0f5RPV0Ei7RwJIP+pm24J8Wtxro9UDARRmePEL02GwKqx/JadU7+k+OZiGd/hdd6scN8jxH8DQ7rL8fvisLJe3qFMc0xVWCy7sBKMNXGXMwBdSk2V7qzPpHiF5Ow7LEv6x1DJiDEKcqwRD71Zw6DS8VS51q8XNsm39GrB8hkSouTxCiE1GGNxVHnPvTCHWx8+LIt2jJRw5B2fLRecWOmCD/3B69ghGHxyD7qvBpeqi7vDXBORfZsqAL2izpxcdvAV0eeAD8rQQQgr2Uc17Cu/xb6nVbBCrZgeVloTMGJpOs7MTwVmcl7ZvMfSYje/wCjSXHV2HCZBwMzcbd2D+ZGzZwSR/guXiHpT7N7DddDddH79GeoQiRSPHzwvJP10uxuzwWTkPPVvDmNfKbn91mZmYMU/waOIGaDgYTDbIuwDb5gpxvW0hp00dcedLYcZrX4iaumR1GTEGiXjbc0ya9qwmjYT8lfDNIPB5sAD+4Q1Qbiknj5ZA1YE1lM0pQrZB7DPjB0s1uNEgvtzpH1cObli1Db4em4xy4RrLTLYoVJM/dH8R9n4EJ37mZLN5PD1LloMf+rAGi+vO5552F6ijs2INCqNZXfHexF6A3cfFz1yCVz6TwpMSbDsI876pGC1Ykm19jb4EDpyGrsVD0V+sU+nbtBU1dAYaJcrjFKXi66saIdac7k/IxcjU4dCxroOBL9Xl8Dk5ztpxcax+7gSxX96JcrF46UJRYPDXEHv9xTX9ndA8HrynJd5G8fNDsVrwpp7H1KcnrF1fup2h202Yhw6StAeDAfeBg5i6dsZ76jS6Zn+fF1kNDkINrkgwjO1aE7L7V9z79uM9chz31u0YWrfEm56O4vWh+XzY5swGjxddVAS6hBrXLOQoD9ea9eTcPgTsogK4127A/+3XsT486prblwzGKaqKLupPKLomI/roSApnvoI7LhbLQyMl9zg5Bff6jRTOeAUlLAz/t2ajFdgxtm+D6ZZev1t9/UfgvXCR/Kcmoa9dC/vrczHfOxRd/XroW7egYPIMvDt2YWjVAutzT+FcugwtPQNjl04SGVYc+ab423Bt3Ixj3vul+3X/sg7Hx0uwTX/+uiMjfxSp21eyfuZAUBQSbhpKSEITPC4HjqzLnF7/eSkxBtA0L/mXTpT+rhpMeIsq/2NzFWTj87gq3f6fhurvX6lN0NAgCf3br6HZ7aj+/hStXU92z9slMguKvfr3Q0CANCcaDOgTEyiY8TLWB4ZX+oJxb9qC33NPlZJjQ6f25D70GN69yVgeHImpb29011gZATix1sFPU7NLf18+OoO7Pgvn7Paiq3Ucjv3soFobMyfWOAiv7eDUBidFeT6MNpV1k8v24R+po/m9/uxYKBcs61/NoVobE0Fx11f74fWnZV+HsFnFzvDQbVIyEVMSIRnZSJStW94ujaTyxbVlY50veHt1OMH+smz/xiPQojaculh53yfOS6RW8zriQ541Ct5+Qso/3l4upKV+vKjCD/aVpe3GiVIBvWp7GbGcMAQOnBQldOIw2ZfZBCN6C5k9UxhDUbWb0V3ZV0qMS6Ce30L7aqnoMw5A9hkSfupGk5+bUm3rCPKttXjb+gN9pgSxajus3CLK46gW+1GvlAsYvpIikWEtRpHWY5kQVEsg5tyr2jEAJf0g7syz/Lhd8oPL4+YWwove+YfkF19drgGixG7YW/n2LzYYudTrG7TG94jSGlwDOkzEO3wjy3OH0fzhADo+IpaBd8cJaT2XVnk/V7LknJN+RIYHy1kalLQUuSBoOabCY7RWY8kPSOL7cs3NjiKJs0tNE2Ic7C950lfjtjZ2lLWTRfEOL3dCFAU6ToSQmlC9A/R4A+77BXt0V5wuIZsAWw4beeiDeH4+HMGC78pIRa4dtqRUfr7Fq0VFzsiR85h61TkwG4XQWq8hKnRrLhnaJdh+WojX0XNyIVPSlAiSYvL0XbBys6jOV7IkojD5nK2UGAMcTVVQ0g+WEWOQf/A/PAqF13iD/o/Ae+kyBc+9QGZSczKTmuM5eBh9owZSdRwXi7HfLRjatUGNicZ0a29yBwylcPpLFE6ejjflEO7d++RK8jqAPjEBy8DbMXRoi75mAp6Dh9EVV0VrWdkUffsD9tlviPr5T4ix5+w53Nt3lRLjEhS+/Dre9Mr2rr8KNTQUfeNGeNMz0NWqiS/1PPZX51A442VpyzPJH4hi0OPevI3CKTPwHj5aIaLur8K9eStFn30Jbjdabi6Ot94lf9RYfBcvYWxYH7/nnkINr0LuXcMxdu6Ill+Aff5CPMWeakOnDugbNShL8CiHon+sRCu4xjLP/wAc2VfYueAJNJ+XpvfOIP3YTna89zg/P3czJ3/+kPCk9iR2v5dGd02m8dApqHoDQeXa9M5t/prgGg1RrmrorHPLI1iCwv/ul1MBzpw0Luxezdkt35CTegisVnw5ORSMm1hKjAE8u/eiRkfhO3EKw6190AoKyB/zBL5jJ1CDKltm1LhYfGnyd2G87RbU6CiKPvoEz5595D84FsfCxWhXKzaAM9/L7kVXhfdrkHfJg95U+cLKYFbwFsl+zm4rIqSGnsQuFnZ8WHEf+Ze96IyKDD8BbruG21n5+f/buKEc/ysIjIVavdCyz2Hvt4yD5/S880ssX3wRwFuPiV/V65VK5q0HhNR+/kvFXQzoCCjic/15hxCoapFw7oosy287BA+8Are0FUKTEAMvfCS3t6wjftWtB2RY7/A5eH+lDEolxsCKWRpbDyp0buymkXEzARcPCeEqD1VHWoNnOVBQn4AW31E77wsuBt7MEUcd/A1OLMZ6PPlpCB0aCjGvFQs1IwoJPv6h+EMPlZs683lQ8y8RWfQjhPeDgOhrx3UFx+Pzelm+Abo3hx6tYOdhUckTY2DzAVE0g2ww8hb4tWKZFjFhkjl8NapHwo+7jBw7B7e2j6B1vT6oKuw6BEOnl5VmXMyQuLwNc+U5xp+ouJ/erSEgY5NYJ3LOVX6izOPQdRpaYk+82ecoNMayOKU5696Mplm5Qi9FgRdHwWe/wOzP5fy99KAQzJKK7uqR0KPWOViyAo7/ALcvkvroojwIrSU/egNc3AuXdoPLTv2IBpyc6UVNSybdUI93d7bgy+1hpfsrfTt8QlCvRu2qYvE4c1k+Z91bSFRdCbLzoU19ycqeMEQGCx1FUjPerZnUfJegWV09+XbJh/YzS971Y3eI/73QKWkXi36s+Pwbk0VRPld+ZbOk7ro8Ci5L3fX/Ubh+WoP9lTdKf/ds20nR0q8w3dkfXXQU7s1bUPys+L/5MoVvzavwWPevmzF2uwnvxWtckf+boXm9oKp/SIk0tmuNLzOLgvHP4pg7D118dayPjSH/mcngdJK9bRfBa77D0Oja9cDerCy8R49XXn4BsFrR7A7cBw+jRoSjC6uck/tnYWzXCq0gH0P9ehQ8P700qs4y+gHcG35FVzMR68OjKJg0DTUyAn2jhpLl/C/Cvas4klFRUPz80AoL0bKycC5cjPm+obh+WY8aG41t8jMUPDsVzW4n8NOF+LJzCLy5K4YWTdFFRWJo2pir/4KMnduj/I9mILsKcylIO0uVum1IO7yN3HNyMaD5fBxeMRdrWCxBVeuza+GTAMS1vY1m977I1rcfxJ5xAbezAK/XRYenlnD427dw5qRRu89DJNw07L/5ssi7eJLNc+7nyv71AARXb0Dz+18lIqoB3pOnKz/A6cR76TJqTDRabm7p59LndmO8pReu71bJdno9ttdmgU9DV7c2rh9/xv7qHPGwfyWT2vZX3sBy39BKNiOdXqHRYBuOLB+KDuyZPvZ9UYAlSKVaaxNXDpk5/auzVDuqd6uVvZ8XoKgQ09RIwk1mzm4pwplTedhWb1ZodreNQyvthCboCYi6/tIrbpDjfxUmf5TIJEwesGowpA+Mu0e8sUaDKGpPvC1pDy3rlQ07udxCfk9eFIV33jdC3u6/BYZOK9t9myRJAJi7XHygby2Hz0btI3HgPhRPIXl+SdSr2oHdx3UVls9PXIDZn/tYOmAZSuZRbOfWgq0K6Q0m4OuaSMS2cVCYxqFWXzDky9s4cFqHqvbh42d6MOYNfekAYpcmGp8+L7Fnm/aLgv3VLwq+rLOodXpKJm3qNvH4th0HqVtRLEF4w+qx92I0jc1h6Fs8BLveky80kz/cNJVjJwu5p4c0000fKfFnuw6Lun57RyH8ySdEOR/STRrzFEUsLgUOGRSMDitruguyQZem4of+8HuxX0wbCQ0SxCbhvkrMuZQpSmbbBqK0v7VcSk6mjoCuNVPhkzug5Wio00/sDCXQm7DHdAU1lF1Ft3LTxIr77dVaBvHSskUFX7FZSjtAjnv2Ulj8nMTHeX2iXq9JNjPU5A9F+dIk5xcOgXFozUehhNWE1B3w5SDIOQOAquqoVlzuEed1M7vRBHp2mM4Pu8xMHCanWVHkOEpSONKKV7X8rTC8l9zvKIKmtaXY5cG+oihbTDDpHli5CUb0geXr4dm75aIlLlz81CW4rT10biw12XuOyevr1hzaN5Tz3akxfLWu8jBrXDiV/MmF/kmVU0yajgD/vy914XqD84uK7YZqlTDcW7aj6PXk3VtmH3D9tAbb7JkUbNpakTR6PCg63X8stcB7+QquVT/h+PhT1OgoLPffi6lr5999jKLXY+nfF32dmnjPpuJa/i35E6eAU5a6tcxMXJu3ok+qi6Kv/PXk3r4L97adGJLqoPj7o+WLKqUm1sD/lenkDRmO98JFTLffimngbZja/2v52GpgIOY7+uM9cYrARg3QsrPRRUehREbgS09Hy8mlaOUqLGMfwtC21W/WUP9ZGFo0AcA+7wNsM6fg+OgTPIeOoEZFYGzbGuv996E5nbKa0KA+uvhq6GolVnqfjV06YejSCfdaqcxW46tjGfPAP1Xnr1f4VYkjtkVvAuPqcnz1wkr3O3OuYPQrsxGlbvmGqEZdaD9hCXqjCUXV48zPwOdy0vGpzzBY/TFfo2zk78b5Hd+VEmOA7DMpnNv6D/z718A8dBCO9yq+Vn271vjWbcTYqQO+7GyZFPf5KBjzBLa5r2EeeBuaw4laNZaCWbPxbq4YuWW6uSslbkbFbJbJ/quQdsTNxtdz8RSruqEJevq8HELOGS/OPB9RDYzUv80PR44Xa6iO87uKCK9joMVwUa6upLiJ72DG44B9X5T9w1d0MlpzaKWdNqMDiG1qxGS7/j6PN8jxvwl6vSzF179qODw7X4aRilzwazI0TIDH74DYKtKU98F3shR97ooobnOvavvdehCeuC2PnYcsXMo08PWY3STuHCG5vUAVVUfXO5ezzdOv0jG1rKfjxX0D+X6bnnYNnqdvksLSbxSqR7ameZs7qBpUwLzVQQTaVKaOEAJ/JUdf4e9k7V6Fjk1EvdU0SdOYN95KofUZAr/pK+Sxs0zCEhALG1+ExvdwKcNLq6dg/qgGjIxNRr3pBfAWSflD4RV2X2rDRz9JjvPOI/DF2rLnXLlZIuMGPC+DX0ueh7b1Zdl+S4os+9ud8OZYUcs9Hkn+sLtEkQRRRc9ckSi+IndlL2yAHxxJhQdelgSR95+EpPj/x955h0dVp1/8c+/0ll5IAkkgCaH3Lr2KWBAsNHsXxYYFURRFLEixYGNRxIKoKBZUQJTee2+hJ7SQnulz7++PN2QIwdX15+66u57n4THO3H7vzJzv+Z73vBVd7XasFdVyxcswYKYkXuz4jPL0/iysOY0XX4xERwhlh0ZCyM/ioTfgu5dg5yGIiYBrK4JMEmPkXHceknvepZk8E2t3gcWUTnHHl4hcdJcsXH4KLSaLnXonjLk2svNWoVQQY0AI5PKXoen1sHE60Vsn0Xnwdczf2IQBo0WJv/FimPa1XJfX7xcPutEox3TXRIlzy6opLbIvagKl5WGrSaRTCkdNJhkMzVsF978mg4m29WHp6+J9z04T+8Xm/dC1uSSv/Lg+rIrPnwhXdpGW5WfjCJPjoGtzhW05OrN/lh/yKzrCwqNNqDN4HqaFD0mr7RY3QfsHwPDn8qH9s6GHQmj5Z8BkxNi0caVPEMC/8CdsD91bpVNW5XtLl2Ns2Zzg+gpritmMsUVT9FCIknsexNKlI6Ye3TDEXmAq4XfCO3sOZfc/Uvn/vrnfEPnZB/g+/QLrDUMxX9TuFwt+TA3qg6LimflxlSljAL2giMC6jZjbt6m2npZzEO+09zBOeRHXtNek4MhiwXLlZQS+X4CamEBg7Xo8r7wh6RhJSaiahhIfiyHq9xVbKYqCMSsDY9Z50XIVMXCm5s0gFEL9A5TqszBd1B7L9UPwzfyY0odHY73zViJmvI2xXt3Ka6pYrZhaNv+72zGkpxE1eybBnbvR/X6M9bN/tRDxzwyjxUazYU9TdGQXcXXbkLdxfpX3zc5otGDV56nwwBayet+KyfrLBZ7/bpzcsazaa2f2byTgKSNy5H1oZWX4Zn2GGheHc8qLWAY9Ij4AACAASURBVLp1xnpJHwB8y1ZgG34HntferOgCeT/2xx/G0KAewc1bqxFjTKYq0YmO556q5jkOeDVWTA0TY4CIZCPHt/oIBSG6lonj2/3kbfGT0cXG8W1+1s8ow5looOykRmo7C1tml7F/sZHuoyJJbW/hwFIP+fsC1OvrYNOsUjyFGj+/UMTAt+Pwloawuv5cBPkvcvw74A8Iqdl7VFS5ltnS3vdCSKshStnRU7Bsq85l9ffTIjGXjDpOCmsr9M1wkOOpDVhw2qoragB64SEW3pfPWm930kLrKokxAFoIw6LHubRTJ54h/MM3sAv8tEFn1Q65xbsOq/y4XpTN8R9Am/pmRg2LQQcyksPtkqNdUjT1yJthtbXULSq4zy8E6e25Ov36rIHeEyRdoDRPpv9/elIaPCS3oNRSm+xUuGtaTdIfG0jXmGWY3LkECo5xNP4q3liaRUEJNK8Lz1XtwkpRmQwWhvQURXnTXtB0UYNTawjB8gWEsA3qIWrosi3ih37rQSHNZhNc+bioybf0k4SPcq80WJn+LUy4O+zH3XkIhjwjLaIb1UEKLAF8pfhXvE5h88eJbnIrS0+3Y+AT4enIdbvg+Ttgza7KBCeSY0V9fextGNBZCOi+YzIgenI6lS2y+7YV9VpRKhrArB3KkwOy0QoOctDRh0258RTtNdM3CvTy01TT/cqOQ1T/igdEY+c+N+/OC5/Pmu1yrQxGIbrRLilsLPNA96Y+SvNL8UfGUjdV4WSB5C1bLaLYN6wjJLp9Q0mrePmT8G7X7JJr/VjFLOS6nRIN175huJjRYpaIuqyaYDXB3Ockdzs2UmxBzbMgO1Xh1sskncVmhnppJkw1+kBqawi4Jd7N8L/19RTcfwD3G+/g++RzDI0aYB9xF2pKMlquWCN0nw9zj64EN2+ttq5is2Pu2onglm0YsrNwjH4YTQdO5aNGRlL66BjsD43A8Q9GOP3isebm4p70WtUXvV6CW7fj+34h3g9mETX/Kyy9e/ziNoyZdbCPHIH7+XBlralrZ0KnThNYvRZTq+aV3cWCu/cS2LYdJT4O7fgJyp4cR8RbUzC1awOBIGXDHyS4aQvGNq2InDUDXdPQy8rxvjIVU+eOaKfzMdRKwdyuzR9KYoFqBYh/BAzJSUS8PpHQvXdKk5e6GRji4399xQtAjYvF3PmiP/gI/32Iy2qFrumkdRxQkTwh1eep7a+gNC8HZ2LVwo4ajTv/qYkxQFLT7hxeXlUZi6vbGosrGmNCGpHT3yA0drQ0CTlvcGNu2xrtxCnUpEQod6NERaImxFM2eiyOp0Zh7tG1Mi8ZwD7yPpTkJGx33IL5skswd2xX7XgCbp2Cg1WnW2s0NKOoYolYOjmcNnFgiZfeY6O56J4ISk+GKDgYxGCCPs9Ek7c5gLtAo/hYkKhaRjK72tg5r5zs3nZUo0LpiSCndgUIeHRi0o1Ep5lQDX+OQtH/rV+fPwhzlsAN48OEaFAPmHAXJF/guyspVrqIjXpH5/W+X1NvzVDYXg5LbSR0eYIE31E6pHdjz+W9WbU9it6tpcvYWVjMkO3YR9TWGWw3dObS9KJq+1CKD5PoLOOGvjG8X2E1apoJc5ZUfcgOnRCCAkLuD52A3q1g2TaxFizeJEr1u/OkRfKnFQ2hopxC6hJj5PX6qRqG3HWQM1+abDS8Gn3z+yipFxHMHsCqgub0fao2bz4EN78AFz+fxcWt0plyyynmb41k+hwnfdpCn1YQEwkWU1hZbJQeonm6m5gIJ5qu8ENF97UHX5fixsRo8VfHRkiCyKvh7qlEOUVJnvKZ5PE+OkTSQK4fLzaXx4aKYj+wsxD+ixoJ0dZ1UUe37IfiUohMaAiNB7E9Yigvr+zEgu8iuam3j7151b2Ey7cJ8Z34ifh4n75Z8pJLyiU15JEhklBx+GSYGN93NUyeLQODxBgh+u1buLjvq250a9mNe8eHByYrt8HnQ1tRLcei6VDYKo1ntPhGzNuTWeXtlTvg2h5w9LREuR07LYrwksfW0frUeCw/bmJv9wW89VVdTAYZxH25TGwsUx+QAUVMhDwn52PZVnis4m9/SAaIT06XAdkl7cXz7vZCfCTMWQq3vBj+rFzTTaLyEmOkmUw12GOAP07d/E+BVl5O2cOj8c2VBija8ROUbNpKxMfT0Q4dAU1HrZ1GKP8M1qsH4Js9J3xRVRVTu9b4Pv0C+wP3YGjcEMVgxL9yNYrXh+/7BVivGYB73ItYr7riD8nfBS5oe1BUVaYoAPeUqZi7d7ngcgCKyYTtxmGocXGEDh1BjY0hdOwYxtpp+ObNx3rjMFSbleDx45Q++iSWnt1Q7DYcE55DMRope3QMisuJ5fJ+6G4PSnw8tiHXUP7mNPAHMNbLxnJVf4LrN6IXFqIXFBJYvQ773bdhSP5tjST+nVBdLtRWf4xN478N3pJ8io/spOmwp1FUAyFvOb6yQhIbdmLTB2Mql6vZ7jISG3f7Nx7pb0PN1v2o1X4BR1d9BUB8/fakdRyIM0GIvmKxYMy4cPyeYjZjvao/of0H0AoKURx2gtt34hh5H4YaidgffRDLVVeinTyFoUE2hqwsTE0bVbHglJ4MkrvRR3FekKhaJuLqGmh4uZ0108LFdLoGJrtCSW7o/Nh9tn1eTkyGgR1zPcRlmgj64Kv7zhAKgDVCpcPwCFa9VYK/TKfzg5EcWe8l5IWgVyOzu41Vb5bQYqiToiMhMrr+OQYyf5HjfxC7DktBUugca+Qni4QgX4gcA7SuD98+koP1b8PCSREBDyx+Bjo+jGXPHB7qWZNpmztQJxlSotx8utxOvZoBXr5qO40230Moti4rdmr4mjfEdp5HQG88mEGTkol0wRsP6qiEqJ1i4PKLFBasCxNPkEKps4iJhDOlMHsRFJTCpe1FEXzhQ+mMZreK5WPhOsnnvaabZNZ+vsSAocE7XFvnCWJ2fgDFR/hb0moOHPPx/Wtmth2U6ZGPF0oB14J1MPRiE92eSKmMstuaAzsOSJLHI0Mk1WHR6J20jliP2Z1LMKI2anZr1p/OYMb34jXedwze+y58/FNGyLT+lhwhuSMHwbiZMmM08RPxxBaXiTI+chDcPkFIm9EAL9wpOcrjP5BmHNf1keQMhw0w1uRk+1e49vE4dh+RQJe5qyw0r8o/AbGiKEjcXoRd9r1sK1x+kRRN7jsGwwfIdTuLhCi4pIMUZ+49Kq+1bSBRfPdOqeqP/m41bOjfljaXTce44nlw56M1vwnSuqBufA+aDuNEw8c48kkcLeqKcnsWVgt8tUzu3VtfwZQhe+m4sZd0qQOylvbn0UFbGfOekaKKosTMFLGmfLJIcrdb1ZMCunPRtVn478RomUkZ0Bk+qxhMZSTDe6NgXy48/k7Vz8qnP8PQ3lJg+hfCCB0+WkmMz0LPzye4biPlo8dWvuac/CKeGR/hen0SgfUbAR1j/Xp4pr5DKOcAgQ2biHj3TXA5Cf7wIyhgalhfisRUtXpB7u+EMSUF+8gRlN79QOVrisuFISsD/YQoefpvSG0w1s0ilH0A3R8Ajwdj7XS8n32J46lR+OZ8BcEAgQ2bUTxudLebwPoNmDtdROlt91RuI/DzUpwvPovudhPMzcPSoS16MISpQ1tC23dAIIB31mdox09gu+MWArv2/EeQ47/wy3DVqM2xoJ+y4zmc3LGcGk26EJ3ejKj0xnQf8wXFR/dgMJmJqFnvD8suLskLcuZggNLjIfYt8hCVaqDRFU4SG/z/CzCdiWl0HvkBZ3I2EfR5cCXVITIl6zevf9b6cxamRufFQPX65XXdBSF+eLKAwyvDmaodhkeQ1dOG+0yI7V+5MTsUYrOMaH6d0hPVP9c64CkQTtLgcjtLJxfLi0gDkTXTSmh6tYN175Wx/NVieo+NZutn5aS0sHJmfwBXDQOqUWHB0wUMm52IK/HfT03//UfwH4bCUrEWnI+ThdVfOxdW3/EqWcgABH0yHNv9FfHNbuK+q8R+cUXtnTzdcAmush1Erv4QQgH2NX2Dn2abeSalPc9c+jdMS8ZA2UloPAh3i/u5pNRIrMNLRlQZJ4qMjJwaidGgM+ZGhbnLwlnLuyUylfQa4LTCDePCh/P1CiGHw3qGGNwunxt7OjHbHDTLgl0HYdQ7ku88rDecLEhlkXkUVxtmE0psycqdKjN/qDric3uhUQZk5omqem7GMwjxu7mf/Lup/UFq7ZuKsvhN0HWMthgG9Z6AxxDLPG8UmTXho4VV13/6XSHImia+5affE8UWJHN3X65E2712v3h/z+b2BkNiG/noSSHGbz5UEW1WLMp5h0aQU5TA7nOCKnYfFjV1zlJZFuScruwsXQPntYCXZslAAqSILcIhlo835orNwGAQomg2QU5umBgDrNkpxZuniyq68DWUgczKHfD9jiTuXXkjr1zfEac5wClDPXpnBGD4NvK8sbz9jZntB0VJH9wTnnpXyGrbBlA7WZTpsTdDm7idsLu4cp9K/i5uTH+V9FH38vMWI+k1FBrXgeGTxdZyZWdolQ3dmsPPFdF5vVvLc6Tr4rXef0yU/54tw10aUSSybsfBcMHkuTj1K5+V/0UoFjPYbJUZtZU4r5LUPWEKlkv7UnrXfRjqZ+N47mkCy1eiB4OYOnXAdsctaF4v2uat2EePxPfFN+i6RnBfDo6nHseQ+Nu6jP0WmPtfSkRkJL45X6HWSMTcqxvlT42vfN/xwD2/qBqfC0ODbAIbN+N+YxooCq6pkwiuWAU6uKe+g14oD0xw9z5c77xO+ZPPVN2ArhM6fBRz/0vxTn8fpXY6anISpXfdj5Z3HKxWnM89RfnT4/H8bQbm3j0IHDyEqXb6H3Yt/sK/FlG16pHRbRjbPp+APbYmIa+XyJpZ2KMljs0W9f97zktPBTm+1Y+vVCOqphFXipHc9V7KToVYNiWcrLNjrpvBHyYQn3VhguwuDOEv06T5xa90gTPZXdRo3Pn/ddznw1sSIm+Ln4IDAeyxBmJqG0hsYKmiGp/Y4a9CjAHWTi+lRiMT3R+Pps2tERhMChFJRvbMLyf9IhtbPi2voh43H+RkwdiCivNQKonxWZTnayS3sJC+L1C5r7zNfvI2+6nby0btTlZ8ZRrl+Rq+Ug3XH/c19bvxFzn+B5GaIGraufm7qir+yr8LVw0wO6pmDBstUroZUQuDNYIIQwkNy76HbbNxZvSEnUcgpQ35jR7ikU974g/AS3Pj+GL9zcwf3QWXyU1sQgwr9qYw9j1QVStP3mCtklqxaR9MewSu6S7EduIncGs/ITIbLpAhPG8V/DgunwafpUCHh6Dnc3z6s5FHzkmKWrpFCOXyPZlcndaJg5kP08MBH8yvWvR23cUQ45TYNXNFXVViDNgt4n81GuS3v8wN9fVtsPaN8MqeAtSVE2jfugUjBzfDahbCaTJKQVvuafEmH8iDSIcUg53bavvKzkKm+7SRgcv5Xm5NgxK3XJux74nFBMTG8sU4UUTPx+RP4f3RsHSzfPbTEkUBd9rEtnCWGMdHybWIjxJy/uC14juf8yzM/EGU1U8WVd/+hr0w8lqw2+CHNXKM428Tkr05R6XLU3V5aBAM6QGYDHi0JB6eEt7W7sNCPBdOEu9zag34/BkZ0MVGgOWIDZZX3WfKlicY2v4Mg/t35qfSPvR5KPze6Gnw7mMS5zasj9y3WgnQLEPjwP4CXpnlYs4KCw8Nkn3k5ApxvrKL2HdSEiRVZPWO8DYVRTrz/YWqMNROxzluDGUPheNPjG1bEzp0uMpy2pkCTL26E9iwieCGTYS27cD//ULMnS9CKyun9L5HcDx4D57xL2O7+zYsAy8DXxAlwoWpyx/rOzUmJWEccg3WgVeg6zrBtesxNKiHWjsN+123/mafq7F2OvaH7sXcrw8oCt5P5uCf+w3WwVdXEmMA2x234Jn0KkpE9RxHxWEntC8HyyV9cE99By3vOJaB/dHLy/G+OxP3lKlYB12FZ9p76D4v/jlfEWrbGm3XbgyptTC2avGHRL/9hV9H6ckgp3YH0II68dlmomr+PhqS0KADnR/5EF9pAdaIOIyWP2Y6vjgvwLcPneH49gCp7SzU7WVFR0c1Kmz8qOoPib9c59TOQDVyXHjET/6+IIdXe7FHG7BGqtRqYyY+K2yQ8xSFKD0RwuJUifyN1+DMgQA5P3s4scNPZncbqe0sOOPC6/rKQ6gGBZNVxe/WWP9+KavfDlsjMrpaaXKNhiVCJamhBYNJwVdSPWot6NVRDQpFx4JoAZ0zOUEia4bQQ3Jul70cw94fvXhLNGq1smCNFvJceDiINUKV6dRzuIA9VkU1gCvRSM8nbBgtCsnNzORt9rNvkYfGAx0sfKaQhAYmnAl/Dlr65ziK/yDUTICJw8UDu2GPEKCX7pKueX8XMZkw8AOJ6gp4hBh3eQI2vAvdnoT4bDj4M3w6SJbf8zWkdYLeL5EX7EBUrJBJl13sDTe9lcGl7YLc1OEwD70uamjnRuGkgHOxZLOoeDf2FcV4+Xb42zzxx56PzGSdzN1jJBFh1RRON36EVz+v+qMRDInq2b6JgdWxXzHiVQcPDZJUhM8Xi43j6m7iWZ29CJpmSMbz3PFC4Lx+SYjYnws1rbnUKdsE7gvksZ7eTXKUj+U/wYcVRftmkzRKeepdaN9Apv637JfEgzopQsRS4oQkOm0yIDhVKGTtTFg0xWiQ5cymMDEG8QVPmAWT7xUrx7lqdcfG8LdvZH+KIirv2Jul0C8lXoreRg4Souzxic/5ZCE8+JokN7SuB5F2KCwTBXbDnqqn27OlXLMrzomH+3kTzB4rNpDUBMiqBUEN5i6Tc4uLkoK+plkwvD/sOAQ/bYD66RUEvobMBmgaFNobE5PSGiX3HFN7qzvg5FbU+AbVItc8PrhzImybIZF3KuD05qAtnUbGtlm8l9mCezuN5vLXWmExidp9Q1/xIIMMAl64A0ZOhfV75B5MHC42I5D7EQxdOIv5fw2KqmK75XqMjRsQXLcRJSUZxemgZPBNVZaz3TiU8nEvYm7dEjUhHi3/DPYHhhPalwP5Z3A8cr80eSgvx79oMfbHR/7ulIbffOwWCwpg7twRc+eOv2sbqt2OuUUzQsePo+UdR7FdIOXC5yO4fReuyc9T/PPS8EjcbkeNi8VQM5niKwdX+ng8r72J7e7bUdPT0A4dRk2Ix9i8KcFde/FMeg3XO6+iFxYTcLvRCgrR8s+gRLgw1E7H3Kbl321f/RfC0HUdb7GG0Sqk7O/h9D4/PzxRwMkdMv3mTDRwxZRYkhpfoDvob4DJ6sBk/WPby+du9HNqd4Aeo6LYv9iDp1Dnm9cKyOpx4edBC+nk7/djsqlEphg5ut5L7kYf3mKNkzsDHN8iglhctolLX4whLtPMyV1+vnv8DGf2BbFEKPR6MpqsnnYURWraFbW6/ak4L8gX95ym+Ig833sXeGh9s4tOIyLxlmjkLPaw4YMy7DEKbW+LJBTUWfO3qg04chZ7qdnKIvsBUppZcSYaMDsU/OVhNlurtZn9P3vYPKuclBZmsnraWfXWGdreFsHyKaXUamOmbm87W2aXs2xKMTUambno3gjyNvnRNJ02t7hYP6MULQhmh0KbWyM4tSvAtjnlbJsDrW50kdpOyDEKFBwKYnap9BkbI+T6T4C/yPHvQMcmosgdPimEqNFvaVOvKBJ7ducGaWesGsUW0X8apLSRdsfbP4WEhnB6l5DTg4th7evUvbwDDdN1xt2mcLpQCuZOF8P9V/op112V7ZXLPEL4zkd8pE5BqcKsRdCpCSysINBaSKK51lQUXZlNMOGq7URu+rJC1VYxqjrOC+TF2yyQFKeyeo+DDXvlWuzPhcE9JK523S5Raz9fLPs8dgpufVGsCyDkdPOruWStHIrx6BK44p3qO0lsQqExvZIYg1gaXpsDz90mZHH45Kqtsl+6S87PaRXiejxflNhXRsiyxWVCJl+4U4r5FEVyfcfNDG/D7RWCGQjKNk4WCuFrWx/umhQ+h6G9hBS//7hYCyaPgHsmVc31fWukENX6afD1cvhwocSeZaZUtSsM6y0Fes+cE6l8Fh8thC+fE8vI+A9hwsfhazj1QSn4PHIKrn8uvE6dZFGsr5e0H97/AR55M5nJw2Zz86XzUU9tRYlIgfJ8yLwYvVZ7ahfCjNEw6ZNwU5Bxt57TEdLvhgWPou6QqmpH8RE6Hf+ZKUPWcfO0LJZsqZ4n3akpfD5O7n9sBNRNFYV/zhIYM10sH48NlQFf3D+Xw/3poUZGYunVA0uvHngX/kRw6zacL43D+9FstGO5WK7oh6lrZzxvv4tnyzYiPnmfwIZN6OVuzH174/n4U3zzFxHYIA+VuVtn1MjqHbN+K0K5eaBpqDVTKqdhNY+H0KGjoAUxJCagxl3gC+f/ATUxEbVmMqbGDaRbXmws+pkKP5bRiJqUSKi4FOeLzxLcuRvF6cDctzcltwzHMXJEVYM7EjdnvXYggRWrIDIC+6iHcL80Bb28HO10PnoggP+7HwgsregkZ7fjGv80frcbS9/ef+i5/Tei+FiQbV+UsfMbNzEZJtrdFkHNlr9MdA+t8FYSY3usSovrHJSfCXFih4+YDCNm678/zqskL0iDyx1s+KAMd0GI+LpmPIUaJceDNLvWyYrXw7YKs0PBGqUyo/9JzE6FLg9FcXiVh/hsM4qqcHzLOQU/ukZJXpCCg0FMdoVO90XgztexRqocWuFB12HbF+XYolSaD3GS3NRSJbnh9N5AJTE+iw0zS8noaiV/X4Afnw0X6x9dd5pLXohBr7o4IGp3yK9zem+A5CYWnAkqfZ+LYd37pZzJCVC7k434TBPLXpUfOoNJQdd06vdzYIs20GKYk/XvlxL0Qf1L7BjMYHaoWCJU0jqYCfl1olKNtLs9Ai0ogyejGULnJOxtnlVGp/vlu6nZIAep7Sw0uNSOLerff//P4i9y/DuRWkP+/WboOhxdCctelBzXtsOhfn9wirlmy36Ymz+VQ/kqA5sfpFP+00QemQuORKyBU4yovxT94BJOpTaj5dDuFBjSaRSZQ40aWQztJVaDjXuFsH2/RhQ5EBJ7TXed5HiFR98StXPkYOmu17oeNMsSsucPaDRMLsHoNvBi1CZquLx0a6njD8QyahjccA7xio0UxfaGcTC+IhnqmRlw70BJR7Bbxfu6aju8ep+Qw582hUklyPEZTmwSYgyw6X3oMlqujxYEVxKhXi+ytzCRSffCoxXRci473HOlkE2DKkkW55Lj5z+Ej8fAwRPSuGT7QbGA+AJilzh4XDq5vfklLK1IxLquT9VbNfxKyVr+fAkoi8XWUOqW6/XTFNmf0ya2jKvHiL8ZpNCsTxshfmcx8wf44lmx3kydK8VsT0yT9/p3EmW1YxPJv/78ZyG858Nqksdn+8EwMQZRhn1++HaVEO9zcSBP7nVOngwI7p0ir985vTZPRt3J4rE51I04iqqoHDE0Z9qXLqZ8Jvu5dyCMGipt0ztU+IsPnwTd7SP15G6qHKK3mEbOnUAWD1xzDpE+B7US5B9AzjFYvxeGPRt+/94pcl/Pvw//ywguX0VgxSosQ67FckU/lLhYQKH0zvsqlwnt2i25t9FRaIePouUcgGAI57gx+NdtwjLwin+o+Udw1278y1eB1YqWm4f7xcnofj+Oxx7Eeu1AtNP5EtP27kzMXTphbN0Cc5eOv5q1+49AUVXsd9yM54NZqPFxuCY9T2DDJkI5BzE2aoD3y29QIyMoffAxlJho8PkxtmiGqXWLyti3c6HGRIPZjOPxkfg3bsX/3XyMDeoR2rMPQ1INgt4jYWIM4HbjnfUZxk4dMHfr/Is5zX8Bgn6dlW8Vs2Oum0b9HSQ0MFJ2OsjR9TpJjc0YLSp527wUHQlxdK10UsvsbqN2Rwt5W/x0eyyKvM1+Vk0Vr2rL65w0HezAFf+vyzYPBXV8xSGMNgWzXb7ZEhuaCfr8FB0N4ohT8ZeJ7SAu08zJnX4uuieCYxt9OGINxNc1kb8vAAr4y3QWji2k432R2KIUjm/1Vu6nRiMTjfo7mDviDFpQtLEuI6PY8lkZZSdD9H0uhgVPF+IplH3t+9HDoJkJJDeRgcaZA37KT12gEE6H4qNBNsysavfQNfC7NeLqmsjfG2altmgVe4zKgWUebNEWdA3ytgRQVWh9s4vTuwNYI1TW/K2EJlc7SMg2YrSpLH+lhLKTIZyJBrqMjOTiZ2Mqt5/eKZqVU4s5sMzDmb0Bjqzx0+OJqMqJHdWosG5GKclNLcRmGDmTEyQU0DHaFC55PkasIfF/Pir65zui/1bkbYT3uoeHT1/fSdBTirHzSHYdht4PQpOMKFwOGPZOU165biI3uNZCs+th+QTsK14GoDaQntIG7yXv4HeksXi7lQFdhLx9tljsB1+M9bFtfwAzfrq2UGjRIJr8YvjkKYn18vml8CwuEmZ8L4V4TpvK6OujGPV2FAM6ixp+57s69dMUWteDT8dK1FtspHSdG/W2EOGzSqHHBy9VELdJ90gDj7M2hn7tIf0CxeHm4DmVWUdWgK9YGopE1YbjGzCseIkvT/dk60Ejbz0kEW1P3STn+dg7ko/7wDXi9z67b7cPftoo1oizKC6TBIkhPUW9PheOCutFYowsd+tl0l75RIGQY12X10HSLNJqCBk+lg8vfhgmxgAfLRDLx7nkuNwLSXFyLbo0ldSIs5i7TP59OV6OY9tBsRzMWVolpYvr+ohdJnSeNaxnS0m8uKiJeH7PR0gTZTznPMfK6SJodH8G+2dlkBIP87+X1I6zeOljiXO7pgfkF8n/j5sJIS2aB/r9zD0NXiZp50uVy5tsNiYOlyi3v4etOfD8Bxd+762vRD22/P8Lv/8roCbG41+0mMC2nbimvYZvxkf4vvy66kJGE6V33U/kR9MpvimcXxxYtoKID/6G8RdaMF8IgW07KOx2CXpREY5JLxBcsx41qQahXbspHzMOijY4vAAAIABJREFU3R/A88obGBvWx/7IA7gnvkroWC7BfftxpaX+odnBxvQ0nI89RCg3D8Vhx3rNAEInT1H68Ggs3ToBOq5pr+Od/j7BnIOETp7CdssNKCYTaq2aaEePVW7L/ugDBNZtpPi623CMGYUaH0dwXw7OiePxLVqMGlPd0xPcux/zFf3+sPP5b0XJ8SA7v3bT6EoH1iiFgpwQSyeWoIV0ml7rpH4/O8e3BFg6uYhQhYC64ys3/SbEEJtpovBQkE0VHl5FhchUI3u+83BkbRFJjc1kdLORkP3P+0LI3+/nxHY/O752E3DrNBvsJLObleSmZgIeDUWV7/+UFha2fl5OcW6Q4twQB5aWkNjATPFRPzu/cXPRPRFV/LVaUOfYhgA1Gps5sEQIcqsbXfwwurCyiE0LwpKXi+j1dDTznyhk8YQiOtwVwaLxRZXvH17lJbmJBS2kk7vJT9HREI54lfLT4R+C7D52Tu0OSBHceSjODdFiiIMDy7wcWe0joZ6ZRlfacRdqJDexEFdXVN6d35QTmWzk+FY/tdpYiEw20OAyB7u+dXNsnULjgQ6aDXJQdDjE9rnlzHu4gCEfh4k7QPdR0RQfDRHoolOrjY/yfI3Vb5VUOR5/mZe6vWycyQnS4DI7DS+zY7T8OSwUF8Jf5PhfheMbq84rAMaVEzieOozdR2ow/g4hUAZFSEKEvQ6nO64l3lgCqyZXWU/JXYv1yAKWll/NJeNlLnpAFynEqptQTKQnh2hzLXadiORgkRnbYcjNlyKvuec04ol2CTEGKWD727fiSU6JFyUYFOavFfI4eYQorcdOhYvbXHaxG7zxoKxb6pZUhmOnq/p7560S3+zUL6peEj2unnwr6hUf9pPbIboO7P0Ojq2hsP7dLNthZPdhWLsbPn9WGlDMrihAK/PAs+/D3x4Nd8Ab0lOK8wAa15HzSa8hg4YIh3ifN+8Xb+/7N2+mtXUpEeUB9rzUmVxTS8p9KlePkQi7c20PTTJgSC9JKpk0Gwb3kgK682E977v8oUFynWxmKc5bshl6t5Hj3XlQsoXP5h9f1Fj8uc/fLi2dNU0KJ79bBd6AkPMeLWHRBlk+LlISLuatlCjBGd+H92s2QZ0k2UZ8JFzVVdJBru4qsXcnCmRAM+vHqs/EWcxZDHf2l/N//BzHywtfxpN+8+3cZnkTfKVoNduR1rQJ9//aLMqpXSzbUpclWwxceYGC7NTEC6vm/6swtW2NISuT0L79eF6YhP2RBwisWoN24iQAtttvIrh+I4asTLyfzKm2vnfWp1iHXPOb9+df+BOWyy/BfOnFKA4HWv4ZHP36gMVCYPVaAouXYWrXGv/Cn9B1HfuD94LfD1Yroby8P4wc+zdsJLhxK3pxMcZmTTA2akDpE8/imfwazpfGUTbycVBVjG1bY7txCI6mTTE2a4xiMhE8eJCI998huGkL2qnTGBvWx/3WdLT9B3A8fD/Bffux3XEzaDplTz5LaF8OER/+jYhGDdBOnkKxWAisXY/u82Ns1fIv1fhXYDApmGwKUTUNhIKw/r0wGdr0URk1GpooOBSoJMYAkSkGQgGdGo3MqCaFzO5W9v/kpeOICA6t8LLne/GkHVjiZc98NwPfiv+nxHqVngpyZI2Pn18sqvz5+WF0AX2fj6HhZQ5qd7bR4V4XRqPK1s9L6TUmms2zS2lytZMlLxdzfKucVHoHC0VHqyq6sXVMnNrtI6WZnYyuVnIWewm4dYK+qhEOWhC0CkpQfCyE2VWVKKpGIbzeYo0T2/zs+LqcjiMiKc6VgsaUZha8JRqbPymj44hITu0K/+haIhTS21v5/vECEuqb6PVUNM5EA8XHAphsCon1zSQ3laYeFpeKrotto+7FNkqOh1j3blhtWfJyMZ0fiMTsVHAmGCg7FaLsVFW/hiPWiCPWSMnxAKUnDPjKzourAGLSjYRCOh3vjaD+pY4/NTGGv8jxvw7GC3zRmp18tdpE7drS1eyNLyVv+NrucEk7WHIghf51z2DUqhuHFF8p2YZ1OGzpjL5O/L7j3odrekQSDLaonEYHKSR7ZGh1Yuw8p77AZRf18ca+8PZ5AlV+MRzIhZv6wqRPhRwnxYo9Y0uOEOk+rUVVzkqFV6v/VnOyEN4dJbaAMg/cfjl8f6gZ3TrOpf7We6DkGHq9y1ESG8PiZ8EWw3rnrZXRc7oumcLfra6+7U17oWcrIcOdmkih28vDxdbh8YkloGW2ENvpjwl57pe2gebLulSmh1gMJtKH/cTMPR05fEIU5mdukcYjKNCyrsSTrdkpKnhRqVzXnzZWPZaUeGmUcvS0NAe5uK0Q1LnLJR2kWZZYP1rXk/s8sCs0qfCsd2gkZPq5mXJ/rukm/y0uF+X7VKFc852HJBbv0HFJkRj3PnRpLmR23kpRt++7Gk4VQItbZNudmsAHo+HhN8VycXM/sXys2SUzAaypeh51U4GyfL5cEg1VjRS8vyaDm26ZitGooKZ2JDL67zBjvxcO/AjFR9l1OJuTBVArXhq2nB1kWcwwYiBV2pb/r8PUsjkRn8yQBhal5WBQifhkBnpxCSgKSlIN9JOn0EtK8C9ZUW19xeH8TZnGutdL6MRJjG1bo6YkoReVSMOB7Cy8sz7DP+8HDLXTcTzzBN73PsTx7BjUmslgtVI6+EZcM95GjbpAtMtvhK5paMdywWQidPQYJTeIAm67bhCBJcvRTp7CM/EVALwzPsL58ngCK1ajuBwY62VjatG00k5hysggUFqO570Psd08TBIqBl+NISsDjEaU/QfQSkpRHA7U1JrYbrmB4PqNlI8J51maevfEMeZRTP+A6v6/hvL8IIVHgpjsKpdOjEHzw4EVHkw2hYAnTIoCHr1K3Jcz0UCrm1wsGCMKqqJCx3sjiKxpJLKmkRVTS7DHqHiKNHQN8vdJ97R/BjkuyQuSvz9QSYzPYsPMUrJ62DDbVTK72vl4yCkCHp2TO4voPioKk02h15NRBLziFVZU+H5UeAa0ThcrKS3MWCNd/PhsEY0G2ElqaiEyxYDZqeA/hzQarQrmCsU3Ot1I/v6weGa0KKS1F2XW7FCISDYQ8sPKqSVc8kIM5fkhts4pw1ci2zu00sOlL8eQu8mPK9FA7Y5W4uuaGTorkfL8EKoRTu32o4d0Tu4IcHSND5PVRWyWidTWVlHKDXB6d4Az+6uKeACFR4Kc2uUnvYOVfYvcFB0JUHgkQHRqVQtMRJKJjK4qRceCJDczkbdZtmWyKXR6IIq4LOOfrk30L+EvcvyvQkprcMRD+enKl/Y3GM+bn8QycrAUJ53FB/OloYTXBw1SMmiQfbmkV5yFKwn85cRYTzP+Nnjm/bBS2ySTyi55Z7F8Gww/p0isfUO4q7+QE5NRrBHfrZK4t/P6i1QiGJIc4YeuFcVy52EhxcOvFOXzrALdpr6o0JNmV12/zC3kdN6LsOsoDJ8Eh46bcNkv4+7ebcmo66Z9Ax3z6S04+3zOpuLGXPta3cr1jQZRfrNqVs0HBsnyTa8B+SVw60uiJF/9ZNh3/d1qKeC7fjy8PiLArV1OEbXzi6qxeqEAxrWvYY/tgOQyhO9JbCQsnyp/RzpkEFHqFuKbmy/qrdEAd14hhYAfj5FECXvFeGjXIbjpeenY99IsGH29qMYPvykd+ybcBbUSxdpx95WiIJ8uhq37JWbubJe6o6eE+P/wslgjvH5RiB+/TgZW9dOE/DfPkvsxfLL83ampKP4nCuHmS2RbjevApv3Spa5tA/j0p3D+cFwkXNvVD1/dSuPEt5hNVfLbNBPya19Hjd+SMrHvW5h9DSgqbVvfwJvYef5DGQQEQ3KN+rSWQcNfqApzi2aYWzRD9/vRQyHUX0hPMKSn4X13JgQqftRUFfvw23/VbxzcvYeyMc/h++IrDFmZ2O64GfeEKeglpTjHjQFVxXrdYLwfzCKwdTumPj1wj38Z3ePBeuMwIj77EM+rb2C98rLfdX6h3Dy83y3AEBNFKO8ESmI8zpeeRfd4KLn+dgiFcDz+cPh4t++g7OHRmNq3xfXMqxjrZlbzGavJSeD3U/7cy1gHXYVeXIJn+kxsNw3DP28+9sdHoh3NxdKrB1pREe5Jr1dZP7DgR3j4PlSn83ed0387Tu/z8/X9Z1BUaDLQybYvyyjP12h4uZ0eo6Moz9fYOqeM4qMhVKNCfLaJpCZmvCUa9fraWTqpuJIw6xosf72EyyfHYrYrdHs0CoNJQTXAgaVe9i7woGkX+DH6A2AwKRjM1T8fZoeCUiFoegq0SrIf9OoseKoQa4RKt8ciCXh0dn1bRuGxIP1fi8VgFqU35NPJ3eTDGqniTFQJlENMHQMHl3vo9nAUSyYW4y3RsLgUuo+KZv0HJdhjVbqMjMRdoJHd14bFoVK/n50aDYQcGy0qqW2tRKe7KTwkpD61jZXy0xontvtJamKmxdAIvn34DGaHQscREcTXlSlMZ7wBi0thycRiVANs/DDsTT6w1Euvp6JQzTpR8QYueT6GggMB3IUaHKiqhltcKkargjVaocPwSFZOLcYSYahGjgFsUQZsUQYunxzH6b0BAm6dmDpG4jL+szxzf5HjfxXi63Hyyp/x7VyIzX+Mw66+PPpNO1pmSyOF8zF3uRDY79ZZSekwDmdMFoac7yXNIrkV/DSGwr6LUIuqWhgcVlEaz4fHJ3FnXy2HO66QZIOa8fD8HfD6HIkz69lSOgDeMyDs4QWxIIQ0UXztVnhwqlgOXrtPXp/xuJC4g8el6UOZW7rCvTtPFMI7r4DFm8XycCxfUjKOnZJtl7rhxblSrfX0zfD0u7X5bgKcVMNNO0CK3YZPgtE3SM7y2Uzj7FpgVOH+qVKcN7QnzD+nILHyei6DHi1gwmwTh0+m8GTUCc6HWn6cBs1DqKqKwypd7iwmyevNTJFlsmqKHzs2UiwJ7RuKZQFEfY+JhHV7ZP9JMaLAHjohg50jJ4WI7jgYVvEPn4BBY2HGKGjfSFImsmvBl0tFNT6/ffOJAvnXq7X8/+Z9sHWfKMiBgOyjRV2xsjxxgxD3d+fB49dLwd9Zm0j3FlIo+caXYoV5b5QoyqoKTWv7af9zY8jfyxXthvFm3ABy89XKZ6FOMjS6Hp670c2gJjuITEiAqLTqD527AH58QkZbeojOfMi9A27hja8MjH0PerXWef52heZ1q6/6F8JQzGb+Hs01tW1N9NL5EuEWDGK98nJMbVv93W1qpaWUjHiYwMKfAAjt3kPZo0/iGDWS8rHj8bz/EYa0VAyZGWJjSEul9J4HK9f3vvMuqsuJZWD/6h+23wjvop9RI10UX3cbtltugK3b8S38GesNg4mc8xHYbejHz/uc6jqYzYQOHSG4PwdT29YYk8KDN0NCPBEfTKNkyM14pr6NEhWF85kncH/wCY7HHsT36Rd43pyGbfgdqAnx6EVFnA+9qLjaa39BCvDWvFNK4eEgHe+LZPHEokqv7YYPymg80MHRDT6aD3ax9t0SnAkqnkINW7RKYkMTNVuZUQwugm6dHV+XU56vSRJCCL64O7/SflGni5WarcxoIY34er+9OM9bomGyKxiMvz5jEpVqJLG+qZra3eaWiMo4Onusimqkivod9Okk1DPhK9WJSDESnWrE4lTY9b0bR7ShkvxaI1S6PhLFpk9KqNEokp3feEhuFqLnU1EEPdIow1sSouX1EVgcCju/dmOwKJSdCNHsPicpzasmfiQ3tXDZxFjy9wXQQjox6UYiUlwEvTpBLywaX0jAoxPw6BxZ46NePwemCttC6fGQ2Pi+cVfZZtCnU3QkxKq3Suj6SBQHlrppNMCBq4aRvE2+yvO2x6iYHQrNhzjZ9a2bDe+XoWtQcKC6wnwunPHGP2Wh3W/Ff+6R/wfCnNSQt1c15L3vhMSkJ8GkB6o2FDmL9BpiC7iq+TGObt/FevcIrm6UhmPnu3B4OfvazmRxbkvU82w7y7dC37ZV7QdWsxDhZnWFzC2raAd87LQ0Bbn8ImjTQEjnZz/D9EehRowQrOxa0LmZkLSvn4exM2T51MRwdJjVLGrt2Pdkavydb0SZ/HSsqJv7joqCeddEIVefPAW3XQZvzg0fY/9Ocr710oTgF5bAzNGS4hHhgLq1RBG/e6J0vPMF5HfyZKGQdZC0jmt7SHzb+VBV0HRRxvflws7UQTTh3SrLeJvfQ4M6Jr55QTy/074RVfyeAfDxj3IODWvDdb3F2vDsrTKYyMmVe5lVS4jqp4vFF5xWA959OEByrJHicoXkOIiPrjrwALFd7Dkq1onX7pO2z1d3E7J6tqveubBVDMBLymHEK7BiW/i9t78Wy8s13aTV9vy1ksWdlx8mxj1aCklvkiG20ZAm5zB9nkTXNS7/BPL34q97FSnGwyy+4zO2ljbitLk+0S6VXYfEMvLpcjvpRgN9vu4M138nA7dzEfBAWZjcpK25g5cyv2PIhOn4FTv1M2zE/v6ksb9QAUVVMbdrg7ldm9+8TuhYbiUxroTfX/mwBXfswtyjK9rpfAwN6hHctqPaNnw//IjzhbGSCPEPwr9+I4YaiXhenIIhLRW9tBTfN9/jePJR3JNfx/3MC6i104h453Wsg6/GO+szANSaKVguv4Tgtu1gMqHa7RhiY6Q9dgXMbVoRvXIRoWO5KIqC7g/gurgXlJXheVOiYgKr1mC+7BJMnS4isOwcW4rZjCH7P2Maw1ceovRkCINJwZVg+Kd7OL3FGodXeVGN4C/XqnVB27fQQ/1+dvb/5ObiZ6PJ3xtk6eRibNEqqW0iWDalmNjaJkpOBml5nYutc8poeb2TwiNB6l1sZ898D0GfzoElXpIam2l5XQRRyb9OjvP3S2Hc3oUe4uuaaHmdk5ot/75f3OoykH6RlcsmxXJ0rY+ATyezm60yhs5TFELToOeT0Sx8phA9JAkTvZ+OJi7TXCWHeNuXZUTXMjJ/TGFlVrC3RGPRc4X0HhvNkXVeaneysv1LN/t+DCs+fZ+PwVOgUXZcJ7Kmke1zy8nqaSOpqblKhNtZJGSbqxQo6prOgmcK2PZ5VdIbm2mqMkBQK6LYjFYFzhv3nVXJ9y4op83NERQcCrJ5VhkX3RMpEWwWBVeiAdWksO69EvI2hQlxrdb/3Z78v8jxvxDREfDIYFFwyzxQP6mcmMB+nNk1qZcaw+4j8kCbTRLJ9sqnGh3aLyChaAn3rhvAG547uaXTZRwPWZn2dg3uvlKaSSREh6fEl26BmU+IYjtvtTRiuK4PXPecNOmYOFsIcp1kuPVSUSHNJrCbxbt7zwBpd/zzJri0PbTIlmSK4nLx337whBCpSx8Nn5fXL+2YZz8VwH9oPSvuL+CAN4OcY9nc/3rVD3nuaSlk8weFXNoscqyHj0MgJF7kcTPD3ebiIoUMTrpXSPKJArhxvKii496vfo3T4rxkpxh5Y66xss0zCPl+6l2xILw8C06c7MA3N3+KdfV4DJqXklajMNftjdUiLZAfeyu87mNvy/6OnhTrQ1ykdEnUNCgtl0HE3qNC6FdsF2J8Y5czPNrmezI3vEkgqQMT7nqBHYcMJMXKQOXIyarHbTIKmX10iNhEGtaGBPNpRgyIYfJnYY9Wv7Z+MmuaWbVdkjnOJcYgxXZFZZCZDAsqrmGDdLm3RgNMuFv80iaTJGDUTpZnwe2BV+6D9bugXnZbTvY7xZRvY9m9UOXabkH6N8vj8H6V4ZPDZP2Gi2FPWQZ90GHrJ9Dz2aoH40qClrfCiomVL5lzvqFNmxWQ3lnaJf8PIHjkGKFDh1BcLgxpqah227+92Et1OFCioqorpxU2BXP3LgRWr8PcpycEgxgyq4e5G9JqoURG/EP71T0e/MtXUTxwKM7XJhI6chTL5Zfgfms69uG3U/70ePRi+QXXDh6m9PGnsd18HY7suhAKoRUW4f1qHqaG9VFjoil/aTJKWi3M2VWnHwwJ8RgSquYK+leGFYPgxs1YrrgU69BrUVxO/PN/xFCvLq7JL2Js1OAfOqd/B4qOBTi6zseR1T7Kz4TI7G6jTmcrUTX/mBi0Mwf8BDw6UTWNWCPl+8caoZDa1sKeBR6MF7AkuJJVXEkGjHYzthiVbV/KFGaj/g4CXo3oVBNH1vqIr2vCaFXo/EAUy14ppvBQkOg0I50ekOl6X6lOyC++21+D3x1i5Rsl7F0gvsGiI0GOrvVx9fR4Euv//Wn8s8pmnU5Vv4fytvhYPKGIvM1+kluYuPL1OBTAWcNATLqpWoOOvC1+kpuYqzTRAPFco4MjxkBmNwueQo2cJV7MdoV2t0ego6EFdIw2KNkTIrmpmVbXuzCaftsgR1EVGl/p5NByH6Un5Es5sYGJtLbWKuQ6MsVAXJYRe4yDlW+EiybtsWqlOtzwcgcrppYQ8uu0GOpi+9xyfOUhGlzmILGBmbL8EM4EI4oawGBW6HBXBCkt/rNsEv8o/iLH/2JYLeLZpPgoLHwctnxIhmpg3q1z2aj1obDcREK0jhYM8s2968n44R7OZN/Bk1fnk1OUwHvfp1PulTSFRrUl63fGKCGzOw5Ku97dh+HZqw7ycG8/mt/DD9vjqZWQwukiabgQ6ZAkg/tfDxOdaJcQ3+c/DBOu2T9JmsJjQ0UpnTwbDhyXDOPzkZcP/rIyuq7rAHV60K52N4JRmUTd2ZIHP86koESU1Po1A1iMJg6fgOnfipKbmSJtmW8YL2T5LDFWVSj1wOUdpXXxc7eF83rX7pRM4flrw8cQFwkty2fgKjzCp08+wRcrbJR7xRu8Zb/C0zfJ8mUeWL3PwWF7N2IG9MHn19h1IoryTeCywmdLqp/f/DVSuLbnCLy8Qgrz7uwvrZu/XSkFgU4blekeY1p/StqauwGwHFvJLcmr2NT5G8o0J5kpRoaNU9AqikFa1xObSYRDji0YBKOqEZ/3JY+51tF1xHA25CZQP7GIDqZvOX1kEB3vT61sS330lBS1TR4uzUCemi5ZxVFO8UfvOSI+8EE9YOU2ua7ndv5r20AGURM+ESvPoNfr8tRNChk1xcP+0iwjqTVq8dwHVVXs93+AvqNNcKgIjl2gUlJVod29Ik9smC5kudvTUKe7NL35H4B/5WpK73+U4LoNqHWzcL34DIH1m1BMJsy9uqGFNPTDRzFk1cGQURst5yBacQmGzDoY69Susi09FJJivPOni34HDKm1cL3yEiU33F75mqlrZ0K79mBs2Rxz106EjuWiJiXienUCuqajpqWiHT4CgOJ0YrmqP6b62f/Y9Vi3Ad/X36GXlhLcug3zZX3RCgpRE+JRzOZKYnwWoXUbUG6+HkxGQqfzUWNjMLVqLlM6JhOBRYuh+AI5hhc658wMDA3rE9ohXqXyp5/DcsMw7M8+ifP5sagpyRhi/zPaNZ7Y7uen8UWVloAjq31ogUha3RAmxyV5QcrPhHDEGYhI+m0/9d6yEPt/9LB0cjHuMxq12ljo8mAkNRpZMFpU2t0RwYkdfvxunahaRuxxKrU7WjHZFOyxBtZOK8VbqhGXGT6OmDpGtn5eXtkQo/REiBM7/DQZ6KDwkLCzwsNBVr9VQuMB0mDCHqsSlfrrx3zmQJCCAwE6PhBB6fEQW2aX4y3RyN/n/1VyfCGUHA/w0wtFnNgmx5q3McCX9+QzeGZCtRbRZ1GjoRmTTcFgpkoyh8EEoYDOrnkeSo4Hic82Ub+fHV2TNtB2zUhmTwuqQSWtrUZEshGT7R/7bCc3tTDwrThO7wugqgqxGUbiMqsep6Io1LvEwandfi4eF03uZsloNhgV1kyXz050upEzOQG0ICx/rZiMrlaskWaiU43UamUlFNBJa2vlonsiMBgVIlOMF+zi99+Ev8jxvwv75sOWD+VvLUT6j5eRfsU7kJIC390HhYfQG17Fzn6buOGtLDbuN6GqMGJAiBsuMWA1iX9Y02HmfPH5NqojJLd/nXUkz+kBPnnw0+vfQVmL1xk+2YiuC1n0B6oSncJSIWbnK5H+gGwzPUkUVxB19/zCvbq1IEnbCfUuB5MNFj2B8f/YO+/4KMr9+79nZntPNp2QBqEkhN6LdEUFsSAq9i72iv1iQ7B3sV699t5FBVFApPcOoYUa0nezvc3vjyfJZkmw36/en57XKy+S2Zlnyi6z5/nM+ZwDnK23UnT1V+xyOTlW+wrm8nmoljGcNXkMQ6b3Y91Omb6dhRvG5ScKgum0iwq2qopUtZgK502H3HSRBre3Upyv0ybI4ferhYzjuvF1FHx1A4R9jNM+wdhO45E6jiNsbovF0J+z79cQiQjO9vhlXh76yM6ZY7RcMENUtEE4UAxspVE91SGOZ9eBuIeyqsbtx9ZuF+4TuekwoW8luVvuT9hed+BH+tU8CEufIXzZemY9mMvSTeK1vRXCCu+u82HcLcJJ4orj/Ti9h0jZ/hJjpZcZVnAy60xXMd89FlNNCl8+KHTX/YvgwgfEU4FFG0Ulu3+x0BJfO1FUs3t3EtepdC+0y070NAZB9I/pK6QjP64HRZZYt100DS7bLCKxdx2UErTtjYgGfeJzVnJ6Kx9yhBZ59AwYcK34XBh/u7PB/xqihyrw3DqVyHLhvWe66FxcZ5wPAfFo1TvtQexvvEzw/Y9AUZCSkwi8LB6HSElJOGZ9iK5/X2L19YSXriA07wdihw6hO2YUupHDUJJ+37U0nHoSSvt2RLduQ0pNERqeUAi9xUJ0wyaiBw+hKQoh5+cTWbwMy7SpqH4/qj+Apl0+mm4lvzohL/ipIMYA/mdewPbGy4TmL8Q0+SJUr69B/9TMQkCrJXbgIN57Z4hKd309aDRYn32MwMuvIWe3Qc5M/0X7VtJSsb/7Gr5HniT09Ry0I4divv5KtCXFP7/xXwghXwxvTZRup5up2xthx/cB1Cis+I+HzmNNmJIVdi0M8NWtNfjrYpiSZY6b4SSnn77Vx/XNUb4+xDf/qm1ycdi7LMgPj7s44QknerNCagcdZ7yWTt2eMJ2ONbJzfoAfn3Jz1HV2Zt0LekN2AAAgAElEQVRc0zTO3Gl1DLrSzvcz6pA1hyXFAb7qWAty5a+LoTNLDLvJQe4AA0b7L3A1UFWKTzSz5SsfRrvMuEec/PhsHSBxcH2Q1E46NNpfTuLcB6JNxLhpF1FBZrO6tZ78lztAz/JXXQy90SGs4aIgKTBsioNwSGX0VAfRMLx/cWVCYt2Y+5OwZmh/1fG1hpT2uhaE+HAYrAo5fYzQR1SJD6wPsvI/HjK6aCk+wUzVjjA5/Q3sXhggElTZ+o2oxBcMFU+4FK2EKUnBlPS/4TTxR+AfcvxnYduXLZdFw/DmCaJjDYhUbOexbbms2i5m4bEYPP6BwqCuQo/6wXwRF91od7avEoL+IBfVT28ixgBbracw/fn4W23Qxclgc4QjiRZbjdAocWIM8PF8YZX2r5fB6xf640eviNDxxzOh90Uw9874ysF6em69gh75w5CXCEsmad8yTAU/svzWW5lbOYhnPjNw1n2CgD40WaTqXfFo/DiMeiHBuPEZkUj38s3w3ly4ZqJwdjh5qCgeGetLIdygvwr7kNa/DSmFaL3ljBs8mC+nR9hbHsZsgMc/NrN0E6zfLRwb3vhGbLZwvXCMSLKKCcPxA4QeO8UmwjwWro2f2pJNgtB+v1q4R2zZAzefCVu2yqC0crOSZAj70QYOMbpPLikOoTcPR+CByfDjOuiUI4hyj0IT4xwFoOgIOzryQvhxbno4u2moC8cK8ur2wafTxeQiywlvrBQa4kmjxYRhykwRnnLrWWJCo1ESuUcjGic6C9YIG8Ete0TT36wlQjbjDwo9eONnDQSXKlIWw4BroHBMy0EbIctgyzry6/+fQY1GUf1+ohWVaAf2RzfsKCRnMtF1G5qIMQCRCJHtO9FfegG463GfcV58jNpa6q+ZguWBe0CjIbJlCxKg5Obif/RpYgfKMV89+Xcdp2Q0ohvYDwb2A8A74xHCq9ai6dUDOSMN/UnjwKhH274AOSWZ6M7dqMEgSn4emoxfRkhbIBhEU9y56Xf36ediuOwitEcNQo1EMcsS3rviE0vzXbcReE8YpDdKQIyTLya0bCXhpcuxf/AmmrbZLXZzJGiLO2N7/klitbXIDkeCVvl/Be6DETzlMbZ+5ceWJVwG5j/satKP1u2J8Pn11U1VZV9NjG/+Vc3wW5IIukWqWWaX1oleza5IC3uzsiVBXPuipHUUxMiSpmBJU6jcFmLx824Mdhn3wcTGiJBH5eC6IMfNSCYaUpEUWkQZS0rLv3MHGMgs0TV5/P4UIsEYZYuDrHrTQ5ueeiq3hvhuei3H3peM60CEssVBIkGVtr1/WsLkq4nid0Ux2GS0Jol2w/XoTAr7VwWbzsuYJFNTFqKqNIJ7fwR7Gw1pRTrsWRoc2VoGX5lE1Y4wp76Qir8uhj1bQ2oHLUoD8Y1FVCY8n8qmz71EAiodx5jI7PrLiXE4EMO9P4KkkXC00fyi63MkSLJEm24GMh/Si/u+CtXbw6QW6vDXRjm0UXgh97/URmbX3/7/o/6QsH+TNBImh4y9jQa9Tf7ZCdpfBf+Q4z8LuUNg8yeJy6LhJmIMUJM2mllzzS02XblVxWSQ6N4+bqHWiM6ZHnRVzRicrBCwduCFm0Tz2tMfCW/eK08WTWfN4fIKf9y7X4kvGzcIOmTDi1PgiffhYI2w3UqywAf3iAS4giwoMm6CWWUQCXA4pENrkTodlji1cy5yx+M5Kt3ImEUDAUEwX58NFx4vNL4WoyByU2aKRsPuhcKdQa8V5PCse+PV74vHqXQbdpiGtf0xsGcx9L8KRYZlWzXc+VLiR351qZAbNMfXS+Ff54qmxC8WC5kCCDI47WKhO67ziCe7c1eKeOqXvxQ2aycPhW7tnbj9D2P75OT4oAY7KDpUey6SI4+9FcLartoNdgukOYReeOVWUYFWZInylGPJGDuT0tpMbpuRSABe/kKkBd79iiDsGU6hqW7EvNUika7RCUUFPv1BvO9Duormv0a0TYPaholI706wcbcIH2lM3fMHIT8T7jhH2Pdt2ysaKx+ZHKJ9jy6QOgY0/3eRr39lhFevxffkTCIrVqMbfzyabiXUX30TlofuRdO3F4ZgkOC33yPpdBgvOo/gx5+hV1Vas6OIrFhFZO0GfPc/jGnKtfhfeZ3YgYMYJ19M8IuvMJx8Akp2mz/s2PWnnUJ4+Sq8t9wJsozxsosw3yGs1BSHA6Vn99+9D92Y0XjvnoblwfsIvPshqs+PprAdktmMrqQYTbcuaIcMIrpnL3KSAykpCe2AfvgefYrotu0YzjkDTa+exA4dwnzpBWi6dvnVxyBptShpab/7XP4MhP0xVr3hYd37Qs9bXx6lfH0No+9OIuIXYQzV2wMJDgx6q0Tvc23MnVaLt1I4KRx9TxLZPXX462IouobH5JKE0dHysb4lXdiBHY5AfQw1CtGQ2uTX2xyufVHMaTKbv/TRdYKZte/GbZRy+usxJSfua8g1dtK7/DQxrtsXoXZ3GK1RRm8Ho0PmqOvsHFgbovPxZjK6aPn02vjEwN5WYfzjKUdM2tu/JsiuhX62fxfAmCTT+xwrqZ00KBqFtn1FwEddWRhnOw0HVoeZe39tk1dx99PNDLrajtEmrMva9jpyVVXWSOT01aM1SlSVholGVLzVUVz7opicQsvciEhYJVQfQ2OUiIZVXPsiLH+lnq1f+1E0Im2vwzFGDHYZNaYS8UMsqmJJUzAl/XJK15ykpjVIUMY/4aRuTxSdWSKtkxZZ+W0SLr87Qm1ZGI1eomJriHWrw3SdYBLuID6wZSukFOhQVZX6iig6i4TB/NeqSv9Djv8sdBoH69+G/SvE32nFYGsj9JhtB4C/FkfVJgZ18vDhokR9ZopdkJnt+6F3x0Rnii/WJPPQBWdh+PEeMDj4ceASbn8vh3U7YUQPUZV9/ztBRGdcJhwZLEahUa33iUpmY6Ncil1UEo+5UZCmW88Sxc+LHojbrGkU+OIBqKoupPPgF8nStWIVkTcMDq5puTzsR+vfT8/CMKtKtTjtolI76Z74KoO7ivCQ5z4RMgqA3EwYf2uiLOTFzyXGDehMx6NuQw3WcyD/YiyRg9h9WyC9Kzot5LTyfZifIazWEg43U3gQv3JrojbXZhYTgannQTAC2SlCTvHi58I+rUchXPaQCE25aOx4xh+3hZJ901EMFiR7NqGtcwiNf5/X5qRRVg4PvxMfe0CxsIlbvkX8zFsNw3smM7D4PAxWtSmmuzkyncIRxO0TOvL7LoY7XxJV4B6FQm7TiFmLhdzirW/h6gmi0XL2MhGOUpQnPKxzM+CoblC6Tzh+qKoo+g7rAelJMPI6OG04nH9cjOJMN4XOakwmA5TOEumGOpP47Lbt3/Jg/waI7NxF3ZiTiFUIn8LIho3ox4/F/tGbuM+6iFhlFZb7pqK0LyAWDhPduYvIilXojz0apbB9i/G0gwciWSzEKirw3HEP5puuxXvvDPwzX8R831ShQf4DocnPw/baC0Sn3gqKjKZdwR/eOChlpGOYcBLRPXsxTJqI3CYT/+vvYGxI81OsVpQRLXPIdQP7ofr9yA7HH3o8/2uo2R1m46eJXp3RMET8Kp2ONQEtLciKx5tZ+qIbX40oCcdiKsH6GFu/9rP2fS/mVIkuJ1qwpCqktNeSN0jP7h+DgLjfD7vJjr0V1wh7lgaTU8ZXHUPRS02/g6gCdzrORM3OCBs+9NF+pJFBV9moLxca6KzuOhbPdHPUDXYkCVI7aMnqpv/JSmr5piAfXlqFv1bso91wA0VjTXx+g5BzZPfSc2BNMGFi4NobZf+qYKvk2HUgwva5fpa/En/Kun9lkBMedfL5jdUYbDJ9L7LSYbSNLV/62bcqSPeJFgL1Mda972XNO14KR5lo00umdlcYX00MW1br3r8Ae5YF+fz6aixpMoOusvP1HXVUbQtjSVcYeZuD9sONVG0Ps+zlevYuD9K2r47Ox5vZtTCeHBgNw9IX6zGnKlRsCWF2KjiyNSh6oUNPzteS3fO3/5+1ZWixZfy+Qoe3Jsqat72seLUeVYWSk8x0nWAiFlVZ/JyHPUuC5A/RM+BSO1tn+9g5P0BqBy3dz7CQ3evnpT//V/iHHP9ZcBbCWV9A5Wbhhm7PhbrdYM2CH2aA0Ym+/1VM61PF4m0WDjRwzlMHeylMU7n0HQs3nC5cDjbsirsfDO0uEe16HnjLKNUNZ+xDHXE33Es/XyQqv1eeJPyK562BZ64XJHPFFjHWmH6iklntEq4QICqmqXbYcUDYlRl0cXIciYpYaI1iZFXphTx9dinDhykY185kd8G1+IztaFvcCeussxPPv+gkQEL2VnFMt3pWlSZz2gjRENgcC9eJx/wXHA+XPATnHCOOtyYxth2AQ3UKC7On8d0qldqdEl0KutAhexQDbTIK0KuTqEq/3KBoMRngwcuFa8P1p4mmvzapotp6/yXCDaI5nr8Rrn2SpvfCYoS3psK014X12pWPx+UJ9/5HxjWhI5t3v8rAdtXkuatxtpvMR+/bSU8WFfzmWLwRjukX/3vVNhg7EL5eJpObHm+8a0RJg9fyZQ83xI4rcPf58HUDOd9UBgOL4auG5Lt1O8Q2E4cLS7whXYU8pbwadh+CN+8U7+v6neK88jNFQ+Yn00TcdSgM/74F1q2r5DzjTNIW3C++fbudI5rrljwldmSww9lfQ87fjyBHNmxqIsaNCH72JfrTJxA7WC48hKc9iFpdjeG8swjNbshBVxRUrwfTNZfje/ZFCIdRCtujP+E4YntEAxyBQIIeRrbb/tCqcdO4ZjPyb6jG/lJoizoS+XExwY8/h1gMNRLF8cnbyKk/rV2W9HokfetSgL8TJFlCb42T0EZoDDIGm6i8mVJkBky2segZN2oMdGa5iRgDlJxsJuiOMf8RF+2GGzFYZWbdUoMag7QiLcfck0TxiRGCbiEPsKSJCuXhGmFbpoYTn0xh9t01LP93PYOutKE1SkQjkNFFi6KTqNkhGPr2uX62f+fH6JCJhVVS2iVxYE2IpFwN5RtDnPhECjpzYpUyHIhRuTWMa18Ee46GJc+5m4gxwI7vA8IPWEK4QqTKVGxu6b3b6ORwOLyVUTZ/mWiDFotA1Y4IqOCtilG9I8K6D71Ul4rzKFsUpP1II2376Nm7PIiKyuo361nwmAs1KkJExj+RQm7/RIIarI+y+UvRLDjyDgcLHnVRWybG9ByK8sVN1Zz+nzQ+v6Ea94FowzYqe5YE2LvssC8ioGJziL0rghSNNTP/0Tp6TLJiSpJZ/5EXexvNfyVV8Jei7McAi2c2ixJ/24PBLpPcXoMpWabbRDNr3/OiNdbjPhCltixCbVmEfauCnPiUk6yufw2LuH/I8Z8JS7r4iYZh6UwoXxVv0vNWwNw7KTzWyqJzZbaZjsfk3U7nLD/+7Ys4feAUHnjTydnHwMzrowQjEjv3y+yvgpUV+Qwe9zzblqpNxLgRK7aIiOoP5wuy7LSJKvSaUkFEQVQk/3We+L1NqkjFe/c74aF7xkhhh3bjM/ExPQFxfyorlxj3UAdOGnw9w3tcy50vanF5RODEk5O/oXP507B3CaSXgCmVqMbCPsMATj/egSVZhGhUtvTkxxsQlewXp0DPQjAZBUkuyhfa2He+FTZwOekw9hao94mbuKJIPHKFhE4L/YpFgtzU80Voh8srNLRJDQ4R1z4FLo9oLLzjHKHPraiL9we1z4a1pXFiDGK7V2fBReOExdzhyYIfLxDnfve7TlLsTj6ZLmQjt54ldLyHo7UshbXbxT6fuAZmvCEa5Lrkw9PXwQUz4gEwY/uLyva59wuHjwuOE0l863cKLToI+7z8LJHg5/LCoMmiubERF40VrhaPvSuuxZSTahiY40GryUGrEfKTCanz0bw/Nb7R6ldEs50pBXxVEHDB5o/+luRYai3/WquFSATJbIZoFLW6GoDo1lI03UoIHTgo9Mllewlt2Iz5ZhG0Ed1/gMAb76IpaWYr1kgONRo0vXu0vr+/OGSTCdPlF6MbNQy1uhYlLwclp+2ffVj/M3DkaOh7gZV5D8W7Yx1tRaBFI3zVMbbM8jH4Gjt6q4Q9S5MQdqEzN0Q0q8L668en4kTGnqWhcmuYJc/XU7dHkDdLutA15/RtSVqyuuk57d9p+GpjmJJkjI7Ez6TWIJHSQUvVtjCo4K+N0e9iK6Vz/eT012NJV+icZ8KenUhFwsEYq9/0sOAxFxqDxNAb7UiyiiSToIn2VkfRGsS57VsZpMPRJmrLEptm2vZtfVKlNUlojS2rlBpd/F5uSVWaiHEjtn/nZ+BkG+1HGfDXRJn/cPy9CHlVvrqthjPfTmsiqDW7wgTcMVz7xQ0+FqWJGDciGoLa3ZEmYgzivTiwNkhyvoaq0kTSn5SjZes3fqpKw5idCqhCW27L0ODaH/nTyLG/LsrWb3wtlu9bGSS9i3DrCHpUDm0OsXN+gO5nWCjfIL4MfdViMpLV9f/6qFvHP+T4r4CaHVCzDbZ+0fI19z7arn6KtkfVwpr7wXEnjk0PMiP9Oy696EpUoGDls3zV7mNK92WxdDM88zHMe1KPvxWyZdAJze4B8R2NLIsmvIXNNKgHq4VDBMClJ8DNz9H0WP/hd0QVtyhPyAB6dRRjSJIY56MF0KuzwtVPxW+S362CqW9Yee36yRhyjwJPBXtDbXlqQRFPfW7CaoJ7LoTxA0GrNFiTaYVNWuk+sBqFPvfuV+HkISItLxgWEoJ2beC5G4W8Y39lPDkPRIX50x+Eg0Qj2qSKn0asLhVuD41FuW17hc/z+MFC1zvtYhGFnZ0qbNJAkHirSZDWXQeFS0hrxDYjWaQDThwhwlQ8AXF+yzcL0vzdqvi6dkuiTGRYD1Ht7d0JPpov7N7euFO8L8lWcZw79sfXP2lovNIP8NSHogL87WPiHDWK0EoX5YLFBIs2JBJjEI2APTvA0M613ND1E3TLHkHapEKfy6HDcZCcj2bH1y1PdPd8yO4L22aJv+v2tFznbwBNj64oJcVEmwVmGM87Szg8eL2o4fgXXHjxUiwP309k9Vp8jz6F+dYbMF9xMYF33ie8aBm6UcPRn3oi3juFd7TxqskEP5+F5HRivv0m1NZ0Nv8jkLRatMV/fS/hvyJ0Rpn2w42YUhQqNoUwpylk99Q3xQUDmBwykiLxw2Mu+pxvxV8TYegNDr6bUUssIu7Vik5Cb5HwVcWZpiRDVncdFZvDTcQYRGVzy1c+UjtqW3WQaIwLbg3OAh1j7k1i34og4WCM9M56LKkynopYk81cVteWoRc1OyPsWRZk5O0OzKkKpXN8hP0w4hYH9ZVRZEmivjxCSqG2ifR7K2PEoio9JllY974HrUnokbO6tZRUuMsjBNxRep9vZc5dtfFzSZJR1bgk5fCCh1gImd21+KpVtEaJk2em4K+NcWhTiPUfevFURPHXxrCmQ/WuMO+dX4E1U6Hj0Sb2Lgui0YsK8+G+yMbDNNgH1gZx5GjIKNaxb0WwqfrvLNTgyNUQ9onwkD1LA8gKIImYa4P9jw2DCXqi+GqEVv1I73Mjdi7wk1ygZce8xN4jW5Yg8LsWBtm5wEf7kSYyinXUlyfex36Jt/X/FX4XOZYk6SFgHBACdgDnq6pa1/DarcCFQBS4WlXVb4440N8d0SC49wnbK1914ms6M0gK2zMu5Id2k9i7NZ2BvUfTr3wqHZafBzoLKwYv4OnPM1m7HYZ2F0ESlz4sgiCOHwhfLooPd/lJIvRj5Vbxd2E2fL+KFli3HU4ZKiqyh38PvzkHnrlOEOp/vRxffsYo8bje17Inj49/gEOXO8jN7AG1O3l7dkce+UhUO6pdwp3CYoTLH41rXaeeJ6rYkSjc8ZKQUmSlipCQrxvkAlvKhAb6vXtoskZrDpdXeACD0GjPXSH+Hdpd6Jl3H2zp3uDyiIrt9v1iMnD6SEH8O+YIzbHTJs4xpgqHjTYp4hiz0+Kx2LIM91wknC4aUe0SDY8PvCmq75lOQZC7tYdbzoJte0RYSWG2uOZFeSIpsWOOIOdajZB+OO2QFxYTgx37RSNfaymLHy0QFfaJI1q+pmtFVibLYDXDeb3noP/ogvgLX14J2pchOR8yWvG5cxZCRbMEtZLTWq7zN4CSlYX1mccIL1hIdNMWNF1LkPNziWzcDKEQst0mKskNJNlz573YXnsR1BiSJBF49yNiNS50I4aiPfZoqKrG+vxTSA47MZcL2WZFzskmsmUbktEIQwb+yWf8+6FGo4RXrCaybAWYTGh69UCtriayYRNKXg7avr1QMjP/8P3WlIWo2BjG746RnKchs5se3a/0mP2zEFNFpG/uAD16h0xKYeJ/ZpNTw7Ab7Xx6bTXLX6mncJSRovFaJr6SirciiiNPg2tPBKNDTmiKM9hlESm8t+XEq6o0TDgQ+2X2aocho1iPGoPVb9az/gMfBUcZ6DHJSsFRRw4BCtbHQFJZ9KybSCBGp+PM2NtIRMOw6VMfnoooSfkaOo8zMfQGOxs/9WFOlWnTQ8+Gjz2c+nLqEeUFVTvCfHxFJa59UUbcZueEx5LZuyKE2SmT0UXHZ9fHv4NjEZXkAg01O+PXZNBVNrZ+7SOzq4HKLSFWvekhFhEuHkOutbPy9XpMTnGd9q8M4q2K4a2K0XWCRN8LrWyd7RM2dw/EH5P2mGQmtaOWTscZ2TJL6IsPbQrT40wLW7720eUkMxq9hDlNweyUWfpyPc4CDbICznZaAvUx0jvrCAejJOf+cc3RFVtCzL2/lv2rQiQXaBh+k4OkAg2ONi33EXDHWPJCPSNvc7DxU2+T9MfokMkdYMBTGaW2LIzJqWHV6x7GTEvi23viE5P0Ym2Lz/Kfid9bOZ4D3KqqakSSpAeAW4GbJUkqAk4HioEs4FtJkjqo6uFmLv8AgKQCiISg+zlwaL2QWQCkd4Wgi7ITlzH+/qymBD3oy0uXPMv51sGUDn6fr0p7kOkUFYFOuYJEef2CkF5wnNDK1riFbjUzBc5tiH0e1gMCQaEjPhyBEPTqACmt9L8kWwUp/M9hRcS3v4UP7hVV3UacPlIckyyLFL/sVBNuUxf+3UoBcuVWUW09WC0I6/1viGCS656Ka4w75SQSchDE9FCNkEQcjlOGCu3w6m3C3WJLQ1Hz0XdFWtzQHi23cftEg9w7cwWhfaZBH7zgKXFc730nKvATR4gKcqNW+GOnsHU7VCsa2Oo84n0w6ERa3dVPiHjml6aI9QYUC/eIkgIwGsTE4oyRwi3iUK1I6duyB+65ADZ64OkPhezkjnNFeuFz10c57W4FtxcyW8kuaJMidNWtoThf2OA197WeNAr6dQb9/LdbbrD+HSg6GQqPhZSZUNUwuzI5oeQM+OIKsOfAsDsgvxU2/jeBZDQQnDUH3bDBhNesI3jXNAynT8A+90uiO3bimPUx/ldeI7phE4ZJE4msWo2mpJjgt/MIvvsBGAzobruJwDMvoBt+FN57pqPt3xfd8WNAVYlVVKEp7oyc/r/ptgAQWr2W6OYtxGrqUNq2wXXKmU2PTaTUVCz3T0VOcUI4QvD7H9AfM+oPDeeoKQsx9746yhbHm86OvT+ZorEtnYH+aqgpEw15S1+sB1Uc+6g7kug6wZygCc4baOSM19Ko3hnGkqGwf2WQZS/ViTaB08yUnGJizP1OfNUR2o80sn2un2B9DFmBtE46ds5PrHDkDf6FvsOtoHxjkG+m1iDLEv7aGGve8XJwfYgJz6cesRLpPhSmy4kWcvpGmuQPeqvMl1NqiARExbV2V4Rv7qyl81gTeYP0VO8KM+uWGmRFuEMcSVqw5Usvrn1Rik4wsWeJkGIoGgm9TebrO2sZdUcSrv3C0i4pR0NmVx27Fgao3hEho4uuydnDXxNjxX/iEg5PRZR1H3k5bkYyllRxXv7aOOWZPbWOwtEGOh9vxpYpM/HlVOr2RbCmK2R21WGwKQy70UGH0SYqt4Wxt1GIRaDfBTY0BgAJrQlc+yP0PMOCpIhI794lVrRGCY0BMoqNv8vmrTl8NVG+mFLdNDGo2Rnhs+ur6XuhFWumhvQiLZs+91K3N0rRWBNZ3XWYkmRc+8OMvM0hpDsxMDkVyhb5kWSJjC46Vr0prtmhTSGOf9DJ7sUBnPlasnrojhi08mfgd5FjVVVnN/tzCTCh4ffxwDuqqgaBXZIkbQf6Aot/z/7+v4XeCsc9AevehlH3i9KpNRPMaWBysmZnZypdEv86T1QNZAl2B/L5YdQObpipZ+U2UV28eZJwWfAHRVDHpl3w71niR6eDGZfA2h1Cr/rKLBEHPeFf8NhVoqL8/GfiO2pQF8jJEDfeTWXxCmUjLj8RNuwUh6nXwSlHCYeHXQeFw0VhWyFLMBsEyXunoe/o7ldEg1dqknBGKN2XeBkynYKYNiIYEhKLRk/mZ87bQYoxhRS7vYU2WavAzE+ELvmFz4Qe+JJx0L6taFis98aJcSPueRUW9YWbJsFDbzWMo4GrTxGOEZedKDTF4ahIjttfnShd+GSh0O9W14lo8Pe+h8feExKGRk3xt48LXfP42wQZ/nC+iJse3BUefBsiUZWFTwTIzRZVFJNRkOnTpoox0pOhbbqoZOu0goj/sE5c5xEpP7Ls8jJKpYFY0tLJTjWzr1LcGA064UqRfQQOleoQqYTfLBN6896dhPNJuzaIz93hMKUI7+aUDnDuHDi0Tjx7TCsGZ3vIGQDIYEltue3fCNrizpguPhf3RVcgJSdjfeJBwgsW4bvvAQynTyA45zuUNlnoTz8VSY0hezx4p96Ppmd3zFNvRXvUIMJbSgkvWIhu6GCUjh3QDuyH9467m/ZhvHoyppuu+RPP8rcjtHotvumPEHz/I3QnjkOtrU3QE6mVlajuelSPh+DHXyBZLWhyslEG/3FV8srN4SZiDELDuuBRFxkluj+06vbfQM2uCMteFsQYxLF//0AdbXro0BgkyjeG2bcqiD1LoWCIgc7Hmdn0uZeFTzihXPUAACAASURBVMR1xStf82B2KvS90AboyelroPp8K8H6GL6aKO4DEYrHm9j8pQ81BkVjTbQbZkRr+PWV9aAnivtglLwBRmp2h8kbbCBQF2PdB162fO3DfSBCp2PNCWl2QU8ESZX4ckp1k744KVfDgMm2JmLciPryKGkdtfhqY2yf5wdVWJ2ldtQmHIOnMobBKmFKVti7Qrz3STkadBaJA2uEZEFvk/AcivL9A3X0nGRh0TNuUjtpaT/MwI7vA9izFTZ85KXXORaiIVBpqbmo2hpuIvyuA2Gc7bVNDYMApXMC2LO0HFyr0m6EAXsbDd7KaAPxlrGkaegwWkOH0Ue+pint/vuNqeFAjENbQgkV87Z99bTpoSelgxbX/gh1eyRCPpWd8/2UzvFz9N1JDLjczsInXRSPF0mA5RtDWJwKepuCuzxCLAphn7gYKYU6CkeaKBxp+q+fz2/BH6k5vgB4t+H3Ngiy3Ih9Dcv+wZGQUiiqbv4aQZY18f8Avs1w+9lCWtAoWZgyCc59wEBZgw3Z1j2iqeyy8fDgW/D4e6JC2b9YuC/4g6JamZYkCNc7d4kI5kgErnpMaFqnnieqvG6vcDywGkUoxklHCQ2w2yua2uwWUUEtKRD64xc+ExZhxfmAJKq/L9wk5Bqnx7/TiUaFo8NtZwny/ON6cVwg5AO5GYmNask26NFejDe6xMU5kStwlY/mhtOu55bn47PjgV1ElXbdDrjiMTi6D/TLEBXlcTcLR4mqVhr9fEHwBsW1PXGwkFHsPCA023sOiQnHtafCUd1jDMnbz/jp2TQ3pI3F4KslIn45Uies0yLRRP3xqg1+Lst7jUXX5bJH24eJM5yoanMpi8TGFdvJ1aiQIToRNpfFr8MNp8Fzn8KSBtXCN8uEo0g4AiGM5C86h3wARcsX1yxkRW0J/oieonyZ/j8T/JWfKT4vjah2C412tzYXkLf2dQiLx3to9OKphq7hJuZoK36aw/IbQyH+P0G0qorwkuVEVq9DO2Io9s/eA8A14Szwi+sY/n4Bloem4Xv2ReR5P6Dp3ZPAzBfF9qXbCQLmGfeg7dEdOS9XeBpfcDae2+9O2Jf/yZkYzz4Dsn95+MXPYY1nE5t821EkmSJTISXmXxcL/UsR3bhZJAIC+lHD8T31XMt1tu8k8OEnaPv1Qdu9K567pmP/+C0Uq/UPOYaAu2UKjqciSsjbSjrOL0AsEsZ9cCdqLIw1vQCN4b/3Ze+vibYI07BlKYT8MWp2R1n8rJvqBkKz4rV6Jr2Zxo4F/hbjbPvWT5cTzehtMt6qKFoDJOXq2D7XT+XWCPmD9RSONoqo4LYKybm/raLnqYiyb2UAg00hvUiHa1+EoFfEUhsdMt4KmV0/ClKbXiT2UV8RY/m/PQmNd7VlETSGlhVRrVEiFhXuDSNudaC3yNgyFbR6QeQrt4X49j4hC7BmKIy5N4lOx5rYvypENARag0xyvpY179YR9MQoOcXM+o+87FgQYOgNdmJR0fDoqajHUyEufNivorfKqLGWx+Nsp2nyiq4qjVC+McSYe5NY8ZqHgCtGx6ON+GqjZHTREQ3C+xc1VH4kOHpqEiWnmJGkn6/87l8dpGp7GK1BIr1Yi7Pgj6u4hoMxVr/tIeiOoTVJhH0qfc63UlUaZsnzbjR6ia6nmilbFKR6R5gh1zqY/3Adi55xc9Z7qYy42UHVjhDGZIXUjmaCnhjEoOLtEFsbJCNpHTXkHKFR8q+CnyXHkiR9C2S08tLtqqp+2rDO7UAEeLNxs1bWb03ajiRJlwCXAOTk5PyCQ/7/GLIM5paWRj06wHVPJmp5tRqaiHEj3F6xvBHLNgtpRfs2opK8ulRYefXq0JBuli8qmAvXxf11n7gGHnknbg13XH+oqIG35wrHiKP7CPK2twI+mw6n3Bl3TNi4Cyb+C5Y9L6qd4VZENGXlUOkS53Jzw9NUWRbyA4tRSDYq6wQhfvgKOHuacM44NqcU00ffYNr3LeOGdqPNbUPYVaEnwxFDq5WbqtChMLhcIZRkL0hJjO4tIpQzksX4nmbfE6cOE3IEq0lMIvIzYcKd8XPfugdcXpUC4250c25Flt85/HSQJDBohXyhQ1sh72iOLO1uzN9eQe9YlE69b2Jol3uYtz5R66CJemDWbXDmZ6C3ktSMA+i0cWLciK+XCkK+t7wH5xSMQbvza4iGKZndj5LiCXDyq0Kr/ivx1hxhVZfq6Md/zl9IV80PpFqCaNr2gtyjfvV4fxeowSDeBx7D/7BIgDQHg6DXIxn0TcS4Eb6nnsN8+03UX3o1+hOOazGWnOIkVlsrGvl8PuS8nCaNcnPEaluZ7f1GLHGv4fzSm3BHxePODG0qL7SfRi9rK/ry34mYO17BVI16DKefgvfu6QnrKHk5qBWVhD6fhWSxoNbVEdtVhvIHWcwl5WlbuB7kDtS3cEz4JfC7Ktn8yeNs+OAhYpEwuYNPofcFD2LNLPhDjvVwOHLjzhMFQw0UjjJQXx7jg4uriIZVisaZyeymZ8PHXrwVMaq2h8kfbCBUr1K2JNDUaOZoqxCJxNj8ro/1H3rRGCSKxppo20ePySmz9Rs/jhwNHUYafxMxrtgSYvdi4V9buTXC3mXCNimts5aCIUbsbRVWv+1h/0pRBYgGQdZBansdagQ8lS2/PAL1Mfqcb2H5K3EpQ9+LrERCMWyZGhbPrCeji46icSas6UJy8NXtNU32bvXlUT6cXMWZb6ZTPN6ExiBxYH2Q/EEGUIVNW0aJysDLbcQikNlNR3ZPAwF3lMHX2Fj8rJtoGCq3hhkw2cbuxQG6nmpm3QdeUMFgkxlytZ0D64KkFmrxVAhphs4q0/MsM5VbwxxYE6RgmJEts3wUjjLS7xIrS18QTwLmPVRHepGW9KKfJo27F/n55KpqIkFBqZLyNIx72ElaJx0BdxTX/ghao0Ry3i9/36pKw1RuD4EKerOEJV3Gma9Bb7GhqmBwSCx/VfhBR4Iqq97wMPhqO7sWBtg220f+EAOHNoZAlcnqriWre/wcAu4YtWVhBl1hp/tpUTQGiZRCLda0v7YfxM8enaqqo37qdUmSzgXGAiNVtam3cx/QvLSUDRw4wvgvAC8A9O7du1UC/XdHZjKUHUpcpqqCOIUO+95sTo5Tk0RD2uKNIuluTD8Y0Oz7JdkGj14pfH+/WSq8eheui5NDEAEjU88XkcTBsCCRH8wTEojSfXFi3IiKWkEq26aLxjJJSuz4HdMPvlspqtfZqWL8jGRRCR/UBZa9IKzHYjGorIUHLxOV2IN0IDNzANqDi+n0/Wg6OfIIJ3VkiXwXw6b35+g+8OhVMC53GbmlD6JUrCPimMCZF09iv9yFhevg2RsEAdx5AEb1hguPCZCtq4CgE/Rm0pPh9Tth/mpYtxP6dIT+bfaQ93Z7kGQmH+fl2xXxQBaNIiYLqfYYUs027jsni2NutTVVfbsXhBign92UemhZ+TAPTTyLPuvjXjVdckN0Ub+FXfPAcwj0Vnp2gJOPEg11rfk5A+g08NliDUMuep8OWffC7nnQZaLQBf8GYrx1bzwZsbIOjnusJxZjT164CU5r96uH+1shsm07/kefii/Q6Qi89R7GS85vsa6k0YhmOkDJz0t4TTugH5LVihoM4b1rGrb330BOS0POaUtsz974GHY7SkHitr8VMTXGm5WfNBFjgPJwJd/W/fhfIcfaLkWC8Ho8qPsOEj1Qjun6qwi8+yGSw47xnEkE3nqvaf3gR59imX43kuWP0wNnddNx3AMictlzKEreQD2DG5LOfi0qNi5k3TtxrVXZwg9JyutK9zP/1WJdNRajavtKXHu3oLcmk1LYG2NSOgF3NZ7yXeisydh+hlSnF2k55t5k1rxTjyVdwVMRY9Ez8ZvE+g+99DnfisEm06aXjrrdUdZ94EFnkTnqOgfrPvDgrYrS5SQzZQuDfD8jPskqXx9i9F1JdD7eRPvhR65+q6rKwXUhKraEkGSJ5AINtiwNvqpog1OCyqfXVeGrjjHoCluCT2/F5jCZJToySrRNxBhg8XNubFkiKc6Ro9DpWCNr3on7kEoaSM7VkNlFR4djjHgqYoR9IpZ690I/Gz8Vk9CDa0PsnOfn5OdSiIVo4Xsci0DVzjCj7kzCfSBMzW4NGh3kD9aza2GQ8vUhyteHcOQqdJtoxlsdZfeiADW7w4y+KwlblrDNk2QJjVEi4IrRbpiRoDtG3b4IX91RQ7BeZfjNdrJ66HAfjLL5cy85/QykFGoJ1assf6WekEclp5+B5Lz4F3bIq+Kt/umnF35XlKUv1jcRYxAWcPtXBYnFVNZ/6GXzlz6MDrnBbs6E3vzTcpgDa4O8f1Flk+tHRomW4hPMzJoSb0xM7agV/sTNUg7dByKYkmUOrAnR7xIr+YMNTVrr5jDYZDJL/tpV4tbwe90qxgA3A0NVVW1ubvcZ8JYkSY8iGvIKgWW/Z19/Z9gtcOboxEa0D+YJP97my847VqSeAXRtJ6qkjeR5TSn07dxy7F4doXt7YVMmyzD48pbrBIKCpIYi8HBDr9bEEcLbt9EHuBGKImKHP5wHg7qKcIlrnxKkeVgP0YB21ePCpSHJCsN7Cm3wqN4NA7jgthfgh7VCC71+J3zyA7TLsnH1KT8wJPcl2i65DOp2EzG25cP1Ills9nK4oP82CmYfLbx2Ac3C6aTVbif92MdIPaoNizfA2UcLu9hjs5ejX3Q/7PoOsnrDiLsgbwh5GZB3bLOTf+9Wwe7VKEeXncXHd7/FG98b0WskThwCA4piSJs/gg/PZpDezpJrn2eTPAyzUaFb1ZO0XX1HfCxVJdvu4eWbIyxY7qdndjXHpHxH1sq7IKUjGET3o9MOT14Lk0aLyUWvjnF3ERBOF5GosJL71+sWJo9/gHbdwjiTtBh/4z0oGEy0wQPx+XF5Wl//H8ShHhbQEZ6/EG2v7ih5uYLs1scTuAwXnkNo8VJ0Y8cQXrcBy7SpqD4/6PWogQCS0UB44WIMZ56GJIH30SewzLgH32NPE1m+EqW4M7bnnkTT7o+pTIbVCDsDe1ss3xXc18ravx/aPr2wv/86nil3EPz0CzSdOxL45lssM+5GRaL+kqsSqu1Kfh6ant1Qf8Gj5l8KjV6m87FmMrvoCHpi2NtomgI0fi3K181rsWzX/LcpOvFadGZbwvK9y75k3rRTiEXETTmzx2h6nDuNpc9eTvW2FRjsqfS99HHyhpyKfIQ4dp1RodMYE9ZMmdn/qm21u79sSYDcAXrSi3QJjggH14U44dFkzCkKKR20LHhMPNJ3ttdgsMkcXBfi4NoQBUMMP+ncsW9FkA8uqWzqG7ekKwybYmf+Qy4C7phoaNsbJaVQy8H1LQ3dD20Kt7QbU0VlN+iKYXIqdD3VgqpCfUWYHqdZkWSJBY+60BolkvK0rHlb3Ji0JonBV9nZNjseme3aH6VqW4SUDlr0NomgO7HmJklCTuEs0OMsAG91hEFX28nqGWD7XD85/fQUn2Am4IpR+p0fCcjuYSAahqAryo7vI+xcEGBLQ2rdsCl25j2YWCX68Wk3Ay634d4fpfTbAKXfBuh5toXyjSFCHhVZI1o4UOOfa1umgjX9pz+HAZeoDB8O98Eovjp/E3kNeaJ8dXstJycrFAw5sitIOBBj8XPuhFTBjCI9i59LrMpUbg3TYVTiOCanQtAdI61IS2aJTgSy/H+E31vXfhrQA3MadDJLVFW9TFXVjZIkvQdsQsgtrvjHqeL3oW8RnDtGyBtMejh5qGhUe3uqaDhr3wb8IRHycNbRQkLxxPvx7QvbiiS44T3jy2rcokprNQlJQTgiKrv//jJx3x1zhB51+htxAmXSC9J66QmiEa4RN58pXCYiUZEyl5cpqtOby4S/7+bdgvRpFLG/LGeiy8SiDYIYD+kqIpQ/XRg/1osfUnjllovI7reRcFInXi8dw1PvxmUoQ9NXw9rEm5S06UPoO5m2BW1oO0LIOBTvAXj1PKhs8H7b9R28swHO+zbRqsxTIWzJ9i0BTzmmXZ9yQpmN0ce+TKDjaSQlGaBqO3x0DkQCSJEAJUtOpERjgIt/hGXPJJbNnYWkFbTjvEIX5xmegsVPwO460Bph3MwESU2mU2i9QVi6vTsX5qyAEb1Eo+L9r4vXVm0TDX53nqtlwRpR5e/TyiTo51CYLdwqmkdOG/VCevMPfhqadgVo+vUhsnQ5AKHv5qE/7RSk1BRsb7xE8LNZqJVVaIcMROlUiNKmDfKEk4ls2Ejwg4+JrFqLdvRwTFdcSqyuDtmZTGjFKnyPPYPxthtR2uXhmP0pamU1crID2en8w45dL+s4IXkka7yJPojD7f+dEBfJaERTUozSszuG48cQc7mIlu0lvHQlBAJoSoqFrRuAVov5jimoHh/UuX564N8AR9vf33yXlNcysSC184AWumP3gR2s/PeUJmIMcHD1HPKHnk71NnG+AVclPzx8NqaUbDJKflrGpNFIeCtj5A1sSabsbTV0Ot6Y0IQHgCp0sB1Gm4mEYqS019DnHCt+d4y6PRFKJpgxOeWGBjU/1gyFzG56zMnxfUSCMZa+5G4ixiB8kCs2hSk6wcTSF+oJecRE0bU/QpeTTGz9OlFa1Lavjl0/JrphKFpBDvU2QZrTOuroNtHMytc9HFgTZvVbHgLuGIOusiWEloR9KqverKfjGBMbPk5MvDLaJPpeaOOHx11Nos7CUUbCfuGHLCsSvtoorv1RanaGcWRrOG56MvZsLXuWBKjaEcKZr2HB4y5cewV9SemoZdAVtiZibM0Qx5zRRdcUZgGiChx0qyQ1qwyvfcfDkGvtbPjYR8kpZnYv9jdJeRxtNQy/2Y6zIPEzGQmr1O4ME6iPEQmq7F7sp3CUkZWvJVYtMkp0fDdd2KI5CzR0PdWMyakQCap4KqOtVnRB+CLX7EqsruttEv66lhXs5l9laZ20hLwxdGaZHqdbqN0T/smnDf+L+L1uFe1/4rVpwLTfM/4/iMNhFprga08VTWyvzhLkpVG6oNfBtNdEU9mUScL/F8RrpwwV/rtpSfHx1u8QCWurtonK9JPXwIShokK9bLNwo5AkoVWes0I0kN1+jnB48AUESe6cC5t2w70XiWWNmuF1O8Q+zrsf3rtbOF1Me00s+2GdcMbISILz+6zD7lqDvL0CfD2hTR92HhCC25G944/5GxEIwbZ9ClXjnyTFDu0M8Vjlolxw2FrRWGkMNL+TKwoisrvyMFNkbwVUbRbkuGYXrPmP8O4tGAHFp4LeBmEvLH0GY1oOxqQG3XD9wXjjWiMiAVG9PvsLmHM77FkIBSNhxD1gbWhcG3gttBsNgVpIbg+pnY785qtCztKrk7Cke/yTxJd9AfHz1VJYuhmWPi/8mH8NjAa4bqKoWn8wT2x/8yThsPEPfhpychL2V5/D98RMgp/PQjt4IOZbrkfbvSuqqqIp6YLq8SCnJDd59kYrK1FratAdPwb9xFPQdCok8PHnBJ59oWlc3dgxKBnp6Do2NMc5WvFVRFR/fouTQCNGOQZxMFTJ6xUfo5U1XJJ+BgOtPX9+w98IOcUJkQiRDZvwPvwExksvQDdkIK6TJ2E4/2z0x4+BSASlY3ukzHSCn83Ces8dPz/wn4DMbsNxFvamulQQXIMjjaLx1+Ct3Idr/1b2L/+KurKNlEy8GfeB7S22D3lqE/5WYzHqy3eht4kJkC2rEEXb8r5mb6slKU9BZ5GxZihNEck6i0TPSRYUHegsLT8TGoNE5bYgyflaik4wUzrb3xDlK3FUrp2K8ggLn4qTyY5jjIy6PQljkiBXkSAJKW6N8NfFSMoVdEKSaWrk0hkl8ocY2PWDIMPpRVrsbTQMukLPnLvr8FREMdhk+k+2kVGiRdGKSmrQG2XBYy52LwzS/1JbUxNltGWSMq59UTodFz9XZ4EGe7bC4ufcpHXWMfgqO5GAiqKDiq0hdCYZ14EwnooYIa/KnLtqm5rt9DaJEx5N4cDaEHq7xN7lwSZiDMKNonp7mJIJZnL76ynfEGbZS/UkF2gYdpODH592EfarpBdrqdkZxtkuTnZjMaG5LvQY8FZG6H2uFaND4pTnUjCnyjgLdAk2bJ7qMJs+9fPD4y4GX23nx6ddxCJw3Ixkik8wsXmWD51ZZsDlVpztFIZPcaBKEHTFcO2LsuBRF9GwIPAnPO4ks0vLyq7RoVA83pwgzSlbHKTTsSY2fxF/lChrhBxpxG0OTMkyWqNE+YYwxePNfP9AHcdO/+OsFv8q+Gsrov9BE7rkCzux658WFddUh7Dquv1FQYwURbzerzOYjcJ9YfZyoRX+frXQHc9/Uozl9grP3VXbxN8uj/A+bpsG424RJPnEIcIy7lCNWL+8Bp78QOxTafBTXrFVEPTnPxVk6oqTxfE1os4DBr14/H/xOHjxc0Hsv1wYZvlt32OafRscWBnfYOwzDCq5vOkYbeaWj/WtJmERJ0kwshcsfk5UldOTQRfrIazFmgdS9L4ErIcxRa0RZKVJCxxfboZIGBY+ABs/gP5XwefNdCaOPDh3NrTt1+yAMkFrgnAzTYLWCMYkcO0RGuBBN0JWD7GsEQYH5A468hvegFgM1myHM+8FuxkeuVJc88MlEI1PnWvcYmL0a8kxQNf28EDD58xubt3j+h+0Dk2njlifehjz3bcj221IDVHPkiShyc9tsb6Smooy4USiFRWg0RAr20vg1TearaBgumoyup7dj7jP2r0hDm2MULc3jCVVIaOLjpT2v755qp0xl9vbXs5pKccjSRKFxjwU6b8XTS3p9VhuuxH3lDuw3Hkz3vsfJjx/IZZHpuN7ciZqbR2GS85H40gisnw15isvRbbZfn7gPwHWzAJG3vUZdbs3gKygt6YQi4bZteBdNn38KIE6kQqUO/gU2vQew76liSmoppREE6eMbsNBgv0rviYa8mNKycZZ2IvkvET9t8EmM+Y+JyvfcFN8oqkhvUxUMEP+GDW7onQ82sTBdaEmoqu3ikaojy6vpsckCwabzPqPRLW13XAj/roYq97wJLTOb/3aT7dTLeT0U5r2W3KymfmPiEp+/lA9RceaMSTJhDzC3eCHJ12c+GQKq9/2sPptD8Nvd9BtoplwQEVnkgn7Y+xfHWhKltMYRCy2OTlOR2p2Rti9UDBhuRlLUVp5cp/SUUtaJy0FQw2kddTSfpSRskVBVr/tZeQdWhQd7F8VwmCXyekjwijq9shs+syDOVXTRIwBgm6Vym0hwr4Y9mwt2za1bIZVdKBoVTZ97mvyg67dHWH/yhA9zrQIacpRBpa+7CYpP37wJSebSW6nwZyqQWeUsGY0vNaKMUzVjhDVO8IseNSFJIsKeSwiAjVWv+Wh/Qg9E/+dSiQoKuDrP/BRsSVMTn89kiSx/JW4lKu+PMqce2qZ+FIaBlvLCVPxeBO+6ijrPvCiNUnkH6Unu6e40Dvm+bFlaehxhoVwMIa9jYIkS8y+uxZPw4Ss6AQTWV3/Ov7EfxT+Icf/IzAahDZ3WA/RBNcmBe57Pe5gEY3Cm7PhqpOhfbZYXusR3sW56SIGuU9DcbK8BhasFV64E4ZBTrqwe9u8W2zX/NE6CO3vl4sFyZ3xhrBO27RbOGHkZwqSuq9SkOS8jLgvMQjd6vghcKgmRrf2EpGIxMSiTZgOLU4kxgBzbqHfJcfxwGV5zPxUWL7d3MzpqWcH4dLQPNgi0yl+BPLglNeg9GuoLoW0ImjTF9IO0xmkd4V+V8Hix+PLik6G1GJw74GVL0Pvi2HZzMTt6naLJEOakWNnIUx4Az44SxBkrQkmfQxf3SDkGo045XXofha/FmWHRLhKLCa03DfPFNX8xko8iIlBqJkMzXyE4I9fAkVp8Dr+B78akkaDkvbrfJ6VNOEprSQnk/zDbEJz56EGQ+hGDUfbJ7F6G4uqVG0PU1sWwZohU7klwnfTa5sejLQbYWDEFAf27F8vF9DKWjqbj/gg8A+HpqgzjtdeIrJvH45Rw1FdblAUbK+9gJycjJSdhaSC4fhj/s+O6bfClJyJe99WNn3yOJ5DuykccwnRoK+JGANs/uTx/8feeYfHUd7b/zNte18Vq1myJcuSe+8dGwPGoUMIJYRQL0lI4Cb0HkgouaSQSkJCCaGGFrptbLCNu+XeZFvFsqyuXW1v8/vjtVdey4Ahye9ewp7n2UfPvpqdeWfLzJnvnO85jLr0PpLRMM01izE6vIy66C40qwtJVtBTSSRZoXT6ecQD3Wx86naSMXFwz62ezMybn8eWl2mf6B2ocdItHiK+JAabnL57cGB9BItLZu0Tfmb8wCncC0wyFq9M1J+k51CSnW+FGHG+Ne1eYbRLSLJweDgW4WPGqhZYiPhT5FYpJCIyK3/rp6s+QfFYI6c/7KF1Z5yCEQbyh7vprE2QiOoYbBLJeApLjow9XyWvSqNtdxx0Ca83kxiDCPE4Un0+uDHGoLlm9iwKU7skzIQr7Kx/qodkTFRFT7nHTf5QA2VTzKhGiVRS5707RUV+yQPdjDzfyqC5ZlQjdDUmWf0HP/PvcWPxqvgO9K2Cy4rEzrdDzBnlpGiMQVxgHAWjXcHkgJrnMmUrEV8Kd5lKV32c+tVRTn/IS+3SMIWjDAw6yUzFXDM2r4btM1RRkZ4kbbvitNeKH7aeEhcI479lJxnX8QxQSUR11vypB1mRCLQmKZ9ponFtlKLRRnT6foat2+OEOpLHJcfOQo3ZN7kZ9007kiIRD6V48co2TA6ZoWdYCbYmWfJAF9Oud5JToTJgmplvPKPRXZ9ANUt4BqiY7P++i+n/LWTJ8ZcIqir0p0dw+6WCJD+3WBDHH31DEGMQBPKUieJxLJw2sfzFJwu98DPvCd9h9Tjfb6dNNPfddqlwwli7Q8REb9wj/r+/WTwALj1FSDKO4LJTYeduH+cXvUlB/S9JFYyBMechh7v6Vm0BYgFMhLnhAlG5jsZheLkIwMh1wchyGPNZ9quFY0T1DWcQ7gAAIABJREFUONAqKrjHscbDaBNV4eIJ0L4b3GVQOBY8ZdDdIAiuwZZu7MvAsRIKSYLqM+G6GiGxsPWDrn2ZxBjg7e/DgFng/HzetBKZiYNt3fDxVhHksmG3SOIzGsRFC8D8CTD03+MilcW/GdqYUWhjRhGPCA0oe5O4+ktp0tOwOsKBDTEkCQwWAxufDQhiLMHYS2xoZpl9yyM4ChLEwyns/RTyqjQ00//NE5fscWPwuD97wf/j6Ny3mUV3nU4iEgRJIupr7bOM78Autr/yKCO+fjuVp1wJsoyjaDC2/DLm/fhd/Af34CgcRMuOlRxY9VqaGAO07fiYjj1r+pBjAEWTsOZknsaNdhl/a5Tx33Kw+P4uVIMgmrP+28WaJwSha90Vx2iTKR5n5MC6KPs/ijDuMjsFIzLJoHzYJeJo2PNUKuaZCbUmeee23sS6A+ujRIMpznosh0hPkr0fRNj1Toi8KoOoSB9e36kPeFj75560k0ROhcbcO9wUj+0tC3vLVSZeYWf5L/3UrYxQOc/M7JtcyCrkVmlUn2YhHtZxFKppPa3RJm6fhbpE+Ebrzjh6isOuF0Fm3OBk/VM9pBKgGCT2Lgsz5iI7exZlHtONdomRF9jY8EyQSVc76NwnGvCQoOo0M9GA0CwrGiSP6TeMh3QmX+3EXaqimUWcdSqlY7Se+G8w0JIkHtFRDVI6QMRRpFDztyAWj0zb7jj9JxjZtyzCpKsc1C4JUzZVVEREZb1v86pngIrJ9cnSK0WT0hr81p2HI6/bUnTsPerWrU66kdLRT8XR7z+bPv5n791/OAYWwr3fFlpRm1lYu50I8t0iOvlrtwh/YYDXV4io55PGwuKjCrp3XiZS9zYcdku46GThnlCUI+zftu4X41azsGIbVCykExVFIg1u7silFCy5CAD5wGpY+1u44MXDLcPHyBEqTwfNiiz3kvyhAwTh+1xQjX1DKo6FZ6B4HAtXf5h9F6x/HEZ8AzY8kbne/OPYW0mSqCArRqh9D6LH8V8Ld2bu6wmiMEdoxt89yutlyQa4/jzRQLetDvY1wQ8vFI2T00eI9LwsvpzwH0qw4jEf214T35UhCy1M+66TrvpEhrep2SUz5hIb7b+KM+p8G3UrItjyFaw5Cot/LBwKFAPMu9NN6VQT9tzMQ300mETRZHECzuKfQuf+TYIYA+g6zv5D6KjdgNndj3BXrxm9t3wM6x6/Ad+BXThLqiideg7eQeMoHD2PwtEnkUomSMTCtO9YibdiDB216aQgQp2Hjt3sJyK30kDPoQStu+LMvMGFbIBkVGfRA12MvdhO82ahKV73VA8zb3Cx480Q9SsjJBM6E75tZ/3TAQ6si+IoUJh7p7uPI0ZXQ4z23XESEb1PYl3bzjg9rQmiPp2lD3Uz6SoHKx7rPR6mErDox10MP8uWJsfttXH2fRjGW6GmI6pVg0zlyWbyhxiIhXSMdgmDVaJgmDEjJvt4CLTq5FRowuqu5XAj3SANd5maTmdrXBemcp4FSYKJV9jZ8GwAPQkjzrdSOMpI5TyFgTNM6MD4y+1MvMpOMqHjLFDpbkyy+P4uhp9to+a5XvKYV62RP0RDMUjpwBLtU1w/PgkGq4zRJlHzXIjJ1zhY+0QP/oNJDm2NMfkaB+ue7CHvcAJgT0sCV4mKooqmxrrlESrmmkWYycviO2mwScy7243FfWIE3VmiUjFHRIofgaRAbqWGt/w/Tz7xSciS4y85JEn4FX9eBMO9xBhEJ+rvXxe37E8aK7TKYyrFMlecBluHCII2ZhD8929g2z742jS45WLY2XhYE/2zXl/e2y4VDheVLX/ou/GGj8FohXkPwNYXhASi+izIqQJFhWRC/P2c2FQr5B5mI4ysEJKPPgj7oHk9dNWBo1DYuB1bXR7zbXAPgFCH0BRveQ7dU0Fkyh2sbx2BJwqDSw439x2Brxneuh52vApz7hHM5OiyQtWZ4PgMwn4cGDThMPKnm+Avb4vq/nVnC+s7k0FIXKb8a7IRsvg/gP0fRdj2agiLV2byNQ5MDpmO/XG2vBzM8DYNd6eI9qSweGQsOTIdexNUnWbJ6ORPxoSllNmjYM9V8R+M42tKEmgTt20798cZd6mdorHGE0rlyuL4ULTeiqfB6sTXuBOzux/Dz7+Z7sadBFvrKZl8Bl37NuE7sAtP+WjKT7qE9X++mVQ8xtx736J4/Kl01G4g2FpPMhHD4i1i4OyL2P7aLwi2NuAu/Xw/8mBHis0vBPE3Z96hiwVTaGYJxSAx/ftO/M0J8qo0qk4102+4EaNV6JajgRQmp0SwNcXuRWFseQq5lRoGi0ygJUUyJuQHk691sOGvPWm7NM0iYfUqtGyNkIz1rayC0PWqxszvW+d+cbcDXVSXe5oT7HgrzNo/91Aw3MCYS62Y3drx48WOgTVHYff7IQbPN2Owykgy9LQkScWFRGHAdCPVC2w4ClVatsWw5cucPT0H1SiRW6mhGgShTcZh8QNdtG6PY8tTmPVDJwXDFMxuhTEX22hYE2Hqdx34DybJGaTScyjFlldC9BuiUbs4haNQxVmikldtQNU+eeKhrgTBthRGhywqsgUqXQ0xRpxrY9MLAcZ+04azSD08Jx1dJ90guevdMNOvd7Lh2R5m3OBi04sBdrwZYuK37Qw6yUwqqeMt13D3P3GpldGqMOVaB4oGu98P4yhUmHmji5KJRhT1izf+ftmQJcdfUXiOU11cvV1UjytLhFb4igehuUOQzBu/DjNGwlUPi2Y5EA16X5sqtLDN7aJ63ekXFczGFkHcU7bjdIaFWsE7WRw5R39LhFasf0J4/e5+CxpXwcT/EhKJE8SHNXD+XUJ2AIIw/u6/D0daH0EiDqsfg8VHdb6PvwbmPyQiu4/A7IQhZ6Wf6lNu4OPdZk6+zkw4Kgjrn26CC+YcJsjxMDStFsQYYN0f4OQHYf0foXMvjLgQZtwChk/2m/w0DCwUj4VTwWwAyxdbTRZfAtQuDolb4De5qPsowvY3QpRNNR1XCxrxpyieYEzHCR+vk7/nUJJge4KmjRF2vRum5rkAqYTo6B92lpUXr2rjrF/n0G+ERiIo0bY7RiKq4+qv4ipR/ykXjK8C/E17SIQDWHKKCbUfIJWIk4gE2PLCT+g/+UwchYOI9XRQv/wlBs66CFfZMA5t+oB1f/oRelIIfve89ydcpUNprlnMhr/cml73gXVvM/ZbP8Voc5NTOe6TpnBcqEYJzSyqrSfd5hIaXpMIrsirMmBxyyx9xJdhP7bgQQ/VC6zY81Xs+bDt9QDv3N6VThKcdLWdqtMsvHNHZ1qra7BKTLnOydKHxIF3xg1OnIUqjgIFSQFZE2Q3dVRPhL2fQtiXSdr7Dddo2RZj6SM+CoYb6D/ByPqnAsy/z8PBmigbng5SPNZId30SR5GCu//xvamPkP85N7to3hIn6k+lI6VzKlS+8ddcap4P8saNHZROMjH+cju5g/pWQwNtCd6/p4v2PaK6HWhN8tYtnZz/p1yKx5oYdYGdsqlmEpEU8WiK57/ZTvlsE5GuFFtf7rWUG/dNO7FAkrIpwuYs4hOhItvfDOEqVhg4U1juteyIUTjSwORrnBSPM1I8xozRHsOW6yTYlsReIFMwUmPXeyFGfd1G8+Yow8+xsvWVIKv/6GfC5XbsBQqn/cSDJEHDmii73glQdaqZnEFaH0ebWFCk1iFJuPurGI4JCsmrMnDqA16mXpfAYJOw5X71qOJXb4+zAGBoGVxzBvzutd6x//46uB2CDL+zRmiL/7ZIaIp//CT89Ko4/mDmFegbK0UoxYotwu1iYCHccznEwiHsaoB94cup3va08AACoQPuNxLe/A6c8Tik4sJWbeBsQSr1FJRMgr/Mg6tXg/ezm4T8Qfj5i73EGIRf8ootx5Djjt3wwd2ZL177OxjzLaE//gTs7fRwys1CLgIiWOXbD8KoQYc14J17oafpqAk1wfs3Q9UZcPpjUDIF1H/+dpQ3K5f4j0fxWBP2fiqBliTb3xDSioM1UcZcZM8gMgDu/hqtO2PkVmlo5t5buUcjd7CG1avQtDGW1n0CdOxLUL8qQsEII00bYnTsFWlXHz3qw9+cxGCTmH2TC3s/hdKJps+8lf2fis66LXTsWU+spxNHUSXu8lHYcoTmKxGLsuGp22n4+FVGXngnqWScUMdB+o2Yze53/sj+Zb1x81Nv+As73/gV9n4DqV/+UsY2jI4ceg7to+6jFzLG9WSCWKCLYefc+Lnn7a3QGHqWFXd/lQ9+2o2/OYkkw/BzrAycYaRlR7zP92npw92UTBApZ217Yiy+vzsjYnvDMwGMdjmjiS0W1GmqiXLaT92YXAp5gzUkScJbrjL5GgfbXg8y9TtO1vzJT7RHx5avcPI9bra9Jr6Lkiwa/IrHGnnxijaSMSgZL9PTkmLct2wse6Sb0OHUuKYNMYaeYaF0qpFtrwUZe7Edd6k4HyXjOo1roqz4jQ9HgYLJIbPphV6SOv0HTiL+JB37RfOanhRuEwc3Rfn6k/l9fIB9BxJpYnwEqQSHGw/Fc9dhj+KtrwVIRHS8A7UMSzRAWMa1qWx+OYA1V0ZWYdXve2ivjTPzv528+aNOwl29+7fovi4WPOKhYKiRfkOMMESs59D2KCPPs9FemyAZT1E530wqoTNojhdJkXCVqLhLNXxNCf52aWtaTnJoa4zmLTFKxhtRDCKu1j1AY++yMGv/KD6DQXNNzPqRC2dh5rldNUh4BvzzXuBfVmTJ8VcUDivce4UIE2nugIpC0XD30jL49csidCSeELZezy4S9mBmKQBkNtBYTKJh7PQpIgb69CnCbo1lP4fFdxCovpzwmS9g6tqMFO4SEoblD4mSQqhdHHGW/jhzcsPOFxrd1q0nRI47/EJScSx2NRwzEOvJLGEcwfEa745Cc0emBAUEQW7uOEyOfY2C1PcbCYc2iQUSUWjbCWbvv4QYZ/HVwKC5ZvYuDadPmCAISFd9nLHftLH99RCqUWLYWVbqVkRoXBulYVWUadc7adsdZdr3HKz5Uw+xoI67VFgwRQM60Z6+lecD62MseNBDe22cSHeKZY90M+tHLpY80E0soLPrnTCJaArVKFE85p+wQPmSwt9Uy8Ynb6dx1evpsfFXPUr5vMsw2Vz4m3ZRv/wl9FSKjU/djsHmonjCAmxFg5hw9c/p3FdDIhLAXlBB4+o3cJUNx1M2nIaPX02HgciagaKxpxDuakE6jpRMkr9YM2VepQFFhUX3daWlFXoKNr8YFHHHgb7HwVBnSkgbEFrdZFyEWARaksTDOmaXTFd939d17UvguFjhpavbcZWoLPipl2iPkP1Mv95JMg4LHvIQ7kphdsu07Rax0wse8iABHfviHFgfZer1TpJhHVmVyKvWCLYn08T4CHa8GaJorBFnscqOt0JMuVZUDA5tjfHSNW2gw8BpDlYcQ1JXPuZjwrcdfPw7P7IK07/vJB5JUbs4SlddPIMcR3vEhcTx0vXM7swKa+uuGInDvZNHn1okBU6+200iotNdn0Q1S2x+MUjRGAODTzNhW6dgtMoZv3OA7sYE/qYEBUN7pTptu2K89r0Ohp9jpXCERk9rkkQMAoeSvHtnN+hg8cqc9ZiwxjtCjIvHGimbZsJRJLPjjRD9J5po3x1n79Io5bNNnHyvC9UgLPi6GxN07BU7kFOh4SjIUsPsO/AVhtch9MUgQkJO+r4IEQFh9Xb2DOFKcdE8YR1Waa9ndIWDjbW9B5KrFoKmwK5GIbuwWxAC5j1vg57Ctv2PsP2PMO4aGHyaIIxzHxCaYl8T7FuUOanCsb0BHcqJkcoir2ja+/3rmePjjs3V8AwU0o32o7KYLTmime5TUJgj9utob2GjQYyLdXihZYuwgyscCwfXib+jLgVv5QntQxZZAHgGaMiqxJ4lmc2bu94NU326hfn3uTFYZZY93E3LYQ/WcHeKDx7s5sxfeVHNEvPvc5OMCXlm0+Yog+aYiPX0JVl5VRp7FoUYMNVMd2OCVAJSiV4y0FUXp3CUkeYtsa8kOe6q30J3/VZGXXw3qWQSszufhtWvk1M5jhZ/B5IkYXLmEe46hKIZGXnhHZiceex99wms+aXsefdPqEYz4a4WAKbd+BSSLDHput/gP1iLarRQMHI2K355NY7CCipPuZKPD6flASgGE/nDPj0p72jEoylatsUIticx2mWsueKOwbHobozjKlH7yB0GzTWn44s1i8SsH7rQU2BySBzaFqfm+QAl441sfjEzia7/JCOvf7+TRBjad4sQkaLRxj4pbrZ8hdm3OKldHOFgTYwd/+itOAycaWLQSSbefVgUKmQVTr6rr5OJJIlaQ09AZ9+HYSZ8245qkGnaGE37MyePU/9IxnsT3lIJ+OjnPhb81Evt4mhG8lu0J8mqx/3sWRRmxvddvH9fV3q9Q063kDtYIxpMCgmTpPPGjR0YbTKTrnIgyaL5TTUKKcumF4I0rOrVOk2+1sG21wIMOd1GyUQjHEexJKtgPCa8pbspzthLbaz9cw+JqM6wM60UjFTwx/X03EIdKZY+3M2Eb4sGJHeZSr/hBpb/wseU6xwUjzWx4jF/OgxlyEILm18KULdCzK90spG8IRpr/xTAUaRw9m9yySn/6laN4bgfTxZfRWyv6yXGR/DKR8JXORyFxy6ro+zgn7l4ns6tl8A1Z8LjPxKx1PMnCH/lsUds1iRJSAqOILcaTHb469fgvR/Ba1cKy7RINxRPFC4QAO6BMPJi2Pm6sGPL7xvNejwYDPDt02HmaPFcVUSS4NRjjSWseXDBCzDoVNHaWzoNLnlTWLl9CsqL4Ok7hKYaRLX8yVuh8ogrW94wkaAnq4JoT/gOjL4MymaCdhzX+iyy+BTY+im4SlSGLOyNY3UUKAw+2UykO0VzTZQhC60ZMorB882gQ8eeeDrBrKM+TsEwIx/93E+gLUn16b1idbNbZvApFna8GSbUncR/2NBfOapxqP8kEwdrosjKV1NSkUokGDT/CrobdiArCi1bP6Rk/EJat6+kZfNSfI07qD7jeyBJDF5wDUZnLorRRMOqV3EUDWLo2TeIhESjhcELrsWWX4okq9j6DWTgrIuoPuMHWHJKCBzai8nhJeJvZ+bNzzFw1jeoWngdM29+noi/7bMnehgH1kYId6dY91SAl65sZ/NLAQpG9i0wmOwyzv4K8+5y4y5TUTSoXmBh9Dds7PswTP2qCH+/tp3F93ez5CfdrH68B1uewsKfefEMUJh9iwuDVUJWYcS5VhJRnVBnbwV0/0eR40p8rDmimS+/uu+ccis1ao6SQaQS4G9O4ijIvKgbfZENZ7HC3mVhyqaY0s1zRrt81GuFu8XRcBYrBNuTRy0jNPsVJ5lZ/5SfdU/6CXcLR4i1TwTobkiya1GQ0x/2MOcWFwsf9TLlOw669id54fI2nj6/hU0vBCkaZeTQ1hjb3wiCDqfe72HyNcL+7WhiDLDhrz0MmmsBSchAdi8OMfyczMjlURfa8JRn1iyTUVj6kI9gW4qoX2f9UwFad8TxHuMicmhLDGuOjLdcpfJkMxufFUEgsgLxUIqq0ywMO9PKuG/aadkeSxNjEKl4qkHG7JbxNyXZ+VbmBdBXEdnKcRZAb8La8camVIWYs+Ec9g+4gRsfUTFo4LLB7w73nz13UwfTS3zAUdZoQ86GvYug9l0Yei58+JPMlS+9F859Fg6sgoveEGRVB3a+Bgseg/KTPpcn8NjB8NydwjnDpAm3CuPxCs/9RsDXXxROFGaXILUngIVTRH/dwQ4hHzkS2w2AwSIcLtp3CjmIs1R4JmeRxReAntRZ+2c/A2eaWfCgh0RMp7sxQcuumIiv3Zdg6BkWJlxuJ5UUCen5Qw28c1snwTZBUmQVFj7i5bUfdIAORofMsDOteEo1kglIxnRW/MqHnhLd/R/81IezRCEWTIEE5TNNGO0y8YhObuVXr4Kkp1KYPf3Y9ur/4C4ZQs0zdwNgzS3Bf3AvobYGrHmlGKxOxlz6Yzzlo1j+s8sYvOAaBs37Fh/cexbDz7+ZCVf/AllRkA0m6pa/TMvmD+iq24IkK0y4+ue4yoYx4sLbCXc0s/2VR4n6O/AMGEkiFmLnG79mwjW/OKH5hjqTxCOw7OFufE2CBDaujjL7Jhdv3dxJsF18L4afa0XXhVdxfpWR3EEqwfYUNc8HeP6yNoadbSHUIWKVj6BjX4JoIIWsQk6FkfxqE/nVGvs+jOAokFl0X6YszV2qoholHEUK/sNzkRSoXmBFT+h4BqoZNmuu/iolE4ysfrwnYz2rHvdz1mM57P8oQld9gn5DDXTVJ2jaEGf0hTYKRhgItCUx2iSKxxixeGRCnSk2vxhkynVONr8UoKM2QeEoA1WnWVj2SG9TiqIJ72DNLLHjH1H2Lo3iKFSJhXtJfsPKGA0rOwE47485hDpSvHxtW1qHveJXfkZfZMOWr4iEv1yZWEgnlewrvwDh0mHNUejcn6BojAGrS2HwfAulE00kEinhBmKTsB3jW924tm+nbd3yCO4yW8ZY4SgjiXCKM36ZQ8feeNopxJYvE2zXqX89hCTB5OscKKrEpKscKEZo3hxj37IIjWuieAaoNHXFOLD+ODYjXzFkyXEWgNDOVpfCjvresfNmibS14YUdyBt1bPEmcpzQ7oPWrt7lPJGd6C/8gMRZz6DlH5YReAbCBc8Lm7aeQ321vqkkWHPh1P/JHB846wvvQ55HPD4TBqt4fE5UFPf6L/eBqkG/43ggZ5HF54Rmkhlxjo13bu/KGJ9/n5tYj86eRRG2vBSi6jQL6GDPV+iuizPyPBt6CgJtSZq3RDm4ufdWc+v2OIEpSTrrEux4s1eyUXmyCDWYc4sLZ4mC2Slz1q+8Qofs05l7h5u8qq8eOQ60NhBsbaBw5By2vPjQMf+rx1VSxf6lf6Py1KvQLA72L3ueiK+Nrv1bsOb1Jx7yZzhPuMqG4eo/hKLxp9JVtwU9lWTz8w/Qf/JZDJj1dbrrtqIYzGx/5X/o3FcDCD2yp3z0Cc030pOic188TYxlFSZf7WTlb/2MutCGLVdBMUq07YqhmiTseSIGuHFdjGWP9JLbZBQ69vaNTA51pDBY5fSdBbNbYcPTAQZMN6XT6wA0s8SI82x88GAXU651YrDJhDqTWNwymlVi7RNBuhvizL3TA7pOMi6cNQ5tjacDL46gdLKJQEuCnW+HsOcraVlB00aFsZfa2LMkxIYng+QPNTDmIiun/sRDd32CWEjH7JKY8QMn3Y1Jwt1JLF4F1SiRjOloZonZN7tY+nAXHbW91eQ9i8OMvdTWZx7uMhVrvkLDx9GMBkWA3e+FmHSlg9bdMQItKVb9QRDwqdc5+rh5jrnUSl6VJpw6JKHn3r8iRP9JFhIdOu/f4yOV0Bl1gU24U4wwYrIraRu3o2HLUzDaRKqhngJniUL5bDNGp4KnVIOUjrdcpbshQaA1xfJfCA22qN4nhff04X2snGdmwDQTuVUa214VFeOqUy19tvlVQ5YcZwFAUS68/GN45UNYsRXmjoWRg4SrRa6vnuiIb+J1D+AX/xXk4p9a0zqtcyb6GBH8K1LTWuR9i9jUUcCwwXZhcWZyQtE44Sls8Ypq7RGYXOAq+/+/o1lk8SVA+Swz8+6E9U/3oJokRl1gw5Kj0N8rs+PNEN2NCVb93o/RIXHqAx6at8REEpguKnEn3+ci5tdx3aXRuFbYuPkahR/r1O84CLan8AxQAZ0tL4Vor40x6Won7XviDD9HnCw1i4yrWP1KOlXIikYs0IVqsqYt1wDql79MycSFWHKKaK5ZzO63/0Dp1HPwNYk+hkObP6D6a9/rsz7NbEdSVKL+3mOg0e6l/5QzCRzaj72gnPzhs7B4C6h978+Yc4oZdvaN5A+ddmLzlQURO6IjLplgJNCaoGl9jKZjqoBnPualflUEb4XaJxq5aUOUirlmNjydqRf2DBDWfunnpSoLHvTw1q2dFI02Mu17DhyFKgaLxLt3dhEPwbKf+Rh+rhXVKG7b9xumMfEKOyanTM3zPdQujiKrwu5s37IIJ9/jZvkvfIQ6UpRNM1I+y0xPi2jKO7oxL9ieJJWAlq0Jwt0p6lZEOLQ1xohzrRzcGmH699w0bYjiO5A8fEEQZ+dbIU59wIOsShgdErVLwhnEWDNLDJhuwtVf5WuPennn9k5iAZ3Bp5qYcLkDV6FKi633okHRoGCkEUuOjLNExd+cZP0zvZXvmucDzLjBxdZXgsiazqSrnHTUxjm0JUbBcAPxEMhKivJZZnwHkrx7R++F8Ht3dzHvTjcRX4Tq06wMnGlm3VM96fdANUlUzDEjyxILH/XQuj1BxJfEliPjKRMXsiaXwqgLbXTuzZR3VJ1mYd1fejLI/+73w8z6oRNZlQh1phhxrpXymV+9HoNjkSXHWaQxuD/cfPFx/hGyYFz0fQDOzhnJxw+8wu66CLlmP6OCfyV3128BUMItNB3cRUIZ16s/BqHpvfAV+Ps3oWu/eH7WX8Az4NgtZZFFFoDZpTDyfBuVJ5vxH0rw3t2dBNt0ymeZGH62FUWT0HVRNeppTlLzt16NYP4wla79Sdb80Y+ehGFnWzntJ2aWPtzNKT/20LQxRskEAzo679zqS6ecJcI6DasjFI404CqBgTO+unp5i7cAXdexF1RQPPF0Dqz+BwCBljri4QCqycaIC4VfusmRQ+f+zXTXbSUW9CEbjBisTmLBwxVZSaJs2rmkkgk2P3e/WL+nkDHf/DEfPnhhmjAPnPUNxl7+EJWnXo2sGlANJ/7+q0aJRDTF6AttrH86gMEio+vHv6jp2JPgo1/4GPUNC/lDNHa/19sY529OkjdYo3qhhZ1vhlBNEuMvs1M2xYQ9r5cuSLLEoLkWLinX6KyPEwvobHwuwIizraRSvcxr26tBzv1DLgXDhONFV0OCLS/1hpMMmmumbmWEiD+F0Qbz73VzaFuc/GqNjr0NDpgmAAAgAElEQVQJZEVCUkh7eQNUzDEjyYLIH0HEJ1xVBs2y8vw3W9M3KnMGqYy60Ma+ZRFMTjndWKoaZDa/GCTqF7+pgTNNhDqSvHxNO5pJYuHDHlSTRP2qKG/d3ImjSGHcpQ5yqzRMdpnSKSbqV0bQk8Iz2OyW0ul7AMH2FB/+rJv597nRU/Da9R1oVon5d7vxNyXp2Jdg51shZv7QSePqvrKJnW+H8Fao5A814D8Y54xfeGnfnSAeSWHPV2jeHOO9uwMsfMTLgOlGzE4Fd2nvhazFreAp02jdEcPer1e3rZlloj16n+2Z3TIWj8LFL+ThHaChGrPtaFlynMVnwztYhGWs/R2G9k2M33Mt42UFNrzVu4yzBCI+VKJsryOTHAOUTYerPoZAq5BT2Pv9/9yDLLL4UsLsUmivjdOyVZztnUUqKx7zpW/XFo0xUD5bNNpVL7CQM1jFnq/y1k2d6XWs/LWfqd9xMPd2N+HuJKsO21lN+S8Hoy8UXfAg0s+9AzU698fJKC39CxHxdxDr6cTkysNg/eeMu6MBH6rJiqL+609jkixTOGYuTevepXrhd3GXDufAmn/gqRhD/8lnEO5uxVM+mlW/upqO2g0MO+8mSqefR/3yl9j+yqNM+u7vCBzaTyzQjbdiDAa7h3i4B1kVlb2x336ILS/8NKOSvG/ps/SfehZl08793POVDVA8zsTBjcLWz1OmEA3qlM82sfeDSHq5CVfYsRfJLHjYg9klgySqzI1rBEHLqdSw5SkYzEKWkDtYI6dCxZZ3fGlNpCfJ9tdDaYLdsSfOhCvsyIpEMi7S2Q7WRFn+Sz/uUpVJVzuQVQnFIG7dl001EupM4SxW2fN+iGB7iuoFVpo3x7B4ZWRVYv49btYcjlAun22ifJYJ1Shz0q0u1v7FT8u2I78NhQ1/DWQo+Nr3JIj1pPj6U7kUDOu92MivNnDRX/Pp2B+jpzlFxKfz0c975SWD5ppp2x1n8+EmwY69CRpXt3Pen3LpqI3z3t29ld69H4SZd5ebghGGjEq8pEhIksRHv/Ix5zYn9lyVpQ8JTbizSGHa9U7iER3N0peIGu0SRWMM7Ho3iMGiYO8HHz7a3YfYtu+NM3CWPSPgI9geJ+LXKRipYc1x4G9OsPu9MPGwTndDgpxKjfbdvVVwWQVXf42C4YavbPPt8ZAlx1l8NoxWEYlceZqwWcutBmd/dE85Ut0y4daQP4x4zYt8kLiVieM/YT22fPHIIossThi2fAWDTSIW0Fn3ZA9Tv+Ok+4AI7bDmKER8KQafaibYkcQdValdHOqzjobVUay5ctomKpUQVtxHYnyHnmGhdWeMijliPUfiaf+VaN3xMev/fAstW5aRUzmBMZfdj2fgKEzOnM9+8VHoathOw8pXqV/+IvaCCqoWXkfBiFn/8vm6SqqRFQP7lz5L7aK/kDNoHB171rF30ZMoRjNz736T6T98hs59NRhsbjSznfI5FyOrBlBUPHYP9R/9nfV/vpn8YTMom3E+U67/E7KqohjMaW3x0Qh1HPxCc7W4VGQtDopOLJAi2CGxb1mE6tPM9BtmoHN/nIEzzXQ3JDiwNkbEnyLcmaJpY5RZP3Ix/CwrskHIC179fgeJw1XQMRfZKBz1yRcxHXsTab0xCE/u5b/wM+9uFz2HUrTuiLHhr0KikYjqHKyJUjTagMUrKsZ7l0aY9SMHkgRlU80Y7TIbn/cx43oPziKFQ9vjRHxJpn3Xgdmj0N0QZ+0TPVSebMEzQGP2TS5W/zGAntKRNfrEZQOEunQiPuGffDQ8AzTMbplFD3QSbOklnYoGJqfwBj4aiajwG9/4XKbkJJUQOu2q0ywY7TINqyN4BmgMP8dKV0OC6oVmzHaFd+/oIuIX0ghfU5KPf+NnzCU2SqcY2flWKB0PLykw9Ewr2/8RomK2ifVPBRh3mR17gUq0J1MPbrTLKIf3S9d19i+PsOIxH111CcpnmRn3TRsDp5u58Jk8WrbFkGQY/Q0ry37m42CNqCrPu8tN4QjDV1I+9WnIkuMsTgy2PKhaKB6HIU38LnH3YLQ9b9DRdJBlhc+w6N08rr3of3GeWWTxHwZ3icbpD3t586YOQh0pVv3Bz8JHvHgHajx57iFKJ5oomWBk8f3dDJhqwuLtS2zNbhmTXWHPYeJs7ydIdcl4I2f80kuwPUnPoSTdjQmseTJFo/+1koqW7StpWPEyjsIKisefyv5lz7HyF1cy8uK7GTD9AlTjiWkc49EIu978HTtf/xUAnXtraFr/DvN/spjcwZ+ccvlFYe83AFthBarJSuPqN9Ljw875IblDpqAajLj6V9O2aw0H1vyDjU/djtHuYdL3fs+GJ26hp1mkEwVa6mjfs5aRF97BxqcfYPwVPyN/2AwObnivz/a+KAqGG5CA1l1x0IWl2oF1UcqmGtEsMr4DCSRJhFFM+S8HK98VTVpLHhBNZJIMk652pIkxiEY/w3Eqm/6DCTrr4+hJcYEVD2dWNA1mmYIRCnoKHIUK7bsTJKI6siKx7fVMm7Btr4YpmWjEnq8QCyYZttCB/2CC1p0xlj3sEzZxEsy60cn6ZwJM/76TFb/24WtMUjLByPjL7UgS7FkSpOoUCxv/lklezS45w8btaEiyBHrvRSIc9kNO6WiWvvulqBLKcYroZpdC8VgjhaMMzL5JBJ/ULY+gWSCv1EjL1niaGKffW3+KVByiwSQL/8dD47oopCB/mIGlj3RTNd+CZpZp3RFn1zshJl1l562bO3slIxUaedVamvQf3BTj9R90pGVSO94MEfYlOf0hL3mDDeQN7rVvOue3GoF2Ea19bDpgFgJZcpzFF0fOIHT7QGrzLqKmwQ6ywov3Qf9scTiLLP6lGDjdzKUv9CPQnsTqlXGVaHTWxYn26Ox+P0z+MHHiq18VZfApZgzWUNqOSzVJlE42EulJsuPNMHlVGkPPtFL7QYgp1zowe2Q69iaI+JMoJgl3sYrJ8a87YXY3bGfJPV/LkBCMvfxBav56Nz0Ha/Ef3I1nwIl5mvsP7GTPO49njCXCAbr2b/63kGNJluk3bAbjLn+Q9t1rCbYfoGjcKRSOmZ/WBMdDPax/4hbadq4CINrTSff+zWlifAS+hh34GndSMnEhNc/ew+hL7iXY3oivYQeSojL8vJvIrZ76hebZ05qg51ASi0dhxDkGYiGdTS8EaN+TYM8iIauY+d9Owof9iI91XUiPHaOmqTqlr2tB2+4Yf7+unZ7mJCff5WLEedaMwA9vuYqsSWx5KUTrzhilk40MnmdhxW/8FIwysPW1Xn2uJAvHFItHQVLgnds6iQV1pn7XQSpO2j9ZNUjEwjrjLrPzzu29BLFxTZREVGf0hVacBQY0swi42Pl2CJNTZvQ3bOx+P8TMG1zsWx5GT6ZQDDKJqI6nTMNTplE5z0rPoST1qyLoKVEJDnSkGHWBjZW/6U3as/VT6DfCwKSrnLz63fb0uGoUv69j/ZuPkNH9y8Mg0Sd0RVKEzZq/MUHgUIqmDVEi3TrrnhTv5a53whSNNVIw0sDepRHKphpZ+DMv/uYkqknCVaLgKe39nXbsi6eJ8RHULY/SVZ+gYHjm79loVzDas6T405Alx1n8UzAYFSoqXFR8dspzFllk8U/AWaziLO49ZNv7KQyeb2bXO2FSMR1ZhbqVEQZMNzLnVhfhrhR6Ejzl6mGvYonz/qjSfSCBYpA55V5vusqcU6EB/x7LtpatH2UQY4C9S56mZNIZoOskwod1nfs2EWipQ08mMdicuPoPweIpyHidrCgompFkLHLM+L/vVGbNKcKaczb5w2ehGs2oRnPG/4PtBzi0eUnG2CfFPkuSxNaXHmbk129D13VOuusNepr3YrC58JaPTmuSPw+aaqIse6SbgzUxvOUqE69wUDLJyNcezaF2SZimjVFyKjRMTjl96z4aSGV4DQMUjTaQM1j49SqaxNTvOCka00v4oj1JIv4kwfYEuYM0Aq1J1j4ZYPI1Dk66zUXb7jjegRrWXJllj3Snw2g69yfoP8nE6Q95iAVTnHq/h9bdURJhKB5jRNLg49/6cZWoqCaJeEQnGQX5qLciEdWx5SskwnofV9DmTTHGX2ZHTyXRdYnCUQYKRhjoqE2w9e9BJl7poLNeROTFQ7Di110kIjrWHJnTH/EyYJqJ5s1R5t/rpnlLDM0iYfXKOEeqOArdNG+O4SxRGTDNhLtEw5arcO4fctn5dhCzV2bI6RYUVSIaSGKwyLTXxuk5lMTslsmr1nAUKuz7MMnYS+xpfT/AtO85qZxvJtSRomFVlOZNmZIJzSoRaEsw9GsWAi1JFv/YR/F4I6MusB6eo5KhBTdY+soijHYJzZyVS3wRZMlxFllkkcWXEJpJZtr3nFg8CjvfDXHSrW7W/qWHpQ/7mHyNnbIpZmx5Co6C3sO8s1Cl9P/zPBORvmlbiVAPJRMXsnfx0ww65Upatq2gc/8mWretIH/YNJrWvU0s2E3FvMvoN3wWmln4kjtLhjDsvB+x4S+3pddlySnGUzHm374fJsfxTdQT0RDW3BKCbY3psdbtKyibcT51H76QHiubfh7Nmz8Q1nCSjLO4CltuMY7C8i88p1Bngnfu6KRrv2CMHXsTLPpxF1971EvZFDOV86C7IcGGZwI4i1XGf8tGxUlmNr0QYNp3nRzcFKN1Z4zy2WZGXWDDU6ZRPNqIJEtYPL0Ev3FthO4DCVJJ6DmUoP9EI8PPsbLovi4+/q2f8ZfbmHS1nYY1UWJBnYo5YhvjvmlHM0tsfSVI/UrR9KcaJWbe6GTNK6Iqu+f9MEPPtKIaJTSTjL2fgsWj0LAqkm4WzBmkoad0LDl9JR4mh0wqpbP8l34mX+tg7wchJl5jJ6/KTMFoTVjcSRKOIpW9S8IkIjpDz7Bgy1P4+Hd+ikYZKZtmxHcwQU9Lgs79CYJtSapPs7J3aQTVDBF/Mn1hoZlkyqaYKJtionFthI8e9dO2K07lKSZKJ5h5/94ueg4lUQww/XonI8+3UnWqlQPrw8z8oRM9CbmDNYrHGtFMMhYXkBIWc1F/b+V36nUO+k8w0bE3zsL/8RBoTREPp0gldNz9DWnbtiPIq9IoHGXgYE1vU+CU/3KSU3G8NKwsPguSrv97upK/CMaNG6evW7fusxfMIossssgCEHG5wc4kBotEMi6spaw5SkYH+/8mDm1Zxrs3n4Se6q1SjvnWT3APHIWiGUQzlaKy9P5zGHbuD6l55u6MyvDs2/9O6dSz0s97Wutp3foRBze8j72wgqKx8/8tkooTRdP69/E17WTtH25IeyIXTzyd8rmXoSfitGz9ELOnH76GHexf9hyq0cK8+98jf+gXk1BkbLsmwt8u7hsxfdJtLkZfaMd/KMGaP/qFBzYiBGLilQ7sBQqqQcJbrqKZZYx2+ROdCjr3x1jxWx/uEo1Vv++tfA6YZmLClXa2vRpCM0v4mhLsWyY+N1u+wtTv2GlYFcMzUGXFr/wZ6/SWq+RWGnCWKLTtEtrl/ct7P/Mp1zkItAk/Y0eBCMJ4+9ZOxl5qxXcgSe2S3mVn3OAk2JFk/ZMBkGDadx2UTjbx+g/amXa9k1QCknGo/ziMLU+leIyB+tXRtBPF8LMtFI42EupI0dWQwOKWkWRIpWDLi8G0VnjkBVZm3ujEYBEXDa07o7x0dXvaf/jU+92s/XOA9trMCvD5T+TSf4KJWChFqDOFNUc+7m+zbXeMupURwl0pBkwz0W+EAe1zWqq118Y4tC1GqDOFd4BGwSgNiytbA/00SJK0Xtf1cceOZ9+1LLLIIosvMWRVyvCgtfwbnCY+D0KdzXTsWU+4sxlX/yF01W1lyvWPs3fx00R8rQycczFRfwdt21ciKwo1z9yNLa+UURfdTbD9QB/JxObn76dwzDw0s4jLteeVYp9TSvmc45myQ9TfSTTQhWp1YHHmfqF9iPg7CBzaj2q04Cga9KlyB2tOIR8/dg3jr/wZkqygmqxIsoqzcBCegSMwewr48CcXEO5uwWj3MPUHT5A3ZMoXmtexMDnkPklsANbDTVaOfiqD5lqoWxmluyEhoqL/FuBrj3rJGXRiFcX22gQVsywZQRUgyGz16Ra2vRZk8rUONj7b+7kFWpJEe3T2fxTBWWQ7dpV07k9QMcdMNJhi4AwTi+7rzvj/yl/7Ofs3XpAkDm6KEe4SF1brnwoy4nwr8+9zE+3RcRUr6LpOPCR0yjV/ExXynpYkA2aYCbYlSUTF+o5g26sS4y+3Y7BKVJ1mYeBME2274hkE3lmsMPICG3nVGg2HfYi3vRZi9Dds5JSL97ZjfyIjnETWpD7EGKD7QIL+E8BgkY/b3HgEuZUGciv/uSpvToUhWyn+FyFLjrPIIosssvhc6KrbRufeDcQjQXIGT8CaU4zJmUvU38nq33yH+hV/ByC3aiKaxUnE38H4Kx+hbvnLbH3xQWIBQYbyhk6l/5SzaFj5CpuevZdxVzzcZ1upZIIT8V32NdXSuXcDG5+6g1D7AfpPPZv+U84ir3pyH+3yp+5b/TY+fOhiuvbVICkqIy+8g+ozvofR5jru8o7iwYy97AFWPHo5iWgIg83NjJuexTNQNBkWjJjJgl+uJdzZjMmVhz2/7ITn8llwl2pM+54zIwK6eqGZ3MG9ZL5knJGFP/PQVZdA1iTyqjVcRSeubTbYJAItqbSs4GjEQzpGm5xBEo+gdWcMV6mCehzN64DpJorGGtizKIzBenzCGI/CwZoI/oMJBp9sYdJVDnRdZ8vLQTa/EGT69x0sebAH/+HIbMUAM25woWgSgZYkJWONHFgvYtRHnGvF5JDZ91GE9j1xJEVnynecbH05QP+JRtY92ZOxbd+BpJB5WCTm3+dGNUrIKhwtJdfMmfPWk8Jr+UiE9xFYvTI73wmSiOq4ilVyqlRM1iz1+r+O7CeURRZZfOkQDfoIttShGq3YC8vRkwmC7U0omhGL98SIUCzUg2IwoXyBJqivMtp3r2X5o5cTamtk9KU/ZuNTd+Jr2EbZ9PMpmXxGmhgD+Bp3Unna1exd9BThzmZ2vfFYxrpat61g5DfupGHlK0R8bUiShKwZSMV7S6Ejvn4bmtn+mXNq3vQBG/5yC3pKELV9S54BPUWsp4PKU648oX1LxKJsevZeug57EOvJBDXP3EXekMkUjp533NfIikrZjPPxVIwm0t2KxVvUx5LNlluCLbfkM7cf9iXpqkugp8Bdpn7mXQBZkRh5no38ao3OuiTWHJn8IRqOgt7vtKxK5Fcbya/utefzNSVIRFLYC9RPrWaCCMxorw2SP1SjZVtvZdRgkzBYJSI9QipwLBSDxMwbXXz4qI9ZPxSBF4mojjVXod9QjdadcSQkFIOEs0TB19hLKnMHa0S6k7TtilM+y0xXvZCraBaJadc7WfErH7IqpYkxiOr5vqVhcoeouAo1EtEUJrfEkAVWNvw1QKgzyeD5FvpPNJJTbuCNGztIxkE1yQyaa8GeryBJ0LY7zp5FYRQNxl1m52BNjGRMJ9qTonZphElX2PEONJBXpdF/ojFdWV71Bz8zbnTyzu1daTeOsZfY2LM4hKfMQPvuGK1OhY59WtqGrXFtlNadMdFsW6TiGaBiyrpI/J9AlhxnkUUWXyp0N+6guWYx4c5mVKMNT/koGtf8gz3vPI7B6mLUJfdSMOoknEWDjvv6YFsjTRvfJxkLk0omyaua9L+qWf0yIREL07JtBd11Wxl18d1sePI24iFxO3rrSw/Rc2gfFSd/i9r3/gxAPNyDvXAQIy++C9VkPe46JUlUFhXNiKQamfK9xzm48X0i3W0Ujz8VxWAi4u/A5PAef07xKJuf/wnusuFpYnwE9Sv+jqN4MD0t9djzP7sVMepvo2nd25g9BSRjEWIBISXwNe35RHJ8ZB+cRZU4iyo/cxvHIh5OoRgkeg4lee+eznTjWsEIjVN/4sVT+ukXbwarTP+JZvpPPLFt7XwrxAcPdxML6JRONTLnZjfeAZ+8DbNLoWK2mbxKAxueDVC3PELOYI3xl9lp3yM8lVt3xhn6NQvb/xFCT0HeEI2xlzjwDlSZf6+LgzVxPny0i5ToRWT6D5w0b45SuyTC1leDzLnFxYF1UQ5uEtZv3nKNRfd1M/cOF/5DSZTDeuhYUMdg0Tn5Xjf7P4r0mavvYJL8oQZ8zQm85SqeUgNv39qbFrn9jRAjzrcS7kyRSsKIc6xoRji0JcbWvwviXzLeyJiLbTgKVV78dlvaHcNdplI+y0zj2ijegQYc/VTm3uGmaUOUzv1xcisNWHNkzvltDl0NCRSDxK63Q+xdGsFVEmfGDU4i/hSSDGuf8jNwmpnlv/Qx6RoHrTvi1C0PY+unMniehbyqrDTifxtZcpxFFll8aRCPhDmw5i02PnUbxeMXkFs9hWhPJ0a7h5EX3sHBje+z6rFrGX/1z+k5WMm2V/6H0ilnUTzhdGx5/UklE3Q378VgdVG37h0UgwlbXim+A7txFn9+YvNVQ6yni1hQEEaDzZMmxkdQv+JlZt36Ypocj7r4Hra/8ii+hu2Un3QJecNm0Lr1w/TyxRMWEGhrwJpfSvnsi9n15m8YeeFdFI09hXjYT6S7jZbNSwl3NDP4tKuOO6dEOEDnvho85aP7/M/iLSQZixIPZ84z2H6AVCKByZ1Pz8E9xIJ+ZFVDs7qYfP3jNNcsIX/IFJLxGOGOgziLB/9T79vxcGB9hO3/CBFoSVIx10w8pKeJMUDz5ji73g4x+Zp/Lmb7aBzaHuPdu3q1w/Uroqz+vZ8p33HQtDFG1J/CXaqSP1TD4u6lB65iDVexhr1AZugZFna+HeTt2zqYeYOLs3+TQ9vuODmVKiMvsCHJ4CpRMbtEBVRWZJb8pDtNMvUULP+Fjzm3uCgYbmTfhxEW3dfNSXe4GHqmFVuuzJonAugpsOUpdNUnWfO0Hz0pqtXTvutEVqUMFxbPAJWKk8x4y4WrxZo/+tFMFiS5r9xjz/thCoYbmHKtg4a1EXa8jSD5h9G4NsqgeWZqlwQzbOO66hJoJonGdVFGXSDuZHjKNFIpncZ1Edbe2ol3gMq4y+z8P/bOOzyqKn/jnzu9t/TeSCUQAgm9F6WoYMW6dnRde19772Utu66uve2uaxcrIgpSAwQCKSQkpPc2mUwv9/fH4EBMrMsq+pvP8/Boztx77rl3JpP3nvs97/vZbX0hL2lzqoycRRpWXtNDwBdMIVxwi5meOjeL7zVj7xYJRIj0NYpEaCTs+dyBJlJAFxl+ovVrEhbHYcKE+c0w0FxF5ftPMOHs+6n78nWSLbHsePU2shaej9ocy+jjryFt1skMtNWh0BhImngUAb+frj2bEQQJLlsPXmsXX95zUqjPui//ydxb3g2L4x+BQh+BNjIZQSIZMdVOplAjU+uIzJ5E2qyTcfQ0Y22sAKB29SvkLbucpOIl9O7bQWz+TFTmWDorN1Bwys1oY9LIWHAWPTXbWPfQGfujysCQkEXqTC1OaxfqERbYKfUWUqefgMvaiTltLH37yoIvCALZiy9EqtSGyjI8Dhvtu76kr3YnYsCPPjELhc6Mq7eNujWv4+xvJ3PB2SRPOobe+jIq3noIhT4CTVQiXdUlmJLzkH/HDPhPob3czVsXdocS2GwdfowJwx+n161zMfE8Qygi+L+lr943rK16lRNLhoyvHxtAHysl7xgNjt4AkaP8RGUrhrhYmJMVmJLkRGcrmHGpiCFehkQqkD5TPazfb3D0+oeFUwR8MNgRTHucc52RjnIPKoOElMkqJFKBOdfJGL1Ui9cZYOuLB+qBPYMiW563ccyjFtJnKVEZzbgdAQZb/Wx72YZULlB0pp4j77CgNkto2uL59nAwxElxDvixdfiRKyV0lA/fpn2Xh/7m4al6HkeAuLEHZnX9XpGW7R4q3g9GaGsipdg6/ehjpSGf5zHHaln3uBVxf3dep8hXD/cz70YzH/25D0dPAKkcJpypp+oTB7mLNVib/GFx/CsTFsdhwoT5zeB1DZK54Cy2vfhnUqYdz54PnyZv6WWhx/tKQwTjTruVuLGzGWirZdsL1xHwBGfjEooXU/iHO6n+5NkhfYp+H42b3idp4pJf45R+M/i8bgaa92BMyaN4xaNIFOqhYhTIOfoS1KZ4is9/hIDfg9/nQZBIQuUOFe/+hdRZpzD+zDtZ+8BpdFdtDu07ZvmfiSuYz45XbwsJY4CBlmpkKg2C8N3BGqMWnE3Vh38nd+mlwbAHpw21JQ6vYxClISK0CK5n7zZ2vHILvbXBmuI5t7yLtWEX21+6KVTnXPKPqyg8824kEinFF/wFxACtpaspeeZKco+5hKRJRzPY2YDf48ScOhZLxrhQaYi9t43B9joIiGhjktFFJY845pZSz5BoYmuzj4zZ6iEWZQAZs9SHTBgDI0aLW9JkdFZ40UZJGHeyjo1PDeBzi0jlMPdGM3Fj5Sg0EtQWCUqNdH8JyY+TDqIYDKc52MM3/zgtxgQpSr2EKX80UL3KSf5xGuILlCEhrjFLSSgQqFjpGNbnYIcfvxciM5SYEuRse9XGtleCqXJ+j8iGvw0QmRlBVLYCpcGLJU1G734faIksWEe85xMH1hY/7oEAqdOCbhUHY0qRoYmQ0Fo6VDgb42WkTDtQu+3o8Q8JUmnf7SGpWEHhqTrWPhJcJKm2SELC+BvSpqtZ+6g1tJDR74Utz9qYdokhWFoy7cfFqYf53xEWx2HChPnNoI9No10ixe92ootORheTytbnrgnZf7kHeih95VbGnnIT2oiEkDAGaCn5iKwjz9/vfjAU0T/cginMAVy2Plq3fcrW567B1d/B2JNvxJyST/EFf6GjbA3Ovnbixy9EoTEw2FHLjldvpW9fGdGjZzLtiufZ/vLNZMw7A5UhEkNiDn315UOEMcCelX9DqbNgTMpmoLlqyGsypfY7QzgABKmUgM/N9hf+jCV9HHnHXwUSKbrYdCLSx4W2a92+KiSMAQJVdCAAACAASURBVFz9XXgGrUMWACIIKLQmSl+6AY89KHBix84mZfrxIAisuftEHN3BwA+JXMGR93xOTP4M+hoqaPj6TfZ+/iJSmYJRR5xNXOER6GJSkKvM9NZ5GWjzo42SDItv9thFRFEkfaaKurXBz3LiBCXZC797RvbnEJsnJ22Gkn3rDgRyTL7AwMpreig6U8+mZwZCrhR+L3xxdx9HPRhBzReD9DX4yFmoIesINfqYHycdGje7+eiGHubdYGbNff1kL9TQvtvD7reDHsNytcDs60zEj1VgjB/ap98LAf9whwxDvDSU+jbQ7qO93ItSL+C2Hdh27xdOEgoVtJd7yJijInexhIA/uEiwcYuLiAwp2Udq6GvwYcmQ07nHQ/uu4HdA2nQVnsEAyZNU+D1Q9uYgapOUaRcbSJmqxBB7YEZXrpGgMkhC0dBeh0hvnZ+oXIFplxjQWqT4XOKw6GhDgpT+d4d/D/ndoDFL0EUdHh7l/58Ji+MwYcL8ZlDoIojMKmbaVS+hMcfi97rIPOJcalY9j98dfLTpGezDY+sl4Bm+YKendjuZR5xN+84Dcb+CRELylGW/2Dn8Fumr28H6R87C7w2Kqh2v3oYhIYuM+X8gbc7p+N12uqtL6KvfScU7j+J12LBkFBI7ZgYe+wDj/3AXm/56ET6XHUEqo+jcBzAl59G/v+QCwOscBEEgoWgRTRvfC7ULEgmRWRNGHJfT2sVg+z62v3gDbTtWA9Bauoru6i1MueRp8PsQD1KiXXuCglxtiSP36ItRmSJDQvebY2UuXEHDuv+EhDFAe9mXTBh/JG5735DtA14PZf+6mzm3vENH2Rp2vHpr6LVtz1/PlEstbHvhOtKmn0LZu7OoXx8UVsueiECmFIbYoynUEubdZGJKbwACQZs2lfHQiiRdtIyFd0XQXePFPRjAkibHaQ0m30mkQshl4Rv8Xuiq8VLxfnAGt22nh75GL3OuNSOVf/+MtsceYP2TVuydAWztfkYv02BJk7PjX4OhbbwukX3rnIyaO3ymVGWUIFMJTFqhp+R5GwEfqE0SZl9jQhclw9rqpW2nB5kSxhynQ6qELc/ZEP1gTpGhNEoJ+EUCHoGS5w/UnEvlcPzTkXxwZS/O/gAIkH+shuJz9BAIjik2X0lkhpzUKSqKz9IjVQhoI4fPuqsMQXeQmVeZKH3VhrXFj73bT9FkLXVr3YiIVH1sZ8qFBjb/w4bPLaLQCsSOlmOIkzLQNnRKWaaC9FmqIfXeYX4dwu9AmDBhDns89gE8g/301u/C3tmIzznApicuwO9xoY1OZsJZ97L1+WsJeD3IlBqUhkjcA8OTw6QKJT17S5l5/b+oXf0SUrmKjHl/wJQ8+lc4q98OtvZ9IWH8DQMt1fjdTqRSGW63k23PXUvOMZfgddjImHcGALv+cz+5Sy+ldtWLoRhp0e9j67NXM+702yl9+eZQf8lTj8UQPwpLRiH5J15H9cfPoI1KoujcB4jMGu4mYm3dy45Xbydu7KyQMP4Gj91Kf2M5TZtXMvmivxKdNyV4jCnLiB83D2PyaHprttJS8jHm9HGoTNFoIxNJm3UygkRCw7o3hh3P53WF/JmHXps6nP2d1K55bdhrbTu/IK5gLhseP4dJF79Jw8aJiAH47PY+jv1rJBUr7dg7/WQv1BBfqMAYL8cY/0Pvxk/DPejH1u5HoRMwxMrRRkjRHlRe4XVLWfKAhf5GHwqdgGfwgECWKhhWErDzDTvjT9MPiy8G8PvEUBmIxyGGLNi8TpGKDxyMW34gECRtuoqE8UqsLT5q17hInqTClDhUkqTPULN3jYNpFxtBgIh0GYYEKV6nn93vONj8jwP1yMYkKYWn6Kj+zEnaDBUyuUDBCcE63qIz9dStdaKPlTL9UiOVKx1BYQwgwu63HUjlArOvNYVS6Rx9fjoqPNi7/JhT5Ch0wojJdgmFSpR6CZGjZEhlAsZEKYY4OUqNlLbdbuIKVOx6286EM4PnLvrA2uZnwh/0rH/SiscuggATz9ETkSUnOi9ca3w4EBbHYcKEOWwRRZGO3WspefYaBpqrSJ56HKnTj+er+09FEIJ/qOydjVR/+iyjj7uK8rceYuypt2BKzcdj6yUmfyYdu9ei0JnIW3Y5DV+/Sdqsk1HoLEw46z4EuRJ7ZwOiGHys/U3taJihqE3DF8LJNXrkGgMKvYW++l147FYkMjkypQZtVDJl/7obAJlChdvWO2RfMRBAF5NKXOECBpqrSJx0NIlFi6hb8zoeh5Wxy28k75hLkao0w8I3+puqqF39Ms2bV5JQtAiJXIlUrhwm3gWJlN7aUirefRTLqEJkChXROVPp2bsVn2uQ8nceweeyk3/CNUy9/Dncth7WP3xWcMa7YA4NX781pD+pTIEhftSw65C1cAX2zqYRg0Y0EfGYU/KJyp2KrWUTUTnT6Kzw4ugO4PeKLLprZHu6Q0XbLjc73xik6iMnGouEGVcYyZyvQaY48DmXKyXkHKmhtyFYn/vpzX147CIylcDsa0xseW6o04dEAj6PSMUHdhpLXGTO1eDzBPB7oWmLC6VOQsYcNQmFSrIXatjxz0F2/HOQKRcZkOxXHKakoKfv148HZ+cbNrpwWQPE5MnRREqJSJUjkQVnawtO1GNt9RLwBOt3FVoJlR85QnXG32Bt8hORIeeoh9TEjg7WBUekyyk6Q8dAh5+xJwa9jOVqKeufsPJt+hp8BLyAMug3/cV9/VR9eKDmecFtZgpOGJ72J0iEYLrdt9rNqXLMqXKiczz4nCJbX7Ch1EuYfrkRXaSUjgoPxzwaid8rojIIaCKlGGJlPzgjH+aXISyOw4QJc9jSV7+LVTctDNUU137+Em5bL8UrHmWgeQ8KnYnytx6mv343OUsuYs7N72JKL8DRUc9XT17I9KtfYfRxV+LoaaGjYn1QGGtNDDRVUvryDahNMcSNm8f2F/9Mwck3EjFqArof4Yf7/w1zxniyFp5P9Sf/CLXln3At8eMXoNSZ0EYnI1Pr6Ni1lrxll9FTszW0nc/tQmmMxG3tDrUJEilSpYbsxReCAP315bis3ez76p/s++qfLLg7loTxRwwbh9dho+QfV9JS8jEASZOPZtcb95G95EIq3n0stF306GkMtFQD0LLtEwbb92FKzsXR20zTlpXEFczB5w4Kn91vPkiBXEVn5QYAemtLSZl6LI7uFrqqNiGVK8k/8Tp0senItSaKzn2Qivcew+uwkr3kIuLHH0Ffwy5Sph1P05aVofpluUZP7Ng5lL/9CPGF81CZYxm7bB11kYU0bFQOq7E91Ax2eSl7y87ud4LnOdDm58NreznxOQkpk4bWMgsSgYg0BRFpCmRKgbadXowJUvz+AN++Xyw6S0/VR3a2PDvIxPP0lL1pI2O2hs9u7wsFGe58w84Jz0QybrkWZ5+f6s+cbPz7AEfcZmLO9UacfQG2vhQUt1HZckbNUbPxqQG8ThFjopRZV5lIKFSGShmM8QdmU539fgbbfQR8w+uRHT1+0qYPLdFQ6qVEfStYI+vIYKT2wWTOVaPUBW+4O8o9Q4QxwFcP9pM4XklE+k+b2Y0cpWDu9XKKz9EjlQvo9kd7f5/DR5hfn7A4DhMmzGFLb93OkDD+hubNH5A++xTK33oImUpLwak3s+ejp+mp2crmpy5hzq3vodJbmHPL+1S99ziWjAJ00SnEjpmNIEjwOgcoeebKUH9tO1Yz9pSbKH/7YVJnnkLOURcikYa/Gg9GF5nAuDPuIGnKMpy9bWgiEzAm5YaCNSIyCpl66TOsf/QcNJGJmNMLadn2KRGjxmNIyKTwjDvZ/sL1eOxWpHIlY08NpurVfPo8qTNPYvd/HmDC2feSd+wVVLzzKK3bPxtRHPc3VYaE8TcMtFSjjUyg8My78HtcSKQyHD0tVH8cFPKWUROQ7bdy66nZirWpkpjR00maeBRNmz/AmJyLQm8eUptc+uqtJE8+hqmXPwsIOHrbqPn0Wdp2rEahM5Ey/QSU+ggSihfhsnax/pGzURoiKT7vYTz2fqQKNSpTNF8/eDoeu5X2sjXEjp1NQtFCkia0U3jauVjS/refsYE2PzWrnMPau6q8w8TxwSQUKvE4RHa+YSdjppIFt5pp2eGhp9ZL+kwVapOEjnIvo+aqkcoF0mdrKH/fPiTh2+sUqV/vYtrFJo64w0LxWV6Q7K8F1kppL3ez6ZlgSUTWEWrWP3Fgdtra7GfDUwPMuspIWuTwcSp0ErxOkZxFGio+OCBgVUYJ8eMUQ/yPv4u0GUqmXmSgZL9N3IQzdEMcIuzdI9i42UUGO33UfO7E0esjZaoaU4KUiIwfDuyQyIT/+c1QmENL+N0KEybMYYtUrhzWptCZQiUVPpedgdZaxp5yMyXPXMGMa17B0d2MrXkP2uhk8o69HL/HRcDnQe7z0lu7naqVfxvSn9/jwu9x0V29hZixs3F0t4Rnj0dAY4lFM3Hxd76eNnM55rSx2DsbUOgs1K/7N6kzl7PpyT+iNEaSufB8pHIlkdmT6KhYj1ypZrCjHolUjlytI+D3IUgkKHRmdNEjX39BIh1iDdey9RPSZp1M3Rev0rp9FZlHnoujr4OWLSsBUOjM5J9wDbqoxOD+ghRb6170cen4vS5Upmj0cRlsf+lGCs+4g/ada4IHEkWaNq8kevR0Kt9/gtQZJ5J7zMWoTNG07ViNraWGlJOOZ80dyxh/5j0EfF6cvW1sfuoSpEo1Y0+6ga8f+sOQsbeXfUnarJNp3/U+o5cdjyDR/LdvyfciV0vQx0pxWYdaY6hM3x9PrDJIyV2kJfsIDYIQnFVOGK+kq8bLR9f10N8YFI4pU5SYkqUEvAxZWPgN31jVKTUSYvOH/h5HjpKTtySYqOd3D9uV7mrvsMVq3yCVCWQfqaH8AzsTz9NT/7WLiEw5Y47TklioJOAX6W/04XUEMCQcCCM5GH20nCl/NJCzWEPAL2JJkyGRBL9TBrt9aKOkSBXBSOpviM6V4+wP4HEEsLb4WfeYldyFGtorvcTmy7EkyxEk4ZKI3wthcRwmTJjDFmNiDpHZE+nesyXUlrv0MqQqTTDRTK3H73Gyd9WLTLzgUUpfvglbWx0A+oRMis97CKUxClvrXjyOAQwJ2SROOpq9nz435DiCIMEyqgh7ZyMSeTi69ecgSCSYU0ZjTgkublz00NeUvngjAG5rN86+dgzxoxhorcaUmI1k/42P3+um+IK/IAb8dFZuIip3CnHj5o94DFNqPplHnk/1x08D0LN3OwnFi5l44eM0rH8TRJG0mScSnTOJgM+DPj6Tvn27SCoOivqEooWU/ftuKt55lLTZpyJT6THEj8LvdlK35jWKznuQjt3rkMgUpE4/nm0v/JmkiUcDQQs3Y1IOSZOPwe924h7oxjPYj0wztA7V73bCd2gkZ28b0blTEcXhYvJQY06RU3y2no9v7A0tqotIlxFf8OM+3weHf7Rsd7PzDXtIGAM0bHSTMVtNwyYHeUdp6Sg/aLGiAGnfUzYgU0qYdrERY5IMhWb4xTLESVEZvltoRmUrGG+Q0N/sJWuBGkOCFI1JhtseYPfbg6x91AoiJE9RMfNKI1Gjhp+zIAgjLirsrvbSuNnJvBvNbHnORn+jj8QiJYWnaqle5UQiEbC1+UmbqqKj0kPAJ8fW6iVhvAqNRUJEevj74/dAWByHCRPmsMWUnMeY5X/G2lCB12lDobegNkXTU72NcWfcgb2zkdixs2kq+Yj++vKQMAawtdTQWroKURQxp41FEAScva2kzVzO3s+eDwVNKA0RSORK0uecitIQOeLCqsMVj91KT20pffW7UWj0mNPGEjFCjPKvgTYiHpU5GoDcpZfSVbWJui9eBUATmRC0WgOMCVls+utFGOJGUXDqrViyijDGpY/Yp0yuZMxJ1xGZVUz7zi8wJOUQkz8DpdaEiEjl249Q89nzoe3Hn3k3Sn1w0ZvXZUcXk8b8Oz6iZtWLeB0DyFQaemtLicyeTPeeTfTW7cSSXoBUoaG3bifGxByyFq9g2wt/RvT76K/fjbWpiqJz7sfR1QxAZ/l60uedQd3qV4IHFQRMKflE58+gc/e60Fhi8mfQu6+M5MlL0UYmHNqLPdK1UgikzVBy/FOR9Nb7UGglxI+RjygIvw+3zU/DJjedlcOT5HrqvIxeqkcMBJh/s4nd7zhQ6gTGn6Encfzwpz4HY0yUMe1PRgY6vNi7/aEaZJlKoPgcPVFZ3y8yDXGyYSUUnZUe1txvZfRSDYY4GX0NPmrXOJEpBcxJwfO293jprvHhsgUXwkVmKIbYtNV+5aJypZMFt6qY+icDBCAQEEGEiAw5216yUXSmnupVTmLHKDAmyvD7RFbf3Y/fI1J4uo6chRo05u+foQ9zeBMWx2HChPlF8Lmd2DsbQJCiNscQCPjo3L0OW1staksc5vQCzMl5Q/YRxQA1Hz+DMTkPqUKNo7uZXf++h/jCBfQ17MbaWMmeD5+i+PyH6azcOOyYA817gsl5OjMyhZqy/9zH9MufZ/4dn9Cw7g00kQlE5QZtvmQqLZGjRvbTPVzp3VeGq78DqVyJTKlh31f/QgwEiMw8PM4jdfoJVH/yLDKVdsjsv6O7hfq1bzDrz/9i1xv3EfB66G+swO91Yu/YN6I49jgG6KrcSMfudehi0ig47Rb0caOQSIMiRJDJ6dy1lsGuRiIyiyk45SacvW3o4zPY+c+7UZmi2bvqRcYuvx6FxkBr6eekTDuOvsZy0maegMYSQ3vZlyi0ZnKO+RMSqRxz6liqP/kH+cdfQ85RF2Fr3YvXMUDle48TlTuZhKJFVH/0NLlLL2H61S/h93pQGSIo+9fdjD7+KqKyJ9FbW4olvQBRFGndvgrNd6Tm/S9QG2WkTpWROvXn9yGKAl01HuLHKbG2DF2kFp0rJ26MHH2MnMEuHxlzVKhNsiFuGD+EIUbO1D8ZyZitxtbhRxspxZwqHRK28WPpb/KRNFGJe1Bk49/31zF/DC3bPSy+z4LPHWDnv+1se3kQv1cMWuiN8+EeDGCMl5E6TYm928fcP5vo2etFkELDJhfNWz0IAix7MhJTkgy/B/KO1mBOkWFr9++/SdCw/dVBvri7H7lKYMyxw50twvx2CIvjMGHC/E+xtddja6uh/J1H6d6zhei8aSROXIJSF8FX950Uqh9Nn3MaY5bfgDnlgECWyhWkTD+R9Y+eM6RPS/o4GtYfsNra8drtzLjqJRq+fnPIdhGZRVS+/wTx44/A2ddO3Lj59NbtZPyZd5JYNHzB12+JvsZKrI0V2LubkSpUVK18itHLLmOwox5NZBKa/bO2vyaRWcUccfen7Pnw6WGvdVdvIaFoYSixTmmIoGfvdho3vkdcwdxhtnr7vvwnG5+4MPSzISmHI+76JFSfbE4ZzeQ//Y3RJ16LraWGNXcuI6F4MT7nIKaU0VSt/CsTzr6Prc9fh7WxEqlCRVT2RBrWvkHD2jeIzComfe7pSORKXL0dlP37bhw9rWQuWoHb2oXCEEF88WIGGitIn3sappR8LBnjyTv2CjyDvRgSsjCnjmGgpYbsRSvo3rOFpIlHgSChZeuHmJLzyT3mYnQxv5w4PhSoDEGrN1ubn+gcOZ1VXhBg7AlaEgqV6GOCIlYX9fPlhEIjIan4v49M1kVLSSpSsuFvQ+3n9q1z0bvPh63Nx6anD3gjV650oDFLSJig4JOb+ig6U0fSBNWBUhQBis/S47KKdFd76aryMPMKI9ZWHwqNhM3P2vC5RDLnq2nY7GbC6Xq+fKifnf+xk7tYg0wZTrr7rRIWx2HChDmk+L1eBlpr8LlsSBQaBjv2senxFTj7OgBo2vQ+tvY6kqceS0LxYpo3BxdP1a15jcRJRzHYXo85LR9ddFBEJE5cwoRz7mfXG/cilSsZfdxVtGz/LCSqAXyuQRQ6M+lzz6BuzasIgkDKtONx9XcSO3Y2HeVfE5lVTPLkY4Ykn/1Wsfe0sOmJC+jY/9hekEiYcM79lL/zMJMv+TsN699EKlNiTh9L5KgJCJKf/0fa0deBq78DudaIxhyH9CfWZEdmFmEdU0nNp88OaU+ceFRocaQgkZB//NXsfvthYvKmD+vD3tXMtheuH9I20FRFb+2OIYv31OZo3LZeSv5xJWIgQETGeHa+fgfRedPw2PuRq3VYGysBkGsMuG09oX27q0vori5BH5dO4R/uwtHTyvQrX6SzYj0b//pHTKn5pM1cTsKERSRPWRraz5SUPWRcpuRctNHJuKxdyDUG9PGZJE86GkEiQRebjsYS+5Ou3+HAqDlq6je4SJ+pYswJWiwpMmLHyFHqDi8JEZ2rwNY2PJYZQJBAU8nw1X8Nm9xE5ypYcl8EA60+1j5qPRB8IsLWl2xM+aOBnr1BH2ikQWeMlu0euqu9eJ0iXXu8TLvYQGe1B0u6DF2EFEEaXpz3W+bw+mSHCRPmN43P5aBh/dsMdjXSvusrMhecDQRCwvgbzKljMMRloI9JQ22MpmbVCyCKuAd6kMiV1K15nbxllyFTqlGbohlz4rWkzToZa/Me+ut301WxYUh/qTNPxuuyYe9qZO6t7+EZ7Kdt5xfI1TpkSg3NJR8RlT2Jrc9dw/w7PvkFr8j/hr66MrqrS0I/i4EAVe8/Sdy4ebSVrmbHK7cAYMkYtz8h7uc9V2/b9RWlL91IV+UmJpz7AKakXAJ+L6aUfAzfURc8EvHjjyB36WVUffAEYiBA/IQjSZp8DNF5U0mbdQpeh5XqT57Fbe0m5+g/DZs19vs8wXjpb/Ftmz8Al7UTV3/ngQZBAESyFq5ADPgRJFLEgB9Xfye66NRh+ycWL0am1BGRWUTvvp1IFSqyFp7PQGsNju4WBlqq0ccO3+9g5CotcpU2+IPegi466Ycu0WGNyiglZ5EW/zwRQTp0sd7hhNYiJXWGmrgCO207vaF2S7oMQ7xsRI9ic7IMuSZYW21OkxGdI6el9EB9tRgInu/SxyPZ/e4g1iY/qVNVBLwi0y81suaBfhCh9ksXadNV9NR6GX+6LpQUGOa3ifS22277tccQ4plnnrltxYoVv/YwwoQJ8zPp2budvvoyyv55F9kLz6O3djsSuZLW7Z+Fthl93JX01u6gauXfaNr0HoGALyQ+UqafwNZnryZ9zqkIEglq04HSAIXWCIIUEEieuhT3QC9iwMeo+WcSmTMJQ2I2HTvXUPavu7F3NpJ/0vV47f1IpHJ00clUvPMX/B4nabNPRh+T+stfnEOAZ7Cf5m2f0l21meQpSxk1/yyszVW4+jvxOm2kzTyZvoZd9NfvBgg6RCRkETN6+GzsD9HXWMGaO4+lv6Gc4hWP0FWxgdKXbsTrtBHwefE6rEiVGsSAD5ni+wMN5GodsQVzSJu5nKyF55Oz+AKMCZkY4kYR8Hlo2fYpanMsky58jOj8GcN8puVqHW5bz5C6ZZlSw9hTbhzyGYGgYO7aswlnTyui30t03lRat39G6oyT6KhYT9zYWXTuv7kKBHxkL1pBb90OxICPtNmnIpUpkKm1GOIzcfS20Vdfxr4v/4m1sZKO3evwe1yY08cR8PmQq/9/1ZVKpMJPsivzeUX6GoIL7pQG4RcR1UqdhMQJSuRqAddAgNyjNMy80oQpUYbSIGHf105c1v2LcfUCM68w0t/sY986N9tfG8SYIGPcSTqat7kJ+EGmFJjwBx0rr+6he48PR0+A1h0eonMUWFt86KJk9Df5MKfKyD1KTdZ8NQnjlOG0zd8It99+e9ttt932zLfbhV/CUubHUlRUJG7duvWHNwwTJsxhSXPJx9StCToSdFdvJX3OqXTt2YxUpqJx4zsodCYyjzyX8rceHrJf3rGXE1+4AFt7Hc7edgCicqcQO2Y2crV2yLZiIIDT2o17oAuXtQvXQC+mxEzMqWOw97TQU70NZ3872qhkvrhjaSixDCBp8lJGH381sfk/XSz+2oiBALvffHBIeUFk9kTyjr2CDY+dT8KEheQecwnWpkrEQACJTE7JP64kddYpTLt0eM3vD9Gw8T3W3LEMqULFhHMeYMvfLyV7yYXY2mrxDPaTMu04ate8RsDrZsxJ15M0ZRkqvWXEvmzt+4IC1O/Hkl6AISHzJ49nsLOR2tUvs3fVixiTcxm7/EaicycP204MBGjZ/hmlr9xMT/VWspdciDl1LAqdGXtnPdaWauLGzcfZ04ImKon2XWuR7o+9bi75iL59Zcy7/QM0kUm07/iCkn9cOewYRec+QM2qFxh/5t0kFi/5yaUmv1dCQlgvQSqHpm0etjw7wGCHn+xFGiaepyfyF7I6s/f48LlEdNESpPIDzhHWVh/N29w4+/wodQIeh0jZf+z01B4ox9DHShk1T03Vhw5mXG7E6wqw5r6h5VjfiGavU6T09UGWPGCh5AUbUqXA4rstmJJ++oLCML88giBsE0Wx6Nvth6SsQhCEq4EHgShRFLuF4C3TY8BiwAGcJYri9kNxrDBhwhy+qExRKPQWBARc/R2IAT8tJR8z49rXSJ15IlKFmtrVLw/br6tqM4lFi6l873EGWmpC7dOvepFR888csq0gkaAxR4+44EwbkYB2StAmy9HXzpSLn6Lyvcexte8jsXgxKdNPQGWK+t5zEEURR3cLglR2WNWH2trr2PH67UPauvdsYbCjnsLT7yAicwJf3Xcyzt42IBiWMvuGN7/Tc/eHUGpNIAgYEnPord2OTKlBoTPTun0VE865n23PXxfadv2j5zJToSZ99inD+uneu50NfzmP3tpSAFSmaObd9gFR2RN/0nh00ckUnHITOUf9CalSg0wxslWYIJEQWzCHaZbncPS1odSZMSWPRpBIqXj3UfateR1ddAo7X7+TaVe9gFyppvztR0L7x40/guatnxBfMBdt9MiL57zOQayNlay563gWPbSOmLxpP/o8/F4Pzr52xIAfmVJLf2N5sFQlKRdt1G+z/MLW4aWv3s+Gv1lp3ubBWuyUhQAAIABJREFUmCBl+qVGVh0UKV3xvgOJFBbcYkEqP/Ch9NgD9DZ5IQAKrYA5WT5k1tVl82Nt9iNTgClZjkTG987KehwBaj53Ur/BQdxYFXtXO9HFShm3XEfcGEUwqU4MJgB2V/sxp8qGCGMAW7ufhPEKshaoGWj1EfAPP55UCZqIYC3//JvMrP/rAH37gv3UfuVkwulhcfxb5r8Wx4IgJAELgMaDmhcBmfv/TQKe2v/fMGHC/I4xJeUSVzCPsn/dTcr0ExAECRMveIzK9x6ne89mZGoducdcCrwzZL+4cfPob6wYIowBSp69mviCuWh+hmjQmGOJzJrEuD/chd/tAEFAG5OCKTF7xO39Ph/OvnZqP3+BhvXvEDFqPAlFC4kffyQKjf4nH/9Q4x7oCQZMfAu/x4UpJZ/mkg9DwhiCJRj7vvwnSdOOQwwEfnBRnhgI4OhpQaEzI1frMGeMI+foi+mr30XEqAl07dmMz+1k/Nn3INcYUFvihhyv8r0nSChahEyhRrpfuIqBAG2ln4eEMYCrv5PK958g4soXflZMt1Jv/sFtZHIllvSxWBg7pF0Xk4Ylo5CO8q+RqbRYGypQGiKYftVL2LtbQPQz0FrDng/+iujzoo6Ix5ich7WxItRH9Ojp9DeW779oIj17t/9ocdyzt5T6r9+kp6YES0YhxsQcGje8Q9PmD9BGJ1O84hGkchXROZNRGiJ+/EX5FRFFkf4mH2VvOWjeFnxKE5uvoK/RNyRSGqDyQwdTLzKG/IldA36atroZbPdTsdKBVC5QeKqOtBkqFBoJvfVeVt3eR1+jj+kXG9j0tA1nv5+8o7XEFyowJQ4XoK07PHz9eD8TzzGw+p4DwSR7PnZw6qsxxOQpMCbIyV+mYaDdx2BHIHgD+a2xeu0inz3Rx8wrjTj7fehjpdjaD4SgFJ6qIypbztaXBqn7cmjte1OJhwmn/xcXNcyvzqGYOX4UuBZ476C2pcDLYrBmY5MgCCZBEOJEUWwbsYcwYcL8LpCptCQWL0Yik+MZ7Ke/eQ9ShZLuPZsB8DkH8Qz2kThxCfbORnQxqYiChNgxs+jYvXZYf167FX/TZtBqQPPTxYI5dTTm1NG4B/uQqXRIZcP/mDr6O+mr20l/YwVetx2JRIo5NZ+O3Wvxuewo9RbiCub+9ItxiPG5nSQUL6Kl5ONQm1xjQBeTghgIDLuxALA2V6Hduw1L2pjvrbPuqS2lbs3rNKx/C31cBmNOup74cfMYc8J1dFVtxNHbQtbC8+lvKKf0gyeRq3XkHHURnRUbaC/7EgCVOYbytx+hvewL0uecQfLUZcgUavoPEpah49VsxeeyB+vIf0GicidT+8UrqIyRwSCRXV/RVbmRmPwZSGRK2nZ8HtpWZYyi7PU7KTjtFuwdDfTUbicqZzKxY2bz1X0nh7ZT6n5YrAM4+zpY/+g59NYFretat68icdJRpEw5luaSj7B3NtK4/h169m4jadIxxIyZgTExF0N8xqG9CIeYwU4/7gGRxs0HBKKtw0/cCEl8uhgpMtWBWdjeei+DHf4hIrZ5m5tj/xpB2nQ1pa8P0lTiZv7NJj67vY/A/gne+g1u5lxnInuRgC7ygIxxWv3s+9rJwrssrLrjoMQ+glHQTVvdiIi4rAE0FikJhSp693kZc6yGXW8f8HDOXqim9isnEil4BkXWPmJlxmVGfB4Ra7OPhEIl2hgJXVVejAnDZVTK5O8PQAlz+PNfiWNBEI4BWkRR3PmtxxwJQNNBPzfvbwuL4zBhfudI5Qo0EQl8ec+JmJJHEz9+wZDXm0s+Ysolf8fe1cxg5z5kKh19+3ahNsUilSvxew/YLWXNOQnthjtBo4Kco372mL5LwPTs3c76x1Zga61h0oWP43c78DismNMK0EanghigueQjjEl5v3qJhcfeT9bCFWgjkmja/D7GpByyFp5P774yNJGJxOTPpHHD0Bn55KnL6Ni9jrRZJ39Hr+Ac6KHinUep3Z/wNti+j66KDSx84CsMiVmUvXEvrv4O0mefSs3+2G3PYD9l/7qH8WfeTXvZl0hkcjKPOId1D56G12Gjs3w9rr52xpx8AxGjJlD7+UtDjpk46WgC/pEttw5msKOB5m2f4BroJjp7Egq9BZUxCm1k4s9a8KSPSWXKn/6GtbmK0pdvRh+XQVflRjx2K/q4oSK0a89mYgvmsuOVW9HHpWNIzKZl68doIhMP9BefEQqR+SH66neHhPE3NG9eSfLkpehiUrC11dFXvwt9/Cgq3n0UiUxG6Us3MfuGtzAkHJ4C2esMIJFAwA+RmXIae4K/u607PBSdqScqW457IEDOEg0RGXIM8VJkygPvmyDAnk+GPw3Zu9pJ3Bgle79worFIEP0iSoMEZ+8B+8by9+xEZsmGiON965xEZspx2wJIRgioE/0iX9zTR+sOL1IFTPmjkcJTtBSfoyd5sore+uAMcWeVhx2v25GrBXzuAAEffPWwFXOylLxjtFib/USkS1GZJRgDkFikpHlr8NxHzVGRPuu/92w+XOiu9dBR7qWzykNkppzUKSr0sb9/o7MfPENBED4HRvqrcCNwAzCSk/5I31ojrvwTBGEFsAIgOfm3ZY4eJkyYkbGkF7Dgzk8offU2YsbMQipTIIoiErkCtSWedQ+chtvWiz4ug9xjLqG7egua6FSmX/0y1R8+xUBbLaOmLiEzRo5kexk0bfyvxPFIOPs7+PLe5dha9zL+zLvZ8vRlIQ9kQSpj4opH2Pz3yzDEZ5JQtOhXF8eW9AIaN7xDV/Vm8k+8DsQApa/dhiE2na6qjWQeeT6jj7uKPR8/jej3MeqIc5FrjcSPPxJNRPx39mtr3Uvdl/8c0uZzO+hvLEcbnYJnoIeY/Jk0rH972L4uaxcFp9yMTKVh+8s3UXTug6Ggjo6K9cRUbEBpsDD7prfY/tKNDDRVkVi8hITxRyJVaL73fL1OO6Wv3kpERiFaSwKDHfV468qQKtUYEkZhyShCZRh+02PvbaO/fjd+twNTymgM8aOGvK6LSUEXk4I2KonBjnpatn1K374yRs0/k5aSj0I3Z11Vm1hw1ycIgkDr9s/wuZ1MXPEoSlMURec9hMoQQfTo6Rjifpxw9bkdI7ZLFSpc1i4AovOm0br9Myac9xDGhGxU5lhsnXUMdtUT8PtQGSIxp475VRYAiqJIe7mHnr1e5GoBlVFCwyYXsWMUKLQSshao6az04rIGBWz5B3bmXG+kuyZ4E9RV5WGgRULLNjdxYxTEj1ci0whDZpK/QaGVINcKzLvJhKs/QF+Dj2l/MiBTCay6rQ+/N+hbbO/y4+jxo4mQ4nEEKHlhkNnXGmkt9ZB7tIb1jx8IA5GrBcypMlp3BC3e/B74+jErcWMUpExWYUlV0F3j4bXTOvE6gnLF6xQxxMuQKQV8bpG+Rj/rnxwgdaqKpGIFcqWAJkOGKUVHwXItKr2E6Fw52ojfh3hsK3PTUenB1u6n+jMn214eJH2WikX3WFAbf9/x2D/4DoqiOH+kdkEQxgBpwDezxonAdkEQJhKcKT64SDARaP2O/p8BnoGgW8VPGXyYMGEOTwRBIHbMTKb86a+suftErPtrNJV6C2OW34Db1guAra2WyvcfJ3bsHEyJ2WijU5i3eBHe9nLUTR9By/6lDNG5h3yM9s4mbK17URojcfS2DgkHEf0+mjZ/QHTuFDorNrDxyYtY9OBXaMy/nkDWx6YRUzAXiULFztdux9XfSfz4I4nOn0HpSzfRXbWZ4j8+zpyb3ibgcWPr3IeAQPz4BSg0hu/sVyqTI1fr8AweeAydPP0ElIZI6tf9h6xFK2jZ/im6mFRsbbVD91Wq2fPx33H1B8Ud++uac5deRkTmBCrefpjmko9AEJh2xfOojFF0VW2i4r3HmF84b0hfto5Gemq24OhpRaEzo41KQqEx0Fa2huQpy/B7XOz5+BlsbXtJnXEiWUuUWNLHodQeOLe++t3sfvPB0KJPpTGSmde+ji4mFeO3HDJMyXmYkvNY8uhG+vbtQqpQs+Dez+ndW4pEKiU6bxqW9ALm3PQ29u4m5BoD2v03GfE/sczGYx+gt24HppR8+ht2h9oTihfjcznwOmzEjZuHTKUla9EF6KOT+fKeEyg65wGat3yMvbMBv89NdO4UHD0taKOSMSZkIVN9/w3GoWTfOhfvXdaNf799cM5iFRlzNHx5nxV9rJQpFxlYcLOJwe4A2kgJ5hQZjZvcuG0itk4f1mZ/aHZVoRU4+pEITMkyxp6gpWGjC3H/pLBUCZnz1VR8MEhnpY+d/7aHxpA5T83sa02svrufnEUaeuq8JBUFZ2klMtBGSGna4qajykPaVDUzLjfSVuZBZZSQOk3Jzv8M98ru3eclZXKwj8hMBcufj6LsbTv9DT4y56tp2+Viwa1mtr1io7/RR9p0FTlLNLhsfrxOiCtQoo/+bYvhgF+ko9KDvcuPxiLFkiGjZaubj27oxe8GBJi8wsCutwep+8pFz14viRP+n4vj70IUxV1AaLm4IAj1QNF+t4r3gYsFQfgXwYV41nC9cZgw//9o3/VlSBirjFGkzlqOXKMnddZy6r/6NwC2tjrS55xGZFYxpuRcMCiQbXgQHN3BTlKmQ/KM/2ocfq+XrqoN9DdUoNAaicgsQqE1IlNqkKt0I6bmuW29qE0xwTG2VNNTsw1/Ui76nxB+caiJzBhHRNpYEosWEfB5kCl1tJZ+RsyYmVjSC4nKnogpMZdAwE+kM5gaKJV//6p5Y1Iuky56Cpe1g4DXjUypQROZwKYn/4i9q4kxy28gYfyRKHRmuio3hmZA9fGj9odpdIX6kspVFK94BJ/LwUBLNZHZE8k44hy2Pn05Jc9cwZRL/s7O125nzPIbgKDrg89lx+d1s+6BU+msWA+ANjqZovMeQq4xEJ07FYlUxq437sWcPo4J59yLvauJhvVvYm2qRBeTSuzoGUjkSjorNw5xQ9FGJtFdXUJPTQnJk5dhOiiaPHT+idkYD1qkGfstT2i5WospKecnvlND8TislL/5AHnHX01i8WK69mwmafJSIjLG4ervZP6dHyORKRjs2IfaFEN39RYUWhMSmRyZUsVgxz40ljjkKi32nhaat3yITKVj7Ck3fqd93qHE2e9nzYP9IWEMoI+R0VXlZaDNz0CbnzfP78aSLiNlspKCE03IlBJad3hwD4oYE2SUv7v/cxMrpfgsPb11Xqo+dJA0Wc4xj0Rg7/Gj0AqYUuWsfbCfsSfq2PnG0N/LmtVOshepmX+TCZlKgtchoosOijSZQsLE8/S8fVEXc/9sZvc7g6TNVJMyWYnSKKCLloT8jQ/m2yUCsflKYvOVOPp97HwjuAahfqOTCX/QIVMK6CIlmFLlCKKAJuK3KRCtLT66qj1I5QKWTCnNm718cW8fCDDmWC2ZC9T4PCLTLjLicYgY4qRsfXmAvKO1bHxqAK/r9z+P+b+63fmIoI3bXoJWbmf/j44TJkyYw5j++qAwjsqZRELRIio/eJI9H/6d9NmnkLfscire/QsKnQljUh6GhKzgTvGFcMFm6KoEmQpi8kEX81+No7lkJV/ecxLi/jrXiMwJTL38ecaffS9b/n4Z2Uv+OGyfxOIllL8d9GNW6Ex4HTaat36MJWMcEemFv+is3cEIEgn62LTQz5lHnM2o+WcOcaOQAvIfMT57dwvtu76kb18ZKkMkCp0ZY2I2rds/xd4VXDbic9po2f4ZGXNOY9pVL2JtrERlikGqULHhL+eE+kosXoLaFIvf46T6k39gbaxEExHP6OOvYeoVz7H9xRvxOgexZIwjedqxdFRuxNnTirOvHZUxirRZJ4fEsb2zEWtTJdqoZPoay9HFpBKRWUTqjJPord2BGPDjsQ8glSvx2Hrp3rsNTWQSzp79DygFgcLTb6e3bgddlRtJKFqIvacFXXwGMvkvv1hKZYwmcdLR7Hz1NtSWOCwZhQgEKHvjXmJGTyfg86KPy8DrsOHoaqKndicTzrqPntptdFVuRBeTQkf517Tt/IKpl/0DbVQSpS/fTGLxIuILR3y4e0jx2EX6G4bWiAsSQiUUAFIF+FwiTSVufG6QKYML8Fz9AQ4uLx9zvBZHr5+yN+1kLdCw6z/OUCKdLkbKwjvNNG/zkH+sOGIxZsAr0rDZhTFeTuFp2iGhJInjlZz0XDRtuz1MPMeA1yWii5ISmSnH6xQpXK6jt64Pty3YceZ8FbH5I988akwyis7Q09fkI3mSEqlCwJwiQ6H5+VHsvzb9TV4G2v2svquPvKO1WJt9pHtUfHZrL8YkGflLtWx7zUbqNBVrH7Fibd7vziHAkXeYsbX5McRLiRz1254p/zEcsjMURTH1oP8XgT8dqr7DhAnz2ySucD5VK//KqAVnh2pRAWpXv0LO0RejjxtFwSk3Ejd+PhLpQbMwlvTgv0PAYGcjpS/dFBLGAD012+it2YpCZ6HwzLuQKlRMu/xZyt99DL/bTtbCFXRWbsDnsiNIJEw45362v3gDWUsuoK9uJ7vfeABjcg7ps0/Dkj72e47+y/BDNm0j4XEMsO3FG6jbP9MqkcnJPPJczGljcVm7Q9vJtUZ6a7bSWzM0oOnI+9cw9bJnsbXXoY1MwjXQjUytpXnrJ4xdfgNytQFBKqV7zxbc1i5yjrkEjSWO6Ve/htfeS/eeErY+dw1iIPgHOGPeH8g5+mKqPngSgPq1bzDlkqfxe52ICOQdewVdlRtx9LbS11hB/rFXYe9qxOe2I5UpCIgi6v114SlTj6Vpy0oisycikSrw2Pvp2PUVzr524grmoD1oUZ1roIe++l149tfAW9ILfvK1/CFkCiUFp9yMvauJzvKvMc46me6abaj0Eex45dbgNmod0y5/np2fPkfM6GmozFFEZk9EH5tGf2Mlo+afhcfWi8vaQ9uO1QDYu5u+77CHDG2khIzZavZ+cWDx3K63Hcy93kTZm3YKlmtRG6UMtPmIL1DgcwcACbGjFbSUurGkBP2JA77gP0Eq4B4MoDJJhkQ1D3b4KX/PQXSOnJ5aL5Z0Gb11B35vddFSFHoJcpUEhR58HpGGTS4McVL08VIGmv2oDBLGnaQb4qUMoDZBxlwBU0oUA20+NGYpUTny762dlaslRGf9NgNeumu91HzuwOsUicmTI1MJrH/CSnyBipyFGrY8N4DbJhKTp8Dvhbyjtaz9i5XE8Uq69ngPCGMAEbY8Z2P2tUZSpkagj/n9ezj//uV/mDBhfjViRk9n6uXP4ehpHvZaw9dvsuDuTzAlj/5Zfrc/Fq/TxmBnw7B210A3puQ8vn7oDCBYP5tYvBh9fCaxBfMI+DzEFczFkJTDrjfuJ2Pu6eAPMNBSjSl1NK2lq6j55FkWP7wBY9LI3smHM9bGqqAwFgTyll2OXK1DE5lEa+lqYvJnUP1xMFF1xPdGEHD1d6HQmenYvS5owyeKLHr4a1KmLKX0pZsY7GzAlJpPwam38PXDZ5J5xLmYUkYjBvwMdjTQsXttSBgD1K5+melXvwQfBH+OHj0dpT6CUQvOpaemhK6qTXjtVnpqS/+PvfOOjqpM//hnep/JZNJ7LyQkQOi9gwqIgr33rmsHe1ndFcuK7aeiYsGG2Bsg0pEaINSQRnpPJpNMppffH4ODY3BXdxHb/ZzDOZ479773fd+Mc773uc/zfRh4/kPUbfmUsuWLCE8rRDrxAhxl24kpnEjOzOtR6MJp2beB5FFz6G0LPBx9P+9hVy8kZ8Z1iMRietsbOfTl8+xd+hh+nxelIZIxdywhftCx6sz/M05rF+bK3fR21Ac8ldMKgy2mw5JymfTgF/S2VON2WGkuWcOuN+8NXuuxW9n9dqAAUWmMRSSSUPnN6zTvXUdYSj7pE87H53WjCo8K2udpo1L+q3n+UqQKMSOu0eG0+qjb5kSpFzP0Uh1iGcx8Mpxd7/VSvz2QG3zgcxstB9xMvCsMXbSU5GEKXL0+TnksnI3PdiORg98LKqOEnua+jiVNe10UnqFhw0IL0x8Jp2K1nbqtTmL6yyk8U8OK+zpRhUkIS1RTtcbBuictFMxVI5aK2LMsMIfCszQMuVgf9FP+HoVWQlyhhLjCP7fVmqXRw0dXtyGRiyg8S43b5qd2q5PcGRoadjpR6uXB6LlIHPjncQQi9dpoMa5eX58x7Z0+tBESovv9uffue/647wcEBAR+9ygNEUTmDEMi62ttpI1NB5HkVxXGEBAQyaNO73NcF5NKb2czQ654CqlCjddpp6t6H3EDJmNtqcGYVojaFI+zu4P20s1E5AzD47JR8c3r7PtgAbroVNInXUBn1a5j3PX3j8cZEBLZJ19N486VlC1/ha6afex+8278fj/Drn0OtSmO5r3ryTrpypBrM6deSlvpZpp2ryJjysUApI4/B4+9l7aDm0mbeD55c27F2d1B8Su3kzPjWkq/eB6HuZnvFl6OSCwmImsI4h/5TrttAXeBmAETyZh8Mfr4DGq/+5BV957EzsXzKf3yBTKnXILd3EzZ1y8jlatIHXc22xfdyt4PHuPbB2agjU4mOn8s4ZlFSORKSr944egN/H52vHYn3U0VAHRU7mTPe48GRbrD0sbWF27A2vbLI7Juey9733uU5fMmsOHxC/j6ttGULV8UYlmn0BgC7bPjstDFZhBTOCFkDEvtQbTRqehj09nx2h00FK/A63LQUbaD3UvuR6bUIhJJEIklDDj/QUzpA3/xPP9bonMVzPqXiXPejmTmk+E07XHx+S2d9LR4qd/uDDl378e9NJYEjikNEpbf00VXrYekoQpUYWK0UWLAT1hC3//3EwYpsLZ5mDAvjJotDgrOVDPnJRMDz9Ng6/Ax7tYwUkcr2fxSN40lLmLyZaiMEna/1xuMTO96u5fyVTZqd9jZ9HwX5ats2Lu8fe71Z6W9LJALPuh8LfhFLL/HTMn7vWxcaCG6nxzJD/TtrvesjLrewPc/w3XbnRiTZYh+pA77z1UTlfvHjKL/NwiRYwEBgV8Na1sdPY2VeFx2DEm5WGoPAiCRKcidcS1uew8Vq97A47ITlpCDMW0ACm3Yzxq7q+YgvZ0N+NyB17LO7nbkunAis4agMh7NUZapNPQ79Sa8Lgc1mz5EaYhi4EWPoDBEse6xc8mcehmTH/4KV283yrAoNj55Ed31hxCJxQy79nmsrXVE54/Fbethz7t/D477vZODSPrHjKTo4zJRR8SjMkbTVbOflLFn0rAj0GBk4xMXUnDe/Yyb/z6O7g46q0oYeOHDACj0Juq2fIZWKqerdj/qiATGz19KVL+RtJcX07j7W3oaK5BrDOTNvZ2qb99CG5UMfj8+n5fe1lrctm6a964nInsorfsDecYisRhjaiEzn9uJPjYDmVqHuXofm56+PJgS47b1sOf9R+k/9w4AEofPomLl4uCafB43O165ndG3vE580bSAka4/NHHV67Tj7O6EeLC1932j0d1Qht3cgvYHXRktDeWYD+8Bv5+w1P7H7LJoqTvIvg8fDzlW/NqdxA+aFlII2FV3iP0fPUHd5k8JS8ln6FX/Yueb9+KxW4nMGY7T2gUiEZ0VO0PGcvVaEEtlaKNTmfncLgwJ2Sfc0k1lkBBfKGH7692UfRNIsVAZjxFj84P5sIf4AT5i8uSc/M9wvnuhm8xJKnrbfRgSJEx9MJyeJi/952rY91Evfh/EDZCjjZZQ/GYPQy/TEVcgJ2WYGr/fz6qHzZR9Y8duPhrVlCpE6OOkNO1x9ZlC5ToH2mgJ4SlSvr67k6GX6xh2mT4kR/nPiv/Id96YJGX90xbkGhFuhx+JXIQmUoLfA1KlCI/DT1upG5nazoTbDfR2qtn9no2OKhdTHzSyc4mV3nYveadqKJyr+a+8xf+oCOJYQEDgf8Jcd5COsh30NFWijUwiImcYxuQ8AGo2fgiiQNOI2AGTSB17Fn6fF7FEBhIppZ8/z+E1bwMBN4sRN75E4rBZofnHx6C9Yif2zia6G8oQicTseuu+YNQxOn8sgy9/AkNidtDCzJRZxMibFtH/rPlIFWoMCdnYOpoITx9E9YalSJVq5BoDu99+MCim/D4f2xfdwvi7lhFXOIGmkm/7zKOheDm5s244bnt5ItFEJjD5wS9o2BnoCmc3N6OJTAp22tvz9oPoopJxdLUiV+vwOG1Y6kqpWr0EhSGCqH6jEEtleJ12UsbMxdHdTvHiefQ0BqKyrl4Lu968l4EX/h2P044mMhHXEQs/t60blSk20NabgOXa8GueIypneDCa7HW7sdSXheSKA9g7GtEcEa5SlRaXrZsfYzc3IVXrCUvMRaJQhbTdVpvi0EYmHdmDvt76+vhM1KY4HD2dmKtKQCxm63PXBVtGa2NSGTfvXSKzh4Vc57Sa+4zl87hx2Y46Ljh7LXy38HJa928EoLlkDebDe8iZcS21331C/7Pmo9CGI1XrEMvkwQe/7zEk5hCZPaTPfU40iYMVyFQi/D5QhUkwJEqw1B2NzMb0l9NW7sZl86M2BvKVjUlSbJ1eelq8FL9lpfWgG02kmCGX6Jj7UgQ9rV7qi53UFzsZfZOB3nYvqWNUQMAasv9cDRFZMlr2uzj4hQ2/HzImquiqD3gr124NjV4bk6SYazyIpZA1Vc2Wl3rIOUl9zJbTfxbMNW4cPT50sRJmP29CFS5ixNV6bJ1eZGox+jgJXXVeLE0eJt1twOMCpU6Mw+Jj1d+70ERKmPWUCXOdG5HUz5T7jXTVulEaxIQl/Xn37VgI4lhAQOC/xmZupXLl6+xbtiB4LH7wSeTO/hsShRpdbDrdDWUkDDmZAx8/RelnzyGWycmcehmmrCHUfne0sYTD0kbt5k8wpQ1EG5PS516O7g4gIEL2LVtAZM4Imvesxd1rCQpjgJZ96+ms3EXZ1y/T7/SbMSYFonZyjSHkNbTaFMuwaxay+dmrcXS1IlWowe8nYdgMTOkDUUck4Lb3BCKG+DGmDugzJ0N8dp/Oan8qCxfmAAAgAElEQVQkwtMGAGJ2L7mPln0bGHrlU7Qe/C4oJg989iwFZ86jp6kKU/pAZCod+rhMfF43Pc1VyI60CwewdzZh+XGraL8fhdZI05419DvtZna+fheIRISnFWJMLUATmUTOjOtQRyWh/0F7a6fVQkdFMTKVBpFEGiKQ5dowRGIpAy54iPIVr5A69iwOfPJ08HORRIrf72ff0n+Sf8Y8xtz2JttevAlbRyO62AyGX/889q5m/GIRpswiCs65h73vP4rf50OhC2f4dS/gdTkofu1OOip2kTxydlAYQ6CDYNXavuJYF5uGXGMIsQXURiWjjU79wbVVQWEcXGt3BxGZg8mbcxsqQyQAPq+XQRc9wo5Xbg+elzzmDKL6hdrM/VbE5Cs4Y1Ek5atsWBo9TJpv5MDnNlpLXcQVKtDFBJpyqMICUWWxREREppz6Ygdf33X0ISJrqpqK1Q7WLrBgSJRQeJaGgjlqFHoJYQmyYFFd0z4nh1bYaNnnJrZQzqnPRODo9tJR7WLHa1Ym3ROGLkZCT3NAoOtiJGgjJTi6/EjVoAoT43X78f9JMyssTS7M1R46KrwcWmFDJIYB52noaYG1C7qwNAQWHpUrI2mYMpDrLZKx/+Neck5Rs+2VniMjuane6GDmkyY8Dj8bn7ZQu81J3mlq0sb8Nu48vxUiv//341c3ePBg/44dO/7ziQICAr8Lmveu55u7p4a0fAYYfesbyDUGtrxwHfj8pIw9A3P1XqL6jcLn9VC7+RPkaj1xRdMpefvB4HVR/UYy6tY3MPygq5nT2kXt5o/Zu/QxZGo96ZMuwG5uQamPoKtmH/XbvsBubgm5f8E591KzYSna6FTG3/1BsCjqWHQ3VdJZsZPOqhLEUlnACUAswZjULyRfNWv6FXjcDqqOtFmWKFRMeXg5Mf3H/k97+Hug9cB3HFq+CGNSHvqELEQiMR2VO3H3Wjjw6TP4fV6GXP4EYrkKU/pAetvqcFo7icgowpRZhEgkore9ns9vKMLR1Roy9qQHv6S3tYbd7zyIUmci/8x5uO1WRGIxtrY6MqddhtftxHx4D16XA2NaId11pfS0HEYklmBI7Mempy7C2d2BVKFm+A0vYq7eS8P2r0gZPRdtTBrW1hqqVi9BFR5L8sjZHPhkIWkTL0BljMbdawkKZolcia29Hn1CNkpDJD6XE0NKHm5rF87uDvTxmRgSc6hcvYSNT1xIxtRLcFjaqd/6eciaInNHcNLjG/q84WjZv4nNz15NV80+InOGM/y65zFlDAp+bqkr5dNrC/B53CHXTV+wrs/3yGXrpqO8mO7GCtSmOCIyB4ekC/1eMNe4qdvhwGHx0VrqpnmvG1OGlFHXG4jKltNW5qK1NNCC2JgqxW3zs/4pC3KtiMIztGxf3BMy3oBztGRPU5I4OBA17m5y8+E17XRUHH1ASh2tpOgCLcuuCriqaCLETJwfRvuRc1y9PkqWWpn2YDhN+53UfucivkjOxPlGJNI/V2qAy+ajar0dS4OXDf86+mA2+/lwylc62P9paGfGoZfpkGtFbFzYTd4sNXU7nHQ3hj41DLtcx853rMFOgZPvNTLgrJ/+Df0jIxKJiv1+/+AfHxcixwICAv8VdnMzbls3Q69eiFwTxs637qWnoRxT5uCAZ6ujl/w5t2MzN6HQGJGp9ez78IlgVDI8rRBNRGLImLGDpqL6QRc6t9NGw/YvMR/eS8roOTgsbbht3RgSshFL5TTuXkXsgMlUHUnN+B5DQjaOng4s9YewtlRjTMn/yXXoY9NRGqIQy9XUb/mUln0bGH/XUtb+46yQ88qWL2LyQ18SmTUUr9uJPi4Dm7mZjspdJ7Qw6tcgLCUfY1I/dr15D163k4isIQy95jmsbbVMG34qIokMsUyOQmdCKlcQmTOsT/6hJiKBUX97lTV/Pz0o/vLm3Eb1hg/oba1mwLn30dtez+ZnryL75KuxdzZxeN17yNR62kq3Ur/9C3xuF+Pv+Yj6HV+hjUrB53HRtOsbRt38GiCirXQzvW117P/wCfD72V1zpPOiIYJRNy/G2nKY7S/djFgmx5Q2gNIvn6f14Gai88aQMfkiSt57hPC0QiJzhnPgk6eJyBjE5ueuYsQNL5M86jRc1i4Ofv4C9iPuKg3FK+k3+ybqt35O7MDJhKcVUrPxQxKGnHzM1J/ovFFMX7AWl9WMwhCJQmMI+VwXm07B2fewe8n9wWOJw2cSlpTXZyy5Wk9s4QRif1S093vDmCwDEXQ3e4jKlVMwB0wZgRbKrWUu9i7rZdc7RzvTRefJGHWDnvJv7HRUuvuM13LAhcvqxZQmRx0uoa3MHSKMAQ5vdJB32tFIZm+7j4Nf2UkoknPgcxtqk5iTHgnH2uFBZZDQb5aanJPUfzphDNBV68Hn9VOz2RFyXKaQ0F7ed3/dDj9d9YH99Lj9x2zfLZIE/KQBYvvLSRn5x6yr+F8QxLGAgMAvpqe1hj3vPUr5EbsviULF6Jtfx+3oobuhnD3vPUL6pAvZ/sptwVfiSkMkY+94G3tnE4jEaKKS8XndSGQK/H4fGVMuJXnEachVWtrLd1CxcjHdjRWkjjsLU9ZQGnZ8Rc3GZXiddgrOuQexXEW/mTdgrtmLvauFpl2rkChUFJw5H4UhAqelHYlChUTe1ynjx8jVOiKzhrD5mSvQxWUEfH6P8Vato3JXiC3YmFvfpPXgZrTRqT+7kPD3SEd5MTteDRS5iSRS8ubcRsvetdg7m9HGpKDQm3D2mJEq1ex8bR45M68nc9qlIX7BAJH9xjD10VV0N1bgsXfTVV9GZM4wKle9HrQfE4nF6OMzOfjpQgrOvgtndwdiiYSCM+fT216Pq6cTbVQyZV+/FHwjcPCzZxg37z2q1ryNMbWAyOyhtJVuDd7XaWnHUncQVVgUfp+XzGmXsfWlG7G1NwDQuHMl3Q1lxBdN49BXL9FZuYuofqPw+7yIJFLWLziXWc/uxNJwiG0v3sCIG14CwN7RgNIQyeSHv6a5ZDUdFbvInH45cQOn/OReKvUmlHrTMT8TS2XkzLqeyJxhdFaVoItLJzJnOErDsc8/XjitZrpq9uOydqGPz8KQkHVcxzcmyTAeIye1rdTFnmWhLZtb9rvJn60hc7IKmVpE1fpQURedI6d8tQ1bZ6CV8U8V0P1Y6FZ8a0euExFbKCMiQ0ZPs5fa7U4Gnq0jdbTqf1zh7xgReF1+ZD8SudbWgDtIy4FQgayJkASdKKrWOhh+lZ4NTx+NOCv0IlLHqogfpEAiFWFKl6KJ+OtJxb/eigUEBP5nOit2BYUxBBwAumr3U7XmHXqaKogdMJFDX/1fSK6ow9JGV+0Byle8irX5MNqoZArOvZcZzxbj9/nQx2chlSvoPLyH5XeMx+MIWI1lTb8Sj6sXY3I+iUNPwdndyY7X7mDIFU/RsPMbUsedRUTWMPLn3B5ohiGS4HXZCU8fSNr4c9HF/LxmIjK1HlPWYNzWLlxWM9qYVKzNh4OfqyPiQwWz30/tlk/Jn3s7to7GP7Q4/r6IDmDw5U9QvHheyNqLLvknfvyIxWJkGgO7l9yPSCym8Jx78LpdtJVuoXL1EvxeNwlDTmHPuw9jbakGIG3iBQy58l9UrVmCXBNGwtBT2P/RU2Sfcg3VGz4IFgACZJ9yDR6HFb/PG5oq4/ezd+k/GHXza+x49Q4yJl1Ab1sdto5GEInIPvkaxBIp+z96ilE3v4qjqzUojL/H2lKNMiwKCKQ3pIw5k+a9azEk5NC6fyM9LYep3vAB+P1IFEpG3PAivW11aCIS2P7yLZir9wLQtHsV7aXbGHP7EuQa3S/ea6UunPiiaQE3jROAvauVbS/dzOG17wCBIsYJdy/DmFKARKFCrjH8Ki4Efp8fp9WPt6+RBH4fbHqum2kPhZEySkn1poBAjsmXI9eJkGvEqMMDkfnofjIis2W0HToq8tLGKxFLA+kUve0B9wqFTkRkuox1T1ooukiLTCWiYK6OhMF/7qinMUlK0x4nKaOVVG1wBPOqDy23U3imhrYKN9UbnSCC7OkqzIfdGBKkwRzt8m/tjL/dQEeVG02EhKypaqKy/zqWbT+FII4FBAR+MfbOpj7HZCotPUf8Y+VaY9C27YdYW2rof+Z8Nj9zJdbWGiy1B9DHZRHTf0zwnI6KnUFhPO7uDzm87p2A68URBpz3AGPnvU/Nuvdo2rMaP37iB02mYccKNJEJOC3tuB1W+p91F7GFE3929zipQkn/OXew7rFz8Lqd5My4jobi5bSVbiEiczD9TruZDUcahnyP12lj/8dPkTPj2p91j98rqvDY4H+LJdIQYQxw6OuXGHjBQ1Stfpu0CedR8vaDlH7xAlnTr6Cr9iAr5k8MikqntYuBFz3CxicuxO/zUr/tC3QxKfSbfTMep4097z1Cb2sNygnnhQhjgPIVrzB23nv0ttX2maPD0o5ILCU6fwxOaxf9z5yPq9eMITGX0q9eQiQWYT68B2trHWpTbJ/rEYkQ/cC8VSQSYUwtoHr90sAeGGMIS8ojacSp1G76iNrvPgECqRDfC+Pvqdv6GeaaPUT3G/XLNvoE0NNUhblmHyKRGGNqf9oPbcdSd5CovFG0H9qG3xMopqxY9QYd5TtIGnkaWdMuRx+feVznIRKLMMRLSRurDIkOKw1ikkcouPiTaGQqMfFFClr2qTHXeOg87GHvsl5OfSYiKI41JikT7wqj5jsHTXtdJA9XElcox9rqZfYzEXTVebA0evA6YfNL3YQlSciYoMKYLEMb+e9db/4MyFRiUseo6DzsYsYT4TSVuBBLRYQlSTHXuRl+lY7BF+qQqkQotGK6GzzoYqXkzlDTvM+Fs9uHTC2i3ywNUTkyFJo//579HARxLCAg8Is5VjRWJD76o9pcEnAn+GEHMAB9XAYdFTuDUVlbRyMeV2jBiIijUSyxWBQijAH2LnuMifd9grPXzKCLH8XaUkNX7QEisobgddmDFf6lX7zA+HnvkzD0FMRSGdb2BqxNFdRu/gRnTyep484hbsBEpEpNcOzw9IEMv+4FnD0ddDeUIxJLyD75KjQRSbjtVty9obZhMYUT2PHqHfg9HiKyhyGV/TGjVKasIaSMOYPqI2krP8bZ3YFYpkAZHkN3Y0DQqsJjkSg0lH7xPPj9FJ53HypjLN0NZTgsrYy9421K3vs7XdX70CfmgEiMTKVl2NULcdt7gg9AP8Tv8yKRKTBlDkEkFuP3HfW0zZx2GYbEHDK0F1O/9UscljZ8bicbn7iI3Fk3ULHqdSQKFX6vm+aSNeTMuC4wtyNkTL6I+iM+zvFDTsJuaUOu1uPoamXA+Q8SlpSHVKFGIldQ/No8TFmDSRw6A6lKy5jbl1C75RNqN330gzn9/vJXOw/vYeVdU4JFkRnTriC+aAraqMD3d8D5DyKWSNnz3iPByPq+DxZgba1h+HUvoNSFH9f5xBbI8bk1aKMl1G51EpkjY+glOkxpoZFJQ5yMzsNuHF0+xvzNgCE+VJrE9JPjsvow13o48Hkvcq2I7ClqVEYJsQUKrG1eOircxA9SYEqXoov+a0kbfYwUfUxgzdlTNP/23MjMo3t/rFQYgQB/rW+QgIDAcSE8azBDrniSXW/ei8dpQxebTmTucDRRyfS21uDs6cSQlEf/s++ictUbyFQ6MiZfRPX699HFpCNTBV5Hh6XkozHFhYxtyixCptYjU+txWbv63Dum/zh6W2txWtrpKN9FdP/RdFXvx+o8THdDBcOv/z+6avZjqS+l5P1Hkaq0tB7ahim1gPWPnxcUf4fXvsvYee+RNu5o4Z1MpUFpiMBpacWQ1A/w4/O4OPDxU0iVGoZc+RTNe9fhczuJHTCJym/fAr+fppJvcXV3IP3RWv4oqI3RZEy5hLDkPDTRyX3s09ImnEdHRTFJI05lzUOnIRKLGXDeA8hUGnweF0mjTqejYif1W78IXpM59VLyTruFLc9dgy42nYoVr1G+4hX8Ph8xBRPod/otqIzRIekTKePOoXrDMgrPvpuJ935CyfuP4uhqIeeUa0mbeB5qYzRqYzQiAg9N3U0VFJ53P8171wIiRt74MogkZEy+GLk2jIRhM7C2VCNVqPF53IilMtInXoBCH4k6Io7e1lpmPL2VsOQ8pEoNxpR82su2EztgIvq4zKDvtdIQyaBL/kF03lh2vnEXMQUTMKb0P4F/of+Mz+vl0JcvhriFROUMZf1j5wb/lk27v2Xkza+GpJwMvvwJ7J1NfHPXFOKKppMx+UIMx2hy8t+gNkrInKwmtlDG0Mv16KIlxyyKk8hERGb99Kt8mUpM+jg18QOV+Lx+1MbQ6KY2UvKXiBILnDgEcSwgIPCLUelN5My6gaj8MbisFvRx6ehiUpny8FdUfvsWrQe+A3yowmKIHzQNj7OXkncexuO0kXvqjTS8uIK8ObcTnTemT6W+MSWf6Y+t4fCGZahM8UiVmmCUUabWEZ0/hu8WXkFE1hD8Xhdr/z43cKFIxJArnsTv9+N1O0kcOgOVMZbm/RtRG6NpO7SlT1R03wePkTj05KBYBzAk5uJ12nH0mAlPG8DmZ67E1WtBrjHg93pIHnUGh754luLX7gxGESOyhyHX/HFzjgEQwe4lD6CNTmHYNc9SsXIxto560ideQMyR9BRbVwuDr3gSfXwmCp0JkUhE5tRLsbbWsO3Fm0KGq1j1BlF5o5j5fAmtBzbRuOub4H4171mDMa2Q8Xcv49CXL2FpKCU6bwxel4NDX/4fcYMmY8oYzLg738XrdiKWSJFrj0Y1w9MKCU8rBMDe1UJ80TQU+gjkGgN2cwsytR6lIYKEwdOx1JXy1e1jcPdaUIZFU/HN6/g8bgrPvY/U8ecQlpgTHLe3vR6X1UxM4cSjhZcE8uUPfrIQU9YQRt+yGJUxBpfVjFz9y3OOfy2srYfprDzaylwTkUBXzf4+TVRcPUd9hosue4yajctoK90CBFKaGnZ8zZS/L0d1JD/7eKCNPD4RSqX+56VICQj8rwjiWEBA4L9CIpURmRXarSssqR9Fl/wDj9PJ9lduxZiSjyY6mep176OLTSdj6qXoE3KYeN9nqMKiCUvKweN2YmuuB78PlTEGmVqHKWMQpoxBmKv3M+qW1yl59yG6qveRe+rNVK0JFBYlDJvB7reOWmLh97PrjXvIPfVGypcvAmDgRY+QMPgkwI8uJg2pUovf68bndnHw8+fweT34faGuFAptGHJdOM37NhCdP5Zx85fSsn8DPreTfR8+QXzRdDRRKUG3BGVYFIMu/DtS5R/bJF8TmUzWSVdS9vXLbP2/G0gYfBK5s2/EbevB5/Oxa/F8zNUliESBdIcRN75MWFI/JApNiP3e9/h9XiRyFXZzE+bDe4jqN4qcU66h4ts36aoO+FOrw+MQSSQodSYqV7+F0xLwrbW11bP7rfvIm3MHMrWWtgPfoY6IJyJ7ONqoJPRxGbSVbqVsxSu4ujvIOvkabJ1N7PtgAc171qCNTWPkDS8SWzgJRVgMhoRcWvdvCGkXrYlKQh8XmmfbVXOAsuWLyDrpqj7rMVfvJXH4LByWQCfA3Jk3oItNI2HIKbhs3YglkpCHrBNNd2MlUXmjaSvdQsbUS8mcegnWlhqSRpxK3bYvgyLZbmkjZcyZdBwuQROZFBTG39NZuYvu+rLjKo4FBP5oCOJYQEDguGG3tGNrq8XW2XzENutFYgsmkHf6LUjVepw9HWz61yVkn3QVW1e+ysgbF7F/2QLay3cQ038cETnDiS0YT9iRrnbGlDwkChXDrnkOEeDzeWnYHnh1/+PWugAepy0k93nfB/9EE5VMV80+JFIFhqQctjx3LSKxmIEXPIRMY0Cu0fcZx5Q+AG1UEr0djVjqDhKWnEfrvo2kTTif+KKpRGQPI2fm9XgcvRgSc9BFJ/86G3oCsbZUY0wtYNQti3FY2lCb4ukoL0YVFolcraWzcicAfgLl8Ac/XUh4WmGwEFNljMFubg6OF5E9DJUxhlX3nRyM2Fdx1A0jImsoHZW7SB17JmsePi14XSAFwoXHacfWXsue9x4NfqZPyCZj0oVE9x/Hyrsm43U5KLrkH1SteYuumv10VATmaG2qYtX9M5ixcDvhqQUMvOBBvn1wFh57wFYsMnck0f3H9fEqFonFWOpK8Xn6frfC0wrpbihDZYwmZ9aNSGRynL0Wyr5+mQOfPI1UpaPwnHuIGzQVqeLEW4eJRGLc9h5G3/om2ugkKlcvoXr9UnQxqQy5/An2LVuAyhhDRPpAlPoIEoacHNJZ8keDndjJCwj8zhDEsYCAwC/G7/OFuED4fT4ad62ietNHxA2YQPnK12nZt47sk69GqtTg6DHTsvkT6rd+QcE5d6MMiyJzyiV0N5Shjkyid9NHVH77FtaWamwdjRScNR/ZkUI5fWwa+thAAWDZysWkjD0rIIJEIiQKVUiqhDoiHpc18NpYFR7LkCufov3QNspXvIJcE0bOzOsZd/cyqla9Tnv5DoZc+TQ/hUIXjkIXjj42HUtDWaBZSFgkmshkRCIRMfm/j1a+xwufx8nWF65HptahNERibakBYOIDn2HvbO1zvq2jAY/ThjE5j57mKkbe/CplX71M+6GtxA89hYzJF1O3+ZM+qSy1mz8maeTppIyZS8U3b+D3+ym6/Amq17+HLiaD2MLx7HzjbtInnk/pFy8QlpJ/RMj1UL1hKRKFitYDm/C6HGgiE+lurEATmRTI//7hetwu2su2I5ZIiew3klOe2oy5ei8ylRZT5hDU4X2j3WHJ+YSnDaBh+1f0m30TBz97Fr8v8EYjY8oldFTuIiJrGG57N/budtw9ZrY8f9SpZPVDs5n0wGeowuMwJOQgU/374qjjhdflwGFuJXbAZOq3fYHbZgm6bXRUmDHX7GP0LW/g97pZv+A8BlzwIFtfvIGiSxcQXzSNhuIVwbFiCidg+EGqiYDAXxFBHAsICPTB3tVGZ1UJ1ubDKPTGI40Komgv205PUyXth7ZhTC1AG5OKNioZr9vFqvtPYeD5D+B12mksXg7AgY//RcE597Dn3b8DkHf6LchUetY/dm7wXjEF48mcdhnlK16lZd8GYgrGY+9sQvaDFtLfowqLwtpSzeDLFtDTfJjh1z7Pzjfuxt7ZhC4uk36n3kjx4nkADDj/AdoObuHgpwsBcNt6KH7tTsbNf4+2Q9voN/smxNL//BMoVagwHclv/TPTWVlCwtBTqN/2JW5boKVvzszrUZsS6W053Of8pJGno4lIRKELJ27QVLrrDpF/xp3g9yGWydj45MVE5Y7sc53X7aTfqTfSXLIGU8YAdr95L+Pu/oDMyRdT/s1iNj97NQASuYrC8x5AqlDR21aHNjqZwrPvRaxU4TtSqKnQhePoakWuMSDXGHD1WkLuJdcYOPDJ0+hi0ogbNI208ef82z1Qh8cw/q6l1G37kt72Oibe/xkeuxWRRIrH0UvG5Itp2PEVrQe/I2fWjez/YEGfMep3rCC2cAKV375F4Tn3oDRE/Lw/wP+Az+9HolCw9f9uJHPqJexd2vdBQapQs+utezGm9qetdAtuWw89TZWYsoYQnjEIS91BDIm5JI087SebmAgI/FUQxLGAgEAIHpeDppJvady5kt62OqJyR2Ku3odSH8G2l/6G3++j36k3BWzZKnex6817CE8fyODLHkehj8DWXtdnTLFUhs/jJqZgPBufvDjks+Y9a8mcdjnlK15FLJUhkat+MnfTlDaAttKtuO1W9PGZuOw9TPn7cvADIhG29npi+o8HsQiPvZe6rZ/1GaOzqgS/x8X2RbcSUzDhuNtX/VFxWjuQawwMvPBhXL1dyLVGRCIJto56Oip3MvJvr7Ln3YdxdLWSMu5skkeehiYy0P5bZYhA9QMRWLX2HSy1B8mccklfS7apl+GwtKGNTqZs+SJ622uRq3QoDSZiC8YHCzD9iOgoL6ZqdUDopU48n7Tx5+KydqIOi2HEjS+z5flrSR5zBqWfP8+A8x9k20t/C94nefQcJHIVPq+X4sXzKf3y/5j4wBd4nb24eszo4zPRH+MBTB+fSd5pR8fxuJz0NJXjslmpWPEq5SteASBp1Fzkx2j8ItcYsHU0BPatoviENPuQKVTgD/iPu209yLVGnN0dIef4vG48Tht+j5vwtAEA7P/oSTKnXYYuKoXk0XMJS8rHlP7nfxAUEPhPCOJYQEAgBHNVCVufvw5nTycATbtWkXvqjcg1Yfh9XgZc8BDgp/ybxVStXgJA+6FtKAwRjLl9CQqdEUNyPpaafUgVamRKLUOvXojD3IrP5w2O+0N8HicAGVMuISJ3BCpj9DHnpo6Ip/8Zd9DdUIbP40YXlxES5TIm5yFT62mv2IWtrRa1Ka5PQwuVMRqbuRmv005PY0WIW8FfmdQxZ/LNfSfjWfMOEpkCxGIm3PMR3Y3lWOoOcuDjp8ibcxv62HSa929k7wcLiB0wCSR9LbTEkoAtV9nyRQy+7HHqt3+Fx2El66SrEEvl7P/4aVr3rQNg0MWPBroPAhFZgznpiU207FuH2hTH2qX/ACBx2Exi8kZT/OrtdNXsByCmcCJT/7GK5pJ1FJx1F2KFmon3fUJ72Q4kcgWdlbtY/eAsxt7xNhXfLKa3tZaOQ1vZ8sJ1+NyuQDvzO98lbuCkf7svUrkCY3I+TXvWULn6zeBxR1czicNm0rJ3PX5fIA9bptYTkTWEim8WY0wtoKep8n/8q/x81JGJiMQSDq9/n7zTb2Xn63cFPwtPK6R1/0ayT7maHYtuw5CYi1SlxWO3Ur78FSQKFZMe+EIQxgICRxDEsYCAQAiW+tI+ArZ85WtMvO9Txt21lO66UjwuB4fXvRdyjtPSjrlqNw3FKxlw7r3YzS2IxBJ2v31/0IVg8sMriBs0hcad3wSvE8vkaKPTGHP7WxiS8ghPLfi385OptJgyBh3zM5FYTGTOcCQyJV0aPZqIONoPbcPnCbSe1UQlIaYX2XEAACAASURBVJGrg3mwCiFqHCQ6fwzTHv2GttJt+H0eTBmDMWUNRiSSIFNqqV6/NMQdZOy8d5HIju1Na8oajDY2ne6Gcna8ejuRuSNJn3QRuoQcJFIZhvgM/D43WdMvJ2HwySHti03phZjSCzm87v3gsdiBk2kv2x4UxgDNJauJHzydpJGzCU/tT2fNftoPbsbe2UjVunfxe70kjZ5LR+Vu4gZOoXHnSjwue/C74LC08d3Cyzn5qc3HzD/+MRKZMqR9eOPOlZjSBzPo4kexdTQglsoxJufjcztpPbAJfVwGihOYnmBKH8jACx5i5xt34+w1M+TKp7CbmwM5/5Y2DnzyNOPvXsbQqxdSu+kjBl/2OB6nDZfVTFhSP8LTfl++zQICvyWCOBYQEAjhh24P3xOePpDWAxtpP7QNW0cD8YOmIZZI8f7IQ9XrtNNcspqWvWsZN38pTSWrg8IYAtG2QJGejoYdX6GLy2TAefdhaSgje/rlP7vV879DLJEQkVWEPi6Dxt3fUHTZAjx2K8qwaMRSGZufuRKArOlX9PFY/qsTmTOcyJzhIcfii6ZgbalhzO1L2P/Rk/jcTvLPuIP4ouk/OY4uOoVJ935Mw47lmGv3E50/hsjsYRhT8gGIyFyEz+tBIv1p/1t9fGbANcHvR6bSBp0ofkhHxU70sen0NJSx/ZVb6W2tRRebxqi/vYrf6+Hwhg9w9nSSM+M69Em5aCOTGXjBQzgs7dRt+RRrSzW9HQ0/SxyHJeWROe1yDn35fwC07t+ELjaD6JhRiGUK5NowfG4X2xfdQu7sm3BbLSHpJL82Epk86D1ua6unee86KlYtDjwMHBH1PreL1gPfkT7lYqo3LqNl71qSR80lInsYSn3kCZurgMDvHUEcCwgIhBCeNhCF3hSSs5hzyjWsf+xcwtMK0cVlULv5EzKmXBIUCgDaqGRcR6yh/D4fTSWrURpCvVJ3vHYno25aRMLQU0gbfy5el53OqhJKP3+epKEzUEccvw5zcq2BlNFzA132RCLEEhmdh0sY9bdXUYXHEp42ALnWcNzu92dGG52MNjqZpBGz8Pt8yDX/ed+Mqf0xph47GikSif6tMIaAc8S4+e+z5dmrsZtbiMwZHtLkAiA8tQA/IjY9fVnQlqynqYptL95E2oTzMFftxjj+XFy9ZtRh0ax//Dw8ditJI2aTN+dWvG4XGmPsz9oDuUZP3tzbCEvOp2bTh5jSB2JIymXD4xeQOGwmxtQCvG4no299A2vzYeq2fk7CsBk/a+zjhVytIyZ/DACGpFzay7fTeeShInnU6ThtFuKHnETxK7eTMu4swlP601a69Zi51wICf2UEcSwgIBCCMSWP8fd8SNXqJVhba4ktGB/0r+2sKiFt4gU07f4Wh6WVAec/QGdVCcaUAsDPnvePetJK5So0UUkhYzvMzfS21VO37TO0kUk071uPpfYg4ekDkfxKTTR+WDQVlTuCqNwRv8p9/gqcyCYXEpmc1DFnEJk9DI+9B2ePmY7KXbQfaVqROPI05FojHntPH79eh6WN8MzBRPUbRc13H7HvoydRHWnWsvONu6nd/AlhyXlUrXkXVVgULmsX0f3H/seUHn1MGvqZ15I781rMtQf47l+X4nU7qd64jOqNy4gdMBlbZyM1G5dRdOkCTJmDf7X9+U+Y0gcw9ZEVdNeX4/d5UYZFoYlKxu/1oDHF07BzJSpjLMOve07IuxcQ+BEiv9//n886QQwePNi/Y8eO33oaAgJ/eRzdHaz751l47FY6K3eTf8ad7P3gn/jcLmRqHbmzbkQsU6AOjyEspQBHVwurHzw1eL1UqWH8/KU0FK/Abm6iZtOHiCUysmdeT+LQU9j24t8wV+8BQCSRMuXhr4kbOPm3Wq7AHwRzzQF6GsvxeT247d3Ub/uS1HHnsO4fZwZTGERiMUOvWohYpsDWXo9EocKQmMuW569FLJESXzSNsq8XkTx6LubDe0gddzYl7zxEWHIe4+9e9rOFot/vp6lkNU27V9Nevp2YvDHo4jLoqj1AfNF0ovNG/ZpbISAgcBwQiUTFfr+/z1OsEDkWEBDog1JvIu/021n90Cx8bhflK19jyBX/ChbXVa1+m6FXP01Py2F2L7kfr9vJ2Hnv0rxnLTKllsh+I+iqK6V++5co9Cb6nzkfv8+LuXofxrPmMenBz+ms3Inb1oMhOY/w1H9fJe91u36y+Evgz4/f76e7sQJndxuayCSs7XX0ttZhSMhh/0dP0u+0W9j/4RNAwH9ZIlOy9cUb8LocAKhNcYy++TWsrTUgEsPXi9DHZdCw/atgMWBXzX46K3f9bHEcSA2R01V7gPCU/pSteIXe1lo0kYlkn9y3/bSAgMAfB0EcCwgIHJP4QVOYsXA7XdX7kGvC0CfmEJaYg72rFXVkAlWr3qRx96qgVVrLnrVEZA0h66QrWfv3ucQOmERE1hAOr3uP9kPbEInFjJv3Hi171qGLzyJpxOz/OIe2su20l27Fbe9BH5+JKaMIXUzqr710gd8Rfr+fui2fsX7BeXgcvUgVakbf9gap48+hq3ovkbkjCEvqR8roOfS21aOOTKJkyX1BYQxg62ikcedKyle9Qfb0Kxhz21u0HdqCMiyaH7479brs+LweLA1l2NrqkWsMgZSfn3LlyCgiY9IFbF8UKAYMS8pj+A0voIlI+JV3RUBA4NdEEMcCAgLHRCQWE55aEJKH+X0b5/riFbSWbiVr+hUhfqq2zqagt2vahHPRxWcT3X8cXpcdXUwaxW/cjcjvJ2Pa5VhqDxCW1A9DYg7iHxRn+TweOip30rJ3PbbORlTGGMqWL8LR1cqwqxci15yOQmc8Qbsg8Fvh6Omk7eBmuhvKkal15M25lZK3H8LjtLHh8QuY9dwukkedHjxfF5NKZA50N1bQ29HQdzxLG3Kllr1L/8nw655HH5dJxpSLgnnxMrUOY2ohTbu/ZfeSB2gr3YJYKiPv9FvJm3PbMbvGSZVqkkedTlhKPi5rF5qoZNQ/4dEtICDwx0EQxwICAr8YtSmewnPu5sCnCym65B9YW2tQRySi1JvY+cbdDDj/AeqLV+Ba8w7Jo+ci1xhZ/dBs9PFZJI85g+JXb8Pv8yESSxh96+ukjT8Xv99H64FNdDeUs33RrcEiK5FYwpArnmDby7dQs/kTwjOHCOL4T47f56Psq5dCHrwSh81iwv2fsmvxfLpqD2DrbMRwjBQIXUwaaePPDbkWwJCYS+W3gW57to5GpAo1u5c8QNKoOeTMvJ6kEaciVWnZ/+HjtB0p+vN53Oxd+k8isoeRPPKn33QY4rOOw6oFBAR+LwjiWEBA4BcTnpKPHyg85156mqqIzhuLOioJdXgso8Lj2PZ/N+D1uCg89z7MVbuRKDQApI47mz3vPxosnvL7vHy38ApMmUNwWc2sffQsUsaeEeI+4Pd5qdv6OdF5o7G11SEW/e9eyAK/bzqrdlPyzkMhx+q2fkbKmLlkTLuUjopdqH7Cgk0kFpM67mw8jl4OfLoQmVJL9slX0VC8PHiORK7C4wo0gvHYexhw4SsotWG0HNhE8551fcY0V5X8W3EsICDw50IQxwICAv8VppR8ONLU4XvcditrHppN0sjZiGQKLHUHEUlkKPSBTnR+nxf/jxuHuBx0N5TRWbkLiVzRx5YLwGXtQhUeS2zhRDTRyb/eogR+Fzh6OkNyhr/H7bBS/No8Jtz7Mfp/E63VxaQy8MKHyT7lanqaq9n09KX0NJQDkD7pAkRiMQ3bvwIg++SrUB6x+1PoIwhLyQ9aFwbHiz9xPsA+j5v28mLay7Yj1xqIzBkeEpm2NJTRWVmCpf4QpoyBROeN/lm+0wICAj8fQRwLCAgcNyRyJcmj59JRvoOG4uXkn3EnVd++Rf4Zd5A4fBbgR6pQ43HagtfINYYjHbz8waKmH5M4bCY+r4fkUXORq0+c167Aiaft0DZc3R2YMoroqCgOHpepdSj0JvxeD05LG027v8Xv8yLXhROZNaRPd0WRSIQmIoHWA5spPOdefC4HIqkMqVJLZ8VOxFIFY+e9S9ygqcFr9LEZ5J9+G51Vu4OdHeOLphPTf9yJWTzQVLKaVfedHHy7oo5IYMK9nxCZVUR7+U7KvnqR5r1rCUvOQypXYO9sJmv6ZSdsfgICfwUEcSwgIHDcEEukxBSMY/fbD4Dfj9dlR6JQ0X5oG3EDp+AXiRl+w/+x7cW/4bKaURgiKDhzPpb6UpSGSKRKDYfXvUvRpY9xeN27eBw2sk+5muj8cRhT8gU7tz85bpeTsq8XcXjt24ybv5TSL1+kefcqwpLzGHD+g1jb60kcNouepko2/etSTFmDSRp+Krb2OlJGzz3mmN+3T65c8za9LdXoE3MpPPde+p9xR0iDGAi0Ho8rmsrUR1bS3VCGTB1GRFYRSn3EiVg+rt5udr5+d0jbaVt7PY07vsLZ3Ybd3IYyLJKUMWfidTuo+OYNkkbOxtbZhDr853X6ExAQ+M8I4lhAQOC4ojREI5ZI8XncHPj4X2TPuBalPgKpWo8+Lp2epmoypl6CVK7C47BSuWYJ0Xlj8HndTF+wjrqtn2MzNzPs6meR6YzoolOQKTW/9bIETgCOjkZqN3+Mx2lnzSNz6H/GfHJnXovP52XTwitwmJtJGDoDkVgCQEfZDvSxGTgsbYglMhp3f4smIoHovNHok/rRWb6DshUv09tSQ8qYMyk4+y6aS9ZQt+UzovuNPOYcRCIRpvSBmNIHnsilA+B1O7F3tfQ57rSa6azchTY2DXVEEuaqXcQVTSdh2Ez8Xg8+j+cYowkICPy3COJYQEDguKKPS6ff7L+xb9nj+H0+Sj97jrDkPAZf/iQ+jwd1eCw+bx6NxStQm+JJGHIKBz99hmmPriIis4iIzKLfegkCvxFKQwT6uEzaujvwuV3sef8RCs+5h91LHgieU7/tC/TxmagjErC119O8dx2DL1vA6oeOFsypjDGMvPk1drx2B6ljzkSm0qIKj8Vt60YXl/GbCN+fgyosktyZ11O8eN7RgyIRKmM0TbtXE+V1Y4jPpnHXKiq+eZ2kkbMJS87H1tFITMF4tJGJv93kBQT+RAjiWEBA4LgilsrIm3M7powiGnevIiwxl8jckYQl5QYLh3weDzH9x2GuKsHjtHHyk5sw/qi4T+Cvh0ytY+CFD/PtAzPxuhxoo5Lobqzsc17jzpVEZBZR215PRGag0cwPsZubcVraSB09l5K3HwikKYhE9D9zHprIZOw9HdR89zGRuSN/V77EHoeNiNwR9D/rLiq+WYxCF07GlIspX/kaCUNO4cBHT5E35zZsHQ3EDzkJRGKsLdU0FK9AoTUK4lhA4DghiGMBAYHjjiosktRxZ5E67qxjfi6WStHHpqOPTT/BMxP4vRM7YBIn/2sLnRU7+7hGfI8pczAdFcWojNFkTL2EjU9e2OccuT6cvc9edTR/1+9n3wcLKDznHiRyBV3Ve6nd/CnDrn4GuUb/ay7pZ1O15m2+e+ZKBl3yT3JPvZGO8mJ2vXkvUXmj8boduHotAIy9420s9YcQSaQoDVGYMouwNJQhSGMBgeODII4FBAQEBH4X9HY2Yu9oAvzEF02nfOVidDEpRGQPof3QdgDUpjjSJ55P7KCpSMQSlKY4Rt/6Bs7uDsq/eZ22A5uQKFT43a4+dnB+nxef14OzuwOZWs/+j54ic+olJ8SNoqetDntHA3JNGD6vB/PhEhS6cMLTBqIOj6G3vYHi1+cDsHPxPIouXUB0/liMyXm0l++g9LPngEAzk/WPn4/XGfBpjsobRe6pN6KSCMWqAgLHC0EcCwgICAj8prisFhqKl9NRUczBT5/B63aSe+qNSBVqNj19KaNvewtLzX78fh9uu5VvH5jJmNuXIFPpOLDscWo2fYhEpqDfaTeTffJVOLvb6WmqQq414rKag/eRKtT4/T7U4bFY6g8B4Owx/9S0jhvNe9dRuXoJTbtWYUjMJXfW9VStfZeGbV8SVzSN0bcsxutyhMy1ePGdDLn8CSrXvENPYzkiiZTCc++jfsfyoDAGaN2/iaThs0kaedqvvg4Bgb8KgjgWEBAQEPhNaT24iZ6mSvYtexwIdLlT6iPY9dZ9ROYMo3z5KzTuXBlyTf3WL9An5VCzcRkQaCaz9/1/MOKGFxGJpdg7myk87z72Lf0ndnMLCr2J/mfOw1x9AJnawP+3d99hUlZnH8e/Z8rO7uxsB5YFdpelLE2qNBsidjTia8Ru1BgVNASNid3YSyS+ljfGJMYeoyE2CBFFxRYVEKRJ77Asbdnepp73j3mygmAoCwzg73NdXMycp93n8DB775nznJOS3ZqkQBaZ+d32a92qN6xk3msPUfr1ewDUblrNlsXTGHrreNbP+BdbFn1B2ZIZpOd3pf2Q81n18avxA61l9l/vYthdE/D6UvGmpOH1p/HWNd13uEYk1IC/Zbv9Wg+RHxIlxyIiklAlMyeT5P92lbfkzFxqNq0CwFq7wwIfALhcbJz9wQ7FZUtnEsgt5JvXH6HVEccyaPTviYTq8QWyqdm0mtwjjmHRhCdpN+hHnHDnG2Tkd9ln9QjWlFO5ZiGRYD0Z+V0JtCqgrmxdU2L8H6HaCurK1nHS/e8SdsYRV61bTLezx+JNSWP1Z/8grXURR4y8mUDrTqTnFgAQDYcoPPocVnz40nbny+7QG4/Xt8/qIfJDp+RYREQSKj2vE+GGmqb3oZpy/DltAShb+hX9rxzH+lnvOSspxnuWC44awbIpz+9wLn9OHhltu9Dviofw57SlZuMKvClpzP3bfdSULqfPxXdRdPwFdD71Z6RkttxndagrW8+0p65l3bSJtD/uPNofdy6r//06/pw2dDr5cpa//4ITu5v8QWcSyC1k4YQnWfflBDCGbiPGktPpSPpc/hDd/+cGjMtDxneWrXZ7k+h1we00Vm1m/cx38frT6XfZA+T2HLrP6iEiYKzzYXMw6N+/v505c2aiwxARkQOocu0iFrz9OI3lG1g3/Z8A9DjnRrYsnsbmhZ+TWdCd4uHXsGXxdIzLRf7AM3Enp4KN8e9HLyNUWwlAVvue9Ln0Hmo3rmblR39l6/Kv6XHOL1n+QfyBvYKjz6Zl16MoOv4CAq0K9mkdVv/7dT5+YCT+Fu0YcNWjfPXML6kvWw9Ay25H0aJ4ACs+eJE+l9zDio/+SsXKueT2PJ62/U5h1gu3YaMRjv31ywRaFtC655D/eq1wYx11m9fiTkohrXX7fVoPkR8SY8wsa23/75ar51hE5DAUXriY6JJlmJxsvD174Mr6dqlkGwxiw2FcgUACI/xWZkE3eo68iZoNK+h08mXYmCW7Q2+OOPfXVK5ZQH15KS63l9SW+QSrt9JQuYmk1Exm/HEsg3/+B0L11RggKZDFpgWfk5SaTufTrqJHaibJTu9wdse+ZBX1IqNd1/2yDHnVusUAdDljNGs+f6MpMQbYsuhLioacz6Brf8/0p8c0JfMbZn9AQ/kGio6/gJVT/0rD1lLSWhXt8lre5FQyC/bvWGmRH7JmJ8fGmDHAz4EI8C9r7U1O+a3AlUAU+IW19r3vP4uIiOwroc++oOLUEaRccSkm1U/DM8/jO/N03AP6QelGwnPmEVu9Fne3YnxnnYG7ZYtEh/y9816nZOUSqq9m6/Kv4wvJBLLIzO9OLBah/1XjKFsynZbdjmbaU9fSpu/JhOqrWP/VZAA8KQFOvm8yA656dLfjaKwup2rdQtzeZDILj8DjS96t47KdVfd8gSwqVs/fYXvF6vlkFfUkVFtJrwvvILOwB1hLuL6GYG15vK7ZefhbttntWEVk/2hWcmyMOQEYAfSy1gaNMa2c8u7ABUAPoA3wgTGm2FobbW7AIiLy/WJVVdTceAtJRw8iumYt7u7dcPlTqH/5VVK9HmrG3kSsZD3uvn1I7VpM4xsTSB11ZaLD/q+S/Onk9Rq6Q3lmflcAVnz0CsHqrWQWdGf2y79p2h5pqGXGn27glAffxxfI3OH47ypfNY/PH7+KrUtngDEUn3oVvS64jUBu4S6PbdllED3Pu4XlH75Em36nUrV20Xbbszv1I1RbyaBrf0/JV+8w79X7ASg4+myKT7+G7mffQHrbYtJyd91zLCL7V3N7jkcDD1trgwDW2s1O+QjgNad8lTFmOTAQ+LKZ1xMRkf/CVlUTmb+QwLgHwFrs1nJcvXqQ3KMbFUNPh5oa/L8cAz4f4WXL8fbrQ3TtOlxt8oiuXoMNhXC3L8Tl9ye6Krstu6g3bq+PaDi4w7bylXMI11fvMjmORaMsnvSHeGIMYC1L33uGgqNHULl2IdFQA5mFPchot/PZLZIzWtDnknvosH4Z4cYaqtYtpHTWFIzLRefTrqJsyQza9DmJmk2rWP/VO03Hrf3ibbKK+lB8xigy2xXvfSM4oqUbiK5bjysnC3fHDhhjmn1OkR+a5ibHxcBxxpgHgEbgV9bar4C2wLRt9itxykREflCstcRKSrE1NcRiEUxKKkQjxDZsIrZxE57uXfH27LHPrufKbUXyJRdgvF7qHvod/rGjCf3rXYL/eIuMZ/+AbWwk9MnneE/oS3jqx4QmTMIkJWFnzSY89RNMIIC7fSFJJ5+Ap8Oh0YuZWdiDk+6bTPmKOTtsyx/0I5Izdj0rRai2Yoe5lPteei+zXriNipVzAfD60zn1oQ9pUbzD8ztAfDaJrPbxf8sTbnudqtJlxEKNhBpqcbk9pLYqZMk7T+9wXNnS6RSfdtUuY9xlHb6YRtXIS4mVbsAEAqQ99b+YnBwanv4znh7dSb7oPLy9ezb7OiKHu10mx8aYD4DWO9l0u3N8FjAYGACMN8Z0AHb2q+pOp8UwxlwNXA1QULBvnx4WEUmk6JYtND73MnX3PwLGkDL6SrAWV15rwrPm4B1wJOHGIOFPPiM49VO8gweQdMqJuNICuAsLMJ49778wPh/+sddRddHlpD36IFUXXA6RCCYri6Thp+DpUIR3QF9iK1biPeYoYmvW0fDUn0i57hrISMf4kolVVhJds47Q1E8gEsE74Eg8/foctL2Qxhjyep9AeruuYAxfv3gb0WADLboMpO9P7sPjS9nlObz+DAZc9Wh8pbr6ajbM+xivP6MpMQYI11cz7+8PMuSWV3c5r7DXn0aLTv12KG/V4zg2L/xiu7KcDn3xpWXtZm13LrpxE1UXX0msdAMAtraW6itGkfH2a5iWLal/5DEa/vIi2V98iKdL52ZdS+Rwt5OZ1bdnrT3JWnvETv5MIN4j/KaNmwHEgBZOef42p2kHlH7P+f9sre1vre3fsuW+m3NSRCTRQlOmUnvLb7C1tdiaGuofeRzjT6X21rvx9ukVT5Knf0XNmF8RemsidTffSfVlVxGZ+w21454gumHjXl3XZATwX38dwclTIBLB1b6AjFeew+TkEProU2qu+QV1v7mfmp+OxpXXGrweakaNxZ2dTd1DvwOPh7rHfk/408+Jba0g+NZEgpOnEKup2fXFEyg1J4/uI37BiD/M46ynZnPy/e+RVbjrXnkbi7Fu+kSmPz2GxZOeIhpqACzVJYt32Ldi1VzKls6goXLzjifaDR2HXbLd0Iysol7kH332biXw/01sw0Ziq9d8pzBGZPpMfCcOJfWBu7Hl5YRnaLpUkV3ZZXK8C28DwwCMMcVAElAGTAQuMMb4jDFFQGdgRjOvJSJyUIuuK6Fx4r9o+Nt4wnPn0/jPd3bYJ/zldDy9exKeMZPIzNkYn4/AE+MwaWnxc8xbQGTxUlweN6F339+rOGx5JXUPjIOYxTv8VNIef4SGV14j9NZEXC1b4DmiB3i9eI8/juCkyYQ/+xL/2Gupu/dhUq6+ApOSjO/kYXiO7IO7UweC739E1Rk/puHpv2DD4Wa10f5mXC7S23Qiu0Of3XoID2Dr8q/59JGLCFaV0eX0awjVVJDZtivpOxlfnD94BJ//70+Z+eyvCTpTsu2JrPZHcMrDUznxnkmcePdEht01gZbFA/b4PNuysRg2GMK02MmsI8nJRJetILxgIUnDhmLr6ojV1TXreiKHu+aOOX4OeM4Y8w0QAi6z8VVFFhhjxgMLiU/xdp1mqhCRw1lk1Woqz7kYV1Ym7nZtqL37AQJ33UbojQkQiTTt52rXlsjipXg6FhGcMIno0mWYzEwCj9xHzejrATC+JKJLlhGaMZOUKy7d81gWLia2chWe0T8j6bSTqfrxxRCNfwQH/zae9PEvE121Gld6Gq7iziT/9FJMSgq+886BZB+xklLqH/pd0/lSf3sfnh5dqb39HpLOOBVvj+7NbK2DS1XJYmLhEP2vHMeqT8dTMmMSAD3Pu4XeF93JgjceJRpuJH/wWbTqdhQL33qMmtLlFJ9+Nbndj9nj66XmtCE1Z99N2Rb+6msqR15C2pPjqL5iFATjDyamjBlNaNJkks4aDpVVJP/sMlwtcohu2oKrQ+o+u77I4aZZPcfW2pC19hJnmEU/a+3UbbY9YK3taK3tYq2d3PxQRUQOXuGv5+K//GIAQp/8G9/QIUTXl+K7+qdN+5icHDydOhDbsBHPkX2JLl0GgK2sBAuuova4O3aID6dwu/H22ruHp0xKfG7exonvEP7o06bE+D8an3keu3kLsa0VhKd/hXG5afjLC0SXr8DTuTPuju1xd/2217Tu9nvwHjWYwH13Yiur9yqmg1lSIJOkQBah+sqmxBhg/viHcfv8HPWLPzNw1JMUHv1jvn7x9qbt4bqqRIS7g9A77+IbdjwNb/2TjLdfI/WOm0m95w7CX04nVl6BSU3Fd+7Z4PNBsg+7ZUuiQxY5qGmFPBGR3RRdV0J43gKM14PFYoyLWEUFsdKNuPJaU3v7PVjnK+uGZ57HN/J/SL7ofHzDhmA3b8FkZBBZvJTAb++j/tEntzu3bWgg7fHfYqtrqL56DIEH7ybpxKF7F2ggQMroqwi9PxXbdcfpwdzFnYmVbaXx0ScJ/PY+DlZZlQAAE+RJREFUqs69uKl3O/zRp6TeeyfJl1xA43MvEV25Kv5An9dD4ztT8F143t7FdBDzJKdxxMibKZ317g7bNsz5kF4X3M6WxdNY+u6fiYVDQPyBu7Q2B8eDbTYSwV1YQON9DxM99SRI9UN9A74zT8dWVeFunUtwyocEX38bd8/uBO64OdEhixzUmjvmWETkByGyajUVZ51PdNlyqn86muiCxdTeeR+htyZRe8PN4Hbhv+NmUu+8mdT778J7wvEE35wI/hRiGzfi7lKMaV+Au6iQ2KbNxNZv84yy348rvx22oZHwp5+T/sKfIDtrr6d48/bsATlZJP/kInw/Oh1c23/UJx1/DI0vvoLJzia6tmS7YR8AwX+8SWTOPJIvHAmASU+PLzldWYmtr9+rmA5mqTltCNVV0WonQyRadTua6X8cS6BVAbk9jsO43LQoHsBJ971LRtuDIzn2DT81/sLrxZaV4W7bhlh1DbGt5XiHHEvtuMcJffgxqbf8EhqDhKZ9ldiARQ5y6jkWEdkNkekzcbdqQfCNCXj69CK6aTP+n19N7e33kvyzyyEcgVAQk5ZOrLwcT68jiNXVxXuVs7JpfPlVjN+P74zTMJ07kpqSTOiDj3G1zsV33jnU/+k5UsdcQ2jqJzQ88zyp996x17G6c1vh/9nlRL6eS6y+kYy/v0jjS69iIxF8555NdM26+I6xGLjdO57A68U2NgLgapOH/9djCb77ASa/Ha7A4TdWNb1tZ4qOG0n5yjlkFHSnau1CID5/cnrbzhQNOZ9oJEz3/7mBfpc/QHrbLrv9sN+B4B08EBuNklaYT8NTfyYyey4mJweT5CX07vskDT2Ohmeep/a2uwk8dA/Bye/D3bclOmyRg5aSYxGR3RAt3YCnXx9cLVuAyxlOUV5B4LGHobGR6ouuaNrX3bEDvovPJ/VXY3EV5mPLtuIdPACTmUH931/Hf+FIzIB+uAryoa6eukceI+XCkdjGENEVK3G1LyTprDOaFa+nsABPYXzu+FhdHe4ORUTXroMUP9brjb9fuQp3Xi6kpEBDQ9OxyeedQ91jT5Ey6kpSO7Sn9oZb8N90A+4O7XHnt2tWXAernE59ySrqSevew6gpXU4sEiQWjbBx3ick+dMI1VXyzevjOOGONw6qxBjAuN34hhxLY1UNkdnxeZnt1q1Niwu4nG8AAKIlpfjOHZGAKEUOHUqORUR2g+fIvkSXLKVu3BN4ijsRXb2W5ItGQjBE3W8e2G7f6IqVuAvagctFZMZMIvMX4M7LIzZrDklH9iXWGMRGo8TKtkJVNb4Th2LS0yE1hfS/PounXx+83Xa+TPHecKWm4urXB2+/PsQ2byE4dz7+X44hOGky9c+9TNrTjxOZPZfY+lK8A/sTnDKVtEcfBJeL+nFPEHjwLkyLHJKGDtlnMR2MXG4PabmFpOUWAlCzcRUNFRspnf0hrXsOodtZvyA5PSfBUX4/V+73rBWwzeItrtyW+M447QBFJHJoMvGZ1w4O/fv3tzNnaoJyETn4xOrq4otqNAaJlqzHlZ9PdMECkk4aRtU5F2Grtp+5IPDoQ3j696XxlfHEVq8hNOXDpm3JV1+Bp09vTKof4/HE5x4ecCTuzIwDUpfQrNnUXHktJi2NpBFnEtu8mdDUjwncfRu2ug5XdiaxcITonLm4CgsITfmQlIvPx3f6KQckPtk7sdpaqn9+I8EXX2kqSxo2FJMWIDhhEq72hWROHL9PlysXOZQZY2ZZa3dYD17JsYjIbrDBIA1/G0/drXeR8qvr8RzRndDHn5F04vGE/vEWDc88/+3ObjcZb/6NyDeLIBal7s77tj+ZMWR+NBlbUYG7IB9Pty6YlOatkLYnYlVVNL71T2quGAVA8k8uwt2pA7HyClyZmUTXleC/6XpiGzYRW78eT3FnPH17Y3Y2PlkOKtH1pYQ+/ZzwF9Pw9O6Ju7gz0aXLcGWk4+nXB0/HDokOUeSg8X3JsYZViIjshsjS5dTd/SD+W26EWIyqs88n8H+PEl29Bu+xR4HLRfDtf+LKa03KmFHEqmuxZWW42hfueDJr46vQHf+jA18RwJWRge+s4bj/9QbByVMwmRm4OxQR+vQLXGkB/L8ei7e4MxQfHLMxyO5zt21DyoUjSdlmnDFD9nyhEpEfMiXHIiK7wdbVYfx+cLvjcxQHg4Qnv0fKlZdR98A4fBedR+DYowBD8N0puNvk4cpthbd3T1wF+cTWrms6l6d/XzydEtuD587Oxj38VDz9+xH67AvCn31Oyuif4e3TC0+H9gmNTUQkkZQci4jsBneHIjyDBkDMEitZD0DwrX9i0zPwXXgutb+8BcJhSE4m8PC9GK+XhlfG4+pSTPpLz9DwzHOEP/sS3xmn4f/FKFzZ2QmuUZy7VUtSfjyClB9rBgMREdCYYxGR3RaaNoPwvG8I/eMtQh981FTu7tyJ9Bf+SGxrOST5CM+bR2z5Knynn4S7qAhPryMgEsFWVWEyMzEe9UuIiCSaxhyLiDRT0uCBuLKy4r2+MUto6se48loTePBuvP37YZKSiFZW4ulQCElJuAvyMf+ZRsvrxbRokdgKiIjILik5FhHZA54unXF37oi3X19sVRWu3Ja42+Q1bXdnZkLmwbVIhIiI7D4lxyIie8i4XPHeYREROey4Eh2AiIiIiMjBQsmxiIiIiIhDybGIiIiIiEPJsYiIiIiIQ8mxiIiIiIhDybGIiIiIiEPJsYiIiIiIQ8mxiIiIiIhDybGIiIiIiEPJsYiIiIiIQ8mxiIiIiIhDybGIiIiIiEPJsYiIiIiIQ8mxiIiIiIhDybGIiIiIiEPJsYiIiIiIw5PoAERE5PBlYzEqVs+nqmQJSYFMsop648/KTXRYIiLfS8mxiIjsNxvmfMj7vxmOjUYAaDtgOMdc/yz+7NYJjkxEZOc0rEJERPZauLGOms1rqStbTzQcaiqvKlnCpgWf8+VT1zYlxgDrv3qH8uVfJyJUEZHdop5jERHZK5u++YzKtYsonf0+WxZ9QW7P4+l13q1EYzE2zfuIYO1Wajeu3OG4uq3rqdmwAk9ygBQNsRCRg4x6jkVEZI9VlSylZNa7WCB/8I/oddFv2DT/Uz68dwSh6i34c9rQps8ptBt45g7HJqVm8s0bj/LlU6NZN30SsUj4wFdAROR7qOdYRET2WPWG5bTsMpCSGe9Q8tU7BHLbM3DUE8x55R5qNq7G40vG5fHQ5cxr8SQHyCzoRiwWxZ+dRyRUTywaoWDwCLYun0WoroLWvU4gtUW7RFdLRETJsYiI7Lmk1EwWTXiC1Z+OB6C+rISty2Zywp1vsmTSHwnWltP+uPNIb9uZ1j2HYIwLX3oLwg01pGS3xuX2sGHuVLLa92T2y3eRnvcSx930CimZrRJcMxH5oVNyLCIieywaDrLm329sXxZqZMuiL1k3fSLG7aH9sT9m6r0jiDkP6mXkd6Vt/9Mp/2AO/uw2rPzoFYzLTf8rH+GrZ26kcvU3UNCdravm0rC1lLS8jrQoHoDHl5KIKorID5SSYxER2WNurw+vP51QbcV25Wl5nWg/5Hzy+pzImi8n0OeSe3B7fUSCdZSvmIs3JY1N8z+lz8V3AWBjUTYv/Jys9j0J1lawaNJTzHv1/vjJjGHQ6P+j6xmjMS49IiMiB4Y+bUREZI+lZOXR55J7tivLyO9KXVkJbfqeROWaRXQ5/SrCdVWULZmOjUbJLOxBSk4b3F4f0XCw6bhoOEhSIAtfeg7z//7gtye0lpl/+RXlq+YdqGqJiKjnWERE9lxqi7akZLXm2BtfpGzZTHyBTCKNdcx5+U4CrTswaNQTfP3C7ZSvnNN0TMeTfkJWUW8iwXo8Pn9TebsBZxCLhGgo34CNxba7TjTUSGPVlgNWLxERJcciIrLH3N4kvP4AFavnsXLqy0Qa65qmZKspXU4k2LBdYgywcuor5A/8EUeNeZrNi6eT1+dEugwfRemcD1j+/vOc8sAUPMmpRBrrmo5JzmhJWusOB7RuIvLDpmEVIiKyV1p1PZpAq0JCtZXbzVXsTkre6RhhG4viTvIx++W7qd24kqLjL6R6wzKWvfcsg6/7A9kd+3PMDc+RkhVfWjq1ZT7H/fqvpLfpeMDqJCLSrJ5jY0wf4I9AMhABrrXWzjDGGOAJYDhQD1xurdV6oSIih5GkQAZtjjyVLsNHs+Sdp5vKu599PRVrFuLPaUv91vVN5e0GnsHaLyfSWLkJ43JRs3EFOZ36ccoDU8jtcSwuj5eiIeeRWdiDYHUZ/pYFpLcuSkTVROQHzFhr9/5gY6YAj1lrJxtjhgM3WWuHOq/HEE+OBwFPWGsH7ep8/fv3tzNnztzreERE5MBrrKmgYuUc6reuJ7VFPjFrqd+yltRWBaz+9O9sXvA57QadSV7vYaz+bDxJqZm4k5KJRkJ0GX4tabkFia6CiPwAGWNmWWv7f7e8uWOOLZDuvM4ASp3XI4CXbDzznmaMyTTG5FlrNzTzeiIicpBJTssir/cJO92W2+M4Gio3Ubl6PrVl6yk87nzqy9fjz8qjZddBJPnTd3qciEiiNDc5vh54zxjzO+Ljl492ytsC67bZr8QpU3IsIvID4nK7Sc1pQ2pOm0SHIiKyW3aZHBtjPgBa72TT7cCJwA3W2jeMMecBzwInAWYn++90/IYx5mrgaoCCAn21JiIiIiKJ09wxx1VAprXWOg/hVVlr040xfwI+tta+6uy3BBi6q2EVGnMsIiIiIgfC9405bu5UbqXA8c7rYcAy5/VE4CcmbjDxpFlDKkRERETkoNbcMcdXAU8YYzxAI87wCOAd4jNVLCc+ldsVzbyOiIiIiMh+16zk2Fr7b+DInZRb4LrmnFtERERE5EDTCnkiIiIiIg4lxyIiIiIiDiXHIiIiIiIOJcciIiIiIg4lxyIiIiIiDiXHIiIiIiIOJcciIiIiIg4lxyIiIiIiDiXHIiIiIiIOJcciIiIiIg4lxyIiIiIiDmOtTXQMTYwxW4A1iY7jMNECKEt0EIchteu+pzbdP9Su+57adN9Tm+4fatfdU2itbfndwoMqOZZ9xxgz01rbP9FxHG7Urvue2nT/ULvue2rTfU9tun+oXZtHwypERERERBxKjkVEREREHEqOD19/TnQAhym1676nNt0/1K77ntp031Ob7h9q12bQmGMREREREYd6jkVEREREHEqOD0PGmDHGmCXGmAXGmEe2Kb/VGLPc2XZqImM8FBljfmWMscaYFs57Y4x50mnTecaYfomO8VBijBlnjFnstN1bxpjMbbbpXt1LxpjTnHZbboy5JdHxHIqMMfnGmI+MMYucz9GxTnm2MeZ9Y8wy5++sRMd6KDLGuI0xs40xk5z3RcaY6U67/t0Yk5ToGA8lxphMY8zrzufpImPMUbpXm0fJ8WHGGHMCMALoZa3tAfzOKe8OXAD0AE4D/mCMcScs0EOMMSYfOBlYu03x6UBn58/VwNMJCO1Q9j5whLW2F7AUuBV0rzaH005PEb83uwMXOu0peyYC3Git7QYMBq5z2vEW4ENrbWfgQ+e97LmxwKJt3v8WeMxp1wrgyoREdeh6AnjXWtsV6E28bXWvNoOS48PPaOBha20QwFq72SkfAbxmrQ1aa1cBy4GBCYrxUPQYcBOw7SD9EcBLNm4akGmMyUtIdIcga+0Ua23EeTsNaOe81r269wYCy621K621IeA14u0pe8Bau8Fa+7XzuoZ4stGWeFu+6Oz2InB2YiI8dBlj2gFnAH9x3htgGPC6s4vadQ8YY9KBIcCzANbakLW2Et2rzaLk+PBTDBznfEX1iTFmgFPeFli3zX4lTpnsgjHmLGC9tXbudzapTfednwKTnddq172nttvHjDHtgb7AdCDXWrsB4gk00CpxkR2yHife0RBz3ucAldv8oqx7ds90ALYAzztDVf5ijElF92qzeBIdgOw5Y8wHQOudbLqd+L9pFvGvAgcA440xHQCzk/01VYljF216G3DKzg7bSZnadBv/rV2ttROcfW4n/jX2K/85bCf7q113j9puHzLGBIA3gOuttdXxTk7ZW8aYM4HN1tpZxpih/yneya66Z3efB+gHjLHWTjfGPIGGUDSbkuNDkLX2pO/bZowZDbxp43P0zTDGxIivsV4C5G+zazugdL8Gegj5vjY1xvQEioC5zg/GdsDXxpiBqE136b/dqwDGmMuAM4ET7bfzSqpd957abh8xxniJJ8avWGvfdIo3GWPyrLUbnCFUm7//DLITxwBnGWOGA8lAOvGe5ExjjMfpPdY9u2dKgBJr7XTn/evEk2Pdq82gYRWHn7eJj9/CGFMMJAFlwETgAmOMzxhTRPwhshkJi/IQYa2db61tZa1tb61tT/yDqJ+1diPxNv2JM2vFYKDqP19jya4ZY04DbgbOstbWb7NJ9+re+wro7Dz9n0T8wcaJCY7pkOOMg30WWGSt/d9tNk0ELnNeXwZMONCxHcqstbdaa9s5n6UXAFOttRcDHwHnOrupXfeA87NonTGmi1N0IrAQ3avNop7jw89zwHPGmG+AEHCZ0yO3wBgznvh/mghwnbU2msA4DwfvAMOJPzBWD1yR2HAOOb8HfMD7Tq/8NGvtKGut7tW9ZK2NGGN+DrwHuIHnrLULEhzWoegY4FJgvjFmjlN2G/Aw8aFqVxKfuWZkguI73NwMvGaMuR+YjfNwmey2McArzi/EK4n/LHKhe3WvaYU8ERERERGHhlWIiIiIiDiUHIuIiIiIOJQci4iIiIg4lByLiIiIiDiUHIuIiIiIOJQci4iIiIg4lByLiIiIiDiUHIuIiIiIOP4fS/2dgX9uHKEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.scatterplot(x =tsne[:, 0], y = tsne[:, 1], hue = train[\"Activity\"],palette=\"bright\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ea1066e8"
   },
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dae8a2f4",
    "outputId": "040613c5-ee1a-4764-eff6-1d71835d6d19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size :  (7352, 561)\n",
      "Test data size :  (2947, 561)\n"
     ]
    }
   ],
   "source": [
    "# Spliting Data into training and test data\n",
    "y_train = train.Activity\n",
    "X_train = train.drop(['subject', 'Activity'], axis=1)\n",
    "y_test = test.Activity\n",
    "X_test = test.drop(['subject', 'Activity'], axis=1)\n",
    "print('Training data size : ', X_train.shape)\n",
    "print('Test data size : ', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "d3e07ace"
   },
   "outputs": [],
   "source": [
    "parameters = {'C':np.arange(10,61,10), 'penalty':['l2','l1']}\n",
    "lr_classifier = LogisticRegression()\n",
    "lr_classifier_rs = RandomizedSearchCV(lr_classifier, param_distributions=parameters, cv=5,random_state = 42)\n",
    "lr_classifier_rs.fit(X_train, y_train)\n",
    "y_pred = lr_classifier_rs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2a0361e3",
    "outputId": "c6efd6bf-e9fd-44df-caa0-87b9ced82902"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy using Logistic Regression :  95.65659993213438\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = np.zeros(5)\n",
    "accuracy_scores [0] = accuracy_score(y_true=y_test, y_pred=y_pred)*100\n",
    "accuracy_scores\n",
    "print(\"Accuracy using Logistic Regression : \", accuracy_scores[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "abc4239c"
   },
   "outputs": [],
   "source": [
    "# function to plot confusion matrix\n",
    "def plot_confusion_matrix(cm,lables):\n",
    "    fig, ax = plt.subplots(figsize=(12,8)) # for plotting confusion matrix as image\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "    yticks=np.arange(cm.shape[0]),\n",
    "    xticklabels=lables, yticklabels=lables,\n",
    "    ylabel='True label',\n",
    "    xlabel='Predicted label')\n",
    "    plt.xticks(rotation = 90)\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, int(cm[i, j]),ha=\"center\", va=\"center\",color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "id": "ff253a8d",
    "outputId": "d9860ccb-8cca-498e-c1cf-c47381016929"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting confusion matrix\n",
    "cm = confusion_matrix(y_test.values,y_pred)\n",
    "plot_confusion_matrix(cm, np.unique(y_pred))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "0a9c69f0"
   },
   "outputs": [],
   "source": [
    "#function to get best random search attributes\n",
    "def get_best_randomsearch_results(model):\n",
    "    print(\"Best estimator : \", model.best_estimator_)\n",
    "    print(\"Best set of parameters : \", model.best_params_)\n",
    "    print(\"Best score : \", model.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b5ef82a3",
    "outputId": "1cc54d21-78ab-426f-c1ba-f81b64bd8758"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator :  LogisticRegression(C=60, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "Best set of parameters :  {'penalty': 'l2', 'C': 60}\n",
      "Best score :  0.9329499576853175\n"
     ]
    }
   ],
   "source": [
    "# getting best random search attributes\n",
    "get_best_randomsearch_results(lr_classifier_rs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K Nearest Neighbors Classifier accuracy: 90.02375296912113%\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier().fit(X_train, y_train)\n",
    "prediction = clf.predict(X_test)\n",
    "accuracy_scores[1] = accuracy_score(y_test, prediction)*100\n",
    "print('K Nearest Neighbors Classifier accuracy: {}%'.format(accuracy_scores[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier accuracy: 92.36511706820495%\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier().fit(X_train, y_train)\n",
    "prediction = clf.predict(X_test)\n",
    "accuracy_scores[2] = accuracy_score(y_test, prediction)*100\n",
    "print('Random Forest Classifier accuracy: {}%'.format(accuracy_scores[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize the Predictor(Feature Set) for SVM training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension of Train set (7352, 561)\n",
      "Dimension of Test set (2947, 561) \n",
      "\n",
      "Number of numeric features: 561\n"
     ]
    }
   ],
   "source": [
    "# Seperating Predictors and Outcome values from train and test sets\n",
    "X_train = pd.DataFrame(train.drop(['Activity','subject'],axis=1))\n",
    "Y_train_label = train.Activity.values.astype(object)\n",
    "X_test = pd.DataFrame(test.drop(['Activity','subject'],axis=1))\n",
    "Y_test_label = test.Activity.values.astype(object)\n",
    "\n",
    "# Dimension of Train and Test set \n",
    "print(\"Dimension of Train set\",X_train.shape)\n",
    "print(\"Dimension of Test set\",X_test.shape,\"\\n\")\n",
    "\n",
    "# Transforming non numerical labels into numerical labels\n",
    "from sklearn import preprocessing\n",
    "encoder = preprocessing.LabelEncoder()\n",
    "\n",
    "# encoding train labels \n",
    "encoder.fit(Y_train_label)\n",
    "Y_train = encoder.transform(Y_train_label)\n",
    "\n",
    "# encoding test labels \n",
    "encoder.fit(Y_test_label)\n",
    "Y_test = encoder.transform(Y_test_label)\n",
    "\n",
    "#Total Number of Continous and Categorical features in the training set\n",
    "num_cols = X_train._get_numeric_data().columns\n",
    "print(\"Number of numeric features:\",num_cols.size)\n",
    "#list(set(X_train.columns) - set(num_cols))\n",
    "\n",
    "\n",
    "names_of_predictors = list(X_train.columns.values)\n",
    "\n",
    "# Scaling the Train and Test feature set \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning using grid search and cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Libraries to Build Ensemble Model : Random Forest Classifier \n",
    "# Create the parameter grid based on the results of random search \n",
    "params_grid = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
    "                     'C': [1, 10, 100, 1000]},\n",
    "                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training SVM model using radial kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],\n",
       "                          'kernel': ['rbf']},\n",
       "                         {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Performing CV to tune parameters for best SVM fit \n",
    "svm_model = GridSearchCV(SVC(), params_grid, cv=5)\n",
    "svm_model.fit(X_train_scaled, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix and Accuracy Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score for training data: 0.938255432696532 \n",
      "\n",
      "Best C: 1000 \n",
      "\n",
      "Best Kernel: rbf \n",
      "\n",
      "Best Gamma: 0.0001 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# View the accuracy score\n",
    "print('Best score for training data:', svm_model.best_score_,\"\\n\") \n",
    "\n",
    "# View the best parameters for the model found using grid search\n",
    "print('Best C:',svm_model.best_estimator_.C,\"\\n\") \n",
    "print('Best Kernel:',svm_model.best_estimator_.kernel,\"\\n\")\n",
    "print('Best Gamma:',svm_model.best_estimator_.gamma,\"\\n\")\n",
    "\n",
    "final_model = svm_model.best_estimator_\n",
    "Y_pred = final_model.predict(X_test_scaled)\n",
    "Y_pred_label = list(encoder.inverse_transform(Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[537   0   0   0   0   0]\n",
      " [  0 436  54   0   0   1]\n",
      " [  0  15 517   0   0   0]\n",
      " [  0   0   0 493   3   0]\n",
      " [  0   0   0   5 398  17]\n",
      " [  0   0   0  16   2 453]]\n",
      "\n",
      "\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "            LAYING       1.00      1.00      1.00       537\n",
      "           SITTING       0.97      0.89      0.93       491\n",
      "          STANDING       0.91      0.97      0.94       532\n",
      "           WALKING       0.96      0.99      0.98       496\n",
      "WALKING_DOWNSTAIRS       0.99      0.95      0.97       420\n",
      "  WALKING_UPSTAIRS       0.96      0.96      0.96       471\n",
      "\n",
      "          accuracy                           0.96      2947\n",
      "         macro avg       0.96      0.96      0.96      2947\n",
      "      weighted avg       0.96      0.96      0.96      2947\n",
      "\n",
      "Training set score for SVM: 0.996872\n",
      "Testing  set score for SVM: 0.961656\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bound method BaseSearchCV.score of GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],\n",
       "                          'kernel': ['rbf']},\n",
       "                         {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making the Confusion Matrix\n",
    "#print(pd.crosstab(Y_test_label, Y_pred_label, rownames=['Actual Activity'], colnames=['Predicted Activity']))\n",
    "print(confusion_matrix(Y_test_label,Y_pred_label))\n",
    "print(\"\\n\")\n",
    "print(classification_report(Y_test_label,Y_pred_label))\n",
    "\n",
    "print(\"Training set score for SVM: %f\" % final_model.score(X_train_scaled , Y_train))\n",
    "print(\"Testing  set score for SVM: %f\" % final_model.score(X_test_scaled  , Y_test ))\n",
    "accuracy_scores[3]=final_model.score(X_test_scaled  , Y_test )*100\n",
    "svm_model.score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "X_train=sc.fit_transform(X_train)\n",
    "X_test=sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()\n",
    "y_train=encoder.fit_transform(y_train)\n",
    "y_train=pd.get_dummies(y_train).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()\n",
    "y_test=encoder.fit_transform(y_test)\n",
    "y_test=pd.get_dummies(y_test).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(n_components=None)\n",
    "X_train=pca.fit_transform(X_train)\n",
    "X_test=pca.transform(X_test)\n",
    "explained_variance=pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.07811723e-01, 6.58068027e-02, 2.80643677e-02, 2.50395275e-02,\n",
       "       1.88828501e-02, 1.72400642e-02, 1.37101147e-02, 1.19907768e-02,\n",
       "       9.95859511e-03, 9.65086590e-03, 8.60051649e-03, 8.00280950e-03,\n",
       "       7.63996369e-03, 6.45328924e-03, 6.32324346e-03, 5.99722090e-03,\n",
       "       5.86750302e-03, 5.75407392e-03, 5.67921047e-03, 5.27295779e-03,\n",
       "       5.00362925e-03, 4.88408406e-03, 4.78205784e-03, 4.68562930e-03,\n",
       "       4.49289260e-03, 4.21027243e-03, 4.17978682e-03, 4.05570369e-03,\n",
       "       3.88524168e-03, 3.87018044e-03, 3.65958878e-03, 3.54553381e-03,\n",
       "       3.47942606e-03, 3.37187109e-03, 3.29676999e-03, 3.28215050e-03,\n",
       "       3.20529478e-03, 2.95614939e-03, 2.86722415e-03, 2.85074548e-03,\n",
       "       2.68766324e-03, 2.65619859e-03, 2.63405989e-03, 2.58850596e-03,\n",
       "       2.49054872e-03, 2.46954034e-03, 2.40679913e-03, 2.36115075e-03,\n",
       "       2.30278857e-03, 2.27567245e-03, 2.20164699e-03, 2.13466984e-03,\n",
       "       2.07704456e-03, 2.02496620e-03, 1.99779441e-03, 1.97750625e-03,\n",
       "       1.94273627e-03, 1.90237160e-03, 1.88064891e-03, 1.86215970e-03,\n",
       "       1.81466544e-03, 1.77380987e-03, 1.76085074e-03, 1.73283909e-03,\n",
       "       1.70738317e-03, 1.68548073e-03, 1.65614929e-03, 1.63102448e-03,\n",
       "       1.58436782e-03, 1.57497542e-03, 1.53022902e-03, 1.52205247e-03,\n",
       "       1.48139151e-03, 1.46071476e-03, 1.44670526e-03, 1.42753738e-03,\n",
       "       1.38656539e-03, 1.35591695e-03, 1.34705548e-03, 1.33078158e-03,\n",
       "       1.30815550e-03, 1.30240739e-03, 1.28229864e-03, 1.24994798e-03,\n",
       "       1.22974483e-03, 1.21552960e-03, 1.19170472e-03, 1.17436497e-03,\n",
       "       1.15283702e-03, 1.13147455e-03, 1.10592054e-03, 1.09326676e-03,\n",
       "       1.07921823e-03, 1.05983611e-03, 1.02711779e-03, 1.02086442e-03,\n",
       "       1.00722080e-03, 9.84274129e-04, 9.64768328e-04, 9.38411210e-04,\n",
       "       9.23386748e-04, 9.17395339e-04, 8.98893380e-04, 8.94995590e-04,\n",
       "       8.75176393e-04, 8.70466802e-04, 8.58500025e-04, 8.34789074e-04,\n",
       "       8.27557558e-04, 8.26355241e-04, 8.11534976e-04, 7.98272745e-04,\n",
       "       7.72818789e-04, 7.58213801e-04, 7.50513383e-04, 7.28136300e-04,\n",
       "       7.18148372e-04, 7.09966903e-04, 6.97854018e-04, 6.78705081e-04,\n",
       "       6.64375012e-04, 6.50269657e-04, 6.39855199e-04, 6.30796301e-04,\n",
       "       6.21437731e-04, 6.15389578e-04, 6.02431674e-04, 5.96996321e-04,\n",
       "       5.82891574e-04, 5.75654343e-04, 5.64485244e-04, 5.57471583e-04,\n",
       "       5.45618542e-04, 5.39341176e-04, 5.29318324e-04, 5.25714158e-04,\n",
       "       5.15133326e-04, 5.05175113e-04, 5.02220396e-04, 4.88906240e-04,\n",
       "       4.84919846e-04, 4.74685040e-04, 4.69585258e-04, 4.61118783e-04,\n",
       "       4.60303801e-04, 4.50249601e-04, 4.42694120e-04, 4.37648533e-04,\n",
       "       4.29693762e-04, 4.22632660e-04, 4.16209744e-04, 4.09031341e-04,\n",
       "       4.07198954e-04, 3.98152708e-04, 3.87819397e-04, 3.85706947e-04,\n",
       "       3.79896552e-04, 3.68934940e-04, 3.62786914e-04, 3.54410253e-04,\n",
       "       3.47723424e-04, 3.46879694e-04, 3.35116336e-04, 3.32705631e-04,\n",
       "       3.26087499e-04, 3.15121893e-04, 3.02513246e-04, 2.88157589e-04,\n",
       "       2.73460298e-04, 2.67374190e-04, 2.59346227e-04, 2.53948264e-04,\n",
       "       2.46786301e-04, 2.41381782e-04, 2.38940673e-04, 2.28360155e-04,\n",
       "       2.25853592e-04, 2.24241681e-04, 2.14799624e-04, 2.08470907e-04,\n",
       "       2.04918069e-04, 1.98169379e-04, 1.96786294e-04, 1.88640947e-04,\n",
       "       1.82513335e-04, 1.80149866e-04, 1.74943506e-04, 1.71658778e-04,\n",
       "       1.67942227e-04, 1.64189168e-04, 1.56091218e-04, 1.55297886e-04,\n",
       "       1.48624880e-04, 1.46546181e-04, 1.43583547e-04, 1.38617453e-04,\n",
       "       1.36686865e-04, 1.31551749e-04, 1.24661068e-04, 1.23390418e-04,\n",
       "       1.20877808e-04, 1.18619996e-04, 1.15197016e-04, 1.11405824e-04,\n",
       "       1.08403383e-04, 1.05223256e-04, 1.03123893e-04, 9.92588958e-05,\n",
       "       9.88228438e-05, 9.69455693e-05, 9.52823210e-05, 9.29974440e-05,\n",
       "       9.09989820e-05, 9.02663923e-05, 8.94427789e-05, 8.86210791e-05,\n",
       "       8.77445408e-05, 8.50704689e-05, 8.21190530e-05, 8.18181796e-05,\n",
       "       8.06130196e-05, 7.94607761e-05, 7.82510991e-05, 7.63689785e-05,\n",
       "       7.51544933e-05, 7.40207555e-05, 7.18409342e-05, 7.10465615e-05,\n",
       "       7.02033879e-05, 6.89494913e-05, 6.80655085e-05, 6.66664300e-05,\n",
       "       6.51964162e-05, 6.48294423e-05, 6.25299230e-05, 6.17140063e-05,\n",
       "       6.09121494e-05, 5.95702112e-05, 5.94037377e-05, 5.85889363e-05,\n",
       "       5.76238866e-05, 5.70355479e-05, 5.61242824e-05, 5.45682738e-05,\n",
       "       5.32075077e-05, 5.30465083e-05, 5.16647520e-05, 5.13750513e-05,\n",
       "       4.99369873e-05, 4.86742430e-05, 4.79054318e-05, 4.71302992e-05,\n",
       "       4.62447979e-05, 4.52463762e-05, 4.41717910e-05, 4.31265005e-05,\n",
       "       4.25229326e-05, 4.20687559e-05, 4.05894218e-05, 4.04843640e-05,\n",
       "       3.99552560e-05, 3.87900310e-05, 3.85995471e-05, 3.80015416e-05,\n",
       "       3.75668437e-05, 3.63161160e-05, 3.56151852e-05, 3.49489273e-05,\n",
       "       3.46557562e-05, 3.35310985e-05, 3.30853549e-05, 3.25462528e-05,\n",
       "       3.21472588e-05, 3.17508450e-05, 3.10102254e-05, 3.04362348e-05,\n",
       "       2.93713307e-05, 2.92026712e-05, 2.88083627e-05, 2.79630436e-05,\n",
       "       2.79183138e-05, 2.76236237e-05, 2.68229223e-05, 2.62488507e-05,\n",
       "       2.59824139e-05, 2.57280496e-05, 2.51909164e-05, 2.46715706e-05,\n",
       "       2.43059206e-05, 2.41169941e-05, 2.38997283e-05, 2.30023015e-05,\n",
       "       2.29245564e-05, 2.25931186e-05, 2.24081881e-05, 2.18782455e-05,\n",
       "       2.15489086e-05, 2.15232796e-05, 2.08601439e-05, 2.07249264e-05,\n",
       "       2.01958696e-05, 1.96945960e-05, 1.95452722e-05, 1.93284986e-05,\n",
       "       1.89562436e-05, 1.86931625e-05, 1.85588477e-05, 1.82242774e-05,\n",
       "       1.80039789e-05, 1.76397623e-05, 1.72440721e-05, 1.70314181e-05,\n",
       "       1.64016994e-05, 1.63435936e-05, 1.60678606e-05, 1.58552449e-05,\n",
       "       1.58010966e-05, 1.54615655e-05, 1.52278135e-05, 1.51563314e-05,\n",
       "       1.48092237e-05, 1.46953487e-05, 1.42420454e-05, 1.42144064e-05,\n",
       "       1.36619284e-05, 1.33798700e-05, 1.30659872e-05, 1.29558943e-05,\n",
       "       1.25900181e-05, 1.23464212e-05, 1.21982128e-05, 1.18778128e-05,\n",
       "       1.15557944e-05, 1.13628988e-05, 1.10719577e-05, 1.09822315e-05,\n",
       "       1.05666034e-05, 1.04422367e-05, 1.02480568e-05, 9.82805368e-06,\n",
       "       9.69092549e-06, 9.53627675e-06, 9.31336629e-06, 9.04589770e-06,\n",
       "       8.75117177e-06, 8.38200492e-06, 8.18681681e-06, 8.15081335e-06,\n",
       "       7.87885163e-06, 7.65430430e-06, 7.50252471e-06, 7.17399929e-06,\n",
       "       6.91564293e-06, 6.73227208e-06, 6.56161178e-06, 6.52174138e-06,\n",
       "       6.21692271e-06, 6.02829911e-06, 5.95882675e-06, 5.81943313e-06,\n",
       "       5.46080815e-06, 5.34404091e-06, 5.32498828e-06, 5.05723893e-06,\n",
       "       5.01036642e-06, 4.72015529e-06, 4.64502736e-06, 4.49348997e-06,\n",
       "       4.37281993e-06, 4.13485798e-06, 4.10642526e-06, 4.00889186e-06,\n",
       "       3.93487802e-06, 3.83746982e-06, 3.74779938e-06, 3.61352061e-06,\n",
       "       3.50801719e-06, 3.47872092e-06, 3.28233620e-06, 3.15085651e-06,\n",
       "       3.08065556e-06, 2.97070882e-06, 2.69378376e-06, 2.60914876e-06,\n",
       "       2.57978556e-06, 2.51491779e-06, 2.29127111e-06, 2.21911987e-06,\n",
       "       2.13682769e-06, 2.06123188e-06, 2.04878243e-06, 2.00866015e-06,\n",
       "       1.94607344e-06, 1.87380562e-06, 1.64088158e-06, 1.58105832e-06,\n",
       "       1.52027815e-06, 1.46509503e-06, 1.35798259e-06, 1.32109360e-06,\n",
       "       1.30549314e-06, 1.24436515e-06, 1.17700506e-06, 1.12820607e-06,\n",
       "       1.07694883e-06, 1.01257449e-06, 9.66671981e-07, 9.26166884e-07,\n",
       "       8.95073667e-07, 8.64892827e-07, 8.16942676e-07, 7.83851669e-07,\n",
       "       7.51111834e-07, 7.23640630e-07, 7.09784805e-07, 6.82083730e-07,\n",
       "       6.74328151e-07, 6.39801988e-07, 6.18445445e-07, 5.98594531e-07,\n",
       "       5.80967584e-07, 5.65441587e-07, 5.41352962e-07, 5.25957596e-07,\n",
       "       4.88288359e-07, 4.80361420e-07, 4.63931067e-07, 4.25119338e-07,\n",
       "       4.10076367e-07, 3.85009426e-07, 3.69754012e-07, 3.59867712e-07,\n",
       "       3.56664245e-07, 3.49621265e-07, 3.09570879e-07, 2.98237486e-07,\n",
       "       2.73805438e-07, 2.64106544e-07, 2.57003377e-07, 2.28527719e-07,\n",
       "       2.06938131e-07, 2.02960791e-07, 1.45318804e-07, 7.17597916e-08,\n",
       "       4.58553418e-08, 4.11180794e-08, 3.48109205e-08, 3.36982085e-08,\n",
       "       2.71295443e-08, 2.64190551e-08, 2.26469504e-08, 2.15367162e-08,\n",
       "       2.04918619e-08, 1.73888902e-08, 1.64903860e-08, 1.15757009e-08,\n",
       "       1.13994859e-08, 1.01919403e-08, 9.22868025e-09, 7.24687376e-09,\n",
       "       6.71738896e-09, 6.01631216e-09, 5.52037578e-09, 4.13158921e-09,\n",
       "       2.41778928e-10, 1.44927754e-10, 1.02644878e-10, 3.71716952e-12,\n",
       "       1.07963161e-12, 2.79609647e-13, 3.05919265e-18, 2.85305833e-18,\n",
       "       2.61777085e-18, 2.19589314e-18, 1.85000601e-18, 1.79946560e-18,\n",
       "       1.70964629e-18, 1.68477125e-18, 1.63807016e-18, 1.52806800e-18,\n",
       "       1.38547145e-18, 1.24618427e-18, 1.16244945e-18, 1.14116910e-18,\n",
       "       1.12994884e-18, 1.07105811e-18, 1.02183566e-18, 8.82089783e-19,\n",
       "       8.68452914e-19, 8.56499394e-19, 8.14750942e-19, 8.05791102e-19,\n",
       "       7.93815101e-19, 7.65918915e-19, 7.42078119e-19, 7.11946831e-19,\n",
       "       6.84805464e-19, 6.34245466e-19, 6.14131091e-19, 6.00408212e-19,\n",
       "       5.81183053e-19, 5.40129483e-19, 5.01766995e-19, 4.70580991e-19,\n",
       "       4.30603121e-19, 4.18487614e-19, 4.15668310e-19, 3.95407526e-19,\n",
       "       3.85968429e-19, 3.68450581e-19, 3.55946359e-19, 3.52230882e-19,\n",
       "       3.45008139e-19, 3.38621346e-19, 3.32820889e-19, 3.24620530e-19,\n",
       "       3.22984448e-19, 3.07309215e-19, 3.01219931e-19, 2.93706725e-19,\n",
       "       2.88633463e-19, 2.77526208e-19, 2.68853035e-19, 2.60571291e-19,\n",
       "       2.54818638e-19, 2.52455842e-19, 2.44925858e-19, 2.42984429e-19,\n",
       "       2.33758422e-19, 2.26436118e-19, 2.09600185e-19, 1.93310876e-19,\n",
       "       1.90445476e-19, 1.80300373e-19, 1.74557377e-19, 1.45830101e-19,\n",
       "       1.37175292e-19, 9.85387141e-20, 7.23230703e-20, 5.67236992e-20,\n",
       "       3.24903205e-33, 3.24903205e-33, 3.24903205e-33, 3.24903205e-33,\n",
       "       3.24903205e-33, 3.24903205e-33, 3.24903205e-33, 3.24903205e-33,\n",
       "       3.24903205e-33, 3.24903205e-33, 3.24903205e-33, 3.24903205e-33,\n",
       "       3.24903205e-33, 3.24903205e-33, 3.24903205e-33, 3.24903205e-33,\n",
       "       3.24903205e-33, 3.24903205e-33, 3.24903205e-33, 8.62685136e-35,\n",
       "       7.19309034e-36])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(units=64,kernel_initializer='uniform',activation='relu',input_dim=X_train.shape[1]))\n",
    "\n",
    "model.add(Dense(units=128,kernel_initializer='uniform',activation='relu'))\n",
    "\n",
    "model.add(Dense(units=64,kernel_initializer='uniform',activation='relu'))\n",
    "\n",
    "model.add(Dense(units=6,kernel_initializer='uniform',activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/22\n",
      "29/29 [==============================] - 1s 7ms/step - loss: 1.5580 - accuracy: 0.5204 - val_loss: 1.0417 - val_accuracy: 0.5965\n",
      "Epoch 2/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.5958 - accuracy: 0.7852 - val_loss: 0.3316 - val_accuracy: 0.8317\n",
      "Epoch 3/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.1719 - accuracy: 0.9544 - val_loss: 0.1462 - val_accuracy: 0.9437\n",
      "Epoch 4/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0549 - accuracy: 0.9816 - val_loss: 0.1285 - val_accuracy: 0.9508\n",
      "Epoch 5/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0329 - accuracy: 0.9897 - val_loss: 0.1295 - val_accuracy: 0.9532\n",
      "Epoch 6/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0249 - accuracy: 0.9920 - val_loss: 0.1311 - val_accuracy: 0.9542\n",
      "Epoch 7/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0170 - accuracy: 0.9948 - val_loss: 0.1161 - val_accuracy: 0.9576\n",
      "Epoch 8/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0146 - accuracy: 0.9954 - val_loss: 0.1194 - val_accuracy: 0.9579\n",
      "Epoch 9/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.1333 - val_accuracy: 0.9586\n",
      "Epoch 10/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 0.9989 - val_loss: 0.1246 - val_accuracy: 0.9617\n",
      "Epoch 11/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0053 - accuracy: 0.9990 - val_loss: 0.1439 - val_accuracy: 0.9606\n",
      "Epoch 12/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0039 - accuracy: 0.9996 - val_loss: 0.1459 - val_accuracy: 0.9613\n",
      "Epoch 13/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0027 - accuracy: 0.9997 - val_loss: 0.1452 - val_accuracy: 0.9623\n",
      "Epoch 14/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1524 - val_accuracy: 0.9620\n",
      "Epoch 15/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1509 - val_accuracy: 0.9634\n",
      "Epoch 16/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1501 - val_accuracy: 0.9640\n",
      "Epoch 17/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 9.1069e-04 - accuracy: 1.0000 - val_loss: 0.1528 - val_accuracy: 0.9644\n",
      "Epoch 18/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 7.9586e-04 - accuracy: 1.0000 - val_loss: 0.1614 - val_accuracy: 0.9634\n",
      "Epoch 19/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 6.2020e-04 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9627\n",
      "Epoch 20/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 5.4343e-04 - accuracy: 1.0000 - val_loss: 0.1637 - val_accuracy: 0.9634\n",
      "Epoch 21/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 4.6983e-04 - accuracy: 1.0000 - val_loss: 0.1638 - val_accuracy: 0.9630\n",
      "Epoch 22/22\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 4.0000e-04 - accuracy: 1.0000 - val_loss: 0.1667 - val_accuracy: 0.9634\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(X_train,y_train,batch_size=256,epochs=22,validation_data=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_scores[4]=max(history.history['val_accuracy'])*100\n",
    "# =(max(history.history['accuracy'])*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 10, 4\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.32556943e-11 1.36117824e-05 9.99972105e-01 1.42641684e-05\n",
      "  3.41130207e-10 1.13713439e-09]\n",
      " [1.66653854e-12 3.46214583e-05 9.99963045e-01 2.37875042e-06\n",
      "  1.54385064e-11 7.53350923e-11]\n",
      " [1.20289286e-07 3.20118554e-02 9.67909038e-01 7.89737969e-05\n",
      "  2.86391888e-08 1.73034294e-07]\n",
      " ...\n",
      " [2.44282837e-05 3.78657091e-06 2.80818659e-16 5.89360013e-07\n",
      "  1.30932995e-05 9.99958158e-01]\n",
      " [3.50996015e-05 1.07480755e-05 2.31748130e-14 4.74385433e-06\n",
      "  3.56326273e-05 9.99913812e-01]\n",
      " [5.40965902e-06 2.26617049e-05 2.95192560e-12 4.41895478e-04\n",
      "  2.87356088e-04 9.99242663e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plotting a bar graph of the accuracies to compare them visually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([95.65659993, 90.02375297, 92.36511707, 96.16559213, 96.43705487])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,  20.,  40.,  60.,  80., 100., 120.]),\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "plt.figure(figsize=(12,8))\n",
    "colors = cm.rainbow(np.linspace(0, 1, 5))\n",
    "labels = ['Logsitic Regression', 'K Nearest Neighbors', 'Random Forest','Support Vector Machine','Neural Networks']\n",
    "plt.bar(labels,\n",
    " accuracy_scores[:5],\n",
    " color = colors)\n",
    "plt.xlabel('Classifiers',fontsize=18)\n",
    "plt.ylabel('Accuracy',fontsize=18)\n",
    "plt.title('Accuracy of various algorithms',fontsize=20)\n",
    "plt.xticks(rotation=45,fontsize=12)\n",
    "plt.yticks(fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clearly seen that SVM and NN performed the best with the highest accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this particular project, we explored the activity recognition dataset. I visualized the data using matplotlib. Then, I applied numerous machine learning algorithms & Neural Networks found out that Neural Netowrks performed the best in classifying different activities with accuracy of almost 96%"
   ]
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "name": "Human_Action_Recognition.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}