gender identification from speech.ipynb

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    "colab": {
      "name": "ietcipher.ipynb",
      "provenance": [],
      "mount_file_id": "17guqTm-uALZT2xuX75blDsY2q5ECOQA8",
      "authorship_tag": "ABX9TyMjUFY48V6E3TnbTDy4Wi2F",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/venkatrohit219/gender-detection-from-speech/blob/main/gender%20identification%20from%20speech.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-jkckB0mIBS"
      },
      "source": [
        "# New section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFl3kl6ooOOK"
      },
      "source": [
        "#------- Importing the Libraries \n",
        "import numpy as np\n",
        "np.set_printoptions(threshold=np.inf) #Showing all the array in Console\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "#------ Importing the DataSet \n",
        "dataset = pd.read_csv('voice.csv')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "ywNkY4C8knU6",
        "outputId": "b8b5b4d6-375d-49c9-f519-add3f7d00147"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meanfreq</th>\n",
              "      <th>sd</th>\n",
              "      <th>median</th>\n",
              "      <th>Q25</th>\n",
              "      <th>Q75</th>\n",
              "      <th>IQR</th>\n",
              "      <th>skew</th>\n",
              "      <th>kurt</th>\n",
              "      <th>sp.ent</th>\n",
              "      <th>sfm</th>\n",
              "      <th>mode</th>\n",
              "      <th>centroid</th>\n",
              "      <th>meanfun</th>\n",
              "      <th>minfun</th>\n",
              "      <th>maxfun</th>\n",
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              "      <th>maxdom</th>\n",
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              "      <th>label</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.059781</td>\n",
              "      <td>0.064241</td>\n",
              "      <td>0.032027</td>\n",
              "      <td>0.015071</td>\n",
              "      <td>0.090193</td>\n",
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              "      <td>274.402906</td>\n",
              "      <td>0.893369</td>\n",
              "      <td>0.491918</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.059781</td>\n",
              "      <td>0.084279</td>\n",
              "      <td>0.015702</td>\n",
              "      <td>0.275862</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>male</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.066009</td>\n",
              "      <td>0.067310</td>\n",
              "      <td>0.040229</td>\n",
              "      <td>0.019414</td>\n",
              "      <td>0.092666</td>\n",
              "      <td>0.073252</td>\n",
              "      <td>22.423285</td>\n",
              "      <td>634.613855</td>\n",
              "      <td>0.892193</td>\n",
              "      <td>0.513724</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.066009</td>\n",
              "      <td>0.107937</td>\n",
              "      <td>0.015826</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.009014</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.054688</td>\n",
              "      <td>0.046875</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>male</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.077316</td>\n",
              "      <td>0.083829</td>\n",
              "      <td>0.036718</td>\n",
              "      <td>0.008701</td>\n",
              "      <td>0.131908</td>\n",
              "      <td>0.123207</td>\n",
              "      <td>30.757155</td>\n",
              "      <td>1024.927705</td>\n",
              "      <td>0.846389</td>\n",
              "      <td>0.478905</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.077316</td>\n",
              "      <td>0.098706</td>\n",
              "      <td>0.015656</td>\n",
              "      <td>0.271186</td>\n",
              "      <td>0.007990</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.046512</td>\n",
              "      <td>male</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.151228</td>\n",
              "      <td>0.072111</td>\n",
              "      <td>0.158011</td>\n",
              "      <td>0.096582</td>\n",
              "      <td>0.207955</td>\n",
              "      <td>0.111374</td>\n",
              "      <td>1.232831</td>\n",
              "      <td>4.177296</td>\n",
              "      <td>0.963322</td>\n",
              "      <td>0.727232</td>\n",
              "      <td>0.083878</td>\n",
              "      <td>0.151228</td>\n",
              "      <td>0.088965</td>\n",
              "      <td>0.017798</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.201497</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.554688</td>\n",
              "      <td>0.247119</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.135120</td>\n",
              "      <td>0.079146</td>\n",
              "      <td>0.124656</td>\n",
              "      <td>0.078720</td>\n",
              "      <td>0.206045</td>\n",
              "      <td>0.127325</td>\n",
              "      <td>1.101174</td>\n",
              "      <td>4.333713</td>\n",
              "      <td>0.971955</td>\n",
              "      <td>0.783568</td>\n",
              "      <td>0.104261</td>\n",
              "      <td>0.135120</td>\n",
              "      <td>0.106398</td>\n",
              "      <td>0.016931</td>\n",
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              "      <td>0.007812</td>\n",
              "      <td>5.484375</td>\n",
              "      <td>5.476562</td>\n",
              "      <td>0.208274</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   meanfreq        sd    median       Q25  ...    maxdom   dfrange   modindx  label\n",
              "0  0.059781  0.064241  0.032027  0.015071  ...  0.007812  0.000000  0.000000   male\n",
              "1  0.066009  0.067310  0.040229  0.019414  ...  0.054688  0.046875  0.052632   male\n",
              "2  0.077316  0.083829  0.036718  0.008701  ...  0.015625  0.007812  0.046512   male\n",
              "3  0.151228  0.072111  0.158011  0.096582  ...  0.562500  0.554688  0.247119   male\n",
              "4  0.135120  0.079146  0.124656  0.078720  ...  5.484375  5.476562  0.208274   male\n",
              "\n",
              "[5 rows x 21 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 6
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        "id": "_CPyLS_8kpdn",
        "outputId": "0778d2be-4f1b-4c0f-bf1e-a5fa87712511"
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      "source": [
        "dataset.corr()\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meanfreq</th>\n",
              "      <th>sd</th>\n",
              "      <th>median</th>\n",
              "      <th>Q25</th>\n",
              "      <th>Q75</th>\n",
              "      <th>IQR</th>\n",
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              "      <th>kurt</th>\n",
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              "      <th>sfm</th>\n",
              "      <th>mode</th>\n",
              "      <th>centroid</th>\n",
              "      <th>meanfun</th>\n",
              "      <th>minfun</th>\n",
              "      <th>maxfun</th>\n",
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              "      <th>meanfreq</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.739039</td>\n",
              "      <td>0.925445</td>\n",
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              "      <td>-0.601203</td>\n",
              "      <td>-0.784332</td>\n",
              "      <td>0.687715</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.460844</td>\n",
              "      <td>0.383937</td>\n",
              "      <td>0.274004</td>\n",
              "      <td>0.536666</td>\n",
              "      <td>0.229261</td>\n",
              "      <td>0.519528</td>\n",
              "      <td>0.515570</td>\n",
              "      <td>-0.216979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sd</th>\n",
              "      <td>-0.739039</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.562603</td>\n",
              "      <td>-0.846931</td>\n",
              "      <td>-0.161076</td>\n",
              "      <td>0.874660</td>\n",
              "      <td>0.314597</td>\n",
              "      <td>0.346241</td>\n",
              "      <td>0.716620</td>\n",
              "      <td>0.838086</td>\n",
              "      <td>-0.529150</td>\n",
              "      <td>-0.739039</td>\n",
              "      <td>-0.466281</td>\n",
              "      <td>-0.345609</td>\n",
              "      <td>-0.129662</td>\n",
              "      <td>-0.482726</td>\n",
              "      <td>-0.357667</td>\n",
              "      <td>-0.482278</td>\n",
              "      <td>-0.475999</td>\n",
              "      <td>0.122660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median</th>\n",
              "      <td>0.925445</td>\n",
              "      <td>-0.562603</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.774922</td>\n",
              "      <td>0.731849</td>\n",
              "      <td>-0.477352</td>\n",
              "      <td>-0.257407</td>\n",
              "      <td>-0.243382</td>\n",
              "      <td>-0.502005</td>\n",
              "      <td>-0.661690</td>\n",
              "      <td>0.677433</td>\n",
              "      <td>0.925445</td>\n",
              "      <td>0.414909</td>\n",
              "      <td>0.337602</td>\n",
              "      <td>0.251328</td>\n",
              "      <td>0.455943</td>\n",
              "      <td>0.191169</td>\n",
              "      <td>0.438919</td>\n",
              "      <td>0.435621</td>\n",
              "      <td>-0.213298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q25</th>\n",
              "      <td>0.911416</td>\n",
              "      <td>-0.846931</td>\n",
              "      <td>0.774922</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.477140</td>\n",
              "      <td>-0.874189</td>\n",
              "      <td>-0.319475</td>\n",
              "      <td>-0.350182</td>\n",
              "      <td>-0.648126</td>\n",
              "      <td>-0.766875</td>\n",
              "      <td>0.591277</td>\n",
              "      <td>0.911416</td>\n",
              "      <td>0.545035</td>\n",
              "      <td>0.320994</td>\n",
              "      <td>0.199841</td>\n",
              "      <td>0.467403</td>\n",
              "      <td>0.302255</td>\n",
              "      <td>0.459683</td>\n",
              "      <td>0.454394</td>\n",
              "      <td>-0.141377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q75</th>\n",
              "      <td>0.740997</td>\n",
              "      <td>-0.161076</td>\n",
              "      <td>0.731849</td>\n",
              "      <td>0.477140</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.009636</td>\n",
              "      <td>-0.206339</td>\n",
              "      <td>-0.148881</td>\n",
              "      <td>-0.174905</td>\n",
              "      <td>-0.378198</td>\n",
              "      <td>0.486857</td>\n",
              "      <td>0.740997</td>\n",
              "      <td>0.155091</td>\n",
              "      <td>0.258002</td>\n",
              "      <td>0.285584</td>\n",
              "      <td>0.359181</td>\n",
              "      <td>-0.023750</td>\n",
              "      <td>0.335114</td>\n",
              "      <td>0.335648</td>\n",
              "      <td>-0.216475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IQR</th>\n",
              "      <td>-0.627605</td>\n",
              "      <td>0.874660</td>\n",
              "      <td>-0.477352</td>\n",
              "      <td>-0.874189</td>\n",
              "      <td>0.009636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.249497</td>\n",
              "      <td>0.316185</td>\n",
              "      <td>0.640813</td>\n",
              "      <td>0.663601</td>\n",
              "      <td>-0.403764</td>\n",
              "      <td>-0.627605</td>\n",
              "      <td>-0.534462</td>\n",
              "      <td>-0.222680</td>\n",
              "      <td>-0.069588</td>\n",
              "      <td>-0.333362</td>\n",
              "      <td>-0.357037</td>\n",
              "      <td>-0.337877</td>\n",
              "      <td>-0.331563</td>\n",
              "      <td>0.041252</td>\n",
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              "    <tr>\n",
              "      <th>skew</th>\n",
              "      <td>-0.322327</td>\n",
              "      <td>0.314597</td>\n",
              "      <td>-0.257407</td>\n",
              "      <td>-0.319475</td>\n",
              "      <td>-0.206339</td>\n",
              "      <td>0.249497</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977020</td>\n",
              "      <td>-0.195459</td>\n",
              "      <td>0.079694</td>\n",
              "      <td>-0.434859</td>\n",
              "      <td>-0.322327</td>\n",
              "      <td>-0.167668</td>\n",
              "      <td>-0.216954</td>\n",
              "      <td>-0.080861</td>\n",
              "      <td>-0.336848</td>\n",
              "      <td>-0.061608</td>\n",
              "      <td>-0.305651</td>\n",
              "      <td>-0.304640</td>\n",
              "      <td>-0.169325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kurt</th>\n",
              "      <td>-0.316036</td>\n",
              "      <td>0.346241</td>\n",
              "      <td>-0.243382</td>\n",
              "      <td>-0.350182</td>\n",
              "      <td>-0.148881</td>\n",
              "      <td>0.316185</td>\n",
              "      <td>0.977020</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.127644</td>\n",
              "      <td>0.109884</td>\n",
              "      <td>-0.406722</td>\n",
              "      <td>-0.316036</td>\n",
              "      <td>-0.194560</td>\n",
              "      <td>-0.203201</td>\n",
              "      <td>-0.045667</td>\n",
              "      <td>-0.303234</td>\n",
              "      <td>-0.103313</td>\n",
              "      <td>-0.274500</td>\n",
              "      <td>-0.272729</td>\n",
              "      <td>-0.205539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sp.ent</th>\n",
              "      <td>-0.601203</td>\n",
              "      <td>0.716620</td>\n",
              "      <td>-0.502005</td>\n",
              "      <td>-0.648126</td>\n",
              "      <td>-0.174905</td>\n",
              "      <td>0.640813</td>\n",
              "      <td>-0.195459</td>\n",
              "      <td>-0.127644</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.866411</td>\n",
              "      <td>-0.325298</td>\n",
              "      <td>-0.601203</td>\n",
              "      <td>-0.513194</td>\n",
              "      <td>-0.305826</td>\n",
              "      <td>-0.120738</td>\n",
              "      <td>-0.293562</td>\n",
              "      <td>-0.294869</td>\n",
              "      <td>-0.324253</td>\n",
              "      <td>-0.319054</td>\n",
              "      <td>0.198074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sfm</th>\n",
              "      <td>-0.784332</td>\n",
              "      <td>0.838086</td>\n",
              "      <td>-0.661690</td>\n",
              "      <td>-0.766875</td>\n",
              "      <td>-0.378198</td>\n",
              "      <td>0.663601</td>\n",
              "      <td>0.079694</td>\n",
              "      <td>0.109884</td>\n",
              "      <td>0.866411</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.485913</td>\n",
              "      <td>-0.784332</td>\n",
              "      <td>-0.421066</td>\n",
              "      <td>-0.362100</td>\n",
              "      <td>-0.192369</td>\n",
              "      <td>-0.428442</td>\n",
              "      <td>-0.289593</td>\n",
              "      <td>-0.436649</td>\n",
              "      <td>-0.431580</td>\n",
              "      <td>0.211477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mode</th>\n",
              "      <td>0.687715</td>\n",
              "      <td>-0.529150</td>\n",
              "      <td>0.677433</td>\n",
              "      <td>0.591277</td>\n",
              "      <td>0.486857</td>\n",
              "      <td>-0.403764</td>\n",
              "      <td>-0.434859</td>\n",
              "      <td>-0.406722</td>\n",
              "      <td>-0.325298</td>\n",
              "      <td>-0.485913</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.687715</td>\n",
              "      <td>0.324771</td>\n",
              "      <td>0.385467</td>\n",
              "      <td>0.172329</td>\n",
              "      <td>0.491479</td>\n",
              "      <td>0.198150</td>\n",
              "      <td>0.477187</td>\n",
              "      <td>0.473775</td>\n",
              "      <td>-0.182344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>centroid</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.739039</td>\n",
              "      <td>0.925445</td>\n",
              "      <td>0.911416</td>\n",
              "      <td>0.740997</td>\n",
              "      <td>-0.627605</td>\n",
              "      <td>-0.322327</td>\n",
              "      <td>-0.316036</td>\n",
              "      <td>-0.601203</td>\n",
              "      <td>-0.784332</td>\n",
              "      <td>0.687715</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.460844</td>\n",
              "      <td>0.383937</td>\n",
              "      <td>0.274004</td>\n",
              "      <td>0.536666</td>\n",
              "      <td>0.229261</td>\n",
              "      <td>0.519528</td>\n",
              "      <td>0.515570</td>\n",
              "      <td>-0.216979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meanfun</th>\n",
              "      <td>0.460844</td>\n",
              "      <td>-0.466281</td>\n",
              "      <td>0.414909</td>\n",
              "      <td>0.545035</td>\n",
              "      <td>0.155091</td>\n",
              "      <td>-0.534462</td>\n",
              "      <td>-0.167668</td>\n",
              "      <td>-0.194560</td>\n",
              "      <td>-0.513194</td>\n",
              "      <td>-0.421066</td>\n",
              "      <td>0.324771</td>\n",
              "      <td>0.460844</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.339387</td>\n",
              "      <td>0.311950</td>\n",
              "      <td>0.270840</td>\n",
              "      <td>0.162163</td>\n",
              "      <td>0.277982</td>\n",
              "      <td>0.275154</td>\n",
              "      <td>-0.054858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>minfun</th>\n",
              "      <td>0.383937</td>\n",
              "      <td>-0.345609</td>\n",
              "      <td>0.337602</td>\n",
              "      <td>0.320994</td>\n",
              "      <td>0.258002</td>\n",
              "      <td>-0.222680</td>\n",
              "      <td>-0.216954</td>\n",
              "      <td>-0.203201</td>\n",
              "      <td>-0.305826</td>\n",
              "      <td>-0.362100</td>\n",
              "      <td>0.385467</td>\n",
              "      <td>0.383937</td>\n",
              "      <td>0.339387</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.213987</td>\n",
              "      <td>0.375979</td>\n",
              "      <td>0.082015</td>\n",
              "      <td>0.317860</td>\n",
              "      <td>0.316486</td>\n",
              "      <td>0.002042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maxfun</th>\n",
              "      <td>0.274004</td>\n",
              "      <td>-0.129662</td>\n",
              "      <td>0.251328</td>\n",
              "      <td>0.199841</td>\n",
              "      <td>0.285584</td>\n",
              "      <td>-0.069588</td>\n",
              "      <td>-0.080861</td>\n",
              "      <td>-0.045667</td>\n",
              "      <td>-0.120738</td>\n",
              "      <td>-0.192369</td>\n",
              "      <td>0.172329</td>\n",
              "      <td>0.274004</td>\n",
              "      <td>0.311950</td>\n",
              "      <td>0.213987</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.337553</td>\n",
              "      <td>-0.243426</td>\n",
              "      <td>0.355390</td>\n",
              "      <td>0.359880</td>\n",
              "      <td>-0.363029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meandom</th>\n",
              "      <td>0.536666</td>\n",
              "      <td>-0.482726</td>\n",
              "      <td>0.455943</td>\n",
              "      <td>0.467403</td>\n",
              "      <td>0.359181</td>\n",
              "      <td>-0.333362</td>\n",
              "      <td>-0.336848</td>\n",
              "      <td>-0.303234</td>\n",
              "      <td>-0.293562</td>\n",
              "      <td>-0.428442</td>\n",
              "      <td>0.491479</td>\n",
              "      <td>0.536666</td>\n",
              "      <td>0.270840</td>\n",
              "      <td>0.375979</td>\n",
              "      <td>0.337553</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.099656</td>\n",
              "      <td>0.812838</td>\n",
              "      <td>0.811304</td>\n",
              "      <td>-0.180954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mindom</th>\n",
              "      <td>0.229261</td>\n",
              "      <td>-0.357667</td>\n",
              "      <td>0.191169</td>\n",
              "      <td>0.302255</td>\n",
              "      <td>-0.023750</td>\n",
              "      <td>-0.357037</td>\n",
              "      <td>-0.061608</td>\n",
              "      <td>-0.103313</td>\n",
              "      <td>-0.294869</td>\n",
              "      <td>-0.289593</td>\n",
              "      <td>0.198150</td>\n",
              "      <td>0.229261</td>\n",
              "      <td>0.162163</td>\n",
              "      <td>0.082015</td>\n",
              "      <td>-0.243426</td>\n",
              "      <td>0.099656</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026640</td>\n",
              "      <td>0.008666</td>\n",
              "      <td>0.200212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maxdom</th>\n",
              "      <td>0.519528</td>\n",
              "      <td>-0.482278</td>\n",
              "      <td>0.438919</td>\n",
              "      <td>0.459683</td>\n",
              "      <td>0.335114</td>\n",
              "      <td>-0.337877</td>\n",
              "      <td>-0.305651</td>\n",
              "      <td>-0.274500</td>\n",
              "      <td>-0.324253</td>\n",
              "      <td>-0.436649</td>\n",
              "      <td>0.477187</td>\n",
              "      <td>0.519528</td>\n",
              "      <td>0.277982</td>\n",
              "      <td>0.317860</td>\n",
              "      <td>0.355390</td>\n",
              "      <td>0.812838</td>\n",
              "      <td>0.026640</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999838</td>\n",
              "      <td>-0.425531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dfrange</th>\n",
              "      <td>0.515570</td>\n",
              "      <td>-0.475999</td>\n",
              "      <td>0.435621</td>\n",
              "      <td>0.454394</td>\n",
              "      <td>0.335648</td>\n",
              "      <td>-0.331563</td>\n",
              "      <td>-0.304640</td>\n",
              "      <td>-0.272729</td>\n",
              "      <td>-0.319054</td>\n",
              "      <td>-0.431580</td>\n",
              "      <td>0.473775</td>\n",
              "      <td>0.515570</td>\n",
              "      <td>0.275154</td>\n",
              "      <td>0.316486</td>\n",
              "      <td>0.359880</td>\n",
              "      <td>0.811304</td>\n",
              "      <td>0.008666</td>\n",
              "      <td>0.999838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.429266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>modindx</th>\n",
              "      <td>-0.216979</td>\n",
              "      <td>0.122660</td>\n",
              "      <td>-0.213298</td>\n",
              "      <td>-0.141377</td>\n",
              "      <td>-0.216475</td>\n",
              "      <td>0.041252</td>\n",
              "      <td>-0.169325</td>\n",
              "      <td>-0.205539</td>\n",
              "      <td>0.198074</td>\n",
              "      <td>0.211477</td>\n",
              "      <td>-0.182344</td>\n",
              "      <td>-0.216979</td>\n",
              "      <td>-0.054858</td>\n",
              "      <td>0.002042</td>\n",
              "      <td>-0.363029</td>\n",
              "      <td>-0.180954</td>\n",
              "      <td>0.200212</td>\n",
              "      <td>-0.425531</td>\n",
              "      <td>-0.429266</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          meanfreq        sd    median  ...    maxdom   dfrange   modindx\n",
              "meanfreq  1.000000 -0.739039  0.925445  ...  0.519528  0.515570 -0.216979\n",
              "sd       -0.739039  1.000000 -0.562603  ... -0.482278 -0.475999  0.122660\n",
              "median    0.925445 -0.562603  1.000000  ...  0.438919  0.435621 -0.213298\n",
              "Q25       0.911416 -0.846931  0.774922  ...  0.459683  0.454394 -0.141377\n",
              "Q75       0.740997 -0.161076  0.731849  ...  0.335114  0.335648 -0.216475\n",
              "IQR      -0.627605  0.874660 -0.477352  ... -0.337877 -0.331563  0.041252\n",
              "skew     -0.322327  0.314597 -0.257407  ... -0.305651 -0.304640 -0.169325\n",
              "kurt     -0.316036  0.346241 -0.243382  ... -0.274500 -0.272729 -0.205539\n",
              "sp.ent   -0.601203  0.716620 -0.502005  ... -0.324253 -0.319054  0.198074\n",
              "sfm      -0.784332  0.838086 -0.661690  ... -0.436649 -0.431580  0.211477\n",
              "mode      0.687715 -0.529150  0.677433  ...  0.477187  0.473775 -0.182344\n",
              "centroid  1.000000 -0.739039  0.925445  ...  0.519528  0.515570 -0.216979\n",
              "meanfun   0.460844 -0.466281  0.414909  ...  0.277982  0.275154 -0.054858\n",
              "minfun    0.383937 -0.345609  0.337602  ...  0.317860  0.316486  0.002042\n",
              "maxfun    0.274004 -0.129662  0.251328  ...  0.355390  0.359880 -0.363029\n",
              "meandom   0.536666 -0.482726  0.455943  ...  0.812838  0.811304 -0.180954\n",
              "mindom    0.229261 -0.357667  0.191169  ...  0.026640  0.008666  0.200212\n",
              "maxdom    0.519528 -0.482278  0.438919  ...  1.000000  0.999838 -0.425531\n",
              "dfrange   0.515570 -0.475999  0.435621  ...  0.999838  1.000000 -0.429266\n",
              "modindx  -0.216979  0.122660 -0.213298  ... -0.425531 -0.429266  1.000000\n",
              "\n",
              "[20 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "jWp3IAgyktz7",
        "outputId": "6d869692-ee07-48a9-f639-119155e30679"
      },
      "source": [
        "import seaborn as sns\n",
        "colormap = plt.cm.viridis\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.title('Pearson Correlation', y=1.05, size=15)\n",
        "plt.yticks(rotation=0)\n",
        "plt.xticks(rotation=90)\n",
        "sns.heatmap(dataset.iloc[:,:-1].astype(float).corr(), linewidths=0.3, vmax=1.0, square=True, cmap=\"YlGnBu\", linecolor='black', annot=True, annot_kws={\"size\": 7})"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ac0dc39d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OJaqd7U0s6NcSc74mKCaZ1eEJADzapRENaznh6VSDBbsicHOw5/6bGgDQu6EHA5aWfS05Zwc7FtzfDXNuPvtOXCJw7zkAZoxuT4v6tUhJz+GDVcdISM5CKfj86b5sPxLLt5siyq3v3F4tMefnExybwppIo74P3dSIhjWd8HC05419kZjzNTO6+eFaw45zKZl8ePBc2Q1RUOf7LHU+mUDgvvNGnUe2p0X9mqSkm/kg8BiN6rkxto8fjjXs2HU0jpV7ys52drBjweSumPPy2XfyEoFBltwR7WjhW4uUjBw++OU4jeq6Mra3JfdYPCv3ll/n8J37iTkaTr45l76PTKCG5f0iOSaeg6s2gtbUbuBDl9GD2fv1SnIys8hJz6T/YxNxcLb9f6Sqybcmbatwyyil/JRSJ5VSXymlTiulliqlApRSu5VS4UqpHkopV6XUEqVUsFLqoFJqpNVjdyqlDlhuvSzl/kqpbUqpFZbspcrwEDAeeNVS5m95fCBwXCllp5R6Rym1Xyl1RCk11ZKnlFIfKqVOKaU2KaXWKqXuqoyGOns+gWnP/7mLT27ZfIC5C6YwZGh3Nm86UFhep647s+fdy2OPj6R7j9a4uTljNuey8qed9O7TodzcO4Z0InD9QZ6a/T23DSz6J3fn0M7MmLWUBYsCmf7AILTWPDNnGY89/y2uLg7lTjlx+5Cb+GX9YZ6e8wPDBhbV446hN/Hky9/z2uI1TH/AH4Bn5/1IRFRChdtiiH8zftsayey3tjOor19h+YpfTjLv7R1EXUjm519PATB0QDPCTlQse/uWo7w0bxyDhnRi2+awwvLEK2lsWn8YJ2cH6tatCUD/gR14fs5YHpk+jMMHosrNPrbnCGOfvpub+nXh6K4jheUmk4kxT05gwvOTyc7KJj8/H/8JAYx5cgJdBnUn8nB4mbnHd4cx+qm76divM8dK5I56cgLjZk4mJ9PI7T9hMKOenECngd05U06utZ1bjvL8vHEMGNKJHVbtkpSYxtb1h3F2dqCOpV0qYsumEOYveIihw3qyeVNIYXmduu7Mmf8Ajz0xlpt7tsXNzYXg4OM898IkXph1Lzt3HLKZOaSvH+t3RDF78U4G9irqFD84/ibSMszofE3c5XSaN/Yg6kIK8/+9m3qezvjUc61wvUv6s/v10E71WXf4IrOWHWJQB5/CcnuTYvytTdh+PB6Adg3d6dasDtnmPBJSs8rNHdbWm7XH4ngp8BiD23gVlm84mcDLa46zeHM4NzfxwM3RnvScXF4KPEamOY9aTjXKr3OzuqyLvMysbacZ1LROYfl/Dl5g9vZwVp2O55YGtYlIymD29nCWHr3IrxGXys/t3pB1wdHMWhJCQNf6heV5eZqc3Hxy8zSpGWYA7gtoydbDseVmAgz2q8v6qEvM3RXOwMaeheVfHLnA/N3h/BKZQM/6tUnMMjN/dzgzt53Ez71iB1GGdmvIuv0XmPVVCAFdrOqcn2+pcz6pGWZCwy8z68sQnv0siIAuDSqQ24B1odHM+jqUgM7WuZqcPKvciCvM+jqUZ78ILvb8ZYkKOoL/tIk079WFqH2HC8trN/BmwPTJDHj8XhLCzwJw65TR9H/0Hrxa+XElKtpGoqhuv7eL2gJYDLSx3CYCfYDngFnAy8AWrXUPYADwjlLKFUgABmutuwITgPetMrsATwHtgGZAb631F0AgMFNrPcmyXlfgSa11K+BBIEVrfTNwM/CwUqopMBpobcm6D+hV2otQSj2ilApRSoWUtryqFHR8fOvXIT4+6Zrlgat2c+dIo8rffLWBeyYNqtD8XPV9PIiJNfLy8os+Dn/4380snDueByf1o7a7CwCtW/jw0cJ7SUnNRJfz0bm+d21iYpON3Lz8wvKP/ruVt+aM5YGJfXCv5VJu/Urj4+VKXHzaNXUGcHCwo5FvLaLOJ1PHw5l2reqyd39MhXILmsunvgeXElIKyy/GXKGWuwuPPTWcuNgkoi9cBuB/X2/n/UWB3NK7dUXSAfDw9iDlcnKxJfHn4vjxnaU4uzpjMhm7VXZmNmE7DtK+V+kjQCViqe3lSWqJ3IRzcaxY9B1Obi7Fco/uOES78nKtn8LyHN6+HlyKt2qX6CvUdHdh6pNGu8RY2qWieb716xIfl3jN8tWrdjJiZF8Abht+K889/QFPPfEeo8f0t5npU9eV2ARjm8i32iZa+nmwamM4738dyvTJXTgecRkHBztemnYLXnVd8an7xztif5ZvbSdikzKB4nV+eFALvt4RSUFJMy83IuOvsjDwOAPa++BYo+y3Xl93J2ItHbaS+8dDvfyYM6wt28MvczU7Fwc7E0smdcXepEjJNJdfZzdHYtOyrqkzgIu9idtbeLEx6kph2YR2vvx4ovxOk4+nC7GJGdfU+eNfjvPcp0HsOhrHhP7NaNmgFiaTIiImxVZUMd4ujsSlZxu5Jd6yXOxNDGtaj03njPp29a7FewPbcjIxrULZPp7OxCZmXlvnNSd47vNgdh2LZ0K/ZoXlDw9rzYpd5X9o8/Gw0Ra/nuC5Lyy5fZsW5Q5txYpdZytU5wJu9TxJS7z2/0jErhAadmpTeD8jKZVLkefxbt3smnXFX8Pv7YhFaa3DtNb5wDFgszb+m4cBfsAQ4EWl1CFgG+AENAZqAJ8rpcKA5RgdpQLBWutoS+YhS05pgrXWBXvAEOA+y/MEAXWAlkA/4HutdZ7W+iKwpbQgrfVnWuvuWuvuv/P1/yG/rN7DO28uIzU1HYC42ES8vT1K1on9wae4uaexA50+eZ5lS7dwNCyKFcvLnu7jYlwS9X2MPJNVx+3AkXM8O3cZP64OLuyonYqIY/oL32IyKRrW9yg1rzA3Ppn6Pu5Grqko92DYeZ6bt5zlgSHExCXbeniZ4hLS8fZys9S5+LJhA5qxYfsZAHp0rU8dD2emP9iNnl0b4NfIvdS8db+E8N7bq7maarypxsUmU8+raN16Xu7UsnxKdqvpTGZGDgATp/Tn1bfv5ftvbE9HFboxmF8++ZnMNOONNTkhCfe6xQ8JejfxYfzMSWitSYpPJCs9i1UfLOe2B0fg6FL64YCDm4L59T/Fc2uVyPVq4sNdz01G6/zC3MAPfmToQ7Zzrf32SwgfWLVLQmwy9bxLtEuta9vFlsDVO1n45rekphp1jou9jLePZ7F1tNbsDzpOj57Gbv7F54F8+fVsvvpmDv/94heb2XGX0/GpZ2wT1h9A4i6lk3I1m4xMM44OdmgN7y4J4c1P9pF6NZvzsanltkNViU3Owre28XewrnPbBu5M6decTk1qc0/vpsQlZ5JiadvMnFwc7O3Kzk3JwqeWkWsq8WHsiz1nmb78EA/18qODby2ikzN5YOkBLiRn0s6n/FHN2LRsfN0cr6mzWw07Xu3firf2niHdnAeAk70Jb1dHzldgFC8uMQMfT+dr6lzwee9KajYuTvb0bu9NYy837g1oybDuDfFwcygtrlB8RjY+ro6W3KJy1xp2zOvdkkX7o8iw1PdAfCpPbTlBV2/3a95XSq9zZjl1zsLF0TiD54EhrbiUklWhkby4pAx8PIwPqSar/7KFuVezrXJbcik1i61Hys49tS2I3UtWkJ1u7Htpl5Nw8yz+Hh6xK4Srl5JoP9T4EJR2JZm936yk78PjMdlV86FBZbp+txvM7z1HLNvq93yr+/mWrDxgrNb6lPWDlFLzgXigE0bnz3qvts7MK6NO6daRwBNa6/Ulnmd4hV7FH+BZ2435z0+gU3s/nps+kkUfVXxOvztH9uLOkb1YtyaI1xd8S3aWmZdmT+LwoUhOnTjP+HsGEBJ8iq7dWha+MS5c/CgAc2ct4a5xtkcRANZsOMzb88YzxL89v20N45N37mPazG8I6N+OYQM74uriyLyFK/HxcuepqUMwmRS5efmFo122/LrhCG/NGctg//as33qMj9+exGPPLyWgX1uGDuyAq4sD8982zm2b9dRwOrZryKJXxjHrtZ/Jsbwx2rJh2xnmPtsX/15N2LLrHO/MHcjMBUa/edjA5jw9dyMA6zZHsm5zJA18ajJ5XAfOXij9U/Rtd3bntju7s2HtQd557Seys808+9IYwg6f5fTJi4yd0ItatVz49zuB5OXm07J1fQJ/DiLiVCxpaZmMGNvTZl27De5Bt8E9OLQllJXv/4g5x8yox+/i3PEoLkbG0L5XR7Yu24TWGpOdCfd6tVn62lfk5eax9fuNdOzfmRadW12T2yWgB10CenB4awirP/gRc7aZEY/fxfnjUVyMjKZdr5vY/sNGdL7GzmSHe73afP/al+Tl5bHt+w107NeF5qXkWht2Z3eG3dmdjWsPsvj1n8jJMvPUrDEcPXyW8JMXGT2hF7XcXfhwUSC5ufm0aF324ZERI/syYmRffl2zh1df+ZLs7BxmzZ7C4UPhnDhxlrvvGcz+4BN07da6cFseFHAz8+Z+AUDvPjfZzN6w8yzzZvTC/5ZGbN17jnde8mfmm9v4ckUYLzzSE43mf4EnAJj/ZG/s7U2EnbpEYnL5nQRb/sx+DbD+8EVeuesmBrT3YcuxOBbf241nvw1lxlfGYPvbk7ry/e4oMnLyuLNbQ14e3YHLqdlcLWfk6rcT8Sy4vS0DW9Vj06kE/jWmI8/8HMaErg1p61OTmo72LAuNJvxSGg/3asqrt7fDw6UGSypwjtH6M5d5pW9LBjSpw5azV1g8qA3Pbj7JO4NaY28yMb1bY36NuMTemGTuaFGPdZHlH5YEWB8Szfz7ujGgU302H7zIoqk9ee7TIKbd2RZfTxc8ajqy4NsDXEox/l4929SjVUN3ktLK7vxvPHuZ2be2oH8jT7aeT2Rh/9a8sP0Ub/Zrjb1JMbVTI36LukxiVg7jW/tiZ1Icu5xGfjnnygGsD41m/uSuDLjJl82HLrLo4R4893kw025vi6+ns1HnpQcZ1Lk+Ewc0Z++JBOrXceHjNSfKyY1h/qQultxYFj3Ug+e+CGba8DaWtnBgwf8OMaiTLxP7N2fvyQTqe7rw8a8nbWa29u9Ja/+ehO/cz45Pl5GbY6bvw+OJO3WGy1HR+LRqyt6vV9Gke3v2fPkTvf5vLL+99Sm1fb0I+m417Yb2pa5fw/IbRVx3qrzDU4UrKuUHrNFad7Dc/8pyf0XBMozDibUwOklaKdVFa31QKfUuEK21XqyU+j9gibFY+QPPaa3vsGR+CIRorb8qkV9yvUeA4cA4rbVZKdUKiAGGAlMty7yA48DDWusVZbwuLZN+G2TS7yIy6XcRmfS7iEz6XUQm/S5yI076rbWuwJhh5Wne7b2KdTYqQWToU9f1tf1ZlT2G9yrGYcgjSqljlvsAHwNTlFKHMc4tS7fx+Ir6AqOTdUApdRT4FGMkbSUQbln2DVD2V+yEEEIIIapRhQ9Naq3PAh2s7t9vY9nUUh4bDlgfk3jBUr4N41yygvUet5Ffcr18jC8HzCqlqoUZllE1IYQQQlSjinzx7J/qxjurTQghhBDib+KvfEHXP816VE0IIYQQ1UMu6GqbtIwQQgghRDX5W4+ICSGEEKL6yaTftknLCCGEEOIfRSk1zDIdYoRS6sVSlr+rlDpkuZ1WSiVbLcuzWhb4p+tS0euI/V0ppf7ZDSCEEOIf53pfR6xVj4+v2//a08GPlfnalFJ2wGlgMBAN7Afu0Voft7H+E0AXrfUDlvtpWmu3yqqvHJqEKrvwalVdUBLg3NU/3Qm/RpOaI8jLP1bpuXam9swO2Vjpua91HwyAd9trPsz8afEn3qLR4lJnyPpTLjw7kMZvVH5bnJ9ltEWLPv+p9OyIXY/SsOMrlZ4bHTaP8JTK345buo8Aqm6/PnzF9jRNf1SnOney5nzlX0D4jsbGZCMzgzZXevY7PQdxNLHy26KD552sj/610nOHNrwdoMrqHFYFuR0976Tnip2Vnht0V99Kz7zB9AAitNZnAJRSy4CRGNcgLc09wLyqqowcmhRCCCFE1TJdv5tS6hGlVIjV7ZEStWkAXLC6H20pu4ZSqgnQlOJzVztZcvcppUb9gdYoRkbEhBBCCPG3obX+DKis+dPuBlZora0nUG6itY5RSjUDtiilwrTWkX/0CWRETAghhBBVS6nrdytfDNDI6n5DS1lp7ga+ty7QWsdYftra6xMAACAASURBVJ7BmPWny+9tDmvSERNCCCHEP8l+oKVSqqlSygGjs3XNCatKqTaAB1bzViulPJRSjpbf6wK9sX1uWYXIoUkhhBBCVK2/0FyTWutcpdTjwHrADliitT6mlFoAhGitCzpldwPLdPHLS7QFPlVK5WMMZr1l69uWFSUdMSGEEEL8o2it1wJrS5TNLXF/fimP2wN0rMy6/G0PTSql/JVSa6q7HkIIIYQQtsiIWCnWrQlif/BJcsy5vDxnMs4ujgBcuZzCJx8ZI5a7d4SxfPUruLk5E346mqkPLmbNb2/i4ur0u57Lr7EXLzw+CvdaLkx89L0/XOct6w5wKCQSszmXGS+NwdnZsXDZfSPepOstLWnZpiG3j7mFc1HxrP5hN3YmE3fcdQtNmvnYzF3zyw6CgsPIycll3rypuLgYry8vL4+XXnwfRycHzDm5vPHmE3z80Y9EnonGvZYrjz02AS9vT5u553bvJ+H4afLNuXR74G7snYrqG/LF/7gSHsXQhS8DkJmUwonA9aCh8a3dqNu6uc1cF+cavDVnFDnmXPbsj+LnNYcAGNSvNRNGdQVg6YoQjp2KZebjAYXL/Ee8R1p6ts1cZ3sTrw9qhTlfs/dCMqtOxgMwpHld/P088a3pxPtBZzkYm8qbAa3oVt+dId/st5lXLLuGideGtsGcp9l3PolVx+KM7Jb16N+sDvVrOfH+7jMcvJjKjkd7sTMqkaPxV/n+kK1zSy25TvbMf6YP5tx8gg9eJHBjBAB33d6am9p44evtysnIRH5ee4pHJnVGKcWZc0l89r/DZec61+D1l4djNuexd/9ZVq09CsCAPi0YN7ITAMt+PsiOvWcAeGP2cDIyzby2uPzrp2377QBHLNvx9BfH4GS1HT846k269GxJ89YNuW3MLaxfHcSpo+e5kpDCozNH4duwrs3cqtynd64/wLEDEZhzcnn4+bHF6gzw2cIVODk7cN+MEaz+bgvxMYlEn42n75CuDB59q83c0M2hRBwKJ8+cy9gnx+Foyc3Pz2fFv5eTm22mhpMD454az29fryPhfDzONV0YMnko7nXdbeZG7wnm8onT5Jlz6XT/PcX2vUNLlpIYfoaBb84BIDbkEAlhx8m8kkSrUbfh2aJZmW2xY/0BjoZGYDbnMrWUtviPpS3unzGCTYFBHA2NIDvLzPgHB9O0ValXDwAgZHMopw9GkGvOZcJTdxVrix/fW0FOthkHpxrc/fR4Uq6ksPF/m9Fa021gF5p1sF3nqqovGNtFQfYjpWR/asmeYsk+ZskeV0a2k52JmV2ak5uvOXAphfUXLgFwq48H45r7sicuiRWRsTR2c+be1g1RwLmrGXx7uuz3iuvibzvs8+fdsE2jlHJVSv2qlDqslDqqlJpgmbLgpFLqADDmj2Zv2XyAuQumMGRodzZvOlBYXqeuO7Pn3ctjj4+ke4/WuLk5YzbnsvKnnfTu0+EPPdfZ8wlMe/7Pf8t219ajPDNnHP0DOrFry9Fiy5xdHMjJMlPP23hzXvHtdlzdnLCzN+FRp1aZuZs27ePVV6czbFgvNm7cV1ienp6Fi6szr746HSdnR1JT07Gzt6NGDXvs7e2pWcu1zNyY/Ye5+eFJNLqlK9H7DxVb1v2hidT09Sq8f+rXTdRwckIphbNn7TJzhwd04JcNYTw3byVDB7QtLO/doxnzFv7K7DfWMKhfay5fSeOFV1bxzgcb2RN0psxOGMBtLeuxNvwSL2w8xeDmdQrLN0ReZtbm0yzafYYeDYz2fWnTac4kZpSZZ21Yay/WnkzgxXUnCGhR1JnYEH6Jl9efZNGOSHo08gAgPScPpxomYlOzys0d0r8p67dFMfvtHQzs7VdYvuLXU8xdvJOoCyn8vPYUURdSeOmt7bz45jY6tfMuN/e2QW1Yu/EEL7yyhiH+rQvLe/XwY8GiDcxfuJ4BfVsAMHxwWw4fu1jRpmDPtqPMmD2OvgGd2LO1lO04u2g7HjqyJzNeHsfQUT2JOFn2P5qq3KeDt4fx6Evj6TWoM0Hbwoot27vlMM3bFn05a+TkgTzywl141qtFr4DOZeYe3X2ECc/eTSf/LoTtOlJYbjKZGP/0BCa+OJnszGzy8/Oxs7PDroY9dnZ2OLs5l5kbG3KYzg9OpkHPbsSGFN/3Oj8wCTffom3At3tnOv3fRNrcNYIrpyLKbYug7WE8Nms8vQd1Zl+Jttiz5TAtrNri2MFIHp9zN6Mm+3P0QNnf/D+8M4yJz02gq39nDu8s3hZ3PzOe+16aRHZmDvn5+Wz+cRuOLo4ok6J23bLfL6qqvgXZ02YZ20XJ7JLbxfGDkUyfczcjJ/tzrIxs/wZ12BpzhTcPRNDXt+iD7t64JL47VbQPnE/L5PXQcF4LDae9Z81y6yqq1w3bEQOGARe11p201h2A34DPgTuBboDtYZ5yKMtJhb716xAfn3TN8sBVu7lzZC8AvvlqA/dMGlT4mOpS8PTevh5cjk8utuyT/z3Ns/MmsPL7XQCcPhHN3VMGMGxkD37+X9lXHy94XQ3qexEfd6Ww3M3NGXOOmalTXyM3N4/atWsydepY3n77KXr17sSK5eWMfljq61LXk8zE5DJXTY2OpUnfnrQfezvHV/5W5rq+PrW4GJcCQF5efmH5qrWH+fzdSXz5wWS+/zmksHzCqG78sDq07LoCvm6OXLxqdNbySkzU8XC3Rsz1b8G2s4nl5pSaXdOpsGOVXzK7R2PmDGrFtjOXARi+JIiZvx7n/25uVDLmGj71XIlNSDNy8/OLLXNwsKOhb02iLqQUlt0+qDm79l+gPL7eVm1slRu47hj/WTSOz98bzw8rD1HX05UObXzYHRRVbmaBgu3Yq5Tt+P3vnubJORNYvWxXYdlXH65lzfI9tO3YpJzcqtunC9ar6+NBYkJReyYnXiXqdAwdb25ZbP3ESyk4ONTAtWbZHaaCncTDy4PkS8XbIu5cHP9buBRnN2dMJhODJgYw6cXJtOrWmn3r9pYWVjLWsu9d2xYlRazdxNGly/G+qX256xa0RT0fD65Yt8WVq0SdiuGm7kVt0WdwF+Y+9jFL3ltNr4Gdysk1fnp6e5B8OaXYstizcXz71lKcXY22iDsbR4/BNzN8yjB++25DtdTXutL1Sm4XV65ypkR278FdmPfYx3z53mpuLSPby9mR+IyC96HyZwwKaFiXoPiy31+vF63UdbvdaG7kjlgYMFgptVAp1RfjyrdRWutwyzccvrP1QOur7lqX/7J6D++8uYzU1HQA4mIT8fb2KPZYrTX7g09xc882AJw+eZ5lS7dwNCyKFcu3V+oLrIiNa0L4eNFq0q5mApAQl0Rd7+KfAk0mE3Z2Jhwc7MnPz8e3gSdOzg641XImM6P0kaBVq7byxhv/JcXSFhdjL+HtUzQSdPzYGeo38OLTT2fTsKEXJ05EYTIZm5OnpzsZGaWP2JzdEcTBb1ZgTjfqm3ElqdxRLmdPDxxcXbB3ciTPbC5z3di4VOpbRkxMpqIdcsbD/oye8hmj7vuMJx7qX1jeu2czdgedKTMTIDYtG18349BCyZ3m89ALPLbmGA93Lb9zVGr21Sx8ahmHv0q+h3wefJ7pq47wUA+jo6ExOmvZufmU93YTdykdHy9jZFKZiq89zL8ZG7YXdZBuH9ScBt5ufL/6RPn1jU/F19sYSTVZVXj6Q70Z939fcdf9XzH9wd7c0r0JdTxdeWpqP269uQlNm9g+VL351xA+W7yatNQKbMeO9oUdy/sfH86U6bexITC41Nyq3Ke3rw3hy3dXkXbVGP28HJ+Ep1fRIcHjByJJTUpjxX83cPRAJBfPG4eRtq4Jxv/2m23m7t8QzMqPfyYzzchNTkiidr3ibeHTxIeJL0xC52sS4xML9z232m7kZOaUmnt+1z7CvvsRc7qRm3ElEWdPj1LXtdZieAA3P/4QEetsT5O0dW0I/313FWmpRval+CTqWLXFsYORpCSl8eOSDRwNNdpi/c97eOOzJ3j+zftZtXRrqblBG/bz04cryUgztovEhCRqlzjs6uvnw70vTkJrTWJcIrXrueNS0xlHZ0fMObnXtb4A29aGsOTdVaSnlr5dHDtobBfLrbI3/LyH1z97gplv3s/qMrITMrPxcnYAiu97pQloWBdfF0dWRsWVuZ6ofjf0pN9KKU9gOPAwsBkI0Fr3sywbATyitb6jnAxdck66dWuCOHDgNNlZZl6aPYnTp6M5deI84+8ZwP6gkxw5HMmDj9xe7DFzZy3hxZcnFp5PUtG5Jj1ruzH/+QkM6tuRL5dtZdFHq8tc39Zck1vWHSDsYBTZ2WaeeHE0UeGxRJyMoUuPlvzwtbFjN2vpy5iJ/ThyIJKNa0Ixm3OZ9GAAjfyMw4ClzTW55pcdhIQeJzsrhzlzH+HUqbOcOBHF6NEDmf3yh9RydyM5KZXX33iCb79ZQ2zcZZKTUpn18kN4eRn/eEuba/Lc7v1cPhlBntlM1/snkHw+huRz0bQY3I+wHwI5vzcU745t6HLfXaTFX+bUmo2AotmAXoXniJU216SLcw3emD2C7Oxcgg6cY0CfVjzx4o+Mvr0TA/u0AmDH3giWBx6kd49mdOvcmPc/23ZNO5eca9LZ3sSrA1uRnZfP/pgU/P08eeq3E9zdwZe29dyo5WjP92EXCY5JYWbvpoxo7c3Oc4nM3xZOjtUQWmlzTTrXMLFgSBuyc/MJiU6mf7M6PP3LMe7uVJ+2XjWp6WjP94diuJKRw7Rb/AA4cekqS6xGr0qba9LZyZ55T/chOyeX0CNx9O3ZiJmvGdvCJ28M5an5m8jOyaNdyzp8+tYwtu45T2Z2Lm9+WHxEpeRck87ONXjtpdvIzskl+MB5/Hu34KmXVzFqeAf69zb+Nrv2RfHTL8YhpIb13bn/nh7XnCNW2lyT2347wLFDUWRnmXnsBWM7jjwVQ+ebW7LiG6Pufi18GTWxHyu+2cql+GTSUjO522o7Lm2uycrYp6H0uSZ3rj/AicNnyMk289BzYzgXEcvZ0zEMHdsbgITYRH5bvov7ZoxAa83rT3/O7PeKz7hS2lyToZtDiQqLxJxjZswTdxEbdZGY8Bg69O7I5u83obVGmRSjHhvNlmWbSU5IIj01ndHTx1CrjvGPv7S5JqP3BHPlVAR5OWZumnI3qRdiSDl3gaYB/Tm+fDUx+0Lw6tCWDpPHEb07iJTzMZgzMvEb0Js6rYtGcUqba3LH+gOcOGS0xcMzjbY4cyqG2+6ytMXFRNau2MX9M0aweuk2Ys4lkJGeRcCInnTuaRzmLm2uyZDNoUQeOUNOjpnxM8Zy8UwsFyKi6dS7Ixv+Z7SFyWRizPRRxJ9PYNMPW1Aoet3es/AcsdLmmqyM+hbUueRckzvXH+B4ieyoUzEMs8pet2IXU2aMILBEdidLdsm5Jp3sTDzXuTk5+fkcvpzKLd4evBJymg6eNZnSuiE1Hez5IfwiMelZLOrVjl1xiWTn5fPvI8VHpYPu6nvdJ/1u2e/T69bZCN8x9YYaFrthO2JKqfpAotY6Syl1B/A40A4YoLWOVEp9D9T8Ix2xyiCTfheRSb+LyKTfRWTS7yIy6XcRmfS7SFVO+i0dsb+OG/lbkx2BdywXVTMD04C6wK9KqQxgJyBnKQohhBDVzXRD9Y2uqxu2I6a1Xo9xVdyS2lzvugghhBBC/BE3bEdMCCGEEDeIG/DbjNfLjfytSSGEEEKIG5qMiAkhhBCiasmAmE0yIiaEEEIIUU1kREwIIYQQVUu+NWnTDXsdscqilPpnN4AQQoh/nOt+HbGAL67fdcQ2PXRD9fpkRAzwaDG90jOTIj6qsouuAlV2sdjm3d6r9NzI0KdoeesnlZ4bvncaQJVdSPHTE2XPa/lHTG07jI+OlXbVlT9nevuhAJxIqvy2aOtxJ0nZlX+BTQ/H22kR8EWl50ZsesjIr6L92u/5ym/js2/fSeO3NlV67vkXAwBo8Z/Kn34t4tH++M1YVem5Z98fRcdvKv9ivGH39QPA75myZy/5I87+ayR+z1b++/3ZxSNYdKTs+TL/iOduGlLpmeWSb03aJOeICSGEEEJUE+mICSGEEEJUEzk0KYQQQoiqJUcmbZIRMSGEEEKIaiIjYkIIIYSoWnL5CptkREwIIYQQoprIiJgQQgghqpYMiNn0lx8RU0ptU0p1t/y+VilVu7rrJIQQQghRGW6oETGt9fCqfg4XZwfemT8BszmXXcHhrAgMAWD08K4M9m9Pbm4eH/53M+FnEli8YAJOjjXIzDLz7Nxl5WZvWXeAQyGRmM25zHhpDM7OjoXL7hvxJl1vaUnLNg25fcwtnIuKZ/UPu7Ezmbjjrlto0sznd78Wv8ZevPD4KNxruTDx0d9/oVZnJ3teeXEAZnM+QaHRBP52CoBxI9vTsZ03vt5unAq/zJffH+LJqbcA4N/bj+ETviMtPafc7PnP9cWcm0/QgYv8siEcgLvuaEPHtvWM7Igr/OvTYF6Z2Q8nRzsys3KZ987OMnN3rj/A0dAIzOZcHnl+LE5WbQzw6cIVODk7MGXGCDYFBnEsNILsLDPjHhxM01YNysw+sT2EC2Hh5JlzCZg2nhpORnZidDzBP28CDZ4NvegxdjBXLsRx8NcdmEwmOg3rTZ3GvjZzT+4IITrsNHm5uQycOqEwNykmnpCfN6HReDbwpvuYwQCcO3iCrZ/9yP2fzCuzvta2rz9AWGgE5pxcpr1QvF2mjnmDTj1a0bx1A4aOvrVCeet/DSV0fwQ5Obm8MPsunF2K8gJXBnH6RDRubs48OmM4ly+l8PUXm9FaM/i2LnTq0qzUTGcne+Y/0Qtzbj7Bh2MJ3BIJQD1PZx69uxMo+HXbGQ6duMTbM/uRlZNHDXsTLy7aQXkThFTVfu1cw45XR3fAnKfZF3mF1YdiCpe19qnJdw/fQv+FW7AzKWbf0Y5Gni5M/Gxfue3rXMPEa4PbYM7PZ9/5JFYdjwdgSMu69G9ah/q1nHh/TxQHL6YC8PqQ1mSY83h9a0T52fYm5vdpiTk/n+CLKQRGJADwSOdGNKrphIeTPa/ticROKT4Z2p5DCansjk5mfdTlsnMd7Hh13E2Yc/PZF3GF1aHRRW3hW5Pvpvem/4KNZOTk8cjAFvjWduZCYjpLtp0pt74v92iBOT+f/fEprI26BECf+h7c06Y+O2MSWXYqFic7E3NvaUm6OZeEzBw+D7tQfls42PHqmJsw5+WzL/Iyqw+U+Ps92ov+b2wiIycPpeCLB3qy/WQ83+w+W4HcjkZbRF5h9cGSubfS/43NVrk92H4yodxcgIid+7l41Hgf6vNI0ftFckw8h1ZtBDS16/vQefRgTm8L4uTmPXQaGUCT7h3Lza5KWi7oalOVjIgppfyUUieVUl8ppU4rpZYqpQKUUruVUuFKqR5KKVel1BKlVLBS6qBSaqTlsc5KqWVKqRNKqZWAs1XuWaVUXcvvq5RSoUqpY0qpR6zWSVNKva6UOqyU2qeU8v49db9jSCcC1x/kqdnfc9vAog33zqGdmTFrKQsWBTL9gUForXlmzjIee/5bXF0cUBXYyHZtPcozc8bRP6ATu7YcLbbM2cWBnCwz9bzdAVjx7XZc3ZywszfhUafW73kJhc6eT2Da85/9occCDB3Ygt82R/Dy65sZ1K/on+fy1ceY++YWzp5P5qc1x7mSmMHcN7fw3n/2si/kQrmdMIAh/k35besZZr+1nUF9mhSWr1hzknnv7CTqfAo/rz2F1jD37R08/+pWXFxqlHtx5qDtYUybNZ5egzqzb1tYsWV7txymedtGhfePH4xk+py7GTnZn2MHIsutc8S+Iwx5/B5a9elC+N7DheWeDb0ZNmMSw56cROzpcwCErNqCo6szJns7XDzK/vtF7jtMwPSJtOzVlYh9RbkeDbwZ/MQkhjwxmbjTZwHITs8g+mg49Zo2LLe+1vZtC+PxWePpE9CZvVuLt4uTswM52Wbqeld8sHnb5jBmzZ9AwNDObN18pLA88cpVNv12EGdnB+rUM1730q+24eLqiDIpvMp4jiF9/Fi/M4rZ7+5i4K2NC8sfHNeRtEwzWkPcpQxcnWuQnmlm9ru7yMrOpZabQ7n1rar9elgHH9aFxfLST0cIaFf0VmNvUky4uTHbTxmdnKtZubyw4gjJGeZy6wowrJUXa08l8OJvJwloUa+wfEP4ZV7ecIpFOyLp0dBoy+GtvTgSl1qhXIAhTeuyPuoSs3eEM9DPs7D8s0MXmLMznNXhCfSsb2Snm/NwsrfjYlpW+XW+yZd1hy7y0g+HCehQ9MHR3qSYcGsTtp8wOpPtGrjTraknWeY8ElKyy80d1KguG89f5pV9Efg3rFNYvutiEl8eK+rsNXV34WxqBq8HR1LP2QFvl/K3i2EdfVl35CIvLT9MQPsSde7ZhO0nEwrLpvRuylbLayg3t4Mv647E8tKKIwS0L7Fd9Gz8h3MBooKO0G/aRJr16kJUUNH7Re0G3vhPn4z/9HtJCD8LQCv/nrQeWLEPV6L6VOWhyRbAYqCN5TYR6AM8B8wCXga2aK17AAOAd5RSrsA0IENr3RaYB3Szkf+A1rob0B2YoZQq2ENdgX1a607ADuDhkg9USj2ilApRSoWUXFbfx4OY2CQA8vKLPmZ/+N/NLJw7ngcn9aO2uwsArVv48NHCe0lJzaQic3YWvKd7+3pwOT652LJP/vc0z86bwMrvdwFw+kQ0d08ZwLCRPfj5f5U/3UdF+Hi5ERt/FSjeFgAODnY0rF+LqHNFr2Psne34ec2JimXXcyMuIc1mdqP6NYk6nwJAcz8PFs4ewNWrOeWOfBQ0cj0fDxITUgqLk69c5cypGG7q3rKwrPfgLsx77GO+fG81tw7sVH6lLX+/WvU8uXol+ZrFJ3eE4te5DQDxkRfoMSaADgG3cCBwWzlVNoJreXmSVkruqZ2hNO7cFoDg5RvoNmpQ+XW18Rz1fDy4YtUuAO9++wxPzJ7ALz+UPdpYPM/46ePrQUJ8UV5M9BVqubsw/ek7iYtNJPrCZc5ExjH8zpt5eNowlnxqe7oWn7ouxF5KByDfapto2cSDVRsjeP+bA0yf3Jm0jBwcatjx+WtDsLc3kXK1/I5/Ve3XPu5OXEzOuib34X7N+Gp3VPnbqw2+NR2JvWrk5pcIefjmxswZ1IptZ65Q18WB9l5u7D6bVOFsH1dHYtOMDlB+fvFlLvYmhjevx6azV4hJy+aewMPM3RnO9K5NSkkqkVvbmYvJmQDkWdX54YEt+Gr7mcK2aOblRkT8VRb+cpyB7b1xrFH2vyFvVwfi0i31LaNBTyam4WBn4rluTS0dMUeb6xbW2d2pqM7Wfz//5ny16wwao6yld01MJkW45f2w3NzaZeUWbRctvd0wKUV4fFqFcqFo36tZz5P0K9f+3SN2hdCwU5sK5103JnX9bjeYquyIRWmtw7TW+cAxYLM23tXCAD9gCPCiUuoQsA1wAhoD/YDvALTWR4Aj10YDRufrMLAPaAQU/IfNAdZYfg+1PFcxWuvPtNbdtdbdSy67GJdEfR8PAExWn4YPHDnHs3OX8ePq4MI39FMRcUx/4VtMJkXD+h42G2LjmhA+XrSatKvGjpkQl3TN6IPJZMLOzoSDgz35+fn4NvDEydkBt1rOZGaU/6mxKsQlpOHjVdNSv+LLbhvUgvUlDoPc0q0h+0KiqYi4S2l413O1ZBffcYYNaMaGbVGF9yPPJvHCa1tRJkV9H7dS87atDWHJu6tIT80A4HJ8Ep5e7oXLjx2MJDUpjeVLNnA0NJKL5y+x4ec9vP7ZE8x8835WL91qs67HtgSz9YufyEoz/n5XLydRs07xv9/JHaGkXkqk0219AHD3rkMNJwecXF3IySz973diaxDb//sTWelGna9eSsStRO6pnaFcvZTITcP6YM7KJiXuEkE//salszGc3LbfZp0LbFkbwhf/WkXaVUu7xCVRx6pd4NptryxrA/fzr4UruZpqtEVcXBJe3kV5Xt7u1KpldGhq1nQmIyMbL293atZyxsXFkZzsXJvZcZcz8KlrbBPWI1Fxl9NJScsmI9OMYw072resS0z8VR6evYHouKu0be5pK7JQVezXAHEpWfi6Oxm5VvtIu/ruTOntR6dGtZnYs/xOTEmxV7PxqWl0JEqOyn2+/zzTV4fxUI/G9GxcmzquDjzZuym3NvagqYdzaXHF65yejY9rQXZRuVsNO17p25KF+6JIN+cVlmfllr1NFOYmZ+Jb23h+6126XQN3pvRrRqcmHkzs7UdcSiaplpHBzJw8HOzL/jcUn55T2Kkqa4RSAx8eOsei0ChSc3KJrsAonvH3K6hzUXa7Bu5M6dOUTo08mHirH71b1aVJHVem9G7KsJvq4+Fa9mhbXLLVdmGdW78WU3o3pVPj2ky8tQm9W9ajSV0XpvT2Y1hHXzzKGMU7vS2IPV+uINvyfpF2OQnXOsW3z4hdIaRdSqLd0L7lvnbx11GV54hZ//fJt7qfb3nePGCs1vqU9YMqcohPKeUPBAC3aq0zlFLbMDpyAGZd9DE2j9/5GtdsOMzb88YzxL89v20N45N37mPazG8I6N+OYQM74uriyLyFK/HxcuepqUMwmRS5efnExF47klFg8B3dGXxHd7asO8C/3/iJ7GwzT7w4muNHzhJxMoYuPVryw9dGR6BT9+aYTCbGTOzLe2/8hNmcy6QHA37PSyjkWduN+c9PoFN7P56bPpJFH/2+yW7Xb4lg3vMD8O/jx5YdUSxaMOT/2Tvv+KiK9f+/Z1M3hFRIo4Uu0jvSO4hXpIeiwlVQEbBjAaniVdGrfu3l2kVUEGnSS+iEJLTQQjrpPSF16/z+2CWNNL0Jwu/O+/XaF+yccz777GRmzjPPnJ2HF5ZbIhr3jmzL00t2lpzbr2dTQs4m1Vp7T2AMy58fyND+LThwNI63lw9n8eoDAIwd3opnl1kSIHs1cuLxh7qj0QhMuAroNQAAIABJREFUJjPJVcwch47rxdBxvTiy+zSfv7URvc7AvMWTCA+LJSY8kbFTBjBgZDfSkrLYufEofs0b07F7az79168UFhQzcnzfKm3tOLwPHYf34fKhEPZ9+gtGvYERj08l6UoMadEJ+N7lz6FvN9OqVycCv9rE0Ecn0XP8UPZ+8gsmo5F+U8dUqtthWF86DOvLlcMhHPjsF4x6PcMem0bylRjSYuLxbd+So99txr9nRw5/vYnBj0ziHy9bArz5mTncNbR3jfU8fFwvho/rxaHdp/nUWi+PL57ElbBYS4Swd1s2fW+p90492qCp6HFXYNz43owb35vdf4Ty1msb0BUbWLx0MmFnYwm/ksCU6QNxcXXi/bWbMRpNtGvfhBkPD+Wjd7eBEEwK6F+l9p6jsaxYeA9D+zbj4MlrvP3SEBa/dYhvNl7gpXm9kRJ+2n6ZyLhsHp3SiZWL+uPu6sC3my7WWA/10a8Bdl1IYdWETgy7y4v9l9J4N6Abz/1ylkU/nQbg7ald+SnIsly9ZmJn7vZzYcl9HfjXH9VHjnddTWP1qPYMb92I/ZHpvHff3Tz7xyWmd/Gjg5czDR1sWX8uieCEHP64kkZTF0dm92xKTHZRjXWxJyaDFQPbMLS5Bwfjsnh7WHsWHwznrWHtsdUInuzRjB1RGRjNZia080Zra8O2yLQadXedT2bVlC4Mu9ub/RdSeffBHjz342kWfWdZeHh7Znd+OhZLod7E+B5NWTqhI+l5xeQVVe2cA+yPz+CV3q0Z3MSDQwmZvD6gHUuPXaVLo4Y83KEJLva2ZBTp2Xctk6V9WmOrEVzKzCeruOZl4F1hyaya2Nli86UU3p3RnefWn2HRD6EWm6fb8dMJi80AfVt70t6nIdk1PH6x60IyqyZ2YlgHb/ZfSi3V/dHaLgK68dOJuJt1C6vWbTe0L+2G9iXySDBHvvgZk97AgLnTSA2PJiMmAe92LQn6fjPNe3bkxLe/cc+cycSFhHE1MAhbezvsHB3w69SuxjqpN+68QNUtQ9RmSe1PiwrhD2yXUnayvv/W+n7jjWPAVsAFWCSllEKI7lLKM0KI54C7pZRzhRCdgLNAPylliBAiFstS5ABgrpTyfiHEXdZzxkopA4UQ+VJKZ+vnTgH+IaWcU42t0r3Ngjqvg+zIj4nL21rnui0ajgfAsdn0Otcujv+Z1j3//EP9NREV+gxt7/m0znUjTswHICxrW51rd/a4n88v76pz3cc7jOXji7vrXHdBR4uzdzm77uuig/v9ZOv+qHNdd4f7aDPyP3WuG7lvrkW/nvq1/4t1X8exa++n+Zv76lz32suWSVybzw7VuXbkE0Pwf2pznevGfjCBzt/X/eMYYQ8PBsD/uT83Ia0Nse8+gP/zdT/ex/57PO+cr3o5/6/yQpfRSClvqWvU5v5v697ZqILIbXPuKLfv79y+4jXADjgvhLhofQ/wKeAshLgMrMayvFiRXYCt9Zw3sSxPKhQKhUKhuB0R4ta97jDqZWlSShkLdCrzfk4Vxx6v5NoioNJwj5TSv8zbe6s4x7nM/zcCG2trt0KhUCgUCsWt5I7aR0yhUCgUCsUdyB34a8ZbxW2/s75CoVAoFArF/68oR0yhUCgUCoXib0ItTSoUCoVCoahf1MpkldTL9hV3EkKI/+0KUCgUCsX/HLd8+4qJ39+67St+f/iOcvtUREyhUCgUCkX9cgduK3GrUI4Y4Nnu6TrXzLz6f5jMNe/4/Wex0XQEqLeNV+tro9iWT/5W57oxn0wGoEJyhjpBiPZsitlR57qTWo5jZ3zd697bbBwAWcV1v9moh+P9FBlP1Lmu1vYeWk/6oc51ozY9BNRfv2714Po6143+cQbN395f57rXFlvyknb6ru43SL0wezAtF2yqc92YjyfR7P3AOteNf2YoAC0X/V7n2jEfTqy3zW1fPFX3G/2u7fPXsrUo6gfliCkUCoVCoahfVESsStSvJhUKhUKhUCj+JlRETKFQKBQKRf2iwj5VoqpGoVAoFAqF4m9CRcQUCoVCoVDUL+oZsSpRETGFQqFQKBSKv4nb1hETQjQVQmwRQkQIIaKFEB8JIRyEEKOEEKFCiDDrv8PLXBMohAgXQpy1vrz+zu+gUCgUCoUCy876t+p1h3FbLk0KIQSwCfhUSvmAEMIG+AJYC3wL3C+lTBJCdAJ2A03KXD5LShnyVz/bSWvP2hVTMBhMHDsVycZtoQBMuLc7o4bejcFo4uOvDhIRncq/V02jd3d/Bo9fWyvt7dsOE3QqDL3eyIoVj+Pk5AiAyWTilZc/wMHRHoPeyL/eWMQnH/9KVHQCri4NePLJALy8ParU1TrasurlYRgMZoJCE9i6y7Kv1tQHOtL5bm98vZ0Jj8jgm/VnefrxfgAMHeDPuIAfyS/Q/+k68m/uxUsLJ+Dq4sTMJ/7afmZaextWB3TDYDITFJHBluD4kmPt/Vz44amBDF2xG40QrH2wJzmFegqKjby+Kaxa3W3bAgkKCkOvN7By5ZPl6vill97H0dEeg8HIG288zW+/7SMsLIKUlAzat/fn+ednV6t99kAoUeciMBqMTHxqKvaODgCYzWY2f7gBg86AvYM9E5+eRuAv+8hKyaQgt4Dx8yfh2titSt2Q/aFEnrXoTn16Kg7aUt0N72/AoDdg52BPwLPT2PndTlKvpaJ1dmLsQ2NwbeRape7uP04TGhyJQW/kxVcno3VyKK2n34MIv5KIs7OWJxbdy5ef7CY2JhUXFyceeXwUjb2q1gXYsf0Ewacuo9cbWLp8Dk5W7cyMXD75yLJP09HD5/ht67/Ys/sUF8NiSE3Jom27pjz93LRKNbUONqya1weD0UzQxVS2HokF4KlpXWjd1IXcfD0fbQjDZJasmNub7OvFXInLYf2eiGpthfrr11oHG1bP7oXBaObklTS2Ho+z2DyxE238XMgt0PPh5gvY2drw2TMDORuZydELKewOSahe107DmpHtMZgkJ+Oz2Xw5FYDRbRoxxN8TPxcHPjgRS26xkfl9moMQRGUV8NmpazXaXPIZthqW9m2DwWwmJCWXP2LSAXi0U1OaODvi5mDHW8FRpBbWbowo6dNGM0ER6Wwp8x3b+7nwwyJLn/bzcGLO0NYADGjfmGGr9v4pm18f1haDWXIiIYfN4WkAjG7lyVB/D3ydHfjgVBxnUvJqb/PUrpZxKDKjvM2+LvywcABDV+2hUG9i3vA2+LprScgs5OvAqBp1X5vaxdIuIjPZElpWtyE/LhjAkNV7cXe254tH+3ImLouj4ensOpdco83xx4PJuHQVs9FA1zkzsHUs7ddnv/qJrIhohr/5aklZbnwix974P0a/uxpbR8da1Yvi1nK7RsSGA8VSym8ApJQm4FngYSBCSplkPe8ioBVCOFQu8+e5b3QXtu0+x7PLfmHs8E4l5f8Y04Wnl65nzb+3s+CRoQA8v+JXImPSaq29b99JXnttAWPH9mfv3pMl5QUFxTg10PLaawtw1Dpw/XoBNrY22NnZYmtrS0OXBtXqjhnehl37I1n6+n5GDG5VUr5hy0WWv3GA2Gs5/Lb9EplZhSx/4wDvf3aCkyHxf8kJA4i9lsb8F7/4S9eW2NzNj51nElny0xlGdPYtKbfVCKb19+fQRcuNx9ddy8WEHJb8dAYfd22Nunv3nmTNmkXce+9A9uw5XlJeUFBEgwZa1qxZhKOjpY6nTh3N6tUL8Pf3Y+LEETVqXzx+nsnPTqfL4O5cOHq+pFyj0TDp6QACXnwQXbEOs9nM0ICRTHo6gO4jehF1rnpHIezoeaY/P51uQ7pzvoJuwHMBPPjyg+iKLLoaGxtsbG2xsbVB61x9fRw6EMaSldMYMaYbgftLHdiszDz27T6LVmtPo8YNAbCx1WBnZ4uNrYaGDWuu5wP7Qlmx+hFGj+3L/n2l8x7PRq4sWzmHJxdNonffDjg7a5k0eQjLVs6hhb83D0wcVKXmmH7N2XXyGks/C2JE76Yl5UaTGYPRjNFk5nqBnl4dGnMgJIEVXwYzoIsPtjY1T4Hrq1+P6dWMncHxLPk6mJHdS+eDJrMZ/Q2bCw0AFBYb0TrYkJRZWKPu2LZe7AhP4+U9VxjZulFJ+Z7IDJbuC+edo9H0aepGdHYhi3dfYfGuy3T3camVzTcY0bwRe+MyWHUikqHNPEvKv7qQwOqTkWyPTqO3T9UTiIqM6Wrt0+sr6dP3tODQJUufjkzJ49Wfz7LuSAx/nE78Uzbf26YROyIzeGn/VUa1KrV5T3QmSw5E8M6JWPr4VT+JuMnmc0ks+fksIzr5VGnz3U1d6dnKE53BRNr14hp1x3bxZefZJF755RwjK+gG3NOCQ1bHGqBAb0RrZ0tSdlGtbE4OOUv3ubNo0rcnSSFnyx3r9uhMnH29S96bjSbiDh7Du0vHWmnXJ1IjbtnrTuN2dcQ6AqFlC6SU14FYoE2Z4snAaSmlrkzZN9ZlyWXWyNpNCCEeE0KECCFuipz5ebuRmJwDgMlkLin/+KuDvLlsMo/MHIiri9Nf+lI3zGni50VqSmZJubOzFoPewOOPr8FoNOHm1pDHH5/M2rXP0H9AVzZuqH7G6OPlTHKqZQZoMpdP52Vvb0NTPxdi4nJKyibffzebtl/+S9+hrvB105KcYxl4zGVsnjeyLd8FRnGj5FpGAV1auPP1k/2JTK55lnvjL+7n50Vqatk6dkKvN/DYY6swGo24uVkcEJ1OT0JCKq1aNa1MrqI6AO7e7uRm5JQ7khqXwq9vr0PbQItGY+lWuiIdYYfP0LF/51oZ7eHtTk56ed2U2BR+fHMdTs4W3VEzR/LQKw/Svmd7Tuyofrf7G+3Nx9edtNTckvKkhCxcXJxY8Mw/SE7KJiE+gzlzR7DyXzPp068dWzYF1VwTJfXsSVpK1k3Ht24+wv0PDCx5r9PpSUzIwL+l703n3sDHw4nkDIuTUrYdf7rpAi98cJxj51KYNrINgaeT6NjSg1dm98ClgT1uDWueh9VXv/bx0JKcebPNn2y9xAufn+TohRQChrYiMaOAgDX7efWbEBZOqPmm6OvsQHKeZVgzV8gHPK9XM5YNa0tgTGn7vr+9F4fjbv47VIe3kz0pBZV/htZWwxj/Rhy4llnZpZXb7K4l2epMlNWbN7It3x2KpmJe44D+Lfj1RNyfstnX2YGkPIsjZKpYL92bsnxwawLjsmuv5+ZYanPZcWhEG747XDoOtfJyJio1j7e2XmJYRx8c7Kq/dfq4aUmyjm9l7Zw3vA3fHormRlFiVhHT/u8oS389x6LR7WpptaXzaRt5UJxV/XeN3LGX1mOGqQflb3NuV0esRoQQHYG3gMfLFM+SUnYGBllfD1V2rZTyCyllLyllr4rHklJz8POxzKg0ZTzrM2HXeGHFBjZsDSExJafiZdWyefNB/vWvr8i9XmD5jOR0vH1KZ3OXLkbj18SLzz9/laZNvbh8OabkZu7h4UphYfUzsJS0fHy8GlptLn/s3hFt2H0wslxZv55NOVnD0kh9k5xThK+bJfJSdozo0NSN2UNa07WFOzMGtmR4Jx92nUnikU+O4+XqiKuTXaV6mzcf4PXXv+S6tY6Tk9Px9i6t44sXo2jSxIsvvlhB06beXL4cDcCuXccYPbp/tbaG7j3Ftk83UZRvueHmpGXj2qh8pMC7hQ/TFs9CSkl2ahbFBcVs/nAD9z46HgenypcDTu05xaaPN1GYZ9HNTsvGrcISpo+/Dw++PAuzlGSlZpW0i4ZuzuiKKo9o7tgawntvbeb6dYtuako2Xt6lUYLG3i64uFqcjoYNtRQV6su0t4YUFepuFrWybctR1r6xrkQ7OTkTL5/yy+ZSSoKDLtOnb4eSsr27gxkxqmeVugApWYX4eFrs0pRpFDduWpm5xTRwtEWnN/H6t6G88d1pCouNZObWHKGoj35tsbkIHw+rzWX6XonN13U4OZS22WK9qVa6yfk6fKwOZsX55Jch8SzYdoG5vZoDFiesiasj684l3aRTHamFerytS8qizIM1DexsWNa3De+GxlBorJ29AMnZRfi63+jTpXodmrgye0irkj4N4Ghng7eblmsZBX/K5uR8Hb7WetFUeBjoyzMJPLnjEvN61GZSZdXLKcbXzbFymwe3pmsLN2YMaElKThG51iXaIr0Re1ubanVTyoxvZQM0dzdxZfZgS13MHOBfUl5sqLmerx05yfkfNmAotPS9oowsHD3cq70mNy6BqD2B5ETFEnPgaI2fUa8Icetedxii4izldkAIMRJYLqUcXKbMBQgHWgGewAHgn1LKY1VozAF6SSkX1vBZsmxOOietPW8um4xObyQoNJrhg+7iyRfXMXJwB8YM70QDJ3tWrt1KWkYeS54Zx8T7enDoeDhL1mxCX6YzVZZrcvu2w4SEXkJXrGfZ8scID4/l8uUYJk4czqtLP8LF1Zmc7Ou8/q9F/PD9dpJTMsjJvs6SpXPx8rLc7CrLNal1tGXFi8PQ6Y2Enk1icP8WvLB8DwCf/fsfPL1kJzqdxbZ+PZvSvYsvn34TfFNd1DbXpIebMytfDGDEoM588/NB3vl4S7XnV5ZrUmtvw6pp3dAZTYREZTL4bm+e/640QLn2oZ6s/PUsTg62rJrWjcw8HY52Nrz4Y2mgtLJck9u2BRIScgmdTsfy5fMJD4/l0qUoJk0awdKlH+Li0oDs7Ou88cYzODk5Mn/+Gt57bzGOjuWjKpXlmjx7IJSYC1EY9AYmLJxCcnQSSVGJdOzfmYM/70NKiUYj+McTE1m35ltMRhOunq50HtKNNt0ss93Kck2G7A8lOiwKg87AlKemkBSdREJkIl0GdGbv+n1Is0RjI5j45ET2rd9PTno2BbkFTFo4CVdPi3NRWa7J3X+c5uzpaHQ6A4uXTCIyIpnwy4lMmT6Azz/aSVGRHqPRxAuvTOLbL/eRmpJDbk4Bz708kUaNS5e5Kss1uWP7CU6HhqPTGXjl1YeJuBrPlctxBMwYwamgy5w/G8ncx+8vOf/pBe/z1r+fxNHRvrQNVMg1qXWwYcXcPugMJkIvpzG4ux8vfHCc+ZM64tOoAR4NHVj9VQj5RXqWP9obG41gX3ACe4Liy9lWWa7JuuzXZXNNah1sWPlwT3QGEyFXMxjc2ZcXPj/J/PvvxtdTi3tDB1Z/f5oW3g2ZNNAfRwcbDpxJYvvJ8s9yVcw1qbXTsHpEe0v/SMxliL8nz+68xPTOvnRo7ExDB1vWn0+i0GDiq4ld2B+VSbHRxGuB5Sdd1eWa1NpqWNKnNTqT5ExaLgP83Fly7CrvDemArUaQVqhnd1w6p1Jyb7oWbs41aenTXS11caNPf1/aX9c+2IOVv56jUG9iSr/m6I1mtlYyIawu16TWVsNrw9qiM5oJTsplaAsPntlzhekdfejQyBkXBxvWX0jhVNLNNleWa1Jrb8OqKV3QGc2ERGcyuIM3z/9QxuZZPVi5wWLzqqld0BvNFBQbeX/nlfI2V8g1WaJrMBESncWQDl489+PpkuNvz+zOio3n6djUlUm9m+Fob8P+C6lsP1N+qbayXJPxx4PJDI/EpDfQdXYA1+MTyYmNp9WoIVz6dQsJJ0Px6nQXnR+aio2dZRIQ+vn3dJ09reQZsbV9RiKlvKUeS+uZ62+ZsxH104w7yhu7XR0xAQQDH0gpv7c+rP8ZlqXJj4FDwCop5aYy19gCblLKDCGEHbAe2Cel/KyGz5Iq6bcFlfS7FJX0uxSV9LsUlfS7FJX0u5Q7Mem3csRuH27LpUlp8Q4nAlOEEBFAJmCWUr4OLMTynNjyCttUOAC7hRDngbNAIvDl3/MNFAqFQqFQlKC2r6iS23L7CgApZTwwHkAI0R9YL4ToIaVcA6yp4rLqH0JRKBQKhUKhuI24bR2xskgpjwMt/m47FAqFQqFQ/AXuwG0lbhW35dKkQqFQKBQKxf8Cd0RETKFQKBQKxR3MHbitxK1CRcQUCoVCoVAo/iZUREyhUCgUCkX9ogJiVXJb7iN2KxFC/G9XgEKhUCj+57jl+4jN/uXW7SP2XcAd5fapiBjQtt8nda4ZcfJJXg2pPkfkX2FNr1EAtL3n0zrXjjgxv942Xq2vjWIBJuyt+80qN48azFvn9tS57ktdR/NycN1v0Phm75EArDxd921uZY9RBCb9Uee6Q/3uo+UT9dDePrNs9Ftf/brn+rpvb6EzBtP600N1rhs1fwgALT4MrHPtuEVDaf5a3feRa8tGE3Cg7uvil+GWumj+Zt33v2svj6w33XfD6r6On+s8us41a0T9arJK1DNiCoVCoVAoFH8TKiKmUCgUCoWiflERsSpRETGFQqFQKBT/UwghxgohwoUQkUKIlys5PkcIkV4mleLcMsdmCyEirK/Z/60tKiKmUCgUCoWiXrm1Pw2oHiGEDfAxMApIAIKFEFullJcqnPqLlHJhhWs9gBVAL0ACodZrs/+qPSoiplAoFAqF4n+JPkCklDJaSqkHfgYeqOW1Y4C9Usosq/O1Fxj73xijHDGFQqFQKBT1i0bculfNNAHiy7xPsJZVZLIQ4rwQYqMQotmfvLbW3LaOmBCiqRBii3UNNloI8ZEQwkEIMavMmu1ZIYRZCNHNek2gdc33xjGvv/t7KBQKhUKhuHUIIR4TQoSUeT32F2S2Af5Syi5Yol7f1a2VpdyWz4gJIQSwCfhUSvmAdT33C2CtlPJpYJ31vM7AZinl2TKXz5JShvzVz9Y62rJy8WAMBhNBp5PYticCgCn330XnDl74ejkTHpXJvz8NAmDVi4MpLDLw1ocnatSOOxZM2qWrmA1Gej4yHVtHh5JjIf/5icyIGMa8tRSAouxcLm/dDRKa39OTRu1bV2/zC4MwGM3lbf7HXXTu0Bhfb2fCIzN59/NTrFo8GEcHG4qKjax4+0j1dWFvw+qAbhhMZoIiMtgSXDoJaO/nwg9PDWToit1ohGDtgz3JKdRTUGzk9U1hNdZFZfg39+KlhRNwdXFi5hPv13i+EAIvLy+S1n1Hg7btce3TD4DipEQydu8AoNGYcTj6NSF180akXo+wt8d7whRyQ0+RfyEMYWOD58jROPj4kX38KMUJ17DRaiv9vKgjwSRdjMBsMNJ/XgB21r9fTmIq57fsBSlxbeJD1wmjiDgURPj+43R5YCTNe3au9nvEHwsm/dJVzEYD3f45o1y7OP3VT2RdjWbkW68CcOm37eQlpmDfwIm7Jo1D6+5WpW7s0WBSL13FZDDS59Hy7e3Ulz+RfjWG+962tLeoA8fJjI6jMDMbt+ZN6Daj+ih90L5Qws9EYjQYmfXsFBy0Fm2z2cy69zZi0Bmwd7DjweenEXbyEsd3nQJg4H396Nj7rko1tfY2rJ7RzdKOr1bS3p4ZyNBlu3Gyt2VFQFey8/VcSczlpyMx1doK9devHW00vNyrDQazmdC0XHbFpQMwwNedaW39OJacxa8RyQCMb+lNe3dn8g1GPg2Lq95eWw2rBrXFYDYTlJTL1og0AB7v1oymLo64O9qy5lgUKQV65nZtiq+zAwl5xXxzPrHmurDVsGZoW/QmycnEHLZctWjP79GMZi6OuGvtWHU4EqNZsnJwG7KLDVzJKGDdxeTqde00rLm3AwaTmZNx2Wy+kALA6HaNGdK6EX4uDnxwNIYziblM6+pHR5+G5OmMvBMYVaVm1qkg8sPDMRsNNJv5IDYOpW048fffSvp0k4mTSdm1E31mBsb8fJpOC8De3QNpNhP9yUe4dOxUtc2j7sJgNnPyWjabL6VabG7biCEtPfFzceSD4zGcSboOwOuj21NoMPH6wcia66IedAEijgSTeMEyDg16rPw4dGazZRxya+JD94mjOPHd7+iLitEXFDHkyZnYax1r1P//ASnlF1h8hqpIBJqVed/UWlZWI7PM2/8Aa8tcO7TCtYF/0VTg9o2IDQeKpZTfAEgpTcCzwMNCCOcy583AsrZbZ4we2opdB6N49c1DjBjkX1K+cdsVVqw9TEx8Dpv+CAdgzLBWhF1Oq7V2YvA5es+bRbN+PUgIPlvuWK+5M2noWxrAC/9jH3aOjggh0HpUfbO12NySXQejLTYPbFFq8/YrrHj7CDHXctm0IxwpYfnaw7z42kGcnOxqzME6ppsfO88ksuSnM4zo7FtSbqsRTOvvz6GLlsHF113LxYQclvx0Bh/3yp2Y2hB7LY35L1bXd8rj7OxMfn4+frNmkxdWWp9ZB/fhGzAL3+mzyArcjyErE2ky4TNtJpglhuws8s6cxu/B2Xg9MInM/Xsw5l3n+ulgNPb22Lq4Vm7fqfMMemImLft3JzboXEm5WxNvBj/5IIMXPER6RCwAbYf0pd2we2r1PZJCztJj3iya9O1JUoV20ePRmTj7epe812hs0NjaImxtsHNyqlY3PvgcfR+bRYt+PYg/VV63z7yZuJRpb62H96fP3Bk09PGi1ZB+Ndp85kgYDy8OoNfQbpw+cr6MfRoeen4ajyyZha5Ij9lsJvxMJNOenEDAoolcOHm5Ss0x3fzYeTqRJevOMKJLhfY2oLS99Wrjyf7zySz/+SwD7vLCthZLEfXVr4c3a8T++AxeD45kcBPPkvJjydl8fyWh5L27gx2jmjemyGgio0hfo+6Ylo3YFZ3O0kMRjGjhUVL++dl4lh2OYMvVNPr5uXG3ZwN6+rhQbDSTVlCzLsDY1o3YEZnBKwevMqplqc2fno5nSWAEm8NTuaepG738XNkfm8myQ5EMaOZeYz2PvcubHZdTefmPy4xs27ikfM/VdJbuvMw7gVH0aeaGp5Md/7jbm0K9ibQ8XbWauWfP0Pyhh3Hv2YvcM6dLyvXWPt00YAaYzeizsvAZey/NZz2ER5++5Idb/pbpgQdx6VT1RGhsOy92hKfx8q4rjGxTxuaIDJbuCeedw1H0aWoZf8e19+J8yvVq7a1vXYCYoPMMnT+T1v27E3Oy/Dg0bMGDDFv4EGnWceie2RMZ8sQMvNov9/GfAAAgAElEQVT5kxmTUIXiLUKIW/eqmWCgrRCipRDCHpgObC1vrvAt83Y8cGPw2g2MFkK4CyHcgdHWsr/M7eqIdQRCyxZIKa8DsUCbMsUBwPoK135jXZZcZo2s/Sl8vBqQkpoPgMlcPiODvb0NzXxdiLmWg6e7lrvbNeJEcM0z0BKs1jg18qAoK6faU68nJNNiUF86Tr6PS7/vqt7mxs6kpFVjs19DYq7lAtDa3523Xh1GXp6emrJb+bppSc4pAsBcRnfeyLZ8FxjFjZJrGQV0aeHO10/2JzI5r3rROsTW1haj0Wh5oyltyuaiImycnLDROmHWFWPIycHO3XIzs/PwwJCdjefI0aT8up6swwcxFRaiz0jHxqkB3hOmYMjKxM7O7qbPu9GanBt5UJh18w9koo6F0KRr5dGearEKW9pF9T+8af/AGHo/OQevTh2IPXisNrI0aOxBYQ3tDcCkN1CQnomLn3eN597Q9vBxJyc9t9yxpNgUvnljHVpnLRqNht7Du/P5ym/59NVvGDCub5Wavu5akrOt7a1M45w3qi3fHYwqaa8HL6TQqbkbSyZ3xtXJDjdn+xrtra9+7aW1J7VQd5PNFWni7Mh1vYGPzsfi28CBJs7VRyZ8nB1IzrfomirIOtlqGNe6MXtjM2np5kRUdiFvB8UwrIUHDjY1D+m+zg4k5xdbtcuLO9lpuK+NF3uiMzkYm0Wnxg1ZOqAVrg62uDtWv4Di29CB5OsW3Yp1Ma9vC5aNbkdgVCbN3Z3IKTby1sFImrhqaV6LyZu9pyf6nNK+YcjJwd7ap+09PDBYj5mKi8kODcG1W3eKkhLBbMbR1696m/OqsLl3c5aNaEdgdCaNnOzp6OXMsdja/TCuvnTL4tzYg/xKxovIoyE0LTMOFWZfJz3qGt7tW/3pz/j/FSmlEViIxYG6DPwqpbwohFgthBhvPe0pIcRFIcQ54ClgjvXaLOA1LM5cMLDaWvaXuV0dsRoRQvQFCqWUF8oUz5JSdgYGWV8PVXFtyfpxxWMpaQV4e1mCbhUngGOHtWLPoWgA+vTww9Ndy4JHe9K3RxP8m1UeRQGIPRzEme83Yiiw3GQKM7NrjHJpPdyxb+CEraMDJoOh2nNT0vPxbtzAYnMFo8cOa8WewNKlm6jYbF5acxChEfj5OFMdyTlF+LpZBsmyLm2Hpm7MHtKari3cmTGwJcM7+bDrTBKPfHIcL1dHXJ1udmLqA6PRiK2t9eZQZrDTaLWYigoxFRWhcXDEzs2tZKA2ZGdh5+6O1r8VvjMewq13P+zcPbBzc8emgbUOtU6U9eEjDgVx8puN6PILAcjPyMbJw72cLVHHQshPz6bD6EG1tj/uyEnO/bABQ4FFtzAzC20F3YoIq8Pp6NIQo67yaEL04SBCv9uI3treCjKycaqhvQFcCzpD0z7dqj3nxO5gfvnwdwrzLNpZqdm4NS7f9v38ffjnK7OQUpKZksXOdft44f8WsviDhez6aX+V2snZ1bS3Ya3p6m9pbzqDmTUbw/jXb2EU6Ixk1hBVgfrp1wBpRXq8nCxLQ5pqshqnF+nI1VsmDXkGE062NtXbm6/Dp4HDTfY629mwenBb1p6MocBgIqWgVLfIaMbepua5Z3K+Dh/nm212trNhzZB2vHE8mgKDCZ3JzGtHo3j9WDSFBhMZhdWPQ8l5OnwaWhzMinPgL4PiWPDbeeb2a05KXjG5RRat6zoDDexurovMk8dJ+PVnTIWWvqHPysLerbRv2Lm5YcjOth7Lxs7NHVNREfHr19Fk0hRsHB3Ju3wZXXo6GYEHyDlzGo3m5tudxWaHym0OvsaCLWHM7dOcvs3d8Gxgz9MDWnJPc3da1uA81odueGAQx77eiK6gdBxyrjBeRB4NIS89m45jLONQfmYOJ77/nUHzpqGphZNer9xeD+sjpdwhpWwnpWwtpXzdWrZcSrnV+v9XpJQdpZRdpZTDpJRXylz7tZSyjfX1zX9bNbflM2LAJWBK2QIhhAvgA4Rbi6ZTIRompUy0/psnhPgJy09Uv68oXnb9uGLS7z2B0Sx/fhBD+7fgwNE43l4+nMWrDwAwdnhrnl1uyeW3c38UO/dH0cSnIQ9O7URsfC5V4T+4L/6D+xJ3LJjQr9ZjMhjoMSeAjKvR5MQl0GbUYMJ+2Up2bAIhX62n+8NTaDduOOfX/w4I2oys/ua+JzCG5c8PrMLmVjy7zJIDzauRE48/1B2NRmAymUm2RgiqYvfZJFZN68awTj4cuJDCv2f34vnvQnjqa8vzPmsf6sn6ozE4Odiyalo3urf0wM5GQ24NA3ZVeLg5s/LFALp29OeFBQ/wzsdbqj0/Pz8fLy8vktf/QMNOXUn89j80mTMXj2EjSfnV0jQ8R43FzsMTYWNDym+/IGxtsXP3IO/CefLDzmHW6fCeOBVbV1dsnBqQ8tsvSJMJvb50maftkL60HdKXqCPBHPviZ0x6A/fMnUZqeDSZsQl4tWvJqe8307xnR05++xv95kzmWmgYkYeCsLG3w87RAd+O7W6yv8WgfrQY1I/4Y8Gc+Xo9Jr2BbnMCyIyIJic2ntajhnDx1y3kxsVz5quf6PLwVCJ27KcoMxt9Xj5dHp5Wab20GtwXBvcl9mgwp/5j0e39SADpV6PJjk2g3ejBnPvZ0t5O/Wc9PWdPwcbOjmsnTzPg6UeqrfN7xvTmnjG9CdoXyrp3N6DXGZj5zGSiLsYSH5FAt4Gd2bluH1JKbGw0uHu50X1wF75/+xcAOvapOmK4+2wSqwK6MayzDwfOp/DvOb14/tsQnvrK2t4etrQ3rb0NKwO6otEINp28VmNkF+qnXwMciM/gxZ6tGejrweGkTFb3a8fyk1fp7NmQWe2b0NDelowiPQcSMrmuN/Jst5bYajRE5BRUq7s7JoMVA9swtIUHB+KyeGd4e144EM5bw9pjpxHM79GMHVEZnEzK4f42Xiy5pxUZhXry9KYa62JXVAarh7RleAtP9sVm8u7Iu3hu3xXeGdkeW42Ghb2asz0inbOp11k1uA02GsHGK6nUVM27rqSyeuxdDG/biP0R6bz3QEee3XKR6d2a0MHbmYYOtqw/k0jydR05RQaWjWyHrY3gctrN45Bnv/7Qrz9Zp4KI/+lHzHo9TWfMoiA6isJr12g8dBjCxoaEDb+gsbXD3sOD6M8+QZpMpOz8A/eevfAaYcm/mhceTnFSIubt2262+Woaq0e1Z3jrRuyPTOe9++7m2T8uMb2LHx28rDafSyI4IYc/rqTR1MWR2T2bEmON3FZZF/Wg235oX9oP7UvEkWAOf/4zRr2BQfOmkRIeTUZMAj7tWnLiu8206NWR49/8Rv9/TmbXm5/j5utF0I9buHvMIBr5N63hr6j4OxCyNqPYLca6pBgMfCCl/N76sP5nQKyU8nUhhAbLz0cHSSmjrdfYAm5SygwhhB0WJ22flPKzGj5LqqTfFlTS71JU0u9SVNLvUlTS71JU0u9S7sSk31Le2i1WWy3YdMucjeiPJ91G28fWzG25NCkt3uFEYIoQIgLIBMw3wofAYCD+hhNmxQHYLYQ4D5zF8suGL2+h2QqFQqFQKBR/itt1aRIpZTyWXyoghOgPrBdC9JBSnpZSBgL9KpxfAPS85YYqFAqFQqGonj//27n/GW5bR6wsUsrjQIsaT1QoFAqFQqG4g7gjHDGFQqFQKBR3MLX8NeP/IrflM2IKhUKhUCgU/wuoiJhCoVAoFIp6RapnxKpERcQUCoVCoVAo/iZuy33EbiUVN3RVKBQKheL/d271PmItn91yy+61Me89cEeF39TSJJBRVP0O7n+FRtoH8O7wcp3rpl5+E4CwrJt3if5v6exxP1KG13zin0SI9vW26SpQb5vFtvms7jeVjHxiCM3+faDOdeOfHw5A6/v+62wbNxH1xz9pM+o/da4buXcux1O317luf+9/APXXryNzt9Z84p+kjet43r/wX+UNrpRnOo0BYN6Rg3Wu/eWgYWyN21HnuuNbjGN3Qt1vIDym6X0A9Wbz5ti6153gP47Ru47Uue6esbVPxaaof5QjplAoFAqFon5Rv5qsEvWMmEKhUCgUCsXfhHLEFAqFQqFQKP4m1NKkQqFQKBSK+kVtX1ElKiKmUCgUCoVC8TehImIKhUKhUCjqF/WwfpWoiJhCoVAoFArF38Qd4YgJIfLL/L+jEOKAECJcCBElhFglhNBYj80RQqQLIc4KIa4IIZ79+6xWKBQKhUIBgLiFrzuMO2ppUgihBbYC86WUe4QQTsBvwNPAe9bTfpFSLhRCeALhQoiNUsr4P/M5e3acITQ4EoPeyOKlk9E62Zcc2/77Ka5eSaJBQ0ceXziWQwcuEHQsnNTUHP752Eg6dWlRpa6T1o43l01AbzByPDiGTdvPAjBicHsCJvQAYN3GEC6GJ7N44ciSY0PHv09+ga5am4/sPs2F0EgMBiOPvTgZR61DueOfv7URR609s58az76tQVwMjURXbGDqo6No2a5JlbrbtgUSFBSGXm9g5concXJyBMBkMvHSS+/j6GiPwWDkjTee5rff9hEWFkFKSgbt2/vz/POzARBCkPj9VwgbWxq0bY9rn34AFCclkrHbsgliozHjcPRrQurmjUi9HmFvj/eEKeSGniL/QhjCxgbPkaNx8PEj+/hRihOu4enpSWZmZrX1cgP/5l68tHACri5OzHzi/VpdUxatrYaVA9tiMJs5lZTL1sg0AB7r1oxmDR1xd7RlzfEobITg0zEdOZt2nWMJOeyOyaiV9usj2mEwS07E57D5SioAo1s3Yqi/B74NHfkgKJbcYgPze7dAAFHZhXwafK16XQdbVj3ZD4PRTFBYClsDowFo7K7liamdEUKw/XAMpy9bvsvqBfdQWGTgza9Dqtd1tGXlov4YDGZOnU9m64Eoi66HliemdwXgj8BoTl9KY8qYtnRo7Ul+oYH3vg2tsS5ucGJvKJdPR2I0mJj9/GQcKrTnb9/ZgKPWgekLxtdKr776NMDBXac5HxKFwWBk4cuTyvW9Rya8Qfe+bWnTvin3TurH7i1BhF+4RkZaLvMXT8C3aaMqda8eDiHxwlVMBiNDHg/AztGim52Yypnf94GUuDXxpsekURz/bjP6wmJ0BYUMWzATe61jlbqpJ0+RcyUcaTTS9qGZ2DiU2hu9cRNmvR6NvT2tpkwi+chR8mLi0GVl0aBZU1pNnlhtXZw+EErU2QiMBiOTnppa8nczm81s+mADBp0BOwd7pjwzjQvHznMl+Ao56TmMmjWaFnf7V6kbsj+Uq2ciMRqMBDwzpZzur+9vRK8zYO9ox/Rnp5Gbmcven/YjpaTn8O606tSqTuwFSI5J4rMXP2HJ98tuapMVOXMglKhzVu1FU7Evo/37Bxsw6C3ak5+21EV4yBVy03MYMWs0LTqUrwsHGw2LOrTGKCXnsnI5mJwOQAtnJ6a3bArAzzEJxOUXAvDU3a0pMpr48mosI/28GNvEmw2xCQSlZ1drs+Lv4Y6IiJVhJnBMSrkHQEpZCCwEFlc8UUqZCUQCvn/2Qw4duMArK6YyYnRXAveHlZRnZeazb/c5HLX2NGrUEIAhwzvx4rLJPLZgLOdOx1SrO25kJ7btCeOFFb8zZliHkvIBfVqx4q0/ePVf2xkxuD0Zmfm8tGozb3+4l+NB0TU6YQBBh8KYv2Qa/Ud042RgWLljJw6co3WHZiXvL52JYsGy6Tzw4FAuno6qVnfv3pOsWbOIe+8dyJ49x0vKCwqKaNBAy5o1i3B0dOD69QKmTh3N6tUL8Pf3Y+LEESXnOjs749K9J36zZpMXdrakPOvgPnwDZuE7fRZZgfsxZGUiTSZ8ps0Es8SQnUXemdP4PTgbrwcmkbl/D8a861w/HYzG3h6j0Vhjvdwg9loa81/8otbnV2R0y0bsjknn1cMRDPf3KCn/4mw8y45EsCUijb5+bpa6MZhwtLUhKb+4Vtr3tm3Mjoh0XtobzqjWniXle6IyWLL/Ku8ci6ZPE1eis4tYvOcKL+y5Qjcflxp1x/Rvzq5jsSz98Dgj+pT+/edO6khBkQGzlKRkFAAwdkALwq7W7DQCjB7gz+4jMbz6/lGG92teUv7olM7kFxqQElIyCvFwc2TckFYUFRtJyyyslfYNQg9f4JGXAugzrCshh8q35+CD52h5V7Mqrqyc+urTACcCL/D0q1MZNLIrxw5eKHdM62SPXmegkbcrAGMe6MtTS6cydkJfIq8kVqsbE3SOYU/OpM2AHkSfPFdS7t7Em+ELZzF80YOkRsQC0H/2BIbOn45Pe38yYhKq1c04fYb2cx6ice+eZISeKSkvzsxCmky0mTkdaTZTnJWF76CBtHt4Flofb3wG3FNjXVw4ep6pz02n65DuXDh6vqRco9Ew5ZkAZrz0IPpiHWazmU4DujDlmWnc+89xRF+Irlb33JEwZr4QQI+h3Th3pLzu9Oem8fArs9AV6TGbzez/NRAHJweERuDWyK3O7DUZTQTtOMldvTtUo1hG+9h5pjw7nS6DuxN2rLz25GcCmP7ig+iLSuti8tPTGDNnHDFhN9fFQC9PjqRm8v7FSO5pXDr+TGzhx0eXo/jwchQPNLfc6gZ6exKeW7KIxL6kNHYnptbK5vpEasQte91p3GmOWEeg3LRaShkFaIUQ5XqcEKI54AicpwJCiMeEECFCiEqn/jd+Zevj5056Wm5JeVJiJi6uTjz5zDhSkrNJiLfcuH767hAfvLOVfgPaV2u8r48LSSkWPZPJXFK+ecc5vnxvFt98+CDrN5WaFDChJ79sqWUUwWp0Yx93ssrYnJOZR3R4Il16tS0pGzCqOyue/IRv3t/CPcO71kYWPz8vUlNLo0/Ozk7o9QYee2wVRqMRNzfLTUyn05OQkEqrVk1LzrW1tcXOzTp4aEqbnLmoCBsnJ2y0Tph1xRhycrBzt5xn5+GBITsbz5GjSfl1PVmHD2IqLESfkY6NUwO8J0zBzs4OOzu72tXPf4lPAweS8y0Osdlc/piTrYZxrRuzLzaTxHwdM7aeY/mRCBb0qD6ScgNfZweS8izapgrZ2Ob1bMbyoW0IjM0qKbu/vRdH4rKoCZ9GDUhOtzhAJnOpcNvm7vx+IIoP1p1l4fSueLo5cncrD46fS66VvT6NnUhOszhw5rK6LdzZvC+SD344zYJZ3Wju05DcPB3vfB1CE29nmvs2rJU+lLY7Tx93stNzSspzs/KIi0jk7p7taq1VVq+u+7RF3PKPl687Gak55Q59+OOzPLMsgK0/Hy0p++ajHWzfcJwOnWtoH1ajGzb2ID8z56bDEUdDadat1CkozL5OWmQ8Pu2rjgBZZC26Dp6e6LJLIyS67GwcPNwBcPT0QJdlOWY2GChOz8DJx6d6e8vY7O7tTk5GeZtT4lJYv3Ydjg20aKzjQOCGg2z59Hfu6n1XbWTx8HYnJyO33LHk2BR+eHMdWqtuSmwKfUb1Ztzssez6cU+d2Ru44QADJw6u9cqXKKOdm15eOzUuhV/WrkPrXFoXhzYeZNtnv9O+krpo5OhAerF1/KG0zzWwtaHAaKLQaMLJ1gY3ezvaNHTmbCXtRXH7cqc5YrUhQAhxHks07BMp5U1hCSnlF1LKXlLKXmXLd24L4f21W8i7XgRASnIOjb1cS4439nLFxVULgHNDLUWFegBmzh7Ca2sfYv331edTTE65jp91Zqwp47U/NW8oE2d/wYSHv2DR3CEl5QP6tuJYUPUzxcAdIXz93mYKrltuuBmp2XiUsfnimSiuZ+ez4es9XAiNIulaOns2Hef1Lxax+I05bFlXeQ66zZsP8PrrX3L9uuWGm5ycjrd3abTm4sUomjTx4osvVtC0qTeXL1vs3LXrGKNH9y+nZTQaMeRYB/wySeY1Wi2mokJMRUVoHByxc3MrOc+QnYWduzta/1b4zngIt979sHP3wM7NHZsGDQBLiF/cor1pUgp0+DSwLC2U/UhnOxtWDWrLWydjKDCYSsqLjeaKElWSnK/D19miXbFDfhkaz5PbLzKvhyUCdH97L5q6OPLj+aSabc4owKeRk0W3THtLySggN19PYbEBB3sb+nbywdNNy8IZXenXxQd/v+qjbSnphfg0tvwNxE26OgqLLLopmYXkWB3M6/l6nLQ1O83HdgWz7oPfKcyz9MHM1BzcG5fOscLPRnE9O4+t3+7h8ulIUq6lVatXn316/x8hfP7vLeRbtdNTsmnkXT4Co9FosLHRYOdgi9nqwf9z4ThmL7iX3VtPVap75WAQR7/6DZ11mSkvPQtnz/K6EUdDyUvLotOYgQDkZ+Zw/PvNDH5sKhqbyof1lOMniFz/C4ZCi64uMwsHd/eS4w7u7uiyLDfw4qxSpyw9JJRGPbtXWQ8AIXtPseWTTRRZbc5Oy74pGuXTwocZL85CSklWqmUiMXTqMB56dQ6HNgZWqhu0J5jfPvqdwnxLHWelZePWyLXcOb7+Pjz0slU3JQu3xq44NdTioHXAoK88av5X7E2KSuTY5sNcC7/Gie3HK5MFIHTvKbZ+uomiPIt2Tlo2ro3La3u38CHgxVlIsyTbWhdDpgzjwaVzOPLbzXWRUayjsaNlOV2UcQULrA6Yk40NhUYTXdxdcXOwY1br5nT1cKOJU9VL1Lccjbh1rzsMIeUtS4j+lxFC5EspnYUQc4HBUsqHyxxrBeyRUrYRQswBelmfEesF7AHullKmVKMtKyYH3rPjDOdOR6PTGXj+lUlERiRx9UoSkwP688VHuygq0mMymnnulQls3RREZHgy+flFjJ/cl249LLPRypJ+O2nt+Ner49HpjASdjmPYwHYsevlXJt7XleEDLTP8wyci2bD1DAP6tKJnt+Z88EVgOY2qkn4f2X2aS2ej0esMzFs8ibjIZGLCExk7ZQAAaUlZ7Nx4lNlPjWfrukAS49IoLChm5Pi+dO1rmfVXlvR727ZAQkIuodPpWL58PuHhsVy6FMWkSSNYuvRDXFwakJ19nTfeeAYnJ0fmz1/De+8txtGx9PkJjeYuej00G42tHU6t25J/6QJN5sylOCmRzL27APAcNdbyjNiW35BGI8LWFu8HJpN34Tz5Yecw63R4T5yKrasrads2Y9briPrtV9LT02uV9NvDzZmVLwYwYlBnvvn5IO98XH1C6IpJv7W2GlYMbIPOaCY05TqDmrmz+GA4H4++G1uNILVAx46oDIxmMxPaeaO1teFAXCZ/RKWX060s6bfWVsNrw9uhM5kJTsxlqL8Hz+y6zPROvnRo7IyLgy3rw5Io0Jv4ekJn9kdnUmQ089qhyBKNypJ+ax1sWfFEX3QGE6GX0hjcowkvvHuENs1cmTe5M1JKft0TUfKMWBMvZx76x103PSNWMem31tGWFQvuQac3EXoxlUG9mrJ47SHaNHdj3rTOSAm/7gzn9KU0npndA62jHbY2gtc+OVm+LqpJ+n1ibyhXz8Wg1xl4+LlJxEclExeRyIiJlvacnpzF/k1HK31GrLKk33XRp6HypN8Hd53m4tkY9MUGnnxpIjERyUSFJ9Ktd1s2fG+Z6LRs48uEmYPZ8P1BMlJzyL9exPRHR9LM3wuoPOn31cMhJF+OwqjXM3jeNDLjksiIjse7fUt2/OsLWvTqiJ2DPQP+OYkNL6zF1a8x9k5aOo0ZSCPrc0OVJf1OPXmK3IgIzHoDbR+cQX58AvnX4mkyfCjRv/2O2WBAY2dX8jzYhQ8/ocPjc7Gxty9nX2VJv08fCCUmLAqDzsDERVNIjkkiMTKRTv07c+DnfUizRGMjGD9/IsG7gkiKTqK4oIi+4+6hVefWQOVJv0P2hxJ1Phq93sC0pyaTFJ1MfGQCXQd0Zs9P+5BSotFomLRgAqnX0tj3ywEEgv739S15RqyypN9/xt4bkauf165j4qIp5Z4Rqyzp95kDocRcsGovLNXuOKAzB29oawT3z59I8O4gkm/Uxb330NJaFzeSfjvYaFjYoTV6k5kLOdfp1cidt8Ou0sLZiWn+lr/1r7Glz4h5OzowvrkvX16NpW9jdyb7N0FnMrMhJpHz2bnsGTsIKeUt9Vj8l+64Zc5G7Ovj7ihv7E5zxLTAReAxKeU+6/sNwHop5bqyjpj1uv8DCqWUr1SjfZMjVhdU5ojVBVU5YnVBZY5YXSBEeybsrT5a+FfYPGowQK0csT9LRUesrqjMEasLKnPE6oqKjlhdUZ0j9t9QmSNWV1TmiNUFlTlidUFljlhdUZkjVhdU5ojVBZU5YnVFZY5YXXDDEatr/hZH7NWdt84RW3PvHeWI3VFLk1LKImA8sFQIcRXIwPLw/roqLnkL+KcQovYPpygUCoVCoVDcIu6I7SuklM5l/n8BGAYghJgAvCuE+ElKGSel/Bb4tsy5SUAtnjBVKBQKhUJRb9xRYZ9byx1dNVLKzVLKVlLKuL/bFoVCoVAoFIo/yx0REVMoFAqFQnEHc4t+4X4nckdHxBQKhUKhUCjuZJQjplAoFAqFQvE3cUdsX1GfCCH+tytAoVAoFP9z3PLtK1btvnXbV6wYc0etg6qImEKhUCgUCsXfhHpYH9gUU/cb8U1qOa5eN+78/PKuOtd+vMPYequLt87VkPPtL/BS19EA9bbxan1tFNugxcM1n/gnKYj7HoA3ztZ9Pb/SbTSDtx2t+cQ/yeH7BxKcXvcbuvZubNnQtb7a8vLQvXWuu7rnKN4Nq/u/3XOdLX3k8aN1PxZ9PnB4vW2O+mg9bED71aBhAPwStbPOtQP+H3vnHR5Vlf7xz5n0kEICafQaOoQWeu8gvYSmogIiRdRFsS/runZXf5ZdF13QXRGp0pVeA6mEJIQQ0ntvpM9M5v7+mCGNZBLYBHD3fJ5nnpk5597vfe+b99w59z0353Se1mS6TRVvD5zf4dJDDwqZEZNIJBKJRCJ5SMiMmEQikUgkkiZFkdNX1InMiEkkEolEIpE8JGRGTCKRSCQSSdMi0z51Il0jkUgkEolE8t9wu6YAACAASURBVJCQGTGJRCKRSCRNi3xGrE4e2YyYECJOCNHyYdshkUgkEolE0lTIjFgtXDsTSHRwJFqNlrnPL8Tc0gIAnU7HgS/3oCnTYG5hztyNizi36xQ5adkU5Rcx67l52Ds1r1PXylTFXya4o9EpXEnM48DNdAAmd27J2A6OuNla8oVvHEGpt3l/ojsDW9kz+V/+DbI5/HwAiaGRlGu0THxuEWYGm3OS0vHbfwoUcGzjjOf8SWQnphF09AIqlYp+U0fQop3bA/dF9EV/UsIi0Wm0DF/lVWFvXnI6IQdPgqJg39qVfnMmEXnel4jTl+k7eyLtBvYx6gcrUxVbRnZFo9Phl5LPoagMAFZ7tKWtrSUOlqa8ezkaEyH4+5ReXMu4jXdSHsdjsxrk55p0aOfM5vVzsLezZumaz+95f2srcz5790nUai0XfW6y++AVAOY/5smUcR5otFq+2PorkbFpbP10NSWlaszNTVmz6TvuZVWM6Ev+pF2PpFyrZdjKSn/np6QTeuAkCgr2rVzpO6f++YUsTVS82LszWkVHUPZtTiVnAtDR1pplXdoAsCMqifiCYl7z6Ipap8NUqPggOJL6LPY+EciNq1FoNeU8tWk+llYW1er/+dEeLK0tWLZ+FucO+xB1I4GcjDyefHEeLm3qvm9rqjgGiPP2JyPsFjqtlkFPL8bUstJm/+9+IutWLNM+egOA6LOXyYmOpzg7l+btWtNvyew6dSMv+pN8Xd9GRq2u3kaCDujbSPPWrvSfO4krP/yCuqQUdVEJY9YuxdzK8i69dB8/cm9GoNNo6fbEUkwsKu2M3vML5Wo1JubmdF44l+yQUFIv6WOx1eiROPbuScQPO8iPisbzz2/XafPVM4FEX9P7ed7zC7GwqvTz/i/0fjazMGfBC4sASI1N4ZtX/sbr/3qrYtuqZPj4kX8zAp1WS5fHq9scu3c/OrUalbk5HRfMQ52XT+KxX1EUBSfPwZjZ2pB07DigYOXqWqu9wWcDiA2JQqvRMmt99bg4/NUeNGoN5hZmzNrgxS2/MHwOX8R9cE+Gzhpdpw/+k2M0hKaKtyZHziNWJ49ERkwI0UwIcVQIESyEuC6E8KpSZyWE+FUIscqw3TYhhJ8QIkgIMduwzVEhRF/D5yAhxNuGz+8IIVbdqz1hl0OY/+Ji+o7uz/VLIRXlKpWKeRu98HplOWWlZeh0OsZ6TWTeRi/6TxhEdHCkUd1pXZ04FpnJ5pMRTOrcoqL8RHQWr5++xSfeMXi2tgfgtVO3iMkpbrDNUT4hTF6/BPeR/Ym8ElxR7tjGhanPL2PqxmWk3ooHIODAGSyaWaEyNcHawe6h+CLOL4RRa5bScXh/4nwr7W3e2oXRa5czet3jZEbGAdB1zBDcxw1rkB8md2zJ8dhM3rwQyfgOjhXlW68l8tbFSA5GZjCklf6HtUhTjqWpCSmFpQ3SrvU8EjJ47pWt973/rKmDOHDMnw2vbWfGpP4V5bOnDmbt5n+y5aM9bFg1DVsbKwqLStnw2nZKStQ0t7O+p+Mk+IUwYs1SOg7rT7xfpb/tW7kwcu1yRq19nMyouAZpjXZtwfnULD4OiWaES6WPF3R04/PQaD4LjWZeBzesTU0o0ZbzcUg0ZeXl2JjVf98XcOE6q171Ysj4fvifD61W53s2mM492lZ8HztzKCs3L2LcrKHE3UoyqttUcQyQ7B+M5+pltB06gET/a9XqBq9ciq2bc8X3zuOGM3jlEmzdnOk4ZqhR3VjfEMY+t5TOw/sT61O9jYxbt5xx6x8nw9BGhj05lzFrluDs3oHs2Np9kXn1Gt1XPI7z4IFkBgZVlJdm56CUl+O+zAtF0VGak0PuzVt0WbyQrku9yA65DkC3J5dh7epi1Obrl0JY+NJi+o25288LXvBiyeblqA1+LteW43vMh+6De9Spl301iK4rHqfl4IFk1WJz56WLUXQ6ynJySDp+AhNLS4RQYeHggLWrK+5PP4n70ysoiI2tVT/8cihzNi6mzygPbnhXt3f2814s2LScshK9ve6evRi1cILR8/9Pj9EQmireJA+PR6IjBkwFUhRF6acoSm/gzrTxNsBhYKeiKN8CbwBnFEXxBMYBHwshmgEXgVFCCHtAC4ww7D8KuFDzYEKI1UKIACFEQO3m6HvuDi4O5GflVatJj09j98c7sGpmhUqld19ZSRmhF4LoNdx4tsbNxoKUgjIAymukBlYNbMvbY7twLi7HqEadGG427JwcKcjOu6v65oVAOnh0159DdCKe8ybSe+JQrh461yDhxvbFnccFbFo6UpyTe1d9tHcArft1r8e2u3FtZkFqod7HNa9r1qYqpnd24lRcNsmFZSw5FMzbFyNZN6D9PR+nsWjt5khSqv5vXl5eafD/ffsrn/5pOasen4CDvTW3C0owNzdl77YXMTU1ITe/6L6O18zJkeLsu/0d4x1A674N87eTpTkZpWoAdFWycs1MTSnUllOkLcfa1IQibTlmKhUfDO6BiUpFgUZbr/aduGjp6kBORmW85ecUEH8rmV6D3Ktt//Pfj3ByvzddeneoTxlo/DiuSrOWjpTU0vZqUq7WUJSZjV0r452aO9g4OVJYSxuJuhRAmyptpDj3NpnRCbh062RUz7JlC8pyK+0sy83DwtFBX+foSFlOHi6egwj7+1auf/UNbqOGN8hOoOIP6ODiQF4NP6fFp7Hzox1YGvx8bs8ZRs4djdE8iUHPskUL1LmVPlDn5lbYbNHCkbKcXIpTUnAePpR2sx8j4cjRim0zfP1x6NWzDn39m72z411xkRGfxr5PfsTKxroiLu6LJjpGU8Vbk6ESD+71O+NR6YiFApOEEB8KIUYpipJvKD8IbFcU5V+G75OBV4UQ14BzgCXQDn1HbDT6DthRwEYIYQ10VBQloubBFEXZqijKIEVRBlUtDzzpx+G/76ekUJ+JysvIxb5l9WEJl/auLHp5GYqikJueQ2lRKQe+3MO0Z2ZhYX33cEBVUgvLcLPRp5FrOv7bwETWHglj1YC2d+9ohLAzfpz9bh+lhSUAFGTlYtuius03LwRyOzOHftNGAmDv0gIzS3Msm1mjLimrVbepfBF53hef7XspM+gWZuVibbig3iHaO4DCzFx6TB51T74ASCsqw7WZ3sdVnw21MTPhT6O68qFPLEWa8oryUm3D7kKbiuTUHFq76s9fVeUCEhgcw8Y3fuDnXy6TlJqDR+/2JCRlseDpz4hPzKRPz3YN0o8674vv93tRF+n9XZSVi3WL6v6OMfi7ewP9nVmqxsnSHKDaj2iRVkszUxOsTU0o1pbjbm9DWkkZr/qHk1pcShe7ZnVqXvzVn39//gtFt/VxnJ2eh6NzZbyFB0WTn1vAL9tPcONqFKkJ+iHnxc89htez0zl3xLdW3aZs07EXfLn6w17URXqbi7NysWphfBgTINE3iDaDPeqsjzjni/e2vZQVVbYRmxptJOpSAAWZufSaov+bFWbnceVfvzBq1SJUJtWvLra2tkT+tBttsV6vNDsHC4dKOy0cmlNm6OSU5uRg4dic+KO/0X/zH+j/6h+IP3a83nMKOOnHwb9V+jk3I5fmNfzs2t6VJa/o/ZyTnkNKdDLeBy6QEJHAlSOXq22bfvkK0Tt3Vdhclp2DuUOlD8wdHCjL0XdC1Dn6Tpm5gwOm1taYWFii02gAfSesLDsbt7FjqukHnfLj2Df7KTXYm595d1w4t3dl/qblKDodeen3foPc2MdoqniTPHzEvTxn0pQIIRyB6cAq4DTwNHAMsAWeUBRFEUIEAktrdq6EEOZAOLAbOAnMAyKB0YqizK/nuErNNemunQkk9no0GrWGOesXkBqTQkp0Mr2G9+Hsz6dQFAWVSvDYmrnsePd7yrXl2Lewp88YD7p46O/Ya1tr0spUxZ/Hu1NWrsM/OZ+xHRx54bdwFvd2o4eTDXYWpuwMTcEvOZ+XR3RkVjcXLsbnsOVcJGpDCq2utSbDzweQfCMarVrDhGcXkhmXQkZMEm7dO3Dg3a10GtQbMwszxj4zj6SwKMLO+FGu1TJ04RQc2+jvlGpba7KxfFFzrcnoi/6khUdTrtYwbOUicuKTyY5Lwtm9Iyc/+AftBvbCxNycoSvmkxAYyvXDZzAxN6Pv7Im49dLr1rbWpJWpij+O7EKZVkdg2m1GtXXg5bMRfD25J6YqQXpRGceis9DqdMxxd8HK1IQz8dkcjc6sZl9D15p0bG7Dlle8mDCqD9t/PssnXx80un3NtSatrcz59J0nKCvTcNn/FpPG9GHVS1uZPLYv0yf2p5m1BW+8v4uCghL+9tEz5OUX0cLBljUvf0txibpCp761JqMv+ZNu8PfQZxaRm5BMdqze36c+/AdtB/TC1MIczyfvbi4115q0NFHxQu9OqMt1hOQWMMSpOX+5FklHW2uWdG4NwM7oZFKKStncrwsFGi325mZ8EBxJaZWsX21rTXqfCCQiOBZ1mYYVf5hHYnQqcbeSmTRPn+TOTM3hxL5LLFs/i8M7zpCTnkdRQTFzVkyiVXt9HNe21mRjxDHUvtZknLc/WTej0Ko1DHrKi7yEZHLjkug6eTQhuw4RfzkQ1z7dGfDkAkzMzLj4yTcMe/5pTM3NKzRqW2sy8qI/qYY2PWrVIrLjk8mKTcLVvSO/vv8P2g/qham5OcOfms/elz+kuZsz5s0s6TllFC076J/Vq7rWZLqPH3m3otBp1LgvX0JhYjKFCQm0Hj+W6L2/oNNqUZma0nnBXNJ9/MgODQPAsWcPXEcMJWbfAdL9AnDs1YOuSxbx7bgpd601efVMILGh0WjKNMzdsIDU2BSSo5LpPbwPZ34+haJTUJkIZj03tyID9PNHO5i7YUHFM2JV15rM8PEjPzISnVpDl+VLKEpMojAhkVbjxxK77xcUjQZhZkbH+XMpSkkh6dcTCAEuo0ZiYm5O2Jdf49i3LybmZpz9+u9A9bUmg88GEH89Bq1aw2PrFpAWm0JqdBI9hvXlwq6ThrhQMe3ZuSRFxHNh1ylKC4sZNmcMvUZWdm6MrTV5L8eomRWrba3Jxoo3RVEeaOqo/SdnHlhnI37T+N9VWuyR6IgJIVoBOYqilAohHgNWAh7AIOBtwFRRlLVCiPcAO2CDoWPWX1GUIIPGOaAN0AeYBXwCfKIoyv/Vc+y7OmKNgVz0uxK56HclctHvSuSi35XIRb8rkYt+V9dtqniTHbFHh0dlaLIP4GcYcvwj8G6Vuo2AlRDiI+DPgBkQIoQIM3y/w0UgQ1GUEsPnNoZ3iUQikUgkkkeSR2L6CkVRjgM1H0ToUOXzU1U+P1uHxlvAW4bPKWD8GVCJRCKRSCQPBuV3+BD9g+JRyYhJJBKJRCKR/M/xSGTEJBKJRCKR/BcjlziqE5kRk0gkEolEInlIyIyYRCKRSCSSpkU+I1YnMiMmkUgkEolE8pB4JOYRe5gIIf63HSCRSCSS/zke9Dxi7b44/8B+axOeH/O7Sr/JoUlgT0zjT8S3sNM02r3X+BPxJbw+CYCvw+pfduReWddrCr8mNv4EjdPaTudV/1ONrvvB4IkATTZxblNNvNpUE8UCvOx7utG1Px4ygeH7G39KvsvzRuGT3vgTug510U/o2lTtuqkmzW3KSY8fP3eu0bX/PXZsk01iuuFy47fpL4frJ8P+/lbjT4a9wn0q2yIaX/fpblObNC4kjwayIyaRSCQSiaRJ+U/WTf9vR7pGIpFIJBKJ5CEhM2ISiUQikUiaFDmNWN3IjJhEIpFIJBLJQ0JmxCQSiUQikTQpMiNWNzIjJpFIJBKJRPKQeGQ7YkKIDkKI6/e5b3MhxNrGtkkikUgkEsm9I4R4YK/fG/91Q5NCCFOgObAW+Nv9aASfDSAmOAqtRsvsDQsxt7QAQKfTcejLPWjUGswszJjzvBfnd50kNy2H4tuFzFgzH3un5nXqWpmpeHdKdzTlCj4JuRwISwNgclcnxnRqQSs7S77wjiEo5TYX1gznYmwO19ML2HktuV6bb14IICn0FuVaLeOf9cLMYHNucjoB+0+hoODY2oVB8/TzkMUHhXN2625W/P2PRnUDTgcSdS0SrUbLwo0LsbCq9MWez+/4whyvFxfx6w+/kp6QjpWNNVMfn4J9S/s6dRO9/cm8cQudVoPHU0swNdgLcPWfP5FzK4aJH74JwI19RyhITsO8mTXd503HysGIj01V/GWCOxqdwpXEPA7cTNf7uHNLxnZwxM3Wki9848gv1fDc4PYIIDq3mL/7Jxj1g7WVOZ+9+yRqtZaLPjfZffAKAPMf82TKOA80Wi1fbP2VyNg0tn66mpJSNebmpqzZ9B33M2lyh3bObF4/B3s7a5au+fye9wdIuuxHVvgtyjVa+q2o7uNr23aQExnD+PffAiA14BoZoTcoyc7Ffc40HLt0qlPX0kTFJo/OaHQKQZn5nEjKBGCYiwPzO7lxJT2XfTGpALzs0Zk+jnY8cSaoQTZfPhlI+NUotJpyVvxhfkW83WH7J3uwtLJgybpZXPrNn/NHfJm+ZBz9R/QyqttUbRog+pI/adcjKddqGbaysu0BeG/dSUZEDHM/fQOAmMuBJF0NQ2ViQu+ZE2jexrVen0Rf9CclLBKdRsvwVZX6ecnphBw8CYqCfWtX+s2ZZFQny9eX2xERKFotHZYtw8Si0s6E/fvRqdWozM1pN28eAMVJSYR/9hkef/kLJpaWpBw/jjo3F4sWLXCbZPxYwWcDiA3R+3vW+ur+PvyV3t/mFmbM2uBV7/lXJeWKHznh+nPo8eRSTKucQ9j3O8iLimbEu283WC/sXADxIfrr29R1iyrszE5K58pe/dyHLdo4M2zBJK7sPUVeWjZZiWn0GjuIAdNG1KudEKrXnrK2urbPvlOg6LWHLpiEz74q2mMG0d+I9r3EQ+R5XyJOX6bv7Im0G9inwX6RPFge2YxYVYQQnYQQQUKIo0KIBVXKCw3vY4UQF4UQh4AbwAdAZyHENSHEx/d6vBveocx9YTF9RnsQdimkolylUjFnoxcLX16OuqQMnU7HGK9JzNnoRb/xg4gJjjSqO7WbM8duZvDqr+FM7NKyovxEZCZvHL/JJxei8WzrAECRuhxLMxWpt0sbZHO0TzAT1y2l6/ABRPkEV5Q7tHZh0oZlTN6wnLRbcQCUFRWTdD0Sp45t6tUNvRTC4j8sxmNMf0Jq+MLrJS+Wv7qcMoMvVCYmmJiaYmJqgpWNlVHdlIBrDFi1jNZDBpLif61a3YBnlmLj5lLlWCaoTE0RpiaYWVsb1Z3W1YljkZlsPhnBpM4tKspPRGfx+ulbfOIdg2dre2JyS3j5xE02nbiJh6tdvX6YNXUQB475s+G17cyY1L+ifPbUwazd/E+2fLSHDaumYWtjRWFRKRte205JiZrmdsbtrYu4hAyee2Xrfe17h9SAYDyeWU7rIQNJDajuY4+nl1XzsdsgD/o9tZTuC2aRHRFlVHdMqxacTc7mw6AoRro5VpRfSc9lR2T1m4aPr0WTUFjSYJsDL1znmc1eeI7rR8D50Gp1fmeD6dS9bcX3kVMHM3q6Z4N0m6pNAyT4hTBizVI6DutPvF9wtboRq5dg36rSz/E+1xi5ZhkDl87i+pGGTb4b5xfCqDVL6Ti8P3G+lfrNW7sweu1yRq97nMzIuHp1coOC6PTEEzgOGkTO1asV5WXZ2Sjl5XRYvBh0OspyctBptWRcukTz3r0BKEpIoDAqCpWZGeb2dd9g3SH8cihzNi6mzygPbnhX9/fs571YsKnyunEvZAReo/fTj+PiOZCMgOqd+14rltHM1aWOPWsn4koI059fQo9R/Ym4XOnbFm1ceOyFZTz2wjKSI+IBGLZgItPWe2Hbwp6eo/rXJVnBLZ8Qpm1YQo+R/blVQ3vGxmXMeGEZKbf02kPnT2TqOr12j3q07yUeuo4Zgvu4YQ32x/8SQoipQogIIUSUEOLVWupfEkLcEEKECCFOCyHaV6krN/Qvrhn6Hf8Rj3xHTAjRDdgHrAAyjWw6ANioKIo78CoQrSiKh6IoL9/7QfVvzZ0duZ2VV60qIz6NvZ/8iKWNNSrDDHVlJWVcv3CNnsON33G42VpWdKx0NZIkqzzb8dYEd87FZAEwfZsvLx+9wVOD29aUqd1kQzrWztmRwuy8u+ojLgbSzqMHAH57TjBwzoQG6d55wtLRxYG8zOq6aXFp/PjBDqxtrFCpVExaOpHHX1tOt4HduHLsSoN0rVs6UpKTa3TTbrOnMHjtCpx79yDurLfRbd1sLEgpKAOgvKaPB7bl7bFdOBeXU1E2s5szF+NzqI/Wbo4kpeq3Ky+v/PH4v29/5dM/LWfV4xNwsLfmdkEJ5uam7N32IqamJuTmF9Wr3WQY4rghPgaIOnaK6zv24NLXeHbJ2cqCjJI7Pm7cVUvujCq0dHUgp0q85ecUEB+ZTM+B7vcprH9r7DZdlWZOjhRnG/dz71kT8Nm2m5vHL6AuLG6Y6QbbbVo6UlzL3zHaO4DW/bo3WMjC0RF1XqUP1Hl5mDvobwDNHR1R5+aSevIkruPHV2xTmpaGpZsb7ebPJzc0FJ1aXc+x9G/2zo7k1+LvfZ/8iFUVfzcYg65VixaU5t59nbtnqthZkHW3Xtj5QDr1r/RtQXY+puZmWNrUf4N1Z4DMztmRglquyTcuBNLxPrQbLR4eMEI8uFf9tggT4GtgGtATWCKE6FljsyBgkKIofYG9wEdV6koM/QsPRVFm/ae+edQ7Yk7AQWCZoijB9WzrpyhKbENEhRCrhRABQoiAquVBp/w4+s1+SgwXyLyMXOxaVh+WcG7vyoJNy1EUHbnpOZQWlXLoy91MWTkLC2tLo8dNLSjF1c7SYEP1um/9Elh3IISVnvpOt4K+s1am1WEsrsLP+nL+n/soLdLbXJCZg02L6jZHXAykIDOHvlNHoiktIz8tE9/dv5EZl8zNc/616vqd8GP/1/spLtDr5mbk0rzGEI1rB1eWv7oMnaKQk55TcVG1bW5DWUntF+r4iz4E/3sPGoO9xdk5WDk6GDlDEAZdSztbtGVlRrdNLSzDzUafqq8Z3N8GJrL2SBirBug7tzO7OdPGzpIfQ1KMagIkp+bQ2lVvp0pV+RcJDI5h4xs/8PMvl0lKzcGjd3sSkrJY8PRnxCdm0qdnu3q1G5uESz6E/rj7nnwM0GX6RAavX0nUr8YzNRklZThZmQOgaqTnMS795s+PX/xCUYE+e5adnodjlXi7eS2a27kFHPj+BOFXo0hNyGiQblO26ajzvvh+vxe1wc9FWblYtzDuZ6cuHRi+egmdR3vSrKXxbSPP++KzfS9lBtsLs3KxrvF3jPYOoDAzlx6TR9WpY2trS/yuXWiL9TplOTmYN6/0gXnz5qhz9T/o6pwczB0cKE5MJP3sWQrj4si4cAFzBwdMDdloE3NzdFptrccKOuXHsW/2U2qwOT8zF/ta/D1/03IUnY689PpvggCSvX0I37G74hxKs3OwNPKIQn2Envbj5NZ9lBqytbczc7GtYWfY+UBuZ+YwYPrIirLgkz70nTjEuPYZP059u4/SoiraNa7JNy4Ecjsjh/7TKrVDTvnQZ0Ld2o0VDxIAPIEoRVFiFEVRAz8Ds6tuoCjKWUVR7twt+QD1DyHdJ4/6M2L5QAIwEv2QoxbD76sQQgWYV9m2wakHRVG2AlsNOhW39P0netJ/oifBZwM4+OVuNGUaZq1fQMKNWFKik+g5vC/nd51E0SmYqEywd2rOzne3U15ezrmdJ+gzuj+dPeq+Y/8tIoN3JndnfOeWnI7K4rOZvXjxcBiL+7Wih7Mttham7LyWTCdHa54b2gEAn4RcjOUceowbQo9xQ7h5IYAz3+xCq1YzbvUiUm/GkhGbiFu3jlz64QAdBvbiwrb9jH56Ho+9ugqAwuw8uo8dXKuu52RPPCd7EnA6kN2f632x4PkFxIbFkhSVTN8RfTi585TeFyYqmjs158SOk+Rl5lKUX8S89fNq1W0/aijtRw0l0dufoG07KVdr8FjhRXZkDHlxiXSeNIaw3QfJj08k6J8/0feJhUQeO01Jdi7qgkL6PrHI2J+WXyMz+fN4d8Z3asGpmGw+n9qDF34LZ3FvN3o42WBnYcrO0BR6Odnw5ujOnI7J5q0xXfjzeePDcYd+C+DTd55g6ngPjp26xrd/Xc2ql7YyeWxfpk/sTzNrC954fxcFBSU8v2oan/35CVo42PL1tvtbE9SxuQ1bXvGiX68ObFo3m0++PtjgfduNHAojh5J02Y/g7T9RrtbQ98nF5ETGkB+fSMeJY7ixR+/j4O0/0Xv5QpK8fclPSEZTXEKHccaffTmfks1L/Toz3MUR79Qc3hrozp8Db9HL0ZbFXVpha2ZKdqmacynZrO7ZHnf7Zrzs0ZnPQ2LQ1EwFGxg5dTAjpw7m8slAvv9kL+oyDU++NI/I63HERyYzce4Ihoz3IDM1h1P7L+HWzpkg7zAu/eaPmYUZltYW9Ojf5S7dpmzTXcYMocuYIURf8ufytz9TrtYw9JlFZNyKITs2iR5TRhO48xA5cUlc3rqTIU8tICX0FomBoWhLyxj0+Fyjfu46Zghdxwwh+qI/3lv1+sNWLiI9IobsuCSc3Tvi968DtBvYC5/v9zF0xfxadQoKCmjv5UWWry+xO3agU6vpsHQpBdHRFCcm4jJ2LMLEhPjduxGmplg4OtJ19WoAtNu34zx6NCoLC7L8/YnfvRsze/uKTpkxfx/6cjdatYbH1i0gITyW1Ogkegzry4VdJ1EUBZWJCXb1PH93h9YjhtJ6xFBSrvhx44efKFer6fHEEvKiYrgdn0C7CWOJ3HuA2/GJhH2/gx7LFqEyM6tTr88ET/pM8CTsXAC/fr0LrVrDlOcWkhQeS3pMEq27d+DMtgN0GdybU9/uZ+KqeSiKQtKNaEYunmLU1j7jPekzXq99/G+70JRpmPzcQpJvxpIenUSr7h04u+0AnQf35vR37uGKkQAAIABJREFU+5mwslJ7hFfd2vcTDwmBoUSd98XE3AwzSwvcet1nRrkReJDP0AshVgOrqxRtNfzu36E1kFjlexJgrIf9DFB1YVVLQyJHC3ygKMqB/8je+3mQ+EEghOgAHEHvnOPoH7zvBNgqirJZCDEH+EVRFCGEGAtsUhTlMcO+LYCriqK0r027xnEUuei3HrnodyVy0e9K5KLflchFvyuRi35X8ntc9FtRlAf674Vd/3HhgXU2Ip8dbfTcDM+aT1UUZaXh++PAEEVR1tey7XJgPTBGUZQyQ1lrRVGShRCdgDPABEVRou/X3kd9aBJFUYqAx4AX0fdgxwghgoFh1JEFUxQlG/AWQly/n4f1JRKJRCKRNB5C9eBeDSAZqPoAdhtDWXWbhZgIvAHMutMJA1AUJdnwHgOcA+r/7w0jPLJDk4qixAG9DZ/zgDtjaD9U2Wyzof4cemdU3X9pU9sokUgkEonkd4c/0FUI0RF9B2wxUK3PIIToD/wDfeYso0q5A1CsKEqZEKIlMILqD/LfM49sR0wikUgkEsl/B4/SPKuKomiFEOvRP/ZkAmxTFCVMCPEOEKAoyiHgY8AG2GOYlSDB8B+SPYB/CCF06EcVP1AU5cZ/Yo/siEkkEolEIvmfQlGUY8CxGmVvV/k8sY79LgONOjuu7IhJJBKJRCJpUlSPUEbsUeORf1hfIpFIJBKJ5L8VmRGTSCQSiUTSpDxKz4g9ajyy84g9KKpO6CqRSCQSyf8CD3oesZ7bHtw8YjeeNj6P2KOGzIgBqcUNn7W8obhZz6bLyG8aXTfq0hoAwnMPN7p2D4eZ5JQ2vq6j5Uy2XG38yW23DNBPbtt5xvZG144++lSTTdzZVJOuAk02Waxrz9cbXTftxnsciGv8CYTndJgONF27jshr/DbSrfnMJpscFeAPPo0fc58OncCOqMa3eVmXaU2mC7A/tvFjbl7H6U2m2/uHC42ue/3J0Y2uWR8yI1Y38hkxiUQikUgkkoeEzIhJJBKJRCJpUoRMidWJzIhJJBKJRCKRPCRkR0wikUgkEonkISGHJiUSiUQikTQpDVyM+38S6RqJRCKRSCSSh4TMiEkkEolEImlS5LP6dfNfmxETQrwghLB+2HZIJBKJRCKR1MV/c0bsBeBHoPh+BU4eCyLIPwqNRstLb8zHysq8ou7oAT8ib6ZgY2PJyvVTG6RnZWnKlpdGotHq8AtK4dDJKAAWzOhG3+7OuLk042Z0DvuPRbB6mQdCCGLic9n6U3CDbT5//CqhgVFo1Fqe2zwfSyuLirpn571HP093OndrzZS5wxqkd/zoVQL99XqvvDkfK+tKvcO/+BJxMxkbGyvWbJjGt387TlxsOnZ21jz97CScnO3r1I275E/6jVuUa7R4PrMYU8tKXb9vfyLzViwzPn4DgOgzl8mOiac4O5fm7VrjsWR2nbpWFqb8ae1QNFodvqFpHDoXA4CTgxVrFvZBCMGRC7FcDc8A4J11wygu0fDBtoAG+QMg+pI/adcjKddqGbbSCzOD7fkp6YQeOImCgn0rV/rOmdQgvaTLfmSF633Rb8WSar64tm0HOZExjH//LQBSA66REXqDkuxc3OdMw7FLpwbbfYcO7ZzZvH4O9nbWLF3z+T3vb2VlxgdvzUKjKeeyXyz7j+rjc/wod7zmDADgp30BhEWk8vK6iQBMGO3O2NlfUFhUZlQ76Ewg0cGRaDVa5m1YiLkhfnU6Hb98sQeNWoOZhTnzNy7iuncIEQE3yc/MY8KyybTv0aFB9jd2uz73W2WbW/tq9Ta3au57eHi607l7a6bOHcaZowGcOOjLvMfH4Tmqp1Hd4LMBxIZEodVombV+IeaWlb44/JXeF+YWZsza4MUtvzB8Dl/EfXBPhs4yPlln0mU/ssNvodNq6bNiCaYWlfYGb99BbmQMY9/Tx1vk4eMUZ2WhLiik9/JFWDk6GNUONdhcrtEyo4rNWUnpeO85DYpCi7YujFw4EZ8D50iPS0EgGP/EDGwc7R647rUq8Tb3+eo+PvDlHjRlGswtzJm7cRHndp0iJy2bovwiZj03D3un5kZ90ZTaAFamKt4Y0gWNTkdAWj5HYzMBeKZ3G1rbWNLcwowP/aNJL1bXq/UgkBmxunmgGTEhRDMhxFEhRLAQ4roQwksIESeE+EgIESqE8BNCdKllPxMhxMdCCH8hRIgQ4llD+VghxDkhxF4hxE0hxA6h53mgFXBWCHH2fu29eOY6r/xxIeMm9+PC6dCK8tycQs4eD8bKypwWLW0brDd5TEeOn4vlzY8uMH5Eh4ryvUcjePvTi8Qm5rP/WASxifm89sF5Xn3/HP16utyTzT7nQln/+iJGTvTgytnQanWWVuaoyzS0dKm/kd/h/JlQXt+yiAlTPDhXxQc52QWcOn4NKytzWjrpfWBiqsLMzBQTUxW2tlZGdRP9gxmyehnthw4g0e9atTrPVUuxc3Ou+N55/HA8Vy7B1tWZTmOGGtWdMrwdv3nH8caXl5ng2baifOW8XhSVaNApCmlZRQBMHdGe0FtZDXNEFRL8QhixZikdh/Un3q+yk2zfyoWRa5czau3jZEbFNVgvNSAYj2eW03rIQFIDqvvC4+ll2LhVxoDbIA/6PbWU7gtmkR0Rdc+2A8QlZPDcK1vva1+AGRN7ceREGJv+eIDJ47pXlI/w7MSWj47x1vtHmTDanazsIja/c5CPvjqFt19MvZ0wgOveISx4cTF9R/cn1DukolylUjH/BS8Wv7IcdUkZOp2O3iP6Mn/jIqasmE5saEyD7W/sdu1zLpQNb+jb3OV62tz4GYOYOGtwg3TDL4cyZ+Ni+ozy4EYNX8x+3osFm5ZTZvCFu2cvRi2c0CDdtMBg+j2znFaed8dbv6eqx1vXmVPo99Qy2gwfQlb4rXq1b14JZdbGxfQa5UF4FZtbtnFh9otLmf3SMpIj4gGID41i9gtLGThtOFePX3koumGXQ5hviLfrl6r7eN5GL7xeWU5Zqd7HY70mMm+jF/0nDCI6OLJeXzSlNsCEdi05GZ/Fn65EMbZti4ryf15P4h2fKI7EZDDYteHXesnD40EPTU4FUhRF6acoSm/gN0N5vqIofYCvgNpu0Z8xbDMYGAysEkJ0NNT1R5/96gl0AkYoivIFkAKMUxRlXE0xIcRqIUSAEMJoGuROD97FzYHM9PyK8pSkbGztrXl243TSUnNJTmzYj7mrUzNSMwoB/V1RVczNTWjjZktsYuVxZkzozCX/xAZpV9qsN9rJ1YHsjPxqdZ/9+yU2vOnF4V0X71nP1c2BjGo+yMHOzpp1LzxGakouSYlZrFg5gS3vLcVzqDsH9/vWo6t/b+bkSHFOXr12lKs1FGVmY9fKeMfUtWUzUjP1SdByXeXSZl3bOfDLmWi+2HGN9Yv70aK5JT07OXI5OLXeY9dFMydHirNz7yqP8Q6gdd/utexRBwZfWLd0pCTnbr2aRB07xfUde3Dp26vhx2hE3FzsSEnTx0JVHx/8NYStf13Cti+WsnN/YEW515wB7D4Y1CDtO/Hm4OJAfmb1uEiPT2PXRzuwsrFCpdJfus7vPcvhb36h2+CG+7ux2/UdQWe3u9vc//34Es+/5cXhnxve5ip19W/2zo7kZ1X3RUZ8Gvs++RErG+sKX9yrrlVLR0obEG/a0lJS/AJxG9CvwYewd3bkdtbd7fr6+at07t8NgAFTh3P0691E+FyvddsHo1sl3rLujrfdH+/AqlllvJWVlBF6IYhew/s0wNqm1AYXa3PSDDc3uhprRluZqpjSoSVnErIbpPUgEOLBvX5vPOihyVDgUyHEh8ARRVEuGi68Ow31O4HPatlvMtBXCLHA8N0e6AqoAT9FUZIAhBDXgA7AJWNGKIqyFdhq2OeuhUh/OxxAZHgyBbdLAMhIzcPJpXKYzcnZHjs7fcbHxtaKkgamftMyi3B1bkZ4VDZCVT1apo7txInzsRXfZ0zoTGsXmwYPS545FkDMzSQKC/SdkKy0XFrUGBq80+DNzU3R6XRGL+DHDgUQEZ7E7dt6vfS0XJyr+sDFDjt7/SN4tgYf3NFzdLQlJiqtVt2YC77kxiaiLtL7tigrF2vH+u/aEnyDaOPpUe92aVlFuLa0Jjw2B1UVH6dlFZFfqKa4VIOFuQlDervSorkV65f0o0dHBzq0siMu5bZR7ajzvmTHJqIuKq60vUX1oZoY7wAKM3MbNCyZcMmH/LgENAa94uyceod+ALpMn0jbEZ6E7TrAgNVP1Lt9Y5Oafhs3FzvCbqaiqnLV27BqDPNWfAfA53+Zz7pXdgP6TNnfthnviASe9CM5KokSQ/zmZeTeNTzj0t4Vr1eW8csXe8hNz8HBxZExC8YxcMIgjv3zMIs2LTV6jMZu12eOBhAdkUSRweZMI23OrAFt7g5Bp/xIjUqitFCvm5+Zi33L6r5wbu/K/E3LOfzVbvLSc2ju4livbmKNeCvJzsGynnjTlJQQ+sPP9PSai6mVZZ3bBZ++22a7GjZfP3+VvIwcRi7UD1d3HdyTroN7EnMtgoy42m+Imko38KQfKVFJlBRWibeWd8fbopeXceBLfbxZ2Vhz8Ou9THtmFhbWdfuiKbWrkl6sxsXagojcIgSV7bCZmQlveHbmr4GxFGvLG6Qlebg80I6Yoii3hBADgOnAu0KIOyvRVu0M1bZCuwA2KIpyvFqhEGOBquMd5TTCOU2dOYipMwdx8lgQn/5lH+pSDS+8Po/rwXFE3kxhrtdw7Oyt+eqTQ2i1Orp0a9Ug3RPnY/njiyMZO6wdZ73j+fjNcbz8rn7kdNrYTryw5RQAPbu24NW1Qzl7OYHX1g/j/a+Mp9cBxk8fxPjpgzh//Cp//3Av6jINz748j5uhccREJNN3cFf2/+sMAL0HdKn3B2H6rEFMnzWI40ev8uGf91JWpuHl1+cRGhxHRHgyCxaPwM7ems8/PohWW07Xbq34/ttTpKflkZ9XxEuvzq1Vt9PoITB6CHGX/PH7biflag2Dn/Yi81YMuXFJuE8eTfDPh8iNS8Lvu50MfHIBJmZmJPhcZcTGp+v1w/HLCfxxzRDGDm7DGb9EPnlpFJv+epFtB8LY/NQgFEVhx7EIroZncOxSHK2dbXj8se71dsIAuowZQpcxQ4i+5M/lb3+mXK1h6DOLyLgVQ3ZsEs7uHfH/9wHaDuiF3w/78HxyvlG9diOHwsihJF32I3j7T5SrNfR9cjE5kTHkxyfSceIYbuw5SH58IsHbf6L38oUkefuSn5CMpriEDuNG1GtzbTg2t2HLK17069WBTetm88nX97Y49tFTYbz3xkwmju7GyXM3+fL9BWx4bS/HToXx6Z/0f/dzl/TDWMM9O+IbGFev5sBJngyc5EnQmUD2f7EbTZmGuesXEH8jluSoZHqN6MPZn0+h6BRUJirsnZrj++sVUmNSKC0qYci0+p95bOx2PX7GIMbPGMS5367ytw/0beS5V+ZxMySO6FvJ9BvclX2GNtdnoL7N+V28wZmjAZhbmGFlbU6fgXc9iUH/iZ70n+hJ8NkADn25G61aw2PrFpAQHktqdBI9hvXlwq6TKIqCysQEO6fmJITH4r3/HKWFxdg62tFr5N03LW1HDqWtId5CvtfHWx9DvN2OT6TDxDGEG+ItZPtP9Fq+kGtb/4VSXk7kod9oNWQALXvWnnnsN8GTfhM8CT0bwJGv9DZPX7uAxPBY0qKTaNOjIye+O4C7Zy+Ob/2FKavnEnzan6SbsWg1Wqatqb2tNJXunXi7diaQX77YjUatYY4h3lKik+k13BBvSmW87Xj3e8q15ZzdeZI+Yzzo4uH+wLWrcjohi9c9OzO6jSPnk7J5b4Q7r3vf4t3h7piqBKv7tON4fCZ+afn1aj0IVL/DTNWDQihKbf2eJjqYEK2AHEVRSoUQjwErAQ/gG0VRPhBCLAe8FEWZWWO/1eg7bwsVRdEIIdyBZPTDlJsURXnMsN1XQICiKN8LIUKBWYqixGIEIYSSWnxvP0QNwc16Nl1GftPoulGX1gAQnnu40bV7OMwkp7TxdR0tZ7Ll6slG190yQJ916jxje6NrRx99ivevnWh03dc8JvOy7+n6N7xHPh6ifz7Isu3iRtcuTfwZ156vN7pu2o33OBB3rNF153SYDkBTteuIvMZvI92az2RX9K+NruvVeRoAf/Bp/Jj7dOgEdkQ1vs3LukxrMl2A/bGNH3PzOk5vMt3eP1xodN3rT45GUZQH2jUa8NPFB9bZuLp01O+q2/eghyb7AB8LIXSABngO2As4CCFC0Ge3lgAIIWYBgxRFeRv4Dv2Q41WhH8vMBObUc6ytwG9CiJTanhOTSCQSiUTyYPg9Prv1oHjQQ5PHgZrDiwAfK4qyuca2h4BDhs864HXDqyrnDK87+6yv8vlL4MtGM14ikUgkEomkkflvnkdMIpFIJBLJI4DMiNXNQ++IKYrS4WHbIJFIJBKJRPIweOgdMYlEIpFIJP/d1JyySVLJf+1akxKJRCKRSCSPOg90+opHkdomdJVIJBKJ5L+ZBz19heeeSw/st9Zv4cjfVfpNZsQkEolEIpFIHhLyGTGgVOvX6JqWpp606fOnRtdNCv0jALllRxtd28FiBiXa+mfxv1esTIdxLqXx7R3bagYAXSZ91+jaUSdXMvqw0ZWy7osLM0cyfP99rDtYD5fnjQJosolXm2qi2Os5jT85am9H/XzQTdWu/TKONLqup/NjnExq/DYyqY2+jfwlqPEnJ36j/2T8MxvfF4OdHmvSCV0Dm8DmgU6PNZkvem473+i6N54e0+iakvtHdsQkEolEIpE0KXL6irqRQ5MSiUQikUgkDwmZEZNIJBKJRNKkyIxY3ciMmEQikUgkEslDQmbEJBKJRCKRNClyPte6kRkxiUQikUgkkofE77IjJoQYJYQIE0JcE0JYPWx7JBKJRCKR1I0QD+71e+P3OjS5DHhfUZQfm0L86JHL+PvdQK3W8ubbK7C2tgQgOyufv321D4CLF4LZf+h9ggJvcfDABQDmLRjH8BF96tS1sjLjL29MR6Mp54p/HAeOXQdg3MguLJzdD4Cf9wdx4UoMAO+9OZ3iEg3vfnrSqL3HjwYS6B+FWq1l85sLsLK2qKg79Isvt8KTsLGxYs3z08nKzOeH706jKAqTpvWnX/9ORrWPHbmCv184arWGN95egbVBW++LXwC4dCGYfYfe48RxP8JCY0lPy6Grexs2vrSoTl3fU4FEBEWh1WhZ9uICLKz0ujqdjh2f7UVTpsHcwozlf1hEqM8NLv+mnxNq5Iyh9BrcvW4fW5qyZcNwNBodfiGpHDoTDYCToxVrFut9fPRcDFdvZLBgSld6dG5BYbGGz74PNOoHSxMVL/bujFbREZR9m1PJmQB0tLVmWZc2AOyISiK+oJjXPLqi1ukwFSo+CI7E2HTSliYqNnl0RqNTCMrM50SSXneYiwPzO7lxJT2XfTGpALzs0Zk+jnY8cSbIqK0VvrAy44O3ZqHRlHPZL5b9R4MBGD/KHa85AwD4aV8AYRGpvLxuIgATRrszdvYXFBaVNegYVenQzpnN6+dgb2fN0jWf3/P+ABeOX+V6YBQajZZnX5mPpZVFtfpvPtyLpZU5K56fxZFdF4iP0vtm6ZppOLSwq1O3qdo0wOUTgdwIikKrLmfFprtt3vbxHiytLFi6fhY/fX2IkqJSigtLWfnqIqwMdtSG/+lAbhnayOIXqreRnz83tBFLM5a8uIj87HyO/3QaFIWB4/vTuXfd7Trmkj9pYZGUa7QMXemFmWWlvVe27iTjVgyzP3kDgNjLgSRdDUNlakKvxybQvI2rUV94nwjkxtUotJpynqrFF//8aA+W1hYsWz+Lc4d9iLqRQE5GHk++OA+XNi3r1A09G0BsSBTlGi0z1i/E3GBzVlI63nv0592irQsjF07E58A50uNSEAjGPzEDG8e648L7RCBhBnufrsXe7wz2Ll8/C4Bg35ts/3Qfn+9+w6gfmsoXVqYq3hrWFU25Dr+0fI7GZACwsk9bWtta4mBhyvu+0RRpytns2Zk2tpY89VtIvbZKHi6PfEZMCNFMCHFUCBEshLguhNgMLAL+LITYIYQYK4Q4L4Q4KISIEUJ8IIRYJoTwE0KECiE63+sxz5wKYMs7K5kydQinTwVUlLdoac9bW55m7Yb5DB7SAxsba/z8brBp8zI2v/44Fy9cM6o7bUJ3jp0MZ/OfjjB5bLeK8uGeHXjnkxNs+fA440Z1AWD6pB4Eh6U0yN5zp0N5fYsXE6d4cPZ0ZaPLyS7g1G9BWFmZ08JJfzHa8f05rJtZIFQCZ5fmDfBFIH9852km1+qLFazdMM/gCyvmzR/DW1tW0L6DC7PnjjKqG3QxlCde9mLQWA+uXqy0WaVS8fgfFvH068soK1Gj0+mICIpi0do5eG2Yy3WfcKO6k0d04PjFWN78/BLjh7arKH9mQR8KizUoCqRlFePY3JLpYzpRUqolI7u4Xj+Mdm3B+dQsPg6JZoSLY0X5go5ufB4azWeh0czr4Ia1qQkl2nI+DommrLwcGzPj9zpjWrXgbHI2HwZFMdKtUvdKei47IpOrbfvxtWgSCkvqtfUOMyb24siJMDb98QCTx1V2Xkd4dmLLR8d46/2jTBjtTlZ2EZvfOchHX53C2y/mvjphAHEJGTz3ytb72vcOvudDWfv6IkZM8MDnXGi1ustngunSo23F9+tXo1n3hheT5wzj1EFfo7pN1aYBAi5cZ+VmL4aM70fA+eo2+50NplMVm5eum8UzryyiS6/2xEcab9/BF0NZtsmLAWM9uFajjSx9aRFPvlbZRk7tPoeltb5dN29pvF0n+IUw/NmldBjWnwS/4Gp1w1Yvwb6VS8X3eN9rjFizjAFLZnHjyOkG+WLVq3pf+Nfwhe/ZYDpX8cXYmUNZuXkR42YNJe5WklHdm1dCmbVxMb1GeRDuXemLlm1cmP3iUma/tIzkiHi9zaFRzH5hKQOnDefqceMTVPtfuM7qV70Y2gB7iwpKuHE1ivZdWxt3goGm8MXE9i05EZfJHy9HMq5d5fXiu9BE/nQ5ksPRGXi6NadQU85b3rfIK9M0yNYHgVA9uNfvjd+DyVOBFEVR+imK0hv4BjgEvKwoyjLDNv2ANUAP4HHAXVEUT+A7YENNQSHEaiFEgBAioGadvl7/7taqJelpOXfVHzxwkVmz9R2NadOHsenFL3lhw+fMnWd8tmI3FztS0vIBKNfpKsoP/RrGN58s5NvPF7Hrl2u0dGxG7+6uePvGGtWraa+rmwMZ6fkV5clJ2djZW7PuxZmkpeaQlJhFTHQa02cOZtVzU9n2j/pn276j3apVCzJq8cWhAxeZOXtkxfeyMjXJSVl06OjWIF1HVwfyMvOr1aXEpbH9/R1Y2VihUqkYPL4//9jyPX9/czsjpg8xquvqZE1qRhEAOl1lLqprewcOnIrii39fZd0yD9q52pJfUMYn2wJo7WJDOzdbo7pOluZklKr1ulXWZ21makqhtpwibTnWpiYUacsxU6n4YHAPTFQqCjRao7rOVhZklOg7PuWNvO5r9Xir1D74awhb/7qEbV8sZef+ykyg15wB7D7YsGxbUyEMgeHk6kB2RmVc5GUXEBuRTN9BXSvKJs8eyj8+2of/xTCya8TQ3br698Zu03px/VtLVwdyMvMqivNzCoi7lUyvge7VNs/Lvk3szUTce3dokK6jiwN5WdXPLzUujX99sAOrZvo2khqXxpBJg5nx5FR++7Gedm3QtXFypDg71+imvWZOwHf7biJOXKCsqP4bFlHVFxnVfRF/K5leg6r74ue/H+Hkfm+61OcLA/bOjtzOyrur/Pr5q3Tur7+5HTB1OEe/3k2Ez/Vat61ucKW92VXszTP87XpXsffADyeZuWxcg+yEpvGFi7UFaYYbpSo/IQBYm6qY2tGJ0/HZDbZR8mjwexiaDAU+FUJ8CBxRFOWiuHsQ2F9RlFQAIUQ0cKLKvne1HEVRtgJbDdtX/EIdOniR8Btx3L6tv+CkpWbh4upYc1/8fW/w1NP6pUO++/YQ2394E4A33/gHH368rs4TSU2/jZuLHTci0lFVOYd1K0ew8KnvAfjru7M5fiaCFo7NeOHZ0fTo5kLH9o7Ext/943HskD83w5MouK3PkqSl5eLsYl9R7+xij52dNQC2tlYUF5fh7GKPrZ0V1tYWqMvq7iQcPniJ8BvxFb5ITc3GuVZfhLPi6ekVZSeP+zNh0sA6da8c9yfhVhLFBXqbc9Jzae5kX22bVh1ceeq1Zez4bC/ZaTn8uuMUm/5vPQDff7CTlW89Xqd+WmYxrk7NCI/JQVT5N520rCLyC8soLtFgYW5CWnYxeQX6C9rtQjXWVmZ1agJklqpxsjQn6nYRVaOvSKulmakJClCsLcfd3oa0kjI+DIlieZc2dLFrRtTtojp1M0rKcLIyJzK/qFpMNAZ34i3sZmo17Q2rxjBvhX5ZqM//Mp91r+wG9Jmyv21r/OWXGsLZYwHERCRRaIi3zPRcWjhXxkVYUDT5uYXs3naCuMhUUhIyGTC8BwOG9yDE/xbx0Wm16jZlm774qz9xVWI5Kz0PR6fKbFR4UDS38wr4ZfsJEqJSSE3IwMLKnF3fHOXJl+aj+n/2zjs8qmL945/Z9BBIIQlJaAFC7xBqKIHQlY6EooIKNrjYAKVJEa8FuHqt91pQVAQFFQJIhwChpAMJkN5I7wnpu8n5/bFLGmlqAnJ/83mefeDMmfPNu++emZ3zzuy8ejU/B3uf8OV2WDyFuuhnVmoWFtZV24i9ox1PvrGQvR9q24iljTmmzU0wMjFCXVJzu448501G9G1KdAOq/PQsTFta1lj3LjZOjtg4OZKbnEbIsdpT7Vw46ktMaDz5ur4oIyUbK9uqvsjJ0voiVucL+3a2zHvhUSJvxuJ52JvZT0+8R/faaR+SIuIpytPanJOWRYtqEb/gcwFkp2b4tPlKAAAgAElEQVQy4jHt9HrnQT3oPKgHUVdDSY1JqtHe80d9ia5kb3pKNi1rsPcX3WcXG55AckI6v+zUHl847sfIic731RcAKQXFtDI1IiQzv8paqGYGeqwf6sQOv2gKNKU1XvugeRjXbt0vhNLIT+FNgRDCCpgCLAVOAx3RDsr2CyFcgZWKojyqq+upO/arfq4WbaV6Trojhy8R4B9KcXEJa9cvIjzsNrduxTBv/nh8vG9y7Wo4S5+bXl73opc2VD50WM/yp+qack2amBiwdc1kiks0+ATE4erixMvrDjBjSi9Gu2hnUL2uRPPLIa1eGwdzFs8fXGWNWE25Jo8f8edqQBTFRWpWrZtNRFgSoSHxzJk3gv98/DtFhSVoNKWsXDubqMhkfth5BoRg+uwhVdaI1ZRr8vfDl3W+ULNm/ZOEh90m5FYs7vPd8PG+xfWrESx5bmp5/ZeWfch7O17E2Niw4n3XkGvS+5Q/EdejKClWs+Dl2cRHJXE7PJ5+I3pzdPcpFEVBpVIxd/kMfM8EcsMnBIDuA7swbOIgoOZckybG+mxcNoziklL8b6Qw0rkNq94/h1M7C5bO7Y2iwM9HQwm4mcrLiwZgYmyAvp7grc+uVLGveq5JYz0VL/fqSElpGdez7jDExoK3r4bTobkp8ztppyr2RCaQmF/E632duKPWYG5owLvXwikqrXh0rZ5r0lhPxat9O2l1M3IZ0sqSt/zD6GnVnCe7tKG5gT4/RybimZjBsz3aM661Nb5p2Xx4PQp1pShXTbkmTUwM+Oe6qRQXa/AJiGXMiM78Y81+Zj7ShzEu2qfxC1ci2OdxleGDO+Dctx0ffXnvl21Dc01aWZixabU7biN7883es2z/9GCd9WvKNXn+eAC3rmrvi6WrZhEbkURUaAKT57gAkJqYye/7vVi8YhqeR/0IDYpFo9bwzKszytfg1JRrsjHaNNSca/LSCX9Cr0dTUqxm0auzuB2ZRExYAuNnaW1OS8rk5C9eLFg+jfVP/wu7tjaYmhnjNmN4+TRXTbkmfU9r24i6RI37itkkRCURHxFPH5fenPhR20aESsWcZTNIiUvl5E9nEAiGPzKkfI1YTbkmo7x8SQmJpLREzZCn55IVl0BmdDzdJo4iYK8H0ZcCcOjdlcGL55AUHMZt/yA0RcU4Pz4TE4uK9VY15Zq8eMKf0GtaXyx+rWZfnPjFi4XLp3Fo9xkyU7LJv1PAjMXjcWivnRKtKddk0Fk/Ym9EoSlRM+XFOaREJ5IcGU+b7h3Ys+kLugzuiYGRIROfncm1077Eh0SjUWuY/Pzs8vVkNeWavHjCnxCdvU+9Nos4nb0TKtl7/Bev8jViAB+s+5ZX3l5cxb6ack02li8q55o00VexbqgTxaVlBKTkMqK1JWsuhPLh2B4YCEFKQTHHY9LxTsrmzWFODHOw5HRcOtt9q86u3Hx6NIqi3Neh0UgPr/s22LgwbcRDNez72w/EhBAOQKaiKEVCiEeBJUA2TTgQawxk0u8KZNLvCmTS7wpk0u8KZNLvCmTS7wqaMun3/R6IjTp08b4NNs5PdXmoBmIPw9Rkb2CbEKIMUAMvAMsfrEkSiUQikUgkf52//UBMUZTjwPFqxYsrnfcEPCsdu9Z2TiKRSCQSyf1HrhGrnYfhV5MSiUQikUgk/5PIgZhEIpFIJBLJA+JvPzUpkUgkEonk4UZOTdaOjIhJJBKJRCKRPCBkREwikUgkEkmTIiNitfO330esqam8s75EIpFIJP8fuN/7iI35/f7tI3Z2itxH7KHDacxfS1RcExFnnyU8x6PRdTuba3d4dhrXBJuYnlpCp1nfN7pu5K9P0OH5XxpdN/o/swG4lNL4GykOb9V0GzReaQJ7h7bS7ll8IOb3Rtee4TilyTZebaqNYqHp2nW7bfUnv/6jxK1yo/2Gxv/sYt/Sph/rv/t8o2sHLhxFh2f3N7pu9Bdz6LWr8e0NXjQKgA4vNkFf9NnsJtN9K+Bk/RX/IBsGjG90zfpQPVRDo/uLXCMmkUgkEolE8oCQETGJRCKRSCRNioyI1Y6MiEkkEolEIpE8IGRETCKRSCQSSZOikr+LqxUZEZNIJBKJRCJ5QMiImEQikUgkkiZFrhGrnf+JiJgQIkYIYf2g7ZBIJBKJRCL5I8iIWDVMjPXZ9JILak0ZPlcT8TgdCYCNlQnPL+gHQnDkTCQ3I9LZ+upI8grUpGYU8NkPgfVqex4L4LpfJGq1hmVvzMLYxKj83DMz3qH/kM506tqGybOGcvygN6HBcWSk5vD8qhnYt6l9nGlirM+mfwzX2nwtCY8zlWye1xcEHPGM4uqtNN5fNYqiklIM9FW8sf08de3na2Kkx+alg1FryvC+kYLHhRgAVsztQ6c2LcjJK+GTfUGUlilsXDKIrNwiQmKz2XMivH4/G+qxZX4/rXZYOgd9b5ef6+rQgu9fHoHrhuOYGuqz0b0vWXklhCTk8OOF6Hq173L5pD+3AiLQqEtZ9NpsjCr5G+Db7fswNjFi3rJpDdK7eMKfmzq9p1bOrvL5AXz9/j6MTY1YuHwanoeuEHEzjszUbBa9MotWdXx+lyrZubgGO7/R2Tl/2TS8jvly7rA3U+aPob9LzzrtDTzjT+S1cDRqDbP+8RiGOt2ysjJ++2gf6hI1BkaGzH5pLsEXrxPqF0JOWjZuCyfQvrtjndrnjwcQ7B+BWq3hudX3+uI/7+3H2MSQxSumcfin88RGJAGw4PnJWLZsUad2dRzb2fL68hmYtzBlwfMf/qFr79JU7drEQMXWcV1RlypcuZ3FgVspAExwsma0Y0scWhjx0eUYcoo0vDC4HQhBZGY+//GJq0dXj62P9qCkVOFKTCYHrydqdbvZMrqzDfbmxnzsGcmtlFzemdaLvGINKXeK+eRcZL2+MNZTsWaQE+qyMvxTczgakwaAi4Ml7l0cuJiYyU9hSVgZG/B87/bl5+YeCSBfU1q7zYZ6bFnQX9em0zjoU61NvzIS1/XHtG16Xj+y8oq1bfp8w9u0ib6KdUO0tvsl53AkWmv7M73a0NrMGAsjA97zjSSloKRheoZ6bHHvh7q0DO/wGvqhFSNw3XgclRC8//hAsgtKyC/S8PavQY2i62BpymLXTgC4dLNlzKYT9doc7eVL8s0wytQaBj8zDwPjirZ35csfSQuLZuq2dQAkBAYTee4KAE5jXHDo271BfmkK/ieiPk3EA/ONEMJRCBEihPhWCBEmhNgthBgnhLgohAgXQgwWQlgJIQ4IIa4LIa4IIfrorm0phDghhLghhPgKEJV0HxdC+Aghrgoh/iuE0Psjdk0Y6cjx89Gs33GBscPbl5c/M7cPeQVqlDKF5PR8OrWzJPp2Dpv+fREbKxPsbJrVq33JM5gV6x9j5Li+XDobXOWciakhJcVqbFqZAzBx+hBWrHuMiTOGEBGSULfNIxw5fiGa9R94MXZYuwqbH+tNXqEaRYHktAKamRiQX6hm/QdeFBVraGFmWKfuxKHtOHYljnX/8cZtUJvyck1pGWpNGZrSMnLzS3DubsMZv3g2fumLSx879PXqj0FP7OfA0YAE1u4OxK2PfXm5vkow18WRcze0X2jOTi05fT2JN/dexaWbLfp/IL7tfz6Yp193Z/CYvvidq9px+p69RodubRusBeB3Ppilb7gzZGxffKvpeZ+9RqfuFXquU4ey5PW5jJk2lJiw+HrtfKYWO33OXqNjJTtHTBrEqCmDG2Rv8MXrzHllHn1G9Sfo4vXycpVKxeyX3Zm3+nFKCospKyujl0sfZr80l4mLpxAdFFWvtve5IF5cOxcXt35c8axq86Uz13Cq5IvggEiWrXNnwoxhnDro3SDbKxMTl8oLq//a5qxN1a4ndbbl99BU3jgRwrhOFYPtExHprDsVynavKAa3sSAqq4BVx0NYdewW/e3qH4hO6tGK32+msMYjmPFdbSt0Q1JZd+gGO06FM6i9JU7WZkSl57Ph8E1szYywb2Fcr7ZbW2tO305nq08Eo1u3LC+/mJjFrpsV92pmkZp/+kbw+fVY/FJy6hyEAUzs31rbpn8IwK1vtTY9osO9bXrPH2/Tbu2sORmbzubLEbi2rbD96+B4tlyJ4HBUKoPsLBqsN7GfA0cDE1j7YyBuvavZPLyiH7K3NOFGfDZrfwzEztKk0XQjku+wfu9VdntFcySg7n7+LnG+1xj27ELaDx3AbZ+rVc4NXbqAFvYV90vyjTAGPjEb50WPkXD1RoP0JfefBz1IdQJ2AN10rwXACGAlsBbYDAQqitJHd/yd7rqNgJeiKD2B34B2AEKI7oA74KIoSj+gFFj4Rwyys25GUmoeAGVlFeGizo6WHDgZzke7/Fn2eH9uRqRjaKjHmheGYmvdDDvr+gdid3Nt2dpbkp6SXeXcRz+8wksb3Dm416u87NtPfufwvkt0792eurCzNiUpLf9em9tbcuBkBB99F8Cyx/uRV1CCoYEeX26dgL6+ipw7dT812lmZkpReAEBpJd3Pfw1m5UeXuHgtmbnjnPAMSKRnByvWLBpAi2aGWDQ3qk2yHHtLE5KyCrU2VwrLLR3fmV1nI8sjdWeDk+nVzoK1s3tjbmqART2Dx8rc9XdLO0uy0ir8nZN5h9jwBHoM7NJgrcp61naWZKZW0wtLoKdzVb29nx/m5K8Xcerl2HDdRrCzQlcrbNnKkpy0qvdbSmwyP72/GxMzE1QqbTdwbv9ZDv3nN7oO6tZgbRs7SzJSc8rLszPuEB2aQB/nzuVlE6YP5b/v/4LvhRtkpOXco3U/aKp2bW9mRNKdYq1utfDyUue2bBjTGc/ojPKyqV1tOR+bWa+99i2MScrRto/SarpLhjuyYXI3zoWncSM5F0N9FesndaNVcyPsGjAQszU1JDm/uEbtmpjWsRWHo1Pqt9nShKRMbX9R2cdLJ3Rh15kI7qbTK2/Tc3pj3szwD7XpVpVsr+5vE30VEx2tOROXUdOlNdtsYUJSduG9No/rzC7PSO6WxKXn06e9JTtfHE5E0p1G072L+3BHfr4U0yCb7/YXzWysyM/MrrOu43Bnzn/4Ned2fIGT67AG6TcVKqHct9fDxoMeiEUrihKkKEoZcAM4rWhbaxDgiHZQ9j2AoihngJZCiBbAKOAHXfkRIEun5wYMBHyFEFd1xx2r/1EhxLNCCD8hhF/1c8np+djZmN2tV1Gelk/OnWIKCtUYGeqhKPDBTj/e+fwKuXeKiUvKrfVNnj7ixxc7DpKXq22YqclZWLeq+tSmUqnQ01NhaKRPWVkZAIuXT2HRssmc8PCpy4ckpxeUf2FUsTk9n5w8nc0GevTsbE1Cyh2Wrj9BfPIduneyqls3swC7lqZa+yrp3u3/MnKKaGasT3FJKW9/6887uwIoKNKQkVNUpy5AUlYh9hYmOpsryru3sWDRmE70dbRk/ogOFKvL2Lo/iH/+EkR+sYYM3ZdeXVw85svuj36j4I7W3xkp2VjaVPg79GokuVl38Pj2BLcCIkiOS61T78JRX77/8Dfycyv0rGwr9G4FRpKTdYffvjnBzYAIknR68154FPfnpuB5uOYokNcxX3746DfyK9lpVcnOEJ2dB3R2JtVj5138T/rg8fmvFN7Rfilmp2ZhblP1fmvV3g731QtRyhSyUrQDg9FzxvD4usVc+MWzVu2zv/vx9QcHyMvVaqelZNHS1rz8/I3ASHKy8vh55wmC/SNJjEtjwPDuPLd6Nr0GdqJ1e9vapJuUpmjXAEl5xdjpHjxEtazGX/rdZtmhYJY4a6PUU7va0trcmN3XEuu1Nym3qHxQVT1g9NWlGJb9fJUlwzugKLDjdDhbj4WQU6QhLqugXu3UghJamRrptOuPRjm3Msc3pf4BdFJWIfaWd9t0hW73tuYsGtuJvo5WzB/ZUdum913nn/uDyC9qWJu+S0ol20XFRAjNDPTYMMSJf/lHU1BP5K6Kzdl19EOjO9G3vbYfGtvLjmOBiTz92SVszY0xNzVoFF0AYwM9WpmbEJeeX6dm5HlvfHftpzhf21/kp2fRzKru6F/wgeNM2PgyEza9QvDB+qc9JQ+GB71GrHILLKt0XIbWNvUf1BPALkVR1tRVSVGUL4Av4N6k3ycuxLBxxXBch7bl7OVYtq1xZdU7nnyzP4jXnx2CgsKPHrcA2PSSC/r6KoJC08jMrn3w4faIM26POON5LIBP3/2F4iI1L74+k1vXY4gMTaDfoM7s/+4sAL0HdEKlUrH/u7OkpWSTl1vIvGfG1fmmT3jFsHH5MFyHtOXslTi2vT6aVe+d45v9wby+dBCKAj8evkVEbBbPzOnFpn8Mx9LciG9/rTtUffxKHBuXDMZ1YGvO+MWzfcVwVn50iRdm9cTOuhlWzY3Y8rUfJkZ6vPnMIPRUgl89o+pcd1aufTWRze79GNPbjjPXk9mx2JnXvvVjxdfaQef7Tw5kj1c0JoZ6bHLvi0ol+PVKXIO0XSYNwmXSIC6f9GfX9v2UFKt58tVZRATHEBuegNtMFwaP7UdaUianf/XCrl3dg4ORkwcxcvIgLp7wZ+c2rd7i12YRHhxDTFgC42e5MNRNq3fiFy/s29lyaPcZMlOyyb9TwIzFNed1GzFpECMmDeLSSX++1dm56FWtbmx4AuNmujBEZ+epX7W6gRdv4HXMFwMjA4xNjeje3+ke3YHjBzNw/GACz/jz60c/oy5WM3P5HGJvRpMQkUBPl96c3XsKpUxBpafC3MYC76OXSYpKpCi/kCGTa39yHjPFmTFTnDl/PID/vqe1eemqWYQGxRAVmsDkOS64jOtHamImv+/3wqGdDZ5H/QgNikWj1vDMqzPq/wCrYWVhxqbV7vTt6cjKZdPZ/unBP6zRFO0a4Fh4KlvcujK2Y0tOR6bzweQevHL0JvN629PdxozmRvrsuZ5IT1sz1rk6cToygw2uTrzlGVG37s0UtjzSg7FdbDkVmsa/ZvXh1V+v4z6gDd3tmtPc2IC9/tp1R2892gN9lYrrCTlk5Ne/Nur07XTecO7EiNZWnE/I4K1hXdhwOYw+1s15vFtrWhjqk1ZYwpnbGTjbmhOYWvdg9C7HAxPYPL8fY3rbc+Z6Ejuecua1b/xY8aWuTS8ayJ4LUdo2Pa+frk3HNqhNl9sel87awZ0Y1caKc/EZ/NOlC2svhrF1eBf0VYJne7fjeGwaPskNi7wev5rI5rn9GNPLjjPByexY5Mxru/xYsVNn8xPafsjUSJ/Nc/vRv4MVBnoqcgrq/mpqqC7AowPbcDSw7uULAJ1GDaHTqCFEe/ni/dUeNCVqBj/tTlpYFJkx8XSdMIrAvR5kxsRz5as9DFo0h3aD+3Hlv7sBcOjXo0E+kdx/hPJHWkFj/mEhHIHDiqL00h1/qzvef/cccAZIUxTlLSGEK/CBoij9hRAfAamKomwVQkwGfgdsAFvgINqpyVQhhBXQXFGU2DrsUGTSby0y6XcFMul3BTLpdwUy6XcFMul3BQ9j0m9FUe7rhhLTT124b4ONg+NGPlSbZTzoqcn62AQMFEJcB94FFunKNwOjhBA3gFlAHICiKDeB9cAJ3TUnAfvqohKJRCKRSP7/IoSYJIQIFUJECCHeqOG8kRDiJ915b12A6O65NbryUCHExL9qywObmlQUJQboVel4cS3n7pnLUBQlA5hQi+5PwE+NZ6lEIpFIJJK/wt8p6qPbTeFTYDwQj3ZduYcumHOXZ4AsRVGchBDzgPcAdyFED2Ae0BNwAE4JIbooitLwxYnV+Dv5RiKRSCQSiaSpGQxEKIoSpShKCbAXmF6tznRgl+7/+wE3of0VynRgr6IoxYqiRAMROr0/zYNerC+RSCQSieR/nL9ZiqPWwO1Kx/HAkNrqKIqiEULkAC115VeqXdv6rxgjI2ISiUQikUj+Z6i8RZXu9eyDtqkuZERMIpFIJBJJkyLu40arlbeoqoUEoHJqlTa6sprqxAsh9AFzIKOB1/4hZERMIpFIJBLJ/yd8gc5CiA5CCEO0i++r7zflQcVODXOAM7oN5z2AebpfVXYAOgN177peDw9sH7G/C9U3dJVIJBKJ5H+d+72P2Nyz5+/bd+3PY0bV+96EEFOADwE9YKeiKG8LIbYAfoqieAghjNFm9ukPZALzFEWJ0l27Dnga0AAvK4py9K/YKwdiQihNtalkvrrxNyVsZqDdlNDSaVmja2dFfErLLi81um5G2L/pPPSzRtcNv/IiAOmFf3yn9fqwNpnOr9GNv8HmrA5T2Bf1l9psjTzWcTIASQWN7wt70+kUaf7SA1+NGOsPbrJNV4Em2yy2wz9+a3Td6I9n0v7Nxr8vYrdo74sOn3o2unb0Mlc6PrG30XWjvp9Ht6/ONbpuyJLRAHRc3Pi7G0V9607Hp39ufN2dc/lXUOOnJnq194T/9wOxvxNyjZhEIpFIJJImRa6Dqh3pG4lEIpFIJJIHhIyISSQSiUQiaVJUcjl2rciImEQikUgkEskDQkbEJBKJRCKRNCl/s531/1bIiJhEIpFIJBLJA+JvMxATQlgIIV78E9c5CyE+quVcjBDC+q9bJ5FIJBKJRNL4/J2mJi2AF4F7NpwSQugriqKp6SJFUfwAvya2Dcd2try+fAbmLUxZ8PyHf0rj6GFvfH1CKFFrWLfhcUxMjQDISM/h80+1m/pePB/EvoObMTMzITwsnuee2cHhY+9g2sy4Vl1TE0O2bXJHrdbg5RPOfg+tO2ZOGcB4155oNKV88vVpwqNS2bHFHWMjAwqL1Lz2Zt17AJmaGPL+xjmo1aVc9Ilg/yF/AGZM7s941x6oNaV8+vVZwqNS2LF5LoP6OzJq2vsN8oWJsT6bVo1CrS7FOyCRQyfCAZgztRu9u9tib2tGaGQGOz73BmDz6lEUFKp57+PLdeqe+D0Qf98I1CUaVq2bjYmpYfm5w7/5EBaSSLPmxjy3fBLnzgTjfTGUlJRsnnp2HL36tK9T++oZfyKvhaNRa5i54jEMjbWfX1lZGQc+3oe6WI2hkSEzX5qL50+nyEzOID8nn2kvzMLcxqJW3Wtn/Yi6FoFGrWH6P6rqeny8D3WJGgMjA2ascOfcTyfJSs6kIDePR56fXaduZU7+HkigbwRqtYZX183GxKTCL0cO+BAekoiZmTFLlk9qkN6Rw5fw9blJSYmG9W8uxtRUe39mpOfw2Se/AHDh/DV+9XiHQP8wDh7Q7qk3a84Yhrv0rlHTxFifTS+5oNaU4XM1EY/TkQDYWJnw/IJ+IARHzkRyMyKdra+OJK9ATWpGAZ/9ENggm2vir7ZrE0M9tjzWF3VpGd4R6Rz0iy8/19W+Bd8vd8F18wkKSkpZOtYJe0sT4jMK2OkZWbeugR5bH+1BSWkZV6IzORiUBMCEbraMdrLG3tyYj89Fci0hhx0z+1CkKcVQT8XKA0HUtzWkib6KLaM6oy5T8E7I5mB4KgDP929LmxbGWBkbsMUrAjNDfRb30eYydmljyZjdde8lZ2Kox5bFA1FryrhyKw2Py7EArJjZEyeHFuTkl/DxgRukZhchBHz5ykjOXU/i+1MR9dr75vDOqMvK8EnK4XCk1t4lfdrSprkxlkb6/PNKJOoyhRUDHWlmoEdsTiGfBMbW7Yi7Nj+pszkkFY8rcVqbp/fEyaE5OflqPva4QVsbM2aPcMTIQA+v4GR+u1S3tomhHlseH4C6tIwrIWl4eOt0p/XAyb4FOQUlfHzoJm2tmzHbRad7I4XfLtdvc/gFXxKCwylTaxj5rDsGuv4iOyGFwAMnQVGwaG1H/5njubzrN0oKiyjJL2T0iwswNKn9e6Sp+dtEff6GNJpvhBBPCiGuCyGuCSG+F0LYCCF+EUL46l4uunqbhBA7hRCeQogoIcQKncS7QCchxFUhxDYhhKsQ4oIQwgO4KYQwFkJ8I4QIEkIECiHG6PRchRCHdf9vKYQ4IYS4IYT4Cmi0WemYuFReWP3XNp88czqAN7csYsJEZ06fCigvb2ltzvqNT/Di8uk4D+6KmZkJarWG3365gMuIXvXqPjqhLx7HA3l5/R4mj634kps6sR8r1u5my3YPlj3thqIovLphLy+u/p5mpoYIUbd7HpnQh0PHr/HKhp+YNLbCjkcn9uGldXvYuuMwy552BeC1jT8TEZ3aYF9McO3IsbORrH/3HG4jHcvL9x8KYeP754m+nc2vR0IBmDimI0G3GqZ97kwwazY+htuEvnieDiovz8zI49TxaxibGGJt3RyA0WN7sXrDbJ5dNolrAdH1at+4dJ3Zr8yjz6j+BHtdLy9XqVTMeskd99WPU1xUTFlZGa7u45j1kjv93ZyJvBZep+7Ni0HMfHkevUf140Y13RkvufPYqscpKdTqjnYfz4yX3Ok71pmoenQrc+FMMKs3PsaYCX05X8kvWZl5nD1+DRMTQ1rq/NIQzpzyY9OWJUycNITTpyqeg1pam7Nh09O8+I/ZDBrSHTMzU3x8brLy9YW8vvYJLpy/WqvmhJGOHD8fzfodFxg7vGJQ/MzcPuQVqFHKFJLT8+nUzpLo2zls+vdFbKxMsLNp1mC7q/NX2/XEvg4cvZbI2r1XcetlV16urxLMHdaeczdTAOjRxpyBHVtSrC4lNbeoXt1J3Vvx+41k1njcYHw32/LyEyGprDt8kx2nwxnU3hIzI33ySzSs8bhBobqUFsYG9dvc0Zqjkems9QzDrUPL8vL/BN5m/blwDoSlMLS1BRFZBaw/F87u4ESORKTVr+vchqM+8azd6ce4AQ7l5aWlCiWaMjSlCrkFagCeHNeZs9eS6tUEGO9ozfHoNN70CmdsO6vy8q+u32bTxXAORaYyxMGCzCI1my6Gs8ozBEdzkwZpTxzYhqO+t1n7rR/j+leyuaxMZ3MZuQVq/MPTWfuNH6994c24/q0boNuao/7xrN3lz7h+lXUVSkor6UZksHaXP6995VPl79dFtPd1XF9YQKfh/Ym+cq283KJ1K8Yse5wxy58gNTwGgB5CBhgAACAASURBVGGLZjL6+fnYdnEkIzq+FkXJg6ZRBmJCiJ7AemCsoih9gZeAfwMfKIoyCJgNfFXpkm7ARGAwsFEIYQC8AUQqitJPUZRVunoDgJcURekCLAMURVF6A/OBXboUBJXZCHgpitIT+A1o1xjvr7G4O/Cxd2hJSkrWPec9Dlxk6vThAHz37QnmL3Srd7AE4GBnSUKSVq+0rOJx+JOvT/Pem3N5ZuEoLMxNAejqZMen7z1BTm4h9WVVcGhlQUJStla3tKy8/NOvz/Luhtk8vWAE5i1M67WvJuxsm5GcknePzQCGhnq0tW9BdFw2LS1N6NHFmsu+Dcupetdddg6WpKXmlJcnJmTQwtyUF1+eQnJSFvG30wH4cdc5PtruwVCXrg1RB8CylSU56dlVzqTEJvPztt2YNDNBpdI2q+LCYoLOB9JzeM0RoGqyWNhakVtNNzU2mf3bf8DYzLSKbvD5q/SoT7fyn9D9jVb2lqSlVPJLfAbNzU157iWtXxJ0fmmonr2DNSnJmfecP3jgAtOmjwRg8pRhrHzlY17+x4fMnDW6Vk0762YkpWrvibJK90RnR0sOnAzno13+LHu8Pzcj0jE01GPNC0OxtW6GnfWfH4j9VewtjEnKKgSq2rzUzYld5yO5W9LR1ozIlDu853GTMT3tMDKou+u1NzcmSTdgq94+lgx3ZMOk7pwLT+dOsQZDPRU7Fw5AXyXIKVTXb7OZEUl5RffYDGCqr+IRJ1tORmeUl7n3sOfnW/UPmuysTEnKLLjH5s8O3WTlf73xCk7GfXRHOrdugUoliEjIqU2qCq1MjUjOL9bqVuuyTPVVTOpgw6lYrb0DWrXgw7HdCcnMa5C2nZUJSZmF99p8+BYrv/TB60YK7qM6lpcvndSV/V71P7TZWdbiiyO3WPmVTndkhwrdiV3Y7xXTIJvvYmZjRV7mvd8jEV5+tOnbrfy4ICuXtMg4WnXteE/d+4lK3L/Xw0ZjRcTGAvsURUkHUBQlExgHfCKEuIo2SWYLIYSZrv4RRVGKdfVTgVa16PooinL3rh8B/KDTDwFigS7V6o+qVOcIcO9dCgghnhVC+AkhmnxKE+DQwUtse2cvubn5ACQnZdKqlWWVOoqi4OsTyqAh2gYUFhLH3t1nCA6KZv++utN9JCZn4WCn1VNVGrgFXI/ltTf38vNBn/KBWmhEMste/x6VStDGwbJGvXLdlGwc7My1upXu7sCgOFZu3Mc+Dz8SkrNru7xOklPzaWVrprO56rlJYzpy4lwUAIMHONDS0oRlzwxkyIDWOLY1r1Hv6CE/Pnz/IHdytZ1qclI2NrYVdW1szWmhe0o2a25CYUEJAAsWjeat959gz3e1p6PyP+nDoc9/pTBP27Fmp2Zhbl11SrBVezvmrlqIoihkpWRSlF/EgY/3MfmZaRiZ1jwdEHjKhyP/qarbopqubXs75qx8HEUpK9f1+PhnJi6pXbcyxw758XElv6QmZWPTqppfWtzrl9rwOHiB9975ntxcrc3JSem0srOqUkdRFHy9bzJ4SA8AvvrSg292refb7zbw9VeHatVOTs/HzkZ7T1R+AElOyyfnTjEFhWqMDPVQFPhgpx/vfH6F3DvFxCXl1uuHpiIpuwh7C+3nUNnm7q3NWTSqE33bWzDfpQPJ2YXk6HxbWKLBUF+vbt2cIuxaaHVV1R7GvroUw7J9V1ky3JFe9i2Izy7k6d0B3M4upIdd/VHNpLxi7M2M7rHZzECPt0Z34d3LUeSrSwEw1lfRqpkRcQ2I4iVnFmBnZXKPzXef9zJyizE11selZyva2ZrxxLjOTHJug6WZYU1y5aQUFGPXzEinW1HezECPjS6d2e4bTYHO3oCUXF4+c4sBrcwb9IWcnFlYj81FmBppV/A8PaELaTlFDYrkJWcVYGepfUhVVfqWLde9U1xJtzNpuUWcvV63bqinNxd37qc4X9v28tKzMLOq2odHePlxJy2LnhO1D0F5Gdlc/u43Ri6di0pPTg7+XWnKNWIqYKiiKFVasK7hF1cqKq3DjvymMExRlC+AL3T2NGiXOSsLMzatdqdvT0dWLpvO9k8bntNv6vThTJ0+nKOHvXl7y/cUF6lZs34h165GEnorjrnzx+DnE8qAgZ3LO8b3djwPwJtrdzLnsdqjCACHT1zj/Y1zmeDak2Nng/h825O8sOo7xo3uwaSxvWlmasTG937Dztacl5+bgEol0JSWlUe7auPIieu8u2E24117cvzsDT57fyEvrt7NuFHdmTi2F81MDdn0vnZt29qXp9C7Rxu2b36MtVt/pUTXMdbGCc8o3nxtJK7D23PGK5Ztb45l1ZYzAEwa24lX3jwJwNHTkRw9HUlru+Y8/lgvYm7X/BQ9eaozk6c6c+L3QLZt/YXiYjWvrZlF0LUYwkISme0+nBYtTPn3Ng9KNWV07uqAx6/eRIQmkZdXyLTZQ2q1deD4wQwcP5irZ/z57aOfUZeombF8DrE3o0mMTKDn8N6c3XsKRVFQ6akwt7Fg99ZvKdWUcnbPSXqP7odTv+rPDNB/3GD6jxvMtbN+HPz4Z9TFaqYtn0PczWgSI+PpMbwP5346iVKmoKfSw9zGgj1bv6G0tBTPPSfoPao/nWrQrcykqc5MmurMyd8D2fH2L5QUqXl57SyCr8UQHpLITPfhtDA35ZPtHmg0ZTh1rXt6ZNr0kUybPpIjhy/x1uZvKC4uYe36RVy7Gs6tWzHMmz8eX59bDBjYtfxedhs3iI1vagPiLiP61Kp94kIMG1cMx3VoW85ejmXbGldWvePJN/uDeP3ZISgo/OhxC4BNL7mgr68iKDSNzOz6Bwm18VfaNcDxa4lsntOHMT3tOHMjmR1PDOS17/1Z8a32Ge/9hQPYczGagpJSpg5sw7qZvUjPLeZOPZGrY7dS2PJId8Z2seFUaCr/mtWbV38Nwn1AG7rbNae5kT57/eMJT8tj6fAOvPVIDyxNDdjZgDVGx6PS2TyyM2Pat+RMTAY73Lrx2ukQtrl1RV+lYtnAdhyJSONyQjaPOtlwNLL+aUmA437xbHpyIGP6OnA6MJHtzw1h5X+9eWFqd+ytTLFsbsSW7wNIy9F+XkO62dCljTlZeXUP/k/GpLN+mBOj21pxNi6T90Z35fVzobwzqiv6KsFzfdtyLDqdzKIS5na1R08luJGeR1kDevbj/vFsenwAY/rYc/pqItuXDmbllz688Eh37K1MtDbvDsStnwMLxnTi8q1UHFqa8tnhW/XoJrBpYX+dbhLblwxm5Vc+vDClm84Xhmz58Spufe1ZMLoTl0NScbAy5bMjIbVqdnUdQlfXIYRf8OX8f/eiKVEzculckkOjSI+Ox65LBy7vOkB7555c+uYXhj81m2Pv/hcLe1u8fzhIj4kjsXZsU79Tmgi5oWvtNErSb93U5G/AMEVRMoQQVsAnQKCiKNt0dfopinJVCLEJyFMUZbuuPBh4FLgDBCiK0l5X7gqsVBTlUd3xq0BPRVGeEUJ0AU6ijYgNu1tP9+vJVEVRtgohJgO/AzZ3I3W12C6TfuuQSb8rkEm/K5BJvyuQSb8rkEm/K5BJv+vn6Que920ktnOk60M1QdkoETFFUW4IId4GzgkhSoFAYAXwqRDiuu7vnAeer0MjQwhxUTcwOwocqVblM+BzIUQQoAEWK4pSXG0N1WZgjxDiBnAJiGuM9yeRSCQSieTP8zCu3bpfNNrUpKIou4Bd1Yrda6i3qdpxr0r/X1Ctumelc0XAUzXoed6tpyhKBjDhj9gtkUgkEolE8qD4O+0jJpFIJBKJ5H8Q+VOB2pG+kUgkEolEInlAyIiYRCKRSCSSJkX+arJ2ZERMIpFIJBKJ5AEhI2ISiUQikUiaFPmrydpplH3EHmYauqGrRCKRSCT/K9zvfcRevHT2vn3XfjZ8zEM17JMRMYlEIpFIJE2KjIjVjhyIQZPtlH0to/a8en+Wvi2nAuC4uvG1Y96fSsfH9zS6btQP8xm4p/GzDPjP12YZiMjxaHRtJ/NpvOl/stF1twwczztXG3+n7DX9tNvnhWY3/n3R1WIqPqmHG113sO2jtNt2utF141a5AU3Xrptqx/52755qdN24N8YB0HbHmUbXvv3a2CbbsX/Aj43fXwQs0PYXjp94Nrp2zHLXJvPFU+fPNrruN6PGNLqm5M8jF+tLJBKJRCKRPCBkREwikUgkEkmTIqM+tSN9I5FIJBKJRPKAkBExiUQikUgkTYrc0LV2ZERMIpFIJBKJ5AEhI2ISiUQikUiaFLl9Re08NBExIcRIIcQNIcRVIYTJg7ZHIpFIJBKJ5K/yMEXEFgLvKIryQ1P+ERNDPbY81hd1aRneEekc9IsvP9fVvgXfL3fBdfMJCkpKWTrWCXtLE+IzCtjpGVmv9oXjAdwIiEBdomHp6tkYmxhVOf/Fe/sxNjHkyRXTOPjDGVISMomPSWHkhAGMnzmsdpsN9HhrZi/UpQpXIjM4eDWhwma75vywdCij3zuDnkqw/tEetLUyZcEXV+r3hZEeWxY5o9aUcSUkFY9LsQCsmNkLJ4cW5OSX8PGBYAz09fjPyyO4GpGBV3Ayxyv5rDaM9VS84eyEuqwM/9QcjsWmAeBib8nczg5cTMrk5/AkAKZ1aEVXSzPy1Bo+D4qtU/fssQCu+0WiVmtY/sasKj5+esY79B/SGaeubZg8ayjHD3oTGhxHemoOL6yagX0b6zq1Yy76knojjDKNBuen56FvXKHt+9WPpIdFM/n9dQBEnr1EZmQsBRlZWLRrTd/502vVjfTyJTk4nFKNhmFL3DGopHvxiz2khkYxc4dWN+qSP/EBN1Dp6dFrqhsWbexq1fU8FkCQv/Z+e/GNqvfb0pn/pN/gLnTq1ppJM4dx5ogfJw56M+uJMQwe2aNOPwBcOuHPzcAINCWlLF557728c9s+jE2MWLB8Gj9+6kFhfhEFeUUseWMuJqbGNWqaGKjYOq6r9j6+ncWBWykATHCyZrRjSxxaGPHR5RhyijS8MLgdCEFkZj7/8Ymr196mbNc14djOlteXz8C8hSkLnv/wD19vYqBi6/huqMvKuBKXxYGbOl90tmZ0h5Y4tDDmo0vRBCbmAvD2hK4UqEt5+2xE/dr6Kt5264K6TOHy7WwOhOi0O1nj6miFfXNjPvKOITApl3fGdWGggzkTvvNtkO6WUZ1Rlyl4J2RzMDwVgOf7t6VNC2OsjA3Y4hWBmaE+i/u0BsCljSVjdvvUqWusp2LNICfUpdq+4mjlvqKLAxcTK/UVHXV9RUn9fcVdm98a3Vl7zyVmczBMZ/OAtrRtYYylsQFbLkSQV1LK+25dySnSkKcu5e2Ldd8XDfVFXkkp74/tSnaxhvySUt6+VLtumrcPOSGhlGk0dHp8AXpGFW0uZv+vlJWUoDI0xHHOLOIOHqIgKQn9Zs1oO/URFI2GkE8/x6xjRyx6dK/XL03BQxP1eQD8ad8IIRyFECFCiG+FEGFCiN1CiHFCiItCiHAhxGAhRDMhxE4hhI8QIlAIMb3StReEEAG613BduasQwlMIsV+nvVtoWQLMBd7SlbkKIQ5XsuUTIcRi3f9jhBCbdbpBQohuf+R9TezrwNFriazdexW3XhVfcvoqwdxh7Tmn6xB7tDFnYMeWFKtLSc0tapC2z7kgnl8zl+Fu/fD2DKpy7vKZa3Tq3rb8ePrjY3n29TlY2bRg+Lh+depO6mXH0aAk1vxynXE9WlWx2X1QO86FajuBO0UaXt9/newCdYPsnejclqO+t1m705dx/VuXl5eWlVGiKUNTWkauTqugSIOJkR6JGQUN0h7b1prTt9N52zeCUa1blpdfTMriu5CKL0lLIwPGt7OhUFNKemFJvbqXPYN5af1jjBzXl4tng6ucMzE1pKRYjXUrc+37mz6EFeseY9KMIUSEJNQkV4UE32sMfnYhbYcO4Lbv1SrnBi1ZQHN72/LjTmOGM2jJfJrb29Jh9NA6deN8ruPy/AI6DOtPrM+1Kudcnp2PuUPFZxp75Sojnl/IwAXTCD5c92aoVzyD+Me6uYwY149LZ6veb8Ymd31hAcDYR5wZN21QnXqV8TsfzJLX3Rkyti9+56pq+5y9RsdK9/KCZdN4ZvVcnHq2JzY8sVbNSZ1t+T00lTdOhDCuU8Wg+EREOutOhbLdK4rBbSyIyipg1fEQVh27RX+7Fg2ytynbdU3ExKXywuov/vT1k7rofHEshHFONuXlJ8LTWXcilO3nIxncRvvZTelqy/Xk3AZrT+5sw+/habx+MpTxnSra3onIdNaeDmP7xSgGt9a2kTWnwojKbFibntjRmqOR6az1DMOtQ4XufwJvs/5cOAfCUhja2oKIrALWnwtnd3AiRyLS6tUd29aaU3HpbK2pr7hVta+Y0M6GIk0p6UX19xUAk3Q2r/EMY5xjJZsDbrPOM5wDoSkMa22BfXMjbqblscYzDHszw0bzhb2ZETfS81jrGYZdPboZAYE4LXoCa+eBZAQElpcXZ2SilJbSccE8FKWM4sxMhJ4Klb4+Kj0V+iamAKiMjCgrKcHIyqpBvpHcP/7qINUJ2AF0070WACOAlcBaYB1wRlGUwcAYYJsQohmQCoxXFGUA4A58VEmzP/Ay0APoCLgoivIV4AGsUhRlYQPsStdpf66zpQpCiGeFEH5CCL/q5+wtjEnKKgSgrKziVx5L3ZzYdT6SuyUdbc2ITLnDex43GdPTDiOD+l0phHaS3NrOkszUnPLy7Mw7RIcl0HtQ5yr1M9NyMDQ0oFnzumdi7cyNSczWfmmUVrZ5VEe+vRjNn00namdlQpJuYFVZ9zOPm6z87xW8gpNxd+1IQno+7ltPs/4bP5bP6NkgbVsTQ1IKigEoq8PA1mbG5Jao+eR6DPbNjGhtVnM0pRzdOgRbe0vSU7KrnPr4h1d4eYM7Hnu9ysu++eR3Du+7RPfe7RtkN0AzaysKM7LrrVdaoiY/LYMWlQZSderaWFGQkVVnnV7T3Liy82dCjp+nJK+eL0jd/WZrb0lGpfsN4N8/vMqKDe4c2nuhQbbdq639x9rOksy0Cl/kZN4hJiyBngO7VKmenZFLdMhtuvRyrFXS3syIpDs13xNLnduyYUxnPKMzysumdrXlfGxmg8xtynbdFNg3NyLpjrZN3+OLQe3Y4NYFz6gMrE0N6WlrxsWYuu+bKtpmRiTq/FxarektHdiWN12d8IxpmF+r6ybl6Wwuqypsqq/iESdbTlb6/Nx72PPzraR6dVuZNryvyClR8/E1bV/Rpr6+ArAzMyJRZ3NpNW1TA63NJ6IziMspok+r5nzzaG/CGzAwbagv4nKL6GPbnJ2P9CYiq2Ht2ahlS0qyKj7v4uwsjKwsteesrCjOyqLNlMl0WfI05j16kHLBC6OWLenzxmo6Pb6Q24eP1Gt/U6AS9+/1sPFXe5loRVGCFEUpA24ApxVtFvEgwBGYALwhhLgKeALGQDvAAPhSCBEE7EM76LqLj6Io8TrNqzqdP8qvun/9a7peUZQvFEVxVhTFufq5pOwi7C20DfjuwAmge2tzFo3qRN/2Fsx36UBydiE5BdqnrsISDYb6erUac+53P7754AB5d7QNLT0lCytb8/LzNwMiyc3KY//XJwgOiCQxTvuUePawD66P1B+lSM4pwt5ca7Oq0ifaw8GcRS6O9G1rwYIhDR9olOtmFmJnZXqP7t3+KiO3GFMjg/LyopLSBmunFpZga6oNrauoveWkFRaTU6IB4I66FNNa/Hz6iB//3XGQvFztl21aclZ5tOcuKpUKPT0VBkb6lJWVAfDU8iksWjaZ4x61T49En/cmYNd+SvK12gXpWZi0tKi1/l1uewfSZlDt0cyIc954f7ufknztfZGfnoVpS8s6NW2cHBn+7Hw6jRpMM+ua65454seX/zpAvu5+S0vOomWl+w0q+cKwwhcN4cJRX77/928U3NH6Ij0lGyubCl/cCowkN/sOv31zgpsBESTFpZKZls2ezw6x6NXZqPRq73KS8oqxa669Jyq3PYAv/W6z7FAwS5zbAdpBWGtzY3Zfqz3CVkW7Cdp1U5J0pw5f+Max7GAQSwa3Y0g7C1o2M+Qllw4Ma2dJB8v6l88m5RVjb3a37VXlS//bvHj4BksHtL33wj+gW9lmMwM93hrdhXcvR5Gv1vYRxvoqWjUzIq4BUceUghJamdbsi8qkFRaTW6zrK0pKMWnAZ5ecV4x9M50vqtm8tZLNYx2tOBaZzlOHg2jVzBBzo7pX9TTUF2PbW3EsKp2njwRha1qzbuqly0Tt/QmNrp8ozszE0LKi7RtZWFKcpX0YKsnMwsjSEqHrsA2bN6e0uKjcBj2j+qN5kvvPX10jVlzp/2WVjst02qXAbEVRQitfJITYBKQAfdH2BZVbY2XN0lps1FC1D6n+6HNXo7bra+X4tUQ2z+nDmJ52nLmRzI4nBvLa9/6s+FYbPHt/4QD2XIymoKSUqQPbsG5mL9Jzi7lTWPt03+gpzoye4syF4wF88f5+SorVLFk5i9CgGGLCEpg424Xh4/qRmpTJsX1eOLSzQVEUbl2LZvZT4+u1+VhwMptn9GJMN1tO30zlX+79ePWnq/zjxwAAtj3Wlx+9teslts7sTQ+HFqx9pDv/PHKrbl/43WbTkwMZ08+e04GJbH9uKCv/e4UXpvbAvqUJls2N2PJdAM5dbJg1whFjIz0OXa5/XQbAmdvprB7YiRH2VpxPzGDL0C68eSWM3i2bs7Bra5ob6pNeWMKZ+AxySzS80q8D+ioV4dn5Neq5PeKM2yPOnD0WwCfv/kJJkZoXX5/JresxRIYm0G9QZ/Z9p83Z1mdAJ1QqFfu+O0t6SjZ5uYXMe2ZcrbZ2GDWEDqOGEHPRF7+v96ApUeP8lDvpYVFkxcTTecIorv/kQVZMPL5f7WHAojnoGRhw2zuAYSuerlXXafQQnEYPIdLLl0tf7qW0RM3QZ+aSGhZFRnQ83SeOwn+PB5kx8Vz6Yg9DnppDYlAYt/2D0BQV4/zEzBp1xz7izNhHnPE8FsBn7+6nuFjNC6tnEXI9hsiwBPoO6swv32lzD/Ye6IRKpcLnwk3OHPHD0MgAE1NDeg90qlF75ORBjJw8iEsn/Plmu/ZeXvTqLMKDtffy+FkuDHXrR1pSJid/8cK+nS3rn/4Xdm1t+Ok/h3GbMZz2nVvXqH0sPJUtbl0Z27ElpyPT+WByD145epN5ve3pbmNGcyN99lxPpKetGetcnTgdmcEGVyfe8qx/XVRTtOu6sLIwY9Nqd/r2dGTlsuls//TgH7r+WFgqW8Z3ZWwna05HpPHBIz145chN5vVxoLutzhfXEvGNz+ZISCptWhizaGAbonVRv7o4Gp7GW2O7MLZjS05FZfDhpO68fOwW83pp/dzCSJ89QdoB7iqXDvS0bc4/3bqwyTOckuohtEocj0pn88jOjGnfkjMxGexw68Zrp0PY5tYVfZWKZQPbcSQijcsJ2TzqZMPRyPqnJUHbV7zu3IkRDlZcSLi3r2hRva/oX3dfUcXPUelsHtWZMY4tOR2Twb/GdePVU1qbDSrZ7JuUw5aRnenXqjn6KhU5ugHfX/WFb1IOm0d1pn+r5hjUoms7fBi2w4eR5u1D5Pe7KVOr6bhwPrmRkeTH3cZ+jCtCT4/on35G6BtgZGXF7SO/U5KZhTovj47z55ETFk7a5SuUlpRgM2Qw7NvfIN83JkLuI1YrQvmT81ZCCEfgsKIovXTH3+qO9989h3Y6sQXwD0VRFCFEf0VRAoUQHwDxiqLsEEI8BezUnhauwEpFUR7VaX4C+CmK8m01/bbABaArYAIEApt19WIAZ0VR0oUQzsB2RVFc63gfikz6rUUm/a5AJv2uQCb9rkAm/a5AJv2u4GFM+q0oyn2dxHvd9/R9G4m9N8jtoZqgbOpfTb4FfAhcF0KogGjgUeAz4BchxJPAMaD+R5dKKIpyWwjxMxCs0wys5xKJRCKRSCQPiIdx7db94k8PxBRFiQF6VTpeXMu552q4NhzoU6nodV25J9q1ZHfrLa9JX3e8Glhdg7Zjpf/7Aa71vReJRCKRSCSSB8HDtI+YRCKRSCSShxC5j1jtSN9IJBKJRCKRPCDkQEwikUgkEonkASGnJiUSiUQikTQpKrl9Ra3IiJhEIpFIJBLJA+JP7yP2v4KQu8xJJBKJ5P8Z93sfsY0Bp+7bd+3mAeMeqs0y5NQk0P7No42uGbtlMofjfm903UfbTQFoss0fm2qDzU6fn2t03cgXRgPwYfDxRtd+uddE/hXU+Buvvtp7Au9da3zd1/tqN3T9KbLx72X3TpM5Gd/4+enGt3mE9hsav43EvqVtI03Vrpuq7TXVRrEAPXY2fvu7+fRo2m861ui6sZsmMeqQV/0V/yDnp44Amm5z26bSnXWq8Te3/XXcqEbXlPx55EBMIpFIJBJJkyI3dK0duUZMIpFIJBKJ5AEhI2ISiUQikUiaFL0HbcDfGBkRk0gkEolEInlAyIiYRCKRSCSSJkXuI1Y7MiImkUgkEolE8oB44AMxIcQ0IcQbDai3TQhxQwix7X7YJZFIJBKJpHFQifv3eth44FOTiqJ4AB4NqPosYKUoSmlT2mNioMfWR3tQUlrGlehMDgYl8X/snXd8VUX6/99z025Cem+EQBq9EzqEXkRERIKgwiq6KrqrLrsWxBXFDqur7rqLrh1RQAWV3kIPEAgpkN4gvYf03HJ+f9xLGmm4Sfjy23m/XveVe2bmfM6T587MnfPMuTMAM/u6MtnfGQ87NR8eTSEqq4yNdw+mRqvD3ETF6h0xtLc27vlD50m+mIROo+WeP96LhaUFAHq9nu1/34a2VoOZ2px7n17M3i/3kH8lD0sbK2bePws7Z7s2bFaxfkZfNHo94VdK2HE5z2BzgDOTezvhaavmg1NpRGZfA+D1mUFUaXS8fiS5HV+oWD89CI1OIfxqCTvijLr+zkz2dcLT1oIPTqdTWBh6IAAAIABJREFUVqPl8WAfEIKU4kr+dfZK+342VbFuYgAavZ4z2WX8nJQPwO+H9sTbVo2D2pT1J1PIraxj5RBvPKwtyCyv4fPorHa1E49FkBWbiE6jZfLvQzFTG/xckpVH5E8HQVGw93Jj+MIZnPpyB3VVNdRWVjFl1VLMLdWt6iYdP0dWbBJ6jZaJjzbolmblEbnjgFHXnWF3z+D0lz9RV11DXWU1k59oW/c6KcfPkX3JoD/ukab60TsN+nZe7gxZMKNdLYCoIxGkRSej1WiZ/+S9mKsb6tsvH21DU6fB3MKM+U+Fknj2EuG/HCdwVH/GzG9/jaFzh86TGGnQXvL0oiZ1+bv3t6Op1WCuNuO+ZxZTVlTGvm8PgaIwYuow/Ab2aVGzoe0phKcXszM6GzC2vQAXQ9sLSyEu7xpvzh9IRa2WvPJaPjqa0q69XdWuu6rttYavjyvPPbkAO1srlj72/m/SsDRVsXZsABqdnrO5ZexKNbS9lYN64mWjxsHClDfPpFCp0fFcsB/eNmp+tze6fV0zE9bP7Ued3vj5XfdxkNHHtmo+PJZCVHYZGxcMokarx1wlWL0zlpZcrDZR8cxAP7SKnsiiaxzMKgCgt40Vy/y9AdicnElGeRUvDA2gTq/HVKh4KyqJ+/y88LBSY2duyoeX0iioqWvVF69PC0SjVzh9tZQd8cbPz8+ZEF9HPGzUfHAmnbIaDY+P6oUAUkqq+Phc231cZ+vqa2vJ/u4bhKkpPQKCsA8eA0BNdhYF+wxr8bnMmova04vcHdvR19WhMjfHfcEiCvbtpq6oEF1FBR73LsHMwbFN2yXdT5dGxIQQvkKIeCHEF0KIRCHEZiHEdCHESSFEkhAiWAixQgjxkbH8F0KID4QQp4QQqUKIRcb0nwFr4LwQItRYblGj61QY/4YIIcKEENuN190shLip8fHsfm7svpTLCz9fYkZf1/r0/fH5rPn1MhsPJTGqlwPWFqZU1ml54edLVGt02KrN2tWOPRlN6J+WMCRkGDEnGjo2lUrF4mdCWfr8/dRW16LX6zExMcHEzBQTExMsrS3btjnQld0J+Ty/N57p/i4NNicVsmZ/AhuOpRDsbQ/A3CBXonOvdcwXAUbd/fFM93Nu0E0uZM3BBDacSCXY257Ukir+vC+eP++NY5i7bYe0Z/V2Zm9qAWuOJjGtV0PH8O+LV1l7LImdifmM8bSnv1MPRrjbUqPVk1/ZcmfanLQzUUx5Yin+44eTGh5Vn+7g5cbUJ5cx9an7yUtKB2Dc8gWEPL4E9yBfCtMy29GNJuTxpfiNG0ZaI117LzemrLqfKU8+QL5Rd+zyu5n82H24BvpS1I7uddLPRjPxsaX0HjeM9DNN9Sc9cT+TVj1AgVG/I8SdimHBH5cwaOJQLp9sWt/u+kMoi1Y31LfA4AFMvHdah7WjjsewbHUow0OGcvF4U+2lzy5m+QvLqK2uQ6/Xc3BrGGorC4RKYO9s36rm7P5u7L6cxws/xzIjqFnb++USGw8a2p6/szWphZWs/fUyrtYWeNi2P8jtqnbdVW2vNdKv5PP4Xzb9VxrTezmzP72Av55KYopPQ9v7NOYq604l8UtKPsEe9lRodKw9mUhpraZDurP7ubI7Lo8XfrnEjMBGvkjIZ82uy2w8YvSxudHHv1yiWtu6jye5O3E0p5B3o1MY79Zg56LeHrwfk8J7MSks9PXAytSEaq2Od6NTqNXpsDYz5duULDbGpHAgq4BhTq3fxM4JcGF3UgHPHUhghp9Tg80phbx4KJENJ1MJ9rIjtaSaP++PZ/X+eIZ2oI/rbN2yixewHTYCr2XLuRZ9sT696MhBPEOX4blkGUVhh6grLkLR6fBcvBT0CnXFxbjMmovX0gexDx5DRWJCu7Z3FTIi1jrdMTXpD2wE+hpfS4EJwGrgxRbKexjz5wFvASiKMh+oVhRlqKIo37dzvWHA00B/oA8wvnkBIcSjQogIIUTEDRe3U5NzrQYAnb7pfdrKcb6snd2Po0mFlNdqMTdR8dmy4ZiqBGXVHemsDDXEwdWB0oLSJjm5Gbl8+/ZmLK0tUalUTFs6nWXP30/giCDC95xuU9XDxoKccoPN+ma374+M8mHttEDCUotwtjJngKs1J9NLOmAreFhbkFNe27LuyJ6snRJAWFpRfdqdQa4cyyjukLa7tQU5FQZtXbPbYStTFXP9XDiQXkRveytSSqp490waU3o5YmHSgSprHHvbuDhSUVR6Q3bSifP0HNqv/riq5Br5yVdxD2o5UtMcaxdHKopv9GHyiQi8h/RtoluQcgW3Dupev2WwdnakqgX9lJMReDXSb1/Q8MfO1ZGywqZ+yM/I5YcN32BpbYVK9Ru6AaO2o5sDpYVlTbJy0nP56q3NWPYw1OWc9FxGzxjFHctns/eb1ncV8LBVk1NWDYBOaaHtzenL0aQCLuVew9xUxUuz++JmY4F7BwZiXdWuu6rtdSVuVhbkVhrbtb5pnpWpitm9XTiUUdTCmW1j+PyMPm7WpleO7cXaWX05mtzIx/cNx1SloqymZR+7qM3JN0ayGvu2h6kpFVodlVodVqYmVGp1mKlUvDWqHyYqFeUaLQCWJiqmeDhzPLf1PsnD2oLs8pb7oUdG9OTlEH/C0hvOvzPIleMd6OM6W1dbUlIfyRKN2quuuhoTKytMLK3Q19SgLS2tL2fm4Ii21FDfdDU1lF2IwHbIsHZtl3Q/3TEQS1MUJUZRFD1wCTikGDa4jAF8Wyi/Q1EUvaIolwG333C9s4qiZBqvd7GlayiKsklRlJGKooxsnpdTVlPfsauaBdM+PZXOqm0XWTnOl4EetmSWVvPQ5gtcLa2mv7tNqwad23+Wn/75I9UVVQCU5pdg79I0MuDey52lzy1D0SsU5xXXfzla21tTV912JCinvBZ3G8PUUPMA4CfnrrBqZwwrg30Y7WOPUw9z/ji+N2N9HOjt0HakLaeiDd2Iq6z6JZaVI30AQ0fiZadmc1R2m5rXya2oxb2HQbvxHYy1mQmvTgrgnfA0KjU6citrKaszdKzVWj3mJq3f7sQfOcOJ//xArdHP5QXFWDs19XPSifOU5xczcJZhu5OKolJOfbWDSY/ei6qVQV5C2BlOfrad2kqDbkVhCdaODk3KJJ+IoLyghAGzJtbrnv7qJyY+srhV3Xqbjp4h/PPt9XZXFJZg1Uw/5WQEFQUl9Js5sU0tgMiDZ9n9rx+pMeqVFZRg1ywS5drLnXtW34+i11Oa17HBM8CZ/efY/tFPVFcYBkwl+SXYN5s29/B158Hnl6EoCkW5xTi42GFlY4mFpQUa42fZEjnXGre9pnmfnkpn1daLrBzXG0WBjYeSWL83nrIaLVdKqtq1uyvaNXRd2+tK8qpqcbO6bnNDeg8zE9aODWBjRBpV2pt/AqTNz+90Bqu2RbFyzHUf1/DQFqOP3Vr2cUFNHS5qc4OdjdIrtVp6mJpgZWpClVZHoJ01udW1PH8ujpyqGvxte2BlasKzg/z4OC6dal3r/0tORS0e1sZ+qFneJ+ev8sSvl3hkeE/A0Md526r5Jrr9Pq6zdU0dHNAYB1WN58pNLC3RVVehq65GpVZjam+PpsRQTlNSjKm9A7rqarK/+wa3BYswUbd/09JVmIjue91udMczYrWN3usbHetbuX7j8q25VIuxfgshVIB5K+frWrlGq+yNy+PVO/oxNdCFgwn5/G3hIJ79MYbQ4d70c7fBxsKU785nklRQwSPjevPaHf1xsDLjs9MZrWqOmhnMqJnBnD90nu3vb0VTp2HhU4tIv5xGVlIWA8cP4tCWgyiKgspEhb2LPQe/PUBpfgmV1yq5e9XCtm1OzOfVGUFM9XPmUHIB793Rn2d2XWbJYE/6uVpjY2HKlqhszmWWsis+H29bNctHeJNWUt22blI+r04LYmofJw6lFPLenP48s+cySwZ50M/FqBudzQBXa9aE+HMopYi1If68Ftb+8y/70gr56wR/Qno5cjijmA1Tg1h9OIG3pwRhphI8Prwnu1MKCc8u5U5/V14c24fCqjrK61rvVPtOGQ1TRpN4LIKj//4ebV0dkx5ZTG5CGoWpV3EL6s2pL3bQa+QATn7+I+N/t5A9b27CztOF01//zMBZE3Du7X2DblDIaIJCRpN0/BzH/v0d2joNEx9ZTG5CKoVpmbgH9ub0lwbdU5//wLjf3cPet/6NvYcrZ77ZSf9ZE3H2vVH3OgGTRxMweTQpx89xctN36Oo0jF25mLyEVIrSM3EN7M3Zr3bgM2IA4V/8wJgV97Tp22HTgxk2PZioIxH8/OFWtHUa5q1axJW4NHJSMuk3djDHvj9grG8m2LrYcyUujZM/hlFTUYWNoy0DJgxtUXv0zFGMnjmKc4fOs+U9w3NmoX+4h9RL6WQmZzJ4/CD2f9tQlx1c7Zm6KISfNv2CQDBx/rhW7d57OY9X7+jP1EBXDiYU8LeFg3n2x+iGtqc247vzVwF4bV5/TFUqorPKKOrAlHVXtGvourbXGo721rzyl1CGDPBl9aq72PCPnTetcTCjkDVj/JnU05Gwq8W8OTGIF44n8PrEIMyE4NHBPdmXXsiZnFJeHutPX0drVo/qzYZzaW37Ii6fV+f0Y2qgMwcTC/jbgkE8uyOG0GFeDT6+YPTxWF9em9sPBytzPgtv2cfHcot4emAfxro6cCq/hDVDA3j9YhLb03L4o/E5wy0pWWRX1hDax5NnBvbBztyM7WnZrBkaiKlK8EBAT45kFxJZVNbiNfYkFfDa1ECm9nHiYGoR78/ux9N741gy0NDH2VqYsiUmmwEu1rw0yY9DqUWsnezPa0fb7uM6W9du6HCyv99MRWw0NoOGcPWLT+m5YiVOU6aT8/0WAJxnzsbc0QlhYkLO9u8RpqaYOzpy5d//QNHpKNi7C7sRI7EO6tfiNSS3DqG094T5fyMuhC/wq6IoA43HXxiPt1/PAzYAIxVFebJxvrF8haIo1i28fwmwURTlOSHEAuAnRVGEECIEWK0oyjxjuY+ACEVRvmjDRkVu+m1AbvrdgNz0uwG56Xcjbbnpdz1y0+8GbsdNvxVF6dbY0caYA922kNifBs24reJit/xXk7+RT4CdQogoYC9QeYvtkUgkEolE0gq340P03UWXDsQURUkHBjY6XtFK3hfN843H1q28zwPGNCr6nDE9DAhrVO7J/+ofkEgkEolEIulCbteImEQikUgkktsEucVR69zylfUlEolEIpFI/leRETGJRCKRSCRdinxGrHVkREwikUgkEonkFiEHYhKJRCKRSLoUk258/TcIIRyFEAeM2zAeEEI4tFBmqBDitBDikhAiWggR2ijvCyFEmhDiovHV8qKMjfW6ch2x2wEh5BOEEolEIvnforvXEfvn5f3d9l37RP+Zv/l/E0K8AxQrivKWEOJ5wEFRlOealQkEFEVRkoQQnsB5oJ+iKKXN10PtCPIZMaD3P8I6XTNtVQh/PtP5i6O+O9qwMbP/vzp/gcbkxyYz8MvOXzwwdvkken0Y1um6GU+FAPDI8SOdrv3JxCn8/kTnL9D47wlTeSAsrNN1vw4JAeBP4Z1f5zaOmcbrkZ2/CO2aYTMZtrnz61vksklA17Xrrlq4s6sWXQW6bLHYrlrc9h+XOn+R5lUDZgHg83rn1+Ura2bi81oX6K6d2WWLKXc3t9EzYncBIcb3X2JYEqvJQExRlMRG77OFEPmAC3Dj5sYdQE5NSiQSiUQi+f8GIcSjQoiIRq9Hb+J0N0VRcozvc2lnz2shRDCGbRZTGiW/bpyyfE8IYdHeBWVETCKRSCQSSZfSneuIKYqyCdjUWr4Q4iDg3kLWmmY6SluPLwkhPICvgeWKouiNyS9gGMCZG214Dni1LXvlQEwikUgkEsn/DIqiTG8tTwiRJ4TwUBQlxzjQym+lnC2wC1ijKEp4I+3r0bRaIcTnwOr27JFTkxKJRCKRSLoUE9F9r/+Sn4HlxvfLgZ3NCwghzIGfgK+aP5RvHLwhhBDAAiC2vQvKgZhEIpFIJBKJgbeAGUKIJGC68RghxEghxKfGMouBScCKFpap2CyEiAFiAGdgfXsXlFOTEolEIpFIupTb5VeTiqIUAdNaSI8AVhrffwN808r5U2/2mrdFREwIYSGEOGgcdYa2f4ZEIpFIJBLJ/31ul4jYMABFUdpdofa/xdJUxauTAtDoFc5klbIzyfCc3mPDeuJtq8ZRbcarJ5KxNjdlxWAvAMZ7OzBl89l2tTNPnaUwLhGdRsuQFfdhqm74VevFzzZTnJTK1DfXApATcZH8mMtUF5UQuGAOjv592rT5lQkBaPR6zmaX8XOyweZHh/akp40aB7Up60+lYCIEH88awMX8a5zMLGVfWuFN+WXNaH80ej0RuWXsSisA4OGB3nhZq7G3MOPtcynkVdV1WG99SAB1OoXwrFJ2Jhpsfnx4T3raqnGwNGPdsWS0eoVXJvlTUqMhvrCSzZdy2tTNCz9LaXwCilZLwANLMbFo8HHq9h/R19WhMjenz6KF5Bw/QXlaBrXFxfTo6U2fe+5uUa8kPgG9RkvQg031Urb9hK6uDhNzc/zuvZui6BhyTpwGwHPSBBwH9ifhy82UJacQ/NrLN2gXnjnDtQSDrb7LljXRvvJjg60+CxcCUJWZSdx77zH09dcxUavJ3rePupISLJycWvRF5qmzFMUlotdqGbTiPkwb6Ud9vpmSpFRC3jDUt6Rf9lFVWEhdeQUD71+MpeMNi0k3IfXEOXIvJaHTaBmzMhSzRnX59KYt5CemctcGww+Q0k6dJ/PCJVSmJgyYNw1775Z+rARqExUvjDLUsfP5ZexJN9Sx8Z4OhAZ6cjK7mO8Tc3BUm/HYoF71eYt3XaBSq2vT3q5q15amKl6fFohGr3D6aik74vMAmOnnTIivIx42aj44k05kzjXenB7ICE87Zn51rk3N67prxwag0ek5m1vGrlSDvSsH9cTLRo2DhSlvnkmhUqPjuWA/vG3U/G5vdLu6beHr48pzTy7AztaKpY+9f9PnW5qpWD+jLxq9nvArJey4bPRFgDOTezvhaavmg1NpRGZfA+D1mUFUaXS8fiS5Xe34YxFkxiSi02qZ+vuG+laSlUfEjwdRUHD0cmPkwhkAZETGcWTTVlZ8/Nf2bZ7VD43OaPOlXIPNgS5M7uOMp60FH5xIIzK7jMWDPRngbkN5rZYNR1Pa151j1M0oYUdsI12/RrpZZSwe0kg3rG1dgHOHzpMYmYxWo2XJ04uwsDT4Qq/X893729HUajBXm3HfM4spKypj37eHQFEYMXUYfgNb/x6R3Do6PSImhPAVQsQbl/lPFEJsFkJMF0KcNG4ZEGx8nRZCRAohTgkhgoznPiOE+Mz4fpAQIlYI4YMhBDjKGBHzE0KkCyGcjeVGCiHCjO9fEUJ8JoQIE0KkCiH+cLP2z+rjzJ6UQl4MS2Ra74YvuH9FXuWlo0nsSMxjjJc9ySVVvHQ0ic2x2exKLuiQdk5EFEMfvh+v0SPIibjYJG/oQ8uw9mhYrsRj5FCG/G4pfRfNpyih7Y5qZm9n9qUV8NKxJKb6Otanb7p4lbXHk9iZlM9oT3sAKjU61KYmZFfUdMjm60zzceZARiHrTicT0rPBL/+JzeTV8GR+Tc1nlLt9h/Vm+zmzO7mQF44kMqORnz++cJUXw5LYkZDHWG97RnracSi9iLVHkxnf0wHTduLbhRciCVrxAC6jRlB4PrI+vaaoGEWnw3/pEhS9npriYjwmTiDwwWVYurvhPn5si3oFFy7Sd8UDuI4aQUELeoHLQlEUg15JfCL+S+4lYGkoRdGG5zODli/Dyr3lZWhKIiPp8+CDOI4cSfGFC/XptUVFKDodvkuWgF5PbXExeq2W/BMnsB84EIDKK1eoSE5GZWaGuZ1di/q556MY8vD9eAbfWN+G/K5pfQu4cxZDfrcM73GjKYxLbC51A1fORjPu90vxHTuMK2ejmuSNffQ+7DwbtDPOXGT8Y8sYft98Lv/a+oKz03o6c+hqIevPJjPZq6FOnMwu4cvLmfXHxTUa3jiXzMfRGUTklbU7CIOua9dzAlzYnVTAcwcSmOHXoLs/pZAXDyWy4WQqwV6Gz+eFg4mkFle1qwkwvZcz+9ML+OupJKb4NLTpT2Ousu5UEr+k5BPsYU+FRsfak4mU1mo6pNsW6Vfyefwvrf7iv11mB7qyOyGf5/fGM93fpT59f1Iha/YnsOFYCsHehj5ibpAr0bnXOqydEh7F9FVLCRg3nOTwhvrm4OXGjKeWMfOp+8lNTAegtrKKzNgkXHp7t29zkBu74/N4fk8c0wMa2ZxYwJq9cWw4mkJwT3ucrMyY19+Nqjod+RW17ev2dWN3XB7P72pBd08cG8Ja0C1vXxcg6ngMy1aHMjxkKBePNwy+VSoVS59dzPIXllFbXYder+fg1jDUVhYIlcDeueP9c1egEt33ut3oqqlJf2Aj0Nf4WgpMwPAzzheBeGCioijDgJeBN4zn/R3wF0LcDXwO/F5RlCsY5mWPK4oyVFGU9m4Z+gKzgGDgr0IIs5sx3MPaghzjIEWvb7p8iJWpijv8XTmQVlSfFtrfg61xbUdp6jFWECtnR6qLS9otnrz7ILGbt+E2eECb5dx7WJBj7Bz0+qZ5VqYq5vq5cDC9iKyKWu77OYqXjyexanivjtlsxM3KnNxK4zWabYtlaapilq8zh68UtXRqizT2s66ZnpWZwc/7U4s4kl7MQBcb1ozvg52FKQ7qtoO4hh+qgIWTE7UlDT6uLSnBwhjlUTs5Umv0v16joaagECv3lqM011E7O1Fb0rBocm1JaYOeoyO1xaW4BY/k0sebiP3oX3hMHNe+E67b6uhIXWmDdl1pKeYOBm1zR0fqSkrIOXAA96kNjx7U5Oai9vDA5557KImJqf+/m+ob/lg6O1LTgfqmrakh++x5PIYP6YDthj/WLo5UFbWtPeDOaZz5fCsJ+49RW9n6QMS1UR1rXidaYn4fN35Ny2vfVrquXXtYW5Bdft3mpnmPjOjJyyH+hKUXd8jGxrhZWTS0txba9OzeLhzK6Hh76w48bCzIKTf6uNnn98goH9ZOCyQstQhnK3MGuFpzMr39Onmd6/Xb1tWRiqIbFy9POH4en6H9ADi7bT8jFtzwiE/rNl9rxebgXqydbrDZx96K0motb4cl42VriY+95W/XHd2LtTMDCUspwsfBitIaLW8fScbLzhIfh7Z1gfq25+jmQGlhWZOsnPRcvnprM5Y9LFGpVOSk5zJ6xijuWD6bvd90/sr/ks6hqwZiaYqixBgXOLsEHFIMm1rGAL6AHbBNCBELvAcMADCWX4FhgbSjiqKc/A3X3qUoSq2iKIUY1v+4IRzReNXd5nk5FbV4WFtcL1efbm1mwmuTA3nrdCqVGsMduNpUhVsPC65cazu6dOVEODHfbEVj/BKqKipud+oHwH/udEY9uZLkPW1vW5NbWYt7j+s2N6Rbm5mwbmIAb4en1dsMUKPVN5dol7yqOtysjNeg4SI9zExYO9qfv51Po6oDkYnr5FTU4m70s4qmfl4/OZA3Txn8XKvT89qJFF4/mUqVRkdhVct3/rmnTpO85Xs0VQYf1xYVY+HQ4GMLBwdqiw0deE1xw6CsIOI8ziOG3aBnY2ND0rdb0Rr1aoqKsXCwb6RnXz/QqykuxsLRnoxdexn23J8Y9vyfyNjd+hYtBadPk/H99/XatcXFmNs3aJvb21Nn1K4rLsbcwYGqq1fJO3KEivR08o8dw9zBAVMrKwBMzM2b1NWrJ8KJbVTfqouKUbdT3zTV1UR/sYX+oXdjaqlutVzK0TOc/WI7dUbtysISrJza1nbx92XsI/fRZ2IwPdoom9+ojqlaGlg2Y6SbHefyytotB13TrpvrNu9MPzl/lSd+vcQjw3t2yMbG5FXVNrS3Rq7oYWbC2rEBbIy4ufbWHeSU1+Juc6OPAT45d4VVO2NYGezDaB97nHqY88fxvRnr40DvNgYfcUfOcPQ/P1BjrG/lBcVYOzWN7CQcP095QTGDZ09AU1NLWW4BZ7bupSA9i/iwtqeBc8prcbdVt2zz2QxW/RTNymAfcstrKKsx9D3XajX0MG97a2mDL1rRPZPBqh+iWTnGqFvdSNesdd0z+8+x/aOfqK6oBqAkvwR756bRcA9fdx58fhmKolCUW4yDix1WNpZYWFqgqdO2aXNXIyNirdNVA7HGMVZ9o2M9hufSXgOOKIoyELgTaNzzBwAVgGcb+loabG/+rdH42jpaeA5OUZRNiqKMVBRlZPO8famFzO7jwmuTAjicXsTGaX0BeHdaELYWpqwa4cNYL0NHMM/fhT0p7U9f+EwYw6D7F+MzaSxRn39L9pkLeIwcSnFSKmkHDfvLXd62k7KMq0R9/i06jYaMsBNEf/U9sVt+pNfklqfNrrM/rZBZfZxZN8GfIxnFvDslCIC3pxhsfmJ4T8Z42jPS3Zb1kwLYOLUvvyS3uEZdqxy6UsiMXk68NNqfo5lFvDE+EID14wKxMTfl0UE+BLu3PEXWEntTCpnj58L6yQEcTC/ib9MNft4w3WDzkyMNfrY0VfHO1EA2Tg9ie3wercVJ3MeNxf++UNzHjyPx6831A6yy5BSyDoehdnJEmJqQ/N1WhEqF2tEw3VMQcQGXkSNu0CsvLydg6WI8Jowj4atvKYg4j8uIYZQlpzbomZiQ9N22ej2XEcOI//xr4j//GqeBhihm6g87KL9ylYSvNqPXGDpcl7Fj6RUaisu4caRt3kzx+fM4Dh9OeUoKeWFhWDg5IUxMyNi6FVQqLBwdCXj0UXqFhmLt64vrpElY+/tTW1xMxtatmNnZoW8UNuk5YQwD719Mz4ljif7iW7LPNtS3dGN9izPWt2hjfbu46Su0VdUk/byXwsvxrX5ufpNHE7xiEf4hYzj96Xekh0fiEzyE/MRU4vcZ9o288N3PFKVncvqTLeg0GjJvJGwkAAAgAElEQVQjL3H60++I2r6bQQtmtqp96Goh03o68cIof45lFfHaWEMdG+xsw/19vZjp48JU47T4SFc7IvM7PrXVFe0aYE9SAXMCXHh9WiAHU4t4f7YhKrNkoAfrpgTw0mR/thqfa/zz+N4McLXhjWmBmLez2NHBjEJm+Dqzdqw/YVeLeXOioU2/PjEIW3NTHh3ck9EeBntfHutPX0drVo/q3WF/tISjvTUfvPEwQwb4snrVXTd9/t7EfOYEubJ+ZhCHkgt4747+ACwZ7Mm66YGsmRLA1ugcdsUbpi//fjKN01dKSCupblWz35TRTH74HvpPHcPhf31P0qlI/McMISc+jag9x8hPvcqJL3dQllfEsc9+xExtwbznH2Hyw/fg4utF35BRbduckGeweXZfDiUV8N6dhna7ZIgX62YGsWZaIFujs8kpr6W0WsPaaYG4WlsQl1/Rtm58HnP6ubJ+jlH3LqPuUC/WzQpizfRAtl7MJueaUXd6+7qjZ45i0ZN3M2Z2MFve28aFo1EMnTiY1EvpHNt5gtLCMrZ+8APf/307KhMVDq72TF0Uwk+bfuHbjVuZOL8DUXrJLUEoHZgCuClBIXwx7Dw+0Hj8hfF4+/U8IAn4RlGUH4QQrwArFEXxFULYAWcxDM4+AjYZzwsBViuKMs+oeRDYqCjKHiHEe8AwRVFCjFoViqJsMJaLBeYpipLehr2K3PTbgNz0uwG56XcDctPvBuSm3w3ITb8buB03/VYUpVtjR9+m7O22PY6W+s2+reJit2r5ineAN4UQkTSNWL0H/MO4s/nDwFtCCNcWzl8H/N04tfh/Kz4vkUgkEolE0kE6ffkKY/RpYKPjFa3kBTY67SVj/kONyl7F8NA/GJ71CmuUd7zZ+dfTX2l2PLB5GYlEIpFIJN3LbbFo6S1C+kYikUgkEonkFnG7LOgqkUgkEonkNuV2/DVjdyEjYhKJRCKRSCS3CBkRk0gkEolE0qXIiFjryIiYRCKRSCQSyS2i09cRu90QQvxvO0AikUgk/3N09zpiP6Xv6bbv2rt959xW8TcZEZNIJBKJRCK5RchnxIA+D3zX6ZqpXy8htviXTtcd6HgnAL5/2NHp2ukfLKD3qh87XTftHwu7bNVpgJ8zdne69vxec7tM9/uUPZ2uG+o3B4DNyZ2vvcx/DucKfu103VEu8+j96PZO103btAjounbdVSv293plb6frZrwyG6DLVsDvqhX7x+843um6JxdMBKDXkDc7XTsj6oUu033twoFO1107fEana7aHfEasdWRETCKRSCQSieQWIQdiEolEIpFIJLcIOTUpkUgkEomkS5FTk60jI2ISiUQikUgktwgZEZNIJBKJRNKlyIhY68iImEQikUgkEskt4v/0QEwIESKE6PzfzUskEolEIuk2TET3vW435NRkMyzNTXh1xQg0Wj3hcQX8fDoDgD/cPQB/T1vKKuv4cMcl8ktrEAI+eWYiR6Nz+Ppgcrvax/ZdIPZ8MhqNlt//5R7UlhZN8v/19nbUluas+MN8Dv58htjzydTWaFj88Ax6B3q1afNr9w422JxcxM7zmfV5QR42fLNqPJNfPUBVnY5Hp/rjYW/J1eJKPgtLbd8XoUPRaPWcSSpgZ0QjXU9bvn5qAiF/3YenoxUrQvwAGB/kwpR17a97Y2mmYv2cfmh0esIzStgRmwvAzEAXJvs542lrwQcn0ojMKmPxEE8GuNtQXqtlQ1hKm7oXDp8n5WISWo2WhX+4Fwujj/V6PT9+sA1NrQYzC3MWPb2Y2JPRxJ+Lp7SglBnLZtKrv2+naQPkpGXzr7/8kxe/Wltfti2ijkSQFp2MVqNl/pP3Yq5u0P/lo21o6jSYW5gx/6nQdrUAYox6Oo2WOxrpFWbmcXLbIVAUnHq6MeHe6YTvCCMvPRuBYOqDd2DtaNum9sn957l8IRmtRsfvVt9Yl//zzjbUVhYse3I+Yb+Ek3z5CsX5pSx/ZiFu3s4talqam/Dq0mGG+pZYwM6zV+vzgjxt+fqZiYS8tBcrc1P+umQoJRW1xGeV8e2xtHZ90VXt2tJUxauTAtDoFc5klbIzKR+Ax4b1xNtWjaPajFdPJGNtbsqKwYY2PN7bgSmbz7ata2bC+rn9qNMrhKcXszMmB4CZQa5M9nfGw1bNh8dSiMouY+OCQdRo9ZirBKt3xtLe8uWWZirWz+iLRq8n/EoJOy7nGbQDnJnc2wlPWzUfnEojMvsaAK/PDKJKo+P1I+33cS3h6+PKc08uwM7WiqWPvf+bNK6jNlHxp8F+aPQKkUVlHMgsAGCMqwP39PHgdF4JP6bldFjPUm3G+jUzqdPoCI+4ws7dlwEImdCHRfMHAfD9T1EcP53OGy/NYvgQL2bf+9kt0wVIO3GO3MuJ6DVagh9egpm6oe2Ff/ItBYlp3PnuGgCyImNJORoOgP+U8XgO6deha0i6l3YjYkIIXyFEvBDiCyFEohBisxBiuhDipBAiSQgRLIToIYT4TAhxVggRKYS4q9G5x4UQF4yvccb0ECFEmBBiu1F7sxBCGPNmG9MuAAsb2eEohNghhIgWQoQLIQYb018RQnxpvE6GEGKhEOIdIUSMEGKvEMLsZhwya6Q3e85m8uJnEUwf7lmfrtMp1Gn1aHUK16o0ADw4PYAjUR1v9GeOxvDEi4sZP20o4WExTfJOHY7Cv1/P+uNLkSk8uXYJC+4PIfZC24OP2YM92HMxmxe+j2L6QPf6dFOVIHRsL47GGTra/l52jOjtSI1GR35Zbbv2zhriyZ7ILF7cEsm0QR5NdBeP7cVRYweenFvOS99dZPPxNHZdyGrfEcDsvm7sjsvj+V1xTA9wqU/fn1jAmj1xbAhLIbinPU5WZszr70ZVnY788vZtjj0Rzb3PLmHI5GHEnoiuT1epVCx6OpT7nrufuppa9Ho9A8cPZtHTi5nzu7mkxrY9KL1ZbZ1Wx5nd4fQd1fGOL+5UDAv+uIRBE4dy+WRT/bv+EMqi1fdTW23Q7wjxp2OY/8clDJg4lLhGes7ebtz1zFLuenYZWQmGAUlGTDJ3Pb2UEXPGcWHf6Xa1I47F8sjzoYyeOoRzR5vW5TNHovBrVJdD7hzDyucWM2X+GNITM5tL1TNrmBd7LmTx4jcXmDakWX2b0Jujlwz1baS/E4eic3h5y0XG93XFtAMPn3RVu57Vx5k9KYW8GJbItN5O9en/irzKS0eT2JGYxxgve5JLqnjpaBKbY7PZlVzQru7sfq7sjsvjhV8uMSOwUftIyGfNrstsPJLEqF4OWJubUlmn5YVfLlGt1WGrbr+7mx3oyu6EfJ7fG890/0baSYWs2Z/AhmMpBHvbAzA3yJXo3Gsd8kVrpF/J5/G/bPqvNK4z2cOJsOwi3olKZoK7Y316eH4J3yZ1rO9pzOxpgew+kMALr+5lxuSA+vSxo3qxfsNh1r1zkCkTDDeZL67fR2p68S3VBbhyLoqxjy6j15jhXD17sUnemEeWYuvhWn+ceymREQ/cw8jl95J18VKHr9EVqITSba/bjY5OTfoDG4G+xtdSYAKwGngRWAMcVhQlGJgCvCuE6AHkAzMURRkOhAIfNNIcBjwN9Af6AOOFEGrgE+BOYATg3qj8OiBSUZTBxmt+1SjPD5gKzAe+AY4oijIIqAbuaP7PCCEeFUJECCEimue5O1qRU1wFgE7f8IH+85fLrP73GU7E5hI6uQ8BXraoVILkrLJ2ndfougC4uDtQlN9wXmlROWkJWQwe2dBgJ8wYxstP/JPP3t/JuKlD2tR1t7cku7TaYHOjvUMfmerPF0dTuZ7Ux9Wa5Lxy3v7lMlMHuGFh1vbH7+FgSU6JQVffWHd6AF8eTaX5PqWh43qx1RhpaA8PGwtyrtXcoA3wyOherJ0ZSFhKET4OVpTWaHn7SDJedpb4OFi2LWz0sYObA6WFpU2ycjNy2fLOZtQ9LFGpDP972LYj7Pz4J/qO6tu+0TehHbbtMBPunsRNRcmNhe1cHSlrpp+fkcsPG77B0tqq3vaOYufqyLVmegCxRy/gNywIgOGzx7HrH1tJCI9tsewNphptdXZ3oDi/oXxZcTkZiVkMGBnYpPx3H//KgR9P4j/Qt1VNDwfL+ranb9T2HpkZyJeHk+vr25HYXAb62PPiokHY9TDH3tq8XXu7ql17WFuQU1Fzg80AVqYq7vB35UBaUX1aaH8Ptsa1P8jzsFWTU2bQ1TX7Xlk5thdrZ/XlaHIh5bVazE1UfHbfcExVKspqNO1r21iQU95K2xvlw9ppgYSlFuFsZc4AV2tOppe0q9lduFhakFdtuCFrbvtvwcPNhpw8w0BT1+gG55e9l/nnuwvY9LeFbN0R3drp3a4LDW2vh4sjlcVtt1XfcSM59v5/OLpxE/4hY3/T9SRdT0d79DRFUWIURdEDl4BDiqFXjAF8gZnA80KIi0AYoAZ8ADPgEyFEDLANw6DrOmcVRck0al406vQ1XivJqP9No/ITgK8BFEU5DDgJIa7Pn+xRFEVjtMcEuL5XyHX7mqAoyiZFUUYqijKyeV5ucRXujoYve5UQjc4x/C26VouV2pTxA9zwcbXmgekBzB7pjUMbXwZHdkfwn/d2UHHN8EVQkFeCk6tdff6lyBTKSirY+tl+Ys+nkH2lgH0/nuKNTU/xlzdXsGPzkVa1AXJLq/Gwv25zQ3p/LzuWT+rDkF4OLB3vS25Zdf1df3WdDnPTtj/+nJJqPIwDH9HIF/287Fg+2aB734TeAKjNTHCzt+RKYWWbmvXa5bW426hv0Ab45EwGq36IZuUYH3LLayirNth8rVZDDzOTFvUiDpxl5z9/pLrC4OOS/BLsne2blHHv5c59f1mGoigU5xnuQEPuncIDL63g6PawVm39LdrZKVmc3HGMKwlXOP3rqTZ9EXnwLLv/9SM1Rv2yghLsmum79nLnntX3o+j1lOa1ffccdegse//dVM+2mV7s0QuU5hcz8o4JAASM6s8dqxbjO8Qf555urWof33OOr9//icprhgF6UV4pjq4N2nGRKZSVlPPT5/u5fCGZnCuGqbolj88j9PdzCfv1TKvarda3nnYsn+rHEF9H7pvYh1qNnvXbonljewyVNVqKOhAp7Yp2DZBTUYuHtcUNNlubmfDa5EDeOp1KpUYHgNpUhVsPC64Yb0Da1L1Wg7ut2mhv07xPT2ewalsUK8f4MtDDlszSGh7acoGrpdX0d7NpX7u8FnebG20G+OTcFVbtjGFlsA+jfexx6mHOH8f3ZqyPA73buwnqBgqqa3G1NHwm4uZuc1okJ68cd1eDzxrXiyceGkvow5tZ/NBmHn9ozP8J3ZRjZzj35XZqKw1tr7KwhB6O9m2eE7tjHzP/+jQzX3mG2J2dv83czaDqxtftRkefEWvc0+kbHeuNGjrgHkVREhqfJIR4BcgDhmDwT+MeqLGm7iZsadU+RVH0QgiN0hCquW5fh9kXkckrD45gyhBPDkVms+H3o1n97zM8fmc/PBytcLCx4NWvL1BgvFsd3deFQG87SirqWtWcMnckU+aO5Ni+C/z77e3U1Wp45M8LSYhJJzUhizmLxjN++lDys4vZvf0Enj4uDBjmxz/f2EpVZQ3T549u0+a90TmsWzSYKf3dOBSbx9/uH86z31zgqS8NAb93lw7j25PpVNXpmD/cmzULBlBQXkN5tbZtX0Rls27xEKYMcONwTA4bHxzBn746zx8+PwfAO/cPZ8sJw/M580Z4sSey41MDe+PzeHV2X6YGOHMoqYD37hrAMzsvsWSoF/3crLGxMGVLZBY512oprdawdnogpiaCuPyKFvVGzghm5IxgLhw+zw9/34qmVsPdTy0i/XIaWclZDBw3iMPfHUTRK5iYqLB3sefM7tNkp2ZTU1nN6Lmt3y3+Fu0HXloBwHfvbGbsvHFt+mLY9GCGTQ8m6kgEP3+4FW2dhnmrFnElLo2clEz6jR3Mse8PoCgKKhMTbF3a7nyHTAtmyLRgYo5E8OtHBr25TyzialwauSmZePfrzf5PdxAYPIB9m35i1qN3E3XoHJnxaWg1WuY8dk+r2hPnjGLinFGc3H+ez9411OUVf1pIUmw66YlZzFg4njHThlKQU8z+H07g4ePKL5sPU5xXSmV5FQtWtL7H3b7ILNbdN5Qpgzw4HJ3Dxt+N5E+fR/CHTwzPU72zfARbjqdiaW7CK0uGolIJfgzPoCOBka5o1wD7UgtZNzGAKb2cOJxexMZpffnToXjenRaEqUrFqhE+7Eou4HRWKfP8XdiT0v60JMDeuHxendOPqYHOHEws4G8LBvHsjhhCh3nRz90GGwtTvruQSVJBBY+M9eW1uf1wsDLns/D2I9J7E/N5dUYQU/2cOZRcwHt39OeZXZdZMtiTfq7GtheVzbnMUnbF5+Ntq2b5CG/SjNHxm8XR3ppX/hLKkAG+rF51Fxv+sfM36QAczSnimcF+jHVz5GReMS8ND2T9hUQGONgQ6ueJjbkpRTV1HM0pal8M2HsokVdfmMHUiX4cPJbM39bP49mXfmXv4UTe+utcwzVPGvq41U9OYkBfN15fM4t17xykzjjA7i5dv0mj8Zs0mrQT5zjz6Ra0dRqCHwqlIDGV4vRMgmZOIvK7nylOzyT80y2MWr4In+ChhP97MwCeQ/u3qCu59Yjm00s3FBDCF/hVUZSBxuMvjMfbr+cBPwO2wFOKoihCiGGKokQKId4DMhVF2SiE+B3wmSFbhACrFUWZZ9T8CIgAvgMSgSmKoqQIIbYANoqizBNCfAAUKIrymvH89xRFGWYc7FUoirLBqFWhKIq18X2TvFb+P0Vu+m1AbvrdgNz0uwG56XcDctPvBuSm3w3cjpt+K4rSrb8vPJy9u9se3prqOfe2+u1kZ0XxXsMwDRkthLhkPAb4J7BcCBGFYdqxzXkrRVFqgEeBXcaH9fMbZb8CjBBCRANvAcs7yXaJRCKRSCSSW0K703aKoqQDAxsdr2gl7/ctnJsEDG6U9JwxPQzDs2TXyz3Z6P1eDIO25lrFwIIW0l9pdmzdWp5EIpFIJJLu53Zc36u7uB2fa5NIJBKJRCL5/wK5oKtEIpFIJJIu5XZc36u7kBExiUQikUgkkluEHIhJJBKJRCKR3CLk1KREIpFIJJIupQM7kf3P0u46Yv+/I4ScuJZIJBLJ/xbdvY7Yybxd3fZdO97tjttq2CcjYkDfT492umb8ysnsy9zV6bqzvA1bZw766lina8c8OIme74d1uu7Vp0MIPdz5Pv5+6mSALvPzw8fb3lrqt/CfiVN46tThTtf9cNxUoOsWdO0q3YFfdn49jl0+Cei6dj382863+cLSSUz65USn6x6707CF1T8u7et07VUDZnXZwqtdtVAswNx9nW/z7lkTWXCg8+vFjhmTurSNdCcyItY68hkxiUQikUgkkluEjIhJJBKJRCLpUmTUp3WkbyQSiUQikUhuETIiJpFIJBKJpEsR8hmxVpERMYlEIpFIJJJbhIyISSQSiUQi6VJkQKx1ui0iJoSYL4R4/ibP+UIIsairbJJIJBKJRCK5lXRbRExRlJ+Bn7vrer8VS1MVL48LQKPXczanjF9T8gFYObgn3jZqHCxMeSM8BY1e4Q8jfOlhZkJGWTUfRWa0qx1x6DyJkcloNVpCn16EhaUFAHq9nq3vb6euVoO52owlzyymrKiMA98eQlEURkwdRp+Bfdq0eU2wPxq9nnN5ZexOKwBggqcD9/X15HhWMd8l5KA2UfHymAAqNVryq+v4JOZqh33y+pQANHqF05ml7Egw+GRmHydCfB3xsLbgg7MZROaWt6tVfPYMFQkJ6LUaei69HxMLi/q8rJ9+QKmrQ5ib43X3PeTu3UNdUSHaigq8F4di7uCIoteT+s+PsB0wsNt8nB9+lrL4BPRaLf4PLG1ic9r2H9HX1aEyN6f3ooXUlZZxdfceFEXBJXgUZjbWZO7eByhYuru36Zvs02cpjktA0Wrpt3wppo2uc+mLzZQmpzB+/cvt+vg6MUciSItORqfRcseT92KuNugVZuZxctshUBScerox4d7phO8IIy89G4Fg6oN3YO1oe8u0wVinRxvqdERuGbuMdfrhgd54WauxtzDj7XMp5FXVdcgXXdWu1SYqXhjlj0an53x+GXsyDHaO93BgcaAnJ7OL2ZqUA8D8Pm4EOVhTUafl45iWddUmKp4Z6IdW0RNZdI2DWQa93jZWLPP3BmBzciYZ5VW8MDSAOr0eU6Hiragk7vPzwsNKjZ25KR9eSqOgpmXfxB+LIDMmEZ1Wy9Tfh2Jm/OxKsvKI+PEgCgqOXm6MXDgDgIzIOI5s2sqKj//aIV+rTVT8abAfGr1CZFEZBzIN/8MYVwfu6ePB6bwSfkzL6ZBWW/j6uPLckwuws7Vi6WPvd/g8CxMVT/TzQ6tXiCkuIyzXYF8vayvu7W3w8ba0TLR6hUW9vRFAZmUV29OzuMfXC3dLNT7WVhzJKWBvZi762lrc3NzI3vwlPQKCsAseA0BNdhaF+3YD4DxrLmpPL/J2bK/v49wWLKI8Npqy8FMA2I+fiNrLm4Jdhq/J8ksxqFStx0o6u410NfIZsdbplIiYEMJXCBFvjGAlCiE2CyGmCyFOCiGShBDBQogVQoiPjOW/EEJ8IIQ4JYRIvR71EgY+EkIkCCEOAq6NrjFNCBEphIgRQnwmhLAwpqcLId4UQlwUQkQIIYYLIfYJIVKEEI/d7P8yw9eZfWkFvHwiiak+jvXpn0Zf5ZWTSfySks9oT3uKazS8cjKJP4fF42tn2SHtqOMxLF0dyvCQoUQdj65PV6lULHl2MQ++sIza6jr0ej2HtoZhYWWBUAnsne3b1J3W05kDVwpZF55MiLdTffqJ7BI+v5RZf9zbzor0a1W8fjYFF0tz3KzMO2T3HH9ndicX8tyhRGb0adDfn1rEi4eT2HA6nWBPuw5plV2MxOeBB3EYMZKyyAv16XXFRSg6Hd6h94FeT11xMe6z5+Cz7AEcg0dTkZAAQEHYEWwHDmpVvyt8XHQhkoAVD+A8agSF5yPr02uKilF0OvyWLkHR66ktLiZz335M1GqEUGHh4ICVuzuBDy0n8KEVlKeltemb/PMXGfjQA7gFjyA/IrJJ3oAVy+jh7tbm+c2JPx3D/D8uYcDEocSdbPCFs7cbdz2zlLueXUZWgmFAkBGTzF1PL2XEnHFc2Hf6lmoDTPNx5kBGIetOJxPSs6HO/Sc2k1fDk/k1NZ9R7m23i8Z0Vbue2tOZg1cKWX8umUleDXaezCnhq7iGtudgYcZMHxdqtDoKWxkgAUxyd+JoTiHvRqcw3q3BzkW9PXg/JoX3YlJY6OuBlakJ1Vod70anUKvTYW1myrcpWWyMSeFAVgHDnFpvjynhUUxftZSAccNJDo9qsNHLjRlPLWPmU/eTm5gOQG1lFZmxSbgYBygdYbKHE2HZRbwTlcwE94b/ITy/hG+Tsjqs0x7pV/J5/C+bbvq8ca5OnMwr4sPLyYx2bbBvvo8nH8el8M+4FO7s6UFWVTV/v5TE+5eSCLKzAeCH9Cz+EZdCUU0dx40DuIqo81RUVOC5bDnlMRfr9YqPHMQjdBkeS5ZRHHYIjbGPc1+8FPQKmpJiqhITcLtnMW73LqHiUgymtnZ43PcALvPuokdgEHq9vtX/o7PbiOTW0ZlTk/7ARqCv8bUUmACsBl5sobyHMX8e8JYx7W4gCOgPPAiMAxBCqIEvgFBFUQZhiOQ93kjriqIoQ4HjxnKLgDHAupYMFUI8ahy0RTTPc7OyILeyFgBdsw0ZrExVzO7twsGMIgCGu9ny/tR+xBdXtOyRG65r+Ovo5kBpYVmTvJz0XL5+azOWPSxRqVTkpucSPGMUc5fPZu83+9vUdethXm+zvo0tq+KLKzA3UbF6RG/jQMyi1bKN8bC2ILu8BgBdM/1Hhnnz8iQ/wjJKOqR1HXMnJ+pKG87RlJZi7mDoFM0dHdEY83Q1NZScj8Bu6DCqs7NAr0ft4dmqbpf42CiqdnKirqTB5rqSEiwcHQCwcHKktriEquxsXMeNweeueVz5tWHF//wz53AY0L9tpxhtt3RyoqaktO2yN4GdqyPXCm/Uiz16Ab9hQQAMnz2OXf/YSkJ4bItlu1vbzar1Om1pqmKWrzOHrxR12M6uatduVubkVbXf9rys1ZTVafgwKh2PHhZ4W6tbLOeiNiffOFBrrNfD1JQKrY5KrQ4rUxMqtTrMVCreGtUPE5WKco0WAEsTFVM8nDmeW9yqLcJYn21dHakouvHzSDh+Hp+h/QA4u20/IxZMa8sFN/4PlhbkVbfvk1uFs9ri/7F33nFVHekffoYOglSpiiCKBRWwYFdUrDEmMUaMJmua+SUxPW6ayWZTN8WUze4mWZMY3cQYuybGXrCLgthA6UV6RzoX7vz+uFeaUkwAw+48fO6He2bO+Z73vnfmnPe859wZciuut6+L3q9l1TWYGxnWlo93duBMPT/ZmZpQqdVSWl0DQHVhAdXVOv9TL4OlLS/H0MICQ3MLtJUVaAoLMdYf44zt7NAUFNB12HBSv/2K1BVfYDNqTO22hSePYTNidLOfo637SHtj0IGvzkZb2pwopbwgpdQCkcB+qZvI8gLgcYP1t0optVLKKODapf54YK2UskZKmQ5cmwumr14/Rr+8Wr/uNa7d8rwAhEopi6WUOUClEOK6SwIp5Qop5TAp5bDGdVlllTh30QUo9adk6GJsyBtj+rD8dCJlGl0HPJN1lWcPXGKIk3Wz0zeE7jnNpn9uoaykHID87AJsHBpesbp4OHP/ywuRUpKfmY9NN2ssrMwxNTdFU1XdtDiQVVpVG1SJZvK/Evjn2WSWhydytaqa1JKKZnWvkVFSiYuV3ieNHrn8OiKVJ3ZEsXhI81fMeSePk7r+J2rKygCoys/HxMa2tt7YxgaNPsipyi/A2MaWmvJyrqxdg9ucuRiamVF86RKVOTnkhtz4hQYAACAASURBVBygMOJMg7R9e/g46/gJ4teuo1pvc2VePia2dTab2NpSmV9Ya7OpnS0mtrYYWVhgaGqGVqMBdEFYZV4eLoETbuibtGMnubRmfe1+KvLyMbP97Vey5/afYte/N1NRotMryimga6OM38VDZyjMzmfYbbopcPoMH8BtS+bh4dsbhx5NZ97aU7s+WWX12nS9NtfF2JDXR/Tmk/BEyvQnwtbptX2/vs7OZvpeTnklVyt1bay4quGJvsF6FVV0M9NlquurlVZX08XIEAsjQ8qqa/C2tiSzvJKXT18io6yC3l27YGFkyPODvPjyUhLlNdf75tLBUA59u4mKUt13V5yTj6V9w+8u+kg4xTn5DJ4+Fk1FJUWZOYSu30VOUhqXQ04374x6n9XR/Npn+OPdj8qtqMTe9Hr7rgW55oa6bCPogjBHM1N2pmbWrjfF1Yl96Vm1y0Y2thgZ6Z/yqRcQGZibU1NeRk15OQamZrpjnP4CU1OQj7GtLbm7d+Lx7It4PPciuXt26iUkpTHRWHj3a/ZztHUfUdw62vIZscp677X1lrVN7Kf++r+3t9bfV2M7buoz7k3K5bVRvZnQw46DKfl8MKEvLx2K5m/j+2JkIPg/3x7sSswlv6KKeX1dMDQQROaWoG3mwm/E1OGMmDqcsP3hrPt0A1VVGuY9fTeJkUlciUvFd8wg9vy4DyklhoYG2DjaMHFuIFtX/IJAMG5281dG+6/k8spwL8a72XEoNY93x3iz7FgMgx2s+FN/N7qaGJFbXsW+lDyWBXhhZCCIyishv0LTKp/sjMvl7Yl9mORhz77EPD6b2o9n91xmvo8z/R0s6WpqyNqLmc1q2I8cDSNHk38qlCs//oC2qoru9y6kNCGespQUugVORBgakrphHQZGxpjY2ZHw1RfImhoyd/6K7dBhOE4OAqA4OpqK9DS0239pVx87jR6F0+hRZJ88Rez3a9BWaeh9371cjYunJOUKrpMCEUaGJPy0HmFsjKmdHW5Tg0jcsBkhwCVwAiXJKSRu2Ijd4MEk/LT+hr5xGzMStzEjST9xiqjVP1JTVUX/P91LYVwCV5NTcJ8cSOzGrVxNvkLkqjX0XzgPA2PjJn3tOzkA38kBXDgYxvZ/rqe6SsPMJ+Zy5VIimfGpdO/vyZ5vtuId4MPuFVuY9uhdnNt/mtTLiVRrqpnx2N23RLs++1NyeTXAi/HddW36vTHevHoshndGe2NkIHh0kDu7k3M4lVnUshjt068BDlzJ5aVhXox1teNIWh5vjfTmLydjGGRvxcK+dX3vQGoeV6uqec7fEyMDA2ILS2+odzgzj2cH9mKUoy3HswtY5teHd8/GsjExg2f0zzCujU8jvbSC4F6uPDewF9YmxmxMTGeZn8439/fpwcH0XCLyGvqm/8QR9J84gsuHwzjw1Tqqq6qY+Og8Mi4nkp14BZe+nhxdvRWPoT4cXrmZ8Q/NYdbLiwEoySukX+DwVvn6UEYezw32YpSTHcey8nltiDfvnInBx9aKYC9XrEyMyKuo4lDG78vW2NlY8tcXg/H18WDpkjtY/q9trdrueHYej/fzYng3O07l5PPCQG8+vhjDzynpPNbPC4BNSan0surCw96enMrJ5xFvT76J0T1a4GPblXWJdc/XWvoOxdLSkoy132M10Je0Vd/g9sAj2E0MInP9WgDsp0zH2M4eYWhI5qZ1CCMjjG3t6Oo3hPQ1q3U6A3TPvZbFRmPh1afZwB7avo+0N0L88bKjfxSEbIPUsRDCA9gupRyoX16lX954rQ5YDgyTUj5Zv16/fomU0lIIMQf4P2AmuufDooDF+u1jgElSyjj99hFSyr8LIZL0urlCiAeu7UOvW1vXjO1STfqtQ036XYea9LsONel3HWrS7zrUpN91dMZJv6WUHZqujMjb3mGRmL/9rD9eKrYZ/mjjiG0BJqELwFKAEwBSygohxIPABiGEEXAa+OqWWalQKBQKhaLVdKrIqINpk0BMSpkEDKy3/EATdasa1+uXLfX/JfBkE/vYD/jfoNyj3vtV1/bRuE6hUCgUCoXij0Zn/IGBQqFQKBQKxX8Ff7RbkwqFQqFQKP7LUAO6No3KiCkUCoVCoVDcIlRGTKFQKBQKRbuiEmJNozJiCoVCoVAoFLeINhlHrDMj1ChzCoVCofgfo6PHEbtY0HHjiA20VeOIdTp6PbCuzTUTVgVzMf+Xlle8SQba3Q6Ax/OtG0X6Zkj65A48n9rS5rqJ/7gL9/f3tbluysu6kfZ/Tt7R5tqze85kXXzbD2Ia7DWDVTG72lz3Ae/pAGxObHtfzPGcSXjO9jbXHdptFp5PbGpz3cQvdKP3t1e/9vhnSJvrJj0ZSI+P236g3ysv6Ab6dX+3+flqfwspy6bS0/dvba6bfO6Vdht0FWi3wWLbS/dUdtv3vQDHWW2uqfjtqEBMoVAoFApFu9KpUlQdjHpGTKFQKBQKheIWoQIxhUKhUCgU7YoQHff6fXYKOyHEXiFErP6/bRPr1QghzupfP9cr9xRChAoh4oQQ64QQJi3tUwViCoVCoVAoFDpeBvZLKfsA+/XLN6JcSumnf82uV/4B8KmUsjdQADzc0g5VIKZQKBQKhaJdER34+p3cAazWv18N3NnaDYUQApgEbLyZ7VUgplAoFAqFQqHDSUqZoX+fCTg1sZ6ZECJMCHFSCHEt2LIHCqWU1frlVMCtpR3+4X81KYRIAoZJKXNvtS0KhUKhUChuno781aQQ4lHg0XpFK6SUK+rV7wOcb7DpsvoLUkrZzFijPaWUaUKIXsABIcQFoOi32PuHD8Q6GnMTQ97601A01VpOXs7m55MpADx9hw+9Xa0oKtXwj58j6dHNkrvHemBqbMjRi5lsOZ7covbh3We4GB6HRlPN/714N2bmpg3qv/pgI2bmJjzw9Gz2/RzKxfA4Kis0zHt4Cp7eTQfV5iaGvD1nMJoaLSfjc9l2Jq22rq+zFT88NpoJ7+2jrKoGIeCbh0Zw6HIW/zmW1LIv7vFFU6MlNC6XbWGpdbouXfn+yTEEvrmHsqoaFk/qjYutOal5ZawMiW/RF+bGBrwzpR8arZaTKQVsjcoCYGofByZ42uPa1YzPjycSkX4VgHen9qVMU8O7B+Oa1T1zIJz4s7FUa6qZ8/Q9mOp9rNVq2fz5BjSVGoxNTZj77DwAMhLT+erFL3j1P6/XrtsU5w6GkXg+jmpNNbOfvAcTszrtX/65AU2VBhNTY2Y/FUzMqUhO/nIE7+EDGDl7fLO6kSFhJJ/X2Tx9ybxa3bzULE5s1I2/Zt/dkVFzp3Bi4z4KM/PIvZKJT+AwhswY06Tu2QPhxJ/T6d71dEN7t/5D5wsTUxPuemYeIev2kZ+ZR2lRKbMfn4N1N5tmbT62J5zIM3FUa2p4aOn1bfmbDzdgZmHKfU/qHp04F3qZ7z7exGfrl91IDtC3t2A/XXuLzWXb6Su1dX1du/L902MJfGM3BkLw4X1DKSyrorSimnc3X2jW1lrtdujX5kYGvD2hD5oaycn0QrbFZAPw2JAe9Ohqhq2ZMW8diaOkqoYPJ/elqKKaEk0N7x5rvo+YGxnw7mRvNFrJiSuFbL2s7x9eDgR62OFiZcbnoUkUVWh4fHhPBBBfUMaXp1Na9oWxAe9M6687XqQUsDUyU6ft3Y0JvRxw7WrK50cTiUgvYt5gV3ycrSiurGb5oRZsNjPmnWVTqdLUcDIshW07ogAIHNuLubMHAbBuyzmOnEjivdemMcTXjen3rGxSz9TQgCf6e1GtlVzILyIkMweAnpYW3OPZHYANialUayVzPbsjgNTSMjYmpXG3hxvO5ma4W1pwMCOHXamZLfqlPh7ujrz05J1Yd7VgwWOf3dS27a17fE84URFxVFfV8MAN+t7KjzZgZm7KgidnE7L9JPGRKeTnFPKn5+bg5ObQFh/jD48+6FrRTH1QU3VCiCwhhIuUMkMI4QJkN6GRpv+fIIQIAfyBTYCNEMJInxXrDqTdaPv6/K5bk0IIDyHEZSHEKiFEjBBijRAiSAhxTP+LgwD964QQIkIIcVwI0Ve/7XNCiJX694OEEBeFEBZCCHshxB4hRKQQ4hvqBdJCiOf1610UQjzbWhtu5jNNG9qdnaev8OqqMIL8XWvLa7Raqqq1VNdouVqmITw2l1e/C+OFFaEE+beYeQQg9NAFnnh1HmMm+3EypOHJ4/iBc/Tu36N2OTIinidfn8+d9wVy8UzzB8Dpg1zYeT6dVzacI8inLsg3MhAEj+jJoct17WjRGE8OXspqlb3TfF3ZeS6dV386y+SBDXXnjerJIX3wNKC7NUN72VOpqSH7akWrtKd7O7IjOpuXd10mqHe32vI9sbks2xPN8sPxBHTXBQMz+zpyPvNqq3QvHj3PPc/Px3eCPxePnq8tNzAwYO6zwdz70n1UVVSi1Wqpqa4hdMdJ+g3v3yrtS8cvcOcz8xk0zo+oYw2173g6mLlL76OyXKftHeDDuHsmt0o3+sR5Zj59L/3H+RN9/FxtuX13J2Y9u5BZzy4kLVoXEIyaG8SMJ4OxsrdmwDj/ZnUjj5/n7ufmM3j89b6Y80wwwS/eR6XeF4HBQcx5Jhj/ycOIPxfbos2nD1/k0ZeDGTnJl9OHGrbl0IPn8KrXlkuLy4k6E0fPPs33k2l+ruyMSOPVHyOYPMilttzIQDBvtAeHInXtzcXWnMjUQl79MQJnW/MWbYX269fTezmwMz6XV0JiCPKwry3/6swVloXEsjU6i1FuNrhYmRKVU8IrITG4WLb4Iypm9OnGjtgcXtobzRSvOt098bm8uj+G5ccSCHCzJqGgnD/vuczSPZfxc+7aKl9M7+vEjstZvLzzEkF96vW9mByW7brE8kPxBPSwwd7CmFkDnCirqiG7pLJl3cne7NgbzStv7WLKhD615aOG9+Sd5Qd488N9TBzrBcCr7+wmISm/Wb3RjvYcy8rjH1FxjHC0qy2f7e7Kl5fi+eJSPLf3cCGtrJy/R8byWWQsfa2tANiUlMa/LsWTV1HFEX0AdzMkpWTz+ItNnsd/M22hG3b4Io+8FMyISb6ENep7pw6eo1e9vhc4ayQPvzSPwNtHkhSd2liqQzEQHff6nfwMLNK/XwRcN3q6EMJWCGGqf+8AjAGipG6qooPA3Oa2v843v9tk6A18DPTTvxYAY4GlwKvAZWCclNIf+Avwnn67vwO9hRB3Ad8B/yelLAPeAI5KKX2ALYA7gBBiKPAgMAIYCSwWQlw7E7VkQ6txtjMnI78cgBptXUbyi+2XWPr1KY5GZhE8vldt+eLpfdl4NLFV2kL/u9puzrbkZddlMAvzikmMTmPwsLqD19gp/vzliS9Y+dk2Rk/ybd5mazPSC6+3eXGgF6uOJiDRlfVxssLAQBCbVdwqe11szMgo0Olq6+tO7s3qw/FcK+nlaEl8VjEf/BzFRB9nTI1bblYuVqZkFOuCNm2jabYWD3fn9cnehCTk4WBhgo+jJceSClpl87XfLts62VKYW9igKjM5k7UfrsGsizkGBgaEbDjA2LvGtz5lrl/R2tGOokba2cmZbFr+A+aWFhgY3GS3qqdb3EgXIPJQOL38+9UuF+cVYWRijJmlRauEbZ1sr7M3KzmT9R+twVzvC4DK8kouHI7AZ/SgVtvs4GxLXnaddmF+MUkxaQwc5l1btnX1Xm5fOLFFSRcbczIKb9DegvqwOqSuvaXkljK4py0rnxhNXEbr2nJ79WtnS1PSS3TtuKZRO7YwNuC23o7sScwjpaiCwU5WfDdrELH5ZS3quliakl5cqddtWLd4aA/+EtibkHqBzO19HTmS3HxgU6ttZUrG1Sb6XkBPXg/S9T13GwsKy6v5ICQOt67muNs0H/S6OFmRkaW7YKrRamvLf9kVxRcf3cmKT+awfuv5pja/DgczU3IrKq+zs4uRIaXVNZRV12BuZFhbPt7ZgTN5dW3RztSESq2W0uqaVu+zU1Cv7+Xn1H3eIn3f8xnq3WD1dV9tZ9/mY/Qe6NGBRnZq3gemCCFigSD9MkKIYfrkEEB/IEwIcQ5d4PW+lDJKX/cS8LwQIg7dM2PftrTDtgjEEqWUF6SUWiAS3c8+JXAB8ACsgQ1CiIvAp4APgH79B4DvgUNSymN6vfHAD/p1fkX380/QBVZbpJSlUsoSYDMwrpU2NEAI8aj+IbuwxnWZ+eU42+kOOAb1BiS5dhzIu1qBhanuju5DU73JKarg4LmMxjINOLgjjG8/3UrJVd0BOCerAHtH69r6yIh4igpKWL9yDxfD40lPyWH35uO8t+IpXvzbA2xdc7BZ/cyiClysr7d5gJs1i8Z64tvDlgWjPBjj7UBP+y4sGuPJ9MGu2HZp/so8o7ACFxszoC6IBOjvZs2i8V749rTh3jGeZBaWU1RWBUB5VTUm9Q6OTWoXV+JsZXqdNsDXp1NYsu0CjwS4M8LdBvsuJjwzxpNR7rZ4NpEBCdt7im1fbKa8ROfjguwCbBwa3l5z7unMvS8uREpJflY+6fFpHNt6mJToFE5sP96krRH7TrHjq81U6LWLcgqwbqTt2NOZu5feh9RqKcxq3Qnxwv5T7F2xiYoSXYBwNacAq0a6kYfCuZqTz5CZY2vLzu09yeCgEU3qhu89xS9f1vmiMPt6e516OjPvzzpfFGTlU1FawdZ/bGDGw7MxtTBrUvvwztOs/mwLpVd1NudmFWLvWKd9KSKeooJiNn23h6gzcSTHppGZlsumlXtIiUvnyO7rulwtGYXluOhP9vWbRP/uNiya4IVvT1vuHevJpIHO7IpI56EvjuNobYa1hXGTmtdoj34NkFlSiUsX0+t0LY0NeWeCN++fSKBUU8MkDzt2xefy4PYLOHUxwdq0+adCMkoqcbHU6zaq+zr8Ck9sj2TxEF3m4/a+jnTvasYP59NbtBf0fa/r9f0a4OtTySzZcp5HAtzJLK6gqEIDwNVKDV1Mmu/XGVnFODvqMlL1ffHEQ6MIfngN8x5aw+MPjWyVjQC5FZXYm+qOUaLe5VJpdQ0WRoaYGxpSrg+yxjs74Ghmys56tyCnuDqxL7112f/OwJGdp/n+71soK67re3bdGva9q4XFbNH3vYwU3Z2Q4MdmMe//ZnJoe+gtsbuzIaXMk1JOllL2kVIGSSnz9eVhUspH9O+PSykHSSl99f+/rbd9gpQyQErZW0p5j5SyxXRyWzwjVn8n2nrLWr3+28BBKeVdQggPIKTe+n2AEsCV30dLNjSg/v3jxg/i7Q5P5a/3DWHiYBf2n01n+eIAln59isdv64+LnTm2Vqa8tSaCyX6uLJjoxYlL2bjaW/DF9ktNGjdx5jAmzhzG4d1n+PcHG6mq1LD4z3OIvpBEQnQaM+aOYUyQH9np+ezYeBRX9274+HvxxXvrKSutIGh20yddgF0XMnjzrkFMHODE/qhMPrnXn+fXRvDU9+EAfDTfmB9PJFFWpTtojfCyp6+zFQWlVc3q7j6XzptzBzPRx5kDkZl8fP9QXvg+nKdX6U6mHy4cwtpjiZRV1XD70O4su2sguVcrKS7XNKsLsCsmm7em9GWSlwP743L49LYBPPdrFPMHu9Lf0RIrUyPWnkvndGohv17OpntXMxYN7U6iPkPXmGFTAhg2JYAzB8LZ9Pf1aCo13PXUXJKiEkmLS2Pg6EEc+GkfUisxNDTAppsN97/2AAA/fbiGUbNGN2mrf1AA/kEBnDsYxs//WE91lYZZS+aScimRjPhU+o8azOF1e5FSYmBoSNduNqRcSuTY5hAqSsqwsuuKz1i/63QHTQ5g0OQAIkPC2PmvdVRXaZj2+D2kXkokKyEVt34eHFi5ld7DB7Lv680ELZ6DlJLUqHjGzp/WpL1DpwQwdEoAZw+Es+Xz9WiqNNz55FySoxJJj0/DZ/QgDv60T2+vAdbdbFjzzipqqms4uHYvgyb40dvP+4ba42cMZ/yM4RzbE863H+na8oMvzCHmYhJJMWlMnTOGUZP9yMnIZ/emo/Ts48YLf3sIgPycIsZNG9ak3bvPpvPmPD8mDnTmwMVMPl40jBdWh/H0ylMAfHj/UNYeTcTC1Ig35/nh72mHsaEBRWUtt7f26NcAuxJyeXN8HyZ62LM/KY9Pgvrx/L7LfDS5L8YGBiwZ6s6vcTmczijirXF98HOywsjAgKLK6mZ1d8bm8PYkbyb1smdfQh6fTe/Ps7suMX+gC/27WdLV1Ii1F9Lx6WbJa+O92J+Qx+sTevP2oeafoQTYFZ3FW1P7Mam3A/tjc/j0dh+e+yWS+b5u9HfS972zaWQUV1JYruH1yd4YGQouZZc0r7s/hrdemcKkcV7sOxzHJ+/M4vnXtrPrQAzvvzETgEPHdFnGpU+Ox6efE+8um8abH+6jSnN91up4dh6P9/NieDc7TuXk88JAbz6+GMPPKek81k93i3NTUiq9rLrwsLcnp3LyecTbk29idPvwse3KusQr1+m2BjsbS/76YjC+Ph4sXXIHy//VNnP7/h7dcTOGM27GcI7vCee75bq+t+j5OcTq+96UOWMYqe97ezcdxcXdke1rDpCXXUhpcRl3LprSJp/ht6KmOGoaIRulpm9qY11gtV1KOVC/vEq/vPFaHRAL/CCl3CSE+CvwgJTSQwhhDZwCbgf+ie5XDRuFEJ8D2VLKd4QQM4AdQDd0tyhXobstKYBQ4H50GbNmbbhW18RnkGrSbx1q0u861KTfdahJv+tQk37XoSb9rqMzTvotpezQ2Ci2aPtvDzZukj7WszpV3NcR44h9CPxNCBFBw+zUp8C/pJQx6EaefV8I4Qi8CYwXQkQCc4AUACnlGXSB2Cl0Qdg3UsqIDrBfoVAoFArF70AI2WGvzsbvujUppUwCBtZbfqCJuvr3OV7T1z9Ub90r6B64v8bUJvb3CfDJb7RBoVAoFAqF4g+FGkdMoVAoFApFu9Kp7hV2MGqKI4VCoVAoFIpbhMqIKRQKhUKhaFeESok1icqIKRQKhUKhUNwiVEZMoVAoFApFu6KyPk3zu8YR+2+gmZnVFQqFQqH4r6SjxxFLKv6lw861Hla3d6oboSojplAoFAqFol1Rz4g1jQrEgF4PrW9zzYSV87jQDiPrD7o2sv4LP7e5dtLHs/F4emvb635+Z7uOrL81qe1Hk7/To/1G1l8Z3fYj6z/Ut31H1j/dDiPrD2/vkfXbqV97/iukzXUTl7TzyPpvt8PI+q+338j6d+493Oa6W6eMBzrfyPrt1fcUfxxUIKZQKBQKhaJdUQmxplHPzykUCoVCoVDcIlRGTKFQKBQKRbuinhFrGpURUygUCoVCobhFqEBMoVAoFAqF4hahbk0qFAqFQqFoV9SdyaZp10BMCPFXoATYDvwESGCulDK+PferUCgUCoVC0RnoqIzYncBGKeU79QuFEALd6P7aDrKjRcxNDHnrviFoarScvJzDz6EpADw9ewC9XbpSVFbFP36JoodDF+4e44GpsSFHI7PYciK5Re0ju89wMTwOjaaaR1+8GzNz0wb1//5gI2bmJix6ejb7fg4lMjyOygoN9zw8BU9vt2ZtfnvOIDTVWk7G57EtIq22rq+zFT88NooJ7+2nrKoGIeCbhwI4dDmb/xxLatEXb98zWKcbl8e28NQ6XRcrflgyhglv7cXW0oQVD48gIjmfo9E57DqX0aIvzI0NeGdKPzRaLSdTCtgalQXA1D4OTPC0x7WrGZ8fTyQi/SoA707tS5mmhncPxjWrG3EgnPhzsVRrqpnz1D2Y6H2s1WrZ8vkGNFUajE1NuPuZeVw8dp7osMsU5RQyeeFUevb3aNFugHMHw0g8H0e1pprZT96DiVndPn75p24fJqbGzH4quFV6kSFhpFzQ2TztiXm1enmpWZzctA8k2Hd3ZOTcKZzctI/CzDxyr2TiM2EY/jPGNKl7tp4v7nq6oZ1b/7EBTaUGE1MT7npmHiHr9pGfmUdpUSmzH5+DdTebZm0+tiecqDNxVGtqeHDp9W352w83YGZhysInZxPyy0niolLIzy5k0XNzcOrucENNcxND3gr2Q1OjJTQ2l22nr9TW9XXtyvdPjyXwjd242lrwQKAXAGP6OTLxry2PkdVe/drcyIC3xvdBo5WEphWyLTYbgMf8e9C9qxl2Zsa8dTSOkqoaPpzUl8LKakqranj3ePPXouZGBrw72RuNVnLiSiFbL+v7h5cDgR52uFiZ8XloEkUVGh4f3hMBxBeU8eXplJZ9YWzAOzP663yRXMDWi5k6be9uTPBywLWrKZ8fTSQirYh5vq74OFtRXFnN8pAWbDYz5p1lU6nS1HAyLIVtO6IACBzbi7mzBwGwbss5jpxI4r3XpjHE143p96y8TkcIQdp/vkUYGtGlT1+sA0YCUJGeRu5u3Th5DtNmYubqRtbWjciqKoSJCU53zqX44nmKTh4HwGbMOMzcupPzq27MRU9PT5KTWz5OA3i4O/LSk3di3dWCBY991qptOkq3PfpeR2CgUmJN0ubPiAkhlgkhYoQQR4G+gAXwLPC4EOKgEMJDCBEthPgPcBHoIYT4UggRJoSIFEK8WU8rSQjxphDijBDighCin768mxBir379b4QQyUIIB33dfUKIU0KIs0KIfwshDG/G/mlD3dgZnsqrq8MJ8nOtLa/RSqpqtFTXaLlapiE8Lo9XV4fzwjenCPJ3bUaxjtBDF3j81XmMnuzHyZALDepOHDiHV/8etctREfEseX0+d9wXSOSZ5g+A0we6sPN8Bq9sPE+Qj1NtuZGBIHiEO4cuZ9eWLRrjycFLWa2yd/pgF3aeTeeVdecIGujcUHdUTw7V0ymtqsbc2Ij0gvLWaXs7siM6m5d3XSaod7fa8j2xuSzbE83yw/EEdNcFAzP7OnI+82qrdC8eO8/c5+YzeLw/F46dry03MDDg7meDmf/ifVSVV6LVahk4ZjB3PzOPaQ/MJPFCQqv0AS4dv8Cdz8xn0Dg/ohrt446ng5m7uc+6bgAAIABJREFU9D4q9ftoDTEnzzPjqXvpP9afmOPnasvtuztx2zMLue3ZhaTH6E4gI+8OYvqSYKzsrek/zr9Z3cjj57lb74uLRxvaOeeZYIJfvI/KCp2dgcFBzHkmGP/Jw4g/F9uizWGHL7L45WBGTPLl9KGGbTn0YMO2HHj7SB55aR4TZ48kKSa1sVQt0/xc2RmRxqs/RjB5kEttuZGBYN5oDw5F6tpbXGYxr/10ljVHE/n1TFpTcg2126lfT+vlwM74XF4NiWGyp31t+VcRV3jtUCxbY7IY6WaDi6UpkbklvBoSg7OlSYu6M/p0Y0dsDi/tjWaKV53unvhcXt0fw/JjCQS4WZNQUM6f91xm6Z7L+Dl3bZUvpvdzYselLF7+9RJBfer1vZgclu28xPKQeAJ62GBvYcysAU6UVdWQXVzZsu5kb3bsjeaVt3YxZUKf2vJRw3vyzvIDvPnhPiaO1QXQr76zm4Sk/BvqWFpa0tV/KK4LF1F84Wxtef7BfbgEL8Rl/kLyQ/ajyc9D1tTgPG8BaCWagnzKYqJxunseTvfMpyTyAkZdrXG59366zbqDsrKyVvfJpJRsHn9xRavWvRnaQrc9+p7i1tKmgZgQYigwH/ADZgLDgTLgK+BTKeVE/ap9gC+klD5SymRgmZRyGDAYmCCEGFxPNldKOQT4EliqL3sDOCCl9AE2Au76/fcHgoExUko/oAZYeAM7H9UHfmGN65xtLcjILwN0B+lrfPHrJZZ+c4qjkVkEj/OsLV88zZuNR5Na6yAAujnbkp9dVFtcmFdMQnQag4fVHbzGTPHnjSe+4LvPtjFqkm+zss42ZqQXll9n8+JAL1YdTeTadKJ9nCwxEILYrJJWmetsY16nW29O0sWTerPqUEKtblp+OfP+fpRl68/x1FTvVmm7WJmSUVwBgLbRfKeLh7vz+mRvQhLycLAwwcfRkmNJBa3SFXof2zrZUpRT2KAuKzmTdR+uwdzSHAMDXdM/tPEgv3y1hb7D+7VKX7cT3T9rRzuKchvuIzs5k03Lf8Dc0qJ2H62Uo6ujHcV5hdfVRx0Ox9O/zr7ivCKMTIwxs7RolbKtk+11dmYlZ7L+ozWYd6nzRWV5JRcOR+AzelDLNuuNdnC2JT+7Trsov5jkmDR8hjVsBz99uZ29m4/Re6BHk5ouNuZk6Nubtn47DurD6pB4Gk9UFzzag/XHk1q0FdqvX7tYmpJRUnGdzQAWRgbc1tuRvYl5pFytYLCjFStvG0RcQVmrdNP1wU9Now++eGgP/hLYm5B6gcztfR05knzjwOY6bStTMq420fdG9OT1qd6ExOfhbmtBYUU1HxyMw83aHHdb8+Z1nazIyNJdMNXUC3h+2RXFFx/dyYpP5rB+6/mmNq/FyMgIYxs73UK9PqQtL8fQwgJDcwu0lRVoCgsxttWtZ2xnh6aggK7DhpP67VekrvgCm1F12eLCk8e4erV1F3N/dNqj73UEogNfnY22zoiNA7ZIKcuklFeBpubhSZZSnqy3PE8IcQaIAHyAAfXqNuv/hwMe+vdj0T1zhpRyF3DtLD0ZGAqcFkKc1S/3arxzKeUKKeUwffDXgMyCMpxtdSe4+ufRa8ervOJKLEx1d3QfmtqHnKsVHDzf/K24kB1hrPx0K6VXdQfg3KwC7Byta+sjI+K5WlDChpV7uBgeT3pKDns2H+fdFU/x5789wLY1B5vVzyyswMXaTGdzvcFaBrh2ZdEYT3zdbVgwqidj+nSjp4MFi8Z4MH2QC7YWzV+ZZxaW42JjrtetKx/gZs2i8b3w7WnLgjEeteUVmppm9eqTUVyJs5UupS4aDTDz9ekUlmy7wCMB7oxwt8G+iwnPjPFklLstnk2cDML3nuLnLzdTXqzzcWF2wXW315x6OhP84kKkVlKQpTtpTZg7kfuWPcCRTSEt2hyx7xQ7vtpMRYluH0U5BVg7NNyHY09n7l56H1KrpTCr+RPjhQOn2Pf1JipKdcHH1ZwCrOwb6kUdDudqdj7+M8bWlp3fd5JBk0c0qRu+9xS/fLmZ8pJ6vnC43hfz/rwQKXW+qCitYOs/NjDj4dmYWpg1qX1k52m+/2wLpVd1NudlFWLnWKd9KSKeooJitny3h6gzcWSk6LKx8x+fRfD/zSRke2iT2hn12lv9JtG/uw2LJnjh29OWe8fqgiUzY0OcrM1JyS1tUq8+7dGvATJKKnGxvL4dWxob8vYEb94/kUCppoZJPe3YlZDLQ79ewNHCBGvT5p8Kqa/b+CD9dfgVntgeyeIhuszH7X0d6d7VjB/Op7doL1zre2bX2QzwdWgySzad55GR7mQWV1BUrgHgaqWGLsbN31zIyCrG2dFKZ3M93SceGkXww2uY99AaHn9oZIv2VVdXoynUH9LrBYoG5ubUlJdRU16OgakZxjY2tetpCvIxtrUld/dOPJ59EY/nXiR3z069hKQ0Jpry8tZl6/+otGffU9xabtWvJmuPnkIIT3SZruFSygIhxCqg/pngWk68hpbtFcBqKeUrv9Ww3eFp/HWhPxMHu7D/bAbLHwlg6TeneHxmP1zsLLC1MuGtH88y2deFBRO8OHE5G1c7C7749XKTmoEzhxE4cxhHdp/h3x9spKpSw+I/zyH6QhKJ0WlMnzuGMUF+ZKfns3PjUVzdu+Hj78WX762nrLSCoNlNn3QBdl3M4M27BjKxvxP7o7L45F5/nl8bwVM/nAHgo2A/fjyRTFmVLlAa4WVPX2crCsqqmtc9n8GbcwczcYAT+y9m8cl9Q3j+hzM8tVqXSPxogT8/HktieC875gzvgZmJIdvCW3eraFdMNm9N6cskLwf2x+Xw6W0DeO7XKOYPdqW/oyVWpkasPZfO6dRCfr2cTfeuZiwa2p3EJm59Dp0SwNApAUQcCGfz5+vRVGq468m5JEclkhaXhs+YQRz8aR9SKzEwNMC6mw2hO0+QkZBORWk5I2aMatFm/6AA/IMCOHcwjJ//sZ7qKg2zlswl5VIiGfGp9B81mMPr9iKlxMDQkK4tPGc1aFIAgyYFEBkSxu4v1qGp1DD18XtIu5xIVnwqrv08OLhyK17DB7L/m81MfmQOUkpSo+IZEzytSd1rvjh7IJwtn69HU6XhTr0v0uPT8Bmt94Ws88Wad1ZRU13DwbV7GTTBj95+N85sjpsxnHEzhnNsTzgrP9K15QdemEPsxSSSYtKYMmcMIyf7kZORz55NR3Fxd+SXNQfIzyqktLiMOx+Y0qTdu8+m8+Y8PyYOdObAxUw+XjSMF1aH8fTKUwB8eP9Q1h5NBGDW0O7sjGj9rZb26NcAuxNyeXNcHyb2tOdAUh4fT+7HC/sv89HkvhgZGLBkqDu/xuVwOqOIN8f3wd/JCmMDA4oqq5vV3Rmbw9uTvJnUy559CXl8Nr0/z+66xPyBLvTvZklXUyPWXkjHp5slr433Yn9CHq9P6M3bh5p/hhJg1+Us3prej0l9HNgfm8Ond/jw3LZI5vu50d9J3/ci0si4WklhuYbXg7wxMhRcym4+k75rfwxvvTKFSeO82Hc4jk/emcXzr21n14EY3n9jJgCHjum+v6VPjsennxPvLpvGmx/uo6reRVxJSQlXz4ZTcvE8VgN9SVv1DW4PPILdxCAy168FwH7KdIzt7BGGhmRuWocwMsLY1o6ufkNIX7MaAMsBAwEoi43GwqsPN4OdjSV/fTEYXx8Pli65g+X/2nZT27eHbnv2vY5AiMY5bcU1hJRt5xwhxBBgFTACXdB0Bvg3YAmUSCmXCyE8gO1SyoH6bXyB/wD+QDfgPPCSlHKVECIJGCalzBVCDAOWSykDhRD/AlKklB8IIaYCu/XbOgLb0N2azBZC2AFW+tufTdks1aTfOtSk33WoSb/rUJN+16Em/a5DTfpdR2ec9FtK2aF38TLLf+6wSMzZfHanukPZphkxKeUZIcQ64ByQDZxuxTbnhBARwGXgCnCsFbt6E1grhLgfOAFkAsX6gO01YI8QwgDQAEuA1v1URqFQKBQKRZvTqSKjDqbNb01KKd8F3m2mPgkY2KjsgSbW9aj3PgwI1C8WAdOklNVCiFHobmtW6tdbB6z7zR9AoVAoFAqFooPorCPruwPr9VmvKmDxLbZHoVAoFApFE6hJv5umUwZiUspYdM+UKRQKhUKhUHRaOmUgplAoFAqFovOgEmJN0+Yj6ysUCoVCoVAoWofKiCkUCoVCoWhXVNanadp0HLHOiFCjzCkUCoXif4yOHkcsr6LjxhGzN/sfHkess/LJhbYf7PD5QVMZsfFIm+uGzh0HwPLzbW/z0sFTefFU2w+8+mFAULv5GGDqrrb3857p4/hL+N42131r6BQ+ONf2vnjJV+eLgavbfiDMi4vGM2DloTbXjXpoAm+faXsfvz5EN4J4e7W5Bw83P+XYb+G78ROZs6/tv7vNQbpBTPem/trm2lO639Zu3197tWOAU9ltP0BqgOOsdht4tb0GilX8cVCBmEKhUCgUinZFDV/RNOq2rUKhUCgUCsUtQmXEFAqFQqFQtDMqJdYUKiOmUCgUCoVCcYtQGTGFQqFQKBTtilAZsSZRGTGFQqFQKBSKW8QfJhATQiQJIRz074/f5LaBQoi2/+2wQqFQKBSK340QBh326mz8IW9NSilH38r9xx45TdrFWLSaasY9GoyxmSkAhWlZRGzdC1Ji4+aM/11TOLF6C1XlFVSVljPhiQWYmJs1qWtmaMCf/b2o1krO5BSx+0oOAKOcbbnHy4XjmQVsjM/A3dKc+/t2RwDJxWV8H5PWos1xR06TfjGWGk01YxvZfHbrXkBi4+qM311TiAkJ5fL+4/jeEUTPYYOa1b1y/DS5UTFoqzX4PnAvRnpdgLPf/kh+bAKT3n+ttqzoShrH/vZ3pn7yFkZmTfuirX1samjAU/29qJaSc/lFHMzQ+banpQXzPbsD8FNiKsklZQA8PcCL8uoavo5JIsjVkeluTmxISiU0p6BJm5OOnSY7MgZtdTXDHprfwBenv/mR3JhEZny4DID4g8fJj0+mLK8AG3c3fO+9o0nd+COnSY/U+WL04oa+OL9N5wtrN2d875xC7KFQovcfZ/AdQbgPbf67u4a5kQHLRvRGo9USllnEr4k63zw8sDtulmbYmBrzwel4ssqqWq33+qg+aGq0nMos4teEbAAeGdQDNyszbE2N+FtoPKWaGl4K8KK7lRkP7jrfKu3Eo6fJjIpBq6km4OH5tb4AOPn1j+TEJHL7Rzofp0VcJP7QSQB6TxyDq2//JnXbo0/nhJ6i6HI02upqvO5bgKFpna1JGzejrarCwMQEj7lzSNn2C2UZGRh16UKP229DVldz+V9fYtmrFzYD6uzWVlaS/tMPCCMjuvTpi03ASAAq0tPI2b0DgG7TZmLm6kbm1o21+3C+cy45u3dQlZdLTUkJLvfMx9jW7oZ2n94fTkxEHNWaauY/OxdTc53dWq2Wnz7biKZSg4mZMfc+N4+ivCJ2/7gfpGToJH+8Bva6Jd/fNdq6LR/fE05URBzVVTU8sPRuzMxNG9Sv/GgDZuamLHhyNiHbTxIfmUJ+TiF/em4OTm4OzWof2xNO1Jk4qjU1PHgD7W8/3ICZhSkLn5xNyC8niYtKIT+7kEXPzcGpe/PajfFwd+SlJ+/EuqsFCx777Ka2Vdx6flfoKITwEEJcFkKsEkLECCHWCCGChBDHhBCxQogAIYSdEGKrEOK8EOKkEGKwflt7IcQeIUSkEOIb6v2kQghRov8fKIQIEUJs1O9njRC60UiEENP1ZWeAOfW2/bsQ4i/699OEEIfFTYbIiaHnCXx8AV6j/Uk8ea623MbNiYlL7mPik/eTHZsEwKhFdzHhsXtx9PYgLzG1Wd1AN3sOpuXxtzNxjHOpO0ieyCzgh+i6YCulpJx3w2N5JzwWHzurVts8/vEF9BrtT2JoQ5sDl9xH4JI6m70DR9B30qhW6WaEncX/kYW4jRhKetjZBnV+Dy/A0sWpdllbXUPywWM4DfZplb1t6eOxjvYcycrjs8g4RnWr8+1dPV3556V4/nEpnjvcXXTrOtkTXVRSu86+9Gx2p2W1aHPa6XMEPLqQHiOHcOV0Q18Mf2QBVi6OtcteE0cz/JF7sXJxxHPCyGZ1k06dZ9xjC/Ac7U9So+9u/BP3MX7J/eTofdFnwgi8J7buu7vGZHcH9ibn8uaJOAJ72NeWf3sxlbdOxrE9IZvhzjat1gvq6cCepBzeOB7LRPc6X39z4QpvHo/ll/hsAlxsKNHU8PqxGAorNa3WTjl9jlGPLqTnyCFcOdXQxyMXL6BrPR9nRsYw9P67GbboHtLORjar2x59Ou9MBL0X3Y/DsKHknYmoLa/My0fW1NBrwXyk1FKZn48wNMDAyAgDQwOMzC0AMDA1RVtVhaldnQ+Lzp6hq/9Q3BYu4ur5us+fd3AfrsELcZ2/kLyQ/VTl5yFranCdtwC0kqr8fLpNm4nbgj9hEzCSkpjoJu0+d+QCC5cGMyTQj7NH6gJkAwMDFjw/j0WvLKSyvAqtVsu+9SGYWZgiDAQ2Di23kfb6/q7R1m057PBFHnkpmBGTfAk7dKFB3amD5+jVv0ftcuCskTz80jwCbx9JUnTzx/pr2otf1mmfbqQdevAcXvW1bx/JIy/NY+LskSTFtKzdmKSUbB5/ccVNb9exiA58dS7aIofXG/gY6Kd/LQDGAkuBV4E3gQgp5WD98n/0270BHJVS+gBbAPcm9P2BZ4EBQC9gjBDCDPgauB0YCjjXW/8VIFgIMRH4HHhQSqmtLyiEeFQIESaECGvug1l2s6Mk//oMSdzRMLr79qtdLiu4Sk58Ck59m79adDQ3JausEoCaVkwtFdTdgdCswhbXg7rB8qy62VGa17LNrUcnbO5gR8UNfNFgHzv24jVt4k2N3NdWPnYwMyWnQudbLXW+7WJkSGl1DWXVNVgYGWJjYkxvK0vO5rXOrzeii4Md5a3YvqZKQ2lOHl1dnZpd75q7LB3sKLuBL+KPheH2m747HU4WJmSW6n3TqN2ZGxkwzcOBAyl5N6FnWqenbVhnYWTAdM9u7E9uvV59rvmiSzc7SvOb97HH6GEc/uxbDn28gt6BrQtO27RP6401tbenqqBOs7KwAFM7W12dnR2VBQV0nzkD70cewnrAALKOHMXU3p7BL7+I130LubK9btT76oKC2kyWMKg7PNeUl2NoYYGhuQXaigqqCwtr1zO2taO6ULf/mooKis6E0dXXvxm7df/snGwpzC1qUJWRlMl/3l+DeRdzDAwMyEjKZMSU4dy2aDq7fmh5poL2/v7aui1f84WDsy35OXX2FuUXkxSThs9Q7warr/tqO/s2H6P3QI+WpetrZzfUTo5Jw2dYQ+2fvtzO3lZqK/67aItbk4lSygsAQohIYL+UUgohLgAeQE/gbgAp5QF9JqwrMB59JktK+asQoqmz/CkpZape/6xes0S/31h9+Q/Ao3qtMiHEYuAw8JyUMr6xoJRyBbBCv21tb44OCSU34QqVpbrbVyW5BVjqD6jXiDsaRnFOAf536aZRKckrJPSHbYxbPA8Dw+bj2uzyShzNTYgtKsWghWAlqLsDLhamLd6WjAkJJTexoc1d7K+3uSSnAD+9za0h5chJCpOuoCnT6Zbn5mPWyBeNKUpOpaKomML4JBIPHKXPzKDr1mkvH+dWVNLNzISE4tIGv84p1QdgSCirrmGwrTU2psYs9HKnl1UX3CzMSCuraPZzJR4OpSDxClWl5QCU5RZgbt/yVfeV0Ai6D/drsj72UCh5CVeoLKnzhUUjX8Qf0313vne2/rtrTFZZFU4WpkQXNPRNF2NDlgV48Ul4ImXVNTehV4mThSmX80sbxNxdjA15bWRvPg67OT2A+MOh5CdeoVLv49LcArrYNe/ji1t3M/WNZwE4/sX3jHv6wevWaY/2ln38BCUpKVTrNSvz8zGxrdM0tbElr0CXIavKL8DU3682qDKxsqIsLQ19Yh9DU5MG2ka2tmgKCzDv4Q71Ag1Dc3NqyssAgYGZGUY2Nmj0wZ+mIB8rX39qystJX7cGpzvnYniDxwJC95zmSkwq5SU6HxdkF2DjYN1gHRcPZ/708kJ++mwjeZn52HazxsLKHFNzUzRV1ddpXqO9vr/GtFVbPrLzNEkxqZQV6+zNzSrErludvZci4rlaWMyW7/aQEpdORko2Lu6OBD82i/ioZA5tD2XOQ9Oa1o5OpfSqTjsvqxA7x4baRQU67eR62vMf12mHbA/l7ia0OzPqV5NN0xYZscp677X1lrW0TaBXX7+mlZqDgDzA9WZ21DdwBGMemku/SSM5/O+fiD8egedIXzKjE7i46zC5CVc4sXorxdm5HP9uEwC73v83skZL6A/byE1qPqUckpbHRDcHXvT34mhGPm/or4gG2llxbx9Xgro7MNHVHm/rLjw9yBOXLmY8M9izWU3vwBGMfnAufSeO5MiKn0g4EYHnCF+yohOI1Nsc+h+dzSdW6WxODrtATEgoUbuPkH4x5oa67uNGMvj+e3AfP4qz360l7VQErsP8yI9NIGGvbt7BqPXbKEy+wtmVP1Kj0TD8qUcYfP892Hh54DlpbIf6+Gh2HmOdHHiqvxcnc/L58yCdb7ckp7OknxdL+nuxLSWDw1m5fBYZx5r4FM7lF5JWVsGIbrZMcXNkVg8XBttaX6ftOX4EQxbNxTNwJGHfriUl9Aw9hvuRG5NA7B7dnHjn1/1MQVIqp79ZS41GdyvuSugZeoxsOjPRZ8IIRj44F++JIzm24ieSTkTgof/uonYfJjfxCqf+s5WS7FxO6r+7lPALxB0K5dLuI2RE3vi7a8z+lFym9LTntRG9OZSax3tjdL55Z7Q3ViZGPDrInQDn6z93U+xLzmWKhwOvj+pNyJV8/jauLwDvjutLVxMjHh3cgxEuuhPPX0b1pp+dJUuHN9+OvcaPYPiiufSeMJLQb9aSfPIMPQL8yIlJIFrv44iffiY/KZWTeh+7B/hx8t9rOPnvNbj6Dbihbnu0N8fRo+g1PxinsaOJ/34NeWHh2A/x52p8PBkHQzC1t0MYGpK4bj0YGGBqZ8eVX3cQ//0a0vbsxWnMGIpiYolb/T3RX39LtxEBtdrWfkO4GhFO+trvsRrky5VV3wBgPzGIjHVryVj3I/aBkzGxs0cYGpKxcR0YGGBiZ0faf1aiLSsjZ9evlERfus7uEVOHM/fJuxg5PYC1n27gzKFz+I0bTEJkEoe3HaUwt4j1n29i3d83YmBogK2jDZPmBrJlxS/8+PF6xs1u+tHd9vr+GtNWbXncjOHc/8xdjJ85nO+Wb+TUwXMMmzCI2ItJ7N18jJGT/XjkpWDuenAqA4b0xsXdke1rDrD6083s3niEkZObvsAaN2M49z97F+NvG87KjzYSeuAcwxtpL365ofYvaw6w+pPN7N5whFHNaDeFnY0ln7/3ML4+Hixd0vTzqIo/JkK24hZZkxsL4QFsl1IO1C+v0i9vvFYHHABypJRvCyECgU+llP5CiM+BbCnlO0KIGcAOoJuUMlcIUSKltNSvv1RKOUuv/08gDPgJiAEmSinjhRBrASsp5SwhRE9gLxCo1/w/KWVoM59Bqkm/dahJv+tQk37XoSb9rkNN+l2HmvS7js446beUskNTVEVVu397sHGTWJtM61Tpt474nedfgaFCiPPA+8AiffmbwHj97cw5QEprBaWUFehuRf6qf1g/G0D/IP+36IK3dOBh4Bv9M2UKhUKhUCgUfyh+161DKWUSMLDe8gNN1N15g23zgKlN6Frq/4cAIfXKn6z3fhe6Hwc0JqjeOuHoblMqFAqFQqG4RXTG8b06CuUZhUKhUCgUiluECsQUCoVCoVAobhF/yJH1FQqFQqFQ/DfRqZ6f71BURkyhUCgUCoXiFqEyYgqFQqFQKNoVNaBr0/yuccT+G6g/sr5CoVAoFP8LdPQ4YsWa/R12rrUyntypor7/+VuTUkpxMy90A8Te1Db/jbqd0Wbli86r2xltVr5Qvvgj+6Jjz7S6jFhH/XU2/ucDsd/Ao0q33bU7m257aivd9tfubLrtqd3ZdNtTu7Pptre2op1Qz4gpFAqFQqFoZ1TepymUZxQKhUKhUChuESojdvOsULrtrt3ZdNtTW+m2v3Zn021P7c6m257anU23vbV/F7qpoBU3Qsj/8V9NKhQKhUKhaF9Kqw91WLDR5f/bO/Mwu6oy3f/ehIQwKtErogyCVxAkzCCIIkJrayOKE4JMFwFBL5N2IyhqgjZXRbBFbFDQxhZQWmgBGQK0gRDmIYOEgFFkbBtRBhXBhBDe+8daJ7XrpKpO5ay1KnXC+j0PT7L3qXrP5mSftb/1jSu9vaesvuoRq1QqlUqlUpieso1GlJojVqlUKpVKpbKcqB6xSqVSqVQqRenF/l4jRTXEOiDpg0O9bvtnI3UtlTxIWs32s4kahwAzbP8m02U1tb9u+/hO57rQnWZ7907nRhMKGb77ARvZ/rKk9YFX276jS72JQ71u+6ludEcKSW8BXkdj7bb9owy6Y4G123QfyaC5B0tf7zdTdNveY8027VH575f7Ph7G+423/XwJ7RWduEb8B+G+fQjY2/bTbT/zDuBfGqfeCOxj+1JJPwTeDvw5vvZ/bM8Z8j1rsv7QSLoSeAtwXTz1DuAW4I+AbX+8C83LgUE/eNvv6+JSm/r/CziMpRfAZb7WqPdK2080jvcHdgDuAc5xlzdRKd2G3muBdYC7bT8v6VXAsYQvxmsStU8C3kb4jGcCM4AbO33hhqk9y/Y2befutr1Fl3oTgFWB64Fd6UvWWBO42vYbEy4XSRsCR7H0/ZZ0H0fts4AXgd1sbyppLeBa29t3qfcg4bsnYH3g6fj3lwOP2N4w8Xo3Bo4DNqD/Z7Fbim7UPg94PTAHWNwn7aMTdY8CJgOPEz7rlm5X91tD9ypgATC3oYvtk1J0o/bhwElRv7VO2PZGibrA6OGrAAAYuElEQVTbASfS9+8n8nwWWe/jNu3phDXtoXi8A2H93DJVOyfPvXDziBkbq660c9fuN0mnAE/Z/pqkE4C1htoER8PtfmBd289FQ+wK2xcP9z2rR6wz44DNbD8GIGkd4Ie2D07QPDX++UHg1cD58XhfwmKYymXAjcAv6FuwU7gW2AZA0hcIBsiPgfcCmwKfHmW6SDqWsKDeD6ws6Uzg68CPgG271W1he3J8n1UIRu9xwLeAsQnX/EngU8DrJd3deGkN4Obur5bDCQboawhGY2uR+gvwnQTdFpcCPwAup/HAzcSbbW8jaTaA7aclje9WrGVoSToHuMT2VfH4PcBeGa73IuC7wDnk+e412Y6wFuV+oB0DbGL7ycy666YaMEPwT8DmzY1cJi4gfJf7GY8ZyHoft/FV4GpJ3wZeC7wHSHk+vdR5P2HDCvDvwHRgqGjEh4Gptp/r9g2rIdaZ9VpGWORxwk66a2zfACDpNNvbNV66XNJdKdqRVVPDWG00dxcfBN5m+1lJPwZmjUJdCKM+NrH9VAwD/BrY2fbMRF1gieG4M7A6MJvwYLgxUfbHwFTCwnpC4/wzKSEX26cDp0s6yvYZidc4EAtsf7uALsCiGOIyLPH25nhA7mj7sNaB7alxJ5zKC7bPyqAzEPcQNm6PdfrBZeRR+sIoOZkq6V22ry2g/Vug6wffEPzR9s8L6Ja6j7F9jaQjgP8CngC2tv37HNq9iqRP0H/c09m2h9tjbe3GM//3hJD9UOwDtIfbT5b0JWAacILthUMJVEOsM9MkXQP8JB5/lOBpysFqkjay/QCApI2A1TLoXiHpH1q7/QysImlrQpXt2FZ+le1FklJ2/aV0IRgHT0W9RyTNz2WERT4IvABcCdwA3Nrpy9YJ23+W9FfCQvpwhmts1z+jUI7R6ZImEzycSz4D26nGNMC3gUuAV0k6mbD7/EIG3f+JxnTLG70f8D8ZdC+X9CnCNTc/ixy5S68E7pV0R5t2agj4AWB6TMNo6qbmct0GXCJpDLCIvjDfmom6AJ8DbpF0O/2vOSlMC0yW9H3CA7Spm5oLXOo+RtIXgb2BXYAtCP+W/2j7yhz6uRjJZP1odA1qeEn6BWFT086JbTqWNKgHOkbIJgHXNE5/jmDAjY/XcDzw5aGutxpiHbB9pKQPEG5yCJb1JZnkjyV8aR6Ix68jz9DWY4DPS1pIngXwMfos/qckrWP7MUmvIBgj3fL7QroA60ZXfYt1msepC3YMM6xJ8Iq9Ezhb0h9svzVRd7Gk+ZLWT02WbmewHCNCuDaFScABwG40cozicRK2L5A0E9idcB/vZfu+VF1CGsBkwsMRQo7fvhl0D4p/Htc4ZyApdykyJYPGQDwS/xsf/8vFN4GdgLkFwqnfI+Tt5g4hHkxIvB5H/3s5yRAreB8DvALYwfbfgFslXQ18n7BJrAyA7b8b7DVJjzeeResAfxhCam9CisOihnbLm7ZQ0rmEaMmQ1GT9YSBpA+ANtn8haVWC9+aZDLofIVjSGwLvIxQFnJjJk1Cc6GpfOSU2XkpX0kFDvW7737vVjvqbE3La3k7I3XmUkKz/pRTdqD0D2Bq4A1hS3Znq+ZB0HwVyjCTdH3WzVWmpx6sbK0vu411t584bRNJs21sX0J1ve5MCugPdz880H+ArOgsW3zpixsaEsTulJOt/A3iykaw/0fZnB/nZ24DP2b6+ca5lxIlQWbnA9gkD/X6L6hHrgKTDCF6qiQRvwmsJybg5Sv6/aPsiSWsQvAenAmcBb04VjlU5bwAmtM7ZnpGoOa7N8l8cDdOuDCZJW9i+u/287cXdajY0lhha0XOF7b+kaLbxNUJO2LeBOzMvqF/MqNWkVI7RPYSqw6F2jsvKTIaobiRsXpYZSd+yfawGqVzOYOweOND5DOFfJD1D3zWPJ3htnk0N9Um6noE/i1SPZivkOZW8IU8I+WefIBSI5AwB3yJpM9v3Juq0MwtYj/738e8lPQ4c1k3axGD3cIsMIeuXKl8DfqrQouhhgterVVF7hO1D4/HrCP+mN7T9/gUxB1CE6MMRnd6wGmKd+b+Elgq3A9j+jUIbhBy0wkN7EMqNr5T0z6mikg4lhCfXJdwIOwK30mWoSKFnynnABEmzgE84lkrTqHzsgtkxLHsh8JPci5+kY4DPEo1RSU8CX7J9oaT1bD/arbbt98aKyfVz72pbxRwFKJVj9HLgV5LuzKXrctWN58U/Tx3yp7qn2Y5gAmHDNov08C+212j9Pe6230/4bqfSDJ1MAD5EemoAwIPxv9whT+gLI3+ucS5HCHhHYI5Cm5OFZGpfQUikv9j2NQCS3kX4nM8FzqS7zXfp6vvM9EZDV4fq4aUcLbbvAg5tHD9EcMy0/9wyP2draLIDkm63/eaWK1zSSsCsDF9MJF0B/I6QY7QN8DfgDif2f5E0l/BAuM32VpLeCPw/20M2px1C705Cn5p5kj5MqOo7wPZtKSEChVLuAwgLx0cJYbifABc2DL2ukDSFYEAf2VYMcTpwE2EX+r8T9PckLITjbW8oaSvgyzl2oQU9H28f6Hy3hp+klW0vzK3b9h5zbU/qdK5L7fHAxvFwfokwkaSXE+7nd+fWjvqlQnR32N4hk9bqALb/mkOvJDENZSlSi2cGuY/vtr2FpDm2t0rQvsv9q+8HPLe8WbD4thEMTe7YG1ZfpHrEOnODpM8TKvzeSejzdHkm7b2BdwOn2v5TTAw8rsPvDIcFthdIaj0sfyUpJe9hvO15ALYvjrlGP5N0PEO4xoeBbd9DqFQ5UaER4T7ATZIesf2WBO39gEm2FzTe7AFJexOa8X4sQRtC4vQOhB4z2J6j0Ng0mVKeD9s3DJTvmCB5K2EDcajtA1KvbxCKVDdK2pXQI+ghwlZ9PUkHpYbvB+BZugyjtqP+Uz7GEHITFwzy48ui28xfGkPos/eyDLqbEzyQE+PxE8CBrbUkUXsc8En6iqimA99LNaZtPyxpS0L+J4S8z1+maEYei+vlhfH4o8DjMR82NYeuvfp+Q/JU32dFdbT1oFRDrDPHE9yRcwmNMa8iVKQkE5PRf9Y4fow8+Tv/HXfilwL/JelpQqy7WxZJerVjb5roGdsduIKQN9ct/XYtDuM+7pD0j/QtsN2yuGmENd7jb5J+5/ReQYsc2k30k0/UXIqYWH+pQnuIIRM+O1Eg33G8pI8Bb9EAo8CcZ/xXqerG04B32Z4PSzri/4TEZr9teTtjgM2An6ZoNtiz8fcXCEbk+zPoNvPxXiCEEw/JoHs28JlWInM0fs8hFCWlchbBU3xmPD4gnjt00N8YBjGd4TD61uXzJZ3t9P57HyPcx5fG45vjubHEHKQEPk1f9b0IUwEOT9SsjCDVEBuCuFuZ5zAC5pzlfT3DxfYH4l+nxETclwFXJ0ieAKwt6S9AK5w3n1AxeGSC7jcAomdmia5DP67UsNbvJO1ue1rzpKTdCOHgVOZFI2SspDcARxNGXyVTyvNB/nzHIwgeqpcTpiGI/sZosiEWk6+PUShoccbw1riWERbf59fRy9IVrTAt/XPPXgAetv3fCdeJ+uaMTrWdy6hD0kdsXwTs3vKmZGY1N6rJbE+XlMtTs31bCsd1knJ4rg4hdMF/FsJnT/D8JhliDhMAjhrk5fsTta+Oa1BrVNmvnNjTsAw9FS0cUaohNgQu2NOpBJLWtP2XtlDD3Pjn6kC3FUUzgFOAAwm7ZRG6DZ9h+2RJW7m7GYsXS/pWu66kMxxKh7vVhWAYXSbpJsKOH4JBszOhVUgqRxFCqgsJHfGvpUPTvmXg44TwKfR5Ps4c9KeHz0KHmZsAxHzHrr14tm8ihJEfBL4b770vElpvJBedxGucREh0b4a3Dooh7RRmKjTubIY8U6ZalAzT/oNCGf0J5POuQUh0vwi4mO4LbobigXg/tAok9idUUuZgsaTX2/4tLMn/zDFSSm06i8lgQUSP6z+xdDPl5F57kW0b2ltKylKpWxkZqiHWmbUI3o+sPZ0K0ZrT2Aw1tEipKDoVWAXYwLF/mkJLiFMVhtm+m+7yYE4lDKPOrdsKn25OcP+/KZ6eARw+UMiyC/a1fSKNTsySvkZi+DDyGkJvmrlRd19C89/zhvytzpTKd9zf9imS3krmNiyExp3t4a2zSQ9vHUHwELYa+95ImrFbMkx7NaHtwerRK90itVHzk5KuBTaUtFSoPsMa93HCYO7W//uN8VwOjgOubwvH5ZiveC5wu6RWKHwvwhzVVFozSL9P5hmkKteoOSttaRyVBrVqchA0AhVhvYJCw843uO1miaHbJ4D32L5ttOiOBJKuAi6wfUE8/g6wiu3k3Jq4u7+IYETuQvAYvtd20jxAhVEzhwDvIjy8rgG+3/75d6Hbqij+KqGL+o+VqZpP0i/bQlADnltGzWbKQRaiEbofId+n3aix7WQDRNJltnPkhLX0xhM8YecxQG7VaF/jJK0MtIqQWikNOXS3AVoTMm60PTuD5kzbSfmHQ2gXadScm+dfvGvErm/8mO16yuqrhtggSJrlMMbmvIIVYVmJC8iguMuO/ZJ+bXvjZX1teenG32+2gOj3Ehnm3Sn0EPs58G8Ez92fbB+TotmmvzEhsfcR4AMO40tGJSrUhiVqX0Low9UMb23byIPsVvcy4KjcKQeSDrGdw4MyYkhatz2PTdIrY15TN3rFGo0O5G1s0+7K86jCkxwU2un8gQIzSCVdBBztvtE6o5LnX5w5gobYtj1liNXQ5OCMREVYbk6Lf04g5EP9kmB4bEHIf9mpS917JR3YnnMgaX8gZV5aKd1+LSBy0rZgH0owlm4GTpI0MWVhVej/1lysJhKqqm6POR/JvesGeM8ptqckypRqwwJ94a3/jMc3kicEVSTlwPYPVGawessI+TrwKsL3OtcQ7askfaLlfZb0IUKvwG43QiUbjbYqR19FCE9PI3wO7yAUy3S7LheZ5NCg5AzSUo2aKyNE9YgNwkiEGkoh6WfA5EaO0ebAFNsf7lLvtYQF7m/0T3xfheCt6aoKsZRuSWJievNL09p5GcB21wurBmkm2cKJTSUHec89befqi5cdhbEiJ9LfsHGqUVoq5WCwfB0nDpmP2vcDezrfsOiW7iSCZ3c6IT/xFYSig9Rqz2KNRmNu20EtL1A0/n9o++8TdQec5GB71LaD6JX0mUUvzh4xY2PcmK17yiNWDbEO9GioYZ7tN3U614XubvQlvt/rttYQo023JAqNYa9uVApuA3yl2/BvZWAkzSdUm91Do/FlqlGqvpYQQ57rQrdYvo6km23vnFs3au9FCP8+A+xiO6mlQtS8D9jD/RuNXmV70xzaTZ2Y/zgvVVtlJzlsTugr15z/O6oS6ktSDbHBqaHJDpQMNRTkbi1dmr/UcO1lxfZ1wHWpOiOlW5gv2P5poUrBIqhvxNNOBKPmVuDTLtNDKhd/LOSxeyehWXOT9wxwblkpNVgd4C5J/0EIhzdDUElpEpJ+QPDibUEIR16h0ELmX1N0KdtodJqkawhNeCF0qv9FBt1SkxwmA7sSDLGrCPfaTSRUNkq6yfZbB8iHzRWyrowQ1SPWgZKhhlJImkD/8R8zgLOcp21DhbKVgqWQdBvwr/Q9vPYhJKyPZuNxd0Ju0TQyGB+SPklo27ER8NvGS2sAt9jer/urBYUGylsB2fN1JJ07wOnkNAlJxwKnt7x4kl4GfNN5KoBXplCj0Zgz1xpFNMP2JUP9/DA1JxI64O9CMG5mEGbIpibrzwW2BGbb3lLS2sD5tt+Zes29wqIX54ygR2yrnvKIVUOsA71SGtxOrOpb343u4ZV8lKwULIXikOG2c0mtIEoj6XzCg3wefaHJro2PaGSsRUhGb/Z8eyZTBVtP5OuMFL0WTZA0oX3DmlJB2tC4w/YOkmYSCgueAe5zQguV0pWeuamG2ODU0GRnSoYaiiDpfYTxQeMJzRq3IuzqahVNPkpWCpZiqkKH9gsJu/2PEirmJsLoW7gj29tOGVjfD4debH8G9lXoJ7Y2YR1cXdLqqe0sShhckj7r0DD3DAZoC5HqnVcYj/NVls5fSqroGyyaQIZGowUrSO+UdFjGCtIWdynM/z2HUJj0V0JqQAqlKz2zojriaFCqIdaZXiwNnkyYKTgdwPacmChbyYTLDWwvSWu48OH0PdBFCFHmKqXPzS2SNrN9b05RSUcCUwjtFJZ42gh5Uim6OxLmEm5K2AiNBZ5NNBBaVZJ3UWCwPKGb/GTgXwjemoMJM05T2Y5y0YRTKFBBSmii/G+SptNXQZo0hkiSgK/a/hPwXUlXA2vaTsrbtb1h1B+w0jNFuzKyVEOsM1OW9wV0wSLbf1b/kRI9FVqtFOF4eq/Sc0dgTmwbspA+z0dqT7VjgU1sP5l6gW18h2DYXkQwRA4k0ZvSKFa4F/g8ba08SPcwrWJ7miTFatQpMYT2pUTdktGExwsYYdieK+lk+leQJrXxsG2FSRyT4vFDyRfanx1tH9Z4v6mSTsn8HsmojjgalGqIdaBHczvmKTSjHRvDDkcTmh1WXtr0XKUnIfxbgkcJIcrs2L5f0ljbi4FzJc0mDNhO5XxC+HsujVYeGVgY2z/8JnoKfwesnkG3ZDSh1ypIZ0na3vadiToDUaTSszJyVEOsA4VCDaU5itAEcyFhEPg1wFeW6xVVRgOtPJ09gHNsXynpn5fnBXUitV/YEDxAaK1wJf0f5N9M1H1OYYbjnOiVeIw8YT4IrTyWGs6dgWOAVQkbtq8QwpMHZtCdkkFjMNYEniPMTW1huu+s32IuoZmtgQclvRlIvScgbHb2k/QwYZJDLs8uhKriyYTxSRAqPffNoJuZXF+DFY9aNdkBSXcxQKjBdo4dbhFUqBt5pbfpxUrPUsS+Tkth+6RE3Q0IeWfjCX20XgacmalBatZWHg3d1nqxATCuT7auF6lI2tD2gxpkakbOjYakNYKk/5pLMyeLfc+IGRtjtXlPxUGrIdYBxZEczdL/HugXVaQbeaW3kbQqIdQ31/ZvYqXnJNvXLudLW25IWjUWXuTULNI6Jncrj4bufAYIeaauFyWiCb1WQSpppu1tJU2zvXvKtQ3xHpMIeYKtdhZPEMY/3VPi/brlRc8bMWNjjN7UU4ZYDU12pmSooRSlupFXepgerfQsgqSdgB8QcqHWl7QlcLjtTyXq7knIvSvROiZrK48GpUKe2QsX6L0K0jGSPg9sLOkz7S9mCIUDfA/4jO3rASTtCpxNGIpe6QGqIdaZAwhfxCMJoYb1gA8t1yvqzGSFEUdZQxiVygrEt4C/B34OYPuXknYZ+leGxRTKtY4p0sqDgutF7sKFHqwg3YfQSmIlgtHf9NTkMiRXaxlhALanS1otk3ZGespJNaJUQ6wDth+OoYZ1UvNHRpCDCSGMcfTvkVQNsUolYvvRtpL6xYP97DJQsnVMqVYepdaLktGEXqkg3QNYBJxJSNIvwQOxHc158Xh/QjFKpUeohlgHCocaSlEqhFGprCg8GsfvWNI4QuVgjr5UJVvHlGrlUWq9KBlN6JUK0jXin5sA2wOXEQzoPQnzSHPwceAk4D/j8Y0E43pUUfuIDU5N1u9AdEvvBkxvJehLmmt70vK9ssFRGA78jQIhjEplhUDSK4HTgb8jPBivBY5OHfMUCyJOpK+twjWEprnZhl3npuR6UbBwoacqSCXNAPaw/Uw8XgO40nZyOLxXquTNfSNmbIhNe8rqqx6xzvRil/pSIYxKZUXhNOBI208DSFornkuqQCRU221GWFtXAt4PvI/E0UmFKbJeFI4mlAqnXkCZkOfawPON4+fjuRxcwABV8qOP0V7jtvyohlhnerFLfakQRqWyorBFywgDsP20pBwtaXrkodiPUuvFFMoVLvRaBemPgDsktZqu7gX8MJN2rZLvcaoh1pme61Jf+4VVKh0ZI2mthkdsInnWw557KBZcL0pGE3qqgtT2yZKmAm+Lpw62PTtFs0Gtku9xqiHWmV4MNVQqlaE5DbhV0kXx+CPAyRl060Oxj5LRhF6rIMX2LGBWqs4A9ESVvGr7ikGpyfodqF3qK5UVE0mbEQpxAK7L4V0p1f2+FylZuFBqZJCk+b1Wcd6L11zpT/WIdabnQg2VSqUz0fDKHdqqrWP6KBZNKLgRLhXyLEkvXnOlQfWIdaBUmXSlUlnxqK1j+ujFaIKk+4DXAz1Tcd6L11zpT/WIdaYn4u+VSmVUUFvH9NGL0YRerDjvxWuuNKgesQ7U+HulUhkupXKXepEaTahUhkf1iHWmxt8rlcqweCkaXENQowmVyjCoHrEO1Ph7pVKpLDs1mlCpDI/qEetMjb9XKpXKslOjCZXKMKgesUqlUqlkp0YTKpXhUQ2xSqVSqWSnFi5UKsOjGmKVSqVSqVQqy4kxy/sCKpVKpVKpVF6qVEOsUqlUKpVKZTlRDbFKpVKpVCqV5UQ1xCqVSqVSqVSWE9UQq1QqlUqlUllO/H9vo2HsviGX8wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "oVTTWsqokzht",
        "outputId": "0fea7d28-dc83-4086-ad13-37ac26543586"
      },
      "source": [
        "# We can compare other features by simply change \"meanfun\" and \"meanfreq\"\n",
        "sns.FacetGrid(dataset, hue=\"label\", size=5)\\\n",
        "   .map(plt.scatter, \"meanfun\", \"meanfreq\")\\\n",
        "   .add_legend()\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fmGddaXUEcHpFFLVej7fWThFqMcRbrYakhXR8cHs6YanQVJ6Tc+y63UfPOYK9uoyQeEQBEEMk+KILnwGWsVB6R9u7MPjMMowd/D36eCJWDi/KMRiAFX5LptI4NKTeEHMk3poNb296EuYO3Zlz7A37mqdvBG/+Xs6OwABoNNT8VAxXvU0Y028Bswm41e0FiSEKgHRWEHzxGkqnanhqb05NiTcRIcbUyH50xu7HgsiGnGv7E0mtEQJcEm9NqGcK5faSczZQTW/5t7zdgAAxQsVAlNu9IKdhaYCOF36dEEwECTKaYdQgOSLBF79ecHmjvBXy2iYawpKGLvQMzVXcIZ+sxDsTQmOw8iOme+jdJz44Lft3KtcAunrC/pkO7AGe/JL196AqNL9+b9u79EWqbmQ0w6hADJHgi6oWyI3tNXVv7cWCgb1KV9vtvXhx28KT0R59EcNPfgUNqcNKIzTIjVg5nLsBvvDbfVmRQ4ojaCAxRqGRGrIapupa/MSagOGEZbyc+Tm/fm9PXWeev5LRDKMCMUSCL85aIJ1nNKUlnhU1zKaJmBrZn3eO7b20xGMg0o8Gb2uJo31mGwZvX4am1OG815mB3kz3bHfeyVmHlIrMw8L0f8Bg3JFQKE5xh9vTcaoPOWXNcQK8m5uaFqsC0hFhFCE5IsEIe37Q6stm5HXKtsNo9gyilcOL8mb9DHIj7m/8JFZfNgMXnXoU+jVGKAJkuy6M1bTjYRDmDt2ZZ4RsEskUvtr1Eraeegse5fMwzBGMEk1OdWNiRLY86D14zjTUFm+tZqGCEBAxREIg3IPn2lriWRm3HZ7rSc/F0uQ12JuehDQT9qYnoemSu9Bxy614fPNuPLxxt7aEtLkphvaZbbilewf60hOV57jzQioupP/EF7a24/LIT/C/aMVDqXPzjKMYpxJgF8cCZkaEU96D50xDbTftEiM0ipDQXIgUWwRarej6wDXHY+hPWJ5OT3puVpjQEo9h2ynz0L21Fy++5l2nc3AwiVu6d+Dhjbvxx8gidMbuzxn1rcoLAcCCyIbMkLv96Md4jEMiO+qhjfbj0ujP8XjqTJwT2YYp9Bb6eCKa6DBa8XbBP4e6wKsLQyRmFcdmzzUYJGgz/059UatfC6bmafrXhJpEDFFIlL2HWgkp1IDqcjH9iSSOWbrWuLDC7pDQk54LJJGdoqqrR1oQ2ZBjsFTGpYmGcE5kW07N0YLIBqyO3Y2I5JDU2NNZVbVCIOADn87tUHHYcJ5P9+eB9u+qC17t+z3xeY1ggSQvNAoRQxQSdr7ESZ7MuQopxoDq8j42hUTCnJ6VE6cHlIaZOk6l2htCFGNRQIeBekKZ+2Gr7xwwYqhM5fLpVL7azonWMyJg9lUSkhuFiCEKCV0RqFdxaDVgakCdXlNzRgVXrpSL2wOKwGwDdOaW7HuMJTFCWhIHra9+I7yDKN2y9/YJ4cm4hrpCDFFI6IpAvQbCVQNeBtQ2Pu73ZeeFysWShq6cvJEJQzQG/5ga2cQ6Yg8Fvsfow6fzuC0c0M1wsl8Pq6hUxjXUDaKaC4nF5x+vlTlXMzpD2dIUw81rdnh2WCgXUyi/RsnNEDfgrfT4rGpvyZGrsS5yJgDLG5pgIFJgjHZlHWOkJZoiUTZ0yAq7eancgMKKSp1qO6HuEUMUEl4y52pGZUAJwOFkyrOzQjk5yOOVx4c5kjU8f5e8FrOG7sV7jzyCuUN3ojs9F4mkFcJb0tBlVOTKTDiCWCmXXh4iUSA2zvBktroeqDyjxIGR1jte/eRUhsp7gblqO6HukdBciIQ27jpE2me2YfPvDuARR60PA9lNvNIsiGzAOyi/28IRjmJx8nPaIlcnJh4VAKRBGIPyhh1LQjoFvKMVOGc1sOaz/ud7tdOxW+/c8GvvmUHJxMg4DT+isu0IuYTqERHRBUT0MhG9SkRLFa/fSES/IaLtRPQ8Eb3H8dpniOiVzJ/PhLlOIZcXfruvameWLmnoQiMN5x0/hLiREQKAPp7kew4z0EDp2m0PNLDHMhyRRv9zfe/lygFlO2c3W4Px7PwRp2A0/SA1JF2zhRxC+2hCRFEAdwE4D8BeAJuIqIeZf+M4bSuA2cw8SERfALASwGVE1ApgOYDZsD6Qb8lcezCs9ZaaWi5mrWZln86baYH3VFV3wWuSCTHKNbfMI7VQNWuAnHQ0l+Y+zhxQXk2R+yOLnXfy+SgjXbMFB2H6yKcDeJWZXwcAIvoBgIsBZA0RM7/gOH8jgE9m/n4+gOeY+UDm2ucAXADg+yGut2TUcjEroFf8TWiK4Y+JYaQqmMHv40mYqjBGqrY/tvFpo/1gIFu42oq3McQN+FM6ivF0BIAVhotStfqBFea4eSN/N5Jqs5VHGtirH+cgXbMFB2GG5toAODWfezPHdFwN4Jkg1xLRtUS0mYg279u3r8jllg6vWpxaQKf4Wz7/RKQrLCPTNVR1t/2x64SmRvaDCHndExppGAN4B6YfeRTTjzwKqtpgZBWw84mRv5t4MvaY8I5+4KPf9VbcCQKqRDVHRJ+EFYZbFeQ6Zr6XmWcz8+zJkyeHs7gCqNViVhsvxV+l66BUDVWXJq/Jyw+Z1Bo5Oy2Y5I3qFmfxaXyC97nRxlwjU+gEV6GuCDM01wvA2Z1wauZYDkR0LoCvA/gQMx9xXPs3rmt/GsoqQ6DQYtZqyivpFH8mQ/LCRtX2x5kD6uNJaDNQxjnDec+nZ+DT9JPRkRsKA3sseOqI93mN4/ONjBSmCj6E6RFtAnAcEU0nokYAHwfQ4zyBiGYCuAfAAmb+g+OldQDmEdEEIpoAYF7mWE1QSDGrnVfq7U+AMZJX6t6aZ7tLTvfWXszpXI/pS9diTud6z2fa3lJLvHrqa5xhuAgBUyP7fQNtznDegsgGfDz6Qm0boXgr0GhaO1QAT3we6Gixily9SNSMnkioIkLziJh5mIi+DMuARAE8wMw7iWgFgM3M3AMrFDcewONk7QK7mXkBMx8gom/AMmYAsMIWLlSCoJ6Kc6Kp6TWVapJaiLBi8+8OYKDMbX28UIXhIpSrgnMyzBE8njoTyxsewnfobgCjQCV30y7ra6mUcm5kdLcQIsSjpIfJ7NmzefPmzSW/r3ujBizvphRdEpwGzutfoS3EMN2czvXKMGJbSxwvLj0b3Vt7cetTO7NjvaMEpKrsV+b1MZcrRznoDFGagSSi2XlFo4J4a8YbqeA/Tiwu+Z8Rav2jTVmpCrFCNROWAs4divMizDCdX5PTxT98KWuEgOozQoBeaJDS/HqnERldRgjICAoq+Y9DYoSEghFD5ENYCjiVgfMiLPm3TkAxpSWOVeteRrIaLY8LnaT7kdTZyvHgUcOxEUIAYk1ihISCEUPkg26jjhAZJfd1FGLIwpB/ewkrakVurpN0Lx++Co+nzkSKR7poE42CfFA1kvQRMQiCB9J90AedXNnuLlBo1wSdxNvvmlJjr9mZBzo8nML1j21DlKiiXRSC4JZ0L4hswJbGa9FKb4vhEYQqRzwiH9zFnVHFrlZI2EzliXgR9iyjw47u2rbtqRUj5GZBZANWxe7BxIiZEarRt1ldyHwhoQjEIzLAWdw5fela5TlBw1hOibeXZ0RAKMWtTsVepIY8HxOWNzwUSIwgHlORRGIyX0goCjFEASnlCHDbwPlJqP0IWufklqSPJiMEAK3kP31VCEgsDpx6OfDKs9bYB3v2UPM0q6WPCBWEIhBDFBBVzqjYsFkx9wxakNq9tRc3dG2TcJTgD0UBTltFqmJshBARQxSQQromhHnPIB0Zurf2YvHjL40aI+TuL7dyeBF60nNxEOPRCvGKiobTVgdtQQgZ6axQ40xfulZZxkgAdnVemHNMFwKsRez+cs7WPmkG/j11Lrak34fvxO6W3I8vBMy+aiTc5ibeOtI6SAiK/PYFQFRzNY5XQaqbWqkLMkHXX+5T0Z8AAB5KnYv06PiMFQ7N04CF9wJHn6FvZDr0ttV1G3CMB2+xvtrHBaEEiCGqcYJ0+q70LKFSohsZHiHLSC0fvgrXJ7+IvelJoyYUWTxkTUwFgD/2AVsftsZ+JzT9hFND1kRWezz4wB4AbH196joxRkLJEENU43gNsXOz+PzjEVN1B61BvAbZ2QPvetJzMXfoTvTK0LsMbOV9AEvxtutn/mO/B/aqx4MnE9ZxQSgBIlYYBeiG2KnOA4COnp3o9xnjEI0QUlUc21o5vAirY3cru247B97Z57rzSYIhzVP148FNxoYLggHiEdUZ7TPbsG35PLzReSFWXzZD290hAmBCU/UMv3PTk56Lf0+dmxd2O8LR7MA7YERZF8cQUkxGYToJ5WWIxS3Ztm7GkMweEkqEGKI6xg7rqYJ1yTSjqbGhqqU/W9LvQ9Ll1JNjxc7JrURAlBhJNPgamvpW22XefPO0kbEO5yyzjJIT20gJQgmQ0Fyd4e7CcNYJk7VTbOxzqlXyvaShC400nHOskYaxpKELPUNzlcq6RhoWj0cJ6QtX7e+fX2GF46TAVSgxUkdUR9zSvQOPbNydY3gI+nFq9mTY6x/bVobVBcdrMivDem/16d1kjIqqNkhH8zQxMqWlLn/zCkVCc3VC99bePCMEeM/0HByyvI2WeHXkihZENuBXY67FrjGXY9eYy7VrJ7Jk3PVphACAgRt+bRkXU0SaLVQQ8YjqhEK7KsRjUVwyqw0/2tIbaKJsKXC28OnHeLwTh9BAub+vzPVscDxYeJ/19anr/CXaKpqnWcZMKBT5rQyAeER1QqFdFRLJFL7/yz1lN0K3NjyA1bG7MTWyHxGyOmq7jRBgGaFR8lmqtDy/wgqvzb8z4xmR9XX21bnf6xBptlBGRKxQQwQd9+CkGNFBucdELIhswKeiP1HmfwRD7PzQKYu88z13nKTOJYk0Wygj4hHVCPa4h97+BBgj4x66t/YaXR90ImwlWdLQFcgIpQx+jZnrzXMiszxPEGm29JsTQkIMUY3gNe5BRffWXszpXI/pS9diTud6AMAls9qyo86jRJhzbHWOd9b1kVNxhKN4JHU2BrnR87xDGIO/zfSeSxdglGrPhjGw5rO5BkNlSFThO7t+yIn0mxNCRMQKIVFMGE2FybgH+5m9/QlPWbZNPBbF4WSq6jbZDY3XYWpEb4zsX9kDPB63Dn8aPem5WWFDG+1XiheYgb9NfhEAPM8bldjTVV961CVcyIyBuOjb/vfQhvA8RA3bu+q59qhefrtKguSIQiDo1FQTdDmeCBGmL12L5ngMh4aGkUxZu7SJcUkkU2iKRTCYTBe0plLiVMgd5PEY4oa8YlXAMigPpc7F8uGrAt2fCFgdu7s+a4uSCWDLg1aj0xwY2PyANQrCz0AE7Tdne1C24bM9KKCejJFgiITmQiBoGM0EXY4nxQwG0J9IZo1QEAaT6aJ/CRZENmBD43V4fczl2NB4HRZENgS+3m7FEyFgYuRtMBgHeHw2t8MMDHMka4RubXgAr475JHaNuRyvjbkC/xT712wrHx11XVuUZ4SyL5h10Q7ab046dgsBEI8oBHRS6d7+BOZ0ri8oXOceJx4hKomaLVrkfdyTUqfSfnTG7geSVmNSE1SteMZQCvvSY/GBoXvzzr+14QF8OvqTrFGJglEbMowKQlG9MTKRap+zLL8myavfnHTsFgIgHlEI6AbQEVCQ6s0WHtyQabVzx2UzkC6BEYrHokUbM5URaaIhLGkwT2LrxAn2XCE3V0TX169nY4TrhxOLA7OuzD9uYyLVNhU1+N1TZOGCAjFEIaAKo6nEA3a4zq1wcxonnWy7uci2O1Gi7EC9YghqRFTohtwRWBnqi6LyOa1qJUUxS4DgNhgXfds6rjJSpl20T1lkCRM6+q2vXrke6dgtBEBCcyHgDKP19ic8w1+9/Qnc8Ni2rJFyCxt0+aaxseI+Q6SZs+t0CiuC0seTMFVhjPp4Yo4AoY8nYeXwImW4Tje4jkgd6kshgoZaM0YUGZmOWiKGOYIExzCejgAADmI87my4Bh0X3aq+4KJvW8KEYpVsJmo46dgtBEDk2yVAJ9V2q+eCECVCOiNEUGEiz/bD7q4NmE1tVeHOEQHAIDfi8dSZuDT687zjS5PXKI2Rn/x6b3oS5g7dCSA/R1QzxOKF9X1TMMwR3Jj8fN7P0inibH0AACAASURBVCnnDwW3Gg6w3pdXmK4+qbXfzooihqhIVMYmHovitoUnZz2iMJjQFMPBweCGw008FsXUCWPxyh8OFXyPEc/nLfTxRDyfnoErouvRQPkegNOgqNCNdkgz4b1HHsl+b2qMwm2KGgUQ4ENGbBzQMAZIHCjusbE4OvhzePDt0/NeamuJ48WlZ1vfhFHHU0g9UX0ihigAEporEi+pdqGNRk04nEwhQkC6yM8RiWSqKCMEWCGzniHrk7ntIamMEOCfO/IK9TlZPnwVtqTfh9Wxuz3bAYXrNQX0dJOHrD/F0DwNOGcZZqTmIK74AGR7uCWr43EbM92MI1HDCUUgYoUi0RkbO0wXFolkumgjFAYqFZ0Tt0Fxs3J4UV67nkFuxMrh/M2zJz23vj522l7HKYuyY97bWuIgWJ7QbQtPHikHKLaOZ3sXcPt0q02Qs62P7idOEelBJxSMeEQaTFv06Doe2NcUIwSoRbz6xOkMipOe9FwgiZxQn07kAAC9Gg+qosRbiw+/uVEoztpntunr0Iqp41HlgbLYs29dn4LsGiXpoCAUgBgiBUFa9KiMjR0isc/9atdLnvU6LfFYQUKBakQXWhvmiFao4MYZ6vNj5fAiz/CcvW2WjVgc+PDtlidRKuKt1j2BTI7GIOejC6PZnot9PQA8c9OI4YxnGuF6iip4ZLQ4RfILZW3PSwyRYIiE5hQEadHjFyJpn9mGf1p0qucIhoFRYoQAfWhNpfAqBT3pufj31Ll5YUpmqynqvw+fCzRPC3cEBEWRV+RJJfqvFRsH3LTL+nuQ7tfHzVMf59TI9U9+CXji87neW+KAvzdnhwg7+vWS9IE9EqYTjBGPSIFX3keFZ4gE+XVFbqow1VMwQUNrpeCb6avx24b340vpR/Oe2dYSx6dvOBvc0QIK6yfNaWtTBkZGLRjVDGVCXF41RslD1v2GDqlzPs/cpPY8XnnW//EpfS5PiztE6ClgkDCdYEbdGiKvHJBXp+vurb2+/eF0926f2aYd5zCaCBJaK5aWeAwdC04EcCrOWzMHiaFUVk6+mv4Vh+ndwNMfCXemeHyCZYCcIS4TYk1AOulvEHQbPWA9z54rlHNNCCo2O0TofNY5y7zDkBKmEwyoyzoiXaHphKYYls8/EYC+24BdI6QzRl51Re0z2zCnc31otUX1RpvrA0T31l5sW3svliTv9lTulZxIFEDEMir+JwOl7gqhquHR1fsUSrx1JETopqPZ52Ia8Rjrh7oSdBZLXeaIVDkgADg4mMyKEm5beHJ2mqkTv3EOfvmlWhrZXe309iew+PGXMHPFs5i+dC1WrXsZS2KPldcIAUA6ZWiECCU3QoDa+1H1eiuGxMHCr41PkBHjgid1aYi8Ck1to9E+s03b4drrer/8UvvMNlwyq037cSnqVZ0p5JFMMw4OJrMNYccO/r7SS/KgyOiDTgCh6mit6pY9++rM90DgD+zOZ9g1Rh3NGW/IawhUDBh6W0aMC57UZY5IlwOysY2GV42QjmYPKfb0pWsxpSWOwaFh5ZYUJcI/XXoqNv/uAL7/yz1IMSNKhDPeOwEvvqbPPcRj0bqqVfKijydWX11RKdCN+/bqaH3KInVuJnDYjkZUeNu7gO4vujxAjYG1peDuvJnkjQQXdekR+YXHbEOjOi+njYoCXUsZzvzp7U9oe8TZHbG/2X4yXrvtI3ij80K8dttH8Mhn/1I7roEAXDKrrehxDtVK0DCmSj5es8RbkTfKwW8mkK3a8wqDBRYysGUA7XY/ujCkU8a+8D4rp6QL6UlLIMGBkUdERAu9XmfmNaVZTnmwk9uqjtNOQ+OeimoyVbW/iEakEaKs1+R+zuLzj88ZF2HDAL7/yz0lGZRXjSSSqUBTZHvSc9Eaa0THuB+NFH4eNw/Dm/+t9kZHqMQBOi8HMO8vpy12jVoycq8iVS8D4pSx+z1LBuQJDkxDc1cD+CsA6zPfnwXgFwD2wdoLa8oQASO1P36tfPxqhNz4hf28sDdbOwl/61M70T+YzK5LtxWXYmR4NaN7f/FYBMNpRjLFjmNRzLjwWmBm7kyer/5iDG5TzDyqWrK5nAD49ZezDUl8AhBtzJWNO0c56FRwA3u82xepjEvQEeNCXWIk3yaiZwF8hpnfzHx/FIAHmfn8kNdnTCXnETkxnUHUEo9h3JgG9PUnEDH4xB+PRTE2FinJ6IfRQltLHGedMBkv/Hafr8fa8c3l+MrQ/WiltwEARxDDGCSrdKZRptA102kbgNk4h44WaPM17llIkRgw5h1W6Cw+wTpm/92rFiraCKSGkaf+izYCF9+lXlcY4yiqn6r8zapWTD2iabYRyvC/AI4OYT01jzuc19IUw9uHh5FM535q71hwYvbc6UvX+t43kUxhTEMEsSjleAD1TG9/Ao/99x6MH+v9a7yp5x7cNHw34pERDyDNVbZPUDQTDnM0FLXb8DCP5GXc4TbnJq8Kqdn3dntK6STQOM4qUHV6LH4FuamhfBGCqtDViVc4URBg7hH9C4DjAHw/c+gyAK8y81dCXFsgqsUjUuEX/jMtciV4q/KE/ILj7q29OK37TLQplHRpFKjWaZ5W2mJRAFaS36NdjmoNqrCXG8+psAGf6byu/gpUg1Jln3SqG+POCkT0UQBnZr79OTM/EdqqCqCaDZETt1E664TJWLv9TaOQW1tLHH39iVHfIqhYnFNK53Sux38mPqofnheJGRajZrBzKaXsrg2MdLM2/tf1MCK26MAOgz2/Qj9VNdAzHde5Ozk8fSOw5UHLI6MoMOtKS+VXv4ghCkCQD4S/ArCWmW8AsI6I3hHSmkYtdv6oN2NMevsTeHjjbiMjZKv5why2N1pwFhX39SfQx5PUJzZPA9rvzsiODYi3WkYoDIYOjeRqTGieqlew2eq1zBA9ZZcFWzAQ5JnO65w8fSOw+XsjYUFOWd8/fWPueSbScqEuMTJERPRZAD8EcE/mUBuA7rAWNVrRtRbyI0qUDTctPv/4uvmo1RSLFPRencZ6SktcWVuUwJiRpLlRp2xYORVgJEdTShIHgIQu3OX6b2obA50E2n1c1WXBNqiHNc+kiHVuvNUaRWHToPggtOVB9T2cx21puXRYEBSYekRfAjAHwB8BgJlfAfCusBZVK3Rv7cWczvWYvnQt5nSuR/fWXs/zvVoDeWEXugKWGOKKM44e9cYoFiUMJtOeQaN4zBJv5B7LLThefP7xeC76ISxNXoO96UlIM6GXJ+HXH/jGSALdtKZlYK9aIu3E9q4oCgYwjAjSDPTjHUiRnzZIYxDjLeoiVoWnk8AYbDrWlbpVqdYAyxDojDCz5VV9+PbcdSUO5BsQlUDCfbzY0eXCqMbUEB1h5qzkiIgaMLrG6ARGFWa7ec0OT2NUaFjNfd0320/GFWccrWzKWqvEooRPjN2IDY3X4fWxV+Bnsa9gQWSD5zWt48Zg1cdO1Q4lBEYGF25553n466E78dfxNdjU/nOctuBzIzcybRAan+CR2CegYwBYfgBYeB8Gx74LzITfp1txffKLmHH4HvwgdVZh/2kSB0cG0dk5n44W4PkVeG3Kxehly8DuTU/CTUNX49Ob3jPye6jzRJ65ydug2iE7EwOiC206jxczulwY9ZjKt39GRF8DECei8wB8EcBT4S2r+vHqsq0rgFWNFfdD1VKoe2svHtu0Z9QUsra1xLH6/a/gtB33ZTe9KdiPztj9QBLaoXp9/Ql9wbHDC2iPT0A7ARh7EBgzFYguA+CQE9uekS6pb6MNnWHEq8ps/E3JBEDAVBp5Hx/C1sI8Wde9nZ0Tjupfg6XJawBYwwhXx+5GH3fhlz2zgZ/uVL+fZMJnFDiAI3+ynmdiQI6ZC+z6Wf45xzj+3fw6LNRnrZGQwVS+TQCuATAPlhpkHYD7uYqGGZVbNacbcEcAdnVeqL1OpZpzFmSaFGjOXPHsqClstWcKnfHkh/Bu7Mt7fW96EuYOeQsE3HOJ8jZsN84uAm68ikK1ELDwXut+moaiwxxBFGnvAtpIzGpWqOt4oLn3W+nxiNNQTtcIZn3fQ2Ps7g46xZ2tnNM1UXWeo/o3sd8boH+tdo3R6AlXlAFfj4iIogB2MvMJAO4LcnMiugDAdwBEYRmuTtfrZwJYDeAUAB9n5h86XksB2JH5djczLwjy7LAppDM3ELxlkIrRYoQA4JiJcdy8Zgd2RvYp/+tOobd879Hbn8CGJ+7GvGd/hKbE7/WFnTZe3Z8LqqvhkXtpPIgG8hFEmHRR0Ny7ld7OMzolidoO7LUMrF+LHhOvKcfrdL23O07Sh/9q1xAJAfA1RMycIqKXiehoZt5teuOMAbsLwHkA9gLYREQ9zPwbx2m7AVwJ4O8Ut0gw8wzT55UbVZjNrzO3kI893qKvcZJyfMMfaFK2kPePh5NIK5yVBZEN+Ae6B2MSDvmwH7rNU9cbrSHu0WPN0RcuqCFTffLXbb4FGckisOcfzb9TbUDscJrOg1Sp91TvTfJHdY9pjmgCgJ1E9N8ADtkHfbyU02F1X3gdAIjoBwAuBpA1RMz8Rua1GmuLXFhnbjd+HRd0OJrAjBpWDi9Cp6spaQJjcNvQpdmfzQ2PbVNeu7zhIYyhgLJ4nVJO98kdUMzhgdVjzekdmHQ7yK5hmn8uxJk7iU/IK8Ad5EYcRiNa8bb/84LCKeu9zL8zv4DVJPxp2thUOnTXPZ6GiIjGMPMRAH9fwL3bADh/u/YC+GCA68cS0WYAwwA6mTmvbomIrgVwLQAcfXT5W98VE2ZzN0e1VXf2fe1zVIZqtBkhICNISFoJ9yn0Fvp4IlYOL7KOZ342LU2xvLDkgsiGbCNTY/w2Sa/eaM/c5N1j7ZRFwO6NwOYH4PlxQdWdwI17s08csAxfvBWcOIg+nojbk9az3Ua8ZOhCZF4ydhMD60Q6dNc9fh7RfwH4AIBrmPlTZViPk/cwcy8RvRfAeiLawcyvOU9g5nsB3AtYYoUyr68odKq7r3a9hBse24bmeAyHhoazDU6dhqqtiFET1UxPei56huYqPT676atzGu2CyAZ0xu73yYdQbnfpIIoslZJLNSPIzSvPKt6Ba00mm6xqs08NAY3jQDftwqatvWhdey+uGXoYY2kIaYogYlqcG4SBPdZo8OEjQPKQz8nkb2DdeOWPhLrAzxA1EtHlAP5KNRzPZyBeLwDnUJWpmWNGMHNv5uvrRPRTADMBvOZ5UQ2hK261Jdmqxqa2PLwQGXglWRDZkPF09qOPJ414Og6iREgze85zGkgkccdlM7Bq3cvo7U/gpliXtxcQbzUzHCpMh8wpF+qV2yBg9lVmm6w2d7IHuOMktA/sQTsIiGSMHqdRcOA23mp1jtDloPy6ctsUGk6TDt2BIaK3mXm8x+vHAHiamU8KcM8HM9f80O/cUuJniD4P4AoALQDmu17zG4i3CcBxRDQdlgH6OIDLTRZFRBMADDLzESKaBKurw0qTa2uFQgfo2bUzAHC9JmdSTdhei20wnHU1TmP0iQ9OwzfbTwag70Y+pSWeGw7tuEL/4GhjpitAgXgVcvptmF7NSD/6XfMNVytOIMdx1czegMTiIz8r0/yW7j4SThMKwLOzAjNvYOYvAFjCzP/H9ecqn2uHAXwZVs3R/wDoYuadRLSCiBYAABGdRkR7AVwK4B4i2pm5/C8AbCailwC8ACtH9Jv8p9Qui88/HvGYYbNNBwxrowasEF21s6Qh32tpoiEsacjtMbZ2+8i4K9XPRqVIHIy/W/1QiuqHtLnQtmkqRsmlazIaxAjp7lNqqYqzZVBOT7oguFoPCWWFiMYT0fNE9Csi2kFEFztebiCiR4jof4joh0TUlLlmFhH9jIi2ENG6zLDTimGkmmPm7xHRXwE4xnkNMz/kc92PAfzYdWyZ4++bYIXs3Nf9AsDJJmurVdyqO5MprTZ2vuiSWW14bNOeqh6UN0UhybaO59YHOUUIJorE7q292HDoEqyge3MM3XB0LBou/mcjJRoP7MVpPBGzkovQi7nZn2vbnqdxmq4WyST0VGDOwy1OWf3+gzitwTFPyGtMtxd+hbLutXsUz+ZhIroQwuYwgI8y8x8zEaSNRNSTee14AFcz84tE9ACALxLRdwD8M4CLmXkfEV0G4B8AeDoXYWJkiIjo3wEcC2AbAPt/JwPwNESCN84wk2rEeCxCGD+2QVnAmkim8PRLb+Z9ODbJx5STPlbXB/XxRM/rdIpEe7O2Qnd/haFIOkdpd3/DJ9HhZ4Qy4ScC0OYKFZ6X+hlO+tX3MPJr7iBI6EmX89C0snH/+8/643M4acv9gNObHE6YGaPYOKCpNV9+HsQwmsjQ3dJ1oVIQgG9lGgSkYSmW/yzz2h5mfjHz94cBXAfgPwCcBOA5q2kOogDeRAUxrSOaDeD91dTSp1pxfqptjsdABPQPJn3rhLy8AF07IbegwTQfU05U9UGD3IiVw7mboEkjAJWxtpV22fsMAR1eN1HkfuxQYc/QXCxp6EIcR/Kvo2jxoScPAcSqdZNy3teShi7E3UKMZMIqrPWcugogOQjc0Jd/PMja7XOdcnUnsXHA/NWF/zykt1wpuQLAZACzmDlJRG8AGJt5TZVEJFjdcv6yfEv0xtQQ/RrAu1Fhq1nt3NK9A49s3J39l3caClWdkBuVF9C9tdc4bNcRe0ibj3Fu1uXEsz7IAcPq3+dlsE3mOfl2ONeEm+wQoi6UCE4Xv1F6CCD6+nOFFdp1JA5abXe8GrTGmjKhtSI8ImDEqyu10ShGkSioaAbwh4wROgvAexyvHU1Ef8nM/wVLLLYBwMsAJtvHiSgG4H3MvDP/1uXB1BBNAvCbTGeF7MfFauv/Vkm6t/bmGCEV7u7cfp0VbA/AxAgtiGzABE11vXZTMyRoetx9vttr0eEcpwHkG2y/eU5GLZYoqsz9pDO6nTcxCW1Q/LxKUeXvIYBwqyh1IU2ALaNw3Dxgy7+p5wklDwEDmXqfgT3Ak1+yuqDaHRmCbvylllYXo0gUVDwC4Cki2gFgM4DfOl57GcCXMvmh3wD4V2YeIqKPAbiTiJph2YHVAKreEHWEuYhawM9orFr3stFmbW+mJp0Vgkx0XdLQpS3stDfZQvNHQYxQLEJIqhrCBUA3TsNL8p7XgVuHpg9dFGm0tcTR9/4laNuxPJwqf49WNov/Jrc2bOXwItweuz8/PAdY99j8PfPnphT3qOTGL73lSoJdQ8TM+wHowmwnaK7dBuBMxfErS7W+IJiq5hTDRuoHE6NhOn3VDh2ZzDMKMtHVy+uJIl22/FGp6vpVBkfXaNY9DE/L9i7o/DtqmYYXbzgbwNnAMRPCyV94tLJpPyU3R7jlneeh788O4Njdj5s1cS0Er40/zByO9JYTXJiq5s6AJff7CwCNsFQWh5j5nSGurWowMRomBarO0JHOyDiP6+45oSmGw8l0zpq0ISUAvTzJs56nlPmjVJHekI3KuQvSaDZfCv0KTtv2dSj9u0gs1+MJq8rfR9bdHn0R7WNWAGP3AjQB2POnAoxQgECqbuMPO4cjveUEF6ahuX+B1RnhcVgKuk8DeF9Yi6o2TIyGSdudRDKFW5/aiY6endqtwpls13kAy+efCABZGXOUCLcnF+EfG+9Bo0t2PMQNWDm8CKtjd6ufZzDvpxIwLGPiNjImjWZVHuyULSsB0sxxGvOOnA220K7oALw9Cb/X3A1Og0JRYNaVwEuP5m7ymrwYjpunXtfQIf8cjvua4+ZZPfZMPCjpLSe4MDVEYOZXiSjKzCkA/0ZEWwHcHN7SqgeTIXj2RnXrUzs9B9d5vRaLEg4dGc5Rj9228OSce45piOQ8z95we3guMGSNRLC7UR/EeHQkP42e9Fws4S5l8vtNeNfzVBKvset+17k/EByl8RYBWEq0DCZhWC1engSgfa07NQdnPPk1vBtFNLJ1FqgefUa+YVEZtleeVa9Zhx3KU13jzFmZeFDSW05wYDoq/OcAzgVwP4Dfw5JxX8nMp4a7PHPCHBWuql/xyk04P1EDZoGSCU0xvH14OCfRH49FccmsNvxoS6/y2SOFnf64c0SAVc+zNHlNRQtevfAbu65DVXe1ofE6TI1ojJGjO4Cuz11bSxwvLj3b+8FeI7MB5WuD8aMw6+3V2Bm5DJFCp6r69bDzGn/ePM182J79c5KuCybIqPAAmHpEn4LVl+7LAG6A1VX7krAWVW0EHYLnDB8ds3St7/0JQFNjfgeFRDKllITb+akgYga7nuemWBeOgr6eJwzsrEVQGbhvTRDUYTSVB7tyWB26dHcHMAnDailADTY28XskkinthNo83GE2XaseJ0bNU31w5nBM1W2ighMMMVXN/Y6I4gCOYuZbQ15TVVLMEDw/prTEtRudbuPu7U9ggmJQnBc96bnoOVJ+74dhjXkw7aUHWEbLryZIF0a7dfpOzEncjaMwIlN/LvohvPSBY3Da/3R6DrbzDMP6Kcl0G358AnB4QJmn6UtboVFVBwpEokDadQ1FgLHN+bOV3JNcU0eskBxgdUFQ4vHvkR0LoXivpiPLRQVX0xDR3wD4O2a+KPRnGYbm5gP4RwCNzDydiGYAWFFNBa1hhuaKYeaKZz2NRSFhNptYhJBG6ZRq1cYburBcZtNND+xFXzrXs1sQ2YDbG7+X06YngTH49Qe+gdMWfM73mbow7EOn/Q6nqeqLTr18JEkfnwAc+VPuOHFVw1HH9R38OTz49unZtdsdKP5Ak/DueEqd23GHvPzGdgfF6WWpjC/g/zwTT210U/OhuXIaIs8xEA46AJwOoB/IFkNND2lNo4rl809ELKr+nWxriWfzTL4dARQk04x0jRgh7ymq+WhHXNib7sAeRMCYGrHqoRZENgCAsldcHEdw2mv/bPTc9pltuG3hyWhriVtNUTP/Rqe99s9qJdnmBzLeAWeMhj2cDlYYraFRbYQyvetmXHhtduRFT3ou5g7diSX8Jbwz3qBXzrlDXl5ju02hKPLGOTh+1gDnihCy4yIy18y+Ovf7+jZCBXHM0rWXH7N07RvHLF2bznw1mt/mBREdQ0S/JaIHiej/ZUZCnEtELxLRK0R0eubPfxHRViL6BRHlbUZENI6IHiCi/86cd7HqeYVimiNKMvMA5e4mtbEDVhjT/FL7zDZfxZ2KWvlHCNIu17NVj0/TUm1hb4B8hTIM+6Tuetcbc4bSODUSHsu7zOpd15751v79+Mz4/8YtfD8aEof1C3SHvEqRi+E00NE/8v32LuCJz+eHE5MJqxGqLmxXLHXaDDVjdO4D0JQ59B4A9x2zdC3e6Lzw0SJv/+ewZr5dBWtg6eUA5gJYAOBrsMpx/pqZh4noXADfQr4G4OsA1jPzVUTUAuC/iegnzOw3O94IU0O0MzMyPEpEx8FqJf6LUiygHjDNLy2ff2JNjQAPgyiRd6cEzaZr10OF1ivONC8S5H4Zcn4/7rgJGPAwQqrCz/gEs7qj2Dhg+LB+zlLWCOyBp7QkcWDkeaUsdq3vZqjfwogRsmnKHC/WEO1i5h0AkBk++jwzc6Y33TGwmqb+38zezgBiinvMA7CAiP4u8/1YAEfDGnpaNKahua8AOBFWw9NHAQwA+NtSLEAYwR0WmtAUQ8xD01vIhNdqJ8XsbbQ1BqWPJ6KtJY7Dx5yLvPB8Kar2z1mWf99C8VqPn3dz6uW5m/L2LmBI3ew2j3QSOGYulD+f4+Y5wnBAIF/bLnYtFq9mqKOfowMeD4IzVp12fJ+G5Yx8A8ALzHwSgPkYGSHhhABcwswzMn+OZuaSGCHA3BC9P/OnIbPIi2G5eEIJcI6rXrXuZSw+/3js6rwQW5fNw6pLT80xTC3xWDZ3ccmstpJsjW0tcay+bEZVjB4nYGRct4pzlmE4mvv/ZDg6FlM/dhte/Mh+HNv3JHI3UcrfvAvhlEUoOBAabzXPn/h5bq88m/v98yvUOSgVqSFg18+h/Pm88mxxeaZShAfruxnq7oDHS0kzAPs/3ZWac9YB+Apl8jNENLOUCzANzT0C4O9gzSUqVV/LmiZIGxivc/0q+b3CenM61xedI7LzMe5ODaXGtCs3w7ujQndqDjYkr8H1+EF2vtHq9McxNzUH7T89X7GZcv7mXSi64k+KWjkWlWouFs+TiHtyzjJgzbXQGj33phx4k1bMSbNVf35QFIiNVee9SiHVru9mqF9Dbo4IAAYzx8NmJazQ3C0AdIWP34A1KmI7EUUA7AJQMjWdqXx7AzNXZ/l9hnLKt4N0WvA7t5hKft3k1qA4n9W9tRdf7XopUM2PCasvm5E1xn53JgB3OM53Gm/3zysreY68hYjmzmkm/HV8TbCecSpUMmm3TLkUyfanb9SPeXBLt027HHhC/jkwW6r+q4dyDa17bcWIC0x+vrVD4GBFRrDwLVjhuN0AvlYCoUJNYOoRLSei+wE8j9zBeGtCWVWVY9KN2/TcYir5m+OxvHHhhdDXn8jx2kqtxGtried4djrja9PSFNN6iX0uI5RXBKqgjycG6xmnw6RZp66HWhADddG3ra+bH0COB6PKLR03L8BsIo0AwV5PXm1Q5nzbwDy/Qm+EgOLFBXXeDDVjdOrC8LgxNUT/B9aApRhGQnMMoC4NkZ/xMNnU7XNNGqrqMK3NiUUIICCZUq/GvfGXmsGh4ZxO2l6dyuOxKJihNd7On5dqtEXes7kRK4cX5dyjKK+okGadhajBLvp2fvNS96a8vcvqtO1LxuNpfW9+jsg2biZGYM21/o8qduCeNEOtS0wN0WnMHLzicpTiZTxUoTjdPQD9qAd3HY0qz+RVc9SWaRtknwsAHT078zwo3cZfKLEooSFCSCRHUokHB5O4/rFt+PoTO/APHz05p7bKHmORYs5OWb3hsW3Ke/f1J3DHZTNwfeZ1r2GAaSZlP70g/flKRqGjsf02ZZNCVjuUZxtDLyGH3/NMJez1IS4QSoipIfoFEb2fmX8T6mpqBC/jYTLe22loTApeVYKGG7vUm+Lf+gAAH5VJREFUmzWgzy+1z2xTGjTdxl8Iqz52qrZd0aGhFBb/8KXsWnSeie76KZkQn21Q+1jdKPT3mIwzjnxHeW8TT7PkhKUG87veOfBPabQCCjmU4TsF9SEuEEqIqXz7DADbiOhlItpORDuIaHuYC6tmdG1gvHI+APLOdd7vxaVnY1fnhXhx6dlGeSYvAZpXuyC7nZDdaHXVupcxNmb2a/DJM47G6stmaLOwtvzbK/+TTDFWrXs577hTwj44NJxXP+U03h0LTkQ8FsXK4UUY5Mac8xLciG8NXap8tmfHhjDRbcylKLL1glNWOM1L0DCwJzNC3YBTFlnCAS9k0qpQAKaqufeojjPz70q+ogKplqanfiq4QmTfQZuhapuFQq3iM8UO9zU1RnFoKPd63ewkFe45Q6o1xaKEcY0NGEgktV7iqnUvY/Yfn8PNjY/jz7Af/4tJ+NbQpcrRFm0+P+tQCUsNFqjZqUenhKBrubVVP8J84X2S47Go+aan5cTIENUC1WKIvOTaQH6dThDZtwlRIrx220e0r/sp1nS4tzGVofAzmjlSa0cyvKhhdA50cvZCB+yVlEJHiJvcV9UTTomHMXIq4/zW0dGsf0THgMmq64GqMEREdB2ALwD4FTNfEcL9OwC8zcz/WMx9jEeFC2Z45XzmdK4vSvZtwic+OM3z9UKS9artK5lijBvTgG3L52WPeeWa8qTWA3uQWPNlLP3BVvRqhvMFNZjFKBBDRyUE2N5lNRB19okLKoG2zzHyjDw+dNrPNVH26Qp7m71/94SK8EUA5zJzVStIxBCFgC4R7yX7dofsTDbhCACQlS+KEuETH5yGb7af7HmN6b1tWjxqldz38bq3SmodxxEsbujCk0NqQ2S3+3F2ofAKa5oqEAui1F2hvcJqQSXQbuk1RTSNTadZXRFUDVIpolb2PXNT/jpUogXJDRVPR3NeQSs6BgquKyKi7wJ4L4BniOgHAI4FcBKsMpwOZn6SiK4E0A5gHIDjkJk7B2sq9xEAH2HmA0T0WQDXZl57FcCnmHnQ9bxjAdwFYDKsrhCfZebfmqzVVKwglADdJ3O7jqc3U3fU258w8uvTAI5qjuONzgvx2m0f8TVCgLVZu5ulej2rP5FEVFOw5O4Lp7q3jU5qbXfNVmG3+wFGQpXOn9HNa3bkPN9LRFIUurk8fkn+7V2WUKCjxfrqPN9Peh1UUXfKIkum3dEPfPS7lmFw4mcoWNO5K3Eg/33aogWZP1Q6LCN0H6zxD5T5el/meEEw8+cB9AE4C5ahWc/Mp2e+X0VE9ujekwAsBHAagH8AMMjMMwH8F6wREQCwhplPY+ZTYXXcvlrxyHsBfIWZZ8FqCXe36VrFEJURnRE4OJjMC8OZZu6ChtpUm7Xfs3TtfpyGwr73JbPUm34fT9Icn+j5bPv9eXWocOKnQCyIQrpC+xkvP0MTRFHnNniA3lAkDprf10b1Pp2G74ZfixEqHq8xEKVgHoClRLQNwE8xMsYBsDpv/4mZ98GarPBU5rg9JgIATiKi/8yMjrgC1jSGLEQ0HsBfAXg884x7ABxlujgJzZURdyGnR+o4y4SmGPoHk4hkij7dFJL/cIcOCxUwAPmG8IXf7lOed1fkctwWuy9nQ3d2PdBhv79iWiEVTSF1QH5FrH7FoQN7LKPiFwLUdW2Yf2duTzob3XPjreYTYYUwCHMMBDAyxiHnkxsRfRD+YyIA4EEA7cz8Uiac9zeu+0cA9DPzjEIWJx5RmbE/sZt4IgDQ1NiAXZ0X4p8WnZrnTanyH7d078CxN/8Yxyxdi2Nv/jFu6d6Rfc1ZqzOnc302rLX4/OO148z9cBtCnWH4weEzsp/SGYRenoSlyWuyUutYhPLW4Hx/OoNbFiFCIXVAfsbLJJ9iEgIM6q2ds0wdtvvw7dbUVRXxCf5rFYol7DEQxY5xeAeAN4koBssjyoGZ/whgFxFdmrk/EdGppjcXQ1QhTD/J2+d55T9sA3PM0rV4eOPurOeUYsbDG3fjlu4dyhzL4sdfwswVz+KGx7ZhXGNw51hlCD0NRiacQx392NT+c2x553nZ97Lq0lOx6mOnavM7qrBm2QpUdZu3lzHxM16nLLK8ED+cRkWVcwrqrXnld6Jj1NckDpoXvQqF8jVYCX4npRwD8Q1YIoXtmSmt3wh4/d8D+CWAFwHoBAhXALiaiF4CsBPW3DojpI6oQpiGw/xqaUzqjaJEeHfz2ILDb+57pZkxpSWOs06YjBd+uy+rYjvrhMlYu/3NvB54drGr89xCikuDFAOXnKCqOdOREUayawIW3qu+HyJAUjUfaJo6NOdFRwtKVvQqBA8xlFg1V0uIIaoQJgZEV+zqxNSgmeSj/HCux7TgtiUew0WnHoXHNu3J6f4dixJWfezUynQ6KBdGRax7MoP1UiNf3dj1OaZzhyIxoD0jWHI/wz0zyLlGnezbuY6gxq1+qYqC1lpBxAoVQlX46vYwTD7xm4b4dGIHUyY0xbB8/ok56zYpuB03pgFrt7+ZN4IimWLc+tTO0W2IvGYTPfmlkRHfnAKijcDMT1ljHVT1OSYjGGzGvMP66vSgbAPjLFTVnaNDF+4rdY2VUHeIIaogXh2oTTEtUC124mpTY0POWoPkuHRP9hpjMap55qYRI2STGgJ2PmGFv1Sbuu3ZmJA46F2n5Mw7GfWpy6DKexUya0kQXIhYocbxKiItJb39iRylnalarSra61QbOpl04oC+PkclmNBFf5qn+kuuB/YGk2XrxBmF1FgJggsxRDWOraYrB739Cdzw2Dbc0r0DZ50w2SgIftYJk9ESjylf0x0fFXh1VSgEldpt9lV6NZ9fQSxFYJw1pKheqBDWrCWhrhCxQhkJU/VVTFFqIcSipB09HoSKjmcwoZD8h59i7vbpaq8o3mrV8wRV56nO396VySuV4v83WR6aCt2sIxE2iFghAGKIyoTXeIhSbMLFzBkqF7Zyz63gK5W8u+QUOkfIaxBd8zTguHnArx4C0o4cWSQGfODTarFCobLpp28ENj+Aoo2Rl1EJa9ZS7SOGKAASmisTpr3S3Oi6IbhxFrx64fd6mDCsOiT3tphIpvDIxt2eDU0rQqH5D6+w1MAey9h84NO5Ybb2u62x3aXMt1z0bav+yPmcoPgV7koDVKEEiEdUJgoZ2laIF9W9tRc3PLZN+ayWeAzbls/ThvGiRUq8S01LPIZxYxpK5iUFDo1qCzwzoSpdWEwXenOi8jL8nlcKTNYWGwckB0WKXRziEQVAPKIyUUivtKBelG24dKbEnuaga5fziQ9OM1LgqfrCmaIbKaGiP5EsmZdkMkYiD682PYWOhrBReU2F9LQLwvYu4Mif/M9rGCNdtYWyIoaoTBTSKy1ox2m/ItP+TN2Orm/dN9tPxm0LT8aEpnw1m20+dH3hVl82A6svm6G81kZn7ExNk0koU0dBoVGvHnNeYTuTUQsq42LS064YNd7zK3LzUjpUM4gEIUSkoLVMeI0Q1xF09LVfkanzOmcx7S3dO/DVrpdw/WPbspNeZ7+nNTuuwg7ZuRVuqrU7j+lCYfa9nR0lfrSl10hoUejYh4LGSLgnnzpDVbpOBwN7rW7VXuEvXd7F63mAZRy6vzhiTAb2WN87r/UiiKRailKFMiI5oiomaI7IS8Ktu+6W7h14eGN+p/lPnnE0Zr+nVanEa4nH0LHgxJKq2txGa3BoWNl5wTaGQWXwup+NX1NZLV6yZd04bvv1QvMuXrLvm3b5X++l5lMhEuxikBxRACQ0V0ZMFXA2QUdfe3VZGNOg/qf+/i/VG9P3f7lHG+rrTyRLrmpzT1ZdPv9EZSjzrBMmB8/1IIQxEl5hNG1ojorLu3h1ZDBB2Z3BAylKFcqEhObKhNu7sTdQQB3islH1o9OFvLwmwNrGw/08nUouxewZtrLzK2HV+uhCmV65Hr+fo+p+Ba/fK4ym6wtXKtFBoajWfNw8YMuDmq7fFV6vUDeIISoTug20oydYB2o/g2b/UYWiVBu2l2S7pSnm2Zg0zDHdOmN7w2PbCl5LKZrM5qDrrn3OMnWRp8lUVi9047y9BuypJObucNvRZ4SzXkEwREJzZUK3UfYnkoFCXKbqL9Pk/Cc+qC9yZIannDushqZeUuuKjgw3Jawizw/fbo2LcBJttI6rMJWYS1GqUGHEEJUJv3ohU0wNjOmG/c12fcPUgURSK+cOc0y3l7Gt6MjwIOi6aBd7z4vvyjUYF9+lv3eQzhBhrFcQDBFDVCYKqRdSYWpggmzYurY/U1riaJ/Zhq3L5mH1ZTOMRRPF4mVsgwo4yk6pu267CWIwStkZO+z3JdQ1kiMqE+0z23DrUzuVOZcgYaXF5x+vlHS7DUyQ5PxZJ0zGIxt35zUidd6z5PkVD/zqp8q5lkBU25C45qmlEU1U2/sSRh3iEZURnSQ5SFgpiEfglkSrzune2osfbenNMUIE4JJZldvsayb85qbIIXFB5f2+mHRqMEGG3wkhIx5RGfHzUkybcpbSI1DlYxjAC7/dV/S9C52/VHKpdbkoIhRWqLzfE79ODabI8DshZEI1RER0AYDvAIgCuJ+ZO12vnwlgNYBTAHycmX/oeO0zAG7JfPtNZv6/Ya61XOiMSCgbkQF+4odCjUmx76dqw29eFBEKK7Q+yhedxDwIpQrxCYKG0EJzRBQFcBeADwN4P4BPENH7XaftBnAlgEdd17YCWA7ggwBOB7CciCaEtdZqoNB5RcWiy0+1NMUwc8WzuP6xbQV1wK7U+6koRYTCCuqFVy5KFeITBA1h5ohOB/AqM7/OzEMAfgDgYucJzPwGM28HkHZdez6A55j5ADMfBPAcgAtCXGvFqdRGpGsLdHAwqRRWmBqTqt5Yw6KIehxPNWS5FWvu5wFSZySESpihuTYATn9+LywPp9Br8+ITRHQtgGsB4Oijjy5slVVC0E7bQOFhMyftM9uw+XcH8Mgvd8O0/62JMSnk/VQNuoF3JhQYCtOpIVe//xXgqeV6xVoxa1WhU8jNv1MaoAqhUdOqOWa+l5lnM/PsyZMnV3o5RRFUKVbQoDfFPWbc+iwe3mhuhAAzY1KzyrdiB94ViE4Nedpr/6xXrIWxVlHICRUgTI+oF4Czf8zUzDHTa//Gde1PS7KqKiWoUqzY5LZqxIQJpsakZpVvXhtxyKEopUDjSQ/FWhhrFYWcUAHCNESbABxHRNNhGZaPA7jc8Np1AL7lECjMA3Bz6ZdYXQRRihWbg/Gb5qoi6ByimlS+VdtG7KVYC2OtopATKkBooTlmHgbwZVhG5X8AdDHzTiJaQUQLAICITiOivQAuBXAPEe3MXHsAwDdgGbNNAFZkjgkZim3+GUQ0QLAG5W1bPq/2DEtQdBtupTZiL8VaGGsVhZxQAULNETHzj5n5fcx8LDP/Q+bYMmbuyfx9EzNPZeZxzDyRmU90XPsAM/955s+/hbnOWqTYHIyfwYpk5ku2tcRxx2UzPJujjioqsRF7qeK8lHhhrFU6cQsVQEaF1zDFqOZ0OaIJTTEsn1/aMeA1RymUaKb3cKvUAMuYmG7+pVbNCaVCRoUHQAxRHVMK+beQz6aee3DSr/4ecRwZOagzLnecpMnJTCufXFqMWRiIIQqAGCKhPglp8+3e2ovTus9EG+3Pe+33mIyNF/8s19h3tADQ/R+k8A1DsR6ZoEMMUQBquo5IEAoixFqhVetexlHIN0IA8C7en1/r5SksKGJtpt0YpG5IqALEEAn1R4ibb19/An08Sf0aT8xvkaQSHLjRrU1nbIIYWq0EfI8MvxPKhhgiof4IsVZoSkscK4cXYZAbc44PciNWDluhrhzpvFulpsO9Ni9jE8TQenlkZegoIQiAGCKhHilF/Y3GG1l8/vF4LvohLE1eg73pSUgzYW96EpYmr0FPei4AhXTeOf67eZr7Seq1eRmbIIbWyyOTEJ1QJmQwnlB/nLNMnaA3rb/xGJ3dPtPyelata8Tc/rkgwHMEe8Fr8zI2Qboj2IKENZ/V308QQkY8IqH+KLZo0yf0ZY9of6PzQtxx2Qyjse6B1+bl1QUtdD1lkbknJgghIPJtQQiKVnJNVnitHPjJroPK00XGXWpEvh0ACc3VEcUWsIZaAFtLRZXV0BjU/tnofmZB5yL53U8QQkQ8olGK22icdcJk/GhLb97gNd9QkeN+qsFtptd7UmufxmttvUIlEI8oAJIjGoWohuY9snG3dn6RCV7zj4qm1ooqpTGoIJQUCc2NQlRGQ+f3mo6DKHb+kSfVNgPIhAJHgguCkI8YohrDJE8TxDiYzi+a0hJHr+K+ptd7Ug05l9FILeXdhLpGQnM1hCrklte7DHrj4A5aB5lfVOz8I09qaRibaQ+3ShNiPz1BKDViiGoIvzxN99ZezOlcj97+hNLoXHHG0cFqWhy0z2zDbQtPLvh6T2ol51JLm3ut5d2EukZCczWEV57GrWpjIFvV31YiqXX7zLbw5hXVQs7Fa3OvtrXXYt5NqFvEI6ohdCG3KS1xrUChJR4DANzw2DbM6VyfF8YTAlBLm3sp+ukJQpkQQ1RDeOVpdN5SfyLpm1MalYSRy6mlzb2W8m5C3SOGqIbwytOYqtdKVvtTzYSVy6mlzb1W8m6CAOmsMGpQdT7wgoDSt+mpFu44SSMHn2aNWygGkUQLZkhnhQCIWGGUYBsTZ43R4NAwDg4mlec7Q3XO60cFYeZyakFUIQg1hhiiUYRb1WbiJdmhOp0hCrXRaVhIgawg1BRiiEYxbi8paJsftyGrGQ+q2MF3NURNflAQBBciVhjl2EPadnVeiDYP+beKUBudhkmdJOpNO20IQrUjHlEdsfj845WjHHRtekJtdBo2dZDL8fqgIF6RUEuIR1RHBG3T41VAK1Semv6gIAgOxCOqM4K06QnqQQnlJdSO6IJQRsQjErSE2uhUKJpQO6ILQhkRj0jwJNRGp0JRqGrHRDUn1CJiiAShhpEPCsJoQEJzgiAIQkURQyQIgiBUFDFEgiAIQkURQyQIgiBUFBErCEZITzNBEMJCDJHgS802PxUEoSaQ0JzgS802PxUEoSYQQyT4Ij3NBEEIEzFEgi/S/FQQhDARQ1SndG/txZzO9Zi+dC3mdK73nGEjPc0EQQgTESvUIUHFB9LTTBCEMCFm3QDp2mL27Nm8efPmSi+jJpjTuV45PqCtJY4Xl55dgRUJwqiDKr2AWkI8oiomrNodL/GB1AsJglBuxBBVKWHW7ugGqrU0xaReSBCEsiNihSolzNodnfiAGVIvJAhC2RFDVKWEWbujm7w6kEiG9kxBEAQdEpqrUnThs1LV7qgGqq1a93KozxQEQVAhHlGVUonaHakXEgShEohHVKVUonZH6oUEQagEUkckCIJQeqSOKAASmhMEQRAqihgiQRAEoaKIIRIEQRAqihgiQRAEoaKIIRIEQRAqSqiGiIguIKKXiehVIlqqeH0MET2Wef2XRHRM5vgxRJQgom2ZP98Nc52CIAhC5QitjoiIogDuAnAegL0ANhFRDzP/xnHa1QAOMvOfE9HHAdwO4LLMa68x84yw1icIgiBUB2EWtJ4O4FVmfh0AiOgHAC4G4DREFwPoyPz9hwD+hYhEf18GZNyDIAjVQpihuTYAexzf780cU57DzMMABgBMzLw2nYi2EtHPiOivVQ8gomuJaDMRbd63b19pVz+KsUdM9PYnwBgZ9+A1LlwQBCEsqlWs8CaAo5l5JoAbATxKRO90n8TM9zLzbGaePXny5LIvslYJc8SEIAhCUMI0RL0Apjm+n5o5pjyHiBoANAN4i5mPMPNbAMDMWwC8BuB9Ia61rghzxIQgCEJQwjREmwAcR0TTiagRwMcB9LjO6QHwmczfPwZgPTMzEU3OiB1ARO8FcByA10Nca12hG+sg4x4EQagEoRmiTM7nywDWAfgfAF3MvJOIVhDRgsxp3wMwkYhehRWCsyXeZwLYTkTbYIkYPs/MB8Jaa70h4x4EQagmpPt2nSKqOUEIFVH/BkDmEdUpqgmtgiAIlaBaVXOCIAhCnSCGSBAEQagoYogEQRCEiiKGSBAEQagoIlaoc0Q9JwhCpRFDVMfYPefsdj92zzkAYowEQSgbEpqrY6TnnCAI1YAYojpGes4JglANiCGqY6TnnCAI1YAYojpGes4JglANiFihjrEFCaKaEwShkoghqnOk55wgCJVGQnOCIAhCRRFDJAiCIFQUMUSCIAhCRRFDJAiCIFQUMUSCIAhCRRFDJAiCIFQUMUSCIAhCRRFDJAiCIFQUMUSCIAhCRSFmrvQaSgIR7QPwO8VLkwDsL/NyvJD1+FNta5L1+FNta6r0evYz8wUVfH5NMWoMkQ4i2szMsyu9DhtZjz/VtiZZjz/VtqZqW4/gjYTmBEEQhIoihkgQBEGoKPVgiO6t9AJcyHr8qbY1yXr8qbY1Vdt6BA9GfY5IEARBqG7qwSP6/+2da4xV1RXHf39FBqtR8JWoVIfxEaOhBYvUqBjwER+JYCqt79CqHyrGxBiTakkTW2LSpB9qG2L4YBTfikQTozFGsRo1ndKCAwNGYQCNKKnx1VgtKLD6Ya/RzfXemXvhnnO4w/olO2ef/Tr/WXvN7HP2ObN3EARBsAcTA1EQBEFQKR01EEm6UNI7kgYk3V4nv0vSE57/D0ndnt4t6X+S+jwszOr8RFK/1/mrJJWg5+pMS5+kHZImed4r3uZg3hFtttHZklZI2iZpdk3eHEnrPMzJ0ou0UV09kiZJ+rukNZJWSbo8y1skaWNmo0lF6/G87dk1n8nSJ3j/Dnh/j25Wz+5okjSjxo+2SLrU84q00a2S3vJ+WSrp2CyvCh+qq6coHwoKwMw6IgD7AuuBHmA0sBI4uabMXGChx68AnvB4N7C6QbvLgNMBAc8DFxWtp6bMRGB9dv4KMKVAG3UDPwIeBGZn6YcAG/w4zuPjSrBRIz0nAid4/ChgMzDWzxflZcuwj+f9t0G7i4ErPL4QuLEsTTX99ynwgxJsNCO7zo1893tWlQ810tN2H4pQTOikJ6KpwICZbTCzr4HHgVk1ZWYBD3h8CXDuUHdeko4EDjKzXkve+SBwacl6rvS67WBYTWb2rpmtAnbU1L0AeNHMPjWzz4AXgQuLtlEjPWa21szWefxD4CPg8Cav23Y9jfD+PIfUv5D6u1n7tFPTbOB5M/uqhWvvqp6/ZdfpBcZ7vCofqqunIB8KCqCTBqKjgfez802eVreMmW0D/gMc6nkTJL0p6VVJ07Lym4Zpsyg9g1wOPFaTdr9PF/yulSmMJjW1WrdoGw2LpKmku+H1WfJdPt3yZ0ldJekZI+lfknoHp8BI/fm59++utNkWG5GeuGv9qAwbXU96whmqbpk+lOv5ljb6UFAAnTQQ7Q6bgWPMbDJwK/CopIMq1oSknwJfmdnqLPlqM5sITPNwbSXi9hD8bvoh4FdmNvhEcAdwEnAaaRroNyXJOdbSsjFXAXdLOq6k6w6J22gi8EKWXLiNJF0DTAH+1O62d4VGevYwHwrq0EkD0QfAD7Pz8Z5Wt4ykUcDBwCdmttXMPgEws+Wku6ITvfz4rH69NtuuJ8v/3l2smX3gxy+AR0lTE83SjKZW6xZto4b4zcJzwDwz6x1MN7PNltgK3E/zNtotPVnfbCC9y5tM6s+x3r8tt7m7mpxfAE+b2TeZ1kJtJOk8YB4w068xVN3CfaiBniJ8KCiCql9SNRuAUaSXnxP47qXlKTVlbmLnjwMWe/xwYF+P95Ac+RA/r32JenHRevx8H9fRU9PmYR7fj/Te4dfttFFWdhHf/1hhI+kl8ziPF26jIfSMBpYCt9Qpe6QfBdwN/LEEPeOALo8fBqzDX5oDT7Lzxwpzy+izLL0XmFGWjUgD8Hr8Q4CqfWgIPW33oQjFhMoFtCQWLgbWutPN87Q/kO6CAMb4H4UBd/weT78MWAP0ASuAS7I2pwCrvc0F+GoTRerxvOlAb017BwDLgVWu9y/4ANpGTaeR5tm/JN3Nr8nqXudaB0jTGGXYqK4e4BrgG++zwTDJ814G+l3Tw8CBJeg5w6+50o/XZ232eP8OeH93ldhn3aQbmn1q2izSRi8B/8765ZmKfaiunqJ8KEL7QyzxEwRBEFRKJ70jCoIgCEYgMRAFQRAElRIDURAEQVApMRAFQRAElRIDURAEQVApMRAFQZNImuYrOfdJ2r9qPUEwUojPt4OgSZS2D3ndzB6uWksQjCTiiSjoWJT2mXrb95ZZK+kRSedJesP3w5kq6QBJ90la5ovezsrqvub7/KyQdIanT1faE2qJt/2IEjeQltKZ72nTJT2baVkg6Zcef1fS773dfkknVWCeIOgYRg1fJAj2aI4Hfk76j/5/khYkPQuYCfwWeAt42cyukzQWWCbpJdKWAOeb2RZJJ5DW/JvibU4GTgE+BN4AzjSzeyWdBTxrZkskTR9G18dmdqqkucBtwA3t+5GDYGQRA1HQ6Ww0s34ASWuApWZmkvpJy9+MB2ZKus3LjwGOIQ0yC3xnzu2kRXAHWWZmm7zNPm/n9RZ1PeXH5cDPWv2hgmBvIgaioNPZmsV3ZOc7SP69HbjMzN7JK0m6k7Q+2Y9JU9RbGrS5nfq/J9vYeWp7TANdjeoHQeDEO6JgpPMCcPPgBoOSJnv6wcBmS/vTXEvakroV3gNOltTlU37ntktwEOxtxEAUjHTmk7bUWOVTd/M9/R5gjqSVpA3SvmylUTN7H1hMWr15MfBm2xQHwV5GfL4dBEEQVEo8EQVBEASVEgNREARBUCkxEAVBEASVEgNREARBUCkxEAVBEASVEgNREARBUCkxEAVBEASV8n/d+T38Z6tAPgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 430.5x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "QP1o8aSMk7NP",
        "outputId": "eddf634b-e39a-44f9-d72b-d19f78d44656"
      },
      "source": [
        "# We can visualize other features by substituting \"meanfun\"\n",
        "sns.FacetGrid(dataset, hue=\"label\", size=6) \\\n",
        "   .map(sns.kdeplot, \"meanfun\") \\\n",
        "   .add_legend()\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 502.5x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3dwqbWok8sX",
        "outputId": "6a3c7317-9ea5-4403-8dd3-6e01fd12bebd"
      },
      "source": [
        "#------ Separating the Independent and Dependent Variables\n",
        "# Getting all Columns, except the last one with the genders\n",
        "X = dataset.iloc[:, : -1].values\n",
        "# Getting the last column\n",
        "y = dataset.iloc[:, 20].values \n",
        "\n",
        "#------ Taking Care of Missing Data\n",
        "# dataset.isnull().sum() \n",
        "# No Need of Taking Care of Missing Data :)\n",
        "#------ Checking the Number of Male and Females\n",
        "print(\"Number of Males {}\".format(dataset[dataset.label == 'male'].shape[0])) # shape returns the dimensions of the array. If Y has n rows and m columns, then Y.shape is (n,m). So Y.shape[0] is n. \n",
        "print(\"Number of Females {}\".format(dataset[dataset.label == 'female'].shape[0]))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Males 1584\n",
            "Number of Females 1584\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckzDRpYZl8jP"
      },
      "source": [
        "#------ Encoding Categorical Data of the Dependent Variable\n",
        "# male -> 1\n",
        "# female -> 0\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder_y = LabelEncoder()\n",
        "y = labelencoder_y.fit_transform(y)\n",
        "\n",
        "#------ Splitting the Dataset into the Training Set and Test Set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 0)\n",
        "\n",
        "#------- Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "\n",
        "# Creating a Dictionaire\n",
        "model_accuracy = {}"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDDMjfpXmB5_",
        "outputId": "38a00afb-b3d4-4ebc-dc1d-5c28a0e1042e"
      },
      "source": [
        "#                                                   #\n",
        "#                Logistic Regression                #\n",
        "#                                                   #\n",
        "#####################################################\n",
        "\n",
        "#------- Fitting Logistic Regression to the Training Set \n",
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state=0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "#------- Predicting the Test Set Results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "#------- Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "#------- Calculating the Performance of Logistic Regression\n",
        "from sklearn import metrics\n",
        "print( \"Accuracy of Logistic Regression: {}\".format(metrics.accuracy_score(y_test,y_pred)) ) # 0.971608832808\n",
        "\n",
        "model_accuracy['Logistic Regression'] = metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Logistic Regression: 0.9716088328075709\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7J3R214lmH0K",
        "outputId": "a730787e-7ff1-4c43-eece-fdd8e2f59884"
      },
      "source": [
        "# Fitting K-NN to the Training set\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors=5, metric = 'minkowski', p = 2 )\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred) \n",
        "\n",
        "#------- Calculating the Performance of K-NN\n",
        "from sklearn import metrics\n",
        "print( \"Accuracy of K-NN: {}\".format(metrics.accuracy_score(y_test,y_pred)) ) # 0.965299684543\n",
        "\n",
        "model_accuracy['K-NN'] = metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of K-NN: 0.9652996845425867\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7v6FoJVmO0V",
        "outputId": "52a70cbb-b2bf-4caf-de97-a3b0e635d410"
      },
      "source": [
        "# Fitting Kernel SVM to the Training set\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "#------- Calculating the Performance of Kernel SVM\n",
        "from sklearn import metrics\n",
        "print( \"Accuracy of Kernel SVM - RBF: {}\".format(metrics.accuracy_score(y_test,y_pred)) ) # 0.9747634069400631\n",
        "\t \n",
        "model_accuracy['Kernel SVM - RBF'] = metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Kernel SVM - RBF: 0.9747634069400631\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQJZrefQmeYm",
        "outputId": "f9779a7c-5bf2-4859-a761-c7a713cc22a5"
      },
      "source": [
        "# Fitting SVM to the Training set\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "#------- Calculating the Performance of SVM\n",
        "from sklearn import metrics\n",
        "print( \"Accuracy of Kernel SVM - Linear: {}\".format(metrics.accuracy_score(y_test,y_pred)) ) # 0.9747634069400631\n",
        "\n",
        "model_accuracy['Kernel SVM - Linear'] = metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Kernel SVM - Linear: 0.9747634069400631\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ps1HecAxmnm9",
        "outputId": "62ad4092-73de-4c3f-8afe-8af25f02ec51"
      },
      "source": [
        "# Fitting Naive Bayes to the Training set\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "#------- Calculating the Performance of Naive Bayes\n",
        "from sklearn import metrics\n",
        "print( \"Accuracy of Naive Bayes Model: {}\".format(metrics.accuracy_score(y_test,y_pred)) ) # 0.8990536277602523\n",
        "\n",
        "model_accuracy['Naive Bayes'] = metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Naive Bayes Model: 0.8990536277602523\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukmkLtgBM2Sz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDmniLCfN0v3",
        "outputId": "c07d7f9f-8a67-43df-ba45-6da34eaa9afd"
      },
      "source": [
        "# Fitting Decision Tree Classification to the Training set\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "#------- Calculating the Performance of Decision Tree Classification\n",
        "from sklearn import metrics\n",
        "print( \"Accuracy of Decision Tree Classification: {}\".format(metrics.accuracy_score(y_test,y_pred)) ) # 0.9463722397476341\n",
        "\n",
        "model_accuracy['Decision Tree'] = metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Decision Tree Classification: 0.9463722397476341\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwcYyzbTN3Cm",
        "outputId": "2f1865b7-c817-4aa5-d0dd-6f26d1b23882"
      },
      "source": [
        "# Fitting Random Forest Classification to the Training set\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier(n_estimators=250, criterion='entropy',random_state=0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "#------- Calculating the Performance of Random Forest Classification\n",
        "from sklearn import metrics\n",
        "print( \"Accuracy of Random Forest Classification: {}\".format(metrics.accuracy_score(y_test,y_pred)) ) # 0.9779179810725552\n",
        "\n",
        "model_accuracy['Random Forest'] = metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Random Forest Classification: 0.9747634069400631\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4xD9ctTN9IN",
        "outputId": "551588e7-2438-41b0-aab3-64d26492c996"
      },
      "source": [
        "header = list(dataset)\n",
        "\n",
        "classifier.fit(X_train, y_train)\n",
        "importances = classifier.feature_importances_\n",
        "std = np.std([tree.feature_importances_ for tree in classifier.estimators_],\n",
        "             axis=0)\n",
        "indices = np.argsort(importances)[::-1] # list in reverse order\n",
        "\n",
        "# Print the feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "header1 = []\n",
        "for f in range(X.shape[1]):\n",
        "    print(\"%d. Feature %s (%f)\" % (f + 1, header[indices[f]], importances[indices[f]]))\n",
        "    header1.append(header[indices[f]])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature ranking:\n",
            "1. Feature meanfun (0.362856)\n",
            "2. Feature IQR (0.183142)\n",
            "3. Feature Q25 (0.113950)\n",
            "4. Feature sd (0.095970)\n",
            "5. Feature sp.ent (0.035143)\n",
            "6. Feature sfm (0.034589)\n",
            "7. Feature mode (0.020395)\n",
            "8. Feature meanfreq (0.019150)\n",
            "9. Feature centroid (0.018943)\n",
            "10. Feature median (0.014553)\n",
            "11. Feature Q75 (0.012724)\n",
            "12. Feature minfun (0.011989)\n",
            "13. Feature maxdom (0.011902)\n",
            "14. Feature dfrange (0.011233)\n",
            "15. Feature skew (0.011195)\n",
            "16. Feature kurt (0.010248)\n",
            "17. Feature meandom (0.008922)\n",
            "18. Feature mindom (0.008845)\n",
            "19. Feature modindx (0.008096)\n",
            "20. Feature maxfun (0.006156)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "OF6bxGg3OCW4",
        "outputId": "50d0ed16-0819-490e-989d-bf249664043a"
      },
      "source": [
        "import random \n",
        "colors = ['#F44336', '#E91E63', '#9C27B0', '#673AB7', '#3F51B5', '#3F51B5', '#2196F3',\n",
        "          '#03A9F4' ,'#00BCD4', '#009688', '#4CAF50', '#8BC34A', '#CDDC39', '#FFEB3B',\n",
        "          '#FFC107', '#FF9800', '#FF5722', '#795548', '#607D8B', '#B71C1C', '#880E4F',\n",
        "          '#4A148C', '#311B92', '#1A237E', '#0D47A1', '#01579B', '#006064', '#004D40',\n",
        "          '#1B5E20', '#33691E', '#827717', '#F57F17', '#FF6F00', '#E65100', '#BF360C',\n",
        "          '#3E2723', '#212121', '#607D8B']\n",
        "\n",
        "random_colors = random.sample(colors, 20)\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.title(\"Feature importances\")\n",
        "plt.bar(range(X.shape[1]), importances[indices],\n",
        "       color=random_colors, align=\"center\")\n",
        "plt.xticks(range(X.shape[1]),header1, rotation=90)\n",
        "plt.xlim([-1, X.shape[1]])\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "tvSA9ZuIOKIO",
        "outputId": "accd597a-f935-4496-8df6-2ba155ec4249"
      },
      "source": [
        "from collections import OrderedDict\n",
        "model_accuracy = OrderedDict(sorted(model_accuracy.items(), key=lambda t: t[1]))\n",
        "\n",
        "plt.figure()\n",
        "plt.title(\"Models Accuracy\")\n",
        "plt.barh(range(len(model_accuracy)), model_accuracy.values(), align='center', color='#009688')\n",
        "plt.yticks(range(len(model_accuracy)), model_accuracy.keys(), rotation = 0)\n",
        "axes = plt.gca()\n",
        "axes.set_xlim([0.8,1.0])\n",
        "plt.xlabel(\"Accuracy\")\n",
        "plt.ylabel('Classifier')\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}