ASTERLAYSE.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHCShbOClFko"
      },
      "source": [
        "# Set up and dataframe creation\n",
        "## Importing Libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NeKlP6_YFM0X"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADUv8udelQpU"
      },
      "source": [
        "### Reading the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "z6out41jRMHY",
        "outputId": "9288b358-9def-48b1-c812-04d942b94e1f"
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      "outputs": [
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
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              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
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              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
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              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
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              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
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              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
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              "\n",
              "  for (const file of files) {\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "    do {\n",
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              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# from google.colab import files\n",
        "\n",
        "# uploaded = files.upload()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXNhNI9AVLp5"
      },
      "source": [
        "copy this zip file of dataset to your google drive in a separate folder named \"*ASTERLAYSE*\" and then run ahead\n",
        "\n",
        "https://drive.google.com/file/d/10Gnj4_JNukuXBkOVpmf0tH64GXfORs3p/view?usp=drive_link\n",
        "\n",
        "cross check zip file path in case of any problem"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDleUXQjR55a",
        "outputId": "fc85d762-dcd9-43c7-b79c-f2eec1936d2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hoChilODUpq3"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "zip_file_path = '/content/drive/MyDrive/ASTERLAYSE/Copy of asteroid_dataset.zip'\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall('/content')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jz0KamZtG5ZD",
        "outputId": "831efb66-7f8e-480f-b547-d26c31a6eba8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-5-b2f25fceea95>:1: DtypeWarning: Columns (15,16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('/content/asteroid_dataset/Train/train_data.csv')\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv('/content/asteroid_dataset/Train/train_data.csv')\n",
        "labels= pd.read_csv('/content/asteroid_dataset/Train/train_labels.csv')\n",
        "df['moid'] = labels['moid']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hFGwujslZNmB"
      },
      "source": [
        "Certainly! Here's a detailed description of each of the features you've listed:\n",
        "\n",
        "1. **Object full name**: This is the full name of the asteroid, which may include additional information about its discovery and observation history.\n",
        "\n",
        "2. **a (semi-major axis)**: The semi-major axis is a fundamental parameter in describing the shape and size of an asteroid's orbit. It represents half of the longest diameter of the elliptical orbit an asteroid follows around the Sun, measured in astronomical units (au).\n",
        "\n",
        "3. **e (eccentricity)**: Eccentricity describes the shape of an asteroid's orbit. It is a measure of how elongated or circular the orbit is. An eccentricity of 0 indicates a perfectly circular orbit, while values between 0 and 1 represent increasingly elongated, elliptical orbits.\n",
        "\n",
        "4. **i (Inclination with respect to x-y ecliptic plane)**: Inclination is the angle between an asteroid's orbital plane and the plane of the Earth's orbit around the Sun (the ecliptic plane). It is measured in degrees.\n",
        "\n",
        "5. **om (Longitude of the ascending node)**: This is the angle measured along the ecliptic plane from the vernal equinox to the point where an asteroid's orbit crosses the ecliptic plane in a northward direction.\n",
        "\n",
        "6. **w (argument of perihelion)**: The argument of perihelion is the angle measured from the ascending node to the point where an asteroid is closest to the Sun in its orbit, known as perihelion.\n",
        "\n",
        "7. **q (perihelion distance)**: The perihelion distance represents the closest approach of an asteroid to the Sun in its orbit, measured in astronomical units (au).\n",
        "\n",
        "8. **ad (aphelion distance)**: The aphelion distance is the farthest point from the Sun in an asteroid's orbit, measured in astronomical units (au).\n",
        "\n",
        "9. **per_y (Orbital period in years)**: This is the time it takes for the asteroid to complete one orbit around the Sun, measured in years.\n",
        "\n",
        "10. **data_arc (data arc-span)**: Data arc-span is the duration, in days, over which observations and data for the asteroid's orbit have been collected.\n",
        "\n",
        "11. **condition_code (Orbit condition code)**: The orbit condition code is a numerical indicator of the accuracy and reliability of an asteroid's orbital parameters. Lower values generally indicate more reliable orbits.\n",
        "\n",
        "12. **n_obs_used (Number of observations used)**: This represents the total number of observations that have been used to calculate and refine the asteroid's orbital parameters.\n",
        "\n",
        "13. **H (Absolute Magnitude parameter)**: The absolute magnitude is a measure of an asteroid's intrinsic brightness, corrected for its distance from both the Sun and Earth.\n",
        "\n",
        "14. **neo (Near Earth Object)**: A binary indicator, typically \"Y\" or \"N,\" that denotes whether the asteroid is classified as a Near Earth Object, which means it has an orbit that brings it into proximity with Earth's orbit.\n",
        "\n",
        "15. **pha (Physically Hazardous Asteroid)**: Similar to \"neo,\" this is a binary indicator (\"Y\" or \"N\") that denotes whether the asteroid is classified as a potentially hazardous asteroid based on its size and orbit.\n",
        "\n",
        "16. **diameter**: This is the estimated diameter of the asteroid in kilometers, providing information about its size.\n",
        "\n",
        "17. **extent**: The object bi/tri axial ellipsoid dimensions represent the dimensions of the asteroid if it were to be approximated as an ellipsoid. It provides information about its shape and size.\n",
        "\n",
        "18. **albedo (geometric albedo)**: Geometric albedo is a measure of an asteroid's reflectivity, indicating how much sunlight it reflects. It is a unitless value between 0 and 1, with 1 indicating perfect reflectivity.\n",
        "\n",
        "19. **rot_per (Rotation Period)**: This is the rotation period of the asteroid, which is the time it takes for the asteroid to complete one full rotation on its axis, measured in hours.\n",
        "\n",
        "20. **GM (Standard gravitational parameter)**: The standard gravitational parameter is a product of the asteroid's mass and the gravitational constant. It is used in calculations related to the asteroid's gravitational effects on other objects.\n",
        "\n",
        "21. **BV (Color index B-V)**: The B-V color index represents the magnitude difference between an asteroid's brightness in blue (B) and visible (V) light. It provides information about the asteroid's color.\n",
        "\n",
        "22. **UB (Color index U-B)**: Similar to B-V, the U-B color index represents the magnitude difference between an asteroid's brightness in ultraviolet (U) and blue (B) light.\n",
        "\n",
        "23. **IR (Color index I-R)**: The I-R color index represents the magnitude difference between an asteroid's brightness in the infrared (I) and red (R) wavelengths.\n",
        "\n",
        "24. **spec_B (Spectral taxonomic type SMASSII)**: SMASSII is a spectral taxonomic classification system for asteroids based on their reflectance spectra. It categorizes asteroids into different types, such as C, S, or M, based on their spectral features.\n",
        "\n",
        "25. **spec_T (Spectral taxonomic type Tholen)**: Tholen is another spectral taxonomic classification system for asteroids, providing information about their composition and characteristics.\n",
        "\n",
        "26. **G (Magnitude slope parameter)**: The magnitude slope parameter is used to model how an asteroid's brightness changes with phase angle.\n",
        "\n",
        "27. **class (asteroid orbit class)**: This represents the classification of the asteroid's orbit, which can include various categories such as Atira, Amor, Apollo, or Centaur, depending on its orbital characteristics.\n",
        "\n",
        "28. **n (Mean motion)**: Mean motion represents the angular speed at which an asteroid moves along its orbit, measured in degrees per day.\n",
        "\n",
        "29. **per (Orbital Period in days)**: This is the time it takes for the asteroid to complete one orbit around the Sun, measured in days.\n",
        "\n",
        "30. **ma (Mean anomaly)**: Mean anomaly is an angular parameter that describes the asteroid's position in its orbit at a specific time.\n",
        "\n",
        "31. **moid (Earth Minimum orbit Intersection Distance)**: MOID represents the closest distance between an asteroid's orbit and the Earth's orbit, measured in astronomical units (au). It indicates how closely the asteroid's path intersects with Earth's orbit.\n",
        "\n",
        "These features are crucial for understanding and characterizing asteroids, their orbits, physical properties, and potential risks to Earth."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBtLvrJclhiz"
      },
      "source": [
        "# Data Preparation\n",
        "## Visualizing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "r981iBW-Hjz2",
        "outputId": "4fb73de8-f7df-4c21-be02-5e682b1c7b76"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>e</th>\n",
              "      <th>i</th>\n",
              "      <th>om</th>\n",
              "      <th>w</th>\n",
              "      <th>q</th>\n",
              "      <th>ad</th>\n",
              "      <th>per_y</th>\n",
              "      <th>data_arc</th>\n",
              "      <th>n_obs_used</th>\n",
              "      <th>...</th>\n",
              "      <th>rot_per</th>\n",
              "      <th>GM</th>\n",
              "      <th>BV</th>\n",
              "      <th>UB</th>\n",
              "      <th>IR</th>\n",
              "      <th>G</th>\n",
              "      <th>n</th>\n",
              "      <th>per</th>\n",
              "      <th>ma</th>\n",
              "      <th>moid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>671769.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
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              "      <td>671770.000000</td>\n",
              "      <td>659481.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>15031.000000</td>\n",
              "      <td>1.300000e+01</td>\n",
              "      <td>820.000000</td>\n",
              "      <td>788.000000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>6.717690e+05</td>\n",
              "      <td>6.717650e+05</td>\n",
              "      <td>671765.000000</td>\n",
              "      <td>6.586820e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.720203</td>\n",
              "      <td>0.155625</td>\n",
              "      <td>8.950426</td>\n",
              "      <td>168.512910</td>\n",
              "      <td>181.122198</td>\n",
              "      <td>2.404063</td>\n",
              "      <td>3.380605</td>\n",
              "      <td>6.830835</td>\n",
              "      <td>5689.675142</td>\n",
              "      <td>259.261430</td>\n",
              "      <td>...</td>\n",
              "      <td>20.868141</td>\n",
              "      <td>8.423614e+00</td>\n",
              "      <td>0.767510</td>\n",
              "      <td>0.364405</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.173204</td>\n",
              "      <td>2.374092e-01</td>\n",
              "      <td>2.494981e+03</td>\n",
              "      <td>180.633420</td>\n",
              "      <td>1.422694e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>127.844968</td>\n",
              "      <td>0.093844</td>\n",
              "      <td>6.673008</td>\n",
              "      <td>103.096193</td>\n",
              "      <td>104.024008</td>\n",
              "      <td>2.227919</td>\n",
              "      <td>12.933745</td>\n",
              "      <td>266.021233</td>\n",
              "      <td>4209.541421</td>\n",
              "      <td>372.040146</td>\n",
              "      <td>...</td>\n",
              "      <td>74.194698</td>\n",
              "      <td>1.731652e+01</td>\n",
              "      <td>0.089088</td>\n",
              "      <td>0.095743</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.125450</td>\n",
              "      <td>8.095870e-02</td>\n",
              "      <td>9.716462e+04</td>\n",
              "      <td>106.519060</td>\n",
              "      <td>2.244968e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-104279.220927</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007546</td>\n",
              "      <td>0.000388</td>\n",
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              "      <td>0.120000</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.250000</td>\n",
              "      <td>2.926897e-08</td>\n",
              "      <td>1.511339e+02</td>\n",
              "      <td>-67.136826</td>\n",
              "      <td>3.437640e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.385572</td>\n",
              "      <td>0.091403</td>\n",
              "      <td>4.068953</td>\n",
              "      <td>80.266871</td>\n",
              "      <td>91.091035</td>\n",
              "      <td>1.972105</td>\n",
              "      <td>2.775585</td>\n",
              "      <td>3.684650</td>\n",
              "      <td>3608.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.218500</td>\n",
              "      <td>2.750000e-03</td>\n",
              "      <td>0.698000</td>\n",
              "      <td>0.289000</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.095000</td>\n",
              "      <td>1.900575e-01</td>\n",
              "      <td>1.345826e+03</td>\n",
              "      <td>86.674078</td>\n",
              "      <td>9.786875e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.644105</td>\n",
              "      <td>0.143694</td>\n",
              "      <td>7.255619</td>\n",
              "      <td>160.346996</td>\n",
              "      <td>181.783987</td>\n",
              "      <td>2.225353</td>\n",
              "      <td>3.038068</td>\n",
              "      <td>4.299582</td>\n",
              "      <td>5807.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>6.650000</td>\n",
              "      <td>7.475000e-01</td>\n",
              "      <td>0.739000</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>2.292371e-01</td>\n",
              "      <td>1.570426e+03</td>\n",
              "      <td>181.493290</td>\n",
              "      <td>1.237690e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.996020</td>\n",
              "      <td>0.199395</td>\n",
              "      <td>12.255971</td>\n",
              "      <td>252.185170</td>\n",
              "      <td>271.460468</td>\n",
              "      <td>2.578095</td>\n",
              "      <td>3.358020</td>\n",
              "      <td>5.185914</td>\n",
              "      <td>7268.000000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>12.582000</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>0.846250</td>\n",
              "      <td>0.439000</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>2.674938e-01</td>\n",
              "      <td>1.894160e+03</td>\n",
              "      <td>274.182490</td>\n",
              "      <td>1.590610e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3043.149073</td>\n",
              "      <td>1.201134</td>\n",
              "      <td>175.188725</td>\n",
              "      <td>359.999800</td>\n",
              "      <td>359.999833</td>\n",
              "      <td>65.081902</td>\n",
              "      <td>6081.841956</td>\n",
              "      <td>167877.712688</td>\n",
              "      <td>72684.000000</td>\n",
              "      <td>9325.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3240.000000</td>\n",
              "      <td>6.262840e+01</td>\n",
              "      <td>1.077000</td>\n",
              "      <td>0.655000</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>2.381994e+00</td>\n",
              "      <td>6.131733e+07</td>\n",
              "      <td>491.618014</td>\n",
              "      <td>6.411030e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3f83196-ea6c-4acc-9919-c2aa6694b36c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f3f83196-ea6c-4acc-9919-c2aa6694b36c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f3f83196-ea6c-4acc-9919-c2aa6694b36c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b4adee9f-0c86-4fe2-b72d-4ca9bdb88a0a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b4adee9f-0c86-4fe2-b72d-4ca9bdb88a0a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b4adee9f-0c86-4fe2-b72d-4ca9bdb88a0a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   a              e              i             om  \\\n",
              "count  671769.000000  671771.000000  671771.000000  671771.000000   \n",
              "mean        2.720203       0.155625       8.950426     168.512910   \n",
              "std       127.844968       0.093844       6.673008     103.096193   \n",
              "min   -104279.220927       0.000000       0.007546       0.000388   \n",
              "25%         2.385572       0.091403       4.068953      80.266871   \n",
              "50%         2.644105       0.143694       7.255619     160.346996   \n",
              "75%         2.996020       0.199395      12.255971     252.185170   \n",
              "max      3043.149073       1.201134     175.188725     359.999800   \n",
              "\n",
              "                   w              q             ad          per_y  \\\n",
              "count  671771.000000  671771.000000  671765.000000  671770.000000   \n",
              "mean      181.122198       2.404063       3.380605       6.830835   \n",
              "std       104.024008       2.227919      12.933745     266.021233   \n",
              "min         0.001666       0.070511       0.793839       0.000000   \n",
              "25%        91.091035       1.972105       2.775585       3.684650   \n",
              "50%       181.783987       2.225353       3.038068       4.299582   \n",
              "75%       271.460468       2.578095       3.358020       5.185914   \n",
              "max       359.999833      65.081902    6081.841956  167877.712688   \n",
              "\n",
              "            data_arc     n_obs_used  ...       rot_per            GM  \\\n",
              "count  659481.000000  671771.000000  ...  15031.000000  1.300000e+01   \n",
              "mean     5689.675142     259.261430  ...     20.868141  8.423614e+00   \n",
              "std      4209.541421     372.040146  ...     74.194698  1.731652e+01   \n",
              "min         0.000000       2.000000  ...      0.004389  2.100000e-09   \n",
              "25%      3608.000000      50.000000  ...      4.218500  2.750000e-03   \n",
              "50%      5807.000000     118.000000  ...      6.650000  7.475000e-01   \n",
              "75%      7268.000000     296.000000  ...     12.582000  7.000000e+00   \n",
              "max     72684.000000    9325.000000  ...   3240.000000  6.262840e+01   \n",
              "\n",
              "               BV          UB    IR           G             n           per  \\\n",
              "count  820.000000  788.000000  1.00  103.000000  6.717690e+05  6.717650e+05   \n",
              "mean     0.767510    0.364405 -0.33    0.173204  2.374092e-01  2.494981e+03   \n",
              "std      0.089088    0.095743   NaN    0.125450  8.095870e-02  9.716462e+04   \n",
              "min      0.580000    0.120000 -0.33   -0.250000  2.926897e-08  1.511339e+02   \n",
              "25%      0.698000    0.289000 -0.33    0.095000  1.900575e-01  1.345826e+03   \n",
              "50%      0.739000    0.360000 -0.33    0.190000  2.292371e-01  1.570426e+03   \n",
              "75%      0.846250    0.439000 -0.33    0.250000  2.674938e-01  1.894160e+03   \n",
              "max      1.077000    0.655000 -0.33    0.510000  2.381994e+00  6.131733e+07   \n",
              "\n",
              "                  ma          moid  \n",
              "count  671765.000000  6.586820e+05  \n",
              "mean      180.633420  1.422694e+00  \n",
              "std       106.519060  2.244968e+00  \n",
              "min       -67.136826  3.437640e-07  \n",
              "25%        86.674078  9.786875e-01  \n",
              "50%       181.493290  1.237690e+00  \n",
              "75%       274.182490  1.590610e+00  \n",
              "max       491.618014  6.411030e+01  \n",
              "\n",
              "[8 rows x 22 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fE_4ZaTfHpen",
        "outputId": "5cbdfcf4-3681-407a-fb3d-f9d0f920ad11"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "name              654128\n",
              "a                      2\n",
              "e                      0\n",
              "i                      0\n",
              "om                     0\n",
              "w                      0\n",
              "q                      0\n",
              "ad                     6\n",
              "per_y                  1\n",
              "data_arc           12290\n",
              "condition_code       703\n",
              "n_obs_used             0\n",
              "H                   2100\n",
              "neo                    6\n",
              "pha                13089\n",
              "diameter          561590\n",
              "extent            671754\n",
              "albedo            562541\n",
              "rot_per           656740\n",
              "GM                671758\n",
              "BV                670951\n",
              "UB                670983\n",
              "IR                671770\n",
              "spec_B            670420\n",
              "spec_T            670984\n",
              "G                 671668\n",
              "class                  0\n",
              "n                      2\n",
              "per                    6\n",
              "ma                     6\n",
              "moid               13089\n",
              "dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjX6HrV_YTUC",
        "outputId": "fc9025a0-2a9c-43d8-9ebf-b62e69a8ec29"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(N    654719\n",
              " Y     17046\n",
              " Name: neo, dtype: int64,\n",
              " N    657067\n",
              " Y      1615\n",
              " Name: pha, dtype: int64,\n",
              " MBA    597956\n",
              " OMB     19822\n",
              " IMB     14076\n",
              " MCA     13874\n",
              " APO      9385\n",
              " AMO      6355\n",
              " TJN      5899\n",
              " TNO      2639\n",
              " ATE      1292\n",
              " CEN       382\n",
              " AST        71\n",
              " IEO        14\n",
              " HYA         4\n",
              " PAA         2\n",
              " Name: class, dtype: int64,\n",
              " 0    508848\n",
              " 1     26306\n",
              " 9     24907\n",
              " 5     20077\n",
              " 6     18409\n",
              " 7     16401\n",
              " 2     16123\n",
              " 8     15735\n",
              " 4     14092\n",
              " 3     10042\n",
              " E       127\n",
              " D         1\n",
              " Name: condition_code, dtype: int64)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['neo'].value_counts(), df['pha'].value_counts(), df['class'].value_counts(), df['condition_code'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eI_YaqHvlsgY"
      },
      "source": [
        "## Feature transformation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fMuvM-EYZ8d8"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "df['class'] = le.fit_transform(df['class'])\n",
        "df['neo']= le.fit_transform(df['neo'])\n",
        "df['pha']= le.fit_transform(df['pha'])\n",
        "df['condition_code']= le.fit_transform(df['condition_code'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ybnz8HuKlygw"
      },
      "source": [
        "## Imputation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U84qxkbt8yMy"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.impute import KNNImputer\n",
        "# Select the features used for imputation\n",
        "impute_features = ['data_arc', 'condition_code', 'H', 'pha']\n",
        "# Create a KNNImputer with k neighbors (you can adjust k as needed)\n",
        "knn_imputer = KNNImputer(n_neighbors=5)\n",
        "\n",
        "# Perform imputation on the selected features\n",
        "df[impute_features] = knn_imputer.fit_transform(df[impute_features])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5viNfDt9Nwm",
        "outputId": "ea6652d5-5066-455f-c23d-f6b4d8dd3a17"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "name              654128\n",
              "a                      2\n",
              "e                      0\n",
              "i                      0\n",
              "om                     0\n",
              "w                      0\n",
              "q                      0\n",
              "ad                     6\n",
              "per_y                  1\n",
              "data_arc               0\n",
              "condition_code         0\n",
              "n_obs_used             0\n",
              "H                      0\n",
              "neo                    0\n",
              "pha                    0\n",
              "diameter          561590\n",
              "extent            671754\n",
              "albedo            562541\n",
              "rot_per           656740\n",
              "GM                671758\n",
              "BV                670951\n",
              "UB                670983\n",
              "IR                671770\n",
              "spec_B            670420\n",
              "spec_T            670984\n",
              "G                 671668\n",
              "class                  0\n",
              "n                      2\n",
              "per                    6\n",
              "ma                     6\n",
              "moid               13089\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPl_JKcTl1up"
      },
      "source": [
        "## Droping Null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EbdSCtbIH_RW"
      },
      "outputs": [],
      "source": [
        "columns_to_drop = ['name','diameter','extent', 'albedo', 'rot_per', 'GM', 'BV', 'UB', 'IR', 'spec_B', 'spec_T', 'G']\n",
        "df = df.drop(columns=columns_to_drop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "r-vF9upvTV5l",
        "outputId": "89fee822-7191-4223-dd38-39884e6ed4ab"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1249b90f-b419-4c32-969c-f38a9f671d69\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>e</th>\n",
              "      <th>i</th>\n",
              "      <th>om</th>\n",
              "      <th>w</th>\n",
              "      <th>q</th>\n",
              "      <th>ad</th>\n",
              "      <th>per_y</th>\n",
              "      <th>data_arc</th>\n",
              "      <th>condition_code</th>\n",
              "      <th>n_obs_used</th>\n",
              "      <th>H</th>\n",
              "      <th>neo</th>\n",
              "      <th>pha</th>\n",
              "      <th>class</th>\n",
              "      <th>n</th>\n",
              "      <th>per</th>\n",
              "      <th>ma</th>\n",
              "      <th>moid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>671769.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671765.000000</td>\n",
              "      <td>671770.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>671771.000000</td>\n",
              "      <td>6.717690e+05</td>\n",
              "      <td>6.717650e+05</td>\n",
              "      <td>671765.000000</td>\n",
              "      <td>6.586820e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.720203</td>\n",
              "      <td>0.155625</td>\n",
              "      <td>8.950426</td>\n",
              "      <td>168.512910</td>\n",
              "      <td>181.122198</td>\n",
              "      <td>2.404063</td>\n",
              "      <td>3.380605</td>\n",
              "      <td>6.830835</td>\n",
              "      <td>5706.508698</td>\n",
              "      <td>1.236400</td>\n",
              "      <td>259.261430</td>\n",
              "      <td>16.793633</td>\n",
              "      <td>0.025393</td>\n",
              "      <td>0.041373</td>\n",
              "      <td>7.927431</td>\n",
              "      <td>2.374092e-01</td>\n",
              "      <td>2.494981e+03</td>\n",
              "      <td>180.633420</td>\n",
              "      <td>1.422694e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>127.844968</td>\n",
              "      <td>0.093844</td>\n",
              "      <td>6.673008</td>\n",
              "      <td>103.096193</td>\n",
              "      <td>104.024008</td>\n",
              "      <td>2.227919</td>\n",
              "      <td>12.933745</td>\n",
              "      <td>266.021233</td>\n",
              "      <td>4240.334952</td>\n",
              "      <td>2.585088</td>\n",
              "      <td>372.040146</td>\n",
              "      <td>1.824612</td>\n",
              "      <td>0.157371</td>\n",
              "      <td>0.280410</td>\n",
              "      <td>1.320693</td>\n",
              "      <td>8.095870e-02</td>\n",
              "      <td>9.716462e+04</td>\n",
              "      <td>106.519060</td>\n",
              "      <td>2.244968e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-104279.220927</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007546</td>\n",
              "      <td>0.000388</td>\n",
              "      <td>0.001666</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.793839</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-1.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.926897e-08</td>\n",
              "      <td>1.511339e+02</td>\n",
              "      <td>-67.136826</td>\n",
              "      <td>3.437640e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.385572</td>\n",
              "      <td>0.091403</td>\n",
              "      <td>4.068953</td>\n",
              "      <td>80.266871</td>\n",
              "      <td>91.091035</td>\n",
              "      <td>1.972105</td>\n",
              "      <td>2.775585</td>\n",
              "      <td>3.684650</td>\n",
              "      <td>3579.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>15.900000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.900575e-01</td>\n",
              "      <td>1.345826e+03</td>\n",
              "      <td>86.674078</td>\n",
              "      <td>9.786875e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.644105</td>\n",
              "      <td>0.143694</td>\n",
              "      <td>7.255619</td>\n",
              "      <td>160.346996</td>\n",
              "      <td>181.783987</td>\n",
              "      <td>2.225353</td>\n",
              "      <td>3.038068</td>\n",
              "      <td>4.299582</td>\n",
              "      <td>5813.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>16.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.292371e-01</td>\n",
              "      <td>1.570426e+03</td>\n",
              "      <td>181.493290</td>\n",
              "      <td>1.237690e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.996020</td>\n",
              "      <td>0.199395</td>\n",
              "      <td>12.255971</td>\n",
              "      <td>252.185170</td>\n",
              "      <td>271.460468</td>\n",
              "      <td>2.578095</td>\n",
              "      <td>3.358020</td>\n",
              "      <td>5.185914</td>\n",
              "      <td>7313.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>17.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.674938e-01</td>\n",
              "      <td>1.894160e+03</td>\n",
              "      <td>274.182490</td>\n",
              "      <td>1.590610e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3043.149073</td>\n",
              "      <td>1.201134</td>\n",
              "      <td>175.188725</td>\n",
              "      <td>359.999800</td>\n",
              "      <td>359.999833</td>\n",
              "      <td>65.081902</td>\n",
              "      <td>6081.841956</td>\n",
              "      <td>167877.712688</td>\n",
              "      <td>72684.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>9325.000000</td>\n",
              "      <td>33.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>2.381994e+00</td>\n",
              "      <td>6.131733e+07</td>\n",
              "      <td>491.618014</td>\n",
              "      <td>6.411030e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1249b90f-b419-4c32-969c-f38a9f671d69')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-1249b90f-b419-4c32-969c-f38a9f671d69 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1249b90f-b419-4c32-969c-f38a9f671d69');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5e507957-abb7-4b51-9c43-04760c3defd2')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "</svg>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5e507957-abb7-4b51-9c43-04760c3defd2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   a              e              i             om  \\\n",
              "count  671769.000000  671771.000000  671771.000000  671771.000000   \n",
              "mean        2.720203       0.155625       8.950426     168.512910   \n",
              "std       127.844968       0.093844       6.673008     103.096193   \n",
              "min   -104279.220927       0.000000       0.007546       0.000388   \n",
              "25%         2.385572       0.091403       4.068953      80.266871   \n",
              "50%         2.644105       0.143694       7.255619     160.346996   \n",
              "75%         2.996020       0.199395      12.255971     252.185170   \n",
              "max      3043.149073       1.201134     175.188725     359.999800   \n",
              "\n",
              "                   w              q             ad          per_y  \\\n",
              "count  671771.000000  671771.000000  671765.000000  671770.000000   \n",
              "mean      181.122198       2.404063       3.380605       6.830835   \n",
              "std       104.024008       2.227919      12.933745     266.021233   \n",
              "min         0.001666       0.070511       0.793839       0.000000   \n",
              "25%        91.091035       1.972105       2.775585       3.684650   \n",
              "50%       181.783987       2.225353       3.038068       4.299582   \n",
              "75%       271.460468       2.578095       3.358020       5.185914   \n",
              "max       359.999833      65.081902    6081.841956  167877.712688   \n",
              "\n",
              "            data_arc  condition_code     n_obs_used              H  \\\n",
              "count  671771.000000   671771.000000  671771.000000  671771.000000   \n",
              "mean     5706.508698        1.236400     259.261430      16.793633   \n",
              "std      4240.334952        2.585088     372.040146       1.824612   \n",
              "min         0.000000        0.000000       2.000000      -1.100000   \n",
              "25%      3579.000000        0.000000      50.000000      15.900000   \n",
              "50%      5813.000000        0.000000     118.000000      16.800000   \n",
              "75%      7313.000000        0.000000     296.000000      17.600000   \n",
              "max     72684.000000       12.000000    9325.000000      33.200000   \n",
              "\n",
              "                 neo            pha          class             n  \\\n",
              "count  671771.000000  671771.000000  671771.000000  6.717690e+05   \n",
              "mean        0.025393       0.041373       7.927431  2.374092e-01   \n",
              "std         0.157371       0.280410       1.320693  8.095870e-02   \n",
              "min         0.000000       0.000000       0.000000  2.926897e-08   \n",
              "25%         0.000000       0.000000       8.000000  1.900575e-01   \n",
              "50%         0.000000       0.000000       8.000000  2.292371e-01   \n",
              "75%         0.000000       0.000000       8.000000  2.674938e-01   \n",
              "max         2.000000       2.000000      13.000000  2.381994e+00   \n",
              "\n",
              "                per             ma          moid  \n",
              "count  6.717650e+05  671765.000000  6.586820e+05  \n",
              "mean   2.494981e+03     180.633420  1.422694e+00  \n",
              "std    9.716462e+04     106.519060  2.244968e+00  \n",
              "min    1.511339e+02     -67.136826  3.437640e-07  \n",
              "25%    1.345826e+03      86.674078  9.786875e-01  \n",
              "50%    1.570426e+03     181.493290  1.237690e+00  \n",
              "75%    1.894160e+03     274.182490  1.590610e+00  \n",
              "max    6.131733e+07     491.618014  6.411030e+01  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAzkJIbnV5tl",
        "outputId": "65db19e1-78b2-4733-b8aa-6ada4eac3a49"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "a                 0\n",
              "e                 0\n",
              "i                 0\n",
              "om                0\n",
              "w                 0\n",
              "q                 0\n",
              "ad                0\n",
              "per_y             0\n",
              "data_arc          0\n",
              "condition_code    0\n",
              "n_obs_used        0\n",
              "H                 0\n",
              "neo               0\n",
              "pha               0\n",
              "class             0\n",
              "n                 0\n",
              "per               0\n",
              "ma                0\n",
              "moid              0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=df.dropna()\n",
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TqRyS-FYDp1",
        "outputId": "5eb3c51d-7cd7-430a-921a-953ed55ddedb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 658672 entries, 0 to 671770\n",
            "Data columns (total 19 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   a               658672 non-null  float64\n",
            " 1   e               658672 non-null  float64\n",
            " 2   i               658672 non-null  float64\n",
            " 3   om              658672 non-null  float64\n",
            " 4   w               658672 non-null  float64\n",
            " 5   q               658672 non-null  float64\n",
            " 6   ad              658672 non-null  float64\n",
            " 7   per_y           658672 non-null  float64\n",
            " 8   data_arc        658672 non-null  float64\n",
            " 9   condition_code  658672 non-null  float64\n",
            " 10  n_obs_used      658672 non-null  int64  \n",
            " 11  H               658672 non-null  float64\n",
            " 12  neo             658672 non-null  int64  \n",
            " 13  pha             658672 non-null  float64\n",
            " 14  class           658672 non-null  int64  \n",
            " 15  n               658672 non-null  float64\n",
            " 16  per             658672 non-null  float64\n",
            " 17  ma              658672 non-null  float64\n",
            " 18  moid            658672 non-null  float64\n",
            "dtypes: float64(16), int64(3)\n",
            "memory usage: 100.5 MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "yXz97atLYKQM",
        "outputId": "602dbc48-591f-41e9-a63c-38e7a033d78f"
      },
      "outputs": [
        {
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b8275c3d-5bc6-4046-8b56-1af10565d232 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          a         e         i          om           w         q        ad  \\\n",
              "0  2.325726  0.024922  5.325786  241.905016  349.792694  2.267765  2.383687   \n",
              "1  2.661547  0.249418  6.347296    3.727897   66.093993  1.997710  3.325384   \n",
              "2  2.634958  0.208986  2.841944  239.743695   19.744937  2.084290  3.185627   \n",
              "3  2.400755  0.229130  5.103050  353.043702  311.703144  1.850669  2.950840   \n",
              "4  2.387261  0.235245  0.333897  358.494635  311.930358  1.825671  2.948852   \n",
              "\n",
              "      per_y  data_arc  condition_code  n_obs_used     H  neo  pha  class  \\\n",
              "0  3.546876    8654.0             0.0         626  16.0    0  0.0      8   \n",
              "1  4.342196    3878.0             0.0         123  17.3    0  0.0      8   \n",
              "2  4.277292     569.0             4.0          39  18.0    0  0.0      8   \n",
              "3  3.719889   10098.0             0.0         390  16.6    0  0.0      8   \n",
              "4  3.688570    8006.0             1.0          38  18.8    0  0.0      8   \n",
              "\n",
              "          n          per          ma      moid  \n",
              "0  0.277886  1295.496594  332.584867  1.255950  \n",
              "1  0.226988  1585.987230  284.878011  1.021440  \n",
              "2  0.230432  1562.280722  212.134292  1.068980  \n",
              "3  0.264961  1358.689334  123.816554  0.839773  \n",
              "4  0.267211  1347.250313  107.305109  0.810448  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVQy0LYMl8D0"
      },
      "source": [
        "# Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J3kOcGRjIZZA"
      },
      "outputs": [],
      "source": [
        "categorical_data = ['condition_code','neo', 'class', 'pha']\n",
        "numerical_data = ['a', 'e', 'i', 'om', 'w', 'q', 'ad',\t'per_y', 'data_arc', 'n_obs_used', 'H', 'n', 'per',\t'ma',\t'moid']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "ZST5MHjaJb-k",
        "outputId": "1584092d-828e-4b10-916c-05deaca03066"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "nrows = 2\n",
        "ncols = 2\n",
        "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12, 8))\n",
        "for i, category in enumerate(categorical_data):\n",
        "    row = i // ncols\n",
        "    col = i % ncols\n",
        "    ax = axes[row, col]\n",
        "    sns.countplot(data=df, x=category, ax=ax)\n",
        "    ax.set_title(f'Count Plot of {category}')\n",
        "plt.tight_layout()\n",
        "# plt.savefig(\"/content/drive/MyDrive/ASTERLAYSE/categorical_data_plot.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "V03lzkUBM_-Z",
        "outputId": "cfef3d55-52ab-4bbb-96d4-c1a8c8abc899"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1500 with 15 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "nrows = 5\n",
        "ncols = 3\n",
        "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 15))\n",
        "for i, feature in enumerate(numerical_data):\n",
        "    row = i // ncols\n",
        "    col = i % ncols\n",
        "    ax = axes[row, col]\n",
        "    sns.histplot(data=df, x=feature, bins=20, kde=True, ax=ax)\n",
        "    ax.set_title(f'Histogram of {feature}')\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"/content/drive/MyDrive/ASTERLAYSE/numerical_data_plot.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tXtkAp5dyK00",
        "outputId": "d4595762-ca9c-416a-eaa2-69f9010d8005"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1500 with 15 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "nrows = 5\n",
        "ncols = 3\n",
        "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 15))\n",
        "for i, feature in enumerate(numerical_data):\n",
        "    row = i // ncols\n",
        "    col = i % ncols\n",
        "    ax = axes[row, col]\n",
        "    sns.boxplot(data=df, y=feature, ax=ax)\n",
        "    ax.set_title(f'Box Plot of {feature}')\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"/content/drive/MyDrive/ASTERLAYSE/numerical_data_Box_plots.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "lknifsZVpBR9",
        "outputId": "bd2ed27a-6b2d-42cc-f702-7ebd68441e2f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# scatter plot for 'a' vs. 'e'\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.scatterplot(data=df, x='a', y='e')\n",
        "plt.title('Scatter Plot of a vs. e')\n",
        "plt.savefig(\"/content/drive/MyDrive/ASTERLAYSE/Scatter_Plot_of_a_vs_e.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "_bn-MlMwrtNV",
        "outputId": "af611a97-7116-434a-951b-05de61c37e03"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Ch5e1tdXAgQM19oL7+Pjk63fatGl55tpnYWZm9sHFwj4Hcrmc0qVLM3PmTLXnsxrUjx49onbt2gQEBDBz5kxcXV0xMDBg7969/P777wXa+ktdD7Emd0WOBctOnjxJkyZN+PLLL1mwYAGOjo7o6+uzYsWKPIuPFTUCAgKAzDUNNKWBf4Km3lx17to+c3VMmjSJUaNG0bFjRyZMmIC1tTUSiYR+/fr9a9u/aUo/ilyL232I1q1bM2zYMLp06YKNjQ316tVTayeTyahevTrm5uaMHz8eb29vjIyMuHLlCkOGDCnQfWvT656TgwcPAplb70VFRWn84CQQCASCz4tovAsEAsFnZMuWLXh5ebFt2zaVXllN+3Fn9Y7n5P79+8rVorOGL+vr62vdq51F1hBfc3PzfP3a2dlhbGysVsu9e/cKdM2CaLt+/Tq1a9dW23udxa5du0hJSWHnzp0qPZ/qhljnF84/YevWrRgZGXHgwAEMDQ2V7itWrFBrr+mZmpiYaOx1BnB3d+fGjRvI5XKV3ves4dHu7u4F1t6wYUN0dXVZu3btBxets7Ozw8TERO0zDw8PRyKR5NvA/hDaPnN1bNmyhZo1a7Js2TIVd5lMhq2trfJ3QcJ1d3fn8OHDxMXFqfS+/5P41gY3NzeqVKnCsWPH6N69O3p66l/Njh07RlRUFNu2bePLL79Uuj958iSP7adM+4sWLeLQoUP8+uuvTJ48mW7duhEWFvbJwhcIBAKB9og57wKBQPAZyeqdy9kbd/78ec6ePavWfseOHSpz1i9cuMD58+dp2LAhAPb29tSoUYPFixfz+vXrPP7fvXunUUtISAje3t5Mnz6d+Ph4jX51dXWpX78+O3bs4NmzZ8rzd+/e5cCBA/nd7kfz3Xff8fLlS5YsWZLnXFJSknLlcnXxGRMTo7bhbGpqikwm++RadXV10dHRURmq/fTpU42ro589e1ZlHvbz588JCwujXr16GntvIXMf8Ddv3rBx40alW3p6OnPnzsXMzIzq1asXWLurqytdunTh4MGDzJ07N895uVzOjBkzePHiBbq6utSrV4+wsDCVreciIiJYt24dVatWVQ7D/xi0febq0NXVzdPDvXnz5jzrPWTtM69NOvjqq6/IyMhg3rx5Ku6///47Ojo6yjz4OZg4cSJjxoyhd+/eGm3Upf3U1FQWLFiQx9bU1LRAw+g18eTJEwYNGkSLFi0YPnw406dPZ+fOnaxevfofhy0QCASCgiN63gUCgeAfsnz5crXbOvXt25fGjRuzbds2mjdvTqNGjXjy5AmLFi2iRIkSahvQPj4+VK1ale7du5OSksKsWbOwsbFh8ODBSpv58+dTtWpVSpcuTZcuXfDy8iIiIoKzZ8/y4sULlb2ucyKRSFi6dCkNGzakZMmSdOjQAWdnZ16+fMlff/2Fubk5u3btAmDcuHHs37+fatWq0aNHD2WjsWTJkty4ceMTxVw2P/30E5s2beLnn3/mr7/+okqVKmRkZBAeHs6mTZs4cOAA5cuXp169ehgYGPD111/TrVs34uPjWbJkCfb29nk+ZoSEhLBw4UImTpyIj48P9vb2ysXQ/gmNGjVi5syZNGjQgDZt2vD27Vvmz5+Pj4+P2rgpVaoU9evXV9kqDjLjOD+6du3K4sWLad++PZcvX8bDw4MtW7Zw+vRpZs2apdWic+qYMWMGjx49ok+fPmzbto3GjRtjZWXFs2fP2Lx5M+Hh4bRq1QrIbFQeOnSIqlWr0qNHD/T09Fi8eDEpKSn89ttvH3X9LLR95upo3Lgx48ePp0OHDnzxxRfcvHmTP//8U2VhPcjs3be0tGTRokUUK1YMU1NTKlasqHbO99dff03NmjUZMWIET58+JSgoiIMHDxIWFka/fv1UFqf71FSvXv2DH2O++OILrKysaNeuHX369EFHR4c1a9aoHaYfEhLCxo0b6d+/P6GhoZiZmfH1118XSJNCoaBjx44YGxuzcOFCALp168bWrVvp27cvderUwcnJqUBhCgQCgeAfUkir3AsEAsF/nqztsTQdz58/V8jlcsWkSZMU7u7uCkNDQ0XZsmUVu3fvzrNNVc7to2bMmKFwdXVVGBoaKqpVq6a4fv16nms/evRI0bZtW0Xx4sUV+vr6CmdnZ0Xjxo0VW7ZsUdqo2/pJoVAorl69qvjmm28UNjY2CkNDQ4W7u7viu+++Uxw5ckTF7vjx44qQkBCFgYGBwsvLS7Fo0SLFmDFjtN4qLvc2Vzkh11ZxCkXmNmhTp05VlCxZUmFoaKiwsrJShISEKMaNG6eIiYlR2u3cuVNRpkwZhZGRkcLDw0MxdepUxfLlyxWA4smTJ0q7N2/eKBo1aqQoVqyYAlBu96Zpi7+se3v37t0H72XZsmUKX19fhaGhoSIgIECxYsUKtXGTdZ9r165V2pctWzbPM9FERESEokOHDgpbW1uFgYGBonTp0mq3PdN2q7gs0tPTFUuXLlVUq1ZNYWFhodDX11e4u7srOnTokGcbuStXrijq16+vMDMzU5iYmChq1qypOHPmjIpNftsmVq9eXVGyZEm1OrR95uq2ihswYIDC0dFRYWxsrKhSpYri7NmziurVq+fZ1i8sLExRokQJhZ6ensq2cbnzoEKhUMTFxSl++eUXhZOTk0JfX1/h6+urmDZtmsqWggqF+vSrTqc6cub1/FCX7k6fPq2oVKmSwtjYWOHk5KQYPHiw4sCBA3nyeXx8vKJNmzYKS0tLBaC8z6wyYfPmzXmul7u8mD17tgJQbN26VcXu2bNnCnNzc8VXX32Vr36BQCAQfHp0FIoCrqwiEAgEAoFAK3R0dOjZs2eeodgCgUAgEAgEBUXMeRcIBAKBQCAQCAQCgaCIIxrvAoFAIBAIBAKBQCAQFHFE410gEAgEAoFAIBAIBIIijmi8CwQCgUDwmVAoFGK+u0AgEAgEhcyJEyf4+uuvcXJyQkdHR+P2rjk5duwY5cqVw9DQEB8fH1auXJnHZv78+Xh4eGBkZETFihW5cOHCpxefA9F4FwgEAoFAIBAIBALB/ywJCQkEBQUxf/58reyfPHlCo0aNqFmzJteuXaNfv3507tyZAwcOKG2ytuQcM2YMV65cISgoiPr16/P27dvPdRuI1eYFAoFAIBAIBAKBQPD/Ah0dHbZv306zZs002gwZMoQ9e/Zw69YtpVurVq2QyWTs378fgIoVKxIaGqocYSeXy3F1daV3794MHTr0s2gXPe8CgUAgEAgEAoFAIPhPkZKSQmxsrMqRkpLyScI+e/YsderUUXGrX78+Z8+eBSA1NZXLly+r2EgkEurUqaO0+RzofbaQBUWSPfr+hS0hXwLu7StsCRqRKOSFLUEjCQqzwpagER2K7uAe7xubCluCRo7+uLCwJeSLTZBFYUvQiPHqXYUtQSNGkuTClqCRZLlRYUvQyMt4q8KWoBFHM1lhS8gXPZ2MwpagEbu0l4UtQSO30ksWtgSNmHWtXtgSNGK6JqywJWjE58bGwpagEaNv+ha2hI+mMNsWF0e0Zty4cSpuY8aMYezYsf847Ddv3uDg4KDi5uDgQGxsLElJSUilUjIyMtTahIeH/+Pra0I03gUCgUAgEAgEAoFA8J9i2LBh9O/fX8XN0NCwkNT8O4jGu0AgEAgEAoFAIBAICoyOvk6hXdvQ0PCzNdaLFy9ORESEiltERATm5uYYGxujq6uLrq6uWpvixYt/Fk0g5rwLBAKBQCAQCAQCgUCgpHLlyhw5ckTF7dChQ1SuXBkAAwMDQkJCVGzkcjlHjhxR2nwORM+7QCAQCAQCgUAgEAgKjESv8HreC0J8fDwPHz5U/n7y5AnXrl3D2toaNzc3hg0bxsuXL1m9ejUAP//8M/PmzWPw4MF07NiRo0ePsmnTJvbs2aMMo3///rRr147y5ctToUIFZs2aRUJCAh06dPhs9yEa7wKBQCAQCAQCgUAg+J/l0qVL1KxZU/k7a658u3btWLlyJa9fv+bZs2fK856enuzZs4dffvmF2bNn4+LiwtKlS6lfv77S5vvvv+fdu3eMHj2aN2/eEBwczP79+/MsYvcpEY13gUAgEAgEAoFAIBD8z1KjRg0UCs07IK1cuVKtn6tXr+Ybbq9evejVq9c/lac1ovEuEAgEAoFAIBAIBIICo6MvllD7NxGxLRAIBAKBQCAQCAQCQRFH9LwLBAKBQCAQCAQCgaDA/FcWrPtfQfS8CwQCgUAgEAgEAoFAUMQRPe8CAKyrlsdrQCcsypXCyMmeSy16ELHzSP5+vqxAielDMSvhS/Lz1zycvJAXq7er2Lh3b4NX/04YFrcj9kY4t/tNIObizY/SuGvXLrZu2YJUKsXTy4vu3bvj7++v0f7kyZOsWb2aiIgInJyd6dihA6EVKqjYPHv2jBXLl3Pz5k0yMjJwc3NjxMiR2NvbF0jbzl272bJ1K1KpFC9PT3p0/zlfbSdOnmT1mrVERETg7OREx44dqBAaqjyflJTE8hUrOXv2LLFxcRR3cKBpkyY0avRVgXQBKBQKNqxdzuEDu0lMiMc/sDRde/bHydklX3/7dm8nbOsGZNJoPDy96fRzX3z9A5XnU1NTWLV0AadOHCU9LY2gcqF07fELllbWH6FvBYfe6wsILKW1vh1KfT50/rlPHn0rly58ry+V4HIV6Nqjn9b6Npy9yaoT14iMT8SvuA1Dm1SjtOuHVw/dd/0BQzccomYJT2b91FDpvvDwBfbfeMgbWTz6urqUcLajV72KlHEr+Iqk7t3a4NmvE4YOtsTdDOf2gInEXFKfr3T09PAe1BXnH5ph5ORAwv0nhI+aTuShU0obXTNT/Eb3oXiTOhjY2RB7/S53Bv1KzOVbBdYG4NDiWxx/+AF9axsSHz7g6cwZJNy5o16fri5O7dpj2/ArDOzsSHr2jOcL5hFz7txHh5kfCoWCjX8u48iBXSQkxBMQWJouPQbg6Oyar7/9u7exc9t6ZNJo3D296ditH77+JQCIi4tl05/LuH71IpHvIjC3sKRCpWp8/2NnTE3NtNa2Z1cY27ZuRiqNxtPTm27de+LnH6DR/tTJ46xds4q3EW9wcnKmfcfOlA+tqNZ2/txZ7N+3h85du9O02Tdaa8pJUY47hULBvs3zOXd0C0kJcXj6l6Vlp1HYObpr9PPo7iWO7lrB8yd3iJW+o+OA2ZQJra1ik5KcyK51v3Pz0lES42RY2zvzZYMfqFL3+wJp26yMtzj8A0vTucfAD8bbgd1b2ZUj3jp0+wWf9/EG8Me837h17RLR0ZEYGZngH1iKNu274+yq+Z7Vafsc9cPBfTs5dfwIjx/eJykpkdUbd2NqVkxrXQDb9h5kw/bdRMti8PZwo2+XdpTw81Fr++TZC5at28z9R0948y6SXh1/4rsmDVVslq/fwsqN21Tc3JwdWTt/RoF0QWa87d64gNOHt5GUGIeXfzCtu47APp/09uDOZQ6FreT547vESN/RdfDvBFeopdF+3eIJnDq0hW/bD6JW4x+11laUy18oumnuU9f5OZmw/RhbLtxhUKMq/Fg1SGtNAoE6RM+7AABdUxNib9zjVp9xWtkbe7gQunMxUcfOc6p8U57MXUXpxROxrVtVaePYsiGB04bxYOJ8TlVoTtyNcCruWYaBXcEadwDHjx9nyR9/0OaHH5g7dy5enp6MGjkSmUym1v7OnTtMnTKFevXrM3fePCpXrsyECRN4+vSp0ub1q1cMGjgQF1dXpk6dyoIFC2jdpg0GBgYF1HaCJUuW8GObNsybOwcvL09GjBqVr7YpU3+jfr16zJ87h8qVKzN+wkQVbX8sWcKly5cZNGggfyxeRLNmTZm/cCFnc1Wo2rBjy3r27tpGt54DmDxzEUZGRkwYNZDU1BSNfk6fOMrKJfP5rk07ps1ZgrunNxNGDSRGJlXarFgyj0sXzjBw2DjGT5mNNDqS334dVWB927esZ8+urfzcsz9TZi7E0MiYCaMG5avv1ImjrFiygO/atGf6nCV4eHozftQgZCr65nPpwhkGDRvLhCmziY6OZOqvo7XStP/GA6bvOU232uXZ0Ksl/o62dF++m6j4xHz9vZTGMnPvGcp5OOY5525rybAm1dja73tW/twcJ6tidF++i+j4JK00ZeHYoiEBU4bycNJ8Tn/xDbE371EhbKnGfOU3pi9unb7nzoCJnCjXiGfLNhCyYR7mQdkvPaUXTMC21hdc6zSEk6FNiDxymgq7V2DoVLCPWADWtevg1qcvL5Yt41b7diQ+eEjA77PRs7JSa+/S7WfsmzXj6cwZ3GjTirfbt+E3ZSomfn4fHWZ+hG1dx75dW+nacyCTZyzG0MiYiaMHfCA/HGHV0nm0bN2eqbOX4u7pw6+jByjzgzQqEml0FG079mTm/NX07Deca5fPs3D2FK11nTx+jKVLFtO6zY/MmrsQTy8vRo8appKmc3L3zm2mTZ1EvXoNmD13IZUqV+HXCWP5++mTPLZnz5zi3r27WNvYaK1HHUU17gCO7FzOif1/0rLzaH6ZuA4DQ2MWTe5GWj7aUpKTcHL359sOIzTa7Fj9G+HXT/Fjz8kMnbGT6g1/YuuKSdy69JfW2nZu/ZN9u7bQuedAfp3xB0ZGxkwa3T/feDtz4girl86jResOTJm9DHdPHyaN7q9SBnv5+PNzv+HMXPgnw8fPQKFQ8OvoX5BnZGit7XPVD6kpKQSXq8A332nf6MzJkVNnmb98Le1bfcPSmb/i4+HGwHFTkMpi1Nonp6TgVNyebm1bYW1lqTFcTzcXtq9YoDzmTR7zUfoO7VjBsb3rad11JIMmrcXQ0Ji5E7rnm95Sk5Nw8fDn+87DPhj+tfNHePrgJhbWdgXSVdTLXyiaae5z1PlZHLn9mJvPI7AzNy2wrv8KOvo6hXb8f0Q03v9j7N+/n6pVq2JpaYmNjQ2NGzfm0aNH/zjcdwdOcH/MLCLCDmtl7961FUlPXnB38FTiwx/z94I/ebP1AJ592yttPPt14PmyTbxYtY34u4+42WMMGYnJuLZvUWB927dvp0HDhtSrVw83d3d69e6NoaEhBw8eVGsfFhZGSPnyfPvtt7i5udG2bVu8vb3ZtWuX0mbVqlWUDw2lU6dOePv44OjkRKVKlbC0tCyQtm3bt9OgQQPq1auLu5sbvXv1wtDQiAMatO0I20n5kBBaftsCNzc32rX9CR9vb3bu2q20uXM3nDq1axNUpgzFHRz4qmFDvLw8uXfvfoG0KRQKdodt5tvvf6JC5ap4eHrTe8BwpNFRXDh7SqO/Xds3UadBY2rV/QpXNw+69RqAoZERRw7uBSAhIZ6jB/fSvnNPSgeVw9vXn579hnLv7i3uh98uoL4tKvr6DBhGdHTkB/Rtpm6DRtSu2/C9vv4YGhlxNIe+Iwf30r5zD6W+Xv2GcO/uLe5poW/Nyet8E1qCZuUD8XawZmSz6hgZ6LHjUrhGPxlyOcM3HqZ7nVBcrM3znP8q2I9KPq64WFvg42DNwEZViE9J5cGbKC1iKhvPPu15vmIzL9ZsIz78Ebd6jyEjKRmXturzlXObpjyatph3B06Q9PQFz5Zs4N2BE3j26QCAxMiQ4s3qET5yOtLTl0h8/IwHv84j8fEz3Lu0LpA2AMfWrXm7M4zIPbtJevqEJ79NQZ6SjF3jr9Xa2zZoyKtVq4g5e4aUV694u30bsjNncWzd5qPD1IRCoWBP2CZafN+W0ErVcPf0oVf/EUijo7h49qRGf7t3bKR2/a+pWbcRrm6edO05EANDI44e2gOAm4cXA4dPpHzFKhR3dKZ0UAit23bl8oUzZGSka6Vtx/at1G/QkDr1GuDm5k6PXn0xNDTk0MEDau13hm2nXEgo33z7Ha5u7vzYtj3e3j7s3hWmYhcVGcnihfMZMGgYerofP9iuKMedQqHgxL411GveldLla+Hk7s8PPScRI33LzUuaR5CVKFuNRt/3oUyFOhptnty/RuiXTfEtWQEbe2e+qNMSJ3d//n6k3QgyhULB3rDNfJMj3nr2H/nBeNuzY4My3lzcPOnccxAGhkb8dSi7nqjToCklSgVj7+CIl48/3//Uhah3b3n79o3W2j5H/QDQuFlLvvnuB/wCSmgMJz82he2lcb2afFW7Bh6uLgzo3gkjQ0P2HDmu1j7Q15se7X+gdrUvMNDTnM51JbrYWFkqD0vzvGX1h1AoFBzd8ycNWnQhqEJNXDz8aNd7IjHSd1y/cFSjv5LlqtKkdS+CK9bWaAMgi4pg07IptO87CV1d/QJpK8rlLxTdNPc56nyAiJh4puw8yaTv66IvEU0uwadBpKT/GAkJCfTv359Lly5x5MgRJBIJzZs3Ry6X/6s6LCsFE3n0rIrbu0OnsKoUDICOvj4W5UoSeeRMtoFCQeTRM1hWKluga6WlpfHwwQOCg4OVbhKJhODgYMLv3lXrJ/zuXcrmsAcICQlR2svlci5evIizszMjR4ygdatW9OvXjzNnzqgJLX9tDx4+VLmWRCKhbHAwd8PVF/p3w8MpWza3tnIq9iUCAzh3/jyRkZEoFAquX7/Oy5evCClXrkD6It68RiaNpkxwiNLN1NQMX/9AjY3YtLQ0Hj28r+JHIpFQJjhE2TB//PA+6enpKjYuru7Y2jlw7672jfcsfUF59JXgXrj6IXmZ+u6p1ZflJ0tfkBp99+/mP9QvLT2Du6/eUcknewifRKJDJW8XbjzT/FK8+MglrEyN+Sb0wy8OaekZbL1wm2JGBvg5at8bqqOvj3nZkkT9lTtfncWqYrBaPxIDAzKSVXs0MpKSsfoiM2509PSQ6OkhV2dTOYSCoKOnh6l/ALEXL6joi7l4kWKlSqv3Y2CAPFePizwlmWJBQR8dpibeRmSmt9LB5ZVupqZm+HwgPzxWmx/K5/uhKjEhHmMTE3S1aDCnpaXx8OF9goKz83dmGVdOYz4ID79DcFnV8qBsSHnCw7PLRLlczszpU/mmRUvc3T0+qCM/imrcAUS9fUGsLBK/0pWVbsYmxXD3KcPT+9e1CkMTnn7B3Lr8F7LoCBQKBQ9uX+Dd66cElPlCK/9vI14hk0ZROjh7WpSJqRk+/iV4EK5+Wkr6+3jLGdcSiYTSweV5oCHekpOTOHZ4L/YOjtjaajdi5nPVD/+UtLR07j96QvkypVSuERJUitv3HvyjsF+8fkPzDj34vltfxs+cR8S7yAKHEfX2JbGySALKZE9RMTYthodvaR7fv/GP9MnlclbOHUGdpu1xclU/RUATRb38haKZ5j5XnS+XKxix6QjtvwzGx6HgI07/S0j0dArt+P+ImPP+H6NFC9XeteXLl2NnZ8edO3coVaqUyrmUlBRSUlQL5TSFHH2df/7NxtDBlpQI1UovJSISfYtiSIwM0beyQKKnR8rbqFw2UZj6exXoWrGxscjlcqxyDdGytLLi+YsXav1IpVIs1dhLpZlDrGQyGUlJSWzetIm27drRoWNHLl++zK8TJzJlyhRKlylTIG2WuYbpWVpa8vz5c83aLPPaZ2kD6N69O3PmzOXHtu3Q1dVFoqND3759KF26FAVBJo3ODD/XPG8LSyvludzExcYgl2dgaWmVx8/L58/ehxuFnp5+nvlkllaaw81Pn0UufZaWVkjz1SfH0jKvnyx9Umm0Rn2aws1CmphMhlyBjZmJirtNMWOevFM/hPnK09dsv3SXTX2+yzfs43efMmTDQZLT0rEtZsqijl9jZWqcr5+cGNhaZeariFz56m0kZv6eav1EHj6FZ+/2RJ/K7FW3rVmZ4k3rgq4uABnxCUjPXcVnaA/i7z0mJSISp+8aYVUxmIRHz7TWBqBnaYmOnh5p0apxnBYdjbG7+rmgMefPUbxVG2KvXiPl5QvMy4diVaMmOu97KT4mTE3IpJnxljttW1paI5Plnx8sLPPmoZcv/lbrJzZGxpYNq6jToIlWumLfp+k8ZZylFS80lCMyteWIav7bunkjEl0JXzdtrpWO/CiqcQcQJ8usi4pZqH4IK2ZhQ6ys4I2znLToMJyNS8YytkdtJLp66Ojo8H3XsXgHlv+wZ3KUcWrKU03xFqsx3qx5lSveDuzZxp8rFmZOAXBxY8TEWejpa9db+7nqh39KTFwcGXI5VpYWKu7WFhY8e/Hqo8Mt4efDsD7dcHN2IkoqZcWGbfQaPp5Vc6ZiYqx9ORwjzUxT5paq6c38E6S3gztWIJHoUvOrNh82zkVRL3+haKa5z1XnrzhxBV2JDm2+0O59UiDQFtF4/4/x4MEDRo8ezfn3vbJZPe7Pnj3L03ifPHky48apzmFvrWPND7q2/5reoopCoQCgUuXKNG+e+WLr7e3N3Tt32Lt3r9aN98/Fzp07uRseztgxo7G3t+fWrVvMX7AQa2trypXVPHLhxF+HWDwve/Gd4WMLNm/0c3M8l74RRUzfx5CQksqITYcZ802NDzbEQ72d2dT7e2SJSWy9eIdB6w+ytkeLPC8Nn5I7g36l1PwJVL+2F4VCQeLj57xYs01lmP31ToMpvWgStR+dQJ6eTuy1O7zatAeLsiU/m64s/v59Jp5DhxO0YSMoFCS/fEnknt3YNW78j8M++ddBFs+frvw9bMzUfxzmh0hMTGDyuMG4uHnwXZuOn/16mnj44D47d25n1pwF6OgUvHeiKMfdpVO72bQku27rOmTBZ9N0Yv+fPH1wg86D5mFt68iju5fZuvxXLKzs8c/R05/Fyb8OsmT+NOXvoWN++2zaAKrVqEeZ4FCk0ih2b1vPrCmjGD9tIQYGhnnvpYjXD5+bSiHByr+9PdwI9PXhu659OHrqHI3r1tTo78KJPaz/Y4Lyd/dh8z6LvmeP7nBs758M/W3DR+XZj+Fzlr/wv5nmtKnz77x8y5+nb7Ch93f/2rMsTP6/zj0vLETj/T/G119/jbu7O0uWLMHJyQm5XE6pUqVITU3NYzts2DD69++v4nbUumDDYDWREhGJoYPqRwBDB1vSYuKQJ6eQGilFnp6Oob1NLhsbUt4U7Mu0ubk5EolEpWcaMnuerDUsmGJlZYVMjX1Wz5a5uTm6urq4ubmp2Li6unK7ACuoZmmTSWWq15LJsLLOR5tMjf17bSkpKaxctZpRI0dQ8f3q+F6enjx69Jit27bl23gPrVhFZfXVtLS0zPCl0VhZZz+LGJkUDy/1Q/KKmVsgkejmWSgrRiZVfi23tLIhPT2NhPg4ld5tmVSa72ruFSpWwU+NvhhpNNY59MlkUjzz1SfJ02sly6HPyspaoz6rD6w2b2VihK5EJ89CNVFxSdgWy9vIfh4VyytpHH1WZ8+9k7//OFRuxELC+rfB1SazB8nEQB83WwvcsKCMW3G+nv4nOy7dpVMN7fKlMl855MpX9nlHwuT0c+X7XkgMDdC3sSTl1Vv8Jwwg8Ul2j27ik+ecr/8TuibG6JmbkfLmHcGrZ5L4VH2vrybSZTIU6enoW6vGsb61NWlR6ntV0mUyHgwdjI6BAXoWFqS9e4drj54kv3z10WFmUb5iVZXVudOz8oNMipV1dvklk0Xj4emrNoys/BCTK71l5gfV55CUmMivowdibGzCoBG/opfP3NucmL9P03nKOJlUYzliqbYcyc4Dt2/fIkYmo2O7H5Tn5XI5y5cuZueObSxbuTZfTUU57kqF1MTdJ/sDa3paZv0XFxOFhVX2Al9xMVE4u2ve9eNDpKYms2fDbDoOmE3JctUBcHL35+Xf4fy1e6Xaxnv5ilWVK+kDpL3XFpMr3mJkUjw81Zdx5hrjLTpPvJmYmmFiaoajsyt+/iXp2KohF8+eoEr1unnC/bfqh3+KRbFi6EokeRani46JyXcxuoJSzMwUVydHXr6JyNeuTGgNPHyzh4inp2c+01iZanqLjYnCxePj09vDu1eIi4lm5M8NlG5yeQZbV8/g6J4/mbhwX77+i1r5C/+NNPc56vwrT14TnZBEg6mrlTYZcgUz9p7hz9M32Dfkp3+sW/D/FzHn/T9EVFQU9+7dY+TIkdSuXZvAwMA8L3s5MTQ0xNzcXOX4FEPmAWTnrmFTq5KKm23tL5CeuwaAIi2NmCu3sa2V4+VGRwebmpWRnbtaoGvp6+vj4+vL9WvXlG5yuZxr164REBio1k9AYCDXctgDXL16VWmvr6+Pn58fL3INu3/58mWBtonT19fH18eHa9fzagsMUL/FU2BAANeuqc7DvHL1qtI+PSOD9PR0JLmelURXgkKuyFePsYkJjk4uysPVzQNLK2tuXr+itElMTODBvbv4B6jvVdXX18fbx4+b1y6r3NONa1fwe+/Hy8cPPT09buQI9+WLZ0S+i8A/UHNvrSZ9N/Lou4O/hkVnMvX5c+Natp9MfZeVfvLT5xeY/5x0fT1dAp3sOP/oZY7wFZx/9IIybsXz2HvaWbKl7/ds7P2d8qgR6EmolzMbe39HcQvN213JFQpS07VfGVqRlkbs1dvY1MidryohPX8tX7/ylFRSXr1FR0+P4s3qEbEn78JKGYlJpLx5h56lOXZ1qhKxW/PiS2r1paeTcC8c8/LZ83vR0cGifChxt/Jf4EuRmkrau3fo6OpiXbMm0pMn/nGYudOby/v0ditH2k5MTODhB/KDl48fN6+r5oeb1y8r80NWOBNG9UdPT48ho6ao7fnUhL6+Pj4+fty4nl02yuVyrl+7qjEfBASU4Po11bL02tUrBARklnE1a9Vh7vzFzJm3SHlY29jQvEVLxk2c/EFNRTnujIxNsSvupjyKu3hjbmnLg1vZu3EkJ8bz98MbePh9/LZM8vR0MjLS0clVFutIdFFoWGfG2MSE4k4uysPFzRNLKxtuXrukcr8P793BN0D9NCg9DfF26/plfDXENYDi/b+sBpI6bf9G/fBP0dfXw8/bk8s3suczy+Vyrty4TUl/9R+KPobEpGRevonA5gMfBIyMTbF3dFMeju/T272b55U2SYnxPH1wEy+/jx+1V6F6Y0bM2Mzw6RuVh4W1HXWbtKP3yIUf9F/Uyl/4b6S5z1HnNy7rz+Y+qjZ25qa0+zKYhR0/zagGwf9fRM/7fwgrKytsbGz4448/cHR05NmzZwwdOvSThK1raoKpT3YvtImnC+ZBAaRGx5D8/DX+E/tj5OzA9Q5DAPj7jw249/iBgMmDeL5yK7Y1K+HYsiEXm3RThvFk1gqClk9FdvkWMRdv4NGnHXqmxjxftS3P9T9E8+bNmTljBr6+vvj5+xO2YwcpKSnUrZvZuzB9+nRsbGzo0CFzBe2mTZsyZPBgtm3dSmiFChw/fpwHDx7Qu08fZZgtWrTInN9eqhRlgoK4fOkS58+fZ+rUgg0P/aZ5c6bPnImvry/+fn5sDwsjOSWZeu+1TZs+AxsbGzp2aA9As6ZNGDRkKFu3baNCaCjHjp/gwYOH9O3dGwBTExNKly7N0uXLMTA0wMHenhs3b3LkyFG6dulcIG06Ojo0btqSLRtW4+jkgn3x4qxfsxwraxsqVM7e1m/s8F+oULkaX32duf/z182/Y+7MyXj7BuDrF8DusC2kJCdRq27mHqampmbUqvcVK5fMx8ysGCYmpixbNBv/gJIFqkwz9X3Llg1rcHRywaG4I+vXLMPa2lZF35jh/alYuWoOfS2ZO3MyPr7++PoFsitsCynJySr6atf7ihVLFmBmZo6JiQlLF83BP6CkxheEnPxULYhRm49S0tmOUq72rD19g6TUdJqFZH5gGbHpMPbmpvRtUBlDfT18i+eaa2uUud1glntiahpL/7pMjUAPbIuZIktMYsPZW7yNTaBu6YItSvRkzkrKLJlCzJVbyC7dwLNXO/RMjHmxJjNflVkyhZRXb7k3ZiYAFqFlMHJyIPb6XYycHPAd0QsdiYTHM5cqw7StUxV0IOH+E0y93QmYNIj4+495sbrgefX1+vV4jxpNQvhd4m/foXirVkiMjHi3O3OVbK/RY0h7947nCzOHOpuWKImBnR2JD+5jYGePc+fOoCPh9do1WoepLTo6OjRq+h1bN66iuLML9g6ObFy7FCtrG0IrV1PajRvelwqVv6Th15lTCxo3+575v0/C2zcAH79A9oRtJiU5iZp1vgIyXz4njupPSkoyfQaOIjEpgcSkBADMzS3Rfb++QH40a96C32f+ho+vH35+/oSFbSc5JZk6desDMHP6VGxsbGnXoRMATZo2Z9iQAWzftpnyoRU5efwYDx/cp1fvfu+vm/nBNid6unpYWVnj4pL//uL/tbjT0dHhy4Y/cXD7H9gVd8fa3pm9m+ZhYWVP6fLZK3vPn9CJMqG1qdYgcz5xSnIi795kz5mNfvuSF0/DMTWzwMrWESMTM7wDy7PzzxnoGxhibefEwzuXuHRiJ01/GqR1vH3VtCXbN67C0dlVY7xNGN6X0Mpf0uB9vDVq1ooFv/+Kt28A3n6B7A3bREpyEjXqNAIg4s1Lzpw4SlC5UMzNLYmKekfY5rUYGBhStnzeEQGatH2O+gFAGh2FTBrNm9eZDaK/nz7G2NgEW3sHihX78Arv3zX9ismzF+Hv40Wgrzebd+0jKTmZr2pnjoD4ddYCbG2s6fZTKyBzkbunzzM/xqelpxMZHc2Dx08xNjbCxTGzATZ/xZ9UCS2Hg50tkVIpK9ZvQSKRUKeadosP5oy3Wo1+YN/WJdg7umNj78yuDfOxsLIjKMe+7bPHdiGoYi1qNMzctSM5STW9RUW85PmTzPRmbeeIWTFLzIpZqlxLV1cfc0tbHJw9tNJWlMvfrLgrimnuU9f5lnq6WJoaqdjoSyTYmpngYfdxW+wVZf6/LhxXWIjG+38IiUTChg0b6NOnD6VKlcLf3585c+ZQo0aNfxy2RUgpKh/JLqxLTB8OwPPV27jRaRiGjnYYu2bvY5n09AUXm3SjxIxhePRuS/KLN9zsNpLIQ9lbfbzevA8DO2v8xvTBsLgdsdfvcqFxZ1JzLWKnDdWrVyc2JoY1a9cijY7Gy9ub8RMmKIeav3v7FkmOeUUlSpRg8JAhrF61ipUrV+Ls7MyoUaPw8PBQ2nxRpQq9evVi06ZNLFq0CBcXF0aMHEnJUgVbFK569S+JiY1hzZq1SKVSvLy8mDh+vFLb23fv0JGoahsyeBCrVq9h5cpVODk7M3rUSBVtw4YMZsXKVfw2bTpxcXHY29vTrm1bGn31VYHjrtm3rUlOTmLR3OkkJMQTUKI0oyZMU+ndevP6FXGx2UMUq3xZi5gYGRvWLkcmjcbTy4eR46epDFHr0KUXEh0J0yeNJi0tjeByoXTp8UuB9TX/tjUpyclKfYElSjNqwm+59L0kNoe+ql/WIjZGxvq1K5T6Ro3/LZe+nujoSJiWQ1/XHv200tSgjC/S+GQWHL5AZFwi/o62LOjQGJv3Q+jeyOJV0tuH0NXR4ck7KTuv3EOWkISliRElXexZ0bVZgVehfb31fb4a1RsDBzvibtzlQrMuynxl7OoEOUZo6Boa4je6LyaermTEJ/L2wHGudx5Cekyc0kbP3Az/8f0xci5OmlTGmx2HuD/2dxTp2m3VlZPoI4fRt7LEpXNX9G1sSHxwn/Bf+pH+fjEiQwcHyNFrKTE0wLXbzxg6OZGRlITs7BkejRtLRny81mEWhKYt2pCcnMTiudNIfJ8fRoyfrpLeIt7kzg+1iY2RsXHtMmTSaDy8fBgxfroyvT15eJ8H9zKn2/Tu0krlevOXbcLeQfMewFlUq16DmFgZf65Z9b4c8Wbc+EnZZdy7tyrlSGCJkgwcPIy1q1eyeuUKnJydGTFqLO4e6hcu/BQU1bgDqN2kI6kpSWxcMpakxDi8/MvRbegi9HNoi4x4Tnxc9mi1Z49uMX9C9tz6HWsy56eHftmUH3r8CkC7vtPZvX4Wa+cNJTE+Bis7J75q1Ycqdb/XShdAkxY/kJKczB9zfyMxIR7/EqUZNn5Grnh7SVysTPn7i/fxtmntUmW8DRs/Qxlv+vqGhN++zr6dm4iPj8PS0pqAkkFMmLYoz+J4+fG56oeD+3ayad1K5e9RQzI/nPfsN1SlwaWJ2lUrI4uJZfn6LURLZfh4ujN9zFCs3y9iF/EuSmVERGS0lE79hyt/b9ixhw079hBcMpA5v44C4F1UFONmzCU2Lh5LC3NKB/qxaOp4LC0Kvl1c3WYdSElJYt3i8SQmxOEdUJZeIxeopLd3ES+Iz/FMnz26zayx2R/gt67KXFOiUo0mtO2VPaf+n1DUy18ommnuU9f5AsHnREeRtXKX4P8Fe/Q/fj7Wv0HAvfzndBUmEsW/ux1fQUhQaB6aXdjoUHSLGO8bmwpbgkaO/vjhYZKFiU2QxYeNCgnj1bsKW4JGjCTJhS1BI8lyow8bFRIv44tub5WjmaywJeSLno72U3P+bezSXn7YqJC4lf75F+z8WMy6Vi9sCRoxXRNW2BI04nNjY2FL0IjRN30LW8JHc6JUwbaA/pR8eatgU3H/FxBz3gUCgUAgEAgEAoFAICjiiMa7QCAQCAQCgUAgEAgERRwx510gEAgEAoFAIBAIBAVGoivWA/g3ET3vAoFAIBAIBAKBQCAQFHFEz7tAIBAIBAKBQCAQCApMzp1QBJ8f0fMuEAgEAoFAIBAIBAJBEUf0vAsEAoFAIBAIBAKBoMDo6Iq+4H8TEdsCgUAgEAgEAoFAIBAUcUTjXSAQCAQCgUAgEAgEgiKOGDYvEAgEAoFAIBAIBIICI7aK+3fRUSgUisIWIfj3ePT4cWFLyJdw/4aFLUEj5W5uLGwJGpHq2Re2hP8kkSkWhS1BI26GLwtbQr6k6hgVtgSNPE8suvkhJONcYUvQSLhR2cKWoJGgVzsLW4JGFM+fFLaEfEkuW7OwJWhk+cMqhS1BI528Txa2BI28M/MsbAkaSVEYFrYEjcSkmhW2BI1ULWFa2BI+mnMVKxTatSudv1Bo1y4sRM+7QCAQCAQCgUAgEAgKjNgq7t9FzHkXCAQCgUAgEAgEAoGgiCMa7wKBQCAQCAQCgUAgEBRxxLB5gUAgEAgEAoFAIBAUGLFg3b+L6HkXCAQCgUAgEAgEAoGgiCN63gUCgUAgEAgEAoFAUGB0RM/7v4roeRcIBAKBQCAQCAQCgaCII3reBQKBQCAQCAQCgUBQYHQkoi/430TEtkAgEAgEAoFAIBAIBEUc0XgXCAQCgUAgEAgEAoGgiCOGzQtU2LVrF1u3bEEqleLp5UX37t3x9/fXaH/y5EnWrF5NREQETs7OdOzQgdAKFVRsnj17xorly7l58yYZGRm4ubkxYuRI7O3ttdJkXbU8XgM6YVGuFEZO9lxq0YOInUfy9/NlBUpMH4pZCV+Sn7/m4eSFvFi9XcXGvXsbvPp3wrC4HbE3wrndbwIxF29qpSk32/ccYMOOXURLZfh4uNOnawcC/XzU2j559pwV6zZx79ETIt6+o2entrRs0kjFJmzfQcL2HeLN23cAeLi50O77FlQMKVtgbXt3b2fH1o3IpNF4eHrT+ec++PkHarQ/ffIY69cu523EGxydXGjboSshoZWU58+ePsGBfbt49PA+8XGxzJyzBE9v9ff6X9YGoFAo2LlhEScPbScxMQ6fgCB+6DocByc3jX7u377MgbDV/P3oLjHSSHoMmUHZijWV59PT09ixbgG3rpzmXcQLjE3MCCxTkRY/9cHS2k4rXTt372HL1m1ES6V4eXrS4+duBPj7abQ/cfIUq9auJSLiLc5OTnTq0J4KoeWV55OSkli2chVnz54jNi6O4g4ONG3yNY2/aqiVntzs2RXGtq2bkUqj8fT0plv3nvj5B2i0P3XyOGvXrOJtxBucnJxp37Ez5UMrqrWdP3cW+/ftoXPX7jRt9k2BtSkUCnZvXMDpw9tISozDyz+Y1l1HYO/ortHPgzuXORS2kueP7xIjfUfXwb8TXKGWis3qeaM4d2yniluJ4C/oNXKh1to2H/iLP3cdIkoWg6+7CwM6tKKkj6da2x1HTrL3xDkeP38FQICnG91bN1Ox/+v8FbYdPkH442fExiewZupI/DxctdaTE4VCwfZ1f3Ds0A4SE+LxDShDu+5DKJ5PXgA4vGcz+3asJUYahauHLz92HYi3X0nleZk0ko0r53L72nmSkhJxdHbn65YdCP2iVj6hqrLh2CVWHTpLVGw8fi4ODPm+PqU9nNXaHrkazrL9p3n2Lpr0DDlu9ta0rVORxhXLqLWfuG4vW05eYeC3dfmxtvo0+SE2XrrHqvN3iYpPws/BiiH1ylPKyVat7c4bjxiz+5yKm4GuhPNDWit/KxQKFp64wfZrD4lLSSPIxY7hDUJxtzYvsLYt+4/y584DRMti8HF3pX/H1pT09VJrG3b4BPuOn+Xx85cA+Hu583Pr5ir2CoWCJRvD2HnkJHEJiZQJ8GFwlx9xdXQosDaFQsHlw3MJv7iZ1KQ4HNzLUrXZGCxsPbTyf+3YEi4emEmpL36i8tfDAUhOlHH58DxePjhNvOw1RqbWeJSoTfl6fTAwKqa1ti37/2LtroPv482FAR1ba86rh0+y78RZZV7193Kje+vmKvYKhYIlm3YSduQk8QlJlA7wZnDnH3D7iHgryuUvZN7rxrXLOXxgF4kJ8fgHlqZrz/44OudfNu3bvY2dWzcgk0bj7ulNp5/74utfAoC4uFg2rV3O9asXiXwXgbmFJaGVqtHqp06YmpoVSFvY+kWcOLydxITMOv+nbvnX+fduX+bAjtU8fV/n9xw6g3I56nyAy2ePcOzAVv5+dJeE+BjGzFyPm6fmd+v/GjoSsWDdv4noeRcoOX78OEv++IM2P/zA3Llz8fL0ZNTIkchkMrX2d+7cYeqUKdSrX5+58+ZRuXJlJkyYwNOnT5U2r1+9YtDAgbi4ujJ16lQWLFhA6zZtMDAw0FqXrqkJsTfucavPOK3sjT1cCN25mKhj5zlVvilP5q6i9OKJ2NatqrRxbNmQwGnDeDBxPqcqNCfuRjgV9yzDwM5aa11ZHD15hgXLV9P++xYsmTkFb093Bo2dhFQWo9Y+JSUFRwcHuv7UGmsrS7U2djY2dG3bhj9mTmbxjEmUK12KEZOm8eTZ8wJpO3XiKCuWLOT7Nu2YMecPPDy9GT9qMDKZVK19+J1bzPxtArXrfcWMOUuoWLkqUyaO4u+nT3LoTyawRCnaduhaIC3/JW1Z7N++iiN71vPjz8MZPmUVBobGzJrQk7TUFI1+UlKScfHwo02XoWrPp6Yk8+xxOI1admbU9HV0HzydiFd/M29yP600HTtxkj+WLOWHNq2ZP2cWXp6ejBg1WmM+vX3nLpN/m0aDevVYMGc2X1SuxLiJv/L06d9Km8VLlnHp8hUGDxzAkkULaN60CfMXLuLsufNaacrJyePHWLpkMa3b/MisuQvx9PJi9KhhGp/r3Tu3mTZ1EvXqNWD23IVUqlyFXyeMVXmuWZw9c4p79+5ibWNTYF1ZHNqxgmN719O660gGTVqLoaExcyd0z/eZpiYn4eLhz/edh+UbdongKkxeckR5dOw3VXtdZy4ye/UWOrVoxKopI/Bxd6HvpDlEx8Sqtb9y+z71vghlwej+LJ0wBHsbK/r8Opu30dnxnJSSSpC/D73afNxLdk72blvNoT0bad99KKOnLcfQyJjpY/uQmk+8nT95iPXLZ9H0+86Mm7kaV09fpo/tQ6wsWmnzx6xxvH75N31HzODXOesJqVyD+dOG8/fje1rpOnDpNjO2HqJbo2qsH94ZPxcHesxZT3Rsglp7c1MjOjeswupBHdg8sgtNKwcxZvUuztx5lMf26LVwbjx5iZ2F9o26PPruPGXGkSt0q1qadR2/ws/eih4b/iI6IVmjHzNDfQ71+UZ57O3ZTOX8ynN3WH/pHsMbVmB1+/oY6+vRc8NfpKRnFEjb4dMXmLNqE51afs3KqaPxdXfll19n5ZPm7lG3agXmjRnIH78Ow8HGin4Tf+dtVHaaWxu2n837jjC4648smzwcY0ND+k38nZTUtAJpA7h+Yim3z6ylarOxNO2xEX0DE/Yt70J6muY0l8W75ze5e2Ej1sVVG0iJsW9JjH1Lxa8G822/nVRvOYnn909yYutIrXVl5tXNdP62MaumjsTX3ZV+v87WHG937lG3SgXmjxnAkolDcLCxpu/EWSp5dU3YATbtO8qQLj+ydNKwzHj7dXaB462ol78AO7asY++urXTtOYBJMxdjaGTEhFED8y1LTp84wqol82nZpj2/zVmKh6cPE0cNJOb9fUmjIomOjqRtpx7MXLCKnr8M49rl8yycrX0ZDLBv+yoO71nPT92GM2LqKgwNjZk5Pv86PzU5s87/sav6Oh8gJSUJ38Bgvm3bp0B6BAJ1iMb7fwy5XM7kyZPx9PTE2NiYoKAgtmzZ8knC3r59Ow0aNqRevXq4ubvTq3dvDA0NOXjwoFr7sLAwQsqX59tvv8XNzY22bdvi7e3Nrl27lDarVq2ifGgonTp1wtvHB0cnJypVqoSlpaXWut4dOMH9MbOICDuslb1711YkPXnB3cFTiQ9/zN8L/uTN1gN49m2vtPHs14HnyzbxYtU24u8+4maPMWQkJuPavoXWurLYHLaHRvVq07BOTTzcXOjfvTNGhgbsPfyXWvsAXx+6d/iR2l9WQV9fX63NFxVCqFS+LC5Ojrg6O9H5p1YYGxlx596DAmnbuX0zdRs0onbdhri6efBzr/4YGhlx5OA+tfa7d26lbEgFmrdohaubO21+6oiXty97d2ePWqhRqx7ft2lHUHBIgbT8l7RB5hf4I7vX0ejbzgRXqIGLhx8d+4xHFv2OqxeOafRXulwVmrfpSblK6nsOTUyL0X/sQkKr1KO4swfe/mVo3XkIfz+6S9S71x/UtW37Dho0qE/9unVwd3OjT68eGBoZcuDgIbX2O3bupHxIOVq2+AY3N1fa/fQjPt7ehO3erbS5E36XurVrEVSmNMUdHPiqYQO8PD25d//+B/Xkud72rdRv0JA69Rrg5uZOj159MTQ05NDBA2rtd4Ztp1xIKN98+x2ubu782LY93t4+7N4VpmIXFRnJ4oXzGTBoGHq6HzdoTKFQcHTPnzRo0YWgCjVx8fCjXe+JxEjfcf3CUY3+SparSpPWvQiuWDvf8PX0DbCwslUeJmba94Su33OYprWr8nXNKni5ODG08w8YGRiw668zau3H9+nEt/Vr4OfhiodzcUb83Ba5QsGlm+FKm6++rETnbxsTWlpzr5s2KBQKDuzawNctO1KuYnXcPHzp2m8ssuhIrpw7rtHf/rB1VK/XjC/rfI2zmxftuw/FwNCIE4ez64iH4Teo2+g7vP1KYl/cmabfdcLE1IwnD+9qpW3NkfN8U6Uszb4IxtvRjpGtv8LIQJ8dZ6+ptQ/186BWcABejra42lnzQ60K+Do7cPWh6ofRCFksUzYeYFKHZujpfvyr0toL4XwT7EPTIG+87SwY0bACRnq67Lie92NBTmzNjJWHjZmx0l2hULDuQjhdqpSipp8rfvZWTPi6Mu/iEvnrXsE+7q7ffYgmtavRuGZVPF2dGNz1RwwNDNh99JRa+3F9u9Cifk38PN3wcHZk2M/tM9PcrbtKbRv3HKZ9i8Z8GVoWH3dXRvfqSKRUxomLVwukTaFQcOv0asrW/BmPErWxcfSnxndTSIx7y9938n8XSEtJ4OjGQXz5zXgMjVXzoHVxP+r+OAf3wJqY27jh7F2J0Pr9+PvuX8gz0rXStn73IZrWrkrjmlXwdHFiSJfMvLr7r9Nq7cf36ZwjrzoyPFdeVSgUbNx7mA7fNOLL0GB83V0Y06vDR8VbUS5/s+51T9hmWnz/ExUqV8PD05veA0YgjY7iwln16Q5g1/ZN1GnQmFp1v8LVzYOuvQZgaGTE0YN7AHDz8GLQiImUr1iF4o7OlA4KoXXbLlw6f4YMLZ+rQqHg8O51NG7ZmbIVa+Dq4Uenvpl1/pXzxzT6Kx1ShW9+0FznA3xRozFNvu9KiaCPG71T1JHo6hTa8f8R0Xj/jzF58mRWr17NokWLuH37Nr/88gs//vgjx49rfoHShrS0NB4+eEBwcLDSTSKREBwcTPhd9S9R4XfvUjaHPUBISIjSXi6Xc/HiRZydnRk5YgStW7WiX79+nDmj/mX0U2FZKZjIo2dV3N4dOoVVpUytOvr6WJQrSeSRHDoUCiKPnsGyUsGGpaelpXPv0WNCgkor3SQSCSFBpQvc0NZERoacIydOk5ycQsl8hkbn1ZbGo4f3VRqyEomEMsHluBd+W62fe+F38jR8g8uFcl+D/cdSlLVlERnxkhhZJIE5KlsT02J4+Zbi8b0bn/RaSYnx6OjoYGKafw9fWloaDx4+pFxwkNJNIpFQNjiYO+HqeyrvhofnzaflynI3PLuRVyIgkHPnzxMZGYVCoeDa9Ru8fPWKkHIFzQ9pPHx4n6Dgcir6goPLcS/8jlo/4eF3CC5bTsWtbEh5wsOzyx25XM7M6VP5pkVL3N09CqQpJ1FvXxIriySgTPYzNTYthodvaR7f/+fP9MHtSwzuWIOxfZqw/o+JxMfJtPKXlp5O+ONnVCidPWVEIpEQWjqAmw8eaxVGckoqGekZmJuZfoz0fHkX8YoYaRQlg7KnRJmYmuHlV5KH99RPNUpPS+Ppo3BKBoUq3SQSCSWDQlX8+ASU4fypQ8THxSCXyzl34iBpqakElv7wB7i09AzuPntNxYDs4ccSiQ4VAzy48fjlB/0rFArOhz/haUQU5Xyzh8XK5QpGrgijXd3K+DhpN5VFrb6MDO6+jqaiR/FsfTo6VPQszo2XkRr9JaWm03DedhrM3U6/zcd59E6mPPdSFk9kQjIVPbPDLGZkQCkn23zDzKMtLZ17j/8mtEyJbG0SCaFlArl1X8s0l5pKeo409+ptJFGyGEJzpGMzUxNK+Hhx617+HytyEyd9QVJcJM4+lZVuBkbFsHMtQ8Sz6/n6PR02AbeA6jj7fKHVtVKT4zAwMkOiRaM0LT2de4+fqdxjZl4N5Ka28ZYrr2bGWyyhZXLEm4kJJX08tQ4Tin75C/D2zWtk0mjKBGdP2zI1NcPXP5D74bfU+klLS+Pxw/sqfiQSCaWDQzS+LwAkJiZgYmKCrpYfGyIjXhIjjVRpYGfV+Y8+cZ0vEPwTxJz3/xApKSlMmjSJw4cPU7lyZoXm5eXFqVOnWLx4MdWrV89jn5KSksfN0NAwT9ixsbHI5XKsrKxU3C2trHj+4oVaPVKpFEs19lJp5jAmmUxGUlISmzdtom27dnTo2JHLly/z68SJTJkyhdJl1M8x/KcYOtiSEqH6EpMSEYm+RTEkRoboW1kg0dMj5W1ULpsoTP3Vz/XTRMz7eLO2tFBxt7K04NmLVx93A+95/PQZPYaMJDU1DWNjIyYMG4iHm4vW/uNiM1+GLSxzPSNLK14+f6bWj0wajaUa+6xn+qkoytqyiJFlpg9zC9WpFMUsbYiRav+S/CHSUlPYumY2oVUbYGyS/9y8rHyaOx6sLC15/lxTPpVhlWuki5WlJVKpTPm7R/duzJ47jx/atUdXVxeJjg59+/SmdKlSBbqX2PfPNU85YmnFi+fqewVlUmmekTiWllbIpNlDq7du3ohEV8LXTZsXSE9usp6buaXqsE9zCxtiZf/smZYI/oLgirWxsXfmXcRzdq6by/xfezDo1zVIdHXz9SuLjSdDLsc61/Bsawtz/n71Rqvrz/9zG7bWFiqNik9FjDQzL1hYquYFc0tr5bncxMXKkMsz8vixsLTm9YvsKRs9B01iwbTh9PyxLrq6uhgYGtFn2G84OH54br40PpEMuQIbc9UPFjbmZjyNUK8LIC4pmXrDZpOWloFEosPw1g2pHJhd9q84eAZdXQltaoZqDEMbpIkpZCgUWJsaqeozNeJplPoh1u7W5oxpXAk/e0viktNYc/4u7VcfZEuXxjiYmxD5fri9tamxij8bUyOiEpK01iaLy0pzuXqmLcz5+6V2aW7B2i3YWVsSWjrzA0DU+6li1pa5wrQ0V57TlqS4zPxobKaaV43NbEmKe6fR36Pre4h8dYdmPTdrdZ3kBClXjy4kIPQ7reyVeTXXPVpZFuPpqw+PnAKY/+dWlbwaJctMC+ryf9Y5bSjq5S+A9H15kfvd0cLSWuWaOcl8X8hQ875grfF9ITZGxpb1q6jToInW2jTV+eaW/7x+EAg+JaLx/h/i4cOHJCYmUrduXRX31NRUypbN20M2efJkxo1TnSfeu08f+vbt+1l1ZqFQKACoVLkyzZtnFvre3t7cvXOHvXv3frbG+/8Krs5OLJ31GwkJiRw/c47Js+cz+9exBWrAC7Tn3PG9rF38q/J37xFzPvs109PTWDx9CCjgx275z6f+nITt3EV4+D3GjR6Fvb0dN2/dZv7CRdhYW1OubHCh6QJ4+OA+O3duZ9acBejoFGyI3IUTe1j/xwTl7+7D5n1qeUrKV81e3M/Z3RcXdz9G92zE/duXVHr6Pwerduzn0JmLLBgzAEMD9VNxCsKZY/tZuXCy8nf/Ub//4zA1sW3dIhIT4hk8fh7FzC25fP44C6YNZ/ikP3D1+PjFJvPD1NCQjcO7kJiSyoV7T5m+5RDOtpaE+nlw5+/XrPvrAuuHdS5wevsUBLnYEeRip/K7xR+72HL1AT2rB+Xj899l9fa9HDp9gQXjBn2SNPfw6i5O7hir/N2gnfYLPWYRL3vN2d2TadhxGXr6eTspcpOaHM/+lT9jae9DSJ2eBb7ex7B6xz4On77I/LEDP0m8fW7+SfkLcOKvg/wxb4by97CxBZuD/jEkJiYwaewQXNw8+O6HDhrtzh3fy+pF2XV+33+hzv9fRSxY9+8iGu//IeLj4wHYs2cPzs6qK+mq600fNmwY/fv3V3F78VL9UEJzc3MkEkmeXkyZVIp1ri+kWVhZWSFTY5/11dfc3BxdXV3c3FRX6XR1deX2HfVDuD4FKRGRGDqoruRr6GBLWkwc8uQUUiOlyNPTMbS3yWVjQ8qbgn1dtXgfb9G5ehWkshiNi9Fpi76+Hi6OmUMj/X28CH/wiK279zKgh3aLsRUzt0AikSgXdMlCJpNiaaV+YT5LK+s8C9vIZNI8X/L/KUVRW3CF6nj5Zfc0p6VlLhQUGxOtsgp8nCwK10+wSmxmw30oUe9eM2D84g/2ukN2Ps0dD1KZTGM8WFlZIs21mF2mvSWQORpn5eo1jB4xnIoVMnsavTw9efz4MVu2bS9Q4938/XPNU47IpFhZq9dnaWWVZ7G9nOng9u1bxMhkdGz3g/K8XC5n+dLF7NyxjWUr12rUUya0Bh6+2VNa0tNTAYiVRWFhlf1MY2OicPH4tCv/2jq4YGZuxbs3zz7YeLc0N0NXIiE6Jk7FPTomNs+ontys3XWQ1WH7mTeyH77un+bDXtkK1fD2z14RPi0tM95iZNFYWmeXrbGyaNw81U/lKWZuiUSiS4xMtTctRhaNhVVm2Rvx+gWH92zm17nrcXHzBsDN04/7t69xZO9m2vfI/4OWlZkJuhIdonItThcVG4+tueb8JJHo4Gafmb4CXIvz5HUky/efIdTPgysPnxEdl0DDHC/yGXIFM7ce5s+jF9j3a+98NanoMzFEV0cnz+J0UQnJ2OTqOdeEvq4Efwdrnksz04bt+1786IQk7HLMhY9KSMbfQfuy0LJYVppT7dmNjonF5gNp7s+dB1izYx9zRg/Axz17hESWv2hZLLY56r9oWewHdzlwK1GLb1yzP+hnZGSmuaT4KEzMs3emSYqPxMZR/eiSyJe3SYqPYvu87LVrFPIMXj+9xO1z6+g44ToSSeYomNSUBPat6IK+oQl1f5yLRFe7hrQyr+bqEZfK4rSIt4Os3rGfuaN+UcmrNu978aNj4lTjLSYW3wLsDlHUyl+A0IpVlSvCQ+Z0Gnj/rpijLImRRePhpf5jXeb7gq6a94XoPO8LSYmJTBw1EGNjEwaPnIienuZmTlCF6ozJUeena6jzYz9RnS8QfCrEnPf/ECVKlMDQ0JBnz57h4+Ojcri65i3gDQ0NMTc3VznUNfIB9PX18fH15fq1a0o3uVzOtWvXCAhUX1EGBAZyLYc9wNWrV5X2+vr6+Pn58SLXsPuXL19qvU3cxyA7dw2bWpVU3Gxrf4H0XKZWRVoaMVduY1srey4dOjrY1KyM7FzBFofR19fD39uLKzey53DK5XIu37hFCX/fj74HdSgUClLTtFt4JVObPt4+fty4dkVF281rV/APKKnWj39ACW5cv6Lidv3qZfw02H8sRVGbkbEp9o5uysPJ1QsLS1vCb1xQ2iQlxvP4wS28/P/ZqJGshvvb18/oP3YRZsUstfKnr6+Pr48PV69lz7/LzKfXKRGg/uUiMCCAa9dV54heuXqNwIDMRczSMzJIT09HkuvLuUQiQaGQF+Cu3pcjPn7cuJ6dj+RyOdevXcU/oIRaPwEBJbh+TTXfXbt6hYCAzHKkZq06zJ2/mDnzFikPaxsbmrdoybiJk9UFqST3M3V08cbc0pZ7N7NX0U9KjOfpg5t4+X3akUDSqAgS4mQqHwk0oa+nR4CXGxdvqs4zvXgrnNIatu2CzBWql2/dw6xhfQj09vgUsgEwNjHFwdFVeTi7emFhZcOdGxeVNkmJ8Ty+fxsf/9Jqw9DT18fDO0DFj1wu586NS0o/qSmZjVqJjuqriEQiQf5+5FZ+6OvpEujmyIV72Stjy+UKLtx7Shkv9VvFqUOuUJCanlm2Nq5Yms0jurJxeBflYWdRjHZ1K7OwdxutwwTQ19Ul0NGa80+zh6HLFQouPH1DGWf1W8XlJkMu5+FbGbbvG+rOlmbYmhpx/mmE0iY+JY1bryK1DhPe111e7lzKleYu3QynlJ/mNLc2bB8rtuzm9xH98qQ5J3tbbCwtlAvYASQkJnHn4WNK+Xvnq8fA0BQLW3flYWXvg3ExW14+yt42LzU5nnfPb+Dgpn4EgpNPZVr0DeOb3tuUh61zKXyCGvNN723ZDffkePYt64Surj712y7Qqpc+C309Pfy93Lh4K3vNkMy8epfS+cTbmrD9LN+6m1nD+2qIN3OV/J+QmMTth0/yDTOPtiJW/gIYm5jg6OSiPFzcPLC0subm9ctKm8TEBB7cu4tfgPppWvr6+nj5+HHzWrYfde8LiYkJTBg1AD19fYaOnoyBQf7P1djYFAdHN+Xh5OqFhZUtd9XU+d7/sM7/X0dHIim04/8jouf9P0SxYsUYOHAgv/zyC3K5nKpVqxITE8Pp06cxNzenXbt2/yj85s2bM3PGDHx9ffHz9ydsxw5SUlKUw/SnT5+OjY0NHTpkDkNq2rQpQwYPZtvWrYRWqMDx48d58OABvftkb4XRokWLzPntpUpRJiiIy5cucf78eaZO1X7olK6pCaY+2b33Jp4umAcFkBodQ/Lz1/hP7I+RswPXOwwB4O8/NuDe4wcCJg/i+cqt2NashGPLhlxs0k0ZxpNZKwhaPhXZ5VvEXLyBR5926Jka83zVtgLHW8umjZg8ewH+Pt4E+nqzZddekpNTaFinBgCTfp+HrY01XdtmvvilpaXz9P385PS0dCKjpDx4/BRjYyNlT/sfq9dRMSQYe1tbkpKSOXziFNdu3WHa2OEF0takeUvmzJyCt68fvn6B7A7bQnJyMrXrNgBg9oxJWNvY8VP7LgA0btKCkUP7EbZtEyGhlTh14iiPHt6je+8ByjDj4mKJfPuW6OjMUQovX2bOObO0ssbKWvut9oqyNgAdHR1qN27Dni1LsXd0w9bBibD1C7G0tqNshRpKuxljulG2Yk1qfdUKgOSkRN6+yZ5fGPn2Jc+e3MPUzBwbO0fS09NYNG0wzx6H03v4bOTyDOVcbFMzC/Q07ECQxTfNmzF95u/4+frg7+fH9rAwkpOTqVe3DgC/zZiJrY0NHdtnlgfNmjRh0NBhbNm2nQqh5Tl+4iQPHj6kX+9emdc0MaFM6VIsWb4CAwNDHOztuHHzFoeP/kXXzp0KFGcAzZq34PeZv+Hj64efnz9hYdtJTkmmTt36AMycPhUbG1vadcgMu0nT5gwbMoDt2zZTPrQiJ48f4+GD+/Tq3Q9A+eExJ3q6elhZWePiUrA9y3V0dKjV6Af2bV2CvaM7NvbO7NowHwsrO4Jy7Ns+e2wXgirWokbDzL21k5MSefcme25lVMRLnj8Jx9TMAms7R5KTEtm7eRFlK9XB3NKGd29esH3t79gVdyUwWLtFs1o3qsP4BSsJ9PaghLcHG/YeITkllcY1Mv2PnbcCO2tLerbJnIK0Omw/f2zaxfg+nXCyt1HOKTY2MsTEKLN3NiY+gYjIaN69X98ga/68jaX5B3sJc8db/a9bsXPTchwcXbFzcGLbukVYWttSrlL2WitTR/WgXKUa1G2UOX+4QdM2LJk9Dk+fQLx8S3Jg1wZSkpOoVqcxAI4uHjg4urJiwWRadeiLWTELrpw/zu3rF/hl5EyttP1UuyKjVu2khJsjpTyc+fPoeZJS0mhaObOBN3JlGPaWxejTLPP5Ltt/mhLujrjaWpGansGp2w/Zc/4mw1tnTnuwNDPB0sxE5Rp6uhJszE3xKF7wLbJ+rBDA6F1nKeFoQyknG9ZdCCcpLYOmZTIbZSN3nsG+mDF9amZOfVt88iZlnG1xtTIjLiWNVefu8Do2geZBPspn0aZCAEtP38LNqhjOlqYsOHEDu2Im1PQvWH5o3bguE+YvJ8DbnZI+nmzYc5jklBQa16wCwLi5y7CztqTHD5k92Wt27GPJxjDG9e2Co50tUdIcac7YCB0dHb5vVIeVW/fgWtwBR3tblmzcga2VJV+GFmzxSx0dHUpVacvVo4uwsHGnmLULlw7NwaSYPe4l6ijt9iztgEeJOpT84gcMDE2xLq46EkTfwBgjE0ule2pyPPuWdyI9LZma3/9Gako8qSmZoxqNTK2VDfwPx9sKAr3cKeHjyca9h0lOSaVRjffxNm95Zry936Jx9Y79LNm0k3F9OuGoJq/q6Ojw/Vd1WLltL66O9jjZ2/LHhrCPireiXP5C5nNt1LQlWzesxtHJBfvijmxYswwraxsqVM7eznfs8H5UrFyNhl9npr2vm3/HvJmT8fb1x8cvkD1hm0lJTqJm3a+A9w33kQNISUlm8MCRJCYmkJiYOSLH3MIS3Q+sO5KlrU7jNuzevBSH93X+9nWZdX65ijWUdtNGd6NcpZrU1lTnR6jW+QDxcTFER75BFp25XsObl08BsLC0wcJK+49uAgGIxvt/jgkTJmBnZ8fkyZN5/PgxlpaWlCtXjuHDC9aoU0f16tWJjYlhzdq1SKOj8fL2ZvyECcrhuO/evkWSY85TiRIlGDxkCKtXrWLlypU4OzszatQoPDw8lDZfVKlCr1692LRpE4sWLcLFxYURI0dSsgALYVmElKLykTXZ152eea/PV2/jRqdhGDraYezqqDyf9PQFF5t0o8SMYXj0bkvyizfc7DaSyEPZ25C83rwPAztr/Mb0wbC4HbHX73KhcWdS32pe5EgTtap9gSw2lhXrNhEtleHj6cFvY4Zh/X4RmIjIKJWvg5HR0XT5ZYjy98Ydu9i4YxdBpUow+9cxAMhiYpk0awHR0VJMTU3wcndj2tjhlA8u2Nffql/WIjYmhg1rVyKVRuPp5c3o8VOVQ83evXuLTo5er4ASpfhl0EjWrVnO2lVLcXR2ZujICbh7ZK/mfPHcGebOyv74MmNq5pzi79u0o9UP7f8ntGXRoHk7UlOSWLNoIokJcfgGBtN31Dz0c3zRf/fmBfGxMuXvvx/dYfro7KkNm1ZkNkIq1/yajr3HIYt+x/WLmbtDjB/QSuV6A8f/gX+p8uRHjS+rERMTw+q1fyKVSvHy8uLX8eOy8+m7dyr5tGSJQIYOGsiqNWtZuWo1Ts5OjBk5Ag8Pd6XNsMGDWb5qFVOnTycuLh57ezvat/2Jxl81zHP9D1Gteg1iYmX8uWbVe33ejBs/KYe+tyrz4wJLlGTg4GGsXb2S1StX4OTszIhRY1We66ekbrMOpKQksW7xeBIT4vAOKEuvkQtUn2mE6jN99ug2s8Z2Vv7eumo6AJVqNKFtrwlIJBJe/n2fc8d2kpQYh4WVPYFBlfm6VU/09Q200/VFKLLYeP7YtJMoWSx+Hi7MGtZHOaQ2IipaZXTEtkMnSEtPZ9jMxSrhdP62MV1afg3AyUvXmbBwlfLcyNlL89hoy1fftCUlOZmVCyaRmBCPb2AQA8fMVundevvmpUq8VaxWl9hYKdvW/UGMNAo3Tz8GjpmNxfsFA/X09Og/+nc2r57PrIkDSE5OxMHRhS59xxBUvopWuuqXL4k0PpGFu48TGZuAv4sDC3q3xub9sPnX0TEqc3WTUlKZtH4fb2VxGOrr4VHcll87NKV++U87ukipr4QH0sQUFp64rhzaPv/7msrt397EJqjk17jkVMbvPUdUQjLmRgYEFrdmZdt6eNtlf2xpX6kESanpTNx3nrjkVIJd7Zn/fU0M9T7cQMlJnSoVkMbGs3RjGFGyzCHav4/op5yqEREZpaJt28FjpKWnM3yG6nz0Ti2/pvN3TQH4sWkDkpJTmLJ4NfGJiZQJ8OX3Ef0+an530JedSU9N4uT2MaQmx+LgXo4GHf5Q6SmPjXpGcqL2i5ZGvrrD2+eZI5c2Tq+vcq7V4MMUs/rwiI3MvBrHkvd51dfDhd+HZ+fVN5HRKmlu26HjmfGWK692+rYxXb7LXFDtp6b1SU5JYcrite/jzYdZw/sWON6KevkL0OzbNqQkJ7N47nQSEuIJKFGakROmq5QlEa9fERubPR2xype1iY2RsWHtcmTSzCH2I8ZPV74vPH54nwf3Mqdj9urcWuV6C5ZvxN7BEW1o2LwdqclJrFqYXef/8oE6/+mjO0wblV3nb3xf539R82s69clcd+raxeOsmDtWabN4RuaUoCbfd6Vpq5+10laUEXPe/110FAotxqYJ/md49Fj7bUcKg3D/gjcW/i3K3dxY2BI0ItX7fNMQ/peJTNG+9/Hfxs3ww1tdFSapOkYfNioknicW3fwQknHuw0aFRLhRwXr5/k2CXu0sbAkaUTx/8mGjQiS5bM3ClqCR5Q+1+0hTGHTyPlnYEjTyzuzzNa7/KSkK7ach/NvEpH54bZnComqJT7/N57/FzcaFV8aU3v1XoV27sPj/OVlAIBAIBAKBQCAQCASC/xBi2LxAIBAIBAKBQCAQCAqMRFcMm/83ET3vAoFAIBAIBAKBQCAQFHFEz7tAIBAIBAKBQCAQCAqMWLDu30X0vAsEAoFAIBAIBAKBQFDEEY13gUAgEAgEAoFAIBAIijhi2LxAIBAIBAKBQCAQCAqMjkT0Bf+biNgWCAQCgUAgEAgEAoGgiCN63gUCgUAgEAgEAoFAUGDEgnX/LqLnXSAQCAQCgUAgEAgEgiKO6Hn/f4ZEIS9sCflS7ubGwpagkSulvy9sCRpxu3uisCVoRELRTXMORtGFLUEjiiL+bVUHRWFL0IikCGtLNLYubAkakRTdaOOaY9PClqARJ7fXhS0hX1J0jAtbgkZqlIwpbAkaeaXrV9gSNGJAamFL0IguGYUtQSP2hlGFLSEfTAtbwEcjet7/XYr226FAIBAIBAKBQCAQCAQC0XgXCAQCgUAgEAgEAoGgqCOGzQsEAoFAIBAIBAKBoMCIYfP/LqLnXSAQCAQCgUAgEAgEgiKO6HkXCAQCgUAgEAgEAkGB0ZGIvuB/ExHbAoFAIBAIBAKBQCAQFHFE410gEAgEAoFAIBAIBIIijhg2LxAIBAKBQCAQCASCAiPRFQvW/ZuInneBQCAQCAQCgUAgEAiKOKLx/h+nRo0a9OvXr7BlCAQCgUAgEAgEgv9n6Eh0Cu34/4gYNv8fZ9u2bejr63+y8Hbu2s2WrVuRSqV4eXrSo/vP+Pv7a7Q/cfIkq9esJSIiAmcnJzp27ECF0FDl+aSkJJavWMnZs2eJjYujuIMDTZs0oVGjrwqsbfueA2zYsYtoqQwfD3f6dO1AoJ+PWtsnz56zYt0m7j16QsTbd/Ts1JaWTRqp2ITtO0jYvkO8efsOAA83F9p934KKIWULpMu6anm8BnTColwpjJzsudSiBxE7j+Tv58sKlJg+FLMSviQ/f83DyQt5sXq7io179zZ49e+EYXE7Ym+Ec7vfBGIu3iyQNgCFQsHGtcs5fGAXiQnx+AeWpmvP/jg6u+brb9/ubezcugGZNBp3T286/dwXX/8SAMTFxbJp7XKuX71I5LsIzC0sCa1UjVY/dcLU1Exrbft2b2fH+2t4ePrQ+ec++PoHarQ/c/IY69cu423EGxydXPipQzdCQiup3OuGtSs4dGA3iQnxBASWomvP/jg5u2itKYs9u8LYvnUTUmk0np7edO3eCz//AI32p04e5881K3kb8QYnJ2fadexC+dCKyvPr1q7i5IljRL57h56+Hj4+vvzYtiP+AZrvNz8+dV5t8FUjtf46dexIy29bFEjb7l072bZ18/u486Jb95745xt3J1i7ZiURERE4OTnTvmNnQkMrqLWdN3c2+/ftoUvXn2na7JsC6YLMNLJr40JOHd5GUmIc3v7BtO46HAdHd41+Hty5zMGwVTx7fJcY6Tt+HjyT4Aq1NNr/uXgiJw9toWX7gdRu/KPW2rbv2c/G7TuJlsrw9nSnT9eOBPr5qrV98uw5K/7cyP1Hj9+Xce35tqnqM/xz83ZOnj3Ps5cvMTQwoGSAP13b/YCbi7PWmnKiUCjYtu4P/jq0g8SEePwCytC++xCKO7nl6+/Qns3s3bGWGGkUrh6+tO06EG+/kgC8i3hF/67N1PrrNXgSFavU0Vrb9vWLOf5em29AGdr+PPSD2g7v3cS+7WuJkUXh5uHLj10G4fVeG8Db1y/YsHI2D+5eIy0tjdJlK/Nj14FYWNpopWvn7j1s3rqd6Pf5tOfPXQnw99Nof+LkKVau/ZOIiLc4OznRuUM7KoSWV56XSqUsXbGKy1evkZAQT+mSJen5czecnZ200pObPbvCcuRVb7p17/nBcm7tmlXKcq59x84q5VxO5s+dxf59e+jctftH59Wt65bw18EwEhLi8QssTcfugz/4TA/u2cKe7WuJkUbj5ulDu64DlOktiwfhN9m0ZhGP7t9GRyLB3dOPoeNmYWBopJW2olx3FeXyd+/u7ezYuvF9vHnT+ec++OUTb6dPHmP92uXKeGvboatKvJ09fYID+3bx6OF94uNimTlnCZ7e6t8LP0RRzgsCQU5Ez/t/HGtra4oVK/ZJwjp+/ARLlizhxzZtmDd3Dl5enowYNQqZTKbW/s6dO0yZ+hv169Vj/tw5VK5cmfETJvL06VOlzR9LlnDp8mUGDRrIH4sX0axZU+YvXMjZc+cKpO3oyTMsWL6a9t+3YMnMKXh7ujNo7CSkshi19ikpKTg6OND1p9ZYW1mqtbGzsaFr2zb8MXMyi2dMolzpUoyYNI0nz54XSJuuqQmxN+5xq884reyNPVwI3bmYqGPnOVW+KU/mrqL04onY1q2qtHFs2ZDAacN4MHE+pyo0J+5GOBX3LMPAzrpA2gB2bFnH3l1b6dpzAJNmLsbQyIgJowaSmpqi0c/pE0dYtWQ+Ldu057c5S/Hw9GHiqIHEyKQASKMiiY6OpG2nHsxcsIqevwzj2uXzLJw9VWtdp04cZcWSBXzXpj3T5yzBw9Ob8aMGIXt/jdyE37nFzN/GU7teI2bMWUqFylWZOnEkfz99rLTZvmU9e3Zt5eee/ZkycyGGRsZMGDUo33tVx8njf7FsySJatfmJ3+cuwsPLizGjhmrUdvfObaZP/ZW69Rowa+4iKlauwqQJY/j76ROljbOzC92692Lugj+YOm0W9vbFGTNyCDExsgJpg8+TV9etXaNy9O/XDx0dHapW+aJA2k4cP8bSJYtp3eZHZs9dgKeXF6NHDc837n6bOom69RowZ+5CKlX+gl8njOVpjrjL4syZU9y7dxdrG+0aTuo4uGMlf+1dR5uuIxgyaQ0GhsbMndCDtHzSSEpyEi4efrTqPOyD4V89f5QnD25gYW1XIF1HT55m4bJVtGvVkj9+n4q3hzuDx/yabxnnVNyerm1/0FjGXb91m2aN6jN/2iSmjR9FekY6g8dMJCk5uUDastizbTUH92ykQ/ehjJ22HEMjY34b2yff/HXu5CHWLZ9F8+87M2Hmatw8ffltbB9iZNEA2Ng6MHflXpXjm9ZdMTIyIaic9mlv7/bVHNq9kXY/D2P0byswNDJmxrje+Wo7f+ogG5bPolmrzoybuQZXD1+mj+tN7HttKclJTBvbCx0dGDx+ISMmLyU9PY1Zv/ZHLpd/UNOxEydZvGQZP7ZpxYI5v+Pl6cHwUWOQasint+/cZdJv02lQry4L58zii8oVGTtxEk+e/g1kNvDGTpzE6zdvGDdqBAvmzMLe3p4hI0Z91DM9mSOvzpq78H1eHZZvXp02dRL16jVg9tyFVKpchV8njFUp57I4+wny6u5taziwexMdug9h/LSlGBoaM2VMv3yf6dmTh/hz2Wy+adWZib+vws3Dlylj+inTG2Q23KeO7UfpshUZP2M5E2asoF7jb7Xe7qoo111FufzNjLeFfN+mHTPm/PE+3gZ/IN4mULveV8yYs4SKlasyZeIolfSWkpJMYIlStO3Q9aM0ZVHU84JAkBPReP+P8ymHzW/bvp0GDRpQr15d3N3c6N2rF4aGRhw4eFCt/Y6wnZQPCaHlty1wc3OjXduf8PH2Zueu3UqbO3fDqVO7NkFlylDcwYGvGjbEy8uTe/fuF0jb5rA9NKpXm4Z1auLh5kL/7p0xMjRg7+G/1NoH+PrQvcOP1P6yisaRCV9UCKFS+bK4ODni6uxE559aYWxkxJ17Dwqk7d2BE9wfM4uIsMNa2bt3bUXSkxfcHTyV+PDH/L3gT95sPYBn3/ZKG89+HXi+bBMvVm0j/u4jbvYYQ0ZiMq7tC9YDqlAo2BO2mRbf/0SFytXw8PSm94ARSKOjuHD2lEZ/u7Zvok6DxtSq+xWubh507TUAQyMjjh7cA4CbhxeDRkykfMUqFHd0pnRQCK3bduHS+TNkZKRrpW3X9s3UbdCI2nUb4urmQbde/d9fY69a+907t1I2pALNWrTCxc2dNj91wtPbl327tyvvdXfYFr79/icqVK6Kh6c3fQYMIzo6Mt97VUfY9q3Ua/AVdeo1wM3NnR69+mFoaMjhg/vV30vYNsqFhPLNt9/j6ubOj2074OXtw55dYUqb6jVrE1w2hOKOTri5e9Cp688kJiby9MljtWHmx+fIq9bW1irH2XPnCCpTBkdHxwJp27F9K/UbNKRuvfq4ubnTs1dfDA0NOXTwgFr7nWE7CAkJpcW33+Hq5sZPbdvj7e3D7l07VewiIyNZvHABAwcNRU/34waNKRQKjuz5k4YtuhBcoSYuHn506D0BmfQd1y6oL0sASpWrStPWvShbUXNvO4A0KoKNy6bQse8kdAuocXPY7hxlnCv9e3TFyNCAfYePqrUP8PXh5w5tqZVPGffbuJE0qF0TTzdXfDw9GNq3JxHvIrn/sOBpTqFQsH/XBpq07EhIxeq4efjSrd9YZNGRXD53XKO/fWHrqFGvGV/W+RpnNy86dB+KoaERJw7vAkCiq4ulla3KcfncMSpUrY2RsYnW2g7uWk+T7zpSrmJ1XD186dJ3HNLoSK6c16ztQNg6qtdrRrXaTXB29aJd92EYGBpx4khm2ntw9zqR717Tuc8YXD18cPXwoUvfsTx9eJe7Ny9+UNfW7WE0bFCP+nXr4O7mRt9ePTA0MuTAQfV1xY6duwgNKcd3Lb7Bzc2V9j/9iI+3Fzt3Z5a7L1+94m74Pfr07IG/ny+uLi706dmdlNRUjh0/oVVcqVzvfV7NLuc+lFe3vy/nvntfzmXl1TAVu6jISBYvnM+AQcP+UV7dv3Mjzb7rQPlKX+Lm6Uv3X8a8T2+a73Vf2Hpq1mtK9TqNcXHzpGOPIRgaGnH8cHZZt2bpLOo3/o4m37bFxc0LJxd3KlWtg76+gVbainLdVZTL35254u3n9/F25OA+tfZZ8da8RStc3dxp81NHvLx92bs7e5RijVr1+L5NO4KCQz5KUxZFOS/8F9CRSArt+P/I/8+7FuQhLS2NBw8fUjY4WOkmkUgoGxzM3fBwtX7uhodTtmywiltISDkV+xKBAZw7f57IyEgUCgXXr1/n5ctXhJQrVwBt6dx79JiQoNIq2kKCShe4oa2JjAw5R06cJjk5hZL5DGn8FFhWCiby6FkVt3eHTmFVKRgAHX19LMqVJPLImWwDhYLIo2ewrFSwIf1v37xGJo2mTHD2sEtTUzN8/QO5H35LrZ+0tDQeP7yv4kcikVA6OIR74bc1XisxMQETExOtGi1paWk8eniPMjkqXIlEQpngEO6F31Hr5374bRV7gLLlKijtI97fa85KPPNeS2gMU5O2hw/vExycnUYlEglBweUI1xBOePgdgsqqpulyIaEa7dPS0jiwbw+mpqZ4enprrS3L7+fIqzmRSqVcuHiR+vXqFVjbw4cPCA7OTqcSiYTg4LKEh99V6yc8/A7BZVXTdbmQ8ir2crmcmdOn8k2Llri7exRIU04i374kVhZJYJnsoY3GpsXw9C3N4/vXPzrcLI0r546kbtN2OLkWbNhmWloa9x8+JiS4jNJNIpFQLqgMt8ML9qEzPxISEgEwL6b91JYs3kW8IkYaRamg7OG0JqZmePmV5OE99dN50tPSePoonJJB2dMzJBIJJYNCNfp58vAufz+5T/U6TQug7SUx0ihKlFHV5u1Xkkf3buSrLaefTG0VePReW1paKjrooJejUadvYICOjoT7d/JPL5rzaZDGfHcnPJyywUEqbuXLZefTtLQ0AAwMsj/WSCQS9PX1uXVb+zIuK6yHD+8TlKucCw4up7G8zMyrquVc2c+UV99FvEImjVJJO1nP9EE+6e3Jw3uUClZNb6WCQnkQnuknRhbNo/u3Mbe0YuzgLnT/qSEThnXn3p1rWukq+nVX0Sx/M+Ptvso9ZsZbOY3vFffC7+RplAeXC+V+Pu8hH6utKOcFwadl/vz5eHh4YGRkRMWKFblw4YJG2xo1aqCjo5PnaNQoe4pa+/bt85xv0KDBZ70H0XgXABAbG4tcLscy1/BLS0tLpNHqhw1JpVIsLdXYS7Ptu3fvjrubGz+2bUfjJk0ZOWo0PXt0p3TpUlpri3mvzdrSQsXdytKCaKlM63DU8fjpMxp835a63/7AzEVLmTBsIB5uBZ9jVhAMHWxJiYhUcUuJiETfohgSI0MMbK2Q6OmR8jYql00UhsVtC3QtqTQzDEsrKxV3C0trZNJodV6Ii41BLs/AwlLVj2U+fmJjZGxZv4o6DZpopSvzGnIsLVWnAVhaWmm8hkwancfeIod91v8WVnnDlGoIUx2xWdqsct+/FTINeUEmlWKZJ74s81z34vlzfPdNY75t9hVhO7Yy/tepmFuopusP6/s8eTUnhw8fwdjYmCoFHDKfrS1v3Emj1T8DqYa4y5kOtmzeiK6uLk2aNiuQnjz6pJn5zjzXfOViFtbEyqLUedGagztWIJHoUuurNgX2GxMbh1wux0pdGadhiHVBkcvlzFu6klKB/ni65z9nWB2y92WJRZ48aE2MVH3cxcXK3pclqn7MLa2V4eXm+OGdOLl44hdYRu15dcTIsrSpPldzCxvN2uI0aLPIvh9v/9IYGhmxadVcUlKSSUlOYsOK2cjlGcRII9UFqyQrL1jlyndWlpYa6y2pVJbH3tLSkuj3+dTVxQV7OzuWr1xNXFw8aWlpbNy8lcjISKWNtmSVc1Zq82p+5Vxufapl9tbNG5HoSvi6afMC6cl7Lc3pTVPa0ZzerJRp5O2bVwBsW7+UmvWaMmTsLDy8/Zk0sjdvXj37oK6iXXcV3fI3K97yvld8KN7U3EsB0/qHKOp54b/Af2XBuo0bN9K/f3/GjBnDlStXCAoKon79+rx9+1at/bZt23j9+rXyuHXrFrq6urRs2VLFrkGDBip269ev/+i41Ib/3TEcAlJSUkhJScnjZmho+K9p2LlzJ3fDwxk7ZjT29vbcunWL+QsWYm1tTbmyBetF/hy4OjuxdNZvJCQkcvzMOSbPns/sX8d+9gb85+LEXwf5Y94M5e9hY7Wfg/6xJCYmMGnsEFzcPPjuhw6f/Xr/ZUoHBTFr3mJiY2M4uH8vUydPZPrvc/O8oBQ2Bw4dolbNGhgYaDeM9HPy8MF9du7cwew5C9DRKVhFff7EHtb9MVH5u+ewuZ9aHgB/P7rD0b3rGP7b+gJr/LeYvWgpT549Z+6UCVrZnz62nxULJyt/Dxj1++eSpiQ1JZmzJw7Q9LtO+dqdOb6PVTm0/TLy82gzt7Ci56AprFo0hcN7NqKjI6FitXq4ewUUynBNPT09Ro8YxszZc2nRqk3m6IzgIELLh6BQKP51PbnJzKvbmfURefX0sf0sW5BdXw0aPSMf649Hochcq6BW/eZUr9MYAA9vf25fv8ixQ7tp1a7HZ7nuf5V/Uv7+f+af5AXB52PmzJl06dKFDh0y31UXLVrEnj17WL58OUOHDs1jb22t+kFtw4YNmJiY5Gm8GxoaUrx48c8nPBei8f4/zOTJkxk3TnURtT69e9Ovb588tubm5kgkEmS5egRkMhlW1uobFlZWVnkWyJLJZMqvlykpKaxctZpRI0dQsULm0EQvT08ePXrM1m3btG68W7zXFp1r4SapLEbjQk3aoq+vh4tjZobz9/Ei/MEjtu7ey4Ae/2zxk/xIiYjE0EG1B93QwZa0mDjkySmkRkqRp6djaG+Ty8aGlDf59/aEVqyqXBEeMocRQuZXYivr7GvGyKLx8FI/tLeYuQUSia5ycbosZLJoLHP1DCQlJjJx1ECMjU0YPHIienraFSmZ15Agk6l+cZfJpHmukYWllXUe+5gc9ln/x0ijsbbOjjuZTIqnhntVh3mWNmnu+5diqSEvWFpZ5VnYJjMvqN6LkZExTk7OODk5ExBQgm6d23HowD5afq99b+3nyKs5uXXrFi9evGD40CFaa8qrLW/cWVmrf65WGuIu63nevn2LGJmMDu1+UJ6Xy+UsW/oHYTu2s3zlGo16gkJr4OmbPd0mPT0VgFhZFBZW2QvKxcVE4+Lx8dNlHt69QlxMNMN/bphDYwZbVs/kyJ4/mbRQ/ZzOLCzMiyGRSPIsTieVxWCdq3fnY5i9aClnL11h9qRx2Nlqt2hSuQrV8PHPXqE7LS0z7mJk0VjmKkvcPdXHXTFzy/dliWq+jZVFY2mVV8eFM0dJSUmmas38dyMpW+FLvP2yR2+lK7VFqWiLjYnCTZO2Yhq0xURjkUNbqbKVmLZ4B3GxMiQSXUzNitGnfX3sHPKfUpKVF3IvTieVyTTWW1ZWlnnsZTIZ1jnyqZ+vD4vmzSYhIYG09HQsLSzo/ctA/HwLNlUjq5zL3YuZmVfzK+dy65Pmyasdc+XV5UsXs3PHNpatXKtRT7kK1VRWhE9Pz6y7YmTReeoudy/1OzBoTm9S5agMS6vMsJxdPVRsnFw9iIp8o1Ff9jWKct1VtMrfnGTFW973ig/Fm5p7UVNv/ROKWl74L1KYW7ap66g0NDTM01GZmprK5cuXGTYse+FZiURCnTp1OHtWdSqrJpYtW0arVq0wNTVVcT927Bj29vZYWVlRq1YtJk6ciM1nXKBQDJv/H2bYsGHExMSoHN1/7qbWVl9fH18fH65dv6Z0k8vlXLt2jcAA9VtlBAYEcO2a6ry/K1evKu3TMzJIT09HoqOazCS6EhRy7XsJ9PX18Pf24sqN7HlucrmcyzduUcJffSX+sSgUClLTtFtw7WORnbuGTa1KKm62tb9Aeu5apoa0NGKu3Ma2VuVsAx0dbGpWRnbuar5hG5uY4Ojkojxc3DywtLLm5vXLSpvExAQe3LuLX4D6qQv6+vp4+fhx81q2H7lczs1rV/APyH65SkxMYMKoAejp6zN09GQMDLQf0aGvr4+3jz83rl1RucaNa5fxDyih1o9fQEluXr+i4nb96iWlvUNxRyytrLmRwybzXu9oDFOTNh8fP65fz63tKgEawgkIKMGNa6rP5trVyxrts1DI5cp5rAXR96nzak72HzyIr48PXl5eBdKVpc3Hx5frubRdv3aNAA1b4gUElOBarri7evWK0r5mrTrMnb+IOfMWKg9rGxu+adGS8RMn5avHyNgUe0c35eHo4o25pS3hN7PnuCUlxvPkwU28/ILyCSl/KlZvzMgZmxkxfaPysLC2o16TdvQZufCD/vX19fHz8eLKddUy7sqNm5QM+PiPCgqFgtmLlnLq3AVmThyDY3EHrf0am5ji4OiqPJxdvbCwsuH2jeyF2pIS43l8/zY+/qXVhqGnr4+HdwB3cviRy+XcvnFJrZ/jh3dSLvRLzC3yfzk3NlbV5vRe251c2h7dv423v/rh95q03blxEW812oqZW2JqVow7Ny4SFyOlbIVq+WpU5tMc+S4zn97QmE9LBARw9brqHP0rV9Xna1NTUywtLHj58hUPHj6kciX1W1Tlp8/Hx48b17PzXmZevaqxvAwIKMH1POVc7ry6mDnzFikPaxsbmrdoybiJk9UFqcTYxJTiTq7Kw9nVE0srG25fz34+iYkJPLp/G9980punj7+KH7lczq0bF/ENyPRj5+CIlbUdr1+qDpF/8/I5tnYfXpyz6NddRaf8za3N28cvT7zlfq/IiX9ACZU4Abh+9TJ+Guw/lqKWFwQFY/LkyVhYWKgckyfnjePIyEgyMjJwcFCtBx0cHHjz5sMf7i5cuMCtW7fo3LmzinuDBg1YvXo1R44cYerUqRw/fpyGDRuSkZHxz24sH0TP+/8w6r48ReUzZP6b5s2ZPnMmvr6++Pv5sT0sjOSUZOrVrQvAtOkzsLGxoWOH9gA0a9qEQUOGsnXbNiqEhnLs+AkePHhI3969ATA1MaF06dIsXb4cA0MDHOztuXHzJkeOHKVrl86aZKilZdNGTJ69AH8fbwJ9vdmyay/JySk0rFMDgEm/z8PWxpqubTN7MNPS0nn6/AUA6WnpREZJefD4KcbGRsqe9j9Wr6NiSDD2trYkJSVz+MQprt26w7SxwwukTdfUBFOf7DmkJp4umAcFkBodQ/Lz1/hP7I+RswPXO2T2ZP79xwbce/xAwORBPF+5FdualXBs2ZCLTbI/rDyZtYKg5VORXb5FzMUbePRph56pMc9XbSuQNh0dHRo1bcnWDatxdHLBvrgjG9Ysw8rahgqVs7emGzu8HxUrV6Ph15mr2X/d/DvmzZyMt68/Pn6B7AnbTEpyEjXrZvaIJSYmMGHkAFJSkhk8cCSJiQkkJiYAYG5hia6u7ge1fd28JXNnTsbH1x9fv0B2hW0hJTmZWnUzey9nz5iEjY0tP7bPHAXRuEkLRg3tS9i2jYSEVuLUiaM8eniPn3sPUN5r46bfsmXDGhydXHAo7sj6NcuwtrZVuVdtaNq8BbNm/oaPrz9+fv7sDNtGckoytetmLkLy+/QpWNvY0q5DZjr+uuk3DB/Sn+3bNhMaWpET/8fefcc3VbUBHP+1tHRBNy1toXuzyiogoAiCqCAbBGXvKbJBloACMmQpIHvvUaaAIEsUEGXI3ksoHUn3pHn/KE2b0pSkAg2vz9dPPpL03HOfnHtGbs69J0d+4cb1a/Tt/wUAyclJbFy/ltDqNbC3cyA2Nobdu8KIioqkVu139IoNXn5bzZKQmMixY8fp0U2/9plT02Yt+G7mNPz8/PD3DyTsWdm9V/99AGZM/xYHBwc6dc68NPrjJk0ZMXwIW7dupmrVUI4eOcyN69fo1/9zIHM2ydraWmMfJkVMsLOzo1Sp0nrFZmRkRL2PPmXvlkU4ubjj6OTGjvXfY2tXgpDQd9Xpvhvfg5BqdXn3g08ASE5KJOJx9of9yPCH3L99BatiNtiXcKFYcVuKFbfV2FeRIiZY2zpQ0s1Tp9haNWnElFnf4+/rQ5C/L5t37CY5OYWG9TLj+ua7uZSwt6f7s5mctLQ07mb1cenpREZHcePWbSzMzXFzzTwJmbVgMQePHmfSl8OwtDBX3xdtZWmp9+1TRkZGNGz8CWEbl1LSpTQlnF3ZvHYBtvaOVK6eXYcnj+lDlep1qP9RawA+aNKOH2d/hZdvEN5+Zdi3cz0pyUm8/eyS5Szhj+5z9eJfDBk7S6+4smJr0LgtOzctpaRraRyd3Ni6dgF29o5UqpYd29Qxvalc/V3eexbb+03asShHbPt3riMlOYna9Rqrtzl2cAcupbywtrbjxtXzrFkykwaN2+Kiw3Ft0awJ02bOws/Pl0B/f7aG7SA5OZn369cD4NsZ3+HgYE/XTh0BaPpxY4aMGMXmrdsy2+nRo1y7cYPP+/dV53n02HFsbGxwKlGC23fuMP/HxbxVvRpVKul/G1pmW/0WXz9//P0DCAvbptFWZ06fioODIx3VbbUZI4cPZtvWTVSpWo1j6rY6EMivrdoXqK02/LgN2zcup6Trs/q25sdn9e1tdbpvRvejSvV3aNAo8zLWD5q0ZeGsiXj5BuHjH8xPOzaQkpzMO/U+Uuf7UbNP2bJuEe5efnh4+XHs0B7+eXiXz0fodjJqyGOXIfe/HzdrxZyZU/Dx88fPP4hdYZtJTs4eV2fP+AZ7hxK079RdXW6jRwwkbOtGjXLr/azcAOLiYol88oTo6MyrEh8++1LG1s5e69UG2svNMNuCyN/IkSMZNGiQxmuv4vbgJUuWUK5cOUJDQzVe/+STT9T/LleuHOXLl8fHx4fDhw9Tr169lx4HyMm7yOGdd94mJjaGVatWo1Ao8Pb2ZtKECepLlJ5ERGhcGhMcHMzwYUNZsXIVy5evwNXNjbFjRuPp6alOM3L4MJYtX8G306YTFxeHk5MTHTt04KMP878sMre6td9CGRvLsrUbiVYo8fXy5NtxI9WXlIZHRmncgxgZHU33L7Iv+92wfScbtu+kQtlgZn89DgBlTCzfzPqB6GgFVlaWeHu4M238KKqE5D1To41N5bLUOJh96Vjw9MyT//srt3K+60jMXEpgUTr7G/2kOw84/XFPgmeMxLN/B5IfPOZCz9FEHsj+SZhHm/ZStIQ9/uMGYFayBLHnLnOqUTdSn+i/qFbTlu1ISU5m4dzpJCTEExhcjtETp2vMlIc/+ofY2OxLdmu+XY/YGCXrVy9Fqci8xP7LCdPVl4TdunGN61czV2Ht162txv5+WLoBJ+cXz2DUersusTFK1q1ehlIRjZe3L2MmfKveR2REOMY57hULDC7LF0PHsHbVEtasWIyLmxvDR0/CwzN7hrhZy7akJCez4Nl7DQoux5iJ3+p1VQBA7XfeJSY2hrWrlj9rCz6MnzBZ3RYiIp5o1Leg4DIMHjaKNSuXsWr5Ulzd3Bg15is8PL0AMDYuwoMH9zn09X5iY2KxtrbG19+fKdO+w70Aq9C+irYKcORI5s9q1amj/xcKWd5+pw4xsTGsXrVSHduECV9rlJ1xjtiCgsswdNhIVq1czsrly3B1c+XLMePxfFZ2L1uDpp1ISUlizcKJJCbE4RtYkf6jf8A0Rx2JCL9PfGz2JZR3b17ku/Hd1c83r8i8H7d6ncZ06qfbPeQvUrd2TWJiYlm+dgPRCiU+3p5MHf+l+hLrJxGRGu0hKlpB94HD1M83bNvJhm2ZfdysbzJvl9qxN/OnA78YNV5jX8M/76P+UkAfHzXvQEpyMkt/+IbEhHj8gyowdNxsjfb15PFD4mKV6ufVa9cnLlbBlrU/EqPIvIx96LjZzy0ud+Tnndg7OFE2RL8Z5CwfNutASnISy3LENnjsnHxjq1arAXExSratW6iObfC4ORqxPXp4l02rvichPhZHJ1cat+zM+x/rdptLnbdrExMTw8rVa9Vt4esJ4zXbaY5jWiY4iJFDB7N81RqWrViFq5sr40ePwsvTQ50mSqFgweKl6svp36v3Lp9+0qZAZVb7nTrExCpZs2qFup/7asI3ufo5zbY6ZNhIVqvbqhtfjhmv7udetkbN25OSnMyS76dkHtPg8gwfP0tz7Hr8QOOY1qhdn7gYJZvXLiJGEYWHtx/Dx3+ncSvEB00+IS0tldVLZpEQF4u7lx8jJ8zG2UW3tW4Meewy5P43s9xiWL96OQpFNF7ePoydMFVdbhERTzDKcaVmZrmNZu2qpax+Vm4jRk/UqG+nfz/B3FnZayXMmJrZH7dp15FPPu2kc2yG3hYMXWH+ZFteE5V5cXR0pEiRIoSHh2u8Hh4e/sL71RMSEli/fj0TJkx44X68vb1xdHTkxo0br+zk3UhlCKuciNfm9s0bhR1CvszT4gs7BK3+LFewD0ivg/tl/X/j93UxJqOwQ9DKxOjV3iLxbxQl5cWJClE6ef+2uCF4kOhU2CFoFVD05fy85avwQOXx4kSF5KnKcBd9cjV9VNgh5CvFyKKwQ9AqNr14YYeglUWR5MIOQauiRqmFHYJWaSrDHRsMecz399H/V0AMxb1ezQtt3+4LdL8itVq1aoSGhjJ3bubitRkZGbi7u9OvX788F6zLsnz5cnr16sXDhw9feC/7gwcPcHd3Z/v27Xz8sW6/wKQvmXkXQgghhBBCCKG3wlywTh+DBg2iY8eOVKlShdDQUGbNmkVCQoJ69fkOHTrg5ub23D3zS5YsoWnTps+duMfHx/PVV1/RokULSpYsyc2bNxk2bBi+vr68//77r+x9yMm7EEIIIYQQQoj/W23atCEiIoKxY8fy+PFjQkJC+Omnn9SL2N27dw/jXLcAXL16lePHj7N///7n8itSpAjnz59nxYoVKJVKXF1dadCgARMnTnylP8stJ+9CCCGEEEIIIf6v9evXj379+uX5t8OHDz/3WkBAANruMLewsGDfvn0vMzydyMm7EEIIIYQQQgi9FeaCdf9FUtpCCCGEEEIIIYSBk5l3IYQQQgghhBD6M3ozFqz7fyEz70IIIYQQQgghhIGTmXchhBBCCCGEEHp7U34q7v+FzLwLIYQQQgghhBAGTk7ehRBCCCGEEEIIAyeXzQshhBBCCCGE0Jv8VNzrJSfv/zEJqmKFHUK+kkwsCzsErdwvHy3sELS6F/R2YYegldVfZws7BK0czGMLOwSt0qV7LjAnC2Vhh6CVEsfCDkErS5IKOwStjFAVdghaJVC8sEPIl0pluPejmhdJKewQtDLkOpeqKlrYIbyRUlRmhR2CEP+afDoUQgghhBBCCKE3WbDu9ZLrHIQQQgghhBBCCAMnJ+9CCCGEEEIIIYSBk8vmhRBCCCGEEELoTRase72ktIUQQgghhBBCCAMnM+9CCCGEEEIIIfQmC9a9XjLzLoQQQgghhBBCGDiZeRdCCCGEEEIIoTeZeX+9ZOZdCCGEEEIIIYQwcHLyLoQQQgghhBBCGDi5bF5oUKlUrF+9lJ/37SIxIZ6AoHL06DsIV7dS+W63d9c2wrasR6mIxtPLh669PscvIEj999TUFFYs/oHjRw+RnpZGhUpV6dHnC2zt7HWObc+ubWzfskG9j269BuCfYx+5/XrsMOtWL+VJ+GNcXEvRoXMPKletrv77b78eZd/endy8cY34uFhmzlmEl4+vzvHkpFKp2LB6KT/v26lRbi5upfPdbu+urex4Vm4e6nILBiAuLpaNq5dy7q/TREaEY21jS9XqtfmkfVesrIrpFJd9rSp4D+6KTaWymLs68UeLPoTvOJj/Nm+HEjx9BMWC/Ui+/4gbk+fzYOU2jTQevdvhPagrZiVLEHv+ChcHTiTm9AWdYspJpVKxc/18jv28laTEOHwCQmjXYxTOrh5at7l28Qz7w1Zw79ZlYhQR9B42k5BqdTXS7Nwwn9PH96GIeoyJiSnu3sE0bdcPL/9yesf3so8rwIG9Ozh25Gdu37hGUlIiKzbsxqpYcb1jW796GQeetdXAoLI6t9Xt6rbqS7deA55rq8sXz3/WVlMJqRRKjz4D9Wqrhhzbi/aR24ljh1m3eom6H2nfuadGP1LQ95oXQ+/jXsXYsH/vDo4fOcitZ21h5YZdercFOaYFO6Zg2GVnyHXOkI+r9L8Fq29Z+RnqmG/Q5KfiXispbaFh++Z17Nm5lZ59BzN55gLMzc2ZOGYIqakpWrf59eghli/6ntbtOjJtziI8vHyYOGYIMUqFOs2yRfP449QJhoz8iglTZqOIjuTbr8foHNfxo4dYtmg+bdp1ZMacH/H08mHCmGEoc+wjpyuX/mbmtxOp1+BDZsxZRLUatZgyaQx379xWp0lJSSYouCwdOvfQOQ5ttm9ey56dW+jRdzDfzFyImU7ldpAVi76nVbtOfDtnMZ5evkzKUW6KqEiioyPp0LUPM39YQd8vRnL2zEnmz56qc1xFrCyJPX+Vvwd8pVN6C89SVN2xkKjDJzlepQm3566g3MJJONavpU7j0uoDgqaN5Pqk7zke2oy481eotnsJRUvoPoBn2bd9OYf2rOXTnl8yYvIqzMwtmDOxD2n5lFtqShKlPP1p232k1jTOrh607TaCsTM3M3TSMhycXJk1sTdxMdF6xfcqjitk1r2KlUJp3vozveLJadvmdezeuYVefQcxZeZ8zMwtmDhmaL6xZbajH2jdrhPT5yx61o6GarSjZYu+549TJxg6cjwTp8wmOjqSqV+P/b+ITZd95JTZj0ygXoOPmDFnMaE1ajF10mju3rn1r96r9tgMuY97NWNDakoKIf+iLcgxLThDLjsw9DpnuMdV+t+C1Tcw7DFfiCxy8l6IUlJSGDBgAE5OTpibm1OrVi1Onz4NwOHDhzEyMmLfvn1UrFgRCwsL6taty5MnT9i7dy9BQUFYW1vTrl07EhMTX0o8KpWKXWGbaNmmPaE1auHp5UP/waNQREdx6rfjWrfbuW0j7zVsRN36H1La3ZOe/QZjZm7Owf17AEhIiOfQ/j106taXchUq4eMXQN+BI7h6+W+uXbmoU2w7tm2ifsOPqFf/A0q7e9Kr36Bn+9ibZ/pdO7ZQsXIozVp8Qml3D9q174K3jx97dmXPINep24A27TpSIaSyHqX0PJVKxe6wTbRo057QGrWflduXepdbj2fldmj/bgDcPb0Z+uUkqlSrSUkXN8pVqEzbDt354+QJnj5N1ym2iH1HuTZuFuFhP+uU3qPHJyTdfsDlYVOJv3KLuz+s4fGWfXh93kmdxmtgZ+4v2ciDFVuJv3yTC33G8TQxmdKdWui0jywqlYqDu9bwYcvuhIS+SylPfzr3n4hSEcHZU79o3a5spVo0bdePirlm23MKrf0hQRWqU6JkKVzdfWnVaTDJifE8uHtdr/hexXEFaNS0Nc1af4ZfYBmd48kd266wzRptdcDgkURHR74gNs121PNZOzqUo60e3L+HTt36qNtqv4HDuXr5b67q2FYNObYX7SO3rH6kaYtPKOXuQbv2XfHy8WPvs36koO81L4bex72KsQGgUdNWNG/9Kf6BwVrzyY8c04Iz5LIz5DpnyMdV+t+C1bes/Ax1zDd0RkZGhfb4L5KT90I0bNgwtmzZwooVK/jzzz/x9fXl/fffJzo6e3Zw/PjxzJs3jxMnTnD//n1at27NrFmzWLt2Lbt372b//v3MnTv3pcQT/vgRSkU05XMMHlZWxfALCNLaOaelpXHzxjWNbYyNjSkfUll9Yn7rxjXS09M10pQq7YFjCWeuXn5xp5+1jwrP7aOS1riuXrn03CAYUqmqzl8W6OOJutyqqF/LKrdrV/7Oc5u0tDRu3bimsY2xsTHlQirnOxAmJiZgaWlJkSKv5o4X2+ohRB76TeO1iAPHsaseAoCRqSk2lcoQefBEdgKVishDJ7CtXlGvfUWGPyRWGUlQ+Wrq1yysiuPlV45bV88V+D3klp6WxrEDW7CwLEZpT3+dt3udx1VfWW21wnNtNZirVy5pje3mjat5ttWsbbLaaoU82uq1y3nn+6bEpss+crt25aJGeoCKlULV6QvyXrXHZrh93KsaG/4tOaYFZ8hllzMvw6xzhntcpf8tWH0Dwx7zhchJTt4LSUJCAvPnz2fatGl88MEHBAcHs2jRIiwsLFiyZIk63aRJk6hZsyYVK1aka9euHDlyhPnz51OxYkVq165Ny5Yt+eWXvGcpU1JSiI2N1Xikpmi/9EepyPzSIPf9Sza2duq/5RYXG0NGxlNsbe20bqNURGFiYvrc/T22dtrzfX4fGdjk2odtPnEpFdHPxWRra4dCkfflWf+GQhGVmb9d7jKwf2G5Pf+etG8TG6Nk87oVvNfw45cQdd7MnB1JCY/UeC0lPBJTm+IYm5tR1NEOYxMTUp5E5UoThVlJR732FavM3I+1rYPG69Y29sQoo/LaRC/n/zjKgE9r0K9tKAd3rWbguAUUs7Z78YbPvK7jWhBZednkaquZdTy/2DKwtX1+m6z8FIporW1VW75vSmy67COv95I7vWbfpv97zS82Q+3jXtXY8G/JMS04Qy67nHkZap0z1OMq/W/B6hsY9pgvRE5y8l5Ibt68SVpaGjVr1lS/ZmpqSmhoKJcvX1a/Vr58efW/nZ2dsbS0xNvbW+O1J0+e5LmPyZMnY2Njo/FYvDB7lv7oLwf4tEVD9UPXS7H/647+sp/PWryvfjx9+vSV7zMxMYFvxg+nlLsnrT/t/Mr39yqcPLqbAZ/WUD9edX0LKFuV0dM3MOybFZQJqcmPM4YRm88974VxXHV15JcDtGvRUP0wpLZqyLGJgpGxQbxuUucKRvrfgjPkMf9NY2RsXGiP/yJZbd7AmZqaqv9tZGSk8TzrtYyMjDy3HTlyJIMGDdJ47cb97G96q1arqbHKZ1paGpD5baadffZsaIxSgad33queFre2wdi4yHMLjsQoFepvzG3tHEhPTyMhPk7jW1ulQvHct+ra92GssfgHgFKpfXtbO/vnYlIqFdjZ6T7zqk3VarU0VhFNV5ebAjv77NnnGGX0C8vt+fcU/dx7SkpMZNKYIVhYWDJs9CRMTF5ds00Jj8TMWXMG3czZkbSYODKSU0iNVJCRno6Zk0OuNA6kPNacsc+tQtU6ePllr/aenpYKQKwyChu7EurXY2Oi9bq8XRszcwucXNxxcnHH2788Y/o25teD2/igedc807/u46qP0Go1NVYyzmqrMYpo7HO0VaVSgVe+sRmjVGp+gZGzHdnZ2Wttq3Za4jfk2PTdR26Z/Yhmes2+zV7v95pfbIbTx72eseHfkmNacIZWdm9anTOU4yr9b8HbqiGP+ULk57/5lYUB8PHxoWjRovz666/q19LS0jh9+jTBwQVbRCU3MzMzrK2tNR5FzczUf7ewtMTFtZT6UdrdE1s7ey6c+1OdJjExgetXLxOgZZENU1NTfHz9uXD2jPq1jIwMzp/9E/9n23j7+mNiYsL5HPk+fHCPyIhwAoJevHhH1j7On83ePiMjgwtn/9QaV0BgsMb+AM79dUYd07+Ru9xKqcstuwyyys0/sKzW9+SdR7nlfk+JiQlMHDMYE1NTRoydTNGiZnll99Iofz+LQ93qGq851nsLxe9nAVClpRHz50Uc69bITmBkhMO7NVD+/le+eZtbWKlPpp1c3HEp7YO1rSNXLpxSp0lKjOf29Qt4B1R4ae8pS4ZKpf7CIC+v87jqS1tbPf9cW71EgJZFmDLbUcBz7ej82TPqbfJrq/5BeedryLHpu4/c/APLaPSHAOf++kOd3rmki97vVXtshtvHvaqx4d+SY1pwhlZ2b1adM5zjKv1vwduqIY/5bxojY6NCe/wXycx7IbGysqJ3794MHToUe3t73N3d+fbbb0lMTKRr166cO/fyFuzSlZGREY2atGLz+pW4uJbCqWRJ1q1aip29A6E1sn8qbPyoLwitUZsPGzcHoHGz1sydORkfv0D8/APZFbaZlOQk6tb/4Nl7LUbdBh+yfNH3FCtWHEtLK5YsmE1AYBmdB6+Pm7Vizswp+Pj54+cfxK6wzSQnJ1OvfkMAZs/4BnuHErTv1B2ARh+3YPSIgYRt3UjlqtU5fvQQN29cpXf/weo84+JiiXzyhOjozBnjhw/vAZnf5trZ6/6NqZGRER81acUWdbm5sH7VkjzKbSDVatTmg8Yt1OU2b+ZkfPwC8PUPYnfYJlKSk3i3/ofAsxP30YNJSUlm2JDRJCYmkJiYAIC1jS1FihR5YWxFrCyx8nVXP7f0KoV1hUBSo2NIvv+IgEmDMHdz5lzn4QDc/XE9Hn0+JXDyUO4v34Lju9VxafUBpz/uqc7j9qxlVFg6FeWZv4k5fR7PAR0xsbLg/oqtOpdZVrnVa/QpezYvwsnFHUcnN8LWfY+tXQlCQt9Vp5s5vgcVQ+vy7oefAJCclEjE43vqv0c+ecj921ewKmaDfQkXUpKT2LNlERWq1sHG1pH4OCWHf9qAMvoJlWvU1yu+V3FcARTRUSgV0Tx+9DCz3O/cwsLCEkcnZ4oXt9YptkZNWrJ5/SpcXEvhXNKFdauWYG/vqBHbuFGDqFajVo622oq5Myfj6xeAn38QO8M2k5KcrNFW6zX4kGWLfqBYMWssLS1ZvGAOAYFldP4gYsixvWgfs2d8g4ODI591yvy5pkYft2DMiM8J27pBox/p9awf0fW96sLQ+7hXMTbAv28LckwLdkwNvewMuc4Z8nGV/rdg9S0rP0Md84XISU7eC9GUKVPIyMigffv2xMXFUaVKFfbt2/dKLpHTVdOWbUlOTmLB3OkkJMQTGFyOMROnacz4Pn70D3GxMernNd+uS0yMkvWrl6JUROPl7cvoCdM0Lhnq3L0fxkbGTP9mLGlpaYRUqkr3Pl/oHFett+sSGxPD+tXLUSii8fL2YeyEqep9REQ8wcgo+0KSwOCyfDF0NGtXLWX1isW4uLkxYvREPDy91GlO/36CubOyfzN9xtSJALRp15FPPu2ke6EBTVu2IyU5mYU5ym30xOka5Rb+6B9iNcqtHrE5ys3T25cvJ0xXv6dbN65x/Wrmiqn9urXV2N8PSzfg5OzywrhsKpelxsFV6ufB00cBcH/lVs53HYmZSwksSmfnk3TnAac/7knwjJF49u9A8oPHXOg5msgD2T+T8mjTXoqWsMd/3ADMSpYg9txlTjXqRuoT/ReZe79pJ1KTk1i9YCKJCXH4BlZkwJgfMM1RbpGP7xMfl31J2t2bF5k5rrv6+ablMwCoUacxnfpPxNjYmMcP7/D74cHExyqxKm6Lp28Zhk5aiqu77pe9wqs5rgD794axae1y9fOxw/sD0HfgSN7N8SEzP81atiUlOVndVoOCyzFm4re52upDjdgy25GSdauXqdvqmAnf5mqrfTEyMmZajrbao89AncvMkGN70T4iI8IxzvHTM5n9yBjWrlrCmmf9yPDRk/DwzF53RJf3qntshtzHvZqxYf/eHWzM0RbGDB8AQN+BIzROuLSRY1rwY2rIZQeGXucM97hK/1uw+gaGPeYbtP/oveeFxUilUqkKOwjx+vx943Fhh5AvY/K+f98QPOXFM92F5V7Q24UdglZWf50t7BC0cjCPLewQtDLktiAKzgjDHXIzDPhOOkMuN0OODUCF4V5aasixGXIfbMjlZsgMuY8r5+tc2CEUWNSEHoW2b4exPxbavguL4dZiIYQQQgghhBBCAHLZvBBCCCGEEEKIAvivLhxXWGTmXQghhBBCCCGEMHAy8y6EEEIIIYQQQm85F2gUr56UthBCCCGEEEIIYeDk5F0IIYQQQgghhDBwctm8EEIIIYQQQgj9yYJ1r5XMvAshhBBCCCGEEAZOZt6FEEIIIYQQQujNyFjmgl8nKW0hhBBCCCGEEMLAycz7f4wRqsIO4Y1lTEZhh6CV1V9nCzsErRIqhhR2CFo5Xj5S2CGIV0CF4d5/Z8h9sCHHZsjHVBSc1Ln/P4ZcboZc395kRnLP+2slM+9CCCGEEEIIIYSBk5N3IYQQQgghhBDCwMll80IIIYQQQggh9Gckc8Gvk5S2EEIIIYQQQghh4GTmXQghhBBCCCGE3mTButdLZt6FEEIIIYQQQggDJyfvQgghhBBCCCGEgZPL5oUQQgghhBBC6M9Y5oJfJyltIYQQQgghhBDCwMnMuxBCCCGEEEIIvRkZyYJ1r5OcvAsNKpWK9auXcWDfLhIT4gkMKkuPvoNwdSuV73Z7d21j+5b1KBXReHr50q3XAPwCgtR/T01NYfni+Rw/eoj0tFRCKoXSo89AbO3sdY5tz65tbN+y4dk+fOjWawD+OfaR26/HDrNu9VKehD/GxbUUHTr3oHLV6uq///brUfbt3cnNG9eIj4tl5pxFePn46hyPPu8/txPHDrNu9RJ1bO0799SIraDHIS8qlYqd6+dz7OetJCXG4RMQQrseo3B29dC6zbWLZ9gftoJ7ty4To4ig97CZhFSrq5Fm54b5nD6+D0XUY0xMTHH3DqZpu354+ZfTKS77WlXwHtwVm0plMXd14o8WfQjfcTD/bd4OJXj6CIoF+5F8/xE3Js/nwcptGmk8erfDe1BXzEqWIPb8FS4OnEjM6Qs6xZRb5nFYys/PjkNAUDmd20OYuj740LXX58+1hxWLf3jWHtKoUKkqPfp8oVd7MOS2avixvfxjun/vDo4fOcitG9dISkpk5YZdWBUrrnNcIH1cQfu47PwMs60actkZcmyG3I9IuRWs3LLjM8w+2JD7ESGyyGXzQsO2zevYvXMLvfoOYsrM+ZiZWzBxzFBSU1O0bnP86CGWLfqB1u06MX3OIjy9fJgwZihKpUKdZtmi7/nj1AmGjhzPxCmziY6OZOrXY3WOK3Mf82nTriMz5vz4bB/DNPaR05VLfzPz24nUa/AhM+YsolqNWkyZNIa7d26r06SkJBMUXJYOnXvoHIf22PJ//8/HNoF6DT5ixpzFhNaoxdRJo7l755Y6TUGOgzb7ti/n0J61fNrzS0ZMXoWZuQVzJvYhLZ+8UlOSKOXpT9vuI7WmcXb1oG23EYyduZmhk5bh4OTKrIm9iYuJ1imuIlaWxJ6/yt8DvtIpvYVnKaruWEjU4ZMcr9KE23NXUG7hJBzr11KncWn1AUHTRnJ90vccD21G3PkrVNu9hKIlCjZAbt+8jj07t9Kz72Amz1yAubk5E8cMyfc4/Hr0EMsXfU/rdh2ZNmcRHl4+TBwzhBiN9jCPP06dYMjIr5gwZTaK6Ei+/XqMXrEZals19Nhe1TFNTUkhpFIozVt/plc8WaSPK3gfB4bbVg257Aw5toLm9fo+j0i5FaT/BcPtg19lbC9jzDdoxsaF9/gP+m++6zfIrl27sLW15enTpwCcPXsWIyMjRowYoU7TrVs3Pvus4J1VFpVKxa6wzbRs057QGrXw9PJhwOCRREdHcuq341q327ltE/UbfkS9+h9Q2t2Tnv0GYWZuzqH9ewBISIjn4P49dOrWh3IVKuHjF0C/gcO5evlvrl65qFNsO3Lto9ezfRzcvzfP9Lt2bKFi5VCatfiE0u4etGvfBW8fP/bsyp6lrVO3AW3adaRCSGU9Skn/968ttqYtPqGUuwft2nfFy8ePvc9iK+hxyItKpeLgrjV82LI7IaHvUsrTn879J6JURHD21C9atytbqRZN2/WjYq7Z9pxCa39IUIXqlChZCld3X1p1GkxyYjwP7l7XKbaIfUe5Nm4W4WE/65Teo8cnJN1+wOVhU4m/cou7P6zh8ZZ9eH3eSZ3Ga2Bn7i/ZyIMVW4m/fJMLfcbxNDGZ0p1a6LSPnDKPwyaN49B/8CgU0VEvaA8bea9hI+rW//BZfRj8rK5mt4dD+/fQqVtfdXvoO3AEVy//zTUd24Mht1XDj+3lH1OARk1b0bz1p/gHBusUS27SxxWsj8vOzzDbqiGXnSHHZsj9iJRbwcotOz7D7IMNuR8RIic5eTdwtWvXJi4ujr/++guAI0eO4OjoyOHDh9Vpjhw5Qp06df71vsIfP0KpiNb4oGdlVQy/gGCuXrmU5zZpaWncvHGV8jm2MTY2pnxIZfU2t25cIz09XSPfUqU9cCzhzLXLeef7/D6uaWyfuY9KWgeNq1cuPfeBNaRS1ZfeUery/nO7duWiRnqAipVC1ekLchy0iQx/SKwykqDy1dSvWVgVx8uvHLeuntMrr/ykp6Vx7MAWLCyLUdrT/6Xlm5Nt9RAiD/2m8VrEgePYVQ8BwMjUFJtKZYg8eCI7gUpF5KET2FavqPf+so5D+eeOQ5DWepdVV/OqD1l1L6s9lM+jPVy9rFv9NNS2+qbE9rKP6b8lfVzB+7ic+RlaWzXksjPk2Aqa1+v7PCLlBvr3vznjM7Q++FXG9jLGfCFykpN3A2djY0NISIj6ZP3w4cN88cUX/PXXX8THx/Pw4UNu3LjBO++889y2KSkpxMbGajxSU7Rf+qNUZF7ubJPrHhxbWzsUirwvhY6LjSEjIwNb2+e3ycpPoYjGxMT0uXuPbO2055vXPmxs7bTuI6/3YptHeoUi78vaCkqX9593bJrpbXKkL8hx0CZWGQmAta2DxuvWNvbEKKP0yisv5/84yoBPa9CvbSgHd61m4LgFFLO2e/GGBWDm7EhKeKTGaynhkZjaFMfY3IyijnYYm5iQ8iQqV5oozEo66r2/rOOQ+540m3yObWZ9ePpc3dM8vlFa24O2fLXFZmht9U2J7WUf039L+rjsPPXt43LmZ2ht1ZDLzpBjK2her/PziJTbszR69L854zO0PvhVxvYyxnxDZ2RsVGiP/yI5eX8DvPPOOxw+fBiVSsWxY8do3rw5QUFBHD9+nCNHjuDq6oqfn99z202ePBkbGxuNx6KFc9V/P/LLAdq1aKh+PH2a/jrflngFTh7dzYBPa6gfr/qYBpStyujpGxj2zQrKhNTkxxnDiNXxnndDc/SXA3zaoqH6YUjtwZDbqiHHZsjHVBScHNf/P4bcjxgyQy83Q26rhhybEPmR1ebfAHXq1GHp0qWcO3cOU1NTAgMDqVOnDocPH0ahUOQ56w4wcuRIBg0apPHazfvZJ1ah1WpqrGSclpYGQIwiGnv77JlapVKBl3feKxQXt7bB2NgYpVLzhE2pVKi/vbSzsyc9PY2E+DiNbx6VCgV2Oqy0mbWPmFyLweTcR262dvbPLR6jVCqws3u5s8K6vP+8Y9NMH5Mjfdb/9TkOWSpUrYOXX/Zq7+lpqQDEKqOwsSuhfj02JvqlXN5uZm6Bk4s7Ti7uePuXZ0zfxvx6cBsfNO/6r/POLSU8EjNnzRl0M2dH0mLiyEhOITVSQUZ6OmZODrnSOJDyWHPGPi9Vq9XUWB02qz0oFdHY5TgOMUoFnvm2hyLP1T3N4+ugtT1oqzOG3FYNObbXdUz/LenjsvN8UR8Hht1Wn9+HYZWdocZmyP2IvvvI7b9cbobcB78p/cgbwUjmgl8nKe03QNZ979999536RD3r5P3w4cNa73c3MzPD2tpa41HUzEz9dwtLS1xcS6kfpd09sbWz5/y5P9VpEhMTuH71EgFaFgAxNTXFxzeA82ezt8nIyOD82TPqbbx9/TExMdHI9+GDe0RGhOMf9OKFRTL34f/cPi6c/ZOAwDJ5bhMQGKyxP4Bzf53BX0v6gtLl/efmH1iGC8/F9oc6vXNJF72PQxZzCyv1ybSTizsupX2wtnXkyoVT6jRJifHcvn4B74AKer/fF8lQqdRfGLxsyt/P4lC3usZrjvXeQvH7WQBUaWnE/HkRx7o1shMYGeHwbg2Uv//1wvy1tYcLzx2Hy1rrXVZdvXD2jPq1zPrwp7ru5dceAoLyzteQ2+qbGNvLPqb/lvRxuvdxYNht9fl9GFbZGWpshtyP6LuP3P7L5WbIffCb0o8IkZvMvL8B7OzsKF++PGvWrGHevHkAvP3227Ru3Zq0tDStM+/6MjIyolGTlmxevwoX11I4l3Rh3aol2Ns7Eloj++e4xo0aRLUatfiwcXMAGjdrxdyZk/H1C8DPP4idYZtJSU6mbv0PgMwFP+o1+JBli36gWDFrLC0tWbxgDgGBZbR2iLl93KwVc2ZOwcfPHz//IHaFbSY5OZl69RsCMHvGN9g7lKB9p+4ANPq4BaNHDCRs60YqV63O8aOHuHnjKr37D1bnGRcXS+STJ0RHZ87KPnx4D8j8FtzOXvdvQ1/0/mfP+AYHB0c+69RDHduYEZ8TtnWDRmy9nsWm63HQhZGREfUafcqezYtwcnHH0cmNsHXfY2tXgpDQd9XpZo7vQcXQurz74ScAJCclEvH4nvrvkU8ecv/2FayK2WBfwoWU5CT2bFlEhap1sLF1JD5OyeGfNqCMfkLlGvV1iq2IlSVWvu7q55ZepbCuEEhqdAzJ9x8RMGkQ5m7OnOs8HIC7P67Ho8+nBE4eyv3lW3B8tzourT7g9Mc91XncnrWMCkunojzzNzGnz+M5oCMmVhbcX7FVr3LLKrtGTVqxef1KXFxL4VSyJOtWLcXO3kHjOIwf9QWhNWrnaA+tmTtzMj5+gfj5B7IrbDMpyUka7aFugw9Zvuh7ihUrjqWlFUsWzCYgsIzOH0QMua0afmwv/5gCKKKjUCqiefzoIQB379zCwsISRydnihe3fmFs0scVrI/Lzs8w26ohl50hx2bI/YiUW8HKLTs+w+yDDbkfESInOXl/Q7zzzjucPXtWPctub29PcHAw4eHhBAQEvLT9NGvZlpTkZBbMnU5CQjxBweUYM/FbihbNnrF//OghsbEx6ue13q5LbIySdauXoVRE4+Xty5gJ32pcDtS5e1+MjIyZ9s1Y0tLSCKlUlR59BuocV+Y+Yli/ejkKRTRe3j6MnTBVvY+IiCcY5bhsJzC4LF8MHc3aVUtZvWIxLm5ujBg9EQ9PL3Wa07+fYO6sqernM6ZOBKBNu4588mknPWPT/v4jI8IxNspeVCMztjGsXbWENc9iGz56Eh6e3uo0uhwHXb3ftBOpyUmsXjCRxIQ4fAMrMmDMD5jmyCvy8X3i47Iv+7p78yIzx3VXP9+0fAYANeo0plP/iRgbG/P44R1+PzyY+FglVsVt8fQtw9BJS3F11+3STZvKZalxcJX6efD0UQDcX7mV811HYuZSAovSLuq/J915wOmPexI8YySe/TuQ/OAxF3qOJvJA9k+4PNq0l6Il7PEfNwCzkiWIPXeZU426kfqkYIvzNW3ZluTkJPVxCAwux5iJ03K1h3+Iy9Eear5dl5gYJetXL1XXh9ETpuVqD/0wNjJmeo720L3PF3rFZqht1dBje1XHdP/eHWxcu1z9fMzwAQD0HThC4wOmNtLHFbyPA8Ntq4ZcdoYcm655Fd7nESm3gvS/YLh98KuM7WWM+QbtP7pwXGExUqlUqsIOQrw+F288KuwQ8mWE4VZHFYbbOUUk2xZ2CFolVAwp7BC08rh8pLBD0MqQ24KhM+S2akxGYYeglSGXmyHHJm1ViGyG3FYNWVnfkoUdQoHFLxhZaPsu1mtyoe27sMjMuxBCCCGEEEIIvRnJgnWvlZS2EEIIIYQQQghh4OTkXQghhBBCCCGEMHBy2bwQQgghhBBCCP3JgnWvlcy8CyGEEEIIIYQQBk5m3oUQQgghhBBC6M3IWOaCXycpbSGEEEIIIYQQwsDJzLsQQgghhBBCCP0ZyT3vr5PMvAshhBBCCCGEEAZOTt6FEEIIIYQQQvxf+/777/H09MTc3Jxq1apx6tQprWmXL1+OkZGRxsPc3FwjjUqlYuzYsbi4uGBhYcF7773H9evXX+l7kJN3IYQQQgghhBD6MzYuvIceNmzYwKBBgxg3bhx//vknFSpU4P333+fJkydat7G2tubRo0fqx927dzX+/u233zJnzhwWLFjAyZMnsbKy4v333yc5OblARakLI5VKpXpluQuDk7x1dmGHkK9TAd0KOwStnM2jCzsErVJVRQs7BK2MMNwu5m7QO4UdglZ1jk4t7BDyFeUYUNghaBWNY2GHoFXxInGFHYJWcU+LF3YIWu0751DYIWhVMzipsEPIl5Wp4cYXeH9vYYeg1RpV+8IOQasG7pcLOwStYo3sCjsErQx5zP8o7Wphh1Bgicu/KrR9W3Yap3PaatWqUbVqVebNmwdARkYGpUuXpn///owYMeK59MuXL2fgwIEolco881OpVLi6ujJ48GCGDBkCQExMDM7OzixfvpxPPvlE/zekA5l5F0IIIYQQQgihPyOjQnukpKQQGxur8UhJSXkuxNTUVM6cOcN7772nfs3Y2Jj33nuP3377Tetbi4+Px8PDg9KlS9OkSRMuXryo/tvt27d5/PixRp42NjZUq1Yt3zz/LTl5F0IIIYQQQgjxRpk8eTI2NjYaj8mTJz+XLjIykqdPn+Ls7KzxurOzM48fP84z74CAAJYuXUpYWBirV68mIyODt956iwcPHgCot9Mnz5dBfipOCCGEEEIIIcQbZeTIkQwaNEjjNTMzs5eSd40aNahRo4b6+VtvvUVQUBALFy5k4sSJL2UfBSEn70IIIYQQQggh9Gak58JxL5OZmZlOJ+uOjo4UKVKE8PBwjdfDw8MpWbKkTvsyNTWlYsWK3LhxA0C9XXh4OC4uLhp5hoSE6PgO9CeXzQshhBBCCCGE+L9UtGhRKleuzMGDB9WvZWRkcPDgQY3Z9fw8ffqUCxcuqE/Uvby8KFmypEaesbGxnDx5Uuc8C0Jm3oUQQgghhBBC6M/ozZgLHjRoEB07dqRKlSqEhoYya9YsEhIS6Ny5MwAdOnTAzc1Nfc/8hAkTqF69Or6+viiVSqZNm8bdu3fp1i3zl7GMjIwYOHAgkyZNws/PDy8vL8aMGYOrqytNmzZ9Ze9DTt6FEEIIIYQQQvzfatOmDREREYwdO5bHjx8TEhLCTz/9pF5w7t69exjnuAVAoVDQvXt3Hj9+jJ2dHZUrV+bEiRMEBwer0wwbNoyEhAR69OiBUqmkVq1a/PTTT5ibm7+y9yEn70IIIYQQQggh9GdsVNgR6Kxfv37069cvz78dPnxY4/l3333Hd999l29+RkZGTJgwgQkTJrysEF/ozbjOQQghhBBCCCGE+A+Tk3chhBBCCCGEEMLAyWXzQsP63y6w4uhZIuMT8S/pwIiPa1OutPMLt9t77joj1h/g3WAvZrX/QP36/J9P8dP5GzxWxmNapAjBbiXo16Aa5d1fnGduKpWKHesXcOzANhIT4/ANrMCnPUbh7OqudZtrF8+wL2wld29eJkYRSZ/hM6hY7V3139PT09i+9gf+/vNXIsIfYGFZjKDy1WjRfgC29iV0jm33zjC2bdmIQhGNl5cPPXr3wz8gUGv648eOsGbVcp6EP8bV1Y2OXbpTpWo19d/Xrl7BsaOHiYyIwMTUBF9fPz7r0IWAwCCdY8qiUqnYsHopP+/bSWJCPAFB5ejRdxAubqXz3W7vrq3s2LIepSIaDy8fuvb6HL+A7Pt8DuzdwbEjP3P7xjWSkhJZsWE3VsWKFyi+9auX8vO+XRrxubqVekF82wh7Fp+nOr7s8klNTWHF4h84fvQQ6WlpVKhUlR59vsDWzv6FMdnXqoL34K7YVCqLuasTf7ToQ/iOg/lv83YowdNHUCzYj+T7j7gxeT4PVm7TSOPRux3eg7piVrIEseevcHHgRGJOX3hhPLlt+PkEK/ceJSomDn93F4Z91oSy3nkfz62HT7LrxJ/cfJD58yhBnm70a9lQI31UTBxzNu7lt4vXiE9MpqK/F8M/a4J7SUe9YwMI27WHTVu3Ea1Q4uPlSd+e3QkM8M8z7Z2791ixZi3Xb9wk/EkEvbt3oXmTj/9VnvlRqVRsWrOEg/t2kpAQR0BQObr1GfLC9rBv1xZ2bl2nbg+de36Bb4728OO8b/n77B9ER0dibm5JQFBZ2nXqjVtpD51j27lzJ1s2b0ahUODl7U3v3r0JCAjQmv7YsWOsWrmS8PBwXN3c6NK5M1VDQzXS3Lt3j2VLl3LhwgWePn2Ku7s7X44ejZOTk85xZTHkslOpVPx1cC5XT28iNTkOJ4+KvPXxOGwcPXXa/tyRRZzZP5Pgt9pT/aNR6td/3T6Of27+RmLsE0yLWuLkXpEqDQdjW8Jbr9jC1i3g6M/bSEzIHLva98x/7Lp68Qz7tq/kzrOxq++IGVTKMXYBnPntIIf3beHuzcskxMcwbuY63L201xdtsW1as5hD6mNanq46H9O1xCiicffyfe6Y5sx/yvghnDvzO4O/nEzVGm/rHNv6w3+w4sBvRMXG41/KmeFt3qecp1ueaQ/+dYUlP/3KvYho0p9m4O5kT4f3qtGoWnl1mjErdrDz9/Ma270V7M0P/dvpHFPO9/Xrrjmc/3UTKUmxuHpXokHb8dg5eWrd5q+jazl7dB2x0Q8BcHDx460P++Bd5h11mn1rx3L3ygkSYp5gamaJm3dF3m46BIeSPjrHZsj9L7y6MX//3h0cP3KQW88+k6zcsEvnzySGPuYbOqM3ZMG6/xdS2kLtp/PXmb77V3rWq8L6fq0IcHGk99JdRMUn5rvdQ0UsM/ecoJKny3N/83C0ZeTHtdkysA3LezXD1a44vZfuJDo+Sf/4tq3g4O51fNZrFKOmrKComQWzJvYlLTVF6zYpKcmU8vSnXfcRef49NSWZe7eu8FGrboyZvpbew6YT/s9d5k0eqHNcx478wpJFC/ikXXu+m7sAT29vxo0ZgVKpyDP95UsXmT71a+o3aMisuQuoVqMm30wcx907t9Vp3NxK0bN3P+b+8CNTp83Cyakk40YPJyZGqXNcWbZvXsuenVvo0Xcw38xciJm5ORPHDCE1n3L79ehBViz6nlbtOvHtnMV4evkyacwQYnK8p5SUZCpWCqV568/0jkkzvnXs2bmVnn0HM3nmAsx1iu8Qyxd9T+t2HZk2ZxEeXj5MzBXfskXz+OPUCYaM/IoJU2ajiI7k26/H6BRTEStLYs9f5e8BX+mU3sKzFFV3LCTq8EmOV2nC7bkrKLdwEo71a6nTuLT6gKBpI7k+6XuOhzYj7vwVqu1eQtESL/4yIad9J88xc/0uejStx9qvBuBX2oW+05cQHRufZ/ozV27RsFoIPw7vwfLRfXC2t6HPtMU8UcQAmR+kBs1ZyYOIaL4b0JG1X32Oi6MtvaYtIiklVa/YAA4fPc7CxUv5rO0nzJ89E28vT0aO/QqFUpln+pSUFFxKlqRrxw7Y29m9lDzzs2PLGvbu3Ey3vkP4esaPmJtb8M3YQfnWtxNHD7Jy8TxatO3MlNlL8PDy5ZuxgzTqm7dvAL0GjmLm/DWMmjADlUrF12O/IOPpU53iOnLkCIt+/JF2n37K3Llz8fbyYszo0Si1vMdLly4xdcoUGrz/PnPnzaNGjRpMnDiRO3fuqNM8+ucfhg4ZQqnSpZk6dSo//PADbdu1o2jRojrFlJuhlh3AhWOLufTbat5qMp7GvTdgamrJvuXdSU/THluWiAcXuHp6A3Ylnz/xdXAtQ+3mX9N84G4adFqEChX7lnUjI0P32PZuW8HPu9fRvucovpy6AjMzC2ZOyH/sSk3OHLs+65H32AWQkpKEX1AILTsM0DmW3HZsWcNPOzfTre9QJs1YhJm5OZNfeEx/ZtXiubRs24XJs5fi4eXL5FzHNMuesA0U5G7YfX9cZMaWA/T8qDbrRnXDv5QzfeasIzo2Ic/01lbmdPugJiuHdmbT6O40qVGBcSt3cuLSTY10NYN9+HnKQPVjSpdmBYgOTh1YxJ+HV1G/7Xg+HbqRomYWbJrbNd/6Vty2JO80HUKHEVtpP3wLHv7V2bagL5H/XFenKelehg/aT6bL2D206rck88uVuV11rm+G3v/CqxvzU1NSCCngZxJDHvOFyE1O3t8wCQkJdOjQgWLFiuHi4sKMGTOoU6cOAwcO/Nd5rzp2juZVg2laJQgfZ3tGN30H86ImbP/jitZtnmZkMGrDz/R+ryql7K2f+/uHIf5U9y1NKXsbfJ3tGfJRTeJTUrn+OEqv2FQqFQd3reWjlt0ICa1DKU9/ugyYgDI6gr9OHda6XblKNWnWri+VqtfN8++WVsUZNH4+VWs2oKSbJz4B5WnbbTh3b14mKuKRTrGFbdtCg4Yf8l6Dhri7e9Cn30DMzMz4ef9PeabfGbaVSpWr0rxlG0q7e/BZh854+/iye2eYOs0779YjpGJlSrq44u7hSdcevUhMTOTO7Vs6xZRFpVKxO2wTLdq0J7RGbTy9fOg/+EsU0VGc+u241u12btvIew0bUbf+h5R296RHv8GYmZtzaP9udZpGTVvTrPVn+AWW0Sum3PHtCttEyzbtCa1R61l8o/SOr+ez+A7u3wNAQkI8h/bvoVO3vpSrUAkfvwD6DhzB1ct/c+3KxRfGFbHvKNfGzSI87Ged3odHj09Iuv2Ay8OmEn/lFnd/WMPjLfvw+ryTOo3XwM7cX7KRByu2En/5Jhf6jONpYjKlO7XQaR9Z1uw7RrN3QmlSuyrebs582bEZ5kVNCTt6Os/0X/dqS+t6NQjwcMXL1YmxXVqiUqk4dekGAPfCI7lw8x6jOjaljHdpPF1KMKpDM1JS0/jp97N6xQawZXsYH7zfgIb16+HhXprP+/bGzMyMfQfynsUI8PejR5dOvPtObUxN874YTN88tVGpVOwJ20TzNh2oWr02Hl6+9B00GkV0FKd/O6Z1u93b11Pv/ca8W/8jSrl70a3vUIqamfPLgV3qNO81bEJw2RCcnF3w9g2gTfvuREU84cmTxzrFtm3bNhp+8AENGjTA3cODfv37Y2Zmxv79+/NMHxYWRuUqVWjZsiXu7u506NABHx8fdu7cqU6zYsUKqlStSteuXfHx9cXF1ZXq1atja2urW4HlYMhlp1KpuPjrSirU6YVHcD3sSwbwdqspJMU94d7l/NtwWkoCRzYOpWbTCZhZPD+GBYa2pqRXVYrbueHoVobK9T8nIeYR8YqHOsf28661NGrVjYrV6lDa05+un2eOXX+ePKx1u3KVa9L8U+1jF8BbdRrxcZseBFeopjXNi2LbG7aRZm06UkV9TMegiI7kj3yP6Qbqvt+YOhrH1IzDOY4pwJ1b19i9bT29Bo7SkpN2qw6epHnNijR9KwQflxKMbvsh5kVN2f7b2TzTV/X3pG5IIN4ujpQuYc+ndUPxc3Pmrxv3NdKZmhbB0aaY+mFtZaF3bCqVijOHVlK9YW/8KryHU6lAPuz4LfExT7h+Tnt98y1fF++y72Dn5Im9sxe1m3xBUTNL/rmd/Z4q1GpDab+q2DiUwtm9DLUaDyRO8YiYKN3qmyH3v/DqxnyARk1b0bz1p/gHPn8FyIsY8pj/RjA2KrzHf5CcvL9hhg4dypEjRwgLC2P//v0cPnyYP//881/nm5b+lMv/RFDdN/uyJWNjI6r7lOL8Pe0foBYe/AM7KwuaV31xZ5mW/pQtpy5S3Lwo/i4OesUXGf6QGGUkQTk+pFhaFcfbryy3rp7PZ0v9JSXGY2RkhKXViy+3SktL48aNa4SEVFK/ZmxsTIWQSly5cinPba5cuUSFipU0XqtUuarW9GlpaezbuxsrKyu8vHS/dA7gyeNHKBXRlA+pon7NyqoYfgFBXLvyt9b93bpxTWMbY2NjyoVU5qoOJ776CFfHV/m5+LTtKy0tjZs3rmlsY2xsTPmQyuoT81s3rpGenq6RplRpDxxLOHP18st9DwC21UOIPPSbxmsRB45jVz0EACNTU2wqlSHy4InsBCoVkYdOYFu9os77SUtP5/Kdh1QL9lO/ZmxsTLUyvpy/eU+nPJJT0kh/+hRrK0sAUtPSAShqaqqRZ1FTE85eu6NzbJB5bK7duEmlkOxLVY2NjakUUoFLV67qlderyPNJ+D8oFVGUC6mqfs3Sqhi+AcFc19Ie0p+1h3LPtYcqXNdSR5OTkzj88x6cnF1wdHzx5elpaWncuH6dkJAQjX2EhIRw5fLlPLe5cvkyFXOkB6hcubI6fUZGBqdPn8bNzY3RX35J208+YeDAgZw4cSKP3F7MUMsOIE7xgKT4SFx9aqhfK2penBKlyvPk3rl8t/1t50RKB7yDm+9bL9xPWmoi189spZhdKaxsSuoUW2T4Q2IUkRon2Flj182XPHbpK/uYZh+frGOqbXxIT0vj9o2rGvUg65jm3CYlOZm5076iS+/B2NrpN96npT/l8r1HVAv0yrEPI6oFenL+1otPYlUqFSev3OZOeBSV/DRvTfjj2l3eHTqTJuN+4Ou1e1C+4MrCvMREPSAhNgKPwOw6Y2ZRHBfPCvxz6y+d8sjIeMrlP3aTlpqIq3feY0BqSiJ//74VG4dSWNu9uL4Zev8Lr27Mf91e15gvRF7knvc3SHx8PEuWLGH16tXUq1cPyJxZKVUq7/uEUlJSSEnRvAxJlZaOWR7frioSk3maocKhmKXG6w7FLbgdkffl33/eecS2Py6zcUDrfOM+cvkOw9fvJzktHcfiVizo0hg7Pb/tjlFmztRb22heblTc1oEYRaReeeUnLTWFLatmU7VWQywsi70wfWxsDBkZGdjmutzM1taOh/fv57mNUqHA1jZ3elsUimiN106f/J1pUyeRkpKCnb09E76eirWNjV7vR6HILLfc8dnY2qPMtb8scbExZGQ8xea5GO15eF+3E0RdZcWQ+z50G1u7F8aXuwxtbO3U8SkVUZiYmD53v5utnfZ8/w0zZ0dSwjXrYUp4JKY2xTE2N8PUzgZjExNSnkTlShOFVYDu984q4xJ5mpGBvY1m3bS3Ls6dRxE65TFn0x5K2FpTLdgXAE8XJ0o62DJv016+7NQcC7OirNl3nPDoGCJiYnWODSAmNo6MjAzscs3s2tnacP/BA73yehV5Zh373HXbxtYOpTLvehGrbg+566g9/zy4q/Havt1bWbNsPinJSbiWcufLSbMwyfGliDaxsbGZ7zF3P2Jnp/U9KhSK5/sdOzsUisz+WqlUkpSUxKaNG+nQsSOdu3ThzJkzfD1pElOmTKFc+fJ5ZauVoZYdQFJcZtuzKKZ5kmhezJGkeO3t4tb53UT9c4nGvTflm//l39dyet8M0lMTsXH0omHnJRQx0e3WA21jl7WtA7HKlzd2FUT2MX3++CiVeV8dFxur1HpMHz7IHh9WLp6Df1BZqlSvrXdcivjEzM8j1lYarztYF+NOuPar9uKSkmkwcjZpaU8xNjZiVNsPqBGU3b/WDPahXkggbo623I9QMC/sF/rOW8/KYZ0oYqz7XFZCTGadsrLWrG9W1g4kxOZ/TCMeXmXN9E9IT0uhqJklTXt8j6OLr0aav46s4cj26aSlJGLv7EWrAct0qm+G3v/CqxvzX7fXNeYLkRc5eX+D3Lx5k9TUVKpVy/4G397eXuuCRpMnT+arrzTv3/my9fuMbvNBnun1kZCSypcbf2Zc8zovPBGv6uPGxv5tUCYmseX0JYau28/qPi2e+6Igp9+P7GH1wq/Vz/t/Oedfx/wi6elpLJw+HFTwWc+Rr3x/L1KuQgVmzVtIbGwM+3/aw9TJk5j+3dznBrCcjv6ynx/nzVA/Hzl+6usIVWdHfznAwhzxjRo/pRCj+e9ZtusX9p08x48jemJWNPPEyNSkCNP7t2fCks3U6fsVRYyNCQ32pWb5AFQqVSFH/O8c+2U/i76fpn4+Yty3r3R/tes0oHxIVRSKKHZtXcesKWOYMG0+RYuavdL95iXr2FWvUYNmzTLv6/Xx8eHypUvs2bPnhSfvhlx2N8/u5New8ern9TvM13t/8cpH/L5rMg27LMHENP/j4xPSGFfft0iKi+DC8WX8sv4LPuqxNs/tfj+yh5ULsseuz1/D2KWr47/s0zimw8dNyyd1wf1x8hgXz51hypxlryR/bazMzNgwqjuJKamcunqH6ZsP4OZoS1V/TwAaVs2+xcvPzQl/Nycajf2eP67d1Zjlz+3SqR3sXzdO/bxF74UFjtHe2YuOI7eTkhzHtT/3sWflcD75YrXGCXxw6Md4BtUkPiaC0z8vYefigbQbsu6F9dQQyZj/HyEL1r1WcvL+f2zkyJEMGjRI4zXV3kV5prWzNKeIsdFzi9NFxSXhWPz5k+z7UbH8o4hjwMrs+40ynn1YrPTlfMIGtaO0Q+YssWVRU9wdbXDHhvLuJWk8fQ3b/7hM1zqVn8s3S0joO3j7l1U/T0tLAyA2JlpjFfg4ZRSl9VxdNy+ZJ+4jiIp4xOAJC3WadQewtrbB2NgYpULz6gSlUoGtfd4n2bZ2ds8tZqdUKrHL9U20ubkFrq5uuLq6ERgYTM9uHTmwby+t2mhfGbdqtVoaK8KnPys3pUKBnX32yuExymg8vX2f2x6guLUNxsZFnlt8SKmM1mml9vxUrVZTY3XYNHV80djZZ89ixCgVL4wvdxnGKBXq+GztHEhPTyMhPk5j9l2pUPzr95CXlPBIzJw1V2Y3c3YkLSaOjOQUUiMVZKSnY+bkkCuNAymPdZ99sy1uSRFjY6JjNBeni46Nw8Em/9s8Vu49wrLdh1kwrDv+pTUXlwz2LMX6iQOJS0wiPf0pdtbF6DBhHkGe+a/+m5uNdXGMjY2fW8hIoYx5blb5deRZJVd7SEvLXIAvRpm7PSjw9Mq7vlmr24PmrFCMMvq5y4EtrYphaVUMF7fS+AeUocsnH3D6t6PUfKd+vnFaW1tnvsfc/YhCoXURKTs7u+f7HYVCXSbW1tYUKVIEd3fNy4ZLly7NxUt536KTkyGXnXtQXUqUzv7y4Wl6ZmxJ8VFYWmdfap8cH4m9S96/0BH1z0WSE6II+z77/lNVxlMe3/mDy7+vpeNX5zA2LgJkXoJf1Lw4No6elChdgTWTqnP30s/4VPjouXwrhL7DuBxjV7qWsSv2JY1d+qhcrRa+AdknsNnHNPq58cHDy++57QGsrW3zOaaZfevFc2cIf/yQLm0aaqSZOflLAoMrMG7KvHzjtCtmmfl5JNfidFGx8Thaax+bjY2NcHfKjCGwdEluP4pk6U8n1CfvuZUqYYddMUvuR0Tne/LuW74uLp4V1M+z6ltCbBTFbLLrW0JsFE6ltP/KDEARk6LYOWX+ikJJ97I8unuBM7+s5P12E9RpzCyKY2ZRHDsnT1y9KjB3SCjXzx4gqGqjfPM2tP4XXt+Y/7q9rjFfiLzIVyVvEB8fH0xNTTl58qT6NYVCwbVr1/JMb2ZmhrW1tcYjr0vmIXP2Lci1BCdvZt9PlpGh4uTNB5R3f/5eK68Stmz+vA0b+rdWP+oEeVHV240N/VtT0kb7AJuhUpGanv/KqeYWVji5uKsfrqW9sbF15Mr5U+o0SYnx3Lr+N94B+l3+mVvWifuTR/cYNH4BxYrb6rytqakpvr7+nDuXve5ARkYG58/+RaCWRVMCA4M5f1bzvrizf53Rmj6LKiNDPfBpY2FpiYtrKfWjlLsntnb2XDh3Rp0mMTGB61cv4x9YNs88TE1N8fb158LZ7G0yMjK4cPZPAv7F4nR5xVdaHV92+WXFp21fpqam+OQR3/mzf+L/bBtvX39MTEw4nyPfhw/uERkRTkDQv3sPeVH+fhaHutU1XnOs9xaKZwu+qdLSiPnzIo51s+/JxcgIh3droPxdt3skAUxNTAjydFMvNgeZ7/3UpRuU99H+s1PL9xxm8Y6DzBvchWAv7SfkxS0tsLMuxr3HkVy6/YA6lfRb+MfU1BR/Xx/+Opd9L29GRgZ/nTtPcGDBTlT+TZ4WlpaUdC2lfpRy98LWzoELZ/9Qp0lMTODG1Uv4aWkPJlnt4Zxmffv73Jl8F2tUPfvvRW026z36+vlx7uxZjX2cPXuWwKC8Tz4Dg4I4myM9wF9//aVOb2pqir+/Pw9yXdr68OFDnX4mzpDLztTMCmsHD/XD1skXi2KO/HPrd3Wa1OR4Ih6cx8m9Qp55uPrUoNmAMJr226p+OLqVxadCI5r226o+cdcWX8bTvH+JwcLCCmcXd/XDtbQ3NnaOXM5j7PL5l2OXviwsrfI8pn+f1Rwfbly9pHV8MDE1xcs3gL/PZdeDrGOatU2TVu35du5Kps5Zrn4AdOg2gN46LF5nalKEIHcXTl3N/gWWjAwVp67eobx33j8Vl5fMzxrpWv8erohFmZCIo3X+X3wWNS+GnZOH+uHg4ouVdQnuXc2+5zklKZ5Hd85pvX9dK1WG+suAPP+syryKJr80WQyt/4XXN+a/bq9rzH9jGBkV3uM/SGbe3yDFihWja9euDB06FAcHB5ycnPjyyy8x1uNerfy0r12BMZsOUcatBGVLO7H61/MkpabTtHLmN8lfbvwZJ2srPm9YAzNTE/xKan6jWNw8856srNcTU9NY/MsZ6gR54ljcCmViEut/+5snsQnUL5f3N6zaGBkZUa9RO3ZvXoyTizuOzq6ErZuPrX0JKobWUaebMa4nFau9S90PPwEgOSmRJ4+z7z2PfPKQe7evYlXMGocSLqSnp7Fg2jDu3bpC/1Gzych4qr6H3qqYjU73XDZp1oJZM7/F1y8Af/8AdoRtJTklmXr1M2cdvps+BXsHRzp27gZA4ybNGTV8ENu2bqJq1WocPfILN65fo2//LzJjTk5i4/q1hFavgb2dA7GxMezeFUZUVCS1ar+jNQ5t5fZRk1ZsWb8SF9dSOJV0Yf2qJdjZOxBaI/snTcaPGki1GrX5oHHmLFTjZq2ZN3MyPn4B+PoHsTtsEynJSbxb/0P1NoroKJSKaB4/yvzC5+6dW1hYWOLo5Ezx4s+v2qwtvkZNWrFZHV9J1q1amkd8XxBaozYfNm6ujm/uzMn4+AXi5x/IrrDNpCQnUbd+5i0hVlbFqNvgQ5Yv+p5ixYpjaWnFkgWzCQgso9NgX8TKEivf7JNhS69SWFcIJDU6huT7jwiYNAhzN2fOdR6e+d5/XI9Hn08JnDyU+8u34PhudVxafcDpj3uq87g9axkVlk5FeeZvYk6fx3NAR0ysLLi/YqtOZZXl0/drM27RRoK9SlHGuxRr9x8nKSWNj2tnLjo15scNONlZ079VZlks332Y+dv2803Ptrg62hOpjMt8T+ZFsTTPvAzzwKnz2BW3oqSDLTcePGbamp3UqVSGGmX1/x3fFk2b8O13s/H38yXA349tYTtJTk7m/fcy1+mYOmMWjg4OdO3UHsicibn7bH2ItPR0IqOiuXHrFhbmFri5uuiUp66MjIz4sEkrtm1YgYtbaZycXdiwejF29g5UrZF9b+7EUZ9TtcbbNHzWHj5q+gk/fPc1Pn6B+PgHsSdsIynJSdR5L3PmNfzxQ04cPUSFSlWxtrYlKiqCsE2rKVrUjIpVauQZS27NmjVj5owZ+Pn54R8QQNj27aSkpFC/fubM8/Tp03FwcKBz584ANGnShOHDhrF1yxaqhoZy5MgRrl+/Tv8B2T8b1qJFi8z728uWpXyFCpz54w9OnjzJ1Kn6305jyGVnZGREmZodOPfLAmwcPChmV4o/f56DRXEn3IPeU6fbu6QzHsHvEVzjU0zNrLBz1qzfJkUtMLO0Vb8eG32f2xf24uZbE3MrOxJiwjl/dBEmJmaU8tft98qNjIx4r1E7dm1ajPOzsWvb2syxq1K1Oup008b2pFL1d6mnbewK1xy7AOLjYoiOfIwyOvMe7McP7wBgY+uAjZ3mrKC22D5o0pptG1ZQ0q0UTs6ubFy9CDt7R6poHNMBz45pSwA+atqG+d99jbdfIL7+wc+OaTLvPDumtnYOeS5S51jCGaeSrjqVW/t61RizYgfB7i6U9XRjzaGTJKWk0aRG5pcxo5eH4WRbnAFNM1fjX/LTrwR7uFDa0Y7U9Kccv3iD3ScvMKptZj+YmJzKgt1Hea9iIA42xXgQoWDW1oOULmHPW8H63YNsZGRE5bod+G3vfOycPLBxKMXxnbMpZuOEX4Xs+rZhdkf8KtSnUp3Mny47un0GXmXextrehdTkBC6f3sW966do1W8JAMrI+1z5Yw+ewTWxLGZPnOIxJ/f/iElRc7zK6jb2G3L/m1V2r2LMh3/3mcSQx3whcpOT9zfMtGnTiI+Pp3HjxhQvXpzBgwcTExPzUvJuWN4PRXwyP/x8isi4RAJcHPmhcyMcnl02/1gZj7Ee33IVMTLidoSCHX9eRZmQhK2lOWVKObGsR1N8nfW/1Klhs46kpiSxasEkEhPi8AsK4fMx8zDNcU9kxOMHxMcq1c/v3rzE9LE91M83LpsJQI13G9Ol/1cooyM4d/oIABMGf6KxvyETfiSgbBVepPY77xITG8PaVctRKBR4e/swfsJk9SVlERFPMMrxBUtQcBkGDxvFmpXLWLV8Ka5ubowa8xUenpmX7RkbF+HBg/sc+no/sTGxWFtb4+vvz5Rp3+Hu4alfoQFNW7YjJTmZhXOnk5AQT2BwOUZPnK5xL2n4o3+Ijc2uRzXfrkdsjJL1q5eiVGReYv/lhOkal6jt3xvGprXL1c/HDu8PQN+BI3m3vu7rKjRt2Zbk5CQW5IhvzMRpGvE9fvQPcRrx1SUmR3xe3r6MnjBNI77O3fthbGTM9G/GkpaWRkilqnTv84VOMdlULkuNg6vUz4OnZ84W3V+5lfNdR2LmUgKLHJeeJ915wOmPexI8YySe/TuQ/OAxF3qOJvJA9k/fPNq0l6Il7PEfNwCzkiWIPXeZU426kfpE+wJMeXm/WgUUcQnM37afqJg4AtxdmTe4i/qy+cdRSo12uunQ76SlP2Xo96s18unR5D16Ncs8MYyMiWPm+l1ExcTjaFucRm9VonsT/T+YAdR5uxbKmBhWrF6HQqHAx9uLbyaMw87OFoAnEREY5fh5l6joaHoPyL69Z9PW7Wzaup3yZcswY8rXOuWpj49bfEpKcjI/zv2WxIR4AoLLMXLCDM328PghcTn6kbeetYeNqxer28PICTPU9c3U1IwrF8+xd8dG4uPjsLW1J7BMBSZOW/DcAm/avPPOO8TGxLBq9WoU0dF4+/gwYeLE7H7kyRON4xocHMyw4cNZuWIFy5cvx83NjTFjxuDp6Zkdd82a9OvXj40bN7JgwQJKlSrFl6NHU6Zs3rOqb2rZAZSr3Y301CR+3T6O1ORYnDwq8X6nHzXuE46LvkdyYt4LsObFxMSM8Dt/cPHXlaQmx2JRzAFnzyo06rnuucXx8vNBs46kJiexYn722PXFC8auOzcvMW1M9ti14dnY9da7jen67Leoz54+wrK549VpFs7IXKvl4zY9aPJJL51iyzymSSxSH9PyjMjzmGb3v2+9/R6xMUo2PTumHt5+jMhxTF+G96uUQRGfyPxdR4iMTSCglDM/9G+Lw7PL5h9Fx2CUoz0kpaTyzbq9PFHGYWZqgmdJR77u3IT3q2R+WWtsbMT1h0/Y+ft54pKSKWFTnBrB3vRt/A5FtVyRmJ/Q+t1JS0li39qxpCTG4uZTmZb9FmvUN2XEfZLis+tbYlwUe1YMJyH2CWbmxXF0C6BVvyV4BtUEwMSkKA9u/sGZX1aQnBiLVXEHSvlV4dMh67Aqrlt9M/T+F17dmL9/7w425vhMMmZ45heZfQeO0DjJz4shj/lvhJc0iSh0Y6R601ckEtSpU4eQkBBmzZr1wrTJW2e/+oD+hVMB3Qo7BK2czV/+KuUvS6pKt5WPC4MRhtvF3A3S70qG16nOUcNabDC3KMfXe7+uPqJ58axjYSleJK6wQ9Aq7umLfx6zsOw7p9/Pjb1ONYOTCjuEfFmZGm58gff3FnYIWq1RtS/sELRq4J73T0gaglijgt1f/zoY8pj/UVrBfs7PECRv+a7Q9m3eQrdJmf8n8lWJEEIIIYQQQghh4OSyeSGEEEIIIYQQ+pOfinut5OT9/8Dhw4cLOwQhhBBCCCGEEK+QnLwLIYQQQgghhNCf8X/zJ9sKi1znIIQQQgghhBBCGDg5eRdCCCGEEEIIIQycXDYvhBBCCCGEEEJ/smDdayWlLYQQQgghhBBCGDiZeRdCCCGEEEIIoT8jWbDudZKZdyGEEEIIIYQQwsAZqVQqVWEHIV6fPZaBhR1CvoLP7yjsELRSGfA9PYkqq8IO4Y3kGXGysEPQ6vDbwws7hHwV87Uo7BC0stuxv7BD0MrMOKWwQ9AqVVW0sEPQSpFSrLBD0MreLK6wQ8iXEYb7Ma+4SlnYIWj1IL1UYYegVVrz2oUdglYOO38q7BC08npyorBD0MrqreaFHUKBJe+aX2j7Nm/Uu9D2XVgM92xECCGEEEIIIYQQgJy8CyGEEEIIIYQQBk8WrBNCCCGEEEIIoT9ZsO61kpl3IYQQQgghhBDCwMnMuxBCCCGEEEII/Rnwgs7/j6S0hRBCCCGEEEIIAycn70IIIYQQQgghhIGTy+aFEEIIIYQQQujPWOaCXycpbSGEEEIIIYQQwsDJyfsb7M6dOxgZGXH27NnCDkUIIYQQQgjxX2NkVHiP/yC5bF6oefRsh9fArpg5OxJ34QoXB08i5o8LeaY1MjHBZ2gP3D5tirmrMwnXbnNlzHQiDxxXpylSzAr/sQMo+fF7FC3hQOy5y1wa+jUxZ/4uUHw7du1m85atRCsUeHt50adXTwID/LWmP3rsOCtWryY8/Alurq507dyJ0KpV1H9PSkpiyfIV/Pbb78TGxVHS2ZkmHzem0Ycf6B/bzl1s3rIFRVZsvXsREBCQT2zHWLlqNeHh4bi5utKlS2dCq1ZV/73hhx/luV3XLl1o1bKFXrGpVCrWr17GgX27SEyIJzCoLD36DsLVrVS+2+3dtY3tW9ajVETj6eVLt14D8AsIUv89NTWF5Yvnc/zoIdLTUgmpFEqPPgOxtbP/v4hvw88nWLn3KFExcfi7uzDssyaU9S6dZ9qth0+y68Sf3HwQDkCQpxv9WjbUSB8VE8ecjXv57eI14hOTqejvxfDPmuBe0lGneLLY16qC9+Cu2FQqi7mrE3+06EP4joP5b/N2KMHTR1As2I/k+4+4MXk+D1Zu00jj0bsd3oO6YlayBLHnr3Bx4ERiTufd/l/EtV0bSnfpSFFHB+KvXOPG11OJu5B3uzcyMcG9RxecmzTGzNmJxNt3uDVjNorjJ9RpXD5phesnrTB3cwUg8cZN7v7wI9HHftU7NpVKxYY1Szi4bycJCfEEBpWje5/BuLjlfWyz/LRrKzu2rkOpiMbDy4cuPQfiFxAMQFxcLBvXLOHcX6eJjAjH2saW0Oq1afNZN6ysiukc2+6dYWzbshGFIhovLx969O6Hf0Cg1vTHjx1hzarlPAl/jKurGx27dKdK1Wrqv69dvYJjRw8TGRGBiakJvr5+fNahCwGBQVrz1EalUrFh9VJ+3reTxIR4AoLK0aPvoBeW295dW9nxrJ16ePnQtdfn6nIDOLB3B8eO/MztG9dISkpkxYbdWBUrXqD4dqxfwLED20hMjMM3sAKf9hiFs6u71m2uXTzDvrCV3L15mRhFJH2Gz6BitXfVf09PT2P72h/4+89fiQh/gIVlMYLKV6NF+wHY2pfQK7b1q5fy87M+LqvsdOnjwtR9XFbZZR+7/Xt3cPzIQW49K7uVG3bpXXaG2v8ChO3aw6at24hWKPHx8qRvz+5ax/w7d++xYs1art+4SfiTCHp370LzJh//qzzzo1Kp2L5uIUcObCMxIR6/wAq07zWCkvnUt6sX/2TvtlXcvXkZpSKS/iOmU6l6nX+db26G3P9mvUdDbA8bDv72bMyPx9+9JMM+/Vj7mH/kFLt+/YubDx8Dz8b8Fu8/P+Zv+onfLl5/NuZ7MvzTj/Ue84XITWbeBQAuLT4gcMoIbnzzPb++1ZzYC1cJDVtM0RJ5D7T+4z7HvWsbLg2exNFKH3FvyXoqr5+HdYXsjrTcDxNxrPsWZ7sO51jVj4k8+Cuhu5Zh5uqkd3yHjx7jx0WL+bRdW76fMwtvLy++HDMWpVKZZ/qLly4z+dtpNGzQgB/mzOatGtX5atLX3LlzV51m4aIl/HHmT4YNGcyiBT/QrMnHfD9/Ab/9flKv2I4cOcqiRYv4rF075s2dg7e3F1+OGaM1tkuXLjFl6re836AB38+dQ40aNZgwcRJ37txRp1m7epXGY9DAgRgZGVGr5lt6xQawbfM6du/cQq++g5gycz5m5hZMHDOU1NQUrdscP3qIZYt+oHW7TkyfswhPLx8mjBmKUqlQp1m26Hv+OHWCoSPHM3HKbKKjI5n69dj/i/j2nTzHzPW76NG0Hmu/GoBfaRf6Tl9CdGx8nunPXLlFw2oh/Di8B8tH98HZ3oY+0xbzRBEDZH5YGTRnJQ8iovluQEfWfvU5Lo629Jq2iKSUVD1KC4pYWRJ7/ip/D/hKp/QWnqWoumMhUYdPcrxKE27PXUG5hZNwrF9Lncal1QcETRvJ9Unfczy0GXHnr1Bt9xKt7T8/JT5ogM/wwdz5fiFnWrQl/uo1yi36AVN7uzzTe37eF5fWLbnx9VRON2rOPxs2U2buTIoFZX/5lfo4nNsz5/Bny3b82aodit9PU2beLCx9ffSOL2zLWvbu3EKPvkOYPGMhZuYWTBo7ON/69uvRg6xYPI9WbTsxdfZiPLx8+XrsYGKe1TdFVCSK6Cg6dOnLzO9X0nfgKM6eOcn82VN0juvYkV9YsmgBn7Rrz3dzF+Dp7c24MSM06nROly9dZPrUr6nfoCGz5i6gWo2afDNxHHfv3FancXMrRc/e/Zj7w49MnTYLJ6eSjBs9nJgYpc5xZdm+eS17dm6hR9/BfDNzIWbm5kwcM+TF5bboe1q168S3cxbj6eXLpDFD1OUGkJKSTMVKoTRv/ZneMeX007YVHNy9js96jWLUlBUUNbNg1sS+pOUTX0pKMqU8/WnXfUSef09NSeberSt81KobY6avpfew6YT/c5d5kwfqFdv2zevYs3MrPfsOZvLMBZjrVHaHWL7oe1q368i0OYvw8PJhYq6yS01JIeRflp0h9r8Ah48eZ+HipXzW9hPmz56Jt5cnI8d+hULLuJqSkoJLyZJ07dgBe7u8+xp988zPnm0rOLBrPR16jWTMt8spam7OzK/651/fkpMo7eXHZz2Hv9R8czL0/hcMsz3sO3memet306NJPdaO75c55s9Ymv+YX708Pw7vzvLRvXG2t6XP9KWaY/7cVZljfv/2rB3fHxcHO3pNX6L3mP9GMDIuvMd/0H/zXRuon376iVq1amFra4uDgwONGjXi5s2b6r+fOnWKihUrYm5uTpUqVfjrr79e2r69BnTi/rJNPFi1lfgrN/m7/zieJiVTqkPes7xu7Zpwc9pCIvYdJenOA+4tWk/EvqN4DegMgLG5GSWbNuDK6Okofv2DxFv3uP71PBJv3cOje1u949u6bTsNG77P+/Xfw8PdnQH9+mBmbsa+/QfyTL99xw6qVK5EqxbNcXcvTcf2n+Hr40PYrl3qNJeuXKZ+vbpUKF+Oks7OfPhBQ7y9vLh67ZqesW2jYcOGNGhQHw93d/r364eZmTn79u/PO7awHVSpXJlWLVvg7u5Oxw7t8fXxYcfO7Njs7e01Hr/9/jsVypfHxcVFr9hUKhW7wjbTsk17QmvUwtPLhwGDRxIdHcmp345r3W7ntk3Ub/gR9ep/QGl3T3r2G4SZuTmH9u8BICEhnoP799CpWx/KVaiEj18A/QYO5+rlv7l65eIbH9+afcdo9k4oTWpXxdvNmS87NsO8qClhR0/nmf7rXm1pXa8GAR6ueLk6MbZLS1QqFacu3QDgXngkF27eY1THppTxLo2nSwlGdWhGSmoaP/1+VufyAojYd5Rr42YRHvazTuk9enxC0u0HXB42lfgrt7j7wxoeb9mH1+ed1Gm8Bnbm/pKNPFixlfjLN7nQZxxPE5Mp3Um/qzwASnVsz6NNWwnfFkbizVtcHz+JjORkSjZvmmd6548/4t6PS4g+epzkBw95tH4T0UePU6pTB3WaqMNHiT56nKS790i6c487s+fxNDER6wrl9IpNpVKxO2wjLdp0oGr12nh4+dJv0JcooqM4/dsxrdvt2r6Beu835t36H1Ha3YsefYdQ1MycQwd2A+Du6c2QUZOoUq0mJV3cKFehMm079ODMqRM8fZquU2xh27bQoOGHvNegIe7uHvTpNxAzMzN+3v9Tnul3hm2lUuWqNG/ZhtLuHnzWoTPePr7s3hmmTvPOu/UIqViZki6uuHt40rVHLxITE7lz+5YepZZVbpto0aY9oTVq4+nlQ//BmeWWfzvdyHsNG1G3/oeUdvekR7/Bz9rpbnWaRk1b06z1Z/gFltErptzxHdy1lo9adiMktA6lPP3pMmACyugI/jp1WOt25SrVpFm7vlSqXjfPv1taFWfQ+PlUrdmAkm6e+ASUp2234dy9eZmoiEc6x7YrbJNGH9d/8Ci9y67ns7I7+KyPA2jUtBXNW3+Kf2Cw1nxeHJvh9b8AW7aH8cH7DWhYvx4e7qX5vG9vzMzM2Hcg76uMAvz96NGlE+++UxtT07wvKtU3T21UKhUHdq6jceuuVKpWh9KefnT/fAKK6Aj+PHlY63blK9ekxad9qFz93Tz/XtB8czLk/jfrPRpie1iz/xjN3q5Kk9pVMsf8Dk0xL1qUsGN/5Jn+656f0LpuDQLcXfFycWJs5+bPxvzMz+yZY/59RnXIOeY3eTbmn9M7PiFykpN3A5KQkMCgQYP4448/OHjwIMbGxjRr1oyMjAzi4+Np1KgRwcHBnDlzhvHjxzNkyJCXsl8jU1OsK5Yh6pfsy6RQqYg89Bt21ULy3Ma4aFGeJmt+S/o0KRm7typn5mligrGJCRl5palRWa/40tLSuH7jBpVCKmTv39iYiiEhXLpyNc9tLl+5QsUQzdgrV6rI5StX1M+DA4P4/eRJIiOjUKlUnD13nof//EPlShX1ji3nvrJiy7mv52KrmCu2ypW0plcoFJw6fZr3GzTQOa4s4Y8foVREUyEku8ytrIrhFxDM1SuX8twmLS2NmzeuUj7HNsbGxpQPqaze5taNa6Snp2vkW6q0B44lnLl2Oe9835T40tLTuXznIdWC/TTyr1bGl/M37+n0vpJT0kh/+hRrK0sAUtMyT+CKmppq5FnU1ISz1+7olGdB2VYPIfLQbxqvRRw4jl31ECCz/dtUKkPkwdzt/wS21XVvC5l5mVC8TBCK33JcvaJSofjtJNYh5fPcxrhoUTJSNPuJjOQUbCpr2bexMSU+fJ8ilhbEnj2vV3xPwjPrW7mQ7NtnrKyK4RsQpPWkIi0tjVs3ruVR36pwLZ8TkcSEeCwsLSlS5MV3p6WlpXHjxjVCQipp7KNCSCWuaGkHV65cokLFShqvVapcVWv6tLQ09u3djZWVFV5e+s2YPXnWTsvnKje/gCCuXcn7ctzscsvextjYmHIhlfX6gk8XkeEPiVFGElQh+5YBS6viePuV5dZV/erIiyQlxmNkZISllW6X44aryy53H5d/nbuZZ52rnG+d05ch9r9Z+7h24yaVcvQZxsbGVAqpoHXMf515RoQ/JEYRRZnyoerXLK2K4eNflhtXC3ar0cvI19D7XzDM9pA55v9DtTK+GvlXC/bh/A19x3wLAFLTngJQNMcXScbGxhQ1MeHs9Tv/Ombx3yb3vBuQFi00Z7mWLl1KiRIluHTpEidOnCAjI4MlS5Zgbm5OmTJlePDgAb1799aaX0pKCim5OuU0VQamuS4zKepoh7GJCSnhUZrbP4mkWIBXnnlH/nwcr/6diD6eOavu+G4NSjapD0WKAPA0PgHF73/hO6IP8VdvkRIeiWvrj7CrFkKCjidAWWJjY8nIyMDWVvOyLztbW+7ff5DnNgqFEjtb2+fSKxRK9fM+vXsye+48Pu3YiSJFimBsZMTnA/pTrmxZ/WOz09yXra0t9+/f1xKbAlvb59MrFHlfHvvzzwexsLCgZgEumVcqogGwyXWfoa2tHYpnf8stLjbmWXk/v83D+/eevYdoTExMn7ufzNZOe75vSnzKuESeZmRgb6N5r7K9dXHuPIp48ZsC5mzaQwlba6oFZ34Y8HRxoqSDLfM27eXLTs2xMCvKmn3HCY+OISImVqc8C8rM2ZGU8EiN11LCIzG1KY6xuRmmdjaZ7f9JrvYfHoVVgLde+zK1tcPIxIS0KM280qKisPTyzHOb6OO/UapTe2L++JOke/exq1ENx/p1MXrWl2Sx8vOl4rqVGJsV5WliEhf7DyLxpn4zyEpFZly5+xJbW3uUyvzq21NsctU3G1s7Hj64m+c2sTFKNq9fwXsNn7/nNs/0WXXaLndcdjzU0o8oFYo83oftc/X79MnfmTZ1EikpKdjZ2zPh66lY29joFFcWRVa55YrPxtZe3YZzyy6358s6q52+LDHKzPisbTSPUXFbB2IUkXltUiBpqSlsWTWbqrUaYmGp21oGWeWT+15vG1u7F5Zd7uNrk6OPexkMsf8FiImNIyMjI48x3Ib7D/Ie819nnur6Zuug8bq1jT0xiqi8Nnkt+Rp6/wuG2R7UY751rjHfpjh3Hus65u/NHPPLZI35JTLH/M37+LJjMyzMTFmz71fCFTFEKOP+dcwG5z+6cFxhkZN3A3L9+nXGjh3LyZMniYyMJCMjA4B79+5x+fJlypcvj7m5uTp9jRo18s1v8uTJfPWV5n2x7Uwc+NT03y+WcWno15T9fiLvnN2DSqUi8dZ9HqzaqnGZ/bmuwyi34Bvq3TxKRno6sWcv8c/G3dhULPjlkS9T2I6dXLlyla/GjsHJqQQX/r7I9/MX4GBvT6VcM+OFad+BA9R9tw5FixZ9Ydojvxxg4bwZ6udfjtf9ntvXwdDjexmW7fqFfSfP8eOInpgVzZxpNzUpwvT+7ZmwZDN1+n5FEWNjQoN9qVk+AJVKVcgRF66b33yL/4SxVN29DVQqku4/4PG2HZRs3kQjXeKdO/zRvA0mxYpR4v33CJg8gXMduuX7AfLYL/tZ+P109fOR46a+svehjjMxgclfDaOUuyet23V55ft7kXIVKjBr3kJiY2PY/9Mepk6exPTv5j73QTino7/s58cc7XTk+Fdfbvr4/cgeVi/8Wv28/5dzXvk+09PTWDh9OKjgs54jtaY7mquPG2VAfdx/of99FX47spcV879RPx84elbhBfOSvcr+Fwy7Pbwsy3YfZt+p8/w4vDtmpjnG/H6fMWHpFur0m/BszPehZjl//tsjvngZ5OTdgDRu3BgPDw8WLVqEq6srGRkZlC1bltTUgi1uMXLkSAYNGqTx2i/OVZ5LlxqpICM9HTNnzW97zZyen7HLuc2fbfphbFYUUwdbUv55QsDEwSTezp4lSrx9n5Pvt6eIpQUm1sVIeRxByMqZJN7JeyZJG2tra4yNjZ9buEmhVGKnZWEaOzvb5xahyUxvC2RelbB85SrGfjmKaqGZq7x7e3lx69YtNm/dpvPJuzq2HDP6AEqlEjstC8TY2dk9t5idUst7+fvvv3nw4AGjRmhf4Can0Go18c+x+mpaWhoAMYpo7O2zj69SqcDL2/e57QGKW9s8K2/Nb8GVSoX623I7O3vS09NIiI/TmF1RKhTY5bOasKHHB2Bb3JIixsZEx2guVBMdG4eDTf6Xyq7ce4Rluw+zYFh3/Etrrk8Q7FmK9RMHEpeYRHr6U+ysi9FhwjyCPPNfYfffSgmPxMxZ8ws7M2dH0mLiyEhOyW7/Trnav7MDKY/1m7VMUypQpadj6qCZl6mDA6mReeeVplBwsf8XGBUtiqmtLalPnuA1+HOSHzzUSKdKSyf5XmbfEX/pMsXLlcGtfTuuj5+kNZ4q1Wrhm2Nl8/Rn9U2pVGBnn10mSmU0nl5+z20PWfWtCDG56luMUoGtneb7TEpM5OuxQ7CwsGTol19jYqLbEGudVadzXX2jVCqw1dKP2NrZPdcnZvYjmvXb3NwCV1c3XF3dCAwMpme3jhzYt5dWbdppjadqtVoaK8Kry02hWW4xymg8822nRTQWlMqMMVrvX6TILST0Hbz9s6+QyupHYmOiNVaBj1NGUdpL+69+6CrzxH0EURGPGDxhYb6z7lWr1dRYATtNXXbR2OXo42KUiheWXe7jG5OjjyuIN6H/BbCxLo6xsXEeY3iM1jH/VeYZEvq2Rn1LT8v8TBarjMI2R3uIjYmmtJf+K9erY3w2417QfA2t/wXDbg9Z1GN+rsXpomPicLB+0Zh/lGW7j7BgaNc8xnw31k8YQFxiMunp6Zlj/sTvX/mYXyiM5S7s10lK20BERUVx9epVRo8eTb169QgKCtK4jDooKIjz58+TnJysfu3333/PN08zMzOsra01HrkvmQdQpaUR+9dFHOrkmMk3MsLh3eooTp7Ndx8ZKamk/PMEIxMTSjZtQPjuQ8+leZqYRMrjCExsrSnxXi3Cdz2fJj+mpqb4+fryV477qzIyMjh79hzBgXl/MAsKDOTsOc1FQf786yxBgZk/u5T+9Cnp6ekYG2te6mNsbIxKlaF3bGfPnc0VW/a+8oztbO7Y/soz/U/79+Pn64u3t26XL1tYWuLiWkr9KO3uia2dPefP/alOk5iYwPWrlwjQsqiLqakpPr4BnD+bvU1GRgbnz55Rb+Pt64+JiYlGvg8f3CMyIhz/IO2LxRh6fACmJiYEebqpF5vLyv/UpRuU99H+cz3L9xxm8Y6DzBvchWAv7YNzcUsL7KyLce9xJJduP6BOpYItNqUr5e9ncahbXeM1x3pvoXi2UJ4qLY2YPy/iWDd3+6+B8nf9FsVUpaUTd/EydtWz79nEyAi76qEvvD9SlZpK6pPMvqRE/XpEHTyc/86MjDF+wdUouetbqWf17e+zZ9RpEhMTuHH1MgFaFkwzNTXF29efC+eyt8nIyODCuTP459gmMTGBiWMGYWJiwvAxUyha1Cz/+HPtw9fXn3PnctfpvwjU0g4CA4M5f1bz+Jz964zW9FlUGRnqD9DaaCu3nGWQ2U4v4x+Y921G6nI7m6vczv6ptax1ZW5hhZOLu/rhWtobG1tHrpw/pU6TlBjPret/4x2Q972+uso6cX/y6B6Dxi+gWHHbfNNr6+MuPNfH5V/nfPIou/Nn/9Soc/p6E/rfrH34+/rw1znNMf+vc+e1jvmvMk8LCyucXUqrH66lvbGxc+DS+ewFTJMS47l57W98A/RfxC1LCWe3f5WvofW/YNjtQZ2/iQlBnq7qxeay8j91+SblffMb84+weOch5g3u/IIx3zzHmP+QOhX1/6lOIXKSmXcDYWdnh4ODAz/++CMuLi7cu3ePESOyf76mXbt2fPnll3Tv3p2RI0dy584dpk+fnk+O+rk9ZznlF00h5s+/Uf5xHq9+HTGxtODBqq0AlF80hZR/nnB13EwAbKqWx9zVmdhzlzF3dcbvy34YGRtza+ZidZ6O79UCI0i4dhsrHw8CvxlK/LVbPFi5Ve/4mjdryvSZ3+Hv50uAvz/bwsJITk6mQf33APh2xkwcHRzo0qkjAE0//pihI0ayees2QqtW4cjRY1y/cYOB/fsBYGVpSflyZVm0dBlFi5rh7FSC8xf+5udDv9CjW1c9Y2vG9Jkz8fPzy44tJZkG9esDMG36DBwcHOjSuVNmbE0+ZujwEWzZupXQqlU5fOQo16/f4PP+/TXyTUhM5Nix4/To1k3v8spiZGREoyYt2bx+FS6upXAu6cK6VUuwt3cktEb2T4WNGzWIajVq8WHj5gA0btaKuTMn4+sXgJ9/EDvDNpOSnEzd+h8AmQvM1GvwIcsW/UCxYtZYWlqyeMEcAgLL6PXB3FDj+/T92oxbtJFgr1KU8S7F2v3HSUpJ4+PamVeujPlxA0521vRvlbm/5bsPM3/bfr7p2RZXR3sin93TZmleFEvzzJO4A6fOY1fcipIOttx48Jhpa3ZSp1IZapTVb6amiJUlVjk+UFh6lcK6QiCp0TEk339EwKRBmLs5c65z5tUad39cj0efTwmcPJT7y7fg+G51XFp9wOmPe6rzuD1rGRWWTkV55m9iTp/Hc0BHTKwsuL9C/7b6YMUqAidPJO7vS8Rd+Bu3Dp9ibGHB422Zq6AHTJlIavgTbn83F4Di5cti5uxE/OWrmDk74dG3Fxgbc2/JcnWeXl/0J/rYryT/8xgTK0ucGn2AbWgVLnTvo1dsRkZGfNSkNVs2rKCkWymcnF3YsHoxdvYOVK1RW53uq1GfE1rjbT5onHkbUKOmbfj+u2/w8QvE1z+I3WGbSElO4t33PgQyP3xOGjOIlJRkBgwZQ2JSAolJCQBYW9tSJNf9o3lp0qwFs2Z+i69fAP7+AewI20pySjL16jcE4LvpU7B3cKRj58z+oHGT5owaPohtWzdRtWo1jh75hRvXr9G3/xcAJCcnsXH9WkKr18DezoHY2Bh27wojKiqSWrXfKUC5tWLL+pW4uJbCqaQL61ctwc7eQaOdjh81kGo1aqvLrXGz1sybORkfvwDNcqv/oXobRXQUSkU0jx9lzvTdvXMLCwtLHJ2cKV7cWuf46jVqx+7Ni3FyccfR2ZWwdfOxtS9BxdA66nQzxvWkYrV3qfvhJ5lllJTIk8fZV4JFPnnIvdtXsSpmjUMJF9LT01gwbRj3bl2h/6jZZGQ8Vd9Db1XMBpMcC1DmF1ujJq3YrC67kqxbtTSPsvuC0Bq1c/RxrZk7czI+foH4+QeyK2wzKclJ6j7uZZSdofa/AC2aNuHb72Y/G/P92Ba2k+TkZN5/rx4AU2fMwtHBga6d2gOZM7p3n60PkZaeTmRUNDdu3cLC3AI3Vxed8tSVkZER9Ru3ZeemJTi7lsbRyY1ta+djZ1+CStXqqNN9O6Y3larX4b2P2gDP6tuj7PoW8eQh925dxaq4DQ4lSuqcb34Muf/NKjtDbA+fNqjNuMWbCPZ0o4x3adbu/5WklFQ+rpW5SN6YRRtxsrWmf6vM/nj57iPM336Ab3p+gqujHZExz8Z8sxxj/ukLmWO+/bMxf+1O6lQK1nvMFyI3OXk3EMbGxqxfv54BAwZQtmxZAgICmDNnDnXq1AGgWLFi7Ny5k169elGxYkWCg4OZOnXqc4vcFdSjLXspWsIe/zH9KepcgrjzlznVtDupzxaxsijtChnZd+oUDGfteQABAABJREFUMTPDf+znWHqV5ml8Ik/2HeFct+Gkx2QvxGFiXYyACYMwdytJmkLJ4+0HuDb+O1Tpuv10Uk513q5NTEwMK1evQaFQ4O3tzdcTvlJf7hYREYFxjgUzygQHMWLoEFasWs3yFStxdXNl3Ogv8fT0UKcZOWwYS1esYOr06cTFxePkVIJOHdrT6MMPntt/ft55521iYmNYtWq1OrZJEyaoY3sSEYFRjhn+4OBghg8byoqVq1i+fAWubm6MHTMaT09PjXyPHDmS+d7r6PdBO7dmLduSkpzMgrnTSUiIJyi4HGMmfqsxM/j40UNiY2PUz2u9XZfYGCXrVi9DqYjGy9uXMRO+1bhErXP3vhgZGTPtm7GkpaURUqkqPfoM/L+I7/1qFVDEJTB/236iYuIIcHdl3uAu6svmH0cpNerbpkO/k5b+lKHfr9bIp0eT9+jVLPNLnMiYOGau30VUTDyOtsVp9FYlujfR70MjgE3lstQ4uEr9PHj6KADur9zK+a4jMXMpgUWOy/eS7jzg9Mc9CZ4xEs/+HUh+8JgLPUcTeSD7Z3kebXrW/scNwKxkCWLPXeZUo27q9q+PiL37MbWzw3NAb4o6OhJ/+SoXevQhLSrzMltzFxeNvsTYzAzPAX2xKF2Kp4mJRB09zpXho3kal92XmDrYEzhlEkVLOJIeF0/CtWtc6N4HxYn8rz7KS5MW7UhOTmLh3GkkJsQTGFyOLydM16hv4Y//IS5Hfav5dj1iY5RsWL0EpSLzUvEvJ0xX17fbN65x/WrmKtr9u3+isb/vl2zEyfnFP/FY+513iYmNYe2q5c/6ER/GT5ico497glGOSxODgssweNgo1qxcxqrlS3F1c2PUmK/w8MxcZNTYuAgPHtzn0Nf7iY2JxdraGl9/f6ZM+w53D0+9y61py3akJCez8Fk7DQwux+iJucrt0T8a7TSr3NavXppnuQHs3xvGprXL1c/HDs/8ErPvwJG8W1/3vrhhs46kpiSxasEkEhPi8AsK4fMx8zDNEV/E4wfExyrVz+/evMT0sT3Uzzcuy/xyusa7jenS/yuU0RGcO53ZD08YrHlch0z4kYCyz9+GlpemLduSnJyk7uMCg8sxZuK0XH1c7jpXl5gcZefl7cvoCdNyld0ONuYouzHDBwDQd+AIjZOa/Bhi/wtQ5+1aKGNiWLF6HQqFAh9vL76ZME5961vucTUqOpreA7JvE9y0dTubtm6nfNkyzJjytU556uPDZh1JTU5m+Q/fkJgQh39QCIPGztGob09y1bc7Ny4xdUwv9fP1S78DoOa7jej2+Xid882Pofe/YJjt4f1q5VHExTN/+8/PxnwX5g3qrH3M/yVrzF+jkU+PJvXo1TRzUilSGcvMdbuJis0a8yvS/eO8f5byTaeSBeteKyPVf321pP+YPZZ5X8ptKILP7yjsELRS5XHLgaFIVFkVdghvJM+Iky9OVEgOv63bOgeFpZivRWGHoJXdjv2FHYJWZsYpL05USFJVL74MtrAoUnRb3b0w2JsZ9urRRga8RFZxlbKwQ9DqQbrh3puc1rz2ixMVEoedPxV2CFp5PTnx4kSFxOqt5oUdQoElHVr14kSviEXd9oW278IiM+9CCCGEEEIIIfRnwJNb/4+ktIUQQgghhBBCCAMnM+9CCCGEEEIIIfQnM++vlZS2EEIIIYQQQghh4OTkXQghhBBCCCGEMHBy2bwQQgghhBBCCL3JT8W9XjLzLoQQQgghhBBCGDiZeRdCCCGEEEIIoT9ZsO61ktIWQgghhBBCCCEMnJy8CyGEEEIIIYQQBk4um/+PcahgU9gh5CvVyLywQ9DKCFVhhyBesijHgMIOQativhaFHUK+4m8kFXYIWjkYZRR2CFplGPB35obcxxUvarj1zZDLzdAlG1sVdghaWZokF3YIWj004P7X0YDbQ1SJoMIOQSvDbQk6kAXrXivD/RQhhBBCCCGEEEIIQGbehRBCCCGEEEIUhLHMBb9OUtpCCCGEEEIIIYSBk5N3IYQQQgghhBDCwMll80IIIYQQQggh9KaSBeteK5l5F0IIIYQQQgghDJzMvAshhBBCCCGE0J+RzAW/TlLaQgghhBBCCCGEgZOZdyGEEEIIIYQQelPJzPtrJaUthBBCCCGEEEIYOJl5FxqcW7TE5dNPMbV3IPHGde7MnEHCpUt5pjUqUgTXjp1w/OBDipYoQdK9e9z/YR4xv/9e4Dzzs3tnGFu3bEKhiMbLy4eevfviHxCoNf3xY0dYvWoFT8If4+rqRqcu3ahStVqeab+fO4uf9u6mW4/eNGnaXO/Ydu3ckSM2b3r27ktAvrEdZfWq5YSHh6tjq1o1NM+08+bO5qe9u+neo1eBYlOpVKxfvYwD+3aRmBBPYFBZevQdhKtbqXy327trG9u3rEepiMbTy5duvQbgFxCk/ntqagrLF8/n+NFDpKelElIplB59BmJrZ/9/EV/Yrj1s2rqNaIUSHy9P+vbsTmCAf55p79y9x4o1a7l+4ybhTyLo3b0LzZt8/K/yzI9ruzaU7tKRoo4OxF+5xo2vpxJ34e880xqZmODeowvOTRpj5uxE4u073JoxG8XxE+o0Lp+0wvWTVpi7uQKQeOMmd3/4kehjv+oVl32tKngP7opNpbKYuzrxR4s+hO84mP82b4cSPH0ExYL9SL7/iBuT5/Ng5TaNNB692+E9qCtmJUsQe/4KFwdOJOb0Bb1iy5JZ35by87P6FhBUTuf6Fqaubz507fW5Rn3bv3cHx48c5NaNayQlJbJywy6sihXXK7Y9O7ezbcsG9T669+6Pf4595PbrscOsXbWMJ+GPcXEtRYcu3alStbr677/9epSf9uzk1o3rxMXFMnPuj3j7+OoVU06vquxSU1NYsfiHZ201jQqVqtKjzxd69SUqlYpNaxZzaN9OEhLiCAgqT9c+Q3BxK53vdvt2bWHn1rXEKKJx9/Klc88v8A0IzjP/KeOHcO7M7wz+cjJVa7ytV2yG2McZemyGPOZn1rclHFTXt3J007m+rUOpiMbDy+e5+vbjvG/5++wfREdHYm5uSUBQWdp16o1baQ+d4npz+mDDq3OGPOYLkZPMvBuIp0+fkpGRUagx2Nd7D/cBn/NgyRL+7tSRxOs3CPxuNiZ2dnmmL9WzF05Nm3Jn5gzOt/uEJ9u24j9lKpb+/gXOU5tjRw6zeNFC2rb7jFlz5+Pl7c3YMSNRKhV5pr986SLTpn5DgwYNmT13PtVr1OTrieO5e+f2c2l/O3Gcq1cvY+/goFdMWY7miG323B+exTYq39i+nfoN9Rs0ZM7c+VSv8RZfTxzPnTxiO/EvYwPYtnkdu3duoVffQUyZOR8zcwsmjhlKamqK1m2OHz3EskU/0LpdJ6bPWYSnlw8TxgzVeE/LFn3PH6dOMHTkeCZOmU10dCRTvx77fxHf4aPHWbh4KZ+1/YT5s2fi7eXJyLFfoVAq80yfkpKCS8mSdO3YAXstdVvfPLUp8UEDfIYP5s73CznToi3xV69RbtEPmNrnvV/Pz/vi0rolN76eyulGzflnw2bKzJ1JsaAAdZrUx+HcnjmHP1u2489W7VD8fpoy82Zh6eujV2xFrCyJPX+Vvwd8pVN6C89SVN2xkKjDJzlepQm3566g3MJJONavpU7j0uoDgqaN5Pqk7zke2oy481eotnsJRUvo9yVRlu2b17Fn51Z69h3M5JkLMDc3Z+KYIfnWt1+PHmL5ou9p3a4j0+YswsPLh4ljhhCTo76lpqQQUimU5q0/K1Bcx4/8wtJF8/mkXQdmzl2Ip7cPX40ZrrUfuXLpb2ZMncR7DT5g5twfqVajJlMmjtXo45KTkwkuU44OnbsXKKbcXlXZLVs0jz9OnWDIyK+YMGU2iuhIvv16jF6x7diyhp92bqZb36FMmrEIM3NzJo8dlG9sJ47+zKrFc2nZtguTZy/Fw8uXyWMHacSWZU/YBgr6Y0iG2McZemyGPOZDZn3bu3Mz3foO4esZP2JubsE3L6xvB1m5eB4t2nZmyuwleHj58k2u+ubtG0CvgaOYOX8NoybMQKVS8fXYL8h4+lSnuN6EPtgQ65whj/lvBCOjwnv8B8nJewHVqVOHfv360a9fP2xsbHB0dGTMmDGoVCogs2EPGTIENzc3rKysqFatGocPH1Zvv3z5cmxtbdmxYwfBwcGYmZlx7949rfs7evQopqamPH78WOP1gQMHUrt27ZfynlzatuXJjjAid+8i6c5tbn87hYyUZEo0apxneseGH/DPihXE/HaClH/+4cm2rShP/IZL23YFzlOb7du28H7DD3ivQUPc3T3o0+9zzMzMOLB/X57pd4Rto1LlqjRv2ZrS7h581qETPj6+7NoZppEuKjKShfO/Z/DQkZgUKdiFKFmx1W/wPu7uHvR9YWzbqVy5Ki1atqa0uzvt1bHt0EgXGRnJwvk/MGToiALHplKp2BW2mZZt2hNaoxaeXj4MGDyS6OhITv12XOt2O7dton7Dj6hX/wNKu3vSs98gzMzNObR/DwAJCfEc3L+HTt36UK5CJXz8Aug3cDhXL//N1SsX3/j4tmwP44P3G9Cwfj083Evzed/emJmZse9A3jMYAf5+9OjSiXffqY2pad7HSt88tSnVsT2PNm0lfFsYiTdvcX38JDKSkynZvGme6Z0//oh7Py4h+uhxkh885NH6TUQfPU6pTh3UaaIOHyX66HGS7t4j6c497syex9PERKwrlNMrtoh9R7k2bhbhYT/rlN6jxyck3X7A5WFTib9yi7s/rOHxln14fd5JncZrYGfuL9nIgxVbib98kwt9xvE0MZnSnVroFRtk1bdNGvWt/+BRKKKjXlDfNvJew0bUrf/hs/o2GDNzcw4+q28AjZq2onnrT/EPfH7WVhdh2zbRoOGH1GuQWad79/sCMzMzDu7fm3dMYVupVDmUZi0/obS7B5926IK3jx97dm5Xp3m3XgPatOtA+YqVCxRTTq+q7BIS4jm0fw+duvVVt9W+A0dw9fLfXNOxL1GpVOwN20izNh2pUr02Hl6+9B00BkV0JH/8dkzrdru3b6Du+42pU/8jSrl70a3vUIqamXH4wC6NdHduXWP3tvX0GjhKp3hyx2aIfZyhx2bIY75KpWJP2Caat+lAVXV9G40iOorT+da39dR7vzHvatQ3c37JUd/ea9iE4LIhODm74O0bQJv23YmKeMKTJ4+15pvTm9EHG16dM+QxX4jc5OT9X1ixYgUmJiacOnWK2bNnM3PmTBYvXgxAv379+O2331i/fj3nz5+nVatWNGzYkOvXr6u3T0xMZOrUqSxevJiLFy/i5OSkdV9vv/023t7erFq1Sv1aWloaa9asoUuXLv/6vRiZmGAVEEjs6VPZL6pUxJw+TfGyeX+ANypalIxc35RmpCRTvEKFAueZl7S0NG7cuEaFkErq14yNjQkJqcTVK3lffn/lyiVCKlbSeK1i5SpcuXI5O9aMDGZOn0rzFq3w8PDUOZ7nY7tOSEjFXLFV1NjX87FV1Hit0iuIDSD88SOUimgqhGR/eLeyKoZfQLDWsktLS+PmjauUz7GNsbEx5UMqq7e5deMa6enpGvmWKu2BYwlnrl3W/ZYIQ4wvLS2NazduUimkvEb+lUIqcOnKVZ3f26vI08jUhOJlglD8djL7RZUKxW8nsc6Rd07GRYuSkZKrnSanYFP5f+yddXgUxx+H37u4EIcQgbgQLLi7W4FSKNAWKVKsQIFS3N2KF3enuLu1uLu7hchdPBe5+/0Rcskld8mFluba37w8+5Db/c7sZ0d3d2a+W0qrPVIp+Rs3wMjSgqjrN/XW9inYVQwm7Pg5jX2hR/7AvmIwABITE2xLFyXsWPoUf1Qqwo6fxa6iDv3ZkFbeSmQpb0V03uCllreHWsubvg+XOaHrHCUzlOnMPLh/lxJZ2rhyuXp5lhs+V9ql1dUSWurqg3v6XcuHkLfIZeEUDy6r3mdpZY1vQBAP72tfTpKclMSzxw8oHlxOQ1vx4LIaYRQJCcybPpbvew7Ezj73I7WG2MYZujZD7vMhY3lLLztp5e1RNuXt6eOHGmU0rbw90lF/EhLiOXl0PwWcXXBy0n1/+FfIqzbYkMqcIff5/xZUEmmebf+P/H9e9d9EoUKF+PXXXwkICOCbb77hxx9/5Ndff+Xly5esXLmSrVu3Uq1aNXx8fBg0aBBVq1Zl5cqV6vBJSUksXLiQypUrExAQgKWlZbbn69Kli0b4PXv2kJCQQJs2bf7ytRjb2SExNiYpIkJjf1JEBCaO2qdGRV44T8G27TFzLwQSCTblymNfsxYmjk6fHKc2oqIiUSqV2GeammRnZ48sQvsUOrlMhp2dXRZ7uSxdy7atm5EaSWnWvKXeWrJqi0KpVGKnVVuE1jAymQw7u8z2dhraft+6GSMjI75o3uKTtQHqOG0zrfmys7NHJtOuL/pjetvZZQ2TFp9MFoGxsUmWNb129rrj/bfoi4yKTi1vmcqPvZ0tMpn28pYTf1ecJnb2qXUqPFxjf1J4OKZOTlrDRPxxDvdO32HhURgkEuwrV8SpXm1M82vaW/n5UvXyWarfuIj/6BHc+XEAcU+e6q3tUzBzdkIREqaxTxESholtPqTmZpg62SM1NkbxITyTTThmBbVfb3aklY/MayBtM7UNGUktbylZ6mx2YXKLukzbZz2HrnZELovQqulTy2hOfK60k8vCddZVfdNX3Y7YZdbmgFweri0IUVFylMoU7WEynHfNsrn4FylG2YqfNsPNENs4Q9dmyH1+6rnSypuWci3Xfn1RH+uC9vKmWUYP7dtOh6/q0fGrely/cp7hE2ZjbGLylzTrIq/aYEMqc4bc5wsE2hAP73+BihUrIsmw3qJSpUo8evSIW7dukZKSgr+/P9bW1urt1KlTPHnyRG1vampKiRLaR8u00alTJx4/fsz5jw7hVq1aRZs2bbCystJqr1AoiIqK0tgS/8Z19S9+nUXCq1eU3LSZ8qf/wHPgIML27QVV3q7d14fHjx6ye/cO+g/4WSMPDYFUbTs/SdupE0do36qhektJSf5MKj8NQ9f3X+TJpGnEP39JuX07qH7zEr4jhvB+x25UmdqCuOfPufzl11z9+jvebtpCwORxWPp455Hqv4fTJ47wTauG6k2UN/0x5LT748QhOn5VV72lJH8ebZcvnOHOjSt07NZP7zCG3MYZsrbPzV/p88+cOEyHr+qpt89V3tKoVrM+U+esYPSU+bi4FmL2lJHZrgc3ZP6fy5zAMFmwYAGenp6Ym5tToUIFLl68qNN26dKlVKtWDXt7e+zt7albt24W+06dOiGRSDS2hg0bftZrEN7mPwMxMTEYGRlx5coVjIyMNI5ZW1ur/7awsMhVJ1KgQAGaNWvGypUr8fLy4sCBAxrr6DMzefJkxo7VdFrSxc2VboWyevRMlstRJSdj4qD5ZtPEwYGkcO1vLZPlch4NGYzE1BRjW1uSQkMp1Ks3CW/efnKc2rCxsUUqlWZ5WymXy7DX4aTLzt4eeSanIHK5TD1qdOfObSLlcr7v+I36uFKpZMWyxezeuZ3lq9bpqc0GqVSKXKs27bML7O3tszjdkcvlWbR1zqRt+bIl7Nq5gxWr1qKL8hWqaHinTkpKAiBSFoGDQ/qUT7lchpe3dq/T+T6md+YRhIzpZ2/vQHJyErEx0RpvuuUyGfbZeHY1dH0Atjb5UstbpvIjk0dmGQnSl78rziS5LLVOZXK0ZOLoSGJYmPYwMhl3fvwJiakpJnZ2JH74gNfAfiS8fqNhp0pKJuHlKwBi7t4jX/GiuH3XnkdjJuitL7coQsIwc9YcvTFzdiIpMhplgoLEMBnK5GTMCjhmsnFE8V779WakXIUqGt6I08qbXBaBfYbyFimX4ZlteTPKUmcjM5S3v4q6TMuynkNXO2Jn76BV06eW0cz8U2lnZ++os67qSt8yFariG1A0g7bEj/FGYO+QXp4i5RF4ePlpjcPGxg6p1IjITO1IpDwivS2+cYWQ92/4/mvNG7FZk4cTGFSS0VPmZ4nXkNs4Q9aWEUPr88tWqIpfBo/w6eVNlqm8yfD00p5uNh/rgvbyptm+WVpZY2lljYtbIfwDivJ920ZcOneaKjXq6dT4qXzuNvjfUOYMuc//12Bgg2C62Lx5MwMGDGDRokVUqFCB2bNn06BBAx48eKB16fLJkydp164dlStXxtzcnKlTp1K/fn3u3LmDm5ub2q5hw4YaM6PNzMw+63WIkfe/wIULFzR+nz9/Hj8/P0qVKkVKSgofPnzA19dXYytYsOBfOmfXrl3ZvHkzS5YswcfHhypVqui0HTp0KJGRkRpbx4+fgsqMKjmZ2Af3sSmbvoYLiQTbsuWIvp3950BUiYkkhYYiMTLCoVYtZGdO/+U4M2JiYoKvrz83b1xT71Mqldy4fo0AHc6hAgODuHH9msa+69euEhiY2onUql2XeQsWM3f+IvXm4OhIy1atGTthci61+XHjxvVM2q6rz6VN2/VM2q5l0baIufN/U28Ojo582ao14yZMylaPhaUlLq7u6q1QYU/s7B24eeOq2iYuLpZHD+7qTDsTExN8fAO4eT09jFKp5Ob1K+ow3r7+GBsba8T75vVLwkJD8C+i22GXoetLi9/f14drN9LXeyuVSq7duElQYEA2IT9/nKqkZKLv3MO+YobPCkok2Fcsn+P6dFViIokfPiAxNiZ/vTqEHzuZ/ckkUqSmpnpr+xTk56/jWLuixj6nOpWRnb8OgCopicird3CqXSmDLgmOtSohP69Zh7Shq7zdylLe7hEQWFRrHKnlzZ9b16+o96WWt6v46wiTW9LOkbG8pp1DVz0ICAzSqAMA169d1nkdueWfSrvs6mpAEe3xWlhaUdDVXb25F/bCzt6R2xnOExcXy+MHd/EPLKY1DmMTE7x8A7h947KGtts3rqjDNG/9HdPmrWHq3FXqDaBD17701OG8zpDbOEPWlvkchtTnW1haai1vt66nl5208uaXTXnz9vXn1g3NunD7xhX8sqmzqo//0h56/27yqg02pDJnyH2+4O9l1qxZdOvWjc6dOxMUFMSiRYuwtLRkxYoVWu3Xr19Pr169CA4OJjAwkGXLlqFUKjl2TNPpoJmZGQULFlRvn/sFjRh5/wu8fPmSAQMG8MMPP3D16lXmzZvHzJkz8ff355tvvqFDhw7MnDmTUqVKERoayrFjxyhRogRNmjT55HM2aNAAGxsbJkyYwLhx47K1NTMzy/L2x1Sq+33Nu40b8Rk5itj794i5c5eCbdsiNTcndG+qJ1TvUaNJCg3l1W8LAbAKKopp/vzEPXqIaf4CuHXtChIp79at1TtOfWnRshW/zpqGr58//v4B7Nq1gwRFAnXrNQBg1oypODo60bFzFwC+aN6Sob8MZMf2rZQtV4Ezp07y+NFD+vzYH0gdMbexsdE4h7GRMfb2Dri7Z/+dVu3apuPn54e/fyC7dm3X0DZzxjQcHR3ppNbWgiG/DGL79t8pV648p9Xa+uWgzT7X2iQSCU2bf8Xvm9bi4uqOc0EXNq5djoODE+UrpX8KZvSwAVSoVJXGzVK/d9usZWvmzZqMr18Afv5F2LPrdxQJCdSu1whIdTBTp35jVi5diLW1DZaWlixbNJeAwKK5engwVH2tWjRn2q9z8PfzJcDfjx27Uv1LNKhbB4CpM2fj5OhIl07fAamjCS9epY5aJyUnExYeweOnT7Ewt8DN1UWvOPXl9eq1BE4eT/Ttu0Tfuo1bh2+QWljwfkeqV+WAKeNJDPnAs1/nAZCvRDHMnAsQc+8BZs4F8OjdA6RSXi5fpY7T66cfiTjzJwlv32NsZUmBpo2wK1+WW9165UqbkZUlVr6F1b8tvdyxKRlIYkQkCa/eETBhAOZuztzo/AsAL5ZswqPXNwRO/plXq7bhVKsiLq0bcemLH9RxPJu9kpIrpiK/cpvISzfx7NsRYysLXq3enittkFbeWvP7pjW4uLpToGBBNq5dgb2Do0Z5GzPsJ8pXqpahvLVh3qzJ+PgF4ucfyN5dv6NIiFeXNwBZRDhyWQTv36XOaHjx/CkWFpY4FXAmXz7N+qyN5i1bM2fWlI9lOpA9u7aRoEigTr3UUd/ZMybj6OjEdx8/+9as+ZcM/+Undm7fQtlyFTlz6jhPHj2k148D1XFGR0cR+uEDERGpI2RvX6eWUXt7B50j+v902llZWVO7fmNWLV2AtXU+LC2tWL5oDgGBRfV+OSKRSGjUvA07Nq+moJs7BZxd2bJuKfYOTpStlL5WffywvpSrVJ2Gzb4CoEmLr/nt14l4+wXi6x/E/l1bUCQkUKNuah9tZ++o1UmdU35nChTU/iJce7oZXhtn6NoMuc+XSCQ0bt6aHZtX4+JWiALOLmxetwx7B0fKaZS3fh/LW6pX9iYt2rLw14n4+AXi41/kY3mLp+bH8hby/g1nTx+nZOly2NjYER4eyq6t6zA1NaNU2UpatWTm39EGG16ZM+Q+/19BHjqOUygUKDI55dX2/JOYmMiVK1cYOnSoep9UKqVu3bqcO6fptFEXcXFxJCUl4ZCp/zx58iQFChTA3t6e2rVrM2HCBBz/wqcoc0I8vP8FOnToQHx8POXLl8fIyIh+/frRvXt3AFauXMmECRMYOHAgb968wcnJiYoVK9K0adO/dE6pVEqnTp2YNGkSHTp0yDlALog4dhQTezvcu3bHxNGRuEcPuf9Tf5I/Ovswc3aGDOtkpWamFPqhB2aurqTExyM/d5YnY8eQEhOjd5z6Uq1GTSKj5KxfuxqZTIa3tw9jx01Sv90KDf2ARJo+badIUFEGDR7KujWrWLNqJa5ubgwfOQYPT6+/kkRaqV6jJpFRkaxbu+ajNm/GjZuooU2aSdvPg4eyVq3NleEjx+D5GbQBtPyqHYqEBBbNm0FsbAxFgoozcvw0TE3TG7b3794QFRWp/l21em2iIuVsXLcSuSwCL29fRo6bpjGNtXO33kgkUqZPGkVSUhLBpcvRvVf//4S+mtWrIo+MZPW6jchkMny8vZg0bjT29nYAfAgN1Shv4RER9Ow7QP176/adbN2+kxLFijJzykS94tSX0AOHMbG3x7NvT0ydnIi594Bb3Xupl6KYu7iAUqW2l5qZ4dm3NxaF3EmJiyP89B/c/2UEKdHRahsTRwcCp0zANL8TydExxD58yK1uvZCdPZ8rbbZlilHpWPrLu6AZqaOTr9Zs52aXoZi55MeikIv6ePzz11z64geCZg7F88cOJLx+z60fRhB2JP2TQe+2HsA0vwP+o/tiVjA/UTfucbFpVxI/aHdElhMtvmpHQkK8urwFBhVn5PjpmcrbW6IzlLcq1WsTGSln07oV6vI2Ytx0jfJ2+MButmxYpf498pe+APTuP0TjIV8XVWvUIjJKzsa1K5HJZHh5+zB63FT1OVLbuPQbpMCgYgwYPJz1a1awbtVyXN3cGDJynEYbd/H8Web9Ok39e8bU8QB83b4D7b7tpGeKpfO50q5ztz5IJVJmZKir3Xr9lCttX7T6BkVCPEvnTSMuNoaAoBIMGTdTQ1vI+zca2ipXr0tUpJyt65Yhl0Xg4e3HkHEz/7blEGkYYhtn6NoMuc+HtPKWwBJ1eSvOUK3lTa7+Xbl6HaIi5Wz5WN48vX0ZmqG8mZiYcf/ODQ7s3kJMTDR2dg4EFi3J+OmLsjjH08W/oQ02xDJnyH2+IHu0LREePXo0Y8aM0dgXFhZGSkoKzs7OGvudnZ25f/++Xuf65ZdfcHV1pW7duup9DRs25Msvv8TLy4snT54wbNgwGjVqxLlz57Isnf67kKjSPkwuyBU1a9YkODiY2bNn/+Pn7tKlC6GhoezevTtn40xcqFThMyj6+7BftzWvJehEguFWlUTV553e/F8ln0qe1xJ08uyLdnktIVtiHsfntQSdeNw7ldcSdGIkSclrCTpJUX2eG42/gySV4Y41mEo+z5Tm/wdMDDjt4pQWeS1BJ2+CquZslEd43juZ1xJ0Ysh9fmE/7Ust/w1EXTmUZ+c2K1ZTr5H3t2/f4ubmxtmzZ6lUKX0my+DBgzl16lSWpdCZmTJlCtOmTePkyZPZOht/+vQpPj4+HD16lDp1Ps8sC8PtDQVZiIyM5NatW2zYsOGTHtwFAoFAIBAIBAKB4L+Atgd1bTg5OWFkZERISIjG/pCQkBz9kc2YMYMpU6Zw9OjRHL8S5u3tjZOTE48fP/5sD+/CYZ0BkfGzcpm3M2fO0Lx5c+rXr0+PHj2oV+/v9zoqEAgEAoFAIBAIBP8lTE1NKVOmjIazuTTncxlH4jMzbdo0xo8fz8GDBylbtmyO53n9+jXh4eG4uLjkaPupiJH3TyS7T7R9KtevX9d5zM3N7bOcUyAQCAQCgUAgEAg+iTx0WJcbBgwYQMeOHSlbtizly5dn9uzZxMbG0rlzZyDVl5mbmxuTJ6d+gWLq1KmMGjWKDRs24Onpyfv374H0wdaYmBjGjh1Lq1atKFiwIE+ePGHw4MH4+vrSoEGDz3Yd4uHdgPD11f6NS4FAIBAIBAKBQCAQfBpff/01oaGhjBo1ivfv3xMcHMzBgwfVTuxevnyJNINj2N9++43ExES++uorjXjSHOIZGRlx8+ZNVq9ejVwux9XVlfr16zN+/PjP+q138fAuEAgEAoFAIBAIBIJco0KSs5GB0KdPH/r06aP1WOYZzs+fP882LgsLCw4d+ued9f075jkIBAKBQCAQCAQCgUDwf4x4eBcIBAKBQCAQCAQCgcDAEdPmBQKBQCAQCAQCgUCQa1T/Eod1/xVEagsEAoFAIBAIBAKBQGDgiJF3gUAgEAgEAoFAIBDkHjHy/o8iUlsgEAgEAoFAIBAIBAIDR6JSqVR5LULwz3Hz0Ye8lpAtoQk2eS1BJ1IMt6oUsJDntQSdGPInRJJVRnktQSeGXN4ApBJlXkvQyYsiNfJagk72T76Q1xJ0MuRpl7yWoJNZQavzWoJOGlUzzWsJ2VLAMjKvJejEOeVNXkvQyQu88lqCTqyN4/Nawr8SQ+7zg/3y57WET0Z241Sendu+pOH2958LMfIuEAgEAoFAIBAIBAKBgSMe3gUCgUAgEAgEAoFAIDBwhMM6gUAgEAgEAoFAIBDkGvGpuH8WkdoCgUAgEAgEAoFAIBAYOGLkXSAQCAQCgUAgEAgEuUdiuI6J/4uIkXeBQCAQCAQCgUAgEAgMHPHwLhAIBAKBQCAQCAQCgYEjps0LBAKBQCAQCAQCgSDXCId1/ywitQUCgUAgEAgEAoFAIDBw/i8f3mvWrEn//v3zWoZAIBAIBAKBQCAQ/GtRIcmz7f8RMW0+B06ePEmtWrWQyWTY2dnltZzPjkqlYvP65Rw7tIfY2BgCixSnW6+BuLgVyjbcwb3b2b19I3JZBB5ePnz/Q3/8AoIAiI6OYsv65dy4domw0BBsbO0oX7EaX3/bFSsr61xp27t5IX8e3U58XDTeAcG06z6cAi4eOsM8unuFI7tW8erpPSJloXQf/CvB5Wtr2KyZP5LzJ3dr7AsKrkyfEb/lStuezb/xx0dtPgHBtOs+DOcctB3etZqXH7X1GDwri7aMrF88gTNHfqd1p0HUafqt3toO7N3Bzm2bkMsi8PTypWuPvvgFFNFpf/bMSTauW86HkPe4uLrzXecfKFOuosa1blq3kiOH9hIXG0NgkWJ07z0AVzd3vTVpxrWCox/jCihSXK+4DuzdwS71NfnQpUc/jWs6fGA3f5w6xtPHD4mPj2PN5r1YWef7JH1b1fUhmoAixenaa1CO9eHQ3m3syVAfOv/wE74f6wPAkvnTuH39MhERYZibWxJQpBjtO/XErZDu8qJNmyHXVUPMV4eqZfEe2AXb0sUwdy3A5Va9CNl9LPsw1csTNGMI1kF+JLx6x+PJv/F6zQ4NG4+e7fEe0AWzgvmJunmfO/3HE3nplt66MtKkijlViptiYSbh6dtkNh2JJ1Su1GnfuLI5TSqba+x7H57C+JXR6t9OtlJa1jTHx80YYyMJ954nseVYPNFxKr11WVdvSL56zTGysSPx9XPkW5aT+OKxVtv8/cdi7l8sy/7421cIWzgJAJsmbbAsUxUje0dISSbx5VMid28g8fkjvTVlpmF5UyoVNcbcTMLzdylsPakgLFL3NTYob0rD8qYa+0JkSqasj1P/7t3SAl83Iw2bs7eT2HpSobculUrF/i0LOHtsG/Gx0XgFBvN115HZ9l2P717m2O5VvHx2lyhZKF0HzaZk+ToaNj+2Ka41bPNvB1D3i856azPUNm7HvkNs2rmHCJkcX08P+nbvTBF/X622z16+YuWGLTx48oyQD6H07tKB1l800bDZdeAwuw4c4f2HUAA8C7vT8etWVChTSm9NaahUKnZsWMLJIzuJi43BL7AEHXv+QkHXwtmGO7pvKwd2riNSFk4hTz++7T4IH/+i6uNyWRibV83jzvULxMfH4eLmQbPWnSlXWfe9gTZthtj+fm59iYkKVi9byB+nj5OclETJ0uXo3usn7OwdcqXNUOuDQJDG/+XI+7+RxMTEf+Q8u7Zt4MCebXTvPYjJMxdjZm7BhFEDSUzUfaPy5+ljrF42n9btOjF1zjI8vHyZOGogkXIZALLwMGQR4XT4vjezFqyhd/9hXL9ygd/mTMmVtiM7V3Jy/0badR/Bz5PWYWZmwbzxPUnKRltiQjzungF83XVotnEHBVdh8tJj6u37/lNzpe3wzlWc2L+B9t2H88uktZiaWTBvfK9stSkS4nH39KdtDtoArl04zrNHN7F1yJ8rXX+cPs7KpQtp074TM+YuxdPLh3Ejf0b+MW8yc//ubWZNG0ed+k2YOXcZ5StVZeqEEbx4/lRts+P3jezbs40evQcwZdZvmJlbMH7kz9mWEV3s/H0j+/ds54feA5k8axHm5uaMHzkoh/J2nFVLF9CmfUemz12Kh5cP40cOUpc3gESFguDS5fmyjf4vObSxe9t6Duz5na69BzFx5hLMzS2YNGpAtvrOnj7GmmXzadWuM1PmLMfDy5dJowZo6PP2DaBH/2HM+m09w8bNRKVSMXHUTyhTUvTWZsh11VDz1cjKkqibD7jdd6xe9hae7pTbvZjwkxf4o2xzns1bTfHFE3CqV1Vt49K6EUWmD+XRhAX8Ub4l0TfvU2Hfckzz63/DmEa98mbULGXGpiNxTF8fTWIS9PnKCmOj7MO9DUth6MJI9TZrU4z6mKkJ9GltBSqYuyWGWRujMZJK6NHSSu8xC4sylbFr1YmofVt4P/lnkt68IP+PI5Fa22i1D18ynTdDuqi3d+P7o0pJIe7qObVNcshbZJuX8X7CAEJmjiA5/EO2ceZE7dImVC9pwtaTCmZvjUeRBD2+sMgx7d6FpzBqRax6m7ctLovNuTtJGja7/8xdW3d01wpOHdjA191GMnDSeszMLFg48Yfs+wdFPG6e/rTpMlynzcQlJzS2b3qOQyKREFyhrt7aDLWNO37mLAtXrKHT161YOmsKPl4e/DxmEjJ5pFZ7hUKBi7Mz3b9rh4O9nVab/I6OdO/QniWzJrN45iRKFy/G8EnTefbylV6aMrJ/+xqO7NtMp55DGDV9BWbmFswY0zfbdLtw5ggbV8ym+dddGTtrDYW8/Jgxpi9R8gi1zZLZY3n35gX9hs9k4tyNlKlUkwXTh/Hi6QO9tRlq+/u59a1cOp/LF88yaOhYxk2ZgywijGkTR+ZKm6HWB0NHJZHm2fb/yH/+qmNjY+nQoQPW1ta4uLgwc+ZMjeNr166lbNmy5MuXj4IFC9K+fXs+fPgAwPPnz6lVqxYA9vb2SCQSOnXqBMDBgwepWrUqdnZ2ODo60rRpU548eaK3rl9++QV/f38sLS3x9vZm5MiRJCUlqY+PGTOG4OBgli1bhpeXF+bmqSMrcrmcH374AWdnZ8zNzSlWrBh79+79K0mkRqVSsW/XFlp93YFyFavh4eVLnwHDkUWEc+ncGZ3h9u7cTJ0GzahVrwmFCnvRvfcgTM3MOX5kHwCFPb0ZNGwCZStUoaCLG8VLlqFdh+5cuXiWlJRkvbUd37eehq26UbJ8Ldw9/en44wQiZaHcuHhcZ7iipavyRbs+BFeoo9MGwNjEFFt7J/VmmYsbSJVKxbF962nUqhvBH7V1/nE8clko1y+e0BmuWOmqNG/Xh1IVsn+jLgsPYfPyKXzfbxJGRrmbLLNnx1bqNWxCnXqNKFTYkx/6DMDM3Jzjh/drtd+7exulypSnRau2uBf2oP13XfDy8ePA3h3qa92763e++vo7yleqiqeXD30HDiUiIoyL5/7IlbbUuLZqxPXjwGHIIsKzjWvPji3UbdiU2vUaf7ymgZiZm3MswzU1bdGaL9t8g39gkM549NG3f9dWvsxQH3oPGJFjfdi3c5O6PrgX9qJr758xNTPnxJH0elq3YXOCigVTwNkFb98Avv6uG+GhH/jw4b3e2gy5rhpqvoYeOs3D0bMJ2XVUL3uP7m2Jf/aae4OnEnP/KS8Wruf9tkN49euktvHq35lXy7fwevV2Yu494Vav0aTEJVCoU6tc66tV2oyD5xO4+SSZt2FKVu+PxdZaSklfk2zDKZUQFadSb7Hx6aPN3q7GONpIWXswjrdhSt6GKVlzIJbCBY3wL6xfe5KvdjNi/jxK7PkTJL9/jWzjYpSJCqwqa29XlXExKKPk6s08sASqRAXxV8+qbeIu/4HiwU1SwkNIfvcK+bZVSC2sMHH7tJGoGiVNOHw5kdvPUngXrmTD0QRsrCQU987+GpVKiI5TqbfYhKw2iUkqDRtFUlYbXahUKk7uX0eDL7tTolxt3DwC+K7PJCJlody8lE3fVaoaTdv2zTLanhEbOyeN7ealE/gVLY+Tc/ajhBm1GWobt3XXPprUr0OjurXwLOzOgJ5dMTczZf9R7X1qoJ8vPTt/S53qVTAx0V5fKpcvQ8WypXB3daGQmytdv2uLhbk5dx/kbraHSqXi0J5NNGv9PaUr1KCwpx/d+49BHhHG1fOndIY7uGsDNeq3oHrdZrgV9qZTzyGYmplz+ugetc3j+zep16QNPv5FKVDQjeZtumBpZc2zx/f01mao7e/n1BcbG8Pxw/vp1LU3xUuWxscvgN79h/Dg3m0e3r+jtzZDrQ8CQUb+8w/vP//8M6dOnWLXrl0cPnyYkydPcvXqVfXxpKQkxo8fz40bN9i5cyfPnz9XP6AXKlSIbdu2AfDgwQPevXvHnDlzgNSXAgMGDODy5cscO3YMqVRKy5YtUSp1T2/MSL58+Vi1ahV3795lzpw5LF26lF9//VXD5vHjx2zbto3t27dz/fp1lEoljRo14s8//2TdunXcvXuXKVOmYGSUw9CCnnwIeYdcFkHx4LLqfVZW1vgGFOGBjsYvKSmJp48fUiK4jHqfVCqlRHDZbBvMuNgYLCwt9X4YDf/whih5GIElKqj3WVjlw9OvOE8f3tQrjux4dOcyg7+vyZi+X7BxyQRiouV6hw37qK1IJm1efsV5+vDGX9KlVCpZNW8E9Zp3xLWQ9umCukhKSuLJ4wda8qYMD+7f1Rrm4f07GvYApUqXV9uHvE8tIyUz2FhZWeMXEKQzTl2kxVUiS1zZl7cnWstbGb07aH35EPIWuSyc4sHl1PssrazxDQji0f3bWsMkf6wPGeuQVCqleHBZHunQl5AQz8mj+yng7IKTUwE9tRluXTX0fM0NdhWDCTt+TmNf6JE/sK8YDIDExATb0kUJO5b+UIpKRdjxs9hVzN1UXEdbKbbWUh68SH9JkpAIz9+l4OWafdrnt5cysYcNY7vmo1NjS+zzpY+pGxuBCkjOMMCTnAIqFfi465GnRsaYFvZB8SBDO6tSobh/EzMvf72uzapyHeKu/IlK1+iVkTHWVeuhjIsl6fVzveLMiKONBBsrKQ9fpV9kQiK8CFHiWTD72xwnOyljOlsy4jtLvq1nhp111vkIZQJMGN/FisHtLGhSyRSTXLxDDf/wmih5GAEl0pceWVjmw9O3OM/+Yv+QkSh5GHeunaFS7ZZ6hzHUNi4pKZkHT55SpmT6sgCpVEqZksVz/aCti5QUJcdO/0lCgoKiAfqV4zRCQ94SKQunaMny6n2WVtZ4+xfl8QPty2WSk5J4/uQ+RUump7VUKqVoyXIaYXwDS3DhjyPEREeiVCo5f/owSYmJFCleRlu0WTD09vdz6Xv6+CHJyckaNu6FPHDK78yDe/pdg6HWB4EgM//pNe8xMTEsX76cdevWUadO6tvr1atX4+6evq7m+++/V//t7e3N3LlzKVeuHDExMVhbW+PgkDr1sUCBAhpr3lu10hxVWbFiBfnz5+fu3bsUK5Z1rV9mRowYof7b09OTQYMGsWnTJgYPHqzen5iYyJo1a8ifP3Wq9OHDh7l48SL37t3D399frVkXCoUChULzZikxUYGpqZlWe7ksHAA7O3uN/XZ2DsgzTOvKSHRUJEplCrZ2mlNEbe3sefP6hdYwUZFyft+0mroNv9CpPTORsjAAbOwcNfbb2DoSJQ/TOx5tBAVXJrhCHRwLuBEa8ordG+axYGIvfp64FqkeL0aidGjLZ+tAlDz8L2k7vHMlUqkRtRu3z3XY1LxRYpcpb+zs7Hnz6qXWMHJZRBZ7Wzt75LII9XEAW/usccpk2suILtLiyrweLeP5MpNW3jKXUdtsrulTUV+rlnPpqg9ROuuDA28z1YdD+7azfuVvKBLicXUvzPAJszHWMWKUVZvh1lVDz9fcYObshCJEs31RhIRhYpsPqbkZJva2SI2NUXwIz2QTjlWA7rZZGzZWqQ+NUXGaL4Cj45TqY9p4/i6ZtQdSCIlIwdZaSuNK5gxol48JK6NQJKU+/CcmQfPqFuw+E49EAs2rWWAklWCbTbxpSK3zITEyIiVKrrE/JToSY2e3HMObevhi6uaBbN3CLMfMi5XB8fufkJiakRIlI3TeWJSx0VpiyZ58lqnXEZNpDX9MnFJ9TBsv3qew8WgKH+QqbKwkNChnyo9fWjBtY5x6dP3qwyQiolVExapwcZTSrLIpBeykrDygZYheC2l9QD7bzP3DX++7MnLx1G7MzS0pWV7/KfOG2sZFRkWhVCpxsLPV2G9vZ8vL129zDJ8dT5+/pNcvI0hMTMLCwpzxQwfhWTh3/loiP7a/mdPAxs5BfSwz0VFynen2LkO69f55EgunD6P3t/UwMjLC1MycvkOn4eyi32wKQ29/P5c+uSwcY2OTLGvw7ex1x6tLm6HVh38Fkv9Px3F5xX/64f3JkyckJiZSoUL6iKiDgwMBAQHq31euXGHMmDHcuHEDmUymHjl/+fIlQUG6pwY9evSIUaNGceHCBcLCwjTC6fPwvnnzZubOncuTJ0+IiYkhOTkZGxvNqdoeHh7qB3eA69ev4+7urn5wz4nJkyczdqzm2s4efQbRs+/PAJw5cZjFC2aojw0dnbt13p9CXFwsk8cOxr2wJ23af6/T7uLpfWxcMl79u+fQ+Z9NU9mqjdR/u3n44e7hz6jeTXh457LGSH8aF07vY8OSCerfvYfO+yy6Xjy5y/H9Gxg2bSOS/0DDePrEERbPT1+2MmxM7tZRf27OnDjM0gXT1b+HjJ72Wc9XrWZ9SgSXQyYLZ+/2jcyeMpJx03/T+nLNkOuqoeeroVKuiAnt6lmqfy/cHpONtW7uPksfqX8bpuT5u1jGd7ehdIAp524nEhOvYtnuWNrWs6BmaVtUKrhyL4mX75NR6e+v7pOxqlyHxDcvtDq3Uzy8TcjkQUit8mFVtR6OXQYSMm0IypiobOMs7W9Mm5rp9WTp3vhP0nb/ZfpI/btwePE+nlEdrQj2NebCvdR0PXcnOYONkqhYFb1bWuBoIyE8KmsCXjqzl01Lxql/9xi64JO05ZZzJ3ZQtloTTHS8nAfDbuP+KQq5ubJs9jRiY+M4dfY8k+csYM7EMdk+wJ89eZBVv01W/x4w8ledtn+V7RsWERcbw+Bx88lnY8eVC6dYOH0YwyYtoZBn1tl3ht7+GrI+UR8E/1b+0w/vOREbG0uDBg1o0KAB69evJ3/+/Lx8+ZIGDRrk6CCuWbNmeHh4sHTpUlxdXVEqlRQrVkwvx3Lnzp3jm2++YezYsTRo0ABbW1s2bdqUZT2+lZWVxm8LC4tcXd/QoUMZMGCAxr6Hr9KdvZStUFXDG2byxzX3crkMewcn9X65PAJPLz+t58hnY4tUakRkpreSkXIZdvaaIw3xcXFMHDUICwtLfh4+EWNj3cWvRLmaePqlT5lLTk5N1yh5OLb26S80oiLDcfcMyBL+r+Dk7I61jT2h719qfXgvWa4mXnpoi46MwN0zd9PxMvL43lWiIyMY1iP95YJSmcLva2ZxbN96Jv12INvwqXkjzfLGWC6X6fS+amefdeQ2MoN92v+RsggcHNLzVy6X4eWd/bT+chWqaHiGTfPxIJdFYJ8hrki5DE8dcaWVt8wO9yKzuSZ9KVuhqtrreqq+RHXcGetDpFyGp5d2fTY660NElvpgaWWNpZU1Lm6F8A8oyvdtG3Hp3Gmq1KinVZuh1lVDz9e/giIkDDNnJ419Zs5OJEVGo0xQkBgmQ5mcjFkBx0w2jijeZz+qevNxEs/fpY8ypzlWs7GUEhWb/lCZz1LK6w/6OzWKV6j4IEshv336dPH7L5IZsywaKwsJSmWqzeSeNlx5kPPibWVMNKqUFIxs7DT2G+WzRZlpND4zElMzLMtWIXLvZq3HVYkKkkPfQ+h7Ep8/ouCY+VhVqUP0oR1a7dO48yyZGSHpaWJslPpy09pSQlSG0XdrSylvw/RPu4RECJUrcbLTPdX+5cfzOtlJCY/KGnfxsrXw9Cuh/p38sR2JjszcP4Tj5hmot7bseHzvCh/ePqdz/xnZ2hlyG5cRWxsbpFIpEZmc08nkkTqd0emLiYkx7i4FAQjw9eb+oyds27ufgb266wxTqnw1fALSPcKnp1sEdhnSLUoeQWEdS0ny2djpTDfbj+kW8u41R/dtZeK8jbgX9gGgsJc/D+9c59j+rXTqldW5raG3v/+UPjt7R5KTk4iNidYYfZfLdF/Dv6U+/BtQ/fdXYRsU/+nU9vHxwcTEhAsXLqj3yWQyHj58CMD9+/cJDw9nypQpVKtWjcDAQLWzujRMTVM/I5OSwSNkeHg4Dx48YMSIEdSpU4ciRYogk2n33q2Ns2fP4uHhwfDhwylbtix+fn68eKF92mpGSpQowevXr9X6c8LMzAwbGxuNLeMbPgtLS1xc3dWbe2FP7OwduH39itomLi6Wxw/uERBYVNspMDExwdvXn1s30sMolUpu3biCf4YwcXGxjB85AGNjY34ZOSXHN43mFlYUcCms3lzcfbCxc+LBrfS8jI+L4fmjW3j7l8gmptwjCw8hNlqucaOlj7b7ty5qaHv26Bbe/iU/WUeFGk0ZMXMrw2dsVm+2Dvmp/0VH+urxGTsTExN8fAO4eT3dx4NSqeTm9SsE6HA44x9YlFs3rmrsu3HtstreuaALdvYO3MxgExcXy6MHd3XGmUbm8lboY3m7lSWu7Mubj68/t65rlreb169qlLdPwcLSkoKu7urNvbAXdvaO3Lp+WUPf4wd38QvUPrvGWEd9uH3jCn7Z6FN9/JfRaWVmbYZaVw09X/8K8vPXcaxdUWOfU53KyM5fB0CVlETk1Ts41a6UbiCR4FirEvLz17KNW5GU+qCYtr0LVxIZoyTAI/1FibkpeLoY8eytfs4CAcxMUj8NFxWT1f9KbLyKeIUK/0LGWFtKuPlYD89rKckkvnyCWUCGz5JJJJgFlEDxLPu+yKJ0ZSTGJsRd1O3EKyMSiQSJcc7TSBVJEBapUm/vI5RExSrxd09f5mRmAh7OUp6/188PDaR65ne0lRIVq3tKgptT6m2TLhtzCyvyFyys3grq6rse38LrL/QPGTl3fDuFvINyfJFtyG1cRkxMjAnw8ebqzfS14Eqlkis3bxMUoP3l5KeiUqlITMq+fllYWuHsUki9uRXyxtbekbs3L6lt4uNiePrwDr4Z60kGjE1M8PQJ1AijVCq5e/OyOkyiInUphjSTB22pVIpSxzQZQ29//yl93r7+GBsba9ybvHn9krDQEAKKaI/331IfBILM/KdH3q2trenSpQs///wzjo6OFChQgOHDhyOVpjaMhQsXxtTUlHnz5tGjRw9u377N+PHjNeLw8PBAIpGwd+9eGjdujIWFBfb29jg6OrJkyRJcXFx4+fIlQ4YM0VuXn58fL1++ZNOmTZQrV459+/axY0f2Iw0ANWrUoHr16rRq1YpZs2bh6+vL/fv3kUgkNGzYMHeJowWJREKT5m3Ytnk1Bd3cKeDswuZ1y7B3cKRcpWpqu7HD+lG+UnUaNUtd99+0xdcs+HUSPn6B+PoXYd+urSgS4qlVtzGQ2vhNGDkAhSKBvoNGEhcfS1x8LAA2NnZ6OdyTSCTUbvINB7YtpYCLB44F3NizaQG29vkpmeHb6HPGdKNkhdrUbNQOgIT4OELfp6/ZCg95w6tn97GytsUhvwsJ8XHs37qIUhXrYmPnSOj71+xY9yv5CxaiSHBlvdOtjlpbYZwKuLF70wLs7PMTXL6W2u7XMd0JrlCbWo3aatUWlkmbdT47rPPZaZzLyMgYGztHCrp56qWtWcvWzJs1GV+/APz8i7Bn1+8oEhKoXS91NH/OzEk4OjrxbafUUYemX7Ri5JB+7Nq+mTLlKvLH6eM8efyAHj8OVF9r0+Zf8fumtbi4uuNc0IWNa5fj4OBE+UpVderQlW5Nm7fm901rcHF1p0DBgmxcuwJ7B0eNuMYM+4nylarRuNmXH6+pDfNmTcbHLxA//0D27vodRUK8+poAZBHhyGURvH/3BoAXz59iYWGJUwFn8uXT70sCEomExs1bs2PzalzcCumsD+OH9aNcpeo0/FgfmrRoy8JfJ+LjF4iPfxH279qCIiGemnVTvzkc8v4NZ08fp2TpctjY2BEeHsqureswNTWjVNlKWrVo02bIddVQ89XIyhIr3/TvMFt6uWNTMpDEiEgSXr0jYMIAzN2cudH5l9T4l2zCo9c3BE7+mVertuFUqyIurRtx6Ysf1HE8m72SkiumIr9ym8hLN/Hs2xFjKwterd6eo57MnLiqoGFFMz7IUgiPVNK0igWRMUpuZHjI7tvaihuPkzh1LXWUqGUNc249SSIiSoWttYQmlc1RquDy/fQwFYuZ8j48hZg4FV6uRnxV24ITVxR8kOn3YBt9fA+OHX4k8cUTEl88Il+tpkjNzIg9l+ot3aHjj6TII4jctV4jnHXl2sTfuIgyVnNJgMTUDJuGrYi/eYmUKDlSq3xY12iIkZ2DxufkcsOpG0nUK2tKqFxJRLSKRhVMiYpVcetp+oNZz+bm3Hqawh+3UtPmiyqm3HmWTES0ClsrCQ3Lm6JSpa5zh1RHeKX9jbn3IoXYBBWujlJaVDPj8ZtUj/b6IJFIqNn4Ww5tX0wBl8I4FnBj76b52Nrnp0S59L5r3riulChfmxoNU/2bKBIy9V0f3vD6+X0srW1xcHJR74+Pi+H6+SO0/G5QrtPMkNu41s2bMHnOQgJ8fSji58Pve/aTkKCgUd2aAEz6dT5Ojg5075CaXklJyTx/9RqA5KRkwsJlPHr6HAsLc/VI+5I1G6hQJpgCTk7Exydw9PQfXL99l+ljhuU63Ro0a8vuLStwdilEfmdXtm9YhJ2DE6Ur1lDbTR3Zi9IVa1KvSRsAGjZvz9I5Y/HyLYK3X1EO7dmEIiGeanWbAuDi7omzSyFWLpxM2879sM5ny9ULp7hz4yI/jZiltzZDbX8/pz4rK2tq12/MqqULsLbOh6WlFcsXzSEgsKjeLyAMuT4IBBn5Tz+8A0yfPp2YmBiaNWtGvnz5GDhwIJGRqVOx8ufPz6pVqxg2bBhz586ldOnSzJgxgy++SHfO5ObmxtixYxkyZAidO3emQ4cOrFq1ik2bNtG3b1+KFStGQEAAc+fOpWbNmnpp+uKLL/jpp5/o06cPCoWCJk2aMHLkSMaMGZNj2G3btjFo0CDatWtHbGwsvr6+TJny960hat6qPQkJ8SyeN5242BgCg4ozfNwMjdG3kPdviY5Kn85WpXodoiLlbF63HLksAk9vX4aPm6GeqvTs8UMePUj1RP5jt7Ya51uwfAsFnF3Qh3otOqNQxLNh8TjiYqPxCSxFnxELNdb4hYa8JibDVM6XT+4we0xX9e9tq1OnFVas+QUd+oxHKpXy5sVDzp/cTXxcNLb2BShSshLN2vbGxMRUz1SD+i06oVDEs37xeOJio/ENLMWPWbS9IiYqfYbGiyd3+HVMN/Xv31fP/KitGZ36aL5E+lSqVq9NVKScjetWIpdF4OXty8hx09R5ExYagjTDevrAoGL89PNINqxdzvrVy3Bxc+OXERPw8Ex3vtXyq3YoEhJYNG8GsbExFAkqzsjx0z5p3VaLr9qRkBCvjiswqDgjx0/XiOv9u8zlrTaRkXI2rVuhvqYR46ZrTI07fGA3WzasUv8e+UtfAHr3H6JxM5ITX7T6BkVCAkvmTSMuNoaAoOIMHTczU314Q3SGMlf5Y33Ysm6Zuj4MHTdTrc/ExIz7d25wYPcWYmKisbNzILBoScZPX5TFUU52GHJdNdR8tS1TjErH1qp/B81IvWl/tWY7N7sMxcwlPxaFMjwYPX/NpS9+IGjmUDx/7EDC6/fc+mEEYUfSP2n0busBTPM74D+6L2YF8xN14x4Xm3YlMZMTO304clGBqYmE9vUtsTCT8ORNMgu2xWp4ineyM8LKIn2HXT4pnZtaYWUuISZexZM3ycxYH0NMhs/FOdtLaV7NHEtzCeGRSg6dV3D8iv7fKo+/cha5tS22TdtiZGNH4utnhM6fgDI6Nf+M7J1AqTkyaFzAFTPfID7MHZslPpVSiXFBNxwr1sTIygZlbDSJLx7zYdYIkt/l/pvbAMevJmFqLKFNLTMszCQ8e5fC4j3xmmlnK8XKIv2h29ZKwncNzNVp9/RtCrO3xqk/F5eiBP9CxtQINsXUGOQxKm4+SebwpZyXx2WkbvPvSVTEs3HxWOLjovEOLEWvYYs0+oewkFfEZuq75o5N9zWxY03q2tzyNb7gu94T1fuvnj2ASqWiTFX927WMGGobV7taZeRRUazcsIUImRxfL0+mjR6Kw0fHwSFh4Uik6SPUYRERdPvpF/XvzTv3sHnnHkoWC2LOxNEAyCOjmDR7IRERMqysLPH2KMz0McMoG5z72XuNv+yAIiGBVQsnERcbg1+RkgwaPUcj3T68f6NxP1KhWj2iomRs37CESFk4hb38GTR6DrYfnd0aGxszYNSvbF2zgNkTBpKQEIezizvd+o2mZNkqemsz1Pb3c+vr3K0PUomUGZNGkZSURHDpcnTr9ZPeusBw64Oho/oP+GX6NyFRqf4JlzUCQ+Hmow85G+UhoQn6f1/9n0aK4VaVAhbyvJagExWG26gnq/6ezyx+Dgy5vAFIJfpPR/6neVGkRs5GecT+yRdyNsojhjztktcSdDIraHVeS9BJo2r6v+jNCwpYRuZslEc4p7zJawk6eYFXXkvQibXxpzlp/H/HkPv8YD/tSzX/DYTcu5Kz0WfCuYh+n1H8L/GfH3kXCAQCgUAgEAgEAsHfj0ryn3ahZnCI1P4MTJo0CWtra61bo0afNrVNIBAIBAKBQCAQCAT/v4iR989Ajx49aNOmjdZjuf3cm0AgEAgEAoFAIBAYIoa8PPK/iHh4/ww4ODjg4JB33yYWCAQCgUAgEAgEAsF/CzFtXiAQCAQCgUAgEAgEAgNHjLwLBAKBQCAQCAQCgSDXCId1/ywitQUCgUAgEAgEAoFAIDBwxMi7QCAQCAQCgUAgEAhyjUoiHNb9k4iRd4FAIBAIBAKBQCAQCAwc8fAuEAgEAoFAIBAIBAKBgSOmzf+fYS5NyGsJ2VIm5W5eS9BJnIXhfv5PjlNeS9CJBFVeS9BJPqO4vJagk2SVSV5LyBalAb/73T/5Ql5L0EnjoRXyWoJOjh15kNcSdDI5cn5eS9BJYkpAXkvIljfSoLyWoJNbcYarrZj5/byWoJNIiWNeS9BJisooryXoJJ9RdF5LyIb8eS3gkxHfef9nMdy7L4FAIBAIBAKBQCAQCASAGHkXCAQCgUAgEAgEAsEnID4V988iUlsgEAgEAoFAIBAIBAIDR4y8CwQCgUAgEAgEAoEg14g17/8sYuRdIBAIBAKBQCAQCAQCA0c8vAsEAoFAIBAIBAKBQGDgiGnzAoFAIBAIBAKBQCDINcJh3T+LSG2BQCAQCAQCgUAgEAgMHDHyLhAIBAKBQCAQCASCXCMc1v2z/CtG3j09PZk9e7b6t0QiYefOndmG6dSpEy1atPisuj43q1atws7OLq9lCAQCgUAgEAgEAoEgj/lXjry/e/cOe3t7AJ4/f46XlxfXrl0jODhYbTNnzhxUKlUeKfz3sm/PLrZv24pMFoGXlw8/9OyNf0CgTvs/zpxi3drVfAh5j6urG52+70rZchW02i6YN5uDB/bRtXtPmrf4Mtfath46wfo9RwiXR+Ln4c7Azm0p6uul1XbnsTPsP32ep6/eAhDoVZie7Vpo2J+4cJXtR09z/+lLomJiWTt1BP6ehXKtC2DHvoNs3rGbCJkcHy8P+nb/niL+flptn718xcr1m3n45CkhH0Lp3aUTXzVvomGzfusOzpy7wMs3bzAzNaVoYADdO35DYXe3XGvbv3cHO7dtRi6LwNPLh649+uIfUESn/Z9nTrJx3Qo+hLzHxdWdDp27U6ZcRfXxc3+e5tCBPTx5/JCY6ChmzV2Kl49vrnUZurY9e/aw7fffkclkeHl707NnTwICAnTanzlzhrVr1hASEoKrmxvfd+5MufLlNWxevnzJyhUruHXrFikpKRQuXJjhI0ZQoECBXOvbt2cXO7ZtUdfV7j375FhX169dpa6rHb/vplFXN6xbzZnTJwkLDcXYxBhfXz++7fA9AYG680MX+/fsZEeGfO3W88cc83XD2pXp+fp9N8pmyteD+/fw9PEjoqOjmDVvCd6fmK8ATaqYU6W4KRZmEp6+TWbTkXhC5Uqd9o0rm9OksrnGvvfhKYxfGa3+7WQrpWVNc3zcjDE2knDveRJbjsUTHadfP+RQtSzeA7tgW7oY5q4FuNyqFyG7j2Ufpnp5gmYMwTrIj4RX73g8+Tder9mhYePRsz3eA7pgVjA/UTfvc6f/eCIv3dJLU0ZUKhVn9szl+pmtKOKjcPcpTYP2Y3Bw9tQZ5uqpDVw9tZHI8DcAOLn4UbVpL3yK1VDbXDu9mbuX9vL+5R0SE2L56ddLmFva5ErbprM3WX3qKmHRcfi7ODGkeXWKFy6YY7gD1x8yZMMhahX1YnbHpgAkpaQw/9B5/rj/gtfhkeQzN6OCnzv9GlWmgK11rnSlseXwKdbtOUZ4ZBR+hd34uVNrivp6arXdcexP9p+5yJPX6X1X76+b6bSfvGwj24/9yU/ftaJ941q51mbIdVWlUrF/ywLOHttGfGw0XoHBfN11JAVcPHSGeXz3Msd2r+Lls7tEyULpOmg2JcvX0bD5sU1xrWGbfzuAul901kubQff5BpynkJqvm9at4OihvcTFxhBQpDjdew/A1c0923AH9u5g17ZN6uvq0qMffhmuKzFRweplC/nj9HGSk5IoWboc3Xv9hJ29g166DL3PFwjS+FeMvGemYMGCmJmZZWtja2srRq1zyZlTJ1m2dDHt2n/L7Hm/4eXtzaiRQ5HLZVrt7929w/Spk6hfvyFz5v1GxUpVmDh+DC+eP8tie+7sHzx4cA8HR8dP0nbk7CXmrPmdLq2asHrKcHw93Ok3aS4RkVFa7a/eeUj9yuVYOGoAy8b/QgFHe/pOnMOHiPRriVckUjLAlz7tc/8iISPHz/zJb8tX07Fta5b8OhUfTw8Gj56ITB6p1V6hUOBasADdO3yDg72dVpsbt+/QokkDFkyfxPRxI0lOSWbw6AnEJyTkStsfp4+zculvfN2+IzPnLsHTy4dxIwfrzNP7d28za9p46tRvzMy5S6lQqSpTJozUyFOFIoEiQcXo0Ll7rrT8m7SdOnWKpUuW0P6bb5g3bx7eXl6MHDECuVyu1f7u3btMnTKF+g0aMG/+fCpVqsT48eN5/vy52ubd27f8PGgQ7oUKMXXqVBYuXEi79u0xNTXNtb4zp06wfOki2rb/jl/nLcLT25vRI4dkW1dnTJ1IvfoNmT1vERUqVWHS+NEaaefm5s4PPfswb+ESpk6fTYECBRk94hciI7Vfsy7+OHWCFUt/o237DsyatxhPbx/Gjvwl23ydOXUCdes3Yta8JVSoVIUp40dpaEtISCCoaHE6dO6WKy3aqFfejJqlzNh0JI7p66NJTII+X1lhbJR9uLdhKQxdGKneZm2KUR8zNYE+ra1ABXO3xDBrYzRGUgk9WlrpPZnQyMqSqJsPuN13rF72Fp7ulNu9mPCTF/ijbHOezVtN8cUTcKpXVW3j0roRRaYP5dGEBfxRviXRN+9TYd9yTPPrdzObkfOHlnL5+FoafjOGjkO2YGJmwea5XUhOUugMk8+uIDVbDqLzsO10GrYNz8CK/L6wN6FvH6ltkhLj8S5ajcqNeuRaE8DB6w+ZsecMP9Qtz6Z+bQlwcaLn8t2Ex8RlG+5NRBSz9v1BaS9Xjf0JicncfxNK9zrl2NyvLbM6NOZ5qJx+q/Z9kr7D564we+0OurZqxNpJv+Dn4caPUxYQERmt1f7KvUfUr1yG30b0Y8XYgTg72tFn8gI+RMiz2J64dINbj5+T3972k7QZel09umsFpw5s4OtuIxk4aT1mZhYsnPgDSYm6y5xCEY+bpz9tugzXaTNxyQmN7Zue45BIJARXqKuXLoPu8w08TwF2/r6R/Xu280PvgUyetQhzc3PGjxxEYjb5+ufp46xauoA27Tsyfe5SPLx8GD9yEJEZrmvl0vlcvniWQUPHMm7KHGQRYUybOFIvTYbe5xs6Kok0z7b/R3J91UqlkmnTpuHr64uZmRmFCxdm4sSJANy6dYvatWtjYWGBo6Mj3bt3JyYm/QYnbSr7jBkzcHFxwdHRkd69e5OUlKS2+fDhA82aNcPCwgIvLy/Wr1+fRUPGafNeXqkjqaVKlUIikVCzZk2Nc6WhUCjo27cvBQoUwNzcnKpVq3Lp0iX18ZMnTyKRSDh27Bhly5bF0tKSypUr8+DBA73TZs+ePZQrVw5zc3OcnJxo2bKl+phMJqNDhw7Y29tjaWlJo0aNePTokUb4VatWUbhwYSwtLWnZsiXh4eFZzrFr1y5Kly6Nubk53t7ejB07luTkZL01ZsfOHdto0LARdes3pHBhD3r16YeZmRlHDh/Sar971w5KlynHl1+1oVBhD77t0AkfH1/27tmlYRceFsbi3xYw8OehGBt92mSPjfuO0rxOVZrVqoK3uytDun6Duakpe06c1Wo/rm8XvmpQE3/PQni6FWR4jw4oVSou37qvtmlcvSJdv2pKueK6Ryv1YeuuvTSpX4dGdWvhWbgQA3p1x9zMlANHj2u1D/TzpUfnDtSuXgUTExOtNtPGjqBhnVp4FS6Er5cnQ/r1JiQ0jIePn+ZK2+4dW6nXsAl16jWiUGFPevQZgJm5OccOH9Bqv3f3NkqVKU/LVm0pVNiD9t99j7ePH/v3po/m1axdn6/bd6RkcJlcafk3aduxYwcNGzWifv36FPbwoM+PP2JmZsbhw4e12u/atYsyZcvy1VdfUbhwYTp06ICPjw979uxR26xevZqy5crRpUsXfHx9cXF1pWLFip/0knHXjm3Ub9g4Q13tj5mZGUcPH9Rqv2fX9o919euPdbUz3j6+7MtQV2vUqkNwqTIUdHGlsIcnXbr3IC4ujufPclfmdu3YSv2GjalTPzVfe/b5CTMzM535mqqtPC2/Ss3Xbzp8zNc9O9U2terU5+v2HShR6q/lK0Ct0mYcPJ/AzSfJvA1Tsnp/LLbWUkr6aq+LaSiVEBWnUm+x8ekj6t6uxjjaSFl7MI63YUrehilZcyCWwgWN8C+sX5sXeug0D0fPJmTXUb3sPbq3Jf7Za+4NnkrM/ae8WLie99sO4dWvk9rGq39nXi3fwuvV24m594RbvUaTEpdAoU6t9DpHGiqVikvH1lClcU/8g+tSwD2Qpp2nES3/wMPruvX6layNb/EaODh74ujsRY0WP2FqZsnbp9fVNuXrdqJSw+64epXMlaY01p65zpcVitKiXBA+zg6M+LIW5ibG7Lx0V2eYFKWSYRsP07NeBdwdNEf581mYsbhbCxqU9MOzgD0lPAoytEUN7r75wDuZ9gfu7Niw7zgtalfmi5qV8HZ3YWiXtpibmrL75Dmt9hP6dKJ1/eoEeLrj6VaQEd2/SU3/25r3Ih8i5MxYtZXxvTthbJTDmycdGHJdValUnNy/jgZfdqdEudq4eQTwXZ9JRMpCuXlJe98KULRUNZq27ZtltD0jNnZOGtvNSyfwK1oeJ2f9Zt4Zcp9vyHkKqfm6d9dWvvr6O8pXqoqnlw8/DhyGLCKci+f+0Bluz44t1G3YlNr1GlOosCc/9Bn48X5hPwCxsTEcP7yfTl17U7xkaXz8AujdfwgP7t3m4f07Oeoy9D5fIMhIrh/ehw4dypQpUxg5ciR3795lw4YNODs7ExsbS4MGDbC3t+fSpUts3bqVo0eP0qdPH43wJ06c4MmTJ5w4cYLVq1ezatUqVq1apT7eqVMnXr16xYkTJ/j9999ZuHAhHz580Knn4sWLABw9epR3796xfft2rXaDBw9m27ZtrF69mqtXr+Lr60uDBg2IiIjQsBs+fDgzZ87k8uXLGBsb8/333+uVLvv27aNly5Y0btyYa9eucezYMcpnmD7TqVMnLl++zO7duzl37hwqlYrGjRurX1xcuHCBLl260KdPH65fv06tWrWYMGGCxjnOnDlDhw4d6NevH3fv3mXx4sWsWrVK/fLkr5CUlMTjxw8pGVxavU8qlRIcXJoH97XfBN2/f5fgUqU19pUqU5b79++pfyuVSmbNmMqXrVrj4eH5adqSk7n/9CXli6dPj5JKpZQrHsitR/p1bAmKRFKSU7CxtvokDTq1JSXx8PFTygSX0NBWumQJ7tx/+LedJzY2dRTJJp/+0zaTkpJ48vihxoOsVCqlRHBpHujozB7cv5vlwTe4dDm9Or/cYOjaHj96pLEMJ7UuBHP/3j2tYe7fu0epDPYAZcqUUdsrlUouXbqEm5sbI4YPp13btvTv35+zZ7W/fMpR3+OHBGeqqyWDS3M/m7paMlNdLV2mnE77pKQkDh3Yh5WVFV5ePrnS9uTxQ0pkyteSwWV0tiMP7t+lRJZ2pJzOcvBXcLSVYmst5cGL9BeeCYnw/F0KXq7ZP2Tnt5cysYcNY7vmo1NjS+zzpY+pGxuBCkhOSbdPTgGVCnzcP8/qNLuKwYQd13wADD3yB/YVgwGQmJhgW7ooYccylDGVirDjZ7GrWCpX55KHvSY2KhTPIpXV+8wt8uHqVZI3T6/pFYdSmcLdS/tISozDzTt359dFUnIK9958oKJv+kOXVCqhol8hbr54rzPc4qMXsbe24MvyRfU6T0yCAokk9cE+d/qSuf/sFeWLpU+9lUqllC8WwK1HWWeoaSNBkUhycgo21pbqfUqlktEL1vBt0zr4FHLJlSa1NgOvq+EfXhMlDyOgRPr0bQvLfHj6FufZwxt/23mi5GHcuXaGSrVb5mzMv6PPN9Q8BQh5/w65LEJDo5WVNX4BRXSeU9d1lQguo+7/nz5+SHJysoaNeyEPnPI78+Be9tdi6H3+vwEVkjzb/h/J1cN7dHQ0c+bMYdq0aXTs2BEfHx+qVq1K165d2bBhAwkJCaxZs4ZixYpRu3Zt5s+fz9q1awkJCVHHYW9vz/z58wkMDKRp06Y0adKEY8dS1/U9fPiQAwcOsHTpUipWrEiZMmVYvnw58fHxOjXlz58fAEdHRwoWLIiDQ9bpgLGxsfz2229Mnz6dRo0aERQUxNKlS7GwsGD58uUathMnTqRGjRoEBQUxZMgQzp49S4Ie05YmTpxI27ZtGTt2LEWKFKFkyZIMHToUgEePHrF7926WLVtGtWrVKFmyJOvXr+fNmzfqGQRz5syhYcOGDB48GH9/f/r27UuDBg00zjF27FiGDBlCx44d8fb2pl69eowfP57FixfnqC8noqIiUSqVal8CadjZ2SOL0D7dSi6TZXmDaGdnj1yW/kJk29bNSI2kNGuuX8eo9TxRMaQolTjY5tPY72BrQ4SOaWqZWbB+O04OtpQrnvv1u9kRGRWdmm52mtMW7e1sidAx3Sq3KJVK5i9bRbEiAXh5FNY7XPTHPLW1y5qnGfMoI3JZBHZa7GUy7WXgUzFkbVFRUdrrgr09ETrOJZPJsNNin6ZNLpcTHx/P1i1bKFO2LBMmTqRy5cpMnDCBWzdv5lJfatplOZ+dPfJs62pmeztkmdL60oXztPmyKV+1aMyundsYN3EqNrb6T8mN1qHN1s4eWYT++Wr7GfIVwMYqtaOPitNc3x4dp1Qf08bzd8msPRDHgt9j2HQ0HkdbKQPa5cPMJO14ColJ0Ly6BSbGqdPoW9awwEgqwTabeP8KZs5OKELCNPYpQsIwsc2H1NwMUyd7pMbGKD6EZ7IJx6ygU67OFRsVCoCVjeayJysbR2Ijw7QFUfPhzQNm9C3FtN7FObh+NF/2WICT66evl82ILDaeFKUKx3yWGvsdrS0Ji9Y+bf7qs7fsuHSX0V/V1usciqRkZu8/S6OS/lib5266a3Z9V7hc+5KvzMzbsAsne1vKF0ufIbZ69xGMjKS0bVgzV3oyYuh1NUqeWm7z2WqWuXy2jkTJsy9zueHiqd2Ym1tSsrx+U+b/DX2+oeZp2vmALOvQbbPp+1OvK0WrzrQwclk4xsYmWFlr1jU7e93xpmHofb5AkJlcDQncu3cPhUJBnTpZpyPdu3ePkiVLYmWVPrJZpUoVlEolDx48wNnZGYCiRYtilGGKl4uLC7du3VLHYWxsTJky6W/OAgMD//IUkydPnpCUlESVKlXU+0xMTChfvjz3Mr1VK1Ei/W2qi0vqG+0PHz5QuHD2Dej169fp1k37eqC066pQId05lKOjIwEBAerz37t3T2OaPUClSpU4eDB9GuyNGzf4888/NUbaU1JSSEhIIC4uDktLzRsYhUKBQqG5hihRocA0B38BfxePHz1k9+4dzJ67EIkk796Ord55kCNnL7Fw9EDMTLOfGmuIzFm0jGcvXzFvyvi8liL4RNKcZ1asVEldz318fLh39y779++neIZ2Jy8pXrIks+cvJioqksMH9zN18gRm/Dovy03Tv4VyRUxoVy+9XVy4PSYba93cfZY+Uv82TMnzd7GM725D6QBTzt1OJCZexbLdsbStZ0HN0raoVHDlXhIv3yfzb/SbevvCbg6uH63+3abPp78gdnT24vsRO1HER/Pg6iH2rvqFbweu+9se4HNDbEIiwzcdYXSr2thbWeRon5SSws/rDqJSwfAvc+8M7q+yatdhjpy7wqKR/dR9172nL9l08CTrJv2Sp/3q382lM3vZtGSc+nePoQv+kfOeO7GDstWaYGL6z9wX6cN/qc8/feIIi+fPVP8eNmZKHqr55/i39PmCfye5eni3sMi5s8uJzOt9JBIJSqVuL7//NBn1pXWM+uj7O9ImJ2JiYhg7dixffpnVwZq5uXmWfZMnT2bsWE3nR31+7M+P/X7KYmtjY4tUKs3ytlUul2HvoP3G3c7ePoszD7lcpn6jeufObSLlcr7v+I36uFKpZMWyxezeuZ3lq9Zpv9DM57GxxkgqzeLgJyIyCge77EcF1+05zJpdB5k/oj9+Htl7Mv0UbG3ypaZbphkAMnkkDn/DuqY5i5Zx7vJV5kwaS36n3Dn7y/cxTyPlWfNUl/dVO3uHLI5t5HJZljfSfxVD1mZjY6O9LshkOOg4l729PXIt9mnabGxsMDIyyvISsFChQty5q3ttrnZ9qWmX5XxyGXbZ1tXM9nLsM6W1ubkFrq5uuLq6ERgYxA9dO3Lk0AFaf91eL235dGiLlMuw1zIrKlVb1nyN/Jvy9ebjJJ6/S2830pzS2VhKiYpNn+Oez1LK6w8pmYPrJF6h4oMshfz26ZPX7r9IZsyyaKwsJCiVqTaTe9pw5UFSNjF9OoqQMMycNUfQzZydSIqMRpmgIDFMhjI5GbMCjplsHFG8z37k0q9kbY016CnJiQDERoVjbZvuJTk2KhznQtn7DDEyNsWhQKp3cBePYrx7fotLx9fQ6Ntx2YbTB3ur1NkN4ZlG2cNj4nDKNBoP8CoikreyKPqu2qvep/x4k116yHx2/fwdhRxT+5S0B/d38iiWdm+Z61F3yL7vcrTL3qP+2r1HWb37CAuG9cHPI93j+LX7T5BFxdDsx1HqfSlKJXPWbWfTgRPsnqdfuhpaXS1ethaefukPNMlJqWUuOjIcW/v86v3RkeG4ef41PzVpPL53hQ9vn9O5/wy9w/wb+nxDyVOAchWqaHiET1sqKpdFYO+Qfn2Rchme3tpf6KVel5FWnWn3C3b2jiQnJxEbE60x+i6X6b6nSMPQ+/x/A6r/0IvEfwO5mjbv5+eHhYWFepp7RooUKcKNGzeIjY1V7/vzzz+RSqXZfmohI4GBgSQnJ3PlyhX1vgcPHuj09giovTampOi+8fLx8cHU1JQ///xTvS8pKYlLly4RFBSkl7acKFGihNZ0gdS0SU5O5sKFC+p94eHhPHjwQH3+IkWKaBwHOH/+vMbv0qVL8+DBA3x9fbNsUmnWrBw6dCiRkZEa2w89emnVaGJigq+vPzdvpK9fVCqV3Lh+jYBA7WkUGBjEjeua6x2vX7tK4MdPS9WqXZd5CxYzd/4i9ebg6EjLVq0ZO2Gy1ji1ajM2JtC7MJduaa6lv3T7PsX9vHWGW7vrECu27WP20L4U8fHU+3y5wcTEBH9fb67eSP/0klKp5OrNWxQN9P/keFUqFXMWLeOP8xeZNWE0LgWdP0mbj68/N69f1dB26/pVAgK1r/UMCAzi5o2rGvtuXLuCvw77T8XQtfn6+XHj+nUNbdevXyewiPZlF4FFinA9gz3AtWvX1PYmJib4+/vz+vVrDZs3b97k+pMxaXX1xg3NtLt5/RqB2dTVm1nq6hWd9mmolEoNh6L6aPPx9dfIp1RtV3W2IwGBQRrlIFXbZZ3lIDcokiBUrlRv78KVRMYoCfBIf29tbgqeLkY8e6u/408zk9RPw0XFZH2xGxuvIl6hwr+QMdaWEm4+/jwP7/Lz13GsXVFjn1OdysjOXwdAlZRE5NU7ONWulG4gkeBYqxLy89mvUzczt8ahgId6c3LxxcomP8/vp6+xV8TH8PbZjVyvX1eplOqXAX8VE2MjirgV4MLj9HqlVKq48PgVJTyyfirOK789vw9oz+b+7dRbzSAvyvm4s7l/Owp+/BRc2oP7yzA5i7u1xE6PUXrt+owJ9Cqk4WxOqVRy6c5Divtp/8wpwJrdR1i+/SBzh/QiyEfzs2iNq5Vjw9ShrJsyRL3lt7fl22Z1mTu0t/7aDKyumltYkb9gYfVW0N0HGzsnHtxKvy+Kj4vh+eNbePl/mnPDzJw7vp1C3kG4e+p3jwr/kj7fQPIUwMLSEhdXd/VWqLAndvYO3MqgMS4ulkcP7uk8Z9p13bqe/myQdl1p/b+3rz/GxsYa1/7m9UvCQkMIKJL9tRh6ny8QZCZXD+/m5ub88ssvDB48mDVr1vDkyRPOnz/P8uXL+eabbzA3N6djx47cvn2bEydO8OOPP/Ldd9+pp8znREBAAA0bNuSHH37gwoULXLlyha5du2Y7ql2gQAEsLCw4ePAgISEhREZmXQNtZWVFz549+fnnnzl48CB3796lW7duxMXF0aVLl9wkgU5Gjx7Nxo0bGT16NPfu3ePWrVtMnToVSH3p0bx5c7p168Yff/zBjRs3+Pbbb3Fzc6N58+YA9O3bl4MHDzJjxgwePXrE/PnzNabMA4waNYo1a9YwduxY7ty5w71799i0aRMjRozQqsnMzAwbGxuNLbsp8y1atuLQwf0cO3qYVy9fsHDBXBIUCdStl7r2ftaMqaxeme4j4IvmLbl65RI7tm/l1auXbFi3hsePHtK0Weo12djY4OHppbEZGxljb++Au3vuvqferklddh3/g32nzvHs9TumLttAgiKRpjVTHSiNmb+SBRvSvY6v2XWQxVt2M6JnR1wLOBIujyRcHklcBv8FkTGxPHz+imdv3gHw4u17Hj5/Rbie6+jTaN28KXsPH+PgsZO8ePWaX39bSkKCgoZ1UqdZTvp1HktXp381ISkpicdPn/H46TOSk5MJiwjn8dNnvHn7Tm0ze9Eyjpw6w/BB/bC0MCdCJiNCJsuyDCInvmjZmiOH9nL86EFevXzB4gW/kpCQQJ16DQGYM3MSa1ctVds3/aIV165cZNf2Lbx+9ZJN61fx5PEDGjdNX9IRHR3FsyePefXyOQBv3rzk2ZPHOtfU/Ru1tWzZkoMHD3L0yBFevnzJgvnzUSgU1KtXD4AZM2awcuVKtX3z5s25cuUK27dt49WrV6xbt45Hjx7RrFkztU2rVq04c/o0Bw8c4O3bt+zZvZsLFy7QtEmTLOfPieYtW3E4Q139bcEcEhTpaffrjCmsXrlMbd+s+Zfquvr61Us2rFvN40cPafKxriYkxLNm1XLu37/Lh5AQHj96yJxfpxMeHkbVajW0atCtrTVHDu7j+NFDvHr5gkULZmtomz1jMmtXpudrs+Zfcu3KJXZ+zNeN61bx5NFDGjdrobaJjo7iaYZ8ffv6FU8/IV8BTlxV0LCiGcV9jHF1ktKhkRWRMUpuZHjI7tvaihql0kdaW9Ywx9fdCAcbKV6uRnRrboVSBZfvp4epWMwUTxcjnGyllCtiQpcvLDlxRcEHmX4zy4ysLLEpGYhNydRRRUsvd2xKBmL+0SFZwIQBlFw5VW3/YskmLL0KETj5Z6wCvPHo0R6X1o14NmeV2ubZ7JUU6tIGt+9aYB3oTbEFYzC2suDVau2OXXUhkUgoV6cDZ/f/xqMbx/jw5gF7Vg4mn10B/IPT1wpvmNWRyyfSZ1Sd3DGTlw8vIQ97zYc3Dzi5YyYvHl6kaPn0ehETGUrIq3vIQl8CEPrmISGv7hEfK9dL23fVgtl+8Q67L9/jaUgEE3acID4xmRZlUx9Whm86zJwDqU6izEyM8SvoqLHlMzfDyswEv4KOmBgbkZSSwqC1B7j7+gOT29VHqVISFh1LWHQsScn6z85Io32T2uw8cZa9p87z7M17pqzYTLxCQbMaqS9eRi9cw/yN6V99WL37CIu27mPUD9/gkt+RMHkUYfIo4hJS2367fNb4FnLV2IyNjHC0tcHTNXcPfIZcVyUSCTUbf8uh7Yu5dfkEb18+ZO38Ydja56dEuXR/BfPGdeXUwQ3q34qEOF4/v8/r56lflgn/8IbXz+8TEfZOI/74uBiunz9C5dq5+/ICGHafb8h5Cqn52rR5a37ftIZL5//kxfMnzJ05CXsHR8pXSv/M5ZhhP7F/T3o71axlG44e2seJowd5/fI5SxbMQpEQT+16jYBUp3e16zdm1dIF3LpxlSePHrDg1ykEBBbV6wW/off5ho5KJcmz7f+RXLvBHTlyJMbGxowaNYq3b9/i4uJCjx49sLS05NChQ/Tr149y5cphaWlJq1atmDVrVq7iX7lyJV27dqVGjRo4OzszYcIERo7U/Z1GY2Nj5s6dy7hx4xg1ahTVqlXj5MmTWeymTJmCUqnku+++Izo6mrJly3Lo0KG/bWpQzZo12bp1K+PHj2fKlCnY2NhQvXp1jevq168fTZs2JTExkerVq7N//371NP2KFSuydOlSRo8ezahRo6hbty4jRoxg/Pj0NU8NGjRg7969jBs3jqlTp2JiYkJgYCBdu3b9W66hWo2aREbJWb92NTKZDG9vH8aOm6ROo9DQD0ik6RWlSFBRBg0eyro1q1izaiWubm4MHzkGD0/dIwqfSr3K5ZBHxbBky27C5VH4e7oze2hf9dTDkPAIpBm0bT9ymqTkZIbO0lyr2fWrpnRrndq4nrl8g/G/rVYfGzFnWRYbfahdrQqRkVGs2rCZCJkcH29Ppo4Zrv6e64fQMKQZphSFR8jo1n+w+vfmHXvYvGMPJYsFMXtS6jKH3QdSP0/y07AxGuf6pV8v9Q2CPlStXpuoyEg2rVuFTBaBl7cPo8ZNVU8jCw39gCTDdzIDg4rx088j2LB2BetWL8PFzY0hI8Zr5Oml82eZNzv9IWLm1NQy+nX7jrT9ptN/QluNGjWIioxk7bp1yCIi8PbxYdz48el14cMHjTwNCgpi8C+/sObjFzTc3NwYOXIknp6eapvKVarQp08ftmzZwqJFi3B3d2f4iBEULVZMb11pVKtRi8ioSDasXaWuq2PGTc5UV9PTrkhQUQYOHsb6NStZu2oFrm5uDBs5Vp12UqkRr1+/4vjEw0RFRmFjY4Ovvz9Tpv9K4Vx+JaJqjVpERsnZuHYlMpkML28fRmfOV6lmvg4YPJz1a1awbtVyXN3cGDJynEa+Xjx/lnm/TlP/nqHO1w60+7ZTrvQduajA1ERC+/qWWJhJePImmQXbYjU8xTvZGWFlkb7DLp+Uzk2tsDKXEBOv4smbZGasjyEmw+finO2lNK9mjqW5hPBIJYfOKzh+Rf8bb9syxah0bK36d9CMYQC8WrOdm12GYuaSH4sMnsXjn7/m0hc/EDRzKJ4/diDh9Xtu/TCCsCPpn1t6t/UApvkd8B/dF7OC+Ym6cY+LTbuS+CHrZ0hzomKDbiQlxnNg3SgS4qIo5FuGNn2XYWyS/kJYHvaK+Jj0qaSx0eHsXfULMZEfMLPIRwG3ANr2XY5XULr/mWunN/HH3vnq3+tmpC6zatJxMiUqZ10ilpmGwf7IYuNZePgCYdGxBLjmZ2GXL9RO7N7LYzTqak58iIzl5N1UT/BtZm/SOLbsh5aU88nd8qv6lcogj4ph8e/7CJdH4+/hxtwhvdV91/uwCI2169uOnCEpOZlfZms60+3WqhHdv/p7b/oNva7Wbf49iYp4Ni4eS3xcNN6Bpeg1bJHG+vSwkFfERsnVv18+ucPcselfCdqxZjoA5Wt8wXe90/0FXT17AJVKRZmqjXKlCQy8zzfwPAVo8VU7EhLiWTRvBrGxMQQGFWfk+OmYZsjX9+/eEh2VPpBSpXptIiPlbFq3ArksAi9vX0aMm64xJb5ztz5IJVJmTBpFUlISwaXL0a1X1mWi2jD0Pl8gyIhEpfo3utMRfCoPn7zMawnZUiA6d980/SeJs8h+3VReIjfKnfdoQSpm0py/JJFXJKsM27miMvdfGv3HmL/j7/0k5N9J46EVcjbKI0KPPMjZKI9oGzk/Z6M8IrGQ/tOu84I3tn/P8sDPwauY/Dkb5RHFzO/ntQSdRBrnbj38P0mKyihnozzCQqr9KxSGgI+37mWghs6jJy/y7Nx+mZYW/T9guHdfAoFAIBAIBAKBQCAQCADx8K43RYsWxdraWuu2fv36nCMQCAQCgUAgEAgEAoHgE8n1mvf/V/bv36/T67K+DvkEAoFAIBAIBAKB4L+Civ9Px3F5hXh41xMPj/+/NRUCgUAgEAgEAoFAIDAMxMO7QCAQCAQCgUAgEAhyjRh5/2cRa94FAoFAIBAIBAKBQCAwcMTDu0AgEAgEAoFAIBAIBAaOmDYvEAgEAoFAIBAIBIJcI6bN/7OIkXeBQCAQCAQCgUAgEAgMHDHyLhAIBAKBQCAQCASCXCNG3v9ZxMi7QCAQCAQCgUAgEAgEBo4Yef8/I0FpntcSsuW+eam8lqATqSqvFejGkvi8lqATCYabcNEp+fJagk6MJMq8lpAthpyvQ552yWsJOjl25EFeS9BJ/noBeS1BJ7/MuJzXEnTybWBKXkvIFkuV4fYPXtbv8lqCTsJVznktQScSleG2v4Y8CmvIff6/GZXKcPP8v4gYeRcIBAKBQCAQCAQCwX+aBQsW4Onpibm5ORUqVODixYvZ2m/dupXAwEDMzc0pXrw4+/fv1ziuUqkYNWoULi4uWFhYULduXR49evQ5L0E8vAsEAoFAIBAIBAKB4L/L5s2bGTBgAKNHj+bq1auULFmSBg0a8OHDB632Z8+epV27dnTp0oVr167RokULWrRowe3bt9U206ZNY+7cuSxatIgLFy5gZWVFgwYNSEhI+GzXIR7eBQKBQCAQCAQCgUCQa1RI8mxTKBRERUVpbAqFQqvOWbNm0a1bNzp37kxQUBCLFi3C0tKSFStWaLWfM2cODRs25Oeff6ZIkSKMHz+e0qVLM3/+/NTrVqmYPXs2I0aMoHnz5pQoUYI1a9bw9u1bdu7c+bmSWzy8CwQCgUAgEAgEAoHg38XkyZOxtbXV2CZPnpzFLjExkStXrlC3bl31PqlUSt26dTl37pzWuM+dO6dhD9CgQQO1/bNnz3j//r2Gja2tLRUqVNAZ59+BcFgnEAgEAoFAIBAIBIJck5dOCocOHcqAAQM09pmZmWWxCwsLIyUlBWdnTUeUzs7O3L9/X2vc79+/12r//v179fG0fbpsPgfi4V0gEAgEAoFAIBAIBP8qzMzMtD6s/5cR0+YFAoFAIBAIBAKBQPCfxMnJCSMjI0JCQjT2h4SEULBgQa1hChYsmK192v+5ifPvQDy8CwQCgUAgEAgEAoEg1+Slwzp9MTU1pUyZMhw7dky9T6lUcuzYMSpVqqQ1TKVKlTTsAY4cOaK29/LyomDBgho2UVFRXLhwQWecfwdi2rxAIBAIBAKBQCAQCP6zDBgwgI4dO1K2bFnKly/P7NmziY2NpXPnzgB06NABNzc3tcO7fv36UaNGDWbOnEmTJk3YtGkTly9fZsmSJQBIJBL69+/PhAkT8PPzw8vLi5EjR+Lq6kqLFi0+23X8Xz28jxkzhp07d3L9+vW8lvK38Pz5c7y8vLh27RrBwcF5LUcgEAgEAoFAIBD8H6FS5Z3Dutzw9ddfExoayqhRo3j//j3BwcEcPHhQ7XDu5cuXSKXpk9IrV67Mhg0bGDFiBMOGDcPPz4+dO3dSrFgxtc3gwYOJjY2le/fuyOVyqlatysGDBzE3N/9s1/F/9fAuyBmVSsXm9cs5dmgPsbExBBYpTrdeA3FxK5RtuIN7t7N7+0bksgg8vHz4/of++AUEARAdHcWW9cu5ce0SYaEh2NjaUb5iNb7+titWVta50rZjwxJOHtlJXGwMfoEl6NjzFwq6Fs423NF9Wzmwcx2RsnAKefrxbfdB+PgXVR+Xy8LYvGoed65fID4+Dhc3D5q17ky5yrVzpW37hiWc+KjNP7AEnfTQdmTfVvZn0NYhg7bQkLcM6N5Ca7g+gydRoUpdrce0adu0bgVHD+0lLjaGgCLF6d57AK5u7tmGO7B3B7u2bUIui8DTy4cuPfrhF1BEffzwgd38ceoYTx8/JD4+jjWb92JlnU8vTRnPsVN9Dl+69uircY7MnD1zko3rlvMh5D0uru581/kHypSrmOlaV3Lk47UGFimm17XqQqVSsVVdH6IJKFKcrr0G5VgfDu3dxp4M9aHzDz/h+7E+ACyZP43b1y8TERGGubklAUWK0b5TT9wKeeRK2+Z1Kzh6aI9Gvuak7cDe7ez+mOYe6nxN13bkwG7OnDrKs4/5unrzvlzn6+cqc4mJClYvW8gfp4+TnJREydLl6N7rJ+zsHfTWZl29IfnqNcfIxo7E18+Rb1lO4ovHWm3z9x+LuX+xLPvjb18hbOEkAGyatMGyTFWM7B0hJZnEl0+J3L2BxOeP9NaUhkql4syeuVw/sxVFfBTuPqVp0H4MDs6eOsNcPbWBq6c2Ehn+BgAnFz+qNu2FT7Eaaptrpzdz99Je3r+8Q2JCLD/9eglzSxu9dTlULYv3wC7Yli6GuWsBLrfqRcjuY9mHqV6eoBlDsA7yI+HVOx5P/o3Xa3Zo2Hj0bI/3gC6YFcxP1M373Ok/nshLt/TWlZlGFU2pVMwECzMJz96msPVEAqFylU77hhVMaVRR09FRSEQKk9bGpV5DPgmjv9feR63cF8/1x8l66VKpVGzbsIQTh3el9g9FStC552C9+od9O9YTKQunsJcfHboP1OgffurWUmu4HwdPokLVOnpr+5Q2M6e2OzFRwaplv32sq4kEly5P9179c1VX9+7ZzfZtW5HJIvDy8uaHnr0JCAjUaf/HmdOsW7uKkJAQXF3d6PR9V8qVK68+/uus6Rw7ekQjTOkyZRk3fpLemtIQ7e+ntb9p+gw57Qy1zxf8PfTp04c+ffpoPXby5Mks+1q3bk3r1q11xieRSBg3bhzjxo37uyTmiFjzLtBg17YNHNizje69BzF55mLMzC2YMGogiYkKnWH+PH2M1cvm07pdJ6bOWYaHly8TRw0kUi4DQBYehiwinA7f92bWgjX07j+M61cu8NucKbnStn/7Go7s20ynnkMYNX0FZuYWzBjTN1ttF84cYeOK2TT/uitjZ62hkJcfM8b0JUoeobZZMnss7968oN/wmUycu5EylWqyYPowXjx9oLe2fdvXcHjfZjr3HMKYj9qm5aDt/JkjbFgxm5Zfd2X8rDUU9vJj2pi+RH7U5ujkzLxV+zW2L9t1x9zckpKlK+utbefvG9m/Zzs/9B7I5FmLMDc3Z/zIQTnk6XFWLV1Am/YdmT53KR5ePowfOUidpwCJCgXBpcvzZZtv9daSkT9OH2fl0oW0ad+JGXOX4unlw7iRPyPPcI6M3L97m1nTxlGnfhNmzl1G+UpVmTphBC+eP1Xb7Ph9I/v2bKNH7wFMmfUbZuYWjB/5c7bXmh27t63nwJ7f6dp7EBNnLsHc3IJJowZkG9/Z08dYs2w+rdp1Zsqc5Xh4+TJp1ACNtPP2DaBH/2HM+m09w8bNRKVSMXHUTyhTUvTWtvP3Dezfs43uvQcyadZizPTK12OsXrqA1u07MW3uMjy9fJmQKV8VigRK/YV8TdX2ecrcyqXzuXzxLIOGjmXclDnIIsKYNnGk3rosylTGrlUnovZt4f3kn0l684L8P45Eaq39QTZ8yXTeDOmi3t6N748qJYW4q+nfb00OeYts8zLeTxhAyMwRJId/yDbO7Dh/aCmXj6+l4Tdj6DhkCyZmFmye24XkJN3pls+uIDVbDqLzsO10GrYNz8CK/L6wN6Fv018eJCXG4120GpUb9ci1JgAjK0uibj7gdt+xetlbeLpTbvdiwk9e4I+yzXk2bzXFF0/AqV5VtY1L60YUmT6URxMW8Ef5lkTfvE+FfcsxzZ+7B4E06pQxpXqwKVuOK/h1cxyJSSp6tLDE2Cj7cO/CUhixNEa9zdkarz4mi1FpHBuxNIb95xQkJKq4+0K/B3eAvdvXcnjvFr7v+Qtjpy/HzMycqaP75dg/rF8+h5ZtuzDh19UU9vRl6uh+Gv3D/NX7NbZW7bthbmFJyTL6r7X8lDZTn7Z75dIFXL54lp+HjmH8lDlERIQxdeIovXWdPnWSZUsX0679t8yZtxAvb29GjRyms3+4d/cO06ZOol79hsyd9xsVK1Vm4vgxPH/+TMOuTJmyrF23Sb0NHjxUb00ZEe1v7tvfdH2Gm3aG3OcbMkokebb9P2JwD+81a9akb9++DB48GAcHBwoWLMiYMWP0Cvvy5UuaN2+OtbU1NjY2tGnTJosHQIDFixdTqFAhLC0tadOmDZGRkepjJ0+epHz58lhZWWFnZ0eVKlV48eJFjufu1KlTlvUN/fv3p2bNmurfv//+O8WLF8fCwgJHR0fq1q1LbGys+viyZcsoUqQI5ubmBAYGsnDhQo34Ll68SKlSpTA3N6ds2bJcu3ZNr3TRF5VKxb5dW2j1dQfKVayGh5cvfQYMRxYRzqVzZ3SG27tzM3UaNKNWvSYUKuxF996DMDUz5/iRfQAU9vRm0LAJlK1QhYIubhQvWYZ2Hbpz5eJZUlL0H7k4tGcTzVp/T+kKNSjs6Uf3/mOQR4Rx9fwpneEO7tpAjfotqF63GW6FvenUcwimZuacPrpHbfP4/k3qNWmDj39RChR0o3mbLlhaWfPs8T29tR3cs4kvWn9PmY/afvio7Uo22g7s2kDNDNo69xyCWQZtUiMj7OydNLYr509SvmodzC0s9da2d9dWvvr6O8pXqoqnlw8/DhyGLCKci+f+0Bluz44t1G3YlNr1GlOosCc/9BmImbk5xw7vV9s0bdGaL9t8g39gkM54smPPjq3Ua9iEOvUafTzHAMzMzTme4RwZ2bt7G6XKlKdFq7a4F/ag/Xdd8PLx48DeHRmu9XeNa+07cCgREWHZXqsuVCoV+3dt5csM9aH3gBE51od9Ozep64N7YS+69v4ZUzNzThzZq7ap27A5QcWCKeDsgrdvAF9/143w0A98+KDfd0FT6+pWWn39HeUrVfuYr8Nzna/dP+br8cP71DZNW7ShZZtv8QssqjOenLR9jjIXGxvD8cP76dS1N8VLlsbHL4De/Yfw4N5tHt6/o5e2fLWbEfPnUWLPnyD5/WtkGxejTFRgVVn7KKUyLgZllFy9mQeWQJWoIP7qWbVN3OU/UDy4SUp4CMnvXiHftgqphRUmbrkbUVGpVFw6toYqjXviH1yXAu6BNO08jWj5Bx5eP6oznF/J2vgWr4GDsyeOzl7UaPETpmaWvH16XW1Tvm4nKjXsjqtXyVxpSiP00Gkejp5NyC7dOjLi0b0t8c9ec2/wVGLuP+XFwvW833YIr36d1DZe/TvzavkWXq/eTsy9J9zqNZqUuAQKdWr1SRprlDLh8EUFt58m8zZMybrDCdhaSSjuk/0EwxQVRMep1FtsQvpIvSrTseg4FSV8jLn+KInEJP10qVQqDu7eRPM2nSlTsQaFvfzo8ZM+/cNGatVvTo20/qFXav9wSqN/cNTYLp87RYUque0fct9m5tR2x8bGcOzwfjp17aWuq336/8KDe7d5oGdd3bljGw0aNqJe/QYULuxB7z79MDMz48jhQ1rtd+/aSZky5Wj1VRsKFS7Mdx064ePjy949uzXsTExMsHdwUG/W+XI3Mgui/f3U9jdNnyGnnaH2+QJBRgzu4R1g9erVWFlZceHCBaZNm8a4ceM4cuRItmGUSiXNmzcnIiKCU6dOceTIEZ4+fcrXX3+tYff48WO2bNnCnj17OHjwINeuXaNXr14AJCcn06JFC2rUqMHNmzc5d+4c3bt3RyL562923r17R7t27fj++++5d+8eJ0+e5Msvv0SlSr1RWL9+PaNGjWLixIncu3ePSZMmMXLkSFavXg1ATEwMTZs2JSgoiCtXrjBmzBgGDRr0l3Vl5EPIO+SyCIoHl1Xvs7KyxjegiM4ONykpiaePH1IiuIx6n1QqpURw2Wwb9LjYGCwsLTEy0m/lRmjIWyJl4RQtmT4FztLKGm//ojx+oH2aZXJSEs+f3KdoyXIa2oqWLKcRxjewBBf+OEJMdCRKpZLzpw+TlJhIkeJltEWrU1uxz6AtI88e3+PFs4fUqNtcL10AIe9T8zRj/lhZWeOXQ54+0ZqnZXLVSWdH6jkeaD3Hg/t3tYZ5eP+Ohj1AqdLl1fZp11oyy7UG6YwzOz6EvEUuC6d4cHoeWVpZ4xsQxKP7t7WGSf5YHzLWIalUSvHgsjzSkXYJCfGcPLqfAs4uODkV0E+bOl8166pfQBEe6tCWXlczayuj9w21PnyuMvf08UOSk5M1bNwLeeCU35kH9/TQb2SMaWEfFA9upu9TqVDcv4mZl79e12ZVuQ5xV/5EpWsUxsgY66r1UMbFkvT6uV5xpiEPe01sVCieRdJn1Zhb5MPVqyRvnur3olapTOHupX0kJcbh5l0qV+f/O7GrGEzY8XMa+0KP/IF9xWAAJCYm2JYuStix9JcgqFSEHT+LXcXc63a0kWBrJeXhy/RRrIREePE+Ba+C2Q+957eTMq6LFSM7WfFdA3Ps8+nu790LSHEvYMS5O3o+uaO7f/DxL8qjbPqHZ4/vUzQ4PYy6f7ifQ/9Q7wu9tX1Km6lP251WV0tqqasP7+XcFiclJfH48SOCg9PLglQqJTi4FPfva3+pfv/+XYJLaZad0mXKZrG/desm37RrzQ/dvmfB/LlERUXlqCczov1NJVft70cMOe0Muc8XCDJikGveS5QowejRowHw8/Nj/vz5HDt2jHr16ukMc+zYMW7dusWzZ88oVCh1bcqaNWsoWrQoly5doly51MqYkJDAmjVrcHNzA2DevHk0adKEmTNnYmpqSmRkJE2bNsXHxweAIkV0r7/NDe/evSM5OZkvv/wSD4/UEZnixYurj48ePZqZM2fy5ZdfAqmfH7h79y6LFy+mY8eObNiwAaVSyfLlyzE3N6do0aK8fv2anj176jynQqFAodC8yUxMVGBqaqbVXi4LB8DOzl5jv52dA/IM08wzEh0ViVKZgq2d5lRHWzt73rzWPmMhKlLO75tWU7eh/jcZkR+1ZT6PjZ2D+lhWbXId2hx4l0Fb758nsXD6MHp/Ww8jIyNMzczpO3Qazi7Zr3FKQ65Dm+0naLOxc+CtjnQ7dXQ3ru5e+BcpoZeuVG2p+ZZ5TZqtnb36WFZtqXmauRzY2tnz5tVLvc+dHannUGKX6frtsjmHXBaRxT7jdaT9b2ufNU6ZjmvNDnV8WtJBV32I0lkfsubroX3bWb/yNxQJ8bi6F2b4hNkYm5jopU2WVlftM2tzyDFfM1+PnZ3D35av8PnKnFwWjrGxSZY1jHb2uuPNiNQ6HxIjI1Ki5Br7U6IjMXZ2yzG8qYcvpm4eyNYtzHLMvFgZHL//CYmpGSlRMkLnjUUZG51jnBmJjQoFwMrGUWO/lY0jsZFh2Yb98OYBa6a2JTlJgamZJV/2WICTq2+uzv93YubshCJEU7MiJAwT23xIzc0wsbdFamyM4kN4JptwrAK8c32+fFapD9zRcZrr26PjVOpj2njxPoUNhxP4IFdiYymhYQUz+n5lyZR1sSi0PJ9XKmrC+/AUnr9T6q0trX+w0dp36aoP2fRdb7T3DyeP7MG1kOcn9Q+5aTP1abtlsgiddVWftjgqKir1HPaZ2yp7Xr96pTWMTCbTcu9ip9E2lC5TlsqVq+LsXJB3796yZvVKRo8azoyZszEyymF9hca5RPubhr7tbxr/hrQzxD7f0MnNJ9sEfx2DfXjPiIuLCx8+fMg2zL179yhUqJD6wR0gKCgIOzs77t27p354L1y4sPrBHVK/4adUKnnw4AE1atSgU6dONGjQgHr16lG3bl3atGmDi4vLX76mkiVLUqdOHYoXL06DBg2oX78+X331Ffb29sTGxvLkyRO6dOlCt27d1GGSk5OxtbVVX1+JEiU0vBfm9A3ByZMnM3as5hrFHn0G0bPvzwCcOXGYxQtmqI8NHT31L19nTsTFxTJ57GDcC3vSpv33Ou3OnjzIqt8mq38PGPnrZ9O0fcMi4mJjGDxuPvls7Lhy4RQLpw9j2KQlFPLMegP858mDrMygbeBn1JZGoiKBc6cP0bxNl2ztTp84wuL5M9W/h43JnV+B/2fOnDjM0gXT1b+HjJ72Wc9XrWZ9SgSXQyYLZ+/2jcyeMpJx03/T+nLt9InDLMmQr0PHfP66qi//L2XOqnIdEt+80OrcTvHwNiGTByG1yodV1Xo4dhlIyLQhKGN0j+rdvrCbg+tHq3+36bP4k7U5Onvx/YidKOKjeXD1EHtX/cK3A9fl6QP856RMgDFf107vCxfvjs/GWjf3XqSP1L8FXryPY/T31pTyN+F8ptF1EyMoHWDC4QvZ+8/48+RBVixMrwODRs36JG25Ia1/aNFGd58KcCpTXR3+H62ruqhRo5b6b08vL7y8vOnapSO3bt3UGOXPjGh/Px1DTjtD7vMFguwwyId3k0xvoiQSCUql/m+6/worV66kb9++HDx4kM2bNzNixAiOHDlCxYoVsw0nlUrVU+DTSEpK7/yNjIw4cuQIZ8+e5fDhw8ybN4/hw4dz4cIFLC1T16ctXbqUChUqaMSRm7fBmRk6dCgDBgzQ2PfwVfr6/rIVqmp4w0z+qFcul2Hv4KTeL5dH4Onlp/Uc+WxskUqN1E500oiUy7Cz1xxBio+LY+KoQVhYWPLz8IkYG+sufqXKV8MnIH3dUlJS4sd4I7DLoC1KHkFhHVNe89nY6dAWge1HbSHvXnN031YmztuIe+HU2RaFvfx5eOc6x/ZvpVOvrM5sSpevhq8e2iLlEXjkUluUPCJLugFcPHschSKBqrUaa40vjXIVqmh4h00rg3JZBPYO6fFGymV4emu/sU/L08yOgVLz9NOcSWk/hzTL22x5Nuews886AySjprT/I2UROGS4VrlchpeOa81I2QpVNTzXpuerZn2IlMvw9NIen43O+pA1Xy2trLG0ssbFrRD+AUX5vm0jLp07TZUaWWcYlcukTV1XZZm1ReSYr5GZ8lUuj/hL+fpPlTk7e0eSk5OIjYnWGP2Ry/Qrl8qYaFQpKRjZ2GnsN8pnizLTaHxmJKZmWJatQuTezVqPqxIVJIe+h9D3JD5/RMEx87GqUofoQzu02kPqWvWMa9BTklPLW2xUONa26VMpY6PCcS6k28M2gJGxKQ4FUmd0uXgU493zW1w6voZG3/5z3m8zoggJw8zZSWOfmbMTSZHRKBMUJIbJUCYnY1bAMZONI4r32c8yALj9NJkX79P9xRgbpY765LOUEJVh9D2fpYQ3ofrfO8QnQqhciZNt1lGkkn7GmBrDxfvZ+2kpXb6axtdMkpNT60OUPEKjrkbJIyjsratfzabvssta1tX9Q+3s+4fyFargr6Wu5qbN1Kfttrd30FlX7fWoqzY2NqnnkGVuq2TYO2gPb29vn6X9kMvl2bYNBV1csLGx5d3bN9k+vIv299PbX0NOO0Pu8/9t/Fs+FfdfwSDXvH8KRYoU4dWrV7zKMKXq7t27yOVygoLSK+fLly95+/at+vf58+eRSqUEBASo95UqVYqhQ4dy9uxZihUrxoYNG3I8f/78+Xn37p3Gvszfk5dIJFSpUoWxY8dy7do1TE1N2bFjB87Ozri6uvL06VN8fX01Ni8vL/X13bx5k4SEBA3t2WFmZoaNjY3GlvENn4WlJS6u7urNvbAndvYO3L5+RW0TFxfL4wf3CNDhAMTExARvX39u3UgPo1QquXXjCv4ZwsTFxTJ+5ACMjY35ZeSUHN80Wlha4exSSL25FfLG1t6RuzcvqW3i42J4+vAOvgHFtcZhbGKCp0+gRhilUsndm5fVYRIVqekplWhWBalUijLTy5ictN35G7TdyaAtI6eO7qZ0uerY2NpnOaapTTNPC33M01s3rqpt4uJieZRDnvr4+nPrumae3rx+VSNP/wqp5wjg5vV0XannuEKADgd4/oFFNa4D4Ma1y2p754Iu2Nk7cDPLtd7VGWdGLCwtKejqrt7cC3thZ+/IreuXNeJ7/OAufoFZPx8GqfmqrT7cvnElWyc6qo//Mr7wy6xNW13NeJ60fPXXoU1dVzPl663rV3WWBX34p8qct68/xsbGGvn75vVLwkJDCCiih/6UZBJfPsEsY/2SSDALKIHi2cPsr7F0ZSTGJsRd1O1gLCMSiQSJcfbTIc3MrXEo4KHenFx8sbLJz/P76WvFFfExvH12I9fr11UqpfplQF4gP38dx9qaL7yd6lRGdv46AKqkJCKv3sGpdobZYxIJjrUqIT+f8/p+RRKERarU2/sIJZGxSvwLpb/sNjMFj4JGPHuvvzdnUxNwtJUSFZu17a9Y1ITbT5OJjdf96TlI7R8KuhZSb26FvFL7hxvpbX1cXAxPHt7BL5v+wcs3UCNMav9wCd/ArGFOHtlD6fLVPrl/yE2bqU/bnV1d9S+Sc1tsYmKCr68fN25c1zjHjevXCQzUvpQxMDCI69c1y861a1d12gOEhYUSHR2l8eJCG6L9/fT219DTzlD7fIEgOwxy5P1TqFu3LsWLF+ebb75h9uzZJCcn06tXL2rUqEHZsumOJMzNzenYsSMzZswgKiqKvn370qZNGwoWLMizZ89YsmQJX3zxBa6urjx48IBHjx7RoUOHHM9fu3Ztpk+fzpo1a6hUqRLr1q3j9u3blProQOXChQscO3aM+vXrU6BAAS5cuEBoaKh6Tf3YsWPp27cvtra2NGzYEIVCweXLl5HJZAwYMID27dszfPhwunXrxtChQ3n+/DkzZszITlKukUgkNGnehm2bV1PQzZ0Czi5sXrcMewdHylWqprYbO6wf5StVp1GzVK/ATVt8zYJfJ+HjF4ivfxH27dqKIiGeWnVTRwHi4mKZMHIACkUCfQeNJC4+lrj41FETGxs7vWYXSCQSGjRry+4tK3B2KUR+Z1e2b1iEnYMTpSumf8946shelK5Yk3pN2gDQsHl7ls4Zi5dvEbz9inJozyYUCfFUq9sUABd3T5xdCrFy4WTadu6HdT5brl44xZ0bF/lphH7THSUSCQ2btWXXlhUU/Kjt94/aymTQNnlkL8pm0NaoeXuWaNFW/aO2NELeveLBnWsMGjVbLz2ZtTVt3prfN63BxdWdAgULsnHtCuwdHClfKf2zTWOG/UT5StVo3CzV50Kzlm2YN2syPn6B+PkHsnfX7ygS4qldr5E6jCwiHLksgvfvUr8v/eL5UywsLHEq4Ey+fDl/JqtZy9bMmzUZX78A/PyLsGfX7ygSEtTnmDNzEo6OTnzbqTsATb9oxcgh/di1fTNlylXkj9PHefL4AT1+HJjhWr/i901rcXF1x7mgCxvXLsfBwUnjWnOTdo2bt2bH5tW4uBXSWR/GD+tHuUrVafixPjRp0ZaFv07Exy8QH/8i7N+1BUVCPDXrNgEg5P0bzp4+TsnS5bCxsSM8PJRdW9dhampGqbL6feIpta62Zps6X13YtHa5lnztT4VK1dR1tVnLNsyfNRkfvwDNulovfcTur+br5ypzVlbW1K7fmFVLF2BtnQ9LSyuWL5pDQGBRvV8qRR/fg2OHH0l88YTEF4/IV6spUjMzYs8dB8Ch44+kyCOI3LVeI5x15drE37iIMjZG81pNzbBp2Ir4m5dIiZIjtcqHdY2GGNk5aHxOTh8kEgnl6nTg7P7fcCjgga2TO6d3zSGfXQH8g+uq7TbM6oh/qXqUrZX6OaSTO2biXbQ6Ng4uJCpiuXtxLy8eXqRt3+XqMDGRocRGhSELTV0fGvrmIabmVtg4uGBhZZejNiMrS6x8079Jbunljk3JQBIjIkl49Y6ACQMwd3PmRudfAHixZBMevb4hcPLPvFq1DadaFXFp3YhLX/ygjuPZ7JWUXDEV+ZXbRF66iWffjhhbWfBq9fZcpVsap64lUb+8GaFyJeFRKhpXMiUyVsWtJ+kj5b2/tODm42TO3Ey9YW5e1Yzbz5KRRSmxsZbQuKIZKqWKKw81R9edbCX4uBmxeFfup+dLJBIaftGWnVtW4uxaiALOrvy+fnGW/mHSiN6UrViT+k1TvyfcqHk7Fs8eh5dvEXz8gzi4exOKhARq1NHsH96/Tesfcr98S982c/SwAVSoVDVDXc2+7baysqZO/casXLoQa2sbLC0tWbZoLgGBRfV+2GrRshW/zpqOn58f/v6B7Nq1nQRFAnXrNQBg5oxpODo60qlz6lKyL5q3YMgvg9i+/XfKlSvP6VMnefzoIX1+7AdAfHw8GzespXKVatjb2/Pu3TtWrliKi4srpcvo55w2Y7qJ9jf37e+/Ie0Mtc8XCDLyn3l4l0gk7Nq1ix9//JHq1asjlUpp2LAh8+bN07Dz9fXlyy+/pHHjxkRERNC0aVP1J9ksLS25f/8+q1evJjw8HBcXF3r37s0PP/yg7ZQaNGjQgJEjRzJ48GASEhL4/vvv6dChA7dupXqGtbGx4fTp08yePZuoqCg8PDyYOXMmjRqlNoxdu3bF0tKS6dOn8/PPP2NlZUXx4sXp378/ANbW1uzZs4cePXpQqlQpgoKCmDp1Kq1afdpndXTRvFV7EhLiWTxvOnGxMQQGFWf4uBkaI+Uh798SHZU+/b5K9TpERcrZvG45clnq1Kfh42aopzM9e/yQRw9SPcz+2K2txvkWLN9CAWf9fAo0/rIDioQEVi2cRFxsDH5FSjJo9BwNbR/evyEmwxTYCtXqERUlY/uGJUTKwins5c+g0XOwtUt9025sbMyAUb+ydc0CZk8YSEJCHM4u7nTrN5qSZavonW5NPmpb8VGbf5GS/KxFW3QGbRWr1SM6Ssa2DNp+zqAtjVNH9+DgWIBiwZpLKvSlxVftSEiIZ9G8GcR+zNOR46draHv/LnOe1iYyUs6mdSuQyyLw8vZlxLjpGlPUDh/YzZYNq9S/R/7SF4De/YdoPOTromr12kRFytm4bqX6HCPHTVOfIyw0BGmGLz0EBhXjp59HsmHtctavXoaLmxu/jJiAh2e6g6uWX7VDkZCgvtYiQcUZOX7aJ68p+6LVNygSElgybxpxsTEE/I+9u46P4ugfOP65i/vF3T24BClS3LUUKVYoDi20UMMKBQoVoEhbSnF3glO0WHFaXINbiN3Fc5G73x+BSy65Sy48fUj6e+bd171o9mbnvruzu7O7MzsbUYmxU2YV2h+0y/Wtl/vDxtWLNfvD2CmzNMtlYmLGzWuX2LtjI6mpKchkDoRVqMLUH34tMlBOcTq92xNlZiYLC5TrhKmF9tXnz0jWsa++KtfC+yrA/r3b2VSgXL/64iMARnw8lsYGlGtebP+dba7/oA+RSqTMnP4V2dnZVK0eyaDhnxi8zjIunERhbYddux4Y2crIenKfuJ+moUrJi8PI3glU2i2rxi4emAVFEDuv6DvO1SoVxm6eONZphJGVLaq0FLIeRhM7ewI5z3UPrFWcOi0HkZ2Vwd7VX5GZnox3UA26jVyMsUn+elPEPyYjNb/7aFpKAruWf0FqUixmFja4eIbSY+QS/CPyj19/H1vPiV0/af5ePbMXAG3fn0Hlt94pMS67GhWpe2iV5u+ImeMAeLxyK5cHjMXM3RkL7/zjeMaDJ5zrMISIWWPx+6gvmU9iuDJkAvEH8l8F9XzTXkydHQiZNBIzN2eSL93gbLuBZBUaxM5Qhy5kYWoC3ZuaY2Em4d6zXH7dlk5OgYZ3RzspVhb5xxSZtYT3W5ljZS4hNUPNvWe5zN6YXqR1vU4FE5JS1dx6aHgrfkHt3umDMjODpT/PyKsfIqrw+eSS64fkJIWmfvANCOHzyXM0j3y98qp+qFTt9eoHQ46ZMc+fah1HSjp2A/QfNAKJRMoPBfbVwcM/Njiuhm83Iik5idWrViKXywkICGDKlG+wfznQWVxcLFJpflmGR1Tgs8/HsmrlclYuX4aHpwfjJ07Gzy+v96JUKuX+/fscOniAtLQ0HBwcqVa9Or379MPExLTU600cf0t//M2Pr/yuu/Jc55dnYsC6N0uiLvygtvD/2uU7xQ/8V9bSc8vvwB3l+RkTS+PXG7DpTZBQfg8x2erye//SSPJmxvl4XeW5XO1mjyjrEPQ61G1LWYegl3Pz0JITlZH9M8+XnKiM9G71ehf1b4qlUfmtH0wlZfd4R0ky1eYlJyoj5fn4W54v5HLV5fdMrmqwc1mH8Nou3C79G33+KTVC/pmxmP5Nyu+ZqyAIgiAIgiAIglBuiQHr3qzyewuqkDVr1mBtba3zU6HCPzOIVnEqVKig9/fXrFlTcgaCIAiCIAiCIAiC8Jr+NS3vHTp0KPIatVcKv1ruv2HPnj16R4V0dXX9r/++IAiCIAiCIAhCeVKeH5X4/+hfc/FuY2ODjY1NyQn/S3x9fcvstwVBEARBEARBEIT/bf+abvOCIAiCIAiCIAiC8L/qX9PyLgiCIAiCIAiCIJQfYsC6N0u0vAuCIAiCIAiCIAhCOSda3gVBEARBEARBEIRSU5V1AP9jRMu7IAiCIAiCIAiCIJRz4uJdEARBEARBEARBEMo50W3+f8zTVPuyDqFYjeTryzoEvS66dyzrEPSSoC7rEPQqz+//3HfJsaxD0KtueEZZh1AsG9PyG9/siBVlHYJeM5J+KusQ9Ppi5vmyDkGvFp/WLOsQ9Iqw7FrWIRQrvnGfsg5Br++jXMs6BL1mSCeXdQh63W0/oaxD0MtEklPWIehVnuv8qsFlHcHrEwPWvVmi5V0QBEEQBEEQBEEQyjnR8i4IgiAIgiAIgiCUWnnuYfn/kWh5FwRBEARBEARBEIRyTrS8C4IgCIIgCIIgCKUmnnl/s0TLuyAIgiAIgiAIgiCUc+LiXRAEQRAEQRAEQRDKOdFtXhAEQRAEQRAEQSg1MWDdmyVa3gVBEARBEARBEAShnBMt74IgCIIgCIIgCEKpqdRlHcH/FtHyLgiCIAiCIAiCIAjlnGh5/5fo168fCoWCbdu2aU0/cuQIjRs3Ri6XI5PJ/uPfUavV7N30M6cPbyYjLQX/0Gp0HTARZ3dfvfPcvXGewzuX8fj+dZLlcXwwZi6VI5tqpVFmprNz7Y9cOX+Y9BQFDi6eNGzVi3rNuxsc2/oj51lx4BQJyamEeLnyRfeWVPLz1Jn20N83WfL7nzyKSyQnV4WPiwN9m9WmXe3KOtNPW7uHzcf/4tN3m9O7aW2DY3pFrVYTtW4hRw9sIz0tleCwyvQd+iVuHj7Fzndwz0b2Rq0mSZGAj18wvQd9RkBIBc33sc+fsH75XO7cuEh2djaVqtWl9+BPsZM5Ghzb3l1RbNuyHoU8ET//IAYOHUlwaLje9CePH2Hd6iXEvojB3cOLPv2HUCOyjtayrl+9jAP7dpGelkpYeEUGjxiNh6eXwTEVlJffUg6+zC80vJJB+e3dFcV2zXIFMmDoKK3lyspSsmLxL5w4dpic7GyqVI9k8PBPkNk7lCq2vw/N59a5TWRlpuDiW423OkzCzsnPoPkvHV3Ehf2ziXirD3XajtNM/3PbJJ7dPUV6ciwmppa4+FSjZqsxyJwDShXbjvW/cvxAFOnpKQSFVaHX4HG4FrPN3b52gX3bV/Lw7g2S5PEM/2IW1Wo31nyfk5PNtrW/cPWvP4l78QQLS2vCK9emS5+RyBycSxXbpjWLObxvJ2lpKYSGV2bA8E9x9/Qudr59u7awc+takuSJ+PgH0X/IJwSFRujM/9vJn3LpwmnGjJ9BZN2GBscG0KqWKXUrGGNuJuHB81w2HVESn6S/6aBlLVNa1TLVmvZCruLbNemav0d0tiDI00grzcmr2Ww6ojQ4rvUnL7Pi6F/Ep6QT4u7Elx0bUsnHrcT59l68zZdr99G4gj9z3m8HQHZuLj/tO82Jmw95kpCEjbkZtYO9GNX6LVzsrA2OqbDWdUypW9EECzMJ95/lsumPTOIU+tddq9qmtK5jpjXtRWIu01flrTsHGwmTPtAdz7LdGVyMzik2Hof6NQkYMwC76hUx93DhfJfhvNhxqPh5GtYiYuaXWEcEk/n4OdEzFvBkZZRWGt9hPQkYPQAzN2eSL9/k2sdTSTp3pdh89dnwdzQrz90iIS2TEGcZnzetRkV33cehHVcfMPn3c1rTTI2knP6ki+bvX/+8xv5bj4lJTsfESEq4qz0jGlSkkrvh9YLm93btZtOWKBLlcgL8/RkxdDBhoSF60x87foLlq9fw4kUsnh4eDOz/PrUia2q+l8vlLF62ggt/XyQtLZVKFSowYugQPD09Sh0bQNt65tSrZIqFmYR7z3JYfyCDOIVKb/o2b5nT9i1zrWkxCblMXZai+dvJTkrnRuYEehpjbCThxoNsNh7KICXd8OZDkyr1MavZBImVDaq4Z2T8sQVVzCPdaSNqYdGqp9Y0dU42KfM+0/wtsbTGrEEHjH1DkZhZkPv0LpmHt6BSxBsckybvcn78/W/V+fv37uDE0UPci75NRkY6KzfswsraptSxldc6XxBeES3vgpZDO5Zy7Pc1dB34FZ9MW4upmQW/zhhCdpb+E1BlZgYevqG823+83jTbVn7PzUsn6D1iBl/O2sHbrfuwZdl0rp7/w6C49p2/xqwtBxjStgHrxg0kxMuV4fPWkZicpjO9rZU5A1vXY+Vn/dk0YRAd61Zh0sqdnLx+t0jawxdvcvn+U5ztSneQL2hP1EoO7NrA+0PH8tX3yzAzt2DW1x+RVcx6O3NiP+uXzqFTj4F8PXsV3n7BzPz6I5IViUDeev1h8odIJPD5lAWMn7GYnJxs5nwzGpVK/8lLQSeOHWbZol/o1rMfM+ctws8/kCkTP0OhkOtMf/P6VWZ/P4WmLdoya95iatWtz3fTJvDwwT1NmqjN69i9cwtDR4zm29kLMDO3YOrEz4pd1uJs27yOPTu3MmTEGGbM/hVzc3OmTvy02Pz+PHaY5Yt+plvP9/lh3iJ8/QOZOvFTkgos17JFP3H+7Ek+Hfs1U76dizwxnu+/mViq2K4cX8z1U6t5q+Nk2g/bgImJJfuWDyInu+RljXtyhVvnNmDvFlrkO0ePCjR45xve+Xg3LfotQo2afcsGolLlGhzb71ErOLR7Hb2HjmPctyswNbNgztQRxe+ryky8/ELoOehLnd9nKTN5dO8mbbsOZOLMtQz7fCYvnj3kpxkfGxwXwI4ta/h952YGjviMabMWYWZuzoyvRhdbpiePHWTV4vm8+94HzJi7FF//IGZ8NVqrTF/Zs33Daw+P06S6CQ2rmLDpiJI5mzJQZsPQDhYYGxU/3/OEXL5amqb5zN+SXiTNqWvZWml2/Gn4PvH7xdvM3HmcIc1qsX5UD0LdnRi2ZAcJqUV/p6CnicnM3n2C6v7aF0eZWTncfBrH4KaRbBjVg9l92/AgTsGo5bsNjqmwpjVMaVjVlI2Hlfy4IZ2sbDVDO1mWvO7ic5mwKFXzmbspQ/OdPFWt9d2ERansOaUkM0vN9YfFX7gDGFlZknz5FldHfm3QMlj4eRG5YyEJR85womZH7s9fQaWF03BqXl+Txr1ra8J/GMudaT9zolZnUi7fpPbuJZg6G37j75V9Nx8z+8glBteNYG2f5gS72DFi8zES0zL1zmNtasz+Ye01n92D22p97+tgwxdNq7GxXwuWvtcYDzsrRmw6hjy9dMfgI8eOs3DREnr37MEv834kwN+PcRMnIVcodKa/dv0G07+fSasWzVkwbw5v1a3N5GnTuf/gIZB34TN52nSex8Tw9cTx/DJvDi4uLnwxfiIZmfqXV5/mtcxoVM2M9QfS+WFNClnZ8OG7ViVub8/icxn7S5LmM3t9quY7UxP4sKsVqGHexlRmr0vBSCphaGcrg48pxiHVMH+7E8rTv5O2eia5cU+xemcoEgv9N8XUygxSfp2o+aQu1t5eLToMRGrnSPr2xaStnokqWY7lu8PB2FRPjvqV5+Mv/Pfq/CylkqrVa/FOt96vHVt5rvPLMzWSMvv8LxIX74KGWq3m2N5VtOg8mEo1m+DhG0qvEdNJksdy5bz+loyIag1o230klWs105vm/u2LRDbsSHCFWji6ePJWs654+Iby8K5hLRmrDp3hnXrV6PRWVQLdnZnwXhvMTU3YduqizvSRIX40qRpGgLsT3s4O9GpSi2BPV/6OfqyV7oUimW837GN6/04YG73e7qBWq9m/cx0dun1A9dpv4+0XzKBRXyNPjOevM0f1zrdv+1rebtGJBk074OkdwPvDxmJqZs6xQzsAuHPjEvFxzxk4chLefkF4+wUxaNRkHkTf4MaVc3rzLWhn1Caat2pL0+at8fbxY8iHozEzN+fw/j060+/asYVqNWrRqUsPvHx86dlnAP6BwezdFaVZ1l3bN/Nu9z7UqlsfP/9ARo4ZS2JiPGdPnSjlmnuV3yat/D4aMw55YkKx+e2M2kizVu1o0rzNy+Uag5m5OYdeLldaWiqH9++h38ARVKpSncDgUEZ8/CW3blzl9s1rBsd27c+VVGk0FN+Ipji4hdKw67dkpMTy6MbBYufNVqZxdONn1Os0BTML2yLfh9Xqhpt/JDb2njh5VqBG81GkJT0nVf7U4NgO7VpL23cHUrVWI7z8Qvhg5BQUiXH8ffaI3vkqVa9H554jqF6nic7vLa1sGD15AZH1WuDm6UdgaGXeG/gFD+/eICHuucGx7d2+kc7d36dmnQb4+gcxYvRE5InxnD91XO98u7dtoEnL9jRq3hYvH38GjvgMUzMzjhzYpZXuwb3b7I5az9CPx+nJqXhvVzFh//ksrt7P5XmCirUHM7G1klApoPiOaCoVpKSrNR9d115Z2WqtNMpsw+Nadfwi79SuQKfICAJdHZjwTmPMTYzZdu663nlyVSrGrdvPsOa18XLQ3s5sLMxYOKgTLasE4+diT2VfN8Z2epvrT2N5Lk/Rk2Px3q5mwv6zSq7ey+FZvIrV+zOxs5JQKbD4dZerLrzu8ls41YW+S0lXUznQmIt3sskyYP3F7TvG7UlzeLG9+H3yFd/BPci4/4Qbn39H6s17PPxlDTFb9uE/qp8mjf/H/Xm8ZCNPVmwl9cZdrgyfRG56Jt79uujPWI8152/TuZI/HSv5E+Bky/jmNTA3MWL71Qf6Z5JIcLIy13wcrbRbkluH+1Db1xUvmTWBTnaMblSF1KwcbscpShXblqjttG7VgpbNm+Hr48OoD4djZm7Gvv261+W2HTuJrFGdbl3ewcfHm359ehMUGMCOXXk3hJ4+e8aNm7cYOWI4oSHBeHt5MXLEMJRZWRw5eqxUsQE0rm7G76czuXw3b3tbsScNO2spVYJMip1PpYLkdLXmk5aRv70FeBjjaCtl1e/pPItX8Sxexcq9afi4GRHiY1hnVLMajci+eorsa2dRJb4g8+Am1DlZmFQspteeGtTpKQU++TcUpDJnjD38yDy0CdWLx6jksWQe3ATGJpiEVTcoJs3PlPPj73+rzgdo16kr73TrRUhY0d4ChsZWXut8QShIXLwLGgmxT0hWxBNSqa5mmoWlDb5BlXlw+9J/lLd/SFWuXvgDReIL1Go1d66dJe75A8Iqv1XivNk5udx49JzaYf6aaVKphNphfly+V/KBT61Wc+bmfR68SKB6cH6XYpVKzYRl23m/eV2CPAzvElxY3IunJMkTiKhcSzPN0sqawJAK3L11Wec8OdnZPLh7U2seqVRKhSq1uHsr74ZGdnYWEiQYm+TfeTcxNUUikXL7esnlkZ2dzd3oW1SuWkPrNypXrcGtm7ovCG7fvKaVHqBa9Vqa9C9inqOQJ1KlQBorK2uCQyP05lmcV/lVLpJfOLf0XGTnLddtncv16sL8XvRtcnJytNJ4efvi5OzKrRuGXbynyJ+QkRqPR2D+/mBqboOzV2ViHxW//k/tnIp36Nt4BhmwfWelc+fCVqztvbCyK7mLNED8i6ckKeIJr5J/smhpZUNAcEXu6dnmXldGeioSiQRLK8N6psS+eIZCnkClqvldaS2trAkKjeD2zas658nJzuZ+9C0qVY3UTJNKpVSqWlNrHmVmJvN/+JoPho1BZl/6LsKOthJsraTcfpzf2pGZBQ9fqPBzK746dJJJmdzfkgl9LOnd3AyZddE7/jVCTZg6wIrP37OgbV1TTAx8MC07J5cbT2OpE5TfrVUqlVAn2JvLD2P0zrfw4FnsrS14p1YFvWkKSs1UIpHkXdiXlqOtBDsrKbcfFVp3Mbn4uxXfFOoskzJlgBUT+1nRp6U59jb6W0u8XKR4uRhx6lop7nyUgqxOVeIPn9KaFnfgBPZ1qgIgMTHBrnoF4g+dzE+gVhN/+CSyOtVK9VvZuSpuvJBT29dVM00qkVDbx5XLzxL0zpeRlUObhbtpvXAXn0T9yd34pGJ/Y+vle1ibmRDiLDM8tuxs7kRHU61q1fzYpFKqVa3CjZs3dc5z/eZNqlWtojWtZvXqmvTZ2XllZmqaf3EtlUoxMTHh6rXS1Q+OdlLsrKXcKtD7IjMLHjzPxd+j+B3L2V7KN0Nt+XqgDf3aWGptb8ZGoAZyCjR45uTm3UQK9DJgh5UaIXX1Iufh7QIT1eQ8vI2Ru5/++UxNsR74FdaDJmHRYQBSxwLHeuO831XnFNzm1ZCbg5Fn6bpVl+fjL/z36vx/Qnmu88s7tVpSZp//ReKZ93+RXbt2YW2t3S0rN1d/lxulUolSqd3VJztLiomp7hO3lJfPVtnYaR+UbewcSX6N564K6tJ/HBsWTWby8KZIjYyRSCR0HzyZwPCaJc4rT00nV6XG0dZKa7qjrTUPXug/AUrJyKTF2LlkZ+cilUoY915r6obnV4TL9p/EyEhKz8aRevMwRJIiL4bCz6Hb2jmSJNcdX0qKApUqFzuZQ6F5HHj+5AEAgaGVMDM3Z+OK+bzbZwSo1Wxc+RMqVS5J8pLLIyU5CZVKhazQb8hk9jx9rPvZPIU8sUh6O5k9Cnmi5nsAO/uiecpfflcar/Ir/Bx6wd8sLG+5cpHJ7IvM82q5FPIEjI1NijzvJrPXn29hGSl569jCWrtcza2dyEiN0zvfvcu7SXh2nfbDNhWb/43Tazm3bxY5WenYOfnTqv8SjAzsIvlqm7O1015vNjJHg7YNQ2VnKdmyai6R9VthYWnYc9KabaTIduSAQqF7f0hO1r0/2MkcePokf1tduXgeIeEVqVmnQWkWQ8PGMq+iTy30bGtqukrznS4PY3JZdzCXWIUaWysJLSNN+egdC75fl65pXf/rdjaJKWqS09S4O0pp/5YpLjIpy/aW3F1YnpaRd4yzsdSa7mhtyf1Y3Y+4/HX/GVHnrrPx4/dKzB9AmZ3DnD0naV0lBGvz0nfFtbHKWz+FnwtOSVdrvtPlYUwua/dnEqtQYWspoVVtM0a+a8m3q9N09kyoW8GEmIRcHjw37NGg0jJzdUL5QnsfUb6Ix8TOBqm5GSb2dkiNjVHGJhRKk4BVaOkupBQZSnLVahwKtZw7WJnzIFF37wdfBxsmtapJsLOMVGU2K8/dov/aw2zq3xLXAtvHsbvPGLvrNJnZuThZm7Pg3YbYWxp+UyY5ORmVSoV9obFy7GUyHj/WfVNcLlcUSS+TyUiU522j3l5euDg7s3T5SkZ9OAJzczO2bttBfHy8Jo2hbF9uU8np2ttBSrpK850uD57nsGpvLi8Sc7GzltKmrjmj37Nh2rJklNl5F/9Z2dCxoQU7jmcgkUDHBhYYSSXYFZPvKxILKyRSI9Tp2uWnTk/ByMFV5zwqeSyZ+9aTG/8MiZk5pjUaY9VjFKkrvkWdmoQq8QWq5ETM67cj4+BGyM7CtEYjpDb2SK2KtuIWpzwffwvG90/X+f+E8lznC0JB4uL9X6Rx48YsWLBAa9qZM2fo3Vv38z0zZszg66+1n6vqOXgCvYd+BcD5E7vYuCj/+8Ff/PIPR5zv2O9reHDnMgM/+wkHJ3fu3rjAlqXfYGfvQmiBlv5/kpWZGRvGDSJdmcXZWw+YufkAnk4yIkP8uP7wOWv/OMu6sQORSEp35+7k0b2sWDBD8/cnE378p0MHwNbOnhGffcuKX7/l4O4NSCRSajdogW9AGBLpv7PTzLE/DrDwp1mav8dN/rYMo9F29+JO/tw+WfN3874L9CfWI1XxnNO7ZtDqgyUYmxR/Ih1YtT0eQW+RkRLHlRPL+GP9J7QdvFbnfKeP7mH1wm80f380fl6pYyutnJxsFs78AtTQe8hYvelO/LGPRT//oPn7i0k/6E37nzh/5jjXLl3g23nLDJ6neogx3Rrlr89FuzKKSa3fzQKtzc8T4GFMBl+9b0XVIGPO3MhrGTx1LadAGhXJaWpGdLbA0VZCQvI/+x6dtMwsxq8/wKQuTbC3sigxfXZuLp+t/h21Gsa/07jE9AA1Qo3p3iT/onPhjtdbdzce5q+7Z8DDmHQmfWBNtRATThdqXTcxguqhJuw/83rjZ/x/UMXDkSoe+RcPlT0c6bLsd7Zcusfw+hU10yO9XVjXtwWKDCVRl+/xxc5TrOzVtMiNgjfJ2NiYr8aPZfbc+XTp0ROpVEr1qlWIrFkDtbr4fSAy3IT3muffnPhla2oxqfW7fj9/P3wWr+LB8zSmDraleqgpp65mkZqhZvGONHo0t6BRdTvUarhwI5tHMTmUEOJry33+gNznDzR/Zzy7j3W/sZhWfgvlyb2gUpG+YykWLd7DdsQM1Kpcch/dJvv+dSQlPNNbno+/IOr8gkpT5//b/Lf2HUE3cfH+L2JlZUVQUJDWtCdPnuhNP3bsWEaPHq017ciN/Iu+ijUa4xuUP/p6TnYWAClJCdjZ53cjT0lKwNO36AAchsrKymT3+rl8MGYuFaq/DYCHbyhPH97kj13LS7x4t7e2xEgqIaHQ4HQJyak42epvDZRKJfi45N3dDfN24/7zeJb+fpLIED/+in5EYkoarQtcBOWq1MzecpA1h8+y95uP9OZbrVZDAkPyT6JerbckRQIyByfN9OSkBHz8dY/aa2MjQyo1Ikmhfac5OSkRuwLd0SpWq8MPC7eRkqxAKjXCytqGkf1a4uzaQm98mt+wtUMqlaIo9BsKhVzviOsye4ci6ZMKpH/1b5I8EQeH/DgVCjn+Adrbpi6RtetpjQ77qpulQp6IfYH8khRy/PTkl7dcRkUG3dOO05GcnGzSUlO0Wt8Vcv3L7hPeBGfv/P0hNyevXDNSE7C0ddFMz0yNx8Fd92j9Cc+ukZmWwPaf85+NVatyiXlwnhun1/L+15eQSvO6GJua22BqboOdkx/O3lVYM60OD68fJLBK2yL5Vq31NgEFtrlX6y05KVFrFPgURQLe/q+/r76Sd+H+JQlxzxkzZWGxre41atcnKDS/23a2Zn9IxL7A/pCkSMTXP1hnHra2uveHJEWipryuXbrAi5infNC9lVaa2TPGExZRhUnf/lQk32v3c5j5Iv/i0dgo70TY2lJCcoEWZGtLKc/iDR84KDML4hQqnGT6b6I9evm7TjIpCcnF521vldfyl5CiPThdQmo6ToVa4wEeJybxTJ7MyOX5z6OqXp49Vf/yJ7Z/1gdvRzsg/8L9uSKZRYM7G9zqfvVeDg9j8o+5r9adTaF1Z2Mp4Wmc4a3kGa/WnV3Ri5IqwcaYGsPZmyUPVPe6lC/iMXN10ppm5upEdlIKqkwlWfFyVDk5mLk4FkrjiDKmdL1aZBZmGEkkRQanS0zLLPIcuz4mRlLCXOx5rNC+mLUwNcbH1Bofe2sqezjScfFetl29zwe19b9JpCBbW1ukUmmRwenkCgUO9jKd89jby4qkVygUONjnt4iGBAfx609zSUtLIzsnB5mdHR998ikhwcXXD5ejs3nwPL81+9WgdLaWUpLT8vcfG0spT2IN31czlGpi5bk42+fvqzcf5jB5cQpWFhJUqrw0M4bZcuFWyY9qqDPSUKtykVhq9+qSWNqgSks2LCiVitzYp0hl+cduVewT0lb/AKbmSIyMUGekYfXeJ+S+KL5luTwff+HN1fmvozzX+YJQHHHx/v+YmZkZZmbad/RMTPMrJ3MLK8wt8ruiq9VqbGVO3Ll6Gi+/MAAy01N5GH2Zes27vXYcqpwccnNzkEi0T3QlUiPUBoyabmJsRLiPO2dv3adJ1bwLE5VKzdlbD+jRqORu95o41GqycvJOCtvVrkSdAs/QAwybv452tSvRsW4VXbNrWFhYYVFovdnZO3L98jl8A/Liy0hP5e7tazRu9a7OPIxNTPALDOP65XPUqNPo5TKpuH75HE3bdC2S3sZWBsD1y+dISZJTrVbJ3dZMTEwIDArl8sW/qF23geY3Ll+8QJt2nXXOExJWgSuX/qJ9p/wYLv19ntCXA8C4urkjs3fg8qW/8A/MOxFIT0/jzq3rtGrTocSYLCwtsbDMvxhRq9XI7B24UiS/G7Rs07GY5QrhysULhZbrL1q/XK6AoBCMjY25fOkv6tbLu2H09Mkj4uNeEBqu+/lgEzMrTMy0y9XC2oln907j6JFXcWdlphL35DJhtXvozMMjsC6dR27XmnZ8y3jsnP2p3HCgphLXRY0aVW6Wzu907at2MiduXj6Lj3/+NnfvzlXeblV0+ymNVxfusc8f8emU37C2kRWb3sLSCgtL7dhk9o5cvXgBv4C8m1fp6WlE37pO89a6tztjExP8g0K5eum85rVDKpWKq5cu0LJd3klRx659aNJCexv77MM+9B04khq16unMV5kNSq1XwKlJTlMR4mXEs/i8Y4+ZCfi6Sjl51fBnrE1N8p7JTb6l/yLT0ynveJecVnKThImxEeGeLpyJfkKTioFA3jHuTPRjerxV9PWW/s72bB6t/eqpn/edIk2ZzecdGuL28lVwry7cH8UrWDzkHWQGtNK/omvdJaWpCPE24umrdWcKvm5GnLjyGutOx3qpU8GEq/dytAYY+6cpTl/EubX2q62cmr6F/PRFANTZ2ST9dQ2nJnXzXzknkeDYuC4Pf1ldqt969Rq3s49iaRyc91pTlVrN2UexdK9W8s1OyLupHB2fRD3/4p+NVavVZOUYfhPFxMSE4KAgLl68RL26ea8CValUXLx4mQ7tdF9MRISF8fely7zTKf/Y/NffFwkPCyuS1soq75jw9Okz7kRH836fXsXGo8ymyCvgklJVhPoa8yQu72Ld3BT83I04ftHwnhlmJnmvhktOLbpuXm1nId7GWFtKuBxtwHasykX14gnGPsHkaAbclWDsE0LWRf0DwmmRSJA6uZNz/0bR77IyUQNSmRNSV28yT+oeXPaV8nz8zYvvzdT5r6M81/mCUBxx8S5oSCQSGrbuw/6o33B288XBxZM9G3/Czt6FSjXz39v+89QBVI5sSoOX7y1VZqYTV+D9pomxT3ny4CZW1nbYO7ljbmlNYHhNdqyZhYmpGQ7OHkRfP8/5Yzvo2OezInHo0qdpbSau2EGEjzsV/TxZc/gMGcpszYX2hOXbcZHZMLJT3ijaS37/kwhfd7yd7MnKyeXEtWh2n7nCuPdaAyCztkRmrd2iZWwkxdHWCj+30g3EIpFIaNH+PXZuWoqbhzdOLp5sXfsr9g5OVK/9tibddxOHUaNOY5q1zbsR0rJjTxbN/Rr/oHACgiuwf+c6lJkZNGjaXjPP8UM7cPfyx9bWnuhbl1mzZDYt2r+Hu6efQbG179yV+bNnEBQcSnBIODu3b0aZmUmT5nnrYe6s6Tg6OtG732AA2nXowsQvR7F96wZqRNbhxLHD3I2+xdCPxmiWtV3Hd9m8fhXuHl64urmzbtUSHBycqFW3vt44ilt37Tp2ZfP6lbh7eOHi5sa6VUuxd3DUym/yuE+oVbcBbdq/83K5ujF/9gwCg8MIDglj1/bNKDMzNMtlZWVNkxZtWL7oZ6ytbbC0tGLJr3MJDatASJhhg3tJJBIq1OvLpT9+xc7RF2t7L/46OA8LGxd8wvPfrLB3SX98I5oRUbcXJmZW2Ltq97YwNrXAzFKmmZ6c+Jj7V/biGVQPcyt70pJecPnYIoyNzfAKMex9uRKJhKbterJ782Jc3H1wcvVg+7oFyBycqVarkSbdrElDqFa7MU3a5J14ZGakExuT/8aF+NinPLp/CytrWxyd3cnJyebXHz7n0b2bfDRurtb4ClbWdhibFD/K86vYWnfsRtSGFbh5euHi6sHG1Yuwd3CiZt38m05Tx40ksm5DWrXPu8HVtlN3Fvz4DQHBYQSFRLBn+0aUmZm83SzvQkJm76hzkCQnZ1dc3Ax/h/TRS9k0r2lKnEJFYoqa1rVNSU5Tc+Ve/oX4sI7mXLmXq7ko7VDPlGv3c0hMUWNnJaFVLVPU6rzn3CFvMLfqIcbceJhLWqYaD0cpnRqYEf00b0R7Q/RpUJWJGw9SwcuFit6urD5xkYysHDrVzLtxNn79flzsrBnV+i3MTIwJLnScsjHPu1H7anp2bi6frtrLjadxzO/fDpVaRXxKXku6nYU5JiW9b0vXuvs7mxa1zIhTqEhIVtOmrilJaWqu3M1fdyPeseBydA7HL+etm471zbh6Pwd5sgpbawlt6pihVqm5cFv7xoeTnYRATyMWbi9d93wjK0usgvIHIrX098K2ShhZiUlkPn5O6LTRmHu6cqn/FwA8/G09vsN7ETbjMx4v34JT4zq4d23NuQ5DNHncn7OMKku/Q3HhKknnLuM38n2MrSx4vGJrqddZr5ohTNp7lghXeyq4O7D2wh0ysnPoUNEPgIl7zuJibcFHDSsB8NvJ61TycMBbZk3Ky2fenyen0blS3vP2GVk5LD5zg7cDPXCyMkeRkcXGi9HEpmbQPLT492QX1qVzR36YPYfg4CDCQkLYun0HmZmZtGyeV99/P+tHHB0dGNDvfQA6dWjPp1+OY/PWKGpFRnLk2DFuR0cz6qMRmjyPHT+BnZ0dLs7O3H/wgAW/LeatOrWpWb10g/0B/PGXklZ1zIiV55KQpKJdPQuSUlVcKnCRPbKrFZeiszn6d95FUOe3zblyN5vEZDV21hLavmWOSg3nb+bPU6eiKTEJuaSmq/H3MOLdJhb8cUFJrNywfVV54QgWrXqS++IxuTGPMK3+NhITU7KvnQHAvFUv1KlJKE/k9YwxrdOS3OcPUCnikZhZYFazCVJbe7Kv5A+caBxcBXVGGqoUOUZO7pg3eoecu1fIfXirVOusvB9//1t1PoA8MQGFPJGY53ljNjx8cA8LC0ucXFyxsSl57IDyXOeXd6r/0Ve2lRVx8S5oadrhA7KUGWxYNJmM9BQCQqsz5MtftQa5i3/xmNSU/O5Lj+5e5eepH2j+3rbqewAiG3ak1/C853TfHzWTXevmsPqnL0lPTcLe2YM2PUZSr3l3g+JqWbMC8tR0Fuw6SnxyGqFervzy0Xs4vuw2/zwxSevZ9QxlFtPX7SVWkYKZiTF+bk58078jLWsaduFWWm0690WZmcGyX6aTnpZKSHgVxnw1D9MC6y025ikpyQrN37XrtyAlSUHUuoUkyfO62I+ZNE9r4LvnTx+yadXPpKUm4+TiQft3+9Oyg3aLW3HqN2xCcpKCdauXoZAn4h8QxMQp32u6msXHvUBaYL2FRVTkk88msnbVEtasWIy7pydfTJiGr1/+QE2d330PZWYmv86fSVpaKuERlZg49XutZS2NTu++R2Zmhia/sIhKTJz6g1Z+Mc+fkZKcP+JyvYZNSEpSsH71Us1yTZjyg1YXuv6DPkQqkTJz+ldkZ2dTtXokg4Z/UqrYKjUYSE5WBn9um0RWZjIuvtVp2e83rWfUUhIfkZlu+GBMxsZmvHhwnmt/riQrMxkLa0dc/WrSbsi6IgPlFKdV5/fJUmaw6tdppKelEBxelVETf9LaV+NinpBaYJt7ePc6M78arPl747LZANRt3J4PPvoaRWIcl87lvd5wyhjtloZPp/xGaEXDerp06NILZWYGi+Z/T3paKqERlflyyiytMn0R81SrTN9q2IzkJAWbVi9GIU/ENyCYL6fM+o+6Repy+K9sTI0ldGtshoWZhPvPc1m4M0Nr9GknOylWFvkn8nZWEvq0NMfKXEJqhpp7z3KZsyld87q4XFVe693bVU0xNQZFqprLd3PYf87wVpVWVUOQp2Xwy/4zxKekEerhzC8DOmgGsYtRpGrtqyWJTUrjyPX7AHSbs17ru8VDOhMZWLoLPYBDF7IwNYHuTc2xMJNw71kuv25L11p3jnZSrCzy45RZS3i/lfa6m70xvUjrep0KJiSlqrn10PAu0QB2NSpS99Aqzd8RM/NeYfV45VYuDxiLmbszFt7umu8zHjzhXIchRMwai99Hfcl8EsOVIROIP5D/mqrnm/Zi6uxAyKSRmLk5k3zpBmfbDSQrVveAX8VpGeaNPF3Jgj+vkZCeSaizjJ/ebaDpNh+TnI60QLEmK7OYuu8CCemZ2JqZEO5qz7L3mhDglHfxIZVKeJCYwq5rJ1FkZGFnbkoFNweW9GhMoJNdqWJr1LABSUlJrFy9FrlcTkBAAN9MmYz9y27wsXFxWvVqhYhwxn42huWr1rBsxSo8PD2YPGEc/n6+mjQJcjm/Ll6q6U7frGljevUwrJ4v7MBZJaYmEnq2sMTCTMLdpzn8vCVNe1+VGWFlkT9BZiOlfzsrzfZ292kOM9ekklpge3O1l9KxgTmW5hISklTsO63k8AXDW/Nzbv9NpqUVZm+1RmJpiyruKelbF2pe/ya1sdc8xgIgMbPAonl3JJa2qJXp5L54TNq6uagSX2jSSK3tMG3UCYmlDeq0ZLKvn0N5ev/rrLZyffyF/16dv3/vDjauXa75e+IXIwEY8fGXWhf5xSnPdb4gvCJRlzSKiPD/yt6//zuv3/mnNJKvLzlRGbnorrtLV3lga5JWcqIyoi7Hd2R3X3QpOVEZqRv+egOEvSk2puU3vpX7ym7QrpLM8F5e1iHo9cWD98s6BL1afGr4I1JvWqNf/rNHVf7b4hv3KesQ9Pp+m+4R2suDGdLJZR2CXnfbTyjrEPQykfz3xqz4T5XnOv+Ld/+dAxEDHLxcdoOMNqv87x/wr7T+vVuKIAiCIAiCIAiCIPyPEBfvgiAIgiAIgiAIglDOiWfeBUEQBEEQBEEQhFITD2C/WaLlXRAEQRAEQRAEQRDKOdHyLgiCIAiCIAiCIJRaeR6Y+P8j0fIuCIIgCIIgCIIgCOWcuHgXBEEQBEEQBEEQhHJOdJsXBEEQBEEQBEEQSk0lBqx7o0TLuyAIgiAIgiAIgiCUc6LlXRAEQRAEQRAEQSg1tVoMWPcmiZZ3QRAEQRAEQRAEQSjnRMv7/xh3a0VZh1As9eX7ZR2CXh4+z8s6BL3SsCnrEP6V6kVklHUIeslMU8o6hGJJKL8PubVuYFvWIeiVlRta1iHo1Tsst6xD0CvCsmtZh6DXkeGbyjqEYjU+Uq2sQ9Dr0tGYsg5BL2kr07IOQS9TSXZZh6CXqhy3CzaokFbWIRTj33sepy6/pwP/L5XfPUwQBEEQBEEQBEEQBEBcvAuCIAiCIAiCIAhCuSe6zQuCIAiCIAiCIAilpkIMWPcmiZZ3QRAEQRAEQRAEQSjnRMu7IAiCIAiCIAiCUGpiwLo3S7S8C4IgCIIgCIIgCEI5Jy7eBUEQBEEQBEEQBKGcE93mBUEQBEEQBEEQhFJTq8WAdW+SaHkXBEEQBEEQBEEQhHJOtLwLgiAIgiAIgiAIpaYSA9a9UeLiXdCiVqvZtGYJh/btJC0thdDwSgwc/inunt7Fzrdv1xZ2bl2HQp6Ir38g/Yd8QlBohOb73376nqsXz5OYGI+5uSWh4RXp2W8Ynt6+Bse24fwtVpy5QUJqBiGu9nzRoiYVPZx0pt1x+S6Tdp3WmmZqJOXMF+9pLeuCY5eJuhhNijKbKl7OjGsVia+DrcExaX5v1242bYkiUS4nwN+fEUMHExYaojf9seMnWL56DS9exOLp4cHA/u9TK7Km5nu5XM7iZSu48PdF0tJSqVShAiOGDsHT06PUse3ZFcW2LRtQyBPx8w9k4NCRhISG603/5/EjrFu9lNgXMbh7eNG3/2BqRNbRfH/qz2Ps27uTu9G3SU1JZva8RfgHBpU6LoC9u6LYtmX9y9iCGDh0JMHFxHby+BHWrV6iia1P/yFasanVatavXsaBfbtIT0slLLwig0eMxsPT67XiU6vVbF/3K8cORpGelkJQWBX6DBmHq4eP3nluXbvAvm0reXD3BknyeEZ8OYvqtRtrpblw6hBH9m3h4d0bpKUmMWn2Onz8Q0sd2/rVSzn4cllDwysZtKx7d0WxXbPOAxkwdJTWOt+/dwcnjh7iXvRtMjLSWblhF1bWNq8RW+nLoaTtIStLyfLFCzhx7DA52VlUrV6LwcM/RmbvUKrY9mz8mZOHtpCRloJ/WFW6D5yIi7v+Y1H09fMc2rGcR/evkyyPY+Cnc6hSq6lWmo+6VdI5b8feo2nWob9BsW3cf5TVOw+RkJRMsI8nn/XrSoUgP51pow79yZ7jZ7n75BkAYf4+jOjeXm/6GYvXsfXQn3zSpws92zTWmaYkarWaLWt/44/920lPSyUkvDL9h32OWzH7A8CB3ZvYHbWGJHkCPv7B9B08hsCQCgDEvXjGJ4M665zvo8+nU7t+U53fFbTh72hWnrtFQlomIc4yPm9ajYruureJHVcfMPn3c1rTTI2knP6ki+bvX/+8xv5bj4lJTsfESEq4qz0jGlSkkrtjibEU5FC/JgFjBmBXvSLmHi6c7zKcFzsOFT9Pw1pEzPwS64hgMh8/J3rGAp6sjNJK4zusJwGjB2Dm5kzy5Ztc+3gqSeeulCq2VzYcOsWK34+TkJRKiLcbX/RqT8UA3fX9oQtXWbLrKI9jE8jJzcXH1Yk+LevT7q1qmjRqtZoF2w4Sdew8KekZVAnyZVzfjvi66q6rSzKglx/tW7hhY2XMlRvJzPzlDk+eZ+hNL5XCB+/50aKxC44yU+ITs9hzKIYVGx5p0pzY+bbOeX9eepd1UU8MimvjpXus/CuahHQlwU62fP52ZSq62etMu+P6I74++LfWNFMjKadGtNeadj8xhXl/XufC03hyVWoCHGz4vm0k7jaWBsX0Snk+/r6Kb8PqpRzct1Or7irpPHPvrq3seBmfr6buyj/PPLB3B8ePHuT+y7prxYbdr1V3bVu3kKMHokhPSyU4rAp9hn5Z7DHu1rW/2Bu1iod3b6CQx/PRlzOpXqfRf5yvIOgjus0LWnZsWcPenZsZOOJTvpn1G+bmFkz/ajRZWUq985w8doiVi3+iy3v9+XbuEnz9g5j+1WiSFHJNmoCgUIZ+PI7ZC9Ywbsos1Go133z1CarcXIPi2nf9AbMO/cWQ+pVY+0EbQlzsGb7+DxLTMvXOY21mwoGR72g+e0Z00vp++enrrDt/i3Gta7GyX0ssTIwZsf4PlDmGxfTKkWPHWbhoCb179uCXeT8S4O/HuImTkCsUOtNfu36D6d/PpFWL5iyYN4e36tZm8rTp3H/wEMg7yE+eNp3nMTF8PXE8v8ybg4uLC1+Mn0hGpv7l1eXEscMsW7SA7j3fZ9a83/DzD2TKxM9RFCibgm5ev8rs76fStEUbZs1bRO269fl22kQePrivSaNUZhIeUZG+/QeXKhbdsf1Ct579mDlv0cvYPishtik0bdGWWfMWU6tufb6bNoGHD+5p0kRtXsfunVsYOmI0385egJm5BVMnflbs9lucvVErOLh7HX2GjGP8dyswM7Ng9pQRZBeTX1ZmJl5+IfQe/KXeNEplBsHhVXm378jXigtg2+Z17Nm5lSEjxjBj9q+Ym5szdeKnxS7rn8cOs3zRz3Tr+T4/zFuEr38gUyd+qrWvZimVVK1ei3e69X7t2F6nHAzZHpYt+pnzZ0/y2djJTP12LomJ8Xz3zVeliu3g9qUc3buW7oMmMmb6GszMLPjlmyHFlqlSmYGnXwjdBozXm+ab3/7Q+vQaNgWJRELV2s0Mimv/qQvMWRXFwC6tWTX9C4J9Pfno259JTErRmf7CjTu0eKsGCyaMYunXY3B1lPHhjJ+JTVQUSfvHuUtciX6As72dQbHos2vrKvbv2sgHw77g6x+WYGZmzneTRhVbrqePH2DNkrl07jGAaT+uwMcviO8mjSJJkQiAo5MrP63Yo/Xp0nMQ5haWVKlRt8SY9t18zOwjlxhcN4K1fZoT7GLHiM3Hiq8bTI3ZP6y95rN7cFut730dbPiiaTU29mvB0vca42FnxYhNx5Cnl+44YmRlSfLlW1wd+bVB6S38vIjcsZCEI2c4UbMj9+evoNLCaTg1r69J4961NeE/jOXOtJ85UaszKZdvUnv3EkydS3cBBbDv7GVmbdjDkA5NWTtpBCHe7gyfvYzE5FSd6e2sLBnYrhErxg9l45SRdKxfnclLt3Dy6m1NmuV7j7Hu4CnG9e3IygnDsDAzZcSsZSizs0sdX68u3rzbzpOZv9xh8Kd/k5GZy+wplTA10f98ba8uPnRq48GPv0bTa/g5Fiy/R693vHm3vacmTYc+J7U+0+fcRKVSc/RkvEFx7b/9lNnHrzG4dihrerxNiJMdH24/RWIx24eVqTH7BrTUfHb1b671/WNFGgM2H8fP3prf3qnH+p6NGVgrBDMjI4NiKqg8H38Btm1ey56dWxg8YgzTZy/EzKC66xArFv1M1579+H7eYvz8g5hWqO5SKjOp9h/WXXuiVnBg13r6Dh3LxO+XY2puzuyvPyq+fsjMwNs/mN5DvvhH8/03UavL7vO/SFy8vyGNGjVi5MiRfP755zg4OODm5sbkyZM13ysUCgYOHIizszO2trY0adKES5cuaeWxYMECAgMDMTU1JTQ0lFWrVv2jMarVavZs38Q73fsSWacBvv5BjBg9AXliAudOHdc73+5t62nasj2Nm7fFy8efgSM+w9TMnD8O7NKkadaqIxEVq+Li6k5AUCjd+wwiIS6W2NgYg2JbffYm71QNomOVQAKd7RjfuhbmxkZsu3S32PmcrC00H0drC61lXXv2JoPqVaRxiDchLvZMbV+XuJR0/rj12KCYXtkStZ3WrVrQsnkzfH18GPXhcMzMzdi3/6DO9Nt27CSyRnW6dXkHHx9v+vXpTVBgADt27Qbg6bNn3Lh5i5EjhhMaEoy3lxcjRwxDmZXFkaPHShXbjqhNNG/VlqbNW+Pt48fQD0djZm7Oof17dabftWML1WrUonOXHnj7+NKzzwcEBAazZ1d+y0+jJi3o3vN9qlStUapYCttZKLYhL2M7vH9PsbF16tIDLx9fevYZgH9gMHtfxqZWq9m1fTPvdu9Drbr18fMPZOSYsSQmxnP21IlSx6dWqzm4ay3tug6kWu1GePuFMGDUFBSJcfx15oje+SrVqMc7vUZQvU4TvWneatSODt0HE1GldqnjehXbru2btJb1ozHjkCcmFLusO6M20qxVO5o0b/NynY95uT3kr/N2nbryTrdehIRF6M2n5NhKXw4lbQ9paakc2r+HfgOHU6lKdQKDQ/nw4y+4deMqt25eMzi2I3tW0/KdwVSObIKnbyh9PpxOkjyOy+cO652vQrUGtOsxskhre0G2Mietz+VzfxBcoRZOrsW3Jr2ydvdhOjV5iw6N6hLg5c7YAT0wNzVlx5FTOtNP+7AfXVs0JNTPCz9PNyYM7oVarebc1Vta6WITFcxcvompI/ph/BoXAq+o1Wp+37Gejt36U6PO2/j4BzP0k8koEuO5cPqo3vn2bl9H4xYdebtZezx9Aug//EvMzMw5enAnAFIjI2T2jlqf86eOUrteU8wtSm5xXHP+Np0r+dOxkj8BTraMb14DcxMjtl99oH8miQQnK3PNx9HKXOvr1uE+1PZ1xUtmTaCTHaMbVSE1K4fbcQpDVpVG3L5j3J40hxfbddcFhfkO7kHG/Sfc+Pw7Um/e4+Eva4jZsg//Uf00afw/7s/jJRt5smIrqTfucmX4JHLTM/Hu10V/xnqs3neCdxpG0rFBDQI9XRnftyPmpqZsO35BZ/qaYQE0qVGBAA8XvF0c6dm8HsFebvx9O//G89oDJxnUvjGNq0UQ4u3O1IFdiVOk8Mdf10sdX9cOnqzc+JATZxK4+yCNaT/exNHBjAZ19LfiVwy35cTpeE6dTyQmVsmRk/GcvSgnPDi/BTZRka31qV/Hib+uKHj2wrCb46v/jqZzRV86RPgS4GjLuCZVMDc2Yvv1h3rnkYD2Nmepvc39cuoG9XxdGVW/AmEuMrxlVrwd4I6DpZlBMb1Sno+/r+LbvX0TXbr3oVbdBi/rrvGlrrsGv6y7Du/frUnTrlM3OnfrTXBYBYPjKRzbgZ3raN9tANVrN8LbL5hBo6YgL6HOr1yjHl16DadGHd09ml43X0HQR1y8v0ErVqzAysqKM2fO8P333zNlyhQOHDgAQNeuXYmNjWXv3r1cuHCB6tWr07RpUxIT81onoqKiGDVqFGPGjOHq1asMGTKE/v3788cff/xj8cW+eIZCnkClqpGaaZZW1gSFRnDn5lWd8+RkZ3Mv+jaVquZ3+ZZKpVSqWpM7eg7omZkZHDm4BxdXd5ycXEqMKzs3lxvPE6nt55b/GxIJtf3duPxU/53yjKwcWv8URav5UXy86Sh3C5x4PVWkEp+WSW3//DxtzE2p6OFUbJ5FYsvO5k50NNWqVs2PTSqlWtUq3Lh5U+c812/epFrVKlrTalavrkmf/bKFwtTURCtPExMTrl4z/AQoOzubu9G3tS6ypVIplatW11vZ3rp5vchFedXqkdwuReVseGy3qFwkthrcuql7GW/fvKaVHqBa9Vqa9C9inqOQJ2rFb2VlTXBohN48ixP/4ilJ8nitC2xLKxsCgity99blUuf3T3q1rJWLLGu43rJ9tT3oWuf/ZPm+TjkYsj3ci75NTk6OVr5e3r44Obty+4Zh5ZsQ+4RkRTyhlfMftbCwtMEvqBL3b18qZs7SSVbEc+3v49Rtors7eGHZOTncvP+YWhXzH52QSqXUqhjKlTv3i5kzX6Yyi5ycXGyt8y94VSoVk35eSe92TQn0di/dQhQS9+IZSfIEKlappZlmaWVNYEgF7tzS3WU7Jzub+9E3qVA1fx6pVEqFKpFE39Q9z/3oGzy8f5u3m3coMabsXBU3Xsip7euan79EQm0fVy4/S9A7X0ZWDm0W7qb1wl18EvUnd+OTiv2NrZfvYW1mQoizrMSY/hOyOlWJP6x9sybuwAns61QFQGJigl31CsQfOpmfQK0m/vBJZHWqURrZOTncePiM2hH5jzxJpVJqRwRy+e6jYuZ89bNqzlyP5kFMHDVC/QB4GicnPimF2hGBmnQ2luZUDPAyKM+CPFzNcXIw49zF/JbVtPRcrt9OpmKY/kfbrt5IpkYVe7w98m7WB/lZUTncjtMXEnWmt5eZ8FZNB3YfMKwhITtXxc3YJGp5O2umSSUSank7c+W57l5jABnZubRdtp82S/cxeucZ7iYka75TqdWceBCDj701I7adpNmivfTdcJQ/7j43KKaCyvPxFyBWU3flnzO+qrtu6znPzH55nlm5yHlmjVLdOChJ3IunJMkTqFC58DGuItF6jnFlma/wv0s88/4GVa5cmUmTJgEQHBzMTz/9xKFDh7CwsODs2bPExsZiZpZ3l3XmzJls27aNzZs3M3jwYGbOnEm/fv0YPnw4AKNHj+b06dPMnDmTxo113+1TKpUoldpdcrKylJia6r6Tq5DnVW52Mu3ntuxk9igUuiu+5OQkVKpc7GQOheZx4NkT7bvQ+3ZvZc2yBSgzM/Dw8mH8tDkYm5hQEnm6kly1GodCrSOOVuY8KFABFuTrYMukdnUIcZGRkpnNqjM36LdyP5sHtcPV1pL4l10qHawstOZztDInIU3/83SFJScno1KpsJfJtKbby2Q8fvxU9/LIFUXSy2QyEuV5Fb+3lxcuzs4sXb6SUR+OwNzcjK3bdhAfH69JY4iU5CRUKlWR8pTJ7Hn6WPeJlEKeiExHenkpfrc0sckKbTclx1Z4O7PXbLea7de+aJ5yue7ttzhJiryTf1s77fxsZY4kKwy/wfPf8GpZCz9rWHB9FJbycl8tXL52xazz/yS20pSDIduDXJ6IsbFJkWcYZfaGl2/yyzK1sdN+dtnG7p8t07NHd2BubkmVWoZ1mVckp5KrUuFgp71sDna2PHj2wqA85q/djpO9HbUqhmmmrdhxACMjKT1aNTI4dr0xyl/uD7LC+4MDSXrLVaG3fnj+VHcr5ZEDO/Hw9iMkvHLJMWXorhscrMx5kKj7cQNfBxsmtapJsLOMVGU2K8/dov/aw2zq3xLXAs8WH7v7jLG7TpOZnYuTtTkL3m2IfSlbQUvLzNUJ5Qvt7VD5Ih4TOxuk5maY2NshNTZGGZtQKE0CVqEBpfoteUp63jZna6013dHWmgfP4/TOl5KeScsx35Kdk4NUImVsnw7UqRAMQHxy3jrXlWdCku6u+Po42JvmxanQ7m4vV2RpvtNl9eZHWFkasWZBJCqVGqlUwm+r7nPgaKzO9K2buJGekcvRk/qXuaBX25xjoW3B0dKMB3Ld25yfvTVfNatKsJMdqcpsVv0VTf9Nx9nUqwmuNhYkpitJz85l+fk7DK8bzsh6FTj58AWf7T7LwnfqUcPL8PECyvPxNy+fBM18BdnJHEqsu4qeyzj8o3WXps6XadcPtnYOJMn13wwsq3zLEzXiVXFvkrh4f4MqV9Y+GXF3dyc2NpZLly6RmpqKo6P2jp2RkcHdu3ndwm/cuMHgwdrPGNerV4+5c+fq/b0ZM2bw9dfaz9oN+fBTho78HIDjf+xn0c8/aL77ctL3pV+oUmjQqAWVq0Yilyewa+s65nw7kSk/LNB7M+E/UcXLmSpezlp/d/ltJ5v/vsOIt6sUM2fZMzY25qvxY5k9dz5devREKpVSvWoVImvWQP2/+oDPG3D66B5W/vqN5u9R4+eVYTTajv1xgIU/zdL8PW7yt2UYjbajhWIbX45iO3d8F+t/m6L5e+jYn9/I7576I4qaDdpi8l84tumyfPt+Dpy6wK8TR2H2ssfOjXuPWP/7EVZP/wKJpPQnVn8e+Z2lv+SX5adfzf7H4tUnS5nJqWP76NTtg//ab1TxcKSKR35dW9nDkS7LfmfLpXsMr19RMz3S24V1fVugyFASdfkeX+w8xcpeTYvcKPhfY2VuyvrJH5GhVHLm+l1mrd+Dl7MDNcNKd/OgsOZvu/DZiPxBXj+f8notkk3qO9P8bRe+nnmD+4/SCQ6wYuTAIOITs/j9cNEbYW2bu7H/SCxZ2f+9urWyuwOVCwygWNndgXdXH2bL1QcMrxuuqdffDnCjV7W8XguhznZcfi5ny9UHxV68l+fjL8CxP/bzW4H4xk7+rgyj0Xbq6F5WLJiu+fvjCXPKLhhBKAVx8f4GmRRqZZZIJKhUKlJTU3F3d+fIkSNF5pEVaqEtjbFjxzJ69GitaTcf57dU16xdX2ukzuzsLACSFHLsHfIriySFHD9/3aOJ29raIZUaaQYfyp8nEZm99s0ISytrLK2scff0JiS0Ah/0aM25U8eo97b2wC2F2VuaYSSRFBmAKCEtE8dCLef6mBhJCXV14PHLO+NOL0/AEtMycC7wLHxCWiahrrpHjNXF1tYWqVRaZHA6uUKBg71M5zz29rIi6RUKBQ4F7kSHBAfx609zSUtLIzsnB5mdHR998ikhwYaP6m5ja4dUKtUa0CXvt+R6R4eV2TsUGTBOoZBjb2/4OilNbIV7dJQcW+HtLD/9q3+T5Ik4OORvewqFHP+AktdblVpvMykk/+Q95+XjC8lJicgc8m8EJSsS8C7lyPD/qcja9bRG/X31aIVCnoh9gWVNUsjx07OsNi/31cLlm1TMOjdErdr1tN5e8Cq20pSDIduDvb0DOTnZpKWmaLX+KORy7PXEX6lmY/yC82+a5rw8xqUkJWBnn1+mKUkJePqFFZn/dUTfuEDsswf0/3imwfPIbK0xkkqLDE6XmJSMo6z4t1+s2nWQFTsO8PO4Dwn2zR+U6++bd5Enp9L+o/wBpXJVKuau3sr6vX+wY/4UXdlpVK/VQDMiPEBOzsv9QZGoVT8kKxLxCQjWmYeNrUxv/VC4NR7g7MnDKJWZ1G/SptjYXpFZ6K4bEtMyizzHro+JkZQwF3seK7Rbhi1MjfExtcbH3prKHo50XLyXbVfv80Ft/W/D+E8pX8RjVmhUdjNXJ7KTUlBlKsmKl6PKycHMxbFQGkeUMaXrOWJvY5m3zRUanC4hORVHO/0jdEulUnxc834/1MeD+8/jWLr7KDXDAnCyzZsvMTkV5wLbbUJyKqE+xT+2ceJsAtdvn9f8bWqS91SnvcyEBHlWftwyU6Lv6W/FH94/gDWbH3PoeF5L+r2Habg5m9Onq0+Ri/fKEXb4elky6TvDu32/2uYSCg1Ol5CuxMnS8G0u1NmOJ0lp+XlKJQQ4aK93fwdrLj4rvlW7PB9/ASILnWfmaOquwueZiSXWXUXPZRL/o7qraq2GBGjV+XnbWbIiAVnBY1xSIt7++t8eVBK7ly3u/3S+5Yl4VdybJZ55LweqV69OTEwMxsbGBAUFaX2cnPJ29PDwcP7880+t+f78808iIvQPKmVmZoatra3Wp2Art4WlJW4eXpqPl48/MntHrlzMr0DT09OIvnWd4LCKun4CYxMTAoJCuHIpf4AblUrF1UsXih00RP3yv2wDRqA1MTIi3N2BMw/yn0lTqdWcfRBDZU/DupPlqlRExypwenmh7imzxsnKnDMP8ivzVGU2V5/FG5wn5N2QCQ4K4uLF/OdlVSoVFy9eJjxM98VARFgYf1/Sfmb6r78v6kxvZWWFzM6Op0+fcSc6mrp1DB/gzMTEhMCgEC5f/EsrtisX/yJUT9mEhkVw+dJfWtMu/X2BkNccAKb42EKLxHb54gVC9QyUFhJWgStFYjuvSe/q5o7M3kEr/vT0NO7cuq43z4IsLKxwdffRfDy8A7Czd+LG5bOaNBnpqdy7c5XA0JK79P6TLCwtcffw0ny8ffyQ2TtorY+8Zb2ht2xfbQ9XLmrvq5cv/vUfla++2EpTDoZsDwFBIRgbG2vl+/TJI+LjXhASrjtfcwsrnN18NB83r0BsZU7cunJGkyYjPZUH0VfwD/lneuScOrwV74AIvPwMv8FjYmxMmL+31mBzKpWKc9duUynYX+98K3ccYMnW35n35XAiArVfddemQSRrvxvL6m+/1Hyc7e3o3b4Z88aOKDEmC0sr3Dy8NR9Pb3/s7B25din/NWvp6ancvX2N4FDdr8kzNjHBPyhMax6VSsW1y+cICis6z5EDO6leqwG2dobdLHz1Grezj/K7RKvUas4+iqWyh2Mxc+bLVamJjk/S3NDVR61Wk5WjMijP16U4fRHHJnW0pjk1fQv56Yt5MWRnk/TXNZyaFBiFXyLBsXFdFKe1X0VWEhNjY8J9PThzI1ozTaVScfbGXSoHGv76qrz1kgOAp7M9TnY2nLmeP5BsakYmV+89KTHPjIxcnj7P1HzuP0onPlFJzSr524KlhRERIbZcvan7cTkAczMjVIV6qOWq1Eh1dD5p18KNm3dSiH6QZsiiAq9u9thx7nF+N3uVWs25x3FUcjdsu81VqYlOSNZc7JsYSangIuOhXPumxEN5Km42xTdQlOfjr674vDR1V3499KruCtFznmny6jyzUN1V3LmMIfLqfG/NJ6/Od+T65fzjVUZ6KndvXyVIzzHOEM6unv+VfIX/rsTERHr16oWtrS0ymYwBAwaQmqr/xmFiYiIfffQRoaGhWFhY4OPjw8iRI0lK0h5TRSKRFPmsX7++VLGJlvdyoFmzZtStW5dOnTrx/fffExISwrNnz9i9ezedO3emZs2afPbZZ3Tr1o1q1arRrFkzdu7cydatWzl40LBRbA0hkUho07ErURtW4O7pjYurOxtWL8bewZHIug006aaOG0Vk3Ya0ap83um3bTj345cdvCAwOIzAknD3bN6LMzKBRs7zX77yIecrJY4epUj0SW1sZCQlxbN+0GlNTM6rVLPlVQAC9a4Xx1c5TRLg7UtHDkbVnb5KRnUvHynld9SbsOImLjQUjG+cN2rPw+BUqezrhbW9NijKbFaev8zw5jc5VgjTL2rNWGIv/vIqPvQ2eMit+OXYZZxtLGocaNjr0K106d+SH2XMIDg4iLCSErdt3kJmZScvmeSNTfz/rRxwdHRjQ730AOnVoz6dfjmPz1ihqRUZy5NgxbkdHM+qj/BPqY8dPYGdnh4uzM/cfPGDBb4t5q05talYv3aBEHTp3Zd7sbwkMDiE4JJxd2zeTmZlJ0+atAJg7azoOjs706TcIgHYdujDhy4/ZvnUjNSLrcOLYYe5G32LYR2M0eaakJBMfG0tiYl5Lz9Onec+cyewdsHcw/C54+85dmT97BkHBoQSHhLNz+2aUmZk0ad5aE5ujoxO9+w3WxDbxy1Fs37pBK7ahL2OTSCS06/gum9evwt3DC1c3d9atWoKDgxO16tbXG4c+EomEZu16smvTYlzdfXBy9SBq7QJkDs5Ur91Ik+6Hr4ZQvU5jmrbpAUBmRjqxMflvLIh/8ZRH929hZW2Lo3Ne61NqShKJ8TEoEvNOAGOePgDy7tDb2Zd88yhvWbuyef1K3D28cHFzY92qpdg7OGot6+Rxn1CrbgPatH/n5TrvxvzZMwgMDiM4JIxd2zejzMzQrHMAeWICCnkiMc/zxmx4+OAeFhaWOLm4YmNTfCtwfmwll8OkcaOpXbd+gdiK3x6srKxp2qINyxb9grW1LZaWliz+dR6hYRUMPoGTSCQ0atObfVsX4uLug6OLJ7vW/4SdvTOVI/PfDjB/ykAq12rC2616AqDMTCcuJv/ZyoTYpzx5cBNLazscnPJbFDPSU7l4+gCd+3xqUDwF9WzbhK8XrCI8wIcKQX6s2/sHGUol7d/Ou5ib9MtKnO3t+PC9jkDe8+wLN+1m2ofv4+7sSLwi74LG0twMS3MzZDbWyGy0nz02NjLC0c4WPw9XSksikdCqQw+2bVyGq4c3Lq4ebF6zEJmDEzXq5L83e/qEEdSs04gW7boC0LrjeyycMwX/oHACQyL4fcd6lJmZvN20nVb+Mc8ec+va33z61Y+liqtXzRAm7T1LhKs9FdwdWHvhDhnZOXSo6AfAxD1ncbG24KOGeSfJv528TiUPB7xleXXDynO38uqGSnl1SUZWDovP3ODtQA+crMxRZGSx8WI0sakZNA8t/j3ZhRlZWWIVlH/RaunvhW2VMLISk8h8/JzQaaMx93TlUv+8V0w9/G09vsN7ETbjMx4v34JT4zq4d23NuQ5DNHncn7OMKku/Q3HhKknnLuM38n2MrSx4vGJrqWID6N2yPl8t3kyEnxcV/b1Ye+BPMpRZdKxfHYAJizbhYm/LyHdbArBk9xEq+Hni5exIVk4OJy7fYvepvxnbJ2+blEgk9Gz+Fot3/YGPqxOezvb8EnUAZ5kNjauX/u0Vm3Y85f3uPjx+lsHzF5kM7O1HQqKS46fzexnMmVaZY6fi2br7GQB/nkugbzdfXsQpuf8ojZAAa7p38mJPoQHpLC2MaFzPmZ+WFP/GGp3rrVoQkw78RbirjIqu9qy9eJeMnFw6ROSV9Vf7L+BsZcFH9fKW+bczt6jkZo+3zIqUl8+8xySn06lC/rbRp0YQY/eep5qnI5FeTpx8GMvx+y9Y2KVeqWIrz8ffV/G17diVLZq6y531q5boqLs+pnbdBrR+eZ7ZvnM3fpo9g8DgUIJCwtm9fRPKzAwaN8/vpfNP1F3N27/Hzk1LcPXwxsnFk6i1C7AvVOd/P3EY1es0olnb7sDLOv95fp0fF/uUR/duYWVjh6Ozm8H5CuVLr169eP78OQcOHCA7O5v+/fszePBg1q5dqzP9s2fPePbsGTNnziQiIoKHDx8ydOhQnj17xubNm7XSLlu2jFatWmn+Lm0va3HxXg5IJBL27NnD+PHj6d+/P3Fxcbi5udGwYUNcXfNOsjp16sTcuXOZOXMmo0aNwt/fn2XLltGoUaN/NJYOXXqhzMzkt/nfk56WSmhEJcZOmaXVYv8i5ikpyQrN3281bEpykoKNqxejkOd1fRo7ZZamO5OJiRk3r11i746NpKamIJM5EFahClN/+LXIACT6tIzwQ56uZMGxS5qu7T93b6x5/VtMchrSAs91pmRmMWXPaRLSMrE1NyXczYHlfVsQ6Jz/nuN+dSLIyMph2t4zpGRmUdXbhZ+7N8bMuHSvU2rUsAFJSUmsXL0WuVxOQEAA30yZrOlqHhsXp/XMaYWIcMZ+Noblq9awbMUqPDw9mDxhHP5++S1nCXI5vy5equlO36xpY3r16F6quADqN2xCclIS61cvRy5PxD8gkK+mfKcpm7i4WCSS/A44YREV+eSzCaxdtZTVKxbj7unJlxOm4uuX3/p37vRJ5s/Jf25t1ndTAeje83169OpXytgUrFu9DIU8Ef+AICZO+V4TW3zcC60yzYttImtXLWHNy9i+mDANX7/8Zy07v/seysxMfp0/k7S0VMIjKjFx6vevPa5C687vk5WZwYoF00hPSyE4vCqfTPxJ61nmuJgnpBbYHx7cvc4PE/PHp9iwLO9Z4bcat2fAy/c9Xzx3lGXzJ2vSLJw1FoAO3QfTscdQg2Lr9O57ZGZmaJY1LKISE6f+oLWsMc+fkZKcf9e3XsMmJCUpWL96qWadT5jyg1bXw/17d7Bx7XLN3xO/yHsX/YiPv9S6yC+OIeUQ8/wpyQViK2l7AOg/aAQSiZQfpn9FdnY2VatHMnj4xwbF9Eqzjh+Qpcxg3cKvyUhPISCsGsPH/apVpvEvHpNWoEwf3b3GvK/zn8OOWpk3VkittzvQZ0T+OAl/ndyLWq2mRn3D1lNBLerWQJGcysLNu0lQpBDi68m8L0dous3HxCdqHUe2HDhOdk4OX8xZopXPoC6tGfyu9nvL/ynt3umDMjODpT/PID0tlZCIKnw+ea5WucYWqh/qNGhOcpKCLWt/I0megG9ACJ9PnoNdoceqjh7ciYOjC5Wqle71iS3DvPPqhj+vkZCeSaizjJ/ebaDpNh+TnK7V6pqszGLqvgskpGdia2ZCuKs9y95rQoBT3nqWSiU8SExh17WTKDKysDM3pYKbA0t6NCbQyU5XCHrZ1ahI3UP5r3SNmDkOgMcrt3J5wFjM3J2xKPAWgIwHTzjXYQgRs8bi91FfMp/EcGXIBOIP5L9C6/mmvZg6OxAyaSRmbs4kX7rB2XYDyYot/cBXLWtVRp6SxoJtB0lISiHU252fP+mv6TYfk6hAWmDlZSqzmL5qB7HyJMxMTfBzc2baoG60rJXfE6lf64ZkKLOYtiKKlPRMqgb78vPo/pgZMDhtYWu2PMbc3IjPPwzB2sqYK9eTGDPpitbz6Z5uFshs8/P+cWE0g3r5MWZYMPZ2JsQnZrHj9+csW689QGKzhi5IJHDwmO6B7IrTIsQTeYaSX0/fJCFNSYizLfM71tG8/i0mJUNrX01RZjHt8EUS0pTYmpsQ5iJjadcGBDjmX1A2CfRgXOMqLDt/h5lHr+Brb833bSKpZmAPkoLK8/EXoNO7PVFmZrKwQN01YepM7fPM58+04qv38jzzVd3lFxDE+CkzC9Vd29lUoO766ouPABjx8VgaG1h3ten8PlmZmSz/ZTrpaSmEhFdl9FfztOqH2MJ1fvR1vpuYX2+vX5p3A7Je43YMHDXZ4Hz/zcpyOCZdg3ObmZlpBgB/HTdu3OD333/n3Llz1KyZ95aD+fPn06ZNG2bOnImHh0eReSpWrMiWLVs0fwcGBvLNN9/Qu3dvcnJyMDbOv+SWyWS4ubkVycNQErUYAet/ysU7ho2oWlZCTi4o6xD0iqtX+ovnNyUN/c8olrXyPAqpPKvku/FlRWaqe+Ti8kJC+a06nqaV/oT3TamTq/+d6GXtllVkyYnKSMQxw8cQeNOODN9U1iEUq/GR6SUnKiMtvjX8MbU37fdWu0tOVEYetvykrEPQS1WOn8hNybYsOVEZeSu8/J7HlWTT6f/uI0XFufb7lCKDc0+aNInJkye/dp5Lly5lzJgxWm9aysnJwdzcnE2bNtG5s2GvgV28eDFjx44lLi7/2ksikeDh4YFSqSQgIIChQ4fSv3//Ug0sK1reBUEQBEEQBEEQhFIry2ZgXYNz/yet7gAxMTG4uLhoTTM2NsbBwYGYmBg9c2mLj49n6tSpRd4UNmXKFJo0aYKlpSX79+9n+PDhpKamMnLkSIPjExfvgiAIgiAIgiAIwr9KabrIf/nll3z3XfGvK7xx48Z/HFNycjJt27YlIiKiSA+AiRMnav6/WrVqpKWl8cMPP4iLd0EQBEEQBEEQBOG/S6Uuv49HFjRmzBj69etXbJqAgADc3NyIjdUeDyMnJ4fExMQSn1VPSUmhVatW2NjYEBUVVeQ14YXVrl2bqVOnolQqDb4JIS7eBUEQBEEQBEEQhP+3nJ2dcXZ2LjFd3bp1USgUXLhwgRo1agBw+PBhVCoVtWvrH1Q1OTmZli1bYmZmxo4dOzA3L/4VpAAXL17E3t6+VF39xcW7IAiCIAiCIAiC8D8vPDycVq1aMWjQIH799Veys7P58MMP6dGjh2ak+adPn9K0aVNWrlxJrVq1SE5OpkWLFqSnp7N69WqSk5NJTs57hauzszNGRkbs3LmTFy9eUKdOHczNzTlw4ADTp0/n009L93pZcfEuCIIgCIIgCIIglNr/x/eWrVmzhg8//JCmTZsilUrp0qUL8+bN03yfnZ3NrVu3SE9PB+Cvv/7izJkzAAQFBWnldf/+ffz8/DAxMeHnn3/mk08+Qa1WExQUxOzZsxk0aFCpYhMX74IgCIIgCIIgCIIAODg4sHbtWr3f+/n5UfBt640aNaKkt6+3atWKVq1a/cexiYt3QRAEQRAEQRAEodT+P7a8l2fSsg5AEARBEARBEARBEITiiYt3QRAEQRAEQRAEQSjnRLf5/zHGktyyDqFYmdUal3UIeiklFmUdgl7qf8k7NssbK5OMsg5BLwmiH9rrcrFMKusQ9HoqjSjrEPSyVJff/SG+cZ+yDkGvxkeqlXUIxfqj0biyDkGvkcdulHUIemXamJZ1CP9KUlRlHYJelsaZZR1CMWzKOoDXphKnK2+UaHkXBEEQBEEQBEEQhHJOtLwLgiAIgiAIgiAIpSZ6f75ZouVdEARBEARBEARBEMo50fIuCIIgCIIgCIIglJp4VdybJVreBUEQBEEQBEEQBKGcExfvgiAIgiAIgiAIglDOiW7zgiAIgiAIgiAIQqmJV8W9WaLlXRAEQRAEQRAEQRDKOdHyLgiCIAiCIAiCIJSaGLDuzRIt7+WEn58fc+bMKeswBEEQBEEQBEEQhHJItLwLWtRqNetXL+Xgvl2kp6USGl6JwSNG4+HpVex8e3dFsX3LehTyRPz8AxkwdBTBoeGa7/fv3cGJo4e4F32bjIx0Vm7YhZW1Tali2/z7Ydbs2EeiIokgX29Gf/AeFYIDdKbdfvAYe4+e4t7jpwCEBvgy9L3OWunVajWLNmxnx6HjpKSlUzksiM8H9cbb3bVUcQHs3rmdrVs2IZcn4u8fyJBhIwgJDdOb/sTxo6xetYLYFzF4eHjS74OB1IysrTPtz/Pn8Pve3QwcPIyOnd4pdWx7d0WxTVM2QQwcOlKrbAo7efwI61YvIfZFDO4eXvTpP4QakXU03+dtI8s48HIbCQuvaNA28m+L7VV+m9Ys5vC+naSlpRAaXpkBwz/F3dO72Pn27drCzq1rSZIn4uMfRP8hnxAUGqEz/28nf8qlC6cZM34GkXUbliq211nWktZ5VpaS5YsXcOLYYXKys6havRaDh3+MzN7h/01sm9Ys4ZCmTCsx0OAyXYdCnoivf2CRMv3tp++5evE8iYnxmJtbEhpekZ79huHp7WtwbHt2biNqywbNcXTQsI8IKWZ/+PP4EdauWqbZH/p+MIiaBfaHU38e4/c9O7kXfYeUlGRmz/+NgMAgg+MprLyW645du9m0JYpEuZwAf39GDB1MWGiI3vTHjp9g+eo1vHgRi6eHBwP7v0+tyJqa7+VyOYuXreDC3xdJS0ulUoUKjBg6BE9PD4PiKWzDoVOs+P04CUmphHi78UWv9lQM0L29HbpwlSW7jvI4NoGc3Fx8XJ3o07I+7d6qpkmjVqtZsO0gUcfOk5KeQZUgX8b17Yivq5PBMTnUr0nAmAHYVa+IuYcL57sM58WOQ8XP07AWETO/xDoimMzHz4mesYAnK6O00vgO60nA6AGYuTmTfPkm1z6eStK5KwbHVZBareZw1HzOH91EZnoKPsHV6NB3Eo5ufnrnObrrN25cOEDc83uYmJjjHVSNFt3G4Ozur0mzZEZfHtw6pzVfZKPudOg32eDYtuw9yNrte/POR/x8+GRAbyL0nI/ce/SUxeu3cuveA2LiEhjZ/z26t2uplSY3V8WSjVHsP3aKBEUSTvYy2jSuT793OyCRSAyOC8rvfvpviK881w+C8IpoeRe0bNu8jj07tzJkxBhmzP4Vc3Nzpk78lKwspd55/jx2mOWLfqZbz/f5Yd4ifP0DmTrxU5IUck2aLKWSqtVr8U633q8V18E/zzJvxUYGdG3P8u++ItjXm0++mUNiUrLO9H9du0Xz+rX4adKn/PbNWFwd7fl42o/EJuTHtHr772zae4jPB/dmyYxxWJiZ8fG0H1FmZZcqtuNHj7B40ULe69mbOfMX4B8QwFcTx6IosPwF3bh+jR++m06LFq2YO38BderW45upk3n44H6RtKdOnuDWrRs4ODqWKqZXThw7zLJFv9CtZz9mzluEn38gUyZ+pje2m9evMvv7KTRt0ZZZ8xZTq259vps2gYcP7mnSRG1ex+6dWxg6YjTfzl6AmbkFUyd+Vuw28m+L7ZUdW9bw+87NDBzxGdNmLcLM3JwZX40uNr+Txw6yavF83n3vA2bMXYqvfxAzvhqttT+8smf7Bkp3WpbvdZbVkHW+bNHPnD97ks/GTmbqt3NJTIznu2+++n8T244ta9i7czMDR3zKN7N+w9zcgukllukhVi7+iS7v9efbuUvw9Q9ieqEyDQgKZejH45i9YA3jpsxCrVbzzVefoMrNNSiuE0f/YOmiBfTo2ZfZ8xfiFxDI1xO/KHZ/mPXdNJq1aM3s+b9Ru249vp36ldZxJDMzk4gKlejbf5CBa6d45bFcjxw7zsJFS+jdswe/zPuRAH8/xk2chFyh0Jn+2vUbTP9+Jq1aNGfBvDm8Vbc2k6dN5/6Dh0DeyfvkadN5HhPD1xPH88u8Obi4uPDF+IlkZGYavrJe2nf2MrM27GFIh6asnTSCEG93hs9eRmJyqs70dlaWDGzXiBXjh7Jxykg61q/O5KVbOHn1tibN8r3HWHfwFOP6dmTlhGFYmJkyYtYylNmG111GVpYkX77F1ZFfG5Tews+LyB0LSThyhhM1O3J//goqLZyGU/P6mjTuXVsT/sNY7kz7mRO1OpNy+Sa1dy/B1Ll0F3evHN+zmNMHVtPh/ckM+WoDpmaWrJg1iOxitrcHN89Rq0lPBk9cz/ufLUGVm82KmQPIUqZrpav5dlc+n3NM82nR/VOD4zr45xnmL1/PB906sfSHr/MaE6bORK7nfESZpcTD1ZlhvbviKLPTmWb1tt1s2/cHowf2Zu3c6Qzv04012/ayec9Bg+N6pTzup/+W+Mpr/VDeqdVl9/lfJC7e35BGjRrx4Ycf8uGHH2JnZ4eTkxMTJ05EXWDLS09P54MPPsDGxgYfHx9+++03rTy++OILQkJCsLS0JCAggIkTJ5Jdisq6JGq1ml3bN/Fu9z7UqlsfP/9APhozDnliAmdPndA7386ojTRr1Y4mzdvg7ePHkA/HYGZuzqH9ezRp2nXqyjvdehESVrT10RDrdh2gQ9MGtGtcH39vDz4f3BszU1N2HdYd19ejBtGlZWNC/H3w83Rn7NB+qNRqzl+9oVnWDbsP0q9LOxpGViPI15uvPvyAeLmCY+f+LlVs26K20LJVa5q1aIWPjy/DPxyFmZkZB/bv05l+x/YoqteI5J13u+Ht40vvvv0IDAxi187tWukS4uNZuOBnxnw2FmOj1+skszNqE81btaVp89Yvy2Y0ZubmHC5QNgXt2rGFajVq0alLD7x8fOnZZwD+gcHs3ZXXupK3jWzW2kZGjhlLYmJ8sdvIvy22V/nt3b6Rzt3fp2adBvj6BzFi9ETkifGcP3Vc73y7t22gScv2NGreFi8ffwaO+AxTMzOOHL22yy4AAPqUSURBVNille7BvdvsjlrP0I/HvVZsr7OsJa3ztLRUDu3fQ7+Bw6lUpTqBwaF8+PEX3LpxlVs3r/2/iG3P9k28070vkZoynYA8MYFzxZbpepq2bE9jrTI1548CZdqsVUciKlbFxdWdgKBQuvcZREJcLLGxMQbFtj1qEy1ataFpi7zlH/bhJ5iZmXFo/17d62v7VqrXqEXnd3vg7eNLr74fEBAYzJ6d2zRpGjdtQfeefalcrYZBMRSnvJbrlqjttG7VgpbNm+Hr48OoD4djZm7Gvv26L3q27dhJZI3qdOvyDj4+3vTr05ugwAB27NoNwNNnz7hx8xYjRwwnNCQYby8vRo4YhjIriyNHj5V6va3ed4J3GkbSsUENAj1dGd+3I+ampmw7fkFn+pphATSpUYEADxe8XRzp2bwewV5u/H07/+bC2gMnGdS+MY2rRRDi7c7UgV2JU6Twx1/XDY4rbt8xbk+aw4vthl0c+g7uQcb9J9z4/DtSb97j4S9riNmyD/9R/TRp/D/uz+MlG3myYiupN+5yZfgkctMz8e7XxeC4XlGr1Zzav5K3OwwlvHpT3LxD6TLoW1Lksdz4S3/M73+6iOoNOuPqGYy7TxjvDJxBUsJznj3Q3pZMTM2xkTlrPuYW1gbHtmHnPto3e5u2TRrg7+3JZ0Pex8zMlF2HdG8f4UEBfPh+D5rVr4OJie66/OqtaBpEVuOtGlVxd3Gmcd1IalWpwPXoezrT61Ne99N/Q3zluX4QhILExfsbtGLFCoyNjTl79ixz585l9uzZLF68WPP9rFmzqFmzJn///TfDhw9n2LBh3Lp1S/O9jY0Ny5cv5/r168ydO5dFixbx448//mPxvYh5jkKeSOWq+Sd6VlbWBIeG6z34ZWdnczf6ttY8UqmUylVrcLsUB/TiZGfncOveQyIr51/4S6VSIiuHc/W2YRVbZlYWOTm52FpbAfAsNp4ERRKRlfK7XFlbWRIRFMDVW3dLEVs20dG3qVK1ulZsVatW59ZN3SdSN29ep2q16lrTqtWoyc2bNzR/q1QqZs/8jne6dMXX18/geArHdjf6ls6y0Rfb7ZvXtNIDVKteS5P+1TZSpcg2EqE3z39bbK/EvniGQp5Apar53WktrawJCo3g9s2rOufJyc7mfvQtKlWN1FquSlVras2jzMxk/g9f88GwMcjsS9+r4nWW1ZB1fi/6Njk5OVr5enn74uTsyu0bhq3D8hxbfpnml8+rMr1TTJnei76ttR28KtM7eo5xmZkZHDm4BxdXd5ycXEqMS99xtEox+8Otm9epXOQ4ElmqE+nSKI/lmp2dzZ3oaKpVraqVf7WqVbhx86bOea7fvEm1qlW0ptWsXl2T/tUNcVNTE608TUxMuHqtdMeR7Jwcbjx8Ru2I/EcVpFIptSMCuXz3UYnzq9VqzlyP5kFMHDVC/QB4GicnPimF2hGBmnQ2luZUDPAyKM/XJatTlfjDp7SmxR04gX2dqgBITEywq16B+EMnCy4A8YdPIqtTjdKSxz0hNSmewIi6mmnmljZ4BVbm8d1LBueTmZECgIWVdov3pdO7mPFhXeaPb8/+TbPJUmYYlF92dg637j4ocj5Ss3IFrt42/NyhsIqhQZy/cp1Hz/Iu5u48eMTlm3eoU61SqfIpj/vpvyW+8lo//Buo1GX3+V8knnl/g7y9vfnxxx+RSCSEhoZy5coVfvzxRwYNyuvS2KZNG4YPHw7ktbL/+OOP/PHHH4SGhgIwYcIETV5+fn58+umnrF+/ns8//1zn7ymVSpRK7a4+WUolpmZmOtMr5IkARZ4PspPZa74rLCU5CZUqF5nMvsg8Tx//MycSipRUclUqHOxstaY72Nny8Klhdy1/Wb0ZZwcZkZXyKtwERVJeHrJCecpsNd8ZIjk5CZVKhb299vLLZPY8efxY5zwKuRyZTFYkfcF1vGXTBqRGUtp37GxwLIWlvIxNJtMuT1kxZaOQJxZJX7D8X/1rZ180T7mebeTfFlvB38v7jcK/6YBCkaBznuRkBSpVrs55nj7JX66Vi+cREl6RmnUalDourdhKsayGrHO5PBFjY5Mi41HI7A1fh/+K2HQcrxQK3XkkvzzG6SrTZ08eak3bt3sra5YtQJmZgYeXD+OnzcHYxISSaJbfvmhcT4rdH4qml8t1d7P/T5XHck1OTs47/hY6ntrLZDx+Od5JYXK5okh6mUxG4sv15u3lhYuzM0uXr2TUhyMwNzdj67YdxMfHa9IYSp6Snld32Wq36jraWvPgeZze+VLSM2k55luyc3KQSqSM7dOBOhWCAYhPzrsY1ZVnQpLurvj/BDNXJ5Qv4rWmKV/EY2Jng9TcDBN7O6TGxihjEwqlScAqVPez4MVJTcr7LWs77ZubVrZOpCbpX3cFqVQq9qydgU9wdVy98sdAqFy3HTJHD2xkLrx4fIv9m2YRH3Ofnh/NLzFPRUpKXpkW6v7uYGfLo6fPDYpLlz6d25KenkHPkWORSqWoVCoG9+xCy4ZvlSqf8rif/lviK6/1gyAUJi7e36A6depoDTxSt25dZs2aRe7LZ14qV66s+U4ikeDm5kZsbKxm2oYNG5g3bx53794lNTWVnJwcbG21Lz4LmjFjBl9/rf0827CPxjB8ZN6zXcf+OMDCn2Zpvhs3+dv/bAHLqZVRezjw51l++fozzEzL/4Ey+s5tduyIYs68X0o9UI3w+k78sY9FP/+g+fuLST8Uk/r1nT9znGuXLvDtvGUGz3O00L46vhztq+U5tuN/7Ncq0y8nff9f/b0GjVpQuWokcnkCu7auY863E5nywwJMTXXfMC3PynO5/jcZGxvz1fixzJ47ny49eiKVSqletQqRNWtoPeb232Rlbsr6yR+RoVRy5vpdZq3fg5ezAzXDSn8R/G9x6eROdqyYrPm79ycL/uM8d62aQuyTOwwcv0ZremSjbpr/d/MOwUbmzLLv+5MY+wgHF5//+Hdfx+GTZ9l//DSTPx6Cv7cnd+4/Yu6ytZqB6/Qp7/tpeY5P1A//nP/VZ8/Lirh4L0dMCt2Bk0gkqFQqAE6dOkWvXr34+uuvadmyJXZ2dqxfv55Zs2bpygqAsWPHMnr0aK1p0Y/zWw4ia9fTGqnzVXdBhTwRe4f8u91JCjl+AbpHKLaxtUMqNSoyqFKSQl7qEUj1kdlYYySVFhmcLjEpWe/gL6+s2bGPVdv2Mu+rMQT55o8W+mq+REUyTvay/DwVyYT4FT+qaEG2tnZIpdIirV0KhRx7B3ud88js7VEUGkxJUWB9Xbt2lSSFgg/e76X5XqVSsXTxQnZs28qS5asNis3mZWyF7xgriikbmb1DkfQFy/LVv0nyRBwKbCMKhRx/PdvIvyW2GrXrExRaQfN3dnbWy99IxN4hfxTnJEUivv7BOvOwtZUhlRqRVCTOxPzyvXSBFzFP+aB7K600s2eMJyyiCpO+/alIvrVq19MadfzVvlqaZTVkndvbO5CTk01aaopWC4ZCLsdeT7mU59hq1q5PcIERf/PLVF6oTOX4+euOzfblMU53mWq3ClpaWWNpZY27pzchoRX4oEdrzp06Rr23m+vMu8jyy4seR+0ditsfdKS3133cKa3yXK6v2Nra5h1/Cx1P5QoFDgWO6wXZ28uKpFcoFDgUWG8hwUH8+tNc0tLSyM7JQWZnx0effEpIcOlG6re3scyruwoNTpeQnIqjnf63rUilUnxc89ZxqI8H95/HsXT3UWqGBeBkmzdfYnIqzgV6jiUkpxLq416q+EpD+SIes0Kj2Zu5OpGdlIIqU0lWvBxVTg5mLo6F0jiijNFusdclrFoTvALzGzBycvL21dSkBGxk+V2L05LjcfPR/waGV3atmsqtS0cZOHYVdg5uxaZ99bsJL0q+eJfZ2OSVaaEeeolJyUVa40vj55Ub6d25Dc3q570tItDXm5j4BFZt3VXsxXt530/Lc3z/lvpBEAoTz7y/QWfOnNH6+/Tp0wQHB2NkZFTivCdPnsTX15fx48dTs2ZNgoODefjwYbHzmJmZYWtrq/Up2GXewtISdw8vzcfbxw+ZvQNXLv2lSZOensadWzcIDaug6ycwMTEhMCiEKxfzB99RqVRcvvgXIXrmKS0TE2NCA3w5f0X7mfDzV25SMUR/S8Tq7XtZtnkXP47/mPBAP63vPFyccJTZaQawA0hLz+B69D0qhgZiKBMTE4KCQrh8KX+QO5VKxaWLfxOqZ3C+sLAILl3UHhTv4t9/ERaWV8E1btKM+T8vZN5Pv2o+Do6OdO7Sla+nzShVbIFBoVy+mF+eeWVzQW9sIWEVtMof4NLf5zXpXd3ckdk7cLnINnJdb57/ltgsLK1w8/DSfLx8/JHZO3K1wLadnp5G9K3rhIRV1JmHsYkJ/kGhXL10Xmu5rl66oJmnY9c+fD9/Jd/NW675APQdOJJhegav07evlmZZDVnnAUEhGBsba+X79Mkj4uNeEBKuO9/yHpuuMr1yMb98XpVpcDFlGhAUwpVL2se4q5cuEFzMMU798j9DBhV9dRwtuGyvjqP61lloWITW+gK4+Pd5vcfq0irP5Vow/+CgIC5ezH8GWqVScfHiZcLDdL+qMyIsjL8vXdaa9tffF3Wmt7KyQmZnx9Onz7gTHU3dOrpf56k3PmNjwn09OHMjWiu+szfuUjnQ8BZetVpNVk4OAJ7O9jjZ2XDmev7z1akZmVy996RUeZaW4vRFHJvU0Zrm1PQt5Kcv5sWYnU3SX9dwapL/jDoSCY6N66I4XfIgsGYWVji6+mo+Lh5BWNs5ce/6aU2azIxUnty9jHdgFb35qNVqdq2ayvULB/ng82XYO5f8mtDnj/LGO7CROZeY1sTEmNBAP85fyX+OWqVSceHydSqGGH7uUFimUolUon1KLpVKS+ztUd730/Ic37+lfhCEwkTL+xv06NEjRo8ezZAhQ/jrr7+YP39+sS3nBQUHB/Po0SPWr19PZGQku3fvJioqquQZS0EikdCuY1c2r1+Ju4cXLm5urFu1FHsHR2rVzb/zO3ncJ9Sq24A27fPeOd6+czfmz55BYHAYwSFh7Nq+GWVmBk2at9bMI09MQCFPJOZ53nOIDx/cw8LCEicXV2xs9Hf9f+W9ds2Z+vNSwgJ9qRDkz/rdB8lUKmnXuB4AX89fgrODjOG98ka1XbVtL4s2bOfrUYNwd3YiQZ53l9zC3AxLC3MkEgnd2zZj+ZbdeLu54u7ixKIN23Cyl9EwsnSD63Tq3IUfZ39PUHAIISGhbN8eRaYyk2bN897jOnvmdzg6OvF+/wEAdOjYmbFfjCFq6yZqRtbm+NEjRN+5zYcffQygudFSkLGRMfb2Dnh5Gd4rAKB9567Mnz2DoOBQgkPC2bl9M8rMTE3ZzJ01HUdHJ3r3GwxAuw5dmPjlKLZv3UCNyDqcOHaYu9G3GPrRGODVNvIum9evwt3DC1c3d9atWoKDg5PWNvJvj+1Vfq07diNqwwrcPL1wcfVg4+pF2Ds4UbNu/rPqU8eNJLJuQ1q1fxeAtp26s+DHbwgIDiMoJII92zeizMzk7WZtAZDZO+ocpM7J2RUXN8PeI23osk4aN5radesX2FeLX+dWVtY0bdGGZYt+wdraFktLSxb/Oo/QsAoGXxSW99jadOxK1IYVuHt64+LqzobVi7F3cCRSq0xHvSzTvONJ2049+OXHbwgMDiMwJPxlmWbQ6GWZvoh5ysljh6lSPRJbWxkJCXFs37QaU1MzqtWsqzOWwjp27src2d++XP4wdm7fQqYyk6bN83pozJk5A0dHJ/q8fO1b+47vMP6LT9i2dSM1I+tw/Ohh7t65zfCX+wNASkoycbGxJCbmtXw+e5I3Doe9vYPeFv3i1l15LNcunTvyw+w5BAcHERYSwtbtO8jMzKRl86YAfD/rRxwdHRjQ730AOnVoz6dfjmPz1ihqRUZy5NgxbkdHM+qjEZo8jx0/gZ2dHS7Oztx/8IAFvy3mrTq1qVm99AOv9W5Zn68WbybCz4uK/l6sPfAnGcosOtbPG2xwwqJNuNjbMvLdvPpiye4jVPDzxMvZkaycHE5cvsXuU38ztk9HTTn0bP4Wi3f9gY+rE57O9vwSdQBnmQ2Nqxt+A9XIyhKroPyLfUt/L2yrhJGVmETm4+eEThuNuacrl/p/AcDD39bjO7wXYTM+4/HyLTg1roN719ac6zBEk8f9OcuosvQ7FBeuknTuMn4j38fYyoLHK7aWer1JJBLqtujLkZ2/4uDmi72TF4e2zsPG3oXw6s006ZZ915/wGs2o0yyvp9quVVO4fGo3PUf9hKm5FSmKvOfjzS1tMDE1JzH2EZdO7SKkyttYWsmIeXKLvWu/xS+0Jm7eoQbF1r19S76Zv4iwQH8iggPYuGs/mUolbZvkHUOmzvsNJwd7hvXuCuQNcnf/Sd65T3ZOLnEJcm7ff4iluTle7q4A1KtZlRVbduLq7IC/tye37z9iw859mjxLs97K4376b4ivPNcP5d3LTsLCGyIu3t+gvn37kpGRQa1atTAyMmLUqFEMHjzYoHk7dOjAJ598wocffohSqaRt27ZMnDiRyZMn/6Mxdnr3PTIzM/h1/kzS0lIJi6jExKk/aD2TE/P8GSnJ+V3G6jVsQlKSgvWrl6KQJ+IfEMSEKT9odX/ev3cHG9cu1/w98YuRAIz4+Euti3x9mtWrhTw5lcUbtpOgSCbYz5sfx3+s6ab2Ij4BaYHnw7fuP0J2Tg7jZmk/Nzega3sGdss7CerdsRUZmUq+XbiS1PR0KocF8+P4j0v9XHyDtxuRlKxgzaoVyOVyAgIC+XrKdE331bi4WCTS/NjCIyrw6edjWb1yOSuXL8PD05PxEyfj6+dfqt81RP2GTUhOUrBu9TJN2Uyc8r2mbOLjXmitt7CIinzy2UTWrlrCmhWLcff05IsJ0/D1y+/h0Pnd91BmZmq2kfCISkyc+n2pn9sqz7G90qFLL5SZGSya/z3paamERlTmyymztPJ7EfNUa394q2EzkpMUbFq9GIU8Ed+AYL6cMusfe4zkFUOWNeb5U5ILxFbSOgfoP2gEEomUH6Z/RXZ2NlWrRzJ4+Mf/b2LLK9NMftOUaSXG6ixThebvtxo2JTlJwcaXZeoXEMTYAmVqYmLGzWuX2LtjI6mpKchkDoRVqMLUH34tMviRPvXfbkxSsoJ1q5Yhl8vxDwhk0pTvNL+RdxzJb5kLi6jI6M/Hs2blUlYvX4KHpydfTpyidRw5e/ok83/Mf45z5ndTAejesy/v9e5XqvUG5bNcGzVsQFJSEitXr315/A3gmymTNcff2Lg4rbFDKkSEM/azMSxftYZlK1bh4enB5Anj8Pfz1aRJkMv5dfFSTXf6Zk0b06tH91KvL4CWtSojT0ljwbaDJCSlEOrtzs+f9Nd0m49JVCAtUD9kKrOYvmoHsfIkzExN8HNzZtqgbrSsld+lvF/rhmQos5i2IoqU9EyqBvvy8+j+mJVi8Cu7GhWpe2iV5u+ImXm9fh6v3MrlAWMxc3fGwju/G37Ggyec6zCEiFlj8fuoL5lPYrgyZALxB/Jf7/V8015MnR0ImTQSMzdnki/d4Gy7gWQVGsTOUA3aDCRbmcGOZZPITE/GJ6Q6fcf8hkmB7S0x9hHpKfmPj5w9vB6Apd++r5VX5wHTqd6gM0ZGJty7fopT+1eSrczA1tGNCjWb83aHYQbH1axebRRJKSxeH0WiIolgfx9mTRijdT5ScJuLl8vp/+kkzd/rdvzOuh2/U61CKD9NGQvAJwN7s2jdVmb+tgp5ct7jfB2bN6J/146lWGMvl7Uc7qf/lvjKa/0gCAVJ1G9qBJb/cY0aNaJq1arMmTOnTOO4Gl2+3ynpkX6nrEPQK97Kt+REZSRbXf4H4iuPssrxejOViO50rytbXX7vS5tJs8o6BL1U6vL7JJ01ySUnKiPOL3S/Rqq8+KOR7sdxyoO0YzdKTlRGmticKTlRGXlh7lfWIfwrlee6oWpwyY9tlFe/7iu73x7asux+u6yU35paEARBEARBEARBEARAXLwLgiAIgiAIgiAIQrlXfvuP/D9z5MiRsg5BEARBEARBEAThHyMewH6zRMu7IAiCIAiCIAiCIJRzouVdEARBEARBEARBKDWVaHl/o0TLuyAIgiAIgiAIgiCUc6LlXRAEQRAEQRAEQSi1sn3ruKQMf7tsiJZ3QRAEQRAEQRAEQSjnxMW7IAiCIAiCIAiCIJRzotu8IAiCIAiCIAiCUGriVXFvlrh4/x/jnP20rEMo1tLoemUdgl6NKiSVdQh6mRspyzoEvSSU36N62OO9ZR2CXnHeNcs6hGJlSq3KOgS9HHJelHUIel1JjyjrEPTyt35e1iHo9X2Ua1mHoNelozFlHUKxRh67UdYh6GXVMLysQ9Br8ndnyzoEvUZ2LL/nI6py3Kk34PHhsg5Bv+BeZR2B8C8hLt4FQRAEQRAEQRCEUlOpyjqC/y3l9/aYIAiCIAiCIAiCIAiAuHgXBEEQBEEQBEEQhHJPdJsXBEEQBEEQBEEQSk0MWPdmiZZ3QRAEQRAEQRAEQSjnRMu7IAiCIAiCIAiCUGoq0fL+RomWd0EQBEEQBEEQBEEo58TFuyAIgiAIgiAIgiCUc6LbvCAIgiAIgiAIglBqYsC6N0u0vP+XPXjwAIlEwsWLF8s6FEEQBEEQBEEQBOFfSrS8C1q27tnP+qhdJCqSCPTzYdSg94kICdKZ9v6jJyxZu4nbd+8TExfPhx/0oVuH1lpplq7bzPINW7Wm+Xi6s/rnWaWOTa1Wc+HgfG6e20RWRgquvtWo32kSdk5+Bs1/8cgizu2bTcW3+lC3/TgAMtMVXDj4E0/v/Emq4jnmVg74RTSlZouRmJrblCq2LWsX8cf+7aSlpRISXokPhn2Om4dPsfPt372Z3VGrSZIn4uMfxPuDxxAYUkErzZ2bV9i46lfu3r6GRCrF1z+EL7+eg6mZucGxrV+9lIP7dpGelkpoeCUGjxiNh6dXsfPt3RXF9i3rUcgT8fMPZMDQUQSHhufHvncHJ44e4l70bTIy0lm5YRdW1oavM+34lnHgZXxh4RUNjm+bJr4gBg4dqRVfVpaS5YsXcOLYYXKys6havRaDh3+MzN7BoLjWHznPigOnSEhOJcTLlS+6t6SSn6fOtIf+vsmS3//kUVwiObkqfFwc6NusNu1qV9akmbhiBztPX9aa762IAH75qKdB8RS0fdceNm2NIlGuINDfjxFDBhEWGqIz7YOHj1ixZi13ou/yIjaOYYM+4J2OHf6jPEuye+d2tm7ZhFyeiL9/IEOGjSAkNExv+hPHj7J61QpiX8Tg4eFJvw8GUjOyts60P8+fw+97dzNw8DA6dnqn1LFF7d7H+m07SZQrCPLzZeTg/oTrPcY9Ztnajdy6e58XsXGMGNCXrh3aaqXZvnc/2/ceICY2DgA/Hy/e796F2jWqlTo2tVrNno0/c/LQFjLSUvAPq0r3gRNxcffVO0/09fMc2rGcR/evkyyPY+Cnc6hSq6lWmo+6VdI5b8feo2nWob9Bse3auaNAmQYwZNgIQost02OsXrWcFy9eaMo0MrKW5vsfZ//AoYMHtOapXqMmU6ZONygeXdrWM6deJVMszCTce5bD+gMZxClUetO3ecuctm9pH0djEnKZuixF87eTnZTOjcwJ9DTG2EjCjQfZbDyUQUp66ZqaBvTyo30LN2ysjLlyI5mZv9zhyfMMvemlUvjgPT9aNHbBUWZKfGIWew7FsGLDI02aEzvf1jnvz0vvsi7qiUFxqdVqDkfN5/zRTWSmp+ATXI0OfSfh6Oand56ju37jxoUDxD2/h4mJOd5B1WjRbQzO7v6aNEtm9OXBrXNa80U26k6HfpNLjMmhfk0CxgzArnpFzD1cON9lOC92HCp+noa1iJj5JdYRwWQ+fk70jAU8WRmllcZ3WE8CRg/AzM2Z5Ms3ufbxVJLOXSkxHn3avmXGWxVNsTCXcO9pLhsOlbC91TWjTd1C21tiLtOWp2r+drKT0vltcwI8jDTb26Y/Mku1vZXn4+/undvYtmUj8pfnFYOHfVRsbH8eP8qaVctexuZF3w8GaWLLyclhzcqlXDh3lpiY51haWVGlanX69h+Io6NTqWNbf+QcK/afLFDnt6aSv746/wZL9p4oVOfXpV2dyjrTT1uzm83HL/Bp1xb0blqn1LGVd+oyHbFOUoa/XTZEy7ugcejEKX5eupp+Pd5h8exvCPLz4dOvv0WuSNKZPlOpxMPNhSF9e+BgL9Obr7+PF1HLftF8fpox6bXiu3RsMddOrqZ+p8l0HL4BE1NL9i4dRE62ssR54x5f4cbZDTi4hWpNT0+OJT05ltptPufdj3fwdtfpPL59nGNbJpQqtl1bV7Fv10b6D/uCKT8sxszMgm8nfUxWlv7YTh0/wJolc3mnx0Cm/bgCH79gvp30MUmKRE2aOzev8N3kj6lUrTZTZi1l6qxltGj3LhKp4bvuts3r2LNzK0NGjGHG7F8xNzdn6sRPi43tz2OHWb7oZ7r1fJ8f5i3C1z+QqRM/JUkh16TJUiqpWr0W73TrbXAsukRtXsfunVsYOmI0385egJm5BVMnflZsfCeOHWbZol/o1rMfM+ctws8/kCkTP0NRIL5li37m/NmTfDZ2MlO/nUtiYjzfffOVQTHtO3+NWVsOMKRtA9aNG0iIlyvD560jMTlNZ3pbK3MGtq7Hys/6s2nCIDrWrcKklTs5ef2uVrp6EYEc/PZjzefbDzobFE9BR46dYOHipfR+rwcL5s4mwN+PsV99jVyh0JleqVTi7ubGgPf74mBv/4/kWZzjR4+weNFC3uvZmznzF+AfEMBXE8dqlU1BN65f44fvptOiRSvmzl9Anbr1+GbqZB4+uF8k7amTJ7h16wYOjo6ljgvg8PGT/LJ0Jf26d2HR7G8J9Pfls8nT9R7jlEol7q6uDO7znt5jnLOjI4P79uS32TNYOGs61StVZPz0H7j/6HGp4zu4fSlH966l+6CJjJm+BjMzC375ZgjZxewLSmUGnn4hdBswXm+ab377Q+vTa9gUJBIJVWs3MyiuYwXKdO78X16W6bhiy/T776bTvEUr5s1fQJ26b/HN1Mk8KFSmNWr8H3vnHd5U9cfhN+nem+69y94gIBtZgoCAgCxZMkQEFEGWgCBThuBg772XTGUIIqLsvXdn0t10JL8/QtOmTdoUhUZ/5+W5D83N95z7yTnfe84996xqrF6zQXN89tlog/ToomkNCxpUtmDDoTRmrk0mMwuGvGuDqUnR4Z7G5TB6UaLmmLMhryFlbgZDOtqACuZvSmHO+mRMpBI+bGdTosfFbh18ebe1N7MW3aL/yL9Iz8hhzqTymJvpj6VbBz/eaenFN9/fptugs3y34i7d2vvy7tt5jYk23U9pHVPnXkepVHHsVJzB2k7sW8Jvh9bQpudEBozfiLmFNStn9yvS5+5fP0uNRl3pP24DPT9dijIni5Wz+pCpSNOyq1a/I5/NPa45mnUeaZAmExtrki7e4PLQLw2ytwrwofquH4j/5Qwnq7Xl3oKVlP9hCq5N62psPDu2IHLmaG5NWcjJGu1IvnidmnuXYu5m2MvcgjSpbk79ShZsOJLOrHUpZGapGNzeQH/7PklzfLMhr04xN4XBHaxRqWDBllS+2ZiCiYmEAe9YG+xvxlz+njj2M8sWf0/nrj2Ys+B7AoOCmThuVJHaZk2fQpNmLfhmwQ/UrF2HaZPHa7QpFBncuX2LTl3eZ86C7xk9diJPHj/iqy/HlVjbgT+uMHvLQQa0rs/6Mf0J8/Fg0IK1+ut8ayv6tqjHqs8+YPO4AbStXYkJq3Zy6srtQrZH/7rOxXuPcXMoeeeGQKAL0Xj/h1AqlcyYMYOQkBAsLCzw8/Pjq6++KmSXk5NDnz59CAwMxMrKivDwcObNm6dl88svv1CjRg1sbGxwdHSkTp06PHjwAIALFy7QsGFD7OzssLe3p2rVqvzxxx//yG/YtHMfrZs1pGXjBgT4+jBiYB8sLSzYe+SYTvvI0GAG9epG43pvYG6qfxCHidQEFydHzeFob19ibSqVisu/rqJyww8JiGqMi2c4DTp9TVpyDA+uHi4ybJYilaMbP+XN9pOwsNK+trNHGE3fn49/ZEPsXfzwDq5F9beG8eDazyhzsg3W9tOujbzTqTfVar2JX2AoAz+ZgDwhjnO/Hdcbbv/O9TRs1pb6TVrj4xfIB4NGYWFhybHDezQ2q5fM5a3WnWjzbg98/ILw8vGnVt0mmJmZG6xtz87NvNu5OzVq1yUgMJiPRoxBlhDP76dP6g23e/smmjRvTaOmLfH1C2DAkBFYWFpy5OA+jU3rdzrSvlM3wiKiDNKiX98WLX1DR4wmISGuGH2badq8FY2btnihbzgWlpYcfaEvNTWFIwf30avvIMpXrEJwaDhDho3ixrXL3Lh+pVhdq4+coX2dyrzzRiWCPd0Y26UlluZm7Dh9Xqd99bAAGlWKIMjTFV83Z7o1qkGotzt/3dZuwJmZmeDqYKs57G2sDE+sF2zdsZMWbzWjedPG+Pv58vHggVhYWHDgkO6eqfCwUPp/0IuG9ethZqb7Pi1pnEWxY/tW3mregibNmuPn58+gIR9jYWHBoYMHdNrv2rmdKlWr0/7dTvj6+fN+j14EB4ewZ/dOLbv4uDh++G4hIz4djanJyw0a27xzL62aNaZFk4YE+PkwfGBfLC3M2Xf4Z532EaEhDOz9Po3frIOZmZlOmzdqVKVWtcr4eHni6+1F3+7vYWVpydUbt0qkTaVS8cu+NbzVvj8VqjfC2z+c7kOmkiiL5eLZo3rDla1cj9bvDS3U254fe0dXrePi2Z8JLVsDV3dfg7Tl5mnTZm/h5+fP4GLzdAdVq1anw7ud8PXzo7smT3dp2ZmZmeHk7Kw5bO1e/uG2YRULfvotg4t3snkap2TlvlQcbKVUDNGdb7kolZCUptIcqel5PUhBXqa42EtZ/VMaT+OUPI1Tsmp/Kn4eJoT5Ge6DHdt4s2rTA06eiefO/VSmfHMdF2cL6tXS3ztYLtKek7/FcfqPBJ7HKPjlVBy/n5cRGZqXRgnyLK2jbi1X/rwk52l0hkG6VCoVpw+uon6bD4ms0hgP33A69PuaZFkM1/7UX6/2HLmYKvXa4e4diqdfBO37TiMx/hlP72uXrWbmltg5umkOSytbg3TFHjjOzQlzid5ZdN2ei3//90i/95hrn00n5fpdHixay/OtBwj8uJfGJnBYbx4t3cTjldtIuXaHS4MmkJOWgW+vDgZdoyANK1tw4EwGl17426qf0nCwlRjkb8lpKs2RmpHP37zV/rbmQJ6/rf4pDT93E8L8inkr8AJjLn93bt9Cs+YtX2gLYOCQYVhYWHD44E867Xfv3PZCW2d8/fzp1qM3QcGh7N29AwAbG1smTZ1J3Tcb4OPjS3hEFAMGfcSd2zeJjYkukbbVh0/Tvk4VdZ3v5cbYrq2wNDNjx6m/dNpXDw+gUeUIgjzd1HV+45rqOv+Odp0fLUvi6437mfpBO0xN/rtNLqWq9I7/R/67nvSaGT16NF9//TXjxo3j6tWrrFu3Dnd390J2SqUSHx8fNm/ezNWrVxk/fjxjxoxh06ZNgHoY0DvvvEP9+vW5ePEip0+fpn///kgk6veu3bp1w8fHh7Nnz3Lu3Dk+//xzvQ+VJSErK5ubd+5RrUI5zTmpVErViuW4UsKH0II8fvacdr0H0XnAx0ya8y3RsYb3CuSSLHtMenIc3iG1NefMLe1w861A9MMLRYb9dedk/CLq4x3yhkHXysxIxtzSFqmBFVRs9FPksnjKVqyuOWdtY0twWFlu3dA9JC87K4t7t29QrlJeGKlUSrmK1bl1XR0mUZ7AnZtXsHd0YuJn/RjYvQWTRw/kxtXzBukCiH7+DLksgQqVqmrO2djYEhoeqbcRm5WVxZ3bN7XCSKVSKlSqyk0DGr4lIVdfxUL6orhx/WoR+m7o1Jcb5u7tm2RnZ2vF6+Prj6ubOzev6Y5XE392DtcePqNmRN4QUKlUQs2IAC7efVLsb1KpVJy5fo/70fFUCdWeNvHHzQc0/HQObScs4qt1+5CnpOmJRY+2rCxu3r5DlUp5Q/OkUilVKlXk6vUbJYrrVcSZlZXF7ds3qVipilZclSpV0Zuf169fpVLlKlrnKletxvXr1zSflUolc2ZNp32Hjvj7B5RIU562bG7cuUvVinlDyNVlXPkSN7T1kZOj5MjxX8nIUFC2hFMO4mMekySPI7xC3pBKK2s7AkLKc+9m0WVcSUiSx3HlrxPUbmTYqA91nt6iUqW8aQDqPK2slUf5Ueep9rSBKgXyFODSpYt069KRAf0+YOG380lKSirhr1Hj4iDFwVbKjQd5L1wzMuH+sxwCvYoux92cpHz1oT1f9rWjV0trnOzy+jhNTUAFZOfk2WfnqBdnCvYxrH7wcrfE1dmCs+fzehdT03K4ejOJchH6X2RfvpZE1YpO+HqpX/CFBNhQIdKB384l6LR3cjTjjWrO7D303CBdALLYx6QkxhEclVevWlrb4RNcgUd3DPe5jHT1NAMrGwet8xd+28O0IbVZ8MXbHNw8h0yF/mkCfwfHWpWIO3pa61zsoZM41aoEgMTMDIcqZYk7cirPQKUi7ugpHGuVfHqLi4MEB1sp1x8W8LfnOQR4Ft3IdnOS8lV/OyZ+YEfPFlaG+5t38f5m3OWv+rmioLaKRWi7cf0qFStX1TpXuWo1vfYAqampSCQSbGwNe1EE+er8yAJ1fmQgF+8WP/1EXeffVdf5IXl1vlKpYuyKHfRs+gYhXmUM1iMQFIeY8/4PkJyczLx58/j222/p2bMnAMHBwdStW5f79+9r2ZqZmfHll3lDwQIDAzl9+jSbNm2iU6dOJCUlkZiYSOvWrQkODgYgMjJvHu/Dhw/59NNPiYhQzxEKDQ39R35DYnIyOUolTo7ala+zgwMPHz996XijwkIYPXQAft5exMtkLN+wjSFjJrFy/nSsrQzvdUxPVjf4rWy1h2tZ2bqSnhyrN9ydC3uJe3qVdwZvNug6Gaky/jr6HRHVOxmsTS6LB8DBUXv4nYOjs+a7giQnyVEqcwqFsXd04umT+wDEPFen+7b1S+jaeyj+gaGc+Hk/U8d+xPRv1xY7n16tTf2gV3Cet4Ojk+a7wtoSUSpzcHR0KhTmyaOHOsO8LLkaHAroc3R0QlakPiWOjoXD5OqTyRIwNTUrNAff0Ul/vLnIUtLIUapwsbfROu9ib8v9aN35CZCcnkGz0fPIyspBKpUwpksLakcGab6vExVM40oReLs68ihWxrc7f2bwtxtY9VkvTAycBpGYlIxSqcTJ0VHrvJOjA48eGzbH9VXGmfQib5wKDM93dHTi8SPdw8jlMhmOBa7tWMA/t27eiNREytttSz7NIJfEpCSUSiXOBco4J8e/V8YB3L3/kEGjxpKZmYWVlSWTR48kwK/oNRsKkiRX+5adg3YZZ+fgQpK85C889fH7sV1YWlpTsYZhQ+aTXqSbYwnyVCaTFSo/HB0dtfK0StVqvPFGXdzdPXj27CmrVi5nwvgvmDV7LiYmhvUy5mJvo24AJaVpzzdOTlNqvtPF/WfZrN6fQ3RCDg62UlrWtmR4FzumLE9CkaVu/GdmQds3rdh1Ih2JBNrWs8JEKsGhiHjz4+ykHiUlk2dpnZfJMzXf6WLNlofYWJuw9rvqKJUqpFIJP66+x6FjMTrtWzTyIC09h2On9NeHBUlJVPuVbQGfs7F3JSXRsHiUSiX71k3DL7QK7j55L6wq1G6No4sXdo5liH50g4ObZxP3/B5dP1pgsD5DsXB3RRGtfY8oouMwc7BDammBmZMDUlNTFDHxBWzisQkPoqTYW6vL64Lz0JNTi/O3HNb8lEa0TImDjZQWtS34pLMtX61M1va3epbsOpmBBPXfJlJJkfHmYszlb662kpQjclmCjnJEfx2emZnJquWLqVe/EdbWNjptdKG3zrez4f5z/WVvcnoGzT7/Jl+d35LaUcGa75cf/BUTqZSujWrojUMgeBlE4/0f4Nq1aygUCho31j9sMT8LFy5k2bJlPHz4kPT0dDIzM6lUqRIAzs7O9OrVi7feeoumTZvSpEkTOnXqhKenJwDDhw+nb9++rF69miZNmtCxY0dNI78gCoUChUJ73poiMxMLc8OGXP8T1KpaSfN3cIAfkaEhdOo/lKMnf6N104Z6w93+azcndkzUfG7e87sSXztF/ozTe6bR4oOlmJpZFGufmZHCTys+xLFMCFWbDNZr9+svP7F00XTN50/Hl3zxPUNQqdQPoo3eakf9Jq0BCAgO58qFs/xyaA/v9RxUKMzxnw/xw7d5esZM/PqVaHtZjhXQ94WR6fs72FhYsHFMP9IUmfx+4z6zthzC29WR6mEBADSvnrcQYah3GcK8y9B6/EL+uPlAq5dfoM3tWzfZtWs7c+cv0oxAMjZ8vb1YMncGqalpHDv1G9PmLWTeVxOLbMCfPbGHDT9O0nz+cPTC1yGV0z9vp1q9VpiZF18mvkrq188r/wMCAwkMDKJvn55cunRRq5dfF9UjzejS1FrzedG2lCKs9XP1Xl7P6dM4JfefpTK5vz1Vws05fTmTlHQVS3al8l5TKxpUcUClgnPXsnj4PFvv1khN65fh08F5jdjPJr3cgmiN6rrRtH4Zvpx1jXsP0wgNsmFo3xDiEjL56WjhIcGtmnpw8JcYMrP0jyO9cGo3u1ZO1Hx+/5OS16sF2bN6EjGPb9H3i7Va56s3yHsB7uEbhp2jG8tn9CYh5iHOZYp/8WxMVIswo0uTvM6G73bongddHFfvF/C359lM6mtPlXAzTl/OIiVdxdI9aXRubEn9yvZqf7uexcPonFLbiuvfUP6CetTqjGmTUKlUDBzy8Wu5po2FBRu/GKCu86/fY9aWg3i7OlE9PICrD56y7ugZ1o/pb9Tp9k8htop7vYjG+z+AVQl6kDds2MDIkSOZPXs2tWvXxs7OjpkzZ3LmzBmNzfLlyxk6dCg//fQTGzduZOzYsRw6dIhatWoxceJEunbtyt69e9m/fz8TJkxgw4YNtGtX+I3otGnTtHr5AUYM6senQwYUsnWws8NEKi20cFNCYmKRi9GVFDtbG3y9PHnyvOj5SH5RjWjvmzeMNycnE4D0lHis7fOGH6WnxOHiGVkoPEDckyukp8Sz/du8OW0qZQ7P7v/Bld/W8cHkC0il6h6eTEUq+5f3w8zCmqbvL0Bqon8qQpUa9bRWhM/OVveoJMoTcHLOm8OYKE/AP0j3yAg7e0ekUhOtxekAkuQyHBzVvSCOTuq4vH0DtGy8fAOIj9M9NLJ6zTpaK65nZam1yWUJODnn9a4kymUEBOleYdvO3gGp1KTQIjKJcpnBK7Xro0bNOoTp0JcoS8A5nz65XEZgkfqkyAuknTyfPicnZ7Kzs0hNSdbqfZfLZDgV8xucbK0xkUqIL7BQTXxSCq72+ofiSaUS/Mqo447w9eDesziW/XRK03gviI+bE0621jyKTTC48e5gb4dUKi20kJxMnliot8VQ/sk47V/kjUym7TtyuQwnZ91xOTo5IS9w7fx5eeXKZRLlcj7o2U3zvVKpZNmSH9i1YxtLV6wxSJuDvT1SqZSEAmWcTP73yzgzM1N8PD0ACA8J4vqtO2zds48Rg/rrDVO+WkMCQvPKuOwsdRmXnBiPg5Ob5nxyYjzeAfpXYy4Jt6+dI+bpfXoPm2VwGPsX6SbXmae67yUnJ6dC5YdcLi+y/PDw9MTe3oFnT58U23i/eDuL+8/yVoTPXSTM3lpKUmremGM7aymPY3IKBtdLukJFjCwHN6e8kTDXH2QzcUkyNlYSlEq1zbSB9py7kaUzjpO/x3P1Zt46NOZm6ricHM2Il2Vqzjs5mnP7rv6XDoN6B7F2yyOOnFD3gN99kIqHmyXdO/oVarxXiHLA38eaCdOLnhIUUbkRPsH5fC5brSclMR47x7x6NTUpDg8/3fVqfvasnsyNC8foO3o1Ds4eRdrmXjc++p9vvCui47Bw114/wMLdlazEZJQZCjLjZCizs7Eo41LAxgVFET2ruVy6k8X953l+lOtvdtYSklLzWi12NiX1N9T+5qjtb18uS8HGUoJSpSJdAVMH2HEuUf8q9rkYc/mbq60k5Yijk7OOcqRwHZ7bcI+NiWbytFkl6nWHIur85NSS1fnP41h24CTVwwP48/ZDEpJTaTFmrsY+R6lizpZDrD1yhv1TX88LBsF/EzHn/R8gNDQUKysrjhwpfnGnX3/9lTfeeINBgwZRuXJlQkJCuHPnTiG7ypUrM3r0aE6dOkW5cuVYt26d5ruwsDA++eQTDh48SPv27Vm+fLnOa40ePZrExEStY2h/3dsCmZmZEhYcyLmLeXOalUolf168Qtnwf2ZoPkBaegZPnkfjUszDsrmFDQ6u/prDqUwIVnauPLnzm8YmMyOF2EcXcferqDMOr5DadPh4J+0/2qY5XL3LEVKxNe0/2pbXcM9IYf/SPpiYmPFWj0XF9tJbWdvg4eWrObx9A3F0cuHKhbxtcdLSUrlz8wqh4bq3aDI1MyMwJFwrjFKp5PLFs4RGqMO4uXvi5OzGsyfaQ9WfP3mEq5unHm3WeHr5aA5fvwAcnZy5dOFPLW23blwjPKKszjjMzMwIDgnj0vlzWtounv+TMD1hDEWfvouF9F0lXM9CeGp94Vw8nxdGre+cJkxQSBimpqZa8T55/JC42GjCIoteYM/M1IRIP09+v5G32q5SqeL3G/epEKR72xhdKFUqMrP1L3oYLUtCnpqGq73hi3SZmZkRFhLMXxfytpxTKpX8deEiURHhRYR8PXGamZkREhLGxQt5i/wolUounP9Lb35GRERx4bz2okDn//qTiAh146FhoyYsWPgD87/9XnM4u7jQrkNHvpwyrQTaTAkPDuLPi3k9oUqlknMXLxP1D5ZxoJ4DmZlV9IKXllY2uHn4aQ4Pn2DsHV25cSnvRW56Wgr3b18iMEx3GVdSTh/dhm9QFD4BhuerOk9DuXDhvOacOk/Pa/KoIBERUZwvkKd/5ctTXcTFxZKcnKT1Ek8fiiyIlSs1x7N4JYkpSsL98/ojLM0hwNOEe08NW3gUwMJMvVVXUkrhhlJquop0hYowX1NsrSVcvK278Z6ensOTZxma497DNOISFFSrmNd4srYyISrMnsvX9c/xt7QwQVmgOytHqUKqoxOvdTMPrt9K5vb9onuELaxscHH31xxlvEKwdXDl7tW8ejUjPYXHdy7iG6zf51QqFXtWT+bqucN88NlynNyKnyLy7OF1AOwc3YqxLDny387j0kh7+y3Xxm8g++28Wm9WFol/XsG1Ud7cfiQSXBrWRv6b7gXJ8qPIgji5UnM8z/U3vwL+5mHC/WeGN97NzcDVUUpiauFuy9QMdcM9zNcEW2sJl+4U78fGXf6qnysKartYhLbwiCitel6t7ZyWfW7D/dnTJ0yaOhN7e4eC0RSvLbfOv16gzr9+jwpBhk9/UqpUZGap8791zQpsHvshG78YoDncHOzo2aw23w3tVkxM/z6USlWpHf+PiJ73fwBLS0tGjRrFZ599hrm5OXXq1CE2NpYrV64UGkofGhrKqlWrOHDgAIGBgaxevZqzZ88SGKjuebt37x4//vgjbdq0wcvLixs3bnDr1i169OhBeno6n376Ke+++y6BgYE8fvyYs2fP0qGD7tVSLSwssLDQboimFzFkvlPblkyb9z3hIUFEhgazefd+0jMyaNlYvZfsV3MX4erizIDu7wHqBaDuP1LPic3KziYuIYFbd+9jZWWp6YVauHwtdapXwd3NlTiZjOXrtyCVSmlSz7DF43KRSCSUq9ODv45+j4OLP3bOPvxxaD7WdmXwj8qbu7l3SW8CoppQ9o1umFvY4OyhvWiUmbkVltaOmvOZGSnsX9aH7KwMGnaeQaYihUyFujfE0sZZ08AvTlvzNp3ZsWkFHl6+uLl7sWXtjzg6u1K11psau6ljh1CtVn2ate4IQIu2Xfhh7mQCQyIJDovip10bUWRkUL9xK028rdp1Y+v6xfgFhqrnvB/dx9MnD/j4c8P2QpZIJLRu25EtG1bh6eVDGQ8P1q9ehpOzCzVq522jM3HMJ9SoXY+Wb6v3bX27XScWzJlGcGgEoWER7Nm5BUVGOo2attCEkSXEI5cl8PyZehG3B/fvYmVljWsZd+zsDNtRQK3vXbZsWI2nlw/uHp6sX70UZ2dXLX0TxgynZu26+fR1ZMGcaYSEhhMaFsnunVtQZGRo9NnY2NK4WUuWL16Era091tbWLPl+PuERZfW+tMhP98Y1GbdyF1F+npQL8Gbt0TOkK7JoW1v9QDt2xU7KONox9J1GACz96Vei/D3xdXUiMzuHk1dus/fMJcZ0UetJy8jk+73HaVI5AhcHWx7Hypi77Qi+bs68EVWyOZcd3mnLjG/mERYaQnhYKNt37iYjI4O3mqjLmumz5+Lq4kKfXt0B9eiGBy/mFGZlZxMXn8Dtu3exsrTC28vToDhLwjvtOvDNnBmEhIYRFhbOzp3byVBk0KTpWwDMmTUdFxdXevbuA0Cbtu0YPWoE27dtplr1mpw49gu3b91kyEfDAHXPr32BHSpMTUxxcnLGx8ew1dJz6di2FdPmLSI8JJjI0GC27N5HRoaCFk0aADD1m29xdXGmf4+uL9Iur4zLzsomLl5WqIz7cdU6alatRBlXV9LTMzh8/CTnL19l5sQxJdImkUho0PJ9Dmz7gTKefriU8WbPhm9xcHKjQvVGGrsFk/pSoUYj6jdXa1RkpBH7PO8FX3zMEx7fv461rQPOrnkv+dLTUjj/2yHadTdsu678qPN0JqGhoYSFRbBz5zatPJ09awYuLi700uTpO3w+aiTbtm2hevUaHNfkqbq3KT09nfXrVvNGnXo4OTnx7Nkzli9bjKenF1WqVtWroyh+/lNB81oWxMhyiE9U0rqOFYkpSi7ka2QP7WjDhdtZHPtL3ePcrr4ll+5kkZCkwsFWQqs3LFGq4I/reWFqlTPneXwOKWkqAr1MeLeRFT+fUxAjK74nNJfNu57Qs7Mfj56m8yw6g77vBxCfoODEb3m9vnOnVOD46Ti27VWvv/Dr2Xh6dPInOlbBvYephAXZ0vkdH/YVWJDO2sqEhnXc+HZp4U6A4pBIJNRu1oNfdn+Ps4c/Tq4+HNk2HzunMkRWyatXl0/vTWTVJtRqom507Fk9iYun99L1428xt7QhWa4eHWBpbYeZuSUJMQ+5cHoPYRXrY23jyPPHN9i/7msCwqvh4Vv8iyMTG2ts8i38ZR3og33FCDITEsl49IzwKcOx9HbnQu9RADz4cQP+g7oRMe1THq3YimvDWnh2bMHZNnmjDO/NXU7FZdORn7tM4tmLBAztiamNFY9WbitxugH8/JeC5jUtiZUpiU9S0uoNSxJTVFr+9tG7an87fv6Fv71pyaW7WSQkqee8t3rDAqVSPTQ+l1plzXieoMzztwaW/Hwu02B/M+byt227d5k3ZzohoWGEhkWwe+dWLW3fzPoaFxdXevTuC8DbbdvzxahP2LFtE9Wq1+LEsZ+5c+smgz8aDqgb7tOnfsmd27cYN/ErlDlKZAnqEXm2dnYlWsy5e5PajFuxgyh/L8oFeKnr/Mws2r5RCYCxy3eo6/x26jpx6U8nifLzxNfNmczsbE5evs3e3y4ypmtLABxtrXG0tda6hqmJFBd7WwI8Sr4HvUCQH9F4/4cYN24cpqamjB8/nqdPn+Lp6cmHH35YyG7AgAH89ddfdO7cGYlEQpcuXRg0aBD79+8HwNramuvXr7Ny5Uri4+Px9PRk8ODBDBgwgOzsbOLj4+nRowfR0dG4urrSvn37QkPjX5bGdWsjT0xi2fotJMjkhAT6M2vC55oFnqJj45FI8gZrxCXI6DM87wF1w469bNixl0plI5n/lXqfzdj4eL6cvYCk5BQcHewpHxnG99Mn4ehQ8u3iKr7Zl+zMdE5sn0BmRhLu/lVo3vtHrZ7ypPiHZKTp3jNUF3FPrxLzSN3juHHWW1rfvffZYeycDOtpbd2+O4qMDJYu/Jq01BTCoiowauJczPPNK41+/pjkJLnmc+16TUlOlLNl3WISZfH4B4UyauI3ODjl9Ty1aPseWVmZrFk6l9TkJPwCQxk9aR7unoa/DX7n3S5kZKTz/YJZpKamEBFVnnGTZ2ppe/7sKclJecOJ67zZiMREORvWLEMuSyAwKISxk2ZqDXs9uH8Xm9at0HweN2ooAIOHfa7VyC+Odu92QZGRodEXGVWecZNnFND3hKR8+uq+2YikRDnr1yzX6Bs3aYaWvt79BiORSJk5dTxZWVlUqlKd/oOGGaTprWplkaWk8d2eY8QlpRLu486ij7rg8mII3bOERK15bOmKTKau30+MPBkLM1MCPFz5qndb3qqmflEglUq49SSG3b9dJDk9AzcHO2pHBTH47fqY69m+TR8N3qyLPDGRlWvWI5PJCA4KZOqkCTi9GM0SExuLJF/3XHxCAgOHDtd83rxtB5u37aBCubLM/vorg+IsCfXqNyAxSc7a1SuRyWQEBQXz5aSpmiH4sbExWvoio8oy8rPRrFm1glUrluPl7c0X4ybiH/DPrwPQqN4byJOSWL5u04syLoAZE0bj/GLBpui4eCTS/GVcAv0+GaX5vHHHbjbu2E3FclHM+2oCAPLEJKbOXURCggwbG2uC/P2YOXEM1fKt3m8oTdp+QKYinfU/fEl6WjJBEZUZNOZ7rfnpcdGPSM1Xjjy8c4X5X36g+bx91UwAatRvQ/fBeduV/nlqPyqViqp1Db83c3mzfgMSkxJZs3rVizwNYtKkr7TyVFogTz/9bDSrNXnqxRfjJhLwIk+lUin37t3jyOFDpKam4uzsQuUqVXi/ey+Dt8EsyKHfFZibSejazBorCwl3nmSzcGuq1srdro4m2FjlnXC0k9K7tQ02lhJS0lXceZLNrLUppOTbLs7dSUrbepZYW0qIT1Ry4DcFR8/p3wNdF2u3PsLS0oTPhoRha2PKpauJjJhwSWt+ureHFY72eY2Nb364Tb9uAYwYGIqTgxlxCZns+ukZyzc80Iq7yZtlkEjg8HHdC9kVR72WfclSpLNr+QQy0pLwC6tCjxE/avlcQsxD0pLz6tXfj24AYNnXPbXiatdnKlXqtcPExIy7V09z+uAqshTp2Lt4ULZaU+q3GWiQJoeq5ah9ZLXmc9Qs9XPGo1XbuNhnNBaeblj55nsxdf8xZ9sMIGr2aAI+6kHG4+dcGjCWuEN5240+27wfczdnwiYMxcLDjaQL1/i9dV8yY/QvQloUh89mYmEmoUtTqxf+lsOibQX8zUGKrVXefeFoK6F3S2usX/jb3SfZzF6fquVvZZyktKmr9reEJCUHzig4+mcmhmLM5W+9+g1JSkpk3eoVyGQyAoOCmTDpa029HaejHBnx2ResWbWM1SuW4eXtzehxkzTa4uPj+P039Q4Cw4ZoT1Ga8vVsyleoZLC2t6qVRZacyne7fyEuKeVFnd/VgDo/Ka/O/6Cdps4XCF4lEpVKLDPw/0T0tXPFG5Uiq6+VfNuW10WDsonFG5USliYle5h8nUgw3iIm6KFh+wiXBrG+1UpbQpFkSEs2r/B1YpdV9I4CpcklRdHTNkqTQNtnpS1BL3O3l3w47OviwrF/biu/V8HQMfVKW4JebN4sfm59abF/+u+lLUEvQ9sa7/OI0ohn5Po/PFbaEvRi1fDfO5x+6kbDp4v804zpXLLdSf4LGO8dJhAIBAKBQCAQCAQCgQAQw+YFAoFAIBAIBAKBQPASiDHcrxfR8y4QCAQCgUAgEAgEAoGRI3reBQKBQCAQCAQCgUBQYgpuayl4tYied4FAIBAIBAKBQCAQCIwc0XgXCAQCgUAgEAgEAoHAyBHD5gUCgUAgEAgEAoFAUGJUytJW8P+F6HkXCAQCgUAgEAgEAoHAyBE97wKBQCAQCAQCgUAgKDEqsWDda0X0vAsEAoFAIBAIBAKBQGDkiMa7QCAQCAQCgUAgEAgERo4YNv9/xuXssqUtoUj6BJ8obQl6eWoSVtoS9CLBeIcsqZCUtgS9rFV1L20JeonKTi5tCUVibZpR2hL0kkBgaUvQSznL66UtQS/xKvfSlqCXadKJpS1BL9Lm5qUtoUgy7IxX38Tpv5e2BL20GFWjtCXoJe3tk6UtQS8mEuNdvWy9qltpS9DLB6Ut4G+gNN4s/08iet4FAoFAIBAIBAKBQCAwckTPu0AgEAgEAoFAIBAISoxYsO71InreBQKBQCAQCAQCgUAgABISEujWrRv29vY4OjrSp08fUlJSigzToEEDJBKJ1vHhhx9q2Tx8+JBWrVphbW1NmTJl+PTTT8nOzi6RNtHzLhAIBAKBQCAQCASCEqP8D3a8d+vWjWfPnnHo0CGysrLo3bs3/fv3Z926dUWG69evH5MmTdJ8tra21vydk5NDq1at8PDw4NSpUzx79owePXpgZmbG1KlTDdYmGu8CgUAgEAgEAoFAIPi/59q1a/z000+cPXuWatWqAbBgwQJatmzJrFmz8PLy0hvW2toaDw8Pnd8dPHiQq1evcvjwYdzd3alUqRKTJ09m1KhRTJw4EXNzwxYXFcPmBQKBQCAQCAQCgUDwr0KhUJCUlKR1KBSKvxXn6dOncXR01DTcAZo0aYJUKuXMmTNFhl27di2urq6UK1eO0aNHk5aWphVv+fLlcXfP29nlrbfeIikpiStXrhisTzTeBQKBQCAQCAQCgUBQYlRKVakd06ZNw8HBQeuYNm3a3/o9z58/p0yZMlrnTE1NcXZ25vnz53rDde3alTVr1vDzzz8zevRoVq9ezfvvv68Vb/6GO6D5XFS8BRHD5gUCgUAgEAgEAoFA8K9i9OjRDB8+XOuchYWFTtvPP/+c6dOnFxnftWvXXlpL//79NX+XL18eT09PGjduzJ07dwgODn7peAsiGu8CgUAgEAgEAoFAICgxpblTnIWFhd7GekFGjBhBr169irQJCgrCw8ODmJgYrfPZ2dkkJCTonc+ui5o1awJw+/ZtgoOD8fDw4Pfff9eyiY6OBihRvKLxLhAIBAKBQCAQCASC/yxubm64ubkVa1e7dm3kcjnnzp2jatWqABw9ehSlUqlpkBvC+fPnAfD09NTE+9VXXxETE6MZln/o0CHs7e2JiooyOF7ReBdooVKp2LNxEb8e3kZ6WjJB4ZXo0v8Lynj66w1z6+o5Du1cwaO710iUxdL/s2+oVKORXvt1P0zm5KEtvNvrUxq1fl+vXUG2/PQza3YfJEGeSIi/DyM+6ELZkECdtjsOn2D/8dPcffQUgPAgPwZ2aadlr1KpWLxpFzuPnCAlNZ3yEcF81rcbfp7uOuMsiv17trNj6wbksgQCAkPo++FQQsMj9dqfOvEL69csJSb6OZ5ePnTvPYCq1WtpaduwZjmHDuwhLTWFiMhy9B88HC9vnxJr27dnOzu2bnyhLZi+Hw4lrAhtv574hfVrlmm09ejdX0vb6V+Pc2D/bu7cvklKchJz5i8mMDikxLrAuNMtN75f98zn4q+bUaQn4RVUhWZdJuJUJkBvmL+Or+P88fUkJTwBwMUzlDdaDiKobH2NzYF143lw/RSpiTGYWVjjHVSZN98ZiYuH4cOqVCoVO9b/wLFD20lLTSE0oiLdP/wcDy8/vWFuXPmT/dtX8+DONeSyOD76fBZVajX42/Hq0rZ57VKOHNhNamoy4ZHl6TtoJJ7evkWGO7BnK7u3rUcuS8A/MJjeAz4hJDyvQvvx2xlcPv8HCQlxWFpaEx5Zjq69BuLtq7980qVt+7of+eXQjhe/rwI9B44q9vcd3ruZ/TvWkCiLxzcglPf7jyQ4rKzme7ksjo0rFnDl/BnS09Pw9Pbn7Y69qf6G/rKwINv3/sTG7btIkMkJDvRnaP8PiAwL1Wl77+Ejlq/dyM07d4mOiWVwn16827aVls3azds5cfoMD588wcLcnLIR4fTv2Q0/H2+DNeWiUqnYuGYZhw/sJi01hfDI8vQfPLzYPN2/Zxu7Xtzj/oHB9PnwY0Lz5emh/bs4ceww927fJD09jZUb92Jja1cibWYV62JRrRESGzuUsU9J/3kryucPddtG1cCqeVft35adRfL8TzWfJda2WNRrg6l/OBILK3Ke3CHj6FaU8rgS6QLYdOEuq/68TXyaglBXez6rX4FyHk46bXddfciXh//SOmduIuX04Le1zt1LSGb+r1c59ySOHKWKIGc7ZrSqjqedNSVl6/7DrNu5X12vBvjxSZ/3iQoN0ml79+ETlmzYxo2793keG8/Q3l3o3PotLZucHCVLN23n4PHTxMsTcXVypGXDuvR6tw0SiaTE+lq9YcEb5cyxspRw90kOG4+kEytX6rVvWduClrUttc49T8hhyoq8/ZldHaS0q29JkJcJpiYSrt3PYvPPGSSnGdZ96Fy3GkEj+uBQpRyWXmX4o8MgoncdKTrMmzWImvU5tlGhZDx6xu1p3/F41XYtG/+BXQka3gcLDzeSLl7nyrDJJJ69ZJCm/Bhz+Zur758uS5KTk9i0ZhkX/jpLXGw09g6OVK9Vj/e698HGxrZE2k7umc+Fk+o63zuoCs26TsS5qDr/2Dr+OrGexHh1ne/6os4PLqeu89NT5Zzcs4D7V0+SJHuGla0zYRWbUK/Nx1hYlaysE7weIiMjad68Of369eP7778nKyuLIUOG8N5772lWmn/y5AmNGzdm1apV1KhRgzt37rBu3TpatmyJi4sLFy9e5JNPPuHNN9+kQoUKADRr1oyoqCi6d+/OjBkzeP78OWPHjmXw4MEGjx4AsWCdoACHdiznl33r6dJ/LJ9OXYOFhRULJg8kK1P/yo2ZGen4BITTue/oYuM/f+YI929dwsG5+DdfWrpOnWXeqs30fbc1K6ePJdTfl2FfzSMhMUmn/Z9Xb9C0Tg0WThjB4imjcHdx5uMpc4lJkGlsVu88wKb9RxnV732WTB2NlYUFw76ahyIzq0TaTh4/yvLFi+jUtRez5i8mIDCYSeM+RS6X6bS/fvUyc2ZMonGzVsyev4QatesyfcpYHty/q7HZvmU9e3dv5cPBw/l6zndYWFoxedynZBaRD/q1fUfnrj2ZPf/HF9o+K0bbZBo3a8ns+YupWbsuX08Zx4P79zQ2CkUGkVHl6NG7v844SqbNONMtl98PLebPX1bTtMtEun26CXMLKzYv6EN2lv747Bw9qP/OSHp8vo3uo7biH1aL7d8PJu7pLY2Nh19ZWnSfxgfj99FxyFL1w9aCPiiVOQZr27d9JYf2bKDHh6MZN2MF5paWzPnyoyLvVUVGOr6Bobw/YNQ/Gm9Bdm1dy/7dW+g7eCRfzf4RS0srpo4fXmQ+nDp+hFVLvqVDl958PW8p/oEhTB0/nMR8/hAUEs6Hw8Yw57u1jJk0G5VKxVfjP0GZU4J027aKQ3s30mvg54yfuQwLSytmTRxapLYzJw6xftlc2nbuy5dzVuEbGMqsiUNJkidobH6c+yXPnjzg4y9m89X89VSt3YCFM8fw4O4Ng3QdPfEr3y1dSc/3OvLjN9MJDvDnswlfIZMn6rRXKBR4eZShf49uODs56rS5cPkK77R6i4UzpzJz0jiyc7L5bMIU0jMyDNKUnx1b1rFv91b6Dx7B1Dk/YGFpyeRxI4tMt1+PH2Hl4oV07NqLGfOXEBAYwpRxI7XyVKHIoHKVGrTvZPiL3PyYhlXGsv47KH77idQ1s8iJfYJN+w+RWOl/YFcp0kn+fpzmSFnypdb3Vm36InVwIW3nElLXzEKZJMP63UFgatg2PrkcvPmEOSeu0L9mOGvfq0+YqwNDdp4mIU1/mtmYm3Kgz1uaY0/vplrfP5Kn0mfLCQKcbPmxfR02dG1I3xphWJiYlEgbwOFfz7BgxQY+6PQOy2Z+SYi/L8Mnz0Kmp15VZCrwcndj4PsdcXF00GmzZsdedhz4meF932fdvKkM6t6JtTv2s2Xf4RLra1LdnPqVLNhwJJ1Z61LIzFIxuL0NpsX81KdxOYz+PklzfLMhVfOduSkM7mCNSgULtqTyzcYUTEwkDHjHGkNfLZjYWJN08QaXh35ZvDFgFeBD9V0/EP/LGU5Wa8u9BSsp/8MUXJvW1dh4dmxB5MzR3JqykJM12pF88To19y7F3M3ZQFV5GHP5C6+mLJHFx5GQEEePPoOYs2glgz8ZzflzZ/huXtHzmwty5uBizv28mre6TqT7Z5sws7Bi0/xi6nwndZ3fc/Q2en6+Ff/wWmz7fjCxL+r8FHkMKfIYGnYYxQfj9tCqxzTuXj3BvtVflEibMaNUqkrteFWsXbuWiIgIGjduTMuWLalbty4//vij5vusrCxu3LihWU3e3Nycw4cP06xZMyIiIhgxYgQdOnRg9+7dmjAmJibs2bMHExMTateuzfvvv0+PHj209oU3BNF4/xfRoEEDhg4dymeffYazszMeHh5MnDjxH4tfpVJxdO9amnfoR8UaDfEJCKPnR1NIlMVy4fejesOVrVKXNl2GUKlm4yLjl8dHs2np1/T6eComJmYl0rZ+zyHaNq5L64Z1CPTxYlS/bliam7Pn51912k8a2pd332pAWIAvAd6ejPmwB0qVij8uXdf81o37DtO7fSverF6JUH8fJgzpTZxMzvGzf+mMUx+7t2+mafNWNG7aAl+/AAYMGY6FpSVHD+7Tab9n11YqV63BOx3ew8fPn67d+xAYHMr+Pds12vbs3MK7nbtTo3ZdAgKDGTpiNAkJcfx++mSJtO0qoO3DF9qOHNxfpLZ2Hd7D18+frt0/ICg4lH178noIGjRqRueuPalYqWqJtBTEmNMtN75zR1dRq/lAQis2oYxPBC17ziAlMYZbF/Q/iIZUaERQufo4lQnA2T2Qem0/wdzCmqf3zmtsKtbtjG9odRxcfHD3K0vdt4eRLHumeXNviLZDu9fzdqc+VKnZAN+AUPp9PAlZQix/nvlFb7gKVevQodsgqtZq+I/GWzCOfTs3075zD6rXqod/YAiDh49FlhDP2dMn9Ibbu2MDjd96m4ZNW+HjF0jfwZ9ibmHJz4f2aGyaNG9LVLlKlHH3JCgknM7d+xEfG0NMjGGrtKpUKg7s3sDbHT+gSs36+AWE0n/YROQJcfz52zG94X7auY76zd7hzSZv4+0XRK+Bn2NuYcnxw3mV8u3rF2naqhPBYWUp4+FN2059sLax5d5twxa/2bxzD62aNaZFk4YE+PkyfFB/LC3M2X9Yd9kbERrCh7170OjNOpiZ6S5PZ3w5luaNGxLo50tIYACffzyY6Ng4bt6+q9NeHyqVir07N9Ohc3dq1K5HQGAwH434AllCfJH31u7tm2jSvDWNmrbE1y+A/kNGvLjH92psWr/TiXad3ic0oqzeeIrComoDsi6fJuvK7ygTosk4vBlVdiZm5YoY2qgCVVpyviOvV1bq6IapVwAZRzajjH6EUhZDxuHNYGqGWUSVEmlb89dt2pXzp02UP0Eu9oxpVBFLUxN2Xn2gN4wEcLWx1Bwu1tq9yItOX6OOvzsf1y1LRBlHfB1tqB/kibO14b02uWzcfYC3m9SnVaN6BPp68+mAnlhYmLPnyHGd9pEhQQzp+R5N6tbCzEz3wM3LN25Tr3pl3qhaCc8ybjSsXZ0aFctytYQ+B9CwsgUHzmRw6U42T+OUrPopDQdbCRVDin5+UCohOU2lOVIz8h7wg7xNcbGXsuZAGk/jlDyNU7L6pzT83E0I8zPsBUjsgePcnDCX6J2GvZDw7/8e6fcec+2z6aRcv8uDRWt5vvUAgR/30tgEDuvNo6WbeLxyGynX7nBp0ARy0jLw7dXBoGvkYszlb66+V1GW+AUE8ekXU6hWsw4ent6Ur1iVLj368ceZU+TkZBus7Y+jq6jdIq/Ob91LXeffPF90nR9crj7OL+r8NwvU+W7eYbQbsICQCo1wcvPDP6I2b7YZxp1LR1EaqE3w+nF2dmbdunUkJyeTmJjIsmXLsLXNeykcEBCASqWiQYMGAPj6+nLs2DHi4+PJyMjg1q1bzJgxA3t7e614/f392bdvH2lpacTGxjJr1ixMTUs2EF403v9lrFy5EhsbG86cOcOMGTOYNGkShw4d+kfijo95QpI8jogKeQ89VjZ2BISW5+7Ni38rbqVSyYoFX9CkbS+8fEs2xDorO5sbdx9SvXzecGqpVEr18pFcumnYA0GGIpOc7BzsbW0AeBoTR7w8ieoV8uK0tbambEigwXGC+s3bnds3qJCvISuVSqlQqSo3rl/VGebm9Sta9gCVq9TQ2Ec/f4ZclqDVOLaxsSU0PEpvnPq13dSKR62tCjeu695P8sb1q4Ua5ZWqVOemHvuXxZjTLZfE+MekJsXiH/GG5pyFlR2eARV5etewFzxKZQ7X/thLVmYaXkGVddpkKtK4/Ns2HFx8sHcybMGS2OgnJMriKVuhhuactY0twWHluH2j5MMs/8l4Y6KfIpfFU75Sda04QsKjuHX9ss4w2VlZ3L19k/KV8vZUlUqllK9UjVt6fC8jI51fDu+jjLsnrq5ldNoUJDb6qfr3VdT+fUFhZfX+vuysLO7fuU7Zinm/RyqVUrZida0wIREVOHPyECnJiSiVSn47fpCszEwiyxf/kisrK4ubt+9StVIFrWtUqViBK9dvGvTbDCE1Vd1DYG9n+DBSgJgX91aFfPmjvrciuaknT7Ne5GmFQnlaVW/5U2KkJkjdfch+kD+NVGQ/uImJZ4D+cObm2PYdj22/CVi16YPUJd999+IhSpWdfwSWCnKyMfHWPZxcF1k5Sq7HJFLDN2+UmVQioYavG5ee6R5dBJCelUOr5QdpuewAw3ef4U58Xi+4UqXi5P3n+DnZMnjHKZos3k+Pjcf4+c4zg3Vp9GVlc+POfapXyBsWLZVKqVahLJdv3ilxfLmUCw/hj0tXefhU3aC7df8hF6/folbl8iWKx8VBgoOtlOsP8xo3GZlw/3kOAZ5FN7LdnKR81d+OiR/Y0bOFFU52eX3qpiagArLzdRZn56gX3Ar2fjUzSR1rVSLu6Gmtc7GHTuJUqxIAEjMzHKqUJe7IqTwDlYq4o6dwrKW73tCHMZe/8HrLkrS0VKytrTExMSxfE+PUdX5AgTrfK7AiT+8ZXudfPauu87311PkAivQUzC1tkRqozdhRqVSldvw/8t/wmv8jKlSowIQJEwAIDQ3l22+/5ciRIzRt2rSQrUKhQKHQHuqTmanC3Fz3G/pEmXo+n72ji9Z5ewcXkl5irl9+Du5YjlRqQsOWXYs3LoA8KYUcpRJnR+23V06Odtx/athDy8K1W3F1dtC8AIiXqx+InB205xs5O9hrvjOE5CT1g7qjo/bQNkdHJ5480j3nUi5LKGTv4OiEXJag+R7AwalwnDJZAoaSq83BUXt+ZfHaCtvLZPofNl8GY063XFITYwGwsde+H2zsXUhNKvp+iH1yg7Wz3iM7S4G5hTXv9F+Iq6f2S6u/jq3l2I5ZZCnScHYPpOPQ5ZgYOCw3UR4P6LpXnUmUxRsUx6uKV5MPBfzIwdEJuVx3PiQlJaJU5uBQKH+defpYu5fywN5trF3+HYqMdLx8/PhiylxM9fQ8FyT3NxS8jr2j/t+XnCTXq+1ZPm2DP53KopljGPx+U0xMTDC3sGTo6Bm4exY9jxMgMSkZpVKJU4GhyE6ODjx8YthojOJQKpV8u2QF5SLDCfQ3fP0CANmLtHF0Kpinzpr8LkiyJk8LlifOeu/xkiKxskEiNUGVlqx1XpWWjImz7rVLlLIYMg5sICfuKRILS8yrNsTmvY9JWfk1qpRElAnRKJMSsKzbmvTDmyArE/OqDZDaOSG1sdcZpy7k6QpyVCpcCvSIu1hbcF+WrDNMgJMt45tUItTVgRRFFqv/vE3vzSfY3K0R7nZWJKQpSMvKYcUftxhUO5Khdcpy6kE0n+79nR/a16Gqj6vh+pKTX9Sr2j7n7GDPwyclfxmQS/d2rUhLS6fr0NFIpVKUSiX9u3bgrTffKD5wPuyt1X1LBeehJ6cqsbfRP8D9/rMc1vyURrRMiYONlBa1Lfiksy1frUxGkaX+PjML2tazZNfJDCSo/zaRSoqM9+9g4e6KIlq7zlBEx2HmYIfU0gIzJwekpqYoYuIL2MRjE274CyMw7vIXXl9ZkpQoZ8v6lTRp3sZgbSlJuut8azvD6vzVM/Pq/HYDCtf5uaSlJHBq/yIq1e1ssDaBID+i8f4vI3fRg1w8PT0LbWeQy7Rp0/jyS+05Wd0//IKeg8YC8Pvxvaz/cbLmu4Gjv/2H1ap5eOcqv+xby+czNrzUgjV/l1U79nP417MsnDgSC/OSDdcX/P9w9fddHFw/QfO5w8AfXjouZ/dAeo7egSIjmZt/HmDfqlG898karco8qkYbAiLrkJIYy9nDS9m9ZBhdR67H1Kzwy7XTx/az8rupms/Dxs59aW3/NCd+PsjihTM1nz+fMOOVXq9eg2ZUqFQdmSyePdvWM/frcUya+Z3Ol5KnfvmJFd9N03wePu6bV6Zr27rvSUtN4bNJ32Jn78i5M8dYNHMMY6b+iG/Ayy3o+E8y7/sl3Hv4iAVfTy7W9vjPB/nx29maz6MnlmzeqDGT8+w+Oc/uaz6nP72Hba/RmFd4A8Wp/aBUkrZrGVbNumA/eBoqZQ45D2+Sde8qEoNnRb8cFTydqeDprPX53TVH2Xr5PoNqR2p6meoHedCtsnpxy3A3By4+k7H18v0SNd5fFUdP/c7BE78xcdgAAn29uXXvIfOWr9MsXKePahFmdGlipfn83Y5UvbZFcfV+Xk/90zgl959nM6mvPVXCzTh9OYuUdBVL96TRubEl9Svbo1LBuetZPIzOKdXtrl4WYy5/oXTKkrS0VKZOHIWPXwCduvXWa3fl910cWJdX57876O/V+b3H7ECRnsyNvw6wd+Uoug5fU6gBr0hPYcvCAbh4BFOn9ZCXvp6xodK/hqTgFSAa7/8yCs5tlEgkKJW675rRo0czfPhwrXO/3sqrnSpUb0BAaN5QtuzsTACS5PE4OOUN9UtKjMcnIPylNd++9ifJiQmM/bC55pxSmcPWVbM5unctU77TPf86F0d7W0ykUhIK9IjL5Ml6F83JZe2ug6za8RMLxn1CqH/eiuMuL3rxExKTcc232FNCYhKhAcX3lOViZ++AVCot9EZbLpfhWKAHWPN7nJwL2Sfms8/9P1GWgLNz3htguVxGYJDhjYBcbYkFFoArXlthe6cCb8n/LsaYbiEVGuEZUFHzOefF/ZCaFI+tQ96wwNSkeMr4RBQZl4mpOU5l1CvweviV49mDS5z7eRVvdc1blMTCyg4LKzucygTgFViRBSNrcOv8ISKrty4UX6UabxIUVk7zOTsr7151dM57YE9KTMA3MKzY36oPhxc97iWJt1rNulqrh2e90JYol+GUL45EuYyAQN35YG/vgFRqQmKh/E3A0alAL4iNLdY2tnh6+xIWXpYP3mvB2dPHqVO/8OijyjXqERyeN5c6T1uC9u+TJ+Cn5/fZ2Tvq1ebwQlv0s8cc3ruZrxasx8dP3ajyCwzj5pXzHNm3mV6Dil7M08HeDqlUWmhxOpk8EWdHxyLDGsK875dw+o8/mTf1S9xcXYq1r14gT7Oz1EPI5bKCeZpAgJ57y06TpwXLkwS993hJUaWnolLmILHWHkElsbZDmWrgCCqlkpyYJ0gd8+o8ZcxjUtfMBHNLJCYmqNJTsenyCTnRho8YcLSywEQiIb7A4nTxaQpcC8xj14eZiZRwNwceJ6bmxSmVEOSs/XsDnW05/7Rko4sc7exe1KvaPpeQmFSoN74kLFy1iffbtaRJXfUuIMH+vjyPi2f1tj1FNt4v3cni/vO8sey5i9LZWUtISs17brGzkfI4xvAF0tIVECPLwc0xb5bo9QfZfLksBRtLCUqVinQFTB1gx7nEV9MCUUTHYeGu/WLFwt2VrMRklBkKMuNkKLOzsSjjUsDGBcXzont8jbn8hddflqSnpTFl3EisrKz5bOyUIucSh1RohFe+Oj9bT52fllzCOt+/HM/uX+KPo6to3i2vzldkpLDp276YW9jQ/sOFJV77SSDIRcx5/w9jYWGBvb291pH/7aillQ1lPP00h6dPMPaOrty4dEZjk56Wwv1blwgKq6DrEgZRo35rvpi9mTGzNmoOB2c3mrbpyUdjvys2vJmpKeFBfpy9fF1zTqlUcvbyNcqH6R9StnrnTyzbuoe5Yz4mMjhA6zuvMq64ONpz9lLeYlKpaelcuX2vyDgLaTMzIzgknIvn/9TSdvH8OcIjdO/ZGBZRlksX/tQ6d+GvPzT27h6eODo5czGfTVpaKrduXNUbp35tYYW0XTr/J+F6FocKj4jSuq5a2znCXnIxqaK1GVe6mVva4lTGX3O4eIZgY+/Gwxt5cxUV6Sk8u39B7/x1vaiUmpcBOr9WqeeM6bOxsrLB3dNXc3j5BuHg5MLVi2c1NulpKdy5eZmQ8JLNLc2Pm7t3ieO1srbGw8tHc/j4BeLo5MKl839obNLSUrl94yqhEeV0xmFqZkZQSBiXLpzTnFMqlVy+cK7IhcxUL/5lZeneIcLKWjvdvPWk292bV/T+PlMzMwKCI7TCKJVKrl78QxMmU6FevV0q0a5SpVIpSgO688zMzAgLCeLPC3lz6JVKJX9evETZiJd/GaNSqZj3/RJO/vY7c6ZMwNPDsG0wrayt8fTy0Rw+fgE4Ojlr5Y/63rpGmJ48NcvN0/PaeVpU+VNilDkoox9j6pd/Oz0Jpn5hWr3rRSKRIHX11N3Yz8xAlZ6K1NEVqbsvWXd0z8nVhZmJlIgyDpx9FJsnV6Xi7KNYynsa9jI0R6nidnySprFvZiKlbBlHHshStOweyFLwsLPSFYV+fWamhAcH8MelvPVAlEol5y5epVyY4VtWFiRDodB5HxQ3N1WRBXFypeZ4Hq8kMUVJuF9e48vSHAI8TLj/zPDGu7kZuDpKSUwtfP3UDHXDPczXBFtrCZfuvJrFw+S/ncelUS2tc66N30D223kAVFlZJP55BddGtfMMJBJcGtZG/lvRc62NufzN1fe6ypK0tFQmjxuBqZkZn4+fpnc0QC4WBep81xd1/oMCdf7TexfwCixZna8qUOcr0lPYNL8PJiZmdBj0nc4RdgKBoYied4EGiURCo1bd2L91MWU8/XEp483uDQtxcHKjYr592+dN7EfFmo1o0KILABnpacTm21M3PvoJj+5dx8bWAWc3T2ztHLG1c9S6lomJGfaOrrh7BxikrUvrpkxeuJzIIH+iQgLZuO8wGYpMWjWoA8CX3y7DzdmRQV3bA7Bqx08s3rSLL4f2wbOMC/EvehesLC2wtrREIpHQuWUTVmzbh69nGbzKuPLjhp24OjnyZvWSFdJvt+vIgjnTCAkNJzQskt07t6DIyKBR0xbq9Jo9FRcXV97vpd5arXWbDoz7/GN2bttI1eq1OHn8KHdu3+DDj0Zo8qF123fZsmE1nl4+uHt4sn71UpydXalRW3/PhS7atOvI/DlfExwaRmhYJHt2biEjI4PGTZtrtDm7uNG9Vz+NtrGfD2Pntk1a2ga+0Abq/VTjYmJISFD3CDx5os57RydnnJwN71Ez5nTLja9qox6c3v8dTmX8cXDx4eTuedg6lCG0YhON3cZ5PQmt2JQqDdRbXR3fMZvAsm9i7+xJZkYq187u4eGt3+k4ZCkA8rhHXP9jHwFRdbC2dSZZ9pwzB3/E1NySwHL1dWrRpa3p213YvXkp7l6+uJbxZvu673BydqNKzQYauxnjBlKlVgOatFLPrctITyPm2SPN97ExT3h49wY2dg64uHkYHG9x2lq27cj2jSvx9PaljLsnG9cswcnZheq162nsJo/5mOq136T52+rVlFu98x6LvvmK4NAIgsMi2bdzE4qMdBo0Ue9dHv38CaeOH6VilerY2zsSHx/Lzs1rMDe3oHK12jq16NL21tvvsWvTMtw9fXFz92Lbuu9xdHalSq28tJ8+bhBVajWgaatOADRv25XF874kMCSSoNCyHNi9AUVGOvWaqEdJePoE4O7py/JF03iv98fY2jnw55ljXLnwO5+MnWOQto5tW/P13IWEhQQTGRbCll17ychQ0LyxemeAqd8swM3ZmX49uwHqRZwePHoMQHZ2NnEJ8dy+ew8rS0u8vTwBmPv9Eo4cP8mULz7D2sqShBdrV9hYW5doT1mJREKrth3ZumEVnl4+lPHwZMPqpTg5u2jdWxPHDKNm7Xq0eJGnb7frxLdzphEcGk5IWCR7d25GkZFOw6YtNWFkCfHIZQk8f6ae2//g/l2srKxxLeOOnV3xc8wV537BqnlXcqIfkfP8IeZV6iMxMyfrivoltGXzbqhSElGcVK+abV7rLXKe3Ucpj0NiYYVFtUZI7Z3IupT3wG4aWhFVeirKZBkmrp5YNmhP9p1L5DwwbNu/XN6vHMKEQ38S6e5IOXcn1p2/Q3p2Dm2i1GsOjD94DjcbKz6qo365+OOZG5T3cMLX0YbkF3Penyel8U7ZvDUKulcNYfT+P6js7UJ1H1dOPYjhxL1ofuhQp0TaADq//RZfLVhMRHAgUaFBbNpzkAyFglaN1Pfp5Pk/4ursxMD3OwLqRe7uPVbnU1Z2DrHxMm7ee4C1pSU+nuoXQ3WqVWLl1t24uzkT6OvNzXsP2bj7gCbOkvDzXwqa17QkVqYkPklJqzcsSUxRceF2XmPxo3dtuHA7i+Pn1Q2ldm9aculuFglJ6jnvrd6wQKlUD43PpVZZM54nKElJUxHoZcK7DSz5+VwmMTLDet5NbKyxCcnLE+tAH+wrRpCZkEjGo2eETxmOpbc7F3qrt+R88OMG/Ad1I2LapzxasRXXhrXw7NiCs20GaOK4N3c5FZdNR37uMolnLxIwtCemNlY8WrmtRGlmzOVvrr5XUZakpaUyeewIFIoMPhs5lrS0VNLS1CNW7B0cMTFgK0WJREK1Rj04te87nNz8cXT14cSLOj+sUl6dv2FuT0IrNaXqizr/2I7ZBOWr86++qPM7faSu8xXpKWyc/wHZWem07j0TRXoKinT1CzhrO2ek0pJv82hsGPKSWvDPIRrvAi2avtMbhSKddT9MIi01meCIygwZuwizfG8wY6Mfk5Ik13x+eOcKcyf21XzeunIWALUatKHHkOLnVxqk643qyJOSWbxpF/HyJEIDfPhmzFDN8PfncQla8+m3HTpGVnY2Y+Zoz2Hq825r+nVSL2DSve1bZCgUfP3DGlLS0qgQEcLcMR+XeF583TcbkZQoZ/2a5chlCQQGhTBu0gzNcK642Gik+bRFRJXjk0/HsW71UtauXIKntzejxk7BPyCvx7/du11QZGTw/YJZpKamEBlVnnGTZxT7Jlm3tkQ2rFmBTJZAYFAw4ydN12iLjY1Bkq+XRK1tLOtWL2PNC22fj52Mf0Cgxubsb6dYMDdv3trs6eo87ty1J+916/WfSLdcajTtR5YinQPrxqNIS8I7uCrvDlmi9dZcHvuI9JS84XxpyfHsWzmK1KQYLCztcPUOp+OQpQREqh+uTU3NeXznD879vJKMtCRs7FzwCa1Gt5HrsbErfkhzLi3b9SQzI4MVi6aSlppMWGQlho+fr3WvxjzXvlfv377K9HEfaj5vWKaeA16nYWv6fjzR4HiLo02HbigyMvhxwQzSUlMIjyrP6EmztfIh+vkTkvNpe+PNxiQlytm0ZglymXoI5ehJszX+YGZmwfUrF9i/axMpKck4OjoTUbYik2d+X2ghoyLTrX0PFJrfl0JoZEVGTpinpS3m+ROtdKtZrylJSTK2rfuRRFk8foFhjJwwTzPNwNTUlOHjv2HzqoXMnTKCjIw03D196PfxBCpWM6xR1aheHRITk1ixbiMJMjnBQQFMn/iFZg/3mNg4rfshPkFGv2GfaT5v3L6bjdt3U7FcFHOnqtc62bX/IACfjJmoda1RHw/SvBQwlHfe7YoiI4MfXtxbEVHlGTt5lnaePntKUlLeMOw6L/J0w5plmjz9YtIsraGuB/fvZPO6FZrP40d9BMDgYaNp+OJFXlFk3/yLDGsbLN5ogcTaHmXsE9K2/aDZ/k1q56T1YCmxsMKqaWck1vaoFGnkRD8idf08lAnRGhuprQPmDd5BYm2HKjWJrKtnUfx2sETpBdAszBtZuoLvf7tOfKqCMDd7FrStpdn+7Xlyula9lazIZMrR88SnKrC3NCOijCPLOtYjyCXvJUajYC/GNKzI8j9uMevYJfydbJnRsjqVvQwvO3JpUqcm8sRklmzYToI8kdBAP2aPHaEZNh8dF6+lL04mo/fIvDnC63f9xPpdP1G5bDjfTlJPDfmk7/ssXr+NWT+uRpaUhKuTI22bNqB3x7Yl1nf4bCYWZhK6NLXCykLCnSc5LNqWqrVSvKuDFFurPI2OthJ6t7TG2lJCSrqKu0+ymb0+lZT0PB8o4ySlTV1LrC0lJCQpOXBGwdE/9Y+MKohD1XLUPrJa8zlq1hgAHq3axsU+o7HwdMPK11Pzffr9x5xtM4Co2aMJ+KgHGY+fc2nAWOIO5W2N9mzzfszdnAmbMBQLDzeSLlzj99Z9ySywiJ0hGHP5C6+mLLl7+ya3bqhHkQzp20XreouWbaSMuyeGULNZP7Iy1XV+RloSPsFV6fSRdp0vK1DnpybHs2dFXp3v5h1Op4+WEviizo9+dIVn9y8A8ON47ekFH045goOLDwJBSZCo/l/X2f8/5ciljNKWUCRVlGeKNyolntq8/PDVV40E472NVa94kae/w+kHhlXopUGUl+4VqY0Fa1PjLUsycgxbtb808JPo3+O7tIk3MWxYfWngv2ta8UalhNTCeP0NIKN+u9KWoJeJBw2fCva6aTGqRvFGpYT3Vf17opc2JhLjXb3s7EPjLeM+aFS8jbEyYtHLLTD5TzB7kE2pXbu0EHPeBQKBQCAQCAQCgUAgMHJE410gEAgEAoFAIBAIBAIjR8x5FwgEAoFAIBAIBAJBiVEqjXfq5n8R0fMuEAgEAoFAIBAIBAKBkSN63gUCgUAgEAgEAoFAUGLE0uevF9HzLhAIBAKBQCAQCAQCgZEjet4FAoFAIBAIBAKBQFBiVGLO+2tF9LwLBAKBQCAQCAQCgUBg5IjGu0AgEAgEAoFAIBAIBEaOGDYvEAgEAoFAIBAIBIISoxQr1r1WJCqVSPH/J87UrlnaEorEac3m0pagFwnGe6tkqsxLW8K/EjuVvLQl6OVemy6lLaFIUm6nl7YEvfhfO1baEvRiIskpbQl6yVGZlLYEvWSpjLevwVySVdoS/rWYGXHapSmtSluCXp5E1S1tCXoJuPZLaUvQizHX+X6hkaUt4aX5aG5SqV17wTD7Urt2aWG8taFAIBAIBAKBQCAQCIwWsWDd60XMeRcIBAKBQCAQCAQCgcDIEY13gUAgEAgEAoFAIBAIjBwxbF4gEAgEAoFAIBAIBCVGDJt/vYied4FAIBAIBAKBQCAQCIwc0fMuEAgEAoFAIBAIBIISIzreXy+i510gEAgEAoFAIBAIBAIjR/S8CwQCgUAgEAgEAoGgxIg5768X0fMuEAgEAoFAIBAIBAKBkSMa7wKBQCAQCAQCgUAgEBg5Yti8QAv3Du/i2a0bZs4upN2+xf05s0m9elWnrcTEBK+evXBt0RJzNzfSHz7k0aJvSfztt5eOsyj27t7Jtq2bkckSCAwMZsDAwYSFR+i1P3niGGtWryQm+jleXt70+qAv1arX1Gm7cMFcftq/l779B9L2nfYl1rZn96582oIYMHAw4UVqO86a1SuIjo7WaKtevYZO228XzOOn/Xvp1//Dl9KmUqnYsGY5hw7sIS01hYjIcvQfPBwvb58iw+3fs50dWzcglyUQEBhC3w+HEhoeqfk+M1PBiiXfcfL4UbKzMqlUpQb9Bw3D0cn5P6Fv5559bN62nQSZnODAAAYP6EdEeJhO2/sPHrJy7Tpu3b5DdEwsA/t9QPu2bf5WnEXh1bUzvh/0xNzVhZTrN7n91XSSL13WaSsxNcWv/we4t30bC/cypN27z93Z85CdPKWx8XyvI17vdcTS2wuAtNt3eLDoRxJO/FoiXc51qxE0og8OVcph6VWGPzoMInrXkaLDvFmDqFmfYxsVSsajZ9ye9h2PV23XsvEf2JWg4X2w8HAj6eJ1rgybTOLZSyXSlova35Zx+IW/hUeWN9jfdmr8LZg+H36s5W8H9+/i5LEj3L19k/T0NFZt3IONrV2JtO3bvYPtWzdqrtFv4EeE5btGQX498QvrVi8nJvo5nl4+9PigH9Wq19J8f/rX4/y0bzd3b98iOTmJOQt+JCg4pESa8vOq0i4zU8HKJYte3KtZVKxSnf6DPilRWaJSqdi8dglHD+wmNTWZ8MgK9Bk0Ek9v3yLDHdizld3b1pEoS8AvMITeAz4hJDxKZ/xfTxzJhXO/MeKLaVSv/WaJtBljGWfs2oy5zlf721KOaPytPH0N9rf1yGUJ+AcGF/K3H7+dweXzf5CQEIelpTXhkeXo2msg3r7+Bun695TBxudzxlznGzsqlRg2/zoRPe//AXJyclAqlX87HufGTfAb+jGPly7lcq+epN26TcQ38zB1ctJp7zPgQ8q88w7358zmYtf3iNm+jbCvp2MdFvbScerjxLFfWLL4B7p0fZ+5C74jMCiI8eNGI5fLdNpfu3qFmdOn0qxZc+Yt+I5atevw1eSJPLh/r5Dt6VMnuXHjGs4uLiXSlMvxfNrmLVj0QtuYIrXNmD6Vps2aM3/Bd9Sq/QZfTZ7IfR3aTv1NbQDbt6xn7+6tfDh4OF/P+Q4LSysmj/uUzEyF3jAnjx9l+eJFdOrai1nzFxMQGMykcZ9q/ablixfyx++n+HT0RCZ/PY+EhDimfzX+P6Hvl+Mn+WHJMt7v8h7fzZtDUGAAo8d/iUwu12mvUCjw9PCgT88eOOvx7ZLGqQ+3Fs0IHjWC+wt/4FyHLqTcuEn5xYswc9Z93YCPB+PZ6V1ufzWds63b83TjFsoumINtZLjGJvN5NPfmzOfPd7vyZ8euyH47S9lv52IdElwibSY21iRdvMHloV8aZG8V4EP1XT8Q/8sZTlZry70FKyn/wxRcm9bV2Hh2bEHkzNHcmrKQkzXakXzxOjX3LsXcrWQviXLZsWU9+3ZvY8DgEUyb8z2WlpZMHjeySH/79fhRVixeSKeuPZk5fzH+gcFMHjeSxHz+lqlQUKlKDdp3ev+ldJ089jPLFn/He117MGfBDwQEBfPluFF6y5HrVy8ze/oUmjRrwZwFP1Kzdh2+njxeq4zLyMggqmx5evTu91KaCvKq0m754m/54/dTjBz9JZO+nocsIY4ZX40rkbZdW9fy0+4t9B38KVNmL8bC0pJp44cXqe3U8cOsXrKAd7t8wLR5y/APDGHa+OFa2nLZt3MjkhIpysMYyzhj12bMdT6o/W3/7i30HTySr2b/iKWlFVOL9bcjrFryLR269ObreUvxDwxhagF/CwoJ58NhY5jz3VrGTJqNSqXiq/GfoMzJMUjXv6EMNkafM+Y6XyAoiGi8lwINGjRgyJAhDBkyBAcHB1xdXRk3bpzmzZVCoWDkyJF4e3tjY2NDzZo1+eWXXzThV6xYgaOjI7t27SIqKgoLCwsePnz4t3V5dulCzK6dxO3dQ/r9e9yb8TVKRQZurd/Wae/avAVPV64k8fQpFE+fErN9G/JTp/Hs0vWl49THju1beat5C5o0a46fnz+DhnyMhYUFhw4e0Gm/a+d2qlStTvt3O+Hr58/7PXoRHBzCnt07tezi4+L44buFjPh0NKYmLzcQJVdb02Zv4efnz+Bite2gatXqdHi3E75+fnTXaNulZRcXF8cP3y1i5Kefv7Q2lUrFnp1beLdzd2rUrktAYDBDR4wmISGO30+f1Btu9/bNNG3eisZNW+DrF8CAIcOxsLTk6MF9AKSmpnDk4D569R1E+YpVCA4NZ8iwUdy4dpkb16/86/Vt3bGTFm81o3nTxvj7+fLx4IFYWFhw4JDuHozwsFD6f9CLhvXrYWamO69KGqc+fHp259nmbURv30nanbvcmjgFZUYGHu3f0Wnv3qYVD39cSsLxk2Q8fsKzDZtJOH4Sn149NDbxvxwn4fhJ0h88JP3+Q+7P+5actDTsK5YvkbbYA8e5OWEu0TsPG2Tv3/890u895tpn00m5fpcHi9byfOsBAj/upbEJHNabR0s38XjlNlKu3eHSoAnkpGXg26tDibRBrr9t1vK3j0aMQZYQX4y/baJJ89Y0atryhb+NwMLSkiMv/A2g9Tsdad+pG2ERhXttDWHn9s00a96Sxs3UPj1wyCdYWFhw5OB+3Zp2bqNK1Rq0e/c9fP386dbjA4KCQ9m3e4fGpmHjZnTu2oMKlau+lKb8vKq0S01N4ejBffTqO1hzrw4e9jk3rl3mpoFliUqlYv/OTbTr3JNqterhHxjC4OHjkCXE8cfpE3rD7d2xkUZvvU2Dpq3w8Quk7+BPMbew4JdDe7Ts7t+9yd7tG/hw2BiD9BTUZoxlnLFrM+Y6X6VSsW/nZtp37kF1jb+NRZYQz9ki/W0Djd96m4Za/mbJz/n8rUnztkSVq0QZd0+CQsLp3L0f8bExxMQ8N0jbv6MMNj6fM+Y6/9+AUqkqteP/EdF4LyVWrlyJqakpv//+O/PmzWPOnDksWbIEgCFDhnD69Gk2bNjAxYsX6dixI82bN+fWrVua8GlpaUyfPp0lS5Zw5coVypQp87f0SExNsQmPIOns73knVSoSz57FrpzuB3iJuTnKAm9KlYoM7CpWfOk4dZGVlcXt2zepWKmK5pxUKqVSpSrcuK57+P3161epVLmK1rnKVatx/fq1PK1KJXNmTad9h474+wcYrKewtltUqlS5gLbKWtcqrK2y1rkqr0AbQPTzZ8hlCVSslPfwbmNjS2h4lN60y8rK4s7tG1TIF0YqlVKhUlVNmLu3b5Kdna0Vr4+vP65u7ty8ZviUCGPUl5WVxc3bd6hSqYJW/FUqVeTq9RsG/7ZXEafEzBS7spHITp/JO6lSITt9Bvt8cedHam6OUlHgPs1Q4FC1sk57pFLcWr6FibUVSecvGqztZXCsVYm4o6e1zsUeOolTrUoASMzMcKhSlrgjeUP8UamIO3oKx1p69BdBrr9VKORvkXof8NT+dlOnvxnauCwOfdeomM+nC3Lj+lUqFCrjqpfo5VlJeFVpl3uvVtBxr964ZthviYl+ilwWT/lK1TTnrG1sCQmP4uZ13dNJsrOyuHf7BuUrVdfSVr5SNa0wiowMFsz8kg8GjsDRqeQ9tcZYxhm7NmOu8yG/v+X5Tq6/3SrC3+7evqnlo7n+dkvP/ZORkc4vh/dRxt0TV9e/94ynj9Iqg43J54y5zhcIdCHmvJcSvr6+fPPNN0gkEsLDw7l06RLffPMNb731FsuXL+fhw4d4eannn44cOZKffvqJ5cuXM3XqVEBdMCxatIiKLxrKulAoFCgKPLRnKpWYSwu/szF1dERiakpWQoLW+ayEBKz8dc+1SjzzGx7vdSXpr/MonjzGvlp1nBo0RPIi/peJUxdJSYkolUqcCgxNcnR04vGjRzrDyGUyHB0dC9nLZXlatm7eiNREyttt2xmspbC2JJRKJY4l0CaTyXB0LGjvqKVty+aNmJiY0KbtOy+tDdDE6VBgzpejoxMyWYKuICS/SG9Hx8Jhnjx6+OI3JGBqalZoTq+jk/54/y36EpOS1f5WwH+cHB149Phxsb/pVcZp5uikvqfi47XOZ8XHYx0YoDNMwsnT+PTqTuIff5L+8BFOtWvi2rQREhMTLTub0BAqr1+F1MKcnLR0rnw0nLQ7dw3W9jJYuLuiiI7TOqeIjsPMwQ6ppQVmTg5ITU1RxMQXsInHJjyoxNfL9beCcyAdCpQN+VH7W06he9Yhn7/9XTQ+7VT4Go/1XEMuS9CpSSbTPaz47/Kq0k4ui9d7r+qLV582B8eC2pyRy+N1BSEpSY5SmaMzzJPHeWm+asl8wiLLUa1WPYO06NVmRGWcsWsz5jpffa1cfyvs13K57t+X9OJe0OVvTx8/0Dp3YO821i7/DkVGOl4+fnwxZS6mZmZ/S7M+SqsMNiafM+Y6XyDQhWi8lxK1atVCIsmbQVe7dm1mz57NpUuXyMnJISxMe0ELhUKBS775Webm5lSooLunLZdp06bx5Zfa8576eHvRz7foRUEM5cE3cwj8fAwVN2wElYqMJ0+I27sHt9at/5H4XyW3b91k167tzJ2/SCsfjAG1th3Mewltx34+xA/fztZ8/mLi1/+0vL+Fsev7L3Jn6gzCJo2n+t7toFKR/ugxz7fvwqN9Wy27tPv3+aN9Z0xtbXF7qwnh0yZxoUffV96Af5UcL+BvY4S/GYwxp93Jnw+weOFMzedRE2YWYf3y/HHmBFcunOPr+csNDmPMZZwxa3vV/J06/8TPB7X87fMJM/5peVrUa9CMCpWqI5PFs2fbeuZ+PY5JM7/D3NzilV73VfD/7HP/T4gF614vovFuZKSkpGBiYsK5c+cwKdAzZmtrq/nbysqq2Apo9OjRDB8+XOvchaaNddpmy+WosrMxc9Z+s2nm7ExWvO63ltlyObc+/wyJuTmmDg5kxcbiO2gwGU+evnScurC3d0AqlRbqUZLLZTjpWaTL0ckJeYFFQeRymabX6MqVyyTK5XzQs5vme6VSybIlP7BrxzaWrlhjoDZ7pFIpcp3adC/m4uTkVGjRHblcXkhb7wLali75kZ07trNsxWq9emrUrKO1OnVWVhYAibIEnJ3zXv7I5TICg3SvOm33Ir0L9iDkTz8nJ2eys7NITUnWetMtl8lwKmJlV2PXB+Bgb6f2twL+I5MnFuoJMpR/Ks4suUx9TxVYaMnMxYXMuDjdYWQyrnz0CRJzc8wcHcmMiSFwxMdkPH6iZafKyibjobpXK+XqNezKl8W7e1duTZxisL6SooiOw8LdVeuchbsrWYnJKDMUZMbJUGZnY1HGpYCNC4rnun9vfqrXrKO1GnGuv8llCTjl87dEuYyAIv3NpNA9m5jP3/4uGp+WFb6GvnLE0clZp6aX9dGCvK60c3Ry0Xuv6kvfqjXrEhJeNp+2zBfxJuDknOdPifIE/ANDdcZhb++IVGpCYoFyJFGekFcWXzhH9PMnfNC5uZbNnGlfEBFVkQlff1soXmMu44xZW36Mrc6vVrMuoflWhM/zN1kBf5MREKg73exf3Au6/U27fLO2scXaxhZPb1/CwsvywXstOHv6OHXqN9Wr8WV51WXwv8HnjLnOFwh0Iea8lxJnzpzR+vzbb78RGhpK5cqVycnJISYmhpCQEK3Dw8OjRNewsLDA3t5e69A1ZB5AlZ1N6o3r2FfLm8OFRIJDteokXy56OxBVZiZZsbFITExwbtgQ2YnjfzvO/JiZmRESEsbFC39pzimVSi6c/4twPYtDRUREceH8X1rnzv/1JxER6kqkYaMmLFj4A/O//V5zOLu40K5DR76cMq2E2kK5cOF8AW3nNdfSpe18AW1/FdL2PfO//U5zOLu40L5DRyZNmVqkHitrazy9fDSHr18Ajk7OXLzwp8YmLS2VWzeu6k07MzMzgkPCuXg+L4xSqeTi+XOaMEEhYZiammrF++TxQ+JiowmL1L9gl7Hry40/LCSYvy7kzfdWKpX8deEiURHhRYR89XGqsrJJvnINp1r5thWUSHCqVaPY+emqzEwyY2KQmJri1rQx8Ud+KfpiEilSc3ODtb0M8t/O49KoltY518ZvIPvtPACqrCwS/7yCa6Pa+XRJcGlYG/lv2veQLvT526VC/naN8IiyOuNQ+1sYl86f05xT+9ufhOkJU1Jyr5HfX3Ovoe8+CI+I0roHAM7/9Yfe31FSXlfaFXWvhkfqjtfK2gYPLx/N4eMXiKOTC5fzXSctLZXbN64SFlFOZxymZmYEhoRz+cIfWtouXzinCdO2Y3dmLFjF9PkrNAdAj75DGahn8TpjLuOMWVvBaxhTnW9lba3T3y6dz/OdXH8LLcLfgkLCuHRB+164fOEcoUXcs6oX/3Ibvf80pVUGG5PPGXOd/29BpVSV2vH/iOh5LyUePnzI8OHDGTBgAH/++ScLFixg9uzZhIWF0a1bN3r06MHs2bOpXLkysbGxHDlyhAoVKtCqVatXpunZ+vUEjxtP6vVrpFy5isd77yG1tCR2j3ol1KDxE8iKjeXRd4sAsIkqi7mbG2m3bmLuVgbvvn1BIuXZmtUGx2ko77TrwDdzZhASGkZYWDg7d24nQ5FBk6ZvATBn1nRcXFzp2bsPAG3atmP0qBFs37aZatVrcuLYL9y+dZMhHw0D0LzMyI+piSlOTs74+BS9T6tubTMJDQ0lLCyCnTu3aWmbPWsGLi4u9NJoe4fPR41k27YtVK9eg+MabR8Xo82pxNokEgmt277Llg2r8fTywd3Dk/Wrl+Ls7EqN2nlbwUwYM5yatevS8m31frdvt+vIgjnTCAkNJzQskt07t6DIyKBR0xaAeoGZxs1asnzxImxt7bG2tmbJ9/MJjyhbosaDserr8E5bZnwzj7DQEMLDQtm+czcZGRm81UQ9cmX67Lm4urjQp1d3QN2b8ODFXMys7Gzi4hO4ffcuVpZWeHt5GhSnoTxeuZqIaZNJvnyV5EuX8e7RDamVFc+3q1dVDv96MpnRMdz7ZgEAdhXKYeFehpRrN7BwL4P/4A9BKuXh0hWaOAM/+YiEE7+S8fQ5pjbWlGndAsca1bjUb1CJtJnYWGMT4qf5bB3og33FCDITEsl49IzwKcOx9HbnQu9RADz4cQP+g7oRMe1THq3YimvDWnh2bMHZNgM0cdybu5yKy6YjP3eZxLMXCRjaE1MbKx6t3FYibZDrbx3ZsmEVnl4+lPHwYP3qZTg5u2j528Qxn1Cjdr18/taJBXOmERwaQWhYBHt2bkGRka7xNwBZQjxyWQLPn6lHNDy4fxcrK2tcy7hjZ6d9P+uibbuOzJvz9QufjmD3zq1kKDJo3FTd6zt31jRcXFzp/mLbt7fbtueLUZ+wY9smqlWvxYljR7lz6yaDPhqhiTM5OYnYmBgSEtQ9ZE8fq33UyclZb4/+6047GxtbGjVryYrFC7G1tcPa2oal388jPKKswS9HJBIJLdp2YvvGlXh4+1DG3YtNaxbj5OxKtdp5c9UnjxlK9dpv0vztdwFo9U5nvvvmK4JCIwgJi2Lfzk0oMjKo30Rdzzo6uehcpM7VzZ0yHl4lSDfjK+OMXZsx1/kSiYSWbTuyfeNKPL19KePuycY1S3BydqG6lr99/MLf1Kuyt3rnPRZ98xXBoREEh0W+8Ld0Grzwt+jnTzh1/CgVq1TH3t6R+PhYdm5eg7m5BZWr1dappSD/jjLY+HzOmOt8gaAgovFeSvTo0YP09HRq1KiBiYkJH3/8Mf379wdg+fLlTJkyhREjRvDkyRNcXV2pVasWrV/xXPKEI4cxc3LEp29/zFxcSLt1k+ufDCP7xWIfFu7ukG8/eamFOb4DPsTCy4uc9HTkp09x58uJ5KSkGBynodSr34DEJDlrV69EJpMRFBTMl5OmaoYfxcbGIJHmTSOIjCrLyM9Gs2bVClatWI6XtzdfjJuIf0Dg30kinbxZvwGJSYmsWb3qhbYgJk36SkubtIC2Tz8bzWqNNi++GDeRgFegDaDdu11QZGTw/YJZpKamEBlVnnGTZ2jNn3v+7AlJSYmaz3XfbERSopz1a5YjlyUQGBTCuEkztIax9u43GIlEysyp48nKyqJSler0HzTsP6GvwZt1kScmsnLNemQyGcFBgUydNAEnJ0cAYmJjtfwtPiGBgUPzpqhs3raDzdt2UKFcWWZ//ZVBcRpK7P6DmDk5ETB0IOaurqRcu8Gl/oM0U1EsPT0h39toqYUFAUMHY+XrQ05aGvHHT3J91FhykpM1NmYuzkR8PQVzN1eyk1NIvXmTS/0GITv1W4m0OVQtR+0jeS/vomapeycfrdrGxT6jsfB0w8rXU/N9+v3HnG0zgKjZown4qAcZj59zacBY4g7lbRn0bPN+zN2cCZswFAsPN5IuXOP31n3JjNG9EFlxvPNuFzIy0jX+FhFVnnGTZxbwt6ck5/O3Om82IjFRzoY1yzT+NnbSTC1/O7h/F5vWrdB8HjdqKACDh32u1cjXR936DUlMkrN+9XJkMhmBQcFMmDRdcw11GZc3cioiqhzDP/uCtauWsWbFUry8vfl83CStMu73306x4Ju8+bmzpk8GoHPXHnR5v5eBKZbHq0q73v2GIJVImZXvXu036JMSaWvToRuKjHQWL5hBWmoK4VEV+HzSbC1t0c+faGl7480mJCXK2bxmCXJZAv5BoXw+afY/Nh0iF2Ms44xdmzHX+ZDrbxn8qPG38ozW6W9yzec33mxMUqKcTS/8LSAohNH5/M3MzILrVy6wf9cmUlKScXR0JqJsRSbP/L7Q4nj6+DeUwcboc8Zc5wsEBZGoxCoDr50GDRpQqVIl5s6d+9qvfaZ2zdd+zZLgtGZzaUvQiwTjvVUyVa92ePN/FTuVvLQl6OVemy6lLaFIUm6nl7YEvfhfO1baEvRiIskpbQl6yVGZFG9USmSpjLevwVzyaoY0/z9gZsRpl6a0Km0JenkSVbd4o1Ii4NovpS1BL8Zc5/uF6p5q+W/ggy9jSu3ayya8mm0UjRkx510gEAgEAoFAIBAIBAIjx3hfZQsEAoFAIBAIBAKBwGhRikHcrxXReC8Ffvnll9KWIBAIBAKBQCAQCASCfxGi8S4QCAQCgUAgEAgEghLz/7plW2kh5rwLBAKBQCAQCAQCgUBg5IjGu0AgEAgEAoFAIBAIBEaOGDYvEAgEAoFAIBAIBIISI3Ydf72InneBQCAQCAQCgUAgEAiMHNHzLhAIBAKBQCAQCASCEqMUC9a9VkTPu0AgEAgEAoFAIBAIBEaOaLwLBAKBQCAQCAQCgUBg5Ihh8/9n2KzeWdoSikShkpS2BL2YkFPaEv6VqDDePE2SOJW2BL247P6ptCUUiSvGO0zOeJVBjsqktCXoxZjvVTNJdmlL0IvSyPtBpChLW4JejDntTCTGm24B134pbQl6uR/ZoLQl6MX/2rHSlvCfROzz/nox3lJTIBAIBAKBQCAQCAQCASB63gUCgUAgEAgEAoFA8BKIreJeL6LnXSAQCAQCgUAgEAgEAiNH9LwLBAKBQCAQCAQCgaDEqJTGuz7EfxHR8y4QCAQCgUAgEAgEAoGRIxrvAoFAIBAIBAKBQCAQGDli2LxAIBAIBAKBQCAQCEqMUmwV91oRPe8CgUAgEAgEAoFAIBAYOaLnXSAQCAQCgUAgEAgEJUZsFfd6ET3vAoFAIBAIBAKBQCAQGDmi512ghUqlYsOaZRw+sIe01BTCI8vTf/BwvLx9igy3f892dm7dgFyWQEBgMH0+/JjQ8EjN9wf37+LksSPcvX2T9PQ0Vm3cg42tXYm1bVyzjMMHdmtp8/T2LUbbNna90Oav0RYFQHJyEpvWLOPCX2eJi43G3sGR6rXq8V73PtjY2Bqsbd+e7ezYulHz+/t+OJSwfL+/IL+e+IX1a5YRE/0cTy8fevTuT9XqtTTfn/71OAf27+bO7ZukJCcxZ/5iAoNDDNaj/fu3s0OTNyH0/XCoVt4U5NSJX1i/ZqlGW/feA7S0qX1kOYde+EhEZDmDfEQXxuxvxq7P+LWV3EeK89XMTAUrlnzHyeNHyc7KpFKVGvQfNAxHJ+cSavvn0y0zU8HKJYteaMuiYpXq9B/0yX9K2z9d/gIc2r+LE8cOc++Fv63cuPc/ea8aa9oZ8726d/cOdmzdhOxF3vQf+BFh4RF67X89cYy1q5cTE/0cLy8fenzQj2rVawKQnZ3N2lXLOHf2d54/f4a1jQ0VK1WhR+++uLi4GqwpF2N+HjHWPHWuW42gEX1wqFIOS68y/NFhENG7jhQd5s0aRM36HNuoUDIePeP2tO94vGq7lo3/wK4EDe+DhYcbSRevc2XYZBLPXjJIU0GMuRwRCHIRPe8CLXZsWc++3dsYMHgE0+Z8j6WlJZPHjSQzU6E3zK/Hj7Ji8UI6de3JzPmL8Q8MZvK4kSTKZRqbTIWCSlVq0L7T+39D2zr27d5K/8EjmDrnBywM0naElYsX0rFrL2bMX0JAYAhT8mmTxceRkBBHjz6DmLNoJYM/Gc35c2f4bt50g3WdPH6U5Yu/o3PXnsye/yMBgcFMGvcZ8ny/Pz/Xr15mzozJNG7WktnzF1Ozdl2+njKOB/fvaWwUigwio8rRo3d/g3Xo17aITl17MWv+4hfaPi1G2yQaN2vF7PlLqFG7LtOnjOXB/bsam+1b1rN391Y+HDycr+d8h4WlFZPHfVpkPujDmP3N2PUZs7aX8RFDfHX54oX88fspPh09kclfzyMhIY7pX40vkbZXlW7LF3/LH7+fYuToL5n09TxkCXHM+Grcf0jbP1/+grqsq/yfv1eNN+2M9V49cexnli3+ns5dezBnwfcEBgUzcdwovXXXtatXmDV9Ck2ateCbBT9Qs3Ydpk0er6lXFYoM7ty+Racu7zNnwfeMHjuRJ48f8dWXJbsPcjHW5xEw3jw1sbEm6eINLg/90iB7qwAfqu/6gfhfznCyWlvuLVhJ+R+m4Nq0rsbGs2MLImeO5taUhZys0Y7ki9epuXcp5m6GvyTKjzGXI8aMSqkqteP/EdF4LyUaNGjARx99xLBhw3BycsLd3Z3FixeTmppK7969sbOzIyQkhP379wOQk5NDnz59CAwMxMrKivDwcObNm/ePalKpVOzZuZl3O3enRu26BAQG89GIMcgS4vn99Em94XZv30ST5q1p1LQlvn4BDBgyAgtLS44c3Kexaf1OR9p36kZYRJTeeIrTtnfnZjp07k6N2vVeaPuixNr6v9B29OBeAPwCgvj0iylUq1kHD09vylesSpce/fjjzClycrIN0rZr+2aaNm9F46Yt8PUL4MMhw1/8/v067ffs2krlqjVo1+E9fP386dr9A4KCQ9m3J+9tcoNGzejctScVK1UtQSrp+v3a2ga80HY0X97o0vZOh/fw8fOna/c+BAaHsv+FNrWPbNHykaEjRpOQEFdkPujCmP3N2PUZv7aS+0hxvpqamsKRg/vo1XcQ5StWITg0nCHDRnHj2mVuXL9SAm3/fLqlpqZw9OA+evUdrNE2eNjn3Lh2mZv/EW2vovwFaP1OJ9p1ep/QiLIGadGnz5jvB2NNO2O+V3du30Kz5i1p0qw5fn4BDBwyDAsLCw4f/Em3pp3bqFK1Ou3f7Yyvnz/devQmKDiUvbt3AGBjY8ukqTOp+2YDfHx8CY+IYsCgj7hz+yaxMdElTjdjfR4x5jyNPXCcmxPmEr3zsEH2/v3fI/3eY659Np2U63d5sGgtz7ceIPDjXhqbwGG9ebR0E49XbiPl2h0uDZpATloGvr06GHSN/BhzOSIQ5Ec03kuRlStX4urqyu+//85HH33EwIED6dixI2+88QZ//vknzZo1o3v37qSlpaFUKvHx8WHz5s1cvXqV8ePHM2bMGDZt2vSP6Yl+/gy5LIEK+RqMNja2hIZH6i2cs7KyuHP7plYYqVRKhUpVDX4wNIQYjbZqhbTdvH5Zr7a7t29qhZFKpZSvVLXIyiYtLRVra2tMTIqfVZL7+ysW+v1V9F7jxvWrhRrllapU/0fTK0/bDZ15c+P6VZ1hbl6/omUPULlKDY19ro9ULOQjUXrj1Icx+5ux6/s3aCuJjxjiq3dv3yQ7O1srXh9ff1zd3Ll5zTDfe1Xplqutgg5tN64ZlrbGrO11lr8vgzHfD8acdsZ6r+bVq1W0rlGxUhW9um5cv0rFygXqrqrViqyXUlNTkUgk2NgaPiQdjPd5BIw3T18Gx1qViDt6Wutc7KGTONWqBIDEzAyHKmWJO3Iqz0ClIu7oKRxrVS7x9Yy5HDF2RM/760U03kuRihUrMnbsWEJDQxk9ejSWlpa4urrSr18/QkNDGT9+PPHx8Vy8eBEzMzO+/PJLqlWrRmBgIN26daN3795FNt4VCgVJSUlaR6ZC/9AfuSwBoND8JQdHJ813BUlOSkSpzMHR0cngMC+DTBb/QlvB6zgXq82hgDbHIsIkJcrZsn4lTZq3MUiX+hpKHdfQ//vlsoRC6eXo6IRMpns44MuSq83RUTs/i9emP/9z/3dwKhynrIT5bcz+Zuz6/g3aSuIjhviqTJaAqalZoXmCjk6G+96rSje5LF6vNkPT1pi1va7y92Ux5vvBmNPOWO/VpNxrOOmoJxNKWq/qts/MzGTV8sXUq98Ia2ubYjXlx1ifR8B48/RlsHB3RREdp3VOER2HmYMdUksLzF2dkJqaooiJL2ATj4VHydcxMOZyRCDIj1iwrhSpUKGC5m8TExNcXFwoX7685py7uzsAMTExACxcuJBly5bx8OFD0tPTyczMpFKlSnrjnzZtGl9+qT23aOBHIxg0dCQAx38+xA/fztZ8N2bi13/7N/1THP/5ID/m0zZ6YsnmfL0MaWmpTJ04Ch+/ADp16/3Kr/f/hjH7Gxi3PmPWdqyAti+MSJsxp5txa3v95W9JEGn3chjzvfo6yc7OZsa0SahUKgYO+bhYe2N+HhF5+vIYcznyb0OpUpa2hP8rROO9FDEzM9P6LJFItM5JJBIAlEolGzZsYOTIkcyePZvatWtjZ2fHzJkzOXPmjN74R48ezfDhw7XO3X6U17tbvWYdrdUws7KyAPXbRydnF835RLmMgCDdK53b2TsglZoUWkQmUS4r0aqyBales67WqrrZGm0ynJzz3qgmyhOK1ZZYQJtcnlBIW3paGlPGjcTKyprPxk7B1NSwW0N9DamOa+j//Y5OzoXSSy6X4VTgLf7fJVebXK799rd4bdr2+fMy9/9EWQLO+XxELpcRqCcfcjFmfzN2fcasrUbNOlo7K+RqK4mPGOKrTk7OZGdnkZqSrNX7I5fJcNKj/3Wlm6OTi15t+tLWuLW93vK3pBjz/WDMaWfM92p+7HOvIdNRTzqXtF7Vts9tuMfGRDN52iyDet2N+Xnk35KnL4MiOg4Ld+0edAt3V7ISk1FmKMiMk6HMzsaijEsBGxcUz7V77HVhzOWIQFAUYtj8v4Rff/2VN954g0GDBlG5cmVCQkK4c+dOkWEsLCywt7fXOswtLDTfW1lb4+nlozl8/QJwdHLm0oU/NTZpaancunGNcD0L4piZmREcEsal8+c055RKJRfP/0nY31iAqKA2H422vOvkaguLKKdXW5AObZfO/6n1e9LSUpk8bgSmZmZ8Pn4a5uYWuqLTe43gkDAuns9LM13XyE94RBQX86UxwIW/zv2t9NKvLbyQtovnzxGuZ9GUsIiyWvmv1vaHxt7dwxNHJ2ct/ep8uKo3zlyM2d+MXd+/UVtJfMQQXw0KCcPU1FQr3iePHxIXG01YpO54X1e6FaUtPFJ3vP8mba+y/H0Z/k33gzGlnTHfq4WvEcbFC38VuMZfenWFR0RpaQI4/5d2XZfbcH/29AmTps7E3t6hWC1g3M8j/5Y8fRnkv53HpVEtrXOujd9A9tt5AFRZWST+eQXXRrXzDCQSXBrWRv7bXxSHMZcjAkFRiJ73fwmhoaGsWrWKAwcOEBgYyOrVqzl79iyBgYH/2DUkEgmt23Zky4ZVeHr5UMbDg/Wrl+Hk7EKN2nlbc0wc8wk1atej5dvtAXi7XScWzJlGcGgEoWER7Nm5BUVGOo2attCEkSXEI5cl8PzZEwAe3L+LlZU1rmXcsbOzN0hbq7Yd2arR5smG1Ut1aBtGzdr1aPF2B422b+dMIzg0nJCwSPbu3IwiI52GTVsCLyrKsSNQKDL4bORY0tJSSUtLBcDewRETE5NitbVp15H5c74mODSM0LBI9uzcQkZGBo2bNgdg3uypOLu40b1XPwBat+nA2M+HsXPbJqpWr8XJ40e5c/sGAz8aoYkzOTmJuJgYEhLUb4+fPHkIqHsX9PU86OLtdh1ZMGcaIaHhhIZFsnvnFhQZGZq8mTd7Ki4urrzfq79G27jPP2bnto1a2j58oU3tI++yZcNqPL18cPfwZP3qpTg7u2rlgyEYs78Zuz7j11a8j0wYM5yatevm01a0r9rY2NK4WUuWL16Era091tbWLPl+PuERZQ1u0LyqdLOxsaVRs5asWLwQW1s7rK1tWPr9PMIjyhr8AGfs2l5F+Qv/H/eqsaadMd+rbdu9y7w50wkJDSM0LILdO7eSocigSdO3APhm1te4uLjSo3dftaa27fli1Cfs2LaJatVrceLYz9y5dZPBH6lHH2ZnZzN96pfcuX2LcRO/Qpmj1Myft7WzKzQSsrh0M9bnEWPOUxMba2xC/DSfrQN9sK8YQWZCIhmPnhE+ZTiW3u5c6D0KgAc/bsB/UDcipn3KoxVbcW1YC8+OLTjbZoAmjntzl1Nx2XTk5y6TePYiAUN7YmpjxaOV2wzSVDjtjLMcMXb+XxeOKy1E4/1fwoABA/jrr7/o3LkzEomELl26MGjQIM1Wcv8U77zbhYyMdL5fMIvU1BQiosozbvJMrbe/z589JTkpUfO5zpuNSEyUs2HNMuSyBAKDQhg7aabWkKGD+3exad0Kzedxo4YCMHjY51oFXNHauqLIyOCHfNrGTp6lpS362VOStLQ1JimftoCgEL6YNEuj7e7tm9y6oV4pdUjfLlrXW7RsI2XcPYvVVffNRiQlJrJhzQpksgQCg4IZP2m65hqxsTFIJHmDXCKiyvHJp2NZt3oZa1YuwdPbm8/HTsY/IO9FzNnfTrFgbt68utnTJwPQuWtP3uvWy6D0ytMmZ/2a5Zq8GTdphkZbXGw00hfTM/K0jWPd6qWsfaFt1Ngp+AcEaWzavdsFRUaGxkcio8ozbvKMEo1YyMWY/c3Y9RmzNkN85PmzJ1r3anG+CtC732AkEikzp44nKyuLSlWq03/QMIM05fKq0q13vyFIJVJm5dPWb9An/yFt/3z5C3Bw/0425/O38aM+AmDwsNE0/M/cq8abdsZ6r9ar35CkpETWrV6BTCYjMCiYCZO+zld3xSCV5tVdkVFlGfHZF6xZtYzVK5bh5e3N6HGTNPVqfHwcv/+mXpV82JD+Wtea8vVsyleoZLA2MN7nETDePHWoWo7aR1ZrPkfNGgPAo1XbuNhnNBaeblj55v3G9PuPOdtmAFGzRxPwUQ8yHj/n0oCxxB3K27bt2eb9mLs5EzZhKBYebiRduMbvrfuSWWARO0Mx5nJE8HpJSEjgo48+Yvfu3UilUjp06MC8efOw1bM7xf379/V2qG7atImOHTsCeVOi87N+/Xree+89g7VJVCqVeF3yf8Tl289LW0KRqCjs1MaCCTmlLUEvxpxuxqxN8PJIMN6qQ/jcy2HM6Sb87eWRYryLSZlIjLdezVIZ3hv/ujHmPL0f2aC0JejF/9qx0pagl3IhHqUt4aV5Z9DNUrv2jkVhryTeFi1a8OzZM3744QeysrLo3bs31atXZ926dTrtc3JyiI2N1Tr3448/MnPmTJ49e6Zp9EskEpYvX07z5s01do6OjlhaWhqsTfS8CwQCgUAgEAgEAoHg/55r167x008/cfbsWapVqwbAggULaNmyJbNmzcLLy6tQGBMTEzw8tF/AbN++nU6dOhXqrXd0dCxkWxLEgnUCgUAgEAgEAoFAIPhXoVAoSEpK0joUCsXfivP06dM4OjpqGu4ATZo0QSqVFrnLV37OnTvH+fPn6dOnT6HvBg8ejKurKzVq1GDZsmWUdBC8aLwLBAKBQCAQCAQCgaDEqFSqUjumTZuGg4OD1jFt2rS/9XueP39OmTJltM6Zmpri7OzM8+eGTT9eunQpkZGRvPHGG1rnJ02axKZNmzh06BAdOnRg0KBBLFiwoET6xLB5gUAgEAgEAoFAIBD8qxg9ejTDhw/XOmdhoXsB5c8//5zp06fr/C6Xa9eu/W1N6enprFu3jnHjxhX6Lv+5ypUrk5qaysyZMxk6dKjB8YvGu0AgEAgEAoFAIBAISoxSWXoLKFpYWOhtrBdkxIgR9OrVq0iboKAgPDw8iImJ0TqfnZ1NQkKCQXPVt2zZQlpaGj169CjWtmbNmkyePBmFQmHw7xCNd4FAIBAIBAKBQCAQ/Gdxc3PDzc2tWLvatWsjl8s5d+4cVatWBeDo0aMolUpq1qxZbPilS5fSpk0bg651/vx5nJycDG64g2i8CwQCgUAgEAgEAoHgJVApjXcrz5chMjKS5s2b069fP77//nuysrIYMmQI7733nmal+SdPntC4cWNWrVpFjRo1NGFv377N8ePH2bdvX6F4d+/eTXR0NLVq1cLS0pJDhw4xdepURo4cWSJ9ovEuEAgEAoFAIBAIBAIBsHbtWoYMGULjxo2RSqV06NCB+fPna77Pysrixo0bpKWlaYVbtmwZPj4+NGvWrFCcZmZmLFy4kE8++QSVSkVISAhz5syhX79+JdImUZV0fXrBv5rLtw1bJbG0UCEpbQl6MSGntCXoxZjTzZi1CV4eCcZbdQifezmMOd2Ev708UkpvPmpxmEiMt17NUpmVtgS9GHOe3o9sUNoS9OJ/7VhpS9BLuZCX3/e7tGnd72qpXXvP4qhSu3ZpIXreBQKBQCAQCAQCgUBQYlQq432Z9F9ENN7/zwi5uLG0JRTJHxF9S1uCXspYxJe2BL0oVIYvdPG6MeYesweR9Utbgl4aHPt7+5S+auLdIktbgl4ScC1tCXqxM0kubQl6Sc6xK20JejlwwaW0JeilXtnU0pZQJNamGaUtQS9Bj46WtgS9rFd1K20Jemni+/e3s3pVGHPvtjHX+eWybpS2BMG/BNF4FwgEAoFAIBAIBAJBifmvLVhn7EhLW4BAIBAIBAKBQCAQCASCohGNd4FAIBAIBAKBQCAQCIwcMWxeIBAIBAKBQCAQCAQlRgybf72InneBQCAQCAQCgUAgEAiMHNHzLhAIBAKBQCAQCASCEqMUW8W9VkTPu0AgEAgEAoFAIBAIBEaO6HkXCAQCgUAgEAgEAkGJEXPeXy+i510gEAgEAoFAIBAIBAIjRzTejZQGDRowbNiwIm0CAgKYO3fua9EjEAgEAoFAIBAIBILSQwybN1K2bduGmZnZa7/uhtOXWHn8PHEpaYR5uPB5m3qU93UvNtz+C7f4fMMhGkYFMrd7C502k7f/wpbfr/Jpqzq8X7diibWpVCp2rv+e44e3k5aaTEhERboPGIO7l5/eMDeunOPAjlXcv3ONRFkcgz+fTZWaDbVszp0+wi8HtvLgzjVSUxKZMGc9foHhJdK2d/dOtm3djEyWQGBgMAMGDiYsPEKv/ckTx1izeiUx0c/x8vKm1wd9qVa9pk7bhQvm8tP+vfTtP5C277QvkS5Qp9vGNcs4fGA3aakphEeWp//g4Xh6+xYZbv+ebezaugG5LAH/wGD6fPgxoeFRmu8P7d/FiWOHuXf7JunpaazcuBcbW7uX0rdhzTIOH9ijpc/L26cYfdvZ+UJfgEZfpOb7zEwFK5cs4uTxo2RnZVGxSnX6D/oERyfnYjU5161G0Ig+OFQph6VXGf7oMIjoXUeKDvNmDaJmfY5tVCgZj55xe9p3PF61XcvGf2BXgob3wcLDjaSL17kybDKJZy8Vq6cgG4+cZtX+48QnphDm58Fn3dpQLkh3fm479jt7fv2LO0+eAxAZ4M2QDm9p2ccnJjN/80+cvnKLlLQMKocFMKpbG/w8XEusDWDnnn1s3radBJmc4MAABg/oR0R4mE7b+w8esnLtOm7dvkN0TCwD+31A+7Zt/lacRaFSqdi8dilHDuwmNTWZ8Mjy9B00stj74cCerezetl5zP/Qe8Akh+e6HH7+dweXzf5CQEIelpTXhkeXo2msg3r7+BmvbvXs3W7dsQSaTERgUxMCBAwkP118WnThxgtWrVhEdHf2/9u47rqmrDwP4E/aGMAVkb1CWo6LFyeuoE1u1aoUq1I1a0TrBPeqedeKse++KoqLiQAURFRRBBQeCTNkj5/0DjUSGSGtukN+3n3xqTm5uHrJuzj0LBoaGGDJ4MJo1by6yTWJiIrZu2YLo6GiUlpbC2NgY06ZPh66ubo1zfSDJzx1jDJEhq/Ho1gEUFbyDrokLWvaYAXVt0xrdPyp0E+4EL4N9y0Fo0XWqsDzs6Ay8ir+OvOwUyMopQdfYBU07+0NDx/yLsh3dswGh544gLzcHVrZOGDR8MhpUe+yKwJkjO/E8PgaZGW/hN3kJXFu0/df7rSybpL6mey/dwvbga0jLzoF1Qz1M6tcFjc0MK902JDIGQWeuIjE1HSWlAhjrasLLww3dWjhWuv3cXadw8ModTOjTEb90aFHjTB8wxnD15CpEXT2AwvxsGJq7ouOAmdDUNa3yPpGhuxF5ZQ+y0l4CALT1rdDyh5GwaNQGAJCfm4mrJ1fj2cOryM54DUUVTVg7ecC9x1jIK9b82CrJ37/A1zvmB585jquhIUh4/5tkx76TNf5NIunHfEnHBDRhnThRy7uE0tTUhKrql1eE/o1/7sVhyakwDOvQFHtH94GNvjZGbDmJtJy8au/3MiMby05fg6upfpXbhDxIQHTSG+ioKdc635kj23H+1B4MGjYV0/7cDnl5RSybPQrFRYVV3qeooAANTa3xy9DJVW5TWJgPKztn/OQ1pla5roRewuZNG9B/wC9YsXodzMzNERgwBZmZGZVuH/PwARb/OR8dO3bGytXr0MKtFebNmYnnz55W2Pb6tat49CgGmlpatcoGAEcP7sbpE4cwdJQ/5i/bAHkFBcwJmICiap63sMsh2L5pLfoM+BWLVm2GqZkl5gZMQFa5v6mwsAAurs3Ru+8vtc5Wlm8PTp84jGGj/LFg2Xoo1CjfBWzbtBZ9B3hj8apNMDGzwJxP8m3dtAa3w69hwpRZmL1wJTLS32LRvIAaZZJWVkL2vUe4P2ZWjbZXNG2IZsc3IO3STVxt2hNPV29H4w1zof2/74Xb6PfpArvFUxA3dy2uNvfEu3ux+O5UEOR0Pn8yobyzN+9h2d5TGNqzA3bPHA0rI32MWroF6dk5lW5/JzYBnVs4YuOk37Bt+gjoaWpg5JItSMnIAlD2Q2r86p14kZqO5X6DsHumH/S1+Bi+JAj5hUVflA0ALl2+ig2bt+CX/j9j3cplMDczxZTAWcjIzKx0+8LCQug3aAAfby9o8vn/yT6rc/zQLpw5cRC+oyZg3tKNUFBQxPzA8dW+365dDsGOzWvwY//BWLgyCCZmlpgfOF7k/WZuaYPh46Zi2bpdmDp7KRhjmBf4OwSlpTXKFRoaik0bN2LAwIFYvXo1zM3MEDB9OjKr+BsfPnyIPxcuRMdOnbB6zRq4ublhzpw5ePbsmXCb169eYeKECWhoZIQ///wTf/31F/oPGAA5ObkaZfqUpD53ABB9ZTMeXv8bLXvORPcR+yArq4Sz235DSXHV2T5IfRGNR7f2gd+g4okSLQMHuPeeh97jTqHjr5vAwHB2qy8EgppnO31kO86d3Auv4VMQsGgb5BQUsGyWX7XHrsKCfBiZWeGXYZP+0/1+SlJf07O3H2DpwWAM69YGe6YOhXXDBhi5ehfSs3Mr3V5NSRG+Xdyx448hOBAwDD3dnDFjxzFce/CkwrYXImNx7+kL6KjX/jfWzeBNuHNxJzoNmIlBf+yHrLwi9q/yqfb9pspvgDa9JsB7ymF4Tz4EE5sWOLx+FFJfxQEAcjJTkJOZgnY/TsKQgJPo6rUACQ+v4PTOaTXOJenfv8DXO+YXFRbCuZa/SST5mE/Ip6jy/h9o27Yt/Pz8MG7cOPD5fOjp6WHTpk3Izc3F4MGDoaqqCktLS5w5c0Z4n9DQUDRv3hzy8vLQ19fH5MmTUVJSIrLP8t3mU1JS0L17dygqKsLMzAy7du36z/+OnVei0LuZPXo1tYOFniam92oDBTkZHL0dW+V9SgUCTN13HiM8mqGhplql27zJysHC41cwv9//ICtVu7ccYwznT+5Gtz6+cPmuLYxMreEzdjYy01MRcfNSlfdr3KQVeg8cBdcW7avcpmXbbujRbyjsnSpv+f6co0cOoVPnLvDo2BnGxiYYOXos5OXlcS74bKXbHz92BK5NmqH3T31hZGyCX7x+hYWFJU6eOCayXdrbt9iwbi38J06BjHTtOskwxnDq2AH82G8Qmru5w9TMAn7+05CRnobw61ervN+JI/vh0bkb2v/vBxgZm2LoaH/IKyjgQvAp4TbdevWFZ99fYGXrUKtsH/KdPHYAP/UbhOZu37/PN/WL8w17ny8k+DQAIDc3BxeCT+NX31Fo7OQKCysbjBo3GY9i7uNx7IPP5ko9exmPZ6zAm2Pna/R3mAz9GflPXyDmjz+RE5uA53/tQvKhszAb+6twG7Nxg5EUtB8vth9GTkw8okfOQGleAYx+/bFGj/HBruAr8GzdDD3dm8LcUA/TvHpBQU4Ox67crnT7ecN+Rt/2brAxNoCZvi4CB/cGYwzhD+MBAIlv3iI6PglTvXrBwdwIpvo6mOrVE4VFxfjnRtQXZQOAQ0ePoUunjuj8vw4wMTbC2FEjIC8vj7PnKm/FsLG2wtAhv6JdG3fIylb+Pv/SfVaFMYbTxw6gdz8vNGvhDhMzS4waPx0Z6Wm4df1Klfc7dXQvOnTqjnb/64qGxmbwHTURcvIKuHjupHAbj849Yd/IGbp6+jC3tEG/Qb8hLTUFKSnJNcp25MgRdO7SBR07doSxiQlG+/lBXl4ewcHBlW5/7NgxNGnaFD/99BOMjY3h5eUFCwsLnDhxQrjN9u3b0bRZM/j4+MDC0hL6BgZo0aIFNDQ0avaElSPJzx1jDA/CdsCp7XCY2HeAZgMbtO6zEPnvUpAYU/1nuLgwF6H7J6JVr9mQV6x4DLNt3hcNzJpBlW8IbUMHNPnfWORmvUZOxssaZzt3Yg+69/WB63dtYWRqhd/GzkbGZ45djk1a4ceBI9GkRbtKb6/tfj/dh6S+pjvPX0fvVq7o1dIZFgY6mD6gKxRkZXH0WmSl2zezMUV7F1uY6+vASEcTAzt8BytDPUTGJ4ls9yYjGwv3ncH8IZ6Qka7975HbF3bArcsIWDl5QLehLbr9ugg5WSl4fLfq95ulY3tYNGoDTV1TaOqZoXXP3yEnr4RXT+8CAHQMreE5bDUsHduDr2MME1s3tO4xDvHRFyAoLalyv+VJ8vcv8PWO+QDQrVcf9O47ENa29lXupyqSfMyvC5iAcXapj6jy/h/Zvn07tLW1ER4eDj8/P4wYMQJ9+vRBy5YtERERgY4dO2LQoEHIy8vDy5cv8cMPP6BZs2aIiorCunXrEBQUhLlz51a5/19//RVJSUm4ePEiDh48iL/++gspKSn/Wf7iklLEvEpFC8uP3ZakpHhoYdEQ9xKrPthuCLkNvrIiejer/MtSIGCYtj8Ev7Z2hqVe7c82vn3zElkZb0Uq2ErKqjC3aoT4R/dqvd9/q7i4GE+ePIaTs6uwTEpKCs7OrngU+7DS+8TGPoSzi6tImUuTpoiNjRFeFwgEWLbkT/T+sQ9MTExrnS8l+TUyM9Lh6NxUWKasrAIrGzs8jr1f5d+U8OSxyH2kpKTQ2LkJHtWg4vsl3gjzNamQr6rHKi4uRvyTxyL3kZKSgqNzE2HFPOHJY5SUlIhs09DIBNo6engU89/+DQCg0cIZby9cFylLPXcV/BbOAACerCzUXR3wNuTaxw0Yw9sL16DRwqXGj1NcUoKYZ6/wnYOlsExKSgrf2Vvg3pPEGu2joLAYJaWlUFNWBAAUFZe1hMmV++EmJSUFORkZ3I17VuNsQNlr8/hJPFydP3ZVlZKSgquzEx7GPvqifX2Nfaa8eYXMjDQ0dm4mLFNSVoGljT3iqvg8lLz/PDSu8Hloirgq3qMFBfm4dP40dPX0oa39+e7pxcXFeBIXB2dnZ5HHcHZ2RmxMTKX3iY2JgUu57QGgSZMmwu0FAgFu3boFQ0NDTJ82Df1//hnjxo3DtWvXKtnb50nqcwcA7zJeID/nLQws3IRlcgqq0GnoiJTE6k9AXT8xB0Y2bWBo2fKzj1NclIe4O4ehwm8IZfUGNcqW+uYlsjLS4OD4cTiDkrIKLKwb4cmj2nef/S/2K6mvaXFJKWISX+M7O7Nyj8HDd3ZmuJfw4rP3Z4zhZmwCnr1Jg6vlxyEEAgHD9G1H4f2/lrA0+PJhIx9kvX2B3OxUmNp+fM/IK6rCwMwJr55WfnLhUwJBKR7eOoXiojwYmld9DCjMz4GcggqkanACX9K/f4Gvd8wXN3Ed8wmpDI15/484OTlh+vTpAIApU6Zg4cKF0NbWxm+//QYACAwMxLp163Dv3j2cOHECRkZGWLNmDXg8HmxtbfHq1StMmjQJgYGBkPqkdfrx48c4c+YMwsPD0axZ2UE2KCgIdnZ2qE5hYSEKC0W7IbHiEshXcnY1I68ApQIGLRUlkXItVUU8Ta28+3fEs9c4cjsG+8f0rTLD1ssRkJbiYUDLysed1VRWZhoAQE1d9ASAmoYWsjPf/qt9/xvZ2VkQCATgf9LdTEODjxdJSZXeJzMjo0LLl4YGH5kZ6cLrhw7sg5S0FLr39PxX+TIyyp43jU/yqWtoijxeee+ysyAQlEJd49O/SRMvk2pWQaypDxk+HYeu/snzUVk+DY1P/ya+MF9mRhpkZGQrjHfT4Fe9339DXk8bhW9E34eFb95CVl0VUgrykOWrQ0pGBoUpaZ9skwZlm5qPnc18l4dSgQCaaioi5ZrqqniWnFqjfaw6cAY6GmrCEwCm+jpooKWBNQfPYpq3JxTlZbHrbBjeZGQhNfNdjbMBQFb2u7LPwyfvb76GOpJefP5H99fe54fX/tP3troGH5mZlb8vsoWfh0/fo5p49eK5SNnZU4exa+s6FBbkw6ChMabNXQGZGsxdkp2dXfn3CJ9f5d+YkZFR4XOtwecjI6Ps+zozMxP5+fk4sH8/vLy9MXjIENy5cwfz5s7FwoUL0djxy76TJfW5A4D8d2WfPUUV0eFFCirayM+p+nORcO8U0l49RPcRB6rdf8yN3bh1dilKivKgrm2GzoODIC1Ts6EHwmOXhmg2NXVNZGWkVXYXse1XUl/TjJy8st8jnwyz01JVxrPkqo/37/IL0HHychQXl0JKioep/X+Am72F8PatwWGQlpLCgPbNq9xHTeRkl72nlNVEn3slVS3kZlf/eyT15SPsXPwzSooLISevBM9ha6Gtb1nptnk56bh25i84f9+vRrkk/fsX+HrHfHET1zGfkMpQ5f0/4ljuh5C0tDS0tLTQuHFjYZmeXtmkbykpKYiJiYGbmxt4PJ7w9latWiEnJwcvXryAsbHoZDMxMTGQkZFBkyYfzzra2tp+tuvjggULMGuW6PidaX07YXq/yieU+xK5hUWYtv88ZvRuC/77FrxPPXyZgl1h97DXr6/I31oTN0JPY8f6ecLrY6et+ld565IncY9x/PgRrFj11xc/b5cvBmPjmqXC61Nm/vlfx/tXLl88hw3l8k2duZDDNPXP1lOXcDb8HjZO+g3y739Ey8pIY8noXzB7yyG0HT0b0lJSaG5vgVaNrVHXO6RduRiMTWsXC69PnrHoqz6ee9uOcHRuhoyMNJw8vAcrFgZg9uJ1kJOT/6qPWxnGyl69Fm5u8PQsOwloYWGBmIcPcfr06c9W3iX5uYu/ewJhx2YKr//Pa90XP15O5mvcOLkAnYcEQUa2+tfHwrk7DCxbIv9dKqKvbsXFvb+j69Ddld7veugZbF83X3h93PQVX5zta5Hk1/S/oCwvj33ThiGvsAjhsU+x5GAwDLX5aGZjiofPX2H3hZvYM3XoFx9XH4Qfx9ndM4TXfxq5odYZNfXMMHjqURTmv8OjyLM4tX0SBoz/u0IFvjA/BwfXDoNWAwu06ja61o/HNTrm1w+M0YR14kSV9//IpzPD83g8kbIPBwuBGGdknDJlCsaPHy9Sxs5sqnRbvpICpKV4FSanS3uXD21VpQrbJ6Vl41XGO4zZ8XG8keD9j0XXaetwbPwARDx9jfTcfHT+c4dwm1IBw9LT17Ar7B7OTBpUZXan5m0ww7qR8HpJcTEAIDsrHRqaOsLy7Mw0GH3hzPD/JTU1dUhJSQlbuz7IzMwAX7PyyV80+PwKk1BlZmYIz0Q/eHAfWZmZGOI9UHi7QCDAls0bcPzoYQRt+7vKPM2++15kRvgPz1tmRgb4mh9nDs/KTIepeeVn+1XV1CElJS0yEUxZxvQazdRenWbftRKZHbZYmC8dfM2PrRhZmRmfzffphIBZ5Z5DDb4WSkqKkZvzTqT1PTMj41//DZUpfPMW8nqiM7PL62mjOOsdBAWFKHqbAUFJCeR1tT7ZRguF1bQkfUpDVQnSUlIVJqdLz3oHLbXqJ1/aceYytp4KxfqJPrA2Ep1c0t7UEHtnj8G7vAKUlJSAr6YCrzlrYWda/ey/n1JXUy37PHzy/s7IzKrQqiyOfTb95PNQXFw2AV9W5qefhwyYmlX+flMTfh5EW4WyMtOhwf+k5U1ZBUrKKtA3NIK1jQOG/NwFt65fRqs2/6s2p5qaWuXfIxkZVU4ixefzkVnJ9h+eEzU1NUhLS1c4GWxkZIQHDysf0lOeJD93xnbtoWP08eRDaUlZtvycNCipfewOXZDzFpr6lfdQS3v1AAW5aTi29uP4UyYoRfKz24i5sRves6IgJSUNoKwLvpyCKtS1TaFj5IRdc1vg+cPzsHDqWmG/zs1bw1zk2FWWLTszDRrlnrfsrHQYmdVutm4AUH/f4v4l+5Xk17Q8vopS2e+RTyanS3uXC+1Peh2VJyXFg7Fu2fe7rVEDPE1+iy1nr6KZjSkiniQi/V0uukxdIdy+VMCw7OA57Aq5iTPzx1a5X0vH9jAw/bhCTsn791tudhpU1D++3/LepUG3YdWrzACAtIwc+LplM+43MGmE18+icfvCDnQeOFu4TWFBDvav8YWcvDJ6D18Laema9UCRtO9fQHzHfHET1zGfkMrQmHcO2NnZ4fr168KWEQAICwuDqqoqGjas+GPZ1tYWJSUluHPnjrDs0aNHVc5C/IG8vDzU1NRELpV1mQfKWt/sDHRwM/7jJDwCAcPN+BdwNK44ts9MRwMHx/bDPr++wktbOzM0MzfEPr++aKCugm4uNjgwRnQbHTVleLd2xroh3arNrqioDD19Y+HFwMgc6nxtxNwLF26Tn5eDhLj7sLD5d13y/w1ZWVlYWlrjXtTHcW4CgQBRdyNhU8WkKba29oi6Kzou7m5kBGxtyw5w7dp7YPXaDVi1Zr3woqmlBc8f+2DW3AXV5lFUUoK+QUPhpaGxKTT4moiO+vjeycvLRdyjGFjbNqp0H7KysjC3tEb03Y/3EQgEiL4bAZt/MTldZfmMhPkiKuSr6rFkZWVhUUm+e3cjYP3+PuaW1pCRkcG9cvt9+SIRb1PfwMbu3/0Nlcm8cRda7UWXG9Lu0BIZN+4CAFhxMbIiHkC7/ccxueDxoNXODZk3ajZGEgBkZWRgZ2ognGwOKPvbw2Pi4WhpXOX9tp0OxeYTF7DGfzDszaqukKsqKYCvpoLE5Ld4+PQl2rpUPzSnQj5ZWVhbWiAy6uM8FAKBAJFR92BvW7uTbP9mn4pKSmhg0FB4aWhsBg2+FqLvfpzcLy8vF08ePYRVFZ8HmQ+fhyjR99v9qDvVTtbI3v/34cfq5/5GSysrRN29K/IYd+/ehW0Vw6Ns7exwt9z2ABAZGSncXlZWFtbW1njxSdfWly9f1miZOEl+7mTllaGmZSK8aOhaQlFFG68Sbgi3KSrIQeqLe9A1rnxZUgMLN3iOOYZeow8LL9qGjWDh1A29Rh8WVtyryicorXwlhrJjl5HwUnbs0sLDe7eE2+Tn5SD+8X1Y2jSudB81oaNn+MX7leTXtDxZGWnYGesjPPbjCiwCAUN47FM4mtf8hKKAMeGcHt2+c8SB6cOxb9ow4UVHXRXeHd2wbszAavcjr6ACvq6J8KKtbwllNR08f/RxzHNhfg5ePY2CgdmXjWdmTCA8+fRhP/tX+UBaWhY/jlz32V4h5Una9y8gvmO+uInrmF9XCASMs0t9RC3vHBg5ciRWrFgBPz8/jB49Go8ePcKMGTMwfvz4CuPdAcDGxgadO3fGsGHDsG7dOsjIyGDcuHFQVKy8u3ptDXJ3QsCBC3Aw1EEjI138HXYP+UUl6NWk7EzytP3noaumjLGd3SAvKwOrBqJnFFUVysYAfijXkJGGhrKCyDayUlLQVlGCqc6XnQXm8Xjw6DYAJw9shp6+MbT1DHBk9zpoaOrA9bu2wu0WBw6Da4t26PDDzwCAgvw8pCR/HHv+9s1LJD59BGUVNWjplLU+5rzLQvrbZGSml41jS375DEBZy4Y6//PrXPfy/BHLly2CpZU1rK1tcOzYERQUFsDjf50AAMuW/AktLW14D/YBAPTo6Ykpk/xx5PABNG32Ha6EXsKTuMcY7TcOAIQnWsqTkZYBn6+Jhg2rX3u3sueta88+OLR3B/QNGkK3gT727gwCX1MLzd0+Lmkyc+o4fOfmji7dy1qhunv2xZplC2BhZQNLazucOnYAhQX5aPe/H4T3yUhPQ2ZGOpJfl53wef4sAYqKStDW1YOqauUrD1SWr1vPPjgozNcAe3ZuqSTf72ju5o4fuvcW5lu9bAEsrGxhZW2Lk8cOorAgH+3/VzYkRFlZBe07/oBtm9ZCRUUVSkrKCFq/Eja2DjU62EsrK0G5XGVYyawh1JxsUZSehYKk17CZOx4KhnqIGly2jNPzjXthMnIgbBdMRNK2Q9Bu1wL6fbrgVo9hwn08XbEVTlv+ROad+8i6dQ+mY7who6yIpO2Ha/RcfTCwoztmbD4Ae1NDOJgbYXdwGPILi9Dj+7JhNQGb9kNXQw1+fToDALadCsW6o+cwf9jPMNDm421W2Th2JXk5KCmU/TA8dysafFVlNNDUwJMXyVi8+wTautrDrdGXtwz+2KsnFi1fCWsrS9hYW+HIsRMoKChAJ48OAIA/l66AtpYWfH4t63lTXFyM5+/nhyguKcHbtHQ8SUiAooIiDA30a7TPmuLxePihZx8c2bcd+oZG0NXTx76/N4OvqYVmbu7C7eZMHYtmbq3R+f3noWuvn/HX8nmwsLKFhbUdTh/bj8KCfLT1KGt5fZP8EtcuX4CTazOoqWkgLS0Vxw78DTk5ebg0das0y6c8PT2xbOlSWFlZwdrGBseOHkVhYSH+97+yVsolS5ZAS0sLgwcPBgD07NkTk/74A4cPHUKz5s0RGhqKuLg4+I35uOTljz/+WDa+vVEjODo54c7t27h58yb+/PPLh9NI8nPH4/Hg0MoLURfXQ13LBCr8hog4vwqKqrowtvMQbncmaDBM7D1g7zYQsvLK4OuJvr9l5BQhr6QhLM9OT8LT6DMwtGwFBWU+crPe4N7lTZCRkUdD69Y1zva/7v1x4kAQ9AyMoK1riCO714H/ybFrUcAIuLZoC4+uZeObC/LzkPL647ErNeUlEhMeQVlVHVo6DWq8389lk9TXdJCHGwK2HYW9iQEamRpg14WbyC8qRs+WzgCA6VuPQldDFWM8y74Dgv65CntjfRjpaKKopARX7z/BqRv3MHVA2TFLQ0UJGp/M6SMjLQUtNRWYNvj8cf7T561pey9cO70OfB0TaGg3xJUTK6Girgtr54/vt70rvGHl/D80aVu2dFno0aUwd2gNNU19FBXk4uGtk0iMC0dfvyAAZRX3fauGoKQ4H90GL0Zhfg4K88t6WSmpalZ7QukDSf7+/fDcfY1jPvDvfpNI8jGfkE9R5Z0DhoaGOH36NCZOnAgnJydoamrCx8dHOOFdZbZu3QpfX1+0adMGenp6mDt3LgICarZmdU11drRCRk4B/jofjrfv8mCjr42/BneD1vtu88mZOZD6wrFi/6Uunt4oKsjH9nVzkZf7DlZ2zvg9YA1ky42fS01+gZzsTOH1Z/EPsThgqPD6vq3LAAAt23WHz/v1PO/eCsXW1TOF22xYOgUA0KPfUPT8efhnc7m3aYus7Ezs2rkdGRkZMDe3wKzZ84VdylJTU8CT+vi82dk7YMIfU/D3jm3YsW0rDAwNMS1gJkxMzap6iH+l108DUFhQgA2rlyA3Nwe29o0xfc4SkXGHb16/QnZ2lvB6q9YdkJ2Vib1/b0FmRlkX+2mzl4h0UQs+cwwHdm8TXg+c5AcAGDVuCtr9r+bzKvT6qT8KCvKxvly+gDmLRfIlv36FdyL52iOrXD4zc0tMn71YJN/g30ZDiieFJfMDUVxcDGfXZvht5O81yqTepBHcQnYKr9svmQoASNpxGPd8pkBeXweK5bqe5z97gVs9hsF+6RSY+nmh4EUyoodNx9tzH5e+eX3gDOR0NGE9YwzkG+ggOyoG4d18UZTyZZNWdfrOERnvcrDu6HmkZb2DjbE+1owfDK33axYnp2WKfE4PXLyB4pJSTFwrurzk0J4dMLxX2Q/Nt5nZWLbnFNKyc6CtoYpuLV3wW4+ql1esTtvW3yMzKwvb/96DjIwMWJibYf7sGeDzNQAAKampIp+HtPR0jBjzcXjPgcNHceDwUTg2csDShfNqtM8v0ePHgSgsKMDG1YuQl5sDG/vGmDJ7qejnIfkl3pX7Hmn5/vOw/+/Nws/DlNlLhe83WVl5xD6Iwpnj+5GT8w4aGpqwdXDCnMXrK0wGVpU2bdogOysLO//+Gxnp6TC3sMDsOXM+fo+kpIi8rvb29vhj0iTs2L4d27Ztg6GhIQICAmBqavoxd6tWGD16NPbv34/169ejYcOGmDZ9OhwaVd6qWlefOwBo7O6LkqJ8hB2dgaKCbOiauKLTrxtFWi7fpSeiIK/yCVgrIyMjjzfPbuNB2A4UFWRDUUULeqZN0W3YngqT41XnB09vFBUUYNtf85GX+w7Wds4YH7hK5NiV8umx68lD/Bnw8fizd8tyAECrdt3gO3Zmjff7OZL6mnZq6oCMd7lYd+IS3mbnwKahHv7yGwCt993mX6dniYxdzy8swvw9Z5CSmQ15WRmYNtDGvCGe6NT067TMftfxNxQX5ePs7kAU5GWjoUUT9PXbLPJ+y0hNQn7Ox/db7rs0nNw2CbnZKZBXUIWOoQ36+gXBzK4VAOBN0gO8fla2OsLGQNGhBcPnhkBd6/O9DiT9+xf4esf84DPHsb/cb5KASWUnMkeNmyxSya+MJB/z6wImxiHBBOCx8n23yTev4PBKriNU67atL9cRqqQrL7lfuIVM/BNi1RRPgqc9e27XhusIVWobWv0QCa6l6XxZl3pxSseXtaSJk6r0l83gL07vSqufN4FLZ6NqXlkWN3eH3M9vxCElmQKuI1TJ5kUw1xGqtIdV352eSx5GlS8hKQmyebUbXy8OknzM71pcu+X8JEHbn65/fqOv5NLBmvXm+ZbQmHdCCCGEEEIIIUTCUbd5QgghhBBCCCFfjNXTieO4Qi3vhBBCCCGEEEKIhKOWd0IIIYQQQgghX4wxmrBOnKjlnRBCCCGEEEIIkXBUeSeEEEIIIYQQQiQcdZsnhBBCCCGEEPLFaMI68aKWd0IIIYQQQgghRMJRyzshhBBCCCGEkC/GBDRhnThRyzshhBBCCCGEECLhqPJOCCGEEEIIIYRIOkZILRUUFLAZM2awgoICrqNUQNlqT5LzUbbaoWy1Q9lqT5LzUbbaoWy1I8nZGJPsfJSNkIp4jDGaIpDUSnZ2NtTV1ZGVlQU1NTWu44igbLUnyfkoW+1QttqhbLUnyfkoW+1QttqR5GyAZOejbIRURN3mCSGEEEIIIYQQCUeVd0IIIYQQQgghRMJR5Z0QQgghhBBCCJFwVHkntSYvL48ZM2ZAXl6e6ygVULbak+R8lK12KFvtULbak+R8lK12KFvtSHI2QLLzUTZCKqIJ6wghhBBCCCGEEAlHLe+EEEIIIYQQQoiEo8o7IYQQQgghhBAi4ajyTgghhBBCCCGESDiqvBNCCCGEEEIIIRKOKu+EEInl5eWFrVu3Ij4+nusohBBCCCGEcIpmmye18vDhQyQmJqKoqEikvEePHhwlKnPlyhVs2LAB8fHxOHjwIAwNDbFz506YmZnh+++/5zQb+XK+vr64fPkynjx5AkNDQ7Rp0wZt27ZFmzZtYGVlxXU8jB8/vsbbLlu27CsmIf+V06dPQ1paGp06dRIpP3v2LAQCAbp06cJRMlEFBQVQUFDgOobEy87OrtF2ampqXzkJ+a9FRERAVlYWjRs3BgAcO3YMW7duhb29PWbOnAk5OTmOE0omxhiSkpKgq6tL3yGE1EEyXAcgdUtCQgI8PT0RHR0NHo+HD+d+eDweAKC0tJSzbIcOHcKgQYMwcOBAREZGorCwEACQlZWF+fPn4/Tp02LPNH78eMyZMwfKysqfrehxVbkrKCjA6tWrcfHiRaSkpEAgEIjcHhERwUkuANi8eTMA4OXLl7h8+TJCQ0OxdOlSDBs2DPr6+njx4gVn2QAgMjISkZGRKC4uho2NDQDg8ePHkJaWhqurq3C7D5+Pr+3evXs13tbR0fErJqlemzZt4OPjgz59+kBRUZGzHJWZPHkyFi5cWKGcMYbJkydzWnkXCASYN28e1q9fjzdv3uDx48cwNzdHQEAATE1N4ePjw1k2SaWhoVHt548xBh6Px+mxi9TOsGHDMHnyZDRu3BgJCQn4+eef4enpiQMHDiAvLw8rVqzgOiJCQkIQEhJS6bF1y5YtnGRijMHS0hIPHjyQiJPghJAvQ5V38kXGjh0LMzMzhISEwMzMDOHh4UhLS4O/vz+WLFnCaba5c+di/fr18PLywt69e4XlrVq1wty5cznJ9KFi9+HfVRFX5a4yPj4+CA4Oxk8//YTmzZtzmqUqfD4fWlpa4PP50NDQgIyMDHR0dLiOhe7du0NVVRXbt28Hn88HAGRkZGDw4MFwd3eHv7+/WPM4OzsLT6p97nXksrLi4uKCCRMmwM/PD3379oWPjw9atGjBWZ7y4uLiYG9vX6Hc1tYWT5484SDRR3PnzsX27duxaNEi/Pbbb8LyRo0aYcWKFZxU3iX9hNHFixeF/2aM4YcffsDmzZthaGgo9izVCQ0NxZIlSxATEwMAsLe3x8SJE+Hu7s5prtLSUixfvhz79++vtLddeno6R8nKTpQ6OzsDAA4cOIDWrVtj9+7dCAsLw88//8x55X3WrFmYPXs2mjZtCn19fYk5tkpJScHKygppaWkSW3k/ePBgle85cTcorFq1qsbbjhkz5ismIeQ9RsgX0NLSYlFRUYwxxtTU1FhsbCxjjLGQkBDm7OzMZTSmqKjInj59yhhjTEVFhcXHxzPGGIuPj2fy8vIcJpNsampq7OrVq1zHqNSUKVOYm5sbU1BQYC4uLmzcuHHs6NGjLD09netojDHGDAwM2P379yuUR0dHM319fbHnefbsmfBy5MgRZmFhwdavX8+ioqJYVFQUW79+PbOysmJHjhwRe7ZPFRcXs0OHDrEePXowWVlZZmdnxxYvXsySk5M5zaWnp8dCQkIqlJ87d47p6OhwkOgjCwsLdv78ecaY6HdcTEwM09DQ4CQTj8djUlJSwv9Xd5EE5Z83SbFz504mIyPD+vbty1auXMlWrlzJ+vbty2RlZdmuXbs4zRYQEMD09fXZkiVLmIKCApszZw7z8fFhWlpabOXKlZxmU1VVZY8fP2aMMebh4cFWrFjBGGPs+fPnTEFBgctojDHGGjRowHbs2MF1jEodP36cff/99yw6OprrKBWsXLmSqaiosNGjRzM5OTk2bNgw5uHhwdTV1dnUqVPFnsfU1FTkoqyszHg8HuPz+YzP5zMej8eUlZWZmZmZ2LOR+okq7+SLaGhosISEBMYYY+bm5uzChQuMMcaePHnCFBUVuYzGzMzM2Llz5xhjoj/Qtm/fzuzs7LiMJtHs7OyEJ2QkDY/HY7q6umzBggXs0aNHXMepQEVFhV28eLFC+YULF5iKior4A5XTrFkzdurUqQrlp06dYq6urhwkqtqbN2/YnDlzmIKCApOVlWU9e/astAItDkOHDmWNGzdmT548EZbFxcUxR0dH5uPjw0mmDxQUFNizZ88YY6LfcQ8ePGDKysqcZKpLJ4wYk8zKu62tLVu2bFmF8qVLlzJbW1sOEn1kbm7OTp48yRgre+4+fC5WrlzJ+vfvz2U01q5dO+bl5cV27NjBZGVlWVxcHGOMsUuXLjETExNOszHGmKampsj3iCTR0NBgcnJyTEpKiikoKAgroh8uXLKxsWG7d+9mjIl+XgMCAtioUaO4jMZ27drFWrVqJWy4Yoyx2NhY5u7uzv7++28Ok5H6hCrv5It8//33wh9h/fv3Z507d2ZXr15lXl5ezMHBgdNs8+fPZ/b29uzGjRtMVVWVXblyhf39999MR0eHrVq1itNskuz06dOsc+fOwkqBJLl79y5buXIl8/T0ZNra2szAwID179+fbdiwQSIq84MGDWKmpqbs0KFDLCkpiSUlJbGDBw8yMzMz5uXlxWk2BQUF9vDhwwrlDx8+lIhWqQ9u3rzJhg8fzjQ0NJixsTELDAxkPj4+TFFRkfn7+4s9T2ZmJmvRogWTkZERtrTIyMiwdu3asYyMDLHnKc/V1ZXt3LmTMSb6o3bWrFns+++/5zIaY6xunDCSxMq7nJycsOJZXlxcHOe9xpSUlNjz588ZY2UtyXfu3GGMlfVoU1NT4zIai4qKYo0aNWJqamps5syZwvLRo0dzfmKBMcb++OMPNnv2bK5jVGrbtm3VXrikqKgo/D2io6PD7t69yxhj7PHjx0xTU5PLaMzc3JxFRERUKL99+zYzNTXlIBGpj2jMO/ki06dPR25uLgBg9uzZ6NatG9zd3aGlpYV9+/Zxmm3y5MkQCATo0KED8vLy0Lp1a8jLywvH1pLKNW3aFAUFBTA3N4eSkhJkZWVFbudyTKOTkxOcnJyE48iioqKwfPlyjBo1CgKBgPNJptavX48JEyZgwIABwrkNZGRk4OPjg8WLF3Oazc7ODgsWLMDmzZuFsy4XFRVhwYIFsLOz4zRbSkoKdu7cia1btyIuLg7du3fHnj170KlTJ+G40F9//RWdO3cW+1wa6urquHbtGs6dO4eoqCgoKirC0dERrVu3FmuOygQGBsLb2xsvX76EQCDA4cOH8ejRI+zYsQMnT57kOh6io6NhZmZWodzMzAwPHz7kIFHlJGXs8QdGRkYICQmBpaWlSPn58+dhZGTEUaoyDRs2xOvXr2FsbAwLCwsEBwfD1dUVt27dgry8PKfZHB0dER0dXaF88eLFkJaW5iCRqIKCAmzcuBHnz5+Ho6NjhWMrlyuQeHt7c/bYn9OgQQOkp6fDxMQExsbGuHHjBpycnPD06VPhJMlcef36NUpKSiqUl5aW4s2bNxwkIvURLRVH/rX09HTw+XyJ+UFUVFSEJ0+eICcnB/b29lBRUeE6kkTz8PBAYmIifHx8oKenV+F15PIgzxhDZGQkLl26hEuXLuHq1avIzs6Go6Mj2rRpg+XLl3OWrbzc3FzhWvQWFhZQVlbmOBEQHh6O7t27gzEmnCjsw+RiJ0+eRPPmzTnLJicnBwsLCwwZMgS//vprpZMPZmdno2fPniITjn1txcXFUFRUxN27d9GoUSOxPe6XuHLlCmbPno2oqCjk5OTA1dUVgYGB6NixI9fR4OrqikaNGlU4YeTr64v79+9zsnJF7969Ra6fOHEC7du3r/AZPXz4sDhjiVi3bh3GjRuHIUOGoGXLlgCAsLAwbNu2DStXrsSwYcM4yzZ58mSoqalh6tSp2LdvH3755ReYmpoiMTERv//+e6UrM4hLUlISeDweGjZsCKDsO2/37t2wt7fH0KFDOcv1Qbt27aq8jcfj4cKFC2JMU1F8fDy2bt2K+Ph4rFy5Erq6ujhz5gyMjY3h4ODAWS5fX18YGRlhxowZWLt2LSZOnIhWrVrh9u3b6N27N4KCgjjL1r17d7x8+RKbN28Wrihz584dDB06FIaGhjh+/Dhn2Uj9QZV3Quo5JSUlXL9+HU5OTlxHqYDP5yMnJwdOTk7CNd7d3d2hoaHBdbQ6ITc3F7t27UJsbCyAstb4AQMGcH5y4cqVKzWaRTssLAxNmzYVawufubk5jhw5IpGfB0lX1QkjHo+HEydOcHLCaPDgwTXabuvWrV85SfWOHDmCpUuXCmebt7Ozw8SJE9GzZ09Oc33q+vXruH79OqysrNC9e3dOs7i7u2Po0KEYNGgQkpOTYWNjAwcHB8TFxcHPzw+BgYGc5pNkoaGh6NKlC1q1aoXLly8jJiYG5ubmWLhwIW7fvo2DBw9ylk0gEEAgEEBGpqxz8L59+xAWFgYrKysMHz68Qg8GcUpNTYW3tzf++ecfYY6SkhJ06tQJ27Ztg66uLmfZSP1BlXdC6jlXV1f89ddfErNUV3mnTp2Cu7s71NTUuI5SZz18+LDS5XZ69OjBUaKaU1NTw927d2Fubi62xwwKCsLhw4exc+dOaGpqiu1xvxWSesKIfHv4fD5u3LgBGxsbrFq1SljJCw4OxvDhw5GQkMB1RInl5uaGPn36YPz48VBVVUVUVBTMzc0RHh6O3r1748WLF5zmKygowL1795CSkgKBQCAs5/F4nJ80AsqWKfzwHWdrawtra2uOE5H6hCrvhNRzwcHBmDVrFubNm4fGjRtXOKtNFee6KSEhAZ6enoiOjq507Xeu5wuoifI/KsXFxcUFT548QXFxMUxMTCpUOsXd9ftLhiRxOT8F+XcyMzNx8OBBJCQkYMKECdDU1ERERAT09PQ4X5M+Li4OFy9erFCRAsBp67aKigru378PU1NT9OjRA61atcKkSZOQmJgIGxsb5Ofnc5ZN0qmoqAjnqCj/Pfvs2TPY2tqioKCAs2z//PMPBg0ahLS0tAq38Xi8OnHsIuRrognrCKnnOnfuDABo3769SCXhQ2WPDpR109ixY2FmZoaQkBCYmZnh5s2bSE9Ph7+/v9gngatLevXqxXUEEStWrBD+Oy0tDXPnzkWnTp3g5uYGoKwb89mzZxEQEMBRQlE7d+7Ehg0bkJCQgOvXr8PExATLly+Hubm5xHUBlxT37t2Dh4cH1NXV8ezZM/j6+kJTUxOHDx9GYmIiduzYwVm2TZs2YcSIEdDW1kaDBg1EjhE8Ho/TyruDgwPWr1+Prl274ty5c5gzZw4A4NWrV9DS0uIsV12goaGB169fV5hgMjIykvOTRX5+fujbty8CAwOhp6fHaRYAGD9+PObMmQNlZWWMHz++2m25nISQ1B/U8k5IPRcaGlrt7W3atBFTEvJf0tbWxoULF+Do6Ah1dXWEh4fDxsYGFy5cgL+/PyIjI7mO+FlctLxLsh9//BHt2rXD6NGjRcrXrFmD8+fP4+jRo9wEe2/dunUIDAzEuHHjMHfuXDx48ADm5ubYtm0btm/fLtbJB+sSDw8PuLq6YtGiRSLv+WvXrmHAgAF49uwZZ9lMTEwwcuRITJo0ibMMVbl06RI8PT2RnZ0Nb29vbNmyBQAwdepUxMbGcjoJoaSbMGECbt68iQMHDsDa2hoRERF48+YNvLy84OXlhRkzZnCWTU1NDZGRkbCwsOAsQ3nt2rXDkSNHoKGhIfGTEJL6gSrvhBBkZmYiKChIOFmSvb09fHx8oK6uznEyUlt8Ph8REREwMzODhYUFNm/ejHbt2iE+Ph6NGzdGXl4e1xE/iyrvolRUVHD37t0KS4o9efIEzs7OyMnJ4ShZGXt7e8yfPx+9evUSee3u37+Ptm3b4u3bt5zmk1Tq6uqIiIiAhYWFyPP2/Plz2NjYcNqFmYt5J75EaWkpsrOzwefzhWXPnj2DkpISTR5WjaKiIowaNQrbtm1DaWkpZGRkUFJSgoEDB2Lbtm2cLrU3ZMgQtGrVCj4+PpxlIESSUbd5Quq527dvo3PnzlBQUBDOBr18+XLMnz9fuKYvqXsaNWqEqKgomJmZ4bvvvsOiRYsgJyeHjRs3SuwP8U9xsfxkaWkpli9fjv3791c60R+X48q1tLRw7Ngx+Pv7i5QfO3ZMIroJP336FC4uLhXK5eXlkZuby0GiukFeXh7Z2dkVyh8/flzpMori1KdPH+EEcJJIWlpapOIOAKamptyEqUPk5OSwadMmBAYGIjo6Grm5uXBxcalwYpALa9asQZ8+fXDlypVK5+EZM2YMR8lEfZjU78NShYSIC1XeCannfv/9d3Tv3h2bNm0SLs1SUlICX19fjBs3DpcvX+Y4IamN6dOnCytMs2fPRrdu3eDu7g4tLS3s27ePs1yMMSQlJUFXVxcKCgqf3VbcZs2ahc2bN8Pf3x/Tp0/HtGnT8OzZMxw9epTzpadmzZoFX19fXLp0Cd999x0A4ObNm/jnn3+wadMmTrMBgJmZGe7evQsTExOR8n/++Qd2dnYcpZJ8PXr0wOzZs7F//34AZSetEhMTMWnSJPz4449iz7Nq1Srhvy0tLREQEIAbN25IZEXq4MGDVZ5oE/fkknVNUFAQli9fjri4OACAlZUVxo0bB19fX05z7dmzB8HBwVBQUMClS5cqzLPA5XtOIBBg7ty5WLp0qbCnk6qqKvz9/TFt2jRISUlxlo3UI4wQUq8pKCiwmJiYCuUPHjxgioqKHCQiX0taWhoTCAScZigtLWWysrLs8ePHnOaoirm5OTt58iRjjDEVFRX25MkTxhhjK1euZP379+cyGmOMsRs3brABAwYwFxcX5uLiwgYMGMBu3LjBdSzGGGObNm1ihoaGbO/evUxZWZnt2bOHzZ07V/hvUrnMzEzm4eHBNDQ0mLS0NDMyMmKysrLM3d2d5eTkiD2PqalpjS5mZmZiz1beypUrmYqKChs9ejSTk5Njw4YNYx4eHkxdXZ1NnTqV02ySLiAggCkrK7PJkyezY8eOsWPHjrHJkyczFRUVFhAQwGk2PT09Nm/ePFZaWsppjspMnjyZ6ejosL/++otFRUWxqKgotnbtWqajo0PvOSI2NOadkHpOT08PO3fuRMeOHUXKz549Cy8vL7x584ajZORb5eDggKCgILRo0YLrKBUoKysjJiYGxsbG0NfXx6lTp+Dq6oqEhAS4uLggKyuL64gSbdeuXZg5cybi4+MBAAYGBpg1axaNX62BsLAwREVFIScnB66urvDw8OA6kogPPxe5GM5SGVtbW8yYMQP9+/cXmSsgMDAQ6enpWLNmDdcRJZaOjg5WrVqF/v37i5Tv2bMHfn5+nM5PoampiVu3bknMhHXlGRgYYP369ejRo4dI+bFjxzBy5Ei8fPmSo2SkPqH+HYTUc/369YOPjw/27duHpKQkJCUlYe/evfD19a1wYCfkv7Bw4UJMnDgR9+/f5zpKBQ0bNsTr168BABYWFggODgYA3Lp1C/Ly8lxGA1A2Jv/QoUOYO3cu5s6diyNHjkjUco4DBw5EXFwccnJykJycjBcvXlDFvQZCQkJw6tQpREREIDY2Frt378aQIUMwZMgQrqMhKCgIjRo1goKCAhQUFNCoUSNs3ryZ61hITExEy5YtAQCKiop49+4dAGDQoEHYs2cPl9EkXnFxMZo2bVqhvEmTJigpKeEg0Ufe3t6cDu2qTnp6OmxtbSuU29racjofCqlfaMw7IfXckiVLwOPx4OXlJTxoy8rKYsSIEVi4cCHH6ci3yMvLC3l5eXBycoKcnBwUFRVFbufyR5CnpydCQkLw3Xffwc/PD7/88guCgoKQmJiI33//nbNcQNms8l27dsWLFy9gY2MDAFiwYAGMjIxw6tQpzluq8vPzwRiDkpISlJSUkJqaihUrVsDe3r5Czx7y0axZszB79mw0bdoU+vr6EtOyDQCBgYFYtmwZ/Pz84ObmBgC4fv06fv/9dyQmJmL27NmcZWvQoAHS09NhYmICY2Nj3LhxA05OTnj69Ckn82XUJYMGDcK6desqrEu+ceNGDBw4kKNUZUpLS7Fo0SKcPXsWjo6OFeZZ4HItdScnJ6xZs0ZkXgigbJI9JycnjlKR+oa6zRNCAAB5eXnCrq4WFhZQUlLiOBH5Vm3fvr3a2729vcWU5PNu3LiBa9euwcrKCt27d+c0yw8//ADGGHbt2gVNTU0AQFpaGn755RdISUnh1KlTnObr2LEjevfujeHDhyMzMxM2NjaQk5PD27dvsWzZMowYMYLTfJJKX18fixYtwqBBg7iOUoEkd6/29fWFkZERZsyYgbVr12LixIlo1aoVbt++jd69eyMoKIizbJLOz88PO3bsgJGRkXD40s2bN5GYmAgvLy+RCrO4K8uSvJZ6aGgounbtCmNjY5GTWYmJiThz5gzc3d05y0bqD6q8E0IIIV+oa9eu2Lx5M/T19cX2mMrKysJZv8uLiopCq1atOF/nXVtbG6GhoXBwcMDmzZuxevVqREZG4tChQwgMDERMTAyn+SSVlpYWwsPDOe85URkNDQ3cunULVlZWIuWPHz9G8+bNkZmZyU0wlM38LRAIhKuk7N27V3iibdiwYZCTk+Msm6SrroJcHteVZUn08uVLrFu3Tvh9Zmdnh5EjR8LAwIDjZKS+oMo7IYQQsYuPj8fWrVsRHx+PlStXQldXF2fOnIGxsTEcHBy4jvdZ5SfIEhdNTU2cPHlSOM73g7CwMHTv3p3zMZdKSkqIjY2FsbEx+vbtCwcHB8yYMQNJSUmwsbFBXl4ep/kk1aRJk6CiooKAgACuo1Tg5+cHWVnZCq2vEyZMQH5+PtauXctRMkK4UVBQgHv37iElJQUCgUDktk8nsiPka6Ax74QQQsQqNDQUXbp0QatWrXD58mXMmzcPurq6iIqKQlBQEA4ePMh1RInUrVs3DB06FEFBQWjevDmAsq6uw4cPl4gfjZaWljh69Cg8PT1x9uxZ4RwBKSkpUFNT4zid5CooKMDGjRtx/vx5iRvjC5RNWBccHFxp9+rx48cLtxNHznv37tV4W0dHx6+YhNRH//zzD7y8vJCWllZhXgUejydRk4eSbxe1vBNCCBErNzc39OnTB+PHjxdpwQ4PD0fv3r3x4sULriN+Fhct75mZmfD29saJEyeEFbySkhL06NED27Ztg7q6utiyVObgwYMYMGAASktL0aFDB+FM/QsWLMDly5dx5swZTvNJKkke4ytp3aulpKTA4/E+OyEdVaTI12BlZYWOHTsiMDAQenp6XMch9RRV3gkhhIiViooKoqOjYWZmJlIJfvbsGWxtbVFQUMB1xM/iovL+wZMnT0TGW1paWoo9Q1WSk5Px+vVrODk5QUqqbDXa8PBwqKmpVbrEEiFf4vnz5zXe1sTE5CsmIfWRmpoaIiMjJXJ+ClJ/ULd5QgghYqWhoYHXr1/DzMxMpDwyMhKGhoYcpao7LC0tJarCXl6DBg3QoEEDkbIPXfwJ+bfKV8gXLFgAPT09DBkyRGSbLVu2IDU1FZMmTRJ3PPKN++mnn3Dp0iWqvBNOUcs7IYQQsZowYQJu3ryJAwcOwNraGhEREXjz5g28vLzg5eWFGTNmcB3xs7hoef/xxx/RvHnzCpWSRYsW4datWzhw4IDYslSmXbt21a5RTrNWk/+Sqakpdu/eXWECx5s3b+Lnn3/G06dPOUpGvlV5eXno06cPdHR00Lhx4wrzU4wZM4ajZKQ+oZZ3QgghYjV//nyMGjUKRkZGKC0thb29PUpKSjBw4EBMnz6d63g1MnXqVOFa6+Jy+fJlzJw5s0J5ly5dsHTpUrFmqYyzs7PI9eLiYty9exf379+Ht7c3N6HINys5ObnSpRp1dHTw+vVrDhKRb92ePXsQHBwMBQUFXLp0SeRkJY/Ho8o7EQuqvBNCCBErOTk5bNq0CYGBgYiOjkZubi5cXFwkqiv4w4cPkZiYiKKiIpHyD7O6T5kyReyZcnJyKl27WlZWFtnZ2WLP86nly5dXWj5z5kzO16An3x4jIyOEhYVVGH4TFhZGa26Tr2LatGmYNWsWJk+eLJzTgxBxo8o7IYQQsQsKCsLy5csRFxcHoGwW33HjxsHX15fTXAkJCfD09ER0dLTIrNYfWli4nMG6cePG2LdvHwIDA0XK9+7dC3t7e45Sfd4vv/yC5s2bY8mSJVxHId+Q3377DePGjUNxcTHat28PAAgJCcEff/wBf39/jtORb1FRURH69etHFXfCKaq8E0IIEavAwEAsW7YMfn5+cHNzAwBcv34dv//+OxITEzF79mzOso0dOxZmZmYICQmBmZkZwsPDkZaWBn9/f84rnwEBAejduzfi4+NFKit79uzhfLx7da5fvw4FBQWuY5BvzMSJE5GWloaRI0cKe8goKChg0qRJnPSMId8+b29v7Nu3D1OnTuU6CqnHaMI6QgghYqWjo4NVq1ahf//+IuV79uyBn58f3r59y1EyQFtbGxcuXICjoyPU1dURHh4OGxsbXLhwAf7+/oiMjOQsGwCcOnUK8+fPx927d6GoqAhHR0fMmDEDbdq04TQXAPTu3VvkOmMMr1+/xu3btxEQEFAnJiIkdU9OTg5iYmKgqKgIKysryMvLcx2JfKPGjBmDHTt2wMnJCY6OjhUmrFu2bBlHyUh9Qi3vhBBCxKq4uBhNmzatUN6kSROUlJRwkOij0tJSqKqqAiiryL969Qo2NjYwMTHBo0ePOM0GAF27dkXXrl2r3WbPnj3o0aMHlJWVxZSqjLq6ush1KSkp2NjYYPbs2ejYsaNYs5D6Q0VFBc2aNeM6BqkHoqOj4eLiAgC4f/++yG3VrbRByH+JWt4JIYSIlZ+fH2RlZSu0UkyYMAH5+flYu3YtR8kAd3d3+Pv7o1evXhgwYAAyMjIwffp0bNy4EXfu3Knwg00Sqamp4e7du2Jdxu5LcHVygRBCCKnrqPJOCCFErPz8/LBjxw4YGRmhRYsWAMrWZk5MTISXl5dIV0Rxd0M8e/YscnNz0bt3bzx58gTdunXD48ePoaWlhb1796JDhw5izVMbXKxB/yUk/eQCIYQQIqmo8k4IIUSs2rVrV6PteDweLly48JXTfF56ejr4fH6d6RYp6ZV3Sc9HCCGESCoa804IIUSsLl68yHWEKg0ZMgQrV64UjnsHAE1NTeTm5sLPzw9btmzhMB0hhBBC6jNaqJAQQgh5b/v27cjPz69Qnp+fjx07dnCQiBBCCCGkDLW8E0IIqfeys7PBGANjDO/evRNZl7y0tBSnT5+Grq4uhwkJIYQQUt9R5Z0QQki9p6GhAR6PBx6PB2tr6wq383g8zJo1i4NkX87ExKTC+sOEEEIIqfuo8k4IIaTeu3jxIhhjaN++PQ4dOgRNTU3hbXJycjAxMYGBgQGHCT8qKipCSkoKBAKBSLmxsTGAiusPSxo6uUAIIYTUDs02TwghhLz3/PlzGBkZQUpK8qaEiYuLw5AhQ3Dt2jWRcsYYeDweSktLOUpWJikpCTweDw0bNgQAhIeHY/fu3bC3t8fQoUM5zUYIIYR8C6jyTgghhHwiLy8PiYmJKCoqEil3dHTkKBHQqlUryMjIYPLkydDX16+wdJ2TkxNHycq4u7tj6NChGDRoEJKTk2FjYwMHBwfExcXBz88PgYGBnOYjhBBC6jqqvBNCCCHvpaamYvDgwThz5kylt3PZuq2srIw7d+7A1taWswzV4fP5uHHjBmxsbLBq1Srs27cPYWFhCA4OxvDhw5GQkMB1REIIIaROk7x+gYQQQghHxo0bh8zMTNy8eROKior4559/sH37dlhZWeH48eOcZrO3t8fbt285zVCd4uJiyMvLAwDOnz+PHj16AABsbW3x+vVrLqMRQggh3wSqvBNCCCHvXbhwAcuWLUPTpk0hJSUFExMT/PLLL1i0aBEWLFjAabY///wTf/zxBy5duoS0tDRkZ2eLXLjm4OCA9evX48qVKzh37hw6d+4MAHj16hW0tLQ4TkcIIYTUfTTbPCGEEPJebm6ucD13Pp+P1NRUWFtbo3HjxoiIiOA0m4eHBwCgQ4cOIuWSMmHdn3/+CU9PTyxevBje3t7CMfjHjx9H8+bNOc1GCCGEfAuo8k4IIYS8Z2Njg0ePHsHU1BROTk7YsGEDTE1NsX79eujr63Oa7eLFi5w+/ue0bdsWb9++RXZ2Nvh8vrB86NChUFJS4jAZIYQQ8m2gCesIIYSQ9/7++2+UlJTg119/xZ07d9C5c2ekpaVBTk4O27dvR79+/biOKPFSUlLw6NEjAGUnQz70ZCCEEELIv0OVd0IIIaQKeXl5iI2NhbGxMbS1tbmOg8zMTAQFBSEmJgZA2TjzIUOGQF1dneNkwLt37zBy5Ejs3btX2IVfWloa/fr1w9q1ayUiIyGEEFKXUeWdEEJIvTZ+/Pgab7ts2bKvmKR6t2/fRqdOnaCoqCgcQ37r1i3k5+cjODgYrq6unGUDgH79+iEyMhKrV6+Gm5sbAOD69esYO3YsnJ2dsXfvXk7zEUIIIXUdVd4JIYTUa+3atRO5HhERgZKSEtjY2AAAHj9+DGlpaTRp0gQXLlzgIiIAwN3dHZaWlti0aRNkZMqmrCkpKYGvry8SEhJw+fJlzrIBZevQnz17Ft9//71I+ZUrV9C5c2fk5uZylIwQQgj5NtCEdYQQQuq18hPBLVu2DKqqqti+fbtw0rWMjAwMHjwY7u7uXEUEUNbyXr7iDgAyMjL4448/0LRpUw6TldHS0qq0a7y6urrIBHaEEEIIqR1a550QQgh5b+nSpViwYIFIZZPP52Pu3LlYunQph8kANTU1JCYmVihPSkqCqqoqB4lETZ8+HePHj0dycrKwLDk5GRMnTkRAQACHyQghhJBvA7W8E0IIIe9lZ2cjNTW1QnlqairevXvHQaKP+vXrBx8fHyxZsgQtW7YEAISFhWHixIno378/J5lcXFzA4/GE1+Pi4mBsbAxjY2MAQGJiIuTl5ZGamophw4ZxkpEQQgj5VlDlnRBCCHnP09MTgwcPxtKlS4WTwt28eRMTJ05E7969Oc22ZMkS8Hg8eHl5oaSkBAAgKyuLESNGYOHChZxk6tWrFyePSwghhNRHNGEdIYQQ8l5eXh4mTJiALVu2oLi4GEDZuHIfHx8sXrwYysrKHCcsyxgfHw8AsLCwgJKSEseJCCGEECIOVHknhBBCPpGbmytSQZaESntdcefOHZF16F1cXDhORAghhHwbqPJOCCGESKjevXtj27ZtUFNT+2y3/cOHD4spVeVSUlLw888/49KlS9DQ0AAAZGZmol27dti7dy90dHQ4zUcIIYTUdTTbPCGEECKh1NXVhRPCqampQV1dvcoL1/z8/PDu3Ts8ePAA6enpSE9Px/3795GdnY0xY8ZwHY8QQgip86jlnRBCCCH/mrq6Os6fP49mzZqJlIeHh6Njx47IzMzkJhghhBDyjaCWd0IIIaQOaN++faUV4OzsbLRv3178gT4hEAggKytboVxWVhYCgYCDRIQQQsi3hVreCSGEkDpASkoKycnJ0NXVFSlPSUmBoaGhcHZ8rvTs2ROZmZnYs2cPDAwMAAAvX77EwIEDwefzceTIEU7zEUIIIXUdrfNOCCGESLB79+4J//3w4UMkJycLr5eWluKff/6BoaEhF9FErFmzBj169ICpqSmMjIwAAElJSWjUqBH+/vtvjtMRQgghdR+1vBNCCCESTEpKSjhpXWWHbEVFRaxevRpDhgwRd7QKGGM4f/48YmNjAQB2dnbw8PDgOBUhhBDybaDKOyGEECLBnj9/DsYYzM3NER4eLrLkmpycHHR1dSEtLc1hwi/TuHFjnD59Wtg6TwghhJCaoW7zhBBCiAQzMTEBgG9m0rdnz55xPj6fEEIIqYuo8k4IIYRIqOPHj6NLly6QlZXF8ePHq922R48eYkpFCCGEEC5Qt3lCCCFEQpWfYV5KqurVXXk8HkpLS8WYrPZUVVURFRUFc3NzrqMQQgghdQq1vBNCCCESqnxX+W+l2zwhhBBCaqfq0/iEEEIIIYQQQgiRCNTyTgghhEioVatW1XjbMWPGfMUkhBBCCOEajXknhBBCJJSZmZnI9dTUVOTl5UFDQwMAkJmZCSUlJejq6iIhIYGDhKJCQkIQEhKClJSUCt38t2zZAgDYvXs3evbsCWVlZS4iEkIIIXUWdZsnhBBCJNTTp0+Fl3nz5sHZ2RkxMTFIT09Heno6YmJi4Orqijlz5nAdFbNmzULHjh0REhKCt2/fIiMjQ+TywYABA6jiTgghhNQCtbwTQgghdYCFhQUOHjwIFxcXkfI7d+7gp59+wtOnTzlKVkZfXx+LFi3CoEGDOM1BCCGEfKuo5Z0QQgipA16/fo2SkpIK5aWlpXjz5g0HiUQVFRWhZcuWXMcghBBCvllUeSeEEELqgA4dOmDYsGGIiIgQlt25cwcjRoyAh4cHh8nK+Pr6Yvfu3VzHIIQQQr5ZNNs8IYQQUgds2bIF3t7eaNq0KWRlZQEAJSUl6NSpEzZv3sxxOqCgoAAbN27E+fPn4ejoKMz4wbJlyzhKRgghhHwbaMw7IYQQUoc8fvwYsbGxAABbW1tYW1tznKhMu3btqryNx+PhwoULYkxDCCGEfHuo8k4IIYQQQgghhEg46jZPCCGE1AGlpaXYtm1bleuoU8s2IYQQ8m2jyjshhBBSB4wdOxbbtm1D165d0ahRI/B4PK4jEUIIIUSMqNs8IYQQUgdoa2tjx44d+OGHH7iOQgghhBAO0FJxhBBCSB0gJycHS0tLrmMQQgghhCNUeSeEEELqAH9/f6xcuRLUYY4QQgipn6jbPCGEEFIHeHp64uLFi9DU1ISDg0OFddQPHz7MUTJCCCGEiANNWEcIIYTUARoaGvD09OQ6BiGEEEI4Qi3vhBBCCCGEEEKIhKOWd0IIIaQOSU1NxaNHjwAANjY20NHR4TgRIYQQQsSBJqwjhBBC6oDc3FwMGTIE+vr6aN26NVq3bg0DAwP4+PggLy+P63iEEEII+cqo8k4IIYTUAePHj0doaChOnDiBzMxMZGZm4tixYwgNDYW/vz/X8QghhBDyldGYd0IIIaQO0NbWxsGDB9G2bVuR8osXL6Jv375ITU3lJhghhBBCxIJa3gkhhJA6IC8vD3p6ehXKdXV1qds8IYQQUg9QyzshhBBSB3To0AFaWlrYsWMHFBQUAAD5+fnw9vZGeno6zp8/z3FCQgghhHxNVHknhBBC6oDo6Gh07twZhYWFcHJyAgBERUVBXl4ewcHBcHBw4DghIYQQQr4mqrwTQgghdUReXh527dqF2NhYAICdnR0GDhwIRUVFjpMRQggh5GujyjshhBBSByxYsAB6enoYMmSISPmWLVuQmpqKSZMmcZSMEEIIIeJAE9YRQgghdcCGDRtga2tbodzBwQHr16/nIBEhhBBCxIkq74QQQkgdkJycDH19/QrlOjo6eP36NQeJCCGEECJOVHknhBBC6gAjIyOEhYVVKA8LC4OBgQEHiQghhBAiTjJcByCEEELI5/32228YN24ciouL0b59ewBASEgI/vjjD/j7+3OcjhBCCCFfG01YRwghhNQBjDFMnjwZq1atQlFREQBAQUEBkyZNQmBgIMfpCCGEEPK1UeWdEEIIqUNycnIQExMDRUVFWFlZQV5enutIhBBCCBEDqrwTQgghhBBCCCESjiasI4QQQgghhBBCJBxV3gkhhBBCCCGEEAlHlXdCCCGEEEIIIUTCUeWdEEIIIYQQQgiRcFR5J4QQQgghhBBCJBxV3gkhhBBCCCGEEAlHlXdCCCGEEEIIIUTC/R/rg+/ggtivjgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Labeled Heatmap of Correlation Matrix\n",
        "correlation_matrix = df.corr()\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title(\"Labeled Heatmap of Correlation Matrix\")\n",
        "plt.savefig(\"/content/drive/MyDrive/ASTERLAYSE/Heatmap.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Y8XZw3yQO8iQ",
        "outputId": "ed5fde0c-458b-44ec-d9fa-9876a193ae6b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "corelateed_with_a = ['per', 'per_y', 'ad']\n",
        "\n",
        "for x in corelateed_with_a:\n",
        "  plt.figure(figsize=(8, 6))\n",
        "  sns.scatterplot(data=df, x='a', y=x)\n",
        "  plt.title(f'Scatter Plot of a vs. {x}')\n",
        "  plt.savefig(f\"/content/drive/MyDrive/ASTERLAYSE/Scatter_Plot_of_a_vs_{x}.png\")\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEHID8JCS638"
      },
      "source": [
        "## Dropping Corelated columns\n",
        "* per\n",
        "* per_y\n",
        "* ad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5o7svMWATTJj"
      },
      "outputs": [],
      "source": [
        "df = df.drop(columns=corelateed_with_a)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euIvUmADb_We",
        "outputId": "2ba37951-dd32-4f45-a398-62683e529459"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 658672 entries, 0 to 671770\n",
            "Data columns (total 16 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   a               658672 non-null  float64\n",
            " 1   e               658672 non-null  float64\n",
            " 2   i               658672 non-null  float64\n",
            " 3   om              658672 non-null  float64\n",
            " 4   w               658672 non-null  float64\n",
            " 5   q               658672 non-null  float64\n",
            " 6   data_arc        658672 non-null  float64\n",
            " 7   condition_code  658672 non-null  float64\n",
            " 8   n_obs_used      658672 non-null  int64  \n",
            " 9   H               658672 non-null  float64\n",
            " 10  neo             658672 non-null  int64  \n",
            " 11  pha             658672 non-null  float64\n",
            " 12  class           658672 non-null  int64  \n",
            " 13  n               658672 non-null  float64\n",
            " 14  ma              658672 non-null  float64\n",
            " 15  moid            658672 non-null  float64\n",
            "dtypes: float64(13), int64(3)\n",
            "memory usage: 85.4 MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eK7gaVFcmD6j"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTYEyVwTmG05"
      },
      "source": [
        "## train test split of training data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bZuN9nhp-Ir",
        "outputId": "6ac3a075-06e4-4e05-ea32-d5e150ad217d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(526937, 15)\n",
            "(131735, 15)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x = df.drop(['moid'], axis=1)\n",
        "y = df['moid']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "DmnAwjZAcl09",
        "outputId": "6b8ab1f6-986c-4e3d-a8c1-4e96cfe335f1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3d0eb59b-3abe-4ee5-9a8b-24502987fb89\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>e</th>\n",
              "      <th>i</th>\n",
              "      <th>om</th>\n",
              "      <th>w</th>\n",
              "      <th>q</th>\n",
              "      <th>data_arc</th>\n",
              "      <th>condition_code</th>\n",
              "      <th>n_obs_used</th>\n",
              "      <th>H</th>\n",
              "      <th>neo</th>\n",
              "      <th>pha</th>\n",
              "      <th>class</th>\n",
              "      <th>n</th>\n",
              "      <th>ma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>288520</th>\n",
              "      <td>2.616660</td>\n",
              "      <td>0.188617</td>\n",
              "      <td>7.836221</td>\n",
              "      <td>181.951704</td>\n",
              "      <td>286.142795</td>\n",
              "      <td>2.123113</td>\n",
              "      <td>6222.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>293</td>\n",
              "      <td>16.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.232854</td>\n",
              "      <td>171.863632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240044</th>\n",
              "      <td>1.214197</td>\n",
              "      <td>0.169346</td>\n",
              "      <td>31.161678</td>\n",
              "      <td>210.711283</td>\n",
              "      <td>198.015265</td>\n",
              "      <td>1.008578</td>\n",
              "      <td>1472.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.736665</td>\n",
              "      <td>138.245131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40687</th>\n",
              "      <td>2.554848</td>\n",
              "      <td>0.106884</td>\n",
              "      <td>11.724590</td>\n",
              "      <td>331.063647</td>\n",
              "      <td>181.330268</td>\n",
              "      <td>2.281774</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>89</td>\n",
              "      <td>16.4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.241355</td>\n",
              "      <td>310.285907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>483757</th>\n",
              "      <td>2.300654</td>\n",
              "      <td>0.204657</td>\n",
              "      <td>5.200765</td>\n",
              "      <td>241.920806</td>\n",
              "      <td>221.337391</td>\n",
              "      <td>1.829809</td>\n",
              "      <td>6713.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100</td>\n",
              "      <td>17.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.282441</td>\n",
              "      <td>173.598831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334555</th>\n",
              "      <td>3.482850</td>\n",
              "      <td>0.063680</td>\n",
              "      <td>8.688176</td>\n",
              "      <td>241.937124</td>\n",
              "      <td>177.301645</td>\n",
              "      <td>3.261061</td>\n",
              "      <td>7359.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>470</td>\n",
              "      <td>14.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.151636</td>\n",
              "      <td>298.132549</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d0eb59b-3abe-4ee5-9a8b-24502987fb89')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3d0eb59b-3abe-4ee5-9a8b-24502987fb89 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3d0eb59b-3abe-4ee5-9a8b-24502987fb89');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d831861f-aa98-4295-9c2e-d80693ba4d28\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d831861f-aa98-4295-9c2e-d80693ba4d28')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d831861f-aa98-4295-9c2e-d80693ba4d28 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "               a         e          i          om           w         q  \\\n",
              "288520  2.616660  0.188617   7.836221  181.951704  286.142795  2.123113   \n",
              "240044  1.214197  0.169346  31.161678  210.711283  198.015265  1.008578   \n",
              "40687   2.554848  0.106884  11.724590  331.063647  181.330268  2.281774   \n",
              "483757  2.300654  0.204657   5.200765  241.920806  221.337391  1.829809   \n",
              "334555  3.482850  0.063680   8.688176  241.937124  177.301645  3.261061   \n",
              "\n",
              "        data_arc  condition_code  n_obs_used     H  neo  pha  class         n  \\\n",
              "288520    6222.0             0.0         293  16.1    0  0.0      8  0.232854   \n",
              "240044    1472.0             0.0          58  21.0    1  1.0      1  0.736665   \n",
              "40687     2000.0             0.0          89  16.4    0  0.0      8  0.241355   \n",
              "483757    6713.0             0.0         100  17.8    0  0.0      8  0.282441   \n",
              "334555    7359.0             0.0         470  14.6    0  0.0     10  0.151636   \n",
              "\n",
              "                ma  \n",
              "288520  171.863632  \n",
              "240044  138.245131  \n",
              "40687   310.285907  \n",
              "483757  173.598831  \n",
              "334555  298.132549  "
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHZCV9pvmOsA"
      },
      "source": [
        "## linear regression model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRffyo5uB3Pi",
        "outputId": "67587919-8754-4eb3-9789-33d5145dd55d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Squared Error: 0.0013448835921428656\n",
            "Root Mean Squared Error: 0.036672654555443156\n",
            "Mean Absolute Error: 0.020814241770678144\n",
            "R-squared: 0.9997452537976232\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error,mean_absolute_error, r2_score\n",
        "\n",
        "# Create and fit the multiple regression model\n",
        "model = LinearRegression()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred_LR = model.predict(x_test)\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred_LR)\n",
        "rmse = np.sqrt(mse)\n",
        "mae = mean_absolute_error(y_test, y_pred_LR)\n",
        "r2 = r2_score(y_test, y_pred_LR)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"Root Mean Squared Error: {rmse}\")\n",
        "print(f\"Mean Absolute Error: {mae}\")\n",
        "print(f\"R-squared: {r2}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRcQsBfpmSa6"
      },
      "source": [
        "## Gradient boosting regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "p6VJtMEwdAj8",
        "outputId": "6f143942-1698-48da-9cc0-b4cc7e8f8e9e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "GradientBoostingRegressor(random_state=42)"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "gbc = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\n",
        "\n",
        "gbc.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8P_MK1IHdujV",
        "outputId": "665c7ee5-4964-4569-bc56-951249ce40cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Squared Error: 0.0006807442439919126\n",
            "Root Mean Squared Error: 0.026091075945462897\n",
            "Mean Absolute Error: 0.014509631039392745\n",
            "R-squared: 0.999871054259298\n"
          ]
        }
      ],
      "source": [
        "y_pred = gbc.predict(x_test)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"Root Mean Squared Error: {rmse}\")\n",
        "print(f\"Mean Absolute Error: {mae}\")\n",
        "print(f\"R-squared: {r2}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5fhe2PCmXgM"
      },
      "source": [
        "## Hist Gradient Boosting regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "cRptltRKL4Bf",
        "outputId": "a124fb0b-946e-48a6-d4c1-5b0583595900"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/experimental/enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>HistGradientBoostingRegressor(max_depth=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HistGradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingRegressor(max_depth=3, random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "HistGradientBoostingRegressor(max_depth=3, random_state=42)"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.experimental import enable_hist_gradient_boosting\n",
        "from sklearn.ensemble import HistGradientBoostingRegressor\n",
        "\n",
        "# Create and train the HistGradientBoostingClassifier model\n",
        "hgbc = HistGradientBoostingRegressor(max_iter=100, learning_rate=0.1, max_depth=3, random_state=42)\n",
        "hgbc.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94ct0sMcMkym",
        "outputId": "548356b3-b71f-4d81-8f7a-5115b96766af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Squared Error: 0.05544783520303665\n",
            "Root Mean Squared Error: 0.23547364014478703\n",
            "Mean Absolute Error: 0.030612144613337926\n",
            "R-squared: 0.9894971389862222\n"
          ]
        }
      ],
      "source": [
        "predictions_hgbc = hgbc.predict(x_test)\n",
        "mse = mean_squared_error(y_test, predictions_hgbc)\n",
        "rmse = np.sqrt(mse)\n",
        "mae = mean_absolute_error(y_test, predictions_hgbc)\n",
        "r2 = r2_score(y_test, predictions_hgbc)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"Root Mean Squared Error: {rmse}\")\n",
        "print(f\"Mean Absolute Error: {mae}\")\n",
        "print(f\"R-squared: {r2}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9M601nHmddt"
      },
      "source": [
        "## model saving"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "emIrhYBGIaaW"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "\n",
        "# saving GradientBoostingRegressor(random_state=42)\n",
        "model_filename = '/content/drive/MyDrive/ASTERLAYSE/gbc_model.pkl'\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(gbc, file)\n",
        "\n",
        "\n",
        "# saving HistGradientBoostingRegressor(max_depth=3, random_state=42)\n",
        "model_filename_hgbc = '/content/drive/MyDrive/ASTERLAYSE/hgbc_model.pkl'\n",
        "with open(model_filename_hgbc, 'wb') as file:\n",
        "    pickle.dump(hgbc, file)\n",
        "\n",
        "\n",
        "# saving Linear regression model\n",
        "model_filename_LR = '/content/drive/MyDrive/ASTERLAYSE/LR_model.pkl'\n",
        "with open(model_filename_LR, 'wb') as file:\n",
        "    pickle.dump(model, file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIM5L_DGIzGS",
        "outputId": "774b8385-d682-46e1-e2ab-9286e5b72a44"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Squared Error: 0.0006807442439919126\n",
            "Root Mean Squared Error: 0.026091075945462897\n",
            "Mean Absolute Error: 0.014509631039392745\n",
            "R-squared: 0.999871054259298\n"
          ]
        }
      ],
      "source": [
        "# Load the model from the pickle file\n",
        "with open(model_filename, 'rb') as file:\n",
        "    loaded_model = pickle.load(file)\n",
        "\n",
        "# Use the loaded model for predictions\n",
        "predictions = loaded_model.predict(x_test)\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "rmse = np.sqrt(mse)\n",
        "mae = mean_absolute_error(y_test, predictions)\n",
        "r2 = r2_score(y_test, predictions)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"Root Mean Squared Error: {rmse}\")\n",
        "print(f\"Mean Absolute Error: {mae}\")\n",
        "print(f\"R-squared: {r2}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6JzpSprmsFw"
      },
      "source": [
        "# Loading actual Test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ukQf3XkkKZR",
        "outputId": "4667da22-8298-448c-a0a1-2045f5e2f0be"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-49-d49ff3a4c664>:1: DtypeWarning: Columns (16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  test = pd.read_csv('/content/asteroid_dataset/Test/test_data.csv')\n"
          ]
        }
      ],
      "source": [
        "test = pd.read_csv('/content/asteroid_dataset/Test/test_data.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vP-CC9Mimwff"
      },
      "source": [
        "## Test data preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7AuNSBw8xodP"
      },
      "outputs": [],
      "source": [
        "test = test.drop(columns=columns_to_drop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jTEpUy9MZWi3"
      },
      "outputs": [],
      "source": [
        "test = test.drop(columns=corelateed_with_a)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKlrf9q6eNZQ",
        "outputId": "732c6c6e-df9c-483d-ed5f-a406082179e5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(167943, 15)"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "nIvyV6I1eXrU",
        "outputId": "c634e1e7-5440-4eb4-c4e1-d818d4e8cf6a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "a                    0\n",
              "e                    0\n",
              "i                    0\n",
              "om                   0\n",
              "w                    0\n",
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        "test.isna().sum()"
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0dececcb-6872-4508-a686-2308f7eef299 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0dececcb-6872-4508-a686-2308f7eef299');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7f650abd-bf29-4d44-b96f-abc82157b39b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f650abd-bf29-4d44-b96f-abc82157b39b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7f650abd-bf29-4d44-b96f-abc82157b39b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          a         e          i          om           w         q  data_arc  \\\n",
              "0  2.886725  0.024609   0.941499  305.739193  317.913991  2.815687   15672.0   \n",
              "1  3.127036  0.170076  26.558403   39.105115  272.566489  2.595201    5370.0   \n",
              "2  2.405329  0.051591   3.407434   42.854786  144.191352  2.281235    5990.0   \n",
              "3  2.379101  0.057795  10.984721  300.313366  313.776277  2.241599      59.0   \n",
              "4  2.970821  0.149538   5.939196  309.085101   62.914581  2.526570    2305.0   \n",
              "\n",
              "  condition_code  n_obs_used       H neo pha class         n          ma  \n",
              "0              0        1988  13.200   N   N   MBA  0.200954  346.115230  \n",
              "1              0         141  15.500   N   N   MBA  0.178240  291.479565  \n",
              "2              0         120  17.300   N   N   MBA  0.264206  241.913283  \n",
              "3              4          15  18.035   N   N   MBA  0.268587  159.781485  \n",
              "4              2          40  17.200   N   N   MBA  0.192482   94.054674  "
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8rw6fa1KLcpq"
      },
      "outputs": [],
      "source": [
        "test['class'] = le.fit_transform(test['class'])\n",
        "test['neo']= le.fit_transform(test['neo'])\n",
        "test['pha']= le.fit_transform(test['pha'])\n",
        "test['condition_code']= le.fit_transform(test['condition_code'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "tawBb9_3cbxn",
        "outputId": "a5fbdf78-eb03-46e7-a01e-3f97b708ea76"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c5442566-d2df-4992-bd26-8253b97be126\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>e</th>\n",
              "      <th>i</th>\n",
              "      <th>om</th>\n",
              "      <th>w</th>\n",
              "      <th>q</th>\n",
              "      <th>data_arc</th>\n",
              "      <th>condition_code</th>\n",
              "      <th>n_obs_used</th>\n",
              "      <th>H</th>\n",
              "      <th>neo</th>\n",
              "      <th>pha</th>\n",
              "      <th>class</th>\n",
              "      <th>n</th>\n",
              "      <th>ma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.886725</td>\n",
              "      <td>0.024609</td>\n",
              "      <td>0.941499</td>\n",
              "      <td>305.739193</td>\n",
              "      <td>317.913991</td>\n",
              "      <td>2.815687</td>\n",
              "      <td>15672.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1988</td>\n",
              "      <td>13.200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.200954</td>\n",
              "      <td>346.115230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.127036</td>\n",
              "      <td>0.170076</td>\n",
              "      <td>26.558403</td>\n",
              "      <td>39.105115</td>\n",
              "      <td>272.566489</td>\n",
              "      <td>2.595201</td>\n",
              "      <td>5370.0</td>\n",
              "      <td>0</td>\n",
              "      <td>141</td>\n",
              "      <td>15.500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.178240</td>\n",
              "      <td>291.479565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.405329</td>\n",
              "      <td>0.051591</td>\n",
              "      <td>3.407434</td>\n",
              "      <td>42.854786</td>\n",
              "      <td>144.191352</td>\n",
              "      <td>2.281235</td>\n",
              "      <td>5990.0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>17.300</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.264206</td>\n",
              "      <td>241.913283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.379101</td>\n",
              "      <td>0.057795</td>\n",
              "      <td>10.984721</td>\n",
              "      <td>300.313366</td>\n",
              "      <td>313.776277</td>\n",
              "      <td>2.241599</td>\n",
              "      <td>59.0</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>18.035</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.268587</td>\n",
              "      <td>159.781485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.970821</td>\n",
              "      <td>0.149538</td>\n",
              "      <td>5.939196</td>\n",
              "      <td>309.085101</td>\n",
              "      <td>62.914581</td>\n",
              "      <td>2.526570</td>\n",
              "      <td>2305.0</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>17.200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.192482</td>\n",
              "      <td>94.054674</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5442566-d2df-4992-bd26-8253b97be126')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c5442566-d2df-4992-bd26-8253b97be126 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c5442566-d2df-4992-bd26-8253b97be126');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2cafa521-c280-476a-9d74-7e177ad8279d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cafa521-c280-476a-9d74-7e177ad8279d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2cafa521-c280-476a-9d74-7e177ad8279d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          a         e          i          om           w         q  data_arc  \\\n",
              "0  2.886725  0.024609   0.941499  305.739193  317.913991  2.815687   15672.0   \n",
              "1  3.127036  0.170076  26.558403   39.105115  272.566489  2.595201    5370.0   \n",
              "2  2.405329  0.051591   3.407434   42.854786  144.191352  2.281235    5990.0   \n",
              "3  2.379101  0.057795  10.984721  300.313366  313.776277  2.241599      59.0   \n",
              "4  2.970821  0.149538   5.939196  309.085101   62.914581  2.526570    2305.0   \n",
              "\n",
              "   condition_code  n_obs_used       H  neo  pha  class         n          ma  \n",
              "0               0        1988  13.200    0    0      7  0.200954  346.115230  \n",
              "1               0         141  15.500    0    0      7  0.178240  291.479565  \n",
              "2               0         120  17.300    0    0      7  0.264206  241.913283  \n",
              "3               4          15  18.035    0    0      7  0.268587  159.781485  \n",
              "4               2          40  17.200    0    0      7  0.192482   94.054674  "
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_UrzRMHgnB__"
      },
      "source": [
        "## Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gqLE8JKqbBSS"
      },
      "outputs": [],
      "source": [
        "predictions = []\n",
        "\n",
        "# Iterate through the rows of the test dataset\n",
        "for index, row in test.iterrows():\n",
        "    if row.isnull().any():\n",
        "        predictions.append(hgbc.predict([row.values])[0])\n",
        "    else:\n",
        "        predictions.append(gbc.predict([row.values])[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxYCqE6QdtV5",
        "outputId": "a1aa438c-fea7-4391-9a3b-359355042e0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "167943"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x5YvtojUrSnM"
      },
      "outputs": [],
      "source": [
        "df_pred = pd.DataFrame({'Predictions': predictions})\n",
        "csv_file_path = '/content/drive/MyDrive/ASTERLAYSE/predictions.csv'\n",
        "df_pred.to_csv(csv_file_path, index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eeq2uGvyfD32"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}