ML_Based_Stock_market_prediction.ipynb

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      "cell_type": "markdown",
      "source": [
        "This project is developed to predict tomorrow's S&P 500 index price using historical data. First start by downloading S&P 500 prices using a package called yfinance.  Then, clean up the data with pandas, and get it ready for machine learning.  "
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      "source": [
        "import pandas as pd\n",
        "import yfinance as yf # importing this to download daily stock index prices\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import precision_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Using Ticker class for downloading the price history for each symbol.\n",
        "\n",
        " The S&P 500 index, or Standard & Poor's 500, is a very important index that tracks the performance of the stocks of 500 large-cap companies in the U.S. The ticker symbol for the S&P 500 index is ^GSPC."
      ],
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      "cell_type": "code",
      "source": [
        "sp_500 = yf.Ticker(\"^GSPC\")  # using GSPC (sp_500 index)"
      ],
      "metadata": {
        "id": "ghUFAJCC1VZi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500 = sp_500.history(period=\"max\") # retriving all the data from beginning"
      ],
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        "id": "Z9blQ7QV1jSJ"
      },
      "execution_count": 3,
      "outputs": []
    },
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        "sp_500"
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              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
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              "1928-01-06 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
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              "2023-09-01 00:00:00-04:00  4530.600098  4541.250000  4501.350098  4515.770020   \n",
              "2023-09-05 00:00:00-04:00  4510.060059  4514.290039  4496.009766  4496.830078   \n",
              "2023-09-06 00:00:00-04:00  4490.350098  4490.350098  4442.379883  4465.479980   \n",
              "2023-09-07 00:00:00-04:00  4434.549805  4457.810059  4430.459961  4451.140137   \n",
              "2023-09-08 00:00:00-04:00  4451.299805  4473.529785  4448.379883  4457.490234   \n",
              "\n",
              "                               Volume  Dividends  Stock Splits  \n",
              "Date                                                            \n",
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              "2023-09-07 00:00:00-04:00  3763760000        0.0           0.0  \n",
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          "metadata": {},
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    {
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        "sp_500.index"
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              "DatetimeIndex(['1927-12-30 00:00:00-05:00', '1928-01-03 00:00:00-05:00',\n",
              "               '1928-01-04 00:00:00-05:00', '1928-01-05 00:00:00-05:00',\n",
              "               '1928-01-06 00:00:00-05:00', '1928-01-09 00:00:00-05:00',\n",
              "               '1928-01-10 00:00:00-05:00', '1928-01-11 00:00:00-05:00',\n",
              "               '1928-01-12 00:00:00-05:00', '1928-01-13 00:00:00-05:00',\n",
              "               ...\n",
              "               '2023-08-30 00:00:00-04:00', '2023-08-31 00:00:00-04:00',\n",
              "               '2023-09-01 00:00:00-04:00', '2023-09-05 00:00:00-04:00',\n",
              "               '2023-09-06 00:00:00-04:00', '2023-09-07 00:00:00-04:00',\n",
              "               '2023-09-08 00:00:00-04:00', '2023-09-11 00:00:00-04:00',\n",
              "               '2023-09-12 00:00:00-04:00', '2023-09-13 00:00:00-04:00'],\n",
              "              dtype='datetime64[ns, America/New_York]', name='Date', length=24040, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cleaning dataset"
      ],
      "metadata": {
        "id": "cTgtdT8V3Hko"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500.plot.line(y=\"Close\", use_index=True) # Visualizing the line graph with closing price at y-axis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "9UkNmJ1x3J0S",
        "outputId": "557a9a05-b7a7-4cc1-a1b7-d35577463f9c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# deleting these two columns\n",
        "del sp_500[\"Dividends\"]\n",
        "del sp_500[\"Stock Splits\"]"
      ],
      "metadata": {
        "id": "XI39JDbB4Zl_"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500[\"Tomorrow\"] = sp_500[\"Close\"].shift(-1)"
      ],
      "metadata": {
        "id": "dp8a-uv64qHY"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "gcitvSPD5EpR",
        "outputId": "066c7465-3a74-4634-b3b9-a47799569ff3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1927-12-30 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "1928-01-03 00:00:00-05:00    17.760000    17.760000    17.760000    17.760000   \n",
              "1928-01-04 00:00:00-05:00    17.719999    17.719999    17.719999    17.719999   \n",
              "1928-01-05 00:00:00-05:00    17.549999    17.549999    17.549999    17.549999   \n",
              "1928-01-06 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "...                                ...          ...          ...          ...   \n",
              "2023-09-07 00:00:00-04:00  4434.549805  4457.810059  4430.459961  4451.140137   \n",
              "2023-09-08 00:00:00-04:00  4451.299805  4473.529785  4448.379883  4457.490234   \n",
              "2023-09-11 00:00:00-04:00  4480.979980  4490.770020  4467.890137  4487.459961   \n",
              "2023-09-12 00:00:00-04:00  4473.270020  4487.109863  4456.830078  4461.899902   \n",
              "2023-09-13 00:00:00-04:00  4462.649902  4479.390137  4453.520020  4467.439941   \n",
              "\n",
              "                               Volume     Tomorrow  \n",
              "Date                                                \n",
              "1927-12-30 00:00:00-05:00           0    17.760000  \n",
              "1928-01-03 00:00:00-05:00           0    17.719999  \n",
              "1928-01-04 00:00:00-05:00           0    17.549999  \n",
              "1928-01-05 00:00:00-05:00           0    17.660000  \n",
              "1928-01-06 00:00:00-05:00           0    17.500000  \n",
              "...                               ...          ...  \n",
              "2023-09-07 00:00:00-04:00  3763760000  4457.490234  \n",
              "2023-09-08 00:00:00-04:00  3259290000  4487.459961  \n",
              "2023-09-11 00:00:00-04:00  3369920000  4461.899902  \n",
              "2023-09-12 00:00:00-04:00  3435740000  4467.439941  \n",
              "2023-09-13 00:00:00-04:00  2254136000          NaN  \n",
              "\n",
              "[24040 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-300d2f56-ac76-4ca7-8780-6411c3dc9510\" 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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Tomorrow</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1927-12-30 00:00:00-05:00</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.760000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-03 00:00:00-05:00</th>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.719999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-04 00:00:00-05:00</th>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>0</td>\n",
              "      <td>17.549999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-05 00:00:00-05:00</th>\n",
              "      <td>17.549999</td>\n",
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              "      <td>17.660000</td>\n",
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              "      <th>1928-01-06 00:00:00-05:00</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
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              "      <th>2023-09-07 00:00:00-04:00</th>\n",
              "      <td>4434.549805</td>\n",
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              "      <td>3763760000</td>\n",
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              "      <th>2023-09-08 00:00:00-04:00</th>\n",
              "      <td>4451.299805</td>\n",
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              "      <th>2023-09-11 00:00:00-04:00</th>\n",
              "      <td>4480.979980</td>\n",
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              "      <td>4487.459961</td>\n",
              "      <td>3369920000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-09-12 00:00:00-04:00</th>\n",
              "      <td>4473.270020</td>\n",
              "      <td>4487.109863</td>\n",
              "      <td>4456.830078</td>\n",
              "      <td>4461.899902</td>\n",
              "      <td>3435740000</td>\n",
              "      <td>4467.439941</td>\n",
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              "    <tr>\n",
              "      <th>2023-09-13 00:00:00-04:00</th>\n",
              "      <td>4462.649902</td>\n",
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              "      <td>4453.520020</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking if the tomorrow price is greater than closing price then create a column target and assign the true or false value but 1 for true and 0 for false. as using astype() function"
      ],
      "metadata": {
        "id": "rHkJQeGj6z_q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500[\"Target\"] = (sp_500[\"Tomorrow\"] > sp_500[\"Close\"]).astype(int)"
      ],
      "metadata": {
        "id": "of0oj9tt5MyP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 995
        },
        "id": "z-PBI2U35fii",
        "outputId": "9f943d35-1248-44c3-9681-6864460a1af6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
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              "                               Volume     Tomorrow  Target  \n",
              "Date                                                        \n",
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              "    (() => {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500 = sp_500.loc[\"1990-01-01\":].copy() #reducing the data after specific date"
      ],
      "metadata": {
        "id": "4T92tcj75whr"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 995
        },
        "id": "6RDGmHqy6FEI",
        "outputId": "b6d9851d-c2b0-44ec-a5da-b526dedaf5bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1990-01-02 00:00:00-05:00   353.399994   359.690002   351.980011   359.690002   \n",
              "1990-01-03 00:00:00-05:00   359.690002   360.589996   357.890015   358.760010   \n",
              "1990-01-04 00:00:00-05:00   358.760010   358.760010   352.890015   355.670013   \n",
              "1990-01-05 00:00:00-05:00   355.670013   355.670013   351.350006   352.200012   \n",
              "1990-01-08 00:00:00-05:00   352.200012   354.239990   350.540009   353.790009   \n",
              "...                                ...          ...          ...          ...   \n",
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              "2023-09-05 00:00:00-04:00  4510.060059  4514.290039  4496.009766  4496.830078   \n",
              "2023-09-06 00:00:00-04:00  4490.350098  4490.350098  4442.379883  4465.479980   \n",
              "2023-09-07 00:00:00-04:00  4434.549805  4457.810059  4430.459961  4451.140137   \n",
              "2023-09-08 00:00:00-04:00  4451.299805  4473.529785  4448.379883  4457.490234   \n",
              "\n",
              "                               Volume     Tomorrow  Target  \n",
              "Date                                                        \n",
              "1990-01-02 00:00:00-05:00   162070000   358.760010       0  \n",
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              "1990-01-08 00:00:00-05:00   140110000   349.619995       0  \n",
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              "2023-09-07 00:00:00-04:00  3763760000  4457.490234       1  \n",
              "2023-09-08 00:00:00-04:00  3259290000          NaN       0  \n",
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              "        document.querySelector('#df-f79fdca5-45bd-4542-b511-04903283132b 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-f79fdca5-45bd-4542-b511-04903283132b');\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-f8de1673-ec0b-4dea-b372-c35e42083a4a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8de1673-ec0b-4dea-b372-c35e42083a4a')\"\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-f8de1673-ec0b-4dea-b372-c35e42083a4a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* n_estimators means the number of decision trees training, the higher the value of n_estimators mostly result in the best accuracy of the model.\n",
        "* min_sample_split helps to prevent overfitting."
      ],
      "metadata": {
        "id": "6jcXq6Nh-HyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(n_estimators=100, min_samples_split=100, random_state=1)"
      ],
      "metadata": {
        "id": "ayao1W-t6d4T"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# time series dataset, so cant use the other splitting methods.\n",
        "# assign row to the training set and excluded last 100 rows and\n",
        "# assigned these last 100 rows to test set\n",
        "\n",
        "train = sp_500.iloc[:-100]\n",
        "test = sp_500.iloc[-100:]"
      ],
      "metadata": {
        "id": "fn5S75rK8E6U"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictors = [\"Close\", \"Volume\", \"Open\", \"High\", \"Low\"]"
      ],
      "metadata": {
        "id": "yhQcku-F8bRc"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train[predictors], train[\"Target\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "wy_jtWuV8vHm",
        "outputId": "7d00880d-18f2-45a6-8bbe-466993957899"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(min_samples_split=100, random_state=1)"
            ],
            "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>RandomForestClassifier(min_samples_split=100, random_state=1)</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_split=100, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = model.predict(test[predictors])"
      ],
      "metadata": {
        "id": "iQDmAywV8-p-"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = pd.Series(preds, index=test.index)"
      ],
      "metadata": {
        "id": "Ji34PP-t9iNo"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-_cPmVw9vJa",
        "outputId": "9c2db125-8c4f-4be0-c1ab-a929bcffcb40"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2023-04-21 00:00:00-04:00    1\n",
              "2023-04-24 00:00:00-04:00    0\n",
              "2023-04-25 00:00:00-04:00    1\n",
              "2023-04-26 00:00:00-04:00    1\n",
              "2023-04-27 00:00:00-04:00    0\n",
              "                            ..\n",
              "2023-09-07 00:00:00-04:00    1\n",
              "2023-09-08 00:00:00-04:00    0\n",
              "2023-09-11 00:00:00-04:00    0\n",
              "2023-09-12 00:00:00-04:00    1\n",
              "2023-09-13 00:00:00-04:00    1\n",
              "Length: 100, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(test[\"Target\"], preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BudbIiEt919j",
        "outputId": "605d91d1-0320-48eb-c6a5-344d9ecea41b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5507246376811594"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined = pd.concat([test[\"Target\"], preds], axis=1)"
      ],
      "metadata": {
        "id": "gI_wzkRYBROk"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "B4_MawH9BpMP",
        "outputId": "2c6a72a3-21e4-4954-9c19-ce050746b315"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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H8AM/8AN43/veh//8n/8z/vt//+948YtfPM0nGBFtnZmMyOEmrm70NVpj4Sq6b6hF0kpGFNJSCYGdecKisyafY7Y7XFz2zlq9T1VnX8gtBJo+Iakh9aJoJyNNZpJjTI9cZHYcXnQXXQn9o28OlUDdj930N/yUUJ/Fze34G/HYaEWfTE6IjCjUqUHVHQ+hzpFUZKR/vZTkPKr092/feEVVYU92icRqvyAZafhkZC26a3cuMygsct77ztvhu0wbkn7nrXXxS+k+J2Ws0H3QTnByb0Ip0YxANYHhmNrHox2Z5HjfLzPJkb75CbIWJCKFNCpPMinb4ZorCr1hMD+f6OdYQabdH0i05HpvxMYxiaTInsVf/MVf4OlPfzqe/vQORrzuuuvw9Kc/HTfccAMA4GMf+5hOTADg4osvxutf/3q86U1vwqWXXopXvOIV+JVf+ZUDIevVycg6k6YRFZo+m6zQGgtwbhV+N5H+JPPQNJUYFp1VI/3vsR4USB0y0i9uTkZNPofMSxqaVg6okGygPrq7wyxf0NR8WWSkr8IvSUaGGyZQoXt96q6gbaWuObE/K/2epgzp+/5GhEKduqSSJCOpNM2KoWlEpaFpDgVY9ijGar+gclEnI2bNxbofMysZ0YhgRRAN/rtMVWrR79ymaXOREfWeoTXEPtdyb0IpQdeEEqRXHdMp14RQZK9han7290XXggJpnPqe7XHV9VuMQPiu0T45qdEMCFliQtYQ6p6i3acysmtGvuzLvix4gnLuql/2ZV+Gd73rXblvtfGQfTJi3MRDQWmaHnWo0IImlvbiljaRxvxpjUVpGqArbD20YHb0Kqmq5kA1oDfOzYzC65m7ilaqZKSBkO1GkBF1w5xxyMgImkbNqRYClVTISNqFuCREsLOIqSRncmRE0TQTJiOtSkaq7ruvPBCwJ9T32bSSpfs4FGAJhYwkJv3GhPtkpDKXqmXVjZmHjAzJk75xWKdYI/2fgwv6nTetxJxclrnnRe25mdGw6bDcm1BKqO9Sym7zs5jlndcDDTLdmhCK3Po0NQ83WSKfswAZ0ZS1Dxkpven70Es5bNqyaRqSjKwPSFeYgzGLUxRDMpJL01T6Rl8RqgIoTUb4mhF1UldWMuK96NTn6KvANUdpjWvSNHkXcivlADdKWjNiP6+fa8kiSXcl1vE8pGmaggJWVdtBakYamXYJ0M/hcMJySHKmDOlLJseM2aokrOqOh+AhYF+o86SVYM+jEDKynpSmKUBGEmgaTa2k1nmQ59mXfu554as5oGHXJ22CpqHXclEBulQ/p6sjS3m/5OJNaf4cxpFYS8+amRBVxa+FkMM1VxRemkbR2TK7dsioGUlcAzcdB2MWpyhkf9MWqcgITUakommaCWgaj5qGLDyzutILqPeiU59jttO/PoGm6YfKo2mEHmcTNA3lM+1jMiQjBYgLpWmkKipLu1HQz+HspjPh+NSQmRRKSrQEMjZqRnLVNNKiacixtUMVm66XBciIh6ZZoft3jbYr3E6JliIj/UMeyi31qzSufS9NkzZWFk3Tf22bKGCln6MI2dQ0jfX4yFoUX+QWxvqQEWNtK9gAqO/ZQbb0NVdawOpJkAidvavpulSaZvh8j9oC1n9S0d+0RZNP0wzIiFkzUuTC6aNpNK3Q/TsKyVrIiM7EvTRNRZKG1CpsclG1hKbxLOpFixkt2LSOyUDT5CMjmqapBATydiv0c7g0Tfdzcpqmn1tb6l/DjdkMC2MrKU2TiIxQRRFD93H0hkIxmmVBzYiPphEEKcmu+aq9/H4+TUNeS64BKaWBxKXEQNP4n9NaaMtO5k0oJei1XDKul6bJXGtSI3fj46sZGSholNE0nrVwPE3DU6kKGRGS0DSpRbyUptkiI6c+xLy7aVepyQhR07T9YlDJKWkaHl2obH7YS9OYyIi6sKRD06hVktA0iTUYNk3jkzaOqpw3aBrz9WPUNJRKqJFJ05Bj7qVpJr6aNoGM0Mr+1ihgzaVpJKvK4m68o5KRCE3T/SPz+jVoGuspmcmISdPQZIR/TnAsraYJ0DStvSbk3YRSgq5hqeuC+fr+58lS02QmOer42se5lTDWttzwFUXLDdE06DdUlTBpmpSiY4o+b5GRAxBilpuMDIjCuqVqmuEpRcZnPjWNxcNH+WEbGUmiafIu5EaaNI1vhzkGkpUBmma3T0aOlyQjhE4RUDfltAtxjyz2fjXNZmiaaWtGNkHTiOAxWOeaC9Lw0DQNKixlv0inICM0waVqGi9Nk+czQl8LmNdDuprGHccOd03YAE0zEhlRx9S7JhQkOKHIXsNa83V0nNaHJieEryga5JorCs81qmtGZKPvDa0E1gmlApqulYKRCZya+NRORhQy0iYWwTGmZxVaCCH0QmIvbkkRpWlsSDYNGWl8/CdjVpUq42utC9ZL04wxPaPz9dA0J0rUNGTXW42haU5WMrKBAtZB2lt19+ep1DSaPnBfo8wFi5IRTdOYyEjTSuz3dSNJyQg9j0Tl9YRQN5Jk0zOqpiHXDx03tR1PkumZNb8kM8TMMJCREprG4+OxMTWNQlwS1wRN04RqRgqQEYWMOshIf20V0yFemoaoaeZU4BCfu+k3VDatqeNTOhmp+mSkbgsKWEkyAsCrKska11bTWNB3ds2IrzKcwuvkT8uEs9KGMiuPKiEXOjXnR5MRm6YZ4zPS/ayEQKV54xKaxvxbrrlVashMpUvSmAoyloqmyVTTrCJqGuYYZLddMF6skBGzZqSVwH4vGU6iaejnq2qdNLmQej7lxpmVGTRNJuUT2hO41O2G1TRFNI2qGeHHXbcS6ynroIppGvvxzdA049U0/Jw4NQ2Q9p21pKi2qLRgA7FNRgDMcmtGqtosRmpbL12RFJHeNGph3J3HaJr+c/Sfa+3bWTMqiOC41pxMmsacq/5IY2gaulBZhZWHNE1T3pumMz1TdEOqmibBZ2RTapoN0DQNqjLTM643TcT0rKm78zHZ6dh4MU/TtFJiLwcZoceQFLD6iixzvktdeDqapomvIbZqSasoJqQ+NkbTkH+nbHxSI5+m4efXyKloGjsZGSiRovDVdakC1h6dX2SgZEMhuyhD8zcQn9rJyKJr1DdL9Spo6U2cHDrZ6OR1Upomlx+2fUbkkDSYAxM1DV14EigVo+K89femGVM5H6JpVG+aMXbwdSU0xLlOlfYm1YxkTykYm6gZUYuQVItQBk2zblrNRzdSsnQfR28MTseJST8N1SiPo2lkDjLio2mstyug3LhaD4OmmbI3jaVa2oltUApi0zQNsBmTtrGmZ0bNyBg1jUfam7rWOBGjafqfOZSdJA0zixu8Thyf0slIPe+SkXlqzQjTm0Y97rspZ43rU9MkF7DaNSO+jFrxFbW18CTAe1Ia9I/P6EcBGkWLTpCmKa8ZYU3PkmtGUtQ0G6oZmZSmoWoakF1X/Lylu1mHprFUHsZ71hkIhh1K5m2paVoptbNrWs2ITdNETM8yvkueppHO3+PjuK+1wzU927S0t6RQvP8ZQEY2QSul96Yxfw6Py3E0ja6fc8cF0ilhd2CP6VmvCKz6n8P9IYWm6RFOtQ4cgMi2g/+nFLOdnqZJRUbUTbKqTQvdtvEWxCWFtzdN9zPZ+tlCRlTBlHSujmFHSz0NUmmahuweOIgaoDzutGqaQ2PUNGT3rkzPUovKggWsgXqJMSF93TrHjKn666DqugHrhS7+HvS8c2iaQK2FMhcsSkY80t62lXk1IzZNE/HHyfkuORt3ExlJGyeF6m1sZGQjpmfD70UF6D7Ts8yNT2pM1puGrm0F15zPzl+MbZQn+GtUISOqdiSnNQAtYN3SNAcgZj1Nk2wpbdA05HEicd1Eb5rkplgeB1ZhX1gjaRpJVDqanmIu7G6u+YuZDNE0U3TtFdC7jFaKJBWRWTNij4t+3Il5GlXQOGnNSIc0SNtnJOE9nISMNJ6jsmnnPXuaJtnpmIaPppE0GclERogDq7/+J32KLE3TjyNEvkw4VE7hrAmb8BnZlOnZyHF9MRlNIyWkprZLCljN+ajQaOTEvWmGAtY+GckwwJMtaZi5TUZOfcx2umRkgVyapnJpGl9fgpxxA71pgGHh2UtARjrIUcHHdgGrq4IAUmkaeEzPbGREjVmw6IRMz+YdmDcGGaksaW9K/kgLBE9ab5pNqmkgstU09Pzwmp5xx0D7+UxJ0yCzZoTSNFXc9CyngJUxK9N0z8hx7HCo2004sI5EMLTpmWdNADZj0parpnF/H6mm8dWMKBS2tGYkoqZRTiHD/SH+ncmtmuZgxbxHRhZylfYCsvgq0zMAHUKQ0FfCG97eNObCmF7AumMVYwVomszdikPTeAoBaRvxXBlfCk1zYpXmNMjNidI0bWIBV5LPyKPMgbXJpWnIMTBNz+qgjbrIbbtAI0jTFKhp+uQrZnqWRdOoa4ChaXLGSVlDTg5NMw7BoNe+8fjIWhRfqPdZJnol0eXILjqehqYxH9c0jayy16xuAF/NSJ+M2DRNps/INhk5ADHfzURGKE1Ds1w5uLAWJSMR07PkYjWCjFCZWpCmISdiSkbdtlIXVtIkLFQ5v5e5oMmEAtZuvnnj0huEcmBNhSkpMvKolvY25TQNPT/M3jSUpmFemNt2gUZITZNF0wxz7ebJ8/u0OWVqcA3uShRW3kZrJHxF7bnXQijotZyyJtgR603TjbshX5SUDRVNGq3fJbEtyA2folKtZy2qsvuDl6bxqWlyfEbE1vTsIMRi53D3UzRo1gm+FbQ3jYemKUoyvWqa7qemaaI+IwMyQiFHtzeNQniqbGSko2kGXtVrB093V7kLWutPRpTXCpDvNUKpBKF2b6k0DUUFnMWm+zm1mkbtiIp2U56Q5HObapoUmsZW0wwF3SEkILvtgn6TBoA6uBPVjPRIUO29cUjj7ynBmZWFpM7ecTxFtTRafeluDhnJvbk7r1fJiEc23Y075XzpWpNQL8HQaUA3702anhUrVzzopVrDNE0zT68f2tI0BywWPTICAMv9hCZe1uKrlRiUpin5YiO9aRyaxndz1wWsu2HNPE2qCnYV9IL1WSCPKYIz1D/WMakroY9Drrx38GggpmcFNI29oGy6N42DbI0J6i9g0DQJyQhV09CaETEsstzNd3A6TkQgVTTk+VYyInNrRmyaxsPvl3yXnD+IvZFICZ9M3pifXhO6f2+iZmSsH4imaTyyaWCD801Icnx+MK3EKNMzn08MVdMU3fg9NE2laZpuzCyahlBH22TkAMTO7mH9+3LvePwFFixtOJH2R3IzNE0/32SaZsdoaJdK06TZCCfSNGN4Z4OmcedU2rnXoFMkhSlTkhGLoqDjFkDySaFMzzZQM6JNz7LUNAGaJkBVVYvMtgv6TUgt12iaZkic6DwnNT3jakYyxkmpGXGp2w137S1AMJJMzzaFjIyhaaw1PTfiyEihciVC04gCmgZN3vp3MuJTOhmZzRca3chCRnpEgRaIDlBtSTLiUdNY/hXppme7kG2gMpyB14PjkrALWH3+KibiMh1NAwz9aXIVNZSmoWqaFJ29gwqQ2Jjp2cZ70yBTTWPTNAP1EaJplLngLBcZaQkNx9I0qoA1Axnpdw2bMT2ju+z8cyLFONGhbjdC04zYSIAAvYGakWmTp+H3tHoJQtNQENZAffPX8SEZsf5g0DQF94eKR7gH0zNTTZODjBTXsWwgPqWTEQB6QVvupSQjpNZCmpXX40zPPGqa/u0G6+d0O3ijz4KvZkTY0t40n5GGScLsj53rX2JMz1gh3M+q+nFkJyNEiSAyFwhHSUJiUwWs2AhNQ9Q0ktI0echIZ3rmnkccTVMvMs0FVSiaRlTDPPuQpTUjMZqm4LusGZQlhBT5wufZw82vtpGRSWmP4fdRahobIBiZ5PjCKJZP8koir7USyCloGntzo5CLRnXKzg0fTWObnuk+RSk1I3nWBicjPuWTkaXokpH1fgpNQzhySzo7qmYk1fQs6sA6mJ5RyFHYNxqDbhoeTrWDN0zPxPA4jVE0TSIyklvpT+kUQaHTpGSEUhT2uOjH3UwycmBoGtqfR/poGvd1da65oH4TvkkeAKs3TYmaZhiHRlAV5IlQb5o8NU2C6ZklI9+4z0iJmuYk0jT2e6Siu77fx9E0/TghmqaoZmToBWY8rGpGNDKSjpINpmcHpzfNp7QdPADd32K1zKNpaE0G2sEWvSjz1ciI+WKbd47TNLbpWQQZqaps6LRpwfamoRe1lNIqVsu8sOlCwNwkSy3hDdv2Nm+3EkJGSiD5lBjs4CfU3hmfmyQjuT4jrUxW0yhzweQeUPpN+mTEqhcBuvOwyA5eqWkiDR5LaBo6lvrKxo5jh7smHKyuvfTadx1Oh9+nomnsG2nqhsr3+xifES86rrv2FlIiHvRSeSUNNSPpKJnc0jQHL1YaGcmhaWqHrvC676WEeo2nZiTZ4IgiI1KilaeGprHP7eyFJ+DACgwFrLnSXsNdM5emsfuy0OkWyEGTQtE0UyIj2oG1/9x6octDh0w1zaDK4m6+uu0CEs0FVWhkxN0zTUbTWKeXXaeVElzhYhFNk1QzYq8Jm+7aW1aXZY8DbIamsd8jdQ1jf2/hp7YTgpN4AwMK21EiJchIzPSsGzMPGVFzSmuHcTJim4yoZCQFGXHUNKSQM8GwyBtRmsauGYkjI+k0Td7C49I07gJqZ9r5u6uwmubQvKxzr7GrzNytGDSNRw6a2oMkOTI8QFLDoGnkVDSNCNIb874hZbK5oH6TAE0jMxvlJZqeaR+PkSqYAYVLHibJONE1PeveYN3mOx1732MEMmJKZU8GTWP+OxXd5eZkbuDK1TTO90cNxkbRNBYyopORHhnJqRmRhKbZIiMHI9ZVt9A1yxzeuUZDs+i2STIs8oa3gNXcpUVlfAQZaamaxr7RUHg9VxaXoKZxdysj1DQBmiZf2tv9dGia3AJWT53B5L1pMopLk8Owg4e3IygX5jGAqaYJIAHz3lxwJ7Xtgn6TEE0jsS9z7OAHahLwm541Bd8lT9OMGCewhtj1SeoGBADLiZKRMaqX0KZkE43yimgaula15uPa0LGAGtX2DtacaLH8ZmiafDWN6t7d1YzkT2kTsU1GemSkXeWpaRyaxifrSgmvtLf7md+bZteoDFcSMOf9RG0WsCZWosdMz0qgU3N+5PVBmibvJm24a6oiu0RXxJDpWYiiGBV9oiAmREbgpWni71FK08z7mpEdsTIN7WIRpGkya0Zsmsbb4LEA0WAonyLTs4Q1xKYEF4QbnKoOY4ws31wHzL8ZdWSTzXVCmkaOpGm8jQ5HKlc8CKmiaVBSwDqWOtpAbJORHGSkpYuvZXqWALF6I2J6ptabdJ+RHcspNdH0LHFXwdI0ngp1oKC47mTSNFP2ppk6GclIFFLDsYHOMj0jNI1tehYw+VoQc8H9lNos/SYRNU1Jb5rKTEbsXWGZP0g/JwYVyJP2xhV59nGe1RVm/esmq8PYFE0zohbFF0VqGvo9Wb97XasTwkfTGMq9UaZntrTXREZ2Y+1CSAxqmq3p2YGJpuo6ispVnjywaQm/2LZJlfDe8KlppFmLEKwZkdJERoyaEZ+apsD0zEpyOO7d4XHHSHuZnfQhbXqWV8Bq3CA2QNNsyoF1UprGTsJyaBrHDj5NTUOdjvdT/HxUeJrkAQU1IyRxAvyUyBjn1Ja5EWfRNB43Y2N+TOI7tfHZGDqFXq7hrr0T0TQFGx+TpjF/H6Wm8SYjRLlScn/w0TR2zUjM+oGEoabZIiMHI9pa0TR5zbZsuiKlEt4/rjTHV3OzaJpdXTPCXCjtenj9bNeAHL3JiIOM5KppJEnCzOfQyKdp0tQ0J5a5hbHdz7oqUNOkSHs3ZXq2MZoGxN0xl6Yhr4mYns3nCzSye3yV4uej36RPNj1qmr2cmpFE0zNNrxQgGtw1kNebxnwtF/aaAOTtiFPCoGly+z8F6Nqxzq78+5n/Tm32yf8eMIpMCF+z1M2pacz7RhFNI7emZwcmWo2M5Khparc3TUJfCW9EaZoEZIQuyL2aptHJSIimGR5O703DmJ4xSgI97hifkVBvmlUmMqKLLOEiBJHwmZ5JKYvqA5JC7YgmdGBVNRuttHxGEmo5gjQNPbZWiKrKczrWb5JK02SoaTRNM4xDw77mUqJm0MExpmet5OoOzPnRcXN2xCkxJmkIIaQboWkKNj7c96R+36zpWRU0tPNGVE3TJyM5Sem2N83Bi7buk5FMF0eDX6RqmlE0Da+mUZugoIyPLsiznXCWv+HeNA6Pm7lIioiaRu0Es03PfDRNZHpNK7EixQW+Ir3pHVinL2AVNjxbrKaRRkF3iKYBgKXozQWzakZCNA3yakZ0vVc3v5jpWVYSwShzxvSm6V7PP4elafRNaDM0Tc5uPrQpyS2WTwnXRiCTprGQnI3TNBOqaWofTbM1PXt0huyTkayK/JDpWck1FmmUZxscAYyMTy3I9U7n+0DVNPbNjCI82TQNWNOzxnODTh3XDDJAkKYpVdPk0TRLa/6+Ir2paRqhJR0TFrDqPhliUjWNytV8x2CJjLYL+k0UTeMmI53pWUajPA9N4zZ4HFEzYuy4zb8ljVPRZMSDjDB0WM5NKCXs986RDIfo2k1Iex1kJEkRyF+/hl1DwabSS9P061lqfZoTvq69MO8bWzXNozzkrDNkEklFcKTWojUrr7mK+vRJhE3P1MKzmA1fl9MQinTsBUwEQ9g3M5pUGQtEmunZULjbsBfgaJrG6E0TomlyfUbIrpJcjLHvzJ6/z0shRw6aFBuU9rbaDj6dCvKanlV1tHPxqkdG1suMZETTND6fEYKMxK47i6bxN3js37LAxp1LUkvGsccy5+eOO3UB65iaL5OmCdWMbMj0LAndpb97kJECmkafB9bxE4QSLuvay7skV04y0j0vpVmg0zDzAMQ2GZl1yIjIpmlAetO0bEV9ckR60yg1TV0JzGsl47MuFq2k6T4PlR4bNSNSQiMPoqQ3jZmECQaaLJHb0TDmy6lp5kpNk0vTdD8pTdNCRL8ze/6+ndXGuvZOqKYRNjxb3JsGsNVlwEAp2jG0XUi4zlSEaBpaMyLbAUXxhaWm8RWLlnTtZe3gi2pGyHQ9Nwh1DgsDGYmYIWaGq1DJQEZCNM0GClgn7U3Tmv3GcoNrmAgQaW9pbxrPNVrbvWm0A2sCMkKcmCfyyhsdn/LJiLp5i2Yimib3XKPJgU9NUyUsPKtB1qteqxAMQS9YepEJs6AqnaYZkjCepjEPQm533ZiaptSB1d+bJvw6+1ibu6nh8alNz0Q1PTJi9qZBlpqGfo8tRUbEUHvkOwbrnLYL+k38yEhLpb1AvG7EY3rmpWmybNzda39MUsPNy54f7YOkbkJ7kyEj5r9zrl9zU+L/W/aa4InxpmfD46bPSIGahnx/lPoQ9jWXGx70Uqtp2vyaEcgBGdnSNAckRH/zrlKSEUdNQ2mauGERG8aN10PT0IXHd8JZyIhXTUPfj8DrQKKaxrpg1dzoOGN704SOCTCCpmnJjUZdjAm7FXuRNxYwStNMjYxswPQMpJiupQWsuWoaWjNS1YNs2nMM1r1qLclcUL+Jv2aklTCTkZg036ZpmIJz2nE2z8a9ny6TpJbSNL5TMlgzMpWaZsT1G5b2lo0ZfL8CnxFbQaPHkiAdyctpGntcRZOn2gg44VkHdAErJCClYYoZTTAoTb0tYD0YIeZ9MtLmNdsyZGBtw1bUJ0VAOcI1YPPyw2sLGSHzM2pGpIWMZEKnSTRNQVEZDZOmcReFQ1pNUyrttWiaWM2INX+fadKmTM82Q9OIAppmeI6UQzEsxNAAzNcsUDkdJ7VdUBGhaQCBpkr0GrFpGk2r2mPC+HtKhGmaMmTEt47wa8K0PiNjar5CvWmM+rSpKKWxyIh1LY+jaUgyYiAjI2W0nmu0Mtb1NqtPkUHTbJGRgxEDMpLQUZQU7NmqkmLTswAloXdXdOHxacmJFbx6LaumMWgaU9q7bNpoMmVIe0kBKz33XTVN3oUtIjSNQkb2VvH50tC7XktNE1sg7Pn7KKmN0TR2b6Ex4dA0OWoaW5VF1DQRmqapMswF9fhhmgaAluZHFTXE6A+gdvA8ZJ9nVubWi8Wkzuw45Km+GwRP3U5dwGr+O6uA1VgHzIFsn5Ep6IGyrr0pNE3+3Oh3Ql+uaPJyNQ1P0xjJSNvo8wBI+M4Ma4NtMnIgouqRkToJGem/tN7kyaBpPFLB+Jh+5Uiwct5R05jICLVt997cCbyuIppR2zSNEPpxFZumaVTNCJBH1Zi9aehNOZaM8EkiAIII+FGB4tA1I9MhI46kuVBNQ8cy1TT8a7PaLugXxZORQZqfWDMSoGlKE8uBonXnV2ecEkKIwUTQl4xwa0JG6/iUGOMTFGqUZ/9tPcFNsKRDOHf9qrFGqWk8NT8VpWlKvqLKTZBk26IW5IPINq9p4tg6lg3ENhlZdDfvWZuAjHh70zTlyIghY22NE47e6FT4aRoLGSGQYxWiaTIXnkaaLoW6gnzCZCRG0+zOxiYjw7gpPK4j7WWK9CavFwEgNmAH70DGiTSNlNI95ywTwG7O/HHQ5oIlyQhL05jjRpERh6bpH/bUD5SoYDgzrdzzgqOPaPBrwsQ0jXP9lhWwujSN+dwp5luy1oR600yhpgECNE0RMuJeo619MHvKPBkl0zTNVtp7YKJedE28ZjKXppGDqkTKpPbfbDjuqO6CVqfww3bNiPSYntGTWNTZ/HDTwkPTkKx9UprGPaBVJYbOvRmKGt70LC5tC6tp3O9oqtiEmsZBhBLVNOtWuue2oabpfvUdh7anaZKcjvWLAshIa9M0iQWstunZBDJtrs+NlpFnUnexzr1h07NT7zMSKmB11poJFDVj7eD9pmcFahpK09BldrTpmYtetnaylKmokaSAf0vTHJCos5CRgSN3aZru13yaxs1wVQQh2Qgy4jU9M2gaV9YVP4lNXjXJ9CwXPo7QNMBA1eR4jRgFrETalk/TcLUBydNID10zMn0yMvSmSVPT8J2iOZqGv/kqc8Ekt1QVm6RpGASC/j62N01pkhrr3Bs2PZuIphmVjJDfPbLpknH975ef4PjUcJPSNDSxpQq2khs/U9fVNFbxvt2fJpmmKTRi20B8yicjs8UhAMA8BRmxerrQ3jTFpmeeDBcgN8+CmhGaLFVcXUoP/eVCsnYzKU5NM56m8RTcktDISBZN0/001TTxBSLkwHpykJEJ1TQkCet606Qtvnyn6HTTs+SkgUaoUZ6TjOTSNAyaQWtGsqS97rVf4lfSzct8vR2qLsVQ06TegBLDpW4LaRqPbFqPOwlNkz9mqDcNXdNzw6BpWGnvdGqa1peMJNM03fNlafO+DcQ2GdnJSEZol1JKV9CGcdlqGg8PD74ewU/TcGoaJe2l25UBrqYdZ5Wza8xKmBbGouX9VdQu2esWGwnBJU9WHF7ky3sNi27avCoRGVGfh6vG32TNSDUlTWNIe4HUzsD2MQDA1oz4buKD03EGMtL6a0bUDSUbGbEcWH2S9JyvUzAUrVEsnRG+njl6jgoZSakjK4zGWhPKaRoyJvlH6brAhRo3Z65eNU07zvSMFiBLAxlJL5bnB3av0aYZR9PkIMMnKz7lk5F5j4wssIo/WatpbJqm9fa6iI/pUSggkx+21TSEpjFuZoxRFTB0wo3TNBgKdyOmZ3TMPBkfea5nUShplmeYyBlyu/Dr1I5TfR72pjO5yQiAqrO9n1Laq8ZqlfOiSJP2qvOCFg/rE4ianvmOg+oBlWIuqEIjIzPnT+o7aGeZ0l6nNw25MZEC5xxl1FSmZ/T5vnOS63kzNU2jrtXUNcGYn684lFtrJkBy3LmmGTeqcEzPNOpbNjeuP41pelYwKKOmcQtYFTKS+J2NTZA2EJ/yychspytgXWTRNJWpKmkbtqI+KUI0DVOP4JXxOcgIKWAFT9PQC0bd3Cehafpx1ZhSAqsm/bgYtJJnx747L6gZoYZRWb1puueqz8PSNBtIRqqNSHsJTZOhplHHYGdeD0oTtjcNfxxEf14mOR2rCNE0+m6valFSTc/MAlZO5pmdQExkekaf7+9N4x7nTalpUtcE47Ue2qtl15oJaBprrVk1MkqFhHvTlNM0ANj1UEt7J+xNIx2apvvbbmp/GqM3zTYZORCx2M1ARgyahu9NM56mcZGRNJqGqRnpEQzbHKf7DKbh2eHFjB/XCj9NQ96iNcfsxk2/uGOmZ93Y+TUjA8Rt0TTRZKTt37P7PFx9zCaAEZWFVhMWsOqmXUpmmErTrAZOWt+sM3rTIKftgoqAA6suYE1GRviaEe67zPWLCSXkpclIvDdNQlF7YQzJSL8mZDXKc8exfx/WmuloGrrWLGNrmGdepulZ2dy4wuhqbLEoS9NYyYimaVKRkS1Nc+BioWpGRIP1KoKOGGoaeGia3GQkRNN0P5PcFh01zQA5Go3yNLRe8TRNZOHxq2nc3UaWI6ARKclIt/jk0TTdTyrtbWS6moajaUr9JFJiE9LeIRnpv38xnMOhUMdgZ14NN2tOTeM5DpVuu5CAQKpQnXhZmsZORsabnmmqKVcBw6AsXF+ptLHM19vRMnOcujeNeusimoY5nvbj09I05phAPMkx52XOsZFp14MvuPNqE71ppOe+MSSm4fkbrrDbAtaDEYvdw/r35X6kb4ajpiE0DVNRnxT2mcDRNGzNSAQZIZBjtVGaxpwrMOwKZ7XAooDPTqFpxkh7hWF6ltKbxqJpGNOzjdI0kyIjPpomlowMO6/uRiiHWhbD9MzzvjlOxyqCNE3/S50oGbZpGoZWjaI7nmDdXMfSNJ6vg1Mtbao3zWiahrlOzHGnUNN077EgiF0KujvMy6ZpLNQvMzgTyIpsAIqUK6yahhc+JBczS0LTPJqRkVe96lW46KKLsLu7i8svvxxve9vbgs9/5Stfic/+7M/GoUOHcOGFF+LFL34x9vYyJH4bDIWMAMD+XiQZoTSNpUkf4NXMCQTVNC4F4JXxqWRkPtjBt1wBq5emSVsgUkzPKB0y7NpKaZqItDdHTUNvNgZNE37dQNN078nx4ptARvSNcwOmZ1pFVEjT1DRBEsMx9N3I675QPMnPR0UCTaPO93TTs25+XG+aWELlCzYhV0BMrulZpPv3yaBp2tZORsoKWH31IyVJji8GFVc6QuSTHzcG2l1I0zCbUkWzFveB4UzPHJrGKmCNoU7/FHrTvPa1r8V1112HG2+8Ee985ztx6aWX4qqrrsK9997LPv+3fuu38IM/+IO48cYb8d73vhe/+qu/ite+9rX4oR/6odGTnyLq2QxL2X2By/3j4SeH1DQReNU/ZoKaJoumGWpGBtMzStMM6A49CdPVNJbZG1e8pw6TEEW7to3VjFAqwVDTZNI0E/hJpISoGdO6sWMaNI3MVtPszCoIATMZ6R2JAX8yUulkJEfa66dp3ALWWM2IoidNmoYmojHjNl9w10BM6uwdKybtZdeEaZER9R4ldIrpxMyjJFPOV5LNQCoq4KOPzDrAshs0p6qkCrapaBrODh5IV1aJfwo1IzfffDNe9KIX4ZprrsEll1yCW265BYcPH8arX/1q9vlvectb8KxnPQvPe97zcNFFF+E5z3kOvvmbvzmKppzMWKKDgVcnYskIUdO0Eq2kaprCmpEU07Okrr2KpqG9aZgCSOK3wFa4R27ujZWEhTwW6koUyQ6Nm+9ENA31VHHUNNFkJKCmactuOilR9dLeSQtYyS6tVE1TV8JMcMm55LuP1/MSZMRP02TXjDg0jVvrFLO09wW3G5aR4+ELzqeCBq+mmbZmZAxN4/MWoccjWfGREBSBSk1yOBQX6Iv+R9M0ATVNcW8aV24sW58DaypN0z2/o2nyp7SJyEpGlssl3vGOd+DKK68cBqgqXHnllbjjjjvY13zxF38x3vGOd+jk40Mf+hDe8IY34Gu+5mu877O/v4+HHnrI+G+TsRQdDLxa5tA08NA0uchIgukZg4zs2ReyhYzQ2g6vmobA0rsZF3KUpiG7wp2ChYf1RbEi12eEfi0uTROrGQnQNIW1ASkhKoZmGzum0SgP2aZnO7MKtbBpmjp6HGY73XmZZC6oIkTTqJvyvExNo71BmO+yWE3DnRelvWl8yAiTMO1OTtN0P4toGgshVUkIlU1PiYw03FoTq3vzIDam6VkpTdP9NDpAnxSaxlTTxMwrjQL+A0LTuPhnIO6//340TYMjR44Yjx85cgTve9/72Nc873nPw/33348v+ZIvgZQS6/Ua3/Ed3xGkaW666Sa87GUvy5naqFDIyDpawErVNLyqZLzpGX9TVxEvYB0cWJsQMlINVdSVcSHHaBpE6amWJDlFNE2CmuZQps+IsTgIoqZJMT1zaBo6bvdzE6ZnYiMFrD1kLBVNk1jAuhpgYGEnI4Ty81EcWW0XVAQa5ekbyiy1ZsRU03Cbh2IFDIcOltI0yTUjw2Mbp2kyxrWTKCm7dYAmeqlrTUpoxKVKR4iklTANv5t2DSXBUXYUjSwzPVM0jVtPqCOzgLX6p0DT5Mbtt9+On/iJn8B//s//Ge985zvxO7/zO3j961+Pl7/85d7XXH/99XjwwQf1f3ffffdG57gSfTISQ0YsNQ2HEEyqpmHqEeJ28G7NCE/T1CaCkXgSG59btnyPj7E0TUpvGiXtTURcDNi0GsZN2a0kmZ5tgqbpayU2gYzo3jQMH83FgIzUqCsrQeql7kAIGVF+PjnIiKoZ4QpY+7dOTUYsmoZr8Fjc3E4tAwYtof5WRvn47g8sdTuxA6tjelZQl6XHUnSaOh45be4TgiJyRTSNlZjQNb0kOGRroGlKTc8Yaa+Ppkk8BiohK57TBiILGTnnnHNQ1zXuuece4/F77rkH5513Hvual770pfjWb/1WfNu3fRsA4PM+7/Nw7Ngx/If/8B/wwz/8w6iYyr+dnR3s7OzkTG1UrMUCkMB6P3VBq0wZmGz0BnOq3jS0sZRZM+IrYO0TKULTyCBNUxEoOb0K23WedQsBaZGdl1YKhJk88a/Lp2nI4kBMz1KkbSmmZxsR0wgmmRw7JuGvW1ozEll8Fey7M+domookzvyB0G0XZIK5oIoEmgap3YB9appNmZ5NOBYNTjKsrt2mlVg1Lea50I79HrpmpDvf9wppGjWneW1vUBKphITgNz7xDZXvd0nW9JLgNme1bAGRVp/GBnONOtLedrg+gfg6TunaRyUyslgscNlll+G2227Tj7Vti9tuuw1XXHEF+5rjx487CUddK5vwg3EQVlWHjDSrADIiJaB9FTZvembUOKQUqzGN8obeNGQwqqZhK9HDJ3HbDs6ukDwipMCeDpLNh3pTakY0TbNKk/a6NSM9l51RMzKYnnHQ/iZpmumuE9f0LBUZsWkaMqeqihbyzncLkJE23rV3qBmJbSQsmoZxTNYCnQn6yQw3yayh2FoWGtwc1Q0ImAYdUe8xqGkyfEY8rDOtT5sSGaEbtlT6x6Rm6O8TqGmYc0Fdv8WUiEIvIfW8pL15yKRpTDVN/pQ2EVnICABcd911eMELXoBnPOMZeOYzn4lXvvKVOHbsGK655hoAwPOf/3xccMEFuOmmmwAAV199NW6++WY8/elPx+WXX44PfvCDeOlLX4qrr75aJyWnOtY9TdOGaBq6WFe1a4teTNPwahqTVkiQ8TF28HGapvs1p9DU5lVDrdhrAczq/IUnR02TiowYx5OoaRolcQ3EnkPTkOltMBnRNM0GpL0NKsiW0jSpBaxKTTOcR8BwHHwS54XqASUaNOs16lnC0uNplEdRQ5HsM2LOVyVNnHPwFNLemCNtbKyYmoauCQuS8eyvGpy+k72ss++hzveYvToNP01DEIzE3XvSXI0NlUJc4muY7/fRNA3jOaNQxK43TcGgglxUsu3WbicZyaNp1AX0qKVpAOC5z30u7rvvPtxwww04evQonva0p+HWW2/VRa133XWXgYS85CUvgRACL3nJS/CRj3wEj3vc43D11Vfjx3/8x6f7FCOj6ZGRdhVY0OiXL4TZVIk2jMv9Xj00jUkrDH9Ot4OXkL30uI7SNBl8qwTbIJBzNTSg04yFR2yCpmmt46lpmgTTM1tNw5hb5cLxKaHUNPWEyQiVGba0gDWqphl2XobpmagM2bSvZkT1gAI6p+NDszPik1XJiEXTGLvO5AJWU03D3TRKKbeQC3E5TcP/nZNQV5XAoq6wbNppkBG7ZqTQ9AwYjgmVTU+qpiEI1FykISMnhabxSHtH0TRAd51WtYuMtDYykkrTHJwC1qIU+tprr8W1117L/u32228332A2w4033ogbb7yx5K1OSjR1T22EkhF6cnJqGnV/noymIZk1WXl2OShSyiAywt7cK9MbIhXeM5Owlqdp1K61cOExaRr+eKqF8ngihOwcTyJti9eMdO8RMj2rp89FNmQHT2kamU7TkJoRw/Ssqo3kwIcE7NC2C3vHcei0hGTEQ9PQG8iAjCTawVs0jZKfCiHId1lK07hJainlEzU9s+a4M5swGXHUNBk0jXUtaWkvVdNMStMwVHNk4+Pv2osJe9MMj412YK0Ig6CSCB9Nk4hwU4T0oCAjG1fTPBqi7ZERGUxGLJqmlboR3bQ0TX+S2LRCH2yhaUN4eGJ6ppIRY2et8W3TNTOdb+W7FftkjSVW1Sk0zW6mtJdy1iKTprHt4FnTs43QNP2Nc5OmZ1Xa4mvQNEJACPc8AvwFrLP5Aut+oY/2gFLhoWmM9xtJ0wDDJVHaZ4hvujeO8smhaYBpLeEd07Ocrr3WtNV8qSR5SvUPj+7GaBryu7WxGE/TmLVIsm1RC0KJFCEjNBnpj5nTmyaPpjGb9+VPaROxTUYwICMytKC1NjICnqbJ/WYdZERlvsNDPjt4vWDReSs1DUFuaiEh1YAcTUMRjIS+DryaJkLT5CAjSTRNd4NartukY049Vei4EiJewHqKetOompF6QmlvReDZRmISmsZMnP1jLNHRLctYDygVXpqmABkhiGA3z2Gi6jss/S7DypysodhGazR8dFhyT5KEGGV6ZtM00kpGaFH7JDUjIOMW0DTWtSxHmp6p3F4dB2rbrhVsuWHTNGCQkUyaZuhRVTinDcQ2GQHQ9v0twsgITUYqi6ZpWXguKRJoGg4ZaSWwVm9GF+Ie0m5pbQfITsvoTdO/JEMWZ35uX2+aYSEu6W+R48AKpHmNcH1pAOK34Yl1MyQ7h+ZdcsCanm2iZkRsQE2j+2SI4t40FZX2VibnHEIC9pWfT6wHFNC768VpmmRkpB2Sp37aznilfYaGIm4y5kjKJ96bxnx8SrRBvYc635dNm3zD4kzP6ONVxlqTEqbNfDx5sj+HjZJM15tGJSPEN2qs6RkwbKKc3jTD9QnkmZ49qrv2/lMLWSdYStMvTNE0cthVFkt7fWoaXwErJ+Oj9SKkgKohX2+jDKQY07McwyAqGe5oGvW4uyvMSXJosL4oVqiGbQBwPKFzL/VUgbVAhL4yejxOFU0zaQGrzRWnqml0zUiNqiIJkjBrRkJJ2QqJbRcAgJo6OTQNeb9kZMSiaSqaqPfjRuTJvjippmeMzwgALKY0ErNoGqBLSFKC8xnpHu/+nWMjkBJskhPYnNjzs2vdpjY9a4ht+zQ0jaJ/fL1pEjd/etN7cApYt8kIoJttiWSaRiEjxPSMqahPe3OPmkYvOmZFvi3jA+AoadQ8Wi4ZoTQN2QmyhbFM+Gma4Tmms2tBAWsCTSOE0F4jKYoamnjRMWM0DZ33oVBvmkeLtBeEpmllNk2za/emcWga/3FYisS2C8BA0QAOMmIoFfoGfFjvhXezBBG056lpmsLv0q4ToGOdDNMzIOMmFAkqm6bJSOq4/mRkSNpLbOZ9YdSnJSQ5djKwadOz1kpGymga8l23ChkZqaaxzQ8PQGyTEUDXWYgmhIyoL18AQpg3+7Zli9iSIoKM2IuOkvEBHmREDUNrWkDgQoOmYZKGBI2+QdMwn9tU04ysGbHhSBJa3ptA0zR012shI+FkpHvuoq4wI7tWbafMSC2niko1dduEtFftiEQiMrKmyIgw1TTUUyFwINapbReAgaIBnJoRAzWcE6fmJmCoRpJwwEpGmN17ToRpmqyh9PN9O2jacI7GVGgDvRYWfX1QN26qn4/5b4emyVDupQRNzlI2Pk73DUrTTFDAavcpsmmaMjt4Abu7duvQNKbib0vTPFpDISPBZGRYfAGXrihORjyN8vTCyCzuzsLDICPGhQUM9sGEOzeg0yI1jQwW79VVutSMhlEjEahlUEhFiqLGOJ5kzJiaRtMTfa2Efp2949tEzYhS09AC5LFjKhQAojsm1fBdhsKuGakITeOjFO1QPaCaZaS+A7CQEb6AtRKAmA/+JcG6kQBN0478LnkX4jL6bqBpfDUj3U+3gHWaGzy9V5ZsJnymZ2zSMEWxLbmuUxRFDk1joVljG+Wpy2mgaWgB6wjlit25V/p606T6jMhhTtPtdUbFNhnBYJxUhZIR0mhLL14GTTO1msa/MDoXHYOMuDSNOoldeN0sKovRNARxIaZnvjqKEpqmTqBpAOBwX2CXR9OYY8Z60wyIQGUkhuprVmvNJmiamtRKODuhwqhG0jQ7PU1TgSa1ipII0xJrbS6YQdNUM0eSYiijKIUTrPmyaRoynkPTxKdHg2uONhyTUpqG/3vrmeNURmJ2MXJukuMzPVMP59gIpISx8UnwGQnRNK2cnqaRBk0zog+M1V1b2ieIRdMs121wk2XQNFtk5OCEkgdWIZiXODg2JKvsfiEFrLnXV8QOnrvHOTsLnYyYyIihptEFrISmYWs7EkzPIr1pTIvmggLWhK69ALCbgYzQolqXpvG/brgJ1+Zu2pGDRqeQHYJU0dNCuDFRaUlfJk2jEaKugNVQ0yhVVuTGq5yO05ARf5M8oymfEKRZXkLNVz9HIYQjozXqijKCcyEuNj1j6k9o+FQ6JQgkOz6l3AyL9bSFzZ63e51Mq6bJrU/zSY+7sWgBa1miZKuh6HU7ymDMKjT32sHPSZ1P4DgY3bu3NSMHJ1QyUrdpNI2+wAyapvt1agdWlqaxdxaaphkga4emUScvQXioF4JaIFaNDJ6c3bhMAStTM1JaOc/20mHi8Dy9ZsQsYCU3jRhNY9ATw+PqGJWaW6VERXo3OYtPYThNu/QilyjtnSuaZqA9Yh17VayrBAm9CqUWqP0de/UNOaVzr/qOiTLB3kCM7U3D9rmZgPKh0bb8sd7dADIiBLPWxF7vSGctmqYyUZyxzVIpApWS5NiHVRoJ5AQ0jVUzIvV6LoBRyIhN04QLWIHwd2ZuSsqmNHVskxEM8sBgMtIOi69evDiaJjsZ8ahpAgujg2IwyEh3sxT9RUALWF14nUKnQLgxFjVT8zmwGjRNic9IKk2j+9PEUQMD2s9R0/TzXlg1Iw60v4GaEdpIsp0KGSHOi02LYtMzg6YJoHg02jqhB5QK7b7KJCM2GpWCjFg0Tfd6E1JvWvPx1AjTNFlD6ef7blrempGJqA/bbDGbprGmzdVWqblK2W1+xgRFoFKOgX2tT0/T9GOpz90oWrSwplCFpmn6TZAHGZlVQs8h9J1taZoDGkoeWLcpNI2I0DSZX6yXpunnxtWM9AuEhk41MmKqabo59vNqrIy6Mu3gDclwaGfRekzPpqRpEtU0WTQNTe6UiyEqIFoz0t+E5xZN009rk8lIZdA00yAjRtOuNo2mkVIadvCd6Zm6K9bJKEBb9clyUs1IgKax0YFZgk+QpabpXm+Ol5pU2TGgg+4cp+5NY6jCSExFfdBroUSa79A09nVCEpxu3LEFt2Tjk1AY60qP6VxN76iS0N+fJe1ViEs5TWOqadxkpPu30Q8scBy2vWkOaNSL7iY+CyYjIZpGbsyBNZSMhJARtbCqObo0zVArIYTAjEhXg5yrxavqBZ2BqE0ZX2kB68Q0TTWMKcWwQ/OFrSJRMbRG7+e8QZ8RYMoC1mERMmka/0FYNYP3REfTAJUgNE2iP0er2y5EDMoADO6rfmREH/MkZMRU0wCMW2YpTaOXAYamKaR8/MgIOY9JTKVQcWiazK7bXpqGrGfmxmcskuNSzTlqGoOmkVPSNCrBHZDI7vGiYV2axtEoD585RVVEEdItMnKAQiUjc5mrphmy1elMz1pjHNv2GWAMjhhkxEZvBmREceeEpunnnlSNTk3PZKsvPimJ98ZYNY0gxzCJpklIRiiCoXYXIr5bofQEq8AoLHpMCaqmkRPTNNpfQPMC/mNIFzWWpvFQB3YMTscpNE2gZsR+v1nCuEGapn/L4gJWhqYhiX5OCBHe1Pg2KVP5jNhmi7kN+LzSXpW0C1HsP8SFaXoWX2scmsaqdZtMTaNqyvraJ41Ql2YjtprGQ9MADHLODUdrRrbIyMGJ2aKjaeYyUU1jS3tb3vwrKTwZbhoyYqtpTGkvnaO2D+ZMz/q57yQY5vh603R/U+/dvwXlcROr/FubjgjcJA/1zfKy1DTE9Ew1xQrSNERFIoRwOOGQ6mlsVGT7O5mapk8iZAZNQxf3Re3vTRNDAWRKoamKFJomp4CVpWnMJMLnbhoL1munuIAVzlg0fPTPVF17bVO1bJrGXs7s66T/fCnW7Slh1qfFExx7ubUbfE6tpnFomlIUwlLTSE+tIZD2nTmbkgMQ22QENBlZ+Z/Ukpu43kkRNU0EXvWGl6bp3y6JpnFNz9Q0BppGVd0SNY3lhZBkp9yaLoUUmncajhXQNM5NN2R6Nh9neib7G3FoZ0BVJACtD1A0TRkcnxKiqnQBsiyEje2oCMXYSiSpaShV1SVklpom8SYuU8wFVXia5AEMapiEjAxIjgp7A0G9MHKC6yfT2nPMHMurpvGsC9P5jHQ/c9YE8/U2TWM+rua9M5ElPJ1vSoJj33jpPyehaSy63kbRN0fTuMhIUgGr3KppDlTMd7pkZIEQMqLO+qFmRJJslauoTwpPhhsqgHMWHlUQyCIjNk1Da1+6X2tn4QlL4zjn2e5vFvduQacpMj5XPx+nafayu/aaNSOhBJLSNIC72GzS9AwgyeTEBay6cM1e5JhQx1cdg46mUXdFWggdefOUHlAqNE0zc/4krfM2Tdo7nPcqXDVNWu2LHby8fRzKwi0jIdv9AZqfCGmw14RUZNMpELXWhMocd+x8c2ka3/wAOMX5JaEob32PaE1kpJgSsZySgzRNAkI0NMzc9qY5UDHfOQwAWISQEaqm0V/eQNOEFpFgeNQ0oZ4nA/Vh14wQaa+uGfHQNESS6UCykWp0yquGnSxNyXDKLsilaQLIiFbTpHftFQIOZB80PSM0DQBHPbRJ0zNg4LCnkvYqekXKXtIn4osv7dgLwOxNIwbTs9iNN8npWEWC6Zmux0hBRjiaxqrzKu0zZFN33RzV38YnNsOYkjzP/NvUvWnKaRobGVFrQvfvqhBx8QVFoNSY61Zi7bmoY6ZnVCFZEjZlZ0t7i5Ur9nWaQtOEakaoxH9L0xycWPTIyA6W/h4gBk3TZ6eab5fTm54Fdml+Nc2AjCgUQmXkklXTkBs0hiRnL4CMmDRNayy2oXbh3XzjC08OTXM4qzcNWWRtmibwne3ZyIh1A5MUcdlAODTbyBA+NQ3gVdQ46JAAWNOzWDKinI5Dfj4qkmgaCxkJ+ZfoJJyoaTQl0r9lcZ2HS9GO7U3D7Vbp+Pa6MFXXXtsfJbvmy6Fp+jXBqq2a2r6eFtuGxnVpGpJAttPTNNKwESi4P6hwTM8CapoEhJuqaaj44FTGNhkBMN/tkJFaSKzXHnSESAPV4iVJ8R9XxJYUPjVNYGF0LmQOGfHSNBy8bkOyYWTET9P0P8lCnOpfosfwdKPkQtWM5PSm6ZIRU00TpGk0KmDTNOOg/dRodTI5LTKibfAJUuDbDdKaEQB9bxr/eeSLARkJ0KEqtOkZR9N4kpGUmpEU07NcmsbylqBjllI+3DlpGJJtyGfEuyYkO7Ca//bSNJPZ10OPmyIZDhawSqoULKVprDok5Rs1NhmxartS1DRhmsbcrB4EpmabjADY2R1s1Pf3jvNPIioUvegQ90pb0pUcPjUNucic+SYgIwomdnxGKE1jJTypvR2kpDSNMP7WvfewUzYLy+ILmiNhDapp0n1GqKeKS9OEakYsmsZORjaNjKiEaQKaRratlk0PvWnIEuBZgO1j4NI0afRGktOxCt0oz6+m0Yc8y/SMJCOWR04xMmLZytM5llI+cZrGl4yMpWm6nzlrgm+OgHudTE7TkO/M9Eriz2VXemz+rSWob0nYRno2MjIZTROorUtT00xEH00Y22QEA00DAEtfMtK6N3EpXIRgatMzzqcgpWZEe37onTWjprG8EFJ2V0bFeWvWjLgNx2CNOzVNk9+1txbDmEUFrBoB66enAIINISNyrD8BHYvu3JUNtEHT8Mda7V4HRRGlaapkdKhKMRfUEwzQNHbSkGV6NszRrv/xdcSNBYdmbKI3jUnTmH/bBO3RjZvpM2LXjLTm41VhkhOfL8z5ejY+9k13c71pTGREt+UovYyjNA1TwBrYpFGahs73VMY2GQFQ1TWWsruxLfc9VtUGTWMnI3zDuKTwqWkCCgWXpgn4jOjeNKqAlappzPdI0ulLU01jeG9Y/LBaWHcT/Ev0+Bk0TVbNCJ2TI+31v86mKLwKjA0hIy26OU5B07AdRAlSkEzTOGqa7tfYjVc7HYf8fPQEQzQN9Dy6AROQEY6msZVRpQoYxhvEkJJnjZWopvH2phlLe3jWhFQHVgd5UOsQ+nHLkpz4fO21xkPThNQ0EuPVNHYy2UxF01hJ0kiahjbMHDWvCWObjPSxJ7od2CqWjFQVT9NodC+XpvGoaSyJHY0UnxHdwlp9xXZvGuGaVSWpaVqpzcLUWJUFU9tKgpwmXg4XGupNk2EHbygwbJomqWZE0TT9eJaZU66fRGpMKe2lyYh2XiyhaYRF0yTexOu+B1QSMpJE02QgIxxNYysfxhadMgWs+WoaGHOiQR/z0jQTFbCW0zTWeNZ1UumakWkLblOTHK/02Nq8jVbT2MjIZDSNUsNNY3q2pWkOYCzRJSPr/QSaxilglRszPWNpmgQHVjWNAYazaZrK2dWn0zRkTm3rFPC5NE36wlOipkmjabqf1GdEXeBh0zPLY8P6nmXhbjo1pJZmj09GqFJMtw5PoWnsY1CJot40SU7HeoIJNI1avZKQEVfa6/am6R6fwhskhGyGIrSO0PEdNc3EpmdVIYLh9qZRPwlNirTde9r7dT81rRRBRmzFrzrM6vuajqZRw0xEh2SoaXYTULLapmmmEeuNim0y0scK3Q5ste/ZXVGaRiMjp8j0zNubhvMZ6WtGAqZn6gKKQZxSdg3TGnraUPdZzw4zZ0HzdaPkQiUjy6b1+gqoMGkapaaJS3vVsVDHRli76U32pgHoYjEtMjL0pqE0ja9mxDwGlaWmSb3x1jsZyYhS3ITUNCU1IwxNM/a7ZO3gS5GRQFuJkGppatojdU3wzVGP56EzU26YSfN1kJwwQhSb33iapn8fXcBqm54VDQvbDt4ZyKBp8rr2Alua5kDFquqRkaWPpiFqmgG26H5OanpmFrCm0TT+mpHBZ8SqGWFNz8K7FZ3tG8lI49SMOJBsxi6odZpb+BcFtVACwPEIVRM0PQt8Z1y9BEDqDAKqpyliKECeIhkZxhhoGjLvKE0z1M1QmiYVHRqcjgPmgnqyyoGV8xnp39oxPctU03hrneLTo8EVnerC5uLExv1bSKFDqdAxnhElcn9ujno823uo0EzNO1+7xiVG09imZ9ovqP87aQIabOftCTsxVdetQjiLDcYsmiZsehZfb2ubptkmIwcnVn3NSONLRii9odU0Q7ZaM7ujpHBoGhMZ4dYyl6Zxu/aqBWlQYzBqGp/cznNjd+pQ+vGchmN2zUjGwtPahZqBHQrtpLsXoWo4nxEk9aZxKQo63nAjDr59ccgppb1GzYgYuvaS2icu9DGYq2Ngq2m6X5OTkRxkJFAzUuQzEuxNM67Og+4uQ8hmKEJqmhDaoq4xKYFVKLuOhJ3wjO3ay7WI6OY7TY2L218rQtNY89PmkDZNAxRRNT7Ts2Z0zUisNw1JRhK+M10zIuNU9cmKbTLSx1onI3GaRu88iBGNLmCdrGYkBMn61DQuTUNluN34LsIzLDxpleiNhYz4FnW98GQYHDmFmoEFQQih5b0xRU1xbxrLDl7dB+wC1k2bnk1B6lKqp6HOi2I4j7kIFrCS8yh249VtF1KQkdavpnFQwyw1zXDu2ihEsekZo6QbTfkw56SjIiJhOh2Xo2hOQ7uRdvDudaLmm66wC8+3++msNd4CVmu+PpoGKEpG3JoyMxkpRq1smsZjlgnkd+3t5ls2rSljm4z0sa4Sk5Fq2Amypme5J1ukNw1fM+JT01Capv+pLwKXprGdLOM0jYm2qPFsimoMTeOjrXxxKFHea5qemTemJNMzhQrY0t5CP4nUaHWR7XiaxvQ8IN9ZpD/NkJD1NE0lDGQkFR1Sfj5z0WC9iqAjAZrG9pYoV9OY4433Bhke08ck27PEnBONUIKT23bBF7bZYn7XXvvfPOqUo7ALzzePVnJpmu7n4MtEi/PzrzmnXUT/BuNpGkUfKcglQdobQJ1UzZdW+WxpmoMTTdXtrtpVAk3j+IwMpmfTFbD2b8csPLt2gZJahOchmkaRoq7pWWrlvJemsSWSPjXNxDQNQCzhV2EawyiytGmaYAGrh6bRi1j3c3NqGpWMjKdp7KZdQP+dVRYEbMVwDIZmgSW9aXYOnaZ/9/r56Ill0DTqvM9V01iUSHmjPHcjouc4oelZqGbEcDoecYN3qVu11pTSNP3PzPq05PladHaUprGOq7SQERv1zQ27ANlGRiJ19v7I6U2TQNPMhK2m2SYjByaaHhmRvmZbDE0jKE1TWsAao2kCxWp7q6a7GzYcMhKjaSqy8PTjRqqwNfJhq2ks741c7b/xHhk0DUDlveHnGSgQ+S67+fpfZ1MUwoLRH400Df3+2hyahjiwltA0htPxCY+EXk8sgaaZqDfN2O/StpUfNZaek/s3pyePFbGar5QYeur0Y2YiGF6aZmSSE5vvWJ8RrT4U5tqWGw5NY11zU9E0wlNrCMTXcWnQtYWI/gZim4z00dYKGfElI6TWQn9xlKYhY+VkJD41TUjaS3cVdDcYqBkZ1DT93ChNkwidDp+Lp2m0Zt/2L8lwcZSBjJ+LgaaJICPUKMtS0wQLWC2Kwu7O3ASSxilCLY5TqGkGZMRORsLeCo6ahjqwiioZHapnMyxlL8eOIiMBmmYyNY25gRjbm0bX4GCMmsacC40YAhWr+UoJ22wxn6axkRG1Qen+vcneNEB8rfFJe/WymOBIHAq7mN9R00xkepbmwOpBuC1V3ah5TRjbZKSPtqdppG93xdI0RE1DkoYs/s3apdt28Lzp2bDoyDVZ1BnTM8c0y6BpfLUdnpOYfC5JYEPXdbD7k+PsmiTt7d57naj3P5zYLM+AzQk6ROdth5TSVZJ4+5lsmqaZwvSsu8G7NE1ETaN607A0TXpvGmAwF1z5zAX1xAI0jZ0AjlXTjCxGpsmBvdPeCE0TQ0bG0DTOmpBXaGpf+667bT/Xie3rUyXDthTf/u4N350SNY1VFO00yiuuGTFpGiHVuC7FGj0GVJlXxanqkxXbZKSPVu2uVp7dFakzGMAFqqYZFoisL1ad8GoHaDmwsj4j8+Fr0ztMUQHVAGnbCZNGHBiEJ7VyvtXoAv3sjbvwOPRPBk3T3zDX6D9LZEFQNSOxAlZDiWAlgL5dwbqV+ntwaBrns24oGYkUl+ZEa7lBAqqANVVNMyAjBk2TgQ4thTIXjCAjmqZxkxEHNZwl1Iy0ZgLa/Wp/l/3jhUZldAxbPZI7FndOysCaAExF0/TzsBCMVSOTds/qXJjXnqL2QpVO7nxjGyrf/AyIu4SmUZer+rLUNafX4ewh+3nZappeDVO562QU4SbISF3HqeqTFdtkpA9ZK6g3oTeNzqIVptoYC1gWva8WSbXo2r1pIjK+5V4/39muYUqikxGnNw1VQcB4j7h7Yf986k0hW3392pr9ooWnP3hGMhK4gg8ldu4dkKbhPdRn8A1P53vKTc+KK9/IWBxN01KaJs1nxHBgzTA9A2jbhVSahklG9OVn0zQpyMjmTM/oWOU0jb/2LFTACkxzg/fRHgCwTEE21c2+MilQfa8vNFOLzldLhsM3YmnNzzY9q0QFTUMXqWlMlHigV0fSIVpN0xo/W8EkI5GklKKsdRXekJ3M2CYjfci6212JxrO7aofFTENtBBmhu5U8mkbt2GbGv0OQ7IKsliudjOwYz9FSNbvmgKNpEjX6OsmohAEbOjtMu3I+o2ZkKLJMg0sPJzbL42gaEbkQ6cXs601TWmeQGkNn4elomoZQFY2UCWoas4i3rmD1pul+TaE3lLmg1+lYTyyFprGQkWbfn1kSRFCFT6adm0DUzCZgUG+VjRWsGfHRNBPIZW1Eh641achm9/pZbVG3dpKzIfv6WAO+xpofK9G3UYiMcBxYdeuJscmIuka7z6VpGpWMZNE0w3Nja+DJjG0yoqK/mQvf7opIAzUEJyhNMzx1GprGvwuiMr7V/rF+/rvGc+zOwi5NUzk7rVSaphJgEzG7XbgNne6l9KbpL5SVIDehQDKSWsDKqWmkuhA935c6DotZpXfhtumZ43kxcWhPlwlomqGYrhpAvQQ1zZ7tM2KbnmUkZGvdHTuAYgAINcpzaRqSiPuoGqtoGaCUSP+UQjSDPt1piVBI+fA0TTjB0dfZFGqaft6zusKsf7+9lEaXmgapjPF8BospY4bnC2O+qfYE3vnRjVYRTTMUM3dv0K81eh0uvOk7NE2//qhkPcMOnjox171abVszcpBCJSONx4yJSAM1Yk52lHQhLlLTaJrGlvaGF57VkkdGdAGVo08fEqlS0zObprEvQG9RWZKahkFGAqjAoURpr7F7V0VgmscNJyOUFrPdZkttv1NjU2qaoRAXwzY4sWuvSdO451EoVA+oxufno6KJS3s1QkATce9mwqVpHJSr8Ls0aJrWPJ/yx4IxJxqaEvSuCRPSNGTeeTVf3U99s7drqyb2Gcn1L1GHVc3PNj2rK4EYUhgKr5pGhDc+0bASJAcZYWtGfDTN8NxKJyNl05oytslIH6I3Tqq8NA2hNyzUoWsW50K1SaGTHJum6R/2QrLdyblWO0wLGdHctZemqby1Hb6dlbl7GBQYvqLOEkhW6/IFuQmFOvdmmp5VgowXgU73LBUJ4O9nsqlkRBcgT+nAisrktmO9aRQyMlc0DVXTiGAfJTsUMtJGaZo+GQmYnulDXs2Gz8AlI1ICpMuwCruFQ6maxqZo6fk0hYGaHjtynKegPrg+QzmSYTVvTdO05uOT0zS5a5g1P5eiQ7SGKhTagVXLGe21JntI4/WOmqYyaw2B4Rj4io6pmqaqzbX7VMY2GelD9Dfzuo2paQaaRtQ+NU3GGxfQNMBwMTdKImkhI62TMDFqGmvhiVZh0wvfUNN0v9r8sNtsKwEZ0dJemoyk0DQxNQ2tGRlQLiBQM8IgI0Pi1f3b8byYODRNM4HpmVSQMarhRtzGaRq3a2+Z6RkAbS7o9fNREaJpbNRQiLC8lyZZlKaJOAenhk3T0N1vselZCU0TqZdICe4Y5BSbJtEgyEtwcuYbW2ui8xOUpsm/QQ+eMyoZOVk0jVvACvBFx2pjs5ZVsEbpZMc2GekjioywahoTzrN3zUlRoKYBhhNuveKREWeO0kZGKLxujtm0EmsmhddqGotXtb03crX/NFRti4GMJNA08d40ZE5qd1GF5XbaX4MoCmqdG5g7qk0XsE6DjPQ+I8JahCIFew5NU5k0TU4RbxMzF9RPDNE00PPQETI+o0kW4zPiQ/RSQwgxKHNaaeSNxT4jzDkZNT2bgPrg1p0smkYhDw6d2f3dru1YrtvyGzQz31Q1zcz+7qkqLuK7E4rN0TQ8MiIDahqA/84MhDTga3OyY5uM9FEtupv5rPXUjFA1jb7Rq8MnASmdJklJ4VPTRBee/uRWjf1sZMT2GbF7GjBmVZSO4C5mc/fg0jTqYxtJC4DdjK69A02TqKZZhGFZPXd6PC0DrFgBa5CmKZSDpsawoxqfjIDUjBg78BhNo+3gedMzR2obiDbWdkHPNYOmAcLICD12jB28r9YpJ3RiI6WxGSl2YOXg9UhR7CQ0DbPuZJkWOjUZ/Trk2fikjhudr4O4JBawcn5Bo2gadR70D+j6tLFqGm1g0v1TF+G7appZXenzkTu2bTOYH4a6RJ/s2CYjfdTzrm9G7UtGDJpG/WreMIs690ZMz/zWz/3FtCQ+IyT0Oe/o013TM7V7W0QWCOMmkGV61luApyAjWtqbSNPMu+cVmZ4V0DSO26xW02yKptF8yuixWrWAQZCGXgjSNFJK/b15Tc8yEjKFjHidjvVkM9Q0QBgZidE0HhVYTtCE3KRp8sYJ3RyMc5iJaQpY3ffIcUsdbvbmBsVX2zF6vh7ExSvt1cmSZ34VRqpp+pd6aZrsIbuwe9P01x+npgGA3cBxaHuYyilkP8WxTUb6qHuaZiYT1DQ6GzGpBK6VeDQ0TbMw/p1K02i421bTqDnaleHE8Ku1dlp1JfRFyi08xmIohpuk49fguC1m1IxoKqFGivnQ4VyaRmTQNBY9oV8PStN0j2+OpplOTaPqcVpRmwhP5d8JcsZvhrRX1FkqFGUumIyMMDQNixoGkRFK01CUqx/PpmkKVkVKVVJU46TawWf4+fjCXhOAiWtG+nHntdD7pDFIjlsYG07ItOmZQm5shJPWw40xPdMwselvM95nxES4uQJWIIwQab8hStNskZGDE/XOYQDA3JeMMGZhwrAOdnu0JIVGRkzuL5WmkZ6aEb2Y+VpPE3idLm4hGa4xJ1IYa3hWwE2kcnZWUt0YRBWtZQCotDetUR5V04gYTWOpSACX0y+Vg6aGtGt+xoyle9P4aBr3OzeTkcH0TBTSNIPTccC6HQjSNCyCEawZidE0/A0zJyg6SO83kzqwJha1j1PTuAlPXs2XdbPXNE33d3WcqFfSmOTJNmnL9hnRiWj398loGv25TRuB0TUj/fpdqZoRxg4eCG8AW13HUjnig1MZ22Skj1lfM+JNRjg1jUPTdL8W0TRWVXSqmkbD3RE1Tag3De0pEjqJjQs/SNP0b2HvVpJ8RlRSUblKICYOJTqwcqZnRTSNh5LalOmZQ7ONCElpGnquBmBptagLMUDbHTKiM9msIl6VjHjNBVVk0zQhNQ1fwOqanpUnIzQhN6S9uWqaFNOzk+Ezwq4JCTSNTzo7Msnxz7f7qb6z3ZiaJmV+U5qeEeFD93jhTd/Tm0YKV00DhL8zhZA2ZFOyLWA9QDGLISOG6Zm6Anw0TcYXW0rTzP3IiJRkd2afxATh4TrO7obgPboYEsTFMQJz+OGMKn9NJVQuqsNEMk1D52TRNPSz0VDHYHdOd9P9lKxF7NHgM8LSNC2CCJRKIHdntUY+KiFQEzv4LHRoFmm7oKLxN8rjaZoCNY1dM0IVFZlBr4Ex3jNBO/iY99AUahrm2O5myHDVHG3kgUN1difo3Gsf69jGRx3WgUZS6yWlacrrtBx0nAgfgAkKWNXa1SOTsvbQNIHjwPkNHYBcpCwZedWrXoWLLroIu7u7uPzyy/G2t70t+PxPfvKT+O7v/m48/vGPx87ODp785CfjDW94Q9GENxXznW6RXCBG01S6TkDQ6rRimkYlI5mmZ2q3rhZfkozQE0voG7pCRii8HuCHg8iITdOYtTI+ud2yaaOJmt69izqJpjmc2iiPFropF0Pao4WZF+vAart2niRpbwl/7YzVo05SpJueDUoaUxJr0jQZ6NAEyQiLGqaoaURlTFK93tfgMSd0YtPGUc1QhNDVKE2ToVrzBbfu5NApA01jroUcqjMFMuKvT2vYDYZdYAt066VxDk9heqbWE90Hy0x+siOXpgkgRK32GxIOqn0qw60Oi8RrX/taXHfddbjllltw+eWX45WvfCWuuuoqvP/978e5557rPH+5XOIrv/Irce655+K3f/u3ccEFF+Af/uEfcPbZZ08x/8livtOpaRZyxT+B3sRVzQi5maFtgxCrN3SBk+UzkkjTgKFpjIXMtvpmTM/oDm4RWHiMxZrSNML8u2tENCALy6bFLqW3rBj6p5CEJ7BDUTTNuu1UH1QRZIxLixOtojI1d/ti2F+5yYivn8mm1DSYsjdNQ9U03WNdzUgAGfEU8Zb2psG8O0+9fj4qAo3ywmqagOmZMM8NH+VWkkQIkqSOqT0JrSHxovYpaRqSNOSoabSPx3DzBXw0zZQFt2rMWr/vupVG0kHnMSMbSfqdGR5KBRuAgabp7xHaD2RYa1pp0mBJUVnUkUryPGqaIE1j1I6pOT0Kk5Gbb74ZL3rRi3DNNdcAAG655Ra8/vWvx6tf/Wr84A/+oPP8V7/61XjggQfwlre8BfN5d+AuuuiicbPeQMx7mmYHK8i2NVEPwKBpdBZt1YwM7nsZbxxxYI3xw2hcZIQuZMK+0bQcMkLGDdI0GOZE1TQWPeX6lxDJ8Ko1aA8nDJOghJqRxTDWiVXjTUZM07MeGTFoGvc1w43YT9OctALWKRxY1bmF2nSKtBc6EpzXiuHAmknTKKfjytcDqpsUQQw5n5F+HmzNSICmEeZ559I0I+gVktxNQ/e4f4vVtExRwDpQt3TcHJpGIQ82TdP9vSpMcrzztWkaguDtr1s9DxV2ga2aszHOiJoRnUyqZIFZa1opUSPz3LASJOXzI31qGm2Lz8j1NU1Tl9lRbCiyaJrlcol3vOMduPLKK4cBqgpXXnkl7rjjDvY1v/d7v4crrrgC3/3d340jR47gcz/3c/ETP/ETRhtjO/b39/HQQw8Z/206FrtdMlIJidWKWSiZ3jRVJ0rvHpfNONMzW00TkfEpvlWwNA1FRkJqmolpGkWT0qQFnduh+hjRhYfq8hNomsVs6CoaompM07P+gqbISIimsSgKwOXCHw2mZ2pHJEVl7sB19SWTjDDoUEfTaK4yCx0SC+XnEyhgbQg6OSVNYyFyld7F9m8rw9dcKCh9N6YQNkjTRBCXnNoOX/CmZxnSfF2TYSI8tulZN+70813UdOPjXjNDV2FK0wwJpJhMTQNjjBglHA2LphF2sp5RwKobZoqDRdNkLaH3338/mqbBkSNHjMePHDmCo0ePsq/50Ic+hN/+7d9G0zR4wxvegJe+9KV4xStegR/7sR/zvs9NN92Es846S/934YUX5kyzKHZ2D+nf9/eOu08gvLNPk25nxUmhXXt8NE0YGakYZMSoGQnSNO5OMHQSG5y1QdNYO0xr7p2ML23hkRRSjziDqlBUzfGAvNf4zjRNQxaIQAHrqTQ9Sz0GKTFYU9s1Iwk0DUnITNOzipxH8TlUc9UDKoCMUNSElfYyqGEIGfHRNJYM0zbQygnO9KysZiRA00QKY6ekPdg1IaEWRdMgtqlYyL9kCtOzfr5VJXRCwm+oYMxPzdFQFo6haSy5uCCI+vB+BTd+2/RM0zRmraGKUM2IZAtYH2XJSEm0bYtzzz0Xv/RLv4TLLrsMz33uc/HDP/zDuOWWW7yvuf766/Hggw/q/+6+++5NTxOLxXAzZ5MRUmdg+HPQHi0WX5gUPpomAvWqk21IRoaaEYOm6U9ifVFQNQ27CwpVYfO8qp+mcecbR0aGG2YqXKq9RgKLpfGdqd0FUUNxhbUDKuB6U7TWDWxjBax6ERq/WAzS3sqkFLNpGsv0LKNmpOqdjr1tFwBoWS/goWkYBCNUM+KjaWjdDMYVIxs+I6PoHvP8osH6q5CYhKZh6ZQMmsaiQXymZ8Z8RxTcsjUuIY8NH01DvzO9gcu/5irrnBrWGrNmJDscmkbdj5QK00ZG4moaafSmKZjTxJFVM3LOOeegrmvcc889xuP33HMPzjvvPPY1j3/84zGfz1HXw5fx1Kc+FUePHsVyucRi4foI7OzsYGdnx3l8kyGqCntyjl2xwopFRgZ6wzDQIpCeA9GlhE9Nk8gP6y7DFBkxXJd4fXqH8PRPYa2fuQsZw5xCpmfMDk7Nl+MwaSiOtRV1cMdO47A2PkuhaTBUpJOdMrdAsGoai9PftLQ39RgkBZFNmzRNgprGUhSZNE06OlTrHlCBAlbS4tyQz/fB3pRTakYqHhlxTM8KtmimtJeZX2IETc+sYk07puiEO56mMW/2PtOzbtwJ52ttfB7e9zWJc5ORth2+s8loGi3tdW0EymgaVQ6gaBq1C4qZnnEFrGqNrRzxwamMrMtusVjgsssuw2233aYfa9sWt912G6644gr2Nc961rPwwQ9+UMuJAOADH/gAHv/4x7OJyKmMpejms1L9XmgwBaxdFk1ompKaEZ+aJgJ9a5pG7TB9ahpH2suZniXSNC3zuRNMz+h8owuPXgAoTRNDRuL9aYydj1og6vACwVIUEUpq8tgITVMP52qMplGdiw1FEazeNN2vKQmZTkZ8fj6AqaRhjmu+6dmABtLwfZclyIiu9Wglj9wkj+OnepNpmkm69nLjZtA0Ee8hILzxSQkppU5yUtHdhjmGrZTmWjiJmqb7t6rtoIKIIoMxC73Um4GomoajafrasUe76dl1112HX/7lX8Zv/MZv4L3vfS++8zu/E8eOHdPqmuc///m4/vrr9fO/8zu/Ew888AC+53u+Bx/4wAfw+te/Hj/xEz+B7/7u757uU0wUS3Rf7GovLA80OzwOMPqkNE0iMqLhbqqmIZp5dREIR01DTM/IWwThPYoukJtkrDcNnW90QeNqRmI0Tb+oBZMRo4B1OAahgkGfkgQgNA2zM5syZOIxSBqLOLAa5+pYmiYDHZr1Baxec0FgoGkYigYoMT3ja0aGJLp/W0rlZQZNIqage1iaJpLkTEl7pN7caVCzRT9Nw8y3kFaihyiZpiHJknpJI22aZoSaxq754SjhIp8Ri6ZRdLbP9CyAkhk1IwH11smObGnvc5/7XNx333244YYbcPToUTztaU/Drbfeqota77rrrl5l0sWFF16IN77xjXjxi1+Mz//8z8cFF1yA7/me78F//I//cbpPMVEsxQKQwHr/mPtHcgMzDLQ0fDaV6Vmvponxw3OVjDA+I7SOwd716s8hyI00j281aRppFO8ZTcI4+idWXKeSsUTTM2AwPtsLLMKm6dlwc6orgbaRfDLiUZIA7iK7qZqRSWkaAxkhN2LNs7nvoY6pa3rG0TTxKQzJiMfPBwgangElahq3iJC+fsreNG3rmV9ihE3P1HN8a0Iv5xxlIta/B3Pt7kWSBjplv5rGTXJi1K1/rsMb0vkuQkoScgxrIbDulTSbMz3rkxFRQYjuGBVRIrbpmUYmI2oaZk1UNI0UlSM+OJWRnYwAwLXXXotrr72W/dvtt9/uPHbFFVfgrW99a8lbndRYqWRkGZYHGt09DZrGyopTwqemSTQ40nA3I+01kRuOpul+TTU48tM0w9/pSc27LUYWtBI1TYIlvPGdkWLk7juTYZomYHo2BpJPiv4YiMnVNN1jpulZCBkxqapS0zNtLuhzOgYQapIH+GiaADLio2ksenFM/Q/diIyiaVJMz3w1I8rpeN1Ckk1CTgQLQiNJA732Z7pmxPwbv/EpRUZ8G59+rVmusWeh3IfrFhecUeO0usUTzphh1bbY399D1a5wwRk1zjkksIcjwOkXAmsBcCh5INQ4jzsksLe3B7F7FvZOvxDy0Fl4wpkztK3E/t4+9vhTOzDwad2cZmcAe3toTjuCPSmx3jkbe6dfCOyea8z1rIXEBWfUWIjGOQYtKuydfiFW1fl4zC5wwRk1ZtI9VqlR1zVms9loRWFRMvJPNdZ9zUjD1YxQmsbw2+DUNBlvGqVp+JepC3muIG1GTdM1tOsGYNU0mQZHbJJjqWnoAiGMcRP5YcO6Ow0uHfrTBKS9Xppm2NHaMfiM+E3POM56yhicGyfYuZDi5XE0DUhvmiqLplHmgl6nYwChJnmAByFIQUZsmsbaFcoxiIbKm+VUvWncv3FIJg3DXHAdMRf0BGuEmFjvRRMoRdMMqBP6cQvQ0shc3flWeNJj5zhzeT/uvPOTxmv++fkSz/i0c3HWoSU+88vOQSuBh+77CJ4wa/Ejzz4Xh+YV7sT/AVz8PECeA9x5Z96k1t04dSVw5513YnHpv8Gd7dU4NDsTN160AymBB+/9CB65P/Pc2P0c4FmvAOaHgTvvxPqLr8edWGO9czbufMxTukJvMtfPP2uNH3n2uTi8AO60PoM460Lc+axXYCXm+BezGb78gnNx9u5x53k5cfjw4dF1oNtkhEQwGaFqGrp7IAhBkR28Y17TGGPEFp45/MhIHUFGgqZnrCQMw5zIDczgy8nLSnZXg81xOk2jO/eG1DT0hqmPQeXQLjRCvWn0bnpEfUBSVGnoUFJIioyQG3GgYI9DhwyahiBsKQnZokdGdrDknY4BQtPwyxN7s09plGe9l8/Abkxvmq6A1Xwsa5zA+Rg712jCWJqMBG3bS2gaVRzM0Jlj1TQGTUPGPX1R4d8+42ycfebpuPD884wd+6FPnsCDeyucc8YOPv7IElJKfMY5p+HYfoP5w3s4fWeGC3AfsDoGnHE+cOjsrDntLRvggWOoqwoXn3s6jt9f43D7CI7NPw3V8jCklPj0c07DYpb53Rx/AHhkBizOAM6+EMt7V1hgjeWhc7E4MQfEDHjcxfrpnzi2xI76PI85bAx14uFP4NCJCnvYwQOL8/Hw3gqPO2MXjz0tP5GQUmK5XOK+++7DnXfeiSc96UlGmUZObJMREutes92uwrzzUPgpDCrBUCikhqYMZsa/o3bw/UKzkJyaBnp+g8+IK+0NmZ5x/LBpDORKmql5kD1uqBuwEfqGKII3SRppPiPkOyOW+KHvbI9VkvA1I5sqYFV2/mKCmhHJ1ozIIEeu62YsRVHNNMpLAQLmvdNxLSRW6xXmC0bCn0jTsDUjqxCqad4AbOfgHFWQHXQjktU40ApdVBkqYPUMPK+FrkvorrNcLsBD0ySqXgyapjKlvVyh92iahm58yHf22EM1ztgVOOPsx+LQoUPGa+pFC7EWmC92UM+7+e3s7GKFNcSJFrPFHLuoASmAnTmwu4usqBuI2QpVVWF3dxfNrMJuK7BezFG1C7RSYmd310gck6JZAHsCWNTA7i6qmcACAmKxwM5KdBcDmetOIyBOtKjnc+xan6Hdn2N3JSBFhflipz8eC+d5qXHo0CHM53P8wz/8A5bLZfE4Gzc9ezRFU3ULI5uMGGqa7leTppGO+15SeGma/j0iapoFFE3j9qahtu0DTRM2q9J8a8D0zE7CvDSNBZ0COTRNTYqDw8fzcErNCEV1KE0TUC9wFIWtHNo8TTOdmkZb7aMyKcVMmqauytU0UadjIE7TBE3PMtQ0ViIqRyAjNCGfxPSMlfb27+UZt3M6Hkl9cAWsiWoak6YxKeuwjcAUNA0dVwBQ//EhyN8kbBJ0/LUsGVpVDH8cMbA0x/Kcq0L4zyOyQjOPlUUpGmKMMXqEf0LRpCAjwirYqwhCYBU3JoWnx0AKTVOhxRx9nQRH03AyNUJRqPcQ7AIRqEQP0jQEGSmBZNXuPYOmUWqaFNOzmqppqjrI0e8zShKn6HFEfUBSjJAZOkES6nTTMwYdEoJU89dZBZuKpgGApS8ZUT4jMZomt2bEUtNM2WeIJqlT0D0cMpJiuz+a+ghRtxEEozWSEQsZ4eifwMYnZ67dfIfHF71/UMvcYukjgkk6jEcKFCah9Ee3McseFUzSYa3d1lwr/mHnwUkSpIlim4yQaOtudyXZZITUWnAOrMU0jVooPTSNz/RsXg+oCMAXsApo5IajadQ0+d40AcMgQ9rbGkWddBHlpb0RSFZNihawRmgaRQEdD9E09Jj4bspW+JQkAIX2y288KTElTWNKe7uHupoRP/rCu9ASaSFFChOOQVXXWMruXF/uM5QKMDiw+mga7v0K1DT27nHMd0ltwMc4sNrUEY0Ug73x1Ie77qQmOHQXPrNrRphjMtVcK2FuqJS0N3QjpodQSvrcMKKSGsN4aj3jk5+Cka1/e5CR/nEuIeNefgBykW0yQkMnI5FmW0ZV+6Z600QWnp1ZhR0jGeGlvdr0TN08mO7DqQuPob7xqmmG5/OIS3hBEwZNk0ZRpNjBG99ZgumZlNJDUfTPb6XxPW8KGJGJVFXSWKQjskEphmgaXTNim54NSWOuyde+Mhf0JSNFNE1C196I6dkYyo02HJuCpgFc6jApGRnpasoWtWfWjAgBh7Lm69PGoTi+pG8+86/DBjLCwALCyEUKkBHrqxHMb2VXspk1uDRNBjLSP1dOkhxNF9tkhIRKRmLNtoxCN6qmKfEZialpEpIRKWoD0jaa7OmdNeMzwtE0AZ8RY6FiVUQepQPy7eBllumZKmD1S3sNTxWqpvFwq0vCtfns4H0o0JThFCCPCckhQshW04yhaYCh7cJ6P0bThE3PDNQwWDOi+UXjYZ9z8NhajzE+IzShsxHWlALb1PoOX3DKKPXdN63EOsBBU7NFG3HkJcPjEifOuwQYaJpQ/k5f0dWMSM9fc0PoMUF+E6B5gzsxIUTwvx/58Z8yxhuyEg8yEqpfNGgae778WK973esCz5gmtmoaEjKJd7bUAwZNU5CM+NQ0kYVnZ1ZjR7iyXsDkfYVTM8LQNIkSXGOxjtA09i45GZIlDrHZNE1Cb5qqgqmm8dA0dIH00TQGZ73hmpFpaJoeGal8NA2XjPAFrFWhmgYAVuh7QO17TJY0TROuGUn2GfHSNOZ4vptbSlBH2zEOrBS8sRPklHGno2lcBKMbt9WGZs5ryfcyeLi4f9PjplK3sbkKOxkJICMUuY29wRg00soZaNLAjfqxj31M//7a174WN9xwA97//vfrx06vV8Dqfv1vB3Gx5rpedfeG8K1I+ICVUxJbZISE7JERkUrTWD4eJ9P0bF4L7IqVMW89VSqjo2oaKS2Ex114dgM9DbymZxSi9sgaUw2OtNNoRdU0E9I0wqZpeGREzVMIYEEWX0pJ0ZdsvFHeBDTNcBzze9PsWkW8hpoms4h3JTrEY835+QAEGYnQNFwy0q7c5DVK0/Sw9Ygkgibko3rTkNfYRnyx3jTAdAoVzmckNi6Vueu6fo06df/m1prlaErJfHzRn6vakV1KHF+ucXy5xollg71V/9+6+3lsf40T+8Pjx1cSx1ctji8b/brU/06s1v043b8luaZCZ8N5552n/zvrrLMghND/PnbsGL7lmm/HkUuvxOmf8TR84Rd+Id78x72jeX9OX3T51+DlL385nv/85+PMM8/Etd/1nQCA//5ffx0XXnghDh8+jG/8xm/EzTffjAsuerIxoT964xvwlf/si7C7u4snPvGJeNnLXob1utsQXHTRRQCAb/zGb4QQQv97E7FFRmj0C5poAsZJ9k28ogjBAOEnh6ZpzGQkVpEvhMAZdY+i1DtGVmnsGOjOmt5siI03L8EN93XgVUTSi+hkd+01aJrEZCTYm4Z8Z1RNo+F18/mUnqA0lkYUWoum2VgysgmaxuMzwtE02mvFdGBlaZrEY6DaLrDmggBijfLYc4wUcGO9DyyI0VNMTWPTNCVJBElSuf4uueMADE2TkORMZSRGp15VAou6wrJpg4gLTcJslDhosDh2rhGa5sSqwSU3vLHgHY4WzYvGX3zXE3HaDD3SC2NeqfHII4/ga676Svz4dddg57Sz8F9+/0/wDS/8Hrz/T34Hj3/MZ+nn/czP/AxuuOEG3HjjjdhfN3j729+KH/3BF+Onfuqn8HVf93V485vfjJe+9KVkZIE/f8uf4SUv/g782E+9Aldf9RX4+7//e/yH//AfAAA33ngj3v72t+Pcc8/Fr/3ar+GrvuqrUNc1NhXbZISE6Be0ik1GPM6lnJomlaYxXHtsmia+2zxdJyPmDpJepLrmAK15Uxci2LyKVdPQSnuyY+eoi2KaRnOhdTJNc2jeHbvkrr1UTeP5zjh6AhgWPirh7B4PTrE4hpqR6aS9kiRhpumZ+x5q12o0yhMClVHA2o2bmoysqwXQAk1MTVPSKA/oqBqajMTUNC2MjrNTde0tomnIaxyaJiHpG019BK7fZdMGkU16/GzUiavzSe1XFZ2rk4wIoLXrQE5NCFocWzjGpZdeikuf/BnAJ+4E5ofx8pc/B7/7P/4bfu8P///49s/9Z/p5X/7lX47v+77vAwCsmhbX/V8/iC959lfi+7//+wEAT37yk/GWt7wFv/+//heAbqW9+T/9BP6P7/pe/Jtv/nd4/NmH8MQnPhEvf/nL8QM/8AO48cYb8bjHPQ4AcPbZZ+O8884r/ARpsU1GaMy7BY1NRsgNzDQVow3jeMjfG3S3W1kFrAmV86fXa6AhhbdqqgTBEJSmoTd1ivAk1oyYpmfh3jRemiamptE0Da1LSXRgDSYjaljBIgS2coHr2AuYRY8G0LRhmmbyAlZKT1X+pI+VN1eC9Kap0bZd8pDqz7EOmQsCA03jkfay10Y9685J2bhFrF7TsyGBGEu5GaZnmbQVNyfAPSdTalo2hTbszCs8vJ9I0xBkRB1XDj0ba9DG9bsBemnvciiDODSv8bc/ehUA4M77j+HY/hpPeMxh3PfwPvbXDS76tM4O/r5H9vDY03ZwPu4DTjwAnH4ecMaRrDmtmxbvO/owAOCS889E9fEP9n8RcItb0+KRRx7Bj7zkpXj9638fH7v341g3LU6cOIG7PnLUWGifcdll+ncB4MMf+iC+/Kv+hdE08ZnPfKZORgCBv/3r9+Dtb70Dv/L/3KyTpabpmusdP34chw+bVvKbjG0yQkLMAskIuYGpNcLsTdMG3TzZoHczu2YkAeo9fdYlI01t2f3S3U0Pq1WytZKfuJrG7vxpLIacmiYga0yu8id0WAlN4+tWasyLwPY1mTsNTdPMzRsYhZ99HYqnDF2APMEuT6j5GrUyMOqe7BjoKlPaq2iaFlVS4kxjSEZKaRrPzX5+CFg+4haxDheT8TCVaY/9Lk2aJu94cOMALlpnrDueCNV8pYRPLpuCYtAkzKasOVRnEzJkAF3fl+VQryKE0MaIh+bdund4UePQooYQ3frRSondeY3DixqHUQPrqrNeX+TdIpt26Al0eD7DvhDdpUvnmHkpf//3fz/e9IdvxM/88LX4rM96Eg5dcAm+6eu/Fsvlyhj3tNNO07/TY9LKwW3YjuPHHsF3ft8P4pv+1b/CkTPN+0iprXtpbJMRElWPjNQt096cqmm8pmfqAkx8Q7oTVQtvYm8aADit6nakyjlWhUHTiO4rdmkan+lZ714ogXUrta2zekzPyaem8Up702gaQfn9zN40TSuxbFq278NwwwRR0wjzpkwiTtNYjbo2LO2tJkRGpKj1jTi5N82Mp2kaiGyTr6DTMZBA03jeb7bTJyM2MkISXBLU9Mz8LiMfgAmzN41nfgkhhHu96X8z9Rx2jFbTcLJppCEuhprG2pi1gbVm2bRoW5l9DfkcaRd1R45we0JuaZaex8s2AMNnkOT/AjRvyBv3z/7sz/DCb/0WfONXfzkwO4RHDp+HD//jRwFc5jVSEwK46Imfhb/5y3f2SVn3vLe//e3GXD/v0qfhw3//QVz0xM/EBWcfYseaz+domgnWn0hs1TQkqoVKRjiahhSwUoSA0jTqppyMjFCkYmY8ltKA7XDdLdqq27CeKrnxatMzjqZhEp5Q5byxI/XRNPo+79tZpZmeCVERNU2sZmS4yfioGgN+JiiXvWiq4OgJgOympWRrbiaPDdSMGDRNm0jTGGoaaDWNFFVSfZMxjZDTMRCnaXyqEp+8l9R70aDyUwqMldEr3c+xpmf0dTbdm+JfMtpnxIPqLBIoFVr8apsJchQTvbaWWT00zLF9c2XZcgUOgq/h6IYSxnNzwh5TSPcvucM+6UlPwu/8z9/Du//6/fjLv3kvnve85w3rlfHZSX2KEHje//Ht+NP//SbcfPPP4u/+7u/wi7/4i/iDP/iDYW0WwHU/8EP4/f/3Nbj5J38cf/M3f4P3vve9eM1rXoOXvOQleqyLLroIt912G44ePYpPfOITmbNPj20yQqKed5khj4y4jfLM3jTSKG5MCo6m6cdKcVs83CMjCvZWQeFSowDSVtNommZ42EhGrCI4Q31D1TRkUfcWwKVCshQZ0TRN+HjO60ojOL4iVsNThbyHt4CV6dgL8DTNJnORoc5hfDIiCLpHER4fTdP2SBMQoWkyTb7aKpKMxGgaH2roMz5rPTUjlFqhxcijakbG9aYBSJLko2lOhrTXuX4TaBqicqLUbfc391ox15qSZATsXFVfHG7ZGHIDYahbjOfq8cZlI5JiLoJPflLi5ptvxmPOfgy++OuvwdXf+l14znOegy/4vKdYc3Xjsi/8Irzkppvxc//3z+LSSy/Frbfeihe/+MXY2VH3C4FnX/mV+Llfew3++I9uwxd+4Rfii77oi/CzP/uz+IzP+Aw9zite8Qq86U1vwoUXXoinP/3phZ8iHluahkTdIyNzGaJpSG8aqioxaJrEk5ijafrHUxaeQ6JLRpRvgwpa2KVhfjQWMsLTNEIILGYVluvWRUboTcBQ0/R/J3y5l6aJVPkL4l+RStMAHTqyatZeea+x42N60zg+Iz6ahiYjI2oDUmMTNA1EbRRv+hAow4XW6E1j0zR5SVkbcksFgCYtGXHez4uM+Gia/u0smXZZbxp1XvDy2JxQb19kehZwUE4JvzQ/k6YhSiWAV9nN6q57dNPKfr789+0L31qjkZFAMtEhI+6BHHslG6+X1l9I8hOKF77whXjhC1+o/33RRRfhf7/x94GPfxCod9A+7rNx7b98FgCg6Y/nh//89cB5n2fORQj8q+e9ANe/+Fpdx/KiF70IT7x4SDQEgGd92Vfg6q/5ajzhsXyx6tVXX42rr746POkJYouMkKgXHTIy45IR2tOFU5VIYv5VUsBK3SZlk3SjO1R1i/YqSNMoZEQaplcQIqG+I5OmCZme5dI0VYVUO3gg3rl3gInB3pS9ycic303T3dQmaZpJpb0MImTSNOZ70N2q7UJr0DSZJl/apK/x0TR9MpJN03iSHB9NQ85b6tY5RgUzBX03IDbm4ylrwia69nbjxpMceo3Zx1YdXkelMwLJ8ZqeBWgaLkExa0YogVNSM+KPcY3yDMiFROV5vFuDf+OWn8e7//Ld+OAHP4if//mfx2/8xm/gec/918PTyxmpyWOLjJCY9e3NeWSE0jTdhWP2pmlN6DslaHJAF0rZJu2CDgtPMkKSBi3tRessyhxNA3QL2sNYOwuP2igLQ0VkmZ5FEpy9GDJCd7GJjfKAQVHjp2nIvIyb8rCjpcH1ZAGG74P2ptkkMqJNzyZR05DCXYoIeWiavf4Y1JUwLMCp6Vkr82kaqdRfPmQkStNAz8sIHzKiNxLm82kCYSIj/rn7QrGWhpqmNBnxIKwpkuHU68wXvnVHJTl7IZ8RYhVAUSe6HnJJzvHeFTV7rl7Ts96BlbtmSKmFfhXZWQgxPKeIpSGfT4Jct9bjBQPrV0urNmQIc+RKCPz1X74T/+WXfh6PPPwwnvjEJ+Lnfu7n8O/+1df21vLT9BGeKrbJCIlZj4wsYjQNNXkaZXpGrc9r4/GUIjhlB7+04E2jN01NYH5CTxg7Qd8uyFp4jEp7Q01DF3XPmCU1Ixk0jYIh02gaelOGnjuNQUUSoGlOQs1IVU/pMzIkeiZNw6tpvF4rlKaR+WoahWAIro8MkNwoz6VpfMiIuiHwahr6XQrhFl+nhNFAMfd4WBGnaTZYM+KjWRPM1DjTMzvRc9eaGsCqaL4+g8VFSs0IYKAC3OOleIGA6BIGaT5mJD9jwqhvCcxDAD/9C7+Gi885DWfsDtfSI/fdTV4rnCFPVWxpGhLznW5ntQBH06iFvPLTNKWmZ4JQEv3jKUVwQzJi0zTQrx1qRlrjZhTqOOtLHMyaEa43TRhtATq5cKjzp9q9C0EbXKTQNMr4jO/ca3xnnJrGWzPio2nGqyaSQhCfmLFDaQqsNj1xPGoaHzokCE3TUJomdTVRyUjDXGcAaZQXoWmcAtZMNQ2h3FpPEp0aZs8idZ0UDeVVeCWpaVShaXHXXv49UpIc+r3QIlx6bdnqwDE1Lto+xk5G5ioZMek3GpSMsR8fXTliUB9yeGwKSkRayIjTf3gI9RW6h0A94KN+Tk1skxES852ugGdHrtw/cmqaSpAbpokQJAVHSfSPp+w2d9DNc99CRkyahsD8ZFEOdZz1GRwZJmme3jQ+vpw2WgvJ+AS9cWTQNIeiNA2GebE0jZ2MeEzPqMHbyB1wShh2/mPHIh2bU0zPfEW8wEDTSOT3phEhp2OA0DR8ozxvEqiRER9Nw8u0TQv3su+yYhLyYprGY8SXYno2vmsv/x4ptSh0syLI8aDrob+wfUTNiGdMwL3HDlMxaRNp3J/H1YzQkQV5dNwqQRMHCrn4sxz1jr6EDKRr70Gwzt8mIyTmfc3IjlhB2tVjBk1D6Yqh+G/ox5D4hl6apk3adfuSEcP0zKBphuSHfjxfsZrNDxtJmPG51d/9skba+Ta08KibrjBomoRkZB5ORgxIlxQj+0zP9qI0zfibTkpsqoDVMD3zIFC+hAwYfEYa5PuMKKdj1s8HIDQNzyJ7UcNMNQ2rjCpcEU2ahqcPsseypb0eky8aYwtYvdLelAJWcj1wjrT8uOXzbTyIHF1rvMgIpeMMOgVjc5Hhve03HDOsUTNCH6YYj2Rf4hxZckwOUs3INhkhsdgdpE37dhMvRk0jDInrUDOSj4xU5ipIaJrQwqPopD1pISOEU6+4RnlCmLuVxIXHVNP4akb4xWxWV5j1Byi08Jg0TY6aRhXYhWtGugK1YafsNz3jKQp1rKQc1yo+NQyabexYXE8eWjPiUdPYx6CTc5fXjESRkQhNE3RgBZJpGto/Zex3Sa3lZebxsMPu66JiqGvZXM2ILykbakZSpL1mEW6sgLWb74gCVmvMOVk03fI9d222buGYDBmR5hhc8lMU/XE20Rw3KrJWseEBW05VbJMREjs0GdmzkhGv6dkEvWnUzYDc4Ns2vvAsemTEl4xQU7YqSNOY4/p4Z9P0jKhpyE4uJHdNWXh0bUSdS9OEO/f6aZruV7c3jQcZIYjCmO6sqWE46I4dS6NOVqO8XJqGPE8W9KZRTsczzlwQGEHTeFQ6PjWNoQLr5zaapplATUPmRSOJptlY194MmqYSRhEuXQ9T69PS5tr9tL8zQdQ89o3YQBXIo5PejEnOIfRDI5ERw6REWuNEkJFQzcgBgka2yQiJ+XyBVnbfzmr/uPlHxvTM8dvwSPK80Vo7NjJWysKjVD92MqJNzyqBqvcvqagdvKiNBcK/W0ksYCW+CKGFeCehiZe+YYoZctQ0MWmv4alC6CpfnY9GBXyN8ia46aTEtMgIUdNQStGDQPnQIfp9NAV28EGnYyCqpvGihl5kpP9ufTTNBN8lZ3qWXNDrjAU9LxpJ1K3q9zK2UZ63gDVE0/SvFcJcE+jGx5r6GPt6n+kZMNxjnRuxOhUsVECRH4LyNIUZCltUKjA6HdGvZupeuGGjyIip8TnlsU1GSIiq0vUXSxsZoWoaihAYNE0uMmIV1pGbQuhCU6H8UE5Ik1s3aBpVM4LGQGLoFMfTNMP7Dny5O9+UYjVN0xgeLmkOrEBcTWPSNG5DLxVemoYqMBJ8H8aGqEijw7Ghj61lekbk6TR8xm8UGVnLfAfWuqdpWHNBgNA0fM2IFzX0ISM+mkabCPv9cVKDMz0bT9OY56RPqUZjMpomcYNCw6BpjELvYd5uz6oRNE2AWhNiuE5pmPUWnoEnupzN9xaexxPD4H5sZIT+jbwkioyEHjn5sU1GrFj21uoru2bEoGlcGmSU6ZnasVGaRsYXnlmv+jneemgaw/TMomkoMuJV09g0TT9NYSYKdIcZWohTFp4hGZkNHz5DTcP5jDieKklqmrjPyMlR0/Q02yTSXqKmSTA983mt0ORQFvSmUU7HrLkgEKdpfKhhrpqGkZ+W5pVcQj6+N435eJLp2Vg7eM97pEiGTRXfMF5INj2FAyt3mDVxEZD2qnCpG572SA1BEgdBHks9HV71qlfhoosuwu7uLi6//HK87W1vM2dsIyOegX21RwNLI6LH6WTGNhmxYh/dgrbOpmnaiWmaFGSk2wEeb80dJN3dDMhIa6ppAouvr1htSMJg7KbNyvn+I7E1I3GapsLghZGjpgnRNI6nClXTeBJIn88INbYb24MkJapJpb3D+Wacq2NoGgh/cuCJeifQAwqI0zS5NSM+NQ2pzRjrpmuOBX5+BWPRSCmyHdu1l2to142bYQcvBCn0Btb99ctvUEaoaUI0jUqGrMclcyM2H6cPZk/JfC9jkDSS5rWvfS2uu+463HjjjXjnO9+JSy+9FFdddRXuvfc+PVFJqB/jF+u+46uboW7OG9xHZcc2GbFCNZ1bLQNqGlrVzqlpimmaARnRF1rgbFGcezAZUTUjHjUNdyHvenxGeJpGmh4LgQUzZdemLpRcmmYwPWOSESplE8JU05AdHA1VAKicXVWcbNOzSWtGiGzaoBT1ys0XsNrHgCIojUHTpB2HeQwZidE0vh1xpumZoaYZmUDwNE3RUMSsykfThJKRkTSN57vcTaj3okghPY7rRiFn7mt2RxTchtYwY8cvJbA8BiyPQayOd/8tj0GsT0CsjkNaj2N5AlidAFbH9ety/qv6sbA81vUEA5zkxxc333wzXvSiF+Gaa67BJZdcgltuuQWHDx/Gq3/9N4YnKTVNZCzt9ZLwvqceF9nawTuxEgtAAs2+f0EzFi62N01qMqJOVEZNoy7swIqm1AjHGvNrNJQjhgOrS9OwuxVPhbsBx3t60+iFmFl4smpGxAw6V06gKHYDPiP063DUNJ5dqB8ZoVy4+dgmoqpVAfL45ULQmhGKCEXVNP6akZaaniVubWbKXJBzOgYIMuKjaXzIiMcOPkbTyAlMzxKLuJPG8iCshs+PJ9R3tWw6RV7uHHzvkVQzYtA0JBnpF0x+g1KOjAxdHTiehpzfq+PAT5wPAHgqecqF/X9Tx2eT35sXvhFY7Bj0j+9SXi6XeMc73oHrr79eP1ZVFa688krc8da3Ai/8GrB6IP35zYHV2S59VbxbB9aDHeu+6VxDkREpreJPsvtnVCVFpmeAHqtthiLM0OKoTKPsZITubjRN46hp/ON7C1jpgs3awYfh7iSaRlmW13m9aXTXXmaH5fTGYNQ0XmmvT03T+neRU4auGcH43jS67qQaEKEgTbNKoGlkGGXjQpkLsj2gAMQa5XlRw6jpme2mO4zXBJLolJjS9Mwv7VXH2f/aHYJihZyOfeHz7ig1PQOAVRNaE8qRnFCx/MGphSDISOR0uP/++9E0DY4cOWI8fuTIERw9es8w4lhkhFJVB4im2SIjVqyrHaABmhVZ0OgJbZiegadpkpGRITkAoG8KDUlGQgta1e8gH/HQNHUFTdPUBk1TBW8gPt7ZqA3wqWkC1EVSAaumaWpAboKmIeNZ3DaNoV7iVNM0hGYbOxaRTRsqoqiahqdpWinQIGBC5gllLrjbOx0LOwNQ53+ka2+6mmZAwmiYNM3YBKKf2yZNzxJoml2SOO6vWpdii4RPNp1Si0JryuhxXPVJEV9HNt70LFgzIgHMDwM/9FEAwN989CG0UuLJR07H/Q8v8cDxJY6cuYNj+w0e2V/jCY85hMfIh4GH/hHYOQt47EXZ8/rAPY9gf93g4nNOw+EHP6RmhKGKZGyCZL+erxnx0X0cunKqUzZgm4w4sa56ZGRFkRFyoVTWjZw0GfMpM7xhw8f9z7YZ3i+0U6t6ZOSRtbngUEWLNs3ymJ6xBaye3UoaTQP93s64CQZHlb5h1mRFSZD2qgLWlSvtpeyDUcAaomm8HWu7nwa0v8FkxChAHhlDo7zK6FhLmz3S8NM03fMadN2fc4s/qdPxarmPxe4h8wmKpmEcWEMGWl5kpLWSfuv1BuU2klqZwvTMd06m1JHN6s5VuGllf4PnEzpfeKW9CdcuRU7pyxUykrPxSZqr9CdnBjIiBLA4rfv3fA0pJcTiNMjFDHI1A+a7kO0asl1DLA53C8b8EDDf1a/LCTlvIUUDzE/jBS+e28M555yDuq5xzz33GI/fc889OO+888jrGZqFCdofyPOMybxPpogtTWNF0ycj7ZIsaPRmaDfKIwhBftdea8fWL5aS3D1DC7yy0354bSMj/d+rgaapwZue8eZkMZoGvJpGhqmLlIWHpWkSjufgM+KOTZERQ9oriAOrr2bEommMtvMnQ01DpdkjQxXTiao2vjNa90QjpqZpUeniRCAdIdohycf+3nH3CQGaxvkuafhqRuxC8T7Y3jQj0YxGYjxNQyk0EkYtWCDGUB++zUSW6VllF7AqNQ0z1wkcWMt8RszbsPG0kdczJ+0F4pTIYrHAZZddhttuu00/1rYtbrvtNlxxxReRZ1o0ja9mJIaM0JKRU5+LbJERO5qqW9Bag6YhF4qozZuQp0dLUjg0jUJGCE3jW3iaNUTbPe9hT81ILQTq2qemgX6OHb7aDgMRMtQ0/fu20qCI3HEzaZoiNY2LjNBEw6RpKoN2oeGlaYgK5aT0pqHJ5Migsmla5+NV03h9RnqaBkLveoH0pGzR28EDwHL/OIBPM58QoGnotSXscyxWM+LQNOQtR9M05LwYmaR6Tc8SvIeA7jo7vmyKqA8fzZrbtdekaVJqRiamadRz7CR+KOEYutrSx41Xj7tDS5DvSpiP++K6667DC17wAjzjGc/AM5/5TLzyla/EsWPHcM0LXwjIHjGZEhnZ1owc3GjrLhmRRjJCaZraLPKiNI0HXvW/2ZAcdD9VASuhaXxnC2ky9vDaXJWptHCQhkpjUQ7atntULw33uanzrEw1PUugaaoqq4B1oGmaDoYl708RHSEsmiZWwOpxYG0lRkP7KWHY+Y8MqqZRhyeJpnEcWAeaZkUSmNTjIKoKe3KOXbFyzQWBCE0z/J6MjERoGgD6c4ylVtoJ6DtqLU8jta6lSxxWTtftlGg8c09Rwg2f23y9OrZBmmaEz0hATMMgI9J5DqSFMqh/FMIFA0VEHxNm8uOJ5z73ubjvvvtwww034OjRo3ja056GW2+9FUeOnAcctZMR6x0LfEZUHABgZJuM2NH2NI2xu3JoGooQDBB3Hc1ErXBomu6Cl5ImI57XkgX3WDND00qnCr/rTdOrMYSEbFZ9i+yI6ZnXDl7NSYBTEdF24aW9adRNt6pmBuoUC0XTSNmNTwv3jO9LElCWqmnIW0gpdW8POxnRN3F5cmiaaX1G1OJdmZRiNk2jkBGLpsnYZi3FArtYYTWGpkmtGSEFyzToOaq9MIrRjH5+E5ie+cwTU1VLY6gPn0cKpW7tZF+FTXVVolsL1ynISEHiFD4eLkpNb8rmK6zHx8IFhKYZHhPJgMu1116La6+91nzQuDYtmsYTWtrrY2no5zwA2ci2ZsQKyVXk2zSNIXGdwvTMVNNQZMS78PQL7lLWaFEZjbE4mgYA2nW/yIsqCEv7kgZDWsj05OlkjfCPqxeeQNde6sCaRdMMn9NW1BgqBOO7rDSdRBcq+rltJYkBx2dKWkuiqpW0dwKahtTjGMlITE3jsYPvaJq0+iY7llDmgkyxqe7c7PqMOJQbDW/NiIKweDUNMCg+pjA9G20tT5Q5NEIFmzTGUB++dUGdA60E1p71zaZ41M9BTcPMdYR9fbg3TfczBdyQ4OoqykMjIFbyM8yy5L1o4mDRNJ6akUHa68lGyGsPQC6yTUbskDXT34LewIjpmaEqoTRN6ontqGn6C76vBREisPD0ap99dAs2vZj12lsJCJqMNKvhMwSgZK+ahiZh1OyNIEIh90k1bgg+Vrv3qqq8N0ku6kpg0Y9/fMUjOjWlaLo3Yak1IxmxaRr2prPJZGQ6aW9FHFhT1DR7Pp+R/npoUJnJSMZqsuz9fBxkRJ2jADgHVqfPEI1cNQ0nPy0tYDXou3Hnha/2LNUltlShIgOqInoO7Hk2E3bxq/qpE71AfVoRpRQwPeMoCuNoihC9k5HJBMJIcMibjb3xp0qDq+jHOEg9e7fJiBOy310JursyaBphZuRUTTMRTYNALwcd/fzUDpPeQE07+OErlmqhJzRNjh+IUStTQNMMltIhZKT/7HWdRdMA/iJWk6YxUS6uZkTNrxLAzPoc9KZDW6ZvKipa8zMyBpqmNs9VL03jqRnxqWkyjsOqT0bWDjJCkhGOpglKe/uNRLseimABv5qG/HM8TTMkdykdt0MRNT3bEDJCb1q+Tt7duPz1aCvp1M/BDj59rUmJkAmc0M8hD1o1HPphOS0yoHEKu2ZkzDJhUCqKplGP8VmH8CS1zkSRnuBsMrbJiB397ko0DDLS34ANjw6qpvHAq97wqmm6x4OLjqJpVDKycpORuoJB08h1XxgoRPBGGjU9q8i2gpiexdwn8wpYZ96bpC8OeSzhDRUC3f2LijU9oyoSe9dFj9d6JLSfEqpmZCampmm6x0zTs1Q1zXiahnU6BkxkhKNpKPTtQ0YAo8A7ZnoGTEDTqEM4AWLmQ0ZCBZs0SmtG6LG15y7EgDx6kxFLSefQNBnKvZQIm5651zU9miZtYj7uoz2SQw8smccmkNHaA3jOBx8yMhSwCnaqpyq2yYgVot9dVcZiNqgvpJQGDTKN6Zni/hRN0+/MQ9+OQkYER9MQZKQmhZxapZDYtTfJ9Iz05ImZniV17R2ohBw1DTAoauyaEWPRMmga3vTMiwjAgvZHelOkhFHz0+TvHmkoB9aK0jS0ZiS1ay+lachxy1GPxJMR4SQPwLCwskmDQkYAs24khaaZyGekldKkcUeNZT6eXMBaSNM0xnfJjRuu+bKVdGqa6tiGm2duxvTMTOjsdXmolxieJgLPT4sBGTHTn/GrhC9J4h8fEjLJ18SQjGx8fjQ+m9kmI3b0uysjGSG1HXSBMHrTFJmeqTu3paZJomk6ZGSlkxG3gLWjacgCrKBrYnoWomlsbtg0PeN604SbhKUUq2k1zQiaxqkZUTcwp4C1NlAdFd5aCdjQvt/MaaqgNFubmJR5x1JLTlXl0TQ2MmKoacoQBeV0bPj5dA90Pz1N8oJ+IFUNLQfmHJSDapqRNSOUphnpP6Ndfh2aRv39JNA0BTLcxkrC1M91ih18Udfefq7Wca7rukswmrVxk7VyAwKAULSt/yMw+g5tFLCK4c5fPqwNdVjH0yftdd6TQUZGxvHjXe3XfJ7n+EtjK+21opr3yUhLmngRmqaxd4JE8WEYSaWER00j2zWAWYSm6ZKlFYOMUGkhS9NUtcPv0qCLDpXxGZw1/dwEXUgyPQvs2Gq9e58hR00DAIfnfbM8j5qmsmtGiPkXp6ZxbsKYFtpPCVqA3DRrzOb8TTolqGza7E3DI1BRnxFZBYsTQ8GaCwKDx0ikSZ4XNZztAsuVVYBuJf0klHX6eJpGHU+KxBUN5d3UpLrEljqwhmialHHtJEz9XIUcWDdA08xmM+zJGdYPfQKHdhfY2+veY920eg3c39vDarmEXC+xWko0qwayabG/v4daLoG17NaKPev8TJnXegm5XmO5bLG37o/p/j7Wq33I9RLrfYm9vYLzbN2lFfvLJcRaYg/9/FZt97f9JVAN821bqT/viRN7+jjtr1oISCyXKwjRzalpK+wVfFYpJY4fP457770XZ599NuravcZSY5uMWCH6ZER1xAVgcM6tccHCUHwYCoWU8KlpGkXTxJGRtXBrRqiihdI0Ui30BOEJ9ZABus6fasEwbuoEtVBDxPjylIVncGDNMz0DgF2FjNg0DUV0LM8YnqbxIyNsN9INJiO1QbONRUb686q2Tc88NE1ETUMdWHNBgMaHjCj0jlHSAK5iw4nZDrB8OImm6cYBGoz/LmkhtDrfYhJcX+jk30ZGEn1tSm/wxkYrRKlEaZp+DF0zEkdh163Eumkxy8jgfI60QgjsL87Cxz52N5bLJdqHujW9aSXufbA73xYnDuHhvRUePLHGsZ0a+6u2kyw/vIMFlsAj93UJ8UP53+H9j+xjb9ViuVvhk/v3dU6sx+7EQ3srPNS/3/EHCjYVD94HyBar+T7mq4ewEgvMj1XAI/d294PDLbD4hPGSez/RIYT1sd2hhufBezCXKyx3WojFA7j3of2uueGxQ85bpsbZZ59t9s8piG0yYkU1776QGUVGSG0HTUb8vWkS38ynppGDmsMb/WK77neYrJpGS40F6t70rHufNJpGjUs9Brp5CbBqmmgyEoePa0NNo3DGRJpG9afx0EsmTdPhtJzpWahmhENGToaaBhhP0+SbnvlomkFNU4oosE7HQJymiVEgnLzXo6YB1Hcng7v3lNAeQxGvnZQYTM/Mx7NNzzKpD2mvbfa4UZrGXHd0zUiogNXa+OQkIyF10WI+xw/ddj8uv+gs/OoLngoA+Pgj+/j2190BIYA3X/dl+K23/QN+9U8+iqs+5zy8664Hce/De3jV874AF7d/B7zx+4CzLwb+3f9Ino+KX/nd9+CtH/o4vuOy0/Gv//r7sJQ1Fv+/P8d/feuH8Wt/9lF8zec9Ht/3nIuzx8Uv/Xtg+RDuvPhbcPGdv4m/m382Lv723wR+72eBu94CfPmNwMVXGy/5zv/5x1g1Lf7bi74I557ZXRsf/oXvx0XNP+B9z3gZdj7ry/Aj//PtOGN3htd995fkzwkdNTMGEVFRlIy86lWvwk//9E/j6NGjuPTSS/HzP//zeOYznxl93Wte8xp88zd/M77+678er3vd60reeuNR930zZhwyYtM0Bl3RGhX1SeEzPesX++Cio5ERhqaxLtIGFWo0QzJSVQTydYdekAVhf9UCu+bnqisMODmRNMfcJ3O69tZ1vprGJ+01aZqhGFl/Flg0jU9FAvN4KclivblcxEC2mtHIyJDomb1pVGI5HOd102pzq1DX3uEYFCYjtkFZMk0TQEYA3rTQQ9MAKP4c9ji0N814yseiaRJVOsU0jbG25Y/ro2lCsml7rTmcARjENlRrCXzskTV2d3u0ex/4yMMN5rXA7u4uVnKGjzzc4JNL4GOPNDj6cIPZYge76wp45G5gZwfY3XXGjsVDK4GPPNzgxHKN3UfuBuQcu7u7WKJ/v33oOWXF3lHg+MdR7X8Cu4/cDbHzad046we7+eKEM9+P70k8vNegqeb6PWePfAy7zd2opMR8ZwcfebjBmStRNqcJI5vVfO1rX4vrrrsON954I975znfi0ksvxVVXXYV77703+LoPf/jD+P7v/3586Zd+afFkT0YMyQitGRluYFT9aDiwjqJp+otJF7AqZCReM9LUbgGrDWW36ms2aBr/oi6EYFEMg7M2aJoBEQrbzMer/KsRNM2hKE0jHGos5DMSp2n8hXlThVHz07hNAHOiIvU4pumZQqCG47YkUJFbMzIUsKpjkHsPbzlzQSCBpolQFRwyEqRprLqGUpqGGB62HvogNSjtSSNVpVNM05B5cxRTDNk0VIbkZwg9m9WV9vLJT57Qz5fb+Lhrja2+4dyUhUW9l4Quil9357Jaf302/8mhOnj363grbETdPX7DuTB8FkH6f/mUW6cispORm2++GS960YtwzTXX4JJLLsEtt9yCw4cP49WvfrX3NU3T4Fu+5Vvwspe9DE984hNHTXjTMds5DACYS46mqRmaZgO9aQKNpXT0i23b0zRU+eLQNDoZYUzPPCsmtwsydiIMTQOMMziSbYtKqNfnq2kOeWkaDPO2PGM4gylfkzygW8jUIVuN3E2nhCHtbfMWazu0i2s12MFLCRaBoot4mKYpQwGU07FwkhGFjERomixkxETDaFQTfZdDN2d/59vcsby9aZKRkTI1jXdNYG7wNNzeNGbNiG9zVTrfkOlZcP3q58FtomrLtqAkBrRtuE6A4fMXq2D7NUt1a9e370DyxLleq0J22r07eQO9wchKRpbLJd7xjnfgyiuvHAaoKlx55ZW44447vK/70R/9UZx77rn49//+3ye9z/7+Ph566CHjv5MVs0VXMzKXxHyJcM5mxTlYVUm66Zl5YxzUNDnICFMzohet/m36r1kSO/iYe+huYGdhd+2li9daJVIFBawN2fmbNE2imsbjM2JQUtaNiTOYUvPbnbs3L/qadYoEe2QIIhtpxiIjRDat5aMGTTMcN3UM5rVwb6raZ0QM33duMjLzICOBJnlAgqKERUb0Ftp5ur5x6K69kYl7wvAZGS3tjdA0kTkONSNlNI13TSg0PYtdJ7sJDTS5CB3nELKre+coBEMSFaCF+paEPhf6pKFVKKzHWTd9YHWdduNKNc9A8sTVD1FjSW3UOYFPyNjIuvTuv/9+NE2DI0eOGI8fOXIER48eZV/zp3/6p/jVX/1V/PIv/3Ly+9x0000466yz9H8XXnhhzjRHxWynT0bASHurofBTQ5kGTdP9OrY3jU5GgqZn3WKrdpismkYtamr8ZmiU10YWNs4TxCxgJTQNGSMEycZqRoydf5HpWYciODQNXWT1jclcIOhbe1UkfTiSxU0ajQBYS5O+K43aS9O4apqBqmISMpamyTwGnJ8PMI2aBuBrRlJomlKfEeINYjRmLAi6Y1dhmC1GkZFxahr/mhBu5+A1PYsUOZd27s3tEG7TZ1wbC3tNLwmdjDSKpnGTn6JQNE1bQtMMf6M0TbY31gZjo6ZnDz/8ML71W78Vv/zLv4xzzjkn+XXXX389HnzwQf3f3XffvcFZmjHvkZGFQdMMNzDHaIelaTKRkcpCRmS6HXyrkRFPbQcITdOm9aYB+JPYS9MYChP/QqwWnaaX8dnRGshIbRTJpsShPtlxHFhZmsbP44Z8RoBhWgNFkTS94lAw71Rqmqq2bPA5miZAVbE0TWEyIpxkJELTxCiQkJqGo2kS5KcpYTZQxLixGNMzx2wxEBujaRILWNW6U1vH1pe0x5IcXwTtCfq5LnuvpO755ndckRux4ew7EU3TNEPSTh8vdivVycja+Hdo08Z9Z4PfEOnNdQBomiw1zTnnnIO6rnHPPfcYj99zzz2sxvjv//7v8eEPfxhXXz3IjdTudzab4f3vfz8+8zM/03ndzs4OdnZ2nMdPRsx3u2RkB2GaRl9YVFWSTdNYhXWqZiRll6Z2fv3ia0p7u5/65Fc+e6yaJn3hMXryGCoiWjMSp2nUuLaMz6Fpsh1Ye9Mzq2bEWCQdmqb7J/3OQtJePQ7Gu3amhtpZtSNpmppY7Ru1MiShhuwKWgdFEXMMDDVNGU2j/HwcZCRG08SoirlKRngHZTvqib5LrmtvsekZs6lxzBYDUUp7xNQ6MQTDXnfc64R/31L1T449we58oKZrYc6vaS0lkKeLdWq4yEifRIy98VeqZqS7RhT9HqKzucRU147VNaEXy6Y0ZWRdLovFApdddhluu+02/VjbtrjttttwxRVXOM9/ylOegve85z1497vfrf/7uq/7Ojz72c/Gu9/97pNKv6TGoi9gXYj1YDLF0DT6/OfUNMU0jYLju5M4xfRMMsmIrWhp7ZqRiOkZwNs0txQW1QqM1qDiQ2jBwlog7KA0zTg1jdW1l8LPtpqGgSlDahqA0jSR+oWJQiMjDJqUE1Q2bVCK9Cbdn+v6GHB1MxOYnon+vK0dZCScjNhUgBMsMhKiabqfY79LrmvveJqGIiNWrVogNkF7dOOm0jRqnhbqNHEBa4qLdDduf77aahpynI0mhBOpaVqrgHXYABQNS5CRPhmx7ht8zYhb+ycIMkITueRN9IYi22fkuuuuwwte8AI84xnPwDOf+Uy88pWvxLFjx3DNNdcAAJ7//OfjggsuwE033YTd3V187ud+rvH6s88+GwCcxw9KLHYHF7rl/gnsHj7dUNM4vTFIFk0toZNC79KtDFf5jCQhI6pmhNZ2mBepzswTTc8AD01DYVwvTeOvIagrgXnd3cC4hYc6jE5ZwGqanploFGcwFfIZASi0X4YK5EajaZpxyIjq/CuqGrVCW1ppwgxtA1R1Bk1TdgzYtgsAQe98BazdT7/pWaaaxvkuIxP3hGn8F5lj4lj0pkV308mmZ8XqFB+dklbAqo6p07U3ttZkJk8hR9p53anepFTHYe4U2MZpmrJkZKBpeGlvcX2GpaaRGlHPpGkIQkqPXSMlqsm61eRHdjLy3Oc+F/fddx9uuOEGHD16FE972tNw66236qLWu+66y2ju9WiLnd3D+vf9vT4ZITSN489B6YrsmpH+eTZNo6RXofOi3/kJlqYxF5XWrhkxetPww3MLj0HtMCoiANooK5TkrJo1u/C0I2kaJe21C1jZ3jSKplGHJlHaCwzHbB1YDKcMqRGz8poR2bZ6mamqCrUk5ypFDGxkhKVpejWNrIbvO/PGW3HmgkC6mianZiRJTTMWGVFvNaHpmRcZiSQjhQWsIY+gbtxwkmObnqVeJ6Wde0PJk/JK2lu1eq2x1TfaD4Q2N6wEWXfKkgZ1bqp1XAkI1NdWnIyoNUvXjNilAnk0TUW6lo+a10RR5MB67bXX4tprr2X/dvvttwdf++u//uslb3nSYjZfYC0rzESL5V7XiZDrTaMXBE5Nk2t6VpkZrrrphH1GukWcS0bsi7QVFSBhqGnSa0YYxKUCS08BZIcZGPeRfX7hoTUjY2ganx08q6Zhpb2KovDQNIk7vqmi0TRbeTLSNGt9sdf1DFVLa0YoTdO9RxAd0siIKDY9q/u2C7UPGfEkI7EbJjjJcI6aptT0jFC0vp4pqcGtI47ZYiDGOrD614QwgmEnYakIYjlNg+h891YtoWlgzIsmfZuhaXoEA+b7FdeMODSNjYykqWk4J2bPy09qPHohjA3GEt1CuNzr25CTG5hjd87RNNl28B6aJqFmRCxUMkJrO/phbZqGqGlkDJJlFh4jyaFFj3Bv0L71MrjwtGrHLbq5Z9M0fNdeAza3jnnY9CzsM1LasTY32gloGiPRq2dm4RqlL/pzL1jEy0h7c1EA7XQs82iaUO+jbkCmgDXJ9Gzcd0n7Uo32GeEKWCN9Y2gM124mTWOvbc64EWm+AnqtmoyYbLoYyYkhLtZa4zNlMyiwgnXHDv396ZoR02CxnKbpN1DSTkZCNSNu/dDgcl0b30mx5Hii2CYjTOz3/V5WS4WMMDSNA5GNaJRn0zStv+5CR7/YKu6dMyfT8k1N0ygPhzqhWC2RpoEEpHT6UPh5Z//C0/Tz01JkXSSbdkAPe+zgDf8Em6bhkJGQkgQE2j9JBayS1neUjkG2PVVVmTe8bJrG7U2TewxqbS5oJSOJNE3cgZWzg2fUNM53GZs5H4PR1WZ602QVsI6kPbxJQ6QWxTZbrFLXhMKC29waF0faa81P/83aaOXG4F3UJyOaplGPFw1L1DQ2TZOrpul+p6ZnwKmX926TESZWPTKy3u+REXIDcxrMaUivNTjIpHBoGlUzogpYA69dd3PTyQjrB9L/2+ppACHiEGeq6Vn/OdQwqZDsHrNrs6VwuTTNLrGDp1r+lN409CuL0TRTQfupodVQEyEjdT0sQi5N032mvUyaJjcZ0eaCDk0TbpRXhoxsnqahjrZ2D5Tc4FR5jtliIELXWChisml1Luwl0zTd41FkZF4438iGatdCd20kpbbmB2Bi07Pu9ZqmyVVb2qHWLC9NwyUjAZqGbEqAEf4nE8U2GWFi2SMj66WiaQYFhpONE5qG7o6SQiMuVoabUTNSzxmaxloMHWSE0jReiNM8ielnMoq8AAMVCpmeAeH+FrJ/D52MFKppAHPBNHbTtpqmhKbRi9g4OD41FMzbjqoZITsjw19AemiaeAHrmN40uu0C9fMBiAOrJxlR+4IcZCRI05jnbel3aSIj3WPjDdSGx2y0MxRjaY9SCa4jnbXWhMlpmkwkx5YC29+9nvNEahpV+yc1TdPPu7hmpEdzpW165qdpdplNZaWOQ23RNFtk5ODFWiUjGhmhNE33a2UnEAZNk5qMKJKV5/7CNE232Nb9DtM0J+t+2jSNoGqaVJpmpeo4LJiYLuykP02oN40xLucz4iAjZWoawPQaMb4zh6ZRz+GSkbDPyNBzI2l6xdEKc3ErCZOmqU1KsavaU08EQKgqtmaE0DRtGaIw3+mS6AUyaZpIEs0iI1bRMg23N83IZGSKmhHmpmUXX4ZCnbdrj9OxL2LvEXVgtZIwu4eTTzZd3iivf7/EJMdOEu356bEM2jL/Bj0kph6aZjI1jdYo93MNICNGzYiS9s6M73pbM3IAY9UnI80yQNOoI0cgPa4dfTBiapqEmhG1wzQKTW1pr1WFbTjJRi/k/oKiyYiNjBg0jUkRueP6Fx67sVQuTVNVQu8EaN2IAc+mmJ7p3jSxAtZwQjdVDDRNeTJiy6ad4j2rP00QHWoVMiLIrjdvPvPeXHBH2shI2A7e6DPERdD0zD0pK+u8La3/oQibocwoCM6pM1asSYMmkMuMZCSqpol07XWls6nIyEj1T2LyZCMpNnIDqLycjFdwzanpDMiITdNkD9lPzlfAGlLTuMdWOTHXlVnEf6rb02yTESbWVbcQtiubpqncXQ+laXThUuIbTaCm0ckIS9P0/9bICKVpYH4OK5ziL/KZ3N0DpWnCqE4IkpXkJgegCC5V6Ajlnw0PAV9vGjKdZcwOPlGyOFVIraYZJ+1V0cGzw9864zPz3EunacpUKPMe0dsRKwO1iTXKiyIEp8j0jCa143vTMDRNhp/LgnyInKLQGKKTTtN0/042PYskObH5RtU0K4um0YCCOT89Zwv1zQ2bpmktSricplHJyCBEoI8H1TTkO1M9qkTdXWPZtY4bim0ywkRTKWSk312RG1hrZ+OjTM94Nc1A0wReq5ARjqaxkBFpIyMZNM2edSEDsNQ0MGTN65j1M9PSWoWGNR2aJv0iOcx07jV2RD41DbkQ9xK79o5VYKSGQorkGCMAIpsGzBuloajRapoAVcWpaTIPwoKaCyo6FCA0jadRXowC0cgIHTOgpnG+y1Kapn8rKaMqj1hw64hjthiIWV1h1j8vB22IvUecprHVKt3j0TWhmKaJqXT6jYlV9+aYstGaEWajlRvDmtK9VtPkDCWcN7BF0yRs2rjNX018Ruh8t8nIAYym6nZXcmUlI8S5dEBGiJqGVNQnhU9NE0NGpNTIyJxJRuwdjroYqpYzPePfwuFbDZoG5gXbtq73RsGCZvdyKKlqP8TIew3aihQj688CX80IT9PYyqHNS3snUNNYsmmHK7YWtKFmxK+maUYgI7bT8TCZiJomWdqbqKaxd++lNA1Z0GN0RyzUnMyuvXkJTskNPndNsMMnnR2ObWyuhTRN4sbHMT2z5qf/xhR058SAjJi083jTsx5h6ZERadH7QTv4/nqWbYtKqKTTRGy2NM0BDJWMtCoZoWoalZfYyQhR00iZKJPyqGmElvZ6rt52rRfYhUpGjN40atg+Q2dqRuKmZzZNY+8eyOvkkIwso6ZnIZrGSkZG0DQnVsON21i0bNMzps4nWsBamZ910zTNlDUjaiy6gLctjKQaSKNpJCp9DHI7QMznC7Q9SrPaPz78IUrTROox2JqRuOnZ2O/S6No7sk0AV1Td2OtOJEJ+Pr5I9u3wSHBts0X7Ook34MtV/3Q/U2tRHNMze35qGMaROCfcmhFzrRlrelbLwbySPs7SNFZS2lqF7N183TXwVMQ2GWGi7SFiqRY0spil9KYBTL7XGz7TMxm+eOlCO+93mLzPiEJGLLMcozdN2sLjNOqienyOpokmOe5F7iQjmWoagFjCLylS1P3ketNwPG6qHXzss04VrS5sLqdplGy6sRZGQNE05rFOomlkuemZqCrs207HQLSANd30jFPTuMnIVN8lPZ6x/kzRsZii6qi/ihUlRmK5N3c73N40mdRtMU3D/92H7nrnZ9s1AGU0jUZG+qTdUdNkD9kPbEp7xVD80s+VQUaspNR2Yga2NSMHOtq6X9BW/YJGakYa+wIgEJkBfad8sa15Y7Sror1rGVlod3pkhMr47DlKdfGRAtbYTsu9kIe/6eJU47P3U4tJexl7YhV6916opgGoCyuV9qpFiIylOmBaC0TTSl1d76dp+kUsxSl3glDV+FMgI5qyI1c+S9MkqGkaCCLbzD8GS9ElI6s9goxEpL1R2WxQTeO+xpZ3ln6VlZGMjKN8OA7f7jgbiyKaJloQOqwJHPJrmy2q9TAmmy51YI0rAi1UwCpgra01y7FrADLUCEPoc1MhIxZNM5XPSI6aRtXBmao6m6bZJiMHLmRtGSdRNY3NUxo0zTBGEhTnU9MomiaGjNQL7CwGONveAWjTM0sSZtI0/Fv4ZHHGnMhu2pbxFalpNPxvo075yQhtlmfSNOaNyeZxl2Re/gLW7ufJMj2bomuvLZs2aRqCjNhqmkBvGklMz0puvEsoc0FK06gi67CaxpsA5qpprPO22PSMHKaxkm/OFTgfGSmgaSKITkwy7JieWdfJ5nrTpG18fL1pnPkZFHQJTWNuHjRNk1tTaEc/7kwOm8ruZwpNo+h2Yn5WmWvBlqY5gCFnHdqgb/pG197+VzuLJvJWIDUZsWkaU9HgXXjUQjvbNWV8trmPrhmxFuGs3jQmTWMs1iRZsBfxcTSNdTyA5OoqZQlvqmn66XI0jcXj0nnFakaGfydNrTjU8ZjC9IynaeCqaUL9eYiaRkVJMrKynY4B0rU3RtN4BqXIiDpnLDSMhvtdjqNWjMdKi2F1+Q6labqf6TUj+chI7D3oucD2lupfr2mQxOtktOlZYi2KvaEKfvcj+tOoz60a0ikEg+uDlTewvY7Ha+vsY2A6Mc+M+W5pmgMYst9diaa/6RNkxOGsjd40uTQNr6ZRJ5U/GemTpNmOJeOzEgd9cVlfc4Lp2a6l/WeNnDwUVffe/NST1DSapqFQU54l/AlG2lsLwdA03T/V51PzmlUCM8+HsHflG6dp1PiF9tTAsCNSqJNDKfpomoiaRkVJQqaTkX3a1C6RponVjMgWuv2BjUCSsM//0u+Su45yi3rtsehONVorY0UJ9RFbExZ1pa8XlmZ1pL3mOFEztY2Znql10ZyHOz/6j3yKWMXQKNCsGRkkv9lDdmEn0wl0tqMoIslI3ScjWnW5VdMcwLCTEYJgOEY7jJoGSDzhHJpGqV4SaZp+F2gvPLbiQNqLsKiyTc8ktwshPiD2VIt600jzhlliPqR8RliapoJzzG2DqVjHXsA9Zpunabrj0Ga4adrhyKZhqTaK1DTD5x6DjDQcMuKjaXRSHKkZAVhk0w77vA02pwwEm4yMTGx4miZtjE3QNEKIsIOybXqWmoyM7NqbKhm2n+8kI+zaVkDTKOQFZjIyXtprXouigKaRBk1j1oxskZEDGKJf0IZkhFHTTErTWBluBk0DEBTDA0eW0TRqTNP0zKRp+ETMeZ4xboCmadRNjqNp0haqQ5qmcQtYOdMzW7kw1Erwxav0Nb5/Tx260eEIZMRRKsH67F41DXMcOJqm4Bisewl9syLISIymiSTRZjKy3+vs42oa379TY1qaRu2gCU0TuV7t4BqkxSLFWC2U5DimZ4nHtmSudL7+DZW58bGTLee7p+OMoWkU8qJoGktOPx1NU5s/IzSNlNJ0YlabX4UOb2tGDl6IvhNunUTTEKqCnMtJX6yjpjFvCH41jQcZ8dZ32LBFnVA5b2bUMZomdVEP0TS2SZDd/yYlWGlvyPTMQ9OEkBF77dswMKJpmnE1IxwyQnZEPtMz7jhwNE3BQdBtF5YZNE2MrhACoAXo9GbC0TTWOOU0DfPYSM8SQ02T2XyvBBmJmZ514wbUcI501vx7ikonJ7JpGqdrrzU/+sAYmkbXjChkxG+wmBVemsYsPqdhFx3LRh0LoaXBWzXNAQ6FjFRt73nA0jQqGRl2rUKIwfI3BfIq7U2jkZFu0bU5V1txIJmTOLVd+LLPqFmTNA9FBZRVztuNpcpoGsb0zEBGTKM5e7cSpCf6ONnIyOAzMoHpmXCTEdP0zFTT7AbVNJSmyZ9TY/eAAggyEu7aG7wn0869NBlhijgcRK/wu6TX/jB20VBDgkx9RiJSfDuKfEYSEp5QYWxjUWj2sSy1mffPF+z7DOPy66JteqbnZ2y0FMddgoz084Kl3GMk21lhJ9MpvWmsomOn5YYxr7JpTRXbZISJykZGNE1TDUVQjOkZpGQdPb1hV/kLc3caNT3z1Yx4etMMH5Axb7OC0hT769ZsNkfG6d6gDV/YxrgBmkY1YLOPB5COjITUNEI4aJQNiQ+IQICmOdk1IxM4sIZomsboTZPgMyJNZQ5QhgI4TscAqRkp9BkBiLx3zzxvOJpG2P8u/y5TFWWp49CdapNAodAoUdM4axs3blCa3/30FbD6Nz51//6DV1LafDNrRmyaJjS/Ao8jFborsIOMjExGrGRaODSNe+zspolcMmIX8Z+q2CYjTFSLPhlRyIh2cKRde/snW/JTagsdDbuwTjn1xYrVbGTEA0c6/XNUENOzGJ3Sjds6ux41DoCOprEX9QKDI2kZc5m2zGkXCtsoj8pBrfoBW7kwqEj8l8ZU0H5y6POifOsyeLh4ClitBS1IV01E0yhzQbmakKYBLHkvTUZSaJrIpAORig5Gx2FuWjG3UTuKClhTkJEAimGbLaaiTvRaK2rsFyvCt1ykK0+yxNI0Bdec+pzCKmCl7UKKokBNYxcdq0L2xlc7dgpjm4wwUffJyEwyNI1jekZOYGJ8VkbTmIWK3kXBQUb4Ala/mkZEK9FnldB/2183vPqGMT1T4dtd2cW2Rig1jX08yN9icWjRvWbPa3pmHnM7eUyhaRwFxqOCprFk0yCf3TI9WzdD8hkqYB2rpml7msZwS43awXc/gwkgNT4zaBq/6ZmKMd+lzQKNdmClaprM5nsl1IftUBocl+lP49I05t993xnnlZQ030hiqtaapU9NE/q+xqhpHGSkr83IQc25YDaVxuOeudJzQdXlcdfuNhk5gFHPO9Ozma4ZGdQ0DlxqdXjM4gXt1ubK+yJG06wGnxHAhWRbC/VwakY4VZAVXUY9VKOzPgcVSUYcmiaMjOwxi5ky5qIQYi5cemjOICOKpqEFrLbpmVPAmqOmSZpaeUzgwDosQgxXbKlp9sgNIeTA2shxNI3sk2lJ3VITG+UFjzlFRqI0zXTJyGQ0jfoqGJ+R3GSEu858kfIe6rrYC9E0npoMn2y6qoROSLLmm2wHb6kMPTUtrOlZCU2jpb2qeM8sYC2naczzN4WmAUzPKEXTNEzt2JamOYAxW3TJyFwhI9QO3kYILPlplQN5qec4hUiRRSFSM+LUd7A0TRzupl4jbOGgQdPk8cPBAlY638xmeVHTM4em6f6paZoEn5GpjLJSY5D2jqBpWlNmCFBkBMaCRne9C+6uP5HpmdN2AUg3PStBRjaopgE4mmbcOCZN0/1MNj0L+Pn4IuU9Qp17HWlvRnJWhuSAfZ9hTMuewKqJSTI9K6FpxEmiaVR2E1DTAOZmlfMb2tI0BzjqHSsZ4WgadeSsdtNZkJemDMwMVyEjyWoa2/LX2uFwpmcpvS4o1xhV09iQp+fMSrGDN+ab2Z/mENMozyh089A0UnY70VjH3m6ctMRrqpiiN40jmwZBhQzTs0afR4u64hEPtv6kBBnpzQUNZCSta2+YpqE1IzGaxvz3mO+SHishxsiE/TUjqQlOicV6ChWUUjOiaYqMpL3Ivj7WS8djBml37VVh0jSK455CTVMZ85yKphEiDRkxvEasfjmAtSk5hbFNRpiY98nIAjZNU7lQpk3T6OKzhDfy0DTpdvCuzwiFdtVFwdI0mZXzuuCVzomqaVKlvQRtcXTtzE0un6ZR0l5SM0KpteGDO5+nlaU0zaaTkbyEjB2D9RnpfjatqaaJeq1oNc24mhHY5oJAAk3T/QwXsBJkRJ83gq1OnZJyo2ONUuWQBFlFKU2T5TOStEEJqWnMjVqq6ZkxbpF9vWdMy0Xa3khunKbRdvBWsfzUapoIekzPhdbqUdXNq/u5NT07gKGTEdXltiXIiEOBWDRNFjLiU9O0/Xt4Xuf4jAw7AHqiB2manMp5Mm7luWDTTc/6Ykw5dMtUMSAj5JgW0jR7q1YvPjJE05B5Nq1MMj2bSjWRHJnHgAutpuEKWC01TRQdYmma/GMg+vO3osnI2N40AK+mYVARYNrvkr621PCMjmP4jKR8bhKj1DQpBawBnxGf6VmpSsc730QX6WXTrQVR0zPPRis3BppG1YwommZ4TpHBmLWpHJKRcOJEi4613xBTO5YkuthgbJMRJuY7hwEAO7BpGobeMGia1nH0DIaXplEOrKnICEEwKDLiTUYqAsn6p0ehU8ntQopMz2jlvHXxqN27QdPkJiPDjvoEJ+mzaRoyzVZKzYXnqGk2DIwMCcQYmsaWTcPiiilNE/NakQzUW3IMlLmg9vORGTRNaFymZoSpFwGmTkb430vHaVmaJrVmZEM0Ddn42GFvWFyaxv/eixJaiVP40bmSa3jZtA6q5hTd01NkVG+a/n08NA1QiI44flEKggojp0ZiahtLgm5K8qc0ZWyTESYWqmZENFivlqaaxnZCpGcwQQiSkBGHplHISIym8fuM0LcdakbcwqecPhR7q5aHcHWiIJOL90JtyCV348iES+n4SlFjfGe2moZMtJVSqwQOVG+aCaS96hzmHFgb6sAq2wyaZpyaRpkLaqdj+vliapqUmpHVCddY0AobBRilppmYpqFrSIohGY2irr0TmZ75HE7DhbEFNE0keaLn796qcZKtU2V6BhRSIj41TYymIXRVq4wltzTNoyMWu4f078t9c0FjoUzSVInruOkNffVaGS4ikKy3ZsSiabi6ln6+0YZjMJMcVn1j0DTma0OdPxceSJYrrsqFS6tK6LoRJROU9DvzmJ4BPU2Tgow4O6qTQ9OIEYuFkk3TxFTvwD00zcJ3DCYyPdM9oFrVA2o1/NGDjKSZnlFkJELTTPhdTkbTMIWOjtliJDZvesbQNDnSWe+40/mizOpKvydFjSvP/KY3PTNpGnpciy5lh6aZmY/HaJo1oWloIbsuYN0mIwcuFDICAMu9EyZNw/ptDDBZVjtmm6ZxkBHP69a2zwjxAzFoGvWbzSswqiAmDLMcTkbHfG79p6RaFJ6m4WtG0ncoh7WixnSkNUzPKjcZMQtYc2iaTSMjvZpmggJWCs8aixBZ0DRN40OHmN1VCaJQ2X4+iqIBvDUjKUWWZs2IeUOww21zH5l0IOi1NIVfCVU3JCVhJIrUNAmKnZSuvXpvZY0TVNOMmG9SkrNqnY2kPR3e0DH/mqttZIRZa4poGhsZccwyI2qa1UDTtKCbkgw0f4OxTUaYmM0XWMm++Gn/hKmm0X4bzIlLOveW0TRWzUhU2msiI3vrxlQyKprGQUZSpb1UTcMVsA4Xgb3QFEG9OukbB5fa8l4DztXH3N0dtUYBa4CmOcnS3piPQEpwsmmjUJIsvqk0TUsSmxIZq3Y6VsgIaW/u701jzp0NTk2TWDMyVW+aKQphDZqGW3cCYStJUiLWBdcYl2vnYNM0Gcd2XJfhtCQnRtOwDqwFeldhJSODjQCZe1EBqzlfobOqNDXN3rrRahrJnKtb07MDGkt0i+Fq/7ilpul+ZflFanqWhIxYahqrgDVuB2/VjFjICGvM1v87y/Rs1RAI1hwHQJbpmTFfH01D51sAl9ryXqOBl94pm6ZnQLdAJPmMhArfNhFWkloUdhNC0JseWJrGm4xMRNOoZGRuIyOi9h5Uo8+QL3LUNFPSNNVEyUj/2VjTs2Rp7wjTs7E0jSpgzZBNh8zUYvNN31D1z/fMzzjlJqVp+vo0StOUXMo2TSMSaRqSQLYto6bJqXPcYGyTEU8sRcdZrygyEqVp2rz+Az6aBjE1jY2MDE6DdAHzqmkS7OC7cUnhE7cLCdE0JQuP3ZsGKDIfsl1YWTWNSvzE0Pq9bWWRA+ujiaYBtwjZNE0MHVLFsJKOlT+nunc61j2gIrJeIG33noOMBHfHmUFfO4VfCb05nFSaprCA1UYqcpyKc2tG6GYvzUW6cWpi7Pmw9XAlNI2tpuFomkkKWPPVNLJRXk6eTckpjG0y4ol90GRkOKl4W3R1NyMOrEWmZ1bNSGoBK2MkZrzUoWlEVh+Krgob7pyGu7jDvxYZJ7F28GNoGrtmBOzNqSYX40E0PdPf3xiLRJam6X52pmcq6WuGIt6Iz8hYmsZpu9D0yYiHogFKakbMgmU7HC+MMUnExDQN3VgkSZpJbJ72MMelyYE6pm5vmuloGqM2LnBMuJoR+vzah2aNoEb9NM3ImhHbgdVW0yQUsEI7MdPPPWJOE8Y2GfHEqkdGGp+aJkbTZPWmMTNcwVEiNLzS3taBSo35kX+nOFnukl0Fa5ImXERIRRFNY1EoAMbRNEtLTSMEe3Oi6gW1k9zNsIPfvOmZOi9GICMcTcOang11M7teZMSV9pYkZPOdLmnQTsdNHBmxm7GxMSM9b3JpmlH0ytQ1I8Nj2aZnBTUjKaZnQ9M181yk652maULIgzNuHpJD3y/dRdpFfrwJ5IS9aYyNT//e05ieWTRNgh28kvZ6/YZOYWyTEU+s+2RkvcyhaQaJ6ziaJhMZYWgats8CmW9W5TxxM+V5VY6myeedRbBRXo6aprtIg6Zn5OZETaZSkJGTb3qWT1U5Y1Bb9D7M3jQMTeNLyDg1TQky0psLDk7HU9E0ChnZJ/VeHppmymTEs+PODU5qmeSvQkJdY00rsU7qTZFoeubZSHBmi66aJjTfvBoXChKm1rhwx1D4vrMxahqVjKmaEWatmZamiRSwkpoR3RaC25RskZGDGauqR0ZWFk2jBR88pDfQNOVqmirqwNojI3OLpllFJLhkvkmV87RmhIPHyed21DShhcdjcCSlS6FMQ9OoOQlHTQOYN+WUmpGTTtNM0JuGk03zpmdNcgGrSdPkT2mx2ycjNjKSRNMEBp5zNI1PTWP+eyrTs1ESYeaGxa47gdglsuxc6iPJgdWhaYbfi0zPMmtGTJombb5cPy7vdzZKTdOPLRgUdoxyxTqHqyKahpH46zrH/ClNGdtkxBMKGWmWe0bSMK3pmUdN08N76aZnLhRJXytCNE3ShRwxPZNtlpOld+HR/VPG0TRDAWtvgW6YnrlUkKZp2rKuvRunaSyVVVGwvWm6n47pWdQOfhqaZqGQEdGgXa8TaZoEuoIiI4/i3jRGAWsChUJjQZ6YXBSaQN1SFNZ8raviy6JuR9E0/udRxIVLtjZC02g1TfdaUbnXSZnpmb3IqmQkg6YJmR9ukZGDGU2PjLRLc3fFwqWTm54pZMTzuoAdPLtztK9WYnqWtvB4kBH6uTMW9RhNYyIjiqLIQEbmFjJC4WeWpiHISEEB68lyYJ2EpuGcF300jddnRCEjI5MR6nS8PJFH06TWjMTUNBPKtA2aZkRSM9A0w2O5apqqEjohSb7BJ60JAwpLgzNbdJadQpVOaK5ABk3DyML9NM0INY1TwDoRMmIl1JW1ifWracjmj5H2co6/pyK2yYgnmrpb0NrVHkyaRlW1MzUZ7dAob4zpWZCmkdJBRnY5NU2wgLXiVUFWmJXocOdEVEQuTZO/8IRpmowCVoWM2DUjBk3D3ZTLHFg3b3o2AU3DyKaNhZGjaTasptnpaRoAWJ44EW2S183VnDsbBjISU9Ok795j4VVmZAbXtTfX9AzI70+Totjx0zQubZKDOpXONXlcz4bqZKppuvfo519UwFqopiFWClKvAzxNfSpjm4x4olXIyIrurmq+qp3cLLIyX4emMQtY2R1KswJUYZRGRggU2Q/JIjd6vh6ExwqepqHjDAqMnDqKXZ+LY5CmybeDH9Q0ZE42GgXzpjz0pgkUsJ5s0zN1XoxplBesGZGEs2nj6BBH05QUsM4XWPdeJcv944MDq6dJHmBRbt6BT5Hp2VQ0DaXP+tC7+pxkJFNRwyrm7DG9NM3wu783TWCumb4obHdyJrT6Z92atWN6Tp7vrGDdscesNE1TO38rokSEBxnJoGn4HlUj5jRhbJMRTyhkRNIFTVR6g+41PcuS9lqZs9UQjc34FSoCMI3yGrYwVdgQ9SZoGkeV4B3Wu6DxNE0+RXGoV9PwvWmsBJDMtaFqmkDNyJRGWUkxBU3D9Ggx+GuyoEWLeFmapmxeyum4a0iZQNMk+Yw8yk3PxPC9qO8tpZ7DDt915guZ8B5eNQ1jtpiFjJCkYaq5GvNdNeyaJwyEgLxwxDWnvr+KVdMMKGx2+Lr2RupbKHI+FLAyNM2IkrQpYpuMeEL2yQhsmoY1PWPUNGNoGuXAyl1oNBnp4Wy16LQSWDZMvQmnpkmAfVkHVk7+1jbG8ahEZNz+4tjzICOj1TSWHfxA0yBI06yaFuv+uTkOrJumaVQB8pgCVk42bUDGhKbZi6ppXGlvCU0DAPvK6XjveCJNk5KMpDfKm5Jym5qmAYbPm0Kr2qF7kmTTNPE1YUnWBIC4txrrQD5Ns5doB98kIkU0yWFNz3wFrKNomn5sjqbJqSl0BrbUNLqAdaDLuaBJKdejSiXOW5rmgIZORqzdFQuXUpomJ8u0IeTKTEbYC43Wi/R/p7t41RyOnR/5N4vwWDHQP42GOFNMz2ILsReSVTQNTZ4moWnIjohJeNR8T5CFMI+m2TAyotU0Y0zPYjQNKWBN7No7lqYBgJXqAbU8kUjT9O8XVNOQazebponP2RfT9aYZXqvuWSU0zaKQ+vCaLcI8J5ZkkePo6xzqNlvam+hIaxhCcqZnUZqmXE3D0jRj6jOcmpFU07OBFpecqm5bM3KwQycjjYmMsDUjRm+a7tc8moZX07BQr6WkAUwZn7oBGzSNvRBXHidZK1SSQ3dBfrO3dL7cV8AqmHqOMpqmV9Os+q69tKiXoWnU3NWxA4aFnAt7sT5pdvAYsVgwSZjhBklpmhgywvamKTsGqgfUOpWmyTI9M+u9uMhxCY0FPS+m8itpNU2Tp6YB/H4+vkiRTdNzgo7LIVb2MEUtIrxzRXSudL5JpmeejVZuDMkIR9N0P4tu/I6apjIf96lpOJpmKoXPhLFNRnzRL2jCqMivNELgV9P0WWaO6ZlViKQz6hgy0geV8andvZk0WF8zMT1LUtMQiNOkpwZ40OReUxcIH03DJCMZaprDjrS3e9ynplGHSh27eS2Ci5xrIZ48tbKwehYVDcF9bgoZG2qaSM3IRKZngOV0nELTpJie6USdKM98NI1dTjURvTKF6RlAaJpM0zNgDNrgf49ZJfT8KOLCIa05qFOuz0hKsS1guUir3MCLjJAXTkjTiI3TNGEq26gd4pyYiZrwVMY2GfFFf7Ovmn1jdzXApeS5HE0Ty3ylhN7pqpOsvxrCNI2LjADDwqNuwJWR/XNqmu7XVIOjcG8aadE03iG7cb1de7VF4vBYAU2jkJG9/lgYEDenpuknrI5diKIB8rjwKUJsmqaRGI55UtfeaUzPANIDarlHHFgT1DQpNSMAsDrej5km7T1IvWkADhlJHydboZJA3QohWBSDlc1m1FZtQoYMmKgAh97ETc/KfUaCappJe9OE0WOalLI9qsagNRPGNhnxxby72YvG5J3jNE1ilklPHKuAdaBp0pARYLjoNE1Dc4YQTZNocJTakweYgqZhClgLHFiPrxSdoKB9Mg7D457QyUj4sjh1dvAT0DTG51bDSuM9oj4jnJqm8Oa71hL6E9qQaTRNQ5GVZZ+M+GiaCb9Legwmo2n6ry23Nw1QQn2kFoW6SQ6HWOX1qyqcawlN41mrfO7SuRGiaZLvD1zYNI1CRiiNy6wRtOhYNhGE9BRGUTLyqle9ChdddBF2d3dx+eWX421ve5v3ub/8y7+ML/3SL8VjHvMYPOYxj8GVV14ZfP5BCdHf7OvGomlY0zOiptGsQuSLpZCaZV4z0DTM63QyYiMjJjWRStMEJbhK9bJu9MXj+9wiYyH27ths2sp6j9QISntZmsZGRsKXRQ4XPklMSdMQeNZL08SkvVpNQ9G3smOgnI47ZCSFpunnHno/IYZkfXVseIwJFxmJz9kXBqA3lZpGKpomTqHY4UUgPZGq2OFUOrxs1nxdiStzbK7xtabWc+XQG6/6J6JQCYU2PROuHbwadiO9aQB2vrTouOmLxA01zaO1gPW1r30trrvuOtx444145zvfiUsvvRRXXXUV7r33Xvb5t99+O775m78Zf/RHf4Q77rgDF154IZ7znOfgIx/5yOjJbzKqvtlW3Vo0zVSmZ/TGomkaS03DIiOKprGQkZlCRrqTzaBpHGlvzV6YdqgLedUMnT99PXlyZI0+Llto2mqcmkZJe5c9NGsslJyaRhewdsfOqyJRz5/wBpYSA00zQW8axvOgob1pTjJN01S9n8/yRB5NE1u5VLK+zKRpRnyZ0/WmGX4fVcBaWDOSeoM3aZruZzFNw7hIp8w1lpyxHkweambq3jTDppKhaaYwPauZZISZL91UrNZ+uvZRZ3p2880340UvehGuueYaXHLJJbjllltw+PBhvPrVr2af/5u/+Zv4ru/6LjztaU/DU57yFPzKr/wK2rbFbbfdNnrymwxBkxFSy8A2mCsxPTNoGrMQSd2Uo9JeEotQzQhH02T0oaDjenvy5Eh7fVX+fcJBdxIlahpF0wBdUaqxULJqmu5nKjJid/osRQWSw2qgWBRMcTA11ypS00xgeqaSkXZNakbG+owABBnJpGlGfJeGrHXEKSHEUCSqrtMUKb4dJ0OhoiImm+3+Fp+rlN3mJ3muyZRSyx5D83fywjE0ja4ZUQvPkFyPMz3zICN0fWc2bbToeL3urjHJ0TSPJmRkuVziHe94B6688sphgKrClVdeiTvuuCNpjOPHj2O1WuGxj32s9zn7+/t46KGHjP9OdtQLlYwsDWif5awJlZAMeQVomq4KW3rUNJ4CVqUg4dQ0jM9IUrtwmoz04/rpKc9FHRjXcWANqmnSkZGdWaXh0OPLtbnjY6SemqZZpdI06YnXFCE0TTPC9IypxzFomv4clBQZifSmochI6XFoVeKx2suS9kZvyhoZUTQN/1nsc3U6B9Zx50Vt3SCSnGetyC9gTUQbGJt5XjZrI4hpa03KfLNRHE/XXi+aRZDC3FA5Q63NKz1oZG7E7OABdr606HitvHyYTcmjSk1z//33o2kaHDlyxHj8yJEjOHr0aNIY//E//kecf/75RkJjx0033YSzzjpL/3fhhRfmTHOSqOZdR9FZuzR2lZyLH929qwswanoWoGmALqvOKmBVPC5TwFo5NM1gax+Ck2d1peewx9WikM/t5V6Z8NI0XAFrAVwqhNDy3hPLxvzOuJuyMD9jVE2T4akyRUyhponZ4KvsrW0bvev00zSutLc8GVFtF/aTaJoUehEAQUZO9GPyn8W5YY4o6a8CN+LcGNYRhYwkSJqt2PH1gPJEiVxWv5aZX4npGZCG5OTWt9ACVnNt9JzDpIYqN3SjQAyIuopxNI15cop65j4e8Rpp1n3NiHHtonxOE8ZJVdP85E/+JF7zmtfgd3/3d7G7u+t93vXXX48HH3xQ/3f33XefxFl2oZCRuVzmqWnURj5J2tuHZXoGdCdyqukZwEh7aSGZs/1La5THjRvryQOkJCM+NY0rhSuFS7XxmZGM+GgaoZ8LhPvSAJZq4iQgI6hN+q4oOJqmUjTNoKZpm2Ex89M0rh18KRIwOB2n0TRs92gu1PWhC1g9NE1GXUMs6GU29rwwKDSU1oyUKlRi47qIy6BY818boe9MCEEcY+PzTVfTDMeAo6YNozojGZmCpnHXM3V8pjA9qx01DaLy3nUz3MvsOZ1qNY1/C8LEOeecg7qucc899xiP33PPPTjvvPOCr/2Zn/kZ/ORP/iTe/OY34/M///ODz93Z2cHOzk7wOZuO2aJHRuQSaIdkgeWsielMcuZr0DRK2ksWd7QemsaHjFg0DTc/8nuSeRS6k/j4siE0Dfkj+dw5EHXcZ2QcTQMMyciJVWP6J7A0Tfcznabhf99UiCnUNBoRcpNGWsCqqu2BuJpmCprGbLug8G0/TZNS6wRguD60tJd/vtvg8YDRNLo3jfsesdgYTcMkDVyy5Kpp4vNdrtsk9Q/bnZwbk/qMMAmTn6YpW3foODXU+3nQyNxwaJrKfdxjDqnuD03jFrA+Kk3PFosFLrvsMqP4VBWjXnHFFd7X/af/9J/w8pe/HLfeeiue8YxnlM/2JMZs5zAAYCEtmoa6eaogkJ6gC3woQpQEemQkq2bEpmnoRWbe3KWUescVoxlUC+49roCVfO4c98kymiZvUTg87/LsEw4ykkDTxNQ0p4imqcaoaRjZ9KD8gj4ebTskZN6bEkvTlE1LKqfjhtI0KV17IwMn0zTmv0epYAI34tzQElClpimhaXLVNIlFobtMh92GWRdzPVy4cb1zze1Ns2rYtdtMTMgLR3bKrgShadj6jAmkvZqmIRP30TT9cWjXqmaErn8on9OEkYWMAMB1112HF7zgBXjGM56BZz7zmXjlK1+JY8eO4ZprrgEAPP/5z8cFF1yAm266CQDwUz/1U7jhhhvwW7/1W7jooot0bcnpp5+O008/fcKPMm3MdzpkZI6VpaZhoMwxpmcMagGk1IwcMh7e1cjIWk11GNZqPEfnlkzT9OPG6KnuvdOhUymHQl1FQ2yKpqkrgGRh+rmapuk/Y1RNM+EOOCWU0+IYmkYfW2NH1P3kaJrgMVA0zQS9aTBXycge0PT0TAgZYXa3bDg+I3GaZjSasQFkRNERJTTN7gZ60wDmDV4F2xHXUdPkIy7j50poGoaa9lKuI2gaNS9N05DFcaBpCga1CppqmoyIqpurj6bpN6vrgM/So4qmAYDnPve5uO+++3DDDTfg6NGjeNrTnoZbb71VF7XeddddA3wE4Bd+4RewXC7xTd/0TcY4N954I37kR35k3Ow3GPMeGdmRlpqGK6CjappcmoajJKBoGuZ1EWQkanomauOkS00c1LiCu2BtmiYROgW6zp/qPYTkjolK2zNpGlXAurL8BUJqmsQC1qlaxafGFDTNUPcUVtO0TYLXCj13EwqhQ6HMBav1fpKapk2E5x2fEa+aZjy6w71+qpoRhTgkOc9aUUrTlDgoc/SZPUxyz6oUmiaxkFnXSrRSdxk2zOl8m6gRaho1r0FNQ6W93c9paBpr0yZb73zVd9Zq0zOOpnmUJSMAcO211+Laa69l/3b77bcb//7whz9c8hanPBY9MrKgyAjpTcPTNK0Dr3ojhaYZpaahUCT5mkVlnHRRSsWif8zdg9CfJUfualfOD8mI21iq1HxIeY2cWK7N7ywgcd1L9BmhH++kJCP9zqoa4TPCyaaNmhGGpvFGoP4ke149MlK1yzSaJttnJF1NM/a7NGmacWM5apoSaW8G7UHfK3VNMGiaiGyW+7czbkbBbep5QDc+7Nrom+8INY0aayhgZdQ0U9A0rB9ThKZh7OAPSjJyUtU0j6aY73bIyEy0kE2PRhCKI96bJtH0jPPUQE/T5NSMWAWs3pqRykxGUiHZwb+E/FF/bpNSiu2sFvXgA0Ih5M2padDPXQR7tOgC1piaJsNTZYoQoqdpRvmM+HvydKZnOclI7+bKcOHZ86JtF5oEZIT2GQpFKk3jMcAqCZOmGTWUo8pjzRYjkVszkmwkxqppup+hmpHYd8YlOdG5JqI4AO/BtAnTMzVuxdDOVer9gQsyTiOFaQ4Z2bSp76xhkhE78T1VsU1GPLGzS2oyCNTLm56VqGlcDXp3h+5en6+mKaRpEncrUZom4wbdmfC4C5p2GJ1ATXOYJCNGFf0UNM2Eu+mUUIvOGAdWRfF425lrmiZyDGi1viEPLJuX2XYhPRlJNz2L0DQbQkamSmzU5z0pNE1iPQ7nM8LTNOY4JUmOd66Jx6OuBOa1eX2n2cGXq2m6saB70+hCU/IeUR8qLsia1dq3brIWc6Hqh9q+GaVkNiVFc5owtsmIJxRNA/SV/gDipmdETZNqemYvkqQ/Tbg3DV8zsux3FSZwY9E09H6SuECocXnTsybL9Kwb14VkuRtmqZpG1YzsrRpTiRCgadRnjNM0JzsZmUBNEzI9IzSNbCPokGHW5yZ0uVFRp+OURnm5NI1GNfnPY1Ju8fmGYqOmZ6kqIhJc0hAKloJmx2VoGobisROFZJomYb7JqioyLrs2boqmoQWsHCUy0vTMTUbC6h+9pqnF35hT/9ItTXMwo6prLOXMfnCwITZWMYqM9L+m2sEzVu1Ah4zk9Kaxd7Le3jTEuA1I9wTRz+c+t2yzd5hDsRpNRgJqmmyfkaFzr1FFzyBS9nHO6U1zMtU042pG3PNN777bQU0jYzSN0VNpvJpGmQvO5BJQHidBB9b+KakFrCpOBk1DDtnoAlZLdZFsg08ih/YAaCfc1HEzTc8mVNO03DocGVfPy4Pisi00PL4dsahJAStH0xTZwVOaxr51V7FkZNjkAqaqLtmOYsOxTUYCsQ8LLibFnyxNU1IzwjSxA4BKSH4zt1LJCO/AqsKs4TBv7vSki3uC2BXcKTRNwgKhCmPJgjbYJ5ObUeQi88VhpmakojUjTDX5MLeYmob+fhKSEe3AOiIZ4fhrWrimaJo2RtMwbQxQjozUtO1Ckpom0/RMT/Bk14xMlYyMKGBVRe0J6pSc92BpGkY2W2VeJzsEzYzPVb1HfjLiq2th1TSlPiOkZoQi08mbVS6CyEh406bWW5Ug8TTNNhk5sLEUFlwsKn5nRlQlycVAPppGDMoJdlGI1Iyo8CIjotILuhBxONkdl851+Nw5pmfduO6CNtA0HFyaWcDaL2zH9gdHUZ+axj4EOTTNSWBpUKlzYopGeZWbQFDTszgyQmp8GBfb3FBOx3OZSNMkm57ZyAj/eYzceiJqpft91FCDKs/q2puXjOSpaVLfg+/a6742f01IR0ayaBprc+Gj03zUe0lQ0zPBXHOjaRr7O0pU0wxGbC6qeYqBkW0yEooVrEWR0jScqqRtnR2NN1JomqyaET+CUdU1/UNWZX4IcTHVNOThHJrGKGDtkySjxqWUpnGTkY6mcb1MHGQkx/TsZGQjtQmxlsQgm3ZNz1pSMwJdM+JBRkhCJAPHMDXqHZqMpNA0mTUjKjw0TY4/TizMnifToCy6gDWRQqFR6jOS3M6BsYM30JBS6jZhvskIGSI0je/7L1x36Fhs115aNJ4bZBwHGYmqadQa4pof2onvqYptMhKIlbBpmppfDClNowuUIoMzTqD032OREdoASlTmzT21ah4I7ypKe9PQ+dIFrVLIiFXjAqCYpnmEIiMVeJrGqRmJ0TQnt2ZE+cRMIe31ygwrVTMSKeJtfchI2XGYUz+fHNOzVDWNnuDJqBnZAE3Tf94yn5HhGkspTpyEpvHQHmlrQjqSk3M83GSE/n4yaBqmTqvkvj9CTaOOAUvTqARpWzNycGPF0TRB07NmnOkZAKlpGslDkBoZSadpzDqPCjl9LkLj+nrTlC48gqk+L4VL2WTES9PYNSMxmob+fhJqRkiCWjwG087csIFWNI3MKWD1nGMZoZORbJomFxnhn29C9pHJRmLK88IuKkxVutBQ15iUwKrJSEZiBawsTcOZnoH8nrLxcYvafZFT0GtvLnx0mo96LwnD9IyhM6evGYkUsPbHthJqTi4itFXTHOBYVwxNw/kcUDVNKidYTNP4ClhDNA0tCK3ZYjNfhHYVZm8anof1jst07q20mmY8TaN09TQZ6br2MuZf1lWQp6bJmlZRKJh3CjWNYHZELelNg1gBK1nsBLPjyw3VdmGBFaFppjA9S1PTbMIbpBtr1FBOoSO77kTCdDpO9+6I+4G4G4mY6VlezUhKAWsGMhJQBHq/s7FqmorQNNRnZGM0TaqaRn1RjJpmS9Mc3FhXbhEcX8DK0DRj1TRo+d2mDxkJXHAOTZNhoBSSDPt700SHZWkagf6GaXbd635m0zTdZ3YLWP0FXMPc0k3PTkbNSKXVNOWLBSeb1mqaFkbdExBAh0gSbULcZfNSfj5z0UCue+v2mq8Zod2m471pEtU0B71mxC5gLU5GUqiPtPdga0YipmfT0zTue/jHDUl7PXUtY2kagQ3QNCPUNBZNw6kJE8CzjcY2GQmEg4xQisPIogdIb4BXI4Mzvg/9A9340ZqRsLTXbHNv0h5jdhV+mmZ4uHThGarPTZM29R45wdM0SKNpstQ0J4OmGdCy0gi1M6emZ4jSNMPxy+lH5IsFdTref7ifGE/T5DgHl6lpwkPGYsrzYgrTMyEEFgXeHclqGoJqxnrT5BWw5tA00acGUWOvgeGEvWkqX51WbpBzWMI6nhFzSE3TsP1y+pduaZqDG42NjFCahsui21ajAuk0jVUzQm48zu5KysFRMmp6Rqdn+nYoGd7YSvTBMrmApmEgWSVdrSz7evUeOaHUNHuEf+5oGkZNY813N6s3zclIRpTpWXnXXkXTmAtj95OanqmdYNRnpKrNBLQ0GaFOxyrR9tA0hj9Ors/ISaFpyO8T+4ykUih2lHTCLaFpOAfWnH5VAE/d+mJMAas5R/o7j/qWhEHTbEJN47GEiHXt5ZCRLU3zKIjWQUZI116f6Vk2TcPDbRUY0zNF0QB5pmdU2utTBHkiyfRMmpRSUi0KU6ymC1gnUNMcslRAQvQXXYrp2T9FmkbLpkkBq2F6ZtJh0QJWh6YpOw6z+QIraR1vj5qGXlKTmZ5NgO5wc5quNw36n+nUKo0ihUpyASuReXNqmsxkNWuuOVRzYs3IpDQNgEqoa84jp88NQ03D1xrG7OC5mpGt6dmjINraXzPC26I36Zkv17UXA/zG0jRq5whEa0ZMlQCB94hXSpmahv5j+Ny57pMsTaOQkXo6mkbPya6ONwq47LnlqGmyplUUCimqR0h7K4YWHGgaODtBf2+aodYptybAF0vb6diTjNBrKl4zYl+7/PMNOnMiamWKsVRCMNA03b9zj3OexXr/3jFkhLGD5+rQhBD6sE9dwNpw67B33DSaxtd3qyTmFTlXmUZ5RaZnZM2S9ueuwvN17OCZQvat6dkBDicZMW7kzIlLCjnTTc/squgATaOREeEs2KFC09ooCKxYTwBfBHvTMJ8bSLtBszQNK+0tg0sPLZjjQakeWsDq1IwcLJ+RSXrTsJBx97Ml0l6ha0YiNI0Q2Q6bvnCcjhNomug5lmx6Nvw+pc/I2NPCXkdyNhA0cqiPVMWOOjdWjdTzUsmBr1Nvjm17Xn1L9KnpdvB0LL22lV1zM0EQ36l606RIez3zte3ghVEzh/I5TRjbZCQQ0kFGah7KZHvTxAbn1TRS0TRCuhcwNTyLFF3Wxv18SEZk5VEEeSKdpqHvXYaMcP1TSuHSRV25ao/EJm8xn5GTbnqmHVjLF4uKU9PQG551nLNpmhFIQCoyQr++TZielVJNeqyJkCL6+jG9aYDNGInRc0N1wtU0jbNh7x7I6U2T4jOSQ1u5hpCeZGRC0zNDEFi7KMRY0zO5AZqmCK2ZMLbJSCDk7JD5gBA8qkAgsnSaJqymmXG7YI8VPJCjpqmzDIPsYk6eV22yZY1czUjF6PJLzYeEEEbdSC2EOQajs9dzi9A0U7aKT4lJfEZU8WvlJmENKWBVCMqu1w6eFLBOlJQ55oI+moZ2m86V9nrVNNOhGcalMZam0Tx+9+8c+3MaqWiDIZuOIiOuf4nPLkD9M2VN2M2iaTK8kpxWGcPv9OWsHXwpTSMoTeMpGs8NMnGnN01MTZNA0xTVsUwY22QkFPSmLypAiAhN06Y77EXUNJRz1KGQkfkh509CCGORMGkamlFXWTJBGxkxjYH0lZV9g+ZompqRnY0xH6JUTUfT8B1n6WdazKro/M3CvOxpZYfaWU0h7a0qhr8m0l4RQ0bIeVtNdPNdCRvFiNeMRN9ufmp704ynabqfraZBypCRXaa+gwtTNh0ec1ZXmPVPUkmOb37qmCZRt/MMFCfDkTZI08SkvYVqGrqZnExNg8GhW3ppmlSfEdfaYEvTHOQwkpHuhNq06ZmiaWrBJSN+ZAQwLzrjIrNpmizTs5DPCKFpDOgzOmzQDr6agKYBzCLWqhIWTcNLe2OoCHAKaBqFjAipe8fkhtCwNkfTQO+6oskIpWkmQhVWtmrNq6YZkuhowpuqppmSpplSTePQNN3jxTRNhPqg98as+o6Vomm6x+2ELoumyakZSTRoo+Oq8Jqe0aeNpmlIckfW31GmZxjuD63HuTtqB89Q4cn91DYc22QkEILurqragNb47rUNgVdT1TT2V/D/tXfuUVJU1/7/VvX0g4EZnsLAOAmPAY3XYVQQhKAiIYIBV7j3YiCweHjVRE3UKElMiC9WiOR6FQSjPySJQU1QgskPfisqV1AwggjyEMQngjAJLyEi48A8errO74/uqj5VXaf61GOm5rE/a7GG6ak6fepU1ald+7v3PhmZxtYYsV8kT4d3R5plGs4YgaBWioBcz4iETONC/rEremZORfYe1W6SaVSxTMOPVb7gVev2ft3xMvAByJrHGcOoUWK3QBYv0+jGiAuZRso4cKCRl2nUAuFrtKuS6GqB+d6SKHrm1zMiXA/KR1vWtWmaSqbR3Ehg4L0Y+WQaxfRTqq8ywbZuip45LPZpt0ZL+g9ByjQCb6QHsp4RdzJNQpdpFBuZhlJ7Wz4K/9BXVHPRJf5asMumyffMEMk0mZvA3hiR94yYJlnTw12RTuEDJCuwauYKrHIyjf7Gxsk0Nsvc+yk+ZJZpIJRpVJeekSAqj7qCmzhSqUaHDR2aYLmGnklSNBZozFOBVb94FMXY3+8YpHjPiMO6NPpkKWX4KIrZaBcYI6b0U5+zYZDXhTUFNOVCWuWxGg0iXFW3Ra6RI8r2iagujBE3Mk1QRc9EBiQnQXuBz6bhY/Z8yzSZRzbL8Yw49zcnm8bUp8yuJNO0XEyeES6TBrBeuLxMk/6vd5kmc9EoNhdVPs+ISKbhLjxmyghqIpnGw2QGZLNprAv76d/hlkJrzAh/TgRFz/Jl0gDBZk3IwBuTmkcN2y5TyVz0LGLazk02jV8vQIrPWnNYsVd6XRod3mgXyDR8e/6LnuW26b0tszvfjbTK48UzImOUWWO+7IqeAdkxdSvT5CtN7qdwo2ihyyCLnhmVuJliLr3u0zOiG9XCtWkE/Y1F9JeN3PpWfr01QUHGiANqjJdpVJPRKSx6JhsMJMim0S3eqKNMI/KM2EsPiqoixZRM+yrn7nbuorXN9D7ByDR2i21FApdpOK02R6YReUZkZBr+/80s03j1jNjE42TdszCuQ9WNTKM/aHw+eE2VjgWL5KX76VKqKDC/TIhwE9fgRKAyjeUN2rbYogTW2A4RwrlN2K45FkUYwJq5V9y++DSknPvrakkL7gVDsUiKwowwnzKN7tlOWR6xviqwIhsrwqyevjwyjR50bGTTRCxzI0imadFE+KyVHJnGzqWXynGvChHJNEZqr5NMI4oZsX/AAllLmkF1FYkec3BxmrOIeB02b7NcAKtN0TPLwn76d7hFmE2jiI/JvUzjuluu4ccjlfK4cJdNSp/pWpWWaXIXyvM7BrKeEf3+k34eW7PhBAQlNwV5XViz8myz+CSwu8/sEM5tonYtLxO6cWDd1801wr8IyHpy5NoVz4vCc+Y3m0bJeIwssR3+14HJvlSaP87f33iBalv0zBTIHiJkjDgQscg0Jl3VlOKam00j7RkRZdOodjJNZol1mWway5k1jBHFXdGziKogyuWvmuUp3WthKXrmKlgtN4A1otrJNO4nhUJTQC+E3iiTTCOTTRNgOqgMZs+Ix4W7bLxORslxTqZRQ5BpTMUFHWJGRFKAkAJzALqI4GSa4OQ7qzvf8Dy4nLF1o6Euj2fEPLfJ3791SatMY97OjfcsGsnG79TlCWIVBcza99U+c876u6iGkhciEHlGOG+kB/TnQ25qb35ZKR6N2GbT+Kp9EiBkjDgQiXGeETVi0jHtZZrsgnF5X+QF2TS6xevJM1KQ+9ark+ICn9y6u4U3s5+1aSz1D5imGenMimVhv8wGUn3l4T0j6aJn9nE6JplGJE/w2weYDioDb4wwzzEjumfELs2Q94zoqb0uZJogjRFHmSaziZeYESmZRq7ZfO0AAXhZLA8t14ZYBtn1Xty2b03NFxkHbgxWvlZS/lRkNxmBucGa2d/zyTQeA1gzqb3W2A69v/liYkToq/XmBLBKGE/xApXzkNrINBQz0nKJxC3ZNKKIc5uskvxFz5yNEecAVplsGvNNqrsLGRRXLk7HdoVr07jXnPmUVVOdER/u0pwA1oBkGn6fZpFpuPH0nE1jxIzkGlK8TBOBBkWByRtmogmyaZisTOMmmwawxIyI93GTfuqEKZwqoLaCk2nyeEY8zglGaq8gZsTtfSLbXzdSc8JBvhaeM79r00A3RqwyTfqn1wd/NpvGctxcqIAIk0xjYzhTAGsLJsp7RpSIWVcVBDtJ52wLZBrdknZO7c1fZ8T6hpPSo7AVwWJ/DgjfLLjjNr9hyLdpvFlxD1n7bBp/dUZMRc98yjT8Ps2zUB4XgOxVprFZEdkUbM2VnI87VaHlpC43aZuOmOQUsUyjuQi8TrcrmU0TkNxk9g76asoUVMgY81H0TC6bxvWcYIkZMeqgWB/2Lu8TWU9OVrbyKdOI5q08q+DmQ/fyapYXH7/BokzkGZGRaQoinDHCzQOy3vwmhowRBwrihdlfVFW8doOdTCOb2ivIprGvwCqf2mu9Sc0xIy5dsiIjhztu12vTZPrakNKgacyUsqo2uUxj8YyYjJH8Mg0Q3Nu0LLrMpnl0G2dTe3PdsxqfTQPmPAac1BWUvGG6nh1kGjeu+Zx2nWSagLKCApVpuLdVfipxLdNIrtoruy6N0a7VsymoiOo2Hscuy84ON9lFTvOisDaMX5km49nWYP/i49UJIYwZkUhFjkdVrnyCTVYdeUZaLtG4JZtGtGy1XdGzfOc1XzZNgEXP0u1y2TQu17nIL9Nopu+Tkmk4A6chpZlSVlW7law8mO2FseyDTVHgINNwxohEnRF+n+YyRvTz5zW11whgVXOvET5mpEDRnL1Dttk0Pscg2lQyjdtsGrlmxe00gTGiMXGxRQmkZRqP3tJcmca8ndtrRLp8vWgudmjTqX85ffSZTRMRyjQ+PSNNkU3jM904KMgYcSCWyBojzEne4LJK9LdEzzINp93nkNczInZHpjjPiJGG59J1CghuWGaOGXFT4AhITzx8ymokIJmGjxkx1RmxyjS8MdICZRqA84z4TO21lWk0ZroOE2LnhFmmCShmRHEt03jwjDSHTMNnlPluK/u26jbThce17OE6ZiQkmcZNNg33gmHth/Cc+c2mMTwj9gGsXr0QhuyTY4zIBLBGjH7Zpfgz5j2wNgjIGHEgyss0SkROppENBhJm0zjJNHk8I3yglkCm0TjPiOyKsyYjR0amkZggCtRsAGR9Y8ocwMobIz7cpYmctWkE2TSeZBr9Z/MYI4bM5nFytFv3x1Tlk7sOE04Gma1M49MLEJWVaeDu+2SzaYKSafi3bL9jwnlYTTJNE8WMuM6msZRuF1V1Noqeufa4SMo0Ev2NRQQvU3A4Z75lmkw/BUXPvD70DZlGmE3jFDOi2la55q+pMAufkTHiQJzzjGiKIl7G23ZtGslsGmudEUOmCa4cPN+u26JngLWYGvcHQTaNTLvpNL7shMZMMk3w2TRK0Nk0RvCm6255QtOvKw8yjSltWsl9S+RlGgDo4OQZaYKiZ6ZlF6RkGsmGJdamAbJu6hZZ9CxHpnEbMyIne4iKlgnbtSxqJ6qI6vYakc6mcRE/pKqKYZCI+pfTR5/ZNBGj6Jn5ugtVpomqgrVpsgceZqkRMkYciHMxIwxOMSO5ZdHly8GbT4Ee8BRxrDMiUw7e/DfDM8JVkvUv0/Bl8MFtI9WsqdYIn01jWtgvKJlGAaSKnknGjOiTYHPJNJoPmYb3OplXEE3/tMo0HQocrl3Oo2cEJ/qVJGKSMo1AChBSYF7OQfj9QR2HyyBumbZSmlWmcddOU8ge5nYtnhFBTIa8x0Uu4NatYar3N7dcveCc+c2m0WNGrNk0hkzjqVlTIoIJLlRAhDCbhjvsMONGyBhxQFFV1LP05KgpEbEr01amydO4MJsmY8H7zaax3HTZYjmqq0j0dLv5ZRqFqzvhVh+uS2pgTJ/UzAtLBSvTGCH/pu3crk2TbiKYB5gsWZnG/TjwmUqKaDlz7jpMOKXH2Mk0fj0K/LILYcg0AWUFNcXaNJpFx28qmcZtppLVgyEKincbj+O6vy6NHKeiZ8HKNIKiZ0Y2jT+ZJud6di3T2M/pJNO0YOqVtDHCoHALzFllmsyJ1bKeEem1aXLqjGQmRtsAVvmYEWsfjWwMRLgVQJ27aLSb1zOiAYy5rjvBT2j6G7+1fLIfd6m46JlDBdYWWPQM8OcZSQkylUzXKu8ZETsnTGPo1vgUYfKMSKxNIy1VSMo0QZWDNy2JEFDNEs0SwOq+Aqs72cOLVxMQL2LoNrbKtUzjMhU5J8CW+9W2urTnbJqMkWa57oyiZx4f+tk1xsTPIBF8No1qE8AKkGekRdOA9OSocTJNTmqh/jvLprh6l2l0z4i/bBqhZwSK5wJHgMWA4fue8Y6k25Vq1uRC1mMhcm4yH+5SPrVXVRThmPNvxNLZNAEFPcqi+Ujt1QQSmKnoGR/AGnGSabJjqD8IpFNtBRSYll3IL9NISxWSRc+CSlE2r3PiqymThMbPJW7H2q3s4dqDkXT2jGSDgyX761JWkh0PoUyTL7XXZ9Eza8yI76Jn+vxovZ4l+huPRuxjRnhjJMTCZ2SM5KFByRojRjaN9QawXZvGZzaNp5gRB5mGixnx+lYBCHRVIJ1R41UfbtSMuIZcz0hARc+csmlMdUbaokxjn6nESwFmmcbhmHiZxjjfrrtkooCv5xORSO315BmRkWmCkVaAILwsWc+I24JkPE2RnZJu1yLTCMoFuJZpomYjJ29/JcdZX308p+iZSabh/uBTpsmX2uvVAaEFlU1jU/wQCLfwGRkjeUgquZ4RR5nGbdEzQTl455iRDrl/g02MBAfLBMZqiLjXh0XyD39DcBKV/NtVNtKfaY2Z/llFXe/u0pxy8AKZhj8mx7RWm32azTOie7Y8jIOohotxrWrMNBPLyTRqYGNgSqF3MEbcvr3L1xnRfwbnGWkKmcbLOCe4FFynOAW3c0LCKtMI9nd7jSRcrk0jawjrLxlOq/aK4uG8WA6iAFZjAUSvdUb0+dFn0TPVJpAdoJiRFk0jb4wwQQS3TfGv/EXP7D0jvmJGHCqw6qmhaZkm89UeAlgVO1cmALCUMS6yb4X8hKZLCdaKhX7cpbECFQV8Cq5ApvHkGQkoA0MW3T3rKbVXkDZtlRT1iS7hNATcdavv71emMRkjEkXPvMWMiPfR2/NrVwZagdUmm8ZLk/yc0OCwbr377BSrZ8Q+5iQr3br1lsoVaZO99vSXjBynNjcV2Mo0gCfviD5/W2Ua6eeDAGN+9JBNkzDJNNljVRQlG2lAnpGWi26MpKDKZdPoBqrntWkCyqYRBbCaZBrnLtq1a/v2AJgKn3mJyNcnM+ubhF93qS7VyK9N4zaAtblkGt1D5C+bRrWpL6BXXtSvvbisTBOQvBGTlmkym0gbI81c9Ez0YPPSFudh9SfTZI/bydvgXroVxIxYZRr9PvEo/4j7q7cva+Rk5gHHOiMBGiN6zIggPs17No2eFelVpsn1jKT7689jEwRkjOShUdWNkaxHIecGsMumyXdS88k0nrJpxOXgda1R44qeuZ14ctq1yjQuvQW8np2VaQQ3mcfIKj2jJi3TCFbt9ZBNky0H76lbrtGNNM2DTKPvk7KkTfPHrTHOM+JUZ4STuoIK4o0msp4RTRGn9qZcB7C6KwcfVAn3YNpK/+SLnnkZ52gk+9brFIdhxHx49GCkBMaB2/vEauSI8DqH5ZaDzyPTAJ4kYr3isVV2Vn0+9FNCmUYmmybrGYmo5vuM98SFBRkjeUip6Qe/xlSxK5NzkcnLNHmyaTytTSMOYDXc/Ca5yd1bRXof7g9BZdMkU0bKaq5Mo/sPvUW163EjzjIN3yeXMk0zeUaM86d5yabRx1YsT6U0zjPiNCtwYxiUvMFXOm50MEbcyzTuFsrzKzcFW4E1+9DyI9OkKx3nlz7cpk0LZRrrM9KtTOM6m0aqWaNd6znOW4EV8DT35JdpXDdpbk8o0zgZI0rW4245UX4Da4OAjJE8pDjPiFimyTVGvBY90x8YqlWm0TQg1ZD+v5RMY/k63jPiuuiZrExjs41ju1xwnWb/wPQv0xRk+ySzNk2LXbXXv0yTY4xwv2qMGX+Py8SMNJFMI2OMeFoor1WvTePyuC3ISB+eZZp8Rc9cx4w0dZ0R8+e8QW2OhzPPbW4RyzT6Q99nnRGrp09KpuG8QBaZhk8lDwsyRvKgRdJvV42cRyF3bZpcmQbIk95ryDSSnhHdKwJ4k2n4hfICk2kUQPdk8DKNW1dvMiV8YPotPmTINIoilU3TUlft9ZNNIyooZ7pWZY0RO5nG74M3EkEDS0+OSSb+crfrp8jWGQlKcmuKcvAmmcazMZI+sDqHWiNZz4a7h3udsTaNfR/d3icyfQXcZ//oc42wf6JyDYCnuUdU9Mx/AKvuGbHWGck/T/KB6daYEb9ZPkFAxkgedGNEYw7FwkzZNNmPHU+sKJtGn+AdjREPAaz6mxZXSdZt5LztPtyxe3fJZj0jVrem3+JDBoPoZQAAHy1JREFUJmMkUJkmd9+mJCvTuB8HPR7HWlDOKtPoEpmzZ0Qfw2z5f7/yBgDUIx242ggHz4hobSgRsgvlGW/Hfr0ZuW36bcucTePRGImavRh2CNfdkmxT5BlRXN4nsgGsXoue5co0+k/BnA548oyoRp0o+7nGe2qvfq3m6GHpn04yDXdfK1aZxqfHJgjIGMmD4RlhDsXCuLd33ghwtH4Fb+ka07NprMZIfXZ7wfodjjEjme9J8Z4Rl28VqtWVCZiO3X2wGl8OPhPA6iEwy4kEH0UvCBo2p/ZKyjTNXfRML3bkyTOSHtuUQ0qzpoHzjDhdt3Zr07juUg56ccGkNYCZw7WHQDZmpCmKnvmcWfk3VX3IvXpb+Ho+ItyvTZM+wJTG0JjShC9qTbU2jREw61qmsRgjRv8sO/DXixfPSGbV3pyF8jiPlxfyyjQOMi5fWdm0GCn8x7IEgadb5vHHH0ffvn2RSCQwfPhwbNu2zXH7VatW4fzzz0cikUBFRQVeeuklT50NA5YxRlJQhFUG+bgG/mJ3NDIF8Qv6Q0e1VmDNE7wKOCxoh6zHgcFLBVZ7FycA87EbNT3cTjwp4yYSyjRgeQbUHrNnJLO/YILg+5SP5i4Hb3iMPMWMmOuI6FhlGl3GiTldF1ysU5BSVTKz7EKSBRnAKplNo+g/fUor3P5BFT1jjLmPlbEgExSqPxvdyjTpdjVhBVe394ls+fpsHI1Us+JsGlH/FEWqdocIff62puCaqh57IGUslOc+to5P2c+NGfEnHwWBa2Nk5cqVuOuuu3D//fdj586dqKysxLhx4/DZZ5/Zbv/mm2/iu9/9Lm644Qbs2rULkyZNwqRJk7B3717fnW8OWIHuGVG4mBHLRjbZNEA+mcZeMkixzAM9xxhxTusFzGl81j7qgVSaj6JnttubZBr9u91OPJohP+QuAOXPXZo1RiBV9CwmObs1ewCrj9TerEwjlqdSppgRues2SJkmmVmQMikl03gwRqRkGrlm87Vj/b+ftvi1abw2KeNtcCvTxDijPW2MBFT0LDPXNAQt00R1mcb8ueN97EMizmbT2EujntemYfq16mFtGu6+zpVp0j/DXChPfOcLWLhwIW666SZcf/31AIClS5fixRdfxFNPPYWf/exnOdsvXrwY48ePx09+8hMAwC9/+UusW7cOv/nNb7B06VKf3W8GMhNaUlOMmzk32Ek/kymoWh3iSGe9nD1bgwLNvohTtLEBkUy7Ke4toDFzsUWRBJJcnEj9l6b+2KGn8dUlNRuZJt3HBk1FMqXnmgubMmG8VdjdsPobZ7IWCSQRRwOirMHcdwEdlEbE0YCGulqkGmoBZD1D2YPiOtlwBk6rutrRMZL+jjgast4lgUwTL1ClJzf9lDf32jRashZ1tWdc7ZusOwsgN4BVr7zIGFDbkEI8c+0l4HD+9Iwufm2aQIyRGMCA2pQiDF407j9PMk1zrE3D/d+vMZLpS6PGjPHwm01TU9cY2NhGVAXRiIJkiqG6NonGlKDomcv7hA9gdQpidVsi35BprEXPnPqnRAA0AslaqfmMp4AlAeR6RiKcMZIvSNeOxoxxI5RpUuJ7N8Easv0QeEZajTHS0NCAHTt24Oc//7nxmaqqGDt2LLZs2WK7z5YtW3DXXXeZPhs3bhxWr14t/J76+nrU19cbv1dXV7vpZrBkHv6ffl6HO5/bBcBBpkmeQfzXffCRbi8szt/8vBc/xB//31rj95sj/8LwKDD89FrgV71s+iP2jADpm64uqeXKNJk+/t/dR/Fc6hAA+Rs5IYhEB5A1Fv7PSLwEAAkAq6SaxQwAMxIAPs38g5NMA+DXZXINc9wD4J4EgPcy/wBhNo2sRMPv01xFz3Rjcuj2nwDbf+Jq34GZnzlji/Q10MgYLn9oA96IKYAKVK6bAqzL02jAMk2jGgM04PG/H8JLG9c6bhv42jQBZQUFKtNkTtUb+07ijX0nc9p3g+4VmLNqN+as2u24rRsjKl4QQTLViNEPbzQ+87s2jd7XI6frcP69ztcB337+vtq/UDlew2oESAF47BKp7+AZkflpTe3VjZ9j1XLHZ2VRtB6XRHI9G8ac9v6a9D8bSrn/t3qZ5uTJk0ilUujVy/yQ7NWrF44dO2a7z7Fjx1xtDwALFixA586djX9lZe4fQkHR9bzL8SXrgLe0C4zPRpX3MG9U3AfocZ7rtmtYAru0ctNnO7RBOMscDI4BYxzbvHLQOSjt0gFf7V5o+rzu3FGoZoXYrQ0AkL45h/TtJtXPc7sWol+Pjhg5oLtNf66SakOW4z0uM38Q6wSce2mA36AA/a4wffKVboU4t2sHXDHoHOlWRg7ogc4doqgo7RJg38RU9/m67zYOdc4dx1EDs9fyG9qFcg1FYsBXR+CSr3RFUaIAl9ldFy75V8konGKd8C7r57idqgAj+kt+X6QA6Hcl0OtCoFC8z8jy7ihKFOCisi4uemzzdaqCy/p3w4WlxejsuNpgfgaf2wVFCfMD4+vWeUeSUeU9pCQeRUlf17JcMci8bfeOMZzfu8j02WX9u6MoXoChfbtKtdm3e0d8pVth/g0B9OgUw3klRfk3BIxrdYTlWv1qt44o69bBfmz7+5vbNCioLxtl+qxfj44o7WK/0KkMW7QL8CXrgC4DLfPBuUPTc6UEe2MXCbNpQrRFoDAXuTxHjhxBaWkp3nzzTYwYMcL4/Kc//Slef/11bN26NWefWCyGp59+Gt/97neNz5544gnMmzcPx48ft/0eO89IWVkZTp8+jeLiYtnuBkZ9MomGjEctoioojNk4lDQNaKgBADAw1NRLuOAK4rayQ4GWRAfVptKmogDx/DefpjHbt8ez9Q1GTEo0oppW+ZVpM6cwEJD28esSEtJuPjdvVw0pzXARq6qKjkVdcjeyfIdbTH1SI0CsY+42gjFzbNfDPn44W3PatAKvGxRFQafi3AcCYww19dlrrYN21lhcUEgkCkTTE2qQY3CmriG3Aq+FAlU11huSgmUCn/OktwR1HIyxzNf5b6uhUTOCThVFQae4a1Xd4GxDY963XuHcJsB67SSiEURtXIVuxzalMZxtyF9puEM0ggIXrklRP4T98zvvKBGo8dy5Rvb4RMQiQDxqY+w21mdjC0V9YgxKvCjHGKmpTwtAiWgkEE8nT3V1NTp37pz3+e3q6u7RowcikUiOEXH8+HGUlJTY7lNSUuJqewCIx+OIx53liOYkHo0inu9FR1WBRHqgFQBF4tAOCfy9VYlu/MK4u3gLmTahKMZxA+4jomOZf45YvsMtMn3y8vBoTkMEAAo7dQ68TUVRUJTgrzd33xHkGHRMeL8+haQt6LybBXUc/AqofokVqKZAUT+4MTJkyb127HE7thFVrl23iPohO7e5/j7B5011fCiI55XxRX3yY+gGhasrPRaLYciQIXj11VeNzzRNw6uvvmrylPCMGDHCtD0ArFu3Trg9QRAEQRDtC9fm0F133YVZs2Zh6NChGDZsGB599FGcOXPGyK6ZOXMmSktLsWDBAgDAHXfcgSuvvBKPPPIIJkyYgOeffx7bt2/HsmXLgj0SgiAIgiBaJa6NkSlTpuDEiRO47777cOzYMVx00UVYu3atEaRaVVUFldOjRo4ciRUrVuCee+7B3LlzMXDgQKxevRoXXigZLEcQBEEQRJvGVQBrWMgGwBAEQRAE0XKQfX7T2jQEQRAEQYQKGSMEQRAEQYQKGSMEQRAEQYQKGSMEQRAEQYQKGSMEQRAEQYQKGSMEQRAEQYQKGSMEQRAEQYRK+AXpJdBLoVRXV4fcE4IgCIIgZNGf2/lKmrUKY+TLL9MrJ5aVlYXcE4IgCIIg3PLll1+ic2fxQpytogKrpmk4cuQIioqKcpewb6VUV1ejrKwM//jHP6iqbEjQOWh+aMzDhcY/fNrbOWCM4csvv0SfPn1MS8VYaRWeEVVVce6554bdjSahuLi4XVyQLRk6B80PjXm40PiHT3s6B04eER0KYCUIgiAIIlTIGCEIgiAIIlTIGAmJeDyO+++/H/F4POyutFvoHDQ/NObhQuMfPnQO7GkVAawEQRAEQbRdyDNCEARBEESokDFCEARBEESokDFCEARBEESokDFCEARBEESokDFCEESg1NTUhN0FgggNygnxBhkjTcD+/fvxwAMP4JNPPgm7K+2SgwcP4pZbbsH//u//ht2VdsWhQ4cwbtw43H333QDSyzgQzcexY8ewfft2HD58OOyutFtOnTplMsbJMJGHjJEAYYzhlltuwcCBA3H06NE2W8K+JTN37lx87Wtfw8mTJ3H27FmaDJoBxhi+//3vo7y8HG+99RZef/11aJrmuA4FESy33347KioqcOONN6KiogLr168Pu0vtjttuuw2XXnoprr32WsyYMQNHjx5tM2upNQc0WwTEc889hx49emDbtm3Ytm0bnnzySSQSCQBkHTcXr732Gl5//XWsXr0aq1atwr//+7/TZNDELFy4EF26dME777yDnTt34sEHH0Q0GsXx48fD7lq7oK6uDlOnTsWOHTvw0ksvYeXKlbjqqqvws5/9LOyutRtqampw7bXXYteuXXjqqacwY8YMfPrpp5gwYQL27t0bdvdaDa1iobzWwNNPP43i4mL87W9/Q+/evbF3714cOXIE5eXlKCkpQWFhIRhj9HBsQpYvX44BAwZg3LhxeOutt/C3v/0NAwYMwKhRozBw4MCwu9fm2LdvH9asWYPFixdj9uzZANJu6t27dyOVSgEAXfNNzL59+/DOO+/gkUcewaWXXgoAmDp1KpYuXYpkMoloNBpyD9s+77zzDg4cOIAVK1agsrISV1xxBa655hr07dsXS5Yswf3334/S0tKwu9niIc9IQDz00ENQVRVPPPEEJk+ejGuvvRZz5szBqFGjcNNNNwEATcpNhKZpOHv2LI4cOYKrr74aixYtwre//W3s3bsX8+fPx5gxY/CXv/wl7G62Ob761a9i48aNhiHCGEOXLl3Qv39/bNiwAQBd802Npmn4+OOPjdLiNTU1ePjhh1FWVoY//OEPFEzcDJw4cQKHDh1CZWWl6bNu3brhtddew8aNG8PrXCuCjBEPLFiwAHfeeSeefPJJNDQ0AAAGDx6Mb33rW3jooYcQi8WwatUq/PGPf8SiRYuwevVqzJ8/HwBJNkFgHX9VVVFYWAgAeOqpp7B7924899xzeOGFF7B//35ccsklxueEd6zjHovFoCiKEaiqKArOOecc1NfXo76+HgBd70FiN+9UVlbimmuuwY033ogJEyaga9euKCoqQteuXXHfffdh+vTp2L59e8g9bzvYnYPS0lL06dMH9913n7HdsmXLMG3aNCQSCbz88ssA6F7ICyOk+fDDD9kFF1zAKioq2JQpU1jXrl3Z6NGj2aZNmxhjjJ0+fZrNnTuXHThwwLTf//zP/7AuXbqwZDIZRrfbDKLxf/PNNxljjD333HMsGo2ysrIy9s9//tPYb8eOHax3795s/fr1YXW9VSMa97feesu0XSqVYowxNmrUKDZr1izGGGOapjV3d9scovHfvHkzY4yx2tpa9sknn7CrrrqKPfDAA8Z+H3/8MRswYABbvnx5WF1vM9idgyuuuILt2rWLpVIptnjxYqYoChs5ciQrLi5m5eXlrLq6mj377LOsa9euYXe/VUCeERe8+OKL6Ny5M3bu3Innn38e77//Pk6dOoUlS5bg448/RnFxMe6++27069fPtF9paSlisRg++OCDkHreNhCN/6JFi1BVVYUxY8Zg9OjRKCgoMMUsXHzxxaivr0dVVVXIR9A6EY37woULsX//fgAwsmcaGhowaNAgnDhxAjU1NSTTBIBo/BcvXoxPPvkEiUQCdXV1OHz4MK6//noA6fMxcOBAnD17Fp9++mnIR9D6sTsHp0+fxoMPPohDhw7h9ttvx4YNGzB9+nSsWLEC+/btQ1FREaqrq9G/f3/861//CvsQWjxkjEjS2NiI9957Dz179kQkEgEAlJSU4Be/+AWqqqqwfPlyAEBxcXHOvlu2bMFll12GioqK5uxymyLf+P/2t79Fz549MWfOHBw/fhyPPfYY/vGPf0BRFLz00ksoLy/H2LFjQz6K1ke+cf/9738PAFBVFZqmIRaLoUePHjh69Cg6depErmmfyI5/cXExPv30Uxw4cABA+ny88sorKCkpwdVXXx1a/9sC+c7BsmXLAABXXnklbr31VkyYMAEAkEqlsHnzZgwePBjdu3cPrf+tBTJGJCkoKEB9fT1qa2uhaZrx5n3ddddhyJAh2Lp1K3bt2mVsX1VVhYMHD+KHP/whVq9ejZkzZwIg3dArTuM/dOhQbNq0CXv27MG4ceOwZMkSrFixAmPGjMHkyZMxdepUjB07liLaPeDmutdjR77xjW9g9+7d2L9/P3lGfCIz/nv27EHv3r0xY8YMjBs3Dt/73vdwww03YPLkyRg7diyGDx8e8lG0bvLNPdu2bTPN/fv27cP+/fvxgx/8AJs2bcKMGTMA0Nyfl3BVotZBY2MjY4yxDRs2MFVV2a5duxhjzIgB2bhxIysvL2d//vOfGWNprXbOnDmspKSEjRgxgu3ZsyeUfrcVZMZ/wIABbOXKlcY+b7/9NnvyySfZ3XffzXbv3t3sfW4LuL3udV544QV2ww03sJMnT1LMiA9kr/tVq1Yxxhirq6tjc+fOZf/1X//Fpk2bRtd9AHi5B5544gk2aNAgNnz4cJr7XaAwRuYakE6J69Spk/E74+ojNDY2oqCgAHV1dRg/fjyi0SjWrVtn2qa8vBwzZ87Efffdh9raWmzduhWapmHMmDGhHE9rI4jxnzVrFu69995Q+t9aCXLcU6kUIpEI1RZxQZDzjo5+Hgg5gp57Pv/8cxw4cABDhw5t/oNpxbR7maahoQG33XYbJk2ahP/4j//AypUrjQstmUwCgBEQefr0acybNw+vv/46li5darjdTp06hY4dOxq6YIcOHTB69GgyRCQIcvy7desW5qG0Kppi3PUHIBki+WmKeUeHDBE5mmru6datGxkiXmhGL0yL45lnnmG9e/dmo0ePZs888wwbO3YsGzFiBHv55ZdN2y1evJjFYjEjRW7+/PmsZ8+e7MYbb2R///vf2Z133sn69evHPvjggzAOo9VC4x8ONO7hQuMfPnQOWh7t1hj56KOP2OTJk9miRYuMzw4ePMh69erF1q1bxxhj7IsvvmDTpk1jffr0YU8//bRJ/16yZAm7/PLLWUVFBausrGRbt25t7kNo1dD4hwONe7jQ+IcPnYOWSbs1Rj7//HO2detWdurUKeOznTt3squvvppt2bLFCFDaunUrO336tLGNXthJ/7+1wBkhB41/ONC4hwuNf/jQOWiZtBtjZNWqVWzdunXsyJEjtn//wQ9+wAoKCthFF13EevTowa655hr2xhtvMMayEdWEd2j8w4HGPVxo/MOHzkHroM0bI8888wzr2bMnGzZsGDvnnHPY17/+dfbXv/6VMWa2dKdOncrWrl3Lampq2ObNm9l3vvMdNmLEiLC63Wag8Q8HGvdwofEPHzoHrYs2a4wkk0n26KOPsq997Wvsd7/7Hauvr2ebN29mM2fOZNdccw2rq6sztmMsdw2Ne+65h1188cXs8OHDzd73tgCNfzjQuIcLjX/40DlonbTZ1N4zZ87gxIkTmDVrFq6//nrEYjGMHDkSF1xwAaqrq9HY2AggnbrFLHURUqkU9u/fjyFDhqBPnz5hHUKrhsY/HGjcw4XGP3zoHLROCsLuQJDs27cP5eXlUBQFnTt3xuTJk1FRUWGsm6GqKsrKynDmzBlEo1FjP/1irK2txeeff44HHngAO3fuxNKlSwGAijhJQuMfDjTu4ULjHz50DtoAYblkgmTlypWsb9++7LzzzmPDhg1jv/vd70x/5/XBadOmsdmzZzPGzMFJf/nLX9jtt9/OevXqxUaPHs327dvXPJ1vA9D4hwONe7jQ+IcPnYO2Q6s3Rl555RXWt29f9vjjj7O1a9eyu+66i0WjUbZs2TJWW1vLGEtrgpqmsdraWjZ48GD27LPP5rTz3nvvsYcffpitX7++uQ+hVUPjHw407uFC4x8+dA7aFq3WGNGDjubNm8eGDBnCGhoajL/deuutbOjQoUbktM7hw4dZ37592ccff8wYSy9o96Mf/aj5Ot2GoPEPBxr3cKHxDx86B22TVhvAqut477//PgYMGIBoNGqsJzB//nwkEgmsWbMGx44dM/ZZv349ysrK0Lt3b9xxxx244IILUFVVhWQyScs7u4TGPxxo3MOFxj986By0UUI1hVzwyiuvsNtuu40tWrTIVH532bJlrKioyNAAdSt52bJlbNCgQWzDhg2MsbQ1fd1117GuXbuy7t27s3/7t39jb7/9drMfR2uFxj8caNzDhcY/fOgctA9avDFy5MgRNnHiRNazZ082ffp0VlFRwTp37mxclB999BErLS1l9957L2OMsfr6emPfkpISY/2BM2fOsIkTJ7Jzzz2XPf/8881+HK0VGv9woHEPFxr/8KFz0L5o0cbImTNn2KxZs9iUKVNM6wAMGzbMiIqurq5m8+fPZx06dGBVVVWMsaymeOWVV7Ibb7zR2G/79u3N2PvWD41/ONC4hwuNf/jQOWh/tOiYkcLCQsTjccyePRv9+vUzitV861vfwgcffADGGIqKijBt2jRccskl+M53voNDhw5BURRUVVXhs88+w6RJk4z2hgwZEtKRtE5o/MOBxj1caPzDh85B+0NhrGVH7ySTSaNIjV68Zvr06ejYsSOWLVtmbHf48GGMHj0ajY2NGDp0KN58802cf/75WLFiBXr16hVW91s9NP7hQOMeLjT+4UPnoH3R4o0RO0aNGoWbbroJs2bNgqZpAABVVfHJJ59gx44d2Lp1KyorKzFr1qyQe9o2ofEPBxr3cKHxDx86B22XVmeMHDhwACNHjsSLL75ouN4aGhoQi8VC7ln7gMY/HGjcw4XGP3zoHLRtWnTMCI9uM23atAmdOnUyLsZ58+bhjjvuwGeffRZm99o8NP7hQOMeLjT+4UPnoH3QahbK0wvdbNu2Df/5n/+JdevW4Xvf+x7Onj2LZ599Fj179gy5h20bGv9woHEPFxr/8KFz0E5o/gQe79TW1rLy8nKmKAqLx+Ps17/+ddhdalfQ+IcDjXu40PiHD52Dtk+rixn55je/iYEDB2LhwoVIJBJhd6fdQeMfDjTu4ULjHz50Dto2rc4YSaVSiEQiYXej3ULjHw407uFC4x8+dA7aNq3OGCEIgiAIom3RarJpCIIgCIJom5AxQhAEQRBEqJAxQhAEQRBEqJAxQhAEQRBEqJAxQhAEQRBEqJAxQhAEQRBEqJAxQhAEQRBEqJAxQhAEQRBEqJAxQhCEb2bPng1FUaAoCqLRKHr16oVvfvObeOqpp6BpmnQ7y5cvR5cuXZquowRBtEjIGCEIIhDGjx+Po0eP4uDBg3j55Zdx1VVX4Y477sDEiRPR2NgYdvcIgmjBkDFCEEQgxONxlJSUoLS0FJdccgnmzp2LNWvW4OWXX8by5csBAAsXLkRFRQU6duyIsrIy3HrrraipqQEAbNy4Eddffz1Onz5teFkeeOABAEB9fT1+/OMfo7S0FB07dsTw4cOxcePGcA6UIIjAIWOEIIgmY8yYMaisrMRf//pXAICqqliyZAnee+89PP3003jttdfw05/+FAAwcuRIPProoyguLsbRo0dx9OhR/PjHPwYA/PCHP8SWLVvw/PPPY8+ePbjuuuswfvx47Nu3L7RjIwgiOGihPIIgfDN79mx88cUXWL16dc7fpk6dij179uD999/P+dsLL7yAm2++GSdPngSQjhn50Y9+hC+++MLYpqqqCv3790dVVRX69OljfD527FgMGzYMDz74YODHQxBE81IQdgcIgmjbMMagKAoAYP369ViwYAE+/PBDVFdXo7GxEXV1dTh79iwKCwtt93/33XeRSqUwaNAg0+f19fXo3r17k/efIIimh4wRgiCalA8++AD9+vXDwYMHMXHiRNxyyy341a9+hW7dumHTpk244YYb0NDQIDRGampqEIlEsGPHDkQiEdPfOnXq1ByHQBBEE0PGCEEQTcZrr72Gd999F3feeSd27NgBTdPwyCOPQFXT4Wp//vOfTdvHYjGkUinTZxdffDFSqRQ+++wzXH755c3Wd4Igmg8yRgiCCIT6+nocO3YMqVQKx48fx9q1a7FgwQJMnDgRM2fOxN69e5FMJvHYY4/h2muvxebNm7F06VJTG3379kVNTQ1effVVVFZWorCwEIMGDcL06dMxc+ZMPPLII7j44otx4sQJvPrqqxg8eDAmTJgQ0hETBBEUlE1DEEQgrF27Fr1790bfvn0xfvx4bNiwAUuWLMGaNWsQiURQWVmJhQsX4r//+79x4YUX4k9/+hMWLFhgamPkyJG4+eabMWXKFJxzzjl46KGHAAB/+MMfMHPmTMyZMwfnnXceJk2ahLfffhtf+cpXwjhUgiAChrJpCIIgCIIIFfKMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKmSMEARBEAQRKv8fycxAgDcd91UAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting function\n",
        "\n",
        "def predict(train, test, predictors, model):\n",
        "  model.fit(train[predictors], train[\"Target\"])\n",
        "  preds = model.predict(test[predictors])\n",
        "  preds = pd.Series(preds, index=test.index, name=\"Predictions\")\n",
        "  combined = pd.concat([test[\"Target\"], preds], axis=1)\n",
        "  return combined"
      ],
      "metadata": {
        "id": "pCNVkfSfByES"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest(data, model, predictors, start=2500, step=250):\n",
        "  all_predictions = []\n",
        "\n",
        "  for i in range(start, data.shape[0], step):\n",
        "    train = data.iloc[0:i].copy()\n",
        "    test = data.iloc[i:(i+step)].copy()\n",
        "    predictions = predict(train, test, predictors, model)\n",
        "    all_predictions.append(predictions)\n",
        "  return pd.concat(all_predictions)\n"
      ],
      "metadata": {
        "id": "33PHGs91Czg8"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = backtest(sp_500, model, predictors)"
      ],
      "metadata": {
        "id": "ZGDvNGbBD2Mu"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Predictions\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24BixZ5Lbf1l",
        "outputId": "849c49ab-9392-44d7-d5ae-065d32dbaf74"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3435\n",
              "1    2555\n",
              "Name: Predictions, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(predictions[\"Target\"], predictions[\"Predictions\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ohSCgWvJbopJ",
        "outputId": "f0d1adfb-050c-4f97-9e5b-aaa9aac84243"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5287671232876713"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Target\"].value_counts() / predictions.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OIy8164Sb0gI",
        "outputId": "4f0cf492-ea49-4a78-8523-5a86daf5fd6e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.533723\n",
              "0    0.466277\n",
              "Name: Target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Including other predictors to increase the accuracy"
      ],
      "metadata": {
        "id": "BUUUqZdrcLYj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "horizons = [2,5,60,250,1000]\n",
        "new_predictors = []\n",
        "\n",
        "for horizon in horizons:\n",
        "  rolling_averages = sp_500.rolling(horizon).mean()\n",
        "\n",
        "  ratio_column = f\"Close_Ratio_{horizon}\"\n",
        "  sp_500[ratio_column] = sp_500[\"Close\"] / rolling_averages[\"Close\"]\n",
        "\n",
        "  trend_column = f\"Trend_{horizon}\"\n",
        "  sp_500[trend_column] = sp_500.shift(1).rolling(horizon).sum()[\"Target\"]\n",
        "\n",
        "  new_predictors += [ratio_column, trend_column]"
      ],
      "metadata": {
        "id": "HkzZ_1hDcCbf"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp_500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 995
        },
        "id": "ZhC3fmoedkGr",
        "outputId": "dd6e922d-4f02-469d-8b55-0d0fc4fabf68"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1990-01-02 00:00:00-05:00   353.399994   359.690002   351.980011   359.690002   \n",
              "1990-01-03 00:00:00-05:00   359.690002   360.589996   357.890015   358.760010   \n",
              "1990-01-04 00:00:00-05:00   358.760010   358.760010   352.890015   355.670013   \n",
              "1990-01-05 00:00:00-05:00   355.670013   355.670013   351.350006   352.200012   \n",
              "1990-01-08 00:00:00-05:00   352.200012   354.239990   350.540009   353.790009   \n",
              "...                                ...          ...          ...          ...   \n",
              "2023-09-07 00:00:00-04:00  4434.549805  4457.810059  4430.459961  4451.140137   \n",
              "2023-09-08 00:00:00-04:00  4451.299805  4473.529785  4448.379883  4457.490234   \n",
              "2023-09-11 00:00:00-04:00  4480.979980  4490.770020  4467.890137  4487.459961   \n",
              "2023-09-12 00:00:00-04:00  4473.270020  4487.109863  4456.830078  4461.899902   \n",
              "2023-09-13 00:00:00-04:00  4462.649902  4479.390137  4453.520020  4467.439941   \n",
              "\n",
              "                               Volume     Tomorrow  Target  Close_Ratio_2  \\\n",
              "Date                                                                        \n",
              "1990-01-02 00:00:00-05:00   162070000   358.760010       0            NaN   \n",
              "1990-01-03 00:00:00-05:00   192330000   355.670013       0       0.998706   \n",
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              "1990-01-05 00:00:00-05:00   158530000   353.790009       1       0.995098   \n",
              "1990-01-08 00:00:00-05:00   140110000   349.619995       0       1.002252   \n",
              "...                               ...          ...     ...            ...   \n",
              "2023-09-07 00:00:00-04:00  3763760000  4457.490234       1       0.998392   \n",
              "2023-09-08 00:00:00-04:00  3259290000  4487.459961       1       1.000713   \n",
              "2023-09-11 00:00:00-04:00  3369920000  4461.899902       0       1.003350   \n",
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              "2023-09-13 00:00:00-04:00  2254136000          NaN       0       1.000620   \n",
              "\n",
              "                           Trend_2  Close_Ratio_5  Trend_5  Close_Ratio_60  \\\n",
              "Date                                                                         \n",
              "1990-01-02 00:00:00-05:00      NaN            NaN      NaN             NaN   \n",
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              "...                            ...            ...      ...             ...   \n",
              "2023-09-07 00:00:00-04:00      0.0       0.991925      1.0        0.998004   \n",
              "2023-09-08 00:00:00-04:00      1.0       0.995566      2.0        0.999097   \n",
              "2023-09-11 00:00:00-04:00      2.0       1.003529      2.0        1.005383   \n",
              "2023-09-12 00:00:00-04:00      1.0       0.999374      2.0        0.999522   \n",
              "2023-09-13 00:00:00-04:00      1.0       1.000527      3.0        1.000547   \n",
              "\n",
              "                           Trend_60  Close_Ratio_250  Trend_250  \\\n",
              "Date                                                              \n",
              "1990-01-02 00:00:00-05:00       NaN              NaN        NaN   \n",
              "1990-01-03 00:00:00-05:00       NaN              NaN        NaN   \n",
              "1990-01-04 00:00:00-05:00       NaN              NaN        NaN   \n",
              "1990-01-05 00:00:00-05:00       NaN              NaN        NaN   \n",
              "1990-01-08 00:00:00-05:00       NaN              NaN        NaN   \n",
              "...                             ...              ...        ...   \n",
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              "2023-09-08 00:00:00-04:00      30.0         1.088754      120.0   \n",
              "2023-09-11 00:00:00-04:00      30.0         1.095670      120.0   \n",
              "2023-09-12 00:00:00-04:00      29.0         1.088867      120.0   \n",
              "2023-09-13 00:00:00-04:00      30.0         1.089664      120.0   \n",
              "\n",
              "                           Close_Ratio_1000  Trend_1000  \n",
              "Date                                                     \n",
              "1990-01-02 00:00:00-05:00               NaN         NaN  \n",
              "1990-01-03 00:00:00-05:00               NaN         NaN  \n",
              "1990-01-04 00:00:00-05:00               NaN         NaN  \n",
              "1990-01-05 00:00:00-05:00               NaN         NaN  \n",
              "1990-01-08 00:00:00-05:00               NaN         NaN  \n",
              "...                                     ...         ...  \n",
              "2023-09-07 00:00:00-04:00          1.152163       530.0  \n",
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              "  .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-99f537bb-4e0f-49b9-8dca-23815e5dbe2b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Droping missing values\n",
        "\n",
        "sp_500 = sp_500.dropna()"
      ],
      "metadata": {
        "id": "O4MlCpb9dlzY"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(n_estimators=200, min_samples_split=50, random_state=1)"
      ],
      "metadata": {
        "id": "p22wfc4HeEC0"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(train, test, predictors, model):\n",
        "  model.fit(train[predictors], train[\"Target\"])\n",
        "  preds = model.predict_proba(test[predictors])[:,1]\n",
        "  preds[preds >= .6] = 1\n",
        "  preds[preds < .6] = 0\n",
        "  preds = pd.Series(preds, index=test.index, name=\"Predictions\")\n",
        "  combined = pd.concat([test[\"Target\"], preds], axis=1)\n",
        "  return combined"
      ],
      "metadata": {
        "id": "eTCqsRPleg9o"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = backtest(sp_500, model, new_predictors)"
      ],
      "metadata": {
        "id": "JA-XC85Ue6zs"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Predictions\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqrnJL2zfhOD",
        "outputId": "5c52f240-9315-477f-e54f-996cb087ad11"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    4167\n",
              "1.0     822\n",
              "Name: Predictions, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(predictions[\"Target\"], predictions[\"Predictions\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyvRgYH3foIB",
        "outputId": "5dce8138-1a94-4afd-daaf-726e142ad22c"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5693430656934306"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    }
  ]
}