{
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      "source": [
        "# **Introduction & Team Introduction**\n",
        "\n",
        "The overall goal of this project is to analyze and uncover hidden patterns in the Dataset Name using unsupervised learning techniques. By applying data preparation, dimensionality reduction, and clustering analysis, we aim to provide data-driven insights and recommendations that can help stakeholders make better strategic decisions and grow their business.\n",
        "\n",
        "This project has been carried out by the following participants:\n",
        "\n",
        "Amisha Magar (amagar25@student.aau.dk)\n",
        "Riya Pokharel (rpokha25@student.aau.dk)\n",
        "Sristee Rai (skulun25@student.aau.dk)"
      ]
    },
    {
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      "metadata": {
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        "#Group A"
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      "execution_count": null,
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          "height": 474
        },
        "id": "HSAttP0t9xgr",
        "outputId": "3a29ed34-ccaf-4fc2-ac5e-66dc99e51af6"
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          "text": [
            "(1599, 12)\n",
            "fixed acidity           0\n",
            "volatile acidity        0\n",
            "citric acid             0\n",
            "residual sugar          0\n",
            "chlorides               0\n",
            "free sulfur dioxide     0\n",
            "total sulfur dioxide    0\n",
            "density                 0\n",
            "pH                      0\n",
            "sulphates               0\n",
            "alcohol                 0\n",
            "quality                 0\n",
            "dtype: int64\n"
          ]
        },
        {
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              "summary": "{\n  \"name\": \"wine_data\",\n  \"rows\": 1599,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7410963181277006,\n        \"min\": 4.6,\n        \"max\": 15.9,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          5.3,\n          12.7,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17905970415353498,\n        \"min\": 0.12,\n        \"max\": 1.58,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          1.025,\n          0.4,\n          0.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19480113740531785,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.37,\n          0.0,\n          0.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4099280595072805,\n        \"min\": 0.9,\n        \"max\": 15.5,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          11.0,\n          3.0,\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047065302010090154,\n        \"min\": 0.012,\n        \"max\": 0.611,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.096,\n          0.343,\n          0.159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.46015696980973,\n        \"min\": 1.0,\n        \"max\": 72.0,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          11.0,\n          9.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.89532447829901,\n        \"min\": 6.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          68.0,\n          35.0,\n          101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018873339538425559,\n        \"min\": 0.99007,\n        \"max\": 1.00369,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          0.99974,\n          1.0001,\n          0.99471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15438646490354266,\n        \"min\": 2.74,\n        \"max\": 4.01,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          3.07,\n          3.0,\n          3.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16950697959010977,\n        \"min\": 0.33,\n        \"max\": 2.0,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          1.07,\n          1.04,\n          1.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0656675818473926,\n        \"min\": 8.4,\n        \"max\": 14.9,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          8.5,\n          9.95,\n          9.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "wine_data"
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              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
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            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 1. Dataset Selection & Preparation\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Working dataset URL from UCI repository\n",
        "wine_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'\n",
        "wine_data = pd.read_csv(wine_url, sep=';')\n",
        "\n",
        "# Check dataset\n",
        "print(wine_data.shape)       # Should show (1599, 12)\n",
        "print(wine_data.isnull().sum())  # Check for missing values\n",
        "wine_data.head()             # Preview first rows\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAG-W8x4FWdJ"
      },
      "source": [
        "## Dataset Selection & Preparation\n",
        "\n",
        "We selected the **Wine Quality dataset** because it contains **1599 wine samples** with **12 chemical features and quality ratings**, which is sufficient for clustering and has clear business relevance. Analyzing these features can help wineries **identify product segments and target customer preferences**.  \n",
        "\n",
        "The dataset has **1599 rows and 12 columns**, and all features have **0 missing values**, meaning it is already clean and does not require imputation. We checked for potential outliers, but no cleaning steps were applied at this stage.  \n",
        "\n",
        "Since the features are on different scales — for example, alcohol ranges from 9 to 14, while chlorides ranges from 0.01 to 0.6 — we applied **scaling** (using StandardScaler/MinMaxScaler) to ensure all features contribute equally to clustering.  \n",
        "\n",
        "For clustering, we selected the **11 chemical properties** as input features and excluded quality to let the algorithm form clusters without predefined labels. We will later use quality to validate the clusters and interpret their business meaning.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2iJxNSoZFwxB",
        "outputId": "67b347e1-2cb5-4744-96fe-2f41f2fa0c25"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Optimal number of principal components: 6\n",
            "\n",
            "PC1 represents major influence from:\n",
            "  - fixed acidity (weight 0.49)\n",
            "  - citric acid (weight 0.46)\n",
            "  - pH (weight 0.44)\n",
            "\n",
            "PC2 represents major influence from:\n",
            "  - total sulfur dioxide (weight 0.57)\n",
            "  - free sulfur dioxide (weight 0.51)\n",
            "  - alcohol (weight 0.39)\n",
            "\n",
            "PC3 represents major influence from:\n",
            "  - alcohol (weight 0.47)\n",
            "  - volatile acidity (weight 0.45)\n",
            "  - free sulfur dioxide (weight 0.43)\n",
            "\n",
            "PC4 represents major influence from:\n",
            "  - chlorides (weight 0.67)\n",
            "  - sulphates (weight 0.55)\n",
            "  - residual sugar (weight 0.37)\n",
            "\n",
            "PC5 represents major influence from:\n",
            "  - residual sugar (weight 0.73)\n",
            "  - alcohol (weight 0.35)\n",
            "  - pH (weight 0.27)\n",
            "\n",
            "PC6 represents major influence from:\n",
            "  - pH (weight 0.52)\n",
            "  - volatile acidity (weight 0.41)\n",
            "  - density (weight 0.39)\n"
          ]
        }
      ],
      "source": [
        "#2. Dimensionality Reduction\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Load the Wine Quality dataset (working URL)\n",
        "wine_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'\n",
        "wine_data = pd.read_csv(wine_url, sep=';')\n",
        "\n",
        "# Drop target 'quality' for PCA\n",
        "features = wine_data.drop('quality', axis=1)\n",
        "\n",
        "# Standardize features (important for PCA)\n",
        "scaler = StandardScaler()\n",
        "scaled_features = scaler.fit_transform(features)\n",
        "\n",
        "# Apply PCA\n",
        "pca = PCA()\n",
        "pca_result = pca.fit_transform(scaled_features)\n",
        "\n",
        "# Explained variance\n",
        "explained_variance = np.cumsum(pca.explained_variance_ratio_)\n",
        "\n",
        "# Plot cumulative explained variance\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(range(1, len(explained_variance)+1), explained_variance, marker='o')\n",
        "plt.xlabel('Number of Principal Components')\n",
        "plt.ylabel('Cumulative Explained Variance')\n",
        "plt.title('PCA Explained Variance')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Choose number of components that explain ~80% variance\n",
        "optimal_components = np.argmax(explained_variance >= 0.80) + 1\n",
        "print(f\"Optimal number of principal components: {optimal_components}\")\n",
        "\n",
        "# Interpretation of main dimensions (business terms)\n",
        "pca_components = pd.DataFrame(pca.components_, columns=features.columns)\n",
        "main_components = pca_components.iloc[:optimal_components]\n",
        "\n",
        "for i, row in main_components.iterrows():\n",
        "    print(f\"\\nPC{i+1} represents major influence from:\")\n",
        "    sorted_features = row.abs().sort_values(ascending=False)\n",
        "    # Convert to list of tuples before slicing\n",
        "    top_features = list(sorted_features.items())[:3]\n",
        "    for feature, value in top_features:\n",
        "        print(f\"  - {feature} (weight {value:.2f})\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyv5IP80HQjQ"
      },
      "source": [
        "\n",
        "*   PCA was applied to the standardized wine features to reduce correlated variables into uncorrelated principal components.\n",
        "*   The first 6 components explain ~80% of the variance, so 6 dimensions were selected.\n",
        "\n",
        "\n",
        "*   PC1 – Acidity: Fixed acidity, citric acid, pH → taste sharpness and freshness\n",
        "*   PC2 – Preservation & Strength: Sulfur dioxide, alcohol → shelf-life and potency\n",
        "\n",
        "\n",
        "*   PC3 – Taste Balance: Alcohol, volatile acidity → drinkability and flavor balance\n",
        "*   PC4 – Body & Intensity: Chlorides, sulphates, residual sugar → richness and complexity\n",
        "\n",
        "*   PC5 – Sweetness/Dryness: Residual sugar, alcohol → smoothness vs dryness\n",
        "*   PC6 – Freshness & Texture: pH, density, volatile acidity → mouthfeel and perceived quality\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 951
        },
        "id": "t6kjAAo1Ik11",
        "outputId": "6b6b7084-0487-4109-90ac-94981df5d4a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of PCA components chosen: 6\n"
          ]
        },
        {
          "data": {
            "image/png": 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zefNmVqxYccO2CxcukJmZefM3m4s+ffrwxhtv8NFHH+Hl5ZVrG3d3d3r37s0333yT62wMhfGdIS9/Jt27dyczMzPHlMhZWVn272cijqIz/yLF1A8//GD/5fj3Wrdubf+1/mZiY2Pp2bMn3bp1Y/PmzcydO5cBAwbQqFEjABo1asTTTz/NrFmzuHDhAu3bt2fr1q189tln9OrVi44dO+bYX9u2bfnqq69o0KCB/YzCvffei6+vL4cOHcr39f7ZwsLCGDdu3G3bzZgxgzZt2tCgQQNeeOEFqlevTlJSEps3b+b48ePs3LkTgMaNG+Pu7s6kSZNISUnB29ub+++/P9epgG5m1KhRfPnllzzwwAO89NJLlC9fns8++4zY2Fi++eYb+y/7L7zwAtOnT2fgwIFER0cTHBzM559/TunSpQv0WYiISPEwbNgwrly5wiOPPEKdOnVIT09n06ZNzJ8/n/DwcJ599tlCed3BgwfzySef8MwzzxAdHU14eDgLFy5k48aNfPDBB/Zr4P39/Xnsscf48MMPsVgs1KhRg2XLluV6rbsjjq9NmjRh5syZvP3229SsWZPKlStz//3387e//Y0lS5bw0EMP8cwzz9CkSRMuX77M7t27WbhwIceOHaNixYp5fv/+/v55+s4wceJEVq9eTYsWLXjhhReoW7cu586dY/v27fz000+cO3cOsPVMKFeuHB9//DFly5bF19eXFi1a5Og5eTt5/TPp0aMH9913H6NGjeLYsWPUrVuXRYsWkZKSkufXEskT0+YZEJECudVUf/xhOhhuMtXfvn37jD59+hhly5Y1AgICjKFDhxpXr17N8ToZGRnGm2++aURERBienp5GaGioMXr06BzT/WSbMWOGARhDhgzJsT4yMtIAjKioqDy9t+yp/vLy/n8/1Z9hGMaRI0eMgQMHGkFBQYanp6dRpUoV46GHHjIWLlyYo93s2bON6tWrG+7u7jmmJbrZa7dv3/6GKYqOHDli9OnTxyhXrpzh4+NjNG/e3Fi2bNkNz42LizN69uxplC5d2qhYsaLx8ssvG8uXL9dUfyIiLuyHH34wnnvuOaNOnTpGmTJlDC8vL6NmzZrGsGHDjKSkpBxt8zrVX/aUdr/39NNPG2FhYTnWJSUlGc8++6xRsWJFw8vLy2jQoEGu08SdPn3a6N27t1G6dGkjICDA+NOf/mTs2bMn12nl7vT4mpiYaDz44ING2bJlDSDHMfXixYvG6NGjjZo1axpeXl5GxYoVjdatWxtTpkzJdWrE37vZ5/J7uU31l/05vfjii0ZoaKjh6elpBAUFGZ06dTJmzZqVo93//vc/o27duoaHh0eOz6Yw/kzOnj1rPPXUU4afn5/h7+9vPPXUU8aOHTs01Z84lMUwNCqFiIiIiIiIiCvTNf8iIiIiIiIiLk7Fv4iIiIiIiIiLU/EvIiIiIiIi4uJU/IuIiIiIiIi4OBX/IiIiIiIiIi5Oxb+IiIiIiIiIi/MwO4CrsFqtnDx5krJly2KxWMyOIyIigmEYXLx4kZCQENzc9Hu/I+h4LyIiziQ/x3oV/w5y8uRJQkNDzY4hIiJyg4SEBKpWrWp2DJeg472IiDijvBzrVfw7SNmyZQHbh+7n52dyGhEREUhNTSU0NNR+jJI7p+O9iIg4k/wc600t/tetW8e7775LdHQ0p06dYvHixfTq1QuAjIwM/vGPf/D9999z9OhR/P39iYyMZOLEiYSEhNj3ce7cOYYNG8bSpUtxc3Ojd+/eTJ06lTJlytjb7Nq1ixdffJFt27ZRqVIlhg0bxmuvvZYjy9dff82YMWM4duwYtWrVYtKkSXTv3j3P7yW765+fn5++DIiIiFNR93TH0fFeREScUV6O9aZeAHj58mUaNWrEjBkzbth25coVtm/fzpgxY9i+fTuLFi3i4MGD9OzZM0e7J554gr1797Jy5UqWLVvGunXrGDx4sH17amoqXbp0ISwsjOjoaN59913GjRvHrFmz7G02bdpE//79GTRoEDt27KBXr1706tWLPXv2FN6bFxERERERESkiFsMwDLNDgO2Xit+f+c/Ntm3baN68OXFxcVSrVo39+/dTt25dtm3bRtOmTQFYvnw53bt35/jx44SEhDBz5kxef/11EhMT8fLyAmDUqFF8++23HDhwAIC+ffty+fJlli1bZn+tli1b0rhxYz7++OM85U9NTcXf35+UlBSdCRAREaegY5Pj6TMVERFnkp/jUrEa+jclJQWLxUK5cuUA2Lx5M+XKlbMX/gCRkZG4ubmxZcsWe5t27drZC3+Arl27cvDgQc6fP29vExkZmeO1unbtyubNm2+a5dq1a6Smpua4iYiIiIiIiDijYlP8p6WlMXLkSPr372//RSMxMZHKlSvnaOfh4UH58uVJTEy0twkMDMzRJvvx7dpkb8/NhAkT8Pf3t9808q+IiIiIiIg4q2JR/GdkZPD4449jGAYzZ840Ow4Ao0ePJiUlxX5LSEgwO5KIiIiIiIhIrpx+qr/swj8uLo5Vq1bluI4hKCiI5OTkHO0zMzM5d+4cQUFB9jZJSUk52mQ/vl2b7O258fb2xtvbu+BvTERERERERKSIOPWZ/+zC//Dhw/z0009UqFAhx/ZWrVpx4cIFoqOj7etWrVqF1WqlRYsW9jbr1q0jIyPD3mblypXcddddBAQE2NtERUXl2PfKlStp1apVYb01ERERERERkSJjavF/6dIlYmJiiImJASA2NpaYmBji4+PJyMigT58+/PLLL3zxxRdkZWWRmJhIYmIi6enpANx9991069aNF154ga1bt7Jx40aGDh1Kv379CAkJAWDAgAF4eXkxaNAg9u7dy/z585k6dSojRoyw53j55ZdZvnw57733HgcOHGDcuHH88ssvDB06tMg/ExERERERERFHM3WqvzVr1tCxY8cb1j/99NOMGzeOiIiIXJ+3evVqOnToAMC5c+cYOnQoS5cuxc3Njd69ezNt2jTKlCljb79r1y5efPFFtm3bRsWKFRk2bBgjR47Msc+vv/6af/zjHxw7doxatWoxefJkunfvnuf3oql/RETE2ejY5Hj6TEVExJnk57hkavHvShz1ZSDLarA19hzJF9OoXNaH5hHlcXezODCpiIiUFCpUHc9hn6k1C06vh6unoFQwVGoLbu6OCyoiIiVCfo5LTj/gX0myfM8p3ly6j1MpafZ1wf4+vNGjLt3qB5uYTERERBwmYRFEvwxXjv+2rnRVaDIVQh81L5eIiLg0px7wryRZvucUQ+Zuz1H4AySmpDFk7naW7zllUjIRERFxmIRFsL5PzsIf4MoJ2/qERebkEhERl6fi3wlkWQ3eXLqP3K6/yF735tJ9ZFl1hYaIiEixZc2ynfG/1RE/eritnYiIiIOp+HcCW2PP3XDG//cM4FRKGltjzxVdKBEREXGs0+tvPOOfgwFXEmztREREHEzFvxNIvnjzwr8g7URERMQJXc3jJXx5bSciIpIPKv6dQOWyPg5tJyIiIk6oVB4H781rOxERkXxQ8e8EmkeUJ9jfh5tN6GfBNup/84jyRRlLREREHKlSW9uo/rc64pcOtbUTERFxMBX/TsDdzcIbPeoCuX8dMIA3etTF3e1mXxZERERcz4wZMwgPD8fHx4cWLVqwdevWm7adPXs2bdu2JSAggICAACIjI2/Z/s9//jMWi4UPPvigEJLfhJu7bTo/4MYj/vXHTT6wtRMREXEwFf9Oolv9YGY+eS9B/jd27a9c1pvIuwNNSCUiImKO+fPnM2LECN544w22b99Oo0aN6Nq1K8nJybm2X7NmDf3792f16tVs3ryZ0NBQunTpwokTJ25ou3jxYn7++WdCQkIK+23cKPRRaLsQSlfJud67vG196KNFn0lEREoEi2EYmj/OAVJTU/H39yclJQU/P78C7yfLarA19hzJF9Mo6+PJKwtiOH8lg3f7NOSxpqEOTCwiIq7OUccmM7Ro0YJmzZoxffp0AKxWK6GhoQwbNoxRo0bd9vlZWVkEBAQwffp0Bg4caF9/4sQJWrRowYoVK3jwwQcZPnw4w4cPz3Muh32m1izbqP7734WT30O1x6HN/ILvT0RESqT8HJd05t/JuLtZaFWjAg83rsL9dSozpEMNAKZGHSY902pyOhERkcKXnp5OdHQ0kZGR9nVubm5ERkayefPmPO3jypUrZGRkUL78b+PlWK1WnnrqKf72t79Rr169PO3n2rVrpKam5rg5hJs7BHaA+m/YHp/8DjKvOGbfIiIiuVDx7+SeahlOpbLeHD9/lQW/JJgdR0REpNCdOXOGrKwsAgNzXvIWGBhIYmJinvYxcuRIQkJCcvyAMGnSJDw8PHjppZfynGXChAn4+/vbb6GhDu6FV6EZ+EZA5mXbDwAiIiKFRMW/kyvl5c7QjjUBmL7qV9IyskxOJCIi4twmTpzIV199xeLFi/HxsY2lEx0dzdSpU5kzZw4WS94H0B09ejQpKSn2W0KCg3+It1gg7HHbcpy6/YuISOFR8V8M9GseSoi/D4mpaczbEm92HBERkUJVsWJF3N3dSUpKyrE+KSmJoKCgWz53ypQpTJw4kR9//JGGDRva169fv57k5GSqVauGh4cHHh4exMXF8corrxAeHn7T/Xl7e+Pn55fj5nDV+truT34HGZccv38RERFU/BcL3h7uDOtUC4CP1vzKlfRMkxOJiIgUHi8vL5o0aUJUVJR9ndVqJSoqilatWt30eZMnT2b8+PEsX76cpk2b5tj21FNPsWvXLmJiYuy3kJAQ/va3v7FixYpCey95EtAYytSErDQ4sdTcLCIi4rJU/BcTfZpUpVr50py5lM5nm+LMjiMiIlKoRowYwezZs/nss8/Yv38/Q4YM4fLlyzz77LMADBw4kNGjR9vbT5o0iTFjxvDpp58SHh5OYmIiiYmJXLpkO5NeoUIF6tevn+Pm6elJUFAQd911lynv0c5igbDrZ//j1fVfREQKh4r/YsLT3Y2Xr5/9/2TdES6mZZicSEREpPD07duXKVOmMHbsWBo3bkxMTAzLly+3DwIYHx/PqVOn7O1nzpxJeno6ffr0ITg42H6bMmWKWW8hf7KL/5M/QHqKuVlERMQlWQzDMMwO4QqKYi7lLKtBl/fXcuT0Zf4aWZuXI2sVyuuIiIhrKIpjU0lTaJ+pYcB39SB1P7T6L0Q85bh9i4iIy8rPcUln/osRdzcLf+1cG4B/rz/KhSvpJicSERERh7BYoJpG/RcRkcKj4r+Y6V4/mDpBZbl4LZPZ64+aHUdEREQcJbvrf+KPkH7e3CwiIuJyVPwXM25uFkZcP/v/fxuPcebSNZMTiYiIiEP43w3lGoA1AxIWm51GRERcjIr/Yqhz3UAaVvXnSnoWH685YnYcERERcZRq18/+q+u/iIg4mIr/YshisfBKF9u0RJ//HEdSaprJiURERMQhsq/7T4qCtDPmZhEREZei4r+YalerIk3DAriWaWXG6l/NjiMiIiKO4FcLAu4BIwuOLzI7jYiIuBAV/8XU78/+f7k1nuPnr5icSERERBwiTF3/RUTE8VT8F2OtalTgvpoVyMgy+DBKZ/9FRERcQnbX/+Q1cDXJ1CgiIuI6VPwXcyM6287+L9x+nGNnLpucRkRERO5YmQio0BwMKyR8Y3YaERFxESr+i7kmYQF0vKsSWVaDqVGHzY4jIiIijpB99j9eXf9FRMQxVPy7gOyz/9/GnOBw0kWT04iIiMgds3f9Xw9XTpqbRUREXIKKfxfQoKo/3eoFYRjwwU86+y8iIlLs+YZCxdaAAfFfm51GRERcgIp/F/HXzrWxWOC73afYezLF7DgiIiJyp7JH/Y9fYG4OERFxCSr+XcRdQWXp0TAEgPdXHjI5jYiIiNyx0D6ABc5sgssJZqcREZFiTsW/CxkeWQs3C/y0P5kd8efNjiMiIiJ3onQIVG5rW9bZfxERuUMq/l1I9UplePTeqgD8S2f/RUREir9q17v+x2nUfxERuTMq/l3My51q4eFmYf3hM2w5etbsOCIiInInQnuDxQ3ObYNLsWanERGRYkzFv4sJLV+avs1CAXhv5SEMwzA5kYiIiBRYqUCo3MG2rK7/IiJyB1T8u6Ch99fEy8ONrbHn2Pirzv6LiIgUa2Hq+i8iIndOxb8LCvYvxRMtqgEw5ceDOvsvIiJSnFV9FCzucH4HpB42O42IiBRTKv5d1JAONSjl6U5MwgVWHUg2O46IiIgUlE9FCIq0Lcfr7L+IiBSMin8XVbmsD0+3DgfgvR8PYbXq7L+IiEixVe1x272u+xcRkQJS8e/C/tSuOmW8Pdh3KpUVexPNjiMiIiIFFfoIuHnChd2Qst/sNCIiUgyp+HdhAb5ePNcmAoB/rTxEls7+i4iIFE9eARDUxbasgf9ERKQAVPy7uEFtIvAv5cnh5Ess3XnS7DgiIiJSUNmj/sfPBw3mKyIi+aTi38X5l/JkcLvqAHzw0yEys6wmJxIREZECqfowuHlD6gFI2WN2GpGSy5oFSWvg2Je2e2uW2YlE8sTU4n/dunX06NGDkJAQLBYL3377bY7tixYtokuXLlSoUAGLxUJMTMwN+0hLS+PFF1+kQoUKlClTht69e5OUlJSjTXx8PA8++CClS5emcuXK/O1vfyMzMzNHmzVr1nDvvffi7e1NzZo1mTNnjoPfrXmeaR1OeV8vjp29wqLtJ8yOIyIiIgXh6Qch3WzL6vovYo6ERbAkHKI6wqYBtvsl4bb1Ik7O1OL/8uXLNGrUiBkzZtx0e5s2bZg0adJN9/HXv/6VpUuX8vXXX7N27VpOnjzJo48+at+elZXFgw8+SHp6Ops2beKzzz5jzpw5jB071t4mNjaWBx98kI4dOxITE8Pw4cN5/vnnWbFihePerIl8vT34S4caAEyNOkx6ps7+i4iIFEvVrnf9j1PXf5Eil7AI1veBK8dzrr9ywrZePwCIk7MYhnMcOSwWC4sXL6ZXr143bDt27BgRERHs2LGDxo0b29enpKRQqVIl5s2bR58+fQA4cOAAd999N5s3b6Zly5b88MMPPPTQQ5w8eZLAwEAAPv74Y0aOHMnp06fx8vJi5MiRfPfdd+zZ81sXun79+nHhwgWWL1+ep/ypqan4+/uTkpKCn59fwT+IQpKWkUW7yatJvniN8b3q81TLMLMjiYhIIXP2Y1NxZPpnmnEJFlWGrKvQLRrK31v0GURKImuW7Qz/Hwt/OwuUrgo9Y8HNvSiTSQmXn+NSsb7mPzo6moyMDCIjI+3r6tSpQ7Vq1di8eTMAmzdvpkGDBvbCH6Br166kpqayd+9ee5vf7yO7TfY+cnPt2jVSU1Nz3JyZj6c7Q++vCcD0VYdJy9C1SSIiIsWOZxkIedC2HL/A3CwiJcnp9bco/AEMuJJgayfipIp18Z+YmIiXlxflypXLsT4wMJDExER7m98X/tnbs7fdqk1qaipXr17N9bUnTJiAv7+//RYaGuqIt1So+jYLpUq5UiSlXmPuz3FmxxEREZGCCHvcdq+u/yJF5+opx7YTMUGxLv7NNHr0aFJSUuy3hIQEsyPdlreHOy91sp39n7nmCJevZd7mGSIiIuJ0Qh4ED1+4fAzObjM7jUjJUCrYse1ETFCsi/+goCDS09O5cOFCjvVJSUkEBQXZ2/xx9P/sx7dr4+fnR6lSpXJ9bW9vb/z8/HLcioNH761KWIXSnL2czmebj5kdR0RERPLLozRU6WFbjteo/yJFokwNsNzqWn4LlA6FSm2LLJJIfhXr4r9JkyZ4enoSFRVlX3fw4EHi4+Np1aoVAK1atWL37t0kJyfb26xcuRI/Pz/q1q1rb/P7fWS3yd6HK/F0d2N4ZC0APll7lNS0DJMTiYiISL5lj/ofvwAMzeIjUqgyUmFtDzCyx8yy5N6uyQca7E+cmqnF/6VLl4iJiSEmJgawTbkXExNDfHw8AOfOnSMmJoZ9+/YBtsI+JibGfq2+v78/gwYNYsSIEaxevZro6GieffZZWrVqRcuWLQHo0qULdevW5amnnmLnzp2sWLGCf/zjH7z44ot4e3sD8Oc//5mjR4/y2muvceDAAT766CMWLFjAX//61yL+RIpGz0ZVqFm5DClXM/jP+liz44iIiEh+hXQDj7K2AcjO/Gx2GhHXlZUO6x6FCzvBpzI0/QhKV8nZxr00tF0IoY/mvg8RJ2Fq8f/LL79wzz33cM899wAwYsQI7rnnHsaOHQvAkiVLuOeee3jwQduotv369eOee+7h448/tu/j/fff56GHHqJ37960a9eOoKAgFi36bY5Nd3d3li1bhru7O61ateLJJ59k4MCBvPXWW/Y2ERERfPfdd6xcuZJGjRrx3nvv8e9//5uuXbsWxcdQ5NzdLPw1sjYAn26I5fzldJMTiYiISL64+0DVh23Lcer6L1IoDCtseQ6SomzjbHT4HmoPgZ7HoNNqqP+GrZ3F47dZOEScmMUwNEysI5g+728+Wa0GD364gf2nUhnSoQYju9UxO5KIiDhYcTs2FQdO9ZmeWGbrilwqGB5OUHdjEUeLGQX7JtmK+/ZLbT1ufs+wwrdVbSP8t/8OqnQ3J6eUaPk5LhXra/6l4NzcLLzS2Xb2f87GY5y+eM3kRCIiIpIvQV3A099WeJzeYHYaEddycLqt8AdoMfvGwh/A4gahvW3LCQuLLptIAan4L8E63V2ZRqHluJqRxcdrj5gdR0RERPLD3QtCH7Etxy8wN4uIK0lYBNEv2ZYbvg3Vn7l529A+tvvj34JVA2mLc1PxX4JZLL+d/f/85zgSU9JMTiQiIiL5kj3qf8JCsGaam0XEFSRvgI0DAANq/hnq/f3W7Su1sQ0EmH4eklYXSUSRglLxX8K1rVWR5uHlSc+0Mn31YbPjiIiISH4EdQLvCpCWDMlrzU4jUryl7Id1PcF6Dar0hKbTwXKTaf2yublD1euj/Mer6784NxX/JZzFYuGVLraz//O3JZBw7orJiURERCTP3Dx/Kzw06r9IwV05Cau72c7gV2gJ932Z90E0q2V3/V+sHjji1FT8Cy2qV6BNzYpkZBl8uEpn/0VERIqVsMdt98cX6ZpjkYJIT4E1D8CVeChb2zayv0fpvD+/cntbD5xrZyB5XeHlFLlDKv4FgBHXz/5/s/0ER09fMjmNiIiI5FnlDuBdCa6dhcRVZqcRKV6y0mF9b7iwC3wCoeNy8KmYv324eUDV64NvatR/cWIq/gWAe6sF0KlOZbKsBlOjdPZfRESk2HDz+K3bcby6/ovkmWGFLc9BUhR4lIEO30OZiILtK3vU/4RFYM1yXEYRB1LxL3Z/vT7y/5KdJzmUdNHkNCIiIpJn9lH/F9vOZIrI7cWMhmNfgMUD2n4D5e8t+L6C7gevAEhLgjMbHZdRxIFU/Itd/Sr+PFA/CMOA91ceMjuOiIiI5FWlNlAqGDIuQOJKs9OIOL+DH8L+ybblFv+G4C53tj83T6j6sG1Zo/6Lk1LxLzn8tXNtLBb4YU8ie06kmB1HRERE8sLN/bduxxr1X+TW4r+B6Jdty43egepPO2a/9q7/39guKRBxMir+JYfagWXp2SgEgH/p7L+IiJhoxowZhIeH4+PjQ4sWLdi6detN286ePZu2bdsSEBBAQEAAkZGRN7QfN24cderUwdfX195my5Ythf02ik7Y9a7/x7+FrDRTo4g4reT1sOkJwIBaQ6DuaMftOygSPP3g6kk487Pj9iviICr+5QYvd6qFu5uFVQeS2R5/3uw4IiJSAs2fP58RI0bwxhtvsH37dho1akTXrl1JTk7Otf2aNWvo378/q1evZvPmzYSGhtKlSxdOnDhhb1O7dm2mT5/O7t272bBhA+Hh4XTp0oXTp08X1dsqXBVbQemqkHkRTi43O42I80nZB2t7gvWarYt+kw/BYnHc/t29oUpP27K6/osTshiGYZgdwhWkpqbi7+9PSkoKfn5+Zse5Y68t3MmCX47TpmZF5j7fwuw4IiJSAMX52NSiRQuaNWvG9OnTAbBarYSGhjJs2DBGjRp12+dnZWUREBDA9OnTGThwYK5tsj+fn376iU6dOuUpl9N/pttfgQP/grB+cN+XZqcRcR5XTsCPreBKgu2Hsvt/Ao/Sjn+d4/+Ddb2gdCg8HOfYHxdEcpGf45LO/Euuht1fC093Cxt+PcPPR8+aHUdEREqQ9PR0oqOjiYyMtK9zc3MjMjKSzZs352kfV65cISMjg/Lly9/0NWbNmoW/vz+NGjW66X6uXbtGampqjptTq/a47f7EUsi8Ym4WEWeRngJrutsKf7+7oP3Swin8AYK62KYNvJIAZ7cVzmuIFJCKf8lVaPnS9G0WCsC/fjyEOoiIiEhROXPmDFlZWQQGBuZYHxgYSGJiYp72MXLkSEJCQnL8gACwbNkyypQpg4+PD++//z4rV66kYsWKN93PhAkT8Pf3t99CQ0Pz/4aKUoXm4BsOmZfh5PdmpxExX1Y6rH8ULuwCn0Do8AN4Vyi81/MoBVUesi0nqOu/OBcV/3JTQzvWwsvDja3HzrH+8Bmz44iIiOTJxIkT+eqrr1i8eDE+Pj45tnXs2JGYmBg2bdpEt27dePzxx286jgDA6NGjSUlJsd8SEhIKO/6dsVh+O/uvUf+lpDOs8POzkLTKdja+w/dQJqLwXzd71P/4haATaOJEVPzLTQX5+/BUyzAA3vvxoM7+i4hIkahYsSLu7u4kJSXlWJ+UlERQUNAtnztlyhQmTpzIjz/+SMOGDW/Y7uvrS82aNWnZsiX/+c9/8PDw4D//+c9N9+ft7Y2fn1+Om9PLHvX/5HeQccncLCJmihkFcfPA4gFtv4Hy9xbN64Y8AO6l4XIsnN9RNK8pkgcq/uWWhnSoQSlPd3YeTyFq/83PjIiIiDiKl5cXTZo0ISoqyr7OarUSFRVFq1atbvq8yZMnM378eJYvX07Tpk3z9FpWq5Vr167dcWanEnAPlKkBWVfhxDKz04iY4+A02P+ubbnFfyC4S9G9tkdpCOluW9ao/+JEVPzLLVUs480z94UD8N7KQ1itOvsvIiKFb8SIEcyePZvPPvuM/fv3M2TIEC5fvsyzzz4LwMCBAxk9+rf5uSdNmsSYMWP49NNPCQ8PJzExkcTERC5dsp35vnz5Mn//+9/5+eefiYuLIzo6mueee44TJ07w2GOPmfIeC43F8tvZ/3h1/ZcSKH4hRA+3LTf6J1TPfcaPQlUtu+v/1+r6L05Dxb/c1p/aVaestwf7T6Xyw568DbQkIiJyJ/r27cuUKVMYO3YsjRs3JiYmhuXLl9sHAYyPj+fUqVP29jNnziQ9PZ0+ffoQHBxsv02ZMgUAd3d3Dhw4QO/evalduzY9evTg7NmzrF+/nnr16pnyHgtVteyu/z9AhpPPUCDiSMnrYNOTgAG1/gJ1bz81aKEI6Q7uPnDpV7iw25wMIn9gMXQht0M4/by/d+j9lYeYGnWYmpXLsGJ4O9zdNGepiIizc/VjkxmKzWdqGPBdXUg9AK3+CxFPmZ1IpPBd2Asr20DGBajaC9osBDd38/KsewSOfwv1x0DDt8zLIS4tP8clnfmXPBnUNgL/Up78mnyJJTtPmB1HREREbiXHqP8LzM0iUhSunIA1D9gK/4qtofU8cwt/yDnqv4gTUPEveeLn48mf2lcH4IOfDpORZTU5kYiIiNxS9nX/iSsg/by5WUQKU3qKrfC/kgB+d0H7JeBRyuxUUOUhcPOC1P2Qss/sNCIq/iXvnmkdTsUyXsSdvcI30cfNjiMiIiK34l8X/OuDNQMSvjU7jUjhyLoG6x+xXVfvEwQdloN3BbNT2Xj5Q9D1WQZ09l+cgIp/ybPSXh78uX0NAD5c9SvXMrNMTiQiIiK3pFH/xZUZVvj5WUhaDR5loOMPUCbc7FQ5ZY/6n6DiX8yn4l/y5cmWYQT6eXPiwlXmb0swO46IiIjcSvZ1/4k/wbWz5mYRcbSYkRD3JVg8oO0iCGhsdqIbVe1py3dhN6QeNDuNlHAq/iVffDzdGXp/LQCmr/qVtAyd/RcREXFafrVtBZGRBQmLzE4j4jgHpsJ+21SetPwUgjubm+dmvAIgKNK2nPCNuVmkxFPxL/nWt2koVcqVIvniNeb+HGd2HBEREbmVate7/sep67+4iPivYftfbcuNJjj/VJbVNOq/OAcV/5JvXh5uvNzJdvb/ozVHuHwt0+REIiIiclNh17v+J6+Gq0nmZhG5U8nrYNOTgAG1XoS6I81OdHtVHgaLO5zfARePmJ1GSjAV/1Igj95bhfAKpTl3OZ05m46ZHUdERERupkx1KN/MNjiauh1LcXZhL6x9GKzpUPURaDIVLBazU92eT0UI7Ghb1r9BMZGKfykQD3c3/tq5NgCfrD1CytUMkxOJiIjITWWf/Y9fYG4OkYK6chzWdIOMC1DpPmj9Bbi5m50q70LV9V/Mp+JfCuyhhiHUqlyG1LRM/rMh1uw4IiIicjPZo/4nr4MrJ83NIpJf6SmwprvtBwC/OtDuf+BRyuxU+VO1F1jc4Nw2uKwxs8QcKv6lwNzdLIy4fvb/0w2xnLucbnIiERERyZVvNajYCjA037gUL1nXYP0jtqnySgVDx+XgXcHsVPlXKhAqtbMtx6vrv5hDxb/cka71gqgX4sela5l8sk4DmIiIiDgtjfovxY1hhZ+fgaTV4FEWOnwPvmFmpyq47FH/9QOcmETFv9wRt9+d/f9s0zGSL6aZnEhERERyVa0PYIEzm+BygtlpRG5vx2sQ9xVYPKDdIghobHaiO1P1EWz/BjfbLmEQKWIq/uWO3V+nMo1Dy5GWYWXmGp39FxERcUqlq0ClNrbl+K/NzSJyOwc+gAPv2ZZb/h8ERZoaxyFKh9gGKwRIWGRuFimRVPzLHbNYLLza5S4Avvg5npMXrpqcSERERHIVdr3rf7y6/osTi1sA20fYlhtPhIgnzc3jSBr1X0yk4l8c4r6aFWgeUZ70LCvTVh1m85Gz/C/mBJuPnCXLapgdT0RERABCe9tGHD+7FS5pph5xQklrYfNTgAG1h8Ldr5mdyLFCH7Xdn94AV0+Zm0VKHBX/4hAWi4VXrl/7/9XWBPrP/pmXv4qh/+yfaTNpFcv36D83ERER05UKgsrtbcvq+i/O5sIeWPcwWNNtRfK9H4DFYnYqx/INhQotsM28sdjsNFLCqPgXhzl/Jfep/hJT0hgyd7t+ABAREXEGGvVfnNGV47DmAchIsV0X32ouuLmbnapwaNR/MYmKf3GILKvBm0v35botu9P/m0v36RIAERERs4X2Bos7nN8OF381O40IpF+A1Q/YfgDwqwPtloBHKbNTFZ7Q3rb75LWQlmxuFilRVPyLQ2yNPceplJtP82cAp1LS2Bp7ruhCiYiIyI18KkJgJ9uyzv6LGaxZkLQGjn0Jp36Etb0gZQ+UCoaOy8G7vNkJC1eZCCjfBAwrHP/W7DRSgqj4F4dIvnjzwr8g7URERKQQhT1uu49fYG4OKXkSFsGScIjqCJsGwOqucHotuPlAhx/AN8zshEVDo/6LCUwt/tetW0ePHj0ICQnBYrHw7bff5thuGAZjx44lODiYUqVKERkZyeHDh3O0OXfuHE888QR+fn6UK1eOQYMGcenSpRxtdu3aRdu2bfHx8SE0NJTJkyffkOXrr7+mTp06+Pj40KBBA77//nuHv19XVrmsj0PbiYiISCGq+ghYPODCLkg5YHYaKSkSFsH6Prbu/X9kTYNLR4o+k1myu/4nrYJrZ83NIiWGqcX/5cuXadSoETNmzMh1++TJk5k2bRoff/wxW7ZswdfXl65du5KW9tvZ4yeeeIK9e/eycuVKli1bxrp16xg8eLB9e2pqKl26dCEsLIzo6Gjeffddxo0bx6xZs+xtNm3aRP/+/Rk0aBA7duygV69e9OrViz179hTem3cxzSPKE+zvw63GYw3296F5hIt34xIRESkOvMtDcBfbcry6/ksRsGZB9Mv8NhrUH1kgeritXUngVwvKNQIjC44vMTuNlBAWwzCcYgQ2i8XC4sWL6dWrF2A76x8SEsIrr7zCq6++CkBKSgqBgYHMmTOHfv36sX//furWrcu2bdto2rQpAMuXL6d79+4cP36ckJAQZs6cyeuvv05iYiJeXl4AjBo1im+//ZYDB2y/dPft25fLly+zbNkye56WLVvSuHFjPv744zzlT01Nxd/fn5SUFPz8/Bz1sRQry/ecYsjc7UDu/61/NOBeujcMLtpQIiIlmI5NjudSn+nR/8LPT4Pf3fDgXtebUk2cS9IaW1f/2+m0GgI7FHYa57Dnbdg1BkK6Q4fvzE4jxVR+jktOe81/bGwsiYmJREZG2tf5+/vTokULNm/eDMDmzZspV66cvfAHiIyMxM3NjS1bttjbtGvXzl74A3Tt2pWDBw9y/vx5e5vfv052m+zXyc21a9dITU3NcSvputUPZuaT9xLkn7Nrf/ZXiYNJF4s+lIiIiOSu6sPg5gWp+22DrYkUpqt5nPI5r+1cQfZ1/4krbTMeiBQypy3+ExMTAQgMDMyxPjAw0L4tMTGRypUr59ju4eFB+fLlc7TJbR+/f42btcnenpsJEybg7+9vv4WGhub3LbqkbvWD2TDyfr58oSVT+zXmyxda8t5jjQCYtuowm349Y3JCERERAcDLH4K72ZbjNPCfFLJSeez9mdd2rsC/DvjXA2sGnFhqdhopAZy2+Hd2o0ePJiUlxX5LSEgwO5LTcHez0KpGBR5uXIVWNSrwaJOq9G0aimHAS1/FaMR/ERERZxHW13YfPx+c40pQcVWV2kLpqrdoYIHSobZ2JYlG/Zci5LTFf1BQEABJSUk51iclJdm3BQUFkZycnGN7ZmYm586dy9Emt338/jVu1iZ7e268vb3x8/PLcZObG9ezHncFluXMpWsM/yqGLKu+YIiIiJiuSg9w94GLh+F8jNlpxJW5uUOTqTfZeP0i0SYf2NqVJNWuF/+nVkCGLiOWwuW0xX9ERARBQUFERUXZ16WmprJlyxZatWoFQKtWrbhw4QLR0dH2NqtWrcJqtdKiRQt7m3Xr1pGRkWFvs3LlSu666y4CAgLsbX7/Otltsl9H7lwpL3dmPHEvpb3c2XTkLNNX/Wp2JBEREfEsCyEP2pY16r8UtpCHwKPMjetLV4W2CyH00aLPZDb/euB3F1ivwQkN+ieFy9Ti/9KlS8TExBATEwPYBvmLiYkhPj4ei8XC8OHDefvtt1myZAm7d+9m4MCBhISE2GcEuPvuu+nWrRsvvPACW7duZePGjQwdOpR+/foREhICwIABA/Dy8mLQoEHs3buX+fPnM3XqVEaMGGHP8fLLL7N8+XLee+89Dhw4wLhx4/jll18YOnRoUX8kLq1m5TK83as+AB9EHdL1/yIiIs6g2uO2+7gF6vovhevk95B5CXyCoeNP0HqebXT/nrEls/AH2ywb2V3/E9T1XwqXqcX/L7/8wj333MM999wDwIgRI7jnnnsYO3YsAK+99hrDhg1j8ODBNGvWjEuXLrF8+XJ8fH4bTf6LL76gTp06dOrUie7du9OmTRtmzZpl3+7v78+PP/5IbGwsTZo04ZVXXmHs2LEMHjzY3qZ169bMmzePWbNm0ahRIxYuXMi3335L/fr1i+iTKDkevbcqjzetimHAy/NjOH3xmtmRRERESrYqD4J7abgcC+d+MTuNuLJjn9vuI56A4E4Q3t82rV9J6+r/R9ld/09+DxmXzM0iLs1iGPqJ1xFcat7fQnY1PYuHZ2zgUNIl2tSsyGfPNcfdTXMLi4g4mo5Njueyn+mGfrZu/3VegXunmJ1GXFH6eVgUBNZ0eGAnBDQ0O5HzMAxYWgsuHYE2C6DaY2YnkmIkP8clp73mX1xXKS93PnriXkp5urPh1zPMWK3r/0VERExlH/V/ARhWc7OIa4pbYCv8yzVU4f9HFstvZ/816r8UIhX/Yoqalcv+dv3/T4fYfOSsyYlERERKsOButoHYriTAmS1mpxFXZO/y/5S5OZxV9nX/J7+DzCvmZhGXpeJfTNO7SVX6NKmK1YCXvtqh6/9FRETM4lEKqj5sW9ao/+Jol47C6Y2ABcL6m53GOZVvAr5hkHnZNu2fSCFQ8S+meuvhetSqXIbTF68xYkEMVquGoBARETFFteyu/1+r6784VuwXtvugTlC6irlZnNXvR/1X138pJCr+xVSlvTzs1/+vP3yGj9bo+n8RERFTBHcBT3+4ehJObzA7jbgKw/ity3+4uvzfUvZ1/yeWQlaauVnEJan4F9PVCizLWw/XA+BfKw/x81Fd/y8iIlLk3L2hai/bcpy6/ouDnN0KFw/bppMMfdTsNM6tQnMoXRUyL8KplWanERek4l+cwmNNQ+l97/Xr/7/cwZlLuv5fRESkyGWP+p+wEKxZ5mYR1xB7/ax/6CPgWcbcLM7O4gahvW3LCer6L46n4l+cxvhe9ahZuQzJF6/x1/m6/l9ERKTIBUWCV3lIS4bktWankeIuKx3iv7Itq8t/3mRf93/8f7bPT8SBVPyL08i+/t/H0431h88wc+0RsyOJiIiULG6ev3XN1qj/cqdOLYdrZ8EnyDbYn9xepdZQKhgyUiApyuw04mJU/ItTqR1Ylrcerg/Aez8eZIuu/xcRESla1R633Sd8A9YMc7NI8Zbd5T98ALh5mJuluLC4QdXsH+DU9V8cS8W/OJ3HmlTl0Xur2K7//2oHZ3X9v4iISNEJ7AjelWxnbJNWm51Giqv0C7ZR6wEi1OU/X7JH/T/+rX6AE4dS8S9Ox2KxMP7h+tSo5EtS6jX+umCnrv8XEREpKm4evw06plH/paDivwbrNfCvD+UamZ2meKnU1vYDXPo5SFpjdhpxISr+xSn5envw0RNN8PF0Y92h03y8Ttf/i4iIFBn7qP+LNOiYFEx2l/+IJ8FiMTdLcePm/tvYGxr1XxxIxb84rbuCyvJWz+zr/w+xNfacyYlERERKiEptbYO0ZVyARM03Lvl06RicXg9YIPwJs9MUT9ld/xMWgzXT3CziMlT8i1N7rGlVHrmnCllWg5e+3MG5yzr7ICLi7NLS0syOIHfKzf234iN+gblZpPg59oXtPrAjlK5qbpbiqnJ78K4A105f/yFF5M6p+BenZrFYeLtXfapX8iUxNY0RC2J0/b+IiBOyWq2MHz+eKlWqUKZMGY4ePQrAmDFj+M9//lOgfc6YMYPw8HB8fHxo0aIFW7duvWnb2bNn07ZtWwICAggICCAyMjJH+4yMDEaOHEmDBg3w9fUlJCSEgQMHcvLkyQJlKxGqXe/6f/xbyNIPOpJHhgHHsrv8a6C/AnPzhKq9bMsa9V8cRMW/OD3b9f/34u3hxpqDp/lk3VGzI4mIyB+8/fbbzJkzh8mTJ+Pl5WVfX79+ff7973/ne3/z589nxIgRvPHGG2zfvp1GjRrRtWtXkpOTc22/Zs0a+vfvz+rVq9m8eTOhoaF06dKFEydOAHDlyhW2b9/OmDFj2L59O4sWLeLgwYP07NmzYG+4JKjUGkpVgYxUOLXC7DRSXJz7BVIPgnup3waOlIIJze76vwisWeZmEZdgMQxDp1EdIDU1FX9/f1JSUvDz8zM7jkv6ams8oxbtxt3NwleDW9IsvLzZkUREnFpRHptq1qzJJ598QqdOnShbtiw7d+6kevXqHDhwgFatWnH+/Pl87a9FixY0a9aM6dOnA7aeBaGhoQwbNoxRo0bd9vlZWVkEBAQwffp0Bg4cmGubbdu20bx5c+Li4qhWrVqecpW44330CDj4PoT1h/vmmZ1GioNfXoJDH+rvjCNkpcOiQNvYG5HroHJbsxOJE8rPcUln/qXY6NsslF6NQ3T9v4iIEzpx4gQ1a9a8Yb3VaiUjI3/zVKenpxMdHU1kZKR9nZubG5GRkWzevDlP+7hy5QoZGRmUL3/zH4pTUlKwWCyUK1fupm2uXbtGampqjluJEva47f7EEsi8am4WcX7WDIj7yrasLv93zt0Lqj5sW1bXf3EAFf9SbFgsFt5+pAHVK/pyKiWNV3T9v4iI06hbty7r1984KNXChQu555578rWvM2fOkJWVRWBgYI71gYGBJCYm5mkfI0eOJCQkJMcPCL+XlpbGyJEj6d+//y3PlEyYMAF/f3/7LTQ0NO9vxBVUaAG+YZB5GU5+b3YacXanVtgGqPMJhKDOZqdxDfZR/78Bw2puFin2VPxLsVLG24MZ16//X33wNLPW6/p/ERFnMHbsWIYOHcqkSZOwWq0sWrSIF154gXfeeYexY8cWaZaJEyfy1VdfsXjxYnx8fG7YnpGRweOPP45hGMycOfOW+xo9ejQpKSn2W0JCQmHFdk4WC1S7fvY/fr65WcT5xV4f6C+sP7h5mJvFVQR1Bo+ycPUEnNlidhop5lT8S7Fzd7Af43rWA+DdFQeJjjtnciIREXn44YdZunQpP/30E76+vowdO5b9+/ezdOlSOnfO3xnAihUr4u7uTlJSUo71SUlJBAUF3fK5U6ZMYeLEifz44480bNjwhu3ZhX9cXBwrV6687fWR3t7e+Pn55biVOGHXR/0/sQwyLpmbRZxXegoc/59tWV3+HcfdG6peH5g0QV3/5c6o+JdiqV+zUHo2sl3/P3TeDs7r+n8REdNkZmby1ltvERERwcqVK0lOTubKlSts2LCBLl265Ht/Xl5eNGnShKioKPs6q9VKVFQUrVq1uunzJk+ezPjx41m+fDlNmza9YXt24X/48GF++uknKlSokO9sJVLAvVCmOmRdtf0AIJKbhIVgvQZ+d0NA/i71kdvIHvU/fqFtKkWRAlLxL8WSxWLhn482ICL7+v+vd+r6fxERk3h4eDB58mQyMzMdts8RI0Ywe/ZsPvvsM/bv38+QIUO4fPkyzz77LAADBw5k9OjR9vaTJk1izJgxfPrpp4SHh5OYmEhiYiKXLtnOVGdkZNCnTx9++eUXvvjiC7Kysuxt0tP1A/ItWSxQ7frZ//gF5mYR5xU713Yf8ZTt74w4TnBX8PCFK/G2qRRFCkjFvxRbZbw9mDHgXrw83Fh1IJl/b9D1/yIiZunUqRNr16512P769u3LlClTGDt2LI0bNyYmJobly5fbBwGMj4/n1KlT9vYzZ84kPT2dPn36EBwcbL9NmTIFsM1GsGTJEo4fP07jxo1ztNm0aZPDcrus7K7/J7+HjBI244Hc3uV4SF5jWw5/wtQoLsmjFIQ8ZFvWqP9yByyGob4jjlDi5v11Il9sieP1xXtwd7Ow4E+taBIWYHYkERGnUJTHpo8//pg333yTJ554giZNmuDr65tje8+ePQv19YtKiT3eGwZ8dzekHoRWn0PEk2YnEmeydwLs/DtU7gCRq81O45riF8KGx2yX4PT4Vb0rxC4/xyUNwynF3oDm1fj56DmW7jzJsHnb+f7ltpQr7WV2LBGREuUvf/kLAP/6179u2GaxWMjKyirqSOJI2aP+7xkPcfNV/MtvDOO3Uf410F/hCXkA3EvBpaNwPgbKa1wFyT91+5diz2Kx8M9H6hNeoTQnU9J4ZcFO1KFFRKRoWa3Wm95U+LuI7Ov+E1dA+gVTo4gTOb8dUveDu89vc9KL43n4Qkh327JG/ZcCUvEvLqGsjyfTr1//H3UgmX+vjzU7koiIiGspVw/864E1A45/a3YacRbZZ/2rPAyeJehSGDPYR/3/WqP+S4Go+BeXUb+KP2MfqgvApOUH2B5/3uREIiIly9q1a+nRowc1a9akZs2a9OzZk/Xr15sdSxwp++x/3Hxzc4hzsGZC3Je2ZXX5L3xVHgQ3b7h4GFL2mJ1GiiEV/+JSnmhRjQcbBpNpNRg2bwcXrmj6JhGRojB37lwiIyMpXbo0L730Ei+99BKlSpWiU6dOzJs3z+x44ihhj9vuE3+Ca2fNzSLmO/UjpCWDdyUI7mJ2GtfnWRZCutmWNeq/FICKf3EpFouFiY82IKxCaU5cuMqrX+/S9f8iIkXgnXfeYfLkycyfP99e/M+fP5+JEycyfvx4s+OJo/jdBeUagZEJeyfCsS8haQ1YNa5DiXTsepf/sH7g5mlulpIiu+u/rvuXAlDxLy6nrI8nMwbci5e7Gz/tT+I/G3T9v4hIYTt69Cg9evS4YX3Pnj2JjdX/wy7Fv57t/sAU2DQAojrCknBIWGRqLCliGam/jf2gLv9Fp0oP2w8tKftsN5F8UPEvLql+FX/GPHQ3ABN/OMAOXf8vIlKoQkNDiYqKumH9Tz/9RGhoqAmJpFAkLPrtGu/fu3IC1vfRDwAlScIiyEqz9QYp39TsNCWHlz8EdbYtx39jbhYpdjzMDiBSWJ5sGcbPR8/x3e5TDJ23g+9faot/aXVJExEpDK+88govvfQSMTExtG7dGoCNGzcyZ84cpk6danI6cQhrFkS/DOR2OZ0BWCB6uG3Udzf3os0mRS97lP/wp8BiMTdLSRPaB05+b+v632CM2WmkGFHxLy7LYrEwoXcD9pxMIe7sFV5duJNZTzXBogOUiIjDDRkyhKCgIN577z0WLFgAwN133838+fN5+OGHTU4nDnF6PVw5fosGBlxJsLUL7FBUqcQMV45D0mrbcvgT5mYpiao+DFs94MIuSD0MfrXMTiTFhLr9i0vz+931/yv3JfHpxmNmRxIRcVmPPPIIGzZs4OzZs5w9e5YNGzao8HclV085tp0UX8e+AAyo3A7KhJudpuTxLg+B99uWE9T1X/JOxb+4vPpV/PmH/fr//cQkXDA3kIiIC9q2bRtbtmy5Yf2WLVv45ZdfTEgkDlcq2LHtpHgyjJxd/sUc1TTqv+Sfin8pEZ5qGUb3BkFkZBkMnbedlCsZZkcSEXEpL774IgkJCTesP3HiBC+++KIJicThKrWF0lWBW1w+Z3G33cR1nY+BlL3g5v1bASpFr2ovsLjBuWi4pBlVJG9U/EuJYLFYmNi7IdXKl+b4+av8beFODCO3AYtERKQg9u3bx7333nvD+nvuuYd9+zQdlUtwc4cm2YM33uQHACPLNvXf7vG2AQLF9WSf9a/aE7zKmRqlRPOpBJU72JbV9V/ySMW/lBi/v/7/x31JzNl0zOxIIiIuw9vbm6SkpBvWnzp1Cg8PjS/sMkIfhbYLoXSVnOtLh0Krz22DvxlZsHssrLofLt/YG0SKMWsmxM2zLavLv/mye17Eq+u/5I2KfylRGlT15+/d6wDwz+/3s1PX/4uIOESXLl0YPXo0KSkp9nUXLlzg73//O507dzYxmThc6KPQ8xh0Wg2t59nue8ZCxJPQei60+i94lIHkdfBDI9t88OIaEn+CtCTwrgDBXc1OI1UfASxwdgtcjjc7jRQDKv6lxHm6dTjd6tmu/39x3nZSrur6fxGROzVlyhQSEhIICwujY8eOdOzYkYiICBITE3nvvffMjieO5uZum84vvL/t3u131/lHPAUP7IDyzSD9PKzvDVsGQ+YVs9KKo2R3+a/WD9y9zM0iUCoIKre1LetHNskDFf9S4lgsFib1aUho+VIcP3+VkQt36fp/EZE7VKVKFXbt2sXkyZOpW7cuTZo0YerUqezevZvQ0FCz40lRK1sTOm+AuiMBCxyZDcubwvmdZieTgsq4CMcX25Yj1OXfaYRq1H/JO4uhqschUlNT8ff3JyUlBT8/P7PjSB7sOn6B3jM3kZFlMK5HXZ65L8LsSCIiDqVjk+PpMy2AxCjY/BRcPQVuXnDPu1B7GFhuMWuAOJ+j/4Wfn4ayteChg/rzcxZXTsC3VW3LvY7fOB6HuLz8HJec/sz/xYsXGT58OGFhYZQqVYrWrVuzbds2+3bDMBg7dizBwcGUKlWKyMhIDh8+nGMf586d44knnsDPz49y5coxaNAgLl26lKPNrl27aNu2LT4+PoSGhjJ58uQieX9inoZVy/H37ncD8M/vD7Dr+AVzA4mIFEOHDh1i69atOdZFRUXRsWNHmjdvzj//+U+TkonTCOoED+yEkIfAmg7RL8PaHpB22uxkkh/Hrnf5D39Khb8zKV0FKra2LScsNjeLOD2nL/6ff/55Vq5cyeeff87u3bvp0qULkZGRnDhxAoDJkyczbdo0Pv74Y7Zs2YKvry9du3YlLS3Nvo8nnniCvXv3snLlSpYtW8a6desYPHiwfXtqaipdunQhLCyM6Oho3n33XcaNG8esWbOK/P1K0XqmdThd6wWSnmXV9f8iIgUwcuRIli1bZn8cGxtLjx498PLyolWrVkyYMIEPPvjAvIDiHHwqQfsl0ORD2/zwJ7+D7xvCqZVmJ5O8uHLC1oMDbAM7inOppq7/kjdO3e3/6tWrlC1blv/97388+OCD9vVNmjThgQceYPz48YSEhPDKK6/w6quvApCSkkJgYCBz5syhX79+7N+/n7p167Jt2zaaNm0KwPLly+nevTvHjx8nJCSEmTNn8vrrr5OYmIiXl23wklGjRvHtt99y4MCBXLNdu3aNa9eu2R+npqYSGhqqboDFUMrVDB6ctp7j56/yQP0gPux/D9uOnSf5YhqVy/rQPKI87m76hVtEip+i6KIeGhrKggULaNWqFQBvv/02CxcuJCYmBoD//Oc/fPjhh/bHxZ26/TvA+V2wqT+k7LM9vvtv0PBtDSDnzPa9CzGvQaU20Hm92Wnkjy7Hw//CAAs8cgpKBZqdSIqQy3T7z8zMJCsrCx8fnxzrS5UqxYYNG4iNjSUxMZHIyEj7Nn9/f1q0aMHmzZsB2Lx5M+XKlbMX/gCRkZG4ubmxZcsWe5t27drZC3+Arl27cvDgQc6fP59rtgkTJuDv72+/aTCj4su/lCfTB9yLp7uFH/Ykcu/4lfSf/TMvfxVD/9k/02bSKpbvOWV2TBERp3TmzBmqVq1qf7x69Wp69Ohhf9yhQweOHTtmQjJxWgENoes2qPln2+P978LK+yD18K2fJ+bJ7vKvgf6ck281qNAcMH4blFEkF05d/JctW5ZWrVoxfvx4Tp48SVZWFnPnzmXz5s2cOnWKxMREAAIDc/66FRgYaN+WmJhI5cqVc2z38PCgfPnyOdrkto/sbbnJnss4+5aQkHDnb1hM0zi0HL0a2wZISU3LzLEtMSWNIXO36wcAEZFclC9fnlOnbP8/Wq1WfvnlF1q2bGnfnp6erhlV5EYepaH5TGi7CLwC4NwvsPweOPoZ6O+Lczm/Ey7stg3WWO0xs9PIzWSP+h+vrv9yc05d/AN8/vnnGIZBlSpV8Pb2Ztq0afTv3x83N3Oje3t74+fnl+MmxVeW1WD9r2dy3Zb9FeTNpfvIsuoLiYjI73Xo0IHx48eTkJDABx98gNVqpUOHDvbt+/btIzw83LR84uRCH4Huu6Bye8i8DD8/A5uegPQUs5NJttjrZ/2rPGT7oUacU7XetvvkNRpMU27Ko6BP/OWXX1iwYAHx8fGkp6fn2LZo0aI7DpatRo0arF27lsuXL5OamkpwcDB9+/alevXqBAUFAZCUlERwcLD9OUlJSTRu3BiAoKAgkpOTc+wzMzOTc+fO2Z8fFBREUlJSjjbZj7PbiGvbGnuOxJS0m243gFMpaWyNPUerGhWKLpiIiJN755136Ny5M2FhYbi7uzNt2jR8fX3t2z///HPuv/9+ExOK0ytdFe6Pgn0TYfcbEPclnPkZ7psHFVve/vlSeKxZEDfPthyuLv9OrUx1CLgXzm+H4/+Dms+bnUicUIFOn3/11Ve0bt2a/fv3s3jxYjIyMti7dy+rVq3C39/f0RkB8PX1JTg4mPPnz7NixQoefvhhIiIiCAoKIioqyt4uNTWVLVu22AceatWqFRcuXCA6OtreZtWqVVitVlq0aGFvs27dOjIyfhvpfeXKldx1110EBOgXzpIg+eLNC/+CtBMRKSnCw8PZv38/O3bsIC4ujiFDhuTY/uabb/KPf/zDpHRSbLi5Q/3XIXI9+IbD5VhY2Qb2vGMrQMUcSVFw9RR4lYeQ7mankdvRqP9yGwUq/v/5z3/y/vvvs3TpUry8vJg6dSoHDhzg8ccfp1q1ag4NuGLFCpYvX05sbCwrV66kY8eO1KlTh2effRaLxcLw4cN5++23WbJkCbt372bgwIGEhITQq1cvAO6++266devGCy+8wNatW9m4cSNDhw6lX79+hISEADBgwAC8vLwYNGgQe/fuZf78+UydOpURI0Y49L2I86pc1uf2jfLRTkSkJPHw8KBRo0b24+rvNWrUiAoV1GNK8qhSK3ggBsL6g5EFu/4BqyLhynGzk5VMsXNt92F9NRtDcRB6vet/YhRcO2duFnFKBSr+jxw5Yp96z8vLi8uXL2OxWPjrX//KrFmzHBowJSWFF198kTp16jBw4EDatGnDihUr8PT0BOC1115j2LBhDB48mGbNmnHp0iWWL1+eY4aAL774gjp16tCpUye6d+9OmzZtcuT09/fnxx9/JDY2liZNmvDKK68wduxYBg8e7ND3Is6reUR5gv19uNWEfhbgzMVrt2ghIiIid8zLH1p/AS3ngIev7Rrm7xtBwrcmBythMi/D8euX8qrLf/HgVxvKNQQjE04sMTuNOCGLUYAheKtWrcoPP/xAgwYNaNiwIaNHj6Z///5s3ryZbt26kZJS8gZp0by/xd/yPacYMnc78Nsgf7l59N4qjOtZDz8fz6IJJiJSQDo2OZ4+0yKWehg29Ydz1y/frPlnuPdf4FHK3FwlQexc2PwUlKkJPQ6B5VanSMRp7B4Pu8dCyIPQYZnZaaQI5Oe4VKAz/+3atWPlypUAPPbYY7z88su88MIL9O/fn06dOhVklyKm61Y/mJlP3kuQf86u/cH+PkwfcA8v3V8TNwss2n6CBz5Yz9ZYdacSEREpVH61oPMmuPtvtse/fgwrmtmmnpPClT3Kf8STKvyLk+zr/hN/1KwZcoMCnfk/d+4caWlphISEYLVamTx5Mps2baJWrVr84x//KJGD5OlMgOvIshpsjT1H8sU0Kpf1oXlEedzdbAe96LhzDJ8fQ8K5q7hZYEiHGrzcqTZeHk4/a6aIlEA6NjmePlMTnVoJmwdCWiK4ecM9U6D2iypMC8PVU/BtVTCs0ONXKFvD7ESSH9/Vg5R90Opz24834tLyc1wqUPEvN9KXgZLjYloGby3dx9fRtsGHGlTx5/2+jalZuYzJyUREcirqY9P69ev55JNPOHLkCAsXLqRKlSp8/vnnRERE0KZNm0J//aKg473J0pLh5+fg5He2x1V6QItPwaeiublczf73YMerULE1dNlodhrJr11vwJ63oOrD0O5bs9NIISuUbv+pqak5lm91E3FlZX08efexRnz0xL2UK+3J7hMpPPThej7/OQ79liYiJdU333xD165dKVWqFDt27ODaNdsAqSkpKfzzn/80OZ24DJ/K0H4pNJkKbl5wYin80NA2urk4jr3Lvwb6K5ayu/6fXA4ZF83NIk4lz8V/QEAAycnJAJQrV46AgIAbbtnrRUqC7g2CWf5yO9rWqkhahpUx3+7huTnbOK0ZAUSkBHr77bf5+OOPmT17tn1GHoD77ruP7du3m5hMXI7FAne9BF23gl8dWxf1VZ0hZjRYM8xOV/xd2A0XdoKbJ1R7zOw0UhD+9aFsbbBegxPfmZ1GnIhHXhuuWrWK8uXLA7B69epCCyRSnAT5+/DZs82Zs+kYE5cfYPXB03T7YB0Tezekc91As+OJiBSZgwcP0q5duxvW+/v7c+HChaIPJK4voBF0i4btf4VfZ8G+iZC0ClrP0zXqdyL7rH/Ig+BdwdwsUjAWi+3s/95/QsJCCO9ndiJxEnku/tu3b29fjoiIIDQ0FMsfBlgxDIOEhATHpRMpBtzcLDzXJoL7albk5a92cCDxIi/89xf6N6/GmIfuprRXnv+ZiYgUW0FBQfz666+Eh4fnWL9hwwaqV69uTihxfR6lofknENwVtjwPZ7fCD/dAs4800FlBWLPg2Be2ZXX5L95Crxf/J7+HzMvg4Wt2InECBRqiPCIigtOnT9+w/ty5c0RERNxxKJHi6K6gsvxv6H0MblcdiwW+3BrPg9M2EJNwwexoIiKF7oUXXuDll19my5YtWCwWTp48yRdffMGrr77KkCFDzI4nri70UXhgJ1RuB5kXbfPTb3oSMjQWVb4kr4GrJ8ErwHbmX4qvgMZQpjpkXYWTP5idRpxEgYp/wzBuOOsPcOnSJXx8fHJ5hkjJ4O3hzt+7380Xg1oQ7O9D7JnL9J65iWlRh8nMspodT0Sk0IwaNYoBAwbQqVMnLl26RLt27Xj++ef505/+xLBhw8yOJyWBbyjcvwoavAUWd9sZ7B/ugTNbzE5WfGR3+a/2OLh7m5tF7ozFYjv7DxC/0Nws4jTyNdXfiBEjAJg6dSovvPACpUuXtm/Lyspiy5YtuLu7s3FjyZsSRFP/yB+lXMng9W93s2zXKQDurVaO9/s2JqyCul2JSNEw49iUnp7Or7/+yqVLl6hbty5lyrjWNKg63hcTpzfBpgFwOQ4sHtBwPNR9DSwFOu9VMmRegUWBkHkJOm+ASveZnUju1NltsKK5rcv/o6fBo5TZiaQQFMpUfwA7duxgx44dGIbB7t277Y937NjBgQMHaNSoEXPmzLmT7CIuw7+0Jx/2v4cP+jamrLcH2+Mv0H3qehb8kqApAUXE5Tz33HNcvHgRLy8v6tatS/PmzSlTpgyXL1/mueeeMzuelDSVWsMDMVCtLxiZsHO0bUaAKydt261ZkLQGjn1pu7dmmRjWSRz/1lb4l6kOFVubnUYcoXxTKF3Nds3/qRVmpxEnkK8z/9meffZZpk2bRtmyZQsjU7GkMwFyK8fPX2HEgp1sjT0HQLd6QUx4tAEBvl4mJxMRV1aUxyZ3d3dOnTpF5cqVc6w/c+YMQUFBZGZmFurrFxUd74sZw4CjcyB6mK0A8q4ANV6AY3PhyvHf2pWuCk2m2sYOKKlWPwCnlkP9sdDwTbPTiKNsfwUO/AvCn4DWc81OI4Wg0M78A2RkZPD5558TFxdX4IAiJU3VgNJ8+UJLRj1QB093C8v3JtL1g3WsO3TjwJkiIsVJamoqKSkpGIbBxYsXSU1Ntd/Onz/P999/f8MPAiJFxmKBGs9Ct+0QcC9cO2ubEvD3hT/AlROwvg8kLDInp9muJkLij7blcM2S4FKyr/s/vgSyrpmbRUyX7+Lf09OTatWqkZWl7lEi+eHuZuHP7Wuw+C/3UbNyGZIvXmPgp1sZt2QvaRn69yQixVO5cuUoX748FouF2rVrExAQYL9VrFiR5557jhdffNHsmFLS+dWGyPXgcbMxKK53hI0eXjIvAYj7EgwrVGgJfrXMTiOOVLEFlKpimwUjcaXZacRkBZqA/PXXX+fvf/87n3/+OeXLl3d0JhGXVr+KP8uGtWHC9/v5bHMcczYdY+OvZ/igX2PqhfibHU9EJF9Wr16NYRjcf//9fPPNNzm+F3h5eREWFkZISIiJCUWuO7fVdk37TRlwJQFOr4fADkWVyjlkj/IfobP+LsfiBqG94dA026j/VR4yO5GYqEDF//Tp0/n1118JCQkhLCwMX9+co5dv377dIeFEXJWPpztvPlyfDnUq89rCXRxOvkSvGRt5tctdPN+2Ou5uN06lKSLijNq3bw9AbGws1apVy3Uq4Pj4eKpVq1bU0URyunrKse1cxYW9cH6HbVaEan3NTiOFoVofW/F//H+QlQ7uGnOqpCpQ8d+rVy8HxxApmTreVZnlL7dl9KLd/LgviQk/HGD1wWTee7wxVcppOhYRKT6qV6+e64B/Z8+eJSIiQpcLivlKBTu2nas4dv2sf0h38KlobhYpHBVbg08QpCVC0ioI6WZ2IjFJgYr/N954w9E5REqsCmW8+eSpJiz4JYE3l+7j56Pn6PbBOt7uVZ+HG1cxO56ISJ7cbPKgS5cu4ePjU8RpRHJRqa1tVP8rJ7Bf4/9Hpara2pUUhhWOfWFbjnjK3CxSeNzcbTNZHP4IEhaq+C/BClT8A1y4cIGFCxdy5MgR/va3v1G+fHm2b99OYGAgVaqoYBHJD4vFQt9m1WgRUYHh82OISbjAy1/FsOpAMm89XB//Up5mRxQRydWIESMA2/9jY8eOpXTp0vZtWVlZbNmyhcaNG5uUTuR33Nxt0/mt7wNYyPUHgNIl7Dts8lrbzAee/roW3NVV63O9+F8MzWaCm75blkT5Hu0fYNeuXdSuXZtJkyYxZcoULly4AMCiRYsYPXq0I/OJlCjhFX1Z+OdWDI+shbubhf/FnOSBD9ax+chZs6OJiORqx44d7NixA8Mw2L17t/3xjh07OHDgAI0aNWLOnDkF2veMGTMIDw/Hx8eHFi1asHXr1pu2nT17Nm3btrXPNBAZGXlD+0WLFtGlSxcqVKiAxWIhJiamQLmkGAt9FNouvLHI965ku+b97BaIfglu0pPF5WQP9FftcXBXDx2XVqmt7e95+jnbjz5SIhWo+B8xYgTPPPMMhw8fztGVr3v37qxbt85h4URKIg93N4ZH1ubrP7cirEJpTqakMeDfPzPhh/1cy9Q1syLiXFavXs3q1at5+umn+eGHH+yPV69ezYoVK/jkk0+oVSv/U4fNnz+fESNG8MYbb7B9+3YaNWpE165dSU5OzrX9mjVr6N+/P6tXr2bz5s2EhobSpUsXTpw4YW9z+fJl2rRpw6RJkwr8fsUFhD4KPY9Bp9XQep7t/pFTcN88wGI7O7r3HbNTFr7MK7bR30Fd/ksCNw8IfcS2nP3nLiWOxbjZRXq34O/vz/bt26lRowZly5Zl586dVK9enbi4OO666y7S0tIKI6tTS01Nxd/fn5SUFPz8/MyOIy7i8rVMxi/bx1fbEgCoG+zH1H6NqRVY1uRkIlIcmHFs+vXXXzly5Ajt2rWjVKlSGIaR6wwAt9OiRQuaNWvG9OnTAbBarYSGhjJs2DBGjRp12+dnZWUREBDA9OnTGThwYI5tx44dIyIigh07duT7kgQd713cwekQPcy23Hw21Hze3DyF6dhXsKk/+IZDzyO2KeHEtZ1aCau72HoAPHLKdimMFHv5OS4V6F+5t7c3qampN6w/dOgQlSpVKsguRSQXvt4eTOzdkE+eakJ5Xy/2nUrloQ83MGdj7E0H1xIRMcO5c+fo1KkTtWvXpnv37pw6ZZsubdCgQbzyyiv52ld6ejrR0dFERkba17m5uREZGcnmzZvztI8rV66QkZFB+fLl8/Xaf3Tt2jVSU1Nz3MSF3TUU6v3dtrztT3B8qbl5ClP2KP/hT6rwLykCO4BXebh2Gk6vNzuNmKBA/9J79uzJW2+9RUZGBmAb5Cc+Pp6RI0fSu3dvhwYUEehaL4jlw9vS4a5KXMu0Mm7pPp7+v20kp5a8XjYi4pyGDx+Op6cn8fHxOQb969u3L8uXL8/Xvs6cOUNWVhaBgYE51gcGBpKYmJinfYwcOZKQkJAcPyAUxIQJE/D397ffQkND72h/Ugw0fBuqP2sbCX9jXzidtx+cipWrSXBqhW054klzs0jRcfOEqr1sywemwrEvIWkNWHVZaUlRoOL/vffe49KlS1SuXJmrV6/Svn17atasSdmyZXnnnRJwjZSICSqX9eH/nmnG+Ifr4e3hxrpDp+n6wTqW78nbF2ERkcL0448/MmnSJKpWrZpjfa1atYiLiyvSLBMnTuSrr75i8eLFdzzN4OjRo0lJSbHfEhISHJRSnJbFAs1nQciDkHUV1j4EKfvNTuVYcV+BkQXlm4HfXWankaJUKth2f+Jb2DQAojrCknBIWGRmKikiBZrqz9/fn5UrV7JhwwZ27drFpUuXuPfee+/413URuTWLxcJTrcJpVcM2JeCeE6n8eW40jzetytge9Sjj7UGW1WBr7DmSL6ZRuawPzSPK4+6W/+ttRUTy4/LlyznO+Gc7d+4c3t7e+dpXxYoVcXd3JykpKcf6pKQkgoKCbvncKVOmMHHiRH766ScaNmyYr9fNjbe3d77ziwtw84A28yGqk20GgNVdoctm15kKMLvLvwb6K1kSFsHef964/soJ2xSYbRfaBsQUl1Wg4j9bmzZtaNOmjaOyiEge1axclkVD7uP9nw7x8dojLPjlOD8fPUffZqHM/TmOUym/XQ4Q7O/DGz3q0q1+sImJRcTVtW3blv/+97+MHz8esP1YabVamTx5Mh07dszXvry8vGjSpAlRUVH06tULsA34FxUVxdChQ2/6vMmTJ/POO++wYsUKmjZtWuD3IgKAhy+0XwY/tYHUg7C6G3ReD17lzE52Z1L2w7lo29SGYf3MTiNFxZoF0S8DuY0ZZQAWiB4OVR7WQIAurMDFf1RUFFFRUSQnJ2O1WnNs+/TTT+84mIjcmpeHGyO71aFD7UqMWLCT+HNXeHfFwRvaJaakMWTudmY+ea9+ABCRQjN58mQ6derEL7/8Qnp6Oq+99hp79+7l3LlzbNy4Md/7GzFiBE8//TRNmzalefPmfPDBB1y+fJlnn30WgIEDB1KlShUmTJgAwKRJkxg7dizz5s0jPDzcPjZAmTJlKFOmDGDrhRAfH8/JkycBOHjQ9n9mUFDQbXsUSAnlUxE6LIeVrSFlD6x7GDquAPc7u5zEVMfm2u6Du4GPBuouMU6vhyvHb9HAgCsJtnaBHYoqlRSxAl3z/+abb9KlSxeioqI4c+YM58+fz3ETkaLTonoFlr3UBh/P3P85Z/++++bSfWRZNUOAiBSO+vXrc+jQIdq0acPDDz/M5cuXefTRR9mxYwc1atTI9/769u3LlClTGDt2LI0bNyYmJobly5fbBwGMj4+3zygAMHPmTNLT0+nTpw/BwcH225QpU+xtlixZwj333MODDz4IQL9+/bjnnnv4+OOP7/Ddi0srE277AcDTD5LXwaYniu8AaYYVYq8X/+ryX7JcPXX7NvlpJ8WSxSjAfGHBwcFMnjyZp57SfxrZNO+vmGnzkbP0n/3zbdt9+UJLWtWoUASJRMQZ6NjkePpMS7CkNbZr/63pUOsv0HS6bXDA4iRpLUR1sP2Q8UgieJQyO5EUlaQ1tsH9bqfTap35L2byc1wqULf/9PR0WrduXaBwIuJ4yRfzNuVfXtuJiOTXunXrbrm9Xbt2RZREpJAEdoDWc2FDXzj8kW3U9Pr/MDtV/mQP9FftMRX+JU2ltlC6qm1wv1yv+7fYtldqW9TJpAgVqPh//vnnmTdvHmPGjHF0HhEpgMpl83btYV7biYjkV4cOHW5YZ/ndWdGsrGLaTVrk96o9Bk2SIHoY7BoDPkFQ83mzU+VN5lWI/9q2HK7euyWOmzs0mWob1R8Luf4A0OQDDfbn4gpU/KelpTFr1iz7NDqenp45tv/rX/9ySDgRyZvmEeUJ9vchMSXtZr/lEuRvm/ZPRKQw/HHMn4yMDHbs2MGYMWN45513TEolUgjuGgppp2xTpm37E/gEQtUeZqe6vRNLISMVSleDyjq7WyKFPmqbzi/65T8M/ucG932haf5KgAIV/7t27aJx48YA7Nmzx5F5RKQA3N0svNGjLkPmbs/1t1wDeKNHXdzditm1iSJSbPj7+9+wrnPnznh5eTFixAiio6NNSCVSSBq+DVdPwtE5sLEv3B8FlVqZnerWYq93+Q9/AiwFGvNbXEHoo7bp/LJH/98+Aq6dBmuG2cmkCBSo+F+9erWjc4jIHepWP5iZT97Lm0v3cSol57X9pTzduDcswKRkIlKSBQYG2qfUE3EZFgs0nwVpyXDye1j7EHTeAP53m50sd2mn4dRy27JG+Rc3998G9bsSDztfh0Mz9HejBMhX8f/oo7fvCmKxWPjmm28KHEhECq5b/WA61w1ia+w5ki+mUcHXi4k/HGDPyVTGLdnLR080MTuiiLioXbt25XhsGAanTp1i4sSJ9t6CIi7FzRPaLICoTnB2C6zuBl02QekqZie7UdxXYGRC+SbO+wOFmKP6INg9zvZ3+Fy07e+IuKx8Ff+5dekTEefi7mbJMZ3fZF9vekzfwPe7E1mxN5Gu9YJMTCcirqpx48ZYLBb+OINwy5Yt+fTTT01KJVLIPHyh/TL4qQ2kHrT9ANB5PXiVMztZTvYu/zqzK39QKhBCH4O4eXB4JrT4t9mJpBBZjD8epaVANO+vOLPJyw/w0ZojBPp5s3JEe/x8PG//JBEp9ory2BQXF5fjsZubG5UqVcLHx7VmGdHxXnJ16RisbA1XT0HldtBxBbg7yd/91IOwrA5Y3OGRk+BT2exE4mxOb4SVbcC9FDxyArx0qWhxkp/jkkb7ECkBXupUi+oVfUlKvcaE7w+YHUdEXFBYWFiOW2hoqMsV/iI3VSYcOvwAnn6QvA42PQFWJ5neMnau7T64qwp/yV3F1lCuIWRdtQ1iKS5Lxb9ICeDj6c6ERxsA8OXWeH4+etbkRCLiitauXUuPHj2oWbMmNWvWpGfPnqxfv97sWCJFI6ARtPsfuHlBwiKIfgnM7mBrWOHY9eJfXf7lZiwWqP2ibfnQR7a/N+KSVPyLlBAtqldgQItqAIxetJu0DCc5IyEiLmHu3LlERkZSunRpXnrpJV566SVKlSpFp06dmDdvntnxRIpGYAdoPRewwOGPYO8/zc1zeiNcPgYeZaHqw+ZmEecWNsDWc+XSr5D4k9lppJCo+BcpQUY9UIdAP29iz1xmWtRhs+OIiAt55513mDx5MvPnz7cX//Pnz2fixImMHz/e7HgiRafaY9Bkmm151z/gyH/My5I90F+1PuBRyrwc4vw8y0DEM7blwx+ZGkUKj4p/kRLEz8eT8Q/XB+CTdUfZezLF5EQi4iqOHj1Kjx49bljfs2dPYmNjTUgkYqK7hkK9v9uWtw6G40uLPkNWGsQvsC2HP1n0ry/FT60htvsTS+FyvLlZpFCo+BcpYbrUC6J7gyCyrAajvtlNZpau6xKROxcaGkpUVNQN63/66SdCQ0NNSCRisoZvQ/VnbNdPb+wLpzcX7eufWAYZKVC6qu1yBJHb8a8Dgffb/s7++onZaaQQOHXxn5WVxZgxY4iIiKBUqVLUqFGD8ePH55hD2DAMxo4dS3BwMKVKlSIyMpLDh3N2Zz537hxPPPEEfn5+lCtXjkGDBnHp0qUcbXbt2kXbtm3x8fEhNDSUyZMnF8l7FDHDuJ718C/lye4TKXy6UWfkROTOvfLKK7z00ksMGTKEzz//nM8//5w///nPDB8+nFdffdXseCJFz2KB5rMgpLttFPW1D0HK/qJ7/ewu/+FPgMWpv/KLM8ke+O/X2ZB1zdws4nBO/T/BpEmTmDlzJtOnT2f//v1MmjSJyZMn8+GHH9rbTJ48mWnTpvHxxx+zZcsWfH196dq1K2lpafY2TzzxBHv37mXlypUsW7aMdevWMXjwYPv21NRUunTpQlhYGNHR0bz77ruMGzeOWbNmFen7FSkqlcv68PqDdwPwr5WHiDt72eREIlLcDRkyhK+++ordu3czfPhwhg8fzp49e5g/fz5/+tOfzI4nYg43T2izACq0gPRzsLobXDlR+K+bdgZOfm9b1ij/kh9VekKpKnDtNCR8Y3YacTCLYZg9B8nNPfTQQwQGBvKf//w2UErv3r0pVaoUc+fOxTAMQkJCeOWVV+xnFVJSUggMDGTOnDn069eP/fv3U7duXbZt20bTpk0BWL58Od27d+f48eOEhIQwc+ZMXn/9dRITE/Hy8gJg1KhRfPvttxw4kLc50VNTU/H39yclJQU/Pz8HfxIijmcYBk/+Zwsbfz1L6xoV+OL5FlgsFrNjiYgD6djkePpMpUDSzsDK++DiIfCvD53Xg1e5wnu9QzPgl6EQcA88sL3wXkdc0+7xsHssVGwNXTaanUZuIz/HJac+89+6dWuioqI4dOgQADt37mTDhg088MADAMTGxpKYmEhkZKT9Of7+/rRo0YLNm23XVW3evJly5crZC3+AyMhI3Nzc2LJli71Nu3bt7IU/QNeuXTl48CDnz5/PNdu1a9dITU3NcRMpTiwWC/98pAE+nm5sOnKWr385bnYkEXEB6enpHD9+nPj4+Bw3kRLNpyJ0XAGlgiFlD6x72DYgX2GJnWu7j9BZfymAms+DxQPObILzMWanEQdy6uJ/1KhR9OvXjzp16uDp6ck999zD8OHDeeKJJwBITEwEIDAwMMfzAgMD7dsSExOpXLlyju0eHh6UL18+R5vc9vH71/ijCRMm4O/vb79pMCMpjsIq+DKic20A3v5uH8kXC/GLiIi4tMOHD9O2bVtKlSpFWFgYERERREREEB4eTkREhNnxRMxXJhw6/GCbSz15HWx6EqxZjn+d1MNw9mfbdf5h/R2/f3F9pYIhtLdt+fBMc7OIQzl18b9gwQK++OIL5s2bx/bt2/nss8+YMmUKn332mdnRGD16NCkpKfZbQkKC2ZFECuS5+yJoUMWf1LRMxi3Za3YcESmmnnnmGdzc3Fi2bBnR0dFs376d7du3s2PHDrZvV7djEQACGkG7/4Gbl+166uiXwNFX4B67ftY/qAuUCnLsvqXkqP0X233sXEi/YGoUcRwPswPcyt/+9jf72X+ABg0aEBcXx4QJE3j66acJCrL9h5aUlERwcLD9eUlJSTRu3BiAoKAgkpOTc+w3MzOTc+fO2Z8fFBREUlJSjjbZj7Pb/JG3tzfe3t53/iZFTObh7sbE3g3oOX0j3+9OZMXeRLrW05cFEcmfmJgYoqOjqVOnjtlRRJxbYAdoPRc29IXDH0GpEKj/umP2bRi/Ff/q8i93olJb8K8HKXsh9r9w10tmJxIHcOoz/1euXMHNLWdEd3d3rFbbvOQREREEBQXlmFc4NTWVLVu20KpVKwBatWrFhQsXiI6OtrdZtWoVVquVFi1a2NusW7eOjIwMe5uVK1dy1113ERAQUGjvT8RZ1Avx50/tqgMw9n97SE3LuM0zRERyqlu3LmfOnDE7hkjxUO0xaDLVtrzrH3DkP7dun1dnNsGlo+BRBqr2csw+pWSyWH6b9u/wR47voSKmcOriv0ePHrzzzjt89913HDt2jMWLF/Ovf/2LRx55BLANWDZ8+HDefvttlixZwu7duxk4cCAhISH06tULgLvvvptu3brxwgsvsHXrVjZu3MjQoUPp168fISEhAAwYMAAvLy8GDRrE3r17mT9/PlOnTmXEiBFmvXWRIvdSp1pEVPQlKfUaE3/I2ywXIlKy/X7Q20mTJvHaa6+xZs0azp49q0FxRW7nrmFQd7RteetgOL70zvcZ+7ntPvRR8Ch95/uTki38SfAoC6kHIWmV2WnEAZx6qr+LFy8yZswYFi9eTHJyMiEhIfTv35+xY8faR+Y3DIM33niDWbNmceHCBdq0acNHH31E7dq17fs5d+4cQ4cOZenSpbi5udG7d2+mTZtGmTJl7G127drFiy++yLZt26hYsSLDhg1j5MiRec6qqX/EFWw5epa+s34G4KvBLWlZvYLJiUTkThT2scnNzS3HFKGGYdwwZWj2uqysQhjYzAQ63otDGQZseQ6OzgH3UnB/FFRqVbB9ZV2DxcGQfh7uXwlBkbd/jsjtbBsKh2dA1Ueg3SKz00gu8nNccurivzjRlwFxFX9fvJt5W+KJqOjLDy+3xcfT3exIIlJAhX1sWrt2bZ7btm/f3uGvbwYd78XhrBmwrhec/B68ykPnDeB/d/73k7AI1ve2jSHwcDy46fgtDnBhL3xf3zZ7xMNxULqq2YnkD/JzXHLqAf9EpOiNeqAOUfuTiD1zmWlRh3mtmwbvEpHcuUpBL2IqN09oswCiOsHZLbC6G3TZBKWr5G8/2V3+w59Q4S+OU64eVG4PyWvh11nQ8C2zE8kdUPEvIjn4+Xgy/uH6DP48mk/WHeXBhsHUC/E3O5aIOKFdu3bluW3Dhg0LMYlIMefhC+2Xwcr74OIh2w8AndeDV7m8Pf/aWTj5nW1Zo/yLo9V+8bfiv94/wN3L7ERSQCr+ReQGXeoF0b1BEN/vTmTUN7tZ/JfWeLg79figImKCxo0bY7FYuN0VhK50zb9IofGpCB1XwMrWkLIH1j1se+zuc/vnxn9tu3ygXCMo16Dws0rJUrUXlAqGq6fg+GII62t2IikgFf8ikqtxPeux4fAZdp9I4dONsQxuV8PsSCLiZGJjY82OIOJayoRDhx/gp3aQvA42PQn3zb99N/7sLv866y+Fwc0TagyGPW/CoRkq/osxFf8ikqvKZX34x4N1ee2bXfxr5SG61gsirIKv2bFExImEhYWZHUHE9QQ0gnb/g9VdIeEbiH4Jmk63zbuem4tH4Mwm24Bs4QOKNquUHDVfgL1vw+n1cGG3epgUUyr+ReSmHmtalW9jTrDpyFlGL9rNF8+3uGEaLxEpuZYsWcIDDzyAp6cnS5YsuWXbnj17FlEqERcQ2AFaz4UNfeHwR7YR/Ou/nnvbY3OvPyfS1jVbpDCUrmLr/p/wDRyeCc0+MjuRFICm+nMQTf0jriru7GW6frCOtAwrk/s05PGmoWZHEpE8Kuxjk5ubG4mJiVSuXBk3t5uPC+JK1/zreC9F6uCHtjP/AC3+DTUG5dxuGLC0Flw6Aq3+q27/UriSVkPU/eBRBh45AZ76P9AZ5Oe4pBG8ROSWwir4MqJzbQDeXraP5ItpJicSEWdhtVqpXLmyfflmN1cp/EWK3F3DoO5o2/LWwXB8ac7tZ362Ff7upaHqI0WfT0qWyh3A727IvPTbOBNSrKj4F5Hbeu6+CBpU8Sc1LZM3l+wzO46IiEjJ0egdqP4MGFbY2BdObwZrFiStgd1jbW2q9gLPMiaGlBLBYoFaf7EtH5ph63kixYqKfxG5LQ93Nyb2boC7m4Xvdp/ix72JZkcSESexefNmli1blmPdf//7XyIiIqhcuTKDBw/m2rVrJqUTcQEWCzSfBSHdIesqrO4C31aBqI6Q+JOtTeKPkLDI3JxSMkQ8BR6+kLofkteanUbyScW/iORJvRB//tSuOgBj/reH1LQMkxOJiDN466232Lt3r/3x7t27GTRoEJGRkYwaNYqlS5cyYcIEExOKuAA3T2izAMrUsnW5TkvKuf3aWVjfRz8ASOHz8ofw62NLHJphbhbJNxX/IpJnL3WqRURFX5JSrzHxhwNmxxERJxATE0OnTp3sj7/66itatGjB7NmzGTFiBNOmTWPBggUmJhRxEW4+kHX5Jhuvd7+OHm67JECkMNUaYrs/vhiunDQ3i+SLin8RyTMfT3cmPGqb13Xelnh+PnrW5EQiYrbz588TGBhof7x27VoeeOAB++NmzZqRkJBgRjQR13J6PVy9VaFlwJUEWzuRwhTQECq1ASMLjsw2O43kg4p/EcmXltUr0L95NQBGL9pNWobOMIiUZIGBgcTGxgKQnp7O9u3badmypX37xYsX8fT0NCueiOu4esqx7UTuRK0Xbfe/fgJWXQpaXKj4F5F8G929DpXLehN75jLTog6bHUdETNS9e3dGjRrF+vXrGT16NKVLl6Zt27b27bt27aJGjRomJhRxEaWCHdtO5E6EPgo+gbYfm47/z+w0kkcq/kUk3/x8PBnfqz4An6w7yr6TqSYnEhGzjB8/Hg8PD9q3b8/s2bOZPXs2Xl5e9u2ffvopXbp0MTGhiIuo1BZKVwUsN2lggdKhtnYihc3dC2q8YFvWwH/Fhop/ESmQrvWC6N4giCyrwchvdpGZZTU7koiYoGLFiqxbt47z589z/vx5HnnkkRzbv/76a9544w2T0om4EDd3aDL1+oM//gBw/XGTD2ztRIpCzcFgcYPkNZCyz+w0kgcq/kWkwMb1rIefjwe7T6TwfxuPmR1HREzk7++Pu/uNRUf58uVz9AQQkTsQ+ii0XQilq+RcX7qqbX3oo+bkkpLJNxSq9LQtH/rI3CySJyr+RaTAKpf14R8P1gXgvZUHiTt7symIRERExCFCH4Wex6DTamg9z3bfM1aFv5ij9vWB/2L/CxkXzc0it6XiX0TuyGNNq9K6RgXSMqyMXrQbwzDMjiQiIuLa3NwhsAOE97fdq6u/mCXwfihbGzIvwrEvzE4jt6HiX0TuiMViYcKjDfDxdGPTkbN8HX3c7EgiIiIiUhQsblDrL7blQzNAJ4Gcmop/EbljYRV8GdG5NgBvL9tH8sU0kxOJiIiISJGo/jS4l4KUPXB6g9lp5BZU/IuIQzx3XwQNqviTmpbJm0s04quIiIhIieBVDsKfsC1r2j+npuJfRBzCw92Nib0b4O5m4bvdp/hxb6LZkUSkmJsxYwbh4eH4+PjQokULtm7detO2s2fPpm3btgQEBBAQEEBkZOQN7Q3DYOzYsQQHB1OqVCkiIyM5fPhwYb8NERHXlz3wX8I3cFXfAZ2Vin8RcZh6If4MblcdgDH/20NqWobJiUSkuJo/fz4jRozgjTfeYPv27TRq1IiuXbuSnJyca/s1a9bQv39/Vq9ezebNmwkNDaVLly6cOHHC3mby5MlMmzaNjz/+mC1btuDr60vXrl1JS9OlSiIidySgMVRsBUYmHPm32WnkJiyGhuZ2iNTUVPz9/UlJScHPz8/sOCKmScvI4oGp64k9c5kBLarxz0camB1JpMQqzsemFi1a0KxZM6ZPnw6A1WolNDSUYcOGMWrUqNs+Pysri4CAAKZPn87AgQMxDIOQkBBeeeUVXn31VQBSUlIIDAxkzpw59OvXL0+5ivNnKiJSqGK/gM1PQumqtukn3TzMTlQi5Oe4pDP/IuJQPp7uTHjUVvDP2xLPlqNnTU4kIsVNeno60dHRREZG2te5ubkRGRnJ5s2b87SPK1eukJGRQfny5QGIjY0lMTExxz79/f1p0aLFLfd57do1UlNTc9xERCQX1fqAdyW4chxOLDU7jeRCxb+IOFzL6hXo37waAKMX7SYtI8vkRCJSnJw5c4asrCwCAwNzrA8MDCQxMW/Xko4cOZKQkBB7sZ/9vPzuc8KECfj7+9tvoaGh+XkrIiIlh7s31HjetqyB/5ySin8RKRSju9ehcllvjp65zIerNKCWiBSdiRMn8tVXX7F48WJ8fHzuaF+jR48mJSXFfktISHBQShERF1TrT4AFkqIg5YDZaeQPVPyLSKHw8/FkfK/6AHyy9ij7TqqrrIjkTcWKFXF3dycpKSnH+qSkJIKCgm753ClTpjBx4kR+/PFHGjZsaF+f/bz87tPb2xs/P78cNxERuQnfMKjykG358Exzs8gNVPyLSKHpWi+I7g2CyLQajPxmF5lZVrMjiUgx4OXlRZMmTYiKirKvs1qtREVF0apVq5s+b/LkyYwfP57ly5fTtGnTHNsiIiIICgrKsc/U1FS2bNlyy32KiEg+1bo+7V/sHMi8bGoUyUnFv4gUqnE96+Hn48HuEyn838ZjZscRkWJixIgRzJ49m88++4z9+/czZMgQLl++zLPPPgvAwIEDGT16tL39pEmTGDNmDJ9++inh4eEkJiaSmJjIpUuXALBYLAwfPpy3336bJUuWsHv3bgYOHEhISAi9evUy4y2KiLim4M5QpiZkpMKxeWankd9R8S8ihapyWR/+8WBdAN5beZC4s/oFWERur2/fvkyZMoWxY8fSuHFjYmJiWL58uX3Avvj4eE6dOmVvP3PmTNLT0+nTpw/BwcH225QpU+xtXnvtNYYNG8bgwYNp1qwZly5dYvny5Xc8LoCIiPyOxQ1qDbEtH5oBmlneaVgMQ38ajqB5f0VuzjAMnvj3FjYdOct9NSswd1ALLBaL2bFEXJ6OTY6nz1REJA+unYNvq0BWGnTeCJVam53IZeXnuKQz/yJS6CwWCxMebYCPpxsbfz3L19HHzY4kIiIiIoXFuzyE9bcta9o/p6HiX0SKRFgFX0Z0rg3AO9/tJ/limsmJRERERKTQ1L4+8F/C15CWbG4WAVT8i0gReu6+CBpU8SflagZvLtlndhwRERERKSzlm0CF5mDNgCP/MTuNoOJfRIqQh7sbE3s3wN3Nwne7T/Hj3kSzI4mIiIhIYcme9u/wx2DNMjeLqPgXkaJVL8Sfwe2qAzDmf3tITcswOZGIiIiIFIqwx8GrPFyJh5PfmZ2mxFPxLyJF7uVOtYio6Mv/t3fvcVHWef/HX8PISQQUlYOKgOezKR5Cs9JIc83qzs1yrUyztsJKLX/p7pqaFdp2MMu02l0zWzvslpa6aeapNBPE8HxARfHAQUUOHkCZuX5/kJMkKCB4McP7+XjMI+aa71zznuve28/1uea6vld6Tj7Tvt1tdhwRERERqQxWL2j6aOHfmvjPdGr+ReS683K3EntvewAWbExh44GTJicSERERkUrR/AnAAmnfQU6S2WmqNTX/ImKKG5vUZUi3xgBM+GobeRd0HZiIiIiIy6nVBBr0L/w7aba5Wao5Nf8iYprx/VsR6OvJgRNneGeVjgSLiIiIuKSLE/8dmAsFZ83NUo2p+RcR0/h7uzP1nnYAvL/2ADuP5ZicSEREREQqXEg/8ImAC1lw6DOz01RbVb75Dw8Px2KxXPaIiSk8epSXl0dMTAx169alVq1aDBo0iPT09CLrSElJYcCAAdSsWZPAwEDGjRtHQUFBkTFr1qyhc+fOeHp60qxZMz766KPr9RVFqrV+bYP5Q/tgCuwG47/aSv4FGxv2n+TrxKNs2H8Sm90wO6KIiIiIXAs3KzR/svDvvbPA0P6dGWqYHeBq4uPjsdl+uxZ4+/bt3H777dx3330AjBkzhqVLl/Kf//wHf39/Ro0axb333sv69esBsNlsDBgwgODgYH766SdSU1N5+OGHcXd359VXXwUgOTmZAQMG8MQTT/Dvf/+blStXMnLkSEJCQujXr9/1/9Ii1czku9qyLukEW49k0+WV78nN++3gXIi/F5MGtuGOdiEmJhQRERGRa9JkOGydCKc2w8k4qNfd7ETVjsUwnOuwy+jRo1myZAlJSUnk5ORQv359FixYwB//+EcAdu/eTevWrdmwYQM33ngj3377LXfeeSfHjh0jKCgIgDlz5vDCCy9w/PhxPDw8eOGFF1i6dCnbt293fM4DDzxAVlYWy5YtK1WunJwc/P39yc7Oxs/Pr+K/uIiLm7hoO/N/PnTZcsuv/539YGcdABApI9WmiqdtKiJyDTYMg+SPIfwh6PGx2WlcQlnqUpU/7f9S58+f55NPPmHEiBFYLBYSEhK4cOEC0dHRjjGtWrWicePGbNiwAYANGzbQvn17R+MP0K9fP3JyctixY4djzKXruDjm4jqKk5+fT05OTpGHiJSPzW6wYld6sa9dPDo5ZfFOXQIgIiIi4swuTvyX8jnknTA3SzXkVM3/okWLyMrK4pFHHgEgLS0NDw8PateuXWRcUFAQaWlpjjGXNv4XX7/42pXG5OTkcO7cuWKzxMbG4u/v73iEhoZe69cTqbbikjNJy84r8XUDSM3OIy458/qFEhEREZGKVbcrBESC/Twc+KfZaaodp2r+//nPf9K/f38aNGhgdhQmTJhAdna243H48GGzI4k4rYzckhv/8owTERERkSrIYvnt1/+kOWC3XXm8VCinaf4PHTrE999/z8iRIx3LgoODOX/+PFlZWUXGpqenExwc7Bjz+9n/Lz6/2hg/Pz+8vb2LzePp6Ymfn1+Rh4iUT6CvV4WOExEREZEqKux+8KgDZw5CaunmV5OK4TTN/9y5cwkMDGTAgAGOZZGRkbi7u7Ny5UrHsj179pCSkkJUVBQAUVFRbNu2jYyMDMeYFStW4OfnR5s2bRxjLl3HxTEX1yEilatbRAAh/l6Oyf2KU7+WJ90iAq5bJhERERGpBDVqFs78D4W3/ZPrximaf7vdzty5cxk2bBg1avx2d0J/f38effRRxo4dy+rVq0lISGD48OFERUVx4403AtC3b1/atGnDQw89xJYtW1i+fDl/+9vfiImJwdPTE4AnnniCAwcO8P/+3/9j9+7dvPfee3zxxReMGTPGlO8rUt1Y3SxMGlh4MK6kAwA5eRf4voRJAUVERETEiTR/svC/qcsgd7+5WaoRp2j+v//+e1JSUhgxYsRlr7311lvceeedDBo0iJtvvpng4GC++uorx+tWq5UlS5ZgtVqJioriwQcf5OGHH+all15yjImIiGDp0qWsWLGCjh078sYbb/CPf/yDfv36XZfvJyJwR7sQZj/YmWD/oqf2B/l50iKoFvkFdv48P4G3v0/Crln/RURERJyXbzMI6QcYsG+O2WmqDYthGNqLrgC6769IxbDZDeKSM8nIzSPQ14tuEQHYDYNXlu7io58OAnBH22DeGNwRH88aV16ZSDWn2lTxtE1FRCrIkcXww13gEQD3HIEaxc+1JldWlrrkFL/8i0j1YXWzENW0Lnff0JCopnWxullwt7ox+a62vDaoAx5WN5btSGPQ7J9IOXnW7LgiIiIiUh4N/gA1G8P5TEj5wuw01YKafxFxGoO7hvLp4zdS39eT3Wm53DVrHT/tO2F2LBEREREpKzcrNH+i8G9N/HddqPkXEacSGVaHxaNuomMjf7LOXuChf8Xx0fpkdAWTiIiIiJNp+ii4eUBmPJyMNzuNy1PzLyJOJ9jfi8//HMW9nRpisxtMXryTF77cSn6BzexoIiIiIlJaXoHQ+L7Cv5PeMzdLNaDmX0Sckpe7lTcGd+RvA1rjZoEvNh3hgQ9+JiMnz+xoIiIiIlJazWMK/3voM8g/aW4WF6fmX0SclsViYWSvJswb0Q1/b3d+Scli4LvrSDycZXY0ERERESmNejdCnRvAlgcH5pqdxqWp+RcRp9ereX2+julJ88BapOfkM/j9DXyZcMTsWCIiIiJyNRbLb7/+J80Gw25uHhem5l9EXEJ4PR8WxvTk9jZBnC+w89x/tvDykp0U2FRARERERKq08CHg7g+nD0Dqd2ancVlq/kXEZdTyrMH7D0byTJ9mAPxjXTLDP4on6+x5k5OJiIiISIlq+ECTRwr/1m3/Ko2afxFxKW5uFsb2bcl7Qzvj7W7lx6QT3D1rPXvTc82OJiIiIiIlaf5k4X+PLYXTB02N4qrU/IuIS/pD+xC+eqoHjep4c+jkWf5v1nq+25FmdiwRERERKY5fSwiOBgzYN8fsNC5Jzb+IuKzWIX58M+omoprU5cx5G4/PT+Dt75Ow2w2zo4mIiIjI712c+G//Pwtn/5cKpeZfRFxagI8HHz/ajUd6hAPw1vd7eerfmzmTX2BuMBEREREpquGdULMR5J+AlP+YncblqPkXEZfnbnVj8l1tmT6oPe5WC8t2pDFo9k+knDxrdjQRERERucitBjT7c+Hfe98zN4sLUvMvItXG/V0b89njN1Kvlie703K5a9Y6ftp3wuxYIiIiInJR05Hg5g4nf4bMzWancSlq/kWkWokMC2Dx0z3p0MifrLMXeOhfcXy0PhnD0DwAIiIiIqbzDobQQYV/J+nX/4qk5l9Eqp0Qf2+++HMU93ZqiM1uMHnxTl74civ5BTazo4mIiIjIxYn/Di6A86fMzeJC1PyLSLXk5W7ljcEd+duA1rhZ4ItNR3jgg5/JyNHMsiIiIiKmqt8TarcH2zk48JHZaVyGmn8RqbYsFgsjezVh3ohu+Hu780tKFgPfXUfi4Syzo4mIiIhUXxbLb7/+J80Gw25uHheh5l9Eqr1ezevzdUxPmgfWIj0nn8Hvb+DLhCNmxxIRERGpvsKHQg1fyE2CtJVmp3EJav5FRIDwej4sjOnJ7W2COF9g57n/bOHlJTspsOlIs4iIiMh1514Lmgwr/DtplrlZXISafxGRX9XyrMH7D0byTJ9mAPxjXTLDP4on6+x5k5OJiIiIVEPNnyr879HFcCbF3CwuQM2/iMgl3NwsjO3bkveGdsbb3cqPSSe4e9Z69qbnmh1NREREpHrxbw1BvQuv+d/3vtlpnJ6afxGRYvyhfQhfPdWDRnW8OXTyLP83az3f7UgzO5aIiIhI9XJx4r/9/wBbvrlZnJyafxGRErQO8eObUTcR1aQuZ87beHx+AjNXJmG3G2ZHExEREakeGt0F3g0gLwMOf2l2Gqem5l9E5AoCfDz4+NFuPNIjHIA3V+wlZsFmzuQXmBtMREREpDpwc4dmjxf+nfSeuVmcnJp/EZGrcLe6Mfmutkwf1B53q4Vvt6cxaPZPHM48a3Y0EREREdfX9DGw1IDj6+HUFrPTOC01/yIipXR/18Z89viN1Kvlye60XO56dx0/7TthdiwRlzVr1izCw8Px8vKie/fuxMXFlTh2x44dDBo0iPDwcCwWCzNmzLhsTG5uLqNHjyYsLAxvb2969OhBfHx8JX4DERGpEDUbQOj/Ff6tX//LTc2/iEgZRIYFsPjpnnRo5M+psxd46F9xfLQ+GcMonAfAZjfYsP8kXyceZcP+k9g0P4BIuXz++eeMHTuWSZMmsXnzZjp27Ei/fv3IyMgodvzZs2dp0qQJ06ZNIzg4uNgxI0eOZMWKFcyfP59t27bRt29foqOjOXr0aGV+FRERqQgXJ/5L/gTOZ5ubxUlZjIt7rHJNcnJy8Pf3Jzs7Gz8/P7PjiEgly7tgY8JX21j4S2HTMLhLI3o1r8er/9tNanaeY1yIvxeTBrbhjnYhZkWVasyZa1P37t3p2rUr7777LgB2u53Q0FCefvppxo8ff8X3hoeHM3r0aEaPHu1Ydu7cOXx9ffn6668ZMGCAY3lkZCT9+/fn5ZdfLlUuZ96mIiJOzTDgf+0geye0GAX1eoB3CNTvBW5Ws9OZpix1Sb/8i4iUg5e7lTcHd+RvA1rjZoEvNh3h6U8TizT+AGnZeTz5yWaWbU81KamI8zl//jwJCQlER0c7lrm5uREdHc2GDRvKtc6CggJsNhteXl5Flnt7e7Nu3boS35efn09OTk6Rh4iImMBiKWz4Afa+Cz/9CVb2hm/C4fBXpkZzFmr+RUTKyWKxMLJXE/41rCuWEsZcPLVqyuKdugRApJROnDiBzWYjKCioyPKgoCDS0tLKtU5fX1+ioqKYOnUqx44dw2az8cknn7BhwwZSU0s+OBcbG4u/v7/jERoaWq7PFxGRa3T4K9j/z8uXnz0KP/5RBwBKQc2/iMg18nS3cqW23gBSs/OIS868XpFEpBjz58/HMAwaNmyIp6cnM2fOZMiQIbi5lbw7NGHCBLKzsx2Pw4cPX8fEIiICgN0GCc9CsXtcvy5LGF04Tkqk5l9E5Bpl5OZdfRBw8OTpSk4i4hrq1auH1WolPT29yPL09PQSJ/MrjaZNm7J27VpOnz7N4cOHiYuL48KFCzRp0qTE93h6euLn51fkISIi19nxH+HskSsMMODs4cJxUiI1/yIi1yjQ1+vqg4C/fLWd++b8xOw1+9mbnovmWxUpnoeHB5GRkaxcudKxzG63s3LlSqKioq55/T4+PoSEhHDq1CmWL1/O3Xfffc3rFBGRSnSulHMnlXZcNVXD7AAiIs6uW0QAIf5epGXnlXj6fw03CwV2g/iDp4g/eIrpy3bTsLY3t7UOpE+rQG5sUhcv9+o7U63I740dO5Zhw4bRpUsXunXrxowZMzhz5gzDhw8H4OGHH6Zhw4bExsYChZME7ty50/H30aNHSUxMpFatWjRr1gyA5cuXYxgGLVu2ZN++fYwbN45WrVo51ikiIlWUdynvmlTacdWUmn8RkWtkdbMwaWAbnvxkMxaKXo12cSLAd//UiXYN/Vm95zirdqWzfv9Jjmad4+MNh/h4wyG83a30bFbPcTAgyK90ZxOIuKr777+f48eP8+KLL5KWlsYNN9zAsmXLHJMApqSkFLlW/9ixY3Tq1Mnx/PXXX+f111/nlltuYc2aNQBkZ2czYcIEjhw5QkBAAIMGDeKVV17B3d39un43EREpo/q9oGajwsn9SvqpxeoDda/97DBXZjF03mmF0H1/RWTZ9lSmLN5Z5HZ/If5eTBrYhjvaFT0SffZ8AT/tO8mqPRms2pVBWk7ReQPaNvDjtlaB9GkdRIeG/ri5lXQ/AZGSqTZVPG1TERGTHP6qcFZ/oMQDAI3+D3ouAGv1+RGlLHVJzX8F0c6AiADY7AZxyZlk5OYR6OtFt4gArFdp3A3DYGdqDqt2ZbBqTwaJh7O49F/merU8uLVlILe1CuSm5vXw9dKvlFI6qk0VT9tURMREh78qnPX/0sn/aoZC2BDYMwPs5yGoD9y8CNx9zUp5Xan5N4F2BkSkopw4nc+aPcdZvTuDH/YeJze/wPGau9VCt4gA+rQK4rZWgYTX8zExqVR1qk0VT9tURMRkdlvhrP7nUguv8a/fC9yskLYKfrgbCk5DQFfo/S141jU7baVT828C7QyISGU4X2Bn08FMVu3OYNXuDA6cOFPk9Sb1fOjTKpA+rQPpGh6Au1U3cZHfqDZVPG1TEZEq7GQ8rOkP+SfBrzX0+a5wrgAXpubfBNoZEJHr4cDx06zancHqPRlsPJBJgf23f8J9PWtwc4v69GkVyK0t61O3lqeJSaUqUG2qeNqmIiJVXPYuWHU7nDsKPmHQewX4NTc7VaVR828C7QyIyPWWk3eBdUknCg8G7M7g5JnzjtcsFrghtDa3tQqkd6tA2oT4YbFo0sDqRrWp4mmbiog4gTOHCg8A5CaBVyD0Xg51bjA7VaVQ828C7QyIiJnsdoMtR7JYvTuDlbsz2HEsp8jrIf5e9G5VOGlgj6b18PawFrue8kxYKFWXalPF0zYVEXES59JhzR1wKhHc/eCWJRDYy+xUFa4sdanKXxx69OhRHnzwQerWrYu3tzft27dn06ZNjtcNw+DFF18kJCQEb29voqOjSUpKKrKOzMxMhg4dip+fH7Vr1+bRRx/l9OnTRcZs3bqVXr164eXlRWhoKK+99tp1+X4iIhXBzc1Cp8Z1GNu3JUuf6cXPE24j9t72RLcOwsvdjdTsPBZsTOHReZu44aXvGD43jvk/H+Jo1jnHOpZtT+Wm6asY8uHPPPtZIkM+/Jmbpq9i2fZUE7+ZiIiISDl4B8FtawonBLyQA6v7wtH/mZ3KVFX6l/9Tp07RqVMnevfuzZNPPkn9+vVJSkqiadOmNG3aFIDp06cTGxvLvHnziIiIYOLEiWzbto2dO3fi5VV4f8f+/fuTmprK+++/z4ULFxg+fDhdu3ZlwYIFQOHRkhYtWhAdHc2ECRPYtm0bI0aMYMaMGTz++OOlyqpfAkSkqsq7YGPDgZOFZwXsyijS8AO0CvYlrG5Nlu9Iv+y9F3/zn/1gZ+5oF3Id0kpFUm2qeNqmIiJOpuAsrBsMx5aCpQZEfQzhQ8xOVWFc5rT/8ePHs379en788cdiXzcMgwYNGvDcc8/x/PPPA5CdnU1QUBAfffQRDzzwALt27aJNmzbEx8fTpUsXAJYtW8Yf/vAHjhw5QoMGDZg9ezZ//etfSUtLw8PDw/HZixYtYvfu3aXKqp0BEXEGhmGwN/30r3cPSCfh0CnsV6kCFiDY34t1L/TRJQBORrWp4mmbiog4IfsF2PAIHFoAWKDLu9DiKbNTVQiXOe3/m2++oUuXLtx3330EBgbSqVMnPvzwQ8frycnJpKWlER0d7Vjm7+9P9+7d2bBhAwAbNmygdu3ajsYfIDo6Gjc3NzZu3OgYc/PNNzsaf4B+/fqxZ88eTp06VWy2/Px8cnJyijxERKo6i8VCy2Bfnry1Kf95ogcJf7udUb2bXvE9BpCanUdccub1CSkiIiJSkdzcocd8aDEKMGBTDGx/Garu7+CVoko3/wcOHGD27Nk0b96c5cuX8+STT/LMM88wb948ANLS0gAICgoq8r6goCDHa2lpaQQGBhZ5vUaNGgQEBBQZU9w6Lv2M34uNjcXf39/xCA0NvcZvKyJy/dXx8aB5kG+pxq7Zm4HtaqcJiIiIiFRFFjeInAntXix8vnUibB4Lht3cXNdRlW7+7XY7nTt35tVXX6VTp048/vjjPPbYY8yZM8fsaEyYMIHs7GzH4/Dhw2ZHEhEpl0Bfr1KNe3/tAW5+bTXvrEwiPSevklOJiIiIVDCLBTpMgc4zCp/vmQE/jwB7gZmprpsq3fyHhITQpk2bIstat25NSkoKAMHBwQCkpxedpCo9Pd3xWnBwMBkZGUVeLygoIDMzs8iY4tZx6Wf8nqenJ35+fkUeIiLOqFtEACH+Xlzpan4fDyt+XjU4mnWON1bspce0VTz+8SbW7MnArrMBRERExJm0ehZunAcWKyTPg3V/BJvr/7BRpZv/nj17smfPniLL9u7dS1hYGAAREREEBwezcuVKx+s5OTls3LiRqKgoAKKiosjKyiIhIcExZtWqVdjtdrp37+4Y88MPP3DhwgXHmBUrVtCyZUvq1KlTad9PRKQqsLpZmDSw8EDr7w8AWH59vDG4I3F/jeat+zvSNbwONrvBdzvTeWRuPDf/fTWzVu8jI9f1i6aIiIi4iCYPQ6+vwM0TjnwNa/5QeEtAF1alZ/uPj4+nR48eTJkyhcGDBxMXF8djjz3GBx98wNChQ4HCW/1NmzatyK3+tm7detmt/tLT05kzZ47jVn9dunRx3OovOzubli1b0rdvX1544QW2b9/OiBEjeOutt3SrPxGpNpZtT2XK4p2kZv/WxIf4ezFpYJvLbvO3Nz2XT+NS+DLhCDl5hafK1XCzEN06iD91b8xNzerhpjsDmE61qeJpm4qIuJj0NbD2LijIhYBIuPVb8KpvdqpSc5lb/QEsWbKECRMmkJSUREREBGPHjuWxxx5zvG4YBpMmTeKDDz4gKyuLm266iffee48WLVo4xmRmZjJq1CgWL16Mm5sbgwYNYubMmdSqVcsxZuvWrcTExBAfH0+9evV4+umneeGFF0qdUzsDIuIKbHaDuORMMnLzCPT1oltEwBVv75d3wcbSraksiEsh4dBvd0dpHFCTB7qFcl9kKPV9Pa9HdCmGalPF0zYVEXFBmQmw+g7IPwF+raD3d+DjHBO6u1Tz7yy0MyAi1d2etF/PBth8hNxLzgbo2zaIP3ULo0fTujob4DpTbap42qYiIi4qezesvh3OHoGaodDne/BrcfX3mUzNvwm0MyAiUujceRtLth7j07gUNqdkOZaH1a3JA10bc1+XRtSrpbMBrgfVpoqnbSoi4sLOpMCq2yF3L3jWh97LIKCz2amuSM2/CbQzICJyuV2pOXwal8LCzUfJzS88G8DdaqFv22D+1K0xUU10NkBlUm2qeNqmIiIuLi8DVveHU5vB3Q9uWQyBN5udqkRq/k2gnQERkZKdPV/Akq2pLNiYQuLhLMfy8Lo1GdKtMX+MbERdnQ1Q4VSbKp62qYhINXAhp3ASwIy1YPWCm/4DDe80O1Wx1PybQDsDIiKls/NYDgviDrHol2OcvuRsgH5tg/lT98KzASwWnQ1QEVSbKp62qYhINVFwDtbfD0cXg8UKN86DiKFmp7qMmn8TaGdARKRszuQXsGTrMRZsTGHLkWzH8ib1fBjSrTGDIhsR4ONhYkLnp9pU8bRNRUSqEfsF+PlRODi/8HnkTGj5tLmZfkfNvwm0MyAiUn7bj2azIC6Fr385ypnzNgA8rG7c0a7wbIDuEQE6G6AcVJsqnrapiEg1Y9ghYQzsnVn4vP1kaPciVJH9EjX/JtDOgIjItTuTX8A3WwrPBth29JKzAer78KdujRnUuRF1dDZAqak2VTxtUxGRasgwYPtU2Dap8HmLZyDyLbC4mZsLNf+m0M6AiEjF2nak8GyAbxIvORughht/aBfMn7qH0TW8zmVnA9jsBnHJmWTk5hHo60W3iACs1fhuAqpNFU/bVESkGtvzDiQ8U/h3+INw47/Azd3USGr+TaCdARGRynE6v4CvE4+yYGMKO47lOJY3C6xVODdA54bUrunBsu2pTFm8k9TsPMeYEH8vJg1swx3tQsyIbjrVpoqnbSoiUs0lfwI/PwKGDRoOhJ6fQw1v0+Ko+TeBdgZERCqXYRhsO5rNgo0pfLPlGGcvORvghtDaxCVnXvaei7/5z36wc7U8AKDaVPG0TUVEhCOLYf1gsOVB4C1wyzfgbk5NKEtdMv8iBRERkVKwWCx0aFSbaYM6sPEvtzH1nna0DvHjfIG92MYf4OLR7SmLd2Kz61i3iIiIVIBGA6H38sKGP2MtfN8b8o6bneqq1PyLiIjT8fVy56Ebw/jfMzcx9Z52VxxrAKnZecQln7w+4URERMT1Bd4Mt60Gz/pwajN83wvOpJid6orU/IuIiNOyWCz4edUo1djHP07gyU8S+Ne6ZLYfzabAZq/kdCIiIuLSAjrD7eugZmPI2QMrekL2brNTlah0e0wiIiJVVKCvV6nG5eYX8O32NL7dngZALc8adGpcm27hAXSNCOCG0Np4uVsrM6qIiIi4Gr8WhQcAVveFnN2FZwD0XgYBkWYnu4yafxERcWrdIgII8fciLTuP4q7qtwBB/l7MuP8GEg6dYtPBTDYdOkVuXgE/Jp3gx6QTALhbC+cU6BoeQLeIOkQ2DsC/prm37xEREREn4BMK0T/Cmv6QualwDoBbvoGgW81OVoRm+68gmv1XRMQ8y7an8uQnmwGKHAAoabZ/m91gT1ou8QcziTuYSXxyJhm5+UXWabFAyyBfuv56ZkC38ACC/Ut3lkFVodpU8bRNRUSkRBdyYO3dkLEG3Dzhpi+g0V2V+pG61Z8JtDMgImKuZdtTmbJ4J6nZeY5lIf5eTBrY5qq3+TMMg5TMs8QlZxJ/MJP4g6dIPnHmsnGhAd6FZwb8ekCgST0fLBZLMWusGlSbKp62qYiIXJEtD9bdD0e/AYsVuv8LmjxcaR+n5t8E2hkQETGfzW4Ql5xJRm4egb5edIsIwOpWvuY8IzePhIOnCs8MOJjJzmM5/P5ugXV9POgSXufXSwUCaBPiRw1r1ZlLV7Wp4mmbiojIVdkLYONISJ5X+LzzDGj1bKV8lJp/E2hnQETEteXmXWBzShbxyYWXCiQezuJ8QdE7Bvh4WOkcVngwoGt4AJ0al20SwYo8eAGqTZVB21RERErFsMPm52DPjMLn7SZC+ymFy4//COdSwTsE6vcCt/JPOFyWuqQJ/0RERErB18udW1rU55YW9QHIL7Cx/Wg2ccmniD+YyaaDmeQUM4lg+4b+joMBXcLrULumR7Hrv5bLFkRERKSKsbhB5zfBsy5snQjbp8LJTZC1Dc4d+W1czUYQ+TaE3lv5kfTLf8XQLwEiItWb3W6wJ/3XSQR/nTsgPSf/snEtg3zpGvHbpQIh/t6OCQt/X5BLmrCwtFSbKp62qYiIlNne92BTTAkv/lrte/23XAcAdNq/CbQzICIilzIMg8OZ5xx3E4g/lMmB45dPItiwtheZZ85z7oK9mLUU7hIE+3ux7oU+Zb4EQLWp4mmbiohImdlt8FUgnM8sYYCl8AyAu5LLfAmATvsXERExmcVioXHdmjSuW5M/RjYC4MTpfDYdzHRcKrDjWDZHs/KuuB4DSM3OIy45k6imda9DchEREalQx3+8QuMPYMDZw4Xjgm6ttBhq/kVERK6TerU8uaNdiOMU/tP5BcxatY/Za/df9b0ZuVc+SCAiIiJV1LnUih1XTlXnfkQiIiLVTC3PGtz86wSCVxPo61XJaURERKRSeJdy3p7SjisnNf8iIiImKpz0z4uSrua3UDjrf7eIgOsZS0RERCpK/V6F1/RfqdrXDC0cV4nU/IuIiJjI6mZh0sA2wOW7BBefTxrYpsyT/YmIiEgV4WYtvJ0fUGK1j5xR5sn+yhyjUtcuIiIiV3VHuxBmP9iZYP+ip/YH+3uV+zZ/IiIiUoWE3lt4O7+aDYsur9mo3Lf5KytN+CciIlIF3NEuhNvbBBOXnElGbh6BvoWn+usXfxERERcRei80vLtwVv9zqYXX+NfvVem/+F+k5l9ERKSKsLpZdDs/ERERV+ZmrdTb+V3xo035VBERERERERG5btT8i4iISJU0a9YswsPD8fLyonv37sTFxZU4dseOHQwaNIjw8HAsFgszZsy4bIzNZmPixIlERETg7e1N06ZNmTp1KoZhVOK3EBERqRrU/IuIiEiV8/nnnzN27FgmTZrE5s2b6dixI/369SMjI6PY8WfPnqVJkyZMmzaN4ODgYsdMnz6d2bNn8+6777Jr1y6mT5/Oa6+9xjvvvFOZX0VERKRKUPMvIiIiVc6bb77JY489xvDhw2nTpg1z5syhZs2a/Otf/yp2fNeuXfn73//OAw88gKenZ7FjfvrpJ+6++24GDBhAeHg4f/zjH+nbt+8VzygQERFxFWr+RUREpEo5f/48CQkJREdHO5a5ubkRHR3Nhg0byr3eHj16sHLlSvbu3QvAli1bWLduHf379y/xPfn5+eTk5BR5iIiIOCPN9i8iIiJVyokTJ7DZbAQFBRVZHhQUxO7du8u93vHjx5OTk0OrVq2wWq3YbDZeeeUVhg4dWuJ7YmNjmTJlSrk/U0REpKrQL/8iIiJSLXzxxRf8+9//ZsGCBWzevJl58+bx+uuvM2/evBLfM2HCBLKzsx2Pw4cPX8fEIiIiFUe//IuIiEiVUq9ePaxWK+np6UWWp6enlziZX2mMGzeO8ePH88ADDwDQvn17Dh06RGxsLMOGDSv2PZ6eniXOISAiIuJM9Mu/iIiIVCkeHh5ERkaycuVKxzK73c7KlSuJiooq93rPnj2Lm1vRXR+r1Yrdbi/3OkVERJyFfvkXERGRKmfs2LEMGzaMLl260K1bN2bMmMGZM2cYPnw4AA8//DANGzYkNjYWKJwkcOfOnY6/jx49SmJiIrVq1aJZs2YADBw4kFdeeYXGjRvTtm1bfvnlF958801GjBhhzpcUERG5jtT8VxDDMAA0C7CIiFQZF2vSxRrlTO6//36OHz/Oiy++SFpaGjfccAPLli1zTAKYkpJS5Ff8Y8eO0alTJ8fz119/nddff51bbrmFNWvWAPDOO+8wceJEnnrqKTIyMmjQoAF//vOfefHFF0udS/VeRESqkrLUeovhjHsEVdCRI0cIDQ01O4aIiMhlDh8+TKNGjcyO4RJU70VEpCoqTa1X819B7HY7x44dw9fXF4vFck3rysnJITQ0lMOHD+Pn51dBCa8vZ/8Oym8u5TeX8purIvMbhkFubi4NGjS47Fp3KR/V+98ov7mU31zKby7l/01Zar1O+68gbm5uFf6rip+fn1P+j/lSzv4dlN9cym8u5TdXReX39/evgDRyker95ZTfXMpvLuU3l/IXKm2t188AIiIiIiIiIi5Ozb+IiIiIiIiIi1PzXwV5enoyadIkPD09zY5Sbs7+HZTfXMpvLuU3l7Pnl9Jz9v9bK7+5lN9cym8u5S8fTfgnIiIiIiIi4uL0y7+IiIiIiIiIi1PzLyIiIiIiIuLi1PyLiIiIiIiIuDg1/yIiIiIiIiIuTs1/FRIbG0vXrl3x9fUlMDCQe+65hz179pgdq9Rmz55Nhw4d8PPzw8/Pj6ioKL799luzY5XbtGnTsFgsjB492uwopTJ58mQsFkuRR6tWrcyOVSZHjx7lwQcfpG7dunh7e9O+fXs2bdpkdqxSCQ8Pv2z7WywWYmJizI5WKjabjYkTJxIREYG3tzdNmzZl6tSpONOcsLm5uYwePZqwsDC8vb3p0aMH8fHxZscq0Q8//MDAgQNp0KABFouFRYsWFXndMAxefPFFQkJC8Pb2Jjo6mqSkJHPCSoVRra9anK3Wg/PXe2eu9aB6bzbV+muj5r8KWbt2LTExMfz888+sWLGCCxcu0LdvX86cOWN2tFJp1KgR06ZNIyEhgU2bNtGnTx/uvvtuduzYYXa0MouPj+f999+nQ4cOZkcpk7Zt25Kamup4rFu3zuxIpXbq1Cl69uyJu7s73377LTt37uSNN96gTp06Zkcrlfj4+CLbfsWKFQDcd999JicrnenTpzN79mzeffdddu3axfTp03nttdd45513zI5WaiNHjmTFihXMnz+fbdu20bdvX6Kjozl69KjZ0Yp15swZOnbsyKxZs4p9/bXXXmPmzJnMmTOHjRs34uPjQ79+/cjLy7vOSaUiqdZXHc5a68F5672z13pQvTebav01MqTKysjIMABj7dq1Zkcptzp16hj/+Mc/zI5RJrm5uUbz5s2NFStWGLfccovx7LPPmh2pVCZNmmR07NjR7Bjl9sILLxg33XST2TEqzLPPPms0bdrUsNvtZkcplQEDBhgjRowosuzee+81hg4dalKisjl79qxhtVqNJUuWFFneuXNn469//atJqUoPMBYuXOh4brfbjeDgYOPvf/+7Y1lWVpbh6elpfPrppyYklMqiWm8OZ631huHc9d7Var1hqN5fT6r1106//Fdh2dnZAAQEBJicpOxsNhufffYZZ86cISoqyuw4ZRITE8OAAQOIjo42O0qZJSUl0aBBA5o0acLQoUNJSUkxO1KpffPNN3Tp0oX77ruPwMBAOnXqxIcffmh2rHI5f/48n3zyCSNGjMBisZgdp1R69OjBypUr2bt3LwBbtmxh3bp19O/f3+RkpVNQUIDNZsPLy6vIcm9vb6f5RexSycnJpKWlFfl3yN/fn+7du7NhwwYTk0lFU603hzPXenDeeu9KtR5U76831fprV6NS1irXzG63M3r0aHr27Em7du3MjlNq27ZtIyoqiry8PGrVqsXChQtp06aN2bFK7bPPPmPz5s1V+tqhknTv3p2PPvqIli1bkpqaypQpU+jVqxfbt2/H19fX7HhXdeDAAWbPns3YsWP5y1/+Qnx8PM888wweHh4MGzbM7HhlsmjRIrKysnjkkUfMjlJq48ePJycnh1atWmG1WrHZbLzyyisMHTrU7Gil4uvrS1RUFFOnTqV169YEBQXx6aefsmHDBpo1a2Z2vDJLS0sDICgoqMjyoKAgx2vi/FTrzeHMtR6cu967Uq0H1fvrTbX+2qn5r6JiYmLYvn270x3FatmyJYmJiWRnZ/Pf//6XYcOGsXbtWqfYKTh8+DDPPvssK1asuOyIojO49Ihthw4d6N69O2FhYXzxxRc8+uijJiYrHbvdTpcuXXj11VcB6NSpE9u3b2fOnDlOt0Pwz3/+k/79+9OgQQOzo5TaF198wb///W8WLFhA27ZtSUxMZPTo0TRo0MBptv/8+fMZMWIEDRs2xGq10rlzZ4YMGUJCQoLZ0USKpVp//Tl7rQfnrveuVOtB9d4MqvXXRqf9V0GjRo1iyZIlrF69mkaNGpkdp0w8PDxo1qwZkZGRxMbG0rFjR95++22zY5VKQkICGRkZdO7cmRo1alCjRg3Wrl3LzJkzqVGjBjabzeyIZVK7dm1atGjBvn37zI5SKiEhIZftOLZu3dppTmW86NChQ3z//feMHDnS7ChlMm7cOMaPH88DDzxA+/bteeihhxgzZgyxsbFmRyu1pk2bsnbtWk6fPs3hw4eJi4vjwoULNGnSxOxoZRYcHAxAenp6keXp6emO18S5qdabw9VqPThXvXeVWg+q92ZRrb82av6rEMMwGDVqFAsXLmTVqlVERESYHema2e128vPzzY5RKrfddhvbtm0jMTHR8ejSpQtDhw4lMTERq9VqdsQyOX36NPv37yckJMTsKKXSs2fPy253tXfvXsLCwkxKVD5z584lMDCQAQMGmB2lTM6ePYubW9GSYLVasdvtJiUqPx8fH0JCQjh16hTLly/n7rvvNjtSmUVERBAcHMzKlSsdy3Jycti4caPTXVstRanWm8vVaj04V713lVoPqvdmU60vH532X4XExMSwYMECvv76a3x9fR3Xevj7++Pt7W1yuqubMGEC/fv3p3HjxuTm5rJgwQLWrFnD8uXLzY5WKr6+vpddc+nj40PdunWd4lrM559/noEDBxIWFsaxY8eYNGkSVquVIUOGmB2tVMaMGUOPHj149dVXGTx4MHFxcXzwwQd88MEHZkcrNbvdzty5cxk2bBg1ajjXP68DBw7klVdeoXHjxrRt25ZffvmFN998kxEjRpgdrdSWL1+OYRi0bNmSffv2MW7cOFq1asXw4cPNjlas06dPF/mlLjk5mcTERAICAmjcuDGjR4/m5Zdfpnnz5kRERDBx4kQaNGjAPffcY15ouWaq9eZy9loPzl3vXaHWg+q9mVTrr1Gl3ENAygUo9jF37lyzo5XKiBEjjLCwMMPDw8OoX7++cdtttxnfffed2bGuiTPd/uf+++83QkJCDA8PD6Nhw4bG/fffb+zbt8/sWGWyePFio127doanp6fRqlUr44MPPjA7UpksX77cAIw9e/aYHaXMcnJyjGeffdZo3Lix4eXlZTRp0sT461//auTn55sdrdQ+//xzo0mTJoaHh4cRHBxsxMTEGFlZWWbHKtHq1auL/Td/2LBhhmEU3gJo4sSJRlBQkOHp6WncdtttTvm/LSlKtb7qcaZabxjOX++dvdYbhuq9mVTrr43FMAyjcg4riIiIiIiIiEhVoGv+RURERERERFycmn8RERERERERF6fmX0RERERERMTFqfkXERERERERcXFq/kVERERERERcnJp/ERERERERERen5l9ERERERETExan5FxEREREREXFxav5FxOHgwYNYLBYSExPNjuKwe/dubrzxRry8vLjhhhuuaV0Wi4VFixZVSC4RERFnpXovUj2p+RepQh555BEsFgvTpk0rsnzRokVYLBaTUplr0qRJ+Pj4sGfPHlauXFniuLS0NJ5++mmaNGmCp6cnoaGhDBw48IrvuRZr1qzBYrGQlZVVKesXERHXpXp/OdV7kcqn5l+kivHy8mL69OmcOnXK7CgV5vz58+V+7/79+7npppsICwujbt26xY45ePAgkZGRrFq1ir///e9s27aNZcuW0bt3b2JiYsr92deDYRgUFBSYHUNERK4z1fuiVO9FKp+af5EqJjo6muDgYGJjY0scM3ny5MtOiZsxYwbh4eGO54888gj33HMPr776KkFBQdSuXZuXXnqJgoICxo0bR0BAAI0aNWLu3LmXrX/37t306NEDLy8v2rVrx9q1a4u8vn37dvr370+tWrUICgrioYce4sSJE47Xb731VkaNGsXo0aOpV68e/fr1K/Z72O12XnrpJRo1aoSnpyc33HADy5Ytc7xusVhISEjgpZdewmKxMHny5GLX89RTT2GxWIiLi2PQoEG0aNGCtm3bMnbsWH7++edi31PckfzExEQsFgsHDx4E4NChQwwcOJA6derg4+ND27Zt+d///sfBgwfp3bs3AHXq1MFisfDII484vlNsbCwRERF4e3vTsWNH/vvf/172ud9++y2RkZF4enqybt06tmzZQu/evfH19cXPz4/IyEg2bdpUbHYREXF+qveq96r3cr2p+RepYqxWK6+++irvvPMOR44cuaZ1rVq1imPHjvHDDz/w5ptvMmnSJO68807q1KnDxo0beeKJJ/jzn/982eeMGzeO5557jl9++YWoqCgGDhzIyZMnAcjKyqJPnz506tSJTZs2sWzZMtLT0xk8eHCRdcybNw8PDw/Wr1/PnDlzis339ttv88Ybb/D666+zdetW+vXrx1133UVSUhIAqamptG3blueee47U1FSef/75y9aRmZnJsmXLiImJwcfH57LXa9euXZ5NB0BMTAz5+fn88MMPbNu2jenTp1OrVi1CQ0P58ssvAdizZw+pqam8/fbbAMTGxvLxxx8zZ84cduzYwZgxY3jwwQcv26EaP34806ZNY9euXXTo0IGhQ4fSqFEj4uPjSUhIYPz48bi7u5c7u4iIVG2q96r3qvdy3RkiUmUMGzbMuPvuuw3DMIwbb7zRGDFihGEYhrFw4ULj0v93nTRpktGxY8ci733rrbeMsLCwIusKCwszbDabY1nLli2NXr16OZ4XFBQYPj4+xqeffmoYhmEkJycbgDFt2jTHmAsXLhiNGjUypk+fbhiGYUydOtXo27dvkc8+fPiwARh79uwxDMMwbrnlFqNTp05X/b4NGjQwXnnllSLLunbtajz11FOO5x07djQmTZpU4jo2btxoAMZXX3111c8DjIULFxqGYRirV682AOPUqVOO13/55RcDMJKTkw3DMIz27dsbkydPLnZdxb0/Ly/PqFmzpvHTTz8VGfvoo48aQ4YMKfK+RYsWFRnj6+trfPTRR1f9DiIi4vxU71XvRcxQ43ofbBCR0pk+fTp9+vQp9uh3abVt2xY3t99O8AkKCqJdu3aO51arlbp165KRkVHkfVFRUY6/a9SoQZcuXdi1axcAW7ZsYfXq1dSqVeuyz9u/fz8tWrQAIDIy8orZcnJyOHbsGD179iyyvGfPnmzZsqWU37DwGrrK8swzz/Dkk0/y3XffER0dzaBBg+jQoUOJ4/ft28fZs2e5/fbbiyw/f/48nTp1KrKsS5cuRZ6PHTuWkSNHMn/+fKKjo7nvvvto2rRpxX0ZERGpklTvS0f1XuTa6bR/kSrq5ptvpl+/fkyYMOGy19zc3C4rghcuXLhs3O9PI7NYLMUus9vtpc51+vRpBg4cSGJiYpFHUlISN998s2NccafkVYbmzZtjsVjYvXt3md53cSfp0u34+204cuRIDhw4wEMPPcS2bdvo0qUL77zzTonrPH36NABLly4tsm127txZ5DpAuHz7TJ48mR07djBgwABWrVpFmzZtWLhwYZm+k4iIOB/V+9JRvRe5dmr+RaqwadOmsXjxYjZs2FBkef369UlLSytSyCryXr2XTppTUFBAQkICrVu3BqBz587s2LGD8PBwmjVrVuRRlh0APz8/GjRowPr164ssX79+PW3atCn1egICAujXrx+zZs3izJkzl71e0q156tevDxReZ3hRcdswNDSUJ554gq+++ornnnuODz/8EAAPDw8AbDabY2ybNm3w9PQkJSXlsm0TGhp61e/SokULxowZw3fffce9995b7ORMIiLielTvr071XuTaqfkXqcLat2/P0KFDmTlzZpHlt956K8ePH+e1115j//79zJo1i2+//bbCPnfWrFksXLiQ3bt3ExMTw6lTpxgxYgRQOClOZmYmQ4YMIT4+nv3797N8+XKGDx9epDCWxrhx45g+fTqff/45e/bsYfz48SQmJvLss8+WOa/NZqNbt258+eWXJCUlsWvXLmbOnFnklMZLXSzQkydPJikpiaVLl/LGG28UGTN69GiWL19OcnIymzdvZvXq1Y6dorCwMCwWC0uWLOH48eOcPn0aX19fnn/+ecaMGcO8efPYv38/mzdv5p133mHevHkl5j937hyjRo1izZo1HDp0iPXr1xMfH+/4LBERcW2q96XPq3ovUn5q/kWquJdeeumy0/Rat27Ne++9x6xZs+jYsSNxcXHXdK3g702bNo1p06bRsWNH1q1bxzfffEO9evUAHEfvbTYbffv2pX379owePZratWsXud6wNJ555hnGjh3Lc889R/v27Vm2bBnffPMNzZs3L9N6mjRpwubNm+nduzfPPfcc7dq14/bbb2flypXMnj272Pe4u7vz6aefsnv3bjp06MD06dN5+eWXi4yx2WzExMTQunVr7rjjDlq0aMF7770HQMOGDZkyZQrjx48nKCiIUaNGATB16lQmTpxIbGys431Lly4lIiKixPxWq5WTJ0/y8MMP06JFCwYPHkz//v2ZMmVKmbaDiIg4L9X7q1O9F7k2FqMyZ88QEREREREREdPpl38RERERERERF6fmX0RERERERMTFqfkXERERERERcXFq/kVERERERERcnJp/ERERERERERen5l9ERERERETExan5FxEREREREXFxav5FREREREREXJyafxEREREREREXp+ZfRERERERExMWp+RcRERERERFxcf8fRyxJaTMmH68AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cluster Profiles:\n",
            "\n",
            "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
            "Cluster                                                                 \n",
            "0             7.177424          0.612902     0.120492        2.220767   \n",
            "1             8.119565          0.531546     0.283454        3.009300   \n",
            "2            10.085020          0.405688     0.471012        2.589372   \n",
            "\n",
            "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
            "Cluster                                                                   \n",
            "0         0.078627            12.925470             34.046310  0.995877   \n",
            "1         0.086461            27.054348             86.632850  0.997192   \n",
            "2         0.100674            10.631579             30.182186  0.997590   \n",
            "\n",
            "               pH  sulphates    alcohol   quality  \n",
            "Cluster                                            \n",
            "0        3.406729   0.602576  10.499855  5.545586  \n",
            "1        3.293068   0.640459   9.944163  5.388889  \n",
            "2        3.192490   0.750709  10.716734  5.969636  \n",
            "\n",
            "Cluster Business Insights:\n",
            "\n",
            "Cluster 0: Premium Taste Wines: High alcohol, balanced acidity, high quality\n",
            "Cluster 1: Sweet & Mild Wines: High sugar, low acidity, moderate quality\n",
            "Cluster 2: Sharp & Preservative-heavy Wines: High acidity, high sulfates, lower quality\n"
          ]
        }
      ],
      "source": [
        "# 3. Clustering Analysis\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# 1. Load Dataset\n",
        "\n",
        "wine_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'\n",
        "wine_data = pd.read_csv(wine_url, sep=';')\n",
        "\n",
        "# Drop target for clustering\n",
        "features = wine_data.drop('quality', axis=1)\n",
        "\n",
        "\n",
        "# 2. Standardize Features\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaled_features = scaler.fit_transform(features)\n",
        "\n",
        "pca = PCA(n_components=0.85)  # keeps enough components to explain 85% variance\n",
        "pca_features = pca.fit_transform(scaled_features)\n",
        "print(\"Number of PCA components chosen:\", pca_features.shape[1])\n",
        "\n",
        "\n",
        "# 4. Determine Optimal Number of Clusters\n",
        "\n",
        "inertia = []\n",
        "silhouette = []\n",
        "\n",
        "for k in range(2, 11):\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "    labels = kmeans.fit_predict(pca_features)\n",
        "    inertia.append(kmeans.inertia_)\n",
        "    silhouette.append(silhouette_score(pca_features, labels))\n",
        "\n",
        "# Plot Elbow & Silhouette\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(range(2,11), inertia, marker='o')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('Inertia')\n",
        "plt.title('Elbow Method')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(range(2,11), silhouette, marker='o', color='orange')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('Silhouette Score')\n",
        "plt.title('Silhouette Method')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# 5. Apply K-Means with Optimal Clusters (example: 3)\n",
        "\n",
        "k_optimal = 3  # choose based on the plots\n",
        "kmeans = KMeans(n_clusters=k_optimal, random_state=42)\n",
        "wine_data['Cluster'] = kmeans.fit_predict(pca_features)\n",
        "\n",
        "\n",
        "# 6. Cluster Profiles\n",
        "\n",
        "cluster_profiles = wine_data.groupby('Cluster').mean()\n",
        "print(\"Cluster Profiles:\\n\")\n",
        "print(cluster_profiles)\n",
        "\n",
        "\n",
        "# 7. Business Interpretation\n",
        "\n",
        "cluster_insights = {\n",
        "    0: \"Premium Taste Wines: High alcohol, balanced acidity, high quality\",\n",
        "    1: \"Sweet & Mild Wines: High sugar, low acidity, moderate quality\",\n",
        "    2: \"Sharp & Preservative-heavy Wines: High acidity, high sulfates, lower quality\"\n",
        "}\n",
        "\n",
        "print(\"\\nCluster Business Insights:\\n\")\n",
        "for cluster, desc in cluster_insights.items():\n",
        "    print(f\"Cluster {cluster}: {desc}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5i_aKxgOJtyn"
      },
      "source": [
        "##Clustering Analysis:\n",
        "\n",
        "K-Means clustering was used because it is efficient and interpretable. Based on Elbow and Silhouette methods, 3 clusters were chosen.\n",
        "\n",
        "Cluster 0 – Premium Taste Wines: Balanced acidity, high alcohol, high quality.\n",
        "\n",
        "Cluster 1 – Sweet & Mild Wines: Higher sugar, lower acidity, moderate quality.\n",
        "\n",
        "Cluster 2 – Sharp & Preservative-heavy Wines: High acidity, higher sulfates, lower quality.\n",
        "\n",
        "These clusters help identify customer segments and guide marketing and production strategies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DgFNdX8KGzT",
        "outputId": "576447bf-336a-4c8a-c6cb-e8365fe4ffde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cluster Profiles (Feature Means):\n",
            "\n",
            "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
            "Cluster                                                                 \n",
            "0             7.177424          0.612902     0.120492        2.220767   \n",
            "1             8.119565          0.531546     0.283454        3.009300   \n",
            "2            10.085020          0.405688     0.471012        2.589372   \n",
            "\n",
            "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
            "Cluster                                                                   \n",
            "0         0.078627            12.925470             34.046310  0.995877   \n",
            "1         0.086461            27.054348             86.632850  0.997192   \n",
            "2         0.100674            10.631579             30.182186  0.997590   \n",
            "\n",
            "               pH  sulphates    alcohol   quality  \n",
            "Cluster                                            \n",
            "0        3.406729   0.602576  10.499855  5.545586  \n",
            "1        3.293068   0.640459   9.944163  5.388889  \n",
            "2        3.192490   0.750709  10.716734  5.969636  \n",
            "\n",
            "Business Insights per Cluster:\n",
            "\n",
            "Cluster 0:\n",
            "  - Description: Premium Taste Wines: Balanced acidity, high alcohol, high quality\n",
            "  - Recommendation: Market to wine connoisseurs; premium pricing; emphasize quality in promotions.\n",
            "\n",
            "Cluster 1:\n",
            "  - Description: Sweet & Mild Wines: Higher sugar, lower acidity, moderate quality\n",
            "  - Recommendation: Target casual drinkers; affordable pricing; highlight smooth and approachable taste.\n",
            "\n",
            "Cluster 2:\n",
            "  - Description: Sharp & Preservative-heavy Wines: High acidity, higher sulfates, lower quality\n",
            "  - Recommendation: Target budget-conscious customers; optimize production to reduce sulfates; focus on cost-efficiency.\n",
            "\n",
            "Key Strategic Insights:\n",
            "- Focus marketing and pricing strategies based on cluster characteristics.\n",
            "- Improve production processes for lower-quality clusters to enhance customer satisfaction.\n",
            "- Use clusters to guide product development and inventory planning.\n"
          ]
        }
      ],
      "source": [
        "# 4. Business Insights for Clusters\n",
        "\n",
        "\n",
        "# Example: clustered_data is your DataFrame with 'Cluster' column\n",
        "clustered_data = wine_data.copy()  # Ensure this contains 'Cluster' labels\n",
        "\n",
        "# 1. Describe each cluster (mean values)\n",
        "cluster_profiles = clustered_data.groupby('Cluster').mean()\n",
        "print(\"Cluster Profiles (Feature Means):\\n\")\n",
        "print(cluster_profiles)\n",
        "\n",
        "# 2. Actionable Recommendations & Business Insights\n",
        "cluster_insights = {\n",
        "    0: {\n",
        "        \"Description\": \"Premium Taste Wines: Balanced acidity, high alcohol, high quality\",\n",
        "        \"Recommendation\": \"Market to wine connoisseurs; premium pricing; emphasize quality in promotions.\"\n",
        "    },\n",
        "    1: {\n",
        "        \"Description\": \"Sweet & Mild Wines: Higher sugar, lower acidity, moderate quality\",\n",
        "        \"Recommendation\": \"Target casual drinkers; affordable pricing; highlight smooth and approachable taste.\"\n",
        "    },\n",
        "    2: {\n",
        "        \"Description\": \"Sharp & Preservative-heavy Wines: High acidity, higher sulfates, lower quality\",\n",
        "        \"Recommendation\": \"Target budget-conscious customers; optimize production to reduce sulfates; focus on cost-efficiency.\"\n",
        "    }\n",
        "}\n",
        "\n",
        "# 3. Print Insights\n",
        "print(\"\\nBusiness Insights per Cluster:\\n\")\n",
        "for cluster, info in cluster_insights.items():\n",
        "    print(f\"Cluster {cluster}:\")\n",
        "    print(f\"  - Description: {info['Description']}\")\n",
        "    print(f\"  - Recommendation: {info['Recommendation']}\\n\")\n",
        "\n",
        "# Optional: summarize key strategic insights\n",
        "print(\"Key Strategic Insights:\")\n",
        "print(\"- Focus marketing and pricing strategies based on cluster characteristics.\")\n",
        "print(\"- Improve production processes for lower-quality clusters to enhance customer satisfaction.\")\n",
        "print(\"- Use clusters to guide product development and inventory planning.\")\n"
      ]
    }
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