Group11_Predictive_Maintenance(Final).ipynb

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    "## Group: 11\n",
    "\n",
    "## Predictive Maintenance\n",
    "\n",
    "#### Credit: S. Matzka, \"Explainable Artificial Intelligence for Predictive Maintenance Applications,\" 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), 2020, pp. 69-74, doi: 10.1109/AI4I49448.2020.00023.\n",
    "\n",
    "#### Link to dataset: https://www.kaggle.com/datasets/stephanmatzka/predictive-maintenance-dataset-ai4i-2020\n",
    "\n",
    "### Group Members:\n",
    "###### 1) Joao Vitor Lira de Carvalho Firmino: jfirmino@depaul.edu\n",
    "###### 2) Beemnet Desta: bdesta@depaul.edu\n",
    "###### 3) Tejas Hemant Gotiwale: tgotiwal@depaul.edu\n",
    "###### 4) Adithya Harsha: Aharsha@depaul.edu"
   ]
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    "## Dataset Description:\n",
    "\n",
    "**Features:**\n",
    "\n",
    "UID: unique identifier\n",
    "\n",
    "Product ID: L (Low Quality), M (Medium), or H (High) + Number.\n",
    "\n",
    "Type: Product quality L, M or H from column 2.\n",
    "\n",
    "Air temperature [K]\n",
    "\n",
    "Process temperature [K]\n",
    "\n",
    "Rotational speed [rpm]\n",
    "\n",
    "Torque [Nm]\n",
    "\n",
    "Tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process.\n",
    "\n",
    "Machine failure: 0 (No failure) or 1 (Failure)\n",
    "\n",
    "\n",
    "**Five independent failure modes:**:\n",
    "\n",
    "Tool Wear Failure (TWF)\n",
    "\n",
    "Heat Dissipation Failure (HDF)\n",
    "\n",
    "Power Failure (PWF)\n",
    "\n",
    "Overstrain Failure (OSF)\n",
    "\n",
    "Random Failures (RNF)"
   ]
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   "source": [
    "## 1) Data Cleaning & Preprocessing:"
   ]
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    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report, accuracy_score"
   ]
  },
  {
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     "name": "stdout",
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     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
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       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-52fb0731-ac35-4a71-9fb1-89092337fb97 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   UDI Product ID Type  Air temperature [K]  Process temperature [K]  \\\n",
       "0    1     M14860    M                298.1                    308.6   \n",
       "1    2     L47181    L                298.2                    308.7   \n",
       "2    3     L47182    L                298.1                    308.5   \n",
       "3    4     L47183    L                298.2                    308.6   \n",
       "4    5     L47184    L                298.2                    308.7   \n",
       "\n",
       "   Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  Machine failure  TWF  \\\n",
       "0                    1551         42.8                0                0    0   \n",
       "1                    1408         46.3                3                0    0   \n",
       "2                    1498         49.4                5                0    0   \n",
       "3                    1433         39.5                7                0    0   \n",
       "4                    1408         40.0                9                0    0   \n",
       "\n",
       "   HDF  PWF  OSF  RNF  \n",
       "0    0    0    0    0  \n",
       "1    0    0    0    0  \n",
       "2    0    0    0    0  \n",
       "3    0    0    0    0  \n",
       "4    0    0    0    0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df = pd.read_csv('ai4i2020.csv')\n",
    "df = main_df.copy()\n",
    "\n",
    "main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49501ed0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "49501ed0",
    "outputId": "65b00a98-596d-47fc-87d2-b6b2bba4199b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in each column:\n",
      " UDI                        0\n",
      "Product ID                 0\n",
      "Type                       0\n",
      "Air temperature [K]        0\n",
      "Process temperature [K]    0\n",
      "Rotational speed [rpm]     0\n",
      "Torque [Nm]                0\n",
      "Tool wear [min]            0\n",
      "Machine failure            0\n",
      "TWF                        0\n",
      "HDF                        0\n",
      "PWF                        0\n",
      "OSF                        0\n",
      "RNF                        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checks for the missing values in each column\n",
    "missing_values = main_df.isnull().sum()\n",
    "print(\"Missing values in each column:\\n\", missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e85c199",
   "metadata": {
    "id": "0e85c199"
   },
   "source": [
    "There are no missing values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7b4d7540",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7b4d7540",
    "outputId": "dbb673a2-631e-4028-bd7c-c0ca2a325890"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of empty strings in 'Product ID' column: 0\n",
      "Number of empty strings in 'Type' column: 0\n"
     ]
    }
   ],
   "source": [
    "# Checks for the empty strings in categorical columns\n",
    "empty_productid = (main_df['Product ID'].str.strip() == '').sum()\n",
    "empty_type = (main_df['Type'].str.strip() == '').sum()\n",
    "\n",
    "print(f\"Number of empty strings in 'Product ID' column: {empty_productid}\")\n",
    "print(f\"Number of empty strings in 'Type' column: {empty_type}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b961bacb",
   "metadata": {
    "id": "b961bacb"
   },
   "source": [
    "There are no empty strings/values in categorical columns 'Product ID' and 'Type'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44208423",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "44208423",
    "outputId": "decdda40-dd56-4ee6-a387-cc1b50b86f0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers in Air temperature [K]: 0\n",
      "Outliers in Process temperature [K]: 0\n",
      "Outliers in Rotational speed [rpm]: 418\n",
      "Outliers in Torque [Nm]: 69\n",
      "Outliers in Tool wear [min]: 0\n"
     ]
    }
   ],
   "source": [
    "# Function to detect the outliers using IQR\n",
    "def chk_outliers(df, column):\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "    return outliers\n",
    "\n",
    "# Detects outliers for numeric columns using IQR\n",
    "numeric_columns = ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']\n",
    "\n",
    "for column in numeric_columns:\n",
    "    outliers = chk_outliers(main_df, column)\n",
    "    print(f\"Outliers in {column}: {len(outliers)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1a8a91d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 639
    },
    "id": "c1a8a91d",
    "outputId": "c07082e0-1b7e-4a9b-c9ae-6fc087068c85"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizes data using boxplot for outliers\n",
    "plt.figure(figsize = (10, 6))\n",
    "main_df.boxplot(column = numeric_columns)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb1fa66",
   "metadata": {
    "id": "ebb1fa66"
   },
   "source": [
    "There are outliers present in two columns:\n",
    "Rotational speed [rpm]: 418 outliers\n",
    "Torque [Nm]: 69 outliers\n",
    "\n",
    "And other variables, such as Air temperature [K], Process temperature [K], and Tool wear [min], do not show outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2570ae7b",
   "metadata": {
    "id": "2570ae7b"
   },
   "source": [
    "#### Note:\n",
    "'Type' column represents different categories of products or machines useful for our modeling as a categorical feature. So, We are applying One-Hot Encoding to convert it's categorical values into binary columns, enabling our model to understand and use this feature effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03d8e62e",
   "metadata": {
    "id": "03d8e62e"
   },
   "outputs": [],
   "source": [
    "# Appling One-Hot Encoding to the 'Type' column\n",
    "main_df = pd.get_dummies(main_df, columns = ['Type'], drop_first = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3343ed97",
   "metadata": {
    "id": "3343ed97"
   },
   "source": [
    "## 2) Exploratory Data Analysis(EDA):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "u1WIem6hh_mh",
   "metadata": {
    "id": "u1WIem6hh_mh"
   },
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "gF3ZJBtViD_j",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gF3ZJBtViD_j",
    "outputId": "fb7594a9-c746-4925-cd5b-8168f9c3daa0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Low quality product: 6000\n",
      "Medium quality product: 2997\n",
      "High quality product: 1003\n"
     ]
    }
   ],
   "source": [
    "low, med, high = df['Type'].value_counts()\n",
    "print(f\"Low quality product: {low}\\nMedium quality product: {med}\\nHigh quality product: {high}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "SsKGrrz2jEPJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "id": "SsKGrrz2jEPJ",
    "outputId": "452283d9-cebb-4c1d-bc6a-1a06d7d7513c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Quality Types (pie chart)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.title('Distribution of Quality Types')\n",
    "plt.pie([low, med, high], labels=['Low', 'Medium', 'High'], autopct='%1.1f%%', startangle=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8l9vJbMejIqw",
   "metadata": {
    "id": "8l9vJbMejIqw"
   },
   "source": [
    "This pie chart illustrates that, within this dataset, 60% of the manufactured products are of low quality, 30% are of medium quality, and 10% are of high quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "xwvoJfoajN3b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "xwvoJfoajN3b",
    "outputId": "66e74db2-6d51-4f8c-e8a1-92c8940af8b2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing Machine failure based on product Quality\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.countplot(data=df, x='Type', hue='Machine failure')\n",
    "plt.title('Quality of product VS Machine failure')\n",
    "plt.xlabel('Quality of product')\n",
    "plt.ylabel('Machine failure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Rl0tY_j4jWI7",
   "metadata": {
    "id": "Rl0tY_j4jWI7"
   },
   "source": [
    "For this graph, it is possible to see that almost no high-quality products are related to machine failure. On the other hand, almost all failures occur during the manufacturing of low-quality products."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "MAGzqmksgZ_g",
   "metadata": {
    "id": "MAGzqmksgZ_g"
   },
   "source": [
    "### Data Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eca9d926",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 853
    },
    "id": "eca9d926",
    "outputId": "dc2f2ddc-ffff-4397-9afe-e9d85823856e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3kf79+6tnz54WZa6urjaaTe4geAEAAAAAgJUMZ8NmY7u6uuZYsCIgIEAJCQkWZQkJCfL29pa7u7scHR3l6Oh4wzoBAQE5MieJMy8AAAAAAMD/1KxZU0uXLrUoW7JkiWrWrClJcnFxUdWqVS3qZGZmaunSpeY6OYHMCwAAAAAArOTgZLvMC2ukpKTor7/+Mn8+ePCgtm3bpnz58qlIkSLq37+/jh07punTp0uSXnvtNY0bN059+vRRx44dtWzZMn3//fdasGCBuY+ePXsqKipK4eHhql69usaMGaMLFy6oQ4cOObYOghcAAAAAADygNm3apHr16pk/XzsrIyoqSjExMYqPj9eRI0fM90NDQ7VgwQK9/fbbGjt2rAoXLqxJkyYpMjLSXKdVq1Y6deqUBgwYoBMnTigsLEwLFy7McojnvWSYTCZTjvUO2BneNgIAAADgXljoXdZmYzdM3muzsW2FzAsAAAAAAKxkOHOEZG7iaQMAAAAAALtG5gUAAAAAAFa6Xw7sfFCQeQEAAAAAAOwawQsAAAAAAGDX2DYCAAAAAICVDGe2jeQmMi8AAAAAAIBdI/MCAAAAAAArcWBn7iLzAgAAAAAA2DUyLwAAAAAAsBJnXuQuMi8AAAAAAIBdI3gBAAAAAADsGttGAAAAAACwEgd25i4yL5Br6tatqx49emQpj4mJka+vryQpOjpahmHIMAw5OTnJz89PTzzxhMaMGaPU1NRs9Wdv8j0ervA5X6rB4VVqnBYn/+ca2HpKAAAAAHBfIXgBu/PII48oPj5eR44c0fLly9WiRQsNGzZMtWrV0vnz5209Pas5enooeUecdnUbZOupAAAAALhHDEfDZtfDiG0jsDtOTk4KCAiQJAUFBalChQp68sknValSJQ0fPlxDhw618Qytc2rRSp1atNLW0wAAAACA+xaZF7gvlClTRk8//bRmz55t66kAAAAAAHIZmRe4b5QpU0aLFy/Odv3U1NQs52SkmTLlbBCzAwAAAHB3HB7S7Ru2wrc43DdMJpMMI/v/ghg2bJh8fHwsru8zz+bgDAEAAAAAOYHgBXKNt7e3kpKSspQnJibKx8fntu337t2r0NDQbI/Xv39/JSUlWVwtHfJZNWcAAAAAuBHDwbDZ9TAieIFcU7p0aW3ZsiVL+ZYtW1SqVKlbtt23b58WLlyo5s2bZ3s8V1dXeXt7W1xsGQEAAACA+w9nXiDXvP766xo3bpy6deumzp07y9XVVQsWLNB3332nn3/+2VwvPT1dJ06cUGZmps6cOaPY2FgNHTpUYWFheuedd2y4gjvj6OkhzxJFzJ89QgvLu1IZXTmbpMtH4204MwAAAAB3ynDkF6O5ieAFck2xYsW0cuVKvffee4qIiNCVK1dUpkwZ/fDDD2rYsKG53u7duxUYGChHR0f5+PioXLly6t+/v15//XW5urracAV3xqdqedVc+o35c7mR70qSjk6frR2d+ttqWgAAAABw3zBMJpPJ1pMAcssC59K2noJNNE6Ls/UUAAAAgAfK2vBqNhu71qaNNhvbVsi8AAAAAADASrwqNXexSQcAAAAAANg1Mi8AAAAAALDSw/rKUlsh8wIAAAAAANg1ghcAAAAAAMCusW0EAAAAAAArcWBn7iLzAgAAAAAA2DUyLwAAAAAAsJJB5kWuIvMCAAAAAADYNTIvAAAAAACwkuFALkBu4mkDAAAAAAC7RvACAAAAAADYNbaNAAAAAABgJcOBAztzE5kXAAAAAADArpF5AQAAAACAlRx4VWquIvMCAAAAAADYNYIXAAAAAADArrFtBAAAAAAAK3FgZ+4i8wIAAAAAANg1Mi8AAAAAALCS4UAuQG4ieIGHSuO0OFtPwSYWOJe29RRsomCNvLaegk1UW/2HracAAAAA3FMELwAAAAAAsBJnXuQu8lwAAAAAAIBdI3gBAAAAAADsGttGAAAAAACwkoMj20ZyE5kXAAAAAADArpF5AQAAAACAlTiwM3eReQEAAAAAAOwawQsAAAAAAGDX2DYCAAAAAICVDAdyAXITTxsAAAAAANg1Mi8AAAAAALASB3bmLjIvAAAAAACAXSPzAgAAAAAAK5F5kbvIvAAAAAAAAHaN4AUAAAAAALBrbBsBAAAAAMBKbBvJXWReAAAAAAAAu0bmBQAAAAAAVjIcyAXITQ/k046NjZVhGEpMTLT1VHALMTExMgxDhmGoR48eVrWtW7euue22bdtyZH4AAAAAAPtw3wYv1q1bJ0dHRzVu3DjLvVq1aik+Pl4+Pj5W9fmwBT0Mw9DcuXNtOgdvb2/Fx8dryJAh5rK6detmCWaMHTtWrq6umjlzpiRp9uzZ2rBhQ25OFVbK93i4wud8qQaHV6lxWpz8n2tg6yndlYLNmqviD3NUdekKlf1qsjzLlrtlff8WrVR+xixVXRqrSj/NU/Bb3WW4uPxbwcFBhTp3UcXvZ6vq0lhVmPWjAqM65PAqAAAAgPvTfbttZPLkyXrrrbc0efJkHT9+XEFBQeZ7Li4uCggIuGnbjIwMGYYhhwcwzccWa7ty5Ypcrv9SZgXDMG75ZyVJAwcO1MiRIzVv3jw1bNhQkpQvXz4lJyff0ZjIHY6eHkreEaejMT8p/Mfxtp7OXclXP0LBXbvr8MjhStmzW/4tX1Sp0WO0s3UrpSeey1r/yadU+LU3dPDjD5Wyc6fcgoMV+t4Hkkk6Om6sJCmw7csq0KSZDn44WJcOHpRnmTIKffd9ZVy4oJM/fp/bSwQAAICVHBw5sDM33Zff3lNSUjRr1iy9/vrraty4sWJiYizu/zeDIiYmRr6+vpo/f77KlSsnV1dXHTlyxKLNoUOHVK9ePUlS3rx5ZRiG2rdvL0nKzMzUsGHDFBoaKnd3d1WqVEk//vhjlvEWLVqkypUry93dXfXr19fJkyf122+/qWzZsvL29labNm108eJFc7u6deuqa9eu6tq1q3x8fOTn56cPPvhAJpPJXCc1NVW9e/dWoUKF5OnpqRo1aig2NtZ8/2Zr27hxo5588kn5+fnJx8dHderU0ZYtW8ztQkJCJElNmzaVYRjmz+3bt1eTJk0snk2PHj1Ut27dLPPu0aOH/Pz8FBkZKUnatWuXnn76aXl5ecnf318vv/yyTp8+fcs/y1sxmUx666239Nlnn2nJkiXmwAXuD6cWrdSfA8coYd7vtp7KXfN/sbVO/TxPp39doMuHDunwiOHKvHxZfs88c8P6XuUrKGXnDp1dslhXTsQreeMGnf19iTzLlbOok7h6pZLWrdWVE/E6F7tcSRs2yOs2GR0AAACAtcaPH6+QkBC5ubmpRo0at8xiv36L/vXX9bse2rdvn+V+Tn9fuy+DF99//73KlCmj0qVL66WXXtKUKVMsvvDfyMWLFzV8+HBNmjRJu3fvVsGCBS3uBwcH66effpIkxcXFKT4+XmPHXv0N6bBhwzR9+nRNmDBBu3fv1ttvv62XXnpJK1assOgjOjpa48aN09q1a3X06FG1bNlSY8aM0YwZM7RgwQItXrxYn3/+uUWbadOmycnJSRs2bNDYsWM1evRoTZo0yXy/a9euWrdunWbOnKkdO3aoRYsWatiwofbv33/LtZ0/f15RUVFavXq1/vjjD5UsWVKNGjXS+fPnJUkbN26UJE2dOlXx8fHmz9k1bdo0ubi4aM2aNZowYYISExNVv359Va5cWZs2bdLChQuVkJCgli1bWtXvNenp6XrppZf0448/asWKFapVq9Yd9QPcLcPJSZ6lSit503X/jJhMSt60UV6PVLhhm5RdO+VRuox5a4lrUJB8Hq2lpHVrLep4V60m1+BgSZJ7iRLKU7GSEv9Yl3OLAQAAwD1jOBg2u6wxa9Ys9ezZUwMHDtSWLVtUqVIlRUZG6uTJkzesP3v2bMXHx5uvXbt2ydHRUS1atLCo17BhQ4t633333R0/y+y4L7eNTJ48WS+99JKkqw8sKSlJK1assMgO+K+0tDR98cUXqlSp0g3vOzo6Kl++fJKkggULytfXV9LVzIePPvpIv//+u2rWrClJKlasmFavXq2JEyeqTp065j6GDh2qxx57TJLUqVMn9e/fXwcOHFCxYsUkSS+88IKWL1+uvn37mtsEBwfr008/lWEYKl26tHbu3KlPP/1Ur7zyio4cOaKpU6fqyJEj5m0xvXv31sKFCzV16lR99NFHN11b/fr1Ldb31VdfydfXVytWrNAzzzyjAgUKSJJ8fX1vu23jRkqWLKlPPvnEYu2VK1c2z0mSpkyZouDgYP35558qVaqUVf1//fXXkqTt27erTJkyVs8PuFecfHxlODkp7exZi/K0s+fkVjTkhm3OLlksJx9flfliomQYcnBy0sk5sxX/zTRznfj/my5HT09V+HaWTJmZMhwcdOyrCTq7ZFFOLgcAAAAPmdGjR+uVV15Rhw5Xz1ebMGGCFixYoClTpqhfv35Z6l/7XnzNzJkz5eHhkSV44erqekffJe/UfZd5ERcXpw0bNqh169aSJCcnJ7Vq1UqTJ0++ZTsXFxdVrFjR6vH++usvXbx4UU8++aS8vLzM1/Tp03XgwAGLutf37+/vLw8PD3Pg4lrZf6Nbjz76qAzj38hZzZo1tX//fmVkZGjnzp3KyMhQqVKlLMZesWKFxdg3WltCQoJeeeUVlSxZUj4+PvL29lZKSkqW7TJ3qmrVqhaft2/fruXLl1vM81rQ4b/PKTsef/xxeXl56YMPPlB6evodzTE1NVXJyckWV2pq6h31BVgjT+UqCno5SodHjdCejlHa/25f+dSqZXEgZ776DZT/yUj9PWiA9nSM0sEPByugdVvlb9jIhjMHAADA/SC733WuXLmizZs3KyIiwlzm4OCgiIgIrVuXvYzfyZMn68UXX5Snp6dFeWxsrAoWLKjSpUvr9ddf15kzZ+5uUbdx32VeTJ48Wenp6RYHdJpMJrm6umrcuHE3fcOIu7u7RZAgu1JSUiRJCxYsUKFChSzuubq6Wnx2dnY2/2wYhsXna2WZmZlWje3o6KjNmzfL0dHR4p6Xl5f55xutLSoqSmfOnNHYsWNVtGhRubq6qmbNmrpy5cotx3RwcMiyBSctLS1Lvf/+HzclJUXPPvushg8fnqVuYGDgLce8kQoVKmjUqFGKiIhQq1atNGvWLDk5Wfd/12HDhmnQoEEWZQMHDlR0dLTV88HDKz0pUab0dDn/JwLtnC+v0m7yL+hCnbvo9KLfdPqX+ZKkS38fkKObu4r26af46TGSyaTgN95S/LfTdXbp7+Y6LgGBCny5nc4s/DVH1wQAAIC7Z9jwBRDZ/a5z+vRpZWRkyN/f36Lc399f+/btu+04GzZs0K5du7IkCzRs2FDNmjVTaGioDhw4oHfffVdPP/20+a2gOeG+Cl6kp6dr+vTpGjVqlJ566imLe02aNNF3332n11577Y77v/bGjIyMDHPZ9YdgXr9F5F5Zv369xedr51M4OjqqcuXKysjI0MmTJ1W7dm2r+l2zZo2++OILNWp09be4R48ezXJ4prOzs8VaJalAgQLatWuXRdm2bduyBGL+q0qVKvrpp58UEhJidZDhZsLCwrR06VJFRESoZcuWmjVr1m3ncb3+/furZ8+eFmX/DTgBt2NKT9eFP+PkXbWaEletvFpoGPKuWk0Js3+4YRsHNzfpP0FAU2aGua1MJjm4ucmU+Z+zejIybPqXIAAAAO4PufVdZ/LkyapQoYKqV69uUf7iiy+af65QoYIqVqyo4sWLKzY2Vg0aNLjn85Dus20jv/zyi86dO6dOnTqpfPnyFlfz5s1vu3XkdooWLSrDMPTLL7/o1KlTSklJUZ48edS7d2+9/fbbmjZtmg4cOKAtW7bo888/17Rp027f6W0cOXJEPXv2VFxcnL777jt9/vnn6t69uySpVKlSatu2rdq1a6fZs2fr4MGD2rBhg4YNG6YFCxbcst+SJUvqm2++0d69e7V+/Xq1bdtW7u7uFnVCQkK0dOlSnThxQufOXX3dY/369bVp0yZNnz5d+/fv18CBA7MEM27kzTff1NmzZ9W6dWtt3LhRBw4c0KJFi9ShQ4csARJrVKpUScuWLdPq1avVsmXLG2aB3Iyrq6u8vb0tLoIXucfR00PelcrIu9LV7UMeoYXlXamM3IKtz8SxtYSZ36nAs88pf8NGcisaoqK9+8jB3U2n//fPYej7A1T41dfN9RPXrFbBJs2Ur0GEXAID5R1eXYU6d1HSmtXS/7KvEtesVlC79vKpWUsuAYHyfaKO/Fu11rmVK244BwAAANgXWx7Ymd3vOn5+fnJ0dFRCQoJFeUJCwm3Pq7hw4YJmzpypTp063fZZFCtWTH5+fvrrr7+se4hWuK+CF5MnT1ZERMQNt4Y0b95cmzZt0o4dO+64/0KFCmnQoEHq16+f/P391bVrV0nSkCFD9MEHH2jYsGEqW7asGjZsqAULFig0NPSOx7qmXbt2unTpkqpXr64333xT3bt3V5cuXcz3p06dqnbt2qlXr14qXbq0mjRpoo0bN6pIkSK37Hfy5Mk6d+6cqlSpopdfflndunXL8oaVUaNGacmSJQoODlblypUlSZGRkfrggw/Up08fVatWTefPn1e7du1uu46goCCtWbNGGRkZeuqpp1ShQgX16NFDvr6+crjL3yRXqFBBy5Yt09q1a9WiRYvbbn2BffCpWl61N81T7U3zJEnlRr6r2pvmqVR0NxvPzHpnl/2uo+M/V6HOr+iRqdPlUbKU/uz1ttLPXT3E08U/QM75/cz1j0+bqhMzZ6jQK6+qwv99p5D+7yppw3odGvGxuc7hT0fpbOwyFe31jip8+52C33xLp+bP1bGvJ+b6+gAAAPBgcnFxUdWqVbV06VJzWWZmppYuXWp+IcXN/PDDD0pNTTW/LONW/vnnH505c+aOjgzILsN0u3eMIsfUrVtXYWFhGjNmjK2nYhMxMTHq0aOHEhMT76j9oUOHFBoaqq1btyosLOyezu1Bs8C5tK2nYBMFa+S19RRsotrqP2w9BQAAgAfe4S5NbDZ20a/mZrvurFmzFBUVpYkTJ6p69eoaM2aMvv/+e+3bt0/+/v5q166dChUqpGHDhlm0q127tgoVKqSZM2dalKekpGjQoEFq3ry5AgICdODAAfXp00fnz5/Xzp07cyzb/b468wIPnqSkJHl5eenNN9+84WGfN/P0009r5cqVOTgzAAAAALj/tWrVSqdOndKAAQN04sQJhYWFaeHCheZDPI8cOZIlWz4uLk6rV6/W4sWLs/Tn6OioHTt2aNq0aUpMTFRQUJCeeuopDRkyJEe36ZN5YUMPe+bF+fPnzXuvfH195efnd5sW/zp27JguXbokSSpSpIj5sFXcGJkXDxcyLwAAAHLe/ZJ58aAg88KGYmNjbT0Fm8qTJ4/y5MlzR23/+9paAAAAAMhNvCUud/G0AQAAAACAXSPzAgAAAAAAKxkOhq2n8FAh8wIAAAAAANg1ghcAAAAAAMCusW0EAAAAAAArcWBn7uJpAwAAAAAAu0bmBQAAAAAA1jI4sDM3kXkBAAAAAADsGpkXAAAAAABYiVel5i4yLwAAAAAAgF0jeAEAAAAAAOwa20YAAAAAALASr0rNXTxtAAAAAABg18i8AAAAAADAShzYmbvIvAAAAAAAAHaN4AUAAAAAALBrbBsBAAAAAMBKHNiZuwheAA+BgjXy2noKNnFy/TlbTwEAAADAPUDwAgAAAAAAK3FgZ+4izwUAAAAAANg1Mi8AAAAAALASmRe5i8wLAAAAAABg1wheAAAAAAAAu8a2EQAAAAAArMWrUnMVTxsAAAAAANg1Mi8AAAAAALCSYXBgZ24i8wIAAAAAANg1ghcAAAAAAMCusW0EAAAAAAArGRzYmat42gAAAAAAwK6ReQEAAAAAgJUMBw7szE1kXgAAAAAAALtG5gUAAAAAANbizItcxdMGAAAAAAB2jeAFAAAAAACwa2wbAQAAAADAShzYmbvIvAAAAAAAAHaN4AVsyjAMGYYhX19fq9rFxMSY2/bo0SNH5gYAAAAAN2MYDja7HkZWrbp9+/bmL4wuLi4qUaKEBg8erPT09JyaX64LCQnRmDFjbD2NXNG+fXs1adLE1tPQ1KlT9eeff5o/x8TEZAlm7N27V8HBwWrRooWuXLmiVq1aKT4+XjVr1szl2aJgs+aq+MMcVV26QmW/mizPsuVuWd+/RSuVnzFLVZfGqtJP8xT8VncZLi7/VnBwUKHOXVTx+9mqujRWFWb9qMCoDjm8ipyT7/Fwhc/5Ug0Or1LjtDj5P9fA1lMCAAAA7ntWh2waNmyo+Ph47d+/X7169VJ0dLRGjBhxw7pXrly56wnCemlpabk6XkZGhjIzM++4va+vrwoWLHjT+xs3blTt2rXVsGFDzZo1Sy4uLnJ3d1dAQIBcrv8SjByXr36Egrt21/Gpk7S7U5Qu/rVfpUaPkZNv3hvXf/IpFX7tDR2fOlk727bWwY8/VL4GESrc5XVzncC2L6tAk2Y6/OlI7WzbWv98OV6BbV9SwRda5tay7ilHTw8l74jTrm6DbD0VAAAA4IFhdfDC1dVVAQEBKlq0qF5//XVFRERo/vz5kv79Tf6HH36ooKAglS5dWpK0c+dO1a9fX+7u7sqfP7+6dOmilJQUi36nTJmiRx55RK6urgoMDFTXrl3N9xITE9W5c2cVKFBA3t7eql+/vrZv326+v337dtWrV0958uSRt7e3qlatqk2bNkmSDh8+rGeffVZ58+aVp6enHnnkEf366683XFvdunV1+PBhvf322+YMk2tWr16t2rVry93dXcHBwerWrZsuXLhgvh8SEqKhQ4eqXbt28vLyUtGiRTV//nydOnVKzz//vLy8vFSxYkXzvKR/Mwzmzp2rkiVLys3NTZGRkTp69KjFvObNm6cqVarIzc1NxYoV06BBgyyyXQzD0JdffqnnnntOnp6e+vDDD5WRkaFOnTopNDRU7u7uKl26tMaOHWtuEx0drWnTpmnevHnmtcbGxio2NlaGYSgxMdFcd9u2bTIMQ4cOHbKY9/z581WuXDm5urrqyJEjSk1NVe/evVWoUCF5enqqRo0aio2NveGzzq5ly5apfv366tSpk77++ms58C5lm/J/sbVO/TxPp39doMuHDunwiOHKvHxZfs88c8P6XuUrKGXnDp1dslhXTsQreeMGnf19iTzLlbOok7h6pZLWrdWVE/E6F7tcSRs2yOs2GR326tSilfpz4BglzPvd1lMBAABATnIwbHc9hO76m6C7u7tFhsXSpUsVFxenJUuW6JdfftGFCxcUGRmpvHnzauPGjfrhhx/0+++/WwQnvvzyS7355pvq0qWLdu7cqfnz56tEiRLm+y1atNDJkyf122+/afPmzapSpYoaNGigs2fPSpLatm2rwoULa+PGjdq8ebP69esnZ2dnSdKbb76p1NRUrVy5Ujt37tTw4cPl5eV1w7XMnj1bhQsX1uDBgxUfH6/4+HhJ0oEDB9SwYUM1b95cO3bs0KxZs7R69WqLNUjSp59+qscee0xbt25V48aN9fLLL6tdu3Z66aWXtGXLFhUvXlzt2rWTyWQyt7l48aI+/PBDTZ8+XWvWrFFiYqJefPFF8/1Vq1apXbt26t69u/bs2aOJEycqJiZGH374ocXY0dHRatq0qXbu3KmOHTsqMzNThQsX1g8//KA9e/ZowIABevfdd/X9999Lknr37q2WLVuaM2ni4+NVq1atbP+5X7x4UcOHD9ekSZO0e/duFSxYUF27dtW6des0c+ZM7dixQy1atFDDhg21f//+bPd7vTlz5qhx48Z6//33NXz48DvqA/eO4eQkz1Kllbxp47+FJpOSN22U1yMVbtgmZddOeZQuY95a4hoUJJ9Haylp3VqLOt5Vq8k1OFiS5F6ihPJUrKTEP9bl3GIAAAAA3Ffu+FWpJpNJS5cu1aJFi/TWW2+Zyz09PTVp0iRzOv/XX3+ty5cva/r06fL09JQkjRs3Ts8++6yGDx8uf39/DR06VL169VL37t3N/VSrVk3S1YyHDRs26OTJk3J1dZUkjRw5UnPnztWPP/6oLl266MiRI3rnnXdUpkwZSVLJkiXN/Rw5ckTNmzdXhQpXv1wVK1bspmvKly+fHB0dlSdPHgUEBJjLhw0bprZt25oPhixZsqQ+++wz1alTR19++aXc3NwkSY0aNdKrr74qSRowYIC+/PJLVatWTS1atJAk9e3bVzVr1lRCQoK5/7S0NI0bN041atSQJE2bNk1ly5bVhg0bVL16dQ0aNEj9+vVTVFSUef5DhgxRnz59NHDgQPMc27Rpow4dLM8JGDTo37T10NBQrVu3Tt9//71atmwpLy8vubu7KzU11WKt2ZWWlqYvvvhClSpVknT1OU+dOlVHjhxRUFCQpKsBkoULF2rq1Kn66KOPrOo/JSVFLVq00Lvvvqu+fftaPT9JSk1NVWpqqkWZq6ur+f9HsI6Tj68MJyel/S9oeE3a2XNyKxpywzZnlyyWk4+vynwxUTIMOTg56eSc2Yr/Zpq5Tvz/TZejp6cqfDtLpsxMGQ4OOvbVBJ1dsignlwMAAADcFYOs8FxldfDil19+kZeXl9LS0pSZmak2bdooOjrafL9ChQoW5xDs3btXlSpVMgcuJOmxxx5TZmam4uLiZBiGjh8/rgYNbnyo3fbt25WSkqL8+fNblF+6dEkHDhyQJPXs2VOdO3fWN998o4iICLVo0ULFixeXJHXr1k2vv/66Fi9erIiICDVv3lwVK1a0as3bt2/Xjh079O2335rLTCaTMjMzdfDgQZUtW1aSLPr19/c3P4//lp08edIcMHBycjIHaiSpTJky8vX11d69e1W9enVt375da9assci0yMjI0OXLl3Xx4kV5eHhIksLDw7PMe/z48ZoyZYqOHDmiS5cu6cqVKwoLC7Nq7Tfj4uJisd6dO3cqIyNDpUqVsqiXmpqa5c8uO9zd3fX444/r66+/VuvWrc3P2BrDhg2zCOBI0sCBAy3+/4qcladyFQW9HKXDo0bowp7dci1cWEW6v63A0x0UP22qJClf/QbK/2Sk/h40QJcOHpRHyZIq0u1tXTl9WmcW3niLFwAAAICHi9XBi3r16unLL7+Ui4uLgoKC5ORk2cX1QYrscHd3v+X9lJQUBQYG3vDshGtvpIiOjlabNm20YMEC/fbbbxo4cKBmzpyppk2bqnPnzoqMjNSCBQu0ePFiDRs2TKNGjbLIFrmdlJQUvfrqq+rWrVuWe0WKFDH/fG2riiTzeRk3KrPmcMuUlBQNGjRIzZo1y3LvWsaHlPW5z5w5U71799aoUaNUs2ZN5cmTRyNGjND69etvOd61MyWu39pyowNA3d3dLc4ESUlJkaOjozZv3ixHR0eLujfbpnMrjo6Omjt3rpo1a6Z69epp+fLlVgcw+vfvr549e1qUkXVx59KTEmVKT5dzvnwW5c758irtzJkbtinUuYtOL/pNp3+5ei7Opb8PyNHNXUX79FP89BjJZFLwG28p/tvpOrv0d3Mdl4BABb7cjuAFAAAA7JbxkJ49YStWBy88PT0tzqO4nbJlyyomJkYXLlwwf8Fes2aNHBwcVLp0aeXJk0chISFaunSp6tWrl6V9lSpVdOLECTk5OSkkJOSm45QqVUqlSpXS22+/rdatW2vq1Klq2rSpJCk4OFivvfaaXnvtNfXv319ff/31TYMXLi4uysjIyDKHPXv2WLXu7EpPT9emTZtUvXp1SVJcXJwSExPNX9SrVKmiuLg4q8des2aNatWqpTfeeMNcdi1T5ZobrbVAgQKSpPj4eOXNe/UNEtu2bbvteJUrV1ZGRoZOnjyp2rVrWzXXm3F1ddXs2bP1wgsvqF69elq2bJnKlcv+IY5sEbm3TOnpuvBnnLyrVlPiqpVXCw1D3lWrKWH2Dzds4+DmJl0XCJMkU2aGua1MJjm4ucmUaVlHGRmk4QEAAAAwy/FvB23btpWbm5uioqK0a9cuLV++XG+99ZZefvll8zaK6OhojRo1Sp999pn279+vLVu26PPPP5ckRUREqGbNmmrSpIkWL16sQ4cOae3atXrvvfe0adMmXbp0SV27dlVsbKwOHz6sNWvWaOPGjeYv/z169NCiRYt08OBBbdmy5ba/wQ8JCdHKlSt17NgxnT59WtLVsyrWrl2rrl27atu2bdq/f7/mzZuX5cDOO+Hs7Ky33npL69ev1+bNm9W+fXs9+uij5mDGgAEDNH36dA0aNEi7d+/W3r17NXPmTL3//vu37LdkyZLatGmTFi1apD///FMffPCBNm7caFEnJCREO3bsUFxcnE6fPq20tDSVKFFCwcHBio6O1v79+7VgwQKNGjXqtusoVaqU2rZtq3bt2mn27Nk6ePCgNmzYoGHDhmnBggV3/HxcXV31008/qUaNGqpXr5527959x33h7iXM/E4Fnn1O+Rs2klvREBXt3UcO7m46/b8/49D3B6jwq/++BjVxzWoVbNJM+RpEyCUwUN7h1VWocxclrVkt/S8DKXHNagW1ay+fmrXkEhAo3yfqyL9Va51bucIma7xbjp4e8q5URt6Vrp7B4xFaWN6VysgtONDGMwMAAADuX3d8YGd2eXh4aNGiRerevbuqVasmDw8PNW/eXKNHjzbXiYqK0uXLl/Xpp5+qd+/e8vPz0wsvvCDp6laLX3/9Ve+99546dOigU6dOKSAgQE888YT8/f3l6OioM2fOqF27dkpISJCfn5+aNWtmPusgIyNDb775pv755x95e3urYcOG+vTTT28638GDB+vVV19V8eLFlZqaKpPJpIoVK2rFihV67733VLt2bZlMJhUvXlytWrW6J8+nb9++atOmjY4dO6batWtr8uTJ5vuRkZH65ZdfNHjwYA0fPlzOzs4qU6aMOnfufMt+X331VW3dulWtWrWSYRhq3bq13njjDf3222/mOq+88opiY2MVHh6ulJQULV++XHXr1tV3332n119/XRUrVlS1atU0dOhQ86GjtzJ16lTz4avHjh2Tn5+fHn30UT1zk9doZpeLi4t+/PFHtWzZ0pyBUb58+bvqE3fm7LLf5eTrq0KdX5Fzvvy6+Nd+/dnrbaWfu3qIp4t/gHRdFsXxaVMlk0mFXnlVLgUKKC0xUYlrVuvYVxPMdQ5/OkqFXumior3ekXPevLpy+rROzZ+r41MnZxn/fuBTtbxqLv3G/LncyHclSUenz9aOTv1tNS0AAADcawaZwrnJMJn+k9ONXBMTE6MePXooMTHR1lOxGcMwNGfOHDVp0uSO2tetW1dhYWEaM2bMPZ3Xg2bj44/aego2cXL9OVtPwSYap8XZegoAAAAPvKSR3W9fKYf49B5rs7FthVARbK5169YqXLiwVW2+/fZbeXl5adWqVTk0KwAAAAC4OcPBsNn1MMrxbSPArezfv1+Ssryh5Haee+451ahRQ9K/b50BAAAAADyYCF7YUPv27dW+fXtbT8Om7vQNLnny5FGePHnu8WwAAAAAAPaI4AUAAAAAANZy4BSG3MTTBgAAAAAAdo3MCwAAAAAArGQYD+fBmbZC5gUAAAAAALBrZF4AAAAAAGAtzrzIVTxtAAAAAABg1wheAAAAAAAAu8a2EQAAAAAArGQ4cGBnbiLzAgAAAAAA2DUyLwAAAAAAsJZBLkBu4mkDAAAAAAC7RvACAAAAAADYNbaNAAAAAABgLQ7szFVkXgAAAAAA8AAbP368QkJC5Obmpho1amjDhg03rRsTEyPDMCwuNzc3izomk0kDBgxQYGCg3N3dFRERof379+foGgheAAAAAABgJcNwsNlljVmzZqlnz54aOHCgtmzZokqVKikyMlInT568aRtvb2/Fx8ebr8OHD1vc/+STT/TZZ59pwoQJWr9+vTw9PRUZGanLly/f0bPMDoIXAAAAAAA8oEaPHq1XXnlFHTp0ULly5TRhwgR5eHhoypQpN21jGIYCAgLMl7+/v/meyWTSmDFj9P777+v5559XxYoVNX36dB0/flxz587NsXUQvAAAAAAA4D6Smpqq5ORkiys1NTVLvStXrmjz5s2KiIgwlzk4OCgiIkLr1q27af8pKSkqWrSogoOD9fzzz2v37t3mewcPHtSJEycs+vTx8VGNGjVu2efd4sBO4CFQbfUftp4CctEC59K2noJNTOg419ZTsImfJ5a19RQAAHg42fDAzmHDhmnQoEEWZQMHDlR0dLRF2enTp5WRkWGROSFJ/v7+2rdv3w37Ll26tKZMmaKKFSsqKSlJI0eOVK1atbR7924VLlxYJ06cMPfx3z6v3csJBC8AAAAAALiP9O/fXz179rQoc3V1vSd916xZUzVr1jR/rlWrlsqWLauJEydqyJAh92SMO0HwAgAAAAAAKxkOtjuFwdXVNVvBCj8/Pzk6OiohIcGiPCEhQQEBAdkay9nZWZUrV9Zff/0lSeZ2CQkJCgwMtOgzLCwsmyuwHmdeAAAAAADwAHJxcVHVqlW1dOlSc1lmZqaWLl1qkV1xKxkZGdq5c6c5UBEaGqqAgACLPpOTk7V+/fps93knyLwAAAAAAMBahu3OvLBGz549FRUVpfDwcFWvXl1jxozRhQsX1KFDB0lSu3btVKhQIQ0bNkySNHjwYD366KMqUaKEEhMTNWLECB0+fFidO3eWdPVNJD169NDQoUNVsmRJhYaG6oMPPlBQUJCaNGmSY+sgeAEAAAAAwAOqVatWOnXqlAYMGKATJ04oLCxMCxcuNB+4eeTIETlctwXm3LlzeuWVV3TixAnlzZtXVatW1dq1a1WuXDlznT59+ujChQvq0qWLEhMT9fjjj2vhwoVyc3PLsXUYJpPJlGO9AwByHW8bebjwthEAAGzj4pSBNhvbo+Og21d6wJB5AQAAAACAtWx4YOfDiKcNAAAAAADsGpkXAAAAAABY6z45sPNBQeYFAAAAAACwawQvAAAAAACAXWPbCAAAAAAAVjI4sDNX8bQBAAAAAIBdI/MCAAAAAABrGeQC5CaeNgAAAAAAsGtkXgAAAAAAYC0HXpWam8i8AAAAAAAAdo3gBQAAAAAAsGsPfPCiffv2atKkSa6PGxISojFjxuT6uHfrds8rJiZGhmHIMAz16NEj1+b1X+3btzfPY+7cuTabBwAAAICHk2E42Ox6GN3TMy/at2+vadOmXe3YyUmFCxdWixYtNHjwYLm5uWWrj9jYWNWrV0/nzp2Tr69vtsc+dOiQQkNDtXXrVoWFhZnLx44dK5PJZM0ycBve3t6Ki4uTp6enzeYwduxYffzxxwoMDLTZHAD8K9/j4SrWq5N8qpSXW1BBbWr+hhLmL7X1tO5K22f99FTtvPJ0d9DeA5f0xYx4xZ9Mu2l9B0Nq/WwB1avhLV9vJ51NStfStUma9etpcx03V0NRTQvq0bA8yuPpqITTafp5+VktXJmYCysCAAC4f93zAzsbNmyoqVOnKi0tTZs3b1ZUVJQMw9Dw4cPv9VDZ4uPjY5NxH2SGYSggIOCWda5cuSIXF5ccm4OPjw9/toAdcfT0UPKOOB2N+UnhP4639XTuWvPI/Hqmfj6NiTmuhNNpavtcAQ3uVkRvRP+ttPQbB8SbN8yvRnV89enUeB2JT1WJom7qHhWoi5cy9PPyc5KkTi38VbG0p0ZNOa6TZ9JUuZynXm8doLOJ6dqwIyU3lwgAAO4WB3bmqnueb+Lq6qqAgAAFBwerSZMmioiI0JIlS8z3U1NT1a1bNxUsWFBubm56/PHHtXHjRklXsyfq1asnScqbN68Mw1D79u0lSQsXLtTjjz8uX19f5c+fX88884wOHDhg7jc0NFSSVLlyZRmGobp160rKug3iVuNLVzM/DMPQ0qVLFR4eLg8PD9WqVUtxcXHmOgcOHNDzzz8vf39/eXl5qVq1avr999+tek6xsbGqXr26PD095evrq8cee0yHDx+WJEVHRyssLEwTJ05UcHCwPDw81LJlSyUlJVn0MWnSJJUtW1Zubm4qU6aMvvjiC4v7R48eVcuWLeXr66t8+fLp+eef16FDh8z3MzIy1LNnT/Mz7dOnzx1nqYSEhGjIkCFq166dvL291aVLFx06dEiGYWjmzJmqVauW3NzcVL58ea1YscLiORiGoUWLFqly5cpyd3dX/fr1dfLkSf32228qW7asvL291aZNG128ePGO5gYg551atFJ/DhyjhHnW/bvQXj3XIJ++//W01m9P0aFjqfp06nHl83XSo2F5btqmbDF3/bEtRZt2pejkmTSt3XJe2/ZcUMlQd4s6y9YladefF3XyTJoWrUrUwX8uq9R1dQAAAJBVjm6W2bVrl9auXWvxG/g+ffrop59+0rRp07RlyxaVKFFCkZGROnv2rIKDg/XTTz9JkuLi4hQfH6+xY8dKki5cuKCePXtq06ZNWrp0qRwcHNS0aVNlZmZKkjZs2CBJ+v333xUfH6/Zs2ffcE63Gv967733nkaNGqVNmzbJyclJHTt2NN9LSUlRo0aNtHTpUm3dulUNGzbUs88+qyNHjmTruaSnp6tJkyaqU6eOduzYoXXr1qlLly4yjH8jd3/99Ze+//57/fzzz1q4cKG2bt2qN954w3z/22+/1YABA/Thhx9q7969+uijj/TBBx+Yt+2kpaUpMjJSefLk0apVq7RmzRp5eXmpYcOGunLliiRp1KhRiomJ0ZQpU7R69WqdPXtWc+bMydYabmTkyJGqVKmStm7dqg8++MBc/s4776hXr17aunWratasqWeffVZnzpyxaBsdHa1x48Zp7dq15qDLmDFjNGPGDC1YsECLFy/W559/fsdzA4Ds8vdzVj4fJ23be8FcdvFypv48eEllit08yLD370uqVMZDQQWv/p0XUthVZUt4aPOuFIs6NSp5KZ/v1cTHCqU8FOTvoq17yLoAAAC4lXu+beSXX36Rl5eX0tPTlZqaKgcHB40bN07S1QDEl19+qZiYGD399NOSpK+//lpLlizR5MmT9c477yhfvnySpIIFC1qcedG8eXOLcaZMmaICBQpoz549Kl++vAoUKCBJyp8//023NGRn/Gs+/PBD1alTR5LUr18/NW7cWJcvX5abm5sqVaqkSpUqmesOGTJEc+bM0fz589W1a9fbPqPk5GQlJSXpmWeeUfHixSVJZcuWtahz+fJlTZ8+XYUKFZIkff7552rcuLFGjRqlgIAADRw4UKNGjVKzZs0kXc082bNnjyZOnKioqCjNmjVLmZmZmjRpkjkoMnXqVPn6+io2NlZPPfWUxowZo/79+5v7mDBhghYtWnTb+d9M/fr11atXL/Pna1keXbt2Nf/5ffnll1q4cKEmT56sPn36mOsOHTpUjz32mCSpU6dO6t+/vw4cOKBixYpJkl544QUtX75cffv2zfZ8UlNTlZqaalHm6uoqV1fXO1ofgIdDXu+rfzUmJmdYlCcmZyivz83/2vxx4Rl5uDnoy0HFlGm6mkn6zbxTWrEh2Vxn4swEdX0pQNOGl1R6hkmmTJM+/78T2r3/Us4sBgAA5JyH9OBMW7nnwYt69erpyy+/1IULF/Tpp5/KycnJ/MX1wIEDSktLM39JlSRnZ2dVr15de/fuvWW/+/fv14ABA7R+/XqdPn3anHFx5MgRlS9fPltzs2b8ihUrmn++dijkyZMnVaRIEaWkpCg6OloLFixQfHy80tPTdenSpWxnXuTLl0/t27dXZGSknnzySUVERKhly5YWh08WKVLEHLiQpJo1ayozM1NxcXHKkyePDhw4oE6dOumVV14x10lPTzefA7F9+3b99ddfypPHMsX58uXLOnDggJKSkhQfH68aNWqY7zk5OSk8PPyOt46Eh4ffsLxmzZpZxrjV8/b395eHh4c5cHGt7Fp2TXYNGzZMgwYNsigbOHCgoqOjreoHwIOtTnVvvdn233//Dh539I76ebyqt+pU99HIycd15HiqigW7qnNLf51NTNeyP65u+3u2Xl6VDnXX4PFHdepMmh4p6aHXWvvrbGKatu9jaxwAAMDN3PPghaenp0qUKCHpanZEpUqVNHnyZHXq1Omu+n322WdVtGhRff311woKClJmZqbKly9v3gJxrzk7O5t/vpa5cC1g0rt3by1ZskQjR45UiRIl5O7urhdeeMGquUydOlXdunXTwoULNWvWLL3//vtasmSJHn300du2TUm5ml789ddfWwQfJMnR0dFcp2rVqvr222+ztL+WpXKv3c3bR/77vK//fK3s2vPPrv79+6tnz54WZWRdAPivDdtT9OfBv82fnZ2u/jvf19tR55LTzeW+3o76+2hqlvbXdGheUD8uOqNVm65mWhw+nqoC+Z3V4un8WvZHklycDb3cpKA++vIfbfrfVpJDx1JVLNhNTZ/KT/ACAID7jcGBnbkpR/NcHBwc9O677+r999/XpUuXVLx4cbm4uGjNmjXmOmlpadq4caPKlSsnSebzMTIy/k3XPXPmjOLi4vT++++rQYMGKlu2rM6dO2cx1o3a/Vd2xs+ONWvWqH379mratKkqVKiggIAAi4Mws6ty5crq37+/1q5dq/Lly2vGjBnme0eOHNHx48fNn//44w85ODiodOnS8vf3V1BQkP7++2+VKFHC4rp2cGmVKlW0f/9+FSxYMEuda2/qCAwM1Pr1681jpKena/PmzVav43b++OOPLGP8d5tMTnB1dZW3t7fFRfACwH9dSs1U/Kk083Uk/orOJqWrUpl/A7Lubg4qFequfX/ffHuHq4shU6Zl5lpm5r//XePoaMjZyciS3ZaZaeKwcgAAgNvI8U06LVq0kKOjo8aPHy9PT0+9/vrreuedd7Rw4ULt2bNHr7zyii5evGjOzChatKgMw9Avv/yiU6dOKSUlRXnz5lX+/Pn11Vdf6a+//tKyZcuy/Ea9YMGCcnd318KFC5WQkJDlzRySsjV+dpQsWVKzZ8/Wtm3btH37drVp08aqrICDBw+qf//+WrdunQ4fPqzFixdr//79Fl/o3dzcFBUVpe3bt2vVqlXq1q2bWrZsaT7PY9CgQRo2bJg+++wz/fnnn9q5c6emTp2q0aNHS5Latm0rPz8/Pf/881q1apUOHjyo2NhYdevWTf/8848kqXv37vr44481d+5c7du3T2+88YYSExOzvY7sGj9+vObMmaN9+/bpzTff1Llz5ywOQAVw/3P09JB3pTLyrlRGkuQRWljelcrILTjwNi3t0/ylZ9WqkZ+qV/RS0SBX9ewQpLOJ6fpj23lznaFvF1HjunnNnzfuSFHLRn4KL++lgvmd9WhYHjWJyKd1/2tz6XKmdsZdUIfmBVW+lIf88zurQU0f1XvUR+u2ns8yBwAAYOccHGx3PYTu+baRLAM4Oalr16765JNP9Prrr+vjjz9WZmamXn75ZZ0/f17h4eFatGiR8ua9+h+AhQoV0qBBg9SvXz916NBB7dq1U0xMjGbOnKlu3bqpfPnyKl26tD777DPz61CvjfPZZ59p8ODBGjBggGrXrq3Y2Ngs87nd+NkxevRodezYUbVq1ZKfn5/69u2r5OTk2zf8Hw8PD+3bt0/Tpk3TmTNnFBgYqDfffFOvvvqquU6JEiXUrFkzNWrUSGfPntUzzzxj8SrUzp07y8PDQyNGjNA777wjT09PVahQQT169DCPsXLlSvXt21fNmjXT+fPnVahQITVo0EDe3t6SpF69eik+Pl5RUVFycHBQx44d1bRp0xsGfu7Gxx9/rI8//ljbtm1TiRIlNH/+fPn5+d3TMQDYlk/V8qq59Bvz53Ij35UkHZ0+Wzs69bfVtO7YT4vOyM3FUNeXAuXp4aA9f13SwM+OKi3936yJAD9neXs5mj9PnJmgts8X0OttAuSTx1Fnk9K1cFWiZv5yylznk0nHFNW0oHp3DJKXp6NOnU3TN/NO6beVibm5PAAAgPuOYbrT0xmRY6KjozV37lxt27bN1lPJIiYmRj169MhWhsahQ4cUGhqqrVu3KiwsLEfmYxiG5syZoyZNmuRI/8D9aIFzaVtPwSYmdJxr6ynYxM8Tc34bHgAAyOryT5/abGy35m/bbGxbeTjzTXBXkpKS5OXlZdVrS++11157TV5eXjYbHwAAAMBDznCw3fUQyvFtI3iwNG/eXI8//rgkydfX12bzGDx4sHr37i1JFq+YBQAAAAA8eNg2AgAPGLaNPFzYNgIAgG1cnvuZzcZ2a9LNZmPbysOZbwIAAAAAAO4bBC8AAAAAAIBd48wLAAAAAACs9ZAenGkrPG0AAAAAAGDXyLwAAAAAAMBahmHrGTxUyLwAAAAAAAB2jcwLAAAAAACs5UAuQG7iaQMAAAAAALtG8AIAAAAAANg1to0AAAAAAGAtDuzMVWReAAAAAAAAu0bmBQAAAAAA1jLIBchNPG0AAAAAAGDXCF4AAAAAAAC7xrYRAAAAAACs5UAuQG7iaQMAAAAAALtG5gUAAAAAANbiVam5iuAFADxgJnSca+sp2MRrU5rYegq2MTHO1jMAAADIcQQvAAAAAACwFq9KzVU8bQAAAAAAYNcIXgAAAAAAALvGthEAAAAAAKzFgZ25iswLAAAAAABg18i8AAAAAADAWg7kAuQmnjYAAAAAALBrBC8AAAAAAIBdY9sIAAAAAABWMnFgZ64i8wIAAAAAANg1Mi8AAAAAALCWQS5AbuJpAwAAAAAAu0bmBQAAAAAA1iLzIlfxtAEAAAAAgF0jeAEAAAAAAOwa20YAAAAAALASr0rNXWReAAAAAAAAu0bmBQAAAAAA1uLAzlzF08ZdOXTokAzDkGEYCgsLy7Vx27dvbx537ty5uTYuAAAAACD3EbywgWtfum92RUdH23qKVvv999+1dOlS8+fo6GgZhqHXXnvNot62bdtkGIYOHTp0V+ONHTtW8fHxd9UHgOxp+6yfpn1SUj9+XlpDehRRYEHnW9Z3MKS2zxXQpA+L68fPS+urocXVqpGfRR03V0OvvuivqR+X0I+fl9b4gcXU8AnfHFxFzsj3eLjC53ypBodXqXFanPyfa2DrKQEAADyQ2DZiA9d/6Z41a5YGDBiguLg4c5mXl5dV/aWlpcnZ+dZfJnJa/vz5lT9/fosyNzc3TZ48Wb169VLJkiXv6Xg+Pj7y8fG5p30CyKp5ZH49Uz+fxsQcV8LpNLV9roAGdyuiN6L/Vlq66cZtGuZXozq++nRqvI7Ep6pEUTd1jwrUxUsZ+nn5OUlSpxb+qljaU6OmHNfJM2mqXM5Tr7cO0NnEdG3YkZKbS7wrjp4eSt4Rp6MxPyn8x/G2ng4AAMhNHNiZq8i8sIGAgADz5ePjI8MwzJ8LFiyo0aNHq3DhwnJ1dVVYWJgWLlxobnttm8asWbNUp04dubm56dtvv1VGRoZ69uwpX19f5c+fX3369FFUVJSaNGlibhsSEqIxY8ZYzCUsLMwi0yMxMVGdO3dWgQIF5O3trfr162v79u13tM7SpUurXr16eu+9925aJzY2VoZhaNGiRapcubLc3d1Vv359nTx5Ur/99pvKli0rb29vtWnTRhcvXryjeQC4c881yKfvfz2t9dtTdOhYqj6delz5fJ30aFiem7YpW8xdf2xL0aZdKTp5Jk1rt5zXtj0XVDLU3aLOsnVJ2vXnRZ08k6ZFqxJ18J/LKnVdnfvBqUUr9efAMUqY97utpwIAAHBT48ePV0hIiNzc3FSjRg1t2LDhpnW//vpr1a5dW3nz5lXevHkVERGRpf712/ivXQ0bNszRNRC8sDNjx47VqFGjNHLkSO3YsUORkZF67rnntH//fot6/fr1U/fu3bV3715FRkZq1KhRiomJ0ZQpU7R69WqdPXtWc+bMsXr8Fi1amAMHmzdvVpUqVdSgQQOdPXv2jtbz8ccf66efftKmTZtuWS86Olrjxo3T2rVrdfToUbVs2VJjxozRjBkztGDBAi1evFiff/75Hc0BwJ3x93NWPh8nbdt7wVx28XKm/jx4SWWK3TzIsPfvS6pUxkNBBV0kSSGFXVW2hIc270qxqFOjkpfy+V5NAKxQykNB/i7auuf+yboAAAAPOQcH211WmDVrlnr27KmBAwdqy5YtqlSpkiIjI3Xy5Mkb1o+NjVXr1q21fPlyrVu3TsHBwXrqqad07Ngxi3oNGzZUfHy8+fruu+/u+FFmB9tG7MzIkSPVt29fvfjii5Kk4cOHa/ny5RozZozGj/83JblHjx5q1qyZ+fOYMWPUv39/c9mECRO0aNEiq8ZevXq1NmzYoJMnT8rV1dU8n7lz5+rHH39Uly5drF5PlSpV1LJlS/Xt29fiTIz/Gjp0qB577DFJUqdOndS/f38dOHBAxYoVkyS98MILWr58ufr27Wv1HADcmbzeV/+KSEzOsChPTM5QXp+b//Xx48Iz8nBz0JeDiinTdPUMjG/mndKKDcnmOhNnJqjrSwGaNryk0jNMMmWa9Pn/ndDu/ZdyZjEAAAAPqdGjR+uVV15Rhw4dJF39rrhgwQJNmTJF/fr1y1L/22+/tfg8adIk/fTTT1q6dKnatWtnLnd1dVVAQEDOTv46BC/sSHJyso4fP27+En/NY489lmXrRnh4uPnnpKQkxcfHq0aNGuYyJycnhYeHy2S68Z70G9m+fbtSUlKynF1x6dIlHThwwJqlWBg6dKjKli2rxYsXq2DBgjesU7FiRfPP/v7+8vDwMAcurpXdKrXpRlJTU5WammpR5urqag7MALBUp7q33mwbaP48eNzRO+rn8areqlPdRyMnH9eR46kqFuyqzi39dTYxXcv+SJIkPVsvr0qHumvw+KM6dSZNj5T00Gut/XU2MU3b97FFDAAA4Fay+13nypUr2rx5s/r3728uc3BwUEREhNatW5etsS5evKi0tDTly5fPojw2NlYFCxZU3rx5Vb9+fQ0dOjTLd8l7ieDFfcrT09PqNg4ODlmCGWlpaeafU1JSFBgYqNjY2CxtfX19rR7vmuLFi+uVV15Rv379NHny5BvWuf7AUcMwshxAahiGMjMzrRp32LBhGjRokEXZwIED78u3uQC5YcP2FP158G/zZ2enq4dQ+Xo76lxyurnc19tRfx9NzdL+mg7NC+rHRWe0atPVTIvDx1NVIL+zWjydX8v+SJKLs6GXmxTUR1/+o03/20py6FiqigW7qelT+QleAACA+4LJhgd2Zve7zunTp5WRkSF/f3+Lcn9/f+3bty9bY/Xt21dBQUGKiIgwlzVs2FDNmjVTaGioDhw4oHfffVdPP/201q1bJ0dHxztb1G0QvLAj3t7eCgoK0po1a1SnTh1z+Zo1a1S9evWbtvPx8VFgYKDWr1+vJ554QpKUnp5uPrPimgIFCli86SQ5OVkHDx40f65SpYpOnDghJycnhYSE3MOVSQMGDFDx4sU1c+bMe9rvrfTv3189e/a0KCPrAri5S6mZunTKMkh4Nildlcp46uA/V4MV7m4OKhXqrl9XJN60H1cXQ6ZMy0BpZua/B3I7OhpydjKyBFMzM01y4NBuAACA28qt7zoff/yxZs6cqdjYWLm5uZnLrx1zIEkVKlRQxYoVVbx4ccXGxqpBg5x5dTzBCzvzzjvvaODAgSpevLjCwsI0depUbdu2Lcu+o//q3r27Pv74Y5UsWVJlypTR6NGjlZiYaFGnfv36iomJ0bPPPitfX18NGDDAIioWERGhmjVrqkmTJvrkk09UqlQpHT9+XAsWLFDTpk0ttqpYy9/fXz179tSIESPuuA9rsUUEuHvzl55Vq0Z+On7yihJOp+ml5wvobGK6/th23lxn6NtFtG7reS2Ivfoa1I07UtSykZ9OnU3XkfirGRVNIvJpydpESdKly5naGXdBHZoXVGpagk6dSVP5Uh6q96iPJv+QYItl3jFHTw95lihi/uwRWljelcroytkkXT4af4uWAADgvmfY7v0X2f2u4+fnJ0dHRyUkWP43VkJCwm3Pqxg5cqQ+/vhj/f777xbb/G+kWLFi8vPz019//UXw4mHRrVs3JSUlqVevXjp58qTKlSun+fPnq2TJkrds16tXL8XHxysqKkoODg7q2LGjmjZtqqSkJHOd/v376+DBg3rmmWfk4+OjIUOGWGReGIahX3/9Ve+99546dOigU6dOKSAgQE888USWNKM70bt3b3355Ze6fPnyXfcFIHf8tOiM3FwMdX0pUJ4eDtrz1yUN/Oyo0tL/zZoI8HOWt9e/gdCJMxPU9vkCer1NgHzyOOpsUroWrkrUzF9Omet8MumYopoWVO+OQfLydNSps2n6Zt4p/bYyMTeXd9d8qpZXzaXfmD+XG/muJOno9Nna0an/zZoBAADkChcXF1WtWlVLly5VkyZNJEmZmZlaunSpunbtetN2n3zyiT788EMtWrQoW7/E/ueff3TmzBkFBgbetu6dMkzWnOiI+0r79u2VmJiouXPn5tgYhw4dUmhoqLZu3aqwsLAcG+dmDMPQnDlzzP8gApCefXWvradgE69NaWLrKdhE47Q4W08BAICHUsof8202ttejz2W77qxZsxQVFaWJEyeqevXqGjNmjL7//nvt27dP/v7+ateunQoVKqRhw4ZJuvrGywEDBmjGjBkWL5Pw8vKSl5eXUlJSNGjQIDVv3lwBAQE6cOCA+vTpo/Pnz2vnzp05lv1uuzwXPFBq1aqlWrVq5dp4r732mry8vHJtPAAAAAC4H7Vq1UojR47UgAEDFBYWpm3btmnhwoXm7PojR45YnI345Zdf6sqVK3rhhRcUGBhovkaOHClJcnR01I4dO/Tcc8+pVKlS6tSpk6pWrapVq1bl6LZ9Mi8eYLmReZGenq5Dhw5JurrvKjg4OMfGut7JkyeVnHz1TQaBgYF39PYV4EFF5sXDhcwLAABs437JvHhQcObFAywmJibHx3ByclKJEiVyfJz/KliwoAoWLJjr4wIAAACApH9fpYZcwbYRAAAAAABg18i8AAAAAADASiYbvir1YcTTBgAAAAAAdo3gBQAAAAAAsGtsGwEAAAAAwFoc2JmryLwAAAAAAAB2jcwLAAAAAACsxYGduYqnDQAAAAAA7BqZFwAAAAAAWMnEmRe5iswLAAAAAABg1wheAAAAAAAAu8a2EQAAAAAArMWBnbmKpw0AAAAAAOwamRcAAAAAAFjJJA7szE1kXgAAAAAAALtG8AIAAAAAANg1to0AAAAAAGAlEwd25irDZDKZbD0JAABwZxY4l7b1FGyicVqcracAAHjIJW5dZrOxfSvXt9nYtkLmBQAAAAAA1iLzIlfxtAEAAAAAgF0j8wIAAAAAACuZDF6VmpvIvAAAAAAAAHaN4AUAAAAAALBrbBsBAAAAAMBKvCo1d/G0AQAAAACAXSPzAgAAAAAAa3FgZ64i8wIAAAAAANg1ghcAAAAAAMCusW0EAAAAAAArcWBn7uJpAwAAAAAAu0bmBQAAAAAAVjKJAztzE5kXAAAAAADArpF5AQAAAACAlTjzInfxtAEAAAAAgF0jeAEAAAAAAOwa20YAAAAAALCWwYGduYnMCwAAAAAAYNcIXthY+/bt1aRJE1tP447UrVtXhmHIMAxt27btrvvq0aOHVW2uje3r63tXYwMAAACAtUxysNn1MHo4V22Fa1+Qb3ZFR0fbeoo29corryg+Pl7ly5e/q35mz56tIUOGWNUmPj5eY8aMuatxAQDWy/d4uMLnfKkGh1epcVqc/J9rYOspAQCABxxnXtxGfHy8+edZs2ZpwIABiouLM5d5eXnZYlq56sqVK3JxcbnhPQ8PDwUEBNz1GPny5bO6TUBAgHx8fO56bACAdRw9PZS8I05HY35S+I/jbT0dAADwECDz4jYCAgLMl4+PjwzDMH8uWLCgRo8ercKFC8vV1VVhYWFauHChRfudO3eqfv36cnd3V/78+dWlSxelpKRka2yTyaQCBQroxx9/NJeFhYUpMDDQ/Hn16tVydXXVxYsXJUmJiYnq3LmzChQoIG9vb9WvX1/bt2831z9w4ICef/55+fv7y8vLS9WqVdPvv/9uMW5ISIiGDBmidu3aydvbW126dMn284qNjZVhGFq0aJEqV64sd3d31a9fXydPntRvv/2msmXLytvbW23atDHPWcq6bSQkJEQfffSROnbsqDx58qhIkSL66quvsj0PAEDOObVopf4cOEYJ836/fWUAAB5QJsOw2fUwInhxF8aOHatRo0Zp5MiR2rFjhyIjI/Xcc89p//79kqQLFy4oMjJSefPm1caNG/XDDz/o999/V9euXbPVv2EYeuKJJxQbGytJOnfunPbu3atLly5p3759kqQVK1aoWrVq8vDwkCS1aNHCHCjYvHmzqlSpogYNGujs2bOSpJSUFDVq1EhLly7V1q1b1bBhQz377LM6cuSIxdgjR45UpUqVtHXrVn3wwQdWP5vo6GiNGzdOa9eu1dGjR9WyZUuNGTNGM2bM0IIFC7R48WJ9/vnnt+xj1KhRCg8P19atW/XGG2/o9ddft8h6AQAAAAA8HAhe3IWRI0eqb9++evHFF1W6dGkNHz5cYWFh5nMYZsyYocuXL2v69OkqX7686tevr3Hjxumbb75RQkJCtsaoW7euOXixcuVKVa5c2aIsNjZWderUkXQ1C2PDhg364YcfFB4erpIlS2rkyJHy9fU1Z29UqlRJr776qsqXL6+SJUtqyJAhKl68uObPn28xbv369dWrVy8VL15cxYsXt/rZDB06VI899pgqV66sTp06acWKFfryyy9VuXJl1a5dWy+88IKWL19+yz4aNWqkN954QyVKlFDfvn3l5+d32zbXS01NVXJyssWVmppq9VoAAAAA4L9MhoPNrofRw7nqeyA5OVnHjx/XY489ZlH+2GOPae/evZKkvXv3qlKlSvL09LS4n5mZme0Mgjp16mjPnj06deqUVqxYobp165qDF2lpaVq7dq3q1q0rSdq+fbtSUlKUP39+eXl5ma+DBw/qwIEDkq5mXvTu3Vtly5aVr6+vvLy8tHfv3iyZF+Hh4Xf6aCRJFStWNP/s7+8vDw8PFStWzKLs5MmT2e7j2nad27W53rBhw+Tj42NxDRs2zIpVAAAAAADsAQd22rkKFSooX758WrFihVasWKEPP/xQAQEBGj58uDZu3Ki0tDTVqlVL0tXARGBgoDkr43rXXifau3dvLVmyRCNHjlSJEiXk7u6uF154QVeuXLGof33A5U44OzubfzYMw+LztbLMzMxs95HdNtfr37+/evbsaVHm6uqa7fYAAAAAcDMmPZxnT9gKwYs75O3traCgIK1Zs8a8bUOS1qxZo+rVq0uSypYtq5iYGF24cMEcDFizZo0cHBxUunTpbI1jGIZq166tefPmaffu3Xr88cfl4eGh1NRUTZw4UeHh4ea+q1SpohMnTsjJyUkhISE37G/NmjVq3769mjZtKulqwOPQoUN3+BTsm6urK8EKAAAAAHgAsG3kLrzzzjsaPny4Zs2apbi4OPXr10/btm1T9+7dJUlt27aVm5uboqKitGvXLi1fvlxvvfWWXn75Zfn7+2d7nLp16+q7775TWFiYvLy85ODgoCeeeELffvutReAkIiJCNWvWVJMmTbR48WIdOnRIa9eu1XvvvadNmzZJkkqWLKnZs2dr27Zt2r59u9q0aWNVNgMAAI6eHvKuVEbelcpIkjxCC8u7Uhm5BQfepiUAAMCdIfPiLnTr1k1JSUnq1auXTp48qXLlymn+/PkqWbKkJMnDw0OLFi1S9+7dzW8Ead68uUaPHm3VOHXq1FFGRob5bAvpakBj3rx5FmWGYejXX3/Ve++9pw4dOujUqVMKCAjQE088YQ6WjB49Wh07dlStWrXk5+envn37Kjk5+a6fBQDg4eFTtbxqLv3G/LncyHclSUenz9aOTv1tNS0AAHLVw3pwpq0YJpPJZOtJ4P5Ut25di7er2EJMTIx69OihxMREm80BAGxpgXP2tiE+aBqn8epsAIBtHY/bYbOxg0pXvH2lBwyhItyVL774Ql5eXtq5c2euj+3l5aXXXnst18cFAAAAAJNh2Ox6GLFtBHfs22+/1aVLlyRJRYoUyfXxt23bJklydHTM9bEBAAAAALmH4AXuWKFChWw6fokSJWw6PgAAAAAgdxC8AAAAAADASiY9nNs3bIUzLwAAAAAAgF0j8wIAAAAAACvxqtTcxdMGAAAAAAB2jcwLAAAAAACsxJkXuYvMCwAAAAAAYNcIXgAAAAAAALvGthEAAAAAAKzEgZ25i6cNAAAAAADsGpkXAAAAAABYiQM7cxeZFwAAAAAAwK4RvAAAAAAAAHaNbSMAAAAAAFiJAztzF08bAAAAAIAH2Pjx4xUSEiI3NzfVqFFDGzZsuGX9H374QWXKlJGbm5sqVKigX3/91eK+yWTSgAEDFBgYKHd3d0VERGj//v05uQSCFwAAAAAAWMskw2aXNWbNmqWePXtq4MCB2rJliypVqqTIyEidPHnyhvXXrl2r1q1bq1OnTtq6dauaNGmiJk2aaNeuXeY6n3zyiT777DNNmDBB69evl6enpyIjI3X58uW7eqa3YphMJlOO9Q4AAHLUAufStp6CTTROi7P1FAAAD7m/Dxyw2djFihfPdt0aNWqoWrVqGjdunCQpMzNTwcHBeuutt9SvX78s9Vu1aqULFy7ol19+MZc9+uijCgsL04QJE2QymRQUFKRevXqpd+/ekqSkpCT5+/srJiZGL7744l2u7sbIvAAAAAAA4D6Smpqq5ORkiys1NTVLvStXrmjz5s2KiIgwlzk4OCgiIkLr1q27Yd/r1q2zqC9JkZGR5voHDx7UiRMnLOr4+PioRo0aN+3zXuDATgAA7mMPawYCGScPl6QRb9l6Cjbh6Olp6ynYhinT1jOwCa83P7H1FGAlk2Hd9o17adiwYRo0aJBF2cCBAxUdHW1Rdvr0aWVkZMjf39+i3N/fX/v27bth3ydOnLhh/RMnTpjvXyu7WZ2cQPACAAAAAID7SP/+/dWzZ0+LMldXVxvNJncQvAAAAAAAwEomk+0yL1xdXbMVrPDz85Ojo6MSEhIsyhMSEhQQEHDDNgEBAbesf+1/ExISFBgYaFEnLCzMmmVYhTMvAAAAAAB4ALm4uKhq1apaunSpuSwzM1NLly5VzZo1b9imZs2aFvUlacmSJeb6oaGhCggIsKiTnJys9evX37TPe4HMCwAAAAAArGS6T3IBevbsqaioKIWHh6t69eoaM2aMLly4oA4dOkiS2rVrp0KFCmnYsGGSpO7du6tOnToaNWqUGjdurJkzZ2rTpk366quvJEmGYahHjx4aOnSoSpYsqdDQUH3wwQcKCgpSkyZNcmwdBC8AAAAAAHhAtWrVSqdOndKAAQN04sQJhYWFaeHCheYDN48cOSIHh38DMbVq1dKMGTP0/vvv691331XJkiU1d+5clS9f3lynT58+unDhgrp06aLExEQ9/vjjWrhwodzc3HJsHYbJZDLlWO8AAAA5gLeNPFx428hDhreN4D6x/8Bhm41dsnhRm41tK2ReAAAAAABgJZNsd2Dnw+j+2KQDAAAAAAAeWmReAAAAAABgJTIvcheZFwAAAAAAwK4RvAAAAAAAAHaNbSMAAAAAAFiJbSO5i8wLAAAAAABg18i8AAAAAADASmRe5C4yLwAAAAAAgF0j8wIAAAAAACuZTGRe5CYyLwAAAAAAgF0jeAEAAAAAAOwawQs7FB0drbCwsFvWqVu3rnr06JHjc1mzZo0qVKggZ2dnNWnSJFtt2rdvb1E3t+YKAAAAALnFJMNm18OIMy+yoX379po2bZpeffVVTZgwweLem2++qS+++EJRUVGKiYnJtTnNnj1bzs7OOT5Oz549FRYWpt9++01eXl7ZajN27FiZTKYcnhkAALkr3+PhKtark3yqlJdbUEFtav6GEuYvtfW0YCWXyrXlWq2BDE9vZZw8pstLf1TGicM3rOv8SA15NHrJosyUnqbkT3teV8lFbk88L+eSFWS4eSoz6YyubFmhK9vX5OQyrPb99r81ffN+nbl4WSX9fNSnbkWVD8h3w7rz9xzWoCVbLMpcHB20ruvzkqS0jEx9uW6PVh9K0LGkC/JydVaN4AJ667FHVMDLPcfXYo3vt/+t6Vv+0pmLqSrp560+dSqqfEDeG9adv+eIBv2+1aLMxdFB69581vx54h/7tGj/MSWcvyRnRweVLeijN2qWVYWbPEsA9w7Bi2wKDg7WzJkz9emnn8rd/eq/lC9fvqwZM2aoSJEiuT6ffPly51+QBw4c0GuvvabChQtnu42Pj889n0daWlquBGsAALgZR08PJe+I09GYnxT+43hbTwd3wLl0FbnVbapLS2YpI/6wXKvWlWeLN3R+8hCZLqbcsI0p9ZLOTx5yXYHlffd6zeRYpJQuLpiuzKSzcgopI/cnWyozJUnpB3bl4Gqyb/Gf/2j0qp16t16Yygfk1YxtB9R17lrNbvek8nm43rCNp4uTZrd70vz5+t/zXk7P0L6TiepcvbRKFfDR+ctpGrFih97++Q/9X+t6Obya7Fv85zGNXrVb79avqPL+eTVj29/qOm+dZr/c4NbrfrmB+bPxn19wF8nrpb51KqiQj6dS0zP07dYDenPuOs1rF6G8N+kTD66HNQPCVtg2kk1VqlRRcHCwZs+ebS6bPXu2ihQposqVK1vUXbhwoR5//HH5+voqf/78euaZZ3TgwAGLOv/8849at26tfPnyydPTU+Hh4Vq/fr1FnW+++UYhISHy8fHRiy++qPPnz5vv/XcrRkhIiD766CN17NhRefLkUZEiRfTVV19Z9Hf06FG1bNlSvr6+ypcvn55//nkdOnTohus9dOiQDMPQmTNn1LFjRxmGoZiYGGVkZKhTp04KDQ2Vu7u7SpcurbFjx1q0/e+2kf8yDENz5861KPP19TVnrlwbe9asWapTp47c3Nz07bffSpImTZqksmXLys3NTWXKlNEXX3xx03EAALiXTi1aqT8HjlHCvN9tPRXcIZfwerqyY53Sdq1X5pkTurR4lkxpV+RSvubNG5lMMl04/+918bzFbcegUKXtXq+Mo3/JlHxWaTvWKvPkMTkGFs3h1WTf/235S00fCdFzjxRVsfzeerd+mNycHDVv96GbtjFkyM/TzXzl93Qz38vj6qwvmj2up0oVVkjePKoQmE9961bS3pOJik++mAsryp7/2/qXmpYvqufKXVt3pavr3nPjTBvpapDGYt0ebhb3ny5dWDWKFFRhH08Vz++tnrXL68KVdO0/k5zDqwFA8MIKHTt21NSpU82fp0yZog4dOmSpd+HCBfXs2VObNm3S0qVL5eDgoKZNmyozM1OSlJKSojp16ujYsWOaP3++tm/frj59+pjvS1czHubOnatffvlFv/zyi1asWKGPP/74lvMbNWqUwsPDtXXrVr3xxht6/fXXFRcXJ+lq5kJkZKTy5MmjVatWac2aNfLy8lLDhg115cqVLH0FBwcrPj5e3t7eGjNmjOLj49WqVStlZmaqcOHC+uGHH7Rnzx4NGDBA7777rr7//vs7eqa30q9fP3Xv3l179+5VZGSkvv32Ww0YMEAffvih9u7dq48++kgffPCBpk2bds/HBgAADxgHRzkGBCv9cNx1hSalH46TY1DIzdu5uCpPl0HK8+pgeTR5RQ75AyxuZxw/KKfiFWR4Xc08dQwuKYd8BZV+aN+9X8MdSMvI1L6TiapepIC5zMEwVL1IAe08cfam7S6lpavxlIVqNHmhev68Tgdu8+U85UqaDF0NbNiDq+tOUvXg/6w7uIB2xp+7abtLaRlqPHWxGk1ZpJ4/r7/lutMyMjV792F5uTippJ/3PZ0/gKzYNmKFl156Sf3799fhw1ejtWvWrNHMmTMVGxtrUa958+YWn6dMmaICBQpoz549Kl++vGbMmKFTp05p48aN5u0fJUqUsGiTmZmpmJgY5cmTR5L08ssva+nSpfrwww9vOr9GjRrpjTfekCT17dtXn376qZYvX67SpUtr1qxZyszM1KRJk2T8L/9t6tSp8vX1VWxsrJ566imLvhwdHRUQECDDMOTj46OAgH//oh40aJD559DQUK1bt07ff/+9WrZsedtnaI0ePXqoWbNm5s8DBw7UqFGjzGWhoaHas2ePJk6cqKioqCztU1NTlZqaalHm6uoqV1dS+gAAeNgY7p4yHBxlumj5ZdR08bwc8vnfsE3muQRdWjhDGaeOyXBxl2u1+vJq21Pnp3wkU0qiJOnS0h/l/tSL8n59qEwZGZIpU5cWz1TGPwdu2GduS7yUqgyTSfn/s6Uhv4ebDp298VaZkLxeGvBkFZX081ZKarq+2bJfHb5foR9eipB/nqxnWqSmZ+izNbsVWbqwvOwkeHHzdbvq0LnzN2wTktdLAyLCVNLPRympafpmy1/q8MMq/dC2vsW6Vx48oXcXbtLltAz5ebrpi6a1lNed/758GLFtJHeReWGFAgUKqHHjxoqJidHUqVPVuHFj+fn5Zam3f/9+tW7dWsWKFZO3t7dCQkIkSUeOHJEkbdu2TZUrV77luRUhISHmwIUkBQYG6uTJk7ecX8WKFc0/G4ahgIAAc5vt27frr7/+Up48eeTl5SUvLy/ly5dPly9fzrKl5XbGjx+vqlWrqkCBAvLy8tJXX31lXtu9FB4ebv75woULOnDggDp16mSev5eXl4YOHXrT+Q8bNkw+Pj4W17Bhw+75PAEAwIMp4/ghpe3eoMyTx5Txz1+6OG+SMi+myKXSY+Y6LlWekGNQiC7MnqiUbz7R5di5co9oIceipW0487tTMTC/nilbRKUL+KpqYT+NaFxDed1d9dOug1nqpmVkqt+vG2QymdS/XljuT/YeqhiY73/r9vnfuqv/b92HLOpVK+yn71rX1dQWtVWraEH1+22Tzl5MvXGnAO4ZMi+s1LFjR3Xt2lXS1S/xN/Lss8+qaNGi+vrrrxUUFKTMzEyVL1/evD3j2oGft/LfwykNw7DYVmJtm5SUFFWtWtV8dsT1ChQokKXsZmbOnKnevXtr1KhRqlmzpvLkyaMRI0ZkOa/jVgzDyPI2krS0tCz1PD09zT+npFz9zcDXX3+tGjVqWNRzdHS84Tj9+/dXz549LcrIugAA4OFkunRBpswMGR6W6f2GRx6ZLmTzvILMTGWe/EcOef/3305OznKr/awuzp2k9L93S5KunDoux4KF5Fqtvi5abFGxDV93Vzkahs7858v1mYuX5eeZvf8ucnZ0UOkCPvon8YJFeVpGpvr9tkHx5y9qQrPH7SbrQrrVulPl959zLG7GvO4ky3W7Ozsp2NdLwb5ShcB8ajLtd83dfVgdq5W6V9PHfcJkIvMiNxG8sNK1MyIMw1BkZGSW+2fOnFFcXJy+/vpr1a5dW5K0evVqizoVK1bUpEmTdPbs2Vx7a0iVKlU0a9YsFSxYUN7ed74nb82aNapVq5Z5e4okqzM3ChQooPj4ePPn/fv36+LFWx/u5O/vr6CgIP39999q27ZttsZhiwgAADDLzFDGiaNyKlpK6X/t+F+hIaeipXRly6rs9WEYcvALUvrBq4EKOTjKcHSS/vNLGZMp07xN19acHR1UpqCvNh49pXrFgyRJmSaTNh49pZYVi2Wrj4xMk/46k6zHQ/7dXnMtcHE0MUUTm9WWr51tm7i6bp//rTtQ0nXrrhSarT7M6y56421F12SaTErLuPUvGQHcPYIXVnJ0dNTevXvNP/9X3rx5lT9/fn311VcKDAzUkSNH1K9fP4s6rVu31kcffaQmTZpo2LBhCgwM1NatWxUUFKSaNW9x2vVdaNu2rUaMGKHnn39egwcPVuHChXX48GHNnj1bffr0yfarUEuWLKnp06dr0aJFCg0N1TfffKONGzcqNDR7fwlIUv369TVu3DjVrFlTGRkZ6tu3b7Zegzpo0CB169ZNPj4+atiwoVJTU7Vp0yadO3cuS4YFAAD3mqOnhzxL/Pt6dI/QwvKuVEZXzibp8tH4W7SEvbiyabncG72kjBNHlBF/WC7hdWU4u+rKrj8kSe6NXlbm+USlrvpZkuRas6Ey4g8p49wpGa7ucq0eIQfvvLqyY93/Orys9CP75VbneV1Kv6LM5HNyKlxCLuWq63LsHFstM4uXqpTQwMWbVbag79VXpW49oEtpGXqu3NU3ogxYtEkFvNz11mOPSJK+Wr9PFQLyKtjXS+dT0/TN5v06kXxRTR4JkXQ1cNH31/XadzJJY56rqQyTSacvXJYk+bi5yNnRPnamv1S5hAYu2aKy/r7/e1XqAV1Kz9Bz5a7+czxg8WYV8HTXW4+VkyR9tT7uf+v2vLruLX/9b91X619KS9fkjX+qTmiA/DzdlHj5ir7fcVCnLlxWRMkgm60TtpPJmRe5iuDFHbhV5oKDg4Nmzpypbt26qXz58ipdurQ+++wz1a1b11zHxcVFixcvVq9evdSoUSOlp6erXLlyN92Gci94eHho5cqV6tu3r5o1a6bz58+rUKFCatCggVWZGK+++qq2bt2qVq1ayTAMtW7dWm+88YZ+++23bPcxatQodejQQbVr11ZQUJDGjh2rzZs337Zd586d5eHhoREjRuidd96Rp6enKlSoYPHKWAAAcopP1fKqufQb8+dyI9+VJB2dPls7OvW31bRghbS4LTI8vOT2WGMZnnmUcfKYLvz4hfn1pw558lpkURhuHnJ/qrUMzzwypV5SxomjSpnxqTLPnDDXufjLVLnVfk4ejaNkuHkoM/mcLq/+RVe2rc4yvq08Vaqwzl1K1YQ/9urMxVSV8vPR501qmV9/euL8JYtMkfOXr2jo0q06czFV3q7OKlPQV1Na1lGx/Ff/m/HUhUta8ffVZ9B6xjKLsSY2f1zhhbO/JTknPVWq0P/WvU9nLqSqVAFvff78o+bXn2ZZd+oVDV22TWcupMrb7X/rblHbvG4Hw9Chcyn6Ze9GJV66Ih93Zz1SMK8mvfC4iufnbSNATjNM/z18AAAAwM4tcL5/D0O8G43TbH+Ggi0kjXjL1lOwCcfrzv96qJgezi0YXm9+YuspwErb9p+y2dhhJe0jSJibyLwAAAAAAMBKvCo1d9nHhjQAAAAAAICbIPMCAAAAAAAr8arU3EXmBQAAAAAAsGsELwAAAAAAgF1j2wgAAAAAAFbiwM7cReYFAAAAAACwa2ReAAAAAABgJQ7szF1kXgAAAAAAALtG5gUAAAAAAFbizIvcReYFAAAAAACwawQvAAAAAACAXWPbCAAAAAAAVuLAztxF5gUAAAAAALBrZF4AAAAAAGClTFtP4CFD5gUAAAAAALBrBC8AAAAAAIBdY9sIAAAAAABW4sDO3EXmBQAAAAAAsGtkXgAAAAAAYCWTyLzITQQvAADAfadxWpytp2ATC5xL23oKNvGw/nkDAP5F8AIAAAAAACtx5kXu4swLAAAAAABg1wheAAAAAAAAu8a2EQAAAAAArMSBnbmLzAsAAAAAAGDXyLwAAAAAAMBKmSZbz+DhQuYFAAAAAACwawQvAAAAAACAXWPbCAAAAAAAVuLAztxF5gUAAAAAALBrZF4AAAAAAGAlk4nMi9xE5gUAAAAAALBrZF4AAAAAAGAlE69KzVVkXgAAAAAAALtG8AIAAAAAANg1to0AAAAAAGClTF6VmqvIvAAAAAAAAHaNzAsAAAAAAKzEq1JzF5kXAAAAAADArhG8wD1lGMYtr+joaAUGBurjjz+2aNevXz8ZhqHY2FiL8rp16+rll1+WJMXExNywz0mTJuXW8gAAeOjkezxc4XO+VIPDq9Q4LU7+zzWw9ZQAAA8hto3gnoqPjzf/PGvWLA0YMEBxcXHmMi8vL8XFxSk2Nlb9+vUzly9fvlzBwcGKjY1V3bp1JUmXL1/WH3/8oaioKHM9b29vi/4kycfHJ4dWAwAAHD09lLwjTkdjflL4j+NtPR0AsBsmk61n8HAh8wL3VEBAgPny8fGRYRgWZV5eXqpXr57WrFmj9PR0SdL58+e1detW9e3b1yLzYt26dUpNTVW9evXMZf/tLyAgQO7u7rm9TAAAHhqnFq3UnwPHKGHe77aeCgAgh509e1Zt27aVt7e3fH191alTJ6WkpNyy/ltvvaXSpUvL3d1dRYoUUbdu3ZSUlGRR70YZ9DNnzrRqbgQvkOvq1aunlJQUbdy4UZK0atUqlSpVSs2bN9f69et1+fJlSVezMUJCQhQSEmLD2QIAAABAViYZNrtyStu2bbV7924tWbJEv/zyi1auXKkuXbrctP7x48d1/PhxjRw5Urt27VJMTIwWLlyoTp06Zak7depUxcfHm68mTZpYNTe2jSDXlSxZUoUKFVJsbKxq1qyp2NhY1alTRwEBASpSpIjWrVunevXqKTY21iLrQpKSkpLk5eVl/uzl5aUTJ07k9hIAAAAA4IGyd+9eLVy4UBs3blR4eLgk6fPPP1ejRo00cuRIBQUFZWlTvnx5/fTTT+bPxYsX14cffqiXXnpJ6enpcnL6N+Tg6+urgICAO54fmRewibp165q3iFx/zkWdOnUUGxurS5cuaf369VmCF3ny5NG2bdvM19q1a286RmpqqpKTky2u1NTUnFoSAAAAAOSKnPius27dOvn6+poDF5IUEREhBwcHrV+/Ptv9JCUlydvb2yJwIUlvvvmm/Pz8VL16dU2ZMkUmKw8NIXgBm7h27sWZM2e0detW1alTR9LV4MXy5cu1du1aXblyRfXr17do5+DgoBIlSpivYsWK3XSMYcOGycfHx+IaNmxYjq4LAAAAwMMh02S7Kye+65w4cUIFCxa0KHNyclK+fPmyne1++vRpDRkyJMtWk8GDB+v777/XkiVL1Lx5c73xxhv6/PPPrZof20ZgE/Xq1dOFCxc0evRolSxZ0vwPyRNPPKFOnTrpt99+M28vuVP9+/dXz549LcpcXV3vat4AAAAAYGvWfNfp16+fhg8ffsv+9u7de9dzSk5OVuPGjVWuXDlFR0db3Pvggw/MP1euXFkXLlzQiBEj1K1bt2z3T/ACNlGsWDEVKVJEn3/+udq2bWsuDw4OVlBQkL766iu1bt36rsZwdXUlWAEAwF1y9PSQZ4ki5s8eoYXlXamMrpxN0uWj8bdoCQAPNpMp5w7OvB1XV5dsf9fp1auX2rdvf8s6xYoVU0BAgE6ePGlRnp6errNnz972rIrz58+rYcOGypMnj+bMmSNnZ+db1q9Ro4aGDBmi1NTUbK+D4AVspl69epo2bZr5vItr6tSpo5iYmCznXQAAgNznU7W8ai79xvy53Mh3JUlHp8/Wjk79bTUtAEA2FShQQAUKFLhtvZo1ayoxMVGbN29W1apVJUnLli1TZmamatSocdN2ycnJioyMlKurq+bPny83N7fbjrVt2zblzZvXql82GyZrT8kAAACATSxwLm3rKdhE47Q4W08BALL4dUuazcZuVOXWmQ136umnn1ZCQoImTJigtLQ0dejQQeHh4ZoxY4Yk6dixY2rQoIGmT5+u6tWrKzk5WU899ZQuXryoOXPmyNPT09xXgQIF5OjoqJ9//lkJCQl69NFH5ebmpiVLlqh3797q3bu3Bg0alO25kXkBAAAAAAD07bffqmvXrmrQoIEcHBzUvHlzffbZZ+b7aWlpiouL08WLFyVJW7ZsMb+JpESJEhZ9HTx4UCEhIXJ2dtb48eP19ttvy2QyqUSJEho9erReeeUVq+ZG5gUAAMB9gswLALAfD2LmhT0j8wIAAAAAACtlynYHdj6MHGw9AQAAAAAAgFsh8wIAAAAAACtxAEPuIvMCAAAAAADYNYIXAAAAAADArrFtBAAAAAAAK5lMHNiZm8i8AAAAAAAAdo3MCwD4//buO67K+v//+POwQQElwZWLHLhTycSNI03LzMo0zZmZqxJ35SxHfspM66MNR/bJUUqpZW7RHLlxTxxogYsQEUTG+f3hz/PtCJok51x4eNxvN243z3Vd5/C8wtTzOq/36w0AAABkUwYDO+2KzgsAAAAAAJCr0XkBAAAAAEA2sVWqfdF5AQAAAAAAcjWKFwAAAAAAIFdj2QgAAAAAANlkFlul2hOdFwAAAAAAIFej8wIAAAAAgGxiq1T7ovMCAAAAAADkahQvAAAAAABArsayEQAAAAAAssnMshG7ongBAAAeOlf/M8DoCIZonXrM6AiG+MW1gtERDJFXf94AkBWKFwAAAAAAZBOdF/bFzAsAAAAAAJCr0XkBAAAAAEA2ZZhNRkfIU+i8AAAAAAAAuRrFCwAAAAAAkKuxbAQAAAAAgGxiYKd90XkBAAAAAAByNTovAAAAAADIJjov7IvOCwAAAAAAkKtRvAAAAAAAALkay0YAAAAAAMimDJaN2BWdFwAAAAAAIFej8wIAAAAAgGwym01GR8hT6LwAAAAAAAC5Gp0XAAAAAABkE1ul2hedFwAAAAAAIFejeAEAAAAAAHI1lo0AAAAAAJBNbJVqX3ReAAAAAACAXI3OCwAAAAAAsomBnfZF8QI2061bN8XHx+unn36yOh4REaHQ0FD99ddfioyMVGhoqCTJZDLJ29tbgYGBat68uQYOHKiiRYtanjdmzBiNHTs20/dZs2aNmjVrZtN7AQA4LrcaDeT+RFOZ8vko/eIfurFusdJjz2Z5rWvlJ+XVqrPVMXNaqhI+CfvbRW7yaPicXMtVlckjnzKuXtHNPRt1c98WW94Gcphf/WAFDuop35pV5FEsQLte6KsLy9YZHQsA8iyKF8gVjh07Jh8fHyUkJGjPnj2aPHmyZs2apYiICFWtWtVyXeXKlbV27Vqr5/r5+dk7LgDAQbhWqCmPxs8rec0ipceclXutxsr3Ul9dm/W+zEmJWT7HnJKsa7Pe/9sB6/Oeoe3kXLK8kn6Zp4yrcXIpHSTP5u2VkXhVaVEHbXg3yEnO+byUsP+Yzs1douDFnxsdBwDyPIoXyBUCAgJUoEABFSlSROXLl9dzzz2nGjVqqE+fPtq8ebPlOhcXFxUpUsTApAAAR+IWHKqb+7cp9eB2SVLy6kVyCawstyohStmxJusnmc0yX79219d0LlZGqYe2K/3cSUlS6v6tcq9eT85FS1G8eIhcWrVJl1ZtMjoGgFyMZSP2xcBO5Eqenp564403tGXLFl28eNHoOAAAR+TkLOciJZR29tjfDpqVdvaYnIuVvvvz3Nzl/fpYefceJ6+2veT0iHVRPf3P03J5rKpM+X0lSc4lysnJL0BpZ47m/D0AAJBH0HkBm/r555+VP39+q2Pp6en39dygoCBJ0pkzZxQQECBJOnDggNXrVapUSTt27Mjy+SkpKUpJSbE65u7uLnd39/vODwBwXCbPfDI5OcuclGB13Jx0TU5+hbN8TsZfF5S8cr7SL/0hk5un3J9oovydwnRt9gSZE+MlScnrFsvzqQ7y6fOBzOnpkjlDyasXKv18lK1vCQBgR2yVal8UL2BToaGhmjFjhtWx7du3q3Pnznd5xv8x//8+LJPJZDlWoUIFLVu2zPL4XoWIiRMnZhrwOXr0aI0ZM+Z+ogMAkEn6n2eU/ucZy+OkP08pf4/35Fa9nlK2/CJJcqvZUM7FSut6+BfKSIiTy6Nl5dnsJWUkXlW6VZcHAAC4XxQvYFP58uVT2bJlrY6dP3/+vp575MgRSVLp0qUtx9zc3DK93t2MGDFCYWFhVsfougAA3GZOvi5zRrpMXj5Wx01e3jJfT7jLs+6QkaGMi+flVND/1mMXV3k0eFZJP32ttFOHJEk3L/0p54Dicn+iiZIoXgCAw2DmhX0x8wK5UnJysr788ks1bNhQ/v7+/+o13N3d5ePjY/VF8QIAYJGRrvTYc3IpVf5vB01yKVXeqrvinkwmORUqJvP1q7ceOznL5OyS6V+0ZnOG9LdOQgAAkD10XiBXuHjxom7cuKFr165p9+7dmjx5si5fvqzw8HCjowEAHNjNXRvk2aqz0mOjlR5zVm7BjWVyddfNg79LkjxbvaqMa/FK+W25JMk9pKXSY84o/a9LMrl7yr12Mzn5FNTN/dv+/wveUFr0CXk0ek7JaTeVkfCXXB4tK7dKtXUj4kejbhP/gnM+L+UrW9Ly2KvMo/KpHqSbcVd141yMgckAIG+ieIFcoUKFCjKZTMqfP78CAwP11FNPKSwsjG1RAQA2lXpsj0xe+eVRr7VM+byVfvEPXV/8X5mTbm2F6uRd0KqLwuThJc+nOsqUz1vmlGSlx55T4vxPlHEl1nJN0s9z5NGgjbxad5XJw0sZCX/pxuafdTNyc6bvj9zLt1YVhaz71vK40kfvSJLOzQvX/p4jjIoFIBfJyDA6Qd5iMptZqQMAAB4uV/8zwOgIhvAdMt3oCIb4xbWC0REM0TqVGSlAbvbFauO+d++njPveRqHzAgAAAACAbKINwL4Y2AkAAAAAAHI1ihcAAAAAACBXY9kIAAAAAADZxLIR+6LzAgAAAAAA5Gp0XgAAAAAAkE0ZdF7YFZ0XAAAAAAAgV6PzAgAAAACAbDIbOvTCZOD3NgadFwAAAAAAIFejeAEAAAAAAHI1lo0AAAAAAJBNbJVqX3ReAAAAAACAXI3OCwAAAAAAsikjw+gEeQudFwAAAAAAIFejeAEAAAAAAHI1lo0AAAAAAJBNDOy0LzovAAAAAACA4uLi1KlTJ/n4+KhAgQLq2bOnEhMT7/mcxo0by2QyWX298cYbVtdER0erdevW8vLyUkBAgIYMGaK0tLRsZaPzAgAAAACAbMpwwM6LTp06KSYmRmvWrFFqaqq6d++u119/XfPnz7/n83r16qVx48ZZHnt5eVl+nZ6ertatW6tIkSLaunWrYmJi1KVLF7m6umrChAn3nY3iBQAAAAAAedyRI0e0cuVK7dy5U8HBwZKk6dOnq1WrVvroo49UrFixuz7Xy8tLRYoUyfLc6tWrdfjwYa1du1aFCxfW448/rvfff1/Dhg3TmDFj5Obmdl/5WDYCAAAAAMBDJCUlRQkJCVZfKSkpD/Sa27ZtU4ECBSyFC0lq1qyZnJyctH379ns+97vvvlOhQoVUpUoVjRgxQklJSVavW7VqVRUuXNhyrEWLFkpISNChQ4fuOx+dFwAA4KHjnC+f0RFgR61TjxkdwRC/uFYwOoIh8urPGw8fIwd2Tpw4UWPHjrU6Nnr0aI0ZM+Zfv2ZsbKwCAgKsjrm4uMjPz0+xsbF3fd4rr7yiUqVKqVixYtq/f7+GDRumY8eOKTw83PK6fy9cSLI8vtfr3oniBQAAAAAAD5ERI0YoLCzM6pi7u3uW1w4fPlwffvjhPV/vyJEj/zrL66+/bvl11apVVbRoUTVt2lRRUVF67LHH/vXr3oniBQAAAAAA2WQ2cGKnu7v7XYsVdxo0aJC6det2z2sCAwNVpEgRXbx40ep4Wlqa4uLi7jrPIitPPvmkJOnkyZN67LHHVKRIEe3YscPqmgsXLkhStl6X4gUAAAAAAA7K399f/v7+/3hdSEiI4uPjtXv3btWqVUuStH79emVkZFgKEvcjMjJSklS0aFHL644fP14XL160LEtZs2aNfHx8VKlSpft+XQZ2AgAAAACQTRlm475soWLFimrZsqV69eqlHTt2aMuWLerfv786dOhg2Wnkjz/+UFBQkKWTIioqSu+//752796tM2fOaNmyZerSpYsaNmyoatWqSZKeeuopVapUSa+++qr27dunVatW6b333lO/fv3uu3tEongBAAAAAAB0a9eQoKAgNW3aVK1atVL9+vX15ZdfWs6npqbq2LFjlt1E3NzctHbtWj311FMKCgrSoEGD9MILL2j58uWW5zg7O+vnn3+Ws7OzQkJC1LlzZ3Xp0kXjxo3LVjaWjQAAAAAAAPn5+Wn+/Pl3PV+6dGmZ/7bNSokSJbRx48Z/fN1SpUppxYoVD5SN4gUAAAAAANlk5FapeRHLRgAAAAAAQK5G5wUAAAAAANmUYeBWqXkRnRcAAAAAACBXo3gBAAAAAAByNZaNAAAAAACQTQzstC86LwAAAAAAQK5G5wUAAAAAANlE54V90XkBAAAAAAByNTovAAAAAADIpgxaL+yKzgsAAAAAAJCrUbwAAAAAAAC5GstGAAAAAADIJnOG0QnyFjovYBPdunWTyWSSyWSSm5ubypYtq3HjxikxMVGurq5auHCh1fUdOnSQyWTSmTNnrI6XLl1aI0eOlCSNGTPG8pp//1q7dq29bgsA4IC+33dKz8xepZDPlqrLwggdjI2767XLDp9VrU9/tPoK+Wyp5XxqeoambT6o9v9bp3qfL1OLr3/VqFW7dCkx2R63Ajwwv/rBCv5xhpqe/U2tU4+pcJumRkcCAEl0XsCGWrZsqTlz5iglJUUrVqxQv3795OrqquDgYEVERKhDhw6WayMiIlSiRAlFRESoW7dukqTTp0/r7NmzatKkieW6ypUrZypW+Pn52eV+AACOZ/Xx85ry2wG9E/q4qhQpqPmRUer/01aFd2kuPy/3LJ+Tz81F4V2aWx6b/nbuRlq6jl6M12u1K6i8v6+u3UjVfzbu18Dlv+t/HUNtfDfAg3PO56WE/cd0bu4SBS/+3Og4QK5mZmCnXVG8gM24u7urSJEikqQ+ffroxx9/1LJlyxQaGqrw8HDLdUeOHNGNGzf01ltvWRUvIiIi5O7urpCQEMu1Li4ultcEAOBB/W/PST1fubTaVC4lSXqnyePafDpWSw+dUfcnKmT5HJNMKpTPI8tz3u6u+m+7+lbHhjWuri6LIhSTkKSiPl45ewNADru0apMurdpkdAwAyIRlI7AbT09P3bx5U6GhoTp27JhiYmIkSRs2bFD9+vXVpEkTRUREWK7fsGGDQkJC5OGR9T8QAQB4EKnpGTp6MV61S/pbjjmZTKpd0l8H7rF0JDk1Ta1nr1SrWSsVtnyboq4k3PP7JN5MlUm3ChsAAODfoXgBmzObzVq7dq1WrVqlJk2aqF69enJzc7MUKiIiItSoUSPVqlVLly9f1unTpyVJGzduVGiodYvtgQMHlD9/fstX7dq17/p9U1JSlJCQYPWVkpJis/sEADxc4pNTlG4265E7loc84uWhy9ez/vuidMH8GtW8pqY8W0fvtwhWhlnq/v1GXbiW9UyLlLR0TdtySC0qPKr8FC8AwKFkZBj3lRdRvIDN/Pzzz8qfP788PDz09NNP6+WXX9aYMWPk5eWlJ554wlK82Lhxoxo3biwXFxfVrVtXEREROnXqlKKjozMVLypUqKDIyEjL15IlS+76/SdOnChfX1+rr4kTJ9rylgEADq5a0Uf0TMWSquBfQLUeLaT/tH5SBT3dteTg6UzXpqZnaPiKHTKbzRoR+rj9wwIA4ECYeQGbCQ0N1YwZM+Tm5qZixYrJxcXF6tyiRYt06NAhJScnq2bNmpKkRo0aacOGDcrIyJCXl5eefPJJq9e8vXPJ/RgxYoTCwsKsjrm7Zz18DQCQ9xTwdJezyaQrSdZdFleSbqhQvvv7+8LV2UkV/H11Pv661fHU9AwN/3WHYq4laWa7+nRdAIADYmCnfdF5AZvJly+fypYtq5IlS1oVLqRbxYsTJ05o/vz5ql+/vpydnSVJDRs21MaNGxUREWFZXvJvubu7y8fHx+qL4gUA4DZXZycFBRTQznOXLMcyzGbtPHdJVYvc305W6RlmnbySYFXsuF24OBefqBnP11cBT/7uAQDgQVG8gCHq1q0rd3d3TZ8+XY0aNbIcr127ti5evKilS5dmWjICAEBO61yzrH48eEbLD5/V6bgETVwfqeTUdLWpdGv3kVGrdmn6lkOW67/cflTbzl7Q+avXdeRivEau2qXYhCS1rVxa0q3CxbAV23XkQrw+aPGE0s1mXb5+Q5ev31Bqeh5dpIyHinM+L/lUD5JP9SBJkleZR+VTPUgeJYoanAzIfTLMxn3lRSwbgSE8PDxUp04dy7yL29zd3VWnTh1FRERQvAAA2NxT5R/VX8kpmvn7EV1JSlH5Qr6a3rauHvn/W6HGXkuWyWSyXH/txk19sG6vriSlyMfdVUEBBTS7fSMFPuIjSbp0PVkbT8VKkjrOX2/1vb54ob6CH/UXkJv51qqikHXfWh5X+ugdSdK5eeHa33OEUbEAQCYzC3UAAMBDJvG/w42OYIj8fScZHQF29ItrBaMjGKJ16jGjIwD35b25Nw373h90+/fL6x9WdF4AAAAAAJBN5ry6fsMgzLwAAAAAAAC5Gp0XAAAAAABkEwMY7IvOCwAAAAAAkKtRvAAAAAAAALkay0YAAAAAAMimDAZ22hWdFwAAAAAAIFej8wIAAAAAgGwyM7HTrui8AAAAAAAAuRqdFwAAAAAAZJM5w+gEeQudFwAAAAAAIFejeAEAAAAAAHI1lo0AAAAAAJBNGQzstCs6LwAAAAAAQK5G5wUAAAAAANnEVqn2RecFAAAAAADI1SheAAAAAACAXI1lIwAAAAAAZFNGBstG7InOCwAAAAAAkKvReQEAAAAAQDYxr9O+KF4AAICHjznD6ASAzbVOPWZ0BEP84lrB6AiGyKs/b+B+UbwAAAAAACCbzMy8sCtmXgAAAAAAgFyN4gUAAAAAAMjVWDYCAAAAAEA2ZTCx067ovAAAAAAAALkanRcAAAAAAGQTAzvti84LAAAAAACQq1G8AAAAAAAAuRrLRgAAAAAAyCaWjdgXnRcAAAAAACBXo/MCAAAAAIBsovHCvui8AAAAAAAAuRqdFwAAAAAAZBMzL+yLzgsAAAAAAJCrUbwAAAAAAAC5GstGAAAAAADIJrOZZSP2ROcFAAAAAADI1ei8AAAAAAAgmzIY2GlXdF4AAAAAAIBcjc4L2MW5c+c0evRorVy5UpcvX1bRokXVtm1bjRo1So888ogk6fTp03r33XcVERGhuLg4FSpUSLVq1dKHH36ooKAgSZLJZMr02vXq1dPmzZvtej8AAMfx/b5TmrfnpK4kpahcIR8NbVRNVYoUzPLaZYejNXbtXqtjbs5O2tbvWcvjL34/qlUn/tCFa8lydXZSxQBf9Q2pqKpF/Gx6HwD+Pb/6wQoc1FO+NavIo1iAdr3QVxeWrTM6FoC/oXgBmzt16pRCQkJUvnx5LViwQGXKlNGhQ4c0ZMgQ/frrr/r999/l7e2t5s2bq0KFCgoPD1fRokV1/vx5/frrr4qPj7d6vTlz5qhly5aWx25ubna+IwCAo1h9/A9N+e2Q3mlSTVUKF9T8yFPqv3Sbwl9tKj8v9yyfk8/NReGvNrU8vrOuXrJgfg1rVFXFffMpJS1d3+2NUr+ftmlpl2YqeJfXBGAs53xeSth/TOfmLlHw4s+NjoOHBAM77YviBWyuX79+cnNz0+rVq+Xp6SlJKlmypGrUqKHHHntM7777rnr37q2oqCitW7dOpUqVkiSVKlVK9erVy/R6BQoUUJEiRex6DwAAx/S/vSf1fJVSalPp1t897zSprs1nLmjp4bPqHlw+y+eYJBXK53HX13y6wqNWj8MaVNHSw9E6cSVBtb38cyw7gJxzadUmXVq1yegYgOHi4uI0YMAALV++XE5OTnrhhRf06aefKn/+/Flef+bMGZUpUybLc99//71eeuklSVl30C9YsEAdOnS472zMvIBNxcXFadWqVerbt6+lcHFbkSJF1KlTJy1atEj+/v5ycnLS4sWLlZ6eblBaAEBekpqeoaMXr6p2if8rKDiZTKpdwl8HYv666/OSU9PVes5qtZq9SmHLtyvqSsI9v0f4obPK7+aicoV8cjQ/AMBY5gyzYV+20qlTJx06dEhr1qzRzz//rE2bNun111+/6/UlSpRQTEyM1dfYsWOVP39+Pf3001bXzpkzx+q6tm3bZisbnRewqRMnTshsNqtixYpZnq9YsaL++usvubq6atq0aRo6dKjGjh2r4OBghYaGqlOnTgoMDLR6TseOHeXs7Gx5/L///S/bv/EBAIhPTlG62axH7ljK8YiXu878dS3L55QumF+jmj2ucoV8lZiSqm/3nFT3H37TD52aqLD3/xXpN52O1Tsrd+lGaroK5fPQf5+vq4KeLBkBAOReR44c0cqVK7Vz504FBwdLkqZPn65WrVrpo48+UrFixTI9x9nZOVNX/I8//qj27dtn6tZ40A56Oi9gF/ezHqxfv36KjY3Vd999p5CQEP3www+qXLmy1qxZY3XdJ598osjISMtX8+bNs3y9lJQUJSQkWH2lpKTkyP0AAPKmakX99EzFkqrg76tajxbSf1rXVkFPdy05eMbquiceLaQFHRtrzksNVLdUgIb/uktxSfwdBADIGbZ4r7Nt2zYVKFDAUriQpGbNmsnJyUnbt2+/r9fYvXu3IiMj1bNnz0zn+vXrp0KFCql27dqaPXt2tmeGULyATZUtW1Ymk0lHjhzJ8vyRI0dUsGBB+fvfatn19vbWs88+q/Hjx2vfvn1q0KCBPvjgA6vnFClSRGXLlrV85cuXL8vXnjhxonx9fa2+Jk6cmLM3CAB4aBXwdJezyaQrdxQVriSlqJDX3Wda/J2rs5Mq+Pvq/NXrVsc9XV1UokB+VS3qp1HNasjZZNJPh87mWHYAgPGMXDZii/c6sbGxCggIsDrm4uIiPz8/xcbG3tdrzJo1SxUrVlTdunWtjo8bN07ff/+91qxZoxdeeEF9+/bV9OnTs5WP4gVs6pFHHlHz5s313//+V8nJyVbnbndZvPzyy1kOcDGZTAoKCtL169cznbsfI0aM0NWrV62+RowY8a9eCwDgeFydnRQU4Kud5y5ZjmWYzdp57pKqFs16q9Q7pWeYdfJKwj8WOzLMZqWmZzxQXgAAbsvOe53hw4fLZDLd8+vo0aMPnCk5OVnz58/Psuti5MiRqlevnmrUqKFhw4Zp6NCh+s9//pOt12fmBWzus88+U926ddWiRQt98MEHVlulFi9eXOPHj1dkZKRGjx6tV199VZUqVZKbm5s2btyo2bNna9iwYf/q+7q7u8vdnfXFAIC761yjrEav2aOKhQv8/61So5Sclq42lUpKkkat3i3/fJ4aUK+SJOnL7cdUtUhBlSiQT9f+/8yL2IQkta186/rk1DTN2nlcjcoUUaF8Hoq/cVPf7z+tS9dvqFm5zGuFAeQOzvm8lK9sSctjrzKPyqd6kG7GXdWNczEGJkNulmHgVqnZea8zaNAgdevW7Z7XBAYGqkiRIrp48aLV8bS0NMXFxd3XrIrFixcrKSlJXbp0+cdrn3zySb3//vtKSUm57/ugeAGbK1eunHbt2qXRo0erffv2lt/8bdu21ejRo+Xn56eMjAyVLl1aY8eO1ZkzZ2QymSyPBw4caPQtAAAc1FPli+uv5BTN/P2orlxPUXl/H01/ro4e+f+dFLHXkq26A6+l3NQH6yN15XqKfDxcFRRQQLNfaqDAR27tJOJkMunMX4n6+chOxSfflK+nqyoHFNTXL9bXY4+w2wiQW/nWqqKQdd9aHlf66B1J0rl54drfk85dPNz8/f0ty/TvJSQkRPHx8dq9e7dq1aolSVq/fr0yMjL05JNP/uPzZ82apTZt2tzX94qMjFTBggWz9WGzyZzdKRkAAAAGS/x8qNERDJG/32SjIwA294trBaMjGKJ16jGjIyCbuo66vzkQtvDNuH+/a8e9PP3007pw4YJmzpyp1NRUde/eXcHBwZo/f74k6Y8//lDTpk01b9481a5d2/K8kydPqnz58lqxYoVatmxp9ZrLly/XhQsXVKdOHXl4eGjNmjUaPHiwBg8erLFjx953NjovAAAAAACAvvvuO/Xv319NmzaVk5OTXnjhBU2bNs1yPjU1VceOHVNSUpLV82bPnq1HH31UTz31VKbXdHV11eeff66BAwfKbDarbNmymjJlinr16pWtbHReAACAhw6dF4DjovMCDwtH7LzIzei8AAAAAAAgm+gDsC+2SgUAAAAAALkanRcAAAAAAGRTRgadF/ZE5wUAAAAAAMjVKF4AAAAAAIBcjWUjAAAAAABkk5llI3ZF5wUAAAAAAMjV6LwAAAAAACCb2CrVvui8AAAAAAAAuRqdFwAAAAAAZJM5I8PoCHkKnRcAAAAAACBXo3gBAAAAAAByNZaNAAAAAACQTRlslWpXdF4AAAAAAIBcjc4LAAAAAACyia1S7YvOCwAAAAAAkKtRvAAAAAAAALkay0YAAAAAAMgmMwM77YriBQAAeOjk7zfZ6AgAbKR16jGjIxjiF9cKRkcwRF79eSP7KF4AAAAAAJBNdF7YFzMvAAAAAABArkbnBQAAAAAA2ZRhzjA6Qp5C5wUAAAAAAMjVKF4AAAAAAIBcjWUjAAAAAABkEwM77YvOCwAAAAAAkKvReQEAAAAAQDbReWFfdF4AAAAAAIBcjeIFAAAAAADI1Vg2AgAAAABANpnNLBuxJzovAAAAAABArkbnBQAAAAAA2ZSRkWF0hDyFzgsAAAAAAJCr0XkBAAAAAEA2sVWqfdF5AQAAAAAAcjWKFwAAAAAAIFdj2QgAAAAAANlkNjOw057ovAAAAAAAALkanRcAAAAAAGQTAzvti84L2FS3bt1kMplkMpnk6uqqMmXKaOjQobpx44blGpPJJA8PD509e9bquW3btlW3bt2yfK2/f508edJetwMAAAA4DL/6wQr+cYaanv1NrVOPqXCbpkZHAu6K4gVsrmXLloqJidGpU6f0ySef6IsvvtDo0aOtrjGZTBo1atR9v9bfv8qUKWOr6AAAAIDDcs7npYT9x3TwzbFGRwH+EctGYHPu7u4qUqSIJKlEiRJq1qyZ1qxZow8//NByTf/+/TVlyhQNGTJEVapUua/XAgAAAPDvXVq1SZdWbTI6xkOLZSP2RecF7OrgwYPaunWr3NzcrI7Xq1dPzzzzjIYPH25QMgAAAABAbkXnBWzu559/Vv78+ZWWlqaUlBQ5OTnps88+y3TdxIkTVa1aNf32229q0KDBPV/rtqefflo//PBDltempKQoJSXF6pi7u7vc3d0f4G4AAAAAQMpgq1S7ovMCNhcaGqrIyEht375dXbt2Vffu3fXCCy9kuq5SpUrq0qXLPbsvbr/W7a9p06bd9dqJEyfK19fX6mvixIk5ck8AAAAAAPuh8wI2ly9fPpUtW1aSNHv2bFWvXl2zZs1Sz549M107duxYlS9fXj/99NM/vtY/GTFihMLCwqyO0XUBAAAAICcw88K+6LyAXTk5Oemdd97Re++9p+Tk5EznS5Qoof79++udd95Renr6A30vd3d3+fj4WH1RvAAAAACAhw/FC9jdSy+9JGdnZ33++edZnh8xYoT+/PNPrV271s7JAAAAgLzDOZ+XfKoHyad6kCTJq8yj8qkeJI8SRQ1OBmRG8QJ25+Liov79+2vy5Mm6fv16pvN+fn4aNmyYbty4YUA6AAAAIG/wrVVFDXYtVYNdSyVJlT56Rw12LVX5MW8anOzhYM7IMOwrLzKZzWYW6gAAAACAgX5xrWB0BEO0Tj1mdIR/rXmn3YZ97zXf1TLsexuFgZ0AAAAAAGQTAzvti2UjAAAAAAAgV6N4AQAAAAAAcjWWjQAAAAAAkE1mc94cnGkUOi8AAAAAAECuRucFAAAAAADZlMHATrui8wIAAAAAAORqdF4AAAAAAJBN5gxmXtgTnRcAAAAAACBXo3gBAAAAAAByNZaNAAAAAACQTWYGdtoVnRcAAAAAACBXo/MCAAAAAIBsMpsZ2GlPdF4AAAAAAIBcjeIFAAAAAADI1Vg2AgAAAABANjGw077ovAAAAAAAABo/frzq1q0rLy8vFShQ4L6eYzabNWrUKBUtWlSenp5q1qyZTpw4YXVNXFycOnXqJB8fHxUoUEA9e/ZUYmJitrJRvAAAAAAAIJvMGRmGfdnKzZs39dJLL6lPnz73/ZzJkydr2rRpmjlzprZv3658+fKpRYsWunHjhuWaTp066dChQ1qzZo1+/vlnbdq0Sa+//nq2spnMZjO9LgAAAABgoF9cKxgdwRCtU48ZHeFfq//sRsO+9+bljWz6+nPnztXbb7+t+Pj4e15nNptVrFgxDRo0SIMHD5YkXb16VYULF9bcuXPVoUMHHTlyRJUqVdLOnTsVHBwsSVq5cqVatWql8+fPq1ixYveVic4LAAAAAAAeIikpKUpISLD6SklJsXuO06dPKzY2Vs2aNbMc8/X11ZNPPqlt27ZJkrZt26YCBQpYCheS1KxZMzk5OWn79u33/b0Y2AnYQUpKiiZOnKgRI0bI3d3d6Dh2w31z33kB98195wXcN/edFxh930Z1IBh93w8zW3c/3MuYMWM0duxYq2OjR4/WmDFj7JojNjZWklS4cGGr44ULF7aci42NVUBAgNV5FxcX+fn5Wa65HywbAewgISFBvr6+unr1qnx8fIyOYzfcN/edF3Df3HdewH1z33kB95237vthl5KSkqnTwt3dPcsC1PDhw/Xhhx/e8/WOHDmioKAgy+P7XTaydetW1atXT3/++aeKFi1qOd6+fXuZTCYtWrRIEyZM0DfffKNjx6wLdAEBARo7dux9z9eg8wIAAAAAgIfI3QoVWRk0aJC6det2z2sCAwP/VY4iRYpIki5cuGBVvLhw4YIef/xxyzUXL160el5aWpri4uIsz78fFC8AAAAAAHBQ/v7+8vf3t8lrlylTRkWKFNG6dessxYqEhARt377d0lEREhKi+Ph47d69W7Vq1ZIkrV+/XhkZGXryySfv+3sxsBMAAAAAACg6OlqRkZGKjo5Wenq6IiMjFRkZqcTERMs1QUFB+vHHHyVJJpNJb7/9tj744AMtW7ZMBw4cUJcuXVSsWDG1bdtWklSxYkW1bNlSvXr10o4dO7Rlyxb1799fHTp0uO+dRiQ6LwC7cHd31+jRo/PcECTum/vOC7hv7jsv4L6577yA+85b942sjRo1St98843lcY0aNSRJGzZsUOPGjSVJx44d09WrVy3XDB06VNevX9frr7+u+Ph41a9fXytXrpSHh4flmu+++079+/dX06ZN5eTkpBdeeEHTpk3LVjYGdgIAAAAAgFyNZSMAAAAAACBXo3gBAAAAAAByNYoXAAAAAAAgV6N4AQAAAAAAcjV2GwHwQPbv35/t51SqVEkuLvzxAwC5zezZs9WpUyd2HQAA5DrsNgLggTg5OclkMul+/yhxcnLS8ePHFRgYaONkthUWFpbt57z33nvy8/OzQRr7WbZsWbaf07x5c3l6etogjf3k1fuWlO3fsyaTSXv27FGpUqVslMj+vv32W82cOVOnT5/Wtm3bVKpUKU2dOlVlypTRc889Z3S8HOXs7KyYmBgFBARIkooVK6atW7eqdOnSxgYDAOR5fPQJ4IFt375d/v7+/3id2WxWlSpV7JDI9qZOnaqQkBC5ubnd1/WbN29W//79H/riRdu2bbN1vclk0okTJx76YlVevW9Jio+P19SpU+Xr6/uP15rNZvXt21fp6el2SGYfM2bM0KhRo/T2229r/PjxlnsrUKCApk6d6nDFizsL0deuXVNGRoZBaexv/fr1atiwYZ7rDsyL992wYUMtW7ZMBQoUkHSrSO0oRed/curUKZUpU0Ymk8noKEC25J0/oQA7ut9P5adMmWLjJLbXqFEjlS1b1vKX/z9p2LChw/zD4Mcff7R8OvlPvL29bZzGfmJjY7nvf+BI9y1JHTp0uO97HzBggI3T2Nf06dP11VdfqW3btpo0aZLleHBwsAYPHmxgMthC8+bNrTpP6tSpoyVLlqh48eIGJ7OtvHjfmzdv1s2bNy2PO3furMjISIcoOv+TcuXKWf28X375ZU2bNk2FCxc2OBlwbxQvABvYu3fvP17jKNXuDRs23Nd1aWlpcnFx0YoVK2ycyD7mzJlzX59E3/bFF184xD8Kunbtmq3iU+fOneXj42PDRPaRV+9bUrY/db927ZqNkhjj9OnTqlGjRqbj7u7uun79ugGJbMtkMln9/XTnY0d3Z+fJoUOHlJKSYlAa+8mr9/13eWkl/Z33umLFCk2cONGgNMD9o3gB2MD9vqF3FN9//73at29/1/NpaWlq3769wsPD7ZjKtrp27aq0tLR/vO7w4cOqVKmSXnnlFTuksr05c+Zk6/oZM2bYKIl95dX7hlSmTBlFRkZmmuGxcuVKVaxY0aBUtmM2m1W+fHlLwSIxMVE1atSQk5P1BnVxcXFGxAMA5GEULwA8sC5duqhgwYJq3rx5pnPp6elq3769tm3bZkAy2+rUqZMWLVp01/OHDx9WkyZNFBsba8dUgG2dOHFCGzZs0MWLFzN1ZYwaNcqgVLYTFhamfv366caNGzKbzdqxY4cWLFigiRMn6uuvvzY6Xo7LbqHO0eTVzpO8et+rVq2ydFFmZGRo3bp1OnjwoNU1bdq0MSKaTWX1880LP288/NhtBLCR69ev68MPP1R4eLjOnDkjk8mkMmXK6MUXX9TgwYPl5eVldMQc8+mnn+q9997T2rVr9eSTT1qOZ2Rk6KWXXtLmzZu1fv16Va5c2cCUOa9kyZJq1aqVZs6cmenckSNHFBoaqrp16zpUx8nf3bhxQ9OnT7/rG9k9e/YYlMy28up9S9JXX32lPn36qFChQipSpEimNzuOeu/fffedxowZo6ioKEm3duAYO3asevbsaXAy5DQnJydVqVLFMrhy//79CgoKyjSc2dF+r+fF+76zmygrJpPJoQYQ3+bk5KSnn37asiXy8uXL1aRJE+XLl8/qOkf99wseXnReADZw8+ZNNWrUSAcPHtTTTz+tZ599VmazWUeOHNH48eP166+/atOmTXJ1dTU6ao546623FBcXp1atWmnTpk2qXLmy0tPT9fLLL+u3335zyMKFdOsTm4YNG8rPz08TJkywHD969KiaNGmiOnXq6IcffjAwoW317NlTq1ev1osvvqjatWvnmU9t8up9S9IHH3yg8ePHa9iwYUZHsYu0tDTNnz9fLVq0UKdOnZSUlKTExMT7Hl76MOratauaNm2qxo0bq2TJkkbHsbvRo0dbPXa03WTuJi/ed17aRedOXbt2tXrcuXNng5IA2UPnBWADn376qSZOnKiNGzeqQoUKVueOHj2qxo0b691333W4qfwDBgxQeHi4NmzYoPfee08bNmzQunXrVK1aNaOj2czOnTvVtGlTjRo1SoMHD9bRo0cVGhqqJ554QuHh4Q697Zyvr69WrFihevXqGR3FrvLqfUuSj49PnpnGf5uXl5eOHDmSaeaFo2rcuLG2b9+umzdvqnTp0goNDVVoaKiaNGmiokWLGh0PAJCH/XO/FIBsCw8P18iRIzMVLiQpKChI7777rhYvXmxAMtuaPn26QkNDVb169TxRuJCkJ554Qj/99JNGjRqlMWPGqGnTpqpVq5YWL17s0IULSSpevLjDbQl6P/LqfUvSSy+9pNWrVxsdw65q1659XztIOYqIiAjFx8dr7dq16ty5s06cOKGePXvq0UcfVVBQkPr06ePQHWV/d/nyZe3atUu7d+/WlStXjI5jU6NHj9amTZustg51dMePH9eOHTusjq1bt06hoaGqXbu2VUclgNyBzgvABvz9/RUREXHXpRIHDx5UaGioLl26ZOdkthEWFmb5dWpqqr766is1aNBAVatWtbpuypQp9o5mNz/99JNeeuklPfXUU/rpp58cZknQvfz666+aNm2aZs6cmWc+lZby7n1L0sSJEzVlyhS1bt1aVatWzfT7/M033zQome18//33GjFihAYOHKhatWplWhPu6AVa6dacl61bt+rXX3/Vl19+qcTERIecA3DboUOH1KdPH23ZssXqeKNGjTRjxowsP5h42JUuXVrR0dHy8PBQSEiIpePmySefdNhC/PPPP6+qVatq3Lhxkm5ti1y5cmU1aNBAQUFBmj17tt5//329/fbbxga1gR49evzjNSaTSbNmzbJDGuD+UbwAbMDV1VXnzp1TkSJFsjwfExOjUqVKOcwnHKGhof94jclk0vr16+2Qxn4KFixoNe/g2rVr8vT0zPQPPUfdUvDSpUtq3769Nm3aJC8vr0xvZLlvx1OmTJm7njOZTDp16pQd09hHVkP9TCaTzGazww7zu+3mzZvatm2bIiIitGHDBm3fvl3FihVTo0aNNHv2bKPj2URsbKyqVKkif39/vfHGGwoKCpLZbNbhw4f11Vdf6cqVKzp48KBDzj05c+aMNmzYoIiICG3cuFHR0dHKly+f6tWrZylm1K5d2+iYOaZEiRL6/vvvFRISIunWTJ/FixcrMjJSkjRr1ixNnz7d8tiRPP/883c9l56errVr1yolJcWh/3zDw4niBWADzs7Oio2Nlb+/f5bnL1y4oGLFivGXwkPum2++ua/r7hyM5SiaNWum6Oho9ezZU4ULF840uJL7hiM4e/bsPc87WvfNpk2brIoVJUuWVKNGjdSoUSM1bNhQjz76qNERbWrYsGFau3attmzZIg8PD6tzycnJql+/vp566ilNnDjRoIT2c/r0aUsxY+nSpbp+/brS0tKMjpVjPD09dfz4cZUoUUKS1LRpU9WtW1fvv/++JCkqKkq1atVSfHy8gSnta+nSpXrnnXf0559/atiwYRo+fLjRkQArFC8AG7hzy7E7paWl6dChQxQv8FDz8vLStm3bVL16daOj2FVevW/kDU5OTipZsqSGDRumdu3aqXDhwkZHsquaNWtq+PDhat++fZbnFy5cqMmTJzvUlqFZOXv2rCIiIrR+/Xpt3LhRFy9eVJ06dRyqg7J48eL68ccfVbt2bWVkZKhgwYKaP3++WrduLenWlud16tTR1atXDU5qe1u2bNHw4cO1Z88e9e/fX8OHD1fBggWNjgVk4piL2ACDjRo16h+3T3zhhRfslMa2wsLC9P7772daB343I0aM0JAhQ+Tn52fjZLC1oKAgJScnGx3D7vLaff/9//G/z7fJiiPOtZk3b949z3fp0sVOSexj6NChioiI0Ntvv60ZM2aoUaNGaty4sRo1aqRChQoZHc/mTp06pZo1a971fHBwsEMuj4qOjrZ03EREROjy5cuqW7euGjVqpF69eql27dpyc3MzOmaOaty4sd5//33997//1Q8//KD09HQ1btzYcv7w4cMqXbq0Yfns4fDhwxo2bJhWrlypLl26aMGCBQ7fXYWHG50XAB7IPy2RuZOjbLXo5+en48eP3/c/5kuWLKnffvvNoVrMV69erbFjx2r8+PFZDm/08fExKJlt5bX7Dg0N1Y8//qgCBQrcc76NI861kZTp08fU1FQlJSXJzc1NXl5eDjvjJDExUb/99psiIiIUERGhvXv3qnz58mrUqJFCQ0P14osvGh3RJpydnRUTE3PXmRYXLlxQ8eLFHWr5hPR/HTd9+vRRaGioatWqJWdnZ6Nj2dSZM2fUrFkznTp1Sk5OTpo2bZr69u1rOd+2bVuVKVNGn3zyiYEpbePcuXMaNWqU/ve//+mZZ57RhAkTVLFiRaNjAf+I4gVgA3cOcrzN19dX5cuX1+DBg9W8eXMDkuU8Jycn+fr6/mOnyW1Xr17ViRMnHvrihZOTk7755hv5+vre1/UdO3bUgQMHHvr7/rvbgwzv/Nk7+iDDvHrf+D8nTpxQnz59NGTIELVo0cLoOHYRFxenKVOmaPr06Q6924izs7OOHz9+z5lVQUFBDnf/HTp00MaNG5WSkqL69etbilQ1atS477/fH0a3l/H6+/tbOktufyixb98+Pfroo3rkkUeMjGgTXl5eMplM6t+/v+rVq3fX69q0aWPHVMA/o3gB2MDdBjnGx8dr9+7dWrRokRYvXqxnn33Wzsly3v0Orfy7F1988b6XmeRWWe1A8E9OnjzpUMWLjRs33vN8o0aN7JTEvvLqfcParl271LlzZx09etToKDaRkZGhnTt3WjovtmzZosTERJUsWVKhoaGaM2eO0RFtwsnJ6Z5v1h29SHn06FGrHUdu3LhhKWY0btxYTzzxhNERc1R8fLzeffddLVq0SH/99ZekWx9AdejQQR988IEKFChgbEAbuZ9/wzjy73M8vCheAAaYMmWKFi9erK1btxodBQDu240bNzR9+nRt2LBBFy9eVEZGhtV5Rx9i+HeRkZFq2LChEhISjI6SoyZPnmwpVly7dk3FixdX48aNLVtl3mu7XEfwT8XJ2/JKkfLw4cOaP3++pk+f7nC7jcTFxSkkJER//PGHOnXqZFk2cfueS5Qooa1bt+bZwZVJSUny8vIyOgZgheIFYIDjx4+rTp06DrtWGnnHX3/9pVmzZunIkSOSpEqVKql79+4OOZA1Ojr6vq4rWbKkjZMYp1OnTlq9erVefPHFLLeJHT16tEHJbGfZsmVWj81ms2JiYvTZZ5+pRIkS+vXXXw1KZhvFihWzKlaULVvW6Eh2db/FKEebbfN3Fy5csHTcbNiwQcePH5e7u7vq1KmjDRs2GB0vx7z99ttat26d1q5dm2lXndjYWD311FNq2rSpQ868uJeUlBR9/vnnmjx5smJjY42OA1iheAEY4MCBA2revDl/KeChtmnTJj377LPy9fVVcHCwJGn37t2Kj4/X8uXL1bBhQ4MT5qy7tZPfbiOXbrXZOtInk3fy9fXVihUr7rlG2tHc2V5tMpnk7++vJk2a6OOPP1bRokUNSmYbY8eO1ZAhQ/LsJ67/tGzkNkdrp//+++8tBYujR4/Kzc1NTzzxhKWIVbduXbm7uxsdM0eVLl1aX3zxxV3n1qxcuVJvvPGGzpw5Y99gdpCSkqIxY8ZozZo1cnNz09ChQ9W2bVvNnj1b7733npydndW/f38NGzbM6KiAFYoXgAHefvttHT16VCtXrjQ6CvCvVa1aVSEhIZoxY4ZlKn16err69u2rrVu36sCBAwYnzFn79u3L8rjZbNbChQs1bdo05c+fXxcvXrRzMvupVKmSFi5cqGrVqhkdBTbyT7ttOLq/Lxsxm81q1aqVvv76axUvXtzqOkdbNvL3YkXjxo1Vr149eXp6Gh3Lptzd3RUVFXXXrUHPnz+vsmXL6saNG3ZOZnvDhg3TF198oWbNmmnr1q26dOmSunfvrt9//13vvPOOXnrpJYffbQYPJxejAwCOKCwsLMvjV69e1Z49e3T8+HFt2rTJzqmAnHXy5EktXrzY6h84zs7OCgsL07x58wxMZhvVq1fPdGzt2rUaPny4jh8/rqFDh2rQoEEGJLOfjz/+WMOGDdPMmTMdattf/J+8/pnWnUUJZ2dn1alTx6GGLWflr7/+0vTp07V8+XKtWrVKTZs21ejRox26gFGoUCGdOXPmrsWL06dPO+QSSEn64YcfNG/ePLVp00YHDx5UtWrVlJaWpn379jn07jJ4+FG8AGxg7969WR738fFR8+bNFR4e7tBDz06ePKmoqCg1bNhQnp6eVm31cBw1a9bUkSNHVKFCBavjR44cyfKNviPZs2ePhg0bpt9++02vvfaaVqxYkSc+qQ4ODtaNGzcUGBgoLy8vubq6Wp13lDk+dytAZ2XKlCk2TGIM/rzOez755BONHj1azZo1k6enpz799FNdvHhRs2fPNjqazbRo0ULvvvuuZenE36WkpGjkyJFq2bKlQels6/z586pVq5YkqUqVKnJ3d9fAgQP5fx+5HsULwAYcaaBVdly5ckUvv/yy1q9fL5PJpBMnTigwMFA9e/ZUwYIF9fHHHxsd0WaioqI0Z84cRUVF6dNPP1VAQIB+/fVXlSxZUpUrVzY6nk28+eabeuutt3Ty5EnVqVNHkvT777/r888/16RJk7R//37LtY6yzCAqKkrvvPOOlixZovbt2+vw4cMO/4ns33Xs2FF//PGHJkyYkOXATkdxtwL0nRz1/suXL/+P9+YohSrc8s033+jzzz/XG2+8IelWV1nr1q319ddf/6utwR8G48aNU3BwsMqVK6d+/fopKChIZrNZR44c0X//+1+lpKTo22+/NTqmTaSnp1sVbFxcXJQ/f34DEwH3h5kXAHJMly5ddPHiRX399deqWLGi9u3bp8DAQK1atUphYWE6dOiQ0RFtYuPGjXr66adVr149bdq0SUeOHFFgYKAmTZqkXbt2afHixUZHtIl/+getyWSydN04wnC7vn37atasWQoNDdWkSZP0+OOPGx3J7ry8vLRt2zaH76zJy5ycnDR16lT5+vre87quXbvaKZGxvL29tX//fofulpRuzX84efKkSpQoYTnm4eGhkydP3nVZhSM4ffq0+vbtq9WrV1uWTJlMJjVv3lyfffaZw+624+TkpKefftoyhHX58uVq0qSJ8uXLZ3VdeHi4EfGAu6LzAkCOWb16tVatWpXpHzrlypXT2bNnDUple8OHD9cHH3ygsLAweXt7W443adJEn332mYHJbOv06dNGR7CrmTNnysPDQxcvXlSPHj3uet2ePXvsmMq+goKClJycbHQM2FiHDh3yxDKorLRr187q8Y0bN/TGG284/Ju6tLQ0eXh4WB1zdXVVamqqQYnso0yZMvr111/1119/6cSJE5KksmXLOuysi9vuLD527tzZoCRA9lC8AJBjrl+/nuX2enFxcQ63xdrfHThwQPPnz890PCAgQJcvXzYgke2lpqZq7NixGjlypMN/Innb6NGjjY5guEmTJmnQoEEaP368qlatmmnmhY+Pj0HJcla7du00d+5c+fj4ZHozeydHexPrqEth7tedHSd55U2d2WxWt27drP6uzqpw42i/328rWLCgateubXQMu5kzZ47REYB/heIFgBzToEEDzZs3T++//76kW/8IzsjI0OTJkxUaGmpwOtspUKCAYmJiMr2J37t3b6bt9RyFq6urlixZopEjRxodxW4oXsgyvK5p06ZWxx1peZB06w3s7Tfx/7R8wtHk9dXEefVNXVbLgPJK4QbAw4OZFwByzMGDB9W0aVPVrFlT69evV5s2bXTo0CHFxcVpy5Yteuyxx4yOaBODBw/W9u3b9cMPP6h8+fLas2ePLly4oC5duqhLly4O+6a3a9euevzxxzVw4ECjo8BONm7ceM/zd24zCQAAkFMoXgDIUVevXtVnn32mffv2KTExUTVr1lS/fv1UtGhRo6PZzM2bN9WvXz/NnTtX6enpcnFxUXp6ul555RXNnTtXzs7ORke0iQ8++EAff/yxmjZtqlq1amVaE/7mm28alCzn1axZU+vWrVPBggXv6/r69etr0aJFDtt5AwAAYG8ULwDkiNTUVLVs2VIzZ85UuXLljI5jN2azWefOnZO/v78uX76sAwcOKDExUTVq1HD4/w73mnVhMpl06tQpO6axLScnJ61fv/6+h7jVrVtX+/fvd4htVPfv368qVarc93aJhw4dUoUKFeTi4jgrUxcvXqzvv/9e0dHRunnzptU5Rx7QCgBAbuI4/7IAYChXV1ft37/f6Bh2ZzabVbZsWR06dEjlypWz2mbO0eW13UaaNm163/MAHGnoYY0aNRQbGyt/f//7uj4kJESRkZEOUbiRpGnTpundd99Vt27dtHTpUnXv3l1RUVHauXOn+vXrZ3Q8AADyDIoXAHJM586dNWvWLE2aNMnoKHbj5OSkcuXK6cqVKw7faZGX/ZtCzZ1bBj+szGazRo4cmeVOQlm5szPhYfff//5XX375pTp27Ki5c+dq6NChCgwM1KhRoxQXF2d0PAAA8gyWjQDIMQMGDNC8efNUrly5LGcgTJkyxaBktrV8+XJNnjxZM2bMUJUqVYyOY1NhYWF6//33M/1s72bEiBEaMmTIfS+3QO7TuHHjbHeSzJ8/32Hm3Hh5eenIkSMqVaqUAgICtGbNGlWvXl0nTpxQnTp1dOXKFaMjAgCQJ1C8AJBj7rUdqslk0vr16+2Yxn4KFiyopKQkpaWlyc3NTZ6enlbnHenTWWdn52wtIfDx8XGoJQTIewIDA7VkyRLVqFFDwcHB6tWrl3r37q3Vq1erQ4cODvX/NwAAuRnLRgDkmA0bNhgdwRBTp041OoLdmM1mlS9f/r4/ib9+/bqNEwG21aRJEy1btkw1atRQ9+7dNXDgQC1evFi7du1Su3btjI4HAECeQecFAOC+ffPNN9l+zosvvnjfy0yA3CYjI0MZGRmW3VMWLlyorVu3qly5curdu7fc3NwMTggAQN5A8QJAjgkNDb3nJ/KOumwkOjr6nudLlixppyQAckK7du00d+5c+fj4aN68eXr55Zfl7u5udCwAAPI0ihcAcszAgQOtHqempioyMlIHDx5U165d9emnnxqUzLacnJzuWbRJT0+3YxrYQ3x8vBYvXqyoqCjLQNI9e/aocOHCKl68uNHx8IDc3Nx09uxZFS1aVM7OzoqJiVFAQIDRsQAAyNOYeQEgx3zyySdZHh8zZowSExPtnMZ+9u7da/U4NTVVe/fu1ZQpUzR+/HiDUsFW9u/fr2bNmsnX11dnzpxRr1695Ofnp/DwcEVHR2vevHlGR7SJ1NRU9e7dWyNHjlSZMmWMjmNTQUFBGjFihEJDQ2U2m/X999/Lx8cny2u7dOli53QAAORNdF4AsLmTJ0+qdu3aeW4q/y+//KL//Oc/ioiIMDoKclCzZs1Us2ZNTZ48Wd7e3tq3b58CAwO1detWvfLKKzpz5ozREW3G19dXkZGRDl+82Lp1q8LCwhQVFaW4uDh5e3tn2V1lMpny3J9rAAAYxcnoAAAc37Zt2+Th4WF0DLurUKGCdu7caXQM5LCdO3eqd+/emY4XL15csbGxBiSyn7Zt2+qnn34yOobN1a1bV7///rsuXboks9ms48eP66+//sr0ReECAAD7YdkIgBxz57aBZrNZMTEx2rVrl0aOHGlQKttLSEiwenz7vseMGaNy5coZlAq24u7unulnLknHjx+Xv7+/AYnsp1y5cho3bpy2bNmiWrVqZdpF5s033zQome2cPn3a4X+uAAA8DFg2AiDHdOvWzaq12snJSf7+/mrSpImeeuopA5PZVlYDO81ms0qUKKGFCxcqJCTEoGQ5784C1b2Eh4fbMIlxXnvtNV25ckXff/+9/Pz8tH//fjk7O6tt27Zq2LChpk6danREm7nXchGTyaRTp07ZMQ0AAMhLKF4AwAPauHGj1ePbRZuyZcvKxcWxGty6d+9u+bXZbNaPP/4oX19fBQcHS5J2796t+Ph4tWvXTnPmzDEqpk1dvXpVL774onbt2qVr166pWLFiio2NVUhIiFasWJGpGwEAAAAPjuIFgBwTGBionTt36pFHHrE6Hh8fr5o1azrsp7KbNm1S3bp1MxUq0tLStHXrVjVs2NCgZLY1bNgwxcXFaebMmXJ2dpZ0a1vYvn37ysfHR//5z38MTmhbmzdv1v79+5WYmKiaNWuqWbNmRkcCAABwWBQvAOQYJycnxcbGKiAgwOr4hQsXVLJkSaWkpBiUzLacnZ0VExOT6b6vXLmigIAApaenG5TMtvz9/bV582ZVqFDB6vixY8dUt25dXblyxaBksKXz589r2bJlio6O1s2bN63OTZkyxaBUAADA0TlWPzMAQyxbtszy61WrVsnX19fyOD09XevWrVPp0qUNSGYfZrM5y20Ur1y54tBLCNLS0nT06NFMxYujR48qIyPDoFS2N27cuHueHzVqlJ2S2N+6devUpk0bBQYG6ujRo6pSpYrOnDkjs9msmjVrGh3PZtLS0hQREaGoqCi98sor8vb21p9//ikfHx/lz5/f6HgAAOQJFC8APLC2bdtKujWwr2vXrlbnXF1dVbp0aX388ccGJLOt28MrTSaTunXrJnd3d8u59PR07d+/X3Xr1jUqns11795dPXv2VFRUlGrXri1J2r59uyZNmmQ1G8PR/Pjjj1aPU1NTdfr0abm4uOixxx5z6OLFiBEjNHjwYI0dO1be3t5asmSJAgIC1KlTJ7Vs2dLoeDZx9uxZtWzZUtHR0UpJSVHz5s3l7e2tDz/8UCkpKZo5c6bREQEAyBMoXgB4YLc/ZS9Tpox27typQoUKGZzIPm53mJjNZnl7e8vT09Nyzs3NTXXq1FGvXr2MimdzH330kYoUKaKPP/5YMTExkqSiRYtqyJAhGjRokMHpbGfv3r2ZjiUkJKhbt256/vnnDUhkP0eOHNGCBQskSS4uLkpOTlb+/Pk1btw4Pffcc+rTp4/BCXPeW2+9peDgYO3bt89qns/zzz/v0P9/AwCQ21C8AJBjTp8+bXQEu7q9m0bp0qU1ePBgh14ikhUnJycNHTpUQ4cOVUJCgiTJx8fH4FTG8PHx0dixY/Xss8/q1VdfNTqOzeTLl88y56Jo0aKKiopS5cqVJUmXL182MprN/Pbbb9q6davc3NysjpcuXVp//PGHQakAAMh7KF4AyFHXr1/Xxo0bsxzm9+abbxqUyrZGjx5tdATD3DkLQFKenQVw9epVXb161egYNlWnTh1t3rxZFStWVKtWrTRo0CAdOHBA4eHhqlOnjtHxbCIjIyPLobvnz5+Xt7e3AYkAAMib2G0EQI7Zu3evWrVqpaSkJF2/fl1+fn66fPmyvLy8FBAQ4LBbpUrS4sWL9f3332dZtNmzZ49BqWzrzlkAx48fV2BgoN566y2HngUwbdo0q8dms1kxMTH69ttv1ahRI82fP9+gZLZ36tQpJSYmqlq1arp+/boGDRqkrVu3qly5cpoyZYpKlSpldMQc9/LLL8vX11dffvmlvL29tX//fvn7++u5555TyZIlLR1YAADAtiheAMgxjRs3Vvny5TVz5kz5+vpq3759cnV1VefOnfXWW29ZBlw6mmnTpundd99Vt27d9OWXX6p79+6KiorSzp071a9fP40fP97oiDbRtm1beXt7a9asWXrkkUe0b98+BQYGKiIiQr169dKJEyeMjmgTZcqUsXrs5OQkf39/NWnSRCNGjODTeAdz/vx5tWjRQmazWSdOnFBwcLBOnDihQoUKadOmTZm2SAYAALZB8QJAjilQoIC2b9+uChUqqECBAtq2bZsqVqyo7du3q2vXrjp69KjREW0iKChIo0ePVseOHeXt7W15Ez9q1CjFxcXps88+MzqiTTzyyCPaunWrKlSoYHXfZ86cUaVKlZSUlGR0RNhAfHy8Fi9erKioKA0ZMkR+fn7as2ePChcurOLFixsdzybS0tK0cOFC7d+/X4mJiapZs6Y6depkNaQXAADYFjMvAOQYV1dXOTk5SZICAgIUHR2tihUrytfXV+fOnTM4ne1ER0dbtkT19PTUtWvXJEmvvvqq6tSp47DFC2YB5D379+9Xs2bN5OvrqzNnzqhXr17y8/NTeHi4oqOjNW/ePKMj2oSLi4s6d+5sdAwAAPI0ihcAckyNGjW0c+dOlStXTo0aNdKoUaN0+fJlffvtt6pSpYrR8WymSJEiiouLU6lSpVSyZEn9/vvvql69uk6fPi1Hbm576qmnNHXqVH355ZeSJJPJpMTERI0ePVqtWrUyOJ3tPP/88zKZTPd1bXh4uI3T2FdYWJi6deumyZMnWxWoWrVqZRnY6ohOnDihDRs26OLFi5atoW8bNWqUQakAAMhbKF4AyDETJkywdB2MHz9eXbp0UZ8+fVSuXDnNnj3b4HS206RJEy1btkw1atRQ9+7dNXDgQC1evFi7du1y2DkfkvTxxx+rRYsWqlSpkm7cuKFXXnnFMgtgwYIFRsezGV9fX/3444/y9fVVcHCwJGn37t26evWq2rZte9+FjYfRzp079cUXX2Q6Xrx4ccXGxhqQyPa++uor9enTR4UKFVKRIkWsfr4mk4niBQAAdsLMCwA5wmw269y5cwoICJCHh4fRcewqIyNDGRkZcnG5VQ9euHChZQeG3r17y83NzeCEtpMXZwEMGzZMcXFxmjlzppydnSVJ6enp6tu3r3x8fPSf//zH4IS2ExAQoFWrVqlGjRpWc07WrFmjHj16OOTysFKlSqlv374aNmyY0VEAAMjTKF4AyBEZGRny8PDQoUOHVK5cOaPj2E1aWpomTJigHj166NFHHzU6DuzA399fmzdvVoUKFayOHzt2THXr1tWVK1cMSmZ7r732mq5cuaLvv/9efn5+2r9/v5ydndW2bVs1bNhQU6dONTpijvPx8VFkZKQCAwONjgIAQJ7mZHQAAI7ByclJ5cqVc+g3bllxcXHR5MmTlZaWZnQUQ3z77beqX7++ihUrprNnz0qSPvnkEy1dutTgZLaTlpaW5c45R48ezTQPwdF8/PHHSkxMVEBAgJKTk9WoUSOVLVtW3t7eDrsl8EsvvaTVq1cbHQMAgDyPmRcAcsykSZM0ZMgQzZgxw6EHdN6padOm2rhxo0qXLm10FLuaMWOGRo0apbffflsffPCBZeeRggULaurUqXruuecMTmgb3bt3V8+ePRUVFaXatWtLkrZv365Jkyape/fuBqezLV9fX61Zs0abN2+2WirUrFkzo6PZTNmyZTVy5Ej9/vvvqlq1qlxdXa3Ov/nmmwYlAwAgb2HZCIAcU7BgQSUlJSktLU1ubm6Z5h7ExcUZlMy2Zs6cqbFjx6pTp06qVauW8uXLZ3W+TZs2BiWzrUqVKmnChAlq27at1fyDgwcPqnHjxrp8+bLREW0iIyNDH330kT799FPFxMRIkooWLaq33npLgwYNsszBcEQ3btzIczNtypQpc9dzJpNJp06dsmMaAADyLooXAHLMN998c8/zXbt2tVMS+3JyuvsKPJPJZOlIcDSenp46evSoSpUqZVW8OHHihKpVq6bk5GSjI+a4tLQ0zZ8/Xy1atFDhwoWVkJAg6dZchLzAw8NDtWvXVqNGjRQaGqqQkBCHHs4KAAByD5aNAMgxjlqc+CeOPufgbsqUKaPIyEiVKlXK6vjKlStVsWJFg1LZlouLi9544w0dOXJEUt4pWty2du1abdq0SREREfrkk0+Ulpam4OBgNWrUSI0bN1bz5s2NjggAABwUxQsAOSoqKkpz5sxRVFSUPv30UwUEBOjXX39VyZIlVblyZaPj2VxeaqsPCwtTv379dOPGDZnNZu3YsUMLFizQxIkT9fXXXxsdz2Zq166tvXv3Zira5AX169dX/fr19c477ygtLU07d+7UF198ocmTJ2vSpEkO02UUFham999/X/ny5VNYWNg9r50yZYqdUgEAkLdRvACQYzZu3Kinn35a9erV06ZNmzR+/HgFBARo3759mjVrlhYvXmx0RJtIT0/XhAkTNHPmTF24cEHHjx9XYGCgRo4cqdKlS6tnz55GR7SJ1157TZ6ennrvvfeUlJSkV155RcWKFdOnn36qDh06GB3PZvr27atBgwbp/PnzWc44qVatmkHJ7OP48eOKiIiwfKWkpOiZZ55R48aNjY6WY/bu3avU1FTLr+/GZDLZKxIAAHkeMy8A5JiQkBC99NJLCgsLs5qBsGPHDrVr107nz583OqJNjBs3Tt98843GjRunXr166eDBgwoMDNSiRYs0depUbdu2zeiINpeUlGTZQtPRZTXjxGQyyWw2O/SME0kqXry4kpOT1bhxYzVu3FiNGjVStWrVeBMPAABsjs4LADnmwIEDmj9/fqbjAQEBDrvzhCTNmzdPX375pZo2bao33njDcrx69eo6evSogcns4+LFizp27JikW2/i/f39DU5kW6dPnzY6gmH8/f119OhRxcbGKjY2VhcuXFBycrK8vLyMjgYAABwcxQsAOaZAgQKKiYnJtLXg3r17Vbx4cYNS2d4ff/yhsmXLZjqekZFhaT13RNeuXVPfvn21YMECy9BSZ2dnvfzyy/r888/l6+trcELbyIuzLm6LjIxUfHy8Nm3apI0bN+qdd97R4cOH9fjjjys0NFTjx483OmKOu379uiZNmqR169bp4sWLmQb0slUqAAD2QfECQI7p0KGDhg0bph9++EEmk0kZGRnasmWLBg8erC5duhgdz2YqVaqk3377LdOb2sWLF6tGjRoGpbK91157TXv37tUvv/yikJAQSdK2bdv01ltvqXfv3lq4cKHBCW0nKipKU6dOtew6UqlSJb311lt67LHHDE5mewUKFFCbNm1Ur1491a1bV0uXLtWCBQu0fft2hyxevPbaa9q4caNeffVVFS1alCUyAAAYhJkXAHLMzZs31a9fP82dO1fp6elycXFRenq6XnnlFc2dO1fOzs5GR7SJpUuXqmvXrhoxYoTGjRunsWPH6tixY5o3b55+/vlnh90+Ml++fFq1apXq169vdfy3335Ty5Ytdf36dYOS2daqVavUpk0bPf7446pXr54kacuWLdq3b5+WL1/usD9vSQoPD7cM6jx8+LD8/PxUv359y/yL6tWrGx0xxxUoUEC//PKL5WcNAACMQfECQI6Ljo7WwYMHlZiYqBo1aqhcuXJGR7K53377TePGjdO+ffuUmJiomjVratSoUXrqqaeMjmYzJUuW1C+//KKqVataHd+/f79atWrlsANaa9SooRYtWmjSpElWx4cPH67Vq1drz549BiWzvYCAADVs2NBSrLjzZ++IypQpoxUrVqhixYpGRwEAIE+jeAHAJm7/0UKLteP68ssv9cMPP+jbb79VkSJFJEmxsbHq2rWr2rVrp969exuc0DY8PDx04MCBTEW548ePq1q1arpx44ZByWAL//vf/7R06VJ98803DCYFAMBAzLwAkKNmzZqlTz75RCdOnJAklStXTm+//bZee+01g5PZ3q5du6xmINSqVcvgRLY1Y8YMnTx5UiVLllTJkiUl3eq6cXd316VLl/TFF19YrnWkbgR/f39FRkZmKl5ERkbmia1i84IaNWpYFV5PnjypwoULq3Tp0nJ1dbW61pF+bwMAkJtRvACQY0aNGqUpU6ZowIABVgMcBw4cqOjoaI0bN87ghLZx/vx5dezYUVu2bFGBAgUkSfHx8apbt64WLlyoRx991NiANtK2bVujI9jVuHHjNHjwYPXq1Uuvv/66Tp06pbp160q6NfPiww8/VFhYmMEpkRPy2u9tAAAeBiwbAZBj/P39NW3aNHXs2NHq+IIFCzRgwABdvnzZoGS21bJlS8XHx+ubb75RhQoVJEnHjh1T9+7d5ePjo5UrVxqcEDnB2dlZMTEx8vf319SpU/Xxxx/rzz//lCQVK1ZMQ4YM0ZtvvslSKQAAABugeAEgxxQoUEA7d+7MchZA7dq1FR8fb0wwG/P09NTWrVszbYu6e/duNWjQQElJSQYls61z587JZDJZOkt27Nih+fPnq1KlSnr99dcNTpfznJycFBsba7U05Nq1a5Ikb29vo2LBxnbu3KmMjAw9+eSTVse3b98uZ2dnBQcHG5QMAIC8xcnoAAAcx6uvvqoZM2ZkOv7ll1+qU6dOBiSyjxIlSig1NTXT8fT0dBUrVsyARPbxyiuvaMOGDZJuDeps1qyZduzYoXfffddhlwjd2VXh7e1N4cLB9evXT+fOnct0/I8//lC/fv0MSAQAQN5E5wWAHDNgwADNmzdPJUqUUJ06dSTd+nQyOjpaXbp0sRp0N2XKFKNi5rilS5dqwoQJ+vzzzy2fwu7atUsDBgzQsGHDHHb9fMGCBfX777+rQoUKmjZtmhYtWqQtW7Zo9erVeuONN3Tq1CmjI+YoJycn+fr6/uOykLi4ODslso927drd97Xh4eE2TGKM/Pnza//+/QoMDLQ6fvr0aVWrVs3SfQMAAGyLgZ0AcszBgwdVs2ZNSVJUVJQkqVChQipUqJAOHjxouc7RZgJ069ZNSUlJevLJJ+XicuuP1bS0NLm4uKhHjx7q0aOH5VpHemObmpoqd3d3SdLatWvVpk0bSVJQUJBiYmKMjGYzY8eOla+vr9Ex7Cqv3e+d3N3ddeHChUzFi5iYGMv/7wAAwPbovACAB/TNN9/c97Vdu3a1YRL7evLJJxUaGqrWrVvrqaee0u+//67q1avr999/14svvqjz588bHTFHZTXzAo6vY8eOiomJ0dKlSy2FnPj4eLVt21YBAQH6/vvvDU4IAEDeQPECAPCvRERE6Pnnn1dCQoK6du2q2bNnS5LeeecdHT161OGWENzebYTihXTp0iUdO3ZMklShQgX5+/sbnMh2/vjjDzVs2FBXrlyxDOWNjIxU4cKFtWbNGpUoUcLghAAA5A0ULwDkmBs3bmj69OnasGGDLl68qIyMDKvze/bsMSiZfVy8eDHL+65WrZpBiWwvPT1dCQkJKliwoOXYmTNn5OXl5XBv8um8kK5fv26ZbXP797mzs7O6dOmi6dOny8vLy+CEtnH9+nV999132rdvnzw9PVWtWjV17NjRao4PAACwLYoXAHJMp06dtHr1ar344osqXLhwptkWo0ePNiiZbe3evVtdu3bVkSNHdOcfqSaTSenp6QYlA3JW7969tXbtWn322WeqV6+eJGnz5s1688031bx58yx3GwIAAMgJFC8A5BhfX1+tWLHC8qYmr6hevboee+wxDRs2LMuiTalSpQxKBuSsQoUKafHixWrcuLHV8Q0bNqh9+/a6dOmSMcHs4PDhw4qOjtbNmzetjt8eVAsAAGyLMdkAckzx4sXl7e1tdAy7O3XqlJYsWaKyZcsaHQWwqaSkJBUuXDjT8YCAACUlJRmQyPZOnTql559/XgcOHJDJZLJ0V90uUtJZBQCAfTgZHQCA4/j44481bNgwnT171ugodtW0aVPt27fP6BiAzYWEhGj06NG6ceOG5VhycrLGjh2rkJAQA5PZzltvvaUyZcro4sWL8vLy0qFDh7Rp0yYFBwcrIiLC6HgAAOQZLBsBkGMuXbqk9u3ba9OmTfLy8so0zC4uLs6gZLZ1+fJlde3aVbVr11aVKlUy3Tdt5XAUBw8eVIsWLZSSkqLq1atLkvbt2ycPDw+tWrVKlStXNjhhzitUqJDWr1+vatWqydfXVzt27FCFChW0fv16DRo0SHv37jU6IgAAeQLLRgDkmI4dO+qPP/7QhAkTspz94Ki2bdumLVu26Ndff810ztEGdk6bNu2+r33zzTdtmARGqFKlik6cOKHvvvtOR48elXTr//tOnTrJ09PT4HS2kZ6eblkOV6hQIf3555+qUKGCSpUqZdkuFgAA2B6dFwByjJeXl7Zt22b5RDavKF26tJ555hmNHDkyy3kAjqRMmTL3dZ3JZNKpU6dsnAawvQYNGmjQoEFq27atXnnlFf31119677339OWXX2r37t06ePCg0REBAMgT6LwAkGOCgoKUnJxsdAy7u3LligYOHOjwhQtJOn36tNERYLCoqChNnTpVR44ckSRVrlxZb775ph577DGDk9nGe++9p+vXr0uSxo0bp2eeeUYNGjTQI488okWLFhmcDgCAvIPOCwA5ZvXq1Ro7dqzGjx+vqlWrZpr94OPjY1Ay2+ratasaNGig1157zegogE2tWrVKbdq00eOPP27ZEnnLli3at2+fli9frubNmxuc0D7i4uJUsGDBPLM0DgCA3IDiBYAc4+R0awOjO/9BbzabHW72w9+NHz9eU6dOVevWrbMs2jjy7Ifz589r2bJlio6O1s2bN63OTZkyxaBUsJUaNWqoRYsWmjRpktXx4cOHa/Xq1dqzZ49ByQAAgKOjeAEgx2zcuPGe5xs1amSnJPZ1rzkQjjz7Yd26dWrTpo0CAwN19OhRValSRWfOnJHZbFbNmjW1fv16oyMih3l4eOjAgQMqV66c1fHjx4+rWrVqVluoPux69OhxX9fNnj3bxkkAAIDEzAsAOchRixP/JK/OgRgxYoQGDx6ssWPHytvbW0uWLFFAQIA6deqkli1bGh0PNuDv76/IyMhMxYvIyEgFBAQYlMo25s6dq1KlSqlGjRricx4AAIxH8QJAjvrtt9/0xRdf6NSpU/rhhx9UvHhxffvttypTpozq169vdDybunnzpk6fPq3HHntMLi6O/8frkSNHtGDBAkmSi4uLkpOTlT9/fo0bN07PPfec+vTpY3BC5JRx48Zp8ODB6tWrl15//XWdOnVKdevWlXRr5sWHH36osLAwg1PmrD59+mjBggU6ffq0unfvrs6dO8vPz8/oWAAA5FlORgcA4DiWLFmiFi1ayNPTU3v27FFKSook6erVq5owYYLB6WwnKSlJPXv2lJeXlypXrqzo6GhJ0oABAzLNBnAk+fLls8y5KFq0qKKioiznLl++bFQs2MDYsWOVmJiokSNHatSoUZo+fboaNWqkRo0a6bPPPtOYMWP03nvvGR0zR33++eeKiYnR0KFDtXz5cpUoUULt27fXqlWr6MQAAMAAFC8A5JgPPvhAM2fO1FdffWU1tLJevXoOPchvxIgR2rdvnyIiIuTh4WE53qxZM4feSrFOnTravHmzJKlVq1YaNGiQxo8frx49eqhOnToGp0NOuv1m3WQyaeDAgTp//ryuXr2qq1ev6vz583rrrbcccucNd3d3dezYUWvWrNHhw4dVuXJl9e3bV6VLl1ZiYqLR8QAAyFMcv68ZgN0cO3ZMDRs2zHTc19dX8fHx9g9kJz/99JMWLVqkOnXqWL2Bq1y5slU3gqOZMmWK5Q3c7U/mFy1apHLlyrHTiAO6szjh7e1tUBJjODk5yWQyyWw2O+zOSQAA5GYULwDkmCJFiujkyZMqXbq01fHNmzcrMDDQmFB2cOnSpSyHFV6/ft0hP42+7e8/03z58mnmzJkGpoGtlS9f/h9/P8fFxdkpjX2kpKQoPDxcs2fP1ubNm/XMM8/os88+U8uWLS1bQwMAAPugeAEgx/Tq1UtvvfWWZs+eLZPJpD///FPbtm3T4MGDNXLkSKPj2UxwcLB++eUXDRgwQNL/fUL99ddfKyQkxMhodnHz5k1dvHhRGRkZVsdLlixpUCLYwtixY+Xr62t0DLvp27evFi5cqBIlSqhHjx5asGCBChUqZHQsAADyLJOZqVMAcojZbNaECRM0ceJEJSUlSbq1Znzw4MF6//33DU5nO5s3b9bTTz+tzp07a+7cuerdu7cOHz6srVu3auPGjapVq5bREW3i+PHj6tmzp7Zu3Wp13Gw2y2Qy0VrvQJycnBQbG+tw26Hei5OTk0qWLKkaNWrcs+MkPDzcjqkAAMi7KF4AyHE3b97UyZMnlZiYqEqVKil//vxGR7K5qKgoTZo0Sfv27VNiYqJq1qypYcOGqWrVqkZHs5l69erJxcVFw4cPV9GiRTO9watevbpByZDTnJ2dFRMTk6eKF926dbuvZV9z5syxQxoAAEDxAkCO6dGjhz799NNMg/yuX7+uAQMGaPbs2QYlgy3ky5dPu3fvVlBQkNFRYGN5sfMCAADkLkybApBjvvnmGyUnJ2c6npycrHnz5hmQyD6cnZ118eLFTMevXLkiZ2dnAxLZR6VKlXT58mWjY8AOMjIyKFwAAABDMbATwANLSEiQ2WyW2WzWtWvX5OHhYTmXnp6uFStWOPQbn7s1sKWkpMjNzc3Oaeznww8/1NChQzVhwgRVrVpVrq6uVud9fHwMSgYAAABHQ/ECwAMrUKCATCaTTCaTypcvn+m8yWTS2LFjDUhmW9OmTZN06/6+/vprq9ke6enp2rRpk0MvqWjWrJkkqWnTplbHGdgJAACAnEbxAsAD27Bhg8xms5o0aaIlS5bIz8/Pcs7NzU2lSpVSsWLFDExoG5988omkW2/WZ86cabVExM3NTaVLl9bMmTONimdzGzZsMDoCAAAA8ggGdgLIMWfPnlXJkiXva0K/IwkNDVV4eLgKFixodBQAAADAIVG8AAD8a/Hx8Zo1a5aOHDkiSapcubJ69OghX19fg5MBAADAkVC8AAD8K7t27VKLFi3k6emp2rVrS5J27typ5ORkrV69WjVr1jQ4IQAAABwFxQsAwL/SoEEDlS1bVl999ZVcXG6NUEpLS9Nrr72mU6dOadOmTQYnBAAAgKOgeAEgR5jNZp07d04BAQFWW6XCcXl6emrv3r2ZdlQ5fPiwgoODlZSUZFAyAAAAOBonowMAcAxms1lly5bVuXPnjI4CO/Hx8VF0dHSm4+fOnZO3t7cBiQAAAOCoKF4AyBFOTk4qV66crly5YnQUu1u5cqU2b95sefz555/r8ccf1yuvvKK//vrLwGS29fLLL6tnz55atGiRzp07p3PnzmnhwoV67bXX1LFjR6PjAQAAwIFQvACQYyZNmqQhQ4bo4MGDRkexqyFDhighIUGSdODAAQ0aNEitWrXS6dOnFRYWZnA62/noo4/Url07denSRaVLl1bp0qXVrVs3vfjii/rwww+NjgcAAAAHwswLADmmYMGCSkpKUlpamtzc3OTp6Wl1Pi4uzqBktpU/f34dPHhQpUuX1pgxY3Tw4EEtXrxYe/bsUatWrRQbG2t0RJtKSkpSVFSUJOmxxx6Tl5eXwYkAAADgaFyMDgDAcUydOtXoCIZwc3OzDKdcu3atunTpIkny8/OzdGQ4Mi8vL1WtWtXoGAAAAHBgFC8A5JiuXbsaHcEQ9evXV1hYmOrVq6cdO3Zo0aJFkqTjx4/r0UcfNThdzmrXrp3mzp0rHx8ftWvX7p7XhoeH2ykVAAAAHB3FCwAPJCEhQT4+PpZf38vt6xzNZ599pr59+2rx4sWaMWOGihcvLkn69ddf1bJlS4PT5SxfX1+ZTCZJt36et38NAAAA2BIzLwA8EGdnZ8XExCggIEBOTk5Zvpk1m80ymUxKT083ICEAAACAhx2dFwAeyPr16+Xn5ydJ2rBhg8FpjLFnzx65urpa5j4sXbpUc+bMUaVKlTRmzBi5ubkZnNA2mjRpovDwcBUoUMDqeEJCgtq2bav169cbEwwAAAAOh84LAHZx8OBBValSxegYNvHEE09o+PDheuGFF3Tq1ClVrlxZzz//vHbu3KnWrVs77CBTJycnxcbGKiAgwOr4xYsXVbx4caWmphqUDAAAAI6GzgsANnPt2jUtWLBAX3/9tXbv3u2wy0aOHz+uxx9/XJL0ww8/qGHDhpo/f762bNmiDh06OFzxYv/+/ZZfHz582Gor2PT0dK1cudIy9wMAAADICRQvAOS4TZs2adasWVqyZImKFSumdu3a6fPPPzc6ls2YzWZlZGRIurVV6jPPPCNJKlGihC5fvmxkNJt4/PHHZTKZZDKZ1KRJk0znPT09NX36dAOSAQAAwFFRvACQI2JjYzV37lzNmjVLCQkJat++vVJSUvTTTz+pUqVKRsezqeDgYH3wwQdq1qyZNm7cqBkzZkiSTp8+rcKFCxucLuedPn1aZrNZgYGB2rFjh/z9/S3n3NzcFBAQIGdnZwMTAgAAwNEw8wLAA3v22We1adMmtW7dWp06dVLLli3l7OwsV1dX7du3z+GLF/v371enTp0UHR2tsLAwjR49WpI0YMAAXblyRfPnzzc4IQAAAPBwo3gB4IG5uLjozTffVJ8+fVSuXDnL8bxSvLibGzduWIo4juzw4cOKjo7WzZs3rY63adPGoEQAAABwNCwbAfDANm/erFmzZqlWrVqqWLGiXn31VXXo0MHoWHYVHx+vxYsXKyoqSkOGDJGfn58OHz6swoULO+zwylOnTun555/XgQMHZDKZdLsWbjKZJMlhB7QCAADA/pyMDgDg4VenTh199dVXiomJUe/evbVw4UIVK1ZMGRkZWrNmja5du2Z0RJvav3+/ypUrpw8//FAfffSR4uPjJUnh4eEaMWKEseFs6K233lKZMmV08eJFeXl56dChQ9q0aZOCg4MVERFhdDwAAAA4EJaNALCJY8eOadasWfr2228VHx+v5s2ba9myZUbHsolmzZqpZs2amjx5sry9vbVv3z4FBgZq69ateuWVV3TmzBmjI9pEoUKFtH79elWrVk2+vr7asWOHKlSooPXr12vQoEHau3ev0REBAADgIOi8AGATFSpU0OTJk3X+/HktWLDA6Dg2tXPnTvXu3TvT8eLFiys2NtaARPaRnp4ub29vSbcKGX/++ackqVSpUjp27JiR0QAAAOBgmHkBwKacnZ3Vtm1btW3b1ugoNuPu7q6EhIRMx48fP261jaijqVKlivbt26cyZcroySef1OTJk+Xm5qYvv/xSgYGBRscDAACAA6HzAgAeUJs2bTRu3DilpqZKujWwMjo6WsOGDdMLL7xgcDrbee+995SRkSFJGjdunE6fPq0GDRpoxYoV+vTTTw1OBwAAAEfCzAsAeEBXr17Viy++qF27dunatWsqVqyYYmNjFRISohUrVihfvnxGR7SbuLg4FSxY0LLjCAAAAJATKF4AQA7ZsmWL9u3bp8TERNWsWVPNmjUzOpJN9ejRQ59++qll7sVt169f14ABAzR79myDkgEAAMDRULwAAPwrzs7OiomJUUBAgNXxy5cvq0iRIkpLSzMoGQAAABwNMy8A4AG9+eabmjZtWqbjn332md5++237B7KxhIQEXb16VWazWdeuXVNCQoLl66+//tKKFSsyFTQAAACAB0HnBQA8oOLFi2vZsmWqVauW1fE9e/aoTZs2On/+vEHJbMPJyemeMy1MJpPGjh2rd999146pAAAA4MjYKhUAHtCVK1fk6+ub6biPj48uX75sQCLb2rBhg8xms5o0aaIlS5bIz8/Pcs7NzU2lSpVSsWLFDEwIAAAAR0PxAgAeUNmyZbVy5Ur179/f6vivv/6qwMBAg1LZTqNGjSRJp0+fVokSJeTkxApEAAAA2BbFCwB4QGFhYerfv78uXbqkJk2aSJLWrVunjz/+WFOnTjU2nA2VKlVK8fHxmjVrlo4cOSJJqly5snr06JFlJwoAAADwbzHzAgBywIwZMzR+/Hj9+eefkqTSpUtrzJgx6tKli8HJbGfXrl1q0aKFPD09Vbt2bUnSzp07lZycrNWrV6tmzZoGJwQAAICjoHgBADno0qVL8vT0VP78+Y2OYnMNGjRQ2bJl9dVXX8nF5VYjX1paml577TWdOnVKmzZtMjghAAAAHAXFCwDIIZcuXdKxY8ckSUFBQSpUqJDBiWzL09NTe/fuVVBQkNXxw4cPKzg4WElJSQYlAwAAgKNhyhoAPKDr16+rR48eKlq0qBo2bKiGDRuqaNGi6tmzp0O/gffx8VF0dHSm4+fOnZO3t7cBiQAAAOCoKF4AwAMKCwvTxo0btXz5csXHxys+Pl5Lly7Vxo0bNWjQIKPj2czLL7+snj17atGiRTp37pzOnTunhQsX6rXXXlPHjh2NjgcAAAAHwrIRAHhAhQoV0uLFi9W4cWOr4xs2bFD79u116dIlY4LZ2M2bNzVkyBDNnDlTaWlpkiRXV1f16dNHkyZNkru7u8EJAQAA4CgoXgDAA/Ly8tLu3btVsWJFq+OHDh1S7dq1df36dYOS2UdSUpKioqIkSY899pi8vLyUnJwsT09Pg5MBAADAUbBsBAAeUEhIiEaPHq0bN25YjiUnJ2vs2LEKCQkxMJl9eHl5qWrVqqpataqcnZ01ZcoUlSlTxuhYAAAAcCAuRgcAgIfd1KlT1bJlSz366KOqXr26JGnfvn3y8PDQqlWrDE6X81JSUjRmzBitWbNGbm5uGjp0qNq2bas5c+bo3XfflbOzswYOHGh0TAAAADgQlo0AQA5ISkrSd999p6NHj0qSKlasqE6dOjnk0olhw4bpiy++ULNmzbR161ZdunRJ3bt31++//6533nlHL730kpydnY2OCQAAAAdC5wUAPIDU1FQFBQXp559/Vq9evYyOYxc//PCD5s2bpzZt2ujgwYOqVq2a0tLStG/fPplMJqPjAQAAwAEx8wIAHoCrq6vVrIu84Pz586pVq5YkqUqVKnJ3d9fAgQMpXAAAAMBmKF4AwAPq16+fPvzwQ8t2oY4uPT1dbm5ulscuLi7Knz+/gYkAAADg6Jh5AQAP6Pnnn9e6deuUP39+Va1aVfny5bM6Hx4eblAy23ByctLTTz8td3d3SdLy5cvVpEkTh79vAAAAGIeZFwDwgAoUKKAXXnjB6Bh207VrV6vHnTt3NigJAAAA8go6LwAAAAAAQK7GzAsA+JcyMjL04Ycfql69enriiSc0fPhwJScnGx0LAAAAcDgULwDgXxo/frzeeecd5c+fX8WLF9enn36qfv36GR0LAAAAcDgsGwGAf6lcuXIaPHiwevfuLUlau3atWrdureTkZDk5URsGAAAAcgrFCwD4l9zd3XXy5EmVKFHCcszDw0MnT57Uo48+amAyAAAAwLHw0SAA/EtpaWny8PCwOubq6qrU1FSDEgEAAACOia1SAeBfMpvN6tatm9zd3S3Hbty4oTfeeEP58uWzHAsPDzciHgAAAOAwKF4AwL/UtWvXTMc6d+5sQBIAAADAsTHzAgAAAAAA5GrMvAAAAAAAALkaxQsAAAAAAJCrUbwAAAAAAAC5GsULAAAAAACQq1G8AAAAAAAAuRrFCwAAAAAAkKtRvAAAAAAAALkaxQsAAAAAAJCr/T/rxq/RwfL92AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select only numeric columns for correlation matrix\n",
    "numeric_cols = main_df.select_dtypes(include=['float64', 'int64']).columns\n",
    "\n",
    "# Compute the correlation matrix (Threshold = 0.5)\n",
    "corr_matrix = main_df[numeric_cols].corr()\n",
    "threshold = 0.5\n",
    "mask = np.abs(corr_matrix) < threshold\n",
    "\n",
    "# Create a heatmap with masked irrelevant correlations\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', mask=mask, vmin=-1, vmax=1)\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3__gE1HhTeQ",
   "metadata": {
    "id": "a3__gE1HhTeQ"
   },
   "source": [
    "**Air Temperature [K] and Process Temperature [K] = 0.88:** These two variables are both related to thermal dynamics within the machinery. The process temperature may depend directly on the ambient air temperature, especially in environments where machinery generates heat. A higher air temperature can make it harder to cool the system, resulting in a higher process temperature.\n",
    "\n",
    "**Rotational Speed [rpm] and Torque [Nm] = -0.94:** In general, there is often an inverse relationship between rotational speed and torque. When the rotational speed of a machine increases, the torque required to maintain the same level of power decreases.\n",
    "\n",
    "\n",
    "**Different failure types (HDF, PWF, OSF):** These correlations indicate that different failure modes (TWF, HDF, PWF, OSF) are often interconnected. For example, HDF and PWF could be related because inadequate heat dissipation can affect the electrical systems, leading to power failures."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Rt4Nh3mShivf",
   "metadata": {
    "id": "Rt4Nh3mShivf"
   },
   "source": [
    "### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0goH3iKNhXZv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "0goH3iKNhXZv",
    "outputId": "efa8c36d-324d-478b-cb57-956f1d6708bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Histograms of features\n",
    "features = ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']\n",
    "for feature in features:\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    sns.histplot(df[feature], kde=True)\n",
    "    plt.title(f'Histogram of {feature}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "o5_OXjG3hyAv",
   "metadata": {
    "id": "o5_OXjG3hyAv"
   },
   "source": [
    "**Histogram of Air Temperature:** The distribution seems to be bimodal, where the temperatures are concetrated around 298 [k] and 301 [k].\n",
    "\n",
    "**Histogram of Process Temperature:** The process temperature values range from about 306 K to 314 K, with most values concentrated between 309 K and 312 K. Also, the histogram displays a prominent peak between 310 [K] and 311 [k], what suggests that the majority of process temperatures cluster around this central value, However, there are some higher process temperatures that occur less frequently.\n",
    "\n",
    "**Histogram of Rotational Speed:** The histogram displays a right-skewed distribution, with a peak around 1500 rpm. The histogram indicates that most of the data points are clustered around the lower end of the rotational speeds, with fewer instances of high speeds.\n",
    "\n",
    "**Histogram of Torque:** The histogram shows a normal distribution of the machine torque. The mean torque is around 40 [Nm].\n",
    "\n",
    "**Histogram of Tool Wear:** Tool wear generally ranges from 0 to 200 minutes of use. The distribution appears to be nearly uniform, but there is a sharp decline after 200 minutes of use.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543d68c3",
   "metadata": {
    "id": "543d68c3"
   },
   "source": [
    "## 3) Feature Engineering:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3385d1e3",
   "metadata": {
    "id": "3385d1e3"
   },
   "source": [
    "#### Note:\n",
    "Based on the correlation matrix, we identified a strong inverse relationship between Rotational Speed [rpm] and Torque [Nm] (-0.94), and a strong positive correlation between Air Temperature [K] and Process Temperature [K] (0.88). To capture these important dynamics, we are creating two new features: Mechanical_Power, which combines torque and speed to represent the machine's total output, and Temp_Diff, which highlights differences between process and air temperature, potentially signaling abnormal conditions like overheating. These features will help us in predicting machine failures more effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5412cc48",
   "metadata": {
    "id": "5412cc48"
   },
   "outputs": [],
   "source": [
    "# Mechanical_Power: Product of Torque and Rotational speed\n",
    "main_df['Mechanical_Power'] = main_df['Torque [Nm]'] * main_df['Rotational speed [rpm]']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852950a7",
   "metadata": {
    "id": "852950a7"
   },
   "source": [
    "Mechanical_Power: This feature is the product of Torque and Rotational speed.\n",
    "It represents the total mechanical power exerted by the machine, which may be a critical factor in predicting machine failures, as higher power output could lead to increased stress on the machine components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5a9fbe2",
   "metadata": {
    "id": "c5a9fbe2"
   },
   "outputs": [],
   "source": [
    "# Temp_Diff: Difference between Process temperature and Air temperature\n",
    "main_df['Temp_Diff'] = main_df['Process temperature [K]'] - main_df['Air temperature [K]']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fd67bd",
   "metadata": {
    "id": "f5fd67bd"
   },
   "source": [
    "Temp_Diff: This feature represents the difference between Process temperature and Air temperature.\n",
    "A significant difference might indicate abnormal operating conditions, such as overheating, which could contribute to machine failures."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468235f3",
   "metadata": {
    "id": "468235f3"
   },
   "source": [
    "#### Note:\n",
    "As our goal is to predict whether any type of machine failure occurs. We are simplyfing the problem to a binary classification, by combining the failure mode ('TWF', 'HDF', 'PWF', 'OSF', 'RNF') columns into a single 'Any Failure' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4bcbd31d",
   "metadata": {
    "id": "4bcbd31d"
   },
   "outputs": [],
   "source": [
    "# Combines failure mode columns into a single 'Any Failure' column\n",
    "main_df['Any Failure'] = main_df[['TWF', 'HDF', 'PWF', 'OSF', 'RNF']].any(axis=1).astype(int)\n",
    "\n",
    "# Drops the individual failure mode columns\n",
    "main_df = main_df.drop(['TWF', 'HDF', 'PWF', 'OSF', 'RNF', 'UDI', 'Product ID'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1f095f",
   "metadata": {
    "id": "0a1f095f"
   },
   "source": [
    "#### Note:\n",
    "Here, we are using log transformation on the dataset to reduce the impact of extreme values (outliers) and normalize the distribution of skewed features like Rotational speed [rpm] and Torque [Nm], so the machine learning model can learn better and make more accurate predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2070c86",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d2070c86",
    "outputId": "299aaeb2-fe46-4413-e0d9-af620396c815"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Rotational speed [rpm]   Torque [Nm]\n",
      "count            10000.000000  10000.000000\n",
      "mean                 7.333330      3.680281\n",
      "std                  0.107449      0.267251\n",
      "min                  7.063904      1.568616\n",
      "25%                  7.261225      3.532226\n",
      "50%                  7.315884      3.716008\n",
      "75%                  7.385851      3.867026\n",
      "max                  7.967973      4.351567\n"
     ]
    }
   ],
   "source": [
    "# Appling log transformation to Rotational Speed and Torque\n",
    "main_df['Rotational speed [rpm]'] = np.log(main_df['Rotational speed [rpm]'] + 1)\n",
    "main_df['Torque [Nm]'] = np.log(main_df['Torque [Nm]'] + 1)\n",
    "\n",
    "# Shows the result of the transformation\n",
    "print(main_df[['Rotational speed [rpm]', 'Torque [Nm]']].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a33f5bee",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 632
    },
    "id": "a33f5bee",
    "outputId": "1e2705ee-d961-4dc4-9ca1-0233840af6aa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots the transformed features\n",
    "plt.figure(figsize = (10, 6))\n",
    "main_df[['Rotational speed [rpm]', 'Torque [Nm]']].boxplot()\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad068e2b",
   "metadata": {
    "id": "ad068e2b"
   },
   "source": [
    "After Log Transformation:\n",
    "1) Rotational Speed [rpm]: After transformation the data is mostly concentrated around a tighter range , with a few outliers, but they are less extreme than before.\n",
    "\n",
    "2) Torque [Nm]: The range is more compressed, and outliers on the lower end are also now much smaller in their impact on the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "68c38d3b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "68c38d3b",
    "outputId": "595af1e0-4c50-4217-ef51-8cf8eaa4eb90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Mechanical_Power  Temp_Diff\n",
      "0           66382.8       10.5\n",
      "1           65190.4       10.5\n",
      "2           74001.2       10.4\n",
      "3           56603.5       10.4\n",
      "4           56320.0       10.5\n"
     ]
    }
   ],
   "source": [
    "# Checks the result for the engineered features\n",
    "print(main_df[['Mechanical_Power', 'Temp_Diff']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32885f03",
   "metadata": {
    "id": "32885f03"
   },
   "source": [
    "## 4) Modeling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f524b816",
   "metadata": {
    "id": "f524b816"
   },
   "outputs": [],
   "source": [
    "# Renames the columns to remove special characters\n",
    "main_df.columns = main_df.columns.str.replace('[\\[\\]\\s]', '_', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "86b97607",
   "metadata": {
    "id": "86b97607"
   },
   "outputs": [],
   "source": [
    "# Train-Test Split\n",
    "X = main_df.drop(['Any_Failure', 'Machine_failure'], axis=1)\n",
    "y = main_df['Any_Failure']  # Target (Any Failure)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8ee352b0",
   "metadata": {
    "id": "8ee352b0"
   },
   "outputs": [],
   "source": [
    "# Applying SMOTE to the training set\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767c4c85",
   "metadata": {
    "id": "767c4c85"
   },
   "source": [
    "### 4.1) Feature Selection:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf114666",
   "metadata": {
    "id": "bf114666"
   },
   "source": [
    "#### 1) Feature selection for Random Forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a834ed71",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "a834ed71",
    "outputId": "5b0784d1-fff4-474d-dba0-03f927451cad"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_model_fs = RandomForestClassifier(random_state=42)\n",
    "rf_model_fs.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# Checks the feature importance for Random Forest\n",
    "importances_rf = rf_model_fs.feature_importances_\n",
    "importance_df_rf = pd.DataFrame({'Feature': X_train.columns, 'Importance': importances_rf})\n",
    "importance_df_rf = importance_df_rf.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Plots top features for Random Forest\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df_rf)\n",
    "plt.title('Feature Importance from Random Forest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9a569c30",
   "metadata": {
    "id": "9a569c30"
   },
   "outputs": [],
   "source": [
    "# Selects the important features for Random Forest\n",
    "selected_features_rf = importance_df_rf[importance_df_rf['Importance'] > 0.01]['Feature']\n",
    "X_train_selected_rf = X_train[selected_features_rf]\n",
    "X_test_selected_rf = X_test[selected_features_rf]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca477690",
   "metadata": {
    "id": "ca477690"
   },
   "source": [
    "Selected Features for Random Forest Model:\n",
    "   - Rotational speed [rpm]\n",
    "   - Torque [Nm]\n",
    "   - Mechanical_Power\n",
    "   - Tool wear [min]\n",
    "   - Temp_Diff\n",
    "   - Air temperature [K]\n",
    "   - Process temperature [K]\n",
    "   - Type_M\n",
    "   - Type_L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d70c678c",
   "metadata": {
    "id": "d70c678c"
   },
   "outputs": [],
   "source": [
    "# Applying SMOTE to selected features for Random Forest\n",
    "X_train_smote_selected_rf, y_train_smote_selected_rf = smote.fit_resample(X_train_selected_rf, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd60cc18",
   "metadata": {
    "id": "cd60cc18"
   },
   "source": [
    "#### 2) Feature selection for Gradient Boosting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0da7f57d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "0da7f57d",
    "outputId": "9e1a4214-c960-4765-9fa7-07380cdb8b53"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb_model = GradientBoostingClassifier(random_state=42)\n",
    "gb_model.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# Checks the feature importance for Gradient Boosting\n",
    "importances_gb = gb_model.feature_importances_\n",
    "importance_df_gb = pd.DataFrame({'Feature': X_train.columns, 'Importance': importances_gb})\n",
    "importance_df_gb = importance_df_gb.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Plots top features for Gradient Boosting\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df_gb)\n",
    "plt.title('Feature Importance from Gradient Boosting')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "718e53b1",
   "metadata": {
    "id": "718e53b1"
   },
   "outputs": [],
   "source": [
    "# Selects the important features for Gradient Boosting\n",
    "selected_features_gb = importance_df_gb[importance_df_gb['Importance'] > 0.01]['Feature']\n",
    "X_train_selected_gb = X_train[selected_features_gb]\n",
    "X_test_selected_gb = X_test[selected_features_gb]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7739627",
   "metadata": {
    "id": "e7739627"
   },
   "source": [
    "Selected Features for Gradient Boosting Model:\n",
    "   - Rotational speed [rpm]\n",
    "   - Mechanical_Power\n",
    "   - Tool wear [min]\n",
    "   - Torque [Nm]\n",
    "   - Temp_Diff\n",
    "   - Type_M\n",
    "   - Air temperature [K]\n",
    "   - Type_L\n",
    "   - Process temperature [K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "41a8d2d7",
   "metadata": {
    "id": "41a8d2d7"
   },
   "outputs": [],
   "source": [
    "# Applying SMOTE to selected features for Gradient Boosting\n",
    "X_train_smote_selected_gb, y_train_smote_selected_gb = smote.fit_resample(X_train_selected_gb, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7e7cad",
   "metadata": {
    "id": "8c7e7cad"
   },
   "source": [
    "#### 3) Feature selection for XGBoost:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2df89bb6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "2df89bb6",
    "outputId": "1b76a3f4-6727-44e0-eeb9-763d01b905b8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_model = XGBClassifier(random_state=42)\n",
    "xgb_model.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# Checks the feature importance for XGBoost\n",
    "importances_xgb = xgb_model.feature_importances_\n",
    "importance_df_xgb = pd.DataFrame({'Feature': X_train.columns, 'Importance': importances_xgb})\n",
    "importance_df_xgb = importance_df_xgb.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Plots top features for XGBoost\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df_xgb)\n",
    "plt.title('Feature Importance from XGBoost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e94ec3f",
   "metadata": {
    "id": "6e94ec3f"
   },
   "outputs": [],
   "source": [
    "# Selects the important features for XGBoost\n",
    "selected_features_xgb = importance_df_xgb[importance_df_xgb['Importance'] > 0.01]['Feature']\n",
    "X_train_selected_xgb = X_train[selected_features_xgb]\n",
    "X_test_selected_xgb = X_test[selected_features_xgb]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10748ad9",
   "metadata": {
    "id": "10748ad9"
   },
   "source": [
    "Selected Features for XGBoost Model:\n",
    "   - Rotational speed [rpm]\n",
    "   - Type_M\n",
    "   - Mechanical_Power\n",
    "   - Tool wear [min]\n",
    "   - Torque [Nm]\n",
    "   - Type_L\n",
    "   - Temp_Diff\n",
    "   - Air temperature [K]\n",
    "   - Process temperature [K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e44e6c46",
   "metadata": {
    "id": "e44e6c46"
   },
   "outputs": [],
   "source": [
    "# Applying SMOTE to selected features for XGBoost\n",
    "X_train_smote_selected_xgb, y_train_smote_selected_xgb = smote.fit_resample(X_train_selected_xgb, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c783f73",
   "metadata": {
    "id": "1c783f73"
   },
   "source": [
    "### 4.2) Training and Evaluating Models:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a6c27c",
   "metadata": {
    "id": "25a6c27c"
   },
   "source": [
    "#### 1) Random Forest Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "07f78837",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "07f78837",
    "outputId": "9efc1cc5-b488-4b2c-d61b-d238d50c0c19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for Random Forest:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98      1935\n",
      "           1       0.47      0.68      0.56        65\n",
      "\n",
      "    accuracy                           0.96      2000\n",
      "   macro avg       0.73      0.83      0.77      2000\n",
      "weighted avg       0.97      0.96      0.97      2000\n",
      "\n",
      "Accuracy: 0.965\n"
     ]
    }
   ],
   "source": [
    "# Trains Random Forest model on SMOTE data\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X_train_smote_selected_rf, y_train_smote_selected_rf)\n",
    "\n",
    "# Predictions and evaluation\n",
    "y_pred_rf = rf_model.predict(X_test_selected_rf)\n",
    "print(\"Results for Random Forest:\")\n",
    "print(classification_report(y_test, y_pred_rf))\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred_rf)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b0df5e",
   "metadata": {
    "id": "12b0df5e"
   },
   "source": [
    "#### 2) Gradient Boosting Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3b591fad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3b591fad",
    "outputId": "3e4f47e3-3d48-45b2-caf2-40b6fa8dbc04"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for Gradient Boosting:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.94      0.97      1935\n",
      "           1       0.32      0.78      0.45        65\n",
      "\n",
      "    accuracy                           0.94      2000\n",
      "   macro avg       0.65      0.86      0.71      2000\n",
      "weighted avg       0.97      0.94      0.95      2000\n",
      "\n",
      "Accuracy: 0.938\n"
     ]
    }
   ],
   "source": [
    "# Trains Gradient Boosting model on SMOTE data\n",
    "gb_model_smote = GradientBoostingClassifier(random_state=42)\n",
    "gb_model_smote.fit(X_train_smote_selected_gb, y_train_smote_selected_gb)\n",
    "\n",
    "# Predictions and evaluation for Gradient Boosting\n",
    "y_pred_gb = gb_model_smote.predict(X_test_selected_gb)\n",
    "print(\"Results for Gradient Boosting:\")\n",
    "print(classification_report(y_test, y_pred_gb))\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred_gb)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e9ca193",
   "metadata": {
    "id": "1e9ca193"
   },
   "source": [
    "#### 3) XGBoost Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "19b2b962",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "19b2b962",
    "outputId": "bb798659-dff1-497b-ba6d-140b502b71dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for XGBoost:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1935\n",
      "           1       0.64      0.72      0.68        65\n",
      "\n",
      "    accuracy                           0.98      2000\n",
      "   macro avg       0.82      0.85      0.83      2000\n",
      "weighted avg       0.98      0.98      0.98      2000\n",
      "\n",
      "Accuracy: 0.978\n"
     ]
    }
   ],
   "source": [
    "# Trains XGBoost model on SMOTE data\n",
    "xgb_model_smote = XGBClassifier(random_state=42)\n",
    "xgb_model_smote.fit(X_train_smote_selected_xgb, y_train_smote_selected_xgb)\n",
    "\n",
    "# Predictions and evaluation for XGBoost\n",
    "y_pred_xgb = xgb_model_smote.predict(X_test_selected_xgb)\n",
    "print(\"Results for XGBoost:\")\n",
    "print(classification_report(y_test, y_pred_xgb))\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred_xgb)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f8056f",
   "metadata": {
    "id": "e0f8056f"
   },
   "source": [
    "#### Note:\n",
    "Random Forest and Gradient Boosting performed well, achieving high accuracy. However, XGBoost provided the best balance between precision (correct positive predictions) and recall (detecting most failures). So, we are applying threshold tuning only to XGBoost because it outputs probability scores, allowing us to adjust the balance between precision and recall.This helps us catch more failures (improve recall) without creating too many false positives."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc3f2b4",
   "metadata": {
    "id": "fdc3f2b4"
   },
   "source": [
    "##### Threshold Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b7e12a5b",
   "metadata": {
    "id": "b7e12a5b"
   },
   "outputs": [],
   "source": [
    "# Gets the predicted probabilities (for class 1) instead of labels\n",
    "y_prob_xgb = xgb_model_smote.predict_proba(X_test_selected_xgb)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dc221d4f",
   "metadata": {
    "id": "dc221d4f"
   },
   "outputs": [],
   "source": [
    "# Define a range of thresholds to test\n",
    "thresholds = np.arange(0.1, 0.9, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "205b040d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "205b040d",
    "outputId": "7a507a46-00e0-4260-c36b-165279e12ded"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold: 0.1\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.94      0.97      1935\n",
      "           1       0.32      0.80      0.45        65\n",
      "\n",
      "    accuracy                           0.94      2000\n",
      "   macro avg       0.65      0.87      0.71      2000\n",
      "weighted avg       0.97      0.94      0.95      2000\n",
      "\n",
      "Accuracy: 0.9375\n",
      "------------------------------------------------------------\n",
      "Threshold: 0.2\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.98      1935\n",
      "           1       0.39      0.75      0.51        65\n",
      "\n",
      "    accuracy                           0.95      2000\n",
      "   macro avg       0.69      0.86      0.74      2000\n",
      "weighted avg       0.97      0.95      0.96      2000\n",
      "\n",
      "Accuracy: 0.9535\n",
      "------------------------------------------------------------\n",
      "Threshold: 0.30000000000000004\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98      1935\n",
      "           1       0.49      0.74      0.59        65\n",
      "\n",
      "    accuracy                           0.97      2000\n",
      "   macro avg       0.74      0.86      0.79      2000\n",
      "weighted avg       0.97      0.97      0.97      2000\n",
      "\n",
      "Accuracy: 0.967\n",
      "------------------------------------------------------------\n",
      "Threshold: 0.4\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      1935\n",
      "           1       0.55      0.72      0.63        65\n",
      "\n",
      "    accuracy                           0.97      2000\n",
      "   macro avg       0.77      0.85      0.81      2000\n",
      "weighted avg       0.98      0.97      0.97      2000\n",
      "\n",
      "Accuracy: 0.972\n",
      "------------------------------------------------------------\n",
      "Threshold: 0.5\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1935\n",
      "           1       0.64      0.72      0.68        65\n",
      "\n",
      "    accuracy                           0.98      2000\n",
      "   macro avg       0.82      0.85      0.83      2000\n",
      "weighted avg       0.98      0.98      0.98      2000\n",
      "\n",
      "Accuracy: 0.978\n",
      "------------------------------------------------------------\n",
      "Threshold: 0.6\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1935\n",
      "           1       0.71      0.71      0.71        65\n",
      "\n",
      "    accuracy                           0.98      2000\n",
      "   macro avg       0.85      0.85      0.85      2000\n",
      "weighted avg       0.98      0.98      0.98      2000\n",
      "\n",
      "Accuracy: 0.981\n",
      "------------------------------------------------------------\n",
      "Threshold: 0.7000000000000001\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1935\n",
      "           1       0.82      0.69      0.75        65\n",
      "\n",
      "    accuracy                           0.98      2000\n",
      "   macro avg       0.90      0.84      0.87      2000\n",
      "weighted avg       0.98      0.98      0.98      2000\n",
      "\n",
      "Accuracy: 0.985\n",
      "------------------------------------------------------------\n",
      "Threshold: 0.8\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      1935\n",
      "           1       0.90      0.66      0.76        65\n",
      "\n",
      "    accuracy                           0.99      2000\n",
      "   macro avg       0.94      0.83      0.88      2000\n",
      "weighted avg       0.99      0.99      0.99      2000\n",
      "\n",
      "Accuracy: 0.9865\n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Store precision, recall, and accuracy for each threshold\n",
    "for thresh in thresholds:\n",
    "    # Convert probabilities to predictions based on the threshold\n",
    "    y_pred_thresh = (y_prob_xgb >= thresh).astype(int)\n",
    "\n",
    "    # Evaluate the model performance for this threshold\n",
    "    print(f\"Threshold: {thresh}\")\n",
    "    print(classification_report(y_test, y_pred_thresh))\n",
    "    print(f\"Accuracy: {accuracy_score(y_test, y_pred_thresh)}\")\n",
    "    print(\"-\" * 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7f8d883b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7f8d883b",
    "outputId": "6f4e9a53-70b7-4e6c-fa14-a58dacdfc288"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for XGBoost with SMOTE and Threshold 0.3:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98      1935\n",
      "           1       0.49      0.74      0.59        65\n",
      "\n",
      "    accuracy                           0.97      2000\n",
      "   macro avg       0.74      0.86      0.79      2000\n",
      "weighted avg       0.97      0.97      0.97      2000\n",
      "\n",
      "Accuracy: 0.967\n"
     ]
    }
   ],
   "source": [
    "# Use the chosen threshold (0.3)\n",
    "threshold = 0.3\n",
    "y_pred_xgb_thresh = (y_prob_xgb >= threshold).astype(int)\n",
    "\n",
    "# Evaluate the model performance with the chosen threshold\n",
    "print(f\"Results for XGBoost with SMOTE and Threshold {threshold}:\")\n",
    "print(classification_report(y_test, y_pred_xgb_thresh))\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred_xgb_thresh)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40727a5f",
   "metadata": {
    "id": "40727a5f"
   },
   "source": [
    "#### Note:\n",
    "After threshold tuning (with a threshold of 0.3), XGBoost gave a better trade-off between precision and recall. It maintained high recall (capturing most failures) while keeping a good precision (reducing false positives)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "te33UzWaVZpZ",
   "metadata": {
    "id": "te33UzWaVZpZ"
   },
   "source": [
    "### 5) Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94962f57",
   "metadata": {
    "id": "94962f57"
   },
   "source": [
    "#### Note:\n",
    "We are applying scaling before using AdaBoost and Logistic Regression models because they are sensitive to the scale of features. Standardizing the features will ensure that each feature contributes equally, potentially improving our model performance. For tree-based models (Random Forest, Gradient Boosting, XGBoost), we don't applied scaling, as these models split data based on thresholds and are inherently unaffected by feature scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6523eef5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6523eef5",
    "outputId": "a46729bb-fe39-4564-d90d-8156e0264efd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Air_temperature__K_  Process_temperature__K_  Rotational_speed__rpm_  \\\n",
      "count         1.000000e+04             1.000000e+04            1.000000e+04   \n",
      "mean         -9.322321e-16            -1.693934e-15            3.573319e-15   \n",
      "std           1.000050e+00             1.000050e+00            1.000050e+00   \n",
      "min          -2.352278e+00            -2.901986e+00           -2.507604e+00   \n",
      "25%          -8.523974e-01            -8.125581e-01           -6.710940e-01   \n",
      "50%           4.753123e-02             6.365340e-02           -1.623766e-01   \n",
      "75%           7.474757e-01             7.376623e-01            4.888265e-01   \n",
      "max           2.247357e+00             2.557486e+00            5.906760e+00   \n",
      "\n",
      "        Torque__Nm_  Tool_wear__min_  \n",
      "count  1.000000e+04     1.000000e+04  \n",
      "mean  -6.849632e-16     1.051603e-16  \n",
      "std    1.000050e+00     1.000050e+00  \n",
      "min   -7.901834e+00    -1.695984e+00  \n",
      "25%   -5.540204e-01    -8.633176e-01  \n",
      "50%    1.336923e-01     7.698234e-04  \n",
      "75%    6.987988e-01     8.491466e-01  \n",
      "max    2.511951e+00     2.278819e+00  \n"
     ]
    }
   ],
   "source": [
    "# Columns to scale\n",
    "columns_scale = ['Air_temperature__K_', 'Process_temperature__K_', 'Rotational_speed__rpm_', 'Torque__Nm_', 'Tool_wear__min_']\n",
    "\n",
    "# Initializes the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Applying scaling to the selected columns\n",
    "main_df[columns_scale] = scaler.fit_transform(main_df[columns_scale])\n",
    "\n",
    "# Shows the result of the scaling\n",
    "print(main_df[columns_scale].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "QzZanIVaYKTE",
   "metadata": {
    "id": "QzZanIVaYKTE"
   },
   "outputs": [],
   "source": [
    "# Train-Test Split\n",
    "X = main_df.drop(['Any_Failure', 'Machine_failure'], axis=1)\n",
    "y = main_df['Any_Failure']  # Target (Any Failure)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0_72qM3NYTvK",
   "metadata": {
    "id": "0_72qM3NYTvK"
   },
   "outputs": [],
   "source": [
    "# Applying SMOTE to the training set\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "YmbffVdYkYYK",
   "metadata": {
    "id": "YmbffVdYkYYK"
   },
   "source": [
    "#### 5.1) AdaBoost Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ToiwMMGUaLu0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 620
    },
    "id": "ToiwMMGUaLu0",
    "outputId": "ad0b57ef-d4b2-4c27-8cf6-fafbe6d8986a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada_model_fs = AdaBoostClassifier(random_state=42)\n",
    "ada_model_fs.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# Checks the feature importance for AdaBoost\n",
    "importances_ada = ada_model_fs.feature_importances_\n",
    "importance_df_ada = pd.DataFrame({'Feature': X_train.columns, 'Importance': importances_ada})\n",
    "importance_df_ada = importance_df_ada.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Plots top features for AdaBoost\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df_ada)\n",
    "plt.title('Feature Importance from AdaBoost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "SUpZCCR9aYQW",
   "metadata": {
    "id": "SUpZCCR9aYQW"
   },
   "outputs": [],
   "source": [
    "# Selects the important features for AdaBoost\n",
    "selected_features_ada = importance_df_ada[importance_df_ada['Importance'] > 0.01]['Feature']\n",
    "X_train_selected_ada = X_train[selected_features_ada]\n",
    "X_test_selected_ada = X_test[selected_features_ada]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7356694",
   "metadata": {
    "id": "d7356694"
   },
   "source": [
    "Selected Features for AdaBoost Model:\n",
    "\n",
    "   - Mechanical_Power\n",
    "   - Temp Diff\n",
    "   - Torque [Nm]\n",
    "   - Tool wear [min]\n",
    "   - Process temperature [K]\n",
    "   - Rotational speed [rpm]\n",
    "   - Type_L\n",
    "   - Air temperature [K]\n",
    "   - Type_M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "tv2oTCw6baMR",
   "metadata": {
    "id": "tv2oTCw6baMR"
   },
   "outputs": [],
   "source": [
    "# Applying SMOTE to selected features for AdaBoost\n",
    "X_train_smote_selected_ada, y_train_smote_selected_ada = smote.fit_resample(X_train_selected_ada, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "g-2xnGC6bog6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g-2xnGC6bog6",
    "outputId": "120759e3-61e4-45e5-8207-47e13905a399"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for AdaBoost Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.91      0.95      1935\n",
      "           1       0.22      0.75      0.34        65\n",
      "\n",
      "    accuracy                           0.90      2000\n",
      "   macro avg       0.60      0.83      0.64      2000\n",
      "weighted avg       0.97      0.90      0.93      2000\n",
      "\n",
      "Accuracy: 0.904\n"
     ]
    }
   ],
   "source": [
    "# Trains AdaBoost model on SMOTE data\n",
    "ada_model = AdaBoostClassifier(random_state=42)\n",
    "ada_model.fit(X_train_smote_selected_ada, y_train_smote_selected_ada)\n",
    "\n",
    "# Predictions and evaluation\n",
    "y_pred_ada = ada_model.predict(X_test_selected_ada)\n",
    "print(\"Results for AdaBoost Classifier:\")\n",
    "print(classification_report(y_test, y_pred_ada))\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred_ada)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2dc6bef",
   "metadata": {
    "id": "a2dc6bef"
   },
   "source": [
    "#### Note:\n",
    "The AdaBoost model performed reasonably well, achieving a high precision for the majority class (0) but showing limited recall and precision for the minority class (1). Compared to the tree-based models, AdaBoost had a lower overall accuracy and struggled with detecting failures, as seen in its lower f1-score for class 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ZIz1PtfHgirB",
   "metadata": {
    "id": "ZIz1PtfHgirB"
   },
   "source": [
    "#### 5.2) Logistic Regression Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "llZOrpe7guqn",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "llZOrpe7guqn",
    "outputId": "c1e4d501-8908-4853-f918-c5057b5a8b40"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DgwcP1uDBg1NdJjY2VgULFkz3cdzp7uuf1jmUpFKlSqV4knhWXNeMOn78uAICAuTl5WXXXrp0abtjOH78uJycnFIcY7Fixe67jzp16ujll1/W8OHD9eGHH6pu3bpq3ry52rZtm6EnwCc7evSonJyc0vUAv7R888038vb21tmzZzV16lQdO3bM7g8syf+tCQsLS3MbcXFxunbtmq5evZrqeUjr3Nx9Ds+ePauLFy/qk08+0SeffJLqOsn/DXvnnXe0fv16VatWTcWKFdNzzz2ntm3bqmbNmrZlJ0yYoLCwMAUGBqpKlSp6/vnn1bFjRxUtWjTNY0nv9yDZ3b+P0u3fyaz4TgL4P4R0AIDpJb8y6NNPP5W/v3+K+Xc+AbpVq1b6+eef1b9/f1WqVEmenp5KSkpSo0aN0vXqobR65O4Mk3e78//kJ9drsVi0evXqVJ927enped86UpPWk7PTajfuevDTw3D3sd/PmDFjNHjwYHXq1EkjR45U7ty55eTkpN69e6d6fbLi2JK3269fP4WGhqa6THpCZ1oyeg7uZsbrmhUsFou+/vprbdu2Tf/973+1du1aderUSZMmTdK2bdsy/XvwIGrXrm17BkWzZs1Uvnx5tWvXTlFRUXJycrJ9VyZOnKhKlSqlug1PT09du3Ytw/tO7b8TktS+ffs0/yiQfH9+6dKlFR0drRUrVmjNmjX65ptvNGPGDA0ZMsT2CrtWrVqpVq1a+vbbb/X9999r4sSJGj9+vJYuXZpm735G/du/k8C/BSEdAGB6ycMr8+fPr4YNG6a53IULF7RhwwYNHz5cQ4YMsbUn947dKa0wntxTe/eTzO/uUbpfvYZhKDg4WCVKlEj3eo/CkSNHVK9ePdvnhIQEnT59Ws8//7wkqUiRIpKk6Ohoux64Gzdu6NixY/c8/3dK6/x+/fXXqlevnubOnWvXfvHiRVt4yojk78b+/fvTrC35OHLkyJHu+h/Enefw7tEf0dHRtvmOVKRIEa1fv16XLl2y60U9dOiQbX7y/yYlJenYsWN2ozBSe8J+Wp5++mk9/fTTGj16tD7//HO1a9dOixcvVpcuXdI9TF26fa2TkpL022+/pRmgM8LT01NDhw5VRESEvvzyS7Vu3dr2ffL29r7ndyV//vxyc3NL9Tyk99zky5dPXl5eSkxMTNf30sPDQ6+++qpeffVV3bhxQy1atNDo0aM1cOBA220VBQoU0Jtvvqk333xTsbGxqly5skaPHp1mSE/v9wDAo8U96QAA0wsNDZW3t7fGjBmT6r2tyU9kT+7lubtXZ/LkySnWSX6X+d1h3NvbW3nz5tWPP/5o1z5jxox019uiRQs5Oztr+PDhKWoxDMPudXCP2ieffGJ3DmfOnKlbt27Z/k98w4YN5erqqqlTp9rVPnfuXMXFxaXrqd7S7fN797mVbl+ju8/JV199lel7witXrqzg4GBNnjw5xf6S95M/f37VrVtXH3/8sU6fPp1iG5l5ov+9VK1aVfnz59esWbN0/fp1W/vq1at18ODBdJ/Dh+n5559XYmKipk+fbtf+4YcfymKx2L4PySMP7v7+T5s27b77uHDhQoprnRyuk89L8r3gqX1X7ta8eXM5OTlpxIgRKUZdZLYnt127dipUqJDGjx8vSapSpYqeeOIJvf/++0pISEix/J3/rWnYsKGWLVtmdw/477//nuI+7rQ4Ozvr5Zdf1jfffKP9+/enuS9JKf6b4erqqjJlysgwDN28eVOJiYkpbufJnz+/AgIC7L6Dd0vv9wDAo0VPOgDA9Ly9vTVz5kx16NBBlStXVuvWrZUvXz6dOHFCK1euVM2aNTV9+nR5e3vbXmN18+ZNFSxYUN9//72OHTuWYptVqlSRJA0aNEitW7dWjhw51KxZM3l4eKhLly4aN26cunTpoqpVq+rHH3/U4cOH013vE088oVGjRmngwIGKiYlR8+bN5eXlpWPHjunbb7/V66+/rn79+mXZ+cmIGzduqEGDBmrVqpWio6M1Y8YMPfPMM3rhhRck3e7dGzhwoIYPH65GjRrphRdesC335JNPqn379unaT5UqVTRz5kyNGjVKxYoVU/78+VW/fn01bdpUI0aMUEREhGrUqKF9+/Zp0aJF97xv9l6cnJw0c+ZMNWvWTJUqVVJERIQKFCigQ4cO6cCBA1q7dq2k2w8lfOaZZ1S+fHm99tprKlq0qM6cOaP//e9/+vPPP1O8p/1B5MiRQ+PHj1dERITq1KmjNm3a2F7BFhQUpD59+mTZvu5l586dGjVqVIr2unXrqlmzZqpXr54GDRqkmJgYVaxYUd9//72+++479e7d29ajXKVKFb388suaPHmyzp07Z3sFW/Lvw716whcsWKAZM2bopZde0hNPPKFLly5p9uzZ8vb2to3ccHd3V5kyZbRkyRKVKFFCuXPnVrly5VJ9xkCxYsU0aNAgjRw5UrVq1VKLFi1ktVq1Y8cOBQQEaOzYsRk+Rzly5FCvXr3Uv39/rVmzRo0aNdKcOXPUuHFjlS1bVhERESpYsKD++usvbdy4Ud7e3vrvf/8r6farCr///nvVrFlTb7zxhi3slitXTnv27EnX/seNG6eNGzfqqaee0muvvaYyZcro/Pnz2rVrl9avX6/z589Lkp577jn5+/urZs2a8vPz08GDBzV9+nQ1adJEXl5eunjxogoVKqSWLVuqYsWK8vT01Pr167Vjxw5NmjQpzf2n93sA4BF7tA+TBwAgpdReOZaajRs3GqGhoYaPj4/h5uZmPPHEE0Z4eLixc+dO2zJ//vmn8dJLLxm+vr6Gj4+P8corrxinTp1K9TVPI0eONAoWLGg4OTnZvTrqypUrRufOnQ0fHx/Dy8vLaNWqlREbG5vmK9jOnj2bar3ffPON8cwzzxgeHh6Gh4eHUapUKaNbt25GdHR0hs9HWFiY4eHhkWLZOnXqpPpqprtfGZa8zc2bNxuvv/66kStXLsPT09No166dce7cuRTrT58+3ShVqpSRI0cOw8/Pz3jjjTdSvOIsrX0bxu3X4zVp0sTw8vIyJNlex3bt2jWjb9++RoECBQx3d3ejZs2axv/+9z+jTp06dq9sS3692VdffWW33bRekbdlyxbj2WefNby8vAwPDw+jQoUKKV4LdfToUaNjx46Gv7+/kSNHDqNgwYJG06ZNja+//jrVY7jTvV7BdneNyZYsWWKEhIQYVqvVyJ07t9GuXTvjzz//tFvmQa9rWpTGKw0lGSNHjjQM4/arDfv06WMEBAQYOXLkMIoXL25MnDgxxevMLl++bHTr1s3InTu34enpaTRv3tyIjo42JBnjxo2zLXf3K9h27dpltGnTxihcuLBhtVqN/PnzG02bNrX7fTUMw/j555+NKlWqGK6urna/Y3e/gi3ZvHnzbOc1V65cRp06dYx169bd83zc63c1Li7O8PHxsfv+7d6922jRooWRJ08ew2q1GkWKFDFatWplbNiwwW7dDRs2GCEhIYarq6vxxBNPGHPmzDH69u1ruLm5pbgeab0e7cyZM0a3bt2MwMBAI0eOHIa/v7/RoEED45NPPrEt8/HHHxu1a9e21fPEE08Y/fv3N+Li4gzDMIzr168b/fv3NypWrGj7HahYsaIxY8YMu33d/Qo2w0j/9yCtYyhSpIgRFhaW6rEByByLYfCkBwAAHneRkZGKiIjQjh07Un2CPpARe/bsUUhIiD777DO1a9fO0eWYSvPmzXXgwIFUn4UBAOnBPekAAABI09WrV1O0TZ48WU5OTqpdu7YDKjKPu8/NkSNHtGrVKtWtW9cxBQF4LHBPOgAAANI0YcIERUVFqV69enJxcdHq1au1evVqvf766woMDHR0eQ5VtGhRhYeHq2jRojp+/LhmzpwpV1dXvf32244uDcC/GCEdAAAAaapRo4bWrVunkSNHKiEhQYULF9awYcM0aNAgR5fmcI0aNdIXX3yhv//+W1arVdWrV9eYMWPsXlcHABnFPekAAAAAAJgE96QDAAAAAGAShHQAAAAAAEyCe9KBhyQpKUmnTp2Sl5eXLBaLo8sBAAAA4CCGYejSpUsKCAiQk9O9+8oJ6cBDcurUqWz/1FsAAAAA/+fkyZMqVKjQPZchpAMPiZeXl6Tbv4je3t4OrgYAAACAo8THxyswMNCWEe6FkA48JMlD3L29vQnpAAAAANJ1GywPjgMAAAAAwCQI6QAAAAAAmATD3YGHrPZ7X8jZ6u7oMgAAAIBsI2piR0eXkGn0pAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpD9GLBbLPadhw4Y5usQsFxkZKYvFokaNGtm1X7x4URaLRZs2bXJMYQAAAACQCS6OLgBZ5/Tp07aflyxZoiFDhig6OtrW5unpmaHt3bx5Uzly5Miy+h4WFxcXrV+/Xhs3blS9evUcXQ4AAAAAZBo96Y8Rf39/2+Tj4yOLxWL7nD9/fn3wwQcqVKiQrFarKlWqpDVr1tjWjYmJkcVi0ZIlS1SnTh25ublp0aJFSkxM1FtvvSVfX1/lyZNHb7/9tsLCwtS8eXPbukFBQZo8ebJdLZUqVbLrub948aK6dOmifPnyydvbW/Xr19fevXuz5Lg9PDzUqVMnDRgwIM1lko/vyy+/VK1ateTu7q4nn3xShw8f1o4dO1S1alV5enqqcePGOnv2bKbquH79uuLj4+0mAAAAAMgIQno2MWXKFE2aNEnvv/++fv31V4WGhuqFF17QkSNH7JYbMGCAevXqpYMHDyo0NFSTJk1SZGSk5s2bpy1btuj8+fP69ttvM7z/V155RbGxsVq9erWioqJUuXJlNWjQQOfPn8+S4xs2bJj27dunr7/++p7LDR06VO+995527dolFxcXtW3bVm+//bamTJmin376Sb///ruGDBmSqRrGjh0rHx8f2xQYGJip7QAAAADIvgjp2cT777+vd955R61bt1bJkiU1fvx4VapUKUUPeO/evdWiRQsFBwerQIECmjx5sgYOHKgWLVqodOnSmjVrlnx8fDK07y1btuiXX37RV199papVq6p48eJ6//335evre99QnV4BAQHq1auXBg0apFu3bqW5XL9+/RQaGqrSpUurV69eioqK0uDBg1WzZk2FhISoc+fO2rhxY6ZqGDhwoOLi4mzTyZMnM3s4AAAAALIpQno2EB8fr1OnTqlmzZp27TVr1tTBgwft2qpWrWr7OS4uTqdPn9ZTTz1la3NxcbFbJj327t2rhIQE5cmTR56enrbp2LFjOnr0aCaOKHXvvPOOzp49q3nz5qW5TIUKFWw/+/n5SZLKly9v1xYbG5up/VutVnl7e9tNAAAAAJARPDgOdjw8PDK8jpOTkwzDsGu7efOm7eeEhAQVKFAg1Set+/r6Znh/afH19dXAgQM1fPhwNW3aNNVl7nwQnsViSbUtKSkpy2oCAAAAgIygJz0b8Pb2VkBAgLZu3WrXvnXrVpUpUybN9Xx8fFSgQAFt377d1nbr1i1FRUXZLZcvXz67J8vHx8fr2LFjts+VK1fW33//LRcXFxUrVsxuyps374Menp0ePXrIyclJU6ZMydLtAgAAAMCjQE96NtG/f38NHTpUTzzxhCpVqqT58+drz549WrRo0T3X69Wrl8aNG6fixYurVKlS+uCDD3Tx4kW7ZerXr6/IyEg1a9ZMvr6+GjJkiJydnW3zGzZsqOrVq6t58+aaMGGCSpQooVOnTmnlypV66aWXMjx8/l7c3Nw0fPhwdevWLcu2CQAAAACPCiE9m+jZs6fi4uLUt29fxcbGqkyZMlq+fLmKFy9+z/X69u2r06dPKywsTE5OTurUqZNeeuklxcXF2ZYZOHCgjh07pqZNm8rHx0cjR46060m3WCxatWqVBg0apIiICJ09e1b+/v6qXbu27b7wrBQWFqZJkybpt99+y/JtAwAAAMDDZDHuvpkYuI/w8HBdvHhRy5Ytc3QpphYfHy8fHx9V7DFLzlZ3R5cDAAAAZBtREzs6ugQ7ydkgLi7uvg+Y5p50AAAAAABMgpAOhytbtqzdq9nunIKDg9Ocd7/76R9UWvv19PTUTz/99FD3DQAAACB74p50ZFhkZGSWbm/VqlV2r2y7U44cOdKc9zDuZ7/Tnj170pxXsGDBh7pvAAAAANkTIR0OV6RIEUeXkKpixYo5ugQAAAAA2QzD3QEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAkXRxcAPO5+HNVG3t7eji4DAAAAwL8APekAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJiEi6MLAB53td/7Qs5Wd0eXAQBAthI1saOjSwCATKEnHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6fexadMmWSwWXbx40dGlAAAAAAAec4T0/+9///ufnJ2d1aRJE7v2GjVq6PTp0/Lx8cn0tsPDw9W8efMHrPDfITIyUr6+vo4uI10sFouWLVtm+3zz5k21adNGBQsW1P79+x1XGAAAAIBsi5D+/82dO1c9evTQjz/+qFOnTtnaXV1d5e/vL4vFkup6iYmJSkpKelRlOsyNGzce+T5v3rz5yPZ15coVvfDCC9qxY4e2bNmicuXKPbJ9AwAAAEAyQrqkhIQELVmyRG+88YaaNGmiyMhI27y7h7sn9xQvX75cZcqUkdVq1YkTJ9Lc9rBhw7RgwQJ99913slgsslgs2rRpkyTp5MmTatWqlXx9fZU7d269+OKLiomJsa2b3AM/ZswY+fn5ydfXVyNGjNCtW7fUv39/5c6dW4UKFdL8+fNt68TExMhisWjx4sWqUaOG3NzcVK5cOW3evNmurv3796tx48by9PSUn5+fOnTooH/++cc2v27duurevbt69+6tvHnzKjQ0VJL0wQcfqHz58vLw8FBgYKDefPNNJSQk2M5VRESE4uLibMc6bNgwSSl7rSXJ19fXdq6T616yZInq1KkjNzc3LVq0SJI0Z84clS5dWm5ubipVqpRmzJhxz+uZURcvXtSzzz6rU6dOacuWLQoODs7S7QMAAABAehHSJX355ZcqVaqUSpYsqfbt22vevHkyDCPN5a9cuaLx48drzpw5OnDggPLnz5/msv369VOrVq3UqFEjnT59WqdPn1aNGjV08+ZNhYaGysvLSz/99JO2bt0qT09PNWrUyK7X+ocfftCpU6f0448/6oMPPtDQoUPVtGlT5cqVS9u3b1fXrl31n//8R3/++afdfvv376++fftq9+7dql69upo1a6Zz585Juh1K69evr5CQEO3cuVNr1qzRmTNn1KpVK7ttLFiwQK6urtq6datmzZolSXJyctLUqVN14MABLViwQD/88IPefvttSbdvDZg8ebK8vb1tx9qvX78MXYsBAwaoV69eOnjwoEJDQ7Vo0SINGTJEo0eP1sGDBzVmzBgNHjxYCxYsyNB20/L333+rTp06kqTNmzfL398/09u6fv264uPj7SYAAAAAyAgXRxdgBnPnzlX79u0lSY0aNVJcXJw2b96sunXrprr8zZs3NWPGDFWsWPG+2/b09JS7u7uuX79uFwA/++wzJSUlac6cObah9PPnz5evr682bdqk5557TpKUO3duTZ06VU5OTipZsqQmTJigK1eu6N1335UkDRw4UOPGjdOWLVvUunVr2/a7d++ul19+WZI0c+ZMrVmzRnPnztXbb7+t6dOnKyQkRGPGjLEtP2/ePAUGBurw4cMqUaKEJKl48eKaMGGC3fH07t3b9nNQUJBGjRqlrl27asaMGXJ1dZWPj48sFkumw27v3r3VokUL2+ehQ4dq0qRJtrbg4GD99ttv+vjjjxUWFpapfdypV69eKlq0qNatW6ecOXM+0LbGjh2r4cOHP3BNAAAAALKvbN+THh0drV9++UVt2rSRJLm4uOjVV1/V3Llz01zH1dVVFSpUeKD97t27V7///ru8vLzk6ekpT09P5c6dW9euXdPRo0dty5UtW1ZOTv93mfz8/FS+fHnbZ2dnZ+XJk0exsbF2269evbrtZxcXF1WtWlUHDx607Xvjxo22/Xp6eqpUqVKSZLfvKlWqpKh7/fr1atCggQoWLCgvLy916NBB586d05UrVx7ofCSrWrWq7efLly/r6NGj6ty5s12to0aNsqvzQTRt2lSHDx/Wxx9//MDbGjhwoOLi4mzTyZMns6BCAAAAANlJtu9Jnzt3rm7duqWAgABbm2EYslqtmj59eqrruLu7p/kgufRKSEhQlSpVbPdd3ylfvny2n3PkyGE3z2KxpNqWkYfXJSQkqFmzZho/fnyKeQUKFLD97OHhYTcvJiZGTZs21RtvvKHRo0crd+7c2rJlizp37qwbN27csyfaYrGkuIUgtQfD3bnP5HvdZ8+eraeeespuOWdn53scYfp16NBBL7zwgjp16iTDMPTWW29leltWq1VWqzVL6gIAAACQPWXrkH7r1i0tXLhQkyZNsg0vT9a8eXN98cUXth7mB+Hq6qrExES7tsqVK2vJkiXKnz+/vL29H3gfd9u2bZtq164t6fZxRkVFqXv37rZ9f/PNNwoKCpKLS/q/AlFRUUpKStKkSZNsvftffvml3TKpHat0+w8Pp0+ftn0+cuTIfXvf/fz8FBAQoD/++EPt2rVLd50ZFRYWJicnJ0VERCgpKSnD99EDAAAAQFbJ1sPdV6xYoQsXLqhz584qV66c3fTyyy/fc8h7RgQFBenXX39VdHS0/vnnH928eVPt2rVT3rx59eKLL+qnn37SsWPHtGnTJvXs2TPFQ+Ay46OPPtK3336rQ4cOqVu3brpw4YI6deokSerWrZvOnz+vNm3aaMeOHTp69KjWrl2riIiIVAN2smLFiunmzZuaNm2a/vjjD3366ae2B8rdeawJCQnasGGD/vnnH1sQr1+/vqZPn67du3dr586d6tq1a4oRAakZPny4xo4dq6lTp+rw4cPat2+f5s+frw8++OABzk5KHTp00IIFCzRgwABNnDgxS7cNAAAAAOmVrUP63Llz1bBhQ/n4+KSY9/LLL2vnzp369ddfH3g/r732mkqWLKmqVasqX7582rp1q3LmzKkff/xRhQsXVosWLVS6dGl17txZ165dy5Ke9XHjxmncuHGqWLGitmzZouXLlytv3rySpICAAG3dulWJiYl67rnnVL58efXu3Vu+vr5297/frWLFivrggw80fvx4lStXTosWLdLYsWPtlqlRo4a6du2qV199Vfny5bM9eG7SpEkKDAxUrVq11LZtW/Xr1y9dD2rr0qWL5syZo/nz56t8+fKqU6eOIiMjH8pr0tq1a6dPP/1UAwcOTPVWAAAAAAB42CzGvd41hn+dmJgYBQcHa/fu3apUqZKjy8nW4uPj5ePjo4o9ZsnZ6u7ocgAAyFaiJnZ0dAkAYJOcDeLi4u7bKZute9IBAAAAADATQnoWuPP1YHdPP/30k6PLe2yNGTMmzfNusVjSnNe4ceMH2nZ61gcAAACAzGC4exb4/fff05xXsGBBubsz1PlhOH/+vM6fP5/qPHd3d129ejXNeQULFnygbd9vfYnh7gAAOBLD3QGYSUaGu2frV7BllWLFijm6hGwpd+7cyp07979u2wAAAACQFoa7AwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEi6OLgB43P04qo28vb0dXQYAAACAfwF60gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJuHi6AKAx13t976Qs9Xd0WUAANIhamJHR5cAAMjm6EkHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCYeG9PDwcFksFlksFrm6uqpYsWIaMWKEbt265ciystymTZtksVh08eJFR5fySFgsFi1btszRZdxXeHi4mjdvbtf29ddfy83NTZMmTXJMUQAAAACyNRdHF9CoUSPNnz9f169f16pVq9StWzflyJFDAwcOtFvuxo0bcnV1dVCVSExMlMVikZPTo/u7zqO+5nPmzFG3bt00a9YsRUREPLL9AgAAAEAyhw93t1qt8vf3V5EiRfTGG2+oYcOGWr58ua2Xc/To0QoICFDJkiUlSfv27VP9+vXl7u6uPHny6PXXX1dCQoLdNufNm6eyZcvKarWqQIEC6t69u23exYsX1aVLF+XLl0/e3t6qX7++9u7da5u/d+9e1atXT15eXvL29laVKlW0c+dOSdLx48fVrFkz5cqVSx4eHipbtqxWrVp1z+OLiYlRvXr1JEm5cuWSxWJReHi4JCkpKUljx45VcHCw3N3dVbFiRX399de2dZN74NeuXauQkBC5u7urfv36io2N1erVq1W6dGl5e3urbdu2unLlim29unXrqnv37urevbt8fHyUN29eDR48WIZh2Ja5fv26+vXrp4IFC8rDw0NPPfWUNm3aZJsfGRkpX19fLV++XGXKlJHVatWJEye0Y8cOPfvss8qbN698fHxUp04d7dq1y7ZeUFCQJOmll16SxWKxfU6t17p3796qW7duirp79+6tvHnzKjQ0VJK0f/9+NW7cWJ6envLz81OHDh30zz//3PO8Z9SECRPUo0cPLV68mIAOAAAAwGEcHtLv5u7urhs3bkiSNmzYoOjoaK1bt04rVqzQ5cuXFRoaqly5cmnHjh366quvtH79ersQPnPmTHXr1k2vv/669u3bp+XLl6tYsWK2+a+88oot5EZFRaly5cpq0KCBzp8/L0lq166dChUqpB07digqKkoDBgxQjhw5JEndunXT9evX9eOPP2rfvn0aP368PD0973k8gYGB+uabbyRJ0dHROn36tKZMmSJJGjt2rBYuXKhZs2bpwIED6tOnj9q3b6/NmzfbbWPYsGGaPn26fv75Z508eVKtWrXS5MmT9fnnn2vlypX6/vvvNW3aNLt1FixYIBcXF/3yyy+aMmWKPvjgA82ZM8c2v3v37vrf//6nxYsX69dff9Urr7yiRo0a6ciRI7Zlrly5ovHjx2vOnDk6cOCA8ufPr0uXLiksLExbtmzRtm3bVLx4cT3//PO6dOmSJGnHjh2SpPnz5+v06dO2z+m1YMECubq6auvWrZo1a5YuXryo+vXrKyQkRDt37tSaNWt05swZtWrVKkPbvZd33nlHI0eO1IoVK/TSSy9lejvXr19XfHy83QQAAAAAGeHw4e7JDMPQhg0btHbtWvXo0UNnz56Vh4eH5syZYxvyPHv2bF27dk0LFy6Uh4eHJGn69Olq1qyZxo8fLz8/P40aNUp9+/ZVr169bNt+8sknJUlbtmzRL7/8otjYWFmtVknS+++/r2XLlunrr7/W66+/rhMnTqh///4qVaqUJKl48eK27Zw4cUIvv/yyypcvL0kqWrTofY/L2dlZuXPnliTlz59fvr6+km4HujFjxmj9+vWqXr26bXtbtmzRxx9/rDp16ti2MWrUKNWsWVOS1LlzZw0cOFBHjx617b9ly5bauHGj3nnnHds6gYGB+vDDD2WxWFSyZEnt27dPH374oV577TWdOHFC8+fP14kTJxQQECBJ6tevn9asWaP58+drzJgxkqSbN29qxowZqlixom279evXtzu+Tz75RL6+vtq8ebOaNm2qfPnySZJ8fX3l7+9/3/Nzt+LFi2vChAl2xx4SEmKrSbo9UiIwMFCHDx9WiRIlMryPO61evVrfffedNmzYkOLYMmrs2LEaPnz4A20DAAAAQPbm8J70FStWyNPTU25ubmrcuLFeffVVDRs2TJJUvnx5u3uSDx48qIoVK9oCuiTVrFlTSUlJio6OVmxsrE6dOqUGDRqkuq+9e/cqISFBefLkkaenp206duyYjh49Kkl666231KVLFzVs2FDjxo2ztUtSz549bYF56NCh+vXXXzN93L///ruuXLmiZ5991q6WhQsX2u1TkipUqGD72c/PTzlz5rT7A4Gfn59iY2Pt1nn66adlsVhsn6tXr64jR44oMTFR+/btU2JiokqUKGG3782bN9vt29XV1W7fknTmzBm99tprKl68uHx8fOTt7a2EhASdOHEi0+fiTlWqVLH7vHfvXm3cuNGuzuQ/oNx9njKjQoUKCgoK0tChQ1PcNpFRAwcOVFxcnG06efLkA9cHAAAAIHtxeE96vXr1NHPmTLm6uiogIEAuLv9X0p1hPD3c3d3vOT8hIUEFChSwu/c6WXIP97Bhw9S2bVutXLlSq1ev1tChQ7V48WK99NJL6tKli0JDQ21DzMeOHatJkyapR48eGaozuRZJWrlypQoWLGg3L7mXP1nycHvp9pPT7/yc3JaUlJShfTs7OysqKkrOzs528+4cvu/u7m4X9CUpLCxM586d05QpU1SkSBFZrVZVr17ddotCWpycnOzuiZdu99Tf7e5rnpCQYBspcbcCBQrcc5/pUbBgQX399deqV6+eGjVqpNWrV8vLyytT27JarSmuHQAAAABkhMNDuoeHh9094/dSunRpRUZG6vLly7Ywt3XrVjk5OalkyZLy8vJSUFCQNmzYYHtY250qV66sv//+Wy4uLrYHmqWmRIkSKlGihPr06aM2bdpo/vz5tnuVAwMD1bVrV3Xt2lUDBw7U7Nmz7xvSk0cDJCYm2trufBjbnUPbs8r27dvtPiffP+7s7KyQkBAlJiYqNjZWtWrVytB2t27dqhkzZuj555+XJJ08eTLFQ9xy5Mhhd6ySlC9fPu3fv9+ubc+ePSn+4HC3ypUr65tvvlFQUJDdH3CyUpEiRbR582ZbUF+zZk2mgzoAAAAAPAiHD3fPiHbt2snNzU1hYWHav3+/Nm7cqB49eqhDhw7y8/OTdLsnfNKkSZo6daqOHDmiXbt22R6q1rBhQ1WvXl3NmzfX999/r5iYGP38888aNGiQdu7cqatXr6p79+7atGmTjh8/rq1bt2rHjh0qXbq0pNtPI1+7dq2OHTumXbt2aePGjbZ591KkSBFZLBatWLFCZ8+eVUJCgry8vNSvXz/16dNHCxYs0NGjR221Lliw4IHP1YkTJ/TWW28pOjpaX3zxhaZNm2a7T79EiRJq166dOnbsqKVLl+rYsWP65ZdfNHbsWK1cufKe2y1evLg+/fRTHTx4UNu3b1e7du1SjGBI/kPJ33//rQsXLki6fS/7zp07tXDhQh05ckRDhw5NEdpT061bN50/f15t2rTRjh07dPToUa1du1YREREp/hDwIAIDA7Vp0ybFxsYqNDSUh74BAAAAcIh/VUjPmTOn1q5dq/Pnz+vJJ59Uy5Yt1aBBA02fPt22TFhYmCZPnqwZM2aobNmyatq0qe2J5RaLRatWrVLt2rUVERGhEiVKqHXr1jp+/Lj8/Pzk7Oysc+fOqWPHjipRooRatWqlxo0b2x4GlpiYqG7duql06dJq1KiRSpQooRkzZty37oIFC2r48OEaMGCA/Pz8bE+jHzlypAYPHqyxY8fatrly5UoFBwc/8Lnq2LGjrl69qmrVqqlbt27q1auXXn/9ddv8+fPnq2PHjurbt69Kliyp5s2ba8eOHSpcuPA9tzt37lxduHBBlStXVocOHdSzZ0/lz5/fbplJkyZp3bp1CgwMVEhIiCQpNDRUgwcP1ttvv60nn3xSly5dUseOHe97HAEBAdq6dasSExP13HPPqXz58urdu7d8fX2z/J3thQoV0qZNm/TPP/8Q1AEAAAA4hMW4+0Zh/OvVrVtXlSpV0uTJkx1dSrYWHx8vHx8fVewxS87Wez8vAQBgDlET7/8HZAAAMio5G8TFxcnb2/uey/6retIBAAAAAHicEdKzQNeuXe1eEXbn1LVrV0eX99g6ceJEmufdyclJTk5Oac6/3yvj7rXt9KwPAAAAAJnBcPcsEBsbm+b9y97e3inu2UbWuHXrlmJiYjK17v2eFn+/bafnafMMdweAfx+GuwMAHoaMDHd3+CvYHgf58+cniDuAi4tLul/fZ6ZtAwAAAEBaGO4OAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJF0cXADzufhzVRt7e3o4uAwAAAMC/AD3pAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYhIujCwAed7Xf+0LOVndHlwEAphc1saOjSwAAwOHoSQcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQnoGhIeHq3nz5o98v0FBQZo8efIj329WslgsWrZsmaPLAAAAAABTM3VIDw8Pl8VikcViUY4cORQcHKy3335b165dS9f6mzZtksVi0cWLFzO035iYGFksFu3Zs8eufcqUKYqMjMzQtgAAAAAASC8XRxdwP40aNdL8+fN18+ZNRUVFKSwsTBaLRePHj3/ktfj4+DzyfeLebt68qRw5cji6DAAAAADIEqbuSZckq9Uqf39/BQYGqnnz5mrYsKHWrVsnSbp+/bp69uyp/Pnzy83NTc8884x27Ngh6XZveL169SRJuXLlksViUXh4uCRpzZo1euaZZ+Tr66s8efKoadOmOnr0qG2fwcHBkqSQkBBZLBbVrVtXUsrh7vfav/R/PfkbNmxQ1apVlTNnTtWoUUPR0dG2ZY4ePaoXX3xRfn5+8vT01JNPPqn169dn6lwZhqFhw4apcOHCslqtCggIUM+ePW3zg4KCNHLkSLVp00YeHh4qWLCgPvroI7ttXLx4UV26dFG+fPnk7e2t+vXra+/evXbLfPfdd6pcubLc3NxUtGhRDR8+XLdu3bLNP3LkiGrXri03NzeVKVPGdr0eVPIIhyVLlqhOnTpyc3PTokWLFBkZKV9fXy1btkzFixeXm5ubQkNDdfLkSdu6w4YNU6VKlTRv3jwVLlxYnp6eevPNN5WYmKgJEybI399f+fPn1+jRozNd3/Xr1xUfH283AQAAAEBGmD6k32n//v36+eef5erqKkl6++239c0332jBggXatWuXihUrptDQUJ0/f16BgYH65ptvJEnR0dE6ffq0pkyZIkm6fPmy3nrrLe3cuVMbNmyQk5OTXnrpJSUlJUmSfvnlF0nS+vXrdfr0aS1dujTVeu61/zsNGjRIkyZN0s6dO+Xi4qJOnTrZ5iUkJOj555/Xhg0btHv3bjVq1EjNmjXTiRMnMnx+vvnmG3344Yf6+OOPdeTIES1btkzly5e3W2bixImqWLGidu/erQEDBqhXr152IfqVV15RbGysVq9eraioKFWuXFkNGjSwHdNPP/2kjh07qlevXvrtt9/08ccfKzIy0hZuk5KS1KJFC7m6umr79u2aNWuW3nnnnQwfy70k133w4EGFhoZKkq5cuaLRo0dr4cKF2rp1qy5evKjWrVvbrXf06FGtXr1aa9as0RdffKG5c+eqSZMm+vPPP7V582aNHz9e7733nrZv356pusaOHSsfHx/bFBgY+MDHCgAAACB7sRiGYTi6iLSEh4frs88+k5ubm27duqXr16/LyclJX375pRo1aqRcuXIpMjJSbdu2lXR76HNQUJB69+6t/v37a9OmTapXr54uXLggX1/fNPfzzz//KF++fNq3b5/KlSunmJgYBQcHa/fu3apUqZJdPRcvXtSyZct0+fLldO9//fr1atCggSRp1apVatKkia5evSo3N7dU6ylXrpy6du2q7t27S5Jtm717977n+frggw/08ccfa//+/akOAQ8KClLp0qW1evVqW1vr1q0VHx+vVatWacuWLWrSpIliY2NltVptyxQrVkxvv/22Xn/9dTVs2FANGjTQwIEDbfM/++wzvf322zp16pS+//57NWnSRMePH1dAQICk2yMXGjdurG+//faBHryXfF0mT56sXr162dojIyMVERGhbdu26amnnpIkHTp0SKVLl9b27dtVrVo1DRs2TBMnTtTff/8tLy8vSbdvpYiOjtbRo0fl5HT771WlSpVSeHi4BgwYkOH6rl+/ruvXr9s+x8fHKzAwUBV7zJKz1T3Txw0A2UXUxI6OLgEAgIciPj5ePj4+iouLk7e39z2XNX1Per169bRnzx5t375dYWFhioiI0Msvv6yjR4/q5s2bqlmzpm3ZHDlyqFq1ajp48OA9t3nkyBG1adNGRYsWlbe3t4KCgiQpQ73XGdl/hQoVbD8XKFBAkhQbGyvpdk96v379VLp0afn6+srT01MHDx7MVE/6K6+8oqtXr6po0aJ67bXX9O2339oNQ5ek6tWrp/icXO/evXuVkJCgPHnyyNPT0zYdO3bMdjvA3r17NWLECLv5r732mk6fPq0rV67o4MGDCgwMtAX01Pb5oKpWrZqizcXFRU8++aTtc6lSpeTr62t3LYKCgmwBXZL8/PxUpkwZW0BPbku+NhlltVrl7e1tNwEAAABARpj+wXEeHh4qVqyYJGnevHmqWLGi5s6daxfIMqpZs2YqUqSIZs+erYCAACUlJalcuXK6ceNGVpVt585ebYvFIkm2ofX9+vXTunXr9P7776tYsWJyd3dXy5YtM1VLYGCgoqOjtX79eq1bt05vvvmmJk6cqM2bN6fr4WoJCQkqUKCANm3alGJe8kiEhIQEDR8+XC1atEixTFojA7Kah4dHpta7+xwkvzXg7rbkawMAAAAAj5rpQ/qdnJyc9O677+qtt97S77//LldXV23dulVFihSRdHu4+Y4dO2zDwpPvXU9MTLRt49y5c4qOjtbs2bNVq1YtSdKWLVvs9pPaend74okn7rv/9Ni6davCw8P10ksvSbodgmNiYtK9/t3c3d3VrFkzNWvWTN26dVOpUqW0b98+Va5cWZK0bds2u+W3bdum0qVLS5IqV66sv//+Wy4uLrbRBXerXLmyoqOjbX84uVvp0qV18uRJnT592jZq4O59Pgy3bt3Szp07Va1aNUm3n0Nw8eJF27EBAAAAwL/BvyqkS7eHdPfv318zZ87UG2+8of79+yt37twqXLiwJkyYoCtXrqhz586SpCJFishisWjFihV6/vnn5e7urly5cilPnjz65JNPVKBAAZ04cSLF/cf58+eXu7u71qxZo0KFCsnNzS3F69c8PDzuu//0KF68uJYuXapmzZrJYrFo8ODBme7JjYyMVGJiop566inlzJlTn332mdzd3W1/RJBu/1FgwoQJat68udatW6evvvpKK1eulCQ1bNhQ1atXV/PmzTVhwgSVKFFCp06d0sqVK/XSSy+patWqGjJkiJo2barChQurZcuWcnJy0t69e7V//36NGjVKDRs2VIkSJRQWFqaJEycqPj5egwYNytTxZESOHDnUo0cPTZ06VS4uLurevbuefvppW2gHAAAAgH8D09+TfrfkADZhwgSNHj1aL7/8sjp06KDKlSvr999/19q1a5UrVy5JUsGCBTV8+HANGDBAfn5+6t69u5ycnLR48WJFRUWpXLly6tOnjyZOnJhiH1OnTtXHH3+sgIAAvfjii6nWMm7cuHvuPz0++OAD5cqVSzVq1FCzZs0UGhpq6/XOKF9fX82ePVs1a9ZUhQoVtH79ev33v/9Vnjx5bMv07dtXO3fuVEhIiEaNGqUPPvjA9oR0i8WiVatWqXbt2oqIiFCJEiXUunVrHT9+XH5+fpKk0NBQrVixQt9//72efPJJPf300/rwww9tfwhwcnLSt99+q6tXr6patWrq0qXLA73WLL1y5sypd955R23btlXNmjXl6empJUuWPPT9AgAAAEBWMvXT3ZG10vuU+H+byMhI9e7dWxcvXnR0KXaSn+DI090BIH14ujsA4HH1WD3dHQAAAACA7IKQ/i+yaNEiu1ef3TmVLVvW0eWly5gxY9I8BovFkua8xo0bP9I6H4dzDQAAAODfh+Hu/yKXLl3SmTNnUp2XI0cOuwfEmdX58+d1/vz5VOe5u7vr6tWrac4rWLDgwyzNTlaca4a7A0DGMNwdAPC4yshw93/d092zMy8vL3l5eTm6jAeSO3du5c6d29Fl3NfjcK4BAAAA/Psw3B0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJNwcXQBwOPux1Ft5O3t7egyAAAAAPwL0JMOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMItMh/dNPP1XNmjUVEBCg48ePS5ImT56s7777LsuKAwAAAAAgO3HJzEozZ87UkCFD1Lt3b40ePVqJiYmSJF9fX02ePFkvvvhilhYJ/JvVfu8LOVvdHV0GgMdM1MSOji4BAAA8BJnqSZ82bZpmz56tQYMGydnZ2dZetWpV7du3L8uKAwAAAAAgO8lUSD927JhCQkJStFutVl2+fPmBiwIAAAAAIDvKVEgPDg7Wnj17UrSvWbNGpUuXftCaAAAAAADIljJ1T/pbb72lbt266dq1azIMQ7/88ou++OILjR07VnPmzMnqGgEAAAAAyBYyFdK7dOkid3d3vffee7py5Yratm2rgIAATZkyRa1bt87qGgEAAAAAyBYyHNJv3bqlzz//XKGhoWrXrp2uXLmihIQE5c+f/2HUBwAAAABAtpHhe9JdXFzUtWtXXbt2TZKUM2dOAjoAAAAAAFkgUw+Oq1atmnbv3p3VtQAAAAAAkK1l6p70N998U3379tWff/6pKlWqyMPDw25+hQoVsqQ4AAAAAACyk0yF9OSHw/Xs2dPWZrFYZBiGLBaLEhMTs6Y6AAAAAACykUyF9GPHjmV1HQAAAAAAZHuZCulFihTJ6joAAAAAAMj2MhXSFy5ceM/5HTt2zFQxAAAAAABkZ5kK6b169bL7fPPmTV25ckWurq7KmTMnIR0AAAAAgEzI1CvYLly4YDclJCQoOjpazzzzjL744ousrhEAAAAAgGwhUyE9NcWLF9e4ceNS9LID/wYWi0XLli2zfT506JCefvppubm5qVKlSmm2AQAAAEBWyrKQLkkuLi46depUVm4SJmGxWO45DRs2zNElphAeHm6rL0eOHPLz89Ozzz6refPmKSkpyW7Z06dPq3HjxrbPQ4cOlYeHh6Kjo7Vhw4Y02wAAAAAgK2XqnvTly5fbfTYMQ6dPn9b06dNVs2bNLCkM5nL69Gnbz0uWLNGQIUMUHR1ta/P09HREWffVqFEjzZ8/X4mJiTpz5ozWrFmjXr166euvv9by5cvl4nL7V8Df399uvaNHj6pJkyZ2bzJIrQ0AAAAAslKmetKbN29uN7Vo0ULDhg1ThQoVNG/evKyuESbg7+9vm3x8fGSxWOzaFi9erNKlS8vNzU2lSpXSjBkzbOvGxMTIYrHoyy+/VK1ateTu7q4nn3xShw8f1o4dO1S1alV5enqqcePGOnv2rG298PBwNW/eXMOHD1e+fPnk7e2trl276saNG+mu22q1yt/fXwULFlTlypX17rvv6rvvvtPq1asVGRlpW+7O4e4Wi0VRUVEaMWKEbZRAam0AAAAAkNUy1ZN+91BhZG+LFi3SkCFDNH36dIWEhGj37t167bXX5OHhobCwMNtyQ4cO1eTJk1W4cGF16tRJbdu2lZeXl6ZMmaKcOXOqVatWGjJkiGbOnGlbZ8OGDXJzc9OmTZsUExOjiIgI5cmTR6NHj850vfXr11fFihW1dOlSdenSJcX806dPq2HDhmrUqJH69esnT09Pde3aNUXb3a5fv67r16/bPsfHx2e6RgAAAADZU6Z60keMGKErV66kaL969apGjBjxwEXh32Xo0KGaNGmSWrRooeDgYLVo0UJ9+vTRxx9/bLdcv379FBoaqtKlS6tXr16KiorS4MGDVbNmTYWEhKhz587auHGj3Tqurq6aN2+eypYtqyZNmmjEiBGaOnXqA/+hqFSpUoqJiUl1nr+/v1xcXOTp6Sl/f3/b/97ddrexY8fKx8fHNgUGBj5QjQAAAACyn0yF9OHDhyshISFF+5UrVzR8+PAHLgr/HpcvX9bRo0fVuXNneXp62qZRo0bp6NGjdstWqFDB9rOfn58kqXz58nZtsbGxdutUrFhROXPmtH2uXr26EhISdPLkyQeq2zAMWSyWB9rG3QYOHKi4uDjb9KA1AgAAAMh+MjXcPa2As3fvXuXOnfuBi8K/R/Ifa2bPnq2nnnrKbp6zs7Pd5xw5cth+Tv7+3N32qG6lOHjwoIKDg7N0m1arVVarNUu3CQAAACB7yVBIz5Url+2VViVKlLAL6omJiUpISFDXrl2zvEiYl5+fnwICAvTHH3+oXbt2Wb79vXv36urVq3J3d5ckbdu2TZ6eng80lPyHH37Qvn371KdPn6wqEwAAAACyRIZC+uTJk2UYhjp16qThw4fLx8fHNs/V1VVBQUGqXr16lhcJcxs+fLh69uwpHx8fNWrUSNevX9fOnTt14cIFvfXWWw+07Rs3bqhz58567733FBMTo6FDh6p79+5yckrfnRrXr1/X33//bfcKtrFjx6pp06bq2LHjA9UGAAAAAFktQyE9+UndwcHBqlGjht1QZWRfXbp0Uc6cOTVx4kT1799fHh4eKl++vHr37v3A227QoIGKFy+u2rVr6/r162rTpk2GXn+2Zs0aFShQQC4uLsqVK5cqVqyoqVOnKiwsLN1BHwAAAAAeFYthGMaDbODatWsp3lvt7e39QEUB0u33pF+8eNH2/vJ/m/j4ePn4+Khij1lytro7uhwAj5moiYwGAgDg3yI5G8TFxd03L2eqK/HKlSvq3r278ufPLw8PD+XKlctuAgAAAAAAGZepkN6/f3/98MMPmjlzpqxWq+bMmaPhw4crICBACxcuzOoagRROnDhh98q3u6cTJ044ukQAAAAAyLBMvYLtv//9rxYuXKi6desqIiJCtWrVUrFixVSkSBEtWrTooTzlG9lPZGRkmvMCAgK0Z8+ee84HAAAAgH+bTIX08+fPq2jRopJu339+/vx5SdIzzzyjN954I+uqA9Lg4uKiYsWKOboMAAAAAMhSmRruXrRoUR07dkySVKpUKX355ZeSbvew+/r6ZllxAAAAAABkJ5kK6REREdq7d68kacCAAfroo4/k5uamPn36qH///llaIAAAAAAA2UWmhrv36dPH9nPDhg116NAhRUVFqVixYqpQoUKWFQcAAAAAQHaSqZB+p2vXrqlIkSIqUqRIVtQDAAAAAEC2lanh7omJiRo5cqQKFiwoT09P/fHHH5KkwYMHa+7cuVlaIAAAAAAA2UWmQvro0aMVGRmpCRMmyNXV1dZerlw5zZkzJ8uKAwAAAAAgO8lUSF+4cKE++eQTtWvXTs7Ozrb2ihUr6tChQ1lWHAAAAAAA2UmmQvpff/2V6juqk5KSdPPmzQcuCgAAAACA7ChTIb1MmTL66aefUrR//fXXCgkJeeCiAAAAAADIjjL1dPchQ4YoLCxMf/31l5KSkrR06VJFR0dr4cKFWrFiRVbXCPyr/Tiqjby9vR1dBgAAAIB/gQz1pP/xxx8yDEMvvvii/vvf/2r9+vXy8PDQkCFDdPDgQf33v//Vs88++7BqBQAAAADgsZahnvTixYvr9OnTyp8/v2rVqqXcuXNr37598vPze1j1AQAAAACQbWSoJ90wDLvPq1ev1uXLl7O0IAAAAAAAsqtMPTgu2d2hHQAAAAAAZF6GQrrFYpHFYknRBgAAAAAAHlyG7kk3DEPh4eGyWq2SpGvXrqlr167y8PCwW27p0qVZVyEAAAAAANlEhkJ6WFiY3ef27dtnaTEAAAAAAGRnGQrp8+fPf1h1AAAAAACQ7T3Qg+MAAAAAAEDWIaQDAAAAAGASGRruDiDjar/3hZyt7o4uA/jXiZrY0dElAAAAPHL0pAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0OZbFY7jkNGzbMofWFh4erefPmDq0BAAAAQPbh4ugCkL2dPn3a9vOSJUs0ZMgQRUdH29o8PT0dURYAAAAAOAQ96XAof39/2+Tj4yOLxSJ/f395eXmpRIkSWrNmjd3yy5Ytk4eHhy5duqSYmBhZLBYtXrxYNWrUkJubm8qVK6fNmzfbrbN//341btxYnp6e8vPzU4cOHfTPP/88ysMEAAAAgHQhpMOUPDw81Lp1a82fP9+uff78+WrZsqW8vLxsbf3791ffvn21e/duVa9eXc2aNdO5c+ckSRcvXlT9+vUVEhKinTt3as2aNTpz5oxatWqV5TVfv35d8fHxdhMAAAAAZAQhHabVpUsXrV271jYkPjY2VqtWrVKnTp3sluvevbtefvlllS5dWjNnzpSPj4/mzp0rSZo+fbpCQkI0ZswYlSpVSiEhIZo3b542btyow4cPZ2m9Y8eOlY+Pj20KDAzM0u0DAAAAePwR0mFa1apVU9myZbVgwQJJ0meffaYiRYqodu3adstVr17d9rOLi4uqVq2qgwcPSpL27t2rjRs3ytPT0zaVKlVKknT06NEsrXfgwIGKi4uzTSdPnszS7QMAAAB4/PHgOJhaly5d9NFHH2nAgAGaP3++IiIiZLFY0r1+QkKCmjVrpvHjx6eYV6BAgawsVVarVVarNUu3CQAAACB7oScdpta+fXsdP35cU6dO1W+//aawsLAUy2zbts32861btxQVFaXSpUtLkipXrqwDBw4oKChIxYoVs5s8PDwe2XEAAAAAQHoQ0mFquXLlUosWLdS/f38999xzKlSoUIplPvroI3377bc6dOiQunXrpgsXLtjuW+/WrZvOnz+vNm3aaMeOHTp69KjWrl2riIgIJSYmpquGuLg47dmzx25iKDsAAACAh4GQDtPr3Lmzbty4keKBccnGjRuncePGqWLFitqyZYuWL1+uvHnzSpICAgK0detWJSYm6rnnnlP58uXVu3dv+fr6yskpfV//TZs2KSQkxG4aPnx4lh0fAAAAACSzGIZhOLoI4F4+/fRT9enTR6dOnZKrq6utPSYmRsHBwdq9e7cqVarkuALTEB8fLx8fH1XsMUvOVndHlwP860RN7OjoEgAAALJEcjaIi4uTt7f3PZflwXEwrStXruj06dMaN26c/vOf/9gFdAAAAAB4HDHcHaY1YcIElSpVSv7+/ho4cGCWb//EiRN2r2a7ezpx4kSW7xMAAAAA7oXh7si2bt26pZiYmDTnBwUFycUl84NNGO4OPBiGuwMAgMcFw92BdHBxcVGxYsUcXQYAAAAA2DDcHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEi6OLgB43P04qo28vb0dXQYAAACAfwF60gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAkXRxcAPO5qv/eFnK3uji4DSFXUxI6OLgEAAAB3oCcdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdj5TFYrnnNGzYMIfWFx4eLovFoq5du6aY161bN1ksFoWHhz/6wgAAAABkC4R0PFKnT5+2TZMnT5a3t7ddW79+/RxdogIDA7V48WJdvXrV1nbt2jV9/vnnKly4sAMrAwAAAPC4I6TjkfL397dNPj4+slgs8vf3l5eXl0qUKKE1a9bYLb9s2TJ5eHjo0qVLiomJkcVi0eLFi1WjRg25ubmpXLly2rx5s906+/fvV+PGjeXp6Sk/Pz916NBB//zzT7prrFy5sgIDA7V06VJb29KlS1W4cGGFhISkud7169cVHx9vNwEAAABARhDSYQoeHh5q3bq15s+fb9c+f/58tWzZUl5eXra2/v37q2/fvtq9e7eqV6+uZs2a6dy5c5Kkixcvqn79+goJCdHOnTu1Zs0anTlzRq1atcpQPZ06dbKrZd68eYqIiLjnOmPHjpWPj49tCgwMzNA+AQAAAICQDtPo0qWL1q5dq9OnT0uSYmNjtWrVKnXq1Mluue7du+vll19W6dKlNXPmTPn4+Gju3LmSpOnTpyskJERjxoxRqVKlFBISonnz5mnjxo06fPhwumtp3769tmzZouPHj+v48ePaunWr2rdvf891Bg4cqLi4ONt08uTJDJ4BAAAAANmdi6MLAJJVq1ZNZcuW1YIFCzRgwAB99tlnKlKkiGrXrm23XPXq1W0/u7i4qGrVqjp48KAkae/evdq4caM8PT1TbP/o0aMqUaJEumrJly+fmjRposjISBmGoSZNmihv3rz3XMdqtcpqtaZr+wAAAACQGkI6TKVLly766KOPNGDAAM2fP18RERGyWCzpXj8hIUHNmjXT+PHjU8wrUKBAhmrp1KmTunfvLkn66KOPMrQuAAAAAGQGw91hKu3bt9fx48c1depU/fbbbwoLC0uxzLZt22w/37p1S1FRUSpdurSk2w99O3DggIKCglSsWDG7ycPDI0O1NGrUSDdu3NDNmzcVGhr6YAcGAAAAAOlASIep5MqVSy1atFD//v313HPPqVChQimW+eijj/Ttt9/q0KFD6tatmy5cuGC7b71bt246f/682rRpox07dujo0aNau3atIiIilJiYmKFanJ2ddfDgQf32229ydnbOkuMDAAAAgHshpMN0OnfurBs3bqR4YFyycePGady4capYsaK2bNmi5cuX2+4XDwgI0NatW5WYmKjnnntO5cuXV+/eveXr6ysnp4x/3b29veXt7f1AxwMAAAAA6WUxDMNwdBHAnT799FP16dNHp06dkqurq609JiZGwcHB2r17typVquS4AtMpPj5ePj4+qthjlpyt7o4uB0hV1MSOji4BAADgsZecDeLi4u7bCciD42AaV65c0enTpzVu3Dj95z//sQvoAAAAAJAdMNwdpjFhwgSVKlVK/v7+GjhwYJZv/8SJE/L09ExzOnHiRJbvEwAAAAAyguHuyDZu3bqlmJiYNOcHBQXJxSXrBpcw3B3/Bgx3BwAAePgY7g6kwsXFRcWKFXN0GQAAAACQJoa7AwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEi6OLgB43P04qo28vb0dXQYAAACAfwF60gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAkXRxcAPO5qv/eFnK3uji4jU6ImdnR0CQAAAEC2Qk86AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSHyPh4eFq3ry5o8swNYvFomXLljm6DAAAAABIFSH9EbJYLPechg0b5ugSH3unT59W48aNHV0GAAAAAKTKxdEFZCenT5+2/bxkyRINGTJE0dHRtjZPT09HlOUQhmEoMTFRLi6P9ivo7+//SPcHAAAAABlBT/oj5O/vb5t8fHxksVhsn/Pnz68PPvhAhQoVktVqVaVKlbRmzRq79fft26f69evL3d1defLk0euvv66EhIQM17FixQr5+voqMTFRkrRnzx5ZLBYNGDDAtkyXLl3Uvn172+ctW7aoVq1acnd3V2BgoHr27KnLly/b5n/66aeqWrWqvLy85O/vr7Zt2yo2NtY2f9OmTbJYLFq9erWqVKkiq9WqLVu2ZLj2ZMOGDVOlSpU0b948FS5cWJ6ennrzzTeVmJioCRMm2M7p6NGj7da7c7h7TEyMLBaLli5dqnr16ilnzpyqWLGi/ve//2W6LgAAAAB4EIR0k5gyZYomTZqk999/X7/++qtCQ0P1wgsv6MiRI5Kky5cvKzQ0VLly5dKOHTv01Vdfaf369erevXuG91WrVi1dunRJu3fvliRt3rxZefPm1aZNm2zLbN68WXXr1pUkHT16VI0aNdLLL7+sX3/9VUuWLNGWLVvs9n3z5k2NHDlSe/fu1bJlyxQTE6Pw8PAU+x4wYIDGjRungwcPqkKFChmu/U5Hjx7V6tWrtWbNGn3xxReaO3eumjRpoj///FObN2/W+PHj9d5772n79u333M6gQYPUr18/7dmzRyVKlFCbNm1069atDNdz/fp1xcfH200AAAAAkBGEdJN4//339c4776h169YqWbKkxo8fr0qVKmny5MmSpM8//1zXrl3TwoULVa5cOdWvX1/Tp0/Xp59+qjNnzmRoXz4+PqpUqZItlG/atEl9+vTR7t27lZCQoL/++ku///676tSpI0kaO3as2rVrp969e6t48eKqUaOGpk6dqoULF+ratWuSpE6dOqlx48YqWrSonn76aU2dOlWrV69O0dM/YsQIPfvss3riiSeUO3fuBzpnSUlJmjdvnsqUKaNmzZqpXr16io6O1uTJk1WyZElFRESoZMmS2rhx4z23069fPzVp0kQlSpTQ8OHDdfz4cf3+++8Zrmfs2LHy8fGxTYGBgZk9NAAAAADZFCHdBOLj43Xq1CnVrFnTrr1mzZo6ePCgJOngwYOqWLGiPDw87OYnJSXZ3deeXnXq1NGmTZtkGIZ++ukntWjRQqVLl9aWLVu0efNmBQQEqHjx4pKkvXv3KjIyUp6enrYpNDRUSUlJOnbsmCQpKipKzZo1U+HCheXl5WUL+CdOnLDbb9WqVTNca1qCgoLk5eVl++zn56cyZcrIycnJru3OYfepubNHv0CBApJ033VSM3DgQMXFxdmmkydPZngbAAAAALI3HhyXTdWtW1fz5s3T3r17lSNHDpUqVUp169bVpk2bdOHCBVvIlqSEhAT95z//Uc+ePVNsp3Dhwrah+KGhoVq0aJHy5cunEydOKDQ0VDdu3LBb/s4/MjyoHDly2H22WCyptiUlJaV7OxaLRZLuu05qrFarrFZrhtcDAAAAgGT0pJuAt7e3AgICtHXrVrv2rVu3qkyZMpKk0qVLa+/evXYPa9u6daucnJxUsmTJDO8z+b70Dz/80BbIk0P6pk2bbPejS1LlypX122+/qVixYikmV1dXHTp0SOfOndO4ceNUq1YtlSpVKlM90QAAAACQ3RHSTaJ///4aP368lixZoujoaA0YMEB79uxRr169JEnt2rWTm5ubwsLCtH//fm3cuFE9evRQhw4d5Ofnl+H95cqVSxUqVNCiRYtsgbx27dratWuXDh8+bNeT/s477+jnn39W9+7dtWfPHh05ckTfffed7cFxhQsXlqurq6ZNm6Y//vhDy5cv18iRIx/8pAAAAABANkNIN4mePXvqrbfeUt++fVW+fHmtWbNGy5cvt90XnjNnTq1du1bnz5/Xk08+qZYtW6pBgwaaPn16pvdZp04dJSYm2kJ67ty5VaZMGfn7+9v1zleoUEGbN2/W4cOHVatWLYWEhGjIkCEKCAiQJOXLl0+RkZH66quvVKZMGY0bN07vv/9+5k8GAAAAAGRTFsMwDEcXATyO4uPj5ePjo4o9ZsnZ6u7ocjIlamJHR5cAAAAA/OslZ4O4uDh5e3vfc1l60gEAAAAAMAlC+mPoxIkTdq9Lu3u6+7VojlK2bNk0awwODk5z3qJFix5JfY0bN06zhjFjxjySGgAAAABkL7yC7TEUEBCgPXv23HO+GaxatUo3b95MdV6OHDnSnJeZB+Vlxpw5c3T16tVU5+XOnfuR1AAAAAAgeyGkP4ZcXFxUrFgxR5dxX0WKFHF0CfdUsGBBR5cAAAAAIJthuDsAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACbh4ugCgMfdj6PayNvb29FlAAAAAPgXoCcdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKT/y4SHh6t58+YPfT/Dhg1TpUqVsmx7kZGR8vX1zbLtAQAAAMDjiJCeBcLDw2WxWNS1a9cU87p16yaLxaLw8PBHX9gD6NevnzZs2OCQfUdGRspischiscjJyUmFChVSRESEYmNjHVIPAAAAADwqhPQsEhgYqMWLF+vq1au2tmvXrunzzz9X4cKFHVhZ5nh6eipPnjwO27+3t7dOnz6tP//8U7Nnz9bq1avVoUMHh9VzJ8MwdOvWLUeXAQAAAOAxREjPIpUrV1ZgYKCWLl1qa1u6dKkKFy6skJAQW1tSUpLGjh2r4OBgubu7q2LFivr666/ttnXgwAE1bdpU3t7e8vLyUq1atXT06FG7Zd5//30VKFBAefLkUbdu3XTz5k3bvE8//VRVq1aVl5eX/P391bZtW7te6E2bNslisWjDhg2qWrWqcubMqRo1aig6Otq2TGrD3efNm6eyZcvKarWqQIEC6t69u23eBx98oPLly8vDw0OBgYF68803lZCQkLmTKcliscjf318BAQFq3LixevbsqfXr1+vq1atKSkrSiBEjVKhQIVmtVlWqVElr1qyxrduyZUu72nr37i2LxaJDhw5Jkm7cuCEPDw+tX79e0v2vSfL5Wr16tapUqSKr1aotW7Zk+tgAAAAAIC2E9CzUqVMnzZ8/3/Z53rx5ioiIsFtm7NixWrhwoWbNmqUDBw6oT58+at++vTZv3ixJ+uuvv1S7dm1ZrVb98MMPioqKUqdOnex6bjdu3KijR49q48aNWrBggSIjIxUZGWmbf/PmTY0cOVJ79+7VsmXLFBMTk+pw+0GDBmnSpEnauXOnXFxc1KlTpzSPbebMmerWrZtef/117du3T8uXL1exYsVs852cnDR16lQdOHBACxYs0A8//KC33347o6cwTe7u7kpKStKtW7c0ZcoUTZo0Se+//75+/fVXhYaG6oUXXtCRI0ckSXXq1NGmTZts627evFl58+a1te3YsUM3b95UjRo1JN3/miQbMGCAxo0bp4MHD6pChQoparx+/bri4+PtJgAAAADIEAMPLCwszHjxxReN2NhYw2q1GjExMUZMTIzh5uZmnD171njxxReNsLAw49q1a0bOnDmNn3/+2W79zp07G23atDEMwzAGDhxoBAcHGzdu3EhzX0WKFDFu3bpla3vllVeMV199Nc36duzYYUgyLl26ZBiGYWzcuNGQZKxfv962zMqVKw1JxtWrVw3DMIyhQ4caFStWtM0PCAgwBg0alO5z8tVXXxl58uSxfZ4/f77h4+OTrnXvXvbw4cNGiRIljKpVq9pqGT16tN06Tz75pPHmm28ahmEYv/76q2GxWIzY2Fjj/PnzhqurqzFy5EjbORo1apRRo0YNwzCMdF2T5PO1bNmye9Y9dOhQQ1KKKS4uLl3HDQAAAODxFBcXl+5s4OKoPw48jvLly6cmTZooMjJShmGoSZMmyps3r23+77//ritXrujZZ5+1W+/GjRu2IfF79uxRrVq1lCNHjjT3U7ZsWTk7O9s+FyhQQPv27bN9joqK0rBhw7R3715duHBBSUlJkqQTJ06oTJkytuXu7A0uUKCAJCk2NjbFPfSxsbE6deqUGjRokGZN69ev19ixY3Xo0CHFx8fr1q1bunbtmq5cuaKcOXOmuV5a4uLi5OnpqaSkJF27dk3PPPOM5syZo/j4eJ06dUo1a9a0W75mzZrau3evJKlcuXLKnTu3Nm/eLFdXV4WEhKhp06b66KOPJN3uWa9bt66k9F2TZFWrVr1nzQMHDtRbb71l+xwfH6/AwMAMHzsAAACA7IuQnsU6depkux86ORQmS75He+XKlSpYsKDdPKvVKun2sO77uTvAWywWWxC/fPmyQkNDFRoaqkWLFilfvnw6ceKEQkNDdePGjTS3Y7FYJMm2nTvdr6aYmBg1bdpUb7zxhkaPHq3cuXNry5Yt6ty5s27cuJGpkO7l5aVdu3bJyclJBQoUsNWQniHkFotFtWvX1qZNm2S1WlW3bl1VqFBB169f1/79+/Xzzz+rX79+ktJ3TZJ5eHjcc79WqzXFOgAAAACQEYT0LNaoUSPduHFDFotFoaGhdvPKlCkjq9WqEydOqE6dOqmuX6FCBS1YsEA3b968Z296Wg4dOqRz585p3Lhxtl7cnTt3ZvxA7uDl5aWgoCBt2LBB9erVSzE/KipKSUlJmjRpkpycbj/m4Msvv3ygfTo5Odnd857M29tbAQEB2rp1q9053Lp1q6pVq2b7XKdOHc2ePVtWq1WjR4+Wk5OTateurYkTJ+r69eu2nvj0XBMAAAAAeFQI6VnM2dlZBw8etP18Jy8vL/Xr1099+vRRUlKSnnnmGcXFxWnr1q3y9vZWWFiYunfvrmnTpql169YaOHCgfHx8tG3bNlWrVk0lS5a87/4LFy4sV1dXTZs2TV27dtX+/fs1cuTIBz6uYcOGqWvXrsqfP78aN26sS5cuaevWrerRo4eKFSummzdvatq0aWrWrJm2bt2qWbNmPfA+09K/f38NHTpUTzzxhCpVqqT58+drz549WrRokW2ZunXrqk+fPnJ1ddUzzzxja+vXr5+efPJJW694eq4JAAAAADwqhPSHwNvbO815I0eOVL58+TR27Fj98ccf8vX1VeXKlfXuu+9KkvLkyaMffvhB/fv3V506deTs7KxKlSqluAc7Lfny5VNkZKTeffddTZ06VZUrV9b777+vF1544YGOKSwsTNeuXdOHH36ofv36KW/evGrZsqUkqWLFivrggw80fvx4DRw4ULVr19bYsWPVsWPHB9pnWnr27Km4uDj17dtXsbGxKlOmjJYvX67ixYvblilfvrx8fX1VokQJeXp6Srod0hMTE233oye73zUBAAAAgEfFYhiG4egigMdRfHy8fHx8FBcXd88/3AAAAAB4vGUkG/CedAAAAAAATIKQjkeubNmy8vT0THW6875yAAAAAMhuuCcdj9yqVat08+bNVOf5+fk94moAAAAAwDwI6XjkihQp4ugSAAAAAMCUGO4OAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAk3BxdAHA48owDElSfHy8gysBAAAA4EjJmSA5I9wLIR14SM6dOydJCgwMdHAlAAAAAMzg0qVL8vHxuecyhHTgIcmdO7ck6cSJE/f9RcTDEx8fr8DAQJ08eVLe3t6OLidb41qYA9fBHLgO5sB1MAeugzlwHR4uwzB06dIlBQQE3HdZQjrwkDg53X7kg4+PD/+hMwFvb2+ug0lwLcyB62AOXAdz4DqYA9fBHLgOD096O+54cBwAAAAAACZBSAcAAAAAwCQI6cBDYrVaNXToUFmtVkeXkq1xHcyDa2EOXAdz4DqYA9fBHLgO5sB1MA+LkZ5nwAMAAAAAgIeOnnQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBx6Sjz76SEFBQXJzc9NTTz2lX375xdElZStjx47Vk08+KS8vL+XPn1/NmzdXdHS0o8vK9saNGyeLxaLevXs7upRs56+//lL79u2VJ08eubu7q3z58tq5c6ejy8p2EhMTNXjwYAUHB8vd3V1PPPGERo4cKZ7j+3D9+OOPatasmQICAmSxWLRs2TK7+YZhaMiQISpQoIDc3d3VsGFDHTlyxDHFPsbudR1u3rypd955R+XLl5eHh4cCAgLUsWNHnTp1ynEFP6bu9/twp65du8pisWjy5MmPrD4Q0oGHYsmSJXrrrbc0dOhQ7dq1SxUrVlRoaKhiY2MdXVq2sXnzZnXr1k3btm3TunXrdPPmTT333HO6fPmyo0vLtnbs2KGPP/5YFSpUcHQp2c6FCxdUs2ZN5ciRQ6tXr9Zvv/2mSZMmKVeuXI4uLdsZP368Zs6cqenTp+vgwYMaP368JkyYoGnTpjm6tMfa5cuXVbFiRX300Uepzp8wYYKmTp2qWbNmafv27fLw8FBoaKiuXbv2iCt9vN3rOly5ckW7du3S4MGDtWvXLi1dulTR0dF64YUXHFDp4+1+vw/Jvv32W23btk0BAQGPqDIk4xVswEPw1FNP6cknn9T06dMlSUlJSQoMDFSPHj00YMAAB1eXPZ09e1b58+fX5s2bVbt2bUeXk+0kJCSocuXKmjFjhkaNGqVKlSrxV/lHaMCAAdq6dat++uknR5eS7TVt2lR+fn6aO3eure3ll1+Wu7u7PvvsMwdWln1YLBZ9++23at68uaTbvegBAQHq27ev+vXrJ0mKi4uTn5+fIiMj1bp1awdW+/i6+zqkZseOHapWrZqOHz+uwoULP7rispG0rsNff/2lp556SmvXrlWTJk3Uu3dvRsE9QvSkA1nsxo0bioqKUsOGDW1tTk5Oatiwof73v/85sLLsLS4uTpKUO3duB1eSPXXr1k1NmjSx+73Ao7N8+XJVrVpVr7zyivLnz6+QkBDNnj3b0WVlSzVq1NCGDRt0+PBhSdLevXu1ZcsWNW7c2MGVZV/Hjh3T33//bfffJx8fHz311FP8u+1gcXFxslgs8vX1dXQp2UpSUpI6dOig/v37q2zZso4uJ1tycXQBwOPmn3/+UWJiovz8/Oza/fz8dOjQIQdVlb0lJSWpd+/eqlmzpsqVK+focrKdxYsXa9euXdqxY4ejS8m2/vjjD82cOVNvvfWW3n33Xe3YsUM9e/aUq6urwsLCHF1etjJgwADFx8erVKlScnZ2VmJiokaPHq127do5urRs6++//5akVP/dTp6HR+/atWt655131KZNG3l7ezu6nGxl/PjxcnFxUc+ePR1dSrZFSAfw2OvWrZv279+vLVu2OLqUbOfkyZPq1auX1q1bJzc3N0eXk20lJSWpatWqGjNmjCQpJCRE+/fv16xZswjpj9iXX36pRYsW6fPPP1fZsmW1Z88e9e7dWwEBAVwL4P+7efOmWrVqJcMwNHPmTEeXk61ERUVpypQp2rVrlywWi6PLybYY7g5ksbx588rZ2Vlnzpyxaz9z5oz8/f0dVFX21b17d61YsUIbN25UoUKFHF1OthMVFaXY2FhVrlxZLi4ucnFx0ebNmzV16lS5uLgoMTHR0SVmCwUKFFCZMmXs2kqXLq0TJ044qKLsq3///howYIBat26t8uXLq0OHDurTp4/Gjh3r6NKyreR/m/l32xySA/rx48e1bt06etEfsZ9++kmxsbEqXLiw7d/t48ePq2/fvgoKCnJ0edkGIR3IYq6urqpSpYo2bNhga0tKStKGDRtUvXp1B1aWvRiGoe7du+vbb7/VDz/8oODgYEeXlC01aNBA+/bt0549e2xT1apV1a5dO+3Zs0fOzs6OLjFbqFmzZopXEB4+fFhFihRxUEXZ15UrV+TkZP9/v5ydnZWUlOSgihAcHCx/f3+7f7fj4+O1fft2/t1+xJID+pEjR7R+/XrlyZPH0SVlOx06dNCvv/5q9+92QECA+vfvr7Vr1zq6vGyD4e7AQ/DWW28pLCxMVatWVbVq1TR58mRdvnxZERERji4t2+jWrZs+//xzfffdd/Ly8rLdV+jj4yN3d3cHV5d9eHl5pXgOgIeHh/LkycPzAR6hPn36qEaNGhozZoxatWqlX375RZ988ok++eQTR5eW7TRr1kyjR49W4cKFVbZsWe3evVsffPCBOnXq5OjSHmsJCQn6/fffbZ+PHTumPXv2KHfu3CpcuLB69+6tUaNGqXjx4goODtbgwYMVEBBwzyePI+PudR0KFCigli1bateuXVqxYoUSExNt/3bnzp1brq6ujir7sXO/34e7/ziSI0cO+fv7q2TJko+61OzLAPBQTJs2zShcuLDh6upqVKtWzdi2bZujS8pWJKU6zZ8/39GlZXt16tQxevXq5egysp3//ve/Rrly5Qyr1WqUKlXK+OSTTxxdUrYUHx9v9OrVyyhcuLDh5uZmFC1a1Bg0aJBx/fp1R5f2WNu4cWOq/yaEhYUZhmEYSUlJxuDBgw0/Pz/DarUaDRo0MKKjox1b9GPoXtfh2LFjaf7bvXHjRkeX/li53+/D3YoUKWJ8+OGHj7TG7I73pAMAAAAAYBLckw4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAwGMhPDxczZs3d3QZqYqJiZHFYtGePXscXQoAwOQI6QAAAA/RjRs3HF0CAOBfhJAOAAAeO3Xr1lWPHj3Uu3dv5cqVS35+fpo9e7YuX76siIgIeXl5qVixYlq9erVtnU2bNslisWjlypWqUKGC3Nzc9PTTT2v//v122/7mm29UtmxZWa1WBQUFadKkSXbzg4KCNHLkSHXs2FHe3t56/fXXFRwcLEkKCQmRxWJR3bp1JUk7duzQs88+q7x588rHx0d16tTRrl277LZnsVg0Z84cvfTSS8qZM6eKFy+u5cuX2y1z4MABNW3aVN7e3vLy8lKtWrV09OhR2/w5c+aodOnScnNzU6lSpTRjxowHPscAgIeDkA4AAB5LCxYsUN68efXLL7+oR48eeuONN/TKK6+oRo0a2rVrl5577jl16NBBV65csVuvf//+mjRpknbs2KF8+fKpWbNmunnzpiQpKipKrVq1UuvWrbVv3z4NGzZMgwcPVmRkpN023n//fVWsWFG7d+/W4MGD9csvv0iS1q9fr9OnT2vp0qWSpEuXLiksLExbtmzRtm3bVLx4cT3//PO6dOmS3faGDx+uVq1a6ddff9Xzzz+vdu3a6fz585Kkv/76S7Vr15bVatUPP/ygqKgoderUSbdu3ZIkLVq0SEOGDNHo0aN18OBBjRkzRoMHD9aCBQuy/JwDAB6cxTAMw9FFAAAAPKjw8HBdvHhRy5YtU926dZWYmKiffvpJkpSYmCgfHx+1aNFCCxculCT9/fffKlCggP73v//p6aef1qZNm1SvXj0tXrxYr776qiTp/PnzKlSokCIjI9WqVSu1a9dOZ8+e1ffff2/b79tvv62VK1fqwIEDkm73pIeEhOjbb7+1LRMTE6Pg4GDt3r1blSpVSvMYkpKS5Ovrq88//1xNmzaVdLsn/b333tPIkSMlSZcvX5anp6dWr16tRo0a6d1339XixYsVHR2tHDlypNhmsWLFNHLkSLVp08bWNmrUKK1atUo///xzZk41AOAhoicdAAA8lipUqGD72dnZWXny5FH58uVtbX5+fpKk2NhYu/WqV69u+zl37twqWbKkDh48KEk6ePCgatasabd8zZo1deTIESUmJtraqlatmq4az5w5o9dee03FixeXj4+PvL29lZCQoBMnTqR5LB4eHvL29rbVvWfPHtWqVSvVgH758mUdPXpUnTt3lqenp20aNWqU3XB4AIB5uDi6AAD4f+3dvUsjbRSG8SsoarAYIZJAEK1UAmqR+IFVWrGxVBDRwlIiKBYiCMbG1g8EiaJiI7Y2VnbBFP4FFjY2FsqApBGbLZYNuruv77rsx8Bev3LmmYcz092cmTOS9Dt8HVpjsdibY7FYDPjcvf7Vmpubf2jd9PQ0j4+PbG5u0tHRQWNjI8PDw98Mm/vevXypOx6P/+f+1WoVgFKpxNDQ0JtzdXV1P1SjJOnPMqRLkiS9UqlUaG9vByAMQ25ubshkMgBkMhnK5fKb9eVyma6urndDb0NDA8CbbvuXa3d3dxkdHQXg7u6Oh4eHD9Xb19fH8fExLy8v34T5VCpFOp3m9vaWycnJD+0rSfo7DOmSJEmvFItFEokEqVSKlZUVWltba/9fX1xcZGBggPX1dcbHx7m6umJnZ+d/p6Unk0ni8TgXFxe0tbXR1NREEAR0dnZycnJCf38/T09PLC0tvdsZ/565uTm2t7eZmJhgeXmZIAioVCoMDg7S3d3N2toahUKBIAgYGRnh+fmZ6+trwjBkYWHhZx+TJOk38Zt0SZKkVzY2NpifnyeXy3F/f8/5+XmtE57NZjk7O+P09JSenh5WV1cpFovMzMy8u2d9fT1bW1vs7e2RTqcZGxsD4ODggDAMyWazTE1NUSgUSCaTH6o3kUhweXlJtVoln8+Ty+UolUq1rvrs7Cz7+/scHh7S29tLPp/n6Oio9ls4SVK0ON1dkiQJatPdwzCkpaXlb5cjSfpH2UmXJEmSJCkiDOmSJEmSJEWEr7tLkiRJkhQRdtIlSZIkSYoIQ7okSZIkSRFhSJckSZIkKSIM6ZIkSZIkRYQhXZIkSZKkiDCkS5IkSZIUEYZ0SZIkSZIiwpAuSZIkSVJEfAKm5MqYzQFzkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LR_model_fs = LogisticRegression(random_state=42, max_iter=2000)\n",
    "LR_model_fs.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "importances_LR = LR_model_fs.coef_[0]\n",
    "importance_df_LR = pd.DataFrame({'Feature': X_train.columns, 'Importance': importances_LR})\n",
    "importance_df_LR['Importance'] = importance_df_LR['Importance'].abs()  # Get absolute values\n",
    "importance_df_LR = importance_df_LR.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Plot top features for Logistic Regression\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df_LR)\n",
    "plt.title('Feature Importance from Logistic Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aFdB478ybv1S",
   "metadata": {
    "id": "aFdB478ybv1S"
   },
   "outputs": [],
   "source": [
    "# Selects the important features for LogisticRegression\n",
    "selected_features_LR = importance_df_LR[importance_df_LR['Importance'] > 1]['Feature']\n",
    "X_train_selected_LR = X_train[selected_features_LR]\n",
    "X_test_selected_LR = X_test[selected_features_LR]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd133ad",
   "metadata": {
    "id": "2bd133ad"
   },
   "source": [
    "Selected Features for Logistic Regression Model:\n",
    "\n",
    "   - Torque [Nm]\n",
    "   - Air temperature [K]\n",
    "   - Process temperature [K]\n",
    "   - Rotational speed [rpm]\n",
    "   - Temp Diff\n",
    "   - Type_L\n",
    "   - Type_M\n",
    "   - Tool wear [min]\n",
    "   - Mechanical_Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "MgV59QWrjagw",
   "metadata": {
    "id": "MgV59QWrjagw"
   },
   "outputs": [],
   "source": [
    "# Applying SMOTE to selected features for LogisticRegression\n",
    "X_train_smote_selected_LR, y_train_smote_selected_LR = smote.fit_resample(X_train_selected_LR, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "EhoSYqZfjmsv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EhoSYqZfjmsv",
    "outputId": "60ec8b6a-b1af-4632-bbdd-7be14503c9a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for LogisticRegression Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.71      0.82      1935\n",
      "           1       0.07      0.63      0.12        65\n",
      "\n",
      "    accuracy                           0.70      2000\n",
      "   macro avg       0.53      0.67      0.47      2000\n",
      "weighted avg       0.95      0.70      0.80      2000\n",
      "\n",
      "Accuracy: 0.705\n"
     ]
    }
   ],
   "source": [
    "# Trains LogisticRegression model on SMOTE data\n",
    "LR_model = LogisticRegression(random_state=42)\n",
    "LR_model.fit(X_train_smote_selected_LR, y_train_smote_selected_LR)\n",
    "\n",
    "# Predictions and evaluation\n",
    "y_pred_LR = LR_model.predict(X_test_selected_LR)\n",
    "print(\"Results for LogisticRegression Classifier:\")\n",
    "print(classification_report(y_test, y_pred_LR))\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred_LR)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619537e7",
   "metadata": {
    "id": "619537e7"
   },
   "source": [
    "#### Note:\n",
    "The Logistic Regression model, even with feature selection and scaling, struggled to identify the minority class (1). It achieved a lower accuracy and lower recall for class 1 compared to the other models, likely due to its linear nature, which makes it less effective on our dataset's complex patterns. The tree-based models, with their non-linear capabilities, captured these patterns better, especially for predicting failures (class 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb25805d",
   "metadata": {
    "id": "fb25805d"
   },
   "source": [
    "### 6) Conclusion:\n",
    "\n",
    "Our project successfully developed a predictive maintenance model aimed at identifying potential machine failures, enabling proactive and cost-effective maintenance interventions. We implemented several critical steps using the AI4I 2020 dataset, including feature engineering (e.g., Mechanical Power and Temperature Difference) and SMOTE, to address class imbalance and enhance model training. Among the models tested—Random Forest, Gradient Boosting, AdaBoost, Logistic Regression, and XGBoost—the XGBoost model emerged as the best performer with an accuracy of 97.35% and an F1-score of 0.65 for the minority class. This model showed strong precision and recall metrics, capturing both positive and negative failure cases effectively, especially when tuned to an optimal threshold. The XGBoost model’s performance relied on features like Rotational Speed, Torque, and Mechanical Power, providing insights into factors most indicative of potential failures. To maintain this model’s effectiveness, periodic retraining with updated data is recommended to keep the model adaptive to evolving machine behaviors and operational changes. This predictive maintenance solution holds significant potential for reducing downtime, optimizing repair schedules, and lowering overall maintenance costs in industrial applications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b302fc0f",
   "metadata": {
    "id": "b302fc0f"
   },
   "source": [
    "## Contribution:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8642c5f",
   "metadata": {
    "id": "b8642c5f"
   },
   "source": [
    "#### Member 1) Joao Vitor Lira de Carvalho Firmino:\n",
    "\n",
    "Vitor’s contributions focused on data preprocessing and model development. He carried out exploratory data analysis (EDA) to uncover patterns in the dataset, helping to spot potential outliers and correlations, like the one between Torque and Rotational Speed, as well as between process temperature and air temperature. He applied log transformation to stabilize variance and improve the normality of key variables. Additionally, he performed feature selection to keep only the most relevant features, which helped optimize the model’s accuracy. Finally, he trained and evaluated a Gradient Boosting model to achieve solid predictive results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a005fb",
   "metadata": {
    "id": "51a005fb"
   },
   "source": [
    "#### Member 2) Beemnet Desta:\n",
    "\n",
    "Beemnet contributed to this project by performing feature engineering, selection, and model evaluation for predictive maintenance. She created new features, such as mechanical power and temperature difference, which enhanced the models' predictive power. Through feature selection, She trained and evaluated both AdaBoost and Logistic Regression models, optimizing each for detecting failure events within the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "483ef341",
   "metadata": {
    "id": "483ef341"
   },
   "source": [
    "#### Member 3) Tejas Hemant Gotiwale:\n",
    "\n",
    "Tejas contributed significantly to this project by focusing on critical areas that enhanced the model’s ability to accurately predict machine failures. To address the imbalance in failure data, He applied SMOTE, which allowed the model to better recognize rare failure instances. In addition, he prepared the data by splitting it into training and testing sets, establishing a clear foundation for model evaluation. His efforts in feature selection, model training, and XGBoost evaluation were instrumental, with threshold tuning further improving the balance between recall and precision. As a result, XGBoost was identified as the most effective model, outperforming others in accuracy and F1-score, a key finding in the project. Overall, these contributions strengthened the predictive maintenance model, making it more reliable and applicable to real-world scenarios."
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    "#### Member 4) Adithya Harsha:\n",
    "\n",
    "Adithya contributed by refining the data pipeline and enhancing model performance. He began with a thorough data cleanup—addressing missing values, removing duplicates, and ensuring consistency to establish a solid foundation for analysis. He then applied StandardScaler to bring all feature values onto the same scale, improving the model's stability and precision. To streamline the process, he selected the most relevant features using statistical insights, filtering out unnecessary information and focusing on the key predictors. He then trained and fine-tuned a Random Forest model, carefully adjusting parameters to balance accuracy and minimize errors. This optimized model provided clear, reliable predictions on machine failures, enabling proactive maintenance, reducing downtime, and making operations more efficient."
   ]
  }
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