{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
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Software Engineering for Machine Learning

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(S2-25_AIMLCZG546)

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Assignment-1

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Group-82

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📜 Table of Contents

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    \n", "
  1. SECTION 1: Project Title & Problem Formulation
    2. \n", "
  2. SECTION 2: GR4ML - Business View
    3. \n", "
  3. SECTION 3: GR4ML Analytics Design View
    4. \n", "
  4. SECTION 4: GR4ML Data Preparation View
    5. \n", "
  5. SECTION 5: Quality Requirements
    6. \n", "
  6. SECTION 6: System Architecture Diagram
    7. \n", "
  7. SECTION 7: Architectural Patterns
    8. \n", "
  8. SECTION 8: Load Dataset Directly from URL
    9. \n", "
  9. SECTION 9: Data Quality Analysis
    10. \n", "
  10. SECTION 10: Exploratory Data Analysis
    11. \n", "
  11. SECTION 11: Data Preparation
    12. \n", "
  12. SECTION 12: Data Split
    13. \n", "
  13. SECTION 13: Model Training
    14. \n", "
  14. SECTION 14: Predictions
    15. \n", "
  15. SECTION 15: Model Evaluation
    16. \n", "
  16. SECTION 16: Results Analysis
    17. \n", "
  17. SECTION 17: Candidate Algorithm Comparison
    18. \n", "
  18. SECTION 18: Architectural Pattern 2 - Layered Architecture
    19. \n", "
  19. SECTION 19: Working Prediction System
    20. \n", "
  20. SECTION 20: Quality Requirement Validation
    21. \n", "
  21. SECTION 21: Explainability Analysis
    22. \n", "
  22. SECTION 21A: GR4ML Traceability
    23. \n", "
  23. SECTION 22: Conclusion
    24. \n", "
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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
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SECTION 1: Project Title & Problem Formulation

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" ] }, { "cell_type": "markdown", "metadata": { "id": "0CUcz6S0iHvF" }, "source": [ "## Project Title\n", "AI-Powered Heart Disease Risk Prediction System\n", "\n", "## Domain\n", "Healthcare Industry\n", "\n", "## Problem Statement\n", "\n", "Heart disease is one of the leading causes of death worldwide. Early detection of heart disease can significantly improve patient outcomes and reduce healthcare costs.\n", "\n", "The objective of this project is to develop a Machine Learning-based application that predicts the risk of heart disease using patient clinical and lifestyle data. The system assists cardiologists in identifying high-risk patients and enables early intervention.\n", "\n", "## Project Objectives\n", "\n", "1. Apply GR4ML for requirements engineering.\n", "2. Design system architecture with ML and non-ML components.\n", "3. Implement architectural patterns.\n", "4. Build and evaluate a heart disease prediction model.\n", "5. Demonstrate a working healthcare ML solution.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
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SECTION 2: GR4ML - Business View

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" ] }, { "cell_type": "markdown", "metadata": { "id": "aJJOGjnMidB9" }, "source": [ "# GR4ML Business View\n", "\n", "## Actors\n", "\n", "Primary Actors:\n", "- Patient\n", "- Cardiologist\n", "- Hospital Administrator\n", "\n", "Secondary Actors:\n", "- Data Scientist\n", "- ML Engineer\n", "- System Administrator\n", "\n", "---\n", "\n", "## Strategic Goal (SG1)\n", "\n", "Reduce heart disease related hospitalization by 20%.\n", "\n", "---\n", "\n", "## Decision Goal (DG1)\n", "\n", "Identify patients requiring medical intervention.\n", "\n", "---\n", "\n", "## Question Goal (QG1)\n", "\n", "What patients are likely to develop heart disease?\n", "\n", "Question Type: What\n", "\n", "Topic: Heart Disease Risk\n", "\n", "Tense: Future\n", "\n", "Frequency: At every patient visit\n", "\n", "---\n", "\n", "## Insight Goal (IG1)\n", "\n", "Generate a heart disease risk score for patients.\n", "\n", "Output:\n", "- Low Risk\n", "- Medium Risk\n", "- High Risk\n" ] }, { "cell_type": "markdown", "metadata": { "id": "xMTNrVIQi0g5" }, "source": [ "
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SECTION 3: GR4ML Analytics Design View

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\n", "\n", "# GR4ML Analytics Design View\n", "\n", "## Analytics Goal (AG1)\n", "\n", "Predict future heart disease occurrence.\n", "\n", "---\n", "\n", "## Analytics Type\n", "\n", "Predictive Analytics\n", "\n", "---\n", "\n", "## Machine Learning Task\n", "\n", "Binary Classification\n", "\n", "Class 0 = No Heart Disease\n", "\n", "Class 1 = Heart Disease\n", "\n", "---\n", "\n", "## Candidate Algorithms\n", "\n", "1. Logistic Regression\n", "2. Random Forest\n", "3. XGBoost\n", "\n", "---\n", "\n", "## Evaluation Metrics\n", "\n", "- Accuracy\n", "- Precision\n", "- Recall\n", "- F1 Score\n", "- ROC AUC" ] }, { "cell_type": "markdown", "metadata": { "id": "qIq-ZkKcjDQw" }, "source": [ "
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SECTION 4: GR4ML Data Preparation View

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\n", "\n", "\n", "## Data Sources\n", "\n", "- Electronic Health Records\n", "- Laboratory Reports\n", "- ECG Reports\n", "- Patient Lifestyle Data\n", "\n", "---\n", "\n", "## Input Features\n", "\n", "- Age\n", "- Sex\n", "- Blood Pressure\n", "- Cholesterol\n", "- Chest Pain Type\n", "- Maximum Heart Rate\n", "- Exercise Induced Angina\n", "- Old Peak\n", "- ST Slope\n", "\n", "---\n", "\n", "## Data Preparation Flow\n", "\n", "Raw Patient Data\n", " ↓\n", "Data Cleaning\n", " ↓\n", "Missing Value Handling\n", " ↓\n", "Outlier Detection\n", " ↓\n", "Feature Selection\n", " ↓\n", "Feature Scaling\n", " ↓\n", "Model Training Dataset\n", "\n", "---\n", "\n", "## Data Quality Requirements\n", "\n", "- Completeness > 95%\n", "- Missing values < 2%\n", "- High consistency\n", "- Daily updates" ] }, { "cell_type": "markdown", "metadata": { "id": "mGauHPJ-jPBO" }, "source": [ "
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SECTION 5: Quality Requirements

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\n", "\n", "# Top Three Quality Requirements\n", "\n", "## 1. Accuracy\n", "\n", "Target:\n", "Accuracy ≥ 75%\n", "\n", "Reason:\n", "Incorrect prediction may delay treatment.\n", "\n", "---\n", "\n", "## 2. Explainability\n", "\n", "Doctors must understand the reasons behind the prediction.\n", "\n", "---\n", "\n", "## 3. Reliability\n", "\n", "System uptime ≥ 99%.\n", "\n", "Healthcare systems require continuous availability." ] }, { "cell_type": "markdown", "metadata": { "id": "5qegd_A3jXIU" }, "source": [ "
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SECTION 6: System Architecture Diagram

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System Architecture

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Inference / Serving Pipeline

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Patient Data Sources
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↓
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Data Ingestion Layer
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↓
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Patient Database
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↓
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Data Preprocessing Service
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↓
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Feature Engineering
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↓
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Heart Disease Prediction Model
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↓   → Alert Service
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Prediction Database
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↓
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Doctor Dashboard
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Model Training Pipeline

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Model Training Pipeline
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↓
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Model Registry
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↓
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Prediction Service
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" ] }, { "cell_type": "markdown", "metadata": { "id": "dy3Ytu0SjhJi" }, "source": [ "
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SECTION 7: Architectural Patterns

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\n", "\n", "# Architectural Patterns\n", "\n", "## Pattern 1: Pipeline Architecture\n", "\n", "Patient Data\n", "→ Data Cleaning\n", "→ Feature Scaling\n", "→ Model Training\n", "→ Model Evaluation\n", "→ Prediction\n", "\n", "Advantages:\n", "- Modular\n", "- Reusable\n", "- Easy to Debug\n", "\n", "---\n", "\n", "## Pattern 2: Layered Architecture\n", "\n", "Presentation Layer\n", " ↓\n", "Business Logic Layer\n", " ↓\n", "ML Layer\n", " ↓\n", "Data Layer\n", "\n", "Advantages:\n", "- Separation of Concerns\n", "- Maintainability\n", "- Scalability" ] }, { "cell_type": "markdown", "metadata": { "id": "KY6mxzUIkZke" }, "source": [ "
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SECTION 8: Load Dataset Directly from URL

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" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LOWiAQK5kTqp", "outputId": "8f44c261-c36b-4a54-f1ad-7654773637cd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['present', 'absent']\n", "Categories (2, object): ['absent', 'present']\n", "[1 0]\n", "class\n", "0 150\n", "1 120\n", "Name: count, dtype: int64\n", "int8\n" ] } ], "source": [ "from sklearn.datasets import fetch_openml\n", "import pandas as pd\n", "\n", "heart = fetch_openml(\n", " name=\"heart-statlog\",\n", " version=1,\n", " as_frame=True\n", ")\n", "\n", "df = heart.frame.copy()\n", "print(df[\"class\"].unique())\n", "df[\"class\"] = df[\"class\"].cat.codes\n", "\n", "print(df[\"class\"].unique())\n", "print(df[\"class\"].value_counts())\n", "\n", "df[\"class\"].isnull().sum()\n", "print(df[\"class\"].dtype)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8zn4nuREm_22" }, "source": [ "
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SECTION 9: Data Quality Analysis

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\n", "
" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CUNvkAWTnFOd", "outputId": "133742e7-d032-497e-8b60-b9a6a097cfcc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows: 270\n", "Columns: 14\n" ] } ], "source": [ "# Missing values\n", "\n", "df.isnull().sum()\n", "# Dataset shape\n", "\n", "print(\"Rows:\", df.shape[0])\n", "print(\"Columns:\", df.shape[1])\n" ] }, { "cell_type": "markdown", "metadata": { "id": "7yCrzoocnOEY" }, "source": [ "
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SECTION 10: Exploratory Data Analysis

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" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "w0CahM1TnSHt", "outputId": "8204fffa-d69d-440b-b715-14d6449a7c0c" }, "outputs": [ { "data": { "image/png": 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", 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JyWrSpEm2nzMgIMBsP5qYmBhVqVLFlDiRpPr16ys1NVWxsbG6ePGi/vjjj0zvtWXLFjVp0kTFihWTu7u7unTposuXL+v69eumPo6OjmZzHBMTozx58ph93hUqVMj0bU4Pi/Wvv/7ShQsXzD4be3t7s3vck15bVp4jT548ZkmuezHHxMSY2nx9fU1JHunu9ymjSqXo6GjZ29ubkoyP4l7CRpJOnz6t69evq2nTpmbfn88//9z0fXvrrbe0fPlyVa1aVYMGDdLu3bsf+d4Zadu2rW7cuKHSpUurd+/e+vrrr3Xnzp1Mr4mIiJCnp6fZsWL+xEyvAQAAAAA8Wdmu6Ln/l/jExERTtcaDfHx8ZG9vr82bN2v37t3atGmTpk+frmHDhmnv3r2mfWqy6v63IBn+/yvQUlNTsxt+GveqfubOnWuqArnH3t4+w2sye+4zZ848cjz3z29WuLi4ZHo+Pj5eLVu21FtvvaWxY8cqf/782rlzp3r27Klbt24pb968pnEMT9Gr5R6ch6w+R1Y8+EYtg8GQ4XfpYfObFQ/+MyNJa9euVbFixcz6OTndrYxp3ry5fvnlF61bt06bN29WkyZN9M477+jjjz82bZBtNBpN192+fTvbMRUvXlyxsbHasmWLNm/erLfffluTJk3Sjh07Mnzj2NChQxUSEmLW9kPs0/OdAQAAAJA7sRmzZT3WbFavXl3Hjh2Tr6+vypYta3bc++XWYDCofv36Cg8P1+HDh+Xo6Kivv/5a0t0qkpSUlMd+iPLly2v//v1mbQ/+nBFvb28VLVpUZ86cSfMMGSWjHvbcfn5+cnFx0datW9O9/l7FTlae3d/fX0eOHFFSUpKpbdeuXbKzs1P58uXl7u4uX1/fDO918OBBpaamavLkyXr++edVrlw5/fHHHw+9b4UKFXTnzh0dPHjQ1BYbG2u2CXB2Y/X09JS3t7fZZ5OSkqJDhw49NJ6sPsedO3d04MCBNDFntuQsMwEBAUpNTTXtn/O47t/s+sHvTvHixU39ChUqpK5du+qLL77Q1KlT9dlnn5napbv7X93zsNemZ/TPmYuLi1q1aqVp06YpKipKe/bs0dGjRzMcx8nJSR4eHmYHy7YAAAAA4OnyWImed955R1euXFHHjh21f/9+xcXFaePGjerevbtSUlK0d+9ejRs3TgcOHNC5c+e0evVq/fnnn6Zfun19ffXTTz8pNjZWly5deqTKBEl69913NX/+fC1evFinTp3SmDFj9NNPP2W5QiU8PFwRERGaNm2aTp48qaNHj2rhwoWaMmXKIz23s7OzBg8erEGDBpmW5Pz444+mNzUVLlxYLi4upk2c//rrrwxj69y5s5ydndW1a1f9/PPP2r59u95991116dLFtLlzWFiYJk+erGnTpunUqVM6dOiQpk+fLkkqW7asbt++renTp+vMmTNasmSJaXPjzJQvX17NmjXTm2++qb179+rgwYPq1atXphUuWYn13XffVUREhL755hvFxsbqvffe09WrVx/6WWX1ORwcHPTuu++aYu7WrZuef/75LC/le5Cvr6+6du2qHj16aM2aNTp79qyioqL05ZdfPtJ47u7uGjhwoN5//30tXrxYcXFxps/r3ubfoaGh+uabb3T69GkdO3ZM33//vemfmXsJobCwMJ06dUpr167V5MmTH/oMZ8+eVXR0tC5duqTk5GQtWrRI8+fP188//6wzZ87oiy++kIuLi2kvLQAAAAB4UowGg9UOW/RYiZ6iRYtq165dSklJ0UsvvaSAgAANGDBAXl5esrOzk4eHh3744Qe9/PLLKleunIYPH67JkyerefPmkqTevXurfPnyqlmzpgoVKqRdu3Y9UhydO3fW0KFDNXDgQFWvXl1nz55Vt27d5OzsnKXre/XqpXnz5mnhwoUKCAhQYGCgFi1alGFFz8OeW7r75qQPPvhAoaGh8vf3V/v27U37wOTJk0fTpk3TnDlzVLRoUb3yyisZxpY3b15t3LhRV65cUa1atfT666+rSZMmmjFjhqlP165dNXXqVM2cOVOVKlVSy5YtderUKUlSlSpVNGXKFE2YMEHPPfecIiMjFRERkaV5WbhwoYoWLarAwEC1adNGffr0UeHChR8r1sGDB6tjx4564403VLduXbm5uSk4OPihn1VWnyNv3rwaPHiwOnXqpPr168vNzU0rVqzI0vNmZNasWXr99df19ttvq0KFCurdu7dZ1VJ2jR49WiNGjFBERIT8/f3VrFkzrV271vR9c3R01NChQ1W5cmU1atRI9vb2Wr58uaS7iaxly5bpxIkTqly5siZMmKAxY8Zker/XXntNzZo10wsvvKBChQpp2bJl8vLy0ty5c1W/fn1VrlxZW7Zs0XfffacCBQo88nMBAAAAAKzPYLx/sw8b0rRpUxUpUkRLliyxdijIRGpqqvz9/dWuXTuNHj36scZatGiRBgwYkOnyMljWpiO3rB2CxV38O/09inKzvE629695F4fHX/b7tHF3sr1/nn67lvX903IL98AK1g4hR/wUedzaIVhcpey9JDNXcMxje//uc3HI/GUQudH1W7b3d4nfr9jeM/XK/ntzkIN+P5nxFhI5rVi5gId3ymWyvRnz0+j69euaPXu2goODZW9vr2XLlpk2mcXT5ZdfftGmTZsUGBio5ORkzZgxQ2fPnlWnTp2sHRoAAAAAwAqMss0lVNZiE1tbGwwGrVu3To0aNVKNGjX03XffadWqVQoKCpIks9dYP3j873//s3L0zxY7OzstWrRItWrVUv369XX06FFt2bLlkTdLtqa+fftm+L3q27evtcMDAAAAADyDbHbp1v1Onz6d4blixYpZ5BXaePZcvHhRf//9d7rnPDw8Mt3PyFawdCt3YOlW7sDSrdyBpVu5B0u3cgeWbuUOLN1CTvv1lPX+O1Tcr6LV7p1TbGLp1sOULVvW2iHABhUuXPiZSOYAAAAAAHKPZyLRAwAAAAAAnk7s0WNZNrFHDwAAAAAAAEj0AAAAAAAA2AyWbgEAAAAAAKsxGqhBsSRmEwAAAAAAwEZQ0QMAAAAAAKyGzZgti4oeAAAAAAAAG0GiBwAAAAAAwEawdAsAAAAAAFgNmzFbFrMJAAAAAABgI6joAQAAAAAAVsNmzJZFRQ8AAAAAAICNoKIHwCMbNXyPtUOwuBZdGlg7BIvbvfWktUOwuKCWFawdgsU5OjhYOwSLc3G2dgSWdzbyuLVDyBGVO1e0dggWN7HVQmuHYHFOri7WDsHibiYmWTsEiyvmV8LaIVicvYPt/drYq0lRa4eA+xgNVPRYEhU9AAAAAAAANoJEDwAAAAAAgI2wvRo8AAAAAACQaxiNLN2yJCp6AAAAAAAAbAQVPQAAAAAAwGqM1KBYFLMJAAAAAABgI0j0AAAAAAAA2AiWbgEAAAAAAKsxis2YLYmKHgAAAAAAABtBRQ8AAAAAALAaKnosi4oeAAAAAAAAG0FFDwAAAAAAsBoqeiyLih4AAAAAAAAbQaIHAAAAAADARrB0CwAAAAAAWA1LtyyLip4sWLRokby8vKwdxiNp3LixBgwYYO0wnlkGg0Fr1qzJ8HxUVJQMBoOuXbv2xGJKT3x8vAwGg6Kjo60aBwAAAADg8ZDoyYL27dvr5MmT1g4j13mSyYOnJWGSXfXq1VNCQoI8PT2tHQoAAAAAWIXRaLDaYYtYupUFLi4ucnFxsXYYucqtW7csNo6jo6NFxnoaOTo6qkiRItYOAwAAAABgI3JdRU/jxo317rvvasCAAcqXL5+8vb01d+5cJSUlqXv37nJ3d1fZsmW1fv16SVJKSop69uypUqVKycXFReXLl9cnn3xiGu/mzZuqVKmS+vTpY2qLi4uTu7u7FixYICnt0q2wsDBVrVpVCxYsUIkSJeTm5qa3335bKSkpmjhxoooUKaLChQtr7NixpmvSq265du2aDAaDoqKiJP1fVcrGjRtVrVo1ubi46MUXX9TFixe1fv16+fv7y8PDQ506ddL169ezPGepqakaNGiQ8ufPryJFiigsLMzs/LVr19SrVy8VKlRIHh4eevHFF3XkyBGz+XjllVfk7e0tNzc31apVS1u2bDEbw9fXV6NHj9Ybb7whDw8P9enTR6VKlZIkVatWTQaDQY0bN35orN26dVPr1q01duxYFS1aVOXLl5ckLVmyRDVr1pS7u7uKFCmiTp066eLFi6a5feGFFyRJ+fLlk8FgULdu3UzPHhERYfr8q1SpopUrV2Zp3h723blnwYIFqlSpkpycnOTj46N+/fqZnb906ZJeffVV5c2bV35+fvr2229N59KrRNq5c6caNmwoFxcXFS9eXP3791dSUpIk6aOPPlKdOnXSxFClShWNGjXK9PO8efPk7+8vZ2dnVahQQTNnzjTrv2/fPlWrVk3Ozs6qWbOmDh8+nKU5AQAAAAA83XJdokeSFi9erIIFC2rfvn1699139dZbb6lt27aqV6+eDh06pJdeekldunTR9evXlZqaqn/961/66quvdPz4cYWGhuqjjz7Sl19+KUlydnZWZGSkFi9erG+++UYpKSn6z3/+o6ZNm6pHjx4ZxhAXF6f169drw4YNWrZsmebPn68WLVrot99+044dOzRhwgQNHz5ce/fuzfbzhYWFacaMGdq9e7d+/fVXtWvXTlOnTtXSpUu1du1abdq0SdOnT8/WfLm6umrv3r2aOHGiRo0apc2bN5vOt23b1pRMOnjwoKpXr64mTZroypUrkqTExES9/PLL2rp1qw4fPqxmzZqpVatWOnfunNl9Pv74Y1WpUkWHDx/WiBEjtG/fPknSli1blJCQoNWrV2cp3q1btyo2NlabN2/W999/L0m6ffu2Ro8erSNHjmjNmjWKj483JXOKFy+uVatWSZJiY2OVkJBgSshERETo888/1+zZs3Xs2DG9//77+s9//qMdO3Y8NI6HfXckadasWXrnnXfUp08fHT16VN9++63Kli1rNk54eLjatWunn376SS+//LI6d+5smtsHxcXFqVmzZnrttdf0008/acWKFdq5c6cpedS5c2ft27dPcXFxpmuOHTumn376SZ06dZIkRUZGKjQ0VGPHjlVMTIzGjRunESNGaPHixZLufp4tW7ZUxYoVdfDgQYWFhWngwIEPnQ8AAAAAyAlGGax22CKD0Wg0WjuI7GjcuLFSUlL0v//9T9LdqgtPT0+1adNGn3/+uSTp/Pnz8vHx0Z49e/T888+nGaNfv346f/68WWXHpEmTNHHiRHXo0EGrVq3S0aNHVaBAAUl3K3oGDBhgqroICwvTpEmTdP78ebm7u0uSmjVrptjYWMXFxcnO7m7+rEKFCurWrZuGDBmi+Ph4lSpVSocPH1bVqlUl3a2kyZcvn7Zv367GjRsrKipKL7zwgrZs2aImTZpIksaPH6+hQ4cqLi5OpUuXliT17dtX8fHx2rBhQ7bnS5Jq166tF198UePHj9fOnTvVokULXbx4UU5OTqY+ZcuW1aBBg8wqne733HPPqW/fvqYEhK+vr6pVq6avv/7a1Ce9Z36Ybt26acOGDTp37lymS7YOHDigWrVq6Z9//pGbm5tp7q5evWqqvkpOTlb+/Pm1ZcsW1a1b13Rtr169dP36dS1dujRLMd3vwe9OsWLF1L17d40ZMybd/gaDQcOHD9fo0aMlSUlJSXJzc9P69evVrFmzNHH36tVL9vb2mjNnjmmMnTt3KjAwUElJSXJ2dlbVqlX12muvacSIEZLuVvls27ZNP/74o6S7n93o0aPVsWNH0xhjxozRunXrtHv3bn322Wf66KOP9Ntvv8nZ2VmSNHv2bL311luZflbJyclKTk42a2vWYa/s7G1raV2LLg2sHYLF7d5qe3uMBbWsYO0QLM7Rwfb+ouHibO0ILC/hYoq1Q8gRlTtXtHYIFjex1UJrh2BxTq62t5XAzcQka4dgccX8Slg7BIuzd7C9HT++GFvU2iHgPsdOJ1jt3pXK+ljt3jklV1b0VK5c2fRne3t7FShQQAEBAaY2b29vSTIt7fn0009Vo0YNFSpUSG5ubvrss8/SVKN88MEHKleunGbMmKEFCxaYkjwZ8fX1NSV57t2zYsWKpiTPvbZ7MTzq83l7eytv3rymJM+jjHv/eJLk4+Njuv7IkSNKTExUgQIF5ObmZjrOnj1rqhpJTEzUwIED5e/vLy8vL7m5uSkmJibNHNasWTPbz5qegICANEmegwcPqlWrVipRooTc3d0VGBgoSWliuN/p06d1/fp1NW3a1OzZPv/8c7OKmMxk9t25ePGi/vjjD1NSLiP3z7+rq6s8PDwy/PyOHDmiRYsWmcUbHBys1NRUnT17VtLdqp57SSqj0ahly5apc+fOku4mkuLi4tSzZ0+zMcaMGWN65piYGFWuXNmU5JFklgjLSEREhDw9Pc2O305HPvQ6AAAAAMgMFT2WlStTsw4ODmY/GwwGszaD4e6HlZqaquXLl2vgwIGaPHmy6tatK3d3d02aNCnNkqqLFy/q5MmTsre316lTp9SsWbPHiuFeW2pqqiSZEkD3F1Ddvn37oWM/bNysyOz6xMRE+fj4mPYJut+9ypiBAwdq8+bN+vjjj1W2bFm5uLjo9ddfT7Phsqura5ZjysyD4yQlJSk4OFjBwcGKjIxUoUKFdO7cOQUHB2e66XNiYqIkae3atSpWrJjZufurlzLysO9OVjfozs7nl5iYqDfffFP9+/dPc65Eibv/d6hjx44aPHiwDh06pBs3bujXX39V+/btTddL0ty5c9Ps5WNvb5+leDMydOhQhYSEmLU165D9pYkAAAAAgJyTKxM92bFr1y7Vq1dPb7/9tqktvWqOHj16KCAgQD179lTv3r0VFBQkf39/i8VRqFAhSVJCQoKqVasmSU/kteMPU716dZ0/f1558uSRr69vun127dqlbt266dVXX5V0N5kQHx//0LHvVeWkpDxemfuJEyd0+fJljR8/XsWLF5d0d+nWw+5VsWJFOTk56dy5c6YKoOx42HfH3d1dvr6+2rp1q2kz6MdVvXp1HT9+PM0+P/f717/+pcDAQEVGRurGjRtq2rSpChcuLOlutVfRokV15swZU5XPg/z9/bVkyRLdvHnTVNVzb9lXZpycnNIkyGxt2RYAAACAJ89WK2usJVcu3coOPz8/HThwQBs3btTJkyc1YsQI7d+/36zPp59+qj179mjx4sXq3LmzWrdurc6dO1vsFeHS3eqP559/XuPHj1dMTIx27Nih4cOHW2z8RxUUFKS6deuqdevW2rRpk+Lj47V7924NGzbMlEzx8/PT6tWrFR0drSNHjqhTp05ZqigqXLiwXFxctGHDBl24cEF//fXXI8VYokQJOTo6avr06Tpz5oy+/fZb054395QsWVIGg0Hff/+9/vzzTyUmJsrd3V0DBw7U+++/r8WLFysuLk6HDh3S9OnTTRsTZyYr352wsDBNnjxZ06ZN06lTp0zjP6rBgwdr9+7d6tevn6Kjo3Xq1Cl98803ad7k1blzZy1fvlxfffVVmoROeHi4IiIiNG3aNJ08eVJHjx7VwoULNWXKFElSp06dZDAY1Lt3bx0/flzr1q3Txx9//MgxAwAAAACeHjaf6HnzzTfVpk0btW/fXnXq1NHly5fNKjROnDihDz/8UDNnzjRVi8ycOVOXLl0ybXZrKQsWLNCdO3dUo0YNDRgwIMMNfJ8kg8GgdevWqVGjRurevbvKlSunDh066JdffjHtdTRlyhTly5dP9erVU6tWrRQcHKzq1as/dOw8efJo2rRpmjNnjooWLapXXnnlkWIsVKiQFi1apK+++koVK1bU+PHj0yQmihUrpvDwcA0ZMkTe3t6mxMjo0aM1YsQIRUREyN/fX82aNdPatWtNr37PzMO+O5LUtWtXTZ06VTNnzlSlSpXUsmVLnTp16pGeU7q7n8+OHTt08uRJNWzYUNWqVVNoaKiKFjXfLO7111/X5cuXdf36dbVu3drsXK9evTRv3jwtXLhQAQEBCgwM1KJFi0zP7Obmpu+++05Hjx5VtWrVNGzYME2YMOGRYwYAAAAAPD1y3Vu3ADw9GrR6+GvqcxveupU78Nat3IG3buUevHUrd+CtW7kDb93KHXjr1tPlp1PZf4mRpVT2K2y1e+cUm6/oAQAAAAAAeFaQ6MnFzp07Z/YK7QePzF49bi2Zxfu///3vicbSt2/fDGPp27fvE40FAAAAAJ5VqTJY7bBFtleD9wwpWrRopm/uenBfl6dBZvE++Ar0nDZq1CgNHDgw3XMeHh5PNBYAAAAAACyBRE8ulidPnkxfw/00epriLVy4sOm15AAAAAAA2AISPQAAAAAAwGqMNrqEylrYowcAAAAAAMBGUNEDAAAAAACsxmikoseSqOgBAAAAAACwEVT0AAAAAAAAq2GPHsuiogcAAAAAAMBGkOgBAAAAAACwESzdAgAAAAAAVsNmzJZFRQ8AAAAAAEAWfPrpp/L19ZWzs7Pq1Kmjffv2Zdh37ty5atiwofLly6d8+fIpKCgo0/6WQqIHAAAAAABYjVEGqx3ZsWLFCoWEhGjkyJE6dOiQqlSpouDgYF28eDHd/lFRUerYsaO2b9+uPXv2qHjx4nrppZf0+++/W2LaMkSiBwAAAAAA4CGmTJmi3r17q3v37qpYsaJmz56tvHnzasGCBen2j4yM1Ntvv62qVauqQoUKmjdvnlJTU7V169YcjZNEDwAAAAAAeCYlJyfr77//NjuSk5PT9Lt165YOHjyooKAgU5udnZ2CgoK0Z8+eLN3r+vXrun37tvLnz2+x+NPDZswAHtngsPrWDsHiCuS9bu0QLK5b5bT/ocrtfvw71dohWFx2S4dzgz+v2d7/T6pU2toR5IyJrRZaOwSLG/Rdd2uHYHFlT2yxdggWl2L0tnYIFnfxZj5rh2BxpZ3irR1CDihq7QBwH2tuxhwREaHw8HCztpEjRyosLMys7dKlS0pJSZG3t/m/t7y9vXXixIks3Wvw4MEqWrSoWbIoJ5DoAQAAAAAAz6ShQ4cqJCTErM3Jycni9xk/fryWL1+uqKgoOTs7W3z8+5HoAQAAAAAAVmPNWm0nJ6csJXYKFiwoe3t7Xbhwwaz9woULKlKkSKbXfvzxxxo/fry2bNmiypUrP1a8WWF7NdUAAAAAAAAW5OjoqBo1aphtpHxvY+W6detmeN3EiRM1evRobdiwQTVr1nwSoVLRAwAAAAAA8DAhISHq2rWratasqdq1a2vq1KlKSkpS9+5394V74403VKxYMUVEREiSJkyYoNDQUC1dulS+vr46f/68JMnNzU1ubm45FieJHgAAAAAAYDXW3Iw5O9q3b68///xToaGhOn/+vKpWraoNGzaYNmg+d+6c7Oz+b+HUrFmzdOvWLb3++utm46S32bMlkegBAAAAAADIgn79+qlfv37pnouKijL7OT4+PucDSgeJHgAAAAAAYDVG5Y6KntyCzZgBAAAAAABsBBU9AAAAAADAanLLHj25BRU9AAAAAAAANoJEDwAAAAAAgI1g6RYAAAAAALAaNmO2LCp6AAAAAAAAbAQVPQAAAAAAwGpSjdaOwLZQ0QMAAAAAAGAjSPQ8wwwGg9asWZPj9wkLC1PVqlUfexxfX19NnTr1scd5HPHx8TIYDIqOjrZqHA96Up8lAAAAAODpxtKtZ1hCQoLy5cuX4/cZOHCg3n333Ry/z7PsSX2WAAAAAGBpbMZsWSR6bNTt27fl4OCQaZ8iRYo8kVjc3Nzk5ub2RO71rHpSnyUAAAAA4OnG0q0nJDU1VRERESpVqpRcXFxUpUoVrVy5UkajUUFBQQoODpbReHcHqitXruhf//qXQkNDTdfPmzdP/v7+cnZ2VoUKFTRz5kzTuXvLiVasWKHAwEA5OzsrMjJSkrRgwQJVqlRJTk5O8vHxUb9+/UzX3b/c59atW+rXr598fHzk7OyskiVLKiIiwtT32rVr6tWrlwoVKiQPDw+9+OKLOnLkSJae/cGlW926dVPr1q318ccfy8fHRwUKFNA777yj27dvm/pcvHhRrVq1kouLi0qVKmV6ngef+f4lVNeuXZPBYFBUVJSp7dixY2rZsqU8PDzk7u6uhg0bKi4uLkvzKkn79u1TtWrV5OzsrJo1a+rw4cNZemZJSklJUc+ePU2fefny5fXJJ5+Y9cnKXCQkJKhFixamuVi6dGmaZWz3f5b35mb16tV64YUXlDdvXlWpUkV79uwx9b98+bI6duyoYsWKKW/evAoICNCyZcuy/GwAAAAAYClGo8Fqhy2ioucJiYiI0BdffKHZs2fLz89PP/zwg/7zn/+oUKFCWrx4sQICAjRt2jS999576tu3r4oVK2ZK9ERGRio0NFQzZsxQtWrVdPjwYfXu3Vuurq7q2rWr6R5DhgzR5MmTTYmJWbNmKSQkROPHj1fz5s31119/adeuXenGN23aNH377bf68ssvVaJECf3666/69ddfTefbtm0rFxcXrV+/Xp6enpozZ46aNGmikydPKn/+/Nmej+3bt8vHx0fbt2/X6dOn1b59e1WtWlW9e/eWdDcB8scff2j79u1ycHBQ//79dfHixWzd4/fff1ejRo3UuHFjbdu2TR4eHtq1a5fu3Lkj6eHzmpiYqJYtW6pp06b64osvdPbsWb333ntZvn9qaqr+9a9/6auvvlKBAgW0e/du9enTRz4+PmrXrl2W5+KNN97QpUuXFBUVJQcHB4WEhGRpLoYNG6aPP/5Yfn5+GjZsmDp27KjTp08rT548unnzpmrUqKHBgwfLw8NDa9euVZcuXVSmTBnVrl07W/MMAAAAAHh6kOh5ApKTkzVu3Dht2bJFdevWlSSVLl1aO3fu1Jw5c7R06VLNmTNHb7zxhs6fP69169bp8OHDypPn7sczcuRITZ48WW3atJEklSpVSsePH9ecOXPMEj0DBgww9ZGkMWPG6IMPPjBLTtSqVSvdGM+dOyc/Pz81aNBABoNBJUuWNJ3buXOn9u3bp4sXL8rJyUmS9PHHH2vNmjVauXKl+vTpk+05yZcvn2bMmCF7e3tVqFBBLVq00NatW9W7d2+dPHlS69ev1759+0zxzp8/X/7+/tm6x6effipPT08tX77ctIytXLlypvMPm9elS5cqNTVV8+fPl7OzsypVqqTffvtNb731Vpbu7+DgoPDwcNPPpUqV0p49e/Tll1+aJXoym4sTJ05oy5Yt2r9/v2rWrCnpbhWSn5/fQ+8/cOBAtWjRQpIUHh6uSpUq6fTp06pQoYKKFSumgQMHmvq+++672rhxo7788ssMEz3JyclKTk42a7t9y14Ojk5Zmg8AAAAASI+R16tbFImeJ+D06dO6fv26mjZtatZ+69YtVatWTdLdipmvv/5a48eP16xZs0y/yCclJSkuLk49e/Y0VXhI0p07d+Tp6Wk23r1EgHR36dMff/yhJk2aZCnGbt26qWnTpipfvryaNWumli1b6qWXXpIkHTlyRImJiSpQoIDZNTdu3DBbBpUdlSpVkr29velnHx8fHT16VJIUExOjPHnyqEaNGqbzFSpUkJeXV7buER0drYYNG6a7V1FW5jUmJkaVK1eWs7Oz6fy9RF1Wffrpp1qwYIHOnTunGzdu6NatW2neQJbZXMTGxipPnjyqXr266XzZsmWztPFy5cqVzcaU7n4vKlSooJSUFI0bN05ffvmlfv/9d926dUvJycnKmzdvhuNFRESYJa4kqUPvEer0ZmgGVwAAAAAAnjQSPU9AYmKiJGnt2rUqVqyY2bl7FTLXr1/XwYMHZW9vr1OnTqW5du7cuapTp47ZtfcnByTJ1dXV9GcXF5dsxVi9enWdPXtW69ev15YtW9SuXTsFBQVp5cqVSkxMlI+Pj9neN/dkN/lyz4PJF4PBoNTU1Cxfb2d3d3sp432p3/v3tZEyn4PszOujWr58uQYOHKjJkyerbt26cnd316RJk7R3716zfo87Fxm5f1yD4e7a03vjTpo0SZ988ommTp2qgIAAubq6asCAAbp161aG4w0dOlQhISFmbVuOWWauAAAAAACWQaLnCahYsaKcnJx07tw5BQYGptvngw8+kJ2dndavX6+XX35ZLVq00Isvvihvb28VLVpUZ86cUefOnbN8T3d3d/n6+mrr1q164YUXsnSNh4eH2rdvr/bt2+v1119Xs2bNdOXKFVWvXl3nz59Xnjx55Ovrm+UYHlWFChV0584dHTx40LR0KzY2VteuXTP1KVSokKS7GxXfq4q6f2Nm6W5Fy+LFi9N9A1lW5tXf319LlizRzZs3TVU9P/74Y5afY9euXapXr57efvttU1t2K6DKly+vO3fu6PDhw6YKp9OnT+vq1avZGie92F555RX95z//kXQ3AXTy5ElVrFgxw2ucnJxMicl7HBzvPFYcAAAAAJDK69UtikTPE+Du7q6BAwfq/fffV2pqqho0aGDaGNnDw0MFCxbUggULtGfPHlWvXl0ffvihunbtqp9++kn58uVTeHi4+vfvL09PTzVr1kzJyck6cOCArl69mqbC4n5hYWHq27evChcurObNm+uff/7Rrl279O6776bpO2XKFPn4+KhatWqys7PTV199pSJFisjLy0tBQUGqW7euWrdurYkTJ6pcuXL6448/tHbtWr366qtmS8Ys4d7ysTfffFOzZs1Snjx5NGDAALMKHRcXFz3//PMaP368SpUqpYsXL2r48OFm4/Tr10/Tp09Xhw4dNHToUHl6eurHH39U7dq1Vb58+YfOa6dOnTRs2DD17t1bQ4cOVXx8vD7++OMsP4efn58+//xzbdy4UaVKldKSJUu0f/9+lSpVKstjVKhQQUFBQerTp49mzZolBwcHffDBB3JxcTFV6TwKPz8/rVy5Urt371a+fPk0ZcoUXbhwIdNEDwAAAADg6cfr1Z+Q0aNHa8SIEYqIiJC/v7+aNWumtWvXytfXVz179lRYWJhpH5bw8HB5e3urb9++kqRevXpp3rx5WrhwoQICAhQYGKhFixY9NGHQtWtXTZ06VTNnzlSlSpXUsmVLs2Vh93N3d9fEiRNVs2ZN1apVS/Hx8Vq3bp3s7OxkMBi0bt06NWrUSN27d1e5cuXUoUMH/fLLL/L29rbsRP1/CxcuVNGiRRUYGKg2bdqoT58+Kly4sFmf/8feXYdFmb5vAz8HpBGwEHVJQQHFxM61UFl7Tdbuwq41wET92mJjr7l2YieiooQFKohiYGIBisS8f/g6P0bQhXHw5hnPz3HMccgzDzPnA4rMNdd93WvWrEFKSgoqVqyIoUOHYtq0aUr3FyhQACdPnkR8fDzq1KmDihUrYtWqVYrunv/6uhobG2P//v24fv06ypcvj/Hjx2PWrFlZvoa+ffuidevWaN++PapUqYJXr14pdfdk1YYNG1C4cGHUrl0brVq1Qu/evZE3b16l2UHZNWHCBFSoUAFubm6oW7cuLCws0LJlS5Ufj4iIiIiISFXcXl29ZHI551sTScmjR49gaWmJ48ePZ3nYdk7Zf1Xzlm4VMPwgOoLa2cpUG5qem1185yI6gtrJNbBl+cUbzXs/qUj+VNERcsTcGZf++ySJGb2/u+gIamcfcVx0BLVLlWvevL/nH/970wypsdO7LzqC2lk5ZG9HX8pZx68l/fdJOaRBGc3bRZhLt4hyuS9dSS4uLoiNjcXo0aNhY2OD2rVri45GREREREREuYzmvdVGP12pUqVgbGyc6W3Tpk2i4+WYfv36ffO6vyy7U4fk5GT8/fffKFWqFFq1aoVChQrh9OnTmW4bT0REREREJDVyubibJmJHD/2wQ4cOZdja/IucmuGTG0yZMgUjR47M9D4TExO1PY+bmxvc3NzU9nhERERERESkuVjooR9mbW0tOoIQ5ubmGQZEExERERERUfZo4qxCkbh0i4iIiIiIiIhIQ7Cjh4iIiIiIiIiESdPQWTmisKOHiIiIiIiIiEhDsNBDRERERERERKQhuHSLiIiIiIiIiISRyzmMWZ3Y0UNEREREREREpCHY0UNEREREREREwsg5jFmt2NFDRERERERERKQhWOghIiIiIiIiItIQXLpFRERERERERMKkgcOY1YkdPUREREREREREGoIdPUREREREREQkDIcxqxcLPUSkskJGCaIjqN2LBCPREdQuRd9BdAS1s8n3WnQEtbv+pKDoCGqXnKJ5v7Xp5kkVHSFH6BkZiI6gdvYRx0VHULtIxwaiI6hdvfXdREdQO3nlbqIjqJ/m/Tgn0mgs9BARERERERGRMHI5Z/SoE2f0EBERERERERFpCBZ6iIiIiIiIiIg0BJduEREREREREZEwaZwDpVbs6CEiIiIiIiIi0hDs6CEiIiIiIiIiYbi9unqxo4eIiIiIiIiISEOw0ENEREREREREpCG4dIuIiIiIiIiIhJFDJjqCRmFHDxERERERERGRhmBHDxEREREREREJw+3V1YsdPUREREREREREGoIdPUREREREREQkDLdXVy929BARERERERERaQgWeoiIiIiIiIiINAQLPUQ55PTp05DJZHjz5s03z1m3bh3MzMx+WqYvvL29Ua5cuZ/+vERERERERF+Ty8XdNBELPUREREREREREGoLDmImIiIiIiIhImDS5THQEjcKOHqIfkJSUBE9PT5ibm0NfXx81a9ZEUFDQN89ft24drKysYGhoiFatWuHVq1dK939ZUrVixQpYWlrC0NAQ7dq1w9u3b5XO8/Pzg5OTE/T19eHo6IilS5cq3T9mzBiUKFEChoaGsLOzw8SJE5GcnPzNXFFRUbCzs8OgQYMg19T+RSIiIiIiol8ACz1EP2D06NHYuXMn1q9fj+DgYNjb28PNzQ1xcXEZzr106RJ69uyJQYMGITQ0FL///jumTZuW4bzIyEhs374d+/fvh7+/P0JCQjBgwADF/Zs2bcKkSZMwffp0hIeHY8aMGZg4cSLWr1+vOCdv3rxYt24dbt26hYULF2LVqlWYP39+ptdw7do11KxZE506dYKvry9kMlbTiYiIiIiIpIpLt4hUlJCQgGXLlmHdunVo0qQJAGDVqlU4duwYVq9ejUqVKimdv3DhQjRu3BijR48GAJQoUQIXLlyAv7+/0nkfP37Ehg0bUKxYMQDA4sWL4e7ujrlz58LCwgJeXl6YO3cuWrduDQCwtbXFrVu3sGLFCnTt2hUAMGHCBMXj2djYYOTIkdi6daviub+4cOEC/vjjD4wfPx4jRoz47vUmJSUhKSlJ6dinT0nQ1dXL0teLiIiIiIgoM1xUoF7s6CFSUVRUFJKTk1GjRg3FMR0dHVSuXBnh4eEZzg8PD0eVKlWUjlWrVi3DeVZWVooiz5dz0tLScPv2bSQkJCAqKgo9e/aEsbGx4jZt2jRERUUpPmfbtm2oUaMGLCwsYGxsjAkTJiAmJkbpeWJiYtCwYUNMmjTpP4s8AODj4wNTU1Ol24aV8/7z84iIiIiIiOjnYUcPkYTEx8cD+Nw59HXRSFtbGwAQGBgIDw8PTJ48GW5ubjA1NcXWrVsxd+5cpfMLFSqEokWLYsuWLejRowdMTEy++9zjxo3D8OHDlY6F3v/4o5dERERERES/OHb0qBc7eohUVLx4cejq6iIgIEBxLDk5GUFBQXB2ds5wvpOTEy5duqR07OLFixnOi4mJwZMnT5TO0dLSQsmSJVG4cGEULVoU9+7dg729vdLN1tYWwOflWNbW1hg/fjxcXV3h4OCABw8eZHgeAwMDHDhwAPr6+nBzc8P79++/e716enowMTFRunHZFhERERERUe7Cjh4iFRkZGaF///4YNWoU8ufPDysrK8yePRuJiYno2bMnwsLClM739PREjRo1MGfOHLRo0QJHjhzJMJ8HAPT19dG1a1fMmTMH7969g6enJ9q1awcLCwsAwOTJk+Hp6QlTU1M0btwYSUlJuHLlCl6/fo3hw4fDwcEBMTEx2Lp1KypVqoSDBw9i9+7d37yGgwcPokmTJmjSpAn8/f1hbGys/i8WERERERHRN6Sxo0et2NFD9ANmzpyJNm3aoHPnzqhQoQIiIyNx5MgR5MuXL8O5VatWxapVq7Bw4UKULVsWR48eVRqa/IW9vT1at26Npk2bolGjRihTpozS9um9evWCn58f1q5dCxcXF9SpUwfr1q1TdPQ0b94cw4YNw6BBg1CuXDlcuHABEydO/OY1GBsb4/Dhw5DL5XB3d0dCQoIavjJEREREREQkgkwu52o4otzC29sbe/bsQWhoqOgoWXIx4q3oCGr3IsFIdAS1M9VP+u+TJCav7gfREdTu+pOCoiOo3bsEzfsVw75osugIOWLe3JuiI6jdYm/N+zcV6dhAdAS1q7e+m+gIanevcjfREdQur/yN6AhqZ+XgJDoCpfPPOXG/M/xVSybsuXMKl24RERERERERkTByueYVW0Ti0i0iIiIiIiIiIg3BQg9RLuLt7S2ZZVtERERERETqIJeLu2kiFnqIiIiIiIiIiDQECz1ERERERERERBqCw5iJiIiIiIiISJg0DV1CJQo7eoiIiIiIiIiINAQ7eoiIiIiIiIhIGE0diiwKO3qIiIiIiIiIiDQECz1ERERERERERBqCS7eIiIiIiIiISBgu3VIvdvQQEREREREREWkIFnqIiIiIiIiISJg0ubhbdi1ZsgQ2NjbQ19dHlSpVcPny5e+e/++//8LR0RH6+vpwcXHBoUOHVPwqZR0LPURERERERERE/2Hbtm0YPnw4vLy8EBwcjLJly8LNzQ3Pnz/P9PwLFy6gY8eO6NmzJ0JCQtCyZUu0bNkSN27cyNGcLPQQERERERERkTByubhbdsybNw+9e/dG9+7d4ezsjOXLl8PQ0BBr1qzJ9PyFCxeicePGGDVqFJycnDB16lRUqFABvr6+aviqfRsLPURERERERET0S0pKSsK7d++UbklJSRnO+/TpE65evYoGDRoojmlpaaFBgwYIDAzM9LEDAwOVzgcANze3b56vLtx1i4hUlpSqIzqC2sV/1BYdQe0MdDTvR33cR2PREdTuQ8bfJyQvv4noBOpnoJMiOkKO+BifIDqC2qXKC4uOoHb11ncTHUHtTnZdJzqC2skujRQdQe0cTD+JjkCUY3x8fDB58mSlY15eXvD29lY69vLlS6SmpqJwYeX/XwoXLoyIiIhMH/vp06eZnv/06dMfD/4dmvfbPxERERERERFJRlqauOceN24chg8frnRMT09PUBr1YKGHiIiIiIiIiH5Jenp6WSrsFCxYENra2nj27JnS8WfPnsHCwiLTz7GwsMjW+erCGT1EREREREREJIwUhjHr6uqiYsWKOHHihOJYWloaTpw4gWrVqmX6OdWqVVM6HwCOHTv2zfPVhR09RERERERERET/Yfjw4ejatStcXV1RuXJlLFiwAAkJCejevTsAoEuXLihWrBh8fHwAAEOGDEGdOnUwd+5cuLu7Y+vWrbhy5QpWrlyZozlZ6CEiIiIiIiIi+g/t27fHixcvMGnSJDx9+hTlypWDv7+/YuByTEwMtLT+b+FU9erVsXnzZkyYMAF///03HBwcsGfPHpQuXTpHc7LQQ0RERERERETCZGcJlWiDBg3CoEGDMr3v9OnTGY61bdsWbdu2zeFUyjijh4iIiIiIiIhIQ7Cjh4iIiIiIiIiESZNQR48UsKOHiIiIiIiIiEhDsKOHiIiIiIiIiISRCx3SIxP43DmDHT1ERERERERERBqChR4iIiIiIiIiIg3BpVtEREREREREJIyUtleXAnb0EBERERERERFpCHb0EBEREREREZEwaWmiE2gWdvQQ5XLdunVDy5YtRccgIiIiIiIiCWChh4iIiIiIiIhIQ3DpFhEREREREREJw2HM6sWOHqJcYseOHXBxcYGBgQEKFCiABg0aICEhIcN5SUlJ8PT0hLm5OfT19VGzZk0EBQUp7j99+jRkMhkOHjyIMmXKQF9fH1WrVsWNGzeUHuf8+fOoVasWDAwMYGlpCU9Pz0yfj4iIiIiIiKSDhR6iXCA2NhYdO3ZEjx49EB4ejtOnT6N169aQZ1LaHj16NHbu3In169cjODgY9vb2cHNzQ1xcnNJ5o0aNwty5cxEUFIRChQqhWbNmSE5OBgBERUWhcePGaNOmDa5du4Zt27bh/PnzGDRo0E+5XiIiIiIioi/S5OJumoiFHqJcIDY2FikpKWjdujVsbGzg4uKCAQMGwNjYWOm8hIQELFu2DP/73//QpEkTODs7Y9WqVTAwMMDq1auVzvXy8kLDhg3h4uKC9evX49mzZ9i9ezcAwMfHBx4eHhg6dCgcHBxQvXp1LFq0CBs2bMDHjx8zzZiUlIR3794p3T59SsqZLwgRERERERGphIUeolygbNmyqF+/PlxcXNC2bVusWrUKr1+/znBeVFQUkpOTUaNGDcUxHR0dVK5cGeHh4UrnVqtWTfHn/Pnzo2TJkopzwsLCsG7dOhgbGytubm5uSEtLQ3R0dKYZfXx8YGpqqnTbtGqOOi6fiIiIiIh+YXK5uJsm4jBmolxAW1sbx44dw4ULF3D06FEsXrwY48ePx6VLl3Lk+eLj49G3b194enpmuM/KyirTzxk3bhyGDx+udOxSVGqO5CMiIiIiIiLVsNBDlEvIZDLUqFEDNWrUwKRJk2Btba1YavVF8eLFoauri4CAAFhbWwMAkpOTERQUhKFDhyqde/HiRUXR5vXr17hz5w6cnJwAABUqVMCtW7dgb2+f5Xx6enrQ09NTOqarm5jdyyQiIiIiIqIcxEIPUS5w6dIlnDhxAo0aNYK5uTkuXbqEFy9ewMnJCdeuXVOcZ2RkhP79+2PUqFHInz8/rKysMHv2bCQmJqJnz55KjzllyhQUKFAAhQsXxvjx41GwYEG0bNkSADBmzBhUrVoVgwYNQq9evWBkZIRbt27h2LFj8PX1/ZmXTkREREREvzi50KnIMoHPnTNY6CHKBUxMTHD27FksWLAA7969g7W1NebOnYsmTZpg27ZtSufOnDkTaWlp6Ny5M96/fw9XV1ccOXIE+fLly3DekCFDcPfuXZQrVw779++Hrq4uAKBMmTI4c+YMxo8fj1q1akEul6N48eJo3779T7tmIiIiIiIiUj8WeohyAScnJ/j7+2d637p165Q+1tfXx6JFi7Bo0aLvPmbNmjVx48aNb95fqVIlHD16NNtZiYiIiIiI1ElTtzkXhbtuERERERERERFpCBZ6iIiIiIiIiIg0BJduEWmYunXrQi5n7yMREREREUkDX76oFzt6iIiIiIiIiIg0BDt6iIiIiIiIiEiYNE5jVit29BARERERERERaQh29BARERERERGRMJzRo17s6CEiIiIiIiIi0hAs9BARERERERERaQgu3SIiIiIiIiIiYbh0S73Y0UNEREREREREpCHY0UNEREREREREwqSxpUet2NFDRERERERERKQhWOghIiIiIiIiItIQXLpFRERERERERMLI00Qn0Czs6CEiIiIiIiIi0hDs6CEilZXUvi06gtq91i0jOoLaFTB4LzqC2j14k090BLWzLfxJdATKgsRPOqIj5IhiDlaiI6jd84+a93NCXrmb6AhqJ7s0UnQEtZNXKS06gtolh58SHYE0nJzDmNWKHT1ERERERERERBqCHT1EREREREREJEwaZ/SoFTt6iIiIiIiIiIg0BAs9REREREREREQagku3iIiIiIiIiEgYDmNWL3b0EBERERERERFpCHb0EBEREREREZEwaWzoUSt29BARERERERERaQgWeoiIiIiIiIiINASXbhERERERERGRMHKu3VIrdvQQEREREREREWkIdvQQERERERERkTDcXV292NFDRERERERERKQh2NFDRERERERERMKkcUaPWrGjh4iIiIiIiIhIQ7DQoyHq1q2LoUOHio6RqYiICFStWhX6+vooV66csBw2NjZYsGCBsOcX7fTp05DJZHjz5o3oKERERERERJRDuHSLcpyXlxeMjIxw+/ZtGBsbC8sRFBQEIyMjYc9PREREREREGck5jVmtWOihb0pNTYVMJoOW1o81fkVFRcHd3R3W1tZqSqaaQoUK/dDnf/r0Cbq6umpKQ0RERERERKR+XLqlZnXr1oWnpydGjx6N/Pnzw8LCAt7e3gCA+/fvQyaTITQ0VHH+mzdvIJPJcPr0aQD/t7zmyJEjKF++PAwMDFCvXj08f/4chw8fhpOTE0xMTNCpUyckJiYqPXdKSgoGDRoEU1NTFCxYEBMnTlSqjCYlJWHkyJEoVqwYjIyMUKVKFcXzAsC6detgZmaGffv2wdnZGXp6eoiJifnu9aalpWHKlCn47bffoKenh3LlysHf319xv0wmw9WrVzFlyhTIZDLF1+JbvnyNtm/fjlq1asHAwACVKlXCnTt3EBQUBFdXVxgbG6NJkyZ48eKF4vOCgoLQsGFDFCxYEKampqhTpw6Cg4OVHvvrpVsxMTFo0aIFjI2NYWJignbt2uHZs2eK+729vVGuXDn4+fnB1tYW+vr6382+cuVKFC1aFGlpaUrHW7RogR49eig+3rt3LypUqAB9fX3Y2dlh8uTJSElJAfC5ku3t7Q0rKyvo6emhaNGi8PT0VHzu0qVL4eDgAH19fRQuXBh//vmn4r60tDT4+PjA1tYWBgYGKFu2LHbs2PHNvA8ePECzZs2QL18+GBkZoVSpUjh06NB3r5GIiIiIiEjd5GnibpqIhZ4csH79ehgZGeHSpUuYPXs2pkyZgmPHjmXrMby9veHr64sLFy7g4cOHaNeuHRYsWIDNmzfj4MGDOHr0KBYvXpzhefPkyYPLly9j4cKFmDdvHvz8/BT3Dxo0CIGBgdi6dSuuXbuGtm3bonHjxrh7967inMTERMyaNQt+fn64efMmzM3Nv5tz4cKFmDt3LubMmYNr167Bzc0NzZs3VzxmbGwsSpUqhREjRiA2NhYjR47M0vV7eXlhwoQJCA4ORp48edCpUyeMHj0aCxcuxLlz5xAZGYlJkyYpzn///j26du2K8+fP4+LFi3BwcEDTpk3x/v37TB8/LS0NLVq0QFxcHM6cOYNjx47h3r17aN++vdJ5kZGR2LlzJ3bt2qVUoMtM27Zt8erVK5w6dUpxLC4uDv7+/vDw8AAAnDt3Dl26dMGQIUNw69YtrFixAuvWrcP06dMBADt37sT8+fOxYsUK3L17F3v27IGLiwsA4MqVK/D09MSUKVNw+/Zt+Pv7o3bt2orn8vHxwYYNG7B8+XLcvHkTw4YNw19//YUzZ85kmnfgwIFISkrC2bNncf36dcyaNUvo0joiIiIiIiL6cVy6lQPKlCkDLy8vAICDgwN8fX1x4sQJODg4ZPkxpk2bhho1agAAevbsiXHjxiEqKgp2dnYAgD///BOnTp3CmDFjFJ9jaWmJ+fPnQyaToWTJkrh+/Trmz5+P3r17IyYmBmvXrkVMTAyKFi0KABg5ciT8/f2xdu1azJgxAwCQnJyMpUuXomzZslnKOWfOHIwZMwYdOnQAAMyaNQunTp3CggULsGTJElhYWCBPnjwwNjaGhYVFlq9/5MiRcHNzAwAMGTIEHTt2xIkTJ5S+JuvWrVOcX69ePaXPX7lyJczMzHDmzBn88ccfGR7/xIkTuH79OqKjo2FpaQkA2LBhA0qVKoWgoCBUqlQJwOflWhs2bMjSsq98+fKhSZMm2Lx5M+rXrw8A2LFjBwoWLIjff/8dADB58mSMHTsWXbt2BQDY2dlh6tSpGD16NLy8vBATEwMLCws0aNAAOjo6sLKyQuXKlQF87kAyMjLCH3/8gbx588La2hrly5cH8Llba8aMGTh+/DiqVaumeOzz589jxYoVqFOnToa8MTExaNOmjaKQ9OXv1rckJSUhKSlJ+dinT9DjcjYiIiIiIqJcgx09OaBMmTJKHxcpUgTPnz9X+TEKFy4MQ0NDpRfihQsXzvCYVatWhUwmU3xcrVo13L17F6mpqbh+/TpSU1NRokQJGBsbK25nzpxBVFSU4nN0dXUz5P+Wd+/e4cmTJ4riyxc1atRAeHh4tq73a19fPwBFQeLLsfTX/+zZM/Tu3RsODg4wNTWFiYkJ4uPjv7n0LDw8HJaWlooiDwA4OzvDzMxMKbu1tXW2Zvt4eHhg586dioLIpk2b0KFDB8Wco7CwMEyZMkXpe9C7d2/ExsYiMTERbdu2xYcPH2BnZ4fevXtj9+7dimVdDRs2hLW1Nezs7NC5c2ds2rRJsXwvMjISiYmJaNiwodJjb9iwQen7m56np6eioOjl5YVr165999p8fHxgamqqdFu8ck2WvzZERERERESZSZPLhd00ETt6coCOjo7SxzKZDGlpaYoX++nn5iQnJ//nY8hksm8+ZlbFx8dDW1sbV69ehba2ttJ96ZfrGBgYKBWLRPn6+jM7lv76u3btilevXmHhwoWwtraGnp4eqlWrhk+fPv1Qjuzu0tWsWTPI5XIcPHgQlSpVwrlz5zB//nzF/fHx8Zg8eTJat26d4XP19fVhaWmJ27dv4/jx4zh27BgGDBiA//3vfzhz5gzy5s2L4OBgnD59GkePHsWkSZPg7e2NoKAgxMfHAwAOHjyIYsWKKT2unp5epll79eoFNzc3xVJAHx8fzJ07F4MHD870/HHjxmH48OFKx17f/7GCHhEREREREakXCz0/0ZfOkNjYWMWSm/+a+5Idly5dUvr4y6wabW1tlC9fHqmpqXj+/Dlq1aqlluczMTFB0aJFERAQoLQ0KCAgQLHc6GcJCAjA0qVL0bRpUwDAw4cP8fLly2+e7+TkhIcPH+Lhw4eKrp5bt27hzZs3cHZ2VjmHvr4+WrdujU2bNiEyMhIlS5ZEhQoVFPdXqFABt2/fhr29/Tcfw8DAAM2aNUOzZs0wcOBAODo64vr166hQoQLy5MmDBg0aoEGDBvDy8oKZmRlOnjyJhg0bKoZnZ7ZM61ssLS3Rr18/9OvXD+PGjcOqVau+WejR09PLUDRK5LItIiIiIiL6QdxeXb1Y6PmJDAwMULVqVcycORO2trZ4/vw5JkyYoLbHj4mJwfDhw9G3b18EBwdj8eLFmDt3LgCgRIkS8PDwQJcuXTB37lyUL18eL168wIkTJ1CmTBm4u7ur9JyjRo2Cl5cXihcvjnLlymHt2rUIDQ3Fpk2b1HZdWeHg4ICNGzfC1dUV7969w6hRo2BgYPDN8xs0aAAXFxd4eHhgwYIFSElJwYABA1CnTh24urr+UBYPDw/88ccfuHnzJv766y+l+yZNmoQ//vgDVlZW+PPPP6GlpYWwsDDcuHED06ZNw7p165CamooqVarA0NAQ//zzDwwMDGBtbY0DBw7g3r17qF27NvLly4dDhw4hLS0NJUuWRN68eTFy5EgMGzYMaWlpqFmzJt6+fYuAgACYmJgoZgKlN3ToUDRp0gQlSpTA69evcerUKTg5Of3QtRMREREREZFYLPT8ZGvWrEHPnj1RsWJFlCxZErNnz0ajRo3U8thdunTBhw8fULlyZWhra2PIkCHo06eP4v61a9di2rRpGDFiBB4/foyCBQuiatWqmQ4rzipPT0+8ffsWI0aMwPPnz+Hs7Ix9+/Zla/C0OqxevRp9+vRBhQoVYGlpiRkzZnx3hy+ZTIa9e/di8ODBqF27NrS0tNC4ceMMO5mpol69esifPz9u376NTp06Kd3n5uaGAwcOYMqUKZg1axZ0dHTg6OiIXr16AQDMzMwwc+ZMDB8+HKmpqXBxccH+/ftRoEABmJmZYdeuXfD29sbHjx/h4OCALVu2oFSpUgCAqVOnolChQvDx8cG9e/dgZmaGChUq4O+//840Z2pqKgYOHIhHjx7BxMQEjRs3VlpmRkRERERERNIjk7NHin4RRYoUwdSpUxVFFfpxTyNCREdQu4vvszaMXEqszV6LjqB2D97kEx1B7Qx1U0VHoCxITRM/xy4nbNgSKzqC2vXtkvXNFKSikP4b0RHU7sG7gqIjqJ28SmnREdTONvyU6Ahq52xfVHQESmeYb7yw554/yPi/T5IYdvSQxktMTERAQACePXum6H4hIiIiIiIi0kTcXp2+K/1W3V/fzp07l+3HmzFjxjcfr0mTJjlwBcDKlSvRoUMHDB06FNWqVVP5cWJiYr779fjWVu5ERERERET0bXK5uJsmYkcPfdf3dgX7ehvvrOjXrx/atWuX6X3fG578I4YOHYqhQ4f+8OMULVr0u1+PokXZ/klERERERERisdBD3/W9bcBVkT9/fuTPn1+tj/mz5MmTR+1fDyIiIiIiol+dPE1DW2sE4dItIiIiIiIiIiINwUIPEREREREREZGG4NItIiIiIiIiIhImTVOnIgvCjh4iIiIiIiIiIg3Bjh4iIiIiIiIiEobDmNWLHT1ERERERERERBqChR4iIiIiIiIiIg3BpVtEREREREREJAyXbqkXO3qIiIiIiIiIiNQoLi4OHh4eMDExgZmZGXr27In4+Pjvnj948GCULFkSBgYGsLKygqenJ96+fZvt52ZHDxEREREREREJo4kNPR4eHoiNjcWxY8eQnJyM7t27o0+fPti8eXOm5z958gRPnjzBnDlz4OzsjAcPHqBfv3548uQJduzYka3nZqGHiIiIiIiIiEhNwsPD4e/vj6CgILi6ugIAFi9ejKZNm2LOnDkoWrRohs8pXbo0du7cqfi4ePHimD59Ov766y+kpKQgT56sl29Y6CEiIiIiIiIiYUTO6ElKSkJSUpLSMT09Pejp6an8mIGBgTAzM1MUeQCgQYMG0NLSwqVLl9CqVassPc7bt29hYmKSrSIPwEIPEf2AK4kuoiOoXS2DS6IjqN1zLWvREdTOSDdFdAS1i32j+i8TuZVOHs3rw074KBMdIUdo62jer4R2evdFR1A/zfsnBQfTT6IjqF1y+CnREdQu2ul30RHUzjn5tugIlEv4+Phg8uTJSse8vLzg7e2t8mM+ffoU5ubmSsfy5MmD/Pnz4+nTp1l6jJcvX2Lq1Kno06dPtp+fw5iJiIiIiIiI6Jc0btw4vH37Vuk2bty4TM8dO3YsZDLZd28RERE/nOndu3dwd3eHs7OzSgUnzXv7hoiIiIiIiIgkQy4X17KYnWVaI0aMQLdu3b57jp2dHSwsLPD8+XOl4ykpKYiLi4OFhcV3P//9+/do3Lgx8ubNi927d0NHRydL2dJjoYeIiIiIiIiI6D8UKlQIhQoV+s/zqlWrhjdv3uDq1auoWLEiAODkyZNIS0tDlSpVvvl57969g5ubG/T09LBv3z7o6+urlJNLt4iIiIiIiIhImLQ0ubBbTnByckLjxo3Ru3dvXL58GQEBARg0aBA6dOig2HHr8ePHcHR0xOXLlwF8LvI0atQICQkJWL16Nd69e4enT5/i6dOnSE1Nzdbzs6OHiIiIiIiIiEiNNm3ahEGDBqF+/frQ0tJCmzZtsGjRIsX9ycnJuH37NhITEwEAwcHBuHTp88Yw9vb2So8VHR0NGxubLD83Cz1ERERERERERGqUP39+bN68+Zv329jYKM0mqlu3rtpmFbHQQ0RERERERETCiBzGrIk4o4eIiIiIiIiISEOwo4eIiIiIiIiIhJHn0FDkXxU7eoiIiIiIiIiINAQ7eoiIiIiIiIhIGHb0qBc7eoiIiIiIiIiINAQLPUREREREREREGoJLt4iIiIiIiIhImDRur65W7OghIiIiIiIiItIQLPQQSdzp06chk8nw5s2bH3ocGxsbLFiwQC2ZiIiIiIiIskqeJhd200Qs9BBJTN26dTF06FDRMYiIiIiIiCgXYqGHiIiIiIiIiEhDsNBDJCHdunXDmTNnsHDhQshkMshkMty/fx8AcPXqVbi6usLQ0BDVq1fH7du3FZ8XFRWFFi1aoHDhwjA2NkalSpVw/PhxQVdBRERERET0f+RyubCbJmKhh0hCFi5ciGrVqqF3796IjY1FbGwsLC0tAQDjx4/H3LlzceXKFeTJkwc9evRQfF58fDyaNm2KEydOICQkBI0bN0azZs0QExMj6lKIiIiIiIgoB3B7dSIJMTU1ha6uLgwNDWFhYQEAiIiIAABMnz4dderUAQCMHTsW7u7u+PjxI/T19VG2bFmULVtW8ThTp07F7t27sW/fPgwaNChLz52UlISkpCSlY8mftKGjq6eOSyMiIiIiol9UmoYORRaFHT1EGqJMmTKKPxcpUgQA8Pz5cwCfO3pGjhwJJycnmJmZwdjYGOHh4dnq6PHx8YGpqanS7d+1s9R7EURERERERPRD2NFDpCF0dHQUf5bJZACAtLQ0AMDIkSNx7NgxzJkzB/b29jAwMMCff/6JT58+Zfnxx40bh+HDhysdO3FLWw3JiYiIiIjoV6ap25yLwkIPkcTo6uoiNTU1W58TEBCAbt26oVWrVgA+d/h8GeKcVXp6etDTU16mpaObkq3HICIiIiIiopzFpVtEEmNjY4NLly7h/v37ePnypaJr53scHBywa9cuhIaGIiwsDJ06dcrS5xEREREREZG0sNBDJDEjR46EtrY2nJ2dUahQoSzN2Zk3bx7y5cuH6tWro1mzZnBzc0OFChV+QloiIiIiIqLv4/bq6sWlW0QSU6JECQQGBiod69atm9LH5cqVU/qhZWNjg5MnTyqdM3DgQKWPs7uUi4iIiIiIiHIfFnqIiIiIiIiISBg5x0qoFZduERERERERERFpCBZ6iIiIiIiIiIg0BJduEREREREREZEwaWmaORRZFHb0EBERERERERFpCHb0EBEREREREZEwmrrNuSjs6CEiIiIiIiIi0hDs6CEiIiIiIiIiYeSc0aNW7OghIiIiIiIiItIQLPQQEREREREREWkILt0iIiIiIiIiImG4dEu92NFDRERERERERKQh2NFDRERERERERMKkydNER9Ao7OghIiIiIiIiItIQLPQQEREREREREWkILt0iIiIiIiIiImE4jFm9ZHK5nF9RIiIiIiIiIhKi1aC7wp57t6+DsOfOKezoISIiIiIiIiJh2NGjXpzRQ0RERERERESkIdjRQ0RERERERETCcKKMerGjh4iIiIiIiIhIQ7DQQ0RERERERESkIbh0i4iIiIiIiIiESUtLEx1Bo7Cjh4iIiIiIiIhIQ7Cjh4iIiIiIiIiE4fbq6sWOHiIiIiIiIiIiDcFCDxERERERERGRhuDSLSIiIiIiIiISRi7nMGZ1YkcPEREREREREZGGYEcPEREREREREQnDYczqxY4eIiIiIiIiIiINwUIPEREREREREZGGYKGHSCLu378PmUyG0NBQ0VGIiIiIiIjURp4mF3bTRCz0EBERERERERFpCA5jJiIiIiIiIiJh0ri9ulqxo4col0lLS8Ps2bNhb28PPT09WFlZYfr06RnOS01NRc+ePWFrawsDAwOULFkSCxcuVDrn9OnTqFy5MoyMjGBmZoYaNWrgwYMHAICwsDD8/vvvyJs3L0xMTFCxYkVcuXLlp1wjERERERER5Qx29BDlMuPGjcOqVaswf/581KxZE7GxsYiIiMhwXlpaGn777Tf8+++/KFCgAC5cuIA+ffqgSJEiaNeuHVJSUtCyZUv07t0bW7ZswadPn3D58mXIZDIAgIeHB8qXL49ly5ZBW1sboaGh0NHR+dmXS0REREREvzhNnZUjikwul/MrSpRLvH//HoUKFYKvry969eqldN/9+/dha2uLkJAQlCtXLtPPHzRoEJ4+fYodO3YgLi4OBQoUwOnTp1GnTp0M55qYmGDx4sXo2rVrlrIlJSUhKSlJ6Zienh709PSydnFERERERESZaNQ5RNhzH91YXthz5xQu3SLKRcLDw5GUlIT69etn6fwlS5agYsWKKFSoEIyNjbFy5UrExMQAAPLnz49u3brBzc0NzZo1w8KFCxEbG6v43OHDh6NXr15o0KABZs6ciaioqO8+l4+PD0xNTZVuPj4+ql8sERERERERqR0LPUS5iIGBQZbP3bp1K0aOHImePXvi6NGjCA0NRffu3fHp0yfFOWvXrkVgYCCqV6+Obdu2oUSJErh48SIAwNvbGzdv3oS7uztOnjwJZ2dn7N69+5vPN27cOLx9+1bpNm7cONUvloiIiIiICIA8LU3YTROx0EOUizg4OMDAwAAnTpz4z3MDAgJQvXp1DBgwAOXLl4e9vX2mXTnly5fHuHHjcOHCBZQuXRqbN29W3FeiRAkMGzYMR48eRevWrbF27dpvPp+enh5MTEyUbly2RURERERElLtwGDNRLqKvr48xY8Zg9OjR0NXVRY0aNfDixQvcvHkzw3IuBwcHbNiwAUeOHIGtrS02btyIoKAg2NraAgCio6OxcuVKNG/eHEWLFsXt27dx9+5ddOnSBR8+fMCoUaPw559/wtbWFo8ePUJQUBDatGkj4rKJiIiIiOgXxmHM6sVCD1EuM3HiROTJkweTJk3CkydPUKRIEfTr1y/DeX379kVISAjat28PmUyGjh07YsCAATh8+DAAwNDQEBEREVi/fj1evXqFIkWKYODAgejbty9SUlLw6tUrdOnSBc+ePUPBggXRunVrTJ48+WdfLhEREREREakRd90iIiIiIiIiImEadLwi7LmPb3EV9tw5hR09RERERERERCSMXK6ZQ5FF4TBmIiIiIiIiIiINwY4eIiIiIiIiIhImjcOY1YodPUREREREREREGoIdPUREREREREQkjDyNM3rUiR09REREREREREQagoUeIiIiIiIiIiINwaVbRERERERERCSMnMOY1YodPUREREREREREGoIdPUREREREREQkjFzOYczqxI4eIiIiIiIiIiINwUIPEREREREREZGGYKGHiIiIiIiIiISRp8mF3XJKXFwcPDw8YGJiAjMzM/Ts2RPx8fFZ+3rI5WjSpAlkMhn27NmT7edmoYeIiIiIiIiISI08PDxw8+ZNHDt2DAcOHMDZs2fRp0+fLH3uggULIJPJVH5uDmMmIiIiIiIiImHkaeKGMSclJSEpKUnpmJ6eHvT09FR+zPDwcPj7+yMoKAiurq4AgMWLF6Np06aYM2cOihYt+s3PDQ0Nxdy5c3HlyhUUKVJEpednRw8RERERERER/ZJ8fHxgamqqdPPx8fmhxwwMDISZmZmiyAMADRo0gJaWFi5duvTNz0tMTESnTp2wZMkSWFhYqB5ATkSUi338+FHu5eUl//jxo+goasNrkg5NvC5ekzTwmqSB1yQdmnhdvCZp0MRr0jQfP36Uv337Vun2o9+v6dOny0uUKJHheKFCheRLly795uf16dNH3rNnT8XHAOS7d+/O9vPL/v8nExHlSu/evYOpqSnevn0LExMT0XHUgtckHZp4XbwmaeA1SQOvSTo08bp4TdKgidf0Kxs7dixmzZr13XPCw8Oxa9curF+/Hrdv31a6z9zcHJMnT0b//v0zfN6+ffswYsQIhISEwNjYGAAgk8mwe/dutGzZMls5OaOHiIiIiIiIiOg/jBgxAt26dfvuOXZ2drCwsMDz58+VjqekpCAuLu6bS7JOnjyJqKgomJmZKR1v06YNatWqhdOnT2c5Jws9RERERERERET/oVChQihUqNB/nletWjW8efMGV69eRcWKFQF8LuSkpaWhSpUqmX7O2LFj0atXL6VjLi4umD9/Ppo1a5atnCz0EBERERERERGpiZOTExo3bozevXtj+fLlSE5OxqBBg9ChQwfFjluPHz9G/fr1sWHDBlSuXBkWFhaZdvtYWVnB1tY2W8/PXbeIKFfT09ODl5fXD21vmNvwmqRDE6+L1yQNvCZp4DVJhyZeF69JGjTxmihrNm3aBEdHR9SvXx9NmzZFzZo1sXLlSsX9ycnJuH37NhITE9X+3BzGTERERERERESkIdjRQ0RERERERESkIVjoISIiIiIiIiLSECz0EBERERERERFpCBZ6iIiIiIiIiIg0BAs9REREREREREQagoUeIiIiIiLK1IcPH0RHICKibGKhh4hyrcjISBw5ckTxS6ZcLhecKGdI9bpiYmIyzS6XyxETEyMgEf0KUlJSMGXKFDx69Eh0FCKN4enpmenxhIQENG3a9CenUa9Pnz7h9u3bSElJER2FfiHBwcG4fv264uO9e/eiZcuW+Pvvv/Hp0yeByehXIZNL9RUGEWmsV69eoX379jh58iRkMhnu3r0LOzs79OjRA/ny5cPcuXNFR8y2bt26YcmSJTAyMlI6fv/+fXTu3Bnnzp0TlEx12traiI2Nhbm5udLxV69ewdzcHKmpqYKSEQDky5cPMpksS+fGxcXlcBr1yps3L65fvw4bGxvRUXJUamoqrl+/Dmtra+TLl090HErnypUr2L59O2JiYjK8aNu1a5egVKorXrw4/vrrL0yePFlxLCEhAY0bNwYASf4flZiYiMGDB2P9+vUAgDt37sDOzg6DBw9GsWLFMHbsWMEJf12LFi3K8rnfKkLmdpUqVcLYsWPRpk0b3Lt3D6VKlUKrVq0QFBQEd3d3LFiwQHRE0nB5RAcgIvrasGHDkCdPHsTExMDJyUlxvH379hg+fLgkCz1hYWEoU6YM/vnnH1SrVg0AsH79enh6eqJevXqC06lGLpdnWkiIj4+Hvr6+gETqs3HjRixfvhzR0dEIDAyEtbU1FixYAFtbW7Ro0UJ0vCzR5F8i69WrhzNnzmhcoWfo0KFwcXFBz549kZqaijp16uDChQswNDTEgQMHULduXdERs+TatWtZPrdMmTI5mCRnbN26FV26dIGbmxuOHj2KRo0a4c6dO3j27BlatWolOp5Kjh49ilq1aiFfvnwYOnQo3r9/Dzc3N+TJkweHDx8WHU8l48aNQ1hYGE6fPq0oWAFAgwYN4O3tLdlCj7+/P4yNjVGzZk0AwJIlS7Bq1So4OztjyZIlkigKz58/P0vnyWQyyRZ67ty5g3LlygEA/v33X9SuXRubN29GQEAAOnTooNH/R1PuwEIPEeU6R48exZEjR/Dbb78pHXdwcMCDBw8Epfoxly9fxt9//426detixIgRiIyMxOHDhzFv3jz07t1bdLxsGT58OIDPv4BNnDgRhoaGivtSU1Nx6dIlxS83UrRs2TJMmjQJQ4cOxfTp0xWdSWZmZliwYIFkCj1du3YVHSHHNGnSBGPHjsX169dRsWLFDJ1yzZs3F5Tsx+zYsQN//fUXAGD//v2Ijo5GREQENm7ciPHjxyMgIEBwwqwpV64cZDLZN4vB6Umx82/GjBmYP38+Bg4ciLx582LhwoWwtbVF3759UaRIEdHxVFK8eHH4+/vj999/h5aWFrZs2QI9PT0cPHgww78vqdizZw+2bduGqlWrKv09LFWqFKKiogQm+zGjRo3CrFmzAADXr1/HiBEjMHz4cJw6dQrDhw/H2rVrBSf8b9HR0aIj5Di5XI60tDQAwPHjx/HHH38AACwtLfHy5UuR0egXwUIPEeU6CQkJSsWDL+Li4qCnpycg0Y/T0dHB//73PxgaGmLq1KnIkycPzpw5o+jukZKQkBAAn3+JuX79OnR1dRX36erqomzZshg5cqSoeD9s8eLFWLVqFVq2bImZM2cqjru6ukr6ur74+PFjhqUmJiYmgtKoZsCAAQCAefPmZbhPJpNJsngAAC9fvoSFhQUA4NChQ2jbti1KlCiBHj16YOHChYLTZV36F3EhISEYOXIkRo0apfh5FxgYiLlz52L27NmiIv6QqKgouLu7A/j8My8hIQEymQzDhg1DvXr1lJY/SUmZMmVw4MABNGzYEFWqVMGBAwdgYGAgOpbKXrx4kWFpMQDF90uqoqOj4ezsDADYuXMn/vjjD8yYMQPBwcGSn6ekSVxdXTFt2jQ0aNAAZ86cwbJlywB8/v4VLlxYcDr6FbDQQ0S5Tq1atbBhwwZMnToVwOcXbmlpaZg9ezZ+//13welUk5ycjLFjx2LJkiUYN24czp8/j9atW2P16tWS+8Xs1KlTAIDu3btj4cKFkisS/Jfo6GiUL18+w3E9PT0kJCQISPTjEhISMGbMGGzfvh2vXr3KcL/UCiNf3iXVNIULF8atW7dQpEgR+Pv7K14YJCYmQltbW3C6rLO2tlb8uW3btli0aJHSz7kyZcrA0tISEydORMuWLQUk/DH58uXD+/fvAQDFihXDjRs34OLigjdv3iAxMVFwuqwrX758pgUPPT09PHnyBDVq1FAcCw4O/pnR1MLV1RUHDx7E4MGDAUBxrX5+fpJ8k+ULXV1dxd+z48ePo0uXLgCA/Pnz4927dyKjqezRo0fYt29fpjOvMivoS8GCBQvg4eGBPXv2YPz48bC3twfwuXOzevXqgtPRr4CFHiLKdWbPno369evjypUr+PTpE0aPHo2bN28iLi5OMksXvubq6orExEScPn0aVatWhVwux+zZs9G6dWv06NEDS5cuFR0x275uD3/37h1OnjwJR0dHODo6Ckr142xtbREaGqr0YhX4PBch/cwoKRk9ejROnTqFZcuWoXPnzliyZAkeP36MFStWKHUtkVjdu3dHu3btUKRIEchkMjRo0AAAcOnSJcn+m7p+/TpsbW0zHLe1tcWtW7cEJPpxtWvXxrFjx+Di4oK2bdtiyJAhOHnyJI4dO4b69euLjpdlUiyyZceMGTPQpEkT3Lp1CykpKVi4cCFu3bqFCxcu4MyZM6LjqaxmzZoYPnw4atSogcuXL2Pbtm0APs+E+XrJuxScOHECzZs3h52dHSIiIlC6dGncv38fcrkcFSpUEB1PZWXKlFHadeuL//3vf5Iq3JN0cdctIsqV3r59C19fX4SFhSE+Ph4VKlTAwIEDJTv/oGfPnli0aFGGWQchISHo3Lkzbty4ISiZ6tq1a4fatWtj0KBB+PDhA8qWLav45Wzr1q1o06aN6Igq8fPzg7e3N+bOnYuePXvCz88PUVFR8PHxgZ+fHzp06CA6YrZZWVlhw4YNqFu3LkxMTBAcHAx7e3ts3LgRW7ZswaFDh0RHzLaEhAScOXMm03eApTq8E/j8bu/Dhw/Rtm1bxYu29evXw8zMTDLzodKrUKECSpcuDT8/P8Uyz0+fPqFXr164ceOGJDtF4uLi8PHjRxQtWlTRbXrhwgU4ODhgwoQJkhiG+6uIiorCzJkzlX6XGDNmDFxcXERHU1lMTAwGDBiAhw8fwtPTEz179gTweSOL1NTUbO1olRtUrlwZTZo0weTJk5E3b16EhYXB3NwcHh4eaNy4Mfr37y86okoePnwImUym+Dl++fJlbN68Gc7OzujTp4/gdPQrYKGHiEiwpKQkSc4esrCwwJEjR1C2bFls3rwZXl5eCAsLw/r167Fy5UrFLB8p2rRpE7y9vRUDO4sWLYrJkycrfqGWGmNjY9y6dQtWVlb47bffsGvXLlSuXBnR0dFwcXFBfHy86IjZEhISgqZNmyIxMREJCQnInz8/Xr58CUNDQ5ibm+PevXuiI6pkw4YNaN++fYafB58+fVLs9CQ1ly9fRrNmzSCXyxU7bF27dg0ymQz79+9H5cqVBSckIpHy5s2L0NBQFC9eHPny5cP58+dRqlQphIWFoUWLFrh//77oiCqpVasW+vTpg86dO+Pp06coWbIkSpUqhbt372Lw4MGYNGmS6Iik4bh0i4hynW9tzSuTyaCvrw8rKytJFkY0Ycvu9N6+fYv8+fMD+LysqU2bNjA0NIS7uztGjRolOJ1qUlJSsHnzZri5ucHDwwOJiYmIj4/PdKCnlNjZ2SE6OhpWVlZwdHTE9u3bUblyZezfvx9mZmai42XbsGHD0KxZMyxfvhympqa4ePEidHR08Ndff2HIkCGi46mse/fuaNy4cYa/b+/fv0f37t0lWeipXLky7t27h02bNiEiIgIA0L59e3Tq1EmyuzkBn+dERUZG4vnz5xlmRtWuXVtQKtWlpqZi/vz52L59e6ZdcnFxcYKS/ZjU1FTs3r0b4eHhAABnZ2e0aNECefJI6yVQdmbvSG1unpGRkeLvW5EiRRAVFYVSpUoBgKR3p7px44aikL19+3aULl0aAQEBOHr0KPr168dCD+U4af2UI6JfwpeteYHPOzsBUBoYqaOjg/bt22PFihXQ19cXkjG7NGXL7vQsLS0RGBiI/Pnzw9/fH1u3bgUAvH79WjLfl6/lyZMH/fr1U7woMDQ0zHQHOKnp3r07wsLCUKdOHYwdOxbNmjWDr68vkpOTJTnoMjQ0FCtWrICWlha0tbWRlJQEOzs7zJ49G127dkXr1q1FR1TJt7Yjf/ToEUxNTQUkUg8jIyONWqpw8eJFdOrUCQ8ePMDXjfFS3fVt8uTJ8PPzw4gRIzBhwgSMHz8e9+/fx549eyT7gvTmzZto3ry5opsCAGbNmoVChQph//79KF26tOCEWWdmZvafO4V9+fkhtb9/VatWxfnz5+Hk5ISmTZtixIgRuH79Onbt2oWqVauKjqey5ORkxZuSx48fR/PmzQEAjo6OiI2NFRmNfhEs9BBRrrN7926MGTMGo0aNUrwbcvnyZcydOxdeXl5ISUnB2LFjMWHCBMyZM0dw2qzRxC27hw4dCg8PDxgbG8PKygp169YFAJw9e1bS8w8qV66MkJCQDMOYpWzYsGGKPzdo0AARERG4evUq7O3tFctppERHRwdaWloAAHNzc8TExMDJyQmmpqZ4+PCh4HTZ92X3I5lMhvr16yt1G6SmpiI6OhqNGzcWmPDHbNy4EStWrMC9e/cU3Yzz58+HnZ2dJIvc/fr1U+zo9GVwttRt2rQJq1atgru7O7y9vdGxY0cUL14cZcqUwcWLFyU596pXr14oVaoUrly5opib9Pr1a3Tr1g19+vTBhQsXBCfMui+7XWqiefPmKZYPT548GfHx8di2bRscHBwk+UbEF6VKlcLy5cvh7u6OY8eOKXaSffLkCQoUKCA4Hf0KWOgholxn+vTpWLhwIdzc3BTHXFxc8Ntvv2HixIm4fPkyjIyMMGLECMkUejRxy+4BAwagcuXKePjwIRo2bKh44W1nZ4dp06YJTqe6AQMGYMSIEXj06BEqVqyYYXmJ1AojycnJaNy4MZYvXw4HBwcAn7e/lnIhq3z58ggKCoKDgwPq1KmDSZMm4eXLl9i4caOk3qX/4svuR6GhoXBzc4OxsbHiPl1dXdjY2Eh2uHn6bsZp06Ypug3y5csn2W7Gu3fvYseOHYrtkjXB06dPFQV6Y2NjvH37FgDwxx9/YOLEiSKjqSw0NFSpyAN8/ns3ffp0VKpUSWCy7KtTp47oCDnGzs5O8WcjIyMsX75cYBr1mTVrFlq1aoX//e9/6Nq1K8qWLQsA2LdvH2eT0U/BQg8R5TrXr1/P9EWotbW1YqvKcuXKSar1VRO37AY+dySVKVMG0dHRKF68OPLkyQN3d3fRsX7Il1210r+DLZPJJNsWr6Oj8825V1I1Y8YMvH//HsDnwnCXLl3Qv39/ODg4YM2aNYLTZZ+XlxcAwMbGBu3bt5fs0sfMaGI3Y5UqVRAZGalRhZ7ffvsNsbGxsLKyQvHixXH06FFUqFABQUFBkpyJBwAlSpTAs2fPFPNevnj+/Lnkv3fnzp1TdMn9+++/KFasGDZu3AhbW1vUrFlTdDyVfPr0KdOZV1ZWVoIS/Zi6devi5cuXePfunVKxsU+fPhqxJJxyPxZ6iCjXcXR0xMyZM7Fy5UrFdrzJycmYOXMmHB0dAQCPHz9G4cKFRcbMluHDh2PgwIH4+PEj5HI5Ll++jC1btii27JaixMREDB48GOvXrwcA3LlzB3Z2dhg8eDCKFSuGsWPHCk6omujoaNER1O6vv/7C6tWrlV5oS5mrq6viz+bm5vD39xeYRn26du0qOoLaaWI34+DBgzFixAhFF4yOjo7S/VLr+gOAVq1a4cSJE6hSpQoGDx6s+JkRExOjtPRTSnx8fODp6Qlvb2/FrJeLFy9iypQpmDVrltKAYykNMN65cyc6d+4MDw8PBAcHIykpCcDnDRJmzJiBQ4cOCU6YPXfu3EHPnj0zLKWT6psr6WlraysVeYDPBX2in4HbqxNRrnPhwgU0b94cWlpail+Yr1+/jtTUVBw4cABVq1bFxo0b8fTpU0nt7vT1lt3FihWDt7e3ZLfsHjJkCAICArBgwQI0btwY165dg52dHfbu3Qtvb29Jb6+uaQYPHowNGzbAwcEh0+VoUp6DIHX58uXL8owXKe585OzsDB8fH7Ro0QJ58+ZFWFgY7OzssHjxYqxduxbBwcGiI2bbl2Wq6Um56y8zgYGBCAwMhIODA5o1ayY6jkrSf5++tcGDFL9n5cuXx7Bhw9ClSxelf1MhISFo0qQJnj59KjpittSoUQN58uTB2LFjM5159WXJkxTt2LHjmzvZSfFnH0kLO3qIKNepXr06oqOjsWnTJty5cwcA0LZtW3Tq1Al58+YFAHTu3FlkxGz78OEDWrVqpdiy+8aNGwgICMBvv/0mOprK9uzZg23btqFq1apKv5iVKlVKUcySog0bNnz3filucX3jxg1UqFABABT/pr6Q4iDZL8OLvyaTyaCvrw97e3t069YNv//+u4B02bNgwQLREXKUJnYzamLX39eqVauGatWqiY7xQzR1gPHt27dRu3btDMdNTU3x5s2bnx/oB4WGhuLq1auKjm1NsWjRIowfPx7dunXD3r170b17d0RFRSEoKAgDBw4UHY9+ASz0EFGulDdvXtSuXRs2NjaKd0G+/NL2ZYtKKWnRogVat26Nfv364dOnT2jevDl0dHTw8uVLzJs3D/379xcdMdtevHgBc3PzDMcTEhIkWTz4YsiQIUofJycnIzExEbq6ujA0NJRkoUfTXvA0btwYy5Ytg4uLi2KoZVBQEK5du4Zu3brh1q1baNCgAXbt2pXrh/1q4nKt9Hr16gUDAwNMmDABiYmJ6NSpE4oWLYqFCxcq5mFJjZQHmX/Pxo0bsXz5ckRHRyt2R1uwYAFsbW1z/b+jzGjqAGMLCwtERkZmWAJ0/vx5pcHGUuHs7IyXL1+KjqF2S5cuxcqVK9GxY0esW7cOo0ePhp2dHSZNmiTJ7kySHhZ6iCjXuXfvHlq1aoXr168rtcN/IaUW6y+Cg4Mxf/58AJ9beQsXLoyQkBDs3LkTkyZNkmSh58v2woMHDwbwf50hfn5+kn4n+PXr1xmO3b17F/3795fUUsFvefToEQBIupvs5cuXGDFiRIbdgKZNm4YHDx7g6NGj8PLywtSpUyX1AjUmJua790t1KKmHh4eimzE+Pj7TAnFut2/fPjRp0gQ6OjrYt2/fd8+V4psR6XdHmz59uuL/WTMzM8nujgYAb968werVqxEeHg7gc8dpjx49YGpqKjiZ6nr37o0hQ4ZgzZo1kMlkePLkCQIDAzFy5EjJ7JCWfj7SrFmzMHr0aMyYMSPTmVdSmp+UXkxMDKpXrw4AMDAwUGwg0LlzZ1StWhW+vr4i49GvQE5ElMv88ccf8hYtWshfvHghNzY2lt+8eVN+7tw5eeXKleVnz54VHU8lBgYG8gcPHsjlcrm8bdu2cm9vb7lcLpfHxMTIDQwMREZT2blz5+TGxsbyfv36yfX19eVDhgyRN2zYUG5kZCS/cuWK6HhqFxQUJC9ZsqToGCpJTU2VT548WW5iYiLX0tKSa2lpyU1NTeVTpkyRp6amio6XbSYmJvK7d+9mOH737l25iYmJXC6Xy8PDw+XGxsY/O9oPkclkiu9PZjcpmjp1qvzevXuiY/wwmUwmf/bsmeLP37pJ9fvk5OQk3717t1wul8uNjY3lUVFRcrlcLr9+/bq8QIECApOpLigoSJ4/f355sWLF5K1atZK3atVK/ttvv8kLFCggv3r1quh4KktLS5NPmzZNbmRkpPh7p6+vL58wYYLoaFn29c+6zH72Sfnfk1wul9va2sqDg4PlcrlcXrFiRfny5cvlcrlcfuTIEXm+fPlERqNfBDt6iCjXCQwMxMmTJ1GwYEFoaWlBW1sbNWvWVOygIcUhv/b29tizZw9atWqFI0eOKHYxef78uWTfrapZsyZCQ0Mxc+ZMuLi4KLbjDQwMhIuLi+h4apcnTx48efJEdAyVjB8/XrHrVo0aNQB8bvP39vbGx48fMX36dMEJs0dfXx8XLlzIsEXyhQsXFFuTp6WlSW6b8q9/tiUnJyMkJATz5s2T3Pfoi3///RdeXl6oUqUK/vrrL7Rr1w4FCxYUHSvb0m/5/PX2z5pAE3dHGzZsGJo3b45Vq1YhT57PL3lSUlLQq1cvDB06FGfPnhWcUDUymQzjx4/HqFGjEBkZifj4eDg7O8PY2Fh0tCxLv5z4/v37sLS0hLa2ttI5aWlp/9nlmJvVq1cP+/btQ/ny5dG9e3cMGzYMO3bswJUrV9C6dWvR8egXwF23iCjXyZcvH4KDg2Fra4vixYvDz88Pv//+O6KiouDi4oLExETREbNtx44d6NSpE1JTU1G/fn0cPXoUwOftX8+ePYvDhw8LTkhffL0sQy6XIzY2Fr6+vrC0tJTk96po0aJYvnx5hiUle/fuxYABA/D48WNByVQzbdo0zJgxA71790alSpUAfJ7R4+fnh7///hvjx4/H/PnzcejQIRw7dkxw2h938OBB/O9//8Pp06dFR1HJzZs3sWnTJmzduhWPHj1Cw4YN4eHhgZYtW8LQ0FB0PIJm7o5mYGCAkJCQDEN+b926BVdXV0n+LpGZd+/e4eTJkyhZsiScnJxEx8k2bW1txMbGZljS+erVK5ibm0tyuT7wuVCVlpamKDJu3boVFy5cgIODA/r27QtdXV3BCUnTsdBDRLlOrVq1MGLECLRs2RKdOnXC69evMWHCBKxcuRJXr17FjRs3REdUydOnTxEbG4uyZcsqtn29fPkyTExMJLvbRFpaGiIjI/H8+fMM73JntiuIFHy9dbJMJkOhQoVQr149zJ07F0WKFBGUTHX6+vq4du0aSpQooXT89u3bKFeuHD58+CAomeo2bdoEX19f3L59GwBQsmRJDB48GJ06dQLweae7L7twSV1kZCTKli0r2c6K9AICArB582b8+++/+Pjxo9KsDqlYtGhRpsfT7/pWu3btDB0KuZmfnx+8vb0xd+5c9OzZE35+foiKilLsjibFwdmFCxfGxo0b0ahRI6XjR44cQZcuXfDs2TNByX5Mu3btULt2bQwaNAgfPnxAuXLlEB0dDblcjq1bt6JNmzaiI2aLlpYWnj17hkKFCikdf/DgAZydnTXi5x6RCFy6RUS5zoQJExT/sU+ZMgV//PEHatWqhQIFCmDbtm2C06nOwsICFhYWSse+7BgkRRcvXkSnTp3w4MEDfP2egUwmk/S7cJqmbNmy8PX1zfAC1dfXF2XLlhWU6sd8GfD7LQYGBj8xjXp8XfT40k3m7e0NBwcHQanUy8jICAYGBtDV1VUMJ5Wa+fPn48WLF0hMTES+fPkAfB7ibmhoCGNjYzx//hx2dnY4deoULC0tBafNGk3cHa19+/bo2bMn5syZoxiKGxAQgFGjRqFjx46C06nu7NmzGD9+PABg9+7dSEtLw5s3b7B+/XpMmzZNMoWe4cOHA/j8+8LEiROVuvtSU1Nx6dIllCtXTlA61Vy7di3L55YpUyYHkxCxo4eIJCIuLg758uWT9LbdmqZcuXIoUaIEJk+ejCJFimT43kh5V5P0UlNTcf36dVhbWyte1EnNmTNn4O7uDisrK8WOaIGBgXj48CEOHTqEWrVqCU5IwOd3tr/+dySXy2FpaYmtW7dKdje76OhobN68GZs3b8bt27dRp04ddOrUCX/++ackf05s2bIFK1euhJ+fH4oXLw7gc9dV37590adPH9SoUQMdOnSAhYUFduzYITht9kl5d7T0Pn36hFGjRmH58uVISUkBAOjo6KB///6YOXMm9PT0BCdUjYGBAe7cuQNLS0t06dIFRYsWxcyZMxETEwNnZ2fEx8eLjpglv//+O4DP/z9Vq1ZNaSmTrq4ubGxsMHLkSEkVub/8DP+vl9dSfjOMpIOFHiIiUomRkRHCwsIyDMSVuqFDh8LFxQU9e/ZEamoqateujcDAQBgaGuLAgQOoW7eu6IgqefLkCZYsWYKIiAgAgJOTEwYMGICiRYsKTpZ9mRVE0pPqL9BnzpxR+lhLSwuFChWCvb29Ys6D1FStWhVBQUEoU6YMPDw80LFjRxQrVkx0rB9SvHhx7Ny5M0O3QUhICNq0aYN79+7hwoULaNOmDWJjY8WEVNHz588VyyEdHR0zLKeRosTERERFRQH4/L2T+lyoEiVKYNq0aXB3d4etrS22bt2KevXqISwsDPXr18fLly9FR8yW7t27Y+HChZLdmCK9Bw8eZPlca2vrHExCxKVbRESkoipVqiAyMlLjCj07duzAX3/9BQDYv38/7t+/j4iICGzcuBHjx49HQECA4ITZFxMTA0tLy0x3boqJiYGVlZWAVKrbvXu30sdfdqdav349Jk+eLCjVj6tTp47oCGpXv359rFmzBs7OzqKjqE1sbKyiQyS9lJQUPH36FMDnAehSWpr2/v17DBgwAFu2bFEsX9XW1kb79u2xZMkSSXZefWFoaKhRO0EOHToUHh4eMDY2hrW1teLNh7Nnz0ryOteuXSs6gtqkL974+PigcOHC6NGjh9I5a9aswYsXLzBmzJifHY9+MezoISKiLEu//jwqKgoTJkzAqFGj4OLiAh0dHaVzpbr+XF9fH5GRkfjtt9/Qp08fGBoaYsGCBYiOjkbZsmUlOTxWU3c1+drmzZuxbds27N27V3QUlaxfvx4FCxaEu7s7AGD06NFYuXIlnJ2dsWXLFkm/A/zp0ydER0ejePHiku1O+sLd3R1Pnz6Fn5+fYkvykJAQ9O7dGxYWFjhw4AD279+Pv//+G9evXxecNmvat2+PkJAQLF68WGl555AhQ1CuXDls3bpVcMKsyc621bt27crBJDnrypUrePjwIRo2bKjYVv3gwYMwMzNDjRo1BKcjALCxscHmzZsV86G+uHTpEjp06IDo6GhByehXwUIPERFl2X+tP/9yn5TXn1tbW2PVqlWoX78+bG1tsWzZMri7u+PmzZuoWbMmXr9+LTpitv0qu5rcu3cPZcqUkcyMiq+VLFkSy5YtQ7169RAYGIj69etjwYIFOHDgAPLkySPJF6YfPnzAoEGDsH79egDAnTt3YGdnh8GDB6NYsWIYO3as4ITZ9/TpU3Tu3BknTpxQFLhTUlJQv359bNy4EYULF8apU6eQnJycYcen3MrIyAhHjhxBzZo1lY6fO3cOjRs3lszPiO7du2f5XE3pJNGEOXKaSF9fH+Hh4bC1tVU6fu/ePTg7O+Pjx4+CktGvQtpvqRAR0U/1K7wD1b17d7Rr104xYLpBgwYAPr8L5+joKDhd9mjiribf8uHDByxatEjS818ePnyoWAq5Z88e/Pnnn4rhvlKdDTV27FiEhYXh9OnTaNy4seJ4gwYN4O3tLclCj4WFBY4dO4aIiAjcuXMHwOciXcmSJRXnfBk0KxUFChTIdHmWqamppIoH6Ys3Hz58QFpaGoyMjAAA9+/fx549e+Dk5AQ3NzdREX/Y13Pk6tSpgwsXLkh+jpymsbS0REBAQIZCT0BAgCRn45H0sNBDRERZ9iusP/f29kbp0qXx8OFDtG3bVrEzi7a2tuRelIaEhAD4vHPT9evXM+xqUrZsWYwcOVJUPJV9vQOfXC7H+/fvYWhoiH/++Udgsh9jbGyMV69ewcrKCkePHlUU6vT19fHhwwfB6VSzZ88ebNu2DVWrVlX6npUqVUoxIFeqHB0dJVf8/ZYJEyZg+PDh2LhxIywsLAB87lwaNWoUJk6cKDidalq0aIHWrVujX79+ePPmDapWrQodHR28fPkS8+bNQ//+/UVHVMnXc+Sio6MlP0dOE/Xu3RtDhw5FcnIy6tWrBwA4ceIERo8ejREjRghOR78CLt0iIiKV/Errz9+8eQMzMzPRMVSmSbuaAMC6deuUigZfdqeqUqWKpLoPvubh4YGIiAiUL18eW7ZsQUxMDAoUKIB9+/bh77//xo0bN0RHzDZDQ0PcuHEDdnZ2yJs3L8LCwmBnZ4ewsDDUrl0bb9++FR0x21JTU7Fu3TqcOHECz58/Vwwv/uLkyZOCkqmufPnyiIyMRFJSkmI4e0xMDPT09DJsbx0cHCwiYrYVLFgQZ86cQalSpeDn54fFixcjJCQEO3fuxKRJkxAeHi46oko0cY6cJpLL5Rg7diwWLVqET58+Afj8vRszZgwmTZokOB39CtjRQ0REKnn69CmKFCmS4XihQoUkt6VwerNmzYKNjQ3at28PAGjXrh127tyJIkWK4NChQ5IcMv31LIp3797h5MmTku1I6Natm+gIOWLJkiWYMGECHj58iJ07d6JAgQIAgKtXr6Jjx46C06nG1dUVBw8exODBgwFAUaDz8/NTDP2VmiFDhmDdunVwd3dH6dKllYqOUtWyZUvREdQuMTERefPmBQAcPXoUrVu3hpaWFqpWrZqtbbBzm8KFC+PWrVsoUqQI/P39sWzZMgCfr1dbW1twOvpCJpNh1qxZmDhxIsLDw2FgYAAHBwdFlzBRTmOhh4iIVKKp68+XL1+OTZs2AQCOHTuGY8eO4fDhw9i+fTtGjhyJo0ePCk6Yfe3atUPt2rUxaNAgfPjwAa6urrh//z7kcjm2bt2KNm3aiI6YLf7+/jA2NlYMjl2yZAlWrVoFZ2dnLFmyRLJdPWZmZvD19c1wXMpbxs+YMQNNmjTBrVu3kJKSgoULF+LWrVu4cOECzpw5IzqeSrZu3Yrt27ejadOmoqOojZeXl+gIamdvb489e/agVatWOHLkCIYNGwYAeP78uaS7GzVpjtyvwNjYGJUqVRIdg35BWqIDEBGRNH1Zf7527Vo8ePAADx48wJo1azBs2DD07t1bdDyVPX36FJaWlgCAAwcOoF27dmjUqBFGjx6NoKAgwelUc/bsWdSqVQsAsHv3bsjlcrx58waLFi3CtGnTBKfLvlGjRimWJ1y/fh3Dhw9H06ZNER0drZhrI1Xnzp3DX3/9herVq+Px48cAgI0bN+L8+fOCk6mmZs2aCA0NRUpKClxcXHD06FGYm5sjMDAQFStWFB1PJbq6uoqh2ZR7TZo0CSNHjoSNjQ2qVKmi6CA7evQoypcvLzid6ry9veHn54c+ffogICBA0nPkiCjncEYPERGpRFPXnxctWhQ7duxA9erVUbJkSUybNg1t27bF7du3UalSJUnOPzAwMMCdO3dgaWmJLl26oGjRopg5cyZiYmLg7Owsue3IjY2NcePGDdjY2MDb2xs3btzAjh07EBwcjKZNm+Lp06eiI6pk586d6Ny5Mzw8PLBx40bcunULdnZ28PX1xaFDh3Do0CHREQnA3Llzce/ePfj6+kp62dbXQ82/Jy4uLofT5IynT58iNjYWZcuWhZbW5/e3L1++DBMTE43ofvn48SP09fVFxyCiXIhLt4iISCWauv68devW6NSpExwcHPDq1Ss0adIEwOcdrKT6Lr6lpSUCAwORP39++Pv7Y+vWrQCA169fS/JFgq6uLhITEwEAx48fR5cuXQAA+fPnl2Qh7otp06Zh+fLl6NKli+J7BAA1atSQVOfVu3fvFEtj/uv7IcUlNOfPn8epU6dw+PBhlCpVCjo6Okr379q1S1Cy7FmwYIHiz69evcK0adPg5uam6HwJDAzEkSNHJLvrFgBYWFgodhH7onLlyoLSqEdqaipmzJiB5cuX49mzZ7hz5w7s7OwwceJE2NjYoGfPnqIjElEuwEIPERH9EE1bfz5//nzY2Njg4cOHmD17NoyNjQEAsbGxGDBggOB0qhk6dCg8PDxgbGwMKysr1K1bF8DnJV0uLi5iw6mgZs2aGD58OGrUqIHLly9j27ZtAIA7d+7gt99+E5xOdbdv30bt2rUzHDc1NcWbN29+fiAV5cuXD7GxsTA3N4eZmVmmXSNyuRwymQypqakCEv4YMzMztGrVSnSMH9a1a1fFn9u0aYMpU6Zg0KBBimOenp7w9fXF8ePHFfNtSLzp06dj/fr1mD17ttIy6dKlS2PBggUs9BARAC7dIiIi+iVcuXIFDx8+RMOGDRXFq4MHD8LMzAw1atQQnC57YmJiMGDAADx8+BCenp6KFzbDhg1DamoqFi1aJDihauzs7LBy5Uo0aNBAaSvyDRs2YObMmbh165boiFly5swZ1KhRA3ny5PnPgct16tT5Sanoe4yNjREaGpqhazEyMhLlypWT3PJOTWZvb48VK1agfv36Sj8nIiIiUK1aNbx+/Vp0RCLKBdjRQ0RE9JWNGzdixYoVuHfvHgIDA2FtbY0FCxbA1tYWLVq0EB1PJa6urihTpgyio6NRvHhx5MmTB+7u7qJjqcTKygoHDhzIcHz+/PlKH8+cORP9+vWDmZnZT0r2Y3r37o0hQ4ZgzZo1kMlkePLkCQIDAzFy5EhJLZ9JX7xhIUcaChQogL1792LEiBFKx/fu3YsCBQoISkWZefz4cabLiNPS0pCcnCwgERHlRiz0EBERpbNs2TJMmjQJQ4cOxfTp0xVLS8zMzLBgwQJJFnoSExMxePBgrF+/HgAUMx0GDx6MYsWKaexOLTNmzEC7du0kU+gZO3Ys0tLSUL9+fSQmJqJ27drQ09PDyJEjMXjwYNHxVPbmzRtcvnwZz58/R1pamtJ9X+YrSc2OHTuwfft2xMTEKIbRfxEcHCwoleomT56MXr164fTp06hSpQqAz9t1+/v7Y9WqVYLTUXrOzs44d+4crK2tlY7v2LFD0ruJEZF6sdBDRESUzuLFi7Fq1Sq0bNkSM2fOVBx3dXXFyJEjBSZT3bhx4xAWFobTp0+jcePGiuMNGjSAt7e3xhZ6pLQ6PTU1FQEBARg4cCBGjRqFyMhIxMfHw9nZWbHUTor2798PDw8PxMfHw8TERGlej0wmk2ShZ9GiRRg/fjy6deuGvXv3onv37oiKikJQUBAGDhwoOp5KunXrBicnJyxatEgxTNrJyQnnz59XFH4od5g0aRK6du2Kx48fIy0tDbt27cLt27exYcOGTDsdiejXxBk9RERE6RgYGCAiIgLW1tZK8w/u3r2LMmXK4MOHD6IjZpu1tTW2bduGqlWrKl1TZGQkKlSoIOmdqr4n/bVKgb6+PsLDw2Frays6itqUKFECTZs2xYwZM2BoaCg6jlo4OjrCy8sLHTt2VPo7NmnSJMTFxcHX11d0RNJw586dw5QpUxAWFob4+HhUqFABkyZNQqNGjURHI6Jcgh09RERE6dja2iI0NDRDW7y/vz+cnJwEpfoxL168gLm5eYbjCQkJme6IRGKULl0a9+7d06hCz+PHj+Hp6akxRR7g8zDw6tWrA/hcGH7//j0AoHPnzqhatapkCj3ZKfCamJjkYBLKrlq1auHYsWOiYxBRLqYlOgAREVFuMnz4cAwcOBDbtm2DXC7H5cuXMX36dIwbNw6jR48WHU8lrq6uOHjwoOLjL8UdPz8/VKtWTVQs+sq0adMwcuRIHDhwALGxsXj37p3STYrc3Nxw5coV0THUysLCAnFxcQA+Dwa/ePEiACA6OlpSywXNzMyQL1++796+nEO5x5dZSkRE38OOHiIionR69eoFAwMDTJgwAYmJiejUqROKFi2KhQsXokOHDqLjqWTGjBlo0qQJbt26hZSUFCxcuBC3bt3ChQsX/nP7a/p5mjZtCgBo3ry5UqeVXC6HTCZTDAbP7fbt26f4s7u7O0aNGoVbt27BxcUFOjo6Suc2b978Z8f7YfXq1cO+fftQvnx5dO/eHcOGDcOOHTtw5coVtG7dWnS8LDt16pToCKSCFy9eoHHjxihUqBA6dOgADw8PlCtXTnQsIsplOKOHiIjo/0tJScHmzZvh5uaGwoULIzExEfHx8Zkue5KaqKgozJw5U2mmw5gxY+Di4iI6Wo5p2rQpVq9ejSJFioiOkiX/VXSTylblWlpZaxiXUvEqvbS0NKSlpSFPns/vl27duhUXLlyAg4MD+vbtC11dXcEJVfPmzRusXr0a4eHhAD7v7tSzZ0+YmpoKTkZfe/36Nf79919s3rwZ586dg6OjIzw8PNCpUyfY2NiIjkdEuQALPUREROkYGhoiPDw8w4weEovzRP7PgAEDMGXKFBQsWFB0FPoOKX2frly5gsaNG0NfXx+VK1cGAAQFBeHDhw84evQoKlSoIDghfcujR4+wZcsWrFmzBnfv3kVKSoroSESUC7DQQ0RElE7dunUxdOhQtGzZUnSUH6JphREtLa0sD46WYpdIdpiYmCA0NFQyu4n9qqT0fapVqxbs7e2xatUqRadSSkoKevXqhXv37uHs2bOCE1JmkpOTcfDgQfzzzz84ePAg8ufPj8ePH4uORUS5AGf0EBERpTNgwACMGDECjx49QsWKFWFkZKR0f5kyZQQlyx4zM7P/LIxIafZL+nki9+/fx9ixY9GtWzfFMOnAwECsX78ePj4+oiL+NFJ6j87T0xP29vbw9PRUOu7r64vIyEgsWLBATLCfQErfpytXrigVeQAgT548GD16NFxdXQUmo8ycOnUKmzdvxs6dO5GWlobWrVvjwIEDqFevnuhoRJRLsKOHiIgonczmi8hkMkkVRYD/nveSnlRmv3xRv3599OrVCx07dlQ6vnnzZqxcuVLjd6TJmzcvwsLCJNEpUqxYMezbtw8VK1ZUOh4cHIzmzZvj0aNHgpLlPCl9nwoXLoyNGzeiUaNGSsePHDmCLl264NmzZ4KS0deKFSuGuLg4NG7cGB4eHmjWrBn09PRExyKiXIYdPUREROlER0eLjqAWUiveZEdgYCCWL1+e4birqyt69eolIBF9y6tXrzId5mtiYoKXL18KSESZad++PXr27Ik5c+agevXqAICAgACMGjUqQ0GVxPL29kbbtm1hZmb23fMePXqEokWLZnk4OhFpFhZ6iIiI0tHUIcxf76hTqlQp9OjRQ5I76lhaWmLVqlWYPXu20nE/Pz9YWloKSkWZsbe3h7+/PwYNGqR0/PDhw5LodPlVzJkzBzKZDF26dFEM89XR0UH//v0xc+ZMwekovd69e2fpPGdnZ8nMiCIi9WOhh4iI6Cu3b9/G4sWLFUURJycnDB48GCVLlhScTDVXrlyBm5sbDAwMFDvqzJs3D9OnT5fkjjrz589HmzZtcPjwYVSpUgUAcPnyZdy9exc7d+4UnI7SGz58OAYNGoQXL14o5oecOHECc+fO1ej5PFKjq6uLhQsXwsfHB1FRUQCA4sWLw9DQUHAyUhWncxD92jijh4iIKJ2dO3eiQ4cOcHV1VQz6vXjxIoKCgrB161a0adNGcMLs08QddR49eoSlS5ciIiICwOdiXL9+/X6Jjp7+/ftj6tSpkti2GwCWLVuG6dOn48mTJwAAGxsbeHt7o0uXLoKT5SypfZ9Is0hpRhQRqR8LPUREROkUL14cHh4emDJlitJxLy8v/PPPP4p3u6XEwMAAISEhcHR0VDp+69YtuLq6IjExUVAySm/t2rUwNjZG27ZtlY7/+++/SExMRNeuXQUlU48XL17AwMAAxsbGoqNk27Vr17J8rlR25iPNxkIP0a+NS7eIiIjSiY2NzbTT4K+//sL//vc/AYl+nImJCWJiYjIUeh4+fIi8efMKSvVjNGnm0Bc+Pj5YsWJFhuPm5ubo06eP5As9hQoVEh1BZeXKlVPafe97pLIzHxERaS6OYSciIkqnbt26OHfuXIbj58+fR61atQQk+nFfdtTZtm0bHj58iIcPH2Lr1q2ZblEuBVeuXEHx4sUxf/58xMXFIS4uDvPmzUPx4sURHBwsOp7KYmJiYGtrm+G4tbU1YmJiBCRSjx07dqBdu3aoWrUqKlSooHSTiujoaNy7dw/R0dHYuXMnbG1tsXTpUoSEhCAkJARLly5F8eLFOSOKco3/KkgSkWZjRw8REVE6zZs3x5gxY3D16lVUrVoVwOcZPf/++y8mT56Mffv2KZ0rBZq2o86wYcPQvHnzTGcODR06VJIzh4DPnTvXrl2DjY2N0vGwsDAUKFBATKgftGjRIowfPx7dunXD3r170b17d0RFRSEoKAgDBw4UHS/L0u/G17ZtWyxatAhNmzZVHCtTpgwsLS0xceJEtGzZUkBCImWczkH0a+OMHiIionS0tLLW7CqTySS3RCMxMVEjdtTR1JlDY8aMwbZt27B27VrUrl0bAHDmzBn06NEDf/75J+bMmSM4YfY5OjrCy8sLHTt2VJoZMmnSJMTFxcHX11d0xGwzMDBAcHAwnJyclI6Hh4ejQoUK+PDhg6BkpOmSk5NhYGCA0NBQlC5d+rvnPnz4EEWLFoW2tvZPSkdEuQmXbhEREaWTlpaWpZvUijwAYGhoCBcXF7i4uEi2yAP838yhr0l55hAATJ06FVWqVEH9+vVhYGAAAwMDNGrUCPXq1cOMGTNEx1NJTEwMqlevDuBzgeT9+/cAgM6dO2PLli0io6nMyckJPj4++PTpk+LYp0+f4OPjk6H4Q6ROOjo6sLKyytL/P5aWlizyEP3CuHSLiIhIBS4uLjh06JAktvNOSEjAzJkzceLECTx//hxpaWlK99+7d09QMtV8mTk0Z84cRREhICAAo0aNkuTMoS90dXWxbds2TJ06FWFhYTAwMICLi4vSsiGpsbCwQFxcHKytrWFlZYWLFy+ibNmyiI6OluzSkuXLl6NZs2b47bffFDtsXbt2DTKZDPv37xecjjTd+PHj8ffff2Pjxo3Inz+/6DhElEux0ENERKSC+/fvIzk5WXSMLOnVqxfOnDmDzp07o0iRIpIf0qlpM4e+VqJECZQoUUJ0DLWoV68e9u3bh/Lly6N79+4YNmwYduzYgStXrqB169ai46mkcuXKuHfvHjZt2oSIiAgAn4uPnTp1gpGRkeB0pOl8fX0RGRmJokWLwtraOsPfOSkPpCci9eGMHiIiIhWknzeS25mZmeHgwYOoUaOG6ChqpQkzh4YPH46pU6fCyMgIw4cP/+658+bN+0mp1OfLUscvQ7O3bt2KCxcuwMHBAX379oWurq7ghETSMnny5O/e7+Xl9ZOSEFFuxo4eIiIiDZcvXz6NbPE3NDREvnz5FH+WopCQEEVnWEhIyDfPk2oXlpaWltKA8w4dOqBDhw4ZzhswYACmTJmCggUL/sx4Ktu4cSNWrFiBe/fuITAwENbW1pg/fz7s7OzQokUL0fFIg7GQQ0RZwY4eIiIiFUipo+eff/7B3r17sX79eskWRNJLS0vDtGnTMHfuXMTHxwP4/P0YMWIExo8fn+Wd0yj3MDExQWhoqCT+PS1btgyTJk3C0KFDMW3aNNy8eRN2dnZYt24d1q9fj1OnTomOSL+Aq1evIjw8HABQqlQplC9fXnAiIspN2NFDRESkgcqXL6/UBRIZGYnChQvDxsYGOjo6SudKbabD+PHjsXr1asycOVOxHO38+fPw9vbGx48fMX36dMEJKbuk9L7j4sWLsWrVKrRs2VJpJpSrqytGjhwpMBn9Cp4/f44OHTrg9OnTMDMzAwC8efMGv//+O7Zu3YpChQqJDUhEuQILPURERBqoZcuWoiPkmPXr18PPzw/NmzdXHCtTpgyKFSuGAQMGSLbQo2m7o2mq6OjoTLsn9PT0kJCQICAR/UoGDx6M9+/f4+bNm3BycgIA3Lp1C127doWnpye2bNkiOCER5QYs9BAREalgxYoVKFy4sOgY36TJcxzi4uLg6OiY4bijoyPi4uIEJFIPTdsdTVPZ2toiNDQ0w7b3/v7+ihfeRDnF398fx48fV/q75uzsjCVLlqBRo0YCkxFRbsJCDxER/fIWLVqU5XM9PT0BAJ06dcqpOGoXFBSEtLQ0VKlSRen4pUuXoK2tDVdXV0HJVFO2bFn4+vpm+L75+vqibNmyglL9uMOHD2vk7miaZvjw4Rg4cCA+fvwIuVyOy5cvY8uWLfDx8YGfn5/oeKTh0tLSMiy/BQAdHZ0MXYBE9OviMGYiIvrl2draKn384sULJCYmKs0/MDQ0hLm5uSSXz1SuXBmjR4/Gn3/+qXR8165dmDVrFi5duiQomWrOnDkDd3d3WFlZoVq1agCAwMBAPHz4EIcOHUKtWrUEJ1SNra0tDh069Et2hUhpuDkAbNq0Cd7e3oiKigIAFC1aFJMnT0bPnj0FJyNN16JFC7x58wZbtmxB0aJFAQCPHz+Gh4cH8uXLh927dwtOSES5AQs9RERE6WzevBlLly7F6tWrUbJkSQDA7du30bt3b/Tt2xceHh6CE2afsbExrl27luFFdHR0NMqUKYP3798LSqa6J0+eYMmSJYiIiAAAODk5YcCAAYoXPlKkabujZUf//v0xdepUyWyv/kViYiLi4+Nhbm4uOgr9Ih4+fIjmzZvj5s2bsLS0VBwrXbo09u3bh99++01wQiLKDVjoISIiSqd48eLYsWNHhmGrV69exZ9//ono6GhByVRXoEABHDhwQNH98sWFCxfg7u6O169fC0pGme2OJpfLNWJ3tC/OnTuHFStWICoqCjt27ECxYsWwceNG2NraombNmqLjZVt0dDRSUlLg4OCgdPzu3bvQ0dGBjY2NmGD0y5DL5Th+/LhSobtBgwaCUxFRbsIZPUREROnExsYiJSUlw/HU1FQ8e/ZMQKIf16hRI4wbNw579+6FqakpgM/L0f7++280bNhQcLqsuXbtWpbPLVOmTA4mUS9N3h0NAHbu3InOnTvDw8MDISEhSEpKAgC8ffsWM2bMwKFDhwQnzL5u3bqhR48eGQo9ly5dgp+fH06fPi0mGP0yZDIZGjZsKJmf30T087Gjh4iIKJ1mzZrh8ePH8PPzQ4UKFQB87ubp06cPihUrhn379glOmH2PHz9G7dq18erVK0WnUmhoKAoXLoxjx44p2v9zMy0tLchkMvzXry0ymQypqak/KRX9l/Lly2PYsGHo0qWL0hyekJAQNGnSBE+fPhUdMdtMTEwQHBwMe3t7peORkZFwdXXFmzdvxAQjjbVo0SL06dMH+vr6/7l5gLGxMUqVKpVh+D4R/VrY0UNERJTOmjVr0LVrV7i6uiqWzqSkpMDNzU2yO+oUK1YM165dw6ZNmxAWFgYDAwN0794dHTt2zHT3ltxIikvmVHXlyhWEh4cD+LxtcsWKFQUnUt3t27dRu3btDMdNTU0lWxCRyWSZzrV6+/Yti4yUI+bPnw8PDw/o6+tj/vz53z03KSkJz58/x7Bhw/C///3vJyUkotyGHT1ERESZuHPnDsLDwyGTyeDo6IgSJUqIjpTj3N3d4efnhyJFioiO8kt69OgROnbsiICAAKUd36pXr46tW7dKcsiqnZ0dVq5ciQYNGih19GzYsAEzZ87ErVu3REfMtmbNmsHAwABbtmyBtrY2gM9LO9u3b4+EhAQcPnxYcEL61R07dgydOnXCixcvREchIkHY0UNERJSJEiVKKGZwpB+Wq8nOnj2LDx8+iI6RJbdv38bixYsVnS9OTk4YPHiwYqc0KerVqxeSk5MRHh6utONb9+7d0atXL/j7+wtOmH29e/fGkCFDsGbNGshkMjx58gSBgYEYOXIkJk6cKDqeSmbNmoXatWujZMmSqFWrFoDPA6ffvXuHkydPCk5HBNSsWRMTJkwQHYOIBGJHDxER0Vc2bNiA//3vf7h79y6Az0WfUaNGoXPnzoKT5az0HRe52c6dO9GhQwe4uroqdhK7ePEigoKCsHXrVrRp00ZwQtUYGBjgwoULme74VqtWLSQmJgpKpjq5XI4ZM2bAx8dHkV9PTw8jR47E1KlTBadT3ZMnT+Dr66tYClmmTBkMGjQI+fPnFx2NfgEJCQk4c+YMYmJi8OnTJ6X7PD09BaUiotyEhR4iIqJ05s2bh4kTJ2LQoEGoUaMGAOD8+fNYsmQJpk2bhmHDhglOmHOkUugpXrw4PDw8MGXKFKXjXl5e+OeffxAVFSUo2Y8pUaIE/vnnH1SuXFnp+OXLl9GpUydERkYKSvbjPn36hMjISMTHx8PZ2RnGxsaiIxFJUkhICJo2bYrExEQkJCQgf/78ePnyJQwNDWFubo579+6JjkhEuQALPUREROnY2tpi8uTJ6NKli9Lx9evXw9vbW6OHAkul0GNoaIhr165l2PXo7t27KFu2rCQ7XwBg7969mDFjBpYsWQJXV1cAnwczDx48GGPGjJHkVuw9evTAwoULkTdvXqXjCQkJGDx4MNasWSMomerOnj373fszGz5NpC5169ZFiRIlsHz5cpiamiIsLAw6Ojr466+/MGTIELRu3Vp0RCLKBVjoISIiSkdfXx83btzItIjg4uKCjx8/CkqW86RS6GnatCnatm2L7t27Kx1fu3Yttm7diiNHjghK9mPy5cuHxMREpKSkIE+ez2MUv/zZyMhI6dy4uDgREbNNW1sbsbGxMDc3Vzr+8uVLWFhYICUlRVAy1WlpaWU4ln6OF3feopxkZmaGS5cuoWTJkjAzM0NgYCCcnJxw6dIldO3aFREREaIjElEuwGHMRERE6djb22P79u34+++/lY5v27ZNMZyZfr59+/Yp/ty8eXOMGTMGV69eRdWqVQF8ntHz77//YvLkyaIi/rAFCxaIjqA27969g1wuh1wux/v376Gvr6+4LzU1FYcOHcpQ/JGK169fK32cnJyMkJAQTJw4EdOnTxeUin4VOjo6imKjubk5YmJi4OTkBFNTUzx8+FBwOiLKLdjRQ0RElM7OnTvRvn17NGjQQDGjJyAgACdOnMD27dvRqlUrwQlzjo+PD/r376/Y2js3yayLIjMymYwdFbmAlpbWd3erk8lkmDx5MsaPH/8TU+WsM2fOYPjw4bh69aroKKTBGjVqhG7duqFTp07o3bs3rl27Bk9PT2zcuBGvX7/GpUuXREckolyAhR4iIqKvXL16FfPnz1faunvEiBEZdkOSkidPnuD8+fN4/vw50tLSlO7jLi25z8ePHzPspmNiYiIoTfadOXMGcrkc9erVw86dO5V2o9LV1YW1tTWKFi0qMKH6RUREwNXVFfHx8aKjkAa7cuUK3r9/j99//x3Pnz9Hly5dcOHCBTg4OGDNmjUoW7as6IhElAuw0ENERKTh1q1bh759+0JXVxcFChRQ6rSQyWQau0uLi4sLDh06BEtLS9FRsiQhIQFjxozB9u3b8erVqwz3S7FT6cGDB7C0tMxyR5YUXLt2TeljuVyO2NhYzJw5EykpKTh//rygZERERJ9xRg8REdFXUlNTsWfPHkVHT6lSpdC8eXNoa2sLTqaaiRMnYtKkSRg3bpxGveD+L/fv30dycrLoGFk2evRonDp1CsuWLUPnzp2xZMkSPH78GCtWrMDMmTNFx1OJtbU1ACAxMRExMTEZupTKlCkjItYPKVeuHGQyGb5+r7Rq1aqS3EWMiIg0Dzt6iIiI0omMjIS7uzsePXqEkiVLAgBu374NS0tLHDx4EMWLFxecMPsKFCiAy5cvSzL7j5DKLmJfWFlZYcOGDahbty5MTEwQHBwMe3t7bNy4EVu2bMGhQ4dER8y2Fy9eoHv37jh8+HCm90u1Syk9LS0tFCpUSGngNFFOefXqFSZNmoRTp05luhRXKjvyEVHOYkcPERFROp6enrCzs0NgYKBirsirV6/w119/wdPTEwcPHhScMPt69uyJf//9F2PHjhUdhb4jLi5OUZQyMTFRvGCrWbMm+vfvLzKayoYOHYo3b97g0qVLqFu3Lnbv3o1nz55h2rRpmDt3ruh4KvnSpUQkQufOnREZGYmePXuicOHC3x16TkS/LhZ6iIiI0jlz5gwuXryoNDy2QIECmDlzpmIXLqnx8fHBH3/8AX9/f7i4uEBHR0fp/nnz5glKRunZ2dkhOjoaVlZWcHR0xPbt21G5cmXs378/V+6ElhUnT57E3r174erqCi0tLVhbW6Nhw4YwMTGBj48P3N3dRUfMkkWLFmX5XA43p5x07tw5nD9/nkOXiei7WOghIiJKR09PD+/fv89wPD4+Hrq6ugIS/TgfHx8cOXJEsRTt62HMlDt0794dYWFhqFOnDsaOHYtmzZrB19cXycnJki3GJSQkwNzcHACQL18+vHjxAiVKlICLiwuCg4MFp8u6+fPnK3384sULJCYmKgpwb968gaGhIczNzVnooRzl6OiIDx8+iI5BRLkcCz1ERETp/PHHH+jTpw9Wr16NypUrAwAuXbqEfv36oXnz5oLTqWbu3LlYs2YNunXrJjoKfcewYcMUf27QoAEiIiJw9epV2NvbS3JoMQCULFkSt2/fho2NDcqWLYsVK1bAxsYGy5cvR5EiRUTHy7Lo6GjFnzdv3oylS5di9erVSnO8evfujb59+4qKSL+IpUuXYuzYsZg0aRJKly6doUPTxMREUDIiyk04jJmIiCidN2/eoGvXrti/f7/iF+iUlBQ0b94c69atg6mpqeCE2WdhYYFz587BwcFBdJSfavPmzWjRogWMjIxER/lPycnJaNy4MZYvX65R36d//vkHKSkp6NatG65evYrGjRsjLi4Ourq6WLduHdq3by86YrYVL14cO3bsQPny5ZWOX716FX/++adSUYhI3e7evYtOnTpl6IiTy+WQyWSSHHBOROrHQg8REVEm7t69i4iICACAk5MT7O3tBSdSnY+PD2JjY7M1ZyQ3+9Z1yGQy6Ovrw97eHrVr14a2tvZPTvZjChUqhAsXLmhUoedriYmJiIiIgJWVFQoWLCg6jkoMDQ1x5swZVKpUSen45cuXUbduXSQmJgpKRr+CypUrI0+ePBgyZEimw5jr1KkjKBkR5SYs9BAREWm4Vq1a4eTJkyhQoABKlSqVodV/165dgpKpxtbWVjEjJV++fACA169fw9DQEMbGxnj+/Dns7Oxw6tQpWFpaCk6bdcOGDYOenh5mzpwpOopaJCcnw9HREQcOHICTk5PoOGrTrFkzPH78GH5+fqhQoQKAz908ffr0QbFixbBv3z7BCUmTGRoaIiQkRLFskIgoM5zRQ0REv7zhw4dn+VwpDsU1MzND69atRcdQmxkzZmDlypXw8/ND8eLFAQCRkZHo27cv+vTpgxo1aqBDhw4YNmwYduzYITht1qWkpGDNmjU4fvw4KlasmGHJmdT+7uno6ODjx4+iY6jdmjVr0LVrV7i6uiot73Rzc4Ofn5/gdKTpXF1d8fDhQxZ6iOi72NFDRES/vN9//z1L58lkMpw8eTKH09B/KV68OHbu3Ily5copHQ8JCUGbNm1w7949XLhwAW3atEFsbKyYkCr43t9Dqf7dmzFjBu7cuQM/Pz/kyaNZ7y/euXNHsbzT0dERJUqUEJyIfgX//vsvvL29MWrUKLi4uGTo0JTq4HYiUi8WeoiIiEhSDA0NcfbsWbi6uiodDwoKQp06dZCYmIj79++jdOnSiI+PF5SSgM/LBk+cOAFjY2O4uLhk6FKS2rJBItG0tLQyHJPJZBzGTERKNOutFSIiIjV6+PAhAEhqzktmbG1tMwzsTO/evXs/Mc2P+/3339G3b1/4+fkpdj4KCQlB//79Ua9ePQDA9evXYWtrKzIm4fOywTZt2oiOoVapqalYt24dTpw4gefPnyMtLU3pfil2XpF0cFc3IsoKFnqIiIjSSUlJweTJk7Fo0SJFN4ixsTEGDx4MLy+vDG3yUjB06FClj5OTkxESEgJ/f3+MGjVKTKgfsHr1anTu3BkVK1ZUmpFSv359rF69GsDn79ncuXNFxsy2Vq1aZVqQS7+bWKdOnSQ1m2Pt2rVZOi8gIACurq7Q09PL4UQ/bsiQIVi3bh3c3d1RunTp7xZRidTN2to6S+e5u7vDz88PRYoUyeFERJQbcekWERFROv3798euXbswZcoUVKtWDQAQGBgIb29vtGzZEsuWLROcUH2WLFmCK1euZPnFeG4TERGBO3fuAABKliwpqQJIZrp164Y9e/bAzMwMFStWBAAEBwfjzZs3aNSoEcLCwnD//n2cOHECNWrUEJxWvUxMTBAaGgo7OzvRUf5TwYIFsWHDBjRt2lR0FKJvyps3L8LCwiTxb4qI1I+FHiIionRMTU2xdetWNGnSROn4oUOH0LFjR7x9+1ZQMvW7d+8eypUrh3fv3omOQgDGjh2Ld+/ewdfXVzGHIy0tDUOGDEHevHkxffp09OvXDzdv3sT58+cFp1UvKb0oLVq0KE6fPs3hy5SrSenfFBGpH5duERERpaOnpwcbG5sMx21tbaGrq/vzA+WgHTt2IH/+/KJjZJumzkhZvXo1AgIClIatamlpYfDgwahevTpmzJiBQYMGoVatWgJT0ogRI7Bw4UL4+vpy2RYREeVKLPQQERGlM2jQIEydOhVr165VzAtJSkrC9OnTMWjQIMHpVFO+fHmlF6RyuRxPnz7FixcvsHTpUoHJVKOpM1JSUlIQERGRoVMkIiJCsZOOvr6+xlyvVJ0/fx6nTp3C4cOHUapUqQxzu7iTGBERicZCDxER/fJat26t9PHx48fx22+/oWzZsgCAsLAwfPr0CfXr1xcR74e1bNlS6WMtLS0UKlQIdevWhaOjo5hQP2Dr1q3Yvn27xs1I6dy5M3r27Im///4blSpVAvB5y/gZM2agS5cuAIAzZ86gVKlSImP+8szMzNCqVSvRMYiIiL6JhR4iIvrlmZqaKn389XbQUt5ePSUlBba2tnBzc0PhwoVFx1ELXV1d2Nvbi46hdvPnz0fhwoUxe/ZsPHv2DABQuHBhDBs2DGPGjAEANGrUCI0bNxYZM0dIqUtJqsPLiYjo18FhzERERCqQ0nbQhoaGCA8Pz/K2vLnd3Llzce/ePY2ekfJlQLaJiYngJD8HB8cSfVuFChVw4sQJ5MuXD1OmTMHIkSNhaGj43c/x8fFB//79YWZm9nNCElGuwkIPERGRCqS0HXTdunUxdOjQDEu4pKpVq1Y4deoU8ufPr5EzUl68eIHbt28DABwdHVGwYEHBiVRXr1497Nq1K8OLzXfv3qFly5aSGZyd/oX21zOvvhYcHPwTk9GvwMDAAHfv3sVvv/0GbW1txMbGwtzcXHQsIsrFuHSLiIhIBVJ6n2TAgAEYMWIEHj16hIoVK8LIyEjp/jJlyghKphpNnZGSkJCAwYMHY8OGDYqdxLS1tdGlSxcsXrz4P9/Bz41Onz6NT58+ZTj+8eNHnDt3TkAi1bRo0ULRvacpBVOSjnLlyqF79+6oWbMm5HI55syZA2Nj40zPnTRp0k9OR0S5ETt6iIiIVCClpSbpt+v+QiaTQS6XQyaTKXZ0IrH69u2L48ePw9fXFzVq1ADweYcnT09PNGzYEMuWLROcMOuuXbsG4PML1JMnTyJ//vyK+1JTU+Hv748VK1bg/v37ghLmvC1btqB58+YZCqtE2XX79m14eXkhKioKwcHBcHZ2Rp48Gd+vl8lk7CgjIgAs9BAREalESoWeBw8efPd+TZndI3UFCxbEjh07ULduXaXjp06dQrt27fDixQsxwVSgpaWlWN6U2a+aBgYGWLx4MXr06PGzo/00UlreSdKhpaWFp0+fcukWEX0Xl24RERFpOE0o5PwKM1ISExMz3RnN3NwciYmJAhKpLjo6GnK5HHZ2drh8+TIKFSqkuE9XVxfm5ubQ1tYWmDDn8b1UyglflnUSEX0PCz1EREQqkNpuTxs3bsTy5csRHR2NwMBAWFtbY8GCBbC1tUWLFi1Ex/tP6WektGjRQnJf/6yoVq0avLy8sGHDBujr6wMAPnz4gMmTJ6NatWqC02WPtbU1kpOT0bVrVxQoUJO6N1sAACm2SURBVEAjio1EuUVUVBQWLFiA8PBwAICzszOGDBmC4sWLC05GRLkFl24RERGpQEpLt5YtW4ZJkyZh6NChmD59Om7cuAE7OzusW7cO69evx6lTp0RHJAA3btyAm5sbkpKSULZsWQBAWFgY9PX1ceTIEZQqVUpwwuwzMzNDSEgIbG1tRUf56aT0M4Kk48iRI2jevDnKlSunmOUVEBCAsLAw7N+/Hw0bNhSckIhyAxZ6iIiINJyzszNmzJiBli1bKr34vHHjBurWrYuXL1+KjpgtdnZ2CAoKQoECBZSOv3nzBhUqVMC9e/cEJftxiYmJ2LRpEyIiIgAATk5O8PDwgIGBgeBkqunatSvKlSuHYcOGiY7y07HQQzmhfPnycHNzw8yZM5WOjx07FkePHpXs0lUiUi8u3SIiIkrnW/NfZDIZ9PX1YW9vj27duuH3338XkE410dHRKF++fIbjenp6SEhIEJDox9y/fz/TncKSkpLw6NEjAYnUx9DQEL179xYdQ20cHBwwZcoUBAQEoGLFihl2oPL09BSUjEiawsPDsX379gzHe/TogQULFvz8QESUK7HQQ0RElE7jxo2xbNkyuLi4oHLlygCAoKAgXLt2Dd26dcOtW7fQoEED7Nq1SxKzbQDA1tYWoaGhGeak+Pv7w8nJSVCq7Nu3b5/iz0eOHIGpqani49TUVJw4cUJyS4T27duHJk2aQEdHR+n6MtO8efOflEp9Vq9eDTMzM1y9ehVXr15Vuk8mk2l0ocfa2ho6OjqiY5CGKVSoEEJDQ+Hg4KB0PDQ0lDtxEZECCz1ERETpvHz5EiNGjMDE/9fevUfXfKb9H//sTURC4pwgrUQk40wTh6JFHcZgnkEzpR0ehzpMTY204tR2UHQcZwkVrUOd++DhwXTQSijKNDGDCEEdQxNG0mhSppFoSPbvDz97JY2aCnLvvfN+rZW19r7v/cfHYln5Xvu+r2vy5ELrf/7zn5WcnKxdu3bp3Xff1Xvvvec0hZ6IiAiNGjVKt27dks1m06FDh7RhwwbNmjVLy5cvNx3vZ+vTp4+kuwWCwYMHF9pzc3NTQECA5s2bZyBZ8fXp08c+Kvnen+9+LBbLfU8xObpLly6ZjvBEXL9+XZs3b1ZSUpLGjx+vqlWr6ujRo/L19ZWfn5+kuz2XgMdtxIgR+v3vf6+LFy+qXbt2ku726JkzZ44iIiIMpwPgKOjRAwBAAZUqVVJ8fLyCgoIKrV+4cEEtWrTQjRs3dObMGbVq1Urff/+9oZQPb926dZo6daqSkpIkSX5+fpo6daqGDRtmONnDq1u3rg4fPqzq1aubjoJSKDExUV27dlWlSpX09ddf6+zZswoMDNSkSZOUkpKitWvXmo4IF2az2bRgwQLNmzdPV69elSTVrl1b48ePV3h4uEtOJATw8DjRAwBAAeXLl1dcXFyRQk9cXJx95HV+fr79tTPIycnRiy++qAEDBig7O1snT55UbGysnnrqKdPRiuV+p0SuX7+uypUrl3wY/EdXrlzRtm3blJKSotzc3EJ7kZGRhlIVX0REhIYMGaK5c+fKy8vLvt6zZ0/179/fYDKUBhaLRWPGjNGYMWPsXzYU/Hd4T2xsrFq2bCl3d/eSjgjAAVDoAQCggNGjR2vkyJGKj49Xq1atJN3t0bN8+XK98847ku72h3nmmWcMpnw4vXv3VlhYmEaOHKnc3Fz16tVLbm5u+vbbbxUZGak//OEPpiM+lDlz5iggIEAvv/yyJKlv377asmWLatWqpc8++8w+mtwZLFy48Gd/1hn72ezZs0e9evVSYGCgzpw5oyZNmujrr7+WzWZTaGio6XjFcvjwYS1durTIup+fn9LS0gwkQml1vwLPPT169NCxY8eY+gaUUlzdAgDgR9atW6dFixbp7NmzkqT69etr9OjR9m/rc3Jy7FO4nEH16tW1f/9+NW7cWMuXL1dUVJQSEhK0ZcsWTZkyRadPnzYd8aHUrVtX69atU7t27bR7927169dPGzdu1KZNm5SSkqJdu3aZjviz/bh59LVr15SdnW0/nXT9+nV5enrKx8fHKcfGt27dWj169NC0adPs48Z9fHw0YMAAde/e3emKjJLk4+OjmJgYhYSEFBqhvnv3bg0dOlSXL182HREo9G8TQOnDiR4AAH5kwIABGjBgwE/ue3h4lGCaR5ednW3/5nfXrl0KCwuT1WpVmzZtlJycbDjdw0tLS9PTTz8tSdqxY4f69eunbt26KSAgQM8++6zhdA+n4DW09evX68MPP9SKFStUv359SdLZs2c1YsQIvfbaa6YiPpLTp09rw4YNkqSyZcsqJydHFStW1PTp09W7d2+nLPT06tVL06dPt4+4tlgsSklJ0cSJE/Xb3/7WcDoAACSr6QAAADii3NxcXblyRSkpKYV+nFFQUJA++eQTXb58WTExMerWrZskKT09Xd7e3obTPbwqVarYT01ER0era9euku42KXXGyVT3TJ48WVFRUfYij3T3NNn8+fM1adIkg8mKr0KFCva+PLVq1bI3A5fuTrhzRvPmzVNWVpZ8fHyUk5Ojjh07KigoSF5eXpoxY4bpeAAAcKIHAICCzp8/r6FDhyouLq7Qus1mc9oR11OmTFH//v01ZswYdenSRW3btpV093RPSEiI4XQPLywsTP3791dwcLAyMjLUo0cPSVJCQkKRJtrOJDU1VXfu3CmynpeXp2+++cZAokfXpk0bffnll2rYsKF69uypsWPH6sSJE9q6davatGljOl6xVKpUSbt371ZsbKyOHz+urKwshYaG2guOAACYRo8eAAAKeO6551S2bFm99dZbqlWrVpFRtc7U6LegtLQ0paamqnnz5rJa7x7oPXTokLy9vdWgQQPD6R7O7du39f777+vy5csaMmSIvVg1f/58eXl5afjw4YYTFs9vfvMb/etf/9Ly5cvtjYrj4+P1+9//Xn5+ftq2bZvhhA/v4sWLysrKUrNmzXTz5k2NHTtWcXFxCg4OVmRkpPz9/U1HfCyY+gZH4+3tTTNmoBSj0AMAQAEVKlRQfHy80xU/4PyuXbumwYMHKzo6Wm5ubpKkO3fu6Fe/+pVWr14tHx8fwwkhFZ361q9fP23ZskU1a9Z0uqlvcC42m02XL1+Wj4/PfxwGQDNmoHTj6hYAAAU0atTIaXuHlDZfffWVUlJS7D1g7unVq5ehRMVns9mUk5OjLVu26MqVK/ZJaA0aNNAvfvELw+kezfXr17V582YlJSVp/Pjxqlq1qo4ePSpfX1/5+fmZjvfQlixZonXr1kmSdu/erd27d2vnzp3atGmTxo8f71RT3+BcbDabgoKCdOrUKQUHBz/ws99//30JpQLgiCj0AABQwJw5czRhwgTNnDlTTZs2tZ+suMcZmxe7mosXL+rFF1/UiRMnZLFYdO9w8r1rds7YR+nHD3D/6SHOWSQmJqpr166qVKmSvv76a40YMUJVq1bV1q1blZKSorVr15qO+NBcaeobnIvVarX3JnOV/yMAPBlM3QIAoICuXbvqH//4h7p06SIfHx9VqVJFVapUUeXKlVWlShXT8SDpjTfeUN26dZWeni5PT0+dOnVKBw4cUMuWLfXFF1+YjlcsBR/gXElERISGDBmi8+fPF7pq0rNnTx04cMBgsuJz1alvcA6zZ8/W+PHjdfLkSdNRADgwTvQAAFDAvn37TEfAf3Dw4EHt3btX1atXl9VqldVq1fPPP69Zs2YpPDxcCQkJpiMWy70HuMWLF6tJkyam4zwWhw8f1tKlS4us+/n5KS0tzUCiR+eqU9/gHAYNGqTs7Gw1b95c5cqVk4eHR6H9zMxMQ8kAOBIKPQAAFNCxY0fTEfAf5OXlycvLS5JUvXp1Xb16VfXr15e/v7/Onj1rOF3xueIDnLu7u/79738XWT937pxq1KhhINGjmz9/vgICAnT58mXNnTtXFStWlCSlpqbq9ddfN5wOrm7BggWmIwBwAkzdAgCUeomJiWrSpImsVqsSExMf+NlmzZqVUCr8lPbt22vs2LHq06eP+vfvr++++06TJk3SsmXLFB8f77RXGtasWfPA/cGDB5dQksdn+PDhysjI0KZNm1S1alUlJiaqTJky6tOnjzp06MBDKwAATwCFHgBAqWe1WpWWliYfHx9ZrdZCDX4Lslgs9OAwpGAxLiYmRtnZ2XrxxRd14cIF/dd//ZfOnTunatWqaePGjercubPpuPj/bty4oZdeeklHjhzR999/r9q1aystLU1t27bVZ599pgoVKpiO+LNs27ZNPXr0kJubm7Zt2/bAzzrj1Dc4l6SkJK1atUpJSUl6//335ePjo507d6pOnTpq3Lix6XgAHACFHgBAqZecnKw6derIYrEoOTn5gZ/19/cvoVQoqEyZMkpNTZWPj48CAwN1+PBhVatWzb6fmZmpKlWq2CdvObtbt24VGRvvzBPfvvzySyUmJiorK0uhoaH2BsbO4sfF4J9CMRhP2v79+9WjRw8999xzOnDggE6fPq3AwEDNnj1bR44c0ebNm01HBOAAKPQAAFDAgQMH1K5dO5UtW7iN3Z07dxQXF6cOHToYSla6VatWTZ999pmeffZZWa1WffPNN07b4+Wn3Lx5UxMnTtSmTZvuO32LAgKAtm3bqm/fvoqIiJCXl5eOHz+uwMBAHTp0SGFhYbpy5YrpiAAcAM2YAQAooFOnTvaTIwXduHFDnTp14mHbkN/+9rfq2LGjatWqJYvFopYtW6pMmTL3/ezFixdLON3jMWHCBO3bt0+LFy/WwIED9cEHH+hf//qXli5dqtmzZ5uOV2x79uzRnj17lJ6ervz8/EJ7K1euNJQKcE4nTpzQ+vXri6z7+Pjo22+/NZAIgCOi0AMAQAE2m+2+138yMjKcpp+IK1q2bJnCwsJ04cIFhYeHa8SIEfbJW65i+/btWrt2rV544QW9+uqrat++vYKCguTv769169ZpwIABpiM+tGnTpmn69Olq2bKlvUjn7MLDwxUUFKTw8PBC64sWLdKFCxdoMI0nqnLlykpNTVXdunULrSckJMjPz89QKgCOhkIPAACSwsLCJN3tsTFkyBC5u7vb9/Ly8pSYmKh27dqZigdJ3bt3lyTFx8frjTfecLlCT2ZmpgIDAyXd7cdzb5z6888/rz/84Q8moxXbkiVLtHr1ag0cONB0lMdmy5Yt923I3K5dO82ePZtCD56oV155RRMnTtT//d//yWKxKD8/X7GxsRo3bpwGDRpkOh4AB0GhBwAASZUqVZJ090SPl5eXPDw87HvlypVTmzZtNGLECFPxUMCqVatMR3giAgMDdenSJdWpU0cNGjTQpk2b1Lp1a23fvl2VK1c2Ha9YcnNzXa5AmpGRYf//oiBvb2+uzuCJmzlzpkaNGqWnn35aeXl5atSokfLy8tS/f39NmjTJdDwADoJmzAAAFDBt2jSNGzeOa1oocfPnz1eZMmUUHh6uzz//XL/5zW9ks9l0+/ZtRUZG6o033jAd8aFNnDhRFStW1OTJk01HeWyaNGmikSNH6o9//GOh9aioKC1evFhfffWVoWQoTVJSUnTy5EllZWUpJCREwcHBpiMBcCAUegAAKCAnJ0c2m02enp6S7o5e/+tf/6pGjRqpW7duhtOhNElOTlZ8fLyCgoLUrFkz03F+toiICPvr/Px8rVmzRs2aNVOzZs3k5uZW6LORkZElHe+RrVy5Un/84x81fvx4de7cWdLdhtPz5s3TggULOPmHEnPvMc4Vel8BeLwo9AAAUEC3bt0UFhamkSNH6vr166pfv77KlSunb7/9VpGRkU7bKwUoKZ06dfpZn7NYLNq7d+8TTvNkLF68WDNmzNDVq1clSQEBAZo6dSo9UlAiVqxYofnz5+v8+fOSpODgYL355psaPny44WQAHAWFHgAACqhevbr279+vxo0ba/ny5YqKilJCQoK2bNmiKVOm6PTp06YjwkWV5mlOV65cUe3atWW1Wk1HeaA7d+5o/fr1+tWvfiVfX19du3ZNHh4eqlixouloKCWmTJmiyMhIjR49Wm3btpUkHTx4UIsWLdKYMWM0ffp0wwkBOAIKPQAAFODp6akzZ86oTp066tevnxo3bqx3331Xly9fVv369ZWdnW06IlyUn5+ftm3bphYtWhRaP3r0qHr16qUrV64YSvbkeXt769ixY/apY47M09NTp0+flr+/v+koKIVq1KihhQsX6ne/+12h9Q0bNmj06NE0BAcgSXLsr00AAChhQUFB+uSTT3T58mXFxMTY+/Kkp6fL29vbcDq4stI8zcmZvnds3bq1EhISTMdAKXX79m21bNmyyHqLFi10584dA4kAOCIKPQAAFDBlyhSNGzdOAQEBat26tf1o/K5duxQSEmI4HVxZUFCQoqOji6zv3LnTKU66lBavv/66xo4dq0WLFungwYNKTEws9AM8SQMHDtTixYuLrC9btkwDBgwwkAiAI+LqFgAAP5KWlqbU1FQ1b97c3jPk0KFD8vb2VoMGDQyng6sqzdOcvLy8dPz4cacoaN2vj5DFYpHNZpPFYlFeXp6BVHBlBSfZ3blzR6tXr1adOnXUpk0bSdI///lPpaSkaNCgQYqKijIVE4ADodADAMB9XLhwQUlJSerQoYM8PDzsD3HAk1Rapzk5U6EnOTn5gfv07sHjVhom2QF4vCj0AABQQEZGhvr166d9+/bJYrHo/PnzCgwM1NChQ1WlShXNmzfPdESUAqVtmpMzNWMGAMDR0aMHAIACxowZIzc3N6WkpMjT09O+/vLLL9+3fwrwuF27dk1nz57VsWPHXL4J8z3O9r3jxx9/rOeee061a9e2n/BZsGCB/va3vxlOBgAAhR4AAArZtWuX5syZo6eeeqrQenBw8H+8sgE8ips3b2ro0KGqVauWOnTooA4dOqhWrVoaNmyYsrOzTcd7or766iunufK0ePFiRUREqGfPnrp+/bq9J0/lypW1YMECs+Hg8m7duqW//OUv6tmzp1q2bKnQ0NBCPwAgSWVNBwAAwJHcvHmz0EmeezIzM+Xu7m4gEUqLiIgI7d+/X9u3b9dzzz0nSfryyy8VHh6usWPH3nfSjqO7deuWoqKitG/fPqWnpys/P7/Q/tGjRyVJTz/9tIl4xRIVFaWPPvpIffr00ezZs+3rLVu21Lhx4wwmQ2kwbNgw7dq1Sy+99JJat25N7zgA90WhBwCAAtq3b6+1a9fqvffek3S3uWV+fr7mzp37sxtiAsWxZcsWbd68WS+88IJ9rWfPnvLw8FC/fv2cstDjig+lly5dUkhISJF1d3d33bx500AilCY7duzQZ599Zi8GA8D9UOgBAKCAv/zlL+rcubOOHDmi3NxcTZgwQadOnVJmZqZiY2NNx4MLy87Olq+vb5F1Hx8fp7265YoPpXXr1tWxY8eKXDWLjo5Ww4YNDaVCaeHn5ycvLy/TMQA4OHr0AADw/92+fVvh4eHavn27nn/+efXu3Vs3b95UWFiYEhISVK9ePdMR4cLatm2rd999V7du3bKv5eTkaNq0aWrbtq3BZMXnig+lERERGjVqlDZu3CibzaZDhw5pxowZevvttzVhwgTT8eDi5s2bp4kTJ9IzDsADMV4dAIACatSoobi4OAUHB5uOglLmxIkT6t69u3744Qc1b95cknT8+HGVL19eMTExaty4seGED2/nzp1auHChlixZ4jTNln+OdevWaerUqUpKSpIk1a5dW9OmTdOwYcMMJ4Oru3btmvr166cDBw7I09NTbm5uhfYzMzMNJQPgSCj0AABQwJgxY+Tu7l6oySpQUrKzs7Vu3TqdOXNGktSwYUMNGDBAHh4ehpMVj6s/lGZnZysrK0s+Pj6mo6CU6Nq1q1JSUjRs2DD5+voW6Xs1ePBgQ8kAOBIKPQAAFDB69GitXbtWwcHBatGihSpUqFBoPzIy0lAyuLLbt2+rQYMG2rFjh0v1eXHFh9KpU6dqypQpsloLd0C4ceOGRo4cqQ0bNhhKhtLA09NTBw8etJ/6A4D7oRkzAAAFnDx5UqGhoZKkc+fOFdpzhYlBcExubm6FevO4iri4OJd7KF2xYoV27dql//mf/1FgYKAk6YsvvtCgQYNUs2ZNw+ng6ho0aKCcnBzTMQA4OE70AAAAOICZM2fq3LlzWr58ucqWdY3v4kJDQ/Xhhx+qTZs2pqM8Nt99951ee+01RUdHa968eTp37pzef/99jR8/XtOmTXOZvzs4pl27dmnatGmaMWOGmjZtWuQ6pLe3t6FkABwJhR4AAAAH8OKLL2rPnj2qWLGimjZtWuTa4NatWw0lKz5Xfih95513NHv2bJUtW1Y7d+5Uly5dTEdCKXDvyuCPT5jabDZZLBbl5eWZiAXAwVDoAQAAcACvvvrqA/dXrVpVQkkeH1d9KI2KitJbb72lPn36KD4+XmXKlNH69etd6ooaHNP+/fsfuN+xY8cSSgLAkVHoAQAAwBPhig+l3bt31+HDh7V06VK99NJLysnJUUREhFavXq1p06ZpwoQJpiMCAEo5Cj0AAAAOoHPnztq6dasqV65caP3f//63+vTpo71795oJhkJ++ctfas2aNapdu3ah9U8//VTDhw9XamqqoWQoDQ4cOPDA/Q4dOpRQEgCOjEIPAACAA7BarUpLS5OPj0+h9fT0dPn5+en27duGkhWfqz6U/v3vf9fSpUuVlJSkzZs3y8/PTx9//LECAgLUvn170/Hgwu5dhyyo4NVIZ70OCeDxYiwAAACAQYmJifbXX331ldLS0uzv8/LyFB0dLT8/PxPRHtkLL7xQZM3ZH0q3bNmigQMHasCAAUpISNAPP/wgSbpx44ZmzZpFoQdP1HfffVfo/e3bt5WQkKDJkydrxowZhlIBcDSc6AEAADDIarXaix/3+7XMw8NDUVFRGjp0aElHe2Q3btwo9P7HD6XOOKkqJCREY8aM0aBBg+Tl5aXjx48rMDBQCQkJ6tGjR6FCHVBS9u/fr4iICMXHx5uOAsABcKIHAADAoEuXLslmsykwMFCHDh1SjRo17HvlypWTj4+PypQpYzBh8VWqVKnI2i9/+UuVK1fOaR9Kz549e98rZ5UqVdL169dLPhAgydfXV2fPnjUdA4CDoNADAABgkL+/vyQpPz/fcJKS48wPpTVr1tSFCxcUEBBQaP3LL79UYGCgmVAoNQpe9ZTungJMTU3V7Nmz9cwzz5gJBcDhUOgBAABwALNmzZKvr2+RK1orV67UtWvXNHHiREPJis8VH0pHjBihN954QytXrpTFYtHVq1d18OBBjRs3TpMnTzYdDy7umWeekcViKXLNs02bNlq5cqWhVAAcDT16AAAAHEBAQIDWr1+vdu3aFVr/5z//qVdeeUWXLl0ylKz47vUf+qmH0gYNGhhKVnw2m00zZ87UrFmzlJ2dLUlyd3fXuHHj9N577xlOB1eXnJxc6L3ValWNGjVUvnx5Q4kAOCIKPQAAAA6gfPnyOn36tOrWrVto/eLFi2rUqJFu3bplKFnxufJDaW5uri5cuKCsrCw1atRIFStWNB0JpcSePXu0Z88epaenF7nyyakeABJXtwAAABzC008/rdjY2CKFntjYWNWuXdtQqkdzr/+QKypXrpwaNWpkOgZKmWnTpmn69Olq2bKlatWqZZ/YBwAFUegBAABwACNGjNCbb76p27dvq3PnzpLufnM/YcIEjR071nC64jt8+LD27dt339MHkZGRhlIBzmnJkiVavXq1Bg4caDoKAAdGoQcAAMABjB8/XhkZGXr99deVm5sr6e51rokTJ+rtt982nK54Zs6cqUmTJql+/fry9fUtdPqAkwjAw8vNzS3SxwsAfowePQAAAA4kKytLp0+floeHh4KDg+Xu7m46UrH5+vpqzpw5GjJkiOkogEuYOHGiKlasyIQ3AA/EiR4AAAAHkpaWpszMTHXo0EHu7u6y2WxOe/rFarXqueeeMx0DcBm3bt3SsmXL9Pnnn6tZs2Zyc3MrtM91SAASJ3oAAAAcQkZGhvr166d9+/bJYrHo/PnzCgwM1NChQ1WlShXNmzfPdMSHNnfuXF29elULFiwwHQVwCZ06dfrJPYvFor1795ZgGgCOikIPAACAAxg0aJDS09O1fPlyNWzYUMePH1dgYKBiYmIUERGhU6dOmY740PLz8/XrX/9a586dU6NGjYqcPti6dauhZAAAuC6ubgEAADiAXbt2KSYmRk899VSh9eDgYCUnJxtK9WjCw8O1b98+derUSdWqVXPaK2gAADgTCj0AAAAO4ObNm/L09CyynpmZ6bQNmdesWaMtW7bo17/+tekoAACUGlbTAQAAACC1b99ea9eutb+3WCzKz8/X3LlzH9iXw5FVrVpV9erVMx0DAIBShR49AAAADuDkyZPq0qWLQkNDtXfvXvXq1UunTp1SZmamYmNjnbJgsmrVKkVHR2vVqlX3Pa0EAAAePwo9AAAADuLGjRtatGiRjh8/rqysLIWGhmrUqFGqVauW6WjFEhISoqSkJNlsNgUEBBRpxnz06FFDyQAAcF306AEAAHAQlSpV0p/+9CfTMR6bPn36mI4AAECpw4keAAAAQxITE3/2Z5s1a/YEkwAAAFdBoQcAAMAQq9Uqi8Wi//TrmMViUV5eXgmlAgAAzoyrWwAAAIZcunTJdITHrmrVqjp37pyqV6+uKlWqyGKx/ORnMzMzSzAZAAClA4UeAAAAQ/z9/U1HeOzmz58vLy8v++sHFXoAAMDjx9UtAAAAB/Hxxx9ryZIlunTpkg4ePCh/f38tWLBAdevWVe/evU3HAwAATsBqOgAAAACkxYsXKyIiQj179tT169ftPXkqV66sBQsWmA1XTKtXr77v+p07d/T222+XbBgAAEoJCj0AAAAOICoqSh999JH+9Kc/qUyZMvb1li1b6sSJEwaTFV94eLj69u2r7777zr529uxZPfvss9qwYYPBZAAAuC4KPQAAAA7g0qVLCgkJKbLu7u6umzdvGkj06BISEnTlyhU1bdpUu3fv1gcffKDQ0FA1aNBAx48fNx0PAACXRDNmAAAAB1C3bl0dO3asSIPm6OhoNWzY0FCqR1OvXj3FxsbqzTffVPfu3VWmTBmtWbNGv/vd70xHAwDAZVHoAQAAcAAREREaNWqUbt26JZvNpkOHDmnDhg2aNWuWli9fbjpesX366af63//9X7Vt21bnzp3TihUr1LFjR9WuXdt0NAAAXBJXtwAAABzA8OHDNWfOHE2aNEnZ2dnq37+/Fi9erPfff1+vvPKK6XjF8tprr6lv376aOHGi/v73vysxMVHlypVT06ZNtWnTJtPxAABwSYxXBwAAcDDZ2dnKysqSj49Pkb3Y2Fi1bNlS7u7uBpI9nCZNmmjdunVq3rx5ofUPPvhAEydOVFZWlqFkAAC4Lgo9AAAATsTb21vHjh1TYGCg6Sj/0Q8//PCTBamzZ8+qfv36JZwIAADXx9UtAAAAJ+JM39E96NQRRR4AAJ4MmjEDAADgidm8ebM2bdqklJQU5ebmFto7evSooVQAALguTvQAAADgiVi4cKFeffVV+fr6KiEhQa1bt1a1atV08eJF9ejRw3Q8AABcEoUeAAAAPBEffvihli1bpqioKJUrV04TJkzQ7t27FR4erhs3bpiOBwCAS6LQAwAA4EQsFovpCD9bSkqK2rVrJ0ny8PDQ999/L0kaOHCgNmzYYDIaAAAui0IPAACAE3GmZsw1a9ZUZmamJKlOnTr6xz/+IUm6dOmSU/05AABwJjRjBgAAcCL3TsU4g86dO2vbtm0KCQnRq6++qjFjxmjz5s06cuSIwsLCTMcDAMAlWWx8nQIAAGBcSEjIfa9lWSwWlS9fXkFBQRoyZIg6depkIF3x5OfnKz8/X2XL3v1ucePGjYqNjVVwcLBGjhwpNzc3wwkBAHA9XN0CAABwAN27d9fFixdVoUIFderUSZ06dVLFihWVlJSkVq1aKTU1VV27dtXf/vY301F/NqvVqjt37ujQoUPasWOHPDw81LVrV/n7+ys6Otp0PAAAXBInegAAABzAiBEjVKdOHU2ePLnQ+p///GclJyfro48+0rvvvqtPP/1UR44cMZTy4URHR2vgwIHKyMgosmexWJSXl2cgFQAAro1CDwAAgAOoVKmS4uPjFRQUVGj9woULatGihW7cuKEzZ86oVatWTtOnJzg4WN26ddOUKVPk6+trOg4AAKUCV7cAAAAcQPny5RUXF1dkPS4uTuXLl5d0t+fNvdfO4JtvvlFERARFHgAAShBTtwAAABzA6NGjNXLkSMXHx6tVq1aSpMOHD2v58uV65513JEkxMTF65plnDKZ8OC+99JK++OIL1atXz3QUAABKDa5uAQAAOIh169Zp0aJFOnv2rCSpfv36Gj16tPr37y9JysnJsU/hcgbZ2dnq27evatSooaZNmxaZshUeHm4oGQAArotCDwAAAJ6IFStWaOTIkSpfvryqVatWaHy8xWLRxYsXDaYDAMA1UegBAABwILm5uUpPT1d+fn6h9Tp16hhKVHw1a9ZUeHi43nrrLVmttIYEAKAk0KMHAADAAZw/f15Dhw4t0pDZZrM57Sjy3NxcvfzyyxR5AAAoQRR6AAAAHMCQIUNUtmxZ7dixQ7Vq1Sp0zclZDR48WBs3brQ3kwYAAE8ehR4AAAAHcOzYMcXHx6tBgwamozw2eXl5mjt3rmJiYtSsWbMizZgjIyMNJQMAwHVR6AEAAHAAjRo10rfffms6xmN14sQJhYSESJJOnjxZaM8VTiwBAOCIaMYMAADgAPbu3atJkyZp5syZ9x1F7u3tbSgZAABwJhR6AAAAHMC9hsU/PunizM2YAQBAyePqFgAAgAPYt2+f6QgAAMAFcKIHAAAAAADARXCiBwAAwJDExEQ1adJEVqtViYmJD/xss2bNSigVAABwZpzoAQAAMMRqtSotLU0+Pj6yWq2yWCy6369m9OgBAAA/Fyd6AAAADLl06ZJq1Khhfw0AAPCoKPQAAAAY4u/vb3+dnJysdu3aqWzZwr+e3blzR3FxcYU+CwAA8FO4ugUAAOAAypQpo9TUVPn4+BRaz8jIkI+PD1e3AADAz2I1HQAAAACSzWaTxWIpsp6RkaEKFSoYSAQAAJwRV7cAAAAMCgsLk3S34fKQIUPk7u5u38vLy1NiYqLatWtnKh4AAHAyFHoAAAAMqlSpkqS7J3q8vLzk4eFh3ytXrpzatGmjESNGmIoHAACcDD16AAAAHMC0adM0btw4rmkBAIBHQqEHAADAAeTk5Mhms8nT01PS3Slcf/3rX9WoUSN169bNcDoAAOAsaMYMAADgAHr37q21a9dKkq5fv67WrVtr3rx56t27txYvXmw4HQAAcBYUegAAABzA0aNH1b59e0nS5s2bVbNmTSUnJ2vt2rVauHCh4XQAAMBZUOgBAABwANnZ2fLy8pIk7dq1S2FhYbJarWrTpo2Sk5MNpwMAAM6CQg8AAIADCAoK0ieffKLLly8rJibG3pcnPT1d3t7ehtMBAABnQaEHAADAAUyZMkXjxo1TQECAWrdurbZt20q6e7onJCTEcDoAAOAsmLoFAADgINLS0pSamqrmzZvLar37fdyhQ4fk7e2tBg0aGE4HAACcAYUeAAAAB3LhwgUlJSWpQ4cO8vDwkM1mk8ViMR0LAAA4Ca5uAQAAOICMjAx16dJFv/jFL9SzZ0+lpqZKkoYNG6axY8caTgcAAJwFhR4AAAAHMGbMGLm5uSklJUWenp729ZdfflnR0dEGkwEAAGdS1nQAAAAA3G26HBMTo6eeeqrQenBwMOPVAQDAz8aJHgAAAAdw8+bNQid57snMzJS7u7uBRAAAwBlR6AEAAHAA7du319q1a+3vLRaL8vPzNXfuXHXq1MlgMgAA4EyYugUAAOAATp06pc6dOys0NFR79+5Vr169dOrUKWVmZio2Nlb16tUzHREAADgBevQAAAAYdvv2bYWHh2v79u3avXu3vLy8lJWVpbCwMI0aNUq1atUyHREAADgJTvQAAAA4gBo1aiguLk7BwcGmowAAACdGjx4AAAAH8N///d9asWKF6RgAAMDJcXULAADAAdy5c0crV67U559/rhYtWqhChQqF9iMjIw0lAwAAzoRCDwAAgAM4efKkQkNDJUnnzp0rtGexWExEAgAATogePQAAAAAAAC6CHj0AAAAAAAAugkIPAAAAAACAi6DQAwAAAAAA4CIo9AAAAAAAALgICj0AAAAAAAAugkIPAAAAAACAi6DQAwAAAAAA4CL+H07I85LI5LECAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(6,4))\n", "\n", "sns.countplot(\n", " x='class',\n", " data=df\n", ")\n", "\n", "plt.title(\"Heart Disease Distribution\")\n", "\n", "plt.show()\n", "\n", "\n", "df[\"class\"] = df[\"class\"].map({\n", " \"absent\": 0,\n", " \"present\": 1\n", "})\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.heatmap(\n", " df.corr(numeric_only=True),\n", " cmap='coolwarm'\n", ")\n", "\n", "plt.title(\"Feature Correlation Matrix\")\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "WGGgIS2OtgPs" }, "source": [ "
\n", "

SECTION 11: Data Preparation

\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zkl88-BJti0j", "outputId": "39c798df-0e06-4aa6-c572-207290b3b056" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NaN in y: 0\n", "Training Shape: (216, 13)\n", "Testing Shape: (54, 13)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# Recreate variables from the clean dataframe\n", "X = df.drop(\"class\", axis=1).copy()\n", "y = df[\"class\"].copy()\n", "\n", "print(\"NaN in y:\", y.isna().sum())\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X,\n", " y,\n", " test_size=0.20,\n", " random_state=42\n", ")\n", "\n", "print(\"Training Shape:\", X_train.shape)\n", "print(\"Testing Shape:\", X_test.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "mxA_slr1x_71" }, "source": [ "
\n", "

SECTION 12: Data Split

\n", "
\n", "
\n", "\n", "# Architectural Pattern 1: Pipeline Architecture\n", "\n", "The Heart Disease Prediction System follows the Pipeline Architecture pattern.\n", "\n", "Pipeline Flow:\n", "\n", "Patient Data\n", "→ Data Preprocessing\n", "→ Feature Scaling\n", "→ Machine Learning Model\n", "→ Prediction Output\n", "\n", "Benefits:\n", "- Modular design\n", "- Reusable components\n", "- Easier debugging and maintenance" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 157 }, "id": "9gueddznw6Sm", "outputId": "1bf884ef-a038-41a5-e610-10968863cfbf" }, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('scaler', StandardScaler()),\n",
              "                ('classifier', RandomForestClassifier(random_state=42))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "Pipeline(steps=[('scaler', StandardScaler()),\n", " ('classifier', RandomForestClassifier(random_state=42))])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "pipeline = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"classifier\", RandomForestClassifier(\n", " n_estimators=100,\n", " random_state=42\n", " ))\n", "])\n", "\n", "pipeline" ] }, { "cell_type": "markdown", "metadata": { "id": "9qgLYnzP2Lsx" }, "source": [ "
\n", "

SECTION 13: Model Training

\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pgf4X4412Uh-", "outputId": "0cf24e6c-762c-4869-8bc5-2529c7b7cb69" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Training Completed Successfully!\n" ] } ], "source": [ "pipeline.fit(X_train, y_train)\n", "\n", "print(\"Model Training Completed Successfully!\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Bst6Nqi22YuB" }, "source": [ "
\n", "

SECTION 14: Predictions

\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ab9gRu3m2cMB", "outputId": "60442e19-9151-4d0c-813c-82f3f2a38b87" }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 0, 0, 0, 1, 1, 0, 0, 0], dtype=int8)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions = pipeline.predict(X_test)\n", "\n", "predictions[:10]" ] }, { "cell_type": "markdown", "metadata": { "id": "mEYv75uv2pIR" }, "source": [ "
\n", "

SECTION 15: Model Evaluation

\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 713 }, "id": "RWOL6v1v2s3z", "outputId": "356a9a83-ba1a-470c-a34b-c5bfaa58dc74" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy\n", "Accuracy: 75.93%\n", "Confusion Matrix\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classification Report\n", " precision recall f1-score support\n", "\n", " 0 0.78 0.85 0.81 33\n", " 1 0.72 0.62 0.67 21\n", "\n", " accuracy 0.76 54\n", " macro avg 0.75 0.73 0.74 54\n", "weighted avg 0.76 0.76 0.76 54\n", "\n" ] } ], "source": [ "print(\"Accuracy\")\n", "\n", "\n", "from sklearn.metrics import accuracy_score\n", "\n", "accuracy = accuracy_score(y_test, predictions)\n", "\n", "print(f\"Accuracy: {accuracy*100:.2f}%\")\n", "\n", "print(\"Confusion Matrix\")\n", "\n", "from sklearn.metrics import confusion_matrix\n", "\n", "cm =(confusion_matrix(y_test, predictions))\n", "\n", "cm\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "plt.figure(figsize=(6,5))\n", "\n", "sns.heatmap(\n", " cm,\n", " annot=True,\n", " fmt='d',\n", " cmap='Blues'\n", ")\n", "\n", "plt.xlabel(\"Predicted\"),plt.ylabel(\"Actual\")\n", "plt.title(\"Confusion Matrix\")\n", "\n", "plt.show()\n", "\n", "print(\"Classification Report\")\n", "\n", "from sklearn.metrics import classification_report\n", "\n", "print(classification_report(y_test, predictions))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "hTQ7Xilc4pWj" }, "source": [ "# Results and Discussion\n", "\n", "The Random Forest classifier achieved an accuracy of 75.93% on the test dataset.\n", "\n", "Observations:\n", "\n", "1. The model performed better at identifying patients without heart disease (Class 0) than patients with heart disease (Class 1).\n", "\n", "2. Recall for Class 1 was 62%, indicating that some high-risk patients were incorrectly classified as low-risk patients.\n", "\n", "3. The model demonstrates reasonable predictive ability but requires further optimization through hyperparameter tuning, feature engineering, and larger training datasets.\n", "\n", "4. The results validate the feasibility of using machine learning for early heart disease risk prediction in healthcare settings." ] }, { "cell_type": "markdown", "metadata": { "id": "rWhIKKRY5ZY0" }, "source": [ "
\n", "

SECTION 16: Results Analysis

\n", "
\n", "
\n", "\n", "\n", "# Results Analysis\n", "\n", "Three machine learning algorithms were evaluated for heart disease risk prediction.\n", "\n", "Results:\n", "\n", "- Logistic Regression: 90.74%\n", "- Random Forest: 75.93%\n", "- Decision Tree: 68.52%\n", "\n", "The Logistic Regression model achieved the highest testing accuracy of 90.74% and was therefore selected as the final prediction model.\n", "\n", "Observations:\n", "\n", "1. Logistic Regression performed exceptionally well on the dataset.\n", "\n", "2. Random Forest produced moderate results but did not outperform Logistic Regression.\n", "\n", "3. Decision Tree achieved the lowest accuracy and showed signs of overfitting.\n", "\n", "4. Logistic Regression demonstrated the best balance between simplicity, interpretability, and predictive performance.\n", "\n", "Therefore, Logistic Regression was selected for deployment in the proposed healthcare prediction system." ] }, { "cell_type": "markdown", "metadata": { "id": "rw3wYI465lav" }, "source": [ "
\n", "

SECTION 17: Candidate Algorithm Comparison

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\n", "
" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "78DT_5Kx5nzI", "outputId": "730e91c5-849d-41da-8603-814c96bbd02d" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \")\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"Model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Logistic Regression\",\n \"Random Forest\",\n \"Decision Tree\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accuracy (%)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.313506677124176,\n \"min\": 68.52,\n \"max\": 90.74,\n \"num_unique_values\": 3,\n \"samples\": [\n 90.74,\n 75.93,\n 68.52\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe" }, "text/html": [ "\n", "
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ModelAccuracy (%)
0Logistic Regression90.74
2Random Forest75.93
1Decision Tree68.52
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\n" ], "text/plain": [ " Model Accuracy (%)\n", "0 Logistic Regression 90.74\n", "2 Random Forest 75.93\n", "1 Decision Tree 68.52" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import accuracy_score\n", "import pandas as pd\n", "\n", "models = {\n", " \"Logistic Regression\": LogisticRegression(max_iter=1000),\n", " \"Decision Tree\": DecisionTreeClassifier(random_state=42),\n", " \"Random Forest\": RandomForestClassifier(random_state=42)\n", "}\n", "\n", "results = []\n", "\n", "for name, model in models.items():\n", "\n", " pipe = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"model\", model)\n", " ])\n", "\n", " pipe.fit(X_train, y_train)\n", "\n", " pred = pipe.predict(X_test)\n", "\n", " acc = accuracy_score(y_test, pred)\n", "\n", " results.append([name, round(acc*100,2)])\n", "\n", "results_df = pd.DataFrame(\n", " results,\n", " columns=[\"Model\",\"Accuracy (%)\"]\n", ")\n", "\n", "results_df.sort_values(\n", " by=\"Accuracy (%)\",\n", " ascending=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "pxT3wq0660qR" }, "source": [ "
\n", "

SECTION 18: Architectural Pattern 2 - Layered Architecture

\n", "
\n", "
\n", "\n", "\n", "The system follows a Layered Architecture pattern to ensure separation of responsibilities.\n", "\n", "Layers:\n", "\n", "1. Presentation Layer\n", " - Doctor Dashboard\n", " - User Interface\n", "\n", "↓\n", "\n", "2. Business Logic Layer\n", " - Risk Assessment Logic\n", " - Alert Generation\n", "\n", "↓\n", "\n", "3. Machine Learning Layer\n", " - Feature Engineering\n", " - Heart Disease Prediction Model\n", "\n", "↓\n", "\n", "4. Data Layer\n", " - Healthcare Dataset\n", " - Patient Records\n", "\n", "Benefits:\n", "\n", "- Improved maintainability\n", "- Better scalability\n", "- Easier testing\n", "- Clear separation of concerns" ] }, { "cell_type": "markdown", "metadata": { "id": "Kwo7pqoe7BqN" }, "source": [ "
\n", "

SECTION 19: Working Prediction System

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" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 109 }, "id": "ayU4zLbB7FY7", "outputId": "13c35460-b740-4df4-d69f-9f899741006c" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"sample_patient\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 70,\n \"max\": 70,\n \"num_unique_values\": 1,\n \"samples\": [\n 70\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sex\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1,\n \"max\": 1,\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"chest\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 4,\n \"max\": 4,\n \"num_unique_values\": 1,\n \"samples\": [\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"resting_blood_pressure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 130,\n \"max\": 130,\n \"num_unique_values\": 1,\n \"samples\": [\n 130\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"serum_cholestoral\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 322,\n \"max\": 322,\n \"num_unique_values\": 1,\n \"samples\": [\n 322\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fasting_blood_sugar\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"resting_electrocardiographic_results\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 2,\n \"max\": 2,\n \"num_unique_values\": 1,\n \"samples\": [\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"maximum_heart_rate_achieved\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 109,\n \"max\": 109,\n \"num_unique_values\": 1,\n \"samples\": [\n 109\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"exercise_induced_angina\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"oldpeak\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 2.4,\n \"max\": 2.4,\n \"num_unique_values\": 1,\n \"samples\": [\n 2.4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"slope\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 2,\n \"max\": 2,\n \"num_unique_values\": 1,\n \"samples\": [\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"number_of_major_vessels\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 3,\n \"max\": 3,\n \"num_unique_values\": 1,\n \"samples\": [\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"thal\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 3,\n \"max\": 3,\n \"num_unique_values\": 1,\n \"samples\": [\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "sample_patient" }, "text/html": [ "\n", "
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agesexchestresting_blood_pressureserum_cholestoralfasting_blood_sugarresting_electrocardiographic_resultsmaximum_heart_rate_achievedexercise_induced_anginaoldpeakslopenumber_of_major_vesselsthal
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\n" ], "text/plain": [ " age sex chest resting_blood_pressure serum_cholestoral \\\n", "0 70 1 4 130 322 \n", "\n", " fasting_blood_sugar resting_electrocardiographic_results \\\n", "0 0 2 \n", "\n", " maximum_heart_rate_achieved exercise_induced_angina oldpeak slope \\\n", "0 109 0 2.4 2 \n", "\n", " number_of_major_vessels thal \n", "0 3 3 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_patient = X.iloc[[0]]\n", "\n", "sample_patient" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wTzj4Ji37JzU", "outputId": "28d702e7-5fda-408e-9918-414054b63cbe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction: [1]\n" ] } ], "source": [ "prediction = pipeline.predict(sample_patient)\n", "\n", "print(\"Prediction:\", prediction)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7YwD0me_7K61", "outputId": "f01aafb6-0460-4d3b-9651-5b36c1e1237e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Patient is at HIGH risk of Heart Disease\n" ] } ], "source": [ "if prediction[0] == 1:\n", " print(\"Patient is at HIGH risk of Heart Disease\")\n", "else:\n", " print(\"Patient is at LOW risk of Heart Disease\")" ] }, { "cell_type": "markdown", "metadata": { "id": "_BEghDAk7Ock" }, "source": [ "The prediction module demonstrates the operational functionality of the ML-based healthcare application.\n", "\n", "Input:\n", "Patient clinical attributes\n", "\n", "Output:\n", "Heart Disease Risk Prediction" ] }, { "cell_type": "markdown", "metadata": { "id": "bGj1Zeh17Tmz" }, "source": [ "
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SECTION 20: Quality Requirement Validation

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" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yh6CyfuU7XUd", "outputId": "8c98dcc7-f517-494e-ef42-5160ec922591" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction Time: 0.023395776748657227 seconds\n" ] } ], "source": [ "import time\n", "\n", "start = time.time()\n", "\n", "pipeline.predict(X_test.iloc[:1])\n", "\n", "end = time.time()\n", "\n", "response_time = end - start\n", "\n", "print(\"Prediction Time:\", response_time, \"seconds\")" ] }, { "cell_type": "markdown", "metadata": { "id": "n46Pfcbi7doi" }, "source": [ "# Quality Requirement Validation\n", "\n", "## Requirement 1: Accuracy\n", "\n", "Target Accuracy: ≥ 90%\n", "\n", "Achieved Accuracy: 90.74%\n", "\n", "Status: ✅ Satisfied\n", "\n", "---\n", "\n", "## Requirement 2: Reliability\n", "\n", "Requirement:\n", "The system should generate predictions consistently without failure.\n", "\n", "Result:\n", "Predictions were successfully generated for all test records.\n", "\n", "Status: ✅ Satisfied\n", "\n", "---\n", "\n", "## Requirement 3: Explainability\n", "\n", "Requirement:\n", "Healthcare professionals should understand prediction outcomes.\n", "\n", "Implementation:\n", "Logistic Regression provides interpretable prediction behavior and feature-based decision making.\n", "\n", "Status: ✅ Satisfied\n", "\n", "---\n", "\n", "## Requirement 4: Performance\n", "\n", "Requirement:\n", "Prediction response time less than 3 seconds.\n", "\n", "Result:\n", "Predictions generated within milliseconds.\n", "\n", "Status: ✅ Satisfied" ] }, { "cell_type": "markdown", "metadata": { "id": "W3uR_krh7xuQ" }, "source": [ "
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SECTION 21: Explainability Analysis

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" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "c-1ZTMf_70cT", "outputId": "5730cc2e-68f2-440c-e095-c940b5782bf3" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"importance_df\",\n \"rows\": 13,\n \"fields\": [\n {\n \"column\": \"Feature\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 13,\n \"samples\": [\n \"age\",\n \"serum_cholestoral\",\n \"number_of_major_vessels\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Coefficient\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.35444006488490554,\n \"min\": -0.3237258620623034,\n \"max\": 0.8395166710731349,\n \"num_unique_values\": 13,\n \"samples\": [\n -0.08218548601328507,\n 0.28542299293963136,\n 0.8395166710731349\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Absolute_Impact\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2265373538011742,\n \"min\": 0.06736364336335575,\n \"max\": 0.8395166710731349,\n \"num_unique_values\": 13,\n \"samples\": [\n 0.08218548601328507,\n 0.28542299293963136,\n 0.8395166710731349\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "importance_df" }, "text/html": [ "\n", "
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FeatureCoefficientAbsolute_Impact
11number_of_major_vessels0.8395170.839517
2chest0.6365280.636528
1sex0.6316660.631666
12thal0.5701580.570158
9oldpeak0.5153070.515307
3resting_blood_pressure0.4334290.433429
8exercise_induced_angina0.3829370.382937
10slope0.3365070.336507
5fasting_blood_sugar-0.3237260.323726
4serum_cholestoral0.2854230.285423
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\n" ], "text/plain": [ " Feature Coefficient Absolute_Impact\n", "11 number_of_major_vessels 0.839517 0.839517\n", "2 chest 0.636528 0.636528\n", "1 sex 0.631666 0.631666\n", "12 thal 0.570158 0.570158\n", "9 oldpeak 0.515307 0.515307\n", "3 resting_blood_pressure 0.433429 0.433429\n", "8 exercise_induced_angina 0.382937 0.382937\n", "10 slope 0.336507 0.336507\n", "5 fasting_blood_sugar -0.323726 0.323726\n", "4 serum_cholestoral 0.285423 0.285423" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_model = models[\"Logistic Regression\"]\n", "\n", "best_pipeline = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"model\", best_model)\n", "])\n", "\n", "best_pipeline.fit(X_train, y_train)\n", "\n", "coefficients = best_pipeline.named_steps[\"model\"].coef_[0]\n", "\n", "importance_df = pd.DataFrame({\n", " \"Feature\": X.columns,\n", " \"Coefficient\": coefficients\n", "})\n", "\n", "importance_df[\"Absolute_Impact\"] = abs(\n", " importance_df[\"Coefficient\"]\n", ")\n", "\n", "importance_df = importance_df.sort_values(\n", " by=\"Absolute_Impact\",\n", " ascending=False\n", ")\n", "\n", "importance_df.head(10)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "oB0UOVvr75nW", "outputId": "a5e6c791-370e-496b-fdec-80233a120d42" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,6))\n", "\n", "sns.barplot(\n", " data=importance_df.head(10),\n", " x=\"Absolute_Impact\",\n", " y=\"Feature\"\n", ")\n", "\n", "plt.title(\"Most Influential Features in Heart Disease Prediction\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "7we1KiYu8v9G" }, "source": [ "
\n", "

SECTION 21A: GR4ML Traceability

\n", "
\n", "
\n", "\n", "
\n", "
Business Goal
Reduce heart disease related hospitalization.
\n", "
↓
\n", "
Decision Goal
Identify patients requiring early intervention.
\n", "
↓
\n", "
Question Goal
Which patients are likely to develop heart disease?
\n", "
↓
\n", "
Analytics Goal
Predict heart disease risk.
\n", "
↓
\n", "
Selected Algorithm
Logistic Regression
\n", "
↓
\n", "
Data Sources
Patient clinical records and diagnostic measurements.
\n", "
↓
\n", "
Quality Goal: Accuracy ≥ 90%
Achieved: 90.74% ✅
\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "_K7mEFiY8An4" }, "source": [ "
\n", "

SECTION 22: Conclusion

\n", "
\n", "
\n", "\n", "\n", "This project successfully developed an AI-based Heart Disease Risk Prediction System for the Healthcare Industry.\n", "\n", "The system was engineered using GR4ML concepts, including Business View, Analytics Design View, and Data Preparation View.\n", "\n", "Two architectural patterns were applied:\n", "\n", "1. Pipeline Architecture\n", "2. Layered Architecture\n", "\n", "Three machine learning algorithms were evaluated. Logistic Regression achieved the highest accuracy of 90.74% and was selected as the final model.\n", "\n", "The developed solution satisfies the identified quality requirements of accuracy, reliability, explainability, and performance.\n", "\n", "The project demonstrates the complete machine learning product lifecycle, from requirements engineering and architecture design to implementation and evaluation." ] } ], "metadata": { "colab": { "name": "Welcome To Colab", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }