diff --git a/Untitled1.ipynb b/Untitled1.ipynb
new file mode 100644
index 0000000..11ae5a3
--- /dev/null
+++ b/Untitled1.ipynb
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1Hi1q37BQmZjp7hNKV5nKFEnAHGDt4UJ6","authorship_tag":"ABX9TyMBvTg5ACXmjvG27VVQWfmE"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"18pqUFiLx-etkSEycYauUnnkreKbVHmI7"},"id":"c4fGXzVCgMlc","executionInfo":{"status":"ok","timestamp":1766762127328,"user_tz":-330,"elapsed":108320,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"62d9ab7b-da85-4d8b-fa97-f0eac2c6b247"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["import cv2\n","import matplotlib.pyplot as plt\n","import os\n","import glob # Import the glob library\n","\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# --- CONFIGURATION PARAMETERS ---\n","# Define the size of the patch to save (in pixels)\n","PATCH_SIZE = 64\n","BW, BH = PATCH_SIZE, PATCH_SIZE\n","\n","# Define the paths\n","template_path = '/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/PCB_USED/04.JPG'\n","folder_path = '/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images/Missing_hole/4'\n","out_dir = \"/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/Missing_hole_4defects\"\n","\n","# Ensure the output directory exists\n","os.makedirs(out_dir, exist_ok=True)\n","\n","# Global counter for saved patches - MUST be initialized outside the loop\n","global_patch_id = 0\n","\n","# Define the area constraints for defect detection\n","MIN_AREA = 0\n","MAX_AREA = 1500\n","\n","\n","# Read template PCB 01 image\n","rgb_template_img = cv2.imread(template_path)\n","plt.figure(figsize=(10,6))\n","plt.imshow(rgb_template_img, cmap=\"gray\")\n","\n","# Calculate the resized dimensions (1/4th of original)\n","a = int(rgb_template_img.shape[0]/4)\n","b = int(rgb_template_img.shape[1]/4)\n","\n","# Read template PCB 01 image as grayscale\n","template_img = cv2.imread(template_path, 0)\n","# display the grayscale template PCB image\n","plt.figure(figsize=(10,6))\n","plt.imshow(template_img, cmap=\"gray\")\n","\n","# Resize template image of PCB\n","template_img_resize = cv2.resize(template_img, (b,a))\n","plt.figure(figsize=(10,6))\n","plt.imshow(template_img_resize, cmap=\"gray\")\n","\n","# Gaussian blur\n","blur_template_img = cv2.GaussianBlur(template_img_resize, (3,3),0)\n","# display the blurred image\n","plt.figure(figsize=(10,6))\n","plt.imshow(blur_template_img, cmap=\"gray\")\n","\n","# Adaptive thresholding\n","template_adap_thresh = cv2.adaptiveThreshold(blur_template_img, 255,\n","                                         cv2. ADAPTIVE_THRESH_MEAN_C,\n","                                         cv2.THRESH_BINARY, 15, 5)\n","# display the thresholded image\n","plt.figure(figsize=(10,6))\n","plt.imshow(template_adap_thresh, cmap=\"gray\")\n","# Note: Initial display code for template is removed here to keep the final script cleaner,\n","# but it can be re-added before the loop if needed for verification.\n","\n","\n","# Use glob to find all files ending with '.jpg' in the specified folder\n","image_files = glob.glob(os.path.join(folder_path, '*.jpg'))\n","\n","print(f\"Found {len(image_files)} images in the folder.\")\n","print(f\"Defect area filter range: {MIN_AREA} < Area < {MAX_AREA}\")\n","\n","\n","\n","# Iterate over each image file path found\n","for image_path in image_files:\n","    # 1. Read and prepare test image\n","    bgr_test_img = cv2.imread(image_path)\n","    filename = os.path.basename(image_path)\n","\n","    # Check if the image was loaded successfully\n","    if bgr_test_img is None:\n","        print(f\"Skipping file: {filename} (Not a readable image).\")\n","        continue\n","\n","    # Convert BGR to RGB for matplotlib display (if needed)\n","    rgb_test_img = cv2.cvtColor(bgr_test_img, cv2.COLOR_BGR2RGB) # Not used below, but good practice\n","     # 2. Display the original test PCB image\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(rgb_test_img, cmap = \"gray\")\n","    plt.title(f\"Original Test PCB Image: {filename}\")\n","\n","    # Read grayscale\n","    test_img = cv2.imread(image_path, 0)\n","\n","    # Resize, Blur, and Threshold the test image\n","    test_img_resize = cv2.resize(test_img, (b, a))\n","    # 3. Display the grayscale resized test PCB image\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(test_img_resize, cmap=\"gray\")\n","    plt.title(f\"Resized Grayscale PCB Image: {filename} ({b}x{a})\")\n","    blur_test_img = cv2.GaussianBlur(test_img_resize, (3,3),0)\n","\n","    test_adap_thresh = cv2.adaptiveThreshold(blur_test_img, 255,\n","                                         cv2. ADAPTIVE_THRESH_MEAN_C,\n","                                         cv2.THRESH_BINARY, 15, 5)\n","\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(test_adap_thresh, cmap=\"gray\")\n","\n","    # 2. Difference Imaging (Core Defect Detection)\n","    sub_img = cv2.subtract(test_adap_thresh, template_adap_thresh)\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(sub_img)\n","    final_img = cv2.medianBlur(sub_img, 3) # Noise reduction\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(final_img, cmap=\"gray\")\n","\n","    # 3. Find Contours\n","    # Using RETR_EXTERNAL focuses on the outline of defect blobs\n","    # Note: final_img is the mask image used for finding contours\n","\n","    orig = test_img_resize\n","    mask_img = final_img\n","    _, thresh = cv2.threshold(mask_img, 127, 255, cv2.THRESH_BINARY)\n","    contours, _ = cv2.findContours(thresh, cv2.RETR_LIST,\n","    cv2.CHAIN_APPROX_SIMPLE) # CHANGE ***\n","\n","    current_defects_count = 0\n","    orig = test_img_resize # The source image for cropping\n","    h_img, w_img = final_img.shape[:2]\n","\n","\n","    # 4. Filter Contours and Save Patches (MUST be inside the image loop)\n","    for cnt in contours:\n","        area = cv2.contourArea(cnt)\n","\n","        # Apply the defect area filter to ensure quality and control quantity\n","        if MIN_AREA < area < MAX_AREA:\n","            current_defects_count += 1\n","\n","            # Get bounding box coordinates\n","            x, y, w, h = cv2.boundingRect(cnt)\n","\n","            # Center of defect\n","            cx = x + w // 2\n","            cy = y + h // 2\n","\n","            # Fixed PATCh_SIZE x PATCh_SIZE box centered on defect\n","            x0 = cx - BW // 2\n","            y0 = cy - BH // 2\n","            x1 = x0 + BW\n","            y1 = y0 + BH\n","\n","            # Clip coordinates to image bounds\n","            x0 = max(0, x0)\n","            y0 = max(0, y0)\n","            x1 = min(w_img, x1)\n","            y1 = min(h_img, y1)\n","\n","            # Crop patch from ORIGINAL resized image\n","            patch = orig[y0:y1, x0:x1]\n","\n","            # Ensure patch is the full desired size\n","            if patch.shape[0] != BH or patch.shape[1] != BW:\n","                continue\n","\n","            # Save patch\n","            base_name = os.path.splitext(filename)[0]\n","            # Use global_patch_id for unique sequential naming across ALL files\n","            out_path = os.path.join(out_dir, f\"defect_{base_name}_{global_patch_id:04d}.png\")\n","            cv2.imwrite(out_path, patch)\n","\n","            # Increment the global counter\n","            global_patch_id += 1\n","\n","    print(f\"Processed {filename}: Found {current_defects_count} defects (Total saved: {global_patch_id})\")\n","\n","print(f\"\\n--- Processing Complete ---\")\n","print(f\"Total patches saved to {out_dir}: {global_patch_id}\")"]},{"cell_type":"code","source":["import cv2\n","import matplotlib.pyplot as plt\n","import os\n","import glob # Import the glob library\n","\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# --- CONFIGURATION PARAMETERS ---\n","# Define the size of the patch to save (in pixels)\n","PATCH_SIZE = 64\n","BW, BH = PATCH_SIZE, PATCH_SIZE\n","\n","# Define the paths\n","template_path = '/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/PCB_USED/01.JPG'\n","folder_path = '/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images/Spur/1'\n","out_dir = \"/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/Spur_1defects\"\n","\n","# Ensure the output directory exists\n","os.makedirs(out_dir, exist_ok=True)\n","\n","# Global counter for saved patches - MUST be initialized outside the loop\n","global_patch_id = 0\n","\n","# Define the area constraints for defect detection\n","MIN_AREA = 1\n","MAX_AREA = 300\n","\n","\n","# Read template PCB 01 image\n","rgb_template_img = cv2.imread(template_path)\n","plt.figure(figsize=(10,6))\n","plt.imshow(rgb_template_img, cmap=\"gray\")\n","\n","# Calculate the resized dimensions (1/4th of original)\n","a = int(rgb_template_img.shape[0]/4)\n","b = int(rgb_template_img.shape[1]/4)\n","\n","# Read template PCB 01 image as grayscale\n","template_img = cv2.imread(template_path, 0)\n","# display the grayscale template PCB image\n","plt.figure(figsize=(10,6))\n","plt.imshow(template_img, cmap=\"gray\")\n","\n","# Resize template image of PCB\n","template_img_resize = cv2.resize(template_img, (b,a))\n","plt.figure(figsize=(10,6))\n","plt.imshow(template_img_resize, cmap=\"gray\")\n","\n","# Gaussian blur\n","blur_template_img = cv2.GaussianBlur(template_img_resize, (3,3),0)\n","# display the blurred image\n","plt.figure(figsize=(10,6))\n","plt.imshow(blur_template_img, cmap=\"gray\")\n","\n","# Adaptive thresholding\n","template_adap_thresh = cv2.adaptiveThreshold(blur_template_img, 255,\n","                                         cv2.ADAPTIVE_THRESH_MEAN_C,\n","                                         cv2.THRESH_BINARY, 25, 5)\n","# display the thresholded image\n","plt.figure(figsize=(10,6))\n","plt.imshow(template_adap_thresh, cmap=\"gray\")\n","# Note: Initial display code for template is removed here to keep the final script cleaner,\n","# but it can be re-added before the loop if needed for verification.\n","\n","\n","# Use glob to find all files ending with '.jpg' in the specified folder\n","image_files = glob.glob(os.path.join(folder_path, '*.jpg'))\n","\n","print(f\"Found {len(image_files)} images in the folder.\")\n","print(f\"Defect area filter range: {MIN_AREA} < Area < {MAX_AREA}\")\n","\n","\n","\n","# Iterate over each image file path found\n","for image_path in image_files:\n","    # 1. Read and prepare test image\n","    bgr_test_img = cv2.imread(image_path)\n","    filename = os.path.basename(image_path)\n","\n","    # Check if the image was loaded successfully\n","    if bgr_test_img is None:\n","        print(f\"Skipping file: {filename} (Not a readable image).\")\n","        continue\n","\n","    # Convert BGR to RGB for matplotlib display (if needed)\n","    rgb_test_img = cv2.cvtColor(bgr_test_img, cv2.COLOR_BGR2RGB) # Not used below, but good practice\n","     # 2. Display the original test PCB image\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(rgb_test_img, cmap = \"gray\")\n","    plt.title(f\"Original Test PCB Image: {filename}\")\n","\n","    # Read grayscale\n","    test_img = cv2.imread(image_path, 0)\n","\n","    # Resize, Blur, and Threshold the test image\n","    test_img_resize = cv2.resize(test_img, (b, a))\n","    # 3. Display the grayscale resized test PCB image\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(test_img_resize, cmap=\"gray\")\n","    plt.title(f\"Resized Grayscale PCB Image: {filename} ({b}x{a})\")\n","    blur_test_img = cv2.GaussianBlur(test_img_resize, (3,3),0)\n","\n","    test_adap_thresh = cv2.adaptiveThreshold(blur_test_img, 255,\n","                                         cv2. ADAPTIVE_THRESH_MEAN_C,\n","                                         cv2.THRESH_BINARY, 25, 5)\n","\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(test_adap_thresh, cmap=\"gray\")\n","\n","    # 2. Difference Imaging (Core Defect Detection)\n","    sub_img = cv2.subtract(test_adap_thresh, template_adap_thresh)\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(sub_img)\n","    final_img = cv2.medianBlur(sub_img, 3) # Noise reduction\n","    plt.figure(figsize=(10,6))\n","    plt.imshow(final_img, cmap=\"gray\")\n","\n","    # 3. Find Contours\n","    # Using RETR_EXTERNAL focuses on the outline of defect blobs\n","    # Note: final_img is the mask image used for finding contours\n","\n","    orig = test_img_resize\n","    mask_img = final_img\n","    _, thresh = cv2.threshold(mask_img, 127, 255, cv2.THRESH_BINARY)\n","    contours, _ = cv2.findContours(thresh, cv2.RETR_LIST,\n","    cv2.CHAIN_APPROX_SIMPLE) # CHANGE ***\n","\n","    current_defects_count = 0\n","    orig = test_img_resize # The source image for cropping\n","    h_img, w_img = final_img.shape[:2]\n","\n","\n","    # 4. Filter Contours and Save Patches (MUST be inside the image loop)\n","    for cnt in contours:\n","        area = cv2.contourArea(cnt)\n","\n","        # Apply the defect area filter to ensure quality and control quantity\n","        if MIN_AREA < area < MAX_AREA:\n","            current_defects_count += 1\n","\n","            # Get bounding box coordinates\n","            x, y, w, h = cv2.boundingRect(cnt)\n","\n","            # Center of defect\n","            cx = x + w // 2\n","            cy = y + h // 2\n","\n","            # Fixed PATCh_SIZE x PATCh_SIZE box centered on defect\n","            x0 = cx - BW // 2\n","            y0 = cy - BH // 2\n","            x1 = x0 + BW\n","            y1 = y0 + BH\n","\n","            # Clip coordinates to image bounds\n","            x0 = max(0, x0)\n","            y0 = max(0, y0)\n","            x1 = min(w_img, x1)\n","            y1 = min(h_img, y1)\n","\n","            # Crop patch from ORIGINAL resized image\n","            patch = orig[y0:y1, x0:x1]\n","\n","            # Ensure patch is the full desired size\n","            if patch.shape[0] != BH or patch.shape[1] != BW:\n","                continue\n","\n","            # Save patch\n","            base_name = os.path.splitext(filename)[0]\n","            # Use global_patch_id for unique sequential naming across ALL files\n","            out_path = os.path.join(out_dir, f\"defect_{base_name}_{global_patch_id:04d}.png\")\n","            cv2.imwrite(out_path, patch)\n","\n","            # Increment the global counter\n","            global_patch_id += 1\n","\n","    print(f\"Processed {filename}: Found {current_defects_count} defects (Total saved: {global_patch_id})\")\n","\n","print(f\"\\n--- Processing Complete ---\")\n","print(f\"Total patches saved to {out_dir}: {global_patch_id}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1BMivZVQlj5yg6Cy3MGt5gdQ6LZ3DQbrt"},"id":"HEXIWQHchMbj","executionInfo":{"status":"ok","timestamp":1766762221799,"user_tz":-330,"elapsed":94295,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"35f63010-2dca-41cb-dd3d-4cf1a3f421a5"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"code","source":["%cd /content/AI_PCB_Defect_Detection_Classification_System\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cyoahIlbtRON","executionInfo":{"status":"ok","timestamp":1766870923586,"user_tz":-330,"elapsed":21,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"6b52c656-5b90-4ca4-94ed-458bd2b70912"},"execution_count":75,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/AI_PCB_Defect_Detection_Classification_System\n"]}]},{"cell_type":"code","source":["!git checkout my-branch\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mQhHAJJCtt03","executionInfo":{"status":"ok","timestamp":1766870936838,"user_tz":-330,"elapsed":66,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"80c9838d-9a11-4236-b639-776e23546ce0"},"execution_count":76,"outputs":[{"output_type":"stream","name":"stdout","text":["Branch 'my-branch' set up to track remote branch 'my-branch' from 'origin'.\n","Switched to a new branch 'my-branch'\n"]}]},{"cell_type":"code","source":["!git branch\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hnEZhqRttx4C","executionInfo":{"status":"ok","timestamp":1766870953291,"user_tz":-330,"elapsed":132,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"d0083afc-6b86-402e-de5e-ba776cf4c7c3"},"execution_count":77,"outputs":[{"output_type":"stream","name":"stdout","text":["  main\u001b[m\n","* \u001b[32mmy-branch\u001b[m\n"]}]},{"cell_type":"code","source":["!ls /content\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cnfjurj8t0gQ","executionInfo":{"status":"ok","timestamp":1766870963358,"user_tz":-330,"elapsed":64,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"9c0f8cb5-49c7-49ba-f2d8-babd7e50a8b6"},"execution_count":78,"outputs":[{"output_type":"stream","name":"stdout","text":["AI_PCB_Defect_Detection_Classification_System\n","drive\n","PCB-DEFECT-DETECTION-AND-CLASSIFICATION\n","sample_data\n"]}]},{"cell_type":"code","source":["!cp /content/drive/MyDrive/Colab Notebooks/Untitled1.ipynb .\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CAsBzu3It5oP","executionInfo":{"status":"ok","timestamp":1766871012416,"user_tz":-330,"elapsed":71,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"6b20a369-5883-4856-aaa2-946bc3cff6ce"},"execution_count":80,"outputs":[{"output_type":"stream","name":"stdout","text":["cp: cannot stat '/content/drive/MyDrive/Colab': No such file or directory\n","cp: cannot stat 'Notebooks/Untitled1.ipynb': No such file or directory\n"]}]}]}
\ No newline at end of file
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"191xbL3N1VtdtunkR4qW9cCh9IBjUjjBb","authorship_tag":"ABX9TyPUGed7kwojl9HVJz2u5Ix1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["!pip install imagehash # Install missing library"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w_idClIYNFkS","executionInfo":{"status":"ok","timestamp":1768065298454,"user_tz":-330,"elapsed":4605,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"f74f54c8-14ee-4115-d144-a28e02c7fea1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: imagehash in /usr/local/lib/python3.12/dist-packages (4.3.2)\n","Requirement already satisfied: PyWavelets in /usr/local/lib/python3.12/dist-packages (from imagehash) (1.9.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from imagehash) (2.0.2)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.12/dist-packages (from imagehash) (11.3.0)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.12/dist-packages (from imagehash) (1.16.3)\n"]}]},{"cell_type":"code","source":["import os\n","import time\n","import numpy as np\n","import torch\n","from torchvision import models, transforms\n","from torchvision.ops import nms\n","from PIL import Image, ImageDraw, ImageFont\n","import matplotlib.pyplot as plt\n","\n","import imagehash\n","from skimage.metrics import structural_similarity as ssim\n","\n","print(\"--- Step 1: Differential detection pipeline configuration ---\")\n","\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","# ===================================================================\n","# 1. LOAD CHECKPOINT (MODEL + CLASS NAMES)\n","# ===================================================================\n","model_path = \"/content/drive/MyDrive/Colab Notebooks/best_resnet50_pcb_defects_50epochs.pth\"\n","\n","checkpoint = torch.load(model_path, map_location=device)\n","\n","# restore class names from checkpoint (preferred)\n","class_names = checkpoint.get(\"class_names\", None)\n","if class_names is None:\n","    class_names = [\n","        'missing_hole',\n","        'mouse_bite',\n","        'open_circuit',\n","        'short',\n","        'spur',\n","        'spurious_copper'\n","    ]\n","\n","if 'normal' in class_names:\n","    class_names.remove('normal')\n","\n","num_classes = len(class_names)\n","\n","# recreate architecture EXACTLY as training (ResNet50)\n","defect_classifier = models.resnet50(weights=None)\n","in_features = defect_classifier.fc.in_features\n","defect_classifier.fc = torch.nn.Linear(in_features, num_classes)\n","\n","# load ONLY model weights\n","defect_classifier.load_state_dict(checkpoint[\"model_state_dict\"])\n","\n","defect_classifier.to(device)\n","defect_classifier.eval()\n","\n","# ===================================================================\n","# 2. CONFIG: GOLDEN IMAGES, WINDOW, NORMALIZATION\n","# ===================================================================\n","golden_images_dir = \"/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/PCB_USED\"\n","if not os.path.exists(golden_images_dir):\n","    raise FileNotFoundError(\n","        f\"Reference images directory '{golden_images_dir}' not found.\"\n","    )\n","\n","WINDOW_SIZE = 128\n","STRIDE = WINDOW_SIZE // 4\n","SIMILARITY_THRESHOLD = 0.95\n","CLASSIFIER_CONFIDENCE_THRESHOLD = 0.80\n","\n","# same normalization as training\n","inference_transform = transforms.Compose([\n","    transforms.ToTensor(),\n","    transforms.Normalize(\n","        mean=[0.485, 0.456, 0.406],\n","        std=[0.229, 0.224, 0.225]\n","    )\n","])\n","\n","# ===================================================================\n","# 3. GOLDEN IMAGE DATABASE\n","# ===================================================================\n","def create_golden_image_database(golden_dir):\n","    db = []\n","    print(\"Creating reference image database...\")\n","    filenames = os.listdir(golden_dir)\n","    for fname in filenames:\n","        path = os.path.join(golden_dir, fname)\n","        try:\n","            img = Image.open(path).convert('RGB')\n","            hash_val = imagehash.phash(img)\n","            db.append({\n","                'filename': fname,\n","                'image': img,\n","                'hash': hash_val\n","            })\n","        except Exception as e:\n","            print(f\"Warning: Could not load '{path}'. Error: {e}\")\n","    return db\n","\n","\n","golden_db = create_golden_image_database(golden_images_dir)\n","print(f\"{len(golden_db)} reference images loaded successfully.\")\n","\n","# ===================================================================\n","# 4. DETECTION FUNCTIONS\n","# ===================================================================\n","def find_best_match(input_image, golden_database):\n","    if not golden_database:\n","        return None\n","\n","    input_hash = imagehash.phash(input_image)\n","    best_match = min(\n","        golden_database,\n","        key=lambda x: input_hash - x['hash']\n","    )\n","    min_dist = input_hash - best_match['hash']\n","\n","    print(\n","        f\"Best match found: '{best_match['filename']}' \"\n","        f\"(hash distance: {min_dist}).\"\n","    )\n","\n","    if min_dist > 10:\n","        print(\"Warning: High hash distance, match may be incorrect.\")\n","\n","    return best_match['image']\n","\n","\n","def detect_anomalies_by_comparison(input_image, golden_image, classifier):\n","    if input_image.size != golden_image.size:\n","        raise ValueError(\n","            \"Input image and reference image must have the same dimensions!\"\n","        )\n","\n","    detections = []\n","    start_time = time.time()\n","\n","    img_width, img_height = input_image.size\n","\n","    for y in range(0, img_height - WINDOW_SIZE + 1, STRIDE):\n","        for x in range(0, img_width - WINDOW_SIZE + 1, STRIDE):\n","\n","            window_input = input_image.crop(\n","                (x, y, x + WINDOW_SIZE, y + WINDOW_SIZE)\n","            )\n","            window_golden = golden_image.crop(\n","                (x, y, x + WINDOW_SIZE, y + WINDOW_SIZE)\n","            )\n","\n","            window_input_gray = np.array(\n","                window_input.convert('L')\n","            )\n","            window_golden_gray = np.array(\n","                window_golden.convert('L')\n","            )\n","\n","            ssim_score, _ = ssim(\n","                window_golden_gray,\n","                window_input_gray,\n","                full=True\n","            )\n","\n","            if ssim_score < SIMILARITY_THRESHOLD:\n","\n","                patch_tensor = inference_transform(\n","                    window_input\n","                ).unsqueeze(0).to(device)\n","\n","                with torch.no_grad():\n","                    outputs = classifier(patch_tensor)\n","                    probabilities = torch.nn.functional.softmax(\n","                        outputs, dim=1\n","                    )\n","                    confidence, predicted_idx = torch.max(\n","                        probabilities, 1\n","                    )\n","\n","                if confidence.item() > CLASSIFIER_CONFIDENCE_THRESHOLD:\n","                    detections.append({\n","                        'box': [x, y, x + WINDOW_SIZE, y + WINDOW_SIZE],\n","                        'label': class_names[predicted_idx.item()],\n","                        'confidence': confidence.item()\n","                    })\n","\n","    print(\n","        f\"Initial detection completed in \"\n","        f\"{time.time() - start_time:.2f}s. \"\n","        f\"{len(detections)} raw anomalies found.\"\n","    )\n","\n","    if not detections:\n","        return []\n","\n","    boxes = torch.tensor(\n","        [d['box'] for d in detections],\n","        dtype=torch.float32\n","    )\n","    scores = torch.tensor(\n","        [d['confidence'] for d in detections],\n","        dtype=torch.float32\n","    )\n","\n","    keep_indices = nms(\n","        boxes,\n","        scores,\n","        iou_threshold=0.2\n","    )\n","\n","    final_detections = [\n","        detections[i] for i in keep_indices\n","    ]\n","\n","    print(\n","        f\"{len(final_detections)} final anomalies \"\n","        f\"after Non-Max Suppression.\"\n","    )\n","\n","    return final_detections\n","\n","\n","def draw_detections_on_image(image, detections):\n","    img_with_boxes = image.copy()\n","    draw = ImageDraw.Draw(img_with_boxes)\n","\n","    try:\n","        font = ImageFont.truetype(\"DejaVuSans.ttf\", 32)\n","    except IOError:\n","        print(\n","            \"Font 'DejaVuSans.ttf' not found. \"\n","            \"Using default font.\"\n","        )\n","        font = ImageFont.load_default()\n","\n","    unique_labels = list(\n","        set([d['label'] for d in detections])\n","    )\n","\n","    colors = plt.cm.get_cmap(\n","        'hsv',\n","        len(unique_labels) + 1\n","    )\n","\n","    color_map = {\n","        label: tuple(\n","            (np.array(colors(i)[:3]) * 255).astype(int)\n","        )\n","        for i, label in enumerate(unique_labels)\n","    }\n","\n","    for det in detections:\n","        box = det['box']\n","        label = det['label']\n","        confidence = det['confidence']\n","\n","        color = color_map.get(label, (255, 50, 50))\n","\n","        draw.rectangle(\n","            box,\n","            outline=color,\n","            width=5\n","        )\n","\n","        text = f\"{label} ({confidence:.2f})\"\n","\n","        try:\n","            text_bbox = draw.textbbox(\n","                (0, 0), text, font=font\n","            )\n","            text_width = text_bbox[2] - text_bbox[0]\n","            text_height = text_bbox[3] - text_bbox[1]\n","        except AttributeError:\n","            text_width, text_height = draw.textsize(\n","                text, font=font\n","            )\n","\n","        background_box = [\n","            box[0],\n","            box[1] - text_height - 5,\n","            box[0] + text_width + 10,\n","            box[1]\n","        ]\n","\n","        draw.rectangle(\n","            background_box,\n","            fill=color\n","        )\n","\n","        draw.text(\n","            (box[0] + 5, box[1] - text_height - 5),\n","            text,\n","            fill=\"white\",\n","            font=font\n","        )\n","\n","    return img_with_boxes\n","\n","\n","# ===================================================================\n","# 5. RUN PIPELINE ON EXAMPLE IMAGE (STANDALONE)\n","# ===================================================================\n","test_image_path = (\n","    \"/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/\"\n","    \"images/Missing_hole/4/04_missing_hole_04.jpg\"\n",")\n","\n","test_image_name = os.path.basename(test_image_path)\n","\n","print(f\"\\n--- Running pipeline on image: {test_image_name} ---\")\n","\n","try:\n","    input_image = Image.open(test_image_path).convert('RGB')\n","\n","    golden_image_ref = find_best_match(\n","        input_image,\n","        golden_db\n","    )\n","\n","    if golden_image_ref:\n","\n","        anomalies = detect_anomalies_by_comparison(\n","            input_image,\n","            golden_image_ref,\n","            defect_classifier\n","        )\n","\n","        result_image = draw_detections_on_image(\n","            input_image,\n","            anomalies\n","        )\n","\n","        plt.figure(figsize=(20, 15))\n","        plt.imshow(result_image)\n","        plt.title(\n","            f\"Detected Anomalies on '{test_image_name}' \"\n","            f\"by Differential Comparison\",\n","            fontsize=20\n","        )\n","        plt.axis('off')\n","        plt.show()\n","\n","    else:\n","        print(\"No matching golden image found.\")\n","\n","except FileNotFoundError:\n","    print(\n","        f\"ERROR: Test file '{test_image_path}' not found.\"\n","    )\n","except Exception as e:\n","    print(f\"An unexpected error occurred: {e}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1igO6HzzePd3za-XGCkOpR2pj9UXe0bRA"},"id":"-ZUB4UtM6KAk","executionInfo":{"status":"ok","timestamp":1768065422313,"user_tz":-330,"elapsed":23397,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"010d42c3-f74b-4150-b08f-7b9d9d683c15"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"code","source":["!git config --global user.name \"Aradhya Stuti\"\n","!git config --global user.email \"aradhya.mutants@gmail.com\"\n"],"metadata":{"id":"G6LLB3yHPFev","executionInfo":{"status":"ok","timestamp":1768372868057,"user_tz":-330,"elapsed":215,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["!ls /content\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I06eEvoVPXtY","executionInfo":{"status":"ok","timestamp":1768372931023,"user_tz":-330,"elapsed":103,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"f6979780-000e-4043-cbc5-fde89bedcca8"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["drive  sample_data\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LX5aea--QM20","executionInfo":{"status":"ok","timestamp":1768373171072,"user_tz":-330,"elapsed":13240,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"35ac3006-1ee4-49c5-8b06-1cf751474e61"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":["!cp \"/content/drive/MyDrive/Colab Notebooks/inference.ipynb\" /content/\n"],"metadata":{"id":"VufiIV2pQqTx","executionInfo":{"status":"ok","timestamp":1768373423155,"user_tz":-330,"elapsed":415,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["!git clone https://github.com/AradhyaStuti/AI_PCB_Defect_Detection_Classification_System.git\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AD3OH5kPRspk","executionInfo":{"status":"ok","timestamp":1768373655578,"user_tz":-330,"elapsed":713,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"7a548c3e-a7d2-429c-93a2-d7a3a89fbb3e"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'AI_PCB_Defect_Detection_Classification_System'...\n","remote: Enumerating objects: 20, done.\u001b[K\n","remote: Counting objects: 100% (3/3), done.\u001b[K\n","remote: Compressing objects: 100% (3/3), done.\u001b[K\n","remote: Total 20 (delta 0), reused 2 (delta 0), pack-reused 17 (from 1)\u001b[K\n","Receiving objects: 100% (20/20), 462.67 KiB | 4.58 MiB/s, done.\n","Resolving deltas: 100% (4/4), done.\n"]}]},{"cell_type":"code","source":["%cd /content/AI_PCB_Defect_Detection_Classification_System"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6srdfFScSjaB","executionInfo":{"status":"ok","timestamp":1768373861789,"user_tz":-330,"elapsed":48,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"12c2d303-2d60-4aa4-d0e2-838ccd120744"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/AI_PCB_Defect_Detection_Classification_System\n"]}]},{"cell_type":"code","source":["\n","!git checkout my-branch"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xF7wdDJeS8ZO","executionInfo":{"status":"ok","timestamp":1768373877780,"user_tz":-330,"elapsed":108,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"719ba0f2-1756-40d9-b67f-507e9ee1235a"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Branch 'my-branch' set up to track remote branch 'my-branch' from 'origin'.\n","Switched to a new branch 'my-branch'\n"]}]},{"cell_type":"code","source":["\n","!git branch"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xbDjbqAKTAzI","executionInfo":{"status":"ok","timestamp":1768373899255,"user_tz":-330,"elapsed":114,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"6243610f-0c78-4aa2-a0c8-1a554b1acffa"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["  main\u001b[m\n","* \u001b[32mmy-branch\u001b[m\n"]}]},{"cell_type":"code","source":["\n","!ls /content"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Oyk5w9L-TF3I","executionInfo":{"status":"ok","timestamp":1768373920038,"user_tz":-330,"elapsed":114,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"e19bf681-bd82-4267-e92d-9bb1ecf1b3ec"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["AI_PCB_Defect_Detection_Classification_System  drive  sample_data\n"]}]},{"cell_type":"code","source":["!cp \"/content/drive/MyDrive/Colab Notebooks/inference.ipynb\" /content/\n"],"metadata":{"id":"jltKp_GETtO0","executionInfo":{"status":"ok","timestamp":1768374068412,"user_tz":-330,"elapsed":411,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"bfoXQR_kTu62"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/trained.ipynb b/trained.ipynb
new file mode 100644
index 0000000..0fed434
--- /dev/null
+++ b/trained.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"vDORRMNZRpwQ"},"outputs":[],"source":["#Importing the standard libraries\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from matplotlib import patches\n","import seaborn as sns\n","import os\n","import random\n","import re\n","import shutil\n","sns.set_style('darkgrid')\n","sns.set_palette('pastel')\n","\n","import warnings\n","warnings.filterwarnings('ignore')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":46,"status":"ok","timestamp":1768051243605,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"RfAzIIERR3es","outputId":"cf11fd03-fad4-445f-8362-3ac298c83df0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['rotate.py',\n"," 'PCB_USED',\n"," 'images',\n"," 'rotation',\n"," 'Annotations',\n"," 'mouse bite defects',\n"," 'open_circuit_defect',\n"," 'short_defect',\n"," 'Spur_defects',\n"," 'Spurious_copper_defect',\n"," 'best_resnet18_pcb_defects_50epochs.pth',\n"," 'Missing_hole_4defects',\n"," 'Spur_1defects',\n"," 'images_combined']"]},"metadata":{},"execution_count":29}],"source":["#Defining the input\n","input_dir='/content/drive/MyDrive/PCB_DATASET/PCB_DATASET'\n","os.listdir(input_dir)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1768051246858,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"mm6Z7CRhgzHt","outputId":"bbb1be2d-7e42-44e3-ee67-fc8921d0b3fd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/PCB_USED'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":30}],"source":["template_dir=os.path.join(input_dir,'/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/PCB_USED')\n","template_dir"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":31,"status":"ok","timestamp":1768051248677,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"Q9xW8moTiDrZ","outputId":"c787a3e9-521f-4518-e6b9-ffbf2b9b038e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Spur',\n"," 'Open_circuit',\n"," 'Spurious_copper',\n"," 'Short',\n"," 'Mouse_bite',\n"," 'Missing_hole']"]},"metadata":{},"execution_count":31}],"source":["#Defining the image directory\n","img_dir=os.path.join(input_dir,'/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images')\n","\n","#Listing the types of defects\n","os.listdir(img_dir)\n","types_defect=os.listdir(os.path.join(input_dir,'/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images'))\n","types_defect"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wo0PjJWviPQg"},"outputs":[],"source":["#Creating an image path list for ready refernce\n","img_path_list=[]\n","#Creating img_path list\n","for sub_cat in types_defect:\n","    for file in os.listdir(os.path.join(img_dir,sub_cat)):\n","        img_path_list.append(os.path.join(img_dir,sub_cat,file))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26,"status":"ok","timestamp":1768051253712,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"mGJSEkVKidxJ","outputId":"2a35756c-043e-4626-fc8a-7552fcbe0ace"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Spurious_copper_angles.txt',\n"," 'Short_angles.txt',\n"," 'Spur_angles.txt',\n"," 'Missing_hole_angles.txt',\n"," 'Open_circuit_angles.txt',\n"," 'Mouse_bite_angles.txt',\n"," 'Spur_rotation',\n"," 'Spurious_copper_rotation',\n"," 'Open_circuit_rotation',\n"," 'Mouse_bite_rotation',\n"," 'Short_rotation',\n"," 'Missing_hole_rotation']"]},"metadata":{},"execution_count":33}],"source":["rotated_dir=os.path.join(input_dir,'/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/rotation')\n","os.listdir(rotated_dir)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1768051255249,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"W8QLUbOEiu60","outputId":"b1800ad0-2697-4eba-fded-b63807fad306"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Spurious_copper_angles.txt',\n"," 'Short_angles.txt',\n"," 'Spur_angles.txt',\n"," 'Missing_hole_angles.txt',\n"," 'Open_circuit_angles.txt',\n"," 'Mouse_bite_angles.txt']"]},"metadata":{},"execution_count":34}],"source":["rotated_angle_list=[j for j in os.listdir(rotated_dir) if j.endswith('.txt')]\n","rotated_angle_list"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26,"status":"ok","timestamp":1768051257427,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"HQnLhKPMi1uA","outputId":"99755b34-93ea-4cb2-fbf7-4cc67e393a9e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Spur_rotation',\n"," 'Spurious_copper_rotation',\n"," 'Open_circuit_rotation',\n"," 'Mouse_bite_rotation',\n"," 'Short_rotation',\n"," 'Missing_hole_rotation']"]},"metadata":{},"execution_count":35}],"source":["types_defect_rotated=[j for j in os.listdir(rotated_dir) if j.endswith('.txt')==False]\n","types_defect_rotated"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1768051259547,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"5OzGQ3Lbi3hl","outputId":"301b2ab6-5e44-449e-b1f4-4924e0a1c979"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/Annotations'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":36}],"source":["annote_dir=os.path.join(input_dir,'Annotations')\n","annote_dir"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1768051260900,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"boZFTPLAjLJ2","outputId":"3b13b7d5-7ac1-4258-f842-7f28f1e92e20"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Spur',\n"," 'Spurious_copper',\n"," 'Short',\n"," 'Open_circuit',\n"," 'Mouse_bite',\n"," 'Missing_hole']"]},"metadata":{},"execution_count":37}],"source":["type_annot=os.listdir(annote_dir)\n","type_annot"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":37,"status":"ok","timestamp":1768051262888,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"ruxtEUpMjQ9d","outputId":"6ceaee28-bb6e-45a8-8aff-a825e3429a3b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['11_missing_hole_08.jpg',\n"," '12_missing_hole_08.jpg',\n"," '12_missing_hole_03.jpg',\n"," '12_missing_hole_04.jpg',\n"," '11_missing_hole_07.jpg']"]},"metadata":{},"execution_count":38}],"source":["#Checking the type of files\n","file_list=os.listdir(os.path.join(annote_dir,'/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images/Missing_hole'))\n","file_list[0:5]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R4wNalXLj5yx"},"outputs":[],"source":["#importing xml ET to parse xml file\n","import xml.etree.ElementTree as ET"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cSndmXKJj9Lx"},"outputs":[],"source":["#Parsing XML to return Bounding box dimensions\n","def parse_xml(xml_file):\n","\n","    data=[]\n","\n","    tree = ET.parse(xml_file)\n","    root = tree.getroot()\n","\n","    filename = root.find('filename').text\n","    width = int(root.find('size/width').text)\n","    height = int(root.find('size/height').text)\n","    for obj in root.findall('object'):\n","        name = obj.find('name').text\n","        xmin = int(obj.find('bndbox/xmin').text)\n","        ymin = int(obj.find('bndbox/ymin').text)\n","        xmax = int(obj.find('bndbox/xmax').text)\n","        ymax = int(obj.find('bndbox/ymax').text)\n","\n","        data.append({\n","            'filename': filename,\n","            'width': width,\n","            'height': height,\n","            'class': name,\n","            'xmin': xmin,\n","            'ymin': ymin,\n","            'xmax': xmax,\n","            'ymax': ymax\n","        })\n","\n","    return data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gVFvlZYJkCl3"},"outputs":[],"source":["#Retrieving data for all files\n","data=[]\n","all_data=[]\n","\n","for x in type_annot:\n","    for file in os.listdir(os.path.join(annote_dir,x)):\n","        xml_file_path=os.path.join(os.path.join(annote_dir,x),file)\n","        data=parse_xml(xml_file_path)\n","        all_data.extend(data)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":35,"status":"ok","timestamp":1768051272875,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"P5wE-wz8kLdT","outputId":"db5ae3ef-aa31-42c3-d740-7b0d7f84e8f9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    filename  width  height         class  xmin  ymin  xmax  \\\n","0             05_spur_03.jpg   2544    2156          spur  1542   670  1628   \n","1             05_spur_03.jpg   2544    2156          spur  1905  1362  1964   \n","2             05_spur_03.jpg   2544    2156          spur   784  1073   845   \n","3             05_spur_03.jpg   2544    2156          spur   792   329   856   \n","4             05_spur_03.jpg   2544    2156          spur  1394   596  1436   \n","...                      ...    ...     ...           ...   ...   ...   ...   \n","2948  11_missing_hole_05.jpg   2282    2248  missing_hole  1755  1749  1834   \n","2949  11_missing_hole_05.jpg   2282    2248  missing_hole  1417  1744  1499   \n","2950  11_missing_hole_05.jpg   2282    2248  missing_hole  2022  1806  2122   \n","2951  11_missing_hole_05.jpg   2282    2248  missing_hole   861  1749   939   \n","2952  11_missing_hole_05.jpg   2282    2248  missing_hole  1145  1868  1217   \n","\n","      ymax  \n","0      715  \n","1     1403  \n","2     1125  \n","3      371  \n","4      690  \n","...    ...  \n","2948  1842  \n","2949  1832  \n","2950  1899  \n","2951  1837  \n","2952  1952  \n","\n","[2953 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-b1b8a97d-d36f-464d-853b-6e2526e2cbc6\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>filename</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>class</th>\n","      <th>xmin</th>\n","      <th>ymin</th>\n","      <th>xmax</th>\n","      <th>ymax</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>05_spur_03.jpg</td>\n","      <td>2544</td>\n","      <td>2156</td>\n","      <td>spur</td>\n","      <td>1542</td>\n","      <td>670</td>\n","      <td>1628</td>\n","      <td>715</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>05_spur_03.jpg</td>\n","      <td>2544</td>\n","      <td>2156</td>\n","      <td>spur</td>\n","      <td>1905</td>\n","      <td>1362</td>\n","      <td>1964</td>\n","      <td>1403</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>05_spur_03.jpg</td>\n","      <td>2544</td>\n","      <td>2156</td>\n","      <td>spur</td>\n","      <td>784</td>\n","      <td>1073</td>\n","      <td>845</td>\n","      <td>1125</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>05_spur_03.jpg</td>\n","      <td>2544</td>\n","      <td>2156</td>\n","      <td>spur</td>\n","      <td>792</td>\n","      <td>329</td>\n","      <td>856</td>\n","      <td>371</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>05_spur_03.jpg</td>\n","      <td>2544</td>\n","      <td>2156</td>\n","      <td>spur</td>\n","      <td>1394</td>\n","      <td>596</td>\n","      <td>1436</td>\n","      <td>690</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2948</th>\n","      <td>11_missing_hole_05.jpg</td>\n","      <td>2282</td>\n","      <td>2248</td>\n","      <td>missing_hole</td>\n","      <td>1755</td>\n","      <td>1749</td>\n","      <td>1834</td>\n","      <td>1842</td>\n","    </tr>\n","    <tr>\n","      <th>2949</th>\n","      <td>11_missing_hole_05.jpg</td>\n","      <td>2282</td>\n","      <td>2248</td>\n","      <td>missing_hole</td>\n","      <td>1417</td>\n","      <td>1744</td>\n","      <td>1499</td>\n","      <td>1832</td>\n","    </tr>\n","    <tr>\n","      <th>2950</th>\n","      <td>11_missing_hole_05.jpg</td>\n","      <td>2282</td>\n","      <td>2248</td>\n","      <td>missing_hole</td>\n","      <td>2022</td>\n","      <td>1806</td>\n","      <td>2122</td>\n","      <td>1899</td>\n","    </tr>\n","    <tr>\n","      <th>2951</th>\n","      <td>11_missing_hole_05.jpg</td>\n","      <td>2282</td>\n","      <td>2248</td>\n","      <td>missing_hole</td>\n","      <td>861</td>\n","      <td>1749</td>\n","      <td>939</td>\n","      <td>1837</td>\n","    </tr>\n","    <tr>\n","      <th>2952</th>\n","      <td>11_missing_hole_05.jpg</td>\n","      <td>2282</td>\n","      <td>2248</td>\n","      <td>missing_hole</td>\n","      <td>1145</td>\n","      <td>1868</td>\n","      <td>1217</td>\n","      <td>1952</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2953 rows × 8 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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2529,\n          3056,\n          2775\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 274,\n        \"min\": 1586,\n        \"max\": 2530,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2530,\n          2464,\n          2159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"spur\",\n          \"spurious_copper\",\n          \"missing_hole\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"xmin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 630,\n        \"min\": 71,\n        \"max\": 2829,\n        \"num_unique_values\": 1642,\n        \"samples\": [\n          843,\n          2631,\n          1843\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ymin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 450,\n        \"min\": 67,\n        \"max\": 2344,\n        \"num_unique_values\": 1343,\n        \"samples\": [\n          1623,\n          1752,\n          1241\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"xmax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 627,\n        \"min\": 124,\n        \"max\": 2854,\n        \"num_unique_values\": 1631,\n        \"samples\": [\n          985,\n          199,\n          1648\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ymax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 451,\n        \"min\": 137,\n        \"max\": 2391,\n        \"num_unique_values\": 1400,\n        \"samples\": [\n          749,\n          357,\n          1962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":42}],"source":["#Creating a dataframe to store the annotations\n","df_annot=pd.DataFrame(all_data)\n","df_annot"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":34226,"status":"ok","timestamp":1768051521202,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"kfNvDIJPsKCT","outputId":"5d94ebc9-0572-4cd5-cd10-90b62eff112e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","✅ Total images copied: 693\n","📁 Images in combined folder: 693\n"]}],"source":["# ⚠️ Do NOT run this cell multiple times – it will duplicate images\n","\n","import os\n","import shutil\n","\n","# ------------------ MOUNT GOOGLE DRIVE ------------------\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# ------------------ PATHS ------------------\n","# Main images directory\n","img_dir = \"/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images\"\n","\n","# Source defect class folders\n","source_dirs = [\n","    os.path.join(img_dir, \"Missing_hole\"),\n","    os.path.join(img_dir, \"Mouse_bite\"),\n","    os.path.join(img_dir, \"Open_circuit\"),\n","    os.path.join(img_dir, \"Short\"),\n","    os.path.join(img_dir, \"Spur\"),\n","    os.path.join(img_dir, \"Spurious_copper\")\n","]\n","\n","# Destination folder (combined images)\n","destination_dir = \"/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images_combined\"\n","\n","# Create destination folder if not exists\n","os.makedirs(destination_dir, exist_ok=True)\n","\n","# ------------------ COPY IMAGES RECURSIVELY ------------------\n","copied_count = 0\n","\n","for source_dir in source_dirs:\n","    if os.path.exists(source_dir):\n","        for root, _, files in os.walk(source_dir):\n","            for file in files:\n","                src_path = os.path.join(root, file)\n","\n","                # Copy only files (images)\n","                if os.path.isfile(src_path):\n","                    dst_path = os.path.join(destination_dir, file)\n","\n","                    # Avoid overwriting files with same name\n","                    if not os.path.exists(dst_path):\n","                        shutil.copy(src_path, destination_dir)\n","                        copied_count += 1\n","    else:\n","        print(f\"❌ Directory not found: {source_dir}\")\n","\n","# ------------------ FINAL COUNT ------------------\n","print(f\"✅ Total images copied: {copied_count}\")\n","print(f\"📁 Images in combined folder: {len(os.listdir(destination_dir))}\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"executionInfo":{"elapsed":181,"status":"ok","timestamp":1768051567073,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"Q8wHjl0ntzeY","outputId":"32e759c0-431f-4b4c-ca05-5750e679ba58"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0, 'No of defects in one PCB')"]},"metadata":{},"execution_count":47},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["#Visualizing the no of defects in each pcb\n","df_multiple_defects=pd.DataFrame(df_annot['filename'].value_counts())\n","sns.countplot(df_multiple_defects,x='count')\n","plt.xlabel('No of defects in one PCB')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nExZBTGnuV67"},"outputs":[],"source":["#Defining a function to view image along with bounding box\n","\n","def draw_bounding_boxes(image_path, bounding_boxes,annotation):\n","    \"\"\"\n","    Draws multiple bounding boxes on an image using Matplotlib.\n","\n","    Args:\n","        image_path: The path to the image file.\n","        bounding_boxes: A list of bounding boxes, each represented as a tuple or list containing\n","                       (min_x, min_y, max_x, max_y).\n","    \"\"\"\n","\n","    # Load the image\n","    img = plt.imread(image_path)\n","\n","    # Create a figure and axis\n","    fig, ax = plt.subplots(figsize=(15,10))\n","\n","    # Display the image\n","    ax.imshow(img)\n","\n","    # Draw each bounding box\n","    for bbox in bounding_boxes:\n","        min_x, min_y, max_x, max_y = bbox\n","        width = max_x - min_x\n","        height = max_y - min_y\n","        rect = patches.Rectangle((min_x, min_y), width, height, linewidth=1, edgecolor='red', facecolor='none')\n","        ax.add_patch(rect)\n","\n","        # Calculate the centroid of the bounding box\n","        centroid_x = (min_x + max_x) / 2\n","        centroid_y = (min_y + max_y) / 2\n","\n","        # Add the annotation to the centroid\n","        ax.annotate( annotation,(centroid_x,centroid_y),(max_x+20,max_y+20),\n","            fontsize=10,color='white',\n","            horizontalalignment='right', verticalalignment='top')\n","\n","    plt.grid(False)\n","    plt.xticks([])\n","    plt.yticks([])\n","\n","\n","\n","    # Show the plot\n","    plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rAP6I77MP58E"},"outputs":[],"source":["#Getting filename from filepath\n","filepath=img_path_list[0]\n","filename=re.sub(r'.+/([\\w_]+\\.jpg)',r'\\1',filepath)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":35,"status":"ok","timestamp":1768051589941,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"j8PXRsXcdNte","outputId":"791b528b-9a53-43b4-c4e8-efb5912f4645"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images/Spur/09_spur_06.jpg'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":50}],"source":["filepath"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1768051591895,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"rPrqCUBQdT2p","outputId":"fda734eb-2291-4b67-ff9c-a572ed5b49e0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'09_spur_06.jpg'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":51}],"source":["filename"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o7ao4WrjdX25"},"outputs":[],"source":["#Defining function to select the a file and return image along with bounding box\n","def visualize_annotations(list_image_path,df):\n","    for i in list_image_path:\n","        filepath=i\n","        filename=re.sub(r'.+/([\\w_]+\\.jpg)',r'\\1',filepath)\n","        df_selected=df[df['filename']==filename]\n","        width=df_selected['width'].values\n","        height=df_selected['height'].values\n","        # Corrected: Use 'class_name' instead of 'class'\n","        class_name=df_selected['class'].values\n","        xmin=df_selected['xmin'].values\n","        ymin=df_selected['ymin'].values\n","        xmax=df_selected['xmax'].values\n","        ymax=df_selected['ymax'].values\n","\n","        bbox=zip(xmin,ymin,xmax,ymax)\n","        draw_bounding_boxes(filepath, bbox,class_name[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"13_Gh8Q3Z8nK0D0nqaxw6mu-PwCfjY-fi"},"executionInfo":{"elapsed":5387,"status":"ok","timestamp":1768051604036,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"R70z5WR2dbwo","outputId":"0c295a88-876f-48b2-b2f7-be458f045751"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["random.shuffle(img_path_list)\n","\n","visualize_annotations(img_path_list[0:5],df_annot)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_zNZ04ADd2MD"},"outputs":[],"source":["destination_dir = \"/content/drive/MyDrive/PCB_DATASET/PCB_DATASET/images_combined\"\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":557},"executionInfo":{"elapsed":2676,"status":"ok","timestamp":1768051664548,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"2GAFjHuredYY","outputId":"62747726-8400-4953-d466-9567ab2d4271"},"outputs":[{"output_type":"stream","name":"stdout","text":["PyTorch Version: 2.9.0+cpu\n","Using device: cpu\n","Preview of the provided annotation DataFrame:\n","         filename  width  height class_name  xmin  ymin  xmax  ymax\n","0  05_spur_03.jpg   2544    2156       spur  1542   670  1628   715\n","1  05_spur_03.jpg   2544    2156       spur  1905  1362  1964  1403\n","2  05_spur_03.jpg   2544    2156       spur   784  1073   845  1125\n","3  05_spur_03.jpg   2544    2156       spur   792   329   856   371\n","4  05_spur_03.jpg   2544    2156       spur  1394   596  1436   690\n","\n","6 classes detected in the DataFrame: ['missing_hole', 'mouse_bite', 'open_circuit', 'short', 'spur', 'spurious_copper']\n","Number of defects - Train: 2054, Validation: 454, Test: 445\n","\n","Visualizing a batch of cropped defect patches...\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x800 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAABukAAAFNCAYAAAANeILsAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsfXmAHUW19+9U9b2zZrISkJCIgBMwC4QlEJYgEREI+xZZIsjmAwFFEUH8UBQFUZ8PEld22VEWxceubEaMAk9ABQSRHQETyJ6Z213n+6OWru7b986dyUwmCfWDyb23u7q61lOnzqlzDjEzIyAgICAgICAgICAgICAgICAgICAgICAgICAgYLVBDHYBAgICAgICAgICAgICAgICAgICAgICAgICAgLebwhKuoCAgICAgICAgICAgICAgICAgICAgICAgICA1YygpAsICAgICAgICAgICAgICAgICAgICAgICAgIWM0ISrqAgICAgICAgICAgICAgICAgICAgICAgICAgNWMoKQLCAgICAgICAgICAgICAgICAgICAgICAgICFjNCEq6gICAgICAgICAgICAgICAgICAgICAgICAgIDVjKCkCwgICAgICAgICAgICAgICAgICAgICAgICAhYzQhKuoCAgICAgICAgICAgICAgICAgICAgICAgICA1YygpAsICAgICAgICAgICAgICAgICAgICAgICAgIWM3olZLurLPOwvjx4zF+/Hjss88+A1WmAcNZZ52FGTNmDHYxAtZQzJ49G7Nnz+73fNeUcTd79ux+n7fjx4/HnDlz+jXPgIAinHXWWZgyZUpDacO4DAjoX8yYMQNnnXXWan/va6+9hvHjx+PWW29d7e/uDwxUuw0Uv9JbzJgxA5/5zGf6Lb+1vb/XVsyZMwfjx48flHevKWN5TUAY/wHvd4S9eO+xLu15Bqoug8XD5jF+/Hh84xvf6Lf85s+fj/Hjx2P+/Pn9lmfAwCHQt95jXaJv/Y01pd8DinH//fc73dn48ePx9NNPN/xsry3phg8fjosuughf/OIXM9dnzJgRJlBAr7AqY+ass84Km/q1GFYQ0Vemcm2gN6syRm+99dZBE5iti3jiiScwZ84cLF68uM95rOv9GeZkfawNffh+wUMPPVRzrK2KgHv27NlrhBAnoG+wwqrXXnutT88H5ciagbfeegtz5szBM888U3Uv0PCA1YlVGTOrylOtLoS9+PsX7we+P6A2An2rj0Df1m4E+hawupEfMxMnTsRFF12EWbNm9TqvqLcPtLa2Yv/99+/1i9YEfPOb3wQzD3YxAtZQXH755QOSbxh3AQGrF0899RSklO73//3f/2Hu3Lk48MAD0dHRMYglCwhYO3H33XeDiFb7e8eMGYOnnnoKUZSyqw899BCuu+46nHrqqau9PL3FQLXbQPErAe9PnHTSSTjxxBMH5d35sfz2229j7ty5GDNmDLbYYotBKdNgoYjeBQS8nxD24u9v5Pdv/YXB4mEDAnwE+hbQnwj9vmZjgw02wP77748kSXDTTTf16tn3xS5g+fLlaG1tRalUGuyivO/BzOjq6kJzc/NgF6UK5XJ5QPIN4+79BUtvAgYPTU1Ng12EtQZxHEMpNdjFGFCEObnqaGR9HIh2JqK1ej4PFF8xUPkGvD8RRVGPiiGlFCqVSr/PxzCW03W4XC6v1fRuXcG6zjOEvXjAmoqBon9hnXn/INC3gHUdQbex5mCg6E2v3V02ikqlgrlz52KPPfbApEmTsP322+Pwww/HvHnzXBobY+jVV1/Fcccdh6222go777wz5s6dm9EK1/K3XOS73+b5yiuv4IQTTsCUKVNwxhlnuHt5v63Lly/HhRdeiF133RUTJ07EJz7xCVx++eWZ99eLEZD3k7t06VJ861vfwowZMzBx4kRMmzYNn/70p/G3v/2tV+23ePFifPvb33b5TJ8+HWeeeSYWLlzo0ixYsABf+cpXsOOOO2LSpEnYb7/9cNtttxW20eWXX46rrroKu+22GyZPnoyjjjoK//jHPzJpG+0PQG/Wr7rqKsycOROTJk3CjjvuiHPPPReLFi3KpLPxSh555BEcdNBBmDx5Mm688cZetUUj8Ot53XXX4WMf+xi23HJLHHvssXjzzTfBzPjhD3+I6dOnY/LkyTjppJPw3nvvZfIo8hN9zTXXYObMmdhyyy2x3Xbb4aCDDsIdd9zh7jfS3/lx55f1pptuwu67746JEyfi4IMPxlNPPVVVt7vuugt77703Jk2ahH322Qf33XffKvkgfuGFFzB79mxsueWW2GWXXXDppZdWpWlkbNXCW2+9hbPPPhs77rgjJk6ciJkzZ+KXv/xln8raKNZEetPfsHNp/vz5bi7tu+++rpz33nsv9t13X0yaNAkHHXQQ/v73v1fl8eijj+KII47AVltthW233RYnnXQS/vnPf2bS1BpbRTFr5s2bh8MPPxzbbrstpkyZgk984hP47//+70ya7u5uXHLJJfj4xz+OiRMnYtddd8VFF12E7u7uPrVDI/TJp8tz5szBRRddBAD42Mc+5vxC+y7RfvWrX7k2nTp1Kk4//XS8+eabfSpfo3j66adx3HHHYfvtt8fkyZMxY8YMnH322e5+b2h3LR/39WjPVVddhd133x2TJk2qGgP9gffDnASA6667DjNnzsTEiROx884747zzzqtyq2p99P/1r3/FJz/5SdffN9xwQ1V+jc4XG9fi/vvvxz777ONo7cMPP9zrOiilcPXVVzv6scMOO+C4447L+E7Px/OwbnL+9Kc/4etf/zqmTZuGXXfd1d1/6KGHcNRRR2HKlCnYeuutcfDBB2fWzlrxQfJjOd/HZ511Fq677jrXBvZvIGDH3Z133om5c+dil112wZQpU3DaaadhyZIl6O7uxre+9S1MmzYNU6ZMwdlnn13VT/l6NjIv3nnnHZx99tmYPn26G1cnnXRShmbl28kv649//GNMnz4dkyZNwtFHH42XX365qm6WT5o8eTIOOeQQPPbYY6sUK+Oxxx7DIYccgkmTJuFjH/sYbr/99qo0r776Kk477TRMnToVW265JQ477DA8+OCDDeX/z3/+0z1r17jf/va3fSprb/D3v/8dxx9/PLbeemtMmTIFRx99NP7yl79k0ti58Oc//xnnnnsutt9+e2y99dY488wzq3hiQM8Nuw5PmTIFJ554Ip5//vlMGkvH3nrrLZx88smYMmUKdthhB3znO99BkiS9rseTTz6JE044Adtttx222mor7Lvvvrj66qvd/aL13dKYX//6147Xf+SRRwBoPu8rX/kKdt55Z0ycOBEzZszA1772NTf+a8W4s21VayzPnz8fhxxyCADg7LPPdvN7oNyQrmnrcK395mDwbo2gJzoP6D2M5a+23357nHHGGXjrrbeqyt4IL9Cb/gAaoxs9rWX9iUZou+X1f//732P//ffHpEmTsPfee+Pee+/N5NWbORb24mEvvjr24nbsPfbYYzj//POxww47YNttt8W5556L7u5uLF68GGeeeSa22247bLfddrjooovq7t+Axvr2pZdewqmnnoqddtoJkyZNwvTp03H66adjyZIlLk0tHvbxxx/HBRdcgB122AFbbbUVPvvZz2ZkbYDmkefMmYOdd94ZW265JWbPno0XXnhhleLcNcK7N8J/1MKTTz6J4447Dttssw223HJLHHXUUXj88cf7VNZGEehboG8W6yJ9WxPlGkG3se7qNgbMkm7u3Ln46U9/ikMPPRSTJ0/G0qVL8de//hV/+9vfsNNOO7l0SZLg+OOPx5ZbbokvfelLeOSRRzBnzhwkSYLPfe5zfXp3HMduYfryl79cU7PJzDjppJPcpnCLLbbAI488gosuushtQHuLr33ta7jnnntw1FFHYdNNN8V7772Hxx9/HP/85z8xYcKEhvJYtmwZjjzySPzzn//EwQcfjI985CN499138bvf/Q5vvfUWRowYgZUrV2L27Nl45ZVXcOSRR2KjjTbC3XffjbPOOguLFy/G0Ucfncnz9ttvx7Jly3DEEUegq6sL11xzDY4++mjccccdGDVqlEvXaH+ce+65uO2223DQQQdh9uzZeO2113Ddddfh73//O2644YaMZv9f//oXvvjFL2LWrFk47LDD8KEPfajX7doo7rjjDlQqFcyePRvvvfceLrvsMnz+85/HDjvsgPnz5+OEE07Ayy+/jGuvvRbf+c53cMEFF9TM6+abb8b555+PT3ziE/jUpz6Frq4uPPfcc3jyySex7777Ali1/v7Nb36DZcuWYdasWSAiXHbZZTj11FNx//33u/Z78MEHcfrpp6OzsxNf/OIXsWjRIpxzzjlYf/31+9Q+ixYtwvHHH4+Pf/zj2GuvvXDPPffge9/7Hjo7O92mtLdjy8d//vMfHHbYYSAiHHnkkRgxYgQefvhhnHPOOVi6dCmOOeaYPpW7J6wN9KY/8PLLL+OLX/wiPvnJT2K//fbDFVdcgf/6r//Ceeedhx/84Ac4/PDDAQA/+9nP8PnPfx533303hNBnMf7whz/ghBNOwEYbbYRTTjkFK1euxLXXXovDDz8ct956KzbaaKNeleX555/HZz7zGYwfPx6nnXYayuUyXn75ZTzxxBMujVIKJ510Eh5//HEcdthh2HTTTfGPf/wDV199NV566SX86Ec/6tU7+9J/H//4x/HSSy/hN7/5Dc4++2wMHz4cADBixAgAwI9//GNcfPHF2GuvvXDIIYdg4cKFuPbaa3HkkUfi9ttvHxD3mAsWLMBxxx2H4cOH48QTT0RHRwdee+013HfffVVpG6XdvcGtt96Krq4uHHbYYSiXyxg6dGi/W9O9H+bknDlzMHfuXOy44444/PDD8a9//Qs33HADnn766ap1cNGiRTjxxBOx1157YebMmbjrrrvw9a9/HaVSyQmleztfHn/8cdx777044ogj0NbWhmuuuQannXYaHnjgATfOG8E555yDW2+9FdOnT8chhxyCJEnw2GOP4cknn8SkSZPqPnveeedhxIgR+OxnP4vly5cD0OPrK1/5Cj784Q/jM5/5DIYMGYJnnnkGjzzyiFs7+4pZs2bh7bffxrx585zyfaDxs5/9DM3NzTjxxBMd/xBFEYgIixcvximnnIInn3wSt956K8aMGYNTTjmlZl6NzItTTz0VL7zwAo466iiMGTMGCxcuxLx58/Dmm2/2SKcvvfRSEBGOPfZYLF26FJdddhnOOOMM/OIXv3Bprr/+enzjG9/Atttui2OOOQavv/46PvvZz6KjowMbbLBBr9vn5Zdfxuc+9zkccsghOPDAA3HLLbfgrLPOwoQJE/DhD38YgOYNPvnJT2LFihWYPXs2hg8fjttuuw0nnXSSU0rXwvPPP4/DDz8c66+/Pk444QS0trbirrvuwmc/+1nMmTOn7rOrgueffx5HHnkk2tracPzxxyOKItx0002YPXs2rr32Wmy55ZaZ9N/4xjfQ0dGBU045xdGCN954A9dcc41zs3X77bfjrLPOws4774wzzjgDK1aswA033IAjjjgCt912W6Z/kyTBcccdh8mTJ+PMM8/Eo48+iiuuuAJjx47FEUcc0XA95s2bh8985jMYPXo0PvWpT2HUqFH45z//iQcffLAuPwcAf/zjH3HXXXfhyCOPxPDhwzFmzBi89dZbOOSQQ7BkyRIcdthh2GSTTfDWW2/hnnvuwcqVK1fppPimm26K0047DZdccglmzZqFbbbZBgCw9dZb9znPWlhb1uHB4N0aLX9PdP7WW2/F2WefjUmTJuELX/gCFixYgJ///Od44oknqvir3vACjfRHb+lG0VrW32iUtr/00ks4/fTT8clPftLR1M997nO47LLLMvxTbxD24sUIe/H+x/nnn49Ro0bh1FNPxZNPPombbroJQ4YMwf/93//hAx/4AE4//XQ8/PDDuPzyy9HZ2YkDDjigZl499W13dzeOO+44dHd346ijjsKoUaPw1ltv4cEHH8TixYsxZMiQHstq1+3XX38dV199Nb7xjW/gf/7nf1ya73//+7jsssuw2267YZdddsGzzz6L4447Dl1dXX1qn0Z4997yHz4effRRnHDCCZg4cSJOOeUUEBFuvfVWHH300bj++usxefLkPpW7JwT6FugbsO7St7VBrhF0G+uQboN7gS9/+cu82267NZR2v/324xNPPLHH/Do7O/mb3/ymu6aU4hNPPJEnTJjACxYsYGbmP/7xj9zZ2cl//OMfM8+/+uqr3NnZybfccktVnt/73vd6LP99993HnZ2d/KMf/SiT7tRTT+Xx48fzyy+/XPM9Fp2dnXzJJZe439tssw2fd955devdEy6++GLu7Ozke++9t+qeUoqZma+66iru7OzkX/3qV+5ed3c3z5o1i7faaitesmRJpuyTJ0/mf//73y7tk08+yZ2dnfztb3/bXWu0P/785z9zZ2cn//rXv86U7eGHH666vttuu3FnZyc//PDDq9IkPcLWc4cdduDFixe769///ve5s7OT99tvP65UKu76F77wBZ4wYQJ3dXW5a0cddRQfddRR7vdJJ53EM2fOrPveRvo7P+5sWadOncrvvfeeu37//fdzZ2cn/+53v3PX9tlnH54+fTovXbrUXZs/fz53dnY2PBctjjrqKO7s7OTbbrvNXevq6uKddtqJTz31VHet0bHFXD3+v/KVr/BOO+3ECxcuzLz79NNP52222YZXrFjRqzI3ijWR3vQ37Fx64okn3LVHHnnEze/XX3/dXb/xxhur6rD//vvztGnT+N1333XXnnnmGd588835zDPPdNdq0flLLrmEOzs73e8rr7ySOzs7XbsV4fbbb+fNN9+c//znP2eu33DDDdzZ2cmPP/54Y5XnxvuPuXpcXnbZZdzZ2cmvvvpqJs/XXnuNt9hiC/7xj3+cuf7cc8/xRz7ykarr/QW79jz11FM10/SGdudpl0Ut2rP11lvX7bf+wLo+JxcsWMATJkzgY489lpMkcdevvfZa7uzs5F/+8pfumqW9V1xxhbvW1dXl5mR3dzcz926+dHZ28oQJExyfwqznc2dnJ19zzTUN1+PRRx+t6gMLy28wa/rz5S9/2f2+5ZZbuLOzkw8//HCO49hdX7x4MU+ZMoUPPfRQXrlyZcP5WeTHclEfn3feeRlaNFCw426fffZxfcSs+Yfx48fz8ccfn0k/a9asKtqZr2dP82LRokXc2dnJl112Wd2y5dvJlnWvvfbK8DVXX301d3Z28nPPPcfMetxNnTqVDz744AxPdOutt3JnZ2chHakHuy75Y3bBggU8ceJEvvDCC921b33rW1Xpli5dyjNmzODddtvNzaGi/j766KN5n332ydRLKcWzZs3iPfbYo1fl7Q1OPvlknjBhAr/yyivu2ltvvcVTpkzhI4880l2zc+HAAw/MjJNLL72UOzs7+f7773f13XbbbfmrX/1q5j3vvPMOb7PNNpnrlo7NnTs3k/aAAw7gAw88sOE6xHHs2njRokWZe/58zK/vzJrGbL755vz8889nrp955pm8+eabF65fNs+i/JjTtvLX4vxYfuqpp2ruufoTa+I6XDT+B4N36wmN0Pnu7m6eNm0a77PPPpk0DzzwAHd2dvLFF1+cKXsjvEBv+qNRulFrLetvNErbLU2955573LUlS5bwTjvtxAcccIC71ps5FvbiYS/OPPB7cTv2jj322Mz6MmvWLB4/fjyfe+657locxzx9+vQqmtlbudrf//537uzs5Lvuuqtu2WrxsMccc0ymrN/+9rd5iy22cOP3nXfe4Y985CN88sknZ/KbM2cOd3Z2FvKx9dAo794o/5HfHymleI899qjqgxUrVvCMGTP405/+dK/K2ygCfQv0jXndpm9rolwj6DbWDt2GpVv19ht5DJi7y46ODjz//PN46aWXekx75JFHuu9WK16pVPDoo4/2+f3WoqQeHn74YUgpq8yOjz32WDBzn9xGdXR04Mknn6xy5dEb3Hvvvdh8880LTwfb07gPP/ww1ltvPeyzzz7uXqlUwuzZs7F8+XL8+c9/zjy3++67Z05ETJ48GVtuuSUeeuihqnf01B933303hgwZgp122gkLFy50fxMmTEBra2uV6e5GG22EXXbZpQ8t0XvsueeemVNT9rTQfvvtl4m3MXnyZFQqlbr91NHRgX//+9+FZuF+mr729957742hQ4e639tuuy0A7RIK0Kbc//jHP3DAAQegra3NpZs6dSo6Ozt7/T4AaG1txf777+9+l8tlTJo0yb0T6P3YsmBm3HvvvZgxYwaYOTM2dt55ZyxZsqTXptGNYm2gN/2BzTbbDFOmTHG/7Um6HXbYARtuuGHVdduvb7/9Np555hkceOCBGDZsmEu3+eabY8cddyykAz3BnoD+7W9/W9MK6+6778amm26KTTbZJDMedthhBwCoohWNoD/777777oNSCnvttVemfKNGjcIHP/jBPpWvEVga9eCDD6JSqdRN2xva3Sj22GMPZ0k4UFjX5+Qf/vAHVCoVfOpTn3LWqgBw6KGHor29vap/oijCrFmz3O9yuYxZs2ZhwYIFji72dr7suOOOGDdunPu9+eabo729PUPPe8K9994LIiq0/rL8Rj0cdthhkFK63/PmzcOyZctw4oknVsUWaSS/NRH7779/5gTd5MmTwcw4+OCDM+kmT56MN998E3Ec18yrp3nR3NyMUqmEP/3pT4WuEnvCQQcdlLFkyvMVf/3rX/Hee+/hsMMOy/BE++67b4Yf6Q0222wz9x5AWyl/6EMfyozDhx56CJMnT86ka2trw6xZs/D666/jhRdeKMz7vffewx//+EfstddeWLp0qZsT7777LnbeeWe89NJLq8Rv10KSJJg3bx523313jB071l0fPXo09tlnHzz++ONYunRp5plZs2Zlxsnhhx+OKIocLfjDH/6AxYsXY+bMmZn5LYTAlltuWbje5OnYNttsk3Hz1BP+/ve/47XXXsOnPvWpKqvwRubjdttth80228z9Vkrh/vvvx2677VZoZbs2zfG1YR0eLN6tJzRC5//6179iwYIFOPzwwzNpPvrRj2KTTTYpdHXbKC/QU3/0hW7k17L+Rm9o++jRozMygPb2dhxwwAH4+9//jnfeeadP7w978WKEvXj/45BDDsmsBZZnsl4jAEBKiYkTJ/bIr/bUt+3t7QCA3//+91ixYkWvy2otciy23XZbJEmC119/HYC2SovjuMp6/aijjur1uyx64t37wn9YPPPMM3jppZew77774t1333V9v3z5ckybNg1//vOfByQOeaBvgb5ZrKv0bW2QawTdxrqj2xgwd5ennXYaTj75ZHziE59AZ2cndt55Z+y///7YfPPNM+mEEJkFCIAzGbQLZG8RRVFDLntef/11jB492i3wFptuummf33/GGWfgrLPOwkc/+lFMmDABu+66Kw444ICqOtbDK6+8gj322KPHsn/wgx/MCAf9sr/xxhuZ6x/84Aer8th4441x1113Za410h8vv/wylixZgmnTphWWbcGCBZnfvXXFsir4wAc+kPltF9Fa1xctWlSzb0444QT84Q9/wKGHHooPfvCD2GmnnbDPPvs49zvAqvV3vkx2EbXxjGwf+oycxQc/+MHCmGM9YYMNNqgSogwdOhTPPfec+93bsWWxcOFCLF68GDfddBNuuummmmkGAmsDvekP1BrH+fdbmpYfS0Xm2Jtuuil+//vfuyC0jWLvvffGL37xC3z1q1/F97//fUybNg0f//jHseeee7qx8/LLL+Of//xnw7SiJ/R3/7300ktg5pr01me2+xNTp07FJz7xCcydOxdXXXUVpk6dit133x377rtvlauwRml3b7A6aPK6PiftnNpkk00y18vlMsaOHVtV9tGjR1fNr4033hiArudWW23V6/mSpweApuf5mHj18Morr2D06NEZAXBvkB9Lr7zyCgA4N4frAvwDEEB9vkIphSVLltR0N9rTvCiXyzjjjDPwne98BzvttBO23HJLfPSjH8UBBxyA9dZbr9dltQL5nviKKIowZsyYHvMvQq1x6Atq3njjjUL3THb+vPHGG4UCgVdeeQXMjIsvvhgXX3xx4fsXLFjQZ7c8tbBw4UKsWLGi5pqplMKbb76ZGed5Wt3W1ob11lvP0QK7sa/lxie/F2lqaqpS4uTbtSdYoUhfhS35+b1w4UIsXbp0nZjfa8M6PFi8W09ohM7XK/smm2xSFR+pN7xAT/3RF7ox0HxRb2j7Bz/4waq9ms8vNLIW5BH24o2VNezFVx294Zl6Ws966tuxY8fi05/+NK688krccccd2HbbbTFjxgzst99+Pbq6LCprozzTsGHD+nywqSfevS/8h4XlM7785S/XfP+SJUv6XPZaCPQt0DeLdZW+rQ1yjaDbWHd0GwOmpNtuu+1w33334be//S3mzZuHX/7yl7j66qtx3nnn4dBDD+1VXrVOZtY6CVIulxveaKzK+4uCt++9997Ydtttcd9992HevHm4/PLLcemll2LOnDkDFoh6dUMphZEjR+J73/te4f28UGEgY3TlUesUZK3xwLmgkT423XRT3H333XjwwQfxyCOP4N5778X111+Pz372szjttNMArFp/1yprvTKtKgbylKidj/vttx8OPPDAwjRFQYD7A+sSvamHWv3Xn2OpUXrX3NyM6667DvPnz3dz5M4778RNN92EK664AlJKKKXQ2dmJs88+uzDP1aXcrAWlFIgIl156aWEb9kbw1RsQES655BL85S9/wQMPPIBHHnkEX/nKV3DllVfipptuypxmWxUUrVHA6qHJ75c52Z/o7XwZjDUkj7wVxaoiSZIBXaf6glpjqS98RSPz4phjjsGMGTNw//334/e//z0uvvhi/OxnP8PVV1+Nj3zkI30q69rOVxx77LE1Ty0WCRfWRNg+uOiiiwqFUPl2XBPmQV/Xit7smwYL69o63J+829qOvtCN/l7LirAqtD2P3s6xsBfvXVnX9jVzMPbiFr3lmeqhkb4966yzcOCBBzq+6vzzz8dPf/pT3HzzzT3uM9cknqk/3mnzOPPMM7HFFlsUphmovW2gb4G+1XtnfyDIGvsHQbeRxZqo2xgwJR2gT5kcfPDBOPjgg7Fs2TIcddRRmDNnTmYgK6Xw6quvZk6L/Otf/wIAd6rXnmpZsmRJJv++aqMtxowZg0cffRRLly7NaJxffPHFzPvzpw4samn5R48ejSOPPBJHHnkkFixYgAMPPBA/+clPGh7I48aNw/PPP99j2Z977jkopTKT1pY9fzLo5ZdfrsrjpZdeqjo53Uh/jBs3Do8++ii23nrr1booDgZaW1ux9957Y++990Z3dzdOPfVU/OQnP8FnPvMZt6Fb1f6uBduH9sSqj6L+7C/0dmxZjBgxAm1tbVBKYccddxyw8tXCmk5vBhO2z2xdfbz44osYPny4Y9o7OjoKLXGK6J0QAtOmTcO0adNw9tln4yc/+Ql+8IMfYP78+c6dx7PPPotp06b1ixusRvqvCLXePW7cODAzNtpoowENMl0LW221FbbaaiucfvrpuOOOO3DGGWfgzjvvzIzZRmj30KFDC13G1FqjVhfW5Tlp59SLL76YOaHV3d2N1157rYoGvv3221UWD/bEq7+29ud8aQTjxo3D73//e7z33nt9tqbL5wfooPdFp9wsaln8vfHGGz2ezlubXOoVoZF5MW7cOBx77LE49thj8dJLL+GAAw7AFVdcUXMD0Sh8vsK6UQV0UPLXX399wDa3G264Yc31xy9XHnYslEql1cpXjBgxAi0tLTXLLISoOqH88ssvZ9p02bJleOeddzB9+nQAaV1Gjhy52upi3/mPf/yjX945YsQItLe397hH8a0RfDebjaxJq3t+r8nr8GDxbj2hETrvlz1/Ovlf//pX1ZzvDX/XU38MFt1oBI3Q9pdffhnMnJkLeX5hVebY2oawF1979uIDgUb6dvz48Rg/fjxOPvlkPPHEEzj88MNxww034PTTT1+ld/v97/Om7777bp9ckjeCvvAfFraM7e3tg9L/gb71HoG+rT30bU2XawTdxrqj2xiwo+bvvvtu5ndbWxvGjRuH7u7uqrTXXXed+87MuO6661AqlRxTP2bMGEgpq3yR3nDDDatUxunTpyNJksz7AeCqq64CEbmNdXt7O4YPH47HHnssk+7666/P/E6SpGqyjRw5EqNHjy6sdy3sscceePbZZ3HfffdV3bMnH6ZPn4533nkHd955p7sXxzGuueYatLa2Yrvttss8d//992d8yT711FN48sknXR199NQfe+21F5IkwY9+9KOqZ+M47pWrrTUZ+TFcLpex6aabgplRqVT6rb9rYf3110dnZyduv/12LFu2zF3/05/+hH/84x+rnH8t9HZsWUgp8YlPfAL33HNPYfkG0r3G2kBvBhOjR4/GFltsgdtvvz0zP//xj39g3rx5mUV23LhxWLJkCZ599ll37e23366iR++9917Ve+ypPdvue+21F9566y3cfPPNVWlXrlyJ5cuX97ouPfVfEVpaWgBUM0N77LEHpJSYO3du1akyZq4aV/2FRYsWVb0v33YWjdDusWPH4sUXX8zMsWeffRZPPPHEQBS/Iazrc3LHHXdEqVTCNddck+nLX/7yl1iyZEkV4xrHccY1R3d3N2666SaMGDECEyZMADAw86Un7LHHHmBmzJ07t+peX05a7rzzzmhra8NPf/pTdHV11cxv7NixePLJJzPj4YEHHsCbb77Z4zvsfF4beY2e5sWKFSuq2m3cuHFoa2vrF75i4sSJGDZsGG6++eZM7Lw77rhjwAROALDrrrviqaeewv/93/+5a8uXL8fNN9+MMWPGZOKe+Rg5ciSmTp2Km266CW+//XbV/YHiK6SU2GmnnfDb3/42EwPuP//5D37zm99gm222qXInc9NNN2Vim91www2I49jR6l122QXt7e346U9/WhgDbSDqMmHCBGy00Ub4+c9/XjVf+jK/hRDYfffd8cADD+Dpp5+uum/ztEocn2YvX74ct99+e4/vWF3ze21YhweLd+sJjdD5iRMnYuTIkbjxxhsz+T700EP45z//iY9+9KNV+TbK3/XUH4NFN+qhN7Q9329Lly7F7bffji222MJZ4a7KHFubEPbia89evL/RSN8uXbq0Kg5wZ2cnhBD90v/Tpk1DFEVV+4287LA/0Rf+w2LixIkYN24crrjiisx4tRio/g/0rW8I9G3toW9rg1wj6DbWHd3GgFnSzZw5E1OnTsWECRMwbNgwPP3007jnnnuqAq02NTXhkUcewZe//GVMnjwZjzzyCB588EH813/9lzMtHDJkCPbcc09ce+21ICKMHTsWDz74YK/jGeUxY8YMbL/99vjBD37gThDPmzcPv/3tb3H00UdnXGEceuih+NnPfoZzzjkHEydOxGOPPVZ1wmXZsmXYdddd8YlPfAKbb745Wltb8Yc//AFPP/00zjrrrIbLddxxx+Gee+7B5z73ORx88MGYMGECFi1ahN/97nc477zzsPnmm2PWrFm46aabcNZZZ+Fvf/sbxowZg3vuuQdPPPEEvvKVr1Qt3uPGjcPhhx+Oww8/HN3d3fj5z3+OYcOG4fjjj8+ka6Q/pk6dilmzZuGnP/0pnnnmGey0004olUp46aWXcPfdd+Occ87Bnnvu2dvuAKD7BAB+97vf9en5/sRxxx2HUaNGYeutt8bIkSPx4osv4tprr8Wuu+6K9vZ2LF68uF/6ux5OP/10nHzyyTj88MNx0EEHYfHixbjuuuvQ2dlZyHz1B3o7tnx88YtfxPz583HYYYfh0EMPxWabbYZFixbhb3/7Gx599FH86U9/qvnsa6+9ho997GM48MADceGFF/aqzGsDvQG0S47bbrsNv/3tb1er/3RAu7444YQTMGvWLBxyyCFYuXIlrr32WgwZMgSnnHKKS7f33nvje9/7Hk455RTMnj0bK1euxA033IAPfehDmWC8P/zhD/HYY49h1113xZgxY7BgwQJcf/312GCDDZwv9f333x933XUXvva1r2H+/PnYeuutkSQJXnzxRdx999247LLLMGnSpIbr0Ej/FcEqQX7wgx9g7733RqlUwm677YZx48bh85//PL7//e/j9ddfx+677462tja89tpruP/++3HYYYfhuOOOq5lvX/vztttuww033IDdd98d48aNw7Jly3DzzTejvb29irlohHYfcsghuOqqq3DcccfhkEMOwYIFC3DjjTdis802WyU6EeZkbYwYMQKf+cxnMHfuXBx//PGYMWMG/vWvf+H666/HpEmTsN9++2XSjx49Gpdeeilef/11bLzxxrjzzjvxzDPP4Jvf/CZKpRKA/p8vjWCHHXbA/vvvj2uuuQYvv/wydtllFyil8Pjjj2P77bev6q+e0N7ejrPPPhtf/epXccghh2CfffZBR0cHnn32WaxcuRLf+c53AGie6p577sHxxx+PvfbaC6+88gruuOOOhlwX2vl8/vnnY+edd4aUEjNnzqyZfvz48Zg6dSquueaaXtVlINDTvHjppZdwzDHHYM8998Rmm20GKSXuv/9+/Oc//6lbx0ZRLpdx6qmn4pvf/CaOPvpo7LXXXnj99ddx6623DqjbyBNPPBH/+7//ixNOOAGzZ8/G0KFDcfvtt+O1117DnDlz6rpx+drXvoYjjjgC++67Lw477DCMHTsW//nPf/CXv/wF//73v/HrX/+65rPz58/Hpz71KZxyyik49dRTe1Xmz3/+8/jDH/6AI444AkcccQSklLjpppvQ3d2NL33pS1XpK5UKjjnmGOy1116OFmyzzTb42Mc+BkDPja9//es488wzcdBBB2HvvffGiBEj8MYbb+Chhx7C1ltvjXPPPbdXZewJQgh8/etfx0knnYQDDjgABx10ENZbbz28+OKLeOGFF3D55Zf3Os8vfOELmDdvHmbPno3DDjsMm266Kd555x3cfffduP7669HR0YGddtoJG264Ic455xy8+OKLkFLilltuwfDhw3s8CT9u3Dh0dHTgxhtvRFtbG1pbWzF58uSaFrbr+jo8GLxbT2iEzpdKJZxxxhk4++yzcdRRR2HmzJlYsGABfv7zn2PMmDE45phjMnn2hr9rpD9WhW7Uw6233oqzzz4bF1xwAQ466KCGn+sNbd94441xzjnn4Omnn8bIkSNxyy23YMGCBbjgggtcmlWZYz0h7MWzCHvxwUEjcrU//vGP+MY3voE999wTG2+8MZIkwa9+9SsnzF9VjBo1Cp/61KdwxRVX4L/+67+wyy674LnnnsPDDz+M4cOHD5jld2/5DwshBM4//3yccMIJ2GeffXDQQQdh/fXXx1tvvYX58+ejvb0dP/nJT2o+H+jb6kWgb2sPfVsb5BpBt7Hm6Tb6igFT0s2ePRu/+93vMG/ePHR3d2PDDTfE5z//+SqBp5QSl112Gb7+9a/ju9/9Ltra2nDKKafgs5/9bCbdV7/6VcRxjBtvvBHlchl77rknzjzzTOyzzz59LqMQAj/+8Y9xySWX4M4778Stt96KMWPG4Mwzz8Sxxx6bSfvZz34WCxcuxD333IO77roL06dPx2WXXZY53dfc3IzDDz8c8+bNw7333gtmxrhx49xGoVG0tbXhuuuuw5w5c3Dffffhtttuw8iRIzFt2jQX5Lq5uRnXXHMNvve97+G2227D0qVL8aEPfajmonrAAQdACIGrr74aCxYswOTJk/H//t//w+jRozPpGu2Pb3zjG5g4cSJuvPFG/OAHP4CUEmPGjMF+++2HrbfeuuG65rF8+fK6LrJWJ2bNmoU77rgDV155JZYvX44NNtgAs2fPxsknnwyg//q7HmbMmIH//u//xpw5c/D9738fG2+8MS644ALcfvvtPZoN9xW9HVs+Ro0ahV/84hf44Q9/iPvuuw833HADhg0bhs022wxnnHFG3WetlUhfAgavDfQG0HVsbm7OuG5YXdhxxx1x2WWX4ZJLLsEll1yCKIqw3Xbb4Utf+lJG+DV8+HDMnTsXF154Ib773e9io402whe+8AW8/PLLGUHPjBkz8Prrr+OWW27Bu+++i+HDh2Pq1Kk49dRTXbBkIQR++MMf4qqrrsKvfvUr3HfffWhpacFGG22E2bNn99rFZKP9l8fkyZPxuc99DjfeeCMeeeQRKKXw29/+Fq2trTjxxBOx8cYb46qrrsIPf/hDADr210477eQY+Vroa39OnToVTz/9NO6880785z//wZAhQzB58mR873vfqxJENkK7N910U3znO9/BJZdcggsuuACbbbYZLrroIvzmN7+py6z2hDAn6+PUU0/FiBEjcO211+KCCy7A0KFDcdhhh+ELX/iCU7xZDB06FBdeeCHOP/983HzzzRg1ahTOPfdcHHbYYS5Nf8+XRnHBBRdg/Pjx+OUvf4mLLroIQ4YMwcSJEzFlypQ+5XfooYdi5MiR+NnPfoYf/ehHiKIIm2yySUYou8suu+Css87ClVdeiW9/+9uYOHEifvKTnzglXj3ssccemD17Nv73f/8Xv/71r8HMNRVYdoPZlzE8EOhpXmywwQaYOXMmHn30Ufz617+GlBKbbLIJ/ud//qdfBE4AcNRRR4GZceWVV+I73/kONt98c/z4xz/G+eefP2BxmUaNGoUbb7wR3/3ud3Httdeiq6sL48ePx09+8pNCixofm222GW655RbMnTsXt912G9577z2MGDECH/nIR3qk/atCwz784Q/juuuuw/e//3389Kc/BTNj8uTJ+O53v4stt9yyKv25556LO+64A5dccgkqlQpmzpyJr371qxkh3r777ovRo0fjZz/7GS6//HJ0d3dj/fXXx7bbbtsrgVhvsMsuu+Dqq6/GD3/4Q1xxxRVgZowdOzZDe3qD9ddfHzfffDMuvvhi3HHHHVi6dCnWX399TJ8+3bmJKZVKmDt3Ls477zxcfPHFWG+99XD00Uejo6OjZsxNi1KphAsvvBD//d//ja9//euI4xgXXHBBTSXdur4ODwbv1ggaofMHHXQQmpubcemll+J73/seWltbsfvuu+NLX/pSVX/1hr9rpD9WhW7UQ19pSm9o+8Ybb4z/9//+Hy666CL861//wkYbbYQf/OAHmfh6qzLHGqlj2IunCHvxwUEjfTt+/HjsvPPOeOCBB/DWW2+hpaUF48ePx6WXXoqtttqqX8pxxhlnoLm5Gb/4xS/w6KOPYquttsLll1+OI444AuVyuV/ekUdv+Q8f22+/PW666Sb86Ec/wrXXXovly5djvfXWw+TJkzFr1qy6zwb6tnoR6NvaQ9/WBrlG0G2sebqNPoN7gS9/+cu866678oIFC3jRokW9ebRmfltttdUq5xNQG6+++ip3dnbyZZdd1mPawe6P559/njs7O/mBBx4YtDKsLdhvv/34mGOOGexi9CuuvfZa3mqrrfidd94ZkPwHe3wzM0+bNo0vvPDCQS1DQP9hIPuzN7R7oBDmZP/gqKOO4pkzZw7oOwKK8eCDD/L48eP52WefHeyirNFIkoSnTp3K55xzzmAXpV/xne98h6dPn85dXV0D9o5bbrmFOzs7+amnnhqwdwTUxrq+Dr8f0CgvsCb0x2mnncYHH3zwgOW/22678Yknnjhg+feEsBdvHGEv/v7FokWLuLOzk3/0ox8NdlH6FYG+BVgE+tZ7rAlyjXUda5Nuo6urixcsWMDXXHNNr/eJvY5J9+abb2LatGn9pr0PCLCYP38+pkyZ0uOp6vcTKpVKla/1+fPn49lnn8XUqVMHqVQDg/nz52P27NkYNWrUYBdlQPD8889j5cqVOOGEEwa7KAH9gPdDf4Y5GbC2449//CNmzpyJ8ePHD3ZR1hh0dXVVxeK6/fbb8d57762TfMXJJ588YKfdAwYXgYYHrE4wM/70pz/h85///GAXZcAQ9uLVCHvx9zdWrlxZde3qq68GgHWq/wN9e38i0LeAgIHBww8/jGnTpuGb3/xmr5/tlbvL448/3sVZaW1t7fXL3u9YuXJlVfDFPIYOHfq+FSYceeSROPLIIwe7GGsU3nrrLXz605/Gfvvth9GjR+PFF1/EjTfeiPXWWw+f/OQnAegg8JVKpWYeUsq68brWFFxyySWDXYQBxYc//GE88cQTg12MNQ5Lliwp3AD5WBPcruTxfujPMCfXXiRJ0mMA7dbWVrS1ta2mEg0OvvzlLw92EdY4/OUvf8EFF1yAPffcE8OGDcPf//53/PKXv0RnZ6fzub9w4UIkSVIzj1KphGHDhq2mEvcdt9xyy2AXYcCwrvB+q4J1mYYPFtaVuT8QICI8+uijg12MAUXYi1cj7MXf37jzzjtx2223Yfr06WhtbcUTTzyB3/zmN9h5551dHM933nmnbh7Nzc29cic8GAj07f2JQN8C+oqg26iPrbfeGldeeaX73ZuwJb1S0m222WbYbLPNevNIgIc777yzRz/KP//5z7H99tuvphIFrOkYOnQoJkyYgF/84hdYuHAhWltbseuuu+KMM87A8OHDAei4SPXiXYwZM2aNCI4bEFCEb33rW7jtttvqpnnuuedWU2kCAtYNvPnmm/jYxz5WN80pp5yCU089dTWVKGBNwZgxY7DBBhvgmmuuwaJFizB06FDsv//+OOOMM9xG6pBDDsHrr79eM4+pU6fimmuuWV1FDihA4P0CBgJh7gcEZBH24u9vjB8/3sU1WrZsGUaOHIlPfepTGYuznXfeuW4eBx54IC688MIBLmlAQO8R6FtAXxF0G/UxYsQI7Ljjjn16ljjv8yZgwPD222/jhRdeqJtmwoQJGDp06GoqUcC6gL/+9a9YvHhxzftNTU3upFdAwJqGF154AW+//XbdNH1d4AIC3q/o6urC448/XjfN2LFjMXbs2NVUooC1CY8//ji6urpq3u/o6MDEiRNXY4kC8gi8X8BAIMz9gIDeI9Dj9zf+8Ic/1L0/evToYOgQsNYi0LeAIgTdxsAhKOkCAgICAgICAgICAgICAgICAgICAgICAgICAlYzxGAXICAgICAgICAgICAgICAgICAgICAgICAgICDg/YZexaQLCAgIWJPw1S/912AXISAgICAgICAgICAgICAgICBgDcH53/3JYBchICAgoFcISrqAgIC1GvP++H94+52Fg12MAQNHBBCB12sBZB3jZ7IPMAA2/zMgqCARAWx+kv5NSNMxe+lIpHdMWnffOEsmzhRAvxd+GvLuI5tnplw1sHIlxAD18Ud32Q6vvf5v/PWZ+j6113oIALJOO4+UQLu5T0J3iXWGzSqXOJ+P7W9On6mVnnJjoVaWVfepYCz7L+P0t/KuCZPWfy3VeBnlCp/3Br4oBl5Lqh7bdqsJaGtrwUPzHivMtukDAJXMDzuFC6Yled/tNHZ/pvx+0dmbZvpC4etd9Qt9JxQ9k8uTOVs2dx16qPjpgOLmdffyr2RAdQGVd2qUPa7uhnURH/rgGGw5cTx+fdcDUGodrXBZ6sExfkP93a9mZsz0MKiJPNoEQJnBaOmU/4gyF5QZoAyQYkcOBPS0JxCYGawYSZJAJbl5zrl8M4PS0j0uvkdI6d5/FgOVahrSH+gY0oaZe+yK+x58FEuXrRiQd6yREB4dKhPkqDyhM23Pdtz4fFB2fHHmmVrfLI9TBM59AkTZ8cxurHBKm/NFdT+9siJ9jvOLBmcHqEsdA8kbjY83IQh7fmwn/PWZF/DaG/VjBTecZ4eAGC1zV+3sqwdbV/8pgt8qLiUzwApM6TyHYIAIJAiQEkIKCPMJMJgSsGNbGEQAkYAQAkIQiAgkNK9aPd1N3pRObWaV4XcJgCDdwV1vdmHlayvAFYaK11H67mHa1MlYtGgJ/vL0s4NdlIEFAaIOX900hNDUXoPnBdL5C6A2A7dugYg8+gZUVigsfbuaRk3YfDNsMHoUfvfIn1Zr+QYbzc0lCLOgCUHYaepH0FQuadolBCQJLRNghXQTntJESQIlWYaQmuYyEsQqsckAISCg91OCCCQkyqUySqUySqUSpJRQUFAAkiRGnBg6zGYtE5p2C7OusVmPIhlBRiVEJQlZilAqlQCp6SiIHWmsJBV0J12oqBgJK8QUI6YEXVxBklSwfOUKLFm+BIuWLcbyrhVYuXIluioxuuMY3ZVuVCoVVF6Iwf/O70ur8YH1R2GnHab0U88EBAQErD4EJV1AQMBajbfeWYCXXn1zsIvR/xhSAkcCPLIJiASwaBmKN3qcXk4UoJR/Ryv2jCQhVbb5gh9fIQeAjRKOBDLCEMrd92RR5P+wv1in1RDee4W3L/W1DL72wfxjJCi0bDno9X/Xaay+Y8fuCha+twgvvvTqgOS/2jBUAs11hF7NBAyPssJkH90MLPS0LCRSYVemnyz8/vI2S740K6PdtULFWkq6BgTzwko080IN75ORCuYBwMoG7WudVM2+M5eHVdRVFYGBJQnwZjfQlb3z4U3HgQg1x1CzAKgMoxHINUEt/aXVtSsvnV99v5hVmq9qkNNIZKtU+N1Po3LX7LtsepFLV5SHn75A0QcG0OyV03tX5S2AK9WPq5VAshTrDNpaW5AkCV7816tQ65pWUgpgzAjgA+sBTSWga0nVHKoexN7vKnJhLtg1xirgVJJOCIahX5x+V2zWSAaBEBFBOLE/AAUkcYy4u1sr6RRnB2SV/sXTTGc+OZeYUrqzvAv497u9aLzGMXLEMADA62++jXffWzwg71gTQGWCGJKuddRGoDZKm92d5/EHjlXSuYUgp7ADbJ+l04/Mv7YP7TXVgJJOZ6Qfsc+yd1tlV9VcUas0d25dSPNnby2kPM2w6bsY8asxGg0/L6VAkij8Z8F7ePm1Nxp6ph6ICEJEEE3+Qmw/6ygvALj+cGJgzZEScoe8GGBODN20ClQFCIYQAhACFAmQjCCjCCIiQChAMpg5Q2+loFQYLgWEkLqMbPvc8tJkvhKEgMlHeQu2fk5aZd96BKwHdL/Rja63V2Llv1eisriyao27BmPK5PFYtHgpXnz5tcEuyiqBQGgeJgw7nDJdloWUJULLiBp8NwNYCVCXzqnOS1YZRbO7H7IdEPhnbAhA9xLGwleq58KYD6yPYcM68K9XXl+t5VudGDqkBWPHrgerBGPF2HLCWHS0twHQ5ESAwUqBpIAkQiQEGGzYHsvPGAUaCDICiGKAND1SUCBS+gCCFBCk+R8yBxiEJJTKEk3lCOVyGTKKADASFUMpicS+h6FpnxAgISBJAiQ1xSWBUilCuamMpqYmlJtKECICpH4nCdIHKIjQlXSjK1mJrqQbFdWNLu7GSnQBLFGJgabuGN2tZcimCOgS6FqcYMnyZVgaL8OyeAVWdK3Ayne6kbzSs5JO0Jo6CwICAgLqIyjpAgICAtYUlAQgCGqDFqApyloOZWSY7AnLGYaLdkJKFoaRdpJ5m4dm4lkYcYc1xbH5Kk954eSSVlAFQNmNKmd2hZz5kr1nj7inp8nz20lbBs5+94VKTWWgtRlYvrJm060TaKqzoRgigNGR6YgCoUCzyK7o+WZOfIG37dy8MNl/yJxsJVH8Pv89RNn+AnJKN/+aJ3DMixFcPj1pnYyUhP10voIuXz7vez5fKzzNW9FVyW4JGCKBFgF09bw5rMrLVr0R2aT3fqsrrdJtUuaj4fwA1JboFOlICt5ZdS+fX613+XUoUApSPg0BpfW99F6+HGvlXeY9Jk2yDKj8B4XgKuVQwIBACqC1CfjIhoCUwJDmGgkpO/b8daCmgMVfYzzlCWkFSvH4NumEAIghmEAkYA+YcGLytco0u56SMdMib9D7Y9hdztNAX6mDdFw3l3W7LA8DsSdQidzSIzoEyFp6S4DKxkLKQC8bOcVoBvlrnB133r1UiMzZ9N4DVFNN510l77fNNHMIZNVE6y4rQsH4MygBNIzA7zampOt3EIBhwqspo9oOrgi+taI33531ht9SnP1mGobsXPb4DoYCM7l5T0TQxnepgpZZgRXARGAopGaa6bsYWgAs3PkzgiRpDrClVifuP9J/rWNb0DquFcmSCpKVCZY8twTdC7vBCkiWJ3WUvwF9AYEgi5Ye08yyldAyWmi+kbP37PySTd6INTxmhlep6ZmhxvcBQE/s3ZqAWlx9qY3Q1C7QtbSXfPVagCHtLSiVtNKLGRgypAXTdtgCSilAJShFEdqHtICVglL60ECECNbqmyw7xEAEgYgiSCkhAChKkMSAQqLHJGmaRArgJAYTQUGBlTle4AY0gYghwCDWcgBOEiRxggq6oZQCM1CpdOnDESQgSOq5FBGEOdokzP5LmUJSooBKNyAFWApAABFKhgJKb68VQURNkCQQswRihaTSjZgZKgGaSEK2tEIQo7yyhEgI7QQmUZBEKEmJaIsVWPL6MqjuwejVgICAgIFHUNIFBAQEDDZKBB5WBo9o8gQCeck1kNniOOsBlQpnjJtCckKJnACTCEy5nWNON5OKILK7Ss5tYLM/qpUtqX1CXthaQ7vn8q/eqLEgYEg7aG1W0g0RwFBRLEgDtCvK9eq5fzLCYoE6wuscbFP2eIo+Nwic4MH2Sx0tTF5ITbmteN41XaHyxxdkkx7bftHcd87Wvdau33+B89Vor4tiGW5RNe07/B99OZlZoJSrVYQen++LsCcva653v941+35b+HpCqIzipaC++WFi/6mn4PO+U0n/uXy8tKIFKI0qLn7lHauQ8d7BWrGnlhc/E9BLjO4APjAc2GCYuVA0AL01LaNsV0jnGaFKee6yyA2ezDVOr0OYPLRrOiEBJErrfhgAkxZisV5H2VnPwZvrdehfvl7eAZfsPatg0pY9ARq+K0hqJ1BL+lsME4B1E+x0WzUIE8EbR7WIZG5dsgOn6jk2d2rwN7XyRErHLB+lc82Ny5xrymwGvhKP0ms1l3AtaC1aY2yBKBpgDUEd0FBh2sMoxYx6k2zhCvrL122yVbjZm7m1hI3izUq09ZQ3uft8hSH0DJV5O4ggzKE1ZjZsjFYQKsNfC2He6AqRKy9phT8AMBtXmpaHIejDAMQQbsgxojaJqFWgaYfhUIpRWZlg2QvLkaxIsOLlFaaqa6q6ZfWhEYVuywYCkaUbOS8fIEbrGKn71RwyZKNoY2NlzawVtWS2UtZjaspXcPpdpT+BvrGDA42abHE/5e2jt+/xtwM2Q5L1XYauqSgq8Qc/OBrjxo2GEBKsGJ2dYzFixBAopYzySw+eRMVIuivorsRQiRmTrBX5ejzqka9Yu9+WJKASBZIKkiJIEiAhUWFCzFoZB6Xd/TJpO20ydFex5akEmBRYaVrGxrqNoeeGihMkICSJglIJ4u4KCIQokkBEKJXLECTM4QO9l2UYGQQSgBQYEkp0awWhkPpdAmCZuAOHsiQQRWWUShFijpySUFUqoFjorCjC8HIbIhmhvbkJreUmNJUiLFq8GAJAMjTGEjGQIz0gICBgcBGUdAEBAQGDgYiAFgm1XrMWKkR55RxlfqYbT7ODZHhKCytA8J713foJYXQT3rbC24RyfrthlS6Z9/o//Aw86VBGPulLjeoIu2w9CuEJR9paUkXQYKBM+q8WmgjY2GoNkH7a8kroFbcobpK1HvP1k7UUJ73S7tTKqAjeeGMY15JklMB+OlG7EBlLOGTHZkbL6z3rx1l0MrsioaSfL6qfKaqLrQMZRV2VRMWT+NVrpn6QH1jhhJXdZcLrZeXD2RJSLklR0zdSPm9uFk6henqIOuWr9a6aWfv55Nu9wXrUzrznfEqjkdI9jwZGsfnuxdizeSdLgfjdXLkN1Pso9FddtDcDG43UCrpyKV3Pqk53GIFRhk5yNq09qEK5+w4+fUigFXFI6Y+lQdYCjgS0PTA50sKJlsgmit2k1Ao6k4+U2bUpY+Hr0Td7CMAqSrRoLTcGcwNyRDuwbOXgrWWDCKJUEcdgiNFCW8zBKJOKhLUe7+GE9rXmeD1abteBwhfk+J3MK/LEiotpWu57dvj686A4n4xCwj1Dteu6FoCGCmfxaN1Wpo2iaUGWZ9QQTqlG7inHp1qrN48f9MMoW6Wbc49t+AArIFfM2mEmpy7fKDOX2VnYwV4mXap06lv+Ov3TwmuAoTQJY1tjXU/FStdWAYACGdKjFCBKAu2bt4MTRusmrYgXx1j2wjKoikKyfGBiWK4pKLUJfXgiBwYgSsCw8dWiKp90ymZj0Wjc2ZJR1Clmz0KS9dquPDaQU57bzlWrI8+yO6nlZWbeN0C+1+KpG5ADAdjwA6MghAAzo1yKMHPPHUCCEHMFlThGa2sTOoa0QQiJOElQ6a5oqzkAkZQgAW0x181QkkGxAnPiQki4rQ8BYAFwAjaKuiROoCLWY1bqRDp+pjkYkACAcooxpYzCztA1wQylGER6T6cMzQJg4tspqDiGIn3IQBJBComSLCGSESITrzNlizQBI8s3iQQcK1SIARUjkmUoSiBFpGUQkY71KZWEYIlSpC3sWmQTEhEjQQUJd4EqDIgYRIyWUgkyArq7y1hRKmGllCgJgbKUYW4FBASs0whKuoCAgIDViRYJ7iiBmyXQGqUbPbPBt/84GU2VoNC6roB3Ip/15hRwO1Ai7dYyFWzYdGlRmIvYXC99/j4D2SBV5ASqZKUZGQGsr3Wyyp3sB/WoeTL3BAFDWoHFy2qkWxWQjom1YYGkwNanQ2h3h42ACYBw/VUs6PUaoREvLwyzg6Pi9HWFkzXu9Vbp6fc/5QOceXn6L3ZHZ/Pap4LHVXZ8VOdd8KwXJ8Q955Rz3nPuXXmlQT7zegXoPWS7tt4h02zWq5JfNPeFsxfzXePmPRf/riu49jPxP3MvqOoWL72lT+zf66+dcr5BGu0Cvz59LAszQBLajZ4d1t60jZqAaCR8ubIjZ/G7xWWNFwFqIEjVmgQCMHYU0NYEbDgcKNXaUvi+xPLIrm0FM6MBeJJVO++dgo706W82a5TSUlo2E4fdoZc0DzJxrCCRHlLITLxqpU2xUq5GnUsRMKQFWLxumm4SZfuO1hN6fgGAAMTw9GCGb2HVuz6nzHkQ/3qt5k/F7AzytfH5tayw2zj33ZTb19NkS5d9rgF6xjn+zI4xsnxYzeYpIty58lgl1WoENQuIlpSgktcqQNofDOsC03Cr1hqK7LgQ5l5O6cJaq8JOs0J6rbUnYEhb0doRZn3HaesSCQEyLiuFc0dJpN+RKoPJ62OGr6hj1uo3PZL0N63cs9aDxjrXxHPSZCRdQCnX31YuH7VHkK0SpfXLiJfG6H67C5V3Y1Te7jb5rN5+XBUQEZpHCrSMEum66idgoHUDCdHkGtXdskutSabzQ+6C/ZEOAdd3Rienxxdpi2kIAvuKOsGarbZeTWvwRXp/xin/w/6SwOn+bQ3BgJQln2lfhmGeTnHVl0GDZh/S9WPsuPWx9dabI5J6n64UY9utxiOShLiilW/CuM+OVYxYxelBH0NDhOUzYOa7gouLi4SRhrEkN25Tq1ylFWyskICgpIJi/SeMIg6kDyUQEbQDS2UsgfU4V4K1ZR4ZC2YTg1OHqtfXhTmowKRplVL6ndaxpYRARFKTP3f+yPJF7MJ+W1qnEkaFE3ACCEoAWQYLASgBKQnaBzODWICRgIgRgVCCRAkCFQa4wkiEQinSMULLQqIsIpSiCOWSRFlGqL/pDQgICFi7EZR0AQEBAQONJn2KTI1o0oo539uVkQCkhnPemU2f/yQr5igQgnnKELKSdLubtBvPIuG8szjwXtLj9s4KKVIhhs7TlCHvDya/zba3fOVKpoAFZWEjvmlvBS1dYdxr9CM2EgCXzIqYr78vhWtQE2A2aa5t7G93z/viCfwzsU+UfYbTNnIxBIukgp70oBZqCulsX5r7olYdvefzrieL8iy670tbGHrD6u55CuC8rD4vnbHWpKpGnTJKOiOgK3Cl6hcXQmTL0w+QzTpbZuOhFul3Ibxu9/QE9faeXNUePT8DZHUMPcp0iwQpVOMZzqXPP5e/3xPyUtx6zxXRtN7kb1BkYJn59O+L7PdopJfGyzsaAX2y2ZJYTwaZLNJKPD9vUStk25qIke1AJIEtxgDlqIb7RkvvLP3K0yyF1M2gP0hyeflrTT0lAwHW3TOEFeD7Da+F5tnlkR2tY6WfI6XXVyGkJi2+pZydpFV0Nje5kL9fgJbyOqWko4hAzaatJSDXlymtipAVYvvrYV7Y7vd35gX2mToEoVD4WwvWBVjBrbrPclqcmglzPE9e01AreVEZHC9hfuTbC8iM8cKmG0HAIgCrOX4PlUnHytW/zL/s/auhZ386HsjEUNLdk7a39fCe9oEyj2jXblZYnSro7ZPstQu7Z+xcJhCktUghOGE5WwWbVzZLz3T5teJTEbQ7S+HE7GmdDdvB3skqMpZ6ThXo9TWBHD9IAKL2EkRLhPKGCZJORuWdblTe7oZankCtXLMt7NrHSGy4SxMoqnZnmBnG/i2Pb7Mxs1Pu2597KbuaPuDfELYX3X5JGEW1W3aUz9iwr3dw2xg/JhiDQMKkq8VDNcrnrI3oDZ9V43F/vNci9asLQhKGrd8GHXNNYOjwdmy5/Xi0NjWhrakFw4YMwfojhqMcNUFAIKkkUN0VxHEMSAUWwtAcQDIhUgKJUkCSIFFamRZJ7RoySRITcw5QSeL+2ISr0PPeHBogAhJt9cZK8yzCKNlYMRKVgCmBVNpDgBQEQEIJAWaly0AMq1jW8ef0LEppmzJ/AkoxZAR9mElo+pskgD0QqhRrhSRrC8LUH2zKZrk9FrTryjhJoEihFDXp+rAE6Sh4YAJiTsCJgEICFtoVcElGKCGChDmAo7QiLxISLeUyWpub0NXdottWEZpHlbHstbU4BEZAQEBAHQQlXUBAQMBAgADuKAFtJXCL1Iq67G7U28qTE0pkZIA5mQxZYaFVyOVzIriYc+wLbgDn6scldBma387FmKgjTCp4zv1UacEzFniGmWffbSLX2fARqpVQJo+WZlBzGejv2HQCQOQLV31BMXvXerGjNO6OXBYAMsLc/A7Vb1O/ndxXu8X1P3uBnhR09n2uGFTwTMF7XbPlrldpPrzfvjIu85i10vP73xekFmhNMkORPIEmMkK1NNYPZ6tsX5V3L9ufMPtaYqM/sEIfcwyfvPL6p7wLplHaHKbctimpSE+C9BmXv5+Xd78o9Je9Z8tbFU+Ec98L2s4fFoXvrpVf0bWiYd8HwRHn86x1r6gsRfUsGjN+v1Kq91asFXhyqM6LpL4f1Yiht8ZACm0xt8n6Otac1fraAD8Asg2hvP4qGhjmn4zinGvMP39C14Dvfk55UlT2VClsk+m0+nS3p6xxa67tMK/T2CgVndA+F180U+4GBmVzWSs4u+Oe064h8K0MUALEKJEeGIoAassSoZqrVMEkc3HK/PiEPk2uQV8K0TBNsAlTe6j0fYz6GRliXoUatS64XGh741gGu0hQ1b1McmqgWXrJuvQHCAQxqpZnAvtvas3orAjZ1h1p3c1UdE+aOW3ZRHLWbkJbwZprjo1kaCWZdXcq/JjJ5u2sBejCWdTpIaCUdZOpB6C1ygKQserTzyiwsabTVXBBgbUyj7XA3Vr12jK7zmGPTSEbl4q1EDsS+hDBmGaUPtCEZEkMXsEgRVjxwhJ9IEQxaiuOVz9IAqKJtFKyaGF17Ygse2g+hfUKYm46dSnbR719hFtrOW17AgRnfXUQAJYwZ0RMHEKGUR6YlJYc+EuYx8OTc5/e15ZZe7Gqo6sBlrHfQGT4K/OWYZu2Y8gHWqBtaAklEWGDsSNQlmWUo2a0NbVhRPtQdLS2ob25GS3lJjSVmp3SjMyaIAkgIQGwOQ/AEImAFIyYAZbeiKNIW0IbpR0r1sq5JDaKOmNVR4CEseoFgSVDQFuzAdaFLxAnCSjWS48SAlIISJdGwLrwVazAZFxcguH83ZjDATqdMAeSrNtM3/0lQIrSOaoAZRR4zoWv6TxhZqgQ+h8FBU6UodGJcaupQIgASLBSSIylHQiAJMhIolmUwKVmJHEXugiQSHQrEmFIcysqQ2IkSkFGJUBEGL11F/712hsDOIICAgICBg9BSRcQEBDQn2iNwBGB12sGyjkJPABfNGOVc36scwvrjktvSAukwkSpYN17nv20+R1RlXIOnpCACqTw+df6JbeZ1hdmcf5bLblm5l5+5w3HrHNzE6i/lXSNbj393XpdKzIDJ+xbhfKwn4cR2pHvCMjeqikSLc634e1x/j3eF1bed/8RK8ymau0R+59VUsvqdDUFtFQgKPEllrnBn9cWuWnpCQILirSqUgRrNQcGVGKaxSrnfCMDIxSEr8v2hIx+2dLTq2kXsKgzJD0XjrZMNjshqq/ndS1V7e9Ne/dIvv249rNFguWqLs6Ri6Lur0LNxD0gXzfbVkVCcU7bvurxlGTXLFqh/H5NFva1NQHrDwU+vKEuqKR0oCigiqBbJRYhW7FC2p74D5rbOc01AxkNcqGw17zLmqy6AwZasZYlJVYIT0YRJ7Rg2wiM0rlHICHAHAGqkpbNSZQ92gcgo0xppD8jCbQ1A91LG0g8eBAtAijr79RCoKEpvSQb86tOhfPkKyUTa44yoZopoeJxlktft+r11rJelk1PgQKiCWR4wFoYFEd8BFCrX+ECHrYA/rjINqHlUf35Dac4ttYcytJnFLCzBQNRu3czzygGCQlmMjHr4C20WjCtyUuqfMy4EWVd/pTsmfoTUvrAdmlhCBuHKlNRbT3DZm9ApMuiSEBQKhyPOkoQQ3U5yqObwAlj+fNLwBVGvLB7DZtf1ahit7w+If+7tw64Zdf1P2V4Cac0pTTDzOgjaEbJxKdjZSyYyC5dnIlJSwCgtBKCjQWdy034NGANXsD7mZ/tb/RkHN0oCEC0oYSIjPtGAM0blNE+rhmRlJBSorncgnIUQTAgFEGqEgQilKVW0A1tHYqRQ0ZhaHsr2pubIZkgFKAqsbYMSxI9ogQQ2UNBpGmIEAQogcgoBaNIImHjzpIZSmmFnI1PZ/fjggAWlq5pnoUoghRC+8mX+vCANDwLs0IcMyhhCCGgpICSEQQRIutJAPYQgmdF6hRw2i2mPaigY3LKVCEuBIhZW/RJAWF8wDOgrfpIgEk5+qIPQ7DLR5Y1TUqSRB/MlQkUAYIFlFIgSAhR1paJSun3GVpLIkISldEclcGIkYCQcAJVIZQoQlupHUk7QZa70aWAkly46gMnICAgYA1FUNIFBAQErCqkFvipDVp0zLnInluz/+qdiP3P8OdVcmAG0hP8mitON6lW4mDjksHb9+c3iU5m4AsOc2kIWkvA2ZO8/nPZR9JykLOeKJBIG+VL5t2ewCtbCv+X8/mYfdYW1V4b2gG8t6T/XV76Ggoq2jn6vUUF94vyzP22wpq8lVpemsRA7UB1tv/9B2yZC/ojIw3J17FGmV1VrWAs/zxQ29KAsp+Fbs28QmWa0W4yi/IuklD67zHvckHM85UCnOLQXmMvD8VpG9bDmAh4t3E3UwxvqCqkLhAVwDIbPi+BNyys8M60fX54cq553QHyApA009wrlK06+zID/5l8JWwCL58qpZ4/PPwwZLnpUjj0OE1a1Auuqwqezc2EqjSUa6v8Q3YIFA7R/IuqXuZ998mG/Z5v34LKreZQUT0jMjHZPjRaK+jaW7KLAQGptlIUDBbyJOXedfYzcBnlaGGezqK6garayzyjrKZNpdcc7dJx6NyUEtpSJaPwJnbKEBIAJ0qfCs/QRCBr/WfKmVEkUvH4yaOjFXh38JV0JLPlFesJoGSEe01kvuv6rbrwnwu/2p9ZR4hVJa3Op15b55fJmmiks7LEI8fheFnVrl+axqz/ZL/Xek++CLXucbYdMk2if8iREvEbq89qU4w0MR1tfxaaL6cdZGzGqlkMf2HLE3e7cBIcD0ymLRgM7YcyTZuxgDJ5sVmgEzDYeZMgQHqKOGjBtK9RsNZ2ZIXdRtlPRqDtV5UgANLu4shbdH3SmGeXFSeZM1B5S08t+Df7AwmQFGjZfAiSSgKxMIJaHiN+o1vzGbVcgq8GEOfmibXMQQ0yDmRZcP+az+sRZecgp3mnzI1eo3x+SU85YfyN63Gp1wGd1urwHGtJxqJP6XZWSUFbNkI++ht1u5Rzv9aseHlFsPOo5vpCAEq5Sx8G0KotwSIh0PqBZkQliZKQiIggSejvMoIUEcoiQsQSpBhSSUQcoS1qxZDyEAxtHoLhbSMwesgotLU2oaUcQcUJ4u5uVJDAGeIKYfgB62o3pQtCiPQwBTEkGEmikFQSSBKIrdWaEpCRNKOXkcT6lIAwceIEacsyYu3VhpRWmimz72alkECBEkKiBMoAWBJIRTq2HFK+RZ9dIk0TfYNx4zaTiKBkAsUSxJxaBQty7n4V6/GvlyplrAfJ0F3DNwlASIEoiiAjqeutYhApqCRBYnkzlCARgcjEBJUCZKyEWShIlojKTYhUjCTpQlJJAMWQUqKt3AqWJcS0BE1Ny7VFXUBAQMA6iqCkCwgICOgrIgJ3lMGjm9PYNv5939+ZuclA5tC9vkyZ9FqYyKhyPel2jiavGlZzNcRH3mbSSiGz0nP/Oco+kBXS+pUpeH8huGAvmxOisbuQf7u3w44EeMRQ0H/e7eGFfUU9IaGfLCcsKrrv+1vy0+bjuVkBQ59lKTlBt9Pc1BIA1qiju1xUfyvNtsLvvOANWSEJiscmMreoxvUasJIdd5o+d9MGrWFklXXZYWWSG0lQlfCQ0vyK0Cp6Ifw1udosZeoFlROtvHMHaP3imD8WntdUzyot07yeJV3dMpgqsVc1JsBFfef0w5eFZspD0Eacfpmo+nlbLl9uVr9ggC8/tagpxMsUtCCh6R/7fiuv8xWg3quzw7a/UDRGbDv6baoAKvIKN1gYMxwYO0orkIz7Iod8e2uJZ5YuqHqVsTTRG1w1G70WHS5oWCc0s/laH7DSLba+zJehhYKsKKUl7jS4FiwLsHOHmfj1Kywve8XtxdpBAJpLwMpKz8/0I4gI1EFOYCc6BNBkbyKdNBnFrBFo5+vX4HLZf6j9Qn9YNpybVQjk4cY6Z6/VokFVby3INLPUrFqjpfG5yCNktV/t+nd1oVl4eiU2/+ZisBmm0ArnC1vErvcA2GnUjZAYbNgAS1SN5awwi6Sx0hBSl4UFaQsoyrq7tAsEQyFJUpImyJi7EwCIdFoYqxQprMs446LOc3+ZVs/QFkFgEi7eGXuLeFpFbx9gLHD0Oqppj3a7mdJjHVOKDDtm3k+AGBYBbUA0jKBWJOCFCtzF4KX9fLCtF+jV2uoT60aWgMz3LCOiSXK6n3Du/ARBGB7d9oWAACvWfJdH6skbtyR0rDD7kn7lGVYBeVa48C4BGW3lmgIG2kcLrFykqs+/lAGMA9AOYCyyTKLUsWNLJFGWElEpQll6SjohUZYlCEhIkoiUhOQIkSKUWKJJNKFNtKC91IaOpqEY1tKB9uY2tDY1oSSBhLr1GFGaNiWJUdaDkcQJWCU6RlySHqa18eGZEzAnUEohjrUlHitlFF9awa4tZmHog3Dx6IQkRJIgEGm6opSWByht9auUdp2rm44RC0BCgiRAShjXuokb16TMmGeYmLv6QAHDxMczeQkhEEmp6SVDK9ZYH0BQItF0ThqlteWVyPRDZNq/XEZUipCoBJWEwJUESnVDqQTMBIJEXEkgSwIiiiBKEWRZgoVCwgxAQqoSoqiE7rjbuPAFFAgcCQgpUY7KaGlqxfDRQ9E6ohnLF4a4dAEBAesegpIuICAgoDcQBLRIqNEtgCBwcz4Oiy/Y8m6YU/vup0sjzG3D5HvxG6oCo/tP5iTLtRVznNmogoQWTprNvrOay7ylOi/yJdz2RHFOSVMo63IS6RruD92G2/rfzytushIoBkBSOqHhgCBjJUaoFqj592toRzLCAr/t/DwsKE2XT29uF+adgdVM2LYG6vtALMrCO0kuvHJVCRdrSQNz9ctVI3VF5xXZFzJm6uJpg0ikAn33TOZYaO065d+Vb3cnjLZ//SzIIl1MQdCxUEr6UlLRRSEJzYmZolECF6shI/M3w509IaKvmyjygJqBL/CyTZmnRzaNhFMcMnvvSMmGG2qFI8E+Y0mOnQq54VI4rZAbcqZbKD+WPCGeL08vkk85BR1X3UqVeJSdyTZ5XlbvhxCybZIpF7LdwEUNxNmENY1SVweIgJYSMP4DQFMEdLR4gmnb+P5akxPy+fVjILXoyd+wv1X205+S2UUopReOtgHVDeUr51Jr7Mzgs0Jvk04RaW9lZN1easEV2bgzRnkOe6pcWoWeV51MG9ryFtGyXH38TyJtpdhV6ff+J6KM5QFJghidjnBqMi7cgOy7BWX5heLc0WiBC1aIutno3vPHi7maYXdsuXta22yfcdV7s14GYIiuTH+TSE9R1KhrvpR9QYY8eEOdnHK5WjHq18Z3hOBoYUFxV6ctDTULiGE5H8sAANZWGWYe27KSVZoRpw4VTHoGpW51fdaBtDtaACBDA3RPqXQtVKwPvAmp+0qQEeybee8pb2w/a4UfuTNW2tKdsm75jDDdKuiEPchgFWuAdvWmv8A6XRTGWkaRtubRyh5KyRrb2HjpvExMLGftWZYy9WfjshHQn0pZRV5qDUMtAhgjQDED3YYU/jsBdzPQ7b1rwJCqX6s8fvQum+rfnP1Jdp6z7yLW0lr3j4e0PS2fZhVyNnYwW22diVHntjAKeix7ueYtQf2btVq5xxaptdb4t3uyNE+n3KqeDRg41CpXK4CPILukEwChlXCRUdJFQqIsJJpkhJIsQYoIkZCISJpY0IaZTRikhFbaoYTmpma0NbeivbUNrc2tKJfLKEURiBMI6IOsQgjIyFipcQLFjAQxkiRBkug4aXZuE0nN3zMjUQpJrFCJY8RxBXESGwszhkpyc90MaM2PKnBCEJGOJacN6hiC2VmuCRLO0k0fLCAk1loYWomolLbstXwOm0M2JIyiEAwlNF3V1nvavbdkaZdNxMoo51hAGGs56x5TSICcFZ3U1n+KQIlWMEoG4gqASqT5JyGgIFEhBRYKpZKJnZewVmoigYCOFcjlZl2/LkYliVFRCRQnkM0S5aiE1tYWrLfxKAzdaEhQ0gUEBKyTCEq6gICAgEbQLIGOErg1AreXjMywxtYrJ1/Kgz1Fhw4Qnwalpowmz0qha0if/U1mvghOAuLlYaTuZHacXKVgKii42fAXWmrVqSbZ48KZo5E9mftwKnjL7SYzh/o72oH3FgNd3fXz60/U22W7fkK6G1aqRp/5DyLbllXPWOEkZS71XMAeduJ5Cwn3jPc9K1urLiuAqmOvGcFJvgzmZsaK0DZYHUmGbd+8RknBxQWpi4wk1UpA7U9fumn7r0gK0kdIQHYAVNKCHfdHAHUZ73xCp7N6UU4AZbySWYWQPX1PDHCMVFAEuLBezo1m0TCI9Tv8eCsoqKq756fz/tjGii9SVlgS5T9jjHUJBenzXc8FaXzvp0BGH+PClrH3vF+HXBv4ijD2b1H6PT/1/ISZ+7UEZwVDPiOEL3qu1rMDDSmAcSOAljIwbhRSrSrlymQ6NqON9GmWlWYyXEAoYTonZ9Xg8syva5m6yzqSxExrFsC/rnReXs+x9zzDnASHZyVBnM4nJ5AlrTgnM+kypqS5shFQrepFNn3+uSEtwJIVWlHXT6BhEYSMQCMErPIhJXVpG/iKG+2qr3jtTTMuul5v4Pr1X7UBXttmhVGlHKXq23XzplRh5B6q8aqqe1Xj1y8E179UOPc9xQKQ8oN1ThAU6h/yiHR8QbVi4K2pSMK5S63HDLs+Ne1KGYVK9hE2aTKsgP+D0uNmDDakRR/ksW7ddLulNE6QSC1gcu2X56yF9OaKVyZ9PY09xXahYXgKO0NZpY0zlxbDnixhxWldzRcSpJULBMOzCy3Ad2slA8Tuq/IC2urftn0YVBKaDyFAbKoHk3onAWJG8k7iuWVeFYanCP2/sHF+sfdZOk4v249CF7o5FpQAFyvYnNMAmTh01uoIBChSIEArN6wVI8j18YDb1vVX9hkmqBiU+zZodoNVy75WkAsIRCQRkYBkAEqB2FrQlREJzUBTomO4kQK4OwGTBJUkmkvNGNY+FCM6hmH40GHoaB+K1qYWCDAqSTfi7m7EcQXMrOOzSYJKCJzEeowohSRRYFZQNrYazHhhfS+OY8RxjCROtFJPKTPXjWtJe7JMKbAgE7NOz2uRAEKWIAQhUZoeREJCJUbpBv1uS8eY9UhlZRSESnsEALTC3rYfKXJKOhCjwpo/UJwq/uzBBKF0ewshtJtKBsC6rKwIWlut36KIUVExOKL04EUioBJpogkQEqXcYQIGQZQjqO5uTeMiU28A5SgCqzLipKLbpFLRdE9qa+RIRGguNyGSQYwdEBCwbiJQt4CAgIBaKBEwpAwe1aSt5kysuVp7FXJCnFTpwH5wBd9SydxPlXPe7jIjVaqWMtXbKmXkhGQ3qDqPVFCdoMqizc85L1BsQClXnYCRtUrqabNOmQ/9ndyljF4JAMoloKu7n7eNBYI12Esq1w5WyuJZdLkuVDB+SVIJGuWfywkaCrUNfqY1JHKF5kn1dt75drbjkpzAqNBSz5kj5a7ZzGo950N4GhMrxPct6xruTTsn6ghBbTsJTgcQwdTVzElPKO1iLLhi1BgLvQCVAFrPyHIkULKuZADEUQzEnBbDG0rC6hZS+aMrl40rISxJYV32uFufqHUCJ/M8J9AubvJeCL2uyFzODx9vaPrvdyEpGan1gx9Dx5XPew+nAsuMG1L7mEJqBZHvS1sOPxQmpd1ppyhseexzdtr6+m/7xXs2M61ygj4rS3Xl9+PyuYZDxtrCH3K+ApK99672U+1SaKu5YS3A2BHaikt61kMZkmQLnI85h8z65WhGkmQbx2pQq6YQeXM+SQec0wDn0vm/M1paCwGnWfMPebj0ZjKZSZTpWmMlB1Yu/guEEaKZZ4i0uzxdTVtvBSTe4Mgo+3N1SN9WUHbz7MghwBsLq+/1EWJYBCpH0Jp9rxxV/eh/7cVgJFTxJNWotY71Apy2cWG8okx/F72Jq+tchXprZdH4zdKRVD/oTf46ryKPVrplxofyxlXVp55TZGOuFdIQnSbTBoC2qmwBsKJ+EVcVBALK/iKS5ZnceuBd1QpifV8/5idiMHvWZvZTeouiv2iRVT5zSoDBWvliOkzLwUlb1UFoAbzQbt1SN5qUzdd0b3Y4KKiEXL2r3F3qH4a2EWDW6lRP6Fl+mWdMKcBE+hidENAuLW1ZCMyaRilPKM+srXPsmsuOx0mV8ZpUpQu/HCV1XUcSkADJa0ZZtyI7glYFultTNVkvOLyqkZxPwICvz3Y37LxK2yC9TmY/VjVtbGQBBW09p4wiDtDtCzYRAtizzDeHKhlG8WFLrseAUwyTV/c+spU1qUsRz12HDDVApWpmVDSj+wN1y2N5J8czpLRBkIAkoT8BCEitTLJuGyHACcBJAk4UOBYoJQJRJNAStWBISztGdIzE8KHD0dHWgbbmFpSoBJVUEFdidHV1wcaI02EM7TyLoZR2ZZm1hLPzTECKCDEl5nnPgl2xi9Ooo6+nRJyMqaYdR4liEKmMcbHlR8jQE4b1BkDQOjMFxQoJ67IlMDEzVdrKdv3Q97WyjgQhYe3uVRmrQWJCwgpCMJgloBKoboCVRBRFYCE1G0+MOKkACSAgkEgFKaWzxEviCmKVAEKXXUCAFCFOKqDuBLGqQJF2gSkjgiwRZKkExQqlJEIcSz3fE4ZKjGtO1gpLscaahgYEBASsGoKSLiAgICCPIcZari0CtxgyaXZm+Q1KZpPobZjqbmTsZsPtCl1uaYIM789uo2vTVckyvHv2JsFsCBSDjVkL+8IQ1huFanlacel73Jxxje9V27scY03+l9pMd0aQM3IYsGRZTyVqDAxPCFJ1I71eZa5DqC6zEajoo5b6ksgJrZxSLP8q+74aLe1t+mG1AlSrbIUZICPZs0XyrbKKFLP+tazMLfvKQgu9WmXyBm9hl+ellPCEbmlVquth2kjYMhhrErK/2cvLbI6dGVu+mEUF64WIQgKqQ3+NIgkRlZxrLFkCUIlhnUEpGJ1uAojEG4626ABUBca1jP4tWMfagACoKwYqyrWnMPXnWJ/yd8osRhpLjvT7bK0I0HEtpNcWXjl8GSr79712s9czvZe7Z0mak73Y55WWZ7q65yz/YOSdbmhK712czcdNC2uBWGBA4q757/Lv5ccXe/d8T7J+Om9YOz0kp2X2Sd1ACb0KMaIVWG8IMLJNK+siCSdAFgLaF6vXcYBRuiovLp0nuPY7E2QGjefjlBmpeWf+eaTXyb/ndUJNqy0uEHT6F3x6Dbd2+tcY+tS3zDwP45PWCtLNzCQAQsd7sVWFopRe+2tSZi2wn/mDKpz9CaSK0n6H7yCv+p77qNXU9bNO5527mH0X5yeQzyw1xExUFyJv/efS1nM16hXDE1XmMrZj2h+flK1F1bgzKb0lNVNCs+6Q17+yHEFVKkgqiXkLmWlDmTqRYRPyLZopslnryI5vW4WqlKuFwjjID0S5d1La9taiIzMUGNXaFrvgkF6qc6yHZkXYCGk5td4FV7W/u274MO0GDkiQgIVWpigAkbEAESRM2ixh18bC1uLUjDk3v0U2vaccA5l4U0ILqJVZxNhZ97F7C5n0IOh4WATtGg+klQRKC6qdgo7ZrOnpIqvXG1Vj7qfj2ykGy1poLz8stXJqgQJiQL2Vxr3qG7JuRbOWkzm4BdKn3TXS5moCwHkkKVo3GKarwI7fsv+QvccwylHd5jbOITM7L8rOtSjBscIi0RcEGfeXZGOI9T9WyTWp1y7FlLWhTMy/tSh+H5F2SDEcmWd37oZY25bqewwFQydIQAgJKSSEIiSxAlcArmirMAkdi669qQ1D24ZiaHsHhrQOQUtTC5pEZBhxQkQlqKgMQGlFudJWa2DDT5uxrf+Emdt+jEpAsNAKMdbP6qGlyykkYD3ZuHloaR10PYgZnCgoSz8NeyTNYUcSBOXFqFTGOk7/KdO05BTzwrmm8MYSMVgpJHGsD0MkMUQc632KoU+lcoKIJDgRQEIglEBQgIzg9leO7xZIkkS74wSQKBOPD5oykym7YgbHCaASVJJuKJGAIUEiQsQSkrSitSxLqMgKpJCIkYCUAnMCsNIKWil6IhMBAQEBayWCki4gICAAAFokEBF4/WagSeog8wBSYQGy8jX3PSc9qLf7cZssT3CXlzwAqHdKvSrmHQOp+Y0pmJUw24P0bJ6SWgxg9jUg9lxeZiTWuWLXqVImgZNGe/Wzkjxf0t8LFInpAABNJWB4B/Du4l7lV/Mt3knl6rhkRZI5/3p6UjqV5JnvfnAu/2GXJbksekRG5unnby/WEYJ4r6oLV3xP4gcYV5z5jAuE6nUt+YqFKMXlSAWP7tMq04wAxVc22ezTfrHlQXUC9vIg4x4r8UQPfj1X4aQmCyAeYoocmUDpwgR8KwOoID2NawRCpACREKSNVQmzoSaAuhkQgBQCBAEJLYxgAhABXImhlHZVI4UACaEDw8eaEFirumwhs59WCaj0PljLC23MPGNBx0WGuOzlb7vOj5ekkB3j/lA1XeF0OnYaJUgNucz9THy8xMkRnJBAS868qaKM/NKfcvYdbO6Z777+yNdBCXikrYbwsGqUUO5aPn2OjA8IWktAJICNhgKtZSAqpWVR7EJwZZXvlhYqr6A5uu2nJbh5lJomcvYTife89feaoxN5K/Mi2LnslGN+mXMN7NNKK/nKuJSzf2ReyWm5/KXAXiIdy4ohTRac0sQM7ctre42lHxWV26AkgaFtwKJ+OnTiYCtR4731HutFWhcLqib6KkIz464ef1VPQsdpkrogqpFHfkwV8wEkTHw0M05IEkTJ+M0TBNnsKekiiaRLACu7kXTHKW1jy4Jkx3Kmeuz91Stuvk1Wp7VBE1VNgaqCkcjq6g0Bz7A3BY+S993Fl2R2Bvr6HmX6ASCQ1LSOBXtrrQKM5UiCGIKFjlVJAiylUfggOzYMnWTTr2SsqihTODgrF6ekJQFJpA/UAFoRRqn3DPZqbdXqxv5Fu6QDUms5VuaPXZF0EvOko1/GNS+nb0h5cc8tJ3R92JZfQMeuZADrAegC1L+Vzn0Fg+PG57Klm7WmbyG/xbVSp5cyun4v/0zMO//ApKu3sV105JoyBUkPMDIgjIWiy9Io63JbG7tt0F1g1pe8Nt9fPjn7fI+oR996fLaBN9TIO2W7yUuan6F9LVgvoQBfEQRiREoZliFtXD3U2fCaDMTaVTXFBE4IUAQZRRDG0k2QRJJoWzNOElQUuRCkkSxBiBYwYggiVCrdSJR1caldV1prSSm0BR+RcG5xyYwDN/0sn2ksdqFYe+tnQCHR14zbSikFoigyLu7N4GLOsA++wwAzozVdMO439WsVrDtvrTi0hQAkaSs8QTZWnVYIxgmgfWsCwhxqYCgkUQQB6fIhLpvyaCY/EcpZngohNa/EQGL2I7p9dGn02TBTaqWQsHFPLGF4fN1vJZZQogzZrD0ZLO1ehuVxgkgIiESzr1vtuzleffJNxN3JAAy8gICAgMFDUNIFBAS8f0HQbiw3bAEPKYFLZPbV/gba2yURjJDb/Pb1L4T0B1mHO277iMxuq4awJ6Oc8/NlWxKRvccM9k4QA2QE1X7+wlkYuTJZyTTDK6d9phen+v1j2xnTkx4f9CrmCzjNV666lG6o3TMEiP4+RdeH3JiNMESkvzPI+cbLCH0K8ioaG56gwP2w2SrvXsPNbzve5KWs6ZERMhOQumgyZVI1Mq+pnMuLYzwBeL4shUoxX2BOxpqnQEJpN+72uz8m89K+vEWgi9NIqZAqX+ZeSayzIAKamgisGCQZSihQpIO0o0VbM2k3SdaEjEEsIJU0J1ltsXV/yYo+fS9In9qNRITIKDdIAugGFCsnNBakY2ioinFqo1gHdjflSyqcuq40NRUCGV2z8yhnFW4JQHHalBnXjwnAFaRd5ynpOPZa0jQrCY9sJMjqcigtFwPOys7FDLP52773yY/flWZ8OGGJneKmXnbI5MMbsqejykwx0yZgM+UpnSYZnbV9xA5ZX7hHuTQF06LPIAIiAj4wBBjaApSE7lRhlAZGSGMkKLpsgryOtOuJyBLi/FzyPlxtKHffakfzktV8ef08at6DN+i8tcDvKKsw8w+K2MYlMvVk56qSvT8naLdpPYG3Hst2rWEoCDMnvTUslWrm2ia39tdaY4a0AEtX6D5ZZfgToVa7o++Dzqetmfr230rcyLtTF5iWUFJ2GHocWJY/y1ysfgHnfueSEwCSQiuBCNqFV5N2q0oEiJJR0gHaxVeT4amYIYVE1BQZaypAQiBWMbjKfaqv+CT3fMqKemX3295bjjlXRzFUgBdx+q4BgBghtGvNqrHgzakc42xZBwX2yEQBXSCTr12fzPqtabKZxUL/CbMGkiC9NgoFJh2rqTp/3dpJolwcVWYtbCZLEwzxJ5NaGOKvSYShl64fUt6BCJCkXfBpixx29dVsc9pLZCelZyWXJNpyJ1FaCJ+YQz2O8pCNc6d5B923liam/pwtf2NHFQvOjCfXDmQisBF0P5YAuZnUc20Za6XdOwq8kl28tnrIbGnYOt8vfiY74smjsWmPFS0jeVLGufdm7lDabh7lz/AbkgiKBBQpzRgwtPU00na0S6jjZcxFu7UqKkB+FciVrN94gMbzSddxv1l1XSg3F1PLUF/RWTjNGylXVYf1XExY/o+BRDCEimFPcRFg3DwmiJMEMRI0QUIaN9YC2j1ms2xGe1M7hrZ0YGjbULQ2taEsJChhxCoGgREJiXJZotTcahRf2r0lKuyUdNaClWyQ6bThUhpO6dhyrk/t/Dax6RjK7W0J1iBYq+dJSDeYWelnYnB6YIihrekIqQJfpUq6lHkxjeezedAHB4g8N55K6XNMKknLDB0nT8YJJEl90BAEQbHhbTUtF0JBSauQM9aHrMslpTTNoV+uWAEx64PQ9hCe0F4/FDEqSAAGIkkoixIEE5pkCd1RCV2JArGCUjFi1Q1RzpCIgICAgHUGQUkXEBDw/oMkcEcJvGGrFtzZeBXwN8veljEj86re2KaWAchsXrJxVAqey2/NCuQS6Q8vY++ULzkptBEUWMm6+WNo3/JQRjjplCn+zofQK+UckEqfWRVusFzNqnafxldcYZ0pleNU3fczMi8cMRRYvBSoxL0re38jI4f1t+FWSGIlMnV2E0X++Fw+Be9TvgYDhX3QUJnds+aCL8Bkvy4o6Biq8T0PQtbtnL8r98ais56rMREym3m/fPa+HZNIlTY2vltGQWfNsWzR3ZfqKviCIjvv2gSwtGdhOhHQFEkjGGSwdrAFRQJC6rgK1h0jsT5hK/UWOHvKm6GHUlnH3yBoS7tIRFrhpxglGYGagdhImoQRDKqEkXQngHHBZOckA5BdMVI/YrbubE6/eu81XgmJABUDqMDpM6xQ01q2oWRGkn92wOTjmt3kCUKqnAPSsGQy7RKn5DJ9avXJVlGmis4G2HIbq7/MMM7NF9+CLjN9Vfr+/NS27rGs1SDDq6v5ZDLVUsicX3BNnR/ivZ2/eUQCGNIMbDjEkHPbKdC0n9hT7NuxbyzBFOnvQmgFn5Bpo+TntZVM2gaxn1abm58rTjJUQwzZG+mkkzB5tM8/9OCs8WBcUlqaRrpu9qCBiTnkkxOd0qYlgIUTdjmqlTk0IAEkTnhbU2jZowTSoBzp9w7igXBy/6z6cBwosCcsBmyf2TleYzA1PL7gjWsFSAKR1DwWAbIs3fAWZQEqpeNNliUo0jeFVeABmkfzTL0ECIoY5eYSlJDagquioOLYpc/MoUz5KENTbVw6F//Kn2ZF47GFMiEc+xskCNRcq7FrjKiM1sVaRbG55c9tS4wNrcrnq6xCRcK6rBVmLpNgTQ/ZKGBg16jsSNLWKAzBSseEMxYewrjBBTxXmn597KJq9w2elZojF8aSJJ1kujzWhaUwbvJs1mwE7ioxbjZVagWoBeFe4FpziIqZoZCA8/F6ydIv07qmfOxcchoLF0sUC8k0g9oIaAfkCK08UP9W4G6A31NO2Zh5hszSkmsuv7UKWjN9n8/jERVO45pTm3L5e+yfXjIYwuP32D8pI/Q81aoS5bYsSimwSJVXKb3UpdVWdZytXIOEtEcS5R8+a+TB/PVceTK8Tq3X2XFTRYdQe92ut54XlcllXacwvidZQ5qV0jtrQRJSRJofJglJkebpEtJxZhMFoQiCJUqihLamVnS0dmBo2zAMbe9AW6kZUgmoRCGOtdVc1EwolZrQ1toExTGWLlkKsHEza3hjPa/JtBVDMSCgzDkRfUAgkhG4VIJSmlHkrq7UDaXQ7lkpEXos2sN2YO3hxnixkCICmJBAH66wynsoMu5yrYWtUfyx58bW0Rt266S+LpyiD7BsoK6DUkorp2Gpgm6bRCSQUQSplCa/zIgkG1qj8xQkEJUkpEggoxIEaYs6KQWElFoRp4Ak0W7+WZgDLkKvsQJaGZl0x2CVQEUCUgCkBEokUBISEQut4EsqiOMKkkr3msusBAQEBKwCgpIuICDg/YGIgJKA+kCLVtK1ZsmfH9Debb4yktn0q39CjgE4fzsZ5QLX2FilP8gJhLznnEQwWy74LmnMRlA/pbLZ2x2xccVj/bGQOfnnAjllHpGeq5gGOF5r+VMDjcrEsn6KvF1hZkdJ3jf2usO0x/B20NvvNfrGHtCgQLXms/mvqTAmK+TOv8+XjNbaXdd+XRX6crQwPxYz4x1eXKqaL/Xy4uz4sEKuelYz9hoVzCVfi5IRBpmJwvm2t4ISX8hjrxv/h8ypokhL4tJ3sZc+028MRAysFwFLu+u2BgCIMiGSWsKQsHarJZWOIaeE0GHAzGuItOJNcEoX7H/2vi4fGwVQAiFIK6mIQSUCRcLNbqvkiiAhS1pBYeNhOAFhuZIKek1RFCeIuytaGMiAigApybjKBShW2nWYUVCR1EJMAWOZV87SUPLCkjlllYBxP2buV/Q1ZxRslXiuLZC62fQUenZ6OYu8XJe54eD9KCLFlL/mDSl/CGUeLiCXnmw2HZoFFn5OqYl0ahQZ0PYIIqClBKzXoZV0rSUYCUq1FE6xvudrX43liIsTRcKruEj//Dzc3DIV8RvMF7g7paCtbB1FXSMrRmZ98qR2rvHMe7RUEU4xKHzhK+mDKrklnf3sneDNlt3UxQrE4LWBLXrhspFPUAtemwxrB95Z1HNbNALyvmT6JV+23Nd8V9Sql6OvRWns7dr9WmW5nCPhVONXTVglcS7b4ifTsZTGDdN5CGncEbO2whLNIh0+zcIp3ERJQETp3KDIO1ihJbg13yokodRcBqSOE8QJQyXLtRIA0MJb8wS7eeVbbZnMPEumDO3RC0Y6RVcXSgQa2hOf4IiHd00Tcj1jGe7oGZs1C+zcx+mDZ3Bjms09q2Ox81PbqCgdh8m8lpndsNWCdC2EtsJsdzjP6qqEgq/4J055UX+Rc0pS81uTIFMO9voIJsaZiStnBeruIJ83B60lmVL6j5WONaXrbmJfWUWd0id9FBSQJE5h53spoAy9TrkLZ71nesAp6rz+4Uxbm7YighijGRheKoAESF6NtQI4Tns6rX06dm07+yOiCI3MfF3UgknvlG6Gf0IukpqJzy1M4xABrLy3ETsLau19RH8n5R27tO0kYPgrLh7atX8W3uuJFajZLnmaU5OWe+tXDdpt0+TJu50BmWqy18eZtq9RtHy5DM/XOlxg6YKk6h5JoddtwVpqSQBFElKWEEUl7VFCCO0inqwpLHSfKYI0Mc6aRJNW0rV0oKO1He3NbShH+lRZEseoVLohiFAqEWIlkcSRmw+CtAt5KQSUlGbeaiszpYzrS/suKSGkgIwiELSFWqVS0Upho6giod3rJmZWSwg9fxlaQW9plUgACH0WSJLOg+Gs58AwSkBO6YnS9F+75U0goF3iC7sYULY/bGw9QVrxJ9hzC8vQFnyJglIMlICEEiTGTQab+LwkSFvMxQBFpJ2CE4NIOLoHBhLWlog6tqd9t14HBel3KRVDxQyOJBBFuiMVIGHjEOo2TyrdiOMYskmgEtxdBgQErGMISrqAgIB1G00CPLwMtUFLdhfkdhjp1t7eVnYT4nYi3qYGNp5GTghSpeQq2mqxe2OtJE6GZu4J5yLGygAYpKzw0dyxnnYISN1/CYB1fA0GATGDKnF6n5ERQKwa0i1bVVYN550KFGxOVTpPK4BRXhs3lfpU4uJC9tXVWJ0daJWM1go5e9qK95BvX1FPgcdAaimT9kex1CCXj+9qLqM049RYkxmpO73MS52Aq7pM3vNeTCnkhc82bdWznrBGMMxx0WxaASu5Q7qDXfX2bpsqkSQxEqWgEq3MIuh4C5D2xKvS8WnIxoBggJUTUlpXlzDPgthZAMRsBW3acs549oGCDlKvSCESemvrYsXBBLtnhpR67ljBH4PRlXRDoZIqvcqAKElAahd/IiZwpBWdgrSg2govkq443ZAbmqliLfQkiXR6EcDSXDPx7VgCbAUwVkln9U2WTBmrOwKgKrrcVk7phNTGqNgp7nzdq2e1V0WmCyRfVPUlfQeA1N0lckPeT2ebwn+PlWMToKyiuBFy4KOjGRjWBrQ3m8MONhM7B80lwVZyZuZDYhTSKi2jNGXi1L2SDk5o5oKQ6Zyx5o42f7LtYc0KrVbWprEdkJ+bDdSxwJIo01hMuhM4Sd9P0pvLMJpfkeZn5lLW5WUqvNJKSwGIBDDCKrYCXgYYwqRPcnxErmKc5l5dL79O9gIBLWVtUde9qpbh3nudySuqxncVek3ydLmralgrn4zGux9QmJ83ke1Q8cYqASApM5NbNkUurWySWhFnxopslm7sypJ0B4vIH2N+eXoAm2SyWQIlAhJCFJdAKwTQrUBe+X264hRL5r1srWEZnkWHeUOdYlArgRf1Yx94EB09Kegs8hPAq7OhVcTQa56vOLFEnvznzNPspTGWJfrJxE0xlbNwc7SY0jY0rQxr0UhklXmpVwxOEjjlILOOR5iWJDWGNH1DQmg31Eaob/PRAn5lrOgoLYOAcVspQMZNtVPmEUGKCFFUgjCW8izMeGCdNuEkPYzgaB688ZnST/Z/Z/qjoLsIyFuyAgC169/R0JKWp7+RgMre/ojTVzvD/VzW9RaEWsO5ysLLrX9I16Yc+VPQ1j+UMYcDrKWRUQkDQis9yeppCWClrQ1dqD/rChOcxqvzhoIdY4U1yzX5qnObdVDAFuevs/c7JZtcmM4Zr1PuIZegRhlqdqSeJ7KpKIEZvFKBjJtpQUIr56KSUdRJlIXU7hhN/5MCpCJj6c6IIFAWJUQUoRSVEUUllESESEaghJFAx6VbmRjvEgIgViiVBFYsX46u7i6oRNMSIQSkjADExuKVnYUdYOa8zKqkhbHulZAQUs8hJSQioZAkunGcW01mQAqIhKE4gXZ9SR6d0WUjYdoDwsWiZpWYvtRMqkq0O0wYS12Cscw1SjHbNZZnT8tsyi0lWGnXlSQFIqHbWLECx7Fjgu0BBhISxApCMSB0GTTbqPkp21YudnYMbbEO0ocLkgRxHIOhoEQMLiWax2eGEAIRCGUSaJElrABBygTjPzYGT97+Uo3BFRAQELB2IijpAgIC1j1EBLRHUOu3ABGBm4TZ33NmX5HZkGZveCdRc+mti5gqwZefsNbOLAeCO5nqGF02Agc2JwKNpJesCwoCIFlvEKXeUHK5BChlhNkRICS4ucm5uaTuBMTdEBV9Ik2RAJNwZU9PtVvWvKets92NpBJm38qt5x2n3Uzndm7GPIbBIJJGvmDECKY/nDqVTGM0l0FDWoAlK3p6aS/QQOeR/8XTBDjpALSgF8i5lyoQghRn3MO1fJJV3Ob7u/S8oq0q//zgz7+bU4u3vODQuQOi4ud9YVsdYVH2nQVSB3fLv5cTD+VP/JIAhBbSklFa2cfY5tUb+aZxWQVrAZYwWCapN1qlY2hogYfe+AJsFP/mFL45pW83yUQwp12BOEn0ZhfCBa9XUDoYO7QyD8QQJA1tIOPdV9dNCk8QSVqApVhpCz9Lfkowcc1MUwqCiKRz0SWjCJGU+iRzOXIbcH2aXwGVBCpmo5RM+09KLchQisGSIWKkZMF6ibUe4IzehZUWvggmUBc7na8go3s13apDsBESZnCi7wuh81PGVSd7eloCtJWemZpWrulIVKZPvfueu8uMkCqXNvMbug6Zg/5FLjuLUJLAyCFAc6T/hMiOX1sY9iqi/DXOFEApb+qRoU+kB6mCUc6ZQeAU7zYbXyDtzSnXIL6VtVtgTP7kSfhqSO2ssr8ImTYiwFu39IfIZutMMwvy8KXFxjcru3hVeo55Ni6mWYVOk1fyF3Zern1qwdFCAJHUirpVVtJ5mftkFsgtP7m29tqOkB/71fXQy3fBdf9Hjb4scl7HNduK0zIhpYku/4y1dHqfRBrXSjbp8awFjwKinGrKZXPq6ls2yUx7iUjm2q/v66xrXlNkq/wRkdSuM2M4xb5mb9LO8v8lJtiYSG7OCzOkGWY+s1ZycbZdaagAFvX1MFJ90IjcetoQ2J3BSccEpbyxUZjaOGwFbzUWT5TyFGTrbFxWGmtzZcaJoNQq0YmjPVLiLMbgpSMTk9IIx1OmgBxfqvtW8+QEXXZBqVs5a+mijFs6U3rnWtGJ9YmcK8r0vItpE1N2/zuzXhxZKXCitFs8ZQ7z2Tnus1pmzmTmuGtcbw7609H0B1kLHf9hP38JiLESaKaU7zVlLGLb3LVaJp9mvrm5by8XLawu06wb07Qq1gJMjw1/CXTNZOrJSitjfB5QV89YBinW4dHMYSByfBuysXKRkki37KUldTyAX9xME3lNnCNzNVGUb9o8nMkXuXR+dW0+VSu1G7tpmd3WgWzfZCtSNczyZa55eEMTNSId01FICSkESlL/dqeMiCCk1GeNEkaFY0SqAqmEUbRKfZCNzZxk3c+CBEAqZd84gVIxKt3dWM4VCGIsX75cu1Y088u6wFXKzFpCdhllNm5qE6Oc0nM1EpFhpRSIIkgwlACE1LEmiRWUsTKzSi57SIGYHF9L7hSbfq8S6ZpAgkCJpfg+3TGHAZHOQ0Wpkj6Nsafbx+6BpIjMYUGjnCRp6B8Z3pWdZwgCTFw8MnFWJQQxoBSEYFNuM/dsfDqVgJNI10eyc2Wq3W5qK2IpCTHH4CixTjggWEGpCpK4C0oNcriLgICAgAFAUNIFBASsOxgSgdsj8NAy0Cqdz3YwG3eP+XOg2e2H28gxUpd4bu+QbjqYctIsAHkrrExYCLcDyBbXbuqJ0z+bTr860SIMI9UnQeCSEUi0lMGlCKpcBksCYgU0RYBiqNZWXf6uCsTKbr1ZNm7NqgRrpDeYOeldzyAvhh0RUtOXRsF1GslsOECwcYHsKWLXD8btEUmAh7WAlq7MSxX7HzkBg+cv0UuQPVXtJa6R0epEwa45D+acS0lzzReKei6kqtL5r/KbgGAEkCav1K8jnHDdF4kX9aWzEvUFcrb9OeuSMzdWXCGE0ZDYiOXujwAS7sSqtjbT7h91m1hLoqThccasnLErnL5W/5Cmjs4qVDCYyAl7LAkCtNBDV0c5QZAyuhClUgW+FkRqxR9Yu98BGV0SWcUjIWZ9ml+RgJTaTQ2DEScxKqyt4chYtomIkAjtlEcRQ8jUmoMEoISCEoSYgFJTBMGMRGnlIVsrRqkgEktntGCLpICUElIBKtIuuqzllhaCKSRdul5R5PcJQSQCFVFBkijjkZF06EFjUSBIGBecCjEzpLFmQMLgmIGEkZj2c4ZGxqLPjkJmXU5lBedm+FEMZ82X0SNXzQU4wV86HuCEYHbI2bR1h9TwNj22R7SlseZcBh798a2kq2DWQiOwTucGjCtTlc59E8tFaw0MnSY7kISXP3vzi9OGsi6n3PqrT1FDyLSMTrNAucpzcVuSTe9fNI1qpU6WtmSWM3Pfc/XkhOv2gzy+ILfkg5AqzV2nmjzc+lW0dhbdK0iXV/q0NAGLV6ziWmbLmaPHPaEqTVEZisStnLlTu0wF76jBI3FB0jzcI6SFlCTJLU1CSoiy1HSQANlSds+QIH3PXBCllJfx48elF/t3vWZD4yVpKyyKCLIsoWKpLahidmM3nW5GDZQ5WeAX0bP0sl3PXmN6bx8IiFZhYvI10nPVpTFcn/mlJ561XmPSSjM3/9IHDBkkd2CBBGnlmEjXKKe9tNYlZpAI7zCSH1/KFYEB/xCLveFcRBJAfnxlR8iVURQLKGHWLHMIhz3LOJUok3f2sAXb99h1lowrULJu6YS2ZknsgRho95lJkpafU3plXWdac3prkWld0DkQZ0pQ0EFpWzG8mHaoM6xyyjlvTXRZe33ZO9Qby4wixZ9VqlQ1uFVgsB47epkk51aQTH2Viyqmx48yWTn325avsEpz+yqfV3BlyRUhV6Wq2hU+YO9l65qd8nXaqaBcnPtVZQRv+4+9MvvLK5m57K+5uYz9LkjZiYJyMoAkARvr50hKRFK7tiQiJIkykZ6FOWsjoGM/K1TiGBwDJUQQkUC5XEZbUwtampvRVCojEtaVMWkFlNB8I4MRqwpUt44PF8cxkjgxfK2dneys43R8SEs7tIIpTrSCjhNtKSdEBIqijIIeMMo+YiCO0wNupt75+HLCKsdg2SbPra1tV6Ngc8u/I5feUQ3WBwdZILNE2IMQJEycOEP3JGnvAUKIlA+3wb5FuvYr1vGsmbXSURgenxmQUkDKkonNmbqGttZ/ugdN3EcTh1PvZypQMaPCFVRUBYmogEnLRZgTxCpGooKry4CAgHUPQUkXEBCwVoObJNAqoDZsAdpK2urDbvdzVjTkzuymzG56H7DmFexd15sJ5xNHXy0Q2hRug2rtOj0hobDKOePbHd42kK1QPmKtYCsLUEerdj3X0QaAjL/+BCwEuGysHwR07KgE4Eh/5zIhUQLUnRg/8jY+kRVW5XZ99fa/RUKrWukLNmjpzlDmrtlNHQNs2Hb7vGt7X9hnTvF1NGlBczwwAqj0fd4PX1kFwPnkc2nNfVY5/WVvRRFU43tDBc2C82O+YHfuC/eK8mab0ORlN5VWcVB0utpKupVKxz7bNIS8nMsJu+zmi/2blLqkc8I5k48UXtsb8RDBKHqdaCUVRhmBntmZAiCwOUVNxIARfOk4L56mJmlMGW3DaMBmb2thNrNw1hC6ajamidt32z8ywyvxKBOnijrd1NrNoHU3qUwdmJLUuyIDygrswGCSAKT2CEqk3XLawPYSUCWAIm1hF7MyepdU4pQYoVWiEhDIWAWyEcRppV4sEn24QOoeYbBWoEnhXHhS2VjPGiEEQWkBpqiAQJCRhIgiRFKAWIAqZvzEFS28kEJbGUrt9seKXnRdEuNOTAAxQzCDE4ZItLDUCRkTfWJXt6R1LabdBYGN7ipRQDec7sr+ZXTTpnk4zk4BZfOoeH1qyUjeko4AlEvGcq5du0D0JZuWVvtKqUxhjITGKfPMffZeQKwHpfCfhR7bnACVio4HkhiJo9LzAVGUzjcyWk5hCy2RUQDagQpvXnrCcmslk0ox3eiGy8DRIy9NZnIgTZuBTe9NKF8Ab80vXTFTgZe//tt3ZFx/ZYqTL7ttX3hrAkMHyClICmTX09Ym3d9dlYKEDYK8v57S+Z/2uxEQ+qi2cuPC53vkjDJK2dya4b2/ZpE9xQVFQlu6mU4RkQSZOHFCCoimnIWcJ+AkKfIZ135pPyId2draWTGDmgSahrSAiBAv74ZSiQsf6RRVXnMzOHseyvU1ufmSOUDgNShJAkWkDyv0I6iNgHL1+9IC9gRfUW69VXj2ZQIwzgozY1EYNou0NFkLgaU9bOMa0VhZ2/5PXUv6zWZpIAMQisHmQE/aZ2bseZPLKukyseXcmq6fV4k5nMPQAmgYi3XzCQASkXGzTy7ks3ZhSZlzB3o8CENG2cS3S7QSIU6gEm3pm+X0vP631xw57N24d5ZlBO9QIafzh/y06afPCuqLlI1L2eBwpMJf2YfJG/fOVbFJny0He4XThIfM+iWMokMCLu4WYPeDZl+mUjfIIDiLOiBd4vziEZCy4JnlzrMSZWTZdFO2quZxtLdG/+V5+ALy7X8WNr9/z1/WMx1aI3+/jLnlPB0/xUWvyjcBILRHiEgIRDKCPeCWcIyECQkJqEg7o4aZG3Eca3fCMkIkIrSUm9Hc0oymchNKUWQONugmlJFEFEkkECDBUCp2VoF6iUrdNFq3ue68IQgktTiVlVZQVbor5qCaZfws1aCU/zcNoQ1h070ReePU6tEJxl0vs7Xl1PHbTAKrSAaMdSA4tRDNbVfY8GC2RD5FVcZlLkMr1nRUz1TxZ8Jf6v4TZkaRHRuah0oYSFQMSgBpDk8oVQIzQcrIbNlSGqyMYk87KVDG6lArCRVrBWlFdSFGjDiqgEWsFaD6lKI7yBsQEBCwLiEo6QICAtZq8EZNSIa0aTd1sFJPoV1umB1FugWy3CSlmwpPSp5uhG1yy4AWSbz8fFF93991eR82X1LCuLdk4xpOC9mVMe1gweASaSVESxkoSdCQVnBLExCZU8sMaMFFBBZkDvZbd2NKWw1FAJcAJASK4CxJ0tg9jUjzegYbyyNXeSskzjVD7qn03f4m31027i8zzcymCzntLAYwtAVYsKyvpW8sSWYseGOKve9V8HaqBUKzYuTHlZd/LSFmvV22+1nvvXZHh6xFmp+VEQBm8vGVcva33+/CuAtTgIsHZxWcBKQWMuaZxOTvJFXe/LHtb9w7wWwcEUnPwohQLbiw7llYv0+aI6ROe2V3nMLVkW154wSIKyZ9nfYvAJvqMXTWtg7WqSu0p500O4ILu+esIUwxcrppozQy+Vt6ocxJb5V2kyIFotTNryYROjMptdWbiPWGOIljF+uIDXlwJ/JtNwgTfwXGCtAOBwISTrQrIUM3maBd81orLCMOSJBakjjtpTCxM6CtnhNWQJOOgSGjCOVSSSv2YiAhBpokpFBAJCBLEUqlyLnhVLHSzyNBk4i0kg6EpKKF36wY0jQyQWgdehxDSF3oCvTzTCauCQjEDJUAJBmo6HpboQgrdoaAVrjHxjLPKjVZGT1XF6en7s1QVQrGMJm0kqa9GRjSknY2/E/TsHYumD5yNN9JT7zx6mhsmkVWic5GUWgnapd+vlsZd5kEVIxmUkpARlqBaMYwIi8OXBV9NITDSYxsWdPiplbZubXAPm8t/NwllebnpH1eWziTSH+dz2k1bPsYoZm/mqexqzitgdWm+vM/QwuK1n/K0i9fWusjTzNHDAHeXIi+gvz1sep9XP3bjuMcbavlfpIyfZvJKfc+RnVlTQEz/JAtVo4HMF8pSmm6KOk4mCCAogiybJUk3j1AW8tFOcvq1aSIawREcAc1hJCQJQlZjpB0xSDfP7a3LAPI0GJ33x42cdOAc8979W4FqJ3A7zW4iDVSF0HajWZN5N9FhbftjHPDlpEKc4nBxueyoaiws1aYuSU4dYnnPMJRGp8uo5jziuIEzY6uwtEEvY4qKBOLmr1BTpRa+1VZLdqKMJwizirVWKUuL60FJLOCdgmp6burmwAY5LFZAmSUdCrR+SSJSmNZsXbn6TwemxJazxOp+0bOjIuUVHijhzwawNX0wPdLQpl2qe5lR0m94ZtxWUnZ5oOXxira/FUFlDuI6ZGkTLnyFfTqaS+TyYeIoKAg3eKs01kLfX3+UbiY5W7tIDhLT2Va0C4XNoRrriFya4dXdq6+7Guz3CGgTF2yWVeNRdcu1e/LJ/PvOdbCKwZ7ZQCypMVbkc2hAp9w5QaFl29RcfMQsCy+5i8Z9hCWnpROgWYP/iSMJFGQSQQpCaVSCc1NzWgqlzWvC0ZFxRCxgBQEWZJo4jJYKjASJCp2h9ksfWWln4uTRO/XzZ5AeAonfRZRH1xL4sStazZ+pWJ9zx0KMkSFjecefebJ0CFzTRFrZVliZrBgQCkkceLKaK1bLX1wS53hBcgUztJBrdQj02eENFKm7Y/0oIci1g5IoPNKBEPC8Ia+y3whNK1MjMWwAITS75DKHjTR3nGsRR3seIKNrQ3trQPQ7kkVtAvQJNH2kaTbXYBQhkQEiaEbNKNtRAnLFq7CwaaAgICANQxBSRcQELB2w7hxTK1yDOOp7I46FbbpCMQFuwQAnDniaJhP76f77j6qBUopqPg7eepCYrNJZc0gC8N8S2hLuVIENJWAtmYttJUSypwMFzqglX6SBJg0k01OEMhAxKAm8zshky9pBYUiOCUdKD2VvQpyGy2TjNJM8sf38u3k4tkV7OgzG/YCYaN9jDhN19EMWrRCu/3sE/pa+YzooMb9XP51X2XbQ6S/62Wf2Yh7Y7231bH9YwU4QCrUtNetj8Dc5j+bh50/1pVdLl+27iZh5iOyJmcZKQWlebpmNDdtEDITmxHkW/h4RbNxVGwhGEgVvXDP6CIZ5YZRKMHeFxJOqUcC2udhz/CbKh+nxK+i8GiMNfDJC2vYK75viOI20gpgM+fyw8C5zMmRQ0WJmUaJFgzE7JSGWvAEFy/NNoUV9iVpkd2LuhMFKeBOyrIAGAxB1m2bEVIytMsa0veYARZk3C6Scy2qWCE2eVVIIDJ61JgTJCIGl1gfPCgBiLSFiBASZCwgiQhSCq00VEAkpKfsMbFJILTgIY4BEGKV6PZg4XUXQYAhEgGIRNMYZUgckT79m7B2lxRJLbBLGDKCE54oI6SJy8opQpWRypECRBmaNm8wPEsynNWc19ZOGiayA4OsVamnAHe0EtmO9NdM+y4bb00BQKx9fdqh3t2tn42i9I9YK+hKSne6NPlahTQjWy47OB2dcGIhuAlJXuUzNEylz6skPUhgtdS+i83cuEwPrsC5xbMCeTefTP+wdziA4Rn5usCBhg64OH528nrldUVgpGaSrjH8BN41D80lraxd3oU+oXBJWoXFPZO1vx6ZehfyQD1mlM1TpuNDlISxqtW/ZXPk3FCKcmotRyCTVt9L45LZjOstnIMHMv+wETMLCB3LyyoVTTWcjrmoHuZeakhrFD5OSZ7jm2z/1NOl9RUSoPbetLWesykFoMz0sAL+jKUVoN0runhwxuLbVNfmlpI6gvarbK2jslUn6AMV1vVj2idWd6/ro2PakXb/zP4c9r56ZMypuGyGVrOn4Cn+crSNgISVF/MNiBWnbtwEGdfVKX+TEfgrbd2vrVB02a2BdGolkxa06v2uGJSSNL+hoPPJ0De3LFk1oFGM5fIlTp2UsvnHX95yr6kmW3lS2RvYZaUqL8osR5Y/EmZCKVLGGknXRysg4NxYStYuCrULQjL8DPsjA0IByq6D9t0M53nVN4Z3ihqbvKiefjvY8dpT3c1nzTwbzcP7SZkvxc/kpwVg5oVdd+09lyhv+ZuDgInfCWNxxXCKITPH9NbCdBAYyhxQjeMKYi4jTiroTrqgoEBCx6pUZo4JIbX79bJEiSLNAwodL1klCrGJ8axglIGsHK8gWJgzg1ZRmCCJYyiVQLG2QxMeLbGH55ySTrG5rvkuAUvD0zlDZt9kzzS5k3zKus7VSi/dJkZB7NE1IuGdmTVrJaXjgkz/MBSs9T8bK1E/NqaWWZgTgMKMf+3z3igxdd/ESQzmRM8jIXWdzGLGzJBCQkoJIYVxiU8gVmb90mOEIm1tqDgBI0GSxIgpRqK0M1PBjBIEJAjtI8po7ghKuoCAgHULQUkXEBCwlsMweYRU6G828Rl/7WaDm24yjOCeCFApY+tO9VcJ3LKv9JNU7wK1hCU9p5YKsgjK/LFJpl3BsSCtmGspAU1l5/aKyxEgScedEwDYiHbsLk8QwArCxeuywhnD0iupBa0xgVkAibGIsFn4Zh2wYo4+wm0Cauy2fAuqbMMZ1Di7747m5nZybvfJ4JYI1FoGFq/sQ8E599kTfJECpV/7Rw6KVIFZB5kimx89KenyFhvufZTOEytXBvQXu1NkeD778n3gFZfhXFMZyRacMoFEqvz2A4CZ8ejKaBV9zvysqN9t2gRIjDs+Ac9SL9tWmR4z7WuDo7OwsW90iozrqkjPP0szSAjwSAm8vQLoqt3hcgNAtKbTy+n4ODUgck+n8qBMTYua1gpBrIdBZTNKveVkHrI6DCc88V7sDBWFERgkSHUhtt1MGje1rVDUDKW8joKhyY0wY0SXSbn3KivsYi1wYZMRG+EjgfRhBcFgqRVy2g5O/0sAEiRISAsBCFrIoZghEwnJUm/uVQImhRgC0kgkIim1hY0R8EiS+tS8YIgmbYVHiYBKgIpSOpYdUmEyJQqK9ElgYoKIdZwhxAwJoV35SG3RTYlK4/cxIzHWdqVIN2gCpYUviQIpBpVNOzoXkLaz/c5DOlD8o9J+LDp73Snh/NHkPeMUeuaaVXrHpK3mkkQf7mAy7l5NIL440YpKMtZ2Lc1AIq0ZIRCVtLtGR5dEtkwsPOmllaB6gymj2Of0HrzfwnvOtZNKrXbzwmKbnx9vzKu3VZZqBSWn70Fq2e7NCq8N4Q1+Px2q32+fgfeMf82nqbatVhUFWWRWWEdYqFbyGnn546rW63K/vMMTFqJkzZ0A2RSZZtGxd2VTysvJpsi1h5CpUs5kXLymrckwY9cavGrBsYAsR4iaI3R1V6qGGvQjcIdQCJoymXmW0n4yh1Y8dolMCjsH7LqXH3N9hBgpe07kwwVW9a/BFhS2PmRUmK5udl1mzkwpZjaG9Zy6lxOmvolxkWnmJ2eGITnLbxI+D2y/WVqkXculY9hLa+7B8vmA20ukcWDh4upZQbVOZy1wyDsgYFxuGwstx7JJvTbai5woJCp2sei8KhkeR//IuKeEF4eO9XvJe859kN9C6TjJK03Ja8yM8p5sd+k1LzMW/ef9F+e+OnDdn7kHuTAP954M2TCKHY9fZvLOlxild/qYvsmK3VgR4DTEK2urR+WVkPTwzbDlbom3h59MH9vlrmhKpkt9NZ3mqi/ePc7e6+ts91crID+HsmWxy7I95FVdPOsy1F701ns7RvNrlJnjSmkrLSUSSFHScUchIMgqgnzFl4JQDIkSIqldZEYyQhRFxorL89BC2lpMTwk2/L/2TKCg3Slq5aAyyr3UVSUAKHPwTKlEx8gzvJ5dy0gIpF5+GErp2NBgq6CzinvWsSu9uOZECUilsdkdvbN/zv2mVixbmYbj881rSRqfEXYs5NgPYV3zIh2LOv4em/NQZGJi6pts6qa7kM1+JHX/qoxXD2YGpASpGDEDKlGIohJYKUilY3ZKU1g2bvEZQofw4BiJqiCJK5rnhq0PQbDeEUhQ1iI3ICAgYB1BUNIFBASs1WAic6pe73xY5ZRzQjrhhmPmnIDP7JCsq6BazF7mur8hzG5mAaTuwSyjzQQggWbLFYAYjFhbmQhprBKkFnaWtXKOm3RsPTYyLLZSeCMI8D2HkNnc6425TqdTCR1TQ+hT2iiRFrBKBpLEnejUG2i9S/S2l73pggLYNuD0uy+kS6UuXnp/a+btLsmrLKxLN/PhC7QZ4OYItHgVi94jvJ2dL9mwuxN7oda+oe6GgpDGP2ygKPadrg2870pVp1fe+Hensm0d/DJ7G2enoDNaINsFOQGA3xw6HQOUGEtXmya1kHCn0jOf0PcpN16MBkoKApHIblKh9KOxiRsXJ/qFLliNqZvQp1QlSa1I8eLXgGCsvoBSqaQ3qOxtMo1rG22ZFSFpi7HyxW6ortoWdXKI1lfYQ6+2GoB+l2sv2+xmKPuuLS1sN/sjz296mG6inBtFAKkLO388CS8Dho5dafrWT+ZOfVM6PKwhpK//cUIXL88kbXY3hGw3O8Wh0tSKST/AxjINzCl9YujTzolAQtq1ZIwYFUqMpUkCCXMiGlJbwKkESay08s2cDCYGtEqOtMUbSUSkFYACOr4VQ5/eTShBYoJC6Xgm0rSxAJWViXsnIBS0BUOFAQgTNy9V0mmdtHCnoRmMpCtB4s8pZWIQlXJ9YlvWt+DKSL7y9NM+73UW2c72pGAwbQxjlgjTqUkCdCdAJQG6Y2MtaCaGP9KSRM9rOyDjRFsAlsxa1qTHE1gZ15gELWG2tFrAWVw7qx9bceGZkarcPfPpDzpXLLaLobmn0nJbgVqhVJHTSWfb2T+skCaEsz50NNQrG9v1voYVt50gmXnorV35MgHa5eWyrh7Wi1rIPWPaqpb7SveMlb7WSJZxR5nJ3H+nxxMRgWT6W5Q9BRsRZFPJNaNskvD5KVlOFT+DFT9uoKBpql5/3BgU2pJOlHQ75BUijq5bXhcAoJybNBghM9nDaEJk1hEbz4iJIDeSiBethO9Vc1VAbQ3yK/nnPIV8fsixGY/M+oCHNZS184F9y1rW7cIqQZIYrxIRuYMsNkYrhLYKIZke1LD9QFZw7/NQLg2MhYpuewZy85KN8iZbN0F6LdCxpRmKVMqPIB0DbOiiUokWbjs30raueu6xoYl6J5Fa80AxkiTWljje2m6Vk9Xz3p+jqKl0yfST3y8uF58eUC5n73nLR9ZM0RiKyBJV/bJtZvra0NwMX+PtKdiULWV1TR6GAXHuqv1XWDLp9Z9VfurU1rLQqz9gDieRGa8pf+Pen6srmS9OUdtjA/VlragDby3IbA/8+/kiwCt3Prm56fhZs2bbdM1DCCsXEbqX+2MsrbvyxrKUQh+yIomIBSS0m9ekooAEkBwhIqAkIjSJJjSVyihHJUQygrCWdJxAMaHSnSBRBFYJ4riCSlwBEGuaqVLrV9fP4MyWS7M2xoWlUWzp8aFd3UobQ9K3/nX8LRv6pD+FOximeWAb8ldRapFn5731cK6b0Wf67Qcb609h4jKnrmzt9opNfuT+Y1cmNms1K9YH1kg65bUyHheUUJAy0tTY7u3MKBCk3Q6XSiVIkuCEjbwiMe7fdX0Tke7PtMWqAiPW8f0SbZ3HkBCk94CKlDvwSKYN+uvASUBAQMCagqCkCwgIWLvhrFCMsA1sTnwZIaSvnEvZUjjptRMeptAsJqe7CfusJ+NMox7oNJl4bP4+nxMQWcs5pTfq0igRWlqBUglobwU3l8GRBCJynnr0u/Um3G5W9AbIumsxG0lXasCEztbbBIrAkkFRAsQxKGKtEIwVOFHeZiovmesPIZgRklpJvXUh6BrQ7VDN+8yx0//P3t892ZIt+YHQz32t2Htn5qnbt9WtljQabEwgmbBBgAFjvPHAn80zmMEDBi+DYQyYMJBpGNFC3a3uW5W5I5Y7D/6xPGLvzJNVt+p2navtVXkyd+yIFevT1/Kvn2dbtEi3h6JjXCTG0a/99gn4y9/9/MJqUpVUpyCf10MJfVT2ZlLwr5QdSut3JXIt74lLRVoMuDot9957T/zavc+V0LBoo8ihoBld4laUTK51KJYxo3e0vCcU4GQ5grrnr9KiREIamgn9tGSRmUPGi+qtoTGbcJ0KLYN82rYNEINSsSTnlIo3s0kwTn3B8/kJrZtxjtgjgJqvOQKenl8AV4AJFEMVMjwJPABmxrpeceW/ek8lvxua6Io62++M6vxdbgpBf7cMUhFUhrGwsxtZXedqrtOqBt7mUiuVo/KbMAN7wkahOut3r02hYECtSy23KK5A05gZubQCapME0KEYuiU00KbDuontRyCGmCqmsZChBaZTsk+72qRtzOhtwSCb5ws6aFBG4L2N1SCPyBwlhpbcQSQe6WfRfRrWSAWkA9IInciAeAgzWlN8j2gEiFg/DgBCaKrgc/C+hv2McF4SziTlMiQGt/KLmAyVl2KyWdgaT+2sqsHNrmKGuTDQDcfrqrwr4QRlDrA6JOaVbG+5DstZ19iMdqcL0IeV3wB0woTjLHWkwjdlxGQ4RJTRnHjVdlj5TZSbfNLLnOEI/kyZyPWn8m5gbrok7pxQF2f5vKtMeVc1xOzYutZCcEONgd88A3/zU/OszirMPw9RCod75ne1rzSftT/230XETn7NNOEomdDPRcxshHbq+Xgr3xnU466yH+yDfwTklhHjoZTTIVpc940giii4iMxQU5ZqJCH1ucotnE8o9wZ2KLLg2T8X7aPLPnPuonkr+fOuaJ7H9AOcX1nadstcaxnxFM+LOG9g91Oain2VmbsJCo/CsX7KvFI8o81yJWt9O+VfCSRpGIjlHE67dRERZ8SUSvz6zqyT7xlQz6OnJYrN+yX4k+qM2VJ2mGoP08+5E0aNXLrk24O3rLCyXYfTXO/z7b7eb2QDultGzoR6Dj6w4E9Nl6+QFh5RI2reBYQkG7fbUbS/In+h9Zs4246IxLIgKbYhizDScIpLuW1y9kT+53LOKjbmaTTU3fZy29jYx0qbMedqvW2/N97p6uOFe8Oq++uHU8a7UuJuFynvqTlPd9xCZz98yPJ9TZB6JBURWJpHVdn63t4GOhq4N5yo4dw7ni9nPJ8vOC0Lmhvix1ixKkC8Odw+gZtCZMMYw+URnx/Rv4qdQc2MVbYHWq5J5F5PBLQw6qtaTrUhubbjPgUcJtIOrAI2+HOYgS9WnWN/zHmd+eV4HpVyTevkG778CQBa2xmzAuXBP7gBOSIHAQ2+RQRqgk7dI/10N5gRrRvRygAZnCUxuJPJbWSGUR0KiGCoQpnN+OgoGYCCHSo+4EJH5AyIeS/ufCfAmTvOrWNhxn/vf/Yb/NX/+4dfTvR/0IMe9KA/MD2MdA960IO+bYowDxDQTSF2kxshBfNQwhXRLA/MmJ8Rghymoqj+TrnXH5Jhx2chKAQqZMphEOCJjpUF2hhYTqDvXoBzh748m1Lg1EzhnB7UikhoxTqDkQIaZ8ZWVHDKECkaItRJw0CWuYK8jxZ2JSz2/XRX0fiV7n/vApeCb4xK9adWoVp+DpLne9U8vvx5AX53/Wq9fxrdkXyrdUQBM2ZFdRifkxpo/sSY3Xt1Car5sKhd9+r+y1B+ckfmjQuoOiIQGlgM6z89Q1Ug24COAe4wKJLN4frKBODecbpc0gAnh4r2ZUEPb0sy4TMVaC70nvuCWGDmje79CODUO5beICLYhhnqhgi2bcM2NkAV3z2/mFDJhDTSu3Hhcjrj5elikXS9mZGuMcAKIVtXl/MZCoc1hGCIYN1WrGODDOuLtZ/w1/w5iK+6PirM5V0ooCpQ347cXuFxUObsDGJaVrD/kWzS2d30wPX3+nWhfR2AYhvS9IOYSjnCTumb3q2a3b9r703bannY66BIkUFbQwTSxNAXtbzT7TFDLSov/QFovjvKGm5hVBpm81KAlUCshpqqgjE2rMOVFDw8OkFcTWJqFMs/YwqVDRsGbWCYEmKoYCNCd89ldoeRwa68OcEULEKgQakupFPkSw3+Df/d5pqtIxPOAUctWuxzUju17HVhnBve+QpgVeCqZpzbxoS+DOi46Mw6chrfj/ndplYWvU3oy+VqUXYM4OkMaDNjXuaUI4D8swqAYZ+zuYEJGJM2omR1r+nLBcHz2bqAIrI2KfaX0o9puZ57PWU/wzzhm1sHnd/s3l/PIpHcMcYFbd4XY0vxfFGWVTr9giLabuPW/cVj3x2eoYaEfCOyvHDRZ9wY7FFwxHtDHJjAvUTIHSE9/5iNcgfanXxy6hLauYNPDcOjtHcOF36WI2aL/gID2wZZJ9wlNQI1z41ZIi+ECORGh5+T6LcM+pKh8rvWvfvM7vRMCEtiOp7lhjX3GcrNS/JcUY86KI4UADJ9LABToMMiOywlpfPme2dMtfqZc4yfQzjglu2G+fZJTBPcnqm0cnf2xVSSp4HOYZyZQTQsCDmU7B4tE8pv6x/2k5U7ifDITZtYJ5S0hHww13S0qy73cmzYjdCOFSjB8lHxlJvKrXe6A9EDAUMY7b9JDPhZ+qnzNtZP8XSyftCbtRBr0q5btLw4PMHQGW0U5YY5z7Ypyu3Dwp/8u3o+8jNWRvdJeW+gGMxpvqv/Z0WyXZv27OUn0btn0U/QTZXvnHnvoCbfEJMZfBoZnHnTBtomlHhkopRNIasZgAQMbQtOyxmn04KlN3Ajh4jdMFTwJisGNYMcZ0VrDMByoFneW4vIF1huOvH8jyqRV5h8HcPP0OriFFvEGBvs4xgbrteRhlwCEqJdNeAq1cd9zGOa7jmNRYbaHBQCmG0yqew71iLo5jUhmAFOJvyurWvFzlCo/iMRSRdvbX5UsYoRKL+bZ/9YNzRzffoetW1iyBppTDUnNWExo19rgJJBxm923hPnX7ZmyJxTVoBpgBYbHhkAKYOU8fJny7vHlgc96EEP+hbpYaR70IMe9E2TKdqXemUP9acTp94+A9NrHlPrHPcixOuiKHBlq99iIrLDW0A3M3ihQSUgm4qWGaZYxsmUlvrbF+DpBHpeLOk4qaXoScWe5yjyUrofikPjbRnlqqKgKBFNXV0Oqn7Qbg471cmUqHFHIqFVcerzx9xUtVRBvSpItUibVVK9EfXvaEqOpFO5MxWj+2orAPz2CfT99Uee1j8pdqZStz5jCqZU2MrhFnpHdMhLRbEbWpMsoyqRfA5fxzS2Mt9Gaji8ato7j6/miDBl07Z6JFlEBjE6OpvARHDFuSq0mZB67ifLN+bXKZTiAFrruFzOFu1UEqyH52jvC06tzbpSziAMh1R5uVzcqzSmPKW3/qkvWHo3D8sxIGNgG56XRQTMwG9evriRztdiWG2YsPRu3rRLA/cOag3KlvhdyAwsBp1CKeizKsDuQUoG13LixTzwvzZVXNcViohBZQoVNL+D3iwpdJa737hVftRou91sq+tE51TMlBpleqWTeCyxw/sq5TveWTZhpGMu7dWin0ulxiwn26Slz4aVVSMJE+rXlV/hDBzPCkwp4Simu2WmdYmpwBHQDIanWVTKppJ5/gTA5knqGQ0RSRf9Zoa6gc3nhWIDQyDKGOrQRVohXq0iAsGAQNwCycymEAJgxhxCRhaTG+o0rKqHzs6JVngAyO4Xns4SpFaehuY6O8KNc5sZ6DY3Lo3Yc5DrdPfSwFoSKgOpthcSAy3KGcCrOFTkAJ6ejO8opuPIbm8uCn8qEzsgbPOaTANhWmPrTKXZ5ro/zYGYfDNfERF4cz4x2KI7VQ2e2mHrVGKMaD5AQMIVa6nncVXHflvrcY+ez8B5Ad7W+9//DLTHA9jVMjyDAJgDBvui4k7gPp/jpc3oucYeFWfP5t9Z8Cf32j9iinxESEWk9QkxsFxOaJeOsW02rXqb69RhGbkzWkRQqEXPk2ga8Kg14ymuQDZ98oz4gKrBn48fdUi6IQKBlsJUd1C8+xbffCQ7b+T9FMYcmjxsfpUOaiCA85yOeX+c1ZnAzX7CCEw0o5MDYpKZZiR9fFsMaPE3wY0vjMMqnrICUUoMxZg4251weTKh7aCwfEwgUGsG34cG3hSQDVAx5G7nTwadCRCxPR8B4kq73jX47zAYTiNd5txT9eivZHV3RkzdMFe+jA7feeTc+X2cUnemWO7h96bF7e13y7Dn739Ro37rOWUGEOquzHrOCcnEfFyMd4vP72kcrn1jqBCWJjXyGcPG+fiChDFF7hcB+x3YrGYTnPvhp1bood/f7ccPaNeVh3E9DvOPKved67G/7qJ6770gzoFwdBwFdHMnKZbJM5Qd7EMxtoFBHWDFclrw3dN3+PL8BV+ennFeTibLr4ptrBAMkAiYBIPDd8hTU7DlsxRVjHWDuHOiDj8P+KFayc50YV2zZjQ3wG3mRLht0BFyDSefIJhRawybCGavC16BRGKICRNdxAhnXS332LyzH3+iFUeAY98HX3I5zV9d+G+MnueOVs2jWh0rDctz4RmiCoyBISPvZzK5EgqI59EkYnBXOyf4YiVSbB5JF5AYJIDQwEYD61jtZxuJmvH77WQPetCDHvTro4eR7kEPetC3TdwM2ickTlcAaEhDNfyjfA7xOfzKFOaJS6lAcIGbAmoCfnC2g6NBDI2EwLLE8lySUdvBNQ10T2fg5Qn03QXU2XV56hF0fhBVO4w2wA+1zWpIhIGiTACg2Hay8z2iUB5GNF3zKLqtmRK1euDtSnLF8F2qor2aEjiVk3GZpiVA63PHw/Tx/Xe+CwVWXKvwlrXeUfUvJ+D59COj6XTfiZ868bsCNutDJdKtiv0hiev8LqMJDwoPn3twWBQk1CRMWBlq0SoNAHVT4DWeijcl08lTA9gj+uoYE6GfI9KNIDlfTYhjYizULLcX2GD7Gqd4R6p4Op0tPwE79IwbsBUGBXk+n6EqJmR5PofwzIQC3z09mU+6K9XUlVkiagiwyxlD3ADOoVhjEAFL76acVIUMyykxxmY5E2BRHC+XJxMUU/nmRkKyXGRL6+DF4HDMSGfDNhQeLbdZxFUVEEEYHtFE5DkRvqJsrt/Kne9zChyeCdS/Wkiiqvq1FLKDMVU7uHftYdnt3+k/JLfPhI0k4SZR/BhCV+C/qiEv339Y0rteurOu0rgYaIdwluJTn8syibqkDgHIyIEw2h3z6+ze7+0MRd3cDgZCxxKv01DYpH4kcoSEelYxINhgRj1iYJAZpg2SSV0RbJBME9LMIjQ3DAwVN0QLRmos9dCxMUClA5O96OHv4JmCVDQXBYo9S5MnDDEj2jpiAXiYIoyHEE2+XvFQ07hU+VoMonfaACCrDw5s73ltQD+B2jDIUIIZ2obOqLGIUI3ItzAGRt3TKEczGi+dQWISx2fF3RyfqYQ/7B3lb4uM91wsTKboZ4EMse7slofFvOO5vO+wUGKRkk+wZPT3uMKBmIAvT7+fke49AyCKys+rxL30LYB+admMdup2bgFAncHL7FBqrRhE7vT1g+5SwBFG4E3A6lE44agZR22ZDENGaG70ZwZJoDXAYTA9io4plbMgtogxEegAyDTeaP/5Cdv/4/X3awAD/I/bPMnUqbY72pSza7AXodyj6TBH9ifF46ZjPIkwo1L8sI5k8MW5Ls4ORABzyygVENxJzniGiEzIy7JmMgpMHAkjzqBxC/u+4O+pz0xb/ISfC6ejOMOAh8Nz+7iqoHGDNoA24+PRvObG1zEUTIpt3YzPbpGfisoGHv0e9fc+JTfQZRtnXaN74ZFzd1nHcY/Hbqj3132/q+y1wnFXytrUra8eXT/gY5+rzZx/Hz05e0Ux1FJ45xe51XlUXURPkkKU7LAilI5zCbXNOs9rui9r7qlzDalvW3G2qUvg/YbN35+JTrv3+L3PdPj9c1E51h0+3N43NuNZxLw7B8PPYxAyg86mwFB0bThTw6Wf8XJ+wndPz/ju6QVfnl5w4hNYbL3rukHdU1Vg0XTKFnEMDDPGwQEDtpGRdCjrWVVvxD3yQ7UhNZj8pGyOiBxCj/Mm4//BEwimW9DkQWGcpHDWPVDNM5cUR4sqS3hpAYkZBjoZmxn9ySddGBkL3zJ+MbAK23k8mKg7gajrSBoTplOlpuHPK+pnbQXEckgPd/JscMeLPt2P17HZuRyS/FtoYMWGK61YdTgsaTT0Y6fJBz3oQQ/61uhhpHvQgx70bZMLo3Zm90Neal/NmBaJy8NQkJIQgIQbc1dZcom4ykUSB/PI/ZE/BIgrrpg90TJNoaoxsDTolyfQ8wn48gQ9N1e2uLjn3oGkDpnJ5r2vMPl76gNq9Nk+IVj95ubATnADHbkA4o1c2BS0VV+otZSj5BRfFmUkwQWBqvQEVHmntLmVGfXrf1fBcyeYFSk3Dua8f4yWH3lgr9GPH0nxR9LDB3LFU1h+3OgaygoFQMPhmnRgRrRNxZIVpZ4XahqBE3pus/oyGI0bwA3KNu8bGCdqICg4cACLEgYAel8whmCIYsC9EMmEt0aMU1vArYGpoXND4+YBLIzGhOfl4sF4plI0GBqPsGDG6XRGzMgQ0gSa7Xg5nV1JQ1kvdaN35MCTERK41Tm83pk544zAZuyQsYDcANcb4XI+x6C6h6i4EGowNK0xGne0ZsZ9gfXDBsuHIEoQsBnuyFSBwoC4ApVFsYp+XSSMIZ1DdlAC3X0kbd7HaV+nRur/48FamWJTOerTjka6fK+vY4EtZXcmtteI65icD+VSKeWEDShtRKWtJOX2UGLURh/YTFXolWkyl355D95po/s/zPKC7ZeOlIAFi7ZVm4kvSUvdFl7KE1o49xs172CQupIdE57MFRIkajmD/Ekluz5kGgkpBqaOS3Z2NGa/JvJm5dLx5WEhh2ui0sc8ByKM/qvlK8XYikGf94Mdk4nq/lcGT2F8zNQtXoZ4EFmZSNsGvF6RuUsUwHa1Z98c8vLk6/d0KpON5ju1Dq63nQgWnRf1KQ4iUZ2dQa9OVH++KN7JX8EMNDhkKdj3eIEwYxBhkMGwqmiJSirjVEM9tXxXX/TedhPj/HIG/n/v3PMTiEBpRwUMjjKMJHwKY5t9bpdpfGPP4wl4d/MxtGi3qh/0KSpGn8IYuTOom1ITzfb6VHiqeoS6R+2QzjMYWVQGe8RpTD8L6LSIu5z7P8dwnaLOOuNFs/gjo68nLI+a8xxxSjrvv9kgQwleNqDEUbbTRSqMAxWAJt9mpjzTc81RS55TyvtUgcxHds8BRzzfaa4OtfMPBtwuJilD2HHHN5SI8gi243zAYOUEqgy1xKYejS3ubKLmJxF7XiACq4DduYpJwbRhgBDYG/aXGwPI2sU697eAqasDohoxOZiSlM4x3BlZ60AeqWxTeatiOmX5V/vUbnooQqc9Efuy5kvuEe3viCbWur536Kr3o2wPsJzEXCocZ+U834FyLtg26Hsv7Dwc0UeaBw3MH9haIOgMRgcsXUIOHMrUnyvo2CM7SNGvNHnX7juP/UE5efCLj+bUBjtDNetvdmM1g9HE8jiyzAl+oo5LO+NpecLT8oTzcsbT6YzLckZTNr6jIZcMXyeep93FMotqEx8qyrpEJK6MiD5ziFmizD3phz+TaTaXP+LMQxPivBL7e4RiJRp/ahTOFgA08sEhoTKPnafuRKbWhD3KhsINgnB+51UN5JImIG4O9a9QifycCh0Dgz1iEGxnIp6Q7sxsUNfBmwNBKHmCRQea/6Ng3bZ0nhwi6NqhylmXIQPR+yB1hwpAhmKQYlOFCoPQsbQFl+cT/un/4k/xb/6Pf/UjJt+DHvSgB/166WGke9CDHvTtkys+Tbj0CBjP51C93vaSSAi1UYQL+kV40fRqEzf4abwO6bHaCrxV6jgJWAj6coF+uQC/fYZ2dgWCH9bZRWlPUF31EBsMbiYO5JT1jboRAIZGfF356qj3C12hNgZ1hXY2I9Zgi+Aj99wex4drKTWaAXnqV5DljIILFqGfLBrv+8K43vy7+/M4XnfqZGf/Ej2RfUDQP38B/c3rBxLqgTT/mW18776dnjUUbMCM2KDsdBKgDYeigsOihNFNgbTgReLBqMJwBbrDqsz8UMj50IixcAO3uY0vrePSFxegTPnK/qi6oqARYVs3bGNgDIOMseqTwVWezui9o/WOxh3djXRmxGu4LKdUfNmP58fhKawBSM/P7B5Yu2W1RO0x50NJZrkbBsbbm0UjheKYCWhsCvMWHpvN8x40j3hitG5GRPY1TDCDmyoXIwnQ2IyKDPZ8BgqGgmVYnjDqBfrR1uDGaoFBMgAdaUh7lzqw/GfYK15QpsY7060srbw3nFyPygwt9yrfmepV6YSiICxzKHTDzGaIrNMXOuu5QzGjWfZRGQekfiqNfambqH2RysH7ijgJnV5d3rVuUa/oh1LujRe51z89+LX0nRsQE26qtC+dNWJPKO+r/UoKNFfcqahBZ/pyhtjYUIncFvJmSemTVMxEZ2ipKPYdUF8eSupdh5QeEWA6MkQvydTQDDUlle9vNww3NT1qBj/SQwdPiKKd5afy69DUiALrBuAHM9YRAB0GsUk+CXsHXq/2+eUFOJPdw1L4q8xwSj68ty5KLXi/Ncq77BMlfet+cqQhz/qc2XiLDiurse/ZBNA2MDAgCCNh7UItZc1hvZnwaXC4Q0xmqPvd2/3v3yMfpzgfUOC/MtAvc89ol2UqfzuhLTOCnpc2jx11/j3oZ6Aa51QcyIjQTgt42zC24YGYBNIGkpkPU3WkEwo8V6MFspI7i9lZmFSBIRjXNQ1HRHOP1vfm3Seo/aOW/Bkoe4y3aRepWf6mYNQepUHZdI8kBPLsazxWfRl7TrSM4vAI1zTUxxJTj2i2WlguulYMdpMNmH3T+wtIY2g6s5VoEHWj4nFDU1FnMep5nQr/Tv7oRDNKTYZCaICGweoxm/JboFASO5832wTJ94hweBIRiI6Ezqw/KcuEAQiAqdfD+WKC5VnZtPPvqE5/O3pn/UdgTX4u5wODb5z7aBWTQCWeuPLFezzyfoX29aC98aP4G9wvlt75HFuBPzB076CjkZ/Qz7+ociEoz/JxbktEgsMuP226ES05YVxNhJpz+j36PZbvr4TqZPngLjbjXGvdfrijoTlqgXjEmskNnRhNCax2JpNhhtZOjN4WqA5sTdAHYYwt80mKmPMUN+tUESnnM+zPu/ExHYMiKtp4mDgcvyYaCqE1czGMM69EJJh6NB18Dvt6NMOX5eOzuEznjSoIJAggzqp2DrI06IFeYnMKgEEhCxCHXoW6s6btCsbuCnQnWVShilqUNuZeYU6T7A4h/jvWHhGGbua8FPcT57oKp0xBpEGwOg2RuX4cpjzrDtOHmDG8w9IPDDQmnJQxGLio4vT0N5+fdg960IMe9Cunh5HuQQ960LdNAckQ0QGuuNRUYMbpEDsFXQiyN8V5mVPo1fm5SnEh7at7xokJErqw5ZF5PgPPJ9DLCdpbphrKBOYVghOAZtns3nAy6wMkDOdUwo5S30/ozzzvWOYz6w04KfA6doLpfGON1huumKDSL+5JXCSrvYK8Cq613DtS5U7LUz8fpGgtCoCbh+NtahCQf/YC+ve/u33XHaKIVIsiDsL9uxTzo948XOMfybdHgUZxJU6C6Ie0lp9dAbTTdlEK/ETA+eWEtnScni7olzNat22ciHDqC56Wi2H/c0vFiIhBragImhI2XnHdNozNhSkoqDUsreG8nNGXBW05eSQdZyRJI/P2nvCXiMx1rnMTrNcKMxrGvBnVt72+YZczEq4YcwiU7Xo1xaNHfTZi9N6BNoAmWLcBZjNQUrMIO2UGoQEMXLfNlersicutfhzlgdGUwWJtCWHRhGROW0bkgVAydFghxSYM4gUnavjtn/0J/vbf/939+dQB/otp2s7lVfQRN88AO+NfREBwsJ8yzRJZr04XYC7fooTcrTq9vc4Mz2EENMxogcyp4tNzty4+oRjKFaFzmu++CPZ5XL7+WwFQcwSvaGvoTUugVHZuafPx/bvPca0aK0OhRii29nSdMEUoLKrqUH0z+HqfRDwCl+jDG55Fh/4IxLr8TLt7pyUzLxwaUhqgmL8VHtWr0wJbI+uGWvTc2DBhdetsGWXgUSZh6czjxD5acytfJ5iRTsSihANPtbwC7LCObbGLP7ya4Y4VWBb70TH3sXgw56YzO9+f9pOASmcXj4gcDElIuLSX+AQh0uQl1hYGa4d6RCTUVWjpDeHvzzNJ7bB9FPx+T7+zsALy8vvrYbF8hWLYALSl4XQ5gxaCNgVXI92p784OES23q9+DfiGaUIa2HARj2B5q/M+jEpgsRyTsZjNkRcxRGJU0z5cB8awqYB2goZBtRRi4qCn6n3fIX27Qv95+Ws0bWV473J+5Wv4N+EQjdiOh11/cUBdK5mxfOTnGGSXWo0PiZn65ZhuDuGFLyQwrtqzJ+StBtXmazXJgvrOmdic6P/NMQ9N02qj3qniuyjA+xuaktVQjGZqROBZpZX3ResPSO7in+xlYyZyJgpePgJGzzKZ2aFA38JRFH6wOBIYjdoTck6M2T+mzO7QMmxsdwuCf7P0OTzgcg6NrVc2JpdapvN6cW7yv8hhct7XSjR9xIt0xMV9ZJdhXdR+tVho/948DVeecuN3qIWjU/JqaIb34uFhVon9t351OUtNhByCHpbXnJM5esZZHqaPOyu7b8K5J9SfRsRt+Lu6/H78692btSYF+Jlx/F3GJ8/ZYe0IWvU4kIGrup2N5IxkNjQldG7qy2beVoMPyyJEArTEGBCwEUXfsY7bOVgV0s3yz5Ea6IvOGTMAqWTvzbZJsSzhDbmOzvNkO+X+EpVQZGM4DCLYOiMkiA5vzMlhbIxdvGO9izc7JGZK4w+iK8ZjgmRaQqdjcgXIMcf7hTs0OkTyYQSTuAGB8mVu+whusUN2g2qEO821HrgERQ4ZZxXKGJ/9xQ17j5s4MjIXZI4kBkKdMyPQL1s8zR2nkNgc2JSg1gLpHEhNINzA6ds5YD3rQgx70jdPDSPegBz3omyfVDXmoy8N2fEtTCEuBKzDhiiKgKjzVflMI2oB7aepUuHkYSgq9TAAzqHdg6WYEc8Fa4fUDsMNV070gkoJ9Hvn3IlLCW9x8s6cbpQnBclx0msa60Hg3Qrh6TiObIpUoUSAwI0+qMqDW56ZS+s7v+JP2fbCTYsoFOrTnQ3KYp/Z58VLd29ykpXtRF4fPVeKUUO7WegMuTZqQVZXboYkQ9SCQUOaa8vdyurhRzA1ipoMBqaAR4U+++xNwZ7SlgZcT2KUoAuG0LLj0kz9LCVUWSciH559aBehK2JQwaFhzmNG5Y0EDC4E38ygl9VyN3sixbkg4FkyvSiJH41z3+ZOORrr19Yc0elQFCqAQEVzdyBfRcI0M1pOaRfqN6wpujN56GuJaZ7TeHfFuoHG3/HWtoTODuYE8em7xyL/o44DIFbVoObjB0JiFMYzOlv9Pu8EQbV3wP/6v/iX+zf/t3344r6QuxA+mY2VVqdfTuUSTb/nv4b/T3p/96L+D39X5Wv9kZK43M3TCbRqUfW/L0xRQ1U6UdvN3FmSFmdwZ7cuaCV6h78jUCZdZ0Rn9WiOY8a4Gfh2rpLjxZg/jW/hu1CVcI9qmIpayzIQPQihq4hWUa09dUbrrC79R8wN2Sr9dxes8qfDBu5vrBMFsZBbkvCYdSKLzAgrKnxd1y/2wSLpd3soo7sCzdxMhJmUZ0F39fR+udY3y4n1hYY53KwAKy+lmdWODiwYzcD4BbbW2nLoZ7zrPeR5K7OqcQ6XTERpPguOY7vs3ovPKfqQ0wePMWIIJ4ckENItuInfUmWOpk8/nXnmYsEeKcb+XUOjlbG3+bG66w/rn1rC8nMFnhrCAKxz0I0Lu7498fljEuymTt+uKbdv8K1sjSgaZm3MP/hwHm7C5LBCQitnb2fa2oWq5ZXvD8AhWYgIvnOeDn0L0QqDvfL+IpVOYlZaoIFT+abUqXWDrJ/hXjbw3GGItR0VxKGaHgAtoS55tkXhvHlFDcW4QoSLkS3lv3IjlUveF+WUYU32s3JB4ROgwGNJbXjrv02QP04FKs4gw+JjhxiJvVOZmRWpoAjoYGDBnowY3Yk7YOvV3WRVsfWvsiXFH3Yt3LfZoQbF6a0Au+piGowKFXHV4ft+nbhgTh6uDscE678K0qLEW7jlEfnTgAGYEXW1GtM8PM9G/t8/uWWBsvUTwg9VEg6hbXO4vFDkJ/Rvalw0x6HklO9/OyPp5jqBGc0h0tjhl0Soyvneg/GBr+YjysVL1PAfe7bGfRvva3hkH9218/jPG9/9Bbr4XcZhfzCisdHhVZCRaA6NRQ6eGhRoW/8xgc6T1s48czggROWvr084RHq9m/KkamDV4nT/v98c6N+OcTEhMVRB7FCy7oU3EkDng8y2cKhlgtXONhBPh2ECeeiDqp2oOAZyeelaQbhs0HK+yugohhail0djGMGdRP1Azx7nWHDnHzjGsHCc9cltFwTrQqEHJeK+hkAhUPEbXkYyCzzEthriSh/sAevXxVQVkQCJKVWNgUeQAwmgMy0DenU82sAiIr9jj/j/oQQ960LdNDyPdgx70oG+apiqxCC8E00JL5KERHAWo/SkURcF2uMUVZ3Z3aH5pKlhcvCeQw/F4uSKQdYMsChoEbe1Y8K0yYNei6vNKu2+maK37x97RuaRswQxaXEm7ifXP5hBIR6XsXjLc1Wd3243MqB/cdKjXR16gn9AfvdtkAvRPLwZ5+fp1T3Ei2hvn7lXqqADYNc2V43W+CRxOLuDi/MFQDoeBzuEc+3LCqS/48uU7nPsJvXlOOLIMhQwzCnz3/GIzh5DahTBosXuRJjSJy1rigqKgYYjlPmRqaASIK08sgXcDD0Bkw1gH1N8vHLMcGNuKXChhwPN6iAjW65odo+owXKVfr2/fJ8TlFMZCCFOMsVpLfY2xhgBtuWJUxCLlHPqmt2bQnL2BFwIYWLp5gy5LB/XFoK6I0Rtj6R29dSQcpyqGCIYOh84BRvSXw252VvQTA61DtoHWCZfl9MGEcgNdEeqPKqeDXiyHs6avGFQQ/SIsD6k7yqnIMRxe3Khy+6FeU3kFnzt8y3HCOBz3cSnrHp/0shXYQXNm8yqPIKTBDJhKsby3KM2GANr25UUfJspU+e7gTJ/VjGbXILP44ujc7/GUCANuQAzNovf7TNwf+Utiy8gC901PVOGsAu0AG/ev0PqhFFoxUWtLKSaKP6fRoX5LKG5kuIFOJ6RuTLoagVY1eIiGHepTJzaAGTJw5/5UKFEqlaYDA5V2CzBe7dobzCB39dx1jYGrR9UtLsKcTmbMy3bo7Jvgt9kWhWOwzfoAmLjA0Rb7XiEQJQyVuc5CKRetC1ioewsjjaTv7nSTPtrzzsuPNtKFo0pEV/VlgTSf1Z/YXx/0h6GYbqHo1QqlqBZxMZ2OfO75vCb2G10hmg4aDJBDhhHgTmT+uxGo8U+2zRII9HzrzBTtOJ7Lcl/AjMrKBzRy58ntCqkMfLcJSBZAngsJjLlngTKyMPsrziPCO+eQMMiEkcV+3ekYghvoPD/gwdFoV9/6WCi/EecbV/yzR89EeWp3iefQZSKgE7Ky4amiAHVgkYblZEa81hpkCNYfXqFDINfwpmPfw9wkOQeisOV9pdMJhadxQuO86nWMaMfSYzdiUwyTDoU2mv1cA4sxIftiP5jl3DlgfET1bFNuj36tjjbv0c5AVw5rx+cU+3ODvU7dWGhjlP4WDENs0MO5oG6NWuefrRGGPRPOI2Gv+7AX7jSQDpdvbqGPPsYp6JenslRvqRGoNRA1dza0rNECRZsrHqxmpFt4wcIdCzU4KGYai8cQrG5EA7lc5bIfM2OI5YgUSJ7hI/fcdFKN6epRkMyAwzuOMTB0TJlLJCFquZmsJSOiZ9XH3kq1stzwNMyIJ2IGNXIkFOsrnfPFZSJDLDeDng4TFoJfkWO7qwKbClYMMxySx9mOAQMisYlW2S1FvWDraECmOkVsT2E0d1xA7l3E01GUHaY4ZVI11BSDxKyGUd+vCO54EfPXnaUYIDQQiRkXO2NhwSqCrit+8+dfsFwa1tcDWsGDHvSgB32D9DDSPehBD/rGaQrsiANzKOtYgOFet3dFFZqS2VGcCaUhwwXlUDDavQmHB3h+K7j+zxJQiwiETC2r4u9h3ktqdwSkWbOqZEQent8TmL4qwBEb5CYbhAe5cSjhVtLz/159Sj8RYa/hq1L2QfJ8t6bIdk2h59AfBy/lm4rFbeFFC1cmhABMgP7ZE+jf/u079TiUd+y8qrvIIQsNVFxwKJIB7KNUyCwlAb8a8yvf4X8wAd2MTefTGU+XC748v+ByOuN0OuHcFjQmC370/GmdGZACNFqVSwBoqHtpikNITmmLRNE2qx8J0kCjMYuVTNgUgahgEEGZU4gFFHJdXaiqGTbs/aKC63pFKLJFXRjUWCmWEDxTWe0kcjMyLotD3Xm3kYT3pnjQjVrKvsHgtmLjhrY19KWjSTclfjzLlr9ugQl9zRPOW+665hF+CiaDARUVMwo5FKjEOlYT7Ntixr3eTzi15etzKqZWna9fWcDexEQ3q0pPgSu5eH4H7I12gC9Vwn7plKXLXqnMRUVF2eYV5IgY8FBO8UCkmHNRz3eb7++pyKaZFy7WJ2BKLEfc9VSb7mmvgRqbCq/F6xUduYMCLe/NnHalrmEIi2Vdl6LC0YhdqcE072ZXEYj/5DaDGQyb0EFkY3OELX2PhSlmUPNey+kdlGu3FLKLDotr4TxScEmTDfP+WuSf0+BVWiLpaFZs9y4fgPIx76u8MzSJyTtjU6wDUr6rEXyZoKdGwkd5V2BbZ1tPZ2BZAc/xgstmhjpVi65pbFF2FaY1F2ThxSU31tGgWNm/qmCMmRMm51xGxtQ9rXTAcXK+R2VO35xDor/+5Bn42x8+VR6F0nJpaN1/ln5/n3vQ3zvFsKsrHtto2NYNsTMDxonS0cfPgkQMamxwbrHhxbERMIWvRy2R+sZBADUGd8b5nz1j/If/uI/++gwRwH9+J2qBdr98Gsda0v1tyQC13rqfnlqi4rhATUbUi1ikV8JjxrZB1je5x1UnE5jxKbcljeU3yz5C02UFD2snFN0gKrav+31pr3Djofd3ay0NXqoKsEfdCEEy1N0b5Ywn+A51RgNBhynIhyevlW3FVa9mRBSBgsEk+/YiOrWyk9kW741syz2j3G3jSj8hhkKRhucw+DEyh9+OdxanMi0lVF6opd6719P9v2O86xF8f+L0a1TuP7aF5pZRr6HWHbdnEEYxPtR9MytZa+Iz2A8DTIAg0CvsXgIcOhE7hJesk5YrP3I516rlGvlpxfz8lGvf+I2KWq7GOCuqyS0W727/dWrovKBhAWvDtine1hX9esXZ+0pEp2OqO/VQI9AgPxJ5zrRmZUvkBK/1grMi9vzXmKgcGodD2PuoGdqHqAA8oIOSZ5EflueRySL9MrecOKqN59ltDoPMRB7dBmTKD2hMEJ+Bknmt1ZkjgzEwXKS3c7SoeFttjSfIC+DGSSubVNPwlj/JHyp3mBGoIrKvnzsiiN8QKATksz46zgGOE7hARA0VRQDWAVBD74whwKqKf/Qv/gL/+rf/Lf7mv/tcmosHPehBD/o108NI96AHPeibJhMsG0ADmc8noweqUPyeyOFiiVbIlP1hc//CkIZo3uUKPjX7iSm2xwbDFVyM0wqQCa/ebQz2ktYHrs4Rh3dHnfB+0Z4zjzfNA3mR2nG3j+LMn8J01aSUfk3h9eN+npVBeS+KRODvIgKqgHu3Ud6CXWTJfJ9e2qd0kgoUuDTs66GYHug3mhrXyJsshFR8D8yQqKooZkY7uXHHIYBM0Flw6gvOywmnpeOyLDgvdq0zey4sAkkIz5YIvXp/h/KYyCA2t6EYm4BSgec1HwISAauijYFtmPVFiSBEGEM8b55Pa7JcAeQCnYTRzL1FAaRXudRkaS6Mha46OvXp5fmukiPyy5zP5+x7GcM8U8eAbAPbupo3vJpHKIEwZKCJ+dU2ErAaFuIYAyqL2VEV1l4FNhAaCG3p6L2DQFig0EUxxmYetuqQNWPguq7Aav3Qlo7nl2ecnjqezh9E0mFOk6Nupy6lqpR02bzMPZ9WWpZBnZ43yqjy3lA86R4QEQhU3jDOUrG336ivTNFElmsnYJ12jbrT5KhbpA0TRqbhIiChJsOQWIsKwx7BlVQTRy3rySXRjEp4JMfzFhnZ4hm1Dgi2kPaoKIImyqFBztl/tT+CywsMMighv7xiWzhPhNLIeb3sSnm/v46Kw923wRPr5KmdlcwpepGR3iIgJIZUqS8CuikgL9NF/2D5DUVYTL74iXLSwBuDgckH84POdtSyb4xi9ky8bpfDTaai0soawOsPwNUHtnWLCnxbkflWl8UiBKHAqU3oafI+Qnl1Wo2jz7FbewSLwGEQ2Pm5Oq8X1VTSkbLPNy9g4HbMKt397s7F+NibGer++usKqOD3y2nB6ekM7ox+bt70z50XHvTz055tVjjIUAoLRuQFZYuQsCXHpqi2x0AwmGfLPcseQWETTuLAxpTKXGJg6QRcfY8mU7T2Pz19PEffa8cLff0sWza3iKKLVu8zzlXQs0NnNT/zWDh95qAlZpBHWEck2hjDz4E6I1Oc9xB5HrxqbfE/uSDmpsEtz6IHNyQlO/Okwtm/c4MgZIBqVLU6BGU572jmzYYruy1yJnomg4yjjsBkWWlUAJQEI55vBF6BfmlY3wZ4oYxiIxWIxKkx9rP52V4zrQ8JR8fwPH52R0SIYTd2e9LD35F2lH3ipjGM3emHMM8UpYTdmQhzfO69h+oDX5uSh7Lfq/vu5mMdKbZMdfTZ/WyOQ435dhrE4ubQgsxkWy7X/Hixl9r8svZEnueIQIL52tRwumowyjP259t/j36NOwMRwK2DmZz3uYOCO/8FX1AlQwURYHM5ZQzFum0Y24CuwNY2NLfYb9uGRorGdkIOtA4lc8YaDq2pwuZEFnl7s17Oz/y5MTZsY3jUHWwCNHJZ0GBqrd7+g70zgKpMQ5/653CYJIWK5bcTUTRltMYGx+1rLGCBhe2MmtG5ATspAmoNiL0j6qmeH5MEEAI3Q1jp3t+qmJ525LDMLptr5KMTmXlF3XgYczr6gxDliEcL29k0HEXj7KckzkORE7KqA1ThuosGpgWtMRoN8LiioePjGf6gBz3oQd8OPYx0D3rQg75tSv1jGOQIeTIOhQVQpJgp9oXuoOYUqB65U0k6lcVTuRgH7IDrmQdsXDeAuj03FLgS0BTKDv+31xPsm5PfhcYzovjCW/cD+JFS4CyXXZysUp2YkqPBlH9DgI1csRlluaJDZnunFvag2cmXHa/pvJSCdEi+VrdUWGR/6r7/gXlKj+/eFbVLJxBBT81gL//q9c59hUJ6OjYjDG8hIRwV1PA2mBWoVEf388bzFbZlweXpKQ0BFqVjkTu99wnRQoxOhE6MhQyuJfOgbJt5W4MSCpJB01PSdUDdh4pCgFNTAo1tg24DQ82YJ2O4QAqA2cubQmiaol1AOp1PUNhzmU8lIkow0JcOdmjXgIWLNaYATpfT7N6q6IIprcy7HA5N40a6bcVgGxDibmPrXuzswm5zODARgz/Ztg3jukLXDbhu0PNm/aWKzg1d4RCY05A7RsMmA+oRCepjbwpUi8p7en7By5fvIKPOgcN0Os823eigKks6PCdq4wUUobT0fZQ5O2xfdOggS2zxLKPWD9Prt14DUIx2QMZKEpmXP2Z9crkcCq/1s/wroWCac0AD8opTp2WGtWLM8dqglTqRzjmZisREMvb/ioc+ubKCyZRdpGpKWXWFmSp0M0OL5vtnO/eKQS1Qba4MDyP1ofFHJWP9ehd/UgKrJ+u4Ha09xR7nmrowrlHzOVEtvdHxJY6QfH/Myk2Fyq4i5HwteXXc62VQhW/25xxCyrTT7/Dm3WXCNCRGnFppMwFAn5MOgGucZj9tJX8dN89XNyyajghYO/B0Bs6hwCmYVfA2UGFQh2qn34aKpS2N/Sq3GYNo4sY573azojKBm80etxMkQ2Dv9N9vPJpuyO13u0rbL2ZGPy0ghwHWnDt6W5cH/Uw03afmlcJnM4JpP/hSot8nz3Wo6IgKA9IIHE4t3BuaAjIYSgYFF5CE3CzSjgSGKBGQzmwRe4zDGv4k0W8ZHz96mPRxgLhzG/mZuTqAhMLcPghmxLfVnRrNHGdkkfXGn03ZzMppGDJluEOnRTQODtM/3xd7i59Gj44EagZGY/3FSYqopJqez6Wi3PORAYZsoB6NUqPGc8yBzMsWkWhQ+MYhWX+AzTnKc0LxQmjUPO/UCToE42qQxrJKOspgt8/C92XfJ2I/jg6iGEnad1q0G/NaOtzU/UsBSDi22DlAVG2bKCz3yCKjXJlvB1E4yxwoHvbfsaUdPtzld1/5en7nxpCbnbgYIAIqnbxnMucgG2pF3OfIiNjtq3F4i3kV27YbdkZTdwyhhGTOLeLQ3b8P/Tq3BEJnQlc2EYuBkBWVbP3oBmAIdBBUGdzM2LQsHUs/oXXb+8dQXNfV4EdFzHDm+bTjvKlhRPLzMZX9PHLChzMYUu5RbGPDCCYQZ1O4Q5xanjdKQ9vMXZlSb5TlPEMSPcQRROJZdwQWRUbAqU9kYjvIRi5PEXcSkOAY5rjBntNTRIwfedlgRlOAGhtfIXuRxHmjca4t0QEdEfmGCdPJDGEGkZ/r0qAXUdEeAX2AB1dMZA/xfpEivMTcNIOlDb45XhJ4KHQdkO0WDehBD3rQg75VehjpHvSgB337RJ4/IaGy0t3dvr/JpZOilF+qCsoitUUYS3xXvt+dBeNwfoycagu0iUPwqUER9qMn8XzdTbmfPnHO9tWSU92s5T4GdGnAJq4oYP8pgjxQFMG7xu/rReUEfWiQhsKhSvJpoKNZ5NFi6dJnPDerfuf9R8pIBi+3EfS3F9B/fNsbIN8juvNbadp8m1dSi3IcpS01Ek/83g6gMfh0wulywZeXL2aIggn17HOqM+OyLHjqC06toYPBQy0pdmpCLM+hiqKRCUUNlIp/UoUMNaFlKLoCw70vR0CODBP+wi3RuskhS1rD09MFS+9wR8psoi0Fwel8gqhgExeKYIITXDDsnXcK6xzFgGZhy/kGaOZ4suHaw/1AAR6MNhibG++4N3CjFJJ9htlcEfL0Wi7cDl9nw9YdAW6Qo8xRZ0LhgtZ6wracaMFyGjidTjidz9i2zSBnFODO+JPv/gQvX77DWN+fT+f/+Z3Ay4OwmV/5faSYwUvz9qnATAVQWZo0p1/cmyyqsLwMDCDsvHcrHbnScbURsylBx21b7j0UrPAYMVeNewpTuligqo1HsE9RZJ6LNODTrKcpMtR1XeUFu4Yj+ctUviI/k1qYZSgFLGsjF+UbJbxmLG/yOsy27vluLP1d39xo97xfQtFd7WoROUZAGtlSc3ro62P0G/wZyhsmz40JJNuBt2Pfd9F9TD4h7/Rn7BtA2SN1B++0e27XX4f3ZF2BdIQgBdDm+8LzwCqG1NDFZJTV9lgOhdRq5TQCXp4sn5vIXJTx3rDw2mTCUSmvPjQzGN95pprCi9nmhwXRDAiFl3ndE3A7/oeu2C3gj26MiMCvpFxRGP+/vr6hP51wOrljRRT/UX0e9PtRTGHAFMhpcDGaZ067OXi2RDRYhgtgRnaV81MYsIhsn23uEENMDmM+PMKTwAuDWwd5NAajQ8ewMhpP5IkfQe/HUd3phJtzYymBynRXmW1DRL4ZnxHTOCOc1gw2kQqcM0ExAIroNt8bRNE4X2BrkveLcd+SyROzfvC1v7uzbKpwYDi16/ZrntUsNbH4+cEjT9whqhUrpwU3+7527C/1/okepeBHxgTEHbYIlrOJl4Zzt9xdug6M68BVrmkYiCi6OBREyyIAOt9Ru0VLu2MM35sGVPpQ4bzKFfZqZ1Qi7HP2FmKfB4Rw3ZjV2FUnukcPW83NtnPoz3IWqlV+j01H1HzCbLtsEmNqdXBnO9h427khnDGtU9nnkcC2pdhbciwQdY25um/o9Fec0Ul1/D7aOb5lIiJ0ahYBB/IjiZ0H2FafyTdjAzZCQwc1mMNjt71v6ScwN6gqxjbAsMOp8jyzWSRcwJZL9i2RWqSexPhMxpXjL3WWVj4QBjR1o1nh8XF7UhjnFIqRcJsR5aphQG8NSopVhyE2OD83h8UGbjZHBA7fP8Qi5VTQQe5AOSyaDlwMhmY83GSDUoOoIb1YV/s529duOiPHgY/gfcK7/es4K9V7J4yre4ckcsjPmb9Ottm2Rs2XCoG2DdwVNBzdYAhkU6gQ/vyf/Sn+47/7uz/a9fCgBz3oPx16GOke9KAHfdtkErn9jGGKuzj4SRVhqPzEcfqOsrIqzFLRVr+ovpyU96XXKxk8IECmQ9zUlLGrupHnYzoKoUfxkXZ3hChrUSN299EEqOVZgzXUxqDOQBegE3Sz3HQYdBuGc0fILS6jfs2VIwd97s46EEpdulO30IZgPzrH+t9T+Ow/eqXKZX1ZTCE1fkQy6Xh9CMrqgvPweqYbMKVn6/zxOrIpidtpwXI543y+4OnpCX/6J7/F0sy0Ri74wPMjLNxw6ScTpgbAGJ7typskAt3EhUdyxUCYNezdY90g22YQJENBbpDz1NsOG2PqBOZu8FGN0XtD7wsuz084dZ5NJxM6hxv6wAbLRYPdEOYCXMiuASunilCiR9SAABhwmLgyZjnmrvC2ITRFBXPDQgTx3EoUeXliuF1BJWOAhwLC5l2vww0elix+2zZs24a1reArT+WaAn1RLMsCbuzwUye0LuhLN+OmO2i2xni+POHlcsHb6SvRmThMdRzUXHT7fc49X9Y39hE4K2rI4KhYc++ujLIkQsDODBI75dBtGfk5K+JKiMp+gJ2tPhV9mHW70SRV249VZEbLVXbs0QoBiRP8KLycB9QjKYMvuhIADk2Zr7P79lWw+ageWSfs8Jy7vggDnvdY6h80FZmmijHeyrEQ6NBHsTxrt5Xgtl2PjzDSFXi2UIgkXGF5aJcf9FAwxUQCPHGmO4zETw2vKHWNNUw8eWANdUnoSOw1qT6WZnU9VLVGJWcHOA/VsulUBRa5A4kQMsQ0J3th0JqDAGwyoTwDc/XcgaWlTiknZTj0HCdoMeymQVbnd2lMANkcBYzP1o0gDH+5B5bXlFfdp/2ev6N/8B3w7/76o4dv9mZbD8hIgP0ND/rZyPmT+joIWLPKXyUVnUAyZj8jqSi2MRySMgxyZrRKAxTgUXLkTiseea6cZYeXALmhgJhBHei+Nu3IKqAL4+lffsH3/9dP5O0NeiLwn/I7X9aJfn+CJxeuRz+qM97r3iKKkDCXltq5RhVAKzmU5vPh/BTK3TD4zJf53xHAWy7RblkEL/JSC4sgtrMDY0Y9mXOQrfvIwZaRq86vTExRTx1oYy+7luu+Qmkt4+Sd4fCUfEvnngfYnCC/3k4LVr5iXN2hKmxlFBGG9rJkp153KuHr+93Qf6sWJ6cPyCY8IqtrOAhZ+zWngdLsj+iOWXJ1f5ndc3d2vXOAmWdUmmXXeUGIRbEjOvyuThRZVY2zVekpsrNJbAN5LznyA5DQrGXHsFF0aym7dxPBzkEJ632AYfhwG/kjIUo5y+VXJj+auHFaBrZtgIafwojRuGPpHb0vLjfYmpAS1SVjAAT03jC2gW1s7ufZABZ3FLNxk8agLVIH0DS0wXkNyOe6pmOGeLJlUXMaFDGDWeTJnDkgjU+oxtk2HBllHu+IynlIoToy9zgTOW6vgCOaDeQAOXbG0SF4215NnmmM3jpawoBbeGdE1gkBzAoeZe2ROSUZzzpwLN+/KmqR2ep08k22PQsgM5Iq3AHDrsCdNCOlQhgziQide36norYfXjcIb9iYcGoLLrTgByz4i3/+Z/jX/4d/84vOxwc96EEP+kPQw0j3oAc96NsmEYsOkIHUpgM7gTxFeeWdfqrkcPdnHM4mogPUz50Ty+Y+UfzjAq9ZI9KAQ4BDcmAqKvcP4yhu7QXRqtQI9U454O9E/SLs35TqN3nEEjqZW2dj+23Zme+0zUuqUqt7KU8hQqNW2N2413zcJyrFU7mwU8jW98SLd4O8/0GMn0J/cwL9+x8+rsNNe/0DF0VL/GS3O25fGOoyuTgBHeinjqeX7/D08oSnyxOeLxf85rvvcG5LegybcdmMzCRAV7LouSgPYUo25dPCLQWb5tfg0WNDBGNdPfqrQMLlnFH0xhAmCBqoG8xmO5kw25cOXrobRiJHlE7loig2HQnBIjEW7sVvwzIVOSrlN8w4N3SbRrqiyASZcKyetyCVbeBUajXqrpgzgS7MEQSCSIdspiiTYf0n/g6BYMjAtm2eBzAUVFFXm/NdO7SxCZFk+X6Yp1TKzDgx4USEP//tn+B/9K/+Bf4v//X//XPzqk4vwi6YKW2+QBqO4LJz2MnDQTVl+oOS8WadC3Y2ntQHEB24BQ5/f1DzYkCpCqosQfdwjru3xB9S+G60JYI67lSJ/L3ToDhVdselCth8E9Wcm6EYHHeilLRWmKgs88lvaddC3e0Xk9vN+QwySCK6Yw9JruZju+uo+B1GOsYc7NBiT9xPv5/e2z6sDIp7YzJ4uzIRU4xp4I/5NfYJGdeY7J6YSFwqXydXNDqjSXTyxlbqSG7FI9hEDmcaif178pV8X1yv/JfaXCAZ0RLP6+TN19VgMdtSFgJyj68GuNmuqbwOWF4bZ941z4ZI5zqVUqZs/r42x/BDiobx/lLdeC4n4LIAr+uHxVAjc8RhpELYpsScM1+tzn/ylBPW/w3eM3vudvmR7ycZA7H/DrdnTriCM6KOwTyNIDQHK+cYkf/Y/eyQq1CBgMEkGYUhoIw+J2LbY9WiQ8AAX94zuN1SOM58HSVTy8Fwd7DLvtL6icL1bDqYRfTGbo0qENG9ygz283xARqZRMiNLOBX5ZpQqE5/m+lUfmQlhWfdpKtWnLC/5noaJLJTzmmKIouacikbzZAVifRltvcfCdxtI9quNZVqAohs9r3K2VQAVQusNo40ZNeOMbcKKYh5ACm8Dwvh3nMVzD/4a7dpPtR/8F81zShhXjc1bPlzOCvq7ab8Hz7oeXnivIveOOeXwVLvzpsDDSyOaurBT57PiZ+ESAXt8NSHb5W5sOdVjuMPAETnPTJYBjjC5fyyUTWfg/B3h9W9jz/V54TnMzCmIoEJmcHL4W2LC5ggfr9cVr9c3bJt4GjRKo5idD91wBoCGTbxtbBaNRzAZoe6T6Yg7P8eP1V0TnnJo5OwuO4AGbKUZ4dKpjGaOt+AhCa1KhIitneer6RMV0W+sAImCiyMmEXvQsOXqFjIny23bzEmyMfpJcSqGwthThp85STY/goaTl+0rIZflKaXyH/Edp3o0eFIHOH+0fKINLLYGbM2ROT4ACYMZsJ9EiiHDc2U6A3FjHYYZ8yQui+xSKDzoQQ960LdMDyPdgx70oG+bEtryQC7gpjHriLmmoZwIxWL86E4xsFfK3v5V9aYhnMGLwSb+4zlyBBY1NZPg4X3psXydf4bgUpQ3WZ8pRn9QmikjmCBLAw8BNTEDXW/AKvMgXAvKAnXqDMhFy6qAScXMHRi2YzNvtVuH6y4sQIs97s4BvOgwapdOJQOALz/CSFdfQZjwmTEnItpQHB506MQKikp4ToTT0wXPL0/48uU7vDw943I64dQWNDaYDg5DMMjzw5jQxUIgs4JZ89xgZME1Xh9xO6ka1Mu2Rf62DSLDq8KWGJ3MSKEEtGXxKCLLi0K9OZQkQRswdHjkkYlWE2gKGBhYYd8PEogr9yOvA2DPD/EE6iIpoKp7mV7HipkWasLFmQAa8CuSCjaGwVAym5e4kk6ZlYuZThSdyTzVBwDqoGGKLCVglQ1v6xXi9Z59tqGvC5Z1xbJYInLmht4dArMxWic0MgijEzMWJnz5zQv+xT//L+4b6TSn727K3l2XZblFnrbjOkmFYRjldD4DmsuOtSiadH6npazyVSotPktEBkWqrJPlSqmuvyui/aIfwvG/soCa+4b4bs8AoPnMnXpWT/577aDaYGDXr3E/M+W9xFOJXpWTU+Xmgxr1zv9CgzO95HcIlXV8yRRRGQEZcyTujfwfqm4Mo129PXlfbcjUmDjPmxMgeq/sj+wVIQ8dHKWShbengSyMfCj1sc6f5cXryAc9nFwESNxcjWe08FX/CWNefKelHjn5a9sOgxn9MIB9UkcB1s323+FOC0ctfPYLzb6MsU+N8lTS1bdOuDIGYJ7tGAIMz5MnA9PoeVzY7xF9fFtj4PSxkc5yVJmDgeWN8bPJvbyrDwJw2+V7dhKK0vlx92REOAHu1DL5hNbCS6Fh1DGeaucF7mJgXyIZnZF51Nz5QB2zVMgUopsaPFkqlV2J7VYEJAcjGBws1PnujL/6MdT+6efy2IWCehq0gp/Wnqv9MZd+RKXZ/jTPEPYMo07l4MoUDjVwAx2z/7iBMhTS/rbbJUa+VGM87SVWJzcf0twF6lYQOZZmnqp96UQEUk7HJ3IjLFwh/ym2gHhp6TAf19hHU+Gv08EqDlcEAnXy3HjW9yFNwOunld/mHrLfW9MASJ+udNa8piulQ1PSQFfamc6VeYaJjfcOAOnN2lTcTO7jWYzmtWx62QLmnLxTFjAjqCjOCxFhP/uNvQ0pJx56JZYs+YEh2mL8AQZVO9Rs0zLzI983KH77RAQsz4RXD+5VGPoGwaD/VRu0C0Q2jK1DWcBkBvsxzBA9toGmjBN3nLijc3eIe8W2zWjmWCNjmGOfiKWAYLJItHDyUUQU3HC4Wt1NL7tNMTRydQf+ydhPm3K0s5cLlCfkALHxEkIEzB0igQFgqKGsKEPFUHs6c85ZUnbeaFIlYOnmVyhWUqhsEBCWjSBMWDrPcy+zw1sKCJqOc0QGdU9iEbQNsVbtzBTrdsgAjdmHBDVHYBI7Zqo7lroDigosEpDJZUXsnBF35/vYL/MzrL/XFUME13W1fOIPI92DHvSgPxJ6GOke9KAH/ZFQFRzV9XmhQPNrgJ+oXfTSetL2w7NGfru4UIWholgsV+a7i9qh6PcwABoC3QjYYAF9NG9L+DmU6mRBkkK/ljv2RkEGlUQ1Ve4nb/KNkMkE7WxJoptCewP1YQrNneKgSK9FiEzLipY3VWk5hfw7ytTaqTeS5juH7KOBLvQIWU4ZF933JU4NeFmA372j2PxAmJ/1K+WLApsCGR3pXzGBFkY/LVjOFzw/P+P5+Rkvl2dc+gkLd7AoMEbqpZsCHWRCuCfBZhe2AMs9FwonEGU+Ocs3oJAxsLlxbts2h1OxqK/WGvqpAQ5/AoroCofPYQWYoRxe+ZpRAORKMfvkUJU68LqtGGoeo6k4Y05PyG0MDNnMc1Q0lWxqYXW4js3a6YOXSjYfwgkARC5mEjoxWBtIN/PUZMtR0OCKONg8zjxRrZsyezPvVvIJIaTYVMBjFPWIC+AyIGND6x2tdajqNNQxYWkdS284nxYsnsPufeOST6OiTDzO8zpdAdykB7pZvzvmMNd09p13204PT/MRIcz8ardL6VPKWgLAzZQh7zl0p6HR/87bwgWYymdGgU58vy/v121WoHIZTUWZKzc01WwHJR3tOpneeX91jOgOKSe+RzB7XEVaWf1XQMrV8QjFZMJHzkd2b1ZJZw7ThvA0IAmQkWUETOCh0ChWXh0YcVFubayXwc2MSnVbu/mJMkr0XfL42PdkTvao49EgVrXRkQc1sOLE60vwaLqpjM0IvuAL4WBT+XLi4XkHRx1J7bt1WDTdqQOtI6MEd/1edmD1cVDs66LIuZXZnUJBD4Oys3ESTMNo4NKWCfKugvuIEVraWOnLE/B3P2AmmbtDCrSlo50MztgOIWW43n/yj5xmLtMPbnHamTKS9jB/h3UFmVEAcGjo5Luz1zPyCoTeLdqpoUOh2MbcGSMqLJxevAKIHJri802pJUdUijf4b5Udt4zjLUjQ/+KE9tIxfrd9vesIoB5Nfmdju+nKyT/j7un4gLncSn3DOQmuwI5canE2IGJni24w4mBFEX3o0TDM4DYdUczwF9U+zIKyVwZscfbn4YwSkSSzjcHvyxdEeX9GBTLZ2Q7O+7yw32st5nG6jIMbHkjM+LssC2QZBn+uJWqn7vyHPZCyj/YG6JiPedPxrHxvOug8sJDD+lWU9t0j9Zqz3uw/H9u8P0U7nWld43mtv/fnnjkHy5yMc4Kz3/2Z4pbI+yPy5sbFnF75Xn+Hn3HSNxTqEWLTkbSp5jk8oqXEc5nRIDvERVD8HxEHLzvv7fRRYLiBrBGb84n3kWJAdYAGg0TRhGzOixuoiNC5Wc5posypZj4z9iZRy1kduSJj3pOaTJZRdzIckSNyek/QFKLgAR4tp5LG8JgC4Sd8XDBa8tZFg8OXicNAl4zIHBBJgM4NnqYNBMJQMzQOAq5elkCxMfDGimuzjBshy6kOQDeoNtN3uIMDO9Rtrg6aspo6zObChAa2PKfs/aoEHbYXcSMwK6g1xDnIzsxmeGQlyPDcz2KpCSo8MTgyPM41KWqmWvKzr0CxiUBWxXUMrJsZ6xQ2tvrR2ehBD3rQg74BehjpHvSgB33bVIVuosCCwP506zeFAFZDV3QKZjtPeq3FF0XMXl2zu3Yj1A01BWFjQNiNdUWYPyjrjvLuVIMU7+xy715NcpSY75eVzzc78GpDQh9FHrWpnIzmFeMcMKWSaPuucC2/CDdWgyPtLAafP1jvvG7vRBXWMrXDIC+/X99/hZbfN/qnMs6qptgOA92AzTlXXi3LgvPlCZeXZ7w8v+DL8wteLk9m2FHLEcdiingSmZFiyhZdJ0BT978ldmObRXTFtJF1QMMwtw5s25pRYYCitWbe5A32d+9AGJWa5R8MW7X48Ikb3TZRE4jCYzS6RAWbCq5jxarDoDVVMEo0AEAJh2k5E1z4dcMGNDKBoaQ1nHBbpvwKyBJbt6xAI86+CKVn54YuzSPfGI0bOnc0ZRfmbF4nBBYooXHgihURwba5wVQEYwj6GOh9ICAMGzdQs8i6y/mMU+SuCy/qe1OpsIkM4D3eevg8hfjCURSZjiYv6mF6HubzvaUYS/S+smkqor9G5MoLZrb5QUi7QraxMMG4xpkHqSz3orz46TQfri3gm++PL9H7nwoPqGPLoMwRE3lCwoRcNIkIpXonizod5TUcf7tyMoyqEf2Y797UDXRTwYEGW6gdHmnHezjM/WblzxW37byv9IPzdYM41sk2Z3AqMvQhy48J6mVl9zLSuDg7cN6j6hGA3phUUJdKh+FQYpJXKGH/DK8PCRITFt6+1K56/4RFVGGOJ9cV2E5mqMtJG2M3i7LqFyvWbmvhOdzZUnuv1kUa+QRbaNGOe9K9SU/7Otwlf8e5G+zl92/v38pA682ijzkMiJL5cv+4yTqxruGfqrKrxvy5gg5nMbG9kohSSRsGtZmXpzpwzX3VppSmIYIaO5qkvSMNdb6pDDs4uCJf87rCohzmK2zdCAvI8zQlw9UJz0jfNdCZgd99vS/4Lxg4fcxLj2fhUqG51G/uqOdu8khkdcg2pAOQnYnscxiY6lmafAzMscbOBrHGVSOXnT3T3LHIouz8J5TENOtYz0C5cnIfVlPey3CHpGBrwXOtXvZaGyd7p+fI2uW3/v0ojZw5Se2PRgwsHdvSDfpuSNYdKnNeZCvr0ExFvf0qfP8TLCTrA8srmFGVwUOLP8W+Cpq+Hrv5RZTG2KinQKfxo0yjmA84FLGvHGLrBjBPQYed8kMaWowxPkmiOtksirp7xBIdxkrs/C/ROWUrDHE2Oo6U5/j9J0CqwDoGVDb0Tjj52laSNGLCDWcRaeWcIY3KnW29kQoaEYR5cvDg1/5klMkxAG5IspT3AtkmbKydf2mObToluk6hsFzj7XFMEYDY6ugDHHXNvYudw2mZlYqU65gbiMxpbIPn3SaDAF0ZCaG5MWE9LxidIVvDGAJSxcYKkQ3r0PSXI2p+TLNyWmtmLGMGoRlCihjkpRChef3NWZJShmEXZEQBUt0dCzX4phtFwROhYjoAMNAaIj+dDotMVFXfGxmsBBKF0rDcggLwAJ6/XPBP/6f/GP/m//z/+aWm5IMe9KAH/UHoYaR70IMe9O1ThqWxaT0DpLwoUKFwD/KDMAfM73UKWEBVJoRyI184f1PBjq9lxt9DHW7LInswGBgKYSoCxR2hcKcHun1fpQnI9oHgVssLfWBni6zqCmxsnzcxRfGhMdZ8V25miE6RrFPxREXxrq5MjWM67a/X+3avKwqwfPLYQ4doirxb7/wN6G9PoP/v99hpzivdfwy7vlcGdJs68LiPATRGWzqeXp7x9PyMp5cXPF/OeD6fcTktaKCMoKOhwDCDnTkOMjrEoC6V0JTQmKaRjhmNLMn3AJmABXIIEXEhb0BlpBHEIAcJLB0dMO/G1sCNQYvldlOPqBsqGBC8rRtEB1YxI93QMQVZ9yJddcV1GKTlFjkAaPbTgBnpbC25wIoZHycRypGzVsqa00zsHmtypr1ziCiYJ/3ggaENTayPemsQUjRtiAg8YjhMZfw0MFs/KuB5++zdIoI2JIXixuwRdTMysXtuLB0CISkC9KT2j4H+55gBvEUvFX9w8J/DlN59LDwoDXhRFu2Xzm7Z0FzqNZViKgrw4yAub8jLYKbkpVXxlhVzWwlo5zS/a+hUhP6cdF+9Rh99OlRgrz72aE7H79wcyhJE0HC1VuvfVB3H3C1lpR4x2l76bfd6m5j+hBu/AHP0iEkj6p7Puv8JbFHA/z7iK0UlYqLQjNILJxcCpiWxwm26Kodq5b3McCmvHZhhpFoMiqVHqLwTALS58ioMZNFenbnqsjNLexF1O/J+mn9um8FDnlczbrXyXY2CjPfRoc8Kf8uW16gZAMQGV2jfk+3zwZ93fXN/ft7U+2ZVlOsE4DfP7xvpCA6fZkq9MCCE1z6T72WfVkX/+mkebzQ/x7DdsruPecQuArhSsrtpXFG1vDhhpAMAGR49Dth1BWZuH8TVOfVDGRkOL43zXXP9mQKzeXsiz6CS7ZkM9gx0zlXdYDgV/AnOWvixgnYHmfeJQLZububv8dmycQW/2nlm7ArdXVVfzypef9KEhQyIxTDCZQ4jiN2/O02HEnu+Z/axw29rQ1OL9ghDLKXCOFoyzy6kChAffMqi7+ygkpCNhYcqIgqkmlUijuvzDjIf0fG4SuptyXlM4M4WcRRRJrrnNruxiTNdDGWcPY5Odl+ruiJ9LXYRZwqkBbackwCdbLeWXfhvzgXAtyMzvuSaKM8rbqWG2wpSNrm+7muccVe9Or1za6WJbkDlfpp/KIlDlMLnJGxsBIa44mgd5LD6KjSjaf8TITNk2TzRcDL0SCsLlB+AMrSZcX+o4G2s+Nsfvsfz3/0dnk8XXM5nPJ0udvbXw4lTBDIoDb8BSRuGM3PeC7QNSUhNwLZQ0mnwC35tPMrnI5f5ipEToTpx5DVSlxtonk3UYt+yfGaA3OmSDD/nKgObDmyiGKq4KgxGkwBpjJUIKg04L2AZ4G0Fu2wmtFk6AHeyIo90GwDOdMbCDeInW9tKKOtiR3xyeEvbHzT6r+4TwRddGBAqaOoIaU2zP4hCeIAbPAOVxd7cG6Ohm8OKSjqsjjEMZaVPZ5UHPehBD/pW6WGke9CDHvRt05RE94qBoOKJpiDstN0AbnQUR4k3Lu7KnQLjVykhsBS0CdShOQ4S8vFjviXBmSj+fu+d++v3ypt3queoIYM7bGLCATOwsEM56l7TlQXXOuheuk2FUKUJw5maagC3qvv9c/NTCNqmJNm39R2F5l0NG6AvC+g/Xm+/uF+F+TnaNaZSHoQC10fo547z5YLnJ4O4fHp+xuV8wrJ09EY4UYeAobKaVO+hNRP1j9BAWJrlBWAitNbMWMSM1jtAhBWKKIKAGZRCyKTZqibWeVo7oMFzqzG4L6ClmU6cCCrmhThEMLBh0xWrrhiymREOE7JyqGCVgVUMWiSUgBLGb2jmtLsZRbL67012U6Fq5NAvUuZVGDw8abmJiwJpjE0cAocIvTUsrGjUwGhobF7kxM3vCQ9cV4q4BikgV1jI4D9hfdlbA3T4+HDmUhlDQC5s67hVmPAL0J4pFYPRBMD6idTSGt5EzZUei2Wym90+xjs92V47MMsoLO5GgYeZJszY2k9TEgbMWPDGtK3Uhtz7KY2cyqtP8NE/KM365Pp0NUIAC5s3vPg1oHuuJFZKW5Pl7rDisn90KvVucst4P9qeFIYpnuF2m85CUzFYVSatTJaYSZXPalH+TOi81JjsdBteZvL/w8Tb8VjdN+6g5MVurtyZ7dViWd8Xxnrm8q6BHd7cTZQg7TtU4fvZsKj2H67Ay2WWXQ0IxTlg1g9l34mrlvspYMcYBvskRBDabPi4mWf92IqPyp3FeuyLz1JnyyO7jbtfj+vA2/ev6JcFoIbIIUsxNu/skd8i5ZTJ7tU8F1aYRLvteE6iXRnTies4x5D5iGwJOTS0GsRzQN/VPJ/mSsOoy4eZENB6NbpmjC0/M3Eqbyt/JmfeZlN36Dy/Jwx8BeQy20tuNAiHmWmktzXe/+GC8Vfbx84bZwL/xb18dO+cwbKDy/v89jkCxhgtivDAMxhu2PQIN27TAOYRVXHGAcQh6tmNQgqVgTGA1vwUTQwiV/aKOrS3ZjU48sTF2EZFdpEs0bd2PQyrMTbKk5lHxAjUo/TDcYm6+z7Iez32M5D1E8PqNK4rxjbsvKJIyMl5tD/wHT/rpL0xpivKulDkujg+W/tAxMeqnHfmHHCexNbn7JUm3yZKb9dVOl+iUQXyvff2PBMYJPd4q03L21H4SaeRnErTOHrkNvPkq7nNkO/Hce6VTQ2XcCs/otBh3x+hVn91R6dP0o+b+wyiJdGxQWRneQU2GeiwPhUYnP06NlyvV7y+vuKH11ds2wY9iTlSqvGwQAoRZZA7N+RM83k0huW3k2HIIQIt6CPqa0nTBylPIJ7Djc1LsKwbRsAUz2NH8Bkki5l+VhYhKAVCGdSxMhCmslUG/u76hlUGRMwJ0lByFNqcf7LlOmUAEKAtdnzAUGxqhj2RgW3dIAIwOpa+mE9qA5Q9UtFyMkAcu57AOafB5gSJyHnpkJnqefUaABV2KHhNNApAoSwF1dyiw8n5szrCicfWghtj6d3QcZpfHW8Tdvxhn3vQgx70R0IPI92DHvSgb55uPVKroiIkvoOBJ/SXmIfkPdb/UYs1paEph05lx1HomHmKCLqJRdE1MqFrBdAc8oI+8PYMbX1+nCLnZ+igujy0Rs370D1s0QToDF3dw5vjrlJY/q13LsbLbr2DE+YmJP3S5/m5Cp83sJhFW7D76tjrx/fOK0oE/OkZ9LfX9yXEo75X1SLv1OukuodTdeVza4zltOB8OeHp6QlPT08WQXc+49Q6esBzmDTj8JUGltcALGoGOhagwbxlmQiNO7ob6Qzfny1STdQ9Ok3xohF9Rg4Z0tly3PSOvnS0ZfEfg77UZrkQNt2wytowemgAAQAASURBVIbrtmLdNqzjinW8YR1XXMeGt4SudNOaqEfQbZYrw5UNkacLKJFwx+FzBeN+rKyz0zdT4FF4WtZPCMPxlCk9eVguOnXj3RgC6YROgkYDig60gOKMHDaA0DR1CyEhOUkJTcxYLcKugJt5bxQKHWqCMwtUuKyDwxQimyuWPN0hytTgu47RbaGsSdsLTUF9x46i8BDg6zXsywRhl+pNCWlnsCpPFdnxFe+TjVUqf2kfCeK6prm8a710Rg/mMo718xlHh79nUjisFhTDlSxAUcQCodvJIcrdhmNOWEFhq9oQSkLsxpWJMKoRLpTcpj3x380svRERltZbASgU6fQ+n9aYLf5TQ04mDi2SKR+8oncaqSimEtXGYb4jrgekJQDomPW8pzBuHmGXxjMqGGCx/wI7C2MtJ55dN+BtBZ5Obi2PhRjRhrD+SwNmQKpRaS+ZwrS8qHUGu1GWySKdjbcYNBbQzREi8/bly+6QlrH86D4YbOfzGfiP39///jBkh0ufXfS/Gsq601SiBsUusueZgThwp6GVh6ZBxnehiF5QJLQyewSVQKZhRpH7rnoUTMSxZdSX18v4HTvPc3O/Snmn5Bo3OMSYe16XejAhgMAgFnM4UT0oiI8bRHwXhgxy1wKDjFUozv/FGW//zQ8f9/+P1RYU/pWn1jmI5QPNfzORGBBp20wD7jlnZyMdLEMRTmDBiyNSXHSAVKHa9m/UEvHozkxmW5v9Ng10XqZvYgLJ6I94Xz3rcET36YT5HkMMJi6qzgbjRhlp/Avsf3XvTTYnbqhzFh9R1wc2fWOkDiCMO1vAzqKQX+yrUo9xRLDhKtM7e1sJwt4vbc6JlO1of2bJ96HOpDhJ+r/v8Li7/PDO589SludbJwvl9dnNkrwp+IyJF3ZB1CNwrwq9KmgDJAxz7rhTt8C/D/qFZuuBE9z/lhuhOWJGb+Z4Z1GUBN2wW4dQk1Ou6xXruroThcP+sjtAgDxHnc6zmTv8iBuHZJiBaKQMhDxP62HuJVwxhZTObriy8bdUuzMieJ5f6ppzlh/XRSAYGUUmCmwk2EyiwBDBJhteZcWAWKQhE5aFQU0B3yMazXyjxAIqUDCUsiOBtIHFnBLRAeEBYct1GoZ0jfMvWQThoJCrLFqXxc9IN0dH2/80rPWcLZ9z2x9iX++xN1ruQQVTtzzrrQGdgA5sIkjEBy+2nxdzmnrkpXvQgx70DdPDSPegBz3oGydCuhfuvHYPWop6Yozzcd5fhPSkKpJMoSu/DZ3jUSityk3EO8iMdJ1BQ6CbwUpqO3ro72Xe+61931Bnvqg/Dg5FHT4EnU2APjXoENAux9ChTR9q+KbwMZOnT6XHbIhmmTECh8dRFVNGpli6bf47irhjfV+65ab7mw+i6eI512PZb7W/B9yzFTZQjdDPC87nE86XC15envHlN7/By9MFl8sFp2VBEwWtCt02E4CY0RtjAWOhhgZCU0McxTAloChmnrXeDN6JmlVlDGzbhnVbsW0W8TY8qbkZ6Bp4OaEvHafLCafLGcvlDF462tIAZhPyNvM4fVuveFvfcB1XbLJh3a64jhVvY8NVBwYkhR3VyMHiCdI9UbeGwQ6A6G4As09tWUxFyzTylPuLd3rq61SK7s6Mi1A3tpFgqCsfh/XPcEjLExSgllB7FjnKBjvqtbAE8lO5QqQOl9I9n1M3eNCErBGIUCpy7y7DMLor0NAyd531mdxCP8KL0mkPEJgdI9AKQ8MoVX9fvtqxG7+XiFy/NldXqARMIKcc088aysS97yMHYSrFxOE/kY604JCbiz42dZKEzEfzLYjRppawNkoq26ZXe8x7n52Q4rSR7CQUQdj7hhw1VGEUEB9Udlja6SGsMMMT39Ga+XdV+Xp8T+55ztzq+3PM4vphAMtrdtrCUNjCn63W2gzr1Nui3tOiJvne3twgvsnsyHvPhzYJuq9jGOl+eAPOC7AsnpvOv5dDf1AOhvGQqVlNBZsCmdOqwn9ya57qTzDA2NbrfB7jE2vtZlAPDYx7APyDL8Df/nDTD5ljdmcEp/zv73vR7abAV69Vp59gbti3OcYjXzDbrfH8oVtD6RrRceTa14BqtkfsD9UJY5sOMaWeCANcVJE5c7SG40yeIXUy9XpExLB6MAwqLSLs5g1z3uf0BDnsYxh9yjnW+0hz3h77mbKMzzh+8T/t70/LrOft5+rEMH8fBsN5DRM5jCemoZP8fBo31jrEMJPBU09HAEk7fNzYmq3V1lpWk4m8vzkNd6kwz3rOazYXSkPV6xm/4wgbSn43Foxhe39fGojU98ZfcBHqnC1jU6xvA8PzNkGwM46Zrbh0lhyc7A77yM6xx3khvcPT9gY6W0dm9NQ0/GV3sq+zpoAQuFHZfiiderSOAZVtx38GYgvaz+xfxBfoZv9KDuv9o8UpS7Pu4XMT8ynOA7qpQVyuaoYnd/64t64+sWR/VvqlZuuxGe+NU0S6kiNnMDMI7mBaDsXpmjEU27qZjLStlotxySlb+Lg7OSQPl5RvzFFjrn2DdGw2D4+woz4mGucmQqKcAHWq6O73jJqznSrPF27oHzodH4daOoEVButpZ1IFloFO8U5C62pOHP6mreTjtDlnkXEM5PwUJqC33J9GizU3nP+RRbwBaGrylMERm8PEcANn8zpY99iqDKjn4JXw/hcFQK618GvhlDL1MfuZJ2KOo506OnWcOnBeVryOFYus0LHhP/+f/BP8t//1f4fv//r1/mR60IMe9KBvgB5Gugc96EHfOFXBuXjthqgUnumpNMzHYBgid5SQfuKex8Pj9+Zpv0/NNgX3+V5/enNIhyHAGJZjQJo77JNX71bgQwgceUUQqZNve4H27b5Hx68IhiPfm+0Gm5pbXCNTjIrcEUTfE9cOisKb2/YXstid+3tVIM6B2sukP0JcJM9XAS9aFdQ/I936OwRwyWj+3rzODaClo5/POH/3gi9PT3h5ecHlcsHT0zPOy4LWCHQd1o/bACvjxIyFGI0IHWZI4apM8+I5PBNbz5FVVWybYL2uuF6v2MbqeePMPZkaoS0LlsuC0+mE0+mEfjqjnU7g3kDNIvHWbZghbr3ibXvDdXvFOjZssuE6zEh3HQObDIsc0lhb5u27DYO0VHHIGK9bdFmFkYqlYV7UDvai4ZHOZaK40SP6wseczN3YdTWU0Xis8Dw8cAOXQgfQSNECps8Tvrv4C2WgE6GBAWpQsshFE/ct7x953j7uzRRFLaxJllCeYGMADPyv/1f/S/xv/3f/p5upQ4PcaON5BdUUTypzHte8ctbOyDlEYAgGabZNijIyDHVHnWdVGSgBixvpAtYq/lWynIdUx+mThrpUZrsCVWN8yPJSRL5HYqQRjrwNsYgpFAn87eDSCNQS1KtF0k1ktjAme2SFj1nAIjHc/sO+Y9C091dKBGECGthzdli/Wi4fKjBLXmiEZQLzYVXT6u0cGcq4xjgASFfnYK5h2JgWa7/xXu6yMiknI8fcc8t9tY5pbPgkhcILOuubHRbay7Z/IOoeWKIRpRTv34ZF1C1tLqrYF48TtRoF8t2mXRZ4pJXnziNF5hfySQ5qChpsHu7bKO/5RLt198f71NlgPOvjzODGGNvA9fUN5y/PUExDxPTi/xFj8TPR8Y33ajChx+ZnABmlcDyTBB9KCO/yfRrWknch96AaJU7h6Y/Y6dTfTBaFQc35HPu+F+8ucW4UzhxaeDQ5JKP6/6XfU2MsFhWf1+y5yiHDOSWmMjvjrwYSDmjN0j1EpUfDmOmGbuPPX8vbBVCnGRF89w7d/VXPavm5GrHuHkSjvjQvFeOm7X2+18Sm57yG2ZAJ0tDmZ4jIH9VawxgeVZ+RNPNdMiSj4LLWnzhm5nj7nIocVsf8VRpOANqsH/0Mk6/4BZahxqFArJ9kSAYN143ApmbMOcbOQBf9kDzQVwdhP2vqPeUyYR4tdsbNMNAV9pqsPPwwyhvC2CG+Vo88Wd3YCsRuQJ6r8cDG35nAP0Ki+CpZgND+4KMxP/xdaeQX/3vAAsqvCqz+90AOU259ZTl8i/RxP881UdcyoJ4rMgx1zfNUAqR7CFxu7DniGIMGXseKv/7b/4jni8ljxITzaYHlOBPIGKA8XrhzINs8G5vLOSOuUyKPe6zXYa1gRtspsDdcx8hbrDUl5LHV2/YuK0vcSKUqELI82yt7+gIoNgADK2QMm1dxbONwOlSDuKUpU25qbck9SBWbDkfvsPPl6nUBG7qPdzCASF9ghxyrO0PAaFAIRYyoO2+iG+Q7mTEwcoKHQxOzyVFw42O8s86QHf+NfUAVioGxAdu2onWProY5tS3U0bljxfhlDPIPetCDHvQHpoeR7kEPetAfDxGlMGbpNhx+JjXcQB6tCSb9RURJCHKhwJ6FlheYNKopgFEKYsdkAUpTgQsFaKjlhNhcw9IZ1AA9ZbGfpPdvvpco/UZ+DqHEWmIBGZ2BRYHNoBLNMHGjSn6nLphQhFVuj66pCpyqf4lvdsVq/QaZnOLY5t1HPSg1S4WKKi0F+j+/gP7q7VZTfrg9JeMwzAmmgW4h8NJxenrC83cv+PLdF7w8P+P56QmX8xmd2aBUVgW2AdoG2hC0Rji5cc6mjUG9jaHTEAWkMil+zPPWBK1t27CuG7ZtOIySwZxQIzB3nC5nnC4XnJYTlvMZ/dzB3RTFQwWbCNZtxdt2xev2huv2hutmBr9NBt62K97Ghk0H1szb4MKTGsxmwDepJPjJ7Lq6DHYe/JrXNjH4FXLIlBBoUy2pVVGr+Rf5DA+jBUMwnzQRdkDBwhhQDOpYVLB1xSKCxeFbmBsGAwuHwG+GCAKZQYUscmqTDetYDZ4UplRpJGhjQKTj+emMdoiGhQC6BjhiQOm5oth9AjLQg+czpISmhu9lgDQjo6kIDl/JBOp75daGEMb3ci2TGw+8z0QnrM9Po8Jb9EaVZ3e40iCVsaEkq6Ww5WViYl//Py7y9w9N0VIhM9BNHSHlXhHkIJiWcxBVFerfx3Xa91kQAWhE09jgRungDIoyhjUksw68+pepaKn8FEVrytjlC81K0B1Y2oN2MNm0Qw3pbP3UGpV9MayUP0V7EnttQN9l2EHcMGAeE1GHHZbabBNg0XTrNi2lHC+AJ2okzD7RWWbsB4cOF1fuQSJt4DQECdRhqmBKqBoNdKzfLBKHwfy4b5iA334B/vJv9tfDiK7qPNq5Wyq78CPPHL8vBf+2f8M4NKNiStXvVCqhLGv9s2gfC0zjOeCwyeXvMMS1MJCQTV8JwxXpNHLVukRfEgrjtbEJxbvdTH7U83ETNxrGdxElENEbbLCXZpOz3KlUlKhhdEyDlF+fw1aYbOnniJD2Rs3VW+5jr058yZeG/hcnrP/u7abvAYD/tIGedm/NV9wjW6Le/5+CdCx1jo/ez7Oa7vSitV+RZ6RqgDtGPAJAay2NagEnWtnRGCMV6MdqZbSszodiPRGFIdfWGsiiXaqSXnWAW0PvHb11EJm73e+1HX+N1M5nYx2QbbihMHaR3W2lsXTzibLdcUKaYzLLmHzvth6H9Xrcs+LwyPMjcfSlOzC638moB53CTs12G+bmGCsCk7rjTJH5ajXe6/yPpusHA1Z7cEZk6vQR2RXj7dj856rQV9uXwkBnkVzIPZvqi/7A9IvOVafwMbr8CeP7/1DOhaRQas5Ca34246NoAHor6BAAL4ZiMcbAtl2xbgNj2zCaGcC2bYN4WgFbK+Y4CDecTuN61IGmyE/wPLlzJVTHxPBrYo+oVjV+EEcvYkYa4oYZ8yuvIra0BHFeM3jN4ScTl3lITMb0usV7hcR9Smc07yYDb3LNeqoqNrGceQHJv8IdQ4kx0CB6RVNGh6GPxLuYCaCOIWyuoeq5+sTqtdBAQzc+owr1MiJqL6oqEmvZ+RKVXK6l2y2yT+ZZYQys6+p93HDlzXIO6kRCOJ4pHvSgBz3oW6SHke5BD3rQt02p8Yad0agozUMbkdah+Fv2z8GVGC6I3mqzNRUednNcK1qhtFTtJbKElhkE2tRwDUNJuA1oo5lbCFX+vH/SvM34ticDj7ijfL1fmMkBzXFVQhm6NI/6k5mT7c6bPovYU41388+9n/i84ai1i/50CQe1z/dt2SmjD8XW1+o/fAL9u0MOlpS31PS0AW9ZLVAMYAH68wmnlyc8P3/Bl+++4LvvvsPT+YxzP+Hcugl+m+WL41XQNkEXxeLypEGJmiJiqJhQPiyxN7PlTYg2CMzrfxubwbeMzSLcAve/kXkULgtO/YRlOeHy9ITldEJbusGvtYZVzej0tl3xw/qK1+0Nr+sb1jTSbVjHwHVbcZXVDHruuSwi2UVDIok6AI9STOEZe9VlOsITzMPVBbExPLrF88OBQ9kVxgWLUHHRrkyVUAa54Smeh61bhoJ1gOCC/HCYwk0hbF6eSooOM2IpG/wnUUcjAnEz4X6xqENqFi1rsFUhHGt6u96b/yoKuvp3IqU/bJx12gQBMlZAHjKnbNE4ZJo+Q8fzZdmYwWi4smR0BAD0RaZiwZUXBAJrS8VsKgRDcelrKhTln6Wq1KyQb3W8OdABKzvMMZrLTP6wVoLfizaWZNkNU1lJoTgrTTGzOTKYq1Lo+ZOCXVX+SHPdVKOn6WBsXxHfwsxoReHevaedwrso9COcL5l/eXEmMjwWcodUkQY6Pdyv5e/Mr/V7qPlC8RV2xdo2ANNA6Htrc75xbB8Aum7Q1yvQm0FeEtyAFm1npDU92hHje6i+QEFicLzi90dUdER8EFtYqYa3uCsXP6f2vHfPYXN7uQB/+z3wuuYdKqagQ0Ma+s2ZwIz/dFSc/z2Q3NGkBURkRN2E0bNGg9lXs/7B0xLueXeusP4T35saxxnCPf2zXH9HwBprYWBgP/uV/8j27lQ01vdlHjk7U9I7EcMEy1kadQyjXUY8+z0x3q5L9ukahqYCvevzdw8NXfhNPFwNTlGXhdH/wXLXSEdEwPnH82nK9R5n768/o76GyNvkBfmXOqNU1XNJqUeHcRjdpqLXHvH9TxTMM5Lu5r2q0+khbAPBQsIIRxFtR4Vf7+MQNWGOI1dgRIw7bDalGcmB6X/+VXjjGKcWRZfn2MI7Jf7Y/Ub5HLxvzsYdHbfwG748iwDmFhCIsrG/EcENcfMcISRzfbCfq1CWZal2QJYHGw9jumYbD4cQL+cnnT7usOQoh3N0vZ0F9jp/0+Fxj6CTV4UGMrKPVfDBd/sZ+ImN+On0y5/a9p1LFA6IlHno1PtI/PBFQmhkqQEaEU444cQndDQwGzTlGAZ92Zs5rI2xIaHe1dYJK+yAdzDwG2u1sTW42oDTpZ0zgK3rOP8FZxiGpAHNHHgBi76JuvOlGQyNPzV01wkwEQSETooN5IGmCtU1c1tCDc5ylLQAb2NgkJicpopNN7zJOvcvA/Rxo5ztJVcZZlRURiNzdmAQujKa60yYCV06Bg00WF5uQkPHhubWtzN3dBL0hB8WMIntVxx7rSaPn3KE7Wcma06g2iGWbkH9uigs2hQKBeOVVly31fOkm1ypfDONHvSgBz3om6OHke5BD3rQt02u1Uy881CuuecaFGZsilMbE0iaKyxRBNeplKuKDa2ewKGRLRF7oKk4Qjms22//S0xhg02gK5kScRsGVZV6zYAkShkTqcBBPXNGlpWPRKX5xD0RPPPpxQUm6EKgjS3vD3l9Gt0EW1SP7Ju3vvMV1b+onqBdIfZVhaXO/il1/iztSidY0uk777C5AjNMhpEulML+HJ87Tl+e8PLlC16+fIcvX77gy/MLzssp88vRtoEGwEPRRdGIsDBbtIUIRmj83VgXBjtF5EWwPlKHwTJv0DUToYtYInQwGTzj0nBaTjidLjidz1jOJ/TlBG4N2hQbBJusuI4VP2xv+H61CLo1oC03ixhbZXheutWeceVmRtJBHfLPJoWmESq6bxdbUBSDACDmpR45TxRT2Vo0JtSmwU78PVOZaWrmUOSR+Xj69Xi7C4IKh04KExl8+pko3UNJy+Y9zx5hx72DerO5T9FmwXDFvULQwBgwJdJuOTCw/CNA3tQMb27MDBbD0R8NqUDd11Mh3f2+Sz6SCFgCFLRiKpqhpqB0pWuwIGs/GexMjoivoOxre3n03+dk2rj3/t2hmEgv8lDQJY8LHYi64fDbkKQjiDZtq0RuQHbKrcMNpTJ1heoFFL2uzV9veg2aAwhMDTMKBbHdmAIoonrqOovJFZa9VG4f1GnFkSU1muIR0wnzOI0G+8V7j18er9/j4bGRvacI/iRRrdOhWtGR1ZkivbRR+sMHQxTYNuD1zR5qbJblXmAza19qfSFsvZV8NOnPEw4NmPu+Ga0Jogy07saAePbQV7vum8rDr+6NTMBvnoHXv5l18v0EjSDrwHa9YqGzBxzOeOTytk9TORr96PtNJxpGzH1JYYJjnzOxBEQsUpy9xjJGwkdGNEVAVprdeEZXx45k8MphYDEHGFYrVzXejroQrWahDcZ+Kvnb4d4R0Pr8oW+CU9elCVeMUu5r4pBu5IpLr7tG7lYrl/Px4LPeUbvDTX0vOZe1fctYvx0ws+ys9jsj2gD+c05ucJ9u94WMQvQ9fmcUuf2j7FF+bzq7CUjs3NgoDKbiETUDygwZo/QOZhSbHkrX+X1VsO8U8kHisyjsRDQj8djzV6Uh1Pd5i0yx+ceupBZxyLfeLdeo7qbYL0cK6KaQ1fIP2x6kse1bT2c9rK674OPkt5KfULp1v72UBtU1Uvf/Ov/9i5nXm2Yq0XBWEgDFUBeOD9MMWttK+z6NfRO03/Zq/Qr7uZEP3qF8noKXUT4Y640w7bxHg6L16Dwv1x8KVOZ6PCMYL/A5mGvDv/82Tk+39NH0r+fFoMaM1hoaMZjt8CwiIEvyDWagEWORhqUzLnpG144mvm8ooGO47OWRWTojHL1AX8PW8buI2hiX4BtEIJKdc2LkVBOH0oy0wQQ1KUUUTQeUBAYX6c9GNJ87Z7bewb0DLeCp3QFFyXyOYU6OGwYir9uqjm7iss6bDmw6c4evuuF1mJEOzrc28X3LnRBWGZ7loqFxGEMVTRmL70edGUsTCHcsCgwwmg77Def5CggRxB0fW2MIKRqr593ztcKADE2nTaKACfZz6CH5tg2VgtyAuG0DGzasWLGNFeoRhqwEEsE//i//If71//7f/MQZ+qAHPehBf//0MNI96EEP+raJyJI5pw6PTEFVQzlSUeiCfCYL8suicHd4VJNYFUxR/qaqHIwXV2FtZgsvHqxqkIahQBsCrAPcGdLa/j03EuWR3leZxDH4RyvAI5JiaZabbiHTTOf7UAx00UfHSt8UOsuuis6pYvoRtfT31WI+/VxRjBCA3y7AX3fgdw67CDKhYKjNhWqci2Y2Ai2E0/MF3z1/wZcvX/Dy/IKn8wWX3rGoeWzqNkCrJeVmsZxnC1NutjJGRicS3JijkXMt6kguiGxQGJzH1fPQvV7fsA5L5k3EIO7oraH1BW3paEt8tkiwDYKhGzYMXGXDdfM8dOsb3sYb3rYV67Zilc2i5MbAihVD1Y10UwEKwA1WJSfPTsEVg7PTZqZWJ43mdQCPY+mKmtR+U8QLuHFOGaoukLV9Aead7uYTAZQEA20GzzLAymB0gyRkQBssmtUNdRY9RwavBMWQgc0VdFYBBjw33dBqGbCqtAugP9jlEd75NJtjXuHBJ7y9KZdaZA7gPMLXH3d7rYpaPstU+iqEZ5Qcubc5u3JTQyHNplgY+Ni0/zl6Z8VGpHEqIQ3KbRQj/wximMbDn6NGvyRFaxuZkbXtFOE1ErEYzmiPRhkUyjvSuX1kEBfFexqYNo+K1HRGqOyLyWOlj9q+um9kXpx4Kl7iIXh53RPgxHNpqIg1WLSZtSZ7LSn2+4FP6J+TmsNLqQChk2cgo+O9KTf9EJFRUft1AK9X87YmtbND95FJWMpD3bOfvd9yP9PMh7kPGVVXOnu0ueegGUNvu/Nden+Pvynk6bz7lnsD3mw/264r+NoB5oShCni4e6P2EX19pR5LKPu/92FGLxzujS5WsjyiIqFItfFUjy4XnZHd8Y4wBhAZjzR4MMk22vHOIqFEFX2xCErdYAYWGRByKEXfpKRG0ykAnXFtquEAZnya3NGjRs1FvaEzb9zeoDv7nog9y5GPi8iMwnUeMLlA5JKbX++ielEcyGBwa1bWziOtdJ9Fl53/9IS35YfJpO7Q1+fHvRPdnDUfzej4NntGLeLCNjCPpOFWjtXm5mFOET5XmqZxDICNCRGaQ01GJB25El5lGumqMRSw+WO/Pbad7dlGnPmV7HsHtR7DzkYFus7Gdr4z+IrG/K0D+TNS7EsqarmrZMatp8GY5t37Y/XkBkTY1U/L97nX56V6452Zovu/I1WobTc6jV4EgC2naxySDJV4nkFrvSKKbnYl5T2zzWVelj9vcvu+Q3T8oNiVEzRglpmICGbVmUe23uteP7oBuunOEfI4JQI6G1TaqbPvvrqN/Krpc5Pf1pvBwNMQiMvX1BiNFMwGqMjcQBJGOXVRzuARr9crlt7QejP4ejL4f9HJL4cY1EUY4O9VzxwWxfcZsTO1D4pBX4cDgckrFg0H21tkmOgnY6K8tgZWzwXZGNwXUGcIEQaRR8IZ+L0oMMiQTK6yYYhgqGKVgVUsMtCQPwa+H1eX1YBVBW+eYiCaZTD5c3VsImConz/Zo0IVHYQFxos7GjYMDAg2CBQNjZo5p4bjoBqqgG2ZzR0aDMC/5xnKDsAKlL2cEFCmYSQNI3vk6zY5OZyZB7CKt8TwK3ItD8Gf/JPvfvx0fNCDHvSgXxE9jHQPetCDvm0yd/VpANC9Agdx4D6euJmmIqXkXdgrI/3AGB8TlL54w4vnAdFQ2vFBciqqiaFmABsCDHZhSy1f3bsCu5e7q5k1tiq498Lmj5P8iQjaCegEXUO/QxZdsIkbC44qljv1DWkyfpUIqN29VZ9Je4ii+xWMx+5l3btHx/KKdBvf/tkZ9Lttfp/z5/A4wZRkC2M5n/H09ITn52dczhc8nc64tI4F5PpiMYjLzY1IFBBH7pmpUX9NpVMjLnlkzPDG1CAiGDIgY2DdNry+XfH29obX9YqhA0KWZ6UvC5bTCafzCeeLRdFR75aDDoLr2HAdV7xuV/ywvuH7tx/w/fUHvI03rNuG6zCokE0HhmwQHbjq5oY4z+eRLtGuJE0FYl1X8fdhnlT3bMVxou4fJbiBDsgFPZMbwgS5GEOFjm33vIDRaTEh3jKIgQVgsajQBraE6ixQFoPAbAppgLIpA+FKPiVyu62CMcBqJkBSNqFQdabjqvQKM6SpB80wQB6kQwSLZtHZPJhjL4QBhkCGQbFBrO6BqKuqkK6ptbFoNU00wRgjf4UJvF1sXYdC3hVhtMvz+NPUPNOj/cB5nFlGLhtmB9XKKISp5HsvIu/XRHU3aL6XNAclje9jnZAiA9Pc9l5Unrjllz5vQ5lMwMxBqQqIKyQaEPnvWC0fIxHM0FQLz42qMLGdJpKwU9aHhhNlPzuu5502FuUdmM/tjFR39trfl8LQxuRQlmFUU1uvuY4Oew1TJDnKokQH6Or3bAM4BewlAadltiHaGcaaHb8qvM6rkzZ8lP05xtePB+xImnPWH9bfcTkSijb9g7XCDHz3lB+XZUHrDUO3dPbgrYGbYdFSjuH+ZXNG33ljGjvnCaMaqD+muNt5hhupdqeXgNRTTUOcuMMCuZHzWF5GPCTcryC8+TO6jsIfy+DGUpkIlEgquJJP8xyXkRal6RRRFhRRyWzRHFSMZGSKRdM9RjmubKRp1NfdvLI6xRFQaH/cjHU5x63w3zhf0BwXuCozJmXdmUEEYbOQiA4/6zGe/9kL/u6//jvo7/bQCfwPGz5NFHvCgeJ4fZct7Pcic8Ax3uQzYrIyVVBjX2eUxhACGyvL6ESbGzVXXRjriMigJ0EQmnvTTZVlnjMix1w4TdT8d9UZMMZEQtmM2PfcgMDTdU7v9sXvT3HMlZKP7r7hlaAcSm8/pwevR8Dy5ad8Zsc6qO7/hy/eI7VI8xDTtB7xmkEXhs9IRMH6xjsPTcFPvTKm3CfPlzXrFWU0O1nZ2U8LCEd992fH43hs8r/LDLaoquD/Rz7rZwTxPHRYYYY6yarPM0N93oeHowtoV+ofLxEA55ui8JC1+Mpkpc7mgadk5ppVVoOdHgY/+f3bD3hbX/CFXiYv3BnPKfcahUWdig6IDgyMPAKY859CdZhzAO/ldoLxGAC2X6nY/uPw/iFGCXze0eQlcMdAa4c5pAyYEXB4ZNyAnQtXEbyODdsYGBoynjl2RT7tN1lNTlTFCoumC/4PkOdspWREq2ywnK3DIhZhc22AMGCRrg3DjYSKjRRheOvMGORcxDYUM06SSex2FlaoMpo7UYajczgTBH+12ojnygOIbNxbrHlIOrQIBCTiWbytZ7XqZh70oAc96Bumh5HuQQ960DdNyn6ohUl+N9FefuBNvWXizZhxTXdYZPMx0xsRbvP9lM/uDkfT7fP2bJgSl873rwK0YSfPzkDjVKV8LgIuGlPF6CreEVJ7+GkyQx0tDCwMDDNmhJfgjfIwfx++SIUo7e99tx2lGVPy/6qs/2F5evi9+86VXJe+G5p9JMS8PbuSCK039OWE3jueljNezhecl8WGUQkYDaKSETedgK4KhivqZEY7dWac+gmnZXHDiU8Pz3UjV8uj8Pa24u36htfrFdd1xRgDvHT01rGcTzifTzifz+inBf1kUCnCwHBPy7ftih88/9zr9obfvX2P312/xypbGue2sWFTi6QTHdjU/CQlvMT3OoaiYbo3QHeuvXdbjPHxe8VOAVaHwj4flMO+pIkEG19N2ESHwPP8CYEam78laXqxOvBNKlTUx5q63Q9mKNs9mwp0bO61D/TW9kq6qPcW9bcXBHph6PVZkTkTPCe6PeOKXWoK9bxuCYUJQIdCPRdDlK8K6OJdGKhnZEpCYdNAUeuleoqZ+Yhqtb9Ct3fcM5bHFabwd1WD7QHc+OxrL5TjP36B/71QwlxmxkCDu5QYJLU50hxilACwKxdS0Q9XssUH+Jxlckgv+4K5o3HHWNf9MiNF5J9jdSMygJ2GMCi3htjrClEptJnyz0I+BTPU596ijHfJ7JF7++ZPY9xfJ+tU2z9rDjn2NoTngJTOjfpYaJUvJPNohyowNkC6LbzQ+p49T91NPszSJzsNKeUlMq1Wni8Ke/J7KNvwORXSR31ZDjVEwJenjLztpw5eOnQIxPnBEEGTgcbF+JGtIkQu34ia1uPZQU0hecx5dVtjKr0SQxBGtzH7Ie6h6S0PmjwhIqri+eBZ7PBm4teYTQEfTD35Y5RSxikiqADz4B9uqJLsxvL22k6dsJBhdCFiCA3I8LkFQhjrgP36z91KZ9tnf2GOIWBnWMQUv02uExEeR8emuys2IxOQRsQY9bqzciisb94G4Mnny3ET/hEUrGh+QLZ31x27I23hQx5JlxB05NHitCRTZSK01sFt7j02XozW+E6/6+GcV74vfRu5oiJCzozA5XtVjG3DWnIG1xy+oYg3Pk9+3qCf1I9fo12RAo8SLcbmGHWiAuNf+RoOx/nJf/Lu2MNJS58eN4Kb2uzrSWWI/TXBotmtdjIMZYIaoF33SyGCJR1OMBwuLao1+JnV2PZq511f47rHrz87RoXZSZz3CZhxPzFnzEAJP8vhzc9+brTM3qZZcPQV6TTQgX6cZPdtks9VVWxjRWsnqAKbDJsXIEPJ0HqfoZjIBmAMtA3ojTKnc++Wo5uZAQG2MXwduDNIZ89fOjA0YsZG7kFhLDTjm3NSNXQOuJNfROWqG/Mo+Lha5B0ElitWzfDfewdxN4AOQhrlrqpYdWCD4IftDdsQbDCZ7HVc8f12xToM5nKVDasa3xmeJOB1rBZ9B7XIN9971XmiqK2dsHsPtVzXDez5r63fVgU6GKwEFsaqghMD3RJp48wLOi1YuIFdThpqeTAXmPMTqRnXVAlCjE6Wbzx4MGHuZ3OPDY2IopEm7ycXrIxXGOSxiLe05B3l4pj1oAc96EHfIj2MdA960IO+adopKlMBjaJoI0x3yRDMq3SoWcruHhDgB9G9ws9fLBGREsqVXW2mEjsFXy9jeDROSKnrAHUGNYI2mrqaLPm+YGkH7JkwbQp4eyG56j1ulThFczK1UFaPhYGN0mC3zzcn2GtXivLyMwqIKCzL1PJMjEXpv7t0KCPborvLu1dq6ZNG0H9QYMJiGhwjFzqATuiXjtPljPPljOfnJ7w8PeH56YwTdzQo2ubDq5sJNACaBCKbOgyKNaeB0LnhfDrhcr6YAgom4G/rwHXdcNUrtjFwXa94W10gg0Aboy8LTuczTpcLzuczzucz2tLM2MvAphvWbcMqK75f3/D9+orX9RWv2xXfr694G1c30Ak2nQa64WKdZDTDvW4nzBCez1BRaB+nzFGfoziMwzuKtTo+8ck712zwAsIKqCkYN6gndFcXYg2upWFg1YGGgYbmeRNsvHlxqBY4XJgIZFOMMeyaK1b1ONEKS1EgPaTVWZHUe0I548ELpAC6KaZCzx+Rdrtk6OU6+rRLALmEoc354ElhJiX6mQEI50trtWIeZ2SY809m9qgXxVHB/GsmBnIukoaJDj6b4MZGI2JCV/LoHfMhVtWMhgzzVhiEieHRHa5U8LFrrYHHMMWKwg1QhpNKEf2mpcCq+K6Rc8BuX9rx+fjMCtMaYaeUrVFTWU4kSYQ/c3zXL0lR58zBSBPukoZ33IS6m1pldQhjmppQiOdebYCu9n1rZrQb5PnpTOmW9+ceXhd47FXYdXH6iHhfzZxkO9PTV+jujn3slPnipxPwg+fwXDpOTxe8Xb3uAMawHDWtUU4dYJqdkSWZWvk9JfB7dZ9bedl/vc1QlHwzbuhgKm/GLlAvFG1h6sg8YDwNMOw8OCLmdlCFznyVaFcn+3Ma9+YRo+RJ3R076r/zetRcMpKUZ1SEWxOMZXh+0XBIKGfNbHlENPhQqndGnOjonT6fd5QmxnJMZXIYN/cjFbMwOj4iBPnSICWSjr9roBd7OuF7y5Tfdco9yuiyULfub6dyxtuxrN3WXzfT8pmQESjsUNW9TeSG6YpjOY96gZTXe3vQ0ehJM2cd4MY6WPSZiPjer6iRnwEXPsbItrXeHSozZs5n1/9PoxpFN64bdBjvqrmk518RcYnJ1sshbM6Sm4Ux5+/98MiPajj7/nCEJwDi0U9zb/He4jolKI/pREhgEyGNoPPcSwlqrD+MAIdOOObRvndUfY92IA+YW7B41FTkrCP4trEpMBRyBXRVg7wcmtCfUcYOIUUx4/Z1t3L/KGl3XLn5RiGeKoCJ0mhp2SMGxlWhbyt4BSAdT3Q2hzdmc1ZrHYQO9f1o81z1zBPaVZw3CNTe0RpGOGIF/97JLrwbPHVGbMe0lhFjcYZTh+kmtf2s9QXcOoQUUIHKhqsOvI0Nb2q5537YVmwyDNhRFW9yxatczcFSBla1SLohAoVgE8WbbiYvwgxwm06ZnbhE+voxRr0jRRmbskWc+9zbIO58xugCrA1YWNBdPgo4ZlYFq90bDmubbGAoSJunERAod3Qyp4XeLH2BuJNVZ3OOzMjlEXy8LPhwJvN1Jhq7jMFuMjV892ff4S/+B/+gpOx40IMe9KBvix5Gugc96EHfOLkiLY0rJXJuL30eKFRVcfDWcj0osOQk33GDprAzDlXVSnl9JQGwDqATsDGo28Ge3MNOQ/2y09EVzV++bSo/3qd7FbhtJbzO2hlYFNjYov1C8dlkRipkufH7qML6iPRWSv6RCnvFwWCot2Xk0N+pV4jqygCd2vyaySMq1DWYriToBDp1LJcLLs9PeHo64+npgtNpQWtsmP1CYBkQ0TTQhedrQGCKK9wtX1zHaVlwOVtZzZKrQUTwRlesm8GUDBFsw7y0DX6S0E9mLLxcLjhdLjidTlhOZzPWwOCB3tYVb9sb3rYrvl/f8MP2A35YX/G2rXjd3nAVF/p0wyaWs05UPKrABMHUjJSh+kn03rT5zP3vfU/HQjTZAMH60QJjLHnVUMaQZoY5FRNudaAJg2Wgy7CE6GRRbNQsnx8xW5RsEdBF1BTGYjkt/uX/8L9/v46FLehBztzpHauSRwCM0rwS8qeY5WSUnvOM3Qok1xssgJKiRf5Dh56lY799ksLAwKCDQryWVmB5KwtzRSYlLN2EBvucOuzvl0gN4hJw3u5KDXF4JQNJUjTihFQL+D24IjfmJVHAHbnXv+eyinxWrXXQtua7I5KDxCM+h+dc6VTmWTFGhLapKFt3n7PbQ+vZZmRaJMeJclT3SqikmIA/Agrv5yJ2JU1uf76nNL9AbSpxYrGEcXGL667BFdiikqt9XrpBXyLKjmg6tr0hQ6Nmf+QcD2hNhjlkRL1S2V3OBLtIu8IUbpajl/EZyEsAeD5b03pDPy/YyPINinvJUyljAi/Pd8ud8hWu4Kd59717dJZUyN+otv9NnyB/e87VMi1dyclMiEgUouq8NA2d6kbweLhCZ1dbbbxjjAHatnmfAgaVWKONkM40N62szl6IoXTjSxgSg3kTg7k6uuyfm4zcXjaPn+JHj4AW3K+/uWe8w8cTYnyWvW+G848wpoo55bAqfvtf/Sn+/f/m3817F4AaYc7h0kfl7/n6g9UCZd5Hf2EOO+UfcwZRRLASeY5IU2aH40MMjkEbWp83NjhJ9ryFlg/KlL/xs6kZZrnxbp5E5AuA3e8bCEzvszGGoXd4XYYanOzYRuZRjHJB5H0bHXBvjf88NEUFH68wMB7etz+OMW7nkdefDnMuzsP5jt+DytZUj0XxGrtHZ0I2hcOBR/M0MSsT/UDU8lnCosxbRl3aON1loe+MxVFEuVf/93rguB0rYI5JotA3QDaPolthOQPjjIfcVTF73AxR7B2V5X1N7PuV0vs7yC2dv1Qsb3N2GGrbMKvxJXa5WMcAaQMrIEpgUfNcc4eUVQRvY8NVBAssWkzdWjY8lxuRWoSjQ1IELzHe6/CWAjdITTwKZkOsGRoQwjr5NzmUbOSoZeNxLWBvmcG9GdQ+FGMoVlFcRfGDDFx1xabDDXLqTpUD17Hi1b8LQ92rrBgiFjUPg/xUKUghuUkCmpDnNgfLQd2dCG0dkcCdYcid/GwdCcLvKazjVhyrOpwlo4MhyhjCWFURObKHMhZqHhntOZ47gcQiyNvSAUR+a3NsVY8GDlnC+lzRWHHWho3EJgYESg3KjNPCOL+cgb/5kZP0QQ960IN+JfQw0j3oQQ/6xokB6pbAyaMBMq8FhguCDtPkebaqMHpjdYuEQsBUogSspVJ5NMSpOOHS7t8dVelETdlDCugmlhdnZVATaD9o3PX4xxRp97eF2qfC2KVKBJFUeS+gY+oySx1nFB0DK0OHWtTZdlQE7N89y92pb+YLqjYnf+n+nh2Ve+l4SWcet3eePo5BKhrKzfqbE/TcgB9iXF1gCS/ehcDnBcvTBZeXJ/95RjstUFYMGdhUgU2weK7BBvPyNxQ2NSXYMCUOuQWlt46l20/vC3gXkSUYY7X8cGLCYSOGtobWCOfzGU/PTzidz1hOJ7Slg7rBF0V+grftDT+sbxY5d/0BP2yveN1eLXeBrFglIC4Hho7MXTRSsVPn8E7b+fvT18o5TomPlBFH7Y5OY1oYlSwvwpsJypsrzkJAPeWscAVew6KLCbYMcG9gJbNdsCvexLx51b3n/9V/+c/x//p//lsAAJ9u66v+T07hgds1HvqkWIOjfKeYkHseeVf1u9gOfUWmnyCF2VA2Uy4EDFT14r8HWfl+V39OxRL5nzinzfQCr1SVo79mMuWEQcGloRbwVPWaKqRODed2csVRsBMp8ICE4ZBDoTghBqhZ2GQYOFtvaFtDa4whkQ/FDBKIPCdCoM149X0tp5ahqpPPb05LBLnRy5U2MtWE09qrs8ybOVBerp+bH78XHQ1FAWlEAVdZ+LeN2F6rKQJzrS8UhjdR4PVqRrreZpnA/P6dtlPpTxtXmob18np4dClxM9jcHCf9uOuy2Yf2H88HXy72mwm0NLB2yABIyPNnakLxETlE124Xrn0bF/2qzvbenCMQ5wDFPf5S83PBDW7EDn8be3oYN6I//f6wywxXpEZ0XP7rxvBqWLnNc6tZjoiYoQX7aKrkRWW57M47vg5FLM9mPMpU5khExUXL07iopeAwPJbruS4p54ixkbjXomhF9307AR6832P+ESFCbMlj4HZGVJ1GSWuTZj8mMdD+UZ/PlD80zlGV/SQ6As2/I5KozP+sfbS/Xs81UvhUZS8T0zT7kSNHXJkXNYNz5BZMSNQYnRw29YjIWRaVecmICGd/QCwyNnLn1vxzUV6labide+MvRQR3DtkE47p5Lqx6Jrd+VV9H01CeBfgth706h+Jn2q8JE1483lvek8sICnVZKYPEw4Ybc8SDzdUtoeJwyCIMIoWQf1e74J0hmE4E79Rb53w/RtBlu/LC5CcqsIDtN4uiizNZwp2X53ePB18Q7FjEL7zLfpWO/P/noCzHCz9/V/vP9mIigpKvViIMIgwoOsyI31pHW9gMPsOeF1i+tR+ub3i9vuG8nCw3qxv5FT5nMGzP96h8gqJ5pC4RQEM9h6V6VjWrNHkOYRrqsNIjI8HNAcTXWWGJiKhwJkiz3G2bQ1WuUKwEDL+2ysCA4g0bhm5YRXAdK97GZnCYLsO9ueOlqjmOjXA4zdGS4rsUk/LOgIrvn/C9F9NIBzb5tqvnySPyeWzwtI0YJ1Y0NEsRIALLaJfLGWyKD08l0sHE6M0MqwSA3clNxOCipThwRFU19kn1PLPaMVjQWKBdIY3NQfPnmpwPetCDHvT3QA8j3YMe9KBvmlLQ1FDK2QFUIemVDR1Qc8XFTkorSoCpLJkfPSFVUSRViSpUKO/XbP8eRUYADPW8dK7EUDVYGuF5AN2rVUpVJ4BRAfecypL0egdKQh2Eoe6272aTiMi8+sgOwtTZ4CYamVdiRNPt3FLvKPZ2n/d9uof60Ru9412qyo0wwkSP3FP0764dpdr5nXaHR3NDGwZNeIwGg7k8n3F6fsLp5Rnn5wtOT2e0kynWt22AZICGgjeAh5gXJzR1VSGwLc0MQafFIC5PywnsytoBhyiSDeu6YV2vGGObOXyYcOoN3E9YTh3LsuC0LOClgxpbnjX1qLttxdu24m274m17xXW8YR0rrjIsT50b5Vb/LWLJx01QDeXevp9+lFbgvXt9eX61vJ+kgajjPeekAj5nBcyCjQcaDzAxVh5oY2DDQCOLJATNqCbiDmodHDCAqpDNWAJGUQCWCNPTfwabN1UnW6q3U3beWSs5zQ9Yc4G6W2GRds0F0tM0yxgAbYBcBe1sEYLVGHS3kLv96Z9pOgDck+3fJ90tXwBp3KJ7/fArIgIyMnb6BGgJtA3TJaMpg5XBzfvI4Y2IXcFLAA/KaBAlBZopasLgC8D19Abf1siiW83DuyhbIh9i5FeNynlkZdRzP1I0r6UWsEVDsNMkp0YU5bn4OxbyvdH/DDP/fYlmNUDIpI8RxkY8Nbkxudgjwz0XzX7Pcv5/XYHzYu3fBFgKw0rL+LEuERE2995cIb6PmyHUIGcREZFq+Vngz3y63bnY4584s+jueyXNnDeZh40M9gpifcMENKIJCVnPP9P641XUHbLpBJfCjpvori7RD0i+mnBbYQiheX4Sqf0Q792XW/1HmDijhVTU81RRGuiq0Q1U6hZrzT+bgbUwobLOKVpJNhfEdZsSfXSHdDc2ANERKhx3GB+lAtUglutUC+OjR29EaB4Zf7E8Zw6rGxMxpz/ZeBPlHI421Zw9qpazaIwJdQkCsEwj6Z0Omnsc1ev7tlGZN2lj323XbjByY5Z62zL/HM8zXjVqG+KkRyiRRbCwzL3Fii7n7/J8ncvp7FWbpWkeTd4d8M1xpI1I6XgHM6MJQzzn45yDzu+LEbpOt5+LYt4pvE/E8kKp+jnUO342t0gvMWdrA+N+zJU+O3Lfrz9qEz8czW3qUPlqGpCn0+XcF8Xb46iBWV9BbFsuO7EgXDVVdMKF121x/ydKcTvScv140jzep4Dv6fOCDougw1WhV907Wt190b6ueRsdfn+jdMv96h9hpC/3b4StE87LGbRcMbYNILIorDhbwaNkhUCNPN0uGZI3zWiy/fDbJ8mDtRnqyGW1hERWi5bDCF5ExteZ0Zg8yF5uETNi/lUHJjL5gps5yZBvDkIEIcJgwirAFcArBCsGXjEsck42bGOY86VcsUKwyQYRxXWsFq1e2nZs7TwmVA+iw28SPxP4uYgYYaiDKgZZigQSc17baGAbAysWLMQYbTFHX7FxEQiIFhB1g9Bkhvk9KhrMiA51uctfGZDUFjELgwatPDTghsGgZhC3DQywQJtAmmaOywc96EEP+lbpYaR70IMe9MdBeUIe9qNFwrGTclE+4SAZFOObYZH5b53PHk58tPsrNCP7byZ2+rGuMEXgRm6sE6CzKfDaXkVzKPFnoeMbdp+ZoAuDNoXKZhEbncwzcZ1Kkg8LJeBe3rm9cPRJyn4/CBzQg6KMDh1VFRDl8/F7wLE7MA10CwELoV0WnF/OePruBU9fXnB6eka/nMCdPT/ZBhoAbwpsii5TIRLKB8Pe7+BuEXSn0wmXywW9L+YN6rmCtm3DNja8vb3h+maCqKqCGQA39GVBO3UspxOW3sEcWP6Wg2CIwaC8rVePmnvDOq7YhsGiCAQbWfSPRZKZOssgMoEZfXJnth0nzE8Rfn7KM1+b+PfKTIVhfMkQ8ZwVo2HwAFPDaIJtDEiTXPK2jEO4dKGQeK5j5ABD1OBdhh4Um/H7rhbik224d8t7dpFaRvmtAHSD5T4Zw9rBpgzvxGV56u73VJsfGeU7Sundp/uc5Rhd8NmovF8DebyGfxrY62Hs3w5TWJhCzqMs1ZQHIFMa7cZIbRxCV6+kqYzuraP3jrEJNhogzOhvFYWCDZIpDGlh3Ggx6e5x9sOYxnwWN1wlxfc+gY789ViuFsjLfPXx/T8z7Yqca/wmT2YaGodH2LmW9nqvjmTOKBFJd2pTMRcvrF1YdW7J69kVbhFJ5yuKCRCDVCRtAA2MUZXdX1sHx3V4WOR3+nsbwwxKZVXHOqzG/PTnUU3lWHRdIh17JF38nlMi4vb3zMdVjlkfgik7d5FrhT8qRT+Urt216w4PIdq9zfI+UhaQhjp/XNWUoarwKDqAiiKSIqooFIPl3Wn4A6Wf1XtkeShnzSyWjX2/cANZGG5jdJRmXeBGFWpmaKh9m4a22SvsTFyljjTNwY1REvXjkVWefS+IuSAioDGgLOAnh/X9Jx3618cWVq53uFr7PupSztjVZgoEixGPWgs+5BFxTIltGF1TgvB8PBmqkS/WoiwnAIbzy12tkbM10X1BO3s+FWMh1PvLr3NA3nr5YRiFR3MApthfaNnBOVfDse475WcnVYJsivV1g46xW0Jz9Vgk3U0UnZUwf5LtTUPdHqkDZWu5x8Pe52u7KLpCFOs6+BEw68EAsWZ+NnUHgx0/IrvHs8Hm2CrDobZntY612x3ZDnW7MbIfnk/OXOZ4nNlIFLqpncXClyTYzL0yD9eFijHys2fKb5HebQ8BZCesEMNV3WDuPM2i1gikDGZ3KhUzEGWObRg8uZDYPk0+pwJG3LERyPcS8+81Jw4LOi+bovNjdrjyzj35RO8Blz1TBZgsYeuNicG9e25dQHTDVRVXGVhl4KqCFYIrBt6w4Q0GZfnDuGLdVqxj4HVc8TquWFUwdLhRfjMn4vruiWp5c/5MOn4PzIVCgO7w94vZTwmQVh5XKHWwElYhdEMqxkINgxmNG7QBwwP4RRWbDsQpiykM6sVDMXgwk0dT+ljFGYasrcxAI4WQGz0dbvzy2zPwd0U+e9CDHvSgb4geRroHPehB3zTZoU1AGUU3FY17mCUAEPecpf09cfIUmspwd8UyBZDdvpcj4tBOuJHSvGY3FFoex4GPpM0YigTcB0wUfk9iPBQ9lULlr7027W4ZR5Xf7hOp7Q4bA12AxtCmoKhnPqw3j7psk2/Qu1LAV+hdw1ylQ98XmjBb9yS/owQelfafTsAzo53POD1dcPnuC16++4Lnlxecz2f0vnjZ5kGIYRF0NKzdlr9qzjliQqeGRh1LX3A6nTyKjiGi5h25bVjXFdu24fX11SLpZABikCuWwJwtBx6z5WPxcR5DsWFgE8tdt4nBn4QxLj2nYw0QYN6SU+ipupmbfvlD010N4Ccp6xsN9fXPliUBMOHb8u5ZniSBmsKSzFtUObxGkYqiyJOjNOPQBAYPeicA5Me346hN/IjKfM0WEmZwRioc7W9iQDvMQBf5jvz+vX+xK5YjukWnovle8z5uSPmc00sTgO2XVlb+nBQ9E969mmuKMwoj1r2IgJr/1sij4xHIpoM3GB/PXaTDjUCk0DbDTJkaWme0rZlxOedi7FeKGTpQKnq3Sw8XQxOosP0mtF2htZLA4OJpo6oTDYzMXXfzYsX+oTvv/1mpamtKR0ToqStxENBHUX2p9/uCUDVD3dsKXE8GebkzYNK83TR4u+hKyg0diISk1AAddXH6nnRUuGY77rUx3usMOhAD8iufA2n8AeBG4jAW5PB6n2icbTDPIFph+yhU4V6uhnGiGC2z3ofG6KxO7bN7bXtvyqZHfTYx+k5nV5AZmwaNaQQpEXQV8tLQR0MdCIMBzC4Lw0z0q+a5YTbNXlijwuYNtUHTUET1HvL8aLD6sU9EdSgwu9/3ltqdvlYNXlJnbp7g4SgGsWohwIwWNIWm5/HxcW1uOAr4zMi1hhfC8k9OVl6brKK22qpX2l3GlmhGoE11LpW54DtBHtHV2U4MdsszU0R/xjtiHsTcOM6cOTcJsbPl/p+ReP5vzhFMOLoyXLb3+9oB5fOgQJOI/jUDZ2A7B+xmKKFnmfQpEIGfTLU/FYAqxnVAPR+t18Lu8b0r+0+PBcnkK6Dj4+U8//Nw9jx6U4UZrcYAv1HmuYUw2SE5tncdfk1UCJ0AKwcedOBiBxhd/KSB2u8qZkDWzRAYMBDpJvNm1Tuv0TrtSxui7F9yO/0DE73z944UwABkWBSXVnhq9nOZWXn8p7mRTiADGGIQlJsMvF2veLtcsbSGpTFYG0gYUFsrNg9iT1RLVcAxUOrHrckzq/jLzOgduf+BTL5T5xNxFmltAffuagDBUGBTxVXEDHU68MPY8Do2vA430G1X/LBdsY4r1jFw3a540w0DCnjOzcrXdn/HpDzuwfU6Dn/fHYw8yPt8NOeEMLQxmcMDD4tmZII5pzWgOUzmRrDoOTU+E4jiQux7m5pMrZ7fmRmRj5bAdqYRdzKNjZDh0ZMOQyxkELek+Cf/6h/iL//yL9+bWQ960IMe9Kumh5HuQQ960DdOCnNTVEBKBF0ImiYB7nR482+d1yQO464QqdCWRO8YvopGJSXKohDw9xZH6UlDHWIxvNitPhZNB3wkjb2j8/oKTSXVfSodZPgRgENyoFFGGNnL7719iqdp+KkS52coy/2onpEPIPp9KnM+lTODMCX2Op4E4ETAwuhPF/TLGeenM5bLGcvlhH7uluSbFG/XDbwJeFvNi1uBiLghh2oK73jlBuKOzguWtqC3Dm4RXSAY60gj3fV6xXW9Yoi4At/KMgNdR28NrXvSbe8ugWDohk1WrGPDKtf8vMkwb0sMz3G3YeiKMTYMsQg+iSTruzx0vwL6qCofTf4KI8eW+4Fd2IMSRMX6YTC2xpaHj8zL1qZWRBvCfxScUDbzxa5u3Cnj8ovPdOWPXcCEG5aTdoEiYIeSMe8RV+C6cf3WrhP8yiFiXNGa8GlzWd+tUm3OXrm0b6b7OufVg0rsV0wlBx2Zv3XE1jEINEwRp8PWGi/saxcAE5jFIhc7mWF42E9Gv6j1eeRHbI28r9i8rokxxij6l3Acofi4H9Dqer+LZi6TJjccdmVXaa7nv8oouzphEOXEJHMtS1UO7X7/gca3aj5376ZZv+Z8jvXO9uL3bsMMddsGDPd2T63UjBqjDL9B/g4DkQV02X5puihNvxwzlth6zC79kA/Uvq4a5sM55NDNM0Y61nwxNGQ30TSSEBCQXLZti0f/2YrNqLxwYfczVZaYHu63jakOALvrGicF2t0XFUyFfdZdMkrVmqxAGtdCaaqZU+fYi+xOGFJyCyaEIalHShDUw26iKewNJoq9QPcK/Tg2AiBHZND6YtQciAfmrfWqrdcw4VlOoIiYjXXsc9GdvdLoR2xQZ6JQdkc0EYzISUY258jzdpG3nclhRtXnjOwVz/fn5p0zMOnNN1ZdnyO0h9xM5yABlN0hQBVEA6AGIs9XSMCNkRlwaDSaRlYbzFmlPNrt533kOBQRM1oint/PQQ0e6V2gss8HmI4WQyxiS9j2TY+OseVfoeY9S94v6Zji03pct5nvEmVuwZFu82ox1H2Fonf2fPbr1fmovMqpa3E2RpTRiUc+mXIVUULP8v+fvT/plSRZ0gWxT0TV3E9E5q2qNzR6Akk0iO4mN1zwL/Dnc8MFAYIAhya7m+zmq/fqVd2bmXHcTEW4kEHFzM1PnMiMyJtx4RI44ZOZmo6iop9MCKWu5LaWXsM8vTF3Jb7VlspqP9OOY6PYuAAivYKsCvlk3nTYdMpsQGY/mHy0vE7Bab99/w0SIViInrdVTV4y4yZARTF0w1DCpoSt3dBAoAZLA6ANCssB/Mu2AlD801/+I5brBR9/+AghM1poxNgo5qJ1OHtlFAoRsjWe3l1UznZIXpLKfFjUBGZ7vgxTwoknVaTWQL2DW8emls98U2AVxToENxG8ysAqpqR7Hav9bRtu24Y1whLDWSnUw6DrlPWiR2Ov/lzHWzH7relIerg23qoCOrACYAl+cgNIQJ6/b6gC1IzfKvvtAx4VHp8UINrQtHmKB5OPB4DLckHn5v0p0DFyn9fwuhXzzhsi2GBKzkHDwnLKWzjCk570pCf9semppHvSk570fZNWxVxK3Aku7s7FRSmnAb4FYBnncs8hQhRllFN/4HVhaZ1l5jG2kIM+lJDLPJ2Rg3iAgYKhsNvIQ1+2/eHtSLsT7rs6CRWZPNZ4V1SEUGp24NDOoK7mUbeyK0IPFdghOwNnwOFdfelw3w5tPlKgNHuA6A6ciTflHJ5QRfU0cIAviclCXDZCu3RcPlzRr1f0y8XSGA3x/HOvGEoYr69oouiiELFwiNwYTRXD50akUjGrcEbrDa11MDfIUEAFY7M8MNu6Yl1vuN1uWG8rtnUkGNn9vnZZ0JeO1pcEmBRiHn1ih7l1rLiNW+aeUxlQtbxWQzdsuvnhUExZdWrK+87p9dYB8Ivm5lcsYz+RDz9FYne1Q+Qg9MEY24ZtG5AxTHk5LPl4gNyJ6UrxYkoA8fDQ39ruz91/XALx9Y7HlSg3auBsU8I2BNzgh1v3osHeiwA7YGwGetT46ezZu9cJ2MYsCo+P2sTvBW+KoY8eszWpqfBlS64BXQXjVTCGgjtlLh0iyzOyEdAvHQoDzodMzzwEqJjOXpTeHUymYGYwRnivqZqjmA9MxfCnhhbYATUxgCfgV05kCuC2GKdUj70d1edEZeJ7OrnmW4LTdd8o+85xwkZI0cCSztaaKnDbgNcNuAzzpiPOaZ1eWrF+UnSwQVQPI2jOKAE4Sipxwig/9oTIlznruBvMqNS+jlR/q22dZF6+YvMq9ahzXCJX1qmtTe06DY+WqYBLbzXV8y48fijbs/r9WjqP6pih8Apyf6w395n9Rn8MiRk/xUc2FH3Xlgi3SRRK1vB6Cm9M44IaMhyOvU8uM9ZsaOW3nZrE5sVcX2JefHBvPv8u16R7cxSXPoRiXKCp/ps4rfWpqucjG5KDqY3cbiX8mS1sbophPkmpRX9W5VXpcsxhnSLXnA+Uc6aOA80bw6jKlYM2buKKa5ub7MY1NjV016/kCr9Q0LXMIWu/M5khzm4UyvyP+bfLW5hjQ+mNllM35rmvc8JU0omKzxJTkLKq1+VkToe88LVZ4exowNeprNvOi04Pzzfjm3Avy5WJDOcdm1Ip9/jQY9/Ztycc4WQLsDyZWqLialTMxoBcSVeXCmaV1ecFDvPTFLee5yqnwAx7eez7DOVZxBaf+scq30VT2fElmp81OlnIDDBX+8PmSsPjYtp1UTVjsofOeXhf/9+Fan1Pnv+eatU+mxzR7s62lbJS3oS/GRtk5ZSpwuOqdcI2GDcAjG4hFX0sNPiGCNYBbMPPRCnDA6GMCyWbs3uY5ZUdzVVHyRdsij2NyBpQbMOMG4gYrbWMRGFT0Ark5jyrtfxjn9A2TdQUdqLYhuU+3VSwDcVtE9w8x7id4QRD7EynET2oLpQ6aGfb0VsDdKS6do/zloAIzyyiePV8y0JiOQDJzpcrFDScPxND2H7vrFB0MJEhBqQeqSRErtgLaNqJaRhFzgqLWq7dIYqBAdEB4ZHhiB+IOE960pOe9Ienp5LuSU960ndOftQimNHyIZzlRAwilGQRYBPkcaAlimyHa3bCXkisM5zS/P6sdkWQJmSuDQXsIO3hOzKUZDGvrIeWY+lVbv58yMsAKAIweCy82iHZ68kEdDbFYby3LNnljnhfk2Yp0oL80YMKUFXruG/h7mS/u26GICrX7wALTMtlmp8plCy1zIWAjxe0pWNZFlw/fsD1w4vlf1sWcANkbLitgG4D2y8/ownjAvOUY1qwLJSHUFXFgIX2UFjegG0MsK6AKjasYGaMbWBdV6zbhvXmnnS3m+W6IwJRQ+OG5bKgXxZwa3bgg4VTCgWc+KFteF4Fa6xnWfCwjiP+xvAQMDrTOBagdPYyuZXmF9If5VTkC0RFXN1mQKxZ6ncLdTksDM4YmyVgHwa8JYgL5xrOAyjK5Tkv75pbD7b06KIvpMeLdf/WwSaWshpFoRssLAxRhggSwPIl+ixVFQhG6jKo/IPqF8yFA3pVKq+n1/zRST1noa2nUI6xAlgV+kmhrwJ5FciKDN1mSnpNdr4tHpZPNIGkVAAzoGs+zreK6SVkESYdRDJ0x73p/GYiqNB+vh1zgt5tJD5ZKj/kyL9oYw6B76nlmqxQ4fcqcBQG+0QovwdFJx7nVHwX3oKem+5USefonKp50a2bedWJ57cJA53dU0LJaup8YwuCGRoXc82EskwB4aiPAplbsNTjEdOo43ikI2BOlpOFAqyORa0EsCmEDFic47XTD+YjZzjF3fd+YeZ+o6MsNHuoeilFuKpojKtlpvIzrOMVuycrZFf+0fPJlGuadYswcUf+GF5Tw8sznHZfbyJXvlObyh0yIDI811Cff6grsF965HnXWvSRaspPJisMKIWSyct1zy0ZVk8mmn3sa3sqgjSfSe5dAQF0yAy1BvO4JzVvXiLe6Wco163g5b/9AXSZSrA6Ke5G+GgdUvU+5fsYk5SwFRamUBQI5STcIyraFLnhCMjoFs5myPcydgUd1xDgRBZ62AH59LY7zFEpCvIZatHHMxSNPoVULAfdHDNX9HkHhgglaspaVm/TUUb9Ztue86cNFuYvFKCHS+qZYuZZLYE4694Q76pg8XVrnNtMylUUvEXnRQ/43jwbaQpqyfOg0Ig2kTywrMvKF4qsM0uG8/vJvz43dDYvYIq5zcIzyk2hw36oR9FdCPHckij5k6by6K6xVVT/8l32HXLkW3S3bb7j+t2HXAc2ERlhsIE5/U7qEh5bQLPw4eRGDSVscyhkpqmEnb2IFIpmZyEZGLK691vEy1CXq6xa3ADViAbj5yoZXm0GoVtUXpr7kkBmJB40hOoeCI9fBjUG82LGOaoYftYYqljHhttYcdtu+DRWvMoNv4xP+Hm84pftEz6NV7zKzfKXq7ULMr76mnzX4Na5CGQdRAa2NGQOAdk80286sOjATTZfAOQGFrZOhcIYhyxoD7oZF/YGpmYSihs2kRutELF3ud2b45582QxR/u6/+PHr9tGTnvSkJ/1O9FTSPelJT/quiQgWljHACwlhzU5FCYygKOHywBMH+7CURrl3HmcDfZgZR0I2vT9R7MAIxOGiAARxGoCDKptYGJTuB7oBO+j1CUadNxwPheodmJjAhB0FrX1mMUsPCiEQlBXaCTTYverYQinFs3fGyicVqQrQR7SrWxRcK1LAgvq5vuaZfo+EhNCvWWG/8ADcAQBdFrQfrrhcLrguF1xfXnC5XtD7AiYLX7dtN+i2QW4bPv35z2ggbNSh/YJ2ESwEbM0O2R1sBzAwWCxYHo8bRAd6swNfaw0igm3bTEk0BrbNQlEGsSfc7q2jNcuRRIAr3tRDn5i3XBzsozsjLr+o56uTDZs/Zzjgdd/PDntUa/5vhyx9fTqtanjA+afwNEhvQnGFZ+m/wOscXBEPdcmYobesZOBf/9t/wD/88CP+8c//CP7T3aPPqnP+ebdov4B8alduBbheR2H5UG4D3BkKs3ZVz9EVYEbwshqGj/2fHrwSfl0F/9hUQa8D7JwAwAazegaApmS5Zj4J5C8C+cWAOXIELhwDCtwIvdl6VIKHM3YYR+0Z5MuemVwHbIUc8O78c12dwYgKKAMzYY9fHHvYsVF5TU70/e/A/C7yvez4eUCNBeDdzesvhfN+C501rrxGuMsIE8jwnDVarotyyMznbxuwDltAHQ4Eaq6PAPCyS/LZ5kWjEaPMB1hdNgkVXip6mJCT6kjZfTI/Z7cexvSsSwgGZnEJ0UXwUIHNpJScH8FAKNuqIoe8YPM3aLTBqxZGDUV5d7xGFJkjDwX8PrZ5Qqzzy+rJl8/x8utfrI1UppXhnUY9agqiqKt7pYsoUmmIGSIvPLSIye7zzk1lICkiVKj7/iVYaLKlyY5RnxNdJsIjIa+RCO3pYd6YPQwbilIBJQ/dDE1MYu8bDAyOfJipaPIcXsSUgSTIFbdMhJb5CP1hdaTqHpkdi12/Jyug40osjS8ymz3B1k1rDcRuiJR9T9kOUQVZYDu7lyzUpynpZp1s/s45WZXAnKEQTWGsqhkOOzzHQQSOOV3KCQ+9nYdGXR/+hYRyMPYBxbcNdenPVR2QdUA3qyeHDIwDN777oMiQpVHPIvvtPcse856j8vx9tS71ONmGds/yaZnryw1J1OcKoa6v2D8JyjADrVjLOqdgc6UuaJ6GYr7uWG6pSm1dbC0umJuC7qaQGzwfnZ8Gi+1KRk5Xmh56WQ+UBhb6tfJhvf+kWOzG9nB5vad0xE7J+Z6qEUwu8veTn5wU4L+3C/Dy95T3OsMDkYUEbyA0Dx1JnstSVLDp5ikILE99Dz5CSIXbMM0piBi9LYB6uHtX6hGThQ+Gq3skeIYHO/e9FURQz5NHMF4xhhRDDpMJmMn5mq3FbRPcdNif56G7yYZfdMUv8oqf5BU/jU/4JJ/wSV9x0xs2Vy7GBE/l9HvnxG8Rxx4tgLn9gMj44wbPlU4DKzU0CAY2bNLA3F0kIjS0HFtVxSCPGgNC5w5SBaudQeD9GwpPY02u6IOiww1S0TBI0VUxiNC44e//m3/zGxr+pCc96Ul/PXoq6Z70pCd939TIPQBMYtVIQh+hW+JksEfUzJqrAgUJ8jgMtzMJJqRWQ/eSMSEO4CU4UIIR04suwrjcnUbs9GCA4FBgA6gJtLWdAB4Q0OdoJ7PvQJVyyD65R88+NVj/NvemWxjYmiP/wNuSf1getzeuqmDQ4yv2Fsnx/uwe2h0g55l3Zr7ZgU5es/5yxYe//xG9LbhwR79cLDylzytZDfwYtxvWXz7h9c9/sbAnbQGWDYsqFiIs1MAUvkkG7osnzlhVM94+M3t+BTXrSM97YK9h/c1gthx0rVs+OpvGalbynjA8vMQUmocecSxOiCBsuUhkWH6AyD2zP4CH5Wd8/Xt7w3xLMhRFHYQeCu+gFesg8NrQuaG/dlzaBS/rFdvlgk02dG0gasZTPHWO5aU3bwtW4MPLFdyv+Pc//yN4QU7pb0bHA3Np5hE3JbWlqq+Cfu1uITwCujJvDQiaK+SIDEhvmUsIfiiuXrJ/WxScWxFhjq3t4R114NiAKxtoALQK9BeF/hRAxQSEylZi1P33BtuaGhIpZAA0DpVyDhJAY6x9gFxBNxew5UDzzxz3In8/nZAZxrmUVRsAJAhq5Z6gpbtnaPn++HzgtA5flQqC6IDeca9OwC+8xHce4TQRV1FT0r2uwJJm88XgZ38blCzUJSJ8mu/1RFCOHH92r7qBxPSKirx3pQ3Zn9EmnV/XPfCuIrMLQhETngdK7FND87eIbEpggAUkh3yyx+cchzaed6552t/kwCa5KBX9dFeGAqbORCQIKnW6a6p9LJ5S1nSd86D8HnIcRTKr7EtyhZjlErMhsbqaKtoU59RMuz5T3bicuJMxq6xZ1omWMd/JIV4GhxsaPDece47lv9lfoZCbpcx1Z6xDvf4+rj63w/seRCBllxkMMOVm8yClo2R6VaA6jrOvB0Xyzzk2xQ8pgWUkY9RSNBHQXNlmHieW+ze0Keoh3mZbw1PZlaIiNkZc+to60qutuZcRkXmbhce893ljToMuBpmRk89bGw9bw+rl5XzNsJwxDxRwIPmkt74BTV6t28gQpzm1y5hMTnzGp3G/2d2NN+1/90Jj/5xXHeaM88jy5XxEuS5CzyKG5Y0OO7LLml9zX3WvWRjPAMnPs140FfO1Le3Qrjs6bC0qgHoOOnlFyvIAZXjPYi/mkUUtrPV+7cz3X7RrvsEjv/S6u+4v2zjt2evb5HPqbtuiIiOd/E5EaMt+HqkotCkaMzpbnu7WGzp1QDnDQJKYsS577jkhxTZW/PTzz/j5w8946Vdce8elN49awplDkngaVgxy761mg0ZF4a9WSatns3xr4Z1quVEVAJuRDkL9zxgK3CD4BMUrBK8Y+IUEv/CGX8YNP+kn/Cy/YJUbVl2xwdMTuAxeV9luhL4Nc7mnOk9jERL8jEAYHia5KWOD50OHYIWAZQOxogmjy8ycO5hsT26LjWljkAAslu8TrOANYFYwCzaZZtBmqGE8m5nA2sBuUHk07nnSk570pO+Jnkq6Jz3pSd83UQNaRyjEVGbQx4KyYCoePIxOAjqUwJUGUJoWypEEQYts6qeOMG3OI2mASQQzvQPCOjNCkgXkkqTk4VEkQ0nSYETi9AANyrFg33TUs1NANgX4KkL9/kBVkiAVcGf3BHIL1KZWt07A6t+xn7AivFIceo8H9TwxHypQD8sPaOfcdXft4XQM914koCozaw6Z2Yf34OPl5YIX+hNYCR2W+JvYFG4G+A2zbl9XjNcbaFiolDEs6fetdVz7BaMNdIYBs2CImmJMB0CiiU6KiCfSjtwm9VXRO4N7R+sMbvanDkAJ1PIEwixvKZOpW9sHEQRkYUQYGI0tb557cCZQqId+3M3L42/fIdHhTcG543CdOQ+Ywa2DW/OcMtOyPkiBVOI0MgVuYHIY43F3/R5nRJrLI/Dn+uhoqxCBlSE0lW5pLUyEpg2EUZaIzgTtf4OUHNmVrgoBa4SdqoGT/DpjBYB70dEnAKspQokw2apvKeo8UgWgYdsU1Las8B2O62LvYQ5APn6T/ZpVB33IgWSCGVLkBJfzubgDVx5M1tQ0hgbR50LukeU1uGp6Wobmsfbu7zVz4lln/D0QXxcEasjLu47yhbRt5k334eKhKeMROxQx77cnhqdc9OFclHmrioe4hoN4gU76PbGf4uw1PlaUtG6SencZCOYpppJg8R5tPevGfVlaL63IbOwl5brTWeXgeXOZycQim/RMYVzlwPwOUNvvR9VLL/8Kks1s3ushqZl+R2cXwb2oxHKxGYBn9SPmlIBScRP8NKc7lT6Eh6QEFFW7Hokia7+E15sPm1buHPNnuDhGjnnGqz1vvh7261AWlT4Uvxek5oXg81DUvDzCs4y8jo0NoKbyb/Z/HYPD/PL+yxWgJpvUpiW7OdyZxQM2Jwip1CY2bUqECVaEB5v4Z5OtFDH/poGFeD9Ert14roWhpBm51tdujIV4TjxTaMfwhxeWGbKQp1qWobvqm6eiXReSZ+avJHrIar8apSITkGEhvGNcbDnPwZjcqFIZHSqz8sAmcifcMQTYWQBIr85cp3F5Fl8Uh8ffYo0VZe5RBK33atzvoXwpKlimLDFZVBIcFIg0X+t6qrOdfB7EfM627CpT6hJvBiCbmqIuwlcz0oDmuBtq7Wg/Kyk0Q/c+mjplaHZXhSwX/fOwAGAqx/aF7Z9xsoVnV7+xjeyur3ttGb+ZjmE//x7NUqhAtw2bAmA2owIyhR3c4GQMgYwBGgM0gGUw0Bdw71h6x9IYDbCztXsWm3fcSMNIypyMYdhheS6tP8hkPIIpXRkg/8LkQ05ZTTAVROJKQ7BajmIwFN1UWM0ZjnAq/O08J77HiHv5IefFbkx+LxGrPutubnn9VECq2CKPgg85YyowgQW9dY80YxFgBJohL+2822HxCHwHqFus8+3qSagKULOzXFPGUAZB0mP2SU960pO+R3oq6Z70pCd938QN1Lpbug47JqsA0jw5k3ieIN4JmTMopYEykQshQY8AQwGYwK4wqZztGWk5P08eabgfOSlg+QXIwZKUa3cnO7IQW8MVdENAW4N2PXgvnFA5ZEWgpqrMCqCqPKk8/OQEVgomL0CZLNTlwqBNgY0tROe4v5cipkko5+qD3yMtU3mJco4/HigPT3lVQjM5zmlGG0J7WrrbwYGZ0Xu3g5QEaMNoTOiNoNIhTcHcLFoUCDIsjMcAMLYVY9sw+mYeFQ4eRO4B9VyDIgzlDa11EOm0qlUHhYjRmlmILktHXzq4N1PQ+VkulZGuGKWNoMMtyskTeZP9DQKEzMbxPGhhOfQdvU7+1oiAQEqZzfqVmoUS7a2hs1nwU+8gbqA20anwbABMgdvc92yiWTKfEV/9GnoAmLz71vrcANzcultX86bzFCm+LG1tKCwEDRRgKbkVYcDA37o1aga6csUHO7SgsPA9w3urCUNvAv0kwE8APmHqpgKMmk6pRiX1D4XNhyOCRJZaRB3gAxlobrxBJhBW44352o+/2gqb39MY5S5U4knL7z3DSuVKOL3peReoSDROD39nD/oNE/uzVBFar8OpmX/pw6ZmdDKOdfZXUeD1BnzqwKUDL0tBcUvB6ZFReLnDUbl9RbzEXQ7Q+kgCtCEMhYC4JsavLMa871H7ZhtDSWDlPRqTanykxQHM8+qdILQ7f36fCzWHZ81Td7gRLjlBSWaoxmzvlFVIQ/FCmDLZvurhBUZZXwvPOfOFCUIU5FBiq4fvzXYaAGtOleRiAs2uVM01xkSlfFfQlfyls98BFU750fjw7EkVzX5KPa9EjjSdIQpNsNnNTMqxLCVqyCsxHrSri71h3/qG52Oa3gfk/c1qhikc4Vc/x/J191K+CKF0mkg9WoqxJ6dHnIf3DAWdef1vGLJCVcDcAB1ZbirZQFMJGjXxDyGKRrjXrGsGC7D5IjJMscMTkCeECG4h10yW1zLHjtMy8geGPFdNPb4RueymQyDhSXf8ubC++a7Wfl/D5APlu/0YqrMmX+slLG6M5a5UZzJHGeWt3eDst2kLqHt27zfk1ucPzRC//vvOwTUFpKmM2e8EqXLdr9+4QEPJj/SIGxHmsmxHWrbgrGu2R8vOGZXzeftG38wWhnhRBviRrHZkxwc5JW87Pvhk7szewl2b7mSfHIM5L3Ibre2kw4N2HWVfqAhUN2zUzECxm7xjRhaKIYJ1rOBN0YZ7QjLQmcz7jtg8diEQ2TCUsA0zwDT7ouBDmmFuAVP+1P4xrCFyZhLIc+XlDCLbk1TMSDP2MMufxrFJ5NgLBEMsb/kQwRgD69iwjg2bDAu9mVF5HvTRX5OcB4FCUrbcp2OYIm6Q5f5euOd6NG9FnWs55JXWfG80YzMmmmdX7/uIThNrOhXbxCASC4FMDBolKsSTnvSkJ31n9FTSPelJT/quiSgUZ+SCmvhpTaZ1WjlRU4TAAcyiTotQHi5JMMFvCuEWzqJazxfYpJx37c0EyHiKiLtrCkXdPOQlDQ9zFNb2RVG39xKrJRH2Xgyov5SjeNp1Iw4kb4qwbjmIpmnlZ151zQDOEM6Bff6a2sz3HiTqGS2E9vrDgWYvPHhoHGgUAAlUaX8JsL9GLeSkbopOliOlt4YOBqnHxe8doy3oF0DHgIqicTMr7iEYm0BIHNRXsIewJFGA/Dr32hFxIFTNZpC4obUOgMCtoXG3sgPk9XkQVt7sh5LODYs0rGhYMUCww6nF/TfwLTy/BrGvl3GcQH+btAMm1AEZBTeYsq2x9XU3hWjvFl60t+5eHpQhigLM81P1bi19ZhX9rhRVTE8tAfAKyC+C9mIKd1bPWeiTQFTQNCBbdvxK/F9AVH+cNv5WCpg72hQgNwUY5P9HX4bXKg9gvCrkJ4B/ArA6B2pIXYxyeU7B3aCYuWsw8QktrAowPkrqYDAspO3ebJ3S+wT1b9fAyjvfs8g1AUsAFj5ajqgq7d7eP7QALr87PWhnBbMSyS11bGxesGdlqe5DXvYafpoOl8feF95FNigz2raFvdSOGWo4EhN57psc25go4S1XgD+UGbmne4R1NwVof2V4DgVQCApgzMqew15WSSr8yp5Za1Am8U5BV97nujpgjQA5AK8+Fe0fC2c+vV2ZQ0tITw9hmCrAk94oQKl4mGglpBc1EaVyrHrYKMg9HgVDKGWc9JBwpVLIiQFGZ1uiMqHEFZMxALjHGGX1MoQicdYh+j5D4eU8jukwZTDR6It4rO0CkW+N1XOlqXqUV+sz83FolhtTgZHrIdZz1LEM+AOWQj5PtUy+NICIOmc/R47nvVGIYIDUvL61qrgIAARKo8hMs8zsK3ZwXpt3jfc3xbzUmV9wdpZ3rUCU0GIdEkHVRsI8NTXFbBHL7Ut+s8jcJxup58ibz/ymopbLnzqG51b09uY4B4WQfdh0opDTbb7I9Yr9m1DOS1nz7+X/wZfu+Mjheyrzporwh7oQCJlK2bdMPoR+ztytCosQQpTjFx6BZmCAWWaZfwq4cpfm3u3nNQwxBV3RpWRTzizk5hS774Qv2EMLxz4Z07Pn+oP58H1sMy4HnM3YB0OWt99dXD6EYvRo1HC3lx4ftnuIGd0qFKsSAEGXYVFJYBFKNhl2hlbChWLdm4KOPddc5AJXJoyxQcV4nuUntcljxiauemN4xJJQ6CtmPsyWnsjJoIksZDzbvgpuQO8QbhiNoWOz1AMqlot8bLiJ/b2OFa/jhlfZcJNh/DjCZf8R6VitmM++J1meQN1ft5NbbS9vYThJcy9kASKctBmlmcFchB4GhceihyIWC9vcmvX/wg1dznGRJz3pSU/6o9NTSfekJz3puyZ2sMWEfw9bFFbpeQiYwIMd8Pxg7VbRu1NfBVtMOjRBWwzU2HkvJGjiB8WqKIvwL0CCtPdeC/4q8Hx0arnfNgFtDO1xWDoPeTkBiobT41N4PZwSTTzQ7z07gCkD6ATqDVisbhlpi2Y7s0O+4LxYq7lr1AOEUfcXlU/ewaq7oZyf6bx75oUO4Pl8YA8z2UNJR9AuWJYLXq4D6FdT0g331qOGsQE3rOBBABMW6mAFaCjYQyKl4pZNacigVB5GDjpxjwv1V1LLk9PcClFg3nFjCKCbe2eU9gfYVxTPe6CrdF8B/e4H5Y3f/ui0OwTGdwYUMDf7ax2NLfTK0jp6X9C6fTbFKwVq6ApzpMJOVD1fyTyT3tHxUPoGvevS4w+PxsfbHesgvLR0Feg6gAZQi1BeSNDBgvW16dVAsHA0mP7Efwu051YOMqq10YJ9Rja6ihOakg6bAjcF3wDaMFG4dJ3aL7HyoOS1EYHufDsgNA+5yuml5KCF82nzqi0TPF1F/Kmx4VBU7Bj+8gwk0/vf2ZXUde86GmIAAHFBIWNDiTJr2acI8FekKDsqfNiXYqBULfxYU3M1Bh2udxR3G/Ynap7urQzc2aOLLJC50XxCmLOc57ZjnVG0ZUDVY6QC3peKqZU5jM3ds+cMvftFi9JNFEIDjU0+UfemiuRq0mIvKs2p3UnYT9biVqB6AF6PXeP374upXns+p8RURplbCp6rCQDBANYIcSjwceFwBzkKEFF1TR5onmojw1Ta78exr6W415yDgOqdQgSIRC40C08NBxmNd7oXgLeTQpGh2HlVxRwBKI26bG8RV525cZiPAe00/MAuv13wCK28epZLZApmkogooIicy6q8y2urI8LrKsogPRzfKjdne1Cnaq6E/N0mRfES9f4QtbBwRAoVj0JBcMMFdi81MoOItJkrnlyE2ebk7dEHKGHaPfRpyEw+ITTqgZlfL8RRYoL6fdEqqGBo6a8Y6DqPgu9/M1LoUMi6QTapHb8ft92gPFqvPod0zp3zpeWFab3v7DrcbwPldwpN4lnX5TXllU6eFc+IXORlP7T5RNPBPOaJOy6juSGOl5sK1bpF+jkyMyYQZWh59VDWuilk9Tx0m+48pt8UoX/LvKh9f/yK9l+kaODrJ7yP4po8n5Z7jwpKX2qAHs6JRfzIYop4cmxmelo+2LpOJJHyhUVwUQ9EujFjG4sZHoYBRmOgDchQbBjpXXfjhk+3G37+9DNerlc0ZjMEcKMNs1tUENt5S1kRzl5zPxT35rOWaij6uTbWIqIQGESW0xrMEI/OoQCGKNax4VU33HTDqwy8jg2vcsNt3HCTFbexYpMNImPKB4+3+z8c2RzzcJ3BJ4MZBJ+uZ9UwgPGTSIhrrBYCM0IOe9Y/DFfOqqopXtmjxpB5L0sz7/hvbB7xpCc96UnfjJ5Kuic96UnfN7USdpIc7amnE3WYWWUa+isAlFBFFDACLHRGBdni1NFcoTJPdQUoCUEdu99pB6Q4mELmKZEWyiFxe8hLkzYVugKIkJefFcoHCA3narbDAQrVm84B1tPyHWwJZSfDch8tDdgIOhQkY3/Ck7OnP6Y4PB5OcThWaA/4xLvy3KN1/xlomCa6+1IAYIhZ+xET0BgbgJsOXCBYlo7GBOUFC5EdDFzhoWKhLNkBRhHC2ABcDGw3Y10GBtza0r2UHFQbap524iFOxhCIqE9pP+xxQ+OG3po7WNhcGhHO66THU+HogMOM6c/plZD5DQ49sqPz6fTHpwNIEN8xMxo3NOpoZMq6xouNVXgvUjMFiXAq5mKNU+SKFJ1OrmfL8wv77N3dfHzQybClR9fYFyqbYvtphW4E/kjoC9uch82hAQdE/TW+A0wh+bdGAUKJG2kws1npJgQUmB+jD4K+CuTPA/gFoJqDDg/4WJBfUPGV2BLOK2YFyaYYwwBTdc8qdcTY7FFoFiZA2mlkZXT//r4DHn2Y9SCeE0niuwf1BjBjfVbYrtLkwd+WHBk/PipAXKjtZQ0eG/hYT7+mhrxcDGyrUUTn7k15H+VeNP1r2b2mlOZ+a/zb5AaNnH8pw2RlT4aOUHOezcm07++0z6k5DXleY2CjpAfMbh+pVSivtUtDhDl6SOwURXQoLIxJSvvmr+p9NQfN+LCFgGweMUHELFcEwxRTYnMuvNkypGbZG41fR1sprfUB876oosQuTChMQSeiUw4CmW5TTaEm7kkXct3cX8lzpIU4EjwlDGmmh6CBukAG9sqwu6XvXRmo7rFFwFSAanTvHF9RgFlNlgxQ3ONg63BFFInZnrknlKjLoGWUckpUr7NjbqQcshj3UJJFML6jxOXMM2JUwlaSAu5FTPlMC08360BkshVHeOqUlew1l7jLWga8R45ZB4Vh4dIo1h7N6BliEDEUBFYNhw0QLBSx8V+b/KRW0wjCkXX0c0IYu3xrEUrFlKtSQl36sL6PvIGKCDF7+C0fdHLfW5/jlmBtp48lD6l9UhTVD/5mZywwX8OLru4xNkw2n1jhQVbK2iYD9pl0t3aOus25pdkb9XkBUcgGU9DdUPKN3vfDt6IdS30kg2D2SnWirLvxnrfPPMVBFA/TFIf3RLuXOVVq3/mDyZ/3lliixy9jYZN6FJcbBITb2sDNZXbPOYduntEqhE023MC4ccM6LlAGBhTbWCFQs9fxcOWZjzTy1ud4E9KizfcPItr1lwJuyOtyEzeAuntMe0hOIqiMzEG+iWAVC2tpHt6RG9UMVDQMdvTYIed995vpHUfCdxVD7rEtgk03vKpHd6GGRhap5MrAwgwhBl8WS1nCZjBiXRgT0o1k1c/psmHdVgwRtB7Yj/3b1BTnEoZsNA0wnvSkJz3pe6Onku5JT3rSd00pV9cwIxHiphzoEnatpxo/gRF5YEo/dKvqFLgxobhpMBsPpRTk8zSqQOa4SyXdzBlicmc1x/XqDLVcb5sAGwGd3eDYQ+z4yaYerA49cfrLziLy7trPS+JEBG1sVoBMIFrt4NAY4LE/uP0aymrs63IMIRgjMVEr7F7vrfl3R9AdUHREDAYUqw7LNJYHfYGohQ8SD1FGABpZjjjqNPOUiILBaNqxeJ6zpVveMvLxa2rhUZoQeut2Zg3wL5qVOCA7CBjecHNuOlIHy3EXngxhd28H4fSgk2n5Wad94Mz7HjuZCw8O0t8FHaa3nbnNupW5g7mjcbf8dNw9N90MMRrhk1QU7DEMg0dg+MGcaeJCQScAyJdUeUdHEPRsLGobDwVkHRTQAWyfBBjA0hfLM0nDr1O3QvXwiqD0ovtux/8Nmn7JHhSO9jwywX24Fb4A+qrQXwD8Yg48TGdr6EC+5nIYjwN8BlKpgRkGPLqyPeNmlnxgoSWO/SdAJQqji4Jw0sFw5XN1zvsYNtnrfbR/H5+DvwQKuPOGRkHmvhU9KHsiafahAm4EH8hEActNvqffNgt5eenAYgCeCwmYgB1cGeL3psLN9x2/3ox+zFOvKWETugdZ0WCe/zOP6gSmvZ5aO/ysybMvTAyRHeCqu4cqRAco/809SI3RTfuWAEr9vjme0V8lF12RrWbPqnuIooDCc4EEjy4lelNtpTYicO9gIgwhrNsKGQOkmt9L9r/X1eWymidvFzpTw+PG+4ibeyD6z2KKPOP33utifRz7s+nUxJ0qitGYr9/I01d5gZZ+DV1Vhkl17X+IlLGu7NkmmyQvCDlUXfnmCisZFqzY8twKKKNM2O0qYiFYoRgY3p5t5jSblSrj6fJkgO0n+02sm5kDjurtpSxrWLYxLLzUPXgZNt4RkrTwuNYaIn9vDvGhHskWaeYloqh0YzQCSBlDxDxiwuiJkL7UkbIyu4GLVErxLELsJMS+ho7GDHWpfE3ysZR1QLYZtvfuUW/uO0X4rDckLz8IUbt7v0xmPMpEBxHebq/jSYdr74zxyrU62TKFQUsUGE92ZWB6OLsgrIC57NTHpvHCfh2q562mTSGbWk7Zopw7DW35e1N09HHRqc3U3ZIu7BxAHllnEZS2ObYVafKs4/Dn2BGlByIwq3KYYW/Old2FXEoJBmyLG0rGw8JLEEQgJnOyVmAbA6+04QMLlE2BEzngVIYr0VrqEKfhh+0Lsa9rmWuRT1RVMUTdyCeU9pR8gMmy1YmfKSJFX2OxaAnDlU8quMmKddxwk4FVNmwaZ+uTCXVcOO/hLZ/r57Pr31NuHUNFhlFVEgwlKAYIjBUDq24YpNDWwP2CdnlBJ4tgEmFDLdIMoakZubJSenCOsVnu923FUHgobPVwSublLJ3AvaE1tRD+39aN+UlPetKTvhk9lXRPetKTvmsKEMpPUDvBdXe+LGc1eNibAEOaH3YBl4mL+auk91yVWgnpSgHM3yPPi6rlvhNJwGsCqvvKVwjDDoAC3GBYXWN/vY+rvq/N20EdqqLuXvauHXMsZYIj2hnUN/ekE2C7K+jL6eR+3b2bYYvyu+MBRY+1PusJ2r+/GwYH0yBpBT2GYKyCgdUw022Ax+bTx/LGRQ5w9sNZ04aFO67XBS/LCxqVvHBeLg3CshiguG0btm3DihWAer4anXUalvNmhmQNQGi2KYHV9BIofxWwdPKUGhMAtYdhNzPK1H7XQfo7ICJGo24W+MRocKtO7lj6gqUt6M28Fg2Y8MO/uS3ZmDODhMwDz/3LrB/34Wg+h2N9MdVFezyYH5GPiknVIjwNIQZAVwUtDUTi4JStMsunKAbYQk94xd8oRRg077vwlABMISc3gXxyvlwdb0Lb/UYn3eGhpz+Ur8XDj5KBPAAcjJp1yj0s3u8Wcwy8zt9PJwvm9TumWsqtv595Jyd5iMgzsDdijb15/9em2NCPiXf8p0AdWU8vyU4VsZx14eXONJuKWDW8H/6CPiomChxf537KBGpkITfr+BFKf8Xg1vhp9XNlBvPzVLdZgyMigI7hYSPjthli0PL6GN8LY6JULmW/Yecxh+hKjd6whnLzPHFal5UbQ5HlIJMAW7W2C2Uv84eGoostB1ljhjBBNwDbBlEFq++RZSqbR1O0EVnnUKCkol4BeK44AqBNAW1ZRtTN8skhFeYW7m6GBzQ7GkqlYkzB9LLA9GgLBwkRDQkLlhkueG4x/nBDkRhOddCYyBRvMQYadQTMOzPnLAOtg0kRXv1YzXhHSLC5t6KNrbhb2JwfcwTjby9PRnDLOfs0xzvnXiklOl1JPXS87ov3hpN7l6rYfEp+zAxqjO5hgckXlkZUArUx4RhnD0MKsKsiHUkmgEgtwkF4UrlXZuUcGvkbfe6T10ldzk9DiuiL0Np7g7+ZzibmtJhSwXLSVdO2g8z7qBBXphxPEJUdHZ+531vq97jfAt6oxeQlJ9e9UeV4Pd5XTzFTtLVPlk/Q56KHkucU23Tutdhvpe6naw9zBR2GRSaQTYFVgQ2mqNO/grj8huxBd//D5u/JjexrMXKmxhZZPcZQ76QpjuS6jbx+2I/D5IGzjPrprAmFFcDWq0e2iDu4nL3LbsfM4A7oNrBtG2gQOgNjGc4jLJd4KGWtnc4TfFJtY9jeJ6YoExHbHzUMSOze8KSGenhLDRGBQM3rTN3qSgwli9EwSDBYsEHwOm74ND753w3ruGETC9d5xDN23fe5Sfa7TsIDhYGHAkrN0jNgYKOBDR7asnW0ZUHjC1rrJVotoTFj8TypLNYPxqMVMjxPn/NYaqYUbUzQRhiNATZFnxBwpeV39Wx90pOe9KSvRU8l3ZOe9KTvnGaomho+Z54i/OAaMr1OAIoEeehGHr7LgQwwgObuwFsPCP6qaof8ULwlIJQZ5FNxIjWHTwVBIy9dbyBls9jehnmy1fBmD/rBIIjDRW4iWL+d1pR2FJrJ0t2KO4EVv4okwz0pAbQwsBLQ2YHM4yk9yj6eKGh3Itvl9zv0cLTJbi1AYQCO+6P4gxKO40bntwQglGAeABGMsWLEfNgMBGmBKRHQyS26YWGZOjou3PHh+oIfXj5gIbMObMRu1WdKHsDyA91uN9xut5w/lo8uADnKsCgq02JfyeamYQ+mOGI0sJhlOYNnInOCAVkS2JHNR1IBswNrA4/n1RsAwB+aKlBEAbIgse0Ed1w5KwlM+sEPhKU1Cx81IoSZhRxt1NC1Y0G3uS+CbWy7x39pt332PH1cSp95QDgJpYdx3CIWhUdFIbfV5vHiAHMDNrGAj1R4xvc6Bc6otiXD6xF5WjX3eEHMDwOYVQBaFfoKA+O8M+/G7BEr2j2zAk/l0soTob79WEg6Cx1kF0W4S3MDoZknzbQfzpgqgBpPpJPv6rPPKh9AWJv8XWJ/rXy83JvfHXmzvqt/fjsdINpQtk7Edn+557ZCo+k2c5zxm7gnXQPoYiGocy+JUEuUYF/kwNKT5xGK99Qdz/UvwgUIlTF7mMxTjzrMgsrvZtVv+V0ibGRe4kYZmR8IIRPMfdOUby6v7OJYh0zlVuyxz3iH2hRl904FIp/blE1cUafingpRfSuLmHaAcSpdQinI7p0e7Q75LTwedkDyNFSJckAhl9H0kPNcR5Eqjfr0hsq9N5eVG3D4d6Y79/42NyyrVuzlCugOJSx+vK74S9nQw16Gp2fWLRRz0Z9qcgBxkeNEocMVgQOWW04EBMFosH2fxKbRuplSB7C8TDLlUSp9mKqn2BuzT2NPjfv2a8bm+AydaKVolpc8SrH3Bs4u8r73OyL8MOW68jJDZqWZG9HCocLkNgAtPF9ZZtm+zkzaFcDF2AT+w2DPQ+IrbJ6qe8Yaf3ZFp4dSNXZpIThDTs4t+2tvos6qVNUMybYxlemYXKOKQd5Th2+QI33/ALtjV+6xHZUdPaC7e2IPq56lle3Sg5uLK9ORDe7qQ/NtFqcp4iMfKnv7GiX1PIfB2301xHRRV8q79xxCQTdgvOP39qKrrH8v2JRrKI0l7DfZX0O7FwTf30clsZ0hH0OH4SJMz1Fi90Itu8lkXPt6nVb4/qPxNh8Ts5JAje2d6S0x5xQ5zxUVrKrgTfCXT4SFCdfe8NI7Lj/8aOFuYUYcnq3V+KIquLUMkyxqZz/E+amE9Y49wHsPlkjN0hQQNX91wxSyMNeDzMt50w2vsuJ13HDbVqyyYpWbheLUkXy4dsWOohrfVKb6DcQH+TPJOBODkmeyKljF845OQyF2Bef1+oJtHeZJp4Ix3HOdm4vEDG6E1ZVzIzbir817n/SkJz3pd6Knku5JT3rSd0074FIcMNI2w4Gph+sSy0JMbjkeMpwoATL8swe6uUsk4VAIEaAlbFCc9MydrxxEQjik3atiethZgfO4E6CeWYgTcBumBNud8E9AxN9MfrBI8Ih3ZzgNkLAp9NJAEZbTLdeIefbfrp7xWupMmCcaYHcIp8NT7fYC5uTb46n82Jbj+0P/nQGbcY7IQ7xaKMkhGDqmslTcqlpMKUdM6NywUEOnhqV1vPQLfvjwET98+IALL1iooTGD1ZRpEMIYG4DpvRAhKQNINACKJhAzBLyJeRBQQH5mGU+kaGohNjcSO+jwSI+x+z7Yd/FD0sPrd0jVo9AOg56Tjht67+iLedAtfcHl8oLLckXvC5Z2QUMHYVgoOGJ0aujU0aljwYIFHTIGBGPO42+xNB9N90esoCyjOqXjWhEArwM6YN6AnaFNsbWR+ZXCetoU0n9bp9wCkc8v/W16RNAMSUibQl8VtO5xpsStH3TPw2VzMm4T/FLc1hVDJcHrqZz3i8Lwg8rdHu4nAdA30dPjXI2JUvlhXOPPYp571l3940OzH5Pn1DbH978HM4n2v7FXVuyIYX03zpgiGbC5bcBtBZZmYZ8dkIx+ytCFBQafIe/UsUOdYfM8Hxk3dsMMws64CMDexc8rvAM7D/tYIU7lVBTLAEmZ+/N9zTWW0wvxOEqPNAUMiIbO3100UETbtSjZ7Fr13HumgHIlUDE8kRGehuThC8mnME3FGizk5NgETBaWknx/G7qZImtzYFVnbawuDGIFZEBETM/s7VRRDDe0oPC4EQGN6Y0eYSrDO5Hcw9UkRZl6awDuHofwmAtg/zCjgNzbgw8RGmF69KpMBZ8iy6CYa15+rCgmMzSRLRS5fu/meZjdm5WCpwwF1uF6W6tz/FZBffUmpdFCKFf89Vz6ignkJXhoOo3GUBEUQ0meRgaFj/nzI6KAeUExTHFmob5DSWd5HYuSNtpFNPe0qHbazE0PRnLFgnldSnk+ube09bnGfNTJDgWxJjz3FFzRAf220X1nB2FsA2Mdzns4f7ojX1d583Efeo9MSCdf1PvK7ztPq0dF1iPTO/srWVbUR8v3xzJKc1O2rscvv0iA5FsCpPdkrH3L+wc/n3moyw2mEC8KOn3QF9+MjtvAw2c6507+eF6U7V1z7dnauJeZgoeGhzNgeTizjBP+cKzySRURvGs3GWJvltiw55wbY3MjOU5+S7CQh9otFLIOwTosl9ltvWHdbtjGht678zxrm/h+oCre/gj/jmIsZeawHPurPQ1MDHalHHM35RxzRMc3pRwGhg4M3TA8168qMGQaC+qx3846q57NfA/+w9BB9FJyKUAVQzassuLn2ydc+xUfLx9wweJRSjrgxo7ElFFoms8+6g2XpWPtC7YuYN7AvaP1Zgu5EYSbiapkzxKWw4J80pOe9KTvh55Kuic96UnfNTEzuLFZ0gaiIIDlUyFX2MlEGyKERFg6jwBE4hCAcugMjyRHSeshIpRyNWt8Ek0wJgoUe66k0B8ea+XerKaVSeuA9g70DboseQhNTxlMGZ4CEEsrwyM4tLeaR7235O04XltLAAHoZMrDpVnfrZsfcWvHlXvTchnltcCZdzkvdFfEr5OxD+hAllnB0PkMSkAS06J+mBX7aMPCCI0NGIqh7Aq6mRdlaQuubcGlL/iwXPHDDz/gxw8fcWkdC3V0jnAoFm5q21azXnfL7zE2B2vtu+E5hAKkFBWMMQxKotllBjTZ/GcP39jE/liaK6TYQVsG8UBGZlScDld+jsl1hl1/B2Rgqh+cyUKo9NY9vOUMcXlZLrhcrrhcrnhZrrguF1zaBU0aRiNT7Hs5zRV1C5kn3VBgGxN8r0bLSb+272q/PxqnRzi97r+ieq0gvcKwCOjSLCSYOjhFADOBB2UIGiH8zdJdrq4CVJMQ8CrAJwPo7PrSt7/Ccv6t6aAKvK433LbNcqa4AknT3dU+pyddRUHpwEfz++PTgwcef4sbw/XygHiqmrdZ7IVVufPwWTBUvPL135V8fzmdv9GfMItvb3oyv0BdiWa4y20AS885E4o3rWVSMXlhAMNzu9U6hecVeWguURvjCjrnRKvoa9mwYi9LpLq0LHn7jBqQzDyUXioZ2jcUEKICFud5EcLZ+ejQogDC3HfMG356VBGxK58kvcCJ1RQYAYKrhXoMr4XI12uKuwFlDyXsSaNCUbPRBmLPRQakJwRcSWrhDj2aATOYuucHijgD7jUW4lzkE/KmJVjrIQ7TcwsAI6z7HaBl9fCCrrgRB3YLoJ1yZR3Y8LQcdn9IYwIyL+fwGMpJVTz8nbGHN+QQ4wtqGkfoCEMiH43NPfXUDHy4GSgNz1WbBmbw57JOb7J4+q4eETo1vMspr9nJks5fwhNtL8xpYRUOujsvy1CkRKmzq8+xuWP5A+Pu8GKsCt30kgZPZYIXIe6BoUQZahRiIcs1xtnXR0QjyFJ9nplzY/SbKaG5MZqHWQNCRisd87Woiq/D1lgoC7KP4F6sRe6m2ZBSJ93Lwymc3/PMx83QNz9+rv3Hc8xnLy7PiKPFLjXm3TPnGo4gFQpMD946PwYyPGpscXacDAWd+h9Mfhr+/TFCw1+D7sbrePZB8sm4nureWNhO3dK1lhHPie2HKZX7IMsJpvAjcQ2be6BqSDG/zKm693iMuif/jDoUA0SVdDRvgOWbBkCtgYUsYwQZPjB0YMiAyIAqg6jPflKdSjmd6z3WTyjqpuEfbO0TucGle8+BZ19nCF0z+BMM32unoi7msFbfzmr0e7Y4/ppzLZ7/qF4pGoYhi2L4frWNDaxqaSCY0CM/ODFYPXpH9MfcLuz8xQ2tNfRmhtLMfmYnNyz28KMCy7P71++kJz3pSU/69fRU0j3pSU/6ronY8oNB7cAkpDBTSQdg8hRmoX+gDERc+uoFN+oJz/7mQcf/qwcePxyrJSFJoClKsOscXI2TPZHFY39L+7GJhZJsAix26qGh0CbYe1Cc0Qw1su8kB1bKqYccLEygZF587GG7ngja2fKZxNmOaYZc2wGR0UTafc7eSQBxAgea/2F3PjunWscj8lAPfnq47LMF5/1mIer5y9wSnUntMOHhvDozWuvoS8dluZiC7voBHz/+gI8fPmDhBQszFm6et46hA3i9MW6vr2jNDh2Nm3l5tQ5VdV9GwXDFnQ6x8VeeoISH7erEUG52MGHLKWZRtxSCgU3NelO0QXVAiSBKc+iP3Vi74Ts+4wScmh6PrqCrr5d+waVfcF1e8LK84Hq54tIu6K2jSbe5zg3Ny2F2RShZTjoIIBlLFOd9dvbdYcq+uaTj/jp1Y+nWcitIVH/ztFjMkw3lLa+KwRtUDLBkQkYMpgT+vuNJ8IDcL6IAsuHt5PklycMYbQS5KfQGYOxT0AUwHfoSL/j4oC+smOJ2M8twsx/x9Ry/M5mBxNHzJEPGHSbE2Xvy/+7YPGHPL32SkedWCf4uPt8jTwwiBFUB0O7a9eD7b0LHB4UMUD7GnhzrlsmTD7pLRHRDoznYmyvpPM9X5DrKEH6xvzhoZPm/xs4GJY0rXFYw5bh5nymzyyuBJBfAPMG64/va3uN46wQ+p+uYzXFiC2EJSgX9NGJC5iWLPWKG6woGYt5HTOwKfEoQM0NVugGKDPdMWwS9RZjw8EvjXareux08jaDsSzNoMZBziOWmCS8zyylHqaRjENBiOBjNXd4UXMI7BiA4QziqKCRUejtZDVFjq3+dJ2p1FejOgyQ9r+KiyGEHuGeO5u1CnOHSp+ecyYs2mmV8azmI6A+usNlM+UZEJn5GfcJwLeb+0AKCas6nHcs6cQVT6G5e1KmX3pkpT0a+twC/FSUQnmuz2bzk3CCBMjy8y/Yc+Q01J4q4UtTqYMqxaCdRvJ9KdguZZh+bh8W0qW59HcYo2Q2+9+XaZpilShpNoYD58Zg5T9IYjg79+bVJFbIOyOoKflc72syoFiR6eCXUGbXzPiz8sypHETzia1NsW5/bH6r7XGV5x62MTt/m9cfTAjYvx8PVJj/0/HUUyhZVD22JDG+J+Kuy1x+AgpvNPUCDRe31PocOyhnxSC4NXu1rExyhHevvBJLJc96SIPN5sW16X9douG59gV0y2CjVQ/gSaR6NGxiNXQHfGSxuVwqAyPZ4U5QNAAvCky6eZ/2k4IxyM8Mj7/55W9llRQCgzMUmfvQnCOy8tWHFpgMrNqy64jZuuI1XbNsNY2z2J/YnEprfz1B0x4y++eX0rUT8WmZMRee9tova2bVxQ+eGRmZQTeFhKJNHQ2doapt/CmU1Lz22z/mMYTKzqHm0M/+aTnnSk570pL8+PZV0T3rSk75rYrIwdmbRymARaFjQssxcJRr50+TOgjpAo72kW4U7dSFTcTztBFYWQA9wuC7NtDEPmgDSYyEPRuVwLArdBFgF6GIHxqUetEuOgEK7vB870t01BQXD/Sk53vsBpd5NgC4M2theB09FndTQEvcn5flImjlO9PyM8PjMcCZwn3x3V8BnUIAc06gsTyUdszuuEBoJmloIDrOeNA8t89JqWJaOtjRsuuE2NjRmDAVYAqyCe2W5rwWbBXZfuiXDBjz90LAwrOSJyT0ECwlZDhkVD9sU4bcaOhSDOxYVbCJorYGHKf/Iga70Cg0Ka+KzuEzvAU7+wGTj5UpWV4T21tHbgt67K+euuF6ueLlc8XIxJd3CFyzooE0hSmhgdDQ0uFckdTAZSE9M6EsHr1yeO+tQl/fjih6ueavPj8vrMEZ6srTT6Sb0Kf5HAOQGD79LaL1DuocmC9aUSM4fCIH6jaTJIy1vRQCyAEAeQo2FoCugfx7QfzElnS+3WtD+/ZeOu9OuSAVu64p1W7HK8Jx0vp8wGdoUnnQ7JV0UVvcIR252k+JxnWdtDvsBwSZPhCMyHY1fcrLP7LaSsrftt5Xfl06fNxUJINdik4NjVQkZ/exKghm2D4e+LXJDAJmtARhT9thdB4BN+R9W9wH8T40BYJ6IDh9W946UGzxfHaZGQJOv2/MMZCxeFLA9g91jLW/N50/ULz2ZQsmfHkT2HzND0XYYes6DMDIBoMJQNg84lWEhFuGhOX2O006GggN1Lu+oQjz3FjPvPPGyVwOoE/OYoNinW0fvHTSM321jhpZ2v7iMsmg54MqYxliG35VSyWOMkhMJHrbagUUiZF7jaJUg5VHLIWfRHszZNHL/edhKwDwVOxeO5Q0UU3oFiK3wZw2x8HsKiyoBYBe+0j37dt56ERY+6G6t0P3HlGUJ05WUph6HrNxpNzAVdMke0sWtlh3yifgjOIHwnYLOSxMRMAHi13T2fHEiPicjDGZRDLosR9EfmHM6+iREoikamYxLIAeRB1L5CYA8Z3REObB7vxGTi/6KPIlV81IuUNpfDqgr5pGKhr2YXhgz4W7YH4rZ5ZZ3y4tHjdaj+4pi9pSqHHT6+/7erG/di8i+ZPi6ZlfWAZnTF7D1rgPQYTnp8jV4bXncX1NaMg9u2oWprd1NPlBHD1WdG8bdWKYRRdhjkkVbQIMryM7nnHftwdv8DSrb2vwCyHN0VMb3s7iBKHJzE5p7QjIAdAapgrdZq23c8NPPf8G1dz+7EZgj5CKDxbxyTezhaYTrmAJny9wwJftyNkBsE/JzG2FjYMQ/vWHIijFWbMPy0G2ymZedh9fUcAus8/TNfnqD3rsmvyZFvd2aN8MJC0Ay86ZbdBJOETb91M3NHaKKTeJuAbeG3hta66BtxSY3dFostKnzXB3Dvejco375a67GJz3pSU/69fRU0j3pSU/6vskt0iHskR7Jcw6IgyBiIZRSpo/wRHaKMVCBZz6QCnbV/45hLWkeRahKzFUxNwLJKdbBBWCboYDiwOwA3NCsK20DunXQJtAW4Texq+PxKDqDVTroHnE0T0kSwqjZB/atotlScoS/sYHFnaGDQRvN/qF5x05xp/fVkN1pgz5/9qiN1cMPFbw8UIJWJwqp7f/1E+THAfrQsk6bDDQQuh8kKGO5BLYlGb5j44ZtbFi3DUvb0Klh8IYNBrBGuC/oAGlLy8kIB2aW/QZqcSMIGDTUsLNMZBRtCCQpTr6cmFkcVht7DjVu6M3DXzaeuXbe08+1f/8aB73fSOYB6eFRuKO1ZiEu+4Jrv+LD5QUfrx/w8foBL8sVl2XBwt3CXg1AbgPYCH00LNxx0Y6mHQyGCjDUvEaWZUFrNm/uQKOzTj5+d4+vvTHH36YKcM85kaxkX5TAwjaJ8SS6Ap0btDkgFYqhwmv+lmgC3A7aenydYG+4KeTVLaIVbrGLXH6BnWhVWuEdXXW2lhTY/gOg/yvF6+uK29gsh4m6EiJBcb+feeZSo1LI3WQqqDOAjCN0CnwWL+2I7VnRx5xMvhdyCc0U/CiUE3ftO35/v2t9faoTtzyvPpbIBpZddhjDPGbCezDa3Jr9UXOPGpr3A2XfYd/u7D4CALYwTqqF5/qiJFeycutgsdw0M+TmsZ6FGRPK55h4cnIfcv+oap5AWuf/8X0J0+XXMyYAChDWkJ9ArkRhDzOOVP4oMMOd5dgXhVuEuSwebAEeQ8mb4rmF3CDCilJs65reUoj664wdUI2Qoj4M9/bQBnElECJ3URqwID1ApoeH18u9a0lN/pLNZEq4Amin7Mkh0AQqc5oMzX7RCKGaVhRq4QvHcA87gvbsklmmaaHm+It5FECQXj45xPEmRNqD/EooPN6vm14xka0tZwfuFE8xZjt51L0goGAtP5TqpNFIhECt/M1ME3y8jblWbqF1Hah5VACYYQpd8aoIyRbu7DuVCbX+Ry4U+lktrZ3NsPI4l6nvH77miajU/ttvm+ZFt7nHUg1ZP/NGJgW/gbWNygUz/1qs0+ALD/i5UxV3CsvI724/CeSm4MuDMuIR724x7utzZoRSr4m1d6x3VDq2wwhLqDHbrFxxZaiIAq6Yk6F7Q4jddkZ4fNb6RlSXBPYhmO9rslfOzasqb/CXIo8S2fYXCql0sI8UcYTS15Sl3uf/O9TpUNFMy5n7AcyTLpmoP9w2GYgQZGxAa+AesRHUzspKIGGMYWe1day4MeNldECGedbFsiEyhSOxb/3OTzA9v3a9pnOPV7KKM5BGAbYTme/ypgMW6HLDwIZNV2y6YowbhmxQHbkfIiTLOiRV1CrjE2mAT88Nfy0qyweA8V5W826DnZdftw23zV4/XkwZJ3CDVz+PAOTGqibaMCzsu4qFshxi+eIxGN21fKKK4Uo69TCb0sYfK2ffk570pCe9k55Kuic96UnfNzE8j4adTEgVSnY8D8DFrLLNso12lqch8JfTej1tVo+4ONAByBjoCHmUphLPwRtLgHCAwaKsu6PTAbQUD3nZyb3U/Luh9t2bIGfEYCnN+cwdyFx2J6R+3MiDAQMXO7BGuBP7owxvkj1Tw6DVPgCKwglZs8dHW9q9nP72znOx5RXYFzT+8RW4CPCxpUfDGIKNCRs2tNYSxAcojZfD8lFUMNRzx/nhYRsbViLL7yMyD5ayYVs3jLE60Fdi5jh4MsOm+HcAmDRzLlZvoKle9WBH4UHWCG24JyAXjzqOXG0MZVeRhsW/Puzk74DmBODWsLSOhZcZUtSVdJZ37oqPLx/xw8cf8MOHH/Dh8oKXdkFXBlbFuK3QVwEPQlNG0waGvQIBxirAHdw9tF2lIzYZn8+W/ZFOuj+LS/7zdjmEAqL4c9UjBsU0zHIFwAroT8OUvBeCdGDkmv+dAadvSOl14+DKKGuIAAPHN19ZNwVesYsaRphbB7kDVm4n71w2p5cpMP5i5bzeNqzYHOBR95Rzgwhm478MpFl3KB2ioBqDqz6gvqrfl/UuCFu+L41yECojGrvX+vnUOKJGB3Ru5+33rXhN7DsHBJUO9UnFpKOQ7AtExJUX3t+9AZcO9O7Kuml0M9sSjzZLeobvj4YezXy5pd/J+b0W3nxUQiQKSPVzaYI6ilUA4/HPK/CjP4Njds/t2J7tHiM7uWcvolCDAZRsCkvSzRUgD0Jx+d6p6h50CbapKahqWCpPEKVM7shVeGiRkezyOYYWjnLkb+zjxs7srOc1+8IMDgQkBtoyCM3QZhAs5C2j2Z7LBhaa8YzLKDo9/SzPMSCbQLYN4nuyShg16dxLw1NFNQ2EdGhGctDNwlhTeMUym3fONmbuRzJ+ZMh1GSAppk0Sc6D8wSUC3gPLsxcNkFaih6HSclkE7K0zzOUp0Sw7ctdJnSfh+e/KuanLsFqFBOgzxpsx/VbyMeEdp3NWpHKUnLc3mspQtRy/gIXCi2uUyMFg7JVX2Ve0Z/j+HDtHeFhTsTB77J6QtrR/p/0yzhjVkyvrrUjX+fqjty5XWvKWu8LdOISnZ+Q76rOXSYHt1Y5D78bIc9v4fWTQmH6eVhDD17zoyC1V0qDTrxvzPQo7NuWSLZDJs36PBhw+E8oeAsxVGd3qn8OIhEoxMdZFzkyFHAFoNG1XQrnFBz7jMmOKvITMPnEnA+cAxL3G55JHAcBPAP4RwH9SrpMBwLynMCycP0hzuYobYfCI3JvDHNswMLBC+uL94eNVjZAo+sf3PTn2Z9S9cNPaX7F21L2aGR71JPZc23F6c8PBraUhaDWvfbjmqvBe6fdZMnv6LG+wAVfYdUpwBdvmeQHd2001bc7Mm1F932TQ8Nyg/iBRG1NRCzvMauuVfB8cY7VcrexlDHkq6Z70pCd9l/RU0j3pSU/6voksn4tCTSh23ywJMCSUZ5FDAuToU4AefqjNA4PO11DmpJMBF+F+WtNGaIsKksBDO+1OQAdhf2ftPK+ahz8HdWgT6MZAG9DO83ClXy6bv+vMrXdvdjdqJ1Any4/UPexlYz/lYp5K7BS3A5g0GzcPRHc59JJod7DUY33u3r/VssPpppS7/yxmVOtgoEiDgKDKEJ3ZVszSsrmChnJ4RcRz52wYIHBTMIcXBoDh1n7DLSjhrjokALkS2RWYmdOGBBmOJhR1Gkag+97j8M6gEq7TFXXNwUxxC/bIiTSVMaWkb4kxnJX9uUPpo2urd6EDcI0JnU0p11oDNTsULx7i8uV6xYfrCz5eX/DhcsW1X9DBYGXzZNjM85Y8T4mKQjzpvHlUAFABKTlc/JnOOpm2e1DjDaKTe87uK4BR6BtAjmegjK4rl3It+/QbnwR8IbS+AFAIWTjIM8/T75kEikZmGz3UAIAGAyNlVegny+aIm858NdFXpZzf1CsTCT6QYh3DFKSsPpA0jSB6Cb+YjhNVISeHcue6SDCM6m9Aam52aCzNfTCQ+ZhUY0yDlNiAMhzjSTvju6pgCo++b05lz6VWFlLtLwK4mQJuxIADidyG5X7rwNJNYcdlQjxclNZnZgNk72tI7DoFwgNaXFGTv6QHXdmzFJjGQy5z7IwsgNv/42fgf7fsGEeGmEZMHyrTQnf8A+UxogqSuR/Fbp26RjXwsYYMzDCVhTWPISAM80jP+RXbWSjqyI2bMNulQHqyeWO05O7jVNDFFEuVu4W9dE+j3loasIBgClERV9J5Li+1ay0ksE4QWoGIvqCikDGw3jYDAkOBFnWIsKbksk8ZN/Ww1Rih7LFytanlIQrPDG+MjgGCmmylpU+jbkfv1GD+db+pgxpvDwLkY+XbRJ93j4r5vC91Xs+UnopSfmNWV0Y3U04CewBVCewg/ZRJbP5T5Ksbw7tgykemGGZfMr5eaUwR35XjIjMKROaFLLwxw/Z5d2TXeV7gMYaBviqpDLAchVO/v+v3b0WikFUg6wbZAsQG3tqV9nobwvGWYP+qReGicKO2N4uexSIC7uvhgW/edPj8hR13uP7u7gebdm2SKMzAkDyyB2Eq5qIpztdEpswUBYUqeSp7f2eqAmLZWnOPyc+Ur4+6OaeFLyMwpa0JNTK5xO1aba3vQ8lqXe+kaT+yE9MP00P9v8iNeTebd3anUdCscUS3EDeYYNUZhtj5iYxh4ROVfC8iC/ecBrfxfxgAzEpGjmLsrgNYNPkYxSIhBdRCXMd+BABCAiGTLc2oExhk6QsGWeSWVTesYr9Pue0BHfj6N6F3rvt9XeqHMIqwfmAiTz/g57FmZ1NyQ6bIQWo212PeD4uMAm7obRo0QSN/LPKcNg0ogM4EGgosX7lfnvSkJz3pd6Cnku5JT3rSd02aFmgA1C2tPOcIIvccDFqiMHuNk0Io1iLE1C6vGiaAAVd6lPAWqrDDuzpoGYJpWOJ5kfWwv5Nj83WPkKSIu4l5T0QbBiwcU+QQKSfnfLcT2k0RWWGSxzJ3nGiaHyxmbffngJC4CWCykCLNcs0wN3PG20qR8UStz5hlPVbOoYT+qJ3yGToWp+c/Kt195T9EqB/4ePN+ZLzbiRjcGlqzdrN7t6h708kYWBWmcJNuylU0S3c0FGMbBtbB8utwYzSx2CVjjAkyhlfCDvCww6NZ7VuFtGmpJWWexvSi85xs5IAmN7fodO8BJY/2kwA17sC+L6IHwMjdb0c6YI5vlr8D8fww7YBJeBRyYyx9sbx/reHSO67LgqV19KVh6R1L72BmDBkJ7xpYZx4QAgUiNCk7qOuKOtYBpQCN39EXx8+fOwDT4f3nHlPmdWLmj/o/XmuO+k8AXR2U5j3g8rdIBPc6hVlM0zagnxzUXZG8jCoL864wK94ChP26CtxRgM+phOvNlHNEBtaHgo6BVNAl0Fw96uqOE+8nMH9XgbMGJLrm14aizr1JQKOE3CQUVHyCp2ehtbT22recW4RdXDLy+IHRWHFkFmN6KzYGZEMqQwKlDWXp0mcbsz1cnkN+qyn5SLVY6kcAPoLAeEgoC0zpQfuxOQLdqbjy37TUg3CwGCf/au5haRdDmWHN61nuIsu1KmI5vywUl4DdW4K5mbJCzRAKmJ5mM4SzGboE4Bndr7VPA7xUBY1QUNGcEn4ZlRy2sS+LjgwzKyqgxrN8iO/b1kc6BENM+cYt1jqBhDKsr5C6A6V6KDuTHzMsHKlhhmKebrIN6OpI/eIyWIDVDhCCLKIDQulByHCXFPmQYV79ll/O3wNzDijcS0/mHpMeZLRfOsTGkHZz8xHplOOq2EpezltUPU60yCI5Qk6MaSDnE0BRQuXZYO7cukystO+YDUTPcKvNDYzYwp8G6A6fY6ICGgRu7hXp64oANCoSrfo4QI1/a+mL2veYnMI876xfw5MjwhCHIdS8ndJz8puQTkXxcENEe9zRu9f50XuVZTs64cmfEwXi3KP7z9+avugp5TiyE6d861S2PMSVISZ79HVd04bm8S3qotNrqKRq/P2obKdv76wKvU9aOO1xOP7MyISbnRmUAem2adm6cFmiWH0RgMaEwR622M+tmf/dbBEniytHDg3v47N2RYPYv3BvUTP6AFQFstkYkJLn4QaosecYHq4IAz693vB6u0HGALpiDEFvphBiahgwpRlg/CjyYDoLSD7C4azaomEE1VDoWWoIC4ITyk0B+RwbMGNJZVPgDVfgzVC57z0MlUt+7Vz7zNni3WXssIzYDcz4MfKDc/coJ62j88yLG32azvaeGxIeYroxg5YFy3bFddkgKnhdzZBQZO69CxN0cGYM4d/csCc96UlP+uvQU0n3pCc96bsmBWXqt7BOlqFFmjZJmrjBg5vP04KGp53M0Duu/CK2t8wW4o4okhM7kIoSvikAzEzMjomDlPcA9qD5ASixRiAvpM3CImEbwMYGGi9ilv/lcBOHh1LwLOsguD+S5e1wUMNZ3aF+3g6GskI7gTsDrYGaQjtAWzyhVCaAlM8BFwWcpOib+prg7gQl6XDQtLNR7Zj6Qy3rQRUogEDKQ0UCrHEIJYDA87uoUijoRLCNAYwBGQMbrViooYHN8N5ifgAwIIqIodJSaadq4Ki9Fw+p2TDjUtkcndOO8rBM8XMBj5orAFOhyAxS+z7UpAoFkbqCICY5zidK7c/o+19zDjorI6acnlzz8BCakGX2Z+KNDvD13nFpDUsjLEvDhw8v+PjyAR9ePuDD9QMWbgbaDgtzFnPA/uIx6rl2BEoOfVfA7mvRoZ159o3pX6/DyXXx01mVCoZ70PMbu1wV8mlYLp8FFlrX51XAB9G3e8zuPqzaH4/07p0d7B1UGZohLt0Y2uaCg1aJNQdLdLCuRoH8HL0LQ4mBVCBDCVfPB0QdBNM8vbZKDp99L4yNyMGMfEby09ikKtDiky6+Yp5lRchL78NfP+q/BV06loPZtvBkilhbFBM+KBZVuJo6AhuKA4XdK4wMYd1svzMhIx5J/ozmi8j4gsCUmQxYSEQqe/Vhy4+wl8TmbTcnW7lIgzlW8D3G5tD/lggHvLiPmAIRopfq2FbZY1ccW8hwAcRDhbEriCXQL2AXelJUTIHlIaCzE3fTQrNpBA+DieHrjNC8Pszh2aZTnIiqinlOiPoYMMzDLbyqoGgmuOWUH5tAxzYVPW7sZLybwa5sNuWieo4cNcWeSubFg6gr8swQzDSRNL1MiryRe0IJf2l8ZspqkY9NAQf47Yd9BEJF3V52XEyBGXcu1u/UIhSRaupFp/i0l4e8s46sZke7H8KIKGTeEqrteP1OoZ2+VqaQC+UOzbaG8QODzMjIla5M5B6Qlg/WlHMzzJ1tyZL8KAzskHJOMVnzuaUuu4FMKavwcYwujS4rY6r1PSmoMai1HMtvvf+pCMZtQNbh55c5sNG7Oz/H5POFN2od/soT7r7Z7/VHdn1k/b9VOfc5Weqs/DfkVD1eEsNP+6qHN93RIC496Yr9ZrD9uo1S7ZivKQ++hw5jsD9qHXh+5kbV3DZmlI5Snk8TbgSOsx4D1Cy/WCjqAOzkUluLups/Oae0XCvzjpyr0X21HsA0/AxyD7/MG6dm3AhSt22yszo1Ros9bRPIWLGOgU+vr/jpl1/w9+vA9WKeV+L78FCxcKeqEA0FoNXPnCun3BQe3eQKSfKoKYTm0Uo2QDeottyb1PtN1daxyjQMJT9zCFPphAPjRvkaJ5d8DVHqN1NsIuYlF2epRuye7IRNN6y6WV8vHboCm4hFuQi5pAhJRMByuZhspQODBEM3HydA3TtRfeJGd7xlCPykJz3pSX9keirpnvSkJ33XlBbfIjP2/c4kUB3IIRALdAwH38YE8CLWiYe1ZHKwP2N+WFiKiRnFG39OZpIHDueN+WF3sKV8rYeYveCtCSRAFDQEKs1CJXmYpXkbYSaOj4o4cIh7Ef9clteT18i1sr/OsCGyHAUBXC6wcJcRDq1mL7+jcqLYHcgOHVXe6uHLu8t1f9X+cY8E9f3z1L+jHP+9Mq4e9EUEgxhNBUMFmwiYBG0MkCq2YWH1OjFuILASOsjCKoLBTLgslhuhNcaizSAksbnGbsUaJ7ioBhc4IBSI1fo0Dq7cCF0bmnZ0GeDW0FuD9L7rM4Ul6I4OTbAt8OA3unVHx2ve6vLjtcdz6PHeOmHzdYJOVMasUUdj85yLv+tywXXpeLmacu7jywd8WF7w4sq7BgNu7ZQX1vecoCAUrhB2YCIAuhEg9tG8+ivSl5aXwAvePNdXPpUg5AqsP23QDegfOvBC2PoAMRn4DvV+RnofU4QAxj4Xy9G74o/glScJ3AKZu1EI2BTy84D+rNAb9hEkCRNzOyCXby6Ls3n+Bpi4u4g0c71k3rlIGUpqHrR8dnOgiIWH78I6l2fkBKnAoiYfmBMp+HTsp+F9hsnnmWDhJGMvPdbtDWD1t+KZta1ZcN1DCNl51ahm91zLNYaGFAcQHuvRJ6rAtgGXhntNRhjyOKgbIfGgGOBd7isFdiDqXC/Oz5t7KiaSpyXsI5CMORHWis5ZpcY/r1j/3xvaf/URCkkeZiGczXu71sBCE3q/lD3FvADswZKIdDAOe65s8bukHKZQ89Iu8HCGH/P+NyWLYNvEPPKILD8dKJV2UPPWM08mxRAp+kivi3iIvTGVXAg9qtg+LRHerIWHndfLDbQGTdlJ0jDG6i9KGb5RxoBGyMMRSkK7Th2sBeB5q7wuMjlhDnT1fkz+iPyuBL2cdPgqHMDuKdbo4eb0Livl1ddZsr/cCa37+8Vj0FVeGEpu8nLSmGhW3vL8CUjZFGwackxOPRtPpTRYmiGmNddOrqWi6ONSzn5JzL6n0vU7ey414zubHupepyaHDWUQqcuEAcoTqFHKiGX21V78uuT8QDbzEI1zSNpNIIwBAvivkSvOa1TNa07FlzLvzri51i+iHp8UeNX7i/9IFKwTBxlI6wXIiMLl20mHvkk+n//N7fOb0COZOdYk7esSP0aVpDbMr6UWoVvJ3jdT0NHFZA5jhXSfxj36C0gv0zhP5w4teZk90tejyuSBg6ZSMWv3/wXwn2IililnuLcclVDI+cog6iBWO+ZHjM5YLxrnJttHxGV4UcuVRmoGKBq8B945KTdZSHgCAPfg7URojaHcwdSyn4cAW+wXUBAJmIClLejouGLBK1+w6YA034No7kHfiJt8AyqT0ZmqmzXaq+d73OTmgQlohj8mz/cqgsEEbMNyuYfdVGN0IghbfvHODdw6SCyo8qaKTWERCtKz+T7//JOe9KQnfS/0VNI96UlP+q4pQgKaAOwADKW2DYqBsG7XQIrUAAbKU3uEuXOLXXbh3QVtISAS1SdgBkdJjl4E5SMBOyvDPNAmRngmfJejz1DQNoBG0GUAg0CjQfvxbNYO9xsoNY+M+m5RX0lA2jD9QyKd9TzwEwBtBO0GKlJzUIgc0Mp4oLOW2arqIZdAEZUrpivV/kB8ry6clT5+UU+QByF9d+KedVSxnCNMBKWGjMnE/t7nVoRWgoepGipoombZxwObmEU+DQGL4nUoSASsQKeGBYyl20GDCOi9gZnQe4NC0DXyFhB4EHj4kZPgYKsBliSCyIvWXbEnCgyCJyJvaIt5DjQPG9V0HmYtxqUpX6x9A6mgI8LMDXTSx2fnni89Sz4q4/jepvN8T/MCX7XWP42tzUTojbH0jmtveOkdL5cLPlxf8PLBPOc+LFdcekfn7h6ONmY6FCxAA2NpnitBAeUJKATO38gUr0Vz/zb9mnP2e8+XRzz1rXIOgNsOk9qQ6TW7hzkCG/+Z1xq/DFCSoGgo4fMAiPsrhPJUoRiiE7/9KyF36eiiYp6mYDN6uAnk5wH8AlPSARlu0rHhCYhLATMJOxh0R4fxprs38zpZC5sKPhqhF5uHYYwxZmAal5RRTF52HNlaMB9+O3l/Cmq4y9KRv+ea9zcPh/UANv3m4df7Zma5um8yysCh2efqMVj7jcQVdTYvwDS951rz3GDBL6lMCq+KA1Ng88rdh0z0WxQ7owoQMpShOfsVsL/uodkWxa7uVN8fK1OBZnVjgjJWDvqbYg3FKEnm7eGh5sqSKStp7oczrKW3V91wgr2/fL6EgiyH0KMe6DDluaqrFXT6pooomM3DKRU2Li8QDGyFg7wRDSHKI29jesgNnm3wUKTWbcHZBFLzwfpTNMHkiLzg15ODgKEsVJljEXKiYD4j3bIp5c7H+2AKiQ9/zs+7L+Zau3Pood3L5Ifx+3Er03JRfKU+cPF7rP9cj7NdzK64D4Oa2ACILZxZTN2QwX39Uj52zj1Jq6EZPjTBefK9humucUW6zzKhigy/gX2bM+BcWacMAMzQ1rLd8TxuzYxY8PuQKqCbea3qNr1wcsxccVwl6OB26o3Rsk4rhWI8OZB3550I/ahyMZ9+Vujrw6tO7suN+fPXHq8helyhcu3dUnhouKeF980b7wx1MD/vyv41ct5voRPZbjehHzRzp1J2XhnZIKiZgo7cg44X2L7I00CQS+G5lcQkiyOckCnJQHk8h4dnjQjRlXUpHlZ3TmKCeUkzu+dZQ0fPk8CskccEVzeoUDe4cJmUPUVDsmSPiJFGd6oYaop5hqcQIKu3zQ1/ku9FTA1M3esE43F0ARFhyMAqagolIjSoiXTBS9XO2LafTXb5docc+qa+fk16bx0eEAVvdiNKi+7SwL2jt26RXqi5+7QZuNIAiASWr51M3BVxHqslnUODnVyRYaGtf10By7Cyn/SkJz3pO6Snku5JT3rSd0303/1H4D+b8ectNpkd5jUt0gE7fIkJcnkIDW8ly3US3wkbCKcOuFjOEJQDWEiuxWK5eo7RGdhRfqP6Q8HX3ATYcpOY0IpB9pgBYDTDr4YmcH7SI+VhHpKM3huQ7aR9h8McvJ4EGNDSACwMbAztDNrE8hD4dXl3KOVoFjYPymevJ4Eqyr2pHMHhwBnV392gOF5y/CyqYBlmqeceDUQzj1tDcyWXAqym2IGrL0lcSSYWCkZhHpvbgKwbdB3QbaAp4cKMhRnXlxcLA9I+oLHljIu8P6Rutd89v4ISWIGFG4SAdQxPkh6HGQKTgHx+t0aeh44nECbmRbquKzYSKBNGI5Csvh78CET3fbPv+5Pv4h46+e0tevSs4+9cPifQaP0Ci8JjoVTY/nrvWPqCS+94WRZ8XC54uVzxcXnBy3LFdblgQQMPBTCgSqDh81ZNSd+oTR286/xjLLqa4rMpQ2VMoKGiYWftqt/t1sJ5v32LM3etSsHxUZxJwBsgqwCvflGj5E0APFdfrR/NfxpH6ZmLS2GABxhod6jWt2whsO9Q5+f156HAqwDuQefR83JMd6x74vM7R5Pf2pzb/wfAf+4f2Pt7afYaHnU5v45hLoG0LM/a1InIOJ9sJ7Wu2sga41OBNCePnK7hPXrKpD8z+e+Any9BgsogHB+br3N/T8+zHDAuFx/3NkfIErljoHXgcpn5AI9rm2pZtEN2bZ4p0uI/PNqA6ZlrmjkAJeQlE3bou3ty7RFz2rdv1wlOogYqQt0TTPb94/UTDxeZoZNDIatmqBA54ITEZhIzOOSiYCTkbXJPW858rmzKLJjhCIHTQ0qG1Uu9bRGqT70+2Yb0VvJmM+cQERwdToY226VQTzNcPKAVBsjWkOjUvUfEohSgMgCz0EieEdq/xm4oU7rT10mKgnUaxghxKEHpfL887hFFNrQximuON5wURqViu42doucKHT6p9RsdrqlBLXegeJENrXgHZhv7PmqAt0SD+HBtRPXdzR8rNPMWKvsQOMhOMO82tlcA92HVi4dwAO42p3zOlTaGB1o8I35jmJzRWwMTYQMMFG4RBpBPevDbkA6PBlKnaf0956qkcngOTUQDmcYDdx6ZhOz3wsXeXz9RyD99ocryoavagc62CL1fZFP8J5zuFcci6pvY8sq6/lwJ9PDDH4t2XXEQi0JJFwo6LBbmEh1Ap8xJF550tW9EgfAir8VrzCXWlKsUhBpSVPeCZJZ51+nBM5LXmOJs8RD+TM2VzBbdAQIMcmPJMYzn+x6tAF5vN9xur9APL2hkBhtDh+1r2ThT2plS0ELlBr8LHtV681xrHS2U+L6Xgy2XWqPwABdTAkZobTXjV1LLb6o+6TLMc+2HR2LVo++/lL5WObW8OHySGTQ0sjyjnS0/eG8LOpuHHBODmk03hgLSLOemWv7bsZEbUKmHzmxoxBhkCk8li6Q0RDy3oT1+6U+Y+0lPetL3SU/u9aQnPem7Jvof/wX8n/9r834L4GuoAV0ZdtETDIVSzUxjA4kwYRyYoEGCmxWOiOJLOEma9+eJOeTrisHUN4EVJtBSgJA8dEtWI5JvRyhJihj2iWv6gdoPGvOBFVAhVHDlDNapZN50tZMPfe7PM28PcqtoshNcZ9AQ0KDpWeAgR3TxvlC6f8B9jQ4fdXfQO1R+f9/xsgpw0RyM9f/4H3D9P/ynno/GEnkL9wTkwJ4AW4GmalGuxEBWdUWZkJSDHGbuQp35CwaA67Lghz/9gA8fX3C9XExBrMAYA+u2QUSxbeaxcblc0HrD9XLFsiwWrkwVW+DOsJj87KHG4PkXOxuQJa1hSMfoHVtfcO0Xu76N3YFZNkCxnUcpfWt4Yi4fr3kvWHG8/zjvcnrYm+mYYjcy7MAWHoPLsmDpC67LBR+Wl/x7CQVdW9CVgU2wjQ3mCcbmpSqERkAXQjMEEMrqEXPNc45AYGVbhzI8r9L7TrfvPgOf9N27730vohRgFArb8+9JAd0E45MdfNsVwNIwQJkLqiEzmQBkaybGhNJjc673ee2XNObL6LDyCyQzeYzCQvdx8I9NoZ8EWF3Zyxn1dE7Lui18y4qrANtfgOvfWWU6J4/N0Ito2IVp3lWqtJWAVEi9ay2eMXyGJYTRguYGokf7ve9hw47lvhP5PK3bkUkc2rabdDS/U4LljCsXZd0Pz0ie4xOgEXDpwLLMy0JhVhV0iSzuO1sDsHNNP7mXlcQ9u0Vh3mfcDh6aEXpT1ZS4MfSZ981DHNZbftqK5xzSA1xlhhDMOmoYfAD55DB2Uue1RMh4ZUIWKq0OfXS3Yno4KeX75qGImU0uEAjGNjCU/DHWL/YYUwBGqMhQ3pirB52sRU3vhxCihmxldZgVv4lvFioQa4CkMV28n0YowU1hqQQLw1lltqJcOk5k4xeU4LQtHa9FihsEHJVJB4pydmC6992R6tJ8bGUzB0p93RTxcdYxrkolziwrvptVogzxeSoDxK1soYWpNQuDykhlXeZ19jEmsnCsGcrb62DK22BrniOwxPOrXttKksqo8IeMh0SQDPVQ+KHUi9ByDDcoyVLnWHDjfb8wgZvJiIr3R6v41aQAhkI2Wzs1r5f9HJv6UV4ub0P5X+8l3M/H0pgz0eytOuq/zCv/8j8L/uF/9WXeLA93k5M59rX7O9l4bFNf+IDsq3ftuV+ZYsw88kNWXY5jS/u3zlpTUdcJvJgCunVXoDcrV0EzhGAYPwSjJeMuqtOg4ihDkZfhq27ut8U5F3fGAXAXP2QlIxQt0kPLjBxV1M/I4mc5mBKvsTnIb6asW9cN29iMhTCBRiSNIIue4nWxZe7nW+cp2Sg/Y7XWwdxtX/PUAVywBg4+KZh7lMYeJymLJO+N1+jfR4xlig1/UDLlWyPGgoaFOhbu6MToZNFKzFBV0bmhjYauClKx86ha7lkdgk023wMGxhjFgMJkCyXPKe6yRez5+9DeT3rSk570/dBTSfekJz3puyZaB/i//2fof/WvDKfyg38mtfdTlsYJYca3MBBIJ7hFeaECGCBq84SRYGSA8m59HgeN3VH2AB5WaZoKTgMk8DBzXnl5ARSoA0eDQOuALhtoYwt52Sykx72MHqeeAPEMFKqy/udk+5qvobZg/xgLeUmDzZtumPJj5gk59AXt73148Dg5Iefh6C38KRGnN36nk4YB0FeZAn0cyNStpVsHs1nudSWQDA97Z9CMq38haoq55vBOZwb6Am4L+AXoxLguHR9fXvCnf/h7XC8LlrYAqq4kW6GwBNo2tyx0Y2sdvTUsrWOIQGjLgyQYECJs4sm5YXOJHbRq3HBhwdoWXPrA9TIwoCAxoMvAW8/rOBiKsR/rR5OEHrx/ODYPrq8T8eG1dpKf4S3NipX9tRGhNXLvuQWXxZSRH5YP+HB5wcfLFS+XK669Y6EGFlhuIQfk1MeVqWfYRgx1w10/vIeXiMJDo6pHzn1DYXzSlC/BfHbXHm/+lYhgdnXF8vy0W62adRjv4YWz/ZbTzfvAwU8msvkUod9ieUVo2GH8rJFzqq+OYibcE1BtgrPzu2izOoZF4Fi0Q82LbrP2htPgFlX1z9UpZSK355RTtgBlD/nt7ku1B/US5rJ60mXnhndYvbd4VKVX+Bs1DDTyqGjIcI4Pak3+rJrcaXdP3AffvI6V+CLI9/w6qoOj+++zj8qzKJLNeVtryOpdtUqbiMyLri3n1wBID8ZQpsihjNjGAPf0CW+DI5DuxhU05ypzc0DUrO3zlnSaC5kk+oBRwdfX/+4XXP7XH9CulF5RkV8uw91lOzyQoFdJZJhXNsGUJl53QShHMPvPxyE96lxjJG4db0Cmw59q34nzBRnqer/wqicPKekGJwHcRg7eWFNi9Q8DiRnK0ysWgB1C7psegubVZY2NMKMO5c7hdUWGggBWG4OYWlrGN+bY3RKZ3wUXmtiu5pCdrgC6e/N+8jFJ46izPaKsl93SPHmchSudHIy8vXdFgnL5WxV0ei9G+EWyucPOyxobDwllHcceEh54/pfeJQAszyOBA9AO3pxinU450V9jjMGhGvA15WNMh4YTLJR1tjTC2iW/MZlDMxfnfb99M8o1jMLO6HiJk7XzuL1UngQc2P+ZnP6FJP9eigLQwtn+LvRebVqsyzp0OcV1x5qP4tbxu0dl/960E6fp8L3zfvuCqjg9ryPbvi0PnXnQUXePuvwtrg7jVMytJ2W+KK+YrcWlZW8j9i3Y+WlMwR1PrB2tAP6dAv/lDDNtu6JiqEDXDUKC1rspfdSC4zZYzmHXL2LzspgJrbvCvRjZkXcGx/6vFm6Z/Fw4dPgcCWPB5spBdpsWS5lgtlGK5prP5h51C18wdMOmmxkDcsOlLVhHx0UbBnVIG5Ax3Lv9aIR1Qr/yHPDV6FSwnXseKdAVuIDzD0OxbZuHFjbZh6lj6WzGpsMtk8MDkcg8I8dIQ9ZtbHYW9pQPFOdYtdDWaOyi81NJ96QnPen7pKeS7klPetL3TZsA//gT9L/6VwA8XxgwQQroPt+PWggFCvO/PCj4YdwP7jRRNztQuMVbgkEaL1oOPn76ScE1Ti/luzjIeJx8Nyze0cRFAjVTd+9wgHEIaCMot52AfOZNZ3QuqFb5+nhO/xwRPBVMI8tP0ygVdbqZpxF5/rQ9uHk8RSKBvfuTmb0G8LM/jBA+fziJk2Q87ACq7CxK7SCFsOS73SAMDDCkCVQbBAQhBrOPO3l4DT8kkCga2YxgJnRasLCFZFlaw6UvuC4LPrxc8eMPP+ByWQzw2RRjtZCkvXUQTLkWocB0E0gXbNvwMyqjcfcDNHkeOkVnC+9FbNaLAlMMCjOurWP0BZuacqqL5WpUMbBThud2HO4VRTRDR51NivccDt9zeHyrnACVCKn4ZQ9ZFXkHmOAKTOvfS7+4F90VH68XfLxcce1XXNgUdE0ZJACLAjoMkKYF1IqCDjBvUrhlrQOICXCZyayFTLsD3H9lW78lHcfvLTBO4cor/7wp6NXWMjegcYOSYpSGRB4n9h4kkPNZhaUlUlNYR/lfmY5sAUBaURvObvNlg+UlXdDRtga9CfSTQsNZjKZpwx14OXGpAti/A7Q7XvA5RvvpL8APPwLXF5PQK6CWr8H7Jo/Mvcxaj0c8f9bhjUUX3hap0ThcSzDLdil76LGBn/EUejelMqrW+S2efrj0+H3+Rph9VIF9y4OSYS4/vgCXxS35ae4ZVUEX9TyEvI43VekAFTeol+KMH2EMDfiPsLpwJQWT5WPD0Dkcu3qcNdJ+m4b7+4ppluEmR66ED0VA5s9xr6bIpVMV4Gm3RAAGUoFn4S0ZYPdShnngKilkNIgM3F5XDAcjQ+KK3HXq8o6KWqix4EnOZwL418hdp0DmJvZm6eY3Bd8JhYoCVPXbMe6BNqtmTjtEeNLwZIwbCC7bPGLomqh2TFO9G6c39tbDlVnN45dV9ttdMNtyTmfy1rHw+Ohyca7zWm6twOEr9+q3SxmjmUeK5i4b4x6PibGj6Ul3qFXyX52w+vwOQBjneYXUlfEKBcawcK1uZBJ1ZvfQ0VDEprwJlwWz8JwmGZcilIjvFZp/I6kq5CYYt833rD2Pn6MZvVWVdKY4D6Oj/X33yrxfy731L++PLPDrHvDg+3ePwbnQk3yyenSd3PXXEN9+Fb25vDXfhgLOjBGBvjDQXVkXyjnCjvPH+opCzISDduOeW2FsU/E9lTOvUhqOEKanrLOM/VD9M4D/krxC9uMQhcoG3TZ0YlxU0XtDcx7jWaVNeeaKeWYzgOHmhieu2IFIsrcwBqxethKGB2J7PimwycCigkYGpTZukGZGLiIreLygtYZLJ3y4AiCCyIKhNyzScOkN1+WCVS646A2vss1x+ZJV9Nc6V9yR77HmTm+pAcC4oOHCYTh5xcfrCz6+fMDL5YrWO+CRN1pjkCo2V042btBOsBOpeQ9v64rX7YbbtrpHXVgWemSTbfOjixmiPulJT3rS90pPJd2TnvSk75/+h3+G/jf/BvLjBe7eYt8TGUijA2ABxgZoyUkHF8gjJEUiop5jogjmEX7JhEGal3lusl2eu1NkFROAUtmlFXJ8oVD90cN3bAJsDGwDtHSosgN5Wp5/RCur9xz7NVP4L0851vSUjmcBIoI286ZDi3BD1jfUyPu6HglPHpSAdy15gqa7+PxR6d2bt2pdANTd+/JaruzM7rUoGK/Duhsd2hSyKDYHZcNLjQELQTUsbJcp6Kxey7LgpS24NsuLdu0XLMuCa++4XBZ8fLniulzBIGw8cMMGGYreOpbe7bAReYQAjHWA0TzcF6OlUanNAcubZ8q9yL1CMOC1t4aLKkZfXKFCaNsKHZYjYUhHH5spBFkRCceUff6novU4Bu8chjvy8h55U9ZR8UOz4ePWRiZGqNQaE5bWcekdS+tYWkPn7onFGZ3N046FQBtALBm6JjzhRFcMYbSu8Dim+XyKXBcAMryPowwEAwAeHqe/9OD8e4B9hS1VnUJdfjrmZfpJsfEK2hjthUHXNsO4essF4WmiaBkKiRzcdq+XXzdR3tGcCvi6955zxOZzp6t5+21kCg/SBtoI9Ekhr5aoPhR0ngJ0F24wMdoHVX+TAxVdStoivEWqwE//AvzpxZvlGoXIEZcbRRiL1L2CJpD+Vvm7/aVWtpTtSiJnMjiNNbZ71mEnudvUSkfkV2/Uc+LM59+f1uV4vd5VK59LjNR45QC7ib+Iu1QyqHVo7zP0aNmXw6p+7i+HOR7ygQOj5IYTILgc4J5kvgkSXDnUmuc7szG3bd5CKT/ojPPOGupAvksAPPsslHTic4YIFlwgcpOqZlhLJtvnYr+hUOgBiAbZ9f4kAZQkF5J4qE5j+WYBv67bVFypKwpia9aZSWsnS4nlRYr8QhFCOsYivRTNesbrIp5IyXg/EN6JhQHuRBPrjP3XmlNk1/Wgg9ySV+/kwahShG3M/e8BFdXVwePnyEvfoEcKumzHye9U/o43EdxDRndfx6DVsYx8SxESlXT4/BUAHdQZ7ErcDLPnRe0U2vU5tZq8v0adT4oEkwX0rJcUHj0jqj0fTF6J8D7LrKtFUWfemQ7Yn8hD3zTEYZnj4X2afBZhDCfzWuwVdLMYmm0HeV8UxeT+8ncTAdCfFPrn98zNLy//rdtPp/NJG3Y5sI/rQ0/fJsnJd2/V7zc28atS5kI9bpmxtbOFY+TOoM5AixCPSCWdpAbN10fd4sn2rlDWAXoWgTnXV9TJQizb3E1lYBiunM5Bd+0L7irBY5wn+lrdOU/5UJt3ttePCMTm0T1kgMCQMax0sugQeR/ghrkoeIDtVevtht4shHNvFyg53ACAiNF7s75vAxcAmw5cRsOmDYts6MLonbGMhi4MFst/bn+Eenr+XemL+EBZUwRETl9SM7TobBFgrv2Cl8sF1+sFHz58wIfrCy7LgoUb2mbHLvZ8rkxsMhAU4vPD2J+FvdzG5gY+w6RgMX7MIf+PARk2zqMPmzZPetKTnvSd0VNJ96QnPen7pz+/gv77f4b+b/6tfY5QNAjkyaDkPD75IZWbgXJcPdBEoRgTRBslbGYIrzzDbuzCTSqmAqBALRgO8gwkIJj/++nYDgAyBV1riJdrgBmELNfbTYA2PB9ce0OWPpxWPiPwn5zj3iTrRgPOaPFkThsDo9l7YIbx3BV8AKn08LoDQo+/vbNiBUCyr9TzkDwAfxTAqsAmkHXg9tMvGO0CVkKHg369A93j5xNbGK9hiaqnRahZBF4vF3y4vuCH5QUfrhd86FdcFlPALUvDwh2XtsBAww0qwGgLLq1j4YbN54ioAkMgYgo19jYQcYlq58m0wWjuBWdHHLP2VzRIBy5Q835UD443BrYxsPJA5+6gsmJQgJXAUIFgGMB27K96CD8D9h7/6Ge6QAnm3AylbITHiXpEm+IA28iAPkvZxZagnRsWtj68NMuB0Kmhg9DEPCSJyQEIyzEIJTsgioEK4XBBGqG3rHzAPcZSySEeIjK+O2/mSbN//bE7yv9a53bCXo9SKHJUqRr4xjLQlgYWNlBPCIPEwI7EgCw0XIOFP2MlMDjzbyVA/WtQwMdNADmfE1geEvZ5AoV5/RXPv0W6Kef+ZYP+ZUBeMdOMMiARIVizcOsPf1i84q0hPxmnLxr39RUW7icZGXaLTetepoeC3xHq8lFNzoameo+Foi4QdeWyHr4y3RXp9c4BOKnrZ8s77Ku5V7shj++xZoqtANgt59n2/EhMliHndP6FQdCQMm+s74xbqHsXwUILp9FPhCCj9NwiOG9XSnBRxI2PIs8c1f44b+8v/5ef8PF//6fZgxQeSh4CEwr2VclkHrI0jNubzOErluxaKt6wIbsY+/O9Hpxhp1QUAwMyJLs5eKoCU4lWxiM98eKhjhJbPjmy74ZAEeEwS3jDIitoVCzmx9DpFRdKM8Bymx3XD/u8LustlH+005jlDXfXzhV2YATl3s+z8gl+7tVNevf7u+mtSz9XnE7unRPcX8Jcg8Iww8HsrG1RaIsIeNheymq+LuxFttbOlXSHqnHOYytTMUHcu9DTRLum1VCkuU6dx5ktgiYoH4Z0YQhCiDnqbUsPy5hQX29fuyNVbK8CeV0hq9WGtPB6916Jet6FwZytN0Vdri/Ziy+/pQkD0PUb7AVOv6XkHHI3ogzPKOsPTL7z4GHHVfdW/b7RjvjZ555RriOaeRNj3ziys8grHs5q5O8rj6VU8mqupbKLwXcK21d2hc+Khvdp6JnJ16DxZJ22SA87u/wY05iyZdCDsZKwGYpoI2Ad+GV9xS+3G37+5ROulys+XK9uiGvheNU1hpJ8JUI+lxp4l4gIhogb/5kRZeNuZ5FLBzVAF2AQ4QLGaAuGLlhFcBuCPiwKSG8dbbiCjn3PKy6/p4YZX0OUPhZ7KqKfyIx6+Inmtdb1jIaG3hhL77gsV1z7B/xw+QE/vPyAy+WK7kaUTC6zu9yu3MzglQlMGwYauKnraM1oWWRg+CEltxcQmBos9L54KoLf2D9PetKTnvRXoqeS7klPetLfBPH/7d9j/G9NSWe52gDywPfEDrwFkAYGqXiSaL8OHk7KhXXzYNKprNuZ+4aSruaD2594WgiHod8begAQHNgIAVz3gvi0IPdTkIe5xLACCTDwqQVQ4GVmOJ+QnuMZgvrtIzrK5589BxCZl8GqUBqgxtDeIF3AQ7zfvaQKjuxA0/I2vJU+e9Kl3cFgXkv3P/u7PJADE1fzz7INfPoP/wz1XDnr6yeMvhoYKYqxbXi5XgEF2uLzKMwmx3AFAXv5AQLYQbdxw2VZcFk6erNcdSSKTTez/hsDGNZfJKb2tXCVksnDdQgGDT9AM4g9EKPnGmSN+wyEYLXDC7sSyryJGi6sEG4Y5PkQ2oa1bRitISz1h4z0UmBliDI2tbwPO8/GnSuWf87xfAOGJJvfxJHXrGVRFq7KQieq2O/kUB67Yo4BXLihsSUg79yxtAVXXnBpF7y0Cy68oIPRBODN5qbALHZb93wVPm+aWoic7mERWa0/iXUH7MLzXFod7RC4pYdA6Y6zuVsOs6fr6eTLs+v00QXvOYxWkDiWzNkw1e/8ACw3QF4HIreWqECbol2sr1YEDm6ebKGoI/c8HRk/8ytTMChyhbZDRB0NCsFQYLgHyEUYfRDkNiC3AZhj9a6s3edKJ4ORvPERg6QHH8v38hMw/gXAf1Yu3DbgL38B/vQjdnwTh8oaA0AO3ueA9l1N6sIt32ejCornHtvnk/SNh+m7K/SO+pbXRwsjtsxH62/3xgc7vbFovroyCNsK4MU19yU0Zj5TTannnmKTL9J8jjMEgc3PmQ+LEC7LZr3fLER0ygKAyoBtP35PGgTsQcozRalZ/E9HsuSjHt8sbhFnWkyuTHfDA61luBwUET9THnIwVWOvo3iKpMdBAMOGy3q7yZ4BMiOSXRMcXPPN04ephBXV8KKT2DAm0z3s6XXsLWSZ2L6yQxjjhXbXnynk1CMD5GVFUXrPfuczKNepff85Vl2lQOh8zuM76eQtFWGnFEy4a9fpb8ED3DhHap9RjDViQvg89XDcyZsyK7LfWeYpzXDVzATiBmZKD/ggkaIA9Wo1Zs9tN9eY4iib+Evuuf4F+1qg2Y4MX8yYhnQur5PLAqLqSjxyIN/a8GjKfRPytbYn4yNKmuw6FZHp1W6dEKIMaYSX9ZD1kfOSYNKV4qjje1/1ti/rha/VZ7sdLCbJYR7E25jWMR+Cd8X1Dx3s6PB5X/SO3pB8f18qvIBmi9MAiS1xG9AA6gR0gLq6p6pzKp/gnIvEvOZGKT/YZ8RzEV+pocyzx06lXXgVk2+1IPgZPSpmz931758V+I8D+Ad3iyr8WRmen07BKhhVLmLz2DVFvv8h1oimAYqoTHbHEUJxzgcKo18m21T9B1ELwwgMCwffCb139L7ger16/65QFgwSLGPBJguWvuEiC666YNMFH2TBTS6mePLnEgHbIE/hEYZZb4z12c9fMgkfyk1vFJKyTFxj3o4RocgM+Ows2hqjtWY51rmhcUNfFiwN4I2w0GL5BIUQ4bfNwM7CWornpNtkmKGTaNqk2TPIDTe5KFH/6qvwSU960pN+FT2VdE960pP+Nug2gH//M/Q/+Whyo4etokBdWwfR8FAMAGsDyUCAdSpiijmxcAlTOicozwMKEhw4eGUBnuvKDophhasVRHJBdoZDKrDLzqK7PM9PQKRqObBWV4StA2geDmseOwo9FuwfyfO/mpghC1wpp8BqAro2cmArDmqHdka/HA/IDytHu5f8oKdXYXoSBpB0vHZeKduGn//dPzluavWWbvNA1w26rtCxTeCpOWjjCb5JBJsO3OSGrgBtAh6KBcBCjK0vpkAbAHoo45qFNdkslr6ODZ0ZS1sgfTiYYkBVgqFDDNTKQzDccZSwuOJpwKM2Krl3SIQ6df+6toC6H7wdiGoK/AJgpQ3bANQtrEXED+SMActXlDgIebg2V+7txsUVybKb17PfqYSYbRE7soxZHOANCzMwL7wXOze8tIsr6Bp677j0i1ls9gXXdsEF3fpDLKfBIOMHzA5SkYLYlJAdjBnp0sPMwQ5+1vGusPdQuqpiSsthVrRSlCeEght/Kb0HGPtaCzfYmGKP++/GEFM/uQHjl+FJ3R20vMByl4DArBluxvgbpULvWxH5/wzyEJfzMxOn0k4ijI4wdBXITSArQCUX3Q7UnYWfP1TfvuSNyt59p5v97UgV+PNPwI8fDp4awGl/psujfr5Sd8z/bDeIyVArXsJt5lyhBIBPUc63FHi/hd5CSt+cbnM/tWt94geQI+W9KvC6Aq834HqxUJdVIaqYCyi8tnKfR2m7/+78WOO3NBiy9xQgpysHZAznRZH3igEas361HSlbHBqvsP04PSQY7OGQAzENcFJBFhXKAVNh9952ZZtgYGB4jjqAmOcT1fuOzaM8FYqis3biijWvhwpchHLZJmWl+IPxWipdG8y1Mtmzpk/UuHyOYZ11sqE4yHAOLu7B+jnmWsZ36q0ZVbtvYo7u5r8mcGxXvM0V6/p7Dx3X/WFNf46noVa1XBTFKOYez3bxnH5xfVFXpSLM9muFeNg3Ny5i48/kxmU2PV1pV3IgmmfLDD0vIqf56lBB+RQRD2sd0zN/76lHqahTsegCOnyfU0VTduMef24A+8H/kIExv91Op4pxE4xPG8ZqYWSnPZTO3MG7/rD6Bb+ZCjz/0ylfmZOj55DGft6+RZX7AMD477+RIc7JM+tz39y+Tu6P7glDusrSj8Uc3x+L/aLt569EVaxTXyPUAHSAO5mCbgGwKLQBROp5Uy18OXsOODtF+Ho+NHzyRX9WjkHxBK6dGBtHnBsUHvXDL+JDX26wSCf5wLiXitLZzifD88oysxuBROSRiCxi8mIaHhCh9TCSqeFgYxmRr382PsRihpUA1m0zBZQouHVc6ILrteFyWXC9LGhLh1ADY7Vwy+sHi4wCUzZ92sJ4xowPLq1hGx2jDVMAKpD5WPXBRH1Eeng9o18rotX76LBSiMFodj6jxaKb9At+uL7gh5cXfHDvxQ+XF3y8XNGV0TfCRQkYA7QBmwr0dsO2DYx1YNsG1m3DNgaGDJPt03syIhNZGgiLnuEKwqcr3ZOe9KTvlJ5Kuic96Ul/G/RpQ/t//hO2f/UC8jw+Brg0O16QAtTQVAEZHi/flTGiFuZwGxmSSgEH0WACaSNMxdwM+wQoMFy4Hx7yJyzhQR5yoaJIEyTa4yrkB5wTZAkOSA0BtOV7ckWd7pJsh0Xt25Z3j0CFs8Norevxvh2AxgTtDFoEKs1AuVH6Cfv3O6Xc52Tpu8MElXvOThqlUxKFf+P0PhT6iynhwN4WXXEThXxaMV5v2F5XbLeB8fEFL9cXgIAWId9UQbJh6MB2W9EVeO2vWG83jNsGiODHDz/g2hhtWIA+CzHZXQllYUFkWfByuYIVaNTQuWGIZP+aZ5Ifm5nT0ryBoNQASKi5oH6wDoUx6/S6azDFXFfGBYyNOoQVrATCAFghm2KEJgMNgIIjT50tEAeIEhnL8aQcozh2z2vCu6rTMg/QeSNZG9yLwnBBdut5V8pxw0u/4MIN3XPQ9b5YbjruWKjjgoYmduBmB3bJQ16CLBU5M6eTTE3pkg3wHH2WtwKWmNw9MrZhuRFmX8xbP3ssPGKMdP/d3bXf8qxZyqa6VPwzKaAbIJ8AuLU8w75vV4aQKTBba9iwQeGKMS8qunXacn89CmttC101wVdRm/2IEJgDoE8CvSlw8zx0NXIgHmD9pw8tF34OtQt61OhH1//yCbitwIcrdglMT+tSK/QegP8wyG9RFLcrtk6Y+Oqd5X0rxR2w74L9mwLi181SCkiotve795pd4kjhNoDBhmwGAhmKtnym7ufEncKp1JND4cnGdwLZ9MiJEbYV7nmXVj/5zAg9TMAbINT4pxXb/++G/m8WcOPpteD5Y2VIyisAzFtOLU9det3JwHBFHWDeCgSgNWuHKd8oDUgscoC1eXrHKcJDjkShzdviBh7Rf+llGPc4M61O2Y/X5MkvdHh/N4d9lzqEMiMikGSMg3n5oVxzMHUGmfLIvXynRWE7M4V9bqN4wGBqhfKn2Fv15Lo3ij7cnmvheGGA1mTXTD7rHmXh+ailiFC6NQZxs+EmuKKOMq9c6KlB2HnQWRH7ttdwmFNRWMP4xf+UCsDgj1N+LQq6qKz/aVkHY7hCXEeG0jbF9MxBq94vVLyEvjrFGvJc1iZPhV+UT2p1RZ2vzfwpqShV/VrzFooHxDuX1FKgP5ZTS9xforfz+azWhaYUequNpw/4/E72VlGn96q3U2dbc6VNVjSdEE+Wn3XvHIHKg/Xknq9Kj+SONy7xlTp/8SAg6AReCHQh29oWAN1jrriNZ6wthoWHHzXRXPRXdFxssQizzMKjTvnRbIQe5qzuf57fnnj0K1w5h5EKZwWBe4eSGUJSYyg1KMzw8rZtuG2bhUFnRieL7AHAvbyDv8OVgNPDtyljgDBcYTRU0FXBIs7vCJdLxw8//oC+LFj1E2h7xTZW9DHsjEWE1hi9NbRmZ6BGBG7NPM5GA/NAE/U84kDNMH8cg1Oqe95b8+ZMhj12c31/KmLGGc4CVy7UcOELXtqCD8sVP1xf8OPLD/jx5Qd8fPmAl8sLPlxf8HK5ogmhscJsYi3MJTbPObet2LYb1nWFyEAYERAHTzcebh7OAJSxoEOJsUEehvN/0pOe9KQ/Oj2VdE960pP+Zoj/r/8e/N/+a+iPi4Ne7ECAgoaCyTzrzNJ7g4hC1m0CS0CCPURwxRxMYZPotQMJFr8KOsS9qQiQYYmOBXsQI4F8Oygds4xMcKF8PgrBCrOKH2oKugGoJ4MiETt1EYC7sHKEo3h/Iur/NiICOkFHB61m/Q62gxFY0vuGvCGlKz5fkSMQ9YjyVEcOYtQnnpVxePAK4C8APsK6UOD5b25YAaw//YJPH37B9dMv+PT6A3744QNAhA/XF0s0zoTbJmjrwNgGaBu40SvWXz4B20AnQgcDlwWXxmjUwF3Q/NBp3kAW9qy3htE6evcDsjgwlFOQML1YkGATwcI4ehA1c25QgGPORXg39fWgigagU8PCDdLEy7B5tbaBNVIsEcDUIWJAlT2PTEGT9UCCqqYvJLBWP7NoAoPJ8vrZiqIESg1ba6ZAg+WPszwNFt5yYcvfcOGGl+XiOQ0sx1/jhoUYXbl4CWrmRZwKdIV5pbAdCFWgSqaEY0DZFd9Q6LZNHwEfhyGCbVuxbpuDje/MTP7o0Ptoap98n95v73vi47IelU276LlT1e/PldVv7QCJeYYSEZZLdwt/m4gD6qHOPKSph5n7fLC3LyOJcQXMQwPsB3PLJaaiGAroCoyfBbQCWGEKWWfniQXLV+aLv5X+8T8C/4v/9H3Xvqn8OiA0DlzP32JHKMqGnUth3TF093J3SeVLfw3aaVUOdT3WrX4fYfViwhMBY3NF6QXmNuoNpdJXUdARBNt5FHp54aKlsP26hPJL1DsAKArA17xALWyXzPplXsLH466bYrwO0NZMUUFugS4KIoZ43ljThxU+LjqL9ws0lATisK0oqDWrilrIZpOragUm6l2dAKYzpvNnwHPZ+UNV9v2pZXrT7uuTD7MrC2qcz97PTXGljyLkOs0N7DBPKEvYkz9nH2avKOMOeRzJlSy7jnhElDWajz946H2Oajek3HUmW2JW8+57xPTdqSb8mqMgF5KX5+wlDw8ZoKrLShZ+DhiiaVREJHeKujNSVePfXGRbQnrizZx1KEpfB4BDObNT1Fk5GdpePOfhJthEIGwSVOvNwH61HKfpRPz+4fhVpEMwXjdsrxs8omh56Ll0Wz/thpxg8g5VKXkGQzwv4Q3yi+R/ktNwl2NV/PIfBT/8G364Xt/7jB39mi1GPRRjNSLAfqnXLeOxsm32GJWKvKtKZxfR6dv95e8o/NiMybIKU7Zox6BGJsMtAC8AGjw6yP6MpEqm9HK5ORVGR5n/UME6xPusnf5d7G8pcmiKHjsmf0aV/Vhl3PscUB6I8MAyBsQNHXlpboczsMrAnz/9jNYILx9ecL1ecbm+2HFfFMMxAsuhCpBWr1/32mL3ehfBEIUI0JtNqt4XXF8+4OPHH9AvHTch6Ctw2xRts3CMTRsWveCiA8t4xTI6unZcNsGgBiGGuPWgQDCK8e/dXDjrp3fI+6f3HPbZ95RlZzczxGBuaNTx0l/w0hZ8XD7g4/UD/vThR/x4/REfrx/xw/KCl8sFV1fS0QYAGwgCRodC0MSUqzYHnYETmxIT7uk5BIq5I808iKb4BFlIzSc96UlP+h7pqaR70pOe9DdF9C834O+uJrQxoSmZ94ya64SKhc3J+PTEhtgyYZqao4S1xATnQBMk89CY+ZrgiwmT94erI5gxsUB/yFutQgB4ULLQHzfPD9UB5Q5lO+18DbzgiDe+TQbBKwjaCVg6yHPnaSPP+Yd5wKgPeFzkyUfdvbu/uJhkf/YReniFKel+AfAhitXpBeghMAdteMUncGtYlo5tWyHLJYEhHYJ1rLi9vkI/rbgpY2uf0BS49gXXtoBUQMtiVqxjGKAEhsiKbRvYtg1D7NXCrsLCEKU1ZdRHTaFE1nY7oIQ3kQFIrAVPFnUligFclqvOcncJC7QtICVs2LC50okxwFgxlNFpQEgxhswk6yA/0Aa4iTxwBzY5Epjcg0INhI4ZmjCANXKQrjN7aDVTaDZuWBpj8XwGl9aw9It5G1KUZYq9Bmt7h3kLRp4+goXGMaBZIFhBbKFRqMFyWbCBz0CDknm0ZE4oH4MxBsa2YYzNntk+Dyy+iz7HAmICfCnIdXJ7fnkAbZk87QYKt4rfHWlRghkJrMD2ywZZFAtfoE3RiSzMDAHDD9ct1+SvQdY+QwUgMgvuwr1cQUsC0A2gT1ZvRDTjAKzogN3/UUjEvLg6f35upCfbQbvx9k1fUJmK3kh5/y06rpb5BTta3iaHz/4heHkisfHqEyDCUoKQySuTd8aEKYqV8JQLdJFLfdO74MAbRtRlgpwEA/8IAHVTZpMDgLE+bYiPIbb38sRplwyBrmaQROhWneZKwJIn1wDM0Gb4fVlN31fDW04Vg9lBzJk3TsVD8aVravoclY2LdyGoIlCVurfdF9ODKbjz9sI0DtjpTt+cWhWxLP386B4Po57mUFXeocpz38tsCqMnev8tuybQbp7lS5my+V2p4wT3QxUhu/SS0/PYQ6W6Evao7KlSloi60Zu6vAiIh4wmUrTGADpUFa3dA6uWw4ryfRQc38UrIwzzZpjM8E59a6mEMnFEDuWY025gNsbm4fHYIoi/S27/jRSIvIQCcVdju4Tgct9Z4+jwf1139WxzkAneQ/W6AfOke+viX7HJfq4qdXV+jiofisAU72Cfh/uNjtlJ36rnTrx657MK+3z3Dnu39M9+IaRybhfq0iOHhAHYTvAjN4RixfAgkQLdr8FH4q/L/ue/IVjbpKK5013HOX1C4Wdx1ra5pTA5nYUhjUAQDCEMVQgRWmd0ZguZ+HrDogPr2DBEp3KfCUoCFpPxrQrug6jB08ltelwxKIp1DDTnf0MFrTGulwUfXq64XC9oAxASvK4DL9Ix1gtWrIhIO3LZLN2BEF6w4CKMyyD8LISVN5ASbkK46QbbYIt8czb56PC+XvdoMv2axRYbB89c4WbwueADv+Dj8oIfLx/wp5cf8cPlAy5tQScztgxMoHHDcu3Y1HCZRh2LAjJuWPkVQIncEu0gIPOHln2t2q90buBuOe+e9KQnPel7pKeS7klPetLfFLX/878D/pd/B6Jm4LwCGDBFhJKFZgIhA+QQDDwg9hCHIQ0WQEyppKbyNxHKUWaoNdD0uLNDoOahYwJzrswL3AtvHeLKQVotBBPdPFxNt7JpVVAX82SD7kK30XmhWfKXHJlrPQMOCR8twLrPwoR4/otOpkzsMOvfitjU98em4uS3u8rXmljf7EG12XbdfTcLvcvjEU3q86AInzsJ7DbBuK1YX1+xXq9Y1xXbdYCJzGKPTamzyTBFhyrWseHTesMvr5/w06dfTAkSU0vjGEjY1hXr7Yb1tuJ2W3Fbb5BtgrSGkcZhJeYVzdwokQfG223RXE2RxurKsHivEW7SQ8AEcAvGRg2be4yxbq6IGxg0ICQQsjxsUPOAG8O8MxSS2LioZDQzAjwZus15ywcX4TaLpxwxmuesYVjeud5MOcccSrpmSrpuIS4v3NEQSjlyNa23DfCcCGZJC3IvK7Hwl+ph3Hwau1JRQc3Pf0QQBwJEPBedIzuimukNh4h7Yrw1X3EOJP8afO/Rwn10CD8+41E9ygJP/MVTysVUTT2lp2DSVwMQcSX0hV1pbGGHBGrzXAINdpjsK2KamV9FC3irSNAm8nj2TwD/GcBtsmIAu8iHR/3AaTVr339DbDbptgJ//hn4Vz/uO26i6Od187l8T0dU52wyFQQ/nqM0J8nZhnU3535L58Tg+HMfIlEPqh91zI91MmMOdO7L5Hu5TCUdkeV7vSzAxw/A5QpzDT7sDWVPt++KMrUil1oHptYv9k7jzpF+EA0Wjs4XXp2zCH7/TsBbN4Fuw5QkSlAWtGbKNRXxcMmWS05G5Bb1MN1QX/fO/0JZGV51cOOR7BP7LcM7nrqq7Ptk7tDqzdPEh3eXR9Przl6m5PH6aEf89HZuGsXcrfxzynFRQz2dhlEqxzNRhybWkr9+8bI4CEp0OuFxN+d3S5zO239YVvfmXbEW1D3ovGAN6WLyXFEt9085LIY98gdpQfStJXadeG7oqqAL4Pxs3KgKuVo+EzDDYfpvOYLx/DI3fC1F7lR2wNmU4YzN3VlFFdi26E4zRjvIm9+MRDFWgdw2yGZjkvvdYS5Q9MGubpRyC/yuh/w57q3r6i1BPIbhF4X8069QsD8qlg7v6xda+MN7WGDhm9PoABll4vSWOZ1Ot/tpFDQfoOXv+Oj3iAw5WpVNvIPNV7Fvt0UAfh6Y9QsPOloAupBbsSEjxqTD0pFVkEVGIJgHqWB6RtetqPbbro4Z6UNnpyrZAMTRQCmjT6uGZ/KhpP9RgP+C7Yy2Y2CEKCiWNlOzMLuRi5XYoopwg7KlubCzi81bgYVihlqoW439DQBI8zqA0uhs1s7OCNu64peff8HPP/2MP/3pR2zbKz68WO41ZfP66g3gnwVgC+l/pQULdXRhdO34GT9Dtg3CduZSVnQZ2IjLfheCuc7mn028HIAH79+id11nWcWhhEZ2nmzK6NqsbXzBpV1xaVd8vP6AHz/8gMtywZUvWLrlqVuWxaK/cAM18XDbAroBUIUMcaNqi79KUDdomgsljERN/rcJ0FpDX+yseBdc6ElPetKTvgN6Kume9KQn/W3Rv/8Z/H//J9B//W9NQHcvKGIGxBVIlhDMASYGhmuRXPmws/5zAEo9vKUp6MrzmDBtKx31dc8h8+Qw8I8ADxVC+7NFPAPIRO72Uxyx/MDihxioQm/DD1cb0BbQGCaIdn4AaZ6faN97zq0Qjr1ruwNhltFgCcgHm1uSZcOGua8cHnz6FKezA0cAYAkgx1H0kMcE+4NLhWjmQ85CjsLqupABtgPzej8TQRUYgm3dsN1WrK833JZXtAuwLJ4zbVlA2wboAIlCNmDbNvxy+4Q/f+p2gGw2lhsPbMPi5q+vN9x++YT104axbbitK7bbCm6eD8XnjU1NUzgT3JWO52xBqa69ZzRYiBUhhcDCaoIamMlz0JmirPPA1lwJB2DlDWvbsMqGQaZ6kWGHW8tRBLCQhxCSDIUjIFdcH2aJK+hIDICL/Hrd8z00bujUXGFn3ooR/tNyN1hYS8tD13Dhjh5z08+sMStMeeehZbhbXgoPtUXD/MQszKZCMSC++HQYENF0WLhWi3djc08MoBQxS1oRU9a1QHv0jfX0O+B576Udj4gO0/1vySd3Ye/8Fi2Al9iyp5t5dqIDTIRBMkOcea6Jb9EHAUGRzzmBe/eIog0Gr4T2M0A/w4wFTrBVCY/ZwCffxxR/H/oP/xH4u4/GmwC8zbUTAcNnOzsGOe85Kzc6pW5Yf6CJfEaB4Cpsv04lkszvUdBaVdv/ZQKGaAoszawplgYs3ftf7p+lh7IARxqzMvvfaxeTA5jOCy0cr+WJo0Zm5FIUNOZl5zlp5X2g+PgfNvCfCNwBkRXCDKHNlBHD8/HCQnSrCIg8tJcDX8kEhhsmRT+pIkzZjdcf+uRIuwtsruXaPSYkenRvCU0Y6/0RVW/uKGOn943rdgrU0NprmfNH+Putqp6soZTtwpMryn9rs3hQtBVUPn9+LX7Jao28gzZ1NQFRk6LEf7Pxmr3mCrqQyXwzTp2tRF/6PKlelETuhd7zM2BKu1CcHVtRPefqX8wmUQErl6XqcqPuvfnztxAe/EpygytmhfKALnCjnMJbH7oOfW3SEq2j7k1FPUA1lHy8PlpP5xNuL9vPLz97Poi5/dM32DDrmt99n0Lw23TcAhX7pQzcRbVF/fmNpR4nkdxp6XDv4erK7T5b7fewhc+woTiyVAWd/UBmi7oweCGgKbTZdFbgPvVttsvLqc89ntHit7JkQ9GmqrZkdHZUpoQglDD05XfcD/2b/RHGfmyh8Zn9jMGRgI8i9j9A5MvKlEDDPeLUjW/Fc09TKMbitOhnnuGK/dYazIiPMYZCMLB+esXPP/2En3/6Gb98+Atelo7lwxXX69WUm9sr1u2CV6wYaqE11yG49gte+hUDG26y4iYDXTY0GD+LKCBKbiF3XJxvLdaTMf3VlM9hCwNKdr680mLRT9Bx4QUf6YIf2wv+/voj/uHj3+EfPvyIP718RG8d18sFL4uFulzaYqkeerejrChULdf3GAPbsIgloqZQFTWlnb2PMKxlrxGAWADxc+TXsx140pOe9KTflZ5Kuic96Ul/W6QAXj0sBMhC15F5zwy1vEnoANbNPOcEADdAhlm0i7jiIoCGCDsVpxACGqdHCTycjv84wTlD8udRLi128eAA6kcU9whKbz8hB8XIPVnUvNQUFopnE2BjUBdo24dFm2enCUIc5fjPyfZ2f8O+4HLwPJzYlAnUmykQmcxKs1HxUpiX73C744PzNYCUA7gS4HHF03YF+8MU09PpmGfmSKQJNJGDteq51fJ6AXTbsN5ueH19RW8dHc29uoDWGmi5gGAhUJgFwoTXbeCX2w2dTdn0yqt5fLUOiGB7XXH75RPGOqCbWDjFTbD0Dulu5U0EEBuIm65NtO8uzCkYZJ5+mfkFRJYAXljRaKDJwKoDmzR0EfcOIyzcsHLDKg2rJ0gf5F51DhxtjQzsBWUYHIJZpooqmtrht0Bz9n7ozoutKUxZR5Y43cJadizcwa0o6mjmpevwZOs+zqzePgALmbdd54aF2ZR0OWdsjC3EZrHsVzXlkm6mtBucykQia5+IYmxiYT6Z9sv/bF6dHIp30/ktpOVRmdgto19PBxAbWkAi76Z8kCYWkp/ZlyZtivHLavj9i4KaB7/xgsj/Lz4pX5USoIUp6SL0EATgDdBXQIsiLtsL7HNv+fe/Gsf4khu9I27/82euC8+lGgHuCECe0XuuOR2Mx7Dk5N8n6OZXo5iIjyp/D9zfXS9lIifw6N/BQd4K9ArNe1yRj87Ay2JedNzszxLRIlHGLEenHJC5YQvaulOoVaQzlAwGWKpmByegqh5Xj8k8jLUThsDz08lehjjrMgFoKEDm7axDpmdTBWU1FB3WjqxKhBBw46Tw5CaQ5aAKA4Zgau9ZAzuQXedz51dvt8mB+rd4SjUKSBOWg2Ishnsq6mR6wyH21gccq7YhGXrN6RQh23TfJnonani6P3hIuliIMYZ3/befE/swwPfVVkUa3eTrrl1ZcE5r6/fYP3UOffId8eumBoBKuFYimoYztU4eZj51MRnKcfYrZWjsqaSrzExkjiN5HsgwCIpn5/7mMksqnlWtDs08W8KTXgEM/z7yUn0zqqxpCGQdJhcOa9F0aXfazZV9f36Ver5VCAHjf/qKriqf3dcO6+kdl55+zvl1uOad1Ti75/Gvny/ts82h849pD1IKqW0IowRj27YOlDXjmhNpOspX46t75WWE0gfS6FEPItUJ37Y+jjk7fyxsw8sgk3HU+fH7um3yEzYlFrlijj1cPrt7utvqgnvH6JY3/HVb8c9//gv+9KefsPSGpgAityo8EsROIa/ZtEaAkkXOYRCGDNtDSbDdNnz65RNeXz9hu93QLh3UG5becbm+4Do2XHXDUMVGAxcduF4WrKNj5QV9LFiWFX1rlrtOmp3P0KEaa02SV2fVimx+HIevQrkpEMjzhncyw8oLX7B4aoIrX/DCCz60C/7u8gF/f/0BH68f8eHyAZel43K1fHTXvoAjF3ljbELYts2iyqybnUN9QoobUJv0tQ+sHDgNxPIQQtxYmRlyka/bB0960pOe9DvRU0n3pCc96W+O9P/0P4P+638N/djMApY87xaZK4U4ECcOcBAE5AIh+3eZs05meBXijohJH5athg9qsTIf5aQDeBxC5InkCKqlcD1P2qo8D5RhuZ6/AbQqtIlZ94uaQfuIExbtPPKOD6uQ0yPA/B4fEiRKHP2Tna37hzlyb8o6Bja3ZGS1OuZ9h9e772o/kVn+1+8ThDmjUhAdLzqeYo518oMYMYitneEhBvem1E2wfbrh5/YToJZzqxPh0ht6Y1w/vKBfzZuLNrGmA/h5vYFgltmN2A97phg2Jd0NugnYAWPmZlaUjiYEsFXbJGphymrX7FroHncdAJGHFGFx7F/Nm0+GhVSRYbkBPLTL6B29bVjGMG86HdjGcAXfwBgCHmxhMMU97OBZK9TCHg6y6DSbA13kFbWQNuHFB/OuE0JjC3V5oY6FrlhaQ+NuVpoewraB0QZbHjpidDJL08ahVAMW7ri0xRR6AdLGdIqQWhk2Kqzl/UAI9aQzAlbLoWB5Ju23QbDE7nBF5NkqenAwPMHR3n3vr6Wz4nbLtjot6Fx+R3wZQFpbB8Dd2Vje+vOADDu+9wsDDM8R4kd6Yu+9rwlvhicksLkiWMmC8DZl0ArILwLc3ICaA6wqS/0RHvRrxuBLET4F5Kd3XPfv/gn4L/7tyUMOffmIoR8fuqsgPfi+3mK5xDLOKb/rQV+B3ujIOyWhTMbngE0Ocv1+h2rG9+U7ImBZ3IuuA73ZX+2m/z97//orWZLkB2I/cz8R92Y9uqu6Z7q5w+WQw8fMcklx9YSgB1bCLiQCWkEChAX0RZ/0LwoQIAgQBEkrQhJ2SYriY8mZ6Z5XV3dXV9crM++NOMfN9MHM3M09TsSNm3mzKrPqWNXNiDjHjx9/mtvbYFJM5vbeipNllIye75KY8JA5CEk9/6UJCLN60icXTBEgu0lx1GKaZ5GVyvs+Cmu4rbZGxJsQnpU2ZNZ2V4RwzctnAtUoJX5NfNWUdGewQ1ia7at1pJuX03rPKahqmXDLFXYxp15D2NJP3WofYHOk51tDpv6C8csDe2htXDtXYMN/9fA/XWvij+BCc0ZoxAPU9z6sDZhA1MPVoa0HPRu4rl8p0FPX2kzm6UITmeGQKQ1s3a+Naux2SmKfqqBL1dOl9YhFzxgyo7Zkitkq0o00kug5L1DjG+UBtB+ZEjhn64vmWU0JSoeuj/YTg+43zw3Zn8+2d09cny7U1eGJftHX/vjaP4NO1k4IeSGvFk7O8d2lJvuXMMkRpV9CO9c423WveQPQdCbfxFnZQ10ZRCDzlqNMSHtCmlRRpzpzaiicoPiEWyW1D4Z2yIzqrl7/lcellevNUMQ/nUIcbadqp74W4GOvAEqPWA54j2gN93w1IwsGq9GdKK8B6w+T4LAccZiPGj2EBWJ5wJPjFziPoGHtk9GyiTxthnqSFcf7rB5g8zJjPs44LAtyKcqvpAk3NzeYUXC0SBNsCRKXZQZzwUKCWSxqCbN5BgI0E1JJOPCMRRiLFKPbuR/niJzG8+Yaw6pYz8kNV40lM37M2GGHnDJu0w325BFOdrjFhB0ybvMO70173GT72+vfNO1RmFHmGRRyHS5ccDgumFmwHBnHY8EyK68pAhBnxelZz1gu+iwKTGlJtp4EzAWllE3SvcEGG7yTsKGuDTbY4LsHi0D+xafAf/9vaN4puH5IEzsLF8tDU9QzrRTLUwVNMM/GWEXmgnaAW/ACJqTzP7M0r55Npsiz31VGUonmJiDzUEEe8sMJzEgox+dqbQzzoiugJUEWDePHKfdhnh4JUQTUgwpmGsMZvtdLGmtfJvWmk8VCBu4IWFTp0fgEapx05QL9lnOE9p6UwnX72jERZ4Q7yYRJJ1LZcyDtvfZuDfkUcqOwQBbBjFkZShbNiQaAbp9hf3OD/f4We7Iwpcy6HhfGIoI7LpDjAbukmQ0ySC0AF2VEwAVJoPkUs+ZJJAvREuXAyuBKVRSxrW8dOpWAiCvBkMxBhDBRNgZVBVrzooq3uSyqqBNT4NlY7NKCpSw4lgUzz0i0YKEFy0IgVulMgTH7yXK0AWAyBRcIs4+kjaFAhc4Tk1qvMiDJvPAImn8uTbihjB3tsMsTMmlYTAhARfNCaO46Qkrar2xJyVVRl40T93Aoba3VqDum/HRRo+ZegobIgVvJwkIoGuMnGtaRGThw0WTnD222NYnG+Mja7wtL9eytVcGuPSO9eLiiN7/onjJuVxD5f9uu7EIVtDrZIgDLDNAikJmR0hBsiVxR+nQCKxfWu2CnygoFqmRdGHwvwKzKRCJDm7EPcZyvwZlXytefFO4OwMs7zY82CKXXF1aU1pyIVcO9M3VEyVy1JPcypqirUvk3AQ/tp7CgfUOL/Q4eMfWafwJ91xURwqRy2i/PRffsGXBz23vGhTOp1UNuVt/aJnGBnemaoJbxrDhJUPGVhwIUaPjLnJLNpmK+xfciuzhTTgWhWi3KZ4z0UzNMgOWocmXgyjl6Timge9iQSKWL0E0XhX+9st4pst9scrIe0S/JMM6tFCG06tzpXxUPsc6LaDV63wlraLH6bOzE2ptDf3xuT162Qpg9hk6rQ071FaGB/Xt8bRJOPAjPQmhew/+iZ/OIuWmYawFEzPMBZuCWzADHjKwI0TPdXxY/+/raO3y5udecaC7b1Dzx9GzSk0AsTzQAFZ5DPVG1PqorTsOlmWdp0YOMYIZTpFERWMTC0QIgUQO/bwr5M6McC8pxAZeVtQO/FNpzsiQd7/VtlnC9RhiI18e6Bqjk8OesHrUXwSgsG7qxieMW8XO53jtpe/uMqCg+v4JV6vvWf7wZeNPvi85UNHyCoDnoJv3EjpD2ACaAs5iyG42nBXTQkqxGGDi7/ICza4ViIcHghUqWuxtmEKtl3UDk5H0M4NcMfDTme1VFXfWvsgWRLB8doLwOLEwiyJRwyDXChhi9k5A0EgQRMk1KXVqYxWa0kixvNdRjL2fs7DzVSBsLlsOMFy9eoBTGR8uM9z58H/tnt9hPN/jhbofd7gbP7l/gsMwAJfX+BatTf9KoJWwe+IswFhYUEkgBkmh6i9KdrQ3n1c3TDb5/j4f7hbkbkD2RplwgM5LcpR0mWPoByphows686XaWsoBYQAxMKauJlwh2lLGf9kgpaahRnlG4qMJ+YZTDEcvhiOPhiLv7Bff3BcvMLR2Bf7LSa5p2wM95qucEQGY4ypuke4MNNngnYUNdG2ywwXcOBAC+PEKEIKz5s3g+aHhIYUAKSBaAFw0FVdiEz5EyNeGcW9OR5ytpnKa4gMxiX0qSWoIqt+FCh8D1GGXtirlqEFuFjaEZ1JiOep0FtKgyDCKaU6cAxHSioDvD2rfqsU6j96BW+H09p4GgCOptpKbICchZw3LlDOxYuRqx0KGdVW8U+OnoSQiN1L1zVVg2/KZQdyegktWnTqojVW2pUTHXnovXuwDCBYsUvDQFLc+M8t4M+eADDdk47cwpY0KaSJOSA4CICVjZfFPUqlwSWUL0BA91KomUERQ7rFOq66Ewalx+RjFlNJnlpa5dsb6ASBOmE2k+PALElHSUCHkhpATMhZBN4afpnAQLCHOdeTaPjKSM62y5ipZiwh71t2QhzVcgAIuG5yzG5GoScDGPOdtjbHMmrJHmMmGShD1N2NOEjMlyy7X9od6Lqh4jMUtaVs8UsICowMOi1Bms86vMr1gMHB0nVcSqjIJ1fJCQ2IW1i7ad2ZxXGS+P9yjCyLcfNO+sYVOMW/eV4CnlgqNsNF4P7xNAw0U62nPUGcsa6qkpqgCgAHxfVEC7Sxamz+bniYWbOvc2v6TKcpB6ZNJRgFmAo063h1eKWCfWc1LxNS//hmS1alDi58cDjYvt8vPnwf4MnZFwfVXSSafPfCMQZ2/IC9Up5nCqrHPhTdeXYXyyeq4hQb3n8qReg1VrZX8qybcQmEB1Ra1hNkM7w5lyep5RU36YglGcnDDlQCLNG5rM87oJFU0RshRAytmpEBHwbAJLkBp9sJ5rlY6xlvR0gwvv0aTrBPTGNGtvHISCnVIjNnLcgRekvBTDeFO3PAECubBylD1KGPJLSzWQZUou+FgMQs8H6mlyZWld6toVxlPQ0yYXERKGm6OwdSQavWgYgEvb9dy9qolp5YTEhPp6MFTayM53sfUvZHSt7dNEhGnKSLusW0pdH0yhZt5v5O+kOreAkz0qHCdq+WzbWJk3UFEhO5vZhrdFz3jtiysJ2Dz+GAIUD2lv1TFbeDyqbWcbyzUHyScHewfHEKTj+UyNHq3LpXUX3Wl7RkF3mqMPQ7lhG/hXf6TI9fnoHHfUrSS1adWu4WIjYgNGMoa6Nl4IUvtuwNjwlSlaLWIX/XiWBFAWpCmBbjQPnUyAZFaUarSde2gj4lEK8xIXWGMsbIzHtXUefdXwtBLqjYg7RRx6oaIz4DSmCEMkKdqidsZ5aGTPXbcjTT1AIIuMQ6Dkhn4ZnseyerfbnqRk0XcoIWdUD+GyFEgSHO6P+PrLr1DKgttn72Fmxo+E8VHKqqjb75BTQiaADvc4lBnH5QalLDjwggPvcJN3mPMOMhUwK67EAiRJKFKQhXAQVeCR8YKusJS2ydrAtFk4d+PkkuN4AMjmPZeSGglNNGFvXnMa7nKHPe00zUBOuE073O72uN1r7rn9tMNEpGkBGYAw+O4eBMEk6g1e5hnL3RHluGC+X3C4P+J4nLEsGtZbjTLMKJAZZWEsSzGSq9EoSmoJpAgkXRleeoMNNtjgLYNNSbfBBht8J0F++TXkL76E/LUPNBE0ACIGaEFVmBGpsGAQHlGnkINxKxZWo1rkc2MyUgakiQ2TC7/M26kJ+Zy7FsC8nGq+mipMTC2/i727I6u9XDFF3Vw0JFZJkEVAk0DyGnfT2FpVajghz7jOv8VDXgrUZ0Xz1J0yw2R56NBy0iX1SiKCflZOzDjJFNpL1rbQ66u85jrFXLhnwjAZpXijwBEAfinA7wiQCYU4pI+xnGoJemouVi8D5TjjTgQ8a566ZZ4hS8Hy7BmmPOG9m1vspx3yLiHnhMRAWlToXswKMImGUtHQZso0JiLkPFmoRRUOFZjHHKvHWgFrCEou6sGWCDlp7jaq+QktE4snVSe3LlXj8V0i5BBSrXALhSmqqQMogUX/lkLVW0rmAlkW8LLALbJNvQkQIVNWBSLpeEIETCpAm/IOtzc3dVUSEaacsdvtsU8aQoU0y5956WmuCW9nrm8y5hlQj0RmZBOEum1tl85L3DMSWLigSSta6CxKBBZCEVU+apRT0VCfXFBYE77fLfdgYXww3dSVWGXaA3TXrhE+PLQhxzouCGDF7lPYGnHpV8xgArMoj47CsPoaE2YXF7B5/QuAOx0rmqDeP64UdsHpQ/2+EghQPEaswkpDGYkJqRDkjkF3rOE2qVmcV3lz7H8M9/kU0r0oqXqqDv/2K+CDZ+uL6wSu6YRL4oA++eBKmbHuTvvxTcGKWNLPZZf4+UIUoCrLOJwlKdRVFXrmf2n5prDf65971lFu5dVnuJ1diRr+8NCXzKhe9nFRVeQSpPxhyEUERUQVTpbzlUwhp1b/ySJJW7gy8+rVMF3J2nZhzlMTuKngkmpfOqGojWFVvrCoNwYZLRO70Ynb2yzFPGNSEc0jN8K55VWlu6FNCWZVcvqa6kh3Jc6NocIf02LtdwsVp3meyOoUa683XRGoMFUh8ml/mzC8u0YuGG+qlpOWrp4/Q04+WukfxXv+H7XlIlAjKafXfLwtmkTz5DBaICVQzkg5V+OhiZKFh7etEnri+eWS5881mtGv1/xzNpktFx3qu5V20new54QWjZDhyu0YUq+iQWld0vpQ17uQh+Gj0Mc3D1zUk45ndzE6R9fHnxTQoe9XXSMdH4E2Xv1hGApR2z+rMAPy1cPjcfc54/ajhLwbiSBHfuvP1W2xuj/ic20nVPSPbjl/ezCM51XlrqySxgv+aRkGKOtZgb2GucSOQMnwpaN/G+DK1kr4Tt0sATAllVCzM0A7bu3VrZ1nJuAkD2Vo/iuTX6LhKxkFkIyFNcRkAjCZ55uIGjymlDTISiIclxnPXzzHs5sb5Jv3NHQ+KW+rYe+lGiDAcQBUCZgttYPjoZTIQixqyMv7l/dYioCmjGe3N/jgvQ+xuyWkaY8dEW6LRlc5LAfMNzfgsuB2OeD+uMNt3qPkgrRzWoIAysh0BBfLdw7CXFQxuHAxg03jyVcjyLRdvzbK9UiX3nNODUk1lCXRhISEfTYlHe1VYUcTbvMee8rY5Yxnuxu8t9tjlywnKJsn4/GAQ1H/Zr4/1rx/zIzj4YD7l3e4u7vH3d0djocDlnlRfiIl86IzPBZDpbqRJ8IZIeg8qjfYYIMN3jXYlHQbbLDBdxNmBg4zkBgtIBqpRDZnCKtyqlqvB9ZHRIbfnvulWc4LJVWsOFFblRTBmlgYUt1Pcq2vggTmsrO0V+FfZFqqYstz5BlnRIuGX0QRYBbQVCBpMqFRGI8qiR4UkPaGh5kjgVAxT7Z0pghVS2XJGq5RcgJN5km3MDBrLH6VH5jHlynuvM3kYxMFfKsM7sidogm9mvQTq0yJl4n1HsN1qXzZCjdsYFKe5bhACqPMBSjqIVeWBTlllPcLnt3e4vbmBjcpawgztwa3kKWeWyRlFcSql1hCThqOpQ6HKc2EgSKMRVg/WT3pcvX+8/FVAbMAkEWQEkNSy7UHhgVPS6A0IUOVaYuw5rkjrkKSRYBcgFQAWoC0ADtkeNYHMc8PF/oQAbuUAWj+JHMUAYzNVSXdbZtBs5TMKWOXJ0wpW9gpXW/GKiJX5RvaHNm4FAAJbAph9bBKVodbw7LlxmMWzEWVdERZ225hrYi1V7Pl2SvM9l3Dfpai4W9eLgewCH7nvY/6dXFWwHxFmXPlV66NVZzbvw+9ilxi6bKAhE5vE4UwQTbfLjizbPoOOgJ0ADDJoIB/oCGPhBAAVgVTnLCbE/A1g79k5FnXq8D06qF/GJq1Kv+McE5Q+E3BcQGe3wEfvj+04yHMLeg0D135Tsw2XMOZ32O934YQZOjz2CUBaihLP1R8jbu0Li5qWJmcNR/dNAG7rAYm/mD0ptPEhy4J1Qa4ly8BNbFVDIE5fiZp73dL/UA7kFiIPsOpLtgkAJQTJtFQfKWYlb2H3V4BuRPIPQO3qY0PwQZjeKab+o4CGQiKKFodK2jS3epJfHGZ1IEL9Q2T6vlDVQIZPKmoP++fCkRU6Rb7H5sbm2mfrY+GmZIbaYWycQwf2LpV2BjeXT2PakOsomEfUkdLQunQ2N7x+8q1hhPd08baL7E9guoRFzqj5Gyq4eDStEO23I7CGqbMPdc1akW/Dl2p1xR1aIo6fzecbqduHOteqePX5oCZIcloTutjoqReRwASi6V6MmUeQXNHwUy1vpEzoAmXzxm2nOSjk2E84t1xf/jWiesKfdeu2VL8+XVeKrwMda3UG73pHmv/EVH5t3EaPRbWMOYlWDuxz1VEBKRJI2bQpMo5Mg861VCJsYDDbPs+iS8ZULKEJee3Lq6A4fjoO9rCOjc05ufrSsdPgNsZnwCxM3ox/Jicv8dOlZVunLjz+4JsiqzCixrykpinmPELvECWoriTWI1vcwsb7ScyEYGyphAoCRAU8MKYeVa+6cUdDu/NWI6z8mFISHkH2gk4ATMvWJYZy3TAe7sblNv3wUVATNilhB122KV7THTAPU3Gq9+VAAEAAElEQVSQLNjhHlOZcYcZwoyCgoUKFlaFfglGcSQ+3jGG6bDq6nFhIS0pIyMZ/2m/06TKOsq4zTd4lnbYU0amCXvK+nvaYz9l3O73uN3t1K95WSBlxv1dQTHD0wwCLy3/H7Pg/qDKufu7e9zdHVCK8l8gghS2JZQRvRvd25iCt72M9MEGG2ywwTsIm5Jugw02+M4C/dNPgD/4IZA1CI5IUqtzi38mlJVp4dNwUeJMgP4I1sMaNlCvuyRKv6vlFmrYqyagGaTCtYGAeKgsAMHUujFlAlXQ1bqM42ExzYnUcGjEqYXGcZmhJGi66/BSFz2Z0GVkAr3UKbjQqmfwTzpF1LzopgTMrERzzoAkOCeljLWxejTWOAhEVoXBcUwGLj1IBWV4ZKyiA3OIABBkmD7H1lyfMvHygjIzhI54UZShuHt5h5Qz7ucZH7z3Pj78kCGZcLvbY8pJFQWLiVUWVS9RUqVCMsavmMcXAMs715hpVdIZG0YZORFyTpimCdNkSlpoXH7NvbKASZn35Mo0n39yZV2uq69APTuSqHo5mzBrRxMkMSZLPl+mgmXZ1TCWdeiIMKXJcl5Ybj2C5cezPA67fZ2CGF4qk+ZgSrmzy7V/g5hCpFnwS1wFxmgmQiHLKyeCshQUUU84FsFioWvUOtMUgSYRFACHZcHMBcd5xnFZMM8LlmXBXDRnxMv5CCRgLot5uTwAF7bMCVyQNq3eOiPQoOFLcIY5W1kU/wXniF7QEoQ4Hr6IxGStCyAHgaQCKgK6SW+E2iSQWnWDkAshcQJmgRxZPTVE0Y/0D+kHreAFGj5fB849H4Xgxw5NPQwv7oAP30OvdAsTNJq31/sn4teVz5UG9gcJ+kXwFIP0GBhPKdGzpJ6NYucuVv580VMT7DWptv6T7Hza79STLme7Z3VDw2S3kMPqfWvEAbrzqk5qGMu6dwRnNcI1vy0ghvtZWPNgiebvqbQHPERf8MQ/A3LHkIMANzG8XaBdEL7SafOb42QU6p/ZLN7HE6Q2rplL6264XtffhXIPLMeL+2x41o7Fk/t9ryRcpFbOvB8R5mn0HPHHzaTrgfYMiIr6WydD3X135S77z9Wluf5b+0CuvbKXqQFb6wNE16FwizZABFDOGj3AjF+S1eGeOtzlUmQkyuZF17whkhnNeLjL1g5tgbC5SaMJa2u/ofQKJz7VQ7PSOJpy08LITtk8zQWJBSUVddJ1ZV2Oh9+bB1kY5b6g3Gso+7Ud1R0DQCOHh2s97TRA9Hp9uFX+kP764jV944dHV20AXr26txrO0WB0eqm7ejIsYUr8WEqZkPYJaZ9AOwATIFkJeQ1X25NyWo3EpdJXP5wRgpB24MKgd8ed+FvQ0gYAzVvVzlkCzIClbu0ePhPgqwL8wHhqN5xNhkvZFOuUwJZDXXNVmnKNNLS/xulISGQhHCkhk4dnh0bRsMgcEAYR9wZnAMg86QkEFPMAFgKbR91SCtKccLg74MWLF3j59UtIASgnPPvwfdzeZuzLHjkb/wZggkYJQSFMknGHjBssmDBpDjjMWJZiDv4JJAlMjEKMmQtmUWXjQqUqsnxfsbCFArZ5JJ91VU7a4CMBuKG98mpmeDvRhIkmZMqY0oT30y3e391iRwnZOMcdZeyIcEMTdpQ14su84OWL58CyaC67nLGfJo2UIgJeuPKpL+5e4sWLlzgc7nG4P4BLMcNUxdOCZFFeGGz5wAFquQdJ84qzG4yEnKUbbLDBBu8abEq6DTbY4LsLL2akP/kC8oc/svB9apXVOYIlGqQXEZScJc89Q2RhjGAcMlcBoTrbmaCwmEsJG6cRZDguyIEVb7KeKDyzb9Hi3//qg9AIV4WBkqqXGnYJtFNvuuYOg+EzSqTohBtaE+uOrLwgMozUFSbS/HyYEqhkYCcqGC2LKkRTacxDtDTvKmnv6lviH01I5i0K4stQPnWPnsBIw3tIS4FyvGxcpruCeXtDnhYUGEMnuC/3WJYZu13GtNuZt8MMZY8AvhXc5gmUMtjCkiZkZYZEGewSuqlOGWI5wdQaUn+qok6ZTvXQm6YJU56Qc2oKYtLwLCysypQilqS8MeZiAit9p1rFk/R/miw8A5OAsAeytqOUgpKVYSqWmF2gSrrdtKsMVEoZlMyzzUNf5V3tp+pCfY1b3hv3Ijkzce6V6PthlPHsdnv1RgEsj4TmKSgWGqaQegHWZ8xzRGCWnWXGXAqOyxH3xyMOxwVlmXFc1LvuqK6IWESwfwwv+JAwSvpbgvFLeCxu3XOC6uEda8K9tfevQkAjsR7HNOqFAMhR8/YJM9I0ab5F37dPyTiL5uwiIqQlgQ8L5AgVbArAObw2tl/6z9ikq4V96xK1K9qsH8dfaTuvhvsZOB6B/U2oKOJyG1vb9+c1E9J/H9dN95vXb1ThfbTOftNg768eXqakYvcks6a4Bx3Qf3pYxPG6OcMhEaq2naDneyxIw2arxjrqQa3tKF2R4AYUnuU2TxXCQuwJA5sB98g37ywP5URy1oPuPLQXnwqxXKlkJ7zRH2MI7jPVodvffr6HR9cVlCNClPO3z4B68A9z+xig1a8D0nBc0uZmVWoe+uhe3Kcoj9a92+J7H+rHg/2MQloCLE9cfewC/nK6wtcc4FPZFHUuWI/KYlXquaItIeXmpa5hxxkipXqO1jZZHinADGxyRjKheKqeP42erbvS9jNb7EzP3UikwmtVmLY+EcyYDk5fcwuvCcsPTMX6K2CSphj/RuS9Uv+tETmGl7twPaYc1Ontx6bVJ7Yt+4N/jbY6v6SaQm7Me311t6T/GRry6LoofF+Di/TNq7zzCeGhtvcGi9Q90+E46R5R1jabkm5HoD2BJqiXV5aWw9CXgXQ19ijnTNtaTkZppIP0CQpii1ueTydLWjhNR6EivrraxMb6OnAjSnIm3koaHyYeQ5eAwmQRQaQaOSZR/JxMITelSY0Bqteunr9k5d1rK0F/k5Diqaz4znPEUlYDWcchS0qQWT3x7u/u8NvPP8M+T/jBsSA/u8G03yPf7rDb7fAMN5hvnmG5PaCUBVwEPGuUHGJgJwVUtH+UEuZU1DhXNOQ/Q8NeZynIaa6hfdki+fg4qoIr1bXCaLhkhwmeh51A2NMOE3Z1LnfIqqRDxoQJO2TcpglTmjScqLQ84VPO2KUJE2VkBmRecOSjKu4yAxOr8QYDvBQsvGCZC17e3eHl/R2OxyOWZVEeLmfA89OLK+gYxXI056znDJlBZzGvXREAE9VINBtssMEG7xpsSroNNtjguwsswK+eg/7ox+iEailp/pmSamIMMeJUOQnPiQElhE3IoWViOEsBKeV78qd8rAvoXNZj3EMVfHD96LgjMYv3qJxbE6K64qiI/onVNcPCcTWhrWUXQZMsxQoTCOVE5jTKObUPLoRqV9uvIPwzxoUyjDAHsM/m9Yc+B8gFYVHfmtOxiO/thV5n2bzL4G5jAmgMPZiijtu7/f3uIBmZThbMxwULL5iKennNxwWH44L7+wPu3v8AHzx7Dx88e0+tzS1P3ISEJKTyVvea8/UEqIALIXwqASln5JSRp4ycJ0w5I2f1UpOijDNzgUgyq0NUy1BmDZVZQ07ZOlU5DxujSjUH3OQhoSbS3ASsQh8uqpwrrPnx2HLaEQG7aaeKOVMievgrovbdmX2fR7f+FAC7aYIK7WDJ3FMNfJlTwpSnbhrioiAA025SPTprWLgq+06NaVcmnOsciqjarjDjxfEeB/OiO85HzPOCwoxlWcAgLJOO24nS61p46KEz++LcY+OuXpELn93gvty9khMZ8Yo074T/td8igMyoHna8Vy9SSaLWxytNeBXw8D2pqKIORwYOovjP0ZT3aZCH17Uw9s+FSg8O5rcA8wK8OKinV4edLzXOD4W16wEuLaq1i99KmMsA1buN2zkoYgo6Pwtj48O5W6XM1oeUgCkDz3bqepkAVdB5XjpATeDDhuw8+Lh6s9dEW56rzgVMVcnmbba2eNtAzXM7K57zMFX6SjYHfX0nFwZLMYMYJ2CuWKQi8aO1IeIan9uTpRU2/Rpu6g7z7qmh0KW1c/2mk3NL/6FXXIIOR9oLTnCBH5RhnEIBzQyESnGlEeddcus7uTV0siPGVh6w4lWI7hELvLnp9JEO6pIMwm+rl0K/tcu6Fmt/kq7hNCXknXnCWy5EzaGIaszUtbd6z/Ud0Vux72E+Qqku9xCheuTBPEE44r9u61Ol2fx5tbUz2gmmUCA68aJ5kyDMKPcLyv0CWbSdHbpd3SJt/5/T/1tNPnuvBfwpQ15eX8943D4Kzj145RH2rcJaoy7M5RipZAxJem4wRIxtJYJkqcdOyqRpvzXwoNG7PfqoGEYAEvXk0iOVezsfp6fg9NP1K4nQjuXY5rOTO67h7p4jaera1PHQgHlVFZRCyu5r1Epk0RzpqqAyUoEZMy/qTYeM6klMpAZ9AiRRoz4CQ1JTKrZw1Koyg6UpKJaWYF4KDvcHPP/qOXgG6GZC3mUkfIib2x3Agv1uh/dubtWojTMwE7AAVBKOfNS0CEY/H8jSeGTNFecGh5MsOCJjQUHChEUWeGyTbHnflOdhDfHLqDzzDd3UNAKU1HNuRzt4Ttl90jCXGZqb7iZPuMk7TDTpM0TYQXnDKWm6gl3KmJCMDtM8dGDjcZNAloL5eMS8KI91OB5R5gUAMCXNYzolnQu2+aw5E524N3zfloYhdxHIwmogvMEGG2zwDsKmpNtggw2+2/DJc/BnL4GPblAzahdjS1xZxy38X0fwSRAcuDCierNBFXQiauUWrV6NcIfn1LLQWK70a4bWJgTswBMjt7x4VZ4nww8XTjJbNK4MT5/XaoshrnrucE0OvSKLH37Fp1yq6Kw/g4IUiJIq6pCS5jcTs+bMTZBa0xqdCK5c2iTttw99x9xJ+PNeNCvLKMOIPXgQCOZZ5eIiF0Shj3LmgyaApYDTps7AvCwoS8HxuOB4POJwf8DhsKD8UEBpAkSwyxmTJeFmJOwoGTNoc9e4Z+NJ2n950mfVg04VdprLxTUNooowY1arIIuVdUtigihu/Wg54CQsOcIEFQjvMiCJQWaNypPUEJICxhK86fZ5Z7LjZMo6z7HUNCcCF4DZXqueJIRpmlTgFsJfJdtbHhKUQFXRWafZujnPcw2HycVz9xUswljAmFEsQh5pnhzxsKKMuRS8OBxwnBccl1nn0kKsMAkKSMPQGQO5unkinAhXXw3q+o31rG3vS+8aNr7LIf1et72s7Ingb3yfPd/ZG1iEQNxr6GCaALql6q3U7avwgnPdGbvF0HCsVAA5FMhBwAfDiQnNgjvMTeybhFevvmDtcmjct8L+f/418NH7p42+qKCJkrgr4cGyNqjfRl46V5CMTn6+eKs0cETSwyLwMSHSPHR7y0U3pTCe5hknvqg8zJaYBxv1XnR27utfDgJFPxjGjWT3E+CL1hV0SYY+Wphe4bjLxBQhaJ8rwL8pyB/SsGbJnC+pnXf1yCXbkmHhBBzRcEiU+FIn/a2kSt1wfi7Fylr5/jOOD7p6u2fHsSS5vNYvLdUTnGo0lruedEJg6cegu5dCE4b2V9wj3e+19kpVSg1jtkITde3u+j/SbKeo4urdK07pEcjovk7h6OvCQ0hnQ8J2zmvO0pUOC6owuF4S6HonATPVpU2mPCc7lNxTbqRzY7OShbSsHjvuXejng9HtKgC2SAUilrfuG8RtlT5DH7UhgqNcnDu3wnqra5NA5OMT/OeuoSFCgY6WLnWHXw+XlNMnr2trvXtqqOJbOYOfCgIK6HAF2nru17WX6Qklgh01E4A9gBto7rUMZXmpD/zhqK1DEQZq29lSEdTciPHYIjTjQbsavTK1Pw8QHAPa6MeC1o8IhxLGQAQQC9WYSEMnoN0qXDADIJmqXQ+xhsInECaycPbzAXx7C8f54vvP32R8gXracjXy8nCdda8anyIgiEUbwVwwHwueP7/DUgjp8x3ypHzZs/kGhRfkG8LNbq9exCVD7gGZNULPDTJQTOnE6l1HorntcppMicjYlQkHyphLAYNxUA4HRIQJGSWpYQ+DNactNe/K9/O+8VBESDQhU1K8TAn7lLFPWd9JCfusvzVspXJ0e0rqRZcn9aRD0rx/RpMJL2AqkKSMMpeC5ThjLppGQIxHTUigyYxXs4Y5WIQtQBHD+d9mMER1CnSqIoH/TmOIDTbY4HsMm5Jugw02+E6DPD8CP/sC/B/9BCoMm5SYn5xDBygTQGzeQUoAV8aBPWxEEHIIgYp7GzUmH0Y4ulVdS2DvTBdQg0+IwBV0vdhi/Fd6hqcmgNLvYsJKYVLGaWZgItBSIPugNTorFGuwxvM3VkysLSasrHV4vDbvmbc3e0QQy4NAEAt96FaI1oXwEhN61hdzbMBKiyX8eYubX1WUWdQ+PcA7whi59sM+o7DUhzXIfa3LFoLU6xDwIjjKEQJRRRZrHi2NxS+42e31b9phP01Y8g47IiQGuLRQJQQVLCV4uj/3ffSQb1Sb4BnWBGrFT8gadkoEKRVwcSanqIOIhSVsXk5tgFSlplpJSqHD0oS7wgkuiivunQYgpwnOrvtYaQg1LVHTJ5JURR5Rqkq5KkwjrYPNA4VAWFhUCQdV4iVrWwqL9sXLOyQAOSmjyMJYWMNdLsyYy1JXDyOp8s68AhdmvDgecFxmLMU9Dwu4Rk8Vc9op2q7HwqU1ONyOAsXXrRfjFEsQ9l1CEYTTBjS5hM3tyvMMyAEgFkzvaewlIVj4V7XsJstXiNIEUjovZ0LqQtdAElKcvQjKnYDvRL3ovLihRN0Idp3R16gyiOvkh2dkZd8oFAY++wr43R+eNqprj+9Vt9p4aGFcA2M9FP6+qcEIm8E9e4JRQjuSohDRLSv8twuOQrVTVqnmbme5U80lvVbjUjn77sYE4vjXxyZBXY7jIWHPxRxyPm45VQkoia5FdUgy71B/pmiBGK5QbQTUK0APh/NKOnm5jqNOdbvW3ioAdvztNM1Qr4/DiLDWBGTuohrPzO69j8CjFfG4IJWGw/1ahDnA2jbpQqRGeipej88P7VhFjnigfV7HSG/51yvHSoA+xuXQ1rCUT55bbZ8qfHSdWyhIDxlPSktTNkLFPXGqB56vWQRjOF1THr2uetKbx4Tn1GuKY0JKouEzrUIiAnEbbxal4euyNPrB36m5ozTXXK/Qbx1u+jELwRlJzDcIIoJyZJTDgnIUe/fawSxhmUU8HIpohfZpA3/pKHhov/icLQL+xSNpnnNur9d4/6Kn8N95eORx2eigsEYpfBDUAGpPoFsC3RBkQs0P6Tip7jhBQ1P2WdkZaWeIhnaUGu0/sq4PtxnrkyWt+96GE/RJD8zzny7Aj6aGNNgNaFrUm0RG91t7NcRuMod3NSdlJnXyck/a6gjPq/Mj4OooX0pBdmWdeSoKGzmCpGOcVYG1E633MM8oeIHyacHxcMDL5y/w7MNnoEz4wQ/fx83thGf7G+QPEqgkZGQkSXjJLzCnBUc6YiHWML55r9FEZFGeZinYCWFiwiILOAE7ysojWqQWFq556XaV8FW8+GyahjHPRlto1BNV0k2axw8JU0oW0nJS/gvAjpJ52yVMSKCiIe8zKR1RAA1TKYAwYyma51t5QfOwtvMiWahkypPygSVgAD8L4OFJYZ7abHnbzfiTztNDG2ywwQZvO2xKug022OA7D/RvPwP+0U+a0T/BpGGT8uAZIErIyZhiVgWcCGtIDCMcCSaPkiGXVQKIqXkNBYGC05QuNGgCh9C+6L0Xrxsro7k6BF0hCkK7IpabroDYrN8EanHo1swy2jaa0A2AYDkzcu5NNTDkJvQRiytHUagn5mUVhB+kEf3VMtnijiiDJqgJ2MI7R+ZxrV3tjo9NY9BO5MjnqliDGfVkXLUUjvWt8QCjPE0AWYAja8LveZ4xzzPu7+5AEDy7ucGz22e42d3gZr/Xv0nDNC7zgp3lSkhEyJKqADcntWifuOjfNGGSjEkYmTWMJow58lwLrnyehTXcSFnUM4w1IJQrqX0IBQClhAwNO8WhX1XWSqSekXZrF+TflCZlwDyXHgBm/y1YiuaG06VYQNkStydVDB7mY+27r1V/ExfG4XgIgrjWV2/8/eGg1phpqtbEDGXmSmHczce6shmq9CzmrTJzwV2ZsYhobjXiJqw2od9CjMIF5THC5WtgTSY3wutIraKQqJOYDPceasP4+vjD9+4C9XQrAE2CxAmcoFa89p8rhxmwLBiEYsql4OQJQAUgSQDiBJoJMheUFwX8UiDzejsqDh764wJjuBAqFHqkHO2bBTufTt0f12AQ3L6WlHN82Afvdet9xKvruo0LVwX5SD4O7HHrhucJp4cvmkQ+Z2A3mXc9oBp5F4gPOXCANg9VExzb5pfiIeae5yY9rR53ej6DxfJ2kinp7FUFADGEzCOdmoeD60jkMTIpiV8oXLYxHbU2ETesSVAfM/c1lGbc1V2jztS58hJByxe2tnlHD89X2dCjstHnNC6/1XqdRtICcc78vnRr40wdtQ0XxuqBGhrNu1LgpP1NsM0SBfuRcrQy1ILzgVDDXVJquegqrRqbQI22IqghUTLvap9OD8+dzOrGFXZiYc7cM6wq/Ej3qo6pVL1qMgsM90qqnkHBS82Nilw5yOw0f8OxEuf6TeI68XagvldpZQIojlpsR5xcij3qGyzxWen78pi9sTyyPGBelD1UHsDHuHtA+nKxwGP38Zs+mx7x+nNNP7uspO2VMWcbYENnIfppB6Q9VFkXjqvIx3QKupPG+F6juoJAVL2t6haIdYTGxOoqT0tDMfF2jLjQ6g4dOzttJ+22zpihSiLNj+Z559z3N6UEKgIRDYFZAHBW7y0PeSsdrlVFkJ7ahhsgSMzqBZZmNbhMGWTqKwjAVAwnEdKkCq6yKF5ZlgV3dwfwvOBwPOD27hlu3tsjJQFwi/17NyAAuzyZAecO834HOiQjGwQpE/Y5IcsEKgkTMRgJExMmyVhogmTBXlRJJyJq1Gn9KyLYpWT5upVnmsjNOn1wNY84rFxOCbc02XgSkiTsMWGChvZMpCOQmTAVM7SU3sCYmTHfH7EsxXLvFRQRpCnX9SJwmtwMSdnXXsNxlFLNfe3NZXCNMiASjZffWip+gw022OAibEq6DTbY4LsPC4N+9QL46fuVTyVygQKBMIFQag4upARxrpIBIla5lVl6iVlqJYhZFZMySS7AsVjpFQQtOsgZdkx1Cyqcc6ajyRH8i0senNPxe6xKwnlRhm229mRXjQCjiu70e880NRgDpWg9TlLX8bT7BLX8leKMGIH3kzkmWMjF42JKxdCMaOUbhBS17+uxfexR8kasdy92aZS3jWX+jIE/aiFTOsHMWP/42zUKCe10FcBdxnhhHPgAYcFynMFcsN/tcXOzx83+Bs/2N7jZ3+B2t0NOCUsp2E877HLGbtLwIxn6NyVVmk1EmGTCBMEuM5gFuyQQTqDJLM/RlgyLp1DSkI7LUsA1TJsp8yyRumsxUtbccalwFf4IPPAIQC4RcFc/G4rFlHFFXBHouZQYRdQTbikaTo4T4OFhk1lS3h/utU1mNV99JM1q8jgvKGWp66Uq80wSeDyqki9nVfw11bpgLgV3x0PbHZRRSOA9XADcz7PlcFDhgTrtkDkRCI7LbCEwiwr41+CSVCaunUfAa8sJxzbR8DkWvcTnnhP+xjoEalV7YNA9AzdQy29TdqbiXsdjaCcA5J4Rppw2LDmVjHSAho+9l+bQe07oKNaOIIeoAuNBlrn6/GPhNeb3QfjyJfDDD4Db3XrlEgfhmo68gqS2FnXJ+hsWhnQ42CeNaqjcau7vCTFdwC6Ox0J7a064MOnLApQCQySo1jvuKddpOsL7CrfDfVTOVW+78Jumdp5RqA/2ShMpGmJtZ4r1idxjTlzxY2d7re8MMCDPBfRBapLWcXx9fDqxrl8epMMnz8XHr1wLNflj/+7+92k79AwYXWPjHJ/rW6jyHA4Mz/QOTAFpRKnzBWQcb0UFXVUQXb1lrih4YepPmzji2PCO2mB1K6mZjM8JzZ2OJqiRTSKkaQLlqQqqq5KOCMmMXar5hdPipCEtk531RATmFsGCSAXMKSU7E8Rtg2ozAGqCdjb1qJGVmoPJqGGPfCFoAt21ESMyL28Cf0MhfWVhlEMBH5eWU9MV2jZPlQKvCoWIS0JvxBexlqu44hxcOqfCvfKXKxq3B+D5rws++v1e5KTzQvX76imyQrev/HxnIaKS03unna+kGqHmoEvZ8tBNBEyNBhePtBJeUrf6MICej0zpeqXsPQJKNVWhxpeNztNNfdwiyMQC56JinwuZSvWfK8DPx/AgmfWKRh5JIMp1vzMzFmM6JiIcpx0OxxkLzxDaVb0xWUhMSRpukslSBwBYlhnMKgVIpKkAMk3GJAhoUq8ySpPmVhNCSYLZJmA5HnH3QjDLjKXssc8AeWSdJaEQAfsdpttbpHLUuTWbHo1PskOBgEgNHWUSzKngyIylCNIELOWIYvwWkYb0L3DcqWEkfVISkil2dY5zSpimvSnllC/bkynkROUUO0yYSL3q/DqxIBUByOgi8rUDzMcF93cHHA8Hi4ICpCljEmqKOj8ai50QTnexKhETiRorYbFUDSGvelC0eujispwzQN5ggw02eLthU9JtsMEG332YGelPPkf5nWeoyioyAjCT6VWypc9QS0IWtyBUYQKbMgBcQvgjZY7FOP4uJI5dY6NSewZUajGHZAy0K5w8/51UyTKqIPs0+o2ogHGy8BUev34xz7XQgDWZkrIyxVo1Mk3JyrsSkizjin2jNp4ilotFLGuIhcuQZQbujsDzl8DCwHEBSarhE11wAqB5JdRmuBAuKkGo/ZtOLodxOf2Uc2WvhSjwre+2waXQ3OhR5xZ/LEAhHMsR5biAF8ZdvsNut8N+v1MF3Y2Gv9xPO4Bg3nU32EMwpYxMGrpkERXUlpTU6cPWS2XQk75LCGbjaWE2uWBhVc7NS8G8LM1q3ZR0aZrgluceRtKFwGqATaHffTQwj8LFIjgWZaSYNZxmYVXYFbNEPRwPGu5EzHHF+WwTCL64ewlX0iVCVd5521xJ5znwtEVq0UkEHI0RJEILyWVrlplxv8yYS1NQsq1XJBVK3punXRWaw/YTKbN/mA/KCBbWXFavCpckTmvy1KeCKLBe2zsy/B6fG+XrF0AKUA4CvDwCkkC3CbucsWi81Tq3LsKt8+mhAMe2FQBHgdwLzKG3pQvz/eYP8SCcsj82GYXEeql97bo29O+ikPAKCSK/BJbfPlzuLPzmK+Cv/05AgmtSfxfcrjVqbcJGrcMZ4bXjagrPra2hNwlu1CGoyi0NHelehsFbzU547cLQUEUsFu6SgKXoXiaoMqzic4G53Ld31/pqo3DqzjYuXuqnZbxnjzhN0T1WvRqkNoMINeTkBd8DSBHIl6xKOh8/f8O1+CcqAashzYAkBMG7bU3sTmdurZQNG4/q++2300InD6x0xi/7Oolztio4Hn/pe9tyJ4gfdDQWHRGFK4H6gifC6XXC7PR6pDHXYOU6jd9oaEvX7n4NenY9wsltVOV8VWYlUE7IWXPjtq2nZzYlC6nNHrZVz942Ek1BR4k0JxIASIs+IdJCvopL08On5ysSsHVJ4IqqSmfaH3sd4nXBaAky9GG5bklHQN6koq56+NmZJO552fbLGobvPaq7G11pGW8B7fCjtsfOkdAVijw6H516MFI7NsJL1pb2dxF8mZ6DOtNnUFh3yrgDtu2TlJOyR0nC/hSN3uL8Z6x3+HS63/dBVXrYmUNEyl+g7ZeTNg0rJ3X7WjoF3fhcT3G0PNwPkxMNN/fQeEo2XoJFozfU47ksYFbz3MM0Ky+yLJAb1sE1xbiiCq64xg0ShcXC76sXG5A0DCWpwmlnz6uSk5CzKgkZBeAZIPWsK0fBQoI7eoHMhMSEIgROOyymkCIBpjRhP+3BXJCL1l1EkChjMf4nTcAkjMUNZGlCyQUzF2QhcGLN7SYCSrlbamrMSHWhZNK8cvHs2FsIzCRqLLeHhbc0nowAiBSNpjObkbHRYEwAmzchF0FZVK6SUtKoGGx5y2H5QC36SknuId3wsY+pQA1BvReUEog1dUKR0vD6BhtssME7CJuSboMNNvheAP3J56A//BHk4x2UqGO45xAIkAQUqBdODZsQFXOlqAuS500S9cqpzIczNiYlVkWDWyHGe618s3RF9yzXsBvWRrigwMpUfx/UK1JEBYxLUoGjaA4yYoZkDTdJ6BUJWl/BJTLWwxwRGg/ohDu7EMWEc7CE1sQJsgAoBfLiDrg/As/v1KOxqLRMmUKrqcaiH8P0BLjE4a/JlAdG9FS4caHTa+WIOia4a4tztJaoHQIQmzJHJT+6hkDAIgCTMiksuqyWI+ZjwXE34/4wYb9ThV2eJswASiJwSthb2JLFkneLMHIhTRouWZnrScBgLJKxzIxpyZiIkCzpNwpjWVS5db8sGnJSM3JXBigDSFktLilpTgJKdj21PnNR5rWAlakiVAafRfDV3Qt4TguIeu8V1vVdCuP+cId5XlR5B/O4c+GCAM/vXpj1fUbOCXnSECyuiJvLgoVLYKJhe06VekVE38eLCRliCFrC/axJywFSZXpSIaMkZSznsqgFffAUrJ4sUOZd4zidWUzj5YelDpdhFOCcEcg+mi09I1OXky9Xwlp5S/FV7gSEgpRtnorhtkmFugULCkrFnzWRvKMKCIACFEGaBbJI8DZa6Vrok3vMVVQSvkssO6KYVxjnTujWVdbGQ17HyHdZcE5Adfr7nCB3TSQ75G5z3H7yuHRFmrD8Ub14GhDbk9WjLfz5lyqsaUJGS1wDTJOF7LX2137ExVElo3bN66H+Pe0BNOsMrIx/KOs0QF3KJuCqLp560z3mCEnPom7dr5yZq2M1vB5A79EW6liZSxd+nq+T+mbQyZeVmxfwZ8VNcXzb7tNvclrev1D/uIzD9Kj1GkSWncLS2rCyR4yswaobSSclfeiVYY5qnyJyG+o4WW40fMrpVA/oxCk/ivMU2isQc1TVRIqajy6Z8gBGN5z21ZVdBDPUsvXruV31/DaPfgCFqYbHI2qVuSIiakCqR3ZKTYeYqO6nuGogonQz/J5YnlT16HPDESFBEsHj/cceB8ICPjJ4boaAFP6NX6mfCIzF2mT6nFVKPZRw2ix45Q54qiNxAciXAnn+aAqjVRLQbLR362qMh/ITwkNb7Y1BfOFDfYr0l8Q10L6QDZ5GI0xImUATgTNDUlNuKa6Tht5X6ZgW/URfLyGU/fDcgGraXgz1kUeDidjaAinGcyOeo1ZW3+vr8MJQLQA+Z+BHacCrhBZS2owGABAbzegGdqQRR5LhbUIwUKTAtRuv0taNhuUlN/pittC4DEiCpIycsnr9TnrQMBeokUExxzLWtiQonzMTDpooD8txwXyvhoNp2oH2ewiAjIzbdAPZF1UqzgcULoBFNhEQijhbogg3i+fJY5RkhsZiSjowkJLxRI0+mdJkEVTUsHPipPy8aN8nyjDnTc1Bh4yMrPnnbIwYlr97sXzTlKCRjwlEGXnaI+eCnBnLvLSRFcW1IA+OQBUnOT1GyYw/iJRP5FKVpiQmEwn4y0UoG2ywwQbvImxKug022OD7A1/dQz7O8LBREjy5SM1qAVKvH5WYMcBFfzODLLSVSFJim5KJYZr1fGM+m/ChE7g50ejEv7QnxRJWu8CnCke8oMdqO2cd5jT3UoBj0tAnmS0UXwjB2fE12pd2yRisIO9MYo4EltdOPY6g7I0T8c4MMgFFIPcL8PIAfP0SOGgoMbJ8IhCYt0sQIKwJR6OgYRQ0j/JQCb/t84l5/CDgGq+Hi6TCp5RrcEZNXVQIIBPzuGIB0LFaTDlxLDgcZkz7I569x7i5uVWvjiNBkLBkzQ9HSJgoYZ6PGnYkT9hPO9zkHXYpYbJQU4d5xpSz/qWMySzapegav1+O6slWdHwJCSkBmRmU1MtJBW0qsMpkzJbN34v7O8xlqYo3DSVTICwoJPj6+Yt+PMj0seZRd384qCKMlXFcCoOlaEhMZixcVBhBap2fJ+0LudeVCIqUkE+iTRORKi/nZcHxOKtCkbnlyQFZuyMXR+qNmrIy9DmhSlkrrmhCAOWKCZeSQZ0Ihsa1c26RrghGTp6P6/5aWCv/GKkVDZ/D42uoqQp8BMBRQ5vSVNS7hy304A2DpqyChGzTYJKaySx1PZ+gGG4ui1rlRqHOiBYutWu8tIpWzwi3Hhr3Ny4IPC7AF18DH38YGjQI4rvVNwqz4qffS/3v+Eitfuh4vf4qi/GRcKl6XwSJmotkBTuLhVEtBpLt9SkD+wnY7aryXcNbpnaeAqjxugSOxEIMa+qHtHrzxfPizNnlQqh6wcPymvBSXBHhAlCtV5CQs5iBAsyT6YHhm0XbnIa2OI67tEROKhs+W/P77zHO2aDY6t5/9iWjhJvQS97ONGTEkyOsvfcskgZOBzdIkan3YlzXYV6xN67ZQkMzyD0NziLe2AqqQulLdSo4/WJ/Yf+LCdJbrioxJ0WqbxJbU01Ab1lfiwpyARfcmmDcc9LBvYMIU1KjMrKQZsxjl0TbIOjmJ0akcFqcCEDOFrJec89qRFypXjHs9Dcpvc7CNWR7JSjfIGL3/El6LmpPomK2GbCtNMXxDXk4WGlLtIa87KkRcdob4XLdqis4AoAc1sKDPqaTY8PH+2fOnteAk3P+gSY8OVx4YUW/nXKhH4OqNPMHyKc6tXyOSb2SdA2b55pFIRB7rqJ0M4AigqYmkFP84eHdPZ+b01e1WHQNHHDjCalg765KlwEjtuXl2AKnxhQRGMAXAvzIO8eW6tWUdmQ0fGiPPkdILCh2fFQMV5Vz1FomzrfrTyLNg5mI1BhTAGJBThlSABGqUR+kAOXIEJmR8tRIB+t7KQsoJcXEzKAFKMuM4/GI+/t7FBHktMN0s0dKWSN/3B+BRTTnm0wWgdsqFUIm/WMAOREmIYDUsLgYbigAkrDyTEQ1wonP1yQZxLq4MiXsoF5zEMUpE7KGuiQCiJWEgnpNJqgsRDhpyN6FlTYBo0ysvCepISlNEyYAOU+Y9rvqeZ2ol0Q0IyUCyAw0XbMfjIqYm9EyeVmCtjH1a3ODDTbY4F2BTUm3wQYbfG8g/4tPwf/++2jkuVhoJrWhVcWcCuCIC1AWJaIFFo9PmedGShZQ4H6c2HeOhp3pdibI6+mgCU+8TadyrtJdXxXmCDQUzaJhL0mM0Q/Kx9rvaoGtDXIbYapcPanTmHguM/U0EhA4qYDQGUcCI4moJZuQeogcFg1rd3cEzQwpRV9fmrxOBSxkcojGHvUjED5XFGSVGeRa5dUwyk0vFrxU74ns1cNxqMWfGINbiNRzJi9t6F0mvvg7VAAyk2BaFtA0Q46mjGJGtnlLALJkHI8HJBD2uwn7vMN+MttGY7LmZQaBsMsZ07THbsqWn0Hben84YlmWKrjyuclZlVT38xGejyCZ5aeL7Vg0HOW8LCilaBgTZhRWpZ0QcHc8WNLxCSknZMo1lZCwYGbzhCuCRQSMonUIo3BBEWO+qYCkYJKMIpOF0iLTo6s1awvHw3UOimh4lZkZZZl1fyRCMwfNGiOmgpiCjszLZpjc+ueSElTB2FNDlPGvQcQRF+ExTZOVv7Uq1+o8VxZtz4sJMHAEOAt4KTXdFy8FNDEkC/IeyDsPa1prAbGAF7NePgJpMQfVC4MQ5ZAXBT+hvf5IxELyEB5Yq2S1MQ+UeQx8/RL48D1VNp1UHCRMVdBBWH/x2Dk6fbZrvwwIlFzSiOsH6YmgTrDtx+qZ49ftIKvCbmufe9GlBEw79abbT82gRQuhuavZVz3YUSV4jkxXFfUmnYufY+OH4ao5ssRzMYrlviVQBpKFORbRUF45Z5ArFNJ5YwEA4C8K0k8y6NlwlhJWDk9Cr+hEVVg0nWL90nWpfYluVKfVj4+u7wnp7pONWSfmpZWya1X40hi8PutT3eY/xesxzHibzmFezePx1XaBCyDXGo92r1N6UusXcPrma/BMGJ948QRbOB61+MG69QSpfkdQbpGtYTNkI7vezRk1NBL/AHieOoLmRxL3JLV3itGl8aCMbdXy1F3Ubao0mZIArLQMqWe/rivFFS5QV4XkSoj5pwZRr3Ce1UjJ0Va3vnycR7xce9eukR1aRKh5+do8U6tDvF6v2SOFAN36JwAF4F++IdeUroHtWodOXrXa4fE3PZWr7Vhrf53Wyy2i4bMd5bZIyFIRqE2b2qgAPf5mqulZAXQeb9o+aeucoMpvU3DwwJH5Khu9LlHv1UV1Fp9VfNUZbrSCF3MnejVdKGqndzz0bgt56XZ4aVLlP1IGJY3QkFLzFGYz6stTqtf0mFcFHUjDtpLxF5QbaaEGMN4nRimkKQcozK+N8VIWJM4WsUM9/sSME5d5UTsaOSBPE/I0gbKmKQAJEidMpNc0vUQBgVWZl8wgAoTMiuckezDODIZmctM82u457B7MgkmS8lY6ithjQl0lIjW0peaRZHiQSxIbrwKLGqOGD6UwmNQwquSMKev6Sllz20mWlj4AUgPqAD5ntoCStqJ6jhcGc1EDvQIz1Cv1/CAQcsoaHeWCAeUGG2ywwdsMm5Jugw02+P7A10fk//oLlD/6EaqpoMWVJ+FBWEEa/q6QEoACZXorf9yEfUTNCB+B4Hcrd3CoG2ZBVxVTnk/ICFBCsCBzqlUfDmq2Xi5Uf4rlomMgF6Bk5diETSzhNWhtqpxzDZEzJoQsGtJDYMolJDB5qBD15VLCvQBCYEkgmYDDEbIU4IuvQS/vgWMBloLEFiLEko415Rya4I1if2MHgyRthZdd4+2vhoe49ZEzjS89eVb6eSALSirSrP+icicDKRPYwi15km3/W7hAlgWFCEspOB6P5omoY5RAmI/3SETYpQm7aac562z+EiUNZckFu7zDbrfDbpqwy5N6paWMw/FgISvZFHUmpCG1bJzLgrkszSrdBNRk/Xp5uMNiuea4FFOYaY42hmAuRcNU5gnTpH81DBWRKuJKS/wtpErgIuptxQtVD70kGoqKUZTBJtKcDmZFWXPLVCGAKelM4QdAPUuTesiph6AqIzl6dCbL70EphDf1eaNujk6lJ2F9XLnELkGzWg4VrQpUe3gKIdS1W+qMjGWQmJrtg8tnxdJyzECyiIA8M5YdIDfAtJuQJKtVOKnQQVhQjgVyL6AZwL2in/quJgc53auPAa/LNvOo1xqn4VuF+6N5cwWha4UoxXLhdnrE4tA8pe35tQcHYZweXt+sFLTi5rjpfOLCOdq1SbR7iYD9vinnsuecG+u0+iq94Gd/zHtn7xU700/OsfCdEBYqndwTy4ubal4tstB7hGyPsefvAkDuXfxoq3EKbXkIfK5leGQNIYVNU7t3rm3SF00uHI20D0G9slC/+z3p1t8VXeiaV2tEn/+LrqjvwpjFJrbXXAfRC3YcWolj5Z6Tl9tx9pIMn/X1QWUg1M1bpUvJh9/b48g9Kd3LAiFGQrZVqpYYImxpHT3sdGhgtCGLayA0MZF78LXXpkqbIOy/uIcbZRaXSUqkXh8JgLAqvxmay8hFxWKh7/1seQPGOP6i6jxVuHmHR54ghs7F6dLoDSSk3RSyQxbhiHB6GyfzPzQMYTWA7+SB8k8EIytwTVPPVPM2wKPb4dMejgZHVxS+VyWd/bQUj+2tNSek8w3GcZKnUFDlhytpTlCC09Pk9Fvbj4KGK0688GyNrR4thE45OK5pWf2xAm5QWztv+4NUJVX3MFC9AEFA3k2YKEGOGSgFCzOOy4z744y7+wNubm7A5PZ5ZggIIFEGVO2DeoCYIULlnVhquH5kNLsgH35Rw92yMPIEgDRQZEqOewQFC+alYEJCTgV5tyAZXUJTwpQmJBIUmUAoyEWNGQmkBrLUbIlIdihkgl6yHOPCKKT58xIBxXBLIuBZ2hs+1QomySgk1fN5gvKXLicQ0Sgw7Gc4c1XSwaObkOJQsFnpQqeINN4nPLwpnIcLZ4uvbEVjCbIUUNIIKGq8pHaXCGuzKVDVvy8Hj8oNNthgg3cJNiXdBhts8P0BAXAsFtLBmSATdiVTYll8+RrCKEEVXSlZOBojSKkpG6g65un9Gn6PtT616lXlFlxAoS9FJ0lxwlQ1ey2vu98Pwix/XSdLVBO8eoPmApmShlRMAJHmg1IoaDlzzLLYrATZrDOZEoQI4uFDqkkuafhKFmAuKiyeC/D8JWhZ9HcRUCGAyTzycms4EdiZzChAdenLNWbLr8uBByHd+r3wXcLnA21S+8TSQqVAY/HXEKpETbFLmitQqqlne/ciAi6LhmwkwrFay7oAB1iOM4QEOVnetqQJvYl1LpelQLhgyhP2uz12uwk3u70q7PKuhpr09cLCyveK5hJYuODoSjpXKi+2RyB4fjyYYs6UbexrX/vA0NyMeZqwKxN2rKFNPMfdPM+mZENbF1AlXWHSXHew3HgWeqqweZUkUuUgc5iaKNi0ZPf+50J596DJ2dY1hfUmrQwhhMYEOgWr5YpEilKTR8C4js6tq7V6x2uh6Y9uxxo8JBxZKd5/6dtjywniDkkCT8nRyRXliDasO8V/7rGBLChLQXnJSHcAHQHMUCUdNTQqJrOqwqYo6LoGQtmT5xxNeZvfFgngF8+Bn/wwXDgRtQ33Lg1IuN/h4ZUO16I+0VGy+KYHZ6jfzxVviyvYE4dFQX15QMt5WNvdZPs54AzXCLQT154L0jcXAtGweFyAWBdhCmPj51zqn7N3VmFo8AzwZ9SIXc+PnAmMbHJYD7H8qkMZ+xeudd5yYiGrlJZwIaYrEuPaaoLbET+OcxfXZCvbe6OZELQaNsXmCk6t5U/3QBT49Zp3J7JW9kVoi5aM++FMny4sfxp+RL0gYrt8vZ5DOCdNlVqM4sidtOMUIXrPenXM6bNREF9bY81Vwx57ryF8ZvfqUFxOpnAmJPXmN2+XmnsUsOgPDCkaTp2Te3XZPrBcs+KHSm2ntyie5cPWqv3o++VRDySFkSACKCMFXfsbx2gs4GNBmS16wAqerjROtx61XPt3BTdWtYgrHFsZ50X63ab/JTfqcHru13yikHkUXDyLpWv6U433uXqeilx6NJzBD6MH7OjE6Mea/rlhaDMQJeOlqLTQszb1q0e3j4DU/9SjtJaRVhJ+FCIokw1fJEv54IZyNGw0X5cVXwwkRvR4a+X128X5+QLAQYAbNGf3RBDRvNeAedO5QajhjwwNS8mcNKfcsYAnxYueJ129gT0fmg8AN2NLvy4CLtB0AWI4khlMypekYpEe0PaZGhZ6GH8AOebgEzNaZMwiqpwDqiEiUUbCpMY44uex5ez0udSmKr9JGq5SCOoFK4p/s5gRkKTqEZiIsEeuprrCAinqEa35ukVzkhs55Xyi5yJ3LzfHD4lU+ZgSYSEGE+kYouWei5jLvTVj6FUdl+ZBpx7a2rdRKVcrdF7V1gNegSTaYIMNNngbYFPSbbDBBt8rSP/mt5A/+Bj4YK9WbATQjkCFYSSkKqYA865b1BktJVBWBUzVkng4vMjIepx3tmTSIEvG7AK5tdBkANCUJVE0UMVC9ADTApPtFFGFWdbETsTKuCGTZpOGskwtkJyHsvR+G6OSMsQ9BVz4UQRUCmgpoJcHbe+Xz3Wc7o5AKRYWFKrwM6ageTYoIyg187Y3ugn0pAow/dLQ69cREugrGnPwkDJwkOXV9xOaEE3WyqCtEwsN2upTxkHErABzqCN8yjyrp5kL35wxZKm6VYmWpLUNomFvTO9HSZBSwn6ZsVsm7JYZu3nClDPmRZV0LvQSYzRVZpkwc8FxKTZ9yhjKsgCWpPtYiimjoTpfb5+3KSlzV5YZCxfMpZgnnTJRx9mUhESgnJFz1jVoOe6Ku1+JCQEIZvVLztWiCihHcOG8SrO1XDZJR7I5cY6zjh+179HS3+vvdOpXinleRRI0roc3CefqX9tmFIqfFTadVutozYe4huEdou/SAuAAlFSAg64BIgLfQD04j1AvuuBBRycvw+q4PYg1VurqZPlDf66v+A3Dizvg+J56gz0IlxbU2oSemeTVx6vk6rryrwwByZLi15M+JRXA1c/WSEOMnlPS2u1VsoVMcuUcS5OMdlJGr87O8urBZouZHUHL6fnlv2VYcG6oUaVsJvijIOD0x7KGrxIuZlij502+wpNOXrCFu2yhBq8/UikI40YEFZCD414QxpCZK1WGz1jeD0KrEv1w2SEV6rmmI2vrn+x/6R5f91Jrv1v0VDm5F5t/0UggNt9+nAxrnPxhLNVLf2zhUGbtdSO9Ekv4q6Ibj7gx1/nxFaghjfY72/RoCGvfYinBBLcmpI0Gaz7XHg5bNJQZ2cN6HOvAKM0qdZ1Vbwzx/I3U9qbTaiOwm4F4uDct2xSIAKXU50g62/vXgKBsdMG41DlXfCIkJ/M6tom6f2XlHrrFGJVtXT60wBd48wjQcH6HVx8BLkA5CvK+p6kEvqwDbj2zpb4zMCz5B4tSI0fJjEJTZKsc11plagAVvMnibaf1k64T3wMtv2QtUnkCYGULhf3rR2YZJy7gsXOUhCv/Tsqcob0q3ANYCLi1M6MmmBPAjCRTSpimHTISJsnNgLEIUhHILMov11C3LXw+ucEhHOMYLy5xD7lho0aUqWMrApaCObHm9yYLI0lkofkZvMwQCCbaIydBStlYO9FxFI0SQoVRSLAII4tgKgKakoYaESVnJiQkZG+Q1l+Ux2Eyj7fialUN8wkIsli+cVBN5+H9IKM/MtRDX2o+X+haYYFYqgIWRiFCziFKUNKcdoUIGalGRGn5EMNZY4PfcK3hMfK7gsKLjmFliFH3RUoEZjUuFjNwFouUdA5vbrDBBhu87bAp6TbYYIPvF7AgLwKkhGxWtAkCJAYvizLEwhreMWUNz6PSgpq/zil6J3s1nIPfMisv95jqJNH+J109lTUhJY7VIjmQrIGJdtn2msik8kQswFIgJQOLgBaAsloyq2ywhe0oREaXOxdnQkkQIMYILGqGR/ezetB9fQc8v1Nh5Hy0hlnfTHgmppCkKnsUJaJjAjlTtJxa5waBH9B/fxWaO1YXhaRr8BzAlwL8cBQ+Sj/oa49HLrOuCV8YlYusZcUFsoTe4tUt6T1MqnunKf/R2j+FdkTLcptn2LyyeeXNEEylWKLujHkppqRrzHVjyqmmM3RGFSaII/P+4zo+0toRc0LZJ7Myv8sy16FsH2LebIwCt8hHr4CMAzzsl7bqh4Xhz2WopwyhF9wRQjhLoPOIOycgkOG6iIWTxUMyzL6O+Dn+DPcvOHe8WVjrx9rYrCGgWD4KaQwFShgrEcCi5+q1Rb8yACIBFRUISFGhC2a09T+iVKAPMfcYiFvd0cO5iiLaftV3CXD85FUrGGBegJf3wP6DcPFC48c1HIufLDjq7w+X2z60QflGFqe/N/y5JDM2IJEvJDMc8IXjCjSolYRQU7KZkK2esime3Wi4Gv4+nPY55qurBgUBuXcefdQvNGaVZlv4LCaApBkPmQlRxcUipIoLIlA2C/UHgD9jpN95KARUOPPCh4+z0iNs77cCUYFkj4sbSfizdXxjff4CanrLMxCXYXXcjPXYtYf3pqwfGSfr+szLL7asPRj1XGsIv2v7WkPqXg2dqlPSzspY9XV46XzHuqPMvaycTutEntJ0YWTenqTfLQiprg97Rh3YVVidk4ZnVYUcBz06gyj3HnBNf9bezxalggByQayRIb686nkzrmNdmCfz755A7lPo+R0JVBUYbwqEBTwzymGx8Od0uq6tH60pLWsXjfSy02qxjnhH0NZOvZ7smVPBtgCQOwG/ePV8dGUWzC+bkk6sMQINXU5OLg901BsZ+Q6fvT1w0qxIO6X2V48UmymxOIdCQBKq4Sw1n6IfM5Yr08ZcFUJq7SRGv9c0YL5dIqoWfxsF/E6NJPdnKayvM+Nb+Q1qp7J39CESvIPEQVuJanQjru5JQCJRJR3UqywJAZJU6WQLrirHF/3jJEp/gpBqBBmuYwjLkekYiZlV+ecIR2ycmVAgyDtSnRqrSpRFQExIIkhSgKK5s0WkRjYRAmYpKAUoYFMeAjJZtBQxTCXqmZyhYTCFLLKJRUUhtJCQybS14iNcw3XGueFKznnoSyVH1LJUKSMbj2JpDoQr3ndvvkQEkOaeKwuDLE2CnhPJcLAvNqr4W8mtYJBc5RM6V2TzTQKk3Dw4U0mWC13nntjW++sa9W6wwQYbfEuwKek22GCD7x3Qf/UJ8j/+u5iySogzK8NCWeOvl+xsrudfS1BNlwBILXSheR2pF5HnnTP2onrODRKkIbdEhSrAGgWe0c5Qgc/Qnc6Au8KMvHApoEWthD0cpiSqVnYAkIQh0tosQkjMwLyA7o7AYQa+eqH13R+sK6HP3tWu3wIOYYTcw6Bj6voBgGpVQo9oLPM4cGu8dYnYChu4AJid27R3RuGdrD12huv352Uo48JZHyZPFxib5RqHseqMxpROIVyaezGuri2z4MSCuRBSKdBk5d1sVIvbKr8iD3dkzA7BBNcJwgUaQ8UKx5SHnXAyNImHuc8UQkZ62BwM48VV+AcgeK3EcYtjPEjpqvBwJR/XJSlAnXufK0FL+DA0QKSbqlr1qwqBVtfYa8IglKzXXhPW9zKCQBSw9BNVV1H1IF7OlhYLaihMCMyTAk3X4c9RqJ+Gd0r4/kR9HLv4FGx/uXuCShxeHoEfcL83gLiRw8VhcZ3rjJy5Wfe24JtXzq2BLw47N+q8kyrnhW2b+sKzPyFgWfSvSMNDABTJujQ0vKrigmH86lqX9hcXoKBJoKoEFL0U1C/GayYUs5PUQgHrLwlEgGfbWr68flHRYzZIbbYKQKtSYFRerC2zDhGeW2wX2jDeGnFjp1imhqfPvKsGdry0iR+9nof3dfvuArE2tOzkxkPDZmvxanw04MuqwKpzG8uSeXGuzLEJ9ZXUs31GOvaU1RMtZWq0lzBSsvBnME/6asxDIGINZUaETMkEseGVdcuop5xvsar/tX3pe0K7ZQHUKPXeiQGvdR4yQCfQrSHXqNFFbxI8VHjHQ/g9quZQdjWuXx+IiKu4b++wjtzoTz0Qw0AKdcXfBHQOmvYu9sYFtPkUZ6wfTSd02TsAdcnZPqvxwalNt/+5cln3VqrKlepUKpFsdVpVOhxd8y6G4zw6ucYIrF0IwlpnC3mbSBU652K/8GBQ6EBAr9i7ZrLcEKTiEwEsUouI5pMXUtMWV+anTEBWA1UG4XC4x4vnL3CTJ83rTRk07THlZMFnErgUeBJk3zeq7CyGuzxvnXolqoKPNA82oabA4MBLaf7uYiH72/51Pmipee11kpL3gxPYPMVUJOGeaW19OK3tToYMtna516C2rUZO8SkhaNQeAciVkVW24X1QD0IR0dzhdq/Y+CRoqgz1wHNc1XAMsxipKm6nq88R4AYfde2Qo4WW292ryzRVBCKkysaFpY5fMAneYIMNNnjnYFPSbbDBBt8/+Pwe9Bdfgf7gY2VskoAkgTkpIUvJQuMRkDyovCaOFklVECceXsehCh2oRbWsIUMWVEGIAJ58WQlff75y0VWI1/gYt0+OvAthuKCMkkBDeCxFBSk5m8V9BmUTWrD2LxnFqwxSUsamCOh4BB0XDad2WFQxNy/Vyv+E821m1XbRPQGd+jeyeRRM0smXC8yZeQ4E/rJxlEObfBz9ZmU4bQznFppzFaLMLMh2L0LkdAgq660ehvaZzIqwJjq3F9Q8A7ENYX6dkwohG+s9QAVliTUTOKH3zKuCKWOsPZzaSfttpTnDB9S1jiq0otAdUqVvlbjY97WIroJ23aUMNddSLGR/nQCYmlAiU6gU2mcgaK7tXgrPRAXdJcbfJRTd4ooChWHhSTfAj4anEkQ9CFf1vf2dq/JBmUl48MQrpgp42v34HCFsDWkoBUBV1nXt9DoeaNSTCFgfO1Gv+77HwvM7YP4QuHGSfmyA9N/PmrjHRwUnyLF6zPoEjWffNwlrCN8hXE+EalKOhsfAAuxSC4UraPnlgIALxvPK9r0ATWMcF6Ljr2B5UW/5AHN/nlRJlfQhOsXzzLR8M4Ua3UFCkJcHyLzg/k8+we722cPDJqKkSFgqFIx1hs42nFeby+YlP3QZaB5Hj9pzVDd7VDcpWTKeiUYDef3kV8bzfTgLhnXSK55OqKlLLT2PCuJ7qwfhFedDHUqq3dKl8DDSqeUeufdazcP6rFYQ7XJdpgSotQVbOEj1iqCcjQ6A0pOJkHJCnnRfkdHM2TzoUlbhbTMo0H3Stog08iGQw1EXq7Qq6rzHXFFdPkcCWjLU+DRAIkgi7Uj3YawKiN6c7knO6jPAM3f56NYc96rjSYB+xTRFnj1xWkl35vpBSyv3hwUlAv7kCcTd43lNtIoqnmqsL+7XbxtOaPMBnJUyBZ2Huez2ArX17h6fvuSrfZ1HKewiNjstb/+s7LPAggZajuo91Mf8vPMmmYKF1Pe7niv1Q3q8MiyAR6GyPwfwD6wmhvFGWouIhstnEjAxCjESEpiAKSdNX2FhHIU13zybopzt/M0pIydlpJgIvPjYNF7cx7RtI50HhikDOyOScJaytauYMg8WYjeMNAtjsfOfkEALGx8vNQ9eAiCiMoxEZLQCQZIaYTaFWgJnZcI8163n8EPxfKF1uTXcytxSeBgfqGElTfEHqDGHGUlQIrPVFAvt6XkQrN9GO7E1vgY3sXmrhh9hNZDJQVjYxsXaT00dp4pjHQeRUqUPm5Jugw02eFdhU9JtsMEG3zuQhYFDMQJTKo0vRBDKkLQArBZdLAmYAJ4FQtm4eRWyqSMBGWNvgjmlg/1NAJzIbALCRob3+WbQP4aeg/GnVgj/ChRIU1Jh30JV4SgaI8KIbGVMyCTdkhMECbhfQC/vgecvQIcCuT8op8fGljAqg1ITNjuTURmX0KbocbjKNgfOnbyevncUn3NrwY65C/e6esOHc4UiwG/uQZ8fgJlB/82CE5CV71Hut8pJSn/fy2e0HFo+d4lUmSZxLgeBibi1ObUxdHNWCu9zDtqVpIQmXao53ey5bl2tdSEMqivyCrdxS7qWWthXe6/n1attGfp7AmcGcXVc6fSzShIEEDcZHSqgoexanXENjdKiqLDzR7owWbagzglZ3lFY7ck4BRIKPkKKKUAvux7e0W1n2yY+vFX+b/eawKl//6pM5KmmZ02i+LbAb74Cfu9Hpzgw4I96LXxcBRaWruKdFTT7zcG4YavUCxWvOHpZU5S4e0FOmsdvl4HbG2C3My2xWNjJIHyMSqmomHfcWl0bHBd5O9xSIuL1IFSUsGgdf7uE1d8t5GIoMATJ0fHdAcsnn+P4l5/VemR/8/DwHQH+bUH+SUyGqv2heo5Y94JQsZ0xK+vn0hoQoFfmniIAiucZhu9Xr6/2zIlub7VN/t0FfuHyQ8+e/JSVZj4eWXiowRV15ePh7LHX74d6TPqVQMYMPxD3fhOK2rLIGiIyTRl5ykgZkGKKI2kedqps8Jx0qAJxApDtvrAghnjXdjVlLCVCFtI6yUPOazi1ZtmBpiCo5ALZWSJADeum+ac0PNpAL3hVbwrHOf4QmBddDbbZiqxq7E4vePi9kTw8T3LL9UtLAHmNfHRrMJJl3x0K6tWhI5v9s+63dq1GIrCzx4+gJKTksLnO1ZD3SfeCe391mKl6HvcNofoyVJw0tNJ45wFXh/KVXQlH9Vo41Q7OHAOrcI+VdU42XgkkqXpZOY6uSqFq6NL6CYGGcDRcMKXJcsmJhowEbM9ayH9QmxcjOqoTPQV2wcfdvehqVQxmV6xZ2NeU4PwuAxYGk5QWpgIuGUSsYXEZYErIrjiDKeQgFgZUny9GQzg/6SglE7SelEAQCw1qRgqiLRBTtHlahJq/E3FN1e7X/hW0cJNdYAGbWAFaOHIy3DziWauLRfME6jNc2Q9KUCWsKWSb/5z9R+dDrm6wwQYbvO2wKek22GCD7yUs//yXSH/wMYrlSNAE8oCSptSYl0QW7UE5I/EQj0RK5QqqoICoEeQA0CktRnmHC0lcECD+j1H3QgByE1ZEZU5VvOCUsPUbc2nvLgk8qQISDFNQFtDCEMu7Q8LA/awec/dH0MxAMU/BaE6ZqDE2lFroExu78wzYitRg5ESjeMLq7H7Hx9ekaX59tMQrAtwtoE9eamiz+Vr7OhnCUAZGIo57NHOuptmN8attc6Gnm7Ym/z7MqY1DIssUYsyQhizx+kwgFfPBecOy1ZugOY68Hb18rv9eJXQSlHzoBEnwNeoWq6tW2OFHLHOR4R5uBgFBN+80lInzsRbibCzTXV9rwyg5ivU5xx2KDVbCj4ZXeO6qRx7LmL5K+325Un+tvn/cmtIuj/qiOqQrqEGFqqd1nbw31kkr14Hz/Rym+WKZV53rNw0vD8CLA/CBe1PFfTdunAtQcWgYPA9tOXq3fmsw7lP7JyiYuqIVZ3sZAvY74L33gJudKuu6cJcqiGuD4Yozv+1jY4rB+j5uC5cMx4PtLKBWl9dx4uUD8+YTW/dNkCgC4LCgfP41Dj//tQqrjsujR64pLvoN5Ao6pzc0PPUpPm3b7JqNcJ4OClWGHyM+vfCOs/SP3Vx5Vy1OA2ofevUQCrj0q5Ila/TJQ0DmHXnuxddWt0ajxMejsdhwhJPNQaNjqe9P9NwjyyGXCDlpmMqUNEdSIkAyLARlyI9G0PI5owbLq9Em7B00RlwwOs7WZwruFxoaDaBCkEQ1skX19DeLOTEc0dBacjVCXZpkHn4u3L3CmfG1QZhRjgXlsHS5oUKJpmR4YDuQ4Y1GRrYN22/3x3WMf8XqffsUQJHGR9/ebwjq+y9t9m8ITkjKyGKEI9g94pwtrcubQh7nJJAkLX8doSp5RaDedH5EnUtfGs5LXz7Ry9dBvH3+O+Ao7ZOtW+nVe5GVeBLo6DtvuCq6cnQHhOWZ02R8ZojDSNAQu9meKayKK2btcbIIOjklM2xVP7GUBSjqkYeJaioHSsnsGwVS9DjvlHNm7JgoNXmB/xGpsYB7ptWB0kkRUb6diWruypzJvOV0/9fsA4QanUXXCmEizVfo15O9k20ioxxDJ8vqZ20Pl6JhO+t8N7wt7vnJgigDaXYSIwHQzpa6zirtYQ+KriO2HKTCeqYUZg0EVHTNMwkWw+Wa9a9mJdxggw02eGdhU9JtsMEG30+4m7H88W9A/8FPGhFNzQpLU00pIc+lKBFe2IQMzgFJZToTQTmgKoOLgiIGITU53RjrrQri+ljsfWwTNIWZ3XfiG+SG/4ERAIDCEJkgTJBj0dCLxztgmVs7UlKlFQlwmC0MpIScUSboiNQ7eUiT5CKQKtTohC2Bc6tedh2Hrn9dn0fo8iwNRL4LZMJYkilrCAQsDPr1HbAw8NW8UvkD4HLYvrntXvclsKISClWPCWdKAovqSjaJz6OuKfeecIa3FTPOxjnieD22heBcYmV6tE8rkhFfD1HxVBm98NsEA6vKr7FCQrXo1DqiFMorXJPS+N7yvtpY1RCW0uoHmvRibFCcr7GtryKcGb3r/JpdOKlyXBpXveMV770qyPAJnKxxiteurZNOm1uHakW4U3NvBYcoaSn/tB2+5Wso4eGVcY++4lhFYVNdnuO1txFEwr5ea2ScVDrfF+Lh0yasK/s2DIK1Qeo/6Ntp7XZlWMVhhkdyVmODm33AndLWD4Xyo6FBEH63BRtjkVELR1zdjL2snLa5tqvlwSNABYoLgz//Cvj0S+Crl5CvnyCZofgRVRHziWeLK+08PJUWbXj34vaq+8/PoXH9DI0JRXvciv7emYvdUo4eQmM7rkQKJ0f7U8ND2ycqUR7ZmCrorOfeaV1rr++mK7bDQ+jVapTY1IjdAhHNp0QpaZjLbN51vq5CjSIaSi6Z97sq3YDk6x5O1nBQ0une6kOg6RpM1GhHMoWcmKLc31tDs8Lxox4gyRV4JEgCcDYfH1/3hCpofjMgdUxcsD3ibiGpSpY6jSe0ol8639aK3uzLo44xEWB5mrF4kGR8Slija8KlDv09daPWaOvwGhnu0Vg0XqvkuHQN9yAcqqCz/I8hLHINMmH71xV0kuJ6onFxtHd6m88RtSvHWC0RaKd1wvIJgalLLQE0Rb5601GXx1iIIZSArEYFHvxm4YKlFMzLjMNxxpRn7KcFKWUkykgkSIlRxA0OzIuOElK2Ou1d7G1hDZNZ22a0hxsy6GWu3mhicgAiAovyskmSDSODeUHhhpsoTVAPPOg8p6S4zMY6FwKJ8pAQQTa8l6z9iusymNyjOeCkMGfdvnW8zwNGYKpj7Hn3YijWSGPFpdfWYcD3rqDrlJv6JBcNZ1lEADNaFdIw4CCgiOJwNmPSN4vDN9hggw3eHGxKug022OB7CQKAP3kO/OGPwewEsoWKYAGkqJJrKUCZAeYWvSpb5H0n2JXCDko4f4tZ4FeBHbXyILMyDsIHf85zddllbY+F1/DSZlWstwPHFBVEAmBxBeOibZkXAAxya2QxoUtktlxoCEADYESLf3+vimHUrnBlcB00FlLj3E5o5oFxu0RTu5Q/MH8nxWcGHQro+QJ8cUSLU/IIGORBlUtxpngUovkgNNPTM7ypcc9eiAYNhDHaGu2f0PLWWZk8SjDtRSdCzKhUtTo994Qziz6G/s5x/pl7Dj0oh0/G52TwAodf/6xd9R3WDvYLoT1ddRLqiL+jJGFsgz/rzOFjhQNrk3dG2hNedfYRGa5fgm9MirUCZ4RLJ5/ntlQUesWpCXKKk71h9VX0tdYGamVGHfdqU2mlnmsgylOGZkad8TV1XAK5x5tJlvGbL9WT7lSyNrYAfUOHzV+9gtH/bhffQmjCnCoFcuOEIs3wZCJgNwH7veEfqVbw8JDVgBknuOIND+xhWxyJ0OL/BU1yt7illa9Kr3rQ1PeRCPDZV+Cf/Qr48oUaCD0R8KcF6ccZuGmd6UKa1XPoNQRcr4Ny60Z/jToeaM86KouCwodeNuyfkz33amPXPTWOwdqYjrh5tS1nnrkA/IsD+JcHpI8nYE+gGxN4+rKo9NyAmMXXkf1Oleq1LWJ0pUggDwKSD3SNC6y9W7pG25iwSEd7JrJw9VUQ3Ojt1sQmdKZEECQIEZJoWDeX6X8TICLgI6McZg0NKu4JOOLmNfwbDz9ZJ8VWwSh3CbVeevAO4N884Yh8C8dHxGTj6L4WXNji50ipi0ChXJhQQTuCg51E3Xp1+yT1AiO3CzGlnHvPSWrkcyW1ZaUbZxp7duxG3H2mcMvkdh4eNTcFwL0At2iTbDmymTLMfACAoAijJAYbb5QzachrFiTJEEBDJjIpu1xE5QIMiBssSbL2MdxDPpEq0SRb6MiKfZxP8Y4NXsAARNjyq/XgSi4fS83tBzAXlCJIVEApI+WClG5AOSMRISdT2BqtY5nsfPABz4VrBkUJCUgNd2hYyWYg4bi3GmEwm7GoGme0BRsJf8XDpeIYx1+nrpsJGBZ8A1dcchgHlzqo3RVDUHQmSLC4ko4s352fKw/SwhtssMEGbydsSroNNtjgewvy2Uvwr59DfvSsCQE03oUJ9rhJZxMGqz0TPgxuHp7jrgkjYDKfFgtec99ACWSn/0lAKQrt/D2Cmh8MqIS/WksaMY1G1HbCyrnUPui7TVFnFRChSiQ08bgS+Uovx/gqOY6aMYfh3Wi/O6I7WhFeEiMExs7lq6SDGJ41on9FcEUswH0BfWZhOu9W8sw9Blyo2nHNWCf4fW2s9QekjhTxmk+4CwJr/Bl/ZRDk9pWda+jQHuq/V+1CaNM4R9LmhqzvIjV5g35yaA+HuU6nzFf3whTbGdaASxmq50PYR7WKoTz1l076dQ7W5ujJoFmgrjUhbtsgf1yVvdUun1Ry5vda2Yd40rXlNAiAXgl6Pl0vDfNUlzm1+65HIaA6I52ruzY9oLhumYwo5kJfV9/xAKw+eu65KBkcYP4tIK/g2PsgLAX48iXw0QdXPhAWIcXf0iYieqJ8GxLWs+DtHDYTJai00vLKFehZ7os7JeDmRvPQ3e5VUCfQ+1wUP7vHW5qG/hvUa+bqGc+6Khjyg1/aXwT3nPNhdTw5F+Df/hV4XoBffX4Z/b8qdDL3Cws1Fqkg3cf1S2JAArEO+/G4rsY12q/Nrh5avfrIpXyms1cM3Wo1I1Q5qq/n1G+3aCATmrI+lHFM1t9z1dwVgL8q4K8KaJ9Azwj4MIF+kGqOK7UZUu8MIg0vp87uLux1LztURRxB5ehkxjmuNFCvCa7KO51eDc8msNB1nrOu0p6WGUpQhdH6qHmbWEXurQY2xaCQoYmkAnBKyKxeMpXWfhP7bgVqvioLqU+dm7jn9Buns1/rr42ZI/0FdHS7lDH06Gu8xvHscMjXZR2W7rVvPNv3uoZWSODwrteBh9o43j+LLsa2rNGRwy2Jd3zwbJ7EwjjW48kUdfXpaisnIRdYGMlIp4bHHj1kj1w2j67/COA3AP66tHVFSYeBxdJVU10Hvvdr0xKBckamjJyzGq6Khldk1rXPhRXnhPEQ9ySrQ59MPMBgsCK4OIYn56cbegb85CNAzhoZMUuqakMS8KI4shAjQ5CmndkAk/oQm4JNzFBC3NtPsiFcy7eXLfNlIqREYJt/LpZ3zvi7Zlt8OjPW8oY7TPnoY5U4GaJH7W+UGXidZF9rwBxRnFgVldWA2OkpWHQZO2MgYKOdFBfaAFro4j4SzwYbbLDBuwObkm6DDTb4/sLdAvqzLyEf39awjZWATKyEctLY8kqQcsceK9noZopSPdtgcehVWOg8kCcxNiGi80ZZmtKjc+FwwV6NOwkldp3mtxx60toSmtbaWagS0IIm08zV407DcDKFcJzs4hTpK+2gcR9NRBqESR1Q/3Wo6iwvF/qnzMxQ631Rr7kvZ+DF8nSClT8T4MfURSrT9oRxjpKFa/iAyggDTTN6RnAY3YVeFVwaBqigOsztyftOBHpkgpLAYTqzw9KkKCeC/DXwNU3Db/seVk/TuIztC2Mcr/nvNWmIf3lbeLSBWT8rrIlSmLW2v8qyuPSMDX/nSbsyBa8MsQL3RMbpVNeLcT5luOd1XNOwx47TOWnn62zFs1K5NwiH4+X7VagSEEFVKkn4jbdn75yF2G5CPVurFLKFn65ra8rqRVe96YJSjgGL7Wblw4JYxTMtTJ8L1PS9sU0rC2h0AV0Y+OXnwN0M/OmvzLPvW4TzEuGGKB5joX6ubLz82H3i52mso3qIj7Byzq6Uu7o5l+ibK2rr0Jqty97E6gxdcMXvq4IZPnJfy5EhR4C+1PBi9FECPiTIDxJoB5DHIRapxmlwQTkJlJAKERhEhdxBTIvm5QETMgNEGclz3ZHRvuSBNKWhMsSwZlpfm2L7VkOwoc+VZYJsJm1TeSKF1DXASwG7F11tdQMPd3kpF10rbJ8dnXRmLazUFzmI+L7yF69p8Bbgxa8Ztz9MSFHyZNPzRo/KN3yOXdvuJ+nfmb6oQgOak45h4RLRor+EIBaRLNY94XsBLdCGNLI7rqIT3PXITvn7H+zfKw1WJGQBiFiuNj+nyaKRaPjclBJoAhJlUM6Gh7UKFrLouBJSjlM9YvqQjgKihJwSOGs4Xy5Ge6gFAVDxlnexhbiM9K4ANeQuucERJcWJGSAxVX5O2O12mgsUFuaxFKtPQClrOE+oEk+EwMyBZSPknFTBl5J6oAmjlGaY3GxDtC2UkkYPSgSPwNNNnfUzgdSbs06DgBeupFgl4ROBco/1VPHm69nz8nkSEgYLqxedGXQwoArTJKa8UzpQw3EWC+O5wQYbbPDuwaak22CDDb7XQD/7Avk/+ikoU1A8sHmVLUb0MohLVdLU0CNmBkyAetmBYRo6EwrAZH/GPFBGy4ehHI4KDRC5JphW0HgO52JTswKstLGH2wzKRb+X9F1eMYE1UbQAaj9MWicBrnokZxiqkLExPRLfFzioVaHSySC3wtbr8LCxLWvxAqUXHgAwT0eAfnPQkJbzGyDCI1caudQTIe3Kc1dVKt3HmwGpU9hJcKICK3x0M0nx2QESGfO5pk0ZhUIuIFsbvPiMKy3pjGde3+QTJQLReU+5Rwtohj5cfP7cID2+HeO0dFVfI9RYg3NDPl7upQet6k7g19exKmx5YJqrQHqtHf5+FyqNQ+tL15fUWvuGes/NzNr12nQ5LRjR1FsPL+6B+xm43dk+XkNSHNaW97xKZIayj1jj3yhExDy1xSFhkfm56gvPj8OcgCm1g7w6r7jHup1H0TigM6UXdOeVbyB/ntKwj8nuBTzHoqGnP/sa+IvPgC9ePPUAXYTyq4L8+2b9HhorZmzUBk8ev+5PytuYrFyOSM5DWV1V7/COJki9AkK5c12rpFRFdAMdclpVbUctc4ZeqEsVqN5eHY1E4Sy+QHNI/PIN4Cb3nJDPC/A5gGcJvGek379F/gFUkLsnFClgLhBI5+F20g3z4kkmTK+uPwHvCixEHPU+ZnE/Og1NrWKlVOt2VS8Tp99FGEyEjBYOr75vxdPr6SHgEYF6yDCMvjcKeW0xr6yF6nDjnlJK4IdtFQLZnt0f3v/2SSA17npC8lrCvJ6M8BrtcAVcJH/e8DTGE6h771CmTvcK+f24F64/NCpYK8nFLQq/vt82ijt/uxGirXn37upoq7EJoSnVhkUuT92jplUQvGUfOUhG2lCCKtwJQPUqUxzCprCSTMBEoKKeugWCuRTkpWDKglIK5lJwi50py9TLlURq+Ev3+KK6Xx3BU8vlHfvh+Kc7Zx16xO4GDTq2OoI5J21DIuQ8ab68pBMhwur9Z+9IHqszJ2RRrzkR6pz6ld1SBCKSkFPGQqX2zdvo4S39Gp/Lae5tRqQp2v2aHoS0PZEddW9CbZ9YKhDDj2hhOBnar0TJDEftlCBAiEFgCBdLr06QTUm3wQYbvKOwKek22GCD7zUQC/DrF6Df+xAAzHqVzKKMIbyApDRBHKSGqUpupugp25gsDIYRmUZwKqGbQIF0JVLLsMoFO/Ucwu3UaPEmMeoikxgoQ+WhCd36TkNkSCxl3nHkCbXrHanCl9q/gQKX4V9auXs1K1aL+3uokwd67fq1cfUkAvr8CPrtETh8w4R3VAasMSdy5v5a2KQqNH6TII35Hq93UoIVMUO2ezVX3Mq8uqltd39t/gmaCOO0Gd2Xalq59i77p0rgLozdqw5rnd8gdcDKZjt57gHpxNimNUllXDvXtj8+85jyj60rDse5+q4QromlNzSZSXtVEBZU+UaQX66264HfFY1dAyfStCvgnHT+bYFSgK9fALcfPeIhn4i1629rR4F6NkZJoSvDuIV6rjh3vwN2u2YIwMXwnZ+75jUSQ1b6gpJhs3a41c9stXrvcr84VAUdgN98DXzyW+CTz7+V4RUR0P3p4pdKi9AV+2E8S06/6u8Bp8ZiD50LZyFswqc6SmM9q/iN6u/TKAMXnl89P2tF6/ceiV+fEh7OGtWA7xi4Y7z8F6pgTjcJ7//t90F7QvqJCv3dCy4KcAFbYj6WZOHaBCDkqiSo5Qd6F3ASpBdsSz1gdNAEFAS+TVDObEooYqSJsEjBwgVLWWyVtrl+UyCFUQ6LhpiroQj7ReSKukYWrVHep4tDQnd9+53S7cMzJzS/QH4tGlLwieHisF5BUq3RLN/WKXXt9nzTFD8A4zfR5jtZ4IsUTyLdH/VYs0/vhAWP6cY46p9WybInwFGNxSLbCSdqnnX4GsCCFvWERMPaZkBYwAQQCRZhHJlVOQTBnhIoJ40Gyqr8YWEUZixSmgM+JeQ0QYLyB0AYODZlW/Ps1QeTkSZSh6fhotZhMsJYMeBwPkLPZI/KQ0RImUw5l+ydLkeAyi5MkVZMkSVEYDNwIGtTSkm9CEk/PTymv9GViA6aC6+gLAUc6Bj3itbHRD2Ck4UMtdDEQDNS7s78qhB2fI3qBcilKUFriEwrkyTVAAYMl6CYF53xa4UEOScU0nDIG2ywwQbvImxKug022OB7DVIE9CefAz95D5JILdKkwInVSnwnqLWqK8GCW4iwh2AQoNhzlkRahQdUGRkxi0Uigpjyz0M61PJOuLokA+h1BtXTSS3SOsbGTGtdbyj+/jTBSP2OCHd+jEJ/V5U7pyN35vqZol01Ph72maKUPhRhAo4L0i/udDzvvwWruFH4dk6wNt4XrAwRw0OdvjloM1p/n0yTc97xkpXnKHAdOuDCyhrmLXL359YI0CUhi/cI6MLunTTzdE2c1Oub4qzi89IajZvKXyD9tRNpxFjFuURqXY0Xm/BasCY1uWJbdkVouBgEPXEpnTRVznxv8tAOLLd7zeJXLXpNgpRsGjksK7eDkGGZdAz/SYdwMqUnEK6tToGjJPnG5eSvD89fAr/7wzM3Q8fGy93nGfw+Dta3PjBBeuiGLosp6qIhSM7Abg88e6Z/mWAJtAB4eVtkKbV9XYeB0W+U8bug4vcK3IrczUBZgH/zC+DrO80997bA6gLXjpMZZKzii/r74XVydo9V5ecjFlJdoyPi0ps01CWhgyO6PHekd22OX+qDw7tXmu/H5Uk1oFZVWL71ubVuPTXEYz3OPz3+tb46yqHgq3/9FSgT8vsZ+cOM9/7gFumGkG8nI5ebQDiRRXSAgKGCZHH61r6r10ap4emiRQdFI5lhEn22pdLX6MOniYDLAknuqeG+O2+OODuZVgZ4CSF5Y/tPFHRDLZFOHkk0oBnD0Om99baZoNxLMSzy7zeA4MP6i3TBNY99qzBMRQsP+FrVnf39EEj4QrDjzGxG6umVoARWIoCp2pQA6BzAwTAFV0/eOsqueDOMQVUOR/oxtCn1P7v+NQ+s+OsK+BLAjFa55Z7kJKqYqmEsyUI6mmKHBckURyyq6FlEMNmKTzkhTxN204RpylgWU15x8zBLlEGcDK+xeg/bIkjBEFYVdWY0YKGBiYIMofY5nBDu2UgAFyWAU0oWstLSV7hXG8TyguocMlO1D2Vmo6FTnctEmosu51TfwyIaSrJuQK7zJB5iEt5uba+wqB2Und8echXCEFMqAqj5+9rxqSFWq9evzZsr5VgYUUkY85CC7LfxaJ6vkwiYkJBzwpwEyNaDbx1JbLDBBhu8GmxKug022GCDv/wa8psXwO8+U2s0t4wHVLgHNElGMos3Z/pLCzOhwkGYwK8xuCQu6CpdupwmPbBrABrVquaPqyGcrC3NCo9q+rEm8MpKL6fGBGgc/dA3vdzEV6MkHOtMukd+vwgyfMafyV4cGxHkXygC+uwAzAz6/A2Y8l4Lo+D6EqzNU+TBeCz4piVxl+qVgUuOUrozwnv/HnM8dfGEzgkrwwOuVKOxYJQkDQLwTlE3ttE0OL7GzynLzkpiR6nKMDdnJcuPg7UnoqPO2jK7alVckjBf8+y1dcepHd8nZ8qvtGt1hQ3vqCsx1JEoKO4IkLXQpi4EcKE3dZcbWjwHD+31x07OuTLfgMwTzMDLe+D9W6yHvBw0A2PfT/bntw1BwnOyeFyZ5h50dr6xqGFHXUwqHNNQlxOwI/OiM0lm1VTYZ+dNFwdqXPDerhCnVdAW4nEB/uw3wJ9/+nYp5ioI2t5zYZhcnv5XxTsXnyFI1FacKetCPgwCTjHJdBUWX4IV3HbykjrlfUMseJoJZtcqHjvQKquvddeUVSXjI2F85RV792T6Iu14rYbkUv1FsHy1oHxVcPyrI6YfZNz89Bb7jyfsf3IDG76OPiJWbQFDOmWH2r2JOr+a5FltuvRhDTXnVVGnQNAAdTBvElShNSBAgQq7lwUzGEX4tY7TRwEDchTIIuBFBiXdmVCXay0j4y2G+T7rhX4B+rx+AI6A/OabPwOueeMqPfXIOr5JeLI1FUOxXsRhwatLqAshTgAkQ9dgtnyHiepeoyTt+DKSXYiaAo/WedIT2jB8j96c/QM9+B4+c/synNB3pCkfkivRTAHEjALNN5cs/GMx3jdzwcILEgQ304Rn+x32+z0mIvAyqzeZKeLcK03IeP1guKiKNKNNEPPWwRRvYYOGFBU6vm3gXIHmsgKunfQk4wU5Z4hkwBRuIFJ6ucDmTOp/epuQsobOTEGJxoUrbtQVwG3OSMwbsyFsMQUbARZIQOUmIqbMKwKXZfi1RG5EjDoGYkq+GE7zREFn41cgcKPOGg1TBATWviQy4yuAs9ZLl9InbLDBBhu8xbAp6TbYYIPvPQgAfH2E/O6tMrhpguQZoGTec8qtaEz1VOOcCzQ8TlXQlRhcQYY3GKdSQ+/E+5YbJnjlSCRma0gJmKCiCQcJgeaPxWCZJdjDWbqgaI1l7IUzXY6DlWtXwyjr6+Vq4QdAiwB3BekXL/WhN5Fr7lVgZDzHYaihH4f7sX/npCZuBvg6UoVLEoAHhTUU7nkfaOhz+CFQwTbbGuzCXtrHSX+CJCHGzamLd6W946Pd71jIleI949cBh0F4CmnJY+o4Vzask6qDHMfiTcATyIRXIeIf7094p68QXnlkrCZOl49TwJ5n39/dd/nItSjkAanfUw6XHIDjr56wwjVgAb56CTy7CQpsBOm1/aiJZboWhr9vG6S5V45NqpJEE46pBL7hJk7owlcCti5Ir+cbxWVkEi1ktJxyub1EJZeoFVTpZwTDhT7AcwG+ugP+9V8ChTVH4FsG8pIhnxfQx6kNZT2nVs6zCK+Kp6KQuV1E56kXcWNfqq3fqqBRAfKpy2586FxbTr9S/ZeG/R8lzvb75MyPL3tiRB737viq1wC5E0hplfFfsoYs+wdPVL81dP5qwfzVc6QpIe0Tnv31W7z3e89AKeH2xzcaDSIDkIxUiuXpa0vFSQxmx1kEElTvkQiuqNPvqOWTCeaLKfE5CWgRcBYwuaIvzPsbmML4XRjgmSGzQDhMbGob4KwXHeKZKEO5fpFcPDfrI/0eomH9f5NwDuXE+28rrG3Pa9pLaz/OPPggST8UFEJz8A6EGCVV/jTlrM55NalLao6g86Fnas37ObycH1gua2lxR5QW+/ba+E0EJKYUMvrHeWDnjYsAJK4SZHApGlWHCV+XL/HhzS3w3vsgEUzTDjztwGVpXrdmMEBEZk+bkZKGhlRPNj1HWJQmkc4TPxL9fsXwWmAIhLmGndAoPazkDbsHnV7PGeZpDAtjGavWHjOzRflO4CKQxGbAq5NHITxlxdxGE1BquesALcfMpoxUowIWbqEw9Y11ZdlCg4iG3vRcew5rnnNtKh2PiRlQ1ylW3IfA/tmViZIGhMnARJuYe4MNNng3YcNeG2ywwQYA8C8/A/7gI7jWizCpgFAAKmyEfzKGmIGy1Oha1XzXLBJV8MeVEG3MiAscTfDn0rGUmtIDaFIJC9Wjkce4UaNBaTcKsJVM1gTXCVKJ2VO+Z2hcEDadMlwxYJT+Xmf+TyQmff3DTRIABwZ9dgAdC/B8OanxW4PVNo/XrhQZEJnXRnxE6po5ARc0X9vOa++tTVq/cAZB6JmyzvDX56jNtXPyVXA5iCyiRKnTUK21f+X6KAERWOINFzQMz1RhapTcPQYe8cA5Ccq561EeHPfgqwgp1p659tpTgq+fgOrqrTOC5jUZe/SEq+juwn6s1bocF+vL/RxUy3GEre+oenjX6wyhCNQQ+k3D1y+Bjz4Anu3Re9P5grMkLrXDbNevXIBPvo5WNklnMBD6IPG7Cb9KUYUY26QxUE29XccmxUzMqYW69O9IannPln/W/6piIGMV4pn9/AD8xWfAiwPw2ddPMShvDIRVQUN1zgeo+HJtounM9fgC9HPnz62BCQPXNm0VIo84whU5aEdOfMZvijR6JlbRhJD+PlfurJwhZyCWWjV+OldNfWfDNjFEZ/22elaH3sSxOkevRAQKgH/BFf/wFww59ANLOb2xM4IXBi+Mr//dc3z9756DQHj/D95H3id8/A9+qPRzIvPw0PCWHka+MIMsDBszkEOECKrCd+twQNiJAKEETgJQAcFC3i0MTkX3dUotJH3Fj28OpAh4Ligzd+HFmzjat0NYF2fnpF/dEq/TFREv6jCShaPWC/zbby+0/JXb72I1b5rMOftiPG75jLTFNRWcRSsdsQVbAnp+VT7SjksL9tLn7PKUDva8JLEjlGqIzJNxDXSTP0d0Ov5jSFYJ36l+e02olQog7tW1GP/saiNb38RYKCGDTPmjOdeoMPYAluOMMi+ah21ShWXKGUmKjot4Dk0N2ZsSGZptnsDeUc8r52EidUAacVzPsqS1wT3oQnGrWRV3BEhRJL4sBbudfs85AyDknDUUsNZq3mmMUjQwkEkG1CCOiykduZYLDQ/KyIScqdaXiKoxhfCYx849nMM1QfMMjGdd8JyjkR+MhsPO4yU/0mItlqcuUWfozMzqNbrBBhts8A7CpqTbYIMNNgA0NNbzGfjBjRL1OSuRWBiUEsiUZMRihKDGDBELBSG7KcZgaM8b09KsyF0S7NS3UZFRysxF5aruVuLWZCblbnS7EaPGepCQB5jwVw8wskdeh0vV0T90kuxieDZKuqpU+0z1sdpDAQ4C+nIGfTUH95l3DYKULAr8HpBJnlXijPcFfSKH1boucPRr2o9O0HemXLx2otyKwtsgEHNFtdj3h9yYuno7qeywBi/UEZnytcJVi34trAnorhO5PEGUsH5bXXh9hz4eqOuqMk8glFyrqlPKjQXXYEW4szYm5PviXLsfcNmrqOqaMXrDAts3Cp9+AfyNn/SCfIcoAXKlF7mA5gK8EVR9oVK2+5LahLkUu5SaA0VDW0pbdL72NE4TMFmYS9eSxtDOBFRFnCvsxOplsTrC2a6uCPrgiwPw8gB88jnw66+A5W0Mafk6EHDikyC5S3Vd+S6nRSgsbKOxPM9PFQ5WZVj/mk4bHw2kHoBTobpWdq1i4AQlXvtgINNM/7KOwwSQr8LFWVD+MqzJBZCnnMfXAIHg+c9UWff85y+Q9gkf/6OPkPcZ7/30mQq+ScO0u0cMi3l8iOZRcrWBhDkgbmJ/BoyWdo8Wfa5w0aBxRQXslTZ/0/heoEq6pWiofAlLdEXx7V5M45qpwTS4XVyPdrHWoUhgxsutrHwlZ+p7PVjuBfsP1gfZ7RX5CfSDD5Eda6TJNwknOOASBOLqwTav8F/V/sFJ9oKWdkH6Oh0VEtBSqtawhqKhLx0HnSJDU8z0feq87wxtO386rsRXmpM/A/BHob9Jaywya4SbRGaDRGqI4x2VEGDZWXMRLAvjMB9xfzjg5uaIZJ5jBM3j5jn+wC1HW1NM0kmfmC0EphkgUKfFlOasHzzNlSQhtR2aFO+hAJr7Tiw0JYGIbd9k86ZTL76Y7458TkyRxszgwiAqta5i4S5ZGELm2eYeg9D6xqPZQ3HW49jn1HFTwCfN847qs3UERi+6weNen9UXCPnUSSufCL/97XPcHxcNiEDA8+f3+Ff/7Of4W7//1/H7//inl9fPBhtssMFbCJuSboMNNtgAAI4F+OPPgf/WXwtchAns3BNBY+xYeCwAFk5DiDSnRhIEE8VODhoJ1hamqxG4+kMAXsyTClXoWOshAMjGz5MxOaNiQhn7M+w+VKA0CLdiaWrlMdzqTAQFp/UZQV8fiV22PtGnB1XMHd6ScJZrwAB+LcC/5/0a7o+cZGUssM55nnsOQPP6WCm75mV3LfhcxRxy59rQP9i8404UhBQ+Bi4bsFBysPXvkiS/EOuI9YTrVZKAxqStyYlOlI/2XmNcO26ycpQe82d872n3uvl7SNqzBlHiICvXL1U5DtcKXLsqavOvFQJdW+mZ32tL7GzVtPq1v+g45iGJ2vDCk+E/M6ZRxr+6JM+06ez+ftugREWUn2dr4PuWVgXFbxZ8v4f2RWUcoBKs6t0G81InVcwVsfPSCzvSsv3uGyBPwG4P3NwC0y5sDHuvuAV2kF4it/EQRo09BVLh389+pYq55/dvYFzePMhvC/CjrIItoG4IWdvUJ3vQN8y166VK9sKV8T1h3oc9PaLS+FyMsBxpD+n9RPqmRAHzQx7dq+8Na3WlcY86Nlb6GpfmCb3mlRVDbAeg/MryFIlAPu/z+rztIBCUQ0E5FPz6v/gUeZfw3u+9h/0P9vj47/8QCQRJCSLBiZ/MWyN4gTRQupzQ7L+YW767IoyFGZIScqSzXoPceriPqLirHBeUwwJZfL23F4vR77p+ZaVZTS25vvXi4n61DsmXpx6WTwECwcvfMvYfrFieRc8ivP1H65NAXHanbNwJ1bz+4xQqCW3H4higBUCzUUkwz9qWn66j+9lWnOURJ0iHJ8mJp0s0c6XlpEWFHvpaDVE7fHcluA2CG24l0uNcGCIE5tQF/wCSKXsUwbZMrKrAX+YFL+/v8PWL58gpQ0pBooTdfgJRQkrSsSACNKVRIpAkHWAOfL955DMEOYS11PmxEJCpDV9kCbVLBMoE9kgB0t7ZPOEYUnPjav1rCjoAKMWy1Xk+OSkQcDtMJZ4htnDCtLDPF9RQIoHUY9nPwxqymMyL0Pt7mnfOy/n9qhAMdZSygEmqaOQv/vzX+PWnX9Zyv/71l7i7P81fvxzeoug8G2ywwQaPgE1Jt8EGG2xgQH/8OeQPPoZ8dIsmuFTCMTkT41wPaR4NgoWqhIWKYAshkniNtkUnEa7eR9zCY1YC3IsTzMwOoOadF1VtCc1y3JkN/47h26l0vDFHQJC5nXDJPhhrA9cqkPgKgubx+2IG/eYI3Jd3g/sWAM/xOGnBWtnHaFPGOXotgZH04Suj2iIy0dHdcq2fvh5dkHlRuWXrlMIz17zj3I3BmrK7Hp8TQZUqsE3CyWOPHMzuFWtSh6GwhKL2uihzjsN/1Tsfau5D99/UHrtQL518uRLOlb/UhxWZalzm1Tr84nq9/Fsu3X8XcBgAzAvw+dfAjz7EeQUdBsHatyQi9Qlck06KoBq/FPNuYwCFQvkU+ijhWYaa1ls4y91OFXZ+38V39Vx3S3u7VhX/ou/91Veac+7f/VIVde8wyEupm6VOf6U9zuDfR71g+HqlQq95GpwvL1ABZNde8dxiD/gAdYjD18p1fV0/DXTP0BUlT94l0o/LqrDbaKvnUpUn/BWrktXKyjsbjeAUysz4+s/Uw+7zf/0Fbj7a4wd/90PsPthh9+Obat5GNbS2C5ybWpa45atjBkopKhxOADgZKW15t5xuwTeA+QTgIuDCmqpplc72T+vLWMRZiHPeqPHb2WV9YW8d+lyFGzwdnCVNh6Prtd7hZPCIZiigO8BThenRWe3bFPGIk/O+xbp2WRlQUzDZ9abyGo7jC/07Ja9ecRBcOVT3spgyScCkPWM0NkHby/aooIhgLjNQGNPzjB0ywIL5eMR+t8d7fIvdfgKsLgiBpSj+gNS8bskMCvzdBMuNR6ihfHFBGVl5eVdmUUJCBoiQyGgSkaooU1TAEGn58Holl5My2h4xD7nMuSnFSENY5pT0vXa26r1sn3ouF1nAzFjmAhE1DtF3aF2e/y8q2TyPXVlKWw7U7qUUNcOEP//LT7GUAhHgOM/4p//iTzvFHnPr/wYbbLDBdxE2Jd0GG2ywgYNAPeogZjivBDHBiGUGIKKhLwkq0MtinnUCFI/xzwMzQpV3EHhS6SB45BgLXhV/ZAyHM0K1fS6wsZKaktp+yal6LrRg5eo5IrcJCkwkdFqEhtIU3lKMwP/kHrQw8OIdDgF2rcKkeo2dgTUljF9LKy+iM9+vgROGerxv1zh8XxMW1XxzJjS9FN/fpU3e1poHiqqlbnvRyLUb4ypW/iHFWDQJrkoFCpKIVqVlPe+Y95UKu4/Tew8xgyYsPrZm+55Iu/B7qKazKn4q5VZr0eWb4xC+KlwQvpyVs/tUvM4ar4LK9hffJ8Ap2npIIUihTPy8QhYwCsTO9fsbhy9fAB++D0zD5q39Cgu2etKNC/MaYcilxRT2flQWkl+TcC/ufWr3XDFXP70IoZqgd+8KOV+FFR/tJ2CiMM+O24bPancvWu64AF/fAT/7JfDb508iSH2b4PplGdbCME312rUwliX/aELMhtPDYwTUfDtVeNzEumSKuquhIqKHHzpBV6T0XNeZ0YgklB+pqrFOAMCCurbL5wz5vLnCyx1Djt+xxXcBBAJZBHe/ucfdb+4xPZuw/+EOAOHH//AjTO9NoH0s3z9blXeV3LGgmDlhMmImJc3j5PevxXaPgbo9RMDHAj4GL7oBNIyl1EY4imw/nIvonjqhUq7SOa+VWQT8m3fb+OBtBFr7QSvz9CoL0J6ppG/Tj3Qr5QQ1JZinnF2qPKrhXEekVm6MrNHW5grXGdD2aneM1KBB0aIhbIGqCLsGfPF72NqunwIWRkkZCwQZGrZyMn5AvQTJvGnNG40Lnt8RJkqacU4Aeg9YdhNSJiBpuEg277WU3PvL3p8AkgzmpQ4EpYSUNX+dK6Zi2E8/gzzbu5yJ+VoVWoImlxBBKcVshwllIWACEqXu+CRS5dbCDGIGZw2VmXNGslCZ1etQ7JwlAiFru0FglKA8hHoNJqqLyxWA9TwPijrvprDguGh+UCLCJ7/+HD/7s1910/mrTz9H+Q4ZnmywwQYbPBY2Jd0GG2ywQYD0T3+J5X/2B03Kq/EgTN7nhKaaHookVdCJWIJ7y3UjaOEv7J/6aI3zbkEpK5OjwkFK/QPqvWfhvTrvqAZVrGoCyRMm5/SLwcghjsqKE9Yfq1bf9oWKgD4/gj49AGeEEN9dCFKVh7re8V90Og3h1qsBmfL4gXfL8DmCnGvbugaC6mI34YArzRJavsb1F4V7qV1zIbtV214Vypw0w3ZDQlPOdVaaK8+c3R/ji8fnBPh0AX7KKAl48c+HJ3fA/t87U20Gdr8bhDSXdByXmveqMApsLulWXqPqs3DN2n7K/l7q3zV9P7Ovu5CZ9txFFPDKe/oVYV4G6dwF8IQfHV733vjf2LtLdUcEUyVp4XY40PwcrIqyoDhxSZbYfUETINaQltI3xUPYZQtDnbM1XdByypmWx+v1+vzdX34NfPJb4Oe/fHrJ/dsAAshXDPrxucSnI2IYDow3spbHdwT8P0iB1/Rh5xywT+CiBPm02LlrRvmYftkl5Q9UOgwr/5rrucyfMuSuPf8uha1807DcLVjuNHzZ3a/uAEDDYd4kvP83nw2eQhTmxoXNwcsHwJQykiQIJyszjPUTr29tH6kg2w2gmnZ6IEMGWtx/ei5g8XCXZ9ZHqPpRIADezei9F2ENi31T71u7UR2qRmQl8mrtc0Wdfaf+ciN1fPkA7Tj2pU9KpidfaxZlmrIelyQCqR5hqPtFiOpxCt9hoS0Rd1fyInSy7zHh8LXg+HnBYc/AB1f0/QWATwH8NbFGW++T8tqUSW17rH9C+j3avqpuTwBhFNGwlKUwlmUGixrhFi5YFlfSLRDhwYPMuHjSPJpecc4JAs1HB+r95zy0o/g4DMae5JZ8NVx/ql5nNXxlCH8p5mFHLGbM4vyY95XBWCwdQUaaCFMiUJqQxBWHbEdZUlwF9xbUexBBTkm97tByxlEJ+ezClB7nBX/681/W9n31/A5//LNftPkfSLcNNthggw02Jd0GG2ywQQ9fH5F+/gX4b/6gUe+kjItbyomFwGL3fmMloj05/aoeYJXnNo6FchUoVEUHszJFXKpQUozaduvIGr8/WI976A2plLlLjUfr29iaIJ8IbiC1jbGt43MFwGFB+uSgfb//DlrhXuTwpS/3ELfxjXEj3pgzGghZKXquHr9XHSLtQkIQokYhPqEJz91D1HM7rrXz9GuTZkSBcOCsXfHmQnb/3tVjbamS25VO3o9er0OxT2fgiwueoC8Z+FsF+MHpLZmBw5+feY6A+ddYnZ79T4H8QV/WId2cb8pQ/fkpHXDRk8oix3V1CR754ir0Gd4j45dzy96fH56t6dBkeEeEtcGk4fMSWJnjX11R9qnhsy+Bn/74tJ1xLDpvNr8ZIZ0Ksc+tsCoBFDRTfZ+U8Xzw/Ws4I7mSLFQhaPvczsKTNkT3xapvI2A3AfsMvP9MQ10mwGJSh8r9oQTcLcCvfgv85afA/QwcTvOcfFdARMCfq5LuZN3XoRnGdJzyx+zhS/uyKxLpD2nhLy2/zkk1A87pmnhF+9bposHfLQjTu/fTcHHArfpDgEP7WT4p7bcAciffqXCV3wS48vK3/+oLEAhf/+w5aCJ89I9+AMqE3fsalo5ETDgOCCUQWv46EQGTqDePbYAaqs3gRIni73+VNi8CPi7gECbX6f3qQReAnOdwzYaVadsy0urU1en8xPlWr1MI/Ml3kH7/huBkpdDKz8hODQqbq0nys3i08WiDA/LJO/xI9jCXFlWxy2NHThCx8ZuJ1Ck9EUjIbVWBJIiOXzwLytHwdjjKfV/xQfD5v5nP9Q7lKFjuBfc/ulJJt9ZBiHn/6ackRgGhQJChOdQ0vzwpzcECZLY0tAlTmpCmhJQzUs52BrlSrChuoQQiaVFvGBAwYh62BEKhVMdybb59a7dx6rtEKUFKP5veHgKqkpSsTV5jY4vUIFhIlWhssoVCDMIEKQ2TuOJvWZbaj2pwTG1xpZxrm1/OC168vEdZCoos+K//5K/wq98oXgaAwowvvnyxKeI22GCDDR4Bm5Jugw022CACC3C/gEwJQCYVoiokVIKXuQC8aHx4XoxbcYbZEyVTCCHinJC+poZ1AgFGxCsPbp57/q7qORAbWbkoeG1N0CmBYT95sK+jk273gihvocTyfkcAHC3X3D0DX32PkjOPwso4vNdyIdF00Bi8NwJhibV3A2FRtnIuJFyTw3eCyBUNh0lGBYyTARLYWu3X//k2rxSg0AiXdEQXphr+jIf6BfisqCLt3Ht/Ma97HL5pEIBfrt+6/9P167QHdr/jP/p7aQfsfopu/rrIhWcEyE+58laF56/ygmv20bDvTkJMXr0X0Y8RcL7dD9W5hhMG4MP5e28M7g7A/RG43a9I/wTdBliTFFbhTBiUurjiwooSOQKIh9/xvSGkZMrQWLqhPoG2y3Glh7iMXnW1kejrzta+ZKGYph0wTeZNZ3jIrOPrZP/qK+Cre+AXvwVefBuT9G1BGIP6+0yxNaHzUOQSdG+4gBdW6xn36HDvIfuP8ec16OEa1HWylRyOIWygAPyr3hhk85B7OhAIDl8cQSD86v/yG9BE+PDvvI98m/HB336/hv8jgnrOeS5DqIKu5jYacGPEbjHv1ivPnMC86ATg5ulXafkQxvXM450QX07uOnl0buWOtJn1rl4WyFFep4fvDLwhivv0PeOLqM3RSRSF+sAaMrvmZajTSbSynAbSvT4S8aovRQtxKbW0rteUCeUe+PKTuSMLIk4//JZx+PwyUf1G1lilP5wg9MsE4gQmAifd70KApAwkz0kpSCxIIsh2TRJQUDAvM47HGYkSKKlnnAfz8Lx9PlLMbOks2rjUkI/W64r7K9sd6CbpPcQpaYjMyuLXgtA1lNTAJmdCTrkq6doQSGiL1JCYLUwmY0FBBlUFXfzUXHKNRhAI/sW//rmFtNR2/+KXn+GvfvmbfipeYfo22GCDDTZosCnpNthggw0GSP/2c8gffAx6bwLErOxYAC5gLsZkLxCZLaa+NKI1CVpIr6ghCaw+ARBNJi1CWlrQBIfsFmzFnuyVZ91X6a9Xpmrg5uPdwO4PMGp1pHuGZgFmU859uXz3Q1quhKLRT/SfV9U1lK+CoTcgLhiF635tbMeaoHBspzOglfOPjDBw6nlTTiupQvoz4zk2IEazvOewzhpjiGTv//nx8jwc5dtRwr0BkCNw/MWZmwQcfxnKhuW1/z0gPUPbzjZ0lAG6fYqGPUEd176DhkvnUNkajFL6tedcYDXee8Q2feuw4lLUI+x2f0EDwmEvy4AnQjGHqLCTcN65ttTdE2No2vpu17gRQBPUS29U0Nl7V3OzxEUchNxA+56SKuamBNxMlq8mPC8MlKKKuX/9S+DFPbB8RxDFK0Eb97Xlf+6RE9LkHFnxmnBWqRWVd3HdXPvOQcfbyKZ4dp6pbBHIvb20CJY/C6FlGd+r/HFvA9RQlovgy//6a1AifP3Hz7H7cIcP/vADcBGk95JKPgL5IiwqbHdheNV26Lwn88iT8J62Xq5baFIEy2EBL6VF+YUbwoXFGzQr1IqhW6hrWhe//Sp7zV/5pUCef5tr9mrM89pveRNQ0Q71U9bhmNXw7aGClcuPATr7o4dyFHCR0C40Ep2Ar37BkBIOBP9CenQuh7fsrBQEOgSNDjHbWUlKkyywqDM7pTl2pLkpZQKwzxAwpCRIETAx5lJwmI/IlJASIU/qWQeczrH5qyEqtKLKu3qldduW6nEjwY7Do3KDxcgqVb55eE0HD31JlNXjzh5UsikNOeGSzTFXZaEwoxg9JgK8vDvg089+i2Uu8Nxy/59//m/x8mUzXPrq+cu3j8bdYIMNNviOwaak22CDDTYY4ViQfv4F8A9/YkH6TWkGJ6I9/5yR4AlQbzhqAh6RzvoWouR443eUG0okJogUpcyZLMG9NO8QD88DQRNoilVbyfwT5j3KVp1tQGAaqP5Lw/P+U2pib/rNDPpyBu7eMubsTYGP+4li6cxnhDXm+ESgSevPviqMgvQq74hrg4bfBuOURmF2lRZRE3RX01lbs5SGCgIjyuiZ564YAc8Z+GJoAIX6v2Dguxtx7ulAAF7JJSO47Jk3/RCr6zXdALufnHnXSvmzup9LsCZM9+UpK9fC96cQtK3JOuvXUb99rl2jXPFtlV48fwl88ExztJ1AUJCBbH/LeVzSnRfUnvGLxMNAWr31a8j75pJBxyGjMqZLNBUUbeFctFQtqGbteVIF3W4CpqyfmdTDDgIcFuBPP9HPX3513fh9R0HuBHghkPeDARGwuo7jnpPxRjgmHoKoV+vastY+4HRNnHm+kkWX2hDbGi51t+OZL9K8zAHIVwz28+ooKJ9fCIW8wbcKwoL5xYL5xYKXv9Qcdje/e4P0LOH2bzxDfi+DiQ015YBn7B9yNKMGc8mVdoDZKJ3ZEdEzBkZxi3rQ8VIgi/MD1HDakBOvKnQiynVan6TzHBVHo93DQ5vOQtjwBW887OpyECx3gunZmU3qfZHrWv9tw4Pozubt0gPd0Xpt5WcGx+t6/mmpnnBrRY8vGMujjQje4hn5HMDvwM54KC0gRXnrei4wFpnAmZCZQShIKaldrUXhkJS6DSXEKKXguCzYlwUFjEmyprYgIBWAkqnZxRVqpKEnnT5ihue7Y1YFWQ3hDEJKqaW58OaLsU62HwkC0kSAQfEmpqTrlXfqAWf1kJUX5f7Z2uDOcV+/uMM//5f/Uj0HRfDFV8/x87/81RuapA022GCDDa6FTUm3wQYbbLAGv3wB/DcShDTkFrsckixoBZEKPGsOusCYE2nYyhqeq3HQnALjLVBmXQBis2wUseTM/uyKQMw+VXg8smE9I0VdWVfLeZmQ/2eoge4LaGak3xwGb6bvCXwmwJcCfOTzGgQ4jxmKTskXJC5pnNgz8BDzXtdSqHu87znHfTGd1PmIDtWlY+v4AOB+Zf1ZHgt8wsD9hXCo7lSzwTcOcgTmT8/cJOD4yfqt/V8H0j5WZDhjD2T32LtWQTfofR5uNKrw9GTZPnJvXlu8duecFiHIOd/aKHZ3B+BwAJ49e0D4Z2dbLCRhTxNQle4SynUDFEY2Ku/qnI9SZbTnaJjclFAVhh7DiYCaTAewszhr2d0O2O9NMZf0ek7AvABfvgA+fw781W+BeVOuAIAcBHIQ0PsPbMCgU109Qy4o99bOq2u3+1Wwhkcuy8XDDUIf7w3AHUMOJvB8ISi/CucXo3mYbPDOweFT9Qg5/OIAJOC9v/cB8rOE/U9ukCaCBI86BzWYExRzgfP7hIygiat8AUXjLtsr5VjAhYMSrF+V0qG9eMi5ssC+eqg6a4ec5Al9RSiE8os3jxOL5StbVdLRsCXfFRhxTTiWLiG6sYvxNy+C5TCUMCsEAvDyt4zl/vwgaSrzd2kQXxO+hvIRVUkHgHK7bxMjzJBEpqwSlKR0hIg96npz429KYcy0IEnCvN+BuWARQjnMyqcTME3mYUeElCckCwXCXEDw9LeClHKX560Iq1EuM1LKSK6IT+bxtnh4TACkuTVTUo++2i3ysOFNSaeg7/j8i6/x1fMXpthj/NP/3x/j08++qGiGhXF/OJ8fcIMNNthgg28HNiXdBhts8E7Df++//Q/xH/7R33n6im8n4L0PoMQujNn2HDmeN+70sSarHBUi0nNwtQyhxr+R8RmMP3DqtXShvCkMoyDhTItPbtLLAhwZ+Ltrz3x34Ic/+AB//w//Nn78o49WbhKwX5EwXsP71jwE/kiUPFypoHsMRB0dedLwR7T3GliLpXQnwMsLL/jDJ3r3Wwy/99OfYLef8L/+z/6Tb7sp3yqkGyC/f2Xha5R43xZcVGJdKPtQmwlYfg+QFZ31xz/8AW5vb/C/+l/8J29OuJYz8OwG6x0M7zy5fUbr6gq4VQ2mDJ8DUPhC3QV0+HE8ZM9Jb5N50aWkSjmNX9fqvz+qku73APyD9SredbjZa87B//h/+N/Bcb5e8EbvE+hmmN9rledPqW07t+yvVQB6mUe3ibpH+IVA7oPlyN9/bH3vLhAIN/sd/tE/+Hv4W3/z977t5nxjcPPDW9AUlHOdkg6oUTO6W01A3gMNa1Y0fN7M4KLGe3HF1VRa1X3MonCEPSgIOcw6uk56NDw2PpZ/QGPEv1ue7Nz98Y8+wgfvv4f3nv1PT+7t3yOk3eW2vEvqpTVbk9eFchTMd+/SKDw9/OR3foT333+G/+X//D9+uDAB+Hh9/55cIEJOCZSShrKF2f6I2k5mIWQQshCSaNlMGdOUsdvtQInApcBj65CFwiQi/USCAMiJMe1Qo/BUlBA83trWjB5yZkbbebVS84oLhlHRPkUrHbqc77FIsusZ//A//KOHx/I7BB+8/9633YQNNthgg1eCTUm3wQYbvNNQSsGyXPDUeVV4vgB/vBI/7lsG2j9CEH62kuH3Gi94Y39vKZSv14Xdj4UuifcIX6y5DFxd89lq3zRcLeTPAH1wejnKlwZ77+7D68CHJyVO66R1HR8FprqWIc3XWP+s5pbxQSJv2xo91F8Tspuw3hnatfHhF4L501ebLTGhWlpToH+PIO8E+zWe+JzQSvq5PCn6kH7ncrHL0MtATyr0tbLedPekCIvaUIVbSYuZQEddOQAwpfVwfB7+OKWAOUYN4Hnp34cf3OJv/P6P6w5xIxIxz2wRAbOAGRp2TQB2T20hsJBF9hNN12Yhkbxs7UZXr6DmZzIBkn+Hl62tN4EWku1pUgEZufDZ96d040Y+zojdP5XiKu5IIAB3d3d48eJ5eMDGLuX+dy86W7l2bh6w8twajHWO7+s6debZc9dOv7f8NI/ERS/t7x2E3/mdH+Fmvw9r08aCEpKHdiWxEGCkHgs5tzOlGlv5/QT6mMAiqlAxSNnuaYWACIgE1YJLBCysLixkdAXp3mMRlLKgMKPwApYCZtY9Bl27GnRBdP9FBXeYZllZMssdYbl/Qk2pnaOFC/jZvG4TNurzpW9bdJRdw821Sgl1+M/hMOhWObX3rTX7tKEqeI9tiu+J77v7eu7adF5ZhxiVsrMD8A5L94AdDUF/0LV/7MtAD63V1316vwTgr59wHbwm+H7LOZ/cKwf9+y4B3QL5Y6n4QWSga2h9qvvlZliJgCRU2S8J9/2stCCHgR4WWwp6riZKqogi9+ryMIeqMBY23Mf6d55wb3u7258r5WUmyBOyzJS0f6Vc6eF5LirECrwrfvT7HwLTM/1+Yq8Uv4xkRTia8g/Cs+NUr7B0ALpU4+foYw/O0vFelY+j5pGIdp+MqKsKSf+ntqNRv62AntG8CO4fMceArvsNNthgg3cRNiXdBhts8E7DP/l//zP86c//8ttuxpuDDJUvWo4o2gH07IrnzskAL8kE176fgzW55TXlL8kRrpW2C3D4U4CfX1n+Avzv/3f/G/zbP/k5/h//5L96/creZgiykvQ7APZQZ5P3ViZkRWtSecAVJn1t6cRaq4NLENwJNecXZ/ISAWnStk67jLzPyLtkwjqBgCFgMAQF0p7PZMxgsypHMqE1IViaezL300Ye/5zx1f9zeaXQm//4P/0f4eOPfoj/w//p//r4h99CSBldpKAItz8R3P411nmszLmqUdI9sJujELTf0JWfH4TOHJUyVvacMBbohaQATpReQqdlRpC4Lkaha7jm1tUejkz7nJCQbH2pwkmKSj9KEcylQDy0GaM6SkOA5/9qwvzladf+4X/w9/A/+R//d/F//D//31V59gjIifCf/2//B5CbPSBFFWtcTDFXsMwLSllwPBbMR8FxZixFsJhOoTChcMJSCMvCOC6Cu+OMZWb9MwcLNsGeKvtMAcFs+zvruvG8cISqNBGTDAk0rNOUM3L41OJJd2dVKjJc8a3KUKlKwKquFwDgquybUkZKCYf7A+7v7yEA/vxnf16NeJalPHps3yX48cc/xN/5W38D/8X/65/hi6+e4HB8C2HKGTln/O0/+H18/NFHyF/fYcpHM9YqKEtBThm7aYfdzU6FgalJIvNuws3trXk86Dp2Lylfk4Dui+NhAbMgp4SbZ3vsbibklA1PCYQXDW0oDMgCkgW8LFDrIYakBcuyYOGC++Mdjscj7g4vMJcj7ueXOC5H3JcCmTIKGEthzKWggCHMetJ1YbalRq5m68+LTxOef5LQ50d7dciJ8O//3k/xX/5//xV+hn+HNDUFgwhgEdkhbOe1d98jtGf9QwpnfeiCC3dH3R8JauhrtoOCCBp+0t5f0NLi+jlNoWw31dYuSzmn9E9u0bWFW6R6j3Zaz6BQX4wIX+ss7Vwg60y2dhYrY9oTfSa1NgItBXXU8baBsD+j+/1MpFBv90wQkJegaK9nGrc64jv87Pa29XSSQhXC+zs9kIjdd8Uj2bzEw/s//9F/hi9+/QL/t3/yX+K7CETGi70H7P6WqPHkAiTzghLWcIpauD0nVbvht061ISKw09BoJnHlm56FlBJ2IGTLI1bAKCggEDISMiXs8oSbaY/9tAcgmJcF8zxj/v+z93fNkiTJlSB2VM3cI+7NrO+u7upuoAHMtwzJJSl8IIX/mi985CspFCFFlrIiXO5SdskZ7gADoAdAd1dV3nA3VT6oqpm6R8TNm1lZ3VUF15KsiOvhbmZuH2pmeuyoXla0i0BWQVsEsjS0VWz9ImMs9IUawed8m3dJqQ+iTdcloP0Tof3dh9FDAPC/+9/8V/j1L7/E/+H/+H/6YGn+EIUq+mGI8xfAw5fjt+kfgXJG1wURQSMW0937N7DVOc2mIWmArK47vH1j7uhjWVNbxrVbStqLwTL0OIl3E9dX6nu4WgmlMLj6oSzSHjKYGWAqII7xon2e0/4fAAp3n+YadPlG8Xf/F3u3l8q/+qvf4C9+88+HEX7IIYf8dOQA6Q455JBDfoBSPrFNYP3SPgE8D3ABLwO5YhP2UrkB2LworzugXJycuwX2vIvMvwLe/Hfv9+w/F6GHAejyl8kI048vJnkbqJHv0fu332LLhf0h7/01vNElUEx5/LmyAKuDbwUQiDEVSKGkYCZwoH7sgdOJLF48AMSnGxmGAURHJ0wy/zlh+gVh+eufrhE/y/yporzamXa9Xs5fKk5f+CUCNOo83cdMUKK+kQ8AhYnQ1OBUUtuAZ2CEUgcZREc7eb4pyzPNcJOJ9o7ybBK7vBuGMTirzwHuBchopo4OYKa8xLvd4182/NN/fQcBfV8h4Oc//wRMbAYOslGk0twIt7qRTrE2oAkgQt1QLSCot4GoGsOnGQC3toYmgDrTLpBKdnSdirOTmLcxeMLI3i30BFUD1Gq/n7sLJ+rxUgFSsxBZ90rsgTDojCMDAJmx0kLEmvuqh4cTzg9nEBSfffLv+2Tzt3/3W3z7zRu0Jvjbv/379516DvkTyGeffoKPP3qNX/z8S3z++aeYpgnTVCCiWNuKJg2tWT8Xsr6rpKhTBSn1vqjLpbO1gxnHzAY4N0FbBbUUY500B7zJRnUpE6Zp6qCHrivQVqg2UHgsZwZpAUGgMqNWu/lhesTysOIP35zw5vINiNTybQu4MlYVLEVQGmMVxSrNWHf+HqFjGIBj1oACr74QfPtbxnr5Hio9vMY6K0JU7T3DqMtb0CYjcF0X7AGiuBzzMQYI1X+Piymdvv7wNUIG5HqivhwI13VdMvAQlzj9plt9HeuG8f4D2AsVyDLKEUBdrGvF26d7r3TjtVLKKyEcPdRmKqdGuqE+NfQptuAejbKWj0ba7KAaKUBh1I50/X5N/0Adz9sAnpGXyEgvDP1EA6gljLoiAqj+NLUrFaD+0g5Nzn8F79+UAEtrJPWGiH7bgW6k5We0P5Aa39ooGO3UERNKwEn4bkAH7tTXXEIK9jVX04amK/rhNO+MzNZw6v/yISNbrPg6jeMSgcmAuhBN5Y+XoNnq511AlH+u8uorgNwC++qXwPQ67YldOvvy1l46dCtt/jSJ/hZj0ZebAc7t92+bfL0fd2A+0tf+c/8+1oPo6z0i15OiUM6JWC+08g70T1P+CZ7r704jeZQz8Oo3wB/+xxsVesghhxzyE5MDpDvkkEMO+VML2QYHAOrPAH4E+AzkuPR+m33SdgF9dRoXN/7OCexlb9C49/zeKHJL0qZhbxi5m8ddxOeZZz+Aof4nI4w+m1MB+Cv/PgGoCRS51X63+k6WG79dXaJdc9xoG4UBAozRX3s/jnvcDkGFQIU7Qy4dTO8ZdKNdGIqYurGpYbgNCwPGW8Hpm+jiD184TuHeaNv5S0H9CCjFwBRl7a/JDwqe1Y1L9rAxknQY8xCbaO3GxTDaKvxkN7nrJR/rLVy2xTOedt+qEzZA3RU4911lZ9BQ7Pp9z9gNjnR1eWvwSOno5m/tZY94k+Iu7ySsrrlY8dz30M0eH08AOZtGqQNq0sxlXz81L4plhTHj1FpdQBBlNAGWpnhaBG8uCy5LQ2uKJmqAHuyZaOsAZQsMcFNvXN29uwEhAbQVMJOx5+AAnDqXQEccJhq16n3S0ra+FoYcs4QztIN9nFwoBSsvYrgQAV/94ktj+inh17/6FUiB//y3f4f/8g//AIW51XqXWG6HfH8yTxPO5xO++urn+NUvv0KtxnJTsQMaAShzAchQNHfp1qAqAJvLRlbTU8boFOhqY5S5oNSC4gCdijiDRUb/Jddf3qeZK6bp5MwWB7cDwAgdrGTzFoBSHZxnBuEBog21ML59mjBNFX/45p9Q2xMWapiYQMtiIAypk1gE6gDAqg0qrTPLAtwWUvAE4PsA6fo8oLlKADhzCgZIBZimQCqf/d7neiT972nEHJMPf3SGV38gZYqRdjYQd0N1Xk/4c5mpn5liWQ1rKu/G4JxAumAv9jkx0ggmWknARbBWYg7N6aey9zUM0FkuUd5g1kR9Uc54t2YjdmAQNqcxART5+9/RNTOgGN8DQOzJxu8+R0K37M3o/wHSkWDMC5zYjj8BIZAddqvA+d8AYEX5ZMQHI1/jBGs+QK5dIlA/KEDAlv2E3XoD6Gw1dSYe+QBSb0SCtbcAmIgBFZsDIQgXmCINSzM9UUoB2BlMTFgxwJFYpxHgsRK9RAQoGFQ2zuUBXHW/LvygkEoHSOdCDLDv63kCPv1XY3zPH8EATWADgN3aW22uJQZxXo/m5/shSJ+TOuBFzlLeM3JvZUzOko6y6ciqf9dRdo05kMZvXUKxKVnHDdqtABoTyQY51NTH1P9+bpN6yCGHHPLTkwOkO+SQQw75U0kF6ud2KrN8lq5ng0H8mTa+3QByK839xVvgxL317q1ns9Dut326NMp6bQkZn7r7+32ECsCvAPn6/Z7/ScjnVg90Avjj3W+0393hw1lOfFO3wbV2ad/CvMKOkZLYGMVKAWgyNyl1qgbUEYMpQB9BE3ObggYDndzFHnng9pbAEivHtiDbOHUuvhM9/Zqx/PWfxsLwXNPUj/Ta/aQzEedPFWUOY47XK6u5HKsErmShuAoc9PStL8HrFggrTR+yXddEuv4XjfhjTNyZTcN9jnaXiKq7zhdGwH2/gRv3kjHgu0hXK8nw8Jz7S0r33E1vYwgZyrm7XOwMtAFQRj1exUL6HsDg/+3//t9iPlU0EWgzRtDqwNy6NqzN/rUVaA2QZg0sDughgQ2r+P1rw7IqmlCvBPaYbxG7q9C4LqTuDq+5IchAD6rmQo9AHaBjNgqOGZV1Z/ilzqADwd0S+rj1/EZvF7ArEMqNtLciRt9yQxEz4+PXr6GqePXqEX/1l7+BqOAP33yN3/72HwAo/u7vfovLZcmPH/I9CwH4+OOP8Vd/+Rf4/LPP8KtffYVlfcKbb5+wrAtEGsCm2/1cPkQIzV3LStPOKMG6gIqCmqI4KzP0XB+HamxSJaCtxlpjMIi0A8kBmMHdutZpNjBYjWG3ioC0DReGrhtLMd06lQkAY55niC44nx/wu6//C6gQRBdUKVh1gUDBqmBpWBQoZUIhsjh2pGiLdJeDHdkiAjPw0S8Fv/1/f2B2rldRqNGYq/scA0A602b83l1Qp/sDlGACSlo3dmAJSc9yf9TApZQH+++bpaV/uQKGYp7hcV+8j8gWUyUAe/UR7LDoLwSgelkMeB35wssZ81ikTYHb5PWwbsu5ObyRsRD/R6nOw3Au0SDpftq9B6nXlZc1g6lE1nZhN89zf7i47Oy4lE/Y0z3pUcx4oc1F/Ggl5pL5NwDNhNNfmIcGAnVGY8R6U1KgqR8Q0AHYkZpXgXgO8LanjWeH3l4p575OwmjmXp2xXvavAvNwUDQcRvtvPvD6kRYikK+lmZxBR1E2m4NtyUx9niTyAwipjJu5kLArHECvFfRE2/v+Gcn8kf0DA/UROH+JDvTn8dfnoRtytXSJOo7zI7rTu9E+WQcoINX1T+jCBgPv05i+cZ5sHHpIe/SsHzJgd7XFJPQDAtKGXu5rOCY7VOPzFwF2EDMmA70Nx5lHhXHI8JBDDjnkpy4HSHfIIYcc8keScAdSf2HAHBgvdmUZi++NceJtoNr7yPuCZ3mjfictvdp93MnnRl30TUG8dwX49T8TkG7GOKX4GsAn43owWt5Z7tW73vh7lwHtryWjzFVZCBuAJOJzhbup/mixa8zuPo+Kn/wcxz47S2dzatN2vQqYoTYMEz3PYNPcqwf1D8H0awX+b3fq5jsKAd5et3+vP2sGOGKbPymBJr0eV2UYLRvGBr0bFj3eQxiz45vmNNIpANszD5c05CBvNiioZ8LeyuE+SdN/gPZus+9KkePeKPASouN3llv68i337oG7vWFidf9PBEqn3s3oYFZRA5TF+2MY1T60iBiDUZuirfZvXQXrolhX+7csZN+bgXOdcee9xgA29ZhTZIC6uNuu1DhEDGZ3WdnpMwRSRZMGacZKikYmENSZnBYvh1DIXKUGQNeZrog+aP2Q3Sjajcjdp1huIOp9dMwz2g2UIX0cqOsJUi/ncKX78HDGn/3ql1AAX375BUSBp8sF/7//+J8Ab8Nvvv7mn60B8vuSx4czHs4P+Ff/+l/gz379azw8PEABrMti4HJbIa1BnKKh3vayiAHE2qz9ixqLg8RjejVz/UoAWcBEBCOTyFhqzf34LevF46wVlFbsnmL9W8RcxspqcR6VCNIs/p0xVUOB2dxVmFEKoRZGmWYQKuo0AzphpW/x0aO5pi1EeLN8gzfrN1jlAmoraAW4AQUEdTCbiUB1gogYIKlwFsYIDlSqoq0fVoPmcz6hAnbYggF1Mcf7kOyAmV8LBj2n55CHMsazPS9TUdvYoRhAURRoMHsRy4T0AiOfflhiTHn9HTu5IxnAeXu7LTnCSu33dd1UhiGe1ebjeJ8AyTr4qNY9r9ZMNjlsWIVRvvx+5IXpQF3UZdRdFJotn+7+mxxATZ4TBdjMcRF7NQNBwJZFGJKy3r5LlBc/PimvCFSB079i8BngB2OCM8YBFU5uma1vW4dpuoJEfE71tWrx9SnCOSVGY+j4MxiOvS362sznyNS3iW1OVrLDCk3d/XPMt5GNKpoquAmYLK6mMkNYbG0noUe196u8JPSJEeFevv+Y3j9eBRhl59eA/PbDtMcPWfhsn0TAw8+B+cHGVzmnvT1jHK5LinO3pAKwrct+v26bo9+Sxyxt7993sw7UA84URz+YslnIbAb0uJST7x6CaejIrieyq9sAJZNOJRlliZeIfcbYb4xChFNz7ftbu+/j3yi++WtB+xaHHHLIIT9pOUC6Qw455JDvWcqnxvoqHwF0er80bq2ndf/DfuGN3W8vSfxWWnvLzDNp6qZwu3TuvkS6l7Z/3i2rAuU1sP493Cry0xKagfK5b5ReAzrT9UbufWXf3vvN4b3fnrv2zD1663pc5GHk4sIgtqPyra1mEGE3ttNwfxeue+CbOxVF876jahG2BjLoWzw3ztqmcrsDVjVQIRtE3keIAP5M7tYPf6T9BH42auXPqBtKf2TjZTagKdBdFEXfoLBIulXNjKbDomib5bD4qDMQB2MJfVMM+w1+/3jLXWH9UlQnM0g1xeQZrmsCLBW9U8G0Uwe7255jxOVnurEr90G92yzj2dQ3r5h1yUhqTMFRqK1DKEIhoOV+xmSAUA8K8uEkjC/rau4t18UYResKLCvQGmFZCWvzOFti/DMAEGUIGCKMS1vxtDasi5iby6bQEQTH3iUHNMJgr4ma4QeCEdsmjbECixtXmVG4oBC7Iz8be8MYZO4vmSgZ9bvVEdQbdxhwtvWg3a1hNnDdOhlu7g2t5USNiaWEzgCd5xOYCa8eH/DpZ58CUDQV/O3f/B2kNfzh91/jH//hn3r+H7ZVf9pCMF3wsy8+xy9+8XP8/Msv8eWXX4ALQ5ricnmCqmJdV6zrgmVdsDaxOSEYTGpuLxub7hZSEDO4Elq4ciUApGB3nTwMmdZaqisChDM3mZa26Ao7NQJ7gAHRhuXyhG+//QbMFnhJW/OOb0xnJgYXc3NZOJijDKJiv3FBdTRIZEGTBxQWVFK8uRCUVzSegfYEgmIBQyIIHEyny+p12KyIYEU9E06fKr75+w+xONg21N5QG58d9Ipqatis+WKsRVyzrjWygTnlcXPdkVC9TIDv+fsFdrWUY2blZwBbL8hubUnpDw4DsmyWJoGdGVsJ6IeL4uxQN8aHYdoN4eJTbGSUAc8Rx/W2bqL4bVe+/LvFDbPvsqujWB/kpVYAhcS7H+JfFEvHexDGb7ks8ZHs7ZsMNQNOP2CJstdfEvgjwvwVo558JhIAYBQ1V8oVbHNTcsscXgREBZACUu6rHQL3uc3YbsNF+1gtaNdTEb/Xb+nP9dhzbPNirNfgM6ioDRSNN0prl5jLoimYyRlJlg4TQYL+GY1uD9pnApOzZ4RbS+Te3BWgR0C/eff2+CELVeD8efwBTJ9aPy8MlAmm9wtA/h08WLahlvKyd7N3342VzJbL7oK7Lsnlys9TSjcYx/58YIUC11W9UGMNae18XabMqMb+k3xqDV22mSRcCepg9HHXOX5oyvdlzFl/a9dr5qqXhp48KYh2iv6QQw455CcoB0h3yCGHHPIhhew0Ic3A7LHB7FT3+yeZ1819gfwSq+D75Hm1A3jHtHX3XXfXbhljFG/P98YOgc/4SQQqp1fDwFK/BDDBNkyVcLUbvrs7fst99649l85+w/ZM3jeLcS+/bNTxmGqF3XViE0DFwKVqbAYGOogX/lYMK3ADgxrrx3AEvQ4JQmmTh+TeB7rZSNIJePj3hG//m3c3ufODYvrzNoyW/p59E52MXVeb2e0tV9cUyWiX2kIVoIYedwbJUEhwN4NipkZSd5npm+GNq6tk1BtmIoxvCnd1afGRwt2clcddGRKhcBgWDN5p4m7pdDDJ9I47m55XvOf3gXrcsi7F9Xv378RYWuR2EI8PAweP3egQdajORIv+1rHSDyhf/vxT/Mt/+WssTw3rumK5NDRZIc1YN60pLiujNQBqAEYoXBVj0zUFmqj9LQRpEdfOXGSBYG4AuxUWNk5RoGL9SR2D1DbGpMWQY2cN+ScXFDJjZ2dzdMMmDdt8GCUJ6PTbMHQi2YTTlziM/ZzrpGCIegtBxF0lquSbunErYt0pCJWAX/ziS4g0fP7FZ/jln38FUcHT0xP+/m+MPvDm2ycsy/pB2vanJqd5xqeffoyfffEFfvPnvwYzY55m1FoNlLusWJ0xJyqQ1rAuK9Z1RWt2TRWotbiBXM21FozdpsX6LKuBZVwYXGhYBn3RFPqL3QjOhVCp2kEFBeoEGKWtgFShKhBlLOsT8C2jlGr6VhQsaocgYHMLhxs5B+niX2UrC6Og8gNqURRSvFmKGfBF0dqCxg2NBKwChoDcfTApULmaa8wW+hjdJWQ5A0TOfP2AsmeUxXhjAFrMSC1q6zBpQ7/tl3z7ZV4w5LoBeKiNNKbH80Cap3SkdVW+Xb5X60rdJt/LEBeCgbZPx79kvaM3rnWmSJqvN+nnSkgsG2rWjvF+myqI512/9eVT11GpfnLeOp6VAOicUOpndDrYGGkGUHevLvv77RRxjmnnU8YPVvgBoLOi/JJQPgPKTKjFmOHFX4SafTIM0CquMTrT0ddErA4pkAHq3SsAWbxIAtAc7dLuZzAOk1jF93B2ceCH1ABBGLAGAor7kQ2wVnxhphggYFyJPiIA1tZ8vp0GQBczrbvIhK/riO2AD2Kmd4Y8NQw2q2ybvo+DuMAKOgP45ofcA+5LmQF2y2h9AM4fp3Vy7OMpxad0/asF0Gr3RDw49XG2AdyAtP5AH0s3vczkv/djbi87hUWAMawB844QOju7+o1kozx+QdOP0cbCQ+9GMUL3im/LghHadY9o7/uxT1Ayjwq2Jh5u0FXj4Jj2+gqWYOg32RTqkEMOOeSnLQdId8ghhxzyXcQX7fULgB9hC+NPcNtycGUx3BowXgS8pWxv3n7TunCnHPvfP8Ti99ZmI+f3nJE8GzL2yebdQd7U/NgW7HGK+hPvLwSUj4FgMOVTl/K+Bv33eG5fjXrr4r2b31aGZFTrBqzo+BETSBVYYJv8OPUNoCqBajFjhbvDNGOWGTPUUakc58OuhIM7mKGhX8U4FRyx7hDlIfD0fpVOZJv73GbdsJU/d0BNHxLJ2JY38d01TmcBplvE3iuIbmGmCWChGEQCCSeL7iOLAkSJE7VRCApXldrZbptT9Qj3h+7yDWyxUNjKF2UFeZmKGaZE4kz4tUrI9ZDf7aqL+YU9m24fH0NTPW/yCePDnb4b+rdXxVv6eM/Tx62Z8MhZarphTpCaMQ+JFPM+Y/RmOYjQhLE+mfu9dW0QESxrw9oszlZbw8UlG6DmFrfWLIajxbIzd5nmAdLYP8awZDemuOlOYW4tRW0MCvkJaHMfqd7/eh+kgokrpjCCEve4X3FKWkW37kL9H49Add3AKGmca2eCjs4haj7ewgia+9W+8UgdVCUFO/MBGEZMZrb3R4wJIEB9M5pO5ia3Vrz6F6/ABHz99be4XC4QEfzN//S3Xb+o3Aenf6pCMEDt9evX+Nf/6l/ifJrxxc++AABIa2mAWH03NbacaHPXjg1NrQ+D1NhppYBLwLkef877XCnWdwEBszEalIwByVBvXziTBCA2c2EpvXdbP2R1t3KDpqwkaNLA6wXqRnEGIYLRFcDjo4YhHKBi8RenqWAu9mnAuAKY0dYTmi54OL8C4H3bgXOsF6y6YEW45LQajcMsYSGNKjx/LvjmbwraB8KHbd7c5rEHg9iN0+QgPdNOrRGu9KjqMGDnwck05rpbz+/ZHL182LliTGvJHG9Od393PUzYsssovXNenGO7Ps9gZF+7y7gQ65tg0xB8+t3rfopDCWney5NkV4bp9SJdz8dJX93VXH9WRv11V505n1THEUsvy2atkeosr2fisM8V2/8HJsQAfw6ULxX0SsGvrPyFjeVaxWJQlngRJ8kSYCAXGSuWlTx+JHqdmOtkBTvoIARji/thsta1VMTMlL7GUjVdU5S8Di02pabKJg4PEr5HCHaS13MwmcYeKQauOyFvxjRmiljCikoKcLGYeaxom9ND+eV8najbAzJ64zOEXwPyT/jhHp7c6aTz58Z+BoB6NqBuf9t+f06pvxPQ2bR50ORnNgBd6LR032ZPe6u4N9aueRzG332JmdIjDH270aG6UVnbcsZzPJ7r6N6+KK4vQyflcyISG1kGipjL1QCnjdk71m+dzKk6dHW8e8rzoz9n/MN/e7DpDjnkkJ+2HCDdIYcccsh7iJ3GBOpntinZBtx4i+wNF/d2Ozfu3T74wvs2u+3d88lQAgzjQvyu+/ufk70F/lZ+bytrekZzndwwlECB6Svg8v99S7n+lDIB9OjfK8Bf2ldy30+719t4xHtJdcV99NKbn03kmd+ekf1j+7/7JtR/JGADiLQVPXZDdL8wHAGjT26YPGGU8A0fuXWKfEdLce5SgWweD5d4PbOwhfRMX/DC7yh5s7lnGuju76s/dpay/pPGaWvqsWYYgDK7EdVProbxEBgne4u7uMRgGA7yUaofJLXUjYT2m8VEMkPTOMXtRiVFZ94B6EZra3TCLWsE+X25bXKXfK5FrgwL+7p7Trw4MQY3bXXr+XTNnFoFYEDd/SJ5YKZubIvYb6QoJ+D0leDNX+9Nou8uBODTzz/B199eQFC0Js4ME4tJ19wo2LxtlMyVpYeMa83GyNIEb940LKuiNXLXlYxKBYWKj1+rpB7HTRTQFSIc/rq8TAZsFWYUJky1oNaCUkp3d2mqj1AACJrFvGFj0XaDV3Zv6wZSkMcqCRCOtIPXm0MbAkScO1UdBjVnRTFv6z7SgPAAWIIF2PlWlgcjcBkFiVjcPtcwTYGHhwc8vnqAEvDR559AIGho+O1/+i2Wy4K2NLz5+uk7t/0PWSozvvrq53j16hH/y//5/wx1mqAA1nWFCtACoBN0l7pKYuy3RpAGY5U1B8aKWpxS71fupNQPEuh4HupeLcOFVvQNcYO2KcJiNDt38Wsag8gMiuTH9wUKZvF7CqAex04btAES4yJiO7ku5aIoVFAqY5pOmErFw3zCPDNqNVRLdcI8n1FKMbYfFegKrEWAidBWAsHi11mMyCcUGJumMuGprVg14uOh65j6oGi/f98FwFZi2rha1tIwSFM1Jl3EYAMD7jl680yfVpGWczHnhuE3GZGz7tddOvfWmOFOrd8SC4mdmu35Rj7+d2aq5Xv7Z6Qd/dbnBycejedkvKf1hzSHyTb9mGM6Q7BE5Yz8xoIoPZeZapqAQEZ3A94BQR75dBXp93fXczeWPR0cxbZOKZeZHKj1tCWx8n4owp/A9ml/7uAc7DiNCaGK6ZWqvoqJztzQ29mYtsVcX/ocAOkwmukZtSiqUXFMpXsdYPejqlA0KJrGgTx3h+mHyMR1mpLEcILCWW7egAqfu32dxr5wjrmpx0NUmxtFDIBjUV/DDWZ4uLvUOFRT7YDDrX7a+8j402owres7mBIxtH9AIF19hQ5gz58aGNfLy1s9F2MhthchGZiLcdZBrNCL6Z7Ns/E8pbTjN725JB5luTGg8vhX2Fjuefj1rC6ZXEeGngOyE5F+bb//zszg3Aee3W+mfqqb57QfyGosICGIiLmPjuT8sF/3guB7Bo3nAZw+/SFpmEMOOeSQ70cOkO6QQw455CXirizmX8M2uGfbjDzrpuKepAVuXxhT+rz32PvktU8vGQE2uxK6cf+VlSSlsf/tqrDpY7/Rufeetwwwkc6NvIjMUPTWsvyxxE8qAwB/Zd9pAuicd24uu0uK61d40VbkllHkXn08U0/7dnpObpWLwuiDsSHMG8Dc5hmg6+kJnIlgF9yrpYMMOk5sN3/YY9UFKwJuyIi8rV5GSTPwZO4ex9/kxzaJCOc/I7z57wXt9y+ri1v1sqmf2KjGZjrKcGuTu+//wIYtAB1sNPL3c2eK/pi6gcYeUA6mCcYOnQfDEMwQDtaXucTsaaV4JCCA3E3O0AE7y423zx4UhWdrm/0wyONmX8tV0vvNvkp06JCNUTYO1roBc2NYvdkwqXyRhqZumvosbW4cwKYZyXjTBt3wo0CDA57qrDOviyvKwneQ3/zVr/DNt6uZ+JoY+0cNoGtuJNNunQHWZqCcimBZLa7NuqqDemFoJTAVFK6obC4tmwikqRnwRKDqQFkERPI8mJ09VytKJUyzf2dCCSYdoTORuBCgzdKNSvY4XAHmcTFwhtiAEXajjYaFmsJ9GMw1lxgrqQmMheVtpV5QkRRTMHVEZnYXm+wGaGP/iQ8q9XSKf0aMIvE2tjwGDbgyIxzDfvHrn6Fpw3JZ8ebrN1aSJvjd3/yjlaIN9uqPUZgIp9OEf/tv/wXOpzO++sXPMdcTapnAbLHmmNhBOYIWi5FITFB431LF2hYsy4KnN296vZapoBRjNpIb8MKY3dkiPrjM5a4xzsgnltCLgKL4OFBpaOTwasT38v6JdNCBqVgZBQAJRJbOfglXccXBY+Ew4BZQmVDqCdNUUeYJpRRM1Q36MMByWS6YlwXagIIKqI8KKfhW30Arg5wmc1kvCBd6JyaQGPNwjYFDwMMXiqf3mLfuya3e2FlT5OOQYC6mHaAbunEYjcNtWdfvbtDuYFOkyWPeieuZSRc6ufMKdXiMRBih/Xv8627evADZaCy0zUsBc13naW88hxKG0TrN0x38SuvqYIEEiJXvjTMqFNcwQDYFuqvNmIcolTHykvyO6TuXlB+7i7rdPL6pU6Cz6Xu9pSSR7utYKo02jTiMyqFfdXgm/hNJuPQuXyn4MwV9BPBMvi5hkANtxSdhBqM07m4u7Z+5vSSYm9qCYi76xFpGVTs4T6kh1Bur/6cE7axuQVN1N7bhFltsTNtRFagGUGdjyvoh9XW0kOk9Y+DRpoNqWuiMdVG46bR3E4318u5fYWgDSGUcWNl1hjgaoXlNne/Ka30A5TPF+rd3Flzfl4RegfWDxy/HUK2P6GzVdPvms39PN2n+IcZOgHF21qP/Hfpvs7TfjdH+Pe9tb70Kbb/ne+5tHWx9cv1+wXAufggg1oWS2iynOfZPz5Q9Z6DjepzD67o41tRqwDKzAM3Yn0LkQJ32edE8bwz341aHutHF+iNeJx1yyCGHvFQOkO6QQw455I7wawPjwMbYykaFK7m1ct7/duv6++xjbq3YQ24tpuO+/C9W0bx77k4+e1ZJ35Dp7t68+XjftXTeJN15vw7mvDJXo+vfv2de31U+AmjyMn0E0OvxU2Z+vVg+wP7jXbrgi2+8kWg3IsXfNICcjZEr9fNuAEtJBsjGYYyK39Q2viuamUPUjJTKfnKUAHVjKQmglSAIAIw2rNAeAyT1zRFXzf7HIBvzM6G9Y0NEt2fA45QMQ2B2dZMZP1c5+IXseUgi4c5U2t7bf+4gh/2/kG/Gi8f2Cjeh4WKGAarm6s+sOoIWhpyd8aBXI3l4TWeBdkMogAAQ4d/7K6v2jXsIuyWluwLsn9t88/delmzkTOXT9MDeqPE2CYC52xdTP7xOxzq2epuIihn23YrL8Vu4+XEwx4C8rbHhQ8ibi0BZQBBokw7+SiOLzQb2Tzu1fFmax/0StNXaZhVgXZNzSGKEu8rOOhCYK0CzFiKOSXMgs+7aq3LBVAvmqWCaK6ZaUIoBc4XMiGylIsfXCEYxNsYfouq8NRiuF9juZ2KL+5gszdSNxB7fRAQCAa2AxdYzEJKgfno8jZTdHEeeXykFxNzL2kcascciMiMpKQ0XhmkgGiuUQA78VTJXY2UuqLVCYG5gHz4xmvXT79/gzR++hari23/89kdjiKqV8dEnZ3zy0Wv85s9/ifP5Fc6nB5Ri8EkTgaw2uJgYXBi1VsDBuXVtaLKgtRWqguWy4LJccFkugKqxzai4/pPOhtT4DgfqVIBwT9r1D49DDtq6oZqlmQ6U0pmWxFY2cy0HKLH3Qe6u7UTE57gGCIGqtW8p1kMaBAsEBQ2nckYtFVOdUadqrvIKUItZdEutxv5WRi0nEP0e6+pjs5ExVLXY/MYFX7+xOamg4tJWyEqArICSjxTFelKUk6I9fRglE2q+SxjBu94fuj2vBQgwA7Zu0+jLAR5pdVDPKhAIRjh2awRc24c7+OZGYU7rc400ycE47V1ow97LLDqKZ3ibeWbHbNb/6R368I/He9pufMaYv7tbOAwGTM/HyEybOXcDPoS6xcizX3c9aY1nld/nyt279qQj7chz/95pDZfTAQNcjeFs7srF2Fqpjf6YQq8B/kTBnxs4F+UnXxsaH5ZR1FzckoNyBYwKNtfgapFXKxd3y2yrQlb1mLMAxGIeC5KrdGUHarhXcri9dc4voAaYKREUFlOzULV1Jtkat0HQlNDIIDGJfuyHfCjWtb5GG2vYwea3UlLa2rGvowXqh0+CaQzYwQewONrtTrvd1SeAzcIqxt9wvnu9hdWYVOcPvNC5I+XBWXEA6gycPkEH0oXw7Hprr1to9+Nz7LY+dtJwi2VfVMzGZWO0JcZhxrxm3ZRpN07jetYxNx69eS3Wwptxje33TZp7JZsTpRuXXcfH+wIeo3SvO+MAR1rrxwE2dW8EIvHP5vaNEkz51xNheg0sf7jx8occcsghPxE5QLpDDjnkkJACUAHKpxZXjmcA0/aWvWHvapX8rjvUtKi/K2mj/E7p3numb/Cu89f9LuBd88rpuNDVlzvluiHdSJSfTbsqInSQ7HuXyfM9A/y5fz/BTru79Op7SXveuPw+Bo68p7rXdPtN2D6fTZnThutefr2s2UjnCfWT3imTe+ypOG2uHMb4UVgNu7imvNzClTe4zdAl2yyWMJJRN1p1g7d/MFKdBWuiH/cnA+bfUfqGPYx/cd3/14sQ2SD6tl1gpW70zAa9HlfmRv1Juo/8lHQ0rqo7diLA0Q1zvcVhXPZYKkibZuyMbGxsoO4C0O+IOBLmEpCSLglHdEisoG2hR/yJbQfTxBLcP/Vcv77OYei2e304LjOPlDUMX7nuMfpaZ3amHCOmGTTYIsNqo87qIQWEBaxGWbBYbh9Gvvqzr6A84bK6f6pw8wg4oOaMsiZQJbQmWBeL97U2wSrkYJGBelYnBYULqDAKzMrVVo9Vp1apFntPnXU24uQUmLvBCrP5VRgToZYCgqJwGKg9hhgFq9P6aWMbmU0aqDkg4v3PQL3BmtWkFBQByCsqsbEMiMCsEAfQyC3pUR8xVNpON3RChRqzNNifgLGsFIoKNbey3s6AxfyzJtiyK7rrRbDVNTeQ119D6+Pk4eNHnD96gELx+OmTsSVA+Oa3f8D6tFjZl3c9OvDhhQDM54pSGV/84hWICK8eH/D64RGlTqilgksBUQGcZUqyTaDWYjEtFVC9WCxFj58oukJk7S7kOObWDXPO2KKmL505Kg0EHmwRtb4TOmBtrTOzS1kBYWe8uZ5UhYqAGCgqjrETQIJKoQMJDIGwYioErgWlaAf2VncfqypY1xVyEj8kwaBaQMX6UynV2rMA83wGUUEpFcyTAbsoxtJs5iuRytdY1obmgbIU5qp2RUOwxwFCfWzm8vIDgHQxbWwnsvgBW/9wrm8LXRtofVj2OVHV5nrvHn0y7gdbcl/J65GUPcGeozRmM3jYi+vXY22jYnnHmiInuLk/r4+HSu/r2wDlkPLNz/T5YvMS/hyl+rhaC+zawO/frHcx6gqKDq5t3t0P46Bs12W4UUfxvrCuPgBL2t7L4zX6mo2YuhWJwhPCVp1+b0IFoBNAnyro1wKefL+m45+5pjRmdIBzAcRRAHNEmKmiKGN2cK5QMea2mi7vMY0VEBJAbOypWIMX12fMBr6NdUf0AHMrSWSeDRh+OMr1jp15sd+JBBFF2DSdoqm6O1cFI5h0AxyJBiGCAYFe+/GeBAa5LoYfHCEyVpOQr8U8fjAJOsC8XYNRX/ebJ8s0KPZ7PQUw2SFX+UBACqc93vkLoJwsM54ArhgM1zSu+7jPeiqNs75X23zag7EelXR/vDy5/mL3roMYa7t7QxdshrZui6np3s1hhqx79/W7e9V7svmdRl4xvinYz3G/P9CBR2/1rL9zun3ppN2hwe5lR5m3deBeB/yfaLhYNSVKoVBvpAMQ6gNw/pyx/OGIS3fIIYf8dOUA6Q455JB/3kJA/dI+yyuAPxrXX7QSfpvcWbhe3XOjXPG52fPpdyxSNk7sr9/YFOR847fNz3cKQ1dfbqR166GwcOzrbbfj6UYZBuY/NyadLnfSfV9hAAHGAaDP0WOn9S0D3a66qyZ/Sx94rlqefS5v7G5c78acW5ndyTQDo1cACNBPRW7ypBEXJm9AJSUgYQjC+BfAXD8JntIL95fsDAkm9FP4zS1U4oZXEEGb2Ilnj3PENOJvRAyrzbuEgSLXFRSv/lcFT//Tel0xz4hGery7hvSyHrMl7CpmQuErEHEYd4LlYe7bgADR1N0tDWOCpZffT9w4BDf+uLGY0eshNr2akPmNWyuYQYs7UGdmo2COAKmfOXulRRl3KL2VMcbM+NzHv+uP5LE+vl4bNHXUea/ruzqJuludyD+XwSOe+fvA62MYvTbJq9Vw6e/j59ldMZmrUfip9wKhBsDAGfpAdLppPqGt5KFfjLkFKEjNHSUgkAasjaBNsYpgXS2e3NqAJm5QDL9vYBAVAy/coKdOTY14QwxjHJXe78yICVV3t0aYisXOKgRUIszMIFZULmB2RhKCOUs+ToFSzJLemkK4WjwwNsUQwDAQ4LQbMRXwqrV+7gZIkKJOxWP0mTslA14URSLGnBlFCRFjz1uRvIGj3ZW6AckIBxajSMnHl5lOIWrMMRClU/TOrGTvCz6exU8yhNtdTfSZ8qqYcVaA6cGAO2krvv0vZvFc/vAG65sx2X2fBvE+zhn4+Z9/DC6En331KdaLoF1WkAIP8wnn+QHn0xnTNIGpjjEOB1pBnValYgDl0hZ3+7hA1Jh0XBh1qt5e1qdKAUDhbs1cuoo2H4vibiubgXsQkAcCE7W+oAo0XaFibavCYGfzsSuaps7GXgXCdQC0CggXlFowUYEWArEApdm7KGBvA6gKLq2BCJinCSiKUhllrgY2Rr8iM5TPZ9MtT28uIC5YLivmesZSFXIi4FKN+Xc2ffTm6QkU4IAzdSMmnfpkPX2suPyTu8P7rm1PAyQib8wOGvH18riP6ay3abDrAXSGvWagj3q1jO9hzN5Ys3dliz88rQCPQpTT3BHzbkp7D9TBSUV5GlZPP9hm2MXc6+vQeG5XIYQBEoRhvL8PjTyifvtzefqMNkh1m+e5zqwjdBcDxDZu4jBCV/H5eRrgG8c8mspFqXzFu1xufyoRKxRoBFt/sQO1+LDSi30Gyq8NAOKvjIUT7QJBB3nJDykFQ65/R8FMFUyMmScUMCauYLVDI5UItRhIx6ZEwK7X4xOsqI3BlSGkKKVCfa4SMf6tijk6Nrd9DCECSHxe8LhbIHMB6POAqmLRFc2PcFi/jTlIHCy0OXKsVIY7y+g4NiePg1SxPgnWa9Kko8+4Z5Xu7tXrO7M186mn4Q3DOpju2pxYISV+fX/hCjx8AZw+x9Zd5W4N+JJMcll2w+BqO6TYJtvHQoyL0DnFxpomSmxej27GNZLe0u31q4Kme+4x+vaXb9X1Rn26/ok5VdvYn0XfAHAFysW1zd4udE/OKOs7Gt0lyMuAu1NnAStBmqB5phE/1tZE3l8R9T4mFMqTyyGHHHLIT1gOkO6QQw75ZyU0oTOvpj+DAQEz3mvRf1durZS/r0Xlu6Z7a42bdyh3dlVvrY5bu5+Xlo9ufN8/tzMcBOsRDEj5AIZKAnDyr58D9OgbsgnYWoS2cm+jtAfv3lf2dqT+xy7PW5/9O+3Kujd83ShrDzTufzdN9gKYMYRzhrqti9jc92fcmMU8Nnz99KvYxnEEKTdjagBu3VhfzKrUQZNkFTPjBVlBgc6QKLCElUfcqrCAjdgu5GUepzvfR3TTSLu+IemdU72EWz/ZPOVOBh1AtD4/2D/h6jPatiZXWP3TDQdlZnCx+uNCHvDeTnLXEpFGyA1rsbvWbhC0mD5hSLIYK4VzTzGjz779QwagNp7pxqJe3xhgq79fGCcyIDcAlKjvFFuM830be5Jdc6us9vdEj3sR1R7tLmTMxnBxtdWVI88C3oAR3SjmEVzMUGGNrm4BZRrPfFdpSlhkuLAKJiOFnzNtaEJYVzHmjbu1FHHml1iZyKkX5GwAouJh4dwAlzQLOTPO+oiGiRBEimkqmApjnitO8wRmwnmaUCezcJdCUD81zWmSDSZQcUMiU7E8w13bHYBOSdxVEjwdBUTMOJ+VHWln1AwHlsYsZLfqyyq9/41+p6NfxD8M4B/srtBKAZSx+jgJJmmMC3Iwxxgw5HGR1M3GDhiSsx2Y0Gg1N5GkPRYQlQmvv/wEIKB9/hoqVvfLtxd8+9vfWevwh+lX1hrA6bGgVMZv/t0XKKVgPhVjVC7WViCLFVg5mGDGBmMqrklsvFaj3YBrgaqgrQ0iDZenJ6zLgsvypus5YnOhKcVdvPk1qMV0Um1et83ZJmJxmrB24zOk+VxTEMwz0/XUXf2aLlixuiu87gKPgMkZb8ZACXPhCqCaZzhqkLZC5QQmQhM2I6PHOmzC+PbpDaZvK+bThBNPAFXTu2hQWW3Cg423UirmCXh8fI3WmjH4vJ9qM9eep7pARQABpFZj9zVYfbJNxg2K6WPcXcO9cx+IeYTS4Y1w7xj3hN6P9RnGZ59Dsh7X0c6b9Vwed521CwPgMdYSm8M8sa4JwC/Yebt+3OfNKG+kTckAHmUtiJCYIPjBImekRV1EnLf8joCXezcEN38HCEi96AMMyHWcyu7dYDB4oroIaF5O7J4hqIFl8V460lJs666PO8U4QJXbhdBdiHed6iw6LoPpqiCrJxnz94cQIpgb+bNi+jdq68EHfxcBSPwgSawp4S6YARSPyDZjAoHwUE5gMM7lBCbGVCYQzEUzqbpBTDFzBTNjIoYmcG4w7Q18Y7IxR8yukyzOlkIhbGu4cIXcVCB+qAN91hxtEnMaQGAVEIq5bFYBe4dd/XBJj0ccY44shp5NmdT1HIC+3iBYOSRWz2zPdX+0Mdb9pJ3GEiLGmFLuMrY/CPemt9qagOkzxdPvKS9u3739J+D0xSbZTcy0rOo28SIx+rLmC9dfN+4785GsvfeP6NYK+MEou1ootwU2dbJXw5S+3AXfdPtO8Vq5zPnRZ9U9pQ+1/ZfEmN/r57ytej45+HK6fw+dFnNBZgD3fV0/lDHexNaDAVrbm5JPPP02Tut+/zI9uML6APPcIYcccsgPUQ6Q7pBDDvlnIfwaKB8D/ArgR1yveF+62Eub+qtr7yM3LdvY7gjyrS/J616a+XN/33Ppvs/73crvbffmP/c7jwAiKvpiX6sZaLQA5Sug/cf3KCcAek2gXwP0SXZU9pbqyQYP3DCu3Lj+orLs8rp1cPC59N6WV+/qKaMBjm33UHFLbNaz3Tv6ogEa9zeNOZZKN+LlXadu/1aFsRgCAOmbyWGoj92tb+UAmKtLkgEaFGCwpohSvYwWcY9qY1PunzxbDMrlb95SmS+QDMp1Y1nPL5hxXp5UkqjfPZ4TcX66baUA07mYwSx1QPUYdOfHCeGScFg3LR9mggQDCAJWM6KPeyjpHN88R5tESbsBkNwdk/04jBhpPANb5lwYTBHMJqujq5gdSAYXbz5KicqosavnupGg56mbe289VPqfty0twSN07pyDeeZWqngrhnO8zn0kA8MaReQ3ul2OF8o0TXj89AssQq4OdfQXH0+kBc0NjGuDsedaMO7YwTkDxZiNIly42riAQoUgzeKEmNtVY04YmEdgEhDb8fZaCPM8YZ4qHs4TTvOMiRnn8wQuhLUtKGSMMG3OSEMYiNRPdKvFDutHuRWajDUKdw/m95q7Q/X+6K5iKfVb6XehdzDAwTCghHETQCMHEBUWQw2pryaALkx6nBQa+72qDgaGq6ys22DnB8x9mr2H6c0oPzwGIIF4ArRBPE4RIfSUHTiocwC9hOk84/zJA1QED9Jp3u8lwcR9/JIxnyq++NVrPDycUbmCO3XKwFlzP1pQuaCWE6ZyRqHJgDpi46yQHRCo1Vxgcq1Y1wXL5YI3T2/wzdd/wLquuKwXcGXUWlCZUaqNJINCxVmFDaKtO4ITbZ0dLSre7sZU0eYHH2QcMzGw3I3VpOZ8VO0eVjvMwYABv1hNZzJbDERtkMZAEbQW7BiCsqBQQS3VGZw2mSztgloJTVY0WWGmcbF4VKI+Zxq/oNSCsooZ/ZlRyoQ6TThDQZWhT+7Skz3y3EVxIXt3ac3Ag2pGzkIMqYrTZ8Cb336nrmASDDIMo3OsvbqeCePsbiGUgbSNGzQfC92VZqQV6QSjR9EZstCRRj4I09cZzg7hHUi3YWPHPJXGZMw3fT3kr9cPByCVjcczgaZl15X5PTYsk319ONDXdfSokjGvllFVI45szsMy6WBipqqQt5uaQT7OoWTgsd+X2pE0mM279VbMVx6LTWFlYYbhzpyyZYLsF4PvKL18BZj+SoAZqH8WP8Z6xMYZm+dJsC9Z2GlgBs4BM01gYjyUs33WMxiMucwgIkwWzBSF7NBEJZuxZ67G7gabPmjiLiqjGIZGEmHMW2SsPvUKVWfGNUpzVPRxb+jokxJrLe/MBAFpQwNBlNC0oagBhg3DpSUzOTeQ+6G6cPcLwBn7tki39QfS4bnhlpPiEJzHhNQokwIbtIkGM07VgTyfd7cdebThZsH7PpLmz0h+c1BLd/dGUbeXNuW4V6QbSY0lIG2/Z5ArxkgU9bkzWPs1bb5+a9jcLNONeyjfcC//nf58m9jabKvQIp8NIBp6L9JOe7446BEHM3I4g1x+RbjVpK5L+u9p3xJZfvyXjH/6H4B2edm7HHLIIYf82OQA6Q455JCfpPArAAyUR6B+gX7S9kpeuqHc3/cdNqLP5rFfuX/XrO5toPJiPf9+K7P9ov7WZmB/z+5v2t17tbGj63sAGFtOMXz+G2nB0ojvlVB+DrT/+H41RQ8AfRzG7uvqeu4gaK6Kt+2R3loObJvhVjo306ZxPYxLb+uuydbTN145Bk22L/fNfKQTe/8wMuiotw0YB3Q3UORlywasOH0pZhXo6Rv5SKANZoBUB4CUzb0MzI1e+KDabHwVDvIhGdnzDdsTwa2z69ItJ6B+QVj+5t36E6V/uRIz8JQu93yNQaSbNLyovT4LJeOYP1wmQpkLzh9X1MlOjcc2NgC1WnttJ4OG5cCsUGUH6QhNxQ2R1lASG2cHH3T3DvtOxZSAJ4qT227I8UHUY8/RLi1smmDUxSZPf7do1myJufV9L29rzkhXnXHjCUW9DiNRwHPh4nJ8zy/CfnK+EKDEzvBSvP6V4s3fA+vTW8rzjHCdUM+vcFmku4tUNfBLg5oIoDVAhDxmGkGNYgkqxQAIJmMSuNtVUnZDXoAY6MwTy4dRCqOwopaCWu3ZaWKc5hnzPOHVwxnn0wlTJcxzBTOwXJ5AELSV0VaL0GauLc0tmPb+ZpkGmJpPwgdkk0cQpf7OTuHQMEZ22ow97Q5IES4mOwCh5lZRQcZWAszg2rTHSrG6Se5ZyfqFgTNsQIqY+yaS1hE6BcxdqHf66NOqcFequayj4wb4KxgV0KMC0ThtrhAwmdu16fXpvfoSQVFeWR08fKWoM3CaCKIr1tXiwxlcCkMaVwaLuUadyozKE5yLBlJGJTYArxaUUjDVAi4FyuRgMEGkYWkrluUJTcx1JKoaYEwVROY6V0ShsjgbxcEpXQEoVm02D8UM7S7HhKL/oBvIwyUysbm/Q3O9ZA2MCkblCqhAyWO9objh38E5Kd7mrmuLoPIJIGesuH9FUsZlXfCHN39A+dqYga8fH0E8Gyuc4CdEAEiw+xhcCubzCWcV8FJAC6ORoF0uWJYLChWrVzAe55PF2bs0CDso4IbS8+fm8lLW912JmHRjNN9YmyWDbf+eXeVlVci79YlisCqQnoet9wKTvGLoRF5l8+iIEeVgXcxNqtu0e9hZtTy6bgOuDOvdBTdSWYPqFmuaYNTR+J0AbFRUpJcndk8jmHcbFo4vgmLd2+uecl7a6yrqRKN8PcPRdlAMZpSOZ/r6wpMkQnffHAcZhMjYXN5d94b3GFtEOuIBC67q821CAPgzgCow/WuBFgVODry6n0OFuaQNrwRw1l4RQz6ro0KnOqOAca5nFBg4V8CYizHqpmLjt5C5tCywd6x+0KjExCDkzFgCxN0odzeSESvV31/FDwVIag+PIUcCEsLq7xnzk3WtOBho45fVgL5VGazNdKPP56oMVgP7iYLtP9x5iioWbWjweLGwRlUOgG50QHIPFcoE6dSqbZxjY6urNbx3gNgLCSXPFNGnb8j0M+Dyn9+tL+z7RdYnQNqzpLxvqYp9kV7SJXdL5M32OcrRD8ndSP8qX72/7Hy33cV9efa9bqyH43CfT3dXjL54j+2ewd+bhoeV2L90PZD0Qm+zpBvtHhp9C7H3s9MY4VY/Tj4Mt/hWlsGg1M5cPeSQQw75qcoB0h1yyCE/DfF91fQLgCagfIJnV6/7uMT7hfVNyddfsuJ/l42q3vn+LuneS2O/UKfNR//jnvuNd6qPGxuCvlBP1njaP0tbo0Ocwuwno9NGQKobNOq4lz+zf/IPd8r6jKQ9xNVrPdcUm83b7pl3tFFsE73x52ajui9Utu/eaMdsH+IwNGUDkg5miDgboQMWSBtVuvHOOtKIHyk9OJhWwwjV04gNn/o+LWLdxcnwAmgz4z0r3P2bxbfaWO/T64imU5gKcwuXjPHX1Xe92aP3b73tMEtGsQj70T1UkRl2R37WiOH+LgxhMQbK5Owlz4MIqDOjnAvqmVDr9i3C8GNGul1j+g3dOBiujNTAHE7l7i4pxd0ekrl4CuNnuF2yMhlQtYp0A/kgI+n4vFW9qfgS+indShg6I+oP0Q02uiQn/oyF5JbkTo5RzwCNOHIp395PvM9Fv1e3PjRxlo+OXkYElLJXgO8uCuDby2LGRmZ38SWoXNyoB2OoiRvh4mQDLO5XKROIne3ExQ3XOgBaIY995fXAg71VmIw5NwHzZK7BTqcZj48Gzr06P+B0rqiFURhQaZiKuSpsC9AMSURbG5jF8zOwjoTcGOhxc3T1940BYRZaAlAYEB/bnGPCUGZXRh/QVHO5D/roYwcIGaCI1ceAinQXm8GasPHiwAqbVYqUPV6dDkN6gI1uWGJnVnJCmRVhsBqQcNQ7gDEW48Uc4SCY69rmBtYN0/gtQr1RgYdfmqV7/ijqkVFKQXEwDWLgGKsZiXUFIAJegcITqk44lRNO9eSMO9cKTMaMKwW1VksKgAFeK5Z1gTQD28jbTrVBhEDaDKAUgUqzeHIeh87qc4Uo7G8oWoAEbTBYrO+Ig3ra+9OECnYmjKhNPBRKVlcQCNpWEFWQrCjEUF7NBavas6Tu4q4pJp+XJnLWHVkNLLJgEsbSLljWJ6zNgEeeKkopg4UFBgnbPEfWY7kWTHwCJkZjxYoL1rbgsi6Yq7HuFm2YlwUNjNWZhA0NxITzxw3fPgJPv3tRd7jXSTrwFeCbXzZVAvSFQJ6Ku77Oi45ID+hW/h63CCONnebuB3f6esLXD/3AFnx9Ee4gy0jH1eFWl7N/SwdiOmhIKa8+n6EDn934rCntODCT7s1zUk47uljkGQz+vBbK6fUCppNjUadMBp6x6k1X7wKk2IBk8cHU1lWS3G36GY0ulpWBV9RjRNmNxuh1oJ5gYxZ+8IGcKc6Dibh3O3pLIo/pXyj4TCg/V6CaO+ZRD6bXlACOOSmBdKSECp+DeEKhgofpAUyMczmDQZi4Wqw5Kqhk7nmZbO4LVU0qAzBT6oeK7GozXe7+Q7kMkM50h4JgwL5ojmLrlR4NTAbARUS56IWDbQ80Ept7ob1/QC0GLIv2vhNtW6iYS3cQVm1Y1VxkWrw5ZyXB54xb0wNRB+yEyYs7/osBGsw78QHQ3zBYgmktNNJGB9Tfe7WT9EjomQ0rFtfZInXdnsZbyqC4fiZYh/sf+zqvOVt139d3y859vt9t5XdfNH253dZe3gLQuntfpHdNf1/pRh1qKbYTG9eWd/K1ITBcyAO+1hTuBwhEjdVuusnH/fbNNn+9RMcccsghh/xY5QDpDjnkkB+18Ctg/jOgfBoXnrk5NtT+v3Gm6/qeu3/n63cWpN9JnlvBU7rn5gr7Lc9hvPs+uY3F4F3TpRuXd+Aa/FrPX3fXeXzvu4D0vIabIABwN5cRSFxZQY8AvQLwD8+U9y1yr0k3cutdX377WyVXqe4v5g3Uvl1iYxqbqFTvdQpggPDweoasguVpNYZHs81TfZggomjrChXB8mYw5CLru5vtuJiMYsH4ik3cjeKi7DfPw3Y9bOkpoD3ET/YSLCCLxxiLPDhcQ8Vm0Nlc4fZOZFsKe0637BjfRE6fMt7UBscIXiapLH0M6DDYdfDJ/x8G7TDR53IohwsvQqmMh4cZKOFm0IwfpQJcFFwVjdvGDaP9n7oRNFd8GFTUjcIBQsTTW4OrGcElgJ/OAIl3DVeaQLAUm7ubG0aeXTV1I+lWEZkxTnvbI/WDOEXbXyMMAskomi033XEtXWl45BfsX29cC724H8PhPnSw6Li/izig2T8DcPFOoBhupr6LvP7qN3jz7WJjtwRNApiqxc4xd4QxwLy0Pi5KsThY5MZKJjZABAqVZmDdih7rx/qc/ZsrY54Y80Q4n6qBdKXg4eGE8+mEh/MZjw8zTlNBYUJhRVsFUgtUCSsbSKeqWBnmUlNkE0sSxCA1ZgCaATLRUwlwZpeNgWESHYbR0ZQBsNl1cpejEZOM3BI32J9uAGR0R6VSqDNX1TubeYRVBwaMzRBMwKIAI+hA9p6tNbBGFD5y/TAsWmbqHozCKEy045YrzMaScEaxEqBstcA3LWRbmV4LqAIPX9knFzOKQ6x/1FpR2GLN2cl2rxNR6CJAg4GUajGdpjqjloKpFHuOCFwYU62opWKaJ4/b1iCrg3NoABoKG2uTmcEFKMXAARs7YnGcHJwTd/8ZdSUYUekAMUAcwaATUDFG3aKCJvYcESCyorgrTpXVx7hAqEDEwEkDQK1/CDHW1lB5wkrNYjJy6cw3wBgtKqUzc6gISAu+vbwBfSMgsjJ9/OojlNDpVAZYUggobPFvV4CFoayoqJh0Qr3MqMuMqU7uQrPhozOZq7/Lgsu6YpHmdQmsqpheCS6/067V30eowNyL+9hIH9u/k3F2z6LKh4eg6HNiZl+M+WO3NCSfz2Nd4GVSB+Py+mjM/Wk9yyNrIvT5UZUMbCBYXL/NS/u98X6+7oz1BWBAF/m79sd8nEi8X3rlmKtKlM/XAw1wRi02YFx/2VQvvX78k6H9/STNfwp0V5fUy8zmmSCS9i9M4z4DqixdA++iEpxd7y8T6wnTPdavQ/8yj9+eOzBQPgX4gTD9he3f7JyB62oPgKeeBomtY5jYiiLqce/M3W4hxswTKhXMdUblghOfMLO5upyIUahgdnCusD1TyPWooh9CGsCfe1gQ8+agi/8LgFlMxzFHHfkREvL6gh3kABkz2FyJK6ofgGtQRIxa9flL1A4VFBBazNPKEAgYFhFz0y2Cyedzpbn4ZDRVrNLc7WWskQhgtTnDrxETqLB3ITuAQW2sRwFjEVKsuX2utDGr2/55c1PgfexBwa8J7Q/373mr0ParYuu9Y39Prie9/mlzu+7+Dp3Uf0vLN/c4jKbocaFV3UHPTn/tq+SZKvrOcu8dv6tEmuFiHxh10/NzXZYLEX/G+cDQv1knGCinYBJb74l5B1DlDs7ZHDAYm+OwmxXo839f8Hf/9+xY/5BDDjnkpyMHSHfIIYf8qGX6hRmrN6vg51bEm9Xs1p3DW5+9lc53WR1/Hyv3e2nSdZFzjK9NkOwbNu27r5kSzcaWjVEhrofRxMke/bR1prEF8FZGet34Qul5wgDu4t90r5AvFN0aiOLaPblVZTfr6QVp3Uq3p+9/EIaBJacVtoGIy8KwMTE9mhFxeiyYTgUMxjRPkEXw9O0CLXZ6kZhRHgv0Irh8rVi+MaYEPDyU4zPj9Hd62ShPkN/CMBQgISFiMe3sTpsddpLUprnNOfJVhbRhVNewcjk4R0wohcGFvbzOmAA27nkGq0674WnUJ+H0Z4yv/x8N7fdvaayQMOSn/q554ISRS8cD0uOTwcvoRpBox0KYasE0W/uVmd0YSX6aXoyJFEa1XaT7MMsNw84oSIY08ql+ew7Izh4LMRBGPs0vieRa1FMjjMbOxtXUX/exfHr9AAbOJKvsPVuQwowHSGW/Gl55IN0Swq4vj8LYWPPT6KrIv1DckdIN16DqxjZxN4nwaxAH8MKC+x3NKktTyGLMn8kNfgRA3H0kM2NycM3srNYhzbBnMeiIijHBumJu1mxib8ROm2AU1GJGzdNU8HAqOJ0Yrx4f8HA+gUvB48MJ82nCeZpwmh2wKQQmRXMmh6qDdsUyWRgGoDTBsqobrR2oFQXUTZQyBg65y2pi7fNIGO2gFl9Roi4wQH1zs2ogXUf+e1vEn5o+1Q+VKArUXO+6UjODLvW+pwDUwU8zMFuhSM2tozJD3MdZgULU3aDC8rIWK904WrxHeTS6Pg5DzM2as12CI0Zw9ttWgmF4+lJQT0B5reCy1XlE1NkAxOTgW8XMxV1AUjfyBzBd2PvYNGGqE+ZqbJZaSgeOa62otXRm4LIueHr6BpenbyGtgSsw1ckAMdYeRxOk7rJSLC6frsay6/HohmaLeG8ChYjDdtoA7lcRrlsJChVCU0ADhnW9JaRoJCjq46e74DLQrqk4CFeM2U0MamYMBwFFzbUnK8FcOZhuXNuKJ4/BZ635CgrFPM8gmAs9ngncAL0ASuoAkunZUgqm04S5nbCsF7TW0JqBkac6QZzVyY0hq7174YKHnzV8/dffTcdwWpdR+szfodgwGpR7lW5Ub38uhl4wYHdzgGLM+1SQqOdDbWoq1954vNf5m/zTDBesrx430svcE+SxRlGMNWjUy9CTqS4olSfWbGktE2sjdfUW4L+BJqM+oyh5Gg08KGNfHYTztUYApLTJL15igP2d/Zfesf8N9DVGMKfNnSM2zHZbc0RhnQvs66/93EgMoADlK0X5HCgfE/gBnbkd3hLI23Gw86kfImHwJv0KA98YDtJxReWKU5kxUcFcJpTEnpvI3fBydZDO2HDqLG6JQzQiNgeJHV7Q5oXyDsbOPC+lOCDs86ovckiDLU3etgSg+cED2tRvrMFE4+iBHeRYoChEWInQYHN4ZXagjtF8HrPuGkAdo4iAm4N7HttV3I8pUXGXpjT6I9scRWKM98b2aRMxnAkOB+lGvOfNejm6QDT5fhFGo19drc9eIjEA6PbzeX14K/u97DVify7pnqwzkvpB89+K+p5G7XswGzOQ/V7v+h3lrra/t0DOOvYZyY8NDTrqrKe1K0Ds8U1v0OaHPt5hY07YvVWo2jpczUV414EaXHj0cXMw6Q455JCfshwg3SGHHPLTEt19AtvVq978+vbnPqTsF8vvs6J/zvicZW+syElcIZS3s+jP5Idp+3ff7OeMEiuOgI0LS7q6x1kEAbimk8UdnAsDUKTj12MPXf8roP1HjGOnL5QwKkXZn62VZDDaGEwwNnTfpdvc7Bq7Xd++TaoDOqfHitNr9xlagfmVGW1LcVdcYc1hxTQVYyS61ZvdoM0glFJRimBd7JFlsRPFgJ8mjcOLaYccBpzcpMF+ykMrbHSkCdiDbRYptTlgJ6y7jUmtv7K4IZB0A/Rw9DOP+RPAXAsDEtDdoAHYgPNxMnhrXNK3dIRnREf62XChqR2tLiQZPnRDHmUGKgOlWoyoemooE5LhTzdGO0pPd1dFVxI5RRk0XTUJj7NmiDGWGEelwwAFQbbW7bPw0+m0ZQdmYO7eo1YnenUtn1Tu9XpvrGarTb/32hVg1n0KNwyEgRDJqNDBXEp3E8Jo1bmI3cCA4eKyu4/EcJ2EsIm8vxmHpxMWVazLakwr5sCUjOVBQGEFcUElwIPh2XjjCqICc39lccVUUqdU7UZZMyYyKhPmYsyq01RwPlecTxWPjzMeziec5gkP58kA5amiVO5x8qANVGHsFbH+VKrHOKsEaQVtbaBCaDDGFBZARLCuCq4FYS010M37Dwcr0Qw+LVwbOigDRGw68Tq3Xl2UzKVSsk53o2MMUA59EPmgM3GICCr+r7d1jCPvdOSsMAkehOmh5pYtxjBCAaa71Ptn0+Zt4L0kA8QUbEgeDMhik6k0wVTHlm56UJST4vypoj4ArUifdON9O8tCXa+TxWeqzH7QweMWuqE6XHYSGLWYYXyejOFVizEqSzF3mdM8Y54m1FKw0gpZGtZ1wbJcLOZbUUy1gNgZ0TB9CATjWKAkABnvTqUhWldVjY2i2l1emqHd7lFtkKbQIt0ADmywXh/nY0yTs1zUXXY2bw/WArCxPYkIVScwr2CtxuhUAREw0YRVF9QyWX0R2QGYi6CJs+mkoRT4+6qBDGxgAVdCPTGKGKugrcYVVFJjGlJFKROKgxGqhMdJQbhARLE0QXGjPqAohTG9Uly+fqlWuRbydVZXqbF2877jS4kNcOfeCG8eJhoJj3+xDoz1VD8DEmlGbDZPigq8Dv2635/PeWH83Mu9Wau65iaywzDBbIOOg0adSLZbf8bk2HVGCI/68SFsv/u6pseTJQLRmBP642Ww95DqL69N9/Mme9lCdfXQYv3f6N9RD/mZAP7GfO/twLAychovmta7BD8gADeeW7sXZypbPDQCv1bUXyv4CwV/7jrVdZ2sPne6C8fNZoOAEi59lcEa7Dc7VFKpoMBAt0IVDMaJZxQuOHG1T6oO0pnGqij9O9Ngv+tqHbYtq80nzeN9OkBnrGnuY70w9+cZ5AcJYK52ATvYxtSZalCLOQwV6ELWH2aYS03XYeSdQQEomdtkZcIKggBoarpkIWMWFxWA3TG6s5rRrM5mrWjasEqzaT91fvJBFoxS7ghTjOUR0DFWzQpfZ4M6KJLH1u0LqR/DYsK339+/533k1hoy3lRzn8boWml67g/0Iu3LRuOjHzzw25rC2JxAB8eRx8efSPp7vkWYAa22rIq9cn/9G23ZdW/So3GtX9+XQXffxQ8kut4gWL2Z7taeeIDIfb2f8+rKMCV8yCGHHPITlQOkO+SQQ37cotiuyDG+50Xi/pG8qNYb99yU91mEvzRdfcvfL83rxnO5DjZkm+c2KTnbvZEj/0037nFjRVhOaPc5rlNi19mG1Jh0ZiAIo0OXQBCcbUX7MlSATng3F4X3JO8FnpGtGWT7OOHOj89kefPWtLmMNCNkCDHw8Kpgfphwej1heixm3IyALp5AnEEUAqRqv2r/vGorAC0oPGMuwHJpWNcG1WXjAi8DDt1w5S8gwxbcN8tCm1t63ThRpxuxKO7T1E9TXw2mUr7W+7XvngWCBW7Md8NKsOxU9zUcxos8UNCN7g//nvCH/+vLGo8Q5dsa37oBk1K9uHHRNrLeFv6MqBnMSQAqClDzxCc7uc65M2jKP4wtGTx6mQwjhlVAhZ0aNlAzDFLSsVlKRrqQfBiXHHAPBlmu87ypz81o7659c89JX0mqv2QnvVb5Wa+Fa6aoGx1pbMq9sSYPhZKdJmr/P/p3IfX4GcM1T/RNCWBo7xaqFw7fTc6PaGCgNWsZURTAXPhpMzeFflpZPI6h9Ut3rcUKEjGXWyqbOiEATAJiRa0G0J2miqkWzLXg1asZD48zagFeP86Yp4KH84zTaUKtBtIwh/GxQbWByF2isXb3lNYPirkOqwRagSaKVbz+mgFC3CQwRu/u6m7WArIxACxYVg0G1IRyMF047lU1d2nkrKhoVCaGBkRM0daWBvVO62yRQhCQnQDX5jHqkiL0zmk2UXfV5u/fgSKFtxF1hRF9tcdG6m9h6RDI3LVxQakMigCjRAAL5mpU8o9/LaClgarVt4Ht/j65sb0cJYzRxQy9U63ddRyHMZlgrK9CqDRhns841RNmZ9EVtvIwGcOuONBHTNCmWNYV67qgyQXSVtdllp7CAS0Mtk8YsINFt+oKVcVqFj9zz6rS2936g7nEFPdhqC0DtD4IRo26PrEYm8Gq0s42tYUJk4CUIWzsOWWAhY33KIK1FKy64qQnEBitNjSpDpQXXHzR8vW3hHVdscqCy7Lg1eMrnE4zmIqBq8TmOtXXRFRgLvZcnzNNKHxCwQWFBaJPKDyBcAGgqAQ0jqBsDVoFp09WXL5+f8txoeG+0btRZ8ln1hf3U1fWp+OwQp6quziDTTl1xTQ3Erkn66zsy/b5wqn73tDp8Vt87vPp6w8e83Uel33g+ZqEIx8vpKZ5t9/udQP1uS8fUOvrG+rMNhIZ8TTJGG8lxYHTcNF9qxJjbUoYsYTtgQ1It60Y7c8QRhvavDXu6nqHxmGSYH8HIEqpHpGe110i/Bkw/Vt1QMrXE+rzp699KACO1MeMLTeYcqyMgoIKjyen7LElGTMmc3lZZhRmY/+Ss8j7kQLyfukNqYRmVHcsl4vpmTiRpjY3bKie6n9pcZDVOk4cDlAxXdAZdWlxwmyupIsSmi9mqEUdkjHfCVBlEAv66s3dWSobSAdSVBGsqmge87fFqkRo089ZGRMVrBQuL8e6Jms/q2XTs+z6Kty7ClmMPeQxgd33XadUUNIL6SZyffa++7KXrJeib2Lonb70Ct3k/4u14uagnt55rXQxF4PU3kcZfU/Uddgtvfc9S1qipAvbG97VzfoVMy4lGXoRqa77ocnQT/F7ukdkHFELfWtzgR9AYh5eUfyFNFLbNdjpU8L5Z4Q3f//Hru1DDjnkkO9fDpDukEMO+WmJbr/fW77Rbs33VnnpAvd914v79F+S39vySpvneN++cH5benvLLdLGPxsA8m/AFpiL72FkqePZYDyFsV/cKi9sBo7uytIX/v10tee5P9UHAlCMTbf8n99SL3dkX+V65/q7yL6eCRg+kVLisdHrl/Om2O8jDRIhmSu5ClBVzJ9UzKcKruQGarPWhHEyGChRf+xpkZohw5K3e2ohd+llBuHlaYFezNguMjzhxHv0jW+nyCUGnW+IwzUKgG7E6OE84EayNtq0A5C7TeZu3z/6sRdKY9fo8dLIgymQwvzS5ArHMF4QaDwL6iyW8hovFoWH1QqDU6qjMNSx11Nrbi5xlkxvATeolEqoM1BOwHwi1FMB1wIqPIDMvnnd1st7Sx9L9qXC2s2jOQEOr5CPzQzSEQD2ziupLAGUkTe+dvCj37HZ5MeGfvM6DDP+eBwyTY/GLl+3iV4NOnNbOZTeAGxHW2kv397KcK1rVI05F8Z90ijFFhqlMOgyOmauYUn9ThIKlpyh5sY4P2EfioR8nIu7++vGErGxriJQGvHm4IYRczOkKATME+M8F8xzxXme8fr1A169KjjPBY8PZ0xTxTTNqNUZCgTA3RSaj8iG4EmRiusBMfNiMA6YAS5gN3QKGFjFD2moDRgNUMbZirBYiYB4zMRgdISG8rp2IEdcLyqaj1FnESrMlOtuyjK/KrWYtSaZfrW4dgFfu/vdpLyzIctcj8JjKwkahSdPdXDPxlgOABopUwdQrQMyUY+nVKma4d9j4kGB0zwDAMpZHeT2udX79ZiyI66ezwHN9HMhAwCZnbFSJ6AJGlaoWMzLSgVTmXEqJ5ymk8V5cp1p49zYd4UJKB6LTgQiK5oY0EZsLEoAg44gA2qzdnQY1l2YdtacGFOuabN4j6RQDrBO0NDQXH+PKTSsgUjxDdEZc8bMM8BjBcA6ohlyxH7SBgKh8WrvV8wfd0GBivW5AkbTilWq/UYFrUxYVquH1iab90CY6gRAUesEUUGtM7gU1NOMsq5o6woRhTQxN5YCMApKmTCRgivApUC04bKu5gbTx5RAwMQ4PRC+qYq2vp++2eg9GnN2d2kYI4MTJzF0juhm3u/tEWq49/X0fa9vs8cFv5eDmebdpmNYu/VBzBWZFResse7IOWLaRvdIa5gObhAhlHdfL8Tl9F4dMKMESMU7cKo318/E4TjVny3uWtUTVnY2rQymbrzYdp1ohm1WArT5mprzLDbqPpU9dJOlqZsbN0AfxhwZ0052kx+sSdF86GocMuCRc38B9fEMWJ33AxgMwF1bsgN0RS3WWritnGlGUQPnCAZGFWJMHHqRUZIWZ5CxPdV0nqhASCCrAf3rcrF6jvzB7sJbQL54pFiUKkAC10s+AqJ9fPHXV5QS6yLXJ0ogcTeaGqw08wzRj5TE/Ilqay1buKKwQFnApGBVNGoeF03RdByQIgUmTFBmNFKUKITrHIR7UbZ5xOJqMrQoVMxFp8ac7DFK44CTYoyP6FtXB8H2na53GmPTXf7z/re3Sx/Hu/F972a5c09SZbFF2K4l8/e9ztrdRvsL+wL/gGRT5ht1mN3s3qvifOwv6+v+lUY+qqFb0n1dXYz1f+jsjU3B7+1rUcD0qdp6NHS+PQjwBPMucsghhxzyE5QDpDvkkEN+/KK7z/33ndDdP9528zP3f8jF+djPvrvkxXZsmmjsw3McjU1et94rrmVjiW/U+2+J3ZbvCeNENrZoABa+e1WYMQL+mxKgBRuQTndp9yrJ5c47kRMsNt3y9qraJHUrvWcfuH05jEYbEJi2N21iuMi2+151t5xOAcpkcX/m1xXlwdKaHs1tlsLiP/X0wsgCYBZ24/RwE0ndOB8GbfU4VPGbuZpbK2PlZie/5UYVpPfcb+Kwuy/eMTzshY022qC709pXXTeajTSykUDdgCIAAs3sjBXEYfwwgrgBx0GJADAlBbZRKGgi0EmhT3feJ79aMp4pBaQFKBnLiWFtLS0MPRr4XXpHM7hRIfDEKCegnAl1smt7o8gmEHu3ft4wnuD563uJdBnw+E3a6y3AdU0DRZH7msJc+gFEDHdSuBlTfZO+LVwkML6nE7Vlz9zLbA0ddbhJN13fGIdvsBOiTwqJZ2155lPY/WQ5GdsRwMaAmo0QgOs78XZzVoi4Pnv9V4R//H++u4InAFRnZ4K5CZRy3DL7F7aMjYGkMxpcLXfXXWbwMwjK7qsMTIVwqoTzqeJ8OuHhfMKrhxmnmfD4MON8rih1wlzNHZhl1SCyAm2F6mpsMyigI34XvI7JaRlm4LY2Kc3iaTZVjwXo7sCaQThNLW5e6+CN8afC7WSOVxadw969eb2sbnQabjFrBzyt3bsxPo7mEzAoJH50nuydRQmcYmftXboaK46AYvol2LbBfBVVkBiLXGBAVBiDSZORm83daCHT/6UUM+xXMreUIMzz7HlvIBQUVQNEi48bGbB7lI2VUEtFKcWAN3K2T1uhq4F0aATlhsKEQoRTrb0sAI2YVRSAm73TIgtWWbC2Ba01kLtnBgIMM6ZjKPFoZ9GGJoKmzfSINkScOsVqB3owWHRQ7x8UTE7X4xj6UbzuDa7d6SUFMiBDIHdVZy7ljJVjTpon40CiyYpGC1ZZjeGICUUnEIBaqvU/LmBSd38LByoZj/KA81mxSgVxgchwJazM5i5vTRMiWVtXKmDMABGmNhvjtRV3Z2vvdyHF/NEKnhTtPRksAW6F3gzgqhvvQ9s4Qyzc5ApgQGzMiTTSy3o+lqJ5PojvNLp9d3OeAbeeeX/An78xEcT8PvLw+YyoHyAhsUMBm7UIGZiijjwGa9pcRqrH9AomHDy+p+fh3gpCfwxXl6Pgo1lpMEe8koLnyTQYSmGgDob2eM4mF/ZJiROI1suT6nSz7lMdaVuWnWZHjE7k7+uuXk70WJN5FhvgZnr5aCfPLwOr4RhAYfrWxlkw5wpmqh5f1RlzNBlTTA2Kq+7216A8/0eEogHQmV4K9EY8CKG0htaaH7DwojZYg+pwl9zngnAHofHO4abTWXTioGq0MaGzSi1jAGJzWaExT6jPGaruSSSqK8aUTxgKc2FdIVhJsIgBdeYKGl3fksfOnDGZ++iIyQpjLVp314GoeqMRkQF2omAxhiCLOTgWcZfEcUowzXG97WNdlfpeHzFEfT94tf98gYx+O/rNXvTO95i+QeNs5HsU4XlxHZTXoZu6+VNJ9DH/s5eJ7LBDKbYsErZPSo/mZU+/EH/Hd78e9wgMlIavcQlwG4DPwHku8LR6F/Z12tAN2jNRFTv0sV/s54XWIYcccshPTA6Q7pBDDvlxi24/t5uG8RX5+v5i/v62lfX3vfJ+30VnbBSw/by7jqXxL++nb226+32BKjAstkIuL+3uh98TG38gAXRIm35040C4BULxjQNvkt68SD/9F+X3cpc/A9qXgPynF9TZrjr2FbWvN736Mt4DQHdzQvt79mnkXaKk63ckjFKlAvPHjPlVxfSq9NgsnWNEQDhegxLmNiCg4m6YBOZyJBg+4owQiR1Xfy83PsdGCuaGqtvZ9+MNY7OW68D35xbHIa7lzWwYqtJz+7bINqdu3IAZMqJfEmHE0CG4SzwADCTbGYAwbAVLYldmKJobvabPCOffAN/+9y/b1vd+4C/BYCg5a8grMQzvOS4ZwdguDeYGanKfYKWWDUAX7jq7sexFpXpH2VkXCrlbJW9LSpvyfHOHBXIn2Immn/dGjI43pvYNTCQAun0MzTC8v7Qe8ri8ObZT2bJeG4zLUXBlONgKqDPV9u+V2bHBoCMYM6ecUmHeRUoFPv6iG0LjxHt3kUjuErGfRqZeT2ZEFyu8G9StH1nZwiDORXCeCs6ngo8ejD33cD7jdJrw6nHCfGac5oJpssMB5gJPHMQM1lqDygoRA1aUxOuJLG+CMT4cJGdy8EUFyg3KC5RXY2A5INOasaQUcMeGA7hrRpNA6+yzMG6Gf95hZDe3j9SvtVS9GvoTxlCzoWxWJ3PnaPU3DLDkBy2s05Lk8eCfBKgzNDqh009qCLnLNPX4dhHbiBXazK0m3BBfCrtLUXN3WQrbJxdwYQPpRrf1/q6md1zPF2arq3SigACAB6PS7NRqseMUYBVn/hnjsMDjztWKWuOAiI9PFQNsmSBouFwu9m+5YF0XA9pEUJQdAJEeT07UXLkJBCLGllMH61YHbRuatT5JB/aMCSfd0BdMOsY4/T/GqPYW3UvXyRLjghzGNYBUiSHNWGpruJUVAkqB0gUNBbQ2nIrgQoRVGqQ1aJkBrUBV8AI81YKnyxuEO8PqwUZLnRBAHDOjGZHK3Ntp9Fx3J+p9gRhY2gXLaqzY0HFCFSs1PHy2YP02oMp3FCIHiYAYMgHwRNwuwMAp8rWHxYq19o3YnOPZrA8JrAHkehspRnuRew3w9YO54iaAteu5mA0jDS9ymsu3bUtBsQc6gzjH7IJIZzUFK9jcsEXtebwytWtBWmavl2AYWmxfbwhSB3u410G8/2Dm0cZl6Kh/IEAfCn0l453J02AycDeDff3ASJxUSvUTZY0sxtoRfX4iB3H27dYBqJ6X9ncK2bgk72MtLrA9k/cmvc081ibsoMDMFTNXcwnLExiMimr6h0xPF39/iztnYF1RAgkbAOu6mYRsvaia1g003Bz3/VMsUqJXcT9JRr4Oijih4gAZHKQDoq3j1Q3As72Kdnft5pEgXl5Boj7DsO9n0jzllSOi5g2CVtRi8eiKaygS8djLgtI3WxMW8kMQqn74iHpb5wU6F4Y2BVq4e/Y1KrGx8UDdLbh6AMh+ZGy/kLsj/ArgM9C+efu9WXp/i78xvugz+b6ra8f7CW3LknUUCQzkWm3vwZsTd3986Xr6uXrBqNN7ZaUb3/tz6fumr0eeeavkFdUPJtBWG2zWyn1vo32NGKzIiEM7Yv9GelGA91hHH3LIIYf8wOUA6Q455JAfv+zWaHT1BdcL0nuL6T/hIvtKXrL+vPV730Wka7y7p2+2/Xs+ZuhgwsZdZfFPZ7gNn4Q6svL8xiZ1+49TPhtiQhgCAqAL6tENu0VP9967v4/sNoJR7y9NPva7UWXPFi02Lvk+3dZf/BD1yA5C1QrUSpgmxVTM/Y1te9xtpdrGkcWMRdxN9+huiQD1eClmHBc3ZjcN1oYb+/1UdmOBVkAroV10G+ou97F9X82/pXe5VR8bklSO7xAnb92+EPcxsbn2JDN2GDin/k6ZxQGQW8VHcUbMIe6bvLF5jF2uPUv3y31DNs2nseFUuNckkMLdqo0hGlU1jJRhTC5moAzmmmo3uBATCim2lMyXl/PF7+OGMAmDDLJhJGW4t5ZIMuwivyelx66Ne6GLkn3Pk9+5yEqnbHNp9oaZjYHhXv2ktsjjv/cH3V6P9ESGscG97plRMB5T3bRrZCAONqjuFfLLRdXcJjIMwC1kMcCM1TH+njw2XRgQuUTMMHeRVgglXq4AJOZCF0w4zYzTVHE6TTifCl49VGPPPVZMc8FU3eUjuRsuhoEocgFkdYO4xbMUB1u6iYWMR8puALd5yC1epRk4RwsaVqy6YNWGVdTZA8agM2DO+kWT5gw0YwIOloJ6ORSbY9wIJpAZO8dUZ4bn8DypmaFI9q7UjUNGSzBWDRljoblxW9CZpNrjFlocJHZwoYaOUZiRVBVNyWOdAdKalzJ0OXWWH3dAzYzawWab6nZLl+clKJKhS7tbwpi/CyzdSua+ktXej0RAqkP3wlw1nk8z5nlGLRXBS6RCKNVZeAC0GfC6LE9obQVYBuCAcWo+3OCKNIiuEBE0EjRp3v4rViwAVihWAAHKuQEa6nEgR2xCwOozWw3jeuadZh28HWOtG83helwgZrxWhkRMQwK4NbCwg2sFYWycaAZIYmqCRFmfCFoID87kmeczlAomBweNDard6EtGjzNQknTMS8SoteJ0mvGwziit4SICFra+hiecP1L8/j0XtQaExfhEZ1FH74rpx8riY8RdaCsJOFzI9nnU+4WzbFQVq+T26UnbBzujGe4vUgDlUQepta6X97sLG9wIMT6due7AFAgDZKfBNhuHakIv+FokrWstFicPMI3MBWz0b0L0exrPxXEATkw6DMYJ6QDymNnePyi2vV65x08U10951s7vPUBH9HTzPG0uQTOjb7RLgEehO9PrR9M4QDrWX1eNGjqnp+dpss09dhdj4ur/JlSaXCcZuy6YcnG4gECoIExgTGAbh0LGYG9+B41DRiBj641DRwoV6q4dIn6e6U2f3zx4YIBz6mCWRiDDtEag8DRBfkCEU3rhPqLZ93AdGqBcpELkeoecR04IZQ1CMZCzNNcT4b5Z/ECHA35EmFH8SIMDjE77VB7tb+UahzbsoCSBCoNVIGyx8oKF19dUGblNTfzcWotmgL55v6VqzN6xlrrSaLT9vNIHeuPinTw2MeXGcB1AdeSvtmTBhO9l/f0nkbRXirYMV5OiGKzk/Ll5YNTP9lBCXBuz7r772Fp5MFL3h/LG4YPRkJ/9u4Jv/kasHQ455JBDfkJygHSHHHLIj1r2a2Pq/8PWCnNrgd53HHcSfT/bxoeV/A570Tvf07WrU3PdOOf/sgvK2GR5nuQx5KgDdHYCVwmgQn7/xrv/ptgdsYq8Uz7hClNLKouDgVzctMZ32jflGBvgKDsA8L8B5K/v1MlLZWsDeqdHbrlcynuZjWhq4q0duddTiTgs1QypBgioM8HcvKxmaDD3PeSGY/Qds7AbyR00iq/NC6FhaAW6Lz8tAJ0APL2sCq66amxqvc+Jb3j7xs43fXlzLBjXu3Gwb/rs1C+KxeQb/deM/UoWWyg20FAdsVLCi1oqowABAABJREFU4Bknr28ao4appJ+k56sWu/3uUV4d7x3m4AgNMgxidgpaIN1ADfVqVxgQKpqMHfaluYWiaJglNf/87pL1XP5+Sy+mjXi4aO23bYwmbhgMv35uFDRYZhhLO/Ou9/dRiFzj0gdG5KW7/LaGxqwXXtpnaffsRlT7KfLd5f7JbmhjMlYVM0PZ4s+0Jh2wg7jLO8F7uX0CALz+BOEeUKEGiNQRl8hAOjPTsrvYjMZiMhDFGFD2z1i3BnARCYgVUy149Tjj4TTjNJcO2J1PFfNcUCZObnENpIuz/0zNSqeLAXSyQOFMOoUzWFyfUevjTzHijYmuxqxCQ9MVi65oAqza0Jwt11Q6w8hcYKo/o4gwgGFUHZbqwT4lhI9KA8YaIrYb9X5hXc/Lm+m/CL5dMCK09+dgVwboZ5OlgYFwu6ak7qSeGtwdYnYnaOqNu86KeHRM7PHsHKhjQi3F3CsiwCB38ZvngM2gjXppiNMw7G3DbpEk/1e5QBmYTg841Vd49fiIeZpQi7HoAJj5nAu4FJQ6gZiwtoblsmBpF4iKgS5u6RU1I3UYBY0Xt5qbSwhWEQPosOKCS3dxCXdrKWHM85eJ8TCAuOj4SXf0b3se8/ZOO3/kRnYzT1p5EQdifASmOE4CATeLuwjve8LN4vmpuZ071YoiBBbBm7VBcfH+oF4ngmma+mQiqh7Pz/JnLhA293PaGogZtVQ8PrzG07JClgUqDdoumCvjUoyJff5Y8e3v8M4S02Oet4DRN0O3Xz8IhEW/Lz8pnh2LtQC7dDcect7BkrKYkdTd93Vjb1qw3l1jJel5sfU/dteO7KNNnG3X1wbeF1iHu+kA6rmz+oJ95msKjfcqHdzdr8PJGUkAtiy6vBAkdKCuu5jcVXgA9waN8shDvQ2wm6xDv/QDPpLmYoYWA5B7IvvKjCUTRb1dT8advacwYCrqUQ287exdwF1zWt0x2FngjMru1pUIxePThYNngunCSY09d4LFrivK5oIjXFMG8NfbkXr5COQHwAgQ6m6Ikapv8z4OSqnrTRuyPr8Q9bVaqFtrxmCOR1qhTzB0obdfrIm0t6q1vXmFYM/T6tzWhjbXTD43EdkcRsm3ayN3Vwmxe2LvpsMzQ4DwuQ3J28P6QbDGR330WwOo2y+e8noqXa+fAu0f8X6iaRp/i2yGEoZuj2GQdU6Ay9Dts7lLR5vmarqpZ3YX764p/8hyq8xEfvgz9tstNaVX1k3dfiOtnEevIyeh2r35WAwGexXj8KiIMUqpqYHN2XYQh36iTKF/ALNRHHLIIYf8BOVQb4cccsiPX26trPe/3ftbd58/cgn7SP8j3FP64pZ2dZVdWHYwDmM/SQEQFTfkVfuMmHEdcUmbmJ5HIotQLgtg7os8nf6g/97Tjt/6pje9qKbfaFwCAP75u9fZc4admw/cuKa7n/ddKm+QgLHp0PQg+Se7q1AmoEyEOhXUU0E9z+BJwaQWLyf+hZGBCBKBAVgHSyHFgeomEjIPOv7DrrC3d8T3jGC5K2zui83vzpDTr+82g3FCPZ+YDeBLREHNdn9CAFUzSmgyFAS7IjJiuEGEyI1y3N3sbTLeGSEiHuKr/wXj2/+xQV4Ql+66srR34Q447/pJt+Fk44fq6JOpc7KfCFeM2CoBEN2T534LM/TNDfmuHXM/HbFN/G8lM+QgYgIijcvRiQjO3MxXI+898pbKqDoYGLlcuai3+uVeZ9yriavpI74EI3B3eeRnBrbCxvcgMrdaTMZ6WJsZQBd11o/HpYEb+t5R65jMD96H1BhVU0GdKiYuA6SDoohaQ4kZ8JgVpQCFYbHMmIz9AYWukZ6p3NNU8Hie8Hia8PHrR3zy8Ss8PDxgOjF48vSCzaH23dihAmWFFgWLwPgnxnwyVokbspl9XrE8F21Y24pLu+DpsuDN5Qlvlgtaa7i0BYsYSHcJt15q8YRWZ0+t2swY3oEbBw9UumEWCCAt/j9YJ6oKBpk7Qzdes8IN0DYRDSMTgwO8ITZgs1Afq9EnwhmZCoOodCZOxO2Cj5lsQCzMqDBATLRBhd2NHTCM6wYolHAzyu7elMtwCxvdF+ggC6XOuyGiigMh7LGgOghoDLpChGmqKFNBwYxTmXue4iApcwFXxlQn1MlcYIblU0WwrsZ0NAZwBTQclTorisMVnLEYVxU0XdFkxaIXNF2MKSfNXMu5ic/0Seb52ltFrVorXY/hbDwd7reGprSR2aOKdr3dc/C+zACax9ECAcwK1hgL1k8Fzea0ZqVtUDOek7nLIxUHgNxYqT6/qfo8ZfNVqcWMna1CW/NCioEbpeDx4RFUnnCRFbwSmqx25okJ0wPeD6QDBu2b0rUAKXrfGpPDhke1mejy/GYXKT6zhTznReiAkjH0bEEUcwjFTQBqWlOJLxj37S5posjAYKSTmfV5QgwAJ622IAlIH64usXk2+t5mDt7VSdRZrGPiP+437V4CAwiM31V9jbRZz+SFM7aVAUpARWI5dmBypLuXDVBL7kISg2mbSy1+SAqqnZWYF4lxyMzimTKKmv6ZakUhO3Rg/Dmb26rr1OL/JhBmZVStrrOKt5PXfxxoS2vkqAsVtfMJgs6e24M1fZqOdkzrDzubQV3npwUPwp2mis8TcVgk7o0/lId74fi9M+wpJgXA53VqCmkMVkWT0vOzmHzmbrrZrNXdvSuMJa3q78rwPQF1ZnVnWJMzNqvdyMxYyeLKaqM05uONk9YkDHCWUvmzMEAnvCjG89vkavV0YzkVY76vo/M9eRJIl8abvaAMvsTS5v/6QcCbyf9JpPdj/57B5N4VYe+SD57tE+nq5uYi2z6p+y1G3z8xrF6IgeLxNS2tAIrtEb6R8WCM+/2dZTfW9nu23SGHHHLIT0UOkO6QQw75UUtfgG8uvEDy2u4Wq+E97KcfRG7Zbu8tjGn33T/jNH+/HnvU0te7Jr54RsQ2c4Bsk2WxxTJX332wG2M6s0CuFvwBxHVgjtADjmv1Qlc3ttC4X8gX91HevKHKrx51dM/OfXI23f/rxm/Pyc6w0/N+YV+41Rx5079Pqx8G31/zOiFvEy6Eaa6YzhNOUzX3M9Fp1YwhitgRITEQtYNzVtV8/SpXF7Y1UCoZODg3rBfFmt1RvlQ0NVUYlDybHgNmV5a4p/flBo93A0DVYgOBjNFpljmL7+S7xMFuij5PfdNHvaJvvfG4KBh98d1lGC/i9H9sXFsYZnl0Doo2E2BtissiqItgWgvoRD707CS6BDMLZgSLV+FuvItkaVOU/fttDIWKjZHKrpnxpm0uYjvWY1OeOn4Lq6G3u2hybbfTXd2+e2c8E8ZGXHveyRgWZccu/fQqYQTrhorIJr1Hfh/yMUm7NDaD28tRmMFcUKgOJgPMUMxCAVEBGkCVPff+pgW3CvnXiI3E1VgF1ctq7iPFXcKaIa9OBuZMlVEroxjK0F+6kMX4qZUxTwWnU8X5POF8OuF8msATAG4gbiD4aX7W7t5Q1NhgFlvOXRNyxOsRZ2KzHUBwvUYMoAkaGlZZzbWhrFhkxdJWPLULlibm7tLvUzFgThyQX9VBCxUHb6j/TWlMZDibfGJiEoRjYFbuLujUmXYEm5cMq7CG1wAou0ESnRlORODmZmH194OBgMQG7zUxEFOSwToMV8wMFvHDBCNu1y0XYoM1hm7wsncbv+b53sZghqNCjJliU4cxQNlfrZIx6aZyxsRnnMqDubgkYwhBCFQZdaqY5wl1mlGmam4dl4bLumBtq4F5hTvzTrShsQJiLttCd5uDTGtbc5MqzrCMWIfa577gzsUn0t9RQ/D3tfGe3CLm+lSgc1louBuMm9knrpi/RE1BKIBSaBg3xeaodQUKCahUgAQXAMYPMIW3risWNqD1G2mwWIrmvvUEQS2Tgc4eO1b9tI6xFCuUBG3xtxagUEUpEwo3VAJWWcAAplpxWRc8fiL4+rdAW99tIhvgTaqupBDHIaOhgIdOjkrJk/p4PvQp+y37dR35fMnhCjIBInkUd0wNZvSN+T1A3I3hOeedfuvjOBhrqQIsLU896X8DDOM2L1PvM9uF5ABxvH56PVC6PbM/R569zl1X0HjK9RwNF43d1eKIQzpYcti8W9YVnWnmbnRvyy2t4YdU6P58Zm4dafNum7Ugk4H8cPYcjB1aqKCwxZ+rGu4ujVFmAB1jJkKR4rqcfb1HffLuJfbFR3f16zHkqO/7Ro1GHZumubGY8PqyOTYOJzlgGemrjXfLOjE8+4sngDfln86WjTWGe1QIrLyor5nVytDUYscpFd8zOOAbTCWP0VpgLFQS6t2pxRpNtoy6WCcrKZgZUhhc2OJrpvIRerfsb6Gx2PIGVngdEQEFqB8Dl7+701nuiW4+NsuwFzzW+9rmgRsdNv88XGZv14yyu7cDdLcdyvzRZV+EW0UijK0ipZv6+p+SKqbxTN+spWHFOY30j4LKv1mT2J+SF9M0rve9lh+YCHewIz+BKndvCaHvX/8F4ff/ww+g8g855JBDPqAcIN0hhxzy45YrK+uNv59bv/l6sQMPaRH6o5BbZWVsgTpgE1MunumGxfgsaaHuHxHgXFj85KxvtP2kJvEwelAG5Ai2s6xuZGFzccGFxkKd0uYw7xh2h0mBtMHdbdhuNlUB6JO31lyXfIrw7j0vTOtmN0ybWiAZiW7dGzuoXLYmgIjFCqICIga0YfENeHAJ+kHolDp/h9hXgDUhMw2D+lskjA37egg84Kq9vMxMyVjnddANdTSMAU0URM1ZB+YyKIDmDli5C6vS+xP1jd/mtHwqM7qRBc5WGWal6eeEp//wwh6QN/bOmFBWKMfpaxubTdV8jZb0fjz2uSKK1tRcqnW/WHBDlbW4uKF0uNZ7WRH3shtafnEY1PbpUr6udm/c0lS7UVf9JP3Yu+927CnvzYVNJqk4FIbBuMVPd8PzyYa01Nd6+rQ1uPRxuS+Wbp/Jrx/FYj9pzlRQYEZFJvJ2E0hr9ikClQYR3ZYP7y5UJ9BUzQCoMLaBj4/KZiydQJDWRrkJQHHWHRsAx+4eFrpC2wppDaU0TJUwc8HjacLD+YRXr8746PUjXr86Y5oqhFcbwzTUVLC5oIoiNk8wK7g6m04UQs3GsbtWomoMH7ABwIs0PC0XXNqTA3QLFlnMXaKsaCJYdfwLV5dQc3G5qnYrmWo3qUM12MZef0mBcYB0iIhTwdCw66W7sBxcK0QcshttmEGebrwHxmEDT8vK5m7QyModMbqsXMbGhETcLZtgw4A+jFba3b9x7qC9XAnK2LjqTGWjmL8VtVScKmNiGLsLAgZQ2NxazrViLhPmUlHrhMoTuE7gUjHNM6Z5xmmaUacZVCtEmsXKqwCxQNoKogJig9GCeasMaBsaAmpG9CYG1oq6C8zuI3bAcKFPchy6+Ldvmz63elMOGU9y/lsNsAaGPo/4rVG7FmcQYFqtLxKBqNpBDEc5m/s6lmYVvrouaG3CKg3nOo9DBGxA6TxbzwQDZWJUZYTrxFILBAUixVkFVp7CBaf5hCI2vtva8O2bJxv+M140d18JYQtaRUXdMOb2mlPFrjtumJubww794q180Q/WgA2IIhnusLOwN27odO2LWItfGQy63Pwa85ZqdyGZ3UpulqV6vaYxN6dplOXJI73H+DNxOmO+oRv11ieJYOvajcE67PH4/FSTarDrR77qhyX6M1HKKE/UR9Sb+53lONEUtafjoFJPa1jSe/nCZaW6/u1D2fXLvp/0cnr+pRQwMWaeUIgx84xKFSfMYBAmZ6mzOusXMf8Ya51g7F/Wsssg9zcJ9WKAb4zjzn4LrRm6wMCuvrZG1NXI27rZ6HcUbUmwmHg7PdR7Ao35QGWkA7jni356z/pDd38ceQdorexMMWOzB0hYIKjeNwQFwSkmUT8o5gxm7ysQf7b3S3I37+JuWWnbeHkMR1fIeiHqwedDpQCcCXQCqODDxxDbVXSsT0O3cqrSvUTRu4vo9MPu3Nsug+us32dd933Js2rWx+aVy8v0nKRnAtTb3LDPLMa9uHpKLi+v7t23l6IfCozDLHEIwPZmwYu3mzubmoDp9UbRHnLIIYf8JOQA6Q455JAfv9zY5N9ds+mdz/R1c2r9T7HqjkXsTaPSDUkba0rX8nsQwVhH4e6kG0LQXV323W86LqjZgp2MfclOsHFhqQTwxB4vhVFrtc1qMJ4KQdzYSjCDqwC2USTEeXM/bWdpX50wfUGd0OzlemHsp1tGhPzbO0tUVd7MJsNP3jBGPfbNvp/KJHb3dIrkiiiMOva9+XaYSa/YaB9KItZKZ/nd2f1dZR114E0fp+ZzEgzfQGfwmNBXJ5ndGRtATWkCOk42s7mMM3TOLTJhIfLvZqQJUHOUmGLXGgwBwDIj4PSXjKf/8HarAqV6iNgpMWYU3Q5jJ6OHDWaw2FI6w11R3GAvHuzTMNZE2eNk6YcUhY74WSl9AiUGxNYQVRDMCwP0jYEwjF1mZBxeH2lkdtPYk+skmA0h0jf26PWaiKP9ud5vc37x+1Bp2ykhFS7aLoODhYsbstzIp2quGEXRWsOyLNDm8eySASwyLY+K0+eEp/+CFwudTgbSiYHzlQm1MObKmJnd5aWiiQ796QZBA9rtRDwTzGWjKtRjfZEKmAqmifD4asZHr8/46PUDHh9POD/MFmNML1BZQRBztxjGXZ8bRJobUwIU0QEE+d/BdTI9T3hqK57WC56WJ7x5uuDNsuDN8gZPlwVLa3haVzRVNAUWMUDOQLrmfcAZdTZr2L9NzJ3RiD2OFeywA4icn+HwDJG7G3SDesSS8wRc0xp4FewR8tGQDK+53wBu8JRgTxhzRNRcHYobU431Z/2xKKO6ArQ4m+42N2JfYbDDyCdyM1i7Puhzs72vhyv1EE1jIJCSseS44jxbP6rVwBASRvF8Cpsry3M94TSfUcsZtUygWlGmGVwrTvMZhQrmeYYyYW1msG/rE4BmQKHzU6y9LH6gSEOThtYW+ycLRCyOW7Dn7H0jNqQkiE69njNIhwEA9n64aZCuK4ZVGmnQ73SS9TJ0N11JLP7iiOtqrupWm+ti3HusyDgAUljA4aiaGSpGw7ADDYQmCoXVN/f+NA46mcuwamB083iPjVCpQEqDUgWrgEvF+fSAdVnQ1gs++tmKf/hP77A4yH1lV3c+DPrfwZ7phn4ay8UeBzHm3Zh7vVYF46DJraWaAKia+Z802Ne5bFFeBx84xhWpMY5vvmTo5qHsO/M2holPzqRjzeVT0XjRntreaeqNHNNBIN3fpWntQaNLUiwM+yIixj6nR6mDSl5yL4/1os06vhvDA7yLtYr9017OkdYQ6oUz/HTE4xMRQO0wXn8qPSq5CJESWcy5qVScyoyCggc6o4BxQh1HKSI/PyZRQeYGkwpIbbWlUAevou2s3nL1kYNS3fWpLyQ3jOtgylLudb7OogHQdfZk6tejmmjz/hRMSHZQNC34emw4jDXtoKnC2yRay3RKMCUNjGOszed8EoAi5jFBiPzwgLtoZdtjtT4OvdR9fTcGb6yP2WORxyGkqwBtoxotqT7FjLHeQdBHwD0ef7+yG36aXg29TG95/s469EqXxHv78iNC2741j+9JcviHXFbvSv2PiHfOxcC65go49iaU/uX23qverirU96r54bEkG337ZqGTKvZ/DerdWdN6boyz4WHjT1HLhxxyyCHfrxwg3SGHHPLjlv2KuRtgNpeef/6eAeKHLndsKPvNsQF0ABc2xhG5UStO/Ju3mMGE22++PI2NTSKt4LXAThVXBhczYNepYqoFZbYT4OI75aZiweTdC5oTIoAIag4AZO7BpAdrSOaCvV3tjvC/BOi/BfTvX1KR94w432GTlS0ROr6HHSuDM7Hx6xtIv7/3YwFkaZC2QpTA7hqMHJgzs94zL/EdhAiYZsZ0LlgXwboKZAmDR3rVWxvaKJKmjXt6BjDDsZBtEsNOlHfSgU+BfOPrLluLA85UCFSM6dcPefumrbOXKLq6uZEDkICDbqpKkpxPvYRmuXtn25Bqd9EV9bipIrpOthv/+mNibt8aAJZk1xmWvESs+U6S9u7b6zfKGSqy27UoPz/eMuo1Gyx6exCuDVu6+4xnCVdtFG2T7QEArnX3Xp/5w3dsMFeG041xx43l/RR9b6Vmhjox93MqDdGHzJhp8cN64gqL2zbvzSj3JRuXCOoIpYBJUEhRSFGd0aahHwBzk0cF0zR5rJ/h0BFqbikJq5WRFLUQpsqYT+6+sDKY1fqfiLuHdEM4B2VbIGvD2hYDBWTFKgtaWx0+kUG7I0GDsWplBZa24rKuuLQFF1lxWRcs64JVVlyaubxcFVhFnUWnaCK9zgXmFtHeyN2YdfPg6HME9NhfBDIDkAJE0ll1lQoamSHY+pxZssJ9nPV7gRI7iG5HxZUEFA2cGJN74xaBwEoWT5PMtWVrxqwjmJWMQSjOgCAii03mLBASMre/MICjcPQD7afQe86uFNSt29I739bAzGQHQUphzLWi9sMYxiqsPKOWGbVMKKVgqhWnqaBMM8AF9XxGrRWn0xkERp0miDZcLm9AtIDIXDC2KkAhNP9PtWEV8X5ifUY7ONcguqBhMRA2QDiSDsgG2DsGKVKbjxFs85SzOa50pGmAMCpSbyX4KMva4Hqs7l0qgsIdlzPc1f53UUXBhAZgYgfH1SCq0zyjkLvy42Iu6MjiJM51BnFBrYA0d/npvle5MIgqWlvN/bUQKpkrbGXglT7izZtvQcX6b30AppNieXq5vmFHjYPl0I2wIOSuNhY0uZ5S/ekgVfRDFQCUpM8fY4wMnWtrT29TkcGio8R6h88Ru7nE0jVw7eYbK0xfk7HV9/fs54buQhLYHKix1zMFM1hZXuYEhr11weq5BuNxMOOpz1foSxEH+zF+N5AzuU3U6Mk01lV5ZKQ1WdSX6IiFe3fZkyZEUuqMx2jAbESPyhpdIwrGPn8UlFJw5gkzTzjRyRl0E8zRpbPnYOA0u/4srpvIF3x2LmTk2g/eaByLiOrvGiON2+iM6VMtDfb3tINRg9lDvQV0tE00hffPrks8Ll5eSOXDbgJxHNn7DI2G6fqKgKFtrK8zxdEFY381X2QZvmjlLCUO15leURr7MHGWnL0TY8xwMUcQWnLZed0Pot2TlswKNq2xen37d34N6NPLlqz79Vi/GCW9sbbrVaZ3nqXr+0eZr92TRnPRvWdWWOhdRuprPx7x6b67VI1r+Y88VDbX04sSbC9Xiut7gtkFaKRxV8/SSNpcPat7FvF4wdBtP+x9QFFfE8qDon37XWrhkEMOOeSHJQdId8ghh/z4Zb943Els5K8u7r8nm08+cXZ7l/9Hklvv9LbypN+DKWebN4sxwFzMyAkdTLq8Exl7+7FJiYW2orOeguGkFaDCKLWg1opSDJwrxalgqlBpfePfADSzN7o7nWTIIPjmM+3Ebrxa36Dd2w0RQK/xMpBuswMb8s4brbe0Szc+he0qNpS5jlOmTYexROqKsrLFdAe7iydgE2fnQ8tbdpvU/7fbbO32Ups9cbo/rrf0WHcF6idS88F2KkAttpvkSqDK3QAVGzhxRko/UR7udpLRz80QyXAUBdCrzSAR4fQLxvwrxeU/PV8ZvU3caGQxm8yIEq4uAT+1quhx6TZEj93YU2g60b01WSikG5/vQY3D0dDtQsdJakr/tzydeeMnz9HrahhmOIw5OyOOaDIH7izjcTqbMNzp5FPkYQzI/aOzBN1+NUxJSO4Eo77Q0w/QdhuLR1OauDI07Cpw06+JPAadd6Zer6oWr8WZc+L1FafPrS+Tge3NgaWd+8u3ChHK558ZSCENxUE1puhfbuANwyniJLyz6DhYdATymDwqdsyZGThNBed5wuvXD/jkk9f4+KNXeHiYUSf2+UEcSFcHC+AKymPfSYOs5tawyYKlPVksMobHxfTjBGQxGUXUALrLBd8uF1zWBU/rYmBd87h0bcFFzGVdgHTGwVo9XhkS28pAeSQm3ABSreOMXjBMt3Y+xZ9RdWMVAVKgXNBUMXn9qpLFGguDJ8wtLkAD9IcPdI/z0w208btS8KgsDpt43DwxABJq7L6KMJIq4KxHiE+ePlDMja6EMunjVFkgZYwS7Ufbx7gyD8HmLrM6u7IyYXK3xiQFlWacpjPO8xmP8xmn0wMephPqfEItE8AVdS4oZcLDg1HXSRq+fvMNvv7DP+KbN99guXwLsDHpRFdn9SlWd2nZ2oqlNYgsaGrgrGKFkLhe014HppfE2Y4xfvyd+2fSGze+X4856x/DnWDMUZlDeS3RttvDKQRBMyYOq/kPc6alwBiE5t6YsAqDVnLQ0fRSU2CKiQ/GuKxsXgmomHNTbeaKT7UNw7i3v8WWAooW1FLw+tUrXJYL1lXQmmB6ECxPN17mnvSTJaNu+8EiZxqZLXUswLv+pphdxjWFdr1HroCzG84dia2LRP7dA6SnrGnNQKMnBBvMW3STbrS/RBOJ6bXh7jDimuVyWY6iQckbE7aGL8zoO6k/BiaVgRfoKCfll/avAWCN+/y764YOHvXS+rfEsBtPbk+sxHonDhF1houPMXW6S9TFFvjfJILYNJDrVfH0Ql3dyneAVITK5j73zDMmmnGmEyaqONGECj8s4IAcxRkPmO5kkLHnhIBG0JYYiGl9R73xdRQkOrHPFQE+b+7vCwr0tADq/WDolbRe8rWjjXc1cBwB0vkqRxLjTtMzedGT45SGVwcdufW4yhoHnWB8Q2fgXuwBwN1fRxxUJentPlyIAkIM0Io49DBa276ZrvV6SaBcMC81yunvcXtPNkajQsGvCe23+3veIm9bK2XFkcdu/jPdk+dBvXXvjfSjK93SUbhz7Ycmveyu2mm/97+6cfzZ2c6uzvrSfiw1Nutlqdt0u66K/Zg/POJ+01ATfnpXCeaOmGytB+RnLbP6EVAecYB0hxxyyE9KDpDukEMO+XHLfqGYNrd9Mbf/3D/vC86+P4p/9575Y8nbNibPSd5khtEkjsvx1h1NZtD1BXsAcP630C5tAjRizxUyI1Jl1LmACkPZ3FVE/KwWLp0gWMPVlS/QZVhx+jvrjZe/Pgn/vJT/NSD/n3d7BniPar/Vv+4lst9ADttFt9dIukf874I40WsMuusjpN+zEMCFLHbBirFR290T42kDSKbf8yaxG6fcZkIyniOFgcBmTQbI4lhxqWAH6goxhIxVI2L1Ig5aUu+/xmKK2Iq2/zODRPETx7opdC6uP1sYPL9bZPgwtMX7QZI9rhuFaNPPw45ngIuztihOQ+tVXaaPIToASg4GjW7MeeNGhAFyuyPPb5pdyoiKubdyRtIenNP4fzzD2jfs9kHpXj8h6w2ewbMbifbvW9YLrpsk+pjfwzxyNeOwsag2j2fDzY0m3jJG3X1UvGq8r7hBy/OopcIYJ9RdcGlTrLRCFhmBkt5hgqFSQGJMtloL5nnCqRZMpWCqBrQ0dzlJpBanhwpKLSiFUHpdDOYVwfpJKaUzpaZaMU3V8pgquAhUFoAXoJl7PgMbyRguIoA0tLaiNXdh3KSPM3X/SQpAld2lH2FtgqWteLpc8M3lCW8uT3i6POFpWbCsDZd1dfeWNpes2gwE1WZ5IhhJBkaLzzGZSbf/jmC2+L9wvRnu54abPOvzRdhi+mmwOQqA5vUW7BXLpRBAPBQjyehYG9Muw8c2UJUhopCVHPxkhKsxCgO1GrPKYpOqz98EEjZ3UB3dNhHCcG/X+9hY0MSBAVXpLClzaWkuqosSSj1hKiecpkec6wnz6Yy5TpjmiloKpnkC1QnnVw8oteLhoVocJlF8+9QgeoHKBVzUAF2va+v1Dma721LVhqYNSgJraXEQygDY7hhRnQvT2XTXI6h7Te4Xt5bGbJRF/74dgy8dkdr/5393JlLoAQCuKyGrtUmKvycQTDSjtGLMIFrRLvYgUwFzAZ0qplJRq+LiOiPABCoEbmyx7JTBDrDMlQA64c3lCVxmcFlQpobTRwve/A7eHu8oAQy7ITVb4/PyLd77Sj/3AzC7evNPpu1hnbF+HWB36G8aP/Z7O2BLsXY0Y+7mUFA85s8JbOwYUO7QV9zUwZ3BThtHWqLfpfeJnpR9zUUv9YMTu2E6CtNLCM8vXC3GO/o96aRhlIV9wkslRYaXh0E8dB3GAZX+/0hft9dHV02vNDwEdBeIOtopu6TL7WRl4QHQ8YxKBSeeUKlg5gkTVUxlQgWjgg1rknDlzs6qs0Yl4T6vSqrHcR4hSqCbgzwdUw1QcdMWo3bj/x2M9mdHeDZK9ZU0BgGAxavtfZUAFUL3xQ30eetKiWSNFmXdu92Mcih1l98UayOffixJY8FPRaHKHWSOWIPEDCKbwQozVLi7n4XPicFa7f/FAU2KGXSnatNaCRRrJxrjEwCKgh4I+p6gyn5bsam6G983IH0qb4BOV03wXJp09XUrfh4p53Uv6T+2ZN2l5HNlAbjaOHOnCFcPeRe8vh7tm13ME3r8bYUfXMwV5X+ThxYghCt8HXs4NPNM4JTYHJ6jJ5P16TuddjvkkEMO+XHIAdIdcsghP35Jm8m8eNb8e7ZR0+56bCzi+avdxw9f9saNWzsDHUeRLUh83lERhttLX2gr2d+ST5cqxiYk7ikG4FAhrKR20htuhPcYYg1mvFUoVmg3EEqc1t23RX4XbOwT2PyUntlvnKgCeA3gDy+txfeQW/0qCpPvubWPoF131O1+I7o1KZywomirok7X9fN9ChVgOjPWC1nsQhrl3JTd/4iNL2HrQiUkG87iWWobG8bYDHt/LJUcbCjgYuBca2KskQCAA9QjN+YV6t8jyW5C0uBoWOnymfNusHeDX4AvL5VuJKFt98geD4epcXSOsYlWr0M1RomPP3LrCHsDELEbqbYmtxAJI2MyUuQKjvrIv9hwjrg0gNIA0ACgaQPc1VTUa5zB1pQyOdAQdbytPnUDlBnrs56+1aXjlPzG/pbfZGTcD1tkkDjeMd226YOakryVfipeqotwdzU6eAdWmC1mlytSA58VKwlYFEyCRu9mKad5hkWZMfeHpA2sK6ANpCvMARg8Llwz8laJeGIV0zyBUcwFZWuAGBBC7gK2FMY02X21FgPDubjx2lzokTdQHLZQUciqNg5lRWsNrTUIGhYRO2HfJ1XrJQbOMEQE0hrW5jHJpKGJYG0N69pwaebu0tjWxqJbtUHFwLoed0wHpBx9Nk8IHdym0Q/DcGvuJJ2N68ZYBUOpDJdp/TfYoQCssGiqJuF2zGEzA4XDYErOIod212LRX9h/t74tUG4gtUhCUIEymWF31ylVARWCNEG7NBRiaDFXiQYA3eu5mQVC5jJNgFIUExPmQqgEVAYqMQozTuWEx4cHnKYTTnXGycG6qZ4xzWfUecbD44Pp5mJurpYnQq12mEOpmSGOPYafEtZ+Ot7GfpMVTVesukClGRDbWz0BsTr+Hlr7jqH4StOh65b4l8f87fraf9v/tTf5xh2Wsvj6x2FfNDVW9erlYyKPwaujLlRx0tlaiCpKnTGpovocxswQZkDdraUCpObq0gAQAQNoylAwpumMx0fFIoAy4fWnit/99Xo7+NvNahgTfawtCbE+1lgKooMVCdTqtRTW1KSTby2H8njrjPQ+jMd8QSib+u9DI9YLOZNemtstvQHYDAG8e/aJGEEtH8/3d8a+G2zK/1KJNQfgOscTsMMe4dxwrBq23OBII5dxvygdMOOm5yo6a8teaRMB8KqMuqn3DF3uxec/mBtYa9vB3j2VirmcMPOMiSeceMLEFQVAcYae6VNBNaUBbc7ea5YOiV6BC2M9oAlciHXfOKCi+7b0Z8OlpV0w3c6qm2v98wpkBYjDITQlhp/rggg1EKzF1O/30pP2+TPr7/6eCj/IARQ1o94qgBTXPgQAguKMXaTuWoTN0QkVALGe3AaLiwMVNmnB2Jf7cZc/sf8cK+Be3wWgMwEvBOny4bbn7oHeue9aTW/LuSnldmV8lU9fS6TXbIAugHh8N7qX3x9ZaPc9v49i+w73xLseFN1ByCax3Ef7Ze/e+zX7hnXeJ5JYQ+31qmDs2ywxyqc/aGROBEyvgcu7sjMPOeSQQ37AcoB0hxxyyE9O8obLvqQfb32PlaSH2Ll7/x9Tbi30n9kLx8fNQ2V2QN1iwak4q0j7iWciuM80dBAm2EvM7MZF+1viNDyNf8QEZWAlBbShOGLTUpHNnZM58BLxuHQB0MlYyN96750HmmS8Ge8fm4mNvALKvwTaf32n3pK8oz2l59s/36WfKPIeZXPie2PE0lEvbQXWizFY3J70R5GWd1odlEkbL6Sy63a/ftWc8W7RnpSMc5JshzEWPaC5ta818toaSAUgHe6n/KSzioKqb+a6YSsyDXdElkFuungnSWhQsMSErI9OfwG8+Y94u4Ez+ibB4+F4nemoj30f3wCzvpldFgEviroApaqxGJVAFqgBHOAQjfiNBlDFyf+xgd30zTsdfRiT8q3DjVH+3pwWGG42432jQoMpRASsO8OtiDNiPM08rjeb+dvVelV8yuXWYTcbJ2237ID+XNyf/36mfq5/21orOkAHQinVgC9HjJWtbxYChKnf+y5Ko3zyEcK1ZCFgdvZcJZhO1wHgsb8cO0DCTM6qI7SmxlLyEwEMxVwLTlPF+Tzj1eMZD+cz5qkYq4rJGU7DiEtsfbE17ZbCIHkF81eVDOAFOqvLQBmFKGFtim+XJ7xZLnizPOHpcsHT5QnrumKVhuZMvEbSXQWKGGPWXApiGAvhbT3OoKR5JxmaxjTmukdB1JBjSSkF+9OAluLgCpNiBaOwMW8ak40BkMfWUiiKMWDIDeo+x1EAicGIEEJR02kFA+yGA98NgDYFyOLdbSd1HUCEv580hayCJvc7bzfsEtzdprn5DBYgQ1ErozIb06VOOE3GqjzPJ0xlxvnhNWqd8fDwGiDG4+tXFuvwRHAn1pAGzOeK+VTBbwjsQKPpZ/IYmwa4CQkaDMBq2iDa0GiFiv3dtI17sfZxTOQgJ5CG0BhLt8ey9V9OE6ekm27pnOcla6RbCnb0KdEGAqPJYACieZQpInAjFCooWkBKaAoQFZRSMdUJZTZXtVgSTNNPXhhjh8EoXAwwVwFQwDyD+dJZsvU04dUXgt/95xegdARn6DjjNi1W1BnewHCQF6BHP+zS5yGMZ2GuMYeb49REaktORYxN15M+Yfb5M+a1UcWe/3Y9SP0u7Ydjotrij806K6e1q4ftw9h6EIiXvDP0gjGfAZ0O0hA6iNn1Uv+S5lav/w70I/W6zWLQQRyKNhnzf54FIwXBKNPWZSg2OibXWX/pCBYbVxSDeZdKU8AWrst130QVtVSceMZcDJSrXP2TYR7y2N1amq4iEHR1/b4Y8FVUzeWzjxc7OIJUO0lHZOlIJNDXqwkMGPN40plAZ/4Sj+Nc0R+3LkGdiQ3q807vLQRY7AHxKuTxrI81YOueuL9NzF+pk5Oau+XW6177uIm1YsQtFCjWND/a7EPoK1QisHKPV05wgNT9OOf3HDGiNSHqSOup6EjZmbrn52nwa4X8jvaY4LMydN/2el5J3Z0Bdbw76fZeQkr7ViK7NQW2zT3Oad1XA39UuTuV7X7Yz1z5eqiGeO8NA12v7w81OPrXjXs15lnXfX6gqTPyItA1xmforw1AB7ir9uhdhI/+Evj6P/xAGuCQQw455APIAdIdcsghP27Z2TqzVzhN17qklWk3zHC6J6/Y39lw8yeSVPZ7RbZT1775JhgYF2w6grHOGED1hTHDDEBgMLFtUt0HoaobM7sBAGgkaGSnMQXqBlPq8TMU4kYqjHhIXqYO0mG7GdJ0odsDNkaAraFHx+t8/0K7z5Cx5+5l2djjo3/q9fNhoNr/tt30hLH8j7MbCWNFZXOFxoVAbC7MwlyRQ67EQ33TlhPCbrOLbdtRrtMYowo7qRqu7BjmXpUNQGM3Eoin0UMzdZ9eaoCWsz8ZxnaJzFYvkBmKpbulFN3WL3+mKSjDDYl8d+/dvMEZwBq/k5VY3Wgh22QsrabQFWakVu6xGcI1KNSMJVRGdWmu925oellP2Y67Mar2oH+kH+bZAJsI5C460WP8KJJxFxGPyBgjnW30Eom+sL9853m6+nLj96Q48m07O+Pmd3PJqt6XqPdZcp8+dpyBu3G5G7ZEIUL9/QlAcXdYL5ZAoaSByZhvhQ1UmQqhkFpcuLYCqj3+XCkErmbkj4MR4mytDQOC4LEeC+bTjOqsVQq2NBTUcqWFQZNBKa5it88Vjy9C1OOISbNxpgIsreFpueDNehmx6NYFT8uCN+uKZW3Gvoa7QlKbPwywG6a/TcMBHaiLGwI4jA4eOjbrLoSLXDUAzhVFN5YqEQoUpOYKUqM5VCEoADWb9wK0877BsPq+6m8c49jah9zgCghWEbAqKuDvHrH2zBpo06WiNECKjykEzHa/Pxm3KuI5YWNYroVRSzEgtzAqFxRmzNOEaZ5xfnjEeXrE4+NHKPWE+fQAIsZ0OmGqBdX1qYiAirGe61wtbmgYYcnjqIoMYFFXKBoaLhA0Y9RJg3Yg39cZGhpJR5+9MchvG1mHRTaaIOoQ2M1b7y3XikjVgGsiK7WosdxscmloNAAXcRe1woKmj6iqYK6olyc8TU+oZcLM1cF2QiMCc7X+oWpucGMx1lZE/LJSKh5PD1jWxUYRAadPVuAFIN2n/47wu6/TTEDuhhluuHdWI0XsRYy1Qnex6Ou2TDay39EZ1LEuDf3BPhZ8WI7a7XPrGOCdye2Lrch/sDusHoh2/LK0Fsl4iB3ouV74bw4PZZaVd6o+w+1PusQc7AZm9ckzrnH45L5aLOU00PvJdjK3PxTUwZ24yiA7XIXEGKOAh7W3Z9RZr0uCx00b+sGWV+wH+lJjbibw6AM61us93QDerO8GKFe5oFJFpYqJKljZgCWyz6J2kMGnKshiipxXW8+xAsy2jojeuAHlcn0Sug6JOTjXYv+M+TyBdF0PORAroc/ThBKgXAYtt82pBu7Fui37VKBoExn92FGLccQrlyleyFMI5E4VJOTHgpzJp0DEDByKb+zbbBlKmEq1g0QQkFhtmpeD3EcyJzUmeXj90whZ0D/SOApwOjfJCaBHQH+P7yT70frcejfCBgBZ39jxkl7s3bfnRHff+8FVbPXKy1P8MDJW3DvRUc4Yo8xArbC1WfFtUwzzeBegA3ZRv1dTbB7zuHUTtjEw42DU6OAjs0jFFXTeQ+TY4/nABv9Ra/iQQw455PuXA6Q75JBDfvySV+m6+wxxX/H7ZzZuGT6IweYDyruuO2/dH4tl8yAFwDbOXM14Suwu9TzIc4B16i5bRAPLGwYR26+SGzLtOel+6IcRFWquMvvC2oG5Hqw9CAa4cVJvu797b+F/AbT/Di92rfIiiX7C28vdGCy7+4DOGsuuEEP6n/t9SvpdGtAuiuUiqLOglF3m35ME64IqY54L5pmxXBrW9fZmdC/55/z+bn+xdlf0uIhRD5udptkgzLBa7CIB7lboRp6ikJ4WQdSBOrI+HUPB3JC5iUnN4N2NJAgT2LDbUQF0vf+eO7vQ6MO43a7vbhbIT2b3S/oBrAC6KcdzyW3V7di9cxhnsXfBiWTsujasXKWfM6dtWWhXx/1fvi/Vu6upzang3iC6fUa3P3UjbtxD4RLMr4XRvQPMnriKYpUV3aWVk6E6SMdAAePVV4qnv7Ox/ZxY0h77TYMBHQaKEXdI/cQDOWuB2dxd1lrAzFiXC9q6QFsDVMEETB53jpiMrclsBkUuDrCZizLLo1g5yNhhVNwcKDYWuxEwykbOInQAR+AstCZY24pFFixttX+yYhVzJhkRS0UHKNe0OUjnYCmAkttcx/jrNmvdxZxJc0pX0TTqeIGY2zDAgQKCUjEAJdpABUQNzIzmNE4hMn1D5pC0syQ5ADhF+GMjGfyMblxmGECq5h6Tme0d2dkVDlQFW1ghADFoVTCbi1FmY6g914dsng3jbQL7YayJqVi8qMITapktniG5a7rTjDpVlFJRqjG85nn2fr9A0ZxpAtSJ8fjqhN9/M+FpuaA52CbOTmzSoGSf4QY74tOJs+oMjBGve+nvYD1e01sl6Q2N7dhO0vyF30td3nrohiK7Lp11TuO5WjzD1XUCu0FduGGlFWgXm6+4oFD5/7P3b8+WJU2eGPRzj7XPyayq/voi9YjRiAZmDBtxEQb/v/EELxggMzAehEmYMAQaYWMzovv7vqo8e61w58Ev4RF77XNOVlVWZXZvrzq5916XuHp4ePgVW9vwdHk2pWdrwH6YYhy2BhUKZU4vq62FKoYA7Thckd8a4+nyhL/4qw/40x8EP/3D60THIrWq88chMHVCF0LcMh6pu+GiWvBNkEin0K2pNFs2GzMIG8S7GotEceoMBDEXer6GpqvEPAu/FeZrkAVXajBlN8PQJZRcU8i1rM9akAqRImCOJ2JPFFekqAKRa3bwvF5henStDQ5k5rwWHlpFfI0ILz0mJBShQ6AdhGA01dvBaV4w9mlSKLE3iwdhDSsH33PEw9FWBWRAs4TAth8pWe65tqWCjpQsXCVrCSdKOVayq3mdXi2sq5EChSqZoVgorzTmrSjVoiy/vjI3MV7BJw4FbVVUaiJKRP2IxHSB0ytZUKdD5DhUB53UPRiJTKsYuKQjpGktL4uIeqY9b8yVswbptZ78jXvUNbKcfl3UI6lQlhU+d+b7L5nnLkMO0piPsaFiRFKgyM+aq8H7EQsJU6/U7/NfAvLH1zjB0u/SrXojxiTPIfXzLXjPc1Q+F/yJawobf+oAd1ho8LYW9NtBDAHFDyCVYc6iob0y7LWbWc4CVJ+h+XqiYJG5BE1VAbgbL9CqS3J4//raGIbCNDri9NzOa1JZOivilfPZAx7wgAd8a/BQ0j3gAQ/4tmHlIlfGOw7i9bxfmcrK6dH83jcFd+RWeY2MiW2NgcbYNjs8SxMwWzx9IjXlnIe6JFjOnE4AScSkL6InMmUIALN+B90IM0BkHhxQEyKGYFAHM7+eg/JTx+Hi5wL9AaALXk1S/p6pvnlmwTPF3FZany2nmsm6fK1kyAmm8kWA3jXDwK0HlC8Js3X68ofSl6X/9bH1EJ3nLr1ddlUAOJRtXi67xbTGUJnAS1y4IN3yQsAFCuReTxG2p4f1vdcXFpmTgB8jFCxgB0a6AB//M+DH/9O3Rhh+GdzFVTJPxvAoU/JcaS750cXLYYU83L81nAU/TmnBIsBCfdbpUOJPFdRhflbre1lg+e54IU6U1FzoMhwr4Ipfl0SoE4RU/io8tJPjVgMuP5hXKt4K+fThCfp0AfoBVuDy9GTKNyI0ciWPL6ZhkKwherZPtj+4gCOEmsymmHt+fsL3332PH77/Hh+en9GY0BhJqMnDrTZt1lP2cVAyZZ3EHktjTRKDKcbMlJRH79j7gZ/2HT9eX/DT9QWf9hfsR8fuOcm6dth/kusxVH/uDAhFyoltihZaDF3mtcxpCoyqjMjvmzqog7qFJlVYqLYGxeG4pGxeC405Pc6tLnKCM4gPwxVtReAbHicUoSDZFKos4kJ9NkUGRWPheGS5+wAFWHB0BnUFd5ixzSt4JLADH3tZXXy3ZkZrDZfWfBxtEEgIG12wNfd62Zorah2/Abx8erHcc3qAuIPI2kasaBfG04cN/GKh6kTV/SIFhxw4+hUv8oIuV4gcOOSw8JZiYS+t0yMPXSrhc6GvC3lV1BRY6cLPgXuF1+vT/qe5v6WQ2+8JBCwMIcWO3UJKxt3NIhf0vuPoB677FS/XqylOicDbBuqHe+1ueLke7sVCYG7QBmxEpogX4Pn5Cd/JdzikA0z4IAcuTz+9abM0wjTedj5C5d4VnEdY1xiT3PMJejJPr/EyQx02sqmlD+LJ5jTxGrmDL2Xd2XemnWotutCKoTWpTI33HSFXpnynekEFzUq6U3DjhmesjaHzvXhivWKMXVlm+izJEPWZhvrEGjHH0/fvcW7SYKLs3uyulnMyChklMpN7dJvyddOGjRqe+GIhLqlhg/2x2CFD3XRBxPZLPQDdFdoV1KWc45zORjj+mA/VCKUw1p73bWZBU400eiOmQk8OUcd4Gl/K/pzT7SglNxwfDR3lsiu5qEQLiXEOD0drr+8dGcJ9nh/Td8SuXve9wGnfd0v7qw8c+fuRQ3VMG0G6WjvFDIxUjGfJfOGE1MIReROTrsGVncXD0Ad5MqfQgrvke+8G0BOgV7wNdT0UuvNzyXnAtH7iWv1RGISgG5WXiGdFU+c6nvm9QSvtu6XTFQ2WoZ2LWbaBiUWmUdRKm6pTcjw7dNbzesx5cM85Zfc+xazerTxAnabtA+Gv/pXiv/+/fxUj/4AHPOABvxgeSroHPOAB3z6sh+6V43SZWXJ1EZufx21audhfyv3/lvAaX1oPfG5tSQJsLtChrVvIv2YnMG1UrCIHIy00QocNK9PymRJOD09BdtAjgn9SHtQij9TZEOch2it2WfC5oiDKoDLVhJsweO1/BRz/+1fG6OfAckh71+N3hEP3yj59XEJIhvHQl4Rsi2J7Yjw9N1xfxCycw5uuPr80erqnt/fzuizzzKZ8yDUaB3t/jhqKgo/QuIFhXibcLPPNWNKU3yPvHCdujxPosDYnz8dhPxtbiFdqxYx4gXv4vMJ0ID27eXrj/eXfrfTNR2gp30eHLEdKeGpIaNfJ3rHP8XaG71zdJGpHHA9EZ3kh4XbtBgTdiefmVs4XY01Wzz7ICCta8+fFuqweHOEJnEqggrOF2hmGucAqrqeS0qR6LjM0wRdhCPBSb/MeUPNCJhGnoWJKtMagxuZhA/dogJhQlO162xq4tSHEJbgnBzw8KZVOhvCwCgMFSuZ7Ki6YC+8S+xSIWmBKkHlSxaQMzw5KoUyHK2zEwlfaX8d+mDfdLh2HuBedqql13JNNaB6W7vhzz2hdpzlD4sgpZhaBFhTmkSCA0NgJOwgs8TBB0cBM6NoBEJjF8tPRLC6l0DAF7QopnnuxAOaJTsxoojhI3KjAPBnMS9H+DK0U2oGNBNIbegd4M6HxTb8KWKYyUxoyjK49bRueL83wicyfgqlh256tfcroh3k/Xp5McHu9vuDokbtIwBuwXRTbhaAeurFtjMvzBe3SgG7efruHNd37C45+RdcDV+lQOSDuQdmDbod3js9biuqoCnzrpGHeW84Erb8FnCFVfDjf1EkAOtzDajNPQReMH/1qStpO4KOBr4T20zOYGz5sz7ZCG4O0Q13B2q87QBbulF2wGV79HeJhqhsu2PB8ecZ/8J98xJ/+/R9HROgTIIaH8V37N7yU4t/qtGRqFo+eUIh7Gg+UNTkrwShpk1Ujy3haeaE8VOSAnk9CEPlCwIkWAlCQZRjlnIzFiVLrLeId78S+wkAarqzl3C8p+hAbjBYcP29tGF6oMFhC6TWHiR16q/wyvMqSbsfOuszhwvCdtr7u6WT71IaGZ77gQhc88RM2Ymy0oSFCtXpYb4h5yYWHXVfIoUBX0OFGQGKeueGhF9oTTYOrOaRl1W7q1MhyrRhSxJ5lwx6BPM0E6YYPKAr2msMx6lUE7xT4Rzl95hRKvmey8w0zk5PKaTWjKCD2DCOE4VFKYQhUpkHFDCO52TxszNCuHiY36o/IKB46VKkYT5oBDsPoEY7Kc3sdCsvtGn2K6+TXSdOw84b1vyj4I6G/R0mnM2+o861sUyrSFiYxng+e82Z7yLYXXCl8Io2i7vKbXzPEmXvigXxQWgPaBuMjxD5rxJd5Sy2zWMbnjM2ffiTviYzQEE6pwVKZDMFWk9FNGQ6uiE+bDPZ5Ck9vYgLx/fPZAx7wgAd8a/BQ0j3gAQ/49mE67COt/dZHaOY2M+YVV8b1W4fpxFKux3mbgW1j8MbQTYFm3g4aOenIQnfZwYqhLizN/yRCA84DHOLBEF6nciUl45oHLQLKQfC2qWt36v2zOaLlMDHJEQigv7xT+GsVvfZoraB8X8P0UW1QKX6V4+VFmsdlkk+peYntLx3bC+Py1LBdfpvT4SQg5fDcOYcpjwyWufALdDKZ1QI1DtGtsQv3ItxcGS6FC3osfxI1hro7Tyezoj7cIpsjuCWbYIeVLG+ajghLKRx0IVVjckvOcbi9/DWDfxDIn877nnmuMEUFSuUQZ2FL9+8RHq/Y+rwIzCoCFhmeYgh6UuhALoSpiHZaVwiFND/zdghI1YVW9fDsz/Qbd6Z4OYRYo6z6EJV7vODGNOel2KknS3fyZ8bqRa4tT084Imsh+jHGsXo7pAePX4iwhib3s/x60Mi9SRlaM+Sp1aMk+ihu7WAesW+HrdXni427CATkNNhDmDFCAoxQnIW1cijjmMi8BI4DIq7gcwH/dtmwPdnf5ck8p2LoJOew0H335A1LexHJPEaWt7RnvlIhxeFKuEMEe+84esdLN685Dc+ptNoXz0dmysbqp5Hrn8Zenp5yi+BwZKN8ZUzrcirrJ94nV3aO3Gg2vh0dKurKOQuXa0HCDnRtIPcaEQgYHaDIGeW0JfNaWq8ILdd2DV1qeet8w3aFn4UPY/daB46uIBJw7yBXpI0ZW/APQFcCdyuxwbznPj49mbdcs3aE0paJsLUNl7ZZ6MW2gfkCUeA4DmgX85QBwBcGMWG7NOwvO7pcsfcDSkDbGGiAHIIuB/pRvOZ6h8hhY6pDGSohRA4RdSUA68yu5Oz3FJiekb+Cm5afzq4JFEyKroJDOqjb2DcowO5NJxdc+462/4TLyxMAxvN2QaMG4makbX9BrE9gKAw2JhAsYSl9+A6HCP780484+oHLh3eEyiZKBZOGt1coIEIxQcP4JfvqdC6E9CFl1bIG6uZ4Zzd6rVmTp1JeL+2wMseuGS8O9UvQ9OJl7XduFHLeoTNFXXoUlmeJRhn53SzVTPl1ptx7l7WGplLnLtBoAzeAmCHSfe4ihyxSqD171IWS1L3DI1rDwq9kWGOn8WNX1mWTtrlnNuXQE2944ic88ZPnoCNsumEDIxy2p24EDfC9oSpkqydlyao3NeVmbKnyM2Fkgunh8TNa4PgfnnT1GQo+axisQOHhLJFMTfI1CguDCHgo2axm8ApkHAQ5v6ZEg5covOB4z14OHGNmNIVFOIHYbZ64ODTfgCxstE49jbyA9qkeGtgZpmYKOyUzEKHitT2xeuX3TWj/EzSn7xT0D/T6EihbQPJs/k9uDYSKCadtizZVHrIQA5+yMl6l7THuQb5WwqWwaCfx1ybe8nVe5LeClUYKPOzlINMzXb0zb1rvLYXHGuHpYTg/irFM1Xd3X1BD2W20SoQn2jMp6pRS2WfrxvjcmZ494AEPeMC3DQ8l3QMe8IBvGyrH/QYnHMK99KpbGPBXy3/tmd8KptPFcg3LvfoZDLIAOAS9dygauDGwMVoDdDNrfghMuKMWXjG95mQIacxiFDfC0Tw3nsxF/ORyr8pJa5On+SgHh5uD3HvnYwPwDODl/PakMNT7cpC76HUHRz5XAFXfycNgbZfEUXKd5C93BNSTfGfm3eF53+5VTSmnmPpkhZ6Mca2G5i+Wz8RyaJGaUNyCfVmopEZsQhgiKIsri+JdTatNIgtdFx5WobYK4RnxEOoRUwq0whp6+2ug/UCQP513+t4Z8c6SsG6/d+pyAF1ZBp0EADeyqfotDrWlqLPiRzWEzBfoQhoRNYtqeGhRADrVMuPJEEbOtVXBCBGN/JX+mOi5MjP+ilH8hP1TLSFEs4YgBJclpQ9Y4bkKxzv1e5a9CC4oeqveWAVUh0Cvw5QrDA+xCkwCXhsnTUXXTWLLM/jwlILptm3Yni7YLhe0zfLHERO0V7WwfWoIZ0O4STQs76uSrjVcLhc8bRuoeaxjWM5SwQ5oN2Wc2t7Re4ccYuEX3aNKYN4NumymCqCL4No7Pu07rseOFw91+eP1BZ/2HS/7gf3YsfeeefsirKUULEtP24IDwM12PnCljmFB8Kqgm5afjj3M6rYYz+Yx6klniIAG855ToCtnnjGlBpBOwi6KcKFaXIVpJGshqHkANxNYNRCauk+w2vuTUjuEiGqeJyCGCKF3DM+o0kHy36ZgZ4gS+t5xaYR2YWzc0Hgz2koE5g1b89xz3KDmUoVDBRcolBjiljfb9oTn5wuePjK2zXLRtQZ8Ov4MM5FQaGtmEAHzmjz6ji4HrseO7t5yIuLjEJFfQ/AtY36qC8fvIfF8B3959spKdKOY8Ig1Gkg41BR1BylYBdgs7ywdlifwUzNl6lNrADfLIQnG5ekJ+3Gg732UDfeoY6NPdLng6XLBdW8QveDj8zP++j++4N/9v/d3dWJlAUauultIoXns8Xc2uDosC7befT726mjDTUNLyaN896Z2S67JU0njiZlGr/Davbvsjys5Q4GiRYE3G9F8HmjO8B1w72gzFnFKqBHeVst5wNo16JTvdY5XoMgtHTtecB55KQ1CTGHre+Gk2Rj7DrPlV7zwhgtd0ITAYnnSIhwoJ28zFCbmhQ4Pz+yGC76vxtlkeNPFpOqMYIkrxftzGX/1jSE91+K6It8xKjb6Vmcxc37RzK+EEiLDtgNzbuoy/qakZ9/fNNsZXRhbmHmrxmZlRh3sHm3eg9SWw8ZvcwVdsSKy/KmhqDPl3EYXw4s8fAjEjeBo40ymqT7lBNzkHotR0dxQl8EuwN8DfRD+U6h7eRoB0vJAfL3Z+OffC3syrhXFf21q0nCar63nF/VxjvzDtwzIbweJ+uueRWM6CGOuyu27bVZgilKT+EjlPRrpCSz0+2hHjmMsT1EzmqwDmczdWP9ntDcU1rOjN+GH/6Thj/+NYP/j78EgPOABD3jArwsPJd0DHvCAbx+S6/TfXM7gecqBCdx4MI+vCn2+Jj5vbcvZ75WXrddc4DmF+WTYDnAB0CKfiVo4lB6WluYlEScPU3DKpKBbz0qT9xjNjH1cr+Euz84Ek/fKa7D0Mc/PNNoHAPTXAP9PAPkv7hc14cRdyctS9/r8ybt5a8XRenOZv7X963t2MNbf7CAYISGZge2p4elZcL0KpGNKoaHjhc9fP3GyA8Za3TuUCQwt4fkYLazFiUGeDypEL1LdmAATOFAIJZAVRH6OqC4srYcghBKhw0r61Ar+tS7dESTk7zfmTgH0QyHdlFktRR+mFKKw3qYhWlpRsZI/yn9vg1tmnTTmU7IT5jkkEtavdm2yOsa98XGBUoE41AcKK1x25Nc7TL68tjBSr/FS1llPoiU1vNFK7pMGrWHfaDyz0i/C8DBjDIFeKCkH2g3FMcMEjer551LB9+7oPLHWaRK0hL+ZqKD3A0c/XPhi68RCAJniSNzbza4jlXsUym01T8hDBUdIJmUMgIhAe0c/BMch6QUloaTTPoS6hPQ8s9BlQBfF0QXXo+Pl2C285dFxdPO0EzFlzdE7Ds+jJBhDNA2VznT1VCZGrwzt2QshPCo/c14RvgewzVNh+eQIOITRhIAGMFrOh4ZyjkbwUOLweLISQezecgCTQD2E79aaK0gJm1roS1EGC6Or5S0SFwiKKroOBVwMijpdiHxl6MCFOPGuPTGeLg2ttaSnJkxXd54gMBq29gRuTwA1CFxBq0YP2tbw/V98j6enhg/fbej9iv0wifq+XwHPL2iuMgIiRZcO9bxzhx6u7HSVvyuURSIX3TKh92b0N9oHfw7Y6i8Q9Itiv/Lwnt0F8ofgsgFdGrocOGjHy/4JjS94ahdsFrsOT+2Ctl1M0cefkCtEfYdQypC3F9qgUBx9x37sNs8XfXWbNo/WsXvo9K9dDyOWIHyV/Kfy5KxsjNkcBhm0CI3tiVPaHkquab8J5i92SBScGBxK8DI1F/UI+3hnMD4D5i7QxAQE2QGNMPIEpKdiVZqNNwqPEhXcmbSg/SC4F7BpT1TE8VDKM4P/ATRl5WbIAQ/rW+Y0mPvCM41wkAsvUJRzDWxhdJ2+NLbQl8TuQ6xAUxO4N7CFXPTrhMjpBj/TheeN35/CNZex0TJMmj20W1TmISmzY0blB8Nr1N8Nsi1ZTsnBBs12qI7nCWuIZn8nvIjq3DnNBREahSdxhI8FguvR/PO2Be3WMXbm+KZGcxuG9RGFAtRynrJ725mCDlAypamFPGYwBEIe7rKRh9m1UQAIdJiRU08Gc3CayXHquHJvffEPQP/783v1tXpGq0ZnPqQ2LratTlB5uXvN0PLczfq60+672w5FxIW5iHexel8I1rbUhhPZ2Y4b0g4p142Od9JoSkcZ5PtZsIp1DJNcxGEiPEhVDV+TSRtevHE2MfZp0IEKNW90VQrzBYbvD3jAAx7wjwAeSroHPOAB/zhg5YDZha8hPKgM+irZXYV1a3m/pwBI73x/7bm1P+vvruhHt9Amzicf7iUh6mHYPBybdHWm2o+1OoaP71XjP+IgX5v0Dr+R8UJhxt/z+GuP8V8OT8F78GY1Z6eydX5O2vwmKp1dLPgaB6Xegf1F0X7q2DbzhGjLYf+LgR/kto3Ty+xzoR7fUT9DzsIzXqnn3yMCaLNGZL3kgnPVNN9M/y5CCh+IdQhmOiANZnEsJSRrpQ26CP/8d1xrfwD2/+4dnV1ojKB4ar0Fam09XhTbxdaghZjUyZI1JRX0usD1ZqreIYRQlKVCIQAswhAdgjktZYQQkMp7p/TVy2shsNQieKoCFxRBbtTxSvtTUFCEjoAr1kLS5h88jeUYTkVEyQwvylI21fx1NHKZitVlRVk4MSYGlDwMK3Iw7gqDzmCzEIpwS3lmdj2RoKtAYCE3JQWKtVir1Ky87SqHtxQzLtuGbbvgcrmgNU6PyaMruHnONx+fUMh30VSudiFXwkX+Rxd4kuXvOQSulOv4tB942Q9cj47r3vFyiCnoevcyNZWOIeQMb5wVx+4N27uWVxXs3NlbQ8BKFCHB1IXc1ijro4UU69pxKIO1W7vR0UFo6BAwBCbsDG9OYoKKIZJ5+ViD1F19idX2ZTCYOxrbCmjS0HvzvISmqFPPHxNefv2om1vJB+gCM41B5fDsw8htqCa0Z7CHtjRPe1UBxJR9e5ec36cPF/zFH37A3/6z/xCXp4bLk2I/XvCnPws+fvwI+od/j/244uiH4akKDhELddk7tHfLw6biXhkCRb+r6P99RZyfCZUHIAXU84I5jVNnSi1sqjqVMXxiVRwd2Inxsn8yfqwTmDZTmG4b2oVwQbOwmNsFHz58B5WfIMdRyrU8rbQxBIqnyxOeLxccz8/o/cB/8B99hz/92z/ipzte4db8GHcKAp0kNJW6cAGtVs/uMOa4U7YXW0X6Y8MgL19z0aeXdt1Py56c+02RLCvRpIizkiiNLm5YuepVFnWuz9THS9vP2EL4mGj5Xlo4yvBNNfPVFeKUCtCTurGUlrRX1fenGBvfj+IcdNrO23umLC87SdnI16PT2M/GnAEwQyvfLNKzEB4iN+gfgu4g7037u1DiQjxnXurDSOu8UzG+t/vhOmbDXze8lGPuQllmhgM15Hqiangt0hiLVFjoOkNUqpehsHBNRuoXyv6kGN7FBKCTQj1EKFd+2JEwsJbYc/JmbRYDgck99chz/zm2ExNYxEO8e8hriT3ZvPC0RSQV9ogrVu8UwTlpQKzIaRBOJ4q/A/rfv3Kg8IGtTlfTTFY+zj+n9bkUfW+91jJKtfn9phC/MdZ4WUtfwXZVefDaZy2faZTn+b8jCq5OyG6fgdtRNquHy8Rcx0QfFKb4a8amVOZXST33t5m7ia/IyOZLgBsxVdppiEBUrmWD6asY9wc84AEP+DXgoaR7wAMe8G3DGUMcXGKE4FhPxlXgG//oxD9+XVDaU63YXn12lSI4hy2HhVPKcFICiHQoex4DtUTtqhbukiLUjBebRplLu2qbQrCL5Vp9+DWFxXLunD3L4iC2PrTUsQ4P/2sA/xcAn87r1DpW9XqtC8v9U8nMefkV1sNS/V6nK/ur45nuytM4HNs7c/6wLwp+iIvwSKjCn2UMU94ksxAjnr3BkboOpwN/HHwtxJsqoN3uiAZ+knmiAMOLBaa/y2oUYBlhM3mpI5piAofzUCsf/jXj03/ZT+d5FSLcPHJ28VwON8bgvUBjnZpn1JStBa8EyLqpOKptZIqa+qoGIalK+8CBOPBjzH2O7501GjnioCa4Ux21vLae1/tVOVfnLdeTmQGf0x0CwBGSDDMx0Bl3p5xDLuSDmoAjctMxWVjJUNKFAir0yeZhZ2P04W8FP/6b+6YL9P0HUGvulQBcLqFU83DFROmdNAJwhbDRaLx0U5Kk0JXqlmDh9tpmIQ7tnv3XewcovJvgSiGCCEG7KehCrJIbDI1QjaKWBy096URwPQ7/23HdD89TZ4qcLuJ53gYS+rSN8GDvWRNvoXrdw85vDZrjIBBT2sEER+KKNBOCR4ECDFGre9ENE3J1r7hEGNX08iWyULzaLKRbA9Cb5QiSsiZSEOgJZboA6KYcPPogvHX9NWrQLpB+QBW4kOX7/PBkec0MjQZBZgK4kXnZtScwNfO0J0C6CcO2bcOH7z7iL//qL7BtDYIr+qcduxy49h0/Xq+4HgeOY0fvB7qHS0X2QR0nDbFsfZhH5tgQ7kz2HZr5xeG1ek+QaYR7HOsyf5PtNCpuPAT15dMQnkldO5oe2PWKT3sDtwva1tDYvC1be8LWGo5tMy8XaZ4TyzZc5obLtkFIwRthP77Hy/UFjQmXC4O3+5yDurIPjGG8EIoLBI33nSXDdlZf9fOBurc0pz1junqbHzUNRKQoBnMfH4QjDHWspGHkstYTSr7IuxrP30x2vUT1CRuRzD9XYKYno7zp2al++z1yNNV2RlWhXBrQ0qLBvJcNx4ZBwcyXjfrOvBKd1UolI0d7S19jPNKgpva3KE83YjRqrlYaaz/4+ZH/lZwHyMEaTJDzd6nQowjJWZg7dUOmatWRzTRl1PBprvOjN3MDIHFZ3Lu3B85RrGP3RGRAXMlA5HmPpXjYh8ddeNoFT+H4Gyo3IoWQed2yDiXFCAfuYSmVIIUu0jJvRvvZe8xGX9knKtYrIY0zzcfWTFA62MKiRsjmMNoAoMRQVvtrtpehk3vcjrYMPpBc6eJ57DCT88kr7gPAHwC5czYDAdQwFEfe2UTpiT+dl6mWi4QJLcYD/vfmllJoR+1PevOLGTPJ4V5pv7NXV4lCCqCQLhR0KAOVW5O4J+3J2s4ydNRRWX2jN2V+BfPA+4KP0LXmtWl4ye5dnmSxTlZO5KC4GSUjcr1C8cN/Qvj3f/97MAcPeMADHvDrwkNJ94AHPOCbBhV4fDTcSnVp/jldX7l1k5nM1742Xk/Hhx38cHsiqW2ufWGY/HAzYXTvAroq0IGdBNIUSh0dsKTgnvx69YRb4d5w5SETN1Mx2n42vqsAxQvW0o435WR3HuC/BeS/Oblx5/kvMf0uYwEwDks3dU4nzPmmiClPD89ptXEc/n8buFwYTx8anl46jl3Qr+PezdzEoa9citA4a3vrgVv11jpX1fDRQrCaRwq5UoddMBPvD8GdhYSchTIhKHKPFFBagwqoys3yPQal4ovf4wp6b7F8QZittE/uTRfOECb9H+ZbMZbq3rUuvLLz9qizCm/teYywQ1WIQjPOh3w6PtZy1vdfuTRu1PqKEIbL9ypZICI0aua1ROZVLIE3U9vWfYTQBFBPjkGOhxTeERBXZCEFdKGcUAKUgfYXCvybe50Zg5SeA40tT5jXmWWKeUYxsStVLNQdQDj6MebONYVE5CEmLeTk0QX73nHdBcz2Z7ITEyNqZ6hoCbMoUDQLoEYKRcs+iiuxjq4WvrIrugwDg/zL8IaK7uEuw7NKgQx3mSHDFrr4JZeZeQeFgDrohrhRAFmISekQYnTuYCGIdAg3a2CJCU1JdFzJpQQ4/REcINoSnwhA2wgkbDnbmgmemd07k61u9Ub1w+aCVLBfj5t1TUTYhCBEcKd4EwSz0bLLRtjCgt5EZpY3ql2wtQ3MBG4N1Nl5B/PQ+PD9M/7iD99he2pojSEC8+zeLkBr1s5+oEvHdd/Re8dxHNAuplB2IwIGW/Y6jbWhLpD+jTzEfym8BwnPklBh0LvIJaVOM0QUVyJgt8KfG+OQDUe/Yt9fsG8N+/GMbfMcX8/PeD4Eu3yCaHdBOWFzJZ0ywMp4ev6AH77/Ab3vOPpf4D/+Vzv+q//zn0+bnN7jHsq1tnfqvOdMjPxBJyzLNFS/xpyGB1LQ51Xplrziuu3dtbaqV9zj6czt7B2Nz3G4dU179b2xr/x8qiZwJaSOQVHSNDK5h6uxt4m6X5WGp6IriRhgtw6sQR7Hu44rOsI0Vt4pPYPJstqaB7or3ULQru5p6oRfDjcW7IrwWM+8dF6xzb/Pmki0xHmhyBNLxUvRx9cVoB0jPPpQyml+xnpUhefPxMA71gw1yc08pbMedu88zxcHDzEdzIg5MZvCy/ZR93BjWFDS8NAH0kBjGAa5sYdGuFB1/nTggFYepDsub2RKo9hnXAGnpGiIsJq+9xKj0YYLI5XF5o1pPIiwAMxQluSnJ0RKBPHN7611c1HQhU4NKImQPHecl+r6zDNUqeM9/CPO7uuJaKDwf3Vpxtl2oj9LO35PuBkDB6qfpW+AjzX87KVzGXVak40+obMEpDKVfC0TYHuI81HkIUmbGr9hOcWd84+oE9kRHbUmKknSNKkTQ8DTX713hB7wgAc84OuGh5LuAQ94wLcNNSyDC2gJGCZkSwIjWrjOPGCsFl9fCbMNYGrXLCTGLReNk98lB112tyuuu6CB0NHTyyIUFmEUnaGBquDbry/ycCBkUeX3nNwZb4/tycng3kHh9N17wOZNd6ak05uOvBPunYQWiPOqxo9FcpXoW8ojb1e1/CaYkm7fFftVcHSFO798UTAL9HoSG99DKENl7kN4VhUkQEalnEJO1XcrbudYjdchaooWVc2DHqfwIizZAzHJBTHIedJhto3qzWRtorQYr9bJgOf+IhPy6Qfgw79ifPqvlripUX5t8K8MqYi7Uz5VRMuxe4eQ5BTmrFCiHoJQxiGaCr0NDx+Tn7hAL4RUBYeLk9QkKHh3G3U5w2P8Tjk4DbyQFDKhnvWXUGiOQy7Y24iw66CJtWnVWEBUcPTxu1lWnfQG7GFlLur51iQFhPnSa4t3Y+D7zXFaQM28bCzvmdECa6ML5nzRxFyICIhM4dgPQe+SOK5EIGwALHcVgf0dNSOAw3KojXmLxeP5aJgAFVA/oNgAD1XY0XGIYlfBy9Hxsndc+4GXw0Jd7r1byMvjMJySoaCJ8GIxR/La2HxBWGWO0zbrF4cngCuvoegqYOmQxhA0x70IGAa4pBaBVaI9iaZo5HpkRHi6DD3lykwLSwbQgSxHIWakpB37cYxGmhw181imzFQsZPHz04YPzxueNgZ7mGGIhVv0rEWmuG4XU/YyechUBZp5xW1Pm9XPBDkE1+sLrtcr/vTnP+On646X6xXSLY9d791oR3jN+Xh1dKiKC8GHB+gNY/GeSfst8OUz60ivglVYrTCBuxNPgeX6FYGFnBPBQR1NOq50BXPD3l/w6djQrg2XyxMulydsrADMbYOoGX50X++iGba0UcMPHz7i06cf0drFFHyXzcLa9jttD7wp7U6PtFCWYGaZpy1wmbakwUAg9viO9dmx0b13a7jZGouyalyay5z64BvY5I23PDdtZBgvx5NU+rVM99TO28Z/PvLWLLrRcgjMczInQEc/fDzqPp3KNbF9IvZru+dKmzA0yeFceqaY+hzfGjE23rDxZqFXc98oTG+8G00VU87JVaEd0B68Z+xrphyDe4gl7tW9j7yOxVOxtnxSwsVejaFkDOVdetrBFW+erC+UdxyBOiszNIY6rB8Gowk3RnBFg6r4PR18LxQdNGLLxtxGLmCY0pQ87J8Ez1GZcPdUitcsfzG5dzcAMJTEzns5PuQGH+pKvFCkWlhRIjFDETCkmQEJMbki1d6PcuLfnJ91Ahbeh/9KIX+iaVll38lLZsNFw+eTdYiyL6NMfxmW28Ix8aej9WXvr/e08Lo6nptejItlT1q7/1uBLp8BlU4rjDXJvHQNzlOcv3u3kvp7LJoxDO78yhadG0RmeJm5nX2DiPMDTfWPQm/GuyDZ5+YNf8ADHvCArxUeSroHPOAB/zggzmTx5zHWM/oULbxkhOCLyG0hf3TGMiN4/d48Xz3p1GtnnwH1+Wh/A+hCoGeGPpFZgqoCh8WzDKF1KBqqPqMy6zVs3ZtihZMH8hClt4/NApZSydnBrfTxl07RG+e3Xw9cdmAh0+bpC6P1e4q6vBB9/tUb937gRmgbozUZnnHr+gLOpVRvIU2RrUxFqHviwMajQTPEYISOYmqgCHVJig4xq2QU0dvqceuixbBktksmULFwRCZ4M/8YhTZg+8OdTuj5zxBO5tTq+P0W2PBSlhEysVFMUW6klXh5X6sgS6dyb1ee3hVmaH1upUelzFDs2TnbHyw0bF3S6S3p7ZtknwuO5xiUdyaymHioI3eSD1jmjovrFEpjsj0A/puGh1cMdrQ5ci/V/G8zUYxwhn7NJX4ZOi6VUrftOYVGwNMYBA2hX3lJDxnhWQuBULh3HXWoKHo3JZ0oTIjJcOUJo3fBsQv2XbBtgstFsfcDuncwH7A8R+zoFZhDgIe5U89JZ4pcQu/AITo89A6rXzynXVfznjuOjl06DhEPd2nzX+RDY4zfC29uSu+DXDNrW+BhGt0b5HDlWXNiTTzPQijqjPAXSyAlqPTyuxImAsjCQDWiYkgwBLwAMoywvW/jF0PApO5pMQSnG5k3xWVrFoKOKGXIgHleNW4WSpHZlbem7OXd52czHOdGeH5+ApiwH1d8evmEnz69uCDbwqwpwUKYZtjLKw7dIXL4p4dozTX7GQzX77j/fT7MzEzQ7dgEIhsPe2hcEgFwYFdk2LlDL+h6QKW796uPadvAxHi6bJB98zXtoUXVQp0yNzAJni9P+O7DR+zHji4df/VXO/7Z333Cv/mvX25bnEoM91SfmJX4GphjfRn2cPcnZpKhpoHN7b/DkIFK1fcX92D7qeQYG/diX1xluLGm6eZfDAVWlEFzC+L+pKBSnZ8NbU3dl+m1npw08vaB06u2fsRiVHsc6QgjO/LHKtjpr7jH3wi96OOkyBxsUd/wEEQyqTRunzQpZtKjFaCZwo42M2XxOOQxb+Suy3Io9LD26eHXYTmEAcuRKeTBL3uwalW5E3uxlZw61cApNwaxvUpwaIfCPOWAkp+OfH6D+faQwKnYpOENbzlYYxL8ZUbmf8vk6D5HShHWV0soSONdmdmUk1raH7yid7I7vYDTBkLMmYVyNwUdbD+CAsdAKYr7UDQwOhTN9ywl82xUUVP0ouEChWJPvpPc+M3yqwLKBDo0+35r8DgRjIV3Ld+fV/wpJeSBoCoBh0L19J0ou7Qn+EldnokmUo7zcn99fml7fhFAdqA3mLHEVxDysi7NX4M1qvMXY6W1cL8RykzBCJ0J9TCrYZukgze3cMnpB4s6CZy/KM8QmPDM6IHAPDIvfyDs//DNMAcPeMADHnAKDyXdAx7wgG8fqPw1P4g4owweTGQauYbElQAS8gOii7vFhGFfpQCIls/4vrZ1PUH4AQIgKJPJAzmEfuWZOKzAGGdL8G5/kRcoc1St9d470eh8icq1E7lPvpMWvXeKOyn+TeC/BeifA3oWXm6Svoxr75IFnozDNFXlkCjlewPS0jAPh6+dpPxBFWD/1HF96tgujNaWSr8AaEwKA9uF8fTUcG2C3YWByrfD8Dly1FMU1nJPbT1rd12aR5WzQx5hY/MWsLApw3vFvEI08XoYbdtaBxMEYmGXhNwbaAihQkBgwnF/+YISj3N0QFfkLH1LPChzvE7XDRrdm8/Ay2IhL4lEBfeqxyO0hJ4NS+QYYMJktlrqjQO0hdkDSE1QVhucoWeASaGsReKhJ52pgqcVAZIcFZmb0qirRsVJxWIc4NXxtQgM8nvpaj4HmKLpEBckFe+7AjSKKdfmEG/rGMc74giotbC31qvnknE/Gyg6RMgVax2dGri53jkFxaZA4hDoHSa0P44O6WJW8AQoCy5bAx8N/ejY9wMv1wNt23HZN3QxJRK55QozuX4wQm/FZLB5kQnhUMH16Pi0H+jouO4dL0d3DzrFLqbEUyC9M6qgTesfzeOcw1YvLvj+JbbsUJ1FOwMnzfPJ8gE2Jog2iAq6dAjbGtEQ0mYHI2QaQTqZoNoZk4EXGtmbQM0UJK0RuAFNCJ0AZstTFwZGsc4sX9ygHWOrttx4ZBEr8Xxp+Pjc8LwRNt87GJEXDxYqlasAX6DsSrouQAM+vfyEHz/9iOePTzgkcgzu+OOf/4x/+POf8Ol6Re8WsFQgED0g6JY7D0ehp+oKpRpIrwz258zqW/vn7w3rhuj0KMhkl248ATNEFMSWp3HvB7hd0aTh0p/Ae8Nlv+B4ukDkAhC7V88oX7ri06crPn488P3zE6g1tAa8HB/x46c/Y2sNW2P89T+74L//73Z8+mn2DK94pTpEp4DtB2kQ50YyRm9j7obi4GY6qNLMsVmE8iAGhAoDuNLbWI+D7afSPs1xrtel7gn3pkfFwlrnGBRPs/Cwjzb6IOUeXAathtmm5fmJda/llDrHJJw1cvQxDWGSlpryh6gjrBPNWMPWmITRCInvp1QUdFposntqY6Z91je/AE2DFMlD1dpUck+6CzbewDBvT2ZGI2SIS5Pm+1j2+PNJC1ZDCJHfUzHGVMseHgyA6RBpjC/FGCu62G7atZvBSO72ZR7ZaK/R0eKdE+vVy1dnLHri6aiPaSiLrZwI5ynuCV9DwGAok5zHpg2mzMBAn+ESRuNZVxCqaHoHsvvAWdhCQsZEqHyQt5UD34nQjMMwPNWYQQtxuaFBSHFQN09fmFedstrnpFge9MJIhK94LX3JnnvXGGh/Axz/7gaNynr2OcqlPlORggJ2/U649TO+LvfLE1SufOjgvU8K+cr2npluvgOWPsR2dbJt3dyre08+o2NsAYzIG/k3FHKVnivNLTecMVVdnscCxwqfyGC0D8DHv9WHku4BD3jANw8PJd0DHvCAbxtc4UTV9avZISfPckVRF5xlJIkPDxrykFWicQD+ipi89zL/d6SVIVMXFlde2km2HjyA+eAx5aLzg8masu/sMHNS/binJ9fegrPD0M+FZ4A+nLTv9NT2TvFgjFORUd2LXDSNL1ZBU7l3pwyG56TbAXmCeceIh0D70gfEeiorB6P1AHf6TnkmDctXZKmHY5nLz+ICAXvIGtwqmRUQ80QgF+KIH+ktcouGXAkkJoxWVhzQzI0ivdu6CAFiupaWXDtqdOH5f8z49F8Kjn8/ej4pFeraORmLeunu7yJfIih6B7gNoU6IVOecbmMQ63ShPHuzhs+aWNpf54lSZqbjxh0akBDvnEk/QsgyunWXvojT9Wherhk9EbykIApDQbeUO+OXCwkx1tG0nmiOqjxCrI55X8TESCGheu4+mAV8cyLR3dPoVXr2Vxf3wOsAMYRM+awikN4hPITKQ27l+fS4QclzxnhISYQQFzDltNgqISZ0b1N3Dzh0QLVnrp+2mVcXVNFcQR5ruau4V4JaKN6946Vf8dN+xafjil0OXI8r9mPHIR374Z5zGp5mZyrR90ENwvklgMoXUxI7FiinIsOcQlzMSy4EJsGWiZQUTJbPz8KCUv6BLNSoCa3c9Z8t/x+7WguAhSkjV6QxYYMp7CAMlVlRXENRs9M8QNEYuHDDpZUwbfCQU2S56LZ2QUPzthC0CwSW664fAJ4EGzN4s0XX5TBcFMEhzjdpCH87RDt2x4GddihJCskjnN4wMNCFCL2BE69uPl8bVKI637FL7kUqYusM5vXa+w4iwkU6unR02XFIw9F3HP2AyAFiD0zneShVLfehRD4vABsziC/4+OEj/vov/hL7/gKRj7j+8IzWfjxt7dBNLEo6J6CTgRdcMVd2Hjrjo12iOyjoa2BlLWzqxC9Nn06fIiRnXvfO6NTouZ6gZfW+vaYe4jnyeGEQvuh6eKnlgHndNLxqJ/apKu7eC1SWCGEYGxa06jDPMFv/EQ4RrqQLMx34bFLuC6PLmjQtIhiaUUt48AYt8eanZ9nczvGVQBoKo4ZGG+w/ApNzaertVABhdNApk2KRaua0sznmMvaxckbVgQfpfQz38Ib4p2Z+uW5BJ60Mf3l4QEUdJV+kjjpFAQk+gPx6mdMOnfS5MSeAWu5SmOKU8n0rk+DKTxo57tbhtavsOD6wa8Zsu8dke5WITaoCoI2M6MebGuMVntXkewcB2AAy/oCpD+U8xdlZzUCv+8hUwnGCE+vlpPIE0NMdkq71w+chcGUpcObBxvgnjpQXKr+X92gqYe7CwhPq7WP5zBc/j/1KUMebG9AaIK4gluP+s/XzbldpfASeh3IOIPdmNQaAnGkRmg2NKJXtwUshaXzw5fECh/fsF+MGH/CABzzgt4OHku4BD3jANw3UMCvqtvlaRJEihDANCKkelQTHYU0a1rGjgt+4Q2/B2p56QtXzZyYFZrdwYxEffvGpSTlZFC0Uz2EooXB7NlnbSHpz6QYWOcN8705f1vc/F9r/GpD/Fyym2kl566HvRqD2SqVnITgV42xx+8IQIjHe6I8W3EURoOlS6ZfE15SIWVi3yzNjfxEcLxjCo3vvvmM+47l73VDNlCDDA5EE1EwL37iBN0bXbodoNS+ftjX3Gh1C4ci7UQVqKpqnSVLzKokQhzphx2R3Pbq1CM2izSm8oCKoK+9lM3TQKagdkvcXRXtStEZQV9LRmmTjtXl/c7G+B6pP2IlUYpUY/kqQKENGxzkP+JYjMNZA6k3iO+A5eW6bmnkR65os78NzHcZaix4H2hERmCjnjNzAY57VUWGEhYILv0KxTESWw+xVaY4C0nN+lbr5h2pzuqGedy7Ery409FCKQpydIdVhoawhLDTPr+Ow/HviXgzH0T30ZAdUTOwh5F7ACpEIcWk571QVhwtc9kNxeE673uPP8md2ERzHgd47RA4PGzlCj3X9DGXbr4tq76pGp78h5EwFraMAkSk0zWOELcxkV6CF14VCdTPhKGgo4MyNzRgYClolwD4aELUSCMyACptBAhEitpZ5wRueMgCQgElx4YbnreH50nBpBBLzWEmHPhCYLdRlCPGFO7SzCbkJQBd0zyn4pz//ER8+XAzF9MB+fMKff/oRP738iKPvUHSABOTicBEduee05CP0vFAJXxvP9QthzdM7SOUt4VZfl42LElgF+7GDqeET/QQCYb+84Dg+4Og7gAvQTTEHBdp2gRymyP/pp0/4/ofvwNTQiKCXhr0BHxphZ8LHpyf83b98xn/xf/3pFNeDPynIh1Cqq7ivUPUEA4pkvHo+GDiFenWKq/BeQ/G20skkwLH2KJVfsSbrs4SggWc1j7CIVboe3nBRdt5JAwzr1eRldwZ36/Xb8QycD9Gxyu8+v2y3isAVu8jSYXnEKPmXweOHh2DwN6OQKEfDUKoVrkeNFyLAzg91O4m2l3YSLK/lhS7YsKHBQrNyY7AISADWbkaEAlPOHTBDKrXApex9CGOqKDmZ5wjp6JeHV7/PDTTpzQHjAbs6HSqWiBQhCgnuTjxqipChEY7XwoTaIUcVY27pZlrGAsq5iZyckvfyfOWKecC909yaTXVsjARCQ0lEHcxMOG0XAzdmdkWtabQkmWa1CDINdhbUGEcknmvBR6JmnnTtkl6ASgJhAjWGsoCFMtR4hFQgijP3GJ/owzQ8cfUHgD4C+hNmKIrg4JNr5IhKRXNt1Ap8UiayezNH82t6Gmt9puX5RDl+qQB0eMjL5ts5Lc//DjCvy5Odh3BqR/ezKohK/DOLLQy3qoc/74CSgltZ3bG82SJ4MLOHhQ3OixC7ULYdkY9Swc90G+nkAQ94wAO+MXgo6R7wgAd82xAyrYt9asNgzENZF0xyHIZ0MIKWKNsPJW4JHjmKXhV+fw0Qh5R6yLjTXhKYICc86BiZNonrc/DDPmyM4jMfyqRTd+oBXlVkTc3Xk2KKYDzrOemTnytHpffaNEuHQB9vy1OMMJQ3UA/eRcB2duC6yedV5qeOy9Kk2+a/gnNxEO+H4tgF/SrgjbH9hnjaGnD5wNg/FSvHcV4aME3kyf3yGK0PpOCnvOtCyyLfsFA8vGHjDWDKkIgCw3MmNsthJsuFJB0kpni4aYxXxvDUX34cnKNaal5foQqtVoFnfDmV1fk4nY2dCcxGaXfRdMgMfzFo/aZI4cuqmKyTFlbd8UwVXtxV3qag9abi0/aEpXnihg48QNCxdeBjL4jHqrAlxt2vsbc5rOrj+YrGpigcVYiY8C29F7xCy0djVvEmWGOgKYTEhOlkNFm/U1y+O++3NS610ebZppbvS0RMEecd92BAgCq2tpnEEYB2cWWb5j7XyJRHox+aCpRDFLxbaEyJMIQEiLDbNQi2Rp5PTDwk346uO7oeuPaO62FhL/fDveYEgJAJZRRnmAR/xAUtY7zvofRvRe6CXwghVoiJ1F1STQDsIcxEoSRlQ3WFengOiQDS7QO2qZKHxCQmtMZmzu73WgNUG7atox2M5mHiYgxzc6IQag9Ezi1bzVuSAWzNwluSCvb9E0gsP1S7NJBsYPd0MVrkgrCQoLsPLoHRtg2XywWtMY6+o4EhcuDl00/Yrz+hh4IO5gl29AO9H+Z1ZwvGvWZcWUCLodBbBOGtCfuaebbYLnP7WcSnrrAmggu+XZEpCuqmNN2x43p9wf50xdEPEBisEQ7M8qu2NpIq9/0APV9AIFyo4fvLR3z6+B368YL9acPzM9+2E/NQaqxbXwyCke91en5RZtVuT91crt0o4eL9YNaLoq7WEZ4WFGW4YnoIhgePcqb0MmpNxWNjhvQ+jzqYR3uNME5lncqqJyYmNqCZBia90/UqzQ8sEDkpLfqCugerjRnz6xuw+ntJ4xZUrKtQ1GIUuHoWEDMusOVL2deiMjM8FxRvOjcuAGcIcmPWzFhAPaZ+hFNl3sIROcuextbd5UKZPbZ051ucSkpwLxQecOp8ohN19mgUkaDT88TF2HbvnICBDIk6vL+ndVLHN8JAxuHBB1njDDadIxSHhrJLM5dw9NrGw5SVqRwEYMwwDS8/HyxiHjit9hwrm1IuPJdu8Eu9fB9ftT40YihtxjuHi2KsGSZwI0A5+VRR8yQc0zVxlBOu2MXCwEV+vDKOfeHz6hLJdbMs3eHxl82dcCTaUFGqRn23B5eW0PgkwhTxI+sqiCCGWqchNn8PWJZ3fhZWeL55Aqd4joFJOdYood9rZVNO8MBDxwXHrViLFmYfbntESNZ/pZE6t/+H/yHjj/9PwvHjz+AfHvCABzzgK4GHku4BD3jANw30BPAHgJofbpozxc7sxXcFkhs1RtKSYwOTTK2epZKZrCHifndm+4TvrKkKpmfi0ODMMS2/gVBGjPfqARv1WR7ykp8jO3urD68+eu90sUJt2706GKC/BHTJfUB3vt8r/6b4eoA7afJZc4YgaQgi8jy/lJNnTnWR6QEchwmGf2sYByIXOnhHpjFcJRd3cDN1NAV/CWHM7AvX12laXHsZJoRgk/OIoB+HjQcrOnV06nboF1NMDMEVmyjpDJnD8t9vpwdViEy8zd//bxh//78t6rvX8O4zD+mro9xNYd6akXPF+jEJyH4FyHleBCN51xVQJtQa3ghJL6v85xcQzgiFRXUcT2Qnb1nOrjRSaTZQqOUqAOUx33ndhX82Bx5+UD0LYnnOFGGhOG7+nAnQDz2ADhzSgSbgDyeD84fNEohFMlAOTxFNWg73DpUOX0/kwiEL0abuwmfCNDXBqkZ+OcLWGp4uF1wuT9i2hkYNDM9jt/f0dCICdHMvLQg6eU4dMiWd4DCPPJhy7pCOowsOD8fbRXGIYO/d/sS8GYZl9ImAaB2Ss31vvfUlN+dJsje+h2elu+dmX27IAVmOMbBbGNABVTO3Z1bLLdjYrMa9cBG4MjaUI0MsLBo6P/HIcNUDp9TsJI4ZaGwGFkyK4zigveNDu0DYBdiHQC8EpmYidV+4yuTKZkAPBbpC+o59v0LlA5Q7jr7j+vIJ//DHf8D15YruHpNG3w3vxL3n0oPOvehQvTwn+DWYja8ddPoWImpRE2CaQrhDqOPadyNxStj3J+yHKekYrXj6CNqFAVw8VC0AFTQmQMmULcy4tIaNCdu24a//6iP+B//iBf/m/3PctA7A8AJPvtgN2qB3lVsJxdMtehnswGeFlF/qMOFvMPrkIT9hXm3u2VbZjuFhtxZLKSA+IzmFVZ6VhLExcGGMQ1t02v7bcscFPR+LEFrHS2dlxtw4w8XMUKfxSfud16kGNFraWg18JjLnA0bOh2UOSQAWwlfGplqIsLEjRhebq+TYAl5aCEUigBuYfJ/anbfr/p6HX+TiJRdbX7buhGHPUNwYvxVAd6VYByCsvqUqiH0P8i6Im07ElqvlE4CdgYTdS89yveWS8EbWTwsnCqjKwMdYP9nGoVS21oc3nY+lz2GrzCgAkBjNh4X3U3JmRiKopppSlAksgEUQHdEkTOlP2JriEKBTVTZT4SUDBxkbbbhw8YQOxXnkOdexVtLgtQ1edj0T2cUZ8dp/qDj+/PpG/ibVKOeIeg453U2W+6/VZDw3RurN6q0VpAiDVFUeuDbt99jRbmiOQw0XGTKTtnnISxkPnLV5wv2T68xjDU2KuniKTJnbYFEamFCUc06TfUyZzGBFJNIX6JiPKDrpwMRRPuABD3jANwkPJd0DHvCAbxraxtieGtCG94wp5xQaMc8K09zibFq5ZfFDRSgAJGURafkVYdIQ139vWE88eiusTIVcK4/qGIPJqwQLsy2lTJM/3pyLP3cYsqxXuqNvXIvfp4LZ9zRoA9r/HDj+d7cF5vwuDbwVuI5705jQ601IMVUc9CqeOX7dzRlGZc5eP8N+QRhC4LYR2hOjXQj8YvmWXKbrwjKMwfDveYnKtbsHZIyJ1jLnIVBpdpikC0Gb5TIIYbaqoKNbKMzNBMzCZk9NIGxMELjVuYfcG3WO0FRDQFfUCa604Oe3jvSvwGuCt/qYRoikBeGqYCEFIF8YKRRu3X0uLQylkfso+lrQMe/vFchWkl1eIdwJYXn++lxGChuXNbw8rAUf4UKD8MwAXDESJuzWqSwjjB2qznyMga3qEHKbMbvk91NIi+MgTAKggblhaw2tRaY7K8Dwfgg34pp1wcIYWh5SArdWhOsupBby8Jfm7nZcLRxlYH4XT0RHikbRfgG1Dgvi2C2oocBCY7pCwBQD8Nx6MUe2EiNHUA14eCZeyXxBMfRa5uvO8P1akIKosl+GMLi5oEgUOZ4mjGY0eFjUKEfEc0MB2Wr2gtOTAxbW1t/paqHhhjdQCFphno+i6Ap0j1c5cIksFx1CwDXyKFouKBvIl5crrnTguwuD1AXp1KCRI0rVHTJdmdZsUTAxNmI0JUg3Zdx+vdpMqilM1NtziKL3juM4Bn75+P0qAsvfS+r5K8AtX2P/MXh4aEoHk/+O0Lb9wH5c8XJ9wcv1BfREeCI7zivZHscbgbXh6cnWfT86ts0MB563D/juw3f49PIn/PnHhtYIlwvfrqW6fwNIBY3mz6kXk7JuUc7VsJexH7ym3Iswjfc87Gh5Nq6nwqNqnmpbKsFNxV0YmJzXBc9JB/g6KvUVVibLv9nrToqtu3ryl1XJXvq9cACnZUV7mKrKxza99Na1ShD55TSMe8p86igqGmJ/wYOV/Tl1wEFrdNAhq8sNDbpCPKqJKf/NcIpELZ9mR6YfoKzxDeUvxphbkyTbZtGnNZV0sbd0iNNbP+h5hQqZPOaTQvlUVyWTsu1lEQJzGi9vu0pEZ7E9Xjz3XG7l5HMdyryJvdMc68KKZN0EdeW7+UePMKt1Hdr4KSk2MJTNWpVcfSESMb0lr0cPCRYm0/AoFIaErW02fhIKPH/HNhck2sUQq3sgFv797mxWOhLejAvvVx8bHu3zc9NglVu5RnUuS3BipOV72Bms8xE/bshkB7QhZQrpHH/W3t8I1h6t8xF4SHznhXdWMo1LWTcdxj7EbwY8n7rhskWxIFfSAUxthAol29fGhIcHfqG7FW3OJ/YBD3jAA74peCjpHvCAB3zTQE8Mfm4gBhieIDw8bwpXnQfR+J6xGGphC6PnP9RkmCn4+t0hJYdIrngICmZGmRqAJ1jakidAt/Lw0v8cG/9z/nmc1Uv9n3PWuPesrt9puXYigFt1FnfhFeEdfQ/gA4BPJ42h5bvfu9sHmh6bPmPs6l9Bq/fjEo1+xzu9A9dPgpenju25YXtuQynwG0BrhHZhtC28dM7bHYfjU1jGdxKEh5AYA92VADQ/uG2Atghh5HH04AIXVnQXzbAAwgIWBl08VxS5ok4FAk4hReB5ClLgQhGMcQ3BEV8U7Q9A/4fPHLhl0j8LD742UDeKoFDSERpMKEaTkNFCCBej41NIOpMSmHI91mARhpUKzpu3PEflWaoXdX5nIq+KVHZNYYGLF0KUJTDrYfZEqJRCBbPRF5cNiohZAzeaCUht0xMXwqHjDyFcHH4RJq+I3DahUvR2U4RnMwEzMVnYRG6Ws8a9wEQEvQv26+E5WgXaBYd0CDrIc86AFI1NuU0QsChstXUIHTjkwK6e4678dTEl51u62kn4FnNd6AKVB7/0uokmuIMHiMLexTohgOfTM/IjqmnlHbn2UmEK77sSVF0p1jZs24Ztu6C1i+dfsdByIWgFQjdmyBBqAZWRz6+LrTcJOhbjTENBZ550ClOfmZA10MnyDxL2q+B47ngWw3aiDUADoXlUOpN+XfeOY+/Y9xdcXywp449//hEvLy84jt1yEIp5UloIUEYXQLs6zY2xKcLTEv9t3n7vGQb8IwMawmcARltDwK2KLh2shI4DrA1H282T7tgh2xN26mCKnUxTSHociuv1wPNTd8E7g7jh0i64bBay9Plywf/oX37Av/3/7ri+jCapBt+r2Z7Kdg4huXvUIWh4LFafwbIXUMznqjDDIHX5m4Z36PqcLmUPej5yat1YW52UszK4dz3aoj3AlH/u/Hn/UExtuMHjd7Tx3UB17OdFlArIqIoI1M39mnxTJBQDEw7aM8ZGfJ+JTbwTQK4QDuVcaLRqj9g9ckkA6r5PQTzHnGl2OKzPfA0MBd3KIOKEYRrcmTj/O8JcAnBDEAtnb7xh8IhmtDIMRMJDLj5jDBVhLOWewE6/6twPL8t4wwY0n0kNkY2XdVcxdCKU/G6M/vA1t78IZUrN+W0PvTkxM82MZCKUKSgaZT8YkesOiBCVzIQGLSw8AW4YMFY1mYIfTxDqEBLn+zx0J5HlcKaYY4x5x5gzDkY/iT8moA3gv1H0f1cmuT5Xma0FC2j9rfONej4747vTU/gd+w3V5q+Pu3IutcS1Pb8j3F1CPwNuWNaVP8OiK4s50Jm990wEppxjQitRHiIkq7hHuaiFZO/aCy1dQhUrwKz46//Zhn/7fzz3DH/AAx7wgG8BHkq6BzzgAd82NAIu9lXjEFS55zg/Fs5cFUCx/KvfLRQIhlB4FDFzuL8Hw72e8WVmitOTq56BCJb8fQPkAmgbXh/lCGy/dRysOL7X8n2cdHlv/aFe+OkQ3cpm8tn18noui+9af6wHuNfmJfr9zwH6G0D/27mYm65UgZEOQfHaqLMqqXy+Vw5DGKhYL1Zj2drWCNMUHhu/C1qug4c7h8DS/owOhdv3ABvnSEMAf75acId8r3eAegcxIERAd2tpkRRWqsI4HbV8G2YIb951NtbFd5FK+Uub0tre8X/7nvDh7xR//r/NOWluxuHmNLsU/DsC5b8jv8pZwwimiFV1K+oqeMoPw0MzhLaxMjnFsNRfhRSrwfKUE+SV5yc6hyKDwskcnAhTbujWCT2C7wNaJOdny1jLDRsbJB5lUS7Qi31FVIaX2sn6wUbAD2zaodjTmMGN0Jp7xcFxt/SJFYgcOJbXihK/I3yV4bfjvyvriNmVdB0HkeUvFXXvN1Os9F3Nkp+BvSuUxFK+koXjs7CXpjhSjFCXhyiOQzzEZYfI4colSU868U7cTIPO43sPFLe49GtBbvc08E4QuQnH5JG7whEYQkM+JwjBlBVADaBuglKmEDDBDQealePT2kVw7B29d/RD0A8P99TNI03VvKqkC4gJKj1bTXDlHjPYc1UBJmxmAhoz+MnwqHGzvFHUQNRMSM9Au2wAbxau1eeVVMBEuGymYBQ90I+O63XHsR8poDUchOUl7B1ialyARoi5EfLydo/PwT9bfPdo5xfCgV8d7mzUcdlMRxiibkIiggaLDCGiEBZc+46X/QU/vfyE1i542i5gmPtGFzMQ2EAQMk/GTy9Xy1XHQKOG5+fv8N2H7/H9h2d8+unPeHrasG2E68tKdEdTQ4lT98FK61KZVb/feNIVJnxS3o1/8/riSZf7zj1PumUsp3dK7TnW7hWvsfHo2Tvl7VL/2PvOnyUfmFDsIRUe9l2XcqIfN6tBa4uX9pR/x9BZmZKd8dakYsTuqytY4LnP1JWwQ6HHZc7sn+rpZ+PnRgMa4T8nZPCzlEK6QFnMsyyMRmI/FCDDj591kygNYtQevXlIHc9t7wkPuqF4U1guWMTdzEk3vLiDXqubOohIckZhoBCKAlVT1ukyW0HLjQ8Ib7fuY2EesoAbXLgyUxBh3TXRYXVmCvQ+IGhgm7cGUJ5TPZec7zPkfG93yyhO96jgBBiWpzZoTuEN/Fojw4E8U8PypR5CidOJwxab3ssz70ioz5tiNoodDSkHgXmNUrsNPTvP9znLljdPnj9/2Pf3W5J3A+szlW2bjFux8Px6XsfXBDEV8Z1g3n/i3oD5DO4fX07POuVe9dKNSDINQFNTWpv3JYHV8qk29/71mAtTQ9J4I5mzUTk7vrbtzGr0AQ94wAO+HXgo6R7wgAd806BNzTsMllg7jDJVLd5/5DqJg2aPk5EfHkgBHJgYdyKkQBU0h1n73eVAWj4VN4JtoFivRR8YkCdAnwG6AMrm8RF94VIm10NisYIMwcwUzk1vmXV//NSxanrgpDv5SeP7m+P9nsPPSSH8z4H+377+zLvKfgMmoRDGQRhA5jS4d4jL/vvLOV8oQuMOyKGQzfOQ/EYISgRcPjQ8f7fheNkhx2wnfoaXee/O93ryzYPdHdlNDU8UFr+uJkjBBbwMdqmxebgQmoZCI+jEKK/KT4dISkfTcohvBQnTXJX21TFb4fOnSxGimPhFU6uXR0N4B7euPq19vE/Lq4GbVOOzYshXspM+Lhby730Lp3o339zzfxKHfAIyVKyO+0Ez7tVa5+FGIPNKUzX/mfeAzFNzMuyWDylyyczlqCp6d0WWRA6p2zKmhkXMZSccSiZStBBa8R9SKOxiMgCmkBkedkAEtuq9g0DoveHoBzbZrGzViL5oIfdcsaIh1EQIJ03QGTmKIB3s1h3qliNWRrSOwSB0X5epnHHBcIRwrEPxu++zdyDaJzrCq6nPbThLMBhE9teIi4W+IS+DTenqoZ1M3iQoU5gFq5rytPcOkY4ugT+CQw4LI6lAazLll1V4gNSoJ8TRIugANmJ8eHrGh6ePaM0UtQBDpKNtF7TLBdw2D1tq7fC0nuhHx6dPLzj6Dr0SjuMFLy+f8HJ9wXU/TEHbO46jo3cFmNFaQxPGYe4trpxT6Ir8evPl5N4rk/O1Is57wOmJoYuL250Ad+kgMJg6WA402czztR+Wl44YG8E8LLsjFTUQBNdDsR2mOG8+z0wNW3vC8/NH/MUPfwGB4j/9X/6A//z/8EdrS9kf2RnCukaTptW2A3ArGP9K063SxZ83PItn3Rpec3l4Iu6JZ0XhQ4MwJn2uzwwv2NFxE2jf9uBmHLD0vyjrxtyWery9yVVQqq2szhsGaDBGuUcv23ns97Gzr/Mg0wZbp9DCZka+Xw2DksKPCZyHwqDzVDdhtRpJCNStHot8YF7lkcuTGlu4X8XApzK8JGW+ymjXGfBomSUEpXs0wxTesW/G4SbWl8RzOpR5XUy92dWVa34nyk4DGyfqEZJVVTyfqIKJ0dVD05Z1zOThkN2YYwSs9j1cT5S0Pq4Syk0Y35C536J8vwcQBIRdxll4I4zoMutkh4LEy+oSjBV5nsfRHgJZXjracNCBo5QVfJkwQZupJk1Zp0BfcPMUBqbyXwLy96e3otOJJ6c8+Ppd59+VX6RluLX8Jc+v68tLmwqrFu+wK6BVbuv4JTTwl0Bhm4Hle9xHA3iDeb0S7jY2aVIZj8rvTq/EXuLvsT9QeXFVBTa4IaWHvWxsMShU4T6g9klwowOr+GaOku98wAMe8IBvFx5Kugc84AHfNAiZgqKB3Jp8cH6WbNgPYGxcOYWsOeMOqlkl8jgIBxNfGc/35EP6TaAKqMu1V2VboZALAXfIcKIsHZ5zULgnBSYOXkrZ1SruDPKg/QqfvDLyihNBxKsVONSDxNqo9QRSfrf/KdD/83fW9962lLqms9xySLzxIqsCCn9wOtgtdUQ4s74rjhfBfu3Ynhj8WyGp497bB+/5wFsFMFr6l5HOQghGA/3CSDd8RHJdurfHGKIh3ggFceSCUEV6BokKDlEwD0vpMDS+Zwl6duBTKD78S8aP/49iasrvOJD7PEei9JtFUF5XBWQH9qtiuwrahUFNZwv4bJ0PoAIjaJKW+2eNwbvm7uz6fEHnxUuz4KeuhXxlfjzXSQgGNPAhBI4LPY5nJpp9p71ZT9S70o+z5+rvWvjJ86GcASLsm4kUTNA6QleFgE9Ehrf2mTXDX0US0VFxWO93dBzt8LVhor5wcVYMQm+WxgLhYdFuQnvzogiFW+YLc886UgUroVFzOmaTEKGspkmIcF4KiI7cdEIdIZkhUVh0rhDisgsj0+dqjGPp8SQDozIPv/E+fBbGqcxKCneHAHeEugwFJOXTJmRKASiHMteIhypwiKCLhbwUsZxkoaAL/EFR6Nn6MA+3VQMdwvatmcoVAuxyYAOwbQ2tWahNpgsaNw+9+cGE8yD0vqML0EE29x4usTUTsIt0yC7o3cS1wXt16d53gWrH0U1xd4jcGF7EnqBOv+4KML8W/utLwcLTmXeMKUdVCUoRzN1W2a4HXuQFl/0JT/uL4VWDr3WFoKF5mb0LrteO1g5s7QmXjbBhw4cPH/Hd9Xu8XD/h+ekJz88XbBvfNCvobzXQioYGvY5wl+M+zZ/TizpdD0+pG6jv6i1un5d//rxdWspY+ejlmQhrSdOs3Acq/9Z26kl7oiSqb1WvwVfqefWZaVEVlc+Zt2FuiNZvj2ZrSjf3pDNjK7ay3PoxWi6+R3Psd2vfvX8aYY+5+z7ASeujrEQLx1+aOmgM8TB2KGPgD8Z/ByQVdbZJSub8hO9hmZMPQPf6dk9IfqhxmuIh9bozIuFJ1/0zaJ34TmYRA8xQjH2txtxzGO4oG19LzpvlUuDEArtEPiQDN0gFHW7VR0CDeUjbWaAGzfSRIEKPPYlMjRpTNIaVnOY6UVa4gZGHMZ2Wn+OEF2K6Nx3MD4/wz2bQMxizQMk0ElsZwqpQD0awjconFllPXseM3kOBOV7O88ay3rONwWeWd5MnPWuzX284f0cPQDdkvsXIMXx3f/uN4Ywn/0WFVH4eZc4VmacQwIjO4+MR0YCIKD1DI3xu8tIeehWqNp7QjFAQ9dT5IwDPPzC+/48aHvCABzzgW4WHku4BD3jAtw3N/tLDgsfRhtnCoBDUDxFuddjVkzob56gyHx5CaP81MNMTFA4/5KXTAaY+Gs91gA+4FwPgMsIhQ9HFsk1jHHErGM8T/219977jHdffgqXaek66gck6r57i1pdfO52UA119bVIivrMzQ4A/5izbGeN90ryzOqZxUDvg1OT1tw/9NkBkCrF+nOBlHEyjn8uJdjpIIwQS4+DN5ftUJ/wwriY4aUUTTeyCghDMQAEh8CEAm0dPJKgXf4Z5TPjZ1Eaoo4AuAv2ASFI1nnsFR84OxmfTVWVRCmQY3vkiXCc3Fu9rXmFfDMhDki3ayRCa5dwxuUDJnmeee6158B75TKoMKKJpzXX7u+Vw/hptqDiZHycTsK6n02ElpGA62pxpTl24YCHFCBC3ZHephboOK/q4/aXOCe8uRWiVLRXP6SY49h1gMe8k2kCkYDZFHalliCMZFsamrxM0Mo1gd9cY5obIdyUQsDQX2hEaWQhFgYCaCd10IWbEvoDctTc9fxI/I8ymuLIhlDTkghsXOtaxfo8gS2/XzJeElfYw4N4Q8V/Lb0HUAteNPvs1kO2/jW5z6qgJslU7RAjHLvj06QUvL1dc993CSR6HK3htkG0dGQFj5jE1TGCLdQl12tZg4y4KKFm+ubY9g/kC4g0W/pScXhv+HXKYwhAwxa4QmDYAHdd9x6efXvD0TLheX3Ddr5aj7nrFse+mWOwdUBckRxC6wAOEN0osQR3zH/vBb7yP/aZwhuQrE0Xw9SkAdxxu2cHaUugtouhHx07XVJjY3mhBM0UUzHDON7zozJqktQuenz7iDz/8wcoiwr/4u5fRFB3hwzQuFFobe3vmBIpPAFVBpMDCRJ4NRu17eeaeEu6NZ09rKZ54hKW9pSWjfyvTsdLlpfVV8RhKy9Png8+ItrwD3trcb/bG87bevBZKRCr9zjkeBiaDrMf8Ahp7jNO98XRpA5myRzQUdQoiyb0BGbqRZh5Gp48bHjHaIzDFW9ehqFMy729TBKifZaxd3fHycG+4Q7t9imUxNo9VoLsvntwo6brVq+5xB0lanw7vRYFs5wbPywcL3SeulGMytz6iyHnLSX9j50Q8A9v7NZXGxm2YoZfkqo+5FBX0bso8UxwODyWw88bQ5KtyuFD4b+9DRO2wvplKFsruVe97HWmOsRJZLnQg97ngkSYk833mxgUUQPvbsTfUjBQFAwfPRxEeP8obfGPF65QR+PVKkoI9q2R5+r3yHDRu2tyMWxq/B2v+VUHlk18FlxVg6Rstn4LAgYk9nM644cgaTDKpeWgrGV1gEYhHGRiUJlZBmL0pGoWpE3skh6hH3LDA62egPf+iYXrAAx7wgN8VHkq6BzzgAd80KCukCdJCHePgTSFIBKBuZaxhgtXNGlLdqy6ZbBp/BIyTy9cE3p6Q6wo8TMoq1y3cOMdhbOHQ1zB/tL7vFpKhMIlq6kFmOVefwpv31pNDEdS995BTi9D640aAAehHoP1npao7dVD+Mx/g7j9cYDntKYbcoB5u6gFwastyGqJyLyzbu7qHmAi68vDO+g1wdtsIT981HFdB3w9kSqR6ENbSp8CfqbMzxJk9QtaqyRRyGUYxLmOwrno4vhKrz56tnna59m3cVMy6OjyeXlvnIaw5uqCXw/+vrRVbp+3eNJqXxfzcr62gW9FIXXg03WMTSGkLpZS6x20V1IVl/n2EjMO26Xpo7gy9TV9o+X53KJYmVG/O19ZLiMSChhodpekBTdw2fxdT3PjtZpRaRSBC5gkZ/WLg8jcC+mlp9YQI4l4MHoZLAGUPPdrgHnDuWemCzg4TNqYiXwQHLF9jWiZHyDH/ND+dZvfJwhsa7fc9FO6RwFo8HkzhQk6PzJqeXDYXXoU8vhEj/svQtDqmnFekD9qrN5d+G0hJoP0Mj/Job7Rd4d4SQ9QJginFQlHVlCxvGNjwHLF2XZmnlnNOBRAjUuMPHmo0TNNhHrVEwLZd0NrFQhkSsD01bLKhgdDYvRZdka5BTcnCBzAaGjcwb+BmeeaIORWq4mFZxXGGN4D5OT2ujmNHlwMRMA6wHGhdOsQNKLp2C80pHaJ9eB+mMO4+nLAE73v4G4SZDyHf31IKidjwTFlwYO8H9uMFL1dzGWcQNt7QlDzMHSW/JyrogV/EYN6wtQuenz/g0E94Pr7D07Hju++f0vMn24EidMVYDqsSdZ6rGoYPtxvUHaK9srCfNXb5Q/1jXE1PojPwPScUVtnzZc+ajBTmAkpRdV+Yw3O+BVXBCbwD398u8JVb7l1LBBYFs+U6VJhCLRQ/Sd8C/ULIHvdiXCd8GUyfhbd0L1A1jzP2sI6FlEHIKFMoUiOjW4kTkH9Rnb3uYS3JcM7q8zWTZyAPv+gGLOp0SSblnBkPdJhhgfhuKFpyrZLMykDEXm99tvQANCk+ATKPNwCWQzYUEZb3zTyFyGgx2dgMlUSsozDA4GDEoFz3Ga+MRhhV88L29qkZlXCE2QwPPy8/59bnLEY8yiVmdBr5YyMvX++2R0Q4wlSAk/0T/HcYwSowKXLuI6fzlkVCuc5/DG8oD+NwNIUiL89q+S1lP1+qndbcm8eo2qizdwqPQO8q8MvDWn1lb+I7N/trPaInjHdXmpSeceUhRbIssUgzelGc7+JcyBQRHWBsSfe573YmS/lNwaHkriq+BY+pZUp+5TPRAx7wgAf8lvBQ0j3gAQ/4pkFpCDGUPexlseAkNuFxZz/AHAo91HJo/aTQFwWuALoL7+NsFwZdP1dq8KVg5YgB5KF4FXwEV3uYN50eAJ7HARfj7fNiCakJnHweikAl8nmdwhuHkmjDpLQpJ4EqGLoLS/lxQNP1fsg/yA+KDcB346XJoPO1xp49Q+PAuMpk6oEx23Air8pu6/2D7HRAVUA70D8prk8dlwujbfwb4aivLRA2VjSO0CSabZ+aUQ5yeXiKa1Pnb6rJ61wunT43CWXg678G1KNUijZXLrH/hcKTHQlMSTi8pCKMDwBTCCpcsEJ4+rtzBI2+vSajU8w4t5Kbe/BzpziCId00okzQeMZDHHqFKh5ij8gURhGLss1ecXJ7jD/H5TIwtQkKQGTp41sdXtbLqsC8qfuk3CqsmIqmcX/ooMMLwB4giRBb8T4lDQoc0tAIThImw4/qxYkPbElkavsUgAsHxd3wVFrOFYfLKHtILQ3h2fBggHtpmZLOPK+2bUMXwdG7e1u51zmZEofUhDTUyemSW/z7d3aprWqHkoVk1BBqwvKfRegxcnxhNMur5cq6vsxX4EDSER33T587md9fFZapCDpvgtgZ0WyaIvSlZt9Fgn4FX2LzLbHxiV0XEUgHDgH2w/LN9d6zHFskHr5NBQQxAXBEPPXiuBG4sefd9NyBYckOyw+3bc2euVh+PGoNFubSxMKicG84eHQC95SQDqC7xI3R1ZRy+7Hjul/No64fOI6eIeOAyIsomd9Oa9Lf6saAW37irlPQ18CP/UJIHsgZhcFmVOKknoMywuV2NN4AkVy/Wz9wUAM18+xkEggLWC3MBJHloWubKWWJCFu74OnyDMEP4G1DB+Ff/y8UlyfONgxvBuexyzyZUgXDa+wOA/VeFu5z1vLtLoMJSYaXpit6FpeQUKZki+8o1NJAwZV49xpZlXM3yr35wWjg3fdXeM+4nLGn2eNiEJB3wgPe96R5q7H9QnjQAlPkMEx5l0VYaRl6G1gZ9sjNOcYR7nFl5h2WIpE8Wbi1YD6bDC/1el7Q8ilBm4r7vab2wI1WVCwYs9OfHoom96jTkmuuo+dnV1fWuTpQCi02JZ2Hhgz20zZ8I2e+VtQJmqU+p8y71WylAiAXxgW2Wl65ZCJslwSDsVFzWhoho3271liHsTgVygI51D3WbfoaA6LNlPCqw03N8YVB6HDFYCNLE9HY93lxYw/bK0gJXbrnJhye0eZVH5ONG1xPEpIWeScYflHwX2o+H1/O+EKlWZk0488M81o8eeAzIF73Kb+pRzss7KWn05iiJfxOUJt5/yw139TlMx4LHjPPOoVXilCutj7L++J7BhsrA7IQ36aYHitLRIEuvhbE+Y6RzzkGPIzA6m4VK/T7v91Af/x54/SABzzgAb83PJR0D3jAA75tIEWnweQJkXt42CF0bx1wJj5Co4gY84wXBb0A+jIExS4r+epCVJyBYIQQobyGtJ7285SFK9uQQuE4WVRhdfQ7YsQHo17zQlWp6DiU//z2v3lGeq90Is6za4HlwFEP+FEx/6cA/QHA3/u96OuddmTxi0QkD+dYzpwnZdVbVTCSXnXxOZ0Al5cLmJHi8Er47cAD7DQLrUZlXP32GLflHL4KkkIIk8Kf+CtnfKovhrzav2uHhUzytW+4oFAyRVMIEkMYlYJFInRSt/J02uH5lrC20ZV+jBH+BwAu/4JvQtf9giWRUA+8FSkpTsBni6dKM6KdyzPRt1u5SBEPF+FdHIxvwJtkys0766aWvCjlapHhBaxwpdKKKzFv5004b5ueLpfRnuVxWm6MeummoggbxSD3DiC33p9bJSojVKl4rq4IseyVNvbwUQHP5Jx5SKAwOuM5vYQAZQJ1C73FqiBHfCLOkF0ZgCwURh1Q6WBq2ETSM6tLx+R2SgB45LEJC3sg1on11az0Y0MoudjgiirPAxmC11jTsQgDN5mKZ8WdCaPyvc5ZpE/91aHUGTQlfqdfIDUTnLqHYCPzQoxmmteteYioGo5EnitNaxEX94oLgYXMA6If6NJNCXN09OPIPHe6LpAFgYmA1sLrQt0LlIC2YWsNzIx22cDN8w4SgVrDdnnCtj2BmM0rMo0W2C3aO1S7eXyEy7bPJzO7R571X1Qh3QTfXTyXnprPP5wXsxBoui6bGehkHf8aBPb3Bp3xdoijnT6nwL6yXkXxqx2HdOzHFQ2MRoxGGxq516QCSh2KLTfSWGPMGwgdShds7QnPbFrkD89XbJcnRC7DFgoBJleqjsbW/Wnao9bN/pQXCrz59SZybH11nxnfbRxpWsfZ3uV3JYPjcoT7vW3z+tzXAPdy4U3PBH13qblS7Ffu8QbN9AGutwPU6bbTMPOgHx4sQNnfiycnlnDiuW3UfyMEJu7vByMH7XgzzyFlk4h1EjzFId2VdJKGV8Ogwrx9uyvqulr45x4GJzTy0OVuprCwmjnGwVfp1Kccad/zQv3NaqEpu3vXdRbLBRt+5yVPnXnIWfazDnhaOnKa7POTPKmmMig86sLAZ4OCOqE1pywaY4DUpwEE4g2NAGU2vWyUd0SZpsSE70Xi/EGarXg4a02FClyBuuzjK024AxltxPmoM0gcWK+dnTteWaJ1Ow3cisfzHFIfLvxIrTfWzLcMSs77lEhDORbx3cl9zE2wkbHunWTkd3aDolDUZUrlLDfWZM8kqArjJRD5INWNxbyS3Hr87EIeyeG7v2Hwj18HPX7AAx7wgM+Fh5LuAQ94wDcNyVCTCQ/ZDwPdLc57xM8ighBBLwRcPRyXAHzFxPhPZ73ChJ9W/Hsx4XcE0Mkcxw8G9AJgs9RB66ElnhsCjrmOM7lKfJcz4dnPgZWH9npX7wk9e379vh78qBwwg5HPUz0snEo5IExjF805kTfda3p9fhnK6Us4aRLsHHJ2oEvHm6VPtSwFzJvuqjiuCvkwx+X/smAtaRfC5Znx9EzYX9SU37eP3QjH1ibWMVkP0yncj+JCIOF/XH6HYi6eaUQZ/jLet7BIaonvfQJC3EEufDALznkyabHGh3sPniHCLyEPQY6qciCUiiFYE412awqNKmhIUmmEjKqDsHalCtDioVDO2aHYaiKweUXVulTrNJ8KMgHYeNdK6tpaFk4KOjCP5U3Jp4tw3FrJQs1vMuHbQm9sbOm2/KVh4TEV16x8TQGZioel7QoSGXQ1BJdEGdJn9Kf+dkR2ohhW/yRALwrXxrb3Wb4ve0cVIFeU2Z97t5F5SfXecfQDgEK5YUtcA4jMnn6E7fQOauBWCOXcn8At7UNRJ0UACJjghGEeAubVY7l52EOhmZCvrO9lHmPs75K29xDn98Ii2It8PWd7UK4arSFAI6CnS7A0RM8ECLuijRCSVWIXdpexVe9sCl5dwcU6hKKAugKtuZePr9dC/+DjTcTYiHAhwlPbwNsF1OAhKbt5kfjki7uy9kMAYvc8RuYy60dPjy5ulpvoZd9x3Xfsx+FruShqoU6/gNbILOdVB5kI65qTOTvd9/8RwdS/GxpeSHZRzok0C02olp/QQs91HH1HY0Zj20H0OAAlC3XXCFtr2LYNrZlnk+7mkUf0DG6EH7rgzy9X0NXXKA+POttb1ZWtlW0aO7ai0M+ko0HQab5BGAr+1yb3Z0i6Z+Xcslg/B6E+h5F657N1FM59zH8hvOahl94nQW2d54H6VmQUXeJw5HNrdJwynrHG3qI6cKPWXYec2el9uGtZzw2XHY/CJRMY+3/h0ytTuPLVCoWywmJNhsLJPeN8Z8lPCQWd/8nIS2dKuQOigr14gyOeI6SiOvjCNafmmNsZ/xTi4WOHp6yQ+r7CWRYnjwU05Qwxrmo8BcOvMUGVIep7Pmh4EbqhGm8Mkg49VsbZH8t1SjWZrvWFjam2KDSWvy8PHKKZk7Vxg/Rudfu+b/sg+ffAwbH0blbzKwZggE0r7/AxWOceiXu5Ja5LqmwtWl+muer6auUbC9tzW8ZNX/y5gqv6Shc/n7L9cniFXQahpLbwkJey4dYSKsbTx3Cmabf1JQubHp9I5Rw1M/RM40r1HKwHoCKektE963xEI/pJ0E/2wgzPIsaqTt6VD3jAAx7wrcFDSfeABzzgm4YQzns2hWQaBZ5/wD1qAE5vg7DESsbePyeZEeFGQTLB78Fhn8DaDMvrZF/0CdBnAM+APAHYynk3Dirl3Zo/LISTca6rXk1fqu1078Yr70zzQyfXUORDcfvs0JRCzdu66nn2tF7cHvjWgiaB28nnimYp8Frr9JdSaKyAdEXval6Qv8nBJMQSIRDRWahyD2ieovegEtM42KW1cBzwapi3kFHkYd49/fwkmTlL2MbJDZhNUT9SNGXvRoi5EMZUwcv41f4GeP5LAj7dol88Vz8nKOtNtYSFKbgY77GS5StRFwJRiHPKS1EHjfpfFQSukgivscraJMvwsHqAhU/SEFW5MtNf4vCQorm/ihA4zbNu/R913qUJb1w7ezdgTTcYsznRwrVcH+cYv6oI1a5Q9pBUlTi4cOBQoLmCIv4yXleRiKhXnsrgRsBfV7ac0gPUaLpjiOdV7Z4EUgEcxwHz7mJQ4zlXiLdLfc4aDwG8dMteR0qQUBaSmuLIYz6b/M+FjD5X5OspOqQgiJj3oKRHQmT28ed8HZF6yC+YOksosu7ojaF8yPpeIb23tORXooEEoz811K5n9TFvI0Senxgb5CapHi4u158CAC/CZ/PGI7oAYMcVwX50y/cjETYzlJiabZIQdsE8XphkUp6rb9hEFvrSpGGWj4jaBW17AjNDOlyR5k3iDeDmukWGiqBBYdYuiuCj2Os+jgNHV0gPVSunt8V+dA/ZeZjADcaXMdWlEITi9xJb/n7w+T22p7t0tN7QqaOr5frb1MOiilMU8vnzCkQOz7sFgBjMtocQNWzNcOnp6RmXyxNwhYeCLm4RCnTqSS+z3U6bwru08oiVv551MLGoP2+haino7M3V0GPkpqa3BzvfDa9fymvv2ZPqu2cwvUPDw/mm7Z8N9T0aZZW+13GZ5s3bbM+54QR0RBZgsT0JSEOP4nI19kPWm70NCt9DPO8pY6GThZ/yvVPIHe6EklaMXt223cJcOl+H4Nnq3lOeyz0plG6KQwWi3XJmugedQlMpZWGcvXy182SO7zQDOniJtH4aCrcx+Jo9Iu3eD8cxsdx94kZVHbaBh+KOwRa6FjE+jAjXygREsMApHzvcAEisHlEBmRs+puyLZPMPMr4mDYe8fRZyWtMgALB2NWZIa+ZZF7ikPj7OY8feqU7jp2VYNDwVdW6g+3Uu75YiKhu2Mgs3uFOng+7U90shlojC6PEBSBvOpL/nTrf2V0++B8n/uWfJ5KnLwSc953Q+u3GjUZk/L568TiXOEo5X8D1rmEAl7irNoS41G/GABzzgAd8mPJR0D3jAA75pSLlYeowYk3YgDlhxiOsgiQOpAk2NAjYA+yjv/gH8rQd+Qzjj8ml8xoEGF1j4tCcCb4BuAuXZIrEq5uL3nF/Bn4nDVD0QrZLTM554lbAubdZajt4+epPvjk6qofkzIomdweRRd+f9Ve5xr77atemZk3G4ac5JX9emJMotB8py9skDydqdL308CQGEAmjPDduHju2T4OWncfaeZK802vwKOtw0/Oag6As+c1WJH/YE4E4pVE8hA3mIIAxlYoSNc6l75pdTkOfxKsITF67ME1Ymo0rxSxNr+yOEbI5HOairwnKK+WF0KAa9rA7su+JpV8iuaM3fpxESiqcmnUgeFqHa7aDfQhVPmsDMi2YCC6BobsEe4VbL4ss5rwubJsFcDmv2+D7O1jVG5cLp8+VirpdyjWHpb07Tg+hcV7ZubSDBBJYp46L0FIpIhFOv1rVOJdyfwuIi1kZMHXWhWV1x4cmA4dnSVUwxqAJScUUSoB6WE8084iIPSAoe1Tz8OoAMsOVKmhQmAyX0UNlvp4QkkXTF25xEOAQnkftn/gNiec24cTI1dfhmgRLmnEV5A7msbgrLeb6zVqj81SVFGIo6+89yfG3MpvwsXnQ0oUB4UcybU8xpCvU0PKPE/g4xzzXJgMCwkY5OuHk6NJV0JiO159lDVW6tYWNCo4atWW4yAkO7Wa7ve0d/MkFseG9QiUmVylI1xdx+HLiICfWv+4FP1yt+ejFvOgtz6jOvIzRVY4b07h66Os3jPzmBGp3g5cnGHescoPTmERC6djTd3KDD8ELkgEh3pbdtjuqKCBOqE1pj95oy3O0KC9vaCB8/KL77+BP2H8OTrg2DEQHITYNCcB8r5IzfmPePk1yo74CZp/KyVoYgtX/n5WcuNMQqmxnSifK6YU4ak6SnchCFUvcdCfaNsm15h5Z7a4tO63oDpjZPxXt9rmRNj/fYi2nwIPm8ejoAhYfm9bxQEbbQ13XwaurhwqHmMWYNCk7E6A+7N11V4oq/PzzpasOj8NHGstlCy0wqCUDhGxeedMhwvZr8iYe1lI4D3T2ITXF3aHfv7571ZhY6DW/x1Wir7PG511DxDh3Pe2/KtLr3mwIKcQ/z4KBirRuT4BkAIaRocOWY0/toh1F6N+JQj6bRGNwY1CMvLOW4E0zhyGou0hG+NBSqcA86c3BW95qWPBc2akATiG4AFPuheV9tOkbEkkKf6jjUC6d78Hotpr/g7Hsoyso33Lup55ffBCp0vLKJBFieezERw9cI09g4ET8b+ntnhKksLcuWCrsfa6NGU3eDSHYDIuYRujW5XF+/ke86eTHfb5gpFdEaZyFfcRGmG6T4y797iLkf8IAHfJvwoF4PeMADvmkQGkmjLY+CMc0snm/KU7NALNwJC5lF4KHmmEDjPoAiYP4dOvNz4aSt5GHWlBT8xNALnBEuR97loFOFRnEgE8as9PoVZGlV4bf2I2Ujq7Bqee4G6KTcs8Nf/Vsh8KFKgN/Ahc8ZjloULdfvAeU/9wuVDlxfOl4+MXhjbL/hqdByDBB4s9x0q9h1EnLr3JV1eIsxO9gFN93fiUOgwnA75pDVvMyaEpoC5BqYEE4FzgtFyCI/BpKOXAk0t6m6u7h4w379TLrAKeTEzQAMudoQZa5jImJzHFGHhtCDyoP5zwlu6ThE500bl7dCbhEASmGihahjZguPFy2uwo4QWrg0JYQqo+ohsI0mD/XDa60YvbmVvJyvpntlTqEL49kQUBQ8qwLMCXyCwsKd2HLJEDwMsLqILsLysIW9BJDemgQXhkIBdGx/CAFnHc9KkB1Zwxw5rdbFcwrBckcRl/aNdcAMEJqvDRf8iaD37oLFZgI6NeGJCNCifX5d9EhRCJGA2FVPlnQNw59geMmZn1eDKJcrksJHKEAa+ZNKdzEUnuvQdxQLfYz5+llQ1kWOl44hX+nVCmMljnxK8cvKJsD7rogEP0F4jB51Melm75Z/zhRyel4Pmbt05pZ0gaw1296xaGUe9pgI2gi0EZgbCA3KhMaupKMxb+ICLxH73RpbKEwRcGvp0adwJeLR0bFDjg6oeXFE+FfJydPhhR9iNLUeVU/Mf6pwi7vzJkPT9TGmRxc0OtDbgS47umzo0tNbyLHNPdwYR+/Yrzv2/Yp+2dDYclxJB9pmipTWOrbtgt29r7qo4wcg0nF0gXRNvlGh2IjNKzbjkxeBb6Vj0S0q6+KVMVnhc7Dkhq2kn6ck/C3B6N/PbWMYAIy1FuGxAYA0VVhLFWOkmMi9q7uFu3RvbWaBxCaeuakSAaxMtmux5KkmqDvpaBpoaOFACJ6ygKoz2tnr1hR/PvdoGvc6PA+d83EC95DDgQOCKw43UPE8oBnesnvZYUTi4S11UK+ksrlRlEZGqOmygQylazxTmOHoC9TDYXaIktNRC4SZ3qxq7bK/aC2DIR49IhSgEY48lG7mnZd1UYRldm92HXNKRJbTks0rL3rDzlNEfjlbx5we+UwWsrqB0UXAZeNO461QtgSb8zk792JRlfgSaJY4f/56Xl544Hs11zNa8q5nTfUuvNaL6d7PODv8llC57PR2IzuLSa792+fr7+kv2BwBwgmUEeEt3cOWUOYj+Huf3Nwr4EaXhtuB17bXjFNFGl8Cxo+S4vKHr3zQH/CABzzgDjyUdA94wAO+aQgFUhxDRKu4G7NsQywXD9RlnQ0g90yZ5An1nJX/jPtfBbP9DulhMsCkGS7wrJgz2YBEjPiMeORCwTquZ+04uZ58My33lvbkYVvnA9eNXGF5P6f43pgo5lx08fukSK34gvF8FvULJMLr8Gv5q5aMZ8+uhVRlguyAPqdoeDmtfjnhlIbwxheJHejPhetTs07GPoqRZe4Bxwctcxy52rSMg/phkDzEJYYXCZGY5SUUh5h3QeQ16Ck3pyHoyXkoQpk6M6QZomnqav1R8eROsvufCzE+d9G9yD+qYC4EGhXfX6dlI9ij1TseruHAatohVS2eHMNKewhRjMDmmK4edgvYfGg2daUFYZ0/Om4P8ZlApQDfuTeNTRH+TQ9E/2l4CKlLa5hNoUqhrYucM+5xkOGifA8SBbABl7+uG87YZEKpMayGYyCKr54rRVQUyhE+0yzvCeQCQBQBR7MQQWoW8N33zA4BsXtN+b0IQ2mW+MPDACTuaeFeXZ6TLtbPTJA1WuJdZPPe0Q6xX2jK7v011uAZqa0lavk+3SwavLe26nUNRZ01TVINO8zuYcSRB44i3FjZYGMASuMFZJ4LGLeM3hnBlH7g2AX7caCL4tg79uPA0fton3uGgDW9Ho0MGSGcUht628g9fbmZMLWxjX0YMPCFQF3BW4MSoXfF1tRySSnQPCGdsREE6YJ+HLi+XPH0/ASFhVm8Ht7ew5Q5sTbZ22HyXRvhVEG+udn9Iwcq+9odSFKumjmowm80xlBVcUhH6zt2frF5bmRKdOffOJP+OA0ixbZd8PLyCf0QtA8bttbwfLngEywE6tHFjFvUvC2lC8SdjaDmIULNcwQZhcjwrx1wwanvQRjrKYwBaGFIb9Cg7jflc0KbE+I87hcGMX5XMntCYAaNoptrEyx7Rlx61ejlZE+a9+F3LARdf1ZmYPL3yp5UsHWMwh+MMptvLjU8c3pSOtqFp5pRceMB3Zl72DkWXm0NNSoaXpj2EEcEg8WbrtJ3u2CboCoyz/T6dCiW1A2wIvtcx+EeqIKObnkdfT2pPyGhyCyfwPCk89YXXmOM+UzqC3EvfM3gg84XfFwaW1cHpTlL8JqlLeHlrpHawdYbhce12tgbHbeJDJ4V5UyXIQK7glhzgpWGb5J47ssYD8Mhz0/sfLY4fzA8bFcTjGDY69iNZ+8OCCH1mpWGxP1T2vkaQS33Y8lNzTmD0sTCwr76+FqXiv3VPNNvNfNLwL2+phEfkHwss+el63kZOn0ZY7jyURXS2ClYpCTLPpAqlqPX+QPy/AbElEpgNA+lD1v/dIYIZXEaD7kaJTzgAQ94wLcDDyXdAx7wgG8eQnESfJqF/SCg68QUkgLaYcmouwtyT3IFBSf7qgzp9+Ky78HCs8ITbssV0GcBLi60wX0mPQ/WYbxZmOrwFDk8bNnEmM9Sirlc3DnQ3LkWwxrn+HrGrw5EWf7g7W/KyQa4YGm6BhOo1NcqQz8JEUuda4iq1w5r06u1zUu/6rl96sY6llTqL+0hNcVU5KCoAai+NIoSEVoDLhfG9Zmx7R3XT8t8Rfvnpmf7p/LKwzEfQp4axQVGVeEaC9WEKwRWNU8RYhC3KdccINbe8KUimNCTwpq5HP7C2juqKgKnVJYs7Z0GW2/79m54U2rw1sulLY5gwzs0RHe/oJJKI4vHTEy6hpaQ5leiVsA98mBzzO6Bc6cnUyunUL1ACo3y+ZN1PhVY6fY7BC16cjHxwIVuIjJyk6ntKxp9Ky+rWyQHiI8DplySjJKd0L+XXq/EMIRuLj1Sz8UDVcP/uC9WNsUigsA83BRQMS8KhWlT3DOCIqSneGtVzMvAR0W6pLV1CEYC76VsJhbqjMHuURajGp5nlt+NUlB0V7ASArWz+7Fs13W5gt7Hp1pWDdcFrzO8U0K4yWho1PyTLewltcSF9K6LsJ9S6H1uMLafSldXconn9gtvE8mcP5qNciGW5yUKJXg2vzUw2HN3Gn0Gq3taEroqrn0H0xM2IjTeTPlInMLrBstXxkzpSQLALdotGsFxHACbQmejhsYX98YxQb+4Irn7EFhUPA9P5f3/2WTuHwkE6zKjdHCx9jnviX6NTDHX5cBBBwgNQhZOVM0F1gxewiNdFPt+xctLw/OFcdk+hHa5OHeawQEAdMFQ0kko6UooRPEIFhJUxRa+oyNYgoqpKxJM2UDOWM6c14wF93CiUo3zkfQnJuWefY+9Zs7jupZwy7BQEoPSMl1eTZpTn8X5O6cbi/MU5eJZL1cW4+ZmlEFxwUodeWWdh0Ssv1EHq69zVyaIaX0sFy5TFmnr2tGFzV+zBy+rgw6pV2bKr+CvxM5lLjy3PVKDuAJElsdTgOG97XgVXC3FPGrSxDomUZd4CEyB46+HfY26e+RwjL/6n0rh/TT/HT07w8BlZpaNyMZj5IxDlj9WO2DRX0jNuCF4UqYFaYjM69Dfsy0+Qmb6AIlHTwiPyvDkD0Ls4QKZyDxmBcABtx4SCA9fN/YyVBRwb131TSwzssa+yGTn7pirgocJdIvv0+iuQ1yXLHkffJ1VI4e6Puj21Rz2vOYk454HXm7R/klRH43Pe5D7/AHIBjMGPmvQNwYr+zydSR1i+la+HAicGBdUBeiAev7K5psROz/VPIw4WtBXezcMAq2MappQv3/zw/2ABzzgnzA8lHQPeMADvm2o3Hge3mjhxu0eOsAHQAfQSjLqkIuGLCqdO75lDs+ZYkYITBXJ494+5gdmFO87v+HC6LDcj8NgfXcq08etyjhvhFDLi1QLWQ9NFEIXLUz6Ulgw/osUg+q1cm9Ye+ZZeS5xFcKsfazt9k5GH6Potd/3UCkOOjlYd6Uw44VJoaqAduD4JHj58cB2YfB3HDKO3wRaI2xPDG7zvAOYBkDP5maRmZ150YkOzyO4dSd4CAZSDJDlmyA4BXOqJlDUIcAbgdvG+uDSxsmCmqKuOCiORioUkfvlLFxs9Os9ytyJ5tTJ0yEseg9RyjXt/fpl1qRaJCdwa2nycXNPifB0e4VuEkJORDdznF1WxToECylKYUl9t4birc/Xd6bunI3xba9nslGEEas3seXr06HwUXJLc0WHplrK6BxlP6N9QgMvBw2o0qESYktoRgOTkJkizAUX0jtAbMro0Bg62lpouuiXJo5I7xBq6ERo3TzczGPLQlqpC+aU1ZVFnnjGFayD1hJUQ9Hoa4MamDbDG4Hnj21QMt8Gs9xnX6898ejeNN1dS29dP5nvFV/GPM3vhFEFafG1qZulhw4jADbyKb60P2GI+DUZ+61oh4ii74L9xb4rGLpsQkO2q+k5KaQZYlgx8JJYzLuxeaOb4RB4KFK7Cq77DmoXEDqUFRRlqQBoqVCZDBY6oIeLh90zwBSM3UJgiofvJIZZSDTz3MPhdRvyVe/YhzStwAkPEHxZCCAj+DCXdSwQ7P0wr1TesXHDhdoI1ReKCLU8lEff0VrD5cMzDnmBdElPSyKFikAOGbm7RNE9hDoZesCXruX/IguxSaSuYJh6YP/6UrhVsuvNr1hjY1xe9VHzR2KRlLHMQoOHxMnN+H2P63uj5gl/X3+28ig3tdzTFpy08O7WVTa8sZs6h0TFq1rHaEStDLVoI+rtFHaabXREoGlAYIoUi4knlSBPDdSilLN3KbVzRkttf4xrmmGhk+cJb5jwjov9my28unjCX3P+c9pV0CC8v0PxZnnpIsyljvvITludpSM6dex89CPlwuBBdHmcnAcuAxSMf/Kd44yjuaZqC3z9K0BhSekbu4IsLCkAaHjfDYxRp8mGHrZfaQe0AXa665aPtAPqntrRp/B8tOgJNti5xHKLozHu2Z+KCrk75XVyXuhUYVfWMLUTfkDrnvgqKzdemtm2ad2ebUETRSg8yaqgWw0u6/vGD+CG5/0mgEYfaJnT8sgElfSubJdSyCNsILlG9Sa1Mx45PhKb4VMzRV0N0x6haON198V1vtKfcb7751tKPuABD3jA7wsPJd0DHvCAbx/GOTStLBHChL48q+5FdwA4TOhUGfipyPmMO26s9X4NcCaYEIC6edShA7ohhdoR7ozJwpzF4TKKUiDdDIRM2GyMN02KOmA+QAVMwk8d1+K8uh6KUvRJ48AWB9YIpxQnnZjfPPAOefHUlgxvFML/aZ5vj2VxyLoXnbD2p8rU40vt89l4nF3/XIjxyx8uWxAxbwyRW/HCl4T0qqnCmeWAHrKCabmU9RoD9+rYUPnLAxtl7iPywz4xg7tCWAY9aAJlRc9AgHBL5BGOyw3Hl4aMgx+lICLwFDMy16aerIf3gC4zptOdnwO3b92KBuPX8NwI8V0qMTEUfebcE94RmmsxJncogHx95pr1WmKNUnwXqJo+QbV41C1C/GltLcKWiY5UPLzzzPnIzPcMR3BOq+IB9xyBjxWphYoktbwvquaNliHffK1mf1zBPAtxdP4rgrax+ANH2VGSLMwlebgrIQhZaC8oQKxoHvxyjEZ4PBaxvwpUCYe/x8zYtg0hZFUGtCvQYkxG/eTugJa3hlKKRZ5HpKFZLjlSNDZBKCtjw4awsyccAMzjYuzlt8Ky3JsrFPrwHrnM2dY90SqanzWyY95mDPOYa7ShUUNDA2mzcVQ3FqjeQr5Xmdep0y31XJMiuF4PHC8d+27KVVOSKHrvTt88JFlBIHVptZJC2bMHuYSbLwxSBm0RZpZwUM89lLhBdgVE0bSl95SSQEkzr1DkNjLFr0BUQBsQrsnknlgCBtkNMF8AejGljpogvCexHzQhBLQPOMHF5cJARc39Mjx+unr4PhwQk7in8YpHqDNhJgOHKF72HfzTC9pG4K2hbQ19N76BN2BrlpdMu0J2Sc/KHuSokCYBQEKwTIfpF3uyNothhtPC1zbIugPdjsHtr9MCJrjns/MWTJxoeevsXbr9Oj32Vn2/lDO0MjLlWWycqL8HPQKo9M3ptz8hAqiwK+nck8uJatfhSSfCma8uDeh0eMllqORunm1QAgncyGfUr0AwBWV8Nf7PvTM7RwCz51mlZkpFsZB5GXI7BPnSzXAg9jBVkHsvl0x9iCDREoNgjcr23MOgm3zGMcQ6TUH2Z8aNhUnB2NeUnCOj2KGdj9LaXlNeKKgoxmZuRSsDldp+21/UnfRT9e/nKFZT+DG50x0zttbMczoYGH/HeDo7ERIRsDH06GXufPfyuaPsm5dwsg/ceGdVlrwydKcb+DIfJ9/vPD54MYz4Bf10Lc9lliG5W1+d7mj6WRd+M1jGt14KPqg18wLk8KCW6fF3Q+1vxoWQeUwCF1ktCortJ85vsaUtyFyWiJUZ/JDvGLEvVir9a5DVBzzgAQ/4HeChpHvAAx7wbUOV3sK9Egq3GZ5GdtOUc+0AaMdQ0EUZE5d6U/Q3wfDlQZFgFL4BuBjTzRqHNg914vmHGAyQToq3KCyuCBSsI5yOKS78eO9Wqq8NTxUPV2+boYgbgcciCTrnPVMKZi6KbsLNejDK8mmeUnh9dMvTT8y8lL6+enJaO3lyiFsFvNP3k4NcOLsAQw5RH30L7RR2yOndhanCaPyOF39FePrQsH+34XgRE/p1P/SuY1HkEjdhbV4BIWdYFOgHQM0UIQA8rJuhurJCm3sMSLfwQRC3wDZFRgguRjgea88BEyg0DEX0OOjdaejvdso+g1m8yRFWzgf6NLTf3VLq7yJ5UqQATMP9eDITNiIjIYDJEqOwJdBZKPH8xJ7ejGdCfF0+o62FXk8Cr/LMcutuv4NwZLlxaX051+mgNDePEFuIYS05DHUUYMIrdYFeHeM6pvckUe7p1JoLMTA8pfzfrpKhNF3kMYrN8SL3nhnekdS7CTWlgQD0MJeP/cO9sqw/phgHW6hZm98OgglGcxMGp1dAwwYlReMOSAOgEGV0NKcX9t5kX1Pn+L1wRkj1dl+f0KrMf1jIRy6UGEMTIjVsxNjQQNhArqiDbFBlV1zFGzGtc944wOi2CHD0juPoYIrcQG54IcWwAIreZXiAkOUbUncTzvVy8WxB28g7GIYDAkFXBknHp66AEPjCuB7PeD46OitYBcIWAnUsbVfWHQAf3crkyDVn+/F+CPphHhkWPtMEuA2MPfZfIyDzHHwDfNUXg1UgfQJjm6wLoKgNfH0rdRzScciOLhcoX8x7gRmtsa3TQtsUwOVygcgzjuMT2tZM8QpAu3nSdXiI3uCdSp3c7a8pgdVyTKpq8mCxoGwtxeqPHeEtaqwTXmiNW/8zNl19K/nfPXCt/TBbuYeuo/DwVnuvaU2l2e9pzrlue+zakwfvyWOVnYl54FCc+TxrzHmHhaCkotJyYX0Y75nSLvYDK2Dsnb7PddtbIi9n5B628JRAC7G700lyRVuEzLV2Bx9juaqoERoZ7RGBt8UN1ZxGRt65KFeKR53lrpPxjM+DQt1b3OfwbLzL8E78TFkny3K5gROWYrqrGHxBzhmPNta9PMpT+Fkt1x0lv1vDS1v4SqAfnreyrFV45BTxMNnxvNGRDf0oYWfCZ5zMa5/cvVrTJdKBYdfLEq4edCvPWQfoFufnOUmW4Rzd5y866iqkdB7j90LlD0s7q6JcfQ3BvRa58DG/19FhHaqVT/4lEMayuTZqZcu6kMKziwAtNeRmSBR8aUXwuoYVyDNcrvGk1yifD3jAAx7w7cFDSfeABzzgHwdQkW2W8z3VfD8hKNLBHMZh9C7cP5V/laAMs3ZvgH4AcAHAdhjiMGOLMQoLWS1CjOV8H13vI4W5Xadx2COhPOSuB1aM6hahkgs9wyrfL5pYc5ZTh7JEXSKYFnVSDgGl8pzbOJjGma7M/zRmVMLVmCTz9AAd8fQn8cdr+LHWU77EoS7qyjJDwPWZpzg5gP1FcP3UcfnY0J7Sjvn9hXw2zIMeXjUhEU6B9NoEnd+OuVkPjnU8Ri4Dv3kYrkNhScU3pJBa1HGFAFHJ8EyhpAul3hSmpkAILIagxi2s67zp/GItQ9+auwVXA08Z7MKjudDegWNX7LtiewIa+xtkefhuDtk0Mn8lWsd6/wwIXyApHjUhSRP1sFBFeDbeuyPXCoVckbrUtTjCvlq4yJqrTtUFHJjpdwodo2l3uriORb1eEU9PvicdOyUKGh+DGFGIJuH5yYbQctCguDYEiqVFr9AVKptc5CRjULPki8pAxMFTMcFqFzXlD+k0WEQt12zknQFsT+yiAAuOjlGHmqBJRYfRB1z42tUXvOfIIYaioWv3gTSFErknGquCfXNWKDYVENzxnSw8WeTbcZR4HV6jwzQ/I8ulCM5ZHGWtf74CMpyt59cbm6j1C0pQMKQzej5PELI8fA0E6exjYu9EyMrjEBx7x74fqZCL0NKGm2UlB46LhZVUBog61JI4QcTUmu7Uhs4d2k0Y3dT8nbruYCY0BnTvEDaBV+8HrtcXPPGzeVd1QWMBkWXwFB7Cb6JmefJUwE3T4IcL7ouI1S32lwLNXOSFWXuA05DKvDrNDR4Gdk2T5prCVVWwawdDsMFyCzbnqTTyyKnjt1quOds7zDumMYMZ0EPQu+LSGgiHGfwcsb4Hna77HwHgTmBhW8uV1/J/k68LgxHoCSF95wD9EshN42dARHIIel3KucXgNXRfGbj3Vvfa83XPO305+GnkhmpdD6WOv1zCIAPkYXwZGc5QFegKiXj5NEz4RMLLyEMkYkTZqHnecqtRmNLGlz27AYPpbTyUvtdjKdNGSNexaXtzPYpu4BSY3QAm9jzH9/DoK+1Jj3bYXqgYSkDjHYrH3CkDczsXuV+U7fvVqU4Nzuorelbd+C9yhtq6tzUUPHH8zRzYzPtTejwilyABNuYC30tgRhuusDV3Khs3buZ1iyPwO/Z5glADO29NpOb1lHxp8KoTwzv3c7CDdWhvf99ZSnfZpGUZxo5d2XedB+6mXFounJIRH6pg/xIVbgr4/eFed19jnwLu4msd0PIQ0fy8li8ZUWPaV4YymLnIF7wg8bDLGboWOgwvAaQsA5WPf/AXD3jAA75NeCjpHvCAB/zjAWfM1CL2hLH/YBqFLOHCAVOOdIxcdLhlQr8y/vo+VA7bD1y4APSRgScCXTTDmpAzxkKeVl11hHesHY6xLNJMRcR5pxTuUnPGWnkIm3WMZXjvhXw/ICzt4+AZZUZoPSK3kAVKiItsSKQ8Gv2JW/UsqHbwlioXUoyGlAlX/31PfhSHxCi/FrGecE4Pcjp/voZn6Q0Y41aEAfOD/rEees+78GXA6xa1nEa8EdpmwhKaH8m25RTEWGjKll6FFK5L+eEFWl4cAE3Q3POSBBASswSHy59I89AHuGyIxviKKvYiZsyp9UkYnk9L3whmeVyvB12pAxD5fOqARPkF6nlXxfI+9Y4SMi/ad2LdXy6Qd/xVJccNMs7PkyP5WRjYKvTIKwQ0Mg+tivb5nI6Jn7zlYhECgCu3WD0XTtTl6yEP41yEKa/08c01QfWzhN16bW3lWtakVwyGUuRzIX+H0dgUjqE8Nhx2oYP/DcIGpAZ5wqhAVP8jTRwQl3g0944GIodQBxHjIFf+kqCBU2iWFvLc3OfLqw9JR4TEojF36t5bCkC7eV4FwWW1fdXmTHGQ9dFksNZu0uaeE2Za3sRNzOWwnrqi2naoMQcRBisd9Hx4qvBnmqCJSM+wyJOy50En7LuH8iL3oGMLb2mhLnkI5cQFvkQQ9aCd4sImYhMOdzLlWCpDLawlESD9E/rRIV3BjRMvji4W8hJhKa7pGRfhMy2yqBEUEcOdAx2f5BMARtcDHKFHQbbnQ0DEUCa8HFc88YaNrtiPZ3Tpk7cn1OcJClCD6o7jOHCRzc3f3TOmm9JORVyBbwJcwypX4IFcQW8jH62C4/A/WQi8jt/LUCiWPcQ9f+KzAZnPBxAwKVi68T4y8CU9qpt7zhKDW8OHDx+xH5+g0sEewlS7eazHIqF1LVH5uk7dyuwF3Trji949RJUH/LxSBpX8OfXWb+8pQV8jO++Cm/38pE23LbG3bslfKHqXl8eUgKAgZpiSzqZPXDNLMnoUUYxJjU86XDmc+686r577WVQ3zgXpAeaGH1ZuhHYMxZNCvdzkH/x8oPDQ6upeW84bpDIuOqBIXi088uK8031vqsEuAdx8njNw8zy8H6nexoaY91TNef5TLsyYbcul0los2R6b8xrz4UrMOG81CvMThXp+YdvnCRRGJWR/yXm5BUtrDU0uno/STcQ8VI2I5fuLnGPJz61IqW/jOGKbiJ/BHt2yZKMKOi+3snZ0cj/Kq6x5Gu7EZ0HGuj3aBCFpW/JeY8gyCkjiC43Xp/J+J1g4zPzNDLQNkG7543qcu+Kln9Pw4Jliv5r47tKmMNA77PzGTB5FwBKhitj1YFMpeeBRkZOJBzzgAQ/4JuGhpHvAAx7wjwPOhHPxXWHWgqpo8TvkofeYuHrSrs+cnQC+BkawctgNwBNAzwR+JqC5eC4VcnY4g3tapJIyBJ4np5no5npAISY0biYg9LAzaRlJyLAUIYgKy+rmx0QOdyuFe8rZYbsDmRTerPcjL884M08HtyimzFvNk1YdVW4O16VjaVl5Iih7c/yj6uUQlgfLk0JCFn+jAAnh2HvqLs/0AzheFNsFqVD98t505kSzPRG2D4ztJwt5eXpAKuMfY5IH4FdO7aollK2vXY+ilzIEpjD6NuUusQl9ugIMzRN4KpJT60d50K8KZSu6CEi0CJ7Cu6Y2kpdDYbTXJ1gxcGPy+PqVpidlI1TH/mRQT/F7LJDMfxJi9Dfb56KloAfFAnbk+oPnDRxCMztED8lHLt3w6FoFMqUrsU5D3pePvELPY43xUpbf9ioohT03Y1TGLXB40MUQGsIEX2Q5XcKjLoRtEjRRLV8diVre0KzLF8RZRwJXSxujG+IeDcLiwjlKukZ6QMFQbq6UI8/J6EpFlczIE0YPypYrzubRhHompB2EUmWELKq5fNJpggDtbu0Mw6f0u9AQFLKXb5+stvLMNw1ontFMUej5Mn2TgG6lwwvtX99ZbQNy3sv4prIug4jF3FuOJQhBQGCYhymRKWpNkUnoQoCwhaVkV14xWzinreGFX0C0A54rScJTTsJiXNyKPJRgauUXC3NgfHYcuOoOFkbX7uvDFIYHOhgHqD1Z39iUiSDG0a94uX7CZXvGxYXoSpaAkLiBes+5hxKu1wMftidcNstdeGnNPOPdy1ZE03jGRo99ZinDUq3z8k8VYi9J/5okAavixWhnowija7SDVdEI2Liq2k2o2XuH9AMCcqWd0Rhm96BtDG6E3tW8lGCh8NIJNhZg4Qtz7425JYY6/karbT26wkHifki0f8akJ7r8TIR5hb94/+Nv1/2Z1ZzXclbNDcNRb817tpdy3tqFAQ5PpzDo0YxmWPZoBZSN3sea7mK4mGjhtN/Y+LK2dZRn3i9rUzRpF1g856lmWM2x3w3vs9gPIkIAsdFW9bRpEnRU7U8wvG+iHRb6fHi0r6B3f2DMw110oOmxwaTMKkBank/v7RIzsYbuDGVEV3EBXhh9+B4VZ9oYdz/3EiwsdXonOTM6WAo7x9k92+kagF7pkYe1Bsc8q4/tqCeVtAuuDk/U0dsY9mju3eH0fiyXxrAWVMv+lHsrTzBCP4/34t36uTZo8IgxZqWPwW7pWAuIOrpFw/BUvBnl4muGGTd/GdwsleCvjI3IbMmqpuTl3kFEaI0tj7FY/skuto7DSivOaxFWO2LuVK/nr3+kH/CABzzgHB5Kugc84AHfNujMXKeA1Q8SEKR1f8aGF/90T7pX+biFW/01mddfHYInDU/BA8Czh8XzQ1nkJ5AitR4HyVFMCqPriaUeftgOxhmiggjMzZhsMmEQAMtt4odEs6KlVLpZXoRQFOp0GM2GRJtCYLBIZ+t8xCEJ5fPmAf/U5fPUTeg1uIczpye9m5/jzB5jGm25oyDIOk+QL5Six1XRnwTS/bD8qvvUrwTeMfacE79mjZPHmtczeUUC4Db+yBUgvWsmIo+/VCK4l60qLBQuW4J0DrNOFwZZ/ihKPErHJsCE8CkOo/l+4usYn2kqfuEA2RIPJKeTe7Xy0fYbIhZf75iajvbr+DEJ1zyErosHjbxQCXtnz3FVbLqRQI88W0ULJ8t4LdXNdOjO2nttaF8TBIXAzxs50x0veMXD7FMUkjlxunsH01CUEbtVOtzLQIzuqYB9D6rOlVMFtXc1YWW5L6poPqaiAHXKfpAWhZx7O4t2QAjUYHnjREHUTVirpnDafHGYJxlSujXG0fCClNJrQsP6WUzp1yFDEHoySeZnpVAPjWkCU0WL0fABVlIcEFOaz7K+sa7nkRs/TmgmnfzVd005HznlhiKR47u/ZSn/1HHcMgGZks7Ci5rmVaHCWYGI7XvtcgFTw9E/ATpCtilM4dqPji6CoxeX8Wh/mOgXRV2Ez7RqjPERmAW6KizPXQjyVaAk4K1BDsFBHR9aRxeGoIM2YHtqsHCoG9QT53Fj9Cvj+mlH2654/vjsa7VQQnUPPxidbGAcbGE/A41AKELoylz8UxeorciqGfp04oNKWN3kXcjCgB1y4CodIAG7UB9qoSyZnO64nFNVAW64NMZ3H3/An/78/0MKPyPSRIFpD+Mh7AY8NHR43jivSU6zGAwWC/UqSlDu6xb12SPz+fBz8Ituvunp3a8L7nAFWMcgA2X7mhU/H+WfDt4ytsUMMe54FwYZBOOnJK7r0g436ggezTzZBIfCDBzUlM7TPpH0HwUBqlegei+GR36ErLQmcvZSoDjijJE72Ph2OoivocvdexNXmIqEUWYwOEOtOnjI0elQR5py0Wj45jTTcsDBDCw8DKYpyr1MiTpt7wgFV4a1dtphzYnfsEgYzJkDXEQtZ5+UcXNvPBAD2j0KgBmQSFdI14Ef1QJmRobprHY7erf7eLBmlRc7XYCFXNbbfOeZs3qTTww2rLCEEz9YLhLmcOzhcRqFqpHhQjO/rt3ul+y+y9SOvYoG357Ky/JOnJmo+aczeJF3Dh6goVMEv9WBq8wmf4jcyASQmqKOpnn5Wqn0Ax7wgAe8Dg8l3QMe8IBvGsLy3344E0h2YNRymCRXzIXSLhi7+QC4MOsnstIpB9pXCGHhHMo4EwjH73EwC5+6OnYpbzuTXJZLUC3MNuHCJlRlBTY2RvogC4UiIBPKL+MlIkNRB03L1zyQYRyOQg4V81tDTuZUaDn0lbomS/BS5s383RHoTuB4lXXrKGYK3bEUe7e4s9OaDhzW5frdthXcDeGbCVV0eAZ+SYgDkZ+8uBHapuj7688u+hCDs3lZvnqKKwMehzyoCRY18D8OiCWPSTajrG1OwUPBqRBeeK01nxtN90a+DQ6vxSJYcnnlvS6VBhHoFcll3lkmdIiE5hwnOr057gxZFy3vh+iERjWL8Gtqbhy865hOA2jl9aJ0T69av5cyQi8w6bUXEV6/Akx9yx+LMCTpxDQ2tzDh0dmNUll8dSeoGcpzCgDdx4U8fyeRewwnAU5PFlEZo01L306hEL+UEMWchVDDVKcUC0tMoGeOM+xK0RC4ufCPmj3qYTgJHjIz0hO5Z6R1vmBVrGGRHBslC9Vo1wQHCXogigK0hulSs9c3lbcF7jPvvhIyLgU0RyG6wzs7WrXmD6z799mCuyvf8wUb6jimZn8IYWjk1HPFmtNbw1NXhJJCSCy3D0VYscB/7ykziAQv+xX7caDL8MI8jo7r9Yrj6O5N7vukigu72MO/WchCqGQbAKA1xubzGnhDTN6+hkMEzKZ0Y21mYbE1oCte+idcjys+QvC0sRneeB4i7Tu6Wq653jv2lysuz5YHbWts9TIPryoM23aB5a0jcY8+7elZe3eS/olBRiCokl8ASKyxh6pBU1dxWuLGUIRpTFUPiB6QfqArQci8NEEwb87m+3VjPD094dP+01g7HbNTb+ENqa63kLgCaXjBwW+q/xFSyd1VPn+2yRUXPxdNSD0f4me/uNCK+ddbUbp/TnNP2YDPPXMUPDDKsO6Odb93L7MOaDfP7lDSKevknTX4EIyw43FbyrUg+24EYmGgkyKZQi741PIHoPB2hJEsGol31Cj5uTD842b0mIQsB7eQmylo8dyv66iMQ53SwoO8e6DPLhXNHE38oN7g0+DJgs+aGYvI2xcpAnINwsbD8ndFzjqPNhL0t3pFttHe+NeWKbnCz2gBMaEp0FSxA57fbxCBmNe4HlERCAQWy0c6nyNp8HxAEpSz8/XpCC9nq2osMPjEk3LulF/ZgnWq37v0kvcdLHNez4Jm1L2t5OeQo18L3qg7usbNjBilWdhLXRnsiWcO/+lR/PRZ6owAOhzh6v2B9BgVNb45lMluUGmFcYlWM6hchNLO0Jfv6egDHvCAB3yl8FDSPeABD/hHAbR+d8Y+wnFBPW+HK+z0xIPuHjM/PUfz/a+KBSQAYZWmgB4CufrJ1j2Kan6DfKfAW+dTM+B3LzombMzYuA3PI3j4IzbrSnLGO4SQkWfO5kTTalO9cgLSgu7e4IZir562VDEr4IqEItn1105nr0F9T0rR5bD8uYgQgrBJqDYdeMrnaxDPqB2i+iHou6AfAjDnYejLglVyuRD6dxuOT4Lry+e6Jr6xnhZBoQkn3IMu8uiQujUmLGTShqGg47mseD+fd/qQucTIBBF2gKTEbZApLUwwMYcSXYNL0cmPe9PJ/lC/cz/kFBHmLsKImaeC4xEbskc4qntgMtoR1nJt7JwD8qwtow2prIn3dAhNOnzdl4JijAjk4UE1w+CmAG2RUoaX2TSeZT7rmE1v1nVO45CvwK2CrvavoO4atoliwSahKW2LRCbeT9JBp4LGmRCijLGn+9IpKZqWOquolReaEEgVeXkkdWnk4YzNu9Xy+3Tp6DDPN2Y30mAAjVCxVwkprEsBWcZv8vmKmKGh+ANBSUqIxiFkzLGEt8uxLy6OKkyIX5V0voNg843NPIk87JE3Xa1Zd53i16lmwEORntObUMGHWiGuTP+FQFgp6cQI7WybkXjnCO7NoKGI7j7FhOt1t3CEIj5dBJGO4zhw3Q/3grNQZUPixSA2RR1yvN0rEgCY0dRGURzxubWRGa47jl+s7F0tv8uG8HIHWmtgJmybScNlt9yGUOA4On768yd8/P6jeVKwhSVoFAYPNmZMhEYbGnf0jJ2IMfDLGvonDdN40FA2k0z8xS2+useqLS4PjWrj3UWAzdewRyyIvIUAp+KgccPH777DLp+gV19FrmyZPJgjUmXwLGzKfe0KFgIaOx82BLUZ1DZQE4SuDJBg3S9fHZ41hMJngeLGC/mzYIia11a8VuTPPR+cjsobhdVdYl5WfoXqxcVjTd14pBO0G31A0itXCldNSOBEGD7WqnpthL0QRkyN2UMpeu47UfTS2Ty35J5NyV+nV070wxizMa1kNJLZPLzS+9vxz5obaruINHEyxivcRdGTyXgDGQa+rCHow0doMP1OBbKf8TKT5Y9t3Eyx1szLuzGDqZkhjHsjabd9ehgq+rqsBmeg3F+IGMQohpO2p3QNbzrfucmipjD35O+ki4W2lnEI0xtv6ehp6dudPWBaZz73Ge2AMJ1rmArO6O1SWUsXVJ71/JlshBdGa6EnMC25siAzdHB4njovSut7vzOU7uaFoPMxBzP/eVuAxNws/FWgXCXDN8aLbqhLJF5fG2y+P5zl+L9a3o11PSuVv5bRfcADHvCAz4OHku4BD3jANw0ED2dZmLeUBRUumNQOn3wAOHAT4x5YzvG6CCjuQJEB/zZwpz0ZRiqYfz8oR1jLUGREIQyyUGRxVr0nfdDb66kkgwvlAKhbeypMCBxeNSLiniPjrxY7LN5KfYtgN+rOw51/imvlQkGHhS9fh0qBoZyV8o4rHavij9Y2rL8JN+NUDxCz9WwZPx1f42HVpek6P1sv3QWyvvVdceyWE649LZV+SfgZ1Zye0U/6HZend13BZk6cOrzrmoXeos3ntGhmE3NMngj2ZwiWLyLLyU/DswgjxEXgVJtbYWrva2Pi624ouTxvyEnhhm8EIUInYCMXrriSoCoclIpyaGnXLf7MV0NRVr/N7fXwMhnGMdZMCMFiTdmCPPOAs1CYpc7wDPFQfauzVVybhC5LR4Le3SybWpULEGpBE9rp7edqYa8x7kVYMeRGgYMmkB7Zy2imAVmzz5tLm3hbNqtpbioxDsQhRFwrdaEnoOjU3XsFILArtYZQwwQYAiijk4XmBIKmA+Fp10XQ2BRi6s9kzhnCEJp7iFsCoNLTg3cYXpDHwq2TOBRxpkvs7oE27rAGHoafXYCF5WQK1a0i1OU+Iq/SyiAT+VkQJNTWI4QYg7GBUTzo0otuhFJjX2/mCc6eF7C7pz6loA7xjJhQU3rHvh/Y9x1HxHvtHUe3PdPCwipYPOhhUZpG2DL19qcnOjDCzvmahSuCGxOeaEs6Y6EQ2UOcKrQRtHdcj0/oelj4s8bAYV5avXtePFX03vHjH/+Mpw9PaM8NzBcQb+4d33z8FcQK7pYrrQujE1uOUCeQNeTthOr/BIFguXo1PBLK8l9IUXodV/8gTzWXXmuBbyoKbR0ihN43D21oe0fmqALhw9NHfLr8BOAw/Mg6vBlhgFCIbH9R7Fu3vHZsNAesiJytQgqG0RDLNWyhVkeP3we/jIP5MkHP3uTJ8MvafcJ6f94L5bsmPtmk6vSgRbvoBwGHFiUdzMgptiPBaIhitorQ5Q9A5D8Nj+MWKltnxMNbX6b9vTQ8JPRKw4sGAJHCQhAj9xBuDaSCGgqYInReKOdSeewnCdWb+RndeQdRunc5oxQMZTtyD47Bo6WWUF8N3iDLgHnRtczfCjOYcP4hIkEMY7I+vOZDs6Wxf4vTF0r+KAwvzTDHaYrYfhG7e/fxi3yi4QVpYS5lnH/cUEkjdOEyXETF7CXwsvCrMXa6MoLeTi1tXietniWKY/kY+2W+AnfetUjfQUCsf5i8QkOGEOk2pDs79DvCe+jWPbjLmsLnkgeZCD7L+MXyPi1zda8yAbSVFalII7m0h6v8pto341FKSP0HPOABD/gG4aGke8ADHvBNQ/CIrMa0pVVd/aRyyA2BWQnLclrgO0GXs8QXFTLp8lmBYAfZDeZN5xwy5YGVxhk9DrfxXnwv0ssppn5U4YOopG7RyXnQIvV8RIgQNhGOqXqQzKFeqqhGgdmKtsruiow68tmlkCosf/1+lDXhQB2zCH06CRRcDHx2LvfDZxwqbpLe34FxDF9gPSh6m2nt772T1FsnrILrVVGg+fKXAyJg2wjtidEuhH7V8ypL/2rYyfNCMR2247BeD3h5EHRhRuBxWKwq1MNZagmX5Mo3KsLMOAhGVe41N5REmnlVWIFjErHczl+06+acWORl7F/IPzOpfAwdmeCid+Doiq2ThysbnnAaSnhYfyNjnlZkWcYx+nmKTieI23P4q/oulDZD7L8Wk4o8H8/w5K3F3ztIT7hR2742OHDJv0/yGxqf8ZfzizHWtc/TdRRhjv/K0JEpgbG3WggQXCgZOWbMW1ATDzIsKlyI6UK2ufLSrmyYlAGojZ6JpgnoCJknDQSCuNPD4YK3BhKCsAlLD4+L1bxtkWeGyMMkhlAwLJqjaWW0BcMbsrtwNJqVQkF/R8nzZcEUXiHUSfVdDm1RG7vHWA6Ht80uWO0p19V5KD0QWNJFez+/+ff8VdRyLT8bGja1X6TN+qUhhHalOTx0qHsvphJNAMsNZuEiidkVdeLKNcOP3s2D7mX3EJgMKCx0Zoo3zd0BBDavpWVNUbe8LBKzImOfVhWQNhNrEQObQA7Gy3FF2xpaeOwxg7fmShz2OWSQz9LRD4g6jnTKPkxeiERgNYWqypGC3QkeMrQBQZucnqwibsPnCH0XHp3zEIoqOhQbLAytKex8ZpwX612gIByH4ZbAwppS48SleUPzS3UfVsPp/kmw84F2uYDMlbLsyQo0VyBThN/zgmNzXOGLClVfY5zuw2stklee+CVdWYMivvFzXB2Okr5XL7s8ofAGMGMDtfya/RBId1oVpHvhybOr69nphGcw/onLNpZclD+uzvNnaxB0cNC6WqCfJZJxpLInqSmstoaGjo5u+4p6njURCDFUu/U9ziU5Ep8xWT8PjZCHifi1jlf8a+zDCG1ZmBqC94dtzYZnHRPbHuG0m8iTL2e1TpnLeFMyNlRa4GGVUcJZkik9xD3qUL2W3GhD1RShaS/EuBmnrNPnrPJ9ayha86qHR4go+7kmGqVCLFGx4GqkwVjTWaxAwC0/efPAzMu/UtxgB1Ha4k24Rw9Ols5XAwQ7ozB7wAWH9EAs/HVEpg1PukR3Le/UzpZ5JBqGR77yUc/kmc8SAEghLujJ3+hjPYuM8LbytY7sAx7wgAe8Dg8l3QMe8IBvGuq5MUN9ETKhOQiWX0eRCdH1ALTjljOmcmmVg1au/ITvW/jOXx90+cRyCAjZhyvq9AmgjdAaQT0ZuEKHhSLpCKlUXQviu48hmbxxgvSeE8u91NHRqAEaQVtoCj8RCrq3ulb7NcmjXXNR8zoAGIrWqnSrhwKychQYVuAl+l4adJ8IpBJODhW13Sta3KDJPYR4rb74pDeercX7WMkBvFw7Li8NT88AX269qr4YEMAXxnYhtI1Mk3UnfqMu753dr31bB1bjwOw4T25JPB26dRz2cWjmMSF279s44cOEhyYTUYTSKUT49eAdQtQIZZeN7QAOgE64qhAm1O5M3p9JuNxCtAgiABdyHoR+KPRQdAG4VaSlMmCr5OzXgQ2x1IpgBaZsEVSFcDSHcv1H28yryxWjNSRNrOkTNKWbL8tvH6sUVtP8zETPaZ6y/AyZX32VTprjdYyDf6GhFCMyPFOGEtVDQnmJEsqKtDS/swBWwjMhUVhSjOuzqDOE+mFhjFSapTKtR/wln0tVXNqGiwLUPIcYmTdVCFbJveKGLD/qBsL1QV0ak6gdnmAqZR5SHe198XZojqIr1uBtZwvlFQveJ45Dya6Urcm25hjE3MCU7mVTp9yxWhEPkYeGNE+6BgJrA6GNDUnZ2ipUvLLdA8r3PpDlAwyFp6hA9hB46ggnGBLAYF6CyPjcKsg9ktSFr3B8EnQVHCI4pKOLEVvxnHjw0GPSFeY730wp24MuqkvgTNh6SAcT4UWueNl/wnf6Azb3MJeuOI4DXUzwRY3w8rLj008v+K59xMab5++j7JR1ceC25aQjsDJE+6v72j9dCEJuYWrXvZtgdERUUrFcvRPUGYF4S/y/DRb+mlgBNjxqLQiiQmFz/92H70D049ScKc+jN0LF+GgAYBb0j4LteTNP4sDh6Aqp7dcippwfSH8LN3tYtOMX7mU/8/Uz1vCNo8Cr774HIpLwDbzWBx1/WhJ0mnFES5oY+2yoUgkEUcJxWAQG6TrKMmSb6x8EubSJxiZ8shETFY+yoNDOF8T5wHfKad8MepjkMXXI3hvHMxPwh1kFgXvkGC5tyLGxwlTHLqklvvUNr/yL0K6aM5WjhPM/I2x/PWTYrET0hsg1lzlRyYweyI0gIgpIhjJWdbpv5ZoHd+Ef1PiX1pxjq6EGcu3ZnLCHtm5E2D2XbpQh+4F+3dE/XW1f873dUiFMvV1GdBCr5I9WPufkrUCx7ErwZ3VeF8/Olfd7C6ZWVEbxrXW3fve6V28/LW2NJn1tW+CZkx+z/akvqa5z2531SaDSv9w7cu3N40tYpidogohXSoM/J4wzg2hGPckQtj64YTxkj31tI/yABzzgAe+Dh5LuAQ94wDcN4R0BzILWiQEmS3zMcW5bDp+VmdbXmHM6/zmxgW8x9b8CrHxn5mtoMKr+TOBnBm0EeG4h0Aj3FqHNxENJUAhrKA4/WkKRWB2p5FKgS+RJUVxVPYTdOPSrC456hFryBsf5nbDIXAojXxVugzH35+Idj+uP7s/EZzmYkcs7I4dcKjGHc8dtuJ56CHxNMvP/Z+/PmmRLlvUw7HOPlbV3n+kOAC8uCBEiCDNKJsrwKj3K9KS/rhcZzGg0GkyCJBKgQNzxnNune1flWuGuBx/CI3JlVe3dffp0903vrp2Za4jRw8PDx5Prp9Out8/lrXLwuIszfj1x+d5zNFBagOkw+MPAOHiHdeObh9CzfsfBu8y5InW041Ea31NRV5EkQ1eG54am4jfDJqmmvD2sPYk1D4QcXnQ0FAUU75X+RVfkE0G+JbTf1JNoGZ6T7r5GKm7CLb4BcUCdhBNTG8thF3HK/hxC9foCCOGTJW0f7UnvGac74Z1Vrahni2r/pLWGIgygIiOk18eqXr+HizVU7iT8icqLLEvKwgrdqjq+mAeLPSygFHgI0wg7XPqmgHlFomfOzpMe1JbWkcDtnKj/O3sgmHGGXQ81VlcLGET9QCTEU1FI6+hbh+gT4LjftGHbMISHqYjx+mKeMQwywvtrCFFN9WUkQjwcnlnPEDVQemJxhrqUHHw1pTpMaBghU2PHUUT5Qxk5wqoFzpDPGSOeZDfRt2dNEDoU9Kak29BAymjEYIkwjoGA/hnhzWLtef8DC0Q1veZ6P8yTSToab+65NsZS3AORJtpTENSrDaGUqJh3iA6FjrK3wZ0pGAQWz1/ULecd1NZjRwMR49oPkO7YmlsyYORx4saZt4ipQdsFqgfEdbyBO60xLtuGCzc0YhwxvuS5ooIuF5P6VNhX9P7HDhm/ffZbNhiGUNUUYBgu2RoWEVBzPPIwqRFibkiJG0QsZF1rBG5tIrxrZIPSBOPBFOiHhdiWQ03xoey4HzzOKEApsJlen+p7xHy1pPgM0Mo0fN6LN/tG4CpNz8y+b3XFfk6rfVU4f/35jVVxuhhhRYjc4M2VKKHAzU44r350HHtH99xZ2QH2DTGIW4laYQ0OGh0hjWVpN5UB0OnVQQsMl0bu2fD08n2iKNzU+QvSkS/YthlNmsnNvMpY2A0DAqWCrs17LakZG32RMVvdkhcYNE0R9PQugxZfJn44fJczLAoauVc3NzRq2FyJ14h8TWqyd6Mhvp9FSEyOEiuTYIYYzDHGikM9UKgqGghQMzphVTQloAu0d9tPSijmiaZHVyo/pUhjUb3p9DyAgXI1CkItU+v3UqGeDGcO8cmafi/UM43qIJf1LHDjPQZ4iGsPd9mQ59NlZ//BYOzAc9v9CJTsTR3fGLcINRlbSSUPOb+YuVRdxoPK/diOEneCLoWxDwhEy6r1l+z95Ibcu1PTGPcR7vIBD3jATxUeSroHPOABPzuoTKefLaBuBC8AcCCFDPNhcxaEjXsnFZyd3NcD2w8ldHK+1pQVLmRsDFwIbeMUbBOr5XHzk0RjzxnSRiI79QOWqFk9S/FwqH00YSwAdAiRM+3kB17jnk2Bouk5QusQrYKfKtVY0pakIJ7KPRcm0E2y+uWZUn4VyHN4QGTL3jn/+urPd017ygzKaekMrabDXin4rTq62KE7+vaD+dMRcPmq4bh27FfF/un8RDz1/w5EOg3z1MLUaa04Aj/09zjMD++hlG/4oTisulXUBAsCpMW52IPMpqALTyJTPmCiEUwWPky+j1FdaUddB3nJFsA9/HgPhOAUwFAQT+Ws0p23gUE4sgAaSuJSrJRTu8sNky6Eki3atApVbppDy6cufTp7Ntp6b6zOrgedc9IQYTorLUvBA3mmNFLsqiAVU2aA0MmFt6apsn47cRTHQxcvnLfndF6CCNJybe3WKEhS0OH4C8AUSMDRD19PYkJ1tXucxhymACKQhTjOhDmUcwmYh5VErDS/Fs+GCJTKoJrHdeS4CdGPWU+TK6omwq0RylV93Cl7KcPv1YSaN9Iv8nuUITXJpX6sobrzut1Lkaml8o5KHjrO2E7x6X1S+BqNDRmIoJ8RGky6QqS7pbgp8RI/tCgynG8R97gIJVl4JMHpUhcx77bCzFiks8gVa20S2H5A7PgmCqIDB2+wvHvA9djBCrT9Cd9ev8Uvjysu7QnMjNbci4MZipahrZ+fX/D08YK2NWzcTBlHjPCtjXVCIGzUcBBb2LmK89Ui4xyV/9FAKraKVJvCoGQ85aErQ41NU7g4FYWy0alDxLwWsSHWY9uaKeW4gVnBLRQewGV78m3ZebrgK/0z1/CyF4tS8bAgsCtRhlCWioIo1sgZvMIURDi/L4Qvlde+lULKxiT8cMe1YFE+t8n5/BcwluHlv3orGu3SfCY8LgkMFUCOjr4r+rGMk8QmXPi4aZ0SMrZ+bNiuqAu+hQrjkdELFlY7QWtIY8vvKmVNKInzDK6q9j2I1RRY4jxb/FkFw2drqLcHA/SdlHN1PO5fwDJoHu559vWuZVeDGCbCRkalG23pWZd9jFDIqaDQ4REZYx3Tk59ueJF5EbyVMvYgUrjRUfOxVhyw6YUo4KEu572WRr4w6MSTLSRsfNGzJ+z78S3w/HfA058DaDAv8NIlxXymO4OkV6/O2QIrf7yWVxpQWVLy9iTdKwQhFF0/NqhdHTzUfARPPXm9gdHn5In9NunQ72eXw6CHxruKcc5rMN7BjMCsQs6E255fvDR0rGk4GlEOfOxXX7K0H/CABzzgxwAPJd0DHvCAnzYkl2j8GC9Mc4320ZtbsTVArzhl4Nbz5+3F8j3qinPG+twrjP5NhbXcUv4ZnAkqFMbXcmPQhdAuDbzR7CXXXFhKgLJJXoTtNxNnaCSFHdZExQ9kGNG3vDITHoYXgh3e2E/mZOaYUDUhoixCOFkbXg81gqyzDkkqqQjmOefPAhhhOeshqJcDXLmfdcT7CuAf1BS3l2XMz+ZumdPPliuuB8UQgtX7de715vGzZgAYoRKPq+L60vHhQ8f2ZAnh9bMa+SVgnWhMOAgpIEl543sOyaVTFddSgICBCzVKTx7Y1LwCqlSNFGAmWwsuP6ZLyU/ngyYU+eGQIRntbFgmpHwXVzKFvOLVfr0FBYnW1zVxZHgqhfIbRRBKVVpQhgYoc+H/5HrCCIroJby7A6s3XHQk24bwqKpCGEX3RZmCjqhd15JmxeyK7zcKt7pOyoOrYOiuom55OMhFyCRCkMf1mTJfOYVukc8wxV4YL6QHmkttUwDCKC7O/lH2s1GZj0AuJp7phAvrgAjbOjfMRPq1i+Zt1p34hqyNVVIR1mEKRwghYtsJNxeiWGGhyOPEUbjQPgR0RgAUpiBIHPPnRcP71zLKmTLQ+mhjZoYl5ilG1hTvSaoOSW/wZ+C8C3L9v8j9R77B53/FrZbEhJesnAo7gDIvihk/2NvmHVmIkZricdANmxML4WR7aj86VBXURqOViiKmCJnHvmCIYeMrUBF0Vey9W/4/6bh+8wlwvOMwQACG4Y73kzDG9ZADl3aBEKEfOza0RBqBWCjrxtguF2xHt1CpB1zQbwY8XTw0GjGetg1PW8OlNewe5pSI0NBwYC9ISUWS9gMakfwEQJ1Az/5mtzs+KQ9BpArIw0mqKzP60fFhMyqmIDDBFHb+jFlMMBBmFkqeMw5Jf274Bh0tqXxoGGfEH7kRy9D4xDpD1vXZ8ApP/B7gL6jyLkztoJvZGbvq5wOhkPm7dd6B5mcg1uRxotA0bkDkSXUaLBbuu7ttxYi+WJRvdaere07e0/FoMqnBs5BjIOZngqUKHnEpHuoKR5X0zoJ7eqGbhy8nec6FYEZWbmjVmNGFPFSkeFhk3x907A9n/p0zRXIjk+zE/Tk5n7pB58JbO6HwZ6HqtTY5vU6lXLN8qGQGEZt70zUyZSZ839XIG7dEi2H3SmweOtOMPzjzSMLf1YgoQZoZEBiERi291JkbtrahcUN3xCG1fNCqFft1wp5wh5t5x/HszcgFCQk8ifx0K+O1zsdyPqiX1kdXqNdX/vGmbYXX0teepXIsKTx4KLJea88fAl7rew5dPMQANYA2gPqgD3f764UoSnnJi4xnkuVSYFLil31i4a7sfqk/Iv8k0PAsX/eoBzzgAQ/4KcFDSfeABzzgpw0pSRgwnRPhYQAvAPaTm/kxWysn8/0lJ+0vPZ2flXHC7N+cIkp/VGF5XzqhfWimlGieV8bjyZiAxi1UncnNA6If3Ou5l/17eLPFOHFhpkniQA6z9PdGqRSB/JrforQZ9U/GZz26ZUiN1TuuRthxhVxYXMb96TPbbH96VYupspXBnSo+mYe1H58Ly8ksPHbyYEOYBM9Uup3CjTvFdjHhxkil/UPAaFBzbzW4p1QqId9YF1X2c3b2XqcklWoKF0jYWyQwYVUzpXXbGO2pmVVmA3gLoYzjrCOGlccjvFsdZKKR58Dr6fC1cnpKL5fWA+2XwAmdew1Wx+B7RcZIvKdwgik8u6h7zHr/MzdaYNvw7AmBSg5ROWAnTiz34uEpZNGyFm7eq+3Uck3n6++BySABQ7gS86w85nUVTiQJS88v97zCEA7ygt9EJf/hyf40VTDdzJaVeyHw03nMk7YstvsEV9RZx5Xc6wqCrh0sQ2AIDAUVCGjMJt4noBE7zeoIBQPB94IIJRZWzlBPK2f12pOcIcyCWA8FWrZoGgkUCscljnH67FGMyFB4mZAzRiE8ECw8mu0tRZlHDHROIVMoysm95wIPG1mYznm6fNBTEQWL5+YKWWomABcbJE8jNGi1GdIQoENpFvOqKuhiIVJFOkSBrh2yhyTMBVuOsMyEJoCoWG4j5vRuSU9IN9J5PnZ8+/KMr56/xcv+CV99/KUJibcN7XLB5uu6d1vb+1Xw8u0nPP3iCao914UpBcPbw8Zs4w2NL5b7Dh0q5EZUEyP2/fBPP3UIKa7/qN50IYRUFQxvRLsTvJR6zp5+CPRieRCVBExA2xiXS0NrzVCU2aSvsLyBQNCAV7zIKq+sphw4MPjEiZeDLaXwX1J9yzftHnOjX4wXtlY//2W605x5LznfWD5zy76t+bNetrFhEggD6eUMLI2laYMc+II01IjHBiMKOPEaOKmwjTCZbIx1mwZammcJuxV7YNRhFVYlTNavEb5ueCgSYEwXA0IWPphb+EBbVlFhhvieRQxwD3pOqaRiNVWT/V/DJt9ScM3OZrPGk5U/Wb6NWXllEpOhmGt1H0HPQ+dhLXnDxhc03nAhCyfM3i9SU7JqD292D/fnyY2rVyXDI0QwZahl9fAG6nmOxeOdEsHG1Me3piuMdrPnxcwwm1rGVefRW3m+KTTnyhOdDBWVm1RyXSeVrLi0ljFI6VTHF61PPflOphwPG4ck31GJAMcBU3Y1oHegbd+xHd8R6pBkU+l2O0ZB04Ws32J8nWxd+qXLJwBnF5FRBOrhb2qH5n81lIWd3zR5PcBvZ/jdf+yMxAMe8ICfKjyUdA94wAN+0nD89wD+WwxGUgfDCYwDAPxcqgTgAtAG6Ms4t4fl6mfLh4I5LKEcvje28ISpjTpvmqHG8OLwvD2NwRuDGlLSQjyYYBOmjSNXMOrsBz4R86IjRX6u7QrL6AwB58+qJ2uqyrdIi3HWPQCpmKtecXmAjzmNeqV8+v2cb5cjkOfLqW2nw5/vZQyjEbVtJwe6MdAn1/AZeJMH1PMiCXd0metDdyAOweHRNHmy/QBARNgujA8fG64fO/ou6HGgXvsQa+7shFpOrrkMfG1FBBQGMhGfEiLKWiY4580E0ryx5fB4smQQAknPH+mGICYEYLTmIYAISMWEC5vW1WLOS/PA/jEO26/BZ099OaVPwrUkkARmeF4YRmSKmBQMKGE5Sef5XRo00Usdv09zisDzE65SH8Wpom59/7XzOq0/6vmeygPePtJiK7BMeghoJkHesu7D4ICywNHhqS9T2bUhPFegQCwEdS8WrYUoQdIzy0MRTvNgChzTTY1JsVx63cN9ljK0hGKECZ4ydw40+2XePjLtN0oo0qxQmHHxdHEVo7vMEjgVSUhlWORCDaX5KE/S88JHikYoTApPOh/7VBCCgQztHHmOYq8kUKchn3b3fFJrg4SXZI6vedNVqWINwUZsyqkuAvQ+vGQB84zzcJbGu1B0wvzcRdD1QFcxrzwV954QmwTAhbJDgZtibvUcc2BsbbN2ikKoY9uaed27B7qlHBK87C/YPn7A9nSBdhvnyIMnMAOHowtov6JLB4NwcU+6627jGqEYM9QqxVgSLIcZQEueqAcUQulfQ7eSOEucs2z7vTM/biPF5F7kxOnJKyq47lfzfiGCErvytkMAHJ7bcN3HXtvXxEgEjkNAUDRVYON8MelFkb+W7f1dEOvqhxS5JqV9s6GhVimGESCIK/6/xGnwrSrr/UFm1IyQEExfPDsb4U08qHtcpUddFKjA8JQLFZZO74/aZXzN97wOUcfdOcBk2QBRthuEpY55+5X+pbd3eG0BnQS0CVprQ4DvCNeY0ZghjbFJg2hPRV3qd7TsqDd8nC+2E0Zdy/unML1G0/XxfSyGGJfYk109Z/nnuOHCGxpt2EI5x7aeW6yHEuLSlB466GvWU3L2xV/koNwcTzdFj+gpdRz8QKEaYVThZXHurSQ6kkeX8ak63VP+q9wvtkxWXzzTByqmQSbDDDHrWN8Z6rP63gUT/o3Paf6DTYnHC52O/scU/JC0671A8Y/eaWOuaSRvWndqqY+UuYxQycGJDLqIMZZRrMaf5tm/evqqKuDhm9MLPCucGx78VdRGp0j3gAc84AE/fngo6R7wgAf8pEF/i/R8CIaw+hfEJxPQnwD9ANAVN9zoevak9csZcx/cqb7+2KuwSitODhzvLkrN06G54oLNFDJFLiP/kQkxUxBOYTXteeYUc5jL0r6wDqTinhGKPSrvBfMuQHrE5Vl+nBsHFIVbKtl0XAuFTLXUDiVdPSDUukhgCrnwqqu561Yk+SsB/rdvWXi/D87waBI4nDxP05e5gHoIrY+sqBJroHfg+ix4eep4emr48FX7wp58DqQYCCALscmRx+CVN+4BYTmAxzu+XmKtZ0QthXk4sYe03ZCCBSjQj8OyQxGwH/vIsdAlU6ooq3naNKtIa0OywTGRb4sZJxqibz09vxPzHO+o2Nruh0K7WT1TtYh3RKnGqPluEa6gjN9ozwniEQCtQtEQRFpuCGaCDOel/Fx13QTK/EhRTR7eQ+ATz/vhW8rFdJ4ov0OPmAf+0plVIJohM4sA4V1QFtW0zmo5C+2v5c/XNNse9E2BxM1JGBiCrjOjgfg+IUZITUJiZBXdPOZzZ2GvFBqhHREW7+4ZSZ6HTi1cKbRD0UYYImAI6poJ6NrkSlqUQtlzBrmKZiUHIdYmf67FcGSQUU0cYDEPMIHhilhsLZ/j4V7NiTDiZVvQUvNYDAgvPRd1KtneJ94A4pwbAkElPO1Q5pxy7zGcNZpn7zqB8Xxx1icXhsK8chWmaJMYfyi6K+nG+qWkP6rm3djFQ1y6ok5VcXx6gTx9ABT45n8B8M/K6s64rJrhorSZAgcKdAiYOmgzZehVd3zz8i1++fIJv/zq17A8MUYkyYXEwmyhUEE4ruapJdTRYfn21BV5JnjTVGhuzOi6QRRg3k2ovODEj1Ka+UeFIHpIW4lYYYD/DgUwbJybh/xTMY87CxPna0oBYtujQYSuDAFjh0KZYxc/p5WFqDDIFcRAv3bQJ4Cagj4ASmweOgDgoVwN4xWhlv3caf6xo0XNs1f33x8SIneYcmwpsVGOUMMR1tSM2ATSjbeI8IizF3oJzZh5Q4OSZyUGKY03GvPtP3zC9XnH04dLGlkMM4axj4AH+VaKf8LLN7wvy4YPWLhf6eAekQ8YTIoGdm86yX3I/mVXVlv/Y78T2PklSX/0pfJ69dqChNXzaJmIU2ZAy3jq8oKbNKARY6MNF9rskzdc2sWVdZRpBaA2DrY/CbSLK+rEeQ1XymWIYzc+Yba9e/P8suxj4YY6aC1zkrPCtetmkKlioZbNWESSLwBge53vLW8MW85LbJ457Yv3HQB8+l+Bj38BtIuXp+7LuI7xOg/rXC1fTtfnwm+eF7Bco/H5Y6dRFaZ+ljOKnHSCG8Ab0DqghxllBNyjc3XeU1lbHl5Dlla+2KKWGN84ewTb32pcDTg9IWehfV0+lHQPeMADfqrwUNI94AEP+EmDPgPH/wBc/jv7HcohCmFwYeLTwLx8Vg86FZOvJQxZ4/khoD6nhQGt76zP4ZVypo6dvP/WswrooZBDcOwdfDWrsq7GUXcVC8NGmPIQgCw3kZ2R1EyjFSM/nI4mR5isPPxGv2X8pQDJioZOp99lSOJ6iVkfZaZXnP9lbrkaDlO8TdWkDzCF3FGerfXT8jkNJBYJ2P22r/eimhiis2reO631mdQRlZe1XL/XPsXcpj80TCKq4pEz1V8PVVimZRnLk1dybVsIVkJYWCc+wMZeD0BJ0aWb0CLWfHOcgYXcY3WhQnMBvp0SoZWQrH30S23pQYZYXV/QWDdLf+4JFgoaxgsqQN/tT3qZ19ogJ2YUYaRqkaGo09sq78FNNKY762Y+a1O+S3nRf6SHngngmofkKjIeaCgSEFM6Ss0DeYzPyVitfQsh3Ps6fFtGFezcTNUqZKDbdpgiiNKzFf5pNHN4GU5jHHuQ1MJKyTVmaCCqVmRyIeCNy6F949wMy0JkIHLziCtVDulgNaUeu0cbu+bchH8ulmYGmP1a7AwwpQCZd5+17owiZRA8V6KTo0sEFRNTBrniUZTQyHAllLz21NjQHQNN6YsR4JNTaFPwEO6pB7KQza6kI8Snj5OY0i4NRGjc00KgLdSatarue6pwhYlPnQp6Fxw0wqypj7mta8o+TMF5vTmq4dEmLtwa2Pn7/xn45T9z/JBY8wMnOrqFC/OxNi88gBqhqYUFJmJ0VTxfn/Hh6ROetl/g8uGCQw6/P/ovqtj3DqUORccGxcemOJrR33BcHwJ69xhRRgs5v2LeAN5Npf4xQGUoR/g+EEaOxVhXToNa0NjG7jnDpjhTQj86np9f8Mve8bSZgk5A/rfQvLpX29I3zye1+QN186K7CsAK3hSsDGkKugyjHSGjKx2mRPpsc6iJ4fn80fsu+FT94+4/E7NU2kjzunwfnDEFt0+8dlOD6QJyX3C2BqGZ0kPN87EzJPiKO7zBbCh11r5zDvP3f/8JL/2K7WkDFOZhSbHMx/Mzm0XDM0lnHiI9RZ0ZkKNDtobBGA+KTkRorWFTgciGTdX3I4Wy7UmW725s2lFn0uPyb7a4jpH35TabXdzE2O7K5SB1lrcxOSbzkiO28Jb+Z9da0swcL4XFtVeySBWHoO/doqAcMngGJYuqAkZrDY2b/W6RWzYph9EVdmZAI1+s3VWPrpK8iw4+jQA3zDSeeuaVzOMcE58a+2VdV5oM1Dru4xVKRd4pnpYq6njPD5xDjGnF5HuPr89NVU44W3hWGKsmhym8xMPxzyGM/whQ27fw0xNP+lrjCg/71g6eSm1ChlMWNTaRaSjnDI1cIEPO5nLxosu22XnH+CL2ZaejrncNwgMe8IAH/PjgoaR7wAMe8NMGBfTb8bOeizw1wwh91gB5AugjLD/dDlPklHdvQgMuB6yba2ftQTl0np9fZ7jHBI+z57s5eAt7qTiuB+RZ0JihUHQa+UvCgj4VdZ4zSEKQ6B5nUy63eu4PUYQiQ+VQt+eoly7rGP9TS7oQ9njhNSRcKuNgZUc70sMvlHphgdthaTNi3KpyMcYw7nndWBWyfH/q747/yfVwXMgql2fWV2j5Ht4ivMx7xe166wy9pAPHLjgOwaUzeKNyQvpDguey+NBw+arh8iI4dgulVKfh5LUR6nC5VUWU/qgd7FCEBF64MiyULUyGAcAFgzAFnSMExynxMt61MHQKiHieHsfLqnFK769bYnD8LWP7036byKd0KmhDncMqv/gciGYNL01X0N2BXLe4j853XpwlE1LrDGUlplOxahHk04zEVdbq6VByXCLlmXlo6FCcnjT8VChzp/mvwU3RNM+vYij6dHkpDfwx+hCeWRsY7Hl7UvHoL5ALwWJNZIDQ8KS726+ouFTqQrNTQpeTFX6QQAdZ7iK4twENXCJiL85y/qhvikoMIvFP76MTKVIGF+wKxYESQZWh5F504cVVLTIiGx+pe+TZM6IRYK2ESXNBjtmVLArOlP9RWk8rFZG5Wu95XYzqOeYEpnSQcW8Ik80zLj0zcuEOvI9cKTZensWHBn6kQI8sB5B094QTxzDHEQmkgNGyEKLaU7EhOw3ypkm02eH4Bvj0N8DTnyko5j9xzZSNogr2+kgJnTq4NTDYlG79ik/Pn/Dx6SvPUQSAzWtFnSamNz0QjABEOvQ4wNrRWHBh8+xWEBpZXjqbN1Mwdh/rsJv/TMr0M4cJc/K7Y3fivaGJjR670DIUeUzhtQlsbQPxBZfLB3DbcpQ4M1QAAOwzSURBVC2b8s6U3Nx44DcK/pZm2LJxfgLmlSVXw99tAxoU+hFA89Bjaiv5gCnqGgY+3of5rvr++8MGvPS6S3NWXq7CmQ7xtedPy8DbK+Ds/j1e054P/pqN3nQ13vBFgEOgO6D7ScHZGFoY6ZN9RnWOZ+rw2//8NX7xm4/oHpqQ2EIsUsWBMm55Bqh7MJX8ZjrqTMWR56aL0MNMDY3tHoeSizoaWQ7GCwS77ereA0ln9JqZjmLsllF/dWt+B1MyFHSDlwxjEQajwejwRg2NI8xlPAPzmPMzjhBSuyoikEOg7vnGMsJmbuwKOvY+u7cSgX2ficaFEYCFwSWJaz7eXbAfO/bjwHF09C5jZNT4/LSigyvops7H3mXPWDhUm18KzsDH8RaVdfxb2aBoe/BOOr+yso/rNE2zNT2AuwEzaDTn9DzGzssG6xw3wgY2fFJ/eGr2/UGwnWf0qHCfAJB5mBWYFZNOA+LMHMZsVq7hT7C2uTIrCfIDhNk01Qm907AHPOABD/iJwENJ94AHPOAnD/otTOG2FeZbZ2awMtEKeO4d5PkzFEk38tHyIxjMPKLeYQLfVNDVa68xknTn+9l7wQEfgHwSaBPQRjg8L52QeJx4y5dDAqDR5J0FD1mpIfQTF05rYaYxngXB89TYs6GoYx/7sLpeItVUOaMJsYHhQQd/X70OHaedqqTL0DwHlokt/Tkbs1uZl8EzgN8p8KfjYhwipvLrZxUu3HwpeIVl7DD/uGcgflLNTT90/pkgHei7WUtHAvk/9IkwguydHd6yjXV8qjDQ/8k+14PcUkiUnYo/gnvuYaTqAhApGOMZcoF3YwLYFHd8YcuxRgC1yN0z94v8hEnFzdZE5vb3ueOqpX93JW7LdaXRh/CyqRWv6HwGVfH0LmFgKndiQQ4PxkMVDUZLPJhgKpdT9KTRLp7KTLEOeVhFu5FW7JKKrNqY2/F4D3w2yue6GhUuOsab5+saDCt+XrwWG5EpIEPYoCMoJNWSHZe9Ebcewnk96O66mcSCWoiVd8LoeHi1SCpSMw+jW2SIWjhDuy4WulbJlOBwPzclqJg3jXlmhXLKZz2UBOEV5sRUJpPtnv0mcI5P5IzMUI0Rfta998jLUjKn6RbOA2Fo4etkeHwpSEMhaNVbeDAxYaeiCJnV/yfH3Zb59sbamydEY8NzCZxGSGkvT9RyvIl0HP0wvBdB9xDUQRAi8PQgbDYek4DK5zOHBor+7RV4+ggAOD4B+nfA5c/GOsoyHA+6dsgR/ofNxoddUCzsHviK5+sLvvn0DRo/YWNT7hCTGcWQQtGhephHnwhIyPMnbfiwXdBFsKugC4PVQh0yQiBtuaQaRVjT6of0Hgr1jwTKErf9id2IZJCIWFuqAvW9DYB7020gagA2MFuo0X4I+n5AP1zQbAmb4mKhM+r1B58YsxKYCfi9bstHFJCmkKvigJinJCk6i7FxChwYCvabDkY/l7nnzfZh/QIjo8Grf/67CjLeAMEP3imDgl5Ubqz4ZL1VdSFMxjd+ZlsnwyDfe4KcRevVJqh3wXEo+hXQXaEv5kk38Wx1+a1zVd2lZoS4gX/469/jn//r/8KK0ULH4DyY092Jf6bR/hGykiBshjsqRqtCQSciaF3TG5zc63vjzaIk2ALB1etnaQif0SxfzSBopKsu+zGKedPEjJbngIUpofVuvma8QbQzcqYSmE1Bd6GGjTfLo5yHVMo9X9C93+HhTkBXyAFIJ6Cb4Qzc8z1y28V+HHNhbJ3kWSo8wCtPIzAjkC62V+77Aekdh+dS7e7JnRG3y55dPSLrcEa/B26OOiva5dgp8M1/AH75Xy846s9WI9Bpdk6uZfkr23T24BsQdRDmqde1LytzXsY3v+vc9x8DrO3hBuhmtFgEd8+20f8IOwmMsVrTFaxnreD51I30hIzvzDxzPpZUxjRW2uRf6RV+vifzAx7wgAf8OOChpHvAAx7wk4f+PwH6fwD4n45rtDLGIbvcAH2ysBPtCjuVrfl/aHkHg6EOQ8GaBw9+r1rdhXfIVKZ/vusM8NYDUZ7eXhvKLIX2bgeYdAxS9DjIFlO+KjzXUNZ1pJIqDluR8wrhAVA92mpoSbo9UBUZoZdJcyg6D0OY85FKw9EviLdrDWNJfn9JXj7hQTnMxeGutg3l4JHX1jF+DWj+ejZFp7AgxL2pBXxMabn2RtkxzTdeSX8AqIciboTtidG2bpb2fj0FiEWQpOVQX5p+VgEU7iWn8BxQ3kcX1POyFq1OD9XHhLYRqJmsqV3Cm0CHxxwplFxBwWzeQjAhSQiTDV0+7wh491mXwdA64asA4LV5q0ICvbmEUFVUN76VRJ61UxdMzOCEGjnOxhF5HIy9vvCoUkWEUszDeikzQ1xqlIuZVtQx+Eyo6H62JRRyVYS598vC8lzF6bB1bxheVxls0UNGKihpLbsFeeSiycJLuMtc41QqrDnsctwD6WurFmJGhbaIopOCWNF8X1CKPKUK0W7CEdhGYHmtGC2lTgyRDpCFrzNVG7vFtLWjeQhMkCuAHPfY3Z8FAjPjd0JAZSMmRuRXiv5F22wMu+OMB4IMhX3sy2RFcwyoE5v0oBaykLjuOZ65kBxBYyaBBqLmnkrkY9JyjBWAWEEZBiwIbtCJoG31r4vlohtrwkI5aRgJRKhgLeFf3ZJFbRC9LTG9M+J+8x8Jv/iXFn6wE4N9hhopiLYcOwGZksxxgVjBYnNzlSv2vuOQji6KthHatqG1DX3f0cui7GLMgoVEg+WI0oantmGXjkM7lBmbKJQ3iAo69fQmecjS3gcx4xHksho8ABaujtiZGc8fZV6LQYcZ7CHvyFW0hqf+2/FoYpt0/B4zrvlfOpQ6ryYHsD93cCccpLm/CtSV074bRdPvWSkVaBfy8HBfhii88BbvBWt3vXBbfygdkGMx6HDlN++71YVhkKb3rbyhpFvvXrYhyA5FoVB4W4ahDdC7WtjsK3Bcgf4C4DpCaJ9ucqWdQzIek04LI0rTa/v1wD/8ze/x63/yS3RVsDC00fDGRuyduFseseHl7Llr96QL+iEg7maEwUBDA5iw+15D7DnruLmRgKK5B5iSeG5TABBknt+1T0HvaRqgfGRQ4vptfi6ve/nNx5NhRhGNGM0935giRx0nbmmcuWA4wq7MiH1LO0F3Aqu9vzFbuFu2vxrekhw3VUYIZds/PaoKPNxl7GHOy0gXHEfHfhzovUO65UW1LVwtd2vmIHAbovW8Mp1B9NytbFmsciDzpety1qxGoAvbj8o2VdQ8IyNaP72NeSbRmZdMHvsOEx0pbVd+UwSWI93P6GH3t7b7jwVViVZh6md8Xxo8KSoxnlUsASKWQUkeyc//6dkKV2AHXQs6RupRgPx7CGSmiuOc9mMY1Qc84AEP+Hx4KOke8IAH/CxA/m5W0iUsBwSPXAXoCCkY/OYQYGIcXPwMFEb2IUyvEUJqHXl2favBn8M7fg4Hr4DuAD0DtMEa+tEPG34giFw9EhaN9UBKOnLBmZwHLKX6EBzLeCfy9KRMWPNnFutFjzCVHv7GFILjRJTPLXVwhK8MpWqtoPS9SlQURW69yq/P4BkjxVNt+BncnGLmz8/JuXJ77F/K/QxcqWMnXXFcBS+fOj5uJuRubxXwPUHbgO1DAz938L1K7/Ut8Gg94ep8WFSeHYmGt5n/NYCYzAuIYYLJRmj+GddNQOFCci9LREEkJlARE26al1dHdwFHHCVfMa6/+RH4nd/PJr0SpPhYxiJ+CjC1u45nFQ9R/uuVnjV6mQ9dChwKufAMG/m+ssmx9imCFVIRwroH1lqv05AUOC99uIf+U/L4L4CboX+tHLp9PtetT1CMr8jICRXtU2+sWRefCRWWhrlXKKqncBLWIFI+j6noCA+sOwWrBtGFaHcap6Y88f4J2TVWBrlg1PY8C1coKuAQA6vvpUGPQ5TnQkhhNa9VsiwjlLGkGaYeis7aLGss5rw0Dzp7DQR4DjpzqWZ1hVfKbDw/HgCgJd3Q2N+65zcSBbp7u5WYXzQy247/XHBKyt4u9hxzAhW2UGMqie+Bt6G0CiVJCBRTfFQJQCgynZHQm/+in0aHLM8XIEe/meqXv6EUwAp1qHIhjB1EBFFg05Y0VTlaRbmYX44rvvn2azxdPqC1DW17AvEGgEo/FNK7jyk8VNsTpAm6HNOYWAA2D0PnG4N5hEhRTDwEawNCxAmjQQRUpRYRuSCcEs8AM0JB7AsTH2OeVP3ouO5XPF0bcHGqLIOXCxJSedppb1CLzhC8McgjIHRYjrPnDr1SGoiBI5y6K6Cq5uuEgK9hLdtFwe2ECL8XJi/Nz4FRp2L2ABy7WlHOERAhpzXHffUcvGnc9H4oY+7ue7hdIfuT09poWLhHgSDojh8emnRXHFdgv/pZ4YiIie9kNgMpdLqAoLV1T+pHx7e/f8av/vwXiLyaAJIekHoes+5zXnnvuumSKeu4Rd4q9dRphs/Uxc4RSsbrkYXWzfHytSEq5rGmjC6SfQ6Pa1a2vN1QENj1X8EJlb12GY7122R0E/uKDrwO7zlyT7cIcxk56DgMfcZg5N5BSnYe8mlWz6VKau+a55zn5ONQ0tFEN4YBiUIZ0K6uLLU944App7uaF13vgi4de9+x9wh12W3fEzckSSW6tTt1qoXVMUMB3JzTco2d0Jr4HWdSlM9TD7p7EONXhnXiv0/qvPH0ii/3iAkV9p3GWg16qgIzDnJD0y8lZ98HlCPGlwPNX0/nkgbr6eyl8UbIrSFJRvAUw2hBIW48BPfKjjDm9Wwf5Q5c0+n3Ax7wgAf81OChpHvAAx7ws4Dr/wi0/xb15Dwx2MmUO4OY3jbzOWhwrv77xmFLbhn3qK9+TSFmbYDfeIfh8PthOVdr7ZcOoQuAFMKElV/8S+FB4Ry2RgZ5t/pL4Y2XsUoJbmRrRc4Xv5tGPgUXVEYymqjHv8cBjuairI47B7MhRCkv3BsuGu/flPNbQP8pbGd8jcGn5RM5dENGjoIHy+uvCV+AWX519g7VC8sh0wQfpig4rpaX7oOOh+1g9Ac+udzr+GuvxDs6DrcZTkVHsaj4jfnwRwwL3cUANRNUsufd4XwhhC0wxYkAgJjCTwQ9kI8A6gBPrqIeJjA8W+4g0ivn93GIPZs/hBBHq1x29mSIvuoQaHXSVAqncrgIRoang3sLvmGlH6q06hU13ScCEzyM4SByq7xjUtClvI1cyeNiyIlIzjDCdd1pZxzsab42+vE25DMnFd0KZOjmsUqnbJ5K37xhuRQmuZ3n2wCGtxRhWH/4PkPrGERc4GxgWRBr5+/2yZRQ6gpoNbN8ABuYYm1YqxlAy7x6SOFecRczRZUCSoSu4rhKRuth3g3WH/O0G+PJTpGiXSWMmppAxvYv8rHnfHZ4AVkbqBKDkA5K4BpM0Z4zKBBxLwTxdkym4jEJMa5lAuN7d0/aULw5Ig7Runka9j68mCIPUBf3Aly0H7YP28SFkMlba+96eEl3ks+5kG+ut1OtwNf/vuFP/rvuilSFsJnuE4+w1grO7hLDjBBYwBDsarnlDvU2+1xdPjzhuF4g1wPilhIEdgUuDLd9nTM3XLjh2l0pzIQNDR2C5i4R5qE4cOEBBsNjPjw7A79MudD878nE+6b4hGGPGaDVnHSCCEnaWkNrm6lLRYAOMKsL8nnwqTqRvrltKFvY2FrNS7QD9Gy5YDWitzrPKR04euDeadGnhHt7snBrX2yRoafFvutFKmtyXWh1X45f1bOwMB2v72XlfQsBrdHsu5+1vO2JjPeJspJO+x5DgIKNbnSkN53sxucIBj8xVTSIMQaDWToPgpl+TYR9gt/956/x5//8T3DZmvVNAdO3UjrBZJvJeJta/TDuQBpfRf1Kzrcd3cOVM1TMIAu+bti96FoTbLhkFJANikMD3cX3FPUgvJrdTdyGhVt9nX0K45CqZnZPee8znB+1/dD+NjIPuog+YFEvBveVHuJKHiIU097YPKQEs+XB3dyoZGsNNU9rND6iF0iwgx7SJMIv737PlHNG/0UsvOXRzZNOi4LuxiG6XKtMFgXPEHO7ro0ytnWYj2+B/jWwfYUMbzmxQTr4LEW5t9CwL6EBgebpsVf5uDMe67VK3iBfd9i2PxrMHArc2NECjES6gZv2+ks1sE3YW010q8wRKzKK7ngovDwLfpHtbtVgK881azMGYX7AAx7wgJ8cPJR0D3jAA34esAPy9wD9uf1MhjB4tAgz8QLwC8AHwBE2UQaj32ACt5IOOw+RyreHgDOI4+obZ7nbk3aFIsGIg8FdgXI9DPmfHgDtAK4AGsyrDqOf8bypHkJ477dcGSbqXnT1UCjl/doOFywDHpYkZMb+R55PIcN81PA6clvWzUFnYt5PxuA98J4TUK1XcTu2r7x3dntSFK/jdVZVCAaWQ2+Eqqw6lqk7ZVzqNc1G1Mxgf3ggApgJbWPwRmibQqrDR8h8aidoHu6bOa9zUA7MBMwKOgqR5njWvDVcWOUCe1VTlIiakq5DhjeelyM18UJt29TZVwZCp49xmcxQIG4SLHwRYagtotqqdNdu4yiiaC0W2VhGdYgEQIS1C4GtFiHNaP4qYXkbqlVsCtdKl4fXHob3h4/9eMg9sig8aMlp0kx7PwfWULCvvv8OgQkQ6y2EbPVOFKPTO2vAq2jX2pZUzqkMhW/m0DlpR24+8dkNUYe7Cm58eG+bguoHbt6hgi4AoSMyhpGG8Mz7TQTCDlWGkClfMu+eNp8zTdoVypoIx6Vikhgh8v20ee2EDChXpWGhSISW9tqgWNir7p4RBFX2sH4x0LbQyWM8m4DTxklc8zs8hgiIPvuYCMRDSVLm/Kn3E+dFzTNFJJkNReyjChEXeoqkgk5TqenefN5lYoJyqFhsDDTKUUXHYfXBYnOGV8g9/yAF4fr3NPAmvOTCw5UAbaaUI76gbWSedF3R6QCjgUhwaMc3+zMun77BVx9/ge3pyTzqWsPBzXLKkjMYV2MSmDff3y/YqLsHoimNIs8Uh1CaWyohTXHTZ+R/QIJhhgnyzfNmhNYNPsNTBZshRWO01kDcDJc8n1ffd8jR3SMpvD8VxJzejJT/3MIZi+RLwnC/j9B08YCyO66qKfHS0eteXQuxvH6yfeJLRa7JT3zuewTz4gh+YymDynMgo2Y3EFvenfpH/91zRICj8HH1M9khnZtyXBRssYjd41nz4bBZUOrm9dgZ6Aw51J2R3cuWwyqvdKxWGIwKrK/QsCTKCjA0GQNevrlaZIIYBK2eMDTKyzOEh8d2JFLPJUpkCgICuaFU0GPfR53WUmOoWk7QxgxIAy7exPhPzfNZFTg0eGNJ2jsrOYfXMKAZ5rzyApqUmKbPEX1g/c9DWhKjuTcdwcJdxjXfwSzPNyhDtpo+LcbElXPUrExlsJKHmmZX0BWvvBwDDCbOkUm6opOYIk4FQsAhln/ukAOHHJAu7kGurqCb8bIaZQ2oTP5AJ5Ki8Dzj7Qse6WEKZf2FkwY6X48rv83r/cpDvQGVdQoWcD17fYkOyLwfx98fPeTlyXgE11V/N49CFOejdxVN4zPscteOrsbUBBsXEdssqDHQTNEc458+sF6Wkd/V//oBD3jAA3668FDSPeABD/hZgH4Cjv8IXP4cAI0E4OGkhQ5gNwVdewZwFEHBycE7BNBUL+p8lsh7cTAoh4zkQ88OBPeurXDCON+cferP2jge7SE/DMS9EKYEk5tn5ehvPWyHvLSEwAQwcsUtDVQs8pVQgNYwJdEelweUyDwuEMY4IegoZ3r21YF5BQivOe8Af6vAP6O5zNNJP6+S7lxfnzmDqRs62lqfn1JTvVGXdmC/KvaXjg9fNdATLxKxP9SR0MrfLsD2gXH5QDie19wu45Nu3hyH4vX5PCTTyXv+Twg3qudP0AILuWpKePG4rsNrRSOSnQmVGzzUJY3QPRiCCOYhyM82dpy2uwrz4gCch3xvnJIJFcynZCi+Yq77oTh2Rd8V8gS0Fh4W7yMpJkiaT8ThWXlmiXoPO9S9CUOIJaKZN8J0JDRySZAJ50OowuR906KcAcX/2cZR+sD5xP26Bk+unUHFq885yA88VEyayCz1fliy6ckqcAq8VAVE0F3ZMzU0qsQdQVB2KKQ8gXxrIeWFOvUEZF4a3+hs3AUm+reHmDxEmBI6GKTihgIKJfsNNdtmJgVxcw9HytCerASi5qo2n+sg8i5MVVeMWbWCYSXd/S27NtY0Q+D5hLweUsOrVLkRj7yr4u91mNdbSngsjGel3ITy2+dIQMNAQi1UpIX6kiEgVlN0if/OvHP+bBcfKxcC+xRPYGvdDQecNnW1nG7iuQFTGQhArwd0vw13CQDXv2V8+s+ED/9MfP2Jh76Dj72NW0dHx2Feh6wlDFxDU3Mrp1TmALQxLh8+QI4DJBb0l31rkX5kuRCggfG0XfChH3jutt6FDMMu3LCroBHjyNEPb8kv8Ko7E8j+VKHsc/mTzJDE1tCWtDHwPQwxTLE8CiCyEJMEhfgcmAIvFAWGddJlGEgUcnFGf/Lawg+lQ7BvWhE6PqhTD97zNZp2b0he49vegCk8/WdA2gsASY+n+xh7dGWt4lps3vn9rA7n8+IlHa9N9ShmtrzC4WErgWHkkjm8gpzFmaArSBna2Whu8lal8Te9XH6ni3fQydKZE23kX/9//w7/1f/+LyGSxwnH01G+gqcNXSGQLmAxIyNiDyJJxUPa918R967plMhFbM8wE1jNk+4Jl1RegRjoV6gcyziPSaPlmu3HwTOOcQg+LFV0boAU+yrryC+XoZOJ0diuNle2bWRqd/OKJQ8P7HsoitLJCzfjGUbjZuFAxeY0c9q5d15yKjE3Nc+Xa/+UjK87xBR1Ag916R5zIsEnUvHEnj3mtH5JPBr4kDmH4zxZ1lTi9R0cr1DX22r8lOvxzmK9Swdy0Y7fhe03MlDW6N1yys21SAUsjcQB8IbhbvZHgrN+OKm4NSq7V0C8VK5VWrjSvlgXwZplG3zSJfaHwnKfqd8UFlqdkhatY/1TZgAe8IAH/GOHh5LuAQ94wM8LGPD4dm4VHII/Hd50O6BXmKKucql5YFg4ShfycWWmF6a0fpfCyE9lYHlnOnCVd9aDxNLGU0jOGuMwHgKGA0PxVbumGBZxpCkktUNYeEHE4Q5z7rk7HnUhqMnnIzyoG8jXnHI3Z/nax6pAfUs4Qyff7/HnfurKdq5wxe18vadMjCl4S5K0RJ6Zy9byVedDYr0e1aRwp7StAZlDEaEAUKQi5n1qhe8DivdJ/nN+rjv7fV7i7e/IK5myI1X0aqnv4ZE4hAs+gOH9E/g9Qr7OQkTBUGIxMyKBOaeQBNmx/W8YT3+5ugl8Hvyhjpafa2e6ChgApHKpi4KkeIApzBOHXPhWBrA7HWGQK/fGgh75JWwSNeYB9r1arX8J1k56tTfwT08u3siazobQG7z4byacj7rjXsHd+jABFiYuIokV2ntTeFpg+EY1UYw7qyyVhEZDhIBDLaBkA0y55u5WFmLIvQnElWoqHo4ygnuJrQiPgUTEFmpLCMot3gZgnh5wgSuhuRKh5ziKwkOe1Q3G+qYDcdJyWkDePjIBmOOfaHjRGVJJJ2h3LxOyfEOxnus4cSiKHP/Mm0HQEwmrslCHd6p7F8TWKGrCThXB0Q90EQ9xas9nOCa28VbflMSFpOpJa0IRKHFNfafxXEL3PHT6ldBfxgh2n4Pc8gETxjYLZSk4fF8UkDI6bei4YD+u+HT9hE8vv8eHp4+4tA8WVo3cg0PNYIDJwqRZGxkbbdDWcdGGrW1gNcsopiG0brxBoZBma6HLMTxXE95Y+YU/CH7uDPN/EnBDcGcCQcVaJBQBIoLeOxrxiHoAw+1QtkMUSgI0hRwdx3V3nIrw14JDdhz9Omj6wodOfAt5yDMdfMi0fwZd8WUvTmNWb2+U7++h71/iufLuwu9WWs4Jb6Ni6q+qxw+99X6MtT9TI7+Vy8gI3Sc8cTUqHPmdZp48GDHNmTDXmOTPXh1fHZVMlVPezkYWqxmF4uXbq3vUW2Oybd5pZXbaWHoeHm0qQIs9xKhXowg3XAbDjTDMOsAUfmEMcmEF4ZIGV4oLugo23WCeeR1Hj13KjQlsR0k+0c1EEF5zVaEdgzKF99bBc5F7yrEfuDYOb1jbfzY25dzmXm+htMsw7YQ0kIzxNY/a5spLe7+5kUwo6Wyt3U5sGMvADaqUXfmvwT+Lh3a38Ja9d4jnoLNIFJQ8sxIyykyOxw0eDaTRDNVZ6DYwzjV3cPD4Grj8KaDN85h5VBa39Rm4X86m2XNa/gqsXEY2904P6nNnS+aMng1c/5HDu4gwbowk66s11GjaAJwoIqf9opZdvgPBJ43xJ0REkKUFNH34VP8UBv0BD3jAA27hoaR7wAMe8LOB498B/N8C/GuANgI6QQSAW9mjKyiEWn5GpThgrBy1fxb5iDGJ9+SeXuYNo7oyszcna/+44ejLs1TacgZx+I1nG5IpDmvFtFyM4krfIgQVDY2dW9j6SxF6IgRAKYW8094qXQgj/wibWYULdzqUAoP6Wd95RdDxys8BmSPp3gNvFXByn3KoUM7pBoE/NA9NMWIeZ8eCL2fn3PCsqwfaome9bWLMf7n3DlnTdwcX4m0fGJePDduzQg6dDUfXhtDoVwj97lrcl4NbenUqIE0ns3nLs+MKByLT6C2a1FjXBEzrRnUoriNs0OY57sQReeToemVMy6n95pnprDn7uqWV8ReeMw2fRvvj06yZrcN8T8K/NPE9EAfoewdjSaQd/TKBFLKPPUJ1lT6EpyOt+PKFkGvuHeNa5G/2T0qZaX7GS5by3fRrRdIQwja2HJ3ShuqKCo7EcDAhHJ5szG5Mj8t3nVbW6x3MvtiCC9/FMGoh84szUWQhLulN4Bb1QnBFrQsWQVDfQ4gJqg0EwrZdAIh5M0BBtIFUTUHGLkjUmGnbX9Q3C3M+izBk3s/EAYam66r7VhBA4HCAM2wMAS4i51s8X/wf6lC5gjCUFV1hofZIhgKpmxIuiH7mh0Mo1Iannah/yjGEwBHizOkREaAeaqxrd282sbBPCI+8qpCTMY+vwNf/44Zf/JdX8FYCe8bYqv0+umB3BR0rofdYqxsa73hqT+iqEOkgEtAmePr4BLl+gF5NVE28gVghx45+mDCbCbjQBr0ouuPXSwc6iYd0M9VghHdjV96KAF3fCHtZhLCaczbf/MmJ55aNZNAeSh4vcoqGB4tUzQyG0YiIKXVFAXQBUTcvVxDa1iwvHREA85QhmAJ4Pw6scM+TLshg5UlWPjb2sMoytpUnfi/c4wfe9+qX7R3Bo72y9ySfsrbtHq0+a9zCS9Z9IF6djgNrOSc8XuDPTfuDjlJkOzxpD4C04LgpJHctTCE7Qup+Mk6fvn7G3//V1/izf/obz0tnf4rwEmO3DoEreE2hDAK01/o0N8xUPiV9h9N/19iYu96IXuBGBRcmOx8CaNRwyI4uHewhhS13I3CQ+DxYh4JXipCZRJpGRnDDrWk0afBDHuASzT3kwmNucyOR5mszlHT1PxCcZo+ZDaUfwCAxjzyC56QTD3x9hq9hQOVJ2al5SHeO/UVzr4l9q/eOLt3z0iVmQJ03mULz51pwIxsdYQlTYVnGR8ebA4XmSwnX3wK/+JdefgkTOfFFt90dw3ZyPXmrpf6JHSi/c+19H3yozmX/UWFpy9ouhrGLrQHabO47I1MYnB0jqjccZvQ99dIzPnDQU8d0qJBF4m0eMjuuV0VdeLQ60ZsjNjzgAQ94wE8PHkq6BzzgAT8f+GQftCFDnRAAdJOMUhPoJpCLorlHl2IIEgKCcZ/4OxrXgRNhQWVyq5DlNdDpY1RFN4/k93tnfJAfWjZjorm5pT3ZgYtOlFIhNKEIrGKSQitTAdXI4yNuCu0vFcUbgNm4tp7h/d7awTO+OR95zwFoFX7cGY/5Qvks5/0beAHwrMDH9VR3+12X63GAC5yqh1Epda3v1SZV3NHpheXhFdYx9mv9CuzPHftzx+WJ0RoVRcIfElxQyrYWmd+e1oorr4YvrALaPN3ZOxLyBx5/DDJlgJ/8xthSwRUd5QEmPHKNKzPjiRqYW74ZmUnqO7VtXvzNYT++rCGu6s2bcfqSkzwNIU1V0MVnerOdCAdPirrtw8lDJcia14MMBdqLMLmGsyyRarKu4dlkdvXxbobB+Vy40+jTyye094aeLE+M4IOJRX7VxHYNI0xXvsUAieGlqGS4UHKrcMu9M2pRF14m7aiNe42e3YOQEBUxWSii8wqPOTNhpCuLZAlJKi09FMJTgJQgLsHpKmiygXDgsjXoJujc8HS5mGqNYJ4DroAgbggtG0Uc2FAm6shLQoFxpC70GcSz+hCkcYjGAIeQ041RqsDXN7lZ4eD9nEz9zRvFhlERvikm2PQ8cr2nZ1jX7p4fLtglQFjBrCPkGGyS1ZO2qpdjSroIf5mzZZ5Kz7dKlQr9W/daU5vflp6/1sGubIFDyf4Ay8t5dEWjHV0PXPsz2r7hH779gF9+9SvzpGsMapYDSUgBz5PGraH3w7zr3EuSIWgcXAbAymgRmkCaK6IFB8QE8iEcL1K+4UFZ6P5CbwZu400e4UcBE4GZ4Zx9GeJ+wNZMS0m1et5H84jh8LIBOZ/rnq/kIWx7tzyOqp7PsePoVxzHS4akO/P+mpaE72FcxnkIxWONFFuu8k7t2Mqe3QxT2SNWsnfvvZt7Ov9e9xwQCl2ZX55w7GyuynM3Blrlvdf20WzvHbwluPcQZl/pm/JyWyeLHvIqKEAddmiIQs42kjL6N5pYGD2FTZKKnjJvIoJj7x4O2Pd00QzN2Ijcg1lApOZprQpWdcOKQn99RMhdqJL0+xdVwJOsJn0HRz3GRDA1bOzh8pTQ2IxIhAQqHUIKkj7oDiHXRfhz99grMHiqygORjyeR8QAMwgbbKzcw2Pc9M1JoaUzWqNnU+JhSMPMUYx2ec5zeyKGAjHDpwUFl8z3HcrozupJOmUBsOVLNUNNxTIGji+ekO3AcB45j0Iwsa1lI5P2tl2N+MoQ15YV8MfnBIPOKG0UOwcMqk9OUJWDFetasZwNg4R1P1ljy5LXdt499NqwrShXQ3be+A2Yk/EfMS/fKap9oIcHaCho0OZ0WtbxzQrvmLwOEBl2bpisuSHivhkHVOiuU9GfQ3oiOcKfSBzzgAQ/4CcBDSfeABzzgZwXH/x2g/5uFXmrMEDHreWYCNUAYJgCLuF7AfPLVIaoLmPi9+neHmw5m0x225jPryXv3hMA17Nt0yL+RMIzvasaVlp+A7aAu6O79MJ/FjcGOwzEBYke/TDovMDcCTyJBB9IrL5uhy/dyyLoZlLXdRWIysd8hm8P8zOn3dRzOpFvr99dgh4W8/Hjb/tVaepVFhMVg9KWmJ0vl3dKk9zRxek/H+NQD5dnBaJqCH/TkN590ie1wzQz0fnKQW+e1CMtucKC+VsaA4GFmT1uj6W2Xh39SE4yHjBMeBsm9zJiAxs3ymTC7l6lVpiUxIq19OYN7cq9VILdINibh3xtV3K33BCZh153Chyh/bvjItVLyk9B4ivydeL97XZF/Jn7X8pN20FgTUiQuHN51d/DgtI+lPWfXz4rR9SLN5Yx51qng2yjItPy+rTBFDgRXjpmQoTXH644M02p5FJ22A0MycroH3SGON5Ki9T2/THOIr45uwkO/pogcbIZHTIRDwuuPICT+PKeQWHfF0Q8wM0QvOERAxOgqub4aczZpYzKhoaqt2e54oiNnVigJKcKjMSZLCAVCNTpGwb1ILdSmK+kiX48GJlLm30nvDI28O5R1j0LVc+i595yaQq13C3EpLuiVsPwncqMFBkUiN/JyYN5zw4PF/jKAZq0W9hiur5tc6AH87n9o+PN/02FhP0uIVVghuxLQdxCASyMQNctNhAOHHrjoht4P7H3Hy/UFHy7P+LD9Ah9/8RXk6ND9Cu0eV1sBiBSFh4Vye9qe8LQdOETAZIJ38XwyDAJcUN1Z0FxxJ+iTIHfMbQSce6Xfr979EcBZA6f9YBCfzMujkf90KJlHyGXyAAouJFdKFYL9tn2LSMFsnnTmGcqQfoD0gMqRe9skKD3Zq9X304B0uA1S4/ylO+tOAtiJ9aNS1x2Y9nYajvBnEQLiyw0tX7pxdi0VZEtZWRSd3Fs2mrV/IJx6xNetLO5Nba7jvrwXxje1eysVIAzvlwl0+Qv6ku4uOtcdkxeNo7P7lVdQFNH9BH/1//kb/Olf/gbb1tA9bG+rsXcLnSM2r14oQ8J9qyoIfUNWZ6zjFpekbXQAygRt7DejePdYa81DJptBCYPQu6AzgyOsM2lWJ+41F3uFeZubEUUxfcjvxCOnXCjpYn2G57CFutxATKmoa2EsGV1NumBjTbwZLWBX1EXo4aiZKMM8V8Wc9WcQf43EY2q9OKTjCO9vEUi30MtyCPZ+ZMjmgWTWsHEuopymmwU0ffNfhDGf7wAF0F+A7auYf4y4sBNRGd9Xlodxe/2NrWR6bCWHN3zmwseu9X9ml//ooBh87xSnwcde+3gurp+N0Qk65L2qHM3xpPrMyDN8O3QrVr3++wEPeMADfirwUNI94AEP+FmBPrsAVE2osYEBtRB11Bh96zg2hRwC9tCNyfhVRrEwmzUx9o1yQefvVcZy9tgE9VCxXNP11luMfRxYuOTZqfEmVPPAHowxiMDKnt6G0SIPHQDpYtao3aQxbMbWU6jQBD1p5z0hMMr4LGVM41aFG2eCopvTEVZ+/U7FbzwXz84nzRsL6TMhUH19bSZQBFm15LN+rGV5QVS+Z7tqgffao7CD9ksHf9XmUC9/YOBG2D40bB8F2/OB3sc9rbjyBt6g3K4RmKqiNOX0Plap3AgxtwqUgeahlUxI7mIVArQgNsGE6ObhY4KZENqrh4qyw3ioW95otAMVOZNXNHldWugenfDhlal9V70hCM21nWGbRpuiHVOlAKrHVLxE8LAzhTiqaNJH8TBQrJweYiEsDoUbwUPoppJ0hBcEQjZoBCAVM2VYVu/n6OfpEJQBvKfIPR3gIX+c9oEqjquwiqPC66XYtd9W6ULDvO2W4sSOu/Hq2V9twoRUgcceAjMHjV0yF+KqpdNebiqr0iLDQllmGEhfWKICFi7hSs2DJz02o18MkCu9DsDC65EZg6SSjoaS7tLM7ZaJwGKePhbyUn1Owq6afBnGjBQFw/otJYkRFjTyzmnBEYKA4E31e4Ku6sE0R1akup60/Cfons9HINI9LK56OxnUZq9edUGw5bMz4WgoQi1I5AhtGD2N0T2Nb7yAgvDtf2L8yb8StF9bOE0KbwxXnItaDqLOHSzN8iNtBBVBh2CnA7Q/45tPX+N3lyf86qtfAVALm7ht2fYuDUT2p+iAANwacERCWg+x6Qq6BnYvSsLFNwGBYI+QppnwCBOuEgaOrr2t0y6Yvbz+aHBCV2/uJ4MRRFtRrYJsrhgaYn8fl/QITe8Vm1MLqxfGEbEmg4DauhYP7ddlB2THS3/By/U5G5yPxhguG1Fepvk7IfYA5z2ias0IhrdQyl7YQgC+WmluTuUZziisrnWdFbxeLx1jL7TjFqh8CVStYbyzPWUPubdaa/WCW/5gKg8Dr1eWNtQn5MxPVTrGM+cKgo4pqVgUrDCGZFqDXkAve1ahza9xKsfe0VXRyGlp7jdIGmdk2AmwW2GZfZTzX+vyCC9rxTAWIrVwxK58DANF26i8jRTGWLbZcjMFN6ugCUNYnE5Z32L1hZGDqIW7FBVXv41BplTIcQmqTPkUe8j0xuxObe5N5+c2Hpg0vFnZxtz2H+dN0cDEGToz1BjBkVLsGclQwPixQlZzfzELE5vLyIHqoS6P48DRu3nSuae49cPKlWIpFzsijR9fFP2A8p8CAnz6X4Ff/SunOSflxl5e002MAm8f1vrOO9qUa7CS0+8ZlmX7g0OukpPOMZkhWW8AbwALUPW2+f6XDkxZ39YG5+3EeMUoeGJ9pwGreSH/2Bv/Ax7wgAd8OTyUdA94wAN+XvAC4B8A+hPKAx6zK6OgaelPYfh9kvqkRvGo8pP4fZMDuTDs03sY3nSvwgkvecqon3Hu9RASwikVkJCHk6IUNATzPEIy2j0SgqpYVhpxYXk34SiFJ50ipRWnZ3FdPk8EH3cPHqsAaO382WDcGbNyNnzt0dcPEX8N4Ne4kWN/7uGpCkRMYBY3zh4+v5SHkZPKb+SP5XAadUoH9hfFy1PH5WN1H/0BgMKD7gsOS4EvdwRoKUda8L+toWNCgKMm6GzsgvJmlsjq4d0EYkpq94oJCZutE+CQnlI/gvULoBR6vNqH2vYvPX2HQEHKXwiqluJXoV8gYlUS93K9ymB5OdieLj1yL6dSr2cNs/uVaOr4CKGQVe2h/6SWMg9RCP4mOcuyHqsQtI5DpQO8PJN4Vct6Y3GvdY+OAhZuJ57zsI8gkLr4zh+LtJ4+YNb2xbOQfIka7jqtXq3F69+EU66AKgILzwJVJLRrAhdakEh97NS8FMi9EFI4YovJ1Cmu/iaAaEOGJVS1w4Vb9AMmiOzUrXhWgMwLITwIidgV4cDVFheeuFmeu+JVqVDPS+LjRoPo0VCfDQFhThX7Xs5DAAS4QNj6Hd6b4SmReZNEcYRXIAAhAoXwlsiVHa7o0o5dDkiXofKPPjbzgIo8SdEfyzun6GL1dh3hNjNUYE550BuCfL2/i57sv2f0T0D7NczLLZWontOIFNoV+35YqOynhsvlCbq7l6wAXQ8c+xUv1xd88/x7tLbh4/YrfPzFL7B/esb1epi3YKyFUK7CQ20r4dI2fNwUn/rh4bdNyauqNi7kHmKtWQ4mNb6k+DB7u4dNff13Inz3+JAfEu7t85/ZpqQ9rkdpIFyoTcJ9JssxtzndMTc2AamiNUUkKm5MeLps2DaGUkfvwHEcEOzYjxf04wpVTi+1oQy5paFVaL2Sz/SlovEbmPkTraTnM/bFWm9OvbeXVuY8SEeloZVm3uvASZ1TM+n8/tpGfeUZXZ65hy7ugGu/dS4nvhfKd3skWJaHrhVq/VGuJcNR3g7POlBx6aufMk/yUub/+u/+Cv/y3/yLQr898mJ9LhutHiZdIX1MdGwHWv6setsrbL/XjJgQmmP1HHXqSG28joWdDK/e8DgzaNNeAxg9Ms8fo/meCRXVqzWUdrGnh9ItQ0Gz3W++DzSOteyKNneZT74lqyeLkKKmlGvcMscdEfl0UCq0Z+OxoTjL1IG+v3UcOKSbt5xYjsreO3rvkG6/5TBlXeyL4XeevNZkaYYc45jJiX96x1p/Tb9S538qvyyAOBu8K5XFVDGSfahr+Evo1GugCugByAFQO6Fbf0QIM6ZYPrX/qe+diPCb5HPAGf3SwamGYSURoN2MPZjN815Fi6L9fXU94AEPeMBPFR5Kugc84AE/K9DfAvIfgO1PyK2NQyGnOF4OyIsAh5rxqP/Zi0imTkKuuh7Ez/jDejJeDv5V2TcxsWen9TcuVQPrqT217vAM9IPtKsiIA8p0sALQSWyMop6uQJcR2lLGwSglj2sfqjQR5ZmFkb/5UQ8/J+NLyzN3E4uf1Pcm3Hu2egrS0oaTqu9dXLv4WYeGE4HjKoBJlI2DKY3rhCLQqefnFML/sBCHOuZheUkoa6QIAt/hIDIKyILKdcfTcXj30GBsgo3Mp7Q1qHYc2k0oTEX4oB7yzkOuhTcPPLeHglzYcjKYr+DyGcoVGQxG5vTbQVABjkOx74Jtb2gXRWsmlKlpIs/klFMjaoWLoq6GqjwvAJPHGzCUGVUQ0yiUFMPTsBdErt5HtZL6q6RE83rPOza1ZhX4hZChPnSzMJF6qJs8KKWOOq657pZiTWdkArva3qCfFAIs9Ys6N4XJnBTS0r3QtSrImerPuMplE5tKLYQzpVRacMErUIVMdvzqeQBHHktr+8iRRtTcGyyeKbGQIzyfekgwtT2HxEM9uiCPmNyAwSZB+mFYI4LufSBvi8LCWEMxwkaGEvpmgOK7l0HDU1zVvWudGKl2y71KSCWdqkDd2yg8O4fSQqcyTeBpSjqEAtoFpaagc7pDkSfP140rsjoEqt3LCo/FsT7NW8U8f6nEwX4vqfzdv2/4y78QM9YRk4QpmRIuRKkiih6uZ0TgRjYubMqcT/sz2jdf41df/Rq/+Pgb4AILm3j5AGiHyBWdGxpfcNDVFJfXbsPP9uwFgDKDjiuuXd2LkC0cHG9gdPMYic3McbRhhG+MuZ7pxy2MbDY/IHzO3nX6rM9G5lmMaw1QC4lnueY4/UEJXMLnuYCfLUcVKad3K3HzuegQ7ZBuY7jvL7j2T9j7M37/7T9A9Te3TTuhmfl7uRd0K6hR3d8nCOetoI3xvdDw1QgntshpG5MsbhLKT+Rw2ZNrN25YSl/j1UNw2qNvuzGVFQqztZ760FrujfccMBxJaVw+846LTynv3ER+uLN3IsdyfiDCCN94yN3MY2G2whrqziJQKPoRhhqjQ8ShnFJMWrdoC8OMDot2UUGpP7Swq5RarfDqj1FWSOZ6zbFssSfYYxE8Fjl26tuqIZ04gyAU/qxWh4q9qfAIDYAZgkCLco7KJ6UnHTuNS2V78krDuGWc+6J9HjozFPI+jMYrxV4Z3R97HSmgDd4XK7NrNyUd2b6lotDe3aO7QzIf3Y7jONCPnvn4QEajLJ2CDiVyuv5Z/eSLKNeP4mZ+PwfCQG2CQoMm27CFlkzl1DV0QtPOWvjufSTm7mSd59DUM/WXD8f3DrW99bPejzWS/C4wvGDvFbpC3SvidxmP4LNCGQ7ni3LiJvpGSa8zdYcC7z9IPuABD3jAjw8eSroHPOABPzuQvwL4IDMeFkE/BLgCsgv0RSyPyw6TbNf0CcH43ZzM31nx8k4UQ3eY/rP3bkJM6Pn3U4XhxOTqOJWSKSxDOBzvCkXQKLOWJ9grJGb9nuWtCri4TuXe0tSb5p1EV3tzXF+VdJzcuzNf60HjTVAA3wL45VyentX1BnwXA0kCUo6/CpXuHohOBFkqgHTFcQh6hg35YYAIuHxgPP2i4XgRHLsMjdIdHM7+xvd7uA6bk65DQAfAZEY13RMBtFkY2La5sHxjy1HZbZTCOngItRTSO0QoJyJycbUGy3NpD07N6l8T5DcE/uXJ7BRp4ISqQR+84yaQ1rG0vsfpCk8vWgT8OVZLZbOiaQh9uii4KucwDtYh1DLxWITdIrC6giLdHs4X0RmpAGYB5d1zv7597YtA7xeT4RfV/0IYBzoh6HPrmG3yaaEUqceL5vu4EmHkpgPmtRQEHIBtfjoKu0cwIsETDKcJdU6DwBOmsJlASmcCi4QsL4/63qMed5bZPQ2IXJhjDeFYT2SeAxphPhGhZxWHUmmeDI+1oqRr3EzRRKaoACI0We26143h8SYYSjLrtQknRx89FGBI0xQj9BmZR4WNEGW5h3R07VYfE7bgAZjAzceA2ZV+prg8xBSB4v8VvW3ZfkORGPs5oF08D9z74PnvRlYXk2FrSgnJpFtAV0g/sB8vuGwXF2xbu4iAQwjP+47fff01ni4fsbUP+MXTr8HK6P1qiMkM3hjtYPMSgOHDBnMR5Qvn+IkIIOGJ0sCkYL7gAvNOjLYavtgczTRreNQNLJ4hDVX+0Fved6Yxyy6f2nkPUUtBne0ZBqGRZbtqjvuNNmxsAn8VQA41Hk8iNCljoyHQVBVcX16w9x2fPn2Ll+MbfPPt76H6a1TSFUrWpD+lpZPun2aaVe/l8ERXdFpqN5/ZfSzDguV6aUzWWfntstHSei/eizat10ufop4VjV5jw1/bq84KCEORei35gyDhOt9b8xJXh8F4psq1p/HzxtX9JQYiKeZa2Do4UyEB97ne3//tN/j6r/8Bf/6Xf2rKKTe0sL1ShhFZREMQQwYmD0Ze2+DbU46JxyBUd8/TPviVDJvp5UpEEWGalOHhEad2Mz21h9MgeXeDOdXsPkdIyfCYi7WaSjq73vx34+FhN3LKldC1QaIFIBnBM1t66432pjFIevVr/qt1//f9WdQUbF09lKUeOOTA3k05Z7zvAZUOUkt/0PeeaRLUg7RIi1DMOuNVYEYiH6UxzXT/zuq4wVOH/e+B458A26/nZ9JrrqDGXNh9KFh804aFKuf3yqOuz6xlfx/s5x8FloaHIi696RrQNgyFI17ZZgsdzt8+OLmM9PbTHit8rNINDeCkWZgJuWImmA94wAMe8BOCh5LuAQ94wM8Ojv+XQv5PivYVW26CXSAvCr0q6NkUdtiNucyDUAgOghFFCdHzWmV0/+eNV8cb5UzCjPdUuR62FaZcE9gBTMQPnJKMcAhAxsHCD1mRsz2Uc/Xwq3Mdtc5XG/iWROO1zr41cOvYvmegXztNrc/9DsAvkAKSfPeN98+6/m4cOGvHnUqo3C/oO3cxDkEd2K+CbRdcRHCxDFM/ClhxLIRK0wEc5exVxyTux4HO8ZhDqEXxXcDqdsqB/BHWlTQ9ElbdYYTZSxkHWYgi81Lw/GpTJz7zQE44wakQGoXA6XMKvIXJyt9DOWWRqpAQIPn9FTQbivQastxy4mMcHh3iAhyN9CcAdOTgI0W/EzOoHLEBzMrJadlVRK991JEjBIQvM6CNtRJnf729fQPlmZp7hqY4bjAB1VmV5II8RcnNV3CfjR5PKUDDay61ULjNERqDlbGyIq+QS1dekT6lsEu9bSGEzAR5pdsLsQtlFRTo5oNgXmFeFhONtQQx7wQFWD3koSvdQgiqLpiNvSuUWNxDccamFCMLY5tyG7LcclBND4PY47sKdrVQjEc/INI9b6Kt81CaRl6ksOIWGQo7IoK4Ms6qcYWf+cOZBxMDzG0IbclVSmQea5EHTlxZ1WOPpiq2HILXCC5mtjcKvQqwvx/R+xX43f+b8Zt/3c3TXgBGGOnYPCshPRqPfsVT+2jKND0ANEgXvEjDt8+fsO8HerdY4dQaLpcPuFw+Qq479EmhfYcch4XM9pCVEQTuAzdQuwCiuKoJghsIGxqEBELdPeXY1oAqWABVxqqoC28VcXyrYCP2hxGTzvRh0K7Tml6T4AasQkxEdin7bvtN87B2nNfJ3Y2NJhuukXvmZD46NY9JJlMW925e4eaVfcXz/gnffvoGn65f43df/w6/1r8cLJ8T3xDMzr0dP+L7pDamcV39ockATse1UEbUPfEsjLEW6XhVuiX5L9cU3ubgk8pem22PvpU2Rt2x5wftoHXOok+4vTaxyXVuaR63gMiVOpHk2qbxMXsI1naUdtfncgXUPhA8/GOpLIy31D1Zq0c/AalhVZoHMDdd/4wBPQGFedcCSNw1+w8CaYMqIBqhxXWMQ3PlmSC9a8TpItcOxtfwvCF1QwB1jzLNQUjDEa/EmhKezu61nBSkeDjbjI2BZSCNIWmEy4wRSCUCjTCYoZQLPhIYOes8/rp5nOfEeXBiinx3Q0E3wmxSDrsmzoRXOJLvFVhI5e47lnnOhee4fe+9o4viuu+4Hgf2vUP8XGlGVuxRtAm5qVdknICyQapn9+/Ae8m2z8MdlBvF3Smv8ph1qa5r+Ixcr2zUG028qVgVFtFHMEVK+MPsWu+DtU/rb2ZYDkf2M1MhEwupe718LdtAoVMZwYcsXQMd7nFPBPZIJpUW53qjYcSiU2se8IAHPOCnBw8l3QMe8ICfHyjQ/wOw/Wuy2B6CEcIxwlz6c68JVYLJTAu9yq3eCFWW90NQ8B5uuwgO6qNUTgJ5Vjs5FUyXFKZocy5YnPsNpjgevvkd78XZeP17DxQBwRi80sjvCnEYW6+dfb8H7z1VVaHRWTtege8lvYDe/0nACDPiv+MAGo5f5Vw8vTmOMT8cEAFtY7QLoW2Wh+Fs+Uyylop3BWdHmKRyoCvgzm62Zg8LrWnKJAFzN6GOmuCyo3suDfeiI/f6cutphYeyg4cPYwzLb6apeZ83IKM/p+hYXca+A6xKt+l3eNKF8MQVcLc56Yoga7lu1uTkXoil6JNvldaUQm7oBU0XkeOviiKMm+GUZr4FpS35ShU2VBq5NH0IdOwKq+eKyYkdPj4hVA85noSYz9cnR4itEC6EsMLlnNXLY2qDj2cNhznT2jLgdbPTJV4x1e9ns7hU7pMRYVHZF+QIMRavjPKjeoHmuBJ0qk66WkizNtQq5G50I0SvJSvpHq+LxIJzsudeVeruTRI5zXR4JHZTHosKdjGPjd53dDnAwmOocsCtDosiKq6sck8P0QyNOdaPeS1REGYmEz57DkwV8y6VLtj7gS4dvSu6dB8b5JiOJbgwIgqMlfZ59EGFcP2GXBlgClGncGBSaHMzBVUTkif+iDFMICgzet/x7cu3+Jvf/j0+Pv0CT5cP+Nh+aZ6MvIGpQbRh2y44tgY5LD6ZCnDZmoVyhXnGNRrCZfaQxA2KCwG6WbAD6h1A9zDJdJehYlc5pmdjiXuY+8T3sPGdK+dGPbh/+1VwX8khqM8ihmdu/IWAf4sweU5vTODP2FzRLd09XsTGpTFj25obrlgI0+v+gn3/hP14xvX6jJfnT/jVZYS0A2DOfF59GsD4beVh3LKOQQ3VuI5H8pj15Shg5R3LLdedmFC7kK4kT4RTV+w0vhjEO6+nd7IrrYJ/Tl3UCUP32j6TrMu9ZyqtPru90PLA3+BrakVnfNRZGyITsfXXaTJ7obD9C1S8r4PR5jEZYWygoVHkOsGhwSWAXukcgP/0//wr/NN/8WdA21y57LmC1b2V0x2SEKE0OaIjaPBlHiKZkRrOuieLapaXyNyHZy61fCHH23dgaHqjVR5PRy63QkxyPG2z962Plin2ULQ2iqmYo0L/LM9v9Dksdsb4pvddlOdlNcxlAEhDqhiTCL8ZyjrzIhd0sbC3Xc1rrqvg6BYCcz8OXPcDzy8W6vLwMKUgtbxgFHvyMCS7P+s6/i081vcFEwsTFS3r51W4tzfo+a0zVva0XYppbFbuKtJt6Bbz5Pe+5/H5ErhHV1aYWBV9+/l7A5fbevC/5Xvle+IEq/49FfzOc9J4Id94wAMe8ICfIjyUdA94wAN+fqDA/u8FH/+bNrhkgXnPHcg8a/Hs2fsrV51yx5XJnCTE5bIf/kRxW8cdIcTahtPX1nfLYQQhvPDrq6xvOl3o+Muc3zEmZWwmYc1ZO97br3PZ2vuhHqjv1HFT/CrPrAKat3h3gU1eCCnW9tP89ebgSbdVlMgvN02cxnTBu5y2Be9SaBD1E0aIkGWc+hXYnzv254Zt4x8uUTmZxWW7MLYnRtsEO+mrB/V7Y8EuWMlLOvqrMJlT4Eha1JeHu4gLjckFOwJhF9i4hWbKnVxRlz4L7EIQHkL0L0LpP+KZMcNV4pz83aCEjo8a6jL+TAkSOch0WlbVCL+ItKY1MDwUIryUv5ySFr9X2p20zfH/jLzWut8DEzlYhDv1uinVUJRxdsuE5lyUoENwDh25aODyrSr4A8I70a/FOg5aFULp0oYcnqD5tSPxJ8sA6DQ7g0BQSOA1n9OUTosLQ7norWbCp0Dt0HQPBBdEek4shBJY0/vVquwDD/oInxp5iERsjaagJokmWURrX68mMDVvq/QGdI9ZEcu72ksISxNSukcf4B5rADebAIWFoQxBpKqHD1SBoOcYV8cSZjJPUqacv/DGEVV0tXw/XcxToYsLP0GYCTwAygxAy3UsLkvvg6//p4bf/KuOy2+Q/eGyUEJ4zESAdCgdABjKDGUBNbYwaP1A7wde9t3CdTYFbRc8PX3A3j8AhwD8hMZPkNZzriKEnVDD1hQXFeys2ODh13ytKCkaGMLmYalCIIiHD1Ucump2YlgolXMjwOn7tvvXYM4N5zBNy8oofnmFxjfa4mmIdWNKyI3sr4Vi2teWBb1EetFZHrpW1h0g3RSwxBZq9Og7wIpDXrAfL7jun3DdP2HvHXpxQtd8j3PDGK78U+kjLe1fxy0UYdOQBD8Xz3eMEIfh2r4uB7+ehgzrxNL4q0LfFPyWdEX1nQy16x6mk06Uy3MobX7PHrPymqVt5OPiRSYk77Z4SgcNqeM48R+uWKTad5n3DlDQJRd062iDKekYw3s4XgoaHpuRNUCl0KvccF3Zx4TVQKiCquL6vGPbGoQZggYE3WktuwPx0JUw3k9p5DNmNmUdT4Z05jEabSffUyxMsZfZ1bzCUwOA5PVyfH3z9d4bDSJX8DkNQpm71bCo0vEIfDk87e15C0vJ6U2Xaj1fBORIkuGw65+XwRSflG2Ie7mlYM4xnPlTRdDFc9FBi+IuFHQd133HsR/Y+zH67HHllRQaqkL9sogPn8unBXz6T8Bvfr3gOjDviZV3Lb9PYaXZevqzkpcMaFCfORuCaY3Gc+Vi8HnVqfXHCDektowZs3m8vfmuzn331/OvXgiv/pzjONSl8rmcw4onnRnDeR7KL8CtBzzgAQ/4scBDSfeABzzg5wkKs9INwQ/GgfRMqEj5zzhET/zkIpi4gZUbxyyweA9Mwo7y/lplFVaf3oiDfXkum+cH90kRF8am67jU8EbrQWZt7Fl7VjgZo8+CV8rX9ST0XeEbAJ+QIS9vK7z9ScuFe804xYtXxoaWH3kWLfNV5CQ3ZWrMsZhV/XcVWn4JTM3SgW/1AFfkQikgGS+OnCQVTcPikv07+2thkMyugAPcW8cFUKqULw0xhqIXT70QesUZMVprCgYFR2Xf07gklAmq1rj1nZSX0H06swr4h9hpWGZPKc1oCNeG5apmf228PbRfUZyt1Z8djleZ9hAijTZNtDZxV1OeRvXBO+vlc4VFUdSNQGYpX5dnBl0dwnS75IK3EpJuQmzMc36IeG40zXrDa1BikrX0HQAOJN3OAmkpOBv7GmH0Tg6t1+nYiAq45rir1bqUrN4hqpMUshVKJThAHl4L/u5QzJnyC4i4diJOuDpygZMvSPbcdKzAQYTmnq7qhEFUzFmki3tUWEjKruLhnM1DLuKzsoveVNnpjCTfICGIjPVIprDKgXDFHDVO77nof+8WQkwE6P0wYwH3xrP1ZOMQmfSGosZzOpYBz9CA37wiFbsD/cU2j8lIRG3+rUvke0jkkewIHCEGeCPoAXTt+Ob5a/zu69/iqw+/xIc//QoXJlBrIG4AGqAbLu0D6KI4yN2TWHGwQo4D1A0fGjNEGy7uHkVoULogyPaBA4rD1od7mzBx0p9TIkCGl0q6YPT74bMUc+eteBtW/hNU/gtzAAsByxHCEqZ8Y2po1NybLpR1VkLmnHWFcir9tobWGNuThWuVvuPoO/bj6oYQhO1yQXgohPtVKOdCYLrypTd0MXCUxu/az1TG0XgvedOyua0eZWhIT7pa4ZqXbRLcV1IU97yubEPcw3gmFWW1k4Rz5Xj0M+hDea22Q5dng7bkPb3d1vI7lzKBVNhUPnNqc9mYkydqwxsuKqWYKA5rkDLJ7lFW+QJEvkOCKdBk7AEgMhrCJxrWAtIV/+nf/zX+1b/537jSKDRvtntCjU8THxDtY6+YlH9k71guWO8vhZeNR0CIa0ogZedd4HVSKkCTtsK3AwpDNlP8RaSFmMRlS4eZEVQPNhp8KYZhDyn7uLr3HLNHzqzr3uuPyVWYQciC53TyGYxL8h5ujRd7GLmxiro3vJIp6nbp2EVwPTr2fuBwJV56h6dzn7rhCUHZNt+zsLTL8Mz3v5QoO8gVI4ztSnjrMAUdurdV0PRx207crsfp2fXmW1DoAzCUfApTPqd351lb/9jg7QnayA1oF2MR5J4rtUPlX0559rhdaGLdGwiwEN9+FkHQo4mvLHhP4VE/8zoPeMADHvBTgoeS7gEPeMDPEo7/JDj+Z8X2LyjOj9NBOjjDSRBwJo+ph+DlmVOojOidQ8Frr569VuGG8VzLjIOmLM+EACC8CKti7rV20vL3WsPfA/cONndPQq/AyQHttUcnJdZ7D0E0NyfHbMGV15p8r2t076GoNw6RBQfD+nmN+vVWG/IhtQPPCGP0AwED21PD0y8spM7+AhcWYF6LKyhQLZerQCuFXP57XXehxDLU5TzkmUBHXZBr0plU5EOReeuoDLErqVg6DkSoOBMyf2l4mrNlZi04W5TRDPU1HIig5c2xvO4u1TLIqprKuvXeeMYVFBihA+dQSjaGBELHGO9JbXMixAl54hB2WeE1zGRV0P1BhBav4d0ZJO7pEKpNbhdRLM3trVIpFxwpXNkpMnDypL4MTtnKuw2ZQ3QK7ZYCvTsdzY1gJYL1c94MQ4GWOdiqvXf0AzV71vw5hsUaayHbrD4qG2oo8uKltPi3+FymUIOCxJ/jwH8bP2wEuJcQE0HZwnZ1GnnfzMdueKeJDyK5kFgYUGIQeuYmyv0/JeCuSVAFsYdYDK85FwYL1MJrO270Q1Ix1z2cmIRyNgg6IQWn5NIqLf9ZE1wwWqV7nwl//W8v+Bf/1yvgecpMCa9jLlxxRo5IpId5ZTW1PHEb0PcOVUHvB45jx/FyoG0fwJcnfPj4CxCZByJrR2OgNYZCICRQdDQBNga6dFxceWfz62FH0aAQXLCBCBAmCFl44kYEaIeKQJOJGRDKOWCEwBxmKZ87YHc2pLOSPmc7zZe5XLL8weblZPlTI68cg7GBwdo8JyqjMaNR7HGcS18By+1Inq2KCNQIvJHndAyE7tj3Z1yfP+HTp2d8ur7guu/mNePCYnIlXep07vBO0xJ3GlVI+njmjPlx5dMatjeUbVxJaYMJ55ecnFFflFN599g/qjdf9SDD8h4V/ZLhXvkdU1bodfUoic+be7lvYpqnGtVxHR9nUabr1CbHvoneR/nh8a8wr1+7Z51nZvdM8wKDloKhxBnLlMoAkXck9jkFFQOWyPUGpyfDa/q19aBQ7C8HPn37DHxl15gbuJmHKLEbSqhp0GI9E8EUvM5oUHiZuZJr2kQVyKB4xDBKJ0bzhDyfHTJfXSjrCAplSiMLIgKa5J6fULx3Alq9HkOo5ON540vn96wfIy9dCXibytJYSEHX0u9u3q1rm/xGI0Zk8lQAHYorOg7qEDpw9I5DOnrvppjrR4ZhFoivgTBW0WTctMGVdKNdK028w9LMz9QhfSd5zrVeKpp4KP/9ZiH+eY9f/hyS/lZVUmlonc4dQDMHUq6s2R8Rkma/Y8tcOch7zU+6W8tei6elzkDjrGTgt9EJpIJu8JKhoHbO9Ucwng94wAMe8CXwUNI94AEP+HlCB3C1AxwEID/YaVj+FU5/ttoaycHPZAqvwhcKzhJ0/k7LNa0NqfznSePi0DIZnNspza7dE/QtQofkfeuJ8F6bPxcWDj/H/EwYdHZNby+9Wg9hCLDv9afC1wD9ylq1pjl5DT/WZq/VvFrt8kJ2+5VTUKJyFeosDVQB5Kroz4L+JNjalvdPQ6p9TxBHpnZh8JOaADCschcBU216SqnKxfAIzBw59Z6MfHythsvyAUlvHRdkRS4RsAnFQ3RyMxLVY0xDPuS5tSwmkwmLpz6PtoWMZYUQ2FH5nfhMoQTSMRYhV+vAsSv2XbHtCt4Gbkr5bHgbQpj3OriAzAmO0SQbs+4NZyKYr5UrdBZt+OSVQgNHkw4XyDQ5/t7kNYHxfD3w17BJNcTa3X4vQoAIY5UC3bmqfKeiXFi9t5wppBCPA2dCqFBpuKJ4I4p7w3mIsQJTeLNK+xYB9c21QIKVtk80YSUUsgxYLKoQd4x/KePqenFaMKgQ71C6UUpevCyV2RNY4R6pUQQlbhAAjtXlaxQeuoyEXMlmQkhRBTykX+SRE89DayElxZtnlSrNypvMK+TzIH59Qr9kFAjmWibZXhC5oF+znJhS6RbaUpU8vKa4EjLK1uzwGN4O1e6CZZgHDJxmXfvwovxM6M+Bj2ONqk8mez0Zhk9tPTOz540CcCFIVxz9ik/f/gO+/eo3+Hb7JZ5+ccl8na1toMsTCALpQGOC6GG5BPXAwQJCB7SBuVseOjEqLbBQl0+8mdAShB2wPIBkCgQmRmsKUXZvyEIMYr5IPaRcCMZRNsgvhRNqufBLd1578+HmlMRE8AwmoMHzWbqizhRypT+uEGEOz10Ck4KUQY0BsfCqaAS+NGyXhrYxwB1dDhzHFdfjBS/XFzy/XPG879h7B57McxLNcSW8uOh2CKee6MCquC4nzykwvCWCzNSX/Hso6kLnIK3c0/J+qbdsOzN/WUNdLtEOw+s+ypqExLFPxaUgmb3wLOs+5mVM10941Zt+R/u1RCuO/pZ7gQMqAKkgUzWSh0cMHGEz6GG4IQ25ks41ksLmvUtgCA2DpRgbJ2u516XPs449T8kMIBAKOuZ85zX4+u9+j29++y2eni7orbmxihrdJZ49yDW8ucfeYpcJZJEywUJ53gk+MIYw9LogxgZ4CGNO71HxGJoaLuyAGWoJ+ctlEjwCAxV6TVFLGI4AoxEZscR5THZlnZpiocHyI0dOOY5+xf6Q/KPTaIymzCinxQgx9t3BHggUB8SMVppAu4dd7t3CWx47dunoR7eQt6rmvUQwxS9MeRmKOTXiPBTM74SkDzf0eubjXuPfpAMvfw88/dmgR2fL/k0o9CZJhc633vN61JvXaH6m8qk3Z4HPavCPDAp9Jcx0eJ2XBJpfjfGpBqnkJIAItr7ZPcWZ00AlDFjSMAHOe83VfLft/gEPeMAD/ojwUNI94AEP+NnCN/+PK/7kL5/sB5kQqB6M7RA6s/gm4CuFJBeJz+f4zp5/g3uMA0zIC9dD/XQIuNeeOOhEaMt4Tkrf7pVxVqbeuR7Pv3bIWN+NZ+X20umFs0PNvffu1R9fq/DlPe9+Kg5nOqMC8PqQvOv6expxK3+c3uciyLgpu/ZdADnUQpMUK+k/9PkwDmvdw+eEJyeHcK0IOnJcS5/V254/AhjTgSyUGUyYrezNdccUaowMDUaeggU0wqKQy2VGXTbb0QeProc5J8nt4tj/isH/asSAoeVwHrKbdVmk0BEKLnG9vvM8lYFSHSGZVsXQPXzkVbhAlG0SspBNrBYOqkdlND8/LrnwiEqYJC39DI9FHQKbmyn5HFgGb1LmOf5xeWaazWXOpj4sdzIvTExkvhsVuQDR+ygairm5kyFUSyFvrU6QoeImWl4TpNS/tfFnkuOKnOoPV63lghTr9jF5KfliDiENyvxqVe7peIcAiBA8xZaHNSpNJwa3UZjJg+fYb6qAHorOCmmmONPuQl+JHHTicxQrWJYBHmvCVOShKg6a4QptalaGS4PTY8XbbsROIdKHN6Bo2csjzJiXnl3TqT+Ug1gJvBsR7F9ODfbfE3777xr+9H/XsZaSDjQUHjKey0mvpnhTQBpZ6ipXfh7HFUff8bw/46unj7g8fUALgTAEB5lA2IwxOrg3bHzB08Xx/FD3WjkAEXRXDJoBBUO1gZhwhIdQJ4gc7nUgEETOqVutZXrgfEF8tXv5kqalDSzLY6JU5cG3sg355hMKOpQcVkWon8q6VIbYXhY56hgXkF5AaCDdLHwgM7aNsbVmXt8QdAEO2fGyv+D5+oyX/Rkv+ws+PX/r3na2N6by/IR/m5yldOzza08raQp9R5Cg2K+DLk7lUql6CUHN5XpdOpNHHcbzBGQOXvMMx/BAJgzvuZVnKuQheIysyxWYqTQpU6/RNsIQzGvuBPlXFYC5//v3aodR+Zvsk/o6FAKrDj1S0qpQzlkDA5ckmBwlsAqgrhzOSozKhOLInIRtgHJvQlHSAWYR5IYLoTR+D3z79TO++s2H4TFPhMu2GfWN8cp90vGoKO+syTzGS2yCWNXzUo2JjNybFkY6cl26FzhbiE03uTA8cgWddkzeiDXv7MDVmOgyY86/5EhQqDoHv8BxhTDWezRa4Yo6AaTkaYUp8LIdxYgMPiaGc3U/MQ/yQw8c2tG7YD86juPAfuzYjx3SBcdx4HBv744OYRt7G0sniKGoi8q+Z3gXqRbg+Aa4/Jn/1GLcVRfYCitPtNalr3cpbKGCpuna3olxuSWb1WDvpp5CQ//gh7LPgIKOA5+dbrfNFKbSgV6c2r9LLrg8xzlNJt8RWS3/JDtdahhh5jn4FRj/IB5d4eZ884AHPOABPyF4KOke8IAH/GxBdkB3O1pqV7N+LQf0ODJV5pIqYxeCmvceRlYJ5j147X7lisuhIRR09W96506ZwVxPL73Wp7ODzNmBZn3nNX74Rhp4fjmvKW4f+Jx5eAvOyr/z3Dp2a8qPt4bl1Yvv6Y9XFnWGAmuSg4RgaBGcTQ1NQdbw4Phex/QuKLrXt10YT08MeRK8HJYb5OZAV9ozeUvF7XhHlldCClgeFj9J55yJh/5igjIBzT1oOAJMhjBdPZeJCVPgAuLWGAK3HH/lRD95ueTEvWuwvl/QaepdkUazAMfvht/ejQV8tn++Q2TCogOAiLh1vwWVIqq10vROXKoK0WyNFAVdfTsEG+vz8czJOj7L5VfvTesk1gYVXFrKjLLsXRfWEbkAmobgmICu6qHrbAJ6+mUNoeMwGPBKfUFH7sgcp9KUKjQ+9T4pCrzTsaIYrLIRaNkQpzfW67cFUiK2Y055ZNRQiFMRuA6i5U0Oj9qTmHojJ5KCeUOE8xL3jiN2pZu6UBUuoHHhZI8wZzlIYuHdNHCSJswXFQubGL3j0YVpLDz/ndENF6gqzIvPw1umZ130iZCTo5PrgI8WWRhK86Jz3iXDPOWIfjGoEvoV7iWnNg4QiCPUyC81gqpZ/iGvmQFtCu0dz8czvvn0NX758Vf4avsl9EmxMaM9XdD4oynWIDgAALsrDwhdFTt1bNwgvKE1RmPCtR+mbFVgB4G4ueDygGgz7zgSNG4QtZB1DAGYzEsPejM6Ex1BeA6/xbDkaGFaK3NhJ7XEo3cfvHmtBq4LIT4R52/Et8QbSoOAhoYLGjZs9qayxWd0jRT7nkWwz7YxiBT7sePoL/bpXjRdDN8Oz7PoOsNE1xAwx/5LlRFahmeFkp4xpdyZGg2YlV2YZyY86cLbrTrJTxUufE5ZombYQDNdzT9XRuqS42pe76NtWY4iBcpOSm55Lx79kyC1S98mGNtjhsWjUo85yTktzP76vssYxguxl5SxUHIll8Q9o++xJmM8mHgxjiEMDefY+xSBD4GvrjRmgr7s0OvbOTP//n/5HX7zX/wKW+vorePoB0QVG5lnHcmgnbEbBT4Smiu3uHhzuXmFzJ7p1kMbG1M8moKus9cROed8UpOGuMWmsT+GsMQ2kNQ8y9waN7VqXmngOPu1CGcZazgUc8ZP+WuOLDop55AKicF/WJ9zjEaMTQCjvK5mqCKq6F1weI7U/aroqjj2A3vv2I+OXRWHh7oUEggHnxY4ol5XxZHx/TWq+ho1PPOsew3Svqmu17UNlR7cFPCeRr3yPpAGG6fexWflrywWkDnduGOkgH292j84RLfPvKATH9ctTgfZe7XthVBOQ+M/BGO/Ib+gTghJbJzYw8OmByrMUzR5a4mc2VJy1z7gAQ94wE8LHkq6BzzgAT9fEGD/q47257AcMZVBpJJLKhn9cwFP/rjH0L+XD3zj4FHLWw8txfD3y8DbTxHZ7L2nqej3d6r8Fk6r/wH56Xfx7h2QvwXaX8RLGF5eWg4q/htYrgW891oppz6T0+VzkLooP4TqvXeXk+uxA/snwfXpwPbEuDx9jxN6ByJElwDgreG4CHjrSKme96la14cwUEv+mTVkYKSBkuhfuRfKvQyVRbCQKP6H+F0O2WGFmeDjTJt7I7QGbg2iBw4oDjf1DsH5jxWsfyFYSbHb+17Mr+ONzEfnn43IrfNtMEyYaHlkVkvWqqQDmQG+uGIiT+V1feF1svMeMjZN6SoICkGA48Ek41og2+ECyk6C5itRYcKu4jtpOKfuaQUXuilMERmCP4wwV7XRNYRU5i3SuVlVcD01EnDPBpQ4X+vAlEEOqbNG5bWW6p6HIcjE3N5lhEY//J0ajnASnmoIP13Y203BG2M3lRz0wYmBhoBWxXPiuS+VuqA2vdiGx1hEL1sJawipNXsJ987yR8S8NSIHiohkvzoE4gq1DBHmivxcd1EQDZVMyL6Hl/DQrKaXnbcoBLIKNT7m+G705u//3YY/+a87Lr+KNWpKZibChc17Zmuh2bBccKwHGJuNtccmE5gn3cv1GV+334KZcfnlR3z4+BH9BSBRSN8hckB7CKcVjRkf2hMamXdXp8PxWSHagW4DQwQIM0ANFzHhtCkaGIcegLpnJQRMnErbMz5uyBKrdPH1cdTyL+W81FLvwRsUNvDYf1iYyqQGICJs5fvwrXOvOWU0WNzoZvHAQNrG5omx57XW0FrDZbM8gyI79v6M5/0Zn16+xaeXb/G8f8LeO7r2NJwg8rDRZSyCXobjVKVTk5fZMhIrm1SVW4nq/n78Dm/Suo9zmbrVYy+iRgSpiXppCX2dz0VRHtYzc99piVDor1UF4tqp8DiM/qcyk5HOVcHPxP06OFFn9hMYHoKrsQ9h7Be5J5CFNxVJRW56z1HhoZKG47ZQ7xy7Emh0gspAWQft6VAUKYTIoxeEZydlmOG3oO+Cv/kPf4e/+K//SXrOPT09Ge8gChZAxVzGI5KkdZ3SWAZMObZDWxoeaJJtD0Oa8GQTT4gs3islmWhD0JHkTRKBPBJDGDWgmHeUvaSO+Njny9j53ZwB37dMOWft5sIcs4cRNZ5z7MexxwIxZW4wkzyGGVFBxPKhimC/HjjkwPV6xdEPy0Upguth619UzIuOytJ03Aojnxrm+vTM8wbc81R+L/RnQA5Y3sxaP83r+y6Z/8w2R/cTxvbs87fcK88SRiTUqSnRTo8uEr+/YDj/aPDaFFbuce3XDc6U33ryvN3Q5NODcA4ZTvBf5kWXYWM/qzcPeMADHvDjgYeS7gEPeMDPFwS4/s+Cj78GsAN6YORk89O88XpFmIaFOTzj8lbO9N4h4HM4RL3/cwgo3wlViLC2JRQeca7UcWtqwitM9PcBhEVg8R5Yn39Pm96Wxb3+esgqQsASOEPl+9LG6QDyVhtfa5uOzykpOpY50zvVTMIC70vGaYn8Iu9o43cBCsGeCyWoWMKXg27KQULgReNgSxiW6FRxmYYQjTB/r+uFHNlSgScAXKiVFpjRmFhrYbFOw4shPWRuTpFvD+B6vv9jgGIoTQG4N818OtYi4UgvuyGDOi0zvHICwktCw2q/lBVv1YN7g4XNrIIVUUXLduTlijJ5bW1bdU46Jd8p7ML5GsYdIVIZh/A8BJBjOB63J8ICWKUocIo3GYUATji9CNWS5rhCr3Sg0uvYp6gIdoMGVMXP4h1Serd8r6NY7mmdJceeyI12MzQzsSk6V8Qgnynr0sONIrcWTX0OgX6Gy4SUEHHDU40cZ7DWh1jSPkc1IaHmTfvq5YrqKF1T/QcSF3yHAhAeytLrNsWcW2+HZ13pSB2PHOnYCKdJpCR0uX/Hm12B63ekIALINaqzOjciNGZszNjIFDSiLuBV9wblHYQGoWbeNHLgun/Cp+ff46unX+I4rlB9AtOG9vTkIejMVYBA6P0wbYgrO0kA0AUHAG6ErbuC8Njx0ilpNuDeHGzKqGYx69CFQDjcc3QovxnVK3iMeBUY5rwA05W4HiFJ53fesVGeuYDQ/GOoDNxTG4Mms6vkIu9Y+OgybG1YKMsGFg97SQ2MBuJmigOG4R1taOwedERozXIKHnJgP15wvX6Ll+snXI8XHPuOQzoOFRyp2MBAQ6c5tHQl9+Il1G6EJIx1VdA5w0yHd5tvrZOn3rQ2Cg1I5yFa20K+j8PzxJZnXEmnJ1rEahhUnMVyT5h4q+gDrL/ayvXFK1AU6WlYPX7iewzZDdaVfgZEbrrwoqs8h/FSClHDkzHWQad8BXBpRyV6pVOmKGbPH+mmJU5YyfsVWjLywTV847HWIiTtO7U2CkXfBcfR0Zp9AldI20ARql88qoHXqcmXeaPIjRvY23e4wlLDU9pWc3iwBfln9t9wgwu0wYOma5rzhTlGjMjiZ8q6oNGUuF49zMbIRjso5y14oIrDqjq8Sq2RPvc27kzuuR37kw5FWmxgmu3258Q8444uOPphoS6vB/a+4+W6o8uB69FxeFho8b02QjijtG/2nIdHm6Bcc4Ye9z29B6dR9rUv9HQ6/sEi5TRX0lU+qDT5LhM49lXcLsTaZrp9pM7bTZ354uv1T+04efXHCoV7Qmv2Jw3ghjwXaX0wvkZHedDbpPt4fZgi8oSFNdWC/45HarQryxcz3AIearoHPOABP114KOke8IAH/Kyhfw3I72EWd1UQoDqbzJavNwz8Xe7x9v3p2TcY9JtyajH33ju7Xk8MtFy/d03Lq/Hb27uczd4Ha79/JmCHXcBTQ8zeNn9o/n8dx3oA4lJ9PTmdNU3HM9oBuSpkB3DBEG6kNOoPAOWU9vHDBf2jYP+0Q3adFF6pfKhm8lXoMcuV6u1J2RwynBS4ufeoiilKWiaXs0pDRZflEjyulnvFKAP9AJPlP4KqhcyEpkDss4fjBwKbXmtg5NaK0amhL4dQ+z5QeW5WNgFQDzHDLsDiEJyVcEz5xYW0LgiPMjKMlgtcJWRxiPfntkRZZ+3Meu50qa7h6Zk36S4h7Ofjoq6PIMRY42YI1yJPGmACeSFA0Q2vyIS/N7qxKNt/a/FAmUi/43oKTA7cSoNf69/NWNYNzntdcsvdFBFzWRaEpsRbp98SBfhY2NwzlBkNNOYkBa3IurNVrggbwv05zuckLqQ6Vj5vy9442IGhtJdSBoFcmTokS6IWOsyUBEt4pcS/UEqrXythRKeJjAEMpXeMu13TVfr1HeCv/u0T/qv/y4vntjIhFxOZFwcxRATMDZ0UKh3ar+bHlRPT0vNt31/w7bdfg2AeH09PjK8unsPuOCBygJjRuoVVpIPA1NCkYWsbDm04sGNnANpBPo/suCEd2NhyF2Ij7HJAAYh2kCsXuwKNrf2mMwofGafxJ2FUB8yLQssfxgy8DieERmnQ1BGYayi5Tfbu950uj3x0HqYS5knXsIGpgbHZNd5g+bgIUDbvnvwjMDdslydcLhcLc+njJWK5/w7puPYrXvZn7McVu3QIA/3obnSBumDyL5XmjKFN8G5Fvlmqjrn1XRgfHgLaSte11JPb/xlhpTJ2ZcbSK7XSsWLwAy6Xy75Afo89rF/1eqvztAqap9CLrU46XPlc/vwaE9JjLB3cVqF28Ddl/5vmwBsdZMOUEzqH2iDf86nsmVnJiqeU/1qQAVML92wYpkYkBpcJULiXVzKl71gvDt/87Sf88p9+sjCZqpC+QS92Nmsgp+sEsOVdFFjo3fAyY58sZvZQx4XqJ2/BqG0a3SG0THLn/FyMqTOUIurGXBYKVMl5Gx8LdeSgUJgpkLmEieb64HM1bREjosNItUCOByO0JbmR2OC70nSkuJh6mV6OuGfR9dhxHB2fXq44pOPT84vnEb1CVCx/aCj2fM1m3txcN6uBkbd04cmmdbLAfdZj4ReWcl+FwEEGKMJFVlIf66+2D+X+nSI/d5c9W1ln/Gctm+D97Ea7tHs/Gv7oMNGdZTA06Y7/OZ11dn+iaVrLYGQ0AoqHoyLfM9IuLWiu41vv3epgQm8M1hIyNpTYQBoecpb1flr0gAc84AE/Jngo6R7wgAf8rEG+BuRboP0aI6yE/0VuiVUIMMEZj6e33yu/Wd89Zd7vwb2TAS2f6+2zNsdhfzrcl3rqNf99I5j5UviSU86XwFtzV09Da3ve2T75HcB/qsCFxmvl8PfF4/Ted4uQpQqz+KT99Yyf+FjmUwAcXdB3wrF3sBDaLO36w4Gf7HrJM5K3/B+CC7MwhAXRh/R6Ks3VZR2mkIFvp7yifgr1I3eJn5bVhXWT14srVYRGmC8ALggNgesbA1hvfw/rwryyxoH4Pl2YhUSpoDPp0nljJqELrZf8tzo+KrqHowqhTigFKaKvwcY52r3WkwpSDItxwDx5DN+L0MabzNP7SNxZu0E0hD25jMqDU46325GYno2RTKFZlOrjqGW8QJQeuICliWKXVouIe7uMMYSOMUopqAw8DgEGgJRgpyww+hdCkxDWulEKVTw5hZWAVvxY7pPVPGOV9TxwwqyjhysfIerXXE8p9Etxio+hey5ICC7dvYaFfCzmlSYqFt7IsMzeVJ48RW06XEFSCSKFLyRle+JfoyOuAIQOPHRkG3MHf8YFolpUeomA5Z3R8llqSNNL9hmC3nUN/v7tMHJvgYIgh+K4EraL1SGqoKO70PkwoTjIaSTQoNB+WPsYoM1c7QSC6/GMl/1bPO0fcBxX9C7AhXC5XMD0CzAD+/UZ+8Hg3g3F+g7qHmJPFRcifNgYGwPthcF0BXWBakcXdSG5rTcmhjKhQdGI0buAuFsuJbI8dYGlI2Rc2T9fIcJjC30/E3NOgiPjccXaqqCjoqQbdIXV1HSbB9MNtZ15OTXzdPTwy43Nk464ofFmT3Ozay7Y58ZpUKLacd2v2Pcrrscz4B4zAsX12EEtcvuhhBb03pArcSKioHvD5XgFvZI6zuO9WH7hSUcxcFqWAZU/zJ4ZOcZlsCdjHU37kLyfYSrZ1nN+L/VyOAe74g0CC1tMo0zAHUAXz2SKNhejIvXw3NnmOvUDFbMvSpg9pgf6ZL+y7CCwYaxQXA9jHxUVtPCoKoUSAepK3NxPxBscwm6Yt1Z35kIljJDY92anpTxobI5t9ufzjJbMm66ji4Ckg7rRnFDIMTEubYMyQZnRYd62safYPBLE845qa9a3Dt+HNfm5WInxHY3c8GPgYCjADPkIzG4wAPO+szktefz85ZjrnK5lDO4PSaX5sHx0gOf6CzxxhVlsOj6hyS+5gnPsrUgDlkM7Xq47rvsV15er5Z677qaUl8jZasMliF3UvcN9D47Q5WO3pBxTq6zk8Yve0Gt9rgNzh/l6B8gOtK9mGgVg8E+F/5vatlxb6ffkPaevPHdy7RR82bI6Wpb3VAHyfHTaYYaT7yr0h4GVC1QMA4UasIHhRqyIdYThmRrvKsACCFso2ygvxjnKXtlV5w7zRyrmUIfKnyC4Eh3jzPCABzzgAT9BeCjpHvCAB/zs4fgrV9IpRiixyglqOWhXpu5ETjlBeT+YzBtmPw7i94rR5fPeg3faRfVaEVAQ5t+n70e9b/Xzp8Torn17C956xhN705bykM+Dey/ckwHeuTahR8hE6vl2yEnmw0kIufw8Lwz05npqBQjqAgGaC/oeQWBCByYLVcbc0T4o+HpMAu/apxCAxcHrFMq6izWsyzvTmvT131XM2j3yWRSvgBSepiQIgKoLKM2aG7QIIT5zfeRBFkWIsAh8wqo7fsdBXxXYd0W7Ci5XQrsIticGa/iOnAsP6J7krOChqiJCSd2EwfQ2rUKLOJBHiMCOqnYJOdoQJOabkSE+D980DtnesFTMFuQO4ebatrNuvUlTz95ZQecfKbDyttgaivXDbt1r+BOeVwA8vJuPrRRPRNE0GCEMQbCtGUdBLm1JFw6/13GjiEycrvT/rLM6fSkPxyayCtBcHBITW4gMgTJUas6jykwzJxzwsmgWWsW/NadbFBqC5CxuFUpCHQOjpDpwyL1Y6zUf5FGOecal4q8ooENIausExSuS8l72Jt0KC5GaU/xhIHtdVLrOQrmrKIkPvxNcf0f4/X9s+LN/LVlRd88LUYEehLY5PVSFoKP5PHPzvGieyKtrx/X4hG++MZ+vj5cNH3jDx48XXC4XWAhMWxsH7QCpKQMaQaShdYLSgU4EkJjinwClHQJgd6OE0GPvAIgErBuUCI0FhxptPuiAaHi6KqAjJyHcyr6yfjG+Q+Q3Rr4+O9+v71WMHO/Z52Aq6zLk4oljT5n3KCuD1ZRsTK6c02Z7T2MwbTb2FPnqbC8K2kPErpRr2C4XbG3znHQWklC6oB87rvuBvnccx46uYmEuVYAdOI70z0klgfr3CXddSZeCWjJUpyplLTx1lkflGQ9pmLwqjP6FcQIFXSxL9TU+WPv4HuRWy19IgAmjXuJQPoZnPEZI8GgTlbYXNIg+geGhCN3rKs4YPl5jH1ua78/R0qfY5wnIMwSX+/mVwjPPjRsUI2dkeLXFfkTmqUxquCMqvpfFvMXe5RWKQLtAqYFY0QhDQRdJ+FzBrlDvc+U+3g+//V9+j8tvPgSzAGFTUoONX6TN6gzvdVG1HG5q1H7zkU26roEHZa8oSiUz2vB+k1OK3M+N0ChpjtkY77Ev1h7KgphEs9IK0a78qnObwvtHkcr1YVQynqd4hyw0tLsW2T5UExgS2cIRhuoBUcV+7Nj7jufrgV06rmLe+4cKOlkflEI5p/kXZjOlcP/3dcbi/G6ZD2/mjWN+ZUfegOf/CHz4P2LmARWm9Cplr6kCKtxUtfAj5dXpev1b751B3dN1aUsqtE4b9MPBTdt1bvdNH9nCXYZxY2M7K09jplNxyedOUQy01BXjHzSWljp98FThuRs9vG6uubGvNlLL2fqABzzgAT9BeCjpHvCAB/zs4fhb4MN/Q3kIDKDKKNbv74GQ+y3SnhCqxiHwVYtCnT4muPvOW6eA+H72XL2my7W1EZ8zFm+156z8HwI+48D3GugVwIfXjmDl2Xc99c4HizBovXbarRAIYXymbNhxwvIMmcBOrh2tte8+128A+z8qAG+M7cMFeO5AI6BrOpYAyFwuEgIu70P1BsplXAfBD3NenOfLGNfDKp865nCaRZAYgj0Q0sNuBEpy4YgLvgSaoe1mLxkAHTj+lrD905NZKji5nstpwdfw9Vleezech7DUMo56dvlGGBpCw6kUHQoMutsyV2zQ/FwIMCO8UxXQUPleL0rXCadj/Zwto3tiwmpZPYUh0tsyVhjyMhl9KMIVInZhnQtCfS2auJTde86EcBKKIXHvK9Zp3RIweXykjIyW7y501r7gh9/LkHStvHfaq9RQY0i1z54fykng5JGcjECmW4+OaGn1xwvRiq1zX1c61qAAQHh1TEgCRLhaW6ucimZT2lWtWCCM5sdMXEbd8W7koBwKwFgzmiGzVWXCxXkBr4ilY5FT+b1K7RyxcuzikX426F8GCsLf/Y8bfvMvd1w+kFcp0K44OlllZAohFg9PhQNKDcoMvii4E0gFIgcO2dF5R5crrscV+7HjIg0fLhd8IELjHcwKPhrwAhxMrgwRkBNraQ2XbQMR4dsXNqWhCg4hUxq4QB1iSq3eCECDkKBxt3YLsMvhnRSoBU9NoXDQoeF9WbGZ1hnMOaBldqtyLpRx9Yr9P1OVYShhHqQMAimhwTyTmi/Y+K/xZs+RhyNlRqOGxg2N/CnPR0a0uYK/IdYzby1z3oke2I8rPl2f8Xz9hE/Xq+cZJGyXC1oXvLy84Hrtgz5T9mYso/CeK4YtAAZd0EFjJWjgwpeE/ctqXBCeemFsk++E0muBmNNoH4UHTQ64ldkLP0zRdh10NsOvwd6visbgByJ6M8oeQtlmyr6pjHfGxJfhK6SVCCNEX+nPSmCj3qADTHD6EDisuU2E4RV87yf4WAb/4kq69AIWNeWu18PeVtWennQRWo7dSMlCPprwW9kMdIgEIvH9dq5eBVH064H+ZHOhwhAVXDYFN8ZG82rN/V8K+cxkhmpKxLg/psCUcxSrj3Ic7VX1saYxSWXbAAbPEP54uReAxvZKXlYyK8uuMPFcg7o0IPc3w4Hx/sinSDkPrZk3scIMWlh4KBZV0QVQHKaEU8G1d1x7x6EduxzYxUIz96KYUwaUFcKKzo5XU445deO1Zf6SQOj8/R4U/Nb8XWopa/otSFu6e89KwZco/7T95T7e7MGEV/eCUrwbKi+phZZ+7jr6AliruOHUdNBYJ//jvtNcZoDbYB1r/tI6NVm2ztcnKGwQodBTBo7ezZDA6W1rDCoGOcLlvONr4QEPeMADfqrwUNI94AEP+EcBGR7ujNsMeC9PF8+dCD5Xxd/dIouAovL3p8/fcLml7JWrfu39+H52Cvmh+dlFEPGDw2ccrPRvAfr1573zvoLx/nFwoZYWvFktsLXI2lOwEO9EOCoF+rPgeBJ8/HgxBQIBk8nj9wzqDZHecVwV+3HgeLEk9iIFjwmzjmDIXay/axO5oG0dCy3CsBA60bCIz+pibKgI2RRuSW2/Iy9MTfuRQvszyaH3V+X7RpYvg1DUnbVVigAplBtp2XwrZ3pd7kJDaAVXj1TvI4LeGjQUWhTedlLKS0UIxrP3QlTeyF0UNySNXnmhKvBeJYUhi1IXIEQuDRYQMZQs7Ba7ED7EBo5hKWQgDYt1U1ZQQbCbYa40oqJVkWbUsa0CZwDwJENjs8n31CTYsQA1Yr/54I0kbTneM1ZXydfNTdzcDA38K5tWzRUrThwmBWbBmXzTQ2GCkGHCoO7ZEnigngfvZP9LpSCG4tm8Q+bfsU5SgaZ1JOoELH95fQiQbvb0JHhRtg9CxvAD8ElPeY4vhf4SfNHoozXJPOp6P6Btg4LAKS0bdJTJ8J+g6HrgKp/w6fqErz894de/+AW++uojiBjbdjHBOANt38EgdNmwH4aYrROO/gJlgjTCRTZcescHebJ28IHWLNwlu/XGISbQFvY5jrVL5DmVBF0j2c8YV6bhMaUqCIXFWc6/wCn2jSFytcUTnEykbay23ivY06tnXUNknWv522hD86caTFhvT25soS03sk+mzQX6plpR9/wBtvR2atzQWgM1yx3YpaPLgf0QHIcpY3ft6fW174LrVXB9OdIjbArjGF9CMUIeKhJjfQFIx1GKYUFR/CzrIdazYhjyMANo7Hu3JLmifrtksrhCH+4ZxU2KN1neKcLdqvSLfqThDg2jneAtjH3Q5E+ij+lBXBobYzC1P5SeS13rs0EHK8836BGCdEA1FGUjr2B6IJOHYHVvrBEvmYcSzg2RDLusDFNoseGexweN/GxC5tEmolAPY3ffy+ocjr3j27/5BPqLr6AqaK1ho0v2RxXp9Scwz97AGRVFh69RocJLumLdmckIT5mBPckU5JNfrdr96ojo1HAa9KEIGBPcCnMhXkZ49sR7N+WU/clC086q/6TIocxzj01y4ps8VwnVEjjXVdHFPA3FhgYHOnY1xd0BU2oYv60QV9ApS4axVQq6gjSQCL54kMrap7JoTiAv6/iiuH2cYPv4u9jnwgvVdVSGbRgDvFLeWTuyMe9px51y6utUf9NoswqgOyANkM080iaDtO8Bzsb47DehjFPgpo7zz8quBLh9hH2nMQ6Vfam0enpd59/VlprLM+S0TUQgYjkaiQEWNhpM8Py6lA3+XFr0gAc84AE/Fngo6R7wgAf87EGvwPU/KC7/JUa4qC/h3SYOtVbgn3zy7GcWffPae8qp7aoH+UUo8keF9bTyfZ0+vqT+eu1zx+eN579ouM/GohyU6rjl1HI5TMVBiDDyvZSDdArOXMBGGobw5ELLqOoPMymkit4Vx4vg5Zsd108d/UWgOzJ3RMoydNYNZBkYQqv1sHcDakIJho1Ha8jceyaUc6Gdnz6ZycKZen6VlJeX8lSBLmI59Qhufc8ZnujHDKI6k6hFlkIpNbArZsmrQ8Fb5SWxjqKMCL9EsDBzrOhkYQ67aobDMmEmZhqAlKF5vTrdNwHGwIKpDdHOz6ElRRhQcSgErOXS3ddd/mffwzsuix4ViA4vrrCO5xTGc+bi2h3ZmEwwHd4QE5yM2ymsgh0XNuaFNR6qwlxO4zspRtaUk4omCdToMUVOmsSjqH8m+JQTtpRNIYzVVALAsSaFRrH2VVPJmcIfHbTB8MbbE7glQPf2TcrqqDey8Wgo3k0AJJGbCfGcYniwDGXglIDwDGqXVylexfkg1r1cC5ejPwSNUeCv/23DP/8/H6gEr4YxhKrnVoPlfUJHp46NXQXdbUw188ddsfcrXo4XfHp5wbZd8OHpgifeIL3h0i5ojXDsL2gNgHa0DrxcBQKBsuCr7aN5dEDQqeFQhrqnqrWNfT25V7jCjCJcg/IExaEWd0uEoJD0ogtcTOwq2vmg/SHeA5BKG6VY0yH4G/NRw7DG1VDYTSo6jZCCJoXdsAEKNM8hN5y6Le9cY0vs2Tz8JaOZJ52HjaYIiUkNvmRsLBSWW84b06Xj6B2fXl5wHDteri+4SschikOAw+nC87dXvLx0ZEdKtNhUHoWne9G3TyQhqiVgK2synkln0Fiz5Rl2r19qvrb68IBPPiaqLcshyA6FcLs8O0HQkNKvIHu571f+WUdd0xIuexaV71N74sstqctrDKRhVeXdmWBRB4D0JCEaPEwMoKqMPbqQWiuD0msslFJACaUbPAxZ/retNVyaK+GkgWVDJ8FGDZfN/ho118zSpKTrkYhPTNnzJaTqeDnQ9wNAhKcLvoIs9+K2AcoeQllt29LRIzO0MGaSCd4+DD6lbHOZc8zHx+bZ6ITmONlDTATJmMw6jIT8nXV+lec5jvfMQzAeUss9F0q6wN2VMfFnNfbNss9VAyvAQrirACKWV/R6HNh7x/O+4yoHrv1qHnRqYYWFFB3q3vausAslHQBTkMceqnN/xgCMNQQgEmUnn6S3iED+xMSjVPzVwNnXWR0A6M/Ap/8MfPwLe5i0nB1iXZTmfg6cPV6amO3T5RPL94lXBm46tQ5nXMtl/oWwjt1Z3ffqrRBcrGLsAaeVOQ6z70EcNDjPlye90fnrzfgFvU/6a2tBuuVRJIYZyUUic5jnrTY3gvEwzw94wAMe8FOEh5LuAQ94wD8K0ANDSPkFh8hTWM9oZ9zuHcZ4feWL2lTfUcxKwjNPwXhuPV28Vfb6/vcE3/UgkoWcfa+/XxHYvAsOQH8P86Z7pehVePR9QByMGpZDIo1E3QhBURyW/HscWldU7AdwXAXHtWP70EBbsRb+nttfyzRPugP7syuAvL01L1UI8KrH273ypmldmz/h9xCjhgcACGaN3NyC2ZV2MRJTFg6FW2zryE/DhI0Z3EyA+hqcJbB/L0zCDKxzSVCLVYQImWTW/uoh5NyLTuxGBGW7Gavli3kUhSjbGjFCerknCqjkTyO3CGewC+1EkBbvgZtTWKKKsxjC0uy3e6243GeUUcYmFM6nApUqGFnGclozC8gr+F/QaZTFLkiOtRfCNFjoRMKQ2qVyJz/LWqXIT1Maun6u0iDCUMIVeqZTR0fb8tk5Nlft/aiEeR4Q10YOwdcYqPTA0sXroGyEsfZI66TMCznDp6ZEaNTgq9eEsqG4yn7SkLwCUC1qfp+L4YUcyudoh6byBRjeseaJZR4p470baaIrf15BxBWo/FUp4lS2QwnFm9LQ7xEUhL6TRbYERmgwAtQSJNmwNkqhcPh3KAAhAbOAGkG1Y1cC90/4dL3gt99+wFcffoFfikBdYMaNAdmwbQ17a9j6hmN/tusADg+bSdrQsOFje4JeLOQodgDYARF0084BLps7YKHfNqeDYIC14ZADvQm6h7+saKMWsNiXDvt43HFT9OkNOm/oYPjGvoADy827ZbwDRSo5PcgeNvekazDlY0vazkVI3cCwcJWRR4wcz5k9/xxZaN3WNgAMUeMTmBu4bWaAIwIlwdEPXK9XXI8dL8eBox/YtVs4vKNjPzr60U3x432eeCTC8EbzPTRzxwU+1X0u9kmUBzBQOfgajhCVBJgOiGCRFK2v6VlLpa51D8EgAb7t5VxP9ea6m5dr/ax6hRA657vL+st9pLwTS7p6o6RxUdCL0hb2CAeiSAOr6TmHCLNNXnj1oIu/8W4ouIzRGUYiY1MMTztiQmuEbWvYts0UYqIWNpwEDMaH7YKNN7ArjUHmDW5KOil7RF0Tnwcvv9vx4c8vpqh3pZbIhi6C4+ju9SZoWv1XPdQdqdFgVRPac6zsMTbmKUxQ4jFWcNxStdCeMLo/5eUl43M0FHRn0VNqpy2Gpu8LXnbZ861Jy4YNG0vz4CvegD7F4u1jJvdY8t2Q3UhBBHu33HNHt7DAz/sVh3R8+/KNrfVuOemO3nEQoTegswLhTcfmNScU+UBjEXkL60IPUAxPu7pAFSBJiojA5JMllGs11mSaOyx7+j2oWz1QttEzPupuIf4ZDVzp1VJEYfdGn047t7yw1rfSE721832t6WejU69VFoppjpReQw2vzUvcDRwsy7saMES/qcGMKzanv+J1JSFbcX+GV6enDLTp4R1H4XWRh4OVKF3RoMhckt8zz/SABzzgAT8UPJR0D3jAA/5RwPGfgfZPgPaEzw8ZdY/Rq0z92SHm5FDzvQCdfF8/V877rIyz9nzBAXuFtdibs4+e3Pwu8I42r8Ki974HwPDl6q/8oeZ0hTIuKezy63HuyfPP2p6CC5Geox6GpQNyKI5dIF3NowH4Xub+BvJAavVdX4CXFyDTBs3ynXc1IQVhpXzy74oxR6oAieczY7UDvf8RA7yZgs4EgcMjbkXPOCCmELu5F2J4MdzTzNI70OPk1XhHytspgPMfJgwmkDRATDkWHkjEnv+NzPeF2F1jNMJe3a8+0WfI83JMR5gnyg8TdpInhPd8YgQPGXor1aSl8pyv2SVtEiRkO4fMKNv5XvlLaXZef4cM6BZiTv1dCe25S7FyfL2tjHE9BVCOuNm2OwTzlDxruRftD6VzNUKJzxphcs1t5w4QEJeC11iiowPzfpkCrVvxlZZ/I8RqCDqj3IrRt/Nn7gQRUnJ0oyg4CeEwAROG0hjPQCFVKMS/symLQ1DrxGPkkzRljRXjZUyKOWB4zw3J3+xBW5D2nkDu7r5RpYkFYux9WNABfHP72HeFb/8z4Zv/H+NX/7yXtW7eK40jLB6B3COto6PRgV1M6abuTaoksEydHb2/4Lo/47ff/D0uzUIwto8f8HTZwGhQbSBSXGTDCwH7YRbvm3RcjwYCm1EVzDPX8gICclV0OrCJpJdGB2Ejde9TCxnJZKFkiQgNHXvgiFqGui6Krh2d3OPIx0KKmwyBBi2DbbZ1d1AKJftg/BKfk0wSWtBpDI842zkU8MCWlkduAEU/YKEvGbC8gM3UfKaga2i8+aeFx3QXIvM09XlTCK7Hjk8vV3z78ozrfsXLvuOQHbuKC+g3bJcnbF99wP7tJ287Jk+6+MxwyLzwJZjsBe7yxHEpwk8HnqcSIF0w7DspMkw3FTq0KruS7Dptq3vYVPHaLJ0+siOTN513ikp5OQ617lpW7o2lLWUQ0gjJ6Sv7ODAwGXpUmhJbjThOatTpf+mR5UrcRuykvuw9vrESXEGqhMaEbWNcLhdcmLEpQ8DodKSS7tIuxkcQ2VpjgrKtP8MDp9mCzIP4ubB/cwDcci66KOQ40LeO7opCpQ0bmzdq5jwE554h2qHSQFRC2SoNPoWR10kBBC0RH6WkCbaO0ELYryAVV16NOTd88bUWPJi53c68KajspWQehzrWqoXpHXtihlUWDyUKXwjeDukE7UYbuype9it6F3zaX6AAPu3POPoVL/sVzy/PuB479t5TGagEaFNXaBr9RoS3rGzAnXU82Zf4v4aWlsPQcmyS4yndPJl8kIxQsQOqb/LbUL3mHL2rfvFN+DJs/UwInNGZpUrS2oG+A23DiPRR2jbxsXe+x49kGxb6PBkmFFoV16pNUBq5+dwXFne8r3NZWV6hicmzTZVVTvG2H5PRZt17QhHuvJy4QVd3nDUPZMunmBE8bg7ID3jAAx7w04CHku4BD3jAPw5QjLx0/vtN7vw1/u5MIIfl93v4w/LMGcP9rjZ+l1NGFWScNuSkrpN+6Z3v9fE8jOlSxOeOWX3nlf7TzZc7db63Spo/7wnSPwveKCDONHLyaCrdQm7gQotquR3XNQRXKZwwQXUXBStlmJ/PdfuqR+kUY4ZQ2w/L+0vHsSv2F8X1WS0PTvSHalnjy2soOK2ZVYAI5GkzhHq627U4QFKzP4FCyay0Q/HmstNsQwxLjh25cMUx+yz32Tw+5UsIEmh4iMVvoBzcXeI3hFD+DsZvVpi1sjSzIoe6os6syHP8EQI5Q4wz1cjc2HhvQHiCrMJIIIRJ8aN2teRvooInOaghvCzJ3v29tXlavtTWV0FHDX2Yj68To3eufwaQ/5OeoP6bqYxvCvFMWmFCdw9z53PSXXEUAuXJMQxFKHJGKwkjl1FxlZ1Cp53R9JCi83jHFHVkIRbzZca0mNIMu0h23tg0pq0iidBQqp3OQUiHFi+6LM3pSVWYEWla7cNfFSeMBEDFNJEEjPCVOpRwEpr8KjxacWiijUV65aSyhkC75QfKhdwA6/C9thaRc431te8JVAndDVAmLyKYkjU8pkblNramBhUIdfMmbYwuHQ0H9v6Cbz99jaf2hJePV+zHAdEndAHa1rChoX0gqBymV9oblIDj2LGpedI8XQwPCQz9YI0QUfSr4gLAgm8GvbABirknJohajsiOBqaOHsnXVNEYFnoyFoQaTVffMNKTOPIwQV05Zr0XqIUedOF/6Lq1CiRzFMmVhW3kmgN5UD+jCflvKAdgarxGm72RnlGmwCQlkLo0Fyastzx0G9ql4fJ0wbZt2ZbeBdfrjuv1wLV37F1wiFiIPKYMkalq80OworUNfg0o32N/prHGp1CU45V5t4l3YxnxKLcueymbuWIosFJJVvkw53dif89KC8MUITozJK4Xr7VfNNqgi4B8hdzT6jtAks1QvAGjj5W6RheStIQHHcM9qSiVF+GEwmOkTaHDZroQTofihgvMgSsexlsLydfYdykrpEYW6nK74GnbsHHD5tnoBGaAdGkXbG0zJR0o91v1dlHSW0Xn/kVKOoXi+bdX4KsN1A/048DT5QloYgxuV+hmZgK6XSAMtHR1DPocURDE92K4Mo7cy5sg3Y1juk9NKOeAsjqRirf0HFTPg+WLIEM5Jk6FJ14Q6tGeikimZ2PELkaOeOLzJrGn+KIMYxJ3+IOI99DL3vcdhwi+fX7G3nf3lL3i6Duer8/Ye3flHIzHbwRtgDSFNFPKaWosdSyQaHdZl2FMVXG4di+V2TBFXURuCGOz8U7s5T4CZR0mrzRIwKvw/DfA058DtI21VcOcvgVUv5yh7WduuPXxOj51zZ/WX36sz4XCce1SsX3K56eYHoU3CfpepzdpNM2/k+QvE5w8XdBOX5oiI8yyzq/4VBuNGPQ3onHcwaP6PZdg4aN9vQ98i2cLstYOPeABD3jATxAeSroHPOAB/2hAfgvwP3sf8z/B+vzKVb727DvKfPVIeyb4C6FC3Dprz+efkz8Lzrr5rmt078b3Azfd/p7GQT8BEAW1ucBTb7b13eX+58owMi8LzYcfYAnLGAea+kgRwFW8OQ7F9UXQngXt0hKR6B2Lo+aaqDavVUn38nxgvx6QQ/H13x2WL+NQHNdXxoumj3OoQkNgwiMtY5KH1ZC/e6otapiUcOEpEwfOHLsqFIxrsdiKIEVJ3pz/tX9R1s3BOw7VCgvptvaTvOkC4GpCq2NTtMsGsIdvEw/pRBG2zMTpQOQfFBdv3zTJBe8pOjIr73KUVrhHSwixZXg9denm8RKNpXmJD4WWeZcAVk4I9MbhnlJILr62quBBly81n2F26M58vFc5NykrTspKhZoLDwI4hKBkQvdY6IxQCBiIWohFkoF/Q5mKEcIpuvLa/sPL/dCv6fJafWeKe1TfD6mcX5tcR/yrKkIp4p0tDa2FxgDNoWAtbBgNAWZpIJdXU5h3M6Eu/HSCFjgeZZuHqU55p0xMSMPbQqrYMPq6ClQrsVWMZJ/xuzxfJVPrXE379z3ELJ+hdHU6RUGzvv3DyZz+6r/f8Kf/UqA8lFKiYsYbqthIAbZcMC5yA3OD0GbeGP5feCMKBHu/4re//1ts/AQhRnt6wq+2CwBTHmz0BEUzITHZmm9bgz53V+R6aEkVC+nWOg7u2OnAoeYhzETYCDi6YFdBB4M9HyYTowHoYBxoaCwQ7emVEoo3wATVDIIqp4W+XSvC5TKPHhnZ73qwTBp7oZZJb6GEow2kjI1NCZYOOWphCTMvHXkoTN7AZJ5ymyMz+T0VMireFcxqIQqZwdxw2TZsrZnCUmw29qPj+eUFL9cdu+zooaSTDoXi5TBvu/040Lvn3XPaEnwDAUk30hjI8SeXrY7tuWyVY/z8GWIYfocRzY1kV8d1Gn9npCZDZldnRB11Rs6vzBWmw0Cm1cJ8v6kGDOvWEu1JFpbGOMHHR9n3ZyeRqUj0eibHZN/Pc79phMhbqqRgEssVloYYVPrjWQ8N2cHi3p9sHq5Mli8XEiE7fU9XVwGn8p3w9HTBhw8XfLxcsFEDWoO2BtlsLTVu4NaSv0tPOoSSDmjMEG7o2rFvV3wJaAf2TwfoAqM1othpR78c2HnHh8sHbHyBKuGpKbht6IoJ92Ki2PcX8mRvCuNXgs/jCMnnyjUCZejZ3LF4IELFOVGYGlNtMJR5USD73kR1rjModHkwjOOCGXReiABFMAKc4S6tOss9t++Hhbncd7zsO/ZjxzefvsVVdlyPFxwiOKRDKIMoGG66gg7s3N6iDSNUXm3mb+p+uRqnTevbv0oxcpj2yjKYNQwoTZW/D+TFlEQZFn1p17vgpItvwUKavhNMtLLSuMJ6TXWsdLa+X8rs89ROa+RuN7Wwh9ME++2oW/1Pbp8JHBgK4FJ4PkrTK7WzuRRKB1NRF0pzIk+PGQTZeL80vohDs3xu2KQHPOABD/hxwENJ94AHPOAfDfS/A7a/sO8TgxtwJmArX7+YGf8DSNdo/eJMbfXywgmT/S7wd99qdr1fDxJ65xmQCzB0vfEFbfz8x78bfAMLwRVW1jQOKxVuw8CcgC4HoLfAhTwZbiQOTOVUFrKBtHoN4ZofpPLw4u8LCOp5bLpYCB2YfOa8CatiTsf3wJXudR1XwTe/veLT1zv6DlPMLYqH6eBZhXnLOJVHbq6djROAFCLWdxRI4WB40oUgfyiKNMc4xzH+CaUdW0Ex9haK7bZR/R8I258B9OFOM7WMQRx4ly6uqa7Ceys8Rsx61ZQORKaYsBB17IIecfUEAHLPiRCY1LYUwYC6dFMRcsYgBi68jjpN6mVPiOWhy3xeNPeh1lPnPDz0FPOhPPHUD/gdJ0KhOqAVcb4Drf0c+VA2Nzqm7gGi6rmmyMMNDe86806ycQqBIRSWw69IcRMnAPO0izGJ7oUgIyS+1dOplwauRDm+p45NkUHUZPRjktJpqTwITI0vhWhIrdQVLBZ7DYTbsEPTbwoFchUzWXmUUv3wDo3naHjVlfZJeEy5dx1Tc5z6/7f3Ls2WHDma2Ad4xLmZZLHIenZXjWb6IVObpIVkWkgr/SD9Ae201E5/QD9IJtNixmxsRtJ0t7qr691VXWSRzNe950Q4oAUAd0ScuDdvkslX0T/y5jknwsPDH3C4O+AAvN1D65CVhCEda526J7ZMxP4mF26qPIIhIQvL+oDW+yYL8iqHZc1EkJeCh970uaDAcgHmp2gK1Cq13Q7lm5W3KweMv1RwKUAxwbpATaGHFbUSbpdbPF3PuMiKu3UxKzaaQWwKqRnueVUr6llQuEAFmGdXViiBaAKIIMK4rBXLqqi6mvtHtwJRNSV5ddprnM4bvsKsaUC1W3A0n68xmUUcSY8/Cp8TMoP0vMVz4DTI2iEVBOUqCk2erlj8L/b4ckoWB6sJHBmMgondJSYXME2YiDfvUJgluoh0GzwmlGLKufk0Y54LSjFiFjVLuloV61pxWVesWrGqYq2KtVY8f3GL5XLBIospuQHjQ+FfMc07jY5jjtwRZZ5PelukH0nXvbXQRLc6y++Md6V1VxYkN3YT83Z+Z8RajPtpDZUtBDdl93c0bwSyqc71A9kq2X8roR8K8vdxWJtgl1+8h8jdVJLzOHO/SvtAVTGfAKDSnSkb2zNeabTETUEFoFn/N2vNoDlmPLk54eZ0ws18wkxsPK0IpFSbz8jWFIBN+RY/z6ztWG0MxHgTrbibPptoSaui3qrrxrqlDESxcoVWReEFWgUyn6CzorAFxGLarFYgTGYZF9aEmdVWp70Q5PtE3g4HiZhlWzZDQvBGK5utu7wvpSIOcShMOaVoM1ZaV7TOtl8x54VyIW8KGFAtxmnU2nWtxrsu64IqgvPlApGKV3e3WJcVt8sZVSpWmAvMCl8nxjTOoSQWVGizHHYC8c+gD1wRfjvol6bHPq91fwjhprUrnLZzfV/g+XPtErU2zOve10JT2dDL+Dps+NKOlx0+frTGpMeV9XplA7dETUs1AVCNnLigk8tDZcc2TdB5dnxwb4F2uK/OlPJV9OWfVOMHtQKy2riyAZD6nvU1/UJ9zvBx36YYSu/bPWGGrj6+gb5mI22H5brHhIGBgYFvHoaSbmBg4FsDXQF9AdB3dmtU3X3iNYvuq532A+ne9N5+I7CROjwCtPvLeT4SR0XbX7tv83CU/soa5Wixnsv62A3WG9brbSCUZZI3DmnTmhVnuXyva68HkYRUuZ1oRxdts+vl4bSpriFk89OHBIZWxXpecEYFSDHNBDwpKPN+dx7va44PW/Du2PCTMuoCnJ+vqBfBq09XLGeEHqeVeV+NzTuO7qU6HpLGrv6BmgRi4sIJsFkqme6ALC5dM4PQJtBLXo7aLnpjsaUCVcZaq7lB2hwn9SQVxz5Kd/XN+o9WXX9PIe6nUT27roRX1FpBK4AFmJVM8egDQ6VLQ7vhk3a62TQhpe9oO+y+XQ6FoG98Pf5c+w1TTAmHJV5/+kpIS12AkPurKXtVwewxY1wia2PtKHZJen5fp5A5pWtbgdk+gweuR9+kNBvBW0ui7v7OrVGcmMgrq9qFd4CiQprCs4bfy8mEH03wtCl8+p75QG5HToeH91Kh9j0EkF6aJg3xv+oPtPd42o3GNZhelp6HZD9NWk0QGWWKjldjDhqvYreCc5eUgFv1Rmy7XUtvxg01xUu7588ImdtDTRLGB+OURH33UrAjxGDdaFDzM7p7VI/pNz+eFQ5s7uhak35BkBX48P8t+On/sJpSzAskKtBqVmZCgiLsMS7J3UhOnf5YMIEwTSYcL1BIXfDs5UfgMmMqBeUHP8LNaYIUd6FKjMKMmexd5tKtAnQBu0XXulYAjCqCJ0+Ay7oY/1kWFzwLCCtUFGtYaaLzLtawDDNLGXPtrI2/dJeZ3gOkmAhg0Z6PhhoCUH9+ArkiBAgOqk3BomYRFZZxFBZ1QKHJ01u8PXULKEJBoYLCs1nWcbi2VB+vPjE5bXMpKEQ4zeba8jTfYJpnFJrMtTGZNeRSV5wvdzhf7nCpC1apWKSiquBSK2pdIXXF5bIaH5+s/Vy/Dm+EvoDJfGg/RnSXNj+DxD58PiXFxgotXpXne+tE75qkrNq4NfayhgvG6E4Cwhhpe3gp8khzYXtvlI168XuCbb1Dkd7YWVJqtneltLyvY7AZfx8TuSm1WUqGm0ZL0pVr3W210yCT5a3w+HPc0ofpXsiww60qF8ZcJpRpwtObGzy9ucHNNKMQW16TQNcKlYo4lCUKs+xzi2XA+ICAu5IOE+bPqKQDAJwJuLG8VlmhpKhUMZcJOgvmMoNdKbeCoMUOZkylx2U0JbdZqbEfKjBLOXTXojFfuMaBfPzHwQ+Ld+WN5kQVh00odbKtSZynOO1Imio13GYi1jN7Gg2lOLmrU39OqHkmuKwVtVbcLRdIrTgvC0QEt+c7iFRclgtELV5oJcEqtg4Tcm8EMTZYsZIr6Fzz0da5ec7bED4aTROwsVTrlLkDpdZJdbVL2p+JNjp4Z7jEfxTymM5fEt+5XvMe53H4zn3i++bi9M6jNokPJ0Mo3JrXaZIpscPoG2zZ6lF+QFqC7CrQllv5WvsHrdvbY0d1S4VoCjpxJZ3/yQpbN8quDGltZLRDB5XJL9msHneFtrvU/mXfy/r6GnHoBegHHR5NRQMDAwNfKwwl3cDAwLcHFZCXAL/nv9P6rbkUBK72K4/GY9aDb7pmPFzQ3oMsGLnv/v73Ln/dfX4WbBb9edOS2zi/hNLv15R9swl7o875/JB/AfinfRO+VwRsNj875E3po7ETgqkf/M/Wkpu9tKIFC4/rLdY29S1QrQqRiloFy61iOgFlBm7emUAToUwF1d9T7yzY/Dy5azIGiBTnZQEUWG4Fl5d2bFwvirtn1WTv664pqNPARvCVNoBXnnU2DXsgJ3uAVjYbW3YFl8uGNxvmEJzs+hTqugpxAaC4Qk/DPZu0E/PyoDXNtg77au/rbJv1JpJDd4QIa390QdC6uFXIKtAbxnRyU0GGi6XstDupu6Jj8frRIc1a/xD2NBxKj3wytVsiWIOa5YG5fluRLPY2lYULbMIt4bWAqPFdQlO8MNQtP7ui7rUuKe/rh12iPY3uEVVs9aat8YRKmLp1oRXQ6Sms6ZoSKdGm+ENd6ACz9NQ0Hym6j7TEL1t23C9zCKQVXXB9qChOD4eSupAT1o5hhKu1sNgLTXHriI1z2dSO4a+R07XWop5/3Uh0NnOPV7Cres3F2MYCTrf322ONX5glirqi5lA5lyf6jfktjgmrlVDvnyRp/z0r67o0rfRfzZARYUE3+e+IyfQFem1SEOqZUBcCZm8rN18lNmExAahqyicRs7oGKQQXKAhlYlBVTNMJMwFFgVUEKhdclle4Pb/AsxcniCyYvj+jTDfgYnHa5lIgAKoI6mzWXJUriCaoEBZyCy8hPJlXEw5ygcBduglQSNxcyTQSjDBmSi57SUDM1pR+aoMyDSmDRZwvJZpxBXRYGRO5U8KmlAuhc5Z4JsulcGFJ5ibUXFaiEWtYaxGbetNiV5pClKvlW6apWR8CMLeZbEq6Mk043ZxQJotJRyRQFdS6Yr1cUGuFyAJoxSorqggusmLRFev5gnUVXG6BemGc/zhB/xv0mHRAVzZlPhRNdt+6LBRYUU3A3Pv6/WaFF0Ld/YI70gAtJmNY9wTrafTrrKtZ/+T5jPrfVRnTnNQtV9GUiPeuR+NdeT1Buyo4eyP2uSCtIzZWSlFH6jSkQT1krmHtUExXzAU9EdjWCsxQdsu7NCdz8HCCHUoSssiIXDCVgmmaMc0znpxOuDnNmMsEBpt7TFVoIWhlkLhSW015rR7vEa7QcyoGyKzI5vLZRUt6QVsj2AX7XlVAMGs6EVPMrdOKeZpRSoFME4gLijLU57JY+xGzK4SpaT9I4IeBxBWc2tY0Eh2GsGI1hV1YiEFdEVCoz0ekUKVmtQa38mmfuW+1r43E+16B5m9dq/GgZTWXlpd1MSXdekGtK+4ud01Jp6pYq624VvFYdU5gdiDPeLWQQBipfADF+M6zE7VG74vDNDG3MbXHXhN0MDlmBZ/mzVNaCBNgHksWdK8Ar8Gr3wLv/OVufPtnm/L3fOFNsF/DJrQlEaxtKT9ysESg9lyMZk/N26xjOZK7Il5/1dJ6/T2nOVqmaKrTnoffw/I6OTjflpr+9h5y0sJmu69y2/QNz481/q7Qm/pvPYDAlXHWTuTzQjoYeN/abGBgYOAbgqGkGxgY+FahPgOmHwF+oPnQDV8TCOxxdO0xi0F98Oc2+7yp2D+0v7Zb1dN99x4C9cU3sBOYPHahu9sYKPpJwU2ddPe3z6PvZQ/Lefj9y4QrnnRX/iYgcuyFNW+EXfpw9ZGVR5tkqRxHm9SmUIy8VE02DkCqQottimUGCivKjeBcgcurasq4lxU8EaYnE8CMOC/96o8LFEBdFPXWykECUE2bvqCt2KTRntjQ6K8pfShVS3d12rV5a4sD2ohrsrsuagJkiLqwQlssGgXA7sIFE9CMF/w9obgMK7Ioh14Rc+qbR4yhZv0YG1J3laUqLa5H5BdxTggAVYXWCq0VrAVKM6i4xLH1u1WAoKCwrkuKuH0MQt0p6Vr0Lg0LLW00GVWMxiWi3ha07TKFthPnud80XHrlYnijt/hivomvD/Cmz8KaNyS1y3dvcdfcnwEbb4ghQAjh6Eb2ABtvFsPPO8SFfeFGT0mbQBawriNX1IWCrcckuq5gHl9tTDCaQJzooB0UJhIhVzcSupKuSZBjYMLnQ90wdm1apZyvpkJpH9TtPYlRtvYQQD02TpuM1ek/T0rdnei+Lrkn930aipXNzaN5tJXNJ/8HFXSEZp6ay7PLdyv06mnDUs1lcy1RE6AWWMyn4E/PYMLrLxDP/7ng9o8r3vkzj21F6O4ZXbhdZYWoWb9VWlFQrCbMFnOtEAQriGcwGNMELJc7nM+f4NkLQmHCNDNWqagETMUqy1xwYwGewKxYljPqUkFkMdLqqigFOM2CJye7Rjxh0YpJK4gKZFVAFz/IYKJThqKCmmtKa2O3nnNLy7oxjzW1qVkNV6h6nE9iV0wAUDGLpxgnZGq1PjgiLzRlStjRMYV7zO4C1srh7jApx/2ytMwFzIx5nkFk3wHFVOz76eaEwgUlYtEheLVgXStub1/i9vwKt+dbXOoFt8sZooqlrlhWwYf/tKAuilcfW1+WUPo7H2IYLxGyz7ZezMr/NORjmG8s0CPdnh/FXH1wkCArsnifHn3YxRQbepCWJlsBuiXR3ott422hoPNzBXqUJlejd/2WteVHPDGTWfU3V5cH9cfudwid7T3snhSTkg5hLWfuKsOqTRQQ9yFK2u3puKcGw+IfTsWUctM843Q6YZ6KKfw0zXPNy4CVq2i8w3Ml3qwFAONZM9/jM/2RkDuAn/q6yudM0YpVAWFr8DNdTBEtAmbGWmeUMqEUBovFZlR3HVuY+vqTvS2KK+BcKa9SLe6baPM2YdZx27mDiGyQeE+oxIGkOEDVlXJU3NKW3VUojDCCNmI+63F9xZVu8VkhMLeWVVa8urs1Zd35DiqK8+ViYz2sfEFQ9rWE0zQzYeXa60Dwk0N9jgtrzY3r6LBmt4J20OYDeUKlfMN/6IY6vA2xPXgG+GGaM6DPyT7PCvlXAN7Da6HLAzc3C4Ldc6/P+vCZlmXia/GRD1BqStL4lEbXZOaXEiQPHLk9r1jFrl6b8uSveXpLfCwXvZU7jf19nWMtFe8Jizp1ZV0Vq0os+fZtq5vPnaJOFTl+XbinjRjhArtmbv2dgYqAqLTDb/kwIwA/bHFUm4GBgYFvBoaSbmBg4FsFeYUu5QY2m/DAlZLoaJ33GVb4V4/kC7RdOB8KE3dp2yKett8Pnzu4lgUouk96sNDGLn3fdG3rQ9gVX3ef+/I04cT1e7abvrcIAeqnMIXIGzyjiwLT1l0LcBU25Bj5+n1tcd9jtN1Q5Q1hjj8Q8qbY3LTX6XVegG2sxOTkeFUr7l4KVAnLIk1BxQUos0BWU+xBAQmhsbpiS608kwvEmtwt9v3N6kC3xHFAf5t672nnMW0VyWnX5K640NotVwho8nZi/37PO5psNgSQeNjA5aEh3AuJllFX/PUNa1aab/QnZMK/Qu4e0ftQ1a1DqgmBGBanTt29Z5z4hov8VLJTSwUkNrxJ8OT/SfWT2rEd3oxbatYlTNQs3/rwvW4JoxkvDx23lToRSWqr1rdJkHHo1eYgww171DRujwQLtL2WaSvyibgiQd8WvN7jrLRM3SonRFaa83TLiGY2YnTY3LDlAgTB5bFwIJiJdtFEL1fjKBckEhO6aUgMYg7pjJ9cVoK4IBMupOlSn9QRbfBKv4cYPFFQ7oxqp6DbjB4ikPhv6sImAzc+qE2b+cDI3DDAREBNyp4nU011yp123+QfdU+NvunINB6C9oIdZkVBATD5yHvb894DuPuE8c6fidvB+pj1phGXwBHUhdlmrSWyunIx+FUBsIKIUQpBWCByh3V5hVd3E/TjC06nCVV/ALz7HubJrXpm4xfifTgXQZlWEBWjveUCqYrTaYWIYpaKc11xqQtUCWsREBhUzVIsBhFBUd0CmdiU/XFYhZTMJTTQrE+UFSS1xXIyRboPSu9TJm7lBMzeptMKOi1pjxnWVCRkFk2A58/GOdkVdN2NIcDEmOYZU5lNEcelz/OuuJumyayJJgKXUKRWrJc7XJYzXrx6iVfnWzx/9Qrn9YyPP76gCvCHn6+QqhZL6IgYXEkHtTmRwgLMSboZAgYyH/JhvpGR+vpgw7pizs3P8259Q9cKuEgec0I73OL33dtnS5e9CSDnGyyF3cuk/2VvwZkVtO/pGYryEro1XDy9pvegu/DLc2djmz7WG3dkcroKfmeNau1ibluJGOwx6Kw9nH40UjMKMSYuKDShJCVdYXNLOZ9mnKYTylTsRdU6yvRZaU5Q+KEZgrqZtln6b2oMAChlZ1n9BlAAWAj6TopJB2ufqhUiinoWrLVicWvAUgpuakWZCubpBHI3mapi6vrqlqcgFCru6jzKD4hUm+64NX1aXJjlLDTiFXelKDRsaAn5I9xZmCU3mjVurLXiU2pFFauLulJdXFkHVVzqAlHB7cWUc+fzGbVabLq1Vl+LwfJudGkDxty/qrnURXfF2abgtkCIOWY7mDfLBELv5c181Ol9s3eLL4lBtLiAbZ3l674zAWdAPzUae9AV9SOw2SZQ5w3t4sE6701fkNf1eWXQLH4jwcHaLGaNqtIVddqf3YRJzddpm0+vZHoBbV8b33P9df8o7f52S51Wleg7ge0tdn/upbw5ZriiG8+j90d4xTDLz8yvJdWD07MKILx5tAOnsW6FH/Yr9kZii7lJ/Nl50cDAwMBXiaGkGxgY+PYhLaKPDlvlTfS9zz/0O55/jSzvCFeboYPPe8tH93yP1z5Qp82tx9YnySA3u7rXlOOqvXMfHAhk3gp887P8AZAXtqmoHwP6379BHheAXgL0gRcxhD7tnwMctcW+jR5AKMqaICiuR9vnPkjZ24YmdUtKk/NplyuwVACXnmEIz+oK1IuCpW+2Z+33XS5kQis/fc8KrNLLEru8+0j/0HoSu+ajRMNHu+QoTt6M05akNjFodnm3/qT0riS4aUobr7PQ1euvcDTmKD+Uvje5SaKrjWCzSS/jrnRBZQXWRUAs4JVwuVsAVTARphNjmhllJlBhaKXWPntSXJaKjYqOotfsZHfEoOtCGbOQMUER2Ul2ZfeaGIL9binWKnbVUGh6oiDWK4FNu28/OfGknP0bDK8eAyQ9ezQfbKaJvcAkMzBFs46LNN0iLNUjTrtLF8wD8Ph0npwSPcRf+Ffa5ddpBCZszuM9FMD5r2VAqeHjPXGNuimuN4wJb+0FGpaVEcMOzhzClA8u+G1WewBUnHyp84VI0yqh7ZOiUJwquUdYIcLc0LY4PyRXabbP5TrvxVe6HbybskV/HhRo80iURHfSqm1yImAtsB1Z/EV/VwCfXr/mi8DHfz/he3+z9oJBUWEWZS3uJgmqVKx1xWmaEJYu5gZSobpCdAGYUEqBzsDdq5dYuOB8LiiF8PLVS0w3T8DThJMInj55ihNPmG6e4KSC4kpqulwgClwuFxSpOJ1mVHkCqYJJBUUmlEsxix5R3N6F4kYQMQiJFUzVDjM4CYl0RV0B4Oo9724xIT4IEONj7AcPiLqVkkCasjiE+OFrlpBJIxR05mqQfawpCibvY3bz2XCFSU0ZY64vC0+mpCvFFOSEppybSgEXBhWFkmCVBct6wd35FufLHV68fIVPXz3Hz//hOc7nFS8+TgceHkCbYigt/agrluHWEw15Dk7zZsxf2VLjaChFHk3BtzO+bVbaaQ44UhZuRjn3i/FeDil5ugeCuRku/VKeQ/LhGKTyhfts4nBriq6oE4UWgYLAboaowQK1n4UQb5w+tZvluLlB5J2ldXd1yYqmpAu3ikU9Aluyzpx5sthzPGEidy9LBaUwTtPJlcDFWL1bbgEMJosN2y12zNWqqrvfJC9TUlg5F/9cSjoA5rFiJdCMvk7xxSy5mwGq1DqpVnMLyStjnlcQA2tdMZcJMlXMxNBiY4gmajQpSu4KUluctjhwtHFR6V/DsqdbZBLCZbRNlX6YScR70niJ7TNNSSdi1n+1Vqyr8dFa7dq62gGDVVaoKpZqsebu1hWLx6Nb1trbg2yOaUVJ07atT+19QgqhUNbF2iqIe8cMdDskI+/NeMifR/NqHpB5ClYybwwVwCdkU/7tPTTwBpAzsD4Hpve8TLvzOW08P4Lv7dH4INKSXRNPixupUXR/bYdm0ZYPNO32AcYbbVzts9msdf09m+W1brLbugZOFaP0yamO0VaZ7qEwqzkBVPwQoKjFpnPFHWBzLPbtkt+vqf4tbU8Q729nJTfEFnuSXNngQwCVAi4xYZgVaRlKuoGBgW8ohpJuYGDgW4fzr4Anf4PtAlDvXVO/Edra/DNsCK6w3xXtr+ffsao9qsRDFcuL6GuZ+GE1spAkVtWbdPsNytHuLqV5YD/z+bAC68f+9SNzdfp5Y/tkZU78ti8PPfT537nJJvVXKOtCiAbtJ8z3fbcXQ++yajeaPCo2owqQ2CFFJrSYPtH9IShTMsUc+e/VM8ubxSbso+37UvH7e9PFEDxQoplc+Mi3PUu7P/gQSVZwG+WwW7JQMcvBZpKYxlXbS9N2E/xZkOvXNqzRb16PEN5B1Y2N1NpdTQQU8W2UADmb0Od8XiyO1EVaH5bCmG8mzE8Kylyw3C0AtIUfy+4AL5fVCtIsBKwwSuZuppW7WRGYG0ouVt6wmixgO5lOurMwuB4MOf5MtnAEWUweqFkEhns4s4KJNtry2w3dPNT411+3144kI3tlW6Tz+pkgeXsq+LAY/gwDTXAb6RtdxYMl3cwKun0ZKbVHtm4L69BMsLn8zYKNE0E6wTO6eQGs/StCvk09m9Y2SSufj4C3Y/T+VCjVFGb9tjFnzoN5Vz+Cn5y2irXYPqkcbiuFPGiDlhql7eafLR7imocNuCljnxzRJsjmRkx7N27mviT03/McCIC7o3K+fcgKnD9m3HzPrai9wELGfwqjMT8VgbiPqyrVXF5yAWuFBRRiV+hWFF4heotVZry6XfHRx8Dt5RUu31/w3fc+wHwzY+YnYDqhTAsgJzAUzAXTNBmNvLR5bZpWTNNsxVhW6EyoqpAbc4VVlgnn5QJzM2v+l4UVYHKXg4RVzWWzWQyaRUOodhXmvpKc4Ahwl4KuAAFBVMDuC7lZvzkTD4fQQAg5CUTmSrD4mGhCfi5W5lKgIDC50tPzNfd9M8p0snh2ZMo4s1KcME3mYtSs0cyq8bKccXt3i1/+8vd4/vwlfvYPH+H21aVZcT0aaW5UoCu1smVHhJRMY4nS8znWUZ7LI4vNuzjN28k6rb0fXbmYdefxNZ8z6M/0clNac9COHyqhWdC36nidOMaj9nwk5RNxAvsBH/tiFkxOHfFyUdRgX54ZowuZ2RVCpOEKGa0R1OfiaEcV2qyhiucjToFhMXcqE+Yym0KXCwoxCswqc5pmzNPkgmxBFTKlNsToDeiuG921plnSoSnp2syuplgUNcv9zwMVtz7M/eSLTFUbp6GYq1LtkENdwYWxygIqhLWumLhA5ieYacLEBZOPmalMKMXdezKZJZ0fOinuZlipu8VlGL+z+Uq39SYA1fjIWk2BVt0tnxazmiWFx4XUpoira0WVirpWrGKWcct6MaWdWykrYHHoxNzTEkwRCHTFocUNRT/04xZ0oZSr1BuwHzLTRjdxAPAhPHg7MggeEGskyes6gr6EKec+7oP686yfN0WogF4Sj2lM4XNkStvHWxvktWPiCa3qjyB9Tc+2C7FMcreRKjC60s6vgo0En9wvk/YG/624Kc1mSZV47P6QW8sqli5eNvtzCzbtsVXV1+5t6bLh3WhWdDn/zeGHXLjMl6OufQnZ9pqh5CQ2C3UUOyQY1nPEhImHmHtgYOCbicG9BgYGvn0Iv+/uzqcJAB/7fJbXJWxEeEeZpb3M1a18YyecvPd7FqQcZbwTXNxXpquvaZF8tDnJyrn7Nlttw4StUGWT725j8FlOOm4zhsUnqMD557Z5kxefM8893HqE7tvZPrRJ+4z1yycPW5dr2kjdt8G6L799u6d7sdePd8Rf29AxgVxptPpLVu1xA7Irun0Z9u6mjsg5byo3G9/7Nn33EV/On9CFgHFC391Vofj3uafLgnJiao2xI3vrgwvuDXC//pEx/1SOScIvMrELXyxD8dPcESeEPPsW6yGFsMhWCqRG7+YWUFwQTL7hF6gsqJcKLoTlYkEJ1ZVv5hrJXqZeZ3bfWyEcNB7J3g7a6ENoK7e9ria1stiFXi/13CSsGzzTfdom7HH3dan57L6mDBPP2ZNGswLZCwP2Zc68zv9pAoFMc9Tr1ay3oBByS4dNJts8Q4Cf39H417wT4mRhZdDoA94cPWyVx9ZKz2h6blPpCDbVe6VX0CQiQe8WnyoJ3xT9RWF11yRc23xaXKBoR2diCpfCbAplBW/dlfqTEuHTvjPjXcmNa2Y4fZ7PRLN9/HUWcu1HPm5OwSSj441B2VjR9ljr63B9x7C4oBOAE0xX2nU1pgi5Ls0XgroQPvlZwY//O2k622gOO6BhTFEIkFWgUsEo4d0NHNZ0WL0JK6aJUeeKKq+g9QbKjKXe4uVtwfziKapWlIkxz4ybacY0n0xbKApSi1FXphllMoXdk5sbE27XCoJZAK1am4tgUXPFKaRQdTd2Pm+FNz8VsQMk7qOLSMzWjTqBVBgvrQp3X+vx5VyYb9ZM7AoJt31zcpI2CbpVHLuSjpOJq3aXexynQpRsfoXx2qkUlGnGNM3JelXAVDBNZlkHAKoVpIqXL+/w8vYO/+f/8Z/w4R+e4XxZPz9RJFbM3GnAov2ZFcU+DtF+ntdtVhuBKwGNN8XYoOBXKb/u7qzn515DNxZEG7YTX6M8gMX79HuM1B25gOjcoSkMtefNuT3crVqrWG43CpeRBBUGkYJJrM2AK2u0bn0dccvIeQgaXTSex2i8JZ7q+kRzazlRwUT9c+aCAnJaJMxs47q4JJ7I5y7YWC6EbulNXSmo3sEcls6Jpwcb/LyQlwR+4m6VmxfjPrmboD6se33MVWCpC4gY52nBqUxYLxVPyo25/ZxNWXmaKsIys7BZuLErwRUFUwlLWoKomnLeFynB2o0W3TpN+qmY1RV0IgIwobrl37KYdVxdXbm4irmllYplXbG60vG8mB/5yGdVQfXYe222SuOLXKmY13BgRSXziCBUAagdVCAkGu9HWUy54rztoU5JU23Q3GYOjduxzhCC/tGsrnD35c1jR2vR/XqRdtd1lzCvhK5ruHvf/uae4cXXoBvyNo92ivUaYBPP4mNI0A4NNj7kmVHO3wuaXXtGv+Z6P7RlzW0S+4zox4inCQF0BWpY0a3J3WXiozFU90upKy8owedpt/Qkt1pN9Wl8XsIa1PkmeazXYocIJi6gwhBSFKLPb9U7MDAw8BVhKOkGBga+dZBXZlk1/Rhd4Hm1Wr/n2hFCWPC6pA9tYI8WsPk27e7d83e1QdnJDe5zeZkX6O3aQWWuFG7H2R1kvHs29tu7sn0WRV39FC3W4OU3+NzWcg9BPgTK+3h7s+cRvWG38dsLnTL2/ZmEXw9uLF9TLJYeQyMgCtTqbo1ckNmEcC5I229M9/vVDQn7RqxtOtNDTeC3K2gTwu2uxya4W4DZd2bf5EY53a3V5s8t1rqgQ1uebrxmgmkvV/0EqB9ZgsvfKS7/o+LdD67bUM901T0ZLeZLaqtQ/prVBiDuHvBofCJO8rt7NoHFjCOltskNxYpWxYpqB/qDV3idOUlVi0liLU8GKCLdE6DuRk4hrY3NzWl39yUQVKhZOQlaSDOKunU5m1dnN+jVhV8EsCpW6Z29aYeM1ng97ys+p10ecvXcEXZCi6DRRrPtddoFLDuBRAhiKFLnsUGJjE3iZ7FZ/i9A/8wT/QW2fCbz+tCkS7rXBObk1hiWrZbdsznuSSv3QeuGRCn91hjwIVWJT6He8PnES1iy9FboApfIpzGQ1IiK1tCa2o5Sn0Wsn8ifIhuJClJ7vr19P0EeHT8Hrppie31HOPt+aenkOs0+f1fS0QxTzk323XXkqD+/pxxfED79WcEHf11x+sDL7oOG3MwvXO1tBYWCWhdUJkyFmqWlEDCXgvkEYKmgskAxYVleokrFhx8v+AH9BO++8w6qvgflCZgYtJ6gqGAIJiY8dUuT6O55XQD0uEtzs4QRUBHwDCgrlrpAiFBFzbWdVQUTK4TYhPwi5iqPFTTNxq88WJwpVTp9wQWDxBabiwkoHqOKnLaUCJxOlpAL/y0+jp0K6zaoNl66y7IgcrP6meenmOcbnOYnIDLrIauLoq6ClSuYgb//+3/C3eWCf/dv/xaXy7JRZH1mUB+a3OZSbkJPVbNgJKDFet0/n5qsW1Yo2gGbJsxPc0QeR62VvD6inW3R7h3tLI1js6yKZuWtG7Xsqj3m9saCgpVNHuYoeE4o1wkgFwwXLk5/lkO4IkTEgiU2K9Qq3p7arNHskA4nYbS6tZRZTAaL3Kz/0ZVFba3ifRHXmQhTKOrYFHRzxLAjQilAIcXkikOo2KcP7mBlTLE+IoSleG5c5k4nJdYb9/HON0GLlejHX6JyiuaykQCI1t4hUKzVLFNpVQgJFq1YJgsOOE8Wh29q1oOK4hbAExGmaQJXxlzMIq5QaUpxTR4i2mEANXeShQi1riAolnUFM2OVakq6apZ+y7JCYUo6hbnoBIDLskBEcakrRLVZ44l35KrdxTgAU+y3cUVNYyzu1UCKLQLElXQg6dNr99PZ0KfD3qZ73rGfMhu2SwMACn1OdrDkE2qH1z43L3oE1hfA6fu+rjja0+//gI0l3FW7ZH4EbCtx1Eg4SHeEzEv89/4VzbmA86hWvLC2z+V6oAjtucR/W4I0VpVgewTnwy3GHACKA82wT13sfhVX1qV4dG39DWzOL+WqR33vk0Ps0cYcUp+0fgu+ZHw4/rYuLt8GMxoYGBj48jGUdAMDAwNAXzzec+/qxv6a/35o/d4WyklgcXQ9f27uHX3Pv9MGOS9mEd+PFs1ZUBvf9y7VHrugPihTk/9i2zZdjPWGmzgF1N1/rX+0Pz0D+hYOjT/2/Z8rzT33Qjiku/44EnjkNr1SJuDN2pTyl7R5k5BaUZfpZ2FWHi+xJdr0KXXhWia9FG7K5EH5/WmTl8kRuK7Phnb348LHjYf6sVgzqbxNeZU24rkOkW8VQF6aW0ipwO1/MMWTnvH4WBr3dETrV1XUgxPM1QnhyjIxNtWEFotGBABkO3x1n5/fU2xcLHrwGxRXoJkQ0aw8iht4kDeUKKFSRQS8D2sCitg1sA5nhIKoSxjCUpCbCD+YQlil5jbQTmu+Qyd/34bFhLAj3tFu4NrSlVy4hgNaRB93uWk08k5pI31uX41gNU7oIVIj9Th9keueGXo983vwLwB+799/ngr0FwA+8Dyepuvh6orQY/RJr8/GzJHStXb4PzTTbocRPlDbIHRpS7aSA7p0Z8+kmsSr/4W1yGZ6i0pryrcpzDIj3Da2bo5o74g83kEE3ZhgWD/A6dL4Urxvxy33PClL0Y4SZGJo6TNzSfwvPxqKb7fmxQzAFXQ0+bgiaq4mvywoCPWWgPejLjB6cqtAJnLLMrcWUcBsUgQiFcLsaQF1F2unG4bqBcQXEJ+gRKj1DCXCs2d/QGHgyZMZ9N0PcDM9sVMVot6fCipslnTzDOYCVeCW71Cr+fudlwXCFQKGCGEGQ1w5UlVQuGBZq30nO1SASbCoYqlizJ0ULATyuHBM5q7XhxOsJBZTrpBC2bmgayjIGRRnt7FAV8RQWNQB8Hh2IupxrtTTwgWOE0q5wTyZgq6UCYBZBhIBr25f4fZW8e//w9/h2fMX+PjjZ1jr26eSNjoSexNNbiPTgN4vU0IxF/MwelIb3pkXpXk8P9fYD5q3w74uoN7MTWmRCt2yTPcya2rnCNxSJIyh8pLCYuz2+GTqJ1/MWsyvM/fKB0tJjRLx6mxeJDc49t/MHo/QFbXuMy44VScj6uZ7eu06Oq708zQ250zEmKngxIyZ2FxdllAuAxMLmFdnVdYA4Vovu62jcFntF0JRZQnNRWaMBVXFWwkDpYC+INB76KekSBO9dMt1wMpD3r5VKiBibrhBqFMFE6OuKy7FFZXscSYBzNMEYsI8zWAAp/kEIsJUJreyK66ANYs9ApuFnALLuqCAsKxnsBKWuoBLgYhZ64lrO9Z1NXfwq1vUOe9ctdqBNzVeEO4pW2RgUggsPmCjg6A1Mr5M4dKXACHxe0k55wso6k27pSDVTm/5hm7XPfm97borcfQTgl4IuOym7i8Jy6doY7+tUqIO6HVuBwSwXRLdi6O65OWC4rrd7nvEC9KWMOhlaY8q3NVlvxbLM61hFd7r1fLW/rlfjrV02p9TRQ/ZS2ntrDBFnHu9h5el8eO1W861wyVRnnYga9sMmSVetUnQGF235dWa2921qB9yCMtyEfFYo+QxNW2OXy6CM39ZgoGBgYGBt4uhpBsYGPhWon4KTD+ECciy+7D7Nhh0cG+/60mbgiag2y2aN+lody1t9tvHwfV87d5Tq/fU47WWaiEjzXk3KWN6Pr9/d23zvl1RHipubEg2ZdT+ufzWNgnL715Thy8Y+gKgD3DV76/d8e02d/FoxsadXnynXRvuGjU2e6/Dazek6IK4nF7jVHvaBLeyprxz0Tf3Duq6Ebilm+13atvX0k+0UWwYfXwoo8WaybSchYBN0eOCJyXg/EtAzgq5Ay6/Onrh54cJ67TVad9+raz5upeb/TQ1wwTlciAVaUJIuqaPiGVHvuFfRWAxkVJ8pdLzAZkYHumvHwbwk+6+2TYLKRMsbXggkwtdr13Fkgv+rK7WmXFanaS3z8Yl8L49gy43pxR6O/aX7caY50t4vAFu8MYm7PCrLjfY0LQVWdyy0d3ihelBk3LcU69n6fsn/llgCjsC8EMA3wNQTDlg/WBWjKFgaDqDDa9yqYz44GiSHzdDVXetF/RZKd2DC/Q8Y04vCYILhV6Yt7i1EaLvd/w9Yv30AqaGjsSt76OBw+9rn0h72ytacJduO9PrkF+wH3BBt073XcofDCsz+2Bs1IkoM8eWT8o+8U8U+5MJ0Bu3+J2BMqG5u30U037L+Jf/OOGvfhJueqk1EwOAWLyjCJUoIljBEF0h64oCwTzNqa8IPE2YTwXACsIZgmqu3i4LmAm3d0/w6tVzPJlPwI2iqMVoM3IuKOUGT25MsbUsF/A6o9QVYIUSYT7dWAyqMkNnBa0rVlcWsFasAlQiqFrkOZqAwoJCDFbGpQJYxQ51uLAPIBTnZUHXDHJ3jz5XANDmCjYsmrnRPABQk1y6u0wAQLF3abUDEOgWAYVmFD7hND/BNN9gnmYQWTv/089/g48++iN+8Yvf4PmLV/AW/lLQLYjM5KLxhqsFzPVvSuliCOVkLc4S0pCLxEhWbmnNmYcmU7oXeWJ7reWdWADBWB2JXc+WGtGFxNRiHBmPl2QxB6gab4ea1XvES7VyUVfmeSw1Znalt7kODZenRGiu3IRdEcjsNnZuLRJ0gnCJasq4AnNPbTZ59u/ExaxYi30vbK5Zi5ersILJXVaqgtw6HuhrC0qt19oONuZ8FWAKOp8MRV3R/RZ4lgKgGrNq7+s2DTg/bgbd6IpOVerujsms1ipZnDoCmqXNVMz36Xkxpd1UJhCA03wxJZ3Hs2K2yZSZIdVKsKwLSIHzcgEDWJezxZ5TwVTM5R4xQ7VCKrBWc9G71jWNgaYaNaUceaRMhbnrdXfkQexxOMqWENIN19nSI9IjWJbPhm1Oel2L92RXyqeWzOfaVwAuHmvuss/hq4HCI1gEbwj+EffTUqJhxzfepA5XSwdct9k+v1h6tsNisY7MiQUW0NuVcm2vOHlc2EgbjE17GgB9nZyWlqGcjGev9ou+ZFIF6gJTttaed3N12z27Nh7ZzuK1sZjGaX6FtiXkNX3l376cals5z1OcT5NbsFoMTdsD1UoABH/8zR3qxd7z7A9n3H3/CfA3GBgYGPjGYSjpBgYGvpWoz4AWly4tdpsw4Wi1frRr2afbyQCvFHH35ZU3UiHEoOvrm+RJCKg4EJgclTVtXq7uyTbZ9Y97ynzf78+ApjQ5w+J9KXD+BaCrXfs6QJ/DLQ3S5ir67DW7vLxRzG1M6V7e8O1F11ck9Hl2xgf9t7fuDMOaJvzay7Uduvveyntf+chPx9+zqz2iv71yMucFQtMzNH1D2Qn0Qnjmz9Q7OxUKUqzPgPOvrSPkDm/NZeqVkj7qors+jc267TWv+j0ndrGZxzt53LC7ajbdCiUlhIssqLDdsCqDFSjFXcshhPPUHjY3X2hKK2lltAY3vmQltrgvHmeMU3S51D9E7C4+zW0eFTSBeMVW5XJIhweKIE2Dro2ZoLMs3Eh5ZqHwvh0pjZOHChOKzcUZNCkgrffMepE8k3tjXO5RAfzMv/8SZoEFgb6voL8yYaI+tf4Cq7VfLqKapRAEICkgNYshhn8H3F1ql/yQB0UhZXMlqYjeMdeoVNwCJCSG8UZ2658wG5NmkaBm8gCk9xy3Y2KuWam5afykRFZ16VKSJmmFSePdxnNvXqPhl0vRtMJAYh5A888XBK47wtl7WMo8KfEvLSlNxMe6AeiJpeWCZjVR/6CQT/bt8cVDVsJyS5jeiSoQtJqCIoT4YJclygqouXUTUlxIwbqinJ6AUTw+W8F8mlBXswYTgVndYcbd5TnohWL6sEDqgu+//0O88+Rdp7XuQpAKMN+cAGIsKzALUERBvGBdKi68QAtDJkDqhILVu2oG+ASVC4hWqHr8OQgEhFMhYAKkCtz0BgBMMeOCS3K6Y7dkysxBqwJsAnkL0FmaRVxDzKViVn0UceeYzPWhEIgKyE+MsbvFfPXyJT65fIJ//NnP8atf/zNub++wrF+SZUBaEytgFmdQP8+m2/VxWq82Hqpp6KQhpxtTZmx12bt8gj0A2PCFnOfGis7z1fRuoM+/G0u6zLjZ1zW79bG5ynZrNzUrpTy1RDxVavEQpbVXU9ARtfmPiJv/YxJyay42t5iAKWlIwXBFk88PxQsTahprM7I4a8QoMN7MWlBQwGQuOEspKKW7uORQ0oUVGVFbC4WVHnyOjncg5iiiTR+F09ZQ3kUn2Nz+OuJ6JO4ALLBYnYC3k17vzdJ8DobF5S3kfaG2nvHpgMLaRmEuKWFuRYlgY5MI52VtMSRJgVJs3mAiLGsFKeG8LACAy7qYZSLM7SUTY4WVQaVCVCBVsIrFlRO1A1HR7OJWxzUr1LyhjdX4OguhgHMLu3BDwZ0l5cWM5f36Jt4N4+3FfHMlW3u8IuCljWN9S2vktwIF5AzoO2jr5xjKwY9adSg1VeIT2cLtCqkt71NgtrVlYi2ULmWvDG0dmZ7VdA87Pqnh/pVgazoJV7S78tLh1837r34LUFc0N5u6mqIuFHKtKOwxXbWxiu2SbNdwx+vzzu83Zdn9ztc2ba025y63gloICwEf/+Kudd56J1v+8P2DQgwMDAx8AzCUdAMDA99OqLlKnP7cf4dAzVf1+0W4tn922K8oD3c9r7mW7mVhw2Y1mwUNwEb4l1/92n3Z4Q4kvcMFHC2zR2z0XpfmyD/91eNervrM4n3VF4C8fMS7vwIo3ArKhRn5OpD6aPdQ3jBe3d5dO9rItM+H+nD3zCFN5A3nfQ9SHxJ5s9lPLqeN2j4fTR/Uszy6vi/cvUMmb6b3uzdXyOVPJAWdbX79oRV49Vt7dn0Gi2X4BeJBJb2mdtGe/srtyy5PVbkSkr62HP6epjQM3uFyaW7+xNy9nipYBQSGELf4S1FwdalXs5qDe+hxggsBpIUq6zFuQtDX6oq+0VfvaCZuFglhgeUiLADhIi4aI7dLXNIND2+fmj7vGUONxuN3EqzkNPf2axorvU+1qYw4RJzNIsIe5kOm8Ros9qcA9JVC/tmUTfwjAE8AzAD9Gx+nSuBCIC0Iax9mE+6aoLmAxKzxSBkkgqpi1h5awPMEAkGqQGq4FnPlAk+Akgs7yRR2LtXRWq3GqlAxsxUVhVRTV1Y3k2lC/DbOE/dqArYsZSrYdGKTDIdkP4hhZyJvZp1OfILNYCDPJxPMvp/bSfYk9WuERfa9aBPGg9Cse9UPD4T1Q7ixKifrK5CfWC+uMBf9SgShyyvCpz8v+MF/bQJpi70UHvfs9Dq7FcqlVqyqKCpYZQWrojBBtZogXwmiFVOZUApQZbH4mCIQNauSZT3j9tUzPCsFsq744P3v4cn0rtFTKW7VMoOoQIVQphVFTuCqUDDmk2BaKlTPmJSgdYUuFlOViEAioKooYONbanE7J7b8+WbCuq4QXd2NHdkYcIVk0G8oaBUEWQXV4+SV0wSCu8l0Lldc0RrxpIjZyc0V9IXNzWI1pbC5l1YwKioW/OM//gK//u3v8eFHH3/5BJCQ2aS4uzHGdr3SeLrPt8nLbB8Dzts9qllX8GwyiTT+XtY2zJpnNh8z3bILwSJ6PpEuX6L+yWkCIvW1nKJZggcixhG5uQg5g2Ky8eCM0+Y977+wBEezomOPPWsFV7d0VlfotHRAc0Ot6rwVscayyjbr2pg5yBS67PeKMgqKubXkgqnYJxdqirn22dxMJ/jYtnahxuoaRw6FI0VPmiLT4qERuMLdL+KtoLFxym2TEjhRUPLvTaCd9Tj1NbAASuYGs+3ziMySkKhZzS6XBWHZSt5mQbB1tTG/+rxm7aJQjz/ITv/k7VDFaKKSuU2vEaQX6G6BwwLOrd/VLdCtiEaYYfxu1/vc0nDwfb/vum/7etXuVjj7cQbwgsyy6mtySPI+3P0OmP8aRp9x/gdI6we/l9YbMfacyXeFWt5jxBzglxJZbg8DoCc4XJ7r5uMQbd3oZVQAVM39pLpnZj/DAWU/0FPgc52/zPlvHNBSdF6ZLS3r4u53RSELsCZFXXN3GdkRDmMMUr6QlkF78DZJz0O311o7+5dQWtZbYHmJdqbq/Exh5obY5zgwMDDwJ4GhpBsYGPjWoj4D5qSkaydpDxCX2yI6r9qPEgYeyDOnJ2ATb+Dez2xKct87PyuudhsdR8Lqzw0F5FNbdF9+7vLU+Ps64xWAFwDeS9dig5IP0MeGSLDdfCF9epomB0i0Rf3rW+viR2WUNnLksskwjrlSYuwevW9otI3X/h0uv2qKk6MK7zfClNopxkS4sQpFHRT1eY+hc/u7rpD70uIX5jJbkfq1RAebhEkvcHXrTYngIKO9YJRSmiiTibJNAK5kp7bJY+9QCA1BUIjHaKlbdzp8IMT1YmgTdroA3QWxFoJKU7l64cz6zp4TaM9cTWDVQoClGBtXDZih6V6idaHra5vvqS5Hr4j0Ryf9Q3Zi9jvcxz4hCV/fDuQPvaz6qyiXgv6aoUWBk6C8O4FdEcHMYBR3wWYCWhUGwywaISb8JQUqQtBO0OqC7FJcCmT59UZh892oFjvErPDsO5MAzGbx5Moys9Ax6VTEM4w4QxTCySwdi1rm5rt3flI0M55mLuy01DqVEk8hbCVwQXNOPFkCSH4/Kea6RhqNP0X8ufjkAvAE8Am2G9NeLVWFvHiDTn/LWF4QsLqgLwnrzBKnoExuabYKFIJVVxQVLHXF7eWMaWKU2SxRRMRi7YHcdaEFtRFRqN7iooJndcWyvML5/Aqr3OH9976HmyfvYC5PTMzOAJgwn054CoCYUatAL4SpCsrdBStVEBRMAqbSrIPNsgiueF7NQk9XQARME4hn0GwmO8uyOL1ZbDhtrrXEhKQewEyqQFazCiRdMb0zm6WSKxVDmdEndTbrFyFwMWIgUWgFahU8e/YK5/OCn//id3j+4iWWZT10Y/ylIzE+RSjq+u29lVqbp3aMUVPaTfaUhl6zOov1gHYDr1yWjPzeHU/fs4ZeHGMipNoUM5kfR33SFNSmDIXJqtsSRRWTCnRf4fhXbeYCegFbW6T5VPcTps940e6t7cTnUHevGkrEAkbRCROKKeaYUZjBxeMhhjVdUtQZicakb4q/cNnalBfYtqEJ+y3GZyhKJy+cFKQDGm8JnxL0nbRYymzXD1Vu3Kc2zdRuMZkX17s1lqjY3OaKHfLxT2wLCqpdkavhqzSmkHaikRvBWKxCUw4Kw/mNxRdV6YeMlLuSDuSfIIAjnlwoXhI9AbsOeUQb0ubjXigAVDLF3NnaPi+3vu7I7LYVOX3fHD5FotP229YGkp9Bajc9XDoetuueBR7d75/b1I1yY924Wp3E+UV1i3sA4MnjSHrcOCZf5jCwTom/+rKsrv0l61l7LE4xJV0UQ9IePPamm/LnJdAeuwbakKteb+8lZSJ3aONz/dR/A93N5sDAwMC3BENJNzAw8K2FvLQ/fg99AfiahSDtfsSG8bU7oHvubzYK+1X/1SYivTOuaZcfPuZ9D5Yv9rBvc5OdkTyQXX4N6AWoH+Prr5TbI8l2A9kgA/nrjq44X0PqduoyhD09NZdE2yLcL7RK2Rzd21w/yCMuSSr7RgT1GvrY32/y7X370HW7XWmmEBYcB+3TxoM24aB8avEmFTBXcV/hxk5XQF4Qynd6Ia7G6hcx1vbtn661Zgtvhd6gTRa02WDb9YJ+2j8SuvMrt6pgqAu6CLahbsHd2ZQ6XUZmAkmLf6ftJK9emar1nxKCW5iA0AwbkivNmgStIRB+qN93QoQkczMaOhhr7WyE7oq3o99wBZSaML/Ka4CkhCR0DfPbhQJmbeff699WABV0Q9D3KgACnxg3/+ZdowNii2+npjDjwm1cUbhDBbp1kQtqC4cFhitu/Nh/CD/FzcEaDXlMIFO8FQATQCYIFS/tWiNaj7nXa07CFGjS2d6k9o8r0CKXrMx1cZc/EMfKE9G4QBwc35u0tt/bEFkQQphzqBOJ9ji30cXhUttj0Gko6NJfc3MWRa7A+gXFw3wMnv2C8cP/CuDvhHu5LigWNbdtTAwtsDhNXv7q6n0VRVWL+1alolZBmSYUKCYhiBJoqSA/DS+iWJaKV68qlvU57u5e4r333se777wPZsap3GDSGWCL68XTBKGKGTeoAjx5uqIutblhqwKwqvszZlc+KKAFVRaQK92I2CyNQBAxl3eAXS88ucGlYq0rZBWsWlGxoqi5tawEnG5O7lawOH/yMa5hxcM+XwtqWKFWRa2C3/zmX/CHDz/BJ5+8wKu7r5GpiiIFc8LmAFu2ZD5S0jUh+Z7PIpQS6T1pjUub8RYKVphSO7mwy1lf/7hnSk18Oyg5ypiVe6TbDIL2g+ZVu3VfsINKiTfC+JxAQR6vztZ81POA80iYa0vyOGQtvh16/traImLgoVn/aWTu/I+Jm/VfIXN32a+ZdWdhV8QFbwr/2vBYcqHIC0u6vJAkq1tYBrbYX2Fd54qCR7tufgx09yWt/6LoMR2Ei87M9sm/5XMZjYV7f4fLbm2VjMM0aHSDIE1FP0wEW+eAAWGFFAI1fm7WbyoCgdgah6Wtc7T9eZxDXwZs/aDv5zjc//sAh2MF2y418rL5Gq8APCfg9vi5rzv6mEmg3m/p0tVzV221W44e3WtLv+CBaZzEGjSzQE1pt23bLfj2xvvZJWUYjMaaQgBwRVPSMWztqoD9qP2AJZF9hpKO1NYYUtO7UqGyYi7KvuHpce2ISNJyad/YCrTYd4Dv1S79vry8apyBgYGBbyWGkm5gYOBbC13Mjz2/d72w3ya853q6dO9pw9dtrihdo93vwxehCxPeNuiLWR/LGWbV9AxYP4yLX8CLvgJcKZ7yjo7SdezSIQm4cn6PfvFxng9Zgz70/B6ir0/zWLxOqRcb3azMC9kPw6w39uOsrgrxUASqwPovkeDtlfvzQgXmIug7j0j7RRYkCbaAMBKitplu7M15j7Ip79hjtVDqD22KDQBq1nQbAUjcdkUdu4Y1BBQb2ncLOWZy4aRCryVs7Z3mdqcT08ZNWBx/1p2g9TUNu3dtuTmVvy3GlYAiWLVZ3rg4MAvwopz+Wdx/kTmcjMGfnV5+OdCzop5NOiMA1t+ZlOT0o6eYvnuCEGH+4CnKbJXQ6gJhBUgVpSnvLC4SAx4zya5Z2Dq3lqomoASAcGTH8PhIzO760VqgSjfDWKfaBNeriCuDFbW5JoPnmSRmcAsPZnu/ZOrOhCD9mWbxpqagy/5921zs99pDnq+KX0/p3WquKeoms5YTIIwNrc4TmtIudLRfN4uFlx8y5nfq5ppQhShhqYS5TAAEyuIGLNb2lQTnuqCsDEwE5gLxmE1UFGVWsKiFUyQB4QzVC9a64PZ8wSrvokoFSHFZLnj65F2s84In/K6Ns4kx8wxeC9aiWFdBmU8o08nj3RHKShBaza0gCBMViz1WK9Qt48zyE6ZnJQIrMHMxF5ZMmKYJzAUKxeV8sdhWuoCVwXNF4QIVwXSazUUd22EFSMS5MutTFWMqqoSXL17gk09f4A8ffYLf/8tH5mrs69bxwPGwCaFz5pFpbkC6tuGVj10b754nIrPiDoUKruW+sd65L6u9YHlfp+wZF1GPNAVVqY1vWdw5E6i7sUoTxjd2kRYqcTgBPrYVPW5d5O/HF7ryLyy2Io2aGi/4XJS3xQxVmGtW58WFCopb0ZXCxp/JXLuWcq2ks/e1SLMWi80tGtkbPc+nzUKUIt5eVNobTuTtKukqzKLre9frgmY013int5JP6jn210bhvHmB9jxd66DtchBiXtt73gRfmBJQvD0nQIui+sENVYsxWEt1d5XaNC39MI/2NUhY1OXixRoovj7UtPt7D4wNAizO3B2AWzLPIF9DNvQmWJ8ByzNgfh9tSm7ttuNjR4u8xgsST0hd3n87BB4vUq/beJ/9Hvka6f3p8r4nlFsKtLNBUu3MU3gb2OTja+bmQABpX5d4VrxnP2w3Pw/mgSvemtfcuc0EWF/0e8tH2IzHgYGBgYFrDCXdwMDAtxpakxDhnkXmXujQH+73j2TCG+Eu7S+m30f36PDr4e/Pi/uqdvSeOH36YGartenlF7CA18sXH/vry4Z+DOA72k/N7wVTSMKNvInbbZqJDsguNluxeczp0el1f9D2UBmWBBVHwrJ7BWixcTzYuN3zmkdjU+5ET5GvezYDN+GIuo8pwvlDNQu1iq99jIzAlZDA8Zg22wsJ7n14JxXYyyRCdxBuruJORbfUjHeZZZmd8lclOw0uprhrLgcZzSoA0O7+Rrc0KqquZHVBosdqMq+I1OgpLOoAHCrqrGjUlGAWk8dO+1cCro4gP4QDhqrotM6uVIxyXAkzchYuHFGol4c2QvcWw3EFSMgtKBg15fHpL1d874MK/LePLP9bhAJNYHL+/S3Ov78FETC9d9t41dN/9R54dkFuod5rZEJgou4CEyBUaw1ThsDcD/bT06aIIyZ/lpubO0ZptMlikqeqCqrVrUz6cfLuUi/o2SSx9pi5bNu4x2zSrYOJldCt3cKaJ0vegW7O2zrcrfFYumkR9bzIDQR5Bni2/NhdXYr4Z3EBW9BIyPQJqL9CiwHzVUBB+PSfCr77b7pv4BC+VwhUV6iIK22dN5i/VCgbDVQ1K5JFK1guYGEwKUohzJOgMmOaLM4hCYGwQJWwrK+wrgsUFTend7Gs7+Fmfor1VFF4xul0YzQ0MRiK6WbGaT1hefoEzahvdXM6MQJgd9cLIrDMFjuKq8cnUyhzs/4lNTeupUwoUzEaRcG6WqwqqQpVQZlsTDBPqKgAAeu6NoUL2FyVnZcLPn72HD//xW9we3vG85e3X3Jvvjk2hxd28/PVGne3KNi4m3ssgqXkN/hvYrT5ZVvI7XDeL+P3aZsgfnc9K/q6a12AnH+4/V9T0B1OTxQf1N/jlnHqShl71za+KiX3iV1Rhy4IJ3M5rO5DOu7t1+DNgtMPP5Tiyjrn0VNY0hV2d5deh9QgjYX5PMacDpA03udfPcgVOwEE9we4H0B5C2gKibTxSKy//9gsoqPH+iVE+TTcbaPFhGvrpWhbinWMduVH0Ih6HDAQKE6P+dxQ2ayLmzWxW8hVd+ecvUY06+S8kMA9a/irBnlk4+3TKdmccoEpPuP7nxLUzs3kuIPhwWXPl9oYx7b/rxKFkquN8X0e12U4unSVfVsjb/PwaWp7QdMn0FxaQoFakdZD169XXN+j3eejcc87AAAVTSEoZwsnAsCsAO/e9EUDAwMD324MJd3AwMC3GpdfAtMP4EfiHPdKI7BdlefFs2yTbbCX8h7lkz4PD6K+vX1vw0OClM/yuvUjQC/A8ltAl89aqm8ILleyqQ02G620Maf0++oZSps47e5NWh7xvPbwhRukjdz+oGI+aRm/D5+/py6fF0eK3eZRDlu5OADoS8XqNFRvgfX5AwX8uuOBYrdb9zTylSznsVnvb9D+tvZ8Mx9L6cQ0Z1iFwKtAmUw3EX51EnGqZxZB5qPcHqbGrOo2klhq79UmmdwWP2h9U/z2OwRtfpmv0yqSgO01bbJRlntZwnhq0we7ZxqYcPdbwnoHQAn7197+krG+3NfQJUAKXP7Lrw9tqwLLs64BXz6173xT8PTHZhZKzHjyg/dQmBGi3LBsLCuAwtb3EwMqWFfT0jEVqCjmwpgKAOEW54jVBMQmZ2UXuIkLTQlVAHMNFzETrZWtG+JKWCawKdDCJ1vWNGyYoV7Hksu/oxdLvoZt+tR14YqKJoBmoJwYVAhcAJoIlSqo9ghWK7oBX4T7IgL0xVfP7dZb4Pwp4+b9cEKKJnwWMYu6EsFuuKAQQchYwwrBXb1Y7BwmcCXMrNASfkAV08SY5xNoLpAKyCJQWdxdruDujiCyQrFiOb3Cuqwg3ODpk3cwn2Ywz9ZGBRbvrRAwEbAStJC54gSAahZ+1OjAhey+BW5WRcUt4ACgAoUnTDSBCkGncElIbec8lZN/zlhlxaWeIWxuLM+XFb/+7e/x8tUtfvGr33YXid9gXPHBvdA6+HlOn7BZVlO/3wyhCRYHMxKkHCiE5LtMrw7YHZSZjn6kKadZ0ARpxv0k1c+KtpzdHuIns7hZZIWrRaO98PhuB2B6DvFdYTSm++uurPMf/e1E7TATwQ6YcOEek44YzEBheAw7U+K5x2FIsxSM3NXUbO7GgLPSjdAWbRbLLlR41nDK4lbVx33xmfECoO8AOHXaAaV+ywqKKzpwpRxnBWusfSyx8QbPIJjwThNryld/opDXt691CG41B0HVzikb8poHSAviLRUdkPibr7/3D1QyS7mVgE/fNLNvHuKgRfsOXPMlp50rxVteJqQ8N2tKXPfRVRlSXvfyJsJhrLXQRzdlcSLFts6lfm37BRveur+1L+OGPz4Er3Sru39fXwDqh4nkJaBDGTcwMDDwVjCUdAMDA99uKKAXgJ7478+qkXjdc3TwPTYIscM+yuOzlucA9y3Wj2KGvRYCyAWQ58DyO790iz8ZN5avhcJicCTlbpb7bhA7m9jQHwl6msIiKed2SgL0bPbyphD3b94TNxVbOXW7lTakuRjtPu3uHdDHYzx17S3lNu0jAFW3xFqAi7tDlQV/8rTUBE64Z2zeIxDc95fuE+zQdA2UhI3oOob40WjT/fioqsVSqeSXTFLK8LhwGw2Yi/jidDr5KXuXjBHC3ZNu+Z3dSELJVGfVDR1TI2Z7N0Ua7coNyvXIzaJb+j2Kmxjyj/pAXIxnf1sgGwtOau9cn29jfNyPRwyarxGitPVc8eJXJuUjAHd/eAkiwpMPvoN3f/hdgBjTzQ2mk9ltzqW6kFmxLrXFRlqXBUyMQtZ/TOamksUEquJMUKHQChRWVJEWn8oEV9rEoGGhZ/1PYC0AFIJuvZB0MwAU5FJqJQvmouE/auP+0ivPCveJuKXbZl3n8OtczIKunArKzWRu0BjQYgxeqcJD7rVXiG6z+cLiwr4BlleMuz8ynn4Qkk4A1ONHVqjFW1JrSwZDiM31IxTKiqoVF7kD6griFZNY7DYoo2AGs1mtkbsn1SIgrSBiqNzhcllQ64LLcsJys2Km74BIUXGDaRKICKQyqi5QFqAoaCLQTCAxET2IwfNkXcYEXlfIskJqdYu54vyDwBMB4qoHnkBcnHecQLyCC4G4mFtAntziiAFhvLqc8fLlgl//8+/xq1//Hs9efoPdB+g1l2rriFhfaOe3kaCx/LiU+G5c3wvGY5qvrlMvWUmC+9fHbUynsrQktC3HVUVSHu2nxzcTeEwmxUbRcuiJYPe7esNE0Vx9ldJuC71ZCoWV12bdFYcfuPE3YmouLsMtdXHFGpNZtRa3Wi+E7r6SqMWmY3fra1bjYWdvse9cBYUCbq6xTccUlvNNvdV0T4Bb01K/+7agAFBhsdNccZr7uac7nle3yrKwqPOHcwX8a4unGwqSWFMwQEx97dF6tH9r1pa5n3fraN3Qw76iD+ChZt0v3MOq6SMv/J+axdw9uPsdcAr38t6HmT8B2K65E8+KtEd7rIxYIufV7+bmAfbsK7/nKPG+HPdmdPBq3V27qn/K6t4yaDNAtHZZ+vX1D50X6uU634GBgYGBz4+hpBsYGPh2Q8zy6/TX17eaMCELfNO1EFo8iKP7+4X2Rlr+drFfPz/kqnK/iUjyj5bR+juYu8E7oH6Iby8UkI+A8hP7uenmfZ/vdk+5PZtCLd3T/S4rvlLfWIWLFN3nQdu8klz6CrmYvLumR4n21Xrd5kx3NJVPjn/cX7I8h5mU/AniPkHhfU3XyOIxG19Pcx+5bYQStP3ZYtvAlWmcHiECRJtrPpA291ilhEDPnHCZ/ZKYrs0FdGa85IIoNQWsCT3Fg9g7kTrzXMXdQ23iueUB0euWXXVuZC4HdHrPYXWIAi9/xs294Ka5FHj5c26ng48wZBLWBssrk/wtL894/puPAADv//QHKKUABHz3h9/D6d0nYDKXhqGku1wmkEpTAlv/WSw7gqKKGv2pgli7ZmANN2KACtmztI3pZcJrK+FWkBv0pm4BYcJuVYL4OzRMe5slXUzyYn9hlbdR0qVBGNcYbj3HKBODTwxle28N5SOJWY8FfXvxmMxyWJ5/UT33Znj1O8YHfyntMEq4ds08RaFYazUFrCpYxKx5IFiwQNWC8vGqWN02p1ABCmGtBScGmBWYFbIymAUCd3EqK5Z6wXllXC5nTNMtzusZN8sNbuZ3IEKAMu4uK1ZRVKzQUkEncb5j4tRSCEQMuYhZ+8EUyKKmmGDlpmwDyBS37pZUnSbNdaFHlKTSFHyffvoC//iLX+L3H36IX//z7wF883lErEP2vFVhJB8W08360+/Xg4rndUJmuZukur3P+aa/4CFPAE1Z6FPL0f3NqzyN7t4TSkXRvia6EnrH9LUr/z5dKHK60qorcMJbHYFQtat5+sxqBQrFnGVhVsdcXeFGbikHcxtcqLuPZlfOFY60hMJdYRdZCtgVdduFaLOMTiwvx8bLtSRwK7umtcJbxccE/FRxlHXu9/jaa4J2caOUoN4rpgQ2bwDNOs8Tamt/SgpqQXf0He0FQHPr7Dd4ecWyK5h2avlMIPTN6nOY5dyzhx7404VccGV99hBdtCXirukPl4667aUgE9GtI57NQ7RNmy+z9vtXhB187PjyNaWk91yV+yEc8C3A9/fu+UGrxZMfGBgYGPjyMJR0AwMDA47NIj4vrqnL6wD0gOXom4B7lXW0W5zfp7TLny3jx6Mlf0hY/ZhMsgCCzDpOV2D5tS3c9fzmZftWIuS7r9s87TeH2j8PhQ3pmuI6QY6v8lhSO1JytM2oXt97jPVcg2g7xSvPAQ3jgj91d6gJj2muLEx4o0wfmXlOHmHhjgQHR6+x5CbkJmYPFkgpjSnmQnDLbAzTvqu7mqImoIx8RaSXIZR6BxWKV+1pWiqwPqcNTcd3WYFP/59yPb78s74abOxtIbfjJ7/9qH1/9rs/gkvBNE/4yd/8aygzpqcnPH06Q6opSVQsXo8qQcRE9KyKUk1wva7qOhMyxS/BlDdQj2uoYJfsS3MzmmIpuaTdlHLcFHHEJpQVIlcKAWFGqqHUY3RGTLvvIWVj7YOkKegAKgyeGKXYmNEJPkYUpQgU5j4R4sLytrgA9GukpHv5e4ZUAk/WyASLoycumldXaITipgKoodiCKW2krk0jMlMBoaKQgIgwV0YtClLrDyoMkQqgWpw4BdZVICuwrAvmUiGiWOoNLqcLzP8XYa2CWhkrBEoMZQHNCibLu8wW/K9wwVQnnHCD5by6wiSE/y5MJ4EsFqduXc3ST5SaG8J1XfHH55/i+YuX+A//6e+wLite3v1p+fnqfDldwHY5cJ9qQRKTzvcpXThau0SiGAu0u57fezRfhudV1z5ty7tbWyvQreiuKuB5pXV+eybltcly1z6xbwDM4rRZzKb6KOAWwfnV6u4rk2IuLOGUfE5lsIZlnCnZmvItlHLoyjlyCzrmbk0XijR7l/mwNn7sxdOspAtuKt4O2wYjNzcj7W6P3zoUxlwidmh+/1F6Ot73XHkOyJlYh9iXkta55IeMAHeBmm3ntqrL7IJZt2+4qs5bWYEsXrCPydpnHeua7Bkmxj75oQv/ecXHcPB9gw0T22YQ45pyOmzHe6OP4G1HG7Q3GDZ7LydHRX0tHagpNaOQ9dMeO05jMh8YGBgY+EowlHQDAwPfetQXgN4B9BRXJ99i35Y38/v1elPE3bcni019liYnZco2s9fjMZuwRyvm9hBri8UOhGP9GKaYG7iC3gI4K3CijULuSGkWl0Mw9Lr9Ge13kinrg33iNp89baV0Rwq5K22Npwll9FE8nSyMsnemFGdA/eSlCqBfE4HzV4H6klHerxu3qBvs2vYxbGAjWDzqy4NLVwcL4lkNC7j8qPasCR7Xhj3WTekKCZiQES5gZxfqdcEgmtDL3AvqNm//JBdKKhTihYyYefUWuP1ZNzPeKOlWwt1v6KjKA18DLJcVwIrz7Rn/8G//FgDwnQ++g6fvvQNA8OTdJ/jgJ9+Hubh0d6qkEAFUImaTohSCwOIGlalChLAs4jo0wlqNVljd8jMNDoK7hzPpNrhQU6YJBAyBsNMxiYvTQ5pHSSHnjLAFzvQXhltMoOULjz/HM1uMNBcsN+G2W2YwzKImlNsAmuLu6wIV4NmvCB/8F3DhtAvwPY5glmSrAJUshuUdEWYAooITM1ZVFCjWsLoVBVXCVBi6rKBifgYZxd8xASgAE6godK1Ya0UVwbIuKOUJbp7covAJ7q8UqzBQCRIHAuKAAK0griCerHsmxnwzASDUtdphAVKIivv4UqxrBZcJTAS4UvjufMb//bd/j7vzGb/49T9/+Z3xZUK7vqJdyutfn/glLCx390KfnZVacb0n3M5l1PVAXegdn7TNJ89XV2sRbNfum4z2EuyD+uVM93Nl+5rv7euO67lWNpmbGsfcyWl/t/Z50EeXpfCM42pTnJHFAw2LOpuWqVnRkVsuk1vRMdm4NaVeL4e5hHU1HLFdIbT3cD5pRvZMLAqJu8tLIpgiEW8fWgF6AdD7u+v5R6x5vS2Pt2NRiXDLTcf0Ec9G/Rth6SZWIXC9/rhej0T81PtS6ebKVZnzhSCsFzAF3StceQT4NkMVWD4FTh94d3pQZAJa4Nd26HbXBfeuI/O9HVFl3gMkvuAX5CjRjs42maV88h7ukM4jr32CfV3yDwHqH3vd5Tm+VuuNgYGBgQHDUNINDAx8o/GTP//RWzm5efoA4Hf77/26dR80/gFdnKXPF9ONe71aHj785ULPwOXXsI3MU7/49KEnvvm4uTnh+997H//5X/3rz/Q8/4iAmwN6ONhxb5QrOxxS8Gs2jg89ux8Srxsh+T2RNmISPIYcCYB8qHb68gTg++nmDx+RwTcY73/3Pbzz9An++i+OaWj+0VZJtx/quX33/OGoXx7EfR2W+FA7xau73/G9ufsDCrG57nMFB5eI5WWuBqHa3HUVjpP0aEK7cAlIFMLH/q6wo1ueAy/+vxBJXtfn/ftqPgH4i8c0ytcfP/7h9zGVgr/6i3+1cQv7pww6E86/Nm3+j//yx7h59wYArP7eBHWN2HOKZV09BqFiWZZGT+taoaIQsXh2LZaUwoTP7c9o2QM3IZzPCbl7RYQjOud8BIDE/VJpZ+CUPrNfPvZHClCmguk02XjxWEYCRZUKFVM2WZlTlnA++gKfia7f+44tYH76kx/j/e9+5zWpH4+novjhtEJU3aIRvuayP4ulpT3mFRVMxCgAJiiKAuyx6AqbiRIpQZRRMUHZFakWTQtAKADNwsd0oWqKW0wQnQApuKsVXFaIKJiKKfh1aozHaIVBSqYcdPqZTgwqEwoz1logtdob44BAFY8/xaAC1Frx7/79f0QVwflsQXj+4j/787fWvl8nMDMKM777+x/hR7/9epi7T+8DT/6qKzKaUiKUY7R12Rz39rFK2y0fzs3VbEbo5RnNhWaTy+/WYy3LKEdiEc5i7pXFb+2/NfF89nFgY8vcb1Jza+nOLVGUMfGEqUyYqaCQ9VvhgpkLpmJ/RMXKwj1OHRHbtMxRP4+c5/EmIy4d09YyTkOtGA1CrjR0bSaTuQ5+/70P8PTJEyiAv/zXPz1ogc8GeldB726vHSm2FHjUIcVQ0vX9o/r/6nnGoigtl3aWj29rr9b2Bq9Zt+tLAHcEvOfpv/d23v91xAfvv4cnNzdvvDebvwM89T1HVn4HTwgajvF6pKw7WnHux/5GkabXNPg6PKSUPeIzmu8d6Hw1p0sZrB+iu29nbGkm79P+BPGTP//RV12EgYGBgc8E0m+LNGBgYOBPDv/L//w/fdVFGBgYGBgYGBgYGBgYGBgYGBj4muB//d/+96+6CAMDAwNvhKGkGxgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBj4knGv57WBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYEvBkNJNzAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDDwJWMo6QYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGvmQMJd3AwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwJeMoaQbGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGPiSMZR0AwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwNfMoaSbmBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGDgS8ZQ0g0MDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMfMkYSrqBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgS8Z/z+DjoWJI/pExgAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["# ===================================================================\n","# 1. IMPORTING LIBRARIES AND CONFIGURATION\n","# ===================================================================\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","from torch.utils.data import DataLoader, Dataset\n","from torchvision import models, transforms, utils\n","\n","from PIL import Image\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix, classification_report\n","import time\n","import copy\n","import os\n","\n","# Basic configuration\n","print(\"PyTorch Version:\", torch.__version__)\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(f\"Using device: {device}\")\n","\n","# ===================================================================\n","# 2. DATA PREPARATION (using df_annot and img_dir)\n","# ===================================================================\n","# ASSUMING 'df_annot' (DataFrame) AND 'img_dir' (str) ALREADY EXIST.\n","\n","img_dir = destination_dir\n","\n","# To avoid conflicts with the Python keyword 'class', rename the column\n","if 'class' in df_annot.columns:\n","    df_annot = df_annot.rename(columns={'class': 'class_name'})\n","\n","print(\"Preview of the provided annotation DataFrame:\")\n","print(df_annot.head())\n","\n","# Get the list of classes and number of classes\n","class_names = sorted(df_annot['class_name'].unique())\n","num_classes = len(class_names)\n","print(f\"\\n{num_classes} classes detected in the DataFrame: {class_names}\")\n","\n","# --- Train/Validation/Test split based on FILE NAMES ---\n","# This is CRUCIAL to avoid data leakage.\n","unique_filenames = df_annot['filename'].unique()\n","# 693 images -> train_files : 485 , test_val_files : 208\n","train_files, test_val_files = train_test_split(unique_filenames, test_size=0.3, random_state=42)\n","# 208 -> val_files : 104 , test_files : 104\n","val_files, test_files = train_test_split(test_val_files, test_size=0.5, random_state=42)  # 0.3 * 0.5 = 0.15\n","\n","\n","# Create DataFrames for each dataset\n","train_df = df_annot[df_annot['filename'].isin(train_files)].reset_index(drop=True)\n","val_df = df_annot[df_annot['filename'].isin(val_files)].reset_index(drop=True)\n","test_df = df_annot[df_annot['filename'].isin(test_files)].reset_index(drop=True)\n","\n","dataset_sizes = {'train': len(train_df), 'val': len(val_df), 'test': len(test_df)}\n","print(f\"Number of defects - Train: {dataset_sizes['train']}, Validation: {dataset_sizes['val']}, Test: {dataset_sizes['test']}\")\n","\n","# --- Data Augmentation and Normalization ---\n","data_transforms = {\n","    'train': transforms.Compose([\n","        transforms.Resize((224, 224)),\n","        transforms.RandomResizedCrop(224, scale=(0.7, 1.0)),\n","        transforms.RandomHorizontalFlip(),\n","        transforms.RandomVerticalFlip(),\n","        transforms.RandomRotation(25),\n","        transforms.ColorJitter(\n","            brightness=0.3,\n","            contrast=0.3,\n","            saturation=0.3,\n","            hue=0.1\n","        ),\n","        transforms.ToTensor(),\n","        transforms.Normalize(\n","            [0.485, 0.456, 0.406],\n","            [0.229, 0.224, 0.225]\n","        )\n","    ]),\n","\n","    'val': transforms.Compose([\n","        transforms.Resize((224, 224)),\n","        transforms.ToTensor(),\n","        transforms.Normalize(\n","            [0.485, 0.456, 0.406],\n","            [0.229, 0.224, 0.225]\n","        )\n","    ]),\n","}\n","\n","\n","\n","# --- Custom Dataset class for cropping images ---\n","class PCBCropDataset(Dataset):\n","    def __init__(self, dataframe, image_dir, class_names, transform=None):\n","        self.df = dataframe\n","        self.image_dir = image_dir\n","        self.transform = transform\n","        self.class_to_idx = {cls_name: i for i, cls_name in enumerate(class_names)}\n","\n","    def __len__(self):\n","        return len(self.df)\n","\n","    def __getitem__(self, idx):\n","        row = self.df.iloc[idx]\n","        img_filename = row['filename']\n","        box = (row['xmin'], row['ymin'], row['xmax'], row['ymax'])\n","        label_idx = self.class_to_idx[row['class_name']]\n","\n","        try:\n","            img_path = os.path.join(self.image_dir, img_filename)\n","            image = Image.open(img_path).convert('RGB')\n","            cropped_image = image.crop(box)\n","        except FileNotFoundError:\n","            print(f\"Warning: File not found {img_path}. Returning an empty tensor.\")\n","            return torch.zeros((3, 224, 224)), -1  # Handle missing file case\n","\n","        if self.transform:\n","            cropped_image = self.transform(cropped_image)\n","\n","        return cropped_image, label_idx\n","\n","# --- Create datasets and dataloaders ---\n","train_dataset = PCBCropDataset(train_df, img_dir, class_names, transform=data_transforms['train'])\n","val_dataset = PCBCropDataset(val_df, img_dir, class_names, transform=data_transforms['val'])\n","test_dataset = PCBCropDataset(test_df, img_dir, class_names, transform=data_transforms['val'])\n","\n","# Use a smaller batch size initially to avoid memory issues\n","batch_size = 16\n","\n","dataloaders = {\n","    'train': DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=os.cpu_count()),\n","    'val': DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=os.cpu_count()),\n","    'test': DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=os.cpu_count())\n","}\n","\n","# --- Visualize a batch of cropped defect patches ---\n","print(\"\\nVisualizing a batch of cropped defect patches...\")\n","inputs, classes_idx = next(iter(dataloaders['train']))\n","out = utils.make_grid(inputs)\n","\n","# Inverse normalization for display\n","def imshow(inp, title=None):\n","    inp = inp.numpy().transpose((1, 2, 0))\n","    mean = np.array([0.485, 0.456, 0.406]); std = np.array([0.229, 0.224, 0.225])\n","    inp = std * inp + mean; inp = np.clip(inp, 0, 1)\n","    plt.figure(figsize=(15, 8)); plt.imshow(inp)\n","    if title is not None: plt.title(title)\n","    plt.axis('off'); plt.show()\n","\n","imshow(out, title=[class_names[x] for x in classes_idx])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"TNxr24YNepdS","executionInfo":{"status":"ok","timestamp":1768061263333,"user_tz":-330,"elapsed":2479176,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"bdf23ba5-f306-447f-8e32-11b443fc5d99"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50 | ----------\n","Train Loss: 0.4401 Acc: 0.8583\n","Val Loss: 0.1606 Acc: 0.9427\n","Validation loss improved (inf -> 0.1606). Saving model...\n","\n","Epoch 2/50 | ----------\n","Train Loss: 0.1245 Acc: 0.9606\n","Val Loss: 0.1319 Acc: 0.9537\n","Validation loss improved (0.1606 -> 0.1319). Saving model...\n","\n","Epoch 3/50 | ----------\n","Train Loss: 0.0819 Acc: 0.9771\n","Val Loss: 0.0482 Acc: 0.9890\n","Validation loss improved (0.1319 -> 0.0482). Saving model...\n","\n","Epoch 4/50 | ----------\n","Train Loss: 0.0642 Acc: 0.9830\n","Val Loss: 0.0474 Acc: 0.9846\n","Validation loss improved (0.0482 -> 0.0474). Saving model...\n","\n","Epoch 5/50 | ----------\n","Train Loss: 0.0574 Acc: 0.9796\n","Val Loss: 0.0195 Acc: 0.9934\n","Validation loss improved (0.0474 -> 0.0195). Saving model...\n","\n","Epoch 6/50 | ----------\n","Train Loss: 0.0411 Acc: 0.9849\n","Val Loss: 0.0489 Acc: 0.9846\n","\n","Epoch 7/50 | ----------\n","Train Loss: 0.0333 Acc: 0.9907\n","Val Loss: 0.0120 Acc: 0.9978\n","Validation loss improved (0.0195 -> 0.0120). Saving model...\n","\n","Epoch 8/50 | ----------\n","Train Loss: 0.0311 Acc: 0.9888\n","Val Loss: 0.0307 Acc: 0.9912\n","\n","Epoch 9/50 | ----------\n","Train Loss: 0.0163 Acc: 0.9956\n","Val Loss: 0.0188 Acc: 0.9956\n","\n","Epoch 10/50 | ----------\n","Train Loss: 0.0290 Acc: 0.9907\n","Val Loss: 0.0213 Acc: 0.9934\n","\n","Epoch 11/50 | ----------\n","Train Loss: 0.0257 Acc: 0.9917\n","Val Loss: 0.0110 Acc: 0.9956\n","Validation loss improved (0.0120 -> 0.0110). Saving model...\n","\n","Epoch 12/50 | ----------\n","Train Loss: 0.0201 Acc: 0.9937\n","Val Loss: 0.0361 Acc: 0.9912\n","\n","Epoch 13/50 | ----------\n","Train Loss: 0.0171 Acc: 0.9932\n","Val Loss: 0.0263 Acc: 0.9912\n","\n","Epoch 14/50 | ----------\n","Train Loss: 0.0331 Acc: 0.9893\n","Val Loss: 0.0240 Acc: 0.9912\n","\n","Epoch 15/50 | ----------\n","Train Loss: 0.0111 Acc: 0.9971\n","Val Loss: 0.0491 Acc: 0.9824\n","\n","Epoch 16/50 | ----------\n","Train Loss: 0.0136 Acc: 0.9966\n","Val Loss: 0.0163 Acc: 0.9956\n","\n","Early stopping triggered after 5 epochs with no improvement.\n","Training complete in 156m 15s\n","Best Validation Loss: 0.011042\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1800x600 with 2 Axes>"],"image/png":"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Final evaluation of model 'ResNet50' on the Test set ---\n","\n","                 precision    recall  f1-score   support\n","\n","   missing_hole     1.0000    1.0000    1.0000        71\n","     mouse_bite     0.9882    0.9882    0.9882        85\n","   open_circuit     1.0000    0.9851    0.9925        67\n","          short     1.0000    1.0000    1.0000        70\n","           spur     0.9882    0.9882    0.9882        85\n","spurious_copper     0.9853    1.0000    0.9926        67\n","\n","       accuracy                         0.9933       445\n","      macro avg     0.9936    0.9936    0.9936       445\n","   weighted avg     0.9933    0.9933    0.9933       445\n","\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# ===================================================================\n","# 3. PRETRAINED MODEL (ResNet50 with fine-tuning)\n","# ===================================================================\n","def get_pretrained_model(num_classes):\n","    model = models.resnet50(\n","        weights=models.ResNet50_Weights.IMAGENET1K_V2\n","    )\n","\n","    # Unfreeze only layer4 and fc (fine-tuning)\n","    for name, param in model.named_parameters():\n","        if \"layer4\" in name or \"fc\" in name:\n","            param.requires_grad = True\n","        else:\n","            param.requires_grad = False\n","\n","    # Replace the final fully connected layer\n","    num_ftrs = model.fc.in_features\n","    model.fc = nn.Linear(num_ftrs, num_classes)\n","\n","    return model.to(device)\n","\n","\n","\n","# ===================================================================\n","# 4. TRAINING AND VALIDATION FUNCTION\n","# ===================================================================\n","def train_model(model, criterion, optimizer, num_epochs=50, patience=5):\n","    since = time.time()\n","    history = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}\n","\n","    best_model_wts = copy.deepcopy(model.state_dict())\n","    best_loss = float('inf')\n","    epochs_no_improve = 0\n","\n","    for epoch in range(num_epochs):\n","        print(f'Epoch {epoch+1}/{num_epochs}' + ' | ' + '-'*10)\n","        for phase in ['train', 'val']:\n","            model.train() if phase == 'train' else model.eval()\n","            running_loss, running_corrects = 0.0, 0\n","\n","            for inputs, labels in dataloaders[phase]:\n","                inputs, labels = inputs.to(device), labels.to(device)\n","                optimizer.zero_grad()\n","                with torch.set_grad_enabled(phase == 'train'):\n","                    outputs = model(inputs)\n","                    loss = criterion(outputs, labels)\n","                    _, preds = torch.max(outputs, 1)\n","                    if phase == 'train':\n","                        loss.backward(); optimizer.step()\n","\n","                running_loss += loss.item() * inputs.size(0)\n","                running_corrects += torch.sum(preds == labels.data)\n","\n","            epoch_loss = running_loss / dataset_sizes[phase]\n","            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n","            print(f'{phase.capitalize()} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n","\n","            history[f'{phase}_loss'].append(epoch_loss)\n","            history[f'{phase}_acc'].append(epoch_acc.item())\n","\n","            if phase == 'val':\n","                if epoch_loss < best_loss:\n","                    print(f\"Validation loss improved ({best_loss:.4f} -> {epoch_loss:.4f}). Saving model...\")\n","                    best_loss = epoch_loss\n","                    best_model_wts = copy.deepcopy(model.state_dict())\n","                    epochs_no_improve = 0\n","                else:\n","                    epochs_no_improve += 1\n","\n","        if epochs_no_improve >= patience:\n","            print(f\"\\nEarly stopping triggered after {patience} epochs with no improvement.\")\n","            break\n","        print()\n","\n","    time_elapsed = time.time() - since\n","    print(f'Training complete in {time_elapsed//60:.0f}m {time_elapsed%60:.0f}s')\n","    print(f'Best Validation Loss: {best_loss:4f}')\n","\n","    model.load_state_dict(best_model_wts)\n","    return model, history\n","\n","\n","\n","# ===================================================================\n","# 5. TRAINING EXECUTION\n","# ===================================================================\n","# Clear the GPU cache to ensure memory is free\n","if torch.cuda.is_available():\n","    # torch.cuda.empty_cache()\n","    pass\n","\n","# Instantiate the model\n","resnet_model = get_pretrained_model(num_classes)\n","\n","# Define the loss function and optimizer\n","criterion = nn.CrossEntropyLoss()\n","# Optimize only parameters of the new layer (those that are not frozen)\n","optimizer = optim.Adam(filter(lambda p: p.requires_grad, resnet_model.parameters()), lr=0.001)\n","\n","# Start training\n","best_resnet_model, history = train_model(resnet_model, criterion, optimizer, num_epochs=50, patience=5)\n","\n","\n","# ===================================================================\n","# 6. RESULT ANALYSIS AND EVALUATION\n","# ===================================================================\n","# --- Plot learning curves ---\n","def plot_history(history, model_name):\n","    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 6))\n","    fig.suptitle(f\"Learning curves for {model_name}\", fontsize=16)\n","    ax1.plot(history['train_acc'], label='Train Acc'); ax1.plot(history['val_acc'], label='Val Acc')\n","    ax1.set_title('Accuracy'); ax1.set_xlabel('Epoch'); ax1.legend(); ax1.grid(True)\n","    ax2.plot(history['train_loss'], label='Train Loss'); ax2.plot(history['val_loss'], label='Val Loss')\n","    ax2.set_title('Loss'); ax2.set_xlabel('Epoch'); ax2.legend(); ax2.grid(True)\n","    plt.show()\n","\n","plot_history(history, \"ResNet50 on cropped patches\")\n","\n","# --- Evaluation on the test set ---\n","def evaluate_model(model, dataloader, model_name):\n","    model.eval()\n","    y_true, y_pred = [], []\n","    with torch.no_grad():\n","        for inputs, labels in dataloader:\n","            outputs = model(inputs.to(device))\n","            _, predicted = torch.max(outputs, 1)\n","            y_true.extend(labels.numpy())\n","            y_pred.extend(predicted.cpu().numpy())\n","\n","    print(f\"\\n--- Final evaluation of model '{model_name}' on the Test set ---\\n\")\n","    print(classification_report(y_true, y_pred, target_names=class_names, digits=4))\n","\n","    cm = confusion_matrix(y_true, y_pred)\n","    plt.figure(figsize=(10, 8))\n","    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n","    plt.title(f'Confusion Matrix - {model_name}', fontsize=16)\n","    plt.xlabel('Predictions'); plt.ylabel('True Labels')\n","    plt.show()\n","\n","evaluate_model(best_resnet_model, dataloaders['test'], \"ResNet50\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":721,"status":"ok","timestamp":1768064352288,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"},"user_tz":-330},"id":"9rwlfva4IQ3I","outputId":"0ebad856-0f06-4ba0-a895-6bf3ad455731"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to /content/drive/MyDrive/Colab Notebooks/best_resnet50_pcb_defects_50epochs.pth\n"]}],"source":["# Path where you want to save\n","MODEL_PATH = \"/content/drive/MyDrive/Colab Notebooks/best_resnet50_pcb_defects_50epochs.pth\"\n","\n","# Save only the state_dict (recommended)\n","torch.save({\n","    \"model_state_dict\": best_resnet_model.state_dict(),\n","    \"class_names\": class_names,\n","}, MODEL_PATH)\n","\n","print(f\"Model saved to {MODEL_PATH}\")"]},{"cell_type":"code","source":["!git config --global user.name \"Aradhya Stuti\"\n","!git config --global user.email \"aradhya.mutants@gmail.com\"\n"],"metadata":{"id":"tpOM_8usHBsJ","executionInfo":{"status":"ok","timestamp":1768371787447,"user_tz":-330,"elapsed":247,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}}},"execution_count":36,"outputs":[]},{"cell_type":"code","source":["\n","%cd /content/AI_PCB_Defect_Detection_Classification_System"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iqo6-t0DLCEI","executionInfo":{"status":"ok","timestamp":1768371879870,"user_tz":-330,"elapsed":48,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"3545de62-3fab-4eaa-bd60-9c69fcea6263"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/AI_PCB_Defect_Detection_Classification_System\n"]}]},{"cell_type":"code","source":["\n","!git checkout my-branch"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oCijR3DsLYnV","executionInfo":{"status":"ok","timestamp":1768371981008,"user_tz":-330,"elapsed":118,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"628feb40-6bc8-4382-97ab-d0f8ec67fea5"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["Already on 'my-branch'\n","Your branch is up to date with 'origin/my-branch'.\n"]}]},{"cell_type":"code","source":["!git branch"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xR0eAevFLxXc","executionInfo":{"status":"ok","timestamp":1768372084299,"user_tz":-330,"elapsed":119,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"afd03447-955f-4714-ed7d-0358b00be6e3"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["  main\u001b[m\n","* \u001b[32mmy-branch\u001b[m\n"]}]},{"cell_type":"code","source":["!ls /content"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pPvftiiLMKmj","executionInfo":{"status":"ok","timestamp":1768372108508,"user_tz":-330,"elapsed":113,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"290be2bb-efb7-4325-c968-915f8c726003"},"execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","text":["AI_PCB_Defect_Detection_Classification_System  drive  sample_data\n"]}]},{"cell_type":"code","source":["\n","!cp /content/drive/MyDrive/Colab Notebooks/trained.ipynb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FctUpSt0MROu","executionInfo":{"status":"ok","timestamp":1768372197742,"user_tz":-330,"elapsed":129,"user":{"displayName":"Aradhya Stuti","userId":"13084479309528900862"}},"outputId":"19251814-9845-4b44-d35c-fc9c485353c7"},"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["cp: cannot stat '/content/drive/MyDrive/Colab': No such file or directory\n"]}]}],"metadata":{"colab":{"provenance":[],"mount_file_id":"1qqENmbszhO4tJ-IT4rnY7osQzo2z640H","authorship_tag":"ABX9TyPE13X78Z5Tm9Kv6XANsG7T"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
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