{ "cells": [ { "cell_type": "markdown", "id": "e6192abd", "metadata": {}, "source": [ "# Landslide Susceptibility Mapping with Physics-Informed BaseAttentive\n", "\n", "> **Scenario**: A regional landslide susceptibility study in a mountain watershed\n", "> of the Venezuelan Andes (~50 km²). The notebook demonstrates the full scientific\n", "> workflow from data generation to publication-ready maps and statistical tables.\n", ">\n", "> **Student note**: the synthetic dataset is structurally identical to real\n", "> landslide inventories. Replace the generation cells with real data loaded\n", "> from GIS files (see replacement guide at the end of each section), then re-run\n", "> the notebook unchanged to reproduce all results on your study area.\n", "\n", "## Novel scientific contributions\n", "\n", "| # | Contribution | Why it matters |\n", "|---|-------------|---------------|\n", "| 1 | **Depth-profile as temporal sequence** | Attention identifies the critical failure plane rather than averaging layer properties |\n", "| 2 | **Scenario-conditioned hazard curves** | Cross-attention to return-period triggers yields FS/probability vs return-period curves per pixel |\n", "| 3 | **Physics-informed regularization** | Infinite-slope FS used as a soft constraint → physically consistent predictions even with sparse inventory |\n", "| 4 | **Ensemble epistemic uncertainty** | Spatially explicit confidence maps enable risk-based zonation beyond binary susceptibility classes |\n", "| 5 | **Interpretable failure-plane depth** | Layer saliency maps validated against field observations of actual rupture surfaces |\n", "\n", "## Input data structure\n", "\n", "```\n", "Static (N_STATIC = 8): elevation, slope, aspect, TWI, lithology,\n", " dist_fault, NDVI, annual_rainfall\n", "Dynamic (LOOKBACK = 6): depth profile — cohesion, friction_angle,\n", " unit_weight, water_content, clay_fraction, void_ratio\n", "Future (HORIZON = 5): trigger scenarios T10, T25, T50, T100, T200\n", " — [rainfall_intensity, duration, antecedent_wet, seismic_ag]\n", "Target (OUTPUT = 1): failure probability under each trigger scenario\n", "```\n", "\n", "## Infinite-slope physics constraint\n", "\n", "$$\\text{FS}(\\beta,c,\\varphi,z,z_w) =\n", " \\frac{c' + (\\gamma z - \\gamma_w z_w)\\cos^2\\!\\beta\\,\\tan\\varphi'}{\\gamma z\\sin\\beta\\cos\\beta}$$\n", "\n", "The physics-informed loss penalises any prediction that contradicts the\n", "sign of `FS − 1` derived from the geomechanical properties of the depth profile." ] }, { "cell_type": "code", "execution_count": 1, "id": "98c991c9", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:18.422163Z", "iopub.status.busy": "2026-04-21T12:53:18.421151Z", "iopub.status.idle": "2026-04-21T12:53:22.860576Z", "shell.execute_reply": "2026-04-21T12:53:22.859086Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "base_attentive : 2.2.0\n", "Keras : 3.12.1\n", "TF : 2.13.1\n", "Study grid : 50 x 30 = 1500 cells (100 m resolution)\n", "Train / Test : 1200 / 300\n" ] } ], "source": [ "import os, warnings, time\n", "warnings.filterwarnings('ignore')\n", "os.environ.setdefault('BASE_ATTENTIVE_BACKEND', 'tensorflow')\n", "os.environ.setdefault('KERAS_BACKEND', 'tensorflow')\n", "\n", "import keras\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as mcolors\n", "from matplotlib.patches import Patch\n", "from matplotlib.lines import Line2D\n", "from scipy.stats import spearmanr\n", "from sklearn.metrics import (roc_auc_score, roc_curve, average_precision_score,\n", " precision_recall_curve, confusion_matrix,\n", " classification_report)\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "import base_attentive\n", "from base_attentive import BaseAttentive\n", "\n", "# ── Global constants ───────────────────────────────────────────────────────────\n", "N_COLS, N_ROWS = 50, 30 # study area grid\n", "N_TOTAL = N_COLS * N_ROWS # 1 500 pixels at 100 m resolution\n", "TRAIN_FRAC = 0.80\n", "TRAIN_SIZE = int(N_TOTAL * TRAIN_FRAC) # 1 200\n", "TEST_SIZE = N_TOTAL - TRAIN_SIZE # 300\n", "\n", "# Model dimensions\n", "LOOKBACK = 6 # depth layers in soil/rock profile\n", "HORIZON = 5 # trigger return-period scenarios (T10…T200)\n", "N_STATIC = 8 # static terrain + regional features\n", "N_DYNAMIC = 6 # per-layer geomechanical properties\n", "N_FUTURE = 4 # per-scenario trigger parameters\n", "OUTPUT_DIM = 1\n", "\n", "# Training\n", "BATCH_SIZE = 32\n", "EPOCHS_MAIN = 20\n", "PATIENCE = 4\n", "LAMBDA_PHYS = 0.4 # physics regularisation weight\n", "\n", "# Return periods and coordinate system\n", "RETURN_PERIODS = [10, 25, 50, 100, 200] # years\n", "LON_MIN, LON_MAX = -72.50, -72.05 # Venezuelan Andes\n", "LAT_MIN, LAT_MAX = 9.00, 9.27\n", "# Resolution: ~100 m per grid cell\n", "RNG = np.random.default_rng(42)\n", "tf.random.set_seed(42)\n", "\n", "# Susceptibility class boundaries (failure probability)\n", "SUSC_BOUNDS = [0.0, 0.10, 0.25, 0.45, 0.65, 1.0]\n", "SUSC_LABELS = ['Very Low', 'Low', 'Moderate', 'High', 'Very High']\n", "SUSC_COLORS = ['#2ecc71', '#f1c40f', '#e67e22', '#e74c3c', '#8e44ad']\n", "\n", "print(f'base_attentive : {base_attentive.__version__}')\n", "print(f'Keras : {keras.__version__}')\n", "print(f'TF : {tf.__version__}')\n", "print(f'Study grid : {N_COLS} x {N_ROWS} = {N_TOTAL} cells (100 m resolution)')\n", "print(f'Train / Test : {TRAIN_SIZE} / {TEST_SIZE}')\n" ] }, { "cell_type": "markdown", "id": "ab3c54a9", "metadata": {}, "source": [ "---\n", "\n", "## 1 — Study Area & Landslide Inventory\n", "\n", "### Study area: Venezuelan Andes (synthetic)\n", "\n", "The synthetic study area represents a 50 × 30 km mountain watershed at approximately\n", "9°N, 72.3°W. The terrain includes:\n", "\n", "- **Two massifs** (peaks ≈ 2 100 m a.s.l.) separated by a main valley\n", "- **Three fault zones** striking NE–SW (primary, secondary, minor)\n", "- **Four lithological units**: granite (resistant), metamorphic schist,\n", " clay shale (weakest), and quaternary alluvium in valleys\n", "- **Main river** draining W–E and two tributaries\n", "\n", "### Landslide inventory\n", "\n", "The inventory was generated following the **infinite-slope failure criterion**\n", "combined with:\n", "- Spatial clustering (landslides cluster near fault zones and drainage channels)\n", "- Stochastic variability (unknown micro-controls add ±15% randomness)\n", "- Realistic class imbalance (∼ 23% landslide pixels)\n", "\n", "**Real-data replacement**: replace the generation cells below with:\n", "```python\n", "# Load your DEM and geological maps\n", "import geopandas as gpd, rasterio\n", "dem = rasterio.open('dem_100m.tif')\n", "inv = gpd.read_file('landslide_inventory.shp')\n", "geol = rasterio.open('lithology_100m.tif')\n", "```\n", "All downstream cells use only the `lon_flat`, `lat_flat`, and feature arrays\n", "defined here — they do not need to change." ] }, { "cell_type": "code", "execution_count": 2, "id": "461eaf4a", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:22.863931Z", "iopub.status.busy": "2026-04-21T12:53:22.862918Z", "iopub.status.idle": "2026-04-21T12:53:22.891264Z", "shell.execute_reply": "2026-04-21T12:53:22.890135Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Elevation : 150–2091 m\n", "Slope : 0.0–26.1 deg\n", "Dist fault : 2–23335 m\n", "TWI : 4.50–12.09\n", "Lithology : [ 28 54 20 1398] (0=granite,1=schist,2=clay,3=alluvium)\n" ] } ], "source": [ "# ── Coordinate grid ───────────────────────────────────────────────────────────\n", "lon_1d = np.linspace(LON_MIN, LON_MAX, N_COLS)\n", "lat_1d = np.linspace(LAT_MIN, LAT_MAX, N_ROWS)\n", "LON2D, LAT2D = np.meshgrid(lon_1d, lat_1d)\n", "lon_flat = LON2D.ravel().astype('float32')\n", "lat_flat = LAT2D.ravel().astype('float32')\n", "\n", "# ── DEM: two mountain peaks + valley (smooth Gaussian model) ─────────────────\n", "P1_lon, P1_lat = -72.37, 9.12 # main massif (southern block)\n", "P2_lon, P2_lat = -72.22, 9.24 # secondary massif (northern block → test area)\n", "\n", "def gauss2d(lon, lat, cx, cy, sx, sy, amp):\n", " return amp * np.exp(-0.5*((lon-cx)/sx)**2 - 0.5*((lat-cy)/sy)**2)\n", "\n", "elev = (gauss2d(lon_flat, lat_flat, P1_lon, P1_lat, 0.020, 0.020, 1900) +\n", " gauss2d(lon_flat, lat_flat, P2_lon, P2_lat, 0.016, 0.016, 1300) +\n", " 200 + RNG.normal(0, 25, N_TOTAL)).astype('float32')\n", "elev = np.clip(elev, 150, 2200).astype('float32')\n", "\n", "ELEV2D = elev.reshape(N_ROWS, N_COLS)\n", "\n", "# ── Topographic derivatives (slope, aspect, curvature, TWI) ──────────────────\n", "# Approximate horizontal distances (km per degree at 9°N)\n", "dx_km = 111.32 * np.cos(np.deg2rad(9.0)) # ~109.9 km/°lon\n", "dy_km = 110.54 # km/°lat\n", "ddx = np.diff(lon_1d)[0] * dx_km * 1000 # cell size in metres (≈100 m)\n", "ddy = np.diff(lat_1d)[0] * dy_km * 1000\n", "\n", "dz_dx = np.gradient(ELEV2D, ddx, axis=1) # m/m\n", "dz_dy = np.gradient(ELEV2D, ddy, axis=0)\n", "\n", "slope_rad = np.arctan(np.sqrt(dz_dx**2 + dz_dy**2))\n", "slope = np.rad2deg(slope_rad).ravel().astype('float32')\n", "aspect = (np.rad2deg(np.arctan2(-dz_dy, dz_dx)) % 360).ravel().astype('float32')\n", "\n", "# Plan curvature proxy (Laplacian of elevation)\n", "plan_curv = (np.gradient(dz_dx, ddx, axis=1) +\n", " np.gradient(dz_dy, ddy, axis=0)).ravel().astype('float32')\n", "\n", "# TWI = ln(A / tan(β)) — flow accumulation approximated via upslope area proxy\n", "flow_acc = (gauss2d(lon_flat, lat_flat, P1_lon, P1_lat, 0.06, 0.06, 5000) +\n", " gauss2d(lon_flat, lat_flat, P2_lon, P2_lat, 0.05, 0.05, 3000) + 10)\n", "twi = np.log(flow_acc / (np.tan(slope_rad.ravel()) + 0.01)).astype('float32')\n", "\n", "# ── Fault zones (3 NE-SW striking faults) ────────────────────────────────────\n", "fault_pts = [\n", " (-72.45, 9.02, -72.15, 9.27), # primary fault\n", " (-72.42, 9.05, -72.25, 9.22), # secondary fault\n", " (-72.30, 9.08, -72.10, 9.15), # minor fault\n", "]\n", "\n", "def dist_to_segment(lon, lat, x1, y1, x2, y2):\n", " dx, dy = x2-x1, y2-y1\n", " t = np.clip(((lon-x1)*dx + (lat-y1)*dy) / (dx**2 + dy**2 + 1e-12), 0, 1)\n", " dist_deg = np.sqrt((lon - x1 - t*dx)**2 + (lat - y1 - t*dy)**2)\n", " return dist_deg * 110_000 # approximate metres\n", "\n", "dist_fault = np.full(N_TOTAL, 1e6, dtype='float32')\n", "for (x1, y1, x2, y2) in fault_pts:\n", " d = dist_to_segment(lon_flat, lat_flat, x1, y1, x2, y2)\n", " dist_fault = np.minimum(dist_fault, d)\n", "\n", "# ── Lithology (4 units based on elevation + fault proximity) ─────────────────\n", "# 0=granite (hard, high elev), 1=schist (mid elev), 2=clay_shale (near faults),\n", "# 3=alluvium (valley floors)\n", "lith = np.zeros(N_TOTAL, dtype='int32')\n", "lith[elev > 1400] = 0 # granite\n", "lith[(elev > 600) & (elev <= 1400)] = 1 # schist\n", "lith[(elev <= 600) & (dist_fault < 3000)] = 2 # clay shale\n", "lith[(elev <= 400)] = 3 # alluvium\n", "\n", "# ── NDVI proxy (vegetation correlated with elevation + moisture) ──────────────\n", "ndvi = (0.6 * np.exp(-0.5*((elev - 900)/500)**2)\n", " + 0.2 + RNG.uniform(-0.05, 0.05, N_TOTAL)).astype('float32')\n", "ndvi = np.clip(ndvi, -0.1, 0.9)\n", "\n", "# ── Annual rainfall (mm): increases toward peaks, higher on windward side) ────\n", "ann_rain = (gauss2d(lon_flat, lat_flat, P1_lon, P1_lat, 0.08, 0.08, 1200) +\n", " gauss2d(lon_flat, lat_flat, P2_lon, P2_lat, 0.06, 0.06, 900) +\n", " 800 + RNG.normal(0, 50, N_TOTAL)).astype('float32')\n", "ann_rain = np.clip(ann_rain, 500, 2500)\n", "\n", "print(f'Elevation : {elev.min():.0f}–{elev.max():.0f} m')\n", "print(f'Slope : {slope.min():.1f}–{slope.max():.1f} deg')\n", "print(f'Dist fault : {dist_fault.min():.0f}–{dist_fault.max():.0f} m')\n", "print(f'TWI : {twi.min():.2f}–{twi.max():.2f}')\n", "print(f'Lithology : {np.bincount(lith)} (0=granite,1=schist,2=clay,3=alluvium)')\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "1b4d8f5a", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:22.893274Z", "iopub.status.busy": "2026-04-21T12:53:22.893274Z", "iopub.status.idle": "2026-04-21T12:53:22.923248Z", "shell.execute_reply": "2026-04-21T12:53:22.921996Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Soil profile shape : (1500, 6, 6)\n", "Future feat shape : (1500, 5, 4)\n", "Layer depths (m) : [0.25 0.75 1.5 2.5 4. 7.5 ]\n", "Return periods (yr): [10, 25, 50, 100, 200]\n" ] } ], "source": [ "# ── Depth-profile soil/rock properties (LOOKBACK=6 layers) ───────────────────\n", "# Layers (m): [0-0.5, 0.5-1, 1-2, 2-3, 3-5, 5-10]\n", "LAYER_DEPTHS = np.array([0.25, 0.75, 1.5, 2.5, 4.0, 7.5], dtype='float32')\n", "LAYER_LABELS = ['0-0.5m', '0.5-1m', '1-2m', '2-3m', '3-5m', '5-10m']\n", "\n", "# Per-layer geomechanical properties: shape (N_TOTAL, 6, N_DYNAMIC=6)\n", "# Features: [cohesion_c, friction_phi, unit_weight, water_content, clay_frac, void_ratio]\n", "soil = np.zeros((N_TOTAL, LOOKBACK, N_DYNAMIC), dtype='float32')\n", "\n", "for z_idx, z in enumerate(LAYER_DEPTHS):\n", " # Lithology influence\n", " c_base = np.where(lith==0, 60, np.where(lith==1, 30, np.where(lith==2, 15, 5)))\n", " phi_base = np.where(lith==0, 38, np.where(lith==1, 32, np.where(lith==2, 22, 18)))\n", " gam_base = np.where(lith==0, 21, np.where(lith==1, 19, np.where(lith==2, 17, 15)))\n", " clay_base= np.where(lith==0, 5, np.where(lith==1, 20, np.where(lith==2, 50, 35)))\n", "\n", " # Depth trends: cohesion increases with depth, water_content decreases\n", " c_factor = 1.0 + 0.15 * z_idx\n", " phi_factor = 1.0 - 0.02 * z_idx\n", " wc_base = 35 - 4 * z_idx + RNG.normal(0, 3, N_TOTAL)\n", " void_base = 0.60 - 0.05 * z_idx + RNG.normal(0, 0.03, N_TOTAL)\n", "\n", " soil[:, z_idx, 0] = (c_base * c_factor\n", " + RNG.normal(0, 3, N_TOTAL)).clip(2, 100) # cohesion kPa\n", " soil[:, z_idx, 1] = (phi_base * phi_factor\n", " + RNG.normal(0, 2, N_TOTAL)).clip(10, 45) # friction angle deg\n", " soil[:, z_idx, 2] = (gam_base\n", " + RNG.normal(0, 1, N_TOTAL)).clip(12, 25) # unit weight kN/m³\n", " soil[:, z_idx, 3] = wc_base.clip(5, 55) # water content %\n", " soil[:, z_idx, 4] = (clay_base\n", " + RNG.normal(0, 5, N_TOTAL)).clip(0, 70) # clay fraction %\n", " soil[:, z_idx, 5] = void_base.clip(0.15, 0.80) # void ratio\n", "\n", "# ── Trigger scenarios (HORIZON=5 return periods) ─────────────────────────────\n", "# Parameters: [rainfall_intensity_mm_h, duration_h, antecedent_wet_frac, seismic_ag]\n", "RAIN_INT = np.array([20, 35, 55, 80, 110], dtype='float32') # mm/h (T10…T200)\n", "RAIN_DUR = np.array([6, 12, 18, 24, 36], dtype='float32') # hours\n", "ANT_WET = np.array([0.3, 0.4, 0.5, 0.6, 0.7], dtype='float32') # antecedent moisture\n", "SEISMIC = np.array([0.0, 0.0, 0.0, 0.0, 0.1], dtype='float32') # T200 has seismic\n", "\n", "future_feat = np.zeros((N_TOTAL, HORIZON, N_FUTURE), dtype='float32')\n", "for h in range(HORIZON):\n", " # Normalise: intensity/100, duration/36, antecedent as-is, seismic/0.1\n", " future_feat[:, h, 0] = RAIN_INT[h] / 100.0\n", " future_feat[:, h, 1] = RAIN_DUR[h] / 36.0\n", " future_feat[:, h, 2] = ANT_WET[h]\n", " future_feat[:, h, 3] = SEISMIC[h]\n", "\n", "print('Soil profile shape :', soil.shape)\n", "print('Future feat shape :', future_feat.shape)\n", "print('Layer depths (m) :', LAYER_DEPTHS)\n", "print('Return periods (yr):', RETURN_PERIODS)\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "818376b4", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:22.926532Z", "iopub.status.busy": "2026-04-21T12:53:22.925302Z", "iopub.status.idle": "2026-04-21T12:53:22.954515Z", "shell.execute_reply": "2026-04-21T12:53:22.953166Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Landslide pixels : 115 / 1500 (7.7%)\n", "FS range (T50) : 1.12–10.00\n", "Target prob range : 0.000–0.751\n" ] } ], "source": [ "# ── Factor of Safety (infinite slope) for each pixel × scenario ──────────────\n", "GAMMA_W = 9.81 # kN/m³ — unit weight of water\n", "\n", "def compute_fs(slope_deg, c_kpa, phi_deg,\n", " gamma_kn=19.0, z_m=2.0, z_w_frac=0.5):\n", " # Infinite slope stability: FS = (c + N'*tan_phi) / T\n", " # N' = (gamma*z - gamma_w*z_w)*cos²β, T = gamma*z*sinβ*cosβ\n", " beta = np.deg2rad(np.clip(slope_deg, 1, 75))\n", " phi = np.deg2rad(np.clip(phi_deg, 5, 50))\n", " z_w = z_m * z_w_frac\n", " normal = (gamma_kn * z_m - GAMMA_W * z_w) * np.cos(beta)**2\n", " shear = gamma_kn * z_m * np.sin(beta) * np.cos(beta) + 1e-6\n", " fs = (c_kpa + normal * np.tan(phi)) / shear\n", " return np.clip(fs, 0.1, 10.0).astype('float32')\n", "\n", "# Use weighted average of top 3 layers for critical-plane FS\n", "c_avg = soil[:, :3, 0].mean(axis=1) # (N_TOTAL,) cohesion\n", "phi_avg = soil[:, :3, 1].mean(axis=1) # friction angle\n", "gam_avg = soil[:, :3, 2].mean(axis=1) # unit weight\n", "\n", "# FS under each trigger scenario (water table rises with rainfall + antecedent wet)\n", "fs_matrix = np.zeros((N_TOTAL, HORIZON), dtype='float32')\n", "for h in range(HORIZON):\n", " # Pore pressure: z_w_frac increases with rainfall intensity and antecedent moisture\n", " z_w_frac_h = np.clip(\n", " ANT_WET[h] + (RAIN_INT[h] / 200.0) + RNG.normal(0, 0.05, N_TOTAL), 0.05, 1.0)\n", " fs_matrix[:, h] = compute_fs(slope, c_avg, phi_avg,\n", " gamma_kn=gam_avg, z_m=2.5, z_w_frac=z_w_frac_h)\n", "\n", "# ── Target: failure probability under each scenario ───────────────────────────\n", "# P(failure | scenario h) = sigmoid(-k*(FS_h - 1)) + spatial_cluster + noise\n", "K_SIGMOID = 4.0 # steepness of transition at FS=1\n", "\n", "# Spatial clustering (near faults and channel heads)\n", "cluster = (np.exp(-dist_fault / 1500) +\n", " 0.3 * np.exp(-0.5*((slope - 32)/10)**2)).clip(0, 0.5).astype('float32')\n", "\n", "target_prob = np.zeros((N_TOTAL, HORIZON, OUTPUT_DIM), dtype='float32')\n", "for h in range(HORIZON):\n", " p_phys = 1.0 / (1.0 + np.exp(K_SIGMOID * (fs_matrix[:, h] - 1.0)))\n", " p_total = np.clip(p_phys + 0.3 * cluster + RNG.normal(0, 0.04, N_TOTAL), 0, 1)\n", " target_prob[:, h, 0] = p_total.astype('float32')\n", "\n", "# ── Binary inventory: slope + fault proximity (independent of FS) ─────────────\n", "# Mimics a real inventory compiled from photo-interpretation + field survey.\n", "# Deliberately separated from the FS-based training target so that AUC measures\n", "# generalisation, not self-correlation.\n", "inv_raw = (\n", " np.clip((slope - 15) / 18, 0, 1) * 0.55 + # steep slopes\n", " np.exp(-dist_fault / 1800) * 0.35 + # fault proximity\n", " RNG.normal(0, 0.05, N_TOTAL) # stochastic variability\n", ").clip(0, 1).astype('float32')\n", "thresh_inv = 0.28 + RNG.uniform(-0.08, 0.08, N_TOTAL).astype('float32')\n", "is_landslide = (inv_raw > thresh_inv).astype('int32')\n", "\n", "n_ls = is_landslide.sum()\n", "print(f'Landslide pixels : {n_ls} / {N_TOTAL} ({100*n_ls/N_TOTAL:.1f}%)')\n", "print(f'FS range (T50) : {fs_matrix[:,2].min():.2f}–{fs_matrix[:,2].max():.2f}')\n", "print(f'Target prob range : {target_prob.min():.3f}–{target_prob.max():.3f}')\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "83c6a5ce", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:22.957600Z", "iopub.status.busy": "2026-04-21T12:53:22.956569Z", "iopub.status.idle": "2026-04-21T12:53:24.454922Z", "shell.execute_reply": "2026-04-21T12:53:24.453858Z" } }, "outputs": [ { "data": { "image/png": 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6iSee6AwePDhimf/hD3+IepwAgMyIUyL9bk6aNGmbdTWWmD59ukmfN29e7W9QQUGBiTGi/TZrXBPuyiuvNL91q1evrn3t22+/dXw+n3P//ffXvqZ5NQ7R308br/HD2rVrzTb1uKLRGMnv95v18/PznUMPPdR5/PHHrcei5fTUU085WVlZzsaNG2tfP/bYY81vcLgDDjjAxFnh2/vmm29MGbzxxhuuMYdav369KfsHHnig9jWNGXXd77//3vX4PvroI7PO559/HlN8Fp4W6ZoJX+q/z7POOst59tlnnWnTpjkTJkxwOnbsaOKWNWvW1K6j5TV27Nht9qdx7gUXXGD+XVNT43Tv3r1OHDR79myzz//85z+erhk9Pxo3jR8/3vX9AwAyV7R7OF7vK+hvoW5n5cqV28REixYtMs///e9/m+dTp06tXUdjq+LiYnN/Ify3cMSIEZ5jrfqxg8ZaGsfcdNNN28Qh4fc5GnJvp/69Ka/3m44//ngTC4U7+OCD68QhscRLJ510Up1t6Xn47W9/G/X86nFojBF+T0XLIfyeXGgfRxxxxDb5zzjjDGfPPfesE2906NDBuf76663l1qdPH+t9JLc4Wr311lvOLbfcYh71WHU7GreGb+/JJ580+ZctW1Ynr8aB+vqHH37oum+3sgr9/fD000/XeT3SdZeXl+fceuutddbT8xF+3ek51OcffPBB7TplZWVOSUlJnc/BqFGjzGsrVqyofa28vNyct/DrTPO0adPG2bBhQ8T3pelXXXWV6/tGeqBnEtKW/r0bPtRJOO0yq10ytdWDtnDV7pxeaY29diXV/NoKVBdtvaEtNOsLb0Ggx6ItFEL70lbJn3zyibWFqw49oi1BtTVAeKtUbdXx9ddfW4dZiURbTmiL0VArlBBtxaqtH7QrbTTaukNbqoQWbVGhtMWEtkjVFi3aAqZr167m9Ujlkix6brSXl9tE1tp6Qrv3aosabXkUiZZtiL4XHb5FWzzreQ7R1j/a6rs+7Q5cvws+ACBzaYvD8N9MXbTns9JeLtqbRoey0N8YbQ0Y/ruprRQ1Vgj9zsZCh3zT2CG8N4m2mhw0aJBMnz69zrraKtQtXkpk/FCfDo+hrYh1qA09Bt2GxkRnnXVWnVhO59/RHudallpO2ptJYyEdWsSNlpv2QtbjDe/to7/fOryHngcbHXpY35u2eg159NFHzTxQ2jPMTSgGcItDotEWo/Wvl/pL+LA0SuNYfZ8aq+gcSNqqWof1+cc//hHTvrWlsV5r2vs61Ppb37/Gu7/97W89XTMa/2mPJ2IhAEC8vN5X0OkGwn9vQ3MUhu63fPrpp2YbGg+F6HMd9jWc9lzRnk133nmnzJkzJ+bj1WGFtWey9gQKpyPYJPPeTrT7TUpHYtF4MtRjWX+ftSe8vh5PvFS/h4yWuZd7aRqXau+Y8Hsqui2NGerHpZFGodHe0xon6jQUSoe2094yOlSejb7feGMy7fE0atQo86jHOn78eNOLTXv7N0acYxuNJ3TdlZeXbzNfaP0eZXoOtZzD5zLVvz3qx3Z6fep8T3qNhq4DvU+pvQLrXwfaky3UG6k+7ollBiqTkLb0R02HR4tEf2j1y1J/bIcNG2Z+KHfffXczjms0OqyLdrHVYfF0G/rFqj9i+kVen35ph9OxaUPracWHdkO1TXqsN250/NFQpVVo0W6x+uUe64+Y7lN16NChzuuh5+Fj1rrRG1FaVqFFh0jRMgjNI6U3xzRg0YoyFalckiW0L7du6dqVWseJtY2BHOmc2c5jON2vBpIAAIRu0If/Zuqif6TpcCRaaaJjwetQIfqbGRqiLvT7okPUKVucYPu9r/9br/S1+r/1kdaLtL2Gxg+R6JxFOma7DpehcZsO96LDpunExUorknQoGP3DWIfR0BsJOjxHtPhCj1dvilx55ZXbxFB6QyVSg5BINy6++OILcyw6rKA2SIl20yJaHBJN7969zc0x26Lr2AwcONDMHxE+xr0OwxtpPi4tp/AKR31/+l71Jo3O/6DnQuPe8OGAo10zxEIAgHjFcl8h0t/o4eu5VSTU/w3TIb10aHsdsk3natJ5knVYPK9C92TqzwNZfz+Jvrejot2n0Ia0etP/6aefNs815tQh30LDKscaL3m9L5LouFQbzGhso8Prhhq76Gvbb7+9db96bPHGZJHoMIVaXqHhfDW+UvVjrFDcHB5jxULvWWmFj41bA6b616Hb56D+enp96nzm9a8D/RzWvw6IA5E6M8MCCaQtFpYsWeI68bPS1hY6Xqv+saxjxOu4vDrOrOZz++LWHyO9maBj91966aW1r4cmn46F/hDrH+faetSN/vjoF7/bpILRJsqOtD1Vf8zT0KSM8f7Y6Q0wbeWjwUnohoPOLVWfBi7agjnSD20ihI5fJ9GMRM+bttTVVhhacRhq5ZQoul/t9QYAgI3GH1oxEN7LRFuKhgv9nmicEOvvlf4e1v+tD/3ea/wTLlqvpND2khE/hNPYS3vW6Fj/OqmyVopoOelNJR2jPmTu3LmeYix9Xxrb1Z+HKtRqMhod913HydcbFmP8d5QAALeUSURBVHpjSWOY+j2zbHGIW4MmG41P6l8H9WkL0VjHotdeaaFx/UP0xofeYAgf51+vM63Q0/esN7b0xsLQoUO32Z7tmiEWAgDEy+t9BS+00as2kKgvFLuE6P608You2ttD54HReETnUQ7vzWHbTyhG0jl83PaT6Hs7Xmivbo2DtDJJ553UR73nFepVkoh4qaFxaf0Y0i3G0Eo37dWuvYN0Pq3w3uO2/brdG0qEUAylMZZWdoXoc61o03mW4uElNg9dd3qNhzc8q1/Obp+D+utpWWkMOGbMmG3WrV8hFy0O1PgZ6Y2eSUg7WuGjFT36hac/ONFobbv+Ya4T1ZWWltZW7kRqZaFDqGnFUajVi9KJrF999dWYj1N/wPVGhQ4n4talWbtg6xe/7q9+y2Zdwo+jvkjHrz9wGsDUn4RRgzVdX1tIx0N742g5hv+oPPXUU9uspzcp9AZROO3dFY3XFi96o0dv+LhNdKhlrsGb3qDTGzb1A7yG0slBw4MIAADcfjfr/4bX/93UGEFbJurwam7qt8IN0SHzpk6dWqfBxrx580wvm9BwerFIdPygsc2W6XfqCg1hE6qI8VJOthhLY45I8VPPnj2t5RfeO0n3py1hddg7tyE9QkIxgFsckoxh7urT1rJ6rvfYY486w9LpMMXhN1T0XOqNuvpD1uh71ps0OtyPxko6zF0s51WHzCEWAgAk876CFxqbaMOJd999t/Y1fa6/h260Z5IOv6vq37ew5dFKm1AP8xDtdZ2oezsNoUPa6TB6Ogyu9vIKDXEXS7zklb4HvWdWn8ae2uslNIyumjJliolLvMalOhSynj8d7ljj4z/84Q9R82g8Em9MFolWxunQbzpMtdLKolAj9XDPPPOMiaGi3a+LVFZeaWWn3v/SnvvhtJzDaTyo5Rw+CtOGDRvM3wn1r09tsKWjN9W/DvQa90LvlWqPNuLA9EfPJDRr+mUV6vasX4jakkTnL9Jx9HXcW7cfP72ZosOm6I0B7RqrP0ra6lXXD3WV1S9Rbe2gQ9rp+PjaKkPT9cv4//7v/8xNFR3DV/+trVkitbSIRvPqmLn6xa2tX7Sb7FdffWX2pUON6Pi52nJEWwhoSxJtpbtx40bT80rnGXj44Yddt63Hr+Po69Bu2hpBWyvoctNNN5k5n7Tli87boOX317/+1QwzE2+vGj1ObcmjlXg6VrB2R9fusPXpD/4ll1wit9xyixkHWSt2dN1o9L1oEKgBh5aRDovjdqz77rtvnaFdIs2DEBoPVstdW/cmojeRnhdtgTJ69OgGbwsAkN70d1OH2dXWf/pHvP4e1v+jTmML/U259tprTbyjQ73p43vvvWduBOgfd/r7qHToN21Rqn9c6x98OlyJVkJpRYHOE6iVJTfeeKNpcGHrte1G/3BOZPyg8YnGCTqsjP5Bru9Le4nr9nbbbbfaGwtaTtpCWMep1z/Wddg1jX+8uOOOO0yMpbGeDm+r8YMOpaexhPa20fHetdJKW+VqrKexhTZE0lgr9Me/Hp+Wv/bQCQ2tYqPb0JhL4xCtwIlVrH98a4XPjz/+aN6Lnhed6+H22283wzeHN6i6+OKLzTA+eo1o62PthX/11Veb1+sPo6hj9GuMq/GZlkssdFhAFU+FJQAgM2hD2ueffz5i5Y/X+wpe6D0UnQ9JKx80vtDfe73no/cD6t8/0H3pzXmNd7Sxr8YBXnolhXp06O+p3tvRSiXdp/5+6u9zuIbc22kI3a/GaXp/ScugfnziJV7ySuNSrTDS2E3v92hZa2yjsag+12H39Nxqo15tzK3nPDSXaDQam2gsrBU3F110kSnraPTcasOneOhcSVouoYoUbUCu9xovv/zyOr3Pb775ZnON6X1EvcekFUk6X1e0KTTcysorPad6b03jPq1U0hEPtGxCDbNCPfv0fOs1efrpp5vrX68B7eFVXFxcZxhj7fGlFbfa0F7fo/7NoJWf+l40VtS/LaLRxkx6X9brZwfNmAM0U6NHj9YmrbVLUVGRs/POOzvDhg1zvv32223W79Gjh0lTK1ascM444wxnu+22c3Jzc5327ds7J554ovP999/Xrr9+/Xrn1FNPddq0aWO2f/bZZ5vXf/jhB+c3v/mNU1BQ4HTr1s254447zLEUFhbW5n3vvfdMns8//7zOMRx77LHOgQceWOe1Dz/80Dn44IPN9oqLi529997beeedd2rTKyoqnFtuucXZYYcdnJycHKddu3Zm/SeeeMJaPosXL3aOOuoop2XLluZY9BhDJk6caLaXnZ3tdO/e3RkzZoxTU1Nj3d6jjz5qtrNq1aqI6X/961+drl27mvdx6KGHmrLU9bV8QqqqqpyrrrrK6dChg1NSUuJcdNFFzuTJk816CxYsMOvooz5/7rnnavPNmTPH2X///U35aJoei5sXXnjBycvLc0pLS2tfi7T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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, 3, figsize=(17, 9))\n", "\n", "def plot_map(ax, data, title, cmap, vmin=None, vmax=None, label=''):\n", " data2d = data.reshape(N_ROWS, N_COLS)\n", " im = ax.imshow(data2d, origin='lower', cmap=cmap,\n", " extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX],\n", " vmin=vmin, vmax=vmax, aspect='auto')\n", " plt.colorbar(im, ax=ax, shrink=0.85, label=label)\n", " ax.set_title(title, fontsize=11)\n", " ax.set_xlabel('Longitude'); ax.set_ylabel('Latitude')\n", "\n", "plot_map(axes[0,0], elev, 'Digital Elevation Model (m)',\n", " 'terrain', 150, 2200, 'm')\n", "plot_map(axes[0,1], slope, 'Slope (deg)',\n", " 'YlOrRd', 0, 70, 'deg')\n", "plot_map(axes[0,2], lith.astype('float32'), 'Lithology',\n", " 'tab10', 0, 3, '0=granite,1=schist,2=clay,3=alluvium')\n", "\n", "plot_map(axes[1,0], dist_fault/1000, 'Distance to Fault (km)',\n", " 'RdYlGn', 0, 12, 'km')\n", "plot_map(axes[1,1], fs_matrix[:, 2], 'Factor of Safety (T=50yr)',\n", " 'RdYlGn', 0.3, 3.0, 'FS')\n", "\n", "# Inventory map\n", "ax = axes[1,2]\n", "ax.imshow(elev.reshape(N_ROWS, N_COLS), origin='lower', cmap='gray_r',\n", " extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX], aspect='auto', alpha=0.4)\n", "ls_lon = lon_flat[is_landslide == 1]\n", "ls_lat = lat_flat[is_landslide == 1]\n", "ax.scatter(ls_lon, ls_lat, s=6, c='red', alpha=0.7, label=f'Landslide ({len(ls_lon)})')\n", "ax.set_title('Landslide Inventory (T50 trigger)', fontsize=11)\n", "ax.set_xlabel('Longitude'); ax.set_ylabel('Latitude')\n", "ax.legend(fontsize=9)\n", "\n", "plt.suptitle('Section 1 — Study Area: Venezuelan Andes (synthetic 50×30 km watershed)',\n", " fontsize=13)\n", "plt.tight_layout(); plt.show()\n" ] }, { "cell_type": "markdown", "id": "ae197e3a", "metadata": {}, "source": [ "### Interpretation — Section 1: Study Area Overview\n", "\n", "**Panel (top-left) — Digital Elevation Model (DEM)**: the DEM shows two mountain\n", "massifs rising to approximately 2 100 m a.s.l. (metres above sea level) separated\n", "by a valley floor at ~200 m. Elevation is the primary topographic driver of all\n", "downstream conditioning factors: slope gradient, lithology (harder rocks outcrop at\n", "altitude), and orographic rainfall all derive from the DEM.\n", "\n", "**Panel (top-centre) — Slope**: slope angle (°) is the single most important\n", "mechanical trigger for shallow landslides. The steep flanks of both peaks\n", "(orange–red zones, > 25°) are the primary instability zones. The flat valley\n", "floor (green, < 5°) is stable under any realistic rainfall scenario. Note that\n", "slopes > 45° in natural terrain are rare and indicate rock cliffs rather than\n", "soil-covered hillslopes.\n", "\n", "**Panel (top-right) — Lithology**: four geological units define the study area.\n", "*Granite* (unit 0, hard, high elevation) resists failure even on steep slopes.\n", "*Schist* (unit 1, medium strength) is the dominant rock at mid-slope.\n", "*Clay shale* (unit 2, near fault zones, weakest) has low cohesion and friction\n", "angle — the most landslide-prone bedrock type.\n", "*Alluvium* (unit 3, valley floors) is susceptible to debris flows and channelised\n", "failures but is rarely mapped as classical rotational or translational slides.\n", "\n", "**Panel (bottom-left) — Distance to fault (km)**: fault zones shatter rock and\n", "create preferential pathways for groundwater infiltration, lowering effective\n", "cohesion. The three NE–SW faults are visible as green corridors (< 1 km distance)\n", "crossing the study area from south-west to north-east. Proximity to a fault is\n", "one of the strongest regional landslide conditioning factors in crystalline-rock\n", "terrains (Keefer, 1984).\n", "\n", "**Panel (bottom-centre) — Factor of Safety, FS (T=50 yr trigger)**: FS is the\n", "ratio of resisting forces (cohesion + friction) to driving forces (gravity component\n", "along the slope). **FS < 1** means failure is mechanically possible; **FS > 1**\n", "means the slope is stable. The FS here is computed using the *infinite-slope model*\n", "(see Section 0 header for the formula) for the T=50-year rainfall return period.\n", "Red pixels near the steep flanks are the zones the model should predict as\n", "high-susceptibility.\n", "\n", "**Panel (bottom-right) — Landslide inventory**: black dots mark pixels classified\n", "as landslides in the binary inventory. Observe that inventory points cluster\n", "near steep slopes AND close to fault lines — confirming that both topographic and\n", "structural controls combine to produce failures. In a real study, these dots would\n", "be derived from aerial photography or LiDAR change-detection." ] }, { "cell_type": "markdown", "id": "72f3db84", "metadata": {}, "source": [ "---\n", "\n", "## 2 — Feature Engineering & Dataset Construction\n", "\n", "### Feature design rationale\n", "\n", "| Input stream | Features | Scientific motivation |\n", "|-------------|----------|----------------------|\n", "| **Static** | slope, elevation, aspect, TWI, lithology, dist_fault, NDVI, ann_rainfall | Standard landslide conditioning factors (Fell et al., 2008) |\n", "| **Dynamic (depth profile)** | cohesion, friction angle, unit weight, water content, clay fraction, void ratio | Geomechanical properties vary with depth — the attention mechanism identifies the **critical failure plane** |\n", "| **Future (trigger)** | rainfall intensity, duration, antecedent wetness, seismic coefficient | Trigger parameters scale systematically with return period |\n", "| **Target** | P(failure \\| scenario) | Continuous probability target derived from FS; validated against binary inventory |\n", "\n", "### Why depth-profile-as-sequence is novel\n", "\n", "Traditional landslide ML flattens all layer features into a single 36-dimensional\n", "vector, losing all information about *which layer* is weak. Treating the 6 depth\n", "layers as a temporal sequence lets the encoder's attention mechanism assign higher\n", "weights to the layer most likely to be the **rupture surface** — a key output that\n", "practitioners cannot obtain from conventional models." ] }, { "cell_type": "code", "execution_count": 6, "id": "58cc1416", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:24.460634Z", "iopub.status.busy": "2026-04-21T12:53:24.459638Z", "iopub.status.idle": "2026-04-21T12:53:24.486375Z", "shell.execute_reply": "2026-04-21T12:53:24.485341Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_static : (1500, 8)\n", "X_dynamic : (1500, 6, 6)\n", "X_future : (1500, 5, 4)\n", "Y (probs) : (1500, 5, 1)\n", "inventory : (1500,) positives: 115\n" ] } ], "source": [ "# ── Normalise static features ─────────────────────────────────────────────────\n", "elev_n = ((elev - elev.mean()) / (elev.std() + 1e-8)).astype('float32')\n", "slope_n = ((slope - slope.mean()) / (slope.std() + 1e-8)).astype('float32')\n", "asp_sin = np.sin(np.deg2rad(aspect)).astype('float32')\n", "asp_cos = np.cos(np.deg2rad(aspect)).astype('float32')\n", "twi_n = ((twi - twi.mean()) / (twi.std() + 1e-8)).astype('float32')\n", "df_n = ((dist_fault - dist_fault.mean()) / (dist_fault.std() + 1e-8)).astype('float32')\n", "ndvi_n = ((ndvi - ndvi.mean()) / (ndvi.std() + 1e-8)).astype('float32')\n", "rain_n = ((ann_rain - ann_rain.mean()) / (ann_rain.std() + 1e-8)).astype('float32')\n", "\n", "X_static = np.stack([slope_n, elev_n, asp_sin, twi_n,\n", " lith.astype('float32')/3.0, df_n, ndvi_n, rain_n], axis=1)\n", "\n", "# ── Normalise dynamic (depth-profile) features ───────────────────────────────\n", "X_dyn = soil.copy()\n", "for feat_i in range(N_DYNAMIC):\n", " f_all = X_dyn[:, :, feat_i]\n", " mu, sg = float(f_all.mean()), float(f_all.std())\n", " X_dyn[:, :, feat_i] = ((f_all - mu) / (sg + 1e-8)).astype('float32')\n", "\n", "# ── Future features already normalised ───────────────────────────────────────\n", "X_future = future_feat.copy()\n", "\n", "# ── Target ───────────────────────────────────────────────────────────────────\n", "Y = target_prob.copy() # (N_TOTAL, HORIZON, 1)\n", "\n", "print('X_static :', X_static.shape)\n", "print('X_dynamic :', X_dyn.shape)\n", "print('X_future :', X_future.shape)\n", "print('Y (probs) :', Y.shape)\n", "print('inventory :', is_landslide.shape, ' positives:', is_landslide.sum())\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "54aeebf8", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:24.490656Z", "iopub.status.busy": "2026-04-21T12:53:24.489556Z", "iopub.status.idle": "2026-04-21T12:53:24.520233Z", "shell.execute_reply": "2026-04-21T12:53:24.517098Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train : 1200 pixels (95 landslide 7.9%)\n", "Test : 300 pixels (20 landslide 6.7%)\n" ] } ], "source": [ "# ── Spatial train/test split (block split to avoid spatial autocorrelation) ────\n", "# Test = northern block (top 20% of rows)\n", "# Motivaton: simulates applying a model trained in one sub-catchment to another\n", "\n", "row_idx = np.arange(N_TOTAL) // N_COLS # row (latitude) index for each pixel\n", "TEST_ROW_CUTOFF = int(N_ROWS * 0.80) # row 24 out of 0..29\n", "\n", "tr_m = row_idx < TEST_ROW_CUTOFF # southern 80%\n", "te_m = row_idx >= TEST_ROW_CUTOFF # northern 20%\n", "\n", "Xs_tr, Xd_tr, Xf_tr, Y_tr = X_static[tr_m], X_dyn[tr_m], X_future[tr_m], Y[tr_m]\n", "Xs_te, Xd_te, Xf_te, Y_te = X_static[te_m], X_dyn[te_m], X_future[te_m], Y[te_m]\n", "inv_tr = is_landslide[tr_m]\n", "inv_te = is_landslide[te_m]\n", "fs_te = fs_matrix[te_m]\n", "\n", "print(f'Train : {tr_m.sum()} pixels ({inv_tr.sum()} landslide '\n", " f'{100*inv_tr.mean():.1f}%)')\n", "print(f'Test : {te_m.sum()} pixels ({inv_te.sum()} landslide '\n", " f'{100*inv_te.mean():.1f}%)')\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "ff1c7306", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:24.524966Z", "iopub.status.busy": "2026-04-21T12:53:24.524966Z", "iopub.status.idle": "2026-04-21T12:53:25.873147Z", "shell.execute_reply": "2026-04-21T12:53:25.870884Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Spearman rho (feature vs binary inventory):\n", " slope : rho=+0.207 p=6.560e-16\n", " dist_fault : rho=-0.405 p=2.661e-60\n", " TWI : rho=+0.054 p=3.598e-02\n", " FS_T50 : rho=-0.234 p=4.088e-20\n" ] } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(17, 5))\n", "\n", "# ── Feature distributions ─────────────────────────────────────────────────────\n", "ax = axes[0]\n", "feat_names = ['slope', 'elevation', 'asp_sin', 'TWI', 'lithology',\n", " 'dist_fault', 'NDVI', 'ann_rain']\n", "feat_vals = [slope, elev, asp_sin, twi, lith.astype('float'),\n", " dist_fault/1000, ndvi, ann_rain/1000]\n", "bp = ax.boxplot([f[is_landslide==1] for f in feat_vals], patch_artist=True,\n", " positions=np.arange(len(feat_vals))*2,\n", " boxprops=dict(facecolor='#e74c3c', alpha=0.7))\n", "bp2 = ax.boxplot([f[is_landslide==0] for f in feat_vals], patch_artist=True,\n", " positions=np.arange(len(feat_vals))*2 + 0.7,\n", " boxprops=dict(facecolor='#3498db', alpha=0.7))\n", "ax.set_xticks(np.arange(len(feat_names))*2 + 0.35)\n", "ax.set_xticklabels(feat_names, rotation=40, ha='right', fontsize=8)\n", "ax.set_title('(A) Static Feature Distributions\\n(red=landslide, blue=stable)',\n", " fontsize=10)\n", "ax.grid(True, alpha=0.25, axis='y')\n", "ax.legend([bp['boxes'][0], bp2['boxes'][0]], ['Landslide', 'Stable'], fontsize=9)\n", "\n", "# ── Depth-profile average by class ───────────────────────────────────────────\n", "ax = axes[1]\n", "dyn_feat_names = ['cohesion', 'friction', 'unit_wt', 'water_ct', 'clay_fr', 'void_r']\n", "for feat_i, fname in enumerate(dyn_feat_names):\n", " vals_ls = soil[is_landslide==1, :, feat_i].mean(axis=0)\n", " vals_stb = soil[is_landslide==0, :, feat_i].mean(axis=0)\n", " if feat_i == 0: # only plot cohesion and friction for clarity\n", " ax.plot(LAYER_DEPTHS, vals_ls, 'o-', lw=2, color='#e74c3c',\n", " label=f'{fname} — LS')\n", " ax.plot(LAYER_DEPTHS, vals_stb, 's--', lw=1.5, color='#3498db',\n", " label=f'{fname} — Stable')\n", " elif feat_i == 1:\n", " ax2 = ax.twinx()\n", " ax2.plot(LAYER_DEPTHS, vals_ls, '^-', lw=2, color='#e74c3c', alpha=0.6,\n", " label=f'{fname} — LS')\n", " ax2.plot(LAYER_DEPTHS, vals_stb, 'v--', lw=1.5, color='#3498db', alpha=0.6,\n", " label=f'{fname} — Stable')\n", " ax2.set_ylabel('Friction angle (deg)', fontsize=9)\n", "ax.set_xlabel('Depth (m)'); ax.set_ylabel('Cohesion (kPa)')\n", "ax.set_title('(B) Cohesion & Friction Depth Profile\\n(LS vs Stable classes)',\n", " fontsize=10)\n", "ax.legend(loc='upper left', fontsize=8); ax.grid(True, alpha=0.25)\n", "\n", "# ── FS histogram with inventory overlay ──────────────────────────────────────\n", "ax = axes[2]\n", "fs_50 = fs_matrix[:, 2]\n", "ax.hist(fs_50[is_landslide==0], bins=40, color='#3498db', alpha=0.6,\n", " density=True, label='Stable')\n", "ax.hist(fs_50[is_landslide==1], bins=40, color='#e74c3c', alpha=0.6,\n", " density=True, label='Landslide')\n", "ax.axvline(1.0, color='black', lw=2, linestyle='--', label='FS = 1.0 (failure)')\n", "ax.set_xlabel('Factor of Safety (T=50yr)')\n", "ax.set_ylabel('Density')\n", "ax.set_title('(C) FS Distribution by Inventory Class\\n(T=50yr trigger scenario)',\n", " fontsize=10)\n", "ax.legend(fontsize=9); ax.grid(True, alpha=0.25)\n", "\n", "plt.suptitle('Section 2 — Feature Engineering: Class Separation', fontsize=13)\n", "plt.tight_layout(); plt.show()\n", "\n", "# Spearman correlations of features with inventory\n", "print('Spearman rho (feature vs binary inventory):')\n", "for fname, fv in [('slope', slope), ('dist_fault', dist_fault),\n", " ('TWI', twi), ('FS_T50', fs_50)]:\n", " rho, p = spearmanr(fv, is_landslide)\n", " print(f' {fname:14s}: rho={rho:+.3f} p={p:.3e}')\n" ] }, { "cell_type": "markdown", "id": "79afec3b", "metadata": {}, "source": [ "### Interpretation — Section 2: Feature Class Separation\n", "\n", "**Panel (A) — Static feature distributions (boxplots)**:\n", "Red boxes = landslide pixels; blue boxes = stable pixels. Key observations:\n", "\n", "- **Slope**: landslide pixels have clearly higher median slope than stable pixels —\n", " confirming slope is a primary discriminator.\n", "- **TWI** (Topographic Wetness Index = ln[upslope drainage area / tan(slope)]): a\n", " higher TWI indicates wetter locations (channel heads, concave hollows) where\n", " pore-water pressure builds up during storms. Landslide pixels tend to have\n", " moderately high TWI, reflecting their position on slopes that receive convergent\n", " drainage.\n", "- **NDVI** (Normalized Difference Vegetation Index, range –1 to +1): positive NDVI\n", " values indicate live green vegetation. Landslide pixels typically have *lower*\n", " NDVI because failures remove vegetative cover and expose bare soil — this acts\n", " as a *negative indicator* of stability (higher NDVI → more root cohesion → more\n", " stable).\n", "- **dist_fault**: landslide pixels are concentrated at smaller fault distances,\n", " consistent with weaker rock near fault zones.\n", "\n", "**Panel (B) — Cohesion and friction depth profile**:\n", "The plot shows how mean cohesion (kPa, left axis) and friction angle (°, right axis)\n", "vary with depth for landslide vs. stable pixels. Landslide pixels have systematically\n", "*lower* cohesion and *lower* friction angle than stable pixels — the geomechanical\n", "signature of unstable terrain. The depth trend (cohesion increasing with depth,\n", "friction slightly decreasing) reflects realistic soil consolidation behaviour.\n", "The attention encoder will learn to assign higher weight to the depth layer whose\n", "cohesion/friction contrast between classes is largest — that is the predicted\n", "**critical failure plane**.\n", "\n", "**Panel (C) — FS distribution by inventory class**:\n", "The histogram shows the Factor of Safety distribution for stable pixels (blue) and\n", "landslide pixels (red) under the T=50 yr trigger. The vertical dashed line at\n", "FS = 1.0 marks the theoretical failure boundary. In a perfectly physics-consistent\n", "dataset, all red bars would be left of FS = 1; in practice, the inventory also\n", "includes pixels with FS > 1 (failures driven by unmapped micro-topography, pipes,\n", "or tree-throw) and excludes some FS < 1 pixels (slopes that are unstable but have\n", "not yet failed due to root reinforcement or lack of a triggering event). The\n", "**Spearman correlation** (ρ) printed below quantifies how strongly each feature\n", "ranks with the binary label; a negative ρ for FS confirms that lower FS → higher\n", "landslide probability." ] }, { "cell_type": "markdown", "id": "d29a84fb", "metadata": {}, "source": [ "---\n", "\n", "## 3 — Single BaseAttentive Model\n", "\n", "### Architecture design for geological sequences\n", "\n", "The depth-profile encoder reads 6 depth layers sequentially (surface → bedrock).\n", "Cross-attention in the decoder allows each trigger scenario (horizon step) to attend\n", "to the **most geomechanically relevant** depth layers:\n", "\n", "- **Shallow failures** (T10, T25): expected attention concentration on layers 1–3\n", " (0–2 m depth) where saturated residual soil sits above bedrock.\n", "- **Deep-seated failures** (T100, T200): expected attention shift to layers 4–6\n", " (3–10 m) where deeper clay-rich interfaces promote rotational sliding.\n", "\n", "The `hierarchical` decoder additionally captures multi-scale interactions between\n", "the surface (rainfall infiltration interface) and deeper impermeable layers." ] }, { "cell_type": "code", "execution_count": 9, "id": "bc1689c0", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:53:25.879532Z", "iopub.status.busy": "2026-04-21T12:53:25.878513Z", "iopub.status.idle": "2026-04-21T12:54:26.244815Z", "shell.execute_reply": "2026-04-21T12:54:26.242564Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters : 349,128\n", "Train time : 54.2 s (stopped at epoch 20)\n", "Best val MSE : 0.00150\n", "\n", "Test AUC-ROC : 0.9462\n", "Test AUC-PR : 0.6658\n" ] } ], "source": [ "# ── Build and train single BaseAttentive model ────────────────────────────────\n", "model_ba = BaseAttentive(\n", " static_input_dim=N_STATIC, dynamic_input_dim=N_DYNAMIC,\n", " future_input_dim=N_FUTURE, output_dim=OUTPUT_DIM,\n", " forecast_horizon=HORIZON, objective='hybrid',\n", " architecture_config={'decoder_attention_stack': ['cross', 'hierarchical']},\n", " embed_dim=32, num_heads=4, dropout_rate=0.15,\n", " name='ba_landslide',\n", ")\n", "_ = model_ba([Xs_tr[:4], Xd_tr[:4], Xf_tr[:4]]) # build\n", "model_ba.compile(optimizer=keras.optimizers.Adam(1e-3), loss='mse', metrics=['mae'])\n", "print(f'Parameters : {model_ba.count_params():,}')\n", "\n", "t0 = time.perf_counter()\n", "history_ba = model_ba.fit(\n", " [Xs_tr, Xd_tr, Xf_tr], Y_tr,\n", " epochs=EPOCHS_MAIN, batch_size=BATCH_SIZE,\n", " validation_split=0.15,\n", " callbacks=[keras.callbacks.EarlyStopping(patience=PATIENCE,\n", " restore_best_weights=True)],\n", " verbose=0,\n", ")\n", "train_time_ba = time.perf_counter() - t0\n", "print(f'Train time : {train_time_ba:.1f} s '\n", " f'(stopped at epoch {len(history_ba.history[\"loss\"])})')\n", "print(f'Best val MSE : {min(history_ba.history[\"val_loss\"]):.5f}')\n", "\n", "# Predict probabilities (T50 = horizon index 2 → primary susceptibility)\n", "Y_pred_ba = model_ba.predict([Xs_te, Xd_te, Xf_te], verbose=0) # (N_test, 5, 1)\n", "prob_ba = np.clip(Y_pred_ba[:, 2, 0], 0, 1) # T50 susceptibility index\n", "auc_ba = roc_auc_score(inv_te, prob_ba)\n", "ap_ba = average_precision_score(inv_te, prob_ba)\n", "print(f'\\nTest AUC-ROC : {auc_ba:.4f}')\n", "print(f'Test AUC-PR : {ap_ba:.4f}')\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "f4e7fff3", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:54:26.247651Z", "iopub.status.busy": "2026-04-21T12:54:26.247651Z", "iopub.status.idle": "2026-04-21T12:54:26.585790Z", "shell.execute_reply": "2026-04-21T12:54:26.584612Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " Stable 0.99 0.88 0.93 280\n", " Landslide 0.35 0.90 0.51 20\n", "\n", " accuracy 0.88 300\n", " macro avg 0.67 0.89 0.72 300\n", "weighted avg 0.95 0.88 0.91 300\n", "\n" ] } ], "source": [ "fpr_ba, tpr_ba, thr_ba = roc_curve(inv_te, prob_ba)\n", "prec_ba, rec_ba, _ = precision_recall_curve(inv_te, prob_ba)\n", "# Optimal threshold (Youden J) — clamp to [0.05, 0.95] so the\n", "# model never collapses all predictions to one class on short runs\n", "j_idx = np.argmax(tpr_ba - fpr_ba)\n", "opt_thr = float(np.clip(thr_ba[j_idx], 0.05, 0.95))\n", "pred_cls_ba = (prob_ba >= opt_thr).astype(int)\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n", "\n", "# ── (A) ROC ────────────────────────────────────────────────────────────────────\n", "ax = axes[0]\n", "ax.plot(fpr_ba, tpr_ba, lw=2.5, color='#3498db',\n", " label=f'BA-Cross+Hier AUC={auc_ba:.3f}')\n", "ax.plot([0,1],[0,1], 'k--', lw=1, label='Random')\n", "ax.scatter(fpr_ba[j_idx], tpr_ba[j_idx], s=120, color='red', zorder=5,\n", " label=f'Optimal thr={opt_thr:.2f}')\n", "ax.set_xlabel('False Positive Rate'); ax.set_ylabel('True Positive Rate')\n", "ax.set_title('(A) ROC Curve', fontsize=11)\n", "ax.legend(fontsize=9); ax.grid(True, alpha=0.3)\n", "\n", "# ── (B) Precision-Recall ──────────────────────────────────────────────────────\n", "ax = axes[1]\n", "ax.step(rec_ba, prec_ba, lw=2.5, color='#2ecc71', where='post',\n", " label=f'AP = {ap_ba:.3f}')\n", "ax.set_xlabel('Recall'); ax.set_ylabel('Precision')\n", "ax.set_title('(B) Precision-Recall Curve', fontsize=11)\n", "ax.axhline(inv_te.mean(), color='gray', lw=1, linestyle='--',\n", " label=f'Baseline (PR = {inv_te.mean():.2f})')\n", "ax.legend(fontsize=9); ax.grid(True, alpha=0.3)\n", "\n", "# ── (C) Confusion matrix ──────────────────────────────────────────────────────\n", "ax = axes[2]\n", "cm_ba = confusion_matrix(inv_te, pred_cls_ba, labels=[0, 1])\n", "im = ax.imshow(cm_ba, cmap='Blues', aspect='auto')\n", "for i in range(2):\n", " for j in range(2):\n", " ax.text(j, i, str(cm_ba[i,j]), ha='center', va='center',\n", " fontsize=16, fontweight='bold')\n", "ax.set_xticks([0,1]); ax.set_yticks([0,1])\n", "ax.set_xticklabels(['Predicted Stable', 'Predicted LS'], fontsize=9)\n", "ax.set_yticklabels(['Actual Stable', 'Actual LS'], fontsize=9)\n", "ax.set_title(f'(C) Confusion Matrix (thr={opt_thr:.2f})', fontsize=11)\n", "plt.colorbar(im, ax=ax, shrink=0.8)\n", "\n", "plt.suptitle('Section 3 — Single BA Model: Classification Performance', fontsize=13)\n", "plt.tight_layout(); plt.show()\n", "\n", "print(classification_report(inv_te, pred_cls_ba,\n", " labels=[0, 1],\n", " target_names=['Stable', 'Landslide']))\n" ] }, { "cell_type": "markdown", "id": "e77ebff6", "metadata": {}, "source": [ "### Interpretation — Section 3: Classification Performance\n", "\n", "**Panel (A) — ROC Curve** (Receiver Operating Characteristic):\n", "The ROC curve plots the **True Positive Rate** (TPR = correctly predicted landslides\n", "/ all actual landslides) against the **False Positive Rate** (FPR = falsely predicted\n", "landslides / all actual stable pixels) as the decision threshold varies from 0 to 1.\n", "A perfect classifier hugs the top-left corner (TPR = 1, FPR = 0). The diagonal\n", "dashed line represents a random classifier (AUC = 0.5).\n", "The **AUC-ROC** (Area Under the ROC Curve) summarises performance in a single number:\n", "values above 0.70 are considered acceptable for susceptibility mapping, above 0.80\n", "is good, and above 0.90 is excellent (Hosmer & Lemeshow, 2000).\n", "The red dot marks the **optimal threshold** selected by Youden's J statistic\n", "(J = TPR − FPR, maximised), which provides the best trade-off between sensitivity\n", "and specificity for this dataset.\n", "\n", "**Panel (B) — Precision–Recall (PR) Curve**:\n", "For *imbalanced* datasets (few landslide pixels relative to stable pixels), the PR\n", "curve is more informative than the ROC curve.\n", "- **Precision** = predicted landslides that are truly landslide / all predicted\n", " landslides (measures how often the model is *right* when it flags a pixel).\n", "- **Recall** = correctly predicted landslides / all actual landslides (measures how\n", " many real landslides the model *finds*).\n", "The gray horizontal line is the **baseline precision** equal to the landslide\n", "prevalence in the test set — a random classifier would match this line.\n", "The **AP** (Average Precision, area under the PR curve) penalises methods that\n", "achieve high recall only by generating many false alarms.\n", "\n", "**Panel (C) — Confusion Matrix** (at the Youden optimal threshold):\n", "The four cells show: True Negatives (top-left), False Positives (top-right),\n", "False Negatives (bottom-left), True Positives (bottom-right).\n", "A good model maximises the diagonal (TN + TP) while minimising off-diagonal cells.\n", "For hazard applications, **False Negatives** (missed landslides) are more dangerous\n", "than False Positives (unnecessary evacuations), so a slightly lower threshold (higher\n", "recall) is often preferred in practice." ] }, { "cell_type": "code", "execution_count": 11, "id": "e7d29628", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:54:26.589107Z", "iopub.status.busy": "2026-04-21T12:54:26.589107Z", "iopub.status.idle": "2026-04-21T12:54:27.146909Z", "shell.execute_reply": "2026-04-21T12:54:27.146909Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ── Susceptibility map (all pixels) ───────────────────────────────────────────\n", "Y_pred_all = model_ba.predict([X_static, X_dyn, X_future], verbose=0)\n", "susc_ba = np.clip(Y_pred_all[:, 2, 0], 0, 1) # T50 primary scenario\n", "\n", "# Classify into 5 classes\n", "def classify_susceptibility(probs, bounds=SUSC_BOUNDS):\n", " classes = np.zeros(len(probs), dtype=int)\n", " for cls_i in range(len(bounds)-1):\n", " mask = (probs > bounds[cls_i]) & (probs <= bounds[cls_i+1])\n", " classes[mask] = cls_i\n", " return classes\n", "\n", "cls_ba = classify_susceptibility(susc_ba)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n", "\n", "# Susceptibility map\n", "ax = axes[0]\n", "cmap_susc = mcolors.ListedColormap(SUSC_COLORS)\n", "norm_susc = mcolors.BoundaryNorm(SUSC_BOUNDS, len(SUSC_COLORS))\n", "im = ax.imshow(susc_ba.reshape(N_ROWS, N_COLS), origin='lower',\n", " cmap='RdYlGn_r', vmin=0, vmax=1,\n", " extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX], aspect='auto')\n", "ax.scatter(lon_flat[is_landslide==1], lat_flat[is_landslide==1],\n", " s=5, c='black', alpha=0.6, label='Inventory')\n", "plt.colorbar(im, ax=ax, label='P(failure | T50)')\n", "ax.set_title('(A) Susceptibility Map (BA single, T50 scenario)', fontsize=11)\n", "ax.set_xlabel('Longitude'); ax.set_ylabel('Latitude')\n", "ax.legend(fontsize=9)\n", "\n", "# Class distribution\n", "ax = axes[1]\n", "class_counts = np.bincount(cls_ba, minlength=5)\n", "bars = ax.bar(SUSC_LABELS, class_counts / N_TOTAL * 100,\n", " color=SUSC_COLORS, edgecolor='white')\n", "for bar, cnt in zip(bars, class_counts):\n", " ax.text(bar.get_x() + bar.get_width()/2,\n", " bar.get_height() + 0.3,\n", " f'{cnt}\\n({cnt/N_TOTAL*100:.1f}%)',\n", " ha='center', fontsize=8)\n", "ax.set_ylabel('% of study area'); ax.set_ylim(0, 50)\n", "ax.set_title('(B) Susceptibility Class Distribution', fontsize=11)\n", "ax.grid(True, alpha=0.3, axis='y')\n", "\n", "plt.suptitle('Section 3 — Susceptibility Map: Single BA Model', fontsize=13)\n", "plt.tight_layout(); plt.show()\n" ] }, { "cell_type": "markdown", "id": "f7b9c138", "metadata": {}, "source": [ "### Interpretation — Section 3: Susceptibility Map\n", "\n", "**Panel (A) — Continuous susceptibility map**:\n", "The colour scale (green → yellow → red) represents the predicted failure probability\n", "P(failure | T50 trigger), where T50 is the 50-year return-period rainfall scenario.\n", "Black dots are inventory landslide locations. A well-performing model should assign\n", "high probabilities (red) in the regions where black dots cluster — check that the\n", "red zones overlap with the dot clusters near the steep slopes and fault corridors.\n", "Note that the model predicts *continuous* probabilities, not just a binary mask;\n", "this is essential for risk-quantitative frameworks (e.g., risk = P(failure) ×\n", "consequence × exposure).\n", "\n", "**Panel (B) — Susceptibility class distribution**:\n", "The five classes follow the standard IUGS (International Union of Geological Sciences)\n", "susceptibility terminology. The bar chart shows what fraction of the study area falls\n", "into each class. A good susceptibility map should NOT classify the entire study area\n", "as \"high\" (that would have no discriminative value for land-use planners); typically,\n", "\"High\" + \"Very High\" classes should cover 15–30% of mountainous terrain.\n", "If the Very High class dominates, the model threshold may be set too low; if only\n", "Very Low dominates, the model may be under-predicting." ] }, { "cell_type": "code", "execution_count": 12, "id": "f880134f", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:54:27.150234Z", "iopub.status.busy": "2026-04-21T12:54:27.150234Z", "iopub.status.idle": "2026-04-21T12:54:27.884008Z", "shell.execute_reply": "2026-04-21T12:54:27.882788Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Most important static feature : dist_fault\n", "Critical failure-plane depth : 1-2m\n", "Most influential trigger : T200 yr\n" ] } ], "source": [ "# ── VSN feature importance via gradient saliency ──────────────────────────────\n", "N_SAL = min(128, TRAIN_SIZE)\n", "\n", "xs_v = tf.Variable(Xs_tr[:N_SAL])\n", "xd_v = tf.Variable(Xd_tr[:N_SAL])\n", "xf_v = tf.Variable(Xf_tr[:N_SAL])\n", "\n", "with tf.GradientTape() as tape:\n", " pred = model_ba([xs_v, xd_v, xf_v], training=False)\n", " scalar = tf.reduce_mean(pred[:, 2, 0]) # T50 output\n", "\n", "g_s, g_d, g_f = tape.gradient(scalar, [xs_v, xd_v, xf_v])\n", "\n", "sal_static = tf.abs(g_s).numpy().mean(axis=0) # (N_STATIC,)\n", "sal_dynamic = tf.abs(g_d).numpy() # (N_SAL, 6, N_DYN)\n", "sal_layer = sal_dynamic.mean(axis=(0, 2)) # (6,) layer importance\n", "sal_dyn_feat= sal_dynamic.mean(axis=(0, 1)) # (N_DYN,)\n", "sal_future = tf.abs(g_f).numpy().mean(axis=0) # (5, N_FUTURE)\n", "sal_scen = sal_future.mean(axis=1) # (5,) scenario importance\n", "\n", "STATIC_NAMES = ['slope', 'elevation', 'asp_sin', 'TWI',\n", " 'lithology', 'dist_fault', 'NDVI', 'ann_rain']\n", "DYN_FEAT_NAMES= ['cohesion', 'friction', 'unit_wt',\n", " 'water_ct', 'clay_frac', 'void_ratio']\n", "FUTURE_NAMES = ['rain_int', 'duration', 'antec_wet', 'seismic']\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(17, 5))\n", "\n", "# (A) Static feature importance\n", "ax = axes[0]\n", "order_s = np.argsort(sal_static)\n", "ax.barh([STATIC_NAMES[i] for i in order_s], sal_static[order_s],\n", " color='#3498db', edgecolor='white')\n", "ax.set_title('(A) Static Feature Importance\\n(gradient saliency)', fontsize=11)\n", "ax.set_xlabel('Mean |gradient|'); ax.grid(True, alpha=0.3, axis='x')\n", "\n", "# (B) CRITICAL FAILURE PLANE: depth-layer saliency\n", "ax = axes[1]\n", "colors_lyr = plt.cm.hot_r(np.linspace(0.2, 0.8, LOOKBACK))\n", "bars = ax.bar(LAYER_LABELS, sal_layer, color=colors_lyr, edgecolor='white')\n", "best_layer = int(np.argmax(sal_layer))\n", "bars[best_layer].set_edgecolor('red'); bars[best_layer].set_linewidth(3)\n", "ax.set_title('(B) Critical Layer — Failure Plane Depth\\n(red border = most critical layer)',\n", " fontsize=11)\n", "ax.set_ylabel('Mean |gradient|'); ax.grid(True, alpha=0.3, axis='y')\n", "ax.set_xlabel('Depth layer')\n", "ax.text(best_layer, sal_layer[best_layer]*1.02,\n", " f'CRITICAL\\n{LAYER_LABELS[best_layer]}',\n", " ha='center', color='red', fontsize=8, fontweight='bold')\n", "\n", "# (C) Trigger scenario importance\n", "ax = axes[2]\n", "tp_labels = [f'T{t}yr' for t in RETURN_PERIODS]\n", "ax.plot(tp_labels, sal_scen, 'o-', lw=2.5, color='#e74c3c', markersize=9)\n", "ax.fill_between(range(HORIZON), sal_scen, alpha=0.2, color='#e74c3c')\n", "ax.set_title('(C) Trigger Scenario Importance\\n(cross-attention to return periods)',\n", " fontsize=11)\n", "ax.set_ylabel('Mean |gradient|'); ax.grid(True, alpha=0.3)\n", "\n", "plt.suptitle('Section 3 — Gradient Saliency: Feature & Layer Importance', fontsize=13)\n", "plt.tight_layout(); plt.show()\n", "\n", "print(f'Most important static feature : {STATIC_NAMES[np.argmax(sal_static)]}')\n", "print(f'Critical failure-plane depth : {LAYER_LABELS[best_layer]}')\n", "print(f'Most influential trigger : T{RETURN_PERIODS[int(np.argmax(sal_scen))]} yr')\n" ] }, { "cell_type": "markdown", "id": "1c623c52", "metadata": {}, "source": [ "### Interpreting Feature Importance\n", "\n", "**(A) Static features**: slope gradient and distance to fault typically rank highest,\n", "consistent with the geomorphological literature. Elevation is secondary because it\n", "acts as a proxy for climate (higher elevations receive more orographic rainfall) rather\n", "than a direct mechanical control. NDVI is a negative indicator — dense vegetation\n", "increases root cohesion and reduces surface runoff, stabilising shallow slopes.\n", "\n", "**(B) Critical failure plane** (the key novel result): the red-bordered bar identifies\n", "the depth layer that the model assigns the highest attention weight — this is the\n", "predicted **rupture interface** (the soil layer where shear stress first exceeds\n", "shear strength). In the field, this can be validated by correlating with observed\n", "failure-scar depths from aerial photography or LiDAR.\n", "\n", "**(C) Trigger scenario importance**: the gradient along the five return-period steps\n", "reveals whether the model's predictions are dominated by moderate-frequency events\n", "(T10–T25, short-duration saturation) or rare extreme events (T100–T200, combined\n", "rainfall + seismic loading). A flat curve implies the model relies on static\n", "conditioning rather than trigger intensity — which may suggest over-fitting to\n", "geomorphological proxies rather than genuine mechanistic relationships." ] }, { "cell_type": "markdown", "id": "570e274e", "metadata": {}, "source": [ "---\n", "\n", "## 4 — Ensemble BaseAttentive: Uncertainty Quantification\n", "\n", "### Epistemic uncertainty in susceptibility mapping\n", "\n", "A single model's susceptibility estimate conflates two sources of uncertainty:\n", "- **Aleatoric**: irreducible noise in the data (measurement errors, unknown micro-factors)\n", "- **Epistemic**: uncertainty in model weights due to limited training data\n", "\n", "Ensemble methods quantify **epistemic uncertainty** by training multiple models\n", "with different architectures or initialisations and measuring the disagreement between\n", "predictions. Pixels where the ensemble disagrees strongly are those where additional\n", "field data collection would most efficiently reduce uncertainty — a practical guide\n", "for prioritising field campaigns.\n", "\n", "### Three-member ensemble\n", "\n", "| Member | Decoder stack | Distinct strength |\n", "|--------|--------------|------------------|\n", "| **BA-Cross** | `['cross']` | Future trigger features (rainfall forecast) |\n", "| **BA-Hier** | `['hierarchical']` | Multi-scale depth-layer interactions |\n", "| **BA-Cross+Hier** | `['cross','hierarchical']` | Combined — used as the reference single model |" ] }, { "cell_type": "code", "execution_count": 13, "id": "b0b1de52", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:54:27.886944Z", "iopub.status.busy": "2026-04-21T12:54:27.886944Z", "iopub.status.idle": "2026-04-21T12:56:39.713777Z", "shell.execute_reply": "2026-04-21T12:56:39.712568Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BA-Cross AUC=0.9248 epochs=20\n", "BA-Hier AUC=0.8941 epochs=11\n", "BA-Cross+Hier AUC=0.9212 epochs=20\n", "\n", "Ensemble AUC : 0.9534\n" ] } ], "source": [ "ENS_CONFIGS = [\n", " dict(name='BA-Cross', stack=['cross'], embed=32, heads=4),\n", " dict(name='BA-Hier', stack=['hierarchical'], embed=32, heads=4),\n", " dict(name='BA-Cross+Hier', stack=['cross','hierarchical'], embed=32, heads=4),\n", "]\n", "\n", "ens_preds_all = [] # (3, N_TOTAL, 5) — predictions for every pixel\n", "ens_preds_te = [] # (3, N_test, 5) — for evaluation\n", "ens_histories = {}\n", "\n", "for cfg in ENS_CONFIGS:\n", " safe = cfg['name'].lower().replace('+','p').replace('-','_')\n", " m = BaseAttentive(\n", " static_input_dim=N_STATIC, dynamic_input_dim=N_DYNAMIC,\n", " future_input_dim=N_FUTURE, output_dim=OUTPUT_DIM,\n", " forecast_horizon=HORIZON, objective='hybrid',\n", " architecture_config={'decoder_attention_stack': cfg['stack']},\n", " embed_dim=cfg['embed'], num_heads=cfg['heads'],\n", " dropout_rate=0.15, name=f'ens_{safe}',\n", " )\n", " _ = m([Xs_tr[:4], Xd_tr[:4], Xf_tr[:4]])\n", " m.compile(optimizer=keras.optimizers.Adam(1e-3), loss='mse')\n", " hist = m.fit(\n", " [Xs_tr, Xd_tr, Xf_tr], Y_tr,\n", " epochs=EPOCHS_MAIN, batch_size=BATCH_SIZE,\n", " validation_split=0.15,\n", " callbacks=[keras.callbacks.EarlyStopping(patience=PATIENCE,\n", " restore_best_weights=True)],\n", " verbose=0,\n", " )\n", " all_pred = np.clip(m.predict([X_static, X_dyn, X_future], verbose=0)[:,:,0], 0, 1)\n", " te_pred = np.clip(m.predict([Xs_te, Xd_te, Xf_te], verbose=0)[:,:,0], 0, 1)\n", " ens_preds_all.append(all_pred)\n", " ens_preds_te.append(te_pred)\n", " ens_histories[cfg['name']] = hist.history\n", " print(f'{cfg[\"name\"]:18s} '\n", " f'AUC={roc_auc_score(inv_te, te_pred[:,2]):.4f} '\n", " f'epochs={len(hist.history[\"loss\"])}')\n", "\n", "ens_preds_all = np.array(ens_preds_all) # (3, N_TOTAL, 5)\n", "ens_preds_te = np.array(ens_preds_te) # (3, N_test, 5)\n", "\n", "# Ensemble statistics\n", "susc_ens_mean = ens_preds_all[:, :, 2].mean(axis=0) # (N_TOTAL,) mean susceptibility\n", "susc_ens_std = ens_preds_all[:, :, 2].std(axis=0) # (N_TOTAL,) epistemic uncertainty\n", "auc_ens = roc_auc_score(inv_te, ens_preds_te[:, :, 2].mean(axis=0))\n", "print(f'\\nEnsemble AUC : {auc_ens:.4f}')\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "51d28c8b", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:56:39.716840Z", "iopub.status.busy": "2026-04-21T12:56:39.716840Z", "iopub.status.idle": "2026-04-21T12:56:40.229645Z", "shell.execute_reply": "2026-04-21T12:56:40.227088Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "High-uncertainty pixels (std > 0.15) : 0 (0.0% of study area)\n", "Perfect agreement (all 3 agree) : 1500 (100.0%)\n" ] } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(17, 6))\n", "\n", "plot_map(axes[0], susc_ens_mean,\n", " '(A) Ensemble Mean Susceptibility (T50)', 'RdYlGn_r', 0, 1,\n", " 'P(failure)')\n", "axes[0].scatter(lon_flat[is_landslide==1], lat_flat[is_landslide==1],\n", " s=4, c='black', alpha=0.6, label='Inventory')\n", "axes[0].legend(fontsize=9)\n", "\n", "plot_map(axes[1], susc_ens_std,\n", " '(B) Epistemic Uncertainty (std of ensemble)', 'Purples', 0, 0.25,\n", " 'Std dev')\n", "# Mark high-uncertainty zones\n", "hi_unc = susc_ens_std > np.percentile(susc_ens_std, 90)\n", "axes[1].scatter(lon_flat[hi_unc], lat_flat[hi_unc],\n", " s=4, c='red', alpha=0.3, label='High uncertainty (top 10%)')\n", "axes[1].legend(fontsize=9)\n", "\n", "# Agreement map: fraction of ensemble members classifying as hazardous (>0.4)\n", "agree = (ens_preds_all[:, :, 2] > 0.40).mean(axis=0)\n", "plot_map(axes[2], agree,\n", " '(C) Ensemble Agreement\\n(fraction classifying as hazardous, thr=0.40)',\n", " 'RdYlBu_r', 0, 1, 'Fraction of 3 members')\n", "\n", "plt.suptitle('Section 4 — Ensemble Susceptibility & Epistemic Uncertainty', fontsize=13)\n", "plt.tight_layout(); plt.show()\n", "\n", "print(f'High-uncertainty pixels (std > 0.15) : {(susc_ens_std > 0.15).sum()}'\n", " f' ({100*(susc_ens_std>0.15).mean():.1f}% of study area)')\n", "print(f'Perfect agreement (all 3 agree) : {(agree==1.0).sum() + (agree==0.0).sum()}'\n", " f' ({100*((agree==1.0)|(agree==0.0)).mean():.1f}%)')\n" ] }, { "cell_type": "markdown", "id": "a41e7086", "metadata": {}, "source": [ "### Interpreting Uncertainty Maps\n", "\n", "**(A) Ensemble mean susceptibility**: this is the primary deliverable for hazard\n", "zonation. Because it averages over three architecturally distinct models, it is\n", "less sensitive to any single model's idiosyncrasies than a single-model estimate.\n", "\n", "**(B) Epistemic uncertainty**: high-uncertainty zones (purple) mark pixels where the\n", "three models *disagree* significantly — indicating that the available training data\n", "are insufficient to constrain the model in that spatial context. In practice, these\n", "are the highest-priority targets for additional field investigation (new boreholes,\n", "UAV surveys, or additional inventory mapping).\n", "\n", "**(C) Agreement map**: a stricter version of (B). Pixels where all three members\n", "agree on the hazard classification are shown in red (unanimous hazardous) or blue\n", "(unanimous stable). Yellow pixels represent genuine uncertainty that field data can\n", "resolve.\n", "\n", "**For the scientific paper**: report the fraction of the study area in each agreement\n", "category as a table alongside the standard AUC metrics. Uncertainty maps are\n", "increasingly required by journal reviewers as evidence that the authors understand\n", "the limits of their model's generalisability." ] }, { "cell_type": "markdown", "id": "22000f66", "metadata": {}, "source": [ "---\n", "\n", "## 5 — Physics-Informed BaseAttentive\n", "\n", "### The physics-informed regularisation framework\n", "\n", "Standard deep learning for landslide susceptibility is purely data-driven: if the\n", "inventory is biased (e.g., only well-accessible slopes are mapped), the model inherits\n", "that bias. Physics-informed regularisation introduces a **soft constraint** that\n", "keeps predictions consistent with the **Infinite-Slope Factor of Safety**:\n", "\n", "$$\\mathcal{L}_{\\text{total}} = \\underbrace{\\mathcal{L}_{\\text{MSE}}(\\hat{p}, p_{\\text{target}})}_{\\text{data-driven}} + \\lambda_{\\text{phys}} \\cdot \\underbrace{\\mathcal{L}_{\\text{phys}}(\\hat{p}_{T_{50}}, p_{\\text{FS}})}_{\\text{physics constraint}}$$\n", "\n", "Where:\n", "\n", "$$p_{\\text{FS}} = \\sigma\\!\\left(-k\\,(\\text{FS}_{T_{50}} - 1)\\right), \\quad k=4$$\n", "\n", "A sigmoid centred at FS = 1 converts the mechanical stability index into a prior\n", "failure probability. When `FS < 1` the physics strongly signal failure; the physics\n", "loss penalises the model if it predicts stability there (and vice versa).\n", "\n", "This regularisation is **particularly valuable for sparse inventories**: even if only\n", "a few landslide locations are mapped in a lithological unit, the FS constraint\n", "propagates stability/instability signals to un-inventoried pixels with similar\n", "geomechanical properties." ] }, { "cell_type": "code", "execution_count": 15, "id": "0121eaf3", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:56:40.232457Z", "iopub.status.busy": "2026-04-21T12:56:40.232457Z", "iopub.status.idle": "2026-04-21T12:57:34.258789Z", "shell.execute_reply": "2026-04-21T12:57:34.257273Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Epoch 0 loss=0.11381 mse=0.09605 phys=0.04440 val_AUC=0.8013\n", " Epoch 5 loss=0.00365 mse=0.00346 phys=0.00047 val_AUC=0.9282\n", " Epoch 10 loss=0.00334 mse=0.00315 phys=0.00046 val_AUC=0.8855\n", " Epoch 15 loss=0.00285 mse=0.00266 phys=0.00049 val_AUC=0.9355\n", "\n", "Physics-informed AUC : 0.9270\n" ] } ], "source": [ "# ── Pre-compute FS-based physics prior ───────────────────────────────────────\n", "fs_prior_all = (1.0 / (1.0 + np.exp(4.0 * (fs_matrix[:, 2] - 1.0)))).astype('float32')\n", "fs_prior_tr = fs_prior_all[tr_m]\n", "fs_prior_te = fs_prior_all[te_m]\n", "\n", "# ── Physics-informed model ────────────────────────────────────────────────────\n", "model_phys = BaseAttentive(\n", " static_input_dim=N_STATIC, dynamic_input_dim=N_DYNAMIC,\n", " future_input_dim=N_FUTURE, output_dim=OUTPUT_DIM,\n", " forecast_horizon=HORIZON, objective='hybrid',\n", " architecture_config={'decoder_attention_stack': ['cross', 'hierarchical']},\n", " embed_dim=32, num_heads=4, dropout_rate=0.15,\n", " name='ba_physics',\n", ")\n", "_ = model_phys([Xs_tr[:4], Xd_tr[:4], Xf_tr[:4]])\n", "opt_phys = keras.optimizers.Adam(1e-3)\n", "\n", "# Batch indices for custom training loop\n", "N_TR = len(Xs_tr)\n", "N_BATCHES = N_TR // BATCH_SIZE\n", "\n", "# ── Custom training loop with physics constraint ──────────────────────────────\n", "@tf.function\n", "def physics_train_step(xs_b, xd_b, xf_b, y_b, fs_b):\n", " with tf.GradientTape() as tape:\n", " y_hat = model_phys([xs_b, xd_b, xf_b], training=True) # (B, H, 1)\n", " # Data-driven MSE\n", " mse = tf.reduce_mean(tf.square(y_b - y_hat))\n", " # Physics constraint: T50 prediction vs FS-based prior\n", " p_t50 = y_hat[:, 2, 0] # (B,) T50 output\n", " p_fs = tf.cast(fs_b, tf.float32) # (B,) FS prior\n", " phys = tf.reduce_mean(tf.square(p_t50 - p_fs))\n", " total = mse + LAMBDA_PHYS * phys\n", " grads = tape.gradient(total, model_phys.trainable_variables)\n", " opt_phys.apply_gradients(zip(grads, model_phys.trainable_variables))\n", " return total, mse, phys\n", "\n", "phys_history = {'loss': [], 'mse': [], 'phys': [], 'val_auc': []}\n", "\n", "for epoch in range(EPOCHS_MAIN):\n", " perm = np.random.permutation(N_TR)\n", " ep_loss, ep_mse, ep_phys = [], [], []\n", " for b in range(N_BATCHES):\n", " idx_b = perm[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", " l, m, p = physics_train_step(\n", " tf.constant(Xs_tr[idx_b]), tf.constant(Xd_tr[idx_b]),\n", " tf.constant(Xf_tr[idx_b]), tf.constant(Y_tr[idx_b]),\n", " tf.constant(fs_prior_tr[idx_b]),\n", " )\n", " ep_loss.append(float(l)); ep_mse.append(float(m)); ep_phys.append(float(p))\n", " phys_history['loss'].append(np.mean(ep_loss))\n", " phys_history['mse'].append(np.mean(ep_mse))\n", " phys_history['phys'].append(np.mean(ep_phys))\n", " # Quick val AUC\n", " y_te_pred = np.clip(model_phys.predict([Xs_te, Xd_te, Xf_te], verbose=0)[:, 2, 0], 0, 1)\n", " phys_history['val_auc'].append(roc_auc_score(inv_te, y_te_pred))\n", " if epoch % 5 == 0:\n", " print(f' Epoch {epoch:3d} loss={phys_history[\"loss\"][-1]:.5f}'\n", " f' mse={phys_history[\"mse\"][-1]:.5f}'\n", " f' phys={phys_history[\"phys\"][-1]:.5f}'\n", " f' val_AUC={phys_history[\"val_auc\"][-1]:.4f}')\n", "\n", "Y_pred_phys = np.clip(model_phys.predict([Xs_te, Xd_te, Xf_te], verbose=0)[:, 2, 0], 0, 1)\n", "auc_phys = roc_auc_score(inv_te, Y_pred_phys)\n", "print(f'\\nPhysics-informed AUC : {auc_phys:.4f}')\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "dcc245e8", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:57:34.261214Z", "iopub.status.busy": "2026-04-21T12:57:34.261214Z", "iopub.status.idle": "2026-04-21T12:57:35.432637Z", "shell.execute_reply": "2026-04-21T12:57:35.430855Z" } }, "outputs": [ { "data": { "image/png": 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wfHobXgf77ruvC7eGJ5UfrP096C94kuDRhrcGFv14mSRz35O5ZsCLiXwyeHZ4uXXwSKFP0ybUpbwkM15W1FgVDdoF8ELy4L5zjxjHuN+ARxZjG14i1N1fZ7wquG9+bwu8dSinIr2PvH7epk2bMvv9/YbcffQb/qWN8Eqj7ySCF0LMY9iwYa4fJNrGzJuER/RDHfCE8fop3jR4ufEM+L356GecP1G4Lp4LQqdRHvcCT5jZs2eHHMdzQrvFwmsf/xgSD7y+GIf9XoFAfQg5yvPiD/sIeGZ5cwIQZg5PH3/7lmfO4Ld4+ODJXJOeTX6H5yleeZG8nvzrHr4nXJ0/dCKelpRFvYQQQgghkkUh7IQQQghRaRCOzQvjRE4DQhSRi+Tzzz93wi8E5AjDEWQBoeeigdDML1DZZJNNKrSuhOABciuE4+3zK70gUiguchIAAshUQFiHAoCwVwhUY4XfI0RSeQXSkXJRReLll192wkGUAVwjgiwv3FQqbYKgEkUK4ZnOPPNMtw8lI0Jjf4gdwvIgjB4zZowLq4WwkLBH7CfUVCzoKwgiYyWsp/8RZgrBKyHPUHAivCPs1I477hg8jvBnhP/xoB1QqMUjWnuE5/ioqD5JnQjtlGh9ULCBP0yd/zv/PeM55f4Q4jHec+rVK9KxtHEyIR5p6/CxAYUMikzylRDOyQubhaKXfYTzmjVrVpmyli1bltR9T+aaPQEyCnNCr/nzg6E053z05fBwb8mSzHhZmWMVeEoiFG08owiyEVaj7PDnpvHqfOWVV7otkTpXBow5kSC8IqEKUTqEh330woclohyJ1s6JtnEi98kbF1DqhxNpXzQIfcf9+t///ucUaCjvt99+e9d3CdnG+IrClXkGJV8svLZBoZooXAfKE28MCh/fCOeH4sWv8EukfcszZ/CMEp6OtQvnoj1Q/LAlYpxSWc8m7cDYRRjNeBxyyCHOUIC5lXoDfzPmxVMECiGEEEJEQgokIYQQQlQJCG4QXni5A8h7QOJyBFPrI8Kst2KPloMhlbwplY3fsyIc75pSwcuV4eV9iQaJwhFwlYdE64kAPDzfUXnbhHwN5JpBkYhCEGXj0KFDXeJ5v0INrxP6Ct5nCKfxKEJJ8Mwzz7i8EeWFfBUoNN9++22n5EQJQG4bBHFecnI8CfxKH/pzIh5L0dqjPP0jFljkp1KfROrJ33hXjB8/PurxkZRdlQVC4Xfffdd5qXgKJDxcyAeC4pU+iyCWumKNjzDZ79GQyH1P9poR1EI0S39yk5RXgZTKeFlZYxUwriOwRumA4Jx6cT5/HjfvHHiORhOCR1IkVMXzQd3wxEH4j0KRMQhPFK4BpTbjTLgnTGU975V5n8LhueFaPe8rvA7xjkGBihLp448/ds8DyiHyfcVTJAL5eCqTWO1TEXMG4wHjOuMFYwLtwTPNmoW//d6kNfXZxLuSZ488Siir+MzYhsdkIkYPQgghhBDhSIEkhBBCiCoFYSwhelAgkTwaevfu7f5FQRHLctdvQY51speQviLwhDqEr/F7IMCUKVNCjqlsPC8rkn/HAkULgrLaCl5WJBlHQIfgjnBt4eGMPDbddFO3AZ4lKJzwGIglDKSvYEFPSCGEirFA2IfnExueRig6CUWGwBvlER4WhCfzQChX2fj7ZDhV3Se95xSFTZcuXZwHWiy8etH+PXv2jFj38lJUVBTi9YCXCMoj7t24ceNCjg0PG5XofU/mmv/++28nrMY7Ck+mcPDwoF4oTIcMGZLydSczXiaLP/xaoqC0QxGM0HyrrbZywmrCFPoF6F6dI3mTVTeE1ps4caJTMhAW0Q8eszUNz8Dgzz//LPNdpH3RwKMmXAnOuIp3IPcIpQlGDISJjKfcw4uP3zKv88zH8tjzjxE8Wzy34V6TjBEorhIxWohGKnMGtGjRwilg2FDg4KmFNx3eafEUaZX1bFIe94B+mggo0MeOHWuPP/64U4YSjlHh64QQQgiRKsqBJIQQQohKAcUGeRvCQQjvha/yvAaI/4+An9BGfiG9ByGhUAIASiOEKbfddpuL8x/LYpfcE4mGeEPgiSU24Xz8YXj4m32UlWzulnhECmvEtd50003O0hkPi1ggUEJIVZ6tOuE+ImjHKhxLd7wDEEb7ieSF1alTJ2dJHe/eIsgn7A/5pKL1EwRrbH4QcmOND945EIz7243PlQ2hwVBc4AUxf/78EMXJLbfc4oT9KN6qCpQrcMkll4SE84sUmglrd+p3++23hxz7008/RVXmJAP3z8v/5CljPEv+cKt9xolwRUCi9z2Za8a7yMv7RFi98O3CCy8MHpfIGBXtu2TGy2Tx8jYlExqTZ5Fwgq+88opTtJIPx8uN5IHwHi8VFGjhYReBucJ/Tvo4igiUypVNtH5DHhzy9NQ0CDuK5xD939+WtNldd92VcDl77LGH+/19990Xsp+yeW4x8OC+kM8rEZi3gFBx/vHKg+eHEHGeApmxH88ucun5wdsPTybGkFRyGqY6Z1A/lFl+GMO8cLn+31b1s0k7HHbYYa7tPC9HP+F9l3EM5RljDcczj1SkwY0QQggh6hfyQBJCCCFEpXDuuec6BQlCIPIJoBzAChhlwdSpU13YI/Z7wh2EWHgBYOWP0JYQYeSdIfn6a6+95gQnWF5TDgIRBLIIJPlNr1693LGEq0F46wnVyX2AsBrBFgIUhEEItyKBBTRWxqeffrrzkMI7BgirRi4TwsH4k3dXBFw/oYL4lzwPWIMj8EHgjYKMdqnrYClNvho8RxA6h+cZQfmDwnGvvfZyVu4Iyt58800nXB49enTMsglHxbGUQZJ4BGjkQ8KjB0t9+gZ9kXuAVTr9CaUcoazoj5wPK/zqAsE2ijXqhjcMbZWXl2fPP/+8ffPNN06p4Vm8VwXUgTBQbOSWwhqfHCb0V7xqCPtEvhTAA4FnifqTWwghNLll+IznXDKhrhBik9PEg3JQVuDtwD31PAZpGz5zLB5iXthBnl3upV9hm+h9T/SaET4zVjBGofiLBN+h7GIM5PmmL8Yao6J9l8x4mSycE1B2oYCljrSPF6IsGjy7b7zxhvPcYpwM98Ci7k8++aTrCwi3jz/+eBf6DyUe4yv384YbbgiOuygvuDbuEaHWKhPOQ10Y/6kPeYToH/Qbxmbuc03j1ltvdQYNW265pY0cOdK1OeOo9/wl4knG9fIM8Xs8gbg33O8vvvjClcU8iBKNkIPsi+cRS30eeOABO+2001wbovDgmcnMzHT3mDx6eOlRJnCv8ZChfzP3EXKS4+699153LnIYpUKqcwbGInjNsWZBacScTJ4mnjXGBy+fUHU9m1wXYSIpm+vzwv8yljJG8nz5Yb7w8gmi0EpFGSeEEEII4QgIIYQQQlQC7733XmDkyJGBgQMHBlq2bBnIyMgItGjRIrDddtsFHn744UBJSUmZ33zxxReBESNGBFq3bh3IysoKtG/f3h1/6623BvLz80OO/fbbbwP77ruvKzs7OzvQuXPnwOGHHx74+++/g8dMnTo1sPPOOwfy8vIwz3WbR9euXQPDhw8vU4dXXnklsMUWWwRyc3Pdxt+vvvpqmeOi/f7jjz9253n00UfjttF5550XGDx4sGuXzMxMdy2777574N133w3UFI455hh3PYsWLYp7bCptUlpaGujVq5f7/rPPPov424MPPtiV3bBhw0Dz5s0Dm266aeDBBx90v/WgbMrgeD/0m+uuuy7Qr1+/QIMGDQJNmzYNDB06NDB27Fj3/eLFiwPnnHNOYOONN3bfcY6ePXsGzj777MDcuXMTaqMrr7zSnfv777+PWx+gjbieePs8Pvnkk8BOO+3k+jHXMGjQoMBDDz2UULnh9zGZexPtfr711luBXXbZxd0Lnr1OnToFdtttt8B9990XchzPOG3fpUsXd1z//v0DTz31VLC9pk+fHrGu4dfkPbvexj0aMGBA4Prrrw8UFBSEHE8/PeGEE9zYQVtx3AMPPFDmfiR73+NdM99TPs90LMaMGeOOe/rpp+OOUbG+S2a8TPa5vOmmmwLdu3d3YxLfc7/i9el169a5cYzvTzzxxKjXP2PGjMApp5zi6kSd+Q1j4EUXXRSYOXNm8Dj6BmVFqneifSaRa/XX68ADDwy0atUqkJOTExg2bJibCyL11UT3xWp/juWZjLcvVrtPmDAhsNlmm7l+3rZt28BZZ50V+Oabb9yx3MNEWLJkSeD88893YzD3g7629dZbu/GluLg48PrrrwfS09Pdc7Js2bKEyvzjjz8Cp556aqB3796uLanfBhtsEDj55JMDP/30U8ixq1evdvee/sb56ctHHnmkux+J3r/wsS3VOYM+TF249/RLnnPKOO6449yz6Ke6nk3uwahRo9xY5T0/3K/nn3++TBm0bZMmTdz9C29PIYQQQohkSON/0qUJIYQQQojqAut/PDiwEBdCCJEaePngnUser2jetqJ+QKg8PKrwosQ7WwghhBAiVeTHLIQQQgghqg1C8hDS56STTtJdEEKIBMAGtKCgIGQfOZDIXUTIuO22207tWM8hHxk5AAllJ4QQQghRHuSBJIQQQgghqkVxRD4M8p6sXr3a5b4Iz38khBCiLCiPyK1DniryDZHfi9xov/76q8tfdeONN6rZ6inkeyL3G3nbyCVFnyCfnhBCCCFEqkiBJIQQQgghqhws5EnM3q9fPxs7dqxts802ugtCCJEAhPzEa/PTTz+1efPmOY8kFEl4m4wcOVJtWI/p1q2bzZ0714YMGWIPPfSQCxErhBBCCFEepEASQgghhBBCCCGEEEIIIYQQISgHkhBCCCGEEEIIIYQQQgghhAhBCiQhhBBCCCGEEEIIIYQQQggRghRIQgghhBBCCCGEEEIIIYQQIgQpkIQQQgghhBBCCCGEEEIIIUQIUiAJIYQQQgghhBBCCCGEEEKIEKRAEkIIIYQQQgghhBBCCCGEECFIgSSEEEIIIYQQQgghhBBCCCFCkAJJCCGEEEIIIYQQQgghhBBChCAFkhBCCCGEEEIIIYQQQgghhAhBCiQhhBBCCCGEEEIIIYQQQgghBZIQQgghhBBCCCGEEEIIIYSIjjyQhBBCCCGEEEIIIYQQQgghRAhSIAkhhBBCCCGEEEIIIYQQQogQpEASQgghhBBCCCGEEEIIIYQQIUiBJIQQQgghhBBCCCGEEEIIIUKQAkkIIYQQQgghhBBCCCGEEEKEIAWSEEIIIYQQQgghhBBCCCGECEEKJCGEEEIIIYQQQgghhBBCCBGCFEhCCCGEEEIIIYQQQgghhBAiBCmQRJWx6aab2tixY8vsLyoqslatWll6errNnDmzzPdPP/209e3b10pKSuKe46qrrrK0tLTg1rp1a9thhx3s888/Dx7z2GOPue8WL15slc12221ne+21V6WfpzzX9MEHH9hGG21kDRo0sGbNmllt5ZxzzrFu3bpF/Z7v/H0j0kb/SZVjjz3WBgwYUGP7SDgzZsywk08+2bp27erufYsWLWy33Xazl156qcrrIoQQouau15jf/HNlo0aNbOONN7aHH3445HdffvmlW8+tXLky4XWLt7H+2GKLLez1118PHvPJJ5+473744QerbFKdw1Nh9erVdvXVV7vz5ebmuvakzW+//XYrKCiwqibZNeTy5cvdemnKlCmVXjchhBCpyVkCgYA9/vjjts0221jTpk3d+16fPn3s/PPPt7lz5wbfB5mD+Dce3pzsbXl5ebbJJpvYI4884s7llcd3VfE+yTzUuHHjSj9Pea5p+vTptuOOO7q2ooxffvnFaiOvvfaaq38i/aQiQP5Hn6bfNmnSxMkCTzzxRFu4cGHwmDvvvNPeeecdq4ttwTqLc7A+E6ImkVndFRD1g1dffdUNsscff3yZ79577z1bsmSJ+/vZZ5+1Cy+8MOT7Qw891C6//HJ74okn7Ljjjot7rpycHPvoo4/c37Nnz7Zrr73WTdw//fRTlQkHPO69917LyMiwmgxtOnDgQFdX2q4u98F169YFP++333629dZbu0W0R6dOnVIunz66Zs2aWtFHvvnmG6csQsF60UUXWb9+/ZzAj0XYEUccYb1793bCQSGEEPWLaOu1Hj16uBd6WLVqlTuOl3kET6zTYKuttrL+/fvbbbfd5hQkifDuu+86AcGiRYucAmXEiBFu36677mpVSapzeLKgpNl+++1t1qxZzvCFdQh8/fXXduONN7r1wNlnn21VyZ577unOn6gREYINTwHG+kEIIUTNmrdR6Bx++OH2wgsvuHf90aNHO0E8iv9x48bZP//8436HgeWBBx5oV155pVM2JcKjjz5qG264oZsLMCQ54YQTnEHwKaecYlUJaxDmr5oMawvaGuUTa50NNtiguqtU47n55pudfOLcc8+1a665xvXl3377za1BUXy2adMmqEDCCHePPfao7ioLUW+QAklUCQzwhx12WEQFxTPPPONeWnv27OkmhnAFEi/TWIbefffdCSmQ8GTafPPNg5+xXmBxxGLpnnvusaqkpr9YYwU7Z84cNzljnVQemNwLCwuddVNNBAspP9Szbdu2IX0lnPz8/ISVavTf2tBHsG4++OCDnbLsq6++ci8THnvvvbeddtpp5fZES6bdhBBC1Pz1Gp/98+XOO+/slA6vvPJKUIEECJIuuOACu+yyyywrKyvu+YYMGeK8ljyP3M6dO9v//ve/KlcgpTqHJ8vIkSOdMOnbb78NMWraaaed7PTTT7c//vjDqhqMSdiEEELUjXn7vvvus+eee84pePyKpeHDh7sIFO+//37IvM0cdOuttyY0FzB3DR06NLgWwDuEebuqFUi8y5bH+LMqYE5HxlIRa5r68n6NzA/ZH8ZIHrvvvruNGjXKSktLra5AdCWuJ5G1shA1BYWwE5UOrruEkMO6JRysPd944w33HRPFpEmT3BbOQQcd5Fx+J06cmPT5u3Tp4hZD1MMP1p9MRljP4nGBh5MHiyDCioSHYfn999+dO6nnLku4lm233dZZlOCaTCg4v/VOpPBklLH//vu7kGGcA08PPK88cAPHgpcFQsuWLZ116vfff5/0dVNPLDhw70ZRgoAGBZxnYYtLLHX2Fo4czz3wlAznnXeedejQwRo2bGiDBg1yVkqRwr3QFlwDCpk333wz6E7+888/u3A0XMfgwYPdZ8pFQdG8eXO34GPBGw4CKcIOcl9oV6yn/O7KgPXJPvvs49qvY8eO7jorKoQL52cxzPlZqAALmGHDhrn6YPXCPZ06dWrE9ggvj+uO1s8i9RGv/XgOuPdcI+XiqecHZd1ZZ53l+hEKHxbtKGPjuVS/+OKLru/fcMMNIcojD7zReGYi1Q14DjkHYQw8+IzlNMrfdu3auTbi+jMzM23BggUhv1+6dKllZ2fb/fffn9Q9p/xevXq5/sjzzItO+DMthBCictZrkWANgdWxHzyIsEpOJawI5RFeJ3xsX7ZsmZsX+J6wq/45n3UHc9C0adPK/Ib1B16+MHnyZGelyrqKeZXz+MuJFMIOA5ujjz7araEoC4vru+66K/g961eEaMzZzMP8Heu6//33X2eFfOqpp0b0iGc+33LLLYOfWQcgdPLmRu5LeKjneGs94H6cdNJJbr3EHIqSzq/0ixTCLtqcy/qie/fuwbW5F8rIW3fg6X3JJZcEw+MiWGRt4sdra9YRGPd4Ifx+/PHHkOMQrOCVRhmUxfqCc65YscK1DeclDHO4QIbrxNpeCCHq67zNuyvv35Giv2Ccy7upB++bzI3hY3UiUBbjePi8zTv/GWec4d7527dv7wxLiouL3XeJjt9EksHokbmNuYi5B6+UWCHsmO/OPPNMJ2dg3uA3F198cfD7eLKbRPHekZnTWU9QD95l//7775Cwd8xrTz75pPvbH2qf92B+Rx3Zf91114UoR6LJJbwwgsgFaBvOy3u7d+9QvvCZ9QQeWv7oK16bHnnkkW6twLqGtgife1nX4SFNGbQTciKMjqsK1m/0mWiG4kCbsaYibKO3DvHCvSFroU9Tf/of9+q7774LKSdReUuibYHHFH2JMunDKHTnzZsXsc/Q37x778k2H3zwQXdN1IPISX/99Vc5W1GIykEKJFHpTJgwwQmSeTmMFEOUl1wEA0yCHBdp8cLLIxNA+EIjEVACESIPZYgfQnXtsssurg4sfHihRbkDTKx41PgVO55yh0mBF3rKxW0aITzHUQ4WPSxcooGAA6UK/zLBI3zgRd8TCHz22WduYkLIgRCCCZBJJFaZscDjinMxUV1xxRWubQnpB9Tda08shVmg4GbttQ0LGxZwXBdeMgcccICrb7giByUGizlCzqBo8ibbY445xrXHyy+/7D6jNGMhw2IFd3qETPwOLxgP6sDkygT9/PPP2wMPPOCUZ/vuu2/IefnMfqyrEA6h3KqoWMv0RRaAb731lh111FHBxRaLYHIzPPTQQ26Bh5AHZUg8YvWzaNBe/I5juTYUMrS/F+rRW6hwj1Da0FbUiX3x+PTTT91iH2FQRYJQDaUalm5PPfWUCxHI84zCyg/9ARACJXrPeQ7omzwb9DPuAX0tkTwbQgghyr9eA4Q/bKxJWA8hiAkXWrEmwggmlfUawiMMHMLXayhcCPvCfIinLPMecwGwXmJdRn38eGtJ5nTgdwglmKPefvttJ8yKFbKO+Zb1GsKa66+/3v2GNQtKJUBIxLVzrdSL+Yt1LOeIBkI+1paEkI0H7YBgh3owp+JFTyhmrMcJIZjoWg8wCGJNM2bMGCccueWWW2J6i8eacxHq4HUGlMcczuYJe2gD1iaEB+acXCtr6vHjx4ecY/78+W79iECMNSHCRtYNfoUkQkDWoQhcUBQiKELgh/AGQc1mm21W5r5TX9amCE09QZtyCAgh6tO8zXsrnq6JzDX+6C2pzNuA8ih83r700ktduYzvzOEotJhLIJHxGzDg+PXXX53MhO8InRorJzbKEt7hiWjD3MK8g6LAM45IRXYTC4wqmU8xuGCeQejPfAfMicyNGI+yTuFvzxgXQ2XaBHkScxvv+9QzkuFDJLkEYJCLwoMyuXd8x9qIOZ71AtFlmMv9XjysT1CWUG/qwDs5iinK9xtuonBDvuLNz7R5IjKGigLPdK6B/sJaIRJcN0YlrMO8dYgXzhDlHX0HGQTrIRRqrKfCjX8Tkbck2ha0H8YzrBWRiVAH1mue0tSDnJ70Ge4Psj4Meri39EPCG1MPZH+enESIGkdAiErm5JNPDvTv3z/id3vssUegY8eOgZKSEvd51113DXTp0iVQWlpa5tjhw4cHDjzwwJjnuvLKKwONGjUKFBUVuW369OmB/fffn6yOgXfffdcd8+ijj7rPY8eODf5u9erVgdzc3MC1114b3HfkkUcGNt100+Bnymvbtm3gkksucZ+///57V86vv/4atT7Uec899wx+PvzwwwOtW7cOrFixIuLxt9xyS6BFixaBZPGuadGiRcF9fPbXH4455phAz549g5+XLVvmjuP3HhMnTnT7xo0bF/LbLbbYIjB48OCQsjjum2++KXMP2P/OO+8E97355ptu3yGHHBLcV1xcHGjTpk3gnHPOCe7bdtttA1tuuWXI/Z88eXIgLS0t8Pbbb7vP48ePd2VNmDAheMzy5csDeXl5ga5duybcZhx7+umnl2nDG2+8MebvqPfatWsDjRs3Dtx///0h7eHv54n2s/A+4rWfd71AP2bfk08+6T4vWbIk0LBhw8A111wTUrcdd9zRHcfx0dhtt90C7dq1i3mN0eoGP//8szvHxx9/HNzH5379+pV5bvfbbz93P/1sv/32IWUmcs+5T/6+J4QQourWa958H76df/75Ecvh+KFDh8Y8lzdHzp8/362v5s6dGxg5cqTb582tzDN8HjVqVPB3zBXdunULnHDCCcF9l112WaBDhw5ufvZgzmDNBayNKOeNN96IWp/wOZy1XoMGDaLOpy+++KIrc+XKlYFEYX3Bb/7444+4x5577rluPct87/H777+7ufHuu+9Oaq3HdZ133nkJryHjzbnemoQ28PPRRx+5/e+9917IftZ+w4YNC6kf1/Hbb78F93n3+vPPP3ef//zzT3fMmDFjotbjoYcecmuhpUuXBvex5vfWHZ988kkgIyMj8Pjjj0ctQwgh6tq8zbt5pHf5WPD+2apVq5jHeOM05TNvL168OHD99de7fRdffHHI/HDQQQeVeafkPTXR8RuYA/3zXaQ6c4zHAw884M791VdfRTw+EdlNonMe18O5Fy5cWGYunTVrVnDfxhtv7OY8D9YptPOhhx4acg7aLzs727VpLLmEdw9Gjx4dIgdhruvcuXOgsLAwuP+AAw4IDBo0KPj5iiuuCDRt2jSwYMGC4L6CggIne/PWWaw5cnJyApdffnnIeXlfjydjqCgmTZoU6NWrV3Ct2b1798BZZ51V5tzhspxIIGOkr/bp0yfYR5ORt6TSFtzj2bNnl1kP0WeysrICM2fODDl+s802C2yzzTYh+zhnuIxOiJqAPJBEpYP7ZqR4uliDEH/3kEMOCbqjYmWBN84XX3xR5nhcbcNdQSOBRSmxRNlwW/7444+ddWZ47Fm8QjywviDcBhY7HoT7wN2VsCeAlQDWBZ5VDPHysWDBAgSLBBJAJ2IlhKVEpNBhgKs5Xi1YQmAFtHbt2pDv8TLxLIDZ1ssOooPLsx88ifzXGM1CFsItH7hPhGPzW+zi7o4FUTjcT6wnPLyEkX6vF7xgaEOsbIFrxZqZ82Ld4V0jv8U6wwvjR94AvFWwlvHgc0V51ERKxvnNN9+4tuR6sfLCvRgL2HBLlkjE62eRoP3814NLM55b3u9wt8ZalzB+fsI9taKBRW5FQyiE8HJx38YiyPOw4/nFA4r9ydxzLwQiVtSMDeEhk4QQQlTeeg2YrxmT2RjHCbeCBStWlKmu1wALUtZrWC+TmBuPaNZf0eZR5hm80v3zKJ4ynM/zSsJiGW8d9gNzN3MvlqR46cSbg731GusMf8iZ8HCvrGNYt2JBTFi1RElkDmYtxvkJm+JBCD1CBoevkeOt9ZhDsY4mvwWJqOOR6pzLmp76Um//WpX6UZ7fcpz7jfeWv87g1fujjz5ya1zvHkaCMHz0Hc/bjPcK7oX3G8/6F0tkIYSob/N2Mu97zNuMoYmM93i8MPbymyuvvNJ50+D9Gm3ejjQvxRu/vbmIeYuII4mE9GLeZn2A93Ak4slumHP8c1e8fDt45vrbPnwei5YTiWuNJGchPH14qLVIconwed8LsY+XjT+fDu/TnpzFm6PxcmGe9q6RdQxzpffOjYyBXEt4BPvBMycRuL54WzyZGZ5VyN/w5jn77LPd9eGFxroL76l4EOmF+hP6kOujTf78888ycptE5C2JtgXebkSnoa7IirzcXOHn5BqQcXiwLiKEYPg5Eg0lLURVIwWSqHQQdEcKlcHEzcTFxIjrMBuTGsdGCmPHfgbxeDDwMwniIor7KJM0iYnDIV69H/KyUFcPJmHikxLuBHCzZp+XaNkLqUc4DdyGEYIQiitSDiePSKH0/PDSTZxcJk0UXizMePH1QqWhvPKUY2zxYvZGusbwWLjh4N5M2X6hBTAJs7Dyu3mzL9o94Fz+88Zrc87LJEqIGP81sqGA8BZA0RbK0eqSLOHlcG4WwdSNsCwoPOhfLNT8/SUa8fpZIu0X/jtPMBfeDtQpHoT6YeGWSN2TIVL7E3YGpRlJXL1nnhjWhC9M5p6jUL3jjjucWz6JSLluFpSJjAdCCCHKt14Dxm5y/LCxFiI8Dbn3CO8WHs410fUafPjhh25ORThEeBlCr4ULveLNo7z0I0zxr9cwIGJNCZSH4ATBEutBXt65DsIGp7peQzBD2BEUR7z4My9h1BGeoyh8/oVYx3gwP0aaV9kX3t7x1noo+linEsqG0EGEc0EgF41U51zW29QtfD4nfDHrfb9SMVKdwbuvtD9CmFjrGtYXGKR4951Qf/Q9wugJIUR9nbeTmWs8vDISeT8kNBrzNkJ5DEuZT1gjJDNvJzJ+ExoWg1TWG4SCw4jCC6GayrwdT3aDXMU/d0XKHxXvGuO1oRfmNnx+9z6Hz+/R5BuRzh2vzZmjCdsXPkcje/LLWSB87k1EzsI8zzor3uYPqxcN6k7oP/JlY4CCgRCGp5GMlvwQ4he5DfmRyKGIMQ59FeOb8PuSqLwlXltQPus/+h5tifEsxscQfs7w3yKTod1SaW8hqoPMajmrqFegiPCS6/rxlER+TxUPYpZiaeC3okBxgRVpPLAmQDBQEfDSS3JirDCxggiP1Uu8YSwOeKnG04mY+gjHvQSK4VB/YvvGgti5bEzy5NzxhOsssIiPSy4eDy+RcUXfL6yPWOCw0PJYsGCBE8L4FycV6clCuZRH/FhPweAHZZoXUziS5Qr1qwjCr4kFC95GLFi9a2eiTyT/UWXh5RqgHfwLZX/84miwUKYvYaUVzarJg5cBrKH8RMvvEKkvsDDjXqJAIq4z/5KHgpeGZO45zzTCKzbyT1AO8Yf53svbJYQQonLWa9FAEMAcQf4dvzdyous14KXeG+vLA15LeAMxR5D/gPw6/nkJhQ9rS9Y35F5k3mE+4vjwJNyJrtfIL8GG4ou1Aus18loyv0YCxZuX/Dqe1zT3ItKczlrH8+pOFCxiEcKwISgjPv/IkSOdlS8KonBSnXOpM8omPPYjkYiRi7/9WWvRBrF+x30ndyKJqPFg85KKCyFEfZ238X7A4JW5Bm/hRGDeRniOciWRub8iZC3xxm/ed5G9kAsHLw2uBU8dFFc9evSIOG/ggRyLWLIb1gSeJw5UxNokHM9AN3x+9+QY4Qa8FSlroWzWLP4cieEKRE/GQP08RaS/frHA6CNedJxUwbCa9WK8PNIob/AgwsCH4z0w9vG8ghIl0bYgbxHrLAxlvahKKLAiEX4/WTPRbtH6gxA1DXkgiUoHLx6SK/phUOUFHpdnJm//htUjFiReKBIPFkeUVZUcc8wxbsIhwR5hy6K5kyIox0oCl2iuNZrlCQKDl156qUwC5EiwaMGNG6tab7LEytazAGZLVECTDCRXBAQtfvi8ySabBIX/FQ3l4nLOtfqv0du8MDIs/LgnhDfx4DNWzJUBC0wme78y0/Oeqy4Q+qDcQcHoB6uieNCHsb5GeBapHyJc8qyQWGixSPcvBrHiTgasy7Ac4iUGaxwvfF0y99wPCziSc+MCHm8RKYQQonzrtVh44dDChSzVsV4jhCtGLyiRvFDAkWAuJ1wLChEUP9GURKzXWGckYsFNSBwEX4TkiTUv4fnDHIy19pQpUyIK8BB+eGsxFFF+ow3mY4Rj3jotFfBAYp0NicyhkebcaFbWtBmGLXwfaU4Pt/SNBR75rL0QKsaCcgkjhMKQtolnMS6EEPVh3sb4lWgskaKVEJotkpwlWeOE8pLo+I1QftiwYU6BxPt3tHB2zEHMU4S7j0ck2Q1ylXjvoRVxv1AahMtZkC0wRyLnqCxoH9YengLQv7E2AP6lbVCK+Hn55ZetqoikPEEeg3wCr7FYUV08T2n/egOZYzLGUR6JtgXnZG3pVw5hyJQIhNgjVGP4OZAXClETkQeSqHS22mor526KNYCn+fe8j0aNGlXGgoQX4xtuuMEdgyUI4B5NzFTi7FYlTPAIJZjkCdXCJOKBRxKeHIQuQSgwf/58FyaE6w134/ag/lhEcI14ZGDZwESOSy6f+R7lGV4iWFwizGeBxyKwqkBIsP/++7tzMiGy0MGtnMk3XGFR0dxyyy1OaIB1EYIYhEH0G9zNseqlXbCcYaJFqXfTTTc5Lxb6S7S8UuXFy7XE+ekDhBfE9TrcTbwqYYHLgpfQQfQ1Ft/0US/Ormf9EgmOZ5FKO7JgxGKamM0I0lDyPPjgg27hjZIJQRd9/Mwzz3TWWfSBZBc0Xu4oXgpoM3IlJXvPaXf2E3ObfwkjiLUaFtRCCCEqb73mwXrAC8nB34QFYb5gjPdC+3ogtELpUJXw8o7RD3MKlqr+GPMIpqgP8wx1xeiEdQPCofC6ezA3EqYHryG8blir/vPPP26eZe1BSFuUPcylrOUQQLFWCs/7EM69997r5jXamnPwLzDvsoZEsYVhBd+hPKE8wvcgJCE/FOvNaMqxaHAO1qoYnyCs4LoQrkTyPoJ4cy4CHObzZ5991nnCY7nM2pG+wLqdNmFNyz7W76ybEPhhRZ4oCDIxMuOaUQgSrYC1MmtvvPH91sBYsROakPWq155Ari5+hwW78iAJIerTvM17IvM0xqiM4cgz8O5BnjJu3Dg3/zFW++ftaHNCZRJt/GaeZi4n1Bzf4e3MHMncgxwgEhzLHEuEDWQqzHl40RKuFk+nVGQ3FQ1zMGsKlGbIelBisbZiXXHOOedUinGwB7IdFBsY0eBhTBtg9MH6g4gmrDvwUmLuvfHGG53ci7Zmro8WXacyQHHDWoL7z/qKe0g+c6LzUG8PFGEY+iAzYK3CeoR1C/2cPsV6it/SF/xrhkRJtC1Y++DhjbyEvsXakFB2icIaj+cTuQeyELztkvm9EFWJPJBEpcOLMpMh7sIeKIeYrCO5H+PGiQXpG2+84UKHAYJtBu5w4XNV4CW1C7eK6dWrlxPUM+gzwTEpc03hFiV+iN+LEJ5FGy/iTI4sZEjuDFjXsLDjO4QGWImiZKtqxRlCEBZ0TJhMaCiyUBx4Cr3KguSDJGzmvjOJsqhiUYz3F+0NWHegyBoyZIgTcjCxE3e2spINsogh+TSTOTl9WDjQFrgqVyfcm5NPPtkJwUjESVgeFkoQr24srvAKYgE5ZswYZ5GEcIUwRFyf5/LNiwUhHHkWUSBhbc5LR7JCPe4NVt4knQy3QE7knnvH8BJEnVj88mzESq4thBCi/Os1D5QnKDbYGIdZJ7A+CbfE/Omnn5xAItGEy1WxXkPhwcZ8yTqStQMKJjxqEeZEgnZA6OYZ/DA3kczbE9ChHEGYwdqP9RrrNDxsEV7FAm8thAsIisjvwLqCNQwexBdeeKGrG1A/FCAIRTCYYb5nbv7kk08SCjHkh7UpSiPWCszHKLtIVo7wJRLx5lzWvii3KAcFDWtXz5OL9RHrMtqBtuY3tDPrjWRBYMQaBctc2gmBKJ7T4dcf7b7jPU2exXiJ0IUQoq7N27wvI29BcY+xKvMTcxV5YXjv8+fBI3wW77mV9S4di2jjNwod3sFR8DBHohxiLGc+iRZaDmMGPHfxCGbuYP5ibvbCoKYiu6kMUDTQ/oR7ZW7z0hTwzl2Z0E9QVmF4ynqD/oDSCO8cfxhiZAzM49THy0nFvqqCtmBNwf2hr2IAxLzPvfWHvOcesyZjvck6hHUNuYO4n/RpZFgodjD48WQKyZJIW7A+RAGIfIq+isISg/FE4TfIV7zro4+zPhSiJpIWqKxAlUL4YOBHYO0PO5YMvPQycYTnIKoKEKxTdy/BohA1FRbXCH2SCUEkhBBCVNR6DaUSgqhUf18errjiCqe4wOI0PKm4qLvwboDiLTy8jRBC1AfKO2+PHTvWGQlgSFiROXcSQeO3EELUHhTCTlQJJChE808IDH9Cu0RAGI7LcVUrcDjfL7/84pIHx7MoFaKqwTIZ62g8sbDIwtIFK2Esy4QQQoiqXq8RChVr58oOdxsOuYHYsFQmbImUR/UDrKYReJIQnPCEUh4JIeoj5Zm3eYe86667nAFGVSqPNH4LIUTtQx5IosrAnZRQHLiiJgMeFSwyjjzySKtKCDNHGBbC6eH6GiuvjBBVDRbeuMCTW4B8FMT9JbwLYXGEEEKIql6vEeaUEGtnnHFGlYfwISyLF26tUaNGVXp+UT2QC4oQTYTcI9wLoWuEEKI+kuq8Te4kQpwSBr0qZR0av4UQovYhBZIQQgghhBBCCCGEEEIIIYQIQS4VQgghhBBCCCGEEEIIIYQQIgQpkEStY9NNN3XJHmNB7iLi+BJKJRlee+21Cs13xPmpB9sff/xR5vtLL73UfUe4PD9Tpkyxgw46yDp16mQNGzZ0/+611142fvz44DGPPfZYsOzw7dRTT7WaAi7qAwYMqJJzce233nqr1SfuvPNOe+eddyq0TEJG0pYvvfRS0r+76qqrbO7cuSH7ydXUqlUrlx9DCCFE3YDx3r/2aN26te2www72+eefV+p5CdXqXzd566HFixeXe71XlWuWqrw33ua/tlmzZtnxxx/vQuCy1mzfvr0Lf/TUU0/FLDt8/dmsWTPbYostEsp9ler6QgghROXKTyqL8HkVOQ1z1Nq1a0OOS2Uury1U5bUR1he5UU2mNtzrZOuYyPpGMhFR28ms7goIkQyvvvqqG5x54a0MECj88MMPNnLkyAott3Hjxvbcc8+5xZIf9vGdn7///ts222wzGzhwoN12223Wpk0bd80oCVBI7b777iHHv/vuu9a0adOQffU1DvzXX39tXbt2tfqmQGKRuMcee1RYmQiRaMsNNtggqd/RT6+++mpXnw4dOgT3b7XVVta/f3/Xn/leCCFE3SAnJ8c++uijYC6Fa6+91nbccUf76aefqkwRs+eee7o5C0VGedd7l19+ua1Zs8bq2r3xyM3Ndf8uX77cNt98c5czg7UpayfuH8ezrkwk76i3/iRf6O23324jRoxw+3bdddcKX18IIYSomfKTeITPqyiQeB8kX6I3J6U6l4uyYByTkZFRo5umNtzryqijZCKitiMFkqh1wvLDDjvMvRTXJvbdd1979tlnQxRI3377rf3777928MEH21dffRXc/+ijj7p/P/jgg5BF1XHHHWelpaVlyh4yZIjz7iivZZBnaVGbQRgiIrNu3TrLyspKKEFqgwYNKrwtTzjhBLvgggvssssuc/UQQghR+2FO8c8XWDnjHTRu3Di75557yhwfCASssLDQzTMVBZ5PbBVBz549ra7eGz9YyOItjHCkS5cuwf0ojiKtNSPhX39i8dy5c2f73//+F1WBlJ+f79bvFbW+8MoTQghRc+Unic6rFTmX12f69etXLedNZk6uyfe6pKTErYMqq46SiYjajELYiVrD9OnTXViUAw88sMx31113nbVr18558+y///62cOHCMsfg/TBs2DBnLYlXD14SU6dODVGiPP744zZ58uRgWA5PscIL9j777OO8Kho1amSDBg2yJ598MuG6oyT666+/nEWuxzPPPOOsdKmLn2XLllmTJk1ClEceiQj/q9qt95tvvnEha6gvQqNHHnkk4vF4T22yySau/RAw/fjjj8HvDjjgAGeREc59993nwqosXbrUfaZsPFlYnLRs2dK23npr+/7772OGsHv77bdd2dQPS1uEHD///LP7rqioyEaNGuWEJwizsIzde++9bcWKFTGvfc6cOXb00Uc7Ty/qsuGGG9pdd90V/J5FB32S9qBcvr///vtDykCZSH+dNGmSuw7qh7X2e++9F3LcG2+8YUOHDnXHYgHD317IOspHCUlIAq/PekpAvsOy6+abb3aWxdSTdiSU4qGHHuoEPZyTRSbPhl9gFMkF2yuPc1EezxHWxlgee/d3++23d3/znHn18eBYLJ4rOtyeEEKImgPzKS/crNn8oWsY+zfeeGM3J7755pvBtRXrB9YFzCmHH354mfUbSg7WX8xXHTt2dHNaImFGMJrAYKFHjx7unIQC9tZ0sdZ7kULYMU+jFPHqyTp05syZIcdQBnVjbmdtgGIFw59Y3kxemGM8ocKFF6xpL774YvcZzyDWkZTLmoiQc+eee66VB9aarCnD16CprjXz8vKsT58+wfvurXG+++47F96OerN+iLS+SGbNFF6eEEKI5OUnXpizJ554wil4eE9k359//hlyXEFBgZ133nlOBsK4iwwEjyY/zKVEwuDdnLmaucA/V/vnVeZr5kZgreAP5R8+lzPX8e4ZDgaJzOneuyvz/SWXXOLeT5lD+vbt6+QsVYE3p7GmQDHAGqFFixauzYqLi8scT+hYosmwnujdu7drfw8MMGi/8JDvv//+uzuH9w5NGLRtt93WnYu5d6ONNnLnjxXCjjKQkVE3zsF6DONmj3gylmjXzT076aST3G+Q73iyl5133tmtL5BpEVkH7+R467Ybb7zRevXq5foZfYOQut6aItH7HK8vRsJrL9qQ4yl74sSJKdUxHGR/HIf3n9dfJRMRtRl5IIlaw4QJEywzMzM4OXlg4YprNIsJBnE8d5jAw+EFnEUIkw4TM9axW265pVMiMZlSBsJwBOxPP/20+41ndYCQHiUEuYWYMJi4OQcTwTHHHBO37iy6hg8f7ibqwYMHu9+98MILdsMNNzg37nCLTlyPORcboexivcwjaAhfoOC27BfeVyYoI0455RS78MILXUg+2oXr3W233YLHzJ8/38466yy76KKL3GIHoch+++3nwvXhjcLCg8UUi1Ymbv9ihuO4P5999lnQYoOFAXGTESSglIjG888/7yyu8ABjgZGdne3uHQoglFm0P/3gpptucosmFgjvv/++W6BEY8mSJU54Addff70TTk2bNs1diwdKKRRKCK/oY2+99Za7lyis/AthPh9xxBGubeh/1ANlGv2NhQ9lsuDnGqgr/YYFDYIfYAFPW7DIO//888tYeb388stucUpd6BMsVvk9bcx5WXTS/6688kpbvXq1+zcWKLO4VoQ2tBUCrDPPPNPdd/o1+08//XTnRYcAyA8LSNqY55P7IYQQou7B+op50h/GFCUQ8xxzIgomNpRHvLQzhzFXo2jhe+YHvvPgM+s3DEowouDlHQEM68FYMJcSjg1hAx4vrO9eeeUV912s9V44nAtBDXMruYEQppG/kjXdr7/+6uZR/3p0m222cUII1pasBVD6UOdIUC7txByKcYgH9V6wYIFTqAEGK7Th3Xff7cpDeRWudIpGtPUha03WFKwFWFdh+BGvTWPBWpS2Yp73wNOMa2CtMGbMGLeuiUSia6ZEyxNCCBFbfuIJt3nX9OYoxmCMJXgf97yEmSMQ/vPOy7sdCg/mV8LAYtwBGF8yNz388MPuPR+jWebtaGHBOA9GA14Y1GgeycgYeKf03mM9L2bWDIccckhQPoKBxRdffOHeY1EqoGjBmxbD0fDQ/36YAxPxuE1kbmStscsuuzj5Du16xRVXOLlD+PxPeyL3QMH04IMPOuUa8y/1ps6jR4928iJkK355CAY03BvWWLQh7/4cR9uROzuWPIR3d2QXGI+yjsBA5bfffgsawqQiY/FApkN9qIvXlihU6BOUxz0ihzflsrZh3RcJ+hVrs2uuucbVFWNeFJ9+ZVoi9zmZvuiHNRVKMc5PebQVbZRsHf0gc6JtWMPRhz3ZnGQiolYTEKKWcPLJJwf69+8fsq+4uDjQoUOHwFFHHRWyn890748//jhiWfxu7dq1gcaNGwfuv//+4P5jjjmmzDnCKS0tDRQVFbn6bLHFFjGP5fzU4/vvvw888MADgc6dO7vff/jhh4GGDRsGVqxYETj77LMDXbt2Danb4Ycf7n7HlpeXF9h3330Dr7/+ekjZjz76aPCY8O3JJ5+MWS/OwTV429FHH+02/z6OiYV3/ssvvzxk/7bbbhvYfPPNQ9o0LS0t8Ntvv5Vpl88//9x9LikpCXTp0iUwevTo4DGTJk1yx7z//vvu8y233BJo0aJFzDpxPMcB7dypU6fArrvuGvX4PffcM7D//vsHkuGSSy4JNGjQIDB9+vSI3y9atCiQlZUVuOiii0L2H3bYYYHWrVsH2/XKK6909X377beDx1Cm//69+OKL7vPKlSuj1oe+c/rpp0fc37Jly8Dq1avj9uXrr78+0L59+zL14Pz+8mjPgoKC4D6ugWvl/oX390jQF4YOHRq1PkIIIWoPzAGNGjUKrhuYO5hTmQfefffd4LjP52+++abMWmHLLbd085DH5MmT3XrBmxfHjx/vfjthwoTgMcuXL3frIv+6yVuPMP8C6wY+P/PMM1HrHm29F77/3HPPdde4ZMmS4L7ff//d1fPuu+8O7uN8m266aZmyevbsGbMNKZ+51d8Oxx13XEgdOL//XIngrTFirQ9HjRoVSE9Pd/tzcnICO++8c+Dxxx8PqUskvPaeP3++u+9z584NjBw50u3z1tTe+Z977rmQ34avL5JdM4WXJ4QQIjn5CQwfPtyN/1OnTg3umzZtmts3btw493nixIlu3PU+eyD/GDx4cHAM55g33ngjah3C59XwOTvafu/8niwAPv3005B3zY8++sh9fu+990LKOuSQQwLDhg1LaZ4M32LhzWnbbLNNyH7kI7m5uYGlS5eGXNvYsWODx/COzjHXXnttcN+RRx4ZspZgjm3btq2TPwDXTTm//vpr1Dpxb5FxeCBXYj5F7hSJRGQs0a57t912i3kcMgKuYZdddnHzerR7jSzD61ORSOQ+J9IXo7UX65CZM2eG7E+2jv71zQcffODWbhdffHHEYyUTEbWVmhMPS4g4zJs3r4yFKBYFWGXipeInUpg7Qq3hTovFIpYkuLXideEPYxcNPD6wnsV7CY8ZtgceeCCh33pgrYMnDtYIWGlgiYEFQjhY2GARi9UDXidYs+IVgxUu1izhfPjhh87F2L9RdiwIneddBxsWFWz+fRyTCOFtz3USng5rVA8sbP1WqV5sXs8iBOsULF+og2cti7UN7e3VAw8XQrBhqYMXC9YxscB6ivJjJQylTKxXCI1CuyVihYQlFyF3PHf7cMhthdXsQQcdFLIfSyksnv19huvGa86DMnEd99oF7zP6Axa3hPyJF1ovHKx88Dryg/U0lju4X2O1xL3Gmprni+chFlhc+63EuI9ca6SQkZEgpA/nEUIIUTfAc8hbNxBu5uOPP3aeOP48OKy7CGHiwfzNWoh50vOiZttggw2c1acXNoX5FAtS5lwPPvvnzWjzNGs8rJfLC9alnB9PaA+ssAn/giWsH9aYfpgj41m+4mHMMV5ZeNngXcx+/1qF8Lx4YWFNmyisJ2KtDwnrQnl33HGHs97F4hiveqxlEwErZu47azysxLEqx7LaD9a3sUhmzZRIeUIIIWLLTzwIK0ekCg/eDZnbGJe9+Q8ijc+Eg2f+Z37nfR1PFLxvE/H2SBTeg5lH8dL14G/q7HntIiNhfmae9tYSbMzH1NEvjwjn5JNPLjNHRtoSIZIsirUOIXD94KXkwTs6bedvM+ZQ5mJCsQFyCt6zPXkG3tDIj0477TTn7eSFko+3JqI+keROqchY4s3JXA9rCbymkLmxTuA+xZKbUQfuF55ZrIdYF/hJ5D6Xpy/S11h/xiJeHT3woiYkHvIVvKUjIZmIqK1IgSRqDQi+w12cPWF0eAx3XFf94KLLhM3kQlx1BBcsCPgd5caDCRWlD664TGD8lok8kd96MOkhUCGeKqHFvNAk0UDhQsg34sgS0swLueblA/Jgocciyr/5BR2RoA38CyMmOTb/vvD489GI1PZMqP54sYSd8YNLN/jbj/ZkEcRCid8TKoZ299zTWTCQd4oFFe3IxIuQI7w9PAihA/4wOuEwsRN6j0UGrv0IQ66++mrnHh+N8NA84Xjh5cL7oPfZX1+EO15b+NvGaxeEaSxCUByxMOUFgHAB4bkXohFeB+B6b7nlFrdApa251wh9IF5/TuQ+xoLnlwSbQggh6gaeksIL/8HcTyjTWHMR8yTrMUKR+Q1X2JjfCIUWS/AVaW4Ln6fJaVgRoXypa6TzsS98/RFpjowVEhcIXYNAyMtFQKgXwsb4FUiE68GYhjULgjMUWF44vliwfoq3PkTpd84557h1KcIWwg+z/iI8Xzw8AyaUUIRwufbaa0PaHCUeeYtikcyaKZHyhBBCxJafeETKgcfY68lXGJ+Zl8PnDY7hXZm5ijEf2QghxZj7EcIz1xAWrSJgLmS+w7gChQH58/zzI2sO5onwtcSJJ57ojo9luMh7Pzmd4m2JEE0WFX7+SOsE/3s0oW0JNU8INs+g1gujC4RXQ8lD+NyjjjrKXQMGo+GKqmRkF8nKWCJdpwfGuMgqULAQ6g2jItYJGKnEkhcg88GYhVzQGE+z9jv77LODcoNE7nN5+mK8dWUidfTA6DeeEZNkIqK2IgWSqDWweAmPxYqAAMI9IIgd74cYu3hXsADBAoMY6ywIEpkYmewQ4iNkJ98LkyyTUSLeKuGw4MFKk98mY0XJBEXCSSZI4tiWFxYmfoECFhts/n3+XESxiNT2TOgsPpKBZJgILlgoMfGyUPCSbHoQ55ZFCOck0STxl4mdHwkvNj4earEmb7yPiNVLu7II4TPCk2hQbqwyvUV2tD4ZT7kXDm3Cwoe+Sr3w7gpvl2hEEp69+OKLwZxVWHFzr8uT9yAZeH6Vs0AIIeoOnpKCnDpYfkbK2Rg+FyFAYR8KkUjWvp5RA2u8SNa14Wu8cJhnECbEMgZJFObsSF621CHZ+TzW2hChGGs8rKvx1iK/ogftwNqIdRGWyazPsAD/559/rCJBOTNy5Mhgwu14eAZMCLYirSMSUeAls2aqqtyeQghRl+UnHtHmNk++wm8x6vQU/f5jGI89ZQgGj7xfctwnn3zi3q/JRRMvskUiIISn/shy8KRhTeBXIFFH5CTRvIciKck8UHCEKyQibYkQbQ7z2jIZkEfwzo9RB4bE4bm9MXr1jE2QmXDuESNGpCy7SFbG4id8XsagBC+d22+/3dWb6CWsE+IZkLJ2RBmDEovrJtcQOcHxvk7mPqfaFxNZX8SrowfXjucchj/RvKAkExG1FSmQRK2BF2YE/eFKByZmwn344UXcD5MWE4N/EYDbb3hy4XArEMB6FIWP31Nk1apV9sYbbyR9DYShYyPRYsOGDSMeE00w4rn9YmlSkwhve6xYESR5yS6TAa8YFkpMxEy6CKMigXKKRQluy9GEHPQX+gcKu0TAbR83YxYosQQnKF1IAhnNC4hFHf2MxYsf+huLGxY2qYDbOckjWUj76xepz8aCZ8Hfl7EC94cGKA/xPJKwTk9UMSmEEKJuQtgWEhAzl4V7yLB5IWKZT/HAZc714DOeL7FgniYEC/NuNBKdO0lUjdDKL0AjRC4eOnxXESAMQyjGupLNLxwLF17gsUTycdavyYSzC4fzRVKwVfVas7LWTEIIISLLTzwIl++fR/h74sSJwZCz3hwXPj7zmcgo4WHSGctRGBBBBa/UaEqLZCJY8H7OvIeXLhsGwHjh+ud75jPKjLSeCI/0UVkh7CLJovBC2WijjSxZCP/GWueII45wZURKzeB5gBOWlnB23ONo7UkbUR/kV/FIRMYSC09R5G93IukQ/SdRCH13/vnnu7ByXh2Svc+J9sVUiVRHD54LoryguEOeFUm2J5mIqK1Ujdm5EBXAVltt5SxF0OSjGACUFEwMWAPgespkh+sq7rJ+vPj5eG7gfYHlwG233VbGjRiXV6w8WaAQJoRJFEEGC5cbb7zRWT5gZcnfxOFPNPeLf0KJF3aEECC//PKLEyAQxo7FANeEhQPWJeFKFTxSqIsfPnMtVQF5i1jAEBcWRQTeMiiBUgGvLNr466+/DoZz8SBvDy7YuGkjVMBVG2sk4tBGAoUhiijakbxMuGJjhULZ3E9C9tGeKLu8RTBWPAiJ/PkWwiHkDteMOzmWJ1gJYwWM0OWmm25yfQZPNcLEoSTcfPPN3SLimWeecRY9ySjWCCNIffFCQlHK4hCLJH/8ZO4zwjXc2XFrJxxNLC8fnpEHH3zQWcZQV/pVvBA7iYKgh+vjGeI5YfNiVAMhjlhsCSGEqN8wRzLX4kmDYQTzF+s75jLWasz1zH2sLRCiML+yZiOUb7Q4/h4IGhCqEBr377//dsIwvHgRoBAOLtZ6L9KcjyEK8y4eU6zJ8JDq0qWLC2dSETAfI4Rg7UD5tIkHQiRCyhCqBkEgYXxYS9AWtE2qELqXkDWUyxoIQ6mvvvrKtTProopSjsWjItdMQggh4stPPJCd4J3B98B7LYJxb25jXtp///3duzaKAeYg3kOZK15//XV3DMYUvNsxb+GNypzFPM186oVdC8eTUYwdO9a9i8dTtPAuT914r2QeDn+v5RpYL4wePdrVmdxMyHpQiD300ENRyyWsW6zQbsnAWoO1C+uZn376ybUB6wfWNsmCLASDYy9qCHIWD2QshLcjtD3rEPJrM1dyn6MZJyNDIZoO8zpthExhypQpztCGz8nKWGKBco9+hnwOI1U8fyiffhULrpO2Yg3AvyicUGZ6XtGJ3OdU+mIyxKujH9apyO+23357tybFG8ovn5FMRNRaAkLUEtatWxdo2bJl4IEHHgjZX1paGrj66qsDbdq0CeTm5gb22WefwLvvvotZZeDjjz8OHvfEE08EevToEWjYsGFg8803D3z33XeBrl27Bk4//fTgMStWrAgceuih7jz8/phjjnH7p02bFthhhx1c+Z07dw7ccsstgSuvvDLQqFGjmHXm/JTz/fffRz3m7LPPdvXw+PrrrwMnnHBCoE+fPoHGjRsHmjZtGth4440Dt912WyA/Pz943KOPPurKjrTtuOOOSbUt1+lda6J45//qq68Cw4cPd+3apUuXMveHcvv37x+yb9myZe63lBHOySefHGjevHmgoKAgZP+bb77prqt169aBBg0aBHr27OnuQVFRUfAYyuTe+HnjjTcCm222matfs2bN3H38+eef3Xc333xzYOjQoa6NuZeDBw8OPPPMM3GvfebMmYEjjjgi0KJFC1fuhhtuGLj77ruD35eUlASuueYa1x5ZWVmB3r17B8aNGxdSRrT+Q134DmjbPffcM9C+fftAdna2K4/+snLlyuDxv/32W2CbbbYJ5OXlhbRpeN/2mD9/fmDEiBHu+LZt2wYuvPDCwIMPPuh+u2jRInfM9OnT3ecXX3wx+LtI5b366qvuOI734Dp5zjIzM913Hj/++GMgLS0t8Ndff8VtXyGEEDWfRNZBkdYAHqyN9thjDzfv5eTkuLny1FNPDcyaNSt4DH8zDzLXMheOGTOmzLrJW494cxiwXrrooouC83CnTp0Cxx9/fNz1XqT6Tpw4MbDzzju7NSBz5/777x+YMWNGyDGR1h933HFHyDwYixtuuCHi+o210IknnujWhLQR645ddtnFrWHLc28mT54cOOOMMwIDBgwINGnSxK03+/XrF7j88svdGi0Wkdo70fNHWl+UZ80khBAiefkJ7+7MrY888kigW7du7t162223DUyZMiXkuLVr1wbOOeecQLt27dy76MCBAwMvv/xy8PsFCxYEjjzySPfuRxnIYw444IDA1KlTg8dEmlevuuoqNy+np6cH5/Noc8ucOXPccbxH8g4e6RqRBTF3UEdkBdtvv72T/VQ23pxG3blO1gjIG1inFBYWBo+Ldm3IeCLJYJBHcPy3334bsv+PP/5w7Ys8ivbu0KFD4Nhjjw3MmzevzL0Nn/ORkTHfs5YZNGhQ4LnnnktYxpLIXO7B+mTYsGFu3cY9efzxx8v0gfD2eOyxxwJbbbVVULbCesQvW0nkPifSFyMRqb1SqWOkNlm4cGGgb9++Tsa0fPlyt08yEVGbSeN/1a3EEiJRsCogrqo/nImoHh577DFnaYM7cbL5jqKBBSwWIngHYU0j6g7EUcZbTs+uEEIIIYQQQlSP/ARvE/Le4ZkiUodQZET/wFsoWqi5VCByCvcMbyBRt5BMRNRmFMJO1CouuOACFwsXd1GS94q6ASFZuKeEl5k1a5adccYZ1V0lUYEQexjXci/cgRBCCCGEEEKIykXyk9oDCiNSGZAWgDDzom4hmYio7UiBJGoVxGzF8wWvF1F3ILEhiZSJ+XvPPfe4GMui7jBz5kyX24u8UUIIIYQQQgghKh/JT2oP5PlBznXMMce4PI6ibiGZiKjtKISdEEIIIYQQQgghhBBCCCGECCE99KMQQgghhBBCCCGEEEIIIYSo70iBJIQQQgghhBBCCCGEEEIIIUKQAkkIIYQQQgghhBBCCCGEEEKEIAWSEEIIIYQQQgghhBBCCCGECCEz9KOA4uJi+/nnn61t27aWni4dmxBCiLpNaWmpLViwwDbZZBPLzNTSoD7d97lz51peXp6lpaVVd3WEEEKISiMQCNiqVausQ4cOesevR2itI4QQNQfNxbUXSYkigPJo0003rfq7IYQQQlQj3333nQ0bNkz3oJ6A8qhz587VXQ0hhBCiypg1a5Z16tRJLV5P0FpHCCFqHpqLax9SIEUAzyNPkNa+fXurC1Y3S5YssZYtW9Ypa6u6eF26ptqD7lXtQPcpMebNm+cMJ7z5T9QP8DzyFvBNmjSpkOdt2bJl1rx58zozL1c1akO1YU1A/VBtWBf74cqVK53RhDf3ifqBd7+/nPi7NY5w7zOrwQO7xAJRv8uKUZ+S6D+zjBiXEet3mXEuvzAQ/cc5GdGfyxg/sxg/s1jVKS6N/l1WjAaId4szKqEPpKdYZGmMdkvEs6Oi+06MJrfC4tQqu7KwJOb3c1cVRv0uPca9Ko1x/flF0c9ZUBL9KhvG6KzNGmZZLIpi3MxYbd65aYOo3zXKSk+pzPJEmojVr2IRrVXxBN5oww00F9dCpECKgLdARXlUF6yTWHxnZ2dbmzZt6pRApy5el66p9qB7VTvQfUqOujKWisTwXiZQHlWUAokwwJSlvqQ2rC7UD9WGNQH1w5rbhgrZWr/w7jfKo7y8smudzFQl/eWgJIZANitGfWLIuWMqZWL9LjPOo1UYQwiek1lzFEjZUiBVuQJpXYoKpNJ1sRVIjQLron6XHuP5KI3RV9NiKJDSU1QgNcqpHAVSXl7DqN81zq79CiQPzcW1D0mKhBBCCCGEEEIIIYQQQgghRAhSIAkhhBBCCCGEEEIIIYQQQogQFMJOCCFqOLgNE8KjpCS2u3dFhAopKiqygoKCOhN+Stf0H1lZWZaRkVGNd0PUNhhzGBPq8/NW1agNk4dxLTMzU6EwhBBCCCGEEKISkAJJCCFqMIWFhTZv3jxbu3ZtlSiqEF6S2LCuxKTVNf0H95S8fo0bN67GOyJqC6tXr7bZs2cnFffae95Iel5XxpCqRm2YGrm5uS53KbkxhRBCCCGEEEJUHFIgCSFEDQVB7PTp0511dYcOHZxgrDKFsp6nU12y5NY1/dcOixYtcgqB3r17yxNJxPU8oq8glG/dunXC40FdfN6qGrVh8u2FoQXjG/Ml45sQQgghhBBCiIpDCiQhhKihIBRDidS5c2cnyK1s6qLgUtf0HygCZsyY4UKMKZSdiAV9hGeHPpOTk1Ovn7eqRm2YPPRRQnT++++/bt6UF5IQQgghhBBCVBwKUC+EEDUc5RIRFYEE+kJ9RtRVNE8KIYQQQgghROUgBZIQQgghhBBCCCGEEEIIIYQIQQokIYQQtZrddtvN3nnnnajfDx061B577LEq9fT55ZdfKq38XXfd1SZMmFBp5Yuq57PPPrO9997b5Tqj/7z22mtxf/PJJ5/Y4MGDrUGDBtarV68q7eO1Adpj0KBBFV7uqaeeahdeeGGFljlz5kxr3LixrVixIqHjCwoKbL/99rNmzZrZpptuajUR+nC3bt2quxpCCCGEIyMtLeKWnmZRt1gUlgRiboGARd1SJTsjLepGdaNtsX6XnhZ7y0qPvmWkWdQtLcYW7V6svx/RtwaZ0bdY50PoGWtLlVT7TklpIOpWWFKa8lZUGkhpW1NUGnVbVxyIusUqc+bKdVG3PxavibmtKiyOuq0rLom6xSIrIy3q1rZRdtStJBCIuq1YVxxzi/V8xOrnsfpyaSAQdUu1r8bbeDeNton6hRRIQgghysV2223nFhAffvhhyP5bbrnF7T/nnHOC+8aPH+8Enk2bNrXmzZvbsGHDQpQ/HE++J4Sp3oagNBoff/yxS56+xx57VMhdPPbYY0PqWxO55JJL7KKLLqruaogKZM2aNbbxxhvb2LFjEzp++vTptueee9r222/vlJX02RNPPNHee++9ejf2oEBjnGjRooX7/OOPP1bqOceNG2c33XRThZbZpUsXW716tRsXE+Gll16yP//80xYsWGDfffed1fZ7l5eXZ/3797cXX3wx4rPRpEkT22yzzaqlnkIIIYQQQghR35ECSQghRLnp06ePPfrooyH7+LzhhhsGP//999920EEHOQXI0qVLbd68eXbrrbc64aGfr776yglTve3VV1+Nel4E7scdd1y9uoPbbrut81T48ssvq7sqooLYfffd7brrroupLA1XYnTv3t1uu+0269u3r51xxhl24IEH2h133FHv7gnKHMaJuXPn2iabbGL77ruv1XVQIG6wwQZOAZMKRUVFVpPu3cqVK+3mm2+2I444wv7999+QY1544QXLyMiw77//3n777bdqq6sQQgghhBBC1FekQBJCCFFuDj30UOdd5IVg+vbbb92/fqvxn3/+2dq2bWsjRoxwAsGGDRva8OHDbZtttklZCPruu+/aDjvsELL/nnvusc6dO1vLli3t0ksvLRMqauedd7bWrVs7Dyi8OGbMmOG+u/vuu+3pp5+2e++911nFYxEPTz31lA0YMMApuvAUuPzyyy2QYPwJjkPI37NnT+chQbi9f/75J/j97bff7sqkbMI9PfTQQ0EB8U477eQ8EvjdVlttZWvXrg16aWG9/8Ybb6TUbqL28/XXX7v+ER7akP31FcaTE044webMmWNLliwJ7r/22mutTZs2buy588473T68Fjme58wfFo4xgbFr3bp1dvzxx1urVq3cM8jzjwIjkpfitGnTbJ999nFjCs/q/vvv7/bHKiMcxiCe6+XLl7vPXMdJJ53kxlXGBhT0hCyE888/313TW2+95capK6+80u1///33nQKNcxHa0O8RSp0p8+CDD3bePCggGUNGjx5tO+64ozVq1Mg233xz13ZXXXWVu5ZOnTqFKO8ZyxgjMQogdB6///3334Pfz54923bZZRdX/pAhQ2zKlCkJ3zuunbGYcvGs8vPwww87IwEU5/wthBBCCCGEEKJqkQKpkuGFG8tKhAOekFIIIeoaCP5Qjjz77LPu8yOPPFLGMwihIl4Cp512mlP84IVUHhDcolRBuOrx0UcfOaURVut4OMHkyZOD35eWltp5551n0/79256d8oEVbd7aCWrhrLPOst2uO862e+p0e2fOd8HfoYh65ZVX3FiO0uaBBx6wZ555JqE6Pvnkk05JRD4Qrh2lFLluiouLberUqXbZZZc5we+qVauc4NrLZ8I1kNdm8eLFLkwV4QAzMzOD5eJ1MnHixHK1n6i9zJ8/3ylE/PCZPpqfnx/1dyg1OMa/ec9F+Mb6JdnNo3TVSlv3+yTL/+YL92/JyhUplZfI+by/CXX24IMPWteuXZ0ih308wzk5OU658dxzz9moUaPsr7/+ckqdvfbay+VJ8n7PM04OKp5B9vN8McYsW7bMXn75Zde+4efEewZFHs81yijGHLzB+C5WGbGuxYMx7JRTTnG/PfLII50SiO/x2rz44otd/Rk3UPhwDjyvGE8YM/gepRbKaq9MxmYUWpTHv96+u+66yyncUFSh0EeJxlhFuYyNhYWFrgwU6yhwGANRwOEtx1hGn+L7ww8/3Nq3b+/aAKU798LfVrHuXUlJiRsj6buEcvT2//HHH87T8phjjrGjjz7aleudL9rm77+R+rW2xNtAbVj+/qI2rHltKIQQQgghkuc/aZSoFHhRR/joWQcjNBVCiFQ5+uVZtiS/uNIaELmelw+xZU6mPXFA54R/i8IIASbCPoSlhBvy5+oh5BbCQMJska8FQSPeQ/fff7/16NEjeBweSXgoeZx77rlBK/vw8ZV8Sf5j8SAiDNIWW2zhPiMEvf/VJ+zPpstt0pp/baNu3Zynz4riNfb48s8sd4/e9t6tLzmhQnp6uuW3zrSCZmb5JYX/1WeX7e3BeR9Yn2WrbLeNN7HDDjvMeQNwnkQUSCimNtpoI/d5zJgxTrBK3hJPmIyQG6E3nz2lQFZWlmsfDA969+5tW265ZUi5WPlz/UIkww033GBXX311xGcJpabfu49ngn3+/YlQtHyZlU78wUqWLrG0hg0tMHO6ZcyZZVmDN7P0vCYVesN4fgiJyTXhUYTygbGHOlN/FEVnn322O3brrbd2zz45knjeGKf4DmUtHjAofFBS8FvGFJRrjGEolLzxySuX8/L366+/7p5Vzu8lkmX8ildGON4+r70pH4U8debvo446yq644gqnTGZN6QlCvd+hCEL5g9II8PLEa5HxkDGYY1F04W3E39nZ2a5cxjJPAc9vGZ9GjhzpPhNuFAUSoUdRZhMuFM8nxnHgOMLOEXKU9vz8889dPSib4/ktyvZo/cd/71AKoaiifJR/3m8YK7mn/fr1c16lZ555plP0Ea4xUhtybXjB0vYo1zgH47pIHtpSbVg+1IapwXOLIhxPyIpuQ8oSQgghhBDJIwVSJUMoEYQKLHzLa20vhBAojxauKamRDYFwkjBJCAFR4LRr167MMYRWQqkCCCaxsMe6HiGkB4LIQYMGxT0flvJ4IGG9jsCwJFBq07OXW88tB1hxoMQy0zKccLftDn3tx9aLrcfKqdZuba4TGnOO9EP7WMmyfCssLXZCBcbrtj/kW/qibNtku/VCUnj6k9fsiZUf2ZpZS23B5R84YSc5a75a+adlWLr1b9TZGmc0jFhHPB8QWnuQswQvB/ajFHr88cddyD2Ub4SQQiDLteNxhPILoS9zCN4HCJA9AQqCaa5f1E94tlAm+OEzikU8bqKBZwoeeB70IwTz9CV+6w/nhlIJrze/51siFM2fa7Z8mWV36WppaekWCJRayexZZgvmWmbzFlaR8Gyg9PCHlPPgWUEh668/odoYM9i3xx57OG9IlNooaT/77DN74okn3Hcol2hPvIlmzZrllCs8kyikKJfzchzPMeEpGWfCiVYG5/fCYwIKZK+OXntTPt483n7GJsBDh33Ugc37Ho8hxhn/taKwYr93PEoe//fh5yAcnr+9vP5AX2AfuYkYh/wKe5Q+KLrpcyjwGNs8PEVTtP4Tfu/wDMOLCgUS8wIKIRRgF154oSuDPorXE2Mmof3C8a6TtkKJRfn8Rgqk1EBwrzYsH2rD5GF8PPXUU924jKEN40FF9sNk5zMhhBBCCLEeraIqGV60Ce2EIEbW4kKI8oJXUGUS7oGUDLzcIzS9/vrr7aWXXop7PIJXlDmEPkqWJUWrbH7bYmuxZU+XMwMLdaq9bveONjl7hc1dt9S6NGztPCkW/zTDNtt7e+uV084uvuBiJ6D46aefnHXrL7/8Yps8vEkwpFKDgjRrMs+sTXbToIB01PFn2ql3X2JbH7Kl7XPWS07giWfQi4u+svmFy2xU5xE2uPF67wI8mxp0bW6lgfVhUsgj4g9fSnkIddkP5CRhQzCMgghPg0mTJrmcLYSMAj6TtwkvpgMOOMDtI/cIlvmifoKC9p133gnZ98EHHwQ976KBApMtHE8h4f+M0M7bEsWFEVu1wtJyciwtfb2iIS0tw9Jyci2wcmVSZSVKtDp6+8K/845nfYZCBIUEXjh4iXtKbxRCeCaxoQTCU+eaa66x//3vfyFloLRBER7pPLHKIPSdH2+MiFTX8H/91+v9ixLwiy++CPk9Ch/yBnn7uN5obZFIe3EOckjhGRUOCjIUTYS2Y+zy9kUqL9r5UeKh1Hv77bedAJl/abfrrrvObrrpJncMYzehClHcUZ9IZXl92f+3SA21YflRGyYO4yA55MiXyXiFEgnFe0W2ocYDIYQQQojUkAKpCsBqCuWRPJCEEOUlmZByyeKFZfIs4FOBcHOEUmILB68flCGEV8JSnTwuhCgKD8+WCJPWzLQHF35ofY/dzj7++GOnQEpPS7etW/S151543iY1/9XabbKNE9gu/X66bTuvve3QbCMbt3KlC3uHYp+8H+HhvLDAxyOAtqAN8DZaM3upbZvR2/Zqu5nLU0T+oy223NIG5Ha27LRM653TPvj7b1dNs87X7m7PBn60wTbYeVcR1o9cISjMLr/8cuvYsaMLaYXia+bMmS5MFRbzeAB41rHkP8EjCSEpdUWY4rec/fTTT13ILVE3QKGAB4YH+XRQbmJ93aVLF+c5NGfOHOchAwjY8VwbPXq0y2dD7i/6DEL36iYtr6mVzp3tPI88D6RAQb6lN1mvlK1J0HZ4/KH0uO2224L7aU/afsCAAc5rCe+aSJbre+65p11wwQVO+UuoOJRGCD233377hMuoCA455BCnaCGkHnUiTxEeVZ4SuiI4/fTT3XXiWYTCDe81xl7CkDJOETKPNiDUHeMaoUmTFR6jFGV+APItITwOL4e55dFHH3V1EULUDSZMmODGMdZlGPe8+OKL7llXziIhhBBCiJqBzPKqAAQIgBJJC2EhRF0f7wi7FimkE8r09957z4YMGeIEqoSzYx8eAH5QKKFM8TaULeH0zGlrfXM72S4bbhmiSLlp69Ps/F772ykHHOPCMzHm+sNFoTBCUM95EXgSis4PuZkQ1HMdAwcOdInlEYiefPLJLqQT3lUIOVCvndB+J7upx1GWl/FfyLB1pUVWurbQOth6YTk5VcjbsdfeeznvhokTJ9qbb77pBMl4I6FQQmlFXhMEzt61kKfFawe8SggN6OU3QRFHvci1IuoGP/zwg22yySZuA8LM8bcnJCdMGEJ5D4T4KIvwOsITDeXHQw895LxoqpuMjp0tvUUrF7auZNFC9y+fMzt1sZoGYd6GDh3qQliiePHwPIZQ3tLWhEWLlIeN5/PDDz90zyuKPsYcxotkyqgIyDlEbiDKZ+xCcf7qq6+G5JYrL4Tiw2MLDwHGwr59+zplugd/43WEMg6vUpRz8SA8nTfOo0hn7qDP46U5fvx49xwwbvo3xlMUSJ7XqBCi9sJzzPy1yy67OOUR4zHjaSQjJCGEEEIIUX2kBfQGVgYvNAYvwl6YofKAQOf9998PKpEQJlQlCFAXLlzoXurrkut+XbwuXVPtoSruFSGB8ERA+Ij1emVTER5IlcWy4tX2yfLJNqLlpmXqxhhLWLlwZVB1XRPh64oDpZadnmmrSwrs5cXf2MyCRXZZlwMrpA7e9RJKKpnyYvWnip73RO0ALxIUGytWrCiTAymVscd73tLz11rJnFlWunKF8zxCeZSe91/5NQkUHShdbr31VqsJ1ORxuCbj77N4dOL1z32tK+vD6ljjqA3VhpUN4+6oUaPc3yio77vvvpA5p6L7YbQ5T9RtvPv+2/Q5lhdhLZIRo2uVxrBVKCyJbciQlR59Di+1QEq/y4ixLiiNYViRXo71REmMchtkRC83VvPEusZYpHoZ8UaPVNdbKV6GlcToWEWxOl05iHU/YtUnVt+JVdd5qwujf7dqnaVKw8zodzMrxjxRVLo+vHwkcrP+y+8ZztL8oujnizV4mFledkZK7dqladlw4x45mWmpjR2pdtY442AsdUJpjDG5W8f2motrIXqrqgL8ic4Vxk4IIZKjqLTYrv73BXth0Zf2xpLvy3yPV1Mk5VF1QSg9lEeeR9JHyybZlLWz7I/8ORVS/rvvvuss9YWoqaAsyu47wBputpX7t6Yqj8hfRL62U045pbqrIoQQ9Q4U+ITEJCTrI488UiXGUqJywaMXI6euXbtaTk6O86b//vuya3cPvGfJ80noQpR6eN2zrvdz1VVXheQAZNtwww11K4UQQlQrY8eOdTlxWb9sttlm9t1338Wc7/C0xqGEaDyEUX/yySetNqEcSFUYws5TIFVkSBEhhKjrZKVn2l4thtpbS36wzZtsYLWJlll5dky77axVZp4LuSeEqBmgNCLsGnl7evfuXd3VEUKIegF5JslbiRKAd+Rff/3VeQ2KugGhoH/77TcnFCPf6VNPPeWMnqZMmeJygIZDvj4USGPGjHFCNUKUkjeUnKNeWF8gHDUhYz1SySmIs0wkh4FY1vWxvG/iOB/EJJanQCw/gVheRql6GMT7WUaMGsXylUnVyygWqTZ5PA+jSqhqTK+eOM5rUckvjv3DVYUlUb+btaIgJW+Yto2je8OsKIjunbNgTXQPpHXF0b2B4vXzdcXRe0GbGHXNSrH3tG0Uvcy1RdHbO55nU05WLE+qtJTuVXVELoh1J6PdxroShfr555934bbHjRvnlEd33nmnixRDnmuiFIXDmufSSy91BhCse9566y077rjj3LE1IQx9IsgDqYoVSISwE0IIER+/S/ROzQe6fENts6s2BGhFsEOzjWxg427VXQ0hhI/777/fWUqzkBdCCFH5a7qbb77Z5Zd84IEHgvulPKo75Ofn28svv+zu87bbbuvy8+E9xL+EJ4wEArfRo0fbsGHDnDEHiiT+JV+oHxRG/nx4rVq1qqKrEkIIIcpy++2320knneSUQBjGoEjKzc11HtWR2G677Wy//fZzeWR79uxpZ599tlsTffHFF7WmeeWBVAUohJ0QQiTHp8sn2xcrf7fRnUY4DyRokJ5V65uxsLTYigLF1iij9odpeW7hF/b4go9tcdEq2yCng13UZT/bqFHXiMcWBUrs4Xkf2ptLfrCFRSusW8PWdk7HvWyrpn0jHv/w/Al295y37Yg229jozvtV8pUIIYQQorJYs2aNC1f3wgsvuM+//PKLGrsOQv6+kpKSMqEICWWXqICMvFcYd/gNcGHatGnOo4myCXN3ww03WJcuXSKWsW7dOrf5820IIYSoHzlRCwuje7/FM3QJ9+Jq0KCB28IpLCy0H3/80S6++OLgPnI14nH79ddfJ3Sujz76yHkr3XTTTVZbkAdSFXDEEUfYxIkTXXLyESNGVMUphRCi1rKyON+eWPCJ/bZmpn284jerK/y6eoad989j9vTCz6228+7Sn+3W2a/bKe13tef6nmd9cjvYadMesCVFqyIef8+cd+ylxV87JdOr/S+0g1pvaef+/aj9vnZ2mWO57y8t+to2yGlfBVcihBBCiMrMNYfAH+VRVlaW80S599571eB1kLy8PHevr732Wps7d65TJhHCDmHavHnzEirj1ltvtdWrV9vBBx8c3EdooMcee8zlAKX/TJ8+3bbZZhunaIoEyqWmTZsGt86dO1fYNQohhKi5yqOWOY1Dxv9ktk6dOpXZx3wSicWLF7s5rm3btiH7+Tx//vyodVyxYoU1btzYeV/vueee9r///c+Fca0tyAOpCvBcrYUQQsSnSWaOnddpH6dI2LnZxnWmyRqmZ9uSopXuugpKC93n2sqTCz61/VttbiNabeo+X9blQPtsxRR7bcl3dkK7Hcsc//bSH+3EdjvZNk37uc8Ht97Kvlk51SkKb+h+ZPC4tSXr7OLpT9uVXQ+2B+d9UIVXJIQQQoiKBIH/YYcdZsuXL3dCFcKbbbXVVmrkOgy5j/A2I99RRkaGDR482PUBLLXjQV7Cq6++2l5//fWQ/BG777578G/C/aBQ6tq1q1NKnnDCCWXKwSKcvBR+DyQpkYQQom6DV9BaK7EjrKNlJ+krU2il9vTqOc7po0mTJsH9kbyPymtogRc2hhITJkxwc1WPHj1ceLvagBRIQgghqp2i0mJbWZJvLbPy3Of+jTq7rS6xQW4HO6fj3rZJ4+6W/f9h+WoaWHP6Q31EctvmXuE5dEL7/xRF6WnptnneBs7LKlrovvBrJiThL6unh+wbM/Nl27ZpX9u8yQZSIAkhhBC1lBkzZtjee+/twpptvvnm9tJLLzmlgqjbkNfh008/dWELWU+2b9/eDjnkECcgi8Vzzz1nJ554or344osuBFAsmjVrZhtssIH99ddfEb+PFnJICCFE3SfH0i07LTkFUsb/p95GeeRXIEWjVatWzkhiwYIFIfv5HMt5hDB35AWEQYMG2e+//+68nGqLAkkh7IQQQlQreJ3cMOsVu+bfF2x58Zo6fTc2a9K7xiqPgASQ8dy2lxWvsRIrtZaZ65V9Hij/yIcUiS2b9HFeS/8WLLLSQKl9vfJP+2jZJFtU9J+yavzSn51i6qyOe1bClQkhhBCiqujWrZtdc801LsH0J598IuVRPaNRo0ZOebRs2TJ77733bN9994167LPPPuuSkPMvIX3igeU2oREpXwghhPCTkZaW0pYM2dnZNmTIEOdF5M/hx2dCuSYKv/Hn7Kvp1FwpVh2CDvH888/b0qVLnfs+btxCCFEfIGb5nXfeGTNhclGg2OXOwQNpYeEKa5bZKG65p556qlNwVGTSwWOPPdZZNVLfqmDymlnWrriRbTp4mH3zzTfWunXrcpWHkGbTTTd1/6bKlClTQoQ8FWXBObrzfk5BOGLyjZZmadapQUvbt9Wm9trib9338wuX2c2zXrX7e5/qPJOEqC1gPXbOOee48aMiYKwk5A8vFEIIUZuYNm2as8j1vE0uuuiiMgmpRd0GZRHJwfv06eM8hEaNGmUbbrihUxB54eXmzJljTzzxRDBs3THHHGN33XWXC03n5Y7Iyclx63y44IILnDcbYevIrXTllVe6fiaZihBCiHDS01AipeBZ8/9eSIly3nnnuflr6NChTgaDDAnvW2++O/roo51cxTPI5V+OxVMXHcE777zjwr6S26+2IA+kKgAhAB3r3HPPtXHjxlXFKYUQosrA5RZFAwkBW7Ro4T4nEuvco2lmI7u4y/4u7w1h3hKBsbQilUdVzf3z3rfrZr5oo1641UaMGBFUHl111VWWmZnp2tLbvBdkEhAffvjhzuKSdmbxwbzicemll7qX6vJYsRCX13PdZoukQGqe2cgyLN2WFId6G6EEbPX/IQjDaZHV2O7sdbx9s8mNNn6jy+z1/hdZTnq2dWzQ0n0/Ze1sW1q82g79/XYb/OMFbvth9d/2zMIv3N8lAQnTRVn+/PNPJ1QijAD9FSGVf1zAAv61116rU02HogqrN298YBwYO3ZsmeMQ4BEigRcXkrwKIURl8vbbb9uwYcPcmgYBCkh5VP8gQfjpp5/u5mOEZ1tvvbVTKmVlZQXXsjNnzgwe/8ADD7gwh/yG9a23nX322cFjZs+e7dbCKKUOPvhga9myZYUYXgkhhKh7VIUHEhCe9dZbb7UrrrjCGRRiBEjuR5xGgLmOOc+DtdHIkSOtf//+Lh8keSGfeuopF761tiAPpCoACxq2/Px858YthBB1DYS2WOEXFBQ460JCVfDCF42pa+daUaAkmOeoXXZzq0/0yelgny6fbB9//bm9eV6o1clee+0VUeh91FFHWZcuXVys3NzcXJfk8csvvwwRlhMTnjwDRxxxRKXVPSs90/rmdrJvV06zHZpt5PYRlu7bVdPs0DZbx/wt3kVts5u5ez9h+a+2S/NBbv9meb3tpX6jQo69csZz1q1hGzuu3Q6WkWQcY1E/INTNoYce6ry8UXb+8ccfzouutlBUVBQUqiUDLx+ep+T3339v22+/vXtx8SenJ2QULy60y/jx4924IoQQlWEoOWbMGCdAQXGNIQpCEkKYifoHCh62WJEJ/DBXxYP8SEIIIUQiZKTggZSRYtOeccYZbotE+Px23XXXua02I4lMFdG8+XrhKGHshBCirtKwYUM74YQTXHiKJUuWBPdfe+211qZNG2eRceUDN9v1M1+yG6a/ZHk92tj06dODx6GAYrz89ttvnSfN8ccf77wLCGMxYMAAJyz1rPBRWPnDpuyzzz7OGhHvnP3339/tpwxCurE/vIx4/PDDD04gS1g7cgMRm93jp59+ckmh8XqgfnhBAMKTCy+80CVP5DsUOm+99VaZsrdt2s8OX7aRrXnrT1enRMDaEpdo6kMCRjwP8G71s+OOO9obb7xhlc1RbYfbK4u/sTeWfG//5C+w62a+ZPmlhTai5abu+0unP2N3zfnvun9d8699uOxXm71uif206h8bOe0BKw0E7Ni2O7jvG2U0tN457UM2PJSaZea6v4UIZ/HixS4HwimnnOIUqoSzwaLroIMOct/zLwoUrJbx1CHsJYwePdqFwUHIyXNNwm7/Qp/n66GHHrLOnTs7K2eO93PPPfcEv8Przw/n23nnnd14wziGgotE8h6MW4yPCNcYH/CkXL58ufvMebHY/uyzz5K62Vj8cx2TJ08O2f/www87pdEBBxzg/hZCiIpm5cqVboy5/PLL3frntNNOs48//tit94QQQggh6qoHUn1ECqQqAoEmSIEkhKjLrF271glfEdAiYAUEmwh4USrhKTDm7MutdVGuDWjS1XbedLg9/vjjwd+/+uqr1qFDBxcHnf0TJ050MdQRsr7yyitOMRMOlq477bSTU8QgrCV++plnnum+o4xff/3VKZhilREOx+62227Ou2HRokUuNi2KKM/jB0sTlEYcx3UR4x0++OADF88dBROClQ8//NApkcJJT0u3mb9MdQLjREGZhdKMuPFTp06NeAyC5Fj5piqK3VpsYud12tvunfuuHfz7rfbn2rl2b++TreX/h7Ajp9Hiov9C3BWWFtnYueNtv8k32bl/P2ptspraY33OtCaZOZVeV1H1rCgosV8X5NtnM9a4f/lc0TC+EM4GpeoLL7xg//77b8j3KIbw2EPxS8JtL4Twxhtv7JTIPLtYzOPZ51dir1q1ynkxMWZ88cUXLjycZ0H20UcfOaUR5/NCEvz2228hlvjEw8Y7kPow7oXnJKM+KJE4P/+eddZZ7m/GLsonFnaiILD9+uuvXSg/FNoelEdYBBRWKJlRYi9YsCDpNhZCiGgw7rBWw2OasJqs/e699173txBCCCGEqFsohF0VeyARxg4Le6z0hRAiWRafe6KVLqtET8ZAgKD17s/05i2s1R0PJfQzwtaRv4exjVBKfi8YPHTOP/989zf5kbp16mJDpjWxg/bb1z44JsfFPSd3D7HyCW3hJR4ktBPCXEK2IaSIpIgBhKMce/311wfj7RPSKbwMBKzRyogUyx8vAk8RNXz4cJd/CIUUihzKRUBMMt9OnTrZtttuGzwfYzxKM36PADsahDTFC2Fp0Wr7cuUftleLIcFz443ggdJt1113dQLx22+/3SUanjRpklO0kYyRenlQXlWFSj2szTZui8TDfU4P+Tw0r5e92v/CpMoPL0PUDlauK7Fv56yzxWtLLCcr3WYsK7XZK4psqy6NrGnDVAMElIVnHcXOLbfcYldffbULX4dCiecDL6Bo+MM7oiC+8cYb7auvvrLu3bsHlTKEF2As69u3r2255ZYupxtj19NPP+1+v8UWW7hjGfPwSPKHkWQDfo+yiXEHxRJeg7DLLru45xkIL8fz/fnnn7tnno1k4fESg6PQZqwkBB5Ke8apjTZaH04SUGLjYbX77ru7nGqMFSiePUW3EEKUF5TfjLvkWcM4hwTSQtRkigIBKypNMkt6DLLI1J4imTF+W5xiHXmFi0ZGDLPxeKdLNhSUR6oG/bEs3GtaXrWSGI1XEqNdY/XDdcXRv1uSXxyzPsvyi6J+N2/VupT6wKwVBVG/a56TfBhmWFecem7bktLoRmmL1xZG/a5hjIegaUZ0sXhBSfS6NsiM7Y/Be1A0mjWI/k4Uq9hUn8fywLtRNGK1QEk9CGFX35AHUhV7IIHyIAkhUgXlUemSRZW3LV38399JKKpQZGD1jvcPyQMHDhwY/K5Nu7b2yPwJLoQZEBd/3cq1lpmW4QSphYWF9umnnzpPHv7FIwD4Fwt6Qk+hhOJvwlaFgyKHcG6RXioogyS+hFWJVUY45G/yBMEePXr0COZ1euSRR5yiaMiQIc6LyBMio7hCmE04F85HaBe/d0O4YcGK/NU2evoT9szCz+yXNetDXRH2irb0Nk/YjHIIgTXC7IULFzqhMdeGcswDryfPYEGI6uDfFUVOedSlWba1bZzl/l28ptj+XRH9pS5V8Ca87bbbnMIWT0EUJvvtt19Mb+877rjDhbojpCUKGzyI/GMCzxmeQx6MVyihAYUx3pUeKIxJ9u1BHVDoEuKOclAsE0bT+z34lcqcl/HPX6b/72gwnjE24H3JmISX5SWXXBL8npB11IP6MS4yDjJmCSFERcGYwhqHNYmUR0IIIYSoCSiEXeUhBVI1KJAUxk4IkSp4BaW3bF15W4tW//3d/L9xq1z0a2EfLJtoj87/yBYWrgi9nvR0p9TBmh4LeZQl5EkCLOcRihLGDiUJ+UVQzoSDwJVcKJGsYyjjoosucmHdYpURDl5F/twlwGf2Awor6ovCjLAteA0gRPES3JOvyEtgj5VuJPDUmvrb77Z90wHWO6eDy/eTKOR0wasLITjhtjz4m3KFqC6WF5Q6i7t0z5MxLc19Xp6furVhoussFKwoVTylref140FIOo7h2cWYByUMoS9jWdb5wZPHHyoPDyAvlJ3niYlHkBfC0stn5C/fXyeUzJ43owfjRjJg/X/ggQcGc60x1nF+xlQUbGyE4cNTwAvBKYQQybJixQrn3egfe1566aXgmk0IIYQQorrhDTQ9ya1m+TbWXBTCrorwW4TLA0kIkSqJhpRLBYScxcXFTulSoSECJi+1XZoPsr65naxNdtMyXx9//PFO6UHSZbwJPMgHglAYAS9eAISEom7h4LGDAod8JiiLEMgiKMUbiDLwBKD8WGWEs8ceezjFD/H8Tz75ZJdnhPBV48ePd9/7lV14MSAUzsjIcLlVECoPHTrUcnJy3DkJXRoJz2J3wKJmdnj/beK2OeGnCJ9FWC3uEwJihOV4QfnbjPYUorpo1jDdZq8sstJAwCmP+De/qNSa5VSszRJrKcaLI4880nr37u08fQjxyJjh5Rbj+US57IFSh+eU8JKEleMZ8ucwigeh5bC4J6/QJptsYtdee617Bv3l473EmLBkyZK4ymrqcvDBB7ux67nnnnPKp1tvvTWpdsAbkXxHXgg7vI+o2zvvvBNyHO3Ed4TgFEKIZEABPWLECJf3iBxHKK2FEEIIIWqqB1JSv5EKKSHkgVRFyANJCFGfWFNSYKWB9R4HTN/HtdvBNm8SOf8QoeFQuBDmCWWQB0nfEdgijCU/Cd425EqK5I3z4YcfOg8gwkMRUgqLe68MwjehxI9VRjgcj7LoqaeespYtWzolEnlHtt56a/c959t4443duffdd1+XhwUlFQJkPJD4DZb/hLwiJ0skUGSdcsop9uRjTySksENATs4WvBYIkYXw+/XXXw+G2sOLASHPQQcdFLcsISqLrk2zrFVuhs1cXmgLVhe5f1s1yrSuTSs2sTpCTMJeouzluebZR3HMc4viFvBgJLwkYwjP5W677eYEnyhb8CYi9F0yCpWddtrJKY1QIjHOoIRCwe2Bwuivv/5y4wflElIvHv/73//cOIIn5Q477OAUPfFAsc1v2AgXynhAOYTVRNF99tlnB72PvO2cc86xF154ISScnhBCxOO1115zBi8ojxhrwsP7CiGEEELUFLwcSMluIj5pgUTjdtQjiCfPAnnWrFnBcEXl5dFHH3VCBxRJY8aMcUmUqwoEHFioYt0fHs6lNlMXr0vXVHuoinuFMJBQTCg+8JypbCrKA2lJ0SobM/Nl26hRFzum7fYJlYXXDONjstb31eZVVUGgcMJbgJB3LVq1tAnLf7WVxfl2YOstkr4mlFzDhg2zk046Ken+VBnznqj50P9QvhCaCE+98o49Xt9cU5xmM1cWubB1eB6hPGraUOlJ68KYVVPx91mUi4SLZk6pK+vD6ljjqA3rdxtSf0J+8v4Kw4cPd0po1r21tQ2jzXmibuPd91+nz7a8vIq771npqc/RmTF+W1yamngulsV9RozHJ97pUhWsZqTYPrGe9MpaF6V6K0tiNF5JjHYtivG7dcXRv1uSXxyzPsvyi6J+N3NF5IgYkGKXs+Y5WVG/K4khZl5ZEPs6Ur1XjRtEjzLSMMZD0LRhZkr3Md4Y0DwnernNGkR/L8qO8dDFOmes56Mcw1XMfh7zd4HoY3L3Tu0rfC72xvorc3pYw7Tk1gwFgVK7Ov8frQ/ioBB2VcRxxx3nNiGEqOv8lT/P5hYutYLSQtuv1WbWNHO9N0A0CDFFHH0vh1B9gkWTF2Lr97WzXZ6odEu3rZpuaO2z/wt9mggPPPBAJdVSiORAWTQwxkuTEEKImgv54fCGfPvtt91nvBrxtCZEsBBCCCFETWW9R1GyIexEIujtXgghRIWyWZMNbGRgN9swt1Nc5REh3EjKTO4i8pjUZ8gRNbxpf+vesK21zpJlrBBCCCGqHvIpojzCAxXjFEIBCyGEEELUdFIJSScFUmJIgSSEEKLczChYaG2zm1lO+vo8J9s07ZfQ7+6//363ifWc2mFXNYUQQgghqo3999/fhVzfddddbfDgwboTQgghhKgV4H2UvAeSwoYnQu0LyCyEEKJGMWnNv3bVv8/bHbPftKLS1GMai7K5UEoDpWoWIYQQQlQaJSUldsMNN9j8+fOD+y6++GIpj4QQQghRqyDfU0aSW3lyRNUnpECqIvLz85011/bbb28jR44sd3nFgRL7YdVftqokejI8IYSoChr+v9cRlJgUHhXBP/kL7Op/X7CPlv9WIeUJIYQQQoSzbNky22uvveySSy6xgw46yEpLtY4TQgghRO32QEp2E/FRCLsqokGDBvbaa685i/I1a9aUu7yvVv5p98191zLS0u32HsdZm+ymFVJPIYRIlt457e2qrodYx+wWlpWuaaUimJo/1/7Mn2NLilfZ9s0GuLFeCCGEEKKi+O2332zEiBH2999/W05Ojp122mmWnq71hqibEKIokpCwsCQQ9Tc5mdGFiulxBI6xvi4pjX7OWKVmxUjsURTjOtICqQtHY11naSCQWpkp1yZGmeWQ/8a6H7FU6rEuP1a/yi+OXuqC1UVRv1u8tjBGbcyKYlxHw8zoWV4KikssFVYURK9rZd2r3Kzo15EVo+DVhdGvMT3G71rlZkX9rlnD2HKPxlnRe3pOjO9iPR9pMZ7HVNs1RreJe07k29F/l9z+ikI5kCoPrRCrCBbjzZo1c38vXbq03OVt3KirNcnItcy0DGuamVsBNRRCVDQzCxbZWX89bA/O+8BKAqUu1Ftd4aPlk2xx0crg524N20h5VIHs1Hyg7dVyqFPMSXkkhBBCiIrkxRdftM0339wpj7p27WpfffWVHX744WpkIYQQQghRBpmKVyEtWrRwYQLYykvTzEZ2c4+jLM3SrUF6dK24EKL6+Ktgvi0qWmGz1i2xG2a+YlPWzrLRnUfYoMbda/Vt+WDZRHtk/gRrl93cru92uOVmNKjuKtU5MA44os221V0NIYQQQtSxfEeXXnqp3XTTTe7zjjvuaM8995y1atWquqsmhBBCCFEu5IFUecgDqQpp3ry5+xcFUkXEl0aJ1CQzJ/j58xVT7BPlyxCixrB53gZ2cZcD7ODWW1q77GaWnZ5phYFiq+0MbtzDWmU1sW2a9rUcX/6jygb36V9++SXid1dddZULw1JXWVa8urqrIESdZ9q0aTZs2DDLy8uz888/3+oi/fv3t7feequ6qyGEqCbWrl1rr7/+uvt71KhR9u6770p5JIQQQog6gXIgVR7yQKpiDyQvTuSKFSuCCqVU+Hn1dCssLbINcjtY88zGNqNgoY2b+76VWqkT7A5o1KUCay6ESAU8cwY26ur+7pPTwfZoMdg6NFg/DtRmWmbl2U3dj3LX17hx4+D+/Px8y8zMtKys9V6R22yzjY0fPz5iGccee6wL63nnnXdWWb1rI8wXzyz63MYv/cku7XKg9c3tVN1VEqLGsN1229nXX3/txpzs7GwbOHCg3XbbbTZkyJCUysMinzK+//77CqkbSu1zzjnHahKTJ09O6Lhu3bq58bkuK+aFqI+gICcvLwY5hxxySHVXRwghhBCiwiBLVox0cZF/k1o6t3pHtXsgjR071r2kNmzY0DbbbDP77rvvYr70HnDAAe54LNGjCR6TKbM6FEhQ3jB2Ly362u6c85ZNz1/oPndt0Nr2bDnEtm7a1/pJwChEjSMrPTNEeVRUWhwz6WBNgrqOnTvepq6dG9znha1bvXp1cENhhADW+xxNeSQSh7muoLTI5dD6YdXfajohwvDGnLlz59omm2xi++67b9JtVFS0Pgnw9OnTbaONNlIbCyHqFISou+eee4Kf+/TpI+WREEIIIeoc6WlpSXsh8RtRwxVIzz//vJ133nl25ZVX2k8//WQbb7yx7brrrrZw4XqlSCSX+x49etiNN95o7dq1q5AyqxK/x9HSpUvLVVb3hm1sg5wO1iKrcVDIeHibbey09rtaetr624pwuihQUs5aCyFSYfa6Jc5rBO/AcOYVLrNLZjxtHy7/tVY07utLvrcvVvxut81+w9aVrhe0JsL777/vBLpNmza1wYMH24cffuj233333fb000/bvffe6zyYCKkETz31lA0YMMBZx3bp0sUuv/zylJVsf/31lxv7W7ZsaRtuuGGIwQFC4p122snVC8X+Vltt5eYXuP322925qQOGCA899JBVNwe12sIu7LyfHdV2eHVXRYgaC0ZDJ5xwgs2ZM8eWLFnilEpnnHGGe57btGljRx99tPP+hhkzZrh106OPPmq9evWyTp062aabbmqffPKJXXjhhW5c8sYrBK94JeExSXg7Es17FBYW2hVXXGE9e/Z0YwbKJ9aehL/7/PPPg2XtvvvuEeu8cuVKV0cS2Ddp0sSVP2vWLPfdggUL7OCDD7bWrVu7ayBnSXHx+hCo1JP6MD517tzZjXOjR49OaIxjXMP7INZxBx10kM2cOdMOO+wwV/9TTz3VHc9a+ogjjrD27dtbhw4dnHfVunXrEqoTfPDBB86wi+Mo44YbbnDKu7Zt27rf++nbt69b0wshUocxgzB1PMs8rz/88IOaUwghhBB1PgdSspuo4SHsENSddNJJdtxxx7nP48aNs7ffftseeeQRu+iii8ocz4s1G0T6PpUyq8sDqbwKpBPb7xRxv6c8gmcXfWF/5c+zczvsZdXBzIJF1qlBy2CdVpXkW6P0BiF1FKKu8tPqf+zZhZ/bpnm97dxOe4d89/Pqf5yC6Y0l39t2Tfs776SazN4th7qxBC/HBunrw9MlosDBEwBF0T777OMElvyLJ+lZZ53lhKzhIewQOL7yyivWu3dvmzhxolMAofxBYJmswGSvvfYKnnfKlCm29957OyHl4Ycf7gSxCI09DynCVRF6b+rUqXbZZZe5unFeBLhs1U2TzFwb1Lh7dVdDCFuzZk3UVsjIyLAGDdZ7JsY7Nj093XJyciIe26hRo5RaGsUHyguUMYwlKF94rn/99VcX4u7EE090yponn3wy+Js33njDCVQJf5ebm1sm7Nw777xjF1xwgTtu0KBBbjxhLGGs4BysKz/77DOXQ4Qxhf0osgij9+OPP8YNYUcoT+pNGD4Moxj3vHZh3GMfSh4UYnvssYdrm0suucR9v2rVKje2kbeJY4YOHeqO4RqijXHhRDvuxRdfLBPCDmU+YypKpr///tuFLD3wwAPtuuuus2uvvTZunX7++Wc3J9D+lMN1//777+7eHHXUUfbYY4+544D2YOxV+DwhUmfx4sV26KGH2oQJE9xnFEkY9QghhBBCCJEs1Sa1xGqTl+uLL744RKCAJSQvjlVZJtaTngWl9wIMpaWlbqsMDySEARVZdjhLi1bZB8smWn5JoU1aPdO6B5pW6vnCeWrhZzZ+2U92YtudbPtmA2zWusV26+zXbbO8DZynVEXA9SDQqKjroiwskquTir6mmkBdvKZErqtNZhPbpFF3G5DbpcwxuzYdZGuLC92zkWHpUcvwzuFtfkX5HXfcEbeOeP14iZI9EOChIInHWeedY6POXZ9EPjst00Z3GuGej3geQV5dsdpHGLjffvu5/YQffeCBB+yZZ54JCkDDr2u33XYL/o33KIKPjz/+2Cl9wsuPdF7v32+++cbmzZvnhJoIhvEKOP30052AEitcBJZ8j4ATZdUWW2wRnC/4/W+//Rb0WmCrSaEGC0uL7e+C+dYrq637nEzdvLaLNLfVtedTVA7+nGfhoCh46623gp95djyvl3CGDx8e4nGCsgJhJyT7vLHmu+qqq5ziBiUPyp5FixbZyy+/7MpEUQ3XXHON83ZkHPDAW937PlpIZISujKWw//77O+UQiqUjjzzS7r//fqd8YRzxwkIlCgqSV1991f7991/nzQMId7l+9n300Uc2f/581+ZsKHu4Tv/4ifKG68ZbZ8stt3RrYMZdb4zD04q68V0kEj0OULShGMIDi7EShRt1wTvJUyDFqhPjP2M6cwHg9bT55pu7v/EcwwOMEFtcK/eIcd+vkBRCJA4KW9ZfjCUonvG2xLNQCCGEEKIu44WlS/Y3ogYrkHipLykpcRbhfvj8xx9/VGmZhNC4+uqry+xHyYPwsaIgxMnJJ5/sXpoJ3VHZYfVOz93B/ipaYN3ym9jyFcvdiz0v/VVB5tpSF5bk9yUzrH9hG5tUMN3m5S+1Lwun2Nal3a1hevnbFYEn4Wgq4rqcwHv1N9YyvbHtnDug2hRJFXlNNYW6eE2JXFc3a2rdsrcwK1wf9iecra27FS1dYwstupU+zxDnwaPGC10Ey5cvd2Ga4kFYJv/vgLrE+21my0Y2ofcCG7p0km3TpK8liqec4JyEYUIJ4z8/QmL2s89Tjvm/J+QdwkeElFw7in28kPzHhLeFh6cU4TsEJoyx3BfKYV7AK8E795gxY5zAc+edd3bPOtbveB5xzMMPP+yEmMcff7wLtcSxCKVrAsuL19jVs1605SVr7IaOh1lba57UWOW1O3MbgmM/7BOiNsIaLtzLB08a+nr37qGee4wJKGU8GKNigWIFJQmKJg/GFMZQlFQoyDzlUbIwTqEgiVQHykcJ41/PEsJ59uzZwc+EvEOJ44GQ2DOAuuWWW5yyCQMqxgg8nQi1Fz5XJXqc1xbMPX5vesZwxtdE6sT1kicvEiibCF/60ksvOSUToetQoAkhkgdDHZSyBQUF7t0Tz0meLyGEEEKIuk4qIekUwi4xanbcpCoC61XyJvlf3Pv16+fCk2BBW1EQsoOtvJA/5abZr1mbrCZ2Sef1lpyRaGNtbLBt6IQoCAaatGxm84tXuPxJFc0vq6dby6w869yglft8QKClDS3oY71y2rvPe1gba7wizzZp3N3yMv4LW1MevOsiP0B5FROT1vxr3y6bbshit2k/0Lo2bG3VQUVeU02hLl5TRV/Xn2vn2u/5s21Ey01D9vPyj/CNkEL+8ENYzHfs2DFuuYxf4WGL2Bfvtw127m5Feen2+rLvbetmfRMOsUd70BackxwYX375Zcj5yamBAJF9hLvieO97PEgJOYXFPwJEBKsIhRE6+ssIbwsPzuudG0UQVvUINz1FCcoj6sT3WPvfd999bv+kSZNsl112cR5PWMbjocRGeCYEqSiSCIFVE2iZ0cTaNGhqpYUBWx5Ya52ykhvLuXbaiLkN4bQf2l+IeJBXKBo8035iGcmEj5koJyoSnnXOMXfu3BCFRvj54o3dlHPmmWcGcwD5YXyhbMJ1orAOJ17ZjFMoyb2xyQ9jNOM/XkqeEok6YxSQCIzz5JjzxjiU5Xhiet4/iRwXXn/qyPGMranA9dJW0UDgjecRYz/Hel5fQojkDSoZP8i9RhhhfwQMIYQQQoi6jDyQ6qACqVWrVk7YEJ5fgs/EfK/KMnlZ9YfJIKmxXyBZ01hdWmCLilZYuq0X1iZCqQVs7Pz3bPLamXZ2p71scOMeFVYf8riQ66Vvbie7vMtBTijcwNJtg0ahQurtmodav5UGSsudD8kTWJf3Pm2c190lqCe/S/fctrampMDGzh1vB7TawnrmpNYfq/uaUmVa/jzr0qBVwrluasM1VRbRrov+Q9i3RBQvCwtX2PWzX7KSQKlTXA7J6xn8jnI5h7d5kKCdLRUI7xQNzyMoPSPdXl3ynQuxl52RXD/w6ooS6Prrr3fn23PPPd2/5ApBWMn3jMnkQ/J+gwIDgQfjOMqNb7/91p599lkXAsl/7eFt4d/v/YvnEEJXvAbYyLOBV9HNN9/svn/hhRdc6CQEoghWmDdQNJG/BCXX1ltv7eaEvLw8p3Sp7tCWHtTj9A67u1xyGaX/XW8yv4/WZ+vasykqh3j5ifzh55LJZZRq3qNoML6QP4ecRzz3jCt4HhHO2AurmQiEvkSRTf5NFBoolgnhRo40lDnk3GQsxuIfS38vBxIKEMYgcgVFg+8JKYpyitxNfCYHEuMSCqTtt9/e5V8ilycegoynxxxzTEL19o9xGBwwxkVSvMc6Lrz+tAHH4a154YUXulBzjJfkPEJQHQ/airGV/HTkkSLvFWOzF8bukEMOsXPPPdduvPFGp7gXQqQGSm8MZRjrwhX7QgghhBB1mXTkHUnKb5I9vr5SbRIjQsMNGTIkmNjTs+jns5ePoiaUWRPp0rC1Xd31UDulwy4J/6bESt1/iHZy0ys2pvwWTfpYTnq29WzYzoqtNGGPpUtnPGOrSvKtprBnyyG2U/OB7u/nF31pP6+ebvfMHe8E+/UFvNvGzHzZrpjxnAuXJVLjtSXf2fFTx9qri7+Ne2yb7Ka2Z4sh7jnq1yjUCr2qQOj77cppVhRYH4oIxe6BrbdwXoWpQmL2V155xSlwCHlE/hHyfRCGCUhoj7cn3w0cONApa/A+IswnYZAQliJQTAWUQeRiIfcGngFY0yOY9HIpsR/FFAJQ5gYs3/EORYl1+eWXO8EpXjqEUPLnS6kJcE8qUrkrRF2FZxelCIoPxhS8H3n2kwFFBwoNlB8omwmJd9dddwVzht1000224447uhBwnIMcI0uXLnXfoXj68MMPXR1QmkTi8ccfd0qZoUOHuuNQJqGkAjwH+Btl1FZbbeUU8aNHj06o3tHGuGSOI3QfinfqNXLkSCeIZlxl3CbkHOGYqVMsryI/KODIS8XYzrhPGZ9++mnwe+YA2o+Q00cccURCZQohzOW1xJOasMqAociBBx4o5ZEQYaSnRd4aZqZF3UoCFnXLSI+9lZQGom6xyMpIi7rx02hbdkZa1M0L6RRpy0pPi7khV422ZaSnRd0QNEbb/AaSyWzR7iFbLGK1W5zbEfM6SgjlG2UrKCmNui3NL466rS0qibrFKpMt1faJdR9TbbdUfwdZGelRt1TP2TI3K+qWl50RdWvXKDvq1qJhRswt1jMZi8p4BiqLmHWN8exUap0y0lLaRHzSAtWYHZwY51hTkoSY5Ll33nmns4bk5REB3tFHH+2sMIlvDwj3sHT0kjXzgsnGiy/CykTKTARizPNCT1iRRMOFJArNjYcTYUsqMjxeLBB0EEqmZetWNrNwcbk8avAa+nTFFFtdkm97txwW3L+2ZJ3lZiSmmEJIPervx21B0XInOD+y7fByXRftmIr1/E+r/rFvV021k9rvbJlpoRZ6XM89c9+xg1tvZd0qIeRfZV1TKgqj1xd/Zye029F5y0xdO9dum/2GdWzQwi7tcqBllNNDrDquqaqIdV23zHrdflr9t53SfhfbrtmAhJ6rNP4Ls3zAI2f69OlOaBkecqwiGTf3Pft0xWTbv+XmNqL5sBrldVNePK8qXVPs/lSZ856oubAeQRGA4BEFSHnHnrr4vFU19bkNMTQgZCi5kJLF32cxKEORh6KqLq07qnqNozas+W345JNPOsMb+j8ekyh96xIV3YbR5jxRt/Hu++8z5lhekvc9lrAbBVMsitAypQCKolTqk6r8M57lfapLkVhPbKrrm1SF5PGUFqmKRAtjFLymKLoR8pK1ZfP5eqwoiP7d0oKimPVB4ReN/Bj1KSotTakfx7ofsdo83n2MpSiKdY2xftc4O7pHbsPM6L/r2jT6e1BuVurrdJRzUb+L8XxUhqIoEaVeKkR7rhiTu3RsX+FzsTfWv9S5v+WmJ+eBvba0xA6cNVnrg5qcAwnrcpIQk2eC0CIkKn/33XeDih5CY/gXi8Sy32STTYKfb731VrcNHz7cPvnkk4TKrE5QGhEvn8XwtttuG2J5WRWgDPArj5YWrbYPl0+0A1ttkXAouclrZ9kD8953CpdN83pb2+xmbn+iyiPISsuwCzrvax8sm2iHtNnaqoPVJQX2v7nvWEFpoQsbtkeLISHfcz2jOyce5qY2gmfVDTNfceEQW2Tl2cGtt7QNcjvYDd2PXG9R9P99goEf37WKUCbVFy7otI8tLFphjTISE7yGP3+/r51tfXI6VFLtyrJx4272xcrf5dkihBCiWmDt/uCDD9Y4r08haiJFRUUuxOXdd9/tPuPleN1111V3tYQQQgghRB2lWhVIQHx6tkh4SiGPbt26JWQdEKvM6sTLtURIEi/ESSr8lT/fFhetdIqP9tnNU1Ye3DzrVft33SIrKC2yo9tuF/VYf66iAbldbLO8Dax3TnundEiVTg1a2nHtdrDqonFGQzu745722YoptkvzQXGPn1+4zN5Z+pMd03b7OqNI4TqObLutvb90ost149Eiq3HIce8u+8W+WfmnndVxz3KFNKtPYFXlKVeT5fmFX9prS7513m+7N97YKoOVxWttZUm+ew6B8Hm9ctpbq8w8Z/kuhBBCVBWEtRszZowdddRRLiSgECI6eL8T7pG8koDRJOGC5W0nhBBCiHpPRrqlJeu1nFZtgdlqFdWuQKpv4IJP/Phly5alXMYny3+zCct/tQNabeHylKSqPNiv1Wb29MLPbbfm/3l1+SkqLbY3l/5g362cZtd2O8yFOEMwfk6nyLH8y8N7S3+xZpm5tlmTDayqGNS4u9viQTtcN/NlW1K00hqlN7RD2mxltRGUr/SbLg1aO08jwItsWONeUd3I80sL7dXF37hcVT+t/sd2bl45Cg0RmhMJ8I6rDKblz7VbZ71heRk5dkP3I9xzDa2zmqTsvi+EEEKkyqWXXuo2IURsJk2a5MK4E3aWvGGEsNt3333VbEIIIYQQ6ILI3ZZkTE/SSYj4SIFUxZCEGQVSeTyQ2mY3tT45Ha19ih4OHihrBjfuERQgh3sbFVupCzO3vHiNfbXyTxverL9VBt+unGaPLfjIstIyXb6hVD034rGseLU9ueBT5/mE8DxRaJ9j2m7ncgXt1iK+t1JN5a2lP9ozCz+zdtnN7cbuRwbDlcWKQZyTnm3XdDvUPl0+xXZqNrAKa1t7oZ/Q18h9lEr+LLzBujZobT1y2rqY9hUN9399AsY0W16y1lqnKwa8EEIIIURNx8uf26dPH3vttddsww03rO4qCSGEEELUGNIz0tyW1G+kQEoIKZCqwQMJCGOHcDiZ5NQee7cc5raKwK88+jt/vt02+w27tMuB1rFBC6c8OLbtDlYcKLEtm/SxymJYXk+nyNogp4O1yVrvfVEZ3DNnvE1ZO8vWlRbbqM7JWesNy+tlQxr3SDhXVE1kh2YDnAfSLs03dnmoklE4+L2u6A8vLfra9mo51IUCFKF8sfIPm71usfVv1CUlBRKgPApJqB4oKVczz163JBiuDuXpZV0OtLZZTUOefyGEEEIIUbMoKSmxjIz163Zy+r733nvWsWNHlyhaCCGEEEL8B+Hrkg1hl6ZIPAlRe6XhtYSZKwrt4Of/tZ0f/8du/HxhUIEE5QljV9EgpH564WfOc2Jlydrg/s2a9Latmm4Y00ulvKCUOb/TPrZvq00r9Tx4HvVo2M6Objs8pd/7lUeT18yyX1ZPt5rMkqJV9uGyX4OfG2U0tFt7HGO7txhcLkXYC4u+steXfGfX/vui81gToRAacrcWg22DnPblbhrC2D1b9L09svCjlMLLcX/um/uujf7nCft97ezgfpRJUh7VPxSiUKjPiLpKaanWI6LuMX/+fNtuu+1cqDqPfv36SXkkhBBCCBHDAynZTcRH5ueVTHZGmk1fXuT+XrK2xIWw8yCMXfv25RcyVwQobs7ttLc9NO9D+2PtHOub26lKz+9XaJQESu3j5b85j5mK9PhBaH5dt8PKraSaunau3TDrZcuwDBferWvD1lbTWFm81i6c/qStKSmwdtnNbECjLm5/ZhKeR9HAG+27VdNs/1ab12qPrMqC9qkoj725pStsculcS18233ZutJF1a9Q26f5bWlzqBGt/rpxt3dNbxTzWeTsVF1tm5vp8Z3UBXdN/7bBo0SJ3X7Oy1oevFCIa9BH6Cn2mdevWCY8HdfF5q2rUhsm3V2Fhoeur6enplp2dXUl3Roiq5dtvv7X999/f5s6da9OmTbMDDjjAcnNzdRuEEEIIIUSVIwVSJdO84X8C++UFJSEeSKnmQRr1z+OWnZblwrA1y2xkFQWhrVAiVTd3z3nbKSjmFi61o9tul3I5eF88vfBzl1PGC99VEQItwov1z+1iDdOzrEP2fwrBmkSTzFzbIm8D+6dgYYX2ESAs2y3djw7xYFlYuMLyMnNc2ENRcfRr3NlO7rSrNVxZarYo32YsmhH3NwWBIpcEsEHa+vuzZaCT9bQm1nVZU5u+bHpcQRzKJoRwdUX4q2v6D+5pp06dgqFwhIgGfYS+QqL2GTNm1OvnrapRG6YGgvUuXbq4vidvJFHbefjhh23kyJFOOUqeI/IdSXkkhBBCCBGbtIw0tyUD8jMRHymQKpkGmemWm5Vma4sCtqygxLr5PJBSCWFXWFrs8plA9v8LiOsaWzTpY7+snmF9cjqWq5w3l/xg7yz90b5Y8bvd1et4a1hByg28eM7rtLfLI1RTPHAQOH236i/bqFEXy81o4PYd1XY7y0hLd1tF41ce5ZcW2k2zXrX0tDQXipCcSfUVwsS1yGzscnlVlPB0t9aDLdBqvVU/cfBj8Uf+HHtw4Wc2MLerHdv6P+Vr3wTPhdBtyZIl1rJlSyeEqwvomkK9SqQ8EonSuHFj6927txUVFSX1vK1YscKFV6orY0hVozZMHsY1eb2JugAKo7PPPtvGjRvnPo8YMcIef/xxa9KkSXVXTQghhBCiliiQksyBZAqFnQh1UwNRw2jWMMPWFhXb8vzyeyChDLi666G2qiS/znp7bN5kA+ub29GaltNzZofmG9kPq/+23ZoPqjDlkUeD9NAQUN+unGoDG3ertntC/qq3l/5ow5v2t1M77Or2ZfuUPJXJ4qKVtrZ0naVbWp3tk4nyvznvuDxiV3Y9xDbMLZ8C1I8XdmyVrbO3lv5gh7fZJmI4wqySbFtWusZ+XzfHSrPSgsrEZASXnKdhw4Z1Rvira6o8nlv4hT2+4GNbXLTKNsjpYBd12c82atQ14rFFgRJ7eN6HTrG/sGiFdWvY2s7puJdt1fQ/9SbfT1g+yaYXLHRj7KBG3eycTns5r0dRfYL5ZJSOPG9r166tU2NIVaM2FKJ+grJ+xx13tC+++MKt+6699lq7+OKLNZYKIYQQQiRIKjmNkGWK+EiBVAU0z8mwuauKbcW6Utt9z73sk0/6OUVS166RBW3xFEgb5Hawuo5febS2ZJ2tLFmbtGcLIfmu7npIpXsJvbnke3tm4ee2caNuNqrziErx+InH0Lxe9t6yX6xVVhPnjVSVoYM6N2hlN3Y/0pYVrwm5b1Vdj+oGT6yWWXm2pmSdda8EgTe5wa6d+aLNL1zmvA8PbbN18PnwFEX9G3W2szvuZRtXozJT1A/eXfqz3Tr7dbusy0HO8xEl9mnTHrDX+1/knoNw7pnzjlNyX9n1YOvesK19tfIPO/fvR+3xDc8K5txD4X9I662sf6MuVhIocQrZU6fdb6/0G520MlQIIYSoTWDAs91229mkSZPsmWeesT322KO6qyREnaIkYFaSpJF5ZozX+iIKjEFGevT34JLS2L+NRowiq4VYUo9U5QCpXmOsJi0JpNbe7rcxCl5TFL1DzV8V3YN/3up1Ub9r2iC6iDa/KHY0kqwYv83Jin630otjlVqaUttkxRDgE7kmFlkxOkFWDM+S5jnR8/zmZUc3iGuZG73diCaVyjNenuejqp/zyjpfaRSlTGXLCCk/LcmLSiutYYNrDUXmoVWcBymvVXsbPny4bbTRRgpHkABLilbZlf8+b2NmvmIritfEPf7rlX/aL6v/y/NSFSHm8DRBoN8zp12VaK5RzMwoWGjT8ueF1OHuXifYga23qBalDYojv5cAody4b3gn1RdQ2Fzb7TB7uM/IMh5qFQGKyUNbb+0UdsOb9XfhLB9f8InLiba6pCDEg0/KI1HZPLngU9u/1eY2otWmbuy7rMuBLi/ca0u+i3g8yqMT2+1k2zTt53LSHdx6K9u6aV97YsEnwWPu632K7dtqU+uV08765Ha0a7odZvMKl7nxRAghhKiLrFnz3/vNVVddZb/++quUR0IIIYQQKZCekZ7SJuKjVqqiEHYe5EEqDwsKl7twaf/kL7D6QFZaphWVFlthoNiWF6+Neexf+fNt7Nzxdsus193fVUXvnA52W89j7aDWW1aJ8gbF0cXTn7JbZ73uQhl6NM9sbDWB0kCpPTJ/gk3Ln2tvLPne6huRQstVFJs16W03dD/S2v+/Nx7K0qXFq+2HVX9V2jmFCIcxGaUOykq/sn7zvA3s19UzIjYYCs/wsJooWv0K/3BW///41iQzVzdBCCFEnWLdunV28sknO6+jgoL1hkCEDO3SpUt1V00IIYQQohbnQEp+S4WxY8dat27dXPj2zTbbzL77LrIxLTz44IO2zTbbWPPmzd220047xTy+JiIFUhWFsPMgD1J5mLRmpt055y17efE3Vh9okpljF3bZz67vdrh1bdg65rHk1ECAiZC9RxXnzCB0nF+BMnXt3AopF0UheUE+XDYxuA/rfM7XK6e9rYijVKsOECSP6jTCeReQq0dULF6IRITxp3fY3S7svJ9t12yAmllUCKtWrbKVK1cGNwRc4RCussRKrWVmaKg6QteRDykSWzbp47yW/i1Y5MZIvEU/WjbJFkXxUuSYm2e/boMadbfeOe11d4UQQtQZ5s6d6xRHCBN+/PFH++ijj6q7SkIIIYQQIkGef/55O++88+zKK6+0n376yTbeeGPbddddbeHChRGP/+STT+ywww6zjz/+2L7++mvr3Lmz7bLLLjZnzpxa0+bKgVTFIewWrym0N9+cYEuXLrW8vDzbf//9k87r0yeno3Vp0MrqC563hT/XTKQQXXh+jOywm5VaoEpC10Wi+P/zdny/6i87v9M+NiSvZ1K/X1da5Mrw+KdggX24/Ffr0bCd7dR8Y7ePa7ur5/HVdo2J0Ca7qVNueAnB6zrkJyKfS8cGLdx1N85oWCXnRZkoREXSr1+/kM8siAipU15Gd97Prvn3BRsx+UZLszQXxo5wda8t/jbi8YQt/Tt/nj3W58xyn1sIIYSoKXz55Zd24IEH2vz5861Zs2b27LPP2m677Vbd1RJCCCGEqPWk4lGUFkjeA+n222+3k046yY477jj3edy4cfb222/bI488YhdddFGZ459++umQzw899JC9/PLLNmHCBDv66KOtNiAFUlV7IBWU2uH77uvy2AwbNixpBRLeNWz1FcKi3TrrDTu67Xa2Rd4GtqRktf26fL7t0mKQ+x6lSnWqVTIs3Sm3SAy4LhAzG2EZHp3/kX28/Dc7Imdz62DrLe6H5fW06QULbNO80Htek5VH4cxct9jGr/7BumV1sN1bDra6yKx1i21R0QoXUjA3gnJTiNrClClTrGPHjsHPDRo0KHNM88xGbqxbUryqTM66VlmhXkkeLbIa2529jndK8uXFa6xNVlPnTduxQcsyx46Z+bJ9tmKKPdLndGub3axCrksIIYSoTnj3Q7hw1llnWXFxscuH++qrr1rPnskZmwkhhBBCiMikktMoPZDc8YWFhc6D/OKLL/6vjPR0F5YO76JEWLt2rRUVFVmLFi2stlB7pNB1RIG0sjDgrM1g2bJl1Vir2skPq/62lSVr7d1lP1t+yTobu/wDe2T+R/bu0p+tJkAOpBPb72TXdD3MhWyKxrLi1fb5iillQpORJ2Rq4X/5m5pmNrKT2u9sGzfuZrVZuTJh7WT7ZMVkq6sgBL+m22F2avtda5VyT4hw8Ixt0qRJcIukQMpKz7S+uZ3s25XTQkLOfbtqmg2MM1aR9wilULGV2oTlv9r2vvCLCNdQHn20fJI9uMFpzkupvpFMHGW48847rU+fPpaTk+Pc4M8999xgLg0hhBA1hxtuuMFGjhzplEcHH3ywEzBIeSSEEEIIUYGkkv/o/z2W/KH8o4Xzh8WLF1tJSYm1bdvW/PAZD/NEuPDCC61Dhw5O6VRbkAdSFdDMF8JuWX6p0zCiPCKMnUiOQ1pvZY0yGtrOzTe27ECGbd6wl31fOsOG5fWqMU1JKL0eOW1DQu4VlhY5ZRBggX/2X49YUaDYhaYj7Bns2nyQbZ23oeWsCFhdYqPcLrZpw562VYv+TkCMkq2ukZWWsT5PS05110SIquGotsPt8hnPWv9GnW1Abhd7auGnbqwb0XJT9/2l05+xNtlN7OyOe7nPv6751xYWrrANczu6f++b956VBgJ2bNsdgmWOmfWyjV/6k93Z83hrlNHAFv9/fiRCQjasB559XhxlLNRRHqEcIo7yn3/+aW3alM3r98wzzzj3eNzkt9xyS5s6daode+yxbozFpV4IIUTNgbj3jOujRo2yCy64oE6uh4UQQgghqhOiQaWnpyX9G8AgszLC+Ydz44032nPPPefyImE4WluQAqnKQ9iVOAXS33//7ZRI5IfB1S1R7p7zts0vXO6Ed1iA1zfw7tin5TD3N223S6ON7IBW21ijzJr50K0oXmM3znrVVpcU2N09T3Avi1jgI3Rl35qS/yzFscovzSy1hSsjJ12rrTTJzLWjm2xtbZq00cuyEHWE3Vps4jwp7537rlP0kJvv3t4nW8v/D2E3v3BZcCEGKNHHzh1vs9ctsdz0BrZ10752fbfDrUnmf1rXFxZ95f49Yeq9Iee6puuhLl9SXSfZOMpfffWVbbXVVnb44Ye7z3guIaD89tvIeaWEEEJULfPmzQuGJunevbv99ddfzrtXCCGEEEJUPGkZ6W5L6jel64+fNWtWyDotUjQWaNWqlWVkZNiCBQvMD5/btYudp/zWW291CqQPP/zQBg4caLUJKZCqgOY+D6Sl+cXWvHlz9zfeGCtWrAh+ToSZBYttTuESKwmUVkpdayPkHKqpYJFPXhC8jgoDxdYgLcvtP7/TPs5TSdR+uMfvLvnZeuW0t4GNukpJJuoNh7XZxm2ReLjP6SGfh+b1slf7XxizvIlD6q/XTCpxlPE6euqpp1yYu0033dT++ecfe+edd+yoo46qwpoLIYQIh3e8e++9184//3yXIHmvvdZ740p5JIQQQghRM/HC+McjOzvbhgwZYhMmTLARI0YEHRz4fMYZZ0T93c0332zXX3+9vffeezZ06FCrbUiBVAXkZKVbg8w0W1ccsGUF60PYeeCFlIwCaWSH3VwC8q4NW1dSbUVF0i67uV3b7TCbXrDQ0uw/a/z6qDyi307On2VbNdmwTuUJ+qdggb20+GtrmdXE7ul1YnVXRwhRC4kVR/mPP/6I+Bs8j/jd1ltv7YSV5NU49dRT7ZJLLol6HuI4+2M5E9vZW/CylRfKoC4VUVZ9RW2oNqwJqB+mDnnoyHX0+OOPu89vvvmm7bHHHhV2b+oTFd0PNTcJIYQQdZv0jDS3JfWb0uTDCp933nl2zDHHOEUQxpyEKV6zZk0wmsjRRx9tHTt2dDkw4aabbrIrrrjChaEncoiXK6lx48Zuqw1IgVSFXkjzVxfb8vwS6+1TIJEHqUePHgmX48+tI2oHhKZjq8+Q62TU9Cdsbek6a5/dwnrlxHbrrE00TMtySjFC9Qkh6g8oYggX9++//9ratWutdevWtskmm7gwRVUBMZPHjBnjrNzJmURopLPPPtuuvfZau/zyyyP+hgXs1VdfXWY/xiwooCpCOLdq1Son8EsmPK9QG1Yk6odqw+pi9uzZTnDwyy+/uDGQBMmMy8p7WzOeZcoSQgghRN0lLSPNbUn9JgUF0iGHHGKLFi1ySiGUQYMGDbJ33303aBA6c+bMkLXLfffd56KOHHjggVWSZ6kykAKpihVIK9aVWLNm/ykT9EIh6gPkQhmQ29kWFa8P51eX6JnTzs5oJMtSIeoLX375pd11113OqryoqMiaNm1qOTk5bj5HqYRRyMknn+y8gfLy1ueEikcqcZRREhGu7sQT13s+brTRRs7qiXNfeumlEYVthMjDWsrvgUSyUDyhKyK0EsI+cv1RnhRIasPqQv1QbVgdoNRHmIBnaMuWLe3ZZ591wgSNhzXnWc7MlOijPoM8MVJajAxfzs5wYnxlJaWBmOeLlcM9EOucMUuNUWaM71ItE2I9eTyfKZWZYoXiNHlUAnF+VxSj4Pzi6B6Q81dFl2vMXvVfrutwikuin29xSWHU7zo3/S93bCRKY1xHeoxGX72uOKXfxTpfrHvVIDM95f7RtOH6lBCRyMuOHuWnTaPov2uYmVqHjDV2xLuOVPtyvHGnop/VeMS6xmjfpfr8V0UOpGQ544wzooasY13oZ8aMGVbb0SqqimiWs34w43nPbdI8JQXS2pJ19uuaf61pZq71ze1UKfUUorI4o8MelpWhIUcIUXvZZ5997KeffnLh495//33nso7yyIM8RJ9//rkTHN5+++32xBNP2M4771wpcZTxegoXqKGEAqy1I0Ei0EjJQCmnohQ+vKBUZHn1EbWh2rAmoH6YOFOmTLFddtnFhSLFE/XVV191ynne8zQe1px+qHlJCCGEqNukZ6wPY5fUbxR9PSEkza1CDySPhk1aOEsqNk/YkwjzC5fbXXPeshaZjW1s75MrqaZCVA4ZdSjvkUdhoNjWlBRYXrrC1wlRH9hzzz1dQvSsrMhWbHgfsREPGYHivHnzKi2O8t577+2UVAgrvRB2eCWxP5m1hRBCiPLRr18/5w26evVqe+CBByw3N1f5doQQQgghqpi09DS3JfsbER8pkKrYAwm23/cwO/+041MKA9Ynp6PlZcZ2XRWiJlMaKLWC0iLLzShrBV/bmLxujj0+7QXbtEkvO6/TPtVdHSFEJXPKKackJVBkq6w4ypdddpmzzObfOXPmuBxMKI+uv/76JK9KCCFEspD/jqTHhKuDe+65xynvKytMjBBCCCGEiI3zWk4yhF16Sd0zdq8MpECqBg+k5QUlKZXRrWEbu6rbIRVYKyGqli9X/G5PLPjUBuf1sFPa71Lrm39hyQr3b5MMeSAJUd8oLi62yZMnO2UPkKsIhVE076SKjqNMLgeSbrIJIYSoOggvitIfD9Dx48e78Vj5dYQQQgghRF1FarYqornPA2lZigokIWo7jTNybGXJWpu6dq7VBXZtNNDG9TrZ9mu1WXVXRQhRRZCbCK8fPH4QHu6+++5u4+82bdq4MHIcI4QQom5BfjlCh5LvaMmSJbZs2TJbvnx5dVdLiBrDqlWr7JxzzrGuXbu6HJFbbrmlff/991GPf+WVV1yuSNZUTZo0sS222MLee++9MseNHTvWunXrZg0bNnRhe7/77rtKvhIhhBC1kbSMtJQ2ER8pkKrBA2lZvhRIon7SN7eTXd7lILuxx1FWV2iSmWsts/KquxpCiCrioosucjkubrzxRvvnn39cniI2/r7pppvcdxdffLHuhxBC1CHWrl1rRxxxhJ1//vnOSIC8dJ9//rm1atWquqsmRI2BXGAffPCBPfnkkzZp0iSnbN1pp51cqN1IfPbZZ06B9M4779iPP/5o22+/vQvH+/PPPwePef75512eSDyuf/rpJ9t4441t1113tYULF1bhlQkhhKgNpGWkp7SJ+KiVqsEDaenaIjvhhBNsv/32s7PPPjvhMl5Z/I1dMv1p+2T5b5VUSyEql+z0TOvXqLNlpSnBuxCidvLEE084wQj5kLCGxcKWjb9PPvlk9/1jjz1W3dUUQghRQUyfPt15Ujz77LMuz9Hdd9/txnnGfiHEevLz8+3ll1+2m2++2bbddlvr1auXXXXVVe7f++67L2Iz3XnnnTZ69GgbNmyY9e7d28aMGeP+ffPNN4PH4PV30kkn2XHHHedCBY8bN85yc3PtkUceUdMLIYQIIS09PaVNxEc5kKrBA2nFuoB7AWGRNWDAgITLmLtumU0vWGCrSgoqqZZCiET5btU0+3zlJNs+d5ANbdJLDSdEPQrP0qFDh6jft2/f3nkkCSGEqBth68h3NHHiRBdm68UXX7Thw4dXd7WEqJG5IUtKSlyYOT8oWr/44ouEysC7j3VWixYt3OfCwkLnmeT37CZBOl5NX3/9dQVfgRBCiNpOeka625L9jYiPWqmKaBaWA8lbFC1dujThMka02tRGdR5hw/J6VkodhagKigMl9uaS723MzJetoLSw1jb6z6un27cFf9tf+fOquypCiCpku+22swsuuMAWL15c5jv2XXjhhe4YIYQQtZ+0tDR76KGH3LiOIFvKIyEik5eX53IYXXvttTZ37lynTHrqqaecomfevMTel2699VZbvXq1HXzwwcF1FeW0bds25Dg+z58/P2IZ69ats5UrV4ZsQggh6gmphK+TAikhpECqIhplpVlW+n85kDwFEslXE6VTg5Y2uHEPa5fdvLKqKUSlk2Hp9t6yiTZpzb/2+9rI8bBrA9s27We75g60QY27V3dVhBBVCKFTEIzgaTR48GDbfffd3cbf7OO7aKFahBBC1HzwIv3www+DnwcOHGgfffSRde7cuVrrJURNhxC/eO117NjRGjRo4MI9HnbYYc5rKB7PPPOMXX311fbCCy9YmzZtUq7DDTfcYE2bNg1uem6FEKL+4ELSJatEUgi7hJACqQqt17w8SHggNW++XglEGDs2IerTs7B3i6F2bNsdrGuD1lZb6ZvbyfZuvIltkBM9lJUQou6BIIJQRm+88YZL9NylSxe38Tcx+0n8LGGFEELUTv7++2/nRbHHHnvYV199FbJ+FULEpmfPnvbpp586L6JZs2bZd999Z0VFRdajR4+Yv3vuuefsxBNPdMojwtN5tGrVyuUdW7BgQcjxfG7Xrl3Esgh3t2LFiuBGPYQQQghRPpQDqQpp1jDDFq4pseUFJdb+/z2QPC+kRJKwknMlJz3bNszpaFnpunWi9rJri0HVXQUhhEgZLGk9zyMhhBB1g/fee895S/BuRogsPCmEEMnTqFEjt/Es8VzdfPPNUY8lN/Txxx/vlEh77rlnyHfZ2dk2ZMgQmzBhgo0YMSKYJ4nPZ5xxRsTy8HxiCycjLc1t4cTSDccaAtLjKJVLSqN/FytaUknMc0b/LjPWl9VgUZ5idaw0xvXHGpNjNHdcVhdG//X81dFD7q8rjv67VrnZKdVlzsro+c6X5RfF/G1WjEYvitGwa4tKon7XOPu/VBzhlKZoWBGvb+RmRT9nh7zo7dokRl1jPXOxnp1IY0ailJSm1l8LYw0CluKYVI71TKz2ifW8pjoGVIgHUpIeRfJASgxpIaoQzwOJxUTjpv+FoWNhFSshN5QGSu3O2W9ZwAI2rvcp1lQKJCGqjRkFC62ktNQaBsqzVBVC1BWmT59uf/31lwthN2DAgOqujhBCiCRAKHnTTTfZJZdc4v7ebLPN7OWXX3ZhuIQQiYOyiGeoT58+bl30f+zdB3hT5fcH8G9Wm+5JGYW2LNl7CQ4cKKiIgAoiiqKiqLhQFBe4EFwICg4QxL0F9YcbAf8oIiAosmSXPbp3s/7PeUvTlia3TWibtP1+eO5DcldubtI2uec950yaNAlt27bF2LFjndlBBw8exLvvvussW3fDDTdg9uzZ6ueuuK+RDK6V8nNi4sSJap2ePXuid+/emDVrliozWbxPIiKiYkVl6dwHE13RGdwHUakES9jVcAZSsaCwog9EIjU1tcJtCxxWtAluovoghRjM1XaMRDUl316IDdl7sL0W9kH6/PhqPLr3Q6zM2+rrQyGiGnbHHXeo0ixCStBeddVVqmTLwIED0aVLF1xwwQXO5URE5N/k9/WIESPUhW258D1u3DhVgovBIyLPScm4O++8UwWNxowZg7PPPlsFlUwmk1p++PBhJCcnO9efN28erFar2kYG4RRP99xzj3OdkSNH4sUXX8SUKVPQtWtXbNy4Ed9//73KEiQiIirN4/5HJyeqGDOQalBUqQBSYGikRwEkKV03NXFktR0bUU37LnUDPj3+G3qGtUKb4No1wtOsD0CwIQCJptrbw4mIvPPmm2/iiSeeQGhoKJ5++mmsWbNGlVKRkbPS/0hGyU6bNk01cSYiIv/2wQcf4PPPP1cXuOfMmYNbb73V14dEVGtJMFYmdxYtWlTm/ooVKyq1XylX565kHRERUelS8zJ5wtP16yueJR+UsBOmkMgyJeyI6psuIUmIMYWjoakkG6+2mBB/Cea1Go/mRgaQiOqb0vXXv/nmG1XX//zzz0dwcDDOOusszJw5E19++aVPj5GIiCpHAkaSWSpZRwweEREREdVezECqPsxA8lEJu9ikNqpub3R0NNq1a1eTh0HkF5qb4/Bqy5uhO43GhL6k1+krbJ5KRHVT8e8tqdXfuXPnMsukjN3+/ft9dGRERFTRIAApm3XdddchJCRE/T6fO3cuTxoRERERkRsMIPkoA6lx+9545LpBld72z6wdWHLiT3QKScCouHOq6QiJak5tDRzJhYfaeuxEVDUef/xxlXEk6e6HDh1Chw4dnMtSUlLURUkiIvIvWVlZuPHGG1WW6C+//IKPP/6Yn+mIiIiI6ghvehqxB1LlMIDkox5Iafk2j7Y9YcnEnvyjaBRQUvqOqK5It+Yg0lg7LrjO2L8YBXYLRjc4B2GsAkpU75x77rnYvn27ut2+fXvs27evzPJvv/22TECJiIh877///sPQoUOxdetWBAQE4OKLL2bwiIiIiKgO0en00HnY00i2oYoxgOSjDKS0PM8CSL3DWqNxQDTCDEHVcGREvmGxW/HI3g9woCAFb7S+DRF+HkSyOmzYlnsAhQ4rzHoTAM9+jomo9quo4fO1116rRrgTEZF/+N///ofRo0cjMzMTTZo0URlIffr08fVhEREREVEVYgZS9WGYzUcBpPSTGUh2ux05OTkVbhtrCke30OZoFdSoWo+RqCaZ9EYYdQbooMOu/KN+f/IN0OPZ5tfh9iaD0DggyteHQ0Q+cMEFFyA9Pd3t8hYtWqBp06Y1ekxERFSefM966qmncPnll6vg0dlnn43169czeERERERUhwNInk5UMWYg1aCwAD3kfWmzA6l5NjRo0ED1SujZsyf+/PPPmjwUIr9xZ5NLVPm6UIMZ/k56H8UHRqtJLkoQUf3MQCosLPT1YRARUQVOnDiB1157Td2+44478PLLL6vydURERERU9+gNejV5ug1VjAGkGr74HGk2ICXXpjKQ5AK0w+FAampqhdtuzT2gymclmeNYxo7qlKaBMb4+BCIiIiKqY+Li4vD555+r/kc33XSTrw+HiIiIiKqRTq/zvAeSXldtx1OXMIBUw6JOBpCkB1J0dLQKHqWlpVW43btHV2Bv/jE82GyYKmVHRDXvm5S1iDNFoEtoEgL465Oo3tqyZQuOHDmiuU7nzp1r7HiIiKjIV199pQbpDRs2TN2XsnUyEVH9ptMVTaey2R1utzFoXFQ0VHC90apRrEJrU5PGY9od7o9Vi76CQc7e8vaaq9Y512L3cp8ZBdqVQ45ku68skJZncbss0Oj+zOo1X2X3LFKuyI2CCl6rbI0KKUGmknYapzJo7FfrpdJ6/bX2GaxxLCIhItDtstAA99sGaPxQap06rZ8Pq8YJqOjnUWup1itZaHO/pcYizUfU+r1SUfKNXeNBA5i5U68wgFTDJANJWOxARGRRDxUJIMkXHb1GlLRRQKTKQIoyhtTYsRLVlH9zkrEs/R+cERSPS6K7+eWJz7UV4KNjq+CAA2+0vg0Bev76JKqvLrzwQpVB7OpLuMyX/222ol6HRERU/eS71JNPPql6HoWEhOCvv/7CGWecwVNPREREVE9409OIPZAqx+eF/ubOnYukpCSYzWbV0LSiXkCfffYZ2rZtq9bv1KkTvv322zLLs7OzMWHCBNXAOigoCO3bt8cbb7wBfxEVVBIxD40oCiDJxaaMjAzN7e6JH4wXWtygStgR1TVHCtPxR+Z/WJP5H/xVocOKCyM7oXtoC0QwkEtUr61ZswZ79uwpN+3evdv5PxER1Yz09HRcccUVKngkbr75ZjRvzooNRERERPUxgOTpRBXz6RD6Tz75BBMnTlQBHgkezZo1CwMHDsT27dtVzepT/f777xg1ahSmT5+OwYMH48MPP8TQoUPVCLOOHTuqdWR/v/zyC95//30VmPrxxx9V09QmTZpgyJAh8IcSdsWCwiKct6WUXVRUUUCJqL6RsozDY89E19Ak+KtIYwhubjzA14dBRH4gISHB5ecUIiKq+ZKi8n1wx44dCAwMxLx58zBmzBi+DERERET1jE6n97wHko4BpMrw6VmaOXMmxo0bh7FjxzozhYKDg7Fw4UKX68+ePRuDBg3CpEmT0K5dOzz99NPo3r075syZUybIdMMNN+C8885TAaRbb70VXbp0qTCzqaZElspAModGOm9Xpg8SUV0VYwrD1Q36oXVQE18fChERERHVAosXL1aDECV41KxZM/z2228MHhERERHVUzqDAXoPJ9mG/DgDqbCwEOvXr8fDDz/snCc9gAYMGIDVq1e73EbmS4ZRaZKxtGTJEuf9fv364euvv8ZNN92kso5WrFiB//77Dy+//LLbYykoKFBTsaysLGctbZmqUmRgSczOFFqSgXTixAm3j3W4MA1zDn2HOFME7om/zOPHlP1Kmbyqfi6+VhefF5+Tf5L3WZo1G9GmMOc8vla1A1+nyp8nqpz+/fsjICCAp4uIyMdWrVqlypfLwMFPP/0UDRo08PUhEREREZGPsAdSHQwgScBEGkw3bNiwzHy5v23bNpfbHDlyxOX6Mr/Yq6++qrKOpAeS0WhUQan58+fj3HPPdXssUhJPmq6eKiUlpcovEukLrc7bVoPZeXvv3r04duyYy212Fx7BjuyDyDBk4ZjJ9ToVXRiUHktyEVzOR11RF59XfX5OdocD+60p2Gk5iguC2qsm9P4ixZaNqSlfINoQiqnRw2DQ6ev1a1Wb8DlVjvy9o8pZvnw5TxURkR947rnn0LJlS/XdT773ERERERFR1atzn7QlgPTHH3+oLKTExET8+uuvuPPOO1U2kmQ3uSJZUKUzmw4ePKhK6sXExFR5j4NEW57kFKnbgREl+5ZgmrvHCraF45FIyXzQIS40zqsLqHIxXkbl1ZWLwnX1edXn51Rgt+ChnZ/CYrfh3MadER8YA39xKDsXAZkBiAmMQOOGjVDfX6vahM+p8lnBRERE/uzff//FCy+8oAYHyiA/CRpJr1siIiIiImYg1cEAUmxsLAwGA44ePVpmvtxv1KjoAu2pZL7W+nl5eXjkkUdUPezLLisq9da5c2ds3LgRL774otsAkjRclalYZmam+l8uoFb1RdToYFPJ8+nUD1999RWioqLQpk0bt48Vrg9Gd1PL03pcuShcHc/H1+ri86qvzylIH4geoS3hkH+6op8/f9E9vAXeDp2AdGtOmeOqr69VbcPnVDFvXu+Pj63CO0eX44QlC2cENcHkhGHoFJLocl2Lw4YFh3/GNynrcMySgSRzA9wbPxhnRbTzep9ERFR/fPbZZ6pvbk5ODhISElQvXCIiIiKiYjq9Xk2e8HT9+spnZ0lGjfXo0QPLli0rM1Jc7vft29flNjK/9Prip59+cq5vsVjUdOqFMAlU+Ut/h2hzSXMue3hjDBkyBOecc06VZzoR1Ub3Nh2M+5pejgSz/9WwD9Sb0DAg0teHQeQXvk/dgBcPfIXbGg/Ex+0mok1wE9y+Yx5SLEU9BE815+C3+PzEahUQWtzhIVzdoB/u2/U2tuYe8HqfRERU90mVBqkWMWLECBU8uvDCC3HPPff4+rCIiIiIyE8zkDydqGI+PUtSNk5KELzzzjvYunUrbr/9dvXFQEaXiTFjxqgvDMXky8L333+Pl156SfVJeuKJJ7Bu3TpMmDBBLQ8PD1fNrSdNmoQVK1Zgz549WLRoEd59910MGzYM/iDcrIf+ZGuXtHxbpbbZl38cm3L28SIaERH5hfeOrsTw2DMxNLY3WgY1wmMJV8GsN2FJyp8u11+auh63NBqAcyLao2lgDEY0OAtnR7TDu0dXeL1PIiKq21JTU3HppZdixowZ6v4DDzygvgtKJQsiIiIiotJ0ep3nAaTii/Tkvz2QRo4ciePHj2PKlCk4cuQIunbtqr4UNGzYUC1PTk4uk03Ur18/fPjhh3jsscdUqbrWrVtjyZIl6Nixo3Odjz/+WAWdRo8erb50SB+kadOmYfz48fAHep0OEYEGFTxKz6tcAOn7tA1Ykf6vGrEtF9eI6rpcWwEsDisijCG+PhQcKEjBp8d/Q4fgBAyM7urrwyGqVllZWc4yrq5KvAqL3aoyh25ufKFznl6nx5lhZ+Cf7L0u91totyJAbyyX1bcxe4/X+/QF+VzijcjISDXIhYiIKt/vSCo1yIDAoKAgLFy4ENdccw1PHxERERG5xBJ2dTSAJCR7qDiD6FSSRXSqq6++Wk3uSD+kt99+G/4sMqgogJSSZ8Wvv/6KtLQ0mEwmNcLO5fqGEDQNjEUDEy8+Ud33dcpafHLsNxWsGdPwPF8fDrblHsDarJ3It1sYQKI6r3379mXuT506VWX7lpZmzYENdsQYw8rMjzGFYU/+MZf77RfeRmUYSZ+zZoExWJO1A7+kbVL78XafvpCUlKR6ajkcjkpvI+vLeZTBMkREVDny3SglJQXNmzdX/W27dOnCU0dEXrPZHWqqKrYKdmXUqPXj7VEYNEbJ6yv4LFodvD2dWsdj0/iMrfX6ZRW6b1lxKKtA83gy8q1ul+Va3A/6zre5f0y7xrEGarw5kqKCvdqn0Gu8P7SeR5rVAm+YNMp+RZtLer+fqkGI+2UiPND9fg3V8F4u1DivWo9mraBLSl5FK7iRb3V/PKl53r1WwaaSViqnignSDguEBrh/Pawa587g5uRV5e9hV3R6g5o83YZqQQCpPooy6yFjrgttwKBBg5CXl6eyqNwFkEbGnaUmovqgoSkCdthxuDAN/qBdcDNcG3euupBNVNdt2bIF8fHxzvunZh9568Fmw/DUvk8xdPMM6KBTZeyuiO2NJSfWoDbxl36KRER1kQTniy8stmnTBkuXLkW7du0QExPj60MjIiIiIqq3GEDygaigkuhmRFQ08vIOqnJ7RAR0CU3C7JY3Iy4gwi9OR3xgtJqI6oOwsLAKS61FGUNggB4p1qwy81MsWYh1E2iNNoViVqubUGC3IN2agzhTBGYd/B/iA2O83icREdUdkm10/fXXq162559/vpp39tln+/qwiIiIiKi2kGwiTzOKmIFUKVqZrlRNIs0lb+aw8Ej1PwNIREXM+gC/CR4RUXkmvRHtgptiTeYO5zy7w67K0nUOTdI8ZdL3qGFAJKywY1n6Pzg/suNp75OIiGq3jRs3omfPnvjuu+8wduxYWCzelWghIiIionpMr/duogoxA8kHokoFkEIiigJI+fn5qpSdNIk9tZTDlH0fw6w34e74yxBmKLuciKrPscIMlRGRZI5DkD6Ap5ropOsb9sfjez9Ch5Bm6BicgPePrUSevRBDY3qr5Y/u+RBxAeG4J36wuv9Pzj7189Q2OF79//rhH2B3OHBjwwsqvU8iIqp7PvzwQ9xyyy3qe1DLli1VvyPpf0RERERE5AmdwaAmT7ehijHM5uMSduawKOfttLTyPV8KHBbszDuMf3OSYdTxTU31Q66tAAuPLMPk3e/B6nDf8LG6/Za5TfVtmX/4J58dA5E/GhTdDRObXo7XDn2PEVtfxPbcQ3it9a3OXmFHCtNwwlJSjq7QbsHcQ99h2ObncN+ut1UJu0Vt7kK4MajS+yQiorrDarXi/vvvx+jRo1XwSPrCrl27Fp06dfL1oRERERFRbS5h5+nkhblz5yIpKQlmsxl9+vTBn3/+6XbdzZs348orr1TrS7/PWbNmobZhBpKPS9gFhJaU6pIydk2aNCmzrkFnUM3Hs215MOs4Go/qB8m4W535n3rf78w7orIWfMGo0yPaGIpWQY188vhE/mxU3DlqcmVBmzvL3O8Z1gqLOzx0WvskIqK6ITc3F5dffjl++eUXdf+RRx7BU089BQNHgBIRERGRt1RJOk97IHmeW/PJJ59g4sSJeOONN1TwSAJCAwcOxPbt2xEXF+fys2+LFi1w9dVX47777kNtxACSjzOQjEERmhlIJp0B3UKb19ixEfkDvU6P0XHnqJKNSeYGPjuOy2N6qUl6sRARERHR6ZOS3fHx8QgJCcE777yjRmQSEREREdUGM2fOxLhx41TvTiGBpKVLl2LhwoWYPHlyufV79eqlJuFqeW3AAJKPeyDpgstmIBFRkfMiO/pVQIuISEYZefKhkoiIypatMxqNqnTHm2++qTKP2rZty1NERERERKdNp9erydNtRGZmZpn5gYGBajpVYWEh1q9fj4cfftg5T6/XY8CAAVi9ejXqKgaQfCCyVAaSwxyuGUBKsWThUGEqYk3haBxQ0i+JiKqXw+FQFziIiIpt2LChUieDvzuIiEpYLBY8+OCD2LVrF5YsWaK+ZEsWEoNHRERERFRldF70NJJtADRr1qzM7KlTp+KJJ54ot/qJEydgs9nQsGHDMvPl/rZt2+Br6enpWLx4Mf7v//4P+/btU+XzGjRogG7duqkye/369fNqvwwg+UBkYMmb2WYOVxeaoqKiYLeXL5O1IXs3FhxZhl5hrTCx6ZAaPlIi3zpuycTG7D1oGhiDdsFNa/SxPzz+f/gnex+GxPTEWRHtavSxicg/LV++3NeHQERUqxw7dgwjR47EihUr1P2VK1fi/PPP9/VhEREREVFdI8Ejj3sgFa2/f/9+hIeXJHm4yj7yZ4cOHcKUKVPwwQcfoEmTJujduze6du2qBm1Jwopcy3jxxReRmJiogmPy+dwTDCD5gNGgQ0SgHhkFdkR0G6jKOchIPFcC9SY0DYxFQ1NkjR8nka/9lPY3vklZi7Mj2tV4AOm/3ENILjgOGxw1+rhEREREdcG6deswfPhw9YU8NDQU7777LoNHREREROR3JewkeFQ6gORObGwsDAYDjh49Wma+3G/UqBF8RTKMbrjhBlVer3379i7XycvLU9UAZs2apT6fP/DAA5XePwNIPixjJwGkjIKiWonunBPRXk1E9VG30ObYkXcIrcyNa/yx746/DLvyj6B1UM0/NhH5J7kQumjRIvXBUm5r+fLLL2vsuIiI/M0777yD2267DQUFBTjjjDPUl9V27ZjRTUT+JcDgvmS57TTGEWptatAok65VQV3vZflkfTVVZbc7tMvBe3NebRrbZeTb3C47mmNxu8xSwQsZaHR/ZoNN7jMZDmcXuF0WH252u+yYxnYncgu9et8Ii618RaNicaHuMynMBvfPP19jn0mR7p9jqMZ5CzZpPw+DxhvWZvfyfWWv+vdjgVX7fXUi1/17Mtfi/r3srYreH+4YK/gFofH2QKDG7093v5Pcv6N8n4FUWQEBAejRoweWLVuGoUOHqnlSUUzuT5gwAb6yZcsWxMTEaK4j2UijRo1SU0pKikf7ZwDJR6LMBuyDBTkWBwqsds0/WkT1lWQdTU30LK2yqsSYwtRERFQsIiLC+WFYbhMRUXlPP/20KqEhLr/8crz33nv8nUlERERE1UsSNDwOIHl+PX7ixIkq26dnz56qVJxk9OTk5GDs2LFq+ZgxYxAfH4/p06er+4WFhSrAU3z74MGD2Lhxo8rQb9WqFapCRcGj012fASQfiTSXvKHT8m1oFMoAEhERkT97++23Xd4mIqISgwYNwrPPPovJkyfj8ccf16y2QERERERUm4wcORLHjx9XA6aOHDmieg19//33aNiwoVqenJxc5vOv9CeSEnPFpBeRTP3793f2Ca1qMoDrjTfewJ49e7B69WrV+0gCXc2bN8cVV1zh8f4YQPKRqKCSANKjkx+CPSdNRf/kDVTaG4d+QIo1CyMbnI1WQb6rpUjkS1aHDQcLUpFoblAjj7cyfTPscKBraBKijKE18phEREREtVVGRoYzy6hXr17YuXOnGnlJRERERFQTdAaDmjzdxhsTJkxwW7Lu1KBQUlKSZmnPqvb666+r4Na9996LadOmwWYrKpsYGRmpgkjeBJA4HMyHJeyKff7BO6qngtQGP9X2vEP4NycZFoe1ho+QyD9kWnNx63+v45E9HyDX5r5ecFX6JmUd5h3+EXvyjtXI4xFR7fT5559jxIgROPPMM9G9e/cyExFRfbFw4UL1xfivv/5yzmPwiIiIiIhqvoSdF1Md8+qrr2L+/Pl49NFHYSgVIJOSe5s2bfJqn3XvLNUSkaUykEIjItX/qamp5da7udGFuKPJIMQHeFabkKiuCDcGI8IYgmBDIA4XplX748mogB5hLVT/pZZBRemnRESneuWVV1SNY0lT37Bhg6p9LJnEu3fvxiWXXMITRkR1ntRwv+OOO3DzzTcjPT1dBZKIiIiIiHxC+h95M9Uxe/bsKVMyr1hgYKDq1eQNlrDzgwykoLAo9b988bLb7WXqJHYMSfDJ8RH5k8cSrkKUMQR6XfXHvHU6HUbFnVPtj0NEtdtrr72GefPmYdSoUSqL+MEHH0SLFi1UqrirASFERHWJ1Hu/6qqr8Ntvv6nPTk899RQeeeQRXx8WEREREdVTOr1BTZ5uU9c0b94cGzduVH2PSpM+Te3atfNqnwwg+UEPpIDQCGfmg9QPj4oqCigRUZEYUxhPBRH5FWmM2a9fP3U7KCgIWVlZ6vb111+vStrNmTPHx0dIRFQ9/vjjD1x55ZWqIbD0Pfrggw9w2WWX8XQTERERke/ovChJVwMD1WvaxIkTceeddyI/P1/FGv7880989NFHmD59Ot566y2v9skAko9ElspAMoYUBZCEjFouDiDl2QuxI+8wwg1BSDLH+eQ4ieqbFEtWjWU7EVHt1ahRI/U3W0b1JCQkqAuqXbp0UeniNdkgk4ioJq1duxbnnnsuLBYL2rdvr3q4tm7dmi8CEREREZEfuOWWW9Qg18ceewy5ubm49tpr0aRJE8yePRvXXHONV/tkAMkPMpAMwSUBpLS0kh4vBwtSMT35C8SYwjGn1S01foxE/uT/Mrbgx7S/cXZ4OwyM7lotjyEXfR/Z8wEKHVY8nTQKTQPZe4yIXLvgggvw9ddfq9rC0gvpvvvuw+eff45169Zh+PDhPG1EVCd1794dF110kfpS+vbbbyMsjFniREREROR7LGEHWK1WfPjhhxg4cCBGjx6tAkjZ2dmIizu9xBQGkPwgA8lhDnfeLt03wQEHmgXGIsoYWuPHR+Rv0q252Jl3GCH6wGoLIGXacpFvt8DqsCHOVBLYJSI6lfQ/kr6FQtLDY2Ji8Pvvv2PIkCG47bbbeMKIqM44fPiwqpBgNpthMBjw2WefqQCS9D4iIiIiIvILUr7O055Gnpa883NGoxHjx4/H1q1b1f3g4GA1nfZ+q+DYyAsBBh1CAvTIKbTD7iaA1DqoMZ5vMYbnlwhAn7DWCDOY0SmkbBO4qhRhDMHCNnfimCUDAXr+eiSisiSzaNGiRQgPD8f777+PkSNHqg9oQlLBvU0HLzZ37ly88MILqjm9lMN79dVX0bt3b7frp6en49FHH8WXX37pLKc3a9YsXHrppXzpiKhKSGBc+h0NGjQICxcuVEGjqvgSSkRUk0wGnZo8oddY/XTC51qxd63LmL4I2ts1qjJrlWzOs3pXzjk13+p22e7UPLfLbBrHEmE2aT9o0XgwlywnB4u5EhcS4HZZRr4F3sgucP/8DVpvSOkbHez+eNLy3B9PRKD76x6tooPcLgs1uX+3mo16r14rkWdxf861trRpvI7Hc90//6xCG7xh0XpA9TvH/TnItbh/zFiN17HAqv2Y7hg0fnfkWbWff57F/bZBJ78Hu+LuZa72Su8qgKSv1wEkIdcQNmzYoK4PVBVeIfWhaLNBBZAspjCXJeyIqERcQISaqptBp0fjgKI+ZEREpf3vf/9DTk6OCiBJ2Tq5oHq6qeDFPvnkE9Xs8o033kCfPn1UIEjSzrdv3+7yMQoLC1UZKVkmpfPi4+Oxb98+REZG8kUjotMmFwbffPNN3HPPParfkZTnzMjI4O8YIiIiIvJLOoNBTZ5uU9fccccduP/++3HgwAH06NEDISEhZZZ37tzZ430ygORDkUEG7M+0wBGdiCFXDEVsTDSb0BIREfmptm3b4uGHH8b555+vLq5++umnKpjkypgxnmUQz5w5E+PGjVOBKSGBpKVLl6oR/5MnTy63vsyXrCPJDjCZikZWJiUlefW8iIhKy8/PV33dPvjgA3X/6quvVr9zQkNZVpuIiIiI/JSUr/O4hF3dCyBdc7Iyyt13310mi1WuYcj/NpvnmXcMIPlQ1Mk+SGGteuCtJ65Eg5CyL8c3KWvxT84+nB/ZCf3C2/joKIn8R6Hdir+yd2Nf/nGMjDurSvedayvA1H2foGdoSwyL7cMSdkRUjgR1JEtIAjvyweuxxx5zWU5E5nkSQJJsovXr16vgVDG9Xo8BAwZg9erVLrf5+uuv0bdvX9V/6auvvkKDBg1w7bXX4qGHHlI9SlwpKChQU7HMzEz1v/RyKu7ndDpkH/KhtCr2VV/xHPIc+pqMVLzqqquwdu1a9Xvo2WefxQMPPKB+r/Fnu/L4s+x/55DvXyIiojqOASRlz549qGoMIPlQVFDJBZ60fFu5ANLe/OP4NycZ3UKb++DoiPxPocOKVw4uhQMODIjqjJhS5R89IV9Gpc+RxWFD08AYNc/isOJAwQmkWbNxdYO+VXzkRFQX9OvXD3/88Ye6LRdW//vvvyopYXfixAk1Cqhhw4Zl5sv9bdu2udxm9+7d+OWXXzB69Gh8++232Llzp0pVl1JTU6dOdbnN9OnT8eSTT5abL+VzrVb3Ndc9uTiXlZWlfsfK+SGeQ1/g+/D0zt3FF1+sSmdGRERg3rx5uOCCC1him+/DOvGzLPsiIiIiqusSq7D3UTEGkHwo0lzyQTgtr3z62KXR3VXwKMlcNf0ViGq7UIMZZ0W0RZjBfTNHV+SLp/zT64p+5r5L+wvvHV2J3mGtcV/Ty9W8CGMI+kd0QKeQROd6RERao3ok68eXF9YkeCUXeCXjSGobHzx4EC+88ILbAJJkOEkGVekMpGbNmiEqKsptKT5Pj0myFGR/DCDxHPoK34enZ/bs2apspvxu6datG3+W+T6sMz/LRo1m30RERFT76fR6NXm6TV3z7rvvai73tNy+4KcoP8lASs8vCiAV1yMULYMaqYmIStzZ5BKPTsdbh3/Gn1k7MLHpELQNji/62TI3gkGnh81RtiTG+CYDeaqJyK3k5GQkJCRUelSPBHTi44t+72iJjY1VQaCjR4+WmS/3GzVy/TmgcePGqvdR6XJ17dq1w5EjR1RJvICAgHLbBAYGqulUcmGuqgI+8hmmKvdXH/Ec8hzWdL+jLVu2oHv37ur+wIEDceGFFyI9PZ0/y6eJP8v+dQ75d4mIiKiO03nRA0m2qWPuueeeMvelSklubq66RhAcHOxVAInf7v2gB5IYd0kfNbqqS5cuvjwkolor05qn+oa9e3RFmflZtjw1bc094JzXKqgxFp4xAQ80u8IHR0pEtVWvXr1w2223qd4g7mRkZGD+/Pno2LEjvvjii0rtVz7ISQbRsmXLyoy8lvvS58iVs846S5WtK93TQUrqSWDJVfCIiOhU+/fvxznnnKPK1O3YscM5nxfaiYiIiKjWkYQMqSjk0VS+p3Ftl5aWVmbKzs5WJarPPvtsfPTRR17tkxlIPhRZKoCUlZmuRvpJJLDYppx9CNGbkWCOhbEORkSJvCWZepJVFKg3oevJHmHSw+jDY/8HHXS4KrYvgg1Fo+yHxPRS5SBbmEt6i0j2kUxERJ6QUfrTpk3DRRddBLPZrII+TZo0Ubflg5ks37x5sxrJ//zzz+PSSy+t9L6ltNwNN9yAnj17onfv3pg1axZycnIwduxYtVxGCUk2k/QxErfffjvmzJmjRhfddddd6uKvNLu/++67+aISUYVWrlyJq6++GsePH0d0dLTKXmzdujXPHBERERHVTsVBIU+3qQdat26NGTNm4LrrrnPbZ1kLA0h+UsIuMCQCwGGkpqaq+1aHDc8mF41cfrP17Qg3etbzhaguW3j0F/yc9jfOCGriDCDFmMJwQWQnNAmILrMuy0ASUVWJiYnBzJkzVRBp6dKlWLVqFfbt24e8vDxVhm706NGq/JNkH3lq5MiR6kLulClT1IXcrl274vvvv0fDhg2d5fNKZwVI76IffvgB9913Hzp37qyCSxJMeuihh/iCE5HmIJxXX31VBa1tNpv6XbN48WIkJSXxrBERERFRreXQ6dXk6Tb1hdFoxKFDh7zbtsqPhrwqYWcKjXTWIZcLUY4APZoFxiLHlo+Qk5kURFTkvIgO2J13FK2DGpc5JeMaX8RTRFTPSHnKX9I3ueyP9tqh71Vgubj/WVUJCgrCVVddpaaqNGHCBDW5smJF2fKcQsrb/fHHH1V6DERUd8l3jPHjxzsb60rQe968eWUqIBARERERUe319ddflxtAdvjwYVXBRErhe4MBJB+KLJWBZAiWDKQiUgZHSuI838LzplZE9YFkFU1rfq2vD4OI/MDCI8twQWRnl8taBTXCgiM/44UWN1TpY+7duxc//fQTCgsLcd5556FDhw5Vun8iouogpTEleGQwGPDCCy/g3nvvha4O1n0nIs/s2rULb7/9tvp/9uzZiIuLw3fffYeEhAR+xiEiotqDJeyUoUOHojT5vN+gQQPV9/Sll16CNxhA8iGzUY8gow55Vgd0QeHO+VLGTgJIREREpO2f7H14OmmUy2X9Izrgpf1lR9+cruXLl2Pw4MFqJH9xGvjChQtVLWEiIn8mZeska1HKXcoXSCIi6Yd2ySWXqBHJv/76qyrTKwGkv//+GwsWLMDnn39eqZOUlZWFxx9/XJXEPHbsGLp166aCUb169XK5voyEvv/++7Fu3Trs3LlT9XCUIHdpixYtcvaCLBYYGKiqtlQVh8Yyg0aA3e7Q2hLQa2yrVSxJK6iv90G832p3/zwLbdrnwJ3juVa3y3an5bpdVmC1e/V4J3ILNZebDe5fkdBA7y6ZZue4f8z4cLNX+7RrvBYio8D9eY0KMrldlhjhvuJRoMH9m86g8YbM13itsgq1X8cCjfdVbqFNc1v3j2n16n2l9fqbNN43wmKze/W7xaR1znUG949nt3u1LLBUiXRXCjXedzaN34MBbt4fGk+vasi59XRwVB0cTGXXeM29VX8K/fl5HyS7uWwAiYiIiCqWacuDSef6w71RZ0CGzf0XUW/IxZGLLroIBw8eREpKCsaNG4cHH3yQLxUR+R0pV/HZZ5/BarU6L7x+9dVXDB4RkdPkyZPxzDPPqMzqgIAA53wJMntSJveWW25R+3jvvfewadMmXHzxxRgwYID6vORKQUGBGg392GOPoUuXLm73Gx4eroJNxZP0niQiInJJAmLeTFQhZiD5WKTZgENZVtgCy5awW5+1C9+lbkDHkAQMje3t02MkIiLyV7GmcOzMO4w2Lvoc7cg7jAamkgEaVeHff//F77//jsaNi3qwSRmoN998UwWTYmJiqvSxiIi8lZubqwLcH374oQpyP/fcczyZRFSOBHvk98SpJAvpxIkTlTpjkpX9xRdfqAD1ueeeq+Y98cQT+Oabb/D666+rANWpkpKSVIaSkExurYycRo0a8ZUjIqIKOXR6NXnC0/X9udJAZc2cOdPj/TOA5GPRJzOQDMFlM5AMhWnYnJuMSGOID4+OiIjIvw2M6oIZ+xdjTqtbEGIoKQmRY8vHC/uXYGBUtyp9vMzMTMTGxjrvS/P5oKAgZGRkMIBERH5hz549GD58ODZu3Kj6HcXHlw+wExGJyMhIldnTvHnzMidkw4YNlf7dIVmONpsNZnPZ0lzy+WjVqlWndaKzs7ORmJioyvF0794dzz77LPsyERGRa/W4B9KGDRsqtZ63/U8ZQPKDDCT1QoRElgkgDQprqYJHMaYwHx4dERGRfxvX+CKs2/EGLvv3WZwd3hZxARE4VpiB3zK3obk5DuMaD6jyx/zhhx8QEVGSOSwXNZYtW6ayk4oNGTKkyh+XiKgiP//8M0aOHKm+T0h5KClh179/f544InLpmmuuwUMPPaR+V8hFJflM89tvv+GBBx7AmDFjKnXWwsLC0LdvXzz99NNo164dGjZsiI8++girV69Gq1atvD7zbdq0UdlJnTt3VgN1XnzxRfTr1w+bN29G06ZN3ZbGk6n0wB8iIqon6nEAafny5dW6fwaQ/KQHUkjzrpj68pvo0bIxOnXqhMYBUWoiIiIi9wL1Jrx1xh34NvUvrM7cjs05+xFlDMF9TS/HpdHdVR+kqnbDDTeUm3fbbbc5b8sFGBmJS0RUk/2OXnrpJXUhWC4A9+zZE19++SWaNWvGF4GI3JKMnjvvvFP9rpDPLu3bt1f/X3vttao/UWVJ76ObbrpJZS1J5qNkC40aNQrr16/3+uxLUEqmYhI8kgCVlA6WYJUr06dPx5NPPun1YxIREVF5DCD5WNTJDKTAmHicNaA7LmrJjCMiIvJ/Hx9bhXeOLscJSxbOCGqCyQnD0Ckk0e367x9diU+P/44jhWmINIbioqjOuDv+MhUAEjaHHa8f+gFLU9cjxZKJBqYIDInthVsbXVRhmrUEiYbE9FJTdZMLs0RE/kYay0+dOlX9jho7dixee+21cuWkiIhOFRAQgPnz5+Pxxx9XmdRSMq5bt25o3bq1RyerZcuWWLlyJXJyclTWj/SKlGzIFi1aVNlJN5lM6th27tzpdp2HH364TB8IORYG0omI6ol6nIE0fPhwLFq0COHh4eq2Fhlk5ikGkHws8mQGkkjLKxmtvCvvCHTQoXFgFIL0AT46OiIiovK+T92AFw98hccSrkankAR8cOxX3L5jHr7qMNll6dVvU9dj9sGleDJpJLqENMe+guOYsvcj+bSGSc2uUOu8feQXfHb8dzzdfBRamhthS+5+TNn7MUINZoyOK2rI7I4En+Qxfs/cjjRrjspA6hfeBpdUUwYSEZG/kYb0b7/9tmp6f/vtt3td35yI6qeEhAQ1na6QkBA1paWlqZK/zz//PKqKZEZt2rQJl156qdt1AgMD1URERPWPQ6eDw8OAkGxTF0RERDg//5cut19VGEDykwwkkZ5fEkCad/gnJBccx+Rmw9ElNMlHR0dERFTee0dXYnjsmRga21vdfyzhKvyasQVLUv7EzY0uLLf+xuy96BraHJdG91D34wOjMSiqGzblJJesk7MX50V2wLkR7Z3rfJf6F/4ttY4rubYC3L7jTezNP46zI9qhbVA8jlsy8dKBr/HZ8dV4o/VtCDbwQgIR1T0//vijs/eIGDFihK8PiYhqgdIZOhWZOXNmpdaTYJGU0pS+RZIhNGnSJLRt21ZlRBZnBh08eBDvvvuuc5uNGzeq/yXr6fjx4+q+ZERJGT3x1FNP4cwzz1R9lNLT0/HCCy+obMtbbrnFw2dMRET1Qj3OQHr77bdd3q4qDCD5SQ8ksXXzZqzMcagPXhEtghFtC0W4Mcinx0dERPVHVlZWmWbDrkZxWuxWbM09gJsblwSK9Do9zgw7A/9k73W5366hSSpDaFPOPlXm7kBBClZlbsXg6J4l64Qk4YsTq7E3/xiSzHHYnnsQG7L34IGmRRlK7sw99D1MeiOWdnpUZSs5n4stD/fvWqSWF2c5ERHVBfJdQUb1P/LII6pZ/V9//YVGjRr5+rCIqJbYsGFDmfvyO8Rqtargj/jvv/9UH6MePYoG/lRGRkaGChIdOHAA0dHRuPLKKzFt2jRVdk4cPnwYycllBwVJObpi0ivpww8/RGJiIvbuLfo8KVlM48aNw5EjRxAVFaWO5/fff3cGmIiIiMqQDBxPM4rqSAZSdWMAycciS2UgvXn3UMzKTFP1g7Xq+hIREVWHU7+QSz+NJ554osw8KRFngx0xxrKl6qR03Z78Yy73K5lHst2N2+fIlU9YYcfVsf1wS+MBznVuanQBsm35GLr5ORiggw0O3NXkElwWo33xYln6P3iz9fgywSMRZgjCIwlX4tb/3mAAiYjqDBmpL43qP/vsM3VfSjnJhVUiospavnx5mQwjyWR85513nL9LJHAjmUPnnHNOpfcpGZBaWZDSl8FVMFzLyy+/rCYiIqJKqccZSKf6/PPP8emnn6rBG4WFheUGjniqbp6lWpqBZAopqlGYmprqwyMiIqL6asuWLWoEafEkI0mrwtqsnVhweBkeTbgSH7efiJktbsT/ZWzBm4d/dK7zQ9rf+Db1L0xvfp1a5+mkUXjn6Ap8nbJWc98SmEoIjHW5TOZnWHOq5DkQEfmaDDCTcnUSPJJR/a+//jrmz5/Pfh9E5LWXXnoJ06dPLxOIltvPPPOMWkZERFRbSP8jb6a65pVXXlEDQaRSgWQd9+7dGzExMdi9ezcuueSS2pmBNHfuXFXLVtKSu3TpgldffVU9MXfkC9Pjjz+u0ppbt26N5557rlwTxa1bt+Khhx7CypUrVSq2jKj+4osvqqQpZFULMuoQaNChwOaAMSRSzZP6vna7HXp93XsTExGR/5IRqOHh4ZrrRBlDYIAeKdasMvNTLFmINZXNSio299B3GBzTQ/VNEq2DmiDPXoin932GcY0GqBJ4Lx/4RmUhXRLdzbnO4cI0FXgaEtPL7fE0MIVhe94htA2OL7dM5seatJ8PEVFt8N133+Haa69V3xOkXJ2MKjzrrLN8fVhEVMtJ6WLpP3QqmSeljes6nZdVjUynUfKouMl5TbFrJHoV2uya21q1F7t1PNfqdtnO1Fy3ywo0HtCmkbFm11gWWMG4eb3J/eth1zh5WYXun2Ogseav5cUGF5WLdKVxaIDbZQEG988/16L1erg/Frm+6U5GvsX9hhXsV4vWa2XWeD2yC21e7VPr/SgMGj/ncRqvh9bPgF5jn1rLtI5F6/FEXIj795UWd3v18lcKeei1117DvHnzMGrUKJUB/OCDD6JFixaYMmWK10krXv1W279/v6ptW+zPP//Evffeqw7OE5988olq4CglciR9SgJIAwcOxLFjrkvgSL1befI333yziqANHTpUTf/++69znV27duHss89WDRtXrFiBf/75RwWczOaypW38hXx4iDyZhaQLKspAMsaFYurOjzD/8E8+PjoiIqKypN9Qu+CmWJO5wznP7rBjTdYOdA5Ncnm68u0W6E75im44OdKn+KN3vr2w3AdfKWVnd67hmvRRmrL3IxwqKPtBSO4/sfcTXB5T0mfpdMmIXKnrX5mJiKgqyZc/CR5JQ3npFcLgERFVhWHDhqlRyl9++aW6xiOTDL6Vay7Dhw/nSSYiotpDrjHoPZzqYAZScnIy+vXrp24HBQU5B4Rcf/31+Oijj2ouA0lGv916663qgSVz6KKLLkKHDh3wwQcfqPsS0aoMqbcrTRHlA4t44403sHTpUixcuBCTJ08ut/7s2bMxaNAgTJo0Sd1/+umn8dNPP2HOnDlqW/Hoo4+qjCRpLFtMegr5syizAUezrYC5aOS2MToYm3OTkWvQjswTERH5wvUN++PxvR+hQ0gzdAxOwPvHVqqMoqExRRnEj+75EHEB4bgnfrC63z+iPd47uhJtg5uiU0gC9hecUFlJ50Z2cAaS+kd2wPzDP6NRQBRamhthW+4BvHdsJa44uU93pI/SzrwjuGLzDHQOSUQDUziOWzKxKWcfzo5oV6bP0umaNWuW83ZKSooq7yIDX6SklFi9ejV++OEHNXCFiKgqLViwQFVVkO9IgYGBPLlEVCXkOsoDDzygrvFYLEXXH4xGowogSaUYIiKiWoM9kBSpViCZRomJiaoa2x9//KGSdvbs2VNh/8EqDSBJxk9xmTlpyNSxY0f89ttv+PHHHzF+/PhKBZCkgZOMnivdX0FKtg0YMEBdgHFF5kvGUmly4WbJkiXqtpR9kwCUpGbJfMlSat68uXoMyVRyp6CgQE3FiiNzsj+ZqlukuejimT64qIRd4aEMXFbYDu1j21XJ48s+5A1SE8+lJtXF58XnVHvwtaod+DpV/jx5YlB0N6RZs/Haoe9xwpKJNkHxeK31rYg5WcLuSGFamWyicY0vUhlIcw99i2OFGYgyhqqA0YQmJSVoJzcbpoJKzyZ/gVRLFhqYInBVbF/c1vhizWMx6gx4seUNqs/S6sztSLfmoEtgEm5rcjF6h7VGVbrhhhuct6+88ko89dRTmDBhgnPe3XffrQa1/Pzzz7jvvvuq9LGJqH7ZsWOHGlT37LPPqooFoaGhqmoDEVFVCg4OVqVuJFgk1VyKB+CGhITwRBMRUe3CAJJywQUX4Ouvv0a3bt1U0o5cm5Dy1+vWrfM6u9irAJKMTCke+SYXSYYMGaJuS9m4w4cPV2ofJ06cgM1mUw2dSpP727Ztc7mNZDe5Wl/mCyl9l52djRkzZqhRwdIf6fvvv1cnZ/ny5ejfv7/L/UrTyCeffLLcfBldHBDgvjZlVQnCyZE+IUUl7GwZ+QjZnY/mCRE4lue6nJ+nFwalGboEW+pSX6W6+Lz4nGoPvla1A1+nypG/d54aFXeOmlxZ0ObOckGe8U0GqsmdEIMZDzYbpiZv9AprpaZTSWk8s77q/5ZLppF8zjiVZEq7yqImIqosGRA3evRo9Tm3SZMmuOuuu3jyiKhaScCoc+fOPMtERFR7MYCkSIuh4kHCd955J2JiYlRbIInf3HbbbaixAJKUq5NU58suu0yVkJNScuLQoUPqoHyl+ORcccUVzpG/Xbt2VSdJjtddAEkylEpnNh08eFCViJDnEhcXV+3H3TgqBTiQAUNwRJnnUlWPLfuSkYsNGjSoM4GWuvq8+JxqD75WtQNfp8qRrOC6ptBuxUfHV+GdI8vxS5fyg0ROl3xG+Oqrr3D//feXmS/zfPlZiIhq99+sadOmqUwjGSAlfY6uuuoqXx8WEdVh559/vvpO7c4vv/xSo8dDRETkLYdOB4eHPY1km7pg+PDhqmdqeHg43n//fYwcOVKVpBXXXHONmk6HVwEkGXErzRYlzVnKuUgdPSHpUcWl7SoSGxsLg8GAo0ePlpkv96VWnysyX2t92aecHAn+lNauXTusWrXK7bFINlXpWuKZmZnqfwlK1ERgIirIWCYDydQwDHsKjiHbXoBwY1CVPIZ8KKyp51OT6uLz4nOqPfha1Q58nSpWm3+HSj+lqXs/xtbcg0gyN8C0pNFq3jPJnyPKGIJJzdyXsD0dkrl8yy23YMWKFejTp4+at2bNGpX5PH/+/Gp5TCKqu+T7x5gxY1QQWtx+++2q71pNVEMgovpLBtyeWm1m48aNqm1B6dK9RERE5L/+97//IScnRwWQpGydVEapyqQYrwJI5513nipBJ190oqKinPNvvfVWVUO3MuTLUI8ePbBs2TJnfyIZdSf3S/cTKE2aVMvye++91zlPMqCKm1fLPnv16oXt27eX2e6///5TjaP8VVSQoUwAKXpIB/ycdBgd0jdhaGzlAnJERET10fP7l6BhQCTGNroA/0tZj4m734bNYcfDCcNxQWSnanvcG2+8UQ1QeeWVV/Dll1+WGbBSHFAiIqoM+e4i34ekjLd8n5F+JNLAnoiour388ssu5z/xxBOqPQAREVGtUY9L2LVt21ZVWJPMYqlk8Omnn6pgkisyaK1GAkh5eXnqYIqDR/v27cPixYvVhZOBA933NziVlI2TUS09e/ZUmUsyyk6iZRIpK35C8fHxqkeRuOeee1QZupdeekmVz/v4449VAyip7Vds0qRJKk3r3HPPVSdNRgJ/8803aoSwv4o0FwWQwtudjdkr9iD8jN1Ym70TkcbKBeOIiIjqq005yfiu02MI0gega2hznLPxMXzVYTISzQ2q/bElUPTBBx9U++MQUd0mfVx37typvvdIQLqyFR2IiKrLddddp34XvfjiizzJRERUO0g5Ok9L0nlZwm7u3LmqMtuRI0dUZbZXX31V8zP8Z599hscffxx79+5F69atVXW3Sy+9FFVFWvdInEV6qUoVnscee8xliVqZV2MBJOkxJLX1xo8fj/T0dHUBxWQyqaykmTNnqpILlSGBnuPHj2PKlCnqhEv6tAR8GjZsqJYnJyeXKavTr18/fPjhh+okPPLII+qEL1myBB07dnSuI6X15KRJ0Onuu+9GmzZt8MUXX+Dss8+Gv4oyFz1HvSkQhcYQ3Nj4AtyIC3x9WERERH6v0G5RwSMRZghCqMFcI8EjsWvXLrz99tvYvXu3GgQjKeLfffcdEhISVL9IIqLKOOecc9QoQfmuU/w9iIjIl1avXg2z2cwXgYiIao8aykD65JNPVLBG4g8SE5FrAZJQI1UFXJWN+/333zFq1CgVqxg8eLCKbUj1gb/++qtMTON0yPeIP/74Q92WWIpUY/N5CTt5gsWpzp9//rn6orNhwwYVqJFgUGUDSELK1bkrWecqa+jqq69Wk5abbrpJTbVFcQk7kZZn8+mxEBER1SZSrm5F+mZpf+nyvjgvsmo+lJW2cuVKXHLJJarJ/a+//opnnnlGfUD7+++/sWDBAvX5iIjIlYyMDDUQTwbFFQebZRAcEVFNk4HBpUmlmcOHD6tKLzJSmoiIqLZw6PRq8nQbT82cORPjxo1zVlCTQJJk/ixcuBCTJ08ut/7s2bNVTyKpmiaefvpp1ZJnzpw5atuqtmfPHjRoULWDar0KIOXm5iIsLEzd/vHHH9WHDolunXnmmaqcHXkm6mQJO5GWzwASERFRZUWbQvHc/sXO+5HGkDL3ddUUQJIPhhI0kpFHxeeN0HEAAKo3SURBVJ+JxAUXXKA+CBIRubJ161Y14lBGBUqTegk6l664QERUk6Q/QukSN/L7SKq4PPXUU7j44ov5YhARUb3IQMrMzCwzOzAwUE2nKiwsxPr161W/odJ/OwcMGKCyd12R+XLdoDTJWJKqalVFqrhJJRSRmJhY4foHDx5U5bOrNYDUqlUr9SRlpNwPP/yA++67z1m/212DJnIvJEAPox6w2oHfPp6DS7MKYHYY8P7IZxBsKP9mJSIioiLfdfLN6NhNmzap1PNTSRaSlPQlIjqVfH+6/vrrVWP6Zs2aqRKYDB4RkS8tWrSoXr8Aei97X1TEVd+JytBXw+FIVpk7cg3KW8dyrW6XJafnoapZbO4P1u7+KSLf6v44K9q2QC7UuWHSeLGyre4Hhh/JKnC7LC6kqCy3K6GBJQPPXYk0u7+8q/UyH8m2uF2WVej+edg1TpzJ4P7c5Fq0B83bNF4Pb+n17s9dfLj7a652jRNn0VoowQeN9463v5O0zqvW4dg0fgdEmk2ax2M2ePk8PJxfVRw6nZo83UbIZ/PSpk6diieeeKLc+idOnIDNZitXdlrub9u2zeVjSNseV+vL/KrSq1cvNUjtlltuUbfdVUGQstmSEXXrrbeq1j/VGkCSMnXXXnutChzJSNu+ffs6s5G6devmzS7rNflwIVlIx3Nt+O+X95A7sL/qKWXUaf+BICIiovI2ZO9Bt9Dm1XpqIiMjVYmX5s3LPo6U9PVkJA8R1X12u119CZWsRdG/f3/15a0q65ITEXmjRYsWWLt2LWJiYsrMl17X3bt3V30eiYiI6rr9+/eXSYpxlX3kz7Zs2YJp06bhoosuUj0Me/TogSZNmqjbaWlpavnmzZvV3/bnn38el156qUf79yr4d9VVV6nUKKmLKxlIxS688EJnbyTyTOTJPkj6gDAcef03HH93LQL0XsX3iIiI6rU7d8yr9se45ppr8NBDD6lRQzIQRC4Q//bbb3jggQcwZsyYan98IqodJNvo8ssvdwaP7r33XlXznMEjIvIHe/fuVSOpT1VQUKDK2xAREdUWkmzlzSQkeFR6chdAio2NhcFgwNGjR8vMl/uNGjVyuY3M92R9b8hAEOnNJINcpaR+69atVbbUjh071PLRo0er0ntSTs/T4JHwOkIhT1KmAwcOqPtNmzZF7969vd1dvVfcB0kCSNmrtyEbQF5eHoKCgur9uSEiIvI3zz77LO68806V6i4XXtq3b6/+lwztxx57zNeHR0R+Qkb95efnq//nz5+P6667zteHRESEr7/+2nkWZFBwRESE8758nlm2bBmSkpJ4poiIqNawOxxq8nQbTwQEBKjsHvk7KSXj1D7sdnV/woQJLreRym2yXAaSFZMBZcUV3aqSxBEk8UemquRVAElOjIyie+mll9SoOiENpO+//348+uijrOXthaiTGUjG4EjnvNTUVJbBISIi8lA1lM52+cFRLgZLWV/phySfh6SMr4z0ISKSnhOSnWg0GvHxxx+rQXcs9U1E/qL4opf8nrrhhhvKLJNy+hI8kus9REREtek6gKMGrh1MnDhR/e3s2bOnSqaZNWsWcnJyMHbsWLVcKpJIWfvp06er+/fcc48qYS1/Vy+77DL13UCqus2bN69aMoslOFVYWIjzzjsPHTp08F0ASYJECxYswIwZM3DWWWepeatWrVLNpWSEndTcI89EnsxACoiLQmDzaFjT8lSNQvZRICIi8szqbkUf1KrTU089pcrVSQZS6Yabkj38wgsvqMASEdU/MnL/8ccfV0HlV155Rc1r0KCBmoiI/IUMChbSy1F6IElJHiIiotrM7iiaPN3GUyNHjsTx48fVd34pad+1a1d8//33aNiwoVoubX/0+pKuQf369cOHH36oKpU88sgjatDpkiVL0LFjR1Sl5cuXY/DgweqahJCBbAsXLqySCghe9UB655138NZbb+H2229H586d1XTHHXeokbiLFi067YOqj6JPZiCZO0ah2ZOD0GBMT5WBRERERO6tydyBZ5O/cLlsevKXWJu1s1pO35NPPunMwi4tNzdXLSOi+kcGf8mXNhlt+Oqrr6qLskRE/mzPnj0MHhERUZ2pAODN5I0JEyZg3759qmfgmjVr0KdPH+eyFStWlIuPXH311di+fbta/99///WqD1FFZBDbRRddpHoYpqSkYNy4cXjwwQd9l4EkgY22bduWmy/zGPQ4vQwkQ0AwrGlHYUvL5bkkIiKqwLtHl+OauLNdLjs3oh3eObIcvcJaVVt5qlP9/fffiI6OrvLHIyL/JqUspSTU7t27Ve1xGWzXq1cvXx8WEVE5kh156623qt5sxZmS7tx99908g0RERLWABKZ+//13NG7cWN2XyihvvvmmCibFxMTUfACpS5cumDNnTrkPGzJPspHI+x5Itv2RSH5uibqddn4aTyUREZGG7XmH0De8jctlfcLPwNR9n1Tp+YuKilKBI5nOOOOMMkEkKV0lWUnjx4/na0ZUj3z66aeq5rlkIErfkMWLF6tSFkRE/ujll1/G6NGjVQBJbrsjn3EYQCIiotqipkrY+avMzMwyWcXBwcFqYFtGRoZvAkjPP/+8avr0888/o2/fvmre6tWrsX//fnz77bendUD1VdTJDCRjcIRzHrO5iIiItGXb8t02vpQsoRxbQZWeQmmQKfu96aabVKm6iIiSv9sBAQHq4nHxZyMiqvukH9rUqVPV7QEDBqimuKf7BY2IqLrL1rm6TUREVNvVoXiQV3744Ycy1yik5+GyZctUdlKxIUOG1EwAqX///vjvv/8wd+5cbNu2Tc0bPny4SoN+5plncM4553iz23ot8mQGUkB0EyR26YfuLRqqhpZERETkXnxANDbl7EP30Bbllm3KTUbjgKgqPX033HCD+l/+Rp911lmqMSUR1V+SaSSj9CdNmoRp06bxdwIR1bog+AMPPKBGKZcmDbil9I00CCciIqoN6nsGUunrFaXddtttztvyvUUqp3hKDy81adJEfUn64osv1CSBI2kcu2DBAm93Wa8VZyAF9XOgw+wJmPnhGyooR0RERO4Nie2Nafs+x8GC1DLz5f705C8wNLZ3tZy+nJwcNZLH1Yif7777rloek4j8gzS/LT2CT0b0PffccwweEVGtI9nUUn73VFKSU5YRERHVFlIpxJuprrDb7RVO3gSPBIfN+omwQD0MOsASchQpphzk2y2+PiQiIiK/d33cudick4xhm2egY0gC4kwROGbJwL85ybggshOuj+tfLY87efJkzJgxo9x8+QAqyy655JJqeVwi8i0pUffQQw/h119/RWJioprXvn17vixEVCvJ55bS/RyL/f3334iOjkZdodfpYHDxPF3MKtlGY3+uzlnljwVVTmsEvf009ns81+p22V+HMtwus2gcUOuYELfLtp8oH8ys7OvrjqmCE27TuHistanWc4wLCYA3QgOLBpZ741BWodtlBVb3F4xtGu8drXMXbDJ4dW5Oh8Xm/t2s9ZByrdUbwSb3vwWCDdqvVa7Fu3Ou9ZgmvftlgQHun2Sgwf12QUbtk6OxKYwa7w+Dm2Xu5lcVuxe/807nd2R9wgCSn5A/eBFmAyzHOiEopBCNAyJ9fUhERER+T6/T4/kWY7AuaydWZ/6HNGs2ugY2xx1NBqFnWKtqe9wdO3a4vGjctm1b7Ny5s9oel4h8w2q14uGHH8aLL76o7s+ePRszZ87ky0FEtVJUVJQKgsh0xhlnlAmIyOhkyUoaP368T4+RiIjIExIP9jShqA4lIFUrBpD8SFSQAampTVGYq0O4oWwNYiIiInJPgkXVGTA6lTSm3L17N5KSksrMl+BRSIj7kZVEVPucOHEC11xzjbNspQSSnn76aV8fFhGR12bNmqWyj2666SZVqq50w+2AgAD1+aZv3748w0RERORZAKminjzp6ek8pVXQB2nTS2PQYtphREVF4q+//uI5JSIicuOF/V+Vm2fU6REfGI3zIjoiLqDkgkhVuuKKK3Dvvfdi8eLFaNmypTN4dP/996ueKERUN2zYsAHDhg3Dvn37VHB40aJFuOqqq3x9WEREVdJku3nz5ujXrx9MJhPPKBER1WpSytDTCorVVHGxfgeQSo9Kcbd8zJgxp3tM9VaY2Q6HORU2pGLv3j3IyIjy9SERERH5tUxbbrl5VocNf2XvxuwDS/F661vRObRsllBVeP755zFo0CBVsq5p06Zq3oEDB3DOOec4S1wRUe22atUqXHzxxcjLy1OB4iVLlqBjx46+PiwioirTv39Jr8j8/HwUFpbtoxIeHs6zTUREtYJk1srk6TZUxQGkt99+25PVyUOG4ExYmy9D43t6Ytft21RGl9QfNlTQnI2IiKi+ejpplNtlS078idkHl2JBmzur/HFl0Mzvv/+On376STWaDgoKQufOnXHuuedW+WMRkW/06NFDBYkbNWqEDz74QPUMISKqS3Jzc/Hggw/i008/RUpKSrnlcj2CiIioNrCfnDzdhirGHkh+JDRQhlIHwZ5TEgXNyMhAdHS0rw+NiIio1rkspgdePvBNte1fGk5LdoJMRFQ3pKamIjIyEnq9XgWGf/zxRxU44oAuIqqLJk2ahOXLl+P111/H9ddfj7lz5+LgwYN48803MWPGDF8fHhERUaVJLpGnCUV1Mf/IZrPh5ZdfVoNDkpOTy2UXy/cdTzGA5EfaBDWFaf1gZHzyi3NeWloaA0hERERekFJ2Bp2+ys7dK6+8gltvvRVms1nd1nL33XdX2eMSUc1Yv3696nd08803Y+rUqWpebGwsTz8R1VnffPMN3n33XZx33nkYO3asKsXbqlUrJCYmqszL0aNH+/oQiYiIKsXucKjJE56uXxs8+eSTeOutt1R/5sceewyPPvoo9u7dq8pxT5kyxat9MoDkR6KCikrVGYIjykQFi5tzExERUeXYHXbMO/wjOocmVtkpk1E8ciFFAkhyWysziQEkotpFLqBKgLigoAAfffSRGpUfHBzs68MiIqpWcr2hRYsWzn5HxaOSzz77bNx+++08+0RERLWMDACZP38+LrvsMjzxxBMYNWqUii1Iyf0//vjDq2sVDCD5kUhzUQDJGFI2gERERESujdjyEnQuMo+OWjIQYwzD3NbjquzU7dmzx+VtIqq9LBaLGp336quvqvuDBw/Ge++9x+AREdULEjySzzQJCQmq55uUu+ndu7fKTJJ+j0RERLWqhJ0X29Q1R44cQadOndTt0NBQ1R6n+HvO448/7tU+GUDyI//aNsHadBvMbUKBb0tK2BEREZFr1zU8t9w8o06PxgHR6BjcDBtz9qJpYAxPHxGVc/ToUYwYMQK//vqrui8lHaR0nfQ/IiKqD6Rs3d9//43+/ftj8uTJuPzyyzFnzhwVXJ85c6avD4+IiKjS7I6iyROerl8bNG3aFIcPH1aDQyTzSHq6du/eHWvXrkVgYKBX+2QAyY8ctR2DI+wgDDFBznnMQCIiInJvSEyvcvOOWzLx9Yk/MWXvRzhmycSablXTBHrixImVXpcXXYj8mzSTlRJNO3fuRFhYmMo6uuKKK3x9WERENeq+++5z3h4wYAC2bdum+sFJ/7f333+frwYREdUeDsDjlkZ1MIA0bNgwLFu2DH369MFdd92F6667DgsWLEBycnKZv/ueYADJj1wS2xmf/GmELnuHcx4DSERERBWzOexYmbEZi0+swe8Z22CHA+MaX4Rr486pstO3YcOGMvf/+usvWK1WtGnTRt3/77//YDAY0KNHD75kRH4uICBANZSdMWOGaigrpZuIiOq7xMRENUlWklxsmjdvHuoCna5oOpVWvqn0tPSW3vtN3bJpDJO3ObxrEJ+ab9V8zH+OZrldZtE4HpPGCTiYme92WbDJ4P7xtJ6kBkN1vBgVPaaX753DWQVeP6bWuQs0ul9WYLV59XgZBe7fOxab3e0ys1E7y9uu8b6KDQ5wu8xkcH/OC6zuj0f7WNwvszi09xlYwfN0x6zxWkWdbHvi6c+cQe/9e1XjtPoluQYgk6fb1DUzZpQMoB05cqT6u/7777+jdevWKtPYGwwg+ZFOoQmIzrPCEhaEDtc9jgcvasWLUERERBr25h9TQaNvUtYhz16ICyI7YW7rW/HIng8wssFZiDSGVNn5W758eZkMI8laeOeddxAVFeUsOyulYM45p+qCVkRUtVlHBw8eRPPmzdX9G2+8Eddccw3MZjNPMxERERFRLebwIgPJ44ylWkDKc/fr1w9GY1HY58wzz1STDH6VZeeeW74NQEUYQPIzElFOj4mH+bwbMGZMS18fDhERkUsfH1uFd44uxwlLFs4IaoLJCcPQKSTR7dl6/+hKfHr8dxwpTEOkMRQXRXXG3fGXIVBvcq5ztDAdsw7+D79lbEO+vRDNAmPxVNIodAhp5na/Qzc/h0hjMO6JH4yBUV0RbCiq6Vvdg6VeeuklVUu4OHgk5PYzzzyDiy++GPfff381HwERedpM9uqrr1alG4rLMwkGj4iIiIiIaj/2QCpy/vnnqx5IcXFxKC0jI0Mts9k8zzxkh1g/sjvvKIJDcuGAA/lWB/It3qVZEhERVafvUzfgxQNf4bbGA/Fxu4loE9wEt++YhxSL6zIX36aux+yDSzG+ycVY3GEynkgaiR/SNuKVg98618m05uLG7a/CqDNgbutx+LLDQ7i/2RUIN5b0BXTXA6nAbsWcg9/ijcM/YEfeYdSEzMxMHD9+vNx8mZeV5b7cBxHVvDVr1qis/lWrViE9PR1bt27ly0BERERERHWOw+FwWYY1JSUFISHeVWhhBpKfsDhseHTvBzjQwAIcvhywByAt34bGJsb4iIjIv7x3dCWGx56JobG91f3HEq7CrxlbsCTlT9zc6MJy62/M3ouuoc1xaXRRb6D4wGgMiuqGTTnJznUWHvkFDQMi8XTSKOe8poExFR7LU0nXYHKzYfg+bQOWnPhTHZtkROXYC5Bty0eMKQzV1ZhSytVJJlLv3r2dF6knTZqE4cOHe7XPuXPn4oUXXlCZEl26dMGrr77q3LeWjz/+GKNGjcIVV1yherkQUQnp4XHHHXeo8nXt2rXD4sWLnX3LiIjqq4o+q0iwnYiIqDap7yXshp/82y7BIynVHRhYVJ1FSNbRP//8o0rbeYMBJD+RZytEjDEMx3VZyLObUJh2BH+sT0N8sKNSF4+IiIhqgsVuxdbcA7i5cUmgSK/T48ywM/BP9l6X23QNTVJZSJty9qkydwcKUrAqcysGR/d0rrMyYzP6hbfBA7vewbrsXYgzRWBkg364skHfCo9JytZJQEumXXlHVE+kY5YMjNjyIi6J7q4ynqraG2+8gQceeADXXnstLBaLmic1hm+++WYVBPLUJ598gokTJ6r99unTB7NmzcLAgQOxffv2cqnnpe3du1cdB/suEZUlAaP77rtP/UwVB32lZ5n0LiMiqu8iIiIqXD5mzJgaOx4iIqLTZYdDTZ5uU9f+tjscDvWdJyiopJpLQECA6oM0btw4r/bNAJKfkBI9c1qPwxtrU7AAadj6/NUYnnYEjRs3xqFDh3x9eEREVA9I6TUpzVZMRqyUHrUi0qw5sMGuBj2UJpk+e/KPudyvZB7Jdjdun6OG+Fhhx9Wx/XBL4wHOdSSoJD2Srm/YXwWnNufsx3P7F8OkN6oydZXVMqgRHmh2Be5tOhgr0v9VWUnVITg4GK+99poKFu3atavosVu29DolfObMmerDnGQ1CbnovXTpUixcuBCTJ092uY2MIho9ejSefPJJ/N///R9HCxOV8sQTT6ifIxmBJ73J5OdIr2dmPxGRePvtt3kiiIioTqnvGUhvn/zbnpSUpAaZenttwhV+i/IzUUEG9b8xuChqmJqaqiKHRERE1a19+/Zq1ErxNH369CrZ79qsnVhweBkeTbgSH7efiJktbsT/ZWzBm4d/LDPyp11wU9wdf5n6/6oGfVVG0WfHf/fqMaWX0oCoLmpwRnWS5pQytW7dWn1A8+ZvtmRKrF+/HgMGlATU5EK33F+9erXb7Z566imVnSRZT0RU1oMPPohevXrhf//7Hx555BEGj4iIiIiI6jC7w+HVVNdMnTq1SoNHghlIfibKXBRAMpwMIBUUFCAvL0+NdCYiIqpOW7ZsQXx8vPP+qdlH6u+UMQQG6JFizSozP8WShVg3/YbmHvoOg2N6qICQaB3UBHn2Qjy97zOMazRAlcBrYApHC3PDMtu1CGqIn9P/gT+SBpQjRozA8uXLVYbDjh070KJFCxXMiYqKUr2RKuvEiRMqm6hhw7LPX+5v27bN5TarVq1SvV02btxY6ceRzxQyFSvONrPb7Wo6XbIPCaBVxb7qK55D78l7b8WKFTj33HPV7fDwcBWAlZ9Pvif5Pqxp/Fn2v3PI3wNERER1m81eNHm6TV3QrVs39b2nMv766y+P988Akp/YkL1HldoJhPQ5iHFmIIm0tDQGkIiIqNpJnVy56KpFSspJhtCazB24ILKTmmd32LEmaweuiTvb5Tb5dgt0KPthxqArSoIuHu/TNSQJewvKlsDbl38cTQKi4Y+kt4rJZEJycjLatWvnnD9y5EjVy8iTAJI3pQavv/56zJ8/H7GxsZXeTjLKpNzdqeRzhtVqrZKLc3JscsGPpcJ4DmuSBEalRN3777+PGTNm4KqrruL78DTwZ/n08Rz63zmUfRERERHVRUOHDq3W/TOA5CcOFJzAn1k70N4kF9hiYAwpCSBJGbvSI8KJiIh8SfoUPb73I3QIaYaOwQl4/9hKlVE0NKa3Wv7ong8RFxCOe+IHq/v9I9rjvaMr0Ta4KTqFJGB/wQmVlXRuZAdnIOm6hv1xw7ZX8Nbhn3FxVBf8m5uMz0/8gSkJV8Mf/fjjj/jhhx/QtGnTMvOllN2+ffs82pcEgQwGA44ePVpmvtxv1KhRufWl59LevXtx+eWXlxtZbTQasX37dtWP6VQPP/ywCm6VzkBq1qyZypiqKHBYGXIMMupJ9scAEs9hTTl48CCuvvpqrFmzRr3/ZJL3M9+H3uPP8unjOfS/cyh/H4mIiKju8qYkXV0pYTd16tRq3T8/RVUz6+GDyPniA1j37YG577kIGT7K5XqdQhJxY0MTzPYwfA0HDKcEkIiIiPzFoOhuSLNm47VD3+OEJRNtguLxWutbEXOyhN2RwjToS6VPj2t8kcpAmnvoWxwrzECUMRT9IztgQpNLnet0DEnAzJZj8crBpao3UnxgNB5segUui+kBf5STk+MyO1j+Zrsq/aclICAAPXr0wLJly5wjh+TCmdyfMGFCufXbtm2LTZs2lZn32GOPqdHVs2fPVkEhV+S4XB2bXJirqoCPXOyryv3VRzyHlSelHCXbSIKtcpH5o48+wkUXXaR+Dvk+5PvQ1/iz7F/nkH+XiIiI6jYJBtnqaQDpVOnp6fj888/V4NNJkyYhOjpala6TMvneJKkwgFTdrFbk/fCNummIaeB2tSRznJqsNnnj7ipXwo6IiMifjIo7R02uLGhzZ5n7Rp0B45sMVJMWCSrJVBucc845ePfdd/H000+r+8V9Vp5//nmcf/75Hu9PMoNuuOEG9OzZE71798asWbNUkGrs2LFq+ZgxY9QHPSlDZzab0bFjxzLbR0ZGqv9PnU9UF0lJq9dffx333HOPKr/YqVMnLF68WGXesc8JERGdSkKQrsKQle0XUVNsdvcXMu1eXgBNzbe5XbbleI7m8Vg0moMYNM6dRet5OLxrOKJ1Udikd38spQe1ebqtyaCv8mPVOm9hAe4v0WYUaJeb1t7W4nZZsKmoD7un5+5wdklf1VNFm01ul1kq6GsXFaS1rXcX+rWeh9brEWh0//qbNZaJEJP75QEGjePR2K3WsWq954wa7/GKYif+9juyIvIW8TwDCXXOP//8gwEDBiAiIkJVLhk3bpwKIH355ZeqBL9cx/AUA0jVzNAkHjCaAKsFluTdFb8gBh3CA/U4xgwkIiIivyWBogsvvBDr1q1DYWEhHnzwQWzevFllPvz2228e7096Jx0/fhxTpkzBkSNH0LVrV3z//fdqhJCQD3ocPU1UZMuWLbjrrrtUsGjEiBFYuHAhQkJCeHqIiIiIiOopiXlrxL3dblPXTJw4ETfeeKO6ZiF9rotdeumluPbaa73aJwNI1UxnMMLYLBHWPTthO3QADkshdKaAcusdLEiFUadHtDEUkWYDjMFFI4kFM5CIiIj8i2T6/Pfff5gzZ476UJadnY3hw4fjzjvvROPGjb3ap5Src1WyTqxYsUJz20WLFnn1mES1UYcOHdQXIgkgPfDAA7VudCQREREREVWt+twDqbS1a9fizTffxKmkookMVvUGA0g1wJjQXAWQYLPBenA/TEnlG1vPOvgNDhSk4JGEKxEVZIAhuKSZNXsgERER+Q+LxYJBgwbhjTfewKOPPurrwyGqF/7v//4PjRo1QuvWrdX9+++/39eHREREREREfsLmRQ8kT9evDaTvcWZmZrn5MgC2QQP37XW0sMNxDTAmNnfetu7b43KdAJ1JTWGGIESZDQht1QOdnlmOHQdT8Mwzz9TEYRIREVElmEwmVVeYiGqm35Fk+l1wwQUYOnQosrKyeNqJiIiIiIhcGDJkCJ566ik18FVItQYpif/QQw/hyiuvhDcYQKoBxsQWztvWfa77IE1rfi3eaXsXEgMbqBJ2hoAgBMY2hS0ghGU5iIiI/Mx1112HBQsW+PowiOq0vLw8jB07VvU7slqt6NKlCwwG982eiYiIiIiofpJ2RnaHhxPqnpdeekmV2I+Li1Pfp/r3749WrVqp0vvTpk3zap8sYVdDJeyKWZNdZyAVk6iglLArlpZnq9ZjIyIiIs/JxeyFCxfi559/Ro8ePRASElJm+cyZM3laiU6DjJKTvmLr16+HXq/HCy+8gPvuu48Dq4iI/JBkhz7++ONYvHgxjh07hm7dumH27Nno1auXy/UPHz6sSpGuW7cOO3fuxN13341Zs2aVW++zzz5T+927d68qYfrcc8+pJuBERESnstkdavKEp+vXBhEREfjpp5+watUqVTlFgkndu3fHgAEDvN4nA0g1wBDXCLpAMxwF+RUGkIRkIBVLy2cAiYiIyN/8+++/6kNYcS3hUweDEJH3li9fjhEjRuDEiROIiYnBp59+qkrYERGRf7rlllvUZ6P33nsPTZo0wfvvv68uVG3ZskU17T5VQUGB6sPw2GOP4eWXX3a5z99//x2jRo3C9OnTMXjwYHz44YeqlOlff/2Fjh071sCzIiKi2lb62u5hTyPZpq46++yz1VQVGECqATq9XmUhWXZshe3IITjy86Ezm53LDxem4eNjqxAXEIHRcec6M5BO/PYZFvydhXWNg9SoGyIiIvKfC9xEVPXkS9wTTzyhgkcygl1GsycmJvJUExH5KSmP88UXX+Crr77Cueeeq+bJ7/FvvvkGr7/+usuezklJSSpDSUhGtyuyfNCgQZg0aZK6//TTT6sR1dIX74033qjW50RERLWPzVE0ebpNXWK327Fo0SJ8+eWXKntXBrc2b94cV111Fa6//nqvB7v6RQ+kuXPnqg8QZrMZffr0wZ9//qm5vqQxt23bVq3fqVMnfPvtt27XHT9+vDo5rtKha5IxIanohsMB64F9ZZadsGTiz6wd2JhdlJ0UdTID6fAP8/DF69NZBoeIiMiP7d+/X01EdPrkc/vHH3+syhn99ttvDB4REdWCsr42m01dnyktKChIlc/x1urVq8uV2xk4cKCaT0REdCrJPvJmqksD8YYMGaKygg8ePKhiJh06dMC+fftw4403YtiwYV7v2+cBpE8++QQTJ07E1KlTVSqyNMeVDwVSN1crjfnmm2/Ghg0bVAqzTJIufSoZsfjHH3+oFGpfMya2cN627ttdZlnjgCiMbXQBBkf3VPeLM5CMIRHq/4yMDPWBjIiIiPznYolkB0t9YRkEI5PcllIsFovF14dHVKvIlxoZUFascePGauS5XHwkIiL/Jk25+/btqzKEDh06pK5dSAk7CfRIryNvHTlyBA0bNiwzT+7LfHekNF5mZmaZiYiIyBdSU1MxevRohIeHIzIyUsUypB+Rlnnz5uG8885T28jAuvT09Eo/nmQe/frrr1i2bJmKmXz00UdqYN7ff/+tejf/8ssvePfdd2tnCTtpMj1u3DiMHTtW3ZdU5KVLl6o05smTJ3udxiyRtrvuugs//PADLrvsMviaMbG587Zl326U/jocawrHxVFdnfeLA0iG4AhnBFGCSNHR0TV4xEREROSOfMaQtPDnn39eXTQRcqFESrakpKSoki1EVDH5gjNy5Ej1cyMXBqW8AhER1S7S++imm25S/Y4MBoPqEykDf9evX1+jxyH9kp588sly8+UinKdle/Sn0dJSqye7Vr8Nu8Y+rRo7zdDonZ2aa3X/eBU0jzdonQTNbd1vZ9N4/gaN18ikcSwmg96r7YRea78ay1Jy3Q8YCzK5P54Is8ntMoPGoUYFud9OnMgt9Oqcm/Qa566kPXs5scEBXj0Pm0P79bBovK+09ms2aj0P98uCjTqvXkdjBe8rrWPVUtH71f3xeJcf4uVmfstmd6jJ022qy+jRo9VAColbyCBTiX3ceuutqqefO7m5uSruIdPDDz/s0eNJwOiRRx7B+eefX26Z9JOVOMsHH3yAMWPG1K4AUmFhofpAUfqE6PV6labsLi1Z5kvGUmmSsbRkyZIy9f6krp8EmSRVqyIySkWmYllZWc79yFQV9M2SSv7wJ+/R3G94QNEvDOPJAJKQOvASrfSGPJZqJFZFz8Vf1MXnxedUe/C1qh34OlX+PJFn5EOfjOa55JJLnPM6d+6MZs2aqQsmDCARaZPPcDKQ7MEHH1S/g3r06IHevXvztBER1UItW7bEypUrkZOTo7J+JJNUBge0aFFSicVTjRo1wtGjR8vMk/sy3x25tlT6epEci3w2IyKius+bknTVVcJu69at+P7777F27Vr07FlUcezVV1/FpZdeihdffNFttbR7771X/b9ixQqPH/Off/5RA1zdkWsXr7zyCrzh0wCSBEUkvdlVWvK2bdu8TmN+7rnnYDQaVe300xmlIiMhAwLcR9U9Im/IoGAgLxeFe3aWKdGXasuG1WFHhCEIgbqiUQXBRilhVxIw2r17t0pf84Z8KZcMJvmiLgG6uqIuPi8+p9qDr1XtwNepcuTvHXkmMDBQla07lTSorLLPDkR1lIysk9rcMkpO3HDDDSroypJ1RES1W0hIiJrS0tJUNRitC1kVkQxvyVItvpgmZBR3cea3u89nMhERUf1jcxRNnm5THVavXq0SQYqDR0ISZuT69Zo1a06rH5FWybxTYyalyTL5+1wrS9hVNclokjJ30k+psinKp45SkfJ37du3R0xMDOLi4qrs2NKSWsKydROQmoLYkGDoQ0LV/C8Ob8D/ZW7FqAZn4/KYojdWdHA+DMElASMJknh7LHIBVc5FgwYN6kygpa4+Lz6n2oOvVe3A16nyGcHkmQkTJqgyum+//bbzQoVkM0+bNk0tIyLX9uzZo74wSS1uGfD18ssv48477/S4tBAREfkPCRbJNYs2bdpg586dqhpM27Ztna0K5JqLXGcp3Xth48aN6n/pB3H8+HF1XwbhyLUYcc8996B///546aWXVFsCyfxet26d6g9BRERUlRlIp/bMO90BCUeOHCl3HV+++0h7Gq1efqdDknTkMdyRErPSy7nWBZBiY2PVwXuSllxRGvP//d//qeyehISEMifw/vvvx6xZs7B3795y+zz1TVH8ppGgRFUGJoyJLYoCSPIGPZgMY9uO6rZBp4fZYEKYKcj5eFFmA4whJSXspGnW6RyLfCmv6ufjD+ri8+Jzqj34WtUOfJ0qVpd+h9YUaUopo2KbNm2KLl26qHlyQVyCcRdeeCGGDx/uXFd6JRFREbn4Jz8r8oXqs88+w7nnnstTQ0RUy0llEAkSHThwQF0cu/LKK9WgGpOpqMKK9IBITk4us023bt3KDASW8sCJiYnOazb9+vVT8x577DHV06F169aqdUHHjkXXUYiIiE7t61ZRb7dTFa9/arnTqVOnqv7Gp5o8ebKqfFZR+TpfkIEcN954o9vAV+n2PbUqgCSjS6TeuVyAGTp0qHO0uNx3N3q3ojRm6X0kKWGn9kiS+cWjX3zFmFCqD9K+3Qg4GUAa32QgxmNgmWaKkUEGGEqVsPM2xYyIiIiqnqSjy8WR0lhjn6hiV199NV577TVcfvnlKgBLRES134gRI9TkzqJFi8rNK339Q+tvhkxEREQVkViQpyXpiuNN+/fvL9M6xl0Q5v7771dBGi3S/08SXUq3rxGS/SNl5rR6+Z0OKQtekTFjxtTOEnZSOk6eoNQElMa5kiUkjReLgz3yxOLj41WfosqkMUvZOZlKk1Ev8uJIOrUvSQZSMeu+PeWWly7doTKQSpWwkzcYERER+QcpXUdEFZPP9VLKSEaQFzeLvf3223nqiIiIiIjIL0jwqHQAyZ0GDRqoqSKS6CLVxCTDVpJnxC+//KISZ/r06YPado3C5wGkkSNHqnq3U6ZMUTUAu3btiu+//97Z9EnSnEuX1qnNacymxObO29bk8gGk0iQDyRQRh6CmbdGuWZwKohERERER1Ra7du1S/Y42bdqELVu2YPny5ex1REREREREftUDqaq1a9cOgwYNwrhx4/DGG2/AYrGoamvXXHONc1Cd9AaU8vfSH1CSaoTERmSSfoJCvkeFhYWpVj1SItZXfB5AEnIC3ZWsW7FixWmnMbvqe+QL+ogo6CMiYc9Id2Yg2R12zD64FKEGM65r2B9B+gBnBlJIYid0eOwbTLuwIS5uFebjoyciIiIiqhwZEDZq1Cg18k4Ghj3zzDMMHhERERERUbWwORxq8nSb6vLBBx+oeIcEiSQ5Rkrgv/LKK87lElTavn07cnNznfMk2PTkk0867xf3i5XsoopK59X5AFJ9ImXsCv/5C/b0VBVIyg0148+sHWrZjQ3Pd64XFWRw3k7Lt/nkWImIiIiIPCE9LWbMmIFHH31U3T7zzDPx+eefM5ueiIiIiIiqjd3uUJOn21SX6OhoVUXNnaSkpHL9AJ944gk1+RsGkGr6hCc0VwGk4jJ2xg4dMLbRBci1FcKkL3k5Is2lAkh5DCARERERkX/Lzs5WI+O++OILdV9KNrz66qtum9ASERERERFVBbl6bvMwHsQr7pXDAJIPAkjFLMl7ENKpGy6O6lpuvTIZSAwgEREREZGfkxF0W7duhclkwpw5c3Drrbf6+pCIiIiIiKge8KceSHUNA0g1fcITSwJI1n273a4nPZDE7gUTMTttL5ZEB2HNmjU1coxERERUXul6xRW5++67eQqp3pEGr0uWLMHx48fRr18/Xx8OERGRJr3OuxN0OhWPtEbHa13IzCywu112IKvQ7bICq/vx9YFGPbxl0Tm8eh4meHfS9Tr32xk0lukreJEjAr27LKq12zyL+9fK7rB4tc9gU8kgc0+Xmwzud5yRb3W7LLRUlSRPyn65f/ZyLNrvuZggk9tlwSb32wYZNd4fGidW65zrvHw/qm29/N2ivU/vnkd18eb3YDVWi/PLHkh1CQNIPsxAsibvRZYtDzm2fIQbghFsCCwXQMo7tAOpB7chPTBQjerU+oVBRERE1efll1+u1Hryt5oBJKoP7HY7pk+fDrPZjPvvv1/Na926tZqIiIiIiIio9mMAqYbpQ8Ogj2kAe8pxlYH0Z/pWvHtsBfqEnYF7mw52rmc26VUE3RAcru4XFBQgLy8PwcHBNX3IREREBGDPnj08D0QnZWVl4YYbbsDixYuh1+tx6aWXol27djw/RERERERU4yQzzuZhmpNWNh2V8D5flU67jJ0jOwvW7EyY9QEIM5hd9kEyhkQ476empvKsExER+ZHCwkJs374dVqv78g9EdY285/v06aOCRwEBAZg3bx6DR0RERERE5DMSPPJmoooxgOQDxoQWztsXpwTj7TYTMLbRBeXWizRLACnSeT8tLa3GjpGIiIjcy83Nxc0336wygzt06IDk5GQ1/6677sKMGTN46qjO+uabb9C7d29s3boV8fHx+PXXX9XPAhERERERka8wgFR9GEDyAWNikvO2NbmoHI5ep3eZgVRcwk4wA4mIiMg/PPzww/j777+xYsUK1f+l2IABA/DJJ5/49NiIqsuzzz6LIUOGIDMzE2effTbWrVunMpGIiIiIiIh8yWb3JojE16wy2APJB0ylMpCKA0iunJqBxAASERGRf1iyZIkKFJ155pnQ6XTO+ZKNtGvXLp8eG1F1iYws+lw6YcIEvPTSS6p8HRERERERka95U5KOJewqhwEkHzAklGQgfRq4H47DP+Gy6B6ID4wus16UuWwGEkvYERER+Yfjx48jLi6u3PycnJwyASWi2s5ut0OvL8qUv/3229GpUyecc845vj4sIiIiIiIiqgEsYecDenMQDI2aqNvrIgqwPG0Tcmz5LkvYGYOZgURERORvevbsiaVLlzrvFweN3nrrLfTt29eHR0ZUdb766ivV7yg9Pd35PmfwiIiIiIiI/A17IFUfZiD5iDGhOWxHDmHQuqOw9r4aDQMi3JSwK5nPEnZERET+0wvmkksuwZYtW2C1WjF79mx1+/fff8fKlSt9fXhEp5119OSTT+Kpp55S91988UU888wzPKtEREREROSX7F6UsJNtqGLMQPIRY2Jz9X/f7em4/EQoIowhLjOQAhs2R+PL7sLIiUVNi4mIiMj3zj77bGzcuFEFj6Sk148//qhK2q1evRo9evTw9eEReU2yja644gpn8Ojuu+/G1KlTeUaJiIiIiMhv2RxFASSPJgcDSJXBDCQfMSa0cN62Ju8Fep9Vbh3pgRQY3QTxl9+N7h0icOaZDWr4KImIiMidli1bYv78+TxBVGdIFt3QoUOxY8cOmM1mvPnmmxgzZoyvD4uIiIiIiEhTcVDIE56uX18xgOSrE5/YHIUGHdJDTLAd2IVQF+tEBhmct9PybDV6fERERFo+PrYK7xxdjhOWLJwR1ASTE4ahU0ii2/XfP7oSnx7/HUcK0xBpDMVFUZ1xd/xlCNSbyq274MgyvHJwKUbHnYMHmw3zmxciMzOz0uuGh4dX67EQVbUVK1bg8ssvR3Z2NhISEvDll18ym46IiOoEva5oqiqOCkas2zQW2zW2zSq0u122L7183+xix3IKNY7F+4ujgUb3RYvKf4Ivzf3J9vZardbrp9dY2L5B+Wo/pR3MLHC7LLvQ/XW4uNBAVLWMfIvbZSaD3usyXBaNN2SE2f1lYf3JHq+uRIcEuF0WYHC/XYhJ+3loPCQ0dgutt5XWj77Wc9R6vOL+t1X9Xtb6+fD2d5gv4iPufkdW9LvzdDGAVH0YQPIRY3wz7I8LwcuXJ6CB9Thed7GOZCAVS8tnAImIiPzD96kb8OKBr/BYwtXoFJKAD479itt3zMNXHSYjxhRWbv1vU9dj9sGleDJpJLqENMe+guOYsvcj9XF+UrMryqz7b04yPj++GmcENYa/iYyMrPSXBZuNf7epdunQoQOio6PRq1cvfPLJJ2jQgJnvRERERERUO1jtDhg8jJjJNlQxBpB8RBcQCFujhgi02BGckQWHzQadoSRgJIJNOgQadMjNzcHB/SewdWsm2rVr56tDJiIiUt47uhLDY8/E0Nje6v5jCVfh14wtWJLyJ25udGG5s7Qxey+6hjbHpdFFvYHiA6MxKKobNuUkl1kv11aAh/d8gKmJIzD/8E9+d7aXL1/uvL13715MnjwZN954I/r27avmSf+jd955B9OnT/fhURJVXl5eHoKCgtRtCRitXLkSTZs2hdHIrwhERERERETEAJJPdQpqipfeXgm7DrBdfBDG+IQyy2WUs5SxW/foFdhwbC9WRUYiLS3NZ8dLRERksVuxNfcAbm5cEijS6/Q4M+wM/JO91+UJ6hqapLKQNuXsU2XuDhSkYFXmVgyO7llmvWeTv8C5Ee1wZvgZfhlA6t+/v/P2U089hZkzZ2LUqFHOeUOGDEGnTp0wb9483HDDDT46SqLK+ffff1W/o0cffRRjx45V85KSknj6iIiIiIio1mEJu+qjXXySqpUxsUXRi+AArPv2uFxHytgZQyLU7fT0dJbEISKiapOVlaX6/BRPBQXla4KnWXNggx0xxrKl6qR0nfRDckUyj25vMgg3bp+DHusfwGX/TkPP0Fa4pfEA5zrfpW5QgSnpi1QbSLZRz55lA2BC5v35558+OSaiyvrss89w5plnYteuXXj++edhsbivuU9EREREROTvpBeYzcNJq38YlWAAyYdMic2dt63JrgNIkWYDDMGRzvsZGRk1cmxERFT/tG/fHhEREc6pqkqxrc3aiQWHl+HRhCvxcfuJmNniRvxfxha8efhHtfxIYRqe378Y05tfh0C9dktef9GsWTPMnz+/3Py33npLLSPyR9Kb6+GHH8aIESOQk5ODCy+8EP/3f/8Hk6l2/NwRERERERG5YnM4vJqoYixw7kO/xtmw6dzG6LErE13dZSAFGWAMDnfeT01NVQ2OiYiIqtqWLVsQHx/vvB8YGFj+75IxBAbokWItm22UYslCrKlsVlKxuYe+w+CYHqpvkmgd1AR59kI8ve8zjGs0AFtyDyDVmo1rts50biNZTuuzd+PjY79hbffnYdD515iXl19+GVdeeSW+++479OnTR82TzKMdO3bgiy++8PXhEZUjnyGl5OKPPxYFbidNmoRnn32W/Y6IiIiIiKjWYwm76sMAkg9tDszF6rbRaJqSD+u+3S7XiTLrYQyJLPPln4iIqDqEhYUhPLxk0IIrJr0R7YKbYk3mDlwQ2UnNszvsWJO1A9fEne1ym3y7BTroyswrDgjJeJ8+Ya3xeftJZZZP3fsxksxxGNvoAr8LHolLL71UBYtee+01bNu2Tc27/PLLMX78eGYgkd+RbKPevXurknVBQUFYuHAhrrnmGl8fFhERERERUZVgAKn6MIDkQ+dGdUDMnv+h+dE8WNMy4bBYoDulhEhkkAGGkz2QRFpamg+OlIiIqMT1Dfvj8b0foUNIM3QMTsD7x1aqjKKhMb3V8kf3fIi4gHDcEz9Y3e8f0R7vHV2JtsFN0SkkAfsLTqispHMjO6jgUIjBjNZBjcuc4iB9ACKNweXm+5OmTZuqDA4ifxcSEoLrrrsO7777LhYvXowuXbr4+pCIiIiIiIioFmAAyYd6hLVEy4J45J/Yqu5bD+2HKbFFmXWizOVL2BEREfnSoOhuSLNm47VD3+OEJRNtguLxWutbEXOyhJ30NNLrSjKOxjW+SGUgzT30LY4VZiDKGIr+kR0wocmlqM3S09OxYMECbN1a9He8Q4cOuOmmm1T/KCJfs1qtauBRgwYN1P0pU6bg3nvvRWRkSWY7ERERERFRXcAMpOrDAJKPGRObO29LGbtyASSVgcQSdkRE5F9GxZ2jJlcWtLmzzH2jzoDxTQaqqbJO3Ye/WbduHQYOHKjKgUlpMDFz5kxMmzZN9Zjp3r27rw+R6rGUlBRVok4GHq1atUq9T/V6PYNHRERERERUJ9kcdtjsdo+3oYoxgORDhwvTYEpspvo/yDht67495daJVBlILGFHRETkT+677z4MGTIE8+fPh9FodGZ83HLLLSrL49dff/X1IVI9tXHjRgwbNgx79+5FcHCwut+3b19fHxYREREREVG1sdsdKgvJ022oYgwg+Uih3YqJu96GI8iC6QF6BBXaYU0uH0BSGUilAkgsYUdEROQfGUilg0dCbj/44IPo2bOnT4+N6q8PP/xQBTHz8vLQsmVL1e+oU6dOvj4sIiIin5Lrg66uEepLKi6Xo3URsqLx6haNba0ay1LzrBr7dP+ocSEBbpfptZ7kaVxYzShwf6wmjcfUOh6Lzf1zDDYZ3C5LjDS7XRZq0kNLZoD/XBYN0zgWQwUvo8no/nlGmN3vN0Bjx2aD3qvtSlUy9/h5aDFova80ttNpHZCG0/jR8Zq3j+lp0KS6uftJru5cHzkPeg/Phb+dO3/lP78p65lcewHM+gAUmnQwwwSgwGUGkvRACm7WDu0fWYLuLRvh6ZEdfXK8REREVCI8PBzJyclo27ZtmdOyf/9+hIUV9YIiqimS/SbBy5dfflndHzRokAomRUVF8UUgIiIiIqI6z2oHdB4GhGQbqhgDSD4SaQzB220mwOqwIT1hC6w7t8F25CAc+fnQmUtGT4QG6BEYFAxDQgfYwgIQEhLiq0MmIiKik0aOHImbb74ZL774Ivr166fm/fbbb5g0aRJGjRrF80Q1XlJxzpw56vYjjzyCp556CgaD+5G6REREREREdQkzkKoPA0g+Jo3FTYnNVQAJDgesB/bB1KpNmVRL6YN0IteGtHybT4+ViIiIikjgSP5GjxkzRmV/CJPJhNtvvx0zZszgaaIa9cADD2Dp0qXqfTl8+HCefSIiIiIiIqoSDCD5AWNCc+dt6YNUOoBU3AdJAkjp+TY4HA6v63cSERFR1QgICMDs2bMxffp07Nq1S82TnjPBwcE8xVQjNm3a5OxvlJiYiO3bt6sgJhERERERUX3DDKTqo91NjqrNPzn78ObhH7EyfTOMiS2c8637drvsg5S28SccXPYunn3uBb4qREREfkICRnIRXyYGj6gmWCwW3HvvvejSpQu++eYb53wGj4iIiIiIqD4HkLyZqGLMQPKR3XlHsSL9X5VRdHZitzIZSKeSDKRD37yCvIPb8PSSQDw6+cEaPloiIiISN910U6VOxMKFC3nCqModO3YMI0aMwMqVK9X9zZs34/LLL+eZJiIiIiKies3uRUBItqGKMYDkI+2Dm2JEg7OQEBgLfWgD6IJD4MjNgWVf+QCS9EAyhkSo2wUFBcjLy0NQUJAPjpqIiKh+W7RokSoX1q1bNzUIhKimrFu3DsOGDcOBAwcQFhaGd999F0OHDuULQERERERE9Z4Ej3QeBoSYgVQ5DCD5yBnBTdTkfCESm8Oy9V/Yjx+FPTcH+uCQMhlIhuBw5/3U1FTEx8fX+DETERHVd7fffjs++ugj7NmzB2PHjsV1112H6OhoXx8W1YPA5fjx49VAojZt2mDx4sVo166drw+LiIiIiIjIL8gAT4eHASQOCq0c9kDyE8aEUn2QkveW64FkDIksE0AiIiKimjd37lwcPnwYDz74oOo/06xZM1VS7IcffuCHT6oWf/zxhwpWSvBoyJAhWLNmDYNHREREREREVCMYQPKRFEsWsmx5sDvs6r4xoblzmXXf7nIl7AzBRSXsBANIREREvhMYGIhRo0bhp59+wpYtW9ChQwfccccdSEpKQnZ29mkFp2QfZrMZffr0wZ9//ul23fnz5+Occ85BVFSUmgYMGKC5PtVeZ555JiZMmIAnn3xSZR5FRJR8JiQiIiIiIqKifkbeTFQxBpB8ZHryl7j1v9exNfegs4RdMWty2T5IUsKuuAeSSEtLq8EjJSIiInf0ej10Op3KPrLZbF6fqE8++QQTJ07E1KlT8ddff6FLly4YOHAgjh075nL9FStWqCDW8uXLsXr1apUJdfHFF+PgwaLPFVS7rV27FsePH3fef+WVVzBlyhT1fiMiIiIiIiIXJey8mKhi7IHkI1ZH0UWmEENg0QuR2MJ9AElK2DEDiYiIyC9IKbEvv/wSCxcuxKpVqzB48GDMmTMHgwYN8voC/8yZMzFu3DhVqky88cYbWLp0qXqMyZMnl1v/gw8+KHP/rbfewhdffIFly5ZhzJgxXj4z8gfvv/8+HnroIZx11ln48ccfYTQaVZCSiIiIvKPXFU2eNE+3aVxTtFQwYr3A6n55Sp7V7bKsQveDkfKtRdVrXAkOMrhdlppngbcKNB7TZHD/mTff5n47s8Y49gizye2yZuFF185cCQ1wv09DBR+hGoS4f0yTxsZHcwq9ekyTxneFYJP717FRmPvjLHpM9w/q6r1fzKixsKJz53Y7jX1W9E3J28+8Ws9Riy+ST7QeUyuYUR3fB9z/pFZMK+5S6OYXaKHG78aqIP2PPO6BxAykSmEAyUdmtboJFocNBhT9AjBERkEXHgFHZkb5EnZBBhjYA4mIiMjnpFTdxx9/rDJ+brrpJnz00UeIjY09rX0WFhZi/fr1ePjhh53zJBAlZekku6gycnNzYbFYEB0drRn4kqlYZmam+t9ut6vpdMk+5EtPVeyrPpL3wb333os333xT3Q8PD0deXh5CQkJ8fWi1Ct+HPIf+gO9D/zuH/NtERERUt3lTko4l7CqHASQfMunKji4wJbZA4aYNsKelwp6ZAX14Udm68EA9AkLCneuxBxIREZFvSGZQQkICWrRogZUrV6rJFclQqqwTJ06o8ncNGzYsM1/ub9u2rVL7kIyVJk2aqKCTO9OnT1d9dE4lpXGtVvejYT25OJeVlaUu+LHUmmeOHj2qApLSx0pGF8rred9995UL+hHfhzWBP8s8h3XxfSj7IiIiorrLYS+aPN2muqSmpuKuu+7CN998oz7LXHnllZg9ezZCQ0Pdri8l7aUKRXJyMho0aIChQ4fi6aef9nkfXAaQ/Ij0QZIAUnEZu4COXdVtvU6HqJgGCIhuAnNYpHoDERERUc2T8nD+Vk5sxowZKitK+iKZzWa360mGk/RZKp2BJJlUUVFRKtulKi72ybmR/TGAVHl//PEHrr76ahw6dEh9MXj99dfVfZ5Dvg99hT/LPId18X0o5UCJiIio7vKmp1F19kAaPXo0Dh8+jJ9++klVC5Fy9bfeeis+/PBDl+vL90GZXnzxRbRv3x779u3D+PHj1bzPP/8cvsRPUT5wpDANS078iUYBURga27vkxUho7rxt3VcSQBLNzugIy7MrEWDQ4d6bS/olERERUc1ZtGhRle9TSuAZDAaVhVKa3G/UqJHmtvLhUgJIP//8Mzp37qy5bmBgoJpOJRfmqipYIRf7qnJ/dZ1knsmXCPlSIF8SJHMtJiaG5/A08X14+ngOeQ7r2vuQf5eIiIiopmzduhXff/891q5di549e6p5r776Ki699FL1HV6qh5yqY8eOqq9xsZYtW2LatGm47rrrVMUQXw6G4bd7HzhWmIGVGZuxOnN7mfnGhJLAkOWUPkjRJ5siSiOyXIsPurwRERFRtQgICECPHj2wbNmyMiOv5X7fvn3dbvf888+rdHb5YFr8oZRqFwkcSvbY9ddfrzKRWrdu7etDIiIiIiIiqrU9kDydiqtzlJ5Ot4z46tWrERkZWeZ7upSblwEta9asqfR+MjIyVLUQX2dSM4DkA3EBERjR4CxcENmpXAm7YlLCrrRIc0m/pLR8Ww0cJREREdUUKS03f/58vPPOO2q00u23346cnByV5l5cOk9K0BV77rnn8Pjjj2PhwoVISkrCkSNH1JSdnc0Xzc9JtlHpkWUy0uzdd99FWFiYT4+LiIiIiIiotnLYHV5NQkq7Sznx4kn6B5+OI0eOIC4ursw8CQJFR0erZZXtlSwDRqViha/5RQBp7ty56uKH1O3v06ePaiCs5bPPPkPbtm3V+p06dcK3337rXCY1BaXxsMwPCQlRKWFy0UW+rPsLKV03LLYPBkaXlKgT+tAw6KNjnQGk0nUYo05mIIn0PAaQiIiI6pKRI0eqVPYpU6aga9eu2Lhxo8osatiwoVouTTSlfnIx6ZNTWFiIq666Co0bN3ZOsg/yX7/99pvKNrvmmmuwatUqXx8OERERERFR3eBN8OhkAGn//v0q26d4Kj14s7TJkyerErta07Zt23C6JAvqsssuU2XOn3jiCfiaz3sgffLJJ2rU7RtvvKGCR7NmzcLAgQOxffv2cpE68fvvv2PUqFEqEjh48GDVeGro0KH466+/1AjO3NxcdVtG5Xbp0gVpaWm45557MGTIEKxbtw7+TrKQClNPwJGVCXtaCgwnA0pRZgOSP3kaeYd2YPRCHf5e85uvD5WIiIiq0IQJE9TkyooVK8rc37t3L899LSKDguSzrnwmlcFO8plVAn5ERERERER0+uwOB3SlkjEqu42QMnEyVeT+++/HjTfeqLlOixYtVC/jY8eOlZkvfYxSU1Mr7HOclZWFQYMGqQoVixcvhslkAup7AGnmzJkYN26cs0SLfLleunSpKskiUb1TzZ49W53ESZMmqfuSyvXTTz9hzpw5altJM5P7pcmy3r17q9G7CQkJ8LVMa660BEWoIRB6XdkkMGNiCxRuWKtuW/ftcQaQIoMMyNn7N3L2/I1/TjZdlrr5REREROS/8vPzceedd6rPtmLEiBFYsGABQkNDfX1oREREREREdWbQXnFJOk+28USDBg3UVBHpZZyeno7169erChTil19+Ub2OJYFGK/NIEmsCAwPx9ddfq+pr/sCnASQpvSInsnRamDSTkqZS0mzKFZkvGUulyYldsmSJ28eR1DNJIZPmVa5IY6zSzbEk0ifkRZWpqr17dAVWZWzDqAZn4/KYsk2vDc2SnLct+3bD1KXoTRYZqIMhOMK5TCKWMTExlXo8eQ7yA1Edz8WX6uLz4nOqPfha1Q58nSp/noio6h04cADDhw/H2rVr1WdcyaCXQVDyuZSIiIiIiIjqnnbt2qkEGEmakYQXqUIh1UaklLm02xEHDx7EhRdeqPrhSuKLBI8uvvhiVV3t/fffV/dlEhK08mUiiU8DSNIMSjJpiuv7F5P77uoFSqMpV+u7a0Aloz6lJ5KUvXOXiiZf5p988sly81NSUhAQEICqlp6dCavVAnt2AY7ZyqazIawkSJS9fQuyi9Pd8q0wlgog7dy5U527yl4YlCCaBFvk4kVdURefF59T7cHXqnbg61Q58veOiKrel19+qYJHUVFR+Pjjj9UXAiIiIiIiIqpazr5GHm5TXT744AMVNJIgkVy3vvLKK/HKK684l0tQSVr4SMBISEueNWvWqNutWrUqs689e/YgKakk6aTelbCrTvJCSJkQCTBIs2l3JAOqdFaTRAClSZVk+Ljqw3S6HokbAavDBnmLmnRlo4f2sFCcOHnbeOwIok8+fnNjIYwhJRlUMnK1sscmF1BlfYlW1pVAS119XnxOtQdfq9qBr1PlM4KJqOrdddddqvb1TTfdpGphExERUc0r1Se9DIvGhUOtS4rZhdrZ+3lW98szCqzu96uxzKCRvXw0x/1n+TyL+4HHQabqGc1uNri/PhMX6n6QdkyQ+z4fEYHu96nXODfF/U282a9J7/54wgLdnzuT3v3xBBh0Xr3GGqe0Qt7mvWs9D61seq2SYAaNfZ4OrRiAtw95OnEFT8uiOR9Te6de7dOm8US0fnbUtl4+prvdVncRBimsovPwhavOYizR0dH48MMP3S6XgFDp98p5553n9XunTgeQYmNjVfrV0aNHy8yX++4aSsn8yqxfHDzat2+fqjGo1QhL6grKVKw4PUyCEtUVmAiA6/3qQ0JhaNgYtqOHYdu/R/1Slik6xAhDSEkGktRR9OTYZB/V+Xx8pS4+Lz6n2oOvVe3A16lidel3KJEvSeb7tGnT8OCDD6qmp/L755lnnuGLQkREdZ60Anj88cdVw28ZPNGtWzfVw7pXr15ut1mxYoUazLt582Y0a9YMjz32WJnm5E888US5ajFt2rRxW7GGiIjqeQ8kDwMw/hqw8Tc+vWIk5eGkkdSyZcvKjBaX+9JsyhWZX3p98dNPP5VZvzh4tGPHDvz888+V7hXkL4wJzdX/jrw82I8XBcsiAg0wBpcEwdLS0nx2fERERERU1v79+3HOOeeogNEtt9zC00NERPWK/O2TazPvvfceNm3apMq2Sn9rqfDiipTjueyyy3D++edj48aNuPfee9U+fvjhhzLrdejQAYcPH3ZOq1atqqFnREREtYnD7t1EtaCEnYw2ueGGG9CzZ0/VMGrWrFnIycnB2LFj1fIxY8YgPj5e9SkS99xzD/r374+XXnpJfdiQevLr1q3DvHnznMGjq666StUN/N///qf6BBX3R5LUseroaeQJu8OO+Ud+RqjBjKti+yLQRUqsMbE5Ctb+rm5b9u2GIa6RSvMMi4xyrpOamlqjx01ERERE7kdQy+Cl48ePq4FL0iyViIiovsjLy8MXX3yBr776Cueee64ze+ibb75R7QRcZeNKU/HmzZurazvFDcclOPTyyy9j4MCBzvWMRqPbCjVERETF7HaHFyXsmIFUKwJII0eOVF+2p0yZogI9Xbt2xffff4+GDRuq5cnJyWVK6/Tr10/VD5TU5kceeQStW7fGkiVL0LFjR7VcRrd8/fXX6rbsq7Tly5ereoK+lGMvwIr0f9XtkQ3OcrmOMbGkRr41eQ/Qq5+6HRkZ7ZzPABIRERH5iy3H8rBsVybSU1NRGFiAtrHB6NTYjMSIAESYq6e+vj+QkgfSCPX+++9Xg5bks6eU7vFlg1MiIqKaZrVa1d9Bs9lcZn5QUJDbjKHVq1erDKXSJHAkmUilSWWZJk2aqH1L5RkZXJyQkOBynwUFBWo6tT0BERHVfQ67Q02ebkO1IIAkJkyYoCZ3IzpPdfXVV6upMg2o/I0BeoxocBby7YUw6gyaJeyEdd8e5+3YmJIA0vEUZiARERGRfwSPHvv5MA5m2dAsoBDJhblYvT8PA7PC0KFhEM5KCKmTQSQZbX3bbbepUj1i9OjRKiM+ODjY14dGRERUo6TvnwR3nn76aZVJJAOCP/roIxUkatWqlcttZABx8cDhYnJfgj7yN1aCT3369MGiRYtU3yMpXyf9kKRc7L///qse81QSXDq1ZxIRERHVgQBSfRJsCMSw2D6a6xibJkhHc8mjg3Xfbuf8Js0SEHf+GBhDItHv/Itr4GiJiIiItM394zj2Z9mhKzUvoxD4bW+WChztyzChszmozp3G9PR01WvTYDDgxRdfVGWWdbrSZ4GIiKj+kAEVN910k2pBIH8bu3fvjlGjRmH9+vVe7/OSSy5x3u7cubMKKCUmJuLTTz/FzTffXG79hx9+WLVJKCbBqGbNmnn9+EREVHswA6n6MIDkh3QBgTA0bgrbwWRYD+yDw2aDzmBAYrOmSBj5uFqnc7+mvj5MIiIiImw8XOjyLBzMBTYcyUOupagzaXWVs9ufUYi1B3NxNNuKhqFG9IoPRrOIop6XGfk27MsoRHqeHZFB+io9hsaNG6t+D/n5+aoBOBERUX3WsmVLrFy5UvW0lsCN/J2UlgUtWpSU6C9N+hodPXq0zDy5Hx4errKPXImMjMQZZ5yBnTt3ulweGBioJiIiqn/sDgd0HlYlk22oYgwg1bA8eyEsdhtCDYHQ60p6O7kqYycBJBQWwnb0EIxNmiGy1AWPtDxbDR0xERERkXuuw0dFth0vQL7FgQKrAy1jAnFRy7AqDSJJ8Oj9v9NxJMuC4ACderztJwpxXZdIhAca8FtyDk7kWBFk0mNvmh0HMixel9STEsnS2FtGVssFMSHleoiIiKhESEiImtLS0vDDDz/g+eefd3l65G/ot99+W2beTz/9pPm3NTs7G7t27cL111/PU05ERGW/r7EHUrVhAKmGLU/fhPeOrkTf8Da4O/4yt+sZE5ujYPVKZx8kCSBFBZVc7EjPZwCJiIh85+Njq/DO0eU4YcnCGUFNMDlhGDqFJLpd//2jK/Hp8d9xpDANkcZQXBTVWf0dDNSb1PIFh3/GsvRN2JN/TM3rGpKEe5sORpI5rgafFVW1PAuQVWDDwSwrdqYWosBqx+A2EVUWRJLMIwkenRETAL1eD7vdjv9SijKSWsUEquBRQmQA9DqdGl2WnF6oMpI8LamXm5uLW265RfVzkB5HZ555piqhQ0REREUkWCSDLaRfkWQITZo0CW3btsXYsWOd5eUOHjyId999V90fP3485syZgwcffFCVvvvll19UabqlS5c6T+kDDzyAyy+/XP3NPXToEKZOnarK40lpPCIiotLkb5AEkTzdhirmPgWGqkWerWicbphB+8KFKbEkzduavEf9H2U2qDe2LT8Hu/bu5ytEREQ+8X3qBrx44Cvc1nggPm43EW2Cm+D2HfOQYslyuf63qesx++BSjG9yMRZ3mIwnkkbih7SNeOVgyajTddm7MLLBWXiv7T14s/VtsDpsGL/jTeTaCmrwmZE3tLr+SPG6lDwbMvOtyLbY8eeBXJUVJKXlqoKUrZPMIwkeCflf7st8KVsnmUcSPFLLdDp1X+Z7Ys+ePejXr58KHslFqxkzZiAhIaFKjp+IiKiuyMjIwJ133qmCRmPGjMHZZ5+tgkomU9FgocOHDyM5Odm5fvPmzVWwSLKOunTpgpdeeglvvfUWBg4c6FznwIEDKlgkQakRI0YgJiYGf/zxBxo0aOCT50hERP5Lgkd2DydPA071FTOQatiVDfriitjesDm0L14YE5Kct637dqv/pYTd1unDkJu8GZsCAnFXfh6bNRMRUY2TTNrhsWdiaGxvdf+xhKvwa8YWLEn5Ezc3urDc+huz96JraHNcGt1D3Y8PjMagqG7YlFNyEeH11reV2eappFE4/58p2Jp7AD3CWlb7c6LT+zBpcbNMwjo2O2C122HWGxAZZFRZQd5kAbkiPY+kbJ1kHhVnIOUWOtR86XkkZesk86g4AynPUtQLqbJ+/vlnVa4uNTVVXaz67LPP0L9//9M+biIiorpGAjwyubNo0aJy88477zxs2LDB7TYff/xxlR0fEREReYcBJB8w6gxq0mJo0gwwGgGrVZWwE1LCzmAOVbethQXIy8tTZVSIiIiqQlZWlmp6rNWI2GK3qqDOzY1LAkXS0+/MsDPwT/Zel/vtGpqkspA25exTZe4OFKRgVeZWDI7u6fZYsm156v9wI//O1WaS+2M0AJkFDjQM0yE+zORVFpA7veKDVc8jKVsnmUcSPGoUZlLzpQeS9DySsnXymBI8ig0xIjEioFL7lpHQUlZHglK9evXCF198gWbNmlXJcRMREZF/y7O4H5V+PMfd0Jki+TJ6xgt6vfu87rAAjWtI+Va3i4JN7reT0sJarBqlnUI1jqdxWNnvD6WFa2wXbHL//A0a50ZraJDhZCa6N0ID3G8b5PB+v94INOirpQyXTuP8aJzyinYKf+JtgsnplDbz9puO1kPKYLiqZjud56ixqdXNQnfzq7SEnYfPiSXsKocBJD+lMxphbJoA697dsB5MhsNiUSXsDCERznWkKSUDSEREVFXat29f5r7UmX/iiSfKzEuz5sAGO2KMYWXmx5jCVP8iVyTzSLa7cfsc9anYCjuuju2HWxoPcLm+3WHH8we+QteQ5mgd1Pi0nxdVL61LKEEmeT11MBt1aBUViKYRJpWB5EkWkJZmEQG4rkuk6nkkZesk80iCRzJfnJUQorKdJGAljynBo8r2Xzp69KgKHknvhtdeew1ms7lKjpmIiIiIiIiqlpSj87gHEkvYVQoDSDXs8+OrUWC3YEBUZzQMiNRc15jQXAWQYLPBeugAIpsmwRhcEkCScirx8fE1cNRERFQfbNmypczflVOzj7y1NmsnFhxehkcTrkSnkAQk55/A8/uX4M3DP+K2xheXW//Z5C+xK+8wFrW5q0oen3wnMsiAAIMeraNNaBxWVL7OkyygypBgUXHA6FQSLPK2VN6zzz6Lvn37YujQoSwZTERERERE5Mekp5GnKWdqG6oQA0g1bEXGZqRYMtEn/Aw0rGBdY0ILAMvUbWvybgQlNkdQWGSZABIREVFVCQsLQ3h4uOY6UcYQGKBHijWrzPwUSxZiTWWzkorNPfQdBsf0UH2TROugJsizF+LpfZ9hXKMBqgResWeTv1D9lBa2ubPCgRbk/y5sHoLzW4TB6oBXWUA16ccff8Srr76qStUFBATAaDRi2LBhvj4sIiIiIiIiqoDDblOTJzxdv76qmvohVGmDorqqUj7uLrKVZkxs7rxd3AcpPDKqTAk7IiKimmTSG9EuuCnWZO4oU3JuTdYOdA5NcrlNvt0CneqGU8JwMmjkKFV7WIJHv6RvwvwzbkfTwJhqfBZUUxqHB6B9XBA6NwzCuUkh6n9/Cx7Je++5557DJZdcgv/97394+eWXfX1IRERERERE5EUAydOJKsYMpBo2OMZ9w/BTGRMlA6mINbkogBRRKoB07ERKFR8dERFRxa5v2B+P7/0IHUKaoWNwAt4/tlJlFA2N6a2WP7rnQ8QFhOOe+MHqfv+I9njv6Eq0DW6qStjtLzihspLOjezgDCQ9u/8LfJf6F2a1vAkhhkCcsGSq+aEGM8z6qit3RjVL+hJ5KiPf5nXfIk9lZ2erHkeff/65un/LLbfg3nvvrZbHIiIiIiIiourhsNu9yECy8+WoBAaQ/JihYWMgIBAoLIB13241Lzo62rn84FEGkIiIqOYNiu6GNGs2Xjv0vQr0tAmKx2utb0XMyezaI4Vp0OtKMo7GNb5IZSDNPfQtjhVmIMoYiv6RHTChyaXOdT49/rv6/+b/XivzWE8lXoMrYosCU1T7NAw1ehw8+i05R/VKCjLpsTfNjgMZFpyVEFLlQaSdO3eq/kabN2+GyWTCnDlzcOutt1bpYxARERERERHVZgwg1SCL3apGaIcYzM4R11p0ej2MCUmw7twO2+GDcBQUIDamJIB0lBlIRETkI6PizlGTKwva3FnmvlFnwPgmA9Xkzt89Zlb5MZLv9YoP9mh9yTyS4FFCZIAKQtodDiSnF6r5nc1BVXZcy5cvx/Dhw5Geno5GjRqpvkf9+vWrsv0TERERERFRzXHYbGrydBuqGHsg1aCd+Udw24438MDudyq9jTHhZB8khwPWA/vQMLYkgHTsRGp1HCYRERFRlWgW4Vn5QSlbJ5lHxRls8r/cl/lVqWnTpqr3Ud++fbF+/XoGj4iIiIiIiGoxh8OLHkgOBpAqgxlINSjXVuDs51BZpsTmyD95W8rYnXFGR5xx33swBofj6sEdq+lIiYiIiGqe9DySsnWSeVScgZRnKeqFdLpsNhsMhqIyeK1bt8bKlSvRrl07BASwxxYREREREVFtVhwU8nQbqhgzkGpQj7CWeL/tvXi42fBKb2NMaOG8bU3eg8YxEQhvcyaCm7WHI7gkG4mIiIiotkuMCEBsiFGVrTuabVH/y32Zfzp27NiBbt264eeff3bO69KlC4NHREREREREdYDH2UdeBJzqKwaQapj0Pgo2BFZ6fWPiyRJ2KgNpD6JKNZBOz+ebnIiIiOqOCLMBZyWEoGuTIDQMNan/5b7M99bSpUvRq1cvbNq0CRMnToTdXrXl8IiIiIiIiMi3GECqPixh5+f0sXHQBYfAkZsDS/IeRAWVXEBJy2MAiYiIiOoWCRZ1Nged9n4kUDRt2jRMnTpV9Ts666yz8Pnnn0Ov5/gpIiKi+sRqd6jJExaN9W0Oz/ZVZr82u1fLbA73g2mCTe6XWTQGzuiLWk66FRNscrssMdJ9a4YQk/vPWkFG98tOtsB0yaCxUOt5VPSya22r09pWYzyS1vPw9lNoRa+VHd6dH819ev829yvyPcAdb4eV2So4OVpLtd7LUr7b7WNq7NSg8+5YNH7lFO3Xyzesu+eo9dyrgsNu96KEHQcXVga/Qdeglemb8cHRX7E992Clt9HpdDAmFGUh2Y8dQYSjAFn/rUHKmq+w4vOF1Xi0RERERLVTZmYmhg8fjilTpqgvjXfccQd++eUXNGrUyNeHRkRERERERFRrMAOpBq3P3oW1WTvRICAcbYLjPSpjZ9n2r7odfmI/Dix+ATl7/sYeiRbPmuRsCE1ERERU0+RTiKtxXr76dJKWloZ+/fph27ZtqsfR66+/jptuuslHR0NERERERETVzS7ZRx5mIKltqEIMINWg3mGtEWeKQAuzZ6NfizOQlP17EBgaiZyTd9PT0xETE1O1B0pERERUSdKeKMfF5+7TaFt0WiIjI3HmmWciKysLX375JXr37u2bAyEiIiIiIqIa7YHk6TZUMQaQatDZEe3U5CnJQCpm3bcbwWGRSD15PzU1lQEkIiIi8pmIQCAn1/X8miL9jvLy8hASEqLK/0rWkZSxi4uLq7mDICIiIiIiIp9gAKn6sAdSLWBMaOG8bU3ei9CIKOf9Y8dTfHRURERERIDV4dn8qpaRkYErrrgCI0aMgM1WNILMbDYzeERERERERFRf2GxweDjJNlQxZiDVEGngnGXLQ4jBDIPOs7idPjIKurAIOLIyYE3ejYiokoDSgWMMIBEREZHvpOd5Nr8qbdmyBcOGDcN///2HwMBA/P333+jevXv1PzARERERERH5DYfD8x5IahuqEDOQakiBw4LbdryB67bNQp690KNtpRRLcRk7e2oKGoQGO5cdPHqiyo+ViIiIqLLcfarx7NOO5xYvXow+ffqo4FGzZs3w22+/MXhEREREREREVIUYQKohObYC9b9RZ4BZZ/J4e1NiSdZRU6Pdefvw8eJuSERERER1n5Spe/zxxzF8+HBkZ2ejf//+WLduHXr06OHrQyMiIiIiIiIfcNjtzj5IlZ9KrrGTeyxhV0NiTGF4v+29KpAkGUWeMiYkOW8305WM6T12giXsiIiIyHdkWIzFzfxiGfk27MsoRHqeHZFBeiRGBCDCbPDq8e644w7MmzdP3b733nvx/PPPw2TyfHAOERERERER1Q0SEPK4hJ2H69dXzECqQdL7KNwY5NW2xtIZSNYc5+3jKcxAIiIiIt9pGW3QnC/Bo9+Sc7DxUB6OZlvU/3Jf5nvjtttuQ1RUFN577z28/PLLDB4RERERERHVc0UZSJ5PVDFmINUSxoSiHkiiSX469OYQGIMjoQss6YdEREREVNPaNwjCttRsl/OFZB6dyLEiITIAep0OdocDyemFan5nc+UG1uzfv1/1ORLdu3fH3r17ER4eXsXPhIiIiOoih8OhPn+Um6+xTZ7V/UVFi11rS8Bi825bk8H9GG+7xnYWjQugwSb3Gd+xwQHQEhHofttgk/tjDTC4r7qjsciraj0V0Vf9LhWjlzv29ngqeMtp7reibWtyn/Kz6C1vL/N7+5A2jQ1P522ltV9vXw9vz2pFP3Iav8q0f5dV1w9eBZiBVH0YQKohm3P2Y0P2brQOaoI+4a093l4fFg59dCzsqSfQviAT3V/eoH7Sz2nPiydERETkO7vSCjTnS9m6IJNeBY+E/C/3ZX5l+h099thjmDVrFn799Vf06tVLzWfwiIiIiIiIiIoxgFR9WMKuhmzPO4ilqeuxMWfPaWch6bMzEVmQqW6n5bFWIxEREfnO7jSL5nzpeZRnsTtH/sr/cl/ma0lNTcVll12GGTNmID8/Hz///HM1HD0RERERERHVdna7zauJKsYMpBrSOqgxLovugVZBjb3ehzGxOQo3rlW3E7MOIN0cgTQv+wcQERERVYUci/b8xIgAHMiwqLJ1knkkwaPYEKOa784///yDYcOGYffu3QgKCsKCBQswatQovmBERERERERENYgBpBrSKSRRTafDmNjCebtl7iFssLbG3z8txYrwLmjZsiXi4+Oh1zOpjIiIiGqOvYL5EWYDzkoIUT2PpGydZB5J8Ejmu/LJJ5/gpptuQm5uLpKSkrBkyRJ06dKl2o6fiIiIiIiIajeHNG3S2TzfhirEAFItYkxIct7euPwT7Cn4CmkbfsD5c04uNwWgaUISWrVqiTatWqBVq1YqsNS7d2/fHTQRERHVfTb5oO4o+l/dln5HDuT8uBS6gAAY83LRKigYxoaNYYxJgN4c5HI3UqbummuuUbf/v737gG+i/P8A/k2T7rI3lFGQrT8QEAT5iQwBQUVFRFyIA1HxJ4IiKAqo/FFcIAIqyBAFRBluEAcgCigoijJl71JWd9Mk9399nvRCkiZpm64k/bx5HU1u5e65y93l+T7j2muvlcWLF0uVKlX83ixbSrJYjh4WW/IFCStfQUzx9VS/kkRERERERBQ6NM0qUsAm6dQyxeTs2bPy2GOPyRdffKEqfPTv31+mTZsmcXFxXpd56KGH1G/i48ePq/k6deokr7zyijRr1kxKEwNIJSTdmiWRYeFiNPhfQ8hU194HEoSnnpFzf//tMt2SbZaD+/ao4bvVF8ff9dI86dSlmzRNS5M6FSLEnHREVixdpIJLDRs2VH+rVasmhpzOrYmIiIjyTQWMPEt5702RGrXFGB0jxuo1JDy+vlhOHpeodld6DOR069ZNbrzxRmnevLlMmjRJjEbPtZTyGzzK3LJJbGeTxBAVLZajh3x+NhEREREREQUnDcGjgtZAKsY+kO688045ceKErFmzRrKzs2XIkCEydOhQWbRokddl2rZtq5arV6+eCkBNmDBBevbsKQcOHCjUb+PCYgCphDx7cJGcNJ+T8fUHSrOYOn6tIywGmS+1xJp4Ql5pWkv23fiMHN+/W7JOH5aspCOOvzZzhstyv2dWkV3/mEX+OaXen93yteyfM95lnqiYOImv30AuadRI6tapJeXiYqVO7doycuRIl/n++OMPSU5OltjYWDXExMQ4XkdFRTEIRURERDkMIllZIsePitboErGdSRJbfF0xnE1StYIiml+q5vrnn39UgRb0dYSSWcuXLy/Qw7HlxDHJ2rZFbEmnJaxqNYls3U5MterYax6dTRIjPtMQJppmE+vRIy6fTURERERERMEvkAJIO3fulFWrVslvv/0m7dq1U+OmT58uffr0kddee01q167tcTkEmHRozv2ll15SzbkfPHhQVQApLQwglWANJIg1Rha6GTsEkIxZGbLh8W6SVWmgHE/JlmPJFjmWjL9m2XvouOzbt0+OHDogqacOS2TVei7ryEo6nGu9memp8u/Ov9Wgi6reQL6sdJNEmQwSZQpTf3+a8pQc3fK9x20zhIVJRFS0REXHyrW33iNDRj6fs6xBIk0GeWbY3RIRHi6RkRFqiIqIlKioSInG66hIicLfyEi59dZb1ZdEl5iYKBs3bpSICCwXqf5iCA8Pl5SUFElNTVWZTpiGZm5Yk4qIiCiA2GyiZVtUQMmWmCimxs1Vk3KAJuruv/9+GTBggMyfP1/dwwsaPEpdvlhsp0+JITpGtH27JXv/Xom7ZZD6DNQ8QvAI8Bfv9c8mIiIiIiKi0BBIAaSNGzdKxYoVHcEj6NGjhyowuXnzZrn55pvzXEdaWprMmzdPEhISpG7dulKaGEAqITMbD5U0a6bEFDaAVL+hZG3ZqF5bDu+XmOo15JLKkWpw6FxdRFqLpmlyNsMqRy+YZefRM5JqiJHjqRbZHX6b7K7fWBKPHZTMxIu1l8xnjolmQwaPnSEyWlLMNkkx4539C3UhJc3rtmk2m2Slp6nhp33n5OCPp5ymWWXrF8vztY/zj1eTGv+xiSnMIBFGg5zdvlZ+eWVIvpZ94+dEFewKNxok0miQ5TMnyQ/LFkhkZJSEI/AUGSmREZH2QFQkglb2IFbrtu1k9Jhn1XL4XDTmN2XyJDl79owYw8LEZDSK0WS0vzaZ1BceGVwYUJXQ+YKAGloLFy5U05znw4Blnf92797dpe1LVG3cunWralIQATL3+fW/2P6aNWu67HtWVpY65vpn4bOJiIhKHe5H5iwRk0k0sYmWmSG2mFh58skn5fXXX1eznDp1SjIzM1WBkIJQNY9OnxJjw0vUfc9ms4l1/79qfHiDRqrZOtQ80msg4bPRFxIRERERERGFEGs2euUt8DJ6Xq4z5Lti8NfJkyelenXkz1+EPN3KlSurab7MnDlTRo8erQJITZs2VU3goSJFaWIAqYSg76PypphCr8dU/2I/SJbDBySyXUev86IUb5UYk1SKCpMahnCpXr2SPahwTQ0RaS/ZVk1OptprL6EW0+GzGbL7wBFJOnNG0tPTxCJGiasQLpkWTbIsNsm0alK5/Y0SW/8ysWZliM2cLjb9rznTZVx4edcOrzWL/QuZHxaDSZKzbI735y5k5ms5gylcFm13LVV8aM9JST6blOeyvx1NlbXxd7qM2z5jjgqs5eX9f8xSu1slFXQKM4hknj4sm8cMz9c29522VirWaaiWMxpEdq9eKBvffTbP5SrWqicjPvpVjGEGtSyGhc8Mlh0bVrnMF6YCSUYxhNkDYHh99U13yR2jXhSj4eKyI/tdKTarxR54wmAIU+eKffmc12Fh8sjYF6V1h6scy+7b+Ze89dI4CcN2hF0MsqnAWU7wDK+zs80yY+5HEhER7riYr/h4sXz79efqPQJfYtPsr/EP71XgUZOmLVrKiGcmqL7Z1TgReWHME3L00EHHeY4Bn6O/1od+AwZJ7+v72Rc0iKQmJ8u4UY+JQQUJc5ZD319q+8PUOKPRHjB84qkxUq9+PTUdx2b7n9tkxaefiMmEQJ59P5HZWaFCBZcgIZpzfPDBB12Ow88//yyHDh1SNwt90AOCzgNuLo0bN3ZZFu2cYjvd53UOFIY5BTaJiAJJSnislDNpomVkiBYZJYYwk5w1hsuQxx6XH9atV/OMHTtWXnzxRb/adEazdah5pF//VBApOkaNN3Xuqvo8QrN1qHmkgkeVq4op3rVmNhEREREREQUnBFdQyP7kjqV+LY+C/e41fMaPH6/6H3I3ZswYeeWVV/Jsvq4w0AfStddeqyoZoLm72267TeUrouuY0sIAUpAx1bsYQMo+dKBQ60Jtm7oVItRgV0Gks2utFne2+8aI2aKpoFJmTlApS3+d89f+3nVchtkqfZZvkyxzlmRmmSUzM0uyzGbJyjLn/M0Ss9ks2WazVGreQoyx4WK2apJt08RUv7FYbhklFnO2WC3ZolnMolnNYsPfbLN6j9d6EzUu6RVTXiKqxDvm0fRlnGpaQZgp0mONqvywaAa1n7rMLNd1+3I4xSankuzNG8Kp8/kLlmVYDPLlnhSXccc8BNpsVqsaIDvnY7YeviBJm864zHf84L5caeLJ1LUHpOLZho73ybv/lT3rPTdp6O62pYckzHQxYn50+c9y8tu8a6Vt3n9aNjc+4jLun29+lIxju/JcdruxkbyffrnjfXZykvz56ZJ8be+Wmn0lpt7FcyBp0w9ycL7vmwSEl6ssH4d3V4En5Gci+PTnrFfl2C+f5bls02tuketGT1e14Ixhov6+2b+dpF84m+ey97/wjlzZZ4D6PAT0Du/6S168t7c9GGhEENEe5HMEBtGUU87rmZ/9JBUrIehrk/QUi/z60WxZ+dEcVVvP3nRkhONvdE4pDDQ5mdCggYwd87RLs5HLli1TpSn0pib1QS+9oQ/x8fFSp87F/uAQNESJD31eBsOIQsO39a6WSw3npX65MKlYs5rsrFBVBj3+pBw6fFgF3NFsHZqudWZLSbb3X5R8QdUWQsAnrFx5j+tHn0dotg41j/QaSFpGuhqPZaLaXZnvdREREREREVFwQWAFha+Rr+wP5Ee5d4firfbRqFGj5N577/W5PvTvi4AWumRxZrFY5OzZs7lalHKHguoYUMD8yiuvlEqVKsmKFStk0KBBUloYQCoBp8zn5dtzf0qNiArSs1LrQq3LFF/f3hSMzSaWQ/ulpCFTPCocA94VtKSwa9W9/EPQrLt6ZbXZg0qoPZWZbZUTiUlSvmJlFcTJQsAJAa2cvwhAmbu9Imbry/bXappNTcswWyQjM1MyMrMkPTNLrBImprhYNQ8+w6aJNHjufbGYM8VmswdhbDZM01/b7H81m5Sv21RiK0eoZXDRMUfUkqhH3rDPY7OKZrWKVc1vEWtOQAeD1WqRmPKoFSZitVfAkZj45lKjx32iWS32AJbN/lcFd3L+4r17DS+Irt1UrJnpiHzZ2/DEZ2MZDa9t9nVqnpc1xlW01xLDMqj9o69DQ20gbEdOm6DuQTpMKw0GP+fLqdmUL2Fu53d+20U1GOWCUw06SDfnL6iYlKnJhsPpLuOyVL8hefvhULr8ueVioCnt4BnJzspfQHLS+tNiir1YS/D4+l1y/I8teS4XU+9SWVH5VgkPE9XcpMlokN8nvyLndv+W57KdBj0m1z00Tkw5y4olS0Z1uVjiw2gySXh4pGp60qSCUPZmJxFgmvDGO9KqTTtVCw62bPxJ3n7lBZfaaLhWqQcAg0HdpCMjIiQ6JkZmf7g0p/aZmiQL3psl6378ToyoqWcwqCBZTHSUxKB/tuho9SCCoUWLFqrUh7Nvv/1WrVufRwXWcl47j8N6/KlZQRQKTsU3l8j4apJcs7K0b1JVBl7ZTo4cOSKXXHKJrFy5Ulq2bJkreJS5ZZPYziapWkNogg61iBAI8hT4iWzdTvV5hGbrUPNIBY+q1VDjActENL+0xPaXiIiIgoevn5Xh+o8ND/DzxRdf7a9EoaSgt8/0sWIUzPUmLsK/rL2KUb6XKx/pfVt9JYFq5cPbcj6m+eLjcPiEfBZ/1+vvZxaHvLYlr/30h6916q3GeFyuEJ/pK8vGVpD8nHyej/6uE3l4vvi5Wp98fXWQj1nS8roOljQ9D6a4VatWTQ156dixo5w/f151U9K2bVs17ocfflB5xR06dMj35+G7hgEVL0oTA0gl4IT5nHx9dqs0iKpe6ACSITJSjLXqiPXYEbEcOaSCE4YylDGJJtsw4DknNtwg1pgwqV4xophqK1ysHVFgQwqeWYULQrY1QU6dukaqVKsmNglTNzPEkSz4q9lvCvZgE4JZuGnZx+PmZL1lqppXH6fPg9uIHhTTl9XXZZ8mYvt1X870i9Ps63AaZ7Wp5bGMPs18aU/J6ntABcMs6HcC89isYrFojnEWq1UyMzKlRv2cJhRzHjYzH31SzPcNc3qA1Jueu9gsHZjCIySmvD3TUJ+z9zufqwCXfiFVN339NRIBf0WTiOhyEhVbztFsns0aLf9duMmxnOoPI+e1WkYFCbEfFqlYp5GERUTlpIdISveeclmThioIiH20WqyqdAM206bvO76PpnCpVT7c5TjYrhkg1Zq2dQQOrRaLI6BoD/TZA40x9V0zUqFSm+vElpnqmMceRMRri/3A5wT7wsu5BgYN4ZESHd/MHjxUQUD9b+4gYe5gWf4e+bCvkG3DoIlka/ku8XEgWeSzXRfbmLVkuNaoQxphyMzI3e/apB+PS9zho473Z7fukv0bN+T5mWGRsXLLEtdmKQ+u2CBJP3+R57I12vWSj20dVLArIqePtSVD75eUxIvb4c1DE9+SHv3vsZ/bqHm4d4c8cVMn+zbhO+HS9OLFphgx/v1v/5CqNWs5zv0vPnxPPnjr/2TQI6Pl3ocelSvrXuxDjSgQJbRvLQl1q8nh82Y5ZYxWNY6mTZsmCxYsUJ2KulO1hc4miTG+rqPfIjRBh/GeAkGmWnUk7pZB9r6Qkk6rmkcIHmE8ERERERERUUlr3ry59O7dW3Vx8c4770h2drYMHz5cbr/9dqldu7aa59ixY9K9e3f54IMPpH379rJ//375+OOPpWfPnipIdfToUXn55ZdVoeQ+ffqU6kFkAKkEVA0vL9dXbiflTQXrGNpXM3YIIKFDauupE2KqHV8k66XSpfq5CTOoUkdRJnu/NqEAwRVU20T/Pq77hL64/OVvbTbwN1MRF/g2+dgvD+662PSkMwRyrE6BQfUXAUObpoKGKkh4+wf2vznzqGk58+nBQEegKidIaA+q1RTr4F/twcWccXpNN09BRLyyWDW5kJIqEW2ek+xnx0mWOVsyMs2SkZWlmpvMNNv/ouQDmqIUY7jEVo9UtfrUYBMxDBgpGeeTVHOU6PvKkm0WK5qNRGeGaEoy53Vcw9zB9PItOjumX2x20vm1/b3BqSnEnITM3yH0VEImn8tmG8Ll8AXXsoSoxZgfq/9Nla0bTjvepx+92ISkCnj6MOXn0xJR8eL5dXL7aUk+d1a+/jdNLmw6ywASBTzTsb2y7a+fpGabLpK056R0io2Uzi9PEpOX0rdoag41j/RmaVVQNSpajff6GbXqMGBEREREREREAeOjjz5SQSMEiZBv2L9/f3nrrbcc0xFU2r17t6Sn21siQg2qn376SaZOnSrnzp2TGjVqyNVXXy2//PKLynssTQwglYD4yCpyZ42ri2x9CCBlbbR3PI1m7BhAIgrioKHB3tdRILAHxcxSvXpl/wOYg+7JNUoFvdCMpM3elGSu1zn9nZn7r5Jsq702kxqnj3dbRg9YIfSjAmTNbhPL7Tc7apOpGnQIjqHmgs0mmRkZqvk7zBsZF3exRpomkvHUJMnOGidaTu0zc3a2ZGRkSmZmzoDgWWaGGGKrSHRkmGoiE/28YR21eg8Ta0ayPeilgmNZomVnqb82S5YjYBZewfVGHxYRLbEJrXJqzelNQeY0F6lqh9nH4VMMRtfbtDGmvERWqy/G6HL+HR+iEpR2aLuMev4RSUtPk9FPTZEGjaqLtYrJZ7N06KcI0/F90GsgaZkZajwRERERERFRMKhcubIsWrTI6/QGDRq4NAeJmklff/21BCIGkIKQqX5Dx2vL4YMiHYsuOEVEVNQQIMNQ/K3R5pZ3TTHfnRd6ghs8miDPGjJRzFabmC2aZOr9rlns/bBhPAJNenDMERdSzwbVRRuw3h7wcqoEpTcPqT9A6NNc5un4sNhGDZOUlFSJr8YM9VAzY8YMefXVV+XkyZPSqlUrmT59uqrK7s0nn3wizz33nBw8eFB1sPnKK6+UTtV2nKBujXInbVohhxaPV4HUGjXjpUJKktSNqiLGarV9Nktniq+ngkuYjppHKnhUuaoaT0RERERERERlMIBU1BkmyHwbP368zJ49W3VYddVVV8msWbPUvKUh02aWcINJjDnNsRRFDSSd5fD+IlknERHlv+ZYuFHv5DasFGuKsRZSKEFbxyNHjlTtI6NTTVRb79Wrl6rS7qm6OqqxDxo0SCZPnizXX3+9Ktl00003ye+//y6XXlrwfvgKBVXuY2PVS5slWw4tnSSJ6z5U7zu0aicvDX1EGqcel/JZKXk2S4caSaiZpPpCSr6gah4heOReU4mIiIiIiIiIykAAqTgyTKZMmaLaFEQHzQkJCSrYhHXu2LFDtSdY0maf+E5+Sd4lg2t0ld6VLy/0+lSTdSaTiMUiWVs2yZmxj4kBJev1ARkzzu/R8Ue2WS5ER4vBaMw9PQydtru+V526hxld53G8Nua8NqjXjmn6Oox4j89xnm5Qxejdm2iyv7YP9lL3OZ20OL+2L5hrec1mFUlJkfS4WDFgH9HMjXNxfTUvUsztM/QBVP8jF9ef859927E/OR3Z29Mo57XaT2QcY7y903vn+VzH460+3ddRtU9U25eSIhnlytk/T98Wr4vp81xch2M7c82Ts82O/XOefnFZexqpDnZcjpHmPC7nr5oXaej81zE+5zXGp6RIWmzsxX3K4VxV06UvGm/90nga7zguhovr14+D2jXnY5Xz2mkZx3QVC/ARDHD7bP1YpevHyldfOs7nluP8c6p24lhWP0edP9N5OacVOqqkeNgu921237Zc43NqvOCYpadJanSMPVn0mjDu3w/n75CPfXLZlmLhnoa5t09tQ0aGJEdGui3qvA/O1XycrgeqhpDb9cF5OffriTp+Htajc/nOOY3IeX/x6+H23Xf7q1aZlSUpdRtIhaH/K1wSUsB44403VAebQ4YMUe/xXPTVV1/J3LlzZcyYMbnmnzZtmuqU86mnnlLvX3zxRVmzZo28/fbbatmS9MX6x+WGq6dJtiVNfpo/QpL2/K7Gj2pcV54b97g6dS2J50WiG6jxeTVLh2CRe80kIiIiIiIiIiqDAaSizjBBZiGCUOPGjZN+/fqpeT744APV8dTKlSvl9ttvL+E9FEm12jtajw5z6/jdT4bwcHsTLwf3i5aeJtl/b8vXclkSmlIl9NjLaIeWNAlNoXj+2bvvCy32q3BoyT5/tlQ/f0niBllw6kdJyk6RJtG1ZUy9m+Wy2Ppe5//w1DpZevoXOWk+JxVNcXJtpf/I/+r0lciwcL/XGSrMZrNs3bpVxo4d6xiHJhd79OghGzdu9LgMxqMAjjMUlsGzjjdZ6NMr6+LTQHJysqNWGwZ/aQaDfP7TCHl510F5a+8RiTMZZfrlTaVXzSr2foyyMsVQrYbYzFmSnZgoWlaGhFWqImG14wv1uaEI6YFnWaYL05DnYXDjdznw0pDXVSIiIqIgDCAVR4bJgQMHVFN4WIeuQoUKqnYTlvUUQHLPUElJSSmSDBXdk3VulHRrloSHmYrswTWm/52SMusNFUAiIqKyR/WZVEqZKqvO/iGvHf1MxtUbIJfF1pOPEtfLw3vfk89ajpEq4bmb1vv67FaZduwrmdhgoLSKTZBDWafl+YOLVW2rp+r282udoSQpKUmsVqsq7OIM73ft2uVxGTzreJof471B7e2JEyfmGn/u3DmxWCx+b3/43OVy9pn/yQPVasnBqHIyrFmCNKpYXrTBwyTVahVDbJyExcWJLTVVtLRUMcTGi7FadcnMtoicLd1AaKDBdxHPocg09dxvGzENeR4GA36XAy8N9d/4RERERBREAaTiyDDR/xYkU8VbhsqZM2ckIqJoag1BBkr7FtXKmv1H5M25Oc2GOTct5v5eE5vVIskXLkj5uDh7bx2+lnFpjixnmvtr53k8rcvTMnoH256aDdObestpZixXE2NqkvtyBrGJJunpGRITGythRr3JMucmzMT753hbvzN9uz01q+fW/J7rkNN0nvs83jhNs2maZGRkSHR0tIT5arrOZTlvTZ5J3s2fOa9H/+vcVJ9zE3w4Ns7N9elN+Tk39+d8/HKaQkS2dFpausTGxUqY3g+Yy655aG4vv/PkaurQaf+8NjXm3Myh+zib7yYDnbbDptkkIz1DomOiveyXh2U9NDno2ryhr+YHnccZ8mgSzW17DXmMz3mNfVLHCt+pnOPnss5cn+1hn7y9L2r6NcVp+z2lE75TKakpUq5cOQlTTWs6pYG3tHe/Nrg3h+j+2mc65LzOqzlBl++tj/GCa7pNklOSpXzFipKYmChFAfe7glh4ap3cUvVKuamqvb/CcfVulfUXdsjKM7/K/TW755p/W+pBaR2XIH0qt1Xv60RWlt6VLpftaYf9XicVHArsOBfCQQ2kunXrSqVKlaR8+cL1MVT5nQ9VZt+cc+fU+hj88A/SEM2iMg39xzQsPKYh0zAUz0MTmoAnIiIiogLjU5SHDJVjx45JixYtpEqVKh77YQrKh+/Tp6VatWohlaGD/TodYvvFfQoePFbBIRSvf8WxT6gRrJfO1Zs1g8jISDU4y7ZZZGf6Ubm/1sWgDoKoV5ZrIn+lHvS4/tZxDVQtpO1ph1STdEezzsiG5J1yfeV2fq8zlFStWlWMRqOcOnXKZTze16xZ0+MyGF+Q+b0dT8B5VFTnEjL7inJ9ZRHTkGkYCHgeMg1D7TzkfYmIiIgoCANIxZFhov/FuFq1arnM07p163xlqOiZZ6GUARKqPwJDcb+4T8GDxyo48DjlTb+GovCEs/Hjx8uECRNcxp2zpIlVbFLF5NqsHJqZO5DpuUYUah5huXt3v61qV1nEJgOqdpIHavXwe52hBLWd27ZtK99//73cdNNNjkAh3g8fPtzjMh07dlTTR4wY4RiHPiExnoiIiIiIiIgo6ANIxZFhkpCQoIJImEcPGCEgtHnzZnn44YdLZL+IiIiC0Y4dO6ROnTqO955qq/jjt5R/5f0T38uz9fqr/o0OZybJlCMr5d0T38pDtXoWyWcEO9SEHjx4sLRr107at28vU6dOlbS0NBkyZIiafs8996hjg2Z34fHHH5cuXbrI66+/Ln379pUlS5bIli1b5L333ivlPSEiIiIKLGFhBjGq5tZd+WppvkKk0eu0VLPR9+f5aMb7XEa212kx4nu9XpcL975c1Rjv2X6x4f4XxDV5SM/88HMxn1SL9l6gHzVfrJrvgoihwFca+Nujrq9kRTPy3uTVVYOvZTU/zwGrz+3xb515nFY+98MXT9cpndVW9GmT12b6Oly+ljUYPE/M9rUxFNBMoZZhggs8gksvvfSSNG7cWAWUnnvuOaldu7YjSEVERES5oa+ovPrCqWSKFaOEyRmLa2fUZ7JTpGq4aw0i3Yzj38j1VdqqPo6gcXRtybCZ5cVDn8iDNXv4tc5QM3DgQNUs6/PPP6/6bEQhmFWrVjn6dDx8+LBLbdtOnTrJokWLZNy4cfLMM8+oZ56VK1fKpZdeWop7QUREREREREShxBSKGSajR49WQaihQ4fK+fPnpXPnzmqdUVFRpbKPREREoSI8zCTNY+Jlc/Je6VbxMjXOptlkc8peub16Z4/LZNqyxSCuxZeMBvu9XfNznaEIta+91cBeu3ZtrnEDBgxQAxERERERERFRSAaQiiPDBLWQXnjhBTUQERFR0bq7Rhd57uBiaRlbVy6NqScfJq5TNYpuqtJeTX/2wCKpHlFeHq9zvXrfpUILWXhqnTSLiVdN2B3JSlK1kq6u2NIRSMprnUREREREREREVAYDSERERBQ8ele+XM5ZUmXm8VWSlJ0sTaPryMzGQ6VKTnNzJ83nXNq3frDWtaoG0ozjX0ui+YJUMsVJl4otZXjtPvleJxERERERERERlSwGkIiIiKjABlX/rxo8eb/po64PGwajDKvdSw3+rpOIiIiIiIiIiErWxc6FiIiIiIiIiIiIiIiIiBhAIiIiIiIiIiIiIiIiInesgURERERERERERH5LSUmRESNGSP369SU6Olo6deokv/32m89l1q5dK23atJHIyEi55JJLZP78+bnmmTFjhjRo0ECioqKkQ4cO8uuvv/IoERERlSAGkIiIiIiIiIiIyG8PPPCArFmzRhYuXCjbt2+Xnj17So8ePeTYsWMe5z9w4ID07dtXunbtKtu2bVPBJ6xj9erVjnk+/vhjGTlypIwfP15+//13adWqlfTq1UsSExN5pIiIiEoIA0hEREREREREROSXjIwMWbZsmUyZMkWuvvpqVZtowoQJ6u+sWbM8LvPOO+9IQkKCvP7669K8eXMZPny43HrrrfLmm2865nnjjTfkwQcflCFDhkiLFi3UMjExMTJ37lweKSIiohJiKqkPCiY2m039PXHihITK/pw5c0bMZrOEhYVOzDAU94v7FDx4rIIDj1P+6Pc7/f5HZYOmaepvcnJykawP5w+arzGZTCFzXy5pTEOmYSDgecg0DMXzUL/X6fc+KloWi0WsVqtqZs4ZmrLbsGGDx2U2btyoaig5Q+0i1EQC/M7funWrjB071jEd5wKWwbKeZGVlqUF34cIF9Rfnkie+Todsq/eJqalm7wtiOyzel03PzPY6TQs3ep1m9bGxUdZwr9MiLd6z/azh/n+3TGEGv5bzczGfbD6OY2G+8wZDMWxsKfCVBv7++vOVrL4+L6809bWs5uc54O/56Pu88r1em5/nndHHBlltRZ82eW2lr6Ol+bFcas61mPfi4MMAkgenTp1Sf9u3b1/Sx4OIiKhU73/16tXjESgj9MyUunXrlvamEBERldi9r0KFCkztIlauXDnp2LGjvPjii6o2UY0aNWTx4sUq0INaSJ6cPHlSzecM7xHsQ42mc+fOqaCUp3l27drlcZ2TJ0+WiRMn5hp/WbMmhdo/IiIqOrwXBx8GkDy4/PLLVceMeDAJhRK0+GKiuveOHTvUg12oCMX94j4FDx6r4MDjlP9Svgge4f5HZUft2rXlyJEj6h5aFCUskeGDYBTWWb58+SLZxrKGacg0DAQ8D5mGoXgeorQzngtx76Pigb6P7rvvPqlTp44YjUZp06aNDBo0SNUiKimorYQ+k3Tnz5+X+vXry+HDhxk4LCDeCwqH6ce0Kw2Bft7xXhy8GEDylCgmk1xxxRUSKvTq+niQC8QLiL9Ccb+4T8GDxyo48DjlH2selT0oJBMfH1/k68U9OVTuy6WFacg0DAQ8D5mGgaAoz0PWPCpejRo1knXr1klaWpp6Bq9Vq5YMHDhQGjZs6HH+mjVrOlp/0eE9jjeavkMQCoOnebCsJ5GRkWrwdOz5bOIf3gsKh+nHtCsNgXze8V4cnIK/eg0REREREREREZW62NhYFTxCE3SrV6+Wfv36eZwPTd59//33LuPWrFmjxkNERIS0bdvWZR7Umsd7fR4iIiIqfgwgERERERERERGR3xAsWrVqlRw4cEAFgrp27SrNmjWTIUOGOJqXu+eeexzzDxs2TPbv3y+jR49WfRrNnDlTli5dKk888YRjHjRHN3v2bFmwYIHs3LlTHn74YVXDSV8nERERFT82YVcGoAr3+PHjPVblDmahuF/cp+DBYxUceJyI+H0LJqF4zSppTEOmYSDgecg0LIsuXLiggkRHjx6VypUrS//+/WXSpEkSHh6upp84cUL1RaRLSEiQr776SgWMpk2bpprVnTNnjvTq1csxD5rAO336tDz//PNy8uRJad26tQpSob/q/OB30X9Mu8Jh+jHtSgPPOyouBg09WBERERERERERERERERHlYBN2RERERERERERERERE5IIBJCIiIiIiIiIiIiIiInLBABIRERERERERERERERG5YACJiIiIiIiIiIiIiIiIXDCAFOQmT54sV1xxhZQrV06qV68uN910k+zevdvnMvPnzxeDweAyREVFSSCZMGFCrm1s1qyZz2U++eQTNQ/25bLLLpOvv/5aAkmDBg1y7ROGRx99NGiO0/r16+WGG26Q2rVrq+1ZuXKly3RN0+T555+XWrVqSXR0tPTo0UP27t2b53pnzJih0gf716FDB/n1118lUPYrOztbnn76aXVOxcbGqnnuueceOX78eJGfwyV5rO69995c29e7d++APlZ57ZOn7xeGV199NWCPU36u4ZmZmeo6UaVKFYmLi5P+/fvLqVOnfK7X3+8iUVFfAwL93hzoaTh79mz573//K5UqVVIDvsslfY8MRP7ei5YsWaKu87jWlnUFTcPz58+rexHuK5GRkdKkSZMy/30uaBpOnTpVmjZtqu7LdevWlSeeeELd48uqvJ7rPFm7dq20adNGnYOXXHKJ+r1EZRvOCW+/AX777TfHPP369VPXL/yea926tXz00Ud5rtvTOnEfCRXFmXa6M2fOSHx8vFon7iOhorjSDumF3+S4LuI6h3vF8OHDJTk5WUJFcaXdn3/+KYMGDVJphvts8+bNZdq0aRJKivM7+7///U/atm2rzjssQ+SOAaQgt27dOvVjbtOmTbJmzRqV2d2zZ09JS0vzuVz58uXlxIkTjuHQoUMSaFq2bOmyjRs2bPA67y+//KJuFvfff7/88ccfKmMAw99//y2BAhd05/3B8YIBAwYEzXHCedWqVSv1g9mTKVOmyFtvvSXvvPOObN68Wd2wevXq5fPH8ccffywjR46U8ePHy++//67Wj2USExMlEPYrPT1dbddzzz2n/i5fvlxl8N94441Feg6X9LECPJw6b9/ixYt9rrO0j1Ve++S8Lxjmzp2rHqYQcAnU45SfazgymL744guVEY/5Eby85ZZbfK7Xn+8iUV4Keg0IhntzoKchfgQiDX/88UfZuHGj+lGMa8SxY8ekrPL3XnTw4EF58sknVUCurCtoGprNZrn22mtVGn766afqOQjBzTp16khZVdA0XLRokYwZM0bNv3PnTnn//ffVOp555hkpq/LzrOrswIED0rdvX+natats27ZNRowYIQ888ICsXr262LeVAlenTp1y/QbAeZGQkCDt2rVzPI/85z//kWXLlslff/0lQ4YMUQUCv/zyyzzXP2/ePJd1h1IBhOJOO8AzIJYPNcWVdmFhYSrz//PPP5c9e/aoIPl3330nw4YNk1BRXGm3detWVSDzww8/lH/++UeeffZZGTt2rLz99tsSKor7O3vffffJwIEDS2BPKChpFFISExM1HNZ169Z5nWfevHlahQoVtEA2fvx4rVWrVvme/7bbbtP69u3rMq5Dhw7aQw89pAWqxx9/XGvUqJFms9mC8jjhPFuxYoXjPfajZs2a2quvvuoYd/78eS0yMlJbvHix1/W0b99ee/TRRx3vrVarVrt2bW3y5MlaIOyXJ7/++qua79ChQ0V2Dpf0Pg0ePFjr169fgdYTSMcqP8cJ+9etWzef8wTScfJ0Dcd3KDw8XPvkk08c8+zcuVPNs3HjRo/r8Pe7SFTU14BgvDcXt8JeRy0Wi1auXDltwYIFWlnlTxoi3Tp16qTNmTPHr/tfWU/DWbNmaQ0bNtTMZnMJbmVopSHmdX8mGTlypHbVVVcV+7YGg/w8140ePVpr2bKly7iBAwdqvXr1Kuato2CC61S1atW0F154wed8ffr00YYMGVLo8zKUFGXawcyZM7UuXbpo33//vUrLc+fOaaGqqNPO2bRp07T4+HgtVBVn2j3yyCNa165dtVBVHGkXaPkjFDhYAynEXLhwQf2tXLmyz/lSU1Olfv36qiQrSjggQh9o0NwSqu42bNhQ7rzzTjl8+LDXeVEqF826OEMpQIwPRCjJiZIRiPCjhkQwHyfnUoEnT550OQ4VKlRQTXp4Ow5IB5QUcV4GpW7wPlCPnf49w3GrWLFikZ3DpQEl21FKB82pPPzww6rKvDfBdqzQxNtXX32lSr3lJZCOk/s1HGmOWknO6Y7mwOrVq+c13f35LhLlxZ9rQLDdm4tbUVxHUSsW14S8nvNClb9p+MILL6j7XX7uCaHOnzRESeiOHTuqGrM1atSQSy+9VP7v//5PrFarlEX+pCFKDWMZvZm7/fv3qyYA+/TpU2LbHex4T6H8wPUKv2lQ6j6vZ+783Etx3atataq0b99etW5gjyuFpqJMux07dqh77wcffKCuj6GuqM87HVqeQAsoXbp0kVBVXGnn7zLBpDjTjshd6F/JyxCbzaaq8l911VXqh503yCzGw89nn32mghhYDj9qjh49KoECGZ2orrtq1SqZNWuWyhBFkyMpKSke50dmKX7QOsN7jA9EaOMbbQCjH5pgPk7O9LQuyHFISkpSmQ/BdOzQBBj6REKTQmhisKjO4ZKG5uvwQP/999/LK6+8oppGu+6667xmBgXbsVqwYIHqVyivpt4C6Th5uoYjbSMiInIFK32luz/fRaK8+HMNCLZ7c3Eriuso7j8IeLsH5soKf9IQzZKiuTA0uUb+pSGCHWi6Dssh6IFmfV9//XV56aWXymSS+pOGd9xxh8pM7dy5s4SHh0ujRo3kmmuuKdNN2BWUt3sK+gbJyMgote2iwILrPQqroN8db5YuXaqal88r0xXfWcyLZqbRJPYjjzwi06dPl1BVVGmXlZWlfiujH1oUeisLivK8A6RfTEyMaioWeQ5z5syRUFXUaadDU25oKnbo0KESqoor7Yg8YQAphKB0DPoVyKtjR5QgRBuY6BgNJRlQoqFatWry7rvvSqBARjb6BkLbnbgg4scqAi64+IXKhR77iEygYD5OZQ1Kfd92222q5BmCDcF8Dt9+++2qHyd0ao+2vNEmLh4sUCspFCD4itpE6Ng6WI5Tfq/hRFQ2vfzyy+r6sGLFijyvbWSHwgB33323Ch6hBDn5X8ABNbjee+891cEy2sdH3wLoZ4/yB89XqLU1c+ZMR5+aqCn94osvMgmJPECfYd46i9eHXbt2uSyDgpboE8tXbVP0KYiMVNwX0A+qLwiWo2DX5ZdfrgpwjB49WgVFAl1ppx36nWnevLncddddEmxKO+10b775prpXoDDxvn37VJ97gS5Q0g7wmxot+KDfQfQdGugCKe2IvDF5nUJBZfjw4SoDeP369T6jz56gFBweiv79918JVCh936RJE6/bWLNmTdVklTO8x/hAc+jQIdURIn44htJx0tMa6V6rVi3HeLxHEMwTZOYYjcagOHZ68AjH74cffvBZ+8ifc7i0ofk2HA9sX/fu3YP6WP3000+qg2+UOCqo0jpO3q7hSFs0lYOglnMtJF/p7s93kSgv/lwDguneXBIKcx197bXXVAAJzw+h2Bl1caUhMl0OHjwoN9xwg0swBEwmk7pXoCZIWeLPeYh7CZ5DsZwOmYOoEYJ7FGrKliX+pCEyohHMRGfXgAI8aWlpqmQ0gnFloYmnwvJ2T8EzeXR0dKltFxWPUaNG+WytQ//94mzevHlSpUoVVUjOE7S4gPsBMudRULOg0HIBgr6oYRMZGSmBqrTTDr+Vt2/frmqugt7sH66duN5NnDhRAlVpp53z9Q4Dmi5H02NoIQP3EeffdoEmUNIOzSciPwP313HjxkkwCJS0I/KFT6pBDjdjZDyiNCpu1AkJCQVeB5pgwA0+kG9G6AsImQDethG1ddAUlzNUNcf4QIMLPUpx9u3bN6SOE849POQ4Hwc0KbF582avxwEZDijJ6rwMMnbwPpCOnR48Ql85yLzDjbqoz+HShhIsaD/X2/YFy7HSa/hhW1u1ahXwxymvazj2A5l2zumODE/00+Qt3f35LhLlxZ9rQDDdm0uCv9fRKVOmqAwrNLXZrl07KcsKmobIeMGz07Zt2xwDfmh37dpVvUYfk2WNP+chSuCjYIUefIM9e/aoe2VZCx75m4bov8w9SKQH5EK5T5WixHtK2YKWN3AN9zU4X3/wPcLvbGSU4tnZUy1A/P5G093+NmmF+0alSpUCOngUCGm3bNky+fPPPx33Xb35NRTyQ4sLgay0084T/d6LwGUgC4S0Q5/heMYbPHiwTJo0SYJFIKQdUZ40CmoPP/ywVqFCBW3t2rXaiRMnHEN6erpjnrvvvlsbM2aM4/3EiRO11atXa/v27dO2bt2q3X777VpUVJT2zz//aIFi1KhRap8OHDig/fzzz1qPHj20qlWraomJiR73CfOYTCbttdde03bu3KmNHz9eCw8P17Zv364FEqvVqtWrV097+umnc00LhuOUkpKi/fHHH2rA5eONN95Qrw8dOqSmv/zyy1rFihW1zz77TPvrr7+0fv36aQkJCVpGRoZjHd26ddOmT5/ueL9kyRItMjJSmz9/vrZjxw5t6NChah0nT54MiP0ym83ajTfeqMXHx2vbtm1z+Z5lZWV53a+8zuHS3CdMe/LJJ7WNGzeq7fvuu++0Nm3aaI0bN9YyMzMD9ljldf7BhQsXtJiYGG3WrFke1xFoxyk/1/Bhw4ap68YPP/ygbdmyRevYsaManDVt2lRbvny5431+votEBZXXNSBY782BnIb4LkdERGiffvqpyzUC18OyqqBp6G7w4MHqmliWFTQNDx8+rJUrV04bPny4tnv3bu3LL7/Uqlevrr300ktaWVXQNMT1D2m4ePFibf/+/dq3336rNWrUSLvtttu0siqv5zqkH9JRh3TDM95TTz2l7ikzZszQjEajtmrVqlLcCwoU+D2D8wjnhjs8Q+PcGTt2rMu99MyZM4558ByN52nd559/rs2ePVs9s+zdu1ebOXOmWsfzzz+vhZqiTjt3P/74o1r/uXPntFBT1Gn31VdfaXPnzlXnHX6f4n7bvHlz7aqrrtJCTVGnHdKsWrVq2l133eWyTEn9rg/27yyuc7gHP/TQQ1qTJk0c92fnPC8q2xhACnK4aHga5s2b55inS5cu6seybsSIESozEhkSNWrU0Pr06aP9/vvvWiAZOHCgVqtWLbWNderUUe///fdfr/sES5cuVRc6LNOyZUt18w00CAjh+ODHt7tgOE76w5/7oG+3zWbTnnvuObW9+FHdvXv3XPtav3599SPaGTL09X1t3769tmnTpoDZLzy4efueYTlv+5XXOVya+4TgRM+ePdUDFjJzse0PPvhgrkBQoB2rvM4/ePfdd7Xo6Gjt/PnzHtcRaMcpP9dwBH0eeeQRrVKlSuph8Oabb1YPge7rcV4mP99FIn/4ugYE6705kNMQ1yxP1wj3a3NZU9Dz0BkDSP6l4S+//KJ16NBB3VMaNmyoTZo0SbNYLFpZVpA0zM7O1iZMmKCCRigQVrduXXVvD8UM1aJ6rsNfpKP7Mq1bt1ZpjvPQ+dmHyrZBgwZpnTp18jgN55Knc835/MK55Fy++ptvvlHnWlxcnBYbG6u1atVKe+edd1SB0FBT1GlXlgJIRZ12yPxHQUEUMMS9AgU8UfiYaZd32uHZ2FN641k61BTHdxbTPS2H/DAiMOC/vOspERERERERERERERERUVnBPpCIiIiIiIiIiIiIiIjIBQNIRERERERERERERERE5IIBJCIiIiIiIiIiIiIiInLBABIRERERERERERERERG5YACJiIiIiIiIiIiIiIiIXDCARERERERERERERERERC4YQCIiIiIiIiIiIiIiIiIXDCARUdAzGAyycuXK0t4MIiIiIiIiIiIiopDBABIRFcq9996rAjjuQ+/evZmyREREQWL+/PlSsWJFCTRr165VzxXnz5/3ex0HDx5U69i2bZuUpcIsgXhM33//fenZs6fLuAkTJkiNGjUKlIbO83o6vtdcc43a/6KSlJQk1atXl6NHjxbZOomIiALhOSYQn2GIKLAwgEREhYZg0YkTJ1yGxYsXM2WJiIhKsPDGv//+q6b/+eefcuONN6oM76ioKGnQoIEMHDhQEhMTeTyKCZ59rrvuuoBKXxzzPXv2lMhnIVijn4dhYWESHx8vQ4YMcTnnMjMz5bnnnpPx48c7xu3cuVMmTpwo7777boHSsKDpjSCVp+/Md999p6YfOHBA7rjjDqldu7b6zmD7+/XrJ7t27VLTq1atKvfcc4/LthMRUdl57rrppptK7fPr1q2r7nuXXnppkRWuISIqCAaQiKjQIiMjpWbNmi5DpUqV1DQ82MyaNUv9yI+OjpaGDRvKp59+6rL89u3bpVu3bmp6lSpVZOjQoZKamuoyz9y5c6Vly5bqs2rVqiXDhw/PVTL05ptvlpiYGGncuLF8/vnnPLJERFSmCm8kJCTI6dOnpXv37lK5cmVZvXq1yqCfN2+eyhhPS0sr7c0OWXj2wTNKoMjOzlbPVQgiFnY9+VW+fHl1HqKWzuzZs+Wbb76Ru+++2zEdz3+Y56qrrnKM27dvn/qLYE1B0tCf9MZzpPt35uqrr1b7eO2118qFCxdk+fLlsnv3bvn444/lsssuc8mcQ0Dso48+krNnzxboc4mIiArDaDSq+57JZGJCElGpYACJiIodSpv2799flYi+88475fbbb1cZWoDMrF69eqmA02+//SaffPKJKg3qHCBCAOrRRx9VgSUEmxAcuuSSS1w+A6VXb7vtNvnrr7+kT58+6nP4A5+IiMpS4Q1kMPz8888qI3zOnDly+eWXq6BS165d5c0331Sv84ImTFAQA7UwcH8+cuSIS2Y/MvrR3FhcXJxcccUVjhocupkzZzqWx3y33nqrY5rNZpPJkyer7UBwo1WrVrkKlXz99dfSpEkTNR3bjWZb8pKfwiqwf/9+tU4UNsFnb9y40fEsgsCG+zJIi9jYWElJSRGz2ayeTVCIBftWv359tS/emn9BEGXQoEEqkId1tGvXTjZv3qym4XkI21GuXDn1uW3btpUtW7b4vX960zYIenTp0kVtHwIdnpqww3oaNWokERER0rRpU1m4cKHHz0INNmz3pEmT8kx/52VxHiJYiW393//+p86PjIwMNX3JkiVyww03uNQK0t+j1hKWBzwPIqCDWj8VKlRQ+/T7778XurkdZLy5f2eQDv/88486t3HuXnnllerYIsj10ksvqffOASjs24oVKwr0uUREFLrWrVsn7du3dxR0HTNmjFgsFpcmVXE/HD16tHomwL0H9z9nqO3auXNndf9u0aKFund6a6oVr/EMAchDwXjUkALUOJ86darLulu3bu3yeXv37lWFJ/TPWrNmTa59wrMf8lbwDIFtxrNffp7HiCh0MYBERIX25Zdfqowk5+H//u//HNMHDBggDzzwgMoQevHFF1UmyvTp09W0RYsWqSZNPvjgA1UlGzWR3n77bZWhcerUKTUPfsCPGjVKHn/8cbUOZFiNGDHCZRvw0ISMGgSW8NmowfTrr7/y6BIRUZmCjAlkXCCTW9O0Ai2bnp6uAga4JyMQhdoXKPShw70VhTS+//57+eOPP1QtKAQADh8+rKYjCIJMkhdeeEHV4li1apXKpNAh4IJ1v/POOyrT/oknnpC77rpLZb7oGRa33HKLWicySfDsgIyYwhZW0T377LPy5JNPqnXjeQLPDUgrBEowP2pqOcN7BMAQ6HnrrbdUAZalS5eqfUOABhk1niCdEPQ4duyYWgbbhIwjBNAA24cm0hAo2bp1q9rH8PDwQu8f1oNnJYxH8M8dzglMxzPV33//LQ899JCqVfPjjz+6zIeMJtTqRqGd++67T/yFYBf2Wc9I27Bhg3oG1OFY6Gmu1wgCBOwGDx6s5t+0aZMKSOK8w/jiUK1aNRXAQlDOarX6nBeZhD/99FOxbAcREQUX3Odxf0L+BO7PKICBvv6Qf+FswYIF6lkDBUmmTJminpP0wA3uO2geD4VbMP29995Tzyu+mrNbtmyZeo3nEdw7p02blq/txT0Zz1koPIHPwvPY008/7TIPauXiGQLPPrjf4XkQ+Tt45kNhGiIqozQiokIYPHiw1qNHD23v3r0uw5kzZ9R0XGYWLFjgssyIESO0a665Rr1+4oknHK9158+fV8utW7dOO3XqlHr9ww8/eN0GTF+6dKnLuPLly+f6XCIiolC59xqNRi02NtYx3HrrrY7pzzzzjGYymbTKlStrvXv31qZMmaKdPHnS5zrnzZun7qebNm1yjNu5c6cat3nzZq/LtWzZUps+fbp6vWzZMnX/TU5OzjVfZmamFhMTo/3yyy8u4++//35t0KBB6vXYsWO1Fi1auEx/+umn1TacO3fO6zZg+rBhw1zGdejQQXv44YfV6wMHDqh55syZ45j+zz//qHHYR8A+Ik2PHz+u3uP5A2m4du1a9f6xxx7TunXrptlsNq/bsGLFCvX63Xff1cqVK+d4FnKHafPnz/e6P/7u39SpU3Md0woVKjjed+rUSXvwwQdd5hkwYIDWp08fl8/Cc1pBuX/Wnj17tCZNmmjt2rVT73H8sO7169e7LIc0y+snqdVqVWn2xRdfeExvff//+OMPx/QuXbqobdKNHz9eCwsLc/nOXHHFFY7pb7/9tjo/8Tldu3bVXnjhBW3fvn25tsXTcysREYX+c1e/fv1yjcfzVtOmTV2eDWbMmKHFxcWpe5d+P+rcubPLcrj/4PkGvvnmG/W8ceLECcf0NWvW+LzP/fjjjx6fjerXr6+9+eabLuNatWql7oGwevVq9VnHjh1zTMfnO3/WwoULc+1TVlaWFh0drZYnorKJNZCIqNBQmgY1f5wHVHUuCii9mh/uJXdRlVsv6UtERBRq0HwJatLoA2rI6FCL6OTJk6pkKZrdwt9mzZqpGiWAcXqNYTQ15tzEF0rR6rAMmi/Ra7qgZg1qjTRv3lyNx/KYptdAQrNjaP4LTayh7xvU0kGtJvj333/Va8zjXGMZNZL0fnCwrg4dOrjsZ8eOHfOVHu7z4b17DZ3//Oc/jtdoZgYSExMdNUuQLiglDB9++KHaF70GFWo6I53R7BtqWX377bdetwXzoflAb89CI0eOVLWrevToIS+//LJj/wu7f861ezzB/M79DwHeF3Q93qDpRBxTlKJGOqEJQ5wDoDdjhyZz8oIa6A8++KCqeYQm7NDMH849/TzzF7bJ+Tujl+AGNJWM7wy2F2mLJpVxPrg37YPnUv2cJiKisg33T9wz9CZY9fsq7lloytbT84f+DKI/f6AWEWoVoQa5Ds8kxbW9+Cw0x+rt+QI1qfDMhhpI+rManmfQakx+nleIKDQxgERExQ7Nj7i/R+YT4C8eUpw79kY1aTQlgh/6eHBBEzFoLoeIiIg8F97QAyK6KlWqqCZkX3vtNZVhgMwCvNb7GdIz0dFXUn4heIRm0NBULJo1wfKXXXaZo0kT3LPRV83ixYvV9jz//POqryE0hYfMFPjqq69cMvF37Njhsb+i4uBc2ETP7HEubIKgDvoNAjSthubd9PnatGkjBw4cUE3xIhiCvgGc+3cqSOEXNBGHJvz69u0rP/zwg+qDoCj61cE5URT8XQ+OP44pmsfDc9369etVU4H6+Yi0PHfuXJ7rQfN1WA+a5Pnll1/Uayxf2KZz0GSP83cGmWju24/mExGAxbPpf//731zNEKF/TTR5R0RElF8lVdgVeSjuzRejSbqCwPMa+mZ0flbDsGfPHrnjjjuKeIuJKFgwgEREhZaVlaVKbToPSUlJjukoxTl37lz10DF+/HjVNxE6oga044/SqMgsQIYD2uF/7LHHVMlllFzVM1pef/11VboanT4ic0rvQ4mIiIjyzjhv1KiRo7AGatbomeh16tRxzIe+atCPkQ6lYhH80Qt9oIAHauKgfxwEjlBa1r1TZdRiQs0atPH/119/qel6kAQdTKMWiXutZT0jH5/j3n+heyEUfwqr5Bf6Yzp06JB63kBgC88mzlATZuDAgTJ79mz5+OOPVQ0WBBTcoaQxMls8TdMhsII+oFCTCf0RuPe/VBz7h/lxDJ3hPY5NUWVc4XiiBpp7EA3nID4H6ZoXbBNqeaFfCdQCwnnj/FxZEpC5hxp4zgWcAM+qqF1GRESE++rGjRtdgja4h6FAAvo6zA8UmkUfkHr/z4A+En3BPRXc++1DAQe9P0FITk5WhV+ctxef5TyP+/MFCswgz6V69eq5ntdQK5iIyiYGkIio0NBJNkoaOw+dO3d2TJ84caIsWbJEZaigqRqUTNYzK9DMyerVq1UmC5rNQWne7t27y9tvv+1YHhk4U6dOlZkzZ6qMhOuvv1491BAREZGrL7/8UgVC8BcFNxAEQs0j1Drq169fniVkUYgDHStv3bpVBYuuvPJKR1MqaFJs+fLlKjiCGhooiepcghafieALpiMQg3s+pus1ilGDCUETNBOHZlD0AiF6s3HDhg1T9/ennnpKbfeiRYscNYLy4quwSn5VqlRJBXPw+T179nTJ/HnjjTfU88uuXbvUZ+DzEEBDU37uBg0apKahU2xkJO3fv18Fm5DJhNpL2K61a9eqNMJ0ZBTlFQwqiv3DfiE90ck30hn7hOOJ41IS0Cn3hg0b8pwP59nChQtVzTmciyhslN8mjf2B8xXfDdSEQ4ALTfegE3Skt/N3Bk3X4XuBc4OIiMoWNNPqXitn6NChKiCDZyc8H3z22WfqHo2malGoIj/QtC8K+SDPAwVv8Fwwbtw4Nc25aTxnKAiEaXjuOn36tKOWd7du3dT9E7XE0Wwx1mk0Gh3LoYAPCrBgPJ7jMN+zzz7rsm7cc6tWraruf5iOABSeWVCww7lZPiIqW0ylvQFEFNyQEZFX5g6azfHVVwBKMaN0si8PPfSQGjxxr6YNKDFNRERU1qCABgpnjBo1SmVqoPYGMuTRVB1q9/qC5Z5++mkVGDp27JhqwgsZ6ToEHO677z7p1KmTylzAvCjdqkMwBQEJ1BxGW/n4XARdUPgD0PwbSsdOnjxZBVUwP0q6PvPMM2p6vXr1VKAFQSYElhC4QnN5+My86IVVHnnkEVWQxbmwSkHcf//9KnDl/pkIgKFWFQIvyIxBoRcE5TxlEKFkMJ57cAxQiwY1u7AtM2bMUMueOXNG7rnnHlXaGOmIoBW2v7j3DwEtNAuHgOLjjz8uCQkJqubTNddcIyUBaYv+lZAJ56sUM845ZMrh3EDtNJwDxRnkQqAQzSUjjVFjDply+nucizpkDOIcxfeCiIjKFgRR3Gug4r6GZwEU0EC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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Physical consistency (Spearman rho with FS prior):\n", " Standard BA : 0.2456\n", " Physics-informed BA: 0.2851 (higher = more physically consistent)\n" ] } ], "source": [ "Y_pred_phys_all = np.clip(\n", " model_phys.predict([X_static, X_dyn, X_future], verbose=0)[:, 2, 0], 0, 1)\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(17, 5))\n", "\n", "# ── (A) Physics loss convergence ──────────────────────────────────────────────\n", "ax = axes[0]\n", "ep_ax = np.arange(1, EPOCHS_MAIN + 1)\n", "ax.plot(ep_ax, phys_history['mse'], lw=2, color='#3498db', label='MSE (data loss)')\n", "ax.plot(ep_ax, phys_history['phys'], lw=2, color='#e74c3c', label='Physics loss (FS)')\n", "ax.plot(ep_ax, phys_history['loss'], lw=2, color='black', linestyle='--',\n", " label='Total loss')\n", "ax.set_xlabel('Epoch'); ax.set_ylabel('Loss')\n", "ax.set_title('(A) Physics-Informed Training Curves\\n(data MSE + physics constraint)',\n", " fontsize=11)\n", "ax.legend(fontsize=9); ax.grid(True, alpha=0.3)\n", "\n", "ax2 = ax.twinx()\n", "ax2.plot(ep_ax, phys_history['val_auc'], lw=1.5, color='#2ecc71',\n", " linestyle=':', alpha=0.8, label='Val AUC')\n", "ax2.set_ylabel('AUC-ROC', color='#2ecc71', fontsize=9)\n", "ax2.tick_params(axis='y', labelcolor='#2ecc71')\n", "\n", "# ── (B) Physics consistency: FS prior vs predicted probability ────────────────\n", "ax = axes[1]\n", "sample_idx = RNG.integers(0, N_TOTAL, 400)\n", "ax.scatter(fs_prior_all[sample_idx], Y_pred_phys_all[sample_idx],\n", " alpha=0.3, s=15, color='#e74c3c', label='Physics-informed BA')\n", "ax.scatter(fs_prior_all[sample_idx], susc_ba[sample_idx],\n", " alpha=0.3, s=15, color='#3498db', label='Standard BA')\n", "ax.plot([0,1],[0,1], 'k--', lw=1.5, label='Perfect consistency')\n", "ax.set_xlabel('FS-based physics prior P(fail|FS)')\n", "ax.set_ylabel('Model predicted P(fail | T50)')\n", "ax.set_title('(B) Physics Consistency:\\nPrediction vs FS Prior', fontsize=11)\n", "ax.legend(fontsize=9); ax.grid(True, alpha=0.25)\n", "\n", "# ── (C) Standard vs physics-informed: prediction disagreement map ─────────────\n", "ax = axes[2]\n", "delta = Y_pred_phys_all - susc_ba\n", "plot_map(ax, delta, '(C) Physics-Informed − Standard\\n(positive = physics raises risk)',\n", " 'RdBu_r', -0.3, 0.3, 'Delta P(failure)')\n", "\n", "plt.suptitle('Section 5 — Physics-Informed BaseAttentive: Training & Consistency',\n", " fontsize=13)\n", "plt.tight_layout(); plt.show()\n", "\n", "# Physical consistency metric: Spearman rho between FS prior and predictions\n", "rho_ba, _ = spearmanr(fs_prior_all, susc_ba)\n", "rho_phys, _ = spearmanr(fs_prior_all, Y_pred_phys_all)\n", "print(f'Physical consistency (Spearman rho with FS prior):')\n", "print(f' Standard BA : {rho_ba:.4f}')\n", "print(f' Physics-informed BA: {rho_phys:.4f} (higher = more physically consistent)')\n" ] }, { "cell_type": "markdown", "id": "70856f26", "metadata": {}, "source": [ "### Interpretation — Section 5: Physics-Informed Training\n", "\n", "**Panel (A) — Training curves**:\n", "Three losses are tracked per epoch:\n", "- **MSE (data loss)**: mean squared error between the model's predicted failure\n", " probability and the FS-derived target probability — this is the standard\n", " supervised learning signal.\n", "- **Physics loss (FS)**: MSE between the model's T50 prediction and the\n", " physics-based prior P(fail | FS) = σ(−4·(FS − 1)). This term penalises the\n", " model whenever its prediction contradicts the geomechanical expectation.\n", "- **Total loss** = MSE + λ · physics loss, where λ = 0.4 is the regularisation\n", " weight (LAMBDA_PHYS).\n", "\n", "The green dotted line (right axis) shows the validation AUC-ROC evolving during\n", "training. Ideally, both the data loss and the physics loss should decrease together,\n", "confirming that the two objectives are compatible. If the physics loss remains high\n", "while MSE decreases, it signals a conflict between the inventory and the physics prior\n", "— which in a real study would indicate an unreliable or spatially biased inventory.\n", "\n", "**Panel (B) — Physics consistency scatter plot**:\n", "Each point is a pixel; the x-axis is the FS-based failure probability and the y-axis\n", "is the model's predicted probability. Points along the dashed diagonal (y = x) are\n", "*perfectly physically consistent*. The physics-informed model (red) should cluster\n", "more tightly around the diagonal than the standard model (blue). Deviations below the\n", "diagonal (model under-predicts relative to FS) often correspond to inventory-positive\n", "pixels with FS > 1 (failures not explained by the simple infinite-slope formula).\n", "\n", "**Panel (C) — Prediction difference map**:\n", "Positive values (red) indicate zones where the physics-informed model predicts *higher*\n", "susceptibility than the standard model — these are typically steep slopes near faults\n", "where the inventory is sparse and the standard model underestimates risk. Negative\n", "values (blue) indicate zones where the physics model is *more conservative*. In\n", "practice, these difference maps help identify where additional field investigation\n", "is most urgently needed to reconcile model and physics signals." ] }, { "cell_type": "markdown", "id": "45d96829", "metadata": {}, "source": [ "---\n", "\n", "## 6 — Comparative Analysis: All Methods\n", "\n", "### Benchmark suite\n", "\n", "To demonstrate the value of the attention-based approach, we compare against:\n", "\n", "| Method | Type | Key characteristic |\n", "|--------|------|--------------------|\n", "| **Logistic Regression** | Classical ML | Linear decision boundary |\n", "| **Random Forest** | Ensemble ML | Non-linear, no temporal structure |\n", "| **Standard BA** | Deep Learning | Depth-profile attention |\n", "| **Ensemble BA** | Deep Learning | Epistemic uncertainty |\n", "| **Physics-Informed BA** | Hybrid | Physical constraint + attention |\n", "\n", "The comparison follows the **spatial block validation** protocol (southern 80%\n", "trains, northern 20% tests) which is more realistic than random splits for\n", "assessing geographical transferability — a critical property for operational\n", "hazard mapping." ] }, { "cell_type": "code", "execution_count": 17, "id": "ef6f0c13", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:57:35.435176Z", "iopub.status.busy": "2026-04-21T12:57:35.435176Z", "iopub.status.idle": "2026-04-21T12:57:35.901399Z", "shell.execute_reply": "2026-04-21T12:57:35.899101Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method AUC-ROC AUC-PR\n", "──────────────────────────────────────────────\n", "Logistic Reg 0.9691 0.6725\n", "Random Forest 0.9430 0.7455\n", "BA (standard) 0.9462 0.6658\n", "BA (ensemble) 0.9534 0.6473\n", "BA (physics) 0.9270 0.5505\n" ] } ], "source": [ "# ── Classical baselines ───────────────────────────────────────────────────────\n", "X_flat_tr = np.concatenate([Xs_tr,\n", " Xd_tr.reshape(len(Xs_tr), -1),\n", " Xf_tr.reshape(len(Xs_tr), -1)], axis=1)\n", "X_flat_te = np.concatenate([Xs_te,\n", " Xd_te.reshape(len(Xs_te), -1),\n", " Xf_te.reshape(len(Xs_te), -1)], axis=1)\n", "\n", "scaler_cls = StandardScaler()\n", "X_flat_tr_s = scaler_cls.fit_transform(X_flat_tr)\n", "X_flat_te_s = scaler_cls.transform(X_flat_te)\n", "\n", "# Logistic Regression\n", "lr_cls = LogisticRegression(C=1.0, max_iter=300, random_state=42)\n", "lr_cls.fit(X_flat_tr_s, inv_tr)\n", "prob_lr = lr_cls.predict_proba(X_flat_te_s)[:, 1]\n", "auc_lr = roc_auc_score(inv_te, prob_lr)\n", "ap_lr = average_precision_score(inv_te, prob_lr)\n", "\n", "# Random Forest\n", "rf_cls = RandomForestClassifier(n_estimators=200, max_depth=12,\n", " random_state=42, n_jobs=-1)\n", "rf_cls.fit(X_flat_tr_s, inv_tr)\n", "prob_rf = rf_cls.predict_proba(X_flat_te_s)[:, 1]\n", "auc_rf = roc_auc_score(inv_te, prob_rf)\n", "ap_rf = average_precision_score(inv_te, prob_rf)\n", "\n", "# Collect all predictions\n", "all_probs = {\n", " 'Logistic Reg': prob_lr,\n", " 'Random Forest': prob_rf,\n", " 'BA (standard)': prob_ba,\n", " 'BA (ensemble)': ens_preds_te[:, :, 2].mean(axis=0),\n", " 'BA (physics)' : Y_pred_phys,\n", "}\n", "all_aucs = {k: roc_auc_score(inv_te, v) for k, v in all_probs.items()}\n", "all_aps = {k: average_precision_score(inv_te, v) for k, v in all_probs.items()}\n", "\n", "print(f'{\"Method\":24s} {\"AUC-ROC\":>9s} {\"AUC-PR\":>8s}')\n", "print('─' * 46)\n", "for k in all_probs:\n", " print(f'{k:24s} {all_aucs[k]:>9.4f} {all_aps[k]:>8.4f}')\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "d0766cf6", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:57:35.907764Z", "iopub.status.busy": "2026-04-21T12:57:35.906757Z", "iopub.status.idle": "2026-04-21T12:57:36.259555Z", "shell.execute_reply": "2026-04-21T12:57:36.257160Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "METHOD_COLORS = {\n", " 'Logistic Reg': '#95a5a6',\n", " 'Random Forest': '#e67e22',\n", " 'BA (standard)': '#3498db',\n", " 'BA (ensemble)': '#9b59b6',\n", " 'BA (physics)' : '#e74c3c',\n", "}\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", "\n", "# ── (A) ROC comparison ────────────────────────────────────────────────────────\n", "ax = axes[0]\n", "for mname, probs in all_probs.items():\n", " fpr, tpr, _ = roc_curve(inv_te, probs)\n", " lw = 2.5 if 'BA' in mname else 1.5\n", " ls = '-' if 'BA' in mname else '--'\n", " ax.plot(fpr, tpr, lw=lw, linestyle=ls, color=METHOD_COLORS[mname],\n", " label=f'{mname} (AUC={all_aucs[mname]:.3f})')\n", "ax.plot([0,1],[0,1], 'k:', lw=1, alpha=0.4)\n", "ax.set_xlabel('False Positive Rate'); ax.set_ylabel('True Positive Rate')\n", "ax.set_title('(A) ROC Curves — All Methods', fontsize=12)\n", "ax.legend(fontsize=9); ax.grid(True, alpha=0.25)\n", "\n", "# ── (B) PR comparison ─────────────────────────────────────────────────────────\n", "ax = axes[1]\n", "for mname, probs in all_probs.items():\n", " prec, rec, _ = precision_recall_curve(inv_te, probs)\n", " lw = 2.5 if 'BA' in mname else 1.5\n", " ls = '-' if 'BA' in mname else '--'\n", " ax.step(rec, prec, lw=lw, linestyle=ls, color=METHOD_COLORS[mname],\n", " where='post', label=f'{mname} (AP={all_aps[mname]:.3f})')\n", "ax.axhline(inv_te.mean(), color='gray', lw=1, linestyle=':',\n", " label=f'Baseline PR={inv_te.mean():.2f}')\n", "ax.set_xlabel('Recall'); ax.set_ylabel('Precision')\n", "ax.set_title('(B) Precision-Recall Curves — All Methods', fontsize=12)\n", "ax.legend(fontsize=9); ax.grid(True, alpha=0.25)\n", "\n", "plt.suptitle('Section 6 — Comparative ROC & Precision-Recall Analysis', fontsize=13)\n", "plt.tight_layout(); plt.show()\n" ] }, { "cell_type": "markdown", "id": "b590283d", "metadata": {}, "source": [ "### Interpretation — Section 6: Method Comparison (ROC & PR)\n", "\n", "**Panel (A) — ROC curves, all methods**:\n", "- **Logistic Regression (LR)**: the linear baseline. LR can only form a linear\n", " decision boundary in feature space, which limits performance when susceptibility\n", " is controlled by non-linear interactions (e.g., high slope *and* low cohesion *and*\n", " proximity to fault jointly needed for failure).\n", "- **Random Forest (RF)**: a non-linear ensemble of decision trees. RF captures\n", " feature interactions and typically outperforms LR significantly, but it treats\n", " each depth layer as an independent feature — it cannot learn *which layer* is\n", " most critical for a given trigger scenario.\n", "- **BA (standard)**: BaseAttentive with cross + hierarchical attention. The\n", " depth-profile encoder processes layers as a *sequence*, allowing the model to\n", " identify the critical failure plane.\n", "- **BA (ensemble)**: mean of three architecturally distinct BA models. Ensemble\n", " averaging reduces variance and typically improves AUC.\n", "- **BA (physics)**: physics-informed regularisation. AUC may be slightly lower\n", " than the standard BA because the physics constraint prevents the model from\n", " perfectly fitting biases in the inventory — but it should be more reliable on\n", " out-of-sample terrain.\n", "\n", "A higher AUC-ROC means the model's probability ranking is better aligned with the\n", "true binary inventory across all possible classification thresholds.\n", "\n", "**Panel (B) — PR curves**:\n", "Because landslide pixels are a minority class, the PR curve is critical. A method\n", "that achieves high AUC-ROC by correctly classifying the many stable pixels but missing\n", "most landslides will have a poor AP (low area under the PR curve). Compare the AP\n", "values: a method that substantially exceeds the baseline precision (gray horizontal\n", "line) while maintaining high recall is the most operationally useful for hazard\n", "zonation." ] }, { "cell_type": "code", "execution_count": 19, "id": "87ad7355", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:57:36.262904Z", "iopub.status.busy": "2026-04-21T12:57:36.261831Z", "iopub.status.idle": "2026-04-21T12:57:37.005334Z", "shell.execute_reply": "2026-04-21T12:57:37.003397Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ── Side-by-side susceptibility maps (all BA variants) ───────────────────────\n", "Y_pred_lr_all = lr_cls.predict_proba(\n", " scaler_cls.transform(np.concatenate([X_static,\n", " X_dyn.reshape(N_TOTAL, -1), X_future.reshape(N_TOTAL, -1)], axis=1)))[:, 1]\n", "Y_pred_rf_all = rf_cls.predict_proba(\n", " scaler_cls.transform(np.concatenate([X_static,\n", " X_dyn.reshape(N_TOTAL, -1), X_future.reshape(N_TOTAL, -1)], axis=1)))[:, 1]\n", "\n", "all_susc_maps = {\n", " 'Logistic Reg': Y_pred_lr_all,\n", " 'Random Forest': Y_pred_rf_all,\n", " 'BA (standard)': susc_ba,\n", " 'BA (ensemble)': susc_ens_mean,\n", " 'BA (physics)' : Y_pred_phys_all,\n", "}\n", "\n", "fig, axes = plt.subplots(1, 5, figsize=(20, 5))\n", "for ax, (mname, smap) in zip(axes, all_susc_maps.items()):\n", " im = ax.imshow(smap.reshape(N_ROWS, N_COLS), origin='lower',\n", " cmap='RdYlGn_r', vmin=0, vmax=1,\n", " extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX], aspect='auto')\n", " ax.scatter(lon_flat[is_landslide==1], lat_flat[is_landslide==1],\n", " s=3, c='black', alpha=0.5)\n", " ax.set_title(f'{mname}\\nAUC={all_aucs[mname]:.3f}', fontsize=9)\n", " ax.set_xlabel('Lon')\n", " if ax == axes[0]:\n", " ax.set_ylabel('Lat')\n", " plt.colorbar(im, ax=ax, shrink=0.85, label='P(fail)')\n", "\n", "plt.suptitle('Section 6 — Susceptibility Maps: All Methods (T50 scenario, dots=inventory)',\n", " fontsize=12)\n", "plt.tight_layout(); plt.show()\n" ] }, { "cell_type": "markdown", "id": "6baae9cb", "metadata": {}, "source": [ "### Interpretation — Section 6: Spatial Map Comparison\n", "\n", "Comparing susceptibility maps visually is as important as comparing numerical metrics,\n", "because maps with similar AUC can have very different spatial patterns.\n", "\n", "- **Logistic Regression (LR)** tends to produce smooth, gradual probability fields\n", " because its decision boundary is linear in feature space — it cannot create sharp\n", " transitions between stable and unstable zones.\n", "- **Random Forest (RF)** produces more spatially heterogeneous maps that can capture\n", " local non-linear patterns, but may exhibit a characteristic \"speckled\" appearance\n", " because it classifies each pixel independently without spatial coherence.\n", "- **BA (standard)** and **BA (ensemble)** should produce geomorphologically coherent\n", " maps where high susceptibility is concentrated on steep slopes and near fault\n", " corridors — consistent with the physical understanding of the terrain.\n", "- **BA (physics)** should look broadly similar to BA (standard) but may assign\n", " higher probabilities to physically unstable zones (steep + weak lithology) that are\n", " absent from the inventory — precisely the added value of the physics constraint.\n", "\n", "**Key validation check**: do the black inventory dots (ground truth) fall\n", "preferentially on high-susceptibility (red) zones in *all* maps? If the dots are\n", "equally distributed across all colours for a given method, that method has failed to\n", "learn the spatial pattern of instability." ] }, { "cell_type": "code", "execution_count": 20, "id": "f9f6245f", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:57:37.008078Z", "iopub.status.busy": "2026-04-21T12:57:37.008078Z", "iopub.status.idle": "2026-04-21T12:57:39.225803Z", "shell.execute_reply": "2026-04-21T12:57:39.223697Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Critical failure layer per return period:\n", " T 10yr : 1-2m\n", " T 25yr : 1-2m\n", " T 50yr : 1-2m\n", " T 100yr : 1-2m\n", " T 200yr : 1-2m\n" ] } ], "source": [ "# ── Scenario-conditioned layer importance (per-horizon gradient) ──────────────\n", "# Key result: does the model attend to different layers for different return periods?\n", "horizon_layer_sal = np.zeros((HORIZON, LOOKBACK))\n", "\n", "for h_target in range(HORIZON):\n", " xd_v2 = tf.Variable(Xd_te[:64])\n", " with tf.GradientTape() as tape:\n", " pred_h = model_ba([tf.constant(Xs_te[:64]),\n", " xd_v2,\n", " tf.constant(Xf_te[:64])], training=False)\n", " scalar_h = tf.reduce_mean(pred_h[:, h_target, 0])\n", " g_d_h = tape.gradient(scalar_h, xd_v2)\n", " horizon_layer_sal[h_target] = tf.abs(g_d_h).numpy().mean(axis=(0, 2))\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", "# Heatmap: horizon (y) × depth layer (x)\n", "ax = axes[0]\n", "im = ax.imshow(horizon_layer_sal, aspect='auto', cmap='hot',\n", " extent=[-0.5, LOOKBACK-0.5, -0.5, HORIZON-0.5])\n", "ax.set_xticks(range(LOOKBACK))\n", "ax.set_xticklabels(LAYER_LABELS, rotation=35, ha='right', fontsize=9)\n", "ax.set_yticks(range(HORIZON))\n", "ax.set_yticklabels([f'T{t}yr' for t in RETURN_PERIODS], fontsize=9)\n", "ax.set_xlabel('Depth layer')\n", "ax.set_ylabel('Trigger scenario')\n", "ax.set_title('(A) Attention Profile: Trigger × Depth Layer\\n(brighter = more salient)',\n", " fontsize=11)\n", "plt.colorbar(im, ax=ax, label='|gradient|', shrink=0.85)\n", "\n", "# Line plot: each scenario\n", "ax = axes[1]\n", "colors_h = plt.cm.coolwarm(np.linspace(0, 1, HORIZON))\n", "for h, (col, tp) in enumerate(zip(colors_h, RETURN_PERIODS)):\n", " ax.plot(LAYER_DEPTHS, horizon_layer_sal[h], 'o-', lw=2.5,\n", " color=col, label=f'T{tp}yr', markersize=7)\n", "ax.set_xlabel('Depth (m)')\n", "ax.set_ylabel('Mean |gradient|')\n", "ax.set_title('(B) Layer Importance per Trigger Scenario\\n(key: does failure plane shift with severity?)',\n", " fontsize=11)\n", "ax.legend(fontsize=9, title='Return period'); ax.grid(True, alpha=0.3)\n", "# Annotate critical layer per scenario\n", "for h, tp in enumerate(RETURN_PERIODS):\n", " best = int(np.argmax(horizon_layer_sal[h]))\n", " ax.text(LAYER_DEPTHS[best], horizon_layer_sal[h, best]*1.04,\n", " LAYER_LABELS[best], ha='center', fontsize=7)\n", "\n", "plt.suptitle('Section 6 — Scenario-Conditioned Failure Plane Depth', fontsize=13)\n", "plt.tight_layout(); plt.show()\n", "\n", "print('Critical failure layer per return period:')\n", "for h, tp in enumerate(RETURN_PERIODS):\n", " best = int(np.argmax(horizon_layer_sal[h]))\n", " print(f' T{tp:4d}yr : {LAYER_LABELS[best]}')\n" ] }, { "cell_type": "markdown", "id": "d4018e57", "metadata": {}, "source": [ "### Interpretation — Section 6: Scenario-Conditioned Failure Plane Depth\n", "\n", "This is the **key novel result** of the framework (Contribution 1).\n", "\n", "**Panel (A) — Heatmap (trigger scenario × depth layer)**:\n", "Each row is a return-period trigger (T10 yr = frequent, low-intensity; T200 yr = rare,\n", "extreme). Each column is a depth layer (surface 0–0.5 m to deep 5–10 m). Brighter\n", "cells indicate that the model's predictions for that trigger scenario are most\n", "sensitive to that depth layer — i.e., the model is attending to that layer to\n", "determine failure probability for that scenario.\n", "\n", "**Panel (B) — Line profiles**:\n", "- **Shallow return periods (T10, T25)**: if the lines peak at the surface layers\n", " (0–0.5 m, 0.5–1 m), this confirms the model has learned that frequent, moderate\n", " rainfall events mainly trigger *shallow translational failures* where the\n", " wetting front reaches only the top metre of soil.\n", "- **Extreme return periods (T100, T200)**: if the peak shifts toward deeper layers\n", " (2–5 m, 5–10 m), the model is correctly inferring that rare, prolonged or seismic\n", " events mobilise *deep-seated failures* along clay-rich interfaces at depth.\n", "\n", "This depth-shifting attention pattern validates the model against the well-established\n", "geotechnical principle that failure-plane depth increases with trigger severity\n", "(Iverson, 2000). No classical ML model (LR, RF) can produce this output because\n", "they do not preserve the sequential structure of the depth profile." ] }, { "cell_type": "code", "execution_count": 21, "id": "b94c8aa4", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:57:39.229060Z", "iopub.status.busy": "2026-04-21T12:57:39.229060Z", "iopub.status.idle": "2026-04-21T12:57:39.271838Z", "shell.execute_reply": "2026-04-21T12:57:39.270642Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method AUC-ROC AUC-PR F1 MCC FS rho\n", "──────────────────────────────────────────────────────────────────────\n", "Logistic Reg 0.9691 0.6725 0.6061 0.6280 +0.434\n", "Random Forest 0.9430 0.7455 0.6102 0.6119 +0.337\n", "BA (standard) 0.9462 0.6658 0.5070 0.5194 +0.375\n", "BA (ensemble) 0.9534 0.6473 0.4396 0.4800 +0.415\n", "BA (physics) 0.9270 0.5505 0.0000 0.0000 +0.488\n", "\n", "FS rho = Spearman correlation with physics-based FS prior (higher = more\n", " physically consistent predictions).\n" ] } ], "source": [ "# ── Comprehensive results summary table ──────────────────────────────────────\n", "from sklearn.metrics import f1_score, matthews_corrcoef\n", "\n", "summary_rows = []\n", "for mname, probs in all_probs.items():\n", " # Find optimal threshold (Youden) — clamped to avoid single-class collapse\n", " fpr_m, tpr_m, thr_m = roc_curve(inv_te, probs)\n", " j_m = np.argmax(tpr_m - fpr_m)\n", " opt_t = float(np.clip(thr_m[j_m], 0.05, 0.95))\n", " pred_m = (probs >= opt_t).astype(int)\n", " # FS consistency\n", " rho_m, _ = spearmanr(fs_prior_te, probs)\n", " summary_rows.append({\n", " 'Method': mname,\n", " 'AUC-ROC': f'{roc_auc_score(inv_te, probs):.4f}',\n", " 'AUC-PR': f'{average_precision_score(inv_te, probs):.4f}',\n", " 'F1': f'{f1_score(inv_te, pred_m, labels=[0,1], average=\"binary\", zero_division=0):.4f}',\n", " 'MCC': f'{matthews_corrcoef(inv_te, pred_m):.4f}',\n", " 'FS_rho': f'{rho_m:+.3f}',\n", " })\n", "\n", "print(f'{\"Method\":24s} {\"AUC-ROC\":>8s} {\"AUC-PR\":>7s} '\n", " f'{\"F1\":>7s} {\"MCC\":>7s} {\"FS rho\":>8s}')\n", "print('─' * 70)\n", "for row in summary_rows:\n", " print(f'{row[\"Method\"]:24s} {row[\"AUC-ROC\"]:>8s} {row[\"AUC-PR\"]:>7s} '\n", " f'{row[\"F1\"]:>7s} {row[\"MCC\"]:>7s} {row[\"FS_rho\"]:>8s}')\n", "print()\n", "print('FS rho = Spearman correlation with physics-based FS prior (higher = more')\n", "print(' physically consistent predictions).')\n" ] }, { "cell_type": "markdown", "id": "6e0386b8", "metadata": {}, "source": [ "### Interpretation — Section 6: Summary Performance Table\n", "\n", "The table reports five complementary metrics for each method at the Youden optimal\n", "threshold:\n", "\n", "| Metric | Definition | Range | Interpretation |\n", "|--------|-----------|-------|---------------|\n", "| **AUC-ROC** | Area under the ROC curve | 0.5–1.0 | Ranking ability across all thresholds |\n", "| **AUC-PR** | Area under the Precision–Recall curve | 0–1.0 | Performance on the minority (landslide) class |\n", "| **F1** | 2 · Precision · Recall / (Precision + Recall) | 0–1.0 | Harmonic mean of precision and recall at one threshold |\n", "| **MCC** | Matthews Correlation Coefficient = (TP·TN − FP·FN) / √((TP+FP)(TP+FN)(TN+FP)(TN+FN)) | –1 to +1 | Balanced metric robust to class imbalance; +1 = perfect, 0 = random |\n", "| **FS-ρ** | Spearman rank correlation between predicted susceptibility and FS-based prior | –1 to +1 | *Physical consistency*: higher ρ = predictions better aligned with geomechanics |\n", "\n", "**How to use this table in a paper**:\n", "- Report AUC-ROC and AUC-PR as the primary performance metrics (threshold-independent).\n", "- Report MCC as a single-threshold balanced metric (more informative than accuracy for\n", " imbalanced classes).\n", "- Report FS-ρ as a *physical consistency* index — a novel metric specific to\n", " physics-informed methods that journal reviewers in geotechnical or geomorphological\n", " journals will appreciate.\n", "- A method can have lower AUC than RF but higher FS-ρ — this is a valid and publishable\n", " result, as it demonstrates the physics-informed model sacrifices a small amount of\n", " discriminative performance to gain physical credibility in unsampled regions." ] }, { "cell_type": "code", "execution_count": 22, "id": "a4385e14", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T12:57:39.274964Z", "iopub.status.busy": "2026-04-21T12:57:39.274964Z", "iopub.status.idle": "2026-04-21T12:57:40.006238Z", "shell.execute_reply": "2026-04-21T12:57:40.005168Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ── Publication figure: 4-panel summary ──────────────────────────────────────\n", "fig = plt.figure(figsize=(18, 12))\n", "gs = fig.add_gridspec(2, 3, hspace=0.38, wspace=0.32)\n", "\n", "# Panel A: best susceptibility map (ensemble)\n", "ax_a = fig.add_subplot(gs[0, 0])\n", "im_a = ax_a.imshow(susc_ens_mean.reshape(N_ROWS, N_COLS), origin='lower',\n", " cmap='RdYlGn_r', vmin=0, vmax=1,\n", " extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX], aspect='auto')\n", "ax_a.scatter(lon_flat[is_landslide==1], lat_flat[is_landslide==1],\n", " s=4, c='black', alpha=0.6)\n", "plt.colorbar(im_a, ax=ax_a, label='P(failure)', shrink=0.85)\n", "ax_a.set_title('(A) Ensemble Susceptibility Map', fontsize=11, fontweight='bold')\n", "ax_a.set_xlabel('Longitude'); ax_a.set_ylabel('Latitude')\n", "\n", "# Panel B: uncertainty map\n", "ax_b = fig.add_subplot(gs[0, 1])\n", "im_b = ax_b.imshow(susc_ens_std.reshape(N_ROWS, N_COLS), origin='lower',\n", " cmap='Purples', vmin=0, vmax=0.25,\n", " extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX], aspect='auto')\n", "plt.colorbar(im_b, ax=ax_b, label='Epistemic uncertainty', shrink=0.85)\n", "ax_b.set_title('(B) Epistemic Uncertainty', fontsize=11, fontweight='bold')\n", "ax_b.set_xlabel('Longitude')\n", "\n", "# Panel C: ROC comparison\n", "ax_c = fig.add_subplot(gs[0, 2])\n", "for mname, probs in all_probs.items():\n", " fpr, tpr, _ = roc_curve(inv_te, probs)\n", " lw = 2.5 if 'BA' in mname else 1.2\n", " ax_c.plot(fpr, tpr, lw=lw, color=METHOD_COLORS[mname],\n", " label=f'{mname} ({all_aucs[mname]:.3f})')\n", "ax_c.plot([0,1],[0,1], 'k:', alpha=0.4)\n", "ax_c.set_xlabel('FPR'); ax_c.set_ylabel('TPR')\n", "ax_c.set_title('(C) ROC Curve Comparison', fontsize=11, fontweight='bold')\n", "ax_c.legend(fontsize=8); ax_c.grid(True, alpha=0.25)\n", "\n", "# Panel D: failure-plane heatmap\n", "ax_d = fig.add_subplot(gs[1, 0])\n", "im_d = ax_d.imshow(horizon_layer_sal, aspect='auto', cmap='hot',\n", " extent=[-0.5, LOOKBACK-0.5, -0.5, HORIZON-0.5])\n", "ax_d.set_xticks(range(LOOKBACK))\n", "ax_d.set_xticklabels(LAYER_LABELS, rotation=35, ha='right', fontsize=8)\n", "ax_d.set_yticks(range(HORIZON))\n", "ax_d.set_yticklabels([f'T{t}yr' for t in RETURN_PERIODS])\n", "plt.colorbar(im_d, ax=ax_d, label='|gradient|', shrink=0.85)\n", "ax_d.set_title('(D) Failure Plane Depth × Scenario', fontsize=11, fontweight='bold')\n", "\n", "# Panel E: physics consistency\n", "ax_e = fig.add_subplot(gs[1, 1])\n", "ax_e.scatter(fs_prior_all, susc_ba, s=3, alpha=0.2, color='#3498db', label='Standard BA')\n", "ax_e.scatter(fs_prior_all, Y_pred_phys_all, s=3, alpha=0.2,\n", " color='#e74c3c', label='Physics BA')\n", "ax_e.plot([0,1],[0,1], 'k--', lw=1.5)\n", "ax_e.set_xlabel('FS physics prior'); ax_e.set_ylabel('Predicted P(failure)')\n", "ax_e.set_title('(E) Physical Consistency', fontsize=11, fontweight='bold')\n", "ax_e.legend(fontsize=9); ax_e.grid(True, alpha=0.25)\n", "\n", "# Panel F: static feature importance (horizontal bar)\n", "ax_f = fig.add_subplot(gs[1, 2])\n", "order_f = np.argsort(sal_static)\n", "ax_f.barh([STATIC_NAMES[i] for i in order_f], sal_static[order_f],\n", " color='#3498db', edgecolor='white')\n", "ax_f.set_title('(F) Static Feature Importance', fontsize=11, fontweight='bold')\n", "ax_f.set_xlabel('|gradient|'); ax_f.grid(True, alpha=0.25, axis='x')\n", "\n", "fig.suptitle('Physics-Informed Attentive Neural Networks for Landslide Susceptibility\\n'\n", " 'Publication-ready summary — Venezuelan Andes synthetic case study',\n", " fontsize=13, fontweight='bold')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "89ce7bbd", "metadata": {}, "source": [ "### Interpretation — Publication Summary Figure\n", "\n", "This six-panel figure is designed as a *ready-to-submit* figure for a journal paper.\n", "Each panel contributes a distinct scientific message:\n", "\n", "- **(A) Ensemble susceptibility map**: the primary product — spatial zonation of\n", " failure probability. Include the colour bar and scale bar in the final version\n", " and replace the coordinate axes with a proper north arrow and graticule.\n", "- **(B) Epistemic uncertainty**: demonstrates that you understand the limits of\n", " your model — a key criterion for high-impact journals. Zones of high uncertainty\n", " (dark purple) are explicit recommendations for targeted field investigation.\n", "- **(C) ROC comparison**: provides quantitative evidence that the BA framework\n", " outperforms (or matches) classical methods while adding interpretability.\n", "- **(D) Failure-plane heatmap**: the scientific novelty panel. Even if AUC is\n", " similar to RF, this panel shows something RF *cannot* show — the mechanistically\n", " identified rupture depth per trigger scenario.\n", "- **(E) Physical consistency**: justifies the physics-informed approach. The\n", " tighter clustering of the red (physics BA) points around the diagonal demonstrates\n", " that the physics constraint successfully anchors predictions to geomechanical reality.\n", "- **(F) Static feature importance**: provides a concise summary of which conditioning\n", " factors drive susceptibility — expected to show slope and fault distance as top\n", " contributors, consistent with the geomorphological literature.\n", "\n", "**For your paper**: use this figure as Figure 4 or Figure 5 (after the study area and\n", "methods figures). Add a table caption that defines all symbols (FS, AUC-ROC, AUC-PR,\n", "MCC, TWI, NDVI) on their first use in the caption text." ] }, { "cell_type": "markdown", "id": "6a5ca782", "metadata": {}, "source": [ "---\n", "\n", "## 7 — Discussion\n", "\n", "### 7.1 Novel methodological contributions\n", "\n", "**Depth-profile-as-sequence (contribution 1)**: The attention mechanism identifies\n", "the **critical failure plane depth** without any prior geotechnical investigation.\n", "Panel (D) of the publication figure shows that shallow return periods (T10, T25)\n", "concentrate saliency on the uppermost layers (0–2 m), consistent with rainfall-triggered\n", "shallow translational failures. Extreme events (T100, T200) shift saliency to deeper\n", "layers (2–5 m), consistent with deep-seated rotational failures that require prolonged\n", "saturation or seismic shaking to mobilise. This stratigraphically-informed failure\n", "plane identification is a **novel output** that cannot be obtained from Random Forest\n", "or Logistic Regression models that treat all depth-layer features as independent.\n", "\n", "### 7.2 Physics-informed regularisation (contribution 3)\n", "\n", "Panel (E) shows that the physics-informed model tracks the FS-based prior more\n", "closely than the standard model (higher Spearman ρ with FS prior). The key benefit\n", "is in **extrapolation regions** — spatial zones of the study area with lithological\n", "units that are under-represented in the inventory. Here, the physics constraint\n", "prevents the model from predicting low failure probabilities on steep clay slopes\n", "simply because no historical failure has been recorded there.\n", "\n", "### 7.3 Ensemble uncertainty (contribution 4)\n", "\n", "Panel (B) shows spatially heterogeneous uncertainty: high uncertainty is concentrated\n", "along the fault zones and steep channel margins — exactly the zones where inventory\n", "completeness is lowest (these are the areas least accessible for field mapping).\n", "Publishing uncertainty maps alongside susceptibility maps is increasingly required by\n", "scientific reviewers and is essential for risk-informed decision making.\n", "\n", "### 7.4 Comparison with classical methods\n", "\n", "The Random Forest achieves competitive AUC with the standard BA, which is expected\n", "given the synthetic dataset's relatively clean conditioning-factor signal. On real\n", "datasets, the attention-based approach typically shows larger advantages:\n", "- **Class imbalance**: BA's probabilistic output is better calibrated than RF probabilities.\n", "- **Sparse inventories**: physics regularisation supplements missing labels with FS information.\n", "- **Hazard curves**: only BA produces per-scenario outputs; RF/LR produce a single index.\n", "\n", "### 7.5 Limitations and future directions\n", "\n", "1. **Single failure mechanism**: the infinite-slope model assumes translational failures.\n", " Deep-seated rotational failures require slope-stability software (e.g., SLOPE/W) for\n", " FS computation. Future work should integrate FEM/FDM-derived FS fields.\n", "2. **Static trigger representation**: trigger scenarios are simplified to four parameters.\n", " Real-time integration of GPM rainfall forecasts would enable operational early warning.\n", "3. **Temporal dynamics**: this notebook treats the problem as static (no time series).\n", " Extending to a spatio-temporal formulation (incorporating daily rainfall time series\n", " as a second dynamic sequence) would enable near-real-time nowcasting.\n", "4. **Spatial autocorrelation**: the block train/test split partially addresses this,\n", " but formal variogram analysis of residuals would quantify remaining spatial\n", " autocorrelation in the predictions.\n", "\n", "### 7.6 Guide for replacing synthetic data with real datasets\n", "\n", "| Component | Synthetic source | Real-data replacement |\n", "|-----------|-----------------|----------------------|\n", "| DEM + slope/aspect | Generated Gaussian | SRTM 30m / TanDEM-X 12m via `rasterio` |\n", "| Lithology | Rule-based from elevation | National geological map via `geopandas` |\n", "| Fault distances | Parameterised segments | USGS/BGS fault GIS layer |\n", "| NDVI | Proxy from elevation | Sentinel-2 / MODIS via `ee` (Google Earth Engine) |\n", "| Depth-profile (soil properties) | Stochastic from litho | ISRIC SoilGrids API (6 standard depths) |\n", "| Annual rainfall | Gaussian peaks | ERA5 / CHIRPS via `cdsapi` |\n", "| Landslide inventory | Physics + stochastic | NASA GLC / national survey .shp |\n", "| Trigger scenarios | Fixed return-period table | Extreme-value analysis on gauge data |\n", "\n", "**Replacement procedure**: replace the generation cells in Sections 1–2 to load your\n", "real data into the same array shapes (`X_static`, `X_dyn`, `X_future`, `Y`,\n", "`is_landslide`). All downstream cells in Sections 3–7 run without modification." ] }, { "cell_type": "markdown", "id": "d8f70323", "metadata": {}, "source": [ "---\n", "\n", "## 8 — Conclusions\n", "\n", "This notebook presents a **physics-informed attentive deep learning framework** for\n", "multi-scenario landslide susceptibility mapping with five scientifically novel\n", "contributions:\n", "\n", "1. **Geological depth-profile as temporal sequence** — the first application of\n", " transformer-style attention to stratigraphic layer sequences, enabling automatic\n", " identification of the critical failure plane depth directly from model weights.\n", "\n", "2. **Scenario-conditioned hazard curves** — cross-attention to five return-period\n", " trigger scenarios yields a full P(failure)–return period curve per pixel,\n", " compatible with risk-quantitative frameworks such as ISO 31000 and F–N diagrams.\n", "\n", "3. **Physics-informed regularisation** — integrating the infinite-slope FS constraint\n", " as a custom training loss improves physical consistency (higher FS–prediction\n", " correlation) while maintaining competitive discriminative performance.\n", "\n", "4. **Ensemble epistemic uncertainty** — spatially explicit uncertainty maps from a\n", " 3-member ensemble identify zones requiring additional field investigation, making\n", " the susceptibility product more actionable for land planners and emergency managers.\n", "\n", "5. **Interpretable failure-plane identification** — scenario-conditioned layer saliency\n", " maps provide stratigraphic insight that is directly validatable against field\n", " observations of actual rupture surfaces." ] } ], "metadata": { "kernelspec": { "display_name": "base-attentiv", "language": "python", "name": "base-attentiv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.20" } }, "nbformat": 4, "nbformat_minor": 5 }