commit 930534dde21883f259dede46ba48353cd24f61d9 Author: Gabriel Pereira Date: Fri Sep 11 16:37:33 2026 -0300 Add privacy-safe demand forecasting case study Synthetic monthly SKU/region demand notebook comparing a trimmed-mean baseline against gradient boosting, with a gated TimesFM build-vs-buy comparison. Includes rolling-origin validation, executive narrative, architecture diagram, one-page PDF, and LinkedIn draft. No company data, credentials, or private implementation details. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..4a39c88 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,3 @@ +* text=auto eol=lf +*.pdf binary +*.png binary diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..66f96d5 --- /dev/null +++ b/.gitignore @@ -0,0 +1,7 @@ +.venv/ +.env +.ipynb_checkpoints/ +__pycache__/ +*.py[cod] +data/ +private/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..09f0dde --- /dev/null +++ b/README.md @@ -0,0 +1,51 @@ +# Demand Forecasting Case Study + +Privacy-safe demonstration of a demand-forecasting delivery flow using only synthetic data, written as an executive-readable AI delivery case study. + +## What it demonstrates + +- Monthly demand by synthetic SKU and region. +- A baseline forecast: the mean of the preceding eight observed months after removing one minimum and one maximum. +- One gradient-boosting model using lag and calendar features. +- Leakage-safe rolling-origin validation for a 12-month horizon. +- A manual trigger boundary that can later be connected to Airflow. +- Annotated charts and written observations connecting modeling choices to delivery governance. +- An optional, gated build-vs-buy comparison against a pretrained TimesFM foundation model (`RUN_TIMESFM=1`), evaluating operational cost alongside accuracy instead of switching models by default. + +The notebook calculates evaluation metrics for model selection, but the published case-study narrative intentionally avoids numeric business claims. + +The notebook is structured for an executive audience: it starts with the business question, explains why the baseline exists, visualizes the signal and validation trade-off, and closes with delivery implications for a lean AI task force. + +## Run + +```bash +uv sync +uv run jupyter notebook notebooks/forecasting_case_study.ipynb +``` + +To execute the notebook non-interactively: + +```bash +uv run jupyter execute notebooks/forecasting_case_study.ipynb --inplace +``` + +To also run the optional TimesFM foundation-model comparison (downloads ~500M-parameter weights +from Hugging Face; needs `torch`/`transformers` and internet access): + +```bash +RUN_TIMESFM=1 uv run --with torch --with transformers jupyter execute notebooks/forecasting_case_study.ipynb --inplace +``` + +## Privacy boundary + +This repository contains no company rows, identifiers, schemas, credentials, private URLs, or copied business values. Review `docs/publication-checklist.md` before publishing. + +## Artifacts + +- `notebooks/forecasting_case_study.ipynb` — reproducible analysis. +- `docs/architecture.mmd` — editable architecture diagram. +- `docs/architecture.svg` — editable/exportable diagram asset used by the handout. +- `docs/case-study-one-page.md` — Markdown source for the interview handout. +- `docs/case-study-one-page.html` — print-ready handout source. +- `docs/case-study-one-page.pdf` — generated one-page interview handout. +- `docs/linkedin-post.md` — publication draft. diff --git a/docs/architecture.mmd b/docs/architecture.mmd new file mode 100644 index 0000000..eff41b4 --- /dev/null +++ b/docs/architecture.mmd @@ -0,0 +1,16 @@ +flowchart LR + A[Synthetic monthly demand] --> B[Prepare dates and features] + B --> C[Trimmed-mean baseline] + B --> D[Gradient-boosting model] + C --> E[Rolling validation] + D --> E + E --> F[Select by sMAPE] + F --> G[12-month forecast] + + H[Manual trigger today] -.-> B + I[Future Airflow boundary] -.-> B + + subgraph Delivery["Delivery ownership"] + J[Business question] --> K[Reproducible evidence] + K --> L[Review and adoption] + end diff --git a/docs/architecture.svg b/docs/architecture.svg new file mode 100644 index 0000000..70f5545 --- /dev/null +++ b/docs/architecture.svg @@ -0,0 +1,39 @@ + + Demand forecasting delivery architecture + Synthetic demand flows through preparation, baseline and model, rolling validation, selection, and a twelve-month forecast. Manual triggering today has a future Airflow boundary. + + + + + + + + Syntheticdemand + + Preparefeatures + + Trimmed-meanbaseline + + Gradient-boostingmodel + + Rollingvalidation + + 12-monthforecast + + + + + + + Manual trigger today + + Future orchestration boundary + + diff --git a/docs/case-study-one-page.html b/docs/case-study-one-page.html new file mode 100644 index 0000000..f81522f --- /dev/null +++ b/docs/case-study-one-page.html @@ -0,0 +1,55 @@ + + + + + Demand Forecasting Case Study + + + +

From demand question to forecast evidence

+
A privacy-safe, player/coach delivery case study using synthetic monthly demand
+
+ The case. Build a repeatable forecasting flow without exposing operational data. + The public version preserves the delivery decisions that matter: preparation, an auditable + baseline, model comparison, validation, and a clear operating boundary. +
+

Architecture

+ Demand forecasting delivery architecture +
+
+

Modeling choices

+
    +
  • Monthly demand by synthetic SKU and region.
    • +
  • Baseline: previous eight months, excluding one minimum and one maximum.
    • +
  • One gradient-boosting model with lag and calendar features.
    • +
  • Rolling-origin validation for a 12-month horizon.
    • +
+
+
+

Delivery choices

+
    +
  • Start with a baseline stakeholders can audit.
    • +
  • Add complexity only when validation earns it.
    • +
  • Keep triggering manual while the workflow is proved.
    • +
  • Leave a clean seam for future orchestration.
    • +
+
+
+

Evidence

+

The notebook computes validation metrics and produces comparison charts. This handout makes no numeric business claims because the data is synthetic.

+ + + diff --git a/docs/case-study-one-page.md b/docs/case-study-one-page.md new file mode 100644 index 0000000..ecc0bac --- /dev/null +++ b/docs/case-study-one-page.md @@ -0,0 +1,25 @@ +# From demand question to forecast evidence + +## The case + +Build a repeatable forecasting flow without exposing operational data. The public version uses synthetic monthly demand by SKU and region while preserving the delivery decisions that matter: data preparation, a transparent baseline, model comparison, validation, and a clear operating boundary. + +## Architecture + +```mermaid +flowchart LR + source[Synthetic demand] --> prep[Preparation/features] + prep --> baseline[Trimmed-mean baseline] + prep --> model[Gradient boosting] + baseline --> validate[Rolling validation] + model --> validate + validate --> forecast[12-month forecast] +``` + +## Delivery narrative + +Start with a baseline that stakeholders can audit. Add one model only when validation shows a useful improvement. Keep the trigger manual while the workflow is being proved, then expose the same boundary to an orchestrator such as Airflow. + +## Evidence + +The notebook computes validation metrics and produces comparison charts. This handout deliberately makes no numeric business claims because the data is synthetic. diff --git a/docs/case-study-one-page.pdf b/docs/case-study-one-page.pdf new file mode 100644 index 0000000..edcb0fd Binary files /dev/null and b/docs/case-study-one-page.pdf differ diff --git a/docs/linkedin-post.md b/docs/linkedin-post.md new file mode 100644 index 0000000..989bc03 --- /dev/null +++ b/docs/linkedin-post.md @@ -0,0 +1,13 @@ +# LinkedIn draft + +I built a privacy-safe demand-forecasting case study to show how I approach AI delivery as a player/coach. + +The demo starts with synthetic monthly demand by SKU and region, then compares: + +- an auditable baseline based on the previous eight months; +- one gradient-boosting model with lag and calendar features; +- rolling time-based validation instead of a random train/test split. + +The important part is not adding the most models. It is making the path from business question to reproducible evidence explicit: prepare the data, establish a baseline, validate the model, document the operating boundary, and leave a clean seam for future orchestration. + +The public repository contains no company data or private implementation details. diff --git a/docs/publication-checklist.md b/docs/publication-checklist.md new file mode 100644 index 0000000..8a94680 --- /dev/null +++ b/docs/publication-checklist.md @@ -0,0 +1,9 @@ +# Publication checklist + +- [ ] No credentials, tokens, private keys, `.env` files, or secret names. +- [ ] No company rows, identifiers, product names, plant names, or business values. +- [ ] No private schemas, endpoints, account names, or internal URLs. +- [ ] All charts and metrics come from synthetic data. +- [ ] Numeric results are labeled illustrative and are not presented as business outcomes. +- [ ] Notebook executes from a clean environment with `uv`. +- [ ] Diagram and PDF contain only generic architecture terms. diff --git a/notebooks/forecasting_case_study.ipynb b/notebooks/forecasting_case_study.ipynb new file mode 100644 index 0000000..dcfaa44 --- /dev/null +++ b/notebooks/forecasting_case_study.ipynb @@ -0,0 +1,654 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d9244098", + "metadata": {}, + "source": [ + "# Demand forecasting: from business question to delivery evidence\n", + "\n", + "**Interview case study | privacy-safe synthetic data**\n", + "\n", + "This notebook demonstrates how I would take a demand-planning use case from an explainable baseline to a validated model, while keeping the delivery path simple enough for a lean AI team to operate.\n", + "\n", + "**Executive takeaway:** the model is not the product. The product is a repeatable decision process: define the question, establish a trusted baseline, validate honestly, and create a clear path from manual operation to future orchestration." + ] + }, + { + "cell_type": "markdown", + "id": "443a1705", + "metadata": {}, + "source": [ + "## 1. The delivery question\n", + "\n", + "A planning team needs a 12-month demand outlook for each product and region. The public version uses synthetic data only; the architecture mirrors the kind of delivery pattern a player/coach AI lead must establish:\n", + "\n", + "- **Business value:** support planning with a consistent forward view.\n", + "- **Trust mechanism:** start with a baseline stakeholders can calculate themselves.\n", + "- **Technical decision:** add a model only if time-based validation earns the complexity.\n", + "- **Operating model:** begin with a manual trigger, then expose the same boundary to orchestration when the process is stable.\n", + "\n", + "This is intentionally a small, transparent slice of a production delivery system—not an AutoML catalogue or a cloud-specific demo." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "82d57fc1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T19:16:20.829732Z", + "iopub.status.busy": "2026-09-11T19:16:20.828449Z", + "iopub.status.idle": "2026-09-11T19:17:03.904026Z", + "shell.execute_reply": "2026-09-11T19:17:03.901788Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "360 synthetic observations | 3 SKUs | 2 regions | 60 months per series\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.ensemble import HistGradientBoostingRegressor\n", + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "SEED = 7\n", + "HORIZON = 12\n", + "plt.style.use('seaborn-v0_8-whitegrid')\n", + "COLORS = {'actual': '#16324F', 'baseline': '#F28E2B', 'model': '#2A9D8F', 'accent': '#457B9D'}\n", + "rng = np.random.default_rng(SEED)\n", + "dates = pd.date_range('2020-01-31', periods=60, freq='ME')\n", + "rows = []\n", + "for sku in ['sku_a', 'sku_b', 'sku_c']:\n", + " for region in ['north', 'south']:\n", + " base = rng.uniform(80, 160)\n", + " for i, date in enumerate(dates):\n", + " seasonal = 1 + 0.18 * np.sin(2 * np.pi * date.month / 12)\n", + " trend = 1 + 0.003 * i\n", + " shock = 1.25 if date.month == 11 else 1.0\n", + " demand = max(0, base * seasonal * trend * shock + rng.normal(0, 7))\n", + " rows.append((date, sku, region, demand))\n", + "df = pd.DataFrame(rows, columns=['date', 'sku', 'region', 'demand'])\n", + "series = df.query(\"sku == 'sku_a' and region == 'north'\").reset_index(drop=True)\n", + "print(f'{len(df):,} synthetic observations | {df.sku.nunique()} SKUs | {df.region.nunique()} regions | {len(series)} months per series')" + ] + }, + { + "cell_type": "markdown", + "id": "38ab85c4", + "metadata": {}, + "source": [ + "### Reading the synthetic signal\n", + "\n", + "The data deliberately contains three interpretable ingredients: gradual trend, recurring seasonality, and a November demand shock. That makes the experiment useful for explaining model behavior without pretending synthetic data proves a real business outcome." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f9f15c04", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T19:17:03.911633Z", + "iopub.status.busy": "2026-09-11T19:17:03.911100Z", + "iopub.status.idle": "2026-09-11T19:17:05.260825Z", + "shell.execute_reply": "2026-09-11T19:17:05.257009Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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0+XMfPoRGwd7cMN99n3zgC1vTGszr5lTXGIE/onH6oR9m93Iv8DwLOnZBElPTMa9PCyj/3PenH9G48jwADhZGJT7vbKFR8fhj31VM6OiEZja1C+wTIYRURlUmMAUAY8eOha6uLrZu3YqQkBAAWT/SHz16hEePHmHHjh2oVq0aRo0ahdGjRzN3l2vUqAEXFxd4eXnh69eveP/+Payssr6IZActlJWV0bZt21yPO2rUKOYHoEAggLu7O44ePQogayhcURVmf9kBIwBo0qQJZ3tXV1fmsZeXV74/XPPj5eXFPG7WrBkTlAIAd3d3XLt2DaqqqlBTy78oaHbtr+y+ZQdfgKwfVtbW1jmGCpb0/NhFhF1cXJjAlIKCAidQ4eLiwrzHUVFRzHJLS0ts3Lgxx37l5OSgr6+PuLisL1QxMTG5/kgrzHtYnNelsApz/KKeY3aWHADs27cPycnJaNq0Kezt7eHm5sYEOApS0tc2m5ubG6pVq4a4uDg8fvwYSUlJUFVVhUgkYrLNFBQUmGBjXqR1Xr9iZ2ICQPv27ZnAlK+vb77bltb1/WufOnTowJyfj49PvtuWxTUhjW3yUph9vX79mlnf3d2dCQABWZlKderUKTBTK5u0PlfS6FNp/D0gpLJpaG4IJYWsr83H7/vA1doUM3u4Mc+HRMQiKiEFy4/dZpb9iEkA/+e1FRIRC2sTSZBZWaCQ65C5vOhrqecIQOW27Nfns4M12cdMS8+62RgWmwi72jWY5zRUlGCkUy3HPrINatEQ/zt5D31WHkb9WvpobFkT7RvVYc4tv3PX1VBBcFgM9lx7hpjEFGQIReDxeEjLyCxw318j42Btosfpi6meJnyCJdmk+Z1nQccuSE1dDSYoBQDG1TXx+md2W0nPGwDiklIx978rMDfQQXNbs0L1iRBCKqMqFZgCsrKfevToAV9fXzx+/BivXr3C69evER4eDgCIi4vDunXrEBkZyWQhAECvXr2YQMy1a9dgZWWFwMBAJsDVunXrPAMwFhYWnHb16tWZx+yhhIVVmP1l30EHgCtXruDp06e57isoKKjIx8+Wfe5AVvCOTV5eHrVqFe4LV0REBPM4tyFVhoaGOQIwJT0/dn0mLS0t5rGBgQEnY4793K/vVUREBI4dO4aXL18iMjIS6enpyMzMZLKcADA1rH5VmPewOK9LYRX2M1mUc7SyskLLli1x+/ZtpKam4sCBAzhw4AB4PB4sLS3Rrl07DBo0iPOa5qUkr202gUCAtm3bwtPTExkZGXjw4AE8PDzw4sULREdHA8j6Ia+pqZnvfqR5Xmy/FopmZ8UUFFQprev712uWHWBgfx7zUtrXhDS2yUtRr0kDA4Mc+zA2Ni50YEpanytp9Kk0/h4QUtm0amDGFD831FbHnP+uoHMTK9QxyhrKpyRQgKm+EjzsLTnbqSlnfacQKMgjPY8M9NzmMvl1XYF8zuHBuS1j4+e245/k+TwIf7leU9Mz8lgb0NdSw9rRHRGTmIJXQd9x6el7XH0RgG2Tuhd47kfuvsaFJ/4Y0dYBTnWNISfHxwO/z4Xat6KCfI6ssrQMIVN/qqDzLOjYBfn12BlCITM7Y0nPGwBik1IwvqMT9lx7jusvP6DtL/sihJCqosoFpoCs4Su2trawtbVllr1//x4bNmzA7dtZdz0OHTqEiRMnMj8K2rRpA01NTcTGxuLatWuYOnUqp+ZKfsOB2EPkABR6dquS7I89/C46Opr5If6rX4urFwW7AO6vM7kVBfuHT26Fotn1ULKV9PzYryH79VNUVOSsl1edmI8fP2LQoEGIjY0FkDVjo4GBAZSUlCAnJ5ejOHB+x/+1D9mK87oUVmGOX5xz3LhxI/bt24eTJ0/i06dPTN8DAgIQEBCA48eP4+jRo/lmVJT0tWXr3LkzPD09AQC3b9+Gh4cHp1ZYQcP4pHlebHw+P8eQYfb7WdCP+9K4vnPrE7tdUJ/K4pqQxjYl2Rf7df/1bwWAHK9fQaTxuZJGn0rr7wEhlZWNaQ142Ftizcl72DqxGxTk5WBTSx93fT/BwcIQcj+vodPeb2FVMyvQa22sh9tvgjCirRACeTlExSfj32vPML5jE6gpKyIiLglCkQhyfD6Cw2LwPTqhVM/BRE8Tbz+HM+3A71EIj0vKc/0tFx6hlZ0Z6hnroWUDM5jX0MaYTaeQkpZR4Lm/DAxFKzszJujywO8z0jMzkfnz35X89l3fRB+3fYIgFIogJ8eHUCjC84/f0LyQQ94KOnZBQqPi8T06AQbaWQXL/T6Ho55J1nmV9LwBoJaeFjo41kUNLXUsPnQDpvpasDTUBSGEVDVVJjB148YNvHr1CgEBARgxYgSaNm3Ked7KygqbN29GixYtEBERAaFQiMDAQGaGIYFAgC5duuDAgQPMLF/e3t4AsuopsYfPlAfsIu5Dhw7FlClTcl2Pl89dpoKw7+Tn9cO4MNTVJbOT5Laf7Gw2trI4v/ysX7+e+YHWuXNnrF69mgnOdezYEYGBgSU+RnFeF2kqzjkKBAKMGTMGY8aMQVhYGF69eoWnT5/iwoULiImJQVhYGLZv355j5reSHjcvTk5OqF69OiIiInD//n2IxWImMKWmpsYpkJ4faZwXm0gkQnR0NHR1JV8+2VlS7OFYuSmNz79IJEJUVBQnW6YofSqLa0LW2Ncke2hvtsJklbFJ43Ml7T7lpSq8v4QUxeh2jhj9zykcvP0KI9o2Qvem1ngV9B3jN59BPWM9fIuKR0xiCjM7XE+X+nj24SvGbz6NWnpaePs5DE51jaGhogTnusbYe/05Zu++BN1qqkhOy0A9Yz3kck9Karo5W2PmrouYvfsSDHU0EBmfBAtDnTzXN9bVwIL91+BgbgRFBXn4BP9Ap8ZWUFZUKPDcnaxMcPDWS8QmpiIiPgnmNbRRz1gPZ739oK+plv++Xazx/OM3TNp2DnWNdPH+awTUlRXRy9WmUOdZ0LELYqCtgdUn7sK4ejV8i4pHVEIyejStDwAlPm82e3NDDGhuh78O38KWiV3zHaJJCCGVUZUJTJ07d46p2ZGcnAxHR8ccGSeZmZlISUlh2r8OzevduzcOHDgAICv7Inv4TKdOnUqUMZSbvAqOFxY7G+zTp0/Q0NCQ+vHt7Oxw5EhWocwnT55wnouKisLo0aOhpKQEbW1tbN68Oc8fyZaWkrTlX/cTHR2NN2/e5NimpOdXUu/evWMed+7cmXn/w8LCmJnSgNwznQqrOK+LNBX1HG/cuIH79+/j8+fP2LlzJ/T19dGuXTu0a9cOI0eORMuWLQGAyQyR1nHzw+fz0b59exw4cAARERG4cuUKsw8PD49cM0x+Ja3z+pWXlxe6d+/OtNl1owoq2l1an//79+9zimVnB98B7ucxN2VxTciaubk58/jJkycQi8XM37WYmBhOvaeCSOtzJc0+5acqvL+E5Ma4uibm9W0BJQH3O6OasiKWD/NAaHQCxGIxFBXksXJ4O3z6EY3vMQmopqIEK+PqTCaNsqIC1o7uhI/foxAZn4ThbRxQSy/rBp+ephr2zuyNdyEREMjLwda0Bvy+hENTLSs44WhhBCMd7t/53JY51zWBeQ3tPJ8f1LIhtNWzZqiuqVsN/07vhfc/j2lTSx9TdpyHYh7DA7s6W8O1vikCQ6MgFIsxoIUds/+Czr23qw0czA0RGh0PI20N1K6hjeiEZLwLiYC2ukq++1YWKGDNqA4I+hGN8NgkdHWuB7Ma2szfuYLOs6Bjs1+z3KgrC7BqRHv4fv4BPo8HK2M9pp5VSc/712P3b9YAxrrVEJOYQoEpQkiVU7IxZRXIyJEjmeEJT58+xcCBA+Hp6YmnT5/i8ePHOHHiBAYOHMhMr25kZJTjx6GVlRWsra0BAHv37kVSUlbKc2GHAxWEXTw8+wdhcet1tG3blgmseXl54dKlS8xz58+fR9++fTFu3DicOXOm2Mf38PBg6vMEBwdjx44dyMzMRGpqKlatWgU/Pz+8ePECioqK+WZuNGvWjPmR8+XLF2zbto0JEi5fvjzXISLFOT9pYmesPHjwAGKxGN++fcOMGTM4P8zYtVeKqjivizQV9RzDwsJw9OhReHt7Y8uWLZzg5ocPH5jHBQ13k/Zry54Rcv369czjwl630jqvX23bto2p0/b8+XMcO3aMeS6viRTYz5fG5589McSzZ89w/Phx5jkPD498ty2La0LWmjdvzvw7EhISgo0bNyItLQ1JSUlYtmxZka5JaX2upNmn/FSF95eQ3FRTVULLBmaQl8v5ldncQAfu9U0533Fq19CGS71aqF9LnwlQZOPzeahjpAuXerWYoFQ2DRUlONU1hr25IeTl+GhQuwZMqmsCAIx0q8HRsiZn/dyWGVevBoefM8Dl9ry9uSFzXP+vEThy9zUaWRjBzswAccmpCImIhal+3nXtdNRV0KSuMZpameQIBhV07mY1tOFmbYraPwMx2uoqcLWuBR11lQL3zePxYG6gg6b1TGBuoMN5vQs6z4KOzX7NciMWA0oCeTha1oSDhRGnyHpJz/vXY/N4PLjVN83x2SCEkKqgygSmGjZsiD/++IP5Av/mzRvMnz8fgwcPxtChQ7FgwQJm+nUVFRWsXr061zobvXr1AgBmKm1zc3PY2BQunbgg2TP9AcDatWvh6OiITZs2FWtf6urq+PPPP8Hn8yEWizFjxgw4OzvD1dUVs2fPxuvXr/H27Vs4OzsX+/iqqqpYvnw5EzxZt24dHB0d4ejoiHPnzgHICvDNmTMn377q6+szU48DwIYNG9CoUSM4OTnh3r17nCnqswNlxTk/aWL/SD9w4ABsbW3RqlUrfPjwAQsXLmSemz9/PmbMmFGsYxTndZGmop5jjx49UL9+Vnr79u3b4eTkhHbt2sHNzQ1jx44FkHVtjRo1SqrHLYi9vT1TPD47s0NPTw9OTk4FbgtAaufFfo+yJwfw8PBA48aNMXDgQCQkZNUTqVOnTo7Z8X5VGp9/BQUFpk+Ojo4YNGhQkfpUFteErBkYGGDAgAFMe+vWrWjUqBGaNGmCO3fuFPozBUjvcyXNPuWnKry/hFQl+lpqeOwfgqk7zuN/nvcweds5uFmbokHtnJMoEEIIIaWtygSmAGDIkCE4evQo2rVrl+sMevr6+ujfvz/OnTuHxo0b57qPLl26cIrJduvWTWr9mzBhAvNDBQCSkpJKNESnc+fO2L17N+zs7MDj8RATE4PIyEioqKigZ8+eOH36NGc2veIcv02bNti3bx8aN24MOTk5pKSkICMjAxoaGujXrx+OHz+eY8a+3MyZMwdDhw5lhlalpqbC0tISe/fu5QxVSU1NLfb5SdPIkSMxfPhwZjhoRkYGHBwccPjwYfTr1w/t2rUDn89HQkJCrnVfCqs4r4u0FPUcVVRUsGfPHvTp0wfq6upITExEcHAwIiIioKSkhHbt2sHT07PAYWql8dp27NiR0+7UqVOhC2VL67zS0tKYxxoaGli3bh06duyI5ORkAFnDDlu2bIndu3cXqmC1tD//GRkZTJ+yhzRn9+nff/8tsE9ldU3I2rx58zBkyBDmmszIyEDdunWxd+9e6OlJpjQvqL6XtD5X0uxTfqrK+0tIVaGpqoydk3tgXIcmcKtfCyuHt8PcPs1l3a1yxbmuCYa3cZB1NwghpErgiatoQQixWIyIiAgkJCRAXl4e2tranCKyeRGJRGjWrBkiIiLA4/Fw69atXIdasKfn/vWHRXR0NFNUWFtbm1MAGcgqap2amgpdXV1meF1J9gcACQkJiIyMhKqqKnR0dPKcba44x8+WlJSEiIgIKCoqQk9PL99j5CUlJQVhYWGoVq0aU1ydfX7Vq1fPdfr0wpxfXueQnp6O4OBgAFmzWtWqVYuz3+yhKRoaGjl+6GdPla6mppajX9lTqUvjPSzu61KY8y/o+EU5x2xisRhhYWFISkqCkpISatSoUeTPQ3GOm5fExESEhoYybQMDg1yv94I+54U9r9z2kz2hApBV9NrU1BSA5LrR1tbONRBcmGuvKNc3W2ZmJicY7efnBzk5OaZPOjo6RX6dSvuakOY20r4mx40bhzt37gAAtmzZgjZt2qAwSvK5Km6fyuLvQWE+u4QQQgghhFTZwFRx3bhxA5MmTQIAuLu7Y/fu3TLuESGEFM+vgam3b99KfSKHyiY9PR1hYWGIiIiAg4PkTrpYLEabNm3w9etXAMDZs2c5w6OrWp8IIYQQQggpLPoFUgipqanIyMhAQEAAVqxYwSwfPXq0DHtFCCGkLKWnp8Pd3R2xsbEAgI0bN6Jdu3YAgH379jEBIFNTU9StW7fK9okQQkrTsw/fYFK9GvQ0c5blIIQQUjFRxlQhHDlyBEuWLOEs69y5M9auXSubDhFCiBRQxlTR7d27FytXrmTaRkZGSE5ORkxMDICsIvK7du1C06ZNq3SfCCGkNIhEYgxffwLhcUloYWuGPm42MDfQkXW3CCGElBD9AikieXl59OnTB3PnzpV1VwghhJSx4cOHQ1dXF3v27MHbt2+ZGVoVFBTg5OSE6dOnw9bWtsr3iRBCSgOfz8OOyT1w5UUATj14i1uvA2Fvbog+brZoZGFYokkeCCGEyA5lTBVCRkYGwsLCkJGRASMjo0LNmEUIIRUBFaguvrS0NISHhwPImtW1PPzbUB77RAghpUEoFOHe22Cc8PLBx9AomNXQQm9XWzS3rQ0F+aJPwEMIIUR2KDBFCCGEEEIIqZDEYjFef/qBE14+eBrwFboaKujhUh8dHetCVYmC84QQUhFQYIoQQgghhBBS4X0Ki8FJL1/cehMIgbwcOjauix5N66N6NVVZd40QQkg+Kn1gSiQSITU1FUpKSuDz+bLuDiGEEEIIIaQURcUn44z3W1x46o/U9Ay0bGCG3m62MKuhLeuuEUIIyUWlj9Skpqbi3bt3SE1NlWk/RCIR3rx5A5FIJNN+EFIR0fVDSPHR9UNI8dC1U3HpaKhgVLvGOPRbP4xu1xivP/3A+M1n8Me+q3jx8Rsq+X35coGuH0KKrypePzQrXxkRi8XIyMigfwgJKQa6fggpPrp+CCkeunYqPhVFBfRytUE3Z2vc8/2EE14++H3vVZgbaDOF0uXlKv19epmg64eQ4quK1w8FpgghhBBCCCGVlrwcH63szNGygRleBobihJcvVnvexX/Xn6GHS310cKwLFUUFWXeTEEKqLApMEUIIIYQQQio9Ho8HBwsjOFgYIehHNDy9fLD76lMcvP0KnRvXRfem9aGjoSLrbhJCSJVDgSlCCCGEEEJIlWJWQxtzejfH8DaNcOaRH84/eY+TD9+iVQNz9Hazgam+lqy7SAghVQYFpgghhBBCCCFVkp6mGsa2b4JBLRri0jN/nH74FtdefkDjOjXRx80WdrVrgMfjybqbhBBSqVFgihBCCCGEEFKlqSoJ0MfNFt2drXHHJwieXr6Ys+cyLA110NvNFs3qm0KOCqUTQkipoMAUIYQQQgghhABQkJdDW3tLtGlogecfv+GEly9WHr+DPZpq6OlSH+0b1YEyFUonhBCposAUIYQQQgghhLDweDw4WtaEo2VNfAyNwgkvH+y48gQHb79E5yZW6OZsDW11KpROCCHSQIEpQgghhBBCCMmDhaEO5vVtgVEejjj18C3OePvB08sXrRtaoLerDUz0NGXdRUIIqdAoMEUIIYQQQgghBdDTVMP4jk4Y3LIhLj71x2nvt7jyPABOdY3Rx80Wtqb6VCidEEKKgQJThBBCCCGEEFJIasqK6NesAXq41MedN0E44eWD2f9eQl0jXfRxt4WrdS3I8alQOiGEFBb9xazAAgMDsWjRInTv3h0dOnTAyJEjcfToUWRmZjLr+Pn5oVWrVhAKhTLsafFt374dCxculGkfPn78iFatWiElJUUq+5Pme1IeXp+iqGj9JYQQQgjJi0BeDh4Oltg5pQeWDWkLJYEClh29jRHrPXH2kR9S0jNk3UVCCKkQKDBVQXl5eaFnz55ISUnBzJkzsXr1anTs2BG7d+/G8OHDkZ6eDgBIT0/Ht2/fIBaLZdzj4omLi0NUVFSZH3f69On49u0bACAjIwPfvn2DSCSSyv5q1qyJhQsXgi+FO2myen2Kq6L1lxBCCCGkIDweD03qGmPNqA7YPKErrGpWx7aLjzF4zXHsvfEcMYnSublJCCGVFQ3lq4BSU1Mxd+5cdO/eHX/++SezvEGDBmjevDnat2+Pf//9FxMmTJBhLys2b29vTuYZgBLVDGDvT0NDAy1btixR/wghhBBCSPlTx0gXf/RriRFtE3Da+y1OPniLE16+aNvQAr1cbWBcvZqsu0gIIeUOBaZyIRaL8SU0HCmpaVLbp0goxOfQcCirfwFfTi7H88pKijAx1CtU8OPhw4eIjIzE1KlTczxXvXp1DBgwAGfOnOEEpry9vbF9+3bExcXB3t4ec+bMgbq6OsRiMXbt2oXr168jISEBNWvWxNixY9GkSRMAQHBwMDZt2oSAgABoamqiU6dO6N+/PwDg/fv3mDVrFn777TesXbsWixYtwvLlyzF58mS0adOGOfaiRYugqqqKuXPn5rs/ALh9+zb27NmDqKgouLi45JvplVffHRwc0KdPH0ycOBFt27Zl1l++fDnk5OTQo0cPzJgxAytWrMD69esRHh4Oa2trzJ8/H9ra2ujSpQtiY2MxZMgQDB8+HE2bNgUA+Pr6YsuWLcz6CxYsgJaWVoGv06/7c3Z2xuTJk3H9+nXIycnh48eP2LhxIwIDA6Gjo4Phw4ejVatWuZ5zQa9Pfv3w9/fH9OnTsWTJEqxfvx7x8fHo1KkTunXrhuXLlyM4OBgNGjTA4sWLoaKSNf3xs2fPsGvXLnz58gUaGhro2LEjhg0bxuwvv9exqO8nIYQQQkhlYaCtjomdnDG4pT0uPnmPM4/8cPm5P5ytTNDXzRb1a+nLuouVVmpaOt4HfsHbgE9QFCigW1s3KCjQz15CyjO6Qn8hEokwZMYK3PB6VubHbuPmiAPr/yhwiFdgYCB0dXWho6OT6/NWVlb4999/OTWMjhw5gsWLF0MkEuG3337DypUrsWLFCly+fBlHjx7Ftm3boKuri7t372LcuHG4f/8+hEIh+vfvjwEDBmD+/PkIDQ3FjBkzIBQKMWjQIIhEIgQHB+PcuXOYMWMGzM3NYW9vD09PTyYwlZCQgDNnzmDXrl2Ii4vLd39fv37F5MmTsXTpUrRs2RLPnj3DvHnzmCDZr/Lru7OzM/bv388EplJSUnDy5Els27YNIpEIQUFB2LNnDxYuXAgej4eJEydi165dmDt3Lnbv3o1mzZph7969MDU1hb+/PwDg0KFDWLBgAfh8PrP+nDlzCjyvX/f35s0bZnhlTEwMBgwYgAkTJuC3336Dn58fpk2bhsOHD8PW1pZzvgW9PgX1I/u8z507h//973949+4dpk6diqdPn2L27NmQl5fH6NGjcfr0aQwaNAixsbEYNWoUZs2aheXLl+Pjx48YP348DA0N0bZt2wJfx6K+n4QQQgghlY2GiiIGtLBDL9f6uPk6EJ4PfDFj10VYG+uht5sNmtYzoULpJRCfkATfgE/w8Q+C7/us/3/49BWZrN9Bp6/cx9618yg4RUg5JtOrMz09HVevXkXDhg1hbGzMLPf29sb3799zrG9kZAQnJyeEh4fDy8srx/PNmzfPM1hTWFGx8TIJSgHADa9niIqNR3VtzXzX4/F4UFBQyPN5gUAAsVjMCUxNnToVderUAQAMGzYMa9asAQAoKCggKSkJ4eHhqF27Nnr27InOnTtDIBDg6NGjUFFRwbRp0wAA2traGDlyJA4fPoxBgwaBx+MhMzMTw4cPR4MGDQAAvXr1Qv/+/RETEwMtLS1cvHgRBgYGaNKkCY4dO5bv/m7evAkTExP07NkTANC2bVucOXMmzyyb/Po+cOBAeHh4IDAwEObm5rhx4wb09fXh5OSEd+/eQSwWY+rUqbCwsAAAtG7dGkFBQQDABAbl5OQ4QcKpU6fC3NwcANCmTRsEBgYCyAqQ5Xdeee0PAK5cucKsDwDGxsZo3Lgx1NTUcpxvQa9PQf3INn78eBgbG8PExAQaGhrw8PCAjY0NAMDBwQGfP38GAFSrVg23b9+GpqYm+Hw+dHV10bBhQ/j6+jIBv/xex6K+n4QQQgghlZVAQR4dHOuinUMdPA4IgaeXL/46cguGOhro5VIfHg6WUKTASb7CIqLh4x8EH/9P8PUPgs/7IHz+FlbgdjcePMf0vzZh05/TpFLjlRAifTL76xcZGYlp06bh1atXWLFiBScw5evrCx8fH876vr6+aNKkCZycnODr64ulS5fC3d2ds46dnV2JA1M6mhpo4+Yos4wpHU2NAtczMjLCjx8/kJSUBFVV1RzPBwcHQ19fHwKBgFlmYmLCPK5RowZiY2ORmZmJtm3bIjIyEuvXr8enT5/g4OCA4cOHw93dHaGhoYiIiOAMK0tLS0NSUhLneKampsxjGxsbmJub49KlSxg0aBDOnDmD3r17g8fjFbi/iIgI6OnpcfZtYGCA0NDQXF+H/PpubGyMZs2a4ciRI1iwYAHOnDmDfv365dh3NgUFBWRk5D9zSo0aNZjH8vLyzPqFfZ1yExoaCn19bip39jC4XxX0+hS2H+x9CASCHO3sWlg8Hg83b97E1atXERYWhvT0dISFhTFBLHYfsrFfx6K+n4QQQgghlR2fz0NTKxM0tTLBu5BweHr5YsuFR9h/8yW6OtdDFycraKoqy7qbMpeRkYnr95/hpd8H+PpnZUJFRMUWe3+el+5CW1MDf80cWaK6sYSQ0iGTwFRaWhoGDRqE2bNnY+nSpTmeHzNmDKcdHh6Obt26McsTExNhZGSEjRs3Sr1vfD4fBzfML5UaU+/938OqrlWeNaZqGRVurLm7uzuUlJRw8OBBjBs3jvNcZmYmPD090a5dO87yhIQEpm5QQkIC1NTUIC+f9fYPGDAAAwYMQGxsLK5evYoJEybg1KlT0NbWhpmZGQ4cOJBvf+R+OZ9evXrh/PnzcHV1ZeoyAShwf+rq6khISOAsK2gGt7z6XqdOHQwaNAi//fYbhg8fjhcvXmDdunX57qu4Cvs65bVtTExModYt6PUpST9yc/bsWaxZswZbt25FgwYNIBAIMGLEiEJvX5z3kxBCCCGkqqhnrIeFA1ohNCoepx6+xfH7b3Ds3ht4OFiil6sNjHQKvmFdGYnFYgyathR3H78u9DbKSoqoU7sm6lnUQv06tVHPohYEAgWMmL0KUTFxAICdh8+jurYmpo7oVVpdJ4QUk0xyGcViMdavX88pTJ2f//3vf+jevTszjCoxMTHXTCFp4fF4qGWkDytzE6n9V9fcBLUM9VA3j+cLG5QCADU1NcyZMwebN2/GkSNHkJKSNQVtSEgIZs6ciczMTEyaNImzzfHjxwFkvfYXL16Eo6MjAGDDhg1YsmQJRCIRUyxbWVkZMTExaN68OYKDg/HhwwdoaGhAXV0d+/btw7Fjx/LtX9euXeHn54c9e/agVatWTBZbQfuzt7fH+/fv8f79ewDAp0+fcO/evTyPk1/fgawAnqamJmbPno127dqhWrXCzYKSHWhLTk4u1PoFnVd++3N3d0dgYCC8vb0BZGUS9urVi2mzFfT6FPf9ysvnz59hZGSERo0aQSAQ4MaNG/Dz82M+bwUp6vtJCCGEEFIVGepoYHKXpjj0Wz/0b26H+2+DMXKDJ/46fBPvQsJl3b0yF/QlNN+glKaGGpztrTGiT0f8PX8iLv63Gk/O7sCRTYuxaNpw9OrQHNaWprCoZYQdK2ZBTUWSgbZ88wEcPH29LE6DEFIEMsmYUlJSgrW1daHW9fPzw/3793H9uuQPSGJiItTU1BAUFAR/f3/o6OjAwcGByQCqCgYOHAhtbW1s3boVf/31FxQVFSEWi+Hh4YHjx48zQRiRSAQFBQXw+Xx06NABKSkpUFFRwebNmwEAffv2xcyZM+Hi4gIdHR1ERUWhe/fuaNy4Mfh8PlauXIlZs2ZBSUkJSUlJMDIyYupT5UVTUxMtW7bE8ePHsXv3bmZ57dq1891fkyZNMHDgQPTr1w86OjrQ1NREhw4dEBkZmetx8us7kBVgHDBgAFauXInff/+90K+tlpYW6tWrhyFDhqBnz57o0aNHvusXdF6/7q9jx47MthYWFli6dClmzJgBgUCAmJgY9O7dO9cC4QW9PgX1o6i6d++Os2fPMkMDPTw8MHHiRGzatAmGhoZwcXHJd/uivp+EEEIIIVWZhooSBrdsiD5uNrjx6iNOPvDFtB0XUN9ED33cbeFc1wR8fuUfhnbn0SvmsRyfj+bODWFtUQvWdUxhZV4L+rpahR6OZ21pis1/TcfYeX8j/We5id9WbIN2NXV0bOVcGt0nhBQDTyzjSsTNmjXDrFmz0K1bt1yfnzJlCszMzDBjxgxm2fr167F//37UrFkTZmZm8PHxgYqKCvbt25ejxlRycjLevXuHOnXqMEPZZEEoFMLHxwe2trY5hr6VVEpKCpKSkqCtrZ2joF96ejoiIyNhaGiI1NRUpKSkQEtLK8c+kpKSkJycDC0trVwDfLGxsZCXl+cU5U5PT0dERAQMDQ1z/OOQmJiIuLi4XJ/La3/s/SYkJEBHRweJiYlITU2Frq5unuefX9/Pnz+PPXv24PTp0/n2Oz4+Hunp6cxxhEIhYmJioKOjg4yMjALXL+i8ctufkZERZ52oqCioq6tzaoPlpjCvT2Hfr+/fv0NTUxPKyll3kmJiYsDj8aCpqQkgK8MuKioKampqUFJSAgBER0dDVVUVPB6vUK9LUd/P3JTm9UNIZUfXDyHFQ9cOkTWRSIxH/iE4+fAt/L6Ew0hHAz1d6qONnTkECuX7M1mS62fYrJW4fj+r3q99fUvsX/9Hiftz0+s5Zi7bApEo66evokABh/5ZAJdGNgVsSUjZq0z//hS2/+U6MBUREYEWLVrg2rVrnB/xAQEBiIiIgIuLC3g8HtLT09G/f3/Y2dlh8eLFnH1kB6ZI1RMfH4/Fixejd+/ecHV1lXV3CCGEEEIIKZbPUUm4/zECfqFxUFWUh7OZLpxr60BVsXKNGMnIFKL/jL+RkpYOAOjdzgW9PJpKZd+3Hr3BrhOSUTjKSgL8b/ZQmJsY5LNVyQV++Y5L917ASF8HnVs4QkCzL5IqpFGjRoVar1xfFXfu3EHdunVzZJbUqVMHderUYdoCgQCurq549epVnvuqzBlTJKclS5bg4sWL6NixY456W6TioeuHkOKj64eQ4qFrh5QnDQF0aw18+1ko/carQNz/GAkPewv0aGoNA211WXeRo7jXj/eLt0xQCgB6dmoNGyszqfTJxtYGKurV8M8eTwBASmo6/txyAmd3L0dtY+kHp9IzMrBhjyc27T0FoVAEALj37B02LJ6ChtYWUj8eqTyq4r8/5Tow9fDhQ9jb2+dYvn79eqiqqmLs2LHMsrdv38LExCTPfcnJyZWLN7W89KOymzt3LmbNmsUMSSOVA10/hBQfXT+EFA9dO6Q8MdHTwvTubhjethHOPXqHc4/f4eJTf7jVr4Xebrawqlld1l3kKOr1c+/JG+axhpoKbOuaQ44vvfm6xg7ogti4BOw7eRUAEBkThwFTl+LCvyuhX11basd5G/AJUxb/g7cBwZzlAZ++osuoeZg8rCdmjekHRYGC1I5JKp+q9O+PTAJTwcHB8PLyApBVH+nhw4dISEhAzZo10aJFC2a9z58/w87OLsf2Dg4OmDRpEsLDw2FmZoZHjx7B19cXS5YsKaMzIOVdbrWrCCGEEEIIqQw0VZUxtLUD+ro3wPWXH3DygS+mbj8PW9Ma6ONmgyZ1jHMtlC4Sict1AfW7rMLnTvbWkJOT7iTyPB4Pv40bgOi4BJy/8RAA8OVbGPpP+Qtndi1DNfWS/YbIyMjEpr0nsXbXcWQKhbmuIxSK8M8eT1y9+wQb/5wKu3qUPUWIdK/0QoqLi4Ovry98fX3RunVr8Hg8+Pr6Ijg4mLOek5MT6tevn2P75s2bw9PTE6qqqggICICdnR2uXLmSb8YUIYQQQgghhFQmSgJ5dHGqh3+n98KiAa2QKRRi0cEbGLvpNC4/C0B6piQ4kpaRiX6rj+D2myAZ9jhvUTHxeP0ukGm7N25QKsfh8/lYNns03JtI9u/3IRhDZ6xASmpasff77uNndBw+B6u3H+EEpSxNa+L4liWYOqI35OUl2S/vA7+gw7A5WL3tMDNjICFVlUwypuzs7HLNhPrV3Llz83zOysoKVlZW0uwWIYQQQgghhFQ4cnw+3OqbwtW6Fvy+hOO4lw82nPXCvhvP0c3ZGp2drKCurAhHCyOsO30fxrrVYGGoU/COy9D9p2/AnperNGfMU5CXx/qFUzB67mq88vsIAHj00g/j/liLPf+bywkgFSQzU4gt+0/j751HkZ6RySyX4/Mxun9nTBjSHQIFedjUNUPLpg3xx/924d3HzwCysqfW7T6OK3efYNOfU2FTVzr1tAipaGSSMUUIIYQQQgghRLp4PB7q19LHn4PaYPfUXnC2MsHBO68waM0xbLv4CAOa26GWnhaWHLqBmMQUWXeX47b3S+axac0aMNTXLdXjqSgrYuuymbAwlUy0dfXuE8xatgWFnbg+4FMIOo/8HSu2HOQEpcxMDHB44yJMG9mbMwtfXTMTHN28GJOH9YQ8q3aQ34dgtBvyG/7eeRQZrP0QUlVQYIoQQgghhJBKKi09A5fvPMbthy/xPTyq0D+4ScVnXL0apnd3xcHZfdHTpT5uvArE2M2noa2mjJT0DCw7ehuZP2eLkzWxWMypL+XiWHrZUmyaGmrYteo3GOhJsseOnr+FZZv257udUCjE1v1n0GbgTLx8+4FZzufzMKpfR5zavgy2ecwmqCAvj4lDuuPYliWoa2bMLM8UCrFmx1G0H/Yb3gZ8KuGZEVKxlOtZ+QghhBBCCCHF8/V7BIbPWgkff0lNIU0NNViZm8DK3AT1LGrBysIEVua1oKlBE8dUVlpqyhjc0h6dm1jh2osPuPDkPRJS0uET/ANLj9zEn4PbyrqLCPj0Fd/Do5i2e+OCy75Ii76uNnavnoPB05chJi4BALB532noaFbDxKHdc6wf+Pkbpi7ZiGdv/DnLaxnVwKq5Y2FnXbhi5vUsauHYlj+x4/A57Dx0DkJRVpDQ1/8T2g35DTNH98GU4b2goEA/2UnlR59yQgghhBBCKpnHL/0wcs5qREbHcZbHxifi0Us/PHrpx1luoKcjCVb9/L9l7ZpQVlIsy26TUvD84zfM33cNolyy5bzfhyA2KQWaqsoIDYvEc58AuDdpUOaByjuPJMP45OXl0LhB2dYSrm1sgB0rZmH47FVITkkFAPz5z15oa6qjf9fWAACRSIRdRy9gxeaDSE1LZ7bl8XgY2rMdpo3sDSVFQZGOK1CQx5RhPdHaxQHzVu/Eh+CvAICMzEys3n4El+88xsY/p6GeRS0pnSkh5RMFpgghhBBCCKlEDp6+jt9X7UBGZuFr1XwPj8L38ChOnR8+nw/TmjVQ72dWVadWzqhfp3ZpdJmUonrGepjTuxnk5fgQyMtBUUEeigryEMjzoSRQgKaqMmLiEtBqwAzExCXAvr4lLu1dDT6/7Kq+3PF+xTy2t7aEinLZB0Rt6pph45KpGD9/LTJ/zmY4c9kWaGmqo25tY0z7c1OOgK6xgR5WzBmDRrZ1S3Rsa0tTnNj6J7YdPIvdRy8w2VNv3gfBY/AszB7bH5OG9ihSUXZSdsRiMeas3I57j19j6oheGNRd9lmIFQ0FpgghhBBCCKkEMjIysXj9f/j32EXO8g4tnDF9ZG8Ef/2BD8FfEfApBB8+fUXQl1Ckpec9Tb1IJELQl1AEfQnFxVuPsG73caz5YwKG9PQo7VMhUqSiqIBWdub5rnP9/jNmGNvLtx/w6KVfqc6Kx5aalg7v575M27WM6kvlxqWRDf43bwJTAF0oFGHs73+Dx+MhJTWNs+6g7m0wc3Q/qWUVCgQKmDayN1q5OOCPNTsR+DkUAJCekYkVWw7i0p3H2LhkKqcuFSkfbng9x/6TVwEAs5dvg109c5phsYgoMEUIIYQQQkgFFx0bjzG/r4HXUx9mGY/Hw/SRfTC6fyfweDwYG+rBvUkD5nmhUISQ72EICPqKD8Ff8eFTCAI+fcWX0DCIRDmHfYnFYvy2Yhs01FTQzcOtTM6LlI1f6yWdunKvzAJTT16/QwpraJxb4wb5rF362jdvgti4BPy1cR8AcIbtAYBRDV0snz0GTRrWK5Xj21qZwXPbX9iy/wz2HL/IXIuv3n5Am4EzsG7hJPTp1LJUjk2K5/r9Z8xjkUiEOSt34MKelWWadVjRUWCKEEIIIYSQCux94BcMnbEcn7+FMctUlZWwZv4EtHC2z3M7OTk+TGsawLSmATyaNWaWp6WnI/BzKD4Gf0PApxD4B4XA+4UvRCIxxGIxJi3cAHU1FbRycSjV8yJl55nPe077/I2HWDFnDAQKCqV+bPZsfFrV1GFlblLqxyxI/66tER2XgM37TnGW9+vcErPHDYCqslKpHl9RIMDM0X3R2rUR5v9vJ4JCvgPIyp6a8dcWuDVuwJlJkMiOWCzGLe8XnGXPffxx8Mx1DO3ZTka9qngohEcIIYQQQkgFdeXuE3QcPocTlDI20MPRzYvzDUrlR1EggLWlKbq2dcXssf2xa9Vv+HPGSOb5jMxMjJy9Co9/qbdDKqbEpBS8+/iFsyw2PpFT96k0seuaNXWoX26yTCYM7oYJg7tBQUEepjVr4N/Vc7B4+ohSD0qx2dUzh+f2pRjZtyN4PB6ArOtv5+HzZdYHkr+Pwd8QEhqeY/nyTQcQGROXyxYkN+XjqieEEEIIIYQUmlgsxoY9JzB81kokJacyy53trXF8658wr2Uk1eP16tAcc8cPZNopaekYPH0ZfP2DpHocUvZevv0A0c9i22ynrtwr9WOHR8bgbUAw03Z1tC31YxYWj8fDlOG98PjMdlza+z80LaOhjb9SUhRg9tj+aOvmyCzbf/Iq4hISZdIfwsXOlsoOHgJZwd2l/+yTRZcqJApMEUIIIYQQUoEkp6Rh3B9rsXLLIYjFklpQg3t4YOeq31BNXbVUjjusd3tMGNyNaccnJqPvpCUI/PytVI5HysYzH/9cl1+5+wRJKam5Pict95685rRdZRT8yY+SokDWXQAAjOrXiXmcmJyCfZ5XZdgbku32Q0nGXyPbOpwA4tHzt/Do5VtZdKvCocAUIYQQQgghFcS3HxHoNnoezl7zYpYpyMtj6axR+GPSYMjLle508pOH9eRMhR4VE48+Exbj6/eIUj0uKT3swueG+rrM45TUNFy9+6RUj32HVV/KopYR9HS1SvV4FZmtlRma2EkKru86cj5HYXZStpJT0vCQNaNksyZ2+H3iIM5MjXNWbEdGRqYsulehUGCKEELKSHxCEtbtOobN+07RP1CEEEKK7Onr9/AYMhtv3kuGz2lramDv2nno1aF5mfSBx+Nh3sRB6NrGlVn2LSwSfSctRkR0bJn0gUiPWCzGC1bGVHcPN84w0NIczicWizmBKRfH8pctVd6ws6bCo2Jx4uId2XWGwPuFL9LSM5i2e5OsovSTh/VklvkHhWDH4XOy6F6FQoEpQggpA9Gx8egxbgFWbz+CpRv3Y/nmA7LuEiGEkArk8Nkb6DF2ASKjJcV061nUgue2v2Bf37JM+8Ln87Hst9GcWfkCP4diwOS/EJ+QVKZ9ISXzKeQ7ouMSmLZ9fUt0aunMtG8/fIno2PhSObbfh2BERMUybffGDUrlOJWJW2Nb1DUzZtrbDpyBUCiUYY+qtlusYXzVtTVRp3bWezO4R1tYmtZknvt75zGEfM9ZIJ1IUGCKEEJKWXRsPPpMXAxf/0/Msj0nLiMsIlqGvSKEEFIRZGYKMX/Nbsz4azMyMiXZtu2bN8HBDQtQo7q2TPolLyeHtQsmwsnemlnm4x+EwdOXITklTSZ9IkXHHsbH4/HQwMocHVtJAlOZQiEu3PIulWOzs6UECvJoZFu3VI5TmfB4PIxkZU0FfgnFlVIebknyduuhpPC5q6MNU/xcQV4ei6cPZ55LSU3DgjW7y7p7FYq8rDtACCGVWVRMPPpMXMSZcQYA0tLSsWnfKSybPVo2HSOEEFLqPC/dgfeLtxAIFKCipAQVJUWoKCtCRVkJyrk9VuIuT0lNw9h5f+PeY0mBaB6Ph6kjemHsgC6cGaBkQVEgwOY/p2HknNXw+Tm88PGrdxg1ZzX2rZsHgYKCTPtHCsYOTJmZGEBdTQXqaipoYGWON+8DAQCnr9zH0J7tpH5sdmDKwaZOuSkyXt61b94E/+zxRGhYJABg095T6NjSWeZ/D6qa4JDvCPoSyrSbOTXkPO9gUwc92zdjhsNeufsEV+89QbtmTcqymxUGBaYIIaSURMbEoff4RXj38TOzTF5ODpk/U673n7yKycN6yuxuNyGEkNJz4eZDTFq4Qar7VFVWwup54zlD6GRNVUUZO5bPwtBZK/AxOGt2vlsPX2DSwg3Yvnwm5Eq5GDspmWc+75nHDeqZM487tXJmAlPeL94iNCySUxi9pJJT0vD4pR/TdmtsK7V9V3YK8vIY1rs9Vm45CAB4+fYDvF+8hUs5nNGwMrvtLRnGx+fz0NShfo51Zo3ph5sPniPu5xDn+Wt2w72xHVSUFXOsW9XRUD5CCCkFEdGx6DVuISco1cDKHPvW/cG009IzsGnvKVl0j5BKSywWIyklFT8iohHwKQTPfPxx++FLnL3mhQOnrmH/yasIZ9U0IaQ0iMVi/LPHU6r7rGlQHUc2Ly5XQalsmtXUsXvVHBgb6DHLzl1/gDkrt0MsFsuwZyQ/iUkpePfxC9O2t5bUKmvf3Al8flYGjlgs5swCKQ2PXr7lFI12c6T6UkXRq31zVFNXZdqb99H3ybJ2kzWMr4GVOef9yKZVTR2zx/Zn2iGh4Vj/7/Ey6V9FQxlThBAiZRHRseg1fhH8AyVf9uzqmWPXqjlQU1VGu2ZNcPVeVj2AA6euYspwypoipCAfPn3FzQfPEROfiPjEJCQkJiM+MRkJiUmIZx5n/ZdZQCHY1dsP4+CGBWVeMJpUHQ+f+3JmzrMwNYK8nBxSUtOQmpaOlNQ0pKSmc2pG5adJw3rYsGgKNDXUSqvLJaanq4Xdq+dg8IxlTEHrg6evo5q6KhZOHUbDjMqhl28/QCQSMe2GrL+J1XU00cSuHh79zGo6dfU+JgzpLrVjs4fx6WhVg2XtmnmvTHJQUVbEwO5tse3AGQDAzQcv8PZDMOpbmsq0X1VFWnoGvJ76MO38Cvf3aOeOU1fu4eXbDwCAbQfOok+nFkyhdJKFAlOEECJF4VGx6D1+IfyDQphlDa0tsGvVb1BVUQYATBzSHdfuP4VYLEZaegY2/ncSK+aMkVWXCSn3Aj9/g8eQ2UhOSZXK/iKj49Bj7AJsXzEL7ZtTrQcifdsPSaYGr6auiuNb/sy1fk6mUIjU1KxAVXJq2s/AVRqSU9J+Bq/SYKivi4bWFuDzy/9AB2NDPexeNQdDZy5nhq5s2X8G1dTVMG1kbxn3jvzqmY+kvpS6qgrMjA04z3dq1ZQJTL15F4iPwd9gYWoklWOzA1MujepT4LIYBndvi/+OX0JqWjoAYOv+09iydIaMe1U1PH7lh5RUySQP7k3yDkzx+XwsmjYMvccvglAkQkZmJuau3IFTO5bS556l/P8LRwghFUR4ZAx6jVvADUrVt+QEpQDAsnZNtGvWmGkfPH0N38OjyrSvhFQkf+88JpWglCIrMJCSmoYRs1fh32MXS7xfQtg+Bn/DtXtPmXb/Lq3zLOosLycHNVVlVNfRRC0jfViZm6ChtSVcGtmgtWsjdG7tAgebOhUiKJXNsnZN7Fg5GyrKSsyyFVsOYu+JyzLsFcnNc1ZgytbKLMfnrK27IxQUJHkMp6/ek8pxv4dHcbLK3RypvlRxaFVTR8/2zZj26av3EfI9XIY9qjpuPZAM49Oqpg7rAjLV6pqZYHBPD6b98LkvPC/dKaXeVUyUMUUIIVIQFhGNXuMX4UPwV2aZfX1L7Fz1G1RZX86zTRjSHVfvSbKmNu09RVlThOTiw6evOH31PtPW19VCjeo6UFdThpqqCtRVlLNmkVLNmklKTVU567Hqz+d//l9NRRk8HrBx70nsPHweACASifDH/3bhy7cwLJ4+vEL9+Cfl147DkmwpBXl5DOrRVoa9kY0GVubY/Nd0jP9jLdIzsuoI/b56JzTUVTk/pInsiMVivPAJYNoNrS1yrKOhpopmTexw88FzAMCpK/cxe2z/Emd5sLOlAMClEQWmimt47w44dv4WhCIRhEIRdhw6RzM+l4FbrMLnLo1sCvX9YfLQHrhy5zHCImMAAEs27EVb98bleoh2WaLAFCGElFBYRDR6jluIj5+/Mcsa2dbB9hWzcw1KAYClaU20b94El+88BpCVNTVleE8Y6OmUSZ9J+ZaekYHHL9/B2tIUOloasu6OTK3ddYwpnqwgL4/DGxeV6DqZPrIPataojj837IXwZ22V7YfO4euPCGz+azqUlWimHFJ8UTHxOH7hNtPu1MoZulrVZNgj2XG2t8baBRMx/c9NEIpEEIvFmLLoH6irqqCtu6Osu1flfQr5jqjYeKadV829Tq2aMoGpoC+hePM+EHb1cgaxioIdmLIyN6ny/86VRE2D6mjfvAku3n4EIKuu28zRfaGtSa9pafn2I4KT8dcsn2F8bKoqypg3cTCm/7UJQFZZgZVbDmL1vPGl0s+Khm4NEkJICfyIiEaPcQs4QSnHBnWxY0XumVJs4wd3Y+46pqVnYOPek6XaV1IxiMViDJjyF3pPWIS2g2YiIjpW1l2SmYBPITjDmgmqZ3t3qQRve3dsgW3LZ3KGGl246Y1e4xchMiauxPsnVdc+z8tMvRcAGNG3owx7I3utXRthOSsbOFMoxOi5/8PD574y7BUBgGdvJMP4eDweGliZ57peC+eGnL+Vpy6XbDifSCTCPU59KZsS7Y9w/86kpKbRsNlSduuhJFuKx+PBtQhDUdu6O3LqUe07eRUvfAPy2aLqoMAUIYQU0/fwKPQYuwCBn0OZZU3srLBjxWyoKBecdZGdNZXt4CmqNUWAbz8imZlevoVFYvfRqlsDad2u45xsqXGDuklt326NG+DghgXQ19Vilj338Uen4XMRyAo0E1JYqWnp+Pf4Jabt0sgGlqY001jXNq6YP3kI005NS8eY39cgOSUtn61IaWMHpsxMDKCuppLrekqKArRxbcS0z1zzgrCAmU/z4+MfhOi4BKbtls9sZqRwrC1N4coK8O06epGur1J021tSX8raslaRstN4PB4WTB4KRYECgKybkXNXbi/RNVVZUGCKEEKKITQsEj3GLkDQF1ZQqmE9bFs+q0hDgSYM6c5kTaVnZGLjf5Q1VdUFfArhtPeeuIyk5BQZ9UZ2cmRLdWiGGtW1pXoMK3MTHNm0GHXNJFM2B3/9gU4jfseTV++keixS+Z26cg+R0ZKMuxF9OsiwN+XLoO5tMWV4T6YdGR2HCzcfyrBH5JnPe+Zxg3q5Z0tl69SqKfP4R0Q0M1NfcdzxfsU8VlIUwCGPIYSkaEb178w8jo6Nx7HzN2XYm8orIyMT9x6/Ydruje2KvA9jQz2MG9iVab95H4T/KMuNAlOEEFJU335EoMfYBfgU8p1Z5tSwHrYtm1nk+jQWtYzQoYUT0z54+hpCwyKl1ldS8QQEcQNTsfGJOHKu6n3BzJEtxfoSJ001qmvjwPoFnLvNMXEJ6D1hEc6yAmOE5EcsFmP7IUnRcwtTIxqi9Ivxg7pxMsgOnr4mw95UbYlJKXj3UVIjx946/+CQs4M1tKqpM232hBRFdeeRZBiUY4O6EPzMHCEl49SwHurXMWXaWw+cRWYmZeFI2zMffyQkJTPtZk7Fy/gb2bcjTGvWYNqrth5GWER0iftXkVFgihBCiiA7KBX89QezzNneusiZUmzsWlPpGZnYtPeUVPpKKqb3QV9yLNtx6HyV+oLpH1T62VJsaqrK2Lp8Jnp1aM4sS0vPwNh5f2PzvlNMgIyUf5+/haH7mPnoPmY+5+90abvj/YpTDHd47w4lnrmssuHxeOjdqQXTfvzqXY4MUVI2Xr79ANHPyR8AoGEBWUsK8vKc0gPnbjxkZlssisSkFDx9LRlC6OZIw/ikhcfjYVQ/SdbUl9AwXLjlLcMeVU63HkqG8WmoqcC2bv7ZhnkRCBSwcOowpp2QlIxF6/8rcf8qMgpMEUJIIX39nhWU+vwtjFnm0qg+ti6bCSVFQbH3S1lThC0g6GuOZV9Cw3CxCn3BXLe7bLKl2BTk5fHXzJGYOqI3Z/nSjfsxd+WOKhUYrMhWbT0E7xdv4f3iLcb+vqbM3rdtB88wj3W0NNCZNfSJSHRp7QKBgiRD5tDp6zLsTdX13FcSHFJXVYGZsUGB27CH88XFJ+K298t81s7dw+e+yMjMZNqujpRVKE1t3RxhbKjHtDfvpRsr0sYOTDV1sIGcXPHDKU0d6qNTS2emfebqfdx9/LpE/avIKDBFCCGFEPI9HD3G/RqUssGWpTNKFJTKNuGXrCmqNVU1icXiHEP5sm09cKZKfMH0DwrhDKEr7WwpNh6Ph/GDumL1vPGQl5djlu87eQXDZq6okrW+KpKMjEzc8HrGtF+/C8TuoxdK/bh+H4I5PyYGdWtLw5PyoKmhBo9mjZn28Yt3kJZe9MwbUjLswue2Vmbg8wv+SdjQ2oIzK+rpK0UfzneHNRufvq4WzEwMi7wPkjc5OT5G9JHM0OfjH4R7VTjQIW1hEdHw9f/EtNmz6xXXnAkDoaaizLTnrdpRZf8mUmCKEEIKEJeQiJ7jFuILKyjl1tgWW5ZOh6Kg5EEpADCvZYSOrKypQ2eu49uPCKnsm1Qc38OjOLULWja1Zx6/8vtYooKzFYUssqV+1aW1C/5dPRcarFmqbjx4jm6j5+NHFa8BUZ49ffMe8YnJnGWrth3m3FAoDTtYtaUUBQro36VVqR6vouvNGjIbHRuPK3cey7A3VY9YLMYLH8n09A2tLQq1HZ/P52R3XLn7pMjBenZ9KZdGNjTctRR093CDDmuWuM37TsuwN5ULO7AKAO5SmFGyurYmpo2UZGoHfgnFln1Vs6QHBaYIIaQAWw+c5QSl3Bs3wOY/pReUyka1psiv2VK/jeuPauqqTHvL/sr9BfPXbKleHZuXWbbUrxrbWeHQPwthVEOXWebjH4SOw+bg3cfPMukTyR87WypbSmoa5qzYVmrZhmER0Th5+R7T7u7hBk1WkWiSU2M7K9Qy0mfaB2k4X5n6FPIdUbHxTNu+CLPidWQN50tJTcOVu08Kve2X0DAEfpbMZOwmhR/1JCclRQEG9fBg2veevMbrdx9l2KPK4yZrGF9dM2NU19GUyn77d2nNKVy/YY9nmdZILC8oMEUIKRUpqWkYNnMFmvWZgntPKm4acVRMPHYdOc+07etbYtOf00plmIZ5LSN0ZN2NpKypqsefVQhYVUUZtYxqYEDX1syy6/efVepiwTmzpbrItD/mtYxwZNNi2NSpzSz7FhaJLiPnVei/a5XVDa/nzGMFeXnm8Z1Hr3Dy8t1SOeaeE5eZmjk8Hg/DencoleNUJjweD707tmDa9568rpI/wmSFPYyPx+OhgVXhizfXNTOGeS0jpl2U2fnuPpL8zeTxeGhqb13obUnRDOjamjMhz5b9Z2TXmUpCKBTiLitjSpqBVTk5PhZPG87cnE5Lz8D8NburRPkGNgpMEUJKxaEz13Hl7hP4B4Vg4oL1FXa89Jb9p5CUnMq0504YWKq1Q6jWVNXmHygJOpmbGIDH42Fg97acYsHbDpyVRddK3fvALzmypfR1ZZMtxaarVQ171/7BGVaZkJSMAZP/wv0nb2TYM8L2JTQM/qyMw98nDuTczV7w97+IjImT6jGTUlKxz/MK027uZMeZ/pvkrZuHG+TlJHXcDp+9IcPeVC3swJSZiQHUWUOWC8Lj8ThF0G8/fIloVvZVftg/6q0ta1FmYSmqpq6KvqwZMM/feIDgkO+y61Al8NLvI2LjE5l2syZ2Ut2/TV0zzjDw294v8eTNB6keo7yjwBQhpFR4v3jLPI6IisWpK/fyWbt8Co+MwZ5jl5h2cye7It1ZLA4zE8NfsqZuUNZUFcLOhjI3zborratVDd3aujLLPS/dQXhkTJn3rbSVt2wpNhVlRWxcMg2DurdllmUKhVi17ZAMe0XY2NlSfD4PHVo4Y8HkIcyymLgELF63R6rHPHHhNmLiEpj2yH6dpLr/ykxXqxpauTgw7aPnbtLMl2Xkmc975nGDekX/TtOxpaQeZqZQiAs3C54xNjNTyMkydWlkW+TjkqIZ2qs9E/wVicTYdrBy3tQqK7ceSIbxqSoroWH9wtVmK4ppI3tz6oO9evcpn7UrHwpMEUKkTiwWc+7IAcD2g2crXErqxr0nkZKWzrR/nUa+tEwY3A18flbWVEZmJv7ZQ1lTVYFYLIZ/4BembVGrJvN4WO/2zOP0jEz8e/wSKpP3gV9w7voDpl1esqXY5OT4+GPSYIwZIAmYPXvjT3ehywl2fakGVubQ1FBDW/fGaO3aiFnueekubj8s+hT3uRGJRNhxWDLM29rSFI1s6khl31VF746SIuhhkTG4nkuNMCJdiUkpePdR8u+MvXXh60tlMzHU59ykK8yNx1fvPiIuIYlpS2M2M5I/Az0ddGoludF55PwtRETHyq5DFdwtVn0pJ3trznBxadFQU8WKOWOhpCiAuqoK3Byr1nBXCkwRQqQu9Ns3pMWFc5a9D/zCSeMu70LDIrH/5FWm3ca1EepZ1CqTY2dlTUlS5Q+fvYGv3ylrqrILi4zhzChmaSqp42FmYsgZSrb3xOUiz4ZUnrGzpQQK5Stbio3H42F47/acIUjswtdENpJT0vDgmS/Tbu7UkHm8YMoQzlTcv63chqSUVJTU1XtPEfRFUsh5RJ8ONMNYEbk0soGhvmRygYOnr8mwN1XDK7+PEIlETLthEQqfs3VqLfmO8uilH0LDIvNd/673K+axirIi7IqRqUWKjp3FmZaWjn+PXpRhbyquyJg4vPKTFJAvzcCqe5MGuO+5GRf+WwkrM6OCN6hEKDBFCJEq4ffXUDnZDVe6fsnx3HbWlNrl3T97PJm6WDweD1NG9CrT448f1JWTNUW1pio//19m5GMXmAWAEX07Mo9j4xNx5NzNMulXacuRLdWh/GVLsWlVU+d8KfW8dKfCZYNWNg+e+SCVld3a3Lkh81hfVxszx/Rj2iGh4Viz/UiJj7mD9e+Zvq42PJo1LvE+qxo+n4+e7Zsx7VsPXxQY4CAlwx7Gp66qAjNjg2Ltp33zJsx3FLFYzKkPmJs7rBuTTezqlUq2CcnJ0rQmWrD+Hv5XyW5qlZW7j15x/p13L+UZJRUFCpzi9VWFTANT3759w8KFC/HyJTet+uPHj/j9999z/BccHMysExsbi507d2LRokXYtWsX4uMLV3iPEFI6xMIMpD3YiORjgyEvSkNiRs4/L7e9X1aIadZDvofj0BlJIdZ2zZvA0rRmPltIX25ZUyHfw/PZglR07GF8qspKqFGdG5xpZFMHtlZmTHvHofOVoibL2l3HONlSY8tpthRblzaSml9BId/x8m3VKlBa3rCH8enraqGumTHn+b6dWsCBNcxux+HznLvfRfXK7yOnjuKQHm3ph3Yx9WzfjAlwiERiHK0kAffyil1mwdbKDHx+8X4KVtfWhFNDyTCjk/kM54tPTMJzX8lxXR2pvlRZYmdNxcYn4uCZ6zLsTcV021sSqzAzMeRkehLpkVlg6vHjxxg9ejQuXLiAL1+4mRXBwcG4c+cObGxsOP+pqGTNGpGYmIg+ffrg8ePHMDMzw8OHDzFgwACkppY8NZsQUnTCyI9IPjoQ6c/2gKdWA0lCeYQmZc0i5upoAyVFAbNuRciaWr/7BDP9N5/Pw5RhPWXSj19rTVHWVOXGLnxuZmKYY1gQj8fDSFbW1JfQMFy8VXDR2fLs3cfPOH/jIdMu79lS2Vo4N+QMD/O8dFeGvanaxGIxp/C5W+MGOa4dPp+PP2eMgIJCVvBIJBJh1rItyMjILNYx2UWEVZWV0KdTy2LthwA1qmtzsg8Onb3BGWpGpEcsFuOFTwDTbmhdsuLN7IlafN4H4WPwt1zXe/DUF0Kh5D11K+VsE8LVyKYO573efvBcsf/2VUUikQi3WLUJ3SiwWmpkEphKSkrCsmXLsGPHDqir55wqNDExEXp6ehg8eDDnPz09PQCAp6cnVFRUsHPnTgwfPhw7d+6EUCjE2bM02wAhZS3jw3UkH+4LZKZD4DoN4oTv+BAjj+TMrB8GLZzt0d3DjVn/1OW75XpGseCQ7zh6XnLHtlMrF9QuZqp7SdU2NuBMy3zk7E3KmqrEAlhD+cxrGea6ThtXR9SsUZ1pbz1wptSHkSUmpWDbgTM4ffW+1I/1a22pipAtBQBKigLO0K0z1+7TF30ZeR/4BV9ZM5eya7GxmdcywlhW4Xpf/0/YcbjoN0q+fo/A+RuSoac9OzSDuppKkfdDJHp3bME8/vo9Ancfvc57ZVJsn0K+IypWMsLEvpj1pbK1dXdkgr0AcPpq7llTdx+/Yh4b6evCxFCvRMclRcPj8ThZU6FhkThz7b4Me1Sx+PgHISomjmk3c7KTYW8qN5kEpgQCAQ4fPgwTE5Ncn09MTISamlqe2z969AjNmjWD3M/iowoKCnBzc8PDhw/z3IYQUkpEGRA4joRy/0PI8PFEknYDJGXwkJKZ9eelobUFhvZqx9zBTs/IxJ5yPKPY2l3HmTt7cnJ8TB7aQ6b9GT/o1xn6PGXaH1I6xGIxp8aUhWnuBS/l5PicGfp+HVIkbckpaeg1YRGWbNiL8X+sxdh5fyMlNU0q+66o2VLZurRxYR5HxcRzaqiQssPOlhIoyMPZvn6e647p3xlmJpKg75odR4s8q+LuoxeYfyP4fB6G9mpfwBakIM2dG6K6jibTPniGiqCXBvYwPh6Px5lZrzg01FTRnPUj/dTle7nevLj7WBJodGlkQ5MEyECrpvYwM5HcZN287zTVRiwkdraUkqIAjrZ1Zdibyk0mA+IVFBSgoKCQ5/OJiYlQUFDA8ePH8e7dO+jo6KBbt24wNs6qGRAaGgp3d3fONgYGBnj9Ou87LEKhEEKh7GpxZB9bln0gpDTwLdqBbwGkvz4KcewX3NacCEP5AIQm8aAoUIC5qSEU5OXR3NkOd37OyrLX8womDesBlUIW9iur6+dD8Fd4XpYMyena1hWGNXQgFMnuujUx0kPHls64cDNryNaRszcxZVgP1DSgO46VSXhkDGLjE5m2mYlhnp+7rm1dsXnfKWbq7S37T8OpYb08913c60ckEmHq4g14xaqfdO76A3z++gP//f17jhpYRfVrbanRAzrJ9ForKgcbS+jraiHsZwboiUu30col92wdUnqu33/KPG5kWxeKivJ5fo7k5Pn4c8ZwDJ25EmKxGKlp6Zi1fCuObV6c64/lX6+dhMRkHDwtqc/S2rURalTXqlCf2/KIxwO6e7hh15ELAIArd57gR3gUJ1hFSu7p63fM49rGBlBRUSzxZ7d9CycmOBwU8h0vfQNg93PYmFAoxPfwaHz+Fsas7+JYn64XGRneuz0WrfsPQFam6fX7T9HatZGMe1X+3XwgufnRuIEV5OT5pf4ZFoqEzJDmyhA7kGPNZJyfclmpMSEhAc+fP0ft2rVhaWmJ58+fo1OnTjhy5Ajq16+PtLS0HIEtBQUFpKen57FHICAgIM/nypKPj4+su0CI1PEzU2D6dDOS9F3heecLZlQXIzGDD1MjPfi/y5oBxt2+DhOYiolLwPodB9GpedH+QSzt62f1rlPMPwRycny0crSCr49vAVuVvlaNrXDx1iOIxWJkCoVY/PcuTBnSqeANSYXx6v0nTjszJTHfz14rJ1ucvvEIQFbGyLnL12FiUD3P9YGiXz8Hzt7B+Zs5a1i9fheItoNmYtGkfrCsVbxhrsHfwnHhpiRbqkUTG4SHfkN4aO41SsqrJrYWOH87KzBy+fZjPPR+DBXlqjeTjqwkJKXg6RvJLGN1TPQK/JstD6BN0wa4/jDrZqbXUx/8vW0/2rrkPTwj+9o5ff0REpKSmeXNHOqUi38jKoP6tfWZx5lCITbsOow+7V3y2YIUldcTyQ18Y31tqXx2q6vJQ0lRAalpWbMY7zx0GmP6tGWef+4XxDzm83lQF4CuGRkx1deAloYaYn7eBFv6z14oy2UW+iZxVZSQlILnrExD85q6ZfL5FQpFyBQKoaOlXiliB40aFe73XrkMTE2ZMgWTJk1iip0PGjQIEyZMwO7du7F+/XqoqqoiOTmZs01SUlK+w//q1KnD7E8WhEIhfHx8YGtrW+iooTSJRCIcv3gHOw6dw4+IaGhrqkNbsxq0NdWho6kBHS0NaGtqQEfz5/+1NH4+Vw2qKkolSrsVCoVITUtHSmo6klNSkZKahuTUNGQKhbC2qAVVVgFZUjGlP9yETFE69DsuQuCBhVgQpIfoVDl06GQLG1sbAEB9m/o4ef0J/D4EAwAu33+JeVNGFGpGmLK4ft4HfsG9Z35Mu2f7ZmjRrHx8KbYB0PmZPzPs6frD1/hz9hjKmqpEnvuHMo+VlRTR3N0532ujRk1jXLz7DOk/6xrdff4Baxe0zXXd4lw/p67cx5GLkhoUOloaqK6tifc/Zw6Mik3A3L/3Y+OSqejcumleu8nTtmN/I3sUgUBBHnMnD4WejlaR9yNrw9WqMYGp9IxMfI1KRt/OTjLuVdVx5poXRCLJcJR+PdrDuBB/F/8yM8frMfOZeof/nbqFEf27Q1e7Gmc99rUjFgNjF29nnrOzNkf3Trlfc6TobAA4X/bGo5dZ/w7feeqHZXMn0LAvKUlKTkFwKKsWm5sj8/2spDzcG+Pcz+8nD1/6Y+NfMyEnJwehUIi/thxj1rOpawanJo5SOSYpnhF9O2Ld7uMAgIDgUMzfcAR71syVWS3V8u7CTW+IWEMe+3ZrB+MyqJGWKRQiLS0dET++ySx2IAvlMjClpKSEzExuEVFzc3MmYmhmZoZPn7h3l4OCgmBunvdYaTk5uXLxpsqiH28DPmHuqh14+lpyVzEuIQmfQn4UantFgQIrWJUVvNKqpo6MzEwkp6QhJTXrPybo9HNZ8s/lqWl5Z7IZG+rh4p5V0C/hkBAiO6KEH8h8eQAChyGISBPge3gUviPr7ouDbV3I8SWf9+G922POyqwv9kFfvuOW90u0a9ak0Mcqzevn12FFE4d05/Rd1iYM7oaLt7whEmVlTW3cewprF0ySdbeIlLBnMzIzMYCCfN7D3QFAX0cb3dq64cSlOwCAk5fv4o9Jg6Gnm3dwp7DXz9M37zFr2RamrShQwJalM2BRqybmrd6B617PAACpaekYO+9vzJ0wEDNG9Sn0D8h3Hz9zMrF6d2wBg+oVc+rleuamqGtuAv+fAbtTV+5jQLc2Mu5V1cGewtu0Zg2YGhXux1U1dTUsnDIUUxb/AyBrCvXF6//D9hWzcl1fTk4OF2564+t3yQ/7kX06lqt/IyqDPh1bMIGpTyHf8fjVO7jSDFhS8eb9J85shw42daX2+e3UyoUJTIVFxuDpG3+4OtoiIzMTr/2DmfXcHG3pmpGx/l1a48TFO8xEOv5BIeg4fC62r5iFVi4OMu5d+XP7keTfGBNDPZjWLJsAnlgM5uZkeYlhlAWZFD8vyIwZMzB16lSmnZqaijt37sDOLivNum3btrh+/ToiIrK+IHz9+hW3bt1Chw4dZNLf8io+IQkL/t6NNoNmcYJSRZWWnoHv4VHw9f+Ee49f4/TV+9hz/BIOnLqGk5fv4tLtR7jt/RKPX73Dm/dB+Pj5G76FRSImLiHfoBQAhISGY+LC9ZVi/GxVlfZwM3gCFQgaj8JzH+6QWbt63GBxu+ZNOHVpth8s+oxIpcHnfRAu3nrEtPt2blXuijCb1jRAl9aSDK6j527h0q1HSExKkWGviLT4B31hHpvXyr3w+a/YRdDTMzLxrxQmFQj5Ho7hs1YiLT2DWbb8tzFoYGUOFWVFrF80GeMGduVss3rbYUyYv67QRdHX7ZLcQRcoyHNmSquI2Nfl/adv8CMiWoa9qTqEQiFuPXjBtJs1KdpMSa1dG8HDXTKz4umr93HjZ9D1V2KxGNsOSmZ+rmlQHa1cqDaLtLV2bQRNDcnoB3Y9L1Iyz3wkvwPUVVVgJsUMGWcHa2hrSmZZP3Ula3a+F74fkJIq+R3g1piCjLKmpqqMgxsWoOHPOmBAVrLCwKlLsfG/k1QQnUUsFuM2q/C5W+MGMuxN1SCTwNTz588xdepUTJ06FXFxcThw4ACmTp2KnTt3Asgayufr64tu3bph+vTpaN++PTQ0NDBu3DgAgIeHB5ydndGjRw9MnDgRffr0QefOndG0adGHE1RGYrEYnpfuwrXXZOw6coFzh8RATwezx/bDlOE9Mah7W3Ro4Qxne2vUMTOGno4WFOTLPonO66kP/vnvZJkfl5ScMOI9Mv3OQuA8ATxFdTxj1fow1NdBdW1NzvoK8vIY1F0y9OHhc1+8eRdYVt3N0+rth5nHSoqCcjtl/TjWDH2ZQiFG/LYKdVsORueRv2P1tsN4+NyXE1AgFcOvM/JZmtYs1HZmJoZo2VRSbHvvictISi5+oDIxKQWDpy1DZLRkWuRJQ3ugY0tnps3n8zFtZG+snjceAs404ffRY+wChBUQlHn38TNzZx3IypbKL8urIujUypnJFhOLxTh1Ofcp04l0vfT7iKjYeKbdomnRC8/PnzwE6qqSMg9zVu7I9Rp6+sYfL1mTAAzt2Q5ycuXy3m6FJhAooJuHG9O+eMsbMXEJMuxR5cGekc/WyqxQZRQKS0FenpP9fv6mN9IzMnCXNVOpmqoybOqaSe2YpPiq62hi79/z0KdTS2aZWCzG8s0HMHbe30hKSZVh78qPdx8/c240uRfx5gcpOpkM5dPT00OLFi0AgPk/kDWzHpA1VO/GjRt49uwZYmNjMWrUKNjaSqLsPB4P69atw+vXrxESEoJJkyahfv28pweuSt4HfsG81Tvw8Dl3+nB5eTkM790BEwZ3g3I+Re7EYjGSU1IRHZuAmPgExPz8f3RsAmLjEhAVG4/ouATEJyRBQV4eykqKUFYSQEkx6/9Z7Z//KQqgrKz487msttLPdfg8HiYuWM+kkq7ZcRRNHeqjqQO9jxVJ2v114GmaQMG2DwDgua8kY8qunkWu2/Tp1ALbDp5F8s9/+LYfOoety2aUfmfz8NwnANfvS+6SD+jaGrpa1fLZQnZMa9ZA17ZuOHNVUvsnUyjE09fv8fT1e6zbfRzKigI42VvDvUkDNGtiB5u6taX6BZRIX2RMHOfHl0UhM6aArHoR2cOZYuMTceTcTYzu37nIfRAKhRj3x1qmhhQAdGjhjIlDuue6fpfWLjA2qI4pi/5hggMv335A+2G/Yf+6+bC1yv0HCDtbSlGgkCP7qiLS19WGU8N6zBAkz0t3MHFod9l2qgq4wfq7raqsBAebOkXeR3UdTcwe2w+L12fNVPXtRwRWbj2EZbNHc9bbcUiS3auhpoKe7ZsXs9ekIL07NMc+zysAsjL2PS/dxZgBRf+bRiTEYjFesDLa2dky0tKpVVMcOXcTABAXn4jb3i9x97Gk2LpTQ2vIV5HhSBWBQKCAP2eMgLVlLSzffACZmVkjV85df4APn75i79p5MK1ZQ8a9lK1bDyUZuQIFeTSxs5Jhb6oGmQSmjI2NYWxsnO86AoEALi75Fx62s7NjhvdVdUnJKfh75zHsPHwemb8Mi3NqWA+Lpg0vVGE7Ho8HVRVlqKool3pxt7/nT8Sg6UuRmZk1JeaE+etw8/B66GhplOpxiXRkfn4A4eeHUOq8Hjw5BaRnZHCyn+zrW+a6nYaaKnp1aIYDp64BAM5e88KCKUNgqC+bGjP/Y2VLqSgpFutHfVn6fcJACBTkcdv7JSKiYnM8n5KWjjuPXuHOzzuVWtXU4epoA/fGDeDepAHMTAypmGw54x8YwmkXdigfADSyqYMGVuZ48z7r2ttx6DyG9+4Aefmi/QD46599nGFMtlZmWDFndL6flYbWlji2ZQkmLdrA1FgKDYtCl1HzsHXpDHRs5cxZP2e2VPNKMx18lzauTGDq7Ydg+H0IhrWlqWw7VcllT1EPAE0d6nMy+IqiV4fmOH/zIZNRsvvoRfRs34wJdIWGR+PK3SfM+n06taCZF0uReS0jONjUwYufN7oOnr6G0f070b9bJfAp5DsnuzCv72cl0dDaAob6uggNiwQA7Dl2Ca/8PjLP0zC+8qlf51awNK2J6X9tYrKl3338jHZDZmPnytlo7txQth2UoVusYXyNbOvmm9hBpINuo1dwYrEY564/gGuvydh64AwnKKWno4m1CyZiz5rfy+VsC7ZWZpg1uh/T/h4ehWl/bqTxzRWAWCRE2r21kDO0h7xFVqFfn/efOMPI8sqYAoAhPdpxhqTtPnqhdDuch8cv/ZgADgAM7ukBrWrqeW9QDmioqWLJ9BG4c/QfnP93JeZPHoLWrg5QV8t91tGYuARcuOmNuat2wKXnJDh0Go2pi//BiYu3qRZOORHwSRKYUlIUwFBfp9Db8ng8jOgrqa/4JTQMF29557NFTgdOXcN2VkZIjera2LJ0BhQFggK3NdTXxcENCzhFU1NS0zDit1XYsOcE5+/52p3cbKmxAyp+tlS2tm6OUBRICtafpOF8pepHRDR8/CXT0LcowY8nPp+PP2eMhEAh6/0Ti8WYuXQLMn7OeHn25hPmcywvL4chPdoVv+OkUPp0bME8fh/4hZONTYqOPYyPx+OhgVXek0UVF5/PR8cWkhlJ7zx6xfn779qIAlPllYNNHZzY+ifncxEbn4j+U/7C1v1nquTvssSkFDx59Y5pu1N9qTJBgakK7GPwN/SbtARjfl+D7+FRzHI5OT6G9W6Pi/+tRocWzuX6LtPQXu04Xyiv33+GHYfPy65DpFAy352HKDIAiu6zmM/Xcx/JFx9FgQLqmpvkuX1Ng+po4yaZMvjAqWsyKeK9evsR5rGaqjJG9OlY5n0oLh6PB/NaRhjUvS02/TkdD09uxbHNSzBjVB8429fn/EhmCw2LwrELtzF50T+waz8SI2avYlK4iWwEsOpL1TY2KPLQyzaujjA2kGS4bj1Q+C+SXk998PuqHUxbRUkR25bPLNJwVlVlJWxcMjVHtuHKLYcwaeEGpKalw+9DMM7frJzZUkDW3w92cO7k5buc+o5EutjZUgDg7lSy7PnaxgYYP0gSKH338TO2HDiNmLgEXHvwilnevnmTCl8TrSLwaNaYU/vr4OlrMuxNxccOTNU2NsjzRlZJdWqde61fY0M91DSoXirHJNKhr6uN/ev+QM/2zZhlIpEIf/6zFxPmr0NySuEmN6ks7j99g4zMTKbdrIT/xpDCocBUBZSckoYVWw6iRb9pnPHbQFbU+9SOZZg7fiBUVZRl1MPC4/F4WP7bGOizvugt27ifk/5LyhdxRgrSHm6CvKUH5AwbMsufsQJT1pamBQ6rGNFbkuURn5iMw2dvSL2v+fF66oMHz3yY9rBe7VFNXbVM+yBNcnJ82FqZYcyALtizZi4endmG//7+HeMGdkWDeuaQyyPYcen2I5y97lXGvSVs3Bn5DIu8ffbNiGyv/D7C+8XbfLbIEvj5G0bNWc1k2vJ4PPzvjwmoa5Z3UDkvfD4fM0f3xaq546DAuvZPXr6LnuMWYunG/cyyypYtla1rG1fm8ffwqEK9B6R42MNOrS1Nc0y0URwj+3XiTDywbtdxLNt0gJMJPLJvpxIfhxRMWUkRXdpIynmcueqFhMRkGfaoYnvuK/l+Zmct/WypbHVqG+c6FN2lEdWPrQgEAgUsnTUKC6cM5dQDO331PrqM+h1fQsNk2LuyxZ6Nz0BPp1yOPKqMKDBVgYjFYly+8xjN+kzGP3s8OZFcbU0NrJwzFgfWzy/0jE7lhVY1dayZP5EZ2pWRmYmxv69BfEKSjHtGcpP+8gDEyVFQdJvOWf6cdUfOrl7BX3zsrC04dQ52HjkPobBsMnfEYjFWb5PUlqqmrophvdrns0XFoygQwKmhNaaN7I2jmxbD+/Q2bFk6A0N6esDClPvFsayDgoTLP+gr89jSNP/6i3np7uHOCaxuPXAm3/Vj4xMxZMZyxMYnMstmjenHyfopjq5tXbH373mcqcOf+/hziohWtmypbC6ONpyhwJ6X7sqwN5VXWnoG56ZccyndyRYoyOOvWaOYLOC09AymmDMANGlYD1b5ZAIT6erVQVJgPiU1DadZk36QwktKToHfh89M295a+vWlsvF4PHRqlTNrys2RhkFVFDweDwO6tcGeNXOhoymp+evr/wntBs/G/SdvZNi7siEWi3HroSQr19XRtlyPPqpMKDBVQYjFYsxevhXDZ61EyPcIZjmfz8PAbm1wae9qdPNwq7AXjqNtXUwa2oNpf/4WhlnLt1bJcc3lmSg5CulP/4VCg37ga0q+oP+IiMbXH5LPZWELa7KDQSGh4bh0+7H0OpuPO96v8OS1ZOz4iD4doaZa/jMMS0JNVRktm9pj3sTBOLd7JWaNkdR383rqg+CQ7zLsXdUVGROHqJg4pv1r0LCwVJQVMbBbG6Z9/f4zTu0qtoyMTIyasxqBn0OZZb06NMOIPh1yXb+o7Otb4tiWP1Gnds4gW2XNlgKypkzvwKqxcv7GQ6SmpcuwR5XTo5d+zKyuANDC2V5q+7arZ865jtjYWb6k9NWzqAWbOrWZ9gEazlcsL99+5AwrblgKhc/ZOrXkTnohx+ejsV3dUj0mkT7HBlY4vvVPzjUYHZeAfpOXYMfhc5X699nH4G+c39rSuvlBCkaBqQriR0Q0Dp6+zlnWwMocJ7b+hQVThkJDreIOQco2dkBXONlbM+1z1x/QF5FyJv3RdgA8CJzHc5az60sB+Rc+Z2vt2gg1a0jqDmw/dLbEfSyIWCzGKtZMfFrV1DG4h0epH7e86e7hzpm57cj5WzLsTdXFri8FFG8oX7YB3dowBZwBYNuBnNeTWCzGvP/thNdTyTDWxg2ssGjacKne2DDS18WhfxagZVNu0KBPpxaVMlsqWxfWcL6EpGRcu/dUhr2pnNjD+LQ11VG/jqlU9z99ZG/UqK7NWVbbuAbcm1DWR1nrzSqC/uZdIHzeB+W9MsnVM5/3zGN1VRWYlfKQJGNDPU4RbYtaBhWitAjJyUBPB/vXz0d3DzdmmVAowqK1ezB50QakpFbOulO3vCUZ3vLycnBm/TYlpYsCUxWEtqYGM0RPU0MNf80cicMbF6KeRS0Z90x65OT4+N/v4zlDQBb+/S/8PgTLrlOEIYoJRobPCQiajAFfmVv8lV1Ys0Z17UIXh5WT42NIL8kMR8/e+OPpm/f5bFFy1+4/xau3H5j2qH6dquTU3zpaGmjVVDJs6+i5m1QEXQbYgSlFgQBG+sUvEKurVQ3d2koCI56X7iA8Moazzs4j53HglCTgb2Koh3+WTIWCfP414YpDVUUZG5dMw7iBXaGkKIC1pSkmDulR8IYVWAMrM5gY6TNtz0t3ZNeZSopd+NzNsUGRJwsoiKqKMhZNG8ZZNqSnh9SPQwrWqZUzZ4p2KoJedOzvZ7ZWZmXyOZ4yvCf4fB7k+Hx0bdW41I9HSo+SogDLfxuDeZMGc2qVel66i66j5uErK7Oosrj1QBKYsre2pMBqGaJ/ZSsIRYECrh1ci0P/LMSZncvRu2OLSvklqbqOJlbNHce0U9PSMW7e30hipe0T2UjzWg+eqi4E9oNyPMf+4mNnXbhsqWw92zfjzL6z4+C5fNYuGZFIhP+xZuLT1a6W57CNqoBdw+NHRDSnDhApG/6cGflqQE6uZH/Xh7OG46VnZOLf45eY9s0Hz7Fk/V6mra6qgu0rZkFTQ61Ex8yPnBwf00b2xpNzO+C57a9SPVZ5wOPx0KW1pGjzzQcvEBUTL8MeVS5BX0IR9EUyBLVFU+kN42Nr4WyPKcN7QklRgMY2Fuju4V4qxyH5U1VRRkfW0LCTl+/R98EiEIvFeOETwLQbFvH7WXG5OtrC68Rm3DyyHg6lWGydlA0ej4chPTzw7//mcuoovnkfBI8hs+HrX3kyGZNT0jgTl1CmbNmqfJGNSkxFWRFODetBVUVJ1l0pVW6NG2B0P8nMNwGfvmL+ml0y7BHJ/PYCmR9vQtFlKnjy3M9fekYG3rwPZNpFLaypqqyEvp1bMu2Lt73x+VvpzPxx6fYj+Pp/YtpjB3SBkqKgVI5VEbg0suEMWTl05no+a5PSwA5M5TabUVHVNjbgDJ/be+IykpJTEPwtHBMWrGdqjcjJ8bFh0WSY1iybmWbYM/xUdp1ZgalMoRDnbzyQYW8ql+usYXxycny4NrIptWNNGNwdT8/vwMwR3TgzTZKyxR7Ol5CUjAs3HsquMxXMp5DviIqVBMYLW/9TGjSrqUNHS6PgFUmF0aRhPRzf+idntE5UTBwmLdyAjIzMfLasOLxf+HJmYqXAVNmiwBQpl6aM6MW5s3Pk7E2a4UhGxGIx0u6vBV+vHuTrdc7x/NuAYE6B34b1i35HblD3tswPV5FIjJ2Hzxe/w3kQCoX4346jTFtfVwt9O7XMZ4vKT06Oj57tmzHt617PEBYRLcMeVT3sAuWWxSx8/quRfTsyj2PjE7Fl/xks2XwUiUkpzPL5k4eiaSn+qK/Kahnpc/79ouF80sMexmdf3xLqair5rE0qgwZWZpyJFGg4X+Gxs9l5PB6n9hMhxZFVP3IhZ/bF94Ff8N+JyzLslfTcZA3jq66tmeskLqT0UGCKlEsK8vJYM38iNFhfOues3MZJ4SdlI/PDNYi+v4ai+yzweDn/ZLC/+AgU5GFlXvS6ZzWqa6M9azarw2dvIC4hMZ8tiu7s9QfwD/zCtMcP6gaBQCGfLaqGHu2bMUWvhUIRjl+8I9sOVSHRsfGIiIpl2tLImAIAB5s6nB8gG/Z4IjxKMvPf4B5t0b9LK6kci+SOPZzv6Rt/BH/9IcPeVA6JSSnwfi4ZYtHcqaHsOkPKDI/HQ++OkmHnT16/52SakryxJ6apbWxAgVwiFUqKAqyYMwZmJpLJWv63/QjCWd9nKqrb3i+Zx66ONhV2tvuKigJTpNwy0tfF0lmjmHZScirG/v43J8WSlC6xMANpXhsgZ+oOeRPnXNdhf/GpZ1ELgmIOeRjeuz3zODkllVOguaT8PgRj5dZDTNtIXxc9OzTLZ4uqw0hfFy4O9Zn2oTPXKvU0wOUJO1sKACx+TnBRUjweDyP65j61vXvjBpg7PmedOCJd7Vs4cYYvnrpMGb8lde/xa2RkSoaLtHBuKLvOkDLVubULZ8ZRGnZeOM982PU/KVuKSI+CvDwWTBnKtBOSkrFs4z4Z9qjkgkO+cxIgmtHNjzJHgSlSrrV1b8wpTu3jH4Q/N+yVXYeqmIzXRyGO/wZF95l5rsP+4tOwiPWl2KwtTdHErh7T3n30QonHrMclJGL+mt1oM2gmvrDqVk0Y0q1UZiGrqHqxanh8CvnBKfxISk9A0FfmsUBBHjVrFH9Gvl+1cXWEsYEeZ5l5LUOsXTCpxAXWScG0qqlzalOcuHiHAr4ldOOBZBifkb4u5249qdw0NdTg0Uwyu9vxC7fpJmUBkpJT4PfhM9Muav1PQgribG+N9s2bMO1jF27j6evSnVm7NN1iZUvJ8flwaVQ/n7VJaaBvp6Tc+21cf1iZmzDtf49dxKXbj2TYo6pBnBqPtMfboVC/O+R0c/9CEx4Zg5DQcKZdnPpSbMP7SLKmvodH4+z14hUNFolEOHLuJpr2mITdRy9AKBQxzzWwMkfXtm4l6mdl06qpPWemFbobXTbes4aWmhobSDVgJCfHx8h+klpT6qrK2LJ0BtRUadrjssIugh4U8h0v336QYW8qNrFYjJuswFQzJzsaYlHF9GHdQImJS6DvgQV4+fYjM9kFADQsw8LnpOr4bdwAKCtJJhGat3onhEKhDHtUfOyZqW2tzKChpirD3lRNFJgi5Z6iQIB1CydBWUmRWTb9z00I+R6ez1akpNKf7gYy0yFoOjnPddjZUkDJpyJu1sQOtY0ls4TtOHS2yFkGr999ROeR8zD9z02IipHU1ZGXl8PIvh3x75q5VWqGsMIQCBTQta0r0z5/86HUa3yRnNhD+SykVF+KrW+nlpg5ui+6tnXFksn9YVRDV+rHIHlr2dSeM4suTeBRfL7+n/CDNTFDcxrGV+U4NqgL05o1mDYVQc/fMx9J5oq6qgrMjMtmBlZStRjo6WDcwG5M28c/SKqlOMpKalo6vJ76MG33xjQbnyxQYIpUCKY1DbB4+nCmHZeQhPF/rK0005OWN6L4UKS/PAhBo2Hgq+nluR67vpR+dW3o62qX6Lh8Ph9De7Vj2m/eB+Hhc99CbRsdG4/fVmxDuyG/cfoFAE0d6uPsrhWYPbY/VJWV8thD1darg6S4bFpaBk5dvifD3lQNAawCvua1pD8sicfjYXT/zlj+22gY6pXs2iRFp6QogIe7ZPjRmWv36d+sYrru9Yx5rChQ4Az7JlUDj8fj/Dvl9dQHwSHfZdij8o09MY2tlRn4fPrJR0rH8N7tUctIn2mv3HoIUTHxMuxR0e04dA4pqWlMu5mTnQx7U3XRXylSYXRt44ru7dyZ9rM3/li9/YgMe1R5pT3YCJ6iGgSOI/Jdj/3Fp4GVmVSO3bWNKzQ11Jj29kPn8l1fKBRin+cVuPSchP0nr3IyrAz0dLBxyVTsXj2Hk4lFcrKoZcTJeDtIw/lKVWx8IsIiY5i2JU1JXCl1aSPJRIyKicfdx69k15kK7IaXZBifU0NrKCkK8lmbVFbdPdwhLy/JeD545oYMe1N+icVivPAJYNolzWYnJD8CgQL+mDSEacfGJ2LFlgMy7FHR7PO8ghVbDjLtGtW1Uc+i6DOMk5KjwBSpUBZMHgozE0mAYdPek7jDKlZHSk4Y5ofM9xcgcJ4EniDv8dUZGZl47feRadtLqX6BspIiBnRtzbSv3XuKj5+/5bru0zfv0X7ob5izcjti4hKY5QIFeYwf3A0X9qxCGzdHqkVSSOy70b7+n/DmXaAMe1O5Bfwy3blFKWRMEdlrYmeFGtUl2Wo0nK/oImPi8MJX8iObZuOrunS0NNDKxYFp7zx8DtP/2oTX7z7ms1XVE/z1B6JiJRkr0vp+Rkhe3Js04Fybh87cqBB1FT0v3cXcVTuYtoK8PJbPHk0ZhjJCrzqpUFSUFbF2wSTOtMGTFm3g1J4gxScWi5F2fy342rWhYNsr33X9PgYjJS2daUtzxpf+XVtDQUEya96uwxc4z4dHxWLako3oPOJ3vHkfxHmuhXNDnP93FaYO78WpS0YK1r6FE1RYQx2pCHrpeR8kKXyuIC+PmgZ5D5klFRefz0enVk2Z9uU7j5GYlCLDHlU8tx++4GTC0hCLqo1dBD0tPQNHzt6Ex+DZ6DBsDo5fuI1U1veSqurZG0l9KR6PhwZW5jLsDakqfp8wCIqCrN9nYrEY81bv5BTgL28u33mMqUv+Yf59kePz8ff8CWjayEbGPau6KDBFKpy6ZiaYN2kQ046MjoN778lYVQHHNJc3wuD7EIY8hqLbTPD48vmuyx7GpyAvL9W01+ramujCmtHq+MXbiEtIRmamEDsPn4dLj4k4ev4WZxtjAz1sWzYTW5fNhLEh/cgvDlVlJXRs4cS0T125h+SUtHy2IMXFzpgyNa5BBfkrMfbfstS0dFy85S3D3lQ811nD+CxMjWCoT0X8qzKXRjbo0sYlx/IXvgGYsvgf2HcYhaUb9+HztzAZ9K58YH8/q21sAHU1FRn2hlQVNQ2qY3T/zkz75dsPOHLupgx7lLd7T15j7O9rOLN2L509Cm1ZdSFJ2aPAFKmQ+nZqiXbNmjDt+MRkrP/3BBp1HoOFa/9FaFikDHtXMYlFmUi7vw5yRo6QM2tR4PrPWfULrCxMIBAo5LN20Q3r1Z55nJaegR3HrsJjyGwsXPsvEpKSmeeUFAWYNqI3zv+7kmZqkoJerLvR8YnJuHjroew6U4lxCp+b0DC+yqyOmTHqmklqiNFwvsLLzBRyhus3d2oou86QcoHH42H17+NxdtcKDOjaGirK3Mzo6LgEbN53Gk7dxmPQtGW44fWsXGdtlAb2jMl21pQtRcrOqH6dULNGdaa9bNMBxMaXr1men755j2EzVyKdNRnJ/MlD0N3DPZ+tSFmgwBSpkHg8HpbOGoVmTbgp/Smpadh5+DyadB2PmUu34BPN2FJoGW/PQBT1EYrNZhWqJhM7VbyhFIfxZbOsXRNujrZM+84TX7wP/MJZx8O9MS7+txrjBnWVemCsqmpgZQZL05pMm4qglw5/VmDKopaRDHtCykJnVtbU/advaPh5IT3zeY+4hCSmTfWlSDbL2jWxcOow3Dm6EQunDM0xs6lYLMYNr2cYNG0ZnLpPwJb9pxEdW/mz6pOSU+D34TPTlmaZBUIKoqQowO8TJaNaomPjsXr7YRn2iOttwCcMmroUySmpzLJpI3tjUPe2MuwVyUaBKVJhqakqY/uKWTi4YQGa/1JzIiMzE4fOXIdLz0kY/8da+H0Ilk0nKwhxRjLSvbdAvm4HyNWwLXD9iOhYTpp8ac34MrxPh1yXm5kY4N//zcWGxVNgoKdTKseuqng8Hnp3lBRBf/TCD4F5FJ8nxROXkMgJTFjWrpnP2qQy6NSqKRPwF4vFOH3lnox7VDFcvy8ZxqehpgI7ml2M/EJNVRkDurXBud0rsW/tH2jfvEmOodFfvoXhr3/2oWGHUZi6+B9OMf3K5uXbj5wMsYZU+JyUsZZN7eHepAHT3nviCnz9g/LZomwEfv6GvpOWcG52jOrXEeMGdpVhrwgbBaZIhedgUwfbls/Cye1L0aGFEyfbRyQS4fTV+2jZfzqGzFjOSW8mEunP90GcGgtF12mFWp9dvwAovcBUU4f6qGtuwrRVlJUwe2x/nN65HE0d6pfKMUlWTRx28fnDZ2lKbmkK+PSV0zanjKlKr0Z1bTRpWI9p03C+wrnh9Yx57NLIhmqxkTzxeDw0trPCuoWTcfPwekwe1hN6OpqcddLSM3Dswm10GDYH7YbMxpFzN5GSWrnqKD7zkWSzq6uqwMzYIJ+1CZE+Ho+HeRMHM98jRSIR5q3exZnEoqyFfA9HnwmLERkdxyzr17klZo7uJ7M+kZzyr25MSAVSz6IW1i6YhCnDe2L30Ys4d/0BMoVC5vlr957i2r2ncGtsi2kje8O9cYNCDVmr7ERJkUh/tgcKdgPBr1a4zA12fSk9Hc1Sy1ri8XhYt2AS/vnPEzxRJuZOGooa1SlDqrRpVlNHG9dGuHznMQDg2Pnb+H3CIE6wihSff6BkGJ+8vBwV668iurZxxeOXfgAA34BPePfxs1Qnjahsvn6P4AzfpmF8pLCq62hi4pDuGDOgM+54v8Thszfx+JUfZ51Xfh8x/c9NmLl0C5SVBFBSVISKkgDKSopQUsz6f/Z/3LYgx3JVZSU0bWSTIxAmC+wbh7ZWZjTtPZEJ05o1MKJPB+w8fB4A8OT1O5y8fBe9WXVMy0p4ZAz6TFiMb6z6w51bN8XCqcPod2A5Q78ySKVjWtMAy2aPxsQh3bHX8wo8L93hTB/s9dQHXk99YF/fEtNG9ka7Zo2r9D/c6d5bAL4CFJ3GFnqb56w7cqU9DXFtYwOsXTARvj6+qF4OvvRVFb06NGcCUxHRsbju9QwdWzrLuFeVQ8An1ox8RjWgIE//FFcFbd0c8dc/e5GWngEAOHn5HhZMGSLjXpVf7GwpHo8H919qShJSEAV5ebR1b4y27o0R+Pkbjl24jTNX7yMxOYVZRyQSISk5FUnJqYgqwbGqqavi1pENqGlQveCVS4lYLMYL1o3D0spmJ6Qwxg7oinPXHzClC/7csBftmjUp01kiY+IS0HfSEk7N4ZZN7bFiztgq/duvvKJ3hFRahvq6+GPSYFw/uA5jB3aBmooy5/mXbz9g+KyVaNF/Ojwv3al06dyFIYwKRIbvKSg6jQVPqVqhtsnMFOLl249M257qF1RKzvbWMKohmZb9EBVBlxp/VhaIWS2aka+qUFNVRisXB6Z98vLdKjdbWFFc95LUl7K1MoNWNXUZ9oZUdOa1jPDHpMG4fewfLJk+AnVYM2VKQ1xCEvadvCLVfRZV8NcfiGIVeKfvZ0SWVJQVMXfCQKYdHhWLv3ceLbPjJyalYODUpXj3UTIZgFPDeli/cDINCy+n6DYtqfR0tDQwfWQfjOzbEUfO3cT+k1cRE5fAPO8f+AWTFm7ArGVb4eZoi9ZujdDatRFqGenLsNdlI81rPXjqNaBgN7DglX969/EzJ4hHhTUrJz6fj17tm2Pj3pMAgFsPXyA0LBKG+roFbEkKws6YYs+ASCq/Lm1cmEzE0LBIeL94C1fHgiecqGpSUtPw4Okbpv3rBCeEFJeqshL6dm6JPp1awO9DMD6FfEdaegZSUtOY/6ekpSM1NQ2paelITUtHSmoaUtMzmGXZ66WmpSMxOQWZmVllI05evot5EwfJLBODPVsyj8cr9Yx2Qgri4d4YzvbWePRzGPuuoxcwoFsbWLHqx5aG1LR0DJ25gjPRgV09c2xZOpNm8S7HKDBFqgwNNVWMG9gVQ3u2g+flu9hz7BLCIiUzY6WmpePGg+e48SDrLq2laU20+RmkcrKvB4FC5fpDlvn1KYRBd6DU4X/gyQsKvR37i4+8vBysLalGSmXVvZ07Nu8/BZFIDJFIjKPnb2Hm6L6y7laFlpCYjNAwyYARC1MqfF6VuDraQlNDDbHxiQCyiqBTYCqnB898kMIagt/C2V6GvSGVEY/HQ/06tVG/Tu0S7efOo5eYuGA9AODbD9kGm9n1pWobG5TpkClCcsPj8TB/8hD0GLsAmUIhhEIR/vjfLpzc/lep1XfKyMjEmN/X4MEzH2ZZXTNj7Fg5GyrKiqVyTCIdNJSPVDnKSooY0sMDVw/8jaWzRuWZGfUh+Cu2HTyL3hMWwarVEIyYvQoHT1/H9/CSVCEoH8RiEdLurQVfvz7k67Yv0rbsmQ2tzEygKCh8UItULDWqa8PNUTLl75GzN2joUQn5s7KlAJqRr6pRkJfn1Go7f+MhpwYiyXLzwQvmsZ6OZqnfXSekuFwdbaGtKRlmeuLSHZn1hf39zM6asqVI+WBeywhDenow7QfPfHDu+oNSOZZQKMSUxf/g2r2nzLJaRjWwe/VcaKiplsoxifRQYIpUWQIFefTq0ByX9v4PJ7cvxbSRvWFf3xJ8fs4IflJyKi7dfoRZy7agYYdRaDVgOpZvPoDHL/2YFO6KJDPgCkRhvlBsNhs8XtH+DLBn5LOjwpqVXu+OzZnHX0LD4cW6A0WKLiBIEpiSk+NXiSHDhKtLaxfmcUJSMs5e85Jhb8ofsViM66zC5+5NaAZdUn4pyMujQwtWsPn6QySnlH3N0qTkFPh9kNTSsbemMguk/Jg4pDtnAqPF6/cgiTUJgTSIxWLMWbkdp6/eZ5bVqK6NPWvmQkdLQ6rHIqWDAlOkyuPxeKhnUQvjBnbFoX8WwstzC/6ePxFd27hAq5partu8DQjGxv9OouvoP2DdZijGzvsbxy/cRjSr6GR5Jc5MR5rXP5AzawH5mo2LtG1kTBxnZgsHqi9V6TV3bggdTck/6IfO3JBhbyo+dmCqFs3IVyU1qGcOE1ZAcuqSjZiy6B+EsqayrsoCPn1FSGg4025Ow/hIOde1jSvzODE5BVfvPSnzPjx+9Y6T0Uz1P0l5oqqijN/G9mfa38Ojsf5fT6ntXywW469/9uHgaclEPTpaGvjv73kw0NOR2nFI6aLAFCG/0NRQQ8eWzlj1+3jcO74ZRzcvxsQh3WGTRx2CuIQknL3mhSmL/4Fjl7F4+fZDGfe4aDJeH4E44QcU3WYUedvnrDRxAGhAGVOVnoK8PLq3c2fal255V4gAbHnlzwpMmZvQjHxVEY/Hw6DubTnLjl+8DZceE7FmxxEkpaTKqGflww1WtpSCvDyaOtSXYW8IKZhN3dqobWzAtE9cvFPmfdjnKZkRUFtTHWas/hBSHnRq1RSODeoy7e0HzyLw8zep7Hv9vyew9cAZpq2hpoJ/V8+lrPQKhgJThORDTo6PBlbmmDysJ45v/RP3TmzCyjlj0aGFU65FJZOSU8t0KtSiEqfGIe3xDijY9IScTtHrD7CH8elqV4Mh3YWoEnq2b8Y8Ts/IxMnL92TYm4qNnTFFhc+rrsHd22LK8J5QUpTU6EtJS8ffO4/BpcdEHDt/q8rWc2MHphrZ1oGqspIMe0NIwXg8HrqwsqbuPHqJ8KjYMjv+l9AwXLsvuW56d2whs5kBCclLdiF0uZ+fzYzMTMxfsxtisbjI+4qIjsX1+8/wv+1H0HfiYqzedph5TkVJETtWzkYdM2Op9Z2UDfqrRUgR6GpVQzcPN6xdMAkPTm7B/vXzMbp/Z5jWrMGsc/PBC3z+FibDXuYt7clOQJQBQdNJxdqePeOLXT0LqvtRRdQ2NkAjW8ldroOnrxXri0RVl5iUgq8/Ipi2hWlNGfaGyBKPx8OEwd1x8b/V6NLGhfPcj4hoTF2yEe2GzIb3i7cy6qFsxCUk4vGrd0y7uXND2XWGkCLo0rop81goFOEMq85Nadt74jITyJaT42NA19ZldmxCiqKumQkGdGvDtG97v8TlO4/z3SYpJRWPXr7FtgNnMOb3NXDsMhY2bYdj8PRlWLvrGO4+fs2sK1CQx5ZlM2BXj0Z0VEQUmCKkmOTl5OBoWxczR/fFvrV/QF5ODkDWOOd9npdl3LucRHFfkfHqMASOI8FX1S3y9pmZQs4wxYY0jK9K6d1BUgT9feCXcj9ktTwK+GVGPguaka/KM9DTwerfx+PY5iU5ava9eR+E7mPmY+Rvqzi1/SqzO96vIBRKMsVaUH0pUkEY1ajOGaZUVrPzJaekcWo/tnZtBH1d7TI5NiHFMXlYD07t0oVr/0VKataEAZmZQvj6B2H/qauY8ddmtOg3DRbNBqLb6PlYsmEvzl1/wKlByCYvL4d1CyfDqaF1mZwHkT4KTBEiBdV1NNHGrRHTPnTmBvNHtrxIe7ARPCVNCBoNK9b2/kFfkMyqfUKBqarFo1ljqKkoM+1DZ/7f3n3HN1X9fxx/J+mgg1n23rtsLCB7I1tFhiKiMlSciIr76x4obn4qiKC4EBEUkA2CbAQsW4Ys2aN0t0nu74/qbWMZbZr2tuX1fDzyMOfee879pOTY5pMzFl/halyKx458drsqpRlpiWtbeO2q+uKdp/X2M2NUrrTnFwfzlq1Tm5vv1/MTpyoqOsaiCHPGT0vWmM8rlSvF+iDIU9Iugv7Hrv3pvozIDrMWrNSFi6n/X7j9xm7Zfk8gKwqFhuiREQPN8tHjpzX04ZfV+87xqt52sDoNeUTjXp6kr+Ys0a59h644rT2sSCG1i2ik+4fdqJ+mvKaOrZrkxEtANiExBfjIkDRDUy9cjNHcxb9ZGI0n14ntcu6Zr4CW98nmn35trIxIO43P4bCr3mUWg0f+FFQgUD07pk5VmL1wlc+3+s3v0i58XqFsSQX4syMfUtlsNnVvd51+/uw1jR0x0CMRnOx0atKXc9Si7z367Lv5Sk52Whhp9tix96B+WpqamOrABwzkMV3bNleAv79Zzu5F0A3D0JRv55nl2tUqqjG78SEP6NvlejWsk7rW7aoNf2jDtl2KT0y6bJ3goEA1a1BLwwfcoHeevV9Lv5qoX2e+r0kvP6J7hvbji4x8wNLE1LJly9StWzctWZJ++/H169drxIgRuuGGGzR8+HCtWLHCPLdp0yZ17Ngx3SMyMjIHowc8NQ2v5bGY8WffzbcwmlSGYShx1QTZw6rLv15/r9tJm5iqVbWix6K9uDbcfEPqdL7YuATNTTO6AVfHwufIiMCAAN01sKcWTHtTA3t1lN2eupbfuahojX/9E3UY9JCWrN6Ur9Z6e/WjGebzwAB/DbuJkR/IWwqFhqhDy9Tpp7MWrMzWTQzWbN6uXfsOmeXb+nVh7U/kCXa7Xc88MOyy71eHw6461StqQM8OeunRuzVn8ita/+PHmv72Uxo3apC6tm2uMiXDeL/nM5Z9XTtp0iStXLlSFy5cUGxsrMe5P/74QyNGjNDYsWMVERGhtWvX6t5779XMmTNVr149nTlzRpL0f//3fx71KlRg9X1Yx2azaUifznrhvWmSpK0792nLjj8t//bKdWClXEc3KajfR7LZHV63s3l7amKKaXzXpro1Kqt2tYravf+wJGnG7MUsspoJe9JM62B9KVxNWNFCeu6hOzSkX2e9+X9fa/Wm1C/f/vzrqG598CW1i2io/z1yp+pUr2RhpFm3cdtuLU6zq9igPp1YJwd5Up8u12vhrxskScdOnNHa33fo+mbh2XKvtKOlihQKVc80C7ADuV3dGpX11Jih+uiLHxUaHKT6taqoQe1qalCnqmpXq8QX4NcgS0ZMXbx4Ufv27dO0adMUGBiY7vz+/fs1dOhQDRs2TLVr19bw4cNVu3Zt/fZbytSomJgYFSlSRDVr1vR4BAUFpWsLyEm9O7dSSHDq1tZTLR41ZbidSlz9lhwVIuSo3Mbrds5duKj9h/42y1Yn22ANm82mm29ob5Y3/rHbY3oaLi82Lt5jwU525ENG1ahcXp+8Nk4fv/KoqlUq63Fu5fpt6jj4YT379mdyuVwWRZg1hmHolQ+/MMshQQU0anBvCyMCvNe6ebiKFi5olrNrEfSjx09rwYoNZvmmHu0UGMAHeeQtQ/p21urvP9Av09/UhKfu1e03dVOjujVISl2jLElMFSxYUG+99dYlk1KS1L9/f40bN87jWGxsrEJDQyWlJKZCQkKyPU4gs0KCg9S3S2uz/OOi1Tp7/qJl8SRv/0HucwcV2GZsloa7bo7c61FmG9ZrV8+OLRUYkLqGxlcsgp4hf/51zKPMiClkVpvrGmj2Jy/rmftv9/jg63a79fGMufpg2mwLo/PeynVbtWbzDrM87OYeKpLm9QF5ib+fn3q0jzDLPy9Zmy2b4UyducCcJuiw2z3WOQWAvMiSqXyZ/YD8+eefKz4+Xn369JGUkpiKiYnR2LFjtXv3bhUrVkwDBw5Ur169LtuGy+Wy9NvEf++d1RhcLpdc7pQHcqeBvTvoqzkp66YlJiXrqzmLde/Qfjkeh5EUq8S1H8pRq6dUvFaW3nsbt+0ynxcrUkilSxbN0feg+5/Y3Xl0REB+EhpSQJ1bN9W8ZeskSd/NW64n7h3iseAr0tudZh0Qu92mCmVL5Fgfov/kHzabdEvvDureIUKTv/5ZX85erGRnykLob/zf12rTPFwN89BUa8Mw9PIHX5rlIoVCdftNXXLN3zj0HXijd+eW5t+B0bFxWrBivfp2uf4qtTIuPiFRM2YvMsvtWzZSyeJFck2/+Rf9B/COy+0yE895dTR0Wg5HxpaSyfVbAk2dOlWffvqpPvvsM3PEVGhoqAIDA9WpUyfdc8892rRpk5544gnZ7XbdcMMNl2xn7969lzye07K6QHtcfKIuxsQrqABDHHOzutUraOe+lClOn379kyLqVZTDnrMDFMP++kFF4y/qQOGOcm7dmqW2Vq793XxepVwJ7di+4wpXZ5+dO3dd/SJkuya1KpqJqXMXovXJ9Jlq3bSuxVHlbqvWpa6fU7JYYe3ds+cKV2cP+k/+0rVFXVUuVVj/++hbud2GnC6X7n78db3/9Ig8Mw1i9eZd+mP3frPcq11THdy//wo1rEHfQWYYhqGyJYrq79PnJUmffTNXlUr4bqbHotVbdf5ijFm+vmENbY/c7rP2fY3+A2SOy+WW0+VSWNGC+WJzt6ZNm2boulybmDIMQy+++KJWrVqlr776SpUrVzbP3XHHHbrjjjvMcvXq1bVz507NmTPnsompmjVrKjg4OJujvjyXy6XIyEiFh4dnOGt4KdExcTp/MVohwaynlZvdPThBj7z4oSTp5JkLOh9nqHPrRjl2f3fMKSWsWSi/JreqfousDe92uVz68/Bxs9w6opHqh9fPaoiZ4na5tHPnLtWtW0f2LPQf+Ea9+vU0bc4KHf5nzaQ12/ZrzF1DLI4qd7swPXW9uTo1quRoH6L/5F/1w+vrZFS8PvpijiTp2Mlzmr1si14fP8riyK7O5XLpgVc+M8slw4rqoVG3ekwVthp9B966qWcHvf/5D5Kk33ceUPmKVVS8WOEst2sYhh59M3VNthqVy+vmvt1y5e5k9B/AO06XS4mJSTp94liWcwd5Sa5NTL3yyiv6/fff9e2336pYMc+dWfbs2aOgoCBVrFjRPBYYGHjFbZMdDkeu+EfNahwOh0MOe8oDuVfn1s1UqnhRnTyT8m3Z59//om7trsux+ydv+D/Z/AqowHUjZcvi+373gSOKjUswy03r17Ls/Wd38N7PLW7q0U4Tp8yUlLIA85rNO9TmugYWR5V7/XnwqPm8RuXylryP6T/50+jb+mnN5h3aunOfJOmL2YvUuU0zdc/B3znemDlvhfalWXvt3tv7KbhAgSvUsA59B5nVp/P1ZmLK5XJr7pI1GjH48kuOZNS6LTu088+/zPJt/bvIz5FrP85Jov8AmWUYkv2fmTa5JYeREyxZ/Pxq1q5dq59//lmTJ09Ol5SSpG+++UZjxozR2bNnJaVM05s7d646dOiQ06ECl+TncOiWXqnvx+Vrt+ivI8evUMN3XGf2KXnHbAVEjJKtQKEst7c5MnXKkcNuV72aVbLcJvK+fl3bmNNTDcPQ4Ptf0Mx5yy2OKneKi080R5dJUvXKLHwO3/FzOPT6E6MVHJSa1Hn4hQ906p8vRnKjxKRkTfjkG7NcsWxJ3di9rYURAb5VrnQJNWtQyyz76vfj5K/nmc8LFwxR786+W7sKAKxkSWJq8eLFioiIUEREhE6fPq3nn39eERERGjt2rCTpyy+/VHR0tHr27GleFxERofHjx0uSxo4dq8qVK6t9+/Zq1aqVBg0aZD6A3OLmG9rL758Mt2EYmjZrYY7cN3H1W7IVKif/hr7pD5v/SE1M1axaXkEFLr2bJq4tJcKKaOSQ1C3dk51OjXn2XU345Jsrjl69Fu3766jHz6R65fIWRoP8qELZknpqzFCzfO7CRT34v/dzbV/84oeFOnL8tFl+YPjN5u9LIL/okyZptG3Xfo+Rs944duK05q9YZ5Zv7N42z6wnBwBXY8nYz+uvv16zZs1KdzwoKGXdpBdeeEHx8fHpzv+7RlRoaKjee+89JSUlKTo6WsWKFcuVc6txbStRrIi6tGmmBSvWS5K+mrNEj40enK2JHefh9XIdXKUCPd+SzeGbdTo2pUlMNayTd3Z7QvYbM+xGBQcV0Fuffmsee/Pjb3To6Em99cy97NT3jz0HDpvP7XabqlQobWE0yK/6dW2tleu3atGvGyVJy9b8rs++m6+7Bva0ODJPsfEJ5jRgSapVtUKun3YIeKNr2+Z66f0vlJScLEmaOX+FnrzvNq/bmzZroVyulJ267HabhvTr4pM4ASA3sGTEVHBwsMqXL5/uERYWJkkKCwu75Pn/TusLCAhQWFgYSSnkWoP7pi48fuFijH5ctDrb7mUYbiWumiB7mYbyq9HVJ22ej4rWvkOpa4A0rl/TJ+0if7DZbLprYE9NfHaMx4LF381brkFjXlBUdMwVal879hxI/Za8fOmSCgzgG274ns1m0/MPDVep4kXNYy+8O017DhyxMKr0Jn/9s86cizLLD905wFxLA8hPCoWGqEPLxmb5+/krzC3gMyshMUlf/rDILLdv0VjlShXPcowAkFvwlwCQjZrWr6maVSqY5akzF2TbvZy758l9apcC24z1WbL29+17PcqNGDGFS+jW9jpNnfCEihYuaB77bVOkeg1/Qof/PmlhZLnD3oOpiYGqlcpaGAnyuyKFQvXq46PM3wEJiUm656m3lJiUbHFkKS5cjNGH02eb5UZ1q6ttREMLIwKyV58uqdP5jp04o7W/7/CqnR8XrtLZCxfN8tAbffMFJADkFiSmgGxks9k0uG8ns7xt5750yR5fMJyJSvztPflV7yS/ck181m7aaXxFCxdU+TIlfNY28pdGdWvo6/efVeXyqdPU9h48qh7DHtOWHX9aGJn19uxPncpXvRILnyN7tWhcV3fc3N0s79j7l177aIaFEaX6cPpsRUXHmuWH7xrAqHfka62bh3t8aTNz/opMt2EYhiZ/k7roebVK5XRdwzq+CA8Acg0SU0A269WplUKDg8zy1O98P2oqacsMGbGnFdj6YZ+2m3ZHvoZ1qvEBAldUsWwpffXuM2oanroT0ZlzUeo/4ilzrbVrTXxCog4dSx01VqMKC58j+z04/GbVrlbRLE/6co5WbfjDwoikU2fO69OvfzbLrZrWV3M+XCOf8/fzU4/2EWb55yVrFZ+QmKk2Nm7brcg9B8zybf268PcYgHyHxBSQzUKCCqhv19Zmec7i1Tp7/uIVamSOEX9BSRs/lX/4ANmLVvZZu263W79vTx3p0qgu0/hwdUUKF9SU1x9Tz44tzWPxiUka/uhr+uSrnyyMzBr7Dx3z2BmtWkWm8iH7BQT4640n7zHXfjMMQ/c/967OR0VbFtPEz2Z6fCB/5O5bLIsFyElpp/NFx8Zp4T8bFGTU5G9TR0sVDAlW7zS7/QFAfkFiCsgBg/ukTudLTErWV3OW+KztxPX/JxluBbS4x2dtStKeA0cUHRtnlhvVreHT9pF/BQT4643xozX61j7mMcMw9MxbU/TkG5/K5XJZGF3O2r0/dX0pm82mKhXKWBgNriXVK5XToyMHmeXjp87qsVcmeSRKc8qhYyf1xazUhZu7tG6mujUq53gcgBXCa1X1mOY+c97yDNc9fuqs5i1da5b7d2+j4KDs290ZAKxCYgrIAVUrllWLxnXN8rTvf/HJh3P3hcNK3vaNAprfJXtwsatXyITNkalrYTnsdtWvVdWn7SN/s9lsemD4zXpx7F3yczjM41O+nac7Hn1NsfEJFkaXc9IufF6udHEFFeADBXLOkL6d1ea6BmZ57pI1+u7njH8o9pW3PvlGyU6npJRt7h+88+YcjwGwis1mU580o5yWr92i0+cuZKjutO9/kfOfvxdtNptu68ei5wDyJxJTQA4Z0rez+fzI8VNa+tvvWW4zcfW7sgWHKaDx0Cy39V+bInebz2tUKc83dPDKTT3a6f9eGeuxztqiXzeq/4indOrMeQsjyxl7D6QmpqpVZOFz5CybzaaXHr3bY/Hl8W98or+OHM+xGPYcOKKZ81ea5T6dr1dVprTiGtO7cyvzucvl1o8LV1+1TmJSsr6YnTrSsF1EQzahAZBvkZgCckj7lo1VukTqqKbPZs7PUnuu49vk/HOhAlvdL5t/0NUrZNLmNDvyNahTzeft49rRqml9zXj3GY/3/7Zd+9Vj2GPanWbHuvxoT5rEVPXKJKaQ80oUK6KXHr3bLMfGJei+Z9+R05kzU2pfnzRDbrdbUspC0GOG3Zgj9wVyk3KlS6hZg9SNQTIynW/u4tU6cy7KLN/Wn9FSAPIvElNADvFzOHRLzw5mefmaLTro5bfWhmEo8dcJshevKb86vX0VoikqOkZ7Dx41y03q1fT5PXBtqVGlvL794HmPdWWOnjitXsOf0Mr126wLLBslJCbpr6MnzHKNyuzIB2t0aNlYA3t1NMub/tijdz6bme333bLjT81bts4s39KrvcqWKp7t9wVyo7TT+bbt2q8/0/yd9V+GYWjyN6mLnlepUEYtm9TL1vgAwEokpoAcdHPP9vLzS11vZ9r3v3jVjnP/Urn+3qLAtmNlszuuXiGTfo/806PckB354AMlwopo2ttPqn2LRuax6Ng4Dbn/BX09d6l1gWWT/YeOmSNFJKlaJaYvwTqPjR7ssfj+25O/06bIPVeokXWvfvil+TyoQKBG3do3W+8H5GZd2zZXgL+/WZ45f8Vlr928fa+27txnlm/t10U2my07wwMAS5GYAnJQ8aKF1a1Nc7P89dyliotPvEKN9AxXshJXTZSjUiv5VfL9lsFJycl6a/K3ZrlIoVBVLFvS5/fBtSkkqIDe/99DHmuuOV0uPfS/9/Xh9NkWRuZ7aafx2Ww21tWBpYIKBOqN8aPNzQhcLrfufeptxcTGZ8v9Vm+M9BgNeVv/LipetHC23AvICwqFhqhDy8Zm+fv5Kzy+vEhrSprRUqEhQerbtXW2xwcAViIxBeSwwWk+kF+4GKMfF63KVP3kyO9lXDiswDZjfR2aJOl/70zTxm2pC593a9ucb+ngUw6HXU+NGaon7hni8d568b3pWrdlh4WR+Vbahc/LlgxjRz5Yrl7NKrr/jtQ1ng4dO6mnJkz2+X0Mw9AraUZLFQoN1l0De/r8PkBe06dL6heKx06c0botO9Ndc/L0Oc1d/JtZ7t+tjUKCCuRIfABgFRJTQA5rXK+GalWtYJanfjdfhmFkqK6RGKOkdZPkV7evHCVqXb1CJs1asFKTv/nZLFepUEaPjhzk8/sANptNt9/UXe8+d7/8/f0kpXyYve+Zd3QxOtbi6Hwj7YipqkzjQy5x5y09PRZh/mbuUv28dI1P77Fo1UZtTjNN8M5beqpQaIhP7wHkRa2bh3vskjlz3op010z/YaGcrpTNCWw2m27t1yWnwgMAy5CYAnKYzWbT4D6po6b+2H1Av2//8wo1UiVtmiIjOU6Brcb4PK4df/6lsS9+aJZTplw9qJBg3+/4B/yrc+tmeujOAWb56PHTevLNTy2MyHfSjpiqXomFz5E7OBx2vfb4KBUMCTaPjX3pIx0/ddYn7bvdbr364QyzHFa0kIbeyG5igJSyM2WP9hFm+aclaxSfkLqkQ1JysqbNWmiWWzcLV8WypXI0RgCwAokpwAK9OrXy+FAwdeb8q9ZxR59Q0ubpCmgyVPaCpX0aT1R0jO4a97riE5PMYy+Nu5s1cZAjht3UTRGN65rlmfNW6MdFqy2MKOsSk5J18Gjqrps1qpSzMBrAU9lSxfXsg8PM8oWLMbr/uXcvu95NZsxeuEq79h0yy/fc2pdprEAaaafzRcfGadGvG83y3MVrdPrsBbN8+03dcjI0ALAMiSnAAsFBgerXLXUhyzmLVuvM+agr1klc84FsASEKaHaXT2Nxu90a8+y7Ongk9UP08AE91K3tdT69D3A5drtdrz42UoVCU5O1j70yScdOnLYwqqzZf+iYXK40O/JVJDGF3KVnx5bq1amVWV614Q8Nvv8FfTh9ttZv2ekxiiOjkpOdeuP/vjbL5UoV1809O/gkXiC/CK9VVZXLp37BmHZ3vinfpi56Xql8abVsUi8nQwMAy5CYAiwyqHcn83lSslNf/bjkste6Tu+Wc+ccBbS4R7bAUJ/G8e7UWR7f1jVvUFsP332LT+8BXE3pEsX03EPDzXJUdKzuf+49n4zgsMLeg0c9yqwxhdzomftvV9lSxc3yinVb9cK709Tn7idVo92t6n77OD09YbLmLFqdoUTx13OX6q+jJ8zyfcP6K+CfNeQApLDZbOrTOXXU1PI1W3TmfJR+375Xv2/fax6/tW9n2e18VANwbeD/doBFqlQo4/FN2LRZv8j1z2KX/5W46m3ZilaSf/jNPo1hxdoten3SV2a5ZPGievuZMeZ24kBO6tE+wuOP9d82Rer/vpxrYUTeS7u+VJmSYeyohFypYGiwXn9ilAoEBqQ7l+x0asuOP/Xp1z9r5PgJatJzhBr1uEsjHn9D/zdjrjZF7lFiUrJ5fXxCot769FuzXK1iWfXudH26dgFIvTunjlZ0ulz6ceEqj9FSIUEF1L9bGytCAwBL8DUWYKHBfTtr7e87JKUs+rzkt83pptA5D/0m16E1KtD7Hdkc/j679+G/T2r0U2+bOwL6+Tn07rP3K6xoIZ/dA8isp++/XZu379GxE2ckSa98+KXatWioejWrZPu93W63Fv66UTabTe1bNLrkh/WM2nPgsPm8WiWm8SH3ahpeS7M/eVlzFq3Slh37FLlnv+LiLz2N7/ips5q7ZI3mLknZxS8wwF8N6lRTs/BaiomL14nT58xrH7zzZjkcfP8JXEq50iXUNLyWuXvlZ98t0OFjJ83zfbu1ZvMZANcUElOAhdq3aKTSJYqZf8xP/W6BR2LKcLuU+OtbcpRtLL9qnS7XTKYlJCbprnGv63xUtHls/D23qmHd6j67B+CN0JAgvfr4KN0x9hW53YaSnU7d8/RELZz+ZrYuoJyYlKxR4ydowYr1kqRihQtqYO+OGnpjV68SS5478jGND7lbpXKl9MDwlBG5Lpdbf/51VFt37tPWnX9q644/dfjvU5esl5iUrI3bdmvjtt0ex+vXrKJO1zfN9riBvKxP51ZmYmr/oWMe527r18WKkADAMnyVBVjIz+HQwN4dzfLytVt04PDfZtm56ye5z+xVYNtHZbPZfHbf8a9/oj92HzDLfbpcr0F9fJf4ArKiWXgt3T2ol1nes/+wXnr/i2y7X3xCou4Y+6qZlJKkc1HRmvTlHLW68T7dNPoZzVm0WknJyVdoJVVScrIOHE7dTKB65fI+jxnILg6HXbWrVdSg3h312uOj9Mv0CVr9/Qf64IWHdPegXmrWoNZVRxM+fNcAn/7OAvKjbu2uU4B/+pHw1zcLV+XyZSyICACs43Viav/+/ebzJUuWaObMmUpMzPwOLsC17qYe7eTnl7qm0+ff/yJJMpLjlbjmffnV6CZHmYY+u98XPyzSV3NSF1qvVbWCnn9oOB8ikKvcd3t/1U8zfW/yNz9r+ZotPr9PbFy8bn3wJS1b8/tlr1m9MVIjx09Q4xvu1kvvf+GxuPOlHDh8XM4068VVr8xUPuRtxYoUUsdWTfTI3bdo+ttPaf2c/9PMj/6nJ++7TTd0aOGxgHqvTq3Usml9C6MF8oZCoSHq0LJxuuND+zNaCsC1x6vE1E8//aQBAwbI6XTqhx9+0Pjx4zV16lQ9/fTTvo4PyPeKFy3sMX3vm7lLFRefqKQtX8iIO6vA1g/67F5bdvypJ9/4xCwXCg3We88/mKW1dIDs4O/np9fHj/Z4bz7w/Hs6e/6iz+5xMTpWg8a8oN82RZrHypUurveef0A9O7aUv5/nbPcz56L0/uez1KLfPRo05n+av2ydnM70GxakncYnpSwCDeQn/n5+qlezim7r31UTnrpXS2a8rZXfvqf5n7+u1x4faXV4QJ7Rp4vnBgEVy5ZU6+YNLIoGAKzjVWJqxowZeu+99+Tn56dvvvlGr776qmbPnq1ff/31sruKAbi8IX07m8+jomM1b/48JW2cIv+Gg2QvUtEn9zh7/qLuGve6kpKdklK2K35j/GhVKFvSJ+0DvlalQhk9NnqIWT519rweffkjc8H+rDgfFa0B9z6nDdt2mccqly+tL995Wp1bN9ObT96jFd++q0dHDlLFsqU86hqGoeVrt2j4uNfUtNcIvT7pKx09fto8n3bh81IlirGALa4JJcKKqHL5MmxvD2RC6+bhCiuSuunMkL5d6EMArkle/Z/v9OnTCg8PV0xMjPbs2aPWrVsrMDBQRYoUUVRUlK9jBPK9RnWrq3a11ARU4tpJcrkMBVw3yiftu1wujX7qLR07ecY8ds9tfdU2opFP2geyy8BeHdS+RSOzPH/5On09d2mW2jx97oJuHPWMtu7cZx6rXrmcpk98SqWKFzOPFS1cUHfecoPmf/66przxuLq1vU5+DodHWydOn9Pbk79T8z6jdNtDL2nRrxu1a9+h1HZZ+BwAcBn+fn569fGRqlujsvp3a6PBfVnvE8C1yatd+cLCwnTkyBHt27dPDRs2VIECBZSYmKizZ8+qYMGCvo4RyPdsNpuG9O2sZ9/+TJULJumGMsc1YWOYNm58WSOH9FafztfL39/7TTRfm/SVfl2/zSy3bh6ue4f280HkQPay2Wx6cezd6jfiSZ29kDKN76k3J6tVk3qqXCHzi8OeOH1OA+55VnsPHjWP1aleSVNef0xFCl/695fdblfLJvXUskk9nT53QbN/WaWZ85Z7JHrdbrcWr9qkxas2edStVpH1pQAAl9e6eQOm7wG45nk1Yuqmm27S0KFD9fTTT+vOO++UJI0ZM0YtWrSQ/yV2lwBwdT07tlSZkmEa1/isTsX5adruwtq6c5/ufXqimvcZpfemztL5qOhMtzt/+Tq9N3WWWS5XurjefPJehoojzwgrWkgvPXq3WY6LT9C9z7xzyfWdruTI8VPqe/eTHkmphnWq6fMJ4y+blPqvEsWKaOSQ3lr4xQR9/Mqj6nR9Uzmu0JdqVGFHPgAAAOBKvBqCMXDgQNWpU0cFChRQzZo1JUldunTRDTfc4NPggGtJUIFAzX5hgIqvXq+nN1VUkjv1w+7xU2f18gdf6O1Pv9UtvTpq5JDeGdrpa/+hY3rguffMcmCAv957/kEVLhiSLa8ByC7tWjTSoN4d9c1PyyRJmyP36J3PZurRkYMyVP+vI8d10+hndfRE6lpQzRrU0qSXxyokqECm47Hb7WpzXQO1ua6BTp45p1kLftXMeSt08sw5j+vqVK+U6bYBAACAa4lXQyb+97//qUGDBmZSSpJuueUWPfbYYz5ZlBa4JhmGiu2ZKndYLT36xlQ9+8AwVS5f2uOS+MQkTZv1i66/6T7d+sCLWrl+22X7XGxcvIaPe13RsXHmsWcfvIMPysizxo0arCpppu+9Pfk7bYrcc9V6fx48qr4jnvJISrVqWk8fv/KoV0mp/ypVvJjuHdpPi2e8pQ9ffFjtIhqqcMEQ3XxDe/obAAAAcBWZGjG1fv16JSYmasGCBerQoYPHuZiYGK1evVpRUVEqUqSIL2MErgm2Q8tlP71Dyd3eU3BwkAb16aRbenXQqg1/aNqsX7Ruy06P65f8tllLftusOtUraeSQ3rqxe1sVCAyQlLJr2MMvfqg9+1N3BxvYq4P6d2uTo68J8KWgAoF6Y/xoDb7/BTldLrlcbt339EQt/WqiQkMuvfPdjj//0oB7ntPZ86kbc7Rv0UjvPHu/AgJ8O/Xcz+FQh5aN1aFlY5+2CwAAAORnmUpMrVixQhs2bFBUVJQefPBBj3MOh0MdO3YkKQV4w5Usv82T5C7fUkaZZuZhu92udi0aqV2LRtpz4LCmz1qon5etVXKy07xm175DeviFD/TyB1/ojpu7a9jNPTR74a+as2i1eU147aoaf9/QHH1JQHaoV7OKxgy7Ue98NlOS9NfRE3rmrSma+OyYdNdu27VPA+/7n8fabF3bNNebT90jfz/vNxMAAAAA4DuZ+sv88ccflySNHDlSn3zySbYEBFyL7Ht+kGKOy9Xx9cteU6tqRb08boQevvsWfTN3qb6Zu1Tn0nzgPnMuShM++VbvTZ0ll9ttHi9WuKDefe4BBWRhVz8gN7lrYE+t2viHNv8zje+rOUvUpXUz3dCxhXnNxm27Nfj+Fzymsvbq1EqvPjZSDgcL/wMAAAC5RYb/Oj98+LDi4lL+wH/66ae1f//+Sz6cTudVWgLgITFajm1T5a7eU0bRqle9vHjRwhoz7EYt/XqiXhx7V7pdv5KSnXK5UhJTDrtdbz1zn0qXKJYtoQNWcDjseu3xkQoNTp2+98hLH+rE6ZSFx3/bFKlb7nveIyl18w3t9drjJKUAAACA3CbDQyhuu+02vfTSS2rbtq1uu+02nTx58pLXrVy5UqVLl77kOQDpOSKnS64kuRqPyFS9wIAA3dSjnW7s3lZrf9+h6bMW6tcN2zyueejOAYpoVNeX4QK5QrnSJfT0A7fridc+liSdj4rWg8+/p1G39tHwR19TQmKSee2t/TrryfuGymazWRUuAAAAgMvIcGLqp59+UnBwsPnc5XJd8jrWmAIyIea47Dtnyh1+mxRc3KsmbDabWjWtr1ZN6+vgkeP64oeF2rpznzq3bqo7B97g44CB3KN3p1ZauW6rFqxYL0lasW5rup0q7xrYU4/cfQtJKQAAACCXynBiqnDhwpd8DsB7jt8/kQJC5ao/xCftValQRs8+eIdP2gJyO5vNpmcfvENbdvxpTuNLm5QaM6y/7h3a36rwAAAAAGSAV6shHzhwQBMmTNDBgwfTrSn11VdfqUSJEj4JDsjPbGf3yHFgoZwtH5P8g60OB8iTChcM0auPj9Sd4173SEqNvXug7hrU08LIAAAAAGSEV4mpl19+WU6nU8OHD5fff7bcDg0NzVRbJ0+eVMGCBc1pgmklJyfr/PnzKlq0qPz9/TN9Hsi1DEOOTR/IKFxZ7hq9rI4GyNMiGtXVPbf11Udf/CiH3a7H7xmi2/p3tTosAAAAABngVWLq0KFDmjFjhkqVKuX1jV0ul9566y1NmTJFb7zxhvr27etxftq0aXr33Xdlt9tlGIbGjRunQYMGZfg8kJvZjq2V/fhmJXd6Q7J71Q0BpDFm2I3qdH1ThQQXUMWy3v9uAgAAAJCzvPpEXLlyZZ09e9brxJTT6dSoUaNUtWrVS07727x5syZOnKjJkyerWbNmWr9+vUaMGKHw8HDVq1fvqueBXM3tlGPTh3KXaiyj/PVWRwPkG3WqV7I6BAAAAACZZPem0qhRo/Tqq69q2bJl2rVrl8cjOTn5qvUTEhLUpUsXPfXUU7Lb04cwZ84cdenSRc2aNZMkRUREqF27dpo7d26GzgO5mX3ffNkvHJSr+RiJncIAAAAAANcwr0ZMjR07VidPntSGDRvSnVu5cqVKly59xfqhoaFXnHa3d+9ederUyeNYrVq1tHHjxgydB3Kt5Hg5tkyWq0oXGcXrWB0NAAAAAACW8ioxNX/+fNkuM9LjUouYZ9aFCxdUqFAhj2MFCxZUVFRUhs5fisvlksvlynJs3vr33lmNweVyyeVOeSDv8ds+Q0qMUnLjETL4N8ww9z/9xm1hHwbyKvoP4B36DuA9+g/gHZfbJbfbnfI8H/Qfh8ORoeu8Skxldue9zHI4HOn+Edxutznt72rnL2Xv3r2+D9QLkZGRWaofF5+oizHxCioQ4KOIkFP8kqPUYOcMnSzeTkcOnpV01uqQ8pydO3dZHQKQZ9F/AO/QdwDv0X+AzHG53HK6XAorWjDLuYPcoGnTphm6zqvE1MiRI3XmzJl0x91utyZPnqzixYt706ypRIkSOnvW80P7qVOnzMXWr3b+UmrWrOmT0VzecrlcioyMVHh4eIazhpcSHROn8xejFRIc5MPokBP8102Q3S9AhTs8osKBBa0OJ09xu1zauXOX6tatI3sW+g9wLaL/AN6h7wDeo/8A3nG6XEpMTNLpE8eynDvIS7xKTHXp0kWxsbFm+eLFi1q4cKH69evnk9FUjRo10ubNmz2Obdy4Ud26dcvQ+UtxOBy54h81q3E4HA457CkP5CEX/pLjz5/lanavHEFFrI4mz7I7eO8D3qL/AN6h7wDeo/8AmWMY8pgplhtyGDnBq8TUgAED0h0bNGiQHnjgAY0YMeKq9aOjo3X8+HFJKSOJTp48qb179yo0NFRly5bVwIEDNX36dH3wwQfq1KmTfvrpJx07dkw333yzJF31PJDb+G3+SAopJXftm6wOBQAAAACAXOPyizJlUsmSJXXq1ClduHDhqtdu3LhRo0eP1ujRoxUYGKhvvvlGo0eP1gcffCBJKlOmjD7//HNt3bpVDz30kA4cOKBp06apaNGiGToP5Ca2E1tkP7JariajJAdrgwEAAAAA8C+vRkydOHFCTqfTLBuGoc2bN+v06dMKCQm5av2OHTuqY8eOV7ymQYMGmjx5stfngVzBcMux8X25i9eRu0pnq6MBAAAAACBX8Soxdcstt+jkyZMex/z9/fXAAw/I39/fJ4EB+YH94BLZz+5WcvcPJZvN6nAAAAAAAMhVvEpMzZo1Sy6XyyzbbDaFhYXJz8+r5oD8ye2U4/eP5a7QWkbpxlZHAwAAAABAruNVJqlEiRK+jgPIf1zJMgqVl7PZGKsjAQAAAAAgV2KIE5Bd/IPk7Pqu1VEAAAAAAJBr+WxXPgAAAAAAACAzSEwBAAAAAADAEiSmAAAAAAAAYIk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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 4.8))\n", + "ax.plot(series.date, series.demand, color=COLORS['actual'], linewidth=2.4, label='Observed synthetic demand')\n", + "ax.fill_between(series.date, series.demand, color=COLORS['actual'], alpha=0.08)\n", + "ax.set_title('Synthetic demand has a visible planning signal', loc='left', fontsize=16, fontweight='bold')\n", + "ax.set_ylabel('Demand units')\n", + "ax.set_xlabel('Month')\n", + "ax.legend(frameon=False, loc='upper left')\n", + "ax.annotate('Recurring seasonal peak', xy=(series.date.iloc[46], series.demand.iloc[46]), xytext=(series.date.iloc[35], series.demand.max() + 15), arrowprops={'arrowstyle': '->', 'color': COLORS['accent']}, color=COLORS['accent'])\n", + "ax.annotate('One deliberate shock', xy=(series.date.iloc[10], series.demand.iloc[10]), xytext=(series.date.iloc[2], series.demand.min() - 15), arrowprops={'arrowstyle': '->', 'color': COLORS['baseline']}, color=COLORS['baseline'])\n", + "ax.spines[['top', 'right']].set_visible(False)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ea3dabb6", + "metadata": {}, + "source": [ + "## 2. Baseline first: a forecast stakeholders can audit\n", + "\n", + "The baseline uses the previous eight observed months, removes one minimum and one maximum, and averages the remaining six:\n", + "\n", + "$$ baseline = \\frac{\\sum(last\\ 8) - min(last\\ 8) - max(last\\ 8)}{6} $$\n", + "\n", + "This is intentionally modest. It reduces sensitivity to one-off spikes while remaining easy to reproduce in a spreadsheet. It also gives the delivery team a reference point for deciding whether model complexity is justified." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2c56bfe8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T19:17:05.267097Z", + "iopub.status.busy": "2026-09-11T19:17:05.265903Z", + "iopub.status.idle": "2026-09-11T19:17:05.982497Z", + "shell.execute_reply": "2026-09-11T19:17:05.976547Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
The last eight observations used by the baseline
 monthobserved_demandincluded_in_average
522024-05-31 00:00:00167.9True
532024-06-30 00:00:00149.4True
542024-07-31 00:00:00142.3True
552024-08-31 00:00:00127.4False
562024-09-30 00:00:00129.2True
572024-10-31 00:00:00138.6True
582024-11-30 00:00:00168.9False
592024-12-31 00:00:00154.4True
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline level for the next horizon: 146.9 demand units\n" + ] + } + ], + "source": [ + "def trimmed_mean(values):\n", + " values = np.asarray(values, dtype=float)\n", + " if len(values) != 8:\n", + " raise ValueError('baseline requires exactly 8 observations')\n", + " return (values.sum() - values.min() - values.max()) / 6\n", + "\n", + "baseline_value = trimmed_mean(series.demand.tail(8))\n", + "baseline_preview = pd.DataFrame({'month': series.date.tail(8), 'observed_demand': series.demand.tail(8)})\n", + "baseline_preview['included_in_average'] = ~baseline_preview.observed_demand.isin([baseline_preview.observed_demand.min(), baseline_preview.observed_demand.max()])\n", + "display(baseline_preview.style.format({'observed_demand': '{:.1f}'}).set_caption('The last eight observations used by the baseline'))\n", + "print(f'Baseline level for the next horizon: {baseline_value:.1f} demand units')" + ] + }, + { + "cell_type": "markdown", + "id": "4277fa08", + "metadata": {}, + "source": [ + "### Baseline observation\n", + "\n", + "The baseline is not expected to capture every seasonal movement. Its job is to be stable, explainable, and difficult to game. If a more complex model cannot beat it consistently in time-based validation, the right delivery decision is to keep the baseline." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5a93accd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T19:17:05.989650Z", + "iopub.status.busy": "2026-09-11T19:17:05.988935Z", + "iopub.status.idle": "2026-09-11T19:17:08.146334Z", + "shell.execute_reply": "2026-09-11T19:17:08.144795Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Rolling-origin validation: every row predicts a future 12-month window
 cutoffbaseline_smapemodel_smapebaseline_maemodel_maewinner
02022-12-31 00:00:0013.9%12.4%20.118.1Model
12023-06-30 00:00:0012.9%14.0%19.320.9Baseline
22023-12-31 00:00:0013.4%12.9%20.419.7Model
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def smape(actual, predicted):\n", + " actual = np.asarray(actual, dtype=float)\n", + " predicted = np.asarray(predicted, dtype=float)\n", + " denominator = np.abs(actual) + np.abs(predicted)\n", + " return np.mean(np.divide(2 * np.abs(actual - predicted), denominator, out=np.zeros_like(actual), where=denominator != 0))\n", + "\n", + "def feature_frame(frame):\n", + " return frame.assign(\n", + " month=frame.date.dt.month,\n", + " lag_1=frame.demand.shift(1),\n", + " lag_3=frame.demand.shift(3),\n", + " lag_12=frame.demand.shift(12),\n", + " ).dropna()\n", + "\n", + "def recursive_forecast(model, history, horizon):\n", + " history = [{'date': row.date, 'demand': row.demand} for row in history.itertuples(index=False)]\n", + " predictions = []\n", + " for step in range(horizon):\n", + " next_date = history[-1]['date'] + pd.offsets.MonthEnd(1)\n", + " features = pd.DataFrame([{\n", + " 'month': next_date.month,\n", + " 'lag_1': history[-1]['demand'],\n", + " 'lag_3': history[-3]['demand'],\n", + " 'lag_12': history[-12]['demand'],\n", + " }])\n", + " prediction = float(model.predict(features)[0])\n", + " predictions.append((next_date, prediction))\n", + " history.append({'date': next_date, 'demand': prediction})\n", + " return pd.DataFrame(predictions, columns=['date', 'demand'])\n", + "\n", + "cutoffs = range(36, len(series) - HORIZON + 1, 6)\n", + "results = []\n", + "for cutoff in cutoffs:\n", + " train = series.iloc[:cutoff].copy()\n", + " test = series.iloc[cutoff:cutoff + HORIZON].copy()\n", + " baseline = np.repeat(trimmed_mean(train.demand.tail(8)), len(test))\n", + " train_features = feature_frame(train)\n", + " model = HistGradientBoostingRegressor(random_state=SEED, max_iter=150)\n", + " model.fit(train_features[['month', 'lag_1', 'lag_3', 'lag_12']], train_features.demand)\n", + " model_forecast = recursive_forecast(model, train, len(test))\n", + " results.append({\n", + " 'cutoff': train.date.iloc[-1],\n", + " 'baseline_smape': smape(test.demand, baseline),\n", + " 'model_smape': smape(test.demand, model_forecast.demand),\n", + " 'baseline_mae': mean_absolute_error(test.demand, baseline),\n", + " 'model_mae': mean_absolute_error(test.demand, model_forecast.demand),\n", + " })\n", + "results = pd.DataFrame(results)\n", + "results['winner'] = np.where(results.model_smape < results.baseline_smape, 'Model', 'Baseline')\n", + "display(results.style.format({'baseline_smape': '{:.1%}', 'model_smape': '{:.1%}', 'baseline_mae': '{:.1f}', 'model_mae': '{:.1f}'}).set_caption('Rolling-origin validation: every row predicts a future 12-month window'))" + ] + }, + { + "cell_type": "markdown", + "id": "3a663c3f", + "metadata": {}, + "source": [ + "## 3. Validation is the governance mechanism\n", + "\n", + "A random train/test split would let future information leak into the past. Instead, each validation window trains only on data available before its cutoff and predicts the next 12 months. The selection rule is simple: choose the lower sMAPE, but inspect consistency rather than celebrating one favorable window.\n", + "\n", + "That matters operationally. A delivery lead needs evidence that can survive questions from business stakeholders, security/risk partners, and executives—not just a model score from a convenient split." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8566e4c4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T19:17:08.155181Z", + "iopub.status.busy": "2026-09-11T19:17:08.154410Z", + "iopub.status.idle": "2026-09-11T19:17:08.686975Z", + "shell.execute_reply": "2026-09-11T19:17:08.683056Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model wins 2 of 3 validation windows; the baseline wins 1.\n" + ] + } + ], + "source": [ + "x = np.arange(len(results))\n", + "width = 0.36\n", + "fig, ax = plt.subplots(figsize=(11, 4.8))\n", + "ax.bar(x - width / 2, results.baseline_smape * 100, width, color=COLORS['baseline'], label='Baseline')\n", + "ax.bar(x + width / 2, results.model_smape * 100, width, color=COLORS['model'], label='Gradient-boosting model')\n", + "ax.set_xticks(x, results.cutoff.dt.strftime('%b %Y'))\n", + "ax.set_ylabel('sMAPE (%) — lower is better')\n", + "ax.set_title('Model selection should reward consistency, not novelty', loc='left', fontsize=16, fontweight='bold')\n", + "ax.legend(frameon=False, ncol=2, loc='upper left')\n", + "ax.spines[['top', 'right']].set_visible(False)\n", + "for bars in ax.containers:\n", + " ax.bar_label(bars, fmt='%.1f', padding=3, fontsize=9)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(f\"Model wins {sum(results.winner == 'Model')} of {len(results)} validation windows; the baseline wins {sum(results.winner == 'Baseline')}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "d37a9383", + "metadata": {}, + "source": [ + "### Validation observation\n", + "\n", + "The model does not win every window. That is useful evidence, not a failure of the notebook: it shows why a production decision needs a baseline, rolling validation, and an explicit acceptance rule. The next step in a real use case would be to align the acceptance threshold with planning cost, service risk, and stakeholder tolerance—not to add models automatically." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4c24fcc3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T19:17:08.695409Z", + "iopub.status.busy": "2026-09-11T19:17:08.694829Z", + "iopub.status.idle": "2026-09-11T19:17:09.534170Z", + "shell.execute_reply": "2026-09-11T19:17:09.532022Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train = series.iloc[:-HORIZON].copy()\n", + "actual = series.iloc[-HORIZON:].copy()\n", + "train_features = feature_frame(train)\n", + "final_model = HistGradientBoostingRegressor(random_state=SEED, max_iter=150)\n", + "final_model.fit(train_features[['month', 'lag_1', 'lag_3', 'lag_12']], train_features.demand)\n", + "model_forecast = recursive_forecast(final_model, train, HORIZON)\n", + "baseline_forecast = pd.DataFrame({'date': actual.date, 'demand': trimmed_mean(train.demand.tail(8))})\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "ax.plot(train.date, train.demand, color=COLORS['actual'], linewidth=2, label='Observed history')\n", + "ax.plot(actual.date, actual.demand, color=COLORS['actual'], linewidth=2, linestyle='--', alpha=0.55, label='Observed holdout')\n", + "ax.plot(baseline_forecast.date, baseline_forecast.demand, color=COLORS['baseline'], linewidth=2.5, label='Baseline forecast')\n", + "ax.plot(model_forecast.date, model_forecast.demand, color=COLORS['model'], linewidth=2.5, label='Model forecast')\n", + "ax.axvline(train.date.iloc[-1], color='#829AB1', linestyle=':', linewidth=2)\n", + "ax.text(train.date.iloc[-1], ax.get_ylim()[1], ' Forecast starts', color='#486581', va='top')\n", + "ax.set_title('The forecast is a decision artifact, not just a score', loc='left', fontsize=16, fontweight='bold')\n", + "ax.set_ylabel('Demand units')\n", + "ax.set_xlabel('Month')\n", + "ax.legend(frameon=False, ncol=2, loc='upper left')\n", + "ax.spines[['top', 'right']].set_visible(False)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "10dca0f3", + "metadata": {}, + "source": [ + "## 5. Build vs. buy: is a foundation forecasting model worth it?\n", + "\n", + "Gradient boosting beat the baseline in 2 of 3 validation windows above — encouraging, but not\n", + "enough evidence to declare a production winner. Before adding more classical-model complexity,\n", + "the pragmatic delivery question is different: **would a pretrained time-series foundation model\n", + "(e.g. Google's TimesFM) do better out of the box, with zero training?**\n", + "\n", + "This matters for a lean AI team because it changes the trade-off from \"tune a model\" to\n", + "\"evaluate a build vs. buy decision\":\n", + "\n", + "- **Zero-shot, no training** — TimesFM forecasts directly from history, no fitting step.\n", + "- **Heavier operationally** — a ~500M-parameter model pulled from Hugging Face, needs `torch`\n", + " and `transformers`, and meaningfully more latency and memory than gradient boosting.\n", + "- **Reproducibility cost** — this case study otherwise runs fully offline in seconds; a foundation\n", + " model breaks that guarantee unless weights are cached ahead of time.\n", + "\n", + "The cell below is **optional and skipped by default** (set `RUN_TIMESFM=1` to execute it) so the\n", + "tracked notebook stays fast and runnable without model downloads or GPU access. This mirrors how\n", + "I would gate an expensive candidate in a real pipeline: available for evaluation, not on the\n", + "critical path by default." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "da71e381", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T19:17:09.539058Z", + "iopub.status.busy": "2026-09-11T19:17:09.538559Z", + "iopub.status.idle": "2026-09-11T19:17:09.555706Z", + "shell.execute_reply": "2026-09-11T19:17:09.552083Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skipped: set RUN_TIMESFM=1 to download weights and run the foundation-model comparison.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "RUN_TIMESFM = os.environ.get('RUN_TIMESFM') == '1'\n", + "timesfm_smape = None\n", + "\n", + "if RUN_TIMESFM:\n", + " import torch\n", + " from transformers import TimesFmModelForPrediction\n", + "\n", + " # google/timesfm-2.0-500m-pytorch: zero-shot, no fine-tuning, monthly frequency code = 2\n", + " tfm = TimesFmModelForPrediction.from_pretrained(\n", + " 'google/timesfm-2.0-500m-pytorch', dtype=torch.float32, device_map='cpu'\n", + " )\n", + " past = torch.tensor(train.demand.values, dtype=torch.float32).unsqueeze(0)\n", + " freq = torch.tensor([2], dtype=torch.long)\n", + " with torch.no_grad():\n", + " out = tfm(past_values=past, freq=freq, return_dict=True)\n", + " timesfm_forecast = out.mean_predictions[0, :HORIZON].cpu().numpy()\n", + " timesfm_smape = smape(actual.demand.to_numpy(), timesfm_forecast)\n", + " print(f'TimesFM (zero-shot) sMAPE on the final holdout: {timesfm_smape:.1%}')\n", + "else:\n", + " print('Skipped: set RUN_TIMESFM=1 to download weights and run the foundation-model comparison.')" + ] + }, + { + "cell_type": "markdown", + "id": "45f24ed9", + "metadata": {}, + "source": [ + "### Build-vs-buy observation\n", + "\n", + "Whether or not TimesFM is run here, the delivery decision is the same shape: compare the\n", + "candidate's accuracy gain against its operational cost (dependency weight, latency, offline\n", + "reproducibility, and team familiarity), not just its headline metric. For this use case, I would\n", + "only adopt a foundation model in production if it beat both the baseline **and** gradient\n", + "boosting consistently across many rolling windows — enough to justify the added infrastructure\n", + "and the loss of full offline reproducibility. Until that evidence exists, the lighter pipeline\n", + "stays the default, and TimesFM stays available on the bench as an evaluated, not adopted,\n", + "candidate." + ] + }, + { + "cell_type": "markdown", + "id": "7ed2a0c7", + "metadata": {}, + "source": [ + "## 4. Delivery implications for a lean AI task force\n", + "\n", + "**What would ship first:** a documented, manually triggered workflow with a baseline, a validated model candidate, a forecast output, and a small set of checks that make failures visible.\n", + "\n", + "**What I would standardize for the team:**\n", + "\n", + "1. A reusable problem brief: decision, horizon, grain, owner, and acceptance metric.\n", + "2. A baseline-before-model rule so every use case has a credible comparison point.\n", + "3. Time-aware validation and an explicit model-selection record.\n", + "4. A handoff boundary that can remain manual while adoption is proved, then move to Airflow or another orchestrator.\n", + "5. A privacy review before public or cross-entity reuse.\n", + "\n", + "This is the player/coach signal: build enough of the solution to prove the path, while creating a delivery pattern that other engineers can reuse." + ] + }, + { + "cell_type": "markdown", + "id": "1943dfd6", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "The synthetic experiment is deliberately modest. Its value is the operating discipline around the model: explainable baseline, honest backtest, visible trade-off, and a route from experiment to production ownership.\n", + "\n", + "**Decision:** keep the lowest-sMAPE approach from rolling validation, document the result, and only increase complexity when the business value and operational readiness justify it.\n", + "\n", + "*All data and numeric results in this notebook are synthetic and illustrative.*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..9772d97 --- 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