From 71b98ee4e4dc95c9fe328a22ecb78dbc20050dbf Mon Sep 17 00:00:00 2001 From: Robert Helewka Date: Wed, 10 Jun 2026 14:28:16 -0400 Subject: [PATCH] Token Calculator --- .../notebooks/ctm_token_calculator.ipynb | 2245 ++++++++++--- .../tests/test_benefit_model.py | 132 + .../tests/test_business_case.py | 2 +- .../tests/test_cost_model.py | 26 +- .../ctm-token-calculator/tests/test_meters.py | 42 +- .../tokencalc/benefit_model.py | 101 +- .../tokencalc/cost_model.py | 26 +- .../tokencalc/defaults.py | 158 +- .../ctm-token-calculator/tokencalc/inputs.py | 6 + .../tokencalc/scenarios.py | 41 +- .../docs/Genesys-Token-Metering.md | 56 + .../notebooks/00_provision.ipynb | 43 +- .../notebooks/01_benefits.ipynb | 1408 +++++++++ .../notebooks/01_business_case.ipynb | 376 --- .../202512_GenesysCX/notebooks/02_costs.ipynb | 1508 +++++++++ .../notebooks/03_business_case.ipynb | 382 +++ .../notebooks/04_export.ipynb | 195 ++ .../notebooks/01_benefits.ipynb | 1110 ++++++- .../notebooks/02_costs.ipynb | 2 +- .../notebooks/03_business_case.ipynb | 2776 ++++++++++++++++- 20 files changed, 9719 insertions(+), 916 deletions(-) create mode 100644 studies/202512_GenesysCX/docs/Genesys-Token-Metering.md create mode 100644 studies/202512_GenesysCX/notebooks/01_benefits.ipynb delete mode 100644 studies/202512_GenesysCX/notebooks/01_business_case.ipynb create mode 100644 studies/202512_GenesysCX/notebooks/02_costs.ipynb create mode 100644 studies/202512_GenesysCX/notebooks/03_business_case.ipynb create mode 100644 studies/202512_GenesysCX/notebooks/04_export.ipynb diff --git a/studies/202512_GenesysCX/ctm-token-calculator/notebooks/ctm_token_calculator.ipynb b/studies/202512_GenesysCX/ctm-token-calculator/notebooks/ctm_token_calculator.ipynb index bbc184f..c2fbb98 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/notebooks/ctm_token_calculator.ipynb +++ b/studies/202512_GenesysCX/ctm-token-calculator/notebooks/ctm_token_calculator.ipynb @@ -109,6 +109,7 @@ " voice_acw_seconds\n", " fully_loaded_agent_cost_annual\n", " fully_loaded_supervisor_cost_annual\n", + " licence_type\n", " languages\n", " \n", " \n", @@ -129,6 +130,7 @@ " 60\n", " 65000\n", " 95000\n", + " named\n", " English, French, Spanish\n", " \n", " \n", @@ -147,6 +149,7 @@ " 60\n", " 60000\n", " 88000\n", + " named\n", " English, French, German, Italian, Spanish\n", " \n", " \n", @@ -165,6 +168,7 @@ " 60\n", " 70000\n", " 100000\n", + " named\n", " English\n", " \n", " \n", @@ -183,6 +187,7 @@ " 60\n", " 55000\n", " 80000\n", + " named\n", " English, Cantonese, Mandarin\n", " \n", " \n", @@ -201,6 +206,7 @@ " 60\n", " 55000\n", " 80000\n", + " named\n", " English, Mandarin, Malay\n", " \n", " \n", @@ -219,6 +225,7 @@ " 60\n", " 35000\n", " 55000\n", + " named\n", " Mandarin\n", " \n", " \n", @@ -237,6 +244,7 @@ " 60\n", " 35000\n", " 55000\n", + " named\n", " Mandarin, Cantonese\n", " \n", " \n", @@ -255,6 +263,7 @@ " 60\n", " 60000\n", " 85000\n", + " named\n", " Japanese\n", " \n", " \n", @@ -273,6 +282,7 @@ " 60\n", " 40000\n", " 60000\n", + " named\n", " Mandarin\n", " \n", " \n", @@ -324,16 +334,16 @@ "7 60000 85000 \n", "8 40000 60000 \n", "\n", - " languages \n", - "0 English, French, Spanish \n", - "1 English, French, German, Italian, Spanish \n", - "2 English \n", - "3 English, Cantonese, Mandarin \n", - "4 English, Mandarin, Malay \n", - "5 Mandarin \n", - "6 Mandarin, Cantonese \n", - "7 Japanese \n", - "8 Mandarin " + " licence_type languages \n", + "0 named English, French, Spanish \n", + "1 named English, French, German, Italian, Spanish \n", + "2 named English \n", + "3 named English, Cantonese, Mandarin \n", + "4 named English, Mandarin, Malay \n", + "5 named Mandarin \n", + "6 named Mandarin, Cantonese \n", + "7 named Japanese \n", + "8 named Mandarin " ] }, "metadata": {}, @@ -428,9 +438,24 @@ "source": [ "## Token meters\n", "\n", - "🟒 confirmed (published Genesys rate) Β· 🟑 estimated Β· πŸ”΄ unknown (working default,\n", - "rate not yet sourced). **Rule:** Agent Copilot includes summarization, so\n", - "AI Summary & Insights is never billed at Copilot sites." + "Source: [Genesys Cloud AI Experience metering details](https://help.genesys.cloud/articles/genesys-cloud-tokens-model/) (2026-06-07)\n", + "\n", + "🟒 confirmed (published Genesys rate) Β· 🟑 estimated Β· πŸ”΄ unknown (working default, rate not yet published).\n", + "\n", + "**Licence variants** β€” Agent Copilot and Speech & Text Analytics each have two published rates:\n", + "- **[named]** β€” 40 tok/user/month (Copilot) Β· 30 tok/user/month (STA)\n", + "- **[concurrent]** β€” 60 tok/user/month (Copilot) Β· 45 tok/user/month (STA)\n", + "\n", + "CTM has **named** licences; the `[named]` variants are used in `CTM_DEFAULT_FEATURE_SCOPES`.\n", + "Override by swapping the feature name in `scopes` for sites with concurrent licences.\n", + "\n", + "**Copilot rule:** Agent Copilot includes interaction summarization, so\n", + "AI Summary & Insights is never billed at Copilot-enabled sites.\n", + "\n", + "**New in 2026-06-07 catalogue:** Digital Bot (51 sessions/token), AI Scoring\n", + "(20 evaluations/token), Predictive Routing (17 routes/token), Genesys Cloud Copilot\n", + "(20 AI actions/token), and AI Translate is now a confirmed consumption meter\n", + "(2 translations/token, previously UNKNOWN)." ] }, { @@ -478,116 +503,176 @@ " 0\n", " 🟒 confirmed\n", " IVR self-service voice bot minutes; 17 min per...\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", " 1\n", + " Digital Bot\n", + " per_interaction\n", + " 51\n", + " 0\n", + " 🟒 confirmed\n", + " Digital (non-voice) bot sessions; 51 sessions ...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", + " \n", + " \n", + " 2\n", " Virtual Agent (legacy)\n", " per_interaction\n", " 2\n", " 0\n", " 🟒 confirmed\n", - " Legacy (non-agentic) virtual agent; 2 interact...\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " Legacy (non-agentic) virtual agent; 0.5 tokens...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", - " 2\n", + " 3\n", " Agentic Virtual Agent\n", " per_interaction\n", " 1\n", " 1\n", " 🟒 confirmed\n", " Agentic VA; 1.2 tokens per interaction.\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", - " 3\n", + " 4\n", + " Agent Copilot [named]\n", + " per_user_per_month\n", + " NaN\n", + " 40\n", + " 🟒 confirmed\n", + " 40 tokens per named user per month. Includes i...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", + " \n", + " \n", + " 5\n", + " Agent Copilot [concurrent]\n", + " per_user_per_month\n", + " NaN\n", + " 60\n", + " 🟒 confirmed\n", + " 60 tokens per concurrent user per month. Inclu...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", + " \n", + " \n", + " 6\n", + " AI Scoring\n", + " per_interaction\n", + " 20\n", + " 0\n", + " 🟒 confirmed\n", + " AI-scored quality evaluations; 20 evaluations ...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", + " \n", + " \n", + " 7\n", " AI Summary & Insights\n", " per_summary\n", " 50\n", " 0\n", " 🟒 confirmed\n", " Supervisor standalone summarization; 50 summar...\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", - " 4\n", + " 8\n", + " Speech & Text Analytics [named]\n", + " per_user_per_month\n", + " NaN\n", + " 30\n", + " 🟒 confirmed\n", + " STA named licence; 30 tokens per named user pe...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", + " \n", + " \n", + " 9\n", + " Speech & Text Analytics [concurrent]\n", + " per_user_per_month\n", + " NaN\n", + " 45\n", + " 🟒 confirmed\n", + " STA concurrent licence; 45 tokens per concurre...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", + " \n", + " \n", + " 10\n", + " Predictive Routing\n", + " per_interaction\n", + " 17\n", + " 0\n", + " 🟒 confirmed\n", + " Predictive routing; 17 routes per token.\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", + " \n", + " \n", + " 11\n", " Direct Messaging\n", " per_message\n", " 400\n", " 0\n", " 🟒 confirmed\n", - " FB/IG/WhatsApp messages; 400 messages per token.\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " Apple Messages for Business, Facebook Messenge...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", - " 5\n", + " 12\n", " Social Listening\n", " per_message\n", " 400\n", " 0\n", " 🟒 confirmed\n", - " 400 messages per token.\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " Genesys Cloud Social; 400 social post ingestio...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", - " 6\n", + " 13\n", " Social Responses\n", " per_message\n", " 400\n", " 0\n", " 🟒 confirmed\n", - " 400 messages per token.\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " Social Post Responses; 400 outbound messages p...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", - " 7\n", - " Speech & Text Analytics\n", - " per_user_per_month\n", - " NaN\n", - " 30\n", + " 14\n", + " AI Translate\n", + " per_interaction\n", + " 2\n", + " 0\n", " 🟒 confirmed\n", - " STA: 30 tokens per named user per month.\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " AI translation; 2 translations per token.\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", - " 8\n", - " Agent Copilot\n", - " per_user_per_month\n", - " NaN\n", - " 40\n", + " 15\n", + " Genesys Cloud Copilot\n", + " per_interaction\n", + " 20\n", + " 0\n", " 🟒 confirmed\n", - " 40 tokens per named user per month. Includes i...\n", - " https://help.mypurecloud.com/articles/genesys-...\n", + " 20 AI actions per token; Genesys Cloud knowled...\n", + " https://help.genesys.cloud/articles/genesys-cl...\n", " \n", " \n", - " 9\n", + " 16\n", " Email AI (Auto-Suggest)\n", " per_user_per_month\n", " NaN\n", - " 30\n", - " πŸ”΄ unknown\n", - " Rate not yet sourced. Working default 30 token...\n", - " \n", - " \n", - " \n", - " 10\n", - " Email AI (Auto-Respond)\n", - " per_message\n", - " 2\n", " 0\n", " πŸ”΄ unknown\n", - " Rate not yet sourced. Working default 0.5 toke...\n", + " Requires Agent Copilot. Token rate not yet pub...\n", " \n", " \n", " \n", - " 11\n", - " AI Translate\n", - " per_user_per_month\n", + " 17\n", + " Email AI (Auto-Respond)\n", + " per_message\n", " NaN\n", - " 20\n", + " 0\n", " πŸ”΄ unknown\n", - " Rate not yet sourced. Working default 20 token...\n", + " Feature not yet available; rate TBD.\n", " \n", " \n", " \n", @@ -595,61 +680,85 @@ "" ], "text/plain": [ - " feature meter_type units_per_token \\\n", - "0 Voice Bot per_minute 17 \n", - "1 Virtual Agent (legacy) per_interaction 2 \n", - "2 Agentic Virtual Agent per_interaction 1 \n", - "3 AI Summary & Insights per_summary 50 \n", - "4 Direct Messaging per_message 400 \n", - "5 Social Listening per_message 400 \n", - "6 Social Responses per_message 400 \n", - "7 Speech & Text Analytics per_user_per_month NaN \n", - "8 Agent Copilot per_user_per_month NaN \n", - "9 Email AI (Auto-Suggest) per_user_per_month NaN \n", - "10 Email AI (Auto-Respond) per_message 2 \n", - "11 AI Translate per_user_per_month NaN \n", + " feature meter_type units_per_token \\\n", + "0 Voice Bot per_minute 17 \n", + "1 Digital Bot per_interaction 51 \n", + "2 Virtual Agent (legacy) per_interaction 2 \n", + "3 Agentic Virtual Agent per_interaction 1 \n", + "4 Agent Copilot [named] per_user_per_month NaN \n", + "5 Agent Copilot [concurrent] per_user_per_month NaN \n", + "6 AI Scoring per_interaction 20 \n", + "7 AI Summary & Insights per_summary 50 \n", + "8 Speech & Text Analytics [named] per_user_per_month NaN \n", + "9 Speech & Text Analytics [concurrent] per_user_per_month NaN \n", + "10 Predictive Routing per_interaction 17 \n", + "11 Direct Messaging per_message 400 \n", + "12 Social Listening per_message 400 \n", + "13 Social Responses per_message 400 \n", + "14 AI Translate per_interaction 2 \n", + "15 Genesys Cloud Copilot per_interaction 20 \n", + "16 Email AI (Auto-Suggest) per_user_per_month NaN \n", + "17 Email AI (Auto-Respond) per_message NaN \n", "\n", " tokens_per_unit confidence \\\n", "0 0 🟒 confirmed \n", "1 0 🟒 confirmed \n", - "2 1 🟒 confirmed \n", - "3 0 🟒 confirmed \n", - "4 0 🟒 confirmed \n", - "5 0 🟒 confirmed \n", + "2 0 🟒 confirmed \n", + "3 1 🟒 confirmed \n", + "4 40 🟒 confirmed \n", + "5 60 🟒 confirmed \n", "6 0 🟒 confirmed \n", - "7 30 🟒 confirmed \n", - "8 40 🟒 confirmed \n", - "9 30 πŸ”΄ unknown \n", - "10 0 πŸ”΄ unknown \n", - "11 20 πŸ”΄ unknown \n", + "7 0 🟒 confirmed \n", + "8 30 🟒 confirmed \n", + "9 45 🟒 confirmed \n", + "10 0 🟒 confirmed \n", + "11 0 🟒 confirmed \n", + "12 0 🟒 confirmed \n", + "13 0 🟒 confirmed \n", + "14 0 🟒 confirmed \n", + "15 0 🟒 confirmed \n", + "16 0 πŸ”΄ unknown \n", + "17 0 πŸ”΄ unknown \n", "\n", " notes \\\n", "0 IVR self-service voice bot minutes; 17 min per... \n", - "1 Legacy (non-agentic) virtual agent; 2 interact... \n", - "2 Agentic VA; 1.2 tokens per interaction. \n", - "3 Supervisor standalone summarization; 50 summar... \n", - "4 FB/IG/WhatsApp messages; 400 messages per token. \n", - "5 400 messages per token. \n", - "6 400 messages per token. \n", - "7 STA: 30 tokens per named user per month. \n", - "8 40 tokens per named user per month. Includes i... \n", - "9 Rate not yet sourced. Working default 30 token... \n", - "10 Rate not yet sourced. Working default 0.5 toke... \n", - "11 Rate not yet sourced. Working default 20 token... \n", + "1 Digital (non-voice) bot sessions; 51 sessions ... \n", + "2 Legacy (non-agentic) virtual agent; 0.5 tokens... \n", + "3 Agentic VA; 1.2 tokens per interaction. \n", + "4 40 tokens per named user per month. Includes i... \n", + "5 60 tokens per concurrent user per month. Inclu... \n", + "6 AI-scored quality evaluations; 20 evaluations ... \n", + "7 Supervisor standalone summarization; 50 summar... \n", + "8 STA named licence; 30 tokens per named user pe... \n", + "9 STA concurrent licence; 45 tokens per concurre... \n", + "10 Predictive routing; 17 routes per token. \n", + "11 Apple Messages for Business, Facebook Messenge... \n", + "12 Genesys Cloud Social; 400 social post ingestio... \n", + "13 Social Post Responses; 400 outbound messages p... \n", + "14 AI translation; 2 translations per token. \n", + "15 20 AI actions per token; Genesys Cloud knowled... \n", + "16 Requires Agent Copilot. Token rate not yet pub... \n", + "17 Feature not yet available; rate TBD. \n", "\n", " source \n", - "0 https://help.mypurecloud.com/articles/genesys-... \n", - "1 https://help.mypurecloud.com/articles/genesys-... \n", - "2 https://help.mypurecloud.com/articles/genesys-... \n", - "3 https://help.mypurecloud.com/articles/genesys-... \n", - "4 https://help.mypurecloud.com/articles/genesys-... \n", - "5 https://help.mypurecloud.com/articles/genesys-... \n", - "6 https://help.mypurecloud.com/articles/genesys-... \n", - "7 https://help.mypurecloud.com/articles/genesys-... \n", - "8 https://help.mypurecloud.com/articles/genesys-... \n", - "9 \n", - "10 \n", - "11 " + "0 https://help.genesys.cloud/articles/genesys-cl... \n", + "1 https://help.genesys.cloud/articles/genesys-cl... \n", + "2 https://help.genesys.cloud/articles/genesys-cl... \n", + "3 https://help.genesys.cloud/articles/genesys-cl... \n", + "4 https://help.genesys.cloud/articles/genesys-cl... \n", + "5 https://help.genesys.cloud/articles/genesys-cl... \n", + "6 https://help.genesys.cloud/articles/genesys-cl... \n", + "7 https://help.genesys.cloud/articles/genesys-cl... \n", + "8 https://help.genesys.cloud/articles/genesys-cl... \n", + "9 https://help.genesys.cloud/articles/genesys-cl... \n", + "10 https://help.genesys.cloud/articles/genesys-cl... \n", + "11 https://help.genesys.cloud/articles/genesys-cl... \n", + "12 https://help.genesys.cloud/articles/genesys-cl... \n", + "13 https://help.genesys.cloud/articles/genesys-cl... \n", + "14 https://help.genesys.cloud/articles/genesys-cl... \n", + "15 https://help.genesys.cloud/articles/genesys-cl... \n", + "16 \n", + "17 " ] }, "metadata": {}, @@ -703,11 +812,32 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "ipywidgets not installed β€” use the plain variables above.\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eff7ea970d66430abeb6bfa7d4774f78", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "ToggleButtons(description='Scenario', index=1, options=('floor', 'realistic', 'stretch'), value='realistic')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f99dcb8205740ae86cbaeb7d313da80", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "ToggleButtons(description='Year', options=(1, 2, 3), value=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -732,6 +862,1457 @@ " print(\"ipywidgets not installed β€” use the plain variables above.\")" ] }, + { + "cell_type": "markdown", + "id": "5a590988", + "metadata": {}, + "source": [ + "## Virtual Agents β€” deflection, costs & benefits\n", + "\n", + "**Both** voice volume cost drivers (token consumption) **and** benefit drivers\n", + "(agent labour avoided). Two mechanisms operate on the same call pool:\n", + "\n", + "| Layer | What it does | Cost meter | Benefit |\n", + "|---|---|---|---|\n", + "| **Voice Bot** | IVR self-service on the full call pool | per-minute (17 min/token) | labour avoided Γ— completion |\n", + "| **Agentic Virtual Agent** | Handles a share of the **residual** (calls the bot did not deflect) | per-interaction (1.2 tok/interaction) | labour avoided Γ— completion |\n", + "\n", + "### Layered (sequential) deflection model\n", + "\n", + "The two mechanisms are **substitutes**, not independent additive benefits:\n", + "\n", + "$$\n", + "\\text{effective deflection} = r_{bot} + (1 - r_{bot}) \\cdot r_{va}\n", + "$$\n", + "\n", + "Realistic scenario: 35% + 65% Γ— 15% = **44.75%** (not the naive 50%).\n", + "\n", + "### Three realization haircuts on labour avoided\n", + "\n", + "Deflected volume does NOT translate 1:1 into labour savings:\n", + "\n", + "1. **Completion rate** β€” share of \"deflected\" calls the bot/VA fully handles\n", + " without escalating mid-session (60-75% voice bot, 50-70% agentic VA Y1).\n", + "2. **Labour realization** β€” staffing flexibility ceiling (minimums, shrinkage,\n", + " occupancy); 70-85% of deflected volume actually reduces FTE need.\n", + "3. **Callback discount** β€” poorly-handled deflections drive 5-10% repeat contacts.\n", + "\n", + "Combined realistic factor: 0.70 Γ— 0.80 Γ— (1 βˆ’ 0.05) β‰ˆ **0.53**\n", + "\n", + "So a naive \"$25M of labour avoided\" is realistically ~$13M. The variables below\n", + "override the scenario defaults β€” adjust them to test sensitivity, then re-run\n", + "the Cost & Benefit cells.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "65773422", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Voice Bot: 35% of full pool Β· Agentic VA: 15% of residual\n", + "Effective deflection: 44.75% Β· Realization factor: 53.2% Β· Net labour avoidance: 23.8%\n" + ] + } + ], + "source": [ + "# ── Virtual Agent inputs (override scenario defaults below) ──────────\n", + "# Cost & benefit drivers β€” applied as deflection_target overrides on the\n", + "# Voice Bot and Agentic VA feature scopes; realization haircuts mutate\n", + "# the scenario object so calculate_va_deflection_benefit picks them up.\n", + "\n", + "# Voice Bot β€” % of FULL voice volume deflected to the bot\n", + "VA_BOT_DEFLECTION = scenario.voice_bot_deflection # cost & benefit\n", + "VA_BOT_AVG_MINUTES = scenario.voice_bot_avg_minutes # cost only\n", + "\n", + "# Agentic VA β€” % of RESIDUAL (1 βˆ’ bot) deflected to the agentic VA\n", + "VA_AGENTIC_DEFLECTION = scenario.agentic_va_deflection # cost & benefit\n", + "\n", + "# Realization haircuts (benefit-side only β€” never reduce token consumption)\n", + "VA_COMPLETION_RATE = scenario.va_completion_rate # bot/VA fully handles call\n", + "VA_LABOUR_REALIZATION = scenario.va_labour_realization # staffing flexibility\n", + "VA_CALLBACK_DISCOUNT = scenario.va_callback_discount # 5-10% repeat contacts\n", + "\n", + "\n", + "def _apply_va_inputs():\n", + " \"\"\"Push notebook variables into the scenario + scopes used downstream.\"\"\"\n", + " global scopes\n", + " # Mutate scenario (regular dataclass, not frozen) β€” picked up by\n", + " # calculate_va_deflection_benefit via _param() and direct attribute reads.\n", + " scenario.voice_bot_deflection = VA_BOT_DEFLECTION\n", + " scenario.voice_bot_avg_minutes = VA_BOT_AVG_MINUTES\n", + " scenario.agentic_va_deflection = VA_AGENTIC_DEFLECTION\n", + " scenario.va_completion_rate = VA_COMPLETION_RATE\n", + " scenario.va_labour_realization = VA_LABOUR_REALIZATION\n", + " scenario.va_callback_discount = VA_CALLBACK_DISCOUNT\n", + " # ALSO patch BENEFIT_PARAMS so the realistic path uses our overrides\n", + " # (calculate_va_deflection_benefit reads via _param()).\n", + " BENEFIT_PARAMS[\"va_completion_rate\"][\"realistic\"] = VA_COMPLETION_RATE\n", + " BENEFIT_PARAMS[\"va_labour_realization\"][\"realistic\"] = VA_LABOUR_REALIZATION\n", + " BENEFIT_PARAMS[\"va_callback_discount\"][\"realistic\"] = VA_CALLBACK_DISCOUNT\n", + " # Override deflection_target on the Voice Bot / Agentic VA scopes\n", + " scopes = [\n", + " dataclasses.replace(\n", + " s,\n", + " deflection_target=(\n", + " VA_BOT_DEFLECTION if s.feature == \"Voice Bot\"\n", + " else VA_AGENTIC_DEFLECTION if s.feature == \"Agentic Virtual Agent\"\n", + " else s.deflection_target\n", + " ),\n", + " )\n", + " for s in scopes\n", + " ]\n", + "\n", + "\n", + "_apply_va_inputs()\n", + "\n", + "bot_pct = VA_BOT_DEFLECTION * 100\n", + "va_pct = VA_AGENTIC_DEFLECTION * 100\n", + "combined = VA_BOT_DEFLECTION + (1 - VA_BOT_DEFLECTION) * VA_AGENTIC_DEFLECTION\n", + "real_factor = (\n", + " VA_COMPLETION_RATE * VA_LABOUR_REALIZATION * (1 - VA_CALLBACK_DISCOUNT)\n", + ")\n", + "print(\n", + " f\"Voice Bot: {bot_pct:.0f}% of full pool Β· \"\n", + " f\"Agentic VA: {va_pct:.0f}% of residual\\n\"\n", + " f\"Effective deflection: {combined*100:.2f}% Β· \"\n", + " f\"Realization factor: {real_factor*100:.1f}% Β· \"\n", + " f\"Net labour avoidance: {combined*real_factor*100:.1f}%\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "872af216", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "16b420088b6e40cf98654573e7887cfd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HBox(children=(FloatSlider(value=0.35, description='Bot %', max=0.7, readout_format='.0%', step…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Optional: ipywidgets sliders for VA inputs (skip if ipywidgets unavailable)\n", + "try:\n", + " import ipywidgets as W\n", + " from IPython.display import display as _disp\n", + "\n", + " _w_bot = W.FloatSlider(value=VA_BOT_DEFLECTION, min=0, max=0.7,\n", + " step=0.01, description=\"Bot %\",\n", + " readout_format=\".0%\")\n", + " _w_botm = W.FloatSlider(value=VA_BOT_AVG_MINUTES, min=0.5, max=5.0,\n", + " step=0.1, description=\"Bot min\")\n", + " _w_va = W.FloatSlider(value=VA_AGENTIC_DEFLECTION, min=0, max=0.5,\n", + " step=0.01, description=\"VA %\",\n", + " readout_format=\".0%\")\n", + " _w_cmp = W.FloatSlider(value=VA_COMPLETION_RATE, min=0.3, max=1.0,\n", + " step=0.01, description=\"Completion\",\n", + " readout_format=\".0%\")\n", + " _w_lab = W.FloatSlider(value=VA_LABOUR_REALIZATION, min=0.3, max=1.0,\n", + " step=0.01, description=\"Labour real.\",\n", + " readout_format=\".0%\")\n", + " _w_cb = W.FloatSlider(value=VA_CALLBACK_DISCOUNT, min=0.0, max=0.30,\n", + " step=0.01, description=\"Callback\",\n", + " readout_format=\".0%\")\n", + "\n", + " def _on_va_change(change):\n", + " global VA_BOT_DEFLECTION, VA_BOT_AVG_MINUTES, VA_AGENTIC_DEFLECTION\n", + " global VA_COMPLETION_RATE, VA_LABOUR_REALIZATION, VA_CALLBACK_DISCOUNT\n", + " VA_BOT_DEFLECTION = _w_bot.value\n", + " VA_BOT_AVG_MINUTES = _w_botm.value\n", + " VA_AGENTIC_DEFLECTION = _w_va.value\n", + " VA_COMPLETION_RATE = _w_cmp.value\n", + " VA_LABOUR_REALIZATION = _w_lab.value\n", + " VA_CALLBACK_DISCOUNT = _w_cb.value\n", + " _apply_va_inputs()\n", + " eff = VA_BOT_DEFLECTION + (1-VA_BOT_DEFLECTION)*VA_AGENTIC_DEFLECTION\n", + " rf = VA_COMPLETION_RATE * VA_LABOUR_REALIZATION * (1-VA_CALLBACK_DISCOUNT)\n", + " print(f\"β†’ effective {eff*100:.2f}% Β· realization {rf*100:.1f}% \"\n", + " f\"Β· net {eff*rf*100:.1f}% β€” re-run cells below.\")\n", + "\n", + " for w in (_w_bot, _w_botm, _w_va, _w_cmp, _w_lab, _w_cb):\n", + " w.observe(_on_va_change, \"value\")\n", + " _disp(W.VBox([\n", + " W.HBox([_w_bot, _w_botm, _w_va]),\n", + " W.HBox([_w_cmp, _w_lab, _w_cb]),\n", + " ]))\n", + "except ImportError:\n", + " print(\"ipywidgets not installed β€” adjust the variables above.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "07ed3a9e", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "decreasing": { + "marker": { + "color": "#C62828" + } + }, + "increasing": { + "marker": { + "color": "#2E7D32" + } + }, + "measure": [ + "absolute", + "relative", + "relative", + "relative", + "total" + ], + "name": "Call flow", + "orientation": "v", + "text": [ + "100%", + "βˆ’35%", + "βˆ’15% of residual", + "", + "55.2%" + ], + "textposition": "outside", + "totals": { + "marker": { + "color": "#1565C0" + } + }, + "type": "waterfall", + "x": [ + "Total calls
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layer% of totalannual callslabour avoided (raw)labour avoided (realized)
0Voice Bot deflection35.0%5,100,30413,282,0417,066,046
1Agentic VA deflection (residual)9.8%1,420,7993,699,9971,968,398
2Combined (effective)44.75%6,521,10216,982,0389,034,444
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" + ], + "text/plain": [ + " layer % of total annual calls \\\n", + "0 Voice Bot deflection 35.0% 5,100,304 \n", + "1 Agentic VA deflection (residual) 9.8% 1,420,799 \n", + "2 Combined (effective) 44.75% 6,521,102 \n", + "\n", + " labour avoided (raw) labour avoided (realized) \n", + "0 13,282,041 7,066,046 \n", + "1 3,699,997 1,968,398 \n", + "2 16,982,038 9,034,444 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NAM realization haircut: $16,982,038 raw β†’ $9,034,444 realized (Γ—0.532 = 70% completion Γ— 80% labour Γ— 95% retained)\n" + ] + } + ], + "source": [ + "# Visualize the deflection cascade for the largest site (NAM) at the\n", + "# current scenario Γ— year. Shows where calls go and how realization\n", + "# haircuts shrink raw deflection into actual labour savings.\n", + "\n", + "_site = next(s for s in sites if s.site_name == \"NAM\")\n", + "_annual_calls = _site.voice_volume_monthly * 12\n", + "_aht_seconds = _site.voice_aht_seconds\n", + "_labour_rate = _site.agent_cost_per_second\n", + "\n", + "bot_calls = _annual_calls * VA_BOT_DEFLECTION\n", + "residual_calls = _annual_calls * (1 - VA_BOT_DEFLECTION)\n", + "va_calls = residual_calls * VA_AGENTIC_DEFLECTION\n", + "to_agent_calls = residual_calls - va_calls\n", + "deflected_total = bot_calls + va_calls\n", + "\n", + "# Raw labour avoided (no haircuts)\n", + "raw_labour_avoided = deflected_total * _aht_seconds * _labour_rate\n", + "# After realization haircuts\n", + "real_labour_avoided = raw_labour_avoided * real_factor\n", + "\n", + "# Waterfall: starting at 100% of calls, show where they go\n", + "fig = go.Figure(go.Waterfall(\n", + " name=\"Call flow\",\n", + " orientation=\"v\",\n", + " measure=[\"absolute\", \"relative\", \"relative\", \"relative\", \"total\"],\n", + " x=[\n", + " f\"Total calls
({_annual_calls:,.0f})\",\n", + " f\"Voice Bot
(βˆ’{bot_calls:,.0f})\",\n", + " f\"Agentic VA
(βˆ’{va_calls:,.0f})\",\n", + " f\"To agent
({to_agent_calls:,.0f})\",\n", + " \"Net to agent\",\n", + " ],\n", + " y=[_annual_calls, -bot_calls, -va_calls, 0, 0],\n", + " text=[\n", + " f\"100%\",\n", + " f\"βˆ’{VA_BOT_DEFLECTION*100:.0f}%\",\n", + " f\"βˆ’{VA_AGENTIC_DEFLECTION*100:.0f}% of residual\",\n", + " \"\",\n", + " f\"{(to_agent_calls/_annual_calls)*100:.1f}%\",\n", + " ],\n", + " textposition=\"outside\",\n", + " increasing={\"marker\": {\"color\": \"#2E7D32\"}},\n", + " decreasing={\"marker\": {\"color\": \"#C62828\"}},\n", + " totals={\"marker\": {\"color\": \"#1565C0\"}},\n", + "))\n", + "fig.update_layout(\n", + " title=f\"NAM deflection cascade Β· {SCENARIO} scenario Β· \"\n", + " f\"{deflected_total/_annual_calls*100:.1f}% effective deflection\",\n", + " yaxis_title=\"Annual calls\",\n", + " height=420,\n", + " margin=dict(l=20, r=20, t=60, b=40),\n", + ")\n", + "fig.show()\n", + "\n", + "# Summary table\n", + "summary = pd.DataFrame([\n", + " {\"layer\": \"Voice Bot deflection\",\n", + " \"% of total\": f\"{VA_BOT_DEFLECTION*100:.1f}%\",\n", + " \"annual calls\": bot_calls,\n", + " \"labour avoided (raw)\": bot_calls * _aht_seconds * _labour_rate,\n", + " \"labour avoided (realized)\": bot_calls * _aht_seconds * _labour_rate * real_factor},\n", + " {\"layer\": \"Agentic VA deflection (residual)\",\n", + " \"% of total\": f\"{(va_calls/_annual_calls)*100:.1f}%\",\n", + " \"annual calls\": va_calls,\n", + " \"labour avoided (raw)\": va_calls * _aht_seconds * _labour_rate,\n", + " \"labour avoided (realized)\": va_calls * _aht_seconds * _labour_rate * real_factor},\n", + " {\"layer\": \"Combined (effective)\",\n", + " \"% of total\": f\"{(deflected_total/_annual_calls)*100:.2f}%\",\n", + " \"annual calls\": deflected_total,\n", + " \"labour avoided (raw)\": raw_labour_avoided,\n", + " \"labour avoided (realized)\": real_labour_avoided},\n", + "])\n", + "display(summary)\n", + "print(f\"NAM realization haircut: ${raw_labour_avoided:,.0f} raw β†’ \"\n", + " f\"${real_labour_avoided:,.0f} realized \"\n", + " f\"(Γ—{real_factor:.3f} = {VA_COMPLETION_RATE:.0%} completion Γ— \"\n", + " f\"{VA_LABOUR_REALIZATION:.0%} labour Γ— {1-VA_CALLBACK_DISCOUNT:.0%} retained)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e7fc3a73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inputvaluedrives
0Voice Bot deflection (full pool)35%Voice Bot tokens (cost) + bot deflection benefit
1Voice Bot avg minutes1.5 minVoice Bot tokens (cost only)
2Agentic VA deflection (residual)15%Agentic VA tokens (cost) + VA deflection benefit
3Effective combined deflection44.75%Total share of voice calls deflected
4Realization factor53.2%Haircut on labour avoided (70% Γ— 80% Γ— 95%)
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" + ], + "text/plain": [ + " input value \\\n", + "0 Voice Bot deflection (full pool) 35% \n", + "1 Voice Bot avg minutes 1.5 min \n", + "2 Agentic VA deflection (residual) 15% \n", + "3 Effective combined deflection 44.75% \n", + "4 Realization factor 53.2% \n", + "\n", + " drives \n", + "0 Voice Bot tokens (cost) + bot deflection benefit \n", + "1 Voice Bot tokens (cost only) \n", + "2 Agentic VA tokens (cost) + VA deflection benefit \n", + "3 Total share of voice calls deflected \n", + "4 Haircut on labour avoided (70% Γ— 80% Γ— 95%) " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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modelY2 benefitvs correctednote
0Original (broken) β€” additive on full base, no ...29,993,898Counts bot + VA on the same call pool, 100% la...
1Corrected β€” layered + 'claim' params (no hairc...24,900,726-17.0%Layered model alone removes double-count
2Corrected β€” layered + realistic haircuts (curr...13,247,186-55.8%Production-calibrated; this is what flows into...
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" + ], + "text/plain": [ + " model Y2 benefit vs corrected \\\n", + "0 Original (broken) β€” additive on full base, no ... 29,993,898 \n", + "1 Corrected β€” layered + 'claim' params (no hairc... 24,900,726 -17.0% \n", + "2 Corrected β€” layered + realistic haircuts (curr... 13,247,186 -55.8% \n", + "\n", + " note \n", + "0 Counts bot + VA on the same call pool, 100% la... \n", + "1 Layered model alone removes double-count \n", + "2 Production-calibrated; this is what flows into... " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Reconciliation: how the inputs above flow into both the Cost Model and\n", + "# Benefit Model. Compare the corrected (layered + haircut) figures\n", + "# against the original Genesys ROI-doc 'claim' assumption to make the\n", + "# correction visible.\n", + "\n", + "from tokencalc.benefit_model import calculate_va_deflection_benefit\n", + "\n", + "# Use a year where both Voice Bot AND Agentic VA are active. In the\n", + "# default rollout that's year 2 onward (Agentic VA phase=2).\n", + "_RECON_YEAR = max(YEAR, 2)\n", + "\n", + "_bot_scope = next(s for s in scopes if s.feature == \"Voice Bot\")\n", + "_va_scope = next((s for s in scopes if s.feature == \"Agentic Virtual Agent\"), None)\n", + "\n", + "_bot_realistic = calculate_va_deflection_benefit(\n", + " sites, _bot_scope, scenario, _RECON_YEAR, params=\"realistic\"\n", + ")[\"annual_value\"].sum()\n", + "_bot_claim = calculate_va_deflection_benefit(\n", + " sites, _bot_scope, scenario, _RECON_YEAR, params=\"claim\"\n", + ")[\"annual_value\"].sum()\n", + "\n", + "if _va_scope is not None:\n", + " _va_realistic = calculate_va_deflection_benefit(\n", + " sites, _va_scope, scenario, _RECON_YEAR, params=\"realistic\"\n", + " )[\"annual_value\"].sum()\n", + " _va_claim = calculate_va_deflection_benefit(\n", + " sites, _va_scope, scenario, _RECON_YEAR, params=\"claim\"\n", + " )[\"annual_value\"].sum()\n", + "else:\n", + " _va_realistic = _va_claim = 0.0\n", + "\n", + "# What the (broken) additive model would have produced β€” naive sum on full base\n", + "_total_calls_yr = sum(s.voice_volume_monthly * 12 for s in sites)\n", + "_avg_labour = sum(\n", + " (s.voice_volume_monthly * 12) * s.voice_aht_seconds * s.agent_cost_per_second\n", + " for s in sites\n", + ") / max(_total_calls_yr, 1)\n", + "_naive_additive_claim = (\n", + " _total_calls_yr * (VA_BOT_DEFLECTION + VA_AGENTIC_DEFLECTION) * _avg_labour\n", + " * scenario.realization(_RECON_YEAR)\n", + ") # original error: rates added on full base, no haircuts\n", + "\n", + "_recon = pd.DataFrame([\n", + " {\"input\": \"Voice Bot deflection (full pool)\",\n", + " \"value\": f\"{VA_BOT_DEFLECTION:.0%}\",\n", + " \"drives\": \"Voice Bot tokens (cost) + bot deflection benefit\"},\n", + " {\"input\": \"Voice Bot avg minutes\",\n", + " \"value\": f\"{VA_BOT_AVG_MINUTES:.1f} min\",\n", + " \"drives\": \"Voice Bot tokens (cost only)\"},\n", + " {\"input\": \"Agentic VA deflection (residual)\",\n", + " \"value\": f\"{VA_AGENTIC_DEFLECTION:.0%}\",\n", + " \"drives\": \"Agentic VA tokens (cost) + VA deflection benefit\"},\n", + " {\"input\": \"Effective combined deflection\",\n", + " \"value\": f\"{(VA_BOT_DEFLECTION + (1-VA_BOT_DEFLECTION)*VA_AGENTIC_DEFLECTION):.2%}\",\n", + " \"drives\": \"Total share of voice calls deflected\"},\n", + " {\"input\": \"Realization factor\",\n", + " \"value\": f\"{real_factor:.1%}\",\n", + " \"drives\": \"Haircut on labour avoided \"\n", + " f\"({VA_COMPLETION_RATE:.0%} Γ— {VA_LABOUR_REALIZATION:.0%} Γ— \"\n", + " f\"{1-VA_CALLBACK_DISCOUNT:.0%})\"},\n", + "])\n", + "display(_recon)\n", + "\n", + "_compare = pd.DataFrame([\n", + " {\"model\": \"Original (broken) β€” additive on full base, no haircuts\",\n", + " f\"Y{_RECON_YEAR} benefit\": _naive_additive_claim,\n", + " \"vs corrected\": \"\",\n", + " \"note\": \"Counts bot + VA on the same call pool, 100% labour avoidance\"},\n", + " {\"model\": \"Corrected β€” layered + 'claim' params (no haircuts)\",\n", + " f\"Y{_RECON_YEAR} benefit\": _bot_claim + _va_claim,\n", + " \"vs corrected\": f\"{((_bot_claim+_va_claim)/_naive_additive_claim - 1)*100:+.1f}%\",\n", + " \"note\": \"Layered model alone removes double-count\"},\n", + " {\"model\": \"Corrected β€” layered + realistic haircuts (current)\",\n", + " f\"Y{_RECON_YEAR} benefit\": _bot_realistic + _va_realistic,\n", + " \"vs corrected\": f\"{((_bot_realistic+_va_realistic)/_naive_additive_claim - 1)*100:+.1f}%\",\n", + " \"note\": \"Production-calibrated; this is what flows into the business case\"},\n", + "])\n", + "display(_compare)\n" + ] + }, { "cell_type": "markdown", "id": "3079316e", @@ -742,7 +2323,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "3ac4fee1", "metadata": {}, "outputs": [ @@ -783,14 +2364,14 @@ " \n", " \n", " 4\n", - " Agent Copilot\n", + " Agent Copilot [named]\n", " NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA...\n", " 1,002,240\n", " confirmed\n", " \n", " \n", " 3\n", - " Speech & Text Analytics\n", + " Speech & Text Analytics [named]\n", " NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA...\n", " 751,680\n", " confirmed\n", @@ -832,34 +2413,26 @@ " \n", " \n", " 8\n", - " Email AI (Auto-Respond)\n", - " β€”\n", - " 0\n", - " unknown\n", - " \n", - " \n", - " 9\n", " AI Translate\n", " β€”\n", " 0\n", - " unknown\n", + " confirmed\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cost_line \\\n", - "0 Genesys CX 3 platform licences \n", - "4 Agent Copilot \n", - "3 Speech & Text Analytics \n", - "1 Voice Bot \n", - "6 Direct Messaging \n", - "2 Agentic Virtual Agent \n", - "5 AI Summary & Insights \n", - "7 Email AI (Auto-Suggest) \n", - "8 Email AI (Auto-Respond) \n", - "9 AI Translate \n", + " cost_line \\\n", + "0 Genesys CX 3 platform licences \n", + "4 Agent Copilot [named] \n", + "3 Speech & Text Analytics [named] \n", + "1 Voice Bot \n", + "6 Direct Messaging \n", + "2 Agentic Virtual Agent \n", + "5 AI Summary & Insights \n", + "7 Email AI (Auto-Suggest) \n", + "8 AI Translate \n", "\n", " scope annual_cost confidence \n", "0 all sites 2,788,232 confirmed \n", @@ -870,8 +2443,7 @@ "2 β€” 0 confirmed \n", "5 β€” 0 confirmed \n", "7 β€” 0 unknown \n", - "8 β€” 0 unknown \n", - "9 β€” 0 unknown " + "8 β€” 0 confirmed " ] }, "metadata": {}, @@ -896,7 +2468,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "fa4c9cdc", "metadata": {}, "outputs": [ @@ -980,21 +2552,21 @@ ], "xaxis": "x", "y": { - "bdata": "AAAAAAAAAAAAAAAAfNpCQQAAAAD27kpB", + "bdata": "AAAAAAAAAAAAAAAAaII4QQAAAAC4gUFB", "dtype": "f8" }, "yaxis": "y" }, { - "hovertemplate": "cost_line=Speech & Text Analytics
year=%{x}
$/yr=%{y}", - "legendgroup": "Speech & Text Analytics", + "hovertemplate": "cost_line=Speech & Text Analytics [named]
year=%{x}
$/yr=%{y}", + "legendgroup": "Speech & Text Analytics [named]", "marker": { "color": "#ab63fa", "pattern": { "shape": "" } }, - "name": "Speech & Text Analytics", + "name": "Speech & Text Analytics [named]", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -1012,15 +2584,15 @@ "yaxis": "y" }, { - "hovertemplate": "cost_line=Agent Copilot
year=%{x}
$/yr=%{y}", - "legendgroup": "Agent Copilot", + "hovertemplate": "cost_line=Agent Copilot [named]
year=%{x}
$/yr=%{y}", + "legendgroup": "Agent Copilot [named]", "marker": { "color": "#FFA15A", "pattern": { "shape": "" } }, - "name": "Agent Copilot", + "name": "Agent Copilot [named]", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -1110,33 +2682,7 @@ ], "xaxis": "x", "y": { - "bdata": "AAAAAAAAAAAAAAAAYHQcQQAAAABgdBxB", - "dtype": "f8" - }, - "yaxis": "y" - }, - { - "hovertemplate": "cost_line=Email AI (Auto-Respond)
year=%{x}
$/yr=%{y}", - "legendgroup": "Email AI (Auto-Respond)", - "marker": { - "color": "#FF97FF", - "pattern": { - "shape": "" - } - }, - "name": "Email AI (Auto-Respond)", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - "Y1", - "Y2", - "Y3" - ], - "xaxis": "x", - "y": { - "bdata": "AAAAAAAAAAAAAAAAwAITQQAAAACAKBtB", + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA", "dtype": "f8" }, "yaxis": "y" @@ -1145,7 +2691,7 @@ "hovertemplate": "cost_line=AI Translate
year=%{x}
$/yr=%{y}", "legendgroup": "AI Translate", "marker": { - "color": "#FECB52", + "color": "#FF97FF", "pattern": { "shape": "" } @@ -1162,7 +2708,7 @@ ], "xaxis": "x", "y": { - "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAhQFB", + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAU6lBB", "dtype": "f8" }, "yaxis": "y" @@ -1977,7 +3523,7 @@ } } }, - "image/png": 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IDp64pNwA9Sv6KmGnf4To3fuP1L7fZNX2pGGdA8TlPXzyQl0gWQiDKiFhLVwlvf8FFlVJEsZTSyKI6BK3zv+t/ZcthW50+cY9dTupG1yJEyumquLh6Rlc1UDP+58PsVA2ql2WpsxbSxt2HFJxjTvZmurOFkYR1/7j0gJt2N8JjafEscmP/6LNoxzfwaJ84pxVKs7Ofz1x8QyPEiN6wJi6kPTZWJ/EVVjWgQtNSczPhykCLaL/3QQ1FnP/LXn0xO+LlTws2FFAAARAAARAAARsn4DVCbR034Pw7z/yc3UMaookBkdKVEfHANXEoiRF3BqlZM+clrYunUAzF29ULmQHjl/koPyL7HK2g/6eOpAypkuh6pnrlx3HYEnRPoNqV0SBFHGXq1etlNGqeb+7tkkM2bVbD6l4wRwsLstwnFZyShg/DtVsNdjodfc5Rk8rkoSgeoUiOiuNHNfW7dIEjFbX/2dIWIu1Q8qFa3coGsfUiIiuVLqQitsSgSZWO7EaSYyWvsue/3tq+1qCFG0/NJ/aPGif0ka9qqXUWnv/sJjszNaLddsOqIQeDfSSm4T0XtpcigVP5sV/0eLaDvKz99vY+Sr2rFf7BiopS0p2bdvN1hQRjSEpYRVzpvY5sD6J2+3OFRMDOx3k8cQJ/ZKhBFkpkJNav8Pj300gt1SHzf235PP32MVoTgH/jgXVD5wDARAAARAAARCwTgJWJ9CyZkqtSIurWdfWdVRGQWPoJTZJYp2kGIvFEauUFEm4oRX5pn7KiC7K8iQJGST2aue+UyopxG8cl6MVWcMorOXZ8zeqidTf40mCak+zIIhgadGgUqBV37x9r8SZvJAunGIYI2fPFiFjRdblmjO+F1tqVtOu/adUTJTEymlFyzIpVq30361+2jn9z5CwFrfNdJzgQyxo4jaYmTM0yhhTJU9E8kK+9/A5ZU3LlS29UYui/n3NtW1sPiRBi1hDtv93guZz3JgI39qcNU9LQBOae2sxfPlzZaLSQbhJimullHGD2isXTu1e0Z2ctM0An5q7pHZCm3NNPGvHQ/ppap8Da1fcYcWtOKJLeP67CW4s5vxborETS6Y2F8HdH+dBAARAAARAAASsl4Bh/nArGIdkVBQrjyTIGDJhEWlJNLSuS7yWpB6XmCqxRojlS5JkSPY9rYh427TziLIUSZyPFLHaSFp6KeLeJm6RLRtWUfv3OEhfiqRdlyxqz1+ZHuukLjTya8XGPepo/tyZjZw1PCQvuBJvJOn9pZ/+i6TulyyPItCkaNZBrd7dh8+UENL29T8l2YrEHM37o68SSMJu4crtuioinqRs2X1Md0w2JMmHfgkJa7lOrGjiFrmf488qcYZMKcJd4gElLlBi6mSJg4gqgc2HluVzLi8NIKVpvYph6lL+3JnU9XOWbFaxjvqNyTOtxWrp5pLjFbUicX7X7zzUdnWfWrZNLT5TO6FZ6K7zshHy70IrstRBSESbqX3W2reUz/D8dxPUGM39t0SWYJCydN1uXcyidn+JaUQBARAAARAAARCwLQI/3v6saFwDujQhecmUFOh1Wg+liqUKqNTkD/gbZkkpL257YkWSIrFNsqaVCLb2zWtQTM4IuIVjzER01eNU/dq6RSJABo9fqCwk2TlznqxVJlY6KbIumVayZUqrYqYG8vpSkmFPEgFoKe61OsY+ZU0rER8SKyP9FvdJET81KhQ1Vj3AsWG9W1KL7uOoz8jZVKdKCcqZJT294vT6khxDUqavnjeCsrKojBcnlsp+2JPT+ufIkpaFzjO2SJ1VSR4CNKp3QNbYWjB5AGd7/J0kG6O83Is7ZY2KxThT3AaVAfITu1pmYYF86fpdld5b73K1aSprqSyxZbLWm5RKpfwEmtpmRrLemhRjCULUCTP8MnU+ZC0xEfmSNVF45marXlhKueL5VIZESatft81QqstulCJuxaXzwLEL6h6SGKMgi2YRr6OnLlVWPBGvhzmuUPuyQL8P2blfUqYtXK/WTPNgt13Jflm2eF6SuCUR05L9Ub6MuHLjvnr+1AUm/jK1zyY2F6HVwvvfjbHBmPtviSzxIa6+8sVFq14TOKFMIU4a4sPurmfUfEqCGRQQAAEQAAEQAAHbIWCVAk0SB2xbOp5m8fpcW1lYydpQWhEBJG5pLRtWVofk5VLSj4+bvpwmz12jjskir015IesBXRprl5Fk5ZNkBppokBMxnaNTn46/GLiiDe3VgmYt/ofkm2tJkS/rUwUl0OReUt598FtkWu3wLxEfk4Z31iWbkBdwVbRPreL3T1kYWtLcT5i1kjbtOqp+5JQsRC0ZFiUFt1j4pozsQgPGzFOZESU7olj8xBIo8VO6e3xvU/b1byduWfMn9qPWvSfQyClL1DpZxTiWbf7EvtRj2AwlGEQ0CJfOv9ZWqb/1rzeVtdxeE1/iviWLUGtFBFF8XsPuI1uTtLg67ZzGUtvXPjXnTTu74A3CWhvBzYfWtnyWKJhTCTR5ZkJShI3mZqh/3XTOlCmp9kWIyvIJWpGskRX4ywYp8qWALHYsbqcSlydFYpvExVLWjNPnXpQzTnZvU4/+2XVEiWupO7hHM/lQyzLImm8iCOVHkoD05+d+Li9mrrGQevrbsu+/mNJn/9dExL7umdYHondjc/+70e6nP6/+j4X1b4n/9mR/Ni99IYvUb+O5l3XwpMjfOsnqigICIAACIAACIGBbBOw4gUD4pIOLQE6SRt7lhataXDpF0kS6lPn6XRAXL1n/TBYLllgqWePJWJEsiC4v36i1t2RRZUmJb6zIC/57/pH1xkQYmVLENVJStkvcm5Zy35Tr/NeRMcj6ZOLCJffX0npr9cR9TdZOkpI2VTKzxHEJP5VQhJ8WiR8LKjGHqay1/v6sT1PmQ9xhqzQdQLLw84EN09S6YObqr/zTk+fBjTNpyvIImquifvsSKynxf4nYoinrvgVVpD15xkVAa+6NUl/GIIuIsxxXc+f/eQmqTf/nTOmz/2ssZT+i/92Ex98ScXMV12ARbbKmYFjm0lLmBf0AARAAARAAARAwJGATAs1wSNgDAfMREDfXQeMWUOvGVdni2sR8DaMlEAABEAABEAABEAABEDBCIHifMCMX4RAIRBYCkphBrBVN6pSPLEPGOEEABEAABEAABEAABH4iAeP+ez+xQ7g1CFgKgW/sKioxPrIQdioTlkOwlH6jHyAAAiAAAiAAAiAAAtZLAC6O1jt36DkIgAAIgAAIgAAIgAAIgICNEYCLo41NKIYDAiAAAiAAAiAAAiAAAiBgvQQg0Kx37tBzEAABEAABEAABEAABEAABGyMAgWZjE4rhgAAIgAAIgAAIgAAIgAAIWC8BCDTrnTv0HARAAARAAARAAARAAARAwMYIQKDZ2IRiOCAAAiAAAiAAAiAAAiAAAtZLAALNeucOPQcBEAABEAABEAABEAABELAxAhBoNjahGA4IgAAIgAAIgAAIgAAIgID1EoBAs965Q89BAARAAARAAARAAARAAARsjAAEmo1NKIYDAiAAAiAAAiAAAiAAAiBgvQQg0Kx37tBzEAABEAABEAABEAABEAABGyMAgWZjE4rhgAAIgAAIgAAIgAAIgAAIWC8BCDTrnTv0HARAAARAAARAAARAAARAwMYIQKDZ2IRiOCAAAiAAAiAAAiAAAiAAAtZLAALNeucOPQcBEAABEAABEAABEAABELAxAhBoNjahGA4IgAAIgAAIgAAIgAAIgID1EoBAs965Q89BAARAAARAAARAAARAAARsjAAEmo1NKIYDAiAAAiAAAiAAAiAAAiBgvQQg0Kx37tBzEAABEAABEAABEAABEAABGyMAgWZjE4rhgAAIgAAIgAAIgAAIgAAIWC8BCDTrnTv0HARAAARAAARAAARAAARAwMYIQKDZ2IRiOCAAAiAAAiAAAiAAAiAAAtZLAALNeucOPQcBEAABEAABEAABEAABELAxAhBoNjahGA4IgAAIgAAIgAAIgAAIgID1EoBAs965Q89BAARAAARAAARAAARAAARsjAAEmo1NKIYDAiAAAiAAAiAAAiAAAiBgvQQg0Kx37tBzEAABEAABEAABEAABEAABGyMAgWZjE4rhgAAIgAAIgAAIgAAIgAAIWC8BCDTrnTv0HARAAARAAARAAARAAARAwMYIQKDZ2IRiOCAAAiAAAiAAAiAAAiAAAtZLAALNeucOPQcBEAABEAABEAABEAABELAxAhBoNjahGA4IgAAIgAAIgAAIgAAIgID1EoBAs965Q89BAARAAARAAARAAARAAARsjAAEmo1NKIYDAiAAAiAAAiAAAiAAAiBgvQQg0Kx37tBzEAABEAABEAABEAABEAABGyMAgWZjE4rhgAAIgAAIgAAIgAAIgAAIWC8BCDTrnTv0HARAAARAAARAAARAAARAwMYIQKDZ2IRiOCAAAiAAAiAAAiAAAiAAAtZLAALNeucOPQcBEAABEAABEAABEAABELAxAhBoNjahGA4IgAAIgAAIgAAIgAAIgID1EoBAs965Q89BAARAAARAAARAAARAAARsjAAEmo1NKIYDAiAAAiAAAiAAAiAAAiBgvQQg0Kx37tBzEAABEAABEAABEAABEAABGyMAgWZjE4rhgAAIgAAIgAAIgAAIgAAIWC8BCDTrnTv0HARAAARAAARAAARAAARAwMYIQKDZ2IRiOCAAAiAAAiAAAiAAAiAAAtZLIIr1dt0yeu7i+sUyOoJegAAIgAAIgAAIWDWB5AmiW3X/0XkQAAHzEIAFzTwc0QoIgAAIgAAIgAAIgAAIgAAIhJkABFqYEaIBEAABEAABEAABEAABEAABEDAPAQg083BEKyAAAiAAAiAAAiAAAiAAAiAQZgIQaGFGiAZAAARAAARAAARAAARAAARAwDwEINDMwxGtgAAIgAAIgAAIgAAIgAAIgECYCUQKgebh6RUoqJev3cj989dAz+MECIAACIAACIAACIAACIAACEQUAZsXaPuPnqfC1ToF4HnvkQtVa/4bVWjUl4rU6EIDx/5FmpC7eO0u5Sjbmjr0n2xw3cHjF9Xx3iNmGxzHDgiAAAiAAAiAAAiAAAiAAAiYg4DNCjRXtw9UuUl/6jFsplFOY6YupfSpk9OpHfNo0+Lf6fDJS7R19zFV18fHR32euXSLrt16qLv+7zU71bavr6/uGDZAAARAAARAAARAAAQMCQwc9xfNXLxRd3DbnuN06MRF3T42QAAEAidgswtVx4sTk/6eNpBOX7hJY6b+z4CA27uPdJbFl5x3jhGNMqVPSRVKFaD/Dp+lhjXL6OrWrFiMFq3aTtNGd6fLN+7TnftPqXKZguTjA4Gmg4QNEAABELAhAnZe78mO/L6ks6FhYSjhSEDeCHztnYnso4bjXX5O089fuVLFRv1o+pjuVKl0wRB14t5DF/LVe1+avnADpUudjMoUyxuidlAZBCIjAZsVaPb29pQyWSK6z66M/svLN27qUJoUSXSn0qRMQldu3tfty0abJtWoXtth9PDJC1rC1rMmdSvQK772k/sXg3rYAQEQAAEQsA0CMT7up+jup2xjMBhFhBDwtYtK7xJ3JW/7pBFyv4i8iSawzOE5tHHxGHLgdzMUEACB4AnYrEALaugfPrqr09GcfnzbJdufPhkKrwxpklPpIrnZAreULWj3aFjvX2nqX+sMmk4Ux8lgHzsgAAIgAALWScDTmy1nr1+So+dT6xwAev1TCHhFSUQxnBwoWqyf9z4gXkFzlmyi2w+eUvRoTpQvZ0bq3b4hpeAvqh89fUnjZ66gS9fvUVTHKFS8UE4a1L0ZxY0dU/F68/Y9/Tl/rfI4+vT5C6VImpBqVSquvqTWwkSmLVhPf6/eSY58/fJZQ0PFeeLsVdx2Iureth69ePWWeo+YRbWrlGC3x0t04eodyp45Lf3asDKVL5lf1/5r13c0cc5qOnPxJn3+8o1yZ09Pg3s0p4xpU+jqYAMEbJFApBRosWOxKwKXbx6eujn9+s2DYsaMrtvXNto2rU4te4ynxnXKUYJ4sbXDuk+3Tx66bWyAAAiAAAhYLwFxX3dwrk5fo5a23kGg5xFOwNfOjry/ONEXu7C/DySOGy3E/Rfx0rr3H8prqE3jauTt7U3rtx+ia7cfUhwWYS26jyU77uOvv1Sm9x/c6Z+dh+nBo+e0Zv4IdXzoH4uUeOvepi7FjulM56/coR37TiqBVqZYHrp59zELowyUNWNqsrcLvQXszoNn3Dc/92F557py84H6qV6hCHVpVYd2HzhNErd2bOscJSS/cZ3m3cZSlCgOqi8iLldt2kedB06lHcsmkJPel+whhoYLQMDCCURKgZYkYTw1LY+evqAkiX5sJ00UP8B05c+VmbrxH606lUsEOCcHvLwRj2YUDA6CAAiAgBUSiH01A0V/5GCFPUeXfxYBb2dfci3lQd5OP+d9QBJxSDz99uUTyDGK32tdq0ZV6etXD9q44zC95bj7ZTMHU4HcWRSitKmS0tjpy+n42WtUgq1pN+48ogL8rtOiQWV1XqxaX75+U9sNa5Shv5Zvowps1apcppDZEY8d2I7qVSul2pW+1Gs7nC15N6hk4Vz0z64j9OzFG9ryv3E6i1mq5ImVQBMRWaxgDrP3Bw2CgKUQsGmB5sEWMs/va6DJtp29nfrjFS9uLP5DlZmWrd9NObOmJxf+A7D3yHnq16mR0Xnp2qqu0eM4CAIgAAIgYHsE7DhNCAoImE7g5wgzrX837z5hQZNTJ87kuIRtyM8d5fIYVVnAtPrFCvgJm7ts0RJRVK5EPtrAFre6bYZR0QLZqRSHdsjxiCjx4sTS3Ub7kvz5S1d1TCx3UvqNnqs+5Zf2Tufy8o3uGDZAwBYJ2KxAk2Qe5Rr20c1ZvsodqGCeLLR0xmB1bESfVtR18DQqXL0zSfBrtfJFqA5/a6QVexZzxoq4CcgPCgiAAAiAAAiAAAj8TAKyLJCnlxfFihnDaDdE0ESN6khRHH5YhcXaJkWukzKybyvKnS097eN1Y7f9d5yWb9hD4nY4eXgXdT6ifjno9VHuKRZAEXA92tYP0IVs7G6JAgK2TMBmBVpidmO8dvB/gc5dxnQpaM+aKcp6Jn/Y9P+4iVvjlf1LjF4r5ngUEAABEAABEAABEPjZBCRjtWShPnLqcoCuSLxXaj73fq+7iiPLlimNqnPy3HX1Ke6CUiT2sgG7MsqPl5c3jZuxnDbsOETDOTGaFuelH7OvLoqAX5KSf9eBU5Sd+52cE5foF3NkldRvD9sgYGkEQh/taWkjCWV/5B+9vjgLZTO4DARAAARAAARAAAQinECTOuV5CaB31Gv4LDrF8VuSNKTPyNlsETtH9aqWVBa00ZyN+vDJSySLRc9dupnEnVASgEip2mwArdm8n+4+fEa37z+he7w8UYzo0dSPJEfLlC4lbf73KF3npCPSRkSV+tVLqYyUXdjbSRa4FpfMfzmRSBtOiHKax4gCArZMwGYtaLY8aRgbCIAACIAACIAACAiBxrXLkdv7j7R41U6Opz+noIj1qSNbyORL6GmjutGwiYupy6Bp6pwkCZk9vpeKUZMDUncsW83EKiVp9CVGf8bvPVT2RDnfrll1mrFoI/3ScZQSexf2LJTDwRb/ISH+96UBY+EkWhiJeEItnNyfxkxbxiEp03X3y5ElLQtMvwRvuoPYAAEbI2DH/yB/bnSrlQN1cTVcO83Kh4PugwAIgECkJhD3tCPFeITvLiP1QxDCwXs5+/hlcYwV9tep5AkCLvdjanckHk0SbESP7kTx4xouCyTnnri8YgHmSMmTJAjQpCRSe/7KlRIliMeWs4DrucmrorQdny1q+mvIBmgonA648/psYiUU0abF0IXTrdAsCFgEAQi0ME4DBFoYAeJyEAABELAgAlFdiKJ8sKAOoSuWT4ATh31OyQk3nH8k4ghtp8Mi0EJ7z5BcJwtH1241JNhLjm+bg4RqwVJCBRAInAAEWuBsTDoDgWYSJlQCARAAAasg4OJ9iN753rCKvqKTlkHAnhwpnUN9crILu9udpQs0Ie7++Wuw4GHlChYRKoBAkATgxxEkHpwEARAAARCITAQ86RN5EkxokWnOwzrWqBSHmwi7e2NY+xFR10N8RRRp3CcyE4j0WRwj8+Rj7CAAAiAAAiAAAiAAAiAAApZFAALNsuYDvQEBEAABEAABEAABEAABEIjEBCDQIvHkY+ggAAIgAAIgAAIgAAIgAAKWRQAxaJY1H+gNCIAACIAACIAACIQLAVc3L3rjZnq8HK9XTamTO4ZLX9AoCIBA4AQg0AJngzMgAAIgAAIgAAIgYDMEXr7xocXLiLx9TBtS9cq+LNBMq4taIAAC5iMAgWY+lmgJBEAABEDAygk4cMp0e4pq5aNA9yOSgB0/MUR2EXnLUN/Ll/vp6elLn7+Y1l9ZoNocxZsV4cvXbylObGdeaDr0i3Gboy/mbkMWAX/x2o3ixIoR5rEJp1eubuTh4UXJkyYgxyihe01/8eotnb9ym6pXKBrq4crC5sfPXqP0qZNRobxZQ91OaC48d/kWRXFwoDw5MpKXlzd98/AIM9vQ9ONnXhO6mf+ZPca9QQAEQAAEQCCcCCSwy0vxfCP2ZSSchoJmI4iAHS9U7eDNIg1vVAGI377/hMbPXEkXr94hT37RlpI6RWJqUqcCtWpUJUB9azrw5u17mjBrJe0/doEFlafqeoa0yalb67okQmvguL9o5ZzhlDtbenXOw9OLGnUcRVkzpaY/hnQMMNTN/x6lcTNWsHj2W2cuftxYNH5wBypVJHeAusEduH7nEY2ZtswkgdZ7xGzq1LIWZcuURtfsqk176c/56yh/rkyUI0u6CBdo67YepOjRnJRA2/bfcRo2cTFdPbAkUi1+jj8nuscRGyAAAiAAApGdQLLL2yjGsxORHQPGHwICXjESkmuhPuQdU9ZDQ9EIPHj8nBp1Gk3lS+SjLf8bRymSJqJXb9xox76TtJN/rFmgiVWnXb9JZM/ifOn0QZQ1Y2p6+vw1rdt2kA4cv6gE2K4Dp1lYLKINC8dQVMcoNG/pFnr/8RMN7tFcQ2TwKXVG9WvNgiwXOTjY06DxC5UADI1AM2g4mJ3DJy9Rw5plDGpt/+8E9WhXn1o3qmpw/GfslCmWh1bNGRapxJlwhkD7GU8b7gkCIAACIAACIAACNkxg8rw1lCldSvpzZFfdy3XypAmpQ/Oa1KxeBd3I9xw6Q7OXbCJxy6tYqgC1bFhZZ81p2GEklS6amw6wleoluxHWqFiU+nRsRDGiO6nrA7v23YdPNGbqUjp94SYvIe6rBNTvA9qSWGPuP3pOE4d10t1/2oL19PWbhxJOS9bsojVb9tPbdx8pZbKE1PnXOlSlbCFdXW1j655j9OjJC9q2bAKlSp5YHU6fJjkN6t5MtSUHRvZtRbVbD6H5y7awSM1Pf6/eSXP/6MOukM5aMwaf/t0RxYLmGjumQR39nal/raPXru/I1e2DcmdMmSwxjRvUni1eafWrqW2xkonLo1jnkiZOQJ1a1KJalYuzdXMFuw96KlYxnaNT7SolVJuXb9yn12wh3Lr7GI3q34acojrS72yRu377IV8fn7q0qkO1KhVXbUs/5LwDuyQeOXWZKpUpqOZvxsINlD93ZtWGuGr27tCQ3N5/pJX/7FVuiy0aVKI2TaoF6Kv/AzJfMxZtpOWzhqhT4no5cfYqunDtLkWLGpVKFM5JY3huxYop9eQLgChRHKhetVLUvH5FissMpY9u7z/RW2Z1+uJNZRmU/mhWw9C0KW6Yk+etpXsPXZR7azn+ImJor5b+ux/qfXGcRgEBEAABEAABEAABEAABsxG4dP0e1alSXCfO9BvW4tAu8Ut2/zHzqH710rR0xmC2NDkqdzat7q17j2n3wTP06y9VaCRbl7buOc5xUVfV6aCunbX4H7p17wnNHNuDZozpwW6VScj13Qflqrd97wkWaS6qjY+fPtOKjf+p46cv3KAp89cqF8VlMwerPj1jq5ixcvXWA8qZNb1OnOnXiebkF8OaMH4cGsYv7ItW7aDeI2fTL7XKUolCOfWrGt2W/vUYNpOOnblKPdmKFVhxeemq3CsL5slKo/u3JXt7O3ZLXGu0es6s6ZR4Wz5rKFUrV5iG/LGQRegHJWIcWcyIBa1f58ZUtlheql25BLsXRqU6/CnHkiSMS50H/skxYNFo9vheVKFkARo0bgFd5vmVIv2Yy9bBs5duKTGdJmUSEq4y/8/53O+/taXSbAUbNH6Bspz27fQLC7OqirW4iQZXPnBbdx48VdVEhHXoP1kJ6DEsHAf1aEa3eZ6liDg7cPwCDe/dUo11/9HzJK6SUqSPYrXNnjkNTRvdTYnoxSyYpYSmTYnNFNGbMW0KkmdlIAtzEYDmLLCgmZMm2gIBEAABEAABEACBSE5ALFLv+IU1UYJ4OhILV24nsVRISRAvDvVq34DWbz9I2Tn2KX+uzCpGrUKp/OqYCKMUyRKpuiPYElUkXza1vWv/KTrBiSvE0hbUtV++flNuhbGcY1Cm9CmpYJ4s6nr5lZn3xRVRrF0i+MTCUq54Pjp6+rISk2JJypguhc66ortQb0OsfYlZuARXxCom1sFHT19S+2Y1gquuzj98/ILZfVRWpncf3IO8RiyKHVvUVHUkAYsIu2/M3n9p07gaXb15ny58jwX08fFVfcqXMxMLO3sWLmkNxKNYvLJxrJwIShE9r968YwvWULYqJqLiBXPSvqPnaAtb13Jnz6BuVbNiMQOr5N4j51RCGLGeSkmbKhmLpQNcpzOJgJMyf9k2Onf5tlELpapg5NeJc9f4GXrNQrG3EkdSpVLpgqrmum0HlMUsUUK/Z65o/uy098hZHZ86VUtQtzb1VN1P7l9o7PTlajs0bbZly59YHsVymIwtkmKJ0/qhGjXDLwg0M0BEEyAAAkyA/5tg7xcDDhwgYDIBHweu6veFs8nXoCIIgIBlExArkrjoPX/lquuovMzKz407j+nMxVtKoMnLtlg3xn1/WZbKOTkphWRF1ASargHeEPEk4ktKUNe2blxVWebqth2mhEIjtl51bVWXovL9G9cuz9aWDcrlbu3W/dS4TjkV81WsQA62+JWgnsNnKRe5kixOBnRtwoIiqbqf/i95Kb/G7n7BFRExr13fs6UtEYkrpb5rZWDXdm9bj7pTPVq+YQ8NmbCQRLSaks1REpSINeguu9zpF7FmdRgwhR6zSCzMQldcFKVI9klTiljB4sWJpcSZVj8XJz7Rn1tnZ14wL4iiuaQSu5tqJTq7qWpzqR0L7vPp8zdqPsVypV/EGvj5yzfad+Q8C/jrulPyvBkrYg3U7h2aNsWF8reuTWninNXKJVYEfXcWf+YUaRBoxmYOx0AABEJMwMnVnuKdxJt2iMFF8gvcinjQt+SmvShEclQYPghYFQHJ/vfPzsPUon4lJXjETVGKCI9Vm/apbXEDFMEwceiPmDB1woRfwV27Zck4eshxYkdPX6E/OQZJrBxVyhbm2Ktial8yJopoaVDDL0GGiDeJ4erXuRHHa92hmYs30nSOo5o2unuA3khSkA3bD6n206YyFHDun7+olPAiYCTGq3+Xxspq17LHeKrM8VkV2PpnSpGYNrHSfPj4mS2OsYO9RISvFOHykpOxaEXiwu7cf0r71k2luHH8YtpkDrQiWUiDWk5B7i2xYyKC4sf164ckgMmcPpXWRKg+TVvowbBpGdt7tipKspnE3y1lUiN2TGeVlr9Pp0YqKY3hVUHvhbZNcQutzXF8ItQlblHcPksUyqWLjwz6rsGfRQxa8IxQAwRAwBQC/I5t78UrAuEHDELwDOh9oWrKU4Y6IAACVkKgH78sP3rykroPnUGSbt/Ty4sk5kiSKmilQsn8JG6LEmcmmRFF1Ei2Q5cXb7QqgX4Gde0cdiu8yPFtEnsmVg2xpLh/9ktfL/FvNSsVU+KxCsdjaeJH3PJEUEZzcqJShXNRWracxYhu3DJUp2pJSslWsR48Nom9EmuMiKA/OHnF72wNFMEjqeEl9qtx7XIkroTN6lWk0Zy4RFw/jRXps4hJ6adwWMQuoeIOqPXP2DXPX75VwkmSeizmWDdxOUyS6IdbqVwjVkdh+4yZisiSRCj6ReKyJOGFLAMg5/2XArmzKNEhLomSfOVfzk55/fYjFW/mv2547xfgpCMyJyKchZEkSZk4Z5X6AkCyPU5fuJ7ucXyhjPfarYcqLi24PoWmzfcf3VWGTYlrzMtrtYk7pQ/PuTH30uDuH9h5WNACI4PjIAACIAACIAACIAACoSIgsV9LOYHCJHYDq99uhM5Kk44XPtayOEqM1uNnr2goJ63o981T1cnAliNJGiJFrDv6lha1z8ekBHXte7Y6teo1QaXBlxf6SqULsPtiSXWdurZ8ERUT1axuBd0xSXMvSUJGTF6i4tfEja9/lya68/obUnfhlAHKQtau7yTy8vZWp2VRZ3FRXLvlACfRuK+WF9Cuk6yBBzmea8LslUYthiJ+ug6eptZQk2uEgxbDpbXh/1MSpZSp34vdFf0yVU4d5RfzJfU0biVZbBZnd81GnUapy2Xfr/jVaMoMRFguXLmDxDV0gIyZTwlrKWJhkvXYRHCu/Oc/dbwVW0Mrl/HLbilLDRgr9nZB24D059L/9Xac8ES/aHvSl8nDO9OoP/+nYuCkjhYHJ7GKo6YsoTqth6pLJWmKlmnSfx8l7k4roWnTga8/ef66SjAjSVbEHXd4718pHrv1mqvYscr/4RBqrlYjUTsurl8i0WgxVBAInIDTM3tKcNwp8Ao4AwJGCLgW/0bfUliOi2Pci4uxDpqRecKhwAn8WAfNL/lB4DWDP5M8QfTgK4WhxoMnnnTrrg9bs7RX3qAbS53Sl/JkC/vfdYmNes6JNeRlWOJ//BeJhxK3PGcWU7EDSUPv/xptP7BrxSL0mtuUtPKyrph+kbTrJ85dp/ULRukfVgJRrDLSRy3TpEEFIztirXnx+i3FZMuc5kJopJpJh4STxN9JFsVECYJOQiLZL2PHikH9OzdRlpvgxIG4BYowkTnwX2RhbVe39+qemjAzVkfWepN+/Ygp818r4vZlniTWMVbMGAY3FSuWWGqln+K2GpIS0jYlvk+sp/ruliG5X1B1YUELig7OgQAImE6A//vn44jve0wHhpqKgOF7E6CAAAiEI4F0qRwpXdhCh0LVO3lR1rL3GWtAhIMk3ghNCexasXIZSzQiySTWcxbH37o1DXA7ESchfdmWhBGS3dAcRTilTuG3rpqp7YlYMkUwBTUuEbBBnZe+SJ2g5tDU/pqrXmAC1olFm7F5N+W+IW1TxKF/gWjKfUypA4FmCiXUAQEQCJbA11ju9Lzk+2DroQIIGBCIFoW9acLXamBwP+yAAAhEagIfOH6oV/uGVJ3dHK25NKxRmmOv8BpvzXMYVN8xs0HRwTkQAAGTCXyI8ZQeRttkcn1UBAEhkNa+HsWhTIABAiAAAhFCQLJGNqlbPkLuFZ43KcrLAqDYLgE4l9ju3GJkIAACIAACIAACIAACIAACVkYg0gs0CZrUFquzsrlDd0EABEAABEAABEAABEAABGyMQKQVaPuPXaBfOo5SqV8lRWnngVN5MUB3Nb2ydkaOsq2pQ//JBtN98PhFdbz3iNkGx7EDAiAAAiAAAiAAAiAAAiAAAuYgEClj0GSxxN9+n0ctGlRSgaKyKOAvHUfSCl7foWururyehF/K5zO8+KAsdJcjS1rF+u81O9UnViYwx6OHNkAABEAABEAABCKSwJe3b8nzzWsydYF4uxjOFCtVyojsIu4FAiDABCKlQJN1JmR9jKSJEqgF92SV9WyZ0qgV7/WfipoVi9GiVdtp2ujuJKu0yyrxlcsUVAsC6tfDNgiAAAiAAAiAAAhYOoFvL1+Sz/ShZOftZVJXfRu2J4JAM4kVKoGAOQlESoEmiw82qVOBxs1cTncfPqPsmdPQucu3adbYngZs2zSpRvXaDqOHT17QEraeNeHV1iVm7ZM7Fqc2AIUdEAABELARAh5x05P9t482MhoMIyII+Do4kq+ddUSM2Pn6kJ3HV7Jz/2ASGl+u/zPKV15sWJbSljWtUEAgMhKIlAJNJrpUkVy0a/9JevT0Ba3Zsp8K5M5MqZIbLg6YIU1yKl0kN42ZupQtaPdoWO9fSVaf1y8JYuOPhz4PbEdeAp/fxyBngitM5H0CQjdyR7sYZCl/R728feieWzl671IhdIPBVZGSgH0UX0qX2Y6f40j7ShVg3peu201zl26mI5tmkiy+rJX3HOtfpl5P+nNkV6pQqoB2OMBnr+Gz1OLL4sFkrjJh1kpasfE/1Zwsuhw7pjPVrFSMBnRpohZhDuo+Eu6yePUOmjqqW1DVcA4EzEYgUv41cXv3kboOnkZzJ/RhoZab7j1yIfljMHzSYpo9vrcB3LZNq1PLHuOpcZ1ylCBebINzsvPxs2luAgEuxAEQsDECMb7FoKyf8tnYqDCc8CbgFTOGxfwd9fH1Jbfb9vT6vHVYQ8J7btC+aQSc4vtSiuI+ZnmOneI4mHZTC69VtVxhmjxvDR06eYkqlS6o6+2eg2coWjQn9e6lO2hkY1D3ZmRvLzY085Z8OTPRH0M7kpeXN52/cpvf+/6mwvmyUfkSQf+3683bd3Tk1BXzdgatgUAQBCKlQLtw9Y6KI8ueOa1CI5ay2pVLKDdG/6zy58pM3drUpTp83ljx8Po55n9jfcExEPiZBJzeP6IEZ5Hh9GfOgTXe27Vgd/oWLZ4Fdd38L4UWNDh0JZwIePv4koeXbzi1bn3NJkkUTwmfHXtPGgi0nftOqlh+sarduveEfp+2jK7ffkiyeHSXVnWoVqXiarBbdh+jaOze2PnX2mp/35FzNG/ZVnri8oqSJ0lAbZtUp1qVi9PjZ69o7PRldOHqXc4lkJp+qVlWHQ+MmNw3ZbJE6rTcc8TkJRSFrWlaWb/toLKUvXZ9T7mypaeRfVtRiqQJaeyMFWpJpvrthquqE4d2okzp4TGiccOn+Qn8eCrN37bFtpghbQr1zcyStbtIEoa8+/CJ5B9/tu+CzX/HJbNjiu//oP2fwz4IgAAIgAAIgAAIgIAhAUm0JhY0LW5fYvglO7aIMIkx6zzwT3KOEY09l3pRhZIFaNC4BXT5+j3VyPOXrvTi1Vu1LV+q92QvJxFMs8f1orpVS5EshyTJ3jr9NoUk0dviPwdQvWqllCfUo6cvDTuit+fy4jV/Gb+L/lq+ldr1nUTFC+agogVyqBonzl6j0RzSUr1CUZrxew8VAyfeVr78v/rctqNjFOrXubH6EXGHAgLhSSBSWtDSpExC4wa2V2n1S7EvtJ2dHZUsnIv6dGioYx2YaV3qyg8KCIAACIAACIAACICAcQKS9fp3tm7t5S/A61YtSTv3n6IkCeNRwTxZ6OCJi5x07R0tnzVUWbSKF8xJ+46eI7Gc5c6ewaDBdVsPkHg6iTVLSqG8WdXnkVOX2aL2mob3+ZX4xYzky3f5kS/cJTzFWPnk/pXOsWujL1s8vXlJJRF6m3Ydoca1y9GWPcdU0rie7RqoSxMliMtr5Q6nS9fuqeNRHByoRKGcxprFMRAwO4FIKdCEYu0qJdSPfEMTN05MZUrX6Ipb45X9S7Rdg8+xA9sZ7GMHBEAABEAABEAABEDAkIBYtsoVz0s79p7wE2js3lijYlH1JbdYyOLFiaVzN5QrxUL2/JWrYSO89/T5GyrAos5/EXdHcU+csXCj7pSIqC9snQusZM6QSlnhtPPL1u/m6zdQg+qlyeXFG8qd7Yc4zMwujE7sEinviXFiO2uX4BMEIoRApBVoGl2YqTUS+AQBEAABEAABEAAB8xGQOLGew2aqpYwkE6J4L0mRpGtu7z/S23cfKH5cvwRsDx4/p8zpUwW4eaIEcdQ6tP5PJIwfR7kdrpgzlByjhO51NmO6FCSZJSXURdq7x0svaeUZC7ZvHAYTL24s8uUEQj9ryQGtP/iMXAQiZQxa5JpijBYEQAAEQAAEQAAEIp6AhI/E4kytA8fOpyxsvdISaxTInUWl0Z+/bJsSR/8eOM3JQh5R6aK5A3SydNE8dOn6Xdq445CKXbt84z4t37CHCuXJyhY0B04SslzFuUms255DZ9hidzJAG9qBr1+/sUXuNYkYPHr6Ck1bsIGyZkytxJlk9b7ALo/ikinZvheu3E5xYjlTHna5lDpfvnrQ1ZsP6OOnzyphiNYmPkEgPAiE7iuH8OgJ2gQBEAABEAABEAABELAZAmLZkpT7a7ccoGb1KurGJdaq8YM70LCJi2nlP/8pt8dWv1ThDI+FVB07vRT7dTgk5c6Dpyrjo2RdlGQdkrxNLFvTOZnHqCn/oyI1uqjrRFAN691Sdx/9DckfcImTkFRpOkAdFiuexMP153XQpMh9RPzJsktSxK1x4rBOFJvblB9JQtKi+1jy5BT9q+eNYHfI9KoefoFAeBCwY7Mt8sKGgayL65cwXI1LQcB2CDi9vIg0+7YznRE2EpVmP0neCLtfcDe6sxbroAXHCOcNCcg6aNnbelN0v+zthidDuJc8QfQQXhGy6h8ePiLPaxfIzvObSRf6ps9OCXLnMqluaCp58+LwYtGShBwxojsF2YSnlxdJ+nsRd1FZpOmXd+8/cVZHT9VOWBO5uX/+Sq5uHzg+LiFn/DZ0NBMrnSQXESGIAgLhScDwCQ/PO6FtEAABEAABEAABEACBn0Ygdto0RPJjIcWBk3xIZm1TiljjZA00Y0WSvZmrSOp/+TFWJPEJCghEBAHDrwYi4o64BwiAAAiAAAiAAAiAAAiAAAiAgFECEGhGseAgCIAACIAACIAACIAACIAACEQ8Abg4Rjxz3BEEQAAEQMBCCcTO4EOcUNtCe4duWSIBe36T8nWQZ8bOEruHPoEACFghAQg0K5w0dBkEQAAEQCB8COzPfJuOpXkRPo2jVZsk4GRnT91j5aZ05Leel00OEoMCARCIUAIQaBGKGzcDARAAARCwZAInv72kTe73LbmL6JuFEUgdJSZ1ix1w/S4L6ya6AwIgYEUEINCsaLLQVRAAARAAARAAARAIAV6lIAAAQABJREFULQGXL+70+Msnky+PEyUqZYsdz+T6qAgCIGAeAhBo5uGIVkAABEAABEAABEDAognc//yRGj/ZQ17kY1I/RyQsCIFmEilUAgHzEoBAMy9PtAYCIAACIAACIAACFklAUuB88fUiNx/TFqqW+rZQvLy86ZuHB69vZt51zMKr3cCYv3j1ls5fuU3VKxQNrAqO2wgBCDQbmUgMAwR+NgEPxyT0KkeXn90N3N/KCHjzc4MCAiBguwTGTFtKa7ccoP6dG1ObJtUiZKC9R8ymTi1rUbZMfotyb/vvOA2buJiuHlhCdnamZdtcum43zV26mY5smklRozrq+v3+ozuVqdeT/hzZlT58+hxsu/77omsoFBvX7zyiMdOWBSnQvL19qPwvfejN2/e0Z/VkSpEsUSjuFLJLlq3fTQ4ODtS8fsWQXYjagRKAQAsUDU6AAAiEhMDbV8np1oqUIbkEdUGAMjf3ofjxbeNbekwnCICAIQFPLy/698BpSskiQURSRAm0wycvUcOaZXSdKVMsD62aM8xkcSYXVi1XmCbPW0OHuK1KpQvq2tpz8AxFi+ZEpYrkpk+fvwTbrv++6BoKp40T564p4ZgwfhzavvcEC9Xa4XSnH83eYOEYJQokxQ8iYd8CzbAzRAsgAAJMwJdDGnw8TftmEsBAQEcA2kyHAhsgYGsEjp6+Qh4enjR+bAf6ted4unP/KWVK/+OLvM3/HqV5S7eQuO7lyJKWvn7zoPkT+1LihPHUdTMWbaQd+07yy78D1atWSllo4saOSVP/Wkdu7z/RW7cPdPriTcqfKxP17tBQWczGz1zB7oyeNGbqUorpHJ1qVylBObOkI2lr+awhCvETl1c0cfYqunDtLkWLGpVKFM5JYwa0NcCfJFE8KpwvG+3Ye9JAoO3k/lQuU1BZ1e7fuG/QbsMOI6lD8xp0+ORlus1jTZsqaYC+pONjqzbto78m9dPdr0H7EfT7b20pe+a0tHbrAVq0aocaW3QWgpX4XkN6NidHEwWQCOFyxfNS6hSJaft/hgLt85dvNHnuaiWavX18FK8kzHrS8M6qL3sOnaHZSzap+ahYqgC1bFhZZ4WUsZUumpsOHLtAL1+7UY2KRalPx0Z0+sINOnj8orr+2q0HJMJwweT+urFhI3QE7EN3Ga4CARAAARAAARAAARAAgcAJbNvDYqFEPiqQOzOlS51MWdG02mdYWA39YxGJdet/MwZRlbKF6da9J+Th6aWqiKA6cPwCDe/dksYNak/7j56ndVsPqnMuL11JhFL2zGlo2uhuStgtXr1TnRMh58iCTixo/ditsmyxvMqidOfBU3VeBGOH/pPp7buPNKZ/GxrUoxnd5vsaKzUrFlMWtE/uX9TpV2/c6MylW1SrUnG1Ly6OWrty4Na9xzRw3AIlKCuWyk81OFbMf1/ef3Cne49c1PXar5t3H5P7569qN2G82NSzbX0lJsewaPt3/yn6Z+cRrWqQn1++flOc5L41KhSj+4+f0/XbD3XXiLDdzwKrW5t6SkTFjuVMD5/6rft4icVq/zHzqH710rR0xmCK6uio3De1i2Vsu9l6+OsvVWhkv9a0lef2+Nmrag6ysbDMnT2D4h0RFjutT7b8CYFmy7OLsYEACIAACIAACIDATyAgokYsKyIUpIhoEGuUr6+f2XzL7mOUma1pQ3q2oHw5M7FAK2TQy3XbDihLVSK28IglqWj+7LT3yFldnTpVSyihUbJwLmpatwKdPHddnZO4M3t7e2WNKlEop7Ji6S7iDXEBfOLymkT8VGArkbgvrpk/Ur+KblssZVL2HjmnPneyWBKLU8E8WdS+sV9TOTZtNAs/ESpl2ZIVVF+MXS99ys7WRBFtIhzjxHam+/4EnbHr5Ng+FrESCybul2KpzJguhYEoFubCqkWDSpQ3R0YlnLW21m8/SNmZXf5cmcmTk6pUYIEpfXj2/LVWhUb0baUsmTJXxQrkoBNnrylrZ5KEcdWn8BYxjhJ2AnBxDDtDtAACIAACIAACIAACIKBHYM+hs8q9Tyxd4jr36s07evH6LZ1lC1ShvFnp+StXysvCzFh5++4DiTveviPnWQT4CS+p56SXrEP/OucY0UisR6aUp8/fKNGTMW2KYKuLi6S4C+7gWK66VUsqq5249gWVaMSZrwlLEcvholXbmVE2FpdJ+F72OlEbXLtisRRGo/78n6rq4eHFfT7FCVqasIXuCzP9SvnYHdRYEdEqlslx05frTotr6At2ZzSWaETYmMpc1yA2TCYAgWYyKlQEARAAARAAARAAARAwhYAkqBArTZJE8VX1BPHi0NPnr5RFRwRarqzpSdzqjJXYMZ0pCluC+nRqROXZRTKkRQSUZqnzf63ESImbobgrSqxbcKVW5eLUc9hMOnf5Nl279ZDGDWwf3CUG5/33RSxcnt/dOPUrSj0RPEvW7KSxfI86HDsnpe+oOfrVAt125Xg8sQ7W5v7GixNb1RNxuZQzLJ48f53EupU6RRJ68Og5FeHYOv9FuCRNHJ8mDu3k/1Sw+9J3H45pQzEfAQg087FESyAAAiAAAiAAAiAQ6QlIEglJHvH31N9Uog0NiIiAecu20LBeLZUb3pI1u2j8zJUs1tJxnNVhrZqK4ZLYtOkL11OalEkoDQsLiU8TV8Ne7Rvo6gW2IbFp5y7foiLsFvnJ/bNBNXHBixE9Gre9gXq0q6+E4N8sigZ2a2ZQT9sRF8pYMWPQwLHzKUuGVAZJTrQ6QX3670v+3JlUCvxTzEcShmhxdSIoHdg1MwZbA5+zJesjx7ddZAF76do95W4Y1D3k3C52v5QEKqP7t2U3xx8RTNLGdk4cIgKtVJFctHDldvLy5qXKfXxJ0uPLnEipUDI/DRq/gAVxfrX9+u072rzrqBKKyZMmVHUC+yVj3LD9sIqjEytdogRxA6uK4yYS+DGDJl6AaiAAAiAAAiAAAiAAAiAQGIFdB06pF3//sVrVyhdmwfSFjpy6rDIvdv61NrswXlVWHslgKCWaU1T1KfFOqZMnpjqth1LeSu2padcxyuolJ+39rWUmcV76ReKsJLFGPr5OSx6i5RgWQTKZsxYe5/ipio36UdkGvVkI3dO/3GBbsidKyv3nnGlSkob4L1q7clwsSfr7csx/X5IlTkANapSmTgOmULmGfXRJRuRaWW+ta6u6qs9Fa3ZVa545Of1Yg03dQ34ZKZLtUvqpL86kmixqLcL2G2fI7NiiFuXJkYGWb/hPZZoUK6YTZ7HU6sm9h/6xUPEWNiL6xOInxf/Y1P73eShfsoC6b+HqnXmeflf18StsBOxYsSPJcRgYurj6ZfYJQxO4FARsgsDb63Z0c6nfH3KbGBAGESEEsrbypvjZLec/Q71eH6EN7oG/rEUIFNzEqgikjhKTViauTOmj+rmVhaXzyROELX4puHtfeudKhz660Fdf7+CqqvMFoiWiComDj9UyqTEjlWRRZU1QiBgYOWUJnd4536CmCAtZdFmsMvoLRhtUMrIjbbu6+V0nYsJYee36TglCsZCFZzHWl3e8TICoObF6+S8SfydjTpksoUoy4v98aPdlXTr9dP2yiLaMXVL8a0VcFV+y+6czWxkly2NIivQ5FsemOX0X2SG5FnUNCcDF0ZAH9kAABEAABEAABEDAJgnkiZuA5McSiiQCqdFiEEmKdgd7O5K0+7Kulv8iL/vGklT4r+d/X4RfcDFmEeWKZ6wvceMEFGbaGGJEd1LrmGn75vpcv+0gW8/2qCUPxA312Ys3tGL2UIPmxRopVr7QFM1dMjTX4hpDAhBohjywBwIgAAIgAAIgAAIgEM4EJBGIpLoXkSCLRfflhCCSIh8l/AjIkgKyZIEbrwEnYqoEx9cl4HXXUCyPAASa5c0JegQCIAACIAACIAACNk0gCi8mLYIBJeIIiMVQFvJGsXwChlGVlt9f9BAEQAAEQAAEQAAEQAAEQAAEbJYALGg8tZLK9P1Hd0qeJIFZgzFt9qnBwEDACAGfhB6Usq3lJHsw0kUcskACPvEkeN8wS5kFdhNdAgEQAAEQAIEIIxCpBdp/h8/SlHlreeHE1wr4+gWjSNK8ypoRzbuNpeIFc9DCKQN0k3Hw+EXqNmS6MslPH9NddxwbIAACROedn1Pbz/uBAgRCROBv5/JUmVKH6BpUBgEQAAEQAAFbJhBhAu3yjft08tx1atOkqkGKz58Fd/fBM/Tb7/Opab0K1LBGGYoXN5YKnJT+aKuhn7l0S60anyNLWtVNWchQClYmUBjwCwQCEID9LAASHAABEAABiyHwwc2LPrma/pfakbP+J0oBC7fFTCA6EmkIRJhAO3nuGq8ev5UXyav50+GKwJqzZJNamX1Qd+Mrx0snZUHCRau207TR3UkE5p37T6lymYJq9fWfPgh0AARAAARAAARAAARCQODDKx86v5C/aDZtGTTKWNOXBVoIboCqIAACZiEQYQItU/qU5OHhSbIoYEStOxEYIVlI794jF0rGMWdtev9BX3gRxEJ5slLX1nV0VjS5tk2TalSv7TB6+OQFLWHrWRNemf4VL973yf3H4tTxYvqtwB7YvXAcBCILgShfkXMossy1OccZhdcqspS/o168sG0g69mac8hoywYJRI9qOc9xkHh97cjHw5e8PhtfuNn/tebyGHr+ypVi84LIzjHCdyFu//3X35c+XLhyh6pXKKp/ONjtdx8+kZeXt0pLH2xlG6vw4tVbOn/ldoiZaRjcP38hJ15CQTJ2ooSMQIQJtNJF8lDOLOlI3AQHdgvcahWy7oeu9nN+4KSIW2OJQjnp/Qd3mvu/zfT67Tv6Y0hHXaMZ0iSn0kVy05ipS9mCdo+G9f6Vpv61TndeNr54eBnsYwcEIisBH7ZMo4BASAnIc2Mpf0d9fPgZxmMc0ilEfSbg6eNjluc4upPtvMhKAjZ5Zzp08hLJoshSkiWOT706NKRalYqr/Yj8de3WQxo7Y7nJYkPE6bCJi2nf0fMUjxeVjhM7Ji3+8zcWmdGMdrtt30l06vx1o+ckVGbdX6OMngvqoPR58eodNHVUt6CqqXOnL9ygNn0mUq6s6WjN/JHB1jelwvU7j2jMtGUmMVu2fjc5ODhQ8/oVVdOfv3yjwtW70J8ju1LVcoVNuR3q6BGIMIG2YMU2unrrgfpZtn6PXhf8NueM701li+cNcDw8D/TjRRE1a549r2I/bcGGAPFlbZtWp5Y9xlPjOuWMLub31cMnPLuItkHAagiol1ur6S06aikE5LmxpL+j0GeW8mRYVz+8+LtaS3qOLYHepLlr6MipyzRlRBe1ALULL0i968ApevTkpSV0L9g+XLh6hzb/e5S2L5tAqVMkobINetGmXUeoRYNKRq8dO7Adff78VZ2bsXgjubO31ZCeLdS+k1Po4vjesOHgyKkrRu/n/+C2/05QymSJ6MrNB8rzK22qpP6rhOv+DRZzUaL8kBXReMyr5gyjNBHcj3AdZAQ2/oNkON+0GGdEjOYUuDtghrTJw7kHP5pPnTyx2nn87KVOoIn52svbO4BAy58rM3VrU5fqVC7xowFsgQAIgAAIgAAIgAAIBEpAXOMqlMxPBfNkUXUk1EV+tHKKLT4zFm6gXNnS078HTqv3r3ZNa1CrRlVUFQmLmbFoI+3Yd1K5yMkCy2KdicuWrODOzWGvKGlTrHiSnXtIz+aqTXnXm7ZgPW3ZfUxZwtrxl/D1q5fWumTwGcs5htoXN7/ECePRVw6HiRHduPVMKspSTVqJEysGiRNpxnQ/Avj2HDpDszn/gbRXsVQBatmwshKufUfNoShseZo4rBO7WNvR9dsPafikv2lIj+Zs8VtBX75+o/rthqumJw7tZMBQu9837pu0P7p/G2a2gbazWOvetp52mhp2GEmli+amA8cuKGtmjYpFqU/HRjweJyXmeo+YTeICKknyxNtteJ9fKT17kemXv5ZvpfuPnqt+aseFpXApViAHSaZzKdfYGJMwfhxaMLk/jZ+5kob2akFxs/vNmbF5cXv/kSZzRvV7D11IuJUrkY+vaandItJ+RphAy5sjI8mPJZS4bKounDcrzfp7E80e10vFxck/1qL5sxtdB61rq7qW0G30AQRAAARAAARAAASsgoC8aK/dsp9ixYxOxQrmpGwZU1PsWM66vot4unT9HqVOmYRG9m1Fe3jpo0lzV1PtysVVCIqIswPHL9Dw3i0pJsevTZqzWmUBl2RzQZ2btnC9EijtmtWg7JnSkFiWJJeAFHe2cMmX8yP7taLL1++zgFihEsJFjRrQwiVismHNMtSVl1dKxIKjJrtlSt8mzFpJjWuXCyBgdAMzsnGJl2/qP2Ye9WXPrSL5sjGXA8p9cuOiMdSpZW1q2mUM5zrYxd5a5anf6HlUqkguypcrE9VnUbpg5Xbq17mxajUpu4gaKwdPXFIJ7MQT7c6Dp7R973EDgXbr3mMl9NozExGZIyb/TUX4nVeEosSHybjkfvYsEIWtCMO/p/5mcKtC/N48c/E/3N9aauwyfys2/qcEW/bMaSgbC2FHbuvXX6roDDJyX2Euxdi8PHj8nEb9+T8qUyyvegZk2SvJso5CFGECTZT3jTuP1bcfMsk/u4zq35p6Dp9FxWt3I28ODM+XMxON6tda1y1xeTRW5NsN+UEBARAAARAAARAAARAwTqAzC4/o0ZxoxYY9tHDlDlWpStlC1L9LE521KU5sZ13sv4gLsfCcvniDqpQtTOu2HVDvjInYeiVFvkTfe+SsygYe2LkOzWvQuq0HqS0neWvdqKq6rjALIil7j5zjODJnlZlb9ksVzq0ydcvat1odOa6Vt+8+0PnLt6kax0/t3H+K0rCQdOCkRiJKKpcppFUz6XP99oNKLIpXlidb8SqUyk9y7BkLkiwZUikr05ipy+gwu4TG5/wIA7o2UQYDET5iXZN8CUGV7XtPsNjKr4RRDU6CMp+zpsu49A0jI1gEiziUsovHc+LsNSXQxC2yfo3SKn7u2fM3as6usRXPf5G+Z2bRum7bQZIM6Fv3HFfWzHLF8ykuSRLGVS6Oxvoq8XzG5kUsmt/YUurEAjlZ4gTKolipdEH/t46U+xEm0LLxtxjreVJbc9ZEmeBm9SpSLVbsQbk9hueMpEmZlLYsGadMuvLwa7Fock95CK/sX2L09uJjjAICIAACIAACIAACIBA4gZjO0alb67rUtVUdtlq9IkliIS5+4oInrnr+i729PaVLnYyu3nxI8kW+JJnYd+Q8C4kfiTfkRV6EU2DnXN0+KJe7At/dKv3fQ39fLEfyDiouhMbK32zREovf+MEdWDAWol7sBuj27qOqKmItJOWJy2tyeelK46Yv110mroQvOHlKChZIDXg9XvHkOnPxJm3537gQrRcsWSaPcCIWsWANmbBQtS/vtdtYQOkLNN2NeUPmRhu33LP70BmUhIVwTk4wIkVbD1jt6P1qXLu8mr/enOhl7db9Kj+DiNbgSmDzInPwW9emNJGto2vY2iouod3b1COItAi0oJUumod2r57CPqoXaNXmfcqkKdl9GrD5uCmbdOUB/RlFFDsKCIAACIAACIAACICA+QjIkkQiBMTrSASN/Nx58IwOn/SLVfJ/J0/OtHLv4TOqXr4Ip+R3VpajPuwSWJ5dJfWLWF1EgAR17i67+YnFLfgSuEfUA463cnT0s2OIC97M33tQz2EzKSu7akqMVUiK1Bf3RGPCVNoRkSTJPZInTahcDGeN7amaF3a+vkEnoxOXQHFbLJg7i65Lxdni9u/B0zSoR7NgxZ5YBAuxoJ3FIT9yP2nvys37urb0N2pVLkZ/8rv7OHaBfPz0pRKW2nm5NjBhJ3GDMmfG5kXcSMXFUqx2ItIGjVvAFsNcKj5OazsyfgYve81IRVR2BfZ3lTSlkhVHRJv43FZp9ptS77KYNQoIgAAIgAAIgAAIgIB1E6jVarBKES9xRhKvdPT0FU74cYKtY35udjI6T08WZbwurawxK1/ay35ZdpkTy0qZYnloOseTyXkRZZJyXuKjgjsnSenEDe/spVsqmYhkXhQBFNKSmV0PpY197Brpwf0SC19MThwi/VnIcWEhKZIsRdwKRfzIWCQhx7ylW0gyW8r6wBKfJpbGeRP60DHmtGTtLtW8iMEvXz3YqvhAMdSsXvr33v7fcapavjDHqTXS/Yzo+yu9e/+J27qqX9Xotojo9x/dSaxcMjbhJWLLWJF17GpWKkb/7DxMVdj1M0G82Lpq4o4pcyQxZzIm/SJzZmxeJLGIxPS5slVUrH0iqmXpFUl6EtlLhLk46oO+zEGhq9mKJgGhUsT3V741addvMsnaY8tnD+VMLj8CSfWvxTYIgAAIgAAIgAAIgIBlExChtYhjz6b+tV51VL6kl0WiB3Zrquu4uCq26DaWPrCAi8WJQMYOas8p7f0ybUvM1KgpS6hO66GqvuQG0NZPC+qcJAAZPH4hteo1QV0n1iuxDhGLEHu7gHaJwMSIJNQQoTHkj0XsUvmV4+YS0l+T+pG4FPZgl0C5TuoEVkTQaUXGLW6eQ/9YSP2+eaqMlfK+K5kp+42eq1z7pC1pU/iM4+QlBTjcJnf2DKpOi+5jVeza6nkjKDdnvdSKuE2e58W3xeVQv4h3WH5O+iGCWGL7pF19yaX2v4swySbZZ+RsKlO/l4oFk6ya+kX/OjkuFs51Ww9Qs7oV9KtR+ZIFWNwd5bXPOlMyzmi5d+2fxDeW/6tibF4mDutMJ3ntOLHiSYIR8aYbzmsOyzrFkb3YceBehCz7Iqp/J6dKXb15PycLeaT+IdatWpKa1i3PZu+kyiwqwaH/W7dbrZmRJJFfUKilT5CL6xdL7yL6BwIRQmCP+2Nq83p/hNwLN7EdAksSlafKzqktZkC9Xh+hDe73LKY/6IjlE0gdJSatTFyZ0kf9YU0Iba+TJ4ge2ktNuu7FY096c8uHfDz9v3Ybvzx2Gl9Km8PJ+EkTjsorpqRRF6uKuPg56q2TJUk7JJvg4X9mqjpijdEXNVrzYk158/a9yhXgP9tiUOfExVKEleQYEEES2iIp/cXCY46QGHEBfMnWQmd2SdTPaBlc32Qs3nxteBkvZJ78lp6KF6xroVg6T5y7TusXjDLabZmrWGyVcwpkaS1j8yIWVtEJspwBih+BCLOgSbDlXF6XQrLVSLZEMZFKdh+tyD9KcX+UHxQQAAEQAAEQAAEQAAHzEkia2pGSRuD3ISKM4seNrX4CG4m4v+knavNfT170A8tTENQ5cd2Tn7AWEYXmEGfSD3nXDU1b5hhHUBxknsRYElwRi6ck/PtNzwrq/5rg4vOMzYtYT+UH5QeBCBNoBXJnpuWzhqgMiT9ujy0QAAEQAAEQAAEQAIHIRkDiqwZwyn0U6yHwgd1Ee7VvqNwcrafX1tnTcBVo+4+eV2btSmUKmphNxzohotcgAAIgAAIgAAIgAAKmE5D1t+QHxXoIiJtqEw5NQgl/Aj8iGMPhXuJ3PGziYhV4OGbaUpWFJhxugyZBAARAAARAAARAAARAAARAwCYIhKsFTRbekyw+W/cco007j9DaLQfUItUNeMXymhWLU9w4MW0CYpgH4fGZyMExzM2ggUhGwJ7/+YYh8DmS0cJwQQAEQAAEQAAEQMAqCISrQBMCkpWnTeNq6ufStbv0D6+vMHPxPzRl/jq1+KCIuGIFshvN3GMVBM3QSQePj5TgzHSK8vm1GVpDE5GFwOcUxehd3naRZbgYJwiAAAiAAAiAAAhECgLhLtD0KebhRejkZ1D3ZrTn0FnauOMQdRwwhTPaxKe6VUvxWg8lA83Uo98OtkEABEAABEAABEAABEJG4NvH9+T1ydXki+yixqAYCYLP7mdyg6gIAiBgEoEIFWhajyS9fp0qJdTPo6cv1arlG3ceonnLttD0Md2pUumCWlV8ggAIgAAIgAAIgAAImIGA54fXFO0YL7Ls621Sa19y8ALIEGgmsUIlEDAngZ8i0PQHkCZlErUCeo+29enYmSsU3PoJ+tdiGwRAAARAAARAAARAwDQCdrwgsZ23Bzl4fjLpAjvyMametVd6ywtR+/j4qrCcsCxqrXFw//yFnKJGJVnj7dzlWxTFwUF5kGnn8QkCwREI1yyO2s3vPXKhhSu3k6vbB+0Qrdi4h1r2GEeDxi9Qxx0c7Kl00TyUPXNaXR1sgAAIgAAIgAAIgAAIWC8ByeKdo2xrWrJmV4QNoveI2XTjzqMg7+ft7UNL1u6iknV6UKm6PVXG8RK1u9P4mSuDvC64k7KYc+HqXWjvkXOq6rqtB9lT7Ghwl6nzpvTbpIZQyeoJRIhA+2fHYdr871GKG9sva+ORU5dpwqxVJA/x4ZOX+B/DCqsHiQGAAAiAAAiAAAiAAAj8IODp5UX/Hjit1jvb9t/xHyfCeUveLfWNAsZuN3fpZpr7v83Ut1MjOrVjHh3cOJ0GdG3CwuqsseomH4vm5Eir5gyjopwAL6TFlH6HtE3Ut04CEeLiePfhM6pQMj+JlUzK6s37KF/OTLRi9lB6/OwV1WkzlN7z6uRxYjlbJ0X0GgRAAARAAARAAARAwIDA0dNXyMPDk8aP7UC/9hxPd+4/pUzpU+rqyJf385ZuoRev3lKOLGnp6zcPmj+xLyVOGE9dN2PRRtqx76RyFaxXrRQ1r19Rfdk/9a915Pb+E71lz6zTF29S/lyZVLhMtkxp1Jf+3/ieY6YupZjO0ak25zxo3aiq7p6y4fbuIy1evZP6dPiF6lcvpc5JXblH1XKF1b7756/0x+xVtP/YebLnJW2qlC1M/bs0pmhOUenUhRs0Y+EGypUtvRKgvuw62q5pDWrVqIrKSi5WuKG9WlDc7AGXk1q/7SDfewe9dn2vrh/ZtxWlS53MpH6rjuFXpCAQrgJt2oL1tO/oeXr24g1dv/2QH/ILCupTl1fkxA94zV8Hq335x9ug3XCKxslD5B/oxKGdIgV8DBIEQAAEQAAEQAAEbJXAtj3HqVyJfFQgd2YlQsSKJhYrKWdYWA39Y5ESXdXKF6HL1+/TpLmrycPTS50XcXbg+AUa3rslxYwZgybNWU2OUaJQxxY1yeWlKx3gd8q2TapR03oVVBiNCK4pI7ookbVu6wFqWLMMv1OmoxRJE6r29H/defD/9s4Cvqnri+OnQltaoIUCxX24DR/u7u4w3GW4DpcBw2XI2LABg238seE2HIa7D6cUh3r5n3NLQlLSNiEJTZvf3SfJk6vf90bf751zz71HwdxOtfLhYkz3nASykzRl3mo6fPICDerWjEJCQ9USUa4u8ZSV7fWbd3Tm4g1Kx3EURGBt339C9b125eKU2CshXbnxH4nAi5gOn7hAo1k4yhgK5MlKS1Ztpm5DptP/fp1gVL8j1of9uEvAqgJN1j9rXKssTeM3HXJT9/i2Hl3l/yl6DJ1JYwe1ogJsRZNUj8VZDw4SUjhfNnLhfEggAAIgAAIgAAIgAAKxl8Cbt/6099Bp+nFUdzWIGhWK0bpN+6hvp0YkgTg2bDtIWdmaNrRXS3U+ZXJvJXI0I167cY8Sb8nYmiapWIGcyv1QxI2kOlVLUHd+rpQkbY2bsVxtixXN0dFRxTQoUTi3Ohbx6yFb7KQPkQWmE4vY/7Yf5PrrKguclL/30JfWsPATN0hJnok8aNLQTmq7bPH8SjAeO31JWdrUQQNfG7jOnFnTU6/2DdTZZN5eVJ+fgU+du0ZFvs4Rbb8NVIlDcZSAVQWal2cCko8sRt1v9Dz19kTmn6VIloSqlCmsxNhdtqbJWwb5Hyp1ymRxFDOGBQIgAAIgAAIgAAL2Q0DWuxVXwy3sorh933F68vQFPfJ9RifOXKHC+bPTwyd+lP/Di/qIVCSqosQp2HXgXzp84qL2tLzsN5Q83N3IPyDQ0CmDx2T9XRFhj58+57V4vT/J84xdIKXveXNk1p4Td8aflm+kQHbDjJhEEIqb4vnLt6MUaA/Yo0y3ThGoMiZx8UQCAV0CVhVomobkrUftyiXUje3Kkye//66t1lK26s+d6i1ElgypNdnxCwIgAAIgAAIgAAIgEIsJbNp5mPLnykI+/FJekndiT7ZCPSFxcxSBlid7Jjpz4brBESZK4KFC0/dld8jy7CJpahLrmAiwyFKWjKlV/SIeZe6YbpIQ+RITQeIm3Lj9QPVVzt/kiOTu8d3UFB3d/LItwVBucLyF6uyqGVUSi53k0ySZAiRCUNwiJUXXb005/MZ9Al9EoMlNPrRXCzbp1ucbO57yIdagbVyrHDWpXV4bQERzHL8gAAIgAAIgAAIgAAKxj8Bj3+d0jANp/PzjQOW6pxmBCJT5yzbQ8N6tqFTRvCr0vgTUyJM9I/2xZb8mmwoKUuabfDRj0e8k6+WmT+3D87ruqtD1vTuEuwdqMxvYEDdCWX+sKBsI3rx9R0m8Eunlkv1WjSrT3KV/UQL3+FSVhZU/W+wO8RyxWUvWq4iOxQvlpj+27qdC+bNRSEgobdx+mJeDyqutR+awyTJSCTm4iITrl/2yxaMWkzJmmYMmIfgL8hw0WYJKxGC+nOGWuuj6rW0cG3GewBcRaBqKEiEnYhKTMBIIgAAIgAAIgAAIgEDcILB1z1E1v6sQxxbQTRKUY8r81STTXcpzdO8urWsrF8h/z12lojwHSyIySpRESSM5+MaoqUupTtthat/R0YFqVSoevs0WMt0kLoa6qVndCioC46KVm6ltk6o0oGv4vDHdPDIPzIMtYj9yQLsx05epUyKWalb6Rm1LcJLevJ6apn2xBg7q3kxbhbhgtuw+jl5xwJCEHMRk3OAOlC518vDz3D9NFx2435pUhyNKnr10k3qPmK0OyTy2ycM7U6IPUcyN6bemLvzGbQK8qHwUNuC4PXaLjO6Bn7/Z9Ti9eUzex2eQ8ztfs+tCBfZD4F3qb+hF/vY2M+Dtb/+jb31320x/0JHYQWBpsvJU2SOdzXS2t+8BWvf2hs30Bx2xfQLpnBPQyuSVKZOLvpXmc3qeyvvTF9mfU09kZd48Yfe6x5fIIfTTeVSGyoQkzkye6fVFlqF8n3tMFovWLMG0dfdR+p4F2bEtC/SqkzlfT5+9JAmoYUogOanb73l4OXEdjCr5+r1QkRp9OCBJRLH3hOepSXlpX5PEAjZyys+0/49ZHO7/NbtvJvqknCavoV+JvSDrtKVJmfSTcqb021DdOBY3CHxRC1rcQIZRgAAIgAAIgAAIgEDsI5AgOc/3l48NJAkEUqPlYMqRNQM5sZVJwu737RQegl+3e7Is0+cEkRPhJ+upGZN0xVfE/FHV4ezspCfcIpaNbF+CmsjHUDKl34bK41jcIACBFjeuI0YBAiAAAiAAAiAAArGGgAQCGTOwnVor183FRa2PJhG9Y0PKniWdQbfJ2NB39DF2EIBAix3XCb0EARAAARAAARAAgThDQKxPlUoXipXjScPLQskHCQSsRQACzVpkTag37H08epuSJ76GhZpQClntnUCIZ1p7R4DxgwAIgAAIgAAIgECcI2D3Ak0mn7btO5kCeIHDP38epy7waV6XowVH5ileKBctmjpAe9H3HjpN3YfOUG98ZozpoT1u7sa7wCR04XAdeh9ibk0ob08EPFK/pwz8QQIBEAABEAABEAABEIg7BOxaoEkAy6GTFtPFK7fVCvCayxoWFqY2j/Nq9xf4XK5sGdT+z6u3qF+LB77kZ2z/Jw4U+CzqKEOqcXyBwAcC8RLKfQqBhhsCBEAABEAABEAABOISAbsWaHOW/knXbt2jTq1q0Y59Jz65rjUrfkOLV22i6aN7qHUrrt28R5XLFKKwMDwUfwILB0AABEAABEAABGyawCP/ELoXwNMpogk7rxlEQieibAnD1yXTHMMvCICA9QnYrUDbuOMQrf3fHlo9fyTt+udfg6S/bVqN6rUbTrfvPuLV7rdQU174UNbDePP249pnXh7xDJY15eD7l5h7Zgov5A0nIGtfJnBzJmcn27C8OgXYRj9wf8QuAk58/1ri31FLjDok9L2xz62WaA51xCECbi6ONnMfR4X1tn8odXsUTMbOqOiT2IkFWlQ14hwIgIA1CNilQJMV68fyqvELJn8X5doamdOnotJF89KYH39lC9oNGt6nNf3401q96xAYEu4OqXfQxJ0QZZHDw62J2Ow+u9hxg3khztD3tnHvYMl7u78lPwuA3DeW+Hf0sxqPUEh5R8BBIgIV7BpDQP6OW+I+djemMTPyyO0dwF8vjLzPzX/CMaOzEYpKzIBnvCh00iSeFM/Zfh5fX7x6QyEhoWrcEZBYdVfaDAwK4vXaol88/QRPCXKJ50x5c2a2ap/sqXL7ucN1ruofW/bzqu+etGnnYfW5cv0uPfZ9RmOm/0q9OzTUyUnUrll1atVzAjWpU06tFK93knf8A823foUY+yorYuPYt2sC6sE22Hb+fML1165vx88evNw3lvh39LM7EKGgkc+tEUph194JhIRY5j5OnCDukZRnqzUb9lD/Lk1IPJM0afS0X2jtxr1q19nJic7sWqI5pfcrD/+/rNlKh09epAAWaZJECPwwvDOlTZVcL29c2pF4B8MnL1FeXok9E5BnogS0ZNrASBe4lrEby9QYTuJpJu2f37OUPQuifhG8esNuSpTQPVKB1mfkHOrM04liyzp3xvCxdh5Hazdgi/WXK/41VStfhLz4ZpdPfDdXcuJ/HGTbyVEfSYE8Wan7t3WpfdPqtjgU9AkEQAAEQAAEQAAEbJJAML+B/nvPMbVmmDzw66ZhvVupl+Lp0/jQ8a0LdE9pt9++C6Cug6eTM1vMfl84ig5vnEu/zhyiXpg/e/5Kmy8ubpw6f43++vsf+m3eCNq0bBLdf+hLf249EOVQjWEaZQU6J8t8k49WzR0erTjTKRLp5v4jZ8gvjl+vSAf/mSfs0oJWoVRBko8mLft9Gz199pJ6tW+gOaT3261NXb197IAACHxKwMXBiZI6un16AkdAIAoCct/YUkrs5Ir72JYuSCzoSzy+h6MxMMSCUVini/8cO0dBQcE0YVxHat1rAkmwta8ypVGNyULVzk6O5Mgvxl1cDM/nf/DoKb3zD6AW9StSJp52IqlQvmzqo3b4a8KsFcqS1qphZXXo8IkLtHDFRlo6YzAdPXWJZi5aRwXyZqX/bTuoXCP7dGxIz9lVcuUfO5XrYMsGlbSWPZnG8uzFa/VMeOLMZcqcPjWN/K4NLV+/nfbxUktZMqZWz4qF82dXbYllSKbNSB9TJPemzi1rUa3KvK4tJ6nLlcclBoADR89S7uwZ6fjpy/TTD/0ombeXyiP9m7ZgjRKdYizQTQk9wh1eHz15RsmTJlbWQ/f4Uf+NNcRU2pi1eL1itoEZeLi7UXv2DqtfvbRq7uTZKzRl/hq6cfsBebIVrFyJr0mE3s07D2kml1s+e6jKd/3WfeVpdu7yLUqRLDEl8UpEjWqVpbpVS6rzd+8/oe5DZtAxHmOBPF+RcBaLmVyfQL4HZLpQAo/4VLtKCVVG9o+dusyxqN9T9izpaOyAdpQqRVJdBHa9bZcCzZgr7igRGAwkMfNGZ+o1UAyHQCDOE8gaEEarwrLG+XFigJYlkJjvG7L2xBsTutwyxJsaheFPownI7D6rg6MLeb8LJPK0exSfANi4/ZB64C/IAiljupQkVrTvOjf+JF9kB0QQyUP7KHaHbM0CTFwbv8qYhi1qH1/s3GPLkq7Ae/XmHV2/fV9V+Zq3z1y8QSl9vGnswHa09/AZGjxhIeVhsfRd50bk6/eCJs5epUSVzG178NiP9hw8RR1b1FSicNqCtdS48yhqVLMs/TCiC63fvJ8kArhY8SSJ6GpQo7SaH7abA84NnbSIShTJrcSL1LV191H6pmAuKl0srxJ3O/efpHWb9lHXNnVU+RXrd1C61D7Kk0sd0PkSIduwZhnqxuvvJuO+1axUnGqz+Js4eyU1qV1OK1h1ihjcFAayvm/ypF70fb82dPbiTSWaJFJ5PJ43JiKzzDf56XsWosJy297jqh7hKJHOJYlradch05Uwmz6qu3ohMZoFlt/zl+q8fP177hq1YxfWZvUq0KKVm2jJb1to6siuVK9aKRWUT8aSK1tGSs3Xc/aSP+jKjbs0a1xPFRl9864j5PfiFQSaliYR/goxjNaNqqiPhou4NZ7bvVSzq/c7blB7vX3sgAAIhBNIc+8uZZn/PXCAgEkEArqOprAkaU0qY83MObesJ+ejO6zZBOqOYwTCkqagwB4T6T0Emt6VlYjXe9nq9CM/0EuqUaGYEid9OzUy+kW3vBCfP6kvzfvlLxo3c7l6mBcrjFiAZD6bMcFCPBN50LTvu6k+ZEibUomFycO7kLhWSlqwbCOdPHuVqpQtrPbrVC1BXVrXVtuXrv3HguUYjerfVu2LRaxj/6kkrpvS9rdNqtH5yzdJ3BGDOaiGzKm9c++xEmhSQETQZJ4rp0nXbt5nBnvVfCxfv5fM55RW7GnyaH6fsWD5l/tVrVwR2sJCT/rrxBZHEXWVy4T3VZM3ul9hIEtGSSpVJK9aQkpEmzzvinVLxpWSLYBi8apUutAn1YnlT6yZC9n6J0Jbkk+yjXr5hFv3b+upY3Ltx81YrralTrGS5syagUoUzq2O+QcEqqAiYiUUISpWUSR9AhBo+jywBwIgAAIgAAIgAAIgYCaB7by+rDz8b2HryPZ9x3mZohf0iAOySdAPjYugMU1kyZBaiTyJ4niVLToyp01c77KwJa08u+OZktzja9wIP4YDis/HRDAYSpJfgnVokrgYhoSGUihHUA4IeEcdB0yl/1iQFfk6B7s4JlHZwsI+Bu/y8NB3SRRr2/xfN9D+I2fpwpVblDVTWiWSNPXr/v68eisH3vCgCUM6KvHYmy1dz9n9UpJGXOrmN3ZbrI9uri5qzLI9sFszmjz3N5JAH2Kx7MEiK6JIe8huliLyNOIsurbEjTIyplK2bZOq1H/MfKrLS1lJvY3ZVVKmE+laQqNrI66fh0CL61cY4wMBEAABEAABEACBL0xAImXnz5WFLS3hwkWiZ997+ES5ORor0MItLTKPy5FcWVTkyZ5JfTbtOMyueteVQJMIkDLPTZNMnQ9oeEKLprbIf2Vemcyp27X2R/LiKIuSlq/bHnkBPiNzzyqUKkC//bWLrt68S93bhlucDBW6xXPAxAVRkrggzhrbk3oNn6Xma4k7pnnp46jF9VBcJy9cva1E2uDxC9nSlUev+rw5MtHLV2+VQEzsZfrCeGIJ1RW6Ikw3LB2v1hmWeYrTeL6eWNqqlC2i16497+iHLLRnEhg7CIAACIAACIAACICA2QQe+z7nABCXOEpjA+rXpbH206xuRbamnVCCStbZCmFLlDy46wos3cbF2tagw0jasf+Emi/28Ikfu+dtVkE8CuULD9QhAUAkMIjMJ7t07Q6LjD26VVhtW1wtZQz32fVP3BGXssXLmNSkTnkSURIQEEQ1KhaLtEjWzGmVtXHXgZMUFByi3AQTsEvgjTsP1BwvQwWNYapb7uXrt2pOm8z/EjFdrEBOCuPrIdZK3ZSN+yKisN/oeSTzCifMWkkXr9zWzRLlds6s6dmN9Ioah7Cay/P4xMVS5t+JtU5cLCViJ9JHArCgfWQRY1uOjsGUqjj/I/XRKh5jfUHDsYdA/OTyBuzjW7DY03P0FARAAARAIC4T2LrnqHqgjzi3SJY4mjJ/tYpqKCJFsw5aoWqd6eyunz9Bki51cq0wELdCSeISN6x3SypZJNzKI/OxNu88QmUb9FGuexJBUDc5OkRti9AN/uYYwfyme063TtmW9ovznCoJIqLZVxsf/i5HrCv8HFFRdodMwlaoWhz0I2LkRk0e+e3QvIYSnUMnLVZRIlP5JFURIGXh6p7DZqp5fJJHN43neXqGmBpiIGOTpaWO/HtRzWuLx+6OqVMmoxF9WpPGSqZ5wpC8MpdOokHOYxfNwvmzceCVJCysXFTzEccqc850U7O6FWjSnFUsLDcr98bg4FBq03siSTlxG61UuiDVqRIeDVK3nD1vO/Cbi4/OtfZM4jPH/sDP/zNLfizm8OQ+uc4fyWFy3n08iC0QiIZAWM5CFNSqXzS5vtxpx7OHyA1BQr4c8DjSkgoSkjc8LLUtDMnllx8QJMQWLkQs6oM2SIhPePh4c7qeyju+OcWjLXv+ZRAdfhtKgUY++eV1daDSSfXnUUXbiBUyiGVIlkMSV0dNiPqIzUgQi6TsQujywS0w4nlr7T95+lxZt4x1O7zIroSNO4+mLSsmswUp+oW2xbooFi4J4mGtJJEexZ1UwvlHluQaaKJniuWtctP+KviKRihHVk5zXAS2RH2U6yeCT6yCvsxOlieQ64qkTwAWNH0eMbMnGjkkmBxfPI2Z9tFqrCQQxlGkkEAABEAABEDAWAK5PV0ot7nTl4xtzIL5RBhognBEVm1MraEVlagx1Ndf1vytohkaI86kvATOsKY4kzYSJnBXH9mOLHUaOI3evfPnRcI96fyHACfFC+WKLPsnx0WE6bISIS0WOyTDBCDQDHPBURAAARAAARAAARAAARCwKIGiBXJQvpxZLFrnl6isb6eGdO7STWX5alirDJUumk9ZDr9E2/bYBgSaPV51jBkEQAAEQAAEQAAEQOCLE2hQo8wXb9MSDWoiaFqiLtQRPQE4fUbPCDlAAARAAARAAARAAARAAARA4IsQgED7IpjRCAiAAAiAAAiAAAiAAAiAAAhETwACLXpGyAECIAACIAACIAACIAACIAACX4QA5qB9EcxoBARAAARAAARAAARilkDg87cU8uw1kZFh9h3cXcg9VZKY7TRaBwE7JACBZocXHUMGARAAARAAARCwPwLBvq/IbfFWcggLX/Q5OgL+1YsSQaBFhwnnQcDiBCDQLI4UFYIACIAACIAACICA7RFw4HVXHXiBYKd3gUZ1TvJbM8nix4FBQeThbt0Fuq05hi9Z91teh8zVxUW7YPSXbBttfVkCmIP2ZXmjNRAAARAAARAAARCI8wQmzl5Jucq2VZ+CVTpR1eYD6Mef1tKr12+1Y9+44xAVqd6V3ltYCF64cpu+GzVX246hjdMXrqu+dew/Re/03kOn1fE+I+foHY/pnXf+gYrVzgMnY7oraP8LEIAF7QtAjrYJBwci53j0PqFXtFmRAQQ0BN57JNBs2sYv7mHbuA6xrRd839hSeu+eAP8W29IFiQ19ceJ7WP6OI31CIH+uLDRxaCd6/eYdnb98kxat3EQnz16lxdMGUHw3VyrzTT5aNXc447Msv6fPXtCBo+c+6Y/ugbAPbp7Hz1whEXS5smVQp39evUX9Wlo06rb9OdturvEUq/RpU3xOcZSJZQQg0Gzggr1x8iS/ukPpfah1XQlsYKjoggUJuCZyIVuSaK8TpiffphMtOEJUZQ8E3BImIg8bGujzAjUpMEtlG+oRumLrBESfxeO/47Z0H9sKM1dXF0qXOrnqjgig4oVzU7OuY2nxqs3Us119unnnIc1cvJ6Wzx6q8jTs+D11bFGD9h85S1dv3qNxg9orITduxjI6df465fgqHTWqWZZqVS6u8gcFBdPcX/6iv/ccUyIwZ9YMNKBrExo3cwX5BwRS/fYjVL7JwzrTV5nSqO2IXzUrfsP92UTTR/egs5du0jVut3KZQhQW9vGZbPu+4zRn6Z/06MkzqliqILVqWJn7kp5evHpDY378lY6dusxxV95T9izpaOyAdpQqRVJaunorrd6wm569eE1pUialLq3rUJWyhWnN//ao8T97/kqNrRK3NbRXC4rn7ExiJZsy7zc1nlAWkNKGT9LE9MOILuTo6EgTZq2kYb1bklfOBMoa+fzlG5J6jp2+TAXyfEV9OjZUZWSM12/dpzHTf6Vzl29RimSJKYlXImpUqyzVrVoyIgLs2yABCDQbuCj+/Ji9fL8nPX9u2TdINjA0dMGKBPLlCaMGWYyb6G3FbmirvvvOh1ZtSqXdxwYIGEOgeZNQym5sSDljKjQzz7bzGejMWXj/m4nRroon9npPrVqEQqAZcdXTpkpOZYvno1Pnrqncr9iydu3WPW3JKzf+o0HjF1KdKiVYCBUgFxdn6jxwqhIdS9jqduPOAxrxwxLKmzMzpU/jQ9MX/U6bdhym9s1rUE4WMxt5++4DX6pfrRQtZGtdvy5NVN0pkkceifLbptWoXrvhdPvuIxZVW6hp3Qr05OlzevPWX5U9w66Q/cfMp+86N6aiX+egNRv20PDJS2j94jE0e8kfdOXGXZo1rqcSdJt3HSG/F6/o3kNfmrpgDU0c0lEJwxNspbvPxyQlTZyIerE4zZwhFT3yfU7DJi5Swq5J7XJKdO0+eIq6f1uPcmfPSEt+20K37z1S5eRL+Lx9F6D2Hzz2oz2ctx33v1m9Cso6KfmnjuxKAYFB1HXIdCXMpo/qrgy8o1lI+j1/qa0LG7ZNAALNtq8PegcCIAACIAACIAACcYZAutQponQ//PH7blS+ZAE13gNHzyrBNaJva+VGmjlDahY2qWkXz8MSYbX2f3uVQGnbuKrKX4QFlKR48ZzI2cmJSrDFLrqUOX0qKl00r7KEnb10g4b3aa2Ekqbc75v2KvFXIE9WCuagJhVYOMoxEVxipXOJ50wJPdyVECuUL5sqtu/waeW2mcAjPmXJmFpr1ZKTFdgCJ0JThN+Tpy/IM5EHWxIfqHIbth2k9s2qU8sGldR+wbxZaQuLvshSnaollJiT8yIox81YrrIeZ4vag0dPaeEP/ShjupTqmE+yjeoXX7GDAARa7LhO6CUIgAAIgAAIgAAIxHoCdx88oa8yGnY3lMF5sKjRJMnr7ORIMxet1xxSwsufLUR+7NonlqKCH0SRNsNnbLRjUdSq5wRqUqccebOFSzeJRU6sVeM/iB85lztbRmX9atukqrKu1WULnAitxuxC2K1NXfqmYC5lBew1YraKuFiSheKAbk3Z6pdCuXSKS2Xh/DkoQ1ofFnKOKkiKBE955x9AX7Or4uckD3c3JRil7EN2xZT+aMTZ59SHMjFLAAItZvmjdRAAARAAARAAARCwCwJidRK3vNaNqhg13qRJPNka5kwr5g5Tc7R0C0mIfrGSXWcXyWIFcuqeUtar9++NnwIg1rHu39alOpVL6NUjO9IHcZGUeWyG0oal45V75D/HztE0jlIp88aqlC1C4wd3YBfLxvQvu3POWrKeZixaRxPY5VHcKMcN6qAEnNSniTaZKKEHz9fzoVs8L09cKc1JeXNkopev3tJznv+W2CuhOVWhbAwRgKN9DIFHsyAAAiAAAiAAAiAQlwkEsAvgnXuPOVDFTZ67tZva9p3M1rPU1LaxcQKtcL7sSoSJ65648MlHAnZs3nlEWaa+KZSL1m7cSzLHSwKG/Ln1AIl7nwTr8A8I4siRt1TwEHFFjC6J5St1ymSfZKvA7pZbdx+lbXuPk4jCh0/8aP6vG5QL4VwOHCLh+kVYVSpdiNcoi6fmiEko/D+27Cc3V1cqVSQPZWDLmXt8N3LiQB/ubOl6yBY5iWwpLpxnLtzQtlmqaB41l2zF+u207Pdt6qM9acJGtsxplbDsN3oebdx+SAUXuciRKpFiDwFY0GLPtUJPQQAEQAAEQAAEQCBWEJDQ+Wcu3qDqLQeRzMVK5u1FNSoUow4c0MOFhYwm6YZHkzK6+2L9mTG2J42a+gsVrdFVFfFkS9PwPq3U9vf92tCQCYuoTe+Jal+sXbPH96bkHPmwHgcKadljnJo39tv8kSRWpYjJ0VG3tY9nVT8+hP6vzn3+7/4TGjZpEfULDFbuiDJvrX710vTy9TvVtiPnFQFWqXRBtoyVpIPHz6kgISOnLFVz1PJw2/27NlXjFiEokStn//yHivboyuHzNalTy1r09NlLWr5uB0lAlTzZM6kokJrzEu3jQ7dI2tRNEuVRk6T/k4d3plnczjwWk4XzZ6OUPknUIteaPPi1bQK8SLyFVwe07fFavD2tjnsAAC5HSURBVHcP/MKj/JhT8VM/ouWrnBDF0RyIdlhWRXGsZ7wLh7URXb7iQKvWOFm7GdQfxwioKI7ZPoazjunhrf/LEVEcY/oixLL2NVEck3qb3/FU3h/nX5lf26c1vPnvKdHV++QQHPrpSQNHQtIlJ89ckc8XM1DEaodecEj5oOBgJfREgOgmsazJ/C0Rgbrn5LiEqxdRZ26SddMec3RHDxZi4o6oSUHBIeTLx1Mk9yYnni+nSfJ47ev3gmRumIe7/nWVcPoixCT8vq6wCg4J0XPllMWyEyZwp7ED22mqNfpXuYA6h/9Nfsnz2yo37U/TOABLSbboIdk+AVjQbP8aoYcgAAIgAAIgAAIgYDaBBOmSEsknFiYvz8hX/hQLnXwiJkPHIuYxdl+EVEoWYRGTRHE05BopQlEseYaSe3xX7fpwuud/Z3fN5eu2q+AejzkE/32OxLhizjDdLEZvdxo4jd698+egJ550/sotypopLRVnl1Ck2EEAAi12XCf0EgRAAARAAARAAARAIA4TkHls8d1cVXAPcdcswdauiFEljR1+304N6RwvvC0Wvoa1yvBSAvn0rHXG1oN8MUPArgVaZCbmmLkUaBUEQAAEQAAEQAAEQMBeCYiLpsyds0SS+WvyQYqdBD46y8bO/n92rzuz6bdwtc5UrcVAKl2vN42fGb64n1QoEXlylW1LHftP0at/76HT6rj4BCOBAAiAAAiAAAiAAAiAAAiAgKUJ2K1Ak5Xdf184ik78/RPJCvWr/txFJ89eVXxlIqik4xy29YJOWNKfee0KSYirojDgCwRAAARAAARAAARAAARAwMIE7FagDeBwpzmzZlC+vlXKFlaRfyQsqm6qWfEbktXeJZ1lP95rN+9R5TKFdLNgGwRAAARAAARAAARAAARAAAQsRsCu56BpKF6/fV+FQpWFDXXTt02rUb12w9UK8bLye9O6FegJh1KVsK2alMj94/oVmmOm/r5+ZVy4W1PrRf64TUCiDHu4OfPCl/rhhmNq1E6OuI9jin1sblfu30TutvGnKDTsPa8tFO5BEZuZou9fnoBrPEe+j21/mZHXAW/obchLowE5O7hRUo9PIxcaXQEyggAIfBYB2/ir+Fldt0whWcn9u1FzKW/OzFShZEG9SmUhwtJF89KYH39lC9oNXhixNf3401q9PLK+hrkp3GXSNh6yzR0Lyn85ArJyVPj9Zxv3znuynbWsvtxVQEvmEpD7xhL/jprbDykvAg13sSVI2l8d8nfcVu7jqOi/Dn5OF9/9zve5cc8u6eKVhUCLCijOgYCVCNi1QJMojj2Hz6LQ0DCawyvP6y4wqOHdrll1atVzAjWpU85gqNO3AeZbDYJCNK3hFwRMIMBPkgFBxv2RNaHWz84aFmYbQvGzB4CCMUJA3nFZ4t9RS3X+/Xu79fy3FEK7rCco5L1F7mPPj+sfW4mjSLMQCqWPnkBRNRRbXlm85fW+XF1cyJkXZj559go5OzlRvlxZohqaRc6dOn9NrVUmU2JsLT1/8ZqF+HtK7JlQb/FuW+tnxP5cuXGXLl//j6qVL0qyxpy9Jrv9S/Ti1Rvq0O8HesWrqy+bNdSg+JKbokCerNT927rUvml1e71HMG4QAAEQAAEQAAEQMInAxNkrVeRriYqt+9mx/4RJ9USXWV62F6nelXYeOKmyrv3fXvpz6z9RFjt26pLqU9Muo/XyHTl5Qa+vuz7UqZfpw46v3wvqOng6hfFLft0UWd26eQxtL/t9G638Y6ehUyYdkzoadx5FJev2pFJ1e6mI5cMnLzapji+ZWSKjX7p2R9ukDy/uvWjlJvr5t/DAfNoTdrZhl9JU3rS06D6OwtidZerIrmpOmcwrEwtampTJPrkFurWp+8kxHAABEAABEAABEAABEIicgEwfGTugnV6GlD6WndPm5hqPVs0dTunTptBrJ6qdjTsOq+e9c5dvqTgDGT6ULVogJ21ePolqtBpMa38aRTm+ShdpNZPmrKJSRfNQ7Sol9PJEVrdeJgM7IlKcnc17LD9w9CxNmLWChvVuST9N7sdeNkF0giOS/7nlgIEWbePQ/iNnqGHNMtrOeHkmoAlDOtK3fSYpK1r6ND7ac/a0Yd6dEEtJvXz1Vv0PKd2XtwyaJGbgfzbMVruOkQRecODIDPJBAgEQAAEQAAEQAAEQiJxAfDdXkmWNDKWGHb+nkkVy0+6Dp+jRk2dUtVwRqlOlJE1bsIYkeFul0oXou86NlYfT7buPSCwtD5/48cv1MMqdLaNaIikTxwpwdHRkUbJSiRKvnAkMNaV3LDAwiLbvO06j+39LMxevo00s1nq0q6fyyPOdxq1OfqVuQ+nOvUf0955jtGXFJL3TUdW97/BptaTTTz/005Zp0GEkjR3YjgPQvSBZa1fShSu3KGkST1o4pT+Ju9/Y6cvo4tXblCJ5Eurapg7VqlRcWz7ihrhcyrNs83oVtackv6ZMUHAIidXwhxFdKEuG8OsybcFaSpTQnTq2qKnK/PX3PzT/1w3qmuTKloECmNeCyd9RcrZsXb91n8ZM/5VE2KZIlpiSeCWiRrXKUt2qJVVZ4Tpn6Z+qbMVSBalVw8osctOTeK1JPIdjpy4rt0sJyifC/Ze1f1NgULA6l8AjvhK7bRtXpbw5MlExFsty/vvv2mjHYk8bdinQUqVIShf2/hLpdRa3xnO7lxo8P25Qe4PHcRAEQAAEQAAEQAAEQOAjgTssrKbMX/3xAG/VYYtT1kxpWXz8RyJoOrWspdaXHfHDz7Rz/0nq3Ko2dU7rww/ty2jb3mNKbMjcstqVi9PXeb7iSKsOLKzW07iZK+jnHwequqWut+8C9NqJbGfv4TPKg6ps8fx07dY92rTzkFagRVYm4vHL1+8q4ZE+jb7VLqq6xThw484DvapkrpX0O2fW9JSDl36Kx+Ns3agKubm6KGHUZdA0xWrOhN50+MRFGjx+IaVP7aMC2+lV9GGnXImv6aflG6kXx1eoxbxEHOl6hr1ncSuizz8gUFv8/iNf3k+k9o+fvkzDJi2mFvUrKuvV2Ys36Yd5v5EIOxFqXYdMV8Js+qjubKwgGs2iy+95eFTQMxeuU/8x85WoLvp1DlqzYQ8Nn7yE1i8eQ7OX/KHanTWup2K/edcR8nvxiupVK0Vr/7dHWdBysehOzc/nmlS9QjFa8ccOza7d/dqlQLO7q4wBgwAIgAAIgAAIgMAXJhAcEkKPfZ/rtRqkExlteN/WJA/zkn7lOVhVyhahNo2rqP2yxc8q9zyxBonIqF+jNB399yLdf/hUrWF7ga1Kn5M27TxMFUsVUCKoBouABcv+R6dZXOQ3IaiIrIubLXPaT5r/3LrFOuWT1Eu5OJYonFvVu+fQKWVZWz57mBp/8UK5adc/J2nDtoNKoC1csYmePgsXRy4uztS/SxPKkz0T/cjiacGyDcriKBVlzpCKBnZrxtbKPJ/0N+IBqTtrpjQ0tFdLdSplcm8l0GRHxNuDR09pIVsAM6ZLqc77JNuofuXr9017KScLQjFyBIeEUgVmLMfuPxQBGKgskwk93Okrrr9QvmzacmKllHWJNePWnJB+XGcBLRFS7dFzDQJNcyfgFwRAAARAAARAAARAwGIEsmRMo+b6G1Ohe3xXzsbhiT8k9/hu9OCxn9oTcdBj2EwWMYkpd/aM6pi4OpqaxNXuAM95EmvV0ImLVHGJ+Lhx+yGTBNrdB0/Ih138dJOl6tbU+ZDHLu6KuhawPOz6J26ekl7yWJ4+e6G2XV0+rslbpWxhFrqFSaI4nmd3ycWrNqvlpP75K3wKjyoQyZfUnT/3VwbPPmQ3VM9EHlpxFjHT3Qe+6nqNn7Fce0pcUR+xQG/bpKqyrtXltYWljsbsFinxHVx0+q0t9GHDO0kikgAwz3gc3onDLXwR88TlfQi0uHx1MTYQAAEQAAEQAAEQiBUEIp/fv2L9DirMVpfZvCSSWFO27T3O86BumjwqKSfCr1Dejxac4myx+ptdKQf3bM4uhsY9FqdOmVRZt3Q7EF3dTiwEg9lVMGLSWIfkV1d0iih5/vI1C5RXaq6XlLv130Pl8ijbA7o1lR+9JEHwPNzjq2OJvRJyEJO8vO+mlosS98rMH+ad6VoxdSsQC5y4KhpKMi9M3DRF+EndEZPMm5N5cpOHdY54Su1vWDpexX/459g5msZrCov7pVhMZdzh6wHrFxNhFt/Nhcf+aVv6OePmnnF3YtwcO0YFAiAAAiAAAiAAAiBgJQLvWDCIO6BuSpXCWysidI9HtS0BJP67/5jnO72il7w80p9bD3yW29umHYeoavki1K9LY21zYjWq2LgfHTx2nsp8k0/Nt5KTMu9KBJOhQCEyh+7QiQvaOmQjuroL5P1KuSQe5RD/GTlqpCwHIEkjTmQe2rpN+9WctHf+AVSQRaRYFRcs20jd2tahIycvcrCQO9SheQ1VztDXxNmrKIiDbsgcMgnOcufeYw5Zv1mJnEzsliiBTySypix1IK6PsiTAybNXVUAWqU8E3dLVW1XQlTxsqfxjy35tM+LSKSKs3+h5au6YBAq5eOW2cheVTBVKFqDBExZS+RIF1LYvW/f+4uUOZM6hXC8RwnlzZOa2XFUgEc2cQRm3rF0nETTfvH2nFaM3OFCMBDLRCFhtR+xkAwLNTi40hgkCIAACIAACIAACX4qAPFjLQ7y4temmGWN6KEEg5yO3mRE/mId/pKxEA+z7/RwqU783L0gdj8TVTy9JXR8qc4gkCre4S/577hr16dhQr6jMsyrAwUc27zrM89LiUft+U9R5ifI9a2xPnktVUC+/7EgUwnOXbpK4OqZNlVy59kVXtwQlacDz6DoPmKrmaEmUQ0kaAVK+ZEG1fluR6l1IliLYuWaaCjcvgTZWcrAMydeGA4hULlNYlTP09U3BXDSf558156WkNEksVfMm9SVXDjwiqTWzlDXqlq/bTuk44Ii4HGrYCYcurWvTFg7i8e+5q2p+4DF2L5WgJdL+5OGdaRYHaJnHUR4L58/G/UyiFgiXeiWox3/3n3CQkUXULzBYCc/MHGWzfvXSLKrfUZveE1WAF7FgVipdUEXslHLN6lYgWbJAhKS4Qg7oGm4Z3LTzCHNOL1nsMjmwcv/o8GuXCMwb9AM/f/Mq4NJP2Z14+Sonev48qn+qzG4GFcQxAvnyhFGDeqb74FsLw+UrDrRqjZO1qke9cZRA8yahlD2b7fwZWv+XI505azi0dhy9BBiWmQQSe72nVi1CKam3mRVx8VTe4e5p5tdkuIYnb56QX9Btnun1qaudoRIJHFNROq8Mhk598WPyuCpWtGTeiZVl6Yt3IEKDvUfMJjd2wYvMpS9Cdu3ui5dvWJUReSUyvCSABP5IyBZDjaAK5YWw73GgjWTeXkaPW6xTUk9SnselcXnUdoA3JHiLL4f2l6jmEZO0J+sCS9q6+yh9P3UpHduyQO2HcPAPiagpSSyZlZv2p2nfd9MLQCJWx8dPn5MHC7FECT1UXvkSi6QvH0/BglhTv+aktCnRIGWMIgQvsGWuVc/xKgKkJiCJJq+9/MKCZi9XGuMEARAAARAAARCwawLJEyQn+S82JnlwjxjWPibHMaRnC6rVZqiyNon1yNgkCzFHlcSNUDeJmDF1sWaZdyafyJLMtTMkzmS+W42Wg1UQFSe2REpwlr6dPrqDdho4jcRt1TuxpwpAIq6exQvl0mtGXELFKhkxiXtlao7GaSjJGCWSpaRXLPyGTFxI7ZpWjzQgiaE64toxCLS4dkUxHhAAARAAARAAARAAAasSkIAYsj7ZPXZzjCspUQIPGsMLZ9/ncPpuLi5qTTNxkdSkvp0aKtdOsYY1rFWGShfNZ3COnib/5/xKNMjmdSuya2SpzykeZ8pAoMWZS4mBgAAIgAAIgAAIgAAIfCkCsoabZh23L9WmNdsR98VKpQtF2oREeZSPNVOubBlIPvae4Ghv73cAxg8CIAACIAACIAACIAACIGAzBCDQbOZSoCMgAAIgAAIgAAIgAAIgAAL2TgACzd7vAIwfBEAABEAABEAABEAABEDAZghgDprNXAp0BARAAARAAARAAASsRyDwVSiFvDZ+WQsHXjrLPRkeFa13RVAzCBgmgP/rDHPBURAAARAAARAAARCIUwSCX4aR2x4ncggzbt1V//yhRIYjo8cpLhgMCNgaAbg42toVQX9AAARAAARAAARAwAoEHHiFZIcQB3IKMO7jYLyxzQq9Nb7Kt7w2lyyiLOnk2St05sJ14wubkfPU+Wu0aedhM2pAUV0CAYFB9Nff/9A7/0B1ePc//9KhE+d1s9jNNgSa3VxqDBQEQAAEQAAEQAAEvgyBibNXUq6ybT/57Nh/wqIdkIf5ItW70s4DJ1W9a/+3l/7c+k+UbRw7dUn1q2mX0Xr5jpy8oNffXR/q1Mv0YcfX7wV1HTydwkLD9E5HVrdeJgM7y37fRiv/2GngjGmHyjXsox1DsZrdVB8fPvEzrZIYyv3i5RsaNmkxyYLZkuK7uVLPYbPo+q37MdSjmGsWLo4xxx4tgwAIgAAIgAAIgECcJZA3Z2YaO6Cd3vhS+njr7Zu74+Yaj1bNHU7p06YwuqqNOw5TmpTJ6NzlW3T77iPK8KFs0QI5afPySVSj1WBa+9MoyvFVukjrnDRnFZUqmodqVymhlyeyuvUyGdi5dO0OOTub/1j+nq2ePdvVp2rli9Ij32c0bvoyGjZxMf08fZCBVm370DeFclHzehVp1LRfaPnsoeTgYJxrrm2PyrjemX8nGNcOcoEACIAACIAACIAACNgRAbGAZMmY2uCIG3b8nkoWyU27D56iR0+eUdVyRahOlZI0bcEaun77vlow+bvOjck7cSIlovqMnENiCQoLC6Pc2TLSiL6tKVP6VOTo6EgTZq2kYb1bklfOBAbb0j0YyG502/cdp9H9v6WZi9fRJhZrPdrVU1lEALjEC380ll+p21C6c+8R/b3nGG1ZMUnvdFR17zt8mlb9uYt++qGftkyDDiNp7MB29OTpC9p76LQ6fuHKLUqaxJMWTulPV27cpbEssC5evU0pkiehrm3qUK1KxbXlI9tIwszSp/FRn/rVS9PPq7dos/53/wmNm7GMTp2/rgRoo5plqVbl8DqXrt5KqzfsZgvWaxawSalL6zpUpWxh+vGntSQWQ7/nr+jfc1f5XHIaP7iDdkHp3zfupSW/beY8LylPjkz0/XdtKGO6lKpNuc6li+WlPXydH/s+pxoVi1HfTo3JPb6rOr9xxyGau/RPFpPPKaMBkd2pZU0qVa8XHWWrZzEW0PaSDN959jJ6jBMEQAAEQAAEQAAEQMAqBO6wdWrK/NV6n6s376q2rtz4j3Yd+Jc6Nq9Jw3u3og1/H2R3tpksCIrQlBFd6MjJi7Rt7zGV19nZiWqziFgw+TtaPHWAsqSMm7lC22ep6+27AO1+VBt7D59hkfeeyhbPT9UrFOM5ZIeiym7w3OXrd1ncpGcBpG+1i6rul6/e0o07D/Tqu3w9vN85s6anHFkzkFgc+3VpQp1b1SaZj9Vl0DTycHejORN6U4WSBWnw+IV09uINvTqi2nnP5jSZJ1cwbzaVLSg4hDoPnEoJPOLTkmkDqF61UjTihyV0595jEtfMqSyOu7etS8tmDSERdvcf+qpyDx77KSFdKF92FrbtWLg6KCEtJw+fuECjf/xVsZw5tifPciTqNmQ6BYeEqLJybbbtPU6tG1Wh7/u1pf9tP6SdVyZjkTFJvXJdpT8RU8IE7mypzEvCyp4SLGj2dLUxVhAAARAAARAAARD4QgTkIV2sJropKCj8wV2ODWcrWNGvc6jTv/IcLBFnbRpXUftli5+lE2euKBc3cUesX6M0Hf33IouGp2pu0gW2Kn1OkqAeFUsVIDdXF6rBAm3Bsv/RaQ4qkj9XFqOru3bzHmXLnPaT/J9bd/KkicknqZdycSxROLeqd8+hU8qytnz2MOWOWbxQbtr1z0nasO2gEnILV2yip89eqrwuLs7Un4WdJolF69Dx83SDLZGSRDhJEn53H/gq6yOrXMqcIbX6yFy7zBlSKeEr4k2sniJAdZNYvsSaJckzkQf1HD6LxGK4YftBEoHZq30DdS6ZtxfVbz+CTp27RkU+XNuRbFHTXOetu48qUVexVEH6Y8sBysSWtnGD2quycp0nz/1Nbet+ZcuUloS5PSUINHu62hgrCIAACIAACIAACHwhAlkypqGpI7sa1Vq4y9vHsJHu8d1ILDeSjp++TD3YuubDQiZ39ozqmLg6mppevHpDB46cUdaqoRMXqeLOTk60ka06pgi0uw+ekE+yxHrNW6puTaUPeeyJPRMqcaY5Ju6DmoAfL3ksT5+9UKdcXeJpsqhf6ZvMnxNBVq7E18oVVE5Iv52dHGnmovXa/DJ+fxZa3xTMxS6mJajXiNksFJ2oJAvFAd2afmIllIIi5oKCgtkV9QE9ePSU8ubIrK0va6Y0JP0Rt1VDSQSgf0B4lMa7D5+w9Szcumcor+aYd5JEdPzMZc2uXfxCoNnFZcYgQQAEQAAEQAAEQMCWCUQeAGLF+h1UmB/kZ4/vraw84jJ37vJNkwcj5UT4Ffrg8icVFGch8je7Ug7u2ZziGRmkIzXPz5J5Y7opurqdWAgFs4thxKQJfCG/uqJT5t49f/laRTRM4pVIFbv130PKytYkSSKeIkuli+WjxrXKqvlgnQdOU4KqfbMaam5bPJ5bt2LuMINjlXll/bo05nlm12jWkvU0Y9E6mj66xyfNXLoW7m4oc+Xko7HUScb7LNgCWbwl9kr4SbmIB75iAS9jii7JnLhUFg4uE12bMX0eAi2mrwDaBwEQAAEQAAEQAIE4SOAdr08W0TUtVQpvnlcV36TRitXlv/uPVZCKl6/fchj9A0qomVQJZ97EASmqli+iRIimrFikKjbuRwePnacy3+QjmaclSX5FMBkKFCIi6RDPvdJN0dVdIO9XyiVRgl1IMAxZDkCSzBOTJG6C6zbtV3Pp3vkHqHljYlVcsGwjdWtbR83Ju3j1DnVoXkPlN+ZL3CIHdW9OsuSBzJcrzHO9xGI2bsZyGtA1XODJOmPBwaHkytEwXzHbquWKUqkieeh/7EopYlaTHj5+psTiPXYxXbJqs3KzFEudzA+TOWiyzEHBPFlp0cpN5JnQg/LxfLroUnGO0rh+834VcEXm3y1ft81gkeu37qn2DJ6Mowch0OLohcWwQAAEQAAEQAAEQCCmCIhFSMLY1203XK8LM8b0UBEa5XzkNjOSKVLqI4VbNaxMfb+fQ2Xq91buc+Lqp5ekrg+VOXAAC0NJ3CXFMtSnY0O90ymTe1OBPF/R5l2HeV5aPGrfb4o637jzKJrFc7cq8FypiCl7lnR07tJN5TKYNlVy5YoZXd0SlKQBz6PrPGAqB9AI5Xlw4fVqLGjlOQiIrN9WpHoXkqUIdq6ZRhOGdKThk5fw+mg7lCBtw4E2KpcpHLE7Ue63bFBJrSM2aNxPtJKXI5jBYxo19RcqWiPc9VTE1PA+rVT0SgkSMnLKUrUtjPt/EHHSgCz+LfwlwIqM/8dR3VS74hZ5lln0ZtdISTI/bfLwzpSI65UU8Tqr/Q8XS+ao5WGX1X6j56m8xQqGR2nUvYJv3vrTPg7s0rhWOZXHXr4cWLl/dPi1l1FbcJwP/PzNru0pu1gvX+VEz5/r3pJmV4sK4jiBfHnCqEE9033wrYXl8hUHWrXGyVrVo944SqB5k1DKns12/gyt/8uRzpxFgOM4ertZZViJvd5TqxahlNTb/OpTeZtmWTK1xTeP2Tr04D05hBr3vBGSNIw8M7iY2oxV8svjqljRknkn1oZot0pDRlYqgsTNzYUmD+tsZInwbLIYsyhTr0SGlwSQwB8J2WLoykFMJIXyQtj3OJqiBN/QhKYPr8m8b+lHUHCwqlcjEoWxhNOXyJG6Vs7+Y+az4HLnQCRNVWAQQ+6LEkVTwvBLeH5DVseoeitWTLHWiViMmMTNUhYQXzVvhMn1RqwrNu3Dghabrhb6CgIgAAIgAAIgAAKfSSCBDz/2+Xxm4RguJiIiYlj7mOzSkJ4tqFabobRl1xEVYt7Yvnh5GhZmmvIyp0s3OXFQD1nTzNLJUD+EsUSUjCyJQIxMJIaLuo8ukZHVYei4WDENJQn9v4yje4rlz1TRZ6i+2HQMAs3IqyUmVvEJjurGNbIqZAMBEAABEAABEAABEIjFBGThaFmf7B5HRozrqSG7ZjobGUDFkiwkOMg0dqWMGPLfkm3Yal0QaNFcGVnjYQiHYpXoPOFvb3xo3sQ+NvUWJ5oh4DQIgAAIgAAIgAAIgICFCcjaXpr1vSxctU1VV4xD8MdEqlquSEw0axNtwtE+msuwfst+kog7G3+dQEc3z6PUKZKq6DfRFMNpEAABEAABEAABEAABEAABEDCZAARaNMh27DvBK9sXVov8yYTJ1hxB5/DJi/T6zbtoSuI0CIAACIAACIAACIAACIAACJhGAC6O0fB65Pucyhb/WpsrXWoftWaFRLlJmMCdLBFxKcg/mKM/caSkRLYTyUw7YGzYLIHUqd9TUk9XcnG2jfcsDxMGUob0thNV0mYvHDqmR8AzoQP/O/p5E8v1KrLATlBIGKVJFUQvX+I+tgBOu6kiXjyiRO7OfB/zBhIIgAAIWIAABFo0EMVSJutiaJJm25IWtAxp4tGgnpoW8AsCsZNAwTyuvEhl7Ow7eg0CQkBedtSr5sYf8AABEAABEACBmCNgG6/eY2780bYsVrLAoGBtvoDA8G05jgQCIAACIAACIAACIAACIAACliQAgRYNzRTJEtOde4+1ue7ce6SiOcqCgUggAAIgAAIgAAIgAAIgAAIgYEkCEGjR0KxYuhD9vfcY3frvIckq6bJgXrECOdX8s2iK4rQVCISEhKo5gFaoGlWCgNUJyP374NFT/rfE3+ptoQEQsBaBAF5+Rl5WylxsJBAAARAAAcsTgECLhmkDXpyvUN5sVLP1ECpaoyvde+hLw3q3jKYUTptCoFGnUdRj2Ey9IkdOXqA85b+lS9fuaI+/ePmGKjT+jvYePq09hg0QsAUCN+88oHwV2tOmnYf1ujN62i9Uu81QCg0No+XrtlOR6l2oUtP+VKJ2D+o0YKr690SvAHZAIAYJLFq5ib6p2Y38nr/S9uKdfyBV5H93Zy1Zr44NGDufClXtTNVbDqayDfpQs25j9fJrC2IDBEAABEDgswlAoEWDzs3VhWaO7UmHN86l7aun0taVP1DGdCmjKYXTphAY0acV7Tl4ivZ9EF5BwSE0dsZyalqnvHb1eHkoKNeoLz199pItaKbUjrwgYH0CmdKnopYNK9GUeavpzdtw69jZSzfp9037aETf1uTk5Ege7m40eXhnOrZlPq1bNIbusyVt2e/brd85tAACRhJo07gqJfZKSNMWrNGWmPfrX+Tg6EidWtZSx9KkTE4r5wyjUzsWq/VB795/Qiv/2KHNjw0QAAEQAAHzCUCgGckwUUIPSuXjbWRuZDOFQN6cmUkslRNmraRAdp1ZunqLesjt3aGhtpqB3ZqphwFXl48RNbUnsQECNkCgW5u65OjoQLN//kNZzMb8+CvVrlycCufPrnpXv3ppqsQu07KeYpaMqalkkTx08Pg5G+g5ugAC4QRc4jmzh0gr2rDtIJ08e5Wu37pPy/klgrxEk5eVknp3aED5cmUhyZsmZTL18sErUYLwCvANAiAAAiBgEQIIs28RjKjEXALfdWpM1VsNojHTl9HW3Udp/OAOlMAjvrZaTVAWBwcH7TFsgIAtERAL2eAezan/mPn8oiFYzTVbNKW/wS6GhYXR0X8vUbbMaQ2ex0EQiCkCJQrnpsplCtFY/rc4YYL4VK7E11S6WD697siLtPnL/scvGM4rL4e6VUvqnccOCIAACICAeQRgQTOPH0pbiICXZwLq27ER/fX3P1Qwb1aqVr6ohWpGNSDw5QhUKVuEin6dk10b91K/Lo2Vu5ih1n9gV8j/7j+mbm3rGjqNYyAQowQGdW/OLri+dPn6XRrSs8UnfQnhOZXXb99Xng6vXr+lV7xeKBIIgAAIgIDlCECgWY4lajKTQMOaZUhcGOtWLWVmTSgOAjFHoEWDiqrxOlUMWxWWrt5Kq//aRdNHd6csGVLHXEfRMghEQiBF8iRUoVRBKlYwJ/nwUjMRk1iL54zvTVtWTGIXRyeauXhdxCzYBwEQAAEQMIMAXBzNgIeiliUg7osSTAFejJblitq+LAFnfmCVZMgdV+an/bLmb5ozoY+ag/Zle4bWQMB4As7yb3E02eUez5guBd2++yianDgNAiAAAiBgCgFY0EyhhbwxRiA4JISCgoJV+8Ec5VGzHWMdQsMgYCIBCYIjYcxH9W9LaVMl53WkHquPzOdBAoHYQEDcGSfMWkHXbt4jibZ74swV2r7vBLulZ4sN3UcfQQAEQCDWEIAFLdZcKvvu6Ld9JtOp89cUhO9GzVW/B/6aRUm8Etk3GIze5ggYspxJJ89evK6iOw4ev1Cvz0unD6IiX+fQO4YdELAFAo4cXl83yb199NQlDqu/U3tY5gt3bhUegl97EBsgAAIgAAJmEXB4z8msGlAYBEAABEAABEDAbgi85qAgvn4v1Pw0WTYCCQRAAARAwLIEINAsyxO1gQAIgAAIgAAIgAAIgAAIgMBnE9D3X/jsalAQBEAABEAABEAABEAABEAABEDAXAIQaOYSRHkQAAEQAAEQAAEQAAEQAAEQsBABCDQLgUQ1IAACIAACIAACIAACIAACIGAuAQg0cwmiPAiAAAiAAAiAAAiAAAiAAAhYiAAEmoVAohoQAAEQAAEQAAEQAAEQAAEQMJcABJq5BFEeBEAABEAABEAABEAABEAABCxEAALNQiBRDQiAAAiAAAiAAAiAAAiAAAiYSwACzVyCKA8CIAACIAACIAACIAACIAACFiIAgWYhkKgGBEAABEAABEAABEAABEAABMwlAIFmLkGUBwEQAAEQAAEQAAEQAAEQAAELEYBAsxBIVAMCIAACIAACIAACIAACIAAC5hKAQDOXIMqDAAiAAAiAAAiAAAiAAAiAgIUIQKBZCCSqAQEQAAEQAAEQAAEQAAEQAAFzCUCgmUsQ5UEABEAABEAABEAABEAABEDAQgQg0CwEEtWAAAiAAAiAAAiAAAiAAAiAgLkEINDMJYjyIAACIAACIAACIAACIAACIGAhAhBoFgKJakAABEAABEAABEAABEAABEDAXALO5laA8iAAAiBgDwS6D5lBqVMmo6G9WmiH+/rNO+o8cBrVq1aKGtUqS6GhYbTkt820YdtBuv/oKaVLlZw6tqhJtSoXV2VOnr1CE2evoodP/MjfP5B8kiWhRjXLULtm1dX5R0+eUZ+Rs6lXhwZ09uJNOnbqEmVIl4JG9m2jbRMbIAACIAACIAACcZsALGhx+/pidCAAAhYi8FWm1PTbXztJRJQmbdj2D525eIOKfJ1DHZo89zdatHITlSicm8YP6kDp06agwRMW0vHTl9X5N2/9KQMf6/FtPRozsB19nTsLTftpLW3ZdUSdDwgMonOXb1H3oTNp7cY95OERn5ydnDTN4RcEQAAEQAAEQMAOCMCCZgcXGUMEARAwn0Dj2uXZOraFft+0l3q2q0/v379nwbabShXNS+nT+JCv3wsl4Lq0rkPd29ZVDVYpW5hK1+tFm3ceocL5s1OZb/Krj5x88fIN5fwqPe09fJpOX7hO1SsU03ayWZ3y1K9LE3Jywjs0LRRsgAAIgAAIgICdEIBAs5MLjWGCAAiYRyCVjzeVLf41rdu0j7q0rq2sYrfvPqLBPZqriq/dukdhYe/pz60HaPu+49rG3gUEsrujr9oXl0ixsu08cJJkW5P8A4I0m+pXLHIQZ3pIsAMCIAACIAACdkMAAs1uLjUGCgIgYC6B5nUr0O5//qVdB/6lTTsPK8tZySJ5VLUBH0RWs7rl+XgKvaaSeCVU+/3HzFcukb3a16ci+XNQiuRJqHn3sXp5sQMCIAACIAACIGDfBCDQ7Pv6Y/QgAAImEChWMKeaQzZn6Z90594jGtS9OTk4OKgaMqZLqX7jOTtTxVIF9WoVd8iwsDA6xnPR6lUrSc3rVdQ7jx0QAAEQAAEQAAEQ0BCAQNOQwC8IgAAIRENAxJhYyCQSo3t8NyW2NEVEoMl8tFlL1pOjoyMVK5CTnvg9p72HTqt9cYXMnzMzHTp+nvYcOkUu8eLRxh2H6MbtB5QvZxZNNfgFARAAARAAARCwcwIQaHZ+A2D4IAACphGoUraIEmh1q5YgD/f4eoUnDe1EP8z7TX0k5L6kpEk8qW+nRmq7GwcPGTXtF+rBURoliXukd+JEalv3y9Ex3CqnewzbIAACIAACIAAC9kHAgV1v3tvHUDFKEAABEDCfwG9/7aJxM5bTpmUTSePWGLHWkJBQtdZZfDdXJdAinr9++z55JvSgZN5eEU9hHwRAAARAAARAwM4JwIJm5zcAhg8CIGA8AZlHtuz37Wqds8jEmdTm7OxEaXmR6shSlgypIzuF4yAAAiAAAiAAAnZOAIvs2PkNgOGDAAgYT+Di1TsU382F2japanwh5AQBEAABEAABEAABEwjAxdEEWMgKAiAAAiAAAiAAAiAAAiAAAtYkAAuaNemibhAAARAAARAAARAAARAAARAwgQAEmgmwkBUEQAAEQAAEQAAEQAAEQAAErEkAAs2adFE3CIAACIAACIAACIAACIAACJhAAALNBFjICgIgAAIgAAIgAAIgAAIgAALWJACBZk26qBsEQAAEQAAEQAAEQAAEQAAETCAAgWYCLGQFARAAARAAARAAARAAARAAAWsSgECzJl3UDQIgAAIgAAIgAAIgAAIgAAImEIBAMwEWsoIACIAACIAACIAACIAACICANQlAoFmTLuoGARAAARAAARAAARAAARAAARMIQKCZAAtZQQAEQAAEQAAEQAAEQAAEQMCaBCDQrEkXdYMACIAACIAACIAACIAACICACQQg0EyAhawgAAIgAAIgAAIgAAIgAAIgYE0CEGjWpIu6QQAEQAAEQAAEQAAEQAAEQMAEAhBoJsBCVhAAARAAARAAARAAARAAARCwJgEINGvSRd0gAAIgAAIgAAIgAAIgAAIgYAIBCDQTYCErCIAACIAACIAACIAACIAACFiTAASaNemibhAAARAAARAAARAAARAAARAwgQAEmgmwkBUEQAAEQAAEQAAEQAAEQAAErEkAAs2adFE3CIAACIAACIAACIAACIAACJhAAALNBFjICgIgAAIgAAIgAAIgAAIgAALWJACBZk26qBsEQAAEQAAEQAAEQAAEQAAETCAAgWYCLGQFARAAARAAARAAARAAARAAAWsSgECzJl3UDQIgAAIgAAIgAAIgAAIgAAImEIBAMwEWsoIACIAACIAACIAACIAACICANQlAoFmTLuoGARAAARAAARAAARAAARAAARMIQKCZAAtZQQAEQAAEQAAEQAAEQAAEQMCaBCDQrEkXdYMACIAACIAACIAACIAACICACQQg0EyAhawgAAIgAAIgAAIgAAIgAAIgYE0CEGjWpIu6QQAEQAAEQAAEQAAEQAAEQMAEAhBoJsBCVhAAARAAARAAARAAARAAARCwJgEINGvSRd0gAAIgAAIgAAIgAAIgAAIgYAIBCDQTYCErCIAACIAACIAACIAACIAACFiTAASaNemibhAAARAAARAAARAAARAAARAwgQAEmgmwkBUEQAAEQAAEQAAEQAAEQAAErEkAAs2adFE3CIAACIAACIAACIAACIAACJhAAALNBFjICgIgAAIgAAIgAAIgAAIgAALWJPB/SsTM30HZQSkAAAAASUVORK5CYII=" 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" }, "metadata": {}, "output_type": "display_data" @@ -2007,7 +3553,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "380914e0", "metadata": {}, "outputs": [ @@ -2043,7 +3589,7 @@ " 0\n", " Voice Bot deflection (labour avoided)\n", " APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A...\n", - " 13,122,330\n", + " 6,981,080\n", " estimated\n", " \n", " \n", @@ -2087,7 +3633,7 @@ "4 Supervisor time (AI summaries/insights) \n", "\n", " scope annual_value confidence \n", - "0 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 13,122,330 estimated \n", + "0 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 6,981,080 estimated \n", "3 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 2,099,573 estimated \n", "2 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 1,237,248 estimated \n", "1 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 562,386 estimated \n", @@ -2101,7 +3647,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Year 1 total benefit: $17,340,037\n" + "Year 1 total benefit: $11,198,787\n" ] } ], @@ -2114,7 +3660,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "ae5fb48b", "metadata": {}, "outputs": [ @@ -2146,7 +3692,7 @@ ], "xaxis": "x", "y": { - "bdata": "O7ETTWMHaUFiJ3YK6QV0QcVObLwEx3dB", + "bdata": "DvzA8XWhWkE/Y5r0901lQdtVd3KWTGlB", "dtype": "f8" }, "yaxis": "y" @@ -2275,57 +3821,7 @@ ], "xaxis": "x", "y": { - "bdata": "ip3YKeLqVkEUO7GR7DZbQQ==", - "dtype": "f8" - }, - "yaxis": "y" - }, - { - "hovertemplate": "benefit_line=Email Auto-Respond (displaced handling)
year=%{x}
$/yr=%{y}", - "legendgroup": "Email Auto-Respond (displaced handling)", - "marker": { - "color": "#FF6692", - "pattern": { - "shape": "" - } - }, - "name": "Email Auto-Respond (displaced handling)", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - "Y2", - "Y3" - ], - "xaxis": "x", - "y": { - "bdata": "ip3YiXW7S0GKndjJTXdQQQ==", - "dtype": "f8" - }, - "yaxis": "y" - }, - { - "hovertemplate": "benefit_line=Email Auto-Suggest (drafting time)
year=%{x}
$/yr=%{y}", - "legendgroup": "Email Auto-Suggest (drafting time)", - "marker": { - "color": "#B6E880", - "pattern": { - "shape": "" - } - }, - "name": "Email Auto-Suggest (drafting time)", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - "Y2", - "Y3" - ], - "xaxis": "x", - "y": { - "bdata": "FDuxE3ufOkEndmInYp0/QQ==", + "bdata": "lkOLvBKzP0EgsPIfU9JCQQ==", "dtype": "f8" }, "yaxis": "y" @@ -3140,7 +4636,7 @@ } } }, - "image/png": 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v4GbvKYMQQcr8S1cDxt6+rKv3mEdkCSWEWb73VmMj0BD2h/YjcUOu7Jllq3rWIGgCllx4IivW75Bsep3ae3+dncMK372gIZ8QgDgHxCTC3izbqGxhCAG1QknxGeGKC1dslnVb9oZZoFXS6+vbkZ+gOmPoK8ZozaY9KkzSBaoX7Xq9SYAHLrTXmlW//etKFcq4lkJradVL3ajO824dFp6/EYwfDG08cuKshgLfNl4ueLEg/EJrCfQahRiHKMPyDTCEjXZt1yy0Vbk1RqGulAeQAAmQAAmQAAlECoEYK9DgeVk1b3ggKAj1QjjiWPWqIH01bgrhVbtw+bqUrtMxUFnrw5VrN623Tl9TpwwI4Xrq7W3242YpvHU6PVEEbsyv814g0C5r3+wFmuP6XKHtC57oj5j8g5lf49hchIXa220Vyx0/GWXYj+zbOdBcMZQ7fS4ggUoV9VgGZxg7CGtnWTkxXwx2TsUhDGIH4gdeCxjm2sEr1vuD1rJ+617ZsG2fEWgH/zlh5hYGN88LxyOZTOqUyYwQwuewGMLdYAhPg6HOlq/UktHf/CSLde5g13eamjXyHqiHEWLO3uNpDnD4BQ8d5jZibtIva38TzJuDKH+1US1b2CLEMqyIztOzN3hdkKzm/KWA/fb73H1vhUbal8cY+Wgm0mKF8oglIO33431orzXH4/F5mq77h7DT0Fr+PNncFmjh+RvB/Lq+I2ao1/aACde1b6e/eThkvyX497hua+mSDQiHbfT254ZtiaJ5pWm9aoIHJaE1d8YotHWyPAmQAAmQAAmQQOQQiLECzRkOeHDatKhrwq026I0NvBUwLJjcrH51Z4dIaQcvimMhK4mItR030rDw1GnVFfmvwWerDE1ftuz4Uz4bMtUkKujRsYUJIcyRLZMmMNhjxIZjX07qvDHLNuu8sgYvVrJ527Adni2YJWDMBye/sN4bLFHChEH2WvNr4GWwrHLZouol2mJCIDf9vt/M1UEilkq6HZ64Tm83Np6rVzRJjDsGweSn4iOs5hUvYAysV9TT7OXqGu63RJbqnLDObV4xXi0klEBCkpAMImj5nK9kwswlJrR0s44LfhDSOmtML7NouTX3EXU6GpjB8+aYiTCwvHY8KvjP1vkQNuvKQnOtuaoDnrunT31c7Xa5PVGioBxcFnbYEZp2Y54lkstgvhg8sflyZTNzABu1/dyhVvc+ju7fReZrSOrPv2438yoxdxKhq1iKo++Hbdyr5Fkpd8YoVBWyMAmQAAmQAAmQQKQRCPudS6Q1KXwVW4IK4Y/I1gjDAtFvaSa5iLDIqNNqF9Y1ighDJkCY1VZXdVr73eGDkDzY0N4dTdinVWfSxIFDEq3tSOQxeVgP9bj9aMIL4fHE3CzLrLBShEHme+ZlsvbZv+Z4lpUQWRwdDV4ZGEJYLYM3CQJtwbKNmtHxuplzh30IgcRcsDkL15qiCNWLCrtw6dlYqJi1DPMG4QlDAoip85abm/pX6lYRhOK5Yxg33LxDZCFRC+byrd642yQZ+UznGOZ8dq4Ll6+ZUFCrTszJQwZOeNGspSYsj91jTeASVoPXEoZEF64sNNeaqzqspSpc7Y+M7e62+/rNO2Yc8eBm+uhPAzUlnpvLegQ6SD/AG/lOy5fND+akwSM8asoCM6/vAw1ztLyZ7nxvuDNGjufnZxIgARIgARIggeghEJBjPnrOHeFnRVp8JCWAlSiSz8xnglcN6esx58nRkFADmc1CY5gjFdF1IvNkUk2rH5q5Tq7ajNBBJO3AjaU1F8pV2dD0BTegMHuvDOYeHT522mx3/IUEEpjv9s3wj1UwZJT5S9bL9O9X2oohgQUMSw7YG+aH2RvCELF8AsIWkRXQMiRw+Xn1diM07EO+rMQfc3VZA3h0rOQsL1Yvq+GDXiarILxIpYsXsKqK1FekuoeVLVko0HmszJNTvvvFbG9ltyxCoIIOH3Ad4zqHQWQh9LPNq/XM5xNnAsL/Sj0XEAK3cPkWs936tXrTLk39/8iW4RLbkd0Shr8Ry5CBFIlW3DUIBYQRwnuE+YD2hr8vzAUMzbVmf3x0v3e33ba/D7227A0ZF8EztAZum1SQWYaQxyb1qspzhfOYcFLMaQvN94Y7Y2Sdi68kQAIkQAIkQALRSyDGetCQov5LTUMNw9N/3LTu1RtBzA+rU72cLSEFQoHe+mCofPTlJHODU7xwPrmq6fWR+AMpx3/8pn+IQsZxiCKjzqIF85iU/r2GfGsyJSIJhLUEgOP57T/v3v+vptWfpxn7csq1m7dNunV4Sj567zX7Yi7fu9uX8iq2cMM4cMwc4/2BONimc20sUeDqBPCcTRv1qWayGyzIxohwQ4SbNtSkDWOnLTbZ7e5r2Ghhbf+Bw8dlx95DQapC4hasH4aEIx3fbGgSwizT+Vc4d7P61UxKfOsgnA9iAXOVkJTECrvDGmsQjBjzEro2FG68I8OwVhy8dbghRhZIhB9CjDbUOZH2VrZEISM8kf0SN91Yr8odg6D9fNh0gccN67hhrTUkGIFhfTIYzo9QSMxf+njAZKmhy06cv3hNZvy4yniT39d5b5ZV0MyX03WduOGTftB5gZeMmIAIRAKT0Fivrq3lvU9HSxfNetla25FbvWoQ1Ghv+zfqG/buXmuhOW9UlHWn3UVUKKdNnVL+0rXhuutSCxjTYzq2CLVGMpzQGr7PsGQDEtkgOySWesAcWzx8wfVbvEheU2VovjfcGaPQtpPlSYAESIAESIAEIp5AjBRoEAdYiHWxLs5sGW78cZOEZAlNX65mbTbeAqRx/2ri9yYFOxbuhcEThAyCyDwHg3fFmVlbvbz+czYihbs7daqPw1TpWLctvMwu9KlPj7dk4sylskYXsEa2OqwzFpxAs+qAEPhlze96Yx0wNw6euEGftgu05pLj+e376W5fcPOPBZKRDfG7n9aYKiACIBSw1phdV4xnx/4zvHlTR3wi73z4lRHVWNcJi0ZPHfGx3oSON8IP4g9egs5vvyJT5y4PVN8LmixhhC6kPHTcPBPihZOjT5iL82mX1+27Y95X1rmHEGgQKvaGzxBolpfN2mexDHTSZzuxz50QNYsx1rxDOnPLKuq6bSP7dXaa/KNq+eJmeQL0w11DlkfcqGOhaMvADYIcQgyGNn878mNNWDFT5wj+z/xgO0JBh/d5L1DYI5aVQPhvgIdzFYqpmCpklgBA6n+r78ExwjGoZ5KGtA6f+IPMXvArNhlDyB/qg7l7rZnCUfzLGj9np3Wn3fBmjf6yi3w66BsTiohwRPwtwruJzJk2fnYnsMJLscnab/GG9xuLwG/fczCQNxPCHstNWMeG9L1hlcM53BkjlKORAAmQAAmQAAlELwEvnccSnvwA0dv6UJ4dE+WRHh7eE8xncZVxLjTVRnSduMG/oz9oH2763DEk0sBizrjJg/fIWqfMnWPty7jTF8z7wryxjCqI7TNE2tcTmvfItGkSiuhViFTi1hxCZ3WgLNY/Qzsxb83yjjkrG53bEKoKDxQEkaswU4Ro1mv1qfH+bl481rbOlbvtRsZAzDVMrstLZNXU60i378zu3nugc8OuGM8lUrS7MoToYc5eDg1HhbcxPIa2IUQvY4Y0kiZVCqdVuXOtOT0wmjeG1G78LZ58FmqaJ2dW2zyxsDYb18nFyzfkgc6pxXeCq3mKof3ecGeMwtpmHkcCJEACJEACJBA+AnFKoIUPFY8mgYgjgLDE3kOnyTuvv2wWx464mlkTCZAACZAACZAACZBATCbwX9xeTO4F204CMYwAsknC4/nGs4WcY1jz2VwSIAESIAESIAESIIFIIuA8LiqSTsZqSYAERJ5oGFzzBtV1EewUtpT45EICJEACJEACJEACJEACIMAQR14HJEACJEACJEACJEACJEACJOAhBBji6CEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQAAUarwESIAESIAESIAESIAESIAES8BACFGgeMhBsBgmQAAmQAAmQAAmQAAmQAAlQoPEaIAESIAESIAESIAESIAESIAEPIUCB5iEDwWaQAAmQAAmQAAmQAAmQAAmQQAIiCB+Bizceha8CHk0CJEACJEACUUwgW/qkUXxGno4ESIAESMBdAvSguUuK5UiABEiABEiABEiABEiABEggkglQoEUyYFZPAiRAAiRAAiRAAiRAAiRAAu4SoEBzlxTLkQAJkAAJkAAJkAAJkAAJkEAkE6BAi2TArJ4ESIAESIAESIAESIAESIAE3CVAgeYuKZYjARIgARIgARIgARIgARIggUgmQIEWyYBZPQmQAAmQAAmQAAmQAAmQAAm4S4ACzV1SLEcCJEACJEACJEACJEACJEACkUyAAi2SAbN6EiABEiABEojJBJau3iZtug31iC5cv3lHps1fIS069pdm7fvKidMX5Kdlm+TkmYvhbl+vod/KhJlLTD03b9+NsHrD3TBWQAIkEOcIcKHqODfk7DAJkEBcJuDr/1T85ElcRhBr+u4lCSSBV+QvOH3txm05dOS0R3AbP2OJbNmxX5q8XE0SxI8nl6/dkkFj58rAnu0kX+5sMvunX2XctMVyYOPMULf3xOmL4u/nb447f/FaoHpDXRkPIAESIIFwEKBACwc8HkoCJEACMY3AU/+7cs5vlfiLb0xrOtvrQCBTvOclrVdRh62x96Ovr59s+v0Pad2sjnR9p6npqI+Pr2z7eYKkTP5MqKq+8vXzCzeEYoXyBK433DWyAhIgARJwnwAFmvusWJIESIAEYgEBP/GWB+Ij92NBX+J6F8IvRNwl6KeiZ/6SdbJwxRaBd6lE0Xwy+LMOkit7JlsVu/f/I+OnL5ajJ89JyhTJpE71cvLRey0lWdLEcvnqTfmw/0R5pV5V2brzgOz/+5hABL39al2pXa2sW3W07fGV3L5zX1au3yHbdx2Q6pVLScvGtaRbn/HSs8sb4u/vr21cb17f6DzQ1Fm1Ygnp1r65rX5339y6c89Wb/lShSOk/e6em+VIgARIgHPQeA2QAAmQAAmQAAkES8BbPVUzf1wttauWkZdfqGgEVre+440YwoF7DxyR9h+NkFQpk8uXH78jjepUkZ9/3S5jvl1o6n385Kn89e8pGTp+vpZJJl3aNpHHj58I5n099fYxZUKqo4YKMhiEXT1tQ4kiecWq9+69B5IpQxopmC+HKYP9+Cn9XAHzObS/7OvFsdbn8LQ/tG1geRIggbhLgB60uDv27DkJkAAJkAAJuEUgYYL4smreCOMNwwEF8mSXr1V8QXSVVG/axFlLpUDe7DJ1xMe2+uCFWrlhp/T9sI1t25BeHaRZ/ermc9UKxTXRRz/Zo563aurpCqmO1xrVlPEzFktNFWrwxMHOXbxqXvErd44sUqlMUfltz1/S7vX6tu0R+SY87Y/IdrAuEiCB2E2AAi12jy97RwIkQAIkQALhJhAvXjybOENlpYsHeKbOq0CCQDty4pw5R5N2fWznunnrrty7/1DuP3hk25Y2dUrb+ywZ05n3l67cMK/u1mGrIBrehKf9Kax5ctHQbp6SBEggZhGgQItZ48XWkgAJkAAJkEC0E9DpXsbiayZFzP1CCCC8YE01u6KjJU6c0HHTs2Pj27a7U4f3s1BI20Eu3qCuqLD48UPX/qhoE89BAiQQOwhQoMWOcWQvSIAESIAESCDKCOzad8icK2+urOLl5SV5c2aRW7fvyYua8AOfLYNYsv9sbXd8jYg6UKclBjGvLVHCqLvFiaj2O3LhZxIggbhJIOq+veImX/aaBEiABDyMAFbPSirxJZGHtYvNCS2B+JIktIeEubyPr6+s2bzHzDPbsuNP+fGXTVKuZCEplC+nqbNty5elz/AZ8sVX06V18zpm259/H5df1vwmS2YMcuu8EVHH8+WeM+f67qc1Ur1SCYHXrWSx/G6dP7yFIqL94W0DjycBEogdBCjQYsc4shckQAIk4BaBpHcSSOFrFUWeLcjr1kEs5JEE/DIkE8kQ+U2zPGBDxs0TJP6AIc3+8D6dbCdHaOODh49l8nc/y/J1O8z2hOrBqlergq0M3sSL9593zdph1e9uHVZ563jz+sxrB49e8wY1ZNr85SahSOO6VdwWaKg3SN123kCcJyLaH6jd/EACJEACTgh4afhB1ARrOzl5bNh08cZ/k59jQ3/YBxIggdhNIMHlm5J+9hqJf+9h7O5oHOjdrZY15VHpgmHqabb0zxZ2DsXR3j4+Zg20JEkSSdZM6V0eee3GbUFa/kzp00gCzf4YFgtvHY80hf/tu/clc4a0KqqifkWh8LY/LMx4DAmQQOwhQA9a7BlL9oQESIAESIAEIo1AwgQJBB6qkCyjCrPwWnjrSJokseAHQumVtl+E2JwdKyYH9Z6FeJTrAuFtv+uauYcESCAuEKBAiwujzD6SAAmQAAmQQBwkAKG0YeGYEHseJLQxxCNYgARIgAQijwAFWuSxZc0kQAIkQAIkQALRTCB5sqhLphLNXeXpSYAEYgmBqA/MjiXg2A0SIAESIAESIAESIAESIAESiGgCFGgRTZT1kQAJkAAJkAAJkAAJkAAJkEAYCVCghREcDyMBEiABEiABEiABEiABEiCBiCbAOWgRTZT1kQAJkAAJkEAcJXDtlo/cvOX+6j3JdSm3HFkSxlFa7DYJkAAJOCdAgeacC7eSAAmQAAmQAAmEksCVq34ya76ug+7n3oFNGvqrQHOvLEuRAAmQQFwhwBDHuDLS7CcJkAAJkAAJRDIBP3WePXkicv++l1s//u472yK05Y+fPNV2Po3QOqO6MvThlzW/ycNHCtwNe/DwkazasEuWrt4ul6/elNUbd7lxVOiK4Bw+ukh5ZNn+v4/Jyg07TfWh6cPeA0fk4OETkdWsaKk3uGs4tNeGfQeOn74gW3f+aTYdOXFOlq39XZ56+9gX4fsoIECBFgWQeQoSIAESIAESIIGIIzBn4Vqp1LCLPH3qHajSO/ceSOk6HWTj9n2Btjt+6NFvovQeNs1xc7g+fzXxe3mu1jvmp+SL7aVak24yfNIP4usbsjvx0JHT8vGAyaE6/+0796XP8Bly8/bdEI+7ePm61GjWQ+YuWisr1v0uh4+elkFj54Z4XHAFUNf3SzfYikAoVmzQRTaEwN52QCjfYNHxLr3Hit8znoePnXG7DwuWbZJf1v4WyjN6dvHgruHQXBuOvfxtz18y+btfzObMGdLK9O9XyqwfVzsW4+dIJsAQx0gGzOpJgARIgARIgAQilsDLL1SUUd8skK27DshLNcrbKl+35X+SJEliqV6ppG2bsze9P2gt8eJ5OdsVrm1liheU4X3eM16kP/46Kv1GzpKKZYpK7aplgq33+s3bsn33X8GWCc/O9dv2SaH8OeXHKf1MNZt+3x+e6syx/6hASpDgv9vIJIkTyg+T+0runJETswqxW71SCXmlXtVwtz02VBBZ17A9mzSpU8iwz9+Vdh8Ol/q1K0nuHJntd/N9JBL47y8rEk/iyVVfuXZLUiRPKlzI0pNHiW0jARIgARIggf8IZM6Y1ggfhOzZCzSE7dWtWV4SJUooCM8arF4ieIuyZEonXdo2kcYvVTGVIGwrSeJE0vntV8xneNy+mbtczl28Ktkyp5f2bzSQxnWryNkLV2XIuLmy/+/jUrRgLnmtUS2z/b+WBH6H8+bImtFsxDn7j5otCeIHBCs9ePjYeNQ2/f6HxPPyknq1KkrPLq+b90PGz5dHj59I8w4BAmpEn05SMF+OwJXrpxXrd8jk2T/LZb13yesghOBNHD9jiaxSBgkSxJdm9avLm83rmP5/O3+5zgv0N/XXrVnBiDX7yoPrJ5iMUHG0/9BxSZIokVStWFwFZ1nZsiMgDO7QkVOSIV1qmTaqpwyb8L306fGWpCmWQlz1F9x37/9HJmhby5cqbELocA/WoVUDad6ghn2zbO/PnL8sazbvkdXzh9u2Ob75sP8kgSh++Oixjnd66fRW40BjderMJXm35ygdy2OSM1smGdq7oxQrlMdUs2jFFpn54yq5duOOlCiaT778uK3kzZXVhPa90XmgjOzXWQrkyW7Kfj11oaRKmUzefbOR6ccU9Ta1e72+/PjLRvHXmF1wcDRXbZuq19xp7dvwL96zHYIxBLsvur8Zqms4LNdGmlQpBOGQo6YsMCGviDhOnzZVoHviksqjctli8t3CNYaLraF8E6kE4kSI47fzlpuQg5NnLtpgntD39d/8TF5s+bEJk+g15FtbjO2f+iWEMAX8IdsbvoywHX9oNBIgARIgARIggegj0KjO88aDdv/BI9OIq9dvyf90rhFEGG46O/f62txoThrWQ16sVk56D51mm4d06coNMw8LB+KGvbuGPOLGfNLQHtL05eqC+wDMu+n02WjzEHfm158awdNv5Ew5c/6Ky05fvHxNZi/4VXDf0eHjkVKl/HNSudxzpjxugnfuOyS93m8lH733mgkFnDhzqRFTzVVMJUyYQD7p/Lr5gbhzNMyhQh/KlyoiM0YHtMe+DG7sN+/YL/0+bGPEx6bf/pCFy7dIoXw5pWTR/FK0QC5TNwSsvQXXT4g+3AvdvH1PBvVsJ727tZajKnyLFcotRVXclCyW39TZqU2A0D1y4qwRF6jfVX+x7979h4bx2QtX5MtP2qqorqDibn6QkFWUhf17/JwK5NzqwXHtnSteJK/p97yJfaS+eli/GD49UPgnQiIrlC4qA3u2Fy8VyGO+XWTq3rn3kAwcM0cavFhZxg/uJvCrvv/5WPH28RF/zXYDoQ/xbNkFHWM83IehH5jfNnzS90bAQ8g4M1dtK1eykIac7pALl66Zw3Dd/vDzBimlXENzDYf12sBJJ85aKmu27JGObzaUycM+lPy5swXpAtj8e/xskO3cEHkEYr1AW6sX3ZTvlgUhOEj/GPPlyia7V30jP88cLNs0TGK5PlGD+T1LP4UvesSFWzZrQUAMLp6Q0EiABEiABEiABKKPgCU0rDlPqzftFsyZgVcGQujq9dvS76O3VSQVVxHR0oRnwXPmaAuXbzY3pfCaVChdRNq2rGeO2/3HYfWoXZNXG9UUvaOX/OpBwU9w89vuP3gs+9SLc/DwSfHVewkIvZ9/3W48K8t17lerprVNiB48Rc0bVDfzouLFi2cET4L48aVqheLmJ2UKXX/AwZDcI596dYb06mD6CFFjbwtXbDbew4zKIKmGeUIsbNi+13i3sqrgy5g+jak7n8MNeHD9BEcwGPRZe3mxejnjrVww9UvJpOfInCGNeUWbITTsDfdJrvprlUudKrmMHfiBvFCljHR9p6k8UTEIXs7s2MnzUlhDNIMzeLFSKTcIbm9NVAKPob2Yblinsrz3ViPBK4QwvG0QoMt0XCA4u3doIdUqllAR+qbxnO7/61hwp7PtQxTWL7OHyofvvibt1QvozFy1DddbruyZTbIXHIdrOb56XF+qUS5U13BYrw2cc+mqbdK66YvqwWxoxrGsw1iiTCH15h4/dd5cx/hMi3wCsTrE8eA/J014AeJnPxsy1Ubzlj4JwhOPWWN7madrCCPAF8/6bXsDvoiflcTTuRk/rDRfIKgLXxD4DwF/9DQSIAESIAESIIHoI4Ab4xeqlNbMhDvV61XNhGjh5hveEXjI0qZOaQs3RCvhIbt09UaQBp+/dF3KqahzNIT2ITxx/PQltl0QUY/Uy+HKMM8LXjjLkEhj/PTFUuv50kaAwJNlGdrz7bwVbmeTPHfpqhFm1vH2r0gUgiQdG7f/ITv3HrbtSqwhlyFZcP0EGwgpK7wvpLqs/fC4QXC521+EZCL00d5TZdWFV7QRYa2uDJ6sdz8dLWfVu4k5f5YH0nrg7ngcvERo37FTFwQJVOzbCTECbsgSCU9WSAZBhba7spDa9mqjGhoeuUneV5H6y6+/SdN61UyIbmiu4bBeG0iqc1fZOQpsx76kT5fKXF8YV4RA0iKfQKwVaBf1y7lbn/HSq2vrIF9oVzQMApZbn1pYhomPf/170vpoXtu9UV+ate8rp89d1pCF1fKGPmFACIUVToFCqZOH/OUXqFJ+IAESIIFoJHAreWK51EQn2TMSIBpHIWJO7Z8htaSL4/8HYZ5Y974TZN/BoybiZWivjgYubiJv3blnQtzSpQm4oTx19pIJ93OknzF9avMA1nE75lUh7HD+5D6S0C4ZhmO54D4XyJtdcBPsr/9wI3/i9EXjpcMxmHaRLGkSSaw39xCV/v5+wVUlBfPmEPTBmaVKkVzFZHz5qFPLEBOSOB4fXD/XatKVO3cfmHsfeM3sDW12JYBSp0webH/t6/nvveukLdmzZjAe0f/KBn63ffdBM4YbF44RJLaAzVu8LnAhu08IxYThOkH/T2hqecsuqGCDeEubJqV4qXcT9vRp2NPMh9Q2hNROmLFUPa2/yS712mLuGSw013B4ro1M6gm9pGI0OIMwS5okkaRTJrSoIRArBRrW4eiq8cOv1K1qQgisWGEL6V39soTZP/HA+/v3A+LYrXJ4wlJDM0EhHPLgPyek74dva8zyQmu3efX2Cf4LNVBhfiABEiCBaCZwNnly6Zo+idzhV1c0j0T4T98vdTxpFsf/D0JIGsIBe2mUDELgrMQa5UoWVvGTWKbOXaGeiSaya99hTZZxRjq2bhgEfI3KpTTb4kxZsmqrhr89L0c1WuaAhtohigaiZ8i4efJplzfMcTv2/i3e3r4mTC5IRbrhsc5VOq/zibx1/hpu9DEvrIjO/cqSMZ0JtVz66zYpX7qwyfK4Yt1OqVG5pKkGZR49fip//3vKhGLCo4QwRXvDfLYlGo6GZBmY+zVv8VrbbpSv+XwpGTd9kTkeD6Axdwohcz06trCVc/amgs5pc9XPSmWLGhE5Tr2A3To0N+Uw3QMPvxEWuHjlNjPnDIk5EEJpGdqD0FJX/bXKufuKeXQ7dK6YK4M3FeuvgbmfCt1la4KGsiLkFR46eKbmqngz46Khn8j4iTloYFWuRCGTVh4CE96zRCrQwRoRVvnzZJM9mtwEDwPsE9O4apO1PaS2QYi9oFk+B42dI8gCaoWghuYaDs+18bzOkVyoSVLAA0tCIBOqo0HAwosKUU6LGgKxUqD9oXHDSAJS+rkC5oK3FnGcMmeZvNqwhj5dCXgCgCcklmEyZooUSa2PtlfEE7fpNkxeb/KCU7fuwye+trJ8QwIkQAKeTsDb11/wrfXY0xvK9oVIAOH2Yf0/KE2AkyHEc3h6AXi2kHL/p2WbpXWzOrbmwiuC6Q19R8zUtbrWmxvLtq/VM8koUMjLLsV+E03bfkzn1yDjI7Iuwmv2ftumxoMyTpNGDBj9nUkmhuNw495Xk3A4M9y8HtBEHvVafWp248Yb8+F6PhN3SN7RQ5OMNXmnj9mPe5ReXVuZ9/BOIeviWx8MMfOnfvymv4bd5Qt0GoTuldBEGJ8MnGK2Vy4XkJDCumXur3PoBoyebasfywhYWSvt+2tVah0HT5GrfoLjKM1gOODr70y2RRwLwQKrrYlX4PWp2KCzZM2cXjb89LWZq2fdwwfXXxwfzytoGgRXAgDi4S+daoJQR2RgtMzqA4R6FZ0L17LTALMLnwMsoASyZv6hwqpC/c5mHhUybU4c2t0UwfhjGgvWFYMhpHNE306SSsca9vardQVr3MEjh/li2G/1Efud9QPbLQupbSiHeY4Qga9hvuMzC801HJ5r460WL2kynK/l1Xe/NA81Cqin1tFWarbUIgVyO27m50gk4KUTOWPdhCqkY7WfCIxJu/iCbtm4lpmcm0ezAFVr2k1m6xw0XNSwviNmmPSq3478xEwchSg7tOU7s2/KnF+kiXrjsusfNBaFRIgjMv3ALt4I7HUzG/mLBEiABDyUwDFvf+l200+u04PmoSPkfrMGp/aS+smC3uS6U0O29EEfSLpzXEhlTpx9KkeO60MAX+vWOfgj8uTylxKFA3uKgj8idHvhEYBHC94deNSCM2TtQ5p13BjDc2JvWPj3qbe3qceViLAvH9x7TJVAHfYeJ6s87i+QXARC0JVhHh1CI12VeaIPnK/fvGPqR9r/0Fhw/cRC0Yg2ckxggnOlVA8WQjWdWXD9dVbe1TYIqCQaZoclCFwZzoWkKxhDZ4b+Yc5VzmwZg3iDkNr+xq27Om8xg6nD/nhzbagHLluWDPabQ/U+pLa5qiw013BYrw3L+5gtS/og4bxIltem21BZMmOQWXrAVTu5PWIJBP4Giti6o602pGFFNh7LEOIIgdZGn4L85zouJJi8W7xIPjNBdINOrP1EY7edGZ6k0UjAkwjc9zsnPvLQk5rEtoSBQAJJJiniBZ+ZLAzV8hASiDYC+XMlkvy5ou30QU6MeV/uLq4LbxzWQHNm1rwmZ/tCu81xLpf98QiHC8my6hpfwRmEEh4oh8WC66czQYlzuBJD1vmD669Vxp3XzzW7YuO2X5hkMEj77sxCOhf656qPWIvN1Zq45toIhzhDW0Nqm7P+YFtoruGwXhsISXX2d4IpQZ9/Nc2sC4h14WhRRyBWCjRHfJYrGk9VLOv/UVuzzgVc83AiYoV0uLktQ2iAM8NTr/A+PXNWL7eRQGgIXPXfJff8T4XmEJb1QAIpvfJKCqFA88ChYZNIgAQ8jAAyM2JNu/Ma5kiLGgJYYqF10zomn0PUnJFnsQjEyhBHq3PuvCK9Ktz1ji57d45FGYY4ukuK5SKSwEnfRRRoEQk0muqCQMsX/7UoPTtDHKMUd6SezBNDHCO1w6ycBEiABOIIgTjhQQtuLMMTTxxcvdxHAiRAAiRAAiRAAiRAAiRAAqEl8F/MX2iPZHkSIAESIAESIAESIAESIAESIIEIJUCBFqE4WRkJkAAJkAAJkAAJkAAJkAAJhJ1AnA9xDDs6HkkC0UfASxIIfmgxm4CXxI/ZHWDrScCBwKMbN8T7+nWHra4/xkueQlLkyO66APeQAAmQQBwkwDu8ODjo7HLMJ5D1aQXJ7F085nckjvcgXkJNqR1yVu04Tondj0kEHl+6JP4T+4mXL5ZDD9l8WnUVoUALGRRLkAAJxCkCFGhxarjZ2dhCINuCvyXJsfOxpTtxth+PC2aXm+1yxNn+s+Oxj4CXvwqzRw/F6/EDtzrn7x8xK6Zjgd5UmpE5ebKY/8QDfdn/1zFxtdbXY12IGgsBuVoY2i3wIRRatWGXLsqcXsoULxhCyfDvvqcLR2/87Q89VwFdiytL+CtkDSQQCwhQoMWCQWQXSIAESIAESCCuEcCN/ZhvF8rWXQfkyrVbpvtZda2sHu++Ko8eP5WBX3/nEsm6H0eZxZwHjZ0jPy3bLD07vy7t3qjvsnxU7jh05LQMGT/PpUDr0W+iJEuaWMYO/CBSmrVs7e+G6w+T+waq3xWrPw8dlze7DpEdKyZL6pTJbcd07jVGcuiC2YXy5wxxLP7+95TMX7Jefpr6pVmY2VYJ35BAHCVAgRZHB57dJgESIAESIIGYTGDklAWyffdBGd2/ixQtmFuwrumvm3fLmXNX5O3X6krZZ96fvQeOyOBxc2XpzMESP15AbrRMGdOKt4+PrNm8x4iIFet3eIxAC2lMen/QWuLFgw8t4u32nfsycsqPMrBnOyNgrTMEx8rPz7kX1N/fX/Dzcq0KIY7FZ11bSbP2fY1Ia9uynnVavpJAnCVAgRZnh54dJwESIAESIIGYS+CPv47Ki9XKSvlShU0nCubLIfixLNUzbw5CBmEF8mQP5J3ZvGO/PH3qLcOGvCtvdx8mx06eD3S8VY/1unH7Pvlm7nI5d/GqZMucXtq/0UAa160iR06ck8Fj58rho6cli3rwurRtIo1fqmIO+2n5Zpnxwyq5eeuuJE2SWF6qWV6+6P6mJEwQcPvlrM6k6h3z8fGVsdMWCbxZyZMlkQ6tGkjzBjVMndiWJHEi6fz2K7J7/z8yYcYSw8BZ2YePnsgoFVwQor4qpCBkM2dIKyP7dba6Feh14YrNZn+d6uUCbf9tz1+hYmV/MMYhpLFA+c5tXpGvJn0vFGj29Pg+rhJgmv24OvLsNwmQAAmQAAnEYAIvVC0jy9f9LuOmLzJC5e499+a9WV1esW6HoI5yJQtJ3lxZBV40V7b/72PSXUMLSxTNJ5OG9pCmL1cXhPZhPljnXl8bETVpWA8VjOWk99BpcvDwCVNVhrSppHv75jJv4hcy6LP2smbTblm6ervZ56pO7Hzw8LGcvXBFvvykrdStWUGGTZhvBBL2XbpyQy5fvYm3gjBPtMNVWYSAbvp9v3Rt10ymjepphNLp85fNsc5+/Xv8rNSvXSnILndYTZq1VEZ9s8D2c+LMxSD1BLehtoptCEqrb8GV5T4SiO0E6EGL7SPM/pEACZAACZBALCQAjwu8UvMXr5Pp368yPayn4XQ9u7xhPFzBdfn+g0eyZcefMmaAZpFUa/hiZVm8cqt89N5r4uUVNHxwoXrC8ufOJl9+3NaUr1C6iHmFF+7q9dsqwPqYUMkq5Ytrwot9xvNVslh+eVE9URAqB1REoVzqVMnl5DPh4qrODeqpQzlrjln1iiXVC7fSCLGKZYqa89r/Cq4svGrwvr3V4iVzCMTo6o277A8P9P6oehGbvlwt0DZ3WWEeYKJECW3Henv72N678waewpw6Z+3oyXPGE+nOMSxDArGVAAVabB1Z9osESIAESIAEYjGBFMmTStd3msr7GlJ49sJV2aPhfpNm/yzjZyyWEX06BdvzdVv3yhMNb4RYWbf1f0Y8Xb52UzBfzRJf9hWcv3Rdyj0LpbTfDm9W2tQpjTiztsPLZoVVjtfwQ4irCqWLSp6cmVX8xTPzslDWVZ1WPdZrggTxTUjjo8dPrE0uX+3LwqP48NFjKVPC/UyM5zV8M33a1IHqd5fV4F4dAiUJ6fTZ14HqcedDOvU4nr90zZ2iLEMCsZoABVqsHl52jgRIgARIgARiJwF4diDS4PHKnSOz+Tl26oJs2/VniB1euWGnlH6ugGTOmM6UhSg5f+mqCXN0JtAypk9t5qg5VpxeBcWtO/fk5u27ki5NKrP71NlLUihfTs0k+URmL1gtQ3p1lCb1qpp9Hw+YbKvCVZ22AoHeBPXqBdod6ENAWcz7ypU9s5w6c0kqOfG8BTrk2YdsWTKY/tjvCy0r+2ND+x4ss2XOENrDWJ4EYh0BCrRYN6TsEAmQAAmQAAnEfgKN235uQvdqVy0rGdKllgM672vVxp2Cz8EZQvHgbZs15jOxDxlEHd/MXSZ9e7QJFKqHumpULiX9Rs6UJau2SsM6z2sY3nkTtoj5Wkh5P3XuCnn/nSaya99hTRZyRjq2bmgyRibTsD142ay5YgcOndCwx4D2uaozqyYgiSirXqmEhn+uFB9fH/Hz85e5i9YaVq7qL6wp8Y+dOi/VKpYwRdxh5aqu0G6HoD1/8ZpJyx/aY1meBGIbAQq02Dai7A8JkAAJkAAJxAECtaqUkRk692zMt4tMb+PHj2fWDuulKdvtzXFOGVLxQ4xZ2R+tsvVrVzQJLpC6H3PH7A0eMAgXZGvsP2q2JEyYQEMrm5p6hn3+rvQdMVO+X7reePPavlbPJPbA8SiDMMeJmkAD3qnEif+bo+WqThwXT0MhHc3qh5dDiv3gyr73VmO5fvOOzFu8XnJmyyQliuRTb989x6ptnwvnz6Vhn7ul3esBa8K5wyptmpTmeGc+PqvN1gkcP1vb8brpt/2SWOewIUMmjQTiOgEvXaPCP65DCE//L954FJ7DeSwJhIlAutlrJMmx82E6lgd5DoHHBbPLzXZRuzjuMW9/6XbTT647X7rIc+CwJSESGJzaS+onC3ojH+KBWiBb+qTuFAt1mTsnT4nP4f3i5ePt3rEFiku64s+5V9ZJKdzCICwOhEDtuwAAQABJREFUWQ+R4t5KX++kaIRswnpg127cMcIskYo0y3x9/czcqYzp0xiPmrUdr8hMCJGUI2sGXb8s6Hi5qtO+jrC+R932TD7sP0lSpkgmgzWjpDNDqGajNp/LkN4d1RNZxlmRSNmGdrbo0N8kKGmvSU1oJBDXCfz37RLXSbD/JEACJEACJEAC4SKQOl9eEfxEkcEjg7lf1vyvyD4txI4zDw+8d5gH58wQApkreyZnu8w2V3W6PCAUOxat2KLes3VmGQGEK17QxbznT+rjsgZw/LhTSxk0Zo6umZZLsmaKGm/W11MXGvH6tnofaSRAAiIUaLwKSIAESIAESIAESCAWEnipRnmzFMEtDWtEWGdVnVuGxCbBWYuGNTRUU7NM6nywqBBoyDaZJ0cWebVRTUEWShoJkAAFGq8BEoiRBLxzaKiMhs3QYjYB7+wZY3YH2HoSIAGPJoCQy2b1q4eqjfBKtmhYM1THhKcwsk2+0bR2eKrgsSQQ6wjQgxbrhpQdigsE1uhT0FNli8eFrsbqPuZN4CUBybdjdTfZORIgARIgARIggVAQoEALBSwWJQFPIfCTTwLZ8dRTWsN2hJXA83ogBVpY6fE4EiABEiABEoidBCjQYue4slckQAIk4JQAcsgl1Pkl6YMmk3Nanhs9l0BiHUcaCZAACZBA7CNAgRb7xpQ9IgESIAGXBDJ535XxCe7rfq6w4hJSDNmR3C+FtjSNR7X2/MP7cv7xA7fblCZBYimSyrP64HbjWZAESIAEIokABVokgWW1JEACJOCJBNL63JUCf4yT+E/ueGLz2KZQELhVqqM8Sl45FEdEftHjD+/Km+fXi4+bDwC+yliJAi3yh4VnIAESiGEEKNBi2ICxuSRAAiQQHgK+kkTu5qqrDjSuVB0ejp5w7NOkztfdis62+akwu+fnLff83Vuo2jeaGvv4yVNBhGjixImiqQURe9pVG3ZJtizppUzxgrqQ9m3Zte+wNK5bJWJP4mZt+w4ekQTx40up5wq4eURAMYzJms17pG7NCkEW+w5VRVFQ+NzFq7Jj7yHJlyurVChdJNRn9PHxlSdPn0ryZOFbMP7e/Yey8bc/dNwL6Dp8WULdDh7guQQo0Dx3bNgyEiABEohwAvcfZpR9yxqIv0+EV80Ko5hArvp+kjF93AxVnbNwrUyZ84ts/3mCJEqU0Eb+jq6pVbNZd/n6y/flxerlbNsd3/ToN9GIgLEDP3DcFe7Pg8bOkZ+WbZaenV+Xdm/Ut9X31cTv5cTpizLj609t2x48fCQVG3SROeM/1/4sk91/HLbts3/zXOE8svDbAfabbO+Xrf1dxny7UH6Y3NdsO3bqvPQbNSvaBNrC5VvM2muhFWi379yXPsNnSPlShXVsPHcJkh9+3iBYWLtsiYLyXOG8YRJoK9bvkL4jZsrfm2frmnNhn0yaMkUy+fvfUzJ/yXr5aeqXggXTabGDAAVa7BhH9oIESIAE3COg9/O+uoSe972w3xS4dyKWinQCcdgJ+vILFWXUNwtk664DgsWYLVu35X+SJEliqV6ppLXJ6WvvD1pLvHgR/zfg7eNjvEA5smYU3ITbCzQ0xM/fuaD21+1DenWQhw8fm/aOn7lEHjx4JF90f8t8Tpz4PxFqNjz7BVEzcsqPMrBnO8mu56RFPoGV63dKtw7N5Z2WL4f5ZDWfL2UEdXjEmXXyz7q2kmbt+xqR1rZlPWszX2M4AQq0GD6AbD4JkAAJkAAJxDUCmTOmlYpligpC++wF2uqNuzRErrzxqh05cU4Gj50rh4+eliyZ0kmXtk2k8UsBYX/wOiXR8MbOb79i0G3cvk++mbtcELqWLXN6af9GA+OBOnvhqgwZN1f2/31cihbMJa81qhWsZ+q3PX/J06feMmzIu/J292Fy7OR5KZgvh1vDg/NaljplMhOCWSBvdmuT09eFKzZL5gxppU4w3sJNv++XybN/lv4fvS2PtW0TZiwxXiowSJ4siXRo1UCaN6hh6n+gAnH4pB9k0+9/SDz17NSrVVF6dnndsProy0lStUIJebVRTUG5tj2+MnWWLJZf/vjrqIyc/KP8MKVfkHau2/o/maTnv3z1pmlnm1frKsvcphxELNp2+dotyZszcIjew0dPZJSKT4Q9+vr5mWPQ15H9Optjg6vXsRGzF/wqC5Ztkpu370mOrBl03Jto3yrIsAnzJWe2TII2wXZq2OK0+Stk9rje5vOr734p777ZULbtOihHMZY6Hgf/OSnXbt6R5cpvgArjf46dkRk/rJKbt+4az+FLev190f1NSZgg4Bbb2bWVVcd6vI7DvIlf2M5To3JJ2axjdUVZNKxTWT56r6Ut1POXNb/JN+phBUN4UxEOOnXEx5JJeSRKmEA6t3lFvpr0vVCgGZyx4hd9obFiGNkJEiABEiABEohbBBrVed540O6rpwl29fot+d+BI0aE4Qa2c6+vjQCZNKyHvFitnPQeOk0OHj5hyl66csPc7OLD/r+PSXcNeSxRNJ9MGtpDmr5cXf48dFyeevtIp89GS4rkSWWmhiU2q19d+o2cKWfOXzF1OPu1Yt0OeaFqGSlXspDk1flJECD2dubcZeP5g/cPP+NnLLXfHer3/x4/K/VrV3J5HARHz4FTpFXTF82cMMxZQt/OXrgiX37S1sz3gkiBqISNmrJAdu47JL3eb6UC4TXZoMJ14syANqZJlUK27NhvykGIQpj8unm3+fy/P/818/nixQt8W3lAz9Vz0DdGACKMM1HChCa0DwdhLDAm5UsVkRmjA/iayp79QtgmxGXXds1k2qiekiplcjl9/rLZG1y99nXg/Z79/8joqT9J13eaytwJn5u2XLh0zRQ7r69X9Lqx7K7yOX76gvVRjpw4K720jQkSxFdxWVbguU2aJJE0qVtVPtEQ1lzZM0mGtKmke/vmRmwN+qy9rNm0W5au3m7qcHVt4TwIRbUM51mr3t+3X6un4/KOLNfraMfev81usEXoJ7xu343vbUQzHj7g+rSsdrWyAkELAUeLHQQC/yXFjj7ZeuHr6yeXrt4wX6YIO3BmeFKBJ0E0EiABEiABEiCBmEMAnjIYRARstd4Yw8OCOUwQGVev35Z+6jWqUr643ky31CQKmQVeI0dbuHyz5M+dTb78uK2ZTwQvBI7DfLBzF68Zj5FOFJL8ebKbH3hEnBmE4pYdf0rDF7EEvehrZePhQ/iiZbgXwX3Hfz/hu6GGV6dw/pxW9YFe4dXq3m+C9NIQOHi9LEudKrlg7t0LVcoY0fJExRlEG9q5fN3vKuZqyyv1qhoh07xBdfll7W/m0Epli8l+LQcDgyrln5P12wJYHFCxBY+moy1auUWKqbesbIlC4q2JMV5UkQNRCYEEEYMkGwjtxJghOYi9YawgLN9q8ZKU1oQjEL2WBVevVcZ6ffT4iZnnBaENjyS8Ze3Va+iujdH5jAgh7aReqhqVSxnPGLypVSsUF4hWzHUspl4t9OuoCifwPXnmoqne1bXl7Nz99frDQwB49p4v95zx5qEcOBRSLyzCXZEEBvsdDZ7QnBrievTkOcdd/BxDCcTaEEe4g4eOn69PFALEV7o0KWXY5+/a4tJP6B/PB1+MM1++GDt8kQ7WLwm4ivFF9WbXIebLZ7o+1bEMX7xd9RiEU4wbFPETi63z8JUESIAESIAESCB4ArjhfqFKaRVBO9XrVU0Q3ojQMMzrgYcsbeqUGs7237wseMjw0NbRzl+6LuVUIDgawh0TaNKF8dOX2HYhO+Ej9c45s3Vb92pmPm/TDoTfQSBevnZT9qpXz8r0VyBvDhndv4vtcCQJsQSmbWMo3pzXNqZPmzrIEd7qXenSe6x6eDKrOKsVZL+1AZ4hhHpCxCD8D+0vWTS/tdt4Fb+dt0KeaJ8rlikimPMGEbBt90GTtKRlpwEmSQUEGkIlHQ0C96KOxdBx82y7imtiDYQ0nrt01Qgz2w67N3c12Qvu38poIg5nFly9jnPxIHaaqOCElxT9rabC6tP333A762Fyvc6CM4QqzvhhpY5xUcmTM7Nef/GM2MUxrq6t4OrDPlzbGBMYrtnSKsxCsnTqyYNHkBY7CMRagQahNUDdxNUrlTBZbXoPmy7IoGRNHB40Zo4+uckmi6cPkouXr5tYccQT4ymTn8Y6wxAqcejIaRPvi8+zFqzGi+0Pz3zgLxIgARIgARIggWghgFTy3ftOkH0Hj5r/r4f26mjakV5vVm/duaei466kS5PKbDt19pJ6IoJ6mzKmT23mijl2IEO61JJQ7yXmT+5jm0/kWMb+80oVivD0ZM6YzmyGcDqvIgRhjpZAsy8fEe+zZclg+umsrg80NBDzlgaO+U4GfdreWZFn2wKSpaTWEEJkAUSmSau98AQlS5rEhC9iSYKCKjCn6ly93Cr8EN4HgYx7o0caXlfCTthZJwNDzP8b0aeTtcn2irowJs4M4YwQl6fOXJJKTjxzwdXrWB+yfA7t3dF4Uf/465hM0AQs46YvNl5ECG4rvBPHhTahIkTUbO3/EL3uIAJhHw+YbF7xy9W1ZSvgxpsSRfIJQjpDMlzv2TJnCKkY98cQArE2xLGBesTwJA1/5FhnAh40uKJht/QpEZ5ova1hDHALYwIvXNTrt+0NNGyIb8dTERgmhWKyrxVSEaggP5AACZAACZAACUQ5gWoVSwhSjfcaMtWE+lkJOcqVRKr2xComVsjtu/dNoonDR89oiFrJIG1E2NqBw8dlyaqtJvkC/r+ft3idVNC5UbiBH6LeH4Qv4geeMSQmcTSELGKuU4+OLYwQQEglflo1raPH7A0kAhyPDc9nhDfaz2Wy6oKwRCjflOEfmfZCkIRk8C4hHHTpr9vMPCyE7K1YtzMQs0pli5q5UnVrBYSX1tVwO8ydgqcLD8Yd7UWdG/Wrhp6iDNb+gjcIohEPxhEiCcGEJCDwss1ZtCbQ4XjAPv37lZqdcJ3MXbTW/FgFgqvXKmO9wkO5dPU29RRqdk+9XvLoemEQnbCyGjaJeXpYOw5z6hbo8gihsfg65y6Z3kfCY4v5fdvVs3jgUMA8R9Tj6toKzTngWACnYRO+1/HYIZ8PmxbkcAjF8+qtLOQi3DXIAdzg8QSC/jV5fJND10A80cIXA/7whujkTZg1IRRPgCxDbPpf/560PppXpMdF6tLTOqkXT0je0FhoTEK2JiSjUJJEsVbjBmLBD55DwM/vv/kMntMqtiSsBBIl8IqUdN+u2vM4Hq8fV2xi2naNpIrz/wchUx4SN2DdsdbN6tiGEB4WTGvAWlPfL11vwh7bagIGa56Tl12KfXg+IHKQ8bH/qNnGa/Z+26aSVh/sjhvcTQaM/k4qNQwIS4SXqe+HbWznsd4gWQbOiblU9la/dsByALhxd5ZSHeFwrswx4YazcoXz59KQyt3S7vX6gXYjAyMMHj2sCYe5aJkypDFZ/+I5OafVtn7atx79J0mTd/rYjsccNsswDw1rbr1UI2AeVFUVdBA7lcoUs4qIPVs8LEcmzD7Dp8snT7xNBBLm+yFrJOaslSiSVz7RJCawyuUC6rAWP3jvrcZyXbMlzlu83mRahCcJYZiw4Oo1Bex+QTgiSQjGFu8R6tqzyxumBK4HCO5aLT40oZ5Y28zewMVqj227brB4wTuHawVhjhNnLdXFwjOotzGhrairawsF7Ot1PI/5/GwM0SZkG0UIL+YVwqO4RxOHIDTVsk2/7ZfE2hb7TKDWPr7GTAJeOik0Vv9vPWnWz7J7f8BEX6x7gi9yPOVq99EI+X3ZJEmTOsCrhqdl3/20RjYuGmP+ANp0GyaHtnxn5qkhM87Bf07I2h9Hm8UgIdDG65c27NET35g58mx1jCXgo8lv2l14IjsCwtNjbD/YcJEqiUVmZ09s5rlEFY9rp31l/1SugxZVvCPzPEVa+0n+qmF7zpo0cfxIadr+W9dl6/2L8tQ/YKpASCepmDST1MqYLaRiYd6PZGGYl5MxfRrjUQuuIiTwuHbjjhFajt4gzL166u1t6rFuzoOrK6r2IYSzUZvPZYiG8NXW7JERZXgYjX6CW0QYpo7g4XhyFXOIbLI3eNUg8iB+7Q3jYaWqx/YPVTjCWzr42cN2bAuuXuy3DLe68JIhagpRVY4Gj14G7avjuDuWc/UZ94kQk0jh70xYB3dtuarTfjuuY2sRangkvxw9W/as1i9yNdTdokN/Mw8zNMlP7Ovne88jELZvds/rh8sWfdC+mXwgzUy4whdfTTcZhKwvB0yGtQwpeVOkCPpHi4sdYu31Ji/oRNyAOHbrGLzeuu98srB9Gb4ngQgnEKsfq0Q4LY+tEI/H7j1ynmE2shqtD7HVIufmPLLazHqdE/D19Q/z/0FJEwf9/875WUK3tUzaDIIfTzHc1CJCxh2DGHDlgbAe5rpTT1SWwfy6jzu1FMyrR2bBrJn+W0stPO3A+loRaRAtrtrmavuiFVvMvRuWK0AI6QUVUfMnBXj2rLYFV69VBq8Qm8H1CZ6v8BjCaTEnz5UFd225OsbaDhHe8K3eUrRQHomvnl+k3ccaaZZ9PXWhEYVI0U+LPQRivUCzhiqfutQhyO7ee2jS8GL7GV1PA4tdWu+zPJvYazY8+4XUsF3bNTVrXthv53sSIAESIAESIAESiG4CLRrWUAGiGQN1DpIrsRPdbQzL+ZExO2mSxCZvAMJHq+r8MWcPysNSd0w6JlWK5IL11SBQkyRKZAS5tdA3sl1iTh0S3GEOIS32EIi1Ag0r05fS2GusGXH3/gOZoRNN8RTN+uPGehqYdFpcY5rh2t6w/Q/5RJ9COTPEF9NIgARIgARIgARIwNMIwDvUomFNT2tWuNuD8EqsCxbXDcILYtWZISLsDV23jhb7CMRagYasTe9/PlYQtwvDpFRMlLWs/0dtzf6KDTqbSav1a1eypUhFmXh2E4itY/CKL0L80EiABEiABEiABEiABEiABEggognE6iQhWNsCiyEmTZLI5URXeM8w6RQ/YbGLNx6F5TAeQwLhItD9hq/s4PTHcDH0hIOf1yRcE9NHbVjKg0sih2fGF+97fNDkCddAeNpQ8HVfyVg2bBNSs6WPnDlo4ekPjyUBEiABEgggEGs9aOge0p8GN2kTZcI7MRR10EiABEggphDwTeAn6TVaxs0kezGlW3GynfHTIULEdZr2OAmFnSYBEiCBWEAgygQaFn7cte+wtHvj5UBpU2MBQ3aBBEiABGIMgcup78iwMn+Itz+XCIkxg+aioW1SFpaXJbeLvdGz+c4NH3lwy32vXiINXsmQ7b91o6Kn1TwrCZAACXgWgSgTaLv2HZJv5i6X995q5FkE2BoSiIEEMsf3kizx3b8JioFdjBNNxjhGtfmIvxx8el2u+DI8O6rZR/T5WqTIH9FVhru+O1f95M8Z7ntoCzXzV4EW7tOyAhIgARKIVQSiTKAVzJdDMCcMCwVG1MKHsWok2BkSCAWBd7yuSVuv+6E4gkU9kYCXVwptlntrNHli+9kmEghCQJ8b+T7Rn8fuPXzAAsLRYVj7FC1MnFgngkaD4fw3b9/T+6HUkRJVtGrDLp3Ckd5kso6G7kXKKR88fCSJNc18RKaTj6jr4MiJc/Lv8bOChHNhXew6UqCx0hhLIMoEWo1KpaR44bwya8Fq6dW1dYwFxoaTgCcQeO70Kkl87W9PaArbEA4CTzI8J7dLdwhHDTyUBOImgTkL18qUOb/I9p8nmPnmFoU7ui5UzWbdTdbmF6uXszYHee3Rb6JgceGxAz8Isi+8GwaNnSM/LdssPTu/rtM66geqbu+BIzJswnw5evK8ySCNjNHVK5U090Xbdx+UqRpptP2XCbZs0U3b9ZV0aVPJrDGfmXqeqLCr3LirjBvYVWo+XzpQ3daHZWt/lzHfLpQfJve1NsX414ePnkjFBl3MuL78QsUw9efQkdMy88dVMmZAV9vxEXUdZNaFvT8ZOEUuXbkhnd9+xVY/35BAWAlEmUCbNn+F/H3klPmZu2hdkPZOHvah1Kri/MsmSGFuIIE4TuBGhvrim/SlOE4h5nc/fvIEErU5HGM+M/aABEAAN+mjvlkgW3cdCLRG1Lot/5MkurgxRE9w1vuD1i6X0wnuuJD2efv4yJrNeyRH1oyyYv2OQALtn2NnpN1Hw83aXlO++kiwhtXho6dl/IzFcvzUealYpoh8NfF7OXH6ohTIm11u37kvx3R7kouJBPUmTJBAMJ/fR9+XLVHIaVNwzMgpP8rAnu0ku7YhtliSxAmN4MydM0uYu3T95m3ZvvuvQMdH1HWQJnUKGfb5u9Luw+HGi4Z1d2kkEB4CUSbQni//nCQJJpQgfx4GoYdnIHls3CJwZI3+532U2dti+qinLuQnz3UIWKsxpveF7SeBqCSQOWNaFTRFBaF89ov4rt64S+rWLG+8agg7Gzx2rhFBWTKlky5tm0jjl6qYZsLLhHsSy9uxcfs+M0/+3MWrki1zemn/RgNpXLeKnL1wVYaMmyv7/z4uRQvmktca1TLbXfX1tz1/mekcw4a8K293HybH1FOGKR6wcdMXS4ki+WTQp+1th5cvVVjmTvhCnugUkMSaeTpdmpSy589/jEDbd/CIlCyWX86cuyzw/pR+roDAA1esYB6XSwMtXLFZ4M2pY+c9RD2jvvnJCL/UKZPJC1XLSJ8ebWTrzj/lh583yrcjP7G1p0XH/jL4s/ZSrFAe44VDGOb1m3f0vP/qerLZpf/HbWXeknWydcefpo3dO7SQCqWLmONfffdLqVaxuGz6fb9cvnrTiOgm9arJ11N/kuOnL5hx+rhTS0mvHsHT2qcP+0+SS1dviJ+fn4mw6vfR25JP16zdvf8fmfLdL9Lu9fry4y8bjadx2qie6nn8Xtv9lvhl85OOn4yytdl6883wj2WL9mnGD6vk5q27usRSYnlJr4Uvur+pWWv9Zcj4+fLo8RNp3qGfOWREn06ySq8X++tg0Yotxst27cYdKVE0n3yp/c2bK6utfzUql5TN2r8ruoRTwzqV5aP3WhpPLAqU1PKVyxaT7xauMceZg/iLBMJIIMoEGr5Y8EMjARIgARIgARIggfASaFTneRms4un+g0eSInlSuXr9lvxPBcz77zQVzC3q3OtrKZQvp0wa1kN27j0svYdOk9zZMxvRg1A03MDD9v99TLpryGPLV17QUMNWKujOyJ+Hjks99dJ1+my0CrPcMvPrT+XEmYvSb+RMc7wrD8mKdTuMACpXspC5sYcXDaIEhiiizm2Chr95eXnZHmBX0ht8iLDWzerIXhVWFVX8ZEiXWv7357/mHgr9gzB1ZdY8KGs/5vhBCCEcEmLj/KVrsla9jLA7dx+YPlll8YrjHzx8bDZdVEYQI+++2UjebF5HhdZCadlpgBGpI/t1liWrtsmk2T/LnPGfm/JHTpwVhGC+91ZjI6r6jZwlG7btk07a5045M8ugMXP13HtM3zCP7BUVwGVKFJR42v/xM5YYAYVQznv3HxoGV64FiLxUKZLb6kfbUiRLKp92ecNs89ekRxBuELdp1YuVQcVf9/bNBQ/9sQ5un6+mS5ECENY1pXn96jLt+5XyiYaewiDa7a+DnXsPycAxc0wyO3goZ6rQe//zsbJ8zjDjvUT/IPA6tm6ooiyJ9B81SzBe9mK4wYuVZf7S9aZ+/iKB8BCIMoH27bzl8s+xs+aP3HraEp6G81gSIAESIAESIIG4SwCeMgi0Der9avpyNVm9abfxHsErBU/K1eu3Zd7EPibcsEr54rLxt30Czxm8Uva2cPlm9Q5ls3k9rHsUzAk7d/GawLOjk8L0pj+7+YG3rX2rBvZVmPcQilvUs2TNcWqoN+uLV25VL8trxquG8MNMGdIEOc5+QyUVXxNmLjGb/vfnEenRsYURaPDMvfP6y3JAhWN7h3lt9sdjbhtYWObr62fzzmXNlN6ITXuPo1XO1WuTl6vavIy4h4PAGtDzHVMcoujdnqNt4ZfY2FdZoQ+wOYvWSr1aFaVty3rmc60qB23iEyGgzRvWkN1/HJYLl64bsXxIwz0tg+D+ZfZQm3C1tuMVa9wiKguGuYjw8C2aNsBsx7xDCGlwwvinTpVcTurnePHiqVcwtySIH1+qVihujnX8tWzd76YMvIIwJLSDt23/X8dsohgeRKt/v+r1BlFnL9AKqbcU4aoQxhDeNBIIK4EoE2h4AgXX8Tsan4sLGE+HED4QXNhjWDvF40iABEiABJwTiKe565J4JZCs8XUBKlqMJpBUxzEuG27iX9C566s27AwQaBquhrAz3BjDM5I2dUojzixGCFlDSJ2jnVeBUE5FnaMh3DFB/HgyfnqAYMJ+3OA/Ui+RM1u3da8RQwizXLf1f0YgXFYvEDxiEH2Yp3RJQ/+Cs8rlismAr78zHjyER5YpXtAItAkzl8rBwyd0/pmvy/lnqPe8tjl92tS2U8BT9dn7rWTE5B9lwbJNJizxg3bNAoWF2gqH8AZJVeyzbsKL5OPrKxCBCZ1ciiivCy7YakV5eOVg8Ah+0Ge8EdTFi+Q12xDqaFl85R7S/eG+g0dl7LSFMm5QN8mZLZM5FJ64GT+sVN5FJY967by84gVqs1W/s9eLl69rmOJ/4h33qhChCNd0Zrj+4FGzt/TpUgkSmiA0FKGcNBIIKwEnf1JhrSr442pULiVrfxytT5f2yw8aU4wvIGQZaqFu51ZNaseqyazBk+BeEiABEog+AtkePJE5T3PrnIz/boairzU8c3gIpHyoN78B0V/hqSZGH4sHvd37ThDcrGOe1tBeHU1/cHN86849vVG+q/O6Am6UT529ZEIeHTuMVPcQQ46G0MKEqjzmT+7jVir8lSoUMZUjc8Z0z9qQWkMKr5pkIRBoRQvkll837pa2r9UL5F2B6Hn0+KmZywShkS1LBvl23gozdw0ioHD+nEYozlu83syLChA+jq0N+Ixj0W97e1XvsxBOCA8VRBpCPatWKCHxVWx6e/vYFzXvI87z49qDNH/Jeqmgonji0B6GBcIu//r3ZJC2uNoAr9nHAyZrEpYGtgRzEEuzNVP4EL0GmtSrag5FGcvQL39/1997GO8TOlfOsgsq2DA3MK3OC3TXIMySJklk5hK6ewzLkYAzAlEm0HByPBGB+xk/+KJEOtnZC36V735aY/7A3tIY58rlAtzWzhrLbSRAAiRAAuEjkPrhQ8k0bbTEuxPUkxC+mnl0VBN48k4v8c2YJ6pP61Hnq1axhEmY0WvIVCNkrIQc5UoWNoJn6twVOietiezad9jMLcP8IUfDA2TMLVuyaqt64J43KfARIoc5bvCYDRk3zzbnacfev1XU+BpPnX09SBqxR5NbYA6V/Rwx3PR/M3eZ9NWkHN01XLH1+4Ol97Bp0k3nSaVJlcIkMIF3rJ2GL1rLAiDRxNLV26TNq3XNKRCeBw/f+m17dT5XY/vTBnkPMYfMj+ACw7IDSLiB8EiIRyTnMJ4+9QKWLVnQhAciKUdezY64cPkWc4y9l8xsiIRfEJ5nL1yRG5rMA238+dftgURrcKeEFxEp7bNqMpe3ldHtuwFrgiZOmFCSJUtivKeYx4Z5hAcOnVCuZU11mIsGIfz3v6cEcwgd11ND5k/MQUPIbDmdgzZd56ul1kybpRxCYoNrGwReAQ2FjTiRG9zZuC82E4hSgWaBhJsemXnW6ZcNDF9muKg7aFYexIHPm9TH/FFY5flKAiRAAiRAAiRAAo4EkHoeKfex7himTlgGYYS0531HzJTvNWkDbpjhuapbs4Ip4qXrj1kGbwtEDTI+9h8123jN3m/b1HhOxg3uJgNGfyeVGnYxxXHD3vfDNtahttdfN+82oYiY/2Zv9WsHLAeA+WwQYN8M/8iEG9Zr9akpBgGIsMaCmszEskplixqBZl9XhVJFTMIOJKUIzgrnz/X/9s4DPsqiCeNDQu+9S5GO9C5dQEFACFUBKYL0riggRQREURCp+gmoVLEAKkVFUBDpKIJU6b33Dgl882x4j8txd7mES3LJPcPvcm/Zd8t/N2HnndlZWapWOkRAhASqcrde13nBYpVA3R0Ren9InzY2q1ATXQfW+Q2sIwuxraWylAsE77AXXLfu2V+3js1968TJN7KzsoTy2fdtDV7SuLdxI4T7qb0EqGviI2LKF9NXcBuFVA7qaUs2a+Jbgn6Dm+PEzxcYS2SiRAls9zNqdMtGGijk5R4jTXu/+mSoOI4DbGOAvdEgWL82enBnsx0Czh3b54zHYo0qWlAtpRQSeFwC8fRNyUMH4cfNzc3zMD3DL/ur73/TYCGHzRsvLGRtEVRD32RkNmFWES3oS13wOWZoV3URSOMmN9+5deL8Td+pDGviNwR2Tg9gmP040NsxEWY/3vEDkkgnMrSgxf4BZCxo5R8qJRFpUdZ0SSKS3OO0Jw/dkXN7NLZeSNjJvasMUuW+LzkLYa1S1AjWRyFyIQI+uHMNROnYawzh1aHcJXRYVIUAH3fu3jX5uFNSPG3F9Rs3zTol1Cu8tVae5ol0cOms33qgjBzwqtTQcPqWwKKEeRiUFEdB23RpqrHoOd6LynNMP2FFy5AuTbh9E5F6YA0YXCCzZ0lvgoM4PotgLiHq4g1l25kgUiQse66ed/YMrsHFtnXPd2X+tOG20Pyu0vI6CYRHINoUtMlqYoeZHeb3FkE1pf6zT9tC3IZXSV++TwXNl3sn7taNClrc6NuYUNDunD4rAYf/E30rFjcg+nEr7mfOLgly5Y4UgahS0CJVGT7kVQKIHInw9199MkQQuZES9QSuqJvmy6qcPVe1rPRo3yjqC2QJcZ5AtLk4Yk8QmJ+xtwSFBEiABEggZghclAwya21muUnjf8x0gBdLbVD/nhS3i5LnxayZVSwmALdFuBIe0y0CqKBFT0diO4aWQbWkcd0q0VMgS4nzBKJUQfvtz7/NhofYyR2LXikkQAIkQAIxSwA+7QhkdveuZy5oMVtblu6WQLQsUHBbA970QQJwwWxSr5oP1izuVumpArkEHwoJeIuAk1WY3spaTKhXLNDFItDh42aYyDney505kQAJkAAJkAAJkAAJkAAJkEDcIhClFjS8walesaT8qLuzL1y62kRZwsZ/ML/Xr1XRbNoYt3CyNSQQPQTSFrsnCdNzDVH00I66UpJljbq8mTMJkAAJkAAJkEDsJBBtQUKAB/uKLNC9Ln76bYNGQwo2EYagxD2tIWaxz0dsFAYJiY29FvvrPPnSNtl8+2zsb4ift+CJwGQyPH2FaKVw6rTIrDmBcvUaXRyjFXwUFNYkKESKF4ucnyODhERBhzBLEiABEvASgSi1oDnWsbhukojPgB4tzUaJ2BSyk+6/kSVjWgmqU0X3p6hs9uhwfI7nJEACYQmsu3Vafr91POxFnsU6AtUT04QW6zqNFXZL4NaVixJy/aLbNPY34yVMJknTZbK/xGMSIAES8HsC0aqgWbSTJE4k2BgSn8PHTpsd5OcvXSWfzPxBPh7eQ56tWsZKym8SIAESIAESIIFYQuDulbOSdO2E0Eg0HtT5ZrEWIlTQPCDFJCRAAv5EIMb9CnNmzyR9OjaV5V9/JJ+831eyZU7vT/zZVhIgARIgARKIMwTiaYjQeMG3JPD2ZY8+opsVe0NOnjmvUaN9d+8I1C04OMQbTY1wHtFd9pbte2Xx8nURrmdcfwD9780xunLtP3Lo6CmDzRt97Gke+F1bumK9V7oL0d7Xbt7ulbziWibRoqDtP3xCps5ZbHZmtwDOnr/M7Lg+YNRn5npgYIBUrVBcCufPZSXhNwmQAAmQAAmQAAk4JXD12g15Z+yXUqNZX6nV/HUpV7erfr8mi35d6zR9TF28cfO2qdvy1X9FeRVmfvuLzFmw3FZOdJaNQs+evyRdB4yTeyFhg1ht3LJLnqreTl7q8o6tbjhY/9cOcx338FkRDYzCVCAaTzAuMUbve+GlxIVLV6T3kIkScu+eeKOPI5LHjj2HZOT4WS7JOY5Blwn1Bjzqeg6aIPsOcsmGI6doUdAWLPlDvv/5T0mdMrkpf/WGbfLexLlmUP2xfquMmjDbsV48JwESIAESIAESIAGXBD6YMk9+VyvCB4O7yMaln8r3n4+UBlg6cVQj4fiQJE6UQOZOHiwVNCBaVMuuvYdl974jtmKis2wU+v6kuVKlfFHTD7ZK6MGiX9dJ9iwZ5N/dB21WH9wvr3vkLpn1vkn6zf+GyTOVSprjuPij2tPFzTjAPnWPK4uVZ2Hddy1PzqzijT72Rh5WmxzHoHXd2ffTZZ6Slo1qyTB90eINxdVZGbH1WrSsQdt36LjUrFxKYCWDfPX9CilZJJ/MnjRIjhw/Iw1fGSSXr16XVCmSxVaOrDcJkAAJkAAJkEA0Evj73//M3KJM8QKm1Hy6jQ8+lqxa94/MXbhC/vfB69YlafLqUBnxZnvjrfPR/74xFp/zF68I8sqeJaO8O+BV24bDy1ZtkklfLJRTZy5IrSqlpXXT56RQvpwmr6Yd35aOrerJH+u3yX8Hjknxwnnkmroxvv9WJ1tZ46fNV5e2W/JWr1b6InqODOr9sqQunFz+2rZHPvzka9l/6ITOe5IapWRQ79bmuW8XrZTpXy3Rel2WooWelLdfayu5c2RxWubI/h2kQJ4nbOXB5Q0fyI49ByV92lTy2Yf9wpSNNl+4dFXOXbgsm7fu1gl+NhmqZcxSr6ZV+mze3NmkV4cmUrZEQZMP5mgjP54pW7bv07bnkGb1q8sLz1U09xx/HD52Sn7+faMsnR2qcFn3b9++o4HhNsk7/V6R8dO+EygXPdo3MrehrCRMEDoVxberiN6umIXXx+inyuWKyG9rtph+rPNMOY1/UFnGfvq1YG6KmAevdW4u6dKklA1q5Rs/9TspVSy//PjLGkkQP75ZgnPx8lVjlYSL4stNnpVXXnre1P3rH3+XaXOXyAUdP7AEPVutjOlrPIe8pnz5vbzy4vNmzgvlo9PLL2j758usiW+Z592xddVei+lCjYgOxQYCZvbjC3188fI1U6+N/+yWUkXzmXZg7F66ck2GfzRDNm7ZLff1X8G8OWTEG+0lqy4vss8DFrUPp3xl+hNWOjybKX0a+WBIF1MmWIz77Fv5QTklS5pYOrSoK43rVjXjz9kY/GLeTzLvh9/M2MueJb10adNQalcva/Lq9HJ9qdKol2FWQRV2SiiBKFXQ0Hkr1L/0+KlzsvO/Q+YXBMUeO3FGEiVKKPXbDDS1uHPnrjTpMEQS6wDHTuyjB3UOrR1/kgAJOCUQlDSXFIyf0uk9Xow9BAomTBt7KsuakoCPEYC15Wud9KVInkSeLlNECulkM6Xdi97LV64LlljYC6xLUJogJ06fF3j0dGhRz0zaoRhh4v75uP5mW6B+wz8xk/fyJQuZfVwHj54u86cNN8/u2X9E+r/7mQl2VqtKKXkia0Z5Y8Sn0vOVRiYa9S1VSuYuXC5D+7a1pUe5mKj3GTpJqj1dwihfx06elV9WbjJp1m3eIe/o5BkT1lJF88t0nfx3GzhOfpwxyigLjmViYmwvhfPnlEK6TCRB/EBp06y2WlYShikbJ2jz76qsdGxVX1o1rqXt/Uaadx5mFC9MvuerxxOU0hnjB5rtkDq/OcZMzqePfcOwHPLBdCmmyijiBzjK7n1HTdqc2TOHubVy3Va5d+++7otbQvYePKbr09baFLQwCV2cuGMWXh+DGRREKEfIZ8gHn8vyP/6Szq0bSOcnMqmyMlP5bzTKDlxmt+7cL1kypTNKPOqNZThFC+bWcdDMKPPw/oKCCuU3vSp1vdo3ljy5ssqpsxdl0HtTjcLzYoNndBuTG6oA75HTZy8IlMKUyZPJFb2G9kOw1ZQrtjmyZXQ5RvAsXAyh3D1fozxOjaCd9uMafdxeFckWjWqaJUbTv1oqY4Z2lYnTF8ie/Udlwsiepk+W6Fqy8+ouCQXNPg8oeVBqu+t4LqLtx/OHVAG3BGUdOX5a3n69rWzbeUCVu9m6v/HT+uLj0TEI99Yx+nv13sCO5gUKuBzXcW9JiuRJ1epazFh+qaBZVESiVEHDm4PmL1SXsdrRiRImkB7a0f/p4Ozx1ngZ0b+1lFIrGqSRKmc9dJCX1bdgCTUdhQRIwD2Bl9ZskoD9XFjrnpLv372fPovcfSn076Dv15Y1JAHfItBFJ9mwXMz+bplOQpeYyuGtfL+uL0lWnWR7IvVqVTAKEdKmSplMeg6eYCb03y5eKYXVagBF6a5aC2qqEoZrmFhmU1c9yEdvd5Ma6h1kycTPF5rlHJjUYr0ZvIaerVraum2+Q3Rt1m19KY05UZaM6YxCY0Wu/mHZGjPBhQULkiFdamms86Mt/+6VcqokQhzLNBcf/MioFo5M6VNLfLXgVCpbxP5WmOOGdSqpBaOBubZr7xGjoAzr186co14d+43RNgfLhr93ytETZ2VI3zYiaunKkyub+WCdWHu1mDjKXrUk2lv0rPsIGAIlFgpjvZoV5NOZP8o/ui9uCd12yRNxx8yT5wdr/aFkQ2boGr3a1ctJ2+a1zXn1irqnqCoMljUKY2Cs9isk1xNZ5Bu1ko1WF1pLIf105iK1gP5nrD811aqKFwDY4/fMuUtm/ByweyGQPFkS+f6Ld22KMhQeS9yxhXLtaozgeVjPYK1D/q4EfYxxCLl2/aZaQUPXjN28ddtYLFMkS2qUJcv67JgPLGOwisFiCCmtVkX7wCDgNO6dHuZelXLF1JK42PQpxqnjGISVE5ZS1BcWWssKbR5+8KPAk08Ixg/lIYEoVdBSp0ou+GAz6tffmWLM9HhblTlDWqldraxRxo6qNQ2aODrM+qP3sHo8IgEScEYAyln8HaFvXZ3d57XYQSC4MLcUiR09xVr6IgFM+Lq3C5JubRsaiwLe1MP6Aze6yHjiwBICj5596noIxQTWpncfTGzR/iIFchtLiTVXSeYwQW5av6q6s/0m3bRO3//0pwSpK53jS+f4at16s1sLGT35K+PyhQkrXl5DSTuh3kbFCuWxoc6v7ppQmOBiaYljmdb1yH4nTZIozNqfpEkSS3BIiEApwvwsviqZ46fOt2UfPzBQbqpFypkgfaYMacLcgkvdao01AMveW2phgiCPRcvWeqyguWMWpjAPTtBe3QPClhLtRT87k9C0uPMwfRJ9HkoOBO6KUEzKliikylwmVUICwrCEgm5ZMc0Ddj/csXXXXozPJarwfjyip11u7g9habXq3O7FOgLLcFD7wUahhBGlW9ugMOP0ii45unHzlpRU10hPBPVFO60yHJ95uvRTxtLcS4OaIG1lfXnwRreXVOl9aGlNlzalbFKXW8pDAlGqoFnFwGTZ4LlK8r9Zi9S1MYGa9dvZBgNcAKCJ59U3M94W+MieOXfR5J8sqfM3DafVLI0/8o6uAt6uC/MjAa8SiJ9A7uuHEssJsA9jeQey+jFJAJYB/P+Nt/OwcOCzV6PB/bE+dB1WoCoCd9WVzFFcBWmANQli3NfUhS1zxrQRUvSC6lSRCdMWqIXjT1mv1iesPXMmTetX0zlRRdmhSz+wLmeAukpWKlvUlLtf10VZguUhsKSkSZ3CuhTuN9p2T9cMeUPAIYGuC5s9eZBxsQwvz2y6tgiWJHuB+yaUoDLFCtguV9QJ+s/qVjigZ0uP8sWDrphFtI/VFGirR2QOrKehjHwxb6mM7P+qUT6Q12vDJnucZXhsXbUXgfXgEljuwRpBjwt8kDC/Wqp+UKsewvP/ufFf4+EGAwmsipbATThHtkxy8PBJm+XRuuf62yIDY2vYMYiXFFjb+XqX5rrWc69MmD5fPta1fpYFDnliXaSnVm/XdYhbd6JFQcNbBPyh6tWhsVHQsIDSkuYvPCMvNqhhCyBiXX/c71nq8oA1cPjjBn9smF2xEBZRhCAwS/d462PzlgznMLuP0AW3WKQK03ur7iOlokaXmTrmDdw2goWP3fUZvOnChtoUEogpAteebSPBTzeLqeJZrpcIxNe1M6GrRLyUIbMhAT8i8ELbgcYFq0alUka5wfqhJSvWCc4hpYrlM8EwELAh9xOZ1V1tpbmOtUiWnDx9QSeHV+TYyXNmzRfWV8EKhMBmWH+EvHB89sIlYxVrqFEisV7HmSDQBNbFDR83wwRCe1Ij7DkKAqIheAQsGXDxw0R52arNxq0S63CwBg3ukaXVtRLbEyF4GgKQeCpYA/Td4j+MZxKsIHCTjKyULV7QWLvgHveGuo1CsGfV3bshAtdQR8Hkf62uo7OXxRpavk6NcmZybl3HPlrYFmHNxu26Fq+4WY+Fe1iXBeXSMVCIO2ae9LFVrje/AzUwR1K1TJ1U6xvWm2HeuHXHfuMK60k57thW1iiYrsYI3BsbPV/FKEGelOOYZrJamKEgw1L7bNVExuJsrV2zT4tInBh/wSHok/uC0PlQKj0RxzGI30tY5eo8U16qlCtqArBAabeXfbr8Cb97lIcEHmpKD69F2RHedDmKFZ3I8frjnsMiNnpwZ6Nk4Q9w76ETdYAts73RQhSbJ3Nkle+mDjduBW16jTKDBm8trLdPm9QvGYsxEbgE8rm+LYHY/3E3F/iDBKKZwLz1+WTf/tCoqNFcNIvzIoG8ee5JG/1QSIAEIk6gesWSMk3Xnn30v2/Nw3gZXFdftvbv3sKcY41Xk3pVpfMbWFMVYiIx4oa9BQ3rh6o17m0moYho99Gw0PVHyAeBGAa9P1Vev33X/L+PkOaIVGfl8dBmYC6ZH5hD/PrHZg26Ue3hxdAHtFwRTOxhXZs9/1fz8hjukkP6tDFWMih/23YdMPtb4RF4F2EeYwU+Qb2dlWlfUI3KpY0Fr1zdLibYxfKvx5r1YygbEmAdhJ4aFvY8Hlw2X7DcwZVu2JgvpXy9ruYaFMbBfUIjTtqnxTH4/av1h/segqbAdRAWkz4dm4ZJin5BZEEo0wjv3uH1D819BCuZoOVhbZe9uGOGdO76ODxmwGGPJEDdFN2JyU8fgFUIroFwc5z4+QKjtMNDzF6c5WX1nzu2rtoLg8M63Tfu7dfb2RcTeqx1strh2Mf2Cu/lqzekbe/3zDiAkoQ1kohqacQuDwRVQaTPWd/9avqyaMEnjZXLKthp2x5UwHEMDtU1gAgSMvTDL4wRBNFJsU7UEljCV2lAFhhsKA8JxFNl4+GrpIfX49zRexPnGHMu9tu4qKbUykE9TaQma+EoojNhg0WE40W43dY9R5m3FNhZHWZY/NHEH3nsY4K3CeMf+P+eOH8zzrFig3yfwMw5AVTQfL+bwq2hUdBaRa+Cduq0yKw5gfrW15oqhFtNJvBRAk2CQqR4scj9F5413aMvTL3RzGunj4qc3iXx7j3qWugs/+B0eSXVE/md3fLoGqYwCIMOKwBcEu09dKwMLmnIcWg21l6s1nWsxUmpYe77dXnJWLCcuRLihe1pXSqRTCezlqJkPf8437C6wE0OgT0cBW1B6H+EI7efXDumc3eOyXUKfSmOiNneEDC8c/eusci5UuhQDjZPTpw4YYRcQz2tnztmrvrY07wjmw7h6MH6cfrKFVvH9n42e5EJjz9NI2o+jsBSeVbHdGZVlPFSw5kgSIz97xIij8K1EltUeCr2YxC/p5hjw3jiuOQI7o7YsHzulCGRHu+e1ik2pYtWC1pMgcEf2A1/77JFF8IfW0hO9bG1BL7r/+4+YJ2ab+x10UgXUsIFAb7GLwXVNGvaoO1bwimORYLf0UUgctOx6Kody4kMAf4diQw1PgMCvjZ2kmfSfbnwiSaBspA2dUrzcVUkgpW5EwSDeBgQImxKKEiw+HhbMNnFx5mETmLDuoA5S+fumqfuaO7ysL8XHkMr7cCereSFtm+ZiH+wQnpT3DHztH7erA/ywrhBWPzHEVd1d2zvwSMnpaVujfC4gqU8VqAbV3lhPz4sFYKXG2I1YD0k9i6OiNiPQfyeOnsZgcA+cJ+coxu5R/ZlRETqFJvS+oWC9sGUeWa/hjFvh5ro4QsLsY+ug+Nr1x4qXrgPd4aq6hMOd8htu/arWb+NulJ8g1s2yZT28f6I2jLiAQl4SODO3Xs6KfPs7bSHWTJZDBHA5DptioTq9uH8LWZUVOvy5btRkS3zjAECCeLHk0xpEZWOEhkCTdX9ESHpKd4jACvmpFG9zX633suVOYHAe3aboEc1EcRawBYW8DiDolVJ145hjaW3BcFBxqpbsbPQ+94uK7blF+f/Mpndy79fYVwSrUiRlpsC/HktwaaSyXXBvqNgrw+4O77Y8Bmng/PUhVuOj/CcBKKcwH2Jvgl9lDfGjwuANfTCVechq6MKi3pPqQRGVfbMNxoJ3A2+L5H9PyiqXByjsfmPXVQFDf9N8T4BLB2xlo94P3fmGB0EEFwGwUiiWrCJN8U5gTitoGHh5pdf/6xvc/pIZdX+Lcn0wOf7sO6Kbu3ZgWPsz+Yo2KSy+ytB0lC3CaCQAAmQAAmQAAmQAAmQAAmQQFQSiLOv4UdNmGNChA7r185EoDl87LTgc1stZVgIjF3R4feKBZ77dN+U5av/tkV4cgSOSD3h+es6PsNzEiABEiABEiABEiABEiABEogogThrQdu2c5+EhNwzG0DaQ/liXP/QPdH6tpVuA8cJQtEiuszzNcrbNhtE+oAA50uvrRCr9nnymARIgARIgARIgARIgARIgAS8QcBvwuy7gnVCI9M4RspxldbZdYbZd0aF16KaAMPsRzXh6MmfYfajh3NcLcUXw+yfuBksJ26FhN1cyk0HpNLlkPk0UA6FBEiABEjgIYE4a0F72ET3R1kzp3efgHdJgARIgARIgAQ8InDwZoj0OHVXVEXzSAakDVQFzaOkTEQCJEACfkMgzq5B85seZENJgARIgARIwEcIYNv16xqe9JweePK5F0c2dgwODtHNssNu1eONLomqfF3V7dSZC2YPM1f3Y9P1fYeOy6p1/3hc5aMnzsjXP/4um/7ZLX9t2yNbd+zz+FlPEkZHXy5Zvl62bN9rqhORNvz4yxqz4bYn7YgtafD7CObOJKJjwz6P1Ru2mdgVuPbbn3/L2s3b7W977ZgKmtdQMiMSIAESIAESIIHoJjB83Ax5qno7wbY60SV9hk6SXXsP24pb9OtaXdPe1axpt10M52DGN79I+Xpd5Y7dlj945LLu1VqiVgdZsfov8SRfx7qEU6zb2zu1TcPHzXSbBuv7qzXubZgfP3nWbVpv3TSbGS9YHqHs/tz4r0z+8nuPnpm7cLkEvTJYlv+xWdZs2i7f/LhSFv70p0fPukrk2C+e9KWrvDy5/oMqWR9M+Uoyaoh8SETaMGj0NNl/+IQnxcSKNAgAiN/H5fo75EwiMjYcn58y4wdZtT5U8cdecT0HTbApbI5pH+ecCtrj0OOzJEACJEACJEACMUbgbnCw/Pz7RsmeJYNRZqKrIn+s3yrnL16xFVft6eIyd/JgQSAxTwV7QF3XjQlXaV72smzlJkmsE78q5YuJJ/k61sU+r6g4XvfXDrly7YbZwHjx8nVRUcQjeUIZ3r3vyCPXvXVh8a/rpGeHxjJ1zBvSp2NTr2Tr2C+e9GVkC750+ZpRzob0bcOo4woxcaIE5vexQunCkUXq0XNPl3lKWjaqJcPGfhmhlzOeZO73a9A8gcQ0JEACJEACJEACvkcAb8JhgRo1sqO06TVK9h44JvmezG6r6Pc//ymf6BtvuO49VSCX3NKtdj4d/Zpk1P1Qjxw/IyM/nqkuYfukUL4c0qx+dXnhuYrm2Y/+941c1EnvBVXCNqrLW6mi+czEvVC+nDJqwmy5rWUO/2iGJE+WRBrUriRFCuSW8dPmy6yJb5nn4S43etJc2aJucokTJpRK5YrI8Dfa2+qFA+zDWk43dYZb2rNVy9juLV2xXp6rVkYSJkwgB3YdCJNv045vS8dW9eSP9dvkP21rricyP1KX3Hpt7sIV8r8PXrfl2eTVoTLizfZSOH8u48Y3be4S0zZYAJ7Vst7q1UoSxPdsSnDlBAkAAEAASURBVAhL0DMVS0iObBkFik3n1g1s5cBy8aFacaA0h9y7p1xzCvae/WBIF5Nm2apNMumLhaY/alUpLa2bPmfS4CbaVrVCMfl9zRY5ffai1KtVQfp2ai4bt+ySlWtDLRY79hw0iuFnH/azlWkdoG8/nDLPuGjCczZdmpSSLGli67bL/v7wk3myTTmfvXBZ4Oo3rN8rtmesA3f1hqXzk5k/Cvo8a6Z00v6luvLv7gOP9IvjGNmz/6iMUGvlzv8OSeaMaaVr24bywrPhjz+rTvbf3yz63XAGU2dy6OgpgUXv5Jnzck/7BXWBMvdkzqy25D+t2CDvfjxLTpw+p/1QXMdLB8MPLxHe17H825q/JUBfQNSuXk76dX1RlaCExoXU3VjD71EiHceBgYEC10CMtXbN69jKxIGruuXOkUXa9XlfmtarZvu9xAuZV/qMlldb1pPqOga/XbRSpn+1RM6evyxFCz0pb7/WVvBcQECA/p7OkUG9X5bUhZOb3/vIjA3U7+CRk0YBwxjJlD61XL5yHZdt0unl+lKlUS/ZoOO0QinvKYSe/TbaqsEDbxFIcHG/JD6xyVvZMZ8YInA/QTK5ma2chCTLFEM1YLEkQAIk4L8EFi1TZaFSSbO3KSZmUB5e69zcAMFaokHvT5NWjWuZrXS27TxgrAx37gYLPp3fHGOUg+lj3zDuXUM+mC7FCueRnNkz6ST1vFEU2r/0vLRoVNPsqzr9q6UyZmhXafR8FXUf+12a1q+mSl9uyabBxg7oJG7vwWOmXCiMHft9KGlTp5ThOtm/p1v5TFeFyJnUr/W0jFAl8dr1m0bZO3Puomzauke6tQsyyWGpsvLFhT37j0j/dz8z2wLVqlJKCuTJIb+q0mNfl2079z/irgbrEybakPSquPRq31jy5Moqp1QRGvTeVCmYN4e82OAZc9/dj5u3bpt1N++/1UkVtEzKZYlRMKD4QTAh/00VrO6vNJIiBXPr5HmpHDp2ytzDmq5+wz8x/VNeFdOvf/hdBo+eLvOnDTf30Tbkj8l30iSJZeiHn0t5nfAW04l3Ic0/QfxAadOstlEMzAMOPyZ+vkB+XrlRXlUFtsRT+WTGNz9r+y6YVO76u8FzlUx/NtTvkqqIQ/G0F3f1xnqvXkMmSnNl1797C2VxWP7RdqI/3I0RKJNd+o+V/E8+IZNG9ZZ1m3eabaFyKlOMQXfjz75u1jH6F9tFuZL4yq6BvnxA+6Bk4WXCyPGz5fOP3rQ9sm3XflX+68uZ8xfls9mLjaLb7IXqRumF1bR/txYSHBIiE6YvMErXG91eMsqKo2uk/VhDO376bYM8Xfopo3zjd8tR3NUtb65sMnv+rzYFbbW+mIA1tUzxAspsh7yjL0mgIJUqmt/8jmH7rB9njDIvGzCerDEf2bGRNXM6syVXyuTJ5IPBnU2+cKm2F0SCh7Ub7aaCZk8mlh7fDUkuwSmcv+mIpU3yz2qrN0vw3cSikaIpJEACJEAC0UgASg0sKx8N625KrVezgny3eJVaXZoZV0Osycmv1rS3er1s7mfJmM4oaDjZ8PdOtXicNVYETazKSjbzgTWkfYu6Jn3DOpWMooETlDVSrQsQWIXwhh5KSaWyRcw1KGiWYDKLvCeN6iOYYELsLWRWOnzDUgYFDWtlgupUlqU6mYXFCRNQV/LR292kRuVSttuOdYGC5k5qqpUFk2ooHmfOXZJUKZPJAQ/XH63QoAiwhmBCCgtf3tzZjFJsKWhg3kH5vdzkWVOF0sXy24KOfLt4pRRWdphM39XgDTVVwcQ1rGPLpi6qkKFqAYHyBsHEHpNwWIVguYivFj6Lt0ng8GPBkj9MuR1a1DN3YMWCNRISXn/DeggrqrP83dUbSlgetULBcgMpW6Kg+cYPx35xHCNgP2viIOOeW7FMEVnx518CflDQIK7Gn7np8APWVIwfVwIX4Mb1qhoOx0+eE1hOd6jlzl7692hpY3/42GlZr78jUDR/XLZGfw+CjKUY6Y9pfyGYChQ0TwQvIUarcuNK3NUN5c/74TezxgtjbaFaxJ+vUc68zPhB61U4f07p1aGJyTqDrr1r3GGIbPl3r7FM25cX2bGB33VY2n/44l0z1pHn/2Yvss/aHBdQRRvWe28KLWjepBmBvM4eySx7vw79wx2Bx5jUxwgkSHFfCncIkWSha3J9rHasDgmQAAnEXQLLVm02bmSYhMMFDRNeWEw2qwUKE2W4c5Uoks8pALijxQ8MkPFT59vux1fF46ZaNpwJXOVg3fFEjukEGEqPpZy5ewYuknAXXKJruYyCpm2Ba5+7tWzJ9JnHEVhPps1drIwKqYtkJi0rwOP1M7BYwmUNa24gd+4EqxK0Qfp1eclEsbxx85ax0pibDj+gtMKiAjc6S+BqByuepaBZ1/ENNp4yR2AVWBuhEDqTiPa3fR7u6o2+Lu1GmbbPx/74pHJIkyqFUc6s63DRw5h1JuGNv2M6ntOlSeXsUXMN1uQeg8Yb5R+WTQhcHV0JXB/nalCWC5eumt+xYoVClUakRz3/N2uR3Hbxu+KYZ7JkiR0vhTl3VzcoSHBNXvjTaunQsq78sW6rzJww0DyPfYzt64WXMRibcGe2l8cZG0dVGU2VQn+XVTl0J+nSplTL9253SSJ8jwpahJHxARIgARIgARIggZgmgAAVJZ7Kq2u50pqqYIJ67OQZY9GBgla04JMuQ6WnT5tKEiSIL7MnD/J47ZV9e6FA3VfXRWeCvLFOBe6KWOsWnmDdW6/BEzS0+3+yY88hebf/q+E9Eua+Y11g4bqrLpyOgnRQeL6Yt1RGahkNde0c5LVhkx2TOj1HUBRYB+EqlyZVSpMGyuWMb38x1hZYn+D2ePDwSZslxj4jcMFaq9GDXFtT7NPbH6Pu7hQKTKIzqpXtpMPk3MrjcfrbXb0zpEvl0nLi2C9WXfCN9XEXL19VBeiKcYXFNax1gstjZAR7+iI/VwI3wbKqSE58t7dR/n/RQDSwMLqS/3R9XDrtL3AN1BcZ+w+dsFkHYW2FC2oiXYPmbqy5ytvxenh1a1q/ukycPt8wgxtzcf2dh6Bf9utWCpYcV4UNa0PTpA67seLjjA24sELxt1yQrbIcv6HIYv2hN4VRHL1Jk3mRAAmQAAmQAAlEOQEEkUDwiN6vNpHXuzS3fVoE1VJr2mYTOARueH+ruxOCBcDyM3DUZ7Z6lS1eUC1ogcZtEZMvfGCFQ8AOTwSuVdhnCmubMMm2F1hxMIH9eOp3xiJy9vwlGT15rn2SMMeVyxUVrGPpP/JTXVP2RJggJ2ESujhxrEupYvnMnlYIWgAlcdLnC82TUCgD1TUzqVoDYcG5qhNPBG7YusO9S6RVLFwOU6dMLu/0a2/j/aauuyquLnmLde0fpEr5oma93uz5ywSh8fGxpKa6ZSIPKAfYnwrWIgRwgSUkPEEbobxiTRF4OhOsc/pm0UqzRmn77oOCaJiWPE5/u6s3gmls3blP5i9ZZQJRIJDErO+WmWId+8WqC75LFyugYySRfDpzkVy6cs0EVcH6NQRJiYxg3NivVXTMA9ZIWJKgZMO9FRYpKJD2ckTdGrGFAvaOQ2TRZyqWVLfSQIH75YKf/hDsHYZ1VouWrbPV091Ys8/b3XF4datXs7x5sTBB1xjC5dES/H4jCA/cgy+qgjR1zmKjUGI8OkpkxwZe8qCfJn+50IxXWOuhSDvKPl1/ml/7wJtCBc2bNJkXCZAACZAACZBAlBP46fcN5g2641otrE+BsgXFA5EXu7RpoOuYthsrj7VOCtHn8Jb94xE9VcnbbfYiw35kw8Z8qZPW0KojkIK9YD2RvbQIqikLlq6Wks++agJh4J71BN7sf6hRC9di/VTz16V6kz4aOMK1EoT1Twi5D+sP1us4ipWvKUPrZX+Oa451wVq7JrreqPMbY+SZpn1tE3dMyLFurFvbIFPnCvW7mT3PEmlIcntxzN+6t0Qnp6gnLCr2UlfX/mGSDJe3Ti+/oBaOPKqk/GoiTWKCmyhhQpMc6VD2oPenSgnlBjZQ2GCFgaB+9mWb8wf9UKNyaVNuubpdpEW3ESa94w+se4OrH6JBvtL3fbG3b4bX3yjYXmGJF/CwJu7qDSskApcgGmPp2p1MJNGbt0LdZB37xbTxQaUxRkYN7Khrzv6USg16mOApbTWf56qVNSnCG3+ObUewGLia2ot9GxAt85xGqcT+dc2Uj6PrKNo+buq3Ulz33+s28GO1thWUts2eM9kN6dNarZf3pWG7QYJooHC3REAUiLuxhvuO7cA1RwmvbsmSJtF1ZxVMXrDeWgL2CNjTW4O0VA7qKb/qHnZY65ZSrX5GMJ4edGNkxwYU1K5tGprxjPGKFwpJEifU4fJwfODvzSp1vSyofeBNiadvVOzHsDfz9ou8Tpy/Gal2nv07nq5BC/2jFKkM+JBPELCtQcsSvdWZOSdA9u0P+59k9NaApXmDQN4896RNq3veyMrjPE6dFpk1J1Dfnj/8D8bjh5nQpwg0CQqR4sUi91941nSPt47JFYhtl27Luhv35K6H1SqROJ5UTud+jYqrsjy5DouApVBAGXh7zBeycemnYR7FHlJ37t4VBBmwn6SHSeTkBHmfv3jZ7XOw9kAhhIUsKsVZXdAuzCNh9XIUhMPHhD17lvQmmIXj/cieIwy6fbh+hHZH2xHi3xK4Kp5Wy14ytTLaJtPWzXC+UecUag2Ce50zgWUOrm6IvmdfD/u0ke1vd/VGuxHqHYpXQnWdtcRZv1j38I37CLqBsQdLTWQFVtz6rQfKyAGvSg2NaupMMN0/cvy0lpXGaVm4D2sm9uCDC6ajwBqL3w/U1VHcjTXHtM7Ow6ubs2esa7CqwjIY3lh+nLEBJeyKWiDhSuoosJSvV9ffuVOGePV36eEociyR5yRAAiRAAiRAAiQQAQLFUieSYo/O3yKQg/eSYtJa7+UBJkR7oFpEEIwA+2o5SupUjyowjmmcnUPxC2+NmbPJrLO8Hveas7q4axeUAcdw8o9bBzyPfang4oe1QnBDhbI0e9KgMFnDGgnLS2QECpA7gcXDWSh3+2fccbFP53jsrt5QBp2tQXLWL/b54n549bVP7+oYWzpgewnszYdolM74QrnKmT2zqyyM8uUsWIv1gLuxHlmmVt7h1c1K5+wbFj18wpPHGRtww8THUeBmDTfeObpJvaOV3TFtRM+poEWUGNOTAAmQAAmQAAn4PAHsXTRcLTdQErBZNCawiApHiToC2E4AIdyxJgjKVCVdX+fMGhN1NfDfnOHWCpe+Yxot05mC5r9koq7lCA4ydli3KPm7QgUt6vrNbc4JUt+X9KVC3KbhTd8nEE+9VO8HwkWN7oa+31usIQmQgD8RwBtzV/uP+ROH6GwrLIZYF0SJfgKwQjWpVy36C/bjErEmM6qEClpUkQ0n391ZTsnsGv+Fk4q3fZ1AAt0/pkvKIlJAfMSnx9eBsX4kQAIkQAIkQAIkQAJuCVBBc4sn6m6eCLkuC68fiLoCmHO0EMgYmEQ6piwcLWWxEBIgARIgARIgARIggbhPgH5Zcb+P2UISIAESIAESIAESIAESIIFYQoAWtFjSUawmCZAACZAACfg6gVsXrkmIfjyVeEkTSdKsaTxNznQkQAIk4BcEqKDFUDdnC0gqLyZ7MoZKZ7HeIpBAg4Mkvsf9pLzFk/mQAAnEbgJ3T1+WpF/8IrqzrUcNudlQN56lguYRKyYiARLwHwJU0GKoryscOyNVVq+OodJZrLcI3A+IL3drZ+cEw1tAmQ8JkECsJhBPN7uNd+uOBNy+61k7NH1skOs3bkoiDdWPyJB/bdsj8QMDpfhTeWND1WNdHW/dviM//75RnqtW1umGylaDtmzfa7ZQqF/raetSmO/VG7aZcPN5c2cT5IlXqa42uA7zoIuT6O73fYeOy3HdxLra0yWc1ujHX9ZIxbJFzHYGThN46aJjPRYtWysVShc2G1bb/148TnG//fm3bpCdUCqWKfI42cSpZ6mgxVB3xrtzSwJOHI6h0lms1wjo5pTBGto2dkwxvNZqZkQCJEACMU7gvYlzZPb8Xx+px8fDe3g1vP6Nm7elXN2uMvbtboKw2t/8uNLs9eVOQcMGtq/0HS1FC+aWeZ++/UgdXV3ApreBqvy1alzLVRKPrj/TtI+cOXfJpE2RPKmULJJPhr7WJlbsj3Xp8jUZ9P40KVO8gCpoGZy29+z5S9J1wDh5q2crp/dxccqMH6RWlVICBa33kIlG2Rv3Tg+X6cO74Um/h5dHRO7/ufFfWbpivUsFbdDoaTJt7JtRrqA51mPIB9Plk/df082hk4T5vYhI2xzTYu+8HoPGy9f6u4L+oohQQYuhUXAmRWE5XfO9GCqdxXqLgL5QlQyByRhk31tAmQ8JkAAJRIBAscJ5ZMQb7cM8kSVTujDnj3uSOFECmTt5sOR8IrPHWS36dZ1kz5JB/t19UA4dPSW5PHx2197DaqV7/KkZDJM92zeW52uUl1NnL8jIcTNl0HvT5PNx/T1ugy8nfH/SXKlSvqg0qF3Jo2oO6NFSAgK4HMEjWB4miszvhausny7zlLRsVEuGjf1SZk18SzfcZl89/l8BV7R53S2B45dTyvzlussxJVYTSJ78vrRpxQ3HY3UnsvIkQAKxlgDevLt6496049tSuVwR+W3NFjl15oKxfjWsXVnGfvq1wG0Lm1i/1rm5pEuT0ihRfYZOkpNnzuvyuXtSpEBuGdK3jTyZM6tO7ANk1IQ5Mqj3y5K6cPJwWd1Wd7plqzbJO/1ekfHTvpPFqqz1aN/IPHfnbrC81OUd+WBIF8mbK9RSMPbTbyRliqSSL3d2Wbn2H5Nux56DxjLy2Yf95PqNWwKF5Lc1f0uATlxrVy8n/bq+KIkT6RtCN5JW25UzeybzaVy3qnw+b6kt9ZHjZ2TkxzNly/Z9UihfDmlWv7q88JyuB1T5Yt5PMu+H3+TCpauqZKaXLm0aapll5aP/fSOwXJ2/eEX+/vc/vZdR3h3wqjxVIJd5bs/+ozJCFcGd/x2SzBnTSte2DeWFZ0PzRF9UrVBMfte+OH32otSrVUH6dmpuc2Fc9OtamfzFQlUmL0rucJTZw8dOGRfIpbPfN+VaPw4eOWkm+Nt2HZBM6VPL5SvXrVvyg7oDgleXNg3k0pVrMvyjGbJxy271frkvBfPmMEp+1szpBfUsX6qQwD3yxKlzqgQWk5H9OxhrkS0zPYDS7Wq8IN3RE2dktPbZlh37JLG+ya2k43C4vkhwxx1umB9OmWesZvDKwbhMljQxsnMpcA18Xy3Jjkzd1W+DWncnTJtvLJTggjI6tKgrGCMQT+vh+HuB8XFRrZ8XdHxs/Ge3lCqaT/p0bKrjK6fJd9/B4zJ83Azz0iJzhjSSNnVKafZCdQmqU9nc7/RyfanSqJegfhVKcfsihtk3w4I/SIAESIAESIAEYhuBwzpR/vCTeWE+/x04apqxZ/8RWbH6b+nYsr4M7t1afvh5jfRUNyooOB+qgrT+r53yy8qNJi3WljVQBeXT0a/JtDFvmDf4I8fPtuFAXlCUPJGV67aqkndfqlcsIXVrVpDFy9faHruvyh8UmZu3btuuHT911kywC+fPKYXy5xJYBV/v8qJ0bt3ApMGkfd1fO6R/txaq1DST5av/konTF9ieD+/gvprTsF6rdLECJimUxM5vjpHkyZLI9LFvSKPnqwjc1g4fO61Kyy4Zowps93ZBMnPCQDNpxzooyInT542yW6Z4QVU+2xuLFJRdCCb1XfqPNZP9SaN6S83KpWXAu5/Jtp37zX3w+2XlJmnTrLa8/Xo7+VHXMa3dvN3cQxqkRb5gj/q4k937jppJf87sDy2ad4ODpdvAcXJL1z9+MLizDOjRSpIkSWTL5qTWHUo6BOzQBxNG9pTxw3tKjmyZ5PylK+Ye6rlu8w7p2Kq+UTDX6jEUbEdxN17u3LkrHft9aBTc4aqkD+jZUv7T8txxR/4TP18gP+t4fLVVPZk8qo/k0ZcD4QncD50xdVe/q9duyD+qOB45flr7oq1Z6zdqwmxBvSERqYf97wXGB1wyMY7HvdPdjInpX4W+FMD46Kr9g7E4blh37Z+W5mXI+YuXbU2EKy4U4t37jtiu+fMBLWj+3PtsOwmQAAmQAAnEYgKYmMN6YC937gTbTgerFax8yULmfIau74Jy1rZ5bXNeveI22bx1j3Gtgjti43pVZcPfOzUwwzmzxmyHWoIiI4uXrzNrn2CxqacK2qczfzQT4hLhBBXJmD6NsfzAxbGSBn+AYEL747I10v2VIJs73zFVmL7+8Xd5o9tL8tnsxXLuQugkN2HC+NJPFTtLvl20UtZu2i771VoIGT+ip/lGG4+eOGsshKqJSh615OGzQhW/PLmyGuUUyhssk5b1wzyoP2D5gqUDkiplMuk5eILAYggFEmveZk0cZFw7EexhxZ9/GcsVFE7I0Nfa2vrip982GEWoVpXSsmDpankyRxZjqUI69MXoyV/h0KnsPXBMCuR5Isy9zf/sMdapH75412ZR/d/sRWHSWCdQjhMmiC8pkqnV8snsxpJk3cN3f1UerDFz8MgpY9F5sWEN+ySmjq7GC1iA7yRVsiwrKay1sMq54t5eLVgLlvwhLzd5Vq1Z9UxZ/+4+YBSeMAU7nLhiGt54Rt9Z6/GqlCsm0+YuNmO0nP6uRKYeVrUa1qmkYzXUWnzt+k210s4ytzapRQ0Wyc8+eF1ya19DMmV4tH8KPPmEoH8pXIPGMUACJEACJEACJBBLCeRVt8AxQ7t6VPukxqIC57FQSZoksbEK4QwTSAQpyKRKUhEN7AGBq2NEBe5zq9dvNZawt96bah5HxEdEvgtPQXNWFtwMb6tlo1ihUCUHaYoWelL+N2uRUYwua3nnLoQGA0mUMEGYLDKpGxncF6GQPVOppHHXRAK438UPDJDxU+fb0qOON1XRerr0U9JQ13X10qAasMJUVkURiqC9tcp6CMocrC77Dp0QWKjSpEphFBfrPuoJl1FnAgXQsiIePXnmESXJ2TPWNdQfbbOXo6q0pkqRzKac2d9zPG73Yh3pN/wTCWo/2CiZzdXNrlvbIEnowA/PQUmdu2C5YxZux8sxVfChAFnKmfWwO+6Xr16XK2rZKl0sv5U8wt/2TCMyntHPeJmA/vBGPayKw3XS6uOTar0EE0s5s9I4fqdLm1I2bd3teNkvz2lB88tuZ6NJgARIgARIwN8IuA48gGiQZTVq4MR3exsLEtzxYMGIqOA5KH5lHrgT4nmEQofrGlzd4ul6Noi9lc9cePADwRHsFUMoHYGqTO1XJahsiYIm1YHDJ0wZCBkP5cmVVK1QXKB8QFHq/OZYYx2DdSZ92lSSQC1IsycPkgROApJgXdnrXZrrOrO9MmH6fPl46nc2a4t9Wbv2hrqiIT+sl7p4+aq69V0xa4uQDmvC8qtFJDzB2juk9VSy6bo4K0Kl9Uw+VaSg4MBqA0XFnaBOsLRhnRZcBMfq2ilYCmFddZRd/x12GiXR3XgBD6x/O3PuosAqakl43DPqujkoMt4Qd/Vznn/o7wbGmzfrYZVVTMcgmFzUFw5pUqewLj/yjRcSWTN5N8jPI4XEkguhfyliSWVZTRIgARIgARIgARKwCNzQ/cngEmX/wd5MERVM6mE9QACM/aoALfxptVHUIprPYg12UadGOaPgQMnBB+HtETp+zcbtxrUOLn+//rHZBKtAMJG/tv1nKwbrd3bsOWTWuyEgB6wbcBdc8NMfJrAJ1ucsWrbOBNywPRTOAZ7v372lBvn41qxfK6trvWAxg/sZFBp8UI8ly9eb+wuW/qEWlURSpVxRyaXrvKBwWnLy9AWjhCEQx/S5S8x6OVizsL4NFspPZy4y7cI+ZjtVuUFgkPCkokbwgzKIZ7COaca3P7t9BAoWgrzYS9GCT5ryJ3+50FjtsBbKldKHYCRYg4W1Z3A9hOXRfn3hXl3DCCaweq5Sa+hzGiDFUdyNF1jBwAyKLSyI6MfRk+fqCwDX3JE/rJffqFsqInlu1+ify1TZj6y4q194eXqzHlZZcEmFgvr6O1MMVwTd2anj3FH2HTwm+R3cVx3T+Mu5X1jQsDAT/sbOBL7rGMjhRcpx9iyvkQAJkAAJkAAJxAwBWJsQxh6uavZi7YOG+65tZoLlV+aDZ1s3fU76vj1JqjXubSbssDqFEeT1ILN4LsK1Q7mAooHIdfaSJWM6E9FuyYp1JnBIGy0Le7jN+m6ZURLg+mXlXUODayz86U/dX6qLYLuA5V+PlSF9WktvjTDZsN0gky1cJft3b2FfRLjHWNuEKHr9R/5P5uiWAR/rerRhY76U8vVC3UNhORms5WCuhCAhQz/8whyDQ7+uD610W1WxASMEQUH0w4+GdTNlY/I9amBHGTx6usxZ8Ku2J5601YAg2Gwa4tgX5vxBo7HuCfvFYfIOwSbIEFd9h3L/VQURLoNPZM1o0kKR7arRJmENm/ntMrOmLYlufGyNAPs+u3z1hrTt/Z6JiAlF6tmqpdWtMzSSIDL7RNcMvjdxrgmCguvWnnT2ebgbL2CBIDQIGY8oiRAo5bAcueKONOgjWDoRSRLKLtx33Yk7pu7qhzwD4j1qn0F+kHDroekeJDW/QNYxIozaC6I8WoK8R2vwFkSPxP50ZUsU0PGd1mz+bqWBUrxKA+w0f+EZ65Jff8fTBagPHbLjIAqEIH1t2GT5Z/n0MK3DG7Ieb31sFmziBhbyjtBQqvjjhDcrrbqP1LdWT8lUjShkCcLfdtdn8MYF/wFATpyP+Js6PLd1WzyZ/30gDimxmIAVZj9zpuhtxMw5AbJv/8M/ftFbOkvzFoG8ee7pNg0RX+fyOOWfOi0ya06gXL0W9j/Tx8mTz8YMgSZBIVK8WOT+C8+azr0bWGRbdO2wRvz777jEC/ZsXAfnyiSpCoWGm49smd56DtMhRLbLkC6NmSB7K19n+SC4yVkNqoHQ7s4EgT9S6MtjuDFaApc5THQzpEttXXrsb1j27ty9a/K0JujgAKsPXlxjM2JLsG4L2wH06/KSWf/mzFUtJOSeIIgJ6hi65s96OvxvWJugMEFZDE+w8XRiVcBGD+ocJikm+VfUEuqKq5UYL+7PKs/MqjzDhdSSojVeMZs/51flCPljGwdX4sl4AUes70KEQntxxh33g4ND5LgG08iaOZ1T91P7PMI79qR+rvLwZj2sMpAnFGkIrNXPvdTPbP5eWS21EFgc12uAlblThpitLcxFP/7h3KwUB4DATaFF1+FmoCd4MCDsm4U9MJ7MkVW+mzrcRJZp02uU/KhvOprWr2bz/96k0Z3gamDt8WHtIYJBTyEBEiABEiABEghLIHnODCL4xEKBguIsGEZUNAVrv9wpEbDCOIr9eibHe5E9T53q0X3dwMFdWVC8XClfUHaw91pkBJZGT2Vgz1byQtu3TJRDbGVgCTyi8AlP8DI+m0aLdCXOlE/HtJ6MF1fKtDPuyB8KTGT5RaZ+js9Y596sh5VnJ7UOwiU5XZpUsl33+YOrKgwhEGzvMFOjrMK6a295s571x++Hrw3iWOvT6B+dz8f1lxFvdnikZVikiNC6bTTULt4QIcxqTQ31Cp9we6lf62kTehTX4G8NH/fnqpWxT8JjEiABEiABEiABEojzBJrqNgR1azxUhmKywdgIG/utWVECvVWXwX3ahLtRtrfK8rd8+nZqaraKKFuyoAzr106+/HiATRlDcJCx6i7ruK2DvzGyb2+ctaBBA8c+EIh25Cin1awNyakLRC3BGwvHiE2vvPS8NFLfdkT6+WLeUnkpqKaJygMTOoUESIAESIAESIAE/IVABQ1i4UuCvcqs/cq8Va8XG3D9k7dYOuaDQC74OJM6zzwaQdNZOn+6FmcVNHedCP9kCPyCLcHxtWthFS/s4l5VdzWHO+S2Xft1AW0bjYL0jfWI+U6f8mEeYW6EcxI/MDicFLwdWwgkSRgo6VNG33rCu7q2QwMxxxY8rKcbAlgFlippfF1rEH3ODJcvhripEW/FJgLxA+Pp356we1/FpvqzriRAAiRAAs4J+KWClvLBAlRs/mjJLd2gMXnyR/2Wsbt7656j5MWGz5h9Pqz01vflG5FTtEI0AhIlbhC4rQrT5RvR15/3dA3kfZfxreIGU39pBUbNtdshEnAn+hTu28EoNfoUQn/py5hoJ/4fiez/QRlSRd9LpZhgwzJJgARIIDYT8EsFLdODjQMPHztl240ex5kzpH2kL0sVzS/dXwmShs9VeuQeLsCaERm5f58R1CLDzRefQbjhu2bSG5214/iJTtpRWVZIyH0JUZU7uuRe5P5kRVf1WE4ECCBeVWT/D4pAMUxKAiRAAiQQzQTitIJ2Ry1kdzWUKgTH2MMCkZMQnQcbCSJiTBH1hz2hIU2Xr/5bXu/c3Cn+bm2DnF7nRRIgARIgARIggYcErty6KjeCrzy8EM5RgniJJV0yz6P3hZMdb5MACZBAnCAQZxU07BfyTNO+tk4q+VxHKVO8gMwYP9BcG9q3rXQbOM5sBomw+c/XKK8bFT60kgW42IgSYVXxoZAACZAACZAACYQlcOXOBdl9c77ahD0z1eZKWIsKWliEPCMBEiABibMKGvbw2LHyS5ddnDd3Nlk2b4yxnmEDQftNBOHW+O9vXzh9dqRuZk0hARIgARIgARJwRgAuu7dVPbvj7OYj17CiNjbIdd2/KVHChGafqr+27ZH4gYFS/Km8Pl91bCsExmlSpYgzL5eXLF9vNnIuWSTfI/z3HToux3Wj7GpPl3jknqcXjp44I2s379C9crNI2RIFH3nMfiw8ctPLFxAfASYB+w3LvVHEb3/+bTbirlimiDeyYx5RQMDvV4pjo0h75SwKGDNLEiABEiABEiABLxN4b+Iceap6u0c+jnuaPm6xN27eVm+brroU4i+T1Tc/rpSFP/3pNltsvIu6vdTlHbfpHG+azXoXLHe8HOHzOZpH887DpHJQT6kS1EvKPt9ZBo+eFuF8ouuBPkMnya69h8Mt7odf1sgHU76SjOlSO03758Z/ZfKX3zu958nFuQuXS9Arg2W57ou7ZtP20M2T7frDcSx4kqenaZz1fe8hE2XAqM88zcLjdEkSJ5KegybIvoPHPX6GCaOXQJy1oEUvRpZGAiRAAiRAAiQQ3QSKFc4jI95oH6bYLJm8u6YtcaIEMnfyYMn5ROYw5bg7WfTrOrMX67+7D5q9VHN5+CyUlPi6Vv5xZPWGbTJqwmwZ1Ptl+d/o1+XWnTuyeeseWbh09eNkG6XP/rF+qzStX81tGZcuXzPK2Tv9XpFsus9tVMhi7beeHRpLu+Z1TPYDVTmy74/IjAVP6+ms7wf0aKmbOXt/Wc3TZZ6Slo1qybCxX8qsiW/FGeuqp6xjQ7rH+ysQG1rIOpIACZAACZAACcRJArAEYMmCM2na8W2pXK6I/LZmi5w6c0GwGW7D2pVl7KdfC1zhnq1aRl7T4GDp0qQ0ShSsOCfPnJd7Guq0SIHcMqRvG3lS90MNCAhQhWeOUXhSF07urKgw126rW9qyVZsEisT4ad8JJv092jcyae5o4DJY1T4Y0kXy5gqt99hPv5GUKZJKvtzZZeXaf0y6HXsOSvq0qeSzD/vJ9Ru35P1Jc7Udf0uAroGvXb2c9Ov6Ypi9XO0rsGX7XuPSiAm4JS88W1HwgbirQ8dW9U2a73/+Uz6Z8YPh9lSBXAJXu09HvyZYPgKry/BxMwTKZ+YMaSRt6pTS7IXqElSnsnkWbZ/0xULzbK0qpaV10+ekUL6ccunKNbOv7MYtu43bZcG8OYxy/eU3Pwu2PcKes8mTJZEGGg/AUpBMhg9+fLPod0EUbuRpCer14ZR5snTFeuMsi75MljSxub1BrZhT1Jr2yovPy1ffrxDEG3ir18viqp8//GSe7nl7QM5euCw/qqWugUbvdtYf9mMBe+NeVMXxwsUrsvGf3VKqaD7p07GpaS8q4Y6j1QZ8oxxnZcFiiH16u7RpYPbhvaAuq+e0fpu37pY8ObPJ0Nfayqz5y2SVPo/fg14dmtjcMhEcb/y0+bJE2cSPHyiNnq8irRrXktQpQ8dwp5frS5VGvQScKpQqbF8dHvsAAb93cfSBPmAVSIAESIAESIAEIkHg8NFTgom1/ee/A0dNTnv2H5EVGqG5Y8v6Mrh3a/nh5zXq1jXeKDgfqoK0/q+d8svKjSYtJrANnqtolJBpY94wFoWR42fbaoS8oCh5IivXbVUl775Ur1hC6tasIIuXr7U9dl+Vvz37j8rNW7dt146fOiunz16UwvlzSqH8uQRWwde7vCidWzcwaaCArPtrh/Tv1kL6dmpmXC0nTl9ge97x4JlKJVVpuCq9Bk8QuHse0zVZ9uKuDki3SRWNQe9P03VcxeXL8QMML9QZih0Uoq4aYA3Kzrhh3QUWHii15y9eNkVs3bFP+g3/RBrXrWqCsiVMkEBdK6ebe6gz8pkwsqeMH95TcmTLJOcvXTGKQwLlDwsa2l3dxfqx3fuOmIBuJrMHPyZ+vkB+1j58tVU9mTyqjyotWW23r167YSyH70+aowpTDqOEuOtnKGRJEic02yqhHpVUuXfWH/Zj4cTp80Y5RN+Ne6e74TP9q6WmDu442ir54MBV35/U/PFyAYKyfvptg5TQtY/j3ulhlFq4sSbWtZFQ+NOlSWUU4wdZGuXs97VbZEif1vLugFcF687gnmsJlvdUKV9MwJXiewRoQfO9PmGNSIAESIAESIAEPCBwNzjYKDf2Se/cCd1eB9cGqxWsfMlC5vYM3VoH1qe2zWub8+oVt5kJPCxN2dVlrnG9qrLh750aZOKcTtQTyY7/Dpl0Ef2xePk6tfKUMpaPeqqgfTrzR/lHFRdMrN0JrFOZ0qc2LnWVyoYGb4Ai9OOyNWY/VliWIFC4vv7xd3mj20vy2ezFxqKC6wkTxpd+qlgU1e2DPlLl6dOZPxhrEe7lyZVV3lQFr3K5ojh1K7Da5H8yu7E2IWGWjOmMayGOoXRga6LPPnhdcmsQDUimDIvMN358u3ilFFZrGYKt3Q0OkZrKAdcQuANKacIE8SVFMrUWav6IrG0JrJSFVTm12m1dt//+78Axm5XOur5gyR/ycpNnpUOLeubSv7sPGIXJug+L3PdfvBvG2uiqnwvkecJsxQRlzqqHY39Y+dp/N6xTSfsn1EJ67fpNGfnxLHPbHUf753HsrO8d0+AcZcGaBtm194h5wTCsXztznihhAunYb4xyDzbtgMURFrMMOq4gsJItX71ZYDmzpMCTT8he5UrxPQJU0HyvT1gjEiABEiABEiABDwjkVbfAMUO7epBSJGmSRJruYdTIpEkSG6sEHobi0UOta3ChK1Iwt8kPro4RFbjxrdb1VLC8vPXeVPM4Ij4uWrY2XAXNWVlwaYP7X7FCeWy3ixZ6Uv43a5HAlfKylnfuwiVzDxN0S2pXL6vKaFlBFMft6i45be4SeW3YZPnz+4lWEpffsIiVcBIhEQ+cVGtOqpTJbMqZYyZHT5w1TN99oKTgPtxFT6mFsN2LdYx1Laj9YJNHc3WLxD6zCe3q7Zif/fkxja4IK5Ell69elytqJcO+tq4kMDAgjHLmrX52VR7cKy3rqDuOrp6PyHWMZyjwlmA8B4eESEjIPbl67YogoAksyOs277SSaCTSh2MEF9OlTSmb1F2S4nsEqKD5Xp+wRiRAAiRAAiRAAl4n4DrYwuz5v0pZtehMfLe3cW/8ZeUmXWN1IMI1wHOYKJcp9tA6VFGtYXDDG9CzpcRTSxHE3spnXwj2WbVXDFOlSCZQMvYfOmFbW3Tg8AlTBkKvw4rmKAgDnyxpEnM5TeoUxo0NikPrnqNkvz6b58HaN1d1gAUOrorOpJgqh5evXDeKH/J2FKyby5wxrYwe1Nnxljn/Qa1Zh9QtFdEWx+r6LaxNg1UT7bZXNpw9jKjbcN20BGwyqsURSqOnEtF+duwPT8tBOnccneXzOGU55pcyeTKzFURfXWNZQ11eXQleAGTNlM7VbV6PQQJcgxaD8Fk0CZAACZAACZBA5AncUGUELlr2HygoERW4wsEic16DPUCJWfjT6khFtlv861qpU6OcrqVqbvsMfa2NIALhmo3bjYsf1phhbRisbQio8de2/2zVxVqkHXsOmfVuZ89fMsEdsFfVgp/+MIFNsF5o0bJ1UrVCMdszjgfvTZwrb4741ChZYLFTXTWnzlmiwTxSmL294Gborg5Yl/T3v3tNYBRY/hDJ0BK4AUIJe/2dKcYqiIAZO7W+ltSsXMqsk4KiGqwujrAiIdgI3CIna+AQuHpi7RkCtMCaY63rQ7uxvxzWuV3QdWnOBGXvPRjWHe/p0k/JN4tWmhD92zVoyTIt151EtJ8d+8Nd3o733HF0TIvzxynLMT+stcMawo+nfmvGM/oC4wpBQ+xln/LMr1wpvkeACprv9QlrRAIkQAIkQAIkEA4BWBwQSRAuc/YfbDIMwX3XNjPcD/0gLSINIjpetca9pVnHt21uarhnBHk9yCyei7DnCOIAxQbrzuwFa7gQ3W/JinXmchstC1EHKzXoIeM++864+1l516hc2ljMytXtIi26jTDpEeQBQUcathskTV4daqIU9u/ewr6IMMdQWnZquP6W3Uea/duadRomUPamvN/XtuGxuzqgrljntG7zdsG6PawNgyCaIJiOHtzZBMOYoorXrdu3JUumtGYTb6RBUBS4LQ56f6qUePZVqdX8daOwBaqb5+WrN6Rt7/ekdO2O0rjDEFXSSpuomniuRVBNWaDbAJTUZ6wgG7huLwXy5FBuG+wvmfVncH1ExM5X+r5v58AamiwgXthpbvj9HDpurEKc9QcGgtVfiKppL1hLZ4k7jlYa+29nZdmPNceyzPh2KN8+P0R4zJE1oxk36IsW3YbLmXMXbUmwXm6VBrQpqFwpvkcgnpqUHzqw+l79fL5GJ85H/E0dGrV1WzyZ/32gz7ePFXRPIHny+9KmVYhkzuQ+nbfvzpwTIPv2P/yPwNv5M7/oIZA3zz0dP/eip7AHpZw6LTJrTqCuUQg7sYjWSrAwrxBoEhQixYtF7r/wrOlCXeC8UhG7TE5fPS0X7h7SiXKI3VXXhykCs8sTqXxjgojp0JHjpyVDujQP1qu5rvfj3kEgh7PnLgnc9pwJlMUUatWDG6MlmFxjUp7BxSbNVjrrG9Yp5JNe1xlZLo/WPXy7qwPWMcG1EoLIgW+P+UI2Lv3UnMMaAwsNBFbH517qJ2Pf7hYmAAncNE9rfZOpu2dKdUW0BBays3o9syqtVv7WPZSJaJBoH9rpKLCs1W89UEZqREJ7tz3U57ha6LJmTmeCYzg+53gemX521h+O+To7d8fRWXpci2xZrvLDWkXkCa726/0+nvqdRjLdIXOnDDFbSbh6ntdjhgDXoMUMd5ZKAiRAAiRAAnGOQKYUmQT/YqNAKciZPXO0VD2BbkbtSjlDBeBG6CiI9BcRwboza08wZ8+5qgMUoXovDzCBTgLVWojAGn07Nbdl0enNsQLXUgTsQACS/BoJsKJufGwvsCTBcugocK90tck0FDZ3bcR+a9i3DvulIdKilT+UxZzZPR9zkelnZ/3h2DbH8/A4Oqa3ziNTlvWss28o+Y7MN+reZzPVOjpHN2C3t/o5e57XYoYAFbSY4c5SSYAESIAESIAESMDnCCDAxPA32xurFPbYglKEYB6W9O3UVP7VDZ1hDWv6QjWpWr54tE3ym+hWCDCuHdNokZaCZtXL177D4xiT9UVwkLHDuoXp15isD8t+lAAVtEeZ8AoJkAAJkAAJkAAJ+CUBWKQQxMOVIDohPjEhsH41qVctJoqOcJnhcYxwhl58oM4z5byYG7OKCgJcxBIVVJknCZAACZAACZAACZAACZAACUSCABW0SEDjIyRAAiRAAiRAAiRAAiRAAiQQFQSooEUFVeZJAiRAAiRAAiRAAiRAAiRAApEgQAUtEtD4CAmQAAmQAAmQAAmQAAmQAAlEBQEqaFFBlXmSAAmQAAmQAAmQAAmQAAmQQCQIUEGLBDQ+QgIkQAIkQAIkQAIkQAIkQAJRQYAKWlRQZZ4kQAIkQAIkQAIkQAIkQAIkEAkCVNAiAY2PkAAJkAAJkAAJkAAJkAAJkEBUEKCCFhVUmScJkAAJkAAJkAAJkAAJkAAJRIIAFbRIQOMjJEACJEACJEACJEACJEACJBAVBKigRQVV5kkCJEACJEACJEACJEACJEACkSBABS0S0PgICZAACZAACZAACZAACZAACUQFASpoUUGVeZIACZAACZAACZAACZAACZBAJAhQQYsEND5CAiRAAiRAAiRAAiRAAiRAAlFBgApaVFBlniRAAiRAAiRAAiRAAiRAAiQQCQJU0CIBjY+QAAmQAAmQAAmQAAmQAAmQQFQQoIIWFVSZJwmQAAmQAAmQAAmQAAmQAAlEggAVNA+hXbt+U86cu+hhaiYjARIgARIgARIgARIgARIggYgTiB/xR/zridu378jA96bKLys3Sbx48SRn9kwy5b0++p3Zv0CwtSRAAiRAAiRAAiRAAiRAAlFOgBa0cBDPX/qHbNiySxbNGCUblkyRbJnTy8iPZ4XzFG+TAAmQAAmQAAmQAAmQAAmQQMQJUEELh9mvqzZL7epl5cmcWSVZ0iTSplltWffXTrl67UY4T/I2CZAACZAACZAACZAACZAACUSMAF0cw+F16uxFqV6xpC1VjmyZ5P79+3L2/CVJkTypZE2XxHYvIgdHUt+WXDnvReQRpvVBAoGBIsmTBOo4SBhttbt9N0Ry5bgrwcEcP9EGPYoKypnjvqRLmVASJdCBFE1y7codyZRJy013P5pKZDFRRSBN6nj6tydxVGXPfEmABEiABGKIABW0cMDDUpY4UQJbKuv4cS1oFUomkgoP9T5b/jwggfAIYDL/UlD0TejDqw/vxy4C+XMnlAG9YledWVsSIAESIAES8CcCdHEMp7dhJbt9564t1a3boce4TiEBEiABEiABEiABEiABEiABbxKgghYOzcwZ0sjhY6dtqQ4fO2WiOWZIl9p2jQckQAIkQAIkQAIkQAIkQAIk4A0CVNDCoVirahn5eeVGOXjkpFy/cUtmfvuLVChV2Kw/C+dR3nYgEBwcYtbvOVzmKQm4JYBxc+LUOf39u+k2HW+SgDMCt3SrFLxYw7phCgmQAAmQAAnEBgJU0MLppSb1qkqZYgWkfpuBUr5eVzl28qwM6v1yOE/5z+1mnYZJj0HjwzR4/V87pGiNV2TX3sO265cuX5OazV+Tlev+sV3jgX8TOHD4hBSv2UEWL18XBsQ7Y7+UBm3fkpCQezLru2VSrm4XefalflKpQQ/p9MYY8zsY5gGe+CWBqXMWy9P1u8n5i1ds7b9x87bU0r8zE6bPN9feGPGJlKnTWeq+PECqN+kjLbqNCJPe9iAPSIAESIAESMCHCFBBC6czEidKKONH9JR1iybLsnlj5Kc5H0juHFnCecp/bg/p01p+X7NFVj1QvO7cDZYRuk/cSw1rSKF8OQ0ITJKeadZXzl24rBY0/2HDlrongK0rXm76rHw4ZZ5cux5qHdu264B8u3iVDOnbRgIDA3Rri8QyenBn2bj0E/lu6nA5rpa0md8uc58x7/oFgbbN60ia1Clk7Kdf29o7Zcb3Ei8gQDq9/IK5lj1LRpkzaZBs+XWa2cvy6PEzMmfBr7b0PCABEiABEiABXyRABc3DXkmZIplkzZTOw9T+k6xY4TwCK+OoCXPktroSfTFvqZls9361qQ3Cm91amMlRooQPo2HabvLArwl0axskAQHxZOLnC4zFbPhHM6TBcxWlbImChkvjulXlWXUzxh6EeXNnk8rlisqaTf/6NTM2PpRAwgTx1Zuhtfzwyxr5a9t/su/gcZmlyjteGuHFGqT3q02k+FN5BWmzZ8lglP7UKZOHZsCfJEACJEACJOCjBBhm30c7JjZV67VOzaVu6/4yfNxM+em3DfLugFclebKH+8NZAVXixYsXm5rFukYDAVjIBvRoKf2Gf6IK/l2z1mzqh/2clnzv3j3Z8PcuKZDnCaf3edH/CFQqW0Seq1ZGRujfnhTJk8gzlUpK1QrFw4DAi6NPZv6oiv12Y9UPqlM5zH2ekAAJkAAJkICvEaAFzdd6JBbWJ3Wq5NK3YzP5/uc/pXSx/PJ8jfKxsBWsckwRqF29nJQvWVhdG1fK612aG7c1Z3X5QF0hjxw/Ld3aBTm7zWt+SqB/95bq+npWdu87KgN7tnqEQrCuZdx36Lix7F+5el2u6N6WFBIgARIgARLwZQJU0Hy5d2JR3ZrWryZwYQyqUyUW1ZpV9RUCrZrUMlVpWNu5deOLeT/JvO9XyLh3ukveXNl8pdqshw8QyJwxrdSsUloqlC4smXRbFEeBlXbSu71l6ez31cUxUMZP+84xCc9JgARIgARIwKcI0MXRp7oj9lYG7osI6kAvxtjbhzFZ8/g6cYY4c4PF+rQvv/5ZJo3qY9agxWQ9WbZvEoiPvz3hVA1jK3eOzHLo6KlwUvI2CZAACZAACcQsAVrQYpa/X5R+NzhY7ty5a9p6V6M8Wsd+0Xg28rEIIPgMwqkP69dOnsia0Wwaj43jsa6IQgLuCMCdcdSE2bL3wDFBdNnNW/fIslWb1Q27gLvHeI8ESIAESIAEYpwALWgx3gVxvwKv9BktW7bvNQ19bdhk8736+wmSNnXKuN94ttAjAs4sZ3hw2859JrrjgHc/C5PPF+P6S7mShcJc44l/EwjQ8Pr2gjG1YcsuDau/3HYZ62M7tw4NwW+7yAMSIAESIAES8DEC8e6r+FidWB0SIAESIAES8AqBqxoU5Oz5S2Z9GrZroJAACZAACZCArxOggubrPcT6kQAJkAAJkAAJkAAJkAAJ+A2BsD4hftNsNpQESIAESIAESIAESIAESIAEfI8AFTTf6xPWiARIgARIgARIgARIgARIwE8JUEHz045ns0mABEiABEiABEiABEiABHyPABU03+sT1ogESIAESIAESIAESIAESMBPCVBB89OOZ7NJgARIgARIgARIgARIgAR8jwAVNN/rE9aIBEiABEiABEiABEiABEjATwlQQfPTjmezSYAESIAESIAESIAESIAEfI8AFTTf6xPWiARIgARIgARIgARIgARIwE8JUEHz045ns0mABEiABEiABEiABEiABHyPABU03+sT1ogESIAESIAESIAESIAESMBPCVBB89OOZ7NJgARIgARIgARIgARIgAR8jwAVNN/rE9aIBEiABEiABEiABEiABEjATwlQQfPTjmezSYAESIAESIAESIAESIAEfI8AFTTf6xPWiARIgARIgARIgARIgARIwE8JUEHz045ns0mABEiABEiABEiABEiABHyPABU03+sT1ogESIAESIAESIAESIAESMBPCVBB89OOZ7NJgARIgARIgARIgARIgAR8j0B836sSa0QCJBBXCHQf+LFky5JB3urVytakq9duSOc3x0qj56tIsxeqS0jIPZn+1RL54Zc1cvzUOcmRNaN0bFVfXniuonnmr2175L2Jc+XkmfNy8+ZtyZQhrTSrX03at6hr7p86c0H6DJ0ovV5tItt2HpCNW3ZJrhyZZWjftrYyeUACJEACJEACJEACsYUALWixpadYTxKIhQTyPZlNvvp+uUCJsuSHX/6UrTv3S7mShcyl0ZO/kqlzFkulskXk3f6vSs4nMsuAUZ/Jpn92m/vXrt+UXHqtxyuNZPib7aVkkbwy9n/fyNIV6839W7fvyL+7D0r3t8bLN4t+l2TJkkj8wECrOH6TAAmQAAmQAAmQQKwiQAtarOouVpYEYheB5g1qqHVsqXy7eKX0bN9Y7t+/rwrbb1KlfDHJmT2TnD1/yShwXdo0lO7tgkzjalcvK1Ub9ZIly9dL2RIFpdojotXnAAAGP0lEQVTTJcwHNy9dviaF8+WUlev+kX927JO6NSvYgLRoWENe7/KiBAbyvZMNCg9IgARIgARIgARiHQEqaLGuy1hhEog9BLJmSifVK5aU7xavki5tGhir2KGjp2RAj5amEXsPHpN79+7Lwp9Wy7JVm2wNu3Hrtro7njXncImElW356r8Ex5bcvHXHOjTfsMhROQuDhCckQAIkQAIkQAKxkAAVtFjYaawyCcQmAi2Daspvf/4tK1b/LYuXrzOWs8rlipom3HqgZLUIqqHXM4dpVtrUKcx5v+GfGJfIXh0aS7kShSRzxrTSsvuIMGl5QgIkQAIkQAIkQAJxhQAVtLjSk2wHCfgogQqlC5s1ZJO+WCiHj52S/t1bSrx48Uxtc+fIYr4TxI8vtaqUDtMCuEPeu3dPNupatEbPV5aWjWqFuc8TEiABEiABEiABEoiLBKigxcVeZZtIwIcIQBmDhQyRGJMmSWyULat6UNCwHm3C9PkSEBAgFUoVljPnL8rKtf+Yc7hCliicR9Zu2i6/r90iCRMkkEW/rpX9h05I8cJ5rWz4TQIkQAIkQAIkQAJxhgAVtDjTlWwICfgugdrVyxkFLahOJUmWNEmYir7/Vif5YMpX5oOQ+5D0aVNJ307NzHE3DR4ybOyX0kOjNELgHpkuTUpzbP8jICDUKmd/jcckQAIkQAIkQAIkENsIxFM3ovuxrdKsLwmQQOwi8NX3K2Tkx7Nk8cz3xHJrdGxBcHCI2essSeJERkFzvL/v0HFJlSKZZEiX2vEWz0mABEiABEiABEggzhCgBS3OdCUbQgK+SQDryGZ+u8zsc+ZKOUPN48cPlCd0k2pXkjdXNle3eJ0ESIAESIAESIAE4gwBbhgUZ7qSDSEB3ySw87/DkiRxQmn3Yh3frCBrRQIkQAIkQAIkQAI+RIAujj7UGawKCZAACZAACZAACZAACZCAfxOgBc2/+5+tJwESIAESIAESIAESIAES8CECVNB8qDNYFRIgARIgARIgARIgARIgAf8mQAXNv/ufrScBEiABEiABEiABEiABEvAhAlTQfKgzWBUSIAESIAESIAESIAESIAH/JkAFzb/7n60nARIgARIgARIgARIgARLwIQJU0HyoM1gVEiABEiABEiABEiABEiAB/yZABc2/+5+tJwESIAESIAESIAESIAES8CECVNB8qDNYFRIgARIgARIgARIgARIgAf8mQAXNv/ufrScBEiABEiABEiABEiABEvAhAlTQfKgzWBUSIAESIAESIAESIAESIAH/JkAFzb/7n60nARIgARIgARIgARIgARLwIQJU0HyoM1gVEiABEiABEiABEiABEiAB/yZABc2/+5+tJwESIAESIAESIAESIAES8CECVNB8qDNYFRIgARIgARIgARIgARIgAf8mQAXNv/ufrScBEiABEiABEiABEiABEvAhAlTQfKgzWBUSIAESIAESIAESIAESIAH/JkAFzb/7n60nARIgARIgARIgARIgARLwIQJU0HyoM1gVEiABEiABEiABEiABEiAB/yZABc2/+5+tJwESIAESIAESIAESIAES8CECVNB8qDNYFRIgARIgARIgARIgARIgAf8mQAXNv/ufrScBEiABEiABEiABEiABEvAhAlTQfKgzWBUSIAESIAESIAESIAESIAH/JkAFzb/7n60nARIgARIgARIgARIgARLwIQJU0HyoM1gVEiABEiABEiABEiABEiAB/yZABc2/+5+tJwESIAESIAESIAESIAES8CECVNB8qDNYFRIgARIgARIgARIgARIgAf8mQAXNv/ufrScBEiABEiABEiABEiABEvAhAlTQfKgzWBUSIAESIAESIAESIAESIAH/JkAFzb/7n60nARIgARIgARIgARIgARLwIQJU0HyoM1gVEiABEiABEiABEiABEiAB/yZABc2/+5+tJwESIAESIAESIAESIAES8CECVNB8qDNYFRIgARIgARIgARIgARIgAf8mQAXNv/ufrScBEiABEiABEiABEiABEvAhAlTQfKgzWBUSIAESIAESIAESIAESIAH/JkAFzb/7n60nARIgARIgARIgARIgARLwIQJU0HyoM1gVEiABEiABEiABEiABEiAB/ybwf0VWBo1aldHEAAAAAElFTkSuQmCC" + "image/png": 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}, "metadata": {}, "output_type": "display_data" @@ -3178,7 +4674,7 @@ "Voice Bot deflection (labour avoided)" ], "y": { - "bdata": "ndiJLaMpIUEAAAAAkHATQU/sxG66BEBBFDuxSwDhMkE7sRNNYwdpQQ==", + "bdata": "ndiJLaMpIUEAAAAAkHATQU/sxG66BEBBFDuxSwDhMkEO/MDxdaFaQQ==", "dtype": "f8" } } @@ -3969,7 +5465,7 @@ } } }, - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -4011,7 +5507,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "id": "7bd6456e", "metadata": {}, "outputs": [ @@ -4064,13 +5560,13 @@ " 2\n", " Agentic Virtual Agent\n", " 0\n", - " 2,471,160\n", - " 3,530,220\n", - " 6,001,380\n", + " 1,606,248\n", + " 2,294,640\n", + " 3,900,888\n", " \n", " \n", " 3\n", - " Speech & Text Analytics\n", + " Speech & Text Analytics [named]\n", " 751,680\n", " 751,680\n", " 751,680\n", @@ -4078,7 +5574,7 @@ " \n", " \n", " 4\n", - " Agent Copilot\n", + " Agent Copilot [named]\n", " 1,002,240\n", " 1,002,240\n", " 1,002,240\n", @@ -4104,36 +5600,28 @@ " 7\n", " Email AI (Auto-Suggest)\n", " 0\n", - " 466,200\n", - " 466,200\n", - " 932,400\n", + " 0\n", + " 0\n", + " 0\n", " \n", " \n", " 8\n", - " Email AI (Auto-Respond)\n", - " 0\n", - " 311,472\n", - " 444,960\n", - " 756,432\n", - " \n", - " \n", - " 9\n", " AI Translate\n", " 0\n", " 0\n", - " 143,520\n", - " 143,520\n", + " 4,434,000\n", + " 4,434,000\n", + " \n", + " \n", + " 9\n", + " NICE IEX (NAM)\n", + " 650,000\n", + " 1,300,000\n", + " 1,300,000\n", + " 3,250,000\n", " \n", " \n", " 10\n", - " NICE IEX (NAM)\n", - " 1,300,000\n", - " 1,300,000\n", - " 1,300,000\n", - " 3,900,000\n", - " \n", - " \n", - " 11\n", " Legacy CC platform\n", " 0\n", " 0\n", @@ -4141,15 +5629,15 @@ " 0\n", " \n", " \n", - " 12\n", + " 11\n", " Voice Bot deflection (labour avoided)\n", - " 13,122,330\n", - " 20,995,729\n", - " 24,932,428\n", - " 59,050,487\n", + " 6,981,080\n", + " 11,169,728\n", + " 13,264,052\n", + " 31,414,859\n", " \n", " \n", - " 13\n", + " 12\n", " STA coaching (AHT)\n", " 562,386\n", " 899,817\n", @@ -4157,7 +5645,7 @@ " 2,530,735\n", " \n", " \n", - " 14\n", + " 13\n", " Voice AHT (knowledge surfacing)\n", " 1,237,248\n", " 1,979,597\n", @@ -4165,7 +5653,7 @@ " 5,567,617\n", " \n", " \n", - " 15\n", + " 14\n", " Voice ACW (summarization)\n", " 2,099,573\n", " 3,359,317\n", @@ -4173,7 +5661,7 @@ " 9,448,078\n", " \n", " \n", - " 16\n", + " 15\n", " Supervisor time (AI summaries/insights)\n", " 318,500\n", " 509,600\n", @@ -4181,68 +5669,52 @@ " 1,433,250\n", " \n", " \n", - " 17\n", + " 16\n", " Agentic Virtual Agent deflection (labour avoided)\n", " 0\n", - " 6,007,689\n", - " 7,134,130\n", - " 13,141,819\n", + " 2,077,459\n", + " 2,466,982\n", + " 4,544,441\n", + " \n", + " \n", + " 17\n", + " TOTAL COSTS\n", + " 5,222,876\n", + " 7,120,784\n", + " 12,243,176\n", + " 24,586,835\n", " \n", " \n", " 18\n", - " Email Auto-Respond (displaced handling)\n", - " 0\n", - " 3,634,923\n", - " 4,316,471\n", - " 7,951,394\n", + " TOTAL TAKEOUTS\n", + " 650,000\n", + " 1,300,000\n", + " 1,300,000\n", + " 3,250,000\n", " \n", " \n", " 19\n", - " Email Auto-Suggest (drafting time)\n", - " 0\n", - " 1,744,763\n", - " 2,071,906\n", - " 3,816,669\n", + " TOTAL BENEFITS\n", + " 11,198,787\n", + " 19,995,517\n", + " 23,744,677\n", + " 54,938,980\n", " \n", " \n", " 20\n", - " TOTAL COSTS\n", - " 5,222,876\n", - " 8,763,368\n", - " 10,099,436\n", - " 24,085,679\n", + " NET\n", + " 6,625,911\n", + " 14,174,734\n", + " 12,801,501\n", + " 33,602,145\n", " \n", " \n", " 21\n", - " TOTAL TAKEOUTS\n", - " 1,300,000\n", - " 1,300,000\n", - " 1,300,000\n", - " 3,900,000\n", - " \n", - " \n", - " 22\n", - " TOTAL BENEFITS\n", - " 17,340,037\n", - " 39,131,434\n", - " 46,468,578\n", - " 102,940,050\n", - " \n", - " \n", - " 23\n", - " NET\n", - " 13,417,161\n", - " 31,668,067\n", - " 37,669,143\n", - " 82,754,371\n", - " \n", - " \n", - " 24\n", " Cumulative net\n", - " 13,417,161\n", - " 45,085,228\n", - " 82,754,371\n", - " 141,256,760\n", + " 6,625,911\n", + " 20,800,644\n", + " 33,602,145\n", + " 61,028,701\n", " \n", " \n", "\n", @@ -4252,56 +5724,50 @@ " line Y1 Y2 \\\n", "0 Genesys CX 3 platform licences 2,788,232 2,788,232 \n", "1 Voice Bot 680,592 972,240 \n", - "2 Agentic Virtual Agent 0 2,471,160 \n", - "3 Speech & Text Analytics 751,680 751,680 \n", - "4 Agent Copilot 1,002,240 1,002,240 \n", + "2 Agentic Virtual Agent 0 1,606,248 \n", + "3 Speech & Text Analytics [named] 751,680 751,680 \n", + "4 Agent Copilot [named] 1,002,240 1,002,240 \n", "5 AI Summary & Insights 0 0 \n", "6 Direct Messaging 132 144 \n", - "7 Email AI (Auto-Suggest) 0 466,200 \n", - "8 Email AI (Auto-Respond) 0 311,472 \n", - "9 AI Translate 0 0 \n", - "10 NICE IEX (NAM) 1,300,000 1,300,000 \n", - "11 Legacy CC platform 0 0 \n", - "12 Voice Bot deflection (labour avoided) 13,122,330 20,995,729 \n", - "13 STA coaching (AHT) 562,386 899,817 \n", - "14 Voice AHT (knowledge surfacing) 1,237,248 1,979,597 \n", - "15 Voice ACW (summarization) 2,099,573 3,359,317 \n", - "16 Supervisor time (AI summaries/insights) 318,500 509,600 \n", - "17 Agentic Virtual Agent deflection (labour avoided) 0 6,007,689 \n", - "18 Email Auto-Respond (displaced handling) 0 3,634,923 \n", - "19 Email Auto-Suggest (drafting time) 0 1,744,763 \n", - "20 TOTAL COSTS 5,222,876 8,763,368 \n", - "21 TOTAL TAKEOUTS 1,300,000 1,300,000 \n", - "22 TOTAL BENEFITS 17,340,037 39,131,434 \n", - "23 NET 13,417,161 31,668,067 \n", - "24 Cumulative net 13,417,161 45,085,228 \n", + "7 Email AI (Auto-Suggest) 0 0 \n", + "8 AI Translate 0 0 \n", + "9 NICE IEX (NAM) 650,000 1,300,000 \n", + "10 Legacy CC platform 0 0 \n", + "11 Voice Bot deflection (labour avoided) 6,981,080 11,169,728 \n", + "12 STA coaching (AHT) 562,386 899,817 \n", + "13 Voice AHT (knowledge surfacing) 1,237,248 1,979,597 \n", + "14 Voice ACW (summarization) 2,099,573 3,359,317 \n", + "15 Supervisor time (AI summaries/insights) 318,500 509,600 \n", + "16 Agentic Virtual Agent deflection (labour avoided) 0 2,077,459 \n", + "17 TOTAL COSTS 5,222,876 7,120,784 \n", + "18 TOTAL TAKEOUTS 650,000 1,300,000 \n", + "19 TOTAL BENEFITS 11,198,787 19,995,517 \n", + "20 NET 6,625,911 14,174,734 \n", + "21 Cumulative net 6,625,911 20,800,644 \n", "\n", " Y3 3-yr Total \n", "0 2,788,232 8,364,695 \n", "1 972,240 2,625,072 \n", - "2 3,530,220 6,001,380 \n", + "2 2,294,640 3,900,888 \n", "3 751,680 2,255,040 \n", "4 1,002,240 3,006,720 \n", "5 0 0 \n", "6 144 420 \n", - "7 466,200 932,400 \n", - "8 444,960 756,432 \n", - "9 143,520 143,520 \n", - "10 1,300,000 3,900,000 \n", - "11 0 0 \n", - "12 24,932,428 59,050,487 \n", - "13 1,068,533 2,530,735 \n", - "14 2,350,772 5,567,617 \n", - "15 3,989,188 9,448,078 \n", - "16 605,150 1,433,250 \n", - "17 7,134,130 13,141,819 \n", - "18 4,316,471 7,951,394 \n", - "19 2,071,906 3,816,669 \n", - "20 10,099,436 24,085,679 \n", - "21 1,300,000 3,900,000 \n", - "22 46,468,578 102,940,050 \n", - "23 37,669,143 82,754,371 \n", - "24 82,754,371 141,256,760 " + "7 0 0 \n", + "8 4,434,000 4,434,000 \n", + "9 1,300,000 3,250,000 \n", + "10 0 0 \n", + "11 13,264,052 31,414,859 \n", + "12 1,068,533 2,530,735 \n", + "13 2,350,772 5,567,617 \n", + "14 3,989,188 9,448,078 \n", + "15 605,150 1,433,250 \n", + "16 2,466,982 4,544,441 \n", + "17 12,243,176 24,586,835 \n", + "18 1,300,000 3,250,000 \n", + "19 23,744,677 54,938,980 \n", + "20 12,801,501 33,602,145 \n", + "21 33,602,145 61,028,701 " ] }, "metadata": {}, @@ -4311,7 +5777,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "NPV @ 8%: $69,476,540 Payback: 0.00 years 3-yr ROI: 344%\n" + "NPV @ 8%: $28,449,896 Payback: 0.00 years 3-yr ROI: 137%\n" ] } ], @@ -4338,7 +5804,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "c869422e", "metadata": {}, "outputs": [ @@ -4358,7 +5824,7 @@ "dtype": "i1" }, "y": { - "bdata": "Jh2fUphIRkF0ls26PqRrQZDCdUr8mn1B", + "bdata": "kAHmzPfxOkFcMJWpTCZjQS9Qu52k7HBB", "dtype": "f8" } }, @@ -4371,7 +5837,7 @@ "dtype": "i1" }, "y": { - "bdata": "4CFRL1mXaUEbRntgkX+FQQJZQwrtupNB", + "bdata": "WN07tp1GWUFNYpBFSNZzQWInjgrTBYBB", "dtype": "f8" } }, @@ -4384,7 +5850,7 @@ "dtype": "i1" }, "y": { - "bdata": "3FW3T0n4fUHmLFufJruVQWoygHZnKKJB", + "bdata": "1qNwJg/IaUHmEqiU71F/QR6FGxpGI4dB", "dtype": "f8" } } @@ -5179,7 +6645,7 @@ } } }, - 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -5208,7 +6674,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "id": "bab4c657", "metadata": {}, "outputs": [ @@ -5224,17 +6690,17 @@ "orientation": "h", "type": "bar", "x": { - "bdata": "ADho8SUOIUGArP2C+/UnwYDS8BGK9jHBQLa9ZjWZM8HAsDQbx3o2wcAuqPeUez7BBOFkAqq/ZsE=", + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJskqLbE8UDAN2jxJQ4hQXDS8BGK9jHBwC6o95R7PsE=", "dtype": "f8" }, "y": [ - "voice_bot_avg_minutes", - "email_auto_suggest_acceptance", - "voice_knowledge_eligibility", - "email_auto_respond_rate", "agentic_va_deflection", - "voice_summarization_eligibility", - "voice_bot_deflection" + "email_auto_respond_rate", + "email_auto_suggest_acceptance", + "voice_bot_deflection", + "voice_bot_avg_minutes", + "voice_knowledge_eligibility", + "voice_summarization_eligibility" ] }, { @@ -5242,17 +6708,17 @@ "orientation": "h", "type": "bar", "x": { - "bdata": "gCS5F+gNIcGArP2C+/UnQUDS8BGK9jFBwDDyxQ2ZM0GAX/Nk0Xo2QYAuqPeUez5BMNL/362/ZkE=", + "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALJrSpjG8cCgJLkX6A0hwUDS8BGK9jFBoC6o95R7PkE=", "dtype": "f8" }, "y": [ - "voice_bot_avg_minutes", - "email_auto_suggest_acceptance", - "voice_knowledge_eligibility", - "email_auto_respond_rate", "agentic_va_deflection", - "voice_summarization_eligibility", - "voice_bot_deflection" + "email_auto_respond_rate", + "email_auto_suggest_acceptance", + "voice_bot_deflection", + "voice_bot_avg_minutes", + "voice_knowledge_eligibility", + "voice_summarization_eligibility" ] } ], @@ -6045,7 +7511,7 @@ } } }, - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -6080,7 +7546,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "id": "2b3dee90", "metadata": {}, "outputs": [ @@ -6088,7 +7554,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Break-even auto-respond rate: 0% (NPV already $64,339,036 at 0%)\n" + "Break-even auto-respond rate: 0% (NPV already $28,449,896 at 0%)\n" ] }, { @@ -6119,7 +7585,7 @@ }, "xaxis": "x", "y": { - "bdata": "vbmd4OKtjkEnJxdzmeyOQZCUkAVQK49BcfdX6gZqj0HbZNF8vaiPQbvHmGF0549BkhoJehUTkEGpmBB3cDKQQbiCqTXMUZBBbDnmfidxkEHd6knxgpCQQZGhhjrer5BBRljDgznPkEG3CSf2lO6QQc6HLvPvDZFB3HHHsUstkUGRKAT7pkyRQfdCJh4CbJFBtpCktl2LkUFqR+H/uKqRQdv4RHIUypFBkK+Bu2/pkUFFZr4EywiSQbUXIncmKJJBas5ewIFHkkHaf8Iy3WaSQQ==", + "bdata": "SGA3gsYhe0FIYDeCxiF7QUhgN4LGIXtBSGA3gsYhe0FIYDeCxiF7QUhgN4LGIXtBSGA3gsYhe0FIYDeCxiF7QUhgN4LGIXtBSGA3gsYhe0FIYDeCxiF7QUhgN4LGIXtBSGA3gsYhe0FIYDeCxiF7QUhgN4LGIXtBSGA3gsYhe0FIYDeCxiF7QUhgN4LGIXtBSGA3gsYhe0FIYDeCxiF7QUhgN4LGIXtBSGA3gsYhe0FIYDeCxiF7QUhgN4LGIXtBSGA3gsYhe0FIYDeCxiF7QQ==", "dtype": "f8" }, "yaxis": "y" @@ -6930,7 +8396,7 @@ } } }, - "image/png": 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" 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}, "metadata": {}, "output_type": "display_data" @@ -6964,21 +8430,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "id": "57611721", "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'openpyxl'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m 7\u001b[39m benefit_params=BENEFIT_PARAMS_MODE))\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;28;01min\u001b[39;00m (\u001b[33m\"floor\"\u001b[39m, \u001b[33m\"realistic\"\u001b[39m, \u001b[33m\"stretch\"\u001b[39m))\n\u001b[32m 9\u001b[39m ])\n\u001b[32m 10\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m xlsx = export_excel({\n\u001b[32m 12\u001b[39m \u001b[33m\"Inputs\"\u001b[39m: sites_dataframe(sites),\n\u001b[32m 13\u001b[39m \u001b[33m\"Meters\"\u001b[39m: meters_dataframe(meters),\n\u001b[32m 14\u001b[39m \u001b[33m\"Cost detail\"\u001b[39m: cost_3y,\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/git/palladium/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/exports.py:56\u001b[39m, in \u001b[36mexport_excel\u001b[39m\u001b[34m(sheets, path)\u001b[39m\n\u001b[32m 54\u001b[39m path = Path(path)\n\u001b[32m 55\u001b[39m path.parent.mkdir(parents=\u001b[38;5;28;01mTrue\u001b[39;00m, exist_ok=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[30;43mpd\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mExcelWriter\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mengine\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mopenpyxl\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;28;01mas\u001b[39;00m writer:\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m name, df \u001b[38;5;129;01min\u001b[39;00m sheets.items():\n\u001b[32m 58\u001b[39m df.to_excel(writer, sheet_name=name[:\u001b[32m31\u001b[39m], index=\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/env/palladium/lib/python3.12/site-packages/pandas/io/excel/_openpyxl.py:58\u001b[39m, in \u001b[36mOpenpyxlWriter.__init__\u001b[39m\u001b[34m(self, path, engine, date_format, datetime_format, mode, storage_options, if_sheet_exists, engine_kwargs, **kwargs)\u001b[39m\n\u001b[32m 45\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m( \u001b[38;5;66;03m# pyright: ignore[reportInconsistentConstructor]\u001b[39;00m\n\u001b[32m 46\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 47\u001b[39m path: FilePath | WriteExcelBuffer | ExcelWriter,\n\u001b[32m (...)\u001b[39m\u001b[32m 56\u001b[39m ) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 57\u001b[39m \u001b[38;5;66;03m# Use the openpyxl module as the Excel writer.\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m58\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mopenpyxl\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mworkbook\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Workbook\n\u001b[32m 60\u001b[39m engine_kwargs = combine_kwargs(engine_kwargs, kwargs)\n\u001b[32m 62\u001b[39m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(\n\u001b[32m 63\u001b[39m path,\n\u001b[32m 64\u001b[39m mode=mode,\n\u001b[32m (...)\u001b[39m\u001b[32m 67\u001b[39m engine_kwargs=engine_kwargs,\n\u001b[32m 68\u001b[39m )\n", - "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'openpyxl'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Excel β†’ /Users/robert/git/palladium/studies/202512_GenesysCX/ctm-token-calculator/exports/ctm_token_calculator_realistic.xlsx\n", + "JSON β†’ /Users/robert/git/palladium/studies/202512_GenesysCX/ctm-token-calculator/exports/ctm_scenario_realistic.json\n" ] } ], diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tests/test_benefit_model.py b/studies/202512_GenesysCX/ctm-token-calculator/tests/test_benefit_model.py index f2b1dd8..1e3b114 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tests/test_benefit_model.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tests/test_benefit_model.py @@ -8,9 +8,11 @@ from tokencalc.benefit_model import ( calculate_acw_summarization_benefit, calculate_email_ai_benefit, calculate_total_benefit, + calculate_va_deflection_benefit, ) from tokencalc.defaults import CTM_DEFAULT_FEATURE_SCOPES, CTM_DEFAULT_SITES from tokencalc.inputs import WORKING_SECONDS_PER_YEAR, FeatureScope, SiteInput +from tokencalc.scenarios import BENEFIT_PARAMS ALL_SITES = [s.site_name for s in CTM_DEFAULT_SITES] @@ -105,3 +107,133 @@ def test_zero_volume_site_is_safe(): def test_working_seconds_constant(): assert WORKING_SECONDS_PER_YEAR == 2_080 * 3_600 + + +# ── Virtual Agent deflection tests ─────────────────────────────────────────── + +def test_va_bot_deflection_hand_check(): + """Voice Bot: 10,000 calls/mo Γ— 12 Γ— 35% bot_rate Γ— 300s AHT + Γ— 50% Y1 realization Γ— realization_factor Γ— $0.01/s. + + realistic realization_factor = 0.70 Γ— 0.80 Γ— (1 βˆ’ 0.05) = 0.532 + """ + site = _small_site() + df = calculate_va_deflection_benefit( + [site], + FeatureScope("Voice Bot", ["Small"], deflection_target=0.35), + "realistic", + year=1, + params="realistic", + ) + completion = BENEFIT_PARAMS["va_completion_rate"]["realistic"] + labour = BENEFIT_PARAMS["va_labour_realization"]["realistic"] + callback = BENEFIT_PARAMS["va_callback_discount"]["realistic"] + real_factor = completion * labour * (1.0 - callback) + expected = ( + 10_000 * 12 # annual calls + * 0.35 # bot deflection rate + * 300 # AHT seconds + * 0.50 # Y1 scenario realization + * real_factor # completion Γ— labour Γ— (1 βˆ’ callback) + * 0.01 # labour rate per second + ) + assert df["annual_value"].sum() == pytest.approx(expected) + + +def test_va_agentic_deflection_uses_residual(): + """Agentic VA must operate on the residual (1 βˆ’ bot_rate) call pool, + not the full volume. + + With bot_rate=0.35 and va_rate=0.15: + residual = 10,000 Γ— (1 βˆ’ 0.35) = 6,500 calls/mo + va_deflected = 6,500 Γ— 0.15 = 975 calls/mo + """ + site = _small_site() + df = calculate_va_deflection_benefit( + [site], + FeatureScope("Agentic Virtual Agent", ["Small"], deflection_target=0.15), + "realistic", + year=1, + params="realistic", + ) + completion = BENEFIT_PARAMS["va_completion_rate"]["realistic"] + labour = BENEFIT_PARAMS["va_labour_realization"]["realistic"] + callback = BENEFIT_PARAMS["va_callback_discount"]["realistic"] + real_factor = completion * labour * (1.0 - callback) + # realistic scenario: voice_bot_deflection = 0.35 + bot_rate = 0.35 + va_rate = 0.15 + expected = ( + 10_000 * 12 # annual calls + * (1.0 - bot_rate) * va_rate # residual Γ— va_rate (layered) + * 300 # AHT seconds + * 0.50 # Y1 scenario realization + * real_factor + * 0.01 + ) + assert df["annual_value"].sum() == pytest.approx(expected) + + +def test_va_no_double_count(): + """Combined bot + VA benefit must be less than the naive additive sum. + + Naive (wrong): volume Γ— (bot_rate + va_rate) Γ— AHT Γ— ... + Correct (layered): volume Γ— (bot_rate + (1βˆ’bot_rate)Γ—va_rate) Γ— AHT Γ— ... + + With bot=35%, va=15%: + naive total deflection = 50% + layered total deflection = 35% + 65%Γ—15% = 44.75% + """ + site = _small_site() + bot_scope = FeatureScope("Voice Bot", ["Small"], deflection_target=0.35) + va_scope = FeatureScope("Agentic Virtual Agent", ["Small"], deflection_target=0.15) + + bot_df = calculate_va_deflection_benefit([site], bot_scope, "realistic", year=1) + va_df = calculate_va_deflection_benefit([site], va_scope, "realistic", year=1) + combined = bot_df["annual_value"].sum() + va_df["annual_value"].sum() + + # Naive additive (the old broken model): both on full volume + completion = BENEFIT_PARAMS["va_completion_rate"]["realistic"] + labour = BENEFIT_PARAMS["va_labour_realization"]["realistic"] + callback = BENEFIT_PARAMS["va_callback_discount"]["realistic"] + real_factor = completion * labour * (1.0 - callback) + naive = ( + 10_000 * 12 * (0.35 + 0.15) * 300 * 0.50 * real_factor * 0.01 + ) + assert combined < naive, ( + f"Combined layered benefit ({combined:.2f}) should be less than " + f"naive additive ({naive:.2f}) β€” double-count not fixed" + ) + + # Also verify the exact layered total + layered_deflection = 0.35 + (1.0 - 0.35) * 0.15 # = 0.4475 + expected_combined = ( + 10_000 * 12 * layered_deflection * 300 * 0.50 * real_factor * 0.01 + ) + assert combined == pytest.approx(expected_combined) + + +def test_va_claim_params_reproduce_no_haircut(): + """params='claim' must apply zero haircuts (all factors = 1.0), + reproducing the original Genesys ROI-doc assumption.""" + site = _small_site() + df_claim = calculate_va_deflection_benefit( + [site], + FeatureScope("Voice Bot", ["Small"], deflection_target=0.35), + "realistic", + year=1, + params="claim", + ) + df_realistic = calculate_va_deflection_benefit( + [site], + FeatureScope("Voice Bot", ["Small"], deflection_target=0.35), + "realistic", + year=1, + params="realistic", + ) + # claim should be strictly higher (no haircuts applied) + assert df_claim["annual_value"].sum() > df_realistic["annual_value"].sum() + + # claim realization_factor = 1.0 Γ— 1.0 Γ— (1 βˆ’ 0.0) = 1.0 + expected_claim = 10_000 * 12 * 0.35 * 300 * 0.50 * 1.0 * 0.01 + assert df_claim["annual_value"].sum() == pytest.approx(expected_claim) diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tests/test_business_case.py b/studies/202512_GenesysCX/ctm-token-calculator/tests/test_business_case.py index 76dcd49..2ddaeb1 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tests/test_business_case.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tests/test_business_case.py @@ -111,7 +111,7 @@ def test_scenario_json_roundtrip(tmp_path): scenario_state_to_json( CTM_DEFAULT_SITES, CTM_DEFAULT_TAKEOUTS, CTM_DEFAULT_FEATURE_SCOPES, p ) - sites, takeouts, scopes = scenario_state_from_json(p) + sites, takeouts, scopes, _rollout = scenario_state_from_json(p) assert [s.site_name for s in sites] == [s.site_name for s in CTM_DEFAULT_SITES] assert takeouts[0].annual_cost == CTM_DEFAULT_TAKEOUTS[0].annual_cost assert scopes[0].adoption_curve == CTM_DEFAULT_FEATURE_SCOPES[0].adoption_curve diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tests/test_cost_model.py b/studies/202512_GenesysCX/ctm-token-calculator/tests/test_cost_model.py index a22929d..e6c5f2c 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tests/test_cost_model.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tests/test_cost_model.py @@ -34,8 +34,8 @@ def test_default_sites_match_contracted_users(): def test_sta_acceptance_number(): """2,088 users Γ— 30 tokens Γ— 12 months Γ— $1 = $751,680.""" df = calculate_per_user_ai_cost( - CTM_DEFAULT_SITES, _scope("Speech & Text Analytics"), - DEFAULT_METERS["Speech & Text Analytics"], DEFAULT_PRICING, + CTM_DEFAULT_SITES, _scope("Speech & Text Analytics [named]"), + DEFAULT_METERS["Speech & Text Analytics [named]"], DEFAULT_PRICING, ) assert df["annual_cost"].sum() == pytest.approx(751_680) @@ -43,16 +43,20 @@ def test_sta_acceptance_number(): def test_agent_copilot_acceptance_number(): """2,088 users Γ— 40 tokens Γ— 12 months Γ— $1 = $1,002,240.""" df = calculate_per_user_ai_cost( - CTM_DEFAULT_SITES, _scope("Agent Copilot"), - DEFAULT_METERS["Agent Copilot"], DEFAULT_PRICING, + CTM_DEFAULT_SITES, _scope("Agent Copilot [named]"), + DEFAULT_METERS["Agent Copilot [named]"], DEFAULT_PRICING, ) assert df["annual_cost"].sum() == pytest.approx(1_002_240) -def test_per_user_not_active_before_phase(): - df = calculate_per_user_ai_cost( - CTM_DEFAULT_SITES, _scope("AI Translate", phase=3), - DEFAULT_METERS["AI Translate"], DEFAULT_PRICING, year=2, +def test_ai_translate_not_active_before_phase(): + """AI Translate (consumption meter) produces zero cost before its phase.""" + scenario = get_scenario("realistic") + apac_sites = [s.site_name for s in CTM_DEFAULT_SITES if s.region_pricing == "APAC"] + df = calculate_consumption_ai_cost( + CTM_DEFAULT_SITES, + _scope("AI Translate", apac_sites, phase=3), + DEFAULT_METERS["AI Translate"], scenario, DEFAULT_PRICING, year=2, ) assert df["annual_cost"].sum() == 0 @@ -63,7 +67,7 @@ def test_copilot_covers_supervisor_summary(): total = calculate_total_cost( CTM_DEFAULT_SITES, [ - _scope("Agent Copilot"), + _scope("Agent Copilot [named]"), _scope("AI Summary & Insights"), ], DEFAULT_METERS, DEFAULT_PRICING, scenario, year=1, @@ -111,8 +115,8 @@ def test_regional_pricing_not_hardcoded(): pricing["APAC"] = TokenPricing(region="APAC", list_rate_per_token=2.0) apac_site = next(s for s in CTM_DEFAULT_SITES if s.region_pricing == "APAC") df = calculate_per_user_ai_cost( - [apac_site], _scope("Speech & Text Analytics", [apac_site.site_name]), - DEFAULT_METERS["Speech & Text Analytics"], pricing, + [apac_site], _scope("Speech & Text Analytics [named]", [apac_site.site_name]), + DEFAULT_METERS["Speech & Text Analytics [named]"], pricing, ) expected = apac_site.named_users * 30 * 12 * 2.0 assert df["annual_cost"].sum() == pytest.approx(expected) diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tests/test_meters.py b/studies/202512_GenesysCX/ctm-token-calculator/tests/test_meters.py index 9124da3..d1b2b85 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tests/test_meters.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tests/test_meters.py @@ -10,10 +10,26 @@ from tokencalc.meters import Confidence, MeterType, TokenMeter, TokenPricing def test_all_spec_meters_present(): expected = { - "Voice Bot", "Virtual Agent (legacy)", "Agentic Virtual Agent", - "AI Summary & Insights", "Direct Messaging", "Social Listening", - "Social Responses", "Speech & Text Analytics", "Agent Copilot", - "Email AI (Auto-Suggest)", "Email AI (Auto-Respond)", "AI Translate", + # Voice / Bot + "Voice Bot", "Digital Bot", + # Virtual Agent + "Virtual Agent (legacy)", "Agentic Virtual Agent", + # Agent Copilot (named + concurrent) + "Agent Copilot [named]", "Agent Copilot [concurrent]", + # AI Quality / Analytics + "AI Scoring", "AI Summary & Insights", + # Speech & Text Analytics (named + concurrent) + "Speech & Text Analytics [named]", "Speech & Text Analytics [concurrent]", + # Routing + "Predictive Routing", + # Messaging + "Direct Messaging", "Social Listening", "Social Responses", + # Language + "AI Translate", + # Genesys Cloud Copilot + "Genesys Cloud Copilot", + # Email AI (rates TBD) + "Email AI (Auto-Suggest)", "Email AI (Auto-Respond)", } assert expected == set(DEFAULT_METERS) @@ -22,17 +38,29 @@ def test_confirmed_rates(): m = DEFAULT_METERS assert m["Voice Bot"].units_per_token == 17 assert m["Voice Bot"].tokens_per_unit == pytest.approx(0.0588, abs=1e-3) + assert m["Digital Bot"].units_per_token == 51 assert m["Agentic Virtual Agent"].tokens_per_unit == 1.2 assert m["AI Summary & Insights"].tokens_per_unit == 0.02 assert m["Direct Messaging"].units_per_token == 400 - assert m["Speech & Text Analytics"].tokens_per_unit == 30 - assert m["Agent Copilot"].tokens_per_unit == 40 + # Named variants + assert m["Speech & Text Analytics [named]"].tokens_per_unit == 30 + assert m["Speech & Text Analytics [concurrent]"].tokens_per_unit == 45 + assert m["Agent Copilot [named]"].tokens_per_unit == 40 + assert m["Agent Copilot [concurrent]"].tokens_per_unit == 60 + # AI Translate is now a confirmed consumption meter + assert m["AI Translate"].tokens_per_unit == 0.5 + assert m["AI Translate"].units_per_token == 2 + assert m["AI Translate"].confidence is Confidence.CONFIRMED + # New meters + assert m["AI Scoring"].units_per_token == 20 + assert m["Predictive Routing"].units_per_token == 17 + assert m["Genesys Cloud Copilot"].units_per_token == 20 def test_unknown_meters_flagged(): unknown = {f for f, m in DEFAULT_METERS.items() if m.confidence is Confidence.UNKNOWN} assert unknown == { - "Email AI (Auto-Suggest)", "Email AI (Auto-Respond)", "AI Translate" + "Email AI (Auto-Suggest)", "Email AI (Auto-Respond)", } assert Confidence.UNKNOWN.icon == "πŸ”΄" assert Confidence.CONFIRMED.icon == "🟒" diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/benefit_model.py b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/benefit_model.py index 0b9a2d0..eb69d10 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/benefit_model.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/benefit_model.py @@ -206,7 +206,7 @@ def calculate_sta_benefit( return _df(rows) -def calculate_bot_deflection_benefit( +def calculate_va_deflection_benefit( sites: list[SiteInput], feature_scope: FeatureScope, scenario: str | Scenario, @@ -214,41 +214,85 @@ def calculate_bot_deflection_benefit( params: str = "realistic", rollout: RolloutPlan | None = None, ) -> pd.DataFrame: - """Agent labour avoided on calls deflected to Voice Bot / Agentic VA. + """Agent labour avoided on calls deflected to Voice Bot or Agentic VA. - Not in the original function list but required for a complete net - case β€” deflected volume never reaches an agent, so the full AHT is - avoided. + **Layered (sequential) deflection model** β€” Voice Bot runs first on + the full call pool; Agentic VA handles a share of the *residual* + (calls the bot did not deflect). The two mechanisms are substitutes + operating on the same call base, not independent additive benefits. + + Effective total deflection: + bot_rate + (1 βˆ’ bot_rate) Γ— va_rate + e.g. 35% + 65% Γ— 15% = 44.75% (not 50%) + + **Three realization haircuts** are applied to convert raw deflected + volume into realizable labour savings: + + 1. ``completion_rate`` β€” share of "deflected" calls that don't + escalate to an agent mid-session (bot/VA fully handles the call). + 2. ``labour_realization`` β€” staffing flexibility factor; deflected + volume doesn't reduce headcount 1:1 due to minimums, shrinkage, + and occupancy ceilings. + 3. ``callback_discount`` β€” fraction of deflected calls that re-enter + as repeat contacts (poorly-handled deflections drive callbacks). + + Combined realistic factor: 0.70 Γ— 0.80 Γ— (1 βˆ’ 0.05) β‰ˆ 0.53 + + The ``params="claim"`` path sets all three factors to their + ``claim`` values (1.0 / 1.0 / 0.0) to reproduce the original + Genesys ROI-doc figures for side-by-side comparison. """ sc = get_scenario(scenario) if isinstance(scenario, str) else scenario ro = rollout or NO_ROLLOUT realization = sc.realization(year) + + # Realization haircuts β€” read from BENEFIT_PARAMS so claim/realistic + # paths are consistent with all other benefit lines. + completion_rate = _param("va_completion_rate", params) + labour_real = _param("va_labour_realization", params) + callback_disc = _param("va_callback_discount", params) + realization_factor = completion_rate * labour_real * (1.0 - callback_disc) + rows = [] for s in sites: if not feature_scope.active(s.site_name, year): continue + if feature_scope.feature == "Voice Bot": - deflection = ( + # Bot operates on the full call pool. + bot_rate = ( feature_scope.deflection_target if feature_scope.deflection_target is not None else sc.voice_bot_deflection ) + deflected_calls = s.voice_volume_monthly * MONTHS_PER_YEAR * bot_rate + else: # Agentic Virtual Agent - deflection = ( + # VA operates on the residual after the bot has deflected its share. + # If Voice Bot is not in scope (VA-only deployment), bot_rate = 0 + # and the VA works on the full pool β€” still correct. + bot_rate = sc.voice_bot_deflection + va_rate = ( feature_scope.deflection_target if feature_scope.deflection_target is not None else sc.agentic_va_deflection ) - seconds_saved = ( - s.voice_volume_monthly * MONTHS_PER_YEAR - * deflection * s.voice_aht_seconds * realization - ) + residual_calls = ( + s.voice_volume_monthly * MONTHS_PER_YEAR * (1.0 - bot_rate) + ) + deflected_calls = residual_calls * va_rate + + seconds_saved = deflected_calls * s.voice_aht_seconds * realization rows.append( { "benefit_line": f"{feature_scope.feature} deflection (labour avoided)", "scope": s.site_name, - "annual_value": seconds_saved * s.agent_cost_per_second - * ro.fraction_live(s.site_name, year), + "annual_value": ( + seconds_saved + * s.agent_cost_per_second + * realization_factor + * ro.fraction_live(s.site_name, year) + ), "confidence": Confidence.ESTIMATED.value, } ) @@ -320,19 +364,30 @@ def calculate_predictive_routing_benefit( #: Which calculator handles which feature scope. +#: Agent Copilot and STA exist in named/concurrent variants β€” both map +#: to the same benefit calculators. +#: Voice Bot and Agentic VA both route to calculate_va_deflection_benefit, +#: which implements the layered sequential model β€” VA operates on the +#: residual after the bot has deflected its share. _BENEFIT_DISPATCH = { - "Agent Copilot": ( + "Agent Copilot [named]": ( + calculate_voice_handle_time_benefit, + calculate_acw_summarization_benefit, + ), + "Agent Copilot [concurrent]": ( calculate_voice_handle_time_benefit, calculate_acw_summarization_benefit, ), "AI Summary & Insights": (), # benefit carried by Copilot where present - "Speech & Text Analytics": (calculate_sta_benefit,), - "Voice Bot": (calculate_bot_deflection_benefit,), - "Agentic Virtual Agent": (calculate_bot_deflection_benefit,), - "Email AI (Auto-Respond)": (calculate_email_ai_benefit,), + "Speech & Text Analytics [named]": (calculate_sta_benefit,), + "Speech & Text Analytics [concurrent]": (calculate_sta_benefit,), + "Voice Bot": (calculate_va_deflection_benefit,), + "Agentic Virtual Agent": (calculate_va_deflection_benefit,), "Predictive Routing": (calculate_predictive_routing_benefit,), } +_COPILOT_FEATURES = {"Agent Copilot [named]", "Agent Copilot [concurrent]"} + def calculate_total_benefit( sites: list[SiteInput], @@ -346,10 +401,18 @@ def calculate_total_benefit( """All benefit lines for one scenario-year, aggregated per line. Returns DataFrame: benefit_line, scope, annual_value, confidence. + + Voice Bot and Agentic VA deflection benefits use the layered + sequential model: the bot deflects from the full call pool; the VA + deflects from the residual. The two features are NOT additive on + the same base β€” see :func:`calculate_va_deflection_benefit`. """ sc = get_scenario(scenario) if isinstance(scenario, str) else scenario frames: list[pd.DataFrame] = [] - copilot_scope = _scope_for(feature_scopes, "Agent Copilot") + # Find whichever Copilot variant is in scope (named or concurrent). + copilot_scope = next( + (s for s in feature_scopes if s.feature in _COPILOT_FEATURES), None + ) for scope in feature_scopes: for fn in _BENEFIT_DISPATCH.get(scope.feature, ()): # type: ignore[arg-type] diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/cost_model.py b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/cost_model.py index c5d4ab5..12f9868 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/cost_model.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/cost_model.py @@ -7,8 +7,8 @@ Correctness rules implemented here (see spec Β§4.1): is enabled at a site, AI Summary & Insights consumption at that site is forced to zero β€” Copilot's per-user token rate already includes interaction summarization. Source: Genesys Cloud AI Experience - tokens FAQ, - https://help.mypurecloud.com/articles/genesys-cloud-ai-experience-tokens-faqs/ + token metering, + https://help.genesys.cloud/articles/genesys-cloud-tokens-model/ 2. **Token rounding.** Genesys rounds consumption up at billing β€” ``math.ceil`` is applied to each site's MONTHLY consumption token total before the rate. Per-user totals (users Γ— tokens/user/month) @@ -133,12 +133,18 @@ def _monthly_units(site: SiteInput, feature: str, scope: FeatureScope, site.voice_volume_monthly * deflection * scenario.voice_bot_avg_minutes ) # minutes if feature == "Agentic Virtual Agent": - deflection = ( + # Layered model: VA operates on the residual volume after the voice bot + # has already deflected its share. Cost base = residual Γ— va_rate. + # This is consistent with the benefit model and avoids double-counting + # the same call pool across both deflection mechanisms. + bot_deflection = scenario.voice_bot_deflection + va_deflection = ( scope.deflection_target if scope.deflection_target is not None else scenario.agentic_va_deflection ) - return site.voice_volume_monthly * deflection # interactions + residual = site.voice_volume_monthly * (1.0 - bot_deflection) + return residual * va_deflection # interactions if feature == "Virtual Agent (legacy)": deflection = scope.deflection_target or 0.0 return site.voice_volume_monthly * deflection @@ -159,6 +165,11 @@ def _monthly_units(site: SiteInput, feature: str, scope: FeatureScope, if feature in ("Direct Messaging", "Social Listening", "Social Responses"): eligibility = scope.eligibility_pct if scope.eligibility_pct is not None else 1.0 return (site.chat_volume_monthly + site.sms_volume_monthly) * eligibility + if feature == "AI Translate": + # Each voice interaction generates one translation; eligibility_pct + # can be used to scope to a subset of interactions (e.g. non-English only). + eligibility = scope.eligibility_pct if scope.eligibility_pct is not None else 1.0 + return site.voice_volume_monthly * eligibility # translations raise KeyError(f"No consumption-volume mapping for feature {feature!r}") @@ -260,11 +271,12 @@ def calculate_total_cost( # Rule 1: Agent Copilot covers Supervisor AI Summary. Sites where # Copilot is active this year are excluded from AI Summary billing β€” - # Copilot's 40 tokens/user/month already includes summarization. - # https://help.mypurecloud.com/articles/genesys-cloud-ai-experience-tokens-faqs/ + # Copilot's per-user token rate already includes interaction summarization. + # https://help.genesys.cloud/articles/genesys-cloud-tokens-model/ + _COPILOT_FEATURES = {"Agent Copilot [named]", "Agent Copilot [concurrent]"} copilot_sites: set[str] = set() for scope in feature_scopes: - if scope.feature == "Agent Copilot": + if scope.feature in _COPILOT_FEATURES: copilot_sites |= { s.site_name for s in sites if scope.active(s.site_name, year) } diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/defaults.py b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/defaults.py index 76bfdb5..020e60f 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/defaults.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/defaults.py @@ -29,8 +29,8 @@ DEFAULT_DISCOUNT_RATE = 0.08 #: analysis horizon in the P&L. ESTIMATED β€” confirm with delivery team. DEFAULT_IMPLEMENTATION_COST = 0.0 -_GENESYS_TOKEN_FAQ = ( - "https://help.mypurecloud.com/articles/genesys-cloud-ai-experience-tokens-faqs/" +_GENESYS_TOKEN_METERS = ( + "https://help.genesys.cloud/articles/genesys-cloud-tokens-model/" ) # ── Token meters ───────────────────────────────────────────────────── @@ -41,6 +41,7 @@ _GENESYS_TOKEN_FAQ = ( DEFAULT_METERS: dict[str, TokenMeter] = { m.feature: m for m in [ + # ── Voice / Bot ─────────────────────────────────────────────── TokenMeter( feature="Voice Bot", meter_type=MeterType.PER_MINUTE, @@ -48,16 +49,26 @@ DEFAULT_METERS: dict[str, TokenMeter] = { tokens_per_unit=1 / 17, # 0.0588 confidence=Confidence.CONFIRMED, notes="IVR self-service voice bot minutes; 17 min per token.", - source_url=_GENESYS_TOKEN_FAQ, + source_url=_GENESYS_TOKEN_METERS, ), + TokenMeter( + feature="Digital Bot", + meter_type=MeterType.PER_INTERACTION, + units_per_token=51.0, + tokens_per_unit=1 / 51, # 0.0196 + confidence=Confidence.CONFIRMED, + notes="Digital (non-voice) bot sessions; 51 sessions per token.", + source_url=_GENESYS_TOKEN_METERS, + ), + # ── Virtual Agent ───────────────────────────────────────────── TokenMeter( feature="Virtual Agent (legacy)", meter_type=MeterType.PER_INTERACTION, units_per_token=2.0, tokens_per_unit=0.5, confidence=Confidence.CONFIRMED, - notes="Legacy (non-agentic) virtual agent; 2 interactions per token.", - source_url=_GENESYS_TOKEN_FAQ, + notes="Legacy (non-agentic) virtual agent; 0.5 tokens per interaction.", + source_url=_GENESYS_TOKEN_METERS, ), TokenMeter( feature="Agentic Virtual Agent", @@ -66,7 +77,42 @@ DEFAULT_METERS: dict[str, TokenMeter] = { tokens_per_unit=1.2, confidence=Confidence.CONFIRMED, notes="Agentic VA; 1.2 tokens per interaction.", - source_url=_GENESYS_TOKEN_FAQ, + source_url=_GENESYS_TOKEN_METERS, + ), + # ── Agent Copilot (named vs concurrent) ─────────────────────── + TokenMeter( + feature="Agent Copilot [named]", + meter_type=MeterType.PER_USER_PER_MONTH, + units_per_token=0.0, + tokens_per_unit=40.0, + confidence=Confidence.CONFIRMED, + notes=( + "40 tokens per named user per month. Includes interaction " + "summarization (covers AI Summary & Insights)." + ), + source_url=_GENESYS_TOKEN_METERS, + ), + TokenMeter( + feature="Agent Copilot [concurrent]", + meter_type=MeterType.PER_USER_PER_MONTH, + units_per_token=0.0, + tokens_per_unit=60.0, + confidence=Confidence.CONFIRMED, + notes=( + "60 tokens per concurrent user per month. Includes interaction " + "summarization (covers AI Summary & Insights)." + ), + source_url=_GENESYS_TOKEN_METERS, + ), + # ── AI Quality / Analytics ──────────────────────────────────── + TokenMeter( + feature="AI Scoring", + meter_type=MeterType.PER_INTERACTION, + units_per_token=20.0, + tokens_per_unit=0.05, + confidence=Confidence.CONFIRMED, + notes="AI-scored quality evaluations; 20 evaluations per token.", + source_url=_GENESYS_TOKEN_METERS, ), TokenMeter( feature="AI Summary & Insights", @@ -78,16 +124,50 @@ DEFAULT_METERS: dict[str, TokenMeter] = { "Supervisor standalone summarization; 50 summaries per token. " "NOT metered where Agent Copilot is assigned β€” see cost model." ), - source_url=_GENESYS_TOKEN_FAQ, + source_url=_GENESYS_TOKEN_METERS, ), + # ── Speech & Text Analytics (named vs concurrent) ───────────── + TokenMeter( + feature="Speech & Text Analytics [named]", + meter_type=MeterType.PER_USER_PER_MONTH, + units_per_token=0.0, + tokens_per_unit=30.0, + confidence=Confidence.CONFIRMED, + notes="STA named licence; 30 tokens per named user per month.", + source_url=_GENESYS_TOKEN_METERS, + ), + TokenMeter( + feature="Speech & Text Analytics [concurrent]", + meter_type=MeterType.PER_USER_PER_MONTH, + units_per_token=0.0, + tokens_per_unit=45.0, + confidence=Confidence.CONFIRMED, + notes="STA concurrent licence; 45 tokens per concurrent user per month.", + source_url=_GENESYS_TOKEN_METERS, + ), + # ── Routing / Engagement ────────────────────────────────────── + TokenMeter( + feature="Predictive Routing", + meter_type=MeterType.PER_INTERACTION, + units_per_token=17.0, + tokens_per_unit=1 / 17, # 0.0588 + confidence=Confidence.CONFIRMED, + notes="Predictive routing; 17 routes per token.", + source_url=_GENESYS_TOKEN_METERS, + ), + # ── Messaging ───────────────────────────────────────────────── TokenMeter( feature="Direct Messaging", meter_type=MeterType.PER_MESSAGE, units_per_token=400.0, tokens_per_unit=0.0025, confidence=Confidence.CONFIRMED, - notes="FB/IG/WhatsApp messages; 400 messages per token.", - source_url=_GENESYS_TOKEN_FAQ, + notes=( + "Apple Messages for Business, Facebook Messenger, Instagram DM, " + "WhatsApp, and X (Twitter) DM; 400 inbound or outbound messages " + "per token. Additional carrier charges apply for WhatsApp and X." + ), + source_url=_GENESYS_TOKEN_METERS, ), TokenMeter( feature="Social Listening", @@ -95,8 +175,8 @@ DEFAULT_METERS: dict[str, TokenMeter] = { units_per_token=400.0, tokens_per_unit=0.0025, confidence=Confidence.CONFIRMED, - notes="400 messages per token.", - source_url=_GENESYS_TOKEN_FAQ, + notes="Genesys Cloud Social; 400 social post ingestions per channel per token.", + source_url=_GENESYS_TOKEN_METERS, ), TokenMeter( feature="Social Responses", @@ -104,53 +184,48 @@ DEFAULT_METERS: dict[str, TokenMeter] = { units_per_token=400.0, tokens_per_unit=0.0025, confidence=Confidence.CONFIRMED, - notes="400 messages per token.", - source_url=_GENESYS_TOKEN_FAQ, + notes="Social Post Responses; 400 outbound messages per channel per token.", + source_url=_GENESYS_TOKEN_METERS, ), + # ── Language / Translation ──────────────────────────────────── TokenMeter( - feature="Speech & Text Analytics", - meter_type=MeterType.PER_USER_PER_MONTH, - units_per_token=0.0, # n/a for per-user meters - tokens_per_unit=30.0, + feature="AI Translate", + meter_type=MeterType.PER_INTERACTION, + units_per_token=2.0, + tokens_per_unit=0.5, confidence=Confidence.CONFIRMED, - notes="STA: 30 tokens per named user per month.", - source_url=_GENESYS_TOKEN_FAQ, + notes="AI translation; 2 translations per token.", + source_url=_GENESYS_TOKEN_METERS, ), + # ── Genesys Cloud Copilot ───────────────────────────────────── TokenMeter( - feature="Agent Copilot", - meter_type=MeterType.PER_USER_PER_MONTH, - units_per_token=0.0, - tokens_per_unit=40.0, + feature="Genesys Cloud Copilot", + meter_type=MeterType.PER_INTERACTION, + units_per_token=20.0, + tokens_per_unit=0.05, confidence=Confidence.CONFIRMED, notes=( - "40 tokens per named user per month. Includes interaction " - "summarization (covers AI Summary & Insights)." + "20 AI actions per token; Genesys Cloud knowledge queries " + "are not charged." ), - source_url=_GENESYS_TOKEN_FAQ, + source_url=_GENESYS_TOKEN_METERS, ), + # ── Email AI (rates not yet published) ──────────────────────── TokenMeter( feature="Email AI (Auto-Suggest)", meter_type=MeterType.PER_USER_PER_MONTH, units_per_token=0.0, - tokens_per_unit=30.0, # TBD β€” working default + tokens_per_unit=0.0, confidence=Confidence.UNKNOWN, - notes="Rate not yet sourced. Working default 30 tokens/user/month.", + notes="Requires Agent Copilot. Token rate not yet published.", ), TokenMeter( feature="Email AI (Auto-Respond)", meter_type=MeterType.PER_MESSAGE, - units_per_token=2.0, # TBD - tokens_per_unit=0.5, # TBD β€” working default - confidence=Confidence.UNKNOWN, - notes="Rate not yet sourced. Working default 0.5 tokens/message.", - ), - TokenMeter( - feature="AI Translate", - meter_type=MeterType.PER_USER_PER_MONTH, units_per_token=0.0, - tokens_per_unit=20.0, # TBD β€” working default + tokens_per_unit=0.0, confidence=Confidence.UNKNOWN, - notes="Rate not yet sourced. Working default 20 tokens/user/month.", + notes="Feature not yet available; rate TBD.", ), ] } @@ -342,14 +417,13 @@ CTM_DEFAULT_FEATURE_SCOPES: list[FeatureScope] = [ FeatureScope("Voice Bot", ALL_SITE_NAMES, phase=1, adoption_curve=_RAMP), FeatureScope("Agentic Virtual Agent", ["NAM", "EMEA"], phase=2, adoption_curve={2: 0.70, 3: 1.0}), - FeatureScope("Speech & Text Analytics", ALL_SITE_NAMES, phase=1), - FeatureScope("Agent Copilot", ALL_SITE_NAMES, phase=1), + # CTM has named licences β€” use the [named] variant for both STA and Copilot. + FeatureScope("Speech & Text Analytics [named]", ALL_SITE_NAMES, phase=1), + FeatureScope("Agent Copilot [named]", ALL_SITE_NAMES, phase=1), FeatureScope("AI Summary & Insights", ALL_SITE_NAMES, phase=1, adoption_curve=_RAMP), FeatureScope("Direct Messaging", ALL_SITE_NAMES, phase=1, adoption_curve=_RAMP), FeatureScope("Email AI (Auto-Suggest)", ["NAM", "EMEA"], phase=2), - FeatureScope("Email AI (Auto-Respond)", ["NAM", "EMEA"], phase=2, - adoption_curve={2: 0.70, 3: 1.0}), FeatureScope("AI Translate", ["APAC HK", "APAC SG", "APAC SH", "APAC GZ", "APAC JP", "APAC TW"], phase=3), diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/inputs.py b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/inputs.py index 4c3d34b..db8e542 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/inputs.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/inputs.py @@ -36,9 +36,15 @@ class SiteInput: voice_acw_seconds: int fully_loaded_agent_cost_annual: float fully_loaded_supervisor_cost_annual: float + licence_type: str = "named" # "named" | "concurrent" languages: list[str] = field(default_factory=list) def __post_init__(self) -> None: + if self.licence_type not in ("named", "concurrent"): + raise ValueError( + f"{self.site_name}: licence_type must be 'named' or 'concurrent', " + f"got {self.licence_type!r}" + ) if self.agents < 0 or self.supervisors < 0: raise ValueError(f"{self.site_name}: agent/supervisor counts must be >= 0") for name in ( diff --git a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/scenarios.py b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/scenarios.py index 91e5162..0ecf65b 100644 --- a/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/scenarios.py +++ b/studies/202512_GenesysCX/ctm-token-calculator/tokencalc/scenarios.py @@ -18,12 +18,28 @@ class Scenario: # ── Cost-side drivers ─────────────────────────────────────────── voice_bot_deflection: float # share of voice volume deflected to bot voice_bot_avg_minutes: float # bot minutes per deflected call - agentic_va_deflection: float # share of voice volume to agentic VA + # Agentic VA deflection is INCREMENTAL β€” applied to the residual volume + # after the voice bot has already handled its share (layered model). + # Effective total deflection = bot_rate + (1 βˆ’ bot_rate) Γ— va_rate. + agentic_va_deflection: float # share of RESIDUAL voice volume to agentic VA voice_summarization_eligibility: float voice_knowledge_eligibility: float email_auto_respond_rate: float # share of email auto-responded email_auto_suggest_acceptance: float + # ── Virtual Agent benefit realization factors ─────────────────── + # Applied to both Voice Bot and Agentic VA deflection benefits. + # completion_rate β€” share of "deflected" calls that don't escalate to an agent + # mid-session (bot/VA fully handles the interaction). + # labour_realization β€” staffing flexibility: deflected volume doesn't reduce + # headcount 1:1 due to minimums, shrinkage, occupancy ceilings. + # callback_discount β€” fraction of deflected calls that re-enter as repeat contacts + # (poorly-handled deflections drive callbacks). + # Combined realistic factor: 0.70 Γ— 0.80 Γ— (1 βˆ’ 0.05) β‰ˆ 0.53 + va_completion_rate: float = 0.70 + va_labour_realization: float = 0.80 + va_callback_discount: float = 0.05 + # year -> fraction of full benefit realized benefit_realization: dict[int, float] = field(default_factory=dict) @@ -59,10 +75,16 @@ BENEFIT_PARAMS: dict[str, dict[str, float]] = { "digital_aht_reduction": {"claim": 0.18, "realistic": 0.085}, # 5-12% Y1 "digital_acw_reduction": {"claim": 1.00, "realistic": 0.40}, # 30-50% Y1 "sta_aht_reduction": {"claim": 0.04, "realistic": 0.015}, # 1-2% Y1 - "email_auto_suggest_time_saving": {"claim": 0.30, "realistic": 0.30}, # Γ— acceptance + "email_auto_suggest_time_saving": {"claim": 0.40, "realistic": 0.30}, # Γ— acceptance; Genesys claims 40% # ESTIMATED lines (no Genesys claim published): "supervisor_copilot_time_saving": {"claim": 0.10, "realistic": 0.05}, "predictive_routing_aht_reduction": {"claim": 0.04, "realistic": 0.02}, + # Virtual Agent realization factors. + # ``claim`` = 100% realization (original model assumption β€” no haircuts). + # ``realistic`` = production-calibrated midpoints per the spec analysis. + "va_completion_rate": {"claim": 1.00, "realistic": 0.70}, # 60-75% voice bot; 50-70% agentic VA Y1 + "va_labour_realization": {"claim": 1.00, "realistic": 0.80}, # 70-85% staffing flexibility + "va_callback_discount": {"claim": 0.00, "realistic": 0.05}, # 5-10% deflected re-enter as repeat contacts } @@ -76,6 +98,11 @@ SCENARIOS: dict[str, Scenario] = { voice_knowledge_eligibility=0.40, email_auto_respond_rate=0.10, email_auto_suggest_acceptance=0.25, + # VA realization: conservative β€” low completion, limited staffing flex + # Combined: 0.60 Γ— 0.70 Γ— (1 βˆ’ 0.05) β‰ˆ 0.40 + va_completion_rate=0.60, + va_labour_realization=0.70, + va_callback_discount=0.05, benefit_realization={1: 0.30, 2: 0.60, 3: 0.80}, ), "realistic": Scenario( @@ -87,6 +114,11 @@ SCENARIOS: dict[str, Scenario] = { voice_knowledge_eligibility=0.60, email_auto_respond_rate=0.20, email_auto_suggest_acceptance=0.40, + # VA realization: production midpoints per spec analysis + # Combined: 0.70 Γ— 0.80 Γ— (1 βˆ’ 0.05) β‰ˆ 0.53 + va_completion_rate=0.70, + va_labour_realization=0.80, + va_callback_discount=0.05, benefit_realization={1: 0.50, 2: 0.80, 3: 0.95}, ), "stretch": Scenario( @@ -98,6 +130,11 @@ SCENARIOS: dict[str, Scenario] = { voice_knowledge_eligibility=0.80, email_auto_respond_rate=0.50, email_auto_suggest_acceptance=0.60, + # VA realization: optimistic β€” high completion, good staffing flexibility + # Combined: 0.75 Γ— 0.85 Γ— (1 βˆ’ 0.03) β‰ˆ 0.62 + va_completion_rate=0.75, + va_labour_realization=0.85, + va_callback_discount=0.03, benefit_realization={1: 0.75, 2: 0.95, 3: 1.00}, ), } diff --git a/studies/202512_GenesysCX/docs/Genesys-Token-Metering.md b/studies/202512_GenesysCX/docs/Genesys-Token-Metering.md new file mode 100644 index 0000000..5674f8f --- /dev/null +++ b/studies/202512_GenesysCX/docs/Genesys-Token-Metering.md @@ -0,0 +1,56 @@ +# Genesys Cloud AI Experience metering details +2026-06-07 +https://help.genesys.cloud/articles/genesys-cloud-tokens-model/ +This table describes the Genesys Cloud AI product’s raw meter and how many tokens are consumed. +Note: Direct Messaging (DM) channels include inbound and outbound messages. Social Responses messages include outbound messages only. + +Genesys Cloud product Units per token +Bots (Voice) 17 minutes per token. For more information, see the AI section of the Genesys Cloud pricing hub. +Bots (Digital) 51 sessions per token. For more information, see the AI section of the Genesys Cloud pricing hub. +Virtual Agent 0.5 tokens per Virtual Agent interaction - An interaction is defined in accordance with existing Genesys Cloud token-based pricing. +Agentic Virtual Agent 1.2 tokens per interaction - An interaction is defined in accordance with existing Genesys Cloud token-based pricing. +Agent Copilot [concurrent] One user requires 60 tokens +Agent Copilot [named] One user requires 40 tokens +AI Scoring 20 evaluations scored with AI scoring per token +AI Translate Two translations per token +AI Summary and Insights +50 AI summaries/insights per token. However, if you enable Agent Copilot simultaneously, then Supervisor Copilot summaries and insights do not consume tokens and instead rely on Agent Copilot functionality. +Apple Messages for Business 400 inbound or outbound messages per token. +Facebook Messenger† 400 inbound or outbound messages per token. +Instagram Direct Messaging† 400 inbound or outbound messages per token. +WhatsApp Messaging† 400 inbound or outbound messages per token. + +Other charges apply for WhatsApp. For more information, see the Messaging section of the Genesys Cloud pricing hub. + +X (formerly Twitter) Direct Messaging 400 inbound or outbound messages per token. + +Other charges apply for X integrations. For more information, see the Messaging section of the Genesys Cloud pricing hub. + +Genesys Cloud Social 400 social post ingestions per channel per token. For more information, see the Messaging section of the Genesys Cloud pricing hub. + +Social Post Responses 400 outbound messages per channel per token. + +Predictive Engagement No charge for token usage. For more information, see Can I use digital user tracking at no additional cost? and Predictive Engagement and digital user tracking. +Predictive routing 17 routes per token. One token is consumed for every 17 interactions routed with predictive routing. For more information, see Predictive routing overview. +Speech and Text Analytics [named] One user requires 30 tokens +Speech and Text Analytics [concurrent] One user requires 45 tokens +Genesys Cloud Copilot 20 AI actions per token, no charge for Genesys Cloud knowledge queries. For more information, see Genesys Cloud Copilot AI actions overview. + +† For Facebook, Instagram, and WhatsApp: If the organization has not moved to the Genesys Cloud AI Experience token pricing, then legacy, conversation-based pricing applies. Other charges apply for X integrations. For more information, see Messaging in the Genesys Cloud pricing hub. + + +## Virtual Agent interactions explained +A single interaction is contained by a single billingID. A billingID represents a single interaction on any channel of any length. A single interaction is delimited by end interaction events. A single billingID can contain multiple end interaction events. + +The following actions trigger end interaction events: + +Exit action in flow (return calling flow) +Disconnect action in the flow +Disconnect when the participant hangs up the phone +Disconnect via a Transfer to ACD action +Exit or disconnect handling for an unexpected error +Exit or disconnect handing for a recognition failure (continuous no matches or no inputs) +Exit or disconnect handling for Max No Input override (if set, overrides recognition failure settings) +Exit handling for agent escalation +Digital expiry after inactivity (72-hour async timeout) +When Genesys Cloud transfers interactions between inbound flows and Virtual Agent flows, the same billingID remains. When an action triggers an end interaction event and transfers no longer occur, then Genesys Cloud closes the billingID. \ No newline at end of file diff --git a/studies/202512_GenesysCX/notebooks/00_provision.ipynb b/studies/202512_GenesysCX/notebooks/00_provision.ipynb index 8737b8b..3156fac 100644 --- a/studies/202512_GenesysCX/notebooks/00_provision.ipynb +++ b/studies/202512_GenesysCX/notebooks/00_provision.ipynb @@ -32,7 +32,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "βœ… Athena connected β€” https://athena.ouranos.helu.ca (1 report templates visible)\n", + "βœ… Athena connected β€” https://athena.ouranos.helu.ca (2 report templates visible)\n", "πŸ“ Study: 202512_GenesysCX\n" ] } @@ -69,7 +69,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Created report template UCb2hSJprSBx\n" + "Found existing report template UCb2hSJprSBx (status: active)\n" ] } ], @@ -114,7 +114,7 @@ "`(1 + risk_adj)`; Year-0 amounts use companion `*_initial` fields.\n", "The `genesys_ai_tokens` line is seeded \\$0 (reproduces the published study) β€”\n", "the annual cost gets entered per deal, from the Genesys quote, in\n", - "`01_business_case.ipynb`." + "`03_business_case.ipynb`." ] }, { @@ -127,8 +127,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "12 fields created, 0 already existed.\n", - "Report template activated.\n" + "0 fields created, 12 already existed.\n" ] } ], @@ -305,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "1e375b54", "metadata": {}, "outputs": [ @@ -445,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "584e01dd", "metadata": {}, "outputs": [ @@ -531,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "e04b1676", "metadata": {}, "outputs": [ @@ -539,7 +538,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Auto-selected proposal 1: Secure Cloud Infrastructure Modernization\n", "Attaching via: {'proposal': 1}\n" ] } @@ -589,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "0655d1fc", "metadata": {}, "outputs": [ @@ -597,7 +595,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Created tool 3rzDgVdsjhVv attached to {'proposal': 1}\n" + "Found existing tool 3rzDgVdsjhVv (status: draft)\n" ] } ], @@ -643,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "86443d76", "metadata": {}, "outputs": [ @@ -696,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "0728b42e", "metadata": {}, "outputs": [ @@ -724,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "aba8fc21", "metadata": {}, "outputs": [ @@ -853,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "d8102590", "metadata": {}, "outputs": [ @@ -861,13 +859,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saved version 1 (baseline).\n", "Saved to /Users/robert/git/palladium/.env:\n", " PALLADIUM_GENESYSCX_REPORT_PUBLIC_ID=UCb2hSJprSBx\n", " PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID=3rzDgVdsjhVv\n", " PALLADIUM_GENESYSCX_PROPOSAL_ID=1\n", "\n", - "Next β†’ 01_business_case.ipynb (AI token cost entry + sensitivity).\n" + "Next β†’ 01_benefits.ipynb (walk through the four Forrester benefits).\n" ] } ], @@ -876,7 +873,7 @@ " client.save_version(TOOL_ID, note=(\n", " \"Baseline β€” published Forrester CX Cloud TEI figures (Dec 2025). \"\n", " \"genesys_ai_tokens at $0 per the published study; set the annual \"\n", - " \"cost from the Genesys quote in 01_business_case before client use.\"\n", + " \"cost from the Genesys quote in 03_business_case before client use.\"\n", " ))\n", " print(\"Saved version 1 (baseline).\")\n", "\n", @@ -893,7 +890,7 @@ "print(f\"Saved to {env_path}:\")\n", "for k, v in ids.items():\n", " print(f\" {k}={v}\")\n", - "print(\"\\nNext β†’ 01_business_case.ipynb (AI token cost entry + sensitivity).\")" + "print(\"\\nNext β†’ 01_benefits.ipynb (walk through the four Forrester benefits).\")" ] }, { @@ -903,6 +900,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13acdc34-71f6-4220-8675-4e1527cb8e39", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/studies/202512_GenesysCX/notebooks/01_benefits.ipynb b/studies/202512_GenesysCX/notebooks/01_benefits.ipynb new file mode 100644 index 0000000..39349cd --- /dev/null +++ b/studies/202512_GenesysCX/notebooks/01_benefits.ipynb @@ -0,0 +1,1408 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "g1-md-intro", + "metadata": {}, + "source": [ + "# 01 \u2014 Benefits Analysis\n", + "\n", + "**Study:** Forrester *Total Economic Impact\u2122 Of CX Cloud \u2014 Cost Savings\n", + "And Business Benefits Enabled By Genesys And Salesforce* (December 2025)\n", + "\n", + "Quantify the four benefit categories Forrester identified for the\n", + "composite organization (\\$2.5B revenue, 600 CX agents / 400 concurrent\n", + "licenses, 80,000 weekly interactions @ 12 min), push them into Athena,\n", + "and verify the totals match the published study\n", + "(Benefits PV \u2248 **\\$14.84M**)." + ] + }, + { + "cell_type": "markdown", + "id": "g1-md-setup", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "We add the project root to `sys.path` so the notebook can import `core` and\n", + "the study's local modules without `pip install -e .`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01-bootstrap", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, pathlib # path shim: works on a fresh kernel\n", + "for _p in [pathlib.Path.cwd(), *pathlib.Path.cwd().parents]:\n", + " if (_p / \"pyproject.toml\").exists():\n", + " sys.path.insert(0, str(_p)); break\n", + "\n", + "from core.bootstrap import init\n", + "\n", + "pal = init(study=\"202512_GenesysCX\")\n", + "client, seed, config = pal.client, pal.seed_data, pal.config\n", + "\n", + "STUDY = pal.root / 'studies' / '202512_GenesysCX'\n", + "ROOT = pal.root\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "g1-code-imports", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Study: 202512_GenesysCX \u2022 discount rate 10% \u2022 3-year horizon
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from core.calculations import npv, risk_adjust_benefit\n", + "from core.notebook_helpers import charts, display, tables\n", + "display.alert(\n", + " f'Study: {config.STUDY_SLUG} \u2022 discount rate {config.DISCOUNT_RATE:.0%} '\n", + " f'\u2022 {config.ANALYSIS_YEARS}-year horizon',\n", + " 'info',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "g1-md-benefits-table", + "metadata": {}, + "source": [ + "## Benefits \u2014 what the published study quantifies\n", + "\n", + "Forrester quantifies four benefit categories for the composite. The MTTR,\n", + "self-service deflection and revenue-lift assumptions are documented in the\n", + "`notes` field of every seed row (full PDF cell references \u2014 A1\u2013A4, B1\u2013B8,\n", + "C1\u2013C6, D1\u2013D3 \u2014 preserved for the audit trail):\n", + "\n", + "| Ref | Benefit | Y1 | Y2 | Y3 | Risk Adj |\n", + "|---|---|---|---|---|---|\n", + "| At | Retirement of legacy systems with CX Cloud adoption | \\$680K | \\$930K | \\$930K | 5% |\n", + "| Bt | Self-service savings (40 FTEs reallocated) | \\$2.33M | \\$2.33M | \\$2.33M | 15% |\n", + "| Ct | CX agent efficiency (MTTR 12\u219210 min, 60k weekly interactions) | \\$2.91M | \\$2.91M | \\$2.91M | 10% |\n", + "| Dt | Incremental sales from agent assist (1.5% lift \u00d7 8% margin \u00d7 \\$500M) | \\$0.6M | \\$0.6M | \\$0.6M | 5% |\n", + "\n", + "All four are seeded in `seed_data.BENEFITS` with full source notes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "g1-code-benefits-table", + "metadata": {}, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 field_keylabelcategoryrisk_adjustmentYear 1Year 1 (RA)Year 2Year 2 (RA)Year 3Year 3 (RA)TotalTotal (RA)
0legacy_retirementRetirement of legacy systems with CX Cloud adoptionCost Savings0.050000$680,000$646,000$930,000$883,500$930,000$883,500$2,540,000$2,413,000
1self_service_savingsCost savings from reallocated workers and avoided seasonal hires with increased customer self-serviceProductivity0.150000$2,329,600$1,980,160$2,329,600$1,980,160$2,329,600$1,980,160$6,988,800$5,940,480
2agent_efficiencyCX agent efficiency gainsProductivity0.100000$2,912,000$2,620,800$2,912,000$2,620,800$2,912,000$2,620,800$8,736,000$7,862,400
3agent_assist_salesIncremental sales from agent assist capabilitiesRevenue0.050000$600,000$570,000$600,000$570,000$600,000$570,000$1,800,000$1,710,000
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = tables.benefits_table(seed.BENEFITS)\n", + "df.style.format({col: '${:,.0f}' for col in df.columns if col not in ('field_key','label','category','risk_adjustment')})" + ] + }, + { + "cell_type": "markdown", + "id": "g1-md-source-notes", + "metadata": {}, + "source": [ + "### Where each benefit comes from\n", + "\n", + "The seed-data notes capture the per-line Forrester math so the model\n", + "stays auditable. Surface them next to the headline numbers:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "g1-code-source-notes", + "metadata": {}, + "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", + "
 RefBenefitRisk Adj3-yr NominalSource / formula
0AtRetirement of legacy systems with CX Cloud adoption5%$2,540,000PDF A1\u2013A4. Telephony $250k Y1 ramping to $500k (legacy sunset completes mid-Y1) + WFM/recording/transcription apps $100k + reduced dev effort $230k (2,400 hrs @ $94) + reduced platform mgmt $100k (1,500 hrs @ $65). Risk adj 5%.
1BtCost savings from reallocated workers and avoided seasonal hires with increased customer self-service15%$6,988,800PDF B1\u2013B8. Self-service completion 15%\u219225% on 80k weekly interactions \u2192 8,000 deflected/week \u2192 40 FTEs @ $58,240 fully burdened. Risk adj 15%. (PDF B7 formula cites B2 where the 12-min interaction length is meant; 40 FTEs is correct.)
2CtCX agent efficiency gains10%$8,736,000PDF C1\u2013C6. MTTR 12\u219210 min on 60k agent-handled interactions per week \u2192 104,000 hrs/yr @ $28 fully burdened. Risk adj 10%.
3DtIncremental sales from agent assist capabilities5%$1,800,000PDF D1\u2013D3. $500M revenue impacted (20% of $2.5B) \u00d7 1.5% lift \u00d7 8% gross margin. Risk adj 5%.
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "notes_df = pd.DataFrame([\n", + " {\n", + " 'Ref': ref,\n", + " 'Benefit': b['label'],\n", + " 'Risk Adj': f\"{b['risk_adjustment']:.0%}\",\n", + " '3-yr Nominal': sum(b['year_values'].values()),\n", + " 'Source / formula': b['notes'],\n", + " }\n", + " for ref, b in zip(['At', 'Bt', 'Ct', 'Dt'], seed.BENEFITS)\n", + "])\n", + "notes_df.style.format({'3-yr Nominal': '${:,.0f}'}).set_properties(**{'text-align': 'left'})" + ] + }, + { + "cell_type": "markdown", + "id": "g1-md-validation", + "metadata": {}, + "source": [ + "## Local validation against the PDF\n", + "\n", + "Re-derive the per-benefit risk-adjusted PV (`value \u00d7 (1 \u2212 risk_adj)` per\n", + "year, NPV at 10%) and confirm we land on Forrester's **\\$14,840,638**\n", + "total within rounding." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "g1-code-validation", + "metadata": {}, + "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", + "
 BenefitY1 (RA)Y2 (RA)Y3 (RA)PV
0Retirement of legacy systems with CX Cloud adoption$646,000$883,500$883,500$1,981,225
1Cost savings from reallocated workers and avoided seasonal hires with increased customer self-service$1,980,160$1,980,160$1,980,160$4,924,365
2CX agent efficiency gains$2,620,800$2,620,800$2,620,800$6,517,542
3Incremental sales from agent assist capabilities$570,000$570,000$570,000$1,417,506
4TOTAL$5,816,960$6,054,460$6,054,460$14,840,637
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = []\n", + "for b in seed.BENEFITS:\n", + " rf = b['risk_adjustment']\n", + " yr = [b['year_values'][str(y)] for y in (1, 2, 3)]\n", + " yr_ra = [risk_adjust_benefit(v, rf) for v in yr]\n", + " pv = npv(yr_ra, config.DISCOUNT_RATE)\n", + " rows.append({\n", + " 'Benefit': b['label'],\n", + " 'Y1 (RA)': yr_ra[0],\n", + " 'Y2 (RA)': yr_ra[1],\n", + " 'Y3 (RA)': yr_ra[2],\n", + " 'PV': pv,\n", + " })\n", + "df_check = pd.DataFrame(rows)\n", + "df_check.loc[len(df_check)] = ['TOTAL', df_check['Y1 (RA)'].sum(), df_check['Y2 (RA)'].sum(), df_check['Y3 (RA)'].sum(), df_check['PV'].sum()]\n", + "df_check.style.format({c: '${:,.0f}' for c in df_check.columns if c != 'Benefit'})" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "g1-code-pv-check", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Computed Benefits PV: $14,840,637
Forrester target: $14,840,638
\u0394 = $-1 (rounding)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "expected_pv = seed.PUBLISHED['total_benefits_pv'] # 14,840,638\n", + "computed_pv = df_check.iloc[-1]['PV']\n", + "delta = computed_pv - expected_pv\n", + "kind = 'success' if abs(delta) < 100 else 'warning'\n", + "display.alert(\n", + " f'Computed Benefits PV: ${computed_pv:,.0f}
'\n", + " f'Forrester target: ${expected_pv:,.0f}
'\n", + " f'\u0394 = ${delta:,.0f} (rounding)',\n", + " kind,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "g1-md-visualize", + "metadata": {}, + "source": [ + "## Visualize\n", + "\n", + "Horizontal bar chart of risk-adjusted three-year totals \u2014 mirrors the\n", + "*Benefits (Three-Year)* exhibit on PDF p.6 of the published study." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "g1-code-bar", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "#2E7D32" + }, + "orientation": "h", + "text": [ + "$2.4M", + "$5.9M", + "$7.9M", + "$1.7M" + ], + "textposition": "auto", + "type": "bar", + "x": [ + 2413000, + 5940480, + 7862400, + 1710000 + ], + "y": [ + "Retirement of legacy systems with CX Cloud adoption", + "Cost savings from reallocated workers and avoided seasonal hires with increased customer self-service", + "CX 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "charts.benefits_bar(seed.BENEFITS).show()" + ] + }, + { + "cell_type": "markdown", + "id": "g1-md-where-tokens", + "metadata": {}, + "source": [ + "### Heads-up: three of these benefits depend on AI consumption\n", + "\n", + "Benefits **Bt** (self-service uplift), **Ct** (AI coaching / agent\n", + "efficiency) and **Dt** (agent-assist sales) are all delivered through\n", + "Genesys AI capabilities that are **billed via AI Experience tokens**.\n", + "The published study modelled \\$0 of token consumption \u2014 costs notebook\n", + "(`02_costs.ipynb`) adds the line back, and `03_business_case.ipynb`\n", + "exposes a sensitivity sweep so you can see what the AI cost does to NPV." + ] + }, + { + "cell_type": "markdown", + "id": "g1-md-push", + "metadata": {}, + "source": [ + "## Push to Athena (optional)\n", + "\n", + "When `config.TOOL_PUBLIC_ID` is set (filled in by `00_provision.ipynb`),\n", + "persist the seed values to the live TEI tool. Otherwise this cell is a\n", + "no-op so the notebook still runs offline." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "g1-code-push", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID set \u2014 skipped Athena push. Run 00_provision.ipynb to provision the tool.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if config.TOOL_PUBLIC_ID:\n", + " from core.tei_client import TEIClient\n", + "\n", + " client = TEIClient()\n", + " client.update_values(config.TOOL_PUBLIC_ID, seed.BENEFITS)\n", + " display.alert(f'Pushed {len(seed.BENEFITS)} benefit rows to '\n", + " f'tool {config.TOOL_PUBLIC_ID}.', 'success')\n", + "else:\n", + " display.alert(\n", + " 'No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID set \u2014 skipped Athena push. '\n", + " 'Run 00_provision.ipynb to provision the tool.',\n", + " 'info',\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "g1-md-next", + "metadata": {}, + "source": [ + "---\n", + "\n", + "Continue with [`02_costs.ipynb`](02_costs.ipynb) \u2192" + ] + } + ], + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/studies/202512_GenesysCX/notebooks/01_business_case.ipynb b/studies/202512_GenesysCX/notebooks/01_business_case.ipynb deleted file mode 100644 index a10f296..0000000 --- a/studies/202512_GenesysCX/notebooks/01_business_case.ipynb +++ /dev/null @@ -1,376 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3da9de99", - "metadata": {}, - "source": [ - "# 01 Β· Business Case β€” Genesys CX Cloud TEI\n", - "\n", - "Working view of the live Athena tool, plus the **Genesys AI Experience token**\n", - "cost line the published study omits. Run `00_provision.ipynb` first.\n", - "\n", - "The published study models \\$0 AI consumption while three of its four benefits\n", - "(self-service uplift, agent efficiency, agent-assist sales) depend on AI\n", - "capabilities that Genesys bills via AI Experience tokens. Token pricing is\n", - "tiered and deal-specific, so the model keeps it simple β€” **one annual cost\n", - "value, entered from the Genesys quote**, exactly as Athena stores it. Quote\n", - "details (volume, unit price, tier) go in the field notes for the audit trail." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89deae70", - "metadata": {}, - "outputs": [], - "source": [ - "import sys, pathlib # path shim: works on a fresh kernel\n", - "for _p in [pathlib.Path.cwd(), *pathlib.Path.cwd().parents]:\n", - " if (_p / \"pyproject.toml\").exists():\n", - " sys.path.insert(0, str(_p)); break\n", - "\n", - "import pandas as pd\n", - "from core.bootstrap import init\n", - "from core.notebook_helpers import charts\n", - "\n", - "pal = init(study=\"202512_GenesysCX\")\n", - "client, seed, config = pal.client, pal.seed_data, pal.config\n", - "\n", - "TOOL_ID = config.TOOL_PUBLIC_ID\n", - "assert TOOL_ID, \"No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID in .env β€” run 00_provision.ipynb first.\"\n", - "tool = client.get_tool(TOOL_ID)\n", - "print(f\"Tool: {tool.get('name')} ({TOOL_ID}) status={tool.get('status')}\")" - ] - }, - { - "cell_type": "markdown", - "id": "09afaf70", - "metadata": {}, - "source": [ - "## Current financial summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d80c19a", - "metadata": {}, - "outputs": [], - "source": [ - "summary = client.calculate(TOOL_ID)\n", - "client.print_summary(TOOL_ID)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "392df0ba", - "metadata": {}, - "outputs": [], - "source": [ - "values = client.get_values(TOOL_ID)\n", - "benefit_rows = [v for v in values if v.get(\"table\") == \"benefits\"]\n", - "cost_rows = [v for v in values if v.get(\"table\") == \"costs\"]\n", - "\n", - "def value_table(rows, *, initial=False):\n", - " out = []\n", - " for v in rows:\n", - " yv = v.get(\"year_values\") or {}\n", - " rec = {\"field\": v.get(\"label\") or v[\"field_key\"]}\n", - " if initial:\n", - " rec[\"Initial\"] = v.get(\"initial\", 0.0)\n", - " rec.update({f\"Year {y}\": yv.get(str(y), 0.0) for y in (1, 2, 3)})\n", - " rec[\"risk_adj\"] = v.get(\"risk_adjustment\")\n", - " out.append(rec)\n", - " return pd.DataFrame(out)\n", - "\n", - "print(\"Benefits (nominal; Athena risk-adjusts at calculate time):\")\n", - "display(value_table(benefit_rows))\n", - "print(\"Costs (stored pre-multiplied by 1 + risk_adj):\")\n", - "display(value_table(cost_rows, initial=True))" - ] - }, - { - "cell_type": "markdown", - "id": "2383f761", - "metadata": {}, - "source": [ - "## Financial visualizations\n", - "\n", - "All figures are risk-adjusted and built from the live Athena values, using\n", - "the shared `core.notebook_helpers.charts` helpers (same ones the Streamlit\n", - "app uses)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ── Visual theme β€” plain values, edit freely (not confidential) ──\n", - "THEME = {\n", - " \"heading_font\": \"Helvetica Neue, Arial, sans-serif\", # chart titles\n", - " \"body_font\": \"Helvetica, Arial, sans-serif\", # axes, legends, labels\n", - " \"font_color\": \"#1F2937\", # hex\n", - "\n", - " # Circle-chart slice colours, applied in order a..j\n", - " \"pie_colors\": {\n", - " \"a\": \"#1565C0\",\n", - " \"b\": \"#2E7D32\",\n", - " \"c\": \"#C62828\",\n", - " \"d\": \"#F9A825\",\n", - " \"e\": \"#6A1B9A\",\n", - " \"f\": \"#00838F\",\n", - " \"g\": \"#EF6C00\",\n", - " \"h\": \"#5D4037\",\n", - " \"i\": \"#37474F\",\n", - " \"j\": \"#AD1457\",\n", - " },\n", - "\n", - " \"bar_green\": \"#2E7D32\", # benefits bars\n", - " \"bar_red\": \"#C62828\", # costs bars\n", - "}\n", - "\n", - "charts.apply_theme(**THEME)\n", - "print(\"Theme applied β€” re-run the chart cells below to restyle.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14d5942a", - "metadata": {}, - "outputs": [], - "source": [ - "# Cost breakdown β€” share of total three-year cost per line item\n", - "charts.cost_breakdown_pie(\n", - " cost_rows, title=\"Cost Breakdown β€” share of 3-year total (risk-adjusted)\"\n", - ").show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d13f342e", - "metadata": {}, - "outputs": [], - "source": [ - "# Benefits β€” three-year risk-adjusted total per category\n", - "charts.benefits_bar(\n", - " benefit_rows, title=\"Benefits (Three-Year, Risk-Adjusted)\"\n", - ").show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ef6caee", - "metadata": {}, - "outputs": [], - "source": [ - "# Benefits vs costs, year by year (Initial = one-time Year-0 costs)\n", - "charts.benefits_vs_costs_by_year(benefit_rows, cost_rows).show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f069df6", - "metadata": {}, - "outputs": [], - "source": [ - "# Cash flow with cumulative net benefits β€” the Forrester-style exhibit\n", - "def _yearly_breakdown(benefit_items, cost_items):\n", - " \"\"\"Risk-adjusted yearly rows + initial, computed from the live values.\"\"\"\n", - " initial = sum(float(c.get(\"initial\") or 0) for c in cost_items)\n", - " rows, cumulative = [], -initial\n", - " for y in (1, 2, 3):\n", - " b = sum(float((v.get(\"year_values\") or {}).get(str(y), 0) or 0)\n", - " * (1 - float(v.get(\"risk_adjustment\") or 0))\n", - " for v in benefit_items)\n", - " c = sum(float((v.get(\"year_values\") or {}).get(str(y), 0) or 0)\n", - " for v in cost_items)\n", - " cumulative += b - c\n", - " rows.append({\"year\": y, \"benefits\": b, \"costs\": c,\n", - " \"net\": b - c, \"cumulative_net\": cumulative})\n", - " return rows, initial\n", - "\n", - "yb, initial_cost = _yearly_breakdown(benefit_rows, cost_rows)\n", - "charts.cashflow_chart(yb, initial_cost=initial_cost).show()\n", - "pd.DataFrame(yb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1ff0315", - "metadata": {}, - "outputs": [], - "source": [ - "# Waterfall β€” Benefits PV down to NPV (from the Athena summary)\n", - "charts.waterfall([\n", - " (\"Benefits PV\", float(summary[\"total_benefits_pv\"])),\n", - " (\"Costs PV\", -float(summary[\"total_costs_pv\"])),\n", - " (\"NPV\", float(summary[\"net_present_value\"])),\n", - "], title=\"Benefits PV β†’ Costs PV β†’ NPV\").show()" - ] - }, - { - "cell_type": "markdown", - "id": "c78f77a9", - "metadata": {}, - "source": [ - "## Genesys AI Experience tokens β€” annual cost\n", - "\n", - "Enter the negotiated annual token cost from the Genesys quote. For sizing\n", - "context, the study's own drivers imply roughly **1,040,000** self-service\n", - "interactions/yr and **3,120,000** agent-assisted interactions/yr would draw\n", - "tokens β€” bring the actual figure from the quote, not a derivation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99b9c665", - "metadata": {}, - "outputs": [], - "source": [ - "# ── Deal inputs ──\n", - "AI_TOKEN_ANNUAL_COST = 0.0 # $/yr from the Genesys quote β€” 0 reproduces the published study\n", - "AI_TOKEN_QUOTE_NOTE = \"\" # e.g. \"Quote #1234: 4.2M tokens/yr @ $0.05, tier 2 commit\"\n", - "\n", - "print(f\"AI token line: ${AI_TOKEN_ANNUAL_COST:,.0f}/yr\")" - ] - }, - { - "cell_type": "markdown", - "id": "b7c6c4c7", - "metadata": {}, - "source": [ - "### Sensitivity β€” what the AI line does to NPV and ROI\n", - "\n", - "Computed locally from the current Athena summary: an annual cost `Ξ”` raises\n", - "costs PV by `Ξ” Γ— 2.4869` (the 3-year, 10% annuity factor) and lowers NPV by\n", - "the same amount." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09ceeea2", - "metadata": {}, - "outputs": [], - "source": [ - "ANNUITY = sum(1 / 1.10**n for n in (1, 2, 3)) # 2.4869\n", - "\n", - "base_benefits_pv = float(summary[\"total_benefits_pv\"])\n", - "base_costs_pv = float(summary[\"total_costs_pv\"])\n", - "\n", - "# Remove any token cost already stored, to get a clean zero-token base.\n", - "current_tokens = next(\n", - " (v for v in values if v[\"field_key\"] == \"genesys_ai_tokens\"), {})\n", - "current_annual = (current_tokens.get(\"year_values\") or {}).get(\"1\", 0.0)\n", - "base_costs_pv -= current_annual * ANNUITY\n", - "\n", - "sweep = [0, 100_000, 250_000, 500_000, 750_000, 1_000_000]\n", - "if AI_TOKEN_ANNUAL_COST and AI_TOKEN_ANNUAL_COST not in sweep:\n", - " sweep = sorted(sweep + [AI_TOKEN_ANNUAL_COST])\n", - "\n", - "rows = []\n", - "for ai_annual in sweep:\n", - " costs_pv = base_costs_pv + ai_annual * ANNUITY\n", - " npv = base_benefits_pv - costs_pv\n", - " rows.append({\n", - " \"AI cost/yr\": f\"${ai_annual:,.0f}\" + (\" ← your input\" if ai_annual == AI_TOKEN_ANNUAL_COST and ai_annual else \"\"),\n", - " \"Costs PV\": f\"${costs_pv:,.0f}\",\n", - " \"NPV\": f\"${npv:,.0f}\",\n", - " \"ROI\": f\"{npv / costs_pv * 100:,.0f}%\",\n", - " })\n", - "\n", - "display(pd.DataFrame(rows))" - ] - }, - { - "cell_type": "markdown", - "id": "6516270c", - "metadata": {}, - "source": [ - "## Push the AI token cost to Athena\n", - "\n", - "Writes the annual cost into `genesys_ai_tokens` (quote details in the notes),\n", - "recalculates server-side, and saves a version." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6caacea", - "metadata": {}, - "outputs": [], - "source": [ - "PUSH = False # ← set True once AI_TOKEN_ANNUAL_COST is final\n", - "\n", - "if PUSH:\n", - " note = (\n", - " f\"AI Experience tokens: ${AI_TOKEN_ANNUAL_COST:,.0f}/yr. \"\n", - " + (f\"{AI_TOKEN_QUOTE_NOTE} \" if AI_TOKEN_QUOTE_NOTE else \"\")\n", - " + \"Line absent from the published Forrester study.\"\n", - " )\n", - " client.update_values(TOOL_ID, [{\n", - " \"field_key\": \"genesys_ai_tokens\",\n", - " \"year_values\": {\"1\": round(AI_TOKEN_ANNUAL_COST, 2),\n", - " \"2\": round(AI_TOKEN_ANNUAL_COST, 2),\n", - " \"3\": round(AI_TOKEN_ANNUAL_COST, 2)},\n", - " \"notes\": note,\n", - " }])\n", - " summary = client.calculate(TOOL_ID)\n", - " client.print_summary(TOOL_ID)\n", - " client.save_version(TOOL_ID, note=f\"AI token cost set: {note}\")\n", - " print(\"βœ… Pushed, recalculated, and versioned. Re-run the visualization \"\n", - " \"cells above to refresh the charts.\")\n", - "else:\n", - " print(\"Dry run β€” set PUSH = True to write to Athena.\")" - ] - }, - { - "cell_type": "markdown", - "id": "c261ad48", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "- Adjust other drivers for the client (interaction volume, agent count,\n", - " self-service delta) via `client.update_values` or the Streamlit app\n", - " (`make app`), saving a version per scenario.\n", - "- Export charts for a deck: any figure object supports\n", - " `fig.write_image(\"chart.png\")` (needs `pip install kaleido`) or\n", - " `fig.write_html(\"chart.html\")`.\n", - "- Export for the report pipeline:\n", - " `python -m palladium export $PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID -o exports/export.json`" - ] - } - ], - "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": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/studies/202512_GenesysCX/notebooks/02_costs.ipynb b/studies/202512_GenesysCX/notebooks/02_costs.ipynb new file mode 100644 index 0000000..8f2ab10 --- /dev/null +++ b/studies/202512_GenesysCX/notebooks/02_costs.ipynb @@ -0,0 +1,1508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "g2-md-intro", + "metadata": {}, + "source": [ + "# 02 \u2014 Costs Analysis\n", + "\n", + "**Study:** Forrester *TEI\u2122 Of CX Cloud \u2014 Genesys + Salesforce* (December 2025)\n", + "\n", + "Three published cost categories on a 3-year horizon at a 10% discount\n", + "rate, **plus a fourth Palladium-added line** for Genesys AI Experience\n", + "token consumption \u2014 which the published study omits despite three of its\n", + "four benefits depending on AI capabilities Genesys bills via tokens.\n", + "\n", + "Published target Costs PV = **\\$4,057,170** (the AI-tokens line is\n", + "seeded at \\$0 here; `03_business_case.ipynb` runs the sensitivity)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02-bootstrap", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, pathlib # path shim: works on a fresh kernel\n", + "for _p in [pathlib.Path.cwd(), *pathlib.Path.cwd().parents]:\n", + " if (_p / \"pyproject.toml\").exists():\n", + " sys.path.insert(0, str(_p)); break\n", + "\n", + "from core.bootstrap import init\n", + "\n", + "pal = init(study=\"202512_GenesysCX\")\n", + "client, seed, config = pal.client, pal.seed_data, pal.config\n", + "\n", + "STUDY = pal.root / 'studies' / '202512_GenesysCX'\n", + "ROOT = pal.root\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "g2-code-imports", + "metadata": {}, + "outputs": [], + "source": [ + "from core.calculations import npv, risk_adjust_cost\n", + "from core.notebook_helpers import charts, display, tables" + ] + }, + { + "cell_type": "markdown", + "id": "g2-md-cost-table", + "metadata": {}, + "source": [ + "## Costs \u2014 what the deal looks like\n", + "\n", + "| Ref | Cost | Initial | Y1 | Y2 | Y3 | Risk Adj |\n", + "|---|---|---|---|---|---|---|\n", + "| Et | CX Cloud licenses (400 concurrent) | \u2014 | \\$840K | \\$840K | \\$840K | \u21915% |\n", + "| Ft | Implementation & deployment (10-week project) | \\$1.19M | \u2014 | \u2014 | \u2014 | \u219110% |\n", + "| Gt | Ongoing management (5 staff @ 30%) | \u2014 | \\$203K | \\$203K | \\$203K | \u219110% |\n", + "| **+** | **Genesys AI Experience tokens** *(Palladium add)* | \u2014 | per quote | per quote | per quote | 0% |\n", + "\n", + "Note **costs are risk-adjusted *upward*** (higher risk \u2192 higher modelled\n", + "cost) \u2014 Forrester convention, opposite of how benefits get adjusted.\n", + "\n", + "**PDF caveat:** the published Total Costs table (p.14) prints the\n", + "implementation initial as \\$1,304,600, but the detail rows, the cash flow\n", + "analysis, and the literal arithmetic (\\$1,190,000 \u00d7 1.10) all give\n", + "\\$1,309,000. The seed uses the correct figure." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "g2-code-table", + "metadata": {}, + "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", + " \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", + "
 field_keylabelcategoryrisk_adjustmentInitialInitial (RA)Year 1Year 1 (RA)Year 2Year 2 (RA)Year 3Year 3 (RA)TotalTotal (RA)
0cx_cloud_licensesCX Cloud solution costs (licenses)Subscription0.050000$0$0$840,000$882,000$840,000$882,000$840,000$882,000$2,520,000$2,646,000
1implementationImplementation and deployment costImplementation0.100000$1,190,000$1,309,000$0$0$0$0$0$0$1,190,000$1,309,000
2ongoing_managementOngoing management costsOperations0.100000$0$0$202,800$223,080$202,800$223,080$202,800$223,080$608,400$669,240
3genesys_ai_tokensGenesys AI Experience token consumptionSubscription0.000000$0$0$0$0$0$0$0$0$0$0
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = tables.costs_table(seed.COSTS)\n", + "df.style.format({c: '${:,.0f}' for c in df.columns if c not in ('field_key','label','category','risk_adjustment')})" + ] + }, + { + "cell_type": "markdown", + "id": "g2-md-source-notes", + "metadata": {}, + "source": [ + "### Where each cost comes from" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "g2-code-source-notes", + "metadata": {}, + "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", + "
 RefCostRisk AdjInitial3-yr Annual (Nominal)Source / formula
0EtCX Cloud solution costs (licenses)+5%$0$2,520,000PDF E1\u2013E3. Genesys Cloud CX 2 $170/user/mo + Salesforce Voice $25/user/mo + connector $25/user/mo, 400 concurrent users, 20% contractual discount \u2192 $650k + $95k + $95k. Risk adj +5%. Seat licenses ONLY \u2014 AI consumption is a separate line (genesys_ai_tokens).
1FtImplementation and deployment cost+10%$1,190,000$0PDF F1\u2013F5. 10-week implementation: 20 FTEs @ $80/hr fully burdened ($640k) + $550k professional services. Risk adj +10% \u2192 $1,309,000 (the p.14 Total Costs table's $1,304,600 is a typo in the study).
2GtOngoing management costs+10%$0$608,400PDF G1\u2013G3. 5 people @ 30% time (12 hrs/wk) @ $65/hr. Risk adj +10%.
3+ AIGenesys AI Experience token consumption+0%$0$0NOT in the published study \u2014 Forrester modeled $0 AI consumption even though benefits B (self-service uplift), C (AI coaching/assist), and D (agent assist upsell) all depend on AI capabilities that Genesys bills via AI Experience tokens. Seeded at $0 to reproduce the published totals. For client cases, enter the negotiated annual token cost from the Genesys quote and document the quote details (token volume, unit price, tier) in these notes.
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "notes_df = pd.DataFrame([\n", + " {\n", + " 'Ref': ref,\n", + " 'Cost': c['label'],\n", + " 'Risk Adj': f\"+{c['risk_adjustment']:.0%}\",\n", + " 'Initial': c.get('initial', 0),\n", + " '3-yr Annual (Nominal)': sum(c['year_values'].values()),\n", + " 'Source / formula': c['notes'],\n", + " }\n", + " for ref, c in zip(['Et', 'Ft', 'Gt', '+ AI'], seed.COSTS)\n", + "])\n", + "notes_df.style.format({'Initial': '${:,.0f}', '3-yr Annual (Nominal)': '${:,.0f}'}).set_properties(**{'text-align': 'left'})" + ] + }, + { + "cell_type": "markdown", + "id": "g2-md-validation", + "metadata": {}, + "source": [ + "## Local validation against the PDF\n", + "\n", + "Reproduce the **\\$4,057,170** Costs PV from the PDF Cash Flow Analysis.\n", + "Forrester applies risk *upward* to costs (`value \u00d7 (1 + risk_adj)`),\n", + "leaves Year-0 *Initial* amounts undiscounted, and discounts Years 1\u20133\n", + "at 10%. The `genesys_ai_tokens` line is seeded at \\$0, so it doesn't\n", + "move the published total." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "g2-code-validation", + "metadata": {}, + "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", + "
 CostInitial (RA)Y1 (RA)Y2 (RA)Y3 (RA)PV
0CX Cloud solution costs (licenses)$0$882,000$882,000$882,000$2,193,403
1Implementation and deployment cost$1,309,000$0$0$0$1,309,000
2Ongoing management costs$0$223,080$223,080$223,080$554,767
3Genesys AI Experience token consumption$0$0$0$0$0
4TOTAL$1,309,000$1,105,080$1,105,080$1,105,080$4,057,170
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = []\n", + "for c in seed.COSTS:\n", + " rf = c['risk_adjustment']\n", + " init_ra = risk_adjust_cost(c.get('initial') or 0, rf)\n", + " yr = [c['year_values'][str(y)] for y in (1, 2, 3)]\n", + " yr_ra = [risk_adjust_cost(v, rf) for v in yr]\n", + " pv = npv(yr_ra, config.DISCOUNT_RATE, initial=init_ra)\n", + " rows.append({\n", + " 'Cost': c['label'],\n", + " 'Initial (RA)': init_ra,\n", + " 'Y1 (RA)': yr_ra[0],\n", + " 'Y2 (RA)': yr_ra[1],\n", + " 'Y3 (RA)': yr_ra[2],\n", + " 'PV': pv,\n", + " })\n", + "df_check = pd.DataFrame(rows)\n", + "totals = df_check.drop(columns='Cost').sum()\n", + "df_check.loc[len(df_check)] = ['TOTAL'] + totals.tolist()\n", + "df_check.style.format({c: '${:,.0f}' for c in df_check.columns if c != 'Cost'})" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "g2-code-pv-check", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Computed Costs PV: $4,057,170
Forrester target: $4,057,170
\u0394 = $0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "expected_pv = seed.PUBLISHED['total_costs_pv'] # 4,057,170\n", + "computed_pv = df_check.iloc[-1]['PV']\n", + "delta = computed_pv - expected_pv\n", + "kind = 'success' if abs(delta) < 100 else 'warning'\n", + "display.alert(\n", + " f'Computed Costs PV: ${computed_pv:,.0f}
'\n", + " f'Forrester target: ${expected_pv:,.0f}
'\n", + " f'\u0394 = ${delta:,.0f}',\n", + " kind,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "g2-md-athena-delta", + "metadata": {}, + "source": [ + "### Athena vs Forrester \u2014 the Year-0 discounting delta\n", + "\n", + "Forrester leaves the \\$1.309M implementation initial undiscounted in\n", + "Year 0. **Athena treats non-annual values as Year-1 cashflows**, so it\n", + "discounts the same amount (\\$1,309,000 / 1.10 = \\$1,190,000), making\n", + "Athena's Costs PV ~\\$119k *lower* than Forrester's. That's methodology,\n", + "not data error \u2014 `seed_data.ATHENA_EXPECTED` records the expected\n", + "server-side total, and `00_provision.ipynb` verifies the pipeline\n", + "matches it tightly before reconciling against the published number." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "g2-code-athena-delta", + "metadata": {}, + "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", + "
 metricForrester (published)Athena (expected)
0total_benefits_pv14,840,63814,840,640
1total_costs_pv4,057,1703,938,170
2net_present_value10,783,46810,902,470
3roi_percentage266277
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta_df = pd.DataFrame([\n", + " {'metric': k,\n", + " 'Forrester (published)': seed.PUBLISHED[k],\n", + " 'Athena (expected)': seed.ATHENA_EXPECTED[k]}\n", + " for k in ('total_benefits_pv', 'total_costs_pv', 'net_present_value', 'roi_percentage')\n", + "])\n", + "delta_df.style.format({\n", + " 'Forrester (published)': '{:,.0f}',\n", + " 'Athena (expected)': '{:,.0f}',\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "g2-md-mix", + "metadata": {}, + "source": [ + "## Cost mix\n", + "\n", + "At seeded values, **CX Cloud licenses dominate** (~62% of 3-year cost),\n", + "with implementation a one-time investment. The AI-tokens slice is zero\n", + "until you enter the quote in `03_business_case.ipynb` \u2014 and that's\n", + "exactly the point: in real deals it can rival the license line." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "g2-code-pie", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hole": 0.35, + "labels": [ + "CX Cloud solution costs (licenses)", + "Implementation and deployment cost", + "Ongoing management costs", + "Genesys AI Experience token consumption" + ], + "marker": { + "colors": [ + "#1565C0", + "#2E7D32", + "#C62828", + "#F9A825", + "#6A1B9A", + "#00838F", + "#EF6C00", + "#5D4037", + "#37474F", + "#AD1457" + ] + }, + "type": "pie", + "values": [ + 2646000, + 1309000, + 669240.0000000001, + 0 + ] + } + ], + "layout": { + "font": { + "color": "#1F2937", + "family": "Helvetica, Arial, sans-serif" + }, + "legend": { + "font": { + 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "charts.cost_breakdown_pie(seed.COSTS).show()" + ] + }, + { + "cell_type": "markdown", + "id": "g2-md-ai-callout", + "metadata": {}, + "source": [ + "## Why the AI-tokens line matters\n", + "\n", + "Genesys bills AI consumption in *AI Experience tokens* \u2014 pricing is\n", + "tiered, capability-dependent, and deal-specific. The Forrester study\n", + "modelled **\\$0** of AI consumption even though benefits **Bt**, **Ct**\n", + "and **Dt** all rely on AI features that draw tokens:\n", + "\n", + "- **Bt** Self-service uplift (15\u219225%) \u2014 voice/digital virtual agents\n", + "- **Ct** Agent efficiency (MTTR -2 min) \u2014 real-time AI coaching/assist\n", + "- **Dt** Agent-assist incremental sales \u2014 next-best-action suggestions\n", + "\n", + "For sizing context, the study's own drivers imply ~1.04M self-service\n", + "interactions/yr (B5 \u00d7 52) and ~3.12M agent-assisted interactions/yr\n", + "(C1 \u00d7 52) would draw tokens. Athena stores a single annual cost value\n", + "per cost line, and so does the seed \u2014 enter the negotiated annual\n", + "figure from the Genesys quote in `03_business_case.ipynb` and document\n", + "the quote details (volume, unit price, tier) in the field notes." + ] + }, + { + "cell_type": "markdown", + "id": "g2-md-push", + "metadata": {}, + "source": [ + "## Push to Athena (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "g2-code-push", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID set \u2014 skipped Athena push.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if config.TOOL_PUBLIC_ID:\n", + " from core.tei_client import TEIClient\n", + "\n", + " client = TEIClient()\n", + " # Push pre-multiplied by (1 + risk_adj) per Palladium convention\n", + " payload = []\n", + " for c in seed.COSTS:\n", + " f = 1 + c['risk_adjustment']\n", + " payload.append({\n", + " 'field_key': c['field_key'],\n", + " 'year_values': {y: round(v * f, 2) for y, v in c['year_values'].items()},\n", + " 'initial': round((c.get('initial') or 0) * f, 2),\n", + " 'notes': c['notes'],\n", + " })\n", + " client.update_values(config.TOOL_PUBLIC_ID, payload)\n", + " display.alert(f'Pushed {len(payload)} cost rows to '\n", + " f'tool {config.TOOL_PUBLIC_ID}.', 'success')\n", + "else:\n", + " display.alert('No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID set \u2014 skipped Athena push.', 'info')" + ] + }, + { + "cell_type": "markdown", + "id": "g2-md-next", + "metadata": {}, + "source": [ + "Continue with [`03_business_case.ipynb`](03_business_case.ipynb) \u2192" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1507f5a-3a42-4c9e-bbce-a86ff1a1dbb7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/studies/202512_GenesysCX/notebooks/03_business_case.ipynb b/studies/202512_GenesysCX/notebooks/03_business_case.ipynb new file mode 100644 index 0000000..2b94810 --- /dev/null +++ b/studies/202512_GenesysCX/notebooks/03_business_case.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "g3-md-intro", + "metadata": {}, + "source": [ + "# 03 \u2014 Business Case\n", + "\n", + "Combine the benefits and costs into the consolidated TEI summary,\n", + "render the cash-flow exhibit, run scenario analysis, **and price the\n", + "Genesys AI Experience tokens line that the published study omits**.\n", + "This notebook should reproduce the headline numbers from the PDF\n", + "Financial Summary:\n", + "\n", + "* **NPV \\$10.78M \u2022 ROI 266% \u2022 Payback \u2248 4 months**\n", + "\n", + "It then exposes a sensitivity sweep for the AI-tokens annual cost so\n", + "you can see exactly what an honest deal looks like before sending it\n", + "to a client." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03-bootstrap", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, pathlib # path shim: works on a fresh kernel\n", + "for _p in [pathlib.Path.cwd(), *pathlib.Path.cwd().parents]:\n", + " if (_p / \"pyproject.toml\").exists():\n", + " sys.path.insert(0, str(_p)); break\n", + "\n", + "from core.bootstrap import init\n", + "\n", + "pal = init(study=\"202512_GenesysCX\")\n", + "client, seed, config = pal.client, pal.seed_data, pal.config\n", + "\n", + "STUDY = pal.root / 'studies' / '202512_GenesysCX'\n", + "ROOT = pal.root\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-imports", + "metadata": {}, + "outputs": [], + "source": [ + "from core.export.report_data import _compute_summary\n", + "from core.notebook_helpers import charts, display, tables" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-summary", + "metadata": {}, + "source": [ + "## Local summary (no Athena round-trip)\n", + "\n", + "Compute the moderate-case TEI summary directly from `seed_data` so the\n", + "notebook produces results even before the Athena tool is provisioned.\n", + "Headline numbers should match the published study." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-summary", + "metadata": {}, + "outputs": [], + "source": [ + "summary = _compute_summary(\n", + " seed.BENEFITS,\n", + " seed.COSTS,\n", + " config.DISCOUNT_RATE,\n", + " config.ANALYSIS_YEARS,\n", + ")\n", + "# `_compute_summary` returns roi_pct; expose it as `roi` for kpi_cards.\n", + "summary['roi'] = summary.get('roi_pct')\n", + "display.kpi_cards(summary, title='Forrester composite \u2014 moderate case')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-cashflow-table", + "metadata": {}, + "outputs": [], + "source": [ + "df_cash = tables.cashflow_table(summary)\n", + "df_cash.style.format({c: '${:,.0f}' for c in df_cash.columns if c != 'Year'})" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-cashflow", + "metadata": {}, + "source": [ + "## Cash flow chart\n", + "\n", + "Mirrors the Forrester *Cash Flow Chart* exhibit: stacked benefits/costs\n", + "by year + cumulative-net line. Payback hits inside Year 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-cashflow-chart", + "metadata": {}, + "outputs": [], + "source": [ + "charts.cashflow_chart(\n", + " summary['yearly_breakdown'],\n", + " initial_cost=summary.get('initial_costs', 0),\n", + ").show()" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-waterfall", + "metadata": {}, + "source": [ + "## Waterfall: Benefits PV \u2192 Costs PV \u2192 NPV" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-waterfall", + "metadata": {}, + "outputs": [], + "source": [ + "charts.waterfall([\n", + " ('Benefits PV', summary['total_benefits_pv']),\n", + " ('Costs PV', -summary['total_costs_pv']),\n", + " ('NPV', summary['npv']),\n", + "]).show()" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-scenarios", + "metadata": {}, + "source": [ + "## Scenario analysis\n", + "\n", + "Apply the default Palladium multipliers (see `core.calculations.SCENARIOS`):\n", + "\n", + "* **Conservative** \u2014 lower adoption, higher risk on benefits / lower on costs\n", + "* **Moderate** \u2014 base case (= the published Forrester study)\n", + "* **Aggressive** \u2014 full adoption, lower risk on benefits / higher on costs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-scenarios", + "metadata": {}, + "outputs": [], + "source": [ + "from core.calculations import apply_scenario\n", + "import pandas as pd\n", + "\n", + "scenario_summaries = {}\n", + "for name in ('conservative', 'moderate', 'aggressive'):\n", + " sb = apply_scenario(seed.BENEFITS, name, table='benefits')\n", + " sc = apply_scenario(seed.COSTS, name, table='costs')\n", + " scenario_summaries[name] = _compute_summary(sb, sc, config.DISCOUNT_RATE, config.ANALYSIS_YEARS)\n", + "\n", + "scen_df = pd.DataFrame([\n", + " {\n", + " 'Scenario': k,\n", + " 'Benefits PV': v['total_benefits_pv'],\n", + " 'Costs PV': v['total_costs_pv'],\n", + " 'NPV': v['npv'],\n", + " 'ROI %': v['roi_pct'],\n", + " 'Payback (mo)': round(v['payback_months'], 1) if v['payback_months'] is not None else None,\n", + " }\n", + " for k, v in scenario_summaries.items()\n", + "])\n", + "scen_df.style.format({\n", + " 'Benefits PV': '${:,.0f}', 'Costs PV': '${:,.0f}', 'NPV': '${:,.0f}', 'ROI %': '{:,.0f}%'\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-scenario-chart", + "metadata": {}, + "outputs": [], + "source": [ + "charts.scenario_comparison(scenario_summaries).show()" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-tokens-intro", + "metadata": {}, + "source": [ + "## Genesys AI Experience tokens \u2014 annual cost\n", + "\n", + "Token pricing is tiered, capability-dependent, and deal-specific \u2014\n", + "Athena stores a single annual cost value per line, and so does the\n", + "seed. Enter the negotiated annual cost from the Genesys quote here.\n", + "Quote details (volume, unit price, tier) go into the field notes for\n", + "the audit trail.\n", + "\n", + "For sizing context, the study's own drivers imply roughly **1,040,000**\n", + "self-service interactions/yr and **3,120,000** agent-assisted\n", + "interactions/yr would draw tokens \u2014 bring the actual figure from the\n", + "quote, not a derivation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-token-input", + "metadata": {}, + "outputs": [], + "source": [ + "# \u2500\u2500 Deal inputs \u2500\u2500\n", + "AI_TOKEN_ANNUAL_COST = 0.0 # $/yr from the Genesys quote \u2014 0 reproduces the published study\n", + "AI_TOKEN_QUOTE_NOTE = \"\" # e.g. \"Quote #1234: 4.2M tokens/yr @ $0.05, tier 2 commit\"\n", + "\n", + "print(f'AI token line: ${AI_TOKEN_ANNUAL_COST:,.0f}/yr')" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-sensitivity", + "metadata": {}, + "source": [ + "### Sensitivity \u2014 what the AI line does to NPV and ROI\n", + "\n", + "An annual cost `\u0394` raises Costs PV by `\u0394 \u00d7 2.4869` (the 3-year, 10%\n", + "annuity factor) and lowers NPV by the same amount. The sweep below\n", + "shows where the deal stops being attractive \u2014 and quantifies how much\n", + "of the published 266% ROI was *contingent on Forrester modelling \\$0\n", + "of token spend*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-sensitivity", + "metadata": {}, + "outputs": [], + "source": [ + "ANNUITY = sum(1 / 1.10**n for n in (1, 2, 3)) # 2.4869\n", + "\n", + "base_benefits_pv = float(summary['total_benefits_pv'])\n", + "base_costs_pv = float(summary['total_costs_pv'])\n", + "\n", + "sweep = [0, 100_000, 250_000, 500_000, 750_000, 1_000_000, 1_500_000, 2_000_000]\n", + "if AI_TOKEN_ANNUAL_COST and AI_TOKEN_ANNUAL_COST not in sweep:\n", + " sweep = sorted(sweep + [AI_TOKEN_ANNUAL_COST])\n", + "\n", + "rows = []\n", + "for ai_annual in sweep:\n", + " costs_pv = base_costs_pv + ai_annual * ANNUITY\n", + " npv_v = base_benefits_pv - costs_pv\n", + " roi_pct = (npv_v / costs_pv * 100) if costs_pv else 0\n", + " rows.append({\n", + " 'AI cost/yr': f\"${ai_annual:,.0f}\" + (' \u2190 your input' if ai_annual == AI_TOKEN_ANNUAL_COST and ai_annual else ''),\n", + " 'Costs PV': f'${costs_pv:,.0f}',\n", + " 'NPV': f'${npv_v:,.0f}',\n", + " 'ROI': f'{roi_pct:,.0f}%',\n", + " })\n", + "\n", + "pd.DataFrame(rows)" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-tokens-push", + "metadata": {}, + "source": [ + "### Push the AI-tokens cost to Athena\n", + "\n", + "When `AI_TOKEN_ANNUAL_COST` is set and `TOOL_PUBLIC_ID` exists, write\n", + "the annual cost into the `genesys_ai_tokens` field, with the quote\n", + "details preserved in the field notes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-tokens-push", + "metadata": {}, + "outputs": [], + "source": [ + "PUSH = False # \u2190 set True once AI_TOKEN_ANNUAL_COST is final\n", + "\n", + "if PUSH and config.TOOL_PUBLIC_ID:\n", + " from core.tei_client import TEIClient\n", + "\n", + " note = (\n", + " f'AI Experience tokens: ${AI_TOKEN_ANNUAL_COST:,.0f}/yr. '\n", + " + (f'{AI_TOKEN_QUOTE_NOTE} ' if AI_TOKEN_QUOTE_NOTE else '')\n", + " + 'Line absent from the published Forrester study.'\n", + " )\n", + " client = TEIClient()\n", + " client.update_values(config.TOOL_PUBLIC_ID, [{\n", + " 'field_key': 'genesys_ai_tokens',\n", + " 'year_values': {'1': round(AI_TOKEN_ANNUAL_COST, 2),\n", + " '2': round(AI_TOKEN_ANNUAL_COST, 2),\n", + " '3': round(AI_TOKEN_ANNUAL_COST, 2)},\n", + " 'notes': note,\n", + " }])\n", + " client.calculate(config.TOOL_PUBLIC_ID)\n", + " client.print_summary(config.TOOL_PUBLIC_ID)\n", + " client.save_version(config.TOOL_PUBLIC_ID, note=f'AI token cost set: {note}')\n", + " display.alert('Pushed, recalculated, and versioned.', 'success')\n", + "else:\n", + " display.alert('Dry run \u2014 set PUSH = True and ensure '\n", + " 'TOOL_PUBLIC_ID is configured to write to Athena.', 'info')" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-crosscheck", + "metadata": {}, + "source": [ + "## Cross-check vs Athena (optional)\n", + "\n", + "When `TOOL_PUBLIC_ID` is set, ask Athena to recalculate the summary on\n", + "the server side and confirm it matches our local computation (modulo\n", + "the documented Year-0 discounting delta \u2014 see `02_costs.ipynb`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g3-code-crosscheck", + "metadata": {}, + "outputs": [], + "source": [ + "if config.TOOL_PUBLIC_ID:\n", + " from core.tei_client import TEIClient\n", + "\n", + " client = TEIClient()\n", + " client.calculate(config.TOOL_PUBLIC_ID)\n", + " server_summary = client.get_summary(config.TOOL_PUBLIC_ID)\n", + " display.kpi_cards(server_summary, title='Athena server-side summary')\n", + "else:\n", + " display.alert('Set TOOL_PUBLIC_ID to compare Athena vs local.', 'info')" + ] + }, + { + "cell_type": "markdown", + "id": "g3-md-next", + "metadata": {}, + "source": [ + "Continue with [`04_export.ipynb`](04_export.ipynb) \u2192" + ] + } + ], + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/studies/202512_GenesysCX/notebooks/04_export.ipynb b/studies/202512_GenesysCX/notebooks/04_export.ipynb new file mode 100644 index 0000000..e77b04c --- /dev/null +++ b/studies/202512_GenesysCX/notebooks/04_export.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "g4-md-intro", + "metadata": {}, + "source": [ + "# 04 \u2014 Export for the report pipeline\n", + "\n", + "Build the structured JSON envelope consumed by the html2docx report\n", + "generation pipeline (Peitho). Output goes to `exports/export.json`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04-bootstrap", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, pathlib # path shim: works on a fresh kernel\n", + "for _p in [pathlib.Path.cwd(), *pathlib.Path.cwd().parents]:\n", + " if (_p / \"pyproject.toml\").exists():\n", + " sys.path.insert(0, str(_p)); break\n", + "\n", + "from core.bootstrap import init\n", + "\n", + "pal = init(study=\"202512_GenesysCX\")\n", + "client, seed, config = pal.client, pal.seed_data, pal.config\n", + "\n", + "STUDY = pal.root / 'studies' / '202512_GenesysCX'\n", + "ROOT = pal.root\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g4-code-imports", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from datetime import datetime, timezone\n", + "from core import __version__\n", + "from core.calculations import apply_scenario\n", + "from core.export.report_data import _compute_summary\n", + "from core.notebook_helpers import display" + ] + }, + { + "cell_type": "markdown", + "id": "g4-md-build", + "metadata": {}, + "source": [ + "## Build the envelope\n", + "\n", + "Two paths:\n", + "\n", + "* **Live** \u2014 `core.export.build_report_data(client, public_id)` pulls\n", + " authoritative values + summary from Athena and stamps it.\n", + "* **Local** \u2014 when no `TOOL_PUBLIC_ID` is configured, build the envelope\n", + " directly from `seed_data` so this notebook is always runnable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g4-code-build", + "metadata": {}, + "outputs": [], + "source": [ + "if config.TOOL_PUBLIC_ID:\n", + " from core.export import build_report_data\n", + " from core.tei_client import TEIClient\n", + "\n", + " client = TEIClient()\n", + " envelope = build_report_data(\n", + " client,\n", + " config.TOOL_PUBLIC_ID,\n", + " include_scenarios=True,\n", + " study_slug=config.STUDY_SLUG,\n", + " )\n", + " source = 'live (Athena)'\n", + "else:\n", + " summary = _compute_summary(\n", + " seed.BENEFITS, seed.COSTS, config.DISCOUNT_RATE, config.ANALYSIS_YEARS\n", + " )\n", + " summary['roi'] = summary.get('roi_pct')\n", + " scenarios = {}\n", + " for name in ('conservative', 'moderate', 'aggressive'):\n", + " sb = apply_scenario(seed.BENEFITS, name, table='benefits')\n", + " sc = apply_scenario(seed.COSTS, name, table='costs')\n", + " scenarios[name] = _compute_summary(sb, sc, config.DISCOUNT_RATE, config.ANALYSIS_YEARS)\n", + " envelope = {\n", + " 'metadata': {\n", + " 'study_slug': config.STUDY_SLUG,\n", + " 'tool_public_id': '',\n", + " 'tool_name': 'CX Cloud (Genesys + Salesforce) TEI (local seed)',\n", + " 'report_name': 'Total Economic Impact\u2122 Of CX Cloud \u2014 Genesys + Salesforce',\n", + " 'report_vendor': 'Genesys',\n", + " 'report_version': '1.0',\n", + " 'generated_at': datetime.now(timezone.utc).isoformat(),\n", + " 'generator': f'palladium core {__version__} (offline)',\n", + " },\n", + " 'report': {\n", + " 'name': 'Total Economic Impact\u2122 Of CX Cloud \u2014 Genesys + Salesforce',\n", + " 'vendor': 'Genesys',\n", + " 'version': '1.0',\n", + " 'discount_rate': config.DISCOUNT_RATE,\n", + " 'analysis_period_years': config.ANALYSIS_YEARS,\n", + " },\n", + " 'values': {'benefits': seed.BENEFITS, 'costs': seed.COSTS},\n", + " 'summary': summary,\n", + " 'scenarios': scenarios,\n", + " 'assumptions': seed.ASSUMPTIONS,\n", + " }\n", + " source = 'offline seed data'\n", + "\n", + "display.alert(f'Envelope built from {source}.', 'info')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g4-code-write", + "metadata": {}, + "outputs": [], + "source": [ + "out_path = STUDY / 'exports' / 'export.json'\n", + "out_path.parent.mkdir(parents=True, exist_ok=True)\n", + "out_path.write_text(json.dumps(envelope, indent=2, default=str))\n", + "size_kb = out_path.stat().st_size / 1024\n", + "display.alert(f'Wrote {out_path.relative_to(ROOT)} ({size_kb:.1f} KB).', 'success')" + ] + }, + { + "cell_type": "markdown", + "id": "g4-md-shape", + "metadata": {}, + "source": [ + "## Envelope shape\n", + "\n", + "Top-level keys consumed by the report pipeline:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g4-code-shape", + "metadata": {}, + "outputs": [], + "source": [ + "for key in envelope:\n", + " sub = envelope[key]\n", + " if isinstance(sub, dict):\n", + " print(f' {key}: dict with keys {list(sub.keys())}')\n", + " elif isinstance(sub, list):\n", + " print(f' {key}: list[{len(sub)}]')\n", + " else:\n", + " print(f' {key}: {type(sub).__name__}')" + ] + }, + { + "cell_type": "markdown", + "id": "g4-md-done", + "metadata": {}, + "source": [ + "Done. Hand off `exports/export.json` to **Peitho** / **html2docx** to produce the final Word report.\n", + "\n", + "**CLI alternative:** `python -m palladium export $PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID -o studies/202512_GenesysCX/exports/export.json`" + ] + } + ], + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/studies/202602_AmazonConnect/notebooks/01_benefits.ipynb b/studies/202602_AmazonConnect/notebooks/01_benefits.ipynb index be2e836..ee3c881 100644 --- a/studies/202602_AmazonConnect/notebooks/01_benefits.ipynb +++ b/studies/202602_AmazonConnect/notebooks/01_benefits.ipynb @@ -35,8 +35,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Project root: /home/robert/notebook/git/palladium\n", - "Study root: /home/robert/notebook/git/palladium/studies/202602_AmazonConnect\n" + "Project root: /Users/robert/git/palladium\n", + "Study root: /Users/robert/git/palladium/studies/202602_AmazonConnect\n" ] } ], @@ -64,17 +64,16 @@ "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'pandas'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m seed_data\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m core.calculations \u001b[38;5;28;01mimport\u001b[39;00m npv, risk_adjust_benefit\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m core.notebook_helpers \u001b[38;5;28;01mimport\u001b[39;00m charts, display, tables\n\u001b[32m 5\u001b[39m \n\u001b[32m 6\u001b[39m display.alert(\n\u001b[32m 7\u001b[39m f'Study: {config.STUDY_SLUG} β€’ discount rate {config.DISCOUNT_RATE:.0%} '\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/notebook/git/palladium/core/notebook_helpers/__init__.py:3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[33;03m\"\"\"Notebook helpers β€” pandas tables, plotly charts, IPython display.\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnotebook_helpers\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m charts, display, tables\n\u001b[32m 5\u001b[39m __all__ = [\u001b[33m\"\u001b[39m\u001b[33mcharts\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mdisplay\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mtables\u001b[39m\u001b[33m\"\u001b[39m]\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/notebook/git/palladium/core/notebook_helpers/tables.py:13\u001b[39m\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m__future__\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m annotations\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Iterable\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcalculations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m risk_adjust_benefit, risk_adjust_cost\n\u001b[32m 18\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_years_in_data\u001b[39m(items: Iterable[\u001b[38;5;28mdict\u001b[39m]) -> \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mint\u001b[39m]:\n", - "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'pandas'" - ] + "data": { + "text/html": [ + "
Study: 202602_AmazonConnect β€’ discount rate 10% β€’ 3-year horizon
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 field_keylabelcategoryrisk_adjustmentYear 1Year 1 (RA)Year 2Year 2 (RA)Year 3Year 3 (RA)TotalTotal (RA)
0ai_contact_resolutionAI-driven contact resolution efficiencyProductivity0.150000$13,911,040$11,824,384$23,932,480$20,342,608$37,797,760$32,128,096$75,641,280$64,295,088
1ai_content_sentimentAI-powered content and sentiment analysis savingsProductivity0.150000$4,586,620$3,898,627$5,358,412$4,554,650$6,291,680$5,347,928$16,236,712$13,801,205
2ai_forecasting_supervisionAI-enabled forecasting, agent scheduling, and supervisionProductivity0.150000$6,651,680$5,653,928$9,133,760$7,763,696$12,391,712$10,532,955$28,177,152$23,950,579
3data_driven_profit_liftData-driven profit lift with increased conversionRevenue0.200000$1,200,000$960,000$1,560,000$1,248,000$2,028,000$1,622,400$4,788,000$3,830,400
4legacy_solution_savingsLegacy solution cost savingsCost Savings0.200000$6,177,600$4,942,080$8,030,880$6,424,704$10,440,144$8,352,115$24,648,624$19,718,899
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = tables.benefits_table(seed_data.BENEFITS)\n", "df.style.format({col: '${:,.0f}' for col in df.columns if col not in ('field_key','label','category','risk_adjustment')})" @@ -134,10 +244,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "8cf32003", "metadata": {}, - "outputs": [], + "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", + " \n", + "
 BenefitY1 (RA)Y2 (RA)Y3 (RA)PV
0AI-driven contact resolution efficiency$11,824,384$20,342,608$32,128,096$51,699,827
1AI-powered content and sentiment analysis savings$3,898,627$4,554,650$5,347,928$11,326,358
2AI-enabled forecasting, agent scheduling, and supervision$5,653,928$7,763,696$10,532,955$19,469,777
3Data-driven profit lift with increased conversion$960,000$1,248,000$1,622,400$3,123,065
4Legacy solution cost savings$4,942,080$6,424,704$8,352,115$16,077,540
5TOTAL$27,279,019$40,333,658$57,983,494$101,696,568
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Computed Benefits PV: $101,696,568
Forrester target: $101,696,791
Ξ” = $-223 (rounding)
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "charts.benefits_bar(seed_data.BENEFITS).show()" ] @@ -213,10 +1264,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "d10a54b6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
No TOOL_PUBLIC_ID set in config.py β€” skipped Athena push. Set PALLADIUM_TOOL_PUBLIC_ID in your environment or edit config.py to enable.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if config.TOOL_PUBLIC_ID:\n", " from core.tei_client import TEIClient\n", diff --git a/studies/202602_AmazonConnect/notebooks/02_costs.ipynb b/studies/202602_AmazonConnect/notebooks/02_costs.ipynb index 8d58950..8c48d08 100644 --- a/studies/202602_AmazonConnect/notebooks/02_costs.ipynb +++ b/studies/202602_AmazonConnect/notebooks/02_costs.ipynb @@ -1128,7 +1128,7 @@ } } }, - "image/png": 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s3yPJKvR7p99DFZxW0gchTz50u7Wb4N3bdyIpvmpJ1fnC6m5uRYHWgE+aLLdWqyF/v1tW/pS8Bzp+17YC+f1JzpiuKlPcNHdMHrwxkQAJkAAJkAAJZC0CQWv5LCaRK1X8qAj1J6lbqt7Mq2XHPVW9Jv6YWlI1aZTT0eOnmzlhf874z8y7VJfVbre0Ma6i7uUD3X9K5tdZLr5aVkWoutCO+nW6EX3vvvSIs0p1TXVPuo6pptdlCQl9uaejx085D+l8vdc++hZzxSKk4sI1OYSHe1Lr6T9ibVROX37QzywFY+U5JkJNBaBaYF3dBa3zru/qYqupmsypdU06P1bF8IHD8ef1nLJVd9zVYq0sKVZsdZtVEaeuxspE2123eaepRvMmlQrmj3dNjom9suyOpzIacVRFvC7RkVbJW188XVcVQuqWqp/Veu0f9dglvbb+5tMK1GVbhbtr0jbUmq5JH7T0f7y7cVNWq60utWOlQNoJ9HPmafxWu/6+6zg0cI8m3db0xQfPJWnF1jmtbWRJHP2c39LjFeMKW7t6JXS96XrUqXG1qcf1j6/vRFJ81eqpSZetUXd+TaHyu1WkUH5jybcCIwXy3TKVpOBPoON3b8rf35/kjkkt0YUL5sepM+fcm+Y+CZAACZAACZBAkBMIWvGp8zjVQqQBhNSqV1hu5nwlnQ+lKUdE4nU01QqoSS0hmnRe1qTRH5jgK2ol0gBD+lJr4PefvoRrKpU1+VLrj1q2XpAoj7qu5L9i9bO98JDPqtVKqEkjeHpaUzKvyzywx18cbALq6Lw2dReuXL6MKXNLz1c8tqE35Jr2S4CinXsPJriRP3P2gjmnlrKkkgpqTWrVdU/KWy3KVuRUtYJpWr1xO3JJ1Fl9SKDrnkaePmvEpwYFUsuwui7qMjVJJdegPL7yWmJcoyWnVfK3L9q+dV3VBVvXp/WU6sn4/c2n5XX5Cp2/55rc5wTqvFtN7kt/BNJOoJ8z1/4kd1s/+/MmDjPFB331i/msvPbRdxj7xWuJXLPd2xj8Rh+MEffvP6YtwKZte8xL3cTVEqzWX9fk6zuRFF9dU1WTWk715Z70fJ+etyGQ75Z7He771ufa/bjrfiDjdy2n29bnIqnfn5SMSSMDM5EACZAACZAACWQ9AomVQRCNUa0JKj5H/zZdLGV3eey53oip66LOK9Sk+d2T5XqnYtZKGhREb9C0vEaW1HmUU2cvxUSZE/bik/da2Uzdzp0UbKgVV2+41LKY1M1jhXLxETvr165iggF5a1ajX27cusfMJ3Vd91Dzq/XFU+oqQY/URfDl975Bn5eG4OcvBoprcnx7louzWjSSSleVKWGyHDxyPMH8O70WR6W8Wj8tt121olaSYEFq+VShpPNElb+63+l8zFli3VIraO3qlVPF8mz1XcWWCtojmcS9T8esSfv0wJ0drG4mereEfVL5tKB+R5Kb/O1Pcj5nye2Tt3LP9+6OHRKVWR8WvfD21/hC3MXVVd5bUouvzlvWl86BVFdvXXZGI+M+Ja63VmAvLe/rO+GL71qZW62/N/owyz2adnRMjIjRKWaetIrPQL5b3sZkfaetB23e8unxQMav31nXZP0eJPX7k9wx6YMpnQuv87uZSIAESIAESIAEshYB73dnQTBOvUlUy9Ko8TNkPufqRD1W18+HJPjMgmXrxBKSy1gsNdiOzsGykgao+WPqAiOELJc7tbTpzY8mFUjqgvpgt5vM/s698S6M1jxGT1FeTcYA/+jcMb0JriZiwZpz6q2K+nXirX9f/PCnc06qlVfrmCeL2GtSUaDJsuyaHfmj89DcrWHWOV2DVOe26pItkeL29tgLg01AJD2vLrN6Q6kM1V3QStrmrstcrGN1a8a7L6o11zVNlcifmr9ezWtcD0Otn+puqpbfDq0bmnPKXpde0Lm4OtdRl7pJzaT1q/VMl6HJDEkt4CpmNmzdDf0MuicrQqq/+dzLB7rvbzvJ+Zy590XnZP/v09F4a/Aosz6v+/mk9lVo6sMifYihEYwHf/Wr1yIa4Es/Z1ZSN1Rd17Rm1YpmHrn7XGhf3wmrDk/vGqBM00MyP1oFputLH5aphVsjcG/evjeg75antvSY5QWxSZaH0vm6VtKo4K6C1N/xe/uN8/f3J5DfC6uv+m79puqDJyYSIAESIAESIIGsRSCoLZ/q/vpEjy5QEabLpbRsUgd1Zc6WPjlXN01ddkBdaXvI2pyadB7hU7J25iP9P8aj99+MfBI19S+Z06mC8vZO1zvXANSgPK+8P1IWhG8uVosKIpaiMPmfRaYOK/ptDblR1TRk5G/Gkhcj7eiT+jbN65njvv5MlmVE1NVPLQo671Atq3rDqy6qA599wFdRc+4Gma+mc/bUytP1oYFimWlpxLWOeY6IcL2Jbt2snlkvT+dOaZTYZ2SdPz2ua3ZqBFmd6+grqUVIA37oWoi6fMiPw1816z9qdE69se8pwZH0hl3XQJ29YJVT6Fp1qmhUi4+6LPZ/6wtjoT1w6Lgs5zHFWPb6SpRV16RzOXUtU00dWsaLT7Mt9agw1+Qp2JA5kYI/jUTQ6vXQBxKe5gOnoOpkFVWXzweees8E0VG+tapWlnnNkSZ6r0ZzHffVG2a+rb/5ktUJl0L+tFNNHs4k93NmNaWCe7xE81UhqA8+kpPUkq0Wz3ueeNt8bivL8kLW2rSu9emDJV33Ul24NUpvSVmCRAWgfp/Uuq5roHpK3r4TnvLqd1uD7eSS+Ys6v9RT6tyuifmd+lvWDNaHDv5+tzzVpcd0KoL+/mnk6D4vDzHzV9dv3oUFS9clKOLv+L39xvn7+6ONJmdMs/+Lf/CiAc+YSIAESIAESIAEshaBoBafeil0uY4Gdaoal7lFKzYkuNHSuYl6A9/kssVMb5p0aZT3hv5k8mt5dXXVeV4DZNkQK7WWdS31ZtQSQ3pcb4r7PX6X081Vo96q5XWizBnTSKKaXnn6PvPu7Y/lZqrRZvWlSW9O1Zqo1pUnHuziFMBWXjG9eqxuqCx/osutqDDT5TCspMtMtJOlCjTpzejgN/tgwNtfGbdCdS3MLXMq1Yo7fvIcp9ur5rVc9pztyjEVAWrVUgvOUyLuRw19GQ/eeaNYCo8bNjoHVpMux6JBiBZLNF2rvL6P+Li/BDv6DroYvb40qWvzhwMfT+CKq8ctYanRh6uI262VNACOBmc5p9ZSt/me3uaFWcRCQpI27Ou1VvGpDxeqXn3lM2C1b71bl8Ean3Xceve3L87yVoVWBZff1SL802ev4oPPfpb5iP+Zl57SBxPtrq8v7srxwZH8zedWvcddqyvOvrnk8qedQD5nzjasRi+3pWs6atK5rq5zli+fTvSm9TjrcjmrLqGfvNXXPDB5Z8iPxtvBymd9xtXNu408nFGPCOt7qFXoZ02XYbLcda38VnnN4+k7ocfd0/K1W4z3hEa29RZcSdf9/HTEb2b92gF97vH7u2W1ZXOxblrHdIkeFdYqpPWVJ3cusyTSl6P+Mr91ms/f8fv6jfPn90fb8vf3QvNaafLMxeYBVQuJ8M1EAiRAAiRAAiSQtQiEyPzCxCFPg3SM6kKrrrZqhdQF1b0FIVKXNHUb1blzah1RAegpaQTPQ0dPmOAlGpTFWuvSNa/i07pUnFpub67n03pb29c1E9VFVpe7sFzlXNtVlzvLLbbiVaW93gy7lklqO3690aMoL3M7XefHeSp3VpbE2COMlI8uZ5GZklqo7pF1XjWC598/fuCce5cZ+qifTw38pO6vOkfWmzu2v/lSOqak2knJ50wt6SvWbsW0nz82831T2ld/yuvvxaEjJyWAziXD19vvhT91pWYef75b+r1vcdtTyCOfjVnjP03QvI5L3XlFnhs3ZG+fG3/H7+s3zp/fH+2cP2PSfDPmLhNPiS/xiKzt21/m8TKRAAmQAAmQAAlkLQJZSnxmrUvD0aQXgQ3ilqwCVIPCDH/3mfRqlu1cJqCittktfdFMLF1ffdiPXLwQUE4677eSRKxW1/lPRow3Uw2+/qi/lxLBdVgfUnXpNdBY+SeNes88dAmuEbC3JEACJEACJEACSREIerfbpAbI8ySQFAGd43ff7e2MC/Pw7ybgmUfuTKoIz6cigVUS5VjnIPuK8JuKzQVtVbp0Sd9Xhjr7ry616vqfFVK0eGc8KfPxNRiSPoBQaz8TCZAACZAACZBA1iNAy2fWu6YcUTIIaATeSTMXmpLdbm5t5ssmoxoWSQYBdf/UtSMLFciXjNLZp4gKNA1OpEsVFS1UwAQVK3V5DnCwU9h74AgWLt9g1prVaNtMJEACJEACJEACWZMAxWfWvK4cFQmQAAmQAAmQAAmQAAmQAAlkKgJJhwPNVN1lZ0iABEiABEiABEiABEiABEiABIKRAMVnMF419pkESIAESIAESIAESIAESIAEgowAxWeQXTB2lwRIgARIgARIgARIgARIgASCkQDFZzBeNfaZBEiABEiABEiABEiABEiABIKMAMVnkF0wdpcESIAESIAESIAESIAESIAEgpEAxWcwXjX2mQRIgARIgARIgARIgARIgASCjADFZ5BdMHaXBEiABEiABEiABEiABEiABIKRAMVnMF419pkESIAESIAESIAESIAESIAEgowAxWeQXTB2lwRIgARIgARIgARIgARIgASCkQDFZzBeNfaZBEiABEiABEiABEiABEiABIKMAMVnkF0wdpcESIAESIAESIAESIAESIAEgpEAxWcwXjX2mQRIgARIgARIgARIgARIgASCjADFZ5BdMHaXBEiABEiABEiABEiABEiABIKRAMVnMF419pkESIAESIAESIAESIAESIAEgowAxWeQXTB2lwRIgARIgARIgARIgARIgASCkQDFZzBeNfaZBEiABEiABEiABEiABEiABIKMAMVnkF0wdpcESIAESIAESIAESIAESIAEgpEAxWcwXjX2mQRIgARIgARIgARIgARIgASCjADFZ5BdMHaXBEiABEiABEiABEiABEiABIKRAMVnMF419pkESIAESIAESIAESIAESIAEgowAxWeQXTB2lwRIgARIgARIgARIgARIgASCkQDFZzBeNfaZBEiABEiABEiABEiABEiABIKMAMVnkF0wdpcESIAESIAESIAESIAESIAEgpEAxWcwXjX2mQRIgARIgARIgARIgARIgASCjADFZ5BdMHaXBEiABEiABEiABEiABEiABIKRQHgwdpp9JgESIIG0IuBwOOA4cwr2E8dgP3kUjlMnYD93Bo4L5+A4f/bK+/lzQGwMHHGxQFyc8x2yP6D0vwiXX9cwebxn3sPi3yPkWJ7cIcibF8ibJ+F2oQIhKFIY8gpBgfxAaGhIWg2R9ZIACZAACZAACZBAhhCg+MwQ7GyUBEggIwkYgXniKGwHdsN2cC/sB/bAduQAHCo4Tx0zYjIl/RP9iljRpPIPiHavSU46k+u286ARrYULAYVFiBYvCpQuGYIypUJQWl7FilCYXiHFLRIgARIgARIggWAiQPEZTFeLfSUBEgiYgFombXt3wrZjE2w7N8dvi+BETCJVGHDdaVXAZgdOnNKXA9t3aitXRKpaUkuXhBGjlcqHoGKFEJQvG4IcOWgpTavrwXpJgARIgARIgARSh0CIWACu3NWkTp2shQRIgAQyjID91HHEbViFuG0bLovNHSm2ZAY6mBdKzwm0SIryh4p7b9nSQOUKoagkYrTqNWIxLUYxmiKoLEwCJEACJEACJJDqBCg+Ux0pKyQBEkhPAvYzkYjbKGJTBeeGlbCL+2xGp/QWn57Gq/NHq18bgmpVQs17oYIUo5448RgJkAAJkAAJkED6EaD4TD/WbIkESCAVCKizhrrPxq74T14LYd9n/FJToebUqyIziE/30ZQqAdSpGYp6tUNwTaUQBjRyB8R9EiABEiABEiCBNCdA8ZnmiNkACZBASgk4ZH5m3PoV8YJz5UI4TsuEyEycMqP4dMWVT6Lt1qkZgnq1QlGzWghy5qRV1JUPt0mABEiABEiABNKGAMVn2nBlrSRAAikk4LDbELduBWL++wexy+YBUZdSWGP6Fc/s4tOVREQEUFeEaNNGoahVPQThYRSirny4TQIkQAIkQAIkkHoEKD5TjyVrIgESSAUCcRKVNmbBTMQumi3rbUamQo3pX0UwiU9XOrr2aKPrQtGkYbxrbkgIhagrH26TAAmknMD02TbExPhfT8d2oYzm7T8u5iSBTE+AS61k+kvEDpJA1idwKi4Ko09uxbzjOzHq7U+z/oAz6QgvXATmLrTLCygm64u2bh6K65uGIn8+itBMesnYLRIIOgLTZ9tx/oL/3b6hpYpP//MzJwmQQOYmQPGZua8Pe0cCWZrAovNH8PXxjfgtcieiHDYz1lWNGqL+8hVZetzBMLgTJ4EJk+34c6odDeuFoHWLUFx7tazpwkQCJEACJJDhBKKiY3Dq9DkUL1oQEboANBMJBAkBflqD5EKxmySQVQjEyFzOsae2Y8ixtVh3KXHgoHHNr6P4zEQX2ybPBJaudMjLhjKlbGjbKhQtGociIoLW0Ex0mdgVEiABLwRsNjva3tUPJ06dwcxxg2RN5OJecgbH4RVrt+L94WOwbdcBaPT30NAQtGxSBy89eR8qlCuZqQaxcesefDduCj5968lM1a+M7MyPv81AWFgY7r+jfUZ2I0Pb5mPsDMXPxkkg+xA4HReNj46sRqUNY/DQ3jkehafSGJ8nFpdy584+YIJopIeOAGPG2/HiW3H4e6YNFy46gqj37CoJkEB2JLB45UacPX8RxYoUxN+zFgc1gs3b9+Khfh+iVrVKmPXrJ1g+bQR+GPIyzsn4duw+kOnGduLUaSxYuj7T9SsjO6TXcMuOfRnZhQxvmwGHMvwSsAMkkLUJHIq5gEFH1+DbE5tw3h7n12C/3XYGd/7xt195M2OmYA04FCjLnDIPq2WzUNx4QyiKFKYlNFB+zE8C2ZHAc6/GBjTnc8h74Smad/7SeyMQGxuH8mVLYPaCVZj84wdO7N0eexPXN66FfxeuxpFjp9Dxhsa47abr8cnXv2LHnoPo0Koh+vfujqKFC2DP/iN47o3PcfjYSdjtdtSqWgmv9+uByhXKYPI/i/DDL9Oc9erG1RXLYNDrfeQhXRQ+/HystLEKoRLE7aY2jfFCn7uRS35Al67ejOHfTkDDulXx14yFyJsnFx65tzPu6NwqQV3WTu8XPzFCc+yXr1uHzLtaQKNjYk2dv02ea6yNx0+eQe3qlfFm/56oVL60yafjbdW0DubIeI8ej8TN7Zui3+PdkSd3ziT7EiP1D5O+Tpm9BOHhYbi9U0tjvStUIJ8EkIrFF6P+xPQ5y0z/alxbEQNkjE+9NhyHj57EtZXLmfY/GtgbVS5vWwOYt3gNxv4xGyM+ft46hDsffQPvvPgwtB7l+stf/xoX43Kli+GJHrcJw0b4ddIcfDt2Ck5FnkXuXDnRoXVDvPrM/U4X5D+n/4evRv9lrmvNqhWhbspff9QfJYoVxr6Dx/Du0B+xesMOVK9SHnfd0ga33tjc2b7rhqexaTt63X2x9tTvnDki8Mr735jqS5csah6IfDPoBdfmssU23W6zxWXmIEkg/Qkcjr2AD8XS+c3xTc75nNqL8EsxKLV6Pw41rAB7Ds8/QWOqlsOd6d9lthggAfm/HLPm2TFngd0EJrr5JhGhhShCA8TI7CRAAmlE4FJUNP79bxU+fPVxEZ8lMfLnKdi0bY8RNdrk1p37EC0/ZI8/cKtxYX394+8xa/5K9H6wC3pfVRJvf/ojZsxdhvtub28EVxcRKNfVrmJEpAqxd4eNwfefvmjEY7HCBc0ojp88jVc//BZtW9Q3+4O+/AVqfX2p772Ik3kMw7+bCBUhA/reY4Tamo07RBAVwpvP98S6TbuMS+0t7ZtJkCVZB8stbdi6G09I39yTRiZXMbt4xUb879PRMp5bUL/2tfhOxFnfV4Zg0uj3jSjT8SqTR++7WQRnLrwx6Hs0qV8D7Vs2SLIvOt45i1bj9eceRL58efDxF+NMndrWkJG/4e9/FuMRqbdGlQoixhdj/6HjuEME6jc//43nn7jbdLlUiSLuXceZsxewc++hBMfVMqiifZmI88HyIOCDVx4zolVdjg8ePm7yFpMHAs88fIcR+UdESA/8YCSqXVMed3e5AcvXbMFAuQbq2tqpbRPD9eMvxyFGHkLoq/eLg0V0VsB3nwwwbb/+8XeoU+Nqj27LnsamDyJUvHtjvXr9do/9vqVDM1QXQR0h4r3HXTeZa5Zg4Nlkx/OdXzYZPIdJAiSQ+gSOxF407rUjJJDQpctBhFxbibgQg6sW7USRHcewrkcz11PO7XmOc9hfsQKu2rPXeYwbmZeATKnCvEV2LFxqR5vrQ9G5QygK5KcIzbxXjD0jgexBYLYIT51fp3MiVcxdU6mssVKqRc1Kr4n1ssl11c3uaJmPp5bJnt1vMvttmq+DCh4Vn+VkrugdN7fC0lWbRACdMNa2jSJkNZUuUdS84uJs4hb7EZqKoOvb6zYjaCfNXIgnH+qKLje1MHkPiHhSq52KT00FC+TFkP89ZbZbNq4j1ry/oYK08eU+mRPyR0Xy6TPnjVC1jrm//yVt1bi2Ap55JP7xbfGihXDHI69DxZBV3xtiCbXGO+3fpUawqvjU5Ksv4yfPMWKuuFgONekYZy1YgcfuvxnjJ83Fw/d0Qq/uHc05q62IiDBZOzoMLRrVMscD/aNCWYV1vry5zbVTwWildtJnFa1rhdWxE6dN33ddFrFqRVZr66vPPGCy6/VR8alJr58KY7VaS+UiXsua1+wFK/GwWJ1dk1qUvY3tZbFgemPtq98l5UFDuASISi4T1/4F6zbFZ7BeOfabBDIZgXO2GCM6Pz261qPotLp7qVg+rHr0ejQdOhvFthzGiWrx7kDWeX3XmYS/3tgaL3zzo+thbmdyAnLfZSyh8xfb0U4CE+n6fHnzUIRm8svG7pFAliUweeYiY2V865NRZowxMXGYOnspXnjiHhGlicOeqPtp/P9A8UjUOnhI3EY1qTXtqYHDUFLEl8651KTut67p0xHjxc30BH775n8SCCgUJ8UlVN1h61S/2plNXWFH/DTZiEnnwcsb6s6qFkwVL+4ppxwvVDCfuP0mDtRn5T105ESCtlSAqZVVXYo9JRV1ntrSvK59OXX6LC5eijZuy4tXbHJWpXXrGNWltYG4Dqd2atagprhBt8Azr39m+nO9iFgV7RXKlTIuwCrUG9WrjopipQ4JCTViX/ugrtH1alXx2J39h46JIA7FsJETnOdVIF9SVx635GtsvljHu2977rdbE9lyl+IzW152DpoEUo+AzWGX+Zyb8eah5Tgad8lZcekVe5D3+HkcaFYZUYXyOI/rRrTsn7y2BIpvOORRfGqeMcVyQmeAULoojeBKuoD8tFl2zBdraJdOoWgjy7SEhfFKBtdVZG9JILgJqHBQd1d1lS1csIAZzA3N60Gtm0vE+uXZ8uT9d2rMhH/QSATWZ+89a6xxM+Yux/otu5yQdH/sn7Px4/BXUbhQfnO8YP68RuTu3HNIRFI1c0ytcypqVUx6Tt77UP2aCpgm4rmnuGyqRdBKaqG7FBVj5hDulLmqVjooYlTFr9Uf67j/7/FtFMiX11gw+8n817YtrktQXK29Kt404JFaQ12T9tEh9wi+klqmdU6ue9Kyaq1+7+VHxW23O1aJ9Xb4dxMwdOTveF/ccH/4ZSrefelRI061bP+3vnBWUbtaZWMRdR5w2dDAUxER4RjzxUDjNuxyKtGmzmf1Njatxxtrb/1WC7eOy/2hRaKGs/iBxI99sviAOTwSIIHUIzD99D7U2TQeT+ybn0B4agulV+3DVQt3oPmgmajz42IRmgcRKk+dNYVFxSLfkbNm29ufvbaLWNi6pbfTKT4eLebVSC//J+q5KDW/MqWIwIWLwLgJdrz1URw2bvECO0UtsDAJkAAJeCagLqUqHv73wsNGvKiAefHJe1FX5vb9LQGCAk1qJTxz7oKx9Km75x/TFjgF4O59h2WO4Uj07XmbCWx0+ux5M4dSrYfNG9bCxGnzTQAjncs4eeZiE/Qn0PY1/zOP3gl19VWXT3XfPX/hkpkX+eDT74v77AbjXrxa3FBniQtppKwBOlLmW6oA1jGnJOk4WjerK8LvN+PqqoJTl1HReaB6rlnDmhgvgY7URVkD9CgbtRTrHEwVxRu27DY8PFlZ69epYpbB0eBLx05E4vPv/zBdVUGt45g4db5Yg3OiZePaqCgWTxXuYWJVziPBmTSYkUb6XbB0nYjNnc4hqpu1itX3h/8svBc5g/xohkZ1qxlB+e7Qnww/ZThz3nJMmbXEWd7a8DU2bcMba2/91nrVVVfZ6ZxWnR+cHRMtn9nxqnPMJJBCAidPOTB2gg0rrzqDTcUjPda254ZqqDNmCZY/0Rol1x1AtT/XIOKSRDksWQB5TpyDXeaCHGjm+z/EMQ1r4Pp5CzzWbx1cHxuKt8+rq1TC9GWBKBQPS6wgz4oG+upiDmyOCzVW1WKhDjyYW9yiIuLF0aSocEyRl5ZsnzMO3XPHC2YVqs+dzYXB+aM91puwde65Ejh8FBjylQ11atpxd9cwlCxx5Ym9az5ukwAJkEBqEdCorOr+6O5e27ldUxFNv+MtcbNUK5SvXyM5LXnie/RgtxvR783P0fqOZ40rq7rPWknnGKrIUjGmL00633LuhKEmQM+zEiX3tl4DzfF6Na+RNTnvNdv6J1TcRd2T9stTqiNtfvVhP3wkwX5uuneAyaKWuaYNakhAnqtkXmoxrNu8C8+Km6omncP50Wu9ZQ5+XrPvPl6z79KWr77oXNG3Bv/gHIeuL3prh+amXg2W9Mr7I9Hz2Q/MvloF1UKskWU1Ku4DT72LWBGs4756Q9yCr3DTzDof806ZS9t7wGCTx5p/qn3LIRZKDTj0xqAfzLYyf6HPPcYi2rdnV8P6s+8nyhrUxcSSfCVAU30JCvVEjy7iYr1EROg2M8d1mYhhdWlWK/DQd56WsYxCk5v7mP6qQH9NAil5St7Gpu7A3lh767fW3/b6BiLO/0Pjzk9AI97qkjnZLXGplex2xTleEkgBgTibA//MsWPydLtEjJOZMaESZKbXH9ga51mANvp8Dk5UL43d7cTdyO5AoT0nUeBgJGwiPI/XLIOY/Ll89iZXSBi2fP0XCp4+4zXfOhGf74j4/FTEpmsqI6LSk6fnNxcisNUWivdFROp/VaMuRWCjCFEVlSo4Hz2TCwPzRSOX/N//4tmcGFUoChGy/e3FCB0CHs8rA08iZZelVpLA4PG0PCRH5xslKFH7UHli7vkGy2NBHiQBEsgSBKbPtol1zP+h6NzxHDkyx2+FWuP2HTwqwrKwWZ7E/1HAWPVUUKkoTY104eIls/yI1qeiyjWpVU3djlWM6tzT1Ewa+OjEqTNmHO4RedWKePGSPPiVPulYraTHbTI/VkWet6TBlPRJgFqrXZMyVwuhLkOTN09u11NmHqr2xdM4bRIJz3rwoFbwN0U4L5v6dYLy2mZMbGyi/ibIdHnH29i8sfbVb61S+51frOne3a899SJrHKPlM2tcR46CBNKcwLaddowZb8OhI1eaCrGHovWa5thaa8qVgy5be26oiuoTVmFvy2vMsiqnKxeDvvxNURIt969O7dFj3JXAAN7KlhOx6fJ/nbdsOOkIQYEQB/S/as1fPdyGOdFhUPvmCXsIzsn5smIx1fMqMw/YJNKe5JsveYYUTChw5TRTgAQ0KNGkaXYsX2VHj3vC5Gl96t4YBdgdZicBEkhnAh3byROoIE0qqDTYTXKSWgFTM6kQcxdjVv3xQs33w10rb6DvKpbKSuRfT0ldk/Xlnjwdc8+jwZQ8JWXujZ0GiNL1W92TBki6+YGXzbImYWKhVRdgXc/UPXlr0z2f7nsbmzfWvvqt9al1OLumsLckZdfBc9wkQAJJE4iSyY8//y7uMjJ375w8mHRPjsP5UaTuaexGYuvnxeL5UFrW9LSHh+JcueT9x3uySGE8KOHcvaWjIhjnx4QbkbgiNkzmcYbgKhGP3oxqKjzVtXatWDtziqj8XSyfbXOKS6i43Wpg1mnR4WgQIU/m5RHsLNnukScOYyRPlXAHmuewG4GqFlJfD+Jn5u/lrbs8fpnA+QuQpVkcOHsOqHJ1iAR+EPhMJEACJEACJJBCAhGylEml8qVl3en8uLbSVbLUze1o3yp+OZkUVs3iqUCAbrepAJFVkEBWJbBlux3f/2zDqcS6MsGQw8qcw+gOvyPaw7qeuU5dQHTB3HB4CGufoBIfO0snr8K1mzZ7zLFPLJPTRUwWFMunCs+FMWGoKULy5Xye/bpOydzN98RNt4iI0G3ifqvpFclbLTx+zuf4S+GYKaJTpVCrHHEy79OGl8/lxDBx61URul4ErlpJb88Vi1tyiRnPQ6LbrQcoPg4VkgfAD90n160araA+MPEUCZAACZAACQQ9AYrPoL+EHAAJpD6BmBgHJky2Y/b8eEHmTwsX7liNX/Kt9Jq10O4TiBIRGlXE+5wPb4WfEbH4vy++93Y6wfHFIj4/vZAD3xe8hPwetMzb53JAgwz1lbmbGtFW53KqYP1O5nZaS1Kek2HryAtK+eEyR1SF6q254vC4zAfVOaDbxGo6Usp9XjDxWmzaGYrPBJfE7522LUPRrUvmmd/ld8eZkQRIgARIgARIwC8CHm7N/CrHTCRAAlmUwM49dvzv47iAhKeiyDe1NsqEeRaWusxKwxHzUXH+tmRRGyeWsbgw/6aoFxFhqSnaQwxDiVtgotxWCosX1RpUqJuIyjjJq4LSSipaVXjuF6vqSrF03iZ5domVtIDkzy2vUtLGUZnvejG+KasY31NI4N8FdrwzOA579xNsClGyOAmQAAmQAAlkSgJX7rYyZffYKRIggfQioJHZps2y4aNhNhw9HnirDnF97byzqceCJ6qVwraba2N7x1rO8xHno1Fu8U5c9+1/aDJ0Fmr/vBT5D3j27z1ui8asG9s6y7puTI8Kw8qYeCGoy6Go22xpicKr1k1NE2T/FYlaq0kDDJWX+aBzZI6o5pXgvZgrVs+CYtmsetnt1mS8/OcXcbO9WZZbUTF6tQjWs5L/vJQ7KO69ZaQNy1LqWobbKSOgy7K8/2kcpvxjk4W4KUJTRpOlSYAESIAESCBzEfDPlJC5+szekAAJpDKB8xcc+G6MDes3pexm3zG/EhpdXRrLbYcT9NAh62vsa1nFeazMst24dsp6hEfH4YwEItrf/GqzDEtjWZplTa/mOCli1T39XLsyOk5zPwoRhCEYJa6xtsuWzrIiCvvlvTLfM1LOa8RaKz0l58aKqHxGXGh1KZZykv9JOaYWTde0Jy4Em8Qaquc0FRAB2kHmf/aTtT71qd1dsjYoU9oQkAj5+ONvO7btcOCxHmESZdDt4qRNs6yVBEggHQhETRorrin+Rw7Pedv9CMmReC3ndOgqmyABEkgDApzzmQZQWSUJBBOB7bvs+GaUDZHel9IMaDjh157EyKZ/ypxJz0K27NJdqDppHXa3rYYT1Uqi3g+LsOGeRoi8pgQq/7NJrKG7sPj5DojNm/BmI0zE5ebR/6D4ETGNuSWZooqTYo3MJRbMwqoM/Ug63/OSvPzNb1Wplk+Nkqtrf3pLnPPpjUzgxwvLknh9HgpD5Yp+XtjAm2AJEiCBdCRw5pGb4Tjn/384Bb79G6EFUmdtzHQcJpsiARLwQoD/m3sBw8MkkB0IqJvtoM9ST3gqs7htRXFrbHWv+Cr/sxlbu9TB7nbVcK5sYexqXx1Vpm4w+Xd1qIGYfDlRYH9i91ubiNnfO7b1WK8ue1Ja3GkDEZI63zOQ/FbD+eRX05fwtPLxPXUIRJ6GcQWfNc9zZOHUaYW1kAAJkEDgBCbPXITjJ+VHKgulOFmM+cLFS5l6RFHRMfhz+n+4eMlz0L9AO6/1RcsrPVIw8E1rDhSfaU2Y9ZNAJiSg0WxHjI4zEW3tYslL7VRiWn0UCs2RuFqZVxoeFYvTFYs5z50Vt9sCh6785728bxuPbrda4Kcy+Z3luJF9CKgb7i8T7RgxKg762WUiARIggaQILFm5ETXb9HK+Zi/wHo09qbq8nX/94++wY/dBb6cz5Phzb3yOzdv3+t22e/7J/yxC4859oHEgMms6feY8Bn74LU6dPpsqXXz29c/w8vvfpEpd7pUEI19rDO59t46n9J3iM6UEWZ4EgoxA5GkHPhpuw/JVafcfi/10Ltx2rFFiMhLx53itMmLpXI/QWBtynL2Ea2ZsxPkSV0SlLVdE4nKXj2y2XcDKJh7q9VqCJ7ISgeWr4z+7p8+k3Wc3K/HiWEggOxNoUr8Gpvz0oUEwfsRbuKHFddkCx/wla3Ey0n9R5p6/dbO6GPvFaxKgz8fckixG8uWn7sNzj3VLk1EFM1/3vqcWIAYcSi2SrIcEgoDALllG5YvvbDjj//9LyR5V2PSqqNprE7bGJXSh3ditAWqPW45W705BmAQcii6QG+seaJKwHY1yGur5P75xzeqhwdLlCfNzL9sQ0GVY3v0kDk8/Fo4KV3n+jGQbGBwoCZCAVwIqnnJExN/m6ntoaLy9pdtjb+L6xrXw78LVOHLsFDre0Bi33XQ9Pvn6V+zYcxAdWjVE/97dUbRwASxdvRnDRv6O2tUrY/qcZcYa+Mi9N6Nn95sStRsTE4th307AlNlLEC5B9m7v1BL339EehQrkc9ZTv861mDRjISLCw43YiTxzDj9PnAV1xXzgzg546J5Opl5fdX06YrzEaDiPUyIwl63Zgvq1q5i6qlepgPeHj0G09OPtT0dLoLbc6HJTC7SR/zPVgnX42EmJIG5HraqV8Hq/HqhcoYzH/Hpex/HTZ6+avmzduR/vDPkRm7btQakSRdCn5224tUNzc85XXxIBkgPaj1Xrt4m7bJTUVRS9H7gVt97oX11qkf3ihz9w5HgkKl2VOCiha3vWdfOX919yTXLlzIEnenQxrryDvhxnrrdNeCnXksUK4+PXnzBN6Ofnsftvxvwl67Bt1wG8+9Ij+Gr0Xx7H5el6pCVf/dx8MepP0/dz5y+ixrUV8eoz96Nk8SL48POx8plfJbdWIbipTWO80OduM+bTZ8+bz8uy1VtkcpMD1a4pj3cGPIxR46cn+iz16t7RFXOyt2n5TDY6FiSB4CKwZIUdH8v8zvQQnkomRNbBbL0m/j8VV1Ia+Xbd/U2wtkczrOzdCgtfvBFnyxdxZlFB2mLQDOQ45zka4m8SZfZS7tzO/NzIfgROS6ySj4bFYeWaNPAZz344OWISyFYEtu7ch9kLVuGx+27Ba88+iL+mL8TTA4eZG/JBIjCWrNyEGXOXGSZ6A792006cOXcBb/bvieaNauFjESaRp88lYqaCbc6i1Xj9uQfx3suP4t//VmH8pLkJ6jl89CTeefFhtBLrorp5ThWh2r/3XSI6O2KwiN8Tp+IDMfmq65DUoeVqXFsBQ/73JHS+4nfjppp2VPBGyP+x3W5pjeefuNsITxXCXUTgff1Rf3w7eICxaL47bIzX/GdlzNt3HzDnte4nXvoEefPkwufvP4t21zfAy+99g3XCRJOvvpgMbn9qVatk2Pz02UB0EtH/6ocjna6zvurS9rTdhnWrmTHoOH0l67r5y1vz6YMITSqo9cHEkw/djm8GvYAC+fNiz4Ejzub08/OS9EW5tm9Z37DxNi5P1yMt+Q4Z+RsmTp2Pe29vh6FvP4XSJYtiz/4jGPTlL1gsbugv9b0X/R6/C7PEBf2z7yaaMem7PmAY/u7TGPb20yhftiROijuzp747IaRwg5bPFAJkcRIIBgIaWGjC5PS/UbetKo12tSpjNnYlxCRWzciriyc8dnnPllPW4KxUDLrsyslrS2LznfUT5Dtrj8WUju3R7Y/JCY5zJ3sRkAe8+OoHG7p1caBju7DsNXiOlgRIIEUEXhPLX5Pr4gPjjf5thhGeljWzTfN1WLF2K+67vb1po2CBvPjw1cfNdpvm9TBHhMmyNZtNGddOjJ88x1g6i4uVTFNTcfudtWAFHn/gFrOv9XzyZl+zXfGq0iJM5+Cj155AhXIlzbGvf5yMleu2Sb2NkFRdt3VsYcSRFjx/4RLeHfqTqUOtdGrhVYtXCxHKVrrj5lZYumoTDh4+gdy5cmKjWDE1ecq/a99hc07/qGA5duK0WEEHolzp4mjesBZm/7cSaimsU+Nqk89bX5yVuGw8dHcnbNiyC6s3bEesWHt1Lee9B46iSKECPuuaOHUBKpcvbayMmlH78tEX41xqTrwZCG/X0jq2R+7tbCzReryBWKtV7LumT+U6tr3+yr2Jt3FdV6tKouuRVnx1jq4+7HhYrOeWhbKxfMb1+AtvfyWfl67GEq7jOHD4OH6Vz9+AvvfgUlS08RDInzcPqlQuJwK/qnOonj5LzpMp2KD4TAE8FiWBzE5Af3R+/cOOWfPSX3habK6e1QgLO+xFlMNmHXK+R1yITrCkis4BrbBgO0quPyjHc+Bc6YKQX04xoyZ0rxxTtRzSZnaGs2vcCBICv0+y46wYIe66LTRbzVEKksvDbpJApieQJ3dO6eOVeeR5cucyFj1PHdeb8UoigjZs2ZNAfGrgG428qhbVxSs2OYvmzOE5hkF8m5rtSru5pR8qBAKtS62SWs5bWi6uuU+JZVddR9VCp0ndb/1JahEsXDC/EXtWfnVBVhdeT8lXX9Qa+diAwdgnYlNFkbrwavLWF9e69h8+lkAUeWrb1zFfvF3LnRULt7oEXyeuzL5SXnFptlKg47LK6Xtq8tV5vmqpbuAiHrWNU2KlV1fsOtXjHxboMb2GI36abCL89rq7oxGnXR9+DSrYu9/aBn17dkUOL59dLZ/SRPGZUoIsTwKZlECczYHvx9iwLA0DC/kzdNuh/Oh6rh5+yZcw0qC61Tb79B8sHHCTiYBbcd42lFmxF5cK58GW2+riyHXl4QjzPDNgvuMs9leqiKt27/GnC8yTxQnMnGOXp/8O9LwnDGFhCR9UZPGhc3gkQAIpJuD/b0ZsXBx2yrzQj2G6gQAAQABJREFUzm0TxikokC8vwsPC0E/mirZNZmAjqxcprUvnurpGqh0z4R80EkHy2XvPmgd0M+Yux3qxPlrJPb91XN913qvOS1VBbFknd4tl9NrKV7lm82t7wdJ12C5zJGeP/xSFCuYzZX76faZfZatUKgdtNzWTxdu1TnWxVbfT3XsPOy3jruc9bSc1rvTiq3OL9TO4Q1ym1epupYIypjC5l9q55xAa1atmDu/aewj6kCWnzHPVa/nXD+8Z99z/lq3HJ+J2rBZxnRfqq+9W/cl593xnl5yaWIYESCDTEIiKdmD4iIwXnhaQfFNro0xYXmvXvMfkz4WTVUqg4Tfz0XzwTBTcdwob7m6Ixf074HDDil6FpxbWZ8W/dGhl6uEfElACi5Y58KUE0+JSLPw8kAAJKAEVYDGxcQaGvnuzsCVFK1bK7pSb9WMnIs18QN1v0zxh5Fyd/6dRYofKnDvNqwGENm7dYwL3JFW/+/mU1qVzQVeu22rGrqJRAw/pnFW1jGnf/pi2IIGXiHt+1/40qFNVREpOqEuwBqbRoEubtu1Fq6Z1XLP5ta39UC4Hj5wwYvaHX6b5VU4zNW9YUwL6bDft69zQ0b9N97tsoBlbNqmNkT//jTETZuJHccnWl6+U1LjSi69+bpoJp/GT5xq3cQ0+pNdaXZzVXXritPkmoNaWHfsweeZi5zXUIE5rNu4woluDbam1/sLF+Jgbvvrui0lS52j5TIoQz5NAkBG4FOXAkK9s2LXnijtPRg/BERWOzrua4tsKsxN0Zc8N1dB02Gysv7cxjtYtl+BcUjtjiuXAC5LJ09PLpMryfNYksHZj/Gf/2d5hyJWLn4yseZU5KhLwj4DOcXzk+UEmc/feb2H4O0+jXcsGRnj5+nXQWR6uMz3UnfaBJ9+FBorJny8P3pVgQuXLlojvhGS08r4hAYneGvwDbus10JwLldgGVlRYPRAa4tveo1YmfWnyVZdGK3VNVhRf69i9XduZyKYjf54Cdal8sNuN6Pfm52h9x7NGWKjLpWtyz6+C02qhWJGCeP+Vx/DaR99JVN5/TP963nUTbmwdv+RZUn1xbef6xrVNwCa9Fpp0Pz7Ft+arLnXTrS0uw8//70tTpGmDeMue1c/LFSV4C4R3iEt0/cclAq8Gfvrp939wVZkS0m5l47pqVW6uk7Uj70mNK734apfefL4nXnl/JHo++4HpoV4/tXhrEKxnJdKw9dmsV/MavPTkvSbPmXMXTX7lr9bQDq0amOjPetK97wP63GPKpPRPiDwZyjx3qCkdDcuTQDYncPGSA0NVeO7NnF/rdQ9NwXJbQteZuqMWIbJycexrlXiORcm1B1By3QHE5M+JrbfUgUbKdU2Tlu5Hy7nzXQ9liu0XSs/JFP3Irp24umII+vWhAM2u15/jztwEoiaNBaLjLSv+9DTnbfcjJIfOy0z/pFFB3xj0PeZPHG7cT9UN1V3sufcqWubdqXgpXrRQiufNJbcum80uls74Pliuk/sOHpU+FTaWTPc+u+f3dF6D1OiYrsyfdM/l375akJWhCqNAk841VYGkrqRpldS1WpfCsZIuD6MPHTRKsa/ka1zpyVf7qEGodO6qXi/rgYYe1z7qvh53TeoZcFzO6fI36qLrmpLqu2tef7cpPv0lxXwkkMkJqPAc8qVN5kVkTuGp+MKvPYmRTf+E3SXIQr4jZxB+KRanJcKtM0kEvDo/L0XhncdxqKFE75P/SHOejcK6B5s6s+hG97h8GPHJiATHMsMOxWfGXwUK0Iy/BuwBCQQ7AUt8Lpr0RbAPhf33k8DYP2aJ1XOmCSx1VNYUVTfhMZ8PxDUVy/pZA7MlReCKtE8qJ8+TAAlkWgIXLzrwiQjPvfszr/BUeHHbiuKWBtUxKeJKNMDzpRI//cwhUXAL7j2Jpc+1Q1ShPCYgUePhCV12tb6/Ii7h40IFUVAXfmQiARcCO8XtXN3PaQF1gcJNEiCBgAhUu6Y8UsvVMKCGmTnDCOi8R12ORtdyVetsC3EPVos3U+oRoPhMPZasiQQyhECUzPEMBuFpwSk5rT4Kdd2B0/YY61Ci97hcEp5eXEPCouNQbNNhVJm6HlGFE7vZRMvyLX927oCeY39PVAcPkIBTgPYVF9ycvmYHkRUJkAAJJCag60nqiyn7EFCX1Ns7tcw+A86AkSZ07M2ADrBJEiCB5BOIjXPgs5GZ3+LpOkL76Vy47Vh8sALX467b9ogwbL79OjT+7F/U+nU5TlQvhTW9mrtmcW6Pqejirus8yg0SiCegAvTzb20SZTFzewXwepEACZAACZBAdiDAOZ/Z4SpzjFmSgF3mRX75vQ1r1gffTbUj1I5Fvf7ElrhTCa5NeFQsbBJUyBEe/1wsRMKym7U+3aL7JSgkO0v+Xo2qG6+48rqfT+99zvlMb+JJt9ewXgge7xkmgS5oAU2aFnOQAAmQAAmQQNoQoNtt2nBlrSSQ5gRG/xKcwlPBhNhD0WpNM2ypNeUKJwm8Xe/7hch35KwJMGQPC4FaQPVlu/yyizC15QzH+vubwLjmXi49tk1T/C8Tic8rg+JWZiGwYo0DefPa8WD3hBGTM0v/2A8SIAESIAESyA4EKD6zw1XmGLMcgfF/2rBwafBZPF0vhG1VabSrVRmzsSv+sFg3V/Rt48wSIhFuQ2NtCJNXgnexhkKsvq5pnMQsei0iHBESLpyJBLwRmLfQLiHzga6dKUC9MeJxEkhrAoOOrMZFu/+/1S+Wug65Q3m7mtbXhfWTQHoR4Lc5vUizHRJIJQL/LrBh5hx7KtWWsdVcPasRFnbYiygJHOSeChyIRMW5W5Hn+Hns6lADR+uWc8/i3D9ui8asDu3QaeoM5zFukIAnAn/PsEMCJKNNCwpQT3x4jATSmsDHR9fgRJz/63w+WaIWxWdaXxTWTwLpSIABh9IRNpsigZQS2LjFjnETsobwVBa2Q/nR9Vy9RFhKr9yLBiPmIzp/bmzvXAuVZm9GoT0nEuVzPfBzrUquu9wmAa8Exv5ux+atWed75HWgPEECJGAI2O12HDh8HBcuXko3InHipZOe7aXbwNgQCaSQAMVnCgGyOAmkF4FDRxz4+gcJwJPQ4zS9mk+zdvJNrY2yYeIL6ZIK7zqOLV3rYcsd1+FEjTI4X6qArPuZMDiRS3azOT3kPI6XKul+mPskkIiA3IfiK/kuHTmWxb5MiUbKAySQvQnExMRi0Fe/oFGnJ3DTvQPQuHMf3PX4W9i8fW+ag5n8zyLTniOr/aed5uRSt4Hn3vg8Xa536vY6a9dG8Zm1ry9Hl0UInDvvwPBv4nDJf0+loBm5IyocnXY1SdDf6AK5UXB/JIptOYx6oxah2LajOFGtVII87jsiy/Fbx7buh7lPAh4JqAFEv1MXLlKAegTEgySQBQio8Jw4ZT4+eq03Vs74BjPGDULxogXR89kPcOr02TQdYetmdTH2i9dkyWpG2E5T0ElUPn/JWpyMTNtrnUQXeNqNAJdacQPCXRLIbATibA588rkN23dl7ZvkdQ9NwXLbYYNfgw1Vn7gaRbYfRWTl4tjZsSaiCuVJ8tJUC8uLxe9/k2S+tM7ApVbSmnDq1V+tSgj69QlDmERXZiIBEkh7AsXX/hDQnM/jdXuhWHjugDt2/ORptOveH6891wPdb23jLH8pKho33vMC7ujcCv0evwufjhiPyDPncUoEyrI1W1C/dhU891g3VK9SwZTZsfsg3h4yGuu37Eap4oVRpFAB3CX1de14vTy8isKHn4/FvwtXIVRE5k1tGuOFPncjV84cWLF2K4Z9OwE/ffaqqafbY2+iVdM6mLNwNY4ej8TN7ZtK+92RJ3dOc/7P6f/hq9F/iUfGKdSsWhFR0TH4+qP+KFGssLPv1ob2+dTpczhx6oy0swVXVyiLN/r3xE8TZmLeojW4plJZPPPInWhUr5opota/Veu34aI8wS5Voih6P3Arbr0xfu1sX+Pfs/8ItOzhYyehrsu1qlbC6/16oHKFMqZeX2zU6qzjnzJ7CcIlUv3tnVri/jvao1CBfFi6ejOGjfwd9etci0kzFiIiPNwwjzxzDj9PnCXrMtvwwJ0d8NA9nUw7vury1f/3h48x9ZUtVQz58uZGl5taoFf3jhZGvmcQAVo+Mwg8myUBfwn89qc9ywtPZVF/YVOEIl4A6Nqem+5qgP9e7YyN9zTyS3hqHVtsF7CySSPdZCIBvwhs2S4W8784/9MvWMxEAkFEYOeeg7DJg8wOLRsk6HXuXDlxfePa2LJjnzl+6OhJTBWBVOPaChjyvyeN6Ptu3FRzTgVgn1eGyHQXB4a89SRefuo+I8RORp4x5wd9+QsWr9yIl/rea4TsrAUr8dl3E825s+cvYvvuA2Zb/2zduQ8z5i5Hj7tuwpvP98KkmYuwaMUGc365iN6BH34LtZaOGvayEbFbd+5HjJcI7trnaf8uRb2a10ifn0K0CL3uvd9Crhw58PHrT6Bo4YL4/Ic/nG3XqlYJ7738qAjhgeh0Q2O8+uFIp+XX1/hVNHYRkaoi+NvBA4wV991hY0y9SbFR4Tln0Wq8/tyDpu1//1uF8ZPmmrLnhM3aTTtxWMbxzosPo5WM+2V5cKzXoX/vu0R0dsTgr3814loL+KrLV/9V8EbIGLrd0hrPP3E32jRLHGPCdIh/0pUAo92mK242RgKBEVix2o7Z87PHjXHctqK4pUF1TIrYFBgkt9xj5T+XBkuXux3lLgl4JzBrnh1VKoegQT0+j/VOiWdIILgIHD1xGqGhIShcKH+ijhcpXEDmAcaLTz15W8cWePKh202+8xcu4d2hP5ltFYWHjpzANx8/j0rlS5tjJYtPNu8qSCfNXCjluhqLmh7UoEa/TpqDAX3vMXnc/6h1ssl11c1hFY+LV2xEexHHf4n179rK5fDqMw+Yc6XFOvnxl+PciyfY1z4/0aOLOaZjmTF3Gd56oZfZz5kjAo+9MBixcXHGqvjQ3Z2wYcsurN6wXY7ZxIrpwN4DR40VVwt4G3+50sVxx82tsHTVJhw8fAIq3Ddu22Pa8MVGM4yfPMdYOotfttw2rV8DsxaswOMP3GLKFyyQF5+82ddsV7yqtAjTOeIe/QQqlIuP3fD1j5Oxct02EeKNkqzLW//Veh0aGioPFiqiRaNapi3+yXgCFJ8Zfw3YAxLwSODIUQd+GJd4CRKPmbPIwZLT6qNw152ItEcne0S/5Y7FO3nyIM/Fi8mugwWzH4EfxtpQrkwISpag+232u/occVYkUFJcZFVk6dxOdZV1TToHsFSJIq6HnNt58+SS+Arx/wcdFhdYFUmW8HRmkg11e1WLY53qVzsP165eGSN+moxosZgmldQN9Eo7J1GvVpWking9r667roGN8uTOhTibzVh+o6Iu4rEBg7FPxGZjEb7WuNWN1lNyHb8KzKcGDkNJEZBqPdVklfPN5qy4+EZj9oJVIrCvPFBWUewpWa7HkNgNVsotY1I+ev0Cqcu1/1ZdfM9cBCg+M9f1YG9IwBCIjnHgyx/i5D+w7AXEfjoXuhxriNHFFiZ74OfssZjasT26TZyU7DpYMPsR0HvNr+Q792q/cOTIQQGa/T4BHHFWI3BNxbIylzsUM+euwD1d2zqHp0Lmv6XrzbxN50EvG3VETJ45ewGRIjTdLagF8+c19e/cc8g5t3LX3kMyhzMXcsqcz0BS7WqVsXbjjkCK+J13wdJ1MnXnAGaP/1TWOI6PLP/T7zP9Kj9mwj9oVLcqPnvvWeNyq27D68WCqskXmwL58iI8LAz9endH2xbX+dWWeybrVzildWnAJ1dh7t4O99OfAH2M0p85WySBJAmMGW/DofjYO0nmzWoZwqZXRbVwz0+k/R3rT1XjgyH4m5/5SEAJHDgE/Px79vI24JUngaxKoFiRgri7yw0Y+u3vZq6lzlHcf+gY+r/1hXFH7dHtxiSHXvXqq6D1PP+/LzFZ5mi+P/xnbNq6x5TT+ZDNG9bCxGnzsUPml+oc0skzF5ugQklW7JahZZM6EhBou6lf23klFQPnqYVVA/gcFPdhtSL+8Ms0t9a972rZM+cumGixO0VY/zFtgTN6b1JsdP7q0JG/Qctp+xuFm87dDDQp55TUpXN5V67baubPpnWE40DHll3zU3xm1yvPcWdaAstW2bF4+RXXk0zb0TTqWIg9FK3WNEtR7Qvs57CvcryLUIoqYuFsR2DhUgf0O8hEAiQQ/AQG9LkHd8qcxVc/GIkGNz2Ojve9CI2CO2roy05LpkapdU06R9BKajXTZVpUuH4pkWijxB2pdMkiyCmBfTRpMB117b2t10Dc+egbUJfPl5681yp+OYRe/K7W5dqS2b/ctkbY1fmbiyUA0ejfZpg5ilpKo+Z6Su59dq3LPb8GV2ou8x01IFHLrs9giczfjE/xvXGvy3X8D4pA14i6re94FndJtF7LTVjLJ8VG57eWL1PCsKnX4VHc2/dtHDsRebltSHTgK5ydB102XMfkqy5f/dfq7u3aDhOnLsB10gcrkJRLM9zMAAJcaiUDoLNJEvBGIPK0A29+GCfzG7zlyD7H9/T4F7MR796TnFG/EhmGF7/5MTlFU1yGS62kGGGGVpBHVnX438vhcnPqequYoV1i4ySQZQgMOrIaF+1xfo/nxVLXIXdoymaJ6TzFAxIwp7C4nebPl/SyXa6dU6udWt80qRVQl2nRQDkq6qykokrFUvGihaxDAb9rZF51E9akwYjeHPwDlk39OuB6vBXQPqqwVEtuIEldVvcdPCpjK+xcFsYq7w8bnf+qAlbZ5PAy59OqL6n35NalbDVCsfZBrxNTxhJI2bc5Y/vO1kkgSxHQH/jvf7ZReF6+qlfPaoSFHfYiypE8N8gxxXLgBflPJlS4MpFAIAT04Y8GINL1P3mjEgg55iWBpAkMEDGZ3klFV/myJZLV7OMvfoKL8qOgy5ds2LpbotJeJe62NRPU5WktzgQZkthRd9CbH3gZ1SUqa5hE6NVAP7oGaGqm5PZRfwMrlCvlsSv+sNH5r2Ulam5qpOTWpaI+ueNPjX6zjoQEaPlMyIN7JJBhBP6Za8Ovf9Ddz/UCXLhzFX7Ju8r1UEDbfy07gFZz5gVUJjUy0/KZGhQzvo577ghF+9bxFo+M7w17QAIkkBEENMDO+s27zJxBXQakVZO6TgtlavVHLYi6JqbOy9S1OuvWvBq6TEhmT+nBJrMzYP8CJ0DxGTgzliCBVCdw6IgDbw+Kk0n5qV51UFcYkisO0+79HQdt55M1ju5x+TDikxHJKpuSQhSfKaGXecqGi2/QGwPCUaYU3bQyz1VhT0iABEiABIKZgO/ZvsE8MvadBIKEgLrbjpb1PCk8E18wR1Q4Ou1qkviEn0f+iriEM4WSPwfHz2aYLYsS0O/kKHG/1YAiTCRAAiRAAiRAAiknQPGZcoasgQRSRGDuf3bs3MObW28QHfMqoVFYaW+nfR6Plvmif3Tu4DMPT5KALwK79jowdyHd4X0x4jkSIAESIAES8JcAxae/pJiPBNKAgEa3nTCZN7ZJoW2wsClCEwSpT6rElfNjKha9ssMtEkgGgYnyHdXvKhMJkAAJkAAJkEDKCFB8powfS5NAigjogvZR0SmqIlsUjt1WFLfEVk/WWFfazmFLrYSRCZNVEQtlWwL6HdXvKhMJkAAJkAAJkEDKCFB8powfS5NAsgmsWmvHmvW0pvgLsOS0+igcmtPf7AnyjW2d/HmjCSriTrYloN9V/c4ykQAJkAAJkAAJJJ8AxWfy2bEkCSSbQGysA7/8QUtKIADtp3Ohy7GGgRRx5h0na2rHRnBZYycQbiSLwLgJNkTH8IFRsuCxEAmQAAmQAAkIAYpPfgxIIAMIzPjXjlORGdBwkDcZNr0qqoUXCXgUJ2zRmNWhXcDlWIAEXAlEngFmzKb105UJt0mABEiABEggEAIUn4HQYl4SSAUCGrhk6izewCYHZYg9FK3WNEtOUYypXSlZ5ViIBFwJTBfxeYrBh1yRcJsESIAESIAE/CZA8ek3KmYkgdQhMGGyDTExqVNXdqzFtqo02iJwITkD53GsdKnsiIxjTkUCMbHAhEl0mU9FpKyKBEiABEggGxGg+MxGF5tDzXgCu/bYsWQF54yl9EpcM6sxcoWEBVSNDQ78dlPbgMowMwl4IrB0pUPW5qX3gic2PEYCJEACJEACvghQfPqiw3MkkMoExv/FG9bUQGo7lB9dz9cNuKqfyuQLuAwLkIAnAr9O5HfZExceIwESIAESIAFfBCg+fdHhORJIRQIbNtuxYxetnqmFNN+UOigbFpiY3Gq7gBVNG6VWF1hPNiawa68DazdQgGbjjwCHTgIkQAIkkAwCFJ/JgMYiJJAcAn9M4Y1qcrh5K+OICkenXYGv3zm2aT1vVfI4CQRE4M+p4szt4AOlgKAxMwmQAAmQQLYmQPGZrS8/B59eBFavs2Pvft6kpjZvx7xKaBRWOqBqf8sdg4t58gRUhplJwBOB/QeBlWv5vfbEhsdIgARIgARIwBMBik9PVHiMBFKRgFpG1ELClDYEGixsKgsWh/hd+Xl7HKZ06uB3fmYkAV8E/pLvtt1OAeqLEc+RAAmQAAmQgEWA4tMiwXcSSCMCy1c7cPBwGlXOahG7rShuia0eEIkx1wZmLQ2ocmbOVgQOHwWWraL4zFYXnYMlARIgARJINgGKz2SjY0ES8I/AtFm0evpHKvm5Sk6rj8KhOf2uYIH9HPZWrux3fmYkAV8Epszk3E9ffHiOBEiABEiABCwCFJ8WCb6TQBoQ2LjFDp0XxpS2BOync6HLsYZ+N6J2ql86tPQ7PzOSgC8Cav1ct5HWT1+MeI4ESIAESIAElADFJz8HJJCGBKbNZoTbNMSboOqw6VVRLbxIgmO+dsYUjYA9xP+5or7q4jkSmP4vv+v8FJAACZAACZBAUgQoPpMixPMkkEwCeyS67ZZttIYkE1/AxULsoWi1ppnf5Q7YLmHBDa38zs+MJOCLwPadDuzaQwHqixHPkQAJkAAJkADFJz8DJJBGBKZzrmcakfVerW1VabRFJe8Z3M78XL+a2xHukkDyCdD6mXx2LEkCJEACJJA9CFB8Zo/rzFGmM4FTkQ6u/5fOzK3mrpnVGLlCwqxdn++Twi/hTBH/XXV9VsaT2Z7A6nUOHDtBb4ds/0EgABIgARIgAa8EKD69ouEJEkg+gXmL7JDlPZkygIDtUH50PV/Xr5ajHTb80bGdX3mZiQSSIqDf+bn/0fU2KU48TwIkQAIkkH0JUHxm32vPkacRgTibAwsW8wY0jfD6VW2+KXVQNiyfX3l/qljUr3zMRAL+EFi0zI7YOD558ocV85AACZAACWQ/AhSf2e+ac8RpTGCNuN6dPZfGjbB6nwQcUeHotKuxzzzWyVW2c9hcu5a1y3cSSBGB8xeAlWsoPlMEkYVJgARIgASyLAGKzyx7aTmwjCIwh253GYU+QbuOeZXRKKx0gmPedsa29k+oeivP4yTgSkDd7plIgARIgARIgAQSE6D4TMyER0gg2QQOH3Vg6w5aPZINMJULNljYVBYzTnotz3H5HYiNCE/l1llddiWgy64cOsLfgex6/TluEiABEiAB7wQoPr2z4RkSCJiAzvdiyjwEYrcVxS1x1ZPs0El7DP7p0D7JfMxAAv4S4Lxvf0kxHwmQAAmQQHYiwEf92elqc6xpSsAhoS6XrqT4TFPIyai85NT6KNx1JyLt0T5Lj6ldEZ2n+swCRORAWPnKCC1VDqEly8p7WYSVKIOQAoUQki8/QnLkBMIj8CXCEWcDYmOBCxfldcGBU6cBXYLnyDEHDh8B9h8Sa6ucZ8qaBPS34K7bQhEamrTlPWsS4KhIgARIgARIIDEBis/ETHiEBJJFYJu42p2KTFZRFkpDAvbTudDlWEOMLrbQZyszcB5Hy5RByUOHruTLkxfhNa5DeLU65hVWuSpCRFwmlXJIBn0hN1CwgG4kFiAaFXnffge274p/bRN37YuXNC9TViCgQce2bHegRtXE1z4rjI9jIAESIAESIIHkEAgRaw0npiSHHMuQgBuB0ePisGAJv05uWDLFriPUjkW9/sSWuFM++/POMeDp8ZMQ0eh6RDRti/A6Df0Smz4r9fOkitHN2xxYsdqONesdxmLqZ1Fmy6QEWjQJwUP38RlvJr087BYJkAAJkEAGEKD4zADobDLrEdB1/fq/FodLtFxl2osbVv8wvqk1xWv/2ucvh5eL1EDbQhVEcGasYLCJEF2/2YHZ8+xGkHrtNE9kagK5cwGfvheOiHBaPzP1hWLnSIAESIAE0o1Axt5hpdsw2RAJpC2B9RsdFJ5pizjFtdtWlUbbWpXwL3Y768oVEoZeRavi6RK1USN3EefxjN4ICwtBvVr6CsXBwyJC59uxeLmdc0Qz+sIE2P6lKGDdBgca1KP4DBAds5MACZAACWRRAox2m0UvLIeVvgRWrWOgofQlnrzWrpnVGCo4w2QO5qPFqmN7rfvwVYXWmUp4uo+sbOkQ9Lg7DB+8Ho6WTUMQQh3jjihT7y8XN2omEiABEiABEiCBeAJ0u+UngQRSSEBdJPsNjGOwmBRyTK/ilXrsQbeaxVA1V+H0ajJV29H1IydMsmGtWNuZMj+BnBIAedj74Qin623mv1jsIQmQAAmQQJoToNttmiNmA1mdgEYrZZTSzH+VC+QH7r8rDA3qVsn8nfXRwzKlQvD04+FYu8GOH3+14cxZH5l5KsMJRMsKP1slknHNajRZZ/jFYAdIgARIgAQynADdbjP8ErADwU5g9TpaoDL7NdSoo++8Gi7CM+v85NWV+aDvvBKO5o0pajL752+tzPtkIgESIAESIAESALLOnRivJglkEIHV6zmnK4PQJ9lsLnF5fPLRMLPcRd48WU+k5ZExPXx/OJ6SMeaWNUWZMieBNWKlZiIBEiABEiABEqD45GeABFJEYP9BB05FpqgKFk4jAmVKAa+9EI7ramf9Z2z1ZIyv9Q9H6ZJpBJPVpoiA/kYcOETrZ4ogsjAJkAAJkECWIJD178r+396dR1dx3Qke/1XV0wpIAgxiM2Aw+77Y4BhwsGMcA17ATuzGacd20k47M5kzPdMn58xk+vQsOfmjTy8z0+mkk3THjud0nJnsibE7mHhpvGDj3cQsxizGrGZHCCS9VzX3Sk1kAZKe3qu6davqW+eA9Ja6y+dXb/np3rqVijDRCVsFtmxnRMPG2MxXl7b4mkrGhg1N32hnd96Nqq9f+485mTszO33uzsLG+9/+He8VNsaFNiGAAAIImBUg+TTrTW0pE9iyndEM20J6/WJXvnSfJ1VV2UvCqlWfH3rAkxuW8NZu23G59T3eK2yLCe1BAAEEEDAvwDcU8+bUmBKBvLrEyna1iiWbPQLLb3RlzZ3qKp4Zvhim7vsf3OHJzZ/i7d2eI1Nkh1oVW79nsCGAAAIIIJBlAb6dZDn69L0sgV27A2lpLasIdg5RYPVKV1av9EIsMdlF3XGLJ7cv5y3elii2tons2kPyaUs8aAcCCCCAQDwCfDOJx51aUyDAlFt7grhcjfItv5HE88KIrLzJE23DZofANqbe2hEIWoEAAgggEJsA30pio6fipAvoC8ezxS+wcL4jq9SoJ9ulBbTNgnnZO//10hrx3st5n/H6UzsCCCCAQPwCfGOLPwa0IIECvh/Irg9IPuMO3eQJjty3JtvnePYWA30O6P3KaNKVJKC9WUX9+Ptqqj7nfUatTPkIIIAAAjYLkHzaHB3aZq3AvgMirZzvGWt8Bg0U+bJa2TXnkVT1FohczpEvf8GTwYN6eyaPRynQps77/HB/lDVQNgIIIIAAAnYLkHzaHR9aZ6nAThYOiTUyrnrn+tLnPamtJfEsNhD9lNWD93qi7djiE9j9Adf7jE+fmhFAAAEE4hbga0jcEaD+RArs3M0XyDgDp1dxHX8Fb199jYE2u40VcPvKFurz9zBdP1RPCkMAAQQQSJYA396SFS9aa4nATnXuFls8AvrcRa5hWbr9zTe4nP9ZOl/Ze+7ey3tH2YgUgAACCCCQWAGSz8SGjobHJdDSEsjBw3HVnu16PXU1lT+8iwWGyjkKXNeRe5WhtmQzL6DPF29rIwE1L0+NCCCAAAI2CJB82hAF2pAogf0HAwn47hhLzD59vSvDhnKeZ7n4jcpQW7KZF/DVjP29+3gDMS9PjQgggAACNgjw7cOGKNCGRAnokQs28wJ6ddvly3jLCkt+hbLUpmzmBT7kPcQ8OjUigAACCFghwDc5K8JAI5IkoEc+2cwL3HmrJ1WVjHqGJV+pLD97G3Nvw/LsSzkHD/Ee0hcvnosAAgggkB4Bks/0xJKeGBLYd4Avjoaof1/N8EaR+bNJPH8PEtIv85SptmUzK3CA5NMsOLUhgAACCFgjQPJpTShoSFIESD7NR2rFMn19SpLPsOUdxxFty2ZWgJFPs97UhgACCCBgjwDJpz2xoCUJEDh3LpATJxPQ0BQ1cehlIlfPJfGMKqTaVhuzmRM4ckyteJtnBoU5cWpCAAEEELBFgOTTlkjQjkQI6C+NbGYFPvVJl1HPCMn1iPKNS/koiJD4oqL1atmHuVzTRS7cgQACCCCQfgG+caQ/xvQwRIEjxxitCJGz16JyOZEF83ib6hWqzCdoY23NZk7g0Ee8l5jTpiYEEEAAAVsE+FZnSyRoRyIEjpJ8Go3T3JmO9Ktlym3U6LU1jmhrNnMCx0+SfJrTpiYEEEAAAVsESD5tiQTtSITAkaOJaGZqGrloIW9RpoJ57QKsTVnreo6fMFkbdSGAAAIIIGCHAN827IgDrUiIANNuzQVqQH+RKRMZjTMlrq3r60zVRj3HTzDyyVGAAAIIIJA9AZLP7MWcHpchcOx4GTuza58Epk12RF8KhM2MgF54SJuzmRE4xsinGWhqQQABBBCwSoDk06pw0BjbBU43MVphKkYzp/L2ZMr6fD3TJmN+3iLqn4x8Ri1M+QgggAACNgrwTcPGqNAmawVON1nbtFQ1TA94TmUUznhMp07So83Gq81khScY+cxk3Ok0AgggkHUBks+sHwH0v2iBltZA2tqKfjpPLEPg8pEi/fuRBZVBWNKuA/o7MnoU7iXh9XGnfEHkXAszKfrIxtMRQAABBBIuQPKZ8ADSfHMCTYx6GsMeQwJkzPrCisaNIfm80CSq281noyqZchFAAAEEELBTgOTTzrjQKgsFON/TXFBGX04CZE67a01jR2PfVSS6W2eboyubkhFAAAEEELBRgOTTxqjQJisFms5Y2axUNoqpn/GFdeQIkk9T+s1nmXZrypp6EEAAAQTsECD5tCMOtCIBAi2tCWhkSpo4ajgJUFyhHDY0rpqzVy/TbrMXc3qMAAIIZF2A5DPrRwD9L1qAxYaKpirribU1IlVVJJ9lIZaxc7WyrxtQRgHsWrQAyWfRVDwRAQQQQCAlAiSfKQkk3YheoJWVbqNHVjU0NBiphkp6EBjYQPLfA09oD7Ww2m1olhSEAAIIIJAMAZLPZMSJVlog0KoutcIWvUBDHYlP9Mo919BQ1/PjPBqOQMEPpxxKQQABBBBAICkCJJ9JiRTtjF2AabdmQtBQb6YeauleoLa2+8d4JDyBgrrWJxsCCCCAAAJZEiD5zFK06WtZAiSfZfEVvXNlBSOfRWNF9MSaamIQEW2XYn1GPrt4cAMBBBBAIP0CJJ/pjzE9RCBRAi7vSrHHK5eLvQmZaADTbjMRZjqJAAIIIPAxAb7mfQyDXxHoScBhMKgnntAeI/kMjbLkgjyv5F3ZsQ8CPtNu+6DFUxFAAAEE0iBA8pmGKNIHIwIOrxYjziSfRph7rITpoD3yhPagzxpmoVlSEAIIIIBAMgT4Op2MONFKCwQY+TQTBM6tNePcUy3EoCed8B7LMcIcHiYlIYAAAggkQoDkMxFhopE2CLhMuzUShqZmhoOMQPdQSQuXFepBJ7yHKirCK4uSEEAAAQQQSIIAyWcSokQbrRBgOqiZMDQ1mamHWroXIAbd24T5CAs7halJWQgggAACSRAg+UxClGijFQKVjFIYiUPTGUY+jUD3UMkp/gDQg054D1XkmE4RniYlIYAAAggkQYDkMwlRoo1WCFRz7UMjcTh12kg1VNKDwNFj/AGgB57QHmLkMzRKCkIAAQQQSIgAyWdCAkUz4xeoro6/DVlowclTIpxzGF+ktT1/ADDjzzmfZpypBQEEEEDAHgGST3tiQUssF6gh+TQWoUOHjVVFRRcIfLifUc8LSCK7yXtKZLQUjAACCCBgqQDJp6WBoVn2CVRX2demtLZo7z4SoLhi+8GH2Juy79+Pcz5NWVMPAggggIAdAiSfdsSBViRAgHM+zQWJBMic9YU17d134T3cjkqgf7+oSqZcBBBAAAEE7BQg+bQzLrTKQgG+KJoLyo5djL6Z0+5a05692HcVie4W7ynR2VIyAggggICdAiSfdsaFVlkooKfI5TwLG5bCJumRz3PnSIJMh1Zf5oZRZzPq+r2kqoppt2a0qQUBBBBAwBYBkk9bIkE7EiFQV5eIZia+kYHKO7e+R/JpOpDvbgtE27NFL9CPKbfRI1MDAggggIB1AiSf1oWEBtks0FDHSIWp+LzzLlmQKevz9Wze4p//lZ8RCzTU814SMTHFI4AAAghYKEDyaWFQaJK9AvX19rYtbS174x1ffJ8E1FRcAzXkuXkL3qa8Bw80VRP1IIAAAgggYI8Ayac9saAlCRBg5NNckE6dFtn+PsmQKfGt2wPR5mxmBAYPYuTTjDS1IIAAAgjYJEDyaVM0aIv1AoMYrTAao42vMg3UFPiGjVibstb18F5iUpu6EEAAAQRsESD5tCUStCMRAkOHMFphMlCbXler3rYw+hm1eXNzIK+/jXPUzh8vf/BA3ks+7sHvCCCAAALZECD5zEac6WVIAo0knyFJFldMS6vIi68wIlecVunP2viaL/l86fuzZ98FmHbbdzP2QAABBBBIvgDJZ/JjSA8MCgy5zGBlVNUu8JunfSkUGJWL6nDQizqtf5YEPyrf7sodMri7R7gfAQQQQACB9AqQfKY3tvQsAoGqSkcGsuJtBLLdF3n0mMgravotWzQCL78WyOEj0ZRNqZcWqFfXC66tZdrtpXW4FwEEEEAgzQIkn2mOLn2LRIDzPiNh7bHQJ54qiL4UCFu4AnrUc+26QriFUlqvAiOGkXj2isQTEEAAAQRSKUDymcqw0qkoBYY18sUxSt9LlX3gkMimN0g+L2VTzn2vKtODh8spgX1LERgxnPeQUtzYBwEEEEAg+QIkn8mPIT0wLDB6FF8cDZO3V/fjXxSkhZVvQ6NvaQ3kJ79m1DM00D4UNJKRzz5o8VQEEEAAgTQJkHymKZr0xYjA6JFGqqGSCwSOnxRZ+xQL41zAUvLNtb/x5djxkndnxzIERgwvY2d2RQABBBBAIMECJJ8JDh5Nj0dg1AhHHAY/Y8Ffp1a+PfQR02/LxT94KJDfPEMiX65jqftzzmepcuyHAAIIIJB0AZLPpEeQ9hsXqKhwZHij8WqpUAnk1SzRR39UEL1QDltpAtru//y/grp8TWn7s1d5AsOGqpVua/jrVXmK7I0AAgggkFQBks+kRo52xyrAeZ/x8W/bEciT6xm1KzUCTyg7bcgWj8AVY0g845GnVgQQQAABGwRIPm2IAm1InMCYy/kCGWfQfvmkL+/vJgHtawx27PLlV8qOLT6BcSSf8eFTMwIIIIBA7AIkn7GHgAYkUeDKK0g+44ybr/Kn7/2gIM3NjOAVG4czyuq7ykzbscUncMVYPnbj06dmBBBAAIG4BfgUjDsC1J9IAT3ttqoykU1PTaOPHBP5u38sSD5PAtpbULXRt5QVq9v2JhXt47mcyKgR0dZB6QgggAACCNgsQPJpc3Rom7UCnufIeEY/Y4+PPnfxkcdYOaenQARBIA//sMB5nj0hGXpsrJqun1PvHWwIIIAAAghkVYDkM6uRp99lC0wYx5fIshFDKGDjq4H8fC0JaHeUP3/cl5dfY3S4Ox+T90+awHuGSW/qQgABBBCwT4Dk076Y0KKECEwYzxdJW0K1dp0va9eRgF4YD22iV7dls0NgykTeM+yIBK1AAAEEEIhLgOQzLnnqTbyAXrXS8xLfjdR04OdrffnZ4ySg5wP6s18X1Igwied5j7h/VlQIU/XjDgL1I4AAAgjELkDyGXsIaEBSBSorHZnI6KdV4XviKV9+9LOC6PMcs7rpvj+mDBjxtOsI0NP0K3KMfNoVFVqDAAIIIGBagOTTtDj1pUpgxlS+TNoW0PXP+fLthwtyriV7Caju89+rvv9WGbDZJcCUW7viQWsQQAABBOIRIPmMx51aUyIwYyovIRtD+fpbgXzjr/Ny6KPsJKC6r7rPr6m+s9knMHUS7xX2RYUWIYAAAgiYFuDT0LQ49aVKYHijI5cNSlWXUtOZ/QdFvv5XeXlzc/pHAd9SfdR91X1ms09gYL3IGHWZFTYEEEAAAQSyLkDymfUjgP6XLcDoZ9mEkRVw9qzIN79XkO//U16am9M3Iqj79PAP8/K3qo+6r2x2CsyawUetnZGhVQgggAACpgVypiukPgTSJqDP+3zm+bT1Kl39efGVQH63NS/33uXJrOnpSAT0aOej/7cgJ0+lK1Zp7M2cGYx6pjGu9AkBBBBAoO8CJJ99N2MPBLoITJnkSE21yNlzXe7mhmUCOknTI4Qzp/ly562ejBiWzIRg/8Gg/ZIyb76TvpFcyw6ZUJpTXSUyaUIyj7VQACgEAQQQQACBjwmQfH4Mg18RKEVAXz5hthrZeGkTyUApfqb3eft3gbzzbl4WLXDktuWeNNQnIzE4fiKQXz1ZkOdfDtSlZEyrUV+pAjOmOZLzknGMldpH9kMAAQQQQKBYAZLPYqV4HgI9CFw1x1XJZ6GHZ/CQTQI6eduwMZCXXs3LNfMd+dR1nowcYWeCsO9AIE9v8OXFV3xpa7NJkbYUIzBvZjqmeRfTV56DAAIIIIBAbwIkn70J8TgCRQhMnexIbY1IM4u+FKFlz1Py+Y4kdMPGvEyeqJLQJa5MV+fwxj1S5fuB6Gm1Ounc+h7DnPYcMX1riZ5yO2u6nX/U6FtPeDYCCCCAAALhCJB8huNIKRkX0MnK3FmOPK9G09iSKbB1eyBbtxfa/4gwZ6Yj89Vo9hSVkJpKRNvygby7NZDX3/bbE88zzcl0pNWdAvNmO1JRQfLZKcJvCCCAAAJZFyD5zPoRQP9DE9BTb5/fyNTb0EBjKkiPXr+gzqt84eWORHTCeEcmqn/655hRjnjqDw1hbHp0c98Bke3v+7Jth16NN5CWljBKpgxbBBbOZ8qtLbGgHQgggAACdgiQfNoRB1qRAgE9StagLiZ/4mQKOkMX2gV0IvrW5qD9n76jslJkeKMjjUNEGofqn44MahCpqXGkWq14rFc9rlJTLUUNgBd8aU8m9fU3TzUF7cfF0WOBHDwcyIGDInv3B5zD2a6czv/0e8GkK8P5Q0U6hegVAggggEAWBUg+sxh1+hyJgOs6cu0CV9auU1kHWyoFWltF9uwN1D/dPaZYpzLIIXVqwTxX9HsCGwIIIIAAAgh0CjAnqNOC3xAoW2DxQl5SZSNSAAIpENB/iGJDAAEEEEAAga4CfDp29eAWAmUJXDbYkamTGO0oC5GdEUi4gD5HeMQw3gcSHkaajwACCCAQgQDJZwSoFJltgcXX8LLK9hFA77Mu8MlFvAdk/Rig/wgggAAClxbgE/LSLtyLQMkCc2Y40r9fybuzIwIIJFigboC0X3YpwV2g6QgggAACCEQmQPIZGS0FZ1Ugl3NkCaOfWQ0//c64wCJ13repa8NmnJruI4AAAggkUIDkM4FBo8n2C1y/xBWPV5f9gaKFCIQo4KjTPK/7BC/8EEkpCgEEEEAgZQJ8SqYsoHTHDoGGekeumsuCI3ZEg1YgYEZg3ixHBg/idW9Gm1oQQAABBJIoQPKZxKjR5kQILFvqJaKdNBIBBMIR+PQNfKSGI0kpCCCAAAJpFeCTMq2RpV+xC4we5cikKxkFiT0QNAABAwKTJzgydjQfqQaoqQIBBBBAIMECfFImOHg03X6BZUt5idkfJVqIQPkCjHqWb0gJCCCAAALpF+CbcfpjTA9jFJg5zZHLR8bYAKpGAIHIBUaNEJk+hY/TyKGpAAEEEEAg8QJ8WiY+hHTAZgFHLX95682c+2lzjGgbAuUKLL+R13i5huyPAAIIIJANAZLPbMSZXsYoMGeGy+hnjP5UjUCUAiOHi1w1h3O7ozSmbAQQQACB9AiQfKYnlvTEYoHbGP20ODo0DYHSBW5f4Yme4cCGAAIIIIAAAr0LkHz2bsQzEChbYLYa/Rw9quxiKAABBCwSGDvaET2zgQ0BBBBAAAEEihPgU7M4J56FQNkCjH6WTUgBCFglsGoFH6FWBYTGIIAAAghYL8Anp/UhooFpEZg13eW6n2kJJv3IvMCE8Y5Mm8xHaOYPBAAQQAABBPokwCdnn7h4MgLlCdy1ilUxyxNkbwTsELjrdj4+7YgErUAAAQQQSJIAn55JihZtTbzA6FGOfOJqFidJfCDpQKYF9Gt47Gg+PjN9ENB5BBBAAIGSBPj0LImNnRAoXWCVWh2zsqL0/dkTAQTiE6iqFFm9khkM8UWAmhFAAAEEkixA8pnk6NH2RAoMbHDkpht46SUyeDQ68wLLl7nSUM/shcwfCAAggAACCJQkwDfgktjYCYHyBD6tks9BA8srg70RQMCswGWDRJYt5WPTrDq1IYAAAgikSYBP0TRFk74kRqCq0pF77mTqXmICRkMRUAJ33+FJRY5RTw4GBBBAAAEEShUg+SxVjv0QKFNAX3pl7iy+yJbJyO4IGBGYp16rs9Vrlg0BBBBAAAEEShfgk7R0O/ZEoGyBNWokpaqq7GIoAAEEIhSoqRFZw0yFCIUpGgEEEEAgKwIkn1mJNP20UkAvXLJ6JS9DK4NDoxD4V4E7b/Wkvo5ZChwQCCCAAAIIlCvAt95yBdkfgTIFli5y5YrRfLEtk5HdEYhEYMI4R5Zcw+szElwKRQABBBDInADJZ+ZCTodtE3BdR77wObWQCdf+tC00tCfjAvp6vJ//A08ch+Qz44cC3UcAAQQQCEmA5DMkSIpBoByBYY2O3HkrL8dyDNkXgbAFPnObK8OGkniG7Up5CCCAAALZFeDbbnZjT88tE7h+sStTJvJF17Kw0JyMCkyf7MjSxVwOKaPhp9sIIIAAAhEJkHxGBEuxCPRVQE/tu3+NJ3plTTYEEIhPoF+tyH3qtciGAAIIIIAAAuEKkHyG60lpCJQlMGigI/ryK2wIIBCfwL13e6JXomZDAAEEEEAAgXAFSD7D9aQ0BMoWuOYqV65dwBffsiEpAIESBBYvdGTeLD4aS6BjFwQQQAABBHoV4BO2VyKegIB5gXvUBe1HDjdfLzUikGWBy0eKrFGvPTYEEEAAAQQQiEaA5DMaV0pFoCyBykpHHro/J1WVZRXDzgggUKRATbXIQw/k1CWPmHVQJBlPQwABBBBAoM8CJJ99JmMHBMwI6MuvfF6de8aGAALRC+jFvoZeRuIZvTQ1IIAAAghkWYDkM8vRp+/WC1w9z5Wli3iZWh+oIhvoqb8lDG8U6d+v+x1yORG18HHRm37u0CEigwd13UXff+F9XZ/BrfMCy5a6MpfzPM9z8BMBBBBAAIHIBNTXHDYEELBZ4K7Vruw/GMi2HYHNzaRtvQgsvsaRu1d5UlXVkVm+udmX7/6gIK2tnTvqZPHrX8vJD39SkA0v9RzvqiqRz33GkzkzHalWZfp+IA/+Sb69sFnTHLnns54ManBk5x5fvvNIQY4eE3HV3zH++3/KyY6dgTzyWKGz4gz/NlldW/eOW/gDT4YPAbqOAAIIIGBQgE9cg9hUhUApAjlPnf/5gCdDBpeyN/vYIDB6lMgfqmTwwKFAvv39vDy+riCzp7tyy02db8Ff/Yon3/gv6pzDnNP+r7d2/4cve7JgniPvqUTye4/mVcLq/36XpYtdOXI0kG/8TV5GDnNk7syOeq692lFTS0XWPUPiqbEa1YjxQ/d74qnXGBsCCCCAAAIIRC/Q+c0n+rqoAQEEShTo38+RrzyYE70oClvyBKZOctWooyM/+bUvr70VyC/W+rJzty9zZnS+BT/5W18e+2lnAtlTL/XI5vixrkoifflff1+Ql18L5NkXOve9cpzTPrq5c3cgx0+IjB7liJ7Oe8vNnry0KVAj6T2Vno3HamtE/p16TfWrJfHMRsTpJQIIIICADQKd33xsaA1tQACBbgVGqBGsB+/z+nQ+YLeF8UAsAi3nOqs9eFhk4MDO2++8G8imNzoTyM5HLv5t0oSOhGnaZFf+x3/OyZ9/NScrbux8O99/IFCjeo5UVqjzS/uLHD4SyPVqNLRO/f7LJwsysF5EJ6hZ3fT0Yz2boHFodg2yGnv6jQACCCAQrwDnfMbrT+0I9ElgxhRX7l4dFD1C1qfCeXJkAu9u86VQcOWP1RTP5zf6arRNZMZUR0pNfQYPciQIAjn8USD7VKI5RZ23uGqlp+4TeWK9Ly+q0c271bnC3/yLnDSfFdm6PZB/80WvfXR0ySdcWf6pjpFYPTX363+Vl6YzkXXdyoLX3Okqs85k3cpG0igEEEAAAQRSKMCnbwqDSpfSLXDDEq89eUh3L9PVuw8+FPnW9wty/GQgixa6Mm6sIwP6O+p26f08eUrk2w8X5Ff/7Mtf/l1BTqiyp03pSGeffd6Xr/55Xv73dwvyp3+Wl6mTOqbdPvGUL4tV/eufU4//1za5bLAj0yaXmgKX3vY491yxzJVPXssljOKMAXUjgAACCGRXgJHP7MaenidYYPUtnpxqCtQoWs8roia4i6lr+lubA3lrc8dCP2Mud+TP/tSV994vLn76sin6Mi0Ftbse3dQjnnPVKrd1A0ROne64r0adw6hOK/39pu/fvCVQSa7IjepSIvr8UL3V1zntqycfO67LC9RlWvROxbWjvYAE/3edGvVdtYLEM8EhpOkIIIAAAgkXIPlMeABpfnYF7r3Lk6amgrypkho2+wWmqxFGvejPFDUKuUBdv7W1NWhf9fZ8yyeoczAbGjpu6QWC9FTaHbsCaWsTueYqRx64Jyc/eCwvG9QfHF5/O5BP3yDyR/d68qSaZjt/titVlY5s2X7xOaMr1Yq6uox1T/tyrkXkIzXVdvIEV44d99tXef3gw2wcP/NmqcvPfIbJPuePN34igAACCCAQhwDJZxzq1IlACAJ69dQvqQWI/vpbhfbLbYRQJEVEKLBYjbrNm+W2X4/zg30ij/wwry6H0lnhfWvUAjjto5DSPjVXT8/9b3/RJnvVc89fC/R8mrhrTyA//qUvq1eqcxe/3PE2/tImX/S02o9v+lqgerTvp2qVXZ146u2f1aq6q1a4snB+TvS5qFvU+aBp3yarBZq+qBJ1/ZphQwABBBBAAIH4BBy1aEX6v3nE50vNCEQucPZc0J6A6oSEzV4BfWkPPU32qJruqkciw9j0SKq+/usJde7o2Y+tpNtb2Xo/veiRPm807du4MY7oa6JWV5N4pj3W9A8BBBBAwH4Bkk/7Y0QLEehVoPlsIH+jRkB3fUAC2isWT8iMgE48/0QlnjUknpmJOR1FAAEEELBbgBNg7I4PrUOgKIHamo4v2VeMZnSnKDCelHoBEs/Uh5gOIoAAAggkUIDkM4FBo8kIXErgfAI6lgT0UjzclyEBEs8MBZuuIoAAAggkSoBpt4kKF41FoHeB5uZA/ud3CrJzN1Nwe9fiGWkTGK+uofrvH2KqbdriSn8QQAABBNIhQPKZjjjSCwS6CLSoy3h8+x8LsnkrCWgXGG6kWmD6FEceesBrv+xMqjtK5xBAAAEEEEioAMlnQgNHsxHoTSBfCOThfyrIy6+RgPZmxePJF1gwzxdIl1sAAAtkSURBVJH77/Ek53Hec/KjSQ8QQAABBNIqQPKZ1sjSLwSUgL6S0o9+5stv/6Xr9R/BQSBNAjdc58rdq1xxHBLPNMWVviCAAAIIpE+A5DN9MaVHCFwk8Pi6gvxiLQnoRTDckXiBVStcWbHMS3w/6AACCCCAAAJZECD5zEKU6SMCSmDTG758X03DbWuDA4HkC+RyIg+s8eTqeSzanvxo0gMEEEAAgawIkHxmJdL0EwElsGuPL9/8h4KcPAUHAskVqBsg8m//yJNxY0g8kxtFWo4AAgggkEUBks8sRp0+Z1rg2IlA/va7edm7L9MMdD6hApePFPnKgzkZ1MD5nQkNIc1GAAEEEMiwAMlnhoNP17Mr0NIStE/Bfe0tVsLN7lGQvJ7PnenIFz6nLqVSReKZvOjRYgQQQAABBERIPjkKEMiwwLpnCvLTX/lSYC2iDB8F9nfdVbNr77jFlZuuZ2Eh+6NFCxFAAAEEEOhegOSzexseQSATAjt2+fKdhwty/GQmuksnEyZQXyfyx/d5MmE853cmLHQ0FwEEEEAAgYsESD4vIuEOBLIncLopkO89WpB3tzENN3vRt7fHkyc68uC9ntQNYJqtvVGiZQgggAACCBQvQPJZvBXPRCDVAr4fyBPrffn1k0zDTXWgE9A5R+Wat9zkykr1z3VJPBMQMpqIAAIIIIBAUQIkn0Ux8SQEsiOwe28g//BoXg4ezk6f6ak9AkOHiHxRLSo0bizTbO2JCi1BAAEEEEAgHAGSz3AcKQWBVAm0tgbyE7UQ0dMbWIkoVYG1vDPXXevKZ293paqS0U7LQ0XzEEAAAQQQKEmA5LMkNnZCIBsCm7f48shjBTnBYkTZCHhMvawbIHL/Gk9mTGW0M6YQUC0CCCCAAAJGBEg+jTBTCQLJFTh7Lmi/HMuzLzAKmtwo2tvyaxc48pnbPOnfj9FOe6NEyxBAAAEEEAhHgOQzHEdKQSD1AvqSLI/+qCD7D6a+q3TQgEDjUJF77/Jk0pWMdhrgpgoEEEAAAQSsECD5tCIMNAKBZAjkC4E8+ZQva9f5ki8ko8200i4BzxNZfqPb/q8ix2inXdGhNQgggAACCEQrQPIZrS+lI5BKgUMfBfLjXxTkzc1cFzSVAY6oU9MmO3L3ak+GN5J0RkRMsQgggAACCFgtQPJpdXhoHAJ2C2zZ5stjP1dTcQ/Y3U5aF6/AMDXF9rO3ezJzGlNs440EtSOAAAIIIBCvAMlnvP7UjkDiBXw/kOde9OWXT/jSdCbx3aEDIQr0qxW59WZXPqkuoeJ5jHaGSEtRCCCAAAIIJFKA5DORYaPRCNgn0Hw2kN887cv6Z31pabWvfbTInEBFhcjSRa6sWOZKv1qSTnPy1IQAAggggIDdAiSfdseH1iGQOIHTTWpRovW+PPO8L21tiWs+DS5DIKcWE7pOjXLqBYXq60g6y6BkVwQQQAABBFIpQPKZyrDSKQTiFzhxMpDH1aq4G17ypcDKuPEHJMIW6BVsFy/sGOkc2EDSGSE1RSOAAAIIIJBoAZLPRIePxiNgv4BOQp9SU3Gfe8GXcy32t5cWFi9QWSmyaIErN13vyuBBJJ3Fy/FMBBBAAAEEsilA8pnNuNNrBIwL6HNCdQK6/jlfTp4yXj0VhihQN0DkhiVqISF1XifndIYIS1EIIIAAAgikXIDkM+UBpnsI2CbQlg9k46ZAnt5QkL37bGsd7elJYMQwkWVLPVk435FcjpHOnqx4DAEEEEAAAQQuFiD5vNiEexBAwJDA+7t9eVYtTLTpjUDyeUOVUk2fBHI5kXmzHLnuE65MvJLrdPYJjycjgAACCCCAQBcBks8uHNxAAIE4BJrOBPLCy778i1qc6NDhOFpAnRcKDG8UWaISzmuucqV/P0Y5L/ThNgIIIIAAAgj0XYDks+9m7IEAAhEK7Nrjy8ZXA3nldV9ON0VYEUVfJNC/n8j82a4sUNNqJ4xjlPMiIO5AAAEEEEAAgbIESD7L4mNnBBCISqBQCOR3W9X5oa/68ubmQFpbo6op2+XWVIvMmenI1XNdmTLREc9jlDPbRwS9RwABBBBAIDoBks/obCkZAQRCEmhrC+TdbYG8+U5HIsqIaHmwerXaGVMdmT3dlenqZwWLB5UHyt4IIIAAAgggUJQAyWdRTDwJAQRsEfD9QHbuCeSNtwPZvMWXfQdsaZnd7RhzuSMzVaI5c5ojY0c74jiMcNodMVqHAAIIIIBA+gRIPtMXU3qEQKYETp0OZMt2/c9v/3n0WKa6321nG4eIWp3WkYnjO6bTNtSTbHaLxQMIIIAAAgggYESA5NMIM5UggIApgcNHAtmxs2N0dKe6lMuH+0V831Tt8dTjqrWB9DU4r7xCXw7FkUnqX30dyWY80aBWBBBAAAEEEOhOgOSzOxnuRwCBVAi0tgay50OVjO4OZO++QE3TDeSgupxLW1syu+d5IiOHi+hptGNGqX/q56gR6rzNCpLNZEaUViOAAAIIIJAdAZLP7MSaniKAwL8K6PNGPzoisv9gRzKqR0uPHBU5ciyQ4ydEgiBeKn06Zn2dSOMQR4YNdaRxaOfvlw0WVqSNNzzUjgACCCCAAAIlCpB8lgjHbgggkE4BfYkXnYAePd7xs+lMIE1nRJrUNUdPN3X83nxWXfpFjZzq0VN9CZi2fMfv56f36uTRVf8cNR1WT4n11L+qKhF9WZOaakeq1c/amo7bA/qrKbL1IgPVOZkN6qc+N1MnnlzyJJ3HF71CAAEEEEAgywIkn1mOPn1HAIFQBfSIqquzTjYEEEAAAQQQQACBiwRIPi8i4Q4EEEAAAQQQQAABBBBAAIGwBdRkMDYEEEAAAQQQQAABBBBAAAEEohUg+YzWl9IRQAABBBBAAAEEEEAAAQSUAMknhwECCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAwP8HFA9xkzXwArEAAAAASUVORK5CYII=" 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}, "metadata": {}, "output_type": "display_data" diff --git a/studies/202602_AmazonConnect/notebooks/03_business_case.ipynb b/studies/202602_AmazonConnect/notebooks/03_business_case.ipynb index db88c05..b05e827 100644 --- a/studies/202602_AmazonConnect/notebooks/03_business_case.ipynb +++ b/studies/202602_AmazonConnect/notebooks/03_business_case.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "3cc4b453", "metadata": {}, "outputs": [], @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "62c56628", "metadata": {}, "outputs": [], @@ -61,10 +61,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "7d295b06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Forrester composite β€” moderate case
NPV
$78.7M
ROI
342%
Payback
<6 months (0.7)
Benefits PV
$101.7M
Costs PV
$23.0M
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "summary = _compute_summary(\n", " seed_data.BENEFITS,\n", @@ -79,10 +92,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "c3fc75fb", "metadata": {}, - "outputs": [], + "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", + "
 YearBenefitsCostsNetCumulative
01$27,279,019$7,281,067$19,997,952$18,801,702
12$40,333,658$8,771,168$31,562,490$50,364,192
23$57,983,494$10,539,889$47,443,605$97,807,797
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 ScenarioBenefits PVCosts PVNPVROI %Payback (mo)
0conservative$71,672,868$17,437,916$54,234,951311%0.800000
1moderate$101,696,568$22,983,076$78,713,492342%0.700000
2aggressive$123,911,705$27,682,369$96,229,337348%0.700000
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "charts.scenario_comparison(scenario_summaries).show()" ] @@ -203,10 +2926,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c8239dbd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Set TOOL_PUBLIC_ID to compare Athena vs local.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if config.TOOL_PUBLIC_ID:\n", " from core.tei_client import TEIClient\n", @@ -226,6 +2962,14 @@ "source": [ "Continue with [`04_export.ipynb`](04_export.ipynb) β†’" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ac0231c-5b57-4544-a464-148d2e74332c", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {