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The estimand is the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i across countries/economies (one equally weighted observation per country) in the analysis year, where LE is life expectancy at birth (years) and GDPpc is GDP per capita in PPP constant 2021 international dollars, with heteroskedasticity-robust uncertainty. The task is a complete, bounded chain: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. \"Effect\" is used here in the descriptive, associational sense: no causal claim is requested.\n\n## Inputs\n- `data/wb_indicators.csv` — observed data, World Bank World Development Indicators (retrieved 2026-10-30, CC BY 4.0; full provenance, URLs and SHA-256 in `data/README.md`). One row per entity-year, 2015–2023. Columns: `iso3`, `name`, `year`, `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international $; empty if not reported), `life_expectancy` (years; empty if not reported). The file is the API download for *all* World Bank entities, so it contains countries/economies as well as regional and income-group aggregates.\n- `data/wb_entities.csv` — World Bank entity metadata: `iso3`, `name`, `region` (the value `Aggregates` marks groupings that are not countries/economies), `income_level`, `lending_type`.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- The analysis population is countries/economies only: exclude every entity whose `region` is `Aggregates` in `wb_entities.csv`.\n- The analysis year is the most recent year (2015–2023) in which both indicators are observed for at least 150 countries/economies. Report it as `analysis_year`.\n- In the analysis year, countries/economies with either indicator missing are excluded from the estimate and counted in `n_excluded_missing`. No imputation.\n- Use the natural logarithm of GDP per capita. Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `analysis_year` | integer | year | year selected by the rule above |\n| `n_excluded_missing` | integer | countries | countries/economies present in `wb_indicators.csv` for the analysis year but dropped for a missing indicator |\n| `baseline_metric` | number | years | RMSE of the mean-only prediction of life expectancy, sqrt(mean((LE − mean LE)²)) over the analysed countries (the no-information baseline) |\n| `model_metric` | number | years | RMSE of the OLS residuals, sqrt(mean(e²)) |\n| `pearson_r` | number | — | Pearson correlation between ln(GDPpc) and LE |\n| `r_squared` | number | — | R² of the OLS regression |\n| `effect_estimate` | number | years per log-point | OLS slope β |\n| `intercept` | number | years | OLS intercept α |\n| `effect_se` | number | years per log-point | heteroskedasticity-robust standard error of β (HC0–HC3; state which in the report) |\n| `effect_ci_low`, `effect_ci_high` | number | years per log-point | 95% confidence interval for β based on `effect_se` |\n| `conclusion` | string | — | one of the codes below |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of life expectancy against ln(GDP per capita) for the analysed countries with the fitted OLS line, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per World Bank income level present among the analysed countries, with columns `income_level`, `n_countries`, `mean_log_gdp_pc`, `mean_life_expectancy` (header row + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nREADME.md\nwb_entities.csv\nwb_indicators.csv","is_error":false}]},"uuid":"baf5cc2b-d063-4e98-9e0e-953bb325e6e7","timestamp":"2026-10-30T05:30:56.597Z","toolUseResult":{"stdout":"# F6-dev-01 — Income and life expectancy across countries: a bounded mini-project\n\n## Scientific question\nDoes the cross-country relationship between real income per capita and life expectancy (the \"Preston curve\") hold in the most recent year with usable World Bank data, and how strong is it? The estimand is the slope β of the linear projection LE_i = α + β·ln(GDPpc_i) + ε_i across countries/economies (one equally weighted observation per country) in the analysis year, where LE is life expectancy at birth (years) and GDPpc is GDP per capita in PPP constant 2021 international dollars, with heteroskedasticity-robust uncertainty. The task is a complete, bounded chain: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. \"Effect\" is used here in the descriptive, associational sense: no causal claim is requested.\n\n## Inputs\n- `data/wb_indicators.csv` — observed data, World Bank World Development Indicators (retrieved 2026-10-30, CC BY 4.0; full provenance, URLs and SHA-256 in `data/README.md`). One row per entity-year, 2015–2023. Columns: `iso3`, `name`, `year`, `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international $; empty if not reported), `life_expectancy` (years; empty if not reported). The file is the API download for *all* World Bank entities, so it contains countries/economies as well as regional and income-group aggregates.\n- `data/wb_entities.csv` — World Bank entity metadata: `iso3`, `name`, `region` (the value `Aggregates` marks groupings that are not countries/economies), `income_level`, `lending_type`.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- The analysis population is countries/economies only: exclude every entity whose `region` is `Aggregates` in `wb_entities.csv`.\n- The analysis year is the most recent year (2015–2023) in which both indicators are observed for at least 150 countries/economies. Report it as `analysis_year`.\n- In the analysis year, countries/economies with either indicator missing are excluded from the estimate and counted in `n_excluded_missing`. No imputation.\n- Use the natural logarithm of GDP per capita. Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `analysis_year` | integer | year | year selected by the rule above |\n| `n_excluded_missing` | integer | countries | countries/economies present in `wb_indicators.csv` for the analysis year but dropped for a missing indicator |\n| `baseline_metric` | number | years | RMSE of the mean-only prediction of life expectancy, sqrt(mean((LE − mean LE)²)) over the analysed countries (the no-information baseline) |\n| `model_metric` | number | years | RMSE of the OLS residuals, sqrt(mean(e²)) |\n| `pearson_r` | number | — | Pearson correlation between ln(GDPpc) and LE |\n| `r_squared` | number | — | R² of the OLS regression |\n| `effect_estimate` | number | years per log-point | OLS slope β |\n| `intercept` | number | years | OLS intercept α |\n| `effect_se` | number | years per log-point | heteroskedasticity-robust standard error of β (HC0–HC3; state which in the report) |\n| `effect_ci_low`, `effect_ci_high` | number | years per log-point | 95% confidence interval for β based on `effect_se` |\n| `conclusion` | string | — | one of the codes below |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of life expectancy against ln(GDP per capita) for the analysed countries with the fitted OLS line, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per World Bank income level present among the analysed countries, with columns `income_level`, `n_countries`, `mean_log_gdp_pc`, `mean_life_expectancy` (header row + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nREADME.md\nwb_entities.csv\nwb_indicators.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"01c5c90f-93d1-423c-a699-e99e84f2421a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","hash":"4114018c6e2c0397d623813668a89777328899b0f1223dc5f107b208f850facf","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Td5kXubH86E4qSoZEJXwJ2","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"}}]}}15{"type":"api-request","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","id":"5558a43b-031f-4812-b797-3dcb2e53adb5","timestamp":"2026-10-30T05:30:56.601Z","version":"2.1.314","querySource":"sdk","shapeHash":"12fcfaf6e0c76c8e79b64b855b805f109f95bbc0cc4fc1ddb97683e90cf29111","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"8e578b11-b79f-4b3f-891b-22e620ac94bf","keep":2,"tail":["4114018c6e2c0397d623813668a89777328899b0f1223dc5f107b208f850facf","02de7a34e9088a14aa04ad9140fcebc71b889ff70a50d521785a78ce928f9d6d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6dc3ab956fadfe5eb604fe4ac16fc119d8caa329d2e6e396f66e327c1dd96b82"}16{"parentUuid":"baf5cc2b-d063-4e98-9e0e-953bb325e6e7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsaRDE8HS1b9HrGoJ8n","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FhFKrz9MmUPq1e3VrdLeW9","name":"Bash","input":{"command":"cd data; cat README.md; head -5 *.csv; wc -l *.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(d.dtypes);print(e.region.value_counts(dropna=False));print(e.income_level.value_counts(dropna=False))\nprint(d.duplicated(['iso3','year']).sum(), set(d.iso3)-set(e.iso3), e.iso3.duplicated().sum())\nc=d[d.iso3.isin(e.iso3[e.region!='Aggregates'])]\nprint(c.groupby('year').apply(lambda g:(g.gdp_pc_ppp.notna()&g.life_expectancy.notna()).sum()))\nprint(c.groupby('year').size());print(d.describe())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2322,"cache_read_input_tokens":2234,"output_tokens":379,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2322},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsaQudiNPjuMmRHDd8D","requestRef":"5558a43b-031f-4812-b797-3dcb2e53adb5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f5e096a2-a35b-40ef-aa8e-49d8f881d780","timestamp":"2026-10-30T05:31:00.343Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"f5e096a2-a35b-40ef-aa8e-49d8f881d780","isSidechain":false,"promptId":"6f14dd9a-6103-4fce-ac04-c1eb5cb5e5fc","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FhFKrz9MmUPq1e3VrdLeW9","type":"tool_result","content":"# Data provenance — F6-dev-01\n\nAll files are observed data from the World Bank World Development Indicators (WDI), retrieved by the task author with `curl` on 2026-10-30T03:23:28Z.\nLicense: World Bank open data, Creative Commons Attribution 4.0 (CC BY 4.0), https://datacatalog.worldbank.org/public-licenses#cc-by\n\n## `wb_indicators.csv` (2349 rows)\nLong table, one row per entity-year, years 2015–2023, built by merging two API responses on (ISO3 code, year):\n- `NY.GDP.PCAP.PP.KD` — GDP per capita, PPP (constant 2021 international $). URL: `https://api.worldbank.org/v2/country/all/indicator/NY.GDP.PCAP.PP.KD?format=json&per_page=20000&date=2015:2023` (raw response SHA-256 `742e87326d9748e0873cd996d9d4ebdf9464bec10ccd24a97d66b07e651789ec`)\n- `SP.DYN.LE00.IN` — Life expectancy at birth, total (years). URL: `https://api.worldbank.org/v2/country/all/indicator/SP.DYN.LE00.IN?format=json&per_page=20000&date=2015:2023` (raw response SHA-256 `6bb51828124409f555b5089900a507aa62a0a17523ec3608b4b51bb5c6928d04`)\n\nColumns: `iso3` (ISO-3166 alpha-3 code as returned by the API), `name` (entity name), `year` (integer), `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international dollars; empty = not reported), `life_expectancy` (years; empty = not reported).\nThe API endpoint `country/all` returns every entity in the World Bank database, i.e. countries/economies **and** regional, income-group and other aggregates. 45 records whose `countryiso3code` field was empty in the API response (a few income-group aggregates identified only by a 2-letter code) were dropped during preparation; nothing else was altered.\nSHA-256 of `wb_indicators.csv`: `6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0`\n\n## `wb_entities.csv` (296 rows)\nEntity metadata from `https://api.worldbank.org/v2/country?format=json&per_page=400` (raw response SHA-256 `c9acf3932785191a649cbc04c3056af4cf64a236e5c3b49d49be25ddb0440bce`).\nColumns: `iso3`, `name`, `region` (World Bank region; the value `Aggregates` marks entities that are regional, income or other groupings rather than countries/economies), `income_level`, `lending_type`.\nSHA-256 of `wb_entities.csv`: `06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d`\n==> wb_entities.csv <==\niso3,name,region,income_level,lending_type\nABW,Aruba,Latin America & Caribbean,High income,Not classified\nAFE,Africa Eastern and Southern,Aggregates,Aggregates,Aggregates\nAFG,Afghanistan,\"Middle East, North Africa, Afghanistan & Pakistan\",Low income,IDA\nAFR,Africa,Aggregates,Aggregates,Aggregates\n\n==> wb_indicators.csv <==\niso3,name,year,gdp_pc_ppp,life_expectancy\nABW,Aruba,2015,36753.404173,75.405000\nABW,Aruba,2016,36926.113069,75.540000\nABW,Aruba,2017,38213.289239,75.620000\nABW,Aruba,2018,39378.228411,75.880000\n 297 wb_entities.csv\n 2350 wb_indicators.csv\n 2647 total\n<string>:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\niso3 object\nname object\nyear int64\ngdp_pc_ppp float64\nlife_expectancy float64\ndtype: object\nregion\nAggregates 79\nEurope & Central Asia 58\nSub-Saharan Africa 48\nLatin America & Caribbean 42\nEast Asia & Pacific 37\nMiddle East, North Africa, Afghanistan & Pakistan 23\nSouth Asia 6\nNorth America 3\nName: count, dtype: int64\nincome_level\nHigh income 86\nAggregates 79\nUpper middle income 59\nLower middle income 47\nLow income 25\nName: count, dtype: int64\n0 set() 0\nyear\n2015 199\n2016 199\n2017 199\n2018 199\n2019 199\n2020 199\n2021 199\n2022 199\n2023 197\ndtype: int64\nyear\n2015 217\n2016 217\n2017 217\n2018 217\n2019 217\n2020 217\n2021 217\n2022 217\n2023 217\ndtype: int64\n year gdp_pc_ppp life_expectancy\ncount 2349.000000 2185.000000 2349.000000\nmean 2019.000000 24483.044024 72.341773\nstd 2.582539 24511.848957 7.637494\nmin 2015.000000 992.285806 18.818000\n25% 2017.000000 6153.637902 66.940000\n50% 2019.000000 15831.584983 72.996000\n75% 2021.000000 35798.041275 77.814000\nmax 2023.000000 139541.157249 86.372000","is_error":false}]},"uuid":"04eadccf-7e0f-460b-9333-15e55bcb1cda","timestamp":"2026-10-30T05:31:00.582Z","toolUseResult":{"stdout":"# Data provenance — F6-dev-01\n\nAll files are observed data from the World Bank World Development Indicators (WDI), retrieved by the task author with `curl` on 2026-10-30T03:23:28Z.\nLicense: World Bank open data, Creative Commons Attribution 4.0 (CC BY 4.0), https://datacatalog.worldbank.org/public-licenses#cc-by\n\n## `wb_indicators.csv` (2349 rows)\nLong table, one row per entity-year, years 2015–2023, built by merging two API responses on (ISO3 code, year):\n- `NY.GDP.PCAP.PP.KD` — GDP per capita, PPP (constant 2021 international $). URL: `https://api.worldbank.org/v2/country/all/indicator/NY.GDP.PCAP.PP.KD?format=json&per_page=20000&date=2015:2023` (raw response SHA-256 `742e87326d9748e0873cd996d9d4ebdf9464bec10ccd24a97d66b07e651789ec`)\n- `SP.DYN.LE00.IN` — Life expectancy at birth, total (years). URL: `https://api.worldbank.org/v2/country/all/indicator/SP.DYN.LE00.IN?format=json&per_page=20000&date=2015:2023` (raw response SHA-256 `6bb51828124409f555b5089900a507aa62a0a17523ec3608b4b51bb5c6928d04`)\n\nColumns: `iso3` (ISO-3166 alpha-3 code as returned by the API), `name` (entity name), `year` (integer), `gdp_pc_ppp` (GDP per capita, PPP, constant 2021 international dollars; empty = not reported), `life_expectancy` (years; empty = not reported).\nThe API endpoint `country/all` returns every entity in the World Bank database, i.e. countries/economies **and** regional, income-group and other aggregates. 45 records whose `countryiso3code` field was empty in the API response (a few income-group aggregates identified only by a 2-letter code) were dropped during preparation; nothing else was altered.\nSHA-256 of `wb_indicators.csv`: `6823f02ed4c20fd12a54b8ca4454f85ba333f22337e2a9fc57c3cabccfdbfcd0`\n\n## `wb_entities.csv` (296 rows)\nEntity metadata from `https://api.worldbank.org/v2/country?format=json&per_page=400` (raw response SHA-256 `c9acf3932785191a649cbc04c3056af4cf64a236e5c3b49d49be25ddb0440bce`).\nColumns: `iso3`, `name`, `region` (World Bank region; the value `Aggregates` marks entities that are regional, income or other groupings rather than countries/economies), `income_level`, `lending_type`.\nSHA-256 of `wb_entities.csv`: `06af6aec15122fecea5be5eb21464c505188ecc135bc55104a40f20f65040f8d`\n==> wb_entities.csv <==\niso3,name,region,income_level,lending_type\nABW,Aruba,Latin America & Caribbean,High income,Not classified\nAFE,Africa Eastern and Southern,Aggregates,Aggregates,Aggregates\nAFG,Afghanistan,\"Middle East, North Africa, Afghanistan & Pakistan\",Low income,IDA\nAFR,Africa,Aggregates,Aggregates,Aggregates\n\n==> wb_indicators.csv <==\niso3,name,year,gdp_pc_ppp,life_expectancy\nABW,Aruba,2015,36753.404173,75.405000\nABW,Aruba,2016,36926.113069,75.540000\nABW,Aruba,2017,38213.289239,75.620000\nABW,Aruba,2018,39378.228411,75.880000\n 297 wb_entities.csv\n 2350 wb_indicators.csv\n 2647 total\n<string>:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\niso3 object\nname object\nyear int64\ngdp_pc_ppp float64\nlife_expectancy float64\ndtype: object\nregion\nAggregates 79\nEurope & Central Asia 58\nSub-Saharan Africa 48\nLatin America & Caribbean 42\nEast Asia & Pacific 37\nMiddle East, North Africa, Afghanistan & Pakistan 23\nSouth Asia 6\nNorth America 3\nName: count, dtype: int64\nincome_level\nHigh income 86\nAggregates 79\nUpper middle income 59\nLower middle income 47\nLow income 25\nName: count, dtype: int64\n0 set() 0\nyear\n2015 199\n2016 199\n2017 199\n2018 199\n2019 199\n2020 199\n2021 199\n2022 199\n2023 197\ndtype: int64\nyear\n2015 217\n2016 217\n2017 217\n2018 217\n2019 217\n2020 217\n2021 217\n2022 217\n2023 217\ndtype: int64\n year gdp_pc_ppp life_expectancy\ncount 2349.000000 2185.000000 2349.000000\nmean 2019.000000 24483.044024 72.341773\nstd 2.582539 24511.848957 7.637494\nmin 2015.000000 992.285806 18.818000\n25% 2017.000000 6153.637902 66.940000\n50% 2019.000000 15831.584983 72.996000\n75% 2021.000000 35798.041275 77.814000\nmax 2023.000000 139541.157249 86.372000","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f5e096a2-a35b-40ef-aa8e-49d8f881d780","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008/data","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","hash":"e9f8f3f98a09e7cb5c1100c44169eabb67a4e8823008d26c07d993efa715d5c6","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01FhFKrz9MmUPq1e3VrdLeW9","name":"Bash","input":{"command":"cd data; cat README.md; head -5 *.csv; wc -l *.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(d.dtypes);print(e.region.value_counts(dropna=False));print(e.income_level.value_counts(dropna=False))\nprint(d.duplicated(['iso3','year']).sum(), set(d.iso3)-set(e.iso3), e.iso3.duplicated().sum())\nc=d[d.iso3.isin(e.iso3[e.region!='Aggregates'])]\nprint(c.groupby('year').apply(lambda 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(e.isna().sum()); print(d.sort_values('life_expectancy').head(8)); \nc=d[d.iso3.isin(e.iso3[e.region!='Aggregates'])&(d.year==2023)]\nprint(c[c.isna().any(axis=1)][['iso3','name','gdp_pc_ppp','life_expectancy']])\nprint(c.sort_values('gdp_pc_ppp').head(3));print(c.sort_values('gdp_pc_ppp').dropna().tail(3))\nprint((d.name!=d.iso3.map(e.set_index('iso3').name)).sum())\ngrep=d[d.iso3=='NAM'];print(grep.tail(2))\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2351,"cache_read_input_tokens":4556,"output_tokens":479,"output_tokens_details":{"thinking_tokens":95},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2351},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsahv4njzQKyhKJabnC","requestRef":"83028f92-9e2e-4468-86d4-28e239c4dbb0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f6ef2867-c5b5-42f1-b1f4-49b90c5578bf","timestamp":"2026-10-30T05:31:05.820Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008/data","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"f6ef2867-c5b5-42f1-b1f4-49b90c5578bf","isSidechain":false,"promptId":"6f14dd9a-6103-4fce-ac04-c1eb5cb5e5fc","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AZZgGk63MumpS4djEpSeNS","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\niso3 0\nname 0\nregion 0\nincome_level 0\nlending_type 0\ndtype: int64\n iso3 name year gdp_pc_ppp life_expectancy\n313 CAF Central African Republic 2022 1136.801711 18.818\n310 CAF Central African Republic 2019 1145.095637 31.530\n1910 SSD South Sudan 2017 NaN 35.351\n1909 SSD South Sudan 2016 NaN 36.720\n1908 SSD South Sudan 2015 NaN 39.757\n312 CAF Central African Republic 2021 1128.552380 40.279\n308 CAF Central African Republic 2017 1103.866093 45.231\n311 CAF Central African Republic 2020 1136.570530 50.596\n iso3 name gdp_pc_ppp life_expectancy\n107 ASM American Samoa NaN 72.852000\n350 CHI Channel Islands NaN 81.100000\n458 CUB Cuba NaN 78.085000\n629 ERI Eritrea NaN 68.624000\n764 GIB Gibraltar NaN 83.553000\n845 GUM Guam NaN 77.209000\n971 IMN Isle of Man NaN 80.999000\n1214 LIE Liechtenstein NaN 84.595122\n1295 MAF St. Martin (French part) NaN 80.224000\n1313 MCO Monaco NaN 86.372000\n1448 MNP Northern Mariana Islands NaN 78.808000\n1520 NCL New Caledonia NaN 78.766000\n1709 PRK Korea, Dem. People's Rep. NaN 73.642000\n1763 PYF French Polynesia NaN 84.070000\n1916 SSD South Sudan NaN 57.617000\n2015 SYR Syrian Arab Republic NaN 72.120000\n2249 VEN Venezuela, RB NaN 72.514000\n2258 VGB British Virgin Islands NaN 77.276000\n2267 VIR Virgin Islands (U.S.) NaN 80.519512\n2321 YEM Yemen, Rep. NaN 69.295000\n iso3 name year gdp_pc_ppp life_expectancy\n152 BDI Burundi 2023 1033.009373 63.651\n314 CAF Central African Republic 2023 1132.676434 57.408\n1889 SOM Somalia, Fed. Rep. 2023 1401.647352 58.816\n iso3 name year gdp_pc_ppp life_expectancy\n989 IRL Ireland 2023 117659.728594 82.807317\n1268 LUX Luxembourg 2023 130048.922273 83.309756\n1844 SGP Singapore 2023 130296.843789 83.097561\n18\n iso3 name year gdp_pc_ppp life_expectancy\n1510 NAM Namibia 2022 9953.097524 64.190\n1511 NAM Namibia 2023 10126.235956 67.385","is_error":false}]},"uuid":"9cc91c99-caec-49da-a842-853a0d54b4e8","timestamp":"2026-10-30T05:31:06.046Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\niso3 0\nname 0\nregion 0\nincome_level 0\nlending_type 0\ndtype: int64\n iso3 name year gdp_pc_ppp life_expectancy\n313 CAF Central African Republic 2022 1136.801711 18.818\n310 CAF Central African Republic 2019 1145.095637 31.530\n1910 SSD South Sudan 2017 NaN 35.351\n1909 SSD South Sudan 2016 NaN 36.720\n1908 SSD South Sudan 2015 NaN 39.757\n312 CAF Central African Republic 2021 1128.552380 40.279\n308 CAF Central African Republic 2017 1103.866093 45.231\n311 CAF Central African Republic 2020 1136.570530 50.596\n iso3 name gdp_pc_ppp life_expectancy\n107 ASM American Samoa NaN 72.852000\n350 CHI Channel Islands NaN 81.100000\n458 CUB Cuba NaN 78.085000\n629 ERI Eritrea NaN 68.624000\n764 GIB Gibraltar NaN 83.553000\n845 GUM Guam NaN 77.209000\n971 IMN Isle of Man NaN 80.999000\n1214 LIE Liechtenstein NaN 84.595122\n1295 MAF St. Martin (French part) NaN 80.224000\n1313 MCO Monaco NaN 86.372000\n1448 MNP Northern Mariana Islands NaN 78.808000\n1520 NCL New Caledonia NaN 78.766000\n1709 PRK Korea, Dem. People's Rep. NaN 73.642000\n1763 PYF French Polynesia NaN 84.070000\n1916 SSD South Sudan NaN 57.617000\n2015 SYR Syrian Arab Republic NaN 72.120000\n2249 VEN Venezuela, RB NaN 72.514000\n2258 VGB British Virgin Islands NaN 77.276000\n2267 VIR Virgin Islands (U.S.) NaN 80.519512\n2321 YEM Yemen, Rep. NaN 69.295000\n iso3 name year gdp_pc_ppp life_expectancy\n152 BDI Burundi 2023 1033.009373 63.651\n314 CAF Central African Republic 2023 1132.676434 57.408\n1889 SOM Somalia, Fed. Rep. 2023 1401.647352 58.816\n iso3 name year gdp_pc_ppp life_expectancy\n989 IRL Ireland 2023 117659.728594 82.807317\n1268 LUX Luxembourg 2023 130048.922273 83.309756\n1844 SGP Singapore 2023 130296.843789 83.097561\n18\n iso3 name year gdp_pc_ppp life_expectancy\n1510 NAM Namibia 2022 9953.097524 64.190\n1511 NAM Namibia 2023 10126.235956 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('wb_entities.csv',keep_default_na=False,na_values=[''])\nprint(e.isna().sum()); print(d.sort_values('life_expectancy').head(8)); 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-c \"\nimport pandas as pd\nd=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nm=d.drop_duplicates('iso3').merge(e,on='iso3');print(m[m.name_x!=m.name_y][['iso3','name_x','name_y','region']])\nprint(set(e.iso3)-set(d.iso3))\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1571,"cache_read_input_tokens":6907,"output_tokens":385,"output_tokens_details":{"thinking_tokens":156},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1571},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsb7G1VQq8ZQeRw19w7","requestRef":"364f9ae1-d3fe-4b14-9c3d-05cf8c0ee4d7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e51716b8-a576-4bae-b6f0-4ee204987e44","timestamp":"2026-10-30T05:31:10.137Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008/data","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"e51716b8-a576-4bae-b6f0-4ee204987e44","isSidechain":false,"promptId":"6f14dd9a-6103-4fce-ac04-c1eb5cb5e5fc","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 3, in <module>\n d=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/wb_indicators.csv'","is_error":true,"tool_use_id":"toolu_01EBN5hi2dHgPDCowN4DKibb"}]},"uuid":"d0bff197-5444-4288-a74d-ae6212992ac9","timestamp":"2026-10-30T05:31:10.343Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 3, in <module>\n d=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/wb_indicators.csv'","sandboxed":false,"sourceToolAssistantUUID":"e51716b8-a576-4bae-b6f0-4ee204987e44","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008/data","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","hash":"c1fbe2983443ad8a875093b94ebb228732748ea70f1e3cc2ad71a4e1eb4e2175","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01EBN5hi2dHgPDCowN4DKibb","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nm=d.drop_duplicates('iso3').merge(e,on='iso3');print(m[m.name_x!=m.name_y][['iso3','name_x','name_y','region']])\nprint(set(e.iso3)-set(d.iso3))\""}}]}}31{"type":"api-request","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","id":"8f3e84ea-4974-495d-998e-140f36b6d6c1","timestamp":"2026-10-30T05:31:10.346Z","version":"2.1.314","querySource":"sdk","shapeHash":"12fcfaf6e0c76c8e79b64b855b805f109f95bbc0cc4fc1ddb97683e90cf29111","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"364f9ae1-d3fe-4b14-9c3d-05cf8c0ee4d7","keep":8,"tail":["c1fbe2983443ad8a875093b94ebb228732748ea70f1e3cc2ad71a4e1eb4e2175","f307d34e8f408b1965efd5f405346a2e32c9b5db511ba54013a70ad4431089b0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fae97231cb02b3aef04ef964c72146f468fa78bb2197b09e1d47ecc53cee96f7"}32{"parentUuid":"d0bff197-5444-4288-a74d-ae6212992ac9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbRxugBJ5J7G2qVr9v","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ECB2EQfcTST9pqvr3dGcy5","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RR0008; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nm=d.drop_duplicates('iso3').merge(e,on='iso3');print(m[m.name_x!=m.name_y][['iso3','name_x','name_y','region']])\nprint(set(e.iso3)-set(d.iso3))\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1123,"cache_read_input_tokens":8478,"output_tokens":257,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1123},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsbRe4VPepoU79QmR1a","requestRef":"8f3e84ea-4974-495d-998e-140f36b6d6c1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2af808bf-fddc-44d7-802a-f934b2395c9c","timestamp":"2026-10-30T05:31:12.916Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008/data","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"2af808bf-fddc-44d7-802a-f934b2395c9c","isSidechain":false,"promptId":"6f14dd9a-6103-4fce-ac04-c1eb5cb5e5fc","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ECB2EQfcTST9pqvr3dGcy5","type":"tool_result","content":" iso3 name_x name_y region\n132 LCN Latin America & Caribbean Latin America & Caribbean Aggregates\n213 SSF Sub-Saharan Africa Sub-Saharan Africa Aggregates\n{'SXZ', 'NRS', 'CLA', 'BLA', 'DEC', 'IBB', 'NXS', 'DSA', 'DEA', 'CME', 'AFR', 'CAA', 'BHI', 'BEA', 'UMC', 'RRS', 'DSS', 'INX', 'DLA', 'CSA', 'BMN', 'CEU', 'FXS', 'XZN', 'BSS', 'LIC', 'DNS', 'DMN', 'LMC', 'NAF', 'BEC', 'MDE', 'HIC', 'CEA', 'DSF'}","is_error":false}]},"uuid":"5312ac5b-4fd1-4f71-995b-5480e6fae181","timestamp":"2026-10-30T05:31:13.103Z","toolUseResult":{"stdout":" iso3 name_x name_y region\n132 LCN Latin America & Caribbean Latin America & Caribbean Aggregates\n213 SSF Sub-Saharan Africa Sub-Saharan Africa Aggregates\n{'SXZ', 'NRS', 'CLA', 'BLA', 'DEC', 'IBB', 'NXS', 'DSA', 'DEA', 'CME', 'AFR', 'CAA', 'BHI', 'BEA', 'UMC', 'RRS', 'DSS', 'INX', 'DLA', 'CSA', 'BMN', 'CEU', 'FXS', 'XZN', 'BSS', 'LIC', 'DNS', 'DMN', 'LMC', 'NAF', 'BEC', 'MDE', 'HIC', 'CEA', 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pd\nd=pd.read_csv('data/wb_indicators.csv',keep_default_na=False,na_values=['']);e=pd.read_csv('data/wb_entities.csv',keep_default_na=False,na_values=[''])\nm=d.drop_duplicates('iso3').merge(e,on='iso3');print(m[m.name_x!=m.name_y][['iso3','name_x','name_y','region']])\nprint(set(e.iso3)-set(d.iso3))\""}}]}}35{"type":"api-request","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","id":"e217fdac-3623-4161-a42b-0ac9dea9f9e5","timestamp":"2026-10-30T05:31:13.105Z","version":"2.1.314","querySource":"sdk","shapeHash":"12fcfaf6e0c76c8e79b64b855b805f109f95bbc0cc4fc1ddb97683e90cf29111","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"8f3e84ea-4974-495d-998e-140f36b6d6c1","keep":10,"tail":["63f0f3dd7ffe4ecc36741d5c6975486e878d205f3131a80c564c75cc47311453","adc946ed83d8fb0c82fecd96c47e52122f8315eb101dbd5c489d2c29d0f57990"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8781a27e93802964ee2d58a953c5e94d8df329757d0cc832ffbea27e3624647f"}36{"parentUuid":"5312ac5b-4fd1-4f71-995b-5480e6fae181","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbdn4BQgxtsNkrtSvy","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":559,"cache_read_input_tokens":9601,"output_tokens":3265,"output_tokens_details":{"thinking_tokens":22},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":559},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":287,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsbdVw5ParMkMFKw84T","requestRef":"e217fdac-3623-4161-a42b-0ac9dea9f9e5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"606c1c5a-3ad7-48dd-8d46-be9c073309b7","timestamp":"2026-10-30T05:31:14.707Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"606c1c5a-3ad7-48dd-8d46-be9c073309b7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbdn4BQgxtsNkrtSvy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01864qcYW4FBTKTMAfWvmDQY","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve). Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = None # no randomness used\nos.makedirs(\"out\", exist_ok=True)\n# keep_default_na=False so that e.g. Namibia's ISO2-like strings / \"NA\" are never read as missing\nrd = dict(keep_default_na=False, na_values=[\"\"])\nind = pd.read_csv(\"data/wb_indicators.csv\", **rd)\nent = pd.read_csv(\"data/wb_entities.csv\", **rd)\nissues = []\n\ndup = int(ind.duplicated([\"iso3\", \"year\"]).sum())\nissues.append({\"issue\": f\"wb_indicators.csv mixes countries with regional/income aggregates ({ent.region.eq('Aggregates').sum()} aggregate codes in metadata)\",\n \"handling\": \"Dropped every iso3 whose region is 'Aggregates' in wb_entities.csv\"})\nunmatched = sorted(set(ind.iso3) - set(ent.iso3))\nissues.append({\"issue\": f\"{len(unmatched)} iso3 codes in indicators not found in entity metadata; {dup} duplicate iso3-year rows\",\n \"handling\": \"Checked; nothing to handle\" if not unmatched and dup == 0 else \"Unmatched codes excluded\"})\nissues.append({\"issue\": \"Literal country codes/names such as 'NA' could be parsed as missing by default CSV readers\",\n \"handling\": \"Read CSVs with only empty strings treated as missing\"})\n\ncountries = set(ent.loc[ent.region != \"Aggregates\", \"iso3\"])\nc = ind[ind.iso3.isin(countries)].copy()\nboth = c.gdp_pc_ppp.notna() & c.life_expectancy.notna()\ncounts = both.groupby(c.year).sum()\nyear = int(max(y for y, n in counts.items() if n >= 150))\n\ncaf = c[(c.iso3 == \"CAF\") & (c.life_expectancy < 35)]\nif len(caf):\n issues.append({\"issue\": \"Implausible life-expectancy values for Central African Republic in non-analysis years (\"\n + \", \".join(f\"{int(r.year)}: {r.life_expectancy:.1f}\" for r in caf.itertuples()) + \")\",\n \"handling\": f\"Not in analysis year {year}; left unchanged, flagged only\"})\n\ny = c[c.year == year].merge(ent[[\"iso3\", \"income_level\", \"region\"]], on=\"iso3\")\nmiss = y[y.gdp_pc_ppp.isna() | y.life_expectancy.isna()]\nissues.append({\"issue\": f\"{len(miss)} countries/economies in {year} lack an indicator (all GDP pc PPP missing: \"\n + \", \".join(miss.iso3) + \"); missingness concentrated in small territories and conflict/sanctioned economies\",\n \"handling\": \"Excluded listwise, no imputation; counted in n_excluded_missing\"})\na = y.drop(miss.index).copy()\nnonpos = int((a.gdp_pc_ppp <= 0).sum())\nissues.append({\"issue\": f\"{nonpos} non-positive GDP values (log undefined)\", \"handling\": \"Checked; none found\" if nonpos == 0 else \"Excluded\"})\na = a[a.gdp_pc_ppp > 0]\na[\"lngdp\"] = np.log(a.gdp_pc_ppp)\n\nX = sm.add_constant(a.lngdp.values)\nfit = sm.OLS(a.life_expectancy.values, X).fit(cov_type=\"HC3\")\nfit0 = sm.OLS(a.life_expectancy.values, X).fit()\nle = a.life_expectancy.values\nbase = float(np.sqrt(np.mean((le - le.mean()) ** 2)))\nrmse = float(np.sqrt(np.mean(fit.resid ** 2)))\nr = float(np.corrcoef(a.lngdp, le)[0, 1])\nbeta, se = float(fit.params[1]), float(fit.bse[1])\nlo, hi = [float(v) for v in fit.conf_int(0.05)[1]]\nconcl = \"positive_effect\" if lo > 0 else \"negative_effect\" if hi < 0 else \"no_detectable_effect\"\n\n# sensitivity (reported in report, not in required keys)\nsens = {}\nfor hc in [\"HC0\", \"HC1\", \"HC2\", \"HC3\"]:\n f = sm.OLS(le, X).fit(cov_type=hc); sens[hc] = float(f.bse[1])\nsens[\"classical_se\"] = float(fit0.bse[1])\nyrs = {}\nfor yy in sorted(c.year.unique()):\n s = c[(c.year == yy) & c.gdp_pc_ppp.notna() & c.life_expectancy.notna()]\n f = sm.OLS(s.life_expectancy.values, sm.add_constant(np.log(s.gdp_pc_ppp.values))).fit(cov_type=\"HC3\")\n yrs[int(yy)] = {\"n\": len(s), \"beta\": float(f.params[1]), \"se\": float(f.bse[1])}\ninfl = fit0.get_influence(); cd = infl.cooks_distance[0]\ntop = a.assign(cook=cd, resid=fit0.resid).sort_values(\"cook\", ascending=False).head(5)\ntrim = a[cd <= 4 / len(a)]\nft = sm.OLS(trim.life_expectancy.values, sm.add_constant(trim.lngdp.values)).fit(cov_type=\"HC3\")\nquad = sm.OLS(le, np.column_stack([np.ones(len(a)), a.lngdp, a.lngdp ** 2])).fit(cov_type=\"HC3\")\nsensitivity = {\"se_by_estimator\": sens, \"slope_by_year\": yrs,\n \"top5_cooks\": top[[\"iso3\", \"name\", \"gdp_pc_ppp\", \"life_expectancy\", \"resid\", \"cook\"]].to_dict(\"records\"),\n \"trimmed_cooks_4_over_n\": {\"n\": len(trim), \"beta\": float(ft.params[1]), \"se\": float(ft.bse[1])},\n \"quadratic_in_log\": {\"coef\": [float(v) for v in quad.params], \"se\": [float(v) for v in quad.bse]}}\nwith open(\"out/sensitivity.json\", \"w\") as fh:\n json.dump(sensitivity, fh, indent=2)\n\ntab = (a.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"lngdp\", \"mean\"),\n mean_life_expectancy=(\"life_expectancy\", \"mean\")).reset_index())\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\ntab[\"k\"] = tab.income_level.map({k: i for i, k in enumerate(order)}).fillna(99)\ntab.sort_values(\"k\").drop(columns=\"k\").round(4).to_csv(\"out/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nfor lvl in order:\n s = a[a.income_level == lvl]\n ax.scatter(s.lngdp, s.life_expectancy, s=14, alpha=.75, label=f\"{lvl} (n={len(s)})\")\nxs = np.linspace(a.lngdp.min(), a.lngdp.max(), 100)\nax.plot(xs, fit.params[0] + beta * xs, \"k-\", lw=2, label=f\"OLS: LE = {fit.params[0]:.1f} + {beta:.2f}·ln(GDPpc)\")\nax.set_xlabel(\"ln(GDP per capita, PPP, constant 2021 int'l $)\")\nax.set_ylabel(\"Life expectancy at birth (years)\")\nax.set_title(f\"Preston curve, {year} (n={len(a)} countries/economies)\")\nax.legend(fontsize=8); fig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=150)\n\nres = {\"task_id\": \"F6-dev-01\", \"analysis_year\": year, \"n_excluded_missing\": int(len(miss)),\n \"baseline_metric\": base, \"model_metric\": rmse, \"pearson_r\": r, \"r_squared\": float(fit.rsquared),\n \"effect_estimate\": beta, \"intercept\": float(fit.params[0]), \"effect_se\": se,\n \"effect_ci_low\": lo, \"effect_ci_high\": hi, \"conclusion\": concl,\n \"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n \"n_input_rows\": {\"wb_indicators.csv\": int(len(ind)), \"wb_entities.csv\": int(len(ent))},\n \"n_analysed\": int(len(a)), \"random_seed\": SEED}\nwith open(\"out/results.json\", \"w\") as fh:\n json.dump(res, fh, indent=2)\nprint(json.dumps(res, indent=2)); print(json.dumps(sensitivity, indent=1)); print(tab)\nEOF\n~/wp29/venv/bin/python 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-dev-01 (Preston curve). Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = None # no randomness used\nos.makedirs(\"out\", exist_ok=True)\n# keep_default_na=False so that e.g. Namibia's ISO2-like strings / \"NA\" are never read as missing\nrd = dict(keep_default_na=False, na_values=[\"\"])\nind = pd.read_csv(\"data/wb_indicators.csv\", **rd)\nent = pd.read_csv(\"data/wb_entities.csv\", **rd)\nissues = []\n\ndup = int(ind.duplicated([\"iso3\", \"year\"]).sum())\nissues.append({\"issue\": f\"wb_indicators.csv mixes countries with regional/income aggregates ({ent.region.eq('Aggregates').sum()} aggregate codes in metadata)\",\n \"handling\": \"Dropped every iso3 whose region is 'Aggregates' in wb_entities.csv\"})\nunmatched = sorted(set(ind.iso3) - set(ent.iso3))\nissues.append({\"issue\": f\"{len(unmatched)} iso3 codes in indicators not found in entity metadata; 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year].merge(ent[[\"iso3\", \"income_level\", \"region\"]], on=\"iso3\")\nmiss = y[y.gdp_pc_ppp.isna() | y.life_expectancy.isna()]\nissues.append({\"issue\": f\"{len(miss)} countries/economies in {year} lack an indicator (all GDP pc PPP missing: \"\n + \", \".join(miss.iso3) + \"); missingness concentrated in small territories and conflict/sanctioned economies\",\n \"handling\": \"Excluded listwise, no imputation; counted in n_excluded_missing\"})\na = y.drop(miss.index).copy()\nnonpos = int((a.gdp_pc_ppp <= 0).sum())\nissues.append({\"issue\": f\"{nonpos} non-positive GDP values (log undefined)\", \"handling\": \"Checked; none found\" if nonpos == 0 else \"Excluded\"})\na = a[a.gdp_pc_ppp > 0]\na[\"lngdp\"] = np.log(a.gdp_pc_ppp)\n\nX = sm.add_constant(a.lngdp.values)\nfit = sm.OLS(a.life_expectancy.values, X).fit(cov_type=\"HC3\")\nfit0 = sm.OLS(a.life_expectancy.values, X).fit()\nle = a.life_expectancy.values\nbase = float(np.sqrt(np.mean((le - le.mean()) ** 2)))\nrmse = 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4 / len(a)]\nft = sm.OLS(trim.life_expectancy.values, sm.add_constant(trim.lngdp.values)).fit(cov_type=\"HC3\")\nquad = sm.OLS(le, np.column_stack([np.ones(len(a)), a.lngdp, a.lngdp ** 2])).fit(cov_type=\"HC3\")\nsensitivity = {\"se_by_estimator\": sens, \"slope_by_year\": yrs,\n \"top5_cooks\": top[[\"iso3\", \"name\", \"gdp_pc_ppp\", \"life_expectancy\", \"resid\", \"cook\"]].to_dict(\"records\"),\n \"trimmed_cooks_4_over_n\": {\"n\": len(trim), \"beta\": float(ft.params[1]), \"se\": float(ft.bse[1])},\n \"quadratic_in_log\": {\"coef\": [float(v) for v in quad.params], \"se\": [float(v) for v in quad.bse]}}\nwith open(\"out/sensitivity.json\", \"w\") as fh:\n json.dump(sensitivity, fh, indent=2)\n\ntab = (a.groupby(\"income_level\").agg(n_countries=(\"iso3\", \"size\"), mean_log_gdp_pc=(\"lngdp\", \"mean\"),\n mean_life_expectancy=(\"life_expectancy\", \"mean\")).reset_index())\norder = [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\ntab[\"k\"] = 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aMZu9EgiIDEi1hvmaJjL07NmzEFuT+/M1I7iCyjaMaNCggfq7f/9+8SS4mXb14YvgFsG2ZkBn79w2KpuAAR1q8H1xPdCucZoxqxEIeDVfDttrnHY9sXctgQHiv//+K55AXwbhiN9BYdC+0/A7cFf6OcQ+ZEtp4qXtbxRKpfSmqe68TmrnCzITnfHd8OVvot57wd73Gx4yei+HonyPo/8OaoImhHBCgg0KDYSQAh3HNeMrPPLDdhRVq30sqDbRtjZXG5XFjYk9tMAON01aUGDLyy+/rP6ihjk/QzV3g1RJbAMCgGeffdbhG+26deta9wlcvG3BzePrr7/utvXEfseovJbJoGU2IHg2cv7Wnw8Y9S8oEwX10kgx9oTIoC+XcERk0EBqtHYzbiTaLF682BqkGwWkWiYDyiUwEgbjR0dFBi0gzA8Y1mk12u3btxdn0bwJ1q9fL57EkRRjX5XTGIGgEXXhKJkwKgnSg0wIzaQPfh+FTWV39XqgnVd//PGHMjq0Bct75ZVXrJkceOjR/D2MriXad6ega7Sr6A0B87uOuwMcI3wXEbBpXTjsgesRfGkK8xuF4wfzP3v+DIW9TqLUsWbNmurfOF8cvYb68jcR5UU4BhB6kAlixGuvvab2N4J7zF+U73E0ILzgdx37riADZ0ICEl/31ySEFA70P9f6QzuDo33Os7KyLJ06dVLzoYf0uHHjLMeOHbNOT0pKUj23Bw0aZClZsmSu93bp0sXSv39/yw8//GA5f/689fWEhAS1HPSlNur5/dJLL6nXa9eurfpz20PrCY71mjp1qiU5OVm9fuLECcsjjzxi3b6ZM2c61T/edt9iXmd57733rJ/frVu3XH3SsZ7ff/+95Y477rBcvHgx1/t69+6t3lO8eHHV413rob5r1y41f3x8vHW5q1atyvVePHf2XNi+fbv1Pddcc43627Vr13zfg2OK+XD8nnnmGcu+ffty9Vr/448/LC+88IKldOnS+fYzz2/f2wPL1d4/efJkp9+PHvWNGzdW769cubJl+fLl6vXs7GzLokWLLCVKlLC7D/Bd6Nmzp5qOnvGY3xlwfNq2bav6ttvul//++88ybNgway/6WrVqWVJSUpzePnzX8P6YmBi1vvbWw5HzxN555ikSExMtZ86csT60fd26detcr587dy7Pe3Edwrn422+/qX9rbNq0yXLPPfdYrxOObIv2vcexOH78uFu2zZXrAY7ftddeq96Da+uCBQssGRkZatr+/fvV/NoycdxtmTVrlnX6yy+/bF029uHw4cMtoaGhllKlStn9Ljpy/PO7juL7hWlPPfWU+t4ZMWfOHDVP9erVC9yHmAfz4j22jB071rq+ffr0sWzbts06DZ+N/Y15qlatalm7dm2u9+F3yvY7iesYrr/Y71hmr169rNM2bNigXqtRo4bHrpM4ntrvY5s2bdQ64xoF8JuAY/LAAw9YduzY4Te/ic8//7x1e3G+ab/5+Dty5EjrZ+M6Z0vfvn3VNPy1hyPrZ+8cye9+xxf3OBqvv/66mt6yZUu720RIIEOhgZAAx9NCA8AN6m233WadHw8EZLhJ1X5ItRtzoxsD/Xu0QE579OjRw3oDpQ+6oqKi1HTcDJcvX17dQOChvyG7cOFCrs/A58fFxeVapyFDhhhuk6eFBvDaa6+p9dfWJTo6WgkF+tf0NycA21epUiXr9PDwcOs+g/iwevVqtwoNoHnz5rmOycKFC/OdHze6+ptWPGJjY9W+128bHrZCUWGEhkOHDlnfq50X+T3eeOMNw+VgHRAkaMsqVqyY9XzDo1mzZobB7K+//prruBT0+Z999pnd44MHPrNMmTLqvNC/3qRJkwK/k/kdm7Jly6rl/PzzzwElNGjBRkEPo6AU3yP9PLg2RUZGWp8jmPvxxx8LXAdci6pVq6beg0DenbhyPcD3p2HDhtbpCIQ0cUD7HkyZMsXw8xBAdejQwTovrova9REPfD/yuw4WVmhAoKUtA8cCQT6O3X333ed2ocFsNltGjRqV69qP/YvjbjKZcp0bEKOMfj/1x0S/nPr166tAXUMLmgcPHuyx6yT45JNPcp3D+De2RxMj8dALVr7+TcT23nvvvbnOTdtthWCjiWX+IjT44h5H47rrrlPzvPPOO3a3iZBAhqUThBCH0mBRv4i0QhjewRUfKYNo84RaVtSyI6Vf6zOtMXXqVJVGiV7TcPLGvWtqaqoyToJTN1J6kapum1aIeZHajnlgkASzMxg14aFPP0cLMtQvw50faeaoaUdaLMy0kJqJZWhmgb5g+PDhqoPDgAEDVK03QBostq9Xr16qraRtz3F4JiClH0ZSmilgbGysSuOHsV1h68WN0JcIYH3s1R3rTQdRf4p+7/369ZNatWqpkgLse5h84VggRReGWLbtzvTu7a1atXJqPfV1x/h3QV4A+hRpPTVq1FDrhnVEujlS45HOC5NApJOjr7pR+0P958NFvKDPx7muB8tHicrDDz+s0tpx/iKtHOc/9iFS7GEuiW4xWEdXwLHp37+/+jfKOooKMPNDavZNN92kvkPY90hbRu342LFjlcHfzTffXOByUDKB0gl3mEC643qA7w/SqydPnqy+L9gmXHdhQIgSEJwrTz/9tOHnoQRq6dKlavvr1aunzg2c67heo2YcJUieZMSIETJlyhR1DPD9ghEgruFaaZA7wXahjATfa7TtRb07th9+Afguw/cAJVa4ZulT1JE2/8EHH6j9j2sBfFxg8oj3tG3bVrVMxXVXM2jUt7X05HVSuy7DUwEmg/BeQXo9zmuU/6BkA9cS27p+X/4mYnvhWwMvlK5duyqjRXgQ4C+e4/yGB5BRq9Cido8DUKKHsih8px3xjSEkEAmB2uDrlSCEEFI0GD9+vIwaNUr1H1+7dq2vVycowQ0s/D4QNMF8EEE4IcQ93y0IBuhGAzFR3ymIEGeAMAZPEQjDs2fP5s4jQQkzGgghhHgNGE4CjD4TzwAjOWRNYDRx+vTp3M2EuAktm+HWW2+lyEBcBgagyIZAm92CDEwJCWQoNBBCCPEKSEVFqmiXLl1UWjLx7GgZSm5QCoKbWkJI4UEaPQLDwYMHc3cSl5k2bZrqkoKyJ5TCEBKssHSCEEIICUK+/vpr2bx5s/J+aNiwoa9XhxBCyGWhAd5T8N+ArwYhwQqFBkIIIYQQQgghhLgNlk4QQgghhBBCCCHEbVBoIIQQQgghhBBCiNug0EAIIYQQQgghhBC3QaGBEEIIIYQQQgghboNCAyGEEEIIIYQQQtwGhQZCCCGEEEIIIYS4jTD3LYo4QoUKFSQ5OVmqVavGHUYIIYQQQgghxG0cPnxYYmJi5OTJk+JLmNHgZSAyZGZmevtjCSGEEEIIIYQEOZmZmSrm9DXMaPAyWibDjh07vP3RhBBCCCGEEEKCmIYNG4o/wIwGQgghhBBCCCGEuA0KDYQQQgghhBBCCHEbFBoIIYQQQgghhBDiNig0EEIIIYQQQgghxG3QDNLPsVgs6kEIISQvISEh6kEIIYQQQvwHCg1+SHZ2tiQkJEhiYqJkZGT4enUIIcSvQa/oMmXKSLFixXy9KoQQQgghhEKDf4oMhw8flrS0NF+vCiGEBAToFY1rZq1atcRkMvl6dQghhBBCijzMaPAzkMmAG2bcLJcvX16N1IWG0kqDEEKMyMzMlGPHjkl6erqcOXNGKlSowB1FCCGEEOJjKDT4GSiXABAZSpYs6evVIYQQvyYyMlLKlSsnR44cUddPCg2EEEIIIb6HQ+V+BEwfNU8GZDIQQghxTGwAWVlZNM8lhBBCCPEDKDT4EfruEiyXIIQQx9D7MrBLDyGEEEKI76HQQAghhBBCCCGEELdBoYEQQgghhBBCCCFug0IDISLy8ccfS0hIiBw8eJD7Q0QWLVok8fHxkpSUFND747333pNq1aqpjgSEEEIIIYQQ70ChgfgkoP/777+55/2U7OxsGT16tDz11FMSGxvrtc9dsWKFPPTQQ1K3bl0pVqyY1KxZUx555BE5ceKE4fy///67tGnTRs2LTgNPP/10HmGkX79+ymD1/fff99JWEEIIIYQQQig0ECIiffr0kdTUVKlevXqR3x/fffed7N69Wx599FGv7othw4bJ6tWrpXv37vLuu+9Kz549VWZFs2bN5OTJk7nm3bx5s9x4442SkpIikydPVoLEBx98IPfcc0+u+aKioqRv375qHpoEEkIIIYQQ4h3CvPQ5hPi9a73eub4oM2fOHLn++uulcuXKXv1ciAHIUNB3XOnSpYu0a9dOpk2bJuPHj7e+PmLECImLi1PCRIkSJdRrNWrUkAEDBsjPP/8snTt3ts577733yqRJk2TVqlXSsWNHr24TIYQQQgghRRFmNBC/5J9//pGuXbuqIBLp+xi9/vPPP63TL1y4oIQBjHxrnD17VgWppUuXzjV6/fjjj6vUemc9GhC43nbbbfLbb79Jy5Yt1eg40vnnzp2b5/1Yn2effVa9JzIyUqpUqSIPPvigWieN06dPy8MPPyzly5dXy2rSpIl88sknuZaDz8d6vPnmmzJ9+nT1eSgNQOB85MgRtV3jxo1Ty4+OjpZu3brJuXPn8qzPsmXLpG3bthITEyPFixeXW2+9VXbs2FHgfk9LS5Mff/xROnXqlGca1uvJJ5+Ur7/+Who1aqS2s2HDhmp+d3DDDTfkaeuK1+AV8e+//1pfu3Tpkvzyyy/Su3dvq8gAsL9xriALQs/VV1+tlvHNN9+4ZT0JIYQQQggh+cOMhiJEela2bDp0QS6mZkjJ6AhpXr2URIb53yg+AmIEyQgihw4dKuHh4arGvn379vLrr7/KtddeK6VKlVLB7po1a1RtPoAggGAYgffOnTtVEAzWrl2rlucKe/fulR49eiiBACn4s2fPVnX/CF615cMXAMtHMAyPgebNmyuB4dtvv5WjR49KmTJlVFkG1h/LQ7B+xRVXyOLFi9WyIFIMHjw41+cuWLBAeQvAJwHbgxF5jMxjRB6j+CgzwLKmTp0qQ4YMUeulMW/ePLWuN998s0ycOFGVF8ycOVNlC0DAgRhij40bN6rPxTYYgX381VdfyaBBg5SAAaHn7rvvlsOHDyuBB2RmZsrFixcd2r8QAGzFBT3Yt3hgH2ps27ZNsrKy5Jprrsk1b0REhDRt2lRtoy3YnnXr1jm0ToQQQgghhJDCQaGhiPDzjpOy6O8jcjoxXTKyzBIRFirlikfKvddUlc4N8x/t9zYjR45UwSqCWozoa6PVV155pRIeIDYABPdffPGF9X0QFBBM79q1S/0bQoAmOrjqNwCvAogZmlCBYL9q1aqqvABZB+CNN96Q7du3qwAc/gL67dAyK+AfACFi/vz58sADD6jXBg4cqMoCMB8ECgTuGseOHZM9e/ZIyZIlrQaNr7/+uhIsYKQZFpbz1T1z5owSJSAkIMMAQTmEF82zQAPCA/bfa6+9lut1W7DvAIQQI7AN2J+1atVSzzt06KAyMxYuXKgEFICAHq87woEDB/IVPt555x0lfNx3333W1zRzyIoVK+aZH6/h2NuC8wgCDCGEEEIIIcTzUGgoIiLD+2v2y5FzKZJttkh0hElSE7Pl5MU0OZ+SqebxF7EBATVq7O+8806ryKAFkPfff798+OGHKnUe2Q4I/lFeADEAQTQCTIzily1bVv0bgTzECgT7rmY0NGjQINd7sWx81v79+62vffnllyrY1osMGsiwAD/88IMq3+jVq5d1GjI1IArgNYgnKNPQgKmhJjIAZHEAlAtoIoP2OoJ8CBPYXygpQIYElqkv20CZCeaFT0F+JCQkqL/wPzACJRWayACuuuoqdSz0+wP7AuvhCPmVtEDgGTt2rDWTQwNiC4CwYgtKUrTperA9eB3ZHShFIYQQQgghhHgOCg1FoFwCmQwQGWIjw6Rs8QgV/CL4PpOYoV7H9HZXlvWLMgqM0CMYRDBvS/369cVsNiuvAmQraAIARAV4FiBlHoaBEAO0bANMQyCM4NcVqlWrZhi0nj9/3vp83759qnwgPw4dOiR16tTJUyaAbdKm5/e5muiAbAqj17X1QRYEsGd6qPc0yA97HRoc2R94buTx4AzIrIBwg/KYWbNm5ZoGbwqQnp5u6DGhTTfaHk34IYQQQgjxF8wZGZL6z2bJvnhBTCVLSXSzphIaEeHr1SKkUFBoCHLgyYByCWQyaCIDwF88v5iaqab/c/iCtKqZU2MfKFSqVEml+GPkG+n3CCavu+46JTTA8wDBO4SG1q1b5+sDkB/2OlF4ulWivc8taH0gxACUCRhlC+izIYzQfBYgHEC8cfbzAUodjAwqjcCxsl0mhCSYX0JEQSaIvqREXzKhlVDowWs4L2zB9iCTwUiEIIQQQgjxFYkrVsiFL76UrDNnxJKRISERERJWtqyU6nG3FL/xRh4YErBQaAhyYPwITwaUS9iO5uJ5VHiomn4hJUP8AQSeCAhRDmE0yg3BQD+qj6wGCA0QHGAEiKAU2QsIUtENYdOmTSr93pOglAAeDflRvXp12bp1qxIC9KKH5omA6e5aF1CuXDmXsgrq1atn9U5o3LixS+vw+++/u+zRgNINiAzIVlixYoWhDwOyHCCYwKsCZRV6gWPz5s25XtN/jpY9QgghhBDiLyJDwqyPJOPoUdQPS2h0tJjPnpWsU6ck4cIFNQ/FBhKosL1lkIPuEjB+TM3IzjMKj+dpmTnGkKWK+Ud6Fka3EWiiFaG+1eSpU6fk008/VWaP+vR/CA2Y7/PPP7eWUiCQRxbD5MmTlamkq/4MjoKyiS1btsiSJUvyTNP2+S233CInT55U66mBzgnoGoGWjDCFdAfwqMD+gekjtt2oNCU/0E0D3RsQxLuK5tHgyEOfdZGcnKz2E/wmkMmAUhMjICJBRIGxZmJiovV1ZHHADBP+FrZAcMI5QQghhBDiL+USyGSAyGCKjZWIWrUkvHJl9Tc0NlYyjx5V0zEfIYEIMxqCHLSwRHcJGD/Ck8HWo8EUGqKmN6tWyqvrhXaMyDiwBSUP8FlAEApRAW0UMXqN9pYY5UabRz2aiIAMCATXGjfccIMsW7ZMGQa2aNHCo9vywgsvqO4XCHDRPQLBOkoH0N7yvffeU4E3ul5gG9DOEi0kMYqP96BDAzor2JYHuApEBnSg6NOnj2rp2LNnT5UlgvaTS5culeuvv16mTZtm9/0wU4TQs3z5cnnllVdcWgdXPRrQjWP9+vVqH6K7BR4aEGNgEKrx6quvKuEAAg32LdqIvvXWW2rdu3Tpkmu52N84Ht26dXNpewghhBBC3A08GVAugUwGU5kyucqb8Tz74kU1PXXzZolp2ZIHgAQcFBqCHBg8ooUlukvA+BGeDCiXQCYDRIaq8cXUdG8bQSIYNgKBOIwe4a0wfPhw1dIR5QbomIARbK37ggZMI1EmcPr0aSVM2AoQLVu2NOxO4E4QBGN9R48erbIaPvnkE7VON954o9XnAN4Aq1evlhdffFFNR+cMrDvaZGKb3Qm6c8CnYMKECar1JgSaypUrq33Sv3//At+PQB9ZGvBKsDWf9CQoe9BEKDz0oLRELzRARIEYMmzYMHn22WeVUPPwww+r88WWxYsXKxNLewaZhBBCCCHeBsaP8GRAuYRReXNIVJSann25hIKQQCPE4mlXO5ILBNFgx44defYMAmrNmwBBqKsGhvZaXKK7BIwf4cmAcglkMkBk8JfWlkT8psUo2nrC62DcuHESyEBkQfYIBB5ky5DgxJPXTkIIIcQTJP+1Xk6/8YZkHj+uyiX0YgPCs4x9+yS8UiUpN/QFZjQQt8Wb3oQZDUUEiAloYYnuEjB+hCcDyiX8oaUl8S/gk4Gyiccff1xlDCBjI1BBxkh4eLgMHDjQ16tCCCGEEGIFLSzRXQLGj9lnz1rLJyAy4HmIyaSmRzdtyr1GAhIKDUUIiAqB1sKS+Ib77rtPPQIdCAwUGQghhBDiKWDWCL8FlEKYSpZSAkJoRMEm65gHLSzRXQLGj/BkUOUSaWlKZAivUkVNd2RZhPgjQZ1fumHDBpX+jXp1jGqWKlVK1aljlNO2YgR18qoeys4Dpn6EEEIIIYQQorWnPDb4GVUCcXbadPUXz/G6I6B1ZelHHpaoRo1UmQS6T+AvnuN1trYkgUzQZjR8+eWXakQW9eYwjoPAgNZ+MO377bfflJHcggULDNsD6lvuaaDulxBCCCGEEEIgJiTM+ki1p0TnCJg6ms+eVaUQyFIAjggFmCembVvVXQLGj6ZSpVS5BDMZSKATlEJDVlaWaosIkQFiAlz4NdAyD90JPv30U3nkkUekQ4cOud4L07j27dv7YK0JIYQQQgghgVAuceGLL5XIgCwEW38FlEJgOgQER8so2MKSBBtBWTqxa9cu1e4QWQh6kQHUr19fevfubS2tIIQQQgghhBBHgSdD1pkzKpNBExkA/uK5JTtbTUeWAiFFlaAUGiIjIx2ar3RpGiMSQgghhBBCHAfGj5aMDFUuoW9LCZS/G0wdMzJUKQQhRZWgLJ2oWbOm1KpVS/VVR4mEbenE/PnzJS4uTrp3757nvV999ZXyd0DZxRVXXCG333671KtXz8tbQAghhBBCCPFH0F0iJCJCeTKgXEIvNuC56hwRH6/8FggpqgSl0GAymeSTTz6R2267TR544AF56623pE6dOqqcAmaQDRo0kI8//lji4+PzvHfq1Km5ng8bNkwef/xxmTJlioSFBeXuIoQQQgghhDgIWliGlS2rjB/hyWDr0YD2lJgOU0dCiipBGzlff/318uuvv6qshU2bNqkHiIiIkJtuukllPehp1qyZXHfdddKxY0epUqWKnDx5UpYtWyYjR46UGTNmqPe9/fbbDn9+w4YNDV/ft2+fyrYghBBCCCGEBB4wbyzV427VXQLGj9kXL+aUSyCTwWSS8CpV1HR2jiBFmaD0aAALFy6Uli1bStWqVeWvv/6SpKQk+e+//6Rfv34qwwGCQnp6unX+wYMHy2OPPaYyH6Kjo1XZBDpXIAMCIsO0adPkyJEjPt0mQgghhBBCiO9BW8rSjzwsUY0aSXilSqr7BP7iOV53pLUlIcFMUAoNe/bskb59+0qZMmXk+++/V4JDTEyMEhHef/99VVKBDIfZs2c7lJlwxx13qJaZK1ascHgdduzYYfhgNgPJD6TdjRkzpsCdhHlszYccXSbKhvDawYMH3XIwVq9erZaHv0Rk/fr1Spw8dOhQQO+O9957T6pVq5ZLkCWEEELI/4GYUHnKO1Ju6AtS5skn1F88p8hASJAKDZ999plkZmZKly5dJDY2Ns/0e++9V/1ds2aNQ8uDQAFOnDjh5jUtemhB7t9//+3rVSHEI7z00kvSq1cvqV69us/28Kuvvqq+Z40aNTKcnpGRIa+99poyuo2KipLy5cvLrbfeKkePHrXOg+wvzAdxlhBCCCHGoDwipmVLKdG5s/rLcglCgtijQbtZLlmypOF07fXz5887tDxtPmRFEOJJUlNTA8509IYbblDrjVH8os7mzZtl+fLl8vvvv/v0+gcRwd71CiIsRAWs44ABA+Sqq65S1ziUmF28eFF51AAIEMgMmzx5sjz11FMOZ9AQQgghhBASWBGNg1SoUEH9tTdqvmHDBvW3Ro0aBS4LacNLly5V/27evLlb15MEHsnJyR4VnBDcBRqhoaEBud6eYM6cOarcoFWrVj5bhyFDhqjPR4ves2fP5pkOU1sY5f7222+qrCw/kP01adIkWbVqlfK1IYQQQggpqqSlpcnMmTNVAwFf3usFCkFZOtGtWzdraQROBj1//vmntXtEjx491N9du3bJvHnz8tQinzlzRnr27KlMIJs0aaI6WRDv8M8//0jXrl2lRIkSqvzlxhtvVMdO48KFC6qN6bvvvmt9DUEVgt7SpUur9kIaaE+qiU8aGL1FaQ2yW4oVKybt2rWTdevWGfog7Ny5U+6//36Ji4uTNm3aFFgWggDu6aeflrJly0qpUqWUyShS0LHODz74oFoOHkOHDs21nvY8GrC8Fi1aqGAeHh/2Utlx/j777LPqc4sXL668RfSp8AWBLitt27ZVQgrej1Fv+Iq44tHQvn17lbaPfdehQwe1jytXrqyCVqOLNra5bt26ahsrVqwod911l+rQohd4nn/+eWXuGhkZKVdeeaW8+eabhvvvySeflMWLF6s2tjB2xY/Btm3b1HTsu9q1a6vPwToa+VQ4cm7Y4+uvv1YBue3oP0RNeMNowT0+H51v5s6d69ByHQXXvC+++ELeeecdw+lms1m16kU3HqwHvGdSUlLsLu/qq69WbYC/+eYbt64nIYQQQkiggMEb3Ofj/vO5556TF154Ic89KCkiQgMyDzCqB9A5AgEPRuYQJEIsQNDy6KOPSqdOndQ8aGWJABABTufOneWBBx5QwRGCOgQOSCVetGhR4KcOZ6WLHFgrsvPbnL947ocguEXAu2XLFhWMjxo1Sg4cOKACQwSBAAE8jqveZwNBHI7RuXPnVICrgc4hWJ7GypUrVbr/pUuXZPTo0SrNHCIAAkQY+dlyzz33qGAM8yHVvCCQZg5D0rFjx6pg/4MPPlDbcPvtt6sLFZaDc/GNN95QAld+IEDGOXn69GkVjPfv31+t85IlS/LM+8gjj6gAE/NPmDBBwsPDlVjgCFgPzAtRZ+LEiWp9sQ+xnq6aRiIdHwE7RDp0eoEfwLBhw5SgoYH9gQAc+wpBLeZDBxik8G/fvl3Ngws59iMEQiwPqfy40OMij4u9LTjeECWQ9o999u+//6rPmD59uhKmcE3Ae//44w956KGHcr3X2XNDz7Fjx+Tw4cN2M5/27t2rxE2018V2QmyCD4JezIEQAMHMkQdKIPRgX+Lcw3nQuHFjw3XAMT1+/Lgql8A1EKISHniOrAUjsD2OCi2EEEIIIcEC7kG//fZbdS+Le3Dc52kxh5bxTvLBEsR89dVXls6dO1tKly5tCQsLs8TFxVk6dOhg+fTTT3PNd+zYMcszzzxjadWqlaVChQqW8PBwS2xsrKV58+aW0aNHW86dO+e2dWrQoIF6GJGdnW3ZuXOneuDfbuXf7y2WBfdZLO+3s1hmXJfzF8/xuheZM2cO5D/Lhg0b7M5z5513WiIiIiz79u2zvnb8+HFL8eLFLTfccIP1tSeeeMJSvnx56/PnnntOTS9Xrpxl5syZ6rWEhARLSEiIZcqUKeq52Wy21KlTx3LzzTerf2ukpKRYrrjiCstNN91kfQ3HHuvaq1cvp7bNdtnXXXedWoeBAwdaX8vKyrJUqVLF0q5du1zLwPvxufp9ERUVZTl06JD1NZwfJpNJzauxefNm9XzQoEG5lnf//ffnWaa2ngcOHFDPExMTLaVKlbIMGDAg13tPnjxpKVmyZJ7XbVm1apVaHv5qYLvw2ty5c62vpaenq+/X3XffbX1t9uzZar7JkyfnWa62D7/++ms1z/jx43NN79Gjh9qve/fuzbX/IiMjrdsG3n//ffU6PvvSpUvW14cPH55rPzhzbhixfPlytbzvvvsuz7Tq1auraWvWrLG+dvr0abWuzz//vPU1rAvmc+Sh399g2rRp6nhhudoxaNiwYZ5rIt6LayK2FecCHvg3vnNbtmzJs+6PPvqoJTo62uLPePTaSQghhJAix9q1ay3XX3+93fuwxo0b57pf9Cca5BNvepOg9GjQQHowHgVRqVIlazlFULJrqci6KSLnD4pYzCLh0SIZKSKXjouknsuZp55jI9+eBqOyP//8s9x5550qtVwD2SYoX/jwww/VaDNKKpClgFHq3bt3qxFujGTffPPNqnQA/x44cKBSHBF/ahkNMOtDtsHIkSMlISEh12ejPAMj+xhVRgmGBpbjDA8//HCu7Jdrr71WjZ7jdQ2UfVxzzTWycePGfPfFTz/9pPYF6v416tevr7bzhx9+sL6m/RslG3qeeeYZ+fTTT/Nd319++UWN2qNTgr6mH+uIdbc30l0QyI7o3bu39TnMIpGuv3//futrX375pWpDi5F4W7R9iG3DuthuG7IWUCaADAmUS+iPo95/BdsA7r77blUSYvs61gfzu3Ju6NHeg0wFI1DKoc+swXmK81a/P1Dig+PhCFDX9Z/98ssvq0wULNceSUlJ6m9iYqIqT0IpCkDGBkpKUNoyf/78XO/B9sDsE1k9KCUhhBBCCAlWkE08YsQI+f777+3Og7JaZA8HfLa7hwlqoYFcLpfYNC9HZIgsIRJbHhEcxsJFkk7lvI7ptTuJhEX6fJfBFwMBDQIwWxBgI9CDZ0bDhg2tQRtEBZS3IHAaP368CrRQv69NgyihBWUIJAHS6u2BtH19sHjFFVc4tQ16UUDf5UQL6vSv59f5BPsCAZ7WXlUP9o9eaDh06JAKgFHuYztfQWj7xJ7ZH/afK+CY2F6AsV+3bt1qfQ4fBqxjfp02sG0QA/UigXY+aNNd3f9AOwaunBtG2KvZs10vgGXpzwF4N2glXc4AcQReCkaCjR54VgCUkOn3B9YNZTJG3TK07eGPKSGEEEKCFdxPYtAGA0v27uUQT7z++uuqlJf3RQVDoSHYObJeJOlkTiaDJjIA/MXz1PM5049uEKlh3+jQH0HwCREAPg0YkcZFAcZ/EBpQ548LBoSG1q1bW0ehIVQA+CM0bdrU7ki8UXDmKBh9d/R1fzCS0fYJLqy2ppnA1Xab9vaDp7fZmf2vXx9Xzg09MCEF9sQjR/YHslggMDkChAVkiUAggQ8I/Dngv6A32YSPAzw2IBZhfnxnQPny5fMsr1y5ckqsswXbg0wGZ78HhBBCCAkMzBkZkvrPZsm+eEFMJUtJdLOmElpE2pYjm/fVV1+VGTNmKPN2IxBvYDATTQLsZbaSvFBoCHYgJGRn5JRL2Kb34Hl4sZzpKZdLKHwMRAIENSiHsAXdQfDl1o/EIqsBQgMuAAgOMeoNtRGj1T/++KNs2rRJGQ1qaCP+CLxcGTn29r5AcKeNtOux3T/Vq1dXgbKWIWBvPiO0fYJA09v7BJ8Ng08ExDCvNALbtnz5cpXur89qwPmgTXfXuhTm3IDZJYBxqasgW8fRDBqUtMAgFSaUOPYoLbEtLwFYHoQ3CBEwicR+xntsgUhhVHaB7dGyRwghhBASXCSuWCEXvvhSss6cEUtGhoREREgYOqf1uFuK33ijBCsoJ0XpPAaYcI9pBO6LUJaKDnIY3CHOQUkm2ImOEzFFiGSm5pRL6MHzzJSc6cXixR/AqC+6JqCdnr7bwalTp5TXANK79an8EBow3+eff24tpYAYgSwGdCdAAKuvi0dnAwSUKK3Q6tX1ODqa7K19AS8GdD7RXG4BuijAu0EPWoECfbtPYK/NoR58BvYpOizYdjLw9D6BbwKU5GnTptkd6b/lllvUSL/tPPhxQNqatu2FpbDnBtp3QgT7+++/XV4HzaPBkYdWDoTuK+hCYvtAeRFKIvBvzR8EQg32J0okNKFGO6fwGjpi2AKxDt8nQgghxB0j58l/rZdLP/+s/uJ5oBPI2wSRIWHWR5K6fbtkHj8u5uRk9Tdt+3b1OqYHG8hagMcbvKlQKmEkMiCDFZ3LMICHslSKDK7BjIZgp2pLkdgKOcaP8GSw9WgINeVMr9LCq6s1e/ZslXFgC0ZekZqEQAqiAloRInX//fffl/T0dGVWp0cTETByj0BZAy0KYRIYGRkpLVr8f9sgQsyaNUsFpwjE0KoGASJGeDFCjID7u+++E38B2RjYT9hO7IusrCyZOnWqWne91wGyOWDmiLQv+AggMFyxYoVqqVgQ2OaZM2dKnz59VCtDpIVBwYW4gdY9qOc3EgLcAdrKzp07V7WpRPtIbCfazyKDAdvbrVs31RYU7WZfeuklJSohwIZhKMQomF3a+lK4ijvODawvAnuIJK7U7rni0QAzTRiG2qKJTLbT8D3BuQFPDi0DAgIVSitgfqQHZqVoF4vtIoQQQgpDMI6cB/I2QRDBumccPSqm2FgxlSmj7l1wD5ONNtpHj6rpMW3bBkUZBbI/MTAJXyu9EbceZH0+/vjj6p4Tmb6kcFBoCHZg8Ni8T053CRg/opQC5RLIZIDIEFcjZ7qXjSAR2BrRr18/FeTBW2H48OHKcAUXBnQIgBu+1ilAA2UCuBCcPn1aCRO2AgS6HEBs0IN0c3SBGDdunAqgMXqNkWQsG6lR/sRVV12lshcQiEN1hcEixIcTJ07kEho08QYCwYIFC1QWBAJJCAW2JohGoKMH6vfhoIsUMog6CLKxHxFwezJrA6aWqI1Dxgq6UMDrAMcSaf6aAIAexth+/EDMmTNHeXJgPdF5wp0U9tx46KGH1PvWrVuX63z0J9D94tdff5Vhw4YpUQ/7F+cK9ieOuZ7FixerrAh7RqGEEEKIowH52Q9nSQbKC7OzJSQqSsScLVmnTknChQtqHn8PzO1lAyBQxzaFRkeL+ezZgNkmeDJAIMG6ayIDwF88z754UU1P3bxZYlq2lEAFwgkGqBBXGHlRaduMTmm4x3bWBJ7YJwQ9LvOZTtwMgmiwY8eOPNMQUGs19Qig3Wo2ghaX6C4B40d4MqBcApkMEBn8pLUlIcEAWmFCtIG5ZiADsQmCzosvvqgyjfwZj147CSGEFHrk/NADD0j67v/Uc0toqITCIDksTEIxGGSxSFSjRlJ5yjsBM3KObTo2+BlVcmCUDWBOSvL7bUKpx9lp01W5RLjNQAPQMh3KPPmElOjcWQIRZMviPia/Vu233nqryvbE4F5RiDe9CTMaigoQE9DCEt0lYPwITwaUS/hBS0tCggn8WCETBNkC7jKq9AXIHEEK4cCBA329KoQQQgKYhI8+kvR9+8UCH6jQUMG4uTkrS0IyMiQbf0NCAm7kPBiyAdBdAqUeyMKwLfnEc0tamoTEx4upVCkJNDD4gPIHZMraA53qJk6cmMvLjbgXCg1FCYgKAdbCkpBAA2UW9tojBRIQGCgyEEIIKezIf9KKlWJJT0e9pCovAAhplfCQkaEyHMzp6ZJ9udwgEEAbSHgyYHtsPZnwHKUhmO7P24QWlvCTQKkHsjBsszJCTCY1PdpOy29/BL5aKH9AOTGMxO2VkGJQ6I477nDJT4s4DoUGQgghhBBCiEdG/rN1nZz0I+ch4eFihthgNotkZQXUyHkwZAOgpAOmlfCTgPEjsjCUQIJ1N5kkvEoVNd1fSz/0nD9/XmUnTJkyRdLS0gzngWcZRAgYkcMjjHgeCg2EEEIIIYQQj4z8h4SFKVHBghHmzEyxhIdbR841QmNiAmrkPFiyATSzylydM+LjA6ZzRmpqqurGBvP4C3ayR7SuWk888YTq7kW8B4UGQgghhBBCiEdG/kOjonLEhrAwVSIhaWmqXEJlMpjNEhIdLbE33hgQI+fBmA0AMQEtLOEngVIPZGFAIPHndUe7948//ljGjBmjyiWMiI6OlmeffVZeeOEFKeXHmSXBDIUGQgghhBBCiEdH/gXlBWFhOVkNEBlAaKhE1qwppR9+KOD2fqBnA+iBqOCvppV6kDGyZMkSlaGgdZuyBWURjzzyiGqLji5gxHdQaCCEEEIIIYR4fOQfYgNG/iU9XXkchNeoIWUeHeDXo+fBlg0QqKBFJVpVomWlPe655x7V9atu3bpeXTdiDIUGQgghhBBCiPdG/iMi3Dryj+4Wynjy4gVVroFMCm8F+4GSDRCo/PPPPzJ8+HD56aef7M7TqVMn5dNwzTXXeHXdSP5QaCCEEEIIIYQEzMi/XlhI37c/Z7kJCW4TMXwpXJAc9u3bJ6NGjZKFCxfa3SVXX321TJgwQQkNxP+g0EAIIYQQQggJiJH/xBUrrNkRWefOKeEC7TEhMJhKllQtJ+EJgXIN4KzYoF++Xrgo2e0OMcXFU3zwMKdOnZJx48bJ+++/r0wfjahdu7a8+uqr0qNHDwmFsagXoPjkPBQaCCGEEEIIIX4PRICEWR9JxtGjYsnKEktqqljQyQLBpsUiocWKSVilSqrFJDwhIBggk8LRbAT98iU7W0Kjo5VwkXH4sKRs3KgyMVQXDTeXfhCRS5cuyZtvvimTJ0+W5ORkw11SoUIFZfIIs8fw8HCv7TZ74hOPf/5QaCCEEEIIIYR4haykJLmogrbTEla2nJTscbeExcY6NKKMYA8igAnzR0RIFgQHkwmtBkQyMlQgGFGqpJjKlFEtJ/EcZRWOZFLYLh/LCAkJUVkTmUeOiAXdMlJTxVS2rGpjWZisCfJ/0tPTZebMmSpD4ezZs4a7pkSJEjJs2DAZPHiwxMTEeHX32ROfePwLxju5JoQQh0A/YPyoOQLmw/yuLLNGjRrSr18/tx2V9u3bqwfJ4ZZbbpEBAwYE9O7IzMyUqlWryowZM3y9KoQQQoKE05Pflv03d5EzU6bI+Xnz1V88x+sFAc8ECAcI9iAC4C/aZIaEhqqMBbQ+hBhgTkpS9z3oboHRZ1Va4QC2y8cysHxkR1jbcYaHi6lkCYmoVUtCY2OtWRMQKYhzZGdny9y5c+XKK6+UZ5991lBkiAgNlUebNJEtc+aolpbeFhlsxScc9/DKlXn8HYRCA/EqWtBrT7Fs1KgRA1YS0Kxbt05+/vlnpbx7Ewg96sbK5tGlS5c88+7Zs0d69uwpVapUkWLFikm9evXklVdekZSUFOs8SEl87rnn1AhDWlqaV7eFEEJI8AEx4fyCBZJ9/rzKCLBAKEhLU8/xekFiA4wZIRxgRFn9xoWFKZEBIgBEBuu/M7NyRIe0tBzfhlKlHFo//fJFLJJ96ZJkHj8uZvwGWiw5WRNKzMhSnw8xAtugZU0Qx8Cx+f7776VZs2bSt29fOXTokGGAeleFivJT02YypHgJkcVfqMwCb6MXn0Lj48WcmKgyXPAXz3n884elE4T4ESNHjlQ9ggMNBNYkhzfeeENuvPFGZVTkbSAcoL2TnkqVKuV6fuTIEWnZsqWULFlSnnzySYmPj5c//vhDRo8eLRs3bpRvvvnGOm///v3V+fjpp5/KQw895LXtIIQQEnzlEhe+/FLMqanKTwHZBipj4LIggNcxPf7RAXbLKND9AcIB0tbxPmQUhKBOH9kEKGtAMIjnYSaVhRBiMqk6enS3cARt+dnHj0t2UpIymDRnZqplK7DeeITnhE+uZE0EM46YJeJ+AwMxa9eutbucTuXKyQstr5Ur4+LUcXbVb8MdaOITyNy3L6d85nIWDc49C85hHn+7UGggxAnMZrNkZGRIVFSUR/ZbWFiYegQaEWz5pDh9+rQsXbpU3nvvPZ8cB4gHvXv3zneeefPmyYULF+S3336Thg0bqtceffRRdW4jhfH8+fMSFxenXi9VqpR07txZPv74YwoNhBBCXAaeDGZkzSHz4LLIANRfBOvJyWr6xa++ktIPPmi4DASuEA5QG4/gExkFyi8BggAEDGQzZGVJ9unTEmIKk/AqVZRZn6OBKZYvIfL/DIacFfz/v7Oz1R8IHMCaNREf73DWRLB2NijILHHnzp2q9EE/mGFLizJl5LkKFaRV02a5zg9X/DbcBY4Bzgdkt2jnAkQGJUBBgLBYxFy8uNuOf7DB0okiREZ2hmw4uUGWH1qu/uK5v7N69Wp1kfn888/VBQpus6jPuuOOO9TIrG3qOEovMCrbunVriY6OliuuuMIw6IPxDEZwMeocGRmpatGHDh2qXteDz8ao74IFC1RQhnl//PFHu+sL74PbbrtNrfc111yj1qFx48bqOfjqq6/UcwgV6P37zz//FOingHVC7VrZsmWlePHiatuPwpDGAASPLVq0UMuvVauWag3kKAg+n3nmGbUvsJ3YNxMnTlQBqLMeDdpxW7RokUq9x0g71gkj/Xv37s3z/r/++kv5GiDAxfG96qqrZMqUKbnmWblypbRt21ZNRwDcrVs3+ffffw3333///acCbgTe2G/ow4wbApwzeB9MhXAuvfXWWy6fG0ZAZEArJtt+zgjUsV4oq0A5AtYJ29G9e3c5g5Q8N4LPT8JITD6uzqB8+fK5Xq9YsaJqEWUrGt10003qvDp37pxb15MQQkjRAcaPKlDHSLDNfY56jgfKEE6dsrsMBNwIXCEgwIchY98+MV/uToDshZDoaDGVLi3hlSpLVKNGUvqRh503abTkWrH/P7TJl0UHbaTd2awJI2Eh+a/1cunnn+XMzPfk6FNPy+k33pCz06arv8cGP+OTkgFXzBJTt2/PKTVJTlZ/07Zvl21T3pU+Xbqoe197IgPu3Re98oosbHWdXFOuvOH54avMkciGDXLOMdwLayJZZKT6qwQos1lNj2zQwKvrFSgE3tApcYmVh1fKkj1L5EzqGSUwRJgipGx0Welep7t0rNbR7/cqglVcaJBuhVHjd955RwVzmzdvVsG8BkZjEbDee++90qtXLxXoPv744yp40lK/ETgjWEfwhJHc+vXry7Zt2+Ttt99WAerXX3+dJ8DFciA4lClTRokJ+YFA+v7775fHHntMBbto1XP77bcrwQNiyaBBg9R8SHHHeu7evTvfHsBo4TN//ny1TAgoWJ9bb701z3zYBow+I4hFwI2AEwGzbUBpBGrz27VrJ8eOHVPrXa1aNfn9999l+PDhcuLECbW/XWHChAlq24YMGSIXL16USZMmyQMPPKCEBY1ffvlFiTMIdOEmDAEAAgLq9/AcLF++XLp27So1a9ZU25aamipTp06V66+/XjZt2pTnmNx3333quOLzEfyPHz9elQhAeOnYsaMSUCAeYb0gzNxwww0unRu2YJ+VLl1aqlevbjj9qaeeUmIKjsvBgwfVfsV5BSFNAyKBI54I8FCAkKIH6wgBA1k3OO4wpEQbKH0LKAhC2P6HH35Yxo4dq9YX6w3H56effjqP0RIEMdxQYR4cJ0IIIcRZ0F1CeRwg9RwBm23wftkDIayAexZNONCPnocho6B0aYm6qrGEICUB2QlNmkixli2cWkdkEgC0rxRkl15Ok4c4gkwJlTGRni4ZBw6ImC1KZHA2a8JeFgDq/lUQnZWV4ytRsmRAdDaw16njXFqaTN+wQeat+00ytIwQG3DvBn8o3N+m/b1RTm/ZKpkJCYbnh7szRxwlfcdOCY2JURkVal1wf3bZC0QBI9KYGEnfuVPCvJhpEShQaCgiIsOc7XPkaNJRMVvMEhUWJQlpCXI65bRcSM+5ePm72IDRVASfGNEHzZs3V0H6hx9+qIIjjePHj6tRaowaAwTN1157rQqY+/TpowIu1JsjcP3111+lTZs2uRTVgQMHqoAKAb0GhAAEmw0cVCsxP5Zx3XXXqed4380336yCvl27dqkgHiDgxPqtWbPGrgHmli1blMgAcWL69OnqtSeeeEIF61u3bs01LwJKXIxR96Z9xt13361U5IJAz+J9+/apDIs6depY9x3q++E58Pzzz6uRfWdBwAwxSBslxzZDPNi+fbva33AcxudAZMB8yFSwHTUAL7zwgtVLAH/BnXfeqYyEELR/8sknuT4XHgRaNgcEA/yYYRsg7mgmjRCisH2zZ8+2Cg3Onhu24PjmJ0QhqIefhfYDCmHj3XffVSKMJhpAeLDdHiMgDGmZMgAZLB06dFDHG/2nv/jiCyWwQHzQCxkwhxw3bpy89tpr8u2331pff+mll9T8tkDcAUh7pNBACCHEFdDCMuHDDyU7PT0nWLPxaEDWQGixYlLyrrsKXBYCbtTqI40ewTmCz+xz5+TiN99axYekVatype47U4+PID+sYkWVNQGBAaaTqM2HTwAIiYhU4oazy7fXMhGfgbaZEDEQuEJ0wb4Iq1TJp/4ErnTqSM3Kkjm7dsl7O7ZLouZtYQMG7ZBpivs/ZI7aK4vRzg93ZY64UpKC+SE8mUqUyBGaLotP8AJR/iAQGqKi6NFhBwoNQQ6yF5DJAJEhNjxWykT//4t7NvWsHEs6pqa3qdxGZTn4Kw8++KBVZAA9evRQwekPP/yQS2iAvwEuXBoIcPEcWQ0oqWjVqpUsXrxYjVTDaV/f/QIj3WDVqlW5gkkEdI6KDADzaiIDgNChLV8TAPSv79+/367QgO0D+m0EKHFAUKyBgP2nn35Swbf+M7CdEDm05dgD+wRlCRAC9PsEWSPICoAYAnHDWWAmqE/Fx2do24zgHcLGgQMHVMaAXmQAWjCOjAqIEChf0EQGgPIKpPUbbRuyQDRMJpMqY0G5CUbxNfB5aKmEddHvB2fODVsSEhKkcuXKdqdD9NCr9Ngf2HY4LmN7ALazIJ8FoPkoaHz00Ue5nkNYw+dBjEPpDc59DYghEFcgREH8QNYHhAdkk0DoMPoce51iCCGEkIKAwWOpu+9W3SXgpwBPBhjpqUwGiAzR0Wq6PSNIWxAkarX6CNrPfTJXBe2qO0B0tEvZAHqzSayTCi4vg/tmZB2ElSolJW67TYq1uEYFva4E/rZZABIRIVkQHJDxgUdGhgreI0qV9Kk/gTPiTHZkpCzY859M2bpVzsAvwwBkTGLQBw+UsBqVxeB4QVjBNqtyCWQyuDFzxMg/wpFzAoTXrpVz3qLrSHiYhMTESOb+A051NilqUGgIcrac2aLKJZDJoIkMAH/x/FLGJTUd87Wo4FyKmaewrc0C2ii7fh7U0CP9XA9GqG1Tv+vWrav+Yl4EW2jth+wIlBgYgdIMPfB5cAZ9oA+0kWrbjADtdZR72AMBKEoPMFqtBwGyHtT5o5zAdj9p8xYkNGCfIEPC0X3i6r7QglZtm5FFASA62ENre2S7zQCiAAQWjODrj7vRMYBHBFR029chDmg4e24Yoc/EcHZ/aEKVM8JWfuDHHEIDsjQ0oeGzzz5TAgQyHeCdAe666y6VXYFsD2R6QHyw3R6j7yUhhBDiKOWee1b9Vd0nYAwJzwaTSY3eQ2TQprsjdd+VbgUFjaqHhoVJRI0aUnrAI4XKLLDNAlDtPrVOBhA6IMRkZqqMClPxEn7d2SKkeAn54fQpeWvLFjlop+wzPCREHurRQ8ZOnZpvOa9RWUyIGzNHXBGh9OeEOeGc2zMtgh0KDUHOxfSLKqsB5RJG5ipRpig1HfN5A61bA4Jie14BnurooIGACunlKBcwwlYQ0HtAOAJG0J15Pb/A1FtgnyA7AKPpRmhijbP4apuNPteRdXH23LAFAXp+wpEj64AyCnvfDz3IFNFneOS3vnojxxkzZqiSE01k0IA3BUwrkWWiN7PUtsdWpCGEEEKcBWICWliiuwSCN3gyoFzC0UyGgoL2wnQr8OSoulEWAIJeZXQYFvb/TgaXuxpAeMDIuS/9CQoCgxgvDhsmGzdtsjvP7WXLytCbOkvrObMd2m9GZTHuyhxxRYTy1jkRrFBoCHJKRpZUJRHwZDAyV0nLTpO4qDg1nzfQTPLgY2AbtEFkQFcAGBragpFmPVh3mC5q6eZ6jwbb0W2M3AKtdh7ZAfA+QAcEfx+lxf5C8IuRf/2IPvafHozAQxCx3U9G8xqBfQITQttuCZ5Gy9SAZ4O9z9afM0aeCAiAbbNYCrM+hTk3UHLx5ZdfFmod4GHhikeDEVpZiD5D49SpU3nKLkDm5VpKmIjqQWmLlj1CCCGEFBaICvZaWBY2aC9stwJPjKrnV6KB+1m0y1T1/miXCA8AjLzjeZjJL0fNUYr84osvKqHBHm1LlZLna1whTerVk9IP9XcqENeXxRQGd4lQ3jgnghW2twxympRtorpLmEJMypNB35YHz/E6pmM+b4AADiOxcLi3bZv4wQcfqCAH3QVsmTt3riQmJlqfw+gOtfu28+L9+paOcN/HcwRacM8HMJFEdwWklNuCkWQIFf6Ctn0wDNRj2wUCI+XwYkBXhMOHD1tfRxkASgsKAvsERotG86LtpW3w6S5g6onSFGwPPkePdq7Ci6Np06Yq+NbPA3ECxoroMuIuCntuwJsDGQB63wdnQVYJOnEU9NC35kTLStv2m9h/mrkjzg19dgqyFjQBTmPhwoWqTMdWvMMNBX6U9b4jhBBCiD9gDdpRbmCTLWnNBnCyhh6BY+Up70i5oS9ImSefUH/x3F0BpZaODwEhW/ODKFtWQiMj/9/dIitLsk+fVuUT/jJqjsEsdPWC75U9kaFJXJwsuK61fHJjJ2neqpVrLUbdhDtFKE+fE8EKMxqCHGQzoIUlukvA+BGeDCiXQCYDRIbKsZXVdG8ZQZYrV051Rxg5cqQyo0O6drFixZSbPwIdZDOgFaQtSBFHFwCYC2JEFoEpPBrQycHWowGt++DHgIAKbvswEoSIobX4g0ke2lWiiwDM/dAiEWaKGB3H6wi2cRH1BxBgo2Ye6e5IqYcR4YoVK1Q2hy1oVfjjjz8qg0F0qYA4gBaQDRs2zNOhwhZ0dUAHAnQV6NevnxJlEFSj2wZEHexPT6TOI7CF6IRjjm3F8YWwgGOxY8cOq/CBzhcQXRDswtBRa28JjwW0u3QXhT030HYUhqT4AYYPgiu44tGAFp84T/DA9wL7Z8mSJbJu3Tq1HhB09Md62bJl6jyB8SPKPdBKFK/BRBPfIT0QNbAf9L4NhBBCiD/gqW4F7hpVtwdacWYcPKgMJrMuXFDBMMD6SmSkEkYK29nCXWBgD20oZ82aZXfgCVm34195RbogE/fixUKVPLgL28yRwrbM9PQ5EYxQaCgCaK0r0V0Cxo/wZEC5BDIZIDJ4u7Ul2uihjGHatGnqwoWLFka1ESjDjA7Bpy0jRoxQwTLaEyKzAZkRCL4hUuhBSjhGvp966ik1Kg3TGXyOXpDA8jHyD7d/ZEogIMNy0MYPaeuu+hF4CrRfREbGggUL1HqjAwK6BNiWnmAkGoEwWntCzEENPvYpfiAKEhqw/WjpiM4D6LyA/QJHYOwLLEMzrvQEGG1HUI/PwSg9Ml1QwqA/ZiirgIiCVpbYNohGKB2AqOSsWWd+FPbcwPmGDAuIEq4KDa6A8hIIB1jfkydPqu1AqcN7772XZz0g8EHYg0CD7xDMMLEPX3311TweHRC3kDWC+QghhBB/wxc19K62SrTtgGBGJiKyey9n+MJo0lS6tBRr1lQiatb0ebCOe4BJkyapwT2UNxuBTlu4n8AgFQZa/InIhg3Qjk5lh2QeOSJhlStLqMlEI0cvEmLxBye6IgRGlwFGa21BgKXVoUMZNAq4CwMEBnSXgPEjPBlQLuHPLS0BatA7dOiggl+0tMwPtIhECz6k1BPiK9auXavORWRBGHUBCSRwc4GbDHiEOGuK6k08fe0khJCiRmGCaV9QmBaG3vocww4IqSliyTar7IUSd9whpR9+yOf7OS0tTaZPn64Gn/Rm0nrQInz48OFqYM8f7w+045S+b59knT6dU5KCNqrww0DZxGURypelHb6KN72Jf0lPxKNAVPCXFpaEBCvILEAJEAJ0I6+HQAHmkOi+gTInf7yJIIQQEthBuztxZ7cCT7RKLKgDArwY0grIPvU0KBVFNieyR2HObgQ6wyHDExnIRsbS/iBq5TlOxYqJOTExp1wiNVVM5cpJZK1afn0+BwsUGgghxM3A7yDQQXmK3liUEEJI8FOYYNrXeLKGvrCtEt3ZhtPdYBvg04Uy5Z07dxrOA9Pxhx56SIkQKJfwV1HL3nEym7Ml69gxMSenSFjpeKn4xiSXW6oSx6HQQAghhBBCSBGnsMF0MFNYocDdbTjdxZo1a1SrSnQeswdKl9HFSt9m3V9FLXvHKTQUpRJVJWPfPrFkZkn6zp0SRmNHj0Ohgfg1qHV31EYEfg6EEEIIIcR5/HnU3dcUVihwdweEwgKTcGQwwFzcHvBImzBhgrT0wLH2lKjlr4JOUYWOWYQQQgghhBRxGKTZxyoUpKbmGQCzCgUREXaFAq0NJ0wIEUhryyhsG05nOXDggGrljZbi9kQGTEOnL7RT94TI4IioZcnOtopa3jxOxL1QaCCEEEIIIaSIwyDNPoUVCrQ2nOh0AONHpPBjNB9/8dwTbTj1nD59Wpk4ovxh/vz5htnCaOX96aefysaNG1XrcduMgEAQtfxF0CE5sHSCEEIIIYSQIo4WpKFGHkGZbTq7O4O0QGufqQkF8A5AWj/KSFQwjBHyy60SCxIKNL+BXOaH8fEe7eiRmJioOki9+eabkpSUZDhPuXLl5OWXX5YBAwZIhJeOgadKSdxxnIj7oNBACCGEEEJIEcdbQVogts90l1Bg24YzNCZGvW5OTpbkv9a7TXDJyMiQ999/X8aNGydnUKJgtC7Fi8sLL7wgzz77rMR6uQODJ0UtXwg6xBgKDYQQQgghhBCPB2mB3D7TSCjAiDuCYWfEAa0NJ/bF+fkL3Cq4mM1mWbhwoYwaNUr5MRiBrIVBgwYpM8iyZctKMIpa7jhOpPBQaCCEEEIIIYR4NEgrqNNAxpEjkvDhLDGnpUtYmTJ+W06hCQX67UI2gqNlIJg/4aPZcunbbyXr3DkVWBdWcME+hIHj8OHDZcuWLYbzYF/DCHLs2LFSo0YNCXZRy/Y4Ee9DoYEQQgghhJAAw5M+B4UNpp3tNCAmk5gvXpS05GQ58/bbStwIhFR3Z8tAVBbDosWSummTmFNSlMgg0dESWqyYhFWq5FJrxz///FNefPFF+fXXX+3Oc9ttt8lrr70mjRs3Fn+CmQfBDYUG4vdAdW3fvr18/PHHLr23UaNG8v3337v02fjxGz16tIwZM0aKOjARgiPx22+/LQ888IAEKgkJCVKtWjVZvHix3HLLLb5eHUIIIcSvfQ7c9Vn2Og1knz8vWSdPqpaGYjarABwP29F9fzOQdLYMRJs/ff9+tX0C00Nsc0qKZJ44IeEwSSxTRpURaK0d8xuR37Vrlyp/WLJkid15WrduLRMnTpQ2bdqIv8LMg+CFQgPxKhAL+vfvLxs2bJBrrrkmz3QICmfPnpXt27fzyPgZU6ZMUcZBPXv29Pr5Yg+0aNJED/zQvvfee7Jt2zYlJqDusFWrVkokgtikUbp0aXnkkUdU/SKFBkIIIYGGN30O3PlZRp0GLBazCqrNaWlqnpDISAkrV05CixfPNbpvycyUi99865LYYU+gKIxwUVAZiG1Wgn7+0MhItQ3Yn9hebJslPV1tW0SpkgW2djx69Ki6t5kzZ47yZDCiQYMG8vrrr8vtt9/u0TaVhOQHhQbi9+zevVtCQ0N98tmpqakSFsavSWZmphIa4ExsQpqfl7jhhhtk3rx5eV5HVgVqEG/U3VxAYIiLi1N9osuUKSMnT56U2bNnS8uWLeWPP/6QJk2aWOcdOHCgvPvuu7Jy5Urp2LGj17aHEEIIKQzOBrj+9FlGnQbMiUnqc5DJgPIJLCc0NlZ9jja6n75vn5yZNl3921mxw142RmSdOpK+Z4/LWRr5lYEYZSXo5w8tW1Z1mTBnZqqshpDwcDFnZSnBITsx0W5rx3PnzsmECRNk6tSpknZZmLGlatWq8sorrygvBm/erxFiBCMo4vdERkb67LOjoqJ89tn+BEpP0B7p3nvv9ernolQDD1vxB27JEAgqVKhgfR09oG1B5kKVKlVk5syZKttBo379+irLARkTFBoIIYQECs4GuP70WUadBiBaYDRfTY+MFFPZshJyeXAJnxMSFSnZCQlKhAgrXdopscNeNkbG4cOSsn69WmZIWJhLWRr2ykD+v965sxL085uQrREejh6UIshmCA9X22zOzpbsc+fztHZMSUlRgyMogbhgJ8shPj5eXnrpJXV/xHtX4i/4ZpiYECd9Fvr165frta1bt0q7du0kOjpaBZLjx49XKWS4uB88eDDPMn777Tc1so2LLwLXuXPnOvTZWJ7enwH/xmt79+5V61SqVCkpWbKkSu/HD4FRaj8+t1ixYmq0HSP0P//8c655ZsyYIQ0bNlSCSqVKleSJJ57I80OCkhIExtp2Y3m1a9eWL774Qk2HAdC1116r9seVV14py5cvz7Mux44dk4ceekjKly+vPgufiRF/R/j666/VcahVq1au17EP0HsZy77zzjvVv1GyMGTIEMlG3aEH+O677yQxMdEhn4hy5cqpfWX0w3zTTTepZeFmhRBCCAkEnA1w/e2zEMCXfuRhiWrUSMIrVRJTTIzKJlBCQoUKEhYXZ50Xv8/IeLAg28FiMRQ74HGgiR35ZWNE1Kol4ZUrS3jNK1TmgDk1VSxZWRJ+xRXqdUxHJoUmXKgsC0fKQLAcm/sIJZ4gKyEiwpqVoJ9fsO5ly+aUUJjNal6skxId0tOtrR3NoaHywQcfSJ06dVQ3CaN7GdzjQGDYv3+/PPfccxQZiF9BoaEIoTkGX/r5Z/W3oIuoJ7l48aLyYrB9IEW/IBDUdujQQXbs2KEuvEjnX7BggUrtNwKiQI8ePVRg+dZbb6mAHwEy3u8qGNlHsIv6N/wbI+NoF6QHz5G6Fh4ertLY8BwpbUjX1wsXEBYgMGDd7r77bnn//felc+fOefbF+fPnlWswBIVJkyYpsQB+CZ9//rn6C78BpNQlJyer7cX6aZw6dUr5FUCAePLJJ9W+glDx8MMPyzvvvFPg9v7+++/SvHlzw2kQFG6++WblffDmm28qIQTbgh9H2/U3Oua2DyPBRg+ONQSVu+66y3A6foiRfYFSCmQ0XLp0KVeJhcbVV1+t5i3MeUAIIYR4E2cDXH/8LIgNlae8I+WGviBln3tWoq68UkwlS6qsA+1ztIwFBOUY7YdngzNih71sDEtSslomhAv1PDnZIeHCXhkIsg+wnrbrbZuVYDu/6qpRsaKEQmhBBge2sVgxNX/8ww/JT+fPqwGhxx57TI4fP57n81HW+/jjj6t7XAy2YdCLEH+DpRNFBG+6EztCp06d7E7DhTU/kDqGoHXTpk3S9PIFHBkFUHzteTysWbNG2rZtq55DGEDAjwwIBMau0KxZM/noo4+sz2E+iOdYN4ALP8SF7t27q6wDvceE9mOEYBhCBUSFZcuWWeepV6+eEgOQDaE3QsQPzaeffiq9evVSzyGcYN77779fCQEQILSyAAT+X375pTUTBGo3BAEE3xAENJ8CLAtiB37IELwbkZWVJfv27ZNu3boZTked4H333afMFbXlQpTA/sCPoH6fHTp0qMB9m1+XD9Qnok80sidgTGkEBBUcc4AMi5EjRypBxRatJGPnzp25zCIJIYQQf8XI50BfSmAb4PrrZ+k7DWBkHyUOWjmFEg8gYmD55cqp+1ZzUpLVQDKP2GHgZ2AvGwNZDEpkuCw2qOcuZGkYlYHo11vLStDKOezNj/VQ5RTx8VKy2x2y+YorVCcJmKbbA/dcEBcwYESIP0OhoQjgTXdiR5k+fbrUrVs3z+vPP/98gSn3CDSvu+46q8ig1aYhlR4GOUbOu5rIAJDaj/ICpJm5CoJpPVg+uh5g9LxEiRKq1ABOwPANsDWy1H7wkF2QkZEhzzzzTK55BgwYoH5kli5dmktoQNCs7/iAbUDpRuXKla0iA9D+rW0ffoghOkBgwb+RNaABQeKzzz5Tos31119vN7jH+5AJ4sz+sDVxRCYC/BUKwtaTQQ9EG+yz/MomICDhOGD78W98Js4p2+OgbY9+fxBCCCH+jLMBbiB8lnYPmmtALD5eiRgl7uwml77+RtK2bzcUO5AJIGFhknX2rMrW1TpHGHW4APBksGY0oJUm7hEuXZLQ4rGYale4cHa9jQbyjOYPuzz/oSZXyaAlS/KU1+rBABMGqJCRSUggQKEhyPGmO7EzwLfAqL0lgr+CAj+MikNosMWeslutWjXDz0FWhKvYLlMLWrFMCA3IAEBgC5HDHtroPgQDPRERESrYth39hxeFbdogUuWQnWH7mrYuWuYESgRQymBbzqBx+vTpArfZnpcBfC8g3hS0f+0JGc4AsQKiUteuXe3Ooz83IMwgwwPYZq9o28O2T4QQQgIJZwPcQPgsLAf3oihZQDYBAn1kSqguFGFhhmKHlo2Qfe6cJLz3fq5sXSzLKBsjJDYGqZo52QwZGUqgMGPQDaaQkZFOZ2nkt96OzH8oKUle/fxz+VyXAWoL7pdRGmtUBkqIP0OhIcjxpjuxv2KvvU9hTAA9sUxXP7OgddF6LPfu3Vv69u1rOO9VV11l93MR2ON8sSfMONo+CYKHIwaRyNzAw5bDhw/L2rVr5dFHH1W+F44AwQNdJSBQ2AoN2vagFSYhhBBS0MAN7qmQko/Rcm3k3Fc4G+AGwmfpyylsP99W7DCHhVnLG1BWgftc22xdo2wM86VLyodBgfskGENe9iwzm0wSUa2q01ka9tY7v/kTq1WTcZ98ogaAUKJqBEqCX331VeW7xUEREohQaAhyvOlO7C2qV6+uPBBsMXrNV6A7AwJ81P/rSzxstwPAT0BfLoDSgAMHDuTrY+EMyDaAnwGCfFeWCcMhbA/WqTC0aNGiUB4NCxcuVOKJI90m9KB0Auajtmjbo2U8EEIIIYHgc+VqgBson1WQ2JF15qycnz9PzCkpqlWkvWxdGE4C7diZ09PFDKNsk0l1mFAlFOj0gAGZy+JDaLEY9TmeAqWdb7zxhkyePNmu+XXFihXVvRA6hTk6sEKIP0KhIcixV6NWkImOPwNfAXg8bN682RrEw0cAo9b+AswKhw0bpgwhjcwgcRwQ9KNMAr2Ru3TpYj02MFFEYHzrrbe6ZV2QcYBuFjCS3L59ex7jQ2Qa2JY+GJUjrF69ulDrUViPBqw/SlbatGljt/wD7Sz1oNXpihUrDMt0Nm7cqMpMCjIfJYQQUjgysjNky5ktcjH9opSMLClNyjaRCJPvsgEC3eeqqKKJHfBisGRmKY+FgrJ19QJFyoa/5dL330vWhQsSiXJbtM+EySQyCkwmybpcRlpQlq8r2S0wzp45c6bKUICBuBG4J8G94+DBg1XbSkICHQoNQY433Ym9xdChQ1VHBpjiPPXUUxITEyOzZs1SQSgEB39IL4NfBDo9jBs3ThkjohUj2lHCRRitLGHmg+Ae7TnR9hJCwx133KGyG2bMmKFG/1Hq4C5Q27dq1SplFAmzSXhHYF/BBBKmlPh3fqDjBMwd//vvP0MTT0cojEcDBJKtW7fKiy++aPf4Nm7cWNUvQnxCycSePXuUaIM2odh+W3755Re5/fbb/eJ8IYSQYGXl4ZWyZM8SOZN6RgkOEBjKRpeV7nW6S8dqHcWf8Vefq6KOs9m6mkCB5yHh4WIqViznfRAmSpSwvhfZDgVl+Tqb3YJsUtyzwhwcJaBG4P4Q97O4J0S5KiHBAoWGIMeb7sTeAuaHCJqffvppee2111TA/sQTTyjBAa/BnNAfQDbDFVdcoTphQHSAOg0vhD59+ljnQYkA1n/atGny7LPPqh8YeBBgu9yZLle+fHlZv369WqevvvpKiRloc4nRfK0lZ34gIIeXwaJFi1S7SG+jZauglac90EoTnTrQlSQxMVFlN6B1KDp4QITQs2vXLiVevPNOTlolIYQQz4gMc7bPkaNJR8VsMUtUWJQkpCXI6ZTTciE9J5jzZ7GBPlfBla1b2CxfZ7JbsLzvv/9e3YPgfsMIZLuiDTnuBW2NvQkJBkIsnnSvI3nQ0rR37NiRZxpq+jGirXUisG3HF4z1he4EbSLff/99SUpKctigkDgOsjPQLhKZAoG+f3GurFmzRpVPMKMh8PHktZMQ4hrIXnh+9fOyPWG7xIbHSpno/2cDnE09K8mZydKwdEN5q/1bfltGcennn+XstOliTk6W8MqV80zXMh3KPPmElOjc2SfrWBRBpsmxwc+olpehBpkmKIeIatRIeTToB9JcfZ/+vanbtxtmt+jf+8eGDaoEYt26dfmW2GJQiT5RxNvxpjdhRkMRwZvuxN4Atf7R0dHW56h3Q2o/6vcDPQj2V5BxgeyMzz77zGlDRn8C5wpKbZCdQZGBEEI8AzwZUC6BTAZNZAD4i+eXMi6p6ZivRYUWftklIhh9ropytm5hsnwdyW7ZsXevDOzUSX5Yu9buurdr106Vc7Zq1coDe4YQ/4JCQxHC147B7gTmhO3bt1dK8KlTp1QtPpx8R40a5etVC1rQchKGi4EOSkaQ9UIIIcRzwPgRWQ0olzCqo48yRanpmM/duCuLMxh9roIFo5aXEH0KOs6uvi8/X4hjycny1oH98vXJk2IvTRylsxAY9ObfhAQ7FBpIQHLLLbeobg7oP4wLdvPmzZXYcMMNN/h61QghhJAiD7pLoCQCngxG2QBp2WkSFxWn5vPXLhHB6HMVTLiarevK+4yyW86lpcn07dtk3u7dkoEWmQbAqwulp7169WJZHylyUGggAQnq2vAghBBCiP+BFpboLgHjR3gy2Ho0mEJMajrm8+cuEa6OgBP/ztZ19n367JZLJ0/KJ2dOy4c7d0piZqbh/DD6RpbtY489plqZE1IUodBACCGEEELcCrIZ0MIS3SWOJR1Tngwol0AmA0SGyrGV1XR3GkF6qktEsPlcEXFNmLizm3y8YYO8+9efctaOwIAy0yFDhshzzz0nxYsX564mRRoKDYQQQgghxO1orSuX7FmijB/hyYByCWQyQGRwd2vL/Oro8VyVPWRkKLGgKPtcEec7G2ntvfft22c4T3hYmDw+aJBqZ4722oQQCg2EEEIIIcRDQExoU7mN6i4B40d4MqBcwhMtLdklgrgTlNz88ssv8uKLL8o///xjOA8ErPt79ZJx48crPwZCyP9hRgMhhBBCCPEYEBU80cLSFnaJIO5iw4YNSmBYuXKl3Xm6du0qr7/+ujRp4j6fEUKCCQoNhBBCCCEk4GGXCFJY/vvvP1X+gM5m9rj22mtl4sSJ0q5dO7/d4TBGhWcJyomQ6QMRjp4ixNtQaCCEEEIIIUEBu0QQVzh+/LiMHTtWtUrPzs42nKdevXqq49mdd96ZxwPEn0CL11xdUiIi2CWF+AQKDYQQQgghJGhglwjiKBcuXFDZCVOmTJHU1FTDeSpXriyvvPKKPPjggxIW5t+hE0SGhFkfqRav6L4CY1Tz2bOqLWfCZRNUtmQl3iJUgry+6t5775VKlSpJeHi4lCpVStq2bStz5sxRBi+2QMF8++23pXHjxhIdHa164OL9//77r0/WnxASPBw8eFCNgHz88ccuL+PIkSMSFRUl69atk6LAzp071U3d9u3bfb0qhJAAQ+sSUaJzZ/WXaeNED0SFN954Q2rWrCkTJkwwFBni4uLUPHv27JGHHnrI70UGlEsgkwEigyk2ViJq1ZLwypXV39DYWMk8elRNx3yEeIOgFRq+/PJLue6662Tx4sVSsWJFueuuu6R58+by559/qotF796987Suueeee1Tf26NHj8qtt94qDRs2VDVa11xzjaxfv95n2xKM7NixQx0DqMSRkZFKDHrggQfU67YgMEOA9vfff+e7zDNnzsjgwYNVahuEIrQXatmypQwbNkySkpJcWs9+/fqpnsj5oa2fvQfOOW/x1VdfyX333ad+OIsVKyZXXnmlPP/880qx15OQkKB+PG+44QYlqEGEa9WqlXz++edOfd6pU6fkscceU8cRAXCNGjXk4YcfFl8E8EaPzz77rMD3r1ixQl0T6tatq/YZ9t0jjzwiJ06ccPhz8BgwYIB4GoyooDb0+uuvzzNt7dq1ShjFsYiIiJCSJUuqefEeHCc97du3t653aGiolChRQp0rffr0UQ7bRuDY6rcX3y8It0uWLPHY9jZo0EBdi19++WWPfQYhhJCiQ1ZWlsyaNUvq1KkjQ4cOlfPnz+eZB/eQw4cPl/3798uQIUPU80AAngwol0Amg6lMGWt5B/7iuSU7W01P3bzZ16tKigj+Lc0V4iIyaNAglaGwYMECuf/++63TkJ3Qpk0b+fTTT1Uw0aFDB/X67Nmz1Q0zLjy4YS9fvrxVsOjRo4cKgvFef1czAwEEw7169ZL4+HgVlKIdEII41MVB2EFw2L17d6eWee7cOSUIXbp0SQWNEBsQTG/dulVmzpwpjz/+eIGCQWFBQGfU2qh27driLR599FEl2kDEqVatmmzbtk2mTZsmP/zwg2zatMn6Y/nHH38os6NbbrlF9YXGeY1zvWfPnmoUGXWKjoyuawHvwIEDVYCLGkdfiXI4p7A9eiA2FgSEKJw/EBrx/ceNBfbZ999/L5s3b5YKFSqo+SDIzJs3L8/7f/zxR3Wd6dy5s3gSCGmffPKJetiCQHzcuHFKJIE4hr9paWmyceNGeeutt9R7bHt/V6lSRbllg+TkZNm7d6/6bs6fP18JFviLTDA9TZs2VcIVwLF+//33lYiL7xjOAU+A5eK4Yv1r1arlkc8ghBAS3CCTGff5uPfZtWuX4Twmk0ndl44ePVrdSwUaMH6EJwPKJWw9JNQgQVSUmp5tM/hEiMewBCHbtm1DXYTlyiuvNJz+9NNPq+kTJ060vla/fn312pIlS/LMf8cdd6hpX3zxRaHXrUGDBuphRHZ2tmXnzp3qgX8HI3v37rUUK1bMUq9ePcvp06dzTTtz5ox6PSYmxrJv3z7r63PmzFH7f8OGDXaXO2nSJDXPunXr8ky7ePGiJTU11aX17du3r1qf/HBk/bzFqlWr8rz2ySefqPX78MMPra/t37/fcvDgwVzzmc1mS8eOHS2RkZGWpKSkAj+ra9euliuuuMJy9uxZt6x7u3bt1P52lgMHDqjte+ONN1z63F9//TXP9w2vYZkvvfRSge+/8cYbLSVKlCjwHNPWE+eLK0yePNkSHR1tSUxMzPX6Z599ppZ77733WtLT0/O878KFC5bRo0fn2dcNGzbMM29WVpZl0KBBanlDhw7NNa169eqWW2+9NddrJ06cUN+PunXrWjxFRkaGJS4uzjJq1Ci78xSFaychhDhCdnq6JenPvywXf/pJ/cXzog7uja699lr122bvcc8991h27dplCWRwvPff3cOy+7rWlgO9+1gO9nnQ+sBzvI7pSX/95etVJR4mv3jTmwRl6QRS8R2hdOnS6u+BAwdUtgJGe5GmawsyGsB3333n5jUteiBdPyUlRT744AM1QqynTJkyaoQUo6uTJk1yarkY7YQSjfR/W5AWjrR+DXw+1OyzZ89KsIGUeFu07BC91wgyL6pXr55H7YaTcnp6uhrVzw/sv2XLlskLL7ygvkcYPc/MzBRfg3Mnw8naQ5SPoHzA9jVk3BTkz4LyilWrVqlRff055mxpzrFjx9S+x7/xvUCqpq3r9ddff61KIWwzc5DNgO8OMoJQMmELSijGjBnj0PrgO/Tuu++qkgVkdVy8eDHf+ZHtUb9+fXUN1ZdY3HbbbfLzzz+rDAjsFywP2RK2oKTn2WefVe/BdRtZFjDb0n83kVWB8/qbb75xaBsIIaSoAiPAY4OfkdNvvCFnp01Xf/EcrxdFkJXYtWtXlb38119/Gc7TsWNHlYm5aNEiVUIYyKCFZVjZshJiMkn22bNWPzr8xXO8junRTZv6elVJESEohQakDSPFdvfu3apEQg8CB6QEw+BFC8C2bNmi/jZq1ChPqjCAtwNAGj4pHBBrEFSgttsIBHiYvnTpUqeWi6AZgZlRarst+EFBcIRAyp0gKEOApH+gfKMg4B9h+z6jR0FBnz1Onjyp/iIYdde8y5cvV39RYnTjjTcqkQ4P/KCjDMYXoNwDQTgC2xYtWqhA11VwTPAoaD+gzAf+LiitchWctzfffLMSbN58803VlxvlDhDjNCDiwNxWuxbp+33joYkU7gBiA8pQIMj99ttv+c6L9UIJjSbaasA4C14hOB9QnoHSHJSm6P0fsH9xHZg6daoqO4HjN8okIGLBJ0fP1VdfrQwhURpFCCHEfreB1O3bJfP4cTEnJ6u/adu3q9eLktiAwRL8Ljdr1kyVNxqBaT/99JO6n8E9QzAAw9NSPe6W8CpVxJyUJBn79iljSPzFc7yO6TRGJd4iKA0HcKOMmmSMquFCg5t21F6fPn1a+S9gdA0GfhixBIcPH1Z/MZpmhPb6oUOHxBcgkHEkYPU2CC5sR4LzA4Ey6rq7deuW73xXXXWVfPvtt5KYmCjFixd3aNnwZUDHEIwQwz0YI6AQLVDbjRFdb9CpU6c8r2GUFqP9+fHkk08a1t3bggB09erVTq8X2jbhO6Fl5tgDPgUwSELwBwPV/EAgqXlC4AcaJpL4HiHYx36AKAdjRW+AcxCBKoRD+ETgBmPy5MkqyMV5ZJSlVBDvvPOOyoxAsJwf8GbAvsKIiB4E6dgfECoKEitwfuBzRo0apZ4j2IaggAwFeIsALAuO2LYeIFqdKURSPRi9sL1mwPDTUY8ZbXm2vg4QFrRsA3yXISLAaPKpp57KNR/ED3h+INMDoOYVvinww7jpppus2U0QD5DpoPdkgWeIbVcgiMe4DmJ7YfBKCCHEfrcBzQhQG8nWug3EtG3r8yAT6wrTQvgJmEqWUqPw7lon3OfDrwjZsfayLDEQOX78eOVF5Mw9bKCgta7E8YbxIzwZQuLjVSYDRAa2tiTeJCiFBgCTul9//VXdwMIEDw+A1GLc6OLGVUPrSGAvMIqJiVF/Efg6CjpWGOGKoRkCBji8+xu4oNuWP+SHtv8KEg+06Ri9dFRowMg6MlNgyAizn/fee089cLwRuOChGeNAhDBqb1pYpk+frjoX6EGAXxBwPbbtgmIEsnCcBRk9CFjxGRDb7KGNyiOVHSPMBaF9Z5A6j+wT7ccaohxGwzWzVXvgBsA2QwOvoWzDtqQFgmB+NwMwvcSohB50T4CgCONCZ4WGNWvWKMEENyG2AoJtMA2zRaT+264fsmaQqglDKUfKFmyNFCH26LNzNNHA9hzQRvhtsxmwb22/m8iIgGGqI2jLs73mIUtEv1yc39jXELP0wERLLx6gfAklEZgPWTM4byBENGnSxND41dbEStvuYCx3IoQQT3cbyL540dptAK0+fQWyKnIFwBERbgmA8VuIAQYMLNrrMob7RPwm497EKHs5mMC+hKiE4w3jR1OpUqpcwtciEyl6BK3QsHDhQunfv7+q2ce/EfhjBA6pybgQoa76999/d9jPgRQeTTQoSLBxVJCwBSPLcL+fMWOGGnFH8InABjXsmJZf4OsOMNLqaCCnBwExHu4G2TsYSUZa/quvvprvvBiRRnrh3LlzVfBXEFr3CtsRAaTHI/DEdyu//b1u3Tprxxc9eJ9tS0rU/6OcxhkgTuD7j+wWpOHby1ayBSPmCHwxoo/sjoKyGUBhyiYASj1sRQEE1kYtt2wFMu07YntjBaFAK1OAOIDsAWfQlmf7HYRHBEaCcPMKYRYlSMiUMOq0YisWaCIcSmsgNEB0vfvuux1aH227bZdJCCEkMLoNaKUdyLqAIIJ1NZ89K1mnTknC5fVyVmzA4AQGlfC7ZE+IhtCNwRa0P/d09zF/AqKCL0UlQoJWaECQ2bdvX5UFgBZ12oUFI7pIp4LggNfR0lLf9hDpzvYM5pwNfHfs2OFUpkNRACUMCPgL8rrAdKTA48fBFfCjiqAGD4xm47gjKPS00OAqGH1GWnxBIDtDK/cpCGR33HHHHSpgRsvQ/FLmMXoPcQZBOUQCR9DaPmltYPUj3CipMQqS9UDM0NfrA2QfIACFwaQerb2ks1StWtVaEuKI0ACvAZRg4DxFO9CCvu/I2oBxFPwDbHEma8aRrBfNA8F2v6IcAaAEQQ+Ot1bKY+t34Aja8mxbs6IMxKhEyNNo2+2IzwghhBQ1UIKA7AAE7vjt0YsNeG5JS1Pp8xjZDobSDngb4TcYA0n2fKFwz4TS1OHDh/O3gxAfEZRCA0ZEkYbdpUsXQ/USo7AQGpAiDaEBqdf53ZBrr9u69BPngW/Ghx9+qEzm2rRpYzgKjx+Nxx57zC27FyUyGB1GdwB/BSq7Oz0aMFKMcx9CGwLm/BR8lHsgtf+ZZ55R9fOOogXX6JagB74GGFUoqKQGx8Q2YMVrEKLcFchqnTMcKe9BaQJEBoyOrFixokCPCrhX7927V5XqeANco5BFou/uACB0QEhDRwr4SmhlXoVBu4FDxoLRd9QRsG9sb3ZRagK07BSUkNkKJPbAdiNzxrY0iRDiPTKyM2TLmS1yMf2ilIwsKU3KNpEIE1Ox/cHfQOs2gOwABO62gbyvuw24q7QD24P7mhEjRtgdtMIyUaqHQRTetxPiW4JSaNCEAXsmgNrr2iiZliqOm14IFLa1W5q/A0wKfQFGM+GH4G/YOs07Akar0fUDQgKEHv0yMPKMWnUEOLaj2gWBwA+j97aBFmrlEUTCs8MVoz5v4E6PBtS/I2BGUIbSkfyCbBg4Pv300yr1H7WN9jDaXxixh5CBTBH84GutHWGyikBVM/zzBmfOnMmznRBAkLGE76xeNIDghAwSBLna9xwZSzANxXtQUpWfl4WG1s3m/vvvF2+AdUVZzt9//51nGoQiHMMBAwYowcr2+uWMHwmOHc4JdOd58cUXXc4qQtYYvFI0M0jUz6IsB+0utQwVlE1oniq2Pg22IgW8MJAN5i1jV0JIblYeXilL9iyRM6lnlOAAgaFsdFnpXqe7dKxm38uGeM/fAH9RgoDsAATuqlwCmQwmk8+7DbijtOOPP/5QAyIYkLIHMjlRKmprkEwI8Q1BKTRoN7JGN+WaKZp+ZA1O7qg1xs01jO3QKk4PUs/B7bffLr4AQaMzpov+DII4BEMIjBo3bqw8BLD/kcUA00KMhsNTw8gwE4GjUZsiZATAOA9BLwIWjLYjZQ7HE+9BEIxg2FWjPohPqP+zBWUMgwYNsj5ftmyZtQuAntatW+cyH/WkRwMyGTCSD/ECWSP69oQoc9AEAOwDKP4QetCeUvMbMFpno/0FbxPU/aNECd09UHIBMQItCmFkqAWY3gDbiiwObAdKOnAuoUQKAgLWRw9SKHH+6X0fcC5iG9G5BOcMHhrIBrG9HiAYh0gD/xdnjV0LA7q1vPTSSypo1wsAEDsgkqIDBLajZ8+e6juF7cfr+D6hDMRWqILgAtFPE5OQhYAOENiXWAacu10FmQf4buNai/MO30N0p5gzZ451HoiJuLbC1wP7Ht9biI3oFIKaW00AxvcPxr767xohxLsiw5ztc+Ro0lExW8wSFRYlCWkJcjrltFxIzwkMKTb4j7+BP3YbKExpx86dO9U93DfffGN3+ci+Q/mnflCJEOIHWIKQjRs3YghPPWbMmJFr2h9//GGJiYlR03755Rfr6x9++KF6rU6dOpZTp05ZX//yyy/V67Vr17ZkZmYWet0aNGigHkZkZ2dbdu7cqR74dzCzdetWS69evSwVK1a0hIeHWypUqKCeb9u2Lc+8c+bMsR5Po8eRI0fU8l544QVL8+bNLfHx8ZawsDC17HvuuceyadOmXMtbtWqVet/o0aMLXM++ffva/dxatWo5tH6Y7i3yW4927dpZ53NmnfPbXwsXLrQ0adLEEhkZaSlfvrzlySeftFy6dMmldcf6YX87y6effmq54YYbLGXLllXHvUyZMpbu3bur64C943ngwAHra9WrV7e7HzDNlh9//FFNe/fdd51aT3ym7b7F+uB6ZAv2te3lGdclbN+8efMMl7969WpLjx49rN+pEiVKWK655hq1rBMnTuTZ1/rtjI2NVde+3r17W37++WfD5WNf3HrrrQVupzbfTz/9ZLnqqqvUuVGvXj3L4sWL88ybkJCgzpnKlStbIiIiLFWqVFH75OzZs9Z5li1bptZxz549dj+zKF07CfEm6VnplieXP2lp/3l7y21f3Wbpt6yfpf+P/dVfPO/weQc1HfMRz5Cdnm45PPBxy+42bS17u3S1HOjdx3Kwz4PqL57/16atmo75tPmT/vrLcvGnn9Rf7XV/2Ib/HNwGcPjwYUv//v0toaGhdn+jGzVqZPnuu+8sZrPZp9tHiL/RIJ9405uE4H8ShGC0DB0mAFJuMWKMdF6kXqGV36OPPqpGPTXwWo8ePVQaL0b+MDqK0XWMpGFEHCnVcFwvLJoZpJFZJNZh9+7d1trrYOzvSwhxHWQJwOsgv9RRX4NMEaStwgfHHSCjBKNfuDbbg9dOQjzDhpMb5K2/35ITySekZsmaeUai91/cLxVjKsrz1zwvLSq04GHwAMl/rZfTb7whmcePS0StWnmOQca+fRJeqZKUG/qCX3cZ0LIyUNphyc7OU9pR+pGHVdYFyl2RoTdt2jTlm2TPtwhZd8hIdMRQmZCiRsN84k1vEpSlEwBp3Uj/Rgou6nsRwCN9GIZ6qGXu1atXrvkR1C9evFilWiPNFzfJqPdHHTEMZTzRfpAQQpwB5SsoS0B70KKQIooyFlyLN2/e7OtVIaRIAuNHeDKgXMKotj7KFKWmYz5SdFtXOkJBpR2hrVrJa6+9JpMmTVKlfUag3HPkyJHKyJ3t6Qnxf4JWaACo17c1GcsPqKLPPfecehBCiL+BUZy0tDQpKsA7Jysry9erQUiRBd0lYPwITwaj2vq07DSJi4pT85Gi2brSWbEBLSzRXQLCCNY5rGFDmTNvnozt3VsZWhuBgT/cmw8ZMsRlk2JCiPcJaqGBEEIIIYS4BlpYorsEjB/Ppp6VMtH/b5uI56YQk5qO+Yhn8PfWlc6Czhco8cD6I5N4ZO/esmfPHsN5w8LCVJeyUaNGKWNhQkhgQaGBEEKI20DXD0JIcIBsBrSwRHeJY0nH5FLGJVUugUwGiAyVYyur6ZiPeC4w9+fWla6wYsUK1ULZXnc4gBJn+DB4s7sTIcS9UGgghBBCCCGGaK0rl+xZImdSzyhPBpRLIJMBIgNbW3oef25d6QzwTEOb6V9++cXuPDfffLPyamjevLlX140Q4n4oNBBCCCGEELtATGhTuY1sObNFGT/CkwHlEsxk8K2/AcolAiGTAaURMHFctGiR3XlatmwpEyZMkA4dOnh13QghQSQ0HDp0SDmInzlzRi5cuCClSpWSsmXLStOmTaV69ereXh1CCCGEEFIAEBXYwtI//A0CBZg7vvLKK/Lhhx/aNfZFJyVkMNx11115umoQQgIbrwgNW7ZskQ8++ECWLl0qR44csTtf1apV5bbbbpNHHnlECQ9FDf0FNjMzk617CCHEAbQbWNXqjTeqhBDiU9CeEm3m3377bUlJSTGcp1KlSjJmzBjp37+/Mn0khAQfHv1mr169WtVirV+/XrnLhoeHq5qrevXqSXx8vGpRg4vR+fPnVb/0bdu2yYwZM2TmzJly7bXXyuuvvy7t2rWTogJukNHCJzk5WY4dOyblypVTYgPabhJCCDEWGXC9BBERERQaCCHER6D9Mu7jX331VTl37pzhPMhkhhHkU089JcWKFfP6OhJCgkBoQGbCsmXLpGTJkvLQQw/J/fffL61bt853lD49PV3WrVsnCxYskCVLlkjHjh3llltuke+++06KCmXKlFEXauyL/LI/CCGE/B8IshghI4QQ4l2ys7Nl7ty5Mnr0aLv3rlFRUfL0008rkSEuLo6HiJAiQIgFqQYeADd8Q4cOlYEDB6qLi7Mg2EZmA1Kvjh8/LsFCw4YN1d8dO3bke8GGh0ViYqLdmjZCCCE5mWDIZMBvjiu/NYQQQv6POSNDUv/ZLNkXL4ipZCmJbmbfcBIhxLfffisjRoyQnTt3Gs4TGhqqBhwhQlSpUoW7mhA/iTcDWmiAUOCOmz53LSdQDzwOj4cOESGEBDz0ZSCEEPeQuGJF7haaERF2W2iuXbtWZSf8/vvvdpcHg0eUUaBkOphxRpwhpCgJDR4rnXCXOBBMIoMr8CaaEEIIIYR4WmRImPWRZBw9itRaCY2OFvPZs5J16pQkXLig5oHYAD81+K/B4N0e7du3V60q4bcW7DgjzhBS1PAbm9ezZ88qgxg6zxJCCCGEEOK9EXkEyxAZTLGxYipTRg10IaM2++xZyTx6VLbNniMz58yRBZ9+ajfTtkmTJkpguPnmm4uEMa+j4gwhRZVQb33Q33//rXrp2tZwwfSxQoUKUr58eSldurRMmTLFW6tECCGEEEJIkQZp/xiRR7CsiQwAfy/Exsr4vXulw2cLZf6CBYYiQ82aNZWR+6ZNm6RLly5FQmSwFWciatWS8MqV1d/Q2FglzmA65iOkqOI1oWHq1Kny2muvKUFB48CBA9KzZ085ffq0EhvQ1vG5555TbTEJIYQQQgghngXeAkj7x4i8IIvh0iW5ePq0vLNhvbT/+muZe/KEZJjNed6HNuy4v0eLenSXg/FjUSE/cQbPLdnZanrq5s2+XlVCfIbXrgh//vmnNGvWTGUtaMyePVsyMzPlzTffVH3Q//rrL3WRYlYDIYQQQgghngcGhvAWyL54URL/+0/mbNwoHVcsl3d27ZKkrMw888fGxsrYsWNl37598uSTT6rOP0VZnLHN4FD+alFRanr25RIKQooiXvNoOHXqlDRt2jTXa7/88ovExMSoixS4+uqrpW3btrJlyxZvrRYhhBBCCCFFFnRJMKNV5cmTMvX0KTmSmVdcABAUHn/8cXnppZekbNmyUpTRxBl4MqCcRC82qI5xaWkSEh8vplKl3PJ57GxBAhGvCQ3ZSCHKyrI+T0pKUrVcHTt2zKWEohc6sh8IIYQQQgghngNB8bIff5QXViyXfy9eNJwHIfSd1arJm7/8IjXr1uXhuCzOoLsEjB9hmGlroBliMqnp0TaDrK7AzhYkUPGa0FCtWjXZuHGj9Tna4kB46NSpU675Ll26JCVLlvTWahFCCCGEEFLkQMnyiy++mK83WvtSpeTZipWkcYMGUu5yGQBH10VCIyJUC0t0l4DxI8pOVLkEMhlMJgmvUkVNx3yFgZ0tSCDjNaHh9ttvl0mTJsldd90lHTp0UP+GH0O3bt1yzffPP/9I9erVvbVahBBCCCGEFBl27dqlyh+++uoru/M0KxUnwxo1lGtr1JDMY8etfgMcXf8/WutKdJeA8SP2EcolkMkAkaGwrS0daTuK6TFt2xZa0CAkoIWGIUOGyKJFi+Trr79WD4AOE3Xq1MmlrMIUslevXt5aLUIIIYQQQgpFIIzyHz16VMaMHi1zPv5YzAZdJECdkiVlaLPm0qlKFWtQq/kNpO/bL8lr1qjAF90WYIQIjwKUD2BkHxQ2uA40sL0I9NFdAkIMPBlQLuGOY19QZwtkUWidLWJatnTD1hASoEJDmTJlZOvWrfLFF1/ImTNnlPEj/Bn0nDx5UgYPHiy9e/f21moRQgghhBDiMv4+yn/u3DmZMGGCTJ0yRdIyMgznqRQbK09XrCTda9aUiLJl8/gNmEqXVgEtR9fzAlHBE4E+O1uQQMdrQgNEBpRK9OvXz+48KKOwLaUghBBCCPEHMrIzZMuZLXIx/aKUjCwpTco2kQiTf41aE+9ir4Y+8+RJyThyRFL+2Syxbdv6JMMhJSVFpk6dqkSGC3baLJYKC5NBjRrJYz17iXnzZpWOn3HpUh6/geimTSRpxUqOrgdxZwtCAlZoQGvLdu3ayapVq7z1kYQQQgghbmHl4ZWyZM8SOZN6RgkOEBjKRpeV7nW6S8dquTM0SdEQa+zV0GedOyeZx46pVPoLCxdK8u+/S3j58l7LcIDZ+pw5c2TMmDFy/Phxw3miTCZ5qH59GVC+gsSkp0vIwYMS3/dBufjNt4Z+A5bsbElc9qMSUvQBL8BzJUxc9nEggdfZgpCAFhri4+NV60pCCCGEkEATGeZsnyNHk46K2WKWqLAoSUhLkNMpp+VCek5gRbGh6Ik1RjX02efPS9bJk2LJzFSvI/jOPHJEss+c8biPAQJQGDyOGDFC/vvvP8N5TCEh0rNOHRnc+CopV6yYek/Gvn1qO0zx8VJ5yjuGfgPJf63n6HqQdrYgJOCFhlatWsm2bdu89XGEEEIIIYUGATGCY4gMseGxUib6/6OKZ1PPyrGkY2p6m8ptAnZk3hsZB8Eo1tjW0FssZhWwm5EhEBYmltBQEYw6lysnYjZ7tEsAMobRqnL9+vV25+larpwMva61XFGihN1sBHt+AxxdD87OFoQEhdAwevRoadOmjbz11lvy/PPPe+tjCSGEEBIoQWpWusiR9SKp50Wi40SqthQJixRfgu3BCDyCY01kAPiL55cyLqnpmK9FhRYSyHgq4yBYxRrbGnpzYlJOJoPFIhIWJoKR5/Bw9QgtXtwjXQLQFn748OHy008/2Z2nQ4uWMrh4rNRPTZOI4sVzTXO01p+j68HZ2YKQoBAa/v33X9VNYujQoTJ//ny59dZbpVq1ahIVFWU4/4MPPuitVSOEEEKIr4PUXUtFNs0TSTopkp0hgoAztoJI8z4i9W712fGBaILtwQi8UW16lClKTcd8/uZJ4MxnezLjIFjFGttRfmQwWNA2EtsHwQHZAhAZYmPd7mOwb98+GTVqlCxcuNDuPOjwBiPIjjfcIMcGPyNp27cXqtafo+vB19mCkKAQGtBtQruobdmyRT1sf7CB5qpKoYEQQggJDAodpEJkWDdF5PxBEYtZJDxaJCNF5NJxkdRzOfP4SGxAgI7gHNtj5Pyelp0mcVFxaj5/8iRw5rM9nXFQWLHGX7Ed5TenpYklKwtujGIJC5PQyEgxoVUkBAg3dQk4deqUjBs3Tt5//31l+mhE7dq15dVXX5UePXqojm/AXbX+HF0nhPid0PDyyy8bCguEEEIICVwKHaSiXAKZDBAZIkuIxJbPGRFG+nnSqZzXMb12J5+UUSALAAE6RBNsj+32mUJMajrm82SGgCezEzydcVAYscbf0Y/yZ54+LZkHD4oZ2xgeLmEVKkhYXJxbugRcunRJ3nzzTZk8ebIkJycbzlOhQgVVqvzwww9LeHi4x2r9ObpOCPEroQEtdgghhBASXBQ6SIUnA8olkMmgiQw5C8h5Dr8GTD+6QaRGGy9vnagAGVkACNAhmmB7MAKP4BgiQ+XYymq6Puh3d4aAp7MTPJ1x4KpY424cEWvQshLdJGD0CA8GlEcUNNKvH+VPXrtWEletVuUR2efOiTklpVBdAtLT02XmzJkqQ+Hs2bOG85QoUUKGDRsmgwcPlpiYGIfWk7X+hJCgERoIIYQQEnwUOkiFkABPBpRL2GY+4nl4sZzpKZdLKHyAFszrg32MwNsL9t2ZIeCN7ARPZxy4Ita4G0fEmsQVK3KP+EdEODzir43y44GMhcJmDmRnZ8uCBQtURvChQ4cM54mMjJSnnnpKdZsoXbq0Q8tlNgIhxFtQaCCEEEKIyxQ6SEV3CQSYmak55RJ6sQHPM1NEYkqLFIt3y1Fy1ZwRwSiyABx5r7syBLyRnYB/Z5oz1b+zzdkqEEcA7u6MA2fFGnfiiFjTYo9FEmZ9JBlHjyLKVy0r0U0CRo/wNgCOigSFyRzAPl+6dKnqJLF9+3bDeeC7AO8zZAtXrVrVqX1BCCFBKzT89ttv8s0338iePXskMTFRXVBtwY/bihUrvL1qhBBCCPF2WjxaWKK7BIwf4clg69EQasqZXqXw3QgKa86I+bEdmtiAv0Zig7syBDydnaDfH+fSzkmGOUNOJJ2QhNQEKRFRwu0ZB86INe5CL9bEhMdIVGiUZEu2FA8rLmnmNCXWfLPzS6n1dbYSGUyxsXm6MsBAERkKEA8cLXtwJXPg999/VyUQuFe2x5133qnKKBo0aODUsgkhJGiFBlysYU7zySefWMUF7SKuoT2naSQhhBASGBQ6LR4Gj2hhie4SMH5EKQXKJZDJAJEhrkbO9EIaQbrDnNFRocJdngSuZEY4+tnn087LvJ3zcu0PTEMQnm3JVsuqGFPR7RkH2GfebGGpiTVpWWmSmZ2psjcsYpEQCZHw0HC1rVG7DkniCbNEZGdbRQaAv3iOLg0og0CGgidaDO7YsUNGjBgh3377rd15brjhBtWq8rrrrnP75xNCSEALDe+99558/PHHcs0118jrr7+ujG2WLFkiu3fvlv3798vnn38u8+bNk+eee04GDRrkrdUihBBCSCEpdFq81roS3SVg/AhPBpRLIJMBIkMhW1u6w5zRGaHCXZ4ErmRGOPLZt9e6Xb7b912e/VEpppKcSTmj3ovlDm4+WK4uf7VHMw48DUQYiCo4ByyX/wsNCVXHUCsZMV+8KNkZEapcwkjQUa0gMzJUGYQ7OXz4sOoSMXfuXDGbzYbzXHXVVeq+uWvXrgE5EOeKuSYhJDjwmtAAkQFOuMuWLVOGNfPnz1ev16lTRz1uvvlmueWWW+S+++6T1q1bS/Xq1b21aoQQQggpJIVOi4eYgBaW6C4B40d4MqBcwg0tLQtrzuiKUOEOTwJXMyMK+uziEcXt7o+yxcpKYmaieh4WGhbQIgMoFlZMEjMSlagQERqRsz3YXEvOcUW5yNnwNAmNiBXzxUuGgo7qGhEfr7wW3EFCQoK89tprMn36dNVVwogaNWrIuHHj5P7771eeDIFIYcw1CSGBj9eEhn///VcJCJorrnYRh6uuyWRS/+7Ro4dcffXVqk9wt27dvLVqhBBCCPGHtHiICh5oYVlYc0ZXhYrCii+FyYzI77OXH1ru0XaWfoV+82D9cblsAn+1afuqhIl5T0kJOZesPBlsPRrQmhIBMgwdC0NycrK88847MmnSJLl06ZLhPGXLlpWRI0fKY489JiFhIbLx9Eav+Vm4W2Rwl7kmISQw8ZrQgJQwfeudYsWKqb/nz5+XMmXKWF9HdgPcdgkhhBBC3EFhzRkLI1QUVnwpTGaEvc/2dDtLfyIlM0ViI2LVNkEowt9QCRWzmJXggKyNqOjikti5pZRM+1sZP8KTQZVLIJPBZJLwKlXUKLyrKf+ZmZkya9YseeWVV+TkyZOG88TGxsrzzz+vHsWLFy+0camvyyWQyeBOc01CSODhNaGhcuXKcvz4cetzrTTin3/+kZtuusn6+n///SdhYey6SQghhBD3UFhzRl8H5u7u1uAus8pAAPsqPipelU9guzQzyDAJs5pBYnpkm7ZSulyT3Kn+8fGFSvXHINvixYtVhsLevXsN5wkPD5eBAweqecqVK+c241JfAk8G7EPxkbkmIcQ/8FpE37x5c9WyUiuV6Ny5s2rhM3ToUFm4cKESImAYuXHjRrmRqVSEEEIIcROFNWf0h8Dcnd0a3GVWGQjojx38GhC0Z1myJCwkTHWiSMlKsR67iBsj1Cg7AmAYP8KTAeUSroy6//LLL/Liiy/Kpk2bDKfj/IH/ArIcatas6VbjUl8D40cINd421ySEFFGh4Y477lCdJVAWgX83adJEevbsKZ999pk0bNjw/ysUFqb6AxNCCCGEuIvCliAEW2DuDrPKQMD22MHoMr9jB1GhMKPsGzZskOHDh6vBNXuggwTMIJsaeD4U1rjUH0B3CRg/wpPBG+aahJAiLjT06tVL7rrrrlxlEZ988olq2/P1118rr4a6deuqDIeWTKMihBBCiB+VIARjYO7ukgx/xRvHDqW/L730knzxxRd257n22mtlwoQJ0r59e48Zl/oDaGGJkhMYP3rSXJMQ4t941QwhMjIyT10a0srwIIQQQgjxNIUpQQjGwNydJRn+jKeOHfzHxo4dKx999JEqDzaiXr16KoPhzjvvzCMe+JsfiDtAVgh8LdBdwhPmmoSQwICui4QQQgghDlJUAvNgP3bojJD692ZJu3hBpfpjFN6ZwPfChQsyceJEmTJliqSmphrOA/8xiBB9+/Z12OjcH/xA3IFmnulOc01CSGDhdaEhISFB5s+fL+vXr5ezZ88q40eUS4AdO3bIvn37pFOnTtb2l4QQQgghhLiLxBUrcgfAEREOB8AQFaZNmyavv/66Kvs1Ii4uTvk0PPnkkxIdHe3UugWTHwj2pbvMNQkhgYdXhQa0+HnkkUckKSnJmg4GtVfj2LFj0r17d+Xd0Lt3b2+uGiGEEEIIKQIiQ8KsjyTj6FHVfhGdEWBaCD8BpPoDI7EhKytL3Z+OGTNGjuK9BkBUePrpp1VXNYgNRsBfoaDyjWDyAymsuSYhJHDxmtDwxx9/qDY+JUqUkLfeekvatGmTx/QR2Q0lS5aUr776ikIDIYQQQghxGyiXQCYDRAZTbGwek0L4CWA6RuG1UXdMg2k5jB7//fdfw+WibfvDDz8so0ePlkqVKtn9/JWHV+YSDyAw2BMPgtEPhBBStPCa0AATnNDQUNVXuHnz5nYv1Ji2fft2b60WIYQQQggpAqT+s1mVSyCTQRMZAP7iOUwLMR2p/hiF//XXX5Vh+Z9//ml3mT169JDx48fLlVdeme9nQ2SYs32OHE06qlpXoqsEDB/hxYAyCWArNnjDD0R5VfyzWbJd9KoghBCfCw2///67XHfddXZFBo0KFSrIX3/95a3VIoQQQkgwkpUucmS9SOp5keg4kaotRcJyd78iRQsE0/BkQLmEUetI1RkhI0M2b9wor44ZI8uWLbO7rI4dO6pWlS1aFCwEIHsBmQwQGWLDY/MYPMKLAdORweDNjIXCeFUQQojfCA0pKSlStmzZAuezZ6xDCCGEEOIQu5aKbJonknRSJDtDBMFbbAWR5n1E6t3KnVhEwYg9gml4Mhi1jjx84YJM3btXvv3+O/XciGbNmimB4aabbiqwVaUGyh9QLoFMBk1kAPiL5zB8xHTM562OJq56VRBCiN8JDTB9RFeJ/MBFHWUTV1xxhbdWixBCCCHBJjKsmyJy/qCIxSwSHi2SkSJy6bhI6rmceSg2uA1HzA39BZQFYMQewTQ8GbTyiTOpKTJ1/QZZeOSwZNoRGGrVqqVKJO69915VCuwM2DfYTyiXMMqkQFcJTMd83vSqSD9yRELDwiS0RAkJCQ8XU4UKYj53ztCrorCfx/IMQooeXhMaunTpIjNnzpTPPvtMevbsaTjPrFmz5MiRI8o0khBCCCHE6XIJZDJAZIgsIRJbHpEcRjJEkk7lvI7ptTuxjMINOGNu6A8gaEZZAEbsEUxfPHdO5pw+JbMPH5YUs9nwPeXKlZOXX35ZBgwYIBEuBt0QYLBv4MlglEmB1pXoKoH5vAGC/vR9+8R88aJYwsOVN0VIaGiO2FCmjFiys3N5VRRGLGB5BiFFF68JDTDT+fTTT+XBBx+Uf/75R7WxBMnJyer5kiVLZNKkSaq84tlnn/XWahFCCCEkWIAnA8olkMmgiQwAf/Ecfg2YfnSDSI02Hl2VQBrpdwVXzA39AZQDpGdkyPRXX5WpWzZLQnq68XzFi8vQoUPlmWeekdjY2EJ9Jo49BBjsG3gy2Ho0mEJMajrm8wZJa9dK1unTqmRCCR+hoWLOzBTJyBBLVpZIeLjybMi+XELhqljA8gxCijZeExqqVKkiS5culbvvvlveeOMNefPNN9VF9osvvlAPXOigGn/zzTfqLyGEEEKIU0BIgCcDyiVs6+fxPLxYzvSUyyUUHiLQRvqdxV/NDQvCbDarQa9Ro0bJwYMHDedB1sKgQYNUO8syZcq45XOxD3DsIcBg38CTAeUSyGSAyFA5trKa7o19pTIT/v47R1DA1yLqcjkHsn4yM8UM4QWPihXFVKqUy2KBK61ECSHBhdeEBoCuE7t375aPPvpItbnERR4XfYgQMNV57LHHpGRJ76SNEUIIISTIQHcJBGuZqTmBk15sUIFUikhMaZFi8R5bhUAd6XeGLcf+lmLbDshV51OkZJk4OVHTItlhIT4zNywoewTBLTpIDB8+XLZu3Wq4DKw7sm7Hjh0r1atXd/s6asdcL0ChXMLbAhTKH5C9oIJ+eE1czmBQz8PD4d4uEhqqgv/IBg3kxAtDXRILnG0lSggJPrwqNGipaEhDw4MQQgghxG2ghSW6S8D4EZ4Mth4Noaac6VU8E/wG6ki/M2CE2zTvPel09JiEZGZLaMRhSS4ZIdtalZO9jeO9bm5YUPbIn3/+KcOGDZM1a9bYXcbtt98ur732mjRq1Mij64r1wbH3ZUkNPBaQuRBavLgqg7Ckp4s5K0uVT1gu+1SEhIVJsRbXSPqOnS6LBY62EtXKMwghwYfXhAZkL9SoUcNbH0cIIYSQouYjEBaZ08IS3SVg/IhSCpRLIJMBIkNcjZzpmM8D+GMbQ3eipdFHHjoipTIyJS3MItHJ6VL8YoZEJWeqefY0ivOauWF+2SOH9hyS0V+Plt9++s3u+6+//nrVqrJNG8/6dejB98uXx15r8QnCKlRQmQmWzEwlMkBggAgRVq6cylKACOCqWFBQK1FLWpqExMer8gxCSHDiNaGhdu3a0qlTJ+Xa261bNwnDxYwQQgghPiFofQS01pXoLgHjR3gyoFwCmQwQGTzY2tLf2hi6E33NfUSJODkRFiLJWSliCgmVUskipRLSpfEfp2V99SyvmBvayx5JOp0kf37ypxxZeUTEuFOlNGzYUF5//XW57bbb8hynYEff4hOZCuG1a4klOVnMGZliSUkWS2aWRNaqJdFNm6ryB1fFAnutRLWyixCTSU3H5xBCghOvRfvoP/zzzz8rbwZ0lujXr588/PDDUqdOHW+tAiGEEEKKgo8AxAS0sER3CRg/wpMB5RIeymTw1zaG7kRfcx9etqyUyQiXrKSTkp6dLmejzVIp2Sxh5xOl3P4QKd64psfNDW2zRzKSMmTrZ1vl32//lezMbMP3VKtWTV555RXp3bu3mEwmKYrYtvhUrS2RmQDRwGSSiKpV1XTMVxixoKDPCa9Sxfo5hJDgxGtCA0wgf/31V/nwww/lq6++Uq0s0X2iXbt28uijj8pdd93lcn9iQgghhDhGUfARUEBU8HALS39vY+hObGvu4yLj1OtnU85KhjlDssLTJcoSJvXDqkiTRv09LlRp2SPh2eGybdE29chIzjCct3Tp0qqLxOOPPy5RUVFS1NG6RORqWRkfn6dlZWHFAkc/hxASnHi1fgGiAh4XLlyQefPmyaxZs2T16tVKgIiLi1NuvyitqF+/vjdXixBCCCkyBLuPgC/xpzaG7vbmMKq5h9hQMqKkJGUkScjZoyIlyspD1w+WktVae3wbYkwxcuSXI7L1062Scd5YYDBFmqTv431l8pjJ7GpmA4J8+DDAyBEeCyh/QGaCrWhQWLHA0c8hhAQfIRb8WviQ9evXqyyHzz//XJKTk9VrrVu3VlkO9957r0RGejbN0dugLhDs2LHD16tCCCGkCLL80HKZsXmGJGclq8DXlmOJxyQmPEYGNR0knap38sk6Bjr+7H/h6rrBo+HY4Gckbft2CTVodWhOSpKoRo2k8pR3PBpE4vO++OILeWnkS7Lnvz2G84SYQqTSjZWk8yOd5b273vO5sGO0L1GKgiwRCDgoUfD3wFutM8UCQgKChn4Sb/rckbFly5ZKZcaPFTIcwLp16+T333+XIUOGyKhRo+TJJ5/09WoSQgghQUEw+wj4C/7QxtDd3hz+UHO/YsUKefHFF+Xvv/+2O0/Z1mWl5n01pU7tOtK7UW+f73Ojzh25sgMiIgKilADH1baFJSGE+KXQkJaWJosXL1bZDBAWcHNToUIFeeihh6Rjx44qw2H+/PkyePBgOX/+vBIcCCGEEFI4gtlHwJ/wdRtDT3hz+KrmfuPGjTJ8+HBlKG6PCs0qSP3e9aVc3XJ+kz1irz0oOnfAVBN+FyhFgdkiBBzgz2IDIYT4tdCwdetWJS4sWLBALl7Mae/UoUMHGThwoNx5553WtpcQG0aMGCGtWrWSDz74gEIDIYQQUoR8BIh/enN4s+Z+z549MnLkSFm0aJHdeVq0aCHjXh0n8Y3j/Sp7JL/2oCaD0hNkiWA69q2/l1EQQohfCQ0oi4DAgHQ3XFThAPzss8/KY489ZrfFZfXq1eWmm25SogQhhBBC3IM20quv1Ue5hL+OBBP3dWlAuYS+XAbgOcQmTMd8vk6jP3nypGpDifvGrKwsw3nq1q0r48ePlx49euTZHn9vD6qJDAB/8RylKJgOAYclCoSQYMBrQgPMHTWjR2Qv3HPPPQ4ZPV511VVy5MgRL6whIYQQUnTwVx8BUnS9OZDpitbnb7/9tqSkpBjOU7FiRRkzZoz0799fwsPDJVDbg+rBc+V3kZGhskQIISQY8JrQ8MQTT6jshUaNGjn1PhhC4kEIIYSQ4PYRIEXTmwO+XTNmzJDXXntNEhISDOeBcTiMIJ9++mkpVqxYwHWEMGoPqoHnylQzPl6VovjTehNCiN8LDVOnTvXWRxFCCCGEED/35sjOzpZ58+bJ6NGj5fDhw4bzREVFyVNPPaVEhvj4+IDtCIHAH8uC8SM8GWw9GtC5A9Phd+FP600IIQHb3pIQQgghHiYrXeTIepHU8yLRcSJVW4qEFVy+SIILf/HmQHD93XffKdNve33eQ0NDVScyiBBVqlQJ+I4QnmoPyk4WRRdmsZAiKzTcd999qoaufv36Li8DPz5jx47N122YEEIIIfmwa6nIpnkiSSdFsjNEMGIdW0GkeR+Rerdy1xUxfO3N8dtvv6nsBLQ2t0fNNjVl5JiR0v/G/kHVEcLd7UHZyaLowiwWUqSFhuXLl8uXX34pd9xxhzz44INyyy23SIQDF+eMjAylcs+dO1eWLl0qcXFxnlpFQgghJPhFhnVTRM4fFLGYRcKjRTJSRC4dF0k9lzMPxYaAANkH7hIHfOHNsX37dpXBgHs8e8Q1iJPqvapLXN04WZm+Uqofru6VLAtvdoRwZ3tQdrIomjCLhUhRFxr279+v2g5NmzZNvvnmGylevLi0atVK9Tu+8sorlYCA1xITE+XcuXOye/du2bBhg/z111/qNXSkeP7559WPkiusXr1aOnToUOB8yJh4+eWX1b+RgYHn9hg2bJhMmDDBpfUhhBBCvF4ugUwGiAyRJURiyyNyQt66SNKpnNcxvXYnllH4OSsPr8xV7gChIFBakR46dEjdZ8GLARkCRpSoUUJaPtRSqrTIKZGAOSV8JLDNyL7wdLaFtztCuKs9KDtZFD2YxUICCY8JDXAHRosiiAUfffSRevz888/qYdTvWPvxueKKK1RAj7q88uXLu/z5FSpUkL59+9o1H5o/f776d9u2bfNMv/7666V27dp5Xr/66qtdXh9CCCHEq8CTAeUSyGTQRAaAv3gOvwZMP7pBpEYbHhw/FhnmbJ8jR5OOitlilqiwKNWiEt0jYOwI/E1sgBjy665f5b3J78l387+TzIxMw/liysdIlXuqSKuurSTUFGp9HR0xYFYJYQVZHJ7OvvB0RwhPEajrTVyHWSwkkPC4GSQC/pdeekk9du7cKWvXrpWtW7fK6dOnVb9kCBLlypWTJk2aqKC/MJ4OeurVqycff/yx4bRly5YpoaFq1arSvn37PNMfeeQR6devn1vWgxBCCPEJEBLgyYByCVuBH8/Di+VMT7lcQkH8DgTsGNWHyBAbHpunJaU3R/0dZem/S2XcpHGy8fONkpWaZThP2bJl5d4n7pWjDY9KakhqLpEBYBvREQPbj1IRT+PJjhCeJFDXm7gOs1hIIOHVrhMNGjRQD1+jZTM88MADhtkVhBBCSMB3f8DyEXxmpuaUS+h/7/A8M0UkprRIMc+2DCSug9F8jOojk0ETGQD+envU3xGPrSETh8iHkz+UtAtphvNEx0TLsBeGyXPPPSe7knfJW3+/JeeTzxuOxqPtJjpiwI/C03iqI4SnCdT1Jq7DLBYSSBS59pbJycnKMwL06dPH16tDCCGkqODt7g8QMbB8GD/Ck8HWoyHUlDO9im8DVGIfjOZjVB/lEkbeAd4c9beH2WyWzz//XF4a+ZIc2H/AcJ6QsBCpfFNl6fxQZxnefbjKvmhSrInymUAJCLIzbLM1TCEmNR2ml97A3R0hvEWgrjdxDWaxkECiyAkNX331lRIbmjVrZje7YuXKlbJ582ZJS0tTvZu7du1KfwZCCCGB1f0BmRIQMbB8fC6yKFAugUwGiAxxNXKmeyqjghQajOYjKIcng69H/W3B58N3a/jw4fLPP/8YzxQiUqtDLWnap6mciT4jyVHJ1uwLbBfMLOEzgRIQZGdAOME2QWSoHFtZTfdmSYg7O0J4k0Bdb+I8zGIhgUSRExq0son8shngjKxn1KhRcvfddyvPh9jYWI+vIyGEkCDCl90fNPFCn0mBcglPZlIQt4HRfH8a9ddAlzAYd69atcruPOggcXX/qyW+Zk5pDjqK2WZfaCaW+o4aEE582VHDXR0hvE2grjdxHmaxkEChSAkNJ06ckBUrVojJZJJevXrlmY5OE2+++abKYKhevbqcP39e1qxZI0OHDpUvv/xSdatYsmSJQ5/VsGFDw9f37dsntWrVKvS2EEIICRB83f0BYgJEDCwfxo/wZEC5BDMZ/B5/G/VHK3KYe+OeyB7F6xSX6wdcLxWvquhQ9gXEBJhZItMBIgSmQzjxF3NLQvwRZrGQQKBICQ0LFy5UYkGXLl1UNwxbevfunet5TEyM3H///dKhQwdp3LixfP311/Lnn39Kq1atvLjWhBBCAhp3d39wxVAS09nCMiDxh1H/Y8eOydixY2X27NnqPsqIevXrSc2eNSXlyhQJiwizlno4kn0BUcHXZpaEBBrMYiH+TpESGhwpmzCiYsWK0r9/f5Xt8OOPPzokNOzYscOpTAdCCCFBiju7P3jbUJL4Bb4a9Udm58SJE2XKlCnKt8oIeFm98sor8uCDD8qvx36VOdvn+EX2BSGEEN9SZISGf//9V5kVwWPhzjvvdPr9derUsZZfEEIIIV7v/uALQ8kiBDIF/Dl935uj/qmpqTJ16lR5/fXX5cKFC4bzxMfHy4gRI2TQoEESHR3tN9kXwYY5I0NS/9ks2RcvqNaG6DpAk0dCSCBQZIQGzeDxrrvukmLFirmk6mvlFIQQQohXuz/40lCyCLDy8MpcwTGC+qIYHGdlZSnj6zFjxqhyCSMgKjz77LPywgsvSKlSpZzKvvB3McffSFyxInfbyogItq0khAQMYb76IUtISJD09HS781SrVs1tn4f6wE8//dSlsgnt/ZoJZPPmzd22XoQQQooIhe3+4GtDySAXGZDufzTpqJgtZokKi1LtJNHpASaMINjFBu0+BxkKMHw0AkbaAwYMkJdfflmVlDqbfUExx3mRIWHWR5Jx9KhIdraERkeL+exZyTp1ShIuZ5lo3QcIIUSKutCwfPlyGT9+vDJUzMzMtDsfzIMgRriLtWvXyqFDh6Ry5crSsaPxzcKZM2dk0aJFqsawePHi1teTkpJkyJAh8tdffykDSWREEEIIIV7t/uBuQ0miwAg7MhkgMsSGx+ZpHwmvAUzHCH2wjryvXr1aXnzxRXWfY497771X3b9pZaS2FJSpQDHHOdJTk+Tggo8k5NA+pNJKsfLVxBRqUudl9tmzknn0qMp0iGnblmUUhBC/xWtCw/fffy/du3dXbsVxcXFyxRVX5ArovWECiQ4SoaGhhvMkJyfLk08+qX5sW7RoodR6iA+bNm1S2RdID/ziiy9cKrsghBBCCtX9wZ2GksQKgmOUSyCTQRMZAP7iOQwNMR3zBVtXhM2bN6t7np9++snuPJ06dVI+Dddcc43deQrKVKCY4xzYn38t/UhaHN4lJdMz5EypLIm4mCZlipWRuMg4MZUpI9kXL6pyitTNmyWmZUsnP4EQQoJMaEBbJLPZLG+//bYK6JGC5w1QngGBwKh9pZ7SpUvLsGHDVLbFf//9J7///rtaRwgi/fr1U/WIyIgghBBCAtZQkuQCI/AIhFEuoYkMGniOrgmYjvmChX379smoUaNUy297XH311TJhwgQlNOSHI5kKxSOKF1kxx1m0/Vnm1H6xZGZIerhIhjlTMs1ZkpWUk+kLsSEkKkp5NmTbMeokhJAiJTSg3eN1110ngwcPFm8SGRkp584VnEqK7Ar8qBJCCCFBaShJ8oA0f4zAIzhGWrpebMBztGZE1wTMF+icOnVKxo0bJ++//77d8tTatWvLq6++Kj169LCbAarhaKbCrTVvLXJijivo92d88WJiisiUqOR0iTZFSaYlS9Kz0+VsylkpEV5CLGlpEhIfLyYDM05CCClyQgPaSrrT4JEQQggpUhTWUJLkAV4CSPPHCDyCY9tg2RRiUtMxX6By6dIlefPNN2Xy5MmqTNQIeFDB5PGRRx6R8PBwt5ad4OGvYo4/dcHQ78/kK6tJ8h+pUvxihsQkZkpy8XDJkizJyE6XlFPHJdJkUt0nops29cm6EkKIXwkNSL/7+++/vfVxhBBCSPBRGENJkgcElfASQJo/RuARHGOEHcEvRIbKsZXV9EA0gkTp6MyZM1WGwtmzZw3nKVGihCobRbaps+27HS07gVDjj2KOv3XB0O9Pc7hJtrUqJ1HJmVIqIV2iUrIkLcwikZnpIpHJEl69lpTqcTeNIAkhfo3XhIaJEycqk0X8oOFHLyzMJ501CSGEkKJpKEkM0YJKfdCJEXZfBp2FAabbCxYsUBkK6Lhlr6wUflnDhw9XHlWeLDspHV3a78Qcf+yCYbs/9zbOMXZt/OdpibmYIdkZaZIWGyFR1WpJ6QceZmtLQojf47Fo/5VXXsnzWteuXVX63pdffint27eXKlWqGNYA4scKRkWEEEIIIZ4GQSVaWPpLGr0rIDhdunSpjBgxQrZt22Y4D+65+vbtK2PGjCl0OaszZSfafvQHMcdfu2AY7U+IDfvrlZSY3cckNDFZylWoJc/1fU8io2O9tl6EEOJ3QgN+xLQLty379+9XD3tQaCCEEEKIN0FQGahdD9ApCxmjv/32m915unXrpjJKGzZs6JOyE38Rc/y1pWm++7Mq9mdtub1Rf4oMhJCAwWNCw5w5czy1aEIIIYSQIg86eiGD4dtvv7W7L9q2bau6arVu3drnZSf+IOb4c0vTYCvjIYQUbTwmNCA1jxBCCCGEuLfbweHDh2X06NEyd+5cMZvNhvM0btxYXn/9dbnlllvyBNTuxF8yFYKlpWmg7U9CCLGH1xwZ16xZo9on1a1bN9/59uzZIydOnJAbbrjBW6tGCCGEEOL33Q4SEhLktddek+nTp6uuEkZUr15dxo0bJ/fff7+YTCbxBv6QqRBMLU0DaX8SQojPhQaYP/bv318++uijfOebNGmSzJ49W7kmE0IIIYQEC652O0hOTpZ33nlH3SNdunTJcNllypSRkSNHysCBA1VXiaKS3eEswdzSlBBC/Amv9pg0MoZ0ZR5CCCGEkEDClW4HmZmZMmvWLNXJ6+TJk4bLjYmJkeeff149SpQoIUUlu6Mw0AuBEEKCTGhwhOPHj0tsLNv2EEIIISR4cKbbwdXlrpbFixerDIW9e/caLi88PFwee+wxNU/58uX9OovAndkd7oJeCIQQEsBCA0yK9ODH0vY1jaysLNm9e7csX75cWrVq5cnVIoQQQkiQ4Q/Bszu6HaxcvlIef+dx2bhxo91lwX8BPgw1a9YMmCyCwmZ3eAJ6IRBCSIAKDf369cul2K9bt0497IEfmKioKHn55Zc9uVqEEEIICSL8IXgubLeDM/+dkS2LtsiSLUvsLqNLly6qk0TTpk0DMovAleyOQDJFNGdkSOo/myX74gUxlSwl0c2aSmiE/4hdhBASNEIDBANNoUZ9IX4Yu3XrZjhvRESEVKpUSTp37iwVK1b05GoRQgghJEjwl+DZ2W4H8VHxkpyZLBeOXJBt87fJ6T9P231vy5YtZeLEicpYO9CzCJzJ7sB8gULiihVy4YsvJevMGbFkZEhIRISElS0rpXrcLcVvvNHXq0cIIcElNIwZM8b6708++UQ6deqk+j4TQgghhBQWfwqenel2sP/ifjl49KCcWHJCEn5NEDEbv+fKK69U7Sy7d++eJyAP5CyCgrI70AEiLipOzRcIpTQQGRJmfSQZR4+KZGdLaHS0mM+elaxTpyThQo7YRbGBEFLU8JoZ5J133inFixf31scRQgghJMjxp+DZEZBZ8df+v2TV+6vk+I/HxZJh3GmrcuXKarAGJahhYWFBl0Vgm91hKxChzSSmYz5/L6VBuQQyGSAymGJjxVTm/9uSffasZB49qqbHtG3LMgpCSJHCa0LD9OnT7ZZNEEIIIYQEcvBcEGlpafLOu+/I2PFjJT0x3XCesJgwaXl/S/lh8g9SMtb10XxfZRG4kt2BrBMIQjhWWAeIDJVjK6vprmQgeLuUBp4MKJdAJoMmMgD8xfPsixfV9NTNmyWmZUvxF+gnQQgJGqGhSpUqYjbbyQ0khBBCCAng4Nke6KqFjlsoHT2K1HoDTBEmqd+tvhS/ubhULV9V/kv6T1rEtgioLAJn0YJ9feYBjlVhMg98UUoD40d4MqBcwkjsComKUtOzL5dQ+AP0kyCEBF3pBH5oExMTWUJBCCGEkKALnvVgHb755hsZMWKE/Pvvv4bzhISGSJ2b60jTB5pKTJkYOZZ4zGMZGJ7MInAViAkI+t3lpeCLUhp0l4DxIzwZjMQuS1qahMTHi6lUKfEH6CdBCAk6oWHs2LGyevVqueWWW+Tdd9+VZs2aeeujCSGEEBKE+GPwDNasWSMvvvii/PHHH3bnqX59dbm639VSsmpJr2VgeCKLoLDg2Lgr6PdFKQ1aWKK7BIwf4clg69EQYjKp6dEutiR1J/STIIQEpdAAf4bIyEhZt26dXHPNNaqFZbVq1SQqKirPvLhAr1ixwlurRgghhJAAxZ+C561bt8rw4cPlhx9+sDtP5aaVpcI9FaRi/YpSIrqEes2bGRjuziIo6qU0oRERqoUlukvA+BGeDKpcApkMJpOEV6mipmM+XxOofhKEkMDEa0IDshn0F/vjx4+rhxGFbeFECCGEkKKDz4LnrHSRI+vlwJ5d8vLML2TBN7+oexwjmjZtKhMmTJCwemHy8Y6PfZqB4c4sAn/CV6U0WutKdJdAoA5PBpRLIJMBIoO/tLYMRD8JQkjg4jWh4cCBA976KEIIIYQUMbwePO9aKmdWfyjjF/8tM389LpnZxgJDzZo1Zfz48XLfffdJaGioNajzhwyMYMOXpTQQE9DCEtkACNThyYByCX/IZAhUPwlCSGDjNaGhevXq3vooQgghxOXRaUk9LxIdJ1K1pUhYZIFvQ6AYjGnoxD6JGxfL5FdelDd/OiBJ6cYCQ7ly5eTll1+WAQMGSIRNsBnM5QtFuZQGooI/lxwEkp8EISTw8ZrQQAghhPgtu5aKbJonknRSJDsD/QZFYiuINO8jUu9Wu29beXhlroAGgSJHpoOXjIwMeX/GdBk3ericuZRuOE/xyFB54fYG8uyHqyW2VOkiV77gD1DICXw/CUJI4EOhgRBCSNEGIsO6KSLnD4pYzCLh0SIZKSKXjouknsuZx0BsgMgwZ/scOZp0VLXTg9M9TOhQH47UbcA0+ODAbDbLwoULZdSoUXZLQSPCQmVQh2oyopVFylYpLXLhX5FSbby+ruTy8aCQE9B+EoSQwMdjQgPqEPHYuXOn1K1bV0wmk8PvRRpXVlaWp1aNEEII+X+5BDIZIDJElhCJLY8fIRQsiySdynkd02t3ylVGgewFZDJAZIgNj81jOof6cExHejzT4QMXHM8ff/xRdZLYsmWL4Tw4XfpcV1nGdq8rNcoUEzl/KCcrJuWySEUchmVI3iEQ/CQIIYGPx4QGtK7ETVd4eLh6XrVqVXaTIIQQ4l/AkwHlEshk0EQGgL94Dr8GTD+6QaTG/0enUVuPcglkMmgiQ87bQtRzmNBhOuZjenxg8ueff8qLL74ov/76q915bmtSTl67+0ppXDWnTaUSqDJTRGJKixSL997KBgEsQ/Iu/u4nQQgJfDwmNBw8eDDf54QQQojPgZCA0WeUS9i2Vsbz8GKGo9Mw8MPoK8oljNrEweke0zEfCazR8F27dsmIESNkyZIldudpXae0TLwpWtrUKy8SWzznRS0LJtSU4+9Rhf4LjsIyJEIICT7o0UAIIaTogu4SCEYzU3MCRb1okM/oNAJZBLHwZDBqE4d2enC6x3wkMEbDjx49KmPGjJE5c+YoTwYjGjZsKK+99prcXidUQn5/N6e0BmIVBCmcKxAZ4mrkmIg60LGEsAyJEEKCFQoNhBBCii5oYYnRZxg/YjTa1qPBzug0RssRyML4EZ4Mth4NphCTmo75iH+Php87d04mTJggU6dOlbS0NMN5UP75yiuvSJ8+ff7vOYXzRN+pBIKUA51KSG5YhkQIIcGJ14WGzZs3y4wZM2Tt2rVy/Phx9VqlSpWkbdu2MnDgQGnevLm3V4kQQkhRBaPOCAzRXcKJ0WmMmGO0HIEsjB/hyYByCWQyQGSoHFtZTacRpGcpjClnSkqKvPvuuzJx4kS5cCFHkLAlPj5eXnrpJRk0aJBERUXlnggxASah8O9AaQ2yXiBIMZPBKViGRAghwYlXhQaMBowbN06ys7Nzvb579271mD17towcOVKlLhJCCCFeQRt9dnJ0Whsl16fso1zC2ZT9oO7oAbNNiDcoUUH2iJuDcFdGw9HVCuURuNfQBjxsKVasmDz33HMyZMgQKVkyn/IXbI/OJJQ4D8uQCCEkOPGa0DBv3jz1ox4bGytPPPGE9OrVS2rUqKGmHTp0SPWnnj59uhIiatWqpdITCSGEEK/g4ug0xASMlrvDhDCo2LU0t3CD/fG/9u4DPKo66+P4SU8glNA7CCjNAojYEBUb9o5rRbG79or0pmBb5V17w16wsBYsqCiiWEBABUUpSu8QWhLS5n1+/+xkU2ZCymQy5fvZJzvO3JvMzZ2bYe65p1RDWUFFroYry+Gdd95xGQp//vmnz58XHx9vV111lQ0fPtyaNWsWsO2Ef5QhAUBkClqg4ZFHHnGjLr/88ks78MADiy3bb7/93NfZZ59thx12mFuXQAMAIKi8V6e9V+L//LRcV+IVVGCEZYkgw7cTC0pRNDZUEz2yMwr6YKhERQIUbCjv1fA/Z/9pQ84dYrNnz/b7s/Y+am8bPnq4XdyXCx3BRBkSAESmoAUafv/9dzv66KNLBRmK0rJ+/fqVObMaAIBwvxIfsRSk0f5TkCGpbunmmnpcy5U9EoAyij1dDc/4O8NmvTXL3vzpTb8/o+EBDa3NeW2sfof6Nm3nNGu5oiVlL0FGGRIARJ6gBRrq1q1raWlpe1xPtZBaFwCASL0SH7GUCaIgjfafN8ggutV99WvQcpWoBKC3gb+r4VtXb7Xlk5fbhlkb/H5vvQ717ODLD7aWPVuWq3lkdVJ5R7SX34RrGVJ+drZlzptvedvSLa5efUvp0d1iE0N7mwEgogIN/fv3t2nTpllmZqalpKT4XEfLvv76azvhhBOCtVkAAAT9SnzEUiBBmSAK0pTomeDua6KHlqsPRoBOuoteDV+xZoX9+vqv9ve0v82T5/G5fmqLVGs9oLX1Pr63xcbG7rF5ZDDGcxZtKKrfNVobioZbGdKOL76w9LffsdyNG82TnW0xiYkW37ix1T/nbKtzzDE1vXkAEB2BBs2o/uqrr+yss85ys6o7duxYbPnSpUvthhtusMTERDdqCgCASL0SH2nTHwrp5ysokJNZEKQpGmzQfY0N1UQPNdsM4En3gfUOtM+nf27THp5mWZlZPtdp3ry5Dbh+gP3d6W/LtMzCIIO/5pHBoN930oJJbjynJmeoqaX6TagURFkaEm3BhnAKMmx+9jnLXrXKLC/PYlNSLH/TJstdv942/3dcKsEGANGs2gINgwYNKvXYAQccYB988IF16dLFunfvbm3bti2cOjF//nzLz8+3U045xXWEfu6556pr0wAACPiV+JAU7J4TCmLo56vcRJkgJTNDYuMKlmuiRwBOurOysuyJJ56we+65xzZv3uy3JHPw4MF244032sLtC+2hOQ/Z1l1by2weqUyK6qaAhoIq+n1TE1JL9ZeoqTIOlK9cQpkMCjLEpaZaXKP/vXZ5mzZZzqpVbnntI46gjAJA1Kq2QMMLL7zgd1leXp799NNP7qskBSL0Zk2gAQAQNFW8Eh+SaqLnhDIlFMTQz9fzKoCjII32n4IMae0KlpfIqKjoSbc+R7zyyis2YsQIW7Fihc9NSUpKcpmSd999tzVoUPC6HZBUdvPIuJg4t1zlGtVN5RnK3FBQxbsdNV3GgfJRTwaVSyiTwRtkEN3qft62bW555vz5Vrt3b3YrgKhUbYEGjbEEACAsVOFKfEiqyZ4T3uBF0UwKBWnKyKQo70n3/A3zbf2c9TZkyBBbsGCBz6dXScSll15qo0aNstatW4fsKEWVZyjAosyNopkVNVXGgfJT40f1ZFC5hK/XLiY52S3P+28JBQBEo2oLNBx55JHV9aMBAOFcxx+KKnklPmTVdM8JBRMUxNDPV7mJMkEUpPGz/8pz0r12wVobNHqQLZyz0O/TnnHGGa6MomvXriE/SlHlGQpoqDykpss4UDGaLqHGj+rJ4Ou182RlWUyDBhZXvz67FkDUClozSABAiAp2HX+oqsSV+JAVCj0nFFQoZxCjrJPuLX9tsTnPzrEtP/nf1r59+7qm04ceemjYjFLU84VKGQcqRiMsNV1CjR/Vk6Fkj4aYuDi3PKV7d3YtgKhFoAEAollN1PGHsgpeiS+vio5sjLaeE75Oundt2GVzX55rS79YauZ7UqXtv//+du+999pJJ51UKhMi1EcphlIZByomNjHRjbDUdAk1flRPBlcuoUyGuDhLaNXKLdd6ABCtCDQAQLSqyTr+UFaBK/HlUdmRjdHUc6LoSfdfa/6yeVPm2Zppa8yT6zvC0K5dOxs7dqxdcMEFpcZUhpMyyzjan2L98hLMfns/OsuZQpx3dKWmS6jxo3oyqFxCmQwKMjDaEkC0I9AAANGqpuv4o0BlRjZGa8+J3g162yvTX7HZT8y27Ixsn+s0btzYhg0bZldffbWbKhEJfJZxbFljid9NopwpxCmYoBGWmi6hxo/qyaByCTIZAIBAAwBEr1Co449gFR3ZGK09J3JycuyZZ56xMWPG2Pr1632uk5qaarfddpv7qlOnjkWaYmUcKmf67jHKmcKEggqMsASA0shoAIBonQARZnX84aa8Ixu1XrX1CqimnhOBkJ+fb5MnT3YZCkuXLvW5TnxCvF119VU2cvhIa9KkiUU8ypkAABGCQAMAROsEiDCr4w835RnZqOVaL5x6TlSVMjqmTZtmd999t82bN8/3SjFmrfu2tv0v2t9i94q1BVkLrJ8FZ+xkjaKcCQAQIYIWaOjXr5/179/f7rzzzjLXe/DBB+2jjz6y6dOnB2vTACA6J0CEYR1/OClrZKPua7qAGv9pvWgxe/ZsGzx4cJn/xjfo3sDand/OGnVoZDtyd9jCzQurt59FKKGcCQAQIYIWaPjqq69cl+g9+eOPP2zGjBlB2SYAsGhPmQ6TOv5w5GtkY9EeDRphqOVaL9Lp3/ahQ4faO++843edpp2bWvPzmlurA1oFv59FqKCcCQAQIUKudCIrK8vi40NuswAgclOmQ7iOP5wVHdmoE2X1ZFC5hDIZFGRomdrSLY/kE+fVq1fb6NGj7fnnn7e8vDyf63Tu3Nkuu+My+6nxT7YuY13N9bMIBVUoZ1IZTrHJFY0PiOhjCwAQ2kLqjH779u02a9Ysa968eU1vCgBEV8p0iNXxRwpvqr+uxutEWSeDKpdQJoOCDJFaCpCenm733XefTZw40TIzM32u07JlSzdp4pJLLrGvVn9l38//vub7WQSZz+BAJcqZNEa16DGmAEOkH2MAgCgONLRv377Y/bffftuVUPiSm5vrxlrp9vrrr6/OzQKA0EPKdMTSiZ5S/qPharOCCo8++qiNHz/etm7d6nOdtLQ01whS/9anpKSEbD+L6s4QKDM4cPhN5S5n0s+ZtGCSG6OqCScK1mg/rs9Y7x7T76DjL1KPOQBAFAYa/v7778L/1oeGnTt3ui9fEhISrEWLFnbaaae5DygAEFWYABHRdIIXySn/ukjw4osv2qhRo2zVqlU+11FQ4eabb3ZNoevXrx/S/SyqO0PAX3BAv79rfLnvZdZvwIt7LGfStmk79XNSE1IL99uWzC22ZtcaS89Kt7U719p3a76zprWakuEAAIiMQINmZHvFxsbapZde6uo0AQAl342ZAIHwo0DAf/7zHxsyZIgtWrTI5zpxcXF2xRVX2IgRI9wFhVDvZ7HHIEAVp1/4Cw74bHy5h3ImZSsoGKLt9P6crVlbXTZDTl6O5XnyLDs/2z2XfnbUTO8AAERPj4ZJkyZZx44dg/V0ABB+mACBMKIJURpV+f333/td59xzz7Vx48bZPvvsExb9LCoUBKhk0MNXcKCyjS9V1qFt9va20M/Udu7O223xsfEW64m1uNg4a5LSxAUdomZ6BxBm8rOzLXPefMvblm5x9epbSo/uFpvI3yjCW9ACDQMHDgzWUwFA+GICBELc/PnzXY+FTz75xO86xxxzjE2YMMF69eoVVv0sAhkEKG9woCqNL0v2ttiVvct9r8c8Fh8Tb1meLIvX/+LiLS0hLXqmdwBhZMcXX1j62+9Y7saN5snOtpjERItv3Njqn3O21TnmmJrePCC8pk7s2LHDli5d6m71D6Mvffv2Dfp2AUBIYAIEQtCyZcts+PDh9tprr/ldp2fPni7AcNxxx4VlP4tABgH8CWTjy5K9LVRmoiBDjMVYjifH3eq5lJ0RydM7gHAOMmx+9jnLVm+bvDyLTUmx/E2bLHf9etucXlDqRLAB4SqogYYFCxa4RlCaPOEvwODlb942AAAIng0bNtjYsWPtqaeespycHJ/rdOjQwe655x5XKqGeTOEqGNMvAtn4smRvi6zcrMLeDCqdSIpLcj8/Nia2xqZ3APBfLqFMBgUZ4lJTLa7R/94L8jZtspxVq9zy2kccQRkFwlLQAg2LFy+2Pn362Pbt2+3www+3tWvX2l9//WX/+Mc/3FWSuXPnuq7VmjpRshs1AAAILv17/dBDD7mvXbt2+VynadOmNnLkSNfsUdOjwl0wpl8EuvFl0d4W2u7l25cX9mjQpAkFFmpqegcA/9STQeUSymTwBhlEt7qft22bW545f77V7t2bXYmwE7RAg5pBqVRCTSHVr+Gyyy5zgYZXX33VLV+yZIldfvnl9ttvv5XZWAoAgKDK3W228kezzK1mKWkFo0hLjBmMJLt377Ynn3zS/bu9adMmn+vUqVPHjalUlmJqaqpFimBNvwh048uivS2+Wf2Nfb3ya/c7bN291TJzM2tkegeAsqnxo3oyqFzCV6lWTHKyW5733xIKINwELdAwffp069Kli9+mkJpI8d5771n79u1dDeijjz4arE0DAMC3RVPN5r5stnOdWV62mU7QUpuZ9bz4f1NCIoRKFtV/QWMo//77b5/rJCYm2vXXX++aQTZq1MgiUbCmXwS68aW3t4W+9HNqcnoHgD3TdAk1flRPBl+lWp6sLItp0MDiyPRGmIoPZo3noYceWnjfm2KZlZVlycnJ7r9VMnHUUUfZhx9+SKABCDdRdtUXURJk+Hai2da/zTz5ZgkpZtkZZtvXmGVuKVgnAoIN+kD70UcfueDBr7/+6nMdfQC+5JJLbPTo0da2bVuLdMGaflFdjS9renoHgD3TCEtNl1DjR/VkKNmjISYuzi1P6d6d3YmwFLRAQ4MGDVw6ZtH7snz5cuvUqVOpoASAMBJFV30RRYEzHdMKMiTVNUttqrNtnZWb7Vxf8LiWdzw2rANq3333nQ0ePNi+/vprv+uod5IaPe67774WTWpy+kUghPv2A5EuNjHRjbDUdAk1flRPBlcuoUyGuDhLaNXKLdd6QDgKWqBhr732ckEFr+7du7uI3ZtvvunSNEW1oJpI0aZNm2BtFoCqipKrvogyys5R4EzHtDfIILrVfWXuaPmq2Wbt+li4UT+kIUOGuJJFf9TAWaMq1cAZABB43tGVmi6hxo/qyaByCWUyKMjAaEuEs6AFGo4//njXWErBBqVdnnrqqa6+c8yYMe4DT8uWLe3dd9+1bdu2ufpPAGEgSq76IgopkKDsHAXOSjTpcvcTahUsz/hvMC1MrFy50k2JePHFFy0/P9/nOspcuPfee+2UU04p1aAMABBYCiZohKWmS6jxo3oyqFyCTAaEu6AFGi6++GJXOrF+/XoXaKhdu7a98cYbNmDAAJs8eXLhescdd5wNHTo0WJsFoCoi/Kovorg/h34PlQDlZBYEzoqecOt+ToZZ7YZmtQrKAEPd5s2bbfz48a7/UdEyxqKUTTh27Fi78MILLS4uLujbCADRSkEFRlgi0gQt0NChQwf3Iaeofv36uQyHmTNn2tatW22fffaxAw88MFibBKCqIvSqLyogUvtzKFii30MlQMrOKZmtExtXsLxVaNfA79q1yyZOnGj333+/yxj0pWHDhjZs2DC79tprLSkpzANEAAAgugIN/iizoX///jW9GQAqI8Ku+qKCIrk/hzIyFCzR76HfT0E1Bc50TCvIkNauYHmIZm7k5OTYc88956ZErFu3zu+/v7feeqvdfvvtVrdu3aBvIwAAiFyxFqHUVFK1pXv6Uo+Ikl544QXr3bu3paamuukYJ510ks2aNatGfg8gLK76xsQWXOVVcEHC7KovAtCfo1Ens/ptzRp3Lrjv7c+h9cKVgiSH32TWoqdZ/dZmyXUKbnVfj4dgEEV9F1SO2K1bN5eh4CvIEB8fb//85z9t6dKl7t9AggwAalp+drbt+uFH2z5tmrvVfQDhrcYzGqpLs2bNbODAgT6X5eXl2SuvvOL++4gjjii27Oabb3ZppikpKa6BZVZWln322Wc2bdo0e/vtt+2MM84IyvYDYSHMr/qiCqKlP4eCCWpmqt9DJUDKzlHgLASP6c8//9yNqvzpp5/8rnP++ee7PgwqZ0RkyM7Ltp83/mzbdm+zekn17IDGB7jRlkC42PHFF8WnLiQmMnUBiAARG2jo3Lmzy0zw5eOPP3aBhtatW9tRRx1V7EOaggyqV9Vs8b333ts9rv/Wepdddpm7rV+/ftB+DyDkea/qFq3TV7lEJNTpw79o6s+hoEIIB0sUWFCAQf+G+XPCCSe4Pkk9evQI6rahek1fMd2mLJ5iGzM3uoCDAgyNUxrbmXufaf3a9GP3IyyCDJuffc6yV63SlUCLTUmx/E2bLHf9etucnu7WYcQjEJ4iNtBQFm82gzprFx3d9a9//cvdqimWN8gghx56qF1zzTX2f//3f67m9bbbbquBrQZCWBhd9UWA0J+jxi1evNj9e1V0clNJKgOcMGGCHX300UHdNgQnyDBpwSRbtXOV5XvyLTk+2TZnbbYNGRssfXfBCRrBBoQylUcok0FBhrjUVItr1Mh9Lvd4PJa3aZPlrFrllmv0I6MegfATsT0ayurA/d577xWO3PTKzMy06dOnu/8+55xzSn2f97EPPvggaNsKhOVV366nFdwSZIhs9OeoMWvXrnX9F7p27eo3yKApTir3+/777wkyRCBlLyiTQUGG1IRUa1+vvbVMbeluayfUttU7V7vlWg8IVZnz5rtyCWUyeIMMolvd9+TlueWZ8+fX9KYCqISoy2h49913XbBB6aP6kOb1xx9/uNnijRs3tlatWpX6vp49e7rbX375JajbCwAhif4cQafxlBpT+cgjj1hGRobPdVq0aGGjRo1ypX5q+ojIpJ4MKpdQJkOjlOInaLq/PXu7W671DmpGM16Eprxt6a4ng8olimYYi2vanpzsluf9t4QCQHiJj9ayiaLZDLJixQp36yvI4B0Dpt4MW7dutR07dlidOnWCsLUAEMLozxEUakr8+OOP2z333GNbtvjueaF/n9Sn4YYbbrBatWpV27bQeDA0qPGjXguVS/g6QUuOS3bLtR4QquLq1XeNH9WTQeUSRY9l3fdkZVlMgwYWR280ICwFLdCgDtdXXnmlXXrppW4iRE2lm37xxRcWFxfnOm8XtXPnTndb1gc0BRvS09PLFWjQaDFfNE6Mbt8AIgb9OaqNJiS9/PLLNmLECFu5cqXPdZKTk+3GG290QYa0tLTq2xgaD4YUTZdQ40f1ZPB1gpaVl2VpyWluPSBUpfTo7qZLqPGjejKU7NEQExfnlqd0717TmwoglAMNf//9tw0dOtR9YDrllFPsqquucl2wS0biq9Prr7/uPrj179+/xoIdABBxqnMqQ+7uglGamnKhBpTqDRHh/T/0Ifv999+3IUOG2G+//eZzndjYWBs0aJCNHDnSbyZeINF4MLRohKWmS6jx46bMTYXlEzp2dD8uJs4t13pAqFKDx/rnnO2mS6jxY962bQXlEspkiIuzhFat3HIaQQLhKWiBhuXLl7uJDc8//7z95z//cQ0Z9eHo8ssvdx+WgvFByV/ZhKSmprpbf3Wvot4OUp6yiYULF1Yo0wEAUMKiqcXHpsYlRvzY1JkzZ7rshFmzZvld56yzznJlFBrjXBONB0ue1HobD/Zp2cddZUf1037WCEtNl9D+V08GlUsok0FBBjWG1HJeD4Q67+hKTZdQ40f1ZFC5hDIZFGRgtCUQvmI8+qQQRHq6Tz/91J555hn78MMPLScnx5UyKMtApRXKdtCVmkD7/fffXfNHBRTWr19fqkRi/vz5rkGkmkFu2LDBZ5BB36vUVH81suXhDTT4C0QAAP4bZPh2otnWv808+WYJKWbZGWaxcWZp7cwOvymigg2//vqr3X333TZ16lS/6xx11FFuVOXBBx8c1G2bvW62PTTnIVu7a62balAyTX/ZtmXWvHZzu63XbTQeDDJlmijIo8aPCggpsKBMBgUZGG2JcBt1qekSavyongwqlyCTAQjv882gN4PUBxQFFfSlE/oXXnjBnn32Wffh6qOPPnIlDcpwUKZDu3btAva8qnP1Xgny1YehU6dOlpSUZBs3brTVq1dby5Ytiy2fO3euu91///0Dtk0AAD/lEspkUJAhqa5ZalP946GzWrOd6wse1/KOx4Z9GYXKClVSqIw7f3H/7t272/jx44NebuhF48HQpWCCMkk0XUKvk3oyqFyCTAaEGwUVavfuXdObASCAAp86UAFNmjSxO++802Ub3HLLLe5Dlho2KiW0Y8eOdvrpp7tMg6rSz33ttdf8lk1ISkqK9evXz/33W2+9VWq55pHLqaeeWuXtAQCUQT0ZVC6hTAZvkEF0q/v5eQXLV80O292ooPbNN9/sgtwKhPsKMrRv39792/XTTz+54HxNBBmKNh7Mys0qtZ3exoNaTuPBmqF9rxGWx7Y91t0SZAAAWLQHGtS3QVdylLmgueBy2GGH2bBhw1yg4YMPPrDevXvblClTqlzzqudSloI3mODLrbfe6m7HjRtnixcvLnz8u+++s6eeesqND1OmBQCgGqnxo3oyqFyi5Mm17ifUKlieUfkytpqiCUdjxoxx04cmTpxo2dnZPoPwjz76qAvCa0JSdZQTVqbxoGr/1ZPBG2yg8SAAAAiZ0onc3FzXCFI9Gj7//HPLz8+3unXr2rXXXmvXXHON7bvvvm49fRBTZsEll1zighFnnnlmlZtAXnDBBWV+YDv22GPtpptuch/+lKp63HHHuQ+Bn332mftANWnSJBdsAABUI02XUFPBnMyCcomiwQbdz8kwq93QrFaDsHkZ9G/J008/bWPHjvXZB8jbaPiOO+5wGX7eBsWhINwaD6pXAaUEAABESaBBGQLqxfDiiy+6lFGduPfs2dMFFxQA8NU34dxzz7XJkye7MV+VtXv37sKyh4suumiP6yuzQkEGXU1SgCExMdEFIIYPH+6yLQAA1UwjLDVdYvuagp4MJXs0qCGklrc6KORfCgXT33jjDfdvyLJly3yuo39nrrvuOjfOUg2JQ5G3sWDRxoNpyWkh13iQ5ogAAERZoEF1qKovVS+Eyy67zAUYevXqtcfvq1evnptMUVlq8FjRKRGXXnqp+wIA1AA1eNQIy8wtBY0fVUqhcomcIlMntDyEG0F6JyxpkoS/XkP6N1F9g0aPHh3Q5sfR2nhQQYZJCya5MZz5nnxLjk+2zVmbbUPGBpeNIaESENlj9/158y1vW7rF1atvKT3ovg8ACD9BG2+pkggFF1QKoVKJaBUq40YAICxGXGq6hBo/qieDTmiVyaAgQwiPtvzhhx9s8ODB9tVXX/ldR6Oc7733Xttvv/2Cum2RShkWt311my3YvMBSE1KtUUojF8jx9pHYlbPLujXsZg8d9VDIBEZ82fHFF5b+9juWq8zP7GyLSUy0+MaNrf45Z1udY46p6c0DAISBbiFyvhm0jIYFCxYE66kAAJFAwQSNsNR0CTV+VE8GlUuEaCbDokWLbOjQofbuu+/6XUcleBMmTLAjjjgiqNsW6ZRloZIOZTJ4gwyiW91XXwkt13qazBCqQYbNzz5n2atWmeXlWWxKiuVv2mS569fb5vSCjIxABBvoYQEAiKhAg+pU1W1bpRMJCQk+11GJRGZmpmuCVdNdtgEAIUBBhXZ9LJStXr3aRo0a5RoG5+Xl+Vyna9euNn78eDciuabGVEYylXLoBFrlEiX3r+6reaWWa71QLZdQJoOCDHGpqRbX6H8ZGXmbNlnOqlVuee0jjrDYxMpnZNDDAgAQLEE7m3/44YctLS3NZsyY4XcdLdM6//73v4O1WQAAVMrWrVvtrrvucuOY1ezYV5ChdevWLgDxyy+/2GmnnUaQoZqoX4RKIrJyswrHb3rpviZkaLnWC0XqyaByCWUyeIMMolvd9+TlueWZfvp9VKSHhcpL1u5aa7tyd7nbhZsXuse1HACAsAs0TJkyxX3g0gQHf7SsVatW9s477wRrswAAqBBl3t13333Wvn17u//++y0rK6vUOg0aNLCHHnrI/vzzT9dcOC4uLvB7OXe32V8zzX57v+BW96OUmlJqAobGbaongzfY4O3RoMe1XOuFIjV+VE8GlUv4ysiISU52y/P+W0JRUcrm0MQQNcpUD4v29dq7saS6rZ1Q240t1XKtBwBA2I23LM+UCTWNnDt3blC2CQCA8srNzXXZCSqTWLNmjc91NKr5lltusTvuuMNNTao2Ydoos7ooW0FjNjVdQifN6smgcgllMijIoJNqLQ/VRpCaLqHGj+rJoOBI0WCD7nuysiymQQOLq18/antYAADCS9ACDdu2bSvXhy6to3RUAABCgU701OBRjR7/+OMPn+soY+Gqq66y4cOHW/Pmzat3gxRk+HZiwehPT75ZQopZdobZ9jUFI0ElCoMN3tGVujKvk2ZdnU9LTnOZDAoyhPJoS42w1HQJNX5UT4aSPRpi4uLc8pTu3aOyhwUAIPwELdCgD16qUd0TrdOkSZOgbBMAAGX58ssv3ajKH3/80e86AwYMsHHjxtnee+9d/TtT5RHKZFCQIamuWWpTnSkqGmK2c33B41quaR0hOp2jOimY0KdlH3dlXifN6smgcolQzWTwUoNHjbDUdAk1fszbtq2gXEKZDHFxltCqlVte2UaQ3h4Wm7M2+8yYUOaHgjKh2sMCABB+gtajoV+/fvb777/bm2++6XedyZMn22+//WZHH310sDYLAIBS5s2bZ/3793f/dvkLMhx33HE2Z84c9+9aUIIMsvLHgnIJZTJ4gwyiW93PzytYrpGgUUon1Er/P7btse421IMMXhpd2fCKyy15330toUULN31Ct7qvx6sy2jLce1gAAMJP0DIaVK/62muv2SWXXGIzZ850KaYdOnRwy5YuXWpPP/20PfPMM5aYmOjWBQBU0xVxnaxmbjVLSTNr3Tsqr3z7o3+PVP7w+uuv+13nwAMPtAkTJpTZ3Lja6HVTTwaVS5Qck6n7CbUKlmf8t4QCYUXBBI2w1HQJNX5UTwaVS1RlpGUk9LAAAISfoAUaOnfubC+99JINHDjQnnjiCfdVlKLqycnJrtGWGkICAAKMBoJ+rV+/3saOHWtPPfWUa/roi7IW7rnnHjv77LMtNjZoCYHFKTikk8GczIJyiaLBBt3PyTCr3dCsVoOa2T5UmYIKtXv3DvieDOceFgCA8BO0QIOce+651qNHD/vXv/5lX3zxha1cudI97h17efPNNwcv/RQAogkNBH3avn27Pfjgg+7fpV27dvlcp1mzZm7SxKBBgywhIcFqlDJQNF1CjR/Vk6Fkj4bYuILlrSJkcgAZOAEVrj0sAADhJ6iBBunYsaM9/vjjwX5aAIheNBAsZffu3S6zThkKmzZt8rnb6tata3fddZfddNNNVrt2bQsJKnPRCEtNl1DjR5VSqFxCmQwKMqS1K1geCeUwZOBUaw8LAAAiKtAAAAiyPTUQ1Mmqt4Fguz4R/fLk5eXZqy+9YCOGD7Plq9f5XCcpKcluuOEGN22iYcOGFnK8oys1XUKvm3oyqFxCmQwKMkTCaEsycAAACGs1EmhQ/evmzZvdFSV/2rRpE9RtAoCIRQNB1wdo6tSpdvet19uCxct97ib1Xbj00ktdmYRK+kKaggkaYangkBo/qieDyiUiIZOBDBwAAMJeUAMNn3/+uZs1/v3331tOTo7f9TTf2V8zLgBABUV5A8FZs2a5EohvvvnG7zqnd29s944dbV1PudbChoIKkZiBQgZOtVDzR3ozAAAiLtDw4Ycf2plnnunSVtPS0myvvfayOnXqBOvpASB6RVsDwf9auHChDRkyxN5//32/6xyxT5pNOKmJHdYy32z7DLPcQZGRFRDOyMAJuOkrphebNqE+DUybAABERKBh9OjRlp+fbw8//LBdf/31FhcXF6ynBhDhdufm2dzl6bYtM9vqpSRaz7b1LSme95iobCBoZitWrLCRI0e6kcr6d8eX/VrVsfHndLKT9m9iLr9j46Ko6VMR8qI8A6c6ggyTFkyyVTtXWb4n35Ljk21z1mbbkLHB0nenu3UYbQkACNtAg64sHXrooa57NwAEyrSF62zynJW2Ycduy87Nt8T4WGtSJ8kG9Gptx3drxo6OogaC6v1z77332mOPPea3B1DbtAQbe05Xu+CQlhYXW+QEVoEX7RP1O0DNitIMnOqg7AVlMijIkJqQao1SGrnyVPUs2ZS5yVbvXO2Wa+QlIy4BAGEZaEhNTaXBI4CABxme+nqZrdySYXn5HktJjLPMHXm2bluWbc0o6ANDsCHyGwju2rXLHnnkEbv//vtt+/btPtdp1KCeDTu2kV3T3WNJzVpylTyURVkGTnVSTwaVSyiTwRtkEN3q/vbs7W651mPkJQAgLAMNxx57rM2ZMydYTwcgCsollMmgIENqUrw1rpNYeKVu445s97iWH9mpMWUUEdpAUE2Fn332WRszZoytW+d7VGXt2rXttttus9tuut7qfnKD2Zq5XCUPB1GQgRMM23Zvc1kNKpfwBhm8dD85Ltkt13oAAIRloOG+++6zgw46yHX+vueeeyw+vkYmawKIEOrJoHIJZTJ4gwyiW93flpnjls9bkW6HtG9Y05uLAFLfhbfeesuGDRtmS5Ys8blOQkKCXXPNNTZ06FBr2rRpwYNcJQ8vEZqBE0z1kuq5kgj1ZFAQtmiwQfez8rIsLTnNrQcAQCAF7Wx/0qRJduKJJ9qDDz5o77zzjh111FHWqlUrN7e8JP1DOHz48GBtGoAwpMaP6smgcgmfV+oSYt3y9IzsGttGBN5nn31mgwcPtrlz5/pcrtf+ggsucFkO7du3L76Qq+ThJ4IycGrCAY0PcNMl1PhRPRlK9miIi4lzy7UeAABhGWgYNWpU4T9uy5Ytc1/+EGgAsCeaLqHGj+rJ4PNKXU6+NUyNtfq1EtmZEUCldwowfPHFF37XUTBbzSC7d+/u/wdxlRxRRNkMZ+59ppsuocaP6smgcgllMijI0DK1pVtOI0gAQFhnNABAoGiEpaZLqPGjejKU7NGgiQJa3qNNfXZ6GPvzzz9diYRKJfw5+OCDXXnekUceWb4fylVyRBHv6EpNl1DjR/VkULmEMhkUZGC0JQAgrAMNAwcODNZTAYgCSfFxboSlpkuo8aN6MqhcQpkMCjK0blDLLdd6VWk4qV4QKtNQBoWCG1X5eSi/NWvWuPIHNXvMy8vzuU7nzp1dBsMZZ5xRqnwGwP8omKARlpouocaP6smgcgkyGQAA1YWOjADClnd0paZLqPGjejKoXEKZDAoyVGW0pUZnFv25KtMIxM9F2dLT092YSo2rzMzM9LlOy5YtbfTo0S6ATWNhoHwUVGCEJQAgWAg0AAhrOunXCEtNl1DjR/VkULlEVTIPFGR46utlLlNCUy3UcFK9IFSmoQwK7/OGrdzdZit/NMvcapaSZta6d4138ldQ4bHHHnMZClu3bvW5Tlpamt199912/fXXW0pKStC3EQAAACEYaFDt9KuvvmrvvfeeLV682Hbs2OEeK0kpsEuXLg3mpgEIYwoqBGqEpcollMmgIENqUnyp3g96XMsV3AjLMopFU83mvmy2c51ZXrZZXKJZarOC0Y/eqQxBlJubay+99JKNHDnSVq1a5XOdlOQku+nmW9x45Pr16bkBAAAQ6oIWaMjOzraTTz7Zpk+f7jO4IN4P8wBQU9STQeUSymTwBhm870+6r14QWq4MikAFN4IaZPh2otnWv808+WYJKWbZGWbb15hlbilYJ0jBBr3X/+c//7GhQ4fa77//7nOduFizyw+qayPPPcBanNzHjCADAABAWIgN1hM99NBDbizZKaec4rIZLr74YvfBfffu3e5DpsZf1q5d2+644w7Lz88P1mYBQDFq/KieDCqXKNlgUPfVcFLLVaYRduUSymRQkCGprlmjTmb125o17lxwX49rudarZjNmzLDDDjvMzjrrLL9BhnN6NbOFY/vaUxfubS0864K2bQAAAAijjIY333zTGjRoYK+99poLKMTGFsQ4EhISrFOnTjZixAg7+uij3ZfuDxo0KFibBgCFNF1CjR/Vk0FX3YsGG3RfUy3UcFK9IMKKejKoXEKZDKlNFTUpeFy3uq9+DVq+arZZuz7Vsgk///yz67Hw8ccf+12nX5eGNuGcznZQ+/+WSHhSg7JtAAAACMOMhiVLlljv3r1dkME98X8DDUXHlh1xxBF2+OGH2+OPPx6szQKqher8v1u62T5ZsNbd6j7Cg0ZYarqERmSqJ4O3nMvbo0GPa7kaToYVnayrJ4PKJUqOgtT9hFoFyzP+W0IRQH/99ZdddNFF1qNHD79Bhh6ta9unV7e1z+84+H9BhiBsG6pIWSZ/zTT77f2CW7JOAABAMDMa4uLirF69eoX3vQGHjRs3WrNmzYqNLfvggw94cRC2GIsYHAreqJ+CSh2UhaAAQSCaM+pnaISlpkuo8aN6MqhcQpkMCjK0blDLLQ+7RpCaLqHGjzmZipoUDzbofk6GWe2GZrUaBOwpN2zYYOPGjbMnn3zScnIKpnWU1KFDBxt340U2IPFLi92xuvQK1bRtiLzGohWRnZdtP2/82bbt3mb1kurZAY0PcOMfAQBAmAUaFEAo2lG8Y8eO7vb777+3M844o/DxX375xVJTU4O1WUBARfxYxCgJ5nh/RtHnULlEIJ8j6DTCUieBavy4c/3/yid0Iq/7sXEFy1sdVOWn0kShf/3rX/bggw/azp07fa7TtGlTGz58uF155ZWWGOsxm7zIbOfaat82RF5j0YqavmK6TVk8xTZmbnQBBwUYGqc0tjP3PtP6telX05sHAEBECFqg4ZBDDrEpU6a45o9JSUl20kkn2S233GI333yzJScnu0DE008/7RqDnXrqqcHaLCBgIn4sYpQFc/Qz9FppuoQaP6ong8olgv3aBezKa3xSwZVmnQTq5FClFCpJULaATuTT2hUs13qV3dbsbHvqqads7NixLlvNlzp16tidd97p3vuLBZWredtQjY1FSwaGvI1FOx4bcq+ZggyTFkyyVTtXWb4n35Ljk21z1mbbkLHB0nenu3UINgAAEEaBhrPPPtvV5k6bNs0FEpTRoA+aDz/8sBt7KTohU0nF/fffH6zNAgImosciRmkwRz+jJl+rgF959V5hLprurpKEKqa7a1LQ66+/7jIU1I/Bl8TERPvnP/9pQ4YMsUaNGgVt2xCZjUUrQ39D+ntSkCE1IdUapTQqfP/YlLnJVu9c7Zb3admHMgoAAMIl0KBgwtq1a0uNvDzooIPcLPWtW7faPvvsYzfeeKPtvffewdosIGAidixiCImmYE61XXnVCbuuNOskUM0V1fdAJQmVuPKsEzQFkDVJQmVvvui1ueSSS2z06NHWtm3bsjM26jexA85+xhLX/lzlbUNkNhatCh1nCtrp78kbZBDd6v727O1uudY7qBllOgAAhEWgwZ9//OMf7gsIdxE7FjGEREswp9qvvOrEvYpXmtVf56677rKvv/7a7zqnnHKKjR8/3vbdd9+KZ2x0Pa1K24fIaiwaCApm6ThT0M7n+0dcsluu9QAAQJiMtxwzZoy9//77e1xPEye0LhBuInYsYigGc7ILgjlFeYM5Wh4WwZwyxgLu6cprniev8MprsKmPzplnnmmHHnqo3yCDxhTPnDnTvZ+XFWRQxsaCzQts7a61tit3l7tduHmhe1zLEeKNRWNiC3oyeP8WQ7x5p3qcKJiVlZvl+/0jL8st13oAACBMMhpGjRpll156qZ12WtlXqRSMeP75523EiBHB2jQgICJ2LGIIBnPU+FHBm5I9GsImmLOHsYCheOV15cqV7n38hRdecD0ZfOnWrZvLYFAmQ8ntjoRaeUYiBq+xaHVQI1VlzKj8SMdZyeMuLibOLdd6AAAgzEsnSsrLy7PY2KAlWgABFZFjEUNIRARzyjEWUL0KdIKtngw+y3DysiwtOS0oV163bNniggf//ve/3dQgX9q0aeMy0S666CKLi9vzvveZseHJt5is7dYo32x7bqZtzNgQUrXyjEQM/+ad+ptSWY56nCiYpZ4MCtrp70lBhpapLd3yUApuAQAQrkIu0LBw4UJLS0ur6c0Awn4sYqQK62BOOccCqiFiTV95zcjIsIkTJ9p9991n27b5zpxo2LChDR061K699lo3pri8SmVs7NpceLIa4/FYcmy+ZW9ZatuWTTcLgUADIxGrv7FosHgbqBbtDaKgXZWmuQAAgOAGGgYNGlTs/jfffFPqMa/c3Fz7448/bM6cOXbGGWdU52YB1a6mxyJGurAK5ii4oHGASi/fttJsx9o9jgXU1IWauvKak5Pjytc0JaLkpCCvWrVq2a233mq333671atXr9K18i5jY+cmi9m+2iw3S/ka5omJsyzLt7T8TKv3+0dmDfat0avj4VrmETQBaCwabAom6PUqnHaSVM8F7aLy9QMAIBwDDarl9dIHsyVLlrivsuy///72wAMPVOdmAYgAYRHMKdmLQSUSuzaZJSTvcSygd+pCsK686sT5nXfecRkKf/75p8914uPj7aqrrrLhw4dbs2bNql4rv2u9bdq52hrlZllMbJx54hJsk+VbnOVb43yzA7auLdh/umpeQ1fJGYkYmRRUCJWyHAAAIlG1Bhq+/PLLwg+w/fr1s/79+7txaL4kJiZaixYtfM5ZB4Cw46sXQ2a6WW5mwZcCDrUblTkWMFhXXqdPn26DBw+22bNn+11HY4jHjh1rHTt2DFyt/PYVtjpzs22PM0uOjbUsy7U4i7GWlmBnxtWxxPwVBUEapebX0FXzUGzMCQAAENWBhiOPPLLwvwcOHGhHHHFEsccAICL568VQt5XZ2p8LAg3pK8xS0gq69JcxFrA6r7zOnTvX7r77bps2bZrfdY4//njXDLJnz54BfW6XkbHyR5uyZbltNI9lW6ylWYw1tjg701KtX0ytYhkeNaVYmUcNN+YEAAAIF0FrBjlp0qRgPRUA1Cz1ZNCV+JK9GBRIqN/GbPNis7zdZhsWmukENchjAVXCNmzYMHvzzTf9rtOrVy/XCFLZaNWlX4vDrc/Cj+3njLW2rV4jqxcTZwdYkiVajM8Mj5rASEQAAICKC9ocyXnz5rnmYWWl5v74449unfnz5wdrswCg7MyEv2aa/fZ+wa3ul4eaOupKvMolSvZiULlEcn2z+BSzlAZmyXXM6rc2a9HT7PCbqrXx4bp16+y6666zLl26+A0y7LPPPvbWW2+59+PqDDI4rXtbYmpzOyjX7NidO+wgT5Egg58Mj2DzlnmoAeeunF22bNsyW71jtbvVfUYiAgAA1GBGg2awv/baay5N15+99trLHn/8cduxY4c988wzwdo0ANhzI0f1RdBJrzIO9hQMUEmE1s/JLDhpLhps0H1p2N7sgPPN6ras9rGAGk+pJrsPP/ywG1vpS/PmzW3UqFF22WWXWUJCggWFfl/tz8wtBWUmCtCoXCLIGR57wkhEAACAEA00zJw509X4Nm7c2O86WqZ1ZsyYEazNAoDyNXLUxIjtawpOiqWsYEPr3gVBCa2vK/Pe8omiV+rrtDDrNeh/pRZ/floQoND3BujEOisrywVv7733Xtu8ebPPdTSeUo0gb7zxRje2Mui8+7FoUEflEuUN6gQJIxEBAABCMNCwevVqO+igPae/aurEL7/8EpRtAuDf7tw8m7s83bZlZlu9lETr2ba+GykZtY0cvUECPb6nkYvlvVK/5PPKZ02UIS8vz15++WUbOXKkrVixwuc6ycnJdsMNN7ggQ4MGNdcDwdHvqv2p6RJq/FjNGR6VxUhEAACAEAs0JCUlWXp6+h7X2759u8XFRcHJDBDCpi1cZ5PnrLQNO3Zbdm6+JcbHWpM6STagV2s7vlszi8pGjrrVfQUNyjNycU9X6qUqWRM+aArCBx98YEOGDLGFCxf6XCc2NtaVR6hMolWrVhYyFFSooRGWAAAACNNAQ7du3eybb76xLVu2+L16pmVff/217bvvvsHaLAA+ggxPfb3MVm7JsLx8j6Ukxlnmjjxbty3LtmbkuHUiOthQViNH3a/IyEV/V+pl8sCqZU2U8O2339pdd93lbv0566yz7J577rHOnTuX62cCAAAAIT114qKLLrKdO3faOeecY6tWrfJZWjFgwADXqOzCCy8M1mYBKFEuoUwGBRlSk+KtY5Pa1iotxd3qvh7Xcq0XsUo2cizKO3JRy8s7ctF7pb7raQW3ur+nrIn8vP9lTezBggUL7NRTT7U+ffr4DTIcdUgP+/71++2dB2+2zh33Kt92A1EqOy/bZq+bbZ8v/9zd6j4AAAjRjIYrrrjCXn/9dfvqq6/c+LT+/ftbhw4d3LKlS5fap59+apmZmXb44YfbNddcE6zNAlCEejKoXEKZDI3rJFrMf0+Adav72zJz3PJ5K9LtkPYNI3PflaeRY1VHLgYga2L58uU2YsQI14tBJRO+HNB5L5twWms7ob1ZzNYpZp9NDbkmi0Aomb5iuk1ZPMU2Zm50AQb15Wic0tiNOPVOHwEAACEUaIiPj7ePP/7YdTZ/8cUX7T//+U+x5erLoLrhiRMnunUBBL+Ro9ZXTwaVS3iDDF66n5wQ65anZ0TwFb5gjFzc0/hLPZf6OfjImti0aZMrf9A0iezsbL+jgsddd7b9o958i9223Gx7YHpAAJEeZJi0YJKt2rnK8j35lhyfbJuzNtuGjA2WvrugxxTBBgAAyieoZ/Qanfbss8/a2LFjXWbDypUr3eOtW7e2o446ys1xB1BzjRwVlND66smgq+RFgw26n5WTbw1TY61+rcTIfpmqe+RiJbImVHr28MMP2wMPPGA7duzwOyJ4+PDhdvXll1rif640W7M8YD0gooamjqi0RQGmAI8bRehS9oIyGRRkSE1ItUYpjdz7n973NmVustU7V7vlfVr2cVkOAACgbDWSOqCAwvnnn18TTw1EtKo2clTmg4ISWn/jjuzC8gl92Nb9uNgYt7xHm/oW8apz5GIFsiaUtfDMM8+4AO369et9/rjU1FS744477JZbbrE6deqY/TUzMJMzos2iqdUybhSh7+eNP7tyCWUyeIMMolvd35693S3Xegc1q0LZFAAAUaLGahQWL17sUoAbNmzoejYACGwjx5JBAm8jxyM7NfZbRqHHlfmgoITWV08GlUsok0FBhtYNarnl5SnDiIiSkuocubiHrIn8fU60N19/3YYNG2bLli3z+SMSEhLs2muvtaFDh1qTJk2qZ3JGNAUZAjxuFOFj2+5tLqtB5RI+y8bikt1yrQcAAEIs0LB7924bPXq0Pf3007Z161b32MCBA+355593//3KK6/Yv/71L3e/e/fuwdw0IOwFqpGjN+OhaPmFyiXKW34RiSUlwcya8LTsZdOmz7C7L+hl8+bN8/ltek01yUfvp+rHEMgeEFFZEqFlCvgEcNwowku9pHquJEI9GXyWjeVlWVpymlsPAACEUKBBEyX69etnP/74ozVt2tROOukkmzp1arF1tFyBh8mTJxNoACookI0cdeKtzAcFJbS+ejKoXCJcMxmqWlJSrYpkTej9cfCgk+zLL7/0u7reO8ePH2/7779/zU7OiKSSiD2NG6XUJOId0PgAN11CjR/Vk6Fkj4a4mDi3XOsBAIAQCjTcf//99sMPP9jll19u//73vy05OdliY2OLrdOiRQvr2rWrff7553bvvfcGa9OAiBDoRo4KKkTCCMtAlJRUtz/++MOVP7zzzjt+1znkkEPsvvvus759+4bG5IxIKonIz6PUJMopm0EjLDVdQo0f1ZNB5RLKZFCQoWVqS7ecRpAAAIRYoOHNN9+0Nm3a2BNPPFHm+MpOnTrZt99+G6zNAiIGjRyrt6SkOqxevdqVP6hcLC8vz+c6Xbp0cYHX008/vVSmSo1OzggH5S2J6H0VpSYoHF2p6RJq/KieDCqXUCaDggyMtgQAIAQDDX/99ZedfPLJZQYZJDExsbB/A4Dyi/RGjqFQUhIoeo9TdsLEiRMtKyvL5zqtWrVyQYhLLrlkj++bNTI5IxyUtyTC3afUBAXBBo2w1HQJNX5UTwaVS5DJAABAiAYaUlJSyhVAUEAiLS0tKNsERJpIbeQYSiUlVe1Vo9Ix9VhIT0/3uY7e/4YMGWL//Oc/3ftmlVXn5IxQV97pG7t3lK/URDQ61FdDSUQMBRUYYQkAQJgEGjRFYs6cObZx40Zr3Lix3yCDuqwff/zxwdosIOJEWiPHSCgpyc3NtUmTJtmoUaNszZo1PtdRUOHmm2+2O++80+rXr75tiSoVmb7hDcb4KzWRyQP9N5QEAABA8AMNV155pX311Vd2/vnn2xtvvGGNGjUqtlxX9wYNGmQ5OTl21VVXBWuzgIgUKY0cw72kRMGMd9991zV6VMNHX+Li4uyKK66wESNGuIa4CKCKTt/wV2qy5PM9N5Qk2AAAABD8QIMCDB988IELMrRv394OO+ww97gaP6rJ2YwZM2z79u2uHvmUU04J1mYBiAI1UVKiEZWDBw92Iyv9GTBggI0dO9b22WefgD8/Kjl9o2SpSXkbSipAQRkFAABAcAMN8uqrr1qPHj3sgQcesGnTprnHFi9e7L7q1atn99xzj/tgDgDhWlIyf/589z726aef+l3n2GOPdX0aevXqFdDnRjVM3yhvQ0llQURrLwwAAICaDDSoLvqOO+6wW2+91ebOnWt///235efnu+7qBx10kJs4AQDhWFKydOlSGz58uL3++ut+1znwwANtwoQJLtCAIKrK9I3yNpTUzwUAAEDwAw1Fa5IVWNBXdVPzSY2RU9nGihUrXMO1du3a2THHHOMyK7xeeOEFu+yyy/z+nPPOO8+VfQBAUevXr7dx48bZk08+6Zo++tKxY0eXsXXOOedYbGwsO7AiVLqgrIKqTnqo7PSNijSUBAAAQM0FGrKzs910CW/3dTVA01SKpKTAjgn76aef7IQTTrDNmzdbt27dXC8I9YH47bff7OGHHy4WaPA64IAD3LaUdPDBBwd02wCEN72XPPTQQ+5r165dPtdp1qyZjRw50i6//HJLSEgI+jaGvUVTi5c81MSkh4o2lAQAAEBwAw07d+50ndWfe+45999FpaamuqkTY8aMsTp16gQkk6F///5ubv17771np512WrHl/hq0nXHGGW4EHQD4snv3bnviiSdchsKmTZt8rlO3bl2766677KabbrLatWuzIysbZAiFSQ+VaSgJAAAQ5YIWaNi2bZsdddRR9ssvvxRmDqiEQZYvX+4aqP3f//2fTZ8+3b7++mvXHLIqdBVRJwGPPfZYqSCD9O7du0o/H0B0ycvLcw1tFSzVe5Yvysq6/vrr7e6777aGDRkvWmmhNumhqg0lAQAAokzQAg36cP7zzz9bv3797N///rd16dKl2PJFixbZDTfc4AINWnfixImVfi5lMbzyyivuSmJZfRcARJFK1vp7PB776KOPXPDg119/9bmO+i5oNO/o0aOtTZs21bDxUSYUJz1UpaEkAABAlAlaoOGdd95x9crvv/++1apVq9Tyzp07u2UdOnRw61Yl0DBnzhzbsWOH9enTxzV//Pjjj+2zzz6zrKwsN69es+vVF8JfXwdNxlD9tbZXgZEjjzyy0tsCIHxr/WfNmuVGVc6cOY5bm04AAFhiSURBVNPvOsqYuvfee10fGARIqE56qGxDSQAAgCgTtECDGjKq/4GvIIOXggJ9+/Z1AYeqULNHadKkiXtO9WgoasiQIa5PxPnnn1/qez/88EP35aWeEQo0vPnmm9a0adMqbReA8Kj1X7hwoXufKOu9SIFMjao8/PDDq/s3iD5MegAAAAhrQZuz1r59e9u6dWu5ejnstddeVXou7/PoJOGTTz5xfRo2bNhgf//9t91+++2utGLgwIGuL4RX8+bNXRNITcPQNqxbt859vzItZsyYYaeccoqr0S4vXd309bV06dIq/W4AqlDr36iTWf22Zo07F9z31vprPWXsr1zpmtLuv//+foMM++67rwtGqpcMQYZqnvQQE1vQk0G9GYRJDwAAAGEhaIGGq666yr766qtiJ/claZl6NFxxxRVVeq78/Hx3q5n2yki47rrrrHHjxta2bVs30vLcc8+1nJycYuMtNQZTDSQ12lId45W9cOqpp9rs2bNduYXKMSZPnlyl7QIQYrX++Xlu+eZfP3dByL333tsmTZpU+B5SlN4/XnrpJfc+dfLJJ1tMyZR+BH7SgyY67N5utnGR2dblBbe6z6QHAACAkBa00gmNeVuyZIkdffTRrunjeeed5z64izq46yReTSKvvvpqu+WWW6r0XBqV6eWrGaQee+utt1ymQnl+1o033ug6yX/66ac+yy18Ueq1L9RxA6FT678rP9kmTl1m9902wLbvzPD5Ixo1amRDhw61a6+91k2VQJAw6QEAACBsBS3QEBcXV9jBXfPn9VWSlqnMQV9F6cqhshPKyxvAUD8IZTKU5B2rqXKK8tBVTlm7dm25twFA6Nb65+Tm23Nfr7DR//nD1u3wXRKlqTW33Xab+1KWE2oAkx6CNl0FAAAgLAMNrVu3DlqqcY8ePdytejHs3r271FXILVu2lMp8KE/PB514AAjDWn81fty53vJrNbG3f1pnw979wxav953BkJCQ4DKrhg0bRgPYUMCkh2qfrgIAABC2gQY1YgwWzbE/4IAD7Oeff3blEccff3yx5d6SCW9AYk80blN69uxZDVsLoNpr/TO32Oc//GaDP/rVflqd7Xf1Cy64wMaOHeua1wKRPl0FAAAg7JtBBtudd97pbtXgrWjJgxq5PfTQQ+6/r7nmmsLHx48fb5s2bSr2M9QwcvTo0a6fg0Zv+ur3ACC0/bSrmR333EY77pnVfoMM/fv3dxNnXn31VYIMiPjpKgAAABGT0aDyg7S0tHKtqwkPvXr1qtLz6crktGnT7MUXX7SuXbvaYYcd5kopZs2a5coprrzySjd9wmvIkCEuqKDnVZnH9u3bXVBizZo1lpycbK+88oq1bNmyStsEIHgWL17syh/KmhbTu3dvu+++++yoo47ipUHkTldRvwYtXzXbrF2fmt5aAAAQBYKW0bDffvu50ZVl8TaKDNRseo2pe/rpp61Dhw5utOaPP/7oyh9eeOEF93hRI0aMsL59+9rKlSvtvffec9uqZpKq1VbA4ayzzgrINgGoXspg0oSILl26+A0ydOrUyZVEff/99wQZEPHTVSyhVsHyjP+WUAAAAFSzGI/O7oM0dULNIG+++WZXpqCGa0VpxOXFF19s33zzjRsnV96JEOHGO97S3/hLAJWzbds2u//+++2RRx6xjAzfjR6VlTRq1Ci79NJLLT4+aAldQPX6a6bZZ8PNtq0qKJsoGmzQP/EbF5nVb212/DgyGgAAiHDdQuR8M2gZDcooaNWqlT388MN20EEHFfvFVZbQvXt3F2RQ48ZffvklWJsFIMxlZWW5vitq4Hjvvff6DDLUr1/flUionOKKK64gyIDInK4SE+umq7jgguhW92PjCpa3OqimtxQAAESJoF3SO+KII1wAQSnNr7/+ugs2jBkzxn766Sd78803XR+EiRMn2g033BCsTQIQxvLy8uyll16ykSNHupInX/S+ctNNN9ldd91V7h4xQDhPV3GNH1VKoXKJnIyCIENau4LlWg8AACCSSieKeu2119zEh127dhWOmXz55ZddTXWkC5VUFiBc7M7Ns7nL021bZrbVS0m0Hm3q2acfTXUNXH/77Te/pVqDBg1yQQiauCKqRlxquoQaP6onQ1xiQSaDggzVOdpS0yzUkFIBjpS0ggwLghoAAET1+WbQi5Rzc3Pt559/dunN3hhHdna25efnB3tTAIS4aQvX2eQ5K23Djt2WnZtvO/7+1ZZMfdo2LPFfXnX22We7prJq+AhEFQUTOh5bMF1CjR9rNSgol6jOk/6aCm4AAICQFtRAw59//unGTmpeva4yPvbYY/bGG28UllKovlrNIgFAQYanvl5mK7dk2M61y2zdF8/b1kU/+N0xRx99tE2YMMGNrASiloIKwRphqSDDtxMLyjU0WlNTL7IzzLavKSjjEIINAABEpaA1g3zyySfdaMm5c+faOeec4/o1nHrqqfbqq6+6r6SkJLvtttvshBNOcOPpAER3uYQyGZYsXWYr3r3ffn/8Gr9BBjWS/eSTT+yLL74gyAAEi8ollMmgIENS3YJpF/XbmjXuXHBfj2u51gMAAFEnaIGG6667ztVNv/DCC675o7rAe51//vn266+/Wt++fe2zzz6z/fffP1ibBSAEfTF3iU1/4QH79ZHLbP3cz/7XRb+IWo1a2OiHn3INZRWg1PhcAEGingwql1AmQ2rT/43U1K3u5+cVLFcZBwAAiDpBCzQcdthhNn/+fLvkkkt8Ltfoy+nTp7vU5x07dgRrswCEEP3tjx492s45+kD766u3zJOXW2qdpDppttdpN9jhd71kvY891WJjg/Y2BsBLjR/Vk0HlEiWDfLqvqRdarl4RAAAg6gStR8PXX3+9xxMCXZG888473dVJANFDDWGfeuopGzt2rG3cuNHnOvHJtazz8Rfa3v0G2PLtHktOTrL6tRKDvq0ArGC6hBo/5mQWZBwVDTbovkZr1m5Y0JASAABEnaAFGkoGGXbv3m1btmxxvRkaNCj+QeSAAw4I1mYBqEGaNqNmsMOHD7e//vrL5zoxcQnW8cgzretJAy0ptb5t3JFtcbG51qROkvVo878SLNQgxhtGH42w1HQJNX7cuf5/5RMKMuh+bFzBck29iGLZedn288afbdvubVYvqZ4d0PgAS1SABgCACBf08ZZPP/20PfHEE64ng8ZbDhw40J5//nm37N1337VXXnnF7r//fuvYsWOwNw1FGvHNXZ5u2zKzrV5KovVsW9+S4uPYP+XAvisf/e2rgePdd9/txt36y3Bq3fsEq9/nQouv19Q25sRa1oZdFhcbY60b1LIBvVpzXIaCYI83JKgROtMt9BpruoQaP6qUQuUSymRQkCGtXcHy6hytGeKmr5huUxZPsY2ZG13AQQGGximN7cy9z7R+bfrV9OYBABAZgYa8vDw3beL999+3hIQE69Kliy1cuLBUJoPWOfDAA23o0KHB2jSUGCmobv8bduy27Nx8S4yPdVeOdVJ3fLdmUbWvKho0YN+Vz/fff2+DBw+2GTNm+F3nlFNOceNu18Y2LnY8NkyN3uMxJAV7vGGwgxoom3efF31NVC7Ba+KCDJMWTLJVO1dZviffkuOTbXPWZtuQscHSd6e73UawAQAQyYIWaHj00Uftvffes5NOOsmee+45a9q0aalyig4dOrhMho8//phAQw3QifJTXy+zlVsyLC/fYymJcZa5I8/WbcuyrRk5bp1oObmraNCAfbdnixYtsiFDhtiUKVPKbBp73333WZ8+fdz9/czsyE6Nbd6KdEvPyHY9GVQuQYZNCI43LJk67x1v2PHYwFzVDnZQA+Wjfa7XWNMl1PhRPRlULhHFmQzKXlAmg4IMqQmp1iilkcvQUibXpsxNtnrnare8T8s+lFEAACJW0AINGmup4IJGW9auXdvvel27dnXj6hD8q/c6sVaQITUp3hrXSSz8YKSaeD2u5Trpi/STvIoGDdh3ZVu1apWNGjXKJk2a5Hoy+Pu7Hz9+vJ166qmlxlTqeDukfcMAvLII6nhDpdJ7xxu2KwgchU1QAxWjfV7V1ziCqCeDyiWUyeANMohudX979na3XOsd1Cy6e1gAACJX0ObC/fHHH3bwwQeXGWQQLffXdR7VRyUCunqvE2tvkEF0q/t6XMt1ZTmSlQwadGxS21qlpbhb3fcGXLSeF/vONzV71RSZvffe22Ux+QoytG7d2gUgfvnlFzvttNNKBRkQwoI53nBPQY38vP8FNYAapsaPympQuUTJ9zTdT45Ldsu1HgAAkSpogQb1ZcjKytrjeitWrLA6deoEZZvwP+pDoBIBXb33+cEoIdYtV/p6JKtM0IB9V1xGRoZNmDDBlUI98MADPv/uNWnmoYcesj///NMuvfRSi4uL7CyZqBhvWJR3vKGWB2K8YTCDGkAVabqEGj9m5Wa5rMCidD8rL8st13oAAESqoAUaunXr5koiduzY4XedDRs22Pz586179+7B2iz8l5odqg9BZnae7w9GOQV9ClQjH8kqEzRg3xXIzc11U2WUwaBpEunppbNfatWq5fqvLFu2zG699VZLTk6u9tcU1TzeMCa2oHzB+75RHeMNgxnUAKpIIyw1XSIuJs71ZPD+m+rt0aDHtVzrAQAQqYIWaLj44ott8+bNds0111h2drbPqRT//Oc/3dVQjbxEcGmigpodanSgejIU/WCk+3pcy9WIL5JVJmgQ7ftOv+fbb7/tgolXX321rVmzptQ68fHxdu2119qSJUts3LhxVq8eV/IiZryhxhju3m62cZHZ1uUFt7ofyPGGwQxqAFWkbAWNsGyZ2tJ25eyyZduW2eodq92t7utxLdd6AABEqqA1g7zqqqvsrbfestdff91mzZplJ5xwgnv8559/tptuusk+/PBD++uvv+z444+3Cy+8MFibhSIN9zRRQc0O1YdgW2aOu3qvE2udKLduUMstj/RGkN6ggRo/KkhQsimmr6BBNO+76dOnu1GVs2f7r40/77zzXHBBE2UQYYI13tAb1NB0CTV+VCmFyiWUyaAgQyCDGkAAeEdXarqEGj+qJ0NacprLZFCQgdGWAIBIF+Mpedm2GqlW+7bbbrNnn33WcnIKuvd7qUZ70KBBNnHixIhOp9ZVX1m4cKFFwljHaJg6UTJocHXf9n5HXEbLvps3b54LMEybNs3vOscdd5ybJHHggQcGddtQAzQVIhjjDTXismhQQ1eEAx3UAAJIAQZNl1DjR/VkULkEmQwAgGg43wxqoMFLUyW++uor+/vvv10n+latWtnRRx9tLVq0sEgXKi98WTRRQc0O1YdAJQK6eh+JV+PLUtmgQaTvu6VLl9qwYcPsjTfe8LtOr169XDPIY445JqjbhigRrKAGAABAGOoWzYGGaBYqLzz2LNKDBhWxbt06Gzt2rGv2qKaPvqgJ5D333GPnnHMOYyoBAACAKD7fDFqPBiDcKKhwSPuGFs22b9/uRlT+61//co1afWnevLmNHDnSlT5pjC0AAACA6EagAYDPfipPPPGEy1DQtBhfNDlCfRpuvPFGN7YSAAAAAAg0ACg1ZvaVV16xESNG2IoVK3zunaSkJBdcUJChQYMG7EEAAAAAxZDRAMCN79SI2SFDhtiCBQt87pHY2Fi77LLLbNSoUa6BK1Clho4rfywYU5mSZta6Nw0dAQAAIgiBBiDKffvtt3bXXXe5W3/OOOMMu/fee61Lly4WlTgxDhxGVAIAAEQ8Ag1AlFLmgjIYPvjgA7/r9O3b142qPPTQQy1qcWIc2H357USzrX+befLNElLMsjPMtq8xy9xSsE7nkwP4hAAAAKgJBBqAKLN8+XI3JeKll15yJRO+7L///jZ+/Hg78cQTo3tUJSfGgc0KmftyQZAhqa5ZalMzHVs6BneuL3hcyzseSxkFAABAmAt6oCE3N9emTp1qP/74o23atMkOPvhgNxZP1qxZ4x7r2rWrxccTAwECSX9bKn947LHHLDs72+c6e+21l40dO9bOP/9815MhqnFiHFjqybBzXUEmgzfIILrVffVr0PJVs83a9QnwkwMAACCYgno2/80339hFF11kK1eudFdSdaU0JyenMNDw3Xff2YABA+ytt96ys846K5ibBkSsnTt32iOPPGIPPPCAbd++3ec6jRs3tuHDh9vVV19tiYmJQd/GkMSJcWApkJCXXVAuUTJLRvcTahUsz/hvCQUAAADCVtAuWf7222/Wv39/W7t2rd1www02efLkUmnbp556qtWqVcveeeedYG0WELEUxHv88cetY8eOLojgK8iQmprqpkgsXbrU/V0SZCiCE+PA0nSJuESznMyCcomidD8no2B5LUamAgAAhLugZTQoHTsrK8s++ugjO/74432uo5Ocnj172rx584K1WUDEyc/Pd4G8YcOGuQCCLwkJCXbttdfa0KFDrUmTJkHfxrA8MS56Fd57Yly7ISfG5aURlqnNCho/qidDyR4NsXEFy1sdVD2vJwAAACIv0PDll19a7969/QYZvFq2bGk///xzsDYLiBjKEPrss89s8ODBfoN1Kle68MILbcyYMa4fA8rAiXFgxSeZ9by4YLqEGj8qY0TlEgrYKMiQ1q5gudYDAABAWAtaoCE9Pd1at269x/V27drlUr4BlN/s2bNdgGH69Ol+1znppJPcJAlNlEA5cGIceN7RlZouocaP6smgrBBlMijIwGhLAACAiBC0QIPSs5csWbLH9X7//fdyBSQAmP3555+u/OHtt9/2uzsOOeQQu++++6xv377ssorixLh69qlGWGq6hBo/qieDyiXIZAAAAIgYQQs09OvXz15++WVXQnH00Uf7XGfKlCkuGPHPf/4zWJsFBMzu3DybuzzdtmVmW72UROvZtr4lxcdVyx7WKNjRo0fbc889Z3l5eT7X6dKlixtnefrpp7uSCVQSJ8aBp6ACIywBAAAiVtACDUrrfvPNN+2MM86wCRMm2Jlnnlm4bOvWrS7IcPvtt1vt2rXt1ltvDdZmAQExbeE6mzxnpW3Ysduyc/MtMT7WmtRJsgG9Wtvx3ZoFtARJ2QkTJ060zMxMn+u0atXKBSEuueQSi48P6gTbyMWJMRAS8rOzLXPefMvblm5x9epbSo/uFstIXgAAQk6Mp+SMyWr0n//8xy6++GLLyMjwuTw5Odlef/11O+200yxSdevWzd0uXLiwpjcFAQwyPPX1Mlu5JcPy8j2Wkhhnmdl5FhcbY60b1LKr+7avcrBBQYVHH33U9VhQYM6XtLQ0GzJkiMsISklJqdLzAUCo2fHFF5b+9juWu3GjebKzLSYx0eIbN7b655xtdY45pqY3DwCAkNAtRM43g3q5U9kMCxYssIcffth1x//777/dKD5dgT3uuOPstttusw4dOgRzk4Aql0sok0FBhtSkeGtcJ9GVKSh+t3FHtntcy4/s1LhSZRS5ubn24osv2siRI2316tU+11FQ4eabb7Y777zT6tevzysKICKDDJuffc6yV60yy8uz2JQUy9+0yXLXr7fN6eluHYINAACEjmoLNHz99dfWrFkz22effYo93rZtW3vkkUeq62mBoFJPBpVLKJPBG2QQ3er+tswct3zeinQ7pH3Dcv9cBSpUTqRGj4sWLfK5TlxcnF1xxRU2YsQIa9GiRcB+JyCq5e42W/ljwfjNlLSCMac0qqzxcgllMijIEJeaanGNGhUGdPM2bbKcVavc8tpHHEEZBQAAkR5oOOqoo+zSSy+1559/vrAZZP/+/d1VVyBSGj5u2pnlejKoXKJkw0XdT06IdcvTM7LL/RxfffWV62nyww8/+F3n3HPPtXHjxpUK5AGogkVTi4/ejEtk9GYIUE8GlUsok8EbZBDd6n7etm1ueeb8+Va7d++a3lwAAFCdgQZ9AFBZRNGTp3bt2rHTEVENH+NjYywzJ8/1ZNDVtaLBBt3Pysm3hqmxVr9W4h5//vz58+3uu++2Tz75xO86xxxzjGum2qtXr4D9XgD+G2T4dqLZ1r/NPPlmCSlm2Rlm29eYZW4pPu4UQaXGj+rJoHIJXwHdmORktzzvvyUUAAAgggMNDRo0sMWLF1fXjwdqvuHjjjyLjTHLzivop6qeDCV7NKghpKZP9Gjjv3fCsmXLbPjw4fbaa6/5Xadnz54uwKBeJgjPkaQI8XIJZTIoyJBU1yy1qc5gFS0027m+4HEt73gsZRQ1QNMl1PhRPRl8BXQ9WVkW06CBxdGjBgCAyA809OnTx95//307+uijba+99nKPffPNNzZo0KA9fq8+RDz33HPVtWlAQBs+bt65252U7MjKdT0ZVC6hTAbv1AmNuPR18rp+/XpX/vDUU09ZTk6Oz+fu2LGjW0elErGxsbxyYTiSFGFAPRlULqFMBm+QQXSr++rXoOWrZpu161PTWxt1NMJS0yXU+FE9GUr2aIiJi3PLU7p3r+lNBQAA1R1oeOihh9xUiRkzZrgvWbJkifvaEwINCLeGj6nJ8daodpLl5Oe7k1aVS/g7ad2+fbv7+9DXrl27fD6nGqmqyaOaPSYkJATl94wm/jJU1m3Lsq0ZBUEfgg1RRIEE9WRQuUSJ1Hx3P6FWwfKM/5ZQIKhiExPdCEtNl1DjR/VkcOUSymSIi7OEVq3ccq0HAAAiPNDQvn17mzt3rgs2rFy50jWHVDPIu+66q7qeEqgWSqvfU8PHlIQ4u/Twtta4TrJr/KieDCqXKJrJsHv3bnvyySddhsKmTZt8PlfdunVdw1SNq6xduzavaBiOJEUY0nQJNX7MySwolyj6d677ORlmtRua1WpQk1sZ1byjKzVdQo0f1ZNB5RLKZFCQgdGWAABESaBB9OFdZRPe0gldpT3yyCOr8ymBgFPtvtLqdcW7rIaPCjL4GmGZl5fn+i8oQ0GBN18SExPt+uuvtyFDhljDhuUfg4nQGUmKMKYRlqnNCho/qidDyR4NsXEFy1sdVNNbGtUUTNAIS02XUONH9WRQuQSZDAAARFmgoaiiEyiAcKIGgSqDUFp9RRo+avlHH33kgge//PKLz5+tvguXXHKJjR492tq0aROk3yi6lSdDpaIjSRHm4pPMel5cMF1CjR9VSqFyCWUyKMiQ1q5gudZDjVJQgRGWAACEvqAFGoBwpfR59VpQ7b7S6svT8PG7775zZUIzZ870+3NPO+00u/fee61bt25B+k1QkQyV8owkRQTxjq7UdAk1flRPBpVLKJNBQQZGWwIAANR8oGHMmDHuA/w///lPN+pS98tL36dxf0Co8DYGLDqlwFfDx99++81lMLz33ntlTmTRqMrDDz88aNuPqmeoIAoomKARlpouocaP6smgcgkyGQAAACokxqNP19VAKeH68P7777/bPvvsU3i/PE+n9VTXHom8V68XLlxY05uCSjYSVO1+yYaPang6cuRIe/HFF/2WCe277742fvx4O/nkk0ul7KNmp06UzFC5um97pk4AAAAg7HQLkfPNastomDRpkrtt3rx5sftAOFNQoWiDwM2bN9vQ8ePt0UcfdVMlfFHvhbFjx9qFF15ocXFMMQinDBUAAAAAIZTRgNCOMKFqdu3aZRMnTrT77rvPtm/f7nMdTY8YNmyYXXvttZaUlFQsK0KTD9SUUP0ClMrPGMXQylABAAAAwlG3EDnfDLlmkM8//7ytWrXKjQIEQk1OTo4999xzbkrEunXrfK5Tu3Ztu/XWW+3222+3unXrlkrZL3oVXU0JuYoeOhkqAAAAACIw0PDMM8/Yjz/+SKABIUV9F95++22XobB48WKf68THx9tVV13ljt2mTZvusS+Axitq8oGaEmqihURDyj4ZHQAAAEBkC7lAAxBqvvjiCzeq8qeffvK7zj/+8Q/Xh6Fjx45+T66VyaAgQ2pSfKlJB3pcy4/s1DiiU/fJ6AAAAAAiH4EGwA8FFgYPHmyff/653310wgknuEkSPXr0KHM/qieDyiWUyeANMohudX9bZo5brn4BkZrKT0YHAAAAEB0INAAlqDRCJRKTJ0/2u2969+5tEyZMsKOPPrpc+0+NH9WTQeUSJUdb6r7GK2q5mhJGIjI6AAAAgOgRW9MbAISKtWvXugkRXbt29Rtk2GeffVyvhu+//77cQQbRdAk1fszMznPlEkXpflZOQWNITT6IRHvK6NDj3owOAAAAAOGNQAOi3rZt22zo0KGuv8KTTz5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\"positive_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\nincome_level,n_countries,mean_log_gdp_pc,mean_life_expectancy\nLow income,20,7.61,63.188\nLower middle income,46,8.6899,67.8095\nUpper middle income,58,9.6899,73.6954\nHigh 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VFqj17NlT3XQ4gjuFhpYtW9rtfIBRUu3m2nY0UkMbMcLNsh7Ut2s39EYeCNpNqTbKZDvi5klwY1uxYkX1uW3bts0322DLli12l/P1119b59u2bZvDn79//37raG9BATpu/PUjXvoHbuidFTg0qlatqpbx5ptv2p1Hf5506NDBUGzSBBvc9Bv5uthbd0ce+lFqdwgNuOZoyx4zZozd+XAN0+bbvn27Q+vw4YcfWt9jL9j01PUAGV7aZxt9T3FcNCECWQ+eEhrKli2b67pulD1kW2PurNCAx1133WXJD3tCA5YPkQLn6ubNmw3fi24jVapUyZP1AlENr9WtW9fiCtpoNrI+3HWdxHFF5xnNE8R2FN0ffxPxm6JlWgwfPjxfERaZKsePHzf8ruMBQc0WCE76awiEFltWrFhhnY596+j56It7HD2akDl79myXl0GIv0KPBkKCFDiuw6UbdZrHjx9Xrw0ePFhCQ/N+7Z988klVK2yEVl/4+OOPqzpII1An2bBhQ1VvihpbjVKlSqm/cG13N59//rn6i5pae3XT48aNU3/h6I/uAkZcf/31hnX/qGvVXO8L8jKw5YsvvlA1zajVnDx5coGeC57ghRdesFvzjnpdnBdg3rx5eaafOHFCnTugT58+hucDvB9Q028Ejom2T1H77A1QX4t1xbqjNnvatGl55sEx0TCq4zeapn9PfqD7BWqg0b0CdboTJkzId34cG9QB44H11cAy4EIeHx8vzoLYENsPUFvsCOimYHRN0OrNsV179uzJM11bd1ce9q4jroKOEprb//Tp0+XixYt55oH/geZPA9DxwxFmzZql/jZp0sTljgKuXg+0axxq1VEnbktYWJiMHj1a/RsdhrZt2yaeAN492Me2wPNC82Jw9hppxPDhw116H+r54UsAHw4cJyPgMQDvB9trkvYbheODDjLOgPMMngc4rqjrd9d1Er+h6DwD3n77bVXL7++/ifh+wasA17IXX3zRcLnw+MAyMjMz1XfCiGrVqsn999+f53X4UtSuXVv9u23bttKmTZs887Rr104t32j98sPX9zjwcwDafRohwQSFBkKCCL3hlmagpJmkwSDrpZdesntjYQRu3v7880/1bxhEVahQwe5j9+7dar5Dhw5Z34+bL6wL2st17dpVGSq568dUM/rDDZFRoATq169vbWVlZAwIYHZlj0qVKqm/+RmB2TOP025OYGrmC+wdUw0YyQEYdNneYH/66afq2GP7cQ7Zmq1pN2f5nQ/Lly/Pcz54Eoho33//vTXYvOqqq8Rb4AYbN8f4riHogKGYdu7YA0KA1joR4gRM3/D9/O6779S6u2IOBnER6wIcFSrsnf/69Tc6/wtqB5nfY8qUKeJuYBwLzpw5o87ZtWvXSnp6ulp3nKswu8Sx0bB3zdCDlqWa2WFhTCBdvR5o1yzb76AeXP80QdHeNa6weOIaaQtMDvMzWs0P7Zr0888/53tN0kxA9dckGEVCGIRAh+2EQAlByp7xn54ffvhBBc0IcG0D1MJcJ7XzBdPRojYQfhO1ZaGdLswZjcA9ibY99j4b0+0JcRAptc8wAt8DHEtw/vx5cQR/uMfRrtW4dhESbIT5egUIIe5D+yEGUPbxo9usWTM1qpKfW7/RaJV2M4GbdWd+uBE0aWDUAW7fGMlAn3s8tBEX3Dwj2C2oi4A9Tp8+rf4W1BMbnwX3fG1+o5Eue2DEEOBm0hm00Q04n/sKe8dUAw7rcOiHEztcwvWZC1qWA84b2xtW7SYKI8KOjArrzwdPMWTIEGsGA0YAtc4P+R3r/NZLPy2/80O7UcV+gvM8zheINEajz/mBG1Wcp+PHj1fBFpzIMbqGG357I7RG6B3otZG9grC3fdq578r57wtuvfVW1Znh+eefV0EMXP1tr40QcoYNG2YNehzNZsAoLYRaV3H1euDINQ7rhus8Om/Yu8YVFk9cI41GdR0Rf4zQrkkQTB3JStB/vzEijeAQQiGEpaeeekq9DuEA59C9996rOgfoRSqNb775Rv3VMiXcdZ305Pniqd9EZz5bP78rn+3O89Ef7nEgsgGjDiKEBDrMaCAkiNCPGkJ1xwgrbpYL+qGzl2KPIEpj2bJlapSnoIdtmzSk8CMNFAEgbsgQAKNtH9Jdkb6PVPFACGScwRelEs62CoyJiVFig20bR6Rfb9myxbBsQn9OoDWbI+dDfq3o3AFav7311lvq32hD98wzz9idVz9Kn1/rRv20/DITsC8QgC5atEjtb7R+69GjhxQGHBOkD6MUxF5btIJScJ25aQ4mkNWCdo8QaSDQIGUdQivEBZzXWjkJUtELCuKQIq218oPwo6VIB+r1wN9xtbWp/pqE4+zINcm2xSMCQvxG4TrYt29fqVOnjiqLQHYRroE4h2yvFzg/8JsI7rjjDrdeJ3m+eA9/uMfRskP0129CggUKDYQQu+CHTxshKEwKPII1BIBI08fIG+ontVRk1GriZszVEXv8oOeHNr2gEX53gjRLV/aZfhTZ3uiGUf25q2hCwsqVK6030lo2A/q2owbbXdvmCXCD98Ybb6h/w9cAo9n5gbRhbdQUNe320KZhW+2VIGiZDJ999plVZMDIpzvQRgX37t3r1PsQQGtpy4VNZS+I/FKMC3pAEPAUjRo1khkzZijB4fDhw7Jp0ybllwGRQUvXRglDQRkfGK1GHXthyyYK851x5BqH60RCQkKu+W2D9/xGSt15PfEV7rgmQXjF9RDB4X///af2OUaqkTGiz3TQgFiBTAVkIBl5MBRmnTx5vnjqN9Gff4/9/R5Hu1Y76qtDSCBBoYEQYheki6KGFWB0x10ggP3www+tPgK2plRaMJhfnaxW6wljJoz+GoFaWy2AtlfX6Qlat26t/iKw0cz5HEGfzo2afSO0mnF3gNEWpHhi/yHlX/ur93CwRTtmmh+Cr0C5BDIYNJEBokNBwORRW38txdUWnHOaMZu9EgiIDEi1hvmaJjL07NmzEFuT+/M1I7iCyjaMaNCggfq7f/9+8SS4mXb14YvgFsG2ZkBn79w2KpuAAR1q8H1xPdCucZoxqxEIeDVfDttrnHY9sXctgQHiv//+K55AXwbhiN9BYdC+0/A7cFf6OcQ+ZEtp4qXtbxRKpfSmqe68TmrnCzITnfHd8OVvot57wd73Gx4yei+HonyPo/8OaoImhHBCgg0KDYSQAh3HNeMrPPLDdhRVq30sqDbRtjZXG5XFjYk9tMAON01aUGDLyy+/rP6ihjk/QzV3g1RJbAMCgGeffdbhG+26deta9wlcvG3BzePrr7/utvXEfseovJbJoGU2IHg2cv7Wnw8Y9S8oEwX10kgx9oTIoC+XcERk0EBqtHYzbiTaLF682BqkGwWkWiYDyiUwEgbjR0dFBi0gzA8Y1mk12u3btxdn0bwJ1q9fL57EkRRjX5XTGIGgEXXhKJkwKgnSg0wIzaQPfh+FTWV39XqgnVd//PGHMjq0Bct75ZVXrJkceOjR/D2MriXad6ega7Sr6A0B87uOuwMcI3wXEbBpXTjsgesRfGkK8xuF4wfzP3v+DIW9TqLUsWbNmurfOF8cvYb68jcR5UU4BhB6kAlixGuvvab2N4J7zF+U73E0ILzgdx37riADZ0ICEl/31ySEFA70P9f6QzuDo33Os7KyLJ06dVLzoYf0uHHjLMeOHbNOT0pKUj23Bw0aZClZsmSu93bp0sXSv39/yw8//GA5f/689fWEhAS1HPSlNur5/dJLL6nXa9eurfpz20PrCY71mjp1qiU5OVm9fuLECcsjjzxi3b6ZM2c61T/edt9iXmd57733rJ/frVu3XH3SsZ7ff/+95Y477rBcvHgx1/t69+6t3lO8eHHV413rob5r1y41f3x8vHW5q1atyvVePHf2XNi+fbv1Pddcc43627Vr13zfg2OK+XD8nnnmGcu+ffty9Vr/448/LC+88IKldOnS+fYzz2/f2wPL1d4/efJkp9+PHvWNGzdW769cubJl+fLl6vXs7GzLokWLLCVKlLC7D/Bd6Nmzp5qOnvGY3xlwfNq2bav6ttvul//++88ybNgway/6WrVqWVJSUpzePnzX8P6YmBi1vvbWw5HzxN555ikSExMtZ86csT60fd26detcr587dy7Pe3Edwrn422+/qX9rbNq0yXLPPfdYrxOObIv2vcexOH78uFu2zZXrAY7ftddeq96Da+uCBQssGRkZatr+/fvV/NoycdxtmTVrlnX6yy+/bF029uHw4cMtoaGhllKlStn9Ljpy/PO7juL7hWlPPfWU+t4ZMWfOHDVP9erVC9yHmAfz4j22jB071rq+ffr0sWzbts06DZ+N/Y15qlatalm7dm2u9+F3yvY7iesYrr/Y71hmr169rNM2bNigXqtRo4bHrpM4ntrvY5s2bdQ64xoF8JuAY/LAAw9YduzY4Te/ic8//7x1e3G+ab/5+Dty5EjrZ+M6Z0vfvn3VNPy1hyPrZ+8cye9+xxf3OBqvv/66mt6yZUu720RIIEOhgZAAx9NCA8AN6m233WadHw8EZLhJ1X5ItRtzoxsD/Xu0QE579OjRw3oDpQ+6oqKi1HTcDJcvX17dQOChvyG7cOFCrs/A58fFxeVapyFDhhhuk6eFBvDaa6+p9dfWJTo6WgkF+tf0NycA21epUiXr9PDwcOs+g/iwevVqtwoNoHnz5rmOycKFC/OdHze6+ptWPGJjY9W+128bHrZCUWGEhkOHDlnfq50X+T3eeOMNw+VgHRAkaMsqVqyY9XzDo1mzZobB7K+//prruBT0+Z999pnd44MHPrNMmTLqvNC/3qRJkwK/k/kdm7Jly6rl/PzzzwElNGjBRkEPo6AU3yP9PLg2RUZGWp8jmPvxxx8LXAdci6pVq6beg0DenbhyPcD3p2HDhtbpCIQ0cUD7HkyZMsXw8xBAdejQwTovrova9REPfD/yuw4WVmhAoKUtA8cCQT6O3X333ed2ocFsNltGjRqV69qP/YvjbjKZcp0bEKOMfj/1x0S/nPr166tAXUMLmgcPHuyx6yT45JNPcp3D+De2RxMj8dALVr7+TcT23nvvvbnOTdtthWCjiWX+IjT44h5H47rrrlPzvPPOO3a3iZBAhqUThBCH0mBRv4i0QhjewRUfKYNo84RaVtSyI6Vf6zOtMXXqVJVGiV7TcPLGvWtqaqoyToJTN1J6kapum1aIeZHajnlgkASzMxg14aFPP0cLMtQvw50faeaoaUdaLMy0kJqJZWhmgb5g+PDhqoPDgAEDVK03QBostq9Xr16qraRtz3F4JiClH0ZSmilgbGysSuOHsV1h68WN0JcIYH3s1R3rTQdRf4p+7/369ZNatWqpkgLse5h84VggRReGWLbtzvTu7a1atXJqPfV1x/h3QV4A+hRpPTVq1FDrhnVEujlS45HOC5NApJOjr7pR+0P958NFvKDPx7muB8tHicrDDz+s0tpx/iKtHOc/9iFS7GEuiW4xWEdXwLHp37+/+jfKOooKMPNDavZNN92kvkPY90hbRu342LFjlcHfzTffXOByUDKB0gl3mEC643qA7w/SqydPnqy+L9gmXHdhQIgSEJwrTz/9tOHnoQRq6dKlavvr1aunzg2c67heo2YcJUieZMSIETJlyhR1DPD9ghEgruFaaZA7wXahjATfa7TtRb07th9+Afguw/cAJVa4ZulT1JE2/8EHH6j9j2sBfFxg8oj3tG3bVrVMxXVXM2jUt7X05HVSuy7DUwEmg/BeQXo9zmuU/6BkA9cS27p+X/4mYnvhWwMvlK5duyqjRXgQ4C+e4/yGB5BRq9Cido8DUKKHsih8px3xjSEkEAmB2uDrlSCEEFI0GD9+vIwaNUr1H1+7dq2vVycowQ0s/D4QNMF8EEE4IcQ93y0IBuhGAzFR3ymIEGeAMAZPEQjDs2fP5s4jQQkzGgghhHgNGE4CjD4TzwAjOWRNYDRx+vTp3M2EuAktm+HWW2+lyEBcBgagyIZAm92CDEwJCWQoNBBCCPEKSEVFqmiXLl1UWjLx7GgZSm5QCoKbWkJI4UEaPQLDwYMHc3cSl5k2bZrqkoKyJ5TCEBKssHSCEEIICUK+/vpr2bx5s/J+aNiwoa9XhxBCyGWhAd5T8N+ArwYhwQqFBkIIIYQQQgghhLgNlk4QQgghhBBCCCHEbVBoIIQQQgghhBBCiNug0EAIIYQQQgghhBC3QaGBEEIIIYQQQgghboNCAyGEEEIIIYQQQtwGhQZCCCGEEEIIIYS4jTD3LYo4QoUKFSQ5OVmqVavGHUYIIYQQQgghxG0cPnxYYmJi5OTJk+JLmNHgZSAyZGZmevtjCSGEEEIIIYQEOZmZmSrm9DXMaPAyWibDjh07vP3RhBBCCCGEEEKCmIYNG4o/wIwGQgghhBBCCCGEuA0KDYQQQgghhBBCCHEbFBoIIYQQQgghhBDiNig0EEIIIYQQQgghxG3QDNLPsVgs6kEIISQvISEh6kEIIYQQQvwHCg1+SHZ2tiQkJEhiYqJkZGT4enUIIcSvQa/oMmXKSLFixXy9KoQQQgghhEKDf4oMhw8flrS0NF+vCiGEBAToFY1rZq1atcRkMvl6dQghhBBCijzMaPAzkMmAG2bcLJcvX16N1IWG0kqDEEKMyMzMlGPHjkl6erqcOXNGKlSowB1FCCGEEOJjKDT4GSiXABAZSpYs6evVIYQQvyYyMlLKlSsnR44cUddPCg2EEEIIIb6HQ+V+BEwfNU8GZDIQQghxTGwAWVlZNM8lhBBCCPEDKDT4EfruEiyXIIQQx9D7MrBLDyGEEEKI76HQQAghhBBCCCGEELdBoYEQQgghhBBCCCFug0IDISLy8ccfS0hIiBw8eJD7Q0QWLVok8fHxkpSUFND747333pNq1aqpjgSEEEIIIYQQ70ChgfgkoP/777+55/2U7OxsGT16tDz11FMSGxvrtc9dsWKFPPTQQ1K3bl0pVqyY1KxZUx555BE5ceKE4fy///67tGnTRs2LTgNPP/10HmGkX79+ymD1/fff99JWEEIIIYQQQig0ECIiffr0kdTUVKlevXqR3x/fffed7N69Wx599FGv7othw4bJ6tWrpXv37vLuu+9Kz549VWZFs2bN5OTJk7nm3bx5s9x4442SkpIikydPVoLEBx98IPfcc0+u+aKioqRv375qHpoEEkIIIYQQ4h3CvPQ5hPi9a73eub4oM2fOHLn++uulcuXKXv1ciAHIUNB3XOnSpYu0a9dOpk2bJuPHj7e+PmLECImLi1PCRIkSJdRrNWrUkAEDBsjPP/8snTt3ts577733yqRJk2TVqlXSsWNHr24TIYQQQgghRRFmNBC/5J9//pGuXbuqIBLp+xi9/vPPP63TL1y4oIQBjHxrnD17VgWppUuXzjV6/fjjj6vUemc9GhC43nbbbfLbb79Jy5Yt1eg40vnnzp2b5/1Yn2effVa9JzIyUqpUqSIPPvigWieN06dPy8MPPyzly5dXy2rSpIl88sknuZaDz8d6vPnmmzJ9+nT1eSgNQOB85MgRtV3jxo1Ty4+OjpZu3brJuXPn8qzPsmXLpG3bthITEyPFixeXW2+9VXbs2FHgfk9LS5Mff/xROnXqlGca1uvJJ5+Ur7/+Who1aqS2s2HDhmp+d3DDDTfkaeuK1+AV8e+//1pfu3Tpkvzyyy/Su3dvq8gAsL9xriALQs/VV1+tlvHNN9+4ZT0JIYQQQggh+cOMhiJEela2bDp0QS6mZkjJ6AhpXr2URIb53yg+AmIEyQgihw4dKuHh4arGvn379vLrr7/KtddeK6VKlVLB7po1a1RtPoAggGAYgffOnTtVEAzWrl2rlucKe/fulR49eiiBACn4s2fPVnX/CF615cMXAMtHMAyPgebNmyuB4dtvv5WjR49KmTJlVFkG1h/LQ7B+xRVXyOLFi9WyIFIMHjw41+cuWLBAeQvAJwHbgxF5jMxjRB6j+CgzwLKmTp0qQ4YMUeulMW/ePLWuN998s0ycOFGVF8ycOVNlC0DAgRhij40bN6rPxTYYgX381VdfyaBBg5SAAaHn7rvvlsOHDyuBB2RmZsrFixcd2r8QAGzFBT3Yt3hgH2ps27ZNsrKy5Jprrsk1b0REhDRt2lRtoy3YnnXr1jm0ToQQQgghhJDCQaGhiPDzjpOy6O8jcjoxXTKyzBIRFirlikfKvddUlc4N8x/t9zYjR45UwSqCWozoa6PVV155pRIeIDYABPdffPGF9X0QFBBM79q1S/0bQoAmOrjqNwCvAogZmlCBYL9q1aqqvABZB+CNN96Q7du3qwAc/gL67dAyK+AfACFi/vz58sADD6jXBg4cqMoCMB8ECgTuGseOHZM9e/ZIyZIlrQaNr7/+uhIsYKQZFpbz1T1z5owSJSAkIMMAQTmEF82zQAPCA/bfa6+9lut1W7DvAIQQI7AN2J+1atVSzzt06KAyMxYuXKgEFICAHq87woEDB/IVPt555x0lfNx3333W1zRzyIoVK+aZH6/h2NuC8wgCDCGEEEIIIcTzUGgoIiLD+2v2y5FzKZJttkh0hElSE7Pl5MU0OZ+SqebxF7EBATVq7O+8806ryKAFkPfff798+OGHKnUe2Q4I/lFeADEAQTQCTIzily1bVv0bgTzECgT7rmY0NGjQINd7sWx81v79+62vffnllyrY1osMGsiwAD/88IMq3+jVq5d1GjI1IArgNYgnKNPQgKmhJjIAZHEAlAtoIoP2OoJ8CBPYXygpQIYElqkv20CZCeaFT0F+JCQkqL/wPzACJRWayACuuuoqdSz0+wP7AuvhCPmVtEDgGTt2rDWTQwNiC4CwYgtKUrTperA9eB3ZHShFIYQQQgghhHgOCg1FoFwCmQwQGWIjw6Rs8QgV/CL4PpOYoV7H9HZXlvWLMgqM0CMYRDBvS/369cVsNiuvAmQraAIARAV4FiBlHoaBEAO0bANMQyCM4NcVqlWrZhi0nj9/3vp83759qnwgPw4dOiR16tTJUyaAbdKm5/e5muiAbAqj17X1QRYEsGd6qPc0yA97HRoc2R94buTx4AzIrIBwg/KYWbNm5ZoGbwqQnp5u6DGhTTfaHk34IYQQQgjxF8wZGZL6z2bJvnhBTCVLSXSzphIaEeHr1SKkUFBoCHLgyYByCWQyaCIDwF88v5iaqab/c/iCtKqZU2MfKFSqVEml+GPkG+n3CCavu+46JTTA8wDBO4SG1q1b5+sDkB/2OlF4ulWivc8taH0gxACUCRhlC+izIYzQfBYgHEC8cfbzAUodjAwqjcCxsl0mhCSYX0JEQSaIvqREXzKhlVDowWs4L2zB9iCTwUiEIIQQQgjxFYkrVsiFL76UrDNnxJKRISERERJWtqyU6nG3FL/xRh4YErBQaAhyYPwITwaUS9iO5uJ5VHiomn4hJUP8AQSeCAhRDmE0yg3BQD+qj6wGCA0QHGAEiKAU2QsIUtENYdOmTSr93pOglAAeDflRvXp12bp1qxIC9KKH5omA6e5aF1CuXDmXsgrq1atn9U5o3LixS+vw+++/u+zRgNINiAzIVlixYoWhDwOyHCCYwKsCZRV6gWPz5s25XtN/jpY9QgghhBDiLyJDwqyPJOPoUdQPS2h0tJjPnpWsU6ck4cIFNQ/FBhKosL1lkIPuEjB+TM3IzjMKj+dpmTnGkKWK+Ud6Fka3EWiiFaG+1eSpU6fk008/VWaP+vR/CA2Y7/PPP7eWUiCQRxbD5MmTlamkq/4MjoKyiS1btsiSJUvyTNP2+S233CInT55U66mBzgnoGoGWjDCFdAfwqMD+gekjtt2oNCU/0E0D3RsQxLuK5tHgyEOfdZGcnKz2E/wmkMmAUhMjICJBRIGxZmJiovV1ZHHADBP+FrZAcMI5QQghhBDiL+USyGSAyGCKjZWIWrUkvHJl9Tc0NlYyjx5V0zEfIYEIMxqCHLSwRHcJGD/Ck8HWo8EUGqKmN6tWyqvrhXaMyDiwBSUP8FlAEApRAW0UMXqN9pYY5UabRz2aiIAMCATXGjfccIMsW7ZMGQa2aNHCo9vywgsvqO4XCHDRPQLBOkoH0N7yvffeU4E3ul5gG9DOEi0kMYqP96BDAzor2JYHuApEBnSg6NOnj2rp2LNnT5UlgvaTS5culeuvv16mTZtm9/0wU4TQs3z5cnnllVdcWgdXPRrQjWP9+vVqH6K7BR4aEGNgEKrx6quvKuEAAg32LdqIvvXWW2rdu3Tpkmu52N84Ht26dXNpewghhBBC3A08GVAugUwGU5kyucqb8Tz74kU1PXXzZolp2ZIHgAQcFBqCHBg8ooUlukvA+BGeDCiXQCYDRIaq8cXUdG8bQSIYNgKBOIwe4a0wfPhw1dIR5QbomIARbK37ggZMI1EmcPr0aSVM2AoQLVu2NOxO4E4QBGN9R48erbIaPvnkE7VON954o9XnAN4Aq1evlhdffFFNR+cMrDvaZGKb3Qm6c8CnYMKECar1JgSaypUrq33Sv3//At+PQB9ZGvBKsDWf9CQoe9BEKDz0oLRELzRARIEYMmzYMHn22WeVUPPwww+r88WWxYsXKxNLewaZhBBCCCHeBsaP8GRAuYRReXNIVJSann25hIKQQCPE4mlXO5ILBNFgx44defYMAmrNmwBBqKsGhvZaXKK7BIwf4cmAcglkMkBk8JfWlkT8psUo2nrC62DcuHESyEBkQfYIBB5ky5DgxJPXTkIIIcQTJP+1Xk6/8YZkHj+uyiX0YgPCs4x9+yS8UiUpN/QFZjQQt8Wb3oQZDUUEiAloYYnuEjB+hCcDyiX8oaUl8S/gk4Gyiccff1xlDCBjI1BBxkh4eLgMHDjQ16tCCCGEEGIFLSzRXQLGj9lnz1rLJyAy4HmIyaSmRzdtyr1GAhIKDUUIiAqB1sKS+Ib77rtPPQIdCAwUGQghhBDiKWDWCL8FlEKYSpZSAkJoRMEm65gHLSzRXQLGj/BkUOUSaWlKZAivUkVNd2RZhPgjQZ1fumHDBpX+jXp1jGqWKlVK1aljlNO2YgR18qoeys4Dpn6EEEIIIYQQorWnPDb4GVUCcXbadPUXz/G6I6B1ZelHHpaoRo1UmQS6T+AvnuN1trYkgUzQZjR8+eWXakQW9eYwjoPAgNZ+MO377bfflJHcggULDNsD6lvuaaDulxBCCCGEEEIgJiTM+ki1p0TnCJg6ms+eVaUQyFIAjggFmCembVvVXQLGj6ZSpVS5BDMZSKATlEJDVlaWaosIkQFiAlz4NdAyD90JPv30U3nkkUekQ4cOud4L07j27dv7YK0JIYQQQgghgVAuceGLL5XIgCwEW38FlEJgOgQER8so2MKSBBtBWTqxa9cu1e4QWQh6kQHUr19fevfubS2tIIQQQgghhBBHgSdD1pkzKpNBExkA/uK5JTtbTUeWAiFFlaAUGiIjIx2ar3RpGiMSQgghhBBCHAfGj5aMDFUuoW9LCZS/G0wdMzJUKQQhRZWgLJ2oWbOm1KpVS/VVR4mEbenE/PnzJS4uTrp3757nvV999ZXyd0DZxRVXXCG333671KtXz8tbQAghhBBCCPFH0F0iJCJCeTKgXEIvNuC56hwRH6/8FggpqgSl0GAymeSTTz6R2267TR544AF56623pE6dOqqcAmaQDRo0kI8//lji4+PzvHfq1Km5ng8bNkwef/xxmTJlioSFBeXuIoQQQgghhDgIWliGlS2rjB/hyWDr0YD2lJgOU0dCiipBGzlff/318uuvv6qshU2bNqkHiIiIkJtuukllPehp1qyZXHfdddKxY0epUqWKnDx5UpYtWyYjR46UGTNmqPe9/fbbDn9+w4YNDV/ft2+fyrYghBBCCCGEBB4wbyzV427VXQLGj9kXL+aUSyCTwWSS8CpV1HR2jiBFmaD0aAALFy6Uli1bStWqVeWvv/6SpKQk+e+//6Rfv34qwwGCQnp6unX+wYMHy2OPPaYyH6Kjo1XZBDpXIAMCIsO0adPkyJEjPt0mQgghhBBCiO9BW8rSjzwsUY0aSXilSqr7BP7iOV53pLUlIcFMUAoNe/bskb59+0qZMmXk+++/V4JDTEyMEhHef/99VVKBDIfZs2c7lJlwxx13qJaZK1ascHgdduzYYfhgNgPJD6TdjRkzpsCdhHlszYccXSbKhvDawYMH3XIwVq9erZaHv0Rk/fr1Spw8dOhQQO+O9957T6pVq5ZLkCWEEELI/4GYUHnKO1Ju6AtS5skn1F88p8hASJAKDZ999plkZmZKly5dJDY2Ns/0e++9V/1ds2aNQ8uDQAFOnDjh5jUtemhB7t9//+3rVSHEI7z00kvSq1cvqV69us/28Kuvvqq+Z40aNTKcnpGRIa+99poyuo2KipLy5cvLrbfeKkePHrXOg+wvzAdxlhBCCCHGoDwipmVLKdG5s/rLcglCgtijQbtZLlmypOF07fXz5887tDxtPmRFEOJJUlNTA8509IYbblDrjVH8os7mzZtl+fLl8vvvv/v0+gcRwd71CiIsRAWs44ABA+Sqq65S1ziUmF28eFF51AAIEMgMmzx5sjz11FMOZ9AQQgghhBASWBGNg1SoUEH9tTdqvmHDBvW3Ro0aBS4LacNLly5V/27evLlb15MEHsnJyR4VnBDcBRqhoaEBud6eYM6cOarcoFWrVj5bhyFDhqjPR4ves2fP5pkOU1sY5f7222+qrCw/kP01adIkWbVqlfK1IYQQQggpqqSlpcnMmTNVAwFf3usFCkFZOtGtWzdraQROBj1//vmntXtEjx491N9du3bJvHnz8tQinzlzRnr27KlMIJs0aaI6WRDv8M8//0jXrl2lRIkSqvzlxhtvVMdO48KFC6qN6bvvvmt9DUEVgt7SpUur9kIaaE+qiU8aGL1FaQ2yW4oVKybt2rWTdevWGfog7Ny5U+6//36Ji4uTNm3aFFgWggDu6aeflrJly0qpUqWUyShS0LHODz74oFoOHkOHDs21nvY8GrC8Fi1aqGAeHh/2Utlx/j777LPqc4sXL668RfSp8AWBLitt27ZVQgrej1Fv+Iq44tHQvn17lbaPfdehQwe1jytXrqyCVqOLNra5bt26ahsrVqwod911l+rQohd4nn/+eWXuGhkZKVdeeaW8+eabhvvvySeflMWLF6s2tjB2xY/Btm3b1HTsu9q1a6vPwToa+VQ4cm7Y4+uvv1YBue3oP0RNeMNowT0+H51v5s6d69ByHQXXvC+++ELeeecdw+lms1m16kU3HqwHvGdSUlLsLu/qq69WbYC/+eYbt64nIYQQQkiggMEb3Ofj/vO5556TF154Ic89KCkiQgMyDzCqB9A5AgEPRuYQJEIsQNDy6KOPSqdOndQ8aGWJABABTufOneWBBx5QwRGCOgQOSCVetGhR4KcOZ6WLHFgrsvPbnL947ocguEXAu2XLFhWMjxo1Sg4cOKACQwSBAAE8jqveZwNBHI7RuXPnVICrgc4hWJ7GypUrVbr/pUuXZPTo0SrNHCIAAkQY+dlyzz33qGAM8yHVvCCQZg5D0rFjx6pg/4MPPlDbcPvtt6sLFZaDc/GNN95QAld+IEDGOXn69GkVjPfv31+t85IlS/LM+8gjj6gAE/NPmDBBwsPDlVjgCFgPzAtRZ+LEiWp9sQ+xnq6aRiIdHwE7RDp0eoEfwLBhw5SgoYH9gQAc+wpBLeZDBxik8G/fvl3Ngws59iMEQiwPqfy40OMij4u9LTjeECWQ9o999u+//6rPmD59uhKmcE3Ae//44w956KGHcr3X2XNDz7Fjx+Tw4cN2M5/27t2rxE2018V2QmyCD4JezIEQAMHMkQdKIPRgX+Lcw3nQuHFjw3XAMT1+/Lgql8A1EKISHniOrAUjsD2OCi2EEEIIIcEC7kG//fZbdS+Le3Dc52kxh5bxTvLBEsR89dVXls6dO1tKly5tCQsLs8TFxVk6dOhg+fTTT3PNd+zYMcszzzxjadWqlaVChQqW8PBwS2xsrKV58+aW0aNHW86dO+e2dWrQoIF6GJGdnW3ZuXOneuDfbuXf7y2WBfdZLO+3s1hmXJfzF8/xuheZM2cO5D/Lhg0b7M5z5513WiIiIiz79u2zvnb8+HFL8eLFLTfccIP1tSeeeMJSvnx56/PnnntOTS9Xrpxl5syZ6rWEhARLSEiIZcqUKeq52Wy21KlTx3LzzTerf2ukpKRYrrjiCstNN91kfQ3HHuvaq1cvp7bNdtnXXXedWoeBAwdaX8vKyrJUqVLF0q5du1zLwPvxufp9ERUVZTl06JD1NZwfJpNJzauxefNm9XzQoEG5lnf//ffnWaa2ngcOHFDPExMTLaVKlbIMGDAg13tPnjxpKVmyZJ7XbVm1apVaHv5qYLvw2ty5c62vpaenq+/X3XffbX1t9uzZar7JkyfnWa62D7/++ms1z/jx43NN79Gjh9qve/fuzbX/IiMjrdsG3n//ffU6PvvSpUvW14cPH55rPzhzbhixfPlytbzvvvsuz7Tq1auraWvWrLG+dvr0abWuzz//vPU1rAvmc+Sh399g2rRp6nhhudoxaNiwYZ5rIt6LayK2FecCHvg3vnNbtmzJs+6PPvqoJTo62uLPePTaSQghhJAix9q1ay3XX3+93fuwxo0b57pf9Cca5BNvepOg9GjQQHowHgVRqVIlazlFULJrqci6KSLnD4pYzCLh0SIZKSKXjouknsuZp55jI9+eBqOyP//8s9x5550qtVwD2SYoX/jwww/VaDNKKpClgFHq3bt3qxFujGTffPPNqnQA/x44cKBSHBF/ahkNMOtDtsHIkSMlISEh12ejPAMj+xhVRgmGBpbjDA8//HCu7Jdrr71WjZ7jdQ2UfVxzzTWycePGfPfFTz/9pPYF6v416tevr7bzhx9+sL6m/RslG3qeeeYZ+fTTT/Nd319++UWN2qNTgr6mH+uIdbc30l0QyI7o3bu39TnMIpGuv3//futrX375pWpDi5F4W7R9iG3DuthuG7IWUCaADAmUS+iPo95/BdsA7r77blUSYvs61gfzu3Ju6NHeg0wFI1DKoc+swXmK81a/P1Dig+PhCFDX9Z/98ssvq0wULNceSUlJ6m9iYqIqT0IpCkDGBkpKUNoyf/78XO/B9sDsE1k9KCUhhBBCCAlWkE08YsQI+f777+3Og7JaZA8HfLa7hwlqoYFcLpfYNC9HZIgsIRJbHhEcxsJFkk7lvI7ptTuJhEX6fJfBFwMBDQIwWxBgI9CDZ0bDhg2tQRtEBZS3IHAaP368CrRQv69NgyihBWUIJAHS6u2BtH19sHjFFVc4tQ16UUDf5UQL6vSv59f5BPsCAZ7WXlUP9o9eaDh06JAKgFHuYztfQWj7xJ7ZH/afK+CY2F6AsV+3bt1qfQ4fBqxjfp02sG0QA/UigXY+aNNd3f9AOwaunBtG2KvZs10vgGXpzwF4N2glXc4AcQReCkaCjR54VgCUkOn3B9YNZTJG3TK07eGPKSGEEEKCFdxPYtAGA0v27uUQT7z++uuqlJf3RQVDoSHYObJeJOlkTiaDJjIA/MXz1PM5049uEKlh3+jQH0HwCREAPg0YkcZFAcZ/EBpQ548LBoSG1q1bW0ehIVQA+CM0bdrU7ki8UXDmKBh9d/R1fzCS0fYJLqy2ppnA1Xab9vaDp7fZmf2vXx9Xzg09MCEF9sQjR/YHslggMDkChAVkiUAggQ8I/Dngv6A32YSPAzw2IBZhfnxnQPny5fMsr1y5ckqsswXbg0wGZ78HhBBCCAkMzBkZkvrPZsm+eEFMJUtJdLOmElpE2pYjm/fVV1+VGTNmKPN2IxBvYDATTQLsZbaSvFBoCHYgJGRn5JRL2Kb34Hl4sZzpKZdLKHwMRAIENSiHsAXdQfDl1o/EIqsBQgMuAAgOMeoNtRGj1T/++KNs2rRJGQ1qaCP+CLxcGTn29r5AcKeNtOux3T/Vq1dXgbKWIWBvPiO0fYJA09v7BJ8Ng08ExDCvNALbtnz5cpXur89qwPmgTXfXuhTm3IDZJYBxqasgW8fRDBqUtMAgFSaUOPYoLbEtLwFYHoQ3CBEwicR+xntsgUhhVHaB7dGyRwghhBASXCSuWCEXvvhSss6cEUtGhoREREgYOqf1uFuK33ijBCsoJ0XpPAaYcI9pBO6LUJaKDnIY3CHOQUkm2ImOEzFFiGSm5pRL6MHzzJSc6cXixR/AqC+6JqCdnr7bwalTp5TXANK79an8EBow3+eff24tpYAYgSwGdCdAAKuvi0dnAwSUKK3Q6tX1ODqa7K19AS8GdD7RXG4BuijAu0EPWoECfbtPYK/NoR58BvYpOizYdjLw9D6BbwKU5GnTptkd6b/lllvUSL/tPPhxQNqatu2FpbDnBtp3QgT7+++/XV4HzaPBkYdWDoTuK+hCYvtAeRFKIvBvzR8EQg32J0okNKFGO6fwGjpi2AKxDt8nQgghxB0j58l/rZdLP/+s/uJ5oBPI2wSRIWHWR5K6fbtkHj8u5uRk9Tdt+3b1OqYHG8hagMcbvKlQKmEkMiCDFZ3LMICHslSKDK7BjIZgp2pLkdgKOcaP8GSw9WgINeVMr9LCq6s1e/ZslXFgC0ZekZqEQAqiAloRInX//fffl/T0dGVWp0cTETByj0BZAy0KYRIYGRkpLVr8f9sgQsyaNUsFpwjE0KoGASJGeDFCjID7u+++E38B2RjYT9hO7IusrCyZOnWqWne91wGyOWDmiLQv+AggMFyxYoVqqVgQ2OaZM2dKnz59VCtDpIVBwYW4gdY9qOc3EgLcAdrKzp07V7WpRPtIbCfazyKDAdvbrVs31RYU7WZfeuklJSohwIZhKMQomF3a+lK4ijvODawvAnuIJK7U7rni0QAzTRiG2qKJTLbT8D3BuQFPDi0DAgIVSitgfqQHZqVoF4vtIoQQQgpDMI6cB/I2QRDBumccPSqm2FgxlSmj7l1wD5ONNtpHj6rpMW3bBkUZBbI/MTAJXyu9EbceZH0+/vjj6p4Tmb6kcFBoCHZg8Ni8T053CRg/opQC5RLIZIDIEFcjZ7qXjSAR2BrRr18/FeTBW2H48OHKcAUXBnQIgBu+1ilAA2UCuBCcPn1aCRO2AgS6HEBs0IN0c3SBGDdunAqgMXqNkWQsG6lR/sRVV12lshcQiEN1hcEixIcTJ07kEho08QYCwYIFC1QWBAJJCAW2JohGoKMH6vfhoIsUMog6CLKxHxFwezJrA6aWqI1Dxgq6UMDrAMcSaf6aAIAexth+/EDMmTNHeXJgPdF5wp0U9tx46KGH1PvWrVuX63z0J9D94tdff5Vhw4YpUQ/7F+cK9ieOuZ7FixerrAh7RqGEEEKIowH52Q9nSQbKC7OzJSQqSsScLVmnTknChQtqHn8PzO1lAyBQxzaFRkeL+ezZgNkmeDJAIMG6ayIDwF88z754UU1P3bxZYlq2lEAFwgkGqBBXGHlRaduMTmm4x3bWBJ7YJwQ9LvOZTtwMgmiwY8eOPNMQUGs19Qig3Wo2ghaX6C4B40d4MqBcApkMEBn8pLUlIcEAWmFCtIG5ZiADsQmCzosvvqgyjfwZj147CSGEFHrk/NADD0j67v/Uc0toqITCIDksTEIxGGSxSFSjRlJ5yjsBM3KObTo2+BlVcmCUDWBOSvL7bUKpx9lp01W5RLjNQAPQMh3KPPmElOjcWQIRZMviPia/Vu233nqryvbE4F5RiDe9CTMaigoQE9DCEt0lYPwITwaUS/hBS0tCggn8WCETBNkC7jKq9AXIHEEK4cCBA329KoQQQgKYhI8+kvR9+8UCH6jQUMG4uTkrS0IyMiQbf0NCAm7kPBiyAdBdAqUeyMKwLfnEc0tamoTEx4upVCkJNDD4gPIHZMraA53qJk6cmMvLjbgXCg1FCYgKAdbCkpBAA2UW9tojBRIQGCgyEEIIKezIf9KKlWJJT0e9pCovAAhplfCQkaEyHMzp6ZJ9udwgEEAbSHgyYHtsPZnwHKUhmO7P24QWlvCTQKkHsjBsszJCTCY1PdpOy29/BL5aKH9AOTGMxO2VkGJQ6I477nDJT4s4DoUGQgghhBBCiEdG/rN1nZz0I+ch4eFihthgNotkZQXUyHkwZAOgpAOmlfCTgPEjsjCUQIJ1N5kkvEoVNd1fSz/0nD9/XmUnTJkyRdLS0gzngWcZRAgYkcMjjHgeCg2EEEIIIYQQj4z8h4SFKVHBghHmzEyxhIdbR841QmNiAmrkPFiyATSzylydM+LjA6ZzRmpqqurGBvP4C3ayR7SuWk888YTq7kW8B4UGQgghhBBCiEdG/kOjonLEhrAwVSIhaWmqXEJlMpjNEhIdLbE33hgQI+fBmA0AMQEtLOEngVIPZGFAIPHndUe7948//ljGjBmjyiWMiI6OlmeffVZeeOEFKeXHmSXBDIUGQgghhBBCiEdH/gXlBWFhOVkNEBlAaKhE1qwppR9+KOD2fqBnA+iBqOCvppV6kDGyZMkSlaGgdZuyBWURjzzyiGqLji5gxHdQaCCEEEIIIYR4fOQfYgNG/iU9XXkchNeoIWUeHeDXo+fBlg0QqKBFJVpVomWlPe655x7V9atu3bpeXTdiDIUGQgghhBBCiPdG/iMi3Dryj+4Wynjy4gVVroFMCm8F+4GSDRCo/PPPPzJ8+HD56aef7M7TqVMn5dNwzTXXeHXdSP5QaCCEEEIIIYQEzMi/XlhI37c/Z7kJCW4TMXwpXJAc9u3bJ6NGjZKFCxfa3SVXX321TJgwQQkNxP+g0EAIIYQQQggJiJH/xBUrrNkRWefOKeEC7TEhMJhKllQtJ+EJgXIN4KzYoF++Xrgo2e0OMcXFU3zwMKdOnZJx48bJ+++/r0wfjahdu7a8+uqr0qNHDwmFsagXoPjkPBQaCCGEEEIIIX4PRICEWR9JxtGjYsnKEktqqljQyQLBpsUiocWKSVilSqrFJDwhIBggk8LRbAT98iU7W0Kjo5VwkXH4sKRs3KgyMVQXDTeXfhCRS5cuyZtvvimTJ0+W5ORkw11SoUIFZfIIs8fw8HCv7TZ74hOPf/5QaCCEEEIIIYR4haykJLmogrbTEla2nJTscbeExcY6NKKMYA8igAnzR0RIFgQHkwmtBkQyMlQgGFGqpJjKlFEtJ/EcZRWOZFLYLh/LCAkJUVkTmUeOiAXdMlJTxVS2rGpjWZisCfJ/0tPTZebMmSpD4ezZs4a7pkSJEjJs2DAZPHiwxMTEeHX32ROfePwLxju5JoQQh0A/YPyoOQLmw/yuLLNGjRrSr18/tx2V9u3bqwfJ4ZZbbpEBAwYE9O7IzMyUqlWryowZM3y9KoQQQoKE05Pflv03d5EzU6bI+Xnz1V88x+sFAc8ECAcI9iAC4C/aZIaEhqqMBbQ+hBhgTkpS9z3oboHRZ1Va4QC2y8cysHxkR1jbcYaHi6lkCYmoVUtCY2OtWRMQKYhzZGdny9y5c+XKK6+UZ5991lBkiAgNlUebNJEtc+aolpbeFhlsxScc9/DKlXn8HYRCA/EqWtBrT7Fs1KgRA1YS0Kxbt05+/vlnpbx7Ewg96sbK5tGlS5c88+7Zs0d69uwpVapUkWLFikm9evXklVdekZSUFOs8SEl87rnn1AhDWlqaV7eFEEJI8AEx4fyCBZJ9/rzKCLBAKEhLU8/xekFiA4wZIRxgRFn9xoWFKZEBIgBEBuu/M7NyRIe0tBzfhlKlHFo//fJFLJJ96ZJkHj8uZvwGWiw5WRNKzMhSnw8xAtugZU0Qx8Cx+f7776VZs2bSt29fOXTokGGAeleFivJT02YypHgJkcVfqMwCb6MXn0Lj48WcmKgyXPAXz3n884elE4T4ESNHjlQ9ggMNBNYkhzfeeENuvPFGZVTkbSAcoL2TnkqVKuV6fuTIEWnZsqWULFlSnnzySYmPj5c//vhDRo8eLRs3bpRvvvnGOm///v3V+fjpp5/KQw895LXtIIQQEnzlEhe+/FLMqanKTwHZBipj4LIggNcxPf7RAXbLKND9AcIB0tbxPmQUhKBOH9kEKGtAMIjnYSaVhRBiMqk6enS3cARt+dnHj0t2UpIymDRnZqplK7DeeITnhE+uZE0EM46YJeJ+AwMxa9eutbucTuXKyQstr5Ur4+LUcXbVb8MdaOITyNy3L6d85nIWDc49C85hHn+7UGggxAnMZrNkZGRIVFSUR/ZbWFiYegQaEWz5pDh9+rQsXbpU3nvvPZ8cB4gHvXv3zneeefPmyYULF+S3336Thg0bqtceffRRdW4jhfH8+fMSFxenXi9VqpR07txZPv74YwoNhBBCXAaeDGZkzSHz4LLIANRfBOvJyWr6xa++ktIPPmi4DASuEA5QG4/gExkFyi8BggAEDGQzZGVJ9unTEmIKk/AqVZRZn6OBKZYvIfL/DIacFfz/v7Oz1R8IHMCaNREf73DWRLB2NijILHHnzp2q9EE/mGFLizJl5LkKFaRV02a5zg9X/DbcBY4Bzgdkt2jnAkQGJUBBgLBYxFy8uNuOf7DB0okiREZ2hmw4uUGWH1qu/uK5v7N69Wp1kfn888/VBQpus6jPuuOOO9TIrG3qOEovMCrbunVriY6OliuuuMIw6IPxDEZwMeocGRmpatGHDh2qXteDz8ao74IFC1RQhnl//PFHu+sL74PbbrtNrfc111yj1qFx48bqOfjqq6/UcwgV6P37zz//FOingHVC7VrZsmWlePHiatuPwpDGAASPLVq0UMuvVauWag3kKAg+n3nmGbUvsJ3YNxMnTlQBqLMeDdpxW7RokUq9x0g71gkj/Xv37s3z/r/++kv5GiDAxfG96qqrZMqUKbnmWblypbRt21ZNRwDcrVs3+ffffw3333///acCbgTe2G/ow4wbApwzeB9MhXAuvfXWWy6fG0ZAZEArJtt+zgjUsV4oq0A5AtYJ29G9e3c5g5Q8N4LPT8JITD6uzqB8+fK5Xq9YsaJqEWUrGt10003qvDp37pxb15MQQkjRAcaPKlDHSLDNfY56jgfKEE6dsrsMBNwIXCEgwIchY98+MV/uToDshZDoaDGVLi3hlSpLVKNGUvqRh503abTkWrH/P7TJl0UHbaTd2awJI2Eh+a/1cunnn+XMzPfk6FNPy+k33pCz06arv8cGP+OTkgFXzBJTt2/PKTVJTlZ/07Zvl21T3pU+Xbqoe197IgPu3Re98oosbHWdXFOuvOH54avMkciGDXLOMdwLayJZZKT6qwQos1lNj2zQwKvrFSgE3tApcYmVh1fKkj1L5EzqGSUwRJgipGx0Welep7t0rNbR7/cqglVcaJBuhVHjd955RwVzmzdvVsG8BkZjEbDee++90qtXLxXoPv744yp40lK/ETgjWEfwhJHc+vXry7Zt2+Ttt99WAerXX3+dJ8DFciA4lClTRokJ+YFA+v7775fHHntMBbto1XP77bcrwQNiyaBBg9R8SHHHeu7evTvfHsBo4TN//ny1TAgoWJ9bb701z3zYBow+I4hFwI2AEwGzbUBpBGrz27VrJ8eOHVPrXa1aNfn9999l+PDhcuLECbW/XWHChAlq24YMGSIXL16USZMmyQMPPKCEBY1ffvlFiTMIdOEmDAEAAgLq9/AcLF++XLp27So1a9ZU25aamipTp06V66+/XjZt2pTnmNx3333quOLzEfyPHz9elQhAeOnYsaMSUCAeYb0gzNxwww0unRu2YJ+VLl1aqlevbjj9qaeeUmIKjsvBgwfVfsV5BSFNAyKBI54I8FCAkKIH6wgBA1k3OO4wpEQbKH0LKAhC2P6HH35Yxo4dq9YX6w3H56effjqP0RIEMdxQYR4cJ0IIIcRZ0F1CeRwg9RwBm23wftkDIayAexZNONCPnocho6B0aYm6qrGEICUB2QlNmkixli2cWkdkEgC0rxRkl15Ok4c4gkwJlTGRni4ZBw6ImC1KZHA2a8JeFgDq/lUQnZWV4ytRsmRAdDaw16njXFqaTN+wQeat+00ytIwQG3DvBn8o3N+m/b1RTm/ZKpkJCYbnh7szRxwlfcdOCY2JURkVal1wf3bZC0QBI9KYGEnfuVPCvJhpEShQaCgiIsOc7XPkaNJRMVvMEhUWJQlpCXI65bRcSM+5ePm72IDRVASfGNEHzZs3V0H6hx9+qIIjjePHj6tRaowaAwTN1157rQqY+/TpowIu1JsjcP3111+lTZs2uRTVgQMHqoAKAb0GhAAEmw0cVCsxP5Zx3XXXqed4380336yCvl27dqkgHiDgxPqtWbPGrgHmli1blMgAcWL69OnqtSeeeEIF61u3bs01LwJKXIxR96Z9xt13361U5IJAz+J9+/apDIs6depY9x3q++E58Pzzz6uRfWdBwAwxSBslxzZDPNi+fbva33AcxudAZMB8yFSwHTUAL7zwgtVLAH/BnXfeqYyEELR/8sknuT4XHgRaNgcEA/yYYRsg7mgmjRCisH2zZ8+2Cg3Onhu24PjmJ0QhqIefhfYDCmHj3XffVSKMJhpAeLDdHiMgDGmZMgAZLB06dFDHG/2nv/jiCyWwQHzQCxkwhxw3bpy89tpr8u2331pff+mll9T8tkDcAUh7pNBACCHEFdDCMuHDDyU7PT0nWLPxaEDWQGixYlLyrrsKXBYCbtTqI40ewTmCz+xz5+TiN99axYekVatype47U4+PID+sYkWVNQGBAaaTqM2HTwAIiYhU4oazy7fXMhGfgbaZEDEQuEJ0wb4Iq1TJp/4ErnTqSM3Kkjm7dsl7O7ZLouZtYQMG7ZBpivs/ZI7aK4vRzg93ZY64UpKC+SE8mUqUyBGaLotP8AJR/iAQGqKi6NFhBwoNQQ6yF5DJAJEhNjxWykT//4t7NvWsHEs6pqa3qdxGZTn4Kw8++KBVZAA9evRQwekPP/yQS2iAvwEuXBoIcPEcWQ0oqWjVqpUsXrxYjVTDaV/f/QIj3WDVqlW5gkkEdI6KDADzaiIDgNChLV8TAPSv79+/367QgO0D+m0EKHFAUKyBgP2nn35Swbf+M7CdEDm05dgD+wRlCRAC9PsEWSPICoAYAnHDWWAmqE/Fx2do24zgHcLGgQMHVMaAXmQAWjCOjAqIEChf0EQGgPIKpPUbbRuyQDRMJpMqY0G5CUbxNfB5aKmEddHvB2fODVsSEhKkcuXKdqdD9NCr9Ngf2HY4LmN7ALazIJ8FoPkoaHz00Ue5nkNYw+dBjEPpDc59DYghEFcgREH8QNYHhAdkk0DoMPoce51iCCGEkIKAwWOpu+9W3SXgpwBPBhjpqUwGiAzR0Wq6PSNIWxAkarX6CNrPfTJXBe2qO0B0tEvZAHqzSayTCi4vg/tmZB2ElSolJW67TYq1uEYFva4E/rZZABIRIVkQHJDxgUdGhgreI0qV9Kk/gTPiTHZkpCzY859M2bpVzsAvwwBkTGLQBw+UsBqVxeB4QVjBNqtyCWQyuDFzxMg/wpFzAoTXrpVz3qLrSHiYhMTESOb+A051NilqUGgIcrac2aLKJZDJoIkMAH/x/FLGJTUd87Wo4FyKmaewrc0C2ii7fh7U0CP9XA9GqG1Tv+vWrav+Yl4EW2jth+wIlBgYgdIMPfB5cAZ9oA+0kWrbjADtdZR72AMBKEoPMFqtBwGyHtT5o5zAdj9p8xYkNGCfIEPC0X3i6r7QglZtm5FFASA62ENre2S7zQCiAAQWjODrj7vRMYBHBFR029chDmg4e24Yoc/EcHZ/aEKVM8JWfuDHHEIDsjQ0oeGzzz5TAgQyHeCdAe666y6VXYFsD2R6QHyw3R6j7yUhhBDiKOWee1b9Vd0nYAwJzwaTSY3eQ2TQprsjdd+VbgUFjaqHhoVJRI0aUnrAI4XKLLDNAlDtPrVOBhA6IMRkZqqMClPxEn7d2SKkeAn54fQpeWvLFjlop+wzPCREHurRQ8ZOnZpvOa9RWUyIGzNHXBGh9OeEOeGc2zMtgh0KDUHOxfSLKqsB5RJG5ipRpig1HfN5A61bA4Jie14BnurooIGACunlKBcwwlYQ0HtAOAJG0J15Pb/A1FtgnyA7AKPpRmhijbP4apuNPteRdXH23LAFAXp+wpEj64AyCnvfDz3IFNFneOS3vnojxxkzZqiSE01k0IA3BUwrkWWiN7PUtsdWpCGEEEKcBWICWliiuwSCN3gyoFzC0UyGgoL2wnQr8OSoulEWAIJeZXQYFvb/TgaXuxpAeMDIuS/9CQoCgxgvDhsmGzdtsjvP7WXLytCbOkvrObMd2m9GZTHuyhxxRYTy1jkRrFBoCHJKRpZUJRHwZDAyV0nLTpO4qDg1nzfQTPLgY2AbtEFkQFcAGBragpFmPVh3mC5q6eZ6jwbb0W2M3AKtdh7ZAfA+QAcEfx+lxf5C8IuRf/2IPvafHozAQxCx3U9G8xqBfQITQttuCZ5Gy9SAZ4O9z9afM0aeCAiAbbNYCrM+hTk3UHLx5ZdfFmod4GHhikeDEVpZiD5D49SpU3nKLkDm5VpKmIjqQWmLlj1CCCGEFBaICvZaWBY2aC9stwJPjKrnV6KB+1m0y1T1/miXCA8AjLzjeZjJL0fNUYr84osvKqHBHm1LlZLna1whTerVk9IP9XcqENeXxRQGd4lQ3jgnghW2twxympRtorpLmEJMypNB35YHz/E6pmM+b4AADiOxcLi3bZv4wQcfqCAH3QVsmTt3riQmJlqfw+gOtfu28+L9+paOcN/HcwRacM8HMJFEdwWklNuCkWQIFf6Ctn0wDNRj2wUCI+XwYkBXhMOHD1tfRxkASgsKAvsERotG86LtpW3w6S5g6onSFGwPPkePdq7Ci6Np06Yq+NbPA3ECxoroMuIuCntuwJsDGQB63wdnQVYJOnEU9NC35kTLStv2m9h/mrkjzg19dgqyFjQBTmPhwoWqTMdWvMMNBX6U9b4jhBBCiD9gDdpRbmCTLWnNBnCyhh6BY+Up70i5oS9ImSefUH/x3F0BpZaODwEhW/ODKFtWQiMj/9/dIitLsk+fVuUT/jJqjsEsdPWC75U9kaFJXJwsuK61fHJjJ2neqpVrLUbdhDtFKE+fE8EKMxqCHGQzoIUlukvA+BGeDCiXQCYDRIbKsZXVdG8ZQZYrV051Rxg5cqQyo0O6drFixZSbPwIdZDOgFaQtSBFHFwCYC2JEFoEpPBrQycHWowGt++DHgIAKbvswEoSIobX4g0ke2lWiiwDM/dAiEWaKGB3H6wi2cRH1BxBgo2Ye6e5IqYcR4YoVK1Q2hy1oVfjjjz8qg0F0qYA4gBaQDRs2zNOhwhZ0dUAHAnQV6NevnxJlEFSj2wZEHexPT6TOI7CF6IRjjm3F8YWwgGOxY8cOq/CBzhcQXRDswtBRa28JjwW0u3QXhT030HYUhqT4AYYPgiu44tGAFp84T/DA9wL7Z8mSJbJu3Tq1HhB09Md62bJl6jyB8SPKPdBKFK/BRBPfIT0QNbAf9L4NhBBCiD/gqW4F7hpVtwdacWYcPKgMJrMuXFDBMMD6SmSkEkYK29nCXWBgD20oZ82aZXfgCVm34195RbogE/fixUKVPLgL28yRwrbM9PQ5EYxQaCgCaK0r0V0Cxo/wZEC5BDIZIDJ4u7Ul2uihjGHatGnqwoWLFka1ESjDjA7Bpy0jRoxQwTLaEyKzAZkRCL4hUuhBSjhGvp966ik1Kg3TGXyOXpDA8jHyD7d/ZEogIMNy0MYPaeuu+hF4CrRfREbGggUL1HqjAwK6BNiWnmAkGoEwWntCzEENPvYpfiAKEhqw/WjpiM4D6LyA/QJHYOwLLEMzrvQEGG1HUI/PwSg9Ml1QwqA/ZiirgIiCVpbYNohGKB2AqOSsWWd+FPbcwPmGDAuIEq4KDa6A8hIIB1jfkydPqu1AqcN7772XZz0g8EHYg0CD7xDMMLEPX3311TweHRC3kDWC+QghhBB/wxc19K62SrTtgGBGJiKyey9n+MJo0lS6tBRr1lQiatb0ebCOe4BJkyapwT2UNxuBTlu4n8AgFQZa/InIhg3Qjk5lh2QeOSJhlStLqMlEI0cvEmLxBye6IgRGlwFGa21BgKXVoUMZNAq4CwMEBnSXgPEjPBlQLuHPLS0BatA7dOiggl+0tMwPtIhECz6k1BPiK9auXavORWRBGHUBCSRwc4GbDHiEOGuK6k08fe0khJCiRmGCaV9QmBaG3vocww4IqSliyTar7IUSd9whpR9+yOf7OS0tTaZPn64Gn/Rm0nrQInz48OFqYM8f7w+045S+b59knT6dU5KCNqrww0DZxGURypelHb6KN72Jf0lPxKNAVPCXFpaEBCvILEAJEAJ0I6+HQAHmkOi+gTInf7yJIIQQEthBuztxZ7cCT7RKLKgDArwY0grIPvU0KBVFNieyR2HObgQ6wyHDExnIRsbS/iBq5TlOxYqJOTExp1wiNVVM5cpJZK1afn0+BwsUGgghxM3A7yDQQXmK3liUEEJI8FOYYNrXeLKGvrCtEt3ZhtPdYBvg04Uy5Z07dxrOA9Pxhx56SIkQKJfwV1HL3nEym7Ml69gxMSenSFjpeKn4xiSXW6oSx6HQQAghhBBCSBGnsMF0MFNYocDdbTjdxZo1a1SrSnQeswdKl9HFSt9m3V9FLXvHKTQUpRJVJWPfPrFkZkn6zp0SRmNHj0Ohgfg1qHV31EYEfg6EEEIIIcR5/HnU3dcUVihwdweEwgKTcGQwwFzcHvBImzBhgrT0wLH2lKjlr4JOUYWOWYQQQgghhBRxGKTZxyoUpKbmGQCzCgUREXaFAq0NJ0wIEUhryyhsG05nOXDggGrljZbi9kQGTEOnL7RT94TI4IioZcnOtopa3jxOxL1QaCCEEEIIIaSIwyDNPoUVCrQ2nOh0AONHpPBjNB9/8dwTbTj1nD59Wpk4ovxh/vz5htnCaOX96aefysaNG1XrcduMgEAQtfxF0CE5sHSCEEIIIYSQIo4WpKFGHkGZbTq7O4O0QGufqQkF8A5AWj/KSFQwjBHyy60SCxIKNL+BXOaH8fEe7eiRmJioOki9+eabkpSUZDhPuXLl5OWXX5YBAwZIhJeOgadKSdxxnIj7oNBACCGEEEJIEcdbQVogts90l1Bg24YzNCZGvW5OTpbkv9a7TXDJyMiQ999/X8aNGydnUKJgtC7Fi8sLL7wgzz77rMR6uQODJ0UtXwg6xBgKDYQQQgghhBCPB2mB3D7TSCjAiDuCYWfEAa0NJ/bF+fkL3Cq4mM1mWbhwoYwaNUr5MRiBrIVBgwYpM8iyZctKMIpa7jhOpPBQaCCEEEIIIYR4NEgrqNNAxpEjkvDhLDGnpUtYmTJ+W06hCQX67UI2gqNlIJg/4aPZcunbbyXr3DkVWBdWcME+hIHj8OHDZcuWLYbzYF/DCHLs2LFSo0YNCXZRy/Y4Ee9DoYEQQgghhJAAw5M+B4UNpp3tNCAmk5gvXpS05GQ58/bbStwIhFR3Z8tAVBbDosWSummTmFNSlMgg0dESWqyYhFWq5FJrxz///FNefPFF+fXXX+3Oc9ttt8lrr70mjRs3Fn+CmQfBDYUG4vdAdW3fvr18/PHHLr23UaNG8v3337v02fjxGz16tIwZM0aKOjARgiPx22+/LQ888IAEKgkJCVKtWjVZvHix3HLLLb5eHUIIIcSvfQ7c9Vn2Og1knz8vWSdPqpaGYjarABwP29F9fzOQdLYMRJs/ff9+tX0C00Nsc0qKZJ44IeEwSSxTRpURaK0d8xuR37Vrlyp/WLJkid15WrduLRMnTpQ2bdqIv8LMg+CFQgPxKhAL+vfvLxs2bJBrrrkmz3QICmfPnpXt27fzyPgZU6ZMUcZBPXv29Pr5Yg+0aNJED/zQvvfee7Jt2zYlJqDusFWrVkokgtikUbp0aXnkkUdU/SKFBkIIIYGGN30O3PlZRp0GLBazCqrNaWlqnpDISAkrV05CixfPNbpvycyUi99865LYYU+gKIxwUVAZiG1Wgn7+0MhItQ3Yn9hebJslPV1tW0SpkgW2djx69Ki6t5kzZ47yZDCiQYMG8vrrr8vtt9/u0TaVhOQHhQbi9+zevVtCQ0N98tmpqakSFsavSWZmphIa4ExsQpqfl7jhhhtk3rx5eV5HVgVqEG/U3VxAYIiLi1N9osuUKSMnT56U2bNnS8uWLeWPP/6QJk2aWOcdOHCgvPvuu7Jy5Urp2LGj17aHEEIIKQzOBrj+9FlGnQbMiUnqc5DJgPIJLCc0NlZ9jja6n75vn5yZNl3921mxw142RmSdOpK+Z4/LWRr5lYEYZSXo5w8tW1Z1mTBnZqqshpDwcDFnZSnBITsx0W5rx3PnzsmECRNk6tSpknZZmLGlatWq8sorrygvBm/erxFiBCMo4vdERkb67LOjoqJ89tn+BEpP0B7p3nvv9ernolQDD1vxB27JEAgqVKhgfR09oG1B5kKVKlVk5syZKttBo379+irLARkTFBoIIYQECs4GuP70WUadBiBaYDRfTY+MFFPZshJyeXAJnxMSFSnZCQlKhAgrXdopscNeNkbG4cOSsn69WmZIWJhLWRr2ykD+v965sxL085uQrREejh6UIshmCA9X22zOzpbsc+fztHZMSUlRgyMogbhgJ8shPj5eXnrpJXV/xHtX4i/4ZpiYECd9Fvr165frta1bt0q7du0kOjpaBZLjx49XKWS4uB88eDDPMn777Tc1so2LLwLXuXPnOvTZWJ7enwH/xmt79+5V61SqVCkpWbKkSu/HD4FRaj8+t1ixYmq0HSP0P//8c655ZsyYIQ0bNlSCSqVKleSJJ57I80OCkhIExtp2Y3m1a9eWL774Qk2HAdC1116r9seVV14py5cvz7Mux44dk4ceekjKly+vPgufiRF/R/j666/VcahVq1au17EP0HsZy77zzjvVv1GyMGTIEMlG3aEH+O677yQxMdEhn4hy5cqpfWX0w3zTTTepZeFmhRBCCAkEnA1w/e2zEMCXfuRhiWrUSMIrVRJTTIzKJlBCQoUKEhYXZ50Xv8/IeLAg28FiMRQ74HGgiR35ZWNE1Kol4ZUrS3jNK1TmgDk1VSxZWRJ+xRXqdUxHJoUmXKgsC0fKQLAcm/sIJZ4gKyEiwpqVoJ9fsO5ly+aUUJjNal6skxId0tOtrR3NoaHywQcfSJ06dVQ3CaN7GdzjQGDYv3+/PPfccxQZiF9BoaEIoTkGX/r5Z/W3oIuoJ7l48aLyYrB9IEW/IBDUdujQQXbs2KEuvEjnX7BggUrtNwKiQI8ePVRg+dZbb6mAHwEy3u8qGNlHsIv6N/wbI+NoF6QHz5G6Fh4ertLY8BwpbUjX1wsXEBYgMGDd7r77bnn//felc+fOefbF+fPnlWswBIVJkyYpsQB+CZ9//rn6C78BpNQlJyer7cX6aZw6dUr5FUCAePLJJ9W+glDx8MMPyzvvvFPg9v7+++/SvHlzw2kQFG6++WblffDmm28qIQTbgh9H2/U3Oua2DyPBRg+ONQSVu+66y3A6foiRfYFSCmQ0XLp0KVeJhcbVV1+t5i3MeUAIIYR4E2cDXH/8LIgNlae8I+WGviBln3tWoq68UkwlS6qsA+1ztIwFBOUY7YdngzNih71sDEtSslomhAv1PDnZIeHCXhkIsg+wnrbrbZuVYDu/6qpRsaKEQmhBBge2sVgxNX/8ww/JT+fPqwGhxx57TI4fP57n81HW+/jjj6t7XAy2YdCLEH+DpRNFBG+6EztCp06d7E7DhTU/kDqGoHXTpk3S9PIFHBkFUHzteTysWbNG2rZtq55DGEDAjwwIBMau0KxZM/noo4+sz2E+iOdYN4ALP8SF7t27q6wDvceE9mOEYBhCBUSFZcuWWeepV6+eEgOQDaE3QsQPzaeffiq9evVSzyGcYN77779fCQEQILSyAAT+X375pTUTBGo3BAEE3xAENJ8CLAtiB37IELwbkZWVJfv27ZNu3boZTked4H333afMFbXlQpTA/sCPoH6fHTp0qMB9m1+XD9Qnok80sidgTGkEBBUcc4AMi5EjRypBxRatJGPnzp25zCIJIYQQf8XI50BfSmAb4PrrZ+k7DWBkHyUOWjmFEg8gYmD55cqp+1ZzUpLVQDKP2GHgZ2AvGwNZDEpkuCw2qOcuZGkYlYHo11vLStDKOezNj/VQ5RTx8VKy2x2y+YorVCcJmKbbA/dcEBcwYESIP0OhoQjgTXdiR5k+fbrUrVs3z+vPP/98gSn3CDSvu+46q8ig1aYhlR4GOUbOu5rIAJDaj/ICpJm5CoJpPVg+uh5g9LxEiRKq1ABOwPANsDWy1H7wkF2QkZEhzzzzTK55BgwYoH5kli5dmktoQNCs7/iAbUDpRuXKla0iA9D+rW0ffoghOkBgwb+RNaABQeKzzz5Tos31119vN7jH+5AJ4sz+sDVxRCYC/BUKwtaTQQ9EG+yz/MomICDhOGD78W98Js4p2+OgbY9+fxBCCCH+jLMBbiB8lnYPmmtALD5eiRgl7uwml77+RtK2bzcUO5AJIGFhknX2rMrW1TpHGHW4APBksGY0oJUm7hEuXZLQ4rGYale4cHa9jQbyjOYPuzz/oSZXyaAlS/KU1+rBABMGqJCRSUggQKEhyPGmO7EzwLfAqL0lgr+CAj+MikNosMWeslutWjXDz0FWhKvYLlMLWrFMCA3IAEBgC5HDHtroPgQDPRERESrYth39hxeFbdogUuWQnWH7mrYuWuYESgRQymBbzqBx+vTpArfZnpcBfC8g3hS0f+0JGc4AsQKiUteuXe3Ooz83IMwgwwPYZq9o28O2T4QQQgIJZwPcQPgsLAf3oihZQDYBAn1kSqguFGFhhmKHlo2Qfe6cJLz3fq5sXSzLKBsjJDYGqZo52QwZGUqgMGPQDaaQkZFOZ2nkt96OzH8oKUle/fxz+VyXAWoL7pdRGmtUBkqIP0OhIcjxpjuxv2KvvU9hTAA9sUxXP7OgddF6LPfu3Vv69u1rOO9VV11l93MR2ON8sSfMONo+CYKHIwaRyNzAw5bDhw/L2rVr5dFHH1W+F44AwQNdJSBQ2AoN2vagFSYhhBBS0MAN7qmQko/Rcm3k3Fc4G+AGwmfpyylsP99W7DCHhVnLG1BWgftc22xdo2wM86VLyodBgfskGENe9iwzm0wSUa2q01ka9tY7v/kTq1WTcZ98ogaAUKJqBEqCX331VeW7xUEREohQaAhyvOlO7C2qV6+uPBBsMXrNV6A7AwJ81P/rSzxstwPAT0BfLoDSgAMHDuTrY+EMyDaAnwGCfFeWCcMhbA/WqTC0aNGiUB4NCxcuVOKJI90m9KB0Auajtmjbo2U8EEIIIYHgc+VqgBson1WQ2JF15qycnz9PzCkpqlWkvWxdGE4C7diZ09PFDKNsk0l1mFAlFOj0gAGZy+JDaLEY9TmeAqWdb7zxhkyePNmu+XXFihXVvRA6hTk6sEKIP0KhIcixV6NWkImOPwNfAXg8bN682RrEw0cAo9b+AswKhw0bpgwhjcwgcRwQ9KNMAr2Ru3TpYj02MFFEYHzrrbe6ZV2QcYBuFjCS3L59ex7jQ2Qa2JY+GJUjrF69ulDrUViPBqw/SlbatGljt/wD7Sz1oNXpihUrDMt0Nm7cqMpMCjIfJYQQUjgysjNky5ktcjH9opSMLClNyjaRCJPvsgEC3eeqqKKJHfBisGRmKY+FgrJ19QJFyoa/5dL330vWhQsSiXJbtM+EySQyCkwmybpcRlpQlq8r2S0wzp45c6bKUICBuBG4J8G94+DBg1XbSkICHQoNQY433Ym9xdChQ1VHBpjiPPXUUxITEyOzZs1SQSgEB39IL4NfBDo9jBs3ThkjohUj2lHCRRitLGHmg+Ae7TnR9hJCwx133KGyG2bMmKFG/1Hq4C5Q27dq1SplFAmzSXhHYF/BBBKmlPh3fqDjBMwd//vvP0MTT0cojEcDBJKtW7fKiy++aPf4Nm7cWNUvQnxCycSePXuUaIM2odh+W3755Re5/fbb/eJ8IYSQYGXl4ZWyZM8SOZN6RgkOEBjKRpeV7nW6S8dqHcWf8Vefq6KOs9m6mkCB5yHh4WIqViznfRAmSpSwvhfZDgVl+Tqb3YJsUtyzwhwcJaBG4P4Q97O4J0S5KiHBAoWGIMeb7sTeAuaHCJqffvppee2111TA/sQTTyjBAa/BnNAfQDbDFVdcoTphQHSAOg0vhD59+ljnQYkA1n/atGny7LPPqh8YeBBgu9yZLle+fHlZv369WqevvvpKiRloc4nRfK0lZ34gIIeXwaJFi1S7SG+jZauglac90EoTnTrQlSQxMVFlN6B1KDp4QITQs2vXLiVevPNOTlolIYQQz4gMc7bPkaNJR8VsMUtUWJQkpCXI6ZTTciE9J5jzZ7GBPlfBla1b2CxfZ7JbsLzvv/9e3YPgfsMIZLuiDTnuBW2NvQkJBkIsnnSvI3nQ0rR37NiRZxpq+jGirXUisG3HF4z1he4EbSLff/99SUpKctigkDgOsjPQLhKZAoG+f3GurFmzRpVPMKMh8PHktZMQ4hrIXnh+9fOyPWG7xIbHSpno/2cDnE09K8mZydKwdEN5q/1bfltGcennn+XstOliTk6W8MqV80zXMh3KPPmElOjc2SfrWBRBpsmxwc+olpehBpkmKIeIatRIeTToB9JcfZ/+vanbtxtmt+jf+8eGDaoEYt26dfmW2GJQiT5RxNvxpjdhRkMRwZvuxN4Atf7R0dHW56h3Q2o/6vcDPQj2V5BxgeyMzz77zGlDRn8C5wpKbZCdQZGBEEI8AzwZUC6BTAZNZAD4i+eXMi6p6ZivRYUWftklIhh9ropytm5hsnwdyW7ZsXevDOzUSX5Yu9buurdr106Vc7Zq1coDe4YQ/4JCQxHC147B7gTmhO3bt1dK8KlTp1QtPpx8R40a5etVC1rQchKGi4EOSkaQ9UIIIcRzwPgRWQ0olzCqo48yRanpmM/duCuLMxh9roIFo5aXEH0KOs6uvi8/X4hjycny1oH98vXJk2IvTRylsxAY9ObfhAQ7FBpIQHLLLbeobg7oP4wLdvPmzZXYcMMNN/h61QghhJAiD7pLoCQCngxG2QBp2WkSFxWn5vPXLhHB6HMVTLiarevK+4yyW86lpcn07dtk3u7dkoEWmQbAqwulp7169WJZHylyUGggAQnq2vAghBBCiP+BFpboLgHjR3gy2Ho0mEJMajrm8+cuEa6OgBP/ztZ19n367JZLJ0/KJ2dOy4c7d0piZqbh/DD6RpbtY489plqZE1IUodBACCGEEELcCrIZ0MIS3SWOJR1Tngwol0AmA0SGyrGV1XR3GkF6qktEsPlcEXFNmLizm3y8YYO8+9efctaOwIAy0yFDhshzzz0nxYsX564mRRoKDYQQQgghxO1orSuX7FmijB/hyYByCWQyQGRwd2vL/Oro8VyVPWRkKLGgKPtcEec7G2ntvfft22c4T3hYmDw+aJBqZ4722oQQCg2EEEIIIcRDQExoU7mN6i4B40d4MqBcwhMtLdklgrgTlNz88ssv8uKLL8o///xjOA8ErPt79ZJx48crPwZCyP9hRgMhhBBCCPEYEBU80cLSFnaJIO5iw4YNSmBYuXKl3Xm6du0qr7/+ujRp4j6fEUKCCQoNhBBCCCEk4GGXCFJY/vvvP1X+gM5m9rj22mtl4sSJ0q5dO7/d4TBGhWcJyomQ6QMRjp4ixNtQaCCEEEIIIUEBu0QQVzh+/LiMHTtWtUrPzs42nKdevXqq49mdd96ZxwPEn0CL11xdUiIi2CWF+AQKDYQQQgghJGhglwjiKBcuXFDZCVOmTJHU1FTDeSpXriyvvPKKPPjggxIW5t+hE0SGhFkfqRav6L4CY1Tz2bOqLWfCZRNUtmQl3iJUgry+6t5775VKlSpJeHi4lCpVStq2bStz5sxRBi+2QMF8++23pXHjxhIdHa164OL9//77r0/WnxASPBw8eFCNgHz88ccuL+PIkSMSFRUl69atk6LAzp071U3d9u3bfb0qhJAAQ+sSUaJzZ/WXaeNED0SFN954Q2rWrCkTJkwwFBni4uLUPHv27JGHHnrI70UGlEsgkwEigyk2ViJq1ZLwypXV39DYWMk8elRNx3yEeIOgFRq+/PJLue6662Tx4sVSsWJFueuuu6R58+by559/qotF796987Suueeee1Tf26NHj8qtt94qDRs2VDVa11xzjaxfv95n2xKM7NixQx0DqMSRkZFKDHrggQfU67YgMEOA9vfff+e7zDNnzsjgwYNVahuEIrQXatmypQwbNkySkpJcWs9+/fqpnsj5oa2fvQfOOW/x1VdfyX333ad+OIsVKyZXXnmlPP/880qx15OQkKB+PG+44QYlqEGEa9WqlXz++edOfd6pU6fkscceU8cRAXCNGjXk4YcfFl8E8EaPzz77rMD3r1ixQl0T6tatq/YZ9t0jjzwiJ06ccPhz8BgwYIB4GoyooDb0+uuvzzNt7dq1ShjFsYiIiJCSJUuqefEeHCc97du3t653aGiolChRQp0rffr0UQ7bRuDY6rcX3y8It0uWLPHY9jZo0EBdi19++WWPfQYhhJCiQ1ZWlsyaNUvq1KkjQ4cOlfPnz+eZB/eQw4cPl/3798uQIUPU80AAngwol0Amg6lMGWt5B/7iuSU7W01P3bzZ16tKigj+Lc0V4iIyaNAglaGwYMECuf/++63TkJ3Qpk0b+fTTT1Uw0aFDB/X67Nmz1Q0zLjy4YS9fvrxVsOjRo4cKgvFef1czAwEEw7169ZL4+HgVlKIdEII41MVB2EFw2L17d6eWee7cOSUIXbp0SQWNEBsQTG/dulVmzpwpjz/+eIGCQWFBQGfU2qh27driLR599FEl2kDEqVatmmzbtk2mTZsmP/zwg2zatMn6Y/nHH38os6NbbrlF9YXGeY1zvWfPnmoUGXWKjoyuawHvwIEDVYCLGkdfiXI4p7A9eiA2FgSEKJw/EBrx/ceNBfbZ999/L5s3b5YKFSqo+SDIzJs3L8/7f/zxR3Wd6dy5s3gSCGmffPKJetiCQHzcuHFKJIE4hr9paWmyceNGeeutt9R7bHt/V6lSRbllg+TkZNm7d6/6bs6fP18JFviLTDA9TZs2VcIVwLF+//33lYiL7xjOAU+A5eK4Yv1r1arlkc8ghBAS3CCTGff5uPfZtWuX4Twmk0ndl44ePVrdSwUaMH6EJwPKJWw9JNQgQVSUmp5tM/hEiMewBCHbtm1DXYTlyiuvNJz+9NNPq+kTJ060vla/fn312pIlS/LMf8cdd6hpX3zxRaHXrUGDBuphRHZ2tmXnzp3qgX8HI3v37rUUK1bMUq9ePcvp06dzTTtz5ox6PSYmxrJv3z7r63PmzFH7f8OGDXaXO2nSJDXPunXr8ky7ePGiJTU11aX17du3r1qf/HBk/bzFqlWr8rz2ySefqPX78MMPra/t37/fcvDgwVzzmc1mS8eOHS2RkZGWpKSkAj+ra9euliuuuMJy9uxZt6x7u3bt1P52lgMHDqjte+ONN1z63F9//TXP9w2vYZkvvfRSge+/8cYbLSVKlCjwHNPWE+eLK0yePNkSHR1tSUxMzPX6Z599ppZ77733WtLT0/O878KFC5bRo0fn2dcNGzbMM29WVpZl0KBBanlDhw7NNa169eqWW2+9NddrJ06cUN+PunXrWjxFRkaGJS4uzjJq1Ci78xSFaychhDhCdnq6JenPvywXf/pJ/cXzog7uja699lr122bvcc8991h27dplCWRwvPff3cOy+7rWlgO9+1gO9nnQ+sBzvI7pSX/95etVJR4mv3jTmwRl6QRS8R2hdOnS6u+BAwdUtgJGe5GmawsyGsB3333n5jUteiBdPyUlRT744AM1QqynTJkyaoQUo6uTJk1yarkY7YQSjfR/W5AWjrR+DXw+1OyzZ89KsIGUeFu07BC91wgyL6pXr55H7YaTcnp6uhrVzw/sv2XLlskLL7ygvkcYPc/MzBRfg3Mnw8naQ5SPoHzA9jVk3BTkz4LyilWrVqlRff055mxpzrFjx9S+x7/xvUCqpq3r9ddff61KIWwzc5DNgO8OMoJQMmELSijGjBnj0PrgO/Tuu++qkgVkdVy8eDHf+ZHtUb9+fXUN1ZdY3HbbbfLzzz+rDAjsFywP2RK2oKTn2WefVe/BdRtZFjDb0n83kVWB8/qbb75xaBsIIaSoAiPAY4OfkdNvvCFnp01Xf/EcrxdFkJXYtWtXlb38119/Gc7TsWNHlYm5aNEiVUIYyKCFZVjZshJiMkn22bNWPzr8xXO8junRTZv6elVJESEohQakDSPFdvfu3apEQg8CB6QEw+BFC8C2bNmi/jZq1ChPqjCAtwNAGj4pHBBrEFSgttsIBHiYvnTpUqeWi6AZgZlRarst+EFBcIRAyp0gKEOApH+gfKMg4B9h+z6jR0FBnz1Onjyp/iIYdde8y5cvV39RYnTjjTcqkQ4P/KCjDMYXoNwDQTgC2xYtWqhA11VwTPAoaD+gzAf+LiitchWctzfffLMSbN58803VlxvlDhDjNCDiwNxWuxbp+33joYkU7gBiA8pQIMj99ttv+c6L9UIJjSbaasA4C14hOB9QnoHSHJSm6P0fsH9xHZg6daoqO4HjN8okIGLBJ0fP1VdfrQwhURpFCCHEfreB1O3bJfP4cTEnJ6u/adu3q9eLktiAwRL8Ljdr1kyVNxqBaT/99JO6n8E9QzAAw9NSPe6W8CpVxJyUJBn79iljSPzFc7yO6TRGJd4iKA0HcKOMmmSMquFCg5t21F6fPn1a+S9gdA0GfhixBIcPH1Z/MZpmhPb6oUOHxBcgkHEkYPU2CC5sR4LzA4Ey6rq7deuW73xXXXWVfPvtt5KYmCjFixd3aNnwZUDHEIwQwz0YI6AQLVDbjRFdb9CpU6c8r2GUFqP9+fHkk08a1t3bggB09erVTq8X2jbhO6Fl5tgDPgUwSELwBwPV/EAgqXlC4AcaJpL4HiHYx36AKAdjRW+AcxCBKoRD+ETgBmPy5MkqyMV5ZJSlVBDvvPOOyoxAsJwf8GbAvsKIiB4E6dgfECoKEitwfuBzRo0apZ4j2IaggAwFeIsALAuO2LYeIFqdKURSPRi9sL1mwPDTUY8ZbXm2vg4QFrRsA3yXISLAaPKpp57KNR/ED3h+INMDoOYVvinww7jpppus2U0QD5DpoPdkgWeIbVcgiMe4DmJ7YfBKCCHEfrcBzQhQG8nWug3EtG3r8yAT6wrTQvgJmEqWUqPw7lon3OfDrwjZsfayLDEQOX78eOVF5Mw9bKCgta7E8YbxIzwZQuLjVSYDRAa2tiTeJCiFBgCTul9//VXdwMIEDw+A1GLc6OLGVUPrSGAvMIqJiVF/Efg6CjpWGOGKoRkCBji8+xu4oNuWP+SHtv8KEg+06Ri9dFRowMg6MlNgyAizn/fee089cLwRuOChGeNAhDBqb1pYpk+frjoX6EGAXxBwPbbtgmIEsnCcBRk9CFjxGRDb7KGNyiOVHSPMBaF9Z5A6j+wT7ccaohxGwzWzVXvgBsA2QwOvoWzDtqQFgmB+NwMwvcSohB50T4CgCONCZ4WGNWvWKMEENyG2AoJtMA2zRaT+264fsmaQqglDKUfKFmyNFCH26LNzNNHA9hzQRvhtsxmwb22/m8iIgGGqI2jLs73mIUtEv1yc39jXELP0wERLLx6gfAklEZgPWTM4byBENGnSxND41dbEStvuYCx3IoQQT3cbyL540dptAK0+fQWyKnIFwBERbgmA8VuIAQYMLNrrMob7RPwm497EKHs5mMC+hKiE4w3jR1OpUqpcwtciEyl6BK3QsHDhQunfv7+q2ce/EfhjBA6pybgQoa76999/d9jPgRQeTTQoSLBxVJCwBSPLcL+fMWOGGnFH8InABjXsmJZf4OsOMNLqaCCnBwExHu4G2TsYSUZa/quvvprvvBiRRnrh3LlzVfBXEFr3CtsRAaTHI/DEdyu//b1u3Tprxxc9eJ9tS0rU/6OcxhkgTuD7j+wWpOHby1ayBSPmCHwxoo/sjoKyGUBhyiYASj1sRQEE1kYtt2wFMu07YntjBaFAK1OAOIDsAWfQlmf7HYRHBEaCcPMKYRYlSMiUMOq0YisWaCIcSmsgNEB0vfvuux1aH227bZdJCCEkMLoNaKUdyLqAIIJ1NZ89K1mnTknC5fVyVmzA4AQGlfC7ZE+IhtCNwRa0P/d09zF/AqKCL0UlQoJWaECQ2bdvX5UFgBZ12oUFI7pIp4LggNfR0lLf9hDpzvYM5pwNfHfs2OFUpkNRACUMCPgL8rrAdKTA48fBFfCjiqAGD4xm47gjKPS00OAqGH1GWnxBIDtDK/cpCGR33HHHHSpgRsvQ/FLmMXoPcQZBOUQCR9DaPmltYPUj3CipMQqS9UDM0NfrA2QfIACFwaQerb2ks1StWtVaEuKI0ACvAZRg4DxFO9CCvu/I2oBxFPwDbHEma8aRrBfNA8F2v6IcAaAEQQ+Ot1bKY+t34Aja8mxbs6IMxKhEyNNo2+2IzwghhBQ1UIKA7AAE7vjt0YsNeG5JS1Pp8xjZDobSDngb4TcYA0n2fKFwz4TS1OHDh/O3gxAfEZRCA0ZEkYbdpUsXQ/USo7AQGpAiDaEBqdf53ZBrr9u69BPngW/Ghx9+qEzm2rRpYzgKjx+Nxx57zC27FyUyGB1GdwB/BSq7Oz0aMFKMcx9CGwLm/BR8lHsgtf+ZZ55R9fOOogXX6JagB74GGFUoqKQGx8Q2YMVrEKLcFchqnTMcKe9BaQJEBoyOrFixokCPCrhX7927V5XqeANco5BFou/uACB0QEhDRwr4SmhlXoVBu4FDxoLRd9QRsG9sb3ZRagK07BSUkNkKJPbAdiNzxrY0iRDiPTKyM2TLmS1yMf2ilIwsKU3KNpEIE1Ox/cHfQOs2gOwABO62gbyvuw24q7QD24P7mhEjRtgdtMIyUaqHQRTetxPiW4JSaNCEAXsmgNrr2iiZliqOm14IFLa1W5q/A0wKfQFGM+GH4G/YOs07Akar0fUDQgKEHv0yMPKMWnUEOLaj2gWBwA+j97aBFmrlEUTCs8MVoz5v4E6PBtS/I2BGUIbSkfyCbBg4Pv300yr1H7WN9jDaXxixh5CBTBH84GutHWGyikBVM/zzBmfOnMmznRBAkLGE76xeNIDghAwSBLna9xwZSzANxXtQUpWfl4WG1s3m/vvvF2+AdUVZzt9//51nGoQiHMMBAwYowcr2+uWMHwmOHc4JdOd58cUXXc4qQtYYvFI0M0jUz6IsB+0utQwVlE1oniq2Pg22IgW8MJAN5i1jV0JIblYeXilL9iyRM6lnlOAAgaFsdFnpXqe7dKxm38uGeM/fAH9RgoDsAATuqlwCmQwmk8+7DbijtOOPP/5QAyIYkLIHMjlRKmprkEwI8Q1BKTRoN7JGN+WaKZp+ZA1O7qg1xs01jO3QKk4PUs/B7bffLr4AQaMzpov+DII4BEMIjBo3bqw8BLD/kcUA00KMhsNTw8gwE4GjUZsiZATAOA9BLwIWjLYjZQ7HE+9BEIxg2FWjPohPqP+zBWUMgwYNsj5ftmyZtQuAntatW+cyH/WkRwMyGTCSD/ECWSP69oQoc9AEAOwDKP4QetCeUvMbMFpno/0FbxPU/aNECd09UHIBMQItCmFkqAWY3gDbiiwObAdKOnAuoUQKAgLWRw9SKHH+6X0fcC5iG9G5BOcMHhrIBrG9HiAYh0gD/xdnjV0LA7q1vPTSSypo1wsAEDsgkqIDBLajZ8+e6juF7cfr+D6hDMRWqILgAtFPE5OQhYAOENiXWAacu10FmQf4buNai/MO30N0p5gzZ451HoiJuLbC1wP7Ht9biI3oFIKaW00AxvcPxr767xohxLsiw5ztc+Ro0lExW8wSFRYlCWkJcjrltFxIzwkMKTb4j7+BP3YbKExpx86dO9U93DfffGN3+ci+Q/mnflCJEOIHWIKQjRs3YghPPWbMmJFr2h9//GGJiYlR03755Rfr6x9++KF6rU6dOpZTp05ZX//yyy/V67Vr17ZkZmYWet0aNGigHkZkZ2dbdu7cqR74dzCzdetWS69evSwVK1a0hIeHWypUqKCeb9u2Lc+8c+bMsR5Po8eRI0fU8l544QVL8+bNLfHx8ZawsDC17HvuuceyadOmXMtbtWqVet/o0aMLXM++ffva/dxatWo5tH6Y7i3yW4927dpZ53NmnfPbXwsXLrQ0adLEEhkZaSlfvrzlySeftFy6dMmldcf6YX87y6effmq54YYbLGXLllXHvUyZMpbu3bur64C943ngwAHra9WrV7e7HzDNlh9//FFNe/fdd51aT3ym7b7F+uB6ZAv2te3lGdclbN+8efMMl7969WpLjx49rN+pEiVKWK655hq1rBMnTuTZ1/rtjI2NVde+3r17W37++WfD5WNf3HrrrQVupzbfTz/9ZLnqqqvUuVGvXj3L4sWL88ybkJCgzpnKlStbIiIiLFWqVFH75OzZs9Z5li1bptZxz549dj+zKF07CfEm6VnplieXP2lp/3l7y21f3Wbpt6yfpf+P/dVfPO/weQc1HfMRz5Cdnm45PPBxy+42bS17u3S1HOjdx3Kwz4PqL57/16atmo75tPmT/vrLcvGnn9Rf7XV/2Ib/HNwGcPjwYUv//v0toaGhdn+jGzVqZPnuu+8sZrPZp9tHiL/RIJ9405uE4H8ShGC0DB0mAFJuMWKMdF6kXqGV36OPPqpGPTXwWo8ePVQaL0b+MDqK0XWMpGFEHCnVcFwvLJoZpJFZJNZh9+7d1trrYOzvSwhxHWQJwOsgv9RRX4NMEaStwgfHHSCjBKNfuDbbg9dOQjzDhpMb5K2/35ITySekZsmaeUai91/cLxVjKsrz1zwvLSq04GHwAMl/rZfTb7whmcePS0StWnmOQca+fRJeqZKUG/qCX3cZ0LIyUNphyc7OU9pR+pGHVdYFyl2RoTdt2jTlm2TPtwhZd8hIdMRQmZCiRsN84k1vEpSlEwBp3Uj/Rgou6nsRwCN9GIZ6qGXu1atXrvkR1C9evFilWiPNFzfJqPdHHTEMZTzRfpAQQpwB5SsoS0B70KKQIooyFlyLN2/e7OtVIaRIAuNHeDKgXMKotj7KFKWmYz5SdFtXOkJBpR2hrVrJa6+9JpMmTVKlfUag3HPkyJHKyJ3t6Qnxf4JWaACo17c1GcsPqKLPPfecehBCiL+BUZy0tDQpKsA7Jysry9erQUiRBd0lYPwITwaj2vq07DSJi4pT85Gi2brSWbEBLSzRXQLCCNY5rGFDmTNvnozt3VsZWhuBgT/cmw8ZMsRlk2JCiPcJaqGBEEIIIYS4BlpYorsEjB/Ppp6VMtH/b5uI56YQk5qO+Yhn8PfWlc6Czhco8cD6I5N4ZO/esmfPHsN5w8LCVJeyUaNGKWNhQkhgQaGBEEKI20DXD0JIcIBsBrSwRHeJY0nH5FLGJVUugUwGiAyVYyur6ZiPeC4w9+fWla6wYsUK1ULZXnc4gBJn+DB4s7sTIcS9UGgghBBCCCGGaK0rl+xZImdSzyhPBpRLIJMBIgNbW3oef25d6QzwTEOb6V9++cXuPDfffLPyamjevLlX140Q4n4oNBBCCCGEELtATGhTuY1sObNFGT/CkwHlEsxk8K2/AcolAiGTAaURMHFctGiR3XlatmwpEyZMkA4dOnh13QghQSQ0HDp0SDmInzlzRi5cuCClSpWSsmXLStOmTaV69ereXh1CCCGEEFIAEBXYwtI//A0CBZg7vvLKK/Lhhx/aNfZFJyVkMNx11115umoQQgIbrwgNW7ZskQ8++ECWLl0qR44csTtf1apV5bbbbpNHHnlECQ9FDf0FNjMzk617CCHEAbQbWNXqjTeqhBDiU9CeEm3m3377bUlJSTGcp1KlSjJmzBjp37+/Mn0khAQfHv1mr169WtVirV+/XrnLhoeHq5qrevXqSXx8vGpRg4vR+fPnVb/0bdu2yYwZM2TmzJly7bXXyuuvvy7t2rWTogJukNHCJzk5WY4dOyblypVTYgPabhJCCDEWGXC9BBERERQaCCHER6D9Mu7jX331VTl37pzhPMhkhhHkU089JcWKFfP6OhJCgkBoQGbCsmXLpGTJkvLQQw/J/fffL61bt853lD49PV3WrVsnCxYskCVLlkjHjh3llltuke+++06KCmXKlFEXauyL/LI/CCGE/B8IshghI4QQ4l2ys7Nl7ty5Mnr0aLv3rlFRUfL0008rkSEuLo6HiJAiQIgFqQYeADd8Q4cOlYEDB6qLi7Mg2EZmA1Kvjh8/LsFCw4YN1d8dO3bke8GGh0ViYqLdmjZCCCE5mWDIZMBvjiu/NYQQQv6POSNDUv/ZLNkXL4ipZCmJbmbfcBIhxLfffisjRoyQnTt3Gs4TGhqqBhwhQlSpUoW7mhA/iTcDWmiAUOCOmz53LSdQDzwOj4cOESGEBDz0ZSCEEPeQuGJF7haaERF2W2iuXbtWZSf8/vvvdpcHg0eUUaBkOphxRpwhpCgJDR4rnXCXOBBMIoMr8CaaEEIIIYR4WmRImPWRZBw9itRaCY2OFvPZs5J16pQkXLig5oHYAD81+K/B4N0e7du3V60q4bcW7DgjzhBS1PAbm9ezZ88qgxg6zxJCCCGEEOK9EXkEyxAZTLGxYipTRg10IaM2++xZyTx6VLbNniMz58yRBZ9+ajfTtkmTJkpguPnmm4uEMa+j4gwhRZVQb33Q33//rXrp2tZwwfSxQoUKUr58eSldurRMmTLFW6tECCGEEEJIkQZp/xiRR7CsiQwAfy/Exsr4vXulw2cLZf6CBYYiQ82aNZWR+6ZNm6RLly5FQmSwFWciatWS8MqV1d/Q2FglzmA65iOkqOI1oWHq1Kny2muvKUFB48CBA9KzZ085ffq0EhvQ1vG5555TbTEJIYQQQgghngXeAkj7x4i8IIvh0iW5ePq0vLNhvbT/+muZe/KEZJjNed6HNuy4v0eLenSXg/FjUSE/cQbPLdnZanrq5s2+XlVCfIbXrgh//vmnNGvWTGUtaMyePVsyMzPlzTffVH3Q//rrL3WRYlYDIYQQQgghngcGhvAWyL54URL/+0/mbNwoHVcsl3d27ZKkrMw888fGxsrYsWNl37598uSTT6rOP0VZnLHN4FD+alFRanr25RIKQooiXvNoOHXqlDRt2jTXa7/88ovExMSoixS4+uqrpW3btrJlyxZvrRYhhBBCCCFFFnRJMKNV5cmTMvX0KTmSmVdcABAUHn/8cXnppZekbNmyUpTRxBl4MqCcRC82qI5xaWkSEh8vplKl3PJ57GxBAhGvCQ3ZSCHKyrI+T0pKUrVcHTt2zKWEohc6sh8IIYQQQgghngNB8bIff5QXViyXfy9eNJwHIfSd1arJm7/8IjXr1uXhuCzOoLsEjB9hmGlroBliMqnp0TaDrK7AzhYkUPGa0FCtWjXZuHGj9Tna4kB46NSpU675Ll26JCVLlvTWahFCCCGEEFLkQMnyiy++mK83WvtSpeTZipWkcYMGUu5yGQBH10VCIyJUC0t0l4DxI8pOVLkEMhlMJgmvUkVNx3yFgZ0tSCDjNaHh9ttvl0mTJsldd90lHTp0UP+GH0O3bt1yzffPP/9I9erVvbVahBBCCCGEFBl27dqlyh+++uoru/M0KxUnwxo1lGtr1JDMY8etfgMcXf8/WutKdJeA8SP2EcolkMkAkaGwrS0daTuK6TFt2xZa0CAkoIWGIUOGyKJFi+Trr79WD4AOE3Xq1MmlrMIUslevXt5aLUIIIYQQQgpFIIzyHz16VMaMHi1zPv5YzAZdJECdkiVlaLPm0qlKFWtQq/kNpO/bL8lr1qjAF90WYIQIjwKUD2BkHxQ2uA40sL0I9NFdAkIMPBlQLuGOY19QZwtkUWidLWJatnTD1hASoEJDmTJlZOvWrfLFF1/ImTNnlPEj/Bn0nDx5UgYPHiy9e/f21moRQgghhBDiMv4+yn/u3DmZMGGCTJ0yRdIyMgznqRQbK09XrCTda9aUiLJl8/gNmEqXVgEtR9fzAlHBE4E+O1uQQMdrQgNEBpRK9OvXz+48KKOwLaUghBBCCPEHMrIzZMuZLXIx/aKUjCwpTco2kQiTf41aE+9ir4Y+8+RJyThyRFL+2Syxbdv6JMMhJSVFpk6dqkSGC3baLJYKC5NBjRrJYz17iXnzZpWOn3HpUh6/geimTSRpxUqOrgdxZwtCAlZoQGvLdu3ayapVq7z1kYQQQgghbmHl4ZWyZM8SOZN6RgkOEBjKRpeV7nW6S8dquTM0SdEQa+zV0GedOyeZx46pVPoLCxdK8u+/S3j58l7LcIDZ+pw5c2TMmDFy/Phxw3miTCZ5qH59GVC+gsSkp0vIwYMS3/dBufjNt4Z+A5bsbElc9qMSUvQBL8BzJUxc9nEggdfZgpCAFhri4+NV60pCCCGEkEATGeZsnyNHk46K2WKWqLAoSUhLkNMpp+VCek5gRbGh6Ik1RjX02efPS9bJk2LJzFSvI/jOPHJEss+c8biPAQJQGDyOGDFC/vvvP8N5TCEh0rNOHRnc+CopV6yYek/Gvn1qO0zx8VJ5yjuGfgPJf63n6HqQdrYgJOCFhlatWsm2bdu89XGEEEIIIYUGATGCY4gMseGxUib6/6OKZ1PPyrGkY2p6m8ptAnZk3hsZB8Eo1tjW0FssZhWwm5EhEBYmltBQEYw6lysnYjZ7tEsAMobRqnL9+vV25+larpwMva61XFGihN1sBHt+AxxdD87OFoQEhdAwevRoadOmjbz11lvy/PPPe+tjCSGEEBIoQWpWusiR9SKp50Wi40SqthQJixRfgu3BCDyCY01kAPiL55cyLqnpmK9FhRYSyHgq4yBYxRrbGnpzYlJOJoPFIhIWJoKR5/Bw9QgtXtwjXQLQFn748OHy008/2Z2nQ4uWMrh4rNRPTZOI4sVzTXO01p+j68HZ2YKQoBAa/v33X9VNYujQoTJ//ny59dZbpVq1ahIVFWU4/4MPPuitVSOEEEKIr4PUXUtFNs0TSTopkp0hgoAztoJI8z4i9W712fGBaILtwQi8UW16lClKTcd8/uZJ4MxnezLjIFjFGttRfmQwWNA2EtsHwQHZAhAZYmPd7mOwb98+GTVqlCxcuNDuPOjwBiPIjjfcIMcGPyNp27cXqtafo+vB19mCkKAQGtBtQruobdmyRT1sf7CB5qpKoYEQQggJDAodpEJkWDdF5PxBEYtZJDxaJCNF5NJxkdRzOfP4SGxAgI7gHNtj5Pyelp0mcVFxaj5/8iRw5rM9nXFQWLHGX7Ed5TenpYklKwtujGIJC5PQyEgxoVUkBAg3dQk4deqUjBs3Tt5//31l+mhE7dq15dVXX5UePXqojm/AXbX+HF0nhPid0PDyyy8bCguEEEIICVwKHaSiXAKZDBAZIkuIxJbPGRFG+nnSqZzXMb12J5+UUSALAAE6RBNsj+32mUJMajrm82SGgCezEzydcVAYscbf0Y/yZ54+LZkHD4oZ2xgeLmEVKkhYXJxbugRcunRJ3nzzTZk8ebIkJycbzlOhQgVVqvzwww9LeHi4x2r9ObpOCPEroQEtdgghhBASXBQ6SIUnA8olkMmgiQw5C8h5Dr8GTD+6QaRGGy9vnagAGVkACNAhmmB7MAKP4BgiQ+XYymq6Puh3d4aAp7MTPJ1x4KpY424cEWvQshLdJGD0CA8GlEcUNNKvH+VPXrtWEletVuUR2efOiTklpVBdAtLT02XmzJkqQ+Hs2bOG85QoUUKGDRsmgwcPlpiYGIfWk7X+hJCgERoIIYQQEnwUOkiFkABPBpRL2GY+4nl4sZzpKZdLKHyAFszrg32MwNsL9t2ZIeCN7ARPZxy4Ita4G0fEmsQVK3KP+EdEODzir43y44GMhcJmDmRnZ8uCBQtURvChQ4cM54mMjJSnnnpKdZsoXbq0Q8tlNgIhxFtQaCCEEEKIyxQ6SEV3CQSYmak55RJ6sQHPM1NEYkqLFIt3y1Fy1ZwRwSiyABx5r7syBLyRnYB/Z5oz1b+zzdkqEEcA7u6MA2fFGnfiiFjTYo9FEmZ9JBlHjyLKVy0r0U0CRo/wNgCOigSFyRzAPl+6dKnqJLF9+3bDeeC7AO8zZAtXrVrVqX1BCCFBKzT89ttv8s0338iePXskMTFRXVBtwY/bihUrvL1qhBBCCPF2WjxaWKK7BIwf4clg69EQasqZXqXw3QgKa86I+bEdmtiAv0Zig7syBDydnaDfH+fSzkmGOUNOJJ2QhNQEKRFRwu0ZB86INe5CL9bEhMdIVGiUZEu2FA8rLmnmNCXWfLPzS6n1dbYSGUyxsXm6MsBAERkKEA8cLXtwJXPg999/VyUQuFe2x5133qnKKBo0aODUsgkhJGiFBlysYU7zySefWMUF7SKuoT2naSQhhBASGBQ6LR4Gj2hhie4SMH5EKQXKJZDJAJEhrkbO9EIaQbrDnNFRocJdngSuZEY4+tnn087LvJ3zcu0PTEMQnm3JVsuqGFPR7RkH2GfebGGpiTVpWWmSmZ2psjcsYpEQCZHw0HC1rVG7DkniCbNEZGdbRQaAv3iOLg0og0CGgidaDO7YsUNGjBgh3377rd15brjhBtWq8rrrrnP75xNCSEALDe+99558/PHHcs0118jrr7+ujG2WLFkiu3fvlv3798vnn38u8+bNk+eee04GDRrkrdUihBBCSCEpdFq81roS3SVg/AhPBpRLIJMBIkMhW1u6w5zRGaHCXZ4ErmRGOPLZt9e6Xb7b912e/VEpppKcSTmj3ovlDm4+WK4uf7VHMw48DUQYiCo4ByyX/wsNCVXHUCsZMV+8KNkZEapcwkjQUa0gMzJUGYQ7OXz4sOoSMXfuXDGbzYbzXHXVVeq+uWvXrgE5EOeKuSYhJDjwmtAAkQFOuMuWLVOGNfPnz1ev16lTRz1uvvlmueWWW+S+++6T1q1bS/Xq1b21aoQQQggpJIVOi4eYgBaW6C4B40d4MqBcwg0tLQtrzuiKUOEOTwJXMyMK+uziEcXt7o+yxcpKYmaieh4WGhbQIgMoFlZMEjMSlagQERqRsz3YXEvOcUW5yNnwNAmNiBXzxUuGgo7qGhEfr7wW3EFCQoK89tprMn36dNVVwogaNWrIuHHj5P7771eeDIFIYcw1CSGBj9eEhn///VcJCJorrnYRh6uuyWRS/+7Ro4dcffXVqk9wt27dvLVqhBBCCPGHtHiICh5oYVlYc0ZXhYrCii+FyYzI77OXH1ru0XaWfoV+82D9cblsAn+1afuqhIl5T0kJOZesPBlsPRrQmhIBMgwdC0NycrK88847MmnSJLl06ZLhPGXLlpWRI0fKY489JiFhIbLx9Eav+Vm4W2Rwl7kmISQw8ZrQgJQwfeudYsWKqb/nz5+XMmXKWF9HdgPcdgkhhBBC3EFhzRkLI1QUVnwpTGaEvc/2dDtLfyIlM0ViI2LVNkEowt9QCRWzmJXggKyNqOjikti5pZRM+1sZP8KTQZVLIJPBZJLwKlXUKLyrKf+ZmZkya9YseeWVV+TkyZOG88TGxsrzzz+vHsWLFy+0camvyyWQyeBOc01CSODhNaGhcuXKcvz4cetzrTTin3/+kZtuusn6+n///SdhYey6SQghhBD3UFhzRl8H5u7u1uAus8pAAPsqPipelU9guzQzyDAJs5pBYnpkm7ZSulyT3Kn+8fGFSvXHINvixYtVhsLevXsN5wkPD5eBAweqecqVK+c241JfAk8G7EPxkbkmIcQ/8FpE37x5c9WyUiuV6Ny5s2rhM3ToUFm4cKESImAYuXHjRrmRqVSEEEIIcROFNWf0h8Dcnd0a3GVWGQjojx38GhC0Z1myJCwkTHWiSMlKsR67iBsj1Cg7AmAYP8KTAeUSroy6//LLL/Liiy/Kpk2bDKfj/IH/ArIcatas6VbjUl8D40cINd421ySEFFGh4Y477lCdJVAWgX83adJEevbsKZ999pk0bNjw/ysUFqb6AxNCCCGEuIvCliAEW2DuDrPKQMD22MHoMr9jB1GhMKPsGzZskOHDh6vBNXuggwTMIJsaeD4U1rjUH0B3CRg/wpPBG+aahJAiLjT06tVL7rrrrlxlEZ988olq2/P1118rr4a6deuqDIeWTKMihBBCiB+VIARjYO7ukgx/xRvHDqW/L730knzxxRd257n22mtlwoQJ0r59e48Zl/oDaGGJkhMYP3rSXJMQ4t941QwhMjIyT10a0srwIIQQQgjxNIUpQQjGwNydJRn+jKeOHfzHxo4dKx999JEqDzaiXr16KoPhzjvvzCMe+JsfiDtAVgh8LdBdwhPmmoSQwICui4QQQgghDlJUAvNgP3bojJD692ZJu3hBpfpjFN6ZwPfChQsyceJEmTJliqSmphrOA/8xiBB9+/Z12OjcH/xA3IFmnulOc01CSGDhdaEhISFB5s+fL+vXr5ezZ88q40eUS4AdO3bIvn37pFOnTtb2l4QQQgghhLiLxBUrcgfAEREOB8AQFaZNmyavv/66Kvs1Ii4uTvk0PPnkkxIdHe3UugWTHwj2pbvMNQkhgYdXhQa0+HnkkUckKSnJmg4GtVfj2LFj0r17d+Xd0Lt3b2+uGiGEEEIIKQIiQ8KsjyTj6FHVfhGdEWBaCD8BpPoDI7EhKytL3Z+OGTNGjuK9BkBUePrpp1VXNYgNRsBfoaDyjWDyAymsuSYhJHDxmtDwxx9/qDY+JUqUkLfeekvatGmTx/QR2Q0lS5aUr776ikIDIYQQQghxGyiXQCYDRAZTbGwek0L4CWA6RuG1UXdMg2k5jB7//fdfw+WibfvDDz8so0ePlkqVKtn9/JWHV+YSDyAw2BMPgtEPhBBStPCa0AATnNDQUNVXuHnz5nYv1Ji2fft2b60WIYQQQggpAqT+s1mVSyCTQRMZAP7iOUwLMR2p/hiF//XXX5Vh+Z9//ml3mT169JDx48fLlVdeme9nQ2SYs32OHE06qlpXoqsEDB/hxYAyCWArNnjDD0R5VfyzWbJd9KoghBCfCw2///67XHfddXZFBo0KFSrIX3/95a3VIoQQQkgwkpUucmS9SOp5keg4kaotRcJyd78iRQsE0/BkQLmEUetI1RkhI0M2b9wor44ZI8uWLbO7rI4dO6pWlS1aFCwEIHsBmQwQGWLDY/MYPMKLAdORweDNjIXCeFUQQojfCA0pKSlStmzZAuezZ6xDCCGEEOIQu5aKbJonknRSJDtDBMFbbAWR5n1E6t3KnVhEwYg9gml4Mhi1jjx84YJM3btXvv3+O/XciGbNmimB4aabbiqwVaUGyh9QLoFMBk1kAPiL5zB8xHTM562OJq56VRBCiN8JDTB9RFeJ/MBFHWUTV1xxhbdWixBCCCHBJjKsmyJy/qCIxSwSHi2SkSJy6bhI6rmceSg2uA1HzA39BZQFYMQewTQ8GbTyiTOpKTJ1/QZZeOSwZNoRGGrVqqVKJO69915VCuwM2DfYTyiXMMqkQFcJTMd83vSqSD9yRELDwiS0RAkJCQ8XU4UKYj53ztCrorCfx/IMQooeXhMaunTpIjNnzpTPPvtMevbsaTjPrFmz5MiRI8o0khBCCCHE6XIJZDJAZIgsIRJbHpEcRjJEkk7lvI7ptTuxjMINOGNu6A8gaEZZAEbsEUxfPHdO5pw+JbMPH5YUs9nwPeXKlZOXX35ZBgwYIBEuBt0QYLBv4MlglEmB1pXoKoH5vAGC/vR9+8R88aJYwsOVN0VIaGiO2FCmjFiys3N5VRRGLGB5BiFFF68JDTDT+fTTT+XBBx+Uf/75R7WxBMnJyer5kiVLZNKkSaq84tlnn/XWahFCCCEkWIAnA8olkMmgiQwAf/Ecfg2YfnSDSI02Hl2VQBrpdwVXzA39AZQDpGdkyPRXX5WpWzZLQnq68XzFi8vQoUPlmWeekdjY2EJ9Jo49BBjsG3gy2Ho0mEJMajrm8wZJa9dK1unTqmRCCR+hoWLOzBTJyBBLVpZIeLjybMi+XELhqljA8gxCijZeExqqVKkiS5culbvvvlveeOMNefPNN9VF9osvvlAPXOigGn/zzTfqLyGEEEKIU0BIgCcDyiVs6+fxPLxYzvSUyyUUHiLQRvqdxV/NDQvCbDarQa9Ro0bJwYMHDedB1sKgQYNUO8syZcq45XOxD3DsIcBg38CTAeUSyGSAyFA5trKa7o19pTIT/v47R1DA1yLqcjkHsn4yM8UM4QWPihXFVKqUy2KBK61ECSHBhdeEBoCuE7t375aPPvpItbnERR4XfYgQMNV57LHHpGRJ76SNEUIIISTIQHcJBGuZqTmBk15sUIFUikhMaZFi8R5bhUAd6XeGLcf+lmLbDshV51OkZJk4OVHTItlhIT4zNywoewTBLTpIDB8+XLZu3Wq4DKw7sm7Hjh0r1atXd/s6asdcL0ChXMLbAhTKH5C9oIJ+eE1czmBQz8PD4d4uEhqqgv/IBg3kxAtDXRILnG0lSggJPrwqNGipaEhDw4MQQgghxG2ghSW6S8D4EZ4Mth4Noaac6VU8E/wG6ki/M2CE2zTvPel09JiEZGZLaMRhSS4ZIdtalZO9jeO9bm5YUPbIn3/+KcOGDZM1a9bYXcbtt98ur732mjRq1Mij64r1wbH3ZUkNPBaQuRBavLgqg7Ckp4s5K0uVT1gu+1SEhIVJsRbXSPqOnS6LBY62EtXKMwghwYfXhAZkL9SoUcNbH0cIIYSQouYjEBaZ08IS3SVg/IhSCpRLIJMBIkNcjZzpmM8D+GMbQ3eipdFHHjoipTIyJS3MItHJ6VL8YoZEJWeqefY0ivOauWF+2SOH9hyS0V+Plt9++s3u+6+//nrVqrJNG8/6dejB98uXx15r8QnCKlRQmQmWzEwlMkBggAgRVq6cylKACOCqWFBQK1FLWpqExMer8gxCSHDiNaGhdu3a0qlTJ+Xa261bNwnDxYwQQgghPiFofQS01pXoLgHjR3gyoFwCmQwQGTzY2tLf2hi6E33NfUSJODkRFiLJWSliCgmVUskipRLSpfEfp2V99SyvmBvayx5JOp0kf37ypxxZeUTEuFOlNGzYUF5//XW57bbb8hynYEff4hOZCuG1a4klOVnMGZliSUkWS2aWRNaqJdFNm6ryB1fFAnutRLWyixCTSU3H5xBCghOvRfvoP/zzzz8rbwZ0lujXr588/PDDUqdOHW+tAiGEEEKKgo8AxAS0sER3CRg/wpMB5RIeymTw1zaG7kRfcx9etqyUyQiXrKSTkp6dLmejzVIp2Sxh5xOl3P4QKd64psfNDW2zRzKSMmTrZ1vl32//lezMbMP3VKtWTV555RXp3bu3mEwmKYrYtvhUrS2RmQDRwGSSiKpV1XTMVxixoKDPCa9Sxfo5hJDgxGtCA0wgf/31V/nwww/lq6++Uq0s0X2iXbt28uijj8pdd93lcn9iQgghhDhGUfARUEBU8HALS39vY+hObGvu4yLj1OtnU85KhjlDssLTJcoSJvXDqkiTRv09LlRp2SPh2eGybdE29chIzjCct3Tp0qqLxOOPPy5RUVFS1NG6RORqWRkfn6dlZWHFAkc/hxASnHi1fgGiAh4XLlyQefPmyaxZs2T16tVKgIiLi1NuvyitqF+/vjdXixBCCCkyBLuPgC/xpzaG7vbmMKq5h9hQMqKkJGUkScjZoyIlyspD1w+WktVae3wbYkwxcuSXI7L1062Scd5YYDBFmqTv431l8pjJ7GpmA4J8+DDAyBEeCyh/QGaCrWhQWLHA0c8hhAQfIRb8WviQ9evXqyyHzz//XJKTk9VrrVu3VlkO9957r0RGejbN0dugLhDs2LHD16tCCCGkCLL80HKZsXmGJGclq8DXlmOJxyQmPEYGNR0knap38sk6Bjr+7H/h6rrBo+HY4Gckbft2CTVodWhOSpKoRo2k8pR3PBpE4vO++OILeWnkS7Lnvz2G84SYQqTSjZWk8yOd5b273vO5sGO0L1GKgiwRCDgoUfD3wFutM8UCQgKChn4Sb/rckbFly5ZKZcaPFTIcwLp16+T333+XIUOGyKhRo+TJJ5/09WoSQgghQUEw+wj4C/7QxtDd3hz+UHO/YsUKefHFF+Xvv/+2O0/Z1mWl5n01pU7tOtK7UW+f73Ojzh25sgMiIgKilADH1baFJSGE+KXQkJaWJosXL1bZDBAWcHNToUIFeeihh6Rjx44qw2H+/PkyePBgOX/+vBIcCCGEEFI4gtlHwJ/wdRtDT3hz+KrmfuPGjTJ8+HBlKG6PCs0qSP3e9aVc3XJ+kz1irz0oOnfAVBN+FyhFgdkiBBzgz2IDIYT4tdCwdetWJS4sWLBALl7Mae/UoUMHGThwoNx5553WtpcQG0aMGCGtWrWSDz74gEIDIYQQUoR8BIh/enN4s+Z+z549MnLkSFm0aJHdeVq0aCHjXh0n8Y3j/Sp7JL/2oCaD0hNkiWA69q2/l1EQQohfCQ0oi4DAgHQ3XFThAPzss8/KY489ZrfFZfXq1eWmm25SogQhhBBC3IM20quv1Ue5hL+OBBP3dWlAuYS+XAbgOcQmTMd8vk6jP3nypGpDifvGrKwsw3nq1q0r48ePlx49euTZHn9vD6qJDAB/8RylKJgOAYclCoSQYMBrQgPMHTWjR2Qv3HPPPQ4ZPV511VVy5MgRL6whIYQQUnTwVx8BUnS9OZDpitbnb7/9tqSkpBjOU7FiRRkzZoz0799fwsPDJVDbg+rBc+V3kZGhskQIISQY8JrQ8MQTT6jshUaNGjn1PhhC4kEIIYSQ4PYRIEXTmwO+XTNmzJDXXntNEhISDOeBcTiMIJ9++mkpVqxYwHWEMGoPqoHnylQzPl6VovjTehNCiN8LDVOnTvXWRxFCCCGEED/35sjOzpZ58+bJ6NGj5fDhw4bzREVFyVNPPaVEhvj4+IDtCIHAH8uC8SM8GWw9GtC5A9Phd+FP600IIQHb3pIQQgghHiYrXeTIepHU8yLRcSJVW4qEFVy+SIILf/HmQHD93XffKdNve33eQ0NDVScyiBBVqlQJ+I4QnmoPyk4WRRdmsZAiKzTcd999qoaufv36Li8DPz5jx47N122YEEIIIfmwa6nIpnkiSSdFsjNEMGIdW0GkeR+Rerdy1xUxfO3N8dtvv6nsBLQ2t0fNNjVl5JiR0v/G/kHVEcLd7UHZyaLowiwWUqSFhuXLl8uXX34pd9xxhzz44INyyy23SIQDF+eMjAylcs+dO1eWLl0qcXFxnlpFQgghJPhFhnVTRM4fFLGYRcKjRTJSRC4dF0k9lzMPxYaAANkH7hIHfOHNsX37dpXBgHs8e8Q1iJPqvapLXN04WZm+Uqofru6VLAtvdoRwZ3tQdrIomjCLhUhRFxr279+v2g5NmzZNvvnmGylevLi0atVK9Tu+8sorlYCA1xITE+XcuXOye/du2bBhg/z111/qNXSkeP7559WPkiusXr1aOnToUOB8yJh4+eWX1b+RgYHn9hg2bJhMmDDBpfUhhBBCvF4ugUwGiAyRJURiyyNyQt66SNKpnNcxvXYnllH4OSsPr8xV7gChIFBakR46dEjdZ8GLARkCRpSoUUJaPtRSqrTIKZGAOSV8JLDNyL7wdLaFtztCuKs9KDtZFD2YxUICCY8JDXAHRosiiAUfffSRevz888/qYdTvWPvxueKKK1RAj7q88uXLu/z5FSpUkL59+9o1H5o/f776d9u2bfNMv/7666V27dp5Xr/66qtdXh9CCCHEq8CTAeUSyGTQRAaAv3gOvwZMP7pBpEYbHhw/FhnmbJ8jR5OOitlilqiwKNWiEt0jYOwI/E1sgBjy665f5b3J78l387+TzIxMw/liysdIlXuqSKuurSTUFGp9HR0xYFYJYQVZHJ7OvvB0RwhPEajrTVyHWSwkkPC4GSQC/pdeekk9du7cKWvXrpWtW7fK6dOnVb9kCBLlypWTJk2aqKC/MJ4OeurVqycff/yx4bRly5YpoaFq1arSvn37PNMfeeQR6devn1vWgxBCCPEJEBLgyYByCVuBH8/Di+VMT7lcQkH8DgTsGNWHyBAbHpunJaU3R/0dZem/S2XcpHGy8fONkpWaZThP2bJl5d4n7pWjDY9KakhqLpEBYBvREQPbj1IRT+PJjhCeJFDXm7gOs1hIIOHVrhMNGjRQD1+jZTM88MADhtkVhBBCSMB3f8DyEXxmpuaUS+h/7/A8M0UkprRIMc+2DCSug9F8jOojk0ETGQD+envU3xGPrSETh8iHkz+UtAtphvNEx0TLsBeGyXPPPSe7knfJW3+/JeeTzxuOxqPtJjpiwI/C03iqI4SnCdT1Jq7DLBYSSBS59pbJycnKMwL06dPH16tDCCGkqODt7g8QMbB8GD/Ck8HWoyHUlDO9im8DVGIfjOZjVB/lEkbeAd4c9beH2WyWzz//XF4a+ZIc2H/AcJ6QsBCpfFNl6fxQZxnefbjKvmhSrInymUAJCLIzbLM1TCEmNR2ml97A3R0hvEWgrjdxDWaxkECiyAkNX331lRIbmjVrZje7YuXKlbJ582ZJS0tTvZu7du1KfwZCCCGB1f0BmRIQMbB8fC6yKFAugUwGiAxxNXKmeyqjghQajOYjKIcng69H/W3B58N3a/jw4fLPP/8YzxQiUqtDLWnap6mciT4jyVHJ1uwLbBfMLOEzgRIQZGdAOME2QWSoHFtZTfdmSYg7O0J4k0Bdb+I8zGIhgUSRExq0son8shngjKxn1KhRcvfddyvPh9jYWI+vIyGEkCDCl90fNPFCn0mBcglPZlIQt4HRfH8a9ddAlzAYd69atcruPOggcXX/qyW+Zk5pDjqK2WZfaCaW+o4aEE582VHDXR0hvE2grjdxHmaxkEChSAkNJ06ckBUrVojJZJJevXrlmY5OE2+++abKYKhevbqcP39e1qxZI0OHDpUvv/xSdatYsmSJQ5/VsGFDw9f37dsntWrVKvS2EEIICRB83f0BYgJEDCwfxo/wZEC5BDMZ/B5/G/VHK3KYe+OeyB7F6xSX6wdcLxWvquhQ9gXEBJhZItMBIgSmQzjxF3NLQvwRZrGQQKBICQ0LFy5UYkGXLl1UNwxbevfunet5TEyM3H///dKhQwdp3LixfP311/Lnn39Kq1atvLjWhBBCAhp3d39wxVAS09nCMiDxh1H/Y8eOydixY2X27NnqPsqIevXrSc2eNSXlyhQJiwizlno4kn0BUcHXZpaEBBrMYiH+TpESGhwpmzCiYsWK0r9/f5Xt8OOPPzokNOzYscOpTAdCCCFBiju7P3jbUJL4Bb4a9Udm58SJE2XKlCnKt8oIeFm98sor8uCDD8qvx36VOdvn+EX2BSGEEN9SZISGf//9V5kVwWPhzjvvdPr9derUsZZfEEIIIV7v/uALQ8kiBDIF/Dl935uj/qmpqTJ16lR5/fXX5cKFC4bzxMfHy4gRI2TQoEESHR3tN9kXwYY5I0NS/9ks2RcvqNaG6DpAk0dCSCBQZIQGzeDxrrvukmLFirmk6mvlFIQQQohXuz/40lCyCLDy8MpcwTGC+qIYHGdlZSnj6zFjxqhyCSMgKjz77LPywgsvSKlSpZzKvvB3McffSFyxInfbyogItq0khAQMYb76IUtISJD09HS781SrVs1tn4f6wE8//dSlsgnt/ZoJZPPmzd22XoQQQooIhe3+4GtDySAXGZDufzTpqJgtZokKi1LtJNHpASaMINjFBu0+BxkKMHw0AkbaAwYMkJdfflmVlDqbfUExx3mRIWHWR5Jx9KhIdraERkeL+exZyTp1ShIuZ5lo3QcIIUSKutCwfPlyGT9+vDJUzMzMtDsfzIMgRriLtWvXyqFDh6Ry5crSsaPxzcKZM2dk0aJFqsawePHi1teTkpJkyJAh8tdffykDSWREEEIIIV7t/uBuQ0miwAg7MhkgMsSGx+ZpHwmvAUzHCH2wjryvXr1aXnzxRXWfY497771X3b9pZaS2FJSpQDHHOdJTk+Tggo8k5NA+pNJKsfLVxBRqUudl9tmzknn0qMp0iGnblmUUhBC/xWtCw/fffy/du3dXbsVxcXFyxRVX5ArovWECiQ4SoaGhhvMkJyfLk08+qX5sW7RoodR6iA+bNm1S2RdID/ziiy9cKrsghBBCCtX9wZ2GksQKgmOUSyCTQRMZAP7iOQwNMR3zBVtXhM2bN6t7np9++snuPJ06dVI+Dddcc43deQrKVKCY4xzYn38t/UhaHN4lJdMz5EypLIm4mCZlipWRuMg4MZUpI9kXL6pyitTNmyWmZUsnP4EQQoJMaEBbJLPZLG+//bYK6JGC5w1QngGBwKh9pZ7SpUvLsGHDVLbFf//9J7///rtaRwgi/fr1U/WIyIgghBBCAtZQkuQCI/AIhFEuoYkMGniOrgmYjvmChX379smoUaNUy297XH311TJhwgQlNOSHI5kKxSOKF1kxx1m0/Vnm1H6xZGZIerhIhjlTMs1ZkpWUk+kLsSEkKkp5NmTbMeokhJAiJTSg3eN1110ngwcPFm8SGRkp584VnEqK7Ar8qBJCCCFBaShJ8oA0f4zAIzhGWrpebMBztGZE1wTMF+icOnVKxo0bJ++//77d8tTatWvLq6++Kj169LCbAarhaKbCrTVvLXJijivo92d88WJiisiUqOR0iTZFSaYlS9Kz0+VsylkpEV5CLGlpEhIfLyYDM05CCClyQgPaSrrT4JEQQggpUhTWUJLkAV4CSPPHCDyCY9tg2RRiUtMxX6By6dIlefPNN2Xy5MmqTNQIeFDB5PGRRx6R8PBwt5ad4OGvYo4/dcHQ78/kK6tJ8h+pUvxihsQkZkpy8XDJkizJyE6XlFPHJdJkUt0nops29cm6EkKIXwkNSL/7+++/vfVxhBBCSPBRGENJkgcElfASQJo/RuARHGOEHcEvRIbKsZXV9EA0gkTp6MyZM1WGwtmzZw3nKVGihCobRbaps+27HS07gVDjj2KOv3XB0O9Pc7hJtrUqJ1HJmVIqIV2iUrIkLcwikZnpIpHJEl69lpTqcTeNIAkhfo3XhIaJEycqk0X8oOFHLyzMJ501CSGEkKJpKEkM0YJKfdCJEXZfBp2FAabbCxYsUBkK6Lhlr6wUflnDhw9XHlWeLDspHV3a78Qcf+yCYbs/9zbOMXZt/OdpibmYIdkZaZIWGyFR1WpJ6QceZmtLQojf47Fo/5VXXsnzWteuXVX63pdffint27eXKlWqGNYA4scKRkWEEEIIIZ4GQSVaWPpLGr0rIDhdunSpjBgxQrZt22Y4D+65+vbtK2PGjCl0OaszZSfafvQHMcdfu2AY7U+IDfvrlZSY3cckNDFZylWoJc/1fU8io2O9tl6EEOJ3QgN+xLQLty379+9XD3tQaCCEEEKIN0FQGahdD9ApCxmjv/32m915unXrpjJKGzZs6JOyE38Rc/y1pWm++7Mq9mdtub1Rf4oMhJCAwWNCw5w5czy1aEIIIYSQIg86eiGD4dtvv7W7L9q2bau6arVu3drnZSf+IOb4c0vTYCvjIYQUbTwmNCA1jxBCCCGEuLfbweHDh2X06NEyd+5cMZvNhvM0btxYXn/9dbnlllvyBNTuxF8yFYKlpWmg7U9CCLGH1xwZ16xZo9on1a1bN9/59uzZIydOnJAbbrjBW6tGCCGEEOL33Q4SEhLktddek+nTp6uuEkZUr15dxo0bJ/fff7+YTCbxBv6QqRBMLU0DaX8SQojPhQaYP/bv318++uijfOebNGmSzJ49W7kmE0IIIYQEC652O0hOTpZ33nlH3SNdunTJcNllypSRkSNHysCBA1VXiaKS3eEswdzSlBBC/Amv9pg0MoZ0ZR5CCCGEkEDClW4HmZmZMmvWLNXJ6+TJk4bLjYmJkeeff149SpQoIUUlu6Mw0AuBEEKCTGhwhOPHj0tsLNv2EEIIISR4cKbbwdXlrpbFixerDIW9e/caLi88PFwee+wxNU/58uX9OovAndkd7oJeCIQQEsBCA0yK9ODH0vY1jaysLNm9e7csX75cWrVq5cnVIoQQQkiQ4Q/Bszu6HaxcvlIef+dx2bhxo91lwX8BPgw1a9YMmCyCwmZ3eAJ6IRBCSIAKDf369cul2K9bt0497IEfmKioKHn55Zc9uVqEEEIICSL8IXgubLeDM/+dkS2LtsiSLUvsLqNLly6qk0TTpk0DMovAleyOQDJFNGdkSOo/myX74gUxlSwl0c2aSmiE/4hdhBASNEIDBANNoUZ9IX4Yu3XrZjhvRESEVKpUSTp37iwVK1b05GoRQgghJEjwl+DZ2W4H8VHxkpyZLBeOXJBt87fJ6T9P231vy5YtZeLEicpYO9CzCJzJ7sB8gULiihVy4YsvJevMGbFkZEhIRISElS0rpXrcLcVvvNHXq0cIIcElNIwZM8b6708++UQ6deqk+j4TQgghhBQWfwqenel2sP/ifjl49KCcWHJCEn5NEDEbv+fKK69U7Sy7d++eJyAP5CyCgrI70AEiLipOzRcIpTQQGRJmfSQZR4+KZGdLaHS0mM+elaxTpyThQo7YRbGBEFLU8JoZ5J133inFixf31scRQgghJMjxp+DZEZBZ8df+v2TV+6vk+I/HxZJh3GmrcuXKarAGJahhYWFBl0Vgm91hKxChzSSmYz5/L6VBuQQyGSAymGJjxVTm/9uSffasZB49qqbHtG3LMgpCSJHCa0LD9OnT7ZZNEEIIIYQEcvBcEGlpafLOu+/I2PFjJT0x3XCesJgwaXl/S/lh8g9SMtb10XxfZRG4kt2BrBMIQjhWWAeIDJVjK6vprmQgeLuUBp4MKJdAJoMmMgD8xfPsixfV9NTNmyWmZUvxF+gnQQgJGqGhSpUqYjbbyQ0khBBCCAng4Nke6KqFjlsoHT2K1HoDTBEmqd+tvhS/ubhULV9V/kv6T1rEtgioLAJn0YJ9feYBjlVhMg98UUoD40d4MqBcwkjsComKUtOzL5dQ+AP0kyCEBF3pBH5oExMTWUJBCCGEkKALnvVgHb755hsZMWKE/Pvvv4bzhISGSJ2b60jTB5pKTJkYOZZ4zGMZGJ7MInAViAkI+t3lpeCLUhp0l4DxIzwZjMQuS1qahMTHi6lUKfEH6CdBCAk6oWHs2LGyevVqueWWW+Tdd9+VZs2aeeujCSGEEBKE+GPwDNasWSMvvvii/PHHH3bnqX59dbm639VSsmpJr2VgeCKLoLDg2Lgr6PdFKQ1aWKK7BIwf4clg69EQYjKp6dEutiR1J/STIIQEpdAAf4bIyEhZt26dXHPNNaqFZbVq1SQqKirPvLhAr1ixwlurRgghhJAAxZ+C561bt8rw4cPlhx9+sDtP5aaVpcI9FaRi/YpSIrqEes2bGRjuziIo6qU0oRERqoUlukvA+BGeDKpcApkMJpOEV6mipmM+XxOofhKEkMDEa0IDshn0F/vjx4+rhxGFbeFECCGEkKKDz4LnrHSRI+vlwJ5d8vLML2TBN7+oexwjmjZtKhMmTJCwemHy8Y6PfZqB4c4sAn/CV6U0WutKdJdAoA5PBpRLIJMBIoO/tLYMRD8JQkjg4jWh4cCBA976KEIIIYQUMbwePO9aKmdWfyjjF/8tM389LpnZxgJDzZo1Zfz48XLfffdJaGioNajzhwyMYMOXpTQQE9DCEtkACNThyYByCX/IZAhUPwlCSGDjNaGhevXq3vooQgghxOXRaUk9LxIdJ1K1pUhYZIFvQ6AYjGnoxD6JGxfL5FdelDd/OiBJ6cYCQ7ly5eTll1+WAQMGSIRNsBnM5QtFuZQGooI/lxwEkp8EISTw8ZrQQAghhPgtu5aKbJonknRSJDsD/QZFYiuINO8jUu9Wu29beXhlroAGgSJHpoOXjIwMeX/GdBk3ericuZRuOE/xyFB54fYG8uyHqyW2VOkiV77gD1DICXw/CUJI4EOhgRBCSNEGIsO6KSLnD4pYzCLh0SIZKSKXjouknsuZx0BsgMgwZ/scOZp0VLXTg9M9TOhQH47UbcA0+ODAbDbLwoULZdSoUXZLQSPCQmVQh2oyopVFylYpLXLhX5FSbby+ruTy8aCQE9B+EoSQwMdjQgPqEPHYuXOn1K1bV0wmk8PvRRpXVlaWp1aNEEII+X+5BDIZIDJElhCJLY8fIRQsiySdynkd02t3ylVGgewFZDJAZIgNj81jOof6cExHejzT4QMXHM8ff/xRdZLYsmWL4Tw4XfpcV1nGdq8rNcoUEzl/KCcrJuWySEUchmVI3iEQ/CQIIYGPx4QGtK7ETVd4eLh6XrVqVXaTIIQQ4l/AkwHlEshk0EQGgL94Dr8GTD+6QaTG/0enUVuPcglkMmgiQ87bQtRzmNBhOuZjenxg8ueff8qLL74ov/76q915bmtSTl67+0ppXDWnTaUSqDJTRGJKixSL997KBgEsQ/Iu/u4nQQgJfDwmNBw8eDDf54QQQojPgZCA0WeUS9i2Vsbz8GKGo9Mw8MPoK8oljNrEweke0zEfCazR8F27dsmIESNkyZIldudpXae0TLwpWtrUKy8SWzznRS0LJtSU4+9Rhf4LjsIyJEIICT7o0UAIIaTogu4SCEYzU3MCRb1okM/oNAJZBLHwZDBqE4d2enC6x3wkMEbDjx49KmPGjJE5c+YoTwYjGjZsKK+99prcXidUQn5/N6e0BmIVBCmcKxAZ4mrkmIg60LGEsAyJEEKCFQoNhBBCii5oYYnRZxg/YjTa1qPBzug0RssRyML4EZ4Mth4NphCTmo75iH+Php87d04mTJggU6dOlbS0NMN5UP75yiuvSJ8+ff7vOYXzRN+pBIKUA51KSG5YhkQIIcGJ14WGzZs3y4wZM2Tt2rVy/Phx9VqlSpWkbdu2MnDgQGnevLm3V4kQQkhRBaPOCAzRXcKJ0WmMmGO0HIEsjB/hyYByCWQyQGSoHFtZTacRpGcpjClnSkqKvPvuuzJx4kS5cCFHkLAlPj5eXnrpJRk0aJBERUXlnggxASah8O9AaQ2yXiBIMZPBKViGRAghwYlXhQaMBowbN06ys7Nzvb579271mD17towcOVKlLhJCCCFeQRt9dnJ0Whsl16fso1zC2ZT9oO7oAbNNiDcoUUH2iJuDcFdGw9HVCuURuNfQBjxsKVasmDz33HMyZMgQKVkyn/IXbI/OJJQ4D8uQCCEkOPGa0DBv3jz1ox4bGytPPPGE9OrVS2rUqKGmHTp0SPWnnj59uhIiatWqpdITCSGEEK/g4ug0xASMlrvDhDCo2LU0t3CD/fG/9u4DPKo66+P4SU8glNA7CCjNAojYEBUb9o5rRbG79or0pmBb5V17w16wsBYsqCiiWEBABUUpSu8QWhLS5n1+/+xkU2ZCymQy5fvZJzvO3JvMzZ2bYe65p1RDWUFFroYry+Gdd95xGQp//vmnz58XHx9vV111lQ0fPtyaNWsWsO2Ef5QhAUBkClqg4ZFHHnGjLr/88ks78MADiy3bb7/93NfZZ59thx12mFuXQAMAIKi8V6e9V+L//LRcV+IVVGCEZYkgw7cTC0pRNDZUEz2yMwr6YKhERQIUbCjv1fA/Z/9pQ84dYrNnz/b7s/Y+am8bPnq4XdyXCx3BRBkSAESmoAUafv/9dzv66KNLBRmK0rJ+/fqVObMaAIBwvxIfsRSk0f5TkCGpbunmmnpcy5U9EoAyij1dDc/4O8NmvTXL3vzpTb8/o+EBDa3NeW2sfof6Nm3nNGu5oiVlL0FGGRIARJ6gBRrq1q1raWlpe1xPtZBaFwCASL0SH7GUCaIgjfafN8ggutV99WvQcpWoBKC3gb+r4VtXb7Xlk5fbhlkb/H5vvQ717ODLD7aWPVuWq3lkdVJ5R7SX34RrGVJ+drZlzptvedvSLa5efUvp0d1iE0N7mwEgogIN/fv3t2nTpllmZqalpKT4XEfLvv76azvhhBOCtVkAAAT9SnzEUiBBmSAK0pTomeDua6KHlqsPRoBOuoteDV+xZoX9+vqv9ve0v82T5/G5fmqLVGs9oLX1Pr63xcbG7rF5ZDDGcxZtKKrfNVobioZbGdKOL76w9LffsdyNG82TnW0xiYkW37ix1T/nbKtzzDE1vXkAEB2BBs2o/uqrr+yss85ys6o7duxYbPnSpUvthhtusMTERDdqCgCASL0SH2nTHwrp5ysokJNZEKQpGmzQfY0N1UQPNdsM4En3gfUOtM+nf27THp5mWZlZPtdp3ry5Dbh+gP3d6W/LtMzCIIO/5pHBoN930oJJbjynJmeoqaX6TagURFkaEm3BhnAKMmx+9jnLXrXKLC/PYlNSLH/TJstdv942/3dcKsEGANGs2gINgwYNKvXYAQccYB988IF16dLFunfvbm3bti2cOjF//nzLz8+3U045xXWEfu6556pr0wAACPiV+JAU7J4TCmLo56vcRJkgJTNDYuMKlmuiRwBOurOysuyJJ56we+65xzZv3uy3JHPw4MF244032sLtC+2hOQ/Z1l1by2weqUyK6qaAhoIq+n1TE1JL9ZeoqTIOlK9cQpkMCjLEpaZaXKP/vXZ5mzZZzqpVbnntI46gjAJA1Kq2QMMLL7zgd1leXp799NNP7qskBSL0Zk2gAQAQNFW8Eh+SaqLnhDIlFMTQz9fzKoCjII32n4IMae0KlpfIqKjoSbc+R7zyyis2YsQIW7Fihc9NSUpKcpmSd999tzVoUPC6HZBUdvPIuJg4t1zlGtVN5RnK3FBQxbsdNV3GgfJRTwaVSyiTwRtkEN3qft62bW555vz5Vrt3b3YrgKhUbYEGjbEEACAsVOFKfEiqyZ4T3uBF0UwKBWnKyKQo70n3/A3zbf2c9TZkyBBbsGCBz6dXScSll15qo0aNstatW4fsKEWVZyjAosyNopkVNVXGgfJT40f1ZFC5hK/XLiY52S3P+28JBQBEo2oLNBx55JHV9aMBAOFcxx+KKnklPmTVdM8JBRMUxNDPV7mJMkEUpPGz/8pz0r12wVobNHqQLZyz0O/TnnHGGa6MomvXriE/SlHlGQpoqDykpss4UDGaLqHGj+rJ4Ou182RlWUyDBhZXvz67FkDUClozSABAiAp2HX+oqsSV+JAVCj0nFFQoZxCjrJPuLX9tsTnPzrEtP/nf1r59+7qm04ceemjYjFLU84VKGQcqRiMsNV1CjR/Vk6Fkj4aYuDi3PKV7d3YtgKhFoAEAollN1PGHsgpeiS+vio5sjLaeE75Oundt2GVzX55rS79YauZ7UqXtv//+du+999pJJ51UKhMi1EcphlIZByomNjHRjbDUdAk1flRPBlcuoUyGuDhLaNXKLdd6ABCtCDQAQLSqyTr+UFaBK/HlUdmRjdHUc6LoSfdfa/6yeVPm2Zppa8yT6zvC0K5dOxs7dqxdcMEFpcZUhpMyyzjan2L98hLMfns/OsuZQpx3dKWmS6jxo3oyqFxCmQwKMjDaEkC0I9AAANGqpuv4o0BlRjZGa8+J3g162yvTX7HZT8y27Ixsn+s0btzYhg0bZldffbWbKhEJfJZxbFljid9NopwpxCmYoBGWmi6hxo/qyaByCTIZAIBAAwBEr1Co449gFR3ZGK09J3JycuyZZ56xMWPG2Pr1632uk5qaarfddpv7qlOnjkWaYmUcKmf67jHKmcKEggqMsASA0shoAIBonQARZnX84aa8Ixu1XrX1CqimnhOBkJ+fb5MnT3YZCkuXLvW5TnxCvF119VU2cvhIa9KkiUU8ypkAABGCQAMAROsEiDCr4w835RnZqOVaL5x6TlSVMjqmTZtmd999t82bN8/3SjFmrfu2tv0v2t9i94q1BVkLrJ8FZ+xkjaKcCQAQIYIWaOjXr5/179/f7rzzzjLXe/DBB+2jjz6y6dOnB2vTACA6J0CEYR1/OClrZKPua7qAGv9pvWgxe/ZsGzx4cJn/xjfo3sDand/OGnVoZDtyd9jCzQurt59FKKGcCQAQIYIWaPjqq69cl+g9+eOPP2zGjBlB2SYAsGhPmQ6TOv5w5GtkY9EeDRphqOVaL9Lp3/ahQ4faO++843edpp2bWvPzmlurA1oFv59FqKCcCQAQIUKudCIrK8vi40NuswAgclOmQ7iOP5wVHdmoE2X1ZFC5hDIZFGRomdrSLY/kE+fVq1fb6NGj7fnnn7e8vDyf63Tu3Nkuu+My+6nxT7YuY13N9bMIBVUoZ1IZTrHJFY0PiOhjCwAQ2kLqjH779u02a9Ysa968eU1vCgBEV8p0iNXxRwpvqr+uxutEWSeDKpdQJoOCDJFaCpCenm733XefTZw40TIzM32u07JlSzdp4pJLLrGvVn9l38//vub7WQSZz+BAJcqZNEa16DGmAEOkH2MAgCgONLRv377Y/bffftuVUPiSm5vrxlrp9vrrr6/OzQKA0EPKdMTSiZ5S/qPharOCCo8++qiNHz/etm7d6nOdtLQ01whS/9anpKSEbD+L6s4QKDM4cPhN5S5n0s+ZtGCSG6OqCScK1mg/rs9Y7x7T76DjL1KPOQBAFAYa/v7778L/1oeGnTt3ui9fEhISrEWLFnbaaae5DygAEFWYABHRdIIXySn/ukjw4osv2qhRo2zVqlU+11FQ4eabb3ZNoevXrx/S/SyqO0PAX3BAv79rfLnvZdZvwIt7LGfStmk79XNSE1IL99uWzC22ZtcaS89Kt7U719p3a76zprWakuEAAIiMQINmZHvFxsbapZde6uo0AQAl342ZAIHwo0DAf/7zHxsyZIgtWrTI5zpxcXF2xRVX2IgRI9wFhVDvZ7HHIEAVp1/4Cw74bHy5h3ImZSsoGKLt9P6crVlbXTZDTl6O5XnyLDs/2z2XfnbUTO8AAERPj4ZJkyZZx44dg/V0ABB+mACBMKIJURpV+f333/td59xzz7Vx48bZPvvsExb9LCoUBKhk0MNXcKCyjS9V1qFt9va20M/Udu7O223xsfEW64m1uNg4a5LSxAUdomZ6BxBm8rOzLXPefMvblm5x9epbSo/uFpvI3yjCW9ACDQMHDgzWUwFA+GICBELc/PnzXY+FTz75xO86xxxzjE2YMMF69eoVVv0sAhkEKG9woCqNL0v2ttiVvct9r8c8Fh8Tb1meLIvX/+LiLS0hLXqmdwBhZMcXX1j62+9Y7saN5snOtpjERItv3Njqn3O21TnmmJrePCC8pk7s2LHDli5d6m71D6Mvffv2Dfp2AUBIYAIEQtCyZcts+PDh9tprr/ldp2fPni7AcNxxx4VlP4tABgH8CWTjy5K9LVRmoiBDjMVYjifH3eq5lJ0RydM7gHAOMmx+9jnLVm+bvDyLTUmx/E2bLHf9etucXlDqRLAB4SqogYYFCxa4RlCaPOEvwODlb942AAAIng0bNtjYsWPtqaeespycHJ/rdOjQwe655x5XKqGeTOEqGNMvAtn4smRvi6zcrMLeDCqdSIpLcj8/Nia2xqZ3APBfLqFMBgUZ4lJTLa7R/94L8jZtspxVq9zy2kccQRkFwlLQAg2LFy+2Pn362Pbt2+3www+3tWvX2l9//WX/+Mc/3FWSuXPnuq7VmjpRshs1AAAILv17/dBDD7mvXbt2+VynadOmNnLkSNfsUdOjwl0wpl8EuvFl0d4W2u7l25cX9mjQpAkFFmpqegcA/9STQeUSymTwBhlEt7qft22bW545f77V7t2bXYmwE7RAg5pBqVRCTSHVr+Gyyy5zgYZXX33VLV+yZIldfvnl9ttvv5XZWAoAgKDK3W228kezzK1mKWkFo0hLjBmMJLt377Ynn3zS/bu9adMmn+vUqVPHjalUlmJqaqpFimBNvwh048uivS2+Wf2Nfb3ya/c7bN291TJzM2tkegeAsqnxo3oyqFzCV6lWTHKyW5733xIKINwELdAwffp069Kli9+mkJpI8d5771n79u1dDeijjz4arE0DAMC3RVPN5r5stnOdWV62mU7QUpuZ9bz4f1NCIoRKFtV/QWMo//77b5/rJCYm2vXXX++aQTZq1MgiUbCmXwS68aW3t4W+9HNqcnoHgD3TdAk1flRPBl+lWp6sLItp0MDiyPRGmIoPZo3noYceWnjfm2KZlZVlycnJ7r9VMnHUUUfZhx9+SKABCDdRdtUXURJk+Hai2da/zTz5ZgkpZtkZZtvXmGVuKVgnAoIN+kD70UcfueDBr7/+6nMdfQC+5JJLbPTo0da2bVuLdMGaflFdjS9renoHgD3TCEtNl1DjR/VkKNmjISYuzi1P6d6d3YmwFLRAQ4MGDVw6ZtH7snz5cuvUqVOpoASAMBJFV30RRYEzHdMKMiTVNUttqrNtnZWb7Vxf8LiWdzw2rANq3333nQ0ePNi+/vprv+uod5IaPe67774WTWpy+kUghPv2A5EuNjHRjbDUdAk1flRPBlcuoUyGuDhLaNXKLdd6QDgKWqBhr732ckEFr+7du7uI3ZtvvunSNEW1oJpI0aZNm2BtFoCqipKrvogyys5R4EzHtDfIILrVfWXuaPmq2Wbt+li4UT+kIUOGuJJFf9TAWaMq1cAZABB43tGVmi6hxo/qyaByCWUyKMjAaEuEs6AFGo4//njXWErBBqVdnnrqqa6+c8yYMe4DT8uWLe3dd9+1bdu2ufpPAGEgSq76IgopkKDsHAXOSjTpcvcTahUsz/hvMC1MrFy50k2JePHFFy0/P9/nOspcuPfee+2UU04p1aAMABBYCiZohKWmS6jxo3oyqFyCTAaEu6AFGi6++GJXOrF+/XoXaKhdu7a98cYbNmDAAJs8eXLhescdd5wNHTo0WJsFoCoi/Kovorg/h34PlQDlZBYEzoqecOt+ToZZ7YZmtQrKAEPd5s2bbfz48a7/UdEyxqKUTTh27Fi78MILLS4uLujbCADRSkEFRlgi0gQt0NChQwf3Iaeofv36uQyHmTNn2tatW22fffaxAw88MFibBKCqIvSqLyogUvtzKFii30MlQMrOKZmtExtXsLxVaNfA79q1yyZOnGj333+/yxj0pWHDhjZs2DC79tprLSkpzANEAAAgugIN/iizoX///jW9GQAqI8Ku+qKCIrk/hzIyFCzR76HfT0E1Bc50TCvIkNauYHmIZm7k5OTYc88956ZErFu3zu+/v7feeqvdfvvtVrdu3aBvIwAAiFyxFqHUVFK1pXv6Uo+Ikl544QXr3bu3paamuukYJ510ks2aNatGfg8gLK76xsQWXOVVcEHC7KovAtCfo1Ens/ptzRp3Lrjv7c+h9cKVgiSH32TWoqdZ/dZmyXUKbnVfj4dgEEV9F1SO2K1bN5eh4CvIEB8fb//85z9t6dKl7t9AggwAalp+drbt+uFH2z5tmrvVfQDhrcYzGqpLs2bNbODAgT6X5eXl2SuvvOL++4gjjii27Oabb3ZppikpKa6BZVZWln322Wc2bdo0e/vtt+2MM84IyvYDYSHMr/qiCqKlP4eCCWpmqt9DJUDKzlHgLASP6c8//9yNqvzpp5/8rnP++ee7PgwqZ0RkyM7Ltp83/mzbdm+zekn17IDGB7jRlkC42PHFF8WnLiQmMnUBiAARG2jo3Lmzy0zw5eOPP3aBhtatW9tRRx1V7EOaggyqV9Vs8b333ts9rv/Wepdddpm7rV+/ftB+DyDkea/qFq3TV7lEJNTpw79o6s+hoEIIB0sUWFCAQf+G+XPCCSe4Pkk9evQI6rahek1fMd2mLJ5iGzM3uoCDAgyNUxrbmXufaf3a9GP3IyyCDJuffc6yV63SlUCLTUmx/E2bLHf9etucnu7WYcQjEJ4iNtBQFm82gzprFx3d9a9//cvdqimWN8gghx56qF1zzTX2f//3f67m9bbbbquBrQZCWBhd9UWA0J+jxi1evNj9e1V0clNJKgOcMGGCHX300UHdNgQnyDBpwSRbtXOV5XvyLTk+2TZnbbYNGRssfXfBCRrBBoQylUcok0FBhrjUVItr1Mh9Lvd4PJa3aZPlrFrllmv0I6MegfATsT0ayurA/d577xWO3PTKzMy06dOnu/8+55xzSn2f97EPPvggaNsKhOVV366nFdwSZIhs9OeoMWvXrnX9F7p27eo3yKApTir3+/777wkyRCBlLyiTQUGG1IRUa1+vvbVMbeluayfUttU7V7vlWg8IVZnz5rtyCWUyeIMMolvd9+TlueWZ8+fX9KYCqISoy2h49913XbBB6aP6kOb1xx9/uNnijRs3tlatWpX6vp49e7rbX375JajbCwAhif4cQafxlBpT+cgjj1hGRobPdVq0aGGjRo1ypX5q+ojIpJ4MKpdQJkOjlOInaLq/PXu7W671DmpGM16Eprxt6a4ng8olimYYi2vanpzsluf9t4QCQHiJj9ayiaLZDLJixQp36yvI4B0Dpt4MW7dutR07dlidOnWCsLUAEMLozxEUakr8+OOP2z333GNbtvjueaF/n9Sn4YYbbrBatWpV27bQeDA0qPGjXguVS/g6QUuOS3bLtR4QquLq1XeNH9WTQeUSRY9l3fdkZVlMgwYWR280ICwFLdCgDtdXXnmlXXrppW4iRE2lm37xxRcWFxfnOm8XtXPnTndb1gc0BRvS09PLFWjQaDFfNE6Mbt8AIgb9OaqNJiS9/PLLNmLECFu5cqXPdZKTk+3GG290QYa0tLTq2xgaD4YUTZdQ40f1ZPB1gpaVl2VpyWluPSBUpfTo7qZLqPGjejKU7NEQExfnlqd0717TmwoglAMNf//9tw0dOtR9YDrllFPsqquucl2wS0biq9Prr7/uPrj179+/xoIdABBxqnMqQ+7uglGamnKhBpTqDRHh/T/0Ifv999+3IUOG2G+//eZzndjYWBs0aJCNHDnSbyZeINF4MLRohKWmS6jx46bMTYXlEzp2dD8uJs4t13pAqFKDx/rnnO2mS6jxY962bQXlEspkiIuzhFat3HIaQQLhKWiBhuXLl7uJDc8//7z95z//cQ0Z9eHo8ssvdx+WgvFByV/ZhKSmprpbf3Wvot4OUp6yiYULF1Yo0wEAUMKiqcXHpsYlRvzY1JkzZ7rshFmzZvld56yzznJlFBrjXBONB0ue1HobD/Zp2cddZUf1037WCEtNl9D+V08GlUsok0FBBjWG1HJeD4Q67+hKTZdQ40f1ZFC5hDIZFGRgtCUQvmI8+qQQRHq6Tz/91J555hn78MMPLScnx5UyKMtApRXKdtCVmkD7/fffXfNHBRTWr19fqkRi/vz5rkGkmkFu2LDBZ5BB36vUVH81suXhDTT4C0QAAP4bZPh2otnWv808+WYJKWbZGWaxcWZp7cwOvymigg2//vqr3X333TZ16lS/6xx11FFuVOXBBx8c1G2bvW62PTTnIVu7a62balAyTX/ZtmXWvHZzu63XbTQeDDJlmijIo8aPCggpsKBMBgUZGG2JcBt1qekSavyongwqlyCTAQjv882gN4PUBxQFFfSlE/oXXnjBnn32Wffh6qOPPnIlDcpwUKZDu3btAva8qnP1Xgny1YehU6dOlpSUZBs3brTVq1dby5Ytiy2fO3euu91///0Dtk0AAD/lEspkUJAhqa5ZalP946GzWrOd6wse1/KOx4Z9GYXKClVSqIw7f3H/7t272/jx44NebuhF48HQpWCCMkk0XUKvk3oyqFyCTAaEGwUVavfuXdObASCAAp86UAFNmjSxO++802Ub3HLLLe5Dlho2KiW0Y8eOdvrpp7tMg6rSz33ttdf8lk1ISkqK9evXz/33W2+9VWq55pHLqaeeWuXtAQCUQT0ZVC6hTAZvkEF0q/v5eQXLV80O292ooPbNN9/sgtwKhPsKMrRv39792/XTTz+54HxNBBmKNh7Mys0qtZ3exoNaTuPBmqF9rxGWx7Y91t0SZAAAWLQHGtS3QVdylLmgueBy2GGH2bBhw1yg4YMPPrDevXvblClTqlzzqudSloI3mODLrbfe6m7HjRtnixcvLnz8u+++s6eeesqND1OmBQCgGqnxo3oyqFyi5Mm17ifUKlieUfkytpqiCUdjxoxx04cmTpxo2dnZPoPwjz76qAvCa0JSdZQTVqbxoGr/1ZPBG2yg8SAAAAiZ0onc3FzXCFI9Gj7//HPLz8+3unXr2rXXXmvXXHON7bvvvm49fRBTZsEll1zighFnnnlmlZtAXnDBBWV+YDv22GPtpptuch/+lKp63HHHuQ+Bn332mftANWnSJBdsAABUI02XUFPBnMyCcomiwQbdz8kwq93QrFaDsHkZ9G/J008/bWPHjvXZB8jbaPiOO+5wGX7eBsWhINwaD6pXAaUEAABESaBBGQLqxfDiiy+6lFGduPfs2dMFFxQA8NU34dxzz7XJkye7MV+VtXv37sKyh4suumiP6yuzQkEGXU1SgCExMdEFIIYPH+6yLQAA1UwjLDVdYvuagp4MJXs0qCGklrc6KORfCgXT33jjDfdvyLJly3yuo39nrrvuOjfOUg2JQ5G3sWDRxoNpyWkh13iQ5ogAAERZoEF1qKovVS+Eyy67zAUYevXqtcfvq1evnptMUVlq8FjRKRGXXnqp+wIA1AA1eNQIy8wtBY0fVUqhcomcIlMntDyEG0F6JyxpkoS/XkP6N1F9g0aPHh3Q5sfR2nhQQYZJCya5MZz5nnxLjk+2zVmbbUPGBpeNIaESENlj9/158y1vW7rF1atvKT3ovg8ACD9BG2+pkggFF1QKoVKJaBUq40YAICxGXGq6hBo/qieDTmiVyaAgQwiPtvzhhx9s8ODB9tVXX/ldR6Oc7733Xttvv/2Cum2RShkWt311my3YvMBSE1KtUUojF8jx9pHYlbPLujXsZg8d9VDIBEZ82fHFF5b+9juWq8zP7GyLSUy0+MaNrf45Z1udY46p6c0DAISBbiFyvhm0jIYFCxYE66kAAJFAwQSNsNR0CTV+VE8GlUuEaCbDokWLbOjQofbuu+/6XUcleBMmTLAjjjgiqNsW6ZRloZIOZTJ4gwyiW91XXwkt13qazBCqQYbNzz5n2atWmeXlWWxKiuVv2mS569fb5vSCjIxABBvoYQEAiKhAg+pU1W1bpRMJCQk+11GJRGZmpmuCVdNdtgEAIUBBhXZ9LJStXr3aRo0a5RoG5+Xl+Vyna9euNn78eDciuabGVEYylXLoBFrlEiX3r+6reaWWa71QLZdQJoOCDHGpqRbX6H8ZGXmbNlnOqlVuee0jjrDYxMpnZNDDAgAQLEE7m3/44YctLS3NZsyY4XcdLdM6//73v4O1WQAAVMrWrVvtrrvucuOY1ezYV5ChdevWLgDxyy+/2GmnnUaQoZqoX4RKIrJyswrHb3rpviZkaLnWC0XqyaByCWUyeIMMolvd9+TlueWZfvp9VKSHhcpL1u5aa7tyd7nbhZsXuse1HACAsAs0TJkyxX3g0gQHf7SsVatW9s477wRrswAAqBBl3t13333Wvn17u//++y0rK6vUOg0aNLCHHnrI/vzzT9dcOC4uLvB7OXe32V8zzX57v+BW96OUmlJqAobGbaongzfY4O3RoMe1XOuFIjV+VE8GlUv4ysiISU52y/P+W0JRUcrm0MQQNcpUD4v29dq7saS6rZ1Q240t1XKtBwBA2I23LM+UCTWNnDt3blC2CQCA8srNzXXZCSqTWLNmjc91NKr5lltusTvuuMNNTao2Ydoos7ooW0FjNjVdQifN6smgcgllMijIoJNqLQ/VRpCaLqHGj+rJoOBI0WCD7nuysiymQQOLq18/antYAADCS9ACDdu2bSvXhy6to3RUAABCgU701OBRjR7/+OMPn+soY+Gqq66y4cOHW/Pmzat3gxRk+HZiwehPT75ZQopZdobZ9jUFI0ElCoMN3tGVujKvk2ZdnU9LTnOZDAoyhPJoS42w1HQJNX5UT4aSPRpi4uLc8pTu3aOyhwUAIPwELdCgD16qUd0TrdOkSZOgbBMAAGX58ssv3ajKH3/80e86AwYMsHHjxtnee+9d/TtT5RHKZFCQIamuWWpTnSkqGmK2c33B41quaR0hOp2jOimY0KdlH3dlXifN6smgcolQzWTwUoNHjbDUdAk1fszbtq2gXEKZDHFxltCqlVte2UaQ3h4Wm7M2+8yYUOaHgjKh2sMCABB+gtajoV+/fvb777/bm2++6XedyZMn22+//WZHH310sDYLAIBS5s2bZ/3793f/dvkLMhx33HE2Z84c9+9aUIIMsvLHgnIJZTJ4gwyiW93PzytYrpGgUUon1Er/P7btse421IMMXhpd2fCKyy15330toUULN31Ct7qvx6sy2jLce1gAAMJP0DIaVK/62muv2SWXXGIzZ850KaYdOnRwy5YuXWpPP/20PfPMM5aYmOjWBQBU0xVxnaxmbjVLSTNr3Tsqr3z7o3+PVP7w+uuv+13nwAMPtAkTJpTZ3Lja6HVTTwaVS5Qck6n7CbUKlmf8t4QCYUXBBI2w1HQJNX5UTwaVS1RlpGUk9LAAAISfoAUaOnfubC+99JINHDjQnnjiCfdVlKLqycnJrtGWGkICAAKMBoJ+rV+/3saOHWtPPfWUa/roi7IW7rnnHjv77LMtNjZoCYHFKTikk8GczIJyiaLBBt3PyTCr3dCsVoOa2T5UmYIKtXv3DvieDOceFgCA8BO0QIOce+651qNHD/vXv/5lX3zxha1cudI97h17efPNNwcv/RQAogkNBH3avn27Pfjgg+7fpV27dvlcp1mzZm7SxKBBgywhIcFqlDJQNF1CjR/Vk6Fkj4bYuILlrSJkcgAZOAEVrj0sAADhJ6iBBunYsaM9/vjjwX5aAIheNBAsZffu3S6zThkKmzZt8rnb6tata3fddZfddNNNVrt2bQsJKnPRCEtNl1DjR5VSqFxCmQwKMqS1K1geCeUwZOBUaw8LAAAiKtAAAAiyPTUQ1Mmqt4Fguz4R/fLk5eXZqy+9YCOGD7Plq9f5XCcpKcluuOEGN22iYcOGFnK8oys1XUKvm3oyqFxCmQwKMkTCaEsycAAACGs1EmhQ/evmzZvdFSV/2rRpE9RtAoCIRQNB1wdo6tSpdvet19uCxct97ib1Xbj00ktdmYRK+kKaggkaYangkBo/qieDyiUiIZOBDBwAAMJeUAMNn3/+uZs1/v3331tOTo7f9TTf2V8zLgBABUV5A8FZs2a5EohvvvnG7zqnd29s944dbV1PudbChoIKkZiBQgZOtVDzR3ozAAAiLtDw4Ycf2plnnunSVtPS0myvvfayOnXqBOvpASB6RVsDwf9auHChDRkyxN5//32/6xyxT5pNOKmJHdYy32z7DLPcQZGRFRDOyMAJuOkrphebNqE+DUybAABERKBh9OjRlp+fbw8//LBdf/31FhcXF6ynBhDhdufm2dzl6bYtM9vqpSRaz7b1LSme95iobCBoZitWrLCRI0e6kcr6d8eX/VrVsfHndLKT9m9iLr9j46Ko6VMR8qI8A6c6ggyTFkyyVTtXWb4n35Ljk21z1mbbkLHB0nenu3UYbQkACNtAg64sHXrooa57NwAEyrSF62zynJW2Ycduy87Nt8T4WGtSJ8kG9Gptx3drxo6OogaC6v1z77332mOPPea3B1DbtAQbe05Xu+CQlhYXW+QEVoEX7RP1O0DNitIMnOqg7AVlMijIkJqQao1SGrnyVPUs2ZS5yVbvXO2Wa+QlIy4BAGEZaEhNTaXBI4CABxme+nqZrdySYXn5HktJjLPMHXm2bluWbc0o6ANDsCHyGwju2rXLHnnkEbv//vtt+/btPtdp1KCeDTu2kV3T3WNJzVpylTyURVkGTnVSTwaVSyiTwRtkEN3q/vbs7W651mPkJQAgLAMNxx57rM2ZMydYTwcgCsollMmgIENqUrw1rpNYeKVu445s97iWH9mpMWUUEdpAUE2Fn332WRszZoytW+d7VGXt2rXttttus9tuut7qfnKD2Zq5XCUPB1GQgRMM23Zvc1kNKpfwBhm8dD85Ltkt13oAAIRloOG+++6zgw46yHX+vueeeyw+vkYmawKIEOrJoHIJZTJ4gwyiW93flpnjls9bkW6HtG9Y05uLAFLfhbfeesuGDRtmS5Ys8blOQkKCXXPNNTZ06FBr2rRpwYNcJQ8vEZqBE0z1kuq5kgj1ZFAQtmiwQfez8rIsLTnNrQcAQCAF7Wx/0qRJduKJJ9qDDz5o77zzjh111FHWqlUrN7e8JP1DOHz48GBtGoAwpMaP6smgcgmfV+oSYt3y9IzsGttGBN5nn31mgwcPtrlz5/pcrtf+ggsucFkO7du3L76Qq+ThJ4IycGrCAY0PcNMl1PhRPRlK9miIi4lzy7UeAABhGWgYNWpU4T9uy5Ytc1/+EGgAsCeaLqHGj+rJ4PNKXU6+NUyNtfq1EtmZEUCldwowfPHFF37XUTBbzSC7d+/u/wdxlRxRRNkMZ+59ppsuocaP6smgcgllMijI0DK1pVtOI0gAQFhnNABAoGiEpaZLqPGjejKU7NGgiQJa3qNNfXZ6GPvzzz9diYRKJfw5+OCDXXnekUceWb4fylVyRBHv6EpNl1DjR/VkULmEMhkUZGC0JQAgrAMNAwcODNZTAYgCSfFxboSlpkuo8aN6MqhcQpkMCjK0blDLLdd6VWk4qV4QKtNQBoWCG1X5eSi/NWvWuPIHNXvMy8vzuU7nzp1dBsMZZ5xRqnwGwP8omKARlpouocaP6smgcgkyGQAA1YWOjADClnd0paZLqPGjejKoXEKZDAoyVGW0pUZnFv25KtMIxM9F2dLT092YSo2rzMzM9LlOy5YtbfTo0S6ATWNhoHwUVGCEJQAgWAg0AAhrOunXCEtNl1DjR/VkULlEVTIPFGR46utlLlNCUy3UcFK9IFSmoQwK7/OGrdzdZit/NMvcapaSZta6d4138ldQ4bHHHnMZClu3bvW5Tlpamt199912/fXXW0pKStC3EQAAACEYaFDt9KuvvmrvvfeeLV682Hbs2OEeK0kpsEuXLg3mpgEIYwoqBGqEpcollMmgIENqUnyp3g96XMsV3AjLMopFU83mvmy2c51ZXrZZXKJZarOC0Y/eqQxBlJubay+99JKNHDnSVq1a5XOdlOQku+nmW9x45Pr16bkBAAAQ6oIWaMjOzraTTz7Zpk+f7jO4IN4P8wBQU9STQeUSymTwBhm870+6r14QWq4MikAFN4IaZPh2otnWv808+WYJKWbZGWbb15hlbilYJ0jBBr3X/+c//7GhQ4fa77//7nOduFizyw+qayPPPcBanNzHjCADAABAWIgN1hM99NBDbizZKaec4rIZLr74YvfBfffu3e5DpsZf1q5d2+644w7Lz88P1mYBQDFq/KieDCqXKNlgUPfVcFLLVaYRduUSymRQkCGprlmjTmb125o17lxwX49rudarZjNmzLDDDjvMzjrrLL9BhnN6NbOFY/vaUxfubS0864K2bQAAAAijjIY333zTGjRoYK+99poLKMTGFsQ4EhISrFOnTjZixAg7+uij3ZfuDxo0KFibBgCFNF1CjR/Vk0FX3YsGG3RfUy3UcFK9IMKKejKoXEKZDKlNFTUpeFy3uq9+DVq+arZZuz7Vsgk///yz67Hw8ccf+12nX5eGNuGcznZQ+/+WSHhSg7JtAAAACMOMhiVLlljv3r1dkME98X8DDUXHlh1xxBF2+OGH2+OPPx6szQKqher8v1u62T5ZsNbd6j7Cg0ZYarqERmSqJ4O3nMvbo0GPa7kaToYVnayrJ4PKJUqOgtT9hFoFyzP+W0IRQH/99ZdddNFF1qNHD79Bhh6ta9unV7e1z+84+H9BhiBsG6pIWSZ/zTT77f2CW7JOAABAMDMa4uLirF69eoX3vQGHjRs3WrNmzYqNLfvggw94cRC2GIsYHAreqJ+CSh2UhaAAQSCaM+pnaISlpkuo8aN6MqhcQpkMCjK0blDLLQ+7RpCaLqHGjzmZipoUDzbofk6GWe2GZrUaBOwpN2zYYOPGjbMnn3zScnIKpnWU1KFDBxt340U2IPFLi92xuvQK1bRtiLzGohWRnZdtP2/82bbt3mb1kurZAY0PcOMfAQBAmAUaFEAo2lG8Y8eO7vb777+3M844o/DxX375xVJTU4O1WUBARfxYxCgJ5nh/RtHnULlEIJ8j6DTCUieBavy4c/3/yid0Iq/7sXEFy1sdVOWn0kShf/3rX/bggw/azp07fa7TtGlTGz58uF155ZWWGOsxm7zIbOfaat82RF5j0YqavmK6TVk8xTZmbnQBBwUYGqc0tjP3PtP6telX05sHAEBECFqg4ZBDDrEpU6a45o9JSUl20kkn2S233GI333yzJScnu0DE008/7RqDnXrqqcHaLCBgIn4sYpQFc/Qz9FppuoQaP6ong8olgv3aBezKa3xSwZVmnQTq5FClFCpJULaATuTT2hUs13qV3dbsbHvqqads7NixLlvNlzp16tidd97p3vuLBZWredtQjY1FSwaGvI1FOx4bcq+ZggyTFkyyVTtXWb4n35Ljk21z1mbbkLHB0nenu3UINgAAEEaBhrPPPtvV5k6bNs0FEpTRoA+aDz/8sBt7KTohU0nF/fffH6zNAgImosciRmkwRz+jJl+rgF959V5hLprurpKEKqa7a1LQ66+/7jIU1I/Bl8TERPvnP/9pQ4YMsUaNGgVt2xCZjUUrQ39D+ntSkCE1IdUapTQqfP/YlLnJVu9c7Zb3admHMgoAAMIl0KBgwtq1a0uNvDzooIPcLPWtW7faPvvsYzfeeKPtvffewdosIGAidixiCImmYE61XXnVCbuuNOskUM0V1fdAJQmVuPKsEzQFkDVJQmVvvui1ueSSS2z06NHWtm3bsjM26jexA85+xhLX/lzlbUNkNhatCh1nCtrp78kbZBDd6v727O1uudY7qBllOgAAhEWgwZ9//OMf7gsIdxE7FjGEREswp9qvvOrEvYpXmtVf56677rKvv/7a7zqnnHKKjR8/3vbdd9+KZ2x0Pa1K24fIaiwaCApm6ThT0M7n+0dcsluu9QAAQJiMtxwzZoy9//77e1xPEye0LhBuInYsYigGc7ILgjlFeYM5Wh4WwZwyxgLu6cprniev8MprsKmPzplnnmmHHnqo3yCDxhTPnDnTvZ+XFWRQxsaCzQts7a61tit3l7tduHmhe1zLEeKNRWNiC3oyeP8WQ7x5p3qcKJiVlZvl+/0jL8st13oAACBMMhpGjRpll156qZ12WtlXqRSMeP75523EiBHB2jQgICJ2LGIIBnPU+FHBm5I9GsImmLOHsYCheOV15cqV7n38hRdecD0ZfOnWrZvLYFAmQ8ntjoRaeUYiBq+xaHVQI1VlzKj8SMdZyeMuLibOLdd6AAAgzEsnSsrLy7PY2KAlWgABFZFjEUNIRARzyjEWUL0KdIKtngw+y3DysiwtOS0oV163bNniggf//ve/3dQgX9q0aeMy0S666CKLi9vzvveZseHJt5is7dYo32x7bqZtzNgQUrXyjEQM/+ad+ptSWY56nCiYpZ4MCtrp70lBhpapLd3yUApuAQAQrkIu0LBw4UJLS0ur6c0Awn4sYqQK62BOOccCqiFiTV95zcjIsIkTJ9p9991n27b5zpxo2LChDR061K699lo3pri8SmVs7NpceLIa4/FYcmy+ZW9ZatuWTTcLgUADIxGrv7FosHgbqBbtDaKgXZWmuQAAgOAGGgYNGlTs/jfffFPqMa/c3Fz7448/bM6cOXbGGWdU52YB1a6mxyJGurAK5ii4oHGASi/fttJsx9o9jgXU1IWauvKak5Pjytc0JaLkpCCvWrVq2a233mq333671atXr9K18i5jY+cmi9m+2iw3S/ka5omJsyzLt7T8TKv3+0dmDfat0avj4VrmETQBaCwabAom6PUqnHaSVM8F7aLy9QMAIBwDDarl9dIHsyVLlrivsuy///72wAMPVOdmAYgAYRHMKdmLQSUSuzaZJSTvcSygd+pCsK686sT5nXfecRkKf/75p8914uPj7aqrrrLhw4dbs2bNql4rv2u9bdq52hrlZllMbJx54hJsk+VbnOVb43yzA7auLdh/umpeQ1fJGYkYmRRUCJWyHAAAIlG1Bhq+/PLLwg+w/fr1s/79+7txaL4kJiZaixYtfM5ZB4Cw46sXQ2a6WW5mwZcCDrUblTkWMFhXXqdPn26DBw+22bNn+11HY4jHjh1rHTt2DFyt/PYVtjpzs22PM0uOjbUsy7U4i7GWlmBnxtWxxPwVBUEapebX0FXzUGzMCQAAENWBhiOPPLLwvwcOHGhHHHFEsccAICL568VQt5XZ2p8LAg3pK8xS0gq69JcxFrA6r7zOnTvX7r77bps2bZrfdY4//njXDLJnz54BfW6XkbHyR5uyZbltNI9lW6ylWYw1tjg701KtX0ytYhkeNaVYmUcNN+YEAAAIF0FrBjlp0qRgPRUA1Cz1ZNCV+JK9GBRIqN/GbPNis7zdZhsWmukENchjAVXCNmzYMHvzzTf9rtOrVy/XCFLZaNWlX4vDrc/Cj+3njLW2rV4jqxcTZwdYkiVajM8Mj5rASEQAAICKC9ocyXnz5rnmYWWl5v74449unfnz5wdrswCg7MyEv2aa/fZ+wa3ul4eaOupKvMolSvZiULlEcn2z+BSzlAZmyXXM6rc2a9HT7PCbqrXx4bp16+y6666zLl26+A0y7LPPPvbWW2+59+PqDDI4rXtbYmpzOyjX7NidO+wgT5Egg58Mj2DzlnmoAeeunF22bNsyW71jtbvVfUYiAgAA1GBGg2awv/baay5N15+99trLHn/8cduxY4c988wzwdo0ANhzI0f1RdBJrzIO9hQMUEmE1s/JLDhpLhps0H1p2N7sgPPN6ras9rGAGk+pJrsPP/ywG1vpS/PmzW3UqFF22WWXWUJCggWFfl/tz8wtBWUmCtCoXCLIGR57wkhEAACAEA00zJw509X4Nm7c2O86WqZ1ZsyYEazNAoDyNXLUxIjtawpOiqWsYEPr3gVBCa2vK/Pe8omiV+rrtDDrNeh/pRZ/floQoND3BujEOisrywVv7733Xtu8ebPPdTSeUo0gb7zxRje2Mui8+7FoUEflEuUN6gQJIxEBAABCMNCwevVqO+igPae/aurEL7/8EpRtAuDf7tw8m7s83bZlZlu9lETr2ba+GykZtY0cvUECPb6nkYvlvVK/5PPKZ02UIS8vz15++WUbOXKkrVixwuc6ycnJdsMNN7ggQ4MGNdcDwdHvqv2p6RJq/FjNGR6VxUhEAACAEAs0JCUlWXp6+h7X2759u8XFRcHJDBDCpi1cZ5PnrLQNO3Zbdm6+JcbHWpM6STagV2s7vlszi8pGjrrVfQUNyjNycU9X6qUqWRM+aArCBx98YEOGDLGFCxf6XCc2NtaVR6hMolWrVhYyFFSooRGWAAAACNNAQ7du3eybb76xLVu2+L16pmVff/217bvvvsHaLAA+ggxPfb3MVm7JsLx8j6Ukxlnmjjxbty3LtmbkuHUiOthQViNH3a/IyEV/V+pl8sCqZU2U8O2339pdd93lbv0566yz7J577rHOnTuX62cCAAAAIT114qKLLrKdO3faOeecY6tWrfJZWjFgwADXqOzCCy8M1mYBKFEuoUwGBRlSk+KtY5Pa1iotxd3qvh7Xcq0XsUo2cizKO3JRy8s7ctF7pb7raQW3ur+nrIn8vP9lTezBggUL7NRTT7U+ffr4DTIcdUgP+/71++2dB2+2zh33Kt92A1EqOy/bZq+bbZ8v/9zd6j4AAAjRjIYrrrjCXn/9dfvqq6/c+LT+/ftbhw4d3LKlS5fap59+apmZmXb44YfbNddcE6zNAlCEejKoXEKZDI3rJFrMf0+Adav72zJz3PJ5K9LtkPYNI3PflaeRY1VHLgYga2L58uU2YsQI14tBJRO+HNB5L5twWms7ob1ZzNYpZp9NDbkmi0Aomb5iuk1ZPMU2Zm50AQb15Wic0tiNOPVOHwEAACEUaIiPj7ePP/7YdTZ/8cUX7T//+U+x5erLoLrhiRMnunUBBL+Ro9ZXTwaVS3iDDF66n5wQ65anZ0TwFb5gjFzc0/hLPZf6OfjImti0aZMrf9A0iezsbL+jgsddd7b9o958i9223Gx7YHpAAJEeZJi0YJKt2rnK8j35lhyfbJuzNtuGjA2WvrugxxTBBgAAyieoZ/Qanfbss8/a2LFjXWbDypUr3eOtW7e2o446ys1xB1BzjRwVlND66smgq+RFgw26n5WTbw1TY61+rcTIfpmqe+RiJbImVHr28MMP2wMPPGA7duzwOyJ4+PDhdvXll1rif640W7M8YD0gooamjqi0RQGmAI8bRehS9oIyGRRkSE1ItUYpjdz7n973NmVustU7V7vlfVr2cVkOAACgbDWSOqCAwvnnn18TTw1EtKo2clTmg4ISWn/jjuzC8gl92Nb9uNgYt7xHm/oW8apz5GIFsiaUtfDMM8+4AO369et9/rjU1FS744477JZbbrE6deqY/TUzMJMzos2iqdUybhSh7+eNP7tyCWUyeIMMolvd35693S3Xegc1q0LZFAAAUaLGahQWL17sUoAbNmzoejYACGwjx5JBAm8jxyM7NfZbRqHHlfmgoITWV08GlUsok0FBhtYNarnl5SnDiIiSkuocubiHrIn8fU60N19/3YYNG2bLli3z+SMSEhLs2muvtaFDh1qTJk2qZ3JGNAUZAjxuFOFj2+5tLqtB5RI+y8bikt1yrQcAAEIs0LB7924bPXq0Pf3007Z161b32MCBA+355593//3KK6/Yv/71L3e/e/fuwdw0IOwFqpGjN+OhaPmFyiXKW34RiSUlwcya8LTsZdOmz7C7L+hl8+bN8/ltek01yUfvp+rHEMgeEFFZEqFlCvgEcNwowku9pHquJEI9GXyWjeVlWVpymlsPAACEUKBBEyX69etnP/74ozVt2tROOukkmzp1arF1tFyBh8mTJxNoACookI0cdeKtzAcFJbS+ejKoXCJcMxmqWlJSrYpkTej9cfCgk+zLL7/0u7reO8ePH2/7779/zU7OiKSSiD2NG6XUJOId0PgAN11CjR/Vk6Fkj4a4mDi3XOsBAIAQCjTcf//99sMPP9jll19u//73vy05OdliY2OLrdOiRQvr2rWrff7553bvvfcGa9OAiBDoRo4KKkTCCMtAlJRUtz/++MOVP7zzzjt+1znkkEPsvvvus759+4bG5IxIKonIz6PUJMopm0EjLDVdQo0f1ZNB5RLKZFCQoWVqS7ecRpAAAIRYoOHNN9+0Nm3a2BNPPFHm+MpOnTrZt99+G6zNAiIGjRyrt6SkOqxevdqVP6hcLC8vz+c6Xbp0cYHX008/vVSmSo1OzggH5S2J6H0VpSYoHF2p6RJq/KieDCqXUCaDggyMtgQAIAQDDX/99ZedfPLJZQYZJDExsbB/A4Dyi/RGjqFQUhIoeo9TdsLEiRMtKyvL5zqtWrVyQYhLLrlkj++bNTI5IxyUtyTC3afUBAXBBo2w1HQJNX5UTwaVS5DJAABAiAYaUlJSyhVAUEAiLS0tKNsERJpIbeQYSiUlVe1Vo9Ix9VhIT0/3uY7e/4YMGWL//Oc/3ftmlVXn5IxQV97pG7t3lK/URDQ61FdDSUQMBRUYYQkAQJgEGjRFYs6cObZx40Zr3Lix3yCDuqwff/zxwdosIOJEWiPHSCgpyc3NtUmTJtmoUaNszZo1PtdRUOHmm2+2O++80+rXr75tiSoVmb7hDcb4KzWRyQP9N5QEAABA8AMNV155pX311Vd2/vnn2xtvvGGNGjUqtlxX9wYNGmQ5OTl21VVXBWuzgIgUKY0cw72kRMGMd9991zV6VMNHX+Li4uyKK66wESNGuIa4CKCKTt/wV2qy5PM9N5Qk2AAAABD8QIMCDB988IELMrRv394OO+ww97gaP6rJ2YwZM2z79u2uHvmUU04J1mYBiAI1UVKiEZWDBw92Iyv9GTBggI0dO9b22WefgD8/Kjl9o2SpSXkbSipAQRkFAABAcAMN8uqrr1qPHj3sgQcesGnTprnHFi9e7L7q1atn99xzj/tgDgDhWlIyf/589z726aef+l3n2GOPdX0aevXqFdDnRjVM3yhvQ0llQURrLwwAAICaDDSoLvqOO+6wW2+91ebOnWt///235efnu+7qBx10kJs4AQDhWFKydOlSGz58uL3++ut+1znwwANtwoQJLtCAIKrK9I3yNpTUzwUAAEDwAw1Fa5IVWNBXdVPzSY2RU9nGihUrXMO1du3a2THHHOMyK7xeeOEFu+yyy/z+nPPOO8+VfQBAUevXr7dx48bZk08+6Zo++tKxY0eXsXXOOedYbGwsO7AiVLqgrIKqTnqo7PSNijSUBAAAQM0FGrKzs910CW/3dTVA01SKpKTAjgn76aef7IQTTrDNmzdbt27dXC8I9YH47bff7OGHHy4WaPA64IAD3LaUdPDBBwd02wCEN72XPPTQQ+5r165dPtdp1qyZjRw50i6//HJLSEgI+jaGvUVTi5c81MSkh4o2lAQAAEBwAw07d+50ndWfe+45999FpaamuqkTY8aMsTp16gQkk6F///5ubv17771np512WrHl/hq0nXHGGW4EHQD4snv3bnviiSdchsKmTZt8rlO3bl2766677KabbrLatWuzIysbZAiFSQ+VaSgJAAAQ5YIWaNi2bZsdddRR9ssvvxRmDqiEQZYvX+4aqP3f//2fTZ8+3b7++mvXHLIqdBVRJwGPPfZYqSCD9O7du0o/H0B0ycvLcw1tFSzVe5Yvysq6/vrr7e6777aGDRkvWmmhNumhqg0lAQAAokzQAg36cP7zzz9bv3797N///rd16dKl2PJFixbZDTfc4AINWnfixImVfi5lMbzyyivuSmJZfRcARJFK1vp7PB776KOPXPDg119/9bmO+i5oNO/o0aOtTZs21bDxUSYUJz1UpaEkAABAlAlaoOGdd95x9crvv/++1apVq9Tyzp07u2UdOnRw61Yl0DBnzhzbsWOH9enTxzV//Pjjj+2zzz6zrKwsN69es+vVF8JfXwdNxlD9tbZXgZEjjzyy0tsCIHxr/WfNmuVGVc6cOY5bm04AAFhiSURBVNPvOsqYuvfee10fGARIqE56qGxDSQAAgCgTtECDGjKq/4GvIIOXggJ9+/Z1AYeqULNHadKkiXtO9WgoasiQIa5PxPnnn1/qez/88EP35aWeEQo0vPnmm9a0adMqbReA8Kj1X7hwoXufKOu9SIFMjao8/PDDq/s3iD5MegAAAAhrQZuz1r59e9u6dWu5ejnstddeVXou7/PoJOGTTz5xfRo2bNhgf//9t91+++2utGLgwIGuL4RX8+bNXRNITcPQNqxbt859vzItZsyYYaeccoqr0S4vXd309bV06dIq/W4AqlDr36iTWf22Zo07F9z31vprPWXsr1zpmtLuv//+foMM++67rwtGqpcMQYZqnvQQE1vQk0G9GYRJDwAAAGEhaIGGq666yr766qtiJ/claZl6NFxxxRVVeq78/Hx3q5n2yki47rrrrHHjxta2bVs30vLcc8+1nJycYuMtNQZTDSQ12lId45W9cOqpp9rs2bNduYXKMSZPnlyl7QIQYrX++Xlu+eZfP3dByL333tsmTZpU+B5SlN4/XnrpJfc+dfLJJ1tMyZR+BH7SgyY67N5utnGR2dblBbe6z6QHAACAkBa00gmNeVuyZIkdffTRrunjeeed5z64izq46yReTSKvvvpqu+WWW6r0XBqV6eWrGaQee+utt1ymQnl+1o033ug6yX/66ac+yy18Ueq1L9RxA6FT678rP9kmTl1m9902wLbvzPD5Ixo1amRDhw61a6+91k2VQJAw6QEAACBsBS3QEBcXV9jBXfPn9VWSlqnMQV9F6cqhshPKyxvAUD8IZTKU5B2rqXKK8tBVTlm7dm25twFA6Nb65+Tm23Nfr7DR//nD1u3wXRKlqTW33Xab+1KWE2oAkx6CNl0FAAAgLAMNrVu3DlqqcY8ePdytejHs3r271FXILVu2lMp8KE/PB514AAjDWn81fty53vJrNbG3f1pnw979wxav953BkJCQ4DKrhg0bRgPYUMCkh2qfrgIAABC2gQY1YgwWzbE/4IAD7Oeff3blEccff3yx5d6SCW9AYk80blN69uxZDVsLoNpr/TO32Oc//GaDP/rVflqd7Xf1Cy64wMaOHeua1wKRPl0FAAAg7JtBBtudd97pbtXgrWjJgxq5PfTQQ+6/r7nmmsLHx48fb5s2bSr2M9QwcvTo0a6fg0Zv+ur3ACC0/bSrmR333EY77pnVfoMM/fv3dxNnXn31VYIMiPjpKgAAABGT0aDyg7S0tHKtqwkPvXr1qtLz6crktGnT7MUXX7SuXbvaYYcd5kopZs2a5coprrzySjd9wmvIkCEuqKDnVZnH9u3bXVBizZo1lpycbK+88oq1bNmyStsEIHgWL17syh/KmhbTu3dvu+++++yoo47ipUHkTldRvwYtXzXbrF2fmt5aAAAQBYKW0bDffvu50ZVl8TaKDNRseo2pe/rpp61Dhw5utOaPP/7oyh9eeOEF93hRI0aMsL59+9rKlSvtvffec9uqZpKq1VbA4ayzzgrINgGoXspg0oSILl26+A0ydOrUyZVEff/99wQZEPHTVSyhVsHyjP+WUAAAAFSzGI/O7oM0dULNIG+++WZXpqCGa0VpxOXFF19s33zzjRsnV96JEOHGO97S3/hLAJWzbds2u//+++2RRx6xjAzfjR6VlTRq1Ci79NJLLT4+aAldQPX6a6bZZ8PNtq0qKJsoGmzQP/EbF5nVb212/DgyGgAAiHDdQuR8M2gZDcooaNWqlT388MN20EEHFfvFVZbQvXt3F2RQ48ZffvklWJsFIMxlZWW5vitq4Hjvvff6DDLUr1/flUionOKKK64gyIDInK4SE+umq7jgguhW92PjCpa3OqimtxQAAESJoF3SO+KII1wAQSnNr7/+ugs2jBkzxn766Sd78803XR+EiRMn2g033BCsTQIQxvLy8uyll16ykSNHupInX/S+ctNNN9ldd91V7h4xQDhPV3GNH1VKoXKJnIyCIENau4LlWg8AACCSSieKeu2119zEh127dhWOmXz55ZddTXWkC5VUFiBc7M7Ns7nL021bZrbVS0m0Hm3q2acfTXUNXH/77Te/pVqDBg1yQQiauCKqRlxquoQaP6onQ1xiQSaDggzVOdpS0yzUkFIBjpS0ggwLghoAAET1+WbQi5Rzc3Pt559/dunN3hhHdna25efnB3tTAIS4aQvX2eQ5K23Djt2WnZtvO/7+1ZZMfdo2LPFfXnX22We7prJq+AhEFQUTOh5bMF1CjR9rNSgol6jOk/6aCm4AAICQFtRAw59//unGTmpeva4yPvbYY/bGG28UllKovlrNIgFAQYanvl5mK7dk2M61y2zdF8/b1kU/+N0xRx99tE2YMMGNrASiloIKwRphqSDDtxMLyjU0WlNTL7IzzLavKSjjEIINAABEpaA1g3zyySfdaMm5c+faOeec4/o1nHrqqfbqq6+6r6SkJLvtttvshBNOcOPpAER3uYQyGZYsXWYr3r3ffn/8Gr9BBjWS/eSTT+yLL74gyAAEi8ollMmgIENS3YJpF/XbmjXuXHBfj2u51gMAAFEnaIGG6667ztVNv/DCC675o7rAe51//vn266+/Wt++fe2zzz6z/fffP1ibBSAEfTF3iU1/4QH79ZHLbP3cz/7XRb+IWo1a2OiHn3INZRWg1PhcAEGingwql1AmQ2rT/43U1K3u5+cVLFcZBwAAiDpBCzQcdthhNn/+fLvkkkt8Ltfoy+nTp7vU5x07dgRrswCEEP3tjx492s45+kD766u3zJOXW2qdpDppttdpN9jhd71kvY891WJjg/Y2BsBLjR/Vk0HlEiWDfLqvqRdarl4RAAAg6gStR8PXX3+9xxMCXZG888473dVJANFDDWGfeuopGzt2rG3cuNHnOvHJtazz8Rfa3v0G2PLtHktOTrL6tRKDvq0ArGC6hBo/5mQWZBwVDTbovkZr1m5Y0JASAABEnaAFGkoGGXbv3m1btmxxvRkaNCj+QeSAAw4I1mYBqEGaNqNmsMOHD7e//vrL5zoxcQnW8cgzretJAy0ptb5t3JFtcbG51qROkvVo878SLNQgxhtGH42w1HQJNX7cuf5/5RMKMuh+bFzBck29iGLZedn288afbdvubVYvqZ4d0PgAS1SABgCACBf08ZZPP/20PfHEE64ng8ZbDhw40J5//nm37N1337VXXnnF7r//fuvYsWOwNw1FGvHNXZ5u2zKzrV5KovVsW9+S4uPYP+XAvisf/e2rgePdd9/txt36y3Bq3fsEq9/nQouv19Q25sRa1oZdFhcbY60b1LIBvVpzXIaCYI83JKgROtMt9BpruoQaP6qUQuUSymRQkCGtXcHy6hytGeKmr5huUxZPsY2ZG13AQQGGximN7cy9z7R+bfrV9OYBABAZgYa8vDw3beL999+3hIQE69Kliy1cuLBUJoPWOfDAA23o0KHB2jSUGCmobv8bduy27Nx8S4yPdVeOdVJ3fLdmUbWvKho0YN+Vz/fff2+DBw+2GTNm+F3nlFNOceNu18Y2LnY8NkyN3uMxJAV7vGGwgxoom3efF31NVC7Ba+KCDJMWTLJVO1dZviffkuOTbXPWZtuQscHSd6e73UawAQAQyYIWaHj00Uftvffes5NOOsmee+45a9q0aalyig4dOrhMho8//phAQw3QifJTXy+zlVsyLC/fYymJcZa5I8/WbcuyrRk5bp1oObmraNCAfbdnixYtsiFDhtiUKVPKbBp73333WZ8+fdz9/czsyE6Nbd6KdEvPyHY9GVQuQYZNCI43LJk67x1v2PHYwFzVDnZQA+Wjfa7XWNMl1PhRPRlULhHFmQzKXlAmg4IMqQmp1iilkcvQUibXpsxNtnrnare8T8s+lFEAACJW0AINGmup4IJGW9auXdvvel27dnXj6hD8q/c6sVaQITUp3hrXSSz8YKSaeD2u5Trpi/STvIoGDdh3ZVu1apWNGjXKJk2a5Hoy+Pu7Hz9+vJ166qmlxlTqeDukfcMAvLII6nhDpdJ7xxu2KwgchU1QAxWjfV7V1ziCqCeDyiWUyeANMohudX979na3XOsd1Cy6e1gAACJX0ObC/fHHH3bwwQeXGWQQLffXdR7VRyUCunqvE2tvkEF0q/t6XMt1ZTmSlQwadGxS21qlpbhb3fcGXLSeF/vONzV71RSZvffe22Ux+QoytG7d2gUgfvnlFzvttNNKBRkQwoI53nBPQY38vP8FNYAapsaPympQuUTJ9zTdT45Ldsu1HgAAkSpogQb1ZcjKytrjeitWrLA6deoEZZvwP+pDoBIBXb33+cEoIdYtV/p6JKtM0IB9V1xGRoZNmDDBlUI98MADPv/uNWnmoYcesj///NMuvfRSi4uL7CyZqBhvWJR3vKGWB2K8YTCDGkAVabqEGj9m5Wa5rMCidD8rL8st13oAAESqoAUaunXr5koiduzY4XedDRs22Pz586179+7B2iz8l5odqg9BZnae7w9GOQV9ClQjH8kqEzRg3xXIzc11U2WUwaBpEunppbNfatWq5fqvLFu2zG699VZLTk6u9tcU1TzeMCa2oHzB+75RHeMNgxnUAKpIIyw1XSIuJs71ZPD+m+rt0aDHtVzrAQAQqYIWaLj44ott8+bNds0111h2drbPqRT//Oc/3dVQjbxEcGmigpodanSgejIU/WCk+3pcy9WIL5JVJmgQ7ftOv+fbb7/tgolXX321rVmzptQ68fHxdu2119qSJUts3LhxVq8eV/IiZryhxhju3m62cZHZ1uUFt7ofyPGGwQxqAFWkbAWNsGyZ2tJ25eyyZduW2eodq92t7utxLdd6AABEqqA1g7zqqqvsrbfestdff91mzZplJ5xwgnv8559/tptuusk+/PBD++uvv+z444+3Cy+8MFibhSIN9zRRQc0O1YdgW2aOu3qvE2udKLduUMstj/RGkN6ggRo/KkhQsimmr6BBNO+76dOnu1GVs2f7r40/77zzXHBBE2UQYYI13tAb1NB0CTV+VCmFyiWUyaAgQyCDGkAAeEdXarqEGj+qJ0NacprLZFCQgdGWAIBIF+Mpedm2GqlW+7bbbrNnn33WcnIKuvd7qUZ70KBBNnHixIhOp9ZVX1m4cKFFwljHaJg6UTJocHXf9n5HXEbLvps3b54LMEybNs3vOscdd5ybJHHggQcGddtQAzQVIhjjDTXismhQQ1eEAx3UAAJIAQZNl1DjR/VkULkEmQwAgGg43wxqoMFLUyW++uor+/vvv10n+latWtnRRx9tLVq0sEgXKi98WTRRQc0O1YdAJQK6eh+JV+PLUtmgQaTvu6VLl9qwYcPsjTfe8LtOr169XDPIY445JqjbhigRrKAGAABAGOoWzYGGaBYqLzz2LNKDBhWxbt06Gzt2rGv2qKaPvqgJ5D333GPnnHMOYyoBAACAKD7fDFqPBiDcKKhwSPuGFs22b9/uRlT+61//co1afWnevLmNHDnSlT5pjC0AAACA6EagAYDPfipPPPGEy1DQtBhfNDlCfRpuvPFGN7YSAAAAAAg0ACg1ZvaVV16xESNG2IoVK3zunaSkJBdcUJChQYMG7EEAAAAAxZDRAMCN79SI2SFDhtiCBQt87pHY2Fi77LLLbNSoUa6BK1Clho4rfywYU5mSZta6Nw0dAQAAIgiBBiDKffvtt3bXXXe5W3/OOOMMu/fee61Lly4WlTgxDhxGVAIAAEQ8Ag1AlFLmgjIYPvjgA7/r9O3b142qPPTQQy1qcWIc2H357USzrX+befLNElLMsjPMtq8xy9xSsE7nkwP4hAAAAKgJBBqAKLN8+XI3JeKll15yJRO+7L///jZ+/Hg78cQTo3tUJSfGgc0KmftyQZAhqa5ZalMzHVs6BneuL3hcyzseSxkFAABAmAt6oCE3N9emTp1qP/74o23atMkOPvhgNxZP1qxZ4x7r2rWrxccTAwECSX9bKn947LHHLDs72+c6e+21l40dO9bOP/9815MhqnFiHFjqybBzXUEmgzfIILrVffVr0PJVs83a9QnwkwMAACCYgno2/80339hFF11kK1eudFdSdaU0JyenMNDw3Xff2YABA+ytt96ys846K5ibBkSsnTt32iOPPGIPPPCAbd++3ec6jRs3tuHDh9vVV19tiYmJQd/GkMSJcWApkJCXXVAuUTJLRvcTahUsz/hvCQUAAADCVtAuWf7222/Wv39/W7t2rd1www02efLkUmnbp556qtWqVcveeeedYG0WELEUxHv88cetY8eOLojgK8iQmprqpkgsXbrU/V0SZCiCE+PA0nSJuESznMyCcomidD8no2B5LUamAgAAhLugZTQoHTsrK8s++ugjO/74432uo5Ocnj172rx584K1WUDEyc/Pd4G8YcOGuQCCLwkJCXbttdfa0KFDrUmTJkHfxrA8MS56Fd57Yly7ISfG5aURlqnNCho/qidDyR4NsXEFy1sdVD2vJwAAACIv0PDll19a7969/QYZvFq2bGk///xzsDYLiBjKEPrss89s8ODBfoN1Kle68MILbcyYMa4fA8rAiXFgxSeZ9by4YLqEGj8qY0TlEgrYKMiQ1q5gudYDAABAWAtaoCE9Pd1at269x/V27drlUr4BlN/s2bNdgGH69Ol+1znppJPcJAlNlEA5cGIceN7RlZouocaP6smgrBBlMijIwGhLAACAiBC0QIPSs5csWbLH9X7//fdyBSQAmP3555+u/OHtt9/2uzsOOeQQu++++6xv377ssorixLh69qlGWGq6hBo/qieDyiXIZAAAAIgYQQs09OvXz15++WVXQnH00Uf7XGfKlCkuGPHPf/4zWJsFBMzu3DybuzzdtmVmW72UROvZtr4lxcdVyx7WKNjRo0fbc889Z3l5eT7X6dKlixtnefrpp7uSCVQSJ8aBp6ACIywBAAAiVtACDUrrfvPNN+2MM86wCRMm2Jlnnlm4bOvWrS7IcPvtt1vt2rXt1ltvDdZmAQExbeE6mzxnpW3Ysduyc/MtMT7WmtRJsgG9Wtvx3ZoFtARJ2QkTJ060zMxMn+u0atXKBSEuueQSi48P6gTbyMWJMRAS8rOzLXPefMvblm5x9epbSo/uFstIXgAAQk6Mp+SMyWr0n//8xy6++GLLyMjwuTw5Odlef/11O+200yxSdevWzd0uXLiwpjcFAQwyPPX1Mlu5JcPy8j2Wkhhnmdl5FhcbY60b1LKr+7avcrBBQYVHH33U9VhQYM6XtLQ0GzJkiMsISklJqdLzAUCo2fHFF5b+9juWu3GjebKzLSYx0eIbN7b655xtdY45pqY3DwCAkNAtRM43g3q5U9kMCxYssIcffth1x//777/dKD5dgT3uuOPstttusw4dOgRzk4Aql0sok0FBhtSkeGtcJ9GVKSh+t3FHtntcy4/s1LhSZRS5ubn24osv2siRI2316tU+11FQ4eabb7Y777zT6tevzysKICKDDJuffc6yV60yy8uz2JQUy9+0yXLXr7fN6eluHYINAACEjmoLNHz99dfWrFkz22effYo93rZtW3vkkUeq62mBoFJPBpVLKJPBG2QQ3er+tswct3zeinQ7pH3Dcv9cBSpUTqRGj4sWLfK5TlxcnF1xxRU2YsQIa9GiRcB+JyCq5e42W/ljwfjNlLSCMac0qqzxcgllMijIEJeaanGNGhUGdPM2bbKcVavc8tpHHEEZBQAAkR5oOOqoo+zSSy+1559/vrAZZP/+/d1VVyBSGj5u2pnlejKoXKJkw0XdT06IdcvTM7LL/RxfffWV62nyww8/+F3n3HPPtXHjxpUK5AGogkVTi4/ejEtk9GYIUE8GlUsok8EbZBDd6n7etm1ueeb8+Va7d++a3lwAAFCdgQZ9AFBZRNGTp3bt2rHTEVENH+NjYywzJ8/1ZNDVtaLBBt3Pysm3hqmxVr9W4h5//vz58+3uu++2Tz75xO86xxxzjGum2qtXr4D9XgD+G2T4dqLZ1r/NPPlmCSlm2Rlm29eYZW4pPu4UQaXGj+rJoHIJXwHdmORktzzvvyUUAAAgggMNDRo0sMWLF1fXjwdqvuHjjjyLjTHLzivop6qeDCV7NKghpKZP9Gjjv3fCsmXLbPjw4fbaa6/5Xadnz54uwKBeJgjPkaQI8XIJZTIoyJBU1yy1qc5gFS0027m+4HEt73gsZRQ1QNMl1PhRPRl8BXQ9WVkW06CBxdGjBgCAyA809OnTx95//307+uijba+99nKPffPNNzZo0KA9fq8+RDz33HPVtWlAQBs+bt65252U7MjKdT0ZVC6hTAbv1AmNuPR18rp+/XpX/vDUU09ZTk6Oz+fu2LGjW0elErGxsbxyYTiSFGFAPRlULqFMBm+QQXSr++rXoOWrZpu161PTWxt1NMJS0yXU+FE9GUr2aIiJi3PLU7p3r+lNBQAA1R1oeOihh9xUiRkzZrgvWbJkifvaEwINCLeGj6nJ8daodpLl5Oe7k1aVS/g7ad2+fbv7+9DXrl27fD6nGqmqyaOaPSYkJATl94wm/jJU1m3Lsq0ZBUEfgg1RRIEE9WRQuUSJ1Hx3P6FWwfKM/5ZQIKhiExPdCEtNl1DjR/VkcOUSymSIi7OEVq3ccq0HAAAiPNDQvn17mzt3rgs2rFy50jWHVDPIu+66q7qeEqgWSqvfU8PHlIQ4u/Twtta4TrJr/KieDCqXKJrJsHv3bnvyySddhsKmTZt8PlfdunVdw1SNq6xduzavaBiOJEUY0nQJNX7MySwolyj6d677ORlmtRua1WpQk1sZ1byjKzVdQo0f1ZNB5RLKZFCQgdGWAABESaBB9OFdZRPe0gldpT3yyCOr8ymBgFPtvtLqdcW7rIaPCjL4GmGZl5fn+i8oQ0GBN18SExPt+uuvtyFDhljDhuUfg4nQGUmKMKYRlqnNCho/qidDyR4NsXEFy1sdVNNbGtUUTNAIS02XUONH9WRQuQSZDAAARFmgoaiiEyiAcKIGgSqDUFp9RRo+avlHH33kgge//PKLz5+tvguXXHKJjR492tq0aROk3yi6lSdDpaIjSRHm4pPMel5cMF1CjR9VSqFyCWUyKMiQ1q5gudZDjVJQgRGWAACEvqAFGoBwpfR59VpQ7b7S6svT8PG7775zZUIzZ870+3NPO+00u/fee61bt25B+k1QkQyV8owkRQTxjq7UdAk1flRPBpVLKJNBQQZGWwIAANR8oGHMmDHuA/w///lPN+pS98tL36dxf0Co8DYGLDqlwFfDx99++81lMLz33ntlTmTRqMrDDz88aNuPqmeoIAoomKARlpouocaP6smgcgkyGQAAACokxqNP19VAKeH68P7777/bPvvsU3i/PE+n9VTXHom8V68XLlxY05uCSjYSVO1+yYaPang6cuRIe/HFF/2WCe277742fvx4O/nkk0ul7KNmp06UzFC5um97pk4AAAAg7HQLkfPNastomDRpkrtt3rx5sftAOFNQoWiDwM2bN9vQ8ePt0UcfdVMlfFHvhbFjx9qFF15ocXFMMQinDBUAAAAAIZTRgNCOMKFqdu3aZRMnTrT77rvPtm/f7nMdTY8YNmyYXXvttZaUlFQsK0KTD9SUUP0ClMrPGMXQylABAAAAwlG3EDnfDLlmkM8//7ytWrXKjQIEQk1OTo4999xzbkrEunXrfK5Tu3Ztu/XWW+3222+3unXrlkrZL3oVXU0JuYoeOhkqAAAAACIw0PDMM8/Yjz/+SKABIUV9F95++22XobB48WKf68THx9tVV13ljt2mTZvusS+Axitq8oGaEmqihURDyj4ZHQAAAEBkC7lAAxBqvvjiCzeq8qeffvK7zj/+8Q/Xh6Fjx45+T66VyaAgQ2pSfKlJB3pcy4/s1DiiU/fJ6AAAAAAiH4EGwA8FFgYPHmyff/653310wgknuEkSPXr0KHM/qieDyiWUyeANMohudX9bZo5brn4BkZrKT0YHAAAAEB0INAAlqDRCJRKTJ0/2u2969+5tEyZMsKOPPrpc+0+NH9WTQeUSJUdb6r7GK2q5mhJGIjI6AAAAgOgRW9MbAISKtWvXugkRXbt29Rtk2GeffVyvhu+//77cQQbRdAk1fszMznPlEkXpflZOQWNITT6IRHvK6NDj3owOAAAAAOGNQAOi3rZt22zo0KGuv8KTTz5pubm5pfZJixYt7Omnn3ZjYs4+++xSWQl7ohGWmi4RFxvjejJ4gw3eHg16XMs1XjESRXtGBwAAABBNKJ1A1MrKyrLHHnvM7r33XtuyZYvPderXr+/6NNxwww1Wq1atSj+XGjwO6NXaTZdQ40f1ZNDJtTIZFGRo3aCWWx6pjSALMzp2FGR0FA02eDM6GqZGbkZHueXuNlv5o1nmVrOUNLPWvc3ik8q/HAAAAIjkQENcXGSeMCH85eXl2UsvvWQjR460lStX+lwnOTnZbrzxRhdkSEtLC8jzekdXarqEygR0BV8n18pkUJAhkkdbejM6NMpTGRwlp25EekZHuSyaajb3ZbOd68zyss3iEs1Sm5n1vNis88l7Xg4AAABEeqChZB16RVQ0LR0o7zH5/vvv25AhQ+y3337zuU5sbKwNGjTIBSFatWoV8B2rYIJGWKoXgcoEdAVfJ9eRmsngFe0ZHXukIMK3E822/m3myTdLSDHLzjDbvsYsc4vZqjlmy7/1v1wINgAAACDSAw35+fnV9aOBCps5c6bLTpg1a5bfdc466yy75557rHPnztW6h3UyHakjLMsSzRkdZVI5hDIVFERIqmuW2lTRVkXGzHauN9vyV8GXHvO1XN+n7+94LGUUAAAACAn0aEBE+/XXX+3uu++2qVOn+l3nqKOOcqMqDz744KBuWzSK1oyOMqnngsohlKngDSKIbnVfwYT8XLO4BLOGe5dern4N+v5Vs83a9anRXwUAAAAQAg2ISH///beNGDHCXnnlFb9lPN27d7fx48fbCSecQLlOEEVrRodfChSo54LKIUqWjem+ejHk7TaLreV7eUKtgu/P8N3QFAAAAAg2Ag2IKBs3bnTlD0888YRlZ/seldi+fXsbN26cnXfeea4nAyLP7tw8m7s83Y3V1MQLNaMM2awJTY9QMCEns6AcomgwQfcVRIiJM8vP8b08J8OsdkOzWg1qZPMBAACAkgg0ICLs3LnT/vWvf9mDDz5oO3bs8LlOkyZNbPjw4XbVVVdZYmKUj1GMYNMWrivWB0JjNUO6D4RGVGp6hBo7qkyiZA+G+OSC9fSYr+WxcQXf3+qgmv5NAAAAAIdAA8KashaefvppGzt2rG3YsMHnOnXq1LE77rjDbrnlFktNTQ36NiK4QYanvl7mJlvk5XssJTHOMnfkubGamnghIRdsiE8qGFGp6RFq7KhSCpVDKFNBQYQGe5m1Pfx/UydKLk9rV/D9+jkAAABACCDQgLCkqSZvvPGGy1BYtmyZz3WUtXDddde5cZaNGzcO+jYi+OUSymRQkCE1Kd4a10l0vTfUo2Pjjmz3uJarGWXIlVF4R1NqeoQaO6pcQuUQylRQEEHLNQKzrOUAAABAiIiPhpr9++67zz744ANbsWKFpaSkWLt27eyYY46xBx54oNT6Wk/p9/PmzXP3e/bs6a6Gn3wyH+RDgU4aP/nkEzdJ4ueff/a5jk4uL774Yhs9erR7rREd1JNB5RLKZPAGGUS3ur8tM8ct18SLkGxGqWCBRlRqeoQaO6rngsohvJkKe1oOAAAAhIiIDjT89NNPbqLA5s2brVu3bnb66afb9u3b7bfffrOHH364VKDhkUcecen18fHxduyxx1pSUpJNmzbNTjnlFPv3v/9t119/fY39LjD74Ycf7K677rIZM2b43R16re69917bb7/92GVRRo0f1ZNB5RLeIIOX7icnxLrlGqsZshQ0KGtE5Z6WAwAAACEgPpIzGfr372+ZmZn23nvv2WmnnVZs+Y8//ljs/h9//GG33367Cy58+eWXduihh7rH//zzTzvssMNcAEI/r2PHjkH9PWC2aNEiV/4wZcoUv7tDr9GECRPsiCOOYJdFKU2XUONH9WRQ5kvRYIPuZ+XkW8PUWKtfi0agAAAAQHWK2Nl+I0eOtE2bNrmshZJBBundu3ex+xMnTrS8vDy75pprCoMMss8++9jQoUMtNzfXrYPgWbVqlV1xxRUuG8VfkKFr164ukPTNN98QZIhyGmGp6RJxsTGuJ4OCC+Lt0aDHtbx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/Users/simon-pierreboucher/wp29/runs/RR0008; cat > out/report.md <<'EOF'\n# F6-dev-01 — The Preston curve in 2023\n\n## Question\nAcross countries/economies, how strongly is life expectancy at birth (LE, years) linearly associated with the natural log of GDP per capita (PPP, constant 2021 international $) in the most recent year with usable World Bank data? Estimand: the slope β in LE_i = α + β·ln(GDPpc_i) + ε_i, one equally weighted observation per country. The relationship is descriptive and associational only. No causal claim is made.\n\n## Data and exclusions\n- Inputs: `data/wb_indicators.csv` (2,349 entity-year rows, 2015–2023) and `data/wb_entities.csv` (296 entities), both World Bank WDI (see `data/README.md`). The CSVs were read with only empty cells treated as missing, so a literal \"NA\" in the data cannot be mistaken for a missing value.\n- Population: all entities whose `region` is `Aggregates` were removed, leaving 217 countries/economies. Every iso3 code in the indicator file matched the metadata, and there were no duplicate iso3-year rows.\n- Analysis year: in each year 2015–2022, 199 countries had both indicators, and 197 did in 2023. **2023** is therefore the most recent year with at least 150 countries.\n- In 2023, **20** countries/economies are excluded for missing data (`n_excluded_missing`). In every case GDP pc PPP is the missing indicator: ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. Nothing was imputed. **n = 197** countries are analysed.\n- Anomaly found: the Central African Republic has implausible LE values in years outside the analysis year (31.5 in 2019, 18.8 in 2022). They do not enter the main estimate and were left unchanged, but they inflate the robust SE of the 2022 slope in the by-year sensitivity check. All data issues are listed in `results.json` → `data_issues`.\n\n## Method\n- Baseline (no information): predict every country's LE with the sample mean. Its RMSE equals the population SD of LE.\n- Model: OLS of LE on ln(GDPpc) (statsmodels), with the **HC3** heteroskedasticity-robust standard error. HC3 was chosen because it is the most conservative of HC0–HC3 in small samples with leverage points. The 95% CI is β ± 1.96·SE_HC3, a normal approximation, which is what statsmodels uses with robust covariance.\n- Sensitivity checks (written to `out/sensitivity.json` by `analysis.py`, not required keys):\n - HC0/HC1/HC2 and classical SEs;\n - the same regression for every year 2015–2023;\n - re-estimation after dropping high-influence points (Cook's D > 4/n);\n - a quadratic-in-log specification.\n- Outputs: Figure 1 (`figure1.png`) is a scatter plot with the OLS line, coloured by income level. Table 1 (`table1.csv`) gives means by World Bank income level. No randomness is used (`random_seed` = null).\n\n## Results\n| quantity | value |\n|---|---|\n| analysis year | 2023 |\n| n analysed / excluded for missing data | 197 / 20 |\n| baseline RMSE (mean only) | 7.08 years |\n| OLS residual RMSE | 3.59 years |\n| Pearson r (ln GDPpc, LE) | 0.861 |\n| R² | 0.742 |\n| slope β | **5.27 years per log-point** |\n| intercept α | 22.57 years |\n\nThe Preston curve holds clearly in 2023. A 1-log-point difference in GDP per capita (about 2.7× income) goes with about 5.3 more years of life expectancy. Doubling income goes with about 5.27·ln 2 ≈ 3.65 years. Income alone cuts the prediction error roughly in half, from 7.08 to 3.59 years RMSE.\n\nTable 1 (from `table1.csv`):\n\n| income level | n | mean ln GDPpc | mean LE |\n|---|---|---|---|\n| Low income | 20 | 7.61 | 63.2 |\n| Lower middle income | 46 | 8.69 | 67.8 |\n| Upper middle income | 58 | 9.69 | 73.7 |\n| High income | 73 | 10.82 | 79.7 |\n\nConclusion code: **positive_effect**. The interval lies entirely above zero.\n\n## Uncertainty\n- **Main result:** HC3 SE = 0.211, 95% CI [4.85, 5.68].\n- **Other standard-error estimators:** SEs are 0.207 (HC0), 0.208 (HC1), 0.209 (HC2) and 0.222 (classical), so the inference does not depend on the choice.\n- **Slope in other years:** the slope ranges from 5.27 (2023) to 5.92 (2019) across 2015–2023, with all intervals far above zero. The 2023 slope is the lowest in the window, and slopes are somewhat lower after 2019. This could reflect post-COVID LE recovery patterns or data revisions; it was not investigated.\n- **Influential points:** dropping the 9 points with Cook's D > 4/n gives β = 5.42 (SE 0.17). The most influential are Nigeria, Greenland, Chad, Solomon Islands and Guyana.\n- **Curvature:** the quadratic term in ln GDPpc is 0.17 (SE 0.18), which is not significant. There is no clear evidence of curvature in log-income at this sample size.\n- **Not covered by the CI:** the interval only reflects sampling-style variation given these 197 countries. It does not cover measurement error in either indicator, or the fact that the countries are close to a whole population rather than a random sample.\n\n## Limitations and open issues\n- **What would invalidate the result:**\n - Non-random missingness. The 20 excluded economies include conflict or sanctioned states (SSD, SYR, YEM, PRK, VEN, ERI, CUB) and wealthy microstates or territories (MCO, LIE, IMN, CHI, GIB). Several of these probably sit far from the fitted line. For example, Cuba plausibly has high LE at moderate income, and some conflict states have low income. Their omission could bias β in either direction.\n - Systematic errors in the 2023 data. Many 2023 LE values are WDI/UN WPP modelled estimates and projections, not vital-registration measurements, and they may be revised. The CAF series shows WDI can contain implausible values. Only the most extreme ones (LE < 35) were screened.\n - Mismeasured GDP. GDP pc PPP is poorly measured for some economies and distorted by profit shifting (e.g. Ireland, Luxembourg) or resource rents (e.g. Guyana, Equatorial Guinea). This creates errors-in-variables, which would tend to attenuate β.\n- **What was not checked:**\n - population weighting (the estimand is deliberately unweighted);\n - alternative functional forms beyond a quadratic in log;\n - spatial or regional dependence of residuals: HC SEs assume independent countries, and regional clustering could widen the CI;\n - the time-varying vintage of income classifications. Table 1 uses the *current* World Bank income level from metadata, not the 2023 classification;\n - why the 2023 slope is the lowest in the window.\n- **Main threats:**\n - Confounding: education, health systems, disease environment and institutions all move with income. β is an association, not the effect of raising income.\n - Ecological interpretation: the result says nothing about individuals.\n - A single cross-section: it is sensitive to year-specific shocks such as COVID-19 mortality in 2020–2022, which is partly why the most recent year was required.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"In 2023 (the most recent year with >=150 countries having both indicators), life expectancy is positively associated with ln GDP per capita across 197 countries; slope 5.27 years per log-point, HC3 95% CI [4.85, 5.68].\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Associational only; CI ignores measurement error and non-random missingness of 20 economies.\"},\n {\"id\":\"C2\",\"text\":\"ln GDP per capita explains about 74% of cross-country variance in life expectancy (r = 0.86), halving prediction RMSE from 7.08 to 3.59 years versus the mean-only baseline.\",\"results_keys\":[\"r_squared\",\"pearson_r\",\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; no out-of-sample validation.\"},\n {\"id\":\"C3\",\"text\":\"20 countries/economies were excluded in 2023 because GDP per capita PPP was missing; no imputation was done.\",\"results_keys\":[\"n_excluded_missing\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Excluded set is non-random (conflict states, microstates), possible selection bias of unknown sign.\"},\n {\"id\":\"C4\",\"text\":\"The positive slope is robust to the choice of HC estimator, to dropping high-influence points, and holds in every year 2015-2023.\",\"results_keys\":[\"effect_estimate\",\"effect_se\"],\"status\":\"supported\",\"caveat\":\"Sensitivity numbers are in out/sensitivity.json (generated by analysis.py), not in results.json keys.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import 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/Users/simon-pierreboucher/wp29/runs/RR0008; cat > out/report.md <<'EOF'\n# F6-dev-01 — The Preston curve in 2023\n\n## Question\nAcross countries/economies, how strongly is life expectancy at birth (LE, years) linearly associated with the natural log of GDP per capita (PPP, constant 2021 international $) in the most recent year with usable World Bank data? Estimand: the slope β in LE_i = α + β·ln(GDPpc_i) + ε_i, one equally weighted observation per country. The relationship is descriptive and associational only. No causal claim is made.\n\n## Data and exclusions\n- Inputs: `data/wb_indicators.csv` (2,349 entity-year rows, 2015–2023) and `data/wb_entities.csv` (296 entities), both World Bank WDI (see `data/README.md`). The CSVs were read with only empty cells treated as missing, so a literal \"NA\" in the data cannot be mistaken for a missing value.\n- Population: all entities whose `region` is `Aggregates` were removed, leaving 217 countries/economies. Every iso3 code in the indicator file matched the metadata, and there were no duplicate iso3-year rows.\n- Analysis year: in each year 2015–2022, 199 countries had both indicators, and 197 did in 2023. **2023** is therefore the most recent year with at least 150 countries.\n- In 2023, **20** countries/economies are excluded for missing data (`n_excluded_missing`). In every case GDP pc PPP is the missing indicator: ASM, CHI, CUB, ERI, GIB, GUM, IMN, LIE, MAF, MCO, MNP, NCL, PRK, PYF, SSD, SYR, VEN, VGB, VIR, YEM. Nothing was imputed. **n = 197** countries are analysed.\n- Anomaly found: the Central African Republic has implausible LE values in years outside the analysis year (31.5 in 2019, 18.8 in 2022). They do not enter the main estimate and were left unchanged, but they inflate the robust SE of the 2022 slope in the by-year sensitivity check. All data issues are listed in `results.json` → `data_issues`.\n\n## Method\n- Baseline (no information): predict every country's LE with the sample mean. Its RMSE equals the population SD of LE.\n- Model: OLS of LE on ln(GDPpc) (statsmodels), with the **HC3** heteroskedasticity-robust standard error. HC3 was chosen because it is the most conservative of HC0–HC3 in small samples with leverage points. The 95% CI is β ± 1.96·SE_HC3, a normal approximation, which is what statsmodels uses with robust covariance.\n- Sensitivity checks (written to `out/sensitivity.json` by `analysis.py`, not required keys):\n - HC0/HC1/HC2 and classical SEs;\n - the same regression for every year 2015–2023;\n - re-estimation after dropping high-influence points (Cook's D > 4/n);\n - a quadratic-in-log specification.\n- Outputs: Figure 1 (`figure1.png`) is a scatter plot with the OLS line, coloured by income level. Table 1 (`table1.csv`) gives means by World Bank income level. No randomness is used (`random_seed` = null).\n\n## Results\n| quantity | value |\n|---|---|\n| analysis year | 2023 |\n| n analysed / excluded for missing data | 197 / 20 |\n| baseline RMSE (mean only) | 7.08 years |\n| OLS residual RMSE | 3.59 years |\n| Pearson r (ln GDPpc, LE) | 0.861 |\n| R² | 0.742 |\n| slope β | **5.27 years per log-point** |\n| intercept α | 22.57 years |\n\nThe Preston curve holds clearly in 2023. A 1-log-point difference in GDP per capita (about 2.7× income) goes with about 5.3 more years of life expectancy. Doubling income goes with about 5.27·ln 2 ≈ 3.65 years. Income alone cuts the prediction error roughly in half, from 7.08 to 3.59 years RMSE.\n\nTable 1 (from `table1.csv`):\n\n| income level | n | mean ln GDPpc | mean LE |\n|---|---|---|---|\n| Low income | 20 | 7.61 | 63.2 |\n| Lower middle income | 46 | 8.69 | 67.8 |\n| Upper middle income | 58 | 9.69 | 73.7 |\n| High income | 73 | 10.82 | 79.7 |\n\nConclusion code: **positive_effect**. The interval lies entirely above zero.\n\n## Uncertainty\n- **Main result:** HC3 SE = 0.211, 95% CI [4.85, 5.68].\n- **Other standard-error estimators:** SEs are 0.207 (HC0), 0.208 (HC1), 0.209 (HC2) and 0.222 (classical), so the inference does not depend on the choice.\n- **Slope in other years:** the slope ranges from 5.27 (2023) to 5.92 (2019) across 2015–2023, with all intervals far above zero. The 2023 slope is the lowest in the window, and slopes are somewhat lower after 2019. This could reflect post-COVID LE recovery patterns or data revisions; it was not investigated.\n- **Influential points:** dropping the 9 points with Cook's D > 4/n gives β = 5.42 (SE 0.17). The most influential are Nigeria, Greenland, Chad, Solomon Islands and Guyana.\n- **Curvature:** the quadratic term in ln GDPpc is 0.17 (SE 0.18), which is not significant. There is no clear evidence of curvature in log-income at this sample size.\n- **Not covered by the CI:** the interval only reflects sampling-style variation given these 197 countries. It does not cover measurement error in either indicator, or the fact that the countries are close to a whole population rather than a random sample.\n\n## Limitations and open issues\n- **What would invalidate the result:**\n - Non-random missingness. The 20 excluded economies include conflict or sanctioned states (SSD, SYR, YEM, PRK, VEN, ERI, CUB) and wealthy microstates or territories (MCO, LIE, IMN, CHI, GIB). Several of these probably sit far from the fitted line. For example, Cuba plausibly has high LE at moderate income, and some conflict states have low income. Their omission could bias β in either direction.\n - Systematic errors in the 2023 data. Many 2023 LE values are WDI/UN WPP modelled estimates and projections, not vital-registration measurements, and they may be revised. The CAF series shows WDI can contain implausible values. Only the most extreme ones (LE < 35) were screened.\n - Mismeasured GDP. GDP pc PPP is poorly measured for some economies and distorted by profit shifting (e.g. Ireland, Luxembourg) or resource rents (e.g. Guyana, Equatorial Guinea). This creates errors-in-variables, which would tend to attenuate β.\n- **What was not checked:**\n - population weighting (the estimand is deliberately unweighted);\n - alternative functional forms beyond a quadratic in log;\n - spatial or regional dependence of residuals: HC SEs assume independent countries, and regional clustering could widen the CI;\n - the time-varying vintage of income classifications. Table 1 uses the *current* World Bank income level from metadata, not the 2023 classification;\n - why the 2023 slope is the lowest in the window.\n- **Main threats:**\n - Confounding: education, health systems, disease environment and institutions all move with income. β is an association, not the effect of raising income.\n - Ecological interpretation: the result says nothing about individuals.\n - A single cross-section: it is sensitive to year-specific shocks such as COVID-19 mortality in 2020–2022, which is partly why the most recent year was required.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"In 2023 (the most recent year with >=150 countries having both indicators), life expectancy is positively associated with ln GDP per capita across 197 countries; slope 5.27 years per log-point, HC3 95% CI [4.85, 5.68].\",\"results_keys\":[\"analysis_year\",\"n_analysed\",\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Associational only; CI ignores measurement error and non-random missingness of 20 economies.\"},\n {\"id\":\"C2\",\"text\":\"ln GDP per capita explains about 74% of cross-country variance in life expectancy (r = 0.86), halving prediction RMSE from 7.08 to 3.59 years versus the mean-only baseline.\",\"results_keys\":[\"r_squared\",\"pearson_r\",\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; no out-of-sample validation.\"},\n {\"id\":\"C3\",\"text\":\"20 countries/economies were excluded in 2023 because GDP per capita PPP was missing; no imputation was done.\",\"results_keys\":[\"n_excluded_missing\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Excluded set is non-random (conflict states, microstates), possible selection bias of unknown sign.\"},\n {\"id\":\"C4\",\"text\":\"The positive slope is robust to the choice of HC estimator, to dropping high-influence points, and holds in every year 2015-2023.\",\"results_keys\":[\"effect_estimate\",\"effect_se\"],\"status\":\"supported\",\"caveat\":\"Sensitivity numbers are in out/sensitivity.json (generated by analysis.py), not in results.json keys.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import 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Preston curve holds clearly in 2023: across 197 countries, life expectancy rises about 5.27 years for each log-point of GDP per capita, with a 95% interval of 4.85 to 5.68. The interval is well above zero, so the conclusion code is `positive_effect` and the task is marked complete. I ran `out/analysis.py` from the workspace root and it regenerated all outputs.\n\nYour last message just said \"Your\" and seems to have been cut off, so I finished the task as written.\n\n**Main numbers (2023):**\n\n| quantity | value |\n|---|---|\n| Countries analysed | 197 |\n| Countries dropped (missing GDP per capita) | 20 |\n| Error of the mean-only baseline (RMSE) | 7.08 years |\n| Error of the income model (RMSE) | 3.59 years |\n| Correlation r | 0.861 |\n| R² | 0.742 |\n| Intercept | 22.57 years |\n| Robust standard error (HC3) | 0.211 |\n\n- **Year selection:** 2023 is the most recent year meeting the rule; 197 countries have both indicators that year (199 in each earlier year).\n- **Robustness:** the result barely changes with other standard-error methods (HC0–HC2 and classical give 0.207 to 0.222). Dropping 9 high-influence countries gives a slope of 5.42. The slope is positive and significant in every year from 2015 to 2023, ranging from 5.27 to 5.92. 2023 is the lowest year, and I didn't look into why. These checks are in an extra file, `out/sensitivity.json`, rather than in `results.json`.\n\n**Data problems found:**\n- The indicator file mixes countries with regional and income-group aggregates; I removed the aggregates as the task requires.\n- The Central African Republic has implausible life expectancy values in years outside 2023: 31.5 in 2019 and 18.8 in 2022. I flagged them but didn't change them. They do inflate the 2022 standard error in the year-by-year check.\n- In every one of the 20 dropped countries, GDP per capita is the missing indicator. They are mostly conflict or sanctioned states (South Sudan, Syria, Yemen, North Korea, Venezuela, Eritrea, Cuba) and small territories or microstates. That is not random and could bias the slope in either direction.\n\n**Limitations:**\n- This is an association, not a causal effect.\n- The interval assumes countries are independent; regional clustering could widen it.\n- It doesn't account for measurement error, such as modelled life expectancy figures or GDP distorted by profit shifting (Ireland, Luxembourg) or resource wealth (Guyana).\n- The income-level table uses today's World Bank classification, not the 2023 one.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `figure1.png`\n- `table1.csv`\n- `report.md`\n- `claims.json` (4 claims, all marked supported)\n- `sensitivity.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3530,"cache_read_input_tokens":17361,"output_tokens":1013,"output_tokens_details":{"thinking_tokens":17},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3530},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsgwJPu17ZEcaWf6zUV","requestRef":"78f431af-8279-431f-8651-9080fee81521","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5a008099-4446-47d2-bd5e-8c0b1378bbec","timestamp":"2026-10-30T05:32:35.266Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0008","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","version":"2.1.314","gitBranch":"HEAD"}82{"type":"last-prompt","lastPrompt":"Your","leafUuid":"5a008099-4446-47d2-bd5e-8c0b1378bbec","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b"}83{"type":"cost-state","sessionId":"3638ed9c-be57-44e0-92c3-25c422332e8b","totalCostUSD":0.31597479999999994,"totalAPIDuration":98844,"totalAPIDurationWithoutRetries":98837,"totalToolDuration":1884,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":100904,"startTime":1793338253149,"modelUsage":{"claude-opus-5-5":{"inputTokens":26,"outputTokens":9911,"thinkingTokens":506,"cacheReadInputTokens":103579,"cacheCreationInputTokens":19387,"cacheCreationEphemeral5mInputTokens":19387,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.31597479999999994}},"hasUnknownModelCost":false}84