{ "cells": [ { "cell_type": "markdown", "id": "addb5b59", "metadata": {}, "source": [ "# SUBSIDE Science Backbone & Time-Burst Notebook\n", "\n", "**Author scaffold:** SUBSIDE / TACC · adapted from the *Semantic Bridge* cookbook and the *EPH × Semantic Bridge × Burst* pipeline.\n", "\n", "This notebook builds a **science backbone** for the SUBSIDE literature corpus and visualizes how topics, data resources, and scientific **domains** evolve over time.\n", "\n", "### What it does, end-to-end\n", "\n", "| Step | What happens | Source / inspiration |\n", "|---|---|---|\n", "| 1 | Setup, paths, imports | new |\n", "| 2 | Load a BibTeX file (`Global_SUBSIDE_2024.bib`) and normalize entries | new |\n", "| 3 | **Auto-extract** candidate keywords / topics from `keywords` + `abstract` fields (TF-IDF + LDA) | adapted from `semantic_bridge_cookbook` §3 |\n", "| 4 | **User edits** the keyword groups — pre-seeded with SUBSIDE-relevant domain groups (Subsidence, Groundwater, InSAR/Geodesy, Coastal, Geohazards, Decision Science, …) | adapted from EPH §3.1 (`_GROUP_REGISTRY`) |\n", "| 5 | **Build the science backbone** via three tiers: (1) ETO Map-of-Science CSV export, (2) `semantic_bridge_pipeline.default_science_backbone()`, (3) inlined SUBSIDE-tuned fallback | adapted from EPH §5.1 |\n", "| 6 | Map every keyword group to backbone domains / subdisciplines | adapted from EPH §5.2 |\n", "| 7 | **Interactive backbone network** — domains (blue) ↔ subdisciplines (gray) ↔ keyword groups (colored by primary domain), with hover details, on Plotly | adapted from EPH §5.2 |\n", "| 8 | **Time-burst graphic** — Kleinberg two-state burst detection on **annual** publication frequencies, with three interactive views: (a) keyword/term bursts, (b) **DOMAIN** bursts, (c) key data-resource bursts (InSAR, GNSS, LiDAR, …) | adapted from EPH §6 |\n", "| 9 | Exports — CSV tables and standalone HTML figures | new |\n", "\n", "### Required inputs\n", "\n", "- A `.bib` file. The TACC default is `/work/01813/sawp33/MySUBSIDE/Global_SUBSIDE_2024.bib`; the cell below also tries a local fallback so the notebook runs anywhere.\n", "- *(Optional)* An ETO Map-of-Science CSV export — leave the path `None` to skip Tier 1 and fall through to the SUBSIDE-tuned inlined backbone.\n", "\n", "---" ] }, { "cell_type": "markdown", "id": "ed414c02", "metadata": {}, "source": [ "## 1. Setup\n", "\n", "Paths, dependency installation, and imports. Run once per kernel session.\n", "\n", "### 1.1 Configure paths\n", "\n", "`BIB_PATH` should point to the SUBSIDE BibTeX file. The TACC path is tried first; a local file is a fallback. `OUTPUT_DIR` receives every export.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "022ade84", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BibTeX source : /work/01813/sawp33/MySUBSIDE/Global_SUBSIDE_2024.bib (exists = True)\n", "Output dir : /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults\n", "Backbone JSON : /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults/science_backbone.json (exists = True)\n", "ETO export : /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/eto-map-of-scienceWaterResources.csv\n" ] } ], "source": [ "from pathlib import Path\n", "import os\n", "\n", "# ── BibTeX source: pick whichever exists in your environment ──────────\n", "CANDIDATE_BIB_PATHS = [\n", " Path(\"/work/01813/sawp33/MySUBSIDE/Global_SUBSIDE_2024.bib\"),\n", " Path(os.getcwd()) / \"Global_SUBSIDE_2024.bib\",\n", " Path(os.getcwd()) / \"data\" / \"Global_SUBSIDE_2024.bib\",\n", "]\n", "BIB_PATH = next((p for p in CANDIDATE_BIB_PATHS if p.exists()),\n", " CANDIDATE_BIB_PATHS[0])\n", "\n", "#OUTPUT_DIR = Path(os.getcwd()) / \"ScienceBackboneResults\"\n", "OUTPUT_DIR = Path(\"/work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults\")\n", "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n", "OUTPUT_DIR.mkdir(exist_ok=True)\n", "\n", "# Tier 0: prebuilt backbone JSON from SUBSIDE_ScienceBackbone_Setup.ipynb\n", "# Defaults to the path that setup notebook writes; set to None to skip Tier 0.\n", "\n", "import os\n", "SCIENCE_BACKBONE_JSON = Path(os.environ[\"WORK\"]) / \"cookbooks\" / \"SUBSIDE SciMap\" / \"ScienceBackboneResults\" / \"science_backbone.json\"\n", "# Tier 1: ETO CSV export via sbp (only used if SCIENCE_BACKBONE_JSON is missing)\n", "\n", "ETO_EXPORT_PATH = Path(os.environ[\"WORK\"]) / \"cookbooks\" / \"SUBSIDE SciMap\" / \"eto-map-of-scienceWaterResources.csv\"\n", "\n", "print(f\"BibTeX source : {BIB_PATH} (exists = {BIB_PATH.exists()})\")\n", "print(f\"Output dir : {OUTPUT_DIR}\")\n", "print(f\"Backbone JSON : {SCIENCE_BACKBONE_JSON} \"\n", " f\"(exists = {Path(SCIENCE_BACKBONE_JSON).exists() if SCIENCE_BACKBONE_JSON else False})\")\n", "print(f\"ETO export : {ETO_EXPORT_PATH}\")\n" ] }, { "cell_type": "markdown", "id": "83e265f8", "metadata": {}, "source": [ "### 1.2 Install and import dependencies\n", "\n", "All packages are mainstream and available on TACC Jupyter kernels. `bibtexparser`, `plotly`, and `networkx` are installed on demand if missing.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "ee23fdbc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Imports complete\n" ] } ], "source": [ "import sys, subprocess, importlib\n", "\n", "def _ensure(pkg, import_name=None):\n", " try:\n", " importlib.import_module(import_name or pkg)\n", " except ImportError:\n", " subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", pkg])\n", "\n", "for _p, _m in [\n", " (\"bibtexparser\", \"bibtexparser\"),\n", " (\"plotly\", \"plotly\"),\n", " (\"networkx\", \"networkx\"),\n", " (\"scikit-learn\", \"sklearn\"),\n", "]:\n", " _ensure(_p, _m)\n", "\n", "import re, json, math\n", "from datetime import datetime, timezone, timedelta\n", "from collections import Counter, defaultdict\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import networkx as nx\n", "\n", "import plotly.graph_objects as go\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "\n", "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", "from sklearn.decomposition import LatentDirichletAllocation\n", "\n", "import bibtexparser\n", "from bibtexparser.bparser import BibTexParser\n", "\n", "print(\"✓ Imports complete\")\n" ] }, { "cell_type": "markdown", "id": "23e81afa", "metadata": {}, "source": [ "## 2. Load the BibTeX corpus\n", "\n", "Parses the `.bib` file into a tidy DataFrame `df` with one row per entry. Keeps the fields needed downstream: `id`, `year`, `title`, `abstract`, `keywords`, `journal`, `author`, `doi`. Empty `text_content` (the title + abstract + keywords concatenation used by NLP) rows are dropped.\n", "\n", "### 2.1 Parse the BibTeX file\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "570992f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Loaded 8,409 entries from Global_SUBSIDE_2024.bib\n" ] } ], "source": [ "def load_bib(path):\n", " \"\"\"\n", " Parse a BibTeX file with bibtexparser, tolerant of common quirks\n", " ('month' as a string, missing fields, accented characters).\n", " Returns a list of dicts, one per entry.\n", " \"\"\"\n", " parser = BibTexParser(common_strings=True)\n", " parser.ignore_nonstandard_types = False\n", " parser.homogenize_fields = True\n", " with open(path, \"r\", encoding=\"utf-8\", errors=\"replace\") as fh:\n", " bib_db = bibtexparser.load(fh, parser=parser)\n", " return bib_db.entries\n", "\n", "\n", "def _clean(text):\n", " \"\"\"Strip BibTeX braces and collapse whitespace.\"\"\"\n", " if not text:\n", " return \"\"\n", " t = re.sub(r\"[{}\\\\]\", \" \", str(text))\n", " t = re.sub(r\"\\s+\", \" \", t).strip()\n", " return t\n", "\n", "\n", "def _year_int(entry):\n", " y = entry.get(\"year\") or \"\"\n", " m = re.search(r\"\\d{4}\", str(y))\n", " return int(m.group(0)) if m else None\n", "\n", "\n", "def normalize_entries(entries):\n", " rows = []\n", " for e in entries:\n", " rows.append({\n", " \"id\": e.get(\"ID\") or e.get(\"id\") or \"\",\n", " \"type\": e.get(\"ENTRYTYPE\") or e.get(\"type\") or \"\",\n", " \"year\": _year_int(e),\n", " \"title\": _clean(e.get(\"title\")),\n", " \"abstract\": _clean(e.get(\"abstract\")),\n", " \"keywords\": _clean(e.get(\"keywords\")),\n", " \"journal\": _clean(e.get(\"journal\") or e.get(\"booktitle\")),\n", " \"author\": _clean(e.get(\"author\")),\n", " \"doi\": _clean(e.get(\"doi\")),\n", " })\n", " return pd.DataFrame(rows)\n", "\n", "\n", "if BIB_PATH.exists():\n", " entries = load_bib(BIB_PATH)\n", " df = normalize_entries(entries)\n", "else:\n", " print(f\" ⚠ {BIB_PATH} not found — using empty DataFrame for structure.\")\n", " df = pd.DataFrame(columns=[\"id\",\"type\",\"year\",\"title\",\"abstract\",\n", " \"keywords\",\"journal\",\"author\",\"doi\"])\n", "\n", "# Concatenate the NLP-relevant fields into one column for downstream analysis.\n", "df[\"text_content\"] = (df[\"title\"].fillna(\"\") + \" . \" +\n", " df[\"abstract\"].fillna(\"\") + \" . \" +\n", " df[\"keywords\"].fillna(\"\"))\n", "df = df[df[\"text_content\"].str.strip().str.len() > 0].reset_index(drop=True)\n", "\n", "print(f\"✓ Loaded {len(df):,} entries from {BIB_PATH.name}\")\n" ] }, { "cell_type": "markdown", "id": "fcc4e966", "metadata": {}, "source": [ "### 2.2 Corpus statistics\n", "\n", "A quick sanity check before topic modeling. Watch for: a missing-year tail (entries with `year=None` are excluded from the time-burst plots but still used for backbone mapping), an obviously skewed journal distribution, and any entries that lack both `keywords` and `abstract`.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "5e69fd95", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Entries with year : 8,409 / 8,409\n", "Entries with abstract : 8,409\n", "Entries with keywords : 0\n", "Year range : 1977 – 2025\n", "\n", "Top 10 journals/venues:\n", "journal\n", "nan 660\n", "REMOTE SENSING 330\n", "ENVIRONMENTAL EARTH SCIENCES 178\n", "JOURNAL OF HYDROLOGY 126\n", "SCIENCE OF THE TOTAL ENVIRONMENT 124\n", "ENGINEERING GEOLOGY 123\n", "NATURAL HAZARDS 119\n", "SUSTAINABILITY 108\n", "WATER 102\n", "HYDROGEOLOGY JOURNAL 101\n" ] } ], "source": [ "if len(df):\n", " n_with_year = df[\"year\"].notna().sum()\n", " n_with_abstract = (df[\"abstract\"].str.len() > 0).sum()\n", " n_with_keywords = (df[\"keywords\"].str.len() > 0).sum()\n", " year_min = int(df[\"year\"].min()) if n_with_year else None\n", " year_max = int(df[\"year\"].max()) if n_with_year else None\n", "\n", " print(f\"Entries with year : {n_with_year:,} / {len(df):,}\")\n", " print(f\"Entries with abstract : {n_with_abstract:,}\")\n", " print(f\"Entries with keywords : {n_with_keywords:,}\")\n", " print(f\"Year range : {year_min} – {year_max}\")\n", " print()\n", " print(\"Top 10 journals/venues:\")\n", " print(df[\"journal\"].replace(\"\", np.nan).dropna()\n", " .value_counts().head(10).to_string())\n", "else:\n", " print(\"(no entries loaded)\")\n" ] }, { "cell_type": "markdown", "id": "848cf9c2", "metadata": {}, "source": [ "## 3. Auto-extract candidate keywords and topics\n", "\n", "This is the first half of the *both* strategy: extract candidate vocabulary automatically from the corpus, then in Section 4 surface it for manual editing.\n", "\n", "Two passes:\n", "\n", "1. **Keyword harvest** — every author-supplied `keywords` field is split on `;`, `,`, and `|`, then ranked by corpus frequency. This is the highest-signal vocabulary because authors chose those terms deliberately.\n", "2. **TF-IDF + LDA over titles + abstracts** — surfaces phrases that *aren't* in author keywords but co-occur across many entries. Both unigrams and bigrams are kept.\n", "\n", "### 3.1 Configure stopwords\n", "\n", "Standard English stopwords plus subsidence-corpus-specific noise (units, generic verbs, manuscript boilerplate). Edit `CUSTOM_STOPWORDS` to suit your corpus.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "f248dda0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ 200 stopwords active\n" ] } ], "source": [ "STOPWORDS_BASE = {\n", " # Standard English (compact)\n", " \"a\",\"about\",\"above\",\"after\",\"again\",\"all\",\"am\",\"an\",\"and\",\"any\",\"are\",\"as\",\"at\",\n", " \"be\",\"because\",\"been\",\"before\",\"being\",\"below\",\"between\",\"both\",\"but\",\"by\",\"can\",\n", " \"did\",\"do\",\"does\",\"doing\",\"don\",\"down\",\"during\",\"each\",\"few\",\"for\",\"from\",\"further\",\n", " \"had\",\"has\",\"have\",\"having\",\"he\",\"her\",\"here\",\"hers\",\"herself\",\"him\",\"himself\",\"his\",\n", " \"how\",\"i\",\"if\",\"in\",\"into\",\"is\",\"it\",\"its\",\"itself\",\"just\",\"let\",\"me\",\"more\",\"most\",\n", " \"my\",\"myself\",\"no\",\"nor\",\"not\",\"now\",\"of\",\"off\",\"on\",\"once\",\"only\",\"or\",\"other\",\n", " \"our\",\"ours\",\"ourselves\",\"out\",\"over\",\"own\",\"same\",\"she\",\"should\",\"so\",\"some\",\"such\",\n", " \"than\",\"that\",\"the\",\"their\",\"theirs\",\"them\",\"themselves\",\"then\",\"there\",\"these\",\n", " \"they\",\"this\",\"those\",\"through\",\"to\",\"too\",\"under\",\"until\",\"up\",\"very\",\"was\",\"we\",\n", " \"were\",\"what\",\"when\",\"where\",\"which\",\"while\",\"who\",\"whom\",\"why\",\"will\",\"with\",\"you\",\n", " \"your\",\"yours\",\"yourself\",\"yourselves\",\n", " # Article / manuscript boilerplate\n", " \"abstract\",\"study\",\"results\",\"using\",\"used\",\"based\",\"paper\",\"article\",\"analysis\",\n", " \"method\",\"methods\",\"approach\",\"approaches\",\"data\",\"also\",\"however\",\"therefore\",\n", " \"thus\",\"one\",\"two\",\"three\",\"four\",\"five\",\"first\",\"second\",\"new\",\"high\",\"low\",\n", " \"different\",\"various\",\"several\",\"many\",\"much\",\"may\",\"might\",\"could\",\"would\",\"also\",\n", " \"et\",\"al\",\"fig\",\"figure\",\"table\",\"section\",\"case\",\"cases\",\"work\",\"propose\",\"proposed\",\n", " \"found\",\"shown\",\"show\",\"present\",\"presented\",\"provides\",\"provide\",\"increase\",\"decrease\",\n", " \"important\",\"significant\",\"significantly\",\"compared\",\"resulting\",\"obtained\",\n", " # Units, generic numbers\n", " \"mm\",\"cm\",\"km\",\"kg\",\"yr\",\"yrs\",\"year\",\"years\",\"day\",\"days\",\"month\",\"months\",\n", "}\n", "\n", "CUSTOM_STOPWORDS = set() # add corpus-specific noise here, e.g. {\"foo\", \"bar\"}\n", "\n", "ALL_STOPWORDS = STOPWORDS_BASE | CUSTOM_STOPWORDS\n", "print(f\"✓ {len(ALL_STOPWORDS):,} stopwords active\")\n" ] }, { "cell_type": "markdown", "id": "f5f8602d", "metadata": {}, "source": [ "### 3.2 Harvest author-supplied keywords\n", "\n", "Splits `keywords` on the common delimiters and ranks by frequency. The top 50 are reported here; the full distribution is available in `keyword_freq`.\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "309f5081", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ 0 unique author-supplied keywords\n", "\n", "Top 50 author-supplied keywords:\n", "Empty DataFrame\n", "Columns: [keyword, count]\n", "Index: []\n" ] } ], "source": [ "def split_keywords(s):\n", " if not s:\n", " return []\n", " raw = re.split(r\"[;,|/]\", s)\n", " out = []\n", " for k in raw:\n", " k = k.strip().lower()\n", " if len(k) < 2 or k in ALL_STOPWORDS:\n", " continue\n", " # Drop pure numbers, very long phrases\n", " if k.replace(\" \", \"\").isdigit():\n", " continue\n", " if len(k.split()) > 5:\n", " continue\n", " out.append(k)\n", " return out\n", "\n", "\n", "df[\"kw_list\"] = df[\"keywords\"].apply(split_keywords)\n", "keyword_freq = Counter()\n", "for kws in df[\"kw_list\"]:\n", " keyword_freq.update(kws)\n", "\n", "kw_df = pd.DataFrame(keyword_freq.most_common(),\n", " columns=[\"keyword\", \"count\"])\n", "print(f\"✓ {len(kw_df):,} unique author-supplied keywords\")\n", "print()\n", "print(\"Top 50 author-supplied keywords:\")\n", "print(kw_df.head(50).to_string(index=False))\n" ] }, { "cell_type": "markdown", "id": "74c9a8b8", "metadata": {}, "source": [ "### 3.3 TF-IDF + LDA over titles + abstracts\n", "\n", "LDA discovers latent topics over the concatenated `title + abstract` text. The top terms per topic are surface candidates for the keyword-group editor in §4. `N_TOPICS` is tunable.\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "afdab9db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ LDA fit on 8,409 docs, 10 topics\n", "\n", " Topic 0: groundwater, land, land subsidence, aquifer, level, water, model, pumping, groundwater level, area, plain, hydraulic\n", " Topic 1: basin, sediment, sea, deposits, level, marine, facies, sequence, formation, sea level, depositional, late\n", " Topic 2: temperature, precipitation, surface, circulation, climate, atmospheric, patients, cloud, impact, rainfall, model, pacific\n", " Topic 3: deformation, insar, ground, time, land, area, radar, monitoring, surface, series, areas, time series\n", " Topic 4: basin, fault, uplift, tectonic, faults, seismic, rift, earthquake, along, evolution, similar, margin\n", " Topic 5: model, models, ground, soil, surface, impact, stress, deformation, numerical, construction, pressure, reservoir\n", " Topic 6: soil, water, peat, vegetation, permafrost, carbon, organic, surface, textbackslash, ice, lake, changes\n", " Topic 7: mining, coal, surface, mine, area, coal mining, underground, areas, rock, surface subsidence, china, impact\n", " Topic 8: coastal, sea, level, sea level, rise, land, level rise, flood, delta, change, river, flooding\n", " Topic 9: water, groundwater, area, management, surface, areas, aquifer, system, karst, development, flow, salt\n" ] } ], "source": [ "N_TOPICS = 10\n", "TOP_TERMS_PER_TOPIC = 12\n", "MAX_VOCAB = 3000\n", "\n", "# Build a corpus suitable for LDA: titles + abstracts (NOT keywords — we want\n", "# to surface phrases the keywords field DIDN'T already capture).\n", "texts = (df[\"title\"].fillna(\"\") + \" . \" +\n", " df[\"abstract\"].fillna(\"\")).tolist()\n", "\n", "vec = CountVectorizer(\n", " max_features=MAX_VOCAB,\n", " stop_words=list(ALL_STOPWORDS),\n", " ngram_range=(1, 2),\n", " min_df=3,\n", " max_df=0.7,\n", ")\n", "X = vec.fit_transform(texts)\n", "vocab = np.array(vec.get_feature_names_out())\n", "\n", "lda = LatentDirichletAllocation(\n", " n_components=N_TOPICS,\n", " random_state=42,\n", " learning_method=\"batch\",\n", " max_iter=20,\n", ")\n", "lda.fit(X)\n", "\n", "# Top terms per topic\n", "topic_top_terms = []\n", "for k in range(N_TOPICS):\n", " top_idx = lda.components_[k].argsort()[::-1][:TOP_TERMS_PER_TOPIC]\n", " topic_top_terms.append(list(vocab[top_idx]))\n", "\n", "lda_topics_df = pd.DataFrame({\n", " \"topic_id\": range(N_TOPICS),\n", " \"top_terms\": topic_top_terms,\n", "})\n", "\n", "# Doc-topic distribution → primary topic per entry\n", "doc_topic = lda.transform(X)\n", "df[\"primary_lda_topic\"] = doc_topic.argmax(axis=1)\n", "\n", "print(f\"✓ LDA fit on {len(texts):,} docs, {N_TOPICS} topics\")\n", "print()\n", "for _, row in lda_topics_df.iterrows():\n", " print(f\" Topic {row['topic_id']:2d}: {', '.join(row['top_terms'])}\")\n" ] }, { "cell_type": "markdown", "id": "11176784", "metadata": {}, "source": [ "## 4. User-editable keyword groups\n", "\n", "This is the second half of the *both* strategy. Section 3 surfaced candidates; here you compose them into **named keyword groups** that map cleanly to SUBSIDE-relevant scientific domains.\n", "\n", "Each group is a list of synonyms / variants — a document matches the group if *any* of its terms appears in `text_content`. The groups are intentionally pre-seeded with SUBSIDE-relevant vocabulary so the notebook produces a meaningful network on first run; **edit them freely** based on what you saw in §3.2 and §3.3.\n", "\n", "> **Tip.** Groups are matched as substrings, case-insensitive. `\"insar\"` matches `\"InSAR\"` and `\"insar-derived\"`. Keep groups tight — overly broad groups will sprawl across multiple backbone domains and weaken the network.\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "46c325c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ 18 keyword groups defined\n", " Subsidence : 6 terms — subsidence, land subsidence, ground subsidence, settlement, compaction…\n", " Uplift_Rebound : 4 terms — uplift, rebound, heave, swelling\n", " Groundwater : 8 terms — groundwater, aquifer, aquifers, pumping, withdrawal…\n", " OilGas : 9 terms — oil, gas, petroleum, hydrocarbon, reservoir…\n", " Mining : 6 terms — mining, mine, coal mine, underground mine, longwall…\n", " Tectonic : 5 terms — tectonic, fault, faulting, fault slip, seismic\n", " Sediment : 6 terms — sediment, sedimentation, deltaic, delta, alluvial…\n", " InSAR : 8 terms — insar, interferometric, interferogram, psinsar, persistent scatterer…\n", " GNSS_GPS : 6 terms — gnss, gps, global positioning, continuous gps, cgps…\n", " LiDAR : 4 terms — lidar, light detection and ranging, airborne lidar, topobathymetric\n", " Leveling : 4 terms — leveling, levelling, extensometer, extensometers\n", " Remote_Sensing : 5 terms — remote sensing, satellite, earth observation, sar, synthetic aperture radar\n", " Coastal : 9 terms — coastal, coast, shoreline, tidal, estuary…\n", " Urban : 5 terms — urban, city, cities, metropolitan, infrastructure\n", " Flood : 4 terms — flood, flooding, inundation, storm surge\n", " Modeling : 8 terms — model, modeling, simulation, physics-based, numerical model…\n", " MachineLearning : 6 terms — machine learning, deep learning, neural network, random forest, lstm…\n", " Policy_Decision : 8 terms — policy, governance, stakeholder, decision support, management…\n" ] } ], "source": [ "# ── Edit this dict to add, remove, or rename keyword groups ───────────\n", "KEYWORD_GROUPS = {\n", " # Core phenomenon\n", " \"Subsidence\": [\"subsidence\", \"land subsidence\", \"ground subsidence\",\n", " \"settlement\", \"compaction\", \"consolidation\"],\n", " \"Uplift_Rebound\": [\"uplift\", \"rebound\", \"heave\", \"swelling\"],\n", "\n", " # Drivers\n", " \"Groundwater\": [\"groundwater\", \"aquifer\", \"aquifers\", \"pumping\",\n", " \"withdrawal\", \"depletion\", \"drawdown\", \"phreatic\"],\n", " \"OilGas\": [\"oil\", \"gas\", \"petroleum\", \"hydrocarbon\", \"reservoir\",\n", " \"extraction\", \"production\", \"co2 storage\", \"sequestration\"],\n", " \"Mining\": [\"mining\", \"mine\", \"coal mine\", \"underground mine\",\n", " \"longwall\", \"subsurface mining\"],\n", " \"Tectonic\": [\"tectonic\", \"fault\", \"faulting\", \"fault slip\", \"seismic\"],\n", " \"Sediment\": [\"sediment\", \"sedimentation\", \"deltaic\", \"delta\",\n", " \"alluvial\", \"natural compaction\"],\n", "\n", " # Observation / measurement methods\n", " \"InSAR\": [\"insar\", \"interferometric\", \"interferogram\", \"psinsar\",\n", " \"persistent scatterer\", \"sentinel-1\", \"alos\", \"envisat\"],\n", " \"GNSS_GPS\": [\"gnss\", \"gps\", \"global positioning\", \"continuous gps\",\n", " \"cgps\", \"geodetic\"],\n", " \"LiDAR\": [\"lidar\", \"light detection and ranging\", \"airborne lidar\",\n", " \"topobathymetric\"],\n", " \"Leveling\": [\"leveling\", \"levelling\", \"extensometer\", \"extensometers\"],\n", " \"Remote_Sensing\": [\"remote sensing\", \"satellite\", \"earth observation\",\n", " \"sar\", \"synthetic aperture radar\"],\n", "\n", " # Settings / hazards\n", " \"Coastal\": [\"coastal\", \"coast\", \"shoreline\", \"tidal\", \"estuary\",\n", " \"estuaries\", \"salt marsh\", \"sea level\", \"sea-level rise\"],\n", " \"Urban\": [\"urban\", \"city\", \"cities\", \"metropolitan\", \"infrastructure\"],\n", " \"Flood\": [\"flood\", \"flooding\", \"inundation\", \"storm surge\"],\n", "\n", " # Modeling / analysis\n", " \"Modeling\": [\"model\", \"modeling\", \"simulation\", \"physics-based\",\n", " \"numerical model\", \"finite element\", \"finite-element\",\n", " \"modflow\"],\n", " \"MachineLearning\":[\"machine learning\", \"deep learning\", \"neural network\",\n", " \"random forest\", \"lstm\", \"convolutional\"],\n", "\n", " # Decision / governance\n", " \"Policy_Decision\":[\"policy\", \"governance\", \"stakeholder\", \"decision support\",\n", " \"management\", \"regulation\", \"groundwater management area\",\n", " \"gma\"],\n", "}\n", "\n", "print(f\"✓ {len(KEYWORD_GROUPS)} keyword groups defined\")\n", "for k, v in KEYWORD_GROUPS.items():\n", " print(f\" {k:18s}: {len(v):2d} terms — {', '.join(v[:5])}{'…' if len(v)>5 else ''}\")\n" ] }, { "cell_type": "markdown", "id": "2679cb12", "metadata": {}, "source": [ "### 4.1 Tag each entry with matching groups\n", "\n", "Vectorized substring matching. Every entry ends up with a boolean column `group_` and a list column `group_matches`. This is the foundation for the network mapping (§6) and the time-burst views (§8).\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "ad1dc4c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Documents matched per keyword group:\n", " group matches pct\n", " Subsidence 6925 82.4\n", " Modeling 3621 43.1\n", " OilGas 2929 34.8\n", " Groundwater 2914 34.7\n", " Urban 2893 34.4\n", " Mining 2692 32.0\n", " Sediment 2395 28.5\n", " Coastal 2230 26.5\n", " Remote_Sensing 2163 25.7\n", " Flood 2086 24.8\n", " Tectonic 1808 21.5\n", "Policy_Decision 1565 18.6\n", " InSAR 1404 16.7\n", " Uplift_Rebound 964 11.5\n", " GNSS_GPS 557 6.6\n", " Leveling 335 4.0\n", "MachineLearning 242 2.9\n", " LiDAR 94 1.1\n" ] } ], "source": [ "def tag_entry(text_lower, groups):\n", " matches = []\n", " for name, terms in groups.items():\n", " if any(t in text_lower for t in terms):\n", " matches.append(name)\n", " return matches\n", "\n", "\n", "text_lc = df[\"text_content\"].str.lower().fillna(\"\")\n", "df[\"group_matches\"] = text_lc.apply(lambda t: tag_entry(t, KEYWORD_GROUPS))\n", "\n", "for name in KEYWORD_GROUPS:\n", " df[f\"group_{name}\"] = df[\"group_matches\"].apply(lambda lst: name in lst)\n", "\n", "# Match-count summary\n", "match_summary = pd.DataFrame([\n", " {\"group\": name,\n", " \"matches\": int(df[f\"group_{name}\"].sum()),\n", " \"pct\": round(100 * df[f\"group_{name}\"].mean(), 1)}\n", " for name in KEYWORD_GROUPS\n", "]).sort_values(\"matches\", ascending=False).reset_index(drop=True)\n", "\n", "print(\"Documents matched per keyword group:\")\n", "print(match_summary.to_string(index=False))\n" ] }, { "cell_type": "markdown", "id": "5bcd5d49", "metadata": {}, "source": [ "## 5. Science backbone — four-tier resolution\n", "\n", "The backbone is the domain/subdiscipline scaffolding that the keyword groups get mapped onto. The notebook tries four sources, in order of preference, and uses the **first one that works**:\n", "\n", "| Tier | Source | When it's used |\n", "|---|---|---|\n", "| 0 | `science_backbone.json` produced by `SUBSIDE_ScienceBackbone_Setup.ipynb` | Recommended. A referenced, citable backbone combining the UCSD canonical 13-discipline scaffold [Börner et al. 2012] with an ETO Map of Science export if one was provided. |\n", "| 1 | ETO Map-of-Science CSV via `semantic_bridge_pipeline` | You've set `ETO_EXPORT_PATH` and `sbp` is installed |\n", "| 2 | `semantic_bridge_pipeline.default_science_backbone()` | The `sbp` package is installed in the environment (normal TACC kernel) |\n", "| 3 | Inlined SUBSIDE-tuned fallback | Always available — no external dependencies |\n", "\n", "Tiers 0 and 3 require no external service. Tier 0 is the preferred path for any publication-bound work; build it once with the setup notebook and commit the JSON alongside the corpus.\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "a14cb800", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Tier 0: prebuilt JSON (science_backbone.json, layer='merged', produced 2026-05-11)\n", " 12 domains loaded:\n", " · Biology\n", " · Biotechnology\n", " · Medical Specialties\n", " · Chemical, Mechanical, & Civil Engineering\n", " · Chemistry\n", " · Earth Sciences\n", " · Electrical Engineering & Computer Science\n", " · Brain Research\n", " · Humanities\n", " · Math & Physics\n", " · Health Professionals\n", " · Social Sciences\n" ] } ], "source": [ "SCIENCE_BACKBONE = None\n", "BACKBONE_SOURCE = None\n", "\n", "# ── Tier 0: prebuilt JSON from SUBSIDE_ScienceBackbone_Setup.ipynb ───\n", "# Most-verified, most-portable: a referenced backbone produced once and\n", "# committed alongside the corpus. See SUBSIDE_ScienceBackbone_Setup.ipynb.\n", "if SCIENCE_BACKBONE_JSON and Path(SCIENCE_BACKBONE_JSON).exists():\n", " try:\n", " with open(SCIENCE_BACKBONE_JSON) as fh:\n", " _payload = json.load(fh)\n", " _selected = _payload.get(\"selected_layer\") or \"merged\"\n", " _layers = _payload.get(\"layers\", {})\n", " _candidate = _layers.get(_selected) or _layers.get(\"merged\") or _layers.get(\"ucsd\")\n", " if _candidate:\n", " # Strip the underscore-prefixed metadata keys for compatibility\n", " # with the existing mapper in §6; keep originals as inspection-only.\n", " SCIENCE_BACKBONE = {d: {k: v for k, v in node.items()\n", " if not str(k).startswith(\"_\")}\n", " for d, node in _candidate.items()}\n", " BACKBONE_SOURCE = (f\"Tier 0: prebuilt JSON ({SCIENCE_BACKBONE_JSON.name}, \"\n", " f\"layer='{_selected}', \"\n", " f\"produced {_payload.get('produced_at','?')[:10]})\")\n", " except Exception as e:\n", " print(f\" ⚠ Tier 0 JSON present but failed to load: {type(e).__name__}: {e}\")\n", "\n", "# ── Tier 1: ETO Map-of-Science CSV export ────────────────────────────\n", "if SCIENCE_BACKBONE is None and ETO_EXPORT_PATH and Path(ETO_EXPORT_PATH).exists():\n", " try:\n", " import semantic_bridge_pipeline as _sbp\n", " _eto_df = _sbp.load_eto_cluster_export(ETO_EXPORT_PATH)\n", " _candidate = _sbp.build_science_backbone_from_eto_export(_eto_df)\n", " if _candidate:\n", " SCIENCE_BACKBONE = _candidate\n", " BACKBONE_SOURCE = f\"Tier 1: ETO export via sbp ({ETO_EXPORT_PATH})\"\n", " except Exception as e:\n", " print(f\" ⚠ ETO export present but failed to load: {type(e).__name__}: {e}\")\n", "\n", "# ── Tier 2: semantic_bridge_pipeline default ─────────────────────────\n", "if SCIENCE_BACKBONE is None:\n", " try:\n", " import semantic_bridge_pipeline as _sbp\n", " _candidate = _sbp.default_science_backbone()\n", " if _candidate:\n", " SCIENCE_BACKBONE = _candidate\n", " BACKBONE_SOURCE = \"Tier 2: semantic_bridge_pipeline.default_science_backbone()\"\n", " except ImportError:\n", " pass\n", " except Exception as e:\n", " print(f\" ⚠ sbp default backbone failed: {type(e).__name__}: {e}\")\n", "\n", "# ── Tier 3: SUBSIDE-tuned inlined fallback ───────────────────────────\n", "if SCIENCE_BACKBONE is None:\n", " SCIENCE_BACKBONE = {\n", " \"Hydrology & Water Resources\": {\n", " \"subdisciplines\": [\"Groundwater Hydrology\", \"Aquifer Mechanics\",\n", " \"Surface Water\", \"Water Management\"],\n", " \"terms\": [\"groundwater\",\"aquifer\",\"water\",\"pumping\",\"withdrawal\",\n", " \"drawdown\",\"recharge\",\"modflow\",\"hydrology\",\"watershed\"],\n", " },\n", " \"Geodesy & Remote Sensing\": {\n", " \"subdisciplines\": [\"InSAR\", \"GNSS Geodesy\", \"LiDAR\",\n", " \"Satellite Earth Observation\"],\n", " \"terms\": [\"insar\",\"interferometric\",\"sentinel\",\"gps\",\"gnss\",\"lidar\",\n", " \"remote sensing\",\"satellite\",\"sar\",\"geodetic\",\"leveling\",\n", " \"persistent scatterer\"],\n", " },\n", " \"Geomechanics & Geotechnical Engineering\": {\n", " \"subdisciplines\": [\"Soil Mechanics\", \"Consolidation Theory\",\n", " \"Foundation Engineering\"],\n", " \"terms\": [\"compaction\",\"consolidation\",\"settlement\",\"soil\",\"foundation\",\n", " \"geotechnical\",\"finite element\",\"stress\",\"strain\",\"clay\"],\n", " },\n", " \"Geology & Geophysics\": {\n", " \"subdisciplines\": [\"Sedimentology\", \"Structural Geology\",\n", " \"Tectonics\", \"Stratigraphy\"],\n", " \"terms\": [\"sediment\",\"sedimentation\",\"tectonic\",\"fault\",\"seismic\",\n", " \"stratigraphy\",\"delta\",\"alluvial\",\"geological\"],\n", " },\n", " \"Coastal & Marine Science\": {\n", " \"subdisciplines\": [\"Coastal Geomorphology\", \"Sea-Level Science\",\n", " \"Estuarine Systems\"],\n", " \"terms\": [\"coastal\",\"coast\",\"shoreline\",\"tidal\",\"estuary\",\"sea level\",\n", " \"sea-level\",\"subsidence rate\",\"marsh\",\"delta\"],\n", " },\n", " \"Energy & Resource Extraction\": {\n", " \"subdisciplines\": [\"Petroleum Engineering\", \"Mining Engineering\",\n", " \"CO2 Storage\"],\n", " \"terms\": [\"oil\",\"gas\",\"petroleum\",\"hydrocarbon\",\"reservoir\",\"mining\",\n", " \"mine\",\"coal\",\"extraction\",\"co2\",\"sequestration\",\"longwall\"],\n", " },\n", " \"Geohazards & Risk\": {\n", " \"subdisciplines\": [\"Hazard Assessment\", \"Risk Mapping\",\n", " \"Damage & Loss\"],\n", " \"terms\": [\"hazard\",\"risk\",\"damage\",\"vulnerability\",\"susceptibility\",\n", " \"infrastructure damage\",\"subsidence hazard\"],\n", " },\n", " \"Climate & Atmospheric Science\": {\n", " \"subdisciplines\": [\"Climate Change\", \"Hydroclimate\",\n", " \"Precipitation Variability\"],\n", " \"terms\": [\"climate\",\"climate change\",\"precipitation\",\"drought\",\"warming\",\n", " \"atmospheric\",\"ipcc\"],\n", " },\n", " \"Computational & Data Science\": {\n", " \"subdisciplines\": [\"Machine Learning\", \"Numerical Modeling\",\n", " \"GIS & Spatial Analytics\"],\n", " \"terms\": [\"machine learning\",\"deep learning\",\"neural network\",\"model\",\n", " \"simulation\",\"numerical\",\"gis\",\"spatial\",\"algorithm\"],\n", " },\n", " \"Public Policy & Decision Science\": {\n", " \"subdisciplines\": [\"Decision Support\", \"Governance & Regulation\",\n", " \"Stakeholder Engagement\"],\n", " \"terms\": [\"policy\",\"governance\",\"stakeholder\",\"decision support\",\n", " \"management\",\"regulation\",\"gma\",\"groundwater management\"],\n", " },\n", " }\n", " BACKBONE_SOURCE = (\"Tier 3: SUBSIDE-tuned inlined fallback \"\n", " \"(neither ETO export nor sbp available)\")\n", "\n", "print(f\"✓ {BACKBONE_SOURCE}\")\n", "print(f\" {len(SCIENCE_BACKBONE)} domains loaded:\")\n", "for d in SCIENCE_BACKBONE:\n", " print(f\" · {d}\")\n" ] }, { "cell_type": "markdown", "id": "d9809525", "metadata": {}, "source": [ "## 6. Map keyword groups to backbone domains\n", "\n", "Every keyword group's term list is scored against every backbone subdiscipline's term list. The top 3 hits (by overlap count) become that group's `backbone_mapping`. A group's **primary domain** is the parent of its highest-scoring subdiscipline.\n", "\n", "This is what wires the keyword groups into the network in §7 and the domain-level burst view in §8.\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "055608cd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Keyword group → primary backbone domain:\n", " group primary_domain n_matches\n", " Subsidence Earth Sciences 6925\n", " Uplift_Rebound Uncategorized 964\n", " Groundwater Earth Sciences 2914\n", " OilGas Chemical, Mechanical, & Civil Engineering 2929\n", " Mining Chemical, Mechanical, & Civil Engineering 2692\n", " Tectonic Earth Sciences 1808\n", " Sediment Earth Sciences 2395\n", " InSAR Earth Sciences 1404\n", " GNSS_GPS Earth Sciences 557\n", " LiDAR Earth Sciences 94\n", " Leveling Earth Sciences 335\n", " Remote_Sensing Earth Sciences 2163\n", " Coastal Earth Sciences 2230\n", " Urban Social Sciences 2893\n", " Flood Uncategorized 2086\n", " Modeling Math & Physics 3621\n", "MachineLearning Electrical Engineering & Computer Science 242\n", "Policy_Decision Social Sciences 1565\n" ] } ], "source": [ "def _node_subs(node):\n", " if not isinstance(node, dict):\n", " return [\"General\"]\n", " return node.get(\"subdisciplines\") or node.get(\"subs\") or [\"General\"]\n", "\n", "def _node_terms(node):\n", " if not isinstance(node, dict):\n", " return []\n", " return node.get(\"terms\") or node.get(\"keywords\") or []\n", "\n", "\n", "def map_terms_to_backbone(terms, backbone, top_n=3):\n", " terms_l = [str(t).lower() for t in terms]\n", " hits = []\n", " for domain, node in backbone.items():\n", " subs = _node_subs(node)\n", " node_terms = _node_terms(node) or [domain.lower()]\n", " for sub in subs:\n", " score = sum(1 for t in terms_l\n", " if any(str(kw).lower() in t for kw in node_terms))\n", " if score > 0:\n", " hits.append((domain, sub, score))\n", " if not hits:\n", " return [(\"Uncategorized\", \"General\", 0)]\n", " return sorted(hits, key=lambda x: -x[2])[:top_n]\n", "\n", "\n", "# Apply to every KEYWORD_GROUPS entry\n", "group_mappings = []\n", "for name, terms in KEYWORD_GROUPS.items():\n", " mapping = map_terms_to_backbone(terms, SCIENCE_BACKBONE)\n", " primary_domain = mapping[0][0]\n", " group_mappings.append({\n", " \"group\": name,\n", " \"primary_domain\": primary_domain,\n", " \"backbone_mapping\": mapping,\n", " \"n_matches\": int(df[f\"group_{name}\"].sum()),\n", " })\n", "\n", "group_mapping_df = pd.DataFrame(group_mappings)\n", "print(\"Keyword group → primary backbone domain:\")\n", "print(group_mapping_df[[\"group\",\"primary_domain\",\"n_matches\"]]\n", " .to_string(index=False))\n" ] }, { "cell_type": "markdown", "id": "41eb006c", "metadata": {}, "source": [ "## 7. Interactive backbone network visualization\n", "\n", "Three node tiers, drawn in Plotly:\n", "\n", "- **Domains** (large blue circles) — anchored from the backbone.\n", "- **Subdisciplines** (gray circles) — children of each domain.\n", "- **Keyword groups** (diamonds, colored by primary domain) — connected to every subdiscipline they map to, with edge width proportional to the mapping score.\n", "\n", "Hover any node to see details. The figure renders inline and is also saved as a standalone HTML file in `OUTPUT_DIR`.\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "d004dd72-fcdc-4338-a6ba-175bc660edc1", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "fill": "toself", "fillcolor": "rgba(255,200,61,0.1)", "hoverinfo": "skip", "line": { "color": "rgba(255,200,61,0.35)", "shape": "spline", "width": 1.2 }, "mode": "lines", "showlegend": false, "type": "scatter", "x": [ -1.9238119559903952, -0.8877193299502154, -0.39999999999999986, -1.9238119559903952 ], "y": [ -2.1357633388844603, -2.7339516954341434, -0.6928203230275509, -2.1357633388844603 ] }, { "fill": "toself", "fillcolor": "rgba(202,178,214,0.1)", "hoverinfo": "skip", "line": { "color": "rgba(202,178,214,0.35)", "shape": "spline", "width": 1.2 }, "mode": "lines", "showlegend": false, "type": "scatter", "x": [ -2.586473670974872, -0.7678203230275509, 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parent: Humanities", "Linguistics
parent: Humanities", "Literary Criticism
parent: Humanities", "Medieval History
parent: Humanities", "Modern Language
parent: Humanities", "Music & Theatre
parent: Humanities", "Opera
parent: Humanities", "Philosophy of Education
parent: Humanities", "Philosophy Psychology
parent: Humanities", "Poetry
parent: Humanities", "Science History
parent: Humanities", "Semiotics
parent: Humanities", "Social History
parent: Humanities", "Socio-Cultural Anthropology
parent: Humanities", "Pre-Columbian Urbanism and Coastal Andean Societies
parent: Humanities", "Computational Analysis of Narrative Character Dynamics
parent: Humanities", "Social Media Data Mining and Analytics
parent: Humanities", "South Asian Historical and Architectural Analysis
parent: Humanities", "Legal Geography and Spatial Law Analysis
parent: Humanities", "Cold War Dynamics and Afro-Asian Solidarity
parent: Humanities", "Archaeological Survey Methods and Historical Analysis
parent: Humanities", "Archaeological Analysis of Ancient Ceramic Artifacts
parent: Humanities", "Democratic Consolidation and Political Regression Trends
parent: Humanities", "Social and Cultural Dimensions of Deindustrialization
parent: Humanities", "Demographic Trends and Rural Settlement Abandonment
parent: Humanities", "Ancient Mediterranean Port and Coastal Landscapes
parent: Humanities", "Historical Water Management and Medieval Landscapes
parent: Humanities", "Astrotourism and Sustainable Land Use Management
parent: Humanities", "Marshall McLuhan and Media Theory Perspectives
parent: Humanities", "Archaeological Analysis of Cave Site Deposits
parent: Humanities", "Settlement Patterns in Prehistoric Societies
parent: Humanities", "Literary Analysis and Cultural History Perspectives
parent: Humanities", "Architectural Design Processes and Tectonic Theory
parent: Humanities", "Shell Mound Societies of Southern Brazil
parent: Humanities", "Archaeoseismology and Seismic Damage in Antiquity
parent: Humanities", "Archaeological and Hydrological Analysis in Brazil
parent: Humanities", "Neolithic and Bronze Age Central Europe
parent: Humanities", "Historical Land Use and Forestry Management
parent: Humanities", "Historical Perspectives on Colonial American Mining
parent: Humanities", "Agricultural Economics
parent: Social Sciences", "Applied Economics
parent: Social Sciences", "BioStatistics
parent: Social Sciences", "Business Ethics
parent: Social Sciences", "Child Abuse
parent: Social Sciences", "Child Development
parent: Social Sciences", "Communication Research
parent: Social Sciences", "Computer-Aided Process Planning
parent: Social Sciences", "Construction & Project Management
parent: Social Sciences", "Criminology
parent: Social Sciences", "Decision Support Systems
parent: Social Sciences", "Developmental Economics
parent: Social Sciences", "Eating Disorders; Sex Roles
parent: Social Sciences", "Econometrics
parent: Social Sciences", "Economics
parent: Social Sciences", "Education
parent: Social Sciences", "Education Psychological Measures
parent: Social Sciences", "Educational Psychology
parent: Social Sciences", "Engineering Education
parent: Social Sciences", "Environmental Law
parent: Social Sciences", "Environmental Management
parent: Social Sciences", "Environmental Policy
parent: Social Sciences", "Ethnic Migration
parent: Social Sciences", "Ethnology
parent: Social Sciences", "Finance
parent: Social Sciences", "Financial Accounting
parent: Social Sciences", "Foreign Policy
parent: Social Sciences", "GeoPolitics
parent: Social Sciences", "Higher Education
parent: Social Sciences", "Human Resource Management
parent: Social Sciences", "Human Rights
parent: Social Sciences", "International Conflict
parent: Social Sciences", "International Development
parent: Social Sciences", "International Economics
parent: Social Sciences", "Language Learning
parent: Social Sciences", "Law
parent: Social Sciences", "Leadership & Organizational Behavior
parent: Social Sciences", "Marital & Family Therapy
parent: Social Sciences", "Marketing
parent: Social Sciences", "Operations Management
parent: Social Sciences", "Operations Research
parent: Social Sciences", "Personality
parent: Social Sciences", "Political Geography
parent: Social Sciences", "Political Science
parent: Social Sciences", "Political Studies
parent: Social Sciences", "Pragmatics & Discourse
parent: Social Sciences", "Psychosomatic Medicine
parent: Social Sciences", "Public Administration
parent: Social Sciences", "Public Policy
parent: Social Sciences", "Pyschiatric & Behavioral Genetics
parent: Social Sciences", "Regional Studies
parent: Social Sciences", "Reliability Engineering
parent: Social Sciences", "Research Policy; Technology Management
parent: Social Sciences", "Rural Studies
parent: Social Sciences", "School Psychology
parent: Social Sciences", "Science Education
parent: Social Sciences", "Social Economics
parent: Social Sciences", "Social Psychology
parent: Social Sciences", "Social Work
parent: Social Sciences", "Sociology
parent: Social Sciences", "Statistics
parent: Social Sciences", "Strategic Management
parent: Social Sciences", "Symbolic Interaction
parent: Social Sciences", "Teacher Education; Evaluation
parent: Social Sciences", "Third World Political Economics
parent: Social Sciences", "Tourism
parent: Social Sciences", "Urban Studies
parent: Social Sciences", "Vocational Counseling
parent: Social Sciences", "World Trade; Law
parent: Social Sciences", "Machine Learning Models for Credit Scoring
parent: Social Sciences", "Social Media Analysis of Vaccine Attitudes
parent: Social Sciences", "Distributed Generation Planning in Power Networks
parent: Social Sciences", "Rural Land Systems and Development Patterns
parent: Social Sciences", "Learning Analytics and Educational Data Mining
parent: Social Sciences", "Economic and Environmental Palm Oil Management
parent: Social Sciences", "Clinical Applications of Aromatherapy and Essential Oils
parent: Social Sciences", "Prevalence and Control of Soil-Transmitted Helminth Infections
parent: Social Sciences", "Association Rule Mining for Sales Patterns
parent: Social Sciences", "Crude Oil Price Volatility and Market Interdependencies
parent: Social Sciences", "Artisanal and Small-Scale Gold Mining Dynamics
parent: Social Sciences", "Dental Radiograph Analysis for Age Estimation
parent: Social Sciences", "Community-Based Tourism Development Strategies
parent: Social Sciences", "Socioeconomic Dynamics of Indonesian Fishing Communities
parent: Social Sciences", "Global Crude Oil Price Market Dynamics
parent: Social Sciences", "Computational Models for Crude Oil Price Forecasting
parent: Social Sciences", "Production Costing Frameworks for Small Businesses
parent: Social Sciences", "Sleep Cycles and Memory Consolidation Processes
parent: Social Sciences", "Computational Models for Tourism Demand Forecasting
parent: Social Sciences", "Electricity Load Profile Clustering Methods
parent: Social Sciences", "Remote Sensing for Urban Informal Settlements
parent: Social Sciences", "Mineral and Coal Mining Regulation in Indonesia
parent: Social Sciences", "Social Forestry and Agroforestry in Indonesia
parent: Social Sciences", "Urban and Agricultural Land Use Metrics
parent: Social Sciences", "Pandemic Influences on Global Fisheries Management
parent: Social Sciences", "Antenna
parent: Electrical Engineering & Computer Science", "Antennae; Mobile Radio
parent: Electrical Engineering & Computer Science", "Applied Optics
parent: Electrical Engineering & Computer Science", "Artificial Evolution
parent: Electrical Engineering & Computer Science", "Artificial Intelligence
parent: Electrical Engineering & Computer Science", "Automatic Control
parent: Electrical Engineering & Computer Science", "Broadband Communication
parent: Electrical Engineering & Computer Science", "Chip Design & Manufacturing
parent: Electrical Engineering & Computer Science", "Circuit Systems
parent: Electrical Engineering & Computer Science", "Circuits
parent: Electrical Engineering & Computer Science", "Computer Graphics
parent: Electrical Engineering & Computer Science", "Computer Modeling and Animation
parent: Electrical Engineering & Computer Science", "Computer Networks
parent: Electrical Engineering & Computer Science", "Computer Systems Design
parent: Electrical Engineering & Computer Science", "Computer Systems Theory
parent: Electrical Engineering & Computer Science", "Consumer Electronics
parent: Electrical Engineering & Computer Science", "Control Systems
parent: Electrical Engineering & Computer Science", "Data Mining
parent: Electrical Engineering & Computer Science", "Database Design & Management
parent: Electrical Engineering & Computer Science", "Dielectrics
parent: Electrical Engineering & Computer Science", "Electrical Networks
parent: Electrical Engineering & Computer Science", "Electronic Imaging
parent: Electrical Engineering & Computer Science", "Electronics
parent: Electrical Engineering & Computer Science", "Fault Tolerant Computing
parent: Electrical Engineering & Computer Science", "Functional Programing
parent: Electrical Engineering & Computer Science", "Fuzzy Logic
parent: Electrical Engineering & Computer Science", "Fuzzy Sets
parent: Electrical Engineering & Computer Science", "Hydraulics
parent: Electrical Engineering & Computer Science", "Image Processing
parent: Electrical Engineering & Computer Science", "Instrumentation
parent: Electrical Engineering & Computer Science", "Integrated Circuit Design
parent: Electrical Engineering & Computer Science", "Library Science; Information Retrieval
parent: Electrical Engineering & Computer Science", "Logic
parent: Electrical Engineering & Computer Science", "Machine Learning
parent: Electrical Engineering & Computer Science", "Medical Image Processing
parent: Electrical Engineering & Computer Science", "Microwaves; Radio Frequencies
parent: Electrical Engineering & Computer Science", "Mobile Networks
parent: Electrical Engineering & Computer Science", "Neural Networks
parent: Electrical Engineering & Computer Science", "Parallel Computing
parent: Electrical Engineering & Computer Science", "Pattern Recognition
parent: Electrical Engineering & Computer Science", "Photo-Optics
parent: Electrical Engineering & Computer Science", "Power Distribution
parent: Electrical Engineering & Computer Science", "Power Systems
parent: Electrical Engineering & Computer Science", "Power Transmission
parent: Electrical Engineering & Computer Science", "Robotic Systems
parent: Electrical Engineering & Computer Science", "Robotics
parent: Electrical Engineering & Computer Science", "Search Engines; Web Crawling
parent: Electrical Engineering & Computer Science", "Security; Cryptography
parent: Electrical Engineering & Computer Science", "Signal Processing
parent: Electrical Engineering & Computer Science", "Software Design and Development
parent: Electrical Engineering & Computer Science", "Solid State Electronics
parent: Electrical Engineering & Computer Science", "Speech Recognition
parent: Electrical Engineering & Computer Science", "Spyware; Malware
parent: Electrical Engineering & Computer Science", "Systems Software
parent: Electrical Engineering & Computer Science", "Test Equipment
parent: Electrical Engineering & Computer Science", "User Interface Design
parent: Electrical Engineering & Computer Science", "Wireless Communication
parent: Electrical Engineering & Computer Science", "Deep Learning Applications in Industrial Fault Diagnosis
parent: Electrical Engineering & Computer Science", "Lithium-Ion Battery Health and Life Estimation
parent: Electrical Engineering & Computer Science", "Machine Learning for Diabetes Prediction
parent: Electrical Engineering & Computer Science", "Deep Learning Architectures for Hyperspectral Image Classification
parent: Electrical Engineering & Computer Science", "Machine Learning in Agricultural Systems
parent: Electrical Engineering & Computer Science", "Computational Approaches to Multi-Modal Image Fusion
parent: Electrical Engineering & Computer Science", "Machine Learning Models for Student Performance Prediction
parent: Electrical Engineering & Computer Science", "Computer Vision for Fire Detection Systems
parent: Electrical Engineering & Computer Science", "LiDAR Inertial Odometry and Mapping Systems
parent: Electrical Engineering & Computer Science", "Remote Sensing Image Change Detection
parent: Electrical Engineering & Computer Science", "Fault Detection Mechanisms for Photovoltaic Systems
parent: Electrical Engineering & Computer Science", "Wireless Technologies for Indoor Positioning Systems
parent: Electrical Engineering & Computer Science", "Reservoir Computing and Physical Neural Networks
parent: Electrical Engineering & Computer Science", "Path Planning Algorithms for Unmanned Vehicles
parent: Electrical Engineering & Computer Science", "Computational Models for Salient Object Detection
parent: Electrical Engineering & Computer Science", "Object Detection in Aerial Remote Sensing
parent: Electrical Engineering & Computer Science", "Wind Turbine Condition Monitoring and Diagnostics
parent: Electrical Engineering & Computer Science", "Neural Network Models for River Forecasting
parent: Electrical Engineering & Computer Science", "Machine Learning for Power Transformer Diagnostics
parent: Electrical Engineering & Computer Science", "Fault Diagnosis Mechanisms for Induction Motors
parent: Electrical Engineering & Computer Science", "Deep Learning Architectures for Maritime Vessel Detection
parent: Electrical Engineering & Computer Science", "Process Mining and Workflow Analysis Methods
parent: Electrical Engineering & Computer Science", "Deep Learning Applications in Remote Sensing Analysis
parent: Electrical Engineering & Computer Science", "Object Pose Estimation Methods
parent: Electrical Engineering & Computer Science", "Data-Driven Monitoring for Industrial Processes
parent: Electrical Engineering & Computer Science", "Algebra
parent: Math & Physics", "Applied Math
parent: Math & Physics", "Astronomy & Astrophysics
parent: Math & Physics", "Cancer Statistics
parent: Math & Physics", "Chaos Fractals & Complexity
parent: Math & Physics", "Computational Math
parent: Math & Physics", "Design & Analysis of Algorithms
parent: Math & Physics", "Discrete Applied Mathematics
parent: Math & Physics", "Functional Analysis
parent: Math & Physics", "Geophysical Science
parent: Math & Physics", "High Energy Physics
parent: Math & Physics", "Mathematical Science (Russia)
parent: Math & Physics", "Mathematics Research
parent: Math & Physics", "Nonlinear Analysis
parent: Math & Physics", "Nuclear Instrumentation
parent: Math & Physics", "Nuclear Physics
parent: Math & Physics", "Optics & Lasers
parent: Math & Physics", "Optimization Theory
parent: Math & Physics", "Photonics
parent: Math & Physics", "Physics; Current Developments
parent: Math & Physics", "Plasma Physics
parent: Math & Physics", "Semiconducting Materials
parent: Math & Physics", "Simulation
parent: Math & Physics", "Space Research
parent: Math & Physics", "Superconductor Science
parent: Math & Physics", "Surface Coating Technology
parent: Math & Physics", "Surface Science
parent: Math & Physics", "Topology
parent: Math & Physics", "Fault-Tolerant Quantum Error Correction
parent: Math & Physics", "Gravitational Wave Detection and Cataloging
parent: Math & Physics", "Camera Calibration and Modeling
parent: Math & Physics", "Hydrodynamics of Flapping and Oscillating Foils
parent: Math & Physics", "Stochastic Resonance for Signal Detection
parent: Math & Physics", "Interferometric Gravitational Wave Detector Design
parent: Math & Physics", "Space Situational Awareness and Surveillance
parent: Math & Physics", "Mathematical Approaches to Ramsey Theory
parent: Math & Physics", "Star Pattern Recognition and Tracking Systems
parent: Math & Physics", "Unsteady Airfoil Stall and Vortex Dynamics
parent: Math & Physics", "Computational Modeling of Shallow Water Flows
parent: Math & Physics", "Computational Methods for Seismic Wave Propagation
parent: Math & Physics", "Fault Diagnosis Mechanisms for Multiprocessor Networks
parent: Math & Physics", "Coherent Doppler Lidar for Wind Measurement
parent: Math & Physics", "Derived Categories and Coherent Sheaf Theory
parent: Math & Physics", "Satellite Orbit Determination Using GNSS Data
parent: Math & Physics", "Mathematical Approaches to Camera Pose Estimation
parent: Math & Physics", "Control Theory for Stochastic Linear Systems
parent: Math & Physics", "Proton Structure and Parton Distribution Analysis
parent: Math & Physics", "Mathematical Analysis of Special Function Inequalities
parent: Math & Physics", "Radio Frequency Interference Mitigation
parent: Math & Physics", "Shortest Path Algorithms and Complexity Analysis
parent: Math & Physics", "Mathematical Analysis of Nonlinear Oscillator Systems
parent: Math & Physics", "Facility Location and Network Optimization Models
parent: Math & Physics", "Computational Frameworks for Dissipative Quantum Dynamics
parent: Math & Physics", "Applied Catalysis
parent: Chemistry", "Atomic Spectrometry
parent: Chemistry", "Carbohydrate Research
parent: Chemistry", "Carbon
parent: Chemistry", "Catalysis
parent: Chemistry", "Chemistry & Material Science
parent: Chemistry", "Chemistry (Russia)
parent: Chemistry", "Chromatography; Electrophoresis
parent: Chemistry", "Colloid
parent: Chemistry", "Computational Chemistry
parent: Chemistry", "Computer Aided Molecular Design
parent: Chemistry", "Crystallography
parent: Chemistry", "Electro Analytical Chemistry
parent: Chemistry", "Environmental Chemistry
parent: Chemistry", "EthnoPharmacology
parent: Chemistry", "Flavors & Fragrance
parent: Chemistry", "Food Chemistry
parent: Chemistry", "Green Chemistry
parent: Chemistry", "Inorganic Chemistry
parent: Chemistry", "Liquid Crystals
parent: Chemistry", "Macromolecules & Polymers
parent: Chemistry", "Mass Spectrometry
parent: Chemistry", "Molecular Physics
parent: Chemistry", "Nanotechnology
parent: Chemistry", "Organic Chemistry
parent: Chemistry", "Paints & Coatings
parent: Chemistry", "Pharmaceutical Design
parent: Chemistry", "Pharmaceutical Research
parent: Chemistry", "Phytochemistry
parent: Chemistry", "Surfactants
parent: Chemistry", "Thermal Analysis
parent: Chemistry", "Toxins
parent: Chemistry", "Membrane Configurations for Oil-Water Separation
parent: Chemistry", "Catalytic Transesterification for Biodiesel Production
parent: Chemistry", "Biodiesel Fuel Utilization in Diesel Engines
parent: Chemistry", "Wireless Power Transfer Systems and Technologies
parent: Chemistry", "Topology Optimization for Structural Design
parent: Chemistry", "Coal Spontaneous Combustion Mechanisms and Monitoring
parent: Chemistry", "Cemented Paste Backfill in Mining Operations
parent: Chemistry", "Nanomaterial Additives for Lubricating Oil Applications
parent: Chemistry", "Additives and Materials for Drilling Fluids
parent: Chemistry", "Biomass Pyrolysis and Bio-Oil Production
parent: Chemistry", "Surface Modification for Pool Boiling Processes
parent: Chemistry", "Catalytic Oxidative Desulfurization of Fuel Oils
parent: Chemistry", "Seismic Response of Precast Concrete Structures
parent: Chemistry", "Fiber Optic Interferometric Sensing
parent: Chemistry", "Hydrothermal Liquefaction of Biomass Feedstocks
parent: Chemistry", "Nanoparticle Integration in Oil Recovery Processes
parent: Chemistry", "Steel and Concrete Composite Shear Walls
parent: Chemistry", "Surfactant Usage in Petroleum Recovery Processes
parent: Chemistry", "Superhydrophobic Porous Materials for Oil Remediation
parent: Chemistry", "Flow Boiling Heat Transfer in Microchannels
parent: Chemistry", "Fiber Reinforcement and Stabilization of Soils
parent: Chemistry", "Corrosion Management Mechanisms for Industrial Pipelines
parent: Chemistry", "Polymer Gel Utilization in Oil Recovery
parent: Chemistry", "Natural Gas Hydrate Flow Management Mechanisms
parent: Chemistry", "Optical Phased Array Beam Steering Systems
parent: Chemistry", "Acoustics
parent: Chemical, Mechanical, & Civil Engineering", "Aeronautics & Astronautics
parent: Chemical, Mechanical, & Civil Engineering", "Aerospace
parent: Chemical, Mechanical, & Civil Engineering", "Agricultural Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Alloys
parent: Chemical, Mechanical, & Civil Engineering", "Applied Geophysics
parent: Chemical, Mechanical, & Civil Engineering", "Automotive Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Bulk Solid Handling
parent: Chemical, Mechanical, & Civil Engineering", "Cement & Concrete
parent: Chemical, Mechanical, & Civil Engineering", "Ceramics
parent: Chemical, Mechanical, & Civil Engineering", "Chemical Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Combustion
parent: Chemical, Mechanical, & Civil Engineering", "Composites
parent: Chemical, Mechanical, & Civil Engineering", "Construction
parent: Chemical, Mechanical, & Civil Engineering", "Corrosion
parent: Chemical, Mechanical, & Civil Engineering", "Dams & Tunnels
parent: Chemical, Mechanical, & Civil Engineering", "Defects & Diffusion in Materials
parent: Chemical, Mechanical, & Civil Engineering", "Digital Printing
parent: Chemical, Mechanical, & Civil Engineering", "Dyes & Pigments
parent: Chemical, Mechanical, & Civil Engineering", "Earthquake Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Electrochemical Development
parent: Chemical, Mechanical, & Civil Engineering", "Electrochemistry
parent: Chemical, Mechanical, & Civil Engineering", "Energy Fuel
parent: Chemical, Mechanical, & Civil Engineering", "Environmental Pollution
parent: Chemical, Mechanical, & Civil Engineering", "Environmental Protection
parent: Chemical, Mechanical, & Civil Engineering", "Filtration Membrane
parent: Chemical, Mechanical, & Civil Engineering", "Fluid Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Fluid Mechanics
parent: Chemical, Mechanical, & Civil Engineering", "Fluid Phase Equilibrium
parent: Chemical, Mechanical, & Civil Engineering", "Fractures & Fatigue
parent: Chemical, Mechanical, & Civil Engineering", "Friction Lubrication & Wear
parent: Chemical, Mechanical, & Civil Engineering", "Gas Turbines
parent: Chemical, Mechanical, & Civil Engineering", "Geotechnical Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Heat Transfer
parent: Chemical, Mechanical, & Civil Engineering", "Hydrology Soil Contamination
parent: Chemical, Mechanical, & Civil Engineering", "Industrial Chemistry
parent: Chemical, Mechanical, & Civil Engineering", "Machine Tools
parent: Chemical, Mechanical, & Civil Engineering", "Material Science
parent: Chemical, Mechanical, & Civil Engineering", "Materials Processing
parent: Chemical, Mechanical, & Civil Engineering", "Mechanical Design Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Mechanics of Solids & Structures
parent: Chemical, Mechanical, & Civil Engineering", "Metallurgy
parent: Chemical, Mechanical, & Civil Engineering", "Military Aviation
parent: Chemical, Mechanical, & Civil Engineering", "Mining
parent: Chemical, Mechanical, & Civil Engineering", "Naval Architecture
parent: Chemical, Mechanical, & Civil Engineering", "Nuclear Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Numerical Methods in Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Ocean Coastal Management
parent: Chemical, Mechanical, & Civil Engineering", "Ocean Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Oceanographic Instrumentation
parent: Chemical, Mechanical, & Civil Engineering", "Oil & Natural Gas
parent: Chemical, Mechanical, & Civil Engineering", "Ore Processing
parent: Chemical, Mechanical, & Civil Engineering", "Petroleum Engineering
parent: Chemical, Mechanical, & Civil Engineering", "Printing
parent: Chemical, Mechanical, & Civil Engineering", "Pulp & Paper
parent: Chemical, Mechanical, & Civil Engineering", "Pulp Paper Science
parent: Chemical, Mechanical, & Civil Engineering", "Safety Management
parent: Chemical, Mechanical, & Civil Engineering", "Sensors & Actuators
parent: Chemical, Mechanical, & Civil Engineering", "Soil Quality
parent: Chemical, Mechanical, & Civil Engineering", "Soil Science
parent: Chemical, Mechanical, & Civil Engineering", "Solar & Wind Power
parent: Chemical, Mechanical, & Civil Engineering", "Sound & Vibration
parent: Chemical, Mechanical, & Civil Engineering", "Textile Art
parent: Chemical, Mechanical, & Civil Engineering", "Textiles
parent: Chemical, Mechanical, & Civil Engineering", "Transportation Research
parent: Chemical, Mechanical, & Civil Engineering", "Vehicle System Design
parent: Chemical, Mechanical, & Civil Engineering", "Waste Management
parent: Chemical, Mechanical, & Civil Engineering", "Water Policy
parent: Chemical, Mechanical, & Civil Engineering", "Water Quality & Resource Management
parent: Chemical, Mechanical, & Civil Engineering", "Water Treatment
parent: Chemical, Mechanical, & Civil Engineering", "Water Utilities
parent: Chemical, Mechanical, & Civil Engineering", "Water Waste
parent: Chemical, Mechanical, & Civil Engineering", "Welding
parent: Chemical, Mechanical, & Civil Engineering", "Wood
parent: Chemical, Mechanical, & Civil Engineering", "Wood Components
parent: Chemical, Mechanical, & Civil Engineering", "Wool
parent: Chemical, Mechanical, & Civil Engineering", "Electric Vehicle Charging Infrastructure Planning Models
parent: Chemical, Mechanical, & Civil Engineering", "Multiphase Motor Drive and Control Systems
parent: Chemical, Mechanical, & Civil Engineering", "Protection and Planning of DC Microgrids
parent: Chemical, Mechanical, & Civil Engineering", "Seismic Resilience and Vulnerability of Bridges
parent: Chemical, Mechanical, & Civil Engineering", "Pipeline Corrosion Assessment and Integrity Management
parent: Chemical, Mechanical, & Civil Engineering", "Dynamic Analysis of High-Speed Railway Bridges
parent: Chemical, Mechanical, & Civil Engineering", "Street Lighting and Control Systems
parent: Chemical, Mechanical, & Civil Engineering", "Artificial Intelligence in Petroleum Industry Operations
parent: Chemical, Mechanical, & Civil Engineering", "Safety Risk Management in Coal Mining
parent: Chemical, Mechanical, & Civil Engineering", "Seismic Performance Assessment and Ground Motion
parent: Chemical, Mechanical, & Civil Engineering", "Machine Learning in Drilling Operations
parent: Chemical, Mechanical, & Civil Engineering", "Wireless Sensor Network Applications for Mine Safety
parent: Chemical, Mechanical, & Civil Engineering", "Artificial Intelligence in Water Resource Management
parent: Chemical, Mechanical, & Civil Engineering", "High Voltage Circuit Breaker Fault Diagnosis
parent: Chemical, Mechanical, & Civil Engineering", "Computational Approaches for Reservoir Production Management
parent: Chemical, Mechanical, & Civil Engineering", "Grid Integration Control for Photovoltaic Systems
parent: Chemical, Mechanical, & Civil Engineering", "Computational Methods for Mine Planning Operations
parent: Chemical, Mechanical, & Civil Engineering", "Autonomous Haulage Systems in Mining Operations
parent: Chemical, Mechanical, & Civil Engineering", "Fault Diagnosis Mechanisms for Sucker Rod Pumps
parent: Chemical, Mechanical, & Civil Engineering", "Artificial Intelligence for Coal Boiler Operation
parent: Chemical, Mechanical, & Civil Engineering", "Building Design and Seismic Resilience Assessment
parent: Chemical, Mechanical, & Civil Engineering", "Electromechanical Actuation Systems for Aircraft Control
parent: Chemical, Mechanical, & Civil Engineering", "Artificial Intelligence in Drilling Operations
parent: Chemical, Mechanical, & Civil Engineering", "Structural Assessment of Masonry Arch Bridges
parent: Chemical, Mechanical, & Civil Engineering", "Fault Detection and Cavitation Monitoring Systems
parent: Chemical, Mechanical, & Civil Engineering", "Air Quality
parent: Earth Sciences", "Archeological Science
parent: Earth Sciences", "Atmospheric GeoPhysics
parent: Earth Sciences", "Atmospheric Science
parent: Earth Sciences", "Climatology
parent: Earth Sciences", "GeoChemistry
parent: Earth Sciences", "Geodesy
parent: Earth Sciences", "Geographic Information Science
parent: Earth Sciences", "Geology & Tectonics
parent: Earth Sciences", "Geology (International)
parent: Earth Sciences", "Geomorphology
parent: Earth Sciences", "GIS (non English)
parent: Earth Sciences", "Glaciology
parent: Earth Sciences", "Mineralogy
parent: Earth Sciences", "Oceanography
parent: Earth Sciences", "Paleobiology
parent: Earth Sciences", "Paleogeography
parent: Earth Sciences", "Quaternary Research
parent: Earth Sciences", "Remote Sensing
parent: Earth Sciences", "Sedimentary Geology
parent: Earth Sciences", "Seismology
parent: Earth Sciences", "Water Resource
parent: Earth Sciences", "Microplastic Presence in Agricultural Soil Environments
parent: Earth Sciences", "Biochar Integration in Soil and Agricultural Systems
parent: Earth Sciences", "Fault Diagnosis Mechanisms for Rolling Bearings
parent: Earth Sciences", "Satellite Radar Interferometry for Surface Deformation
parent: Earth Sciences", "Land Use and Ecosystem Service Valuation
parent: Earth Sciences", "Rock Mechanics and Stability in Mining
parent: Earth Sciences", "Microbially Induced Carbonate Precipitation in Soils
parent: Earth Sciences", "Automated Irrigation Systems for Precision Agriculture
parent: Earth Sciences", "Heavy Metal Contamination in Urban Soils
parent: Earth Sciences", "Machine Learning for Flood Risk Mapping
parent: Earth Sciences", "Satellite Remote Sensing for Crop Classification
parent: Earth Sciences", "Unmanned Aerial Vehicle Imagery in Agriculture
parent: Earth Sciences", "Natural Gas Hydrate Extraction and Characterization
parent: Earth Sciences", "Hydraulic Fracturing Simulation and Experimental Analysis
parent: Earth Sciences", "Internet-Connected Sensor and Artificial Intelligence Systems for Water Resource Monitoring
parent: Earth Sciences", "Urbanization and Carbon Emission Dynamics
parent: Earth Sciences", "Microbial Community Dynamics in Rhizosphere Soil
parent: Earth Sciences", "Gas Adsorption and Sequestration in Coal
parent: Earth Sciences", "Soil Microbial Community Structure and Function
parent: Earth Sciences", "Satellite Precipitation Estimation and Data Analysis
parent: Earth Sciences", "Geological Characteristics of Shale Gas Reservoirs
parent: Earth Sciences", "Groundwater Potential Mapping via Geospatial Technologies
parent: Earth Sciences", "Natural Radioactivity in Environmental and Building Materials
parent: Earth Sciences", "Green Roof Systems for Urban Environments
parent: Earth Sciences", "Integration of Solar Energy and Agricultural Land Use
parent: Earth Sciences", "Applied Genetics
parent: Biology", "Aquaculture
parent: Biology", "Aquatic Disease
parent: Biology", "Australian Ecology
parent: Biology", "Biological Conservation
parent: Biology", "Botany
parent: Biology", "Comparative Animal Physiology
parent: Biology", "Crop Science
parent: Biology", "Crustaceans
parent: Biology", "Ecological Modeling
parent: Biology", "Ecology
parent: Biology", "Entomology
parent: Biology", "Environmental Contamination
parent: Biology", "Environmental Microbiology
parent: Biology", "Fish Biology
parent: Biology", "Fish Research
parent: Biology", "Forest Science
parent: Biology", "Freshwater Biology
parent: Biology", "Genetics
parent: Biology", "Horticulture
parent: Biology", "Human Evolution
parent: Biology", "Insect Physiology
parent: Biology", "Insects
parent: Biology", "Mammals
parent: Biology", "Marine Biology
parent: Biology", "Marine Pollution
parent: Biology", "Molecular Biochemical Parasitology
parent: Biology", "Molecular Biological Evolution
parent: Biology", "Molecular Ecology
parent: Biology", "Mycology
parent: Biology", "Parasitology
parent: Biology", "Pest Management Science
parent: Biology", "Plant Disease
parent: Biology", "Plant Ecology
parent: Biology", "Plant Physiology
parent: Biology", "Rangeland Ecology
parent: Biology", "Sociobiology
parent: Biology", "Soil Analysis
parent: Biology", "Weed Management
parent: Biology", "Wetlands
parent: Biology", "Wildlife Management
parent: Biology", "Wildlife Research
parent: Biology", "Zoology
parent: Biology", "Silicon Roles in Plant Growth Processes
parent: Biology", "Computational Methods for Molecular Structure Analysis
parent: Biology", "Probiotic and Feed Additive Poultry Nutrition
parent: Biology", "Antibiotic Resistance Genes in Soil Environments
parent: Biology", "Microbial Processes in Soil Carbon Cycling
parent: Biology", "Microbial Biosurfactant Production and Industrial Applications
parent: Biology", "Selenium Roles in Plant Soil Systems
parent: Biology", "Arbuscular Mycorrhizal Fungi and Plant Symbiosis
parent: Biology", "Plant Growth-Promoting Rhizobacteria in Agriculture
parent: Biology", "Cadmium Accumulation in Soil-Rice Ecosystems
parent: Biology", "Phytogenic Feed Additives in Poultry Nutrition
parent: Biology", "Heat Stress Effects on Poultry Production
parent: Biology", "Plant-Derived Extracts for Mosquito Population Management
parent: Biology", "Plant Essential Oils for Pest Management
parent: Biology", "Gas Diffusion Layers in Fuel Cells
parent: Biology", "Microbial Degradation of Petroleum Hydrocarbon Contaminants
parent: Biology", "Essential Oil Antimicrobial Applications in Food
parent: Biology", "Coating Materials for Controlled Nutrient Release
parent: Biology", "Biological and Environmental Factors in Decomposition
parent: Biology", "Terrestrial Ecosystem Nutrient Stoichiometry and Cycling
parent: Biology", "Respiratory Rate Monitoring Systems
parent: Biology", "Phosphate Solubilizing Microorganisms in Agricultural Soils
parent: Biology", "Soil Organic Matter Formation and Persistence
parent: Biology", "Zinc Biofortification and Crop Nutrition Management
parent: Biology", "Plant Physiological Responses to Salinity Stress
parent: Biology", "BioInformatics
parent: Biotechnology", "Biotechnology Bioengineering
parent: Biotechnology", "Biotechnology Trends
parent: Biotechnology", "Enzyme Microbiological Techniques
parent: Biotechnology", "Food Engineering
parent: Biotechnology", "Food Protection
parent: Biotechnology", "Genomics & Nucleic Acids
parent: Biotechnology", "Microbiology Biotechnology
parent: Biotechnology", "Protein Science
parent: Biotechnology", "Proteomics
parent: Biotechnology", "Systematics & Evolutionary Microbiology
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Tier 0: prebuilt JSON (science_backbone.json, layer='merged', produced 2026-05-11)" }, "xaxis": { "range": [ -3.9, 3.9 ], "showgrid": false, "showticklabels": false, "zeroline": false }, "yaxis": { "range": [ -3.9, 3.9 ], "scaleanchor": "x", "scaleratio": 1, "showgrid": false, "showticklabels": false, "zeroline": false } } }, "image/png": 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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ Saved: /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults/science_backbone_network.html\n" ] } ], "source": [ "\"\"\"\n", "§7 replacement v2 — softer, more elegant treatment.\n", "\n", "Same Klavans/Boyack radial layout as v1. Differences:\n", " - No more diamond markers. Keyword groups are now circles with a soft glow\n", " (an oversized, low-opacity backing disk behind a crisp foreground disk).\n", " - Each domain that has keyword groups gets a \"petal\" polygon: the convex\n", " hull of (domain_node, *its_keyword_groups), filled in the domain color\n", " at low opacity. This visually groups them without harsh lines.\n", " - Subdisciplines are still dot-markers, hover-only labels.\n", " - Domains keep their published UCSD colors and on-canvas labels.\n", "\n", "Drops into the same place in SUBSIDE_Science_Backbone.ipynb §7. Uses the\n", "identical input variables (SCIENCE_BACKBONE, group_mapping_df, OUTPUT_DIR,\n", "BACKBONE_SOURCE) and writes the same HTML output path.\n", "\"\"\"\n", "\n", "import math\n", "import networkx as nx\n", "import plotly.graph_objects as go\n", "import plotly.express as px\n", "\n", "# ── 1. Klavans/Boyack-inspired circular ordering ──────────────────────\n", "KLAVANS_BOYACK_ORDER = [\n", " \"Humanities\", \"Social Sciences\",\n", " \"Electrical Engineering & Computer Science\",\n", " \"Math & Physics\", \"Chemistry\",\n", " \"Chemical, Mechanical, & Civil Engineering\",\n", " \"Earth Sciences\", \"Biology\", \"Biotechnology\",\n", " \"Infectious Disease\", \"Medical Specialties\",\n", " \"Health Professionals\", \"Brain Research\",\n", "]\n", "UCSD_HEX = {\n", " \"Math & Physics\": \"#7570B3\",\n", " \"Chemistry\": \"#1F78B4\",\n", " \"Earth Sciences\": \"#B15928\",\n", " \"Biology\": \"#33A02C\",\n", " \"Biotechnology\": \"#FB9A99\",\n", " \"Infectious Disease\": \"#A6CEE3\",\n", " \"Medical Specialties\": \"#E31A1C\",\n", " \"Health Professionals\": \"#FDBF6F\",\n", " \"Brain Research\": \"#FF7F00\",\n", " \"Electrical Engineering & Computer Science\": \"#CAB2D6\",\n", " \"Chemical, Mechanical, & Civil Engineering\": \"#6A3D9A\",\n", " \"Social Sciences\": \"#FFC83D\",\n", " \"Humanities\": \"#FFE066\",\n", "}\n", "\n", "domains_in_backbone = list(SCIENCE_BACKBONE.keys())\n", "ordered_domains = (\n", " [d for d in KLAVANS_BOYACK_ORDER if d in domains_in_backbone] +\n", " [d for d in domains_in_backbone if d not in KLAVANS_BOYACK_ORDER]\n", ")\n", "\n", "# ── 2. Layout: domains → subdisciplines → keyword groups ──────────────\n", "R_DOMAIN = 1.0\n", "R_SUB = 1.7\n", "R_GROUP = 2.7\n", "ARC_SUBS = math.radians(24)\n", "ARC_GROUPS = math.radians(20)\n", "\n", "pos = {}\n", "domain_angle = {}\n", "for i, d in enumerate(ordered_domains):\n", " theta = -math.pi/2 - 2 * math.pi * i / max(len(ordered_domains), 1)\n", " domain_angle[d] = theta\n", " pos[d] = (R_DOMAIN * math.cos(theta), R_DOMAIN * math.sin(theta))\n", "\n", "\n", "def _fan(base, n, arc, r):\n", " if n <= 0: return []\n", " if n == 1: return [(r * math.cos(base), r * math.sin(base))]\n", " return [(r * math.cos(base - arc/2 + (j/(n-1)) * arc),\n", " r * math.sin(base - arc/2 + (j/(n-1)) * arc)) for j in range(n)]\n", "\n", "\n", "for d in ordered_domains:\n", " subs = SCIENCE_BACKBONE[d].get(\"subdisciplines\") or [\"General\"]\n", " for sub, xy in zip(subs, _fan(domain_angle[d], len(subs), ARC_SUBS, R_SUB)):\n", " pos[f\"{d} :: {sub}\"] = xy\n", "\n", "group_by_domain = {}\n", "for _, row in group_mapping_df.iterrows():\n", " group_by_domain.setdefault(row[\"primary_domain\"], []).append(row[\"group\"])\n", "unmatched = [d for d in group_by_domain if d not in domain_angle]\n", "for k, ud in enumerate(unmatched):\n", " domain_angle[ud] = math.pi/2 + (k - (len(unmatched)-1)/2) * math.radians(10)\n", "\n", "for d, groups in group_by_domain.items():\n", " n = len(groups)\n", " arc = max(ARC_GROUPS, math.radians(8 * n))\n", " coords = _fan(domain_angle[d], n, arc, R_GROUP)\n", " if n > 3:\n", " for j in range(n):\n", " stagger = 0.35 if (j % 2 == 1) else 0.0\n", " x, y = coords[j]; ro = math.hypot(x, y); sc = (ro + stagger) / ro\n", " coords[j] = (x * sc, y * sc)\n", " for g, xy in zip(groups, coords):\n", " pos[g] = xy\n", "\n", "# ── 3. Build the graph (for edge drawing) ─────────────────────────────\n", "G = nx.Graph()\n", "for d in ordered_domains:\n", " G.add_node(d, kind=\"domain\")\n", " for sub in SCIENCE_BACKBONE[d].get(\"subdisciplines\") or [\"General\"]:\n", " sid = f\"{d} :: {sub}\"\n", " G.add_node(sid, kind=\"subdiscipline\", label=sub, parent=d)\n", " G.add_edge(d, sid, edge_kind=\"scaffold\")\n", "for _, row in group_mapping_df.iterrows():\n", " g = row[\"group\"]\n", " G.add_node(g, kind=\"group\",\n", " primary_domain=row[\"primary_domain\"],\n", " n_matches=int(row[\"n_matches\"]))\n", " for domain, sub, score in row[\"backbone_mapping\"]:\n", " sid = f\"{domain} :: {sub}\"\n", " if sid in G:\n", " G.add_edge(g, sid, edge_kind=\"hit\", weight=float(score))\n", " if row[\"primary_domain\"] in G:\n", " G.add_edge(g, row[\"primary_domain\"], edge_kind=\"anchor\")\n", "\n", "# ── 4. Color resolver + helpers ───────────────────────────────────────\n", "QUAL = px.colors.qualitative.Bold + px.colors.qualitative.Vivid\n", "def domain_color(d):\n", " if d in UCSD_HEX:\n", " return UCSD_HEX[d]\n", " idx = (ordered_domains + unmatched).index(d) if d in ordered_domains or d in unmatched else 0\n", " return QUAL[idx % len(QUAL)]\n", "DOMAIN_COLORS = {d: domain_color(d) for d in ordered_domains + unmatched}\n", "DOMAIN_COLORS.setdefault(\"Uncategorized\", \"#9a9a9a\")\n", "\n", "\n", "def _hex_to_rgba(color, alpha):\n", " \"\"\"Convert any of '#RRGGBB', '#RGB', 'rgb(r,g,b)', or 'rgba(r,g,b,a)' to rgba string.\"\"\"\n", " s = str(color).strip()\n", " if s.startswith(\"rgb\"):\n", " # 'rgb(r,g,b)' or 'rgba(r,g,b,a)' — replace alpha and return\n", " inner = s[s.index(\"(\")+1:s.rindex(\")\")]\n", " parts = [p.strip() for p in inner.split(\",\")]\n", " r, g, b = int(float(parts[0])), int(float(parts[1])), int(float(parts[2]))\n", " return f\"rgba({r},{g},{b},{alpha})\"\n", " h = s.lstrip(\"#\")\n", " if len(h) == 3:\n", " h = \"\".join(c * 2 for c in h)\n", " r, g, b = int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)\n", " return f\"rgba({r},{g},{b},{alpha})\"\n", "\n", "\n", "def _convex_hull(points):\n", " \"\"\"Andrew's monotone chain. Returns hull in CCW order (or short list for ≤2 pts).\"\"\"\n", " pts = sorted(set(points))\n", " if len(pts) < 2:\n", " return pts\n", " def cross(o, a, b):\n", " return (a[0]-o[0]) * (b[1]-o[1]) - (a[1]-o[1]) * (b[0]-o[0])\n", " lower = []\n", " for p in pts:\n", " while len(lower) >= 2 and cross(lower[-2], lower[-1], p) <= 0:\n", " lower.pop()\n", " lower.append(p)\n", " upper = []\n", " for p in reversed(pts):\n", " while len(upper) >= 2 and cross(upper[-2], upper[-1], p) <= 0:\n", " upper.pop()\n", " upper.append(p)\n", " return lower[:-1] + upper[:-1]\n", "\n", "\n", "def _inflate_hull(hull_pts, margin=0.18):\n", " \"\"\"Push each hull vertex slightly outward from the centroid. Smooths the shape.\"\"\"\n", " if len(hull_pts) < 2:\n", " return hull_pts\n", " cx = sum(p[0] for p in hull_pts) / len(hull_pts)\n", " cy = sum(p[1] for p in hull_pts) / len(hull_pts)\n", " inflated = []\n", " for x, y in hull_pts:\n", " dx, dy = x - cx, y - cy\n", " dist = math.hypot(dx, dy) or 1.0\n", " inflated.append((x + dx/dist * margin, y + dy/dist * margin))\n", " return inflated\n", "\n", "\n", "# ── 5. Draw ───────────────────────────────────────────────────────────\n", "traces = []\n", "\n", "# (a) Domain \"petal\" polygons — convex hull of (domain_node, *its_groups)\n", "for d in ordered_domains + unmatched:\n", " groups = group_by_domain.get(d, [])\n", " if not groups:\n", " continue\n", " if d in pos:\n", " pts = [pos[d]] + [pos[g] for g in groups if g in pos]\n", " else:\n", " pts = [pos[g] for g in groups if g in pos]\n", " if len(pts) < 2:\n", " continue\n", " hull = _convex_hull(pts)\n", " hull = _inflate_hull(hull, margin=0.20)\n", " if len(hull) < 3:\n", " # 2 points: draw a thin lozenge by perpendicular offset\n", " (x1, y1), (x2, y2) = hull\n", " dx, dy = x2 - x1, y2 - y1\n", " ll = math.hypot(dx, dy) or 1.0\n", " nx_, ny_ = -dy/ll * 0.15, dx/ll * 0.15\n", " hull = [(x1+nx_, y1+ny_), (x2+nx_, y2+ny_), (x2-nx_, y2-ny_), (x1-nx_, y1-ny_)]\n", " xs = [p[0] for p in hull] + [hull[0][0]]\n", " ys = [p[1] for p in hull] + [hull[0][1]]\n", " color = DOMAIN_COLORS.get(d, \"#888\")\n", " traces.append(go.Scatter(\n", " x=xs, y=ys, mode=\"lines\",\n", " line=dict(color=_hex_to_rgba(color, 0.35), width=1.2, shape=\"spline\"),\n", " fill=\"toself\", fillcolor=_hex_to_rgba(color, 0.10),\n", " hoverinfo=\"skip\", showlegend=False))\n", "\n", "# (b) Edges, split by kind\n", "backbone_x, backbone_y = [], []\n", "anchor_x, anchor_y = [], []\n", "hit_x, hit_y = [], []\n", "for u, v, ed in G.edges(data=True):\n", " ux, uy = pos.get(u, (0, 0)); vx, vy = pos.get(v, (0, 0))\n", " kind = ed.get(\"edge_kind\")\n", " if kind == \"hit\":\n", " hit_x += [ux, vx, None]; hit_y += [uy, vy, None]\n", " elif kind == \"anchor\":\n", " anchor_x += [ux, vx, None]; anchor_y += [uy, vy, None]\n", " else:\n", " backbone_x += [ux, vx, None]; backbone_y += [uy, vy, None]\n", "\n", "traces.append(go.Scatter(x=backbone_x, y=backbone_y, mode=\"lines\",\n", " line=dict(color=\"#e8e8e8\", width=0.5),\n", " hoverinfo=\"none\", showlegend=False))\n", "traces.append(go.Scatter(x=anchor_x, y=anchor_y, mode=\"lines\",\n", " line=dict(color=\"#cccccc\", width=0.5, dash=\"dot\"),\n", " hoverinfo=\"none\", showlegend=False))\n", "traces.append(go.Scatter(x=hit_x, y=hit_y, mode=\"lines\",\n", " line=dict(color=\"#9a9a9a\", width=1.0),\n", " hoverinfo=\"none\", showlegend=False))\n", "\n", "# (c) Subdisciplines — small dots, hover-only labels\n", "sub_nodes = [n for n, d in G.nodes(data=True) if d.get(\"kind\") == \"subdiscipline\"]\n", "sub_colors = [DOMAIN_COLORS.get(G.nodes[n][\"parent\"], \"#b0b0b0\") for n in sub_nodes]\n", "traces.append(go.Scatter(\n", " x=[pos[n][0] for n in sub_nodes], y=[pos[n][1] for n in sub_nodes],\n", " mode=\"markers\",\n", " marker=dict(size=7, color=sub_colors, opacity=0.6,\n", " line=dict(color=\"white\", width=0.5)),\n", " hovertext=[f\"{G.nodes[n]['label']}
parent: {G.nodes[n]['parent']}\"\n", " for n in sub_nodes],\n", " hoverinfo=\"text\",\n", " name=\"Subdiscipline\"))\n", "\n", "# (d) Domains — colored disks with white labels\n", "dom_nodes = [n for n, d in G.nodes(data=True) if d.get(\"kind\") == \"domain\"]\n", "traces.append(go.Scatter(\n", " x=[pos[n][0] for n in dom_nodes], y=[pos[n][1] for n in dom_nodes],\n", " mode=\"markers+text\",\n", " marker=dict(size=22, color=[DOMAIN_COLORS.get(n, \"#888\") for n in dom_nodes],\n", " line=dict(color=\"white\", width=2)),\n", " text=[n.split(\" & \")[0] if len(n) > 22 else n for n in dom_nodes],\n", " textposition=\"middle center\", textfont=dict(size=8, color=\"white\"),\n", " hovertext=[f\"{n}
(domain)
\"\n", " f\"{len(SCIENCE_BACKBONE[n].get('subdisciplines', []))} subdisciplines\"\n", " for n in dom_nodes],\n", " hoverinfo=\"text\",\n", " name=\"Domain\"))\n", "\n", "# (e) Keyword groups — soft glow + crisp circle, one trace pair per domain\n", "for d in ordered_domains + unmatched:\n", " grp_nodes = [n for n, dd in G.nodes(data=True)\n", " if dd.get(\"kind\") == \"group\" and dd.get(\"primary_domain\") == d]\n", " if not grp_nodes:\n", " continue\n", " color = DOMAIN_COLORS.get(d, \"#888\")\n", " sizes = [12 + math.sqrt(max(G.nodes[n].get(\"n_matches\", 0), 1)) * 1.8\n", " for n in grp_nodes]\n", " # Glow layer (drawn first, larger, faded)\n", " traces.append(go.Scatter(\n", " x=[pos[n][0] for n in grp_nodes], y=[pos[n][1] for n in grp_nodes],\n", " mode=\"markers\",\n", " marker=dict(size=[s * 2.0 for s in sizes],\n", " color=_hex_to_rgba(color, 0.20),\n", " line=dict(width=0)),\n", " hoverinfo=\"skip\", showlegend=False))\n", " # Foreground circle + label\n", " def _lp(x, y):\n", " if x >= 0.3 and y >= 0.3: return \"top right\"\n", " elif x <= -0.3 and y >= 0.3: return \"top left\"\n", " elif x <= -0.3 and y <= -0.3: return \"bottom left\"\n", " elif x >= 0.3 and y <= -0.3: return \"bottom right\"\n", " elif x >= 0.3: return \"middle right\"\n", " elif x <= -0.3: return \"middle left\"\n", " elif y >= 0.3: return \"top center\"\n", " else: return \"bottom center\"\n", " positions = [_lp(pos[n][0], pos[n][1]) for n in grp_nodes]\n", " traces.append(go.Scatter(\n", " x=[pos[n][0] for n in grp_nodes], y=[pos[n][1] for n in grp_nodes],\n", " mode=\"markers+text\",\n", " marker=dict(size=sizes, color=color, symbol=\"circle\",\n", " line=dict(color=\"white\", width=1.6)),\n", " text=grp_nodes, textposition=positions,\n", " textfont=dict(size=9, color=color),\n", " hovertext=[f\"{n}
primary domain: {d}\"\n", " f\"
matched docs: {G.nodes[n].get('n_matches', 0)}\"\n", " for n in grp_nodes],\n", " hoverinfo=\"text\",\n", " name=f\"Group → {d}\"))\n", "\n", "fig_network = go.Figure(traces, layout=go.Layout(\n", " title=(\"SUBSIDE Science Backbone — radial layout, soft-petal styling\"\n", " f\"
{BACKBONE_SOURCE}\"),\n", " showlegend=True, hovermode=\"closest\", height=820,\n", " xaxis=dict(showgrid=False, zeroline=False, showticklabels=False,\n", " range=[-3.9, 3.9]),\n", " yaxis=dict(showgrid=False, zeroline=False, showticklabels=False,\n", " scaleanchor=\"x\", scaleratio=1, range=[-3.9, 3.9]),\n", " plot_bgcolor=\"white\",\n", " legend=dict(orientation=\"v\", yanchor=\"top\", y=1.0,\n", " xanchor=\"left\", x=1.02, font=dict(size=10)),\n", " margin=dict(l=20, r=20, t=80, b=20),\n", "))\n", "fig_network.write_html(str(OUTPUT_DIR / \"science_backbone_network.html\"),\n", " include_plotlyjs=\"cdn\")\n", "fig_network.show()\n", "print(f\"✓ Saved: {OUTPUT_DIR / 'science_backbone_network.html'}\")\n" ] }, { "cell_type": "markdown", "id": "05485257", "metadata": {}, "source": [ "## 8. Time-burst graphic — annual bursts for terms, **DOMAINS**, and data resources\n", "\n", "Kleinberg's two-state burst model identifies time windows where a term's frequency is anomalously high relative to its baseline rate. Here it's applied to **annual publication frequencies** computed from the BibTeX `year` field.\n", "\n", "Three views, all interactive Plotly heatmaps:\n", "\n", "- **8.2 — Term-level bursts.** The most distinctive terms (by TF-IDF) across the corpus, plotted year × term, color = burst intensity.\n", "- **8.3 — Domain-level bursts.** Annual counts of papers whose `text_content` matches at least one term in each backbone domain, run through the same burst detector. Lets you see at a glance when *Geodesy & Remote Sensing* surged (likely with InSAR availability), when *Coastal & Marine Science* started bursting (likely as sea-level concerns ramped), etc.\n", "- **8.4 — Data-resource bursts.** Same view, but for named data resources (InSAR, GNSS, LiDAR, SAR, leveling, extensometers, MODFLOW, …).\n", "\n", "### 8.1 Annual Kleinberg burst detector\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "23f5ce30", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Annual Kleinberg burst detector defined\n" ] } ], "source": [ "def kleinberg_bursts_annual(year_counts, s=2.0, gamma=1.0):\n", " \"\"\"\n", " Annual-binned Kleinberg two-state burst detector.\n", "\n", " Parameters\n", " ----------\n", " year_counts : dict[int, int]\n", " Year → number of occurrences in that year.\n", " s : float\n", " Burst-state rate multiplier (higher = fewer, larger bursts).\n", " gamma : float\n", " State-change penalty.\n", "\n", " Returns\n", " -------\n", " list[tuple[int, int, float]]\n", " (start_year, end_year, intensity) per detected burst window.\n", "\n", " Adapted from the Kleinberg detector in the EPH × Semantic Bridge × Burst\n", " notebook. Here counts are converted to a sequence of \"events\" with\n", " sub-year jitter so the same Viterbi machinery applies; this preserves the\n", " original algorithm while letting us run it on annual bins.\n", " \"\"\"\n", " if not year_counts:\n", " return []\n", "\n", " years_sorted = sorted(year_counts)\n", " timestamps = []\n", " rng = np.random.default_rng(42)\n", " for y in years_sorted:\n", " n = int(year_counts[y])\n", " if n <= 0:\n", " continue\n", " # Evenly spread the n events through the year (deterministic, reproducible)\n", " offsets = (np.arange(n) + 0.5) / n\n", " timestamps.extend([y + float(o) for o in offsets])\n", "\n", " if len(timestamps) < 4:\n", " return []\n", "\n", " times = sorted(timestamps)\n", " gaps = [max(times[i+1] - times[i], 1e-6) for i in range(len(times)-1)]\n", " mean_gap = float(np.mean(gaps))\n", " if mean_gap <= 0:\n", " return []\n", "\n", " rates = [1.0 / mean_gap, s / mean_gap]\n", " n = len(gaps)\n", " cost = [[0.0, 0.0] for _ in range(n)]\n", " back = [[0, 0] for _ in range(n)]\n", " for k in (0, 1):\n", " cost[0][k] = -np.log(rates[k] * np.exp(-rates[k] * gaps[0]))\n", " for i in range(1, n):\n", " for k in (0, 1):\n", " best, best_prev = None, 0\n", " for j in (0, 1):\n", " trans = (k - j) * gamma * np.log(n) if k > j else 0.0\n", " c = cost[i-1][j] + trans - np.log(rates[k] * np.exp(-rates[k]*gaps[i]))\n", " if best is None or c < best:\n", " best, best_prev = c, j\n", " cost[i][k] = best\n", " back[i][k] = best_prev\n", "\n", " path = [0] * n\n", " path[-1] = 0 if cost[-1][0] <= cost[-1][1] else 1\n", " for i in range(n-2, -1, -1):\n", " path[i] = back[i+1][path[i+1]]\n", "\n", " bursts = []\n", " in_burst, start_idx = False, 0\n", " for i, st in enumerate(path):\n", " if st == 1 and not in_burst:\n", " in_burst, start_idx = True, i\n", " elif st == 0 and in_burst:\n", " intensity = sum(1/gaps[j] for j in range(start_idx, i)) / max(i-start_idx, 1)\n", " bursts.append((int(times[start_idx]), int(times[i]), float(intensity)))\n", " in_burst = False\n", " if in_burst:\n", " intensity = sum(1/gaps[j] for j in range(start_idx, n)) / max(n-start_idx, 1)\n", " bursts.append((int(times[start_idx]), int(times[-1]), float(intensity)))\n", "\n", " return bursts\n", "\n", "\n", "print(\"✓ Annual Kleinberg burst detector defined\")\n" ] }, { "cell_type": "markdown", "id": "38cac24e", "metadata": {}, "source": [ "### 8.2 Term-level burst heatmap\n", "\n", "Pick the N most distinctive terms in the corpus (by TF-IDF over `text_content`, weighted by year coverage), compute per-year occurrence counts for each, and run the burst detector. The heatmap encodes burst intensity; cells outside any burst window are zero.\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "e956506a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Window: 1977–2025 (49 years)\n", " Top 20 distinctive terms identified\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_3873435/1550925816.py:52: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "colorbar": { "title": { "text": "burst
intensity" } }, "colorscale": [ [ 0, "rgb(255,245,235)" ], [ 0.125, "rgb(254,230,206)" ], [ 0.25, "rgb(253,208,162)" ], [ 0.375, "rgb(253,174,107)" ], [ 0.5, "rgb(253,141,60)" ], [ 0.625, "rgb(241,105,19)" ], [ 0.75, "rgb(217,72,1)" ], [ 0.875, "rgb(166,54,3)" ], [ 1, "rgb(127,39,4)" ] ], "hovertemplate": "Year %{x}
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fo7RtdEq+Ts3XI64AUUfhPla7g+TJlU0WTh0Y31eDzxFAAAEEEEAAAQQQQMDhAgSIbjqAuwDR2rxA/1j7cER315QvHYmif1zr2mO6Bpn1B7DeVkezNa5TzYwKOnf+ovkD0PpjUkcqadiVKmUKV3in1+hIEP0DUN+3NnbQ3XN1owlvjtihne7C+Xzz3iaM0j+AddMHPX4/dERqNX7ThALWH48vtR9g/ljWAO3OnNnMefoHqXroaDhdX86X+/kaIGoA0q97c1dYqqMxdQqnBlIaoukuvrcTIOof/93bvSiPPny/pEqZ0ti2e3OUqeuSmUNco4X0j+25i1abKaq6U6+nacEbt+6Wlt2GS62nysqwXm1czaIjCXWUlrVunrv2iitAVOMBPVq4ps6On/65jJv+uWudvrjaP74pzBqu6YjHli/XMiO9Tpw6awIvbd+fVk01AYS3fTyucjTqMNiMHtVDw0sdqabrhha9L/8tIx1vN0DUMmvAo8eFi5dMcKuHFYZb5dPdrh+p2da81D7wdJVH5f5C+Ux59HucIkXyW6pyuwGiTl99pGY747lj1RQTHlkBoj4k+tqI1kM17LMCIU+msacwu+s/nqYO+/JdtQIxd98VDZo0RNR/ILEO7ev12vSNsfFRXFOYfQ0Q3f3+vDNXNq9/l3nytPrc0J6t3X5PNVi/I1tmSRvxX9isAb32i+jT4y0v/R3/ZoeGkitHFvO7UYNs/Ueg6FPgrd/58X3/fAkQtR8cPnJM7s0f9ftcDw0wazV6Qy5fuebaMCmuAFGveWPwB2aX9m+/GGN+53EggAACCCCAAAIIIIAAAp4ECBDdyLgLEKfPWy7Dx8+Rd3q3NTuuWsf5i5ek3P+9Ykb2zRrT0xUgRt8sIvojPG1oUbluZzNiMfa6i48/28EEL9ZusBpe7bi5YcR/QURm19Ti2KGdTrWu37afGdXy1muNYtRWRyVqSLd95WRJmTKF2alWw9COLV4w06n1sBbb7/VaIzOtzpf7+RogutuFuW2Pd81oSWtk2e0EiLPH9zbTkKMfVtBljaBy9wXxFMppQNC88zAz/Xv42+1umQYZ16+buALEtzs3lhdrV3ZdbgXI6q7+cR3xBYjudmG2NpJY++l7kj1rJvG2j8dVDh2BNfC9mWZ6ZOyjU6s60vCFqq41AhMSIGrYqoeGNhrOWyN9e3ZsIA2ef8r1aO3f/UfOcIWM0b8z3dvVNwGYBtPWcbsBol5vhX3WiFErQNRRYrnviArkox9ZMqd37YzryTQhAaIv31UrEHP3XbHKpgGVjvQ9duK08R743qwY/wDhzwDR3e9PX+rjyXPG/BXyzrjZ8tHYXq4Q2t25Opr2jz//Fl2qQEcX63IFzerXkG5tXzSnW16fTxsoBfPncd1i9uerjItOydZR6np4+p0f+/vnS4BoPVCnfP/6x2H55+gpOXnmrMyav9L8Y5E1PTu+ANH6vaH/iKT/mMSBAAIIIIAAAggggAACCHgSIEB0I+MuQLQ2afAEqSHB6vmjXAGiuzAsrj8mn2v+lvx15Lhr5Ij1nJoNe5iAxAoWdQq1TjONfmg4pn/46xE7tLMCt7i+ArqIfu6c2VyjDbUuX88daYIVHWmnoeV3X4w1oy59uZ8/AkSrvlawcTsBYuw/8tXi0y+/kbeHR63LpiNDH36wsFQqX0IqlSvu2rzGUyh37d/rUrlOJ1dopWHHQw/cKy/UrBBjlJY7c18CRCtQ0HXNdP3MuI7bCRCtPm21v7d93JtfpzrCcdfPv8u+A4fMDszfbNphLtNRgBrC6xFfgGjVKfYuzO42UdFRiBo2a4BpLScQvZw65VOnxO/55aCZfmtNj+/branUrVXRdWpCAkQN+zVYswL5QExh1oJ7Cu58+a56CsT0/hoc9nt3htkoJvYRfQSzPwNEd78/famPpz763uQFZrq/1edjn6cBaY9BH7j6R/TPrVHm+p4nL6uMOjJZRyjH9Ts/9vfPlwBRdxif9NFis0u8u8P6fR1fgGj9o9G0UW+YdWE5EEAAAQQQQAABBBBAAAFPAgSIbmTcBYi6OYRuEtGh+fNmMfvYh0790zX54ttR2NNoFE8B4jNNeooumG8FiPpH5vETp2M8PiJNKtfU5NihnW4ioZtJ6LTaUg8WdtsPtNzW+nC6EcncL1bLjPfflNx3ZJWn6neLMVXXl/v5M0CcM+FtszmJvwJEhdBgVMMEa91JfU/Xnvxo3FtmE4K4Qrmz5y/KBx8tlqWrNpnQyjpG9XvVbOTi6fAlQLTWa0usAHHAqJky54vVrjDF2z5+O79OrTUq9dqtyz8woxCt3XCjbzwS/d464ldHRUafDhrXBkefLFwlg96fFWOqqaeyWsGJbnqhSxJYx+0GiBocaoCoG/vo+o96JHWA6Mt3Na4A0RoFqWulal+8O09OyZI5g5kuq78LrSUQEjtA9KU+ntrdqmf0tTKtc0+fOS/la79qXmpY+MSjD0me3Nnl7LkLUrd1X9cyFfq5Jy8NWbv0HW+WNogvQIz9/fMlQLR+t+pIXN0sp+A9eUxb6OhK/e8UAeLt/JbiGgQQQAABBBBAAAEEEIhLgADRjY67kMJaj27Ku6+btfQ8HYkdIMbXnWOHdpu27ZEWXd4xO8NqUBPfYU0T1M01dOMOHbETfXSKL/fzR4BoTfPTAFXXkbNGrelGJVpG64hrExV3IxCjO+iIQl0PUUMBswv28G5merIVIEYPsNz56TTHJV9vNFPco48GdXduYgeI7srqKbSOHWB428c99SGdThzXbuFNOw0V3UXbWotTp1pqQK7htq5JF/vQnY11R2gd2aujYvWIK0C0AjvdQELXEY2rPLpcQPGnojZW2bJsouvRtxMg6vqHOvX/8+XfyitNn5X2TZ819wt0gGjV26qML99VT4GY/uOFbrShwfrcSX1iNJEGpu4CxNjl0It0DURdHzL2Eg2eNlFxNwLRl/p46qPWNH3rHySin6c7KXd4a7QJ5HS6vXVYG5Z4MwIxIQGi/kOELmXh7vsQexMV/d5En6psldXaBMbbAHHSrMUyesqn8tmUATF2mY7vvxN8jgACCCCAAAIIIIAAAs4TIEB00+buQgprtJRu3jDtvTfMCDXrMBsn7D4gOpop2AJEKwDQoEQ3DLGCGC37jRuRsnbDdqn8WMkYCtYfpxrY6XXWBiZ6ki/3S2iAaO1sq1OMrTUgNYDSIErXCtQ1A/XQAGf256vN6DN3uzC7CxB1JONTFUrFaMePP/taBo/+SKx19KwRbSP7tjdr5VmHbiCh02itDWn0fQ0hdS1M7Qu71073+JsksQJET2XVgngbIHrbxz2GM3OXm52Qu7Sua6bERz90TTndJVt9fvx6inHX9dsef7ajeU/bV9vZOn7+9ZC80PJt01+/mvOu2eRFD08Boo4ea9Z5qFnrUHcR192QO/YeLYXvyWvWrou9A/O6jTuk/ZujzCizCUM7u57ra4CoIw91FKtutKEh27T3erieFagA0bKKvVamL99VTwGiFczr7z1dN9A6Dv71j+jyCtGnMHsqh15jhcfRw2AdYdvm9RGmzWLvwuwuQPSlPp76qI7O05G2sb/Tev68xWul37vTY4TA+r71jxaJHSBaobZ+D5bMHOpam3Pn3t+kfrv+ZuMfa9Md/R7o92bTkvGuzYl0VHSb1981/xjibYCowbeOxl332ftuR9Y77/8losYIIIAAAggggAACCCDgSYAA0Y2Mp5CiQ6/3zeYQOm1Mp/KljUgjP/96UJav+V5KFCtoNkQItgBRq/fxZ1/J4NEfm2BDwxTdYVl3YV638Ufzx3vswMvaCECv1U0D9Jroh7f38zVA1PLVr13ZhBLHTp42az1qQBN9oxNdj+3Jel3NH8+N6lSVjBnSyjebfjJ/NOvhbYCobawB6bM1HpN77solh/8+bp6nO5gu+3iY+WN628790qjDIBNiNXuxhuhutA8Uulv+PHLMBA06UujhBwtJ6pQpZd2mHSZoiG+kZ2IFiJ7KqqG2twGi+nnTxz39Mpk+d7kMnzDHfKzBnFqlTp1SfvjpF9caiP27N5cXnv5v5Ki1FqW2feO6Vc2O2xpOaSinR/TNKPS1FZxYG6VcunxFTp4+a0Yq6hF9jcV2b4wyz9V7Vyj7kAlgdNfmjT/scfUXa+SVhlPLVn9v2lB3kda+peW3dtS2wkCdolww/51y5uwFs8GGNf1dy62jP7NmzuDisa7RUan58+a8hS1jhnTS45WX4vyvkzebqOh3ocLzncx3Qndxz5AuQsLDw813ydvvqqcAUUMtHRWn30Ozi3Xhu2X/b3+Z0ZZ6RA8Q4yrHhJlfmPVZ9fxaT5WTPw8fFZ2SbB3eBIi387ssNq4G2U++2DVGP7HOsTYt0v7ybPXykjNHVtny416ziZMeiR0g6jOsoLVy+RJSvGhB2bHnV1m1fpt5fvQAUadJ62hHDXZ17VYzCvqrDa51Wb0JELVtyz3zqmTPmtH1DzRxdkY+RAABBBBAAAEEEEAAAUcLECC6aX5PAaKOmJo2d5lMnb3M/LFuHTpipG3jZ+SZquXlwsXL8kjNtmbEmo5yiX14CnPqtOpjgpPo0yn1Wl0bUf84jD31z1Ov1UX1dept9ClpOkJPQ04Nd6Kv16d/KL9Yu5JrZ1HrnhqOlHvmFfPS2qE3+vO8vZ+7srgrt2WmgZ4GeNFto68lZl371TdbpdPbY1230vCmwfNPio4ea/ny09K5dV3zmbVhwhfTBsm9+e+M8eh3J84z6//FbsfenRqbkaTWoaGinvfXkWPmLd10Q/+QHzrmEzPdOfqhm3G82bGBpEqZwuMvFStAfOqJUvJe/6j11qyRf326NDG7ZVuHtQaivqefxXe4K6uWyVOf0xGbOnLx63kjJVeOLOb23vRxT+U4dPioLFiy1oxosnZFts7V0F2nhcYe7aqjYPX8oWM/idEW2hfe6NBAalYu49rURu9lBYjRy6Dn5suTU158ppLUqFLGNY1ag8vPln7jCruiX6Mb33RqVVfuu/cu87YVHkU/J/rIV2t0mvW5fnc0eNGRlhqsVa1QWtJGpI5BE/ua2G7WxktxtaunADF6/9HrdUSlhq5Wn7SCVG+/q3F9VzScfq336BhtqlO1p81dLnlyZXOtgRhXOfQfVnQ5Au3r1qHfWd3pWN+zRtLF9/vT2/rEZWqNsI4+es86X7/rOrXfOrSN6tSqaDYraVqvunRvX9985MnLmsI8vHc7syauHr58/3TEZ/s333P9vtF+1qVNXRk5ab75hw5rGrn+btCwX0f8Woe2uRVqb1g0zvzjirvfN9b51ijc2FO24/s9w+cIIIAAAggggAACCCDgTAECxNtod/0jVv9Q06BN/8BMny7iNu6SNJfoCL6jx09J5ozpzWipsLCwBBXE3/fTUX665liysDDJlzenxzX1Ll66IjrFWUdb5boj623VQdtRdwvW/9MQKmvmjK5pg9FvqOdpgJgubRrjZh36x/nfR0+YlzpaSTcGSerDU1l9LVdC+7juHH7s5Bm5cf2GCdl0o5+4Dg0Sj544JSdOnjWjP7NnzeS2LXyth56vdTl15rwcO3HatFGuHFklRYrkt3OroL9GgyWtrzu/hHxX9Xup/8ChR97cOeLt6+7KoeX6++hJs6vz3XlzSupUCfu+3G591m/+Sdr2GHnLWodW4+pU4L/+d1TSpE5lgmndjT6Qh67dqWG8PjXvnTk8/g7U74z+DtR/BMl9RzYTGHp76DPqt+1v2nTF7OHm9x8HAggggAACCCCAAAIIIBCXAAEi/QMBBBBAwFEC1hRg3UFap1U77bDWTB3Yo4U8V+Nxp1Wf+iKAAAIIIIAAAggggMBtCBAg3gYalyCAAAIIhK6AtTyAjiCfPT5qsx6nHNaSCSWLFZIZ778Z8BGWTnGmnggggAACCCCAAAII2E2AANFuLUp9EEAAAQTiFdDNcnQNxoL580ixIvfEe75dTtA1ZHWJgUdKFDGbFnEggAACCCCAAAIwAu03AAAgAElEQVQIIIAAAt4IECB6o8Q5CCCAAAIIIIAAAggggAACCCCAAAIIOFSAANGhDU+1EUAAAQQQQAABBBBAAAEEEEAAAQQQ8EaAANEbJc5BAAEEEEAAAQQQQAABBBBAAAEEEEDAoQIEiA5teKqNAAIIIIAAAggggAACCCCAAAIIIICANwIEiN4ocQ4CCCCAAAIIIIAAAggggAACCCCAAAIOFSBAdGjDU20EEEAAAQQQQAABBBBAAAEEEEAAAQS8ESBA9EaJcxBAAAEEEEAAAQQQQAABBBBAAAEEEHCoAAGiQxueaiOAAAIIIIAAAggggAACCCCAAAIIIOCNAAGiN0qcgwACCCCAAAIIIIAAAggggAACCCCAgEMFCBAd2vBUGwEEEEAAAQQQQAABBBBAAAEEEEAAAW8ECBC9UeIcBBBAAAEEEEAAAQQQQAABBBBAAAEEHCpAgOjQhqfaCCCAAAIIIIAAAggggAACCCCAAAIIeCNAgOiNEucggAACCCCAAAIIIIAAAggggAACCCDgUAECRIc2PNVGAAEEEEAAAQQQQAABBBBAAAEEEEDAGwECRG+UOAcBBBBAAAEEEEAAAQQQQAABBBBAAAGHChAgOrThqTYCCCCAAAIIIIAAAggggAACCCCAAALeCBAgeqPEOQgggAACCCCAAAIIIIAAAggggAACCDhUgADRoQ1PtRFAAAEEEEAAAQQQQAABBBBAAAEEEPBGgADRGyXOQQABBBBAAAEEEEAAAQQQQAABBBBAwKECBIgObXiqjQACCCCAAAIIIIAAAggggAACCCCAgDcCBIjeKHEOAggggAACCCCAAAIIIIAAAggggAACDhUgQHRow1NtBBBAAAEEEEAAAQQQQAABBBBAAAEEvBEgQPRGiXMQQAABBBBAAAEEEEAAAQQQQAABBBBwqAABokMbnmojgAACCCCAAAIIIIAAAggggAACCCDgjQABojdKnIMAAggggAACCCCAAAIIIIAAAggggIBDBQgQHdrwVBsBBBBAAAEEEEAAAQQQQAABBBBAAAFvBAgQvVHiHAQQQAABBBBAAAEEEEAAAQQQQAABBBwqQIDo0Ian2ggggAACCCCAAAIIIIAAAggggAACCHgjQIDojRLnIIAAAggggAACCCCAAAIIIIAAAggg4FABAkSHNjzVRgABBBBAAAEEEEAAAQQQQAABBBBAwBsBAkRvlDgHAQQQQAABBBBAAAEEEEAAAQQQQAABhwoQIDq04ak2AggggAACCCCAAAIIIIAAAggggAAC3ggQIHqjxDkIIIAAAggggAACCCCAAAIIIIAAAgg4VIAA0aENT7URQAABBBBAAAEEEEAAAQQQQAABBBDwRoAA0RslzkEAAQQQQAABBBBAAAEEEEAAAQQQQMChAgSIDm14qo0AAggggAACCCCAAAIIIIAAAggggIA3AgSI3ihxDgIIIIAAAggggAACCCCAAAIIIIAAAg4VIEB0aMNTbQQQQAABBBBAAAEEEEAAAQQQQAABBLwRIED0RolzEEAAAQQQQAABBBBAAAEEEEAAAQQQcKgAAaJDG55qI4AAAggggAACCCCAAAIIIIAAAggg4I0AAaI3SpyDAAIIIIAAAggggAACCCCAAAIIIICAQwUIEB3a8FQbAQQQQAABBBBAAAEEEEAAAQQQQAABbwQIEL1R4hwEEEAAAQQQQAABBBBAAAEEEEAAAQQcKkCA6NCGp9oIIIAAAggggAACCCCAAAIIIIAAAgh4I0CA6I0S5yCAAAIIIIAAAggggAACCCCAAAIIIOBQAQJEhzY81UYAAQQQQAABBBBAAAEEEEAAAQQQQMAbAQJEb5SceM7F406sNXVGAAEEEEAAAQQQQAABhwtEHtvrcAGq73SBsOxF/E8Qkc3/9+SOARUgQAwodwg9jAAxhBqLoiKAAAIIIIAAAggggIC/BAgQ/SXJfUJVgAAxVFsucctNgJi4vqF7dwLE0G07So4AAggggAACCCCAAAK3LUCAeNt0XGgTAQJEmzSkn6tBgOhnUNvcjgDRNk1JRRBAAAEEEEAAAQQQQMB7AQJE7604054CBIj2bNeE1ooAMaGCdr2eANGuLUu9EEAAAQQQQAABBBBAIA4BAkS6h9MFCBCd3gPc158AkX5hBM6cuyAbtuyS6pUekbCwMBECRHoGAggggAACCCCAAAIIOFCAANGBjU6VYwgQINIh3AkQINIvjMDufX9IvTZ9ZceqKZI8PJwAkX6BAAIIIIAAAggggAACjhQgQHRks1PpaAIEiHQHAkT6gEcBAkQ6BwIIIIAAAggggAACCCAgQoBIL3C6AAGi03uA+/ozAjFE+8WRf07I8AlzZcuPeyVFiuTy5OMPS8+ODc1U5HfGzZaV67ZK+nRppE6titK6YS0zqnDrjn3Sf+QMOXL0pKl1pXLFpVenRpIxfVpGIIZoP6DYCCCAAAIIIIAAAggg4F8BAkT/enK30BMgQAy9NgtEiQkQA6Hs52dcu/av1G7WS3JkyywtXqopN25EyuSPl8hHY3vJ6wMmys+/HpIuberJydNnZciYT6RTqzrS4PknZde+32X/b39JkYL55NLlK9Jn+DSpWK64OZcRiH5uJG6HAAIIIIAAAggggAACISlAgBiSzUah/ShAgOhHTBvdigAxBBtz49bd0rLbcFn60TDJl+cOVw0uXrospWu0leG920nNKmXM+0PHfiKbt+2RhVMHmtfHTpyWbTv3y9Hjp8woxQzpI2Tc4E4EiCHYDygyAggggAACCCCAAAII+F+AANH/ptwxtAQIEEOrvQJVWgLEQEn78TmffvmNCQa3LJsY466/HzoitRq/GSNYXPLVRuk3coY5d9nqzdKt/wQpWayQFCl4l/zy21+SOlUKmTisKwGiH9uHWyGAAAIIIIAAAggggEDoChAghm7bUXL/CBAg+sfRbnchQAzBFl274Ud5ped7su6z9yVbloyuGpw5e0HKPfOKGVGoU5P1GDt1oSxdvcmEis806SnVK5eR9k1qm8+mzlkq32/fS4AYgn2AIiOAAAIIIIAAAggggEDiCBAgJo4rdw0dAQLE0GmrQJaUADGQ2n561qkz56Rq/e5S66my0q5xbUmePFxmzl9h1jps+OogSZc2tfTp0lT0vM59xknVCqWla9t65rOC9+SRLq3ryl9HjknfEdMlc6Z0BIh+ahdugwACCCCAAAIIIIAAAqEvQIAY+m1IDRImYMcA8a1hU+TuvDml5ctPJwzHwVcTIIZo4+s6iL2GfSj/HDtlaqDTkmeN6Sk6jfm13mPkwMH/mfd1JOLQnq0lfboI+W7LLnlj0CQ5efqcRKRJLYUL5DXvTxjaWfb88ofUbd1XdqyaYnZslovHQ1SGYiOAAAIIIIAAAggggAACty9AgHj7dlxpDwE7Boh1WvWRYkXukT5dmgS0ka5cvSYlq7aSwW+2ktrVygf02f5+GAGiv0UDfL8Tp85K6lQpJW1E6hhPPnr8tKRKlUIypk8b4/1/r1+XI/+ckJw5skqK5OGeS0uAGOCW5HEIIIAAAggggAACCCAQDAIEiMHQCpQhKQUIEP2nf/nKVXm4WmsZ2KOFPFfjcf/dOAnuRICYBOgh8UgCxJBoJgqJAAIIIIAAAggggAAC/hUgQPSvJ3cLPQG7Boi5cmSRnDmyyPI138vlK9fkxdqV5LWWdczgKl0CrkOv0Wb5txJFC5pG0wFYTV8bKi1erimVypWQHXsOyPDxc6Rf92aydNUm87py+ZJS5+knZOKsRea+x06cEX2Ozgbt0qae2b9C97HIkyu7ZM+aydx38ojukiZ1ypDrGASIIddkASowAWKAoHkMAggggAACCCCAAAIIBJMAAWIwtQZlSQoBuwaIe/cflLKlHpDHSheTr9f/INt37TchX4uXaprl4SrX7Szjh3SWCmUfcgWID1VpIf26NZM6tSrI+s0/SdseI81nBfLlliKF8slD998rJ0+dlQkzv5Du7epLntzZZd+vh2T6vBWyZdlEmb9krdl/4ukqj0qJYlHBZJ1aFeOeEZoUje7FMwkQvUBy5CkEiI5sdiqNAAIIIIAAAggggIDTBQgQnd4DqL9dA0TdRGXE2+1cDawbzR47cVpWzB7uU4A4pGcreabqf+sZtu3xrhw6fFSWzBwqyZKFmftfunzVjDJkCjPfJ/sLECDav42pIQIIIIAAAggggAACCNwiQIBIp3C6gF0DxNibqIycNE+mzF5qNpM9cfKs1yMQv5430kxTto55i9dKv3enm2nKlR8rKaUfKiwVyhaX8PBkBIhO/zI5ov4EiI5oZiqJAAIIIIAAAggggAACMQUIEOkRThdwcoA4dvBrZr1DPXQNRHdTmGMHiHrutp2/yIIl62TTtj1mNGPRwvllzsS3RXdhZhMVp3+j7F5/AkS7tzD1QwABBBBAAAEEEAhxgciz/wvxGlB8BBAIRoGwDLn9X6yIbP6/pw93rNOqj8Qegfhc87fk+vUbsmjGYDl/4ZKUebpdjN2Sr137V4o/1fKWNRBjB4h6Dx1tqEdkZKTMW7RG+o+aKfM/6CuFCuQ1IeTbnRvLi7Ur+1Di4DuVNRCDr02Co0QEiMHRDpQCAQQQQAABBBBAAAEPAgSIdA0EEEgMAbsGiClSJJc3Xn3ZbGDy+fJv5ePPvnaFg+rYsttwOX3mvPR6raGcPHVOPpz9pfy050C8AWLzzsPkibIPSfnSRSVlihQybc4ys3nKmgXvSY5smUTXSDx/4bK575lzF6TUQ4UleXh4YjRdot6TADFReUP45gSIIdx4FB0BBBBAAAEEEEDACQIEiE5oZeqIQOAF7Bggvtimn/zvn+Ny8vQ5F2jLl5+Wji1ecI0e3PTDHhk8+iM5cDBqdHfz+jVl6pyl0r97c3nh6SdcuzCvmj9Scmb/bw3EdyfOM+dZR8lihaRZ/RpSuXzUVOiNW3fLkDEfu+6ruzNHpEkd+IZN4BMJEBMIaNvLCRBt27RUDAEEEEAAAQQQQMAeAgSI9mhHaoFAsAnYMUC0jC9cvCx/HztpNkHxFOLpjsp3ZM8sqVKm8LppdL3EYyfOSNqI1JIhXYTb644ePy3p00WY3ZlD8SBADMVWC0SZCRADocwzEEAAAQQQQAABBBC4bQECxNum40IEEIhDwM4BIg1/+wIEiLdvF9ArV6zdYubJZ82cIVGeq/PwN2zZJdUrPSJhYWEiBIiJ4sxNEUAAAQQQQAABBBDwlwABor8kuQ8CCEQXIECkP7gTIEAMkX7xQMWmMnN0T3n4wUKJUuLd+/6Qem36yo5VU6IW8yRATBRnbooAAggggAACCCCAgL8ECBD9Jcl9EECAAJE+EJ8AAWJ8QkHyOQFikDQExUAAAQQQQAABBBBAIEgECBCDpCEoBgI2E2AEos0a1E/VIUD0A6Tu1DPqg/ny26Ejkj1rRnmuxuPSqkEtiYyMlHmL1siM+Svk3PmL8nzNJ+Sl56qY3Xp0a/B2b46SX38/bErwQOG75c0ODaRwgbxuSxQ9QPR03zSpUknr7iOkd5fGUrRwfnOfYydOy6s935fhb7eVvLlzeCwPIxD90BG4BQIIIIAAAggggAACARQgQAwgNo9CwEECBIgOamwfqkqA6AOWu1MvX7kqD1drLW0a/Z88XeVR+ePPf2TTtt3S67VG8uWqTdJ3xHTp162Z5L8rp0yY+YVkTJ9OBrzeXHTNwYXL1kvJogUlZcoUMnX2UhNALpjcL94AMa77aoCYJ3cOebtzY3OfmfNXyIIl62TRjMFxlocAMYEdgcsRQAABBBBAAAEEEAiwAAFigMF5HAIOESBAdEhD+1hNAkQfwWKffv7CJSnzdDvp2OIFaVTnqRjbgDd8dZDky3OHNHzhKXPZ3v0HZciYT2TjknFmncFLl6/KT3sPyB+HjsjOn383geLutdPjDRDjuu+a77ZLp7fHytblH5itwZ9p0tOMenzp2SoS13X7fv2TNRAT2Be4HAEEEEAAAQQQQACBQAoQIAZSm2ch4BwBOwaIujFthnQRUrbUA0HdkBcvXZEVa7+Xovfll4L58wRVWQkQ/dAcnyxcJYPen2XuVKJoQenUqo7ZMfnxZzuYQDF71kwxnvJe/1fNFOZmnYdK+nQRUrr4fXLl6jVZvHKDVwFiXPfVL0T52h2kb7emJrx8sU0/2bB4nGRMnzbO8vxz7BQBoh/6ArdAAAEEEEAAAQQQQCBQAgSIgZLmOQg4S8COAWLlup2lSMF8Mm5wJ68aUzOaklVbyeA3W0ntauW9usYfJx3554Q8+WJXef2Vl6RJ3Wqyeftead55mCz7+B25684c/njEbd+DAPG26WJeqFOZ9x34U2bMWyFbftwraz99X15s2890tEZ1qt7ylGHjZpsRiVPefV3Cw5PJjj0H5OX2A7wKEOu06uPxvvqgkZPmya6ff5d7898pFy5elkFvtDTPj+s6pjD7qSNwGwQQQAABBBBAAAEEAiRAgBggaB6DgMME7Bggnj1/UcKTJZO0Eam9ak1rubqBPVqYfS4Cddy4ESlnz12QNGlSSaqUKUT33GjR9R1Z9vEwuevOOwJVDLfPIUBMIL+mw1+s+E5erF3JrG8454vVZkOVDYvGyrS5y2TWgpUyfkhnub/Q3XL47+OyYMla6dKmnoybtlDWbPhRJgztLP/+e13GTf/c6ynMH3y02ON9tTq/HzoitRq/aWo2a0wvKVmsoPnfcV1HgJjAjsDlCCCAAAIIIIAAAggEWIAAMcDgPA4BhwjYMUDsN3KG5L4jq9nwVpeTa9VtuNR6qqxs3bFP1m3cIffde5cZ/FW1QinTyq/0fE/WbvhR8uTK7ppVOnlEd7NU3PrNO2XSrEWyfdd+83nt6o+Z+6ZIHm4Ghw0fP0defu5Jmbd4jWjWUqlccWlSr7rZPFePq1evycRZi2T5mu/l2IkzkitHFqlYrrjJijS4bNl1uLRrUlvuvftOM3P14F//mNGTqVOlNO/dX/huWbpqk4wf0inGMno6mOzEqbOuQWT+7q4EiAkU1V2Om7w2xDSoHtqoHZo/LxXKPmQ6xajJC8xGJtah05Wnv/eGHDl6Ujr0et+MQtTj8TLFTCeMaw3EWWN6SsliheK8r/WcRh0Gy4lTZ+TLWUMlLCzM1Uk9lWfPL39I3dZ9ZceqKWZ9Rrl4PIEyXI4AAggggAACCCCAAAKJKUCAmJi63BsB5wrYMUB8qf0AE77pprbnzl+UR2u1Nw2sm+GWKFZQ1m380WQyG5eMN2slzl+y1myKa32u59apVVE2/bBb2vYYKf9XtZw8+fjD8tOeAzJl9lLp2raeNK9fU9Zv/sl8rkfjutUkb+4cMmPecsmUIZ3MndTHvD926kKzyW73dvUlT+7ssu/XQzJdZ7Mumyi6BmLpGm1kaM/WJlQcPeVT0WXz2jepLVkyZ5AsmdLLPflyy7PN3jIb9tapVcHc8+jx01KpTifp2bGBNHg+ah8Ofx8EiH4S1eGw169fl8wZ099yx3+vX5cTJ89KhvRpTVod/fjf38clU8b0EpEmlc8lieu+cd3Mq+sIEH1uDy5AAAEEEEAAAQQQQCCQAgSIgdTmWQg4R8ApAWKv1xrJy89VMQ178vQ5s2/EyL6vSLWKpc1IwIertZbYU5ifa/6WGZH4wfBurg7Rpe84+fX3w7JoxmBXgPjph/3NqEY9Vq3fJh17j5Y1C96THNkySdse78qhw0dlycyhkixZ1IAvHRWpeVH0AFFDSk9TmJt2Gipnzp6XhVMHmusnzVpswkZrD4zE6K0EiImhaod7EiDaoRWpAwIIIIAAAggggICNBQgQbdy4VA2BJBRwSoA4vHc7qVmljEv6gYpNpXv7+tK0XnW3AeK1a/9K8adamlGAd2TP4rpOZ6RevHTZzCi1RiB+PW+kmZqsx86ff5f6bfvJnIl9pNh9+WXe4rXS793pZvpz5cdKSumHCkuFssXN/hjeBohffbNVOr09Vj4a20uK3nePVK7TSapXekQ0FE2sgwAxsWRD/b4EiKHegpQfAQQQQAABBBBAwOYCBIg2b2Cqh0ASCRAgug8QdZPaR2q2lbq1KkqVx0vGap2wm0vTRU1hjh4g6tJ1uqmtFSDqhdt2/iILlqyTTdv2yD/HTknRwvllzsS3zUhEawpzXCMQr/173YSG5UoXNVOpNUz8Ytogs5luYh0EiIklG+r3JUAM9Rak/AgggAACCCCAAAI2FyBAtHkDUz0EkkiAALG66NJvD1VpIW93biwv1q7sagmd5ly6eBEZ2TdqDUXriIyMNPtPuBuBGDtAvH79hhltqIdeN2/RGuk/aqbM/6Cv3J03V4wAUTdqafjqIPl82kApmD9PjGda05YL5MstObJnlg9HdE/UHkOAmKi8IXxzAsQQbjyKjgACCCCAAAIIIOAEAQJEJ7QydUQg8AIEiNUNuq5VeP7CZen1WkM5c+6ClHqosMxbtFYGvT9LWrxU02ykcvXqv/Lj7v1mJ2ddF9GbALF552HyRNmHpHzpopIyRQqZNmeZ2bRF10hMlzZNjABRN+ctUbWVNHj+SbOJy/kLl6RksYKmfLqpb8UXOpn/PXbwa1KpXIlE7SwEiInKG8I3J0AM4caj6AgggAACCCCAAAJOECBAdEIrU0cEAi9g9wBRQ7gyT7cTd2sgvv7KS9KkbjWDvnHrbhky5mM5cPB/5rXukpwqZUr5+LOvZMzUhWbdQ+vQQLFLm3quAHHV/JGS8+Y6idYIRN2FWacqvztxnkyds9R1bclihaRZ/RpSuXwJM4W5VPXWZhdmDSj1mDF/hXz48RKz0cuD9xeQ2eN7u67VzVQOHf5HVs4ZIcnDwxO1sxAgJipvCN+cADGEG4+iI4AAAggggAACCCAQXAKRl04FV4EoDQIIeBQIS5PZ/zoR2fx/zwDd8ejx05I+XYTZJdk6dOrx8ZNnJDJSJGvmDK4pyd4WSadIHztxRtJGpJYM6SLivUzP1+dly5LRFRTq6wrPv+ba+CXemyTwBALEBALa9nICRNs2LRVDAAEEEEAAAQQQQCDQAgSIgRbneQjcvgAB4u3bBfLK8TO+kHHTFsqGReMkY4a0if5oAsREJw7RBxAghmjDUWwEEEAAAQQQQAABBIJPgAAx+NqEEiHgSYAAMfj7ho6AbPfGSCla+B55tflzASkwAWJAmEPwIQSIIdhoFBkBBBBAAAEEEEAAgeAUIEAMznahVAi4EyBApF+47ReRGltyIBBbgACRPoEAAggggAACCCCAAAJ+EiBA9BMkt0EgAAIEiAFADsFHMAIxBBstIEUmQAwIMw9BAAEEEEAAAQQQQMAJAgSITmhl6mgXAQJEu7Skf+tBgOhfT/vcjQDRPm1JTRBAAAEEEEAAAQQQSGIBAsQkbgAej4APAgSIPmA56FQCRAc1tk9VJUD0iYuTEUAAAQQQQAABBBBAwLMAASK9A4HQESBADJ22CmRJCRADqe3nZy1euUHWbdohGdOnlUUrN8h9995ldt8pU6KIedKsBStl2txl8s+xU5IlU3p56dkq0q5JbQkLC5P4rhUCRD+3FrdDAAEEEEAAAQQQQMC5AgSIzm17ah56AnYLEPvel0LCwkR0B5BA/uyz91roNX4cJSZADOHmnD53uQyfMEea1a8hjz1STJat3iy79/0hCyb3M7VauW6rJE8eLnlzZ5c/Dx+VDm+NlvFDOkuFsg9JfNcSIIZwx6DoCCCAAAIIIIAAAggEmQABYpA1CMVBIK6gKE1m//tEZPP/Pb28Y78iKUR0++AwCejPPj8TIHrZRJyW2AIaAn67Zad8OKK7edTvh45IrcZvyoZF4yRjhrTmvQN/HJY9vxyUYydPy7Q5y6Rlg1rSpG41EyDGeS0jEBO7+bg/AggggAACCCCAAAKOESBAdExTU1EbCNhtBGL/IimiskNrCKKVJSby696MQLTBt8EmVYgdAh49floq1ekkq+aPlJzZs8jQsZ+YacyVy5eQfHlzytJVm6TRC1XNiMX4rmUEok06CdVAAAEEEEAAAQQQQCAIBAgQg6ARKAICXgrYLUAcoCMQk+AgQEwCdB7pXiCuEDBF8uTyxHMdZeqoHq41Edv2eFfKlLifAJEOhQACCCCAAAIIIIAAAgEVIEAMKDcPQyBBAnYLEAdqgBht+rKORNS9ITxNa/bX52/tYQpzgjoiF/tPIK4AMSJNailbq70M7NFCqlYoLVt37JNu/SdI+ya1CRD91wTcCQEEEEAAAQQQQAABBLwQIED0AolTEAgSAbsFiIPuT5oRiL0IEIOkR1MMmT5vuWzYsks+GN7NaBw7cVoqvtBJVs8fJXdkzyxTZi+VkZPmmc8K5MstV65eMzsxN32xerzXMoWZDoYAAggggAACCCCAAAL+EiBA9Jck90Eg8QXsGCBG7b4cKWESFjXwMACve+5mBGLi91ae4DeBCxcvy9nzFyVXjiy+3ZNNVHzz4mwEEEAAAQQQQAABBBDwKECASOdAIHQE7BYgDnkghVj7pQTyZ09GIIZOp6ekCRAgQEwAHpcigAACCCCAAAIIIIBAdAECRPoDAqEjYLsA8f7kZs1Da23DQP18IxFGIJ47f1H+vX5dMmdMH/AOFRZp9rHmQCCWAAEiXQIBBBBAAAEEEEAAAQT8JECA6CdIboNAAATsFiAOfSBp1kD0FCBevXpNWnQdLpcuX5EFk/uZFj15+pw8/myHW1p3ysjX5dGS98vFS5elx8BJsvq77eacB+8vIGMGdpRsWTIGoEdEPYIAMWDUIfYgAsQQazCKiwACCCCAAAIIIIBA8AoQIAZv21AyBGIL2C1AHPZACteahzf/h2tEYtTr/9ZE9Ofnr++6dQ1EHcP31rAp8vnyb6VIwXyuAPHEqbPyxHMdZeKwrnLXnTlcTZIjW2ZJkzqlfPjJlzJ/8VqZNaaXed3ujVGS/65cMuD15gHrwASIAaMOsfNJGO4AACAASURBVAcRIIZYg1FcBBBAAAEEEEAAAQSCV4AAMXjbhpIhYPcA8Z2iSTMC0V2AOPnjJbJ01Sap9VQ5WbZ68y0B4pKZQ0wwGPuo06qPVKtYWlo1qGU+WrH2e+nSd7zsWjPNhKGBOAgQA6Ecis8gQAzFVqPMCCCAAAIIIIAAAggEpQABYlA2C4VCwK2A3UYgvlPUzRqIoqFb1NBDt2si+uHz7rFGIK5ct1UGjJoh8yf3k2827pB5i9feEiBWLl9CMmZIJ4XuySO1qz8mGdOnNW1UukZbGdijhQkR9djzyx9St3Vf2bB4nOucxO7OBIiJLRyq9ydADNWWo9wIIIAAAggggAACCCRI4MbBbxN0PRcjgEBoCyTL95j/KxCRzf/39PKOI4qlMFmhlQkG6me3aAHizp9/l+adh8nUUT2k2H35Zd6iNTECxPMXLsn7Hy4QnbKsG6UsXLberG84d2IfSZEiuRSt1EzGD+ksFco+ZGp94I/D8kzTXvL13Hcl1x1ZvZRI2GkEiAnzs+/VBIj2bVtqhgACCCCAAAIIIIBAHAIEiHQPBJwtYL8AMbmESbSRhhIZkNddd/63BuKAUTNl4w+7pWLZ4qZz7dl/UHbv+0Pq1qog7ZrUlvTpImJ0ut8PHZFajd+U2eN7mw1TdATioDdaStUKpaKuZwSis7+kQVV7AsSgag4KgwACCCCAAAIIIIBAoAQIEAMlzXMQCE4BuwWII4ulMAMQo46b4WEAXkcPENdv/kn27j/oeuqOPQfkpz0HpFGdqtLwhackIk3qGJ3hwsXL8kjNtmbEYpkSRUTXQKxe6RFp+fLT5jzWQAzO744zS0WA6Mx2p9YIIIAAAggggAACjhcgQHR8FwDA4QL2CxDdrIHoae1DP77f+adbd2G2ulbsKczrNu6Qy1euyKMPPyApkofLe5M/NdOYv573rlnjUDdfWbBkndmFOSJNKmnbYyS7MDv8exo81SdADJ62oCQIIIAAAggggAACCARQgAAxgNg8CoEgFLBbgDjqQd2F2VoE0QJP/Nedf/rXY+vGDhC/+mar9BzyoVy8dNlckyVTehneu508+vD95rWOSOzWf4J8s2mHeV20cH4ZM+g1yZEtU8B6EGsgBow6+B507d/rcv36dUmdKqWcOXdBNmzZZYbEmi3ACRCDr8EoEQIIIIAAAggggAACARAgQAwAMo9AIIgF7BYgvvfgfyMQXTupaO4R+d/OKpqD6G7M/vy80w7PIxDdNf+/16/LiZNnzUcaDJpsJtah2c21a/+aDVYCfRAgBlo8iJ43dupCWfXtD7Jw6kCzeGe9Nn1lx6opkjw8nAAxiNqJoiCAAAIIIIAAAgggEEgBAsRAavMsBIJPwG4B4vsPRd+FOVLEhIdWVph4r1+LYwpz8LV6/CUiQIzfyLZnHD1+Ws6dvyAF7r6TANG2rUzFEEAAAQQQQAABBBDwTYAA0TcvzkbAbgK2CxCjjUC0Rhqan7F3Y46MNKP+dCSiPz7v6OMIxGDvRwSIwd5CXpbvk4Wr5KNPV8qxE2ckX5475NVmz0nFcsXN1OR3xs2Wleu2Svp0aaROrYrSumEtM8rwy1Wb5IeffpG3OzcmQPTSmdMQQAABBBBAAAEEELC7AAGi3VuY+iEQt4DdAsQxOgLxvyGHZuihFR7Gft967Y/PO+7wvAZiKPZBAsRQbLVYZd6+a780fHWQjOzbXu7Jl1u27/pV/v33urz8XBV5fcBE+fnXQ9KlTT05efqsDBnziXRqVUcaPP+kzJy/QtZu+NFsC84UZht0BKqAAAIIIIAAAggggIAfBAgQ/YDILRAIYQH7BYhJswvzqz/6tgZisHcZAsRgbyEvyrdx625p2W24TBzWVcqWuj9qDUMRs3tP6Rptzc49NauUMe8NHfuJbN62x6x7SIDoBS6nIIAAAggggAACCCDgMAECRIc1ONVFIJaA3QLEccVTmBGH1gYpYTdHICb261d/ZAQiX64gE9AdeIaM/UTmfrHalKxaxUekS5u6ZmeeWo3flKUfDTPTmvVY8tVG6TdyhmxZNpEAMcjakeIggAACCCCAAAIIIBAMAgSIwdAKlAGBpBOwX4CYNCMQ229nBGLS9WKeHKfAmbMX5Ke9B2TkpHlS+N675M1XG0i5Z16RcYM7mfUQ9dCdl5eu3mRCRUYg0qEQQAABBBBAAAEEEEAgtgABIn0CAWcL2C1AHF9Cd2G2dlsO3M/2jEB09hcpGGu/fvNPcvb8RalcvqSEJwuTt4ZNkXTpIszmKLo2Yrq0qaVPl6Zy6sw56dxnnFStUFq6tq1HgBiMjUmZEEAAAQQQQAABBBBIYgECxCRuAB6PQBIL2C1AnFAiedTGKTd3V3ZtnGKFijenN/v783aMQEzinszjbxHY9MMe6fDWaLPmoR7lSxeVvl2bSu6c2eT3Q0fktd5j5MDB/5nPdCTi0J6tJX26CJm1YKWs+W672URlzy9/SN3WfWXHqilRayhePI40AggggAACCCCAAAIIOFCAANGBjU6VEYgmYLcAcaKOQDQhoXWEBeR12+2sgcgXKwgFNCk/ceqsRKRJLRFpUt1SwqPHT0uqVCkkY/q03pWeANE7J85CAAEEEEAAAQQQQMBmAgSINmtQqoOAjwL2CxCTZg3ENttYA9HHrsfpISlAgBiSzUahEUAAAQQQQAABBBBIqAABYkIFuR6B0BawW4D4Qcnk0fZgtvZe1p9hZndmazzirT8T9nmbbYxADO1vAqX3ToAA0TsnzkIAAQQQQAABBBBAwGYCBIg2a1Cqg4CPAnYMEMPCbq6BaIWGAXjd6gdGIPrY9Tg9JAUIEEOy2Sg0AggggAACCCCAAAIJFSBATKgg1yMQ2gJ2CxA/fDiF2UDFGmpohYmJ/boVIxBD+4tA6b0UIED0EorTEEAAAQQQQAABBBCwlwABor3ak9og4KuA7QLEkkmzBmILRiD62vU4PyQFCBBDstkoNAIIIIAAAggggAACCRUgQEyoINcjENoCdgsQpzycPEkapMUPrIGYJPA8NMACBIgBBudxCCCAAAIIIIAAAgjYV+DGxy3tWzlqhoDNBJI1+ND/NYrI5v97ennHqRogWmseunZKubkmYliYiJnebL0WidpxJeGfN9/KGoheNhGnhbQAAWJINx+FRwABBBBAAAEEEEAgmAQIEIOpNSgLAnEL2C1AnFYqaUYgNtvKCES+a04QIEB0QitTRwQQQAABBBBAAAEEAiJAgBgQZh6CgF8E7BggunZh9rT7crQRh7rhSljs3Zpv4/OmjED0S3/kJsEuQIAY7C1E+RBAAAEEEEAAAQQQCBkBAsSQaSoKioDYLUCcUTq5NUs5oD+bMgKRb5MjBAgQHdHMVBIBBBBAAAEEEEAAgUAIECAGQplnIOAfATsGiGZEoY4s1CUPdYnDALxuvIU1EP3TI7lLcAsQIAZ3+1A6BBBAAAEEEEAAAQRCSIAAMYQai6I6XsBuAeLM0kmzBmLjLayB6PgvkyMACBAd0cxUEgEEEEAAAQQQQACBQAgQIAZCmWcg4B8BuwWIszRAvLn7srUWovU69k9/ft7oewJE//RI7hLcAgSIwd0+lA4BBBBAAAEEEEAAgRASIEAMocaiqI4XsFuA+NEjSTMCsSEBouO/S4kO0GPQJNmwZZecPH1OCuTLLa80e06qVSxtnjt07Cdy1513yJlz52XDlt3y0rNVpEblR2TeojUyY/4KOXf+ojxf8wl56bkqkjN7Ftm6Y5/0HzlDjhw9aa6vVK649OrUSDKmTyuXr1yVdyfOleVrvpfLV67JQw8UkF4dG0r+u3KJECAmejvzAAQQQAABBBBAAAEEnCJAgOiUlqaedhCwW4D4sQaIrt2Xb7ZQAF432MwaiHb4PgR1HT7+7Cu5N38eyZopg6zd+KOM+mC+bFg0TjJmSCvt3hgl32zaIdUqPmICv2L33SNHjp6QviOmS79uzST/XTllwswvJGP6dDLg9eaya9/vsv+3v6RIwXxy6fIV6TN8mlQsV1y6tKknH37ypcyYt1zGDu4k4eHJZM132+XRkvdL6eL3ESAGdQ+hcAgggAACCCCAAAIIhJYAAWJotReldbaA3QLET8okt3ZOce3CfHMnFdf71uvISJM13vL+7Xz+MiMQnf1FCkTtr1+/IfsOHJKffz0kR4+fljFTP5O5k/pI0cL5TYBYuEBe6dSqjqsoDV8dJPny3CENX3jKvLd3/0EZMuYT2bhknCQPD5djJ07Ltp375ejxU7Jy3VbJkD5Cxg3uJGOnLpTFX22Q0QM7SqF78ojO9XcdjEAMRFPzDAQQQAABBBBAAAEEHCFAgOiIZqaSNhGwXYD4SPKo3ZdvhoOun2Y3ZjfvW+cl8POXNrMGok2+EsFZjQsXL0vbHiNNeFj5sRKSK0dWmfzxEpk9vrc8eH8BEyCWLFZQWjWo5arA4892kIg0qSV71kwxKvVe/1dly48/S7f+E6RksUJSpOBd8stvf0nqVClk4rCuZlpzryGTZfP2veb6l56tLG0b15aINKkYgRic3YNSIYAAAggggAACCCAQkgIEiCHZbBTaoQJ2CxDnlEnuacBhor5PgOjQL1Cgqr1q/Tbp2Hu0bFg8zqxTqMcDFZvGGSDWadVHalcrL43qVL2lmM806SnVK5eR9k1qm8+mzlkq32/fawJE6zjyzwn5/sefZeB7s+TNDi+bNRRZAzFQLc5zEEAAAQQQQAABBBCwvwABov3bmBraR8COAWLUCMRIM/Pyv5GIifv6xU2MQLTPtyIIa7Lphz3Sous78tmUAWYTlC9XbZJB78+KM0D84KPFMmvBShk/pLPcX+huOfz3cVmwZK1Z51CnNxe8J490aV1X/jpyzKyVmDlTOhMg6lqLujaijmzUkY/PNX9LurerLzUqlyFADMK+QZEQQAABBBBAAAEEEAhVAQLEUG05yu1EAbsFiPMeTZpdmOvdRoB49eo1OXXmvOTIlinmMnM3O6JunPvv9euSOWP6gHfNsEiNYDmCRuDGjUjp0necfPXNVlOmyuVLyOrvtsucCW9LsSL3mCnMDz9YSFq+/LSrzNrBRk1eIDPnr3C9pxuhTH/vDfluyy55Y9Aks6OzTlPW9RPTp4uQCUM7m9GI706cZ67Rz6pWKCX9ujcz6yYyAjFougQFQQABBBBAAAEEEEAg5AUIEEO+CamAgwTsFiDOfzQ8amcUTb8Sc9HDWPevu9H9CETNcFp0HW42ul0wuZ/pWRrNTZi5SMZNW2heZ8mU3mx4+9D9Bczri5cuS4+Bk0w+pIcOBBszsKNky5IxYD2TADFg1L496PjJM2ZnZF9SZU2hT5w8KxnSp5U0qVO6Hqjv6zTlnDmySork4TEKYl2TNUuGqODQOthExbcG42wEEEAAAQQQQAABBBDwKECASOdAIHQE7BYgLiibNCMQ67gJEDUofGvYFPl8+bdmRqgVIG7ftd/MIJ01pqcUu+8eGT3lM/ly1Ub5eu5ISZYsTD785EuZv3itzBrTy+Q9Orgs/125ZMDrzQPWsQgQA0YdYg8iQAyxBqO4CCCAAAIIIIAAAggErwABYvC2DSVDILaA3QLET8uG31z70DVE0PU6ak1Eay1E/37+/IZbRyDqJrlLV22SWk+Vk2WrN7sCRJ0duvfXg/LhiO6mOY4ePy2V6nQyn2vQqHtfVKtY2rWh7oq130uXvuNl15ppbqc6J0avJkBMDFU73JMA0Q6tSB0QQAABBBBAAAEEEAgKAQLEoGgGCoGAVwJ2CxAXlovahdkcVljoev3f7GZ/fx47QFy5bqsMGDVD5k/uJ99s3CHzFq91BYjd+k+QzBnTSa/XGrnaSDfU1b0uKpR9SErXaCsDe7QwIaIee375Q+q27htjA16vGjcBJxEgJgDP1pcSINq6eakcAggggAACCCCAAAKBFCBADKQ2z0IgYQL2CxDDJUzCJFIiA/rz2WgjEHf+/Ls07zxMpo7qIcXuyy/zFq2JESC27j5CChe4S7q2redqPA0N+3ZrKjUrl5GilZq5wkQ94cAfh+WZpr3k67nvSq47siaswb28mgDRSyjHnUaA6Lgmp8IIIIAAAggggAACCCSWAAFiYslyXwT8L2C3APGL8snNyEPXRio6EFHDRLcbq0R5+uPzZzdcdzXOgFEzZeMPu6Vi2eLmvT37D8rufX9I3VoVpF2T2tJv5AyzcUrPjg1d18QegTjojZZm81tzPSMQ/d/xueNtChAg3iYclyGAAAIIIIAAAggggEBsAQJE+gQCoSNgvwDx5ghE1/TlmyMRE/n1M9/9twbi+s0/yd79B12dYMeeA/LTngPSqE5VafjCUzJhxiLZd+CQfDC8mznH3RqI1Ss9Ii1fftp8zhqIofN9sn9JCRDt38bUEAEEEEAAAQQQQAABNwKRZ/70u0tYxrx+v6fe8MbBbxLlvtwUAScLJMv3hP+rH5HN//f08o6LdQSiWQUxLNpP6+LY71uvE/75/0ULEGMXNfYU5v92Ye4lxYrcI+9/uMBstmLtwqybryxYss7swhyRJpW07TGSXZi9bH9OS2wBAsTEFub+CCCAAAIIIIAAAggEpQABYlA2C4VCIGACdgsQl5QPvzl92ZrGHJiftb69dRdmqxFjB4i6E/TYaQtl4sxF5pSINKnlg+FdpUTRgub1hYuXRTda+WbTDvO6aOH8MmbQa5IjW6aA9QvWQAwYdYg9iAAxxBqM4iKAAAIIIIAAAggg4B8BAkT/OHIXBEJVwG4B4pePuRuB6Gnkof/ef/rb/9ZA9LYvXL5yVU6eOis5c2SVZMl0xGTM48y5C3Lt2r+SLUtGb2/pt/MIEP1GabMbESDarEGpDgIIIIAAAggggAAC3gkQIHrnxFkI2FXAbgHi0sfCzYYpOsovkD9rrPc8AjEU+w4BYii2WiDKTIAYCGWegQACCCCAAAIIIIBA0AkQIAZdk1AgBAIqYLcAcfnjyc2uytYRJmEBeV1jve8jEAPa0D4+jADRRzC7nq7DYDds2SW6q4/ZypwA0a5NTb0QQAABBBBAAAEEEIhTgACRDoKAswXsFyAmzQjEat8wAtHZ36QQqf1fR47JyEnz5J3ebSV5eHi8pd697w+p16av7Fg1Jep8AsR4zTgBAQQQQAABBBBAAAE7ChAg2rFVqRMC3gvYLUBc+UT4rZsw+2+z5ShYN0snVmUEovedjjOTTmDv/oNSp1Uf+fGrDyVFCl0wNO6DADE+IT5HAAEEEEAAAQQQQMAZAgSIzmhnaomAJwG7BYhfaYB4c9qyTl+20j6d1vzfNiVR05r9+flTjEDkS5YQgW07f5FRHyyQn389JHlyZZNGdarK8zWfkAMH/yeD3pslm7fvlQL5csurzZ+XqhVKmUfNWrBSps1dJv8cOyVZMqWXl56tIu2a1DZTjf/831EZOvYT+X77z5I6VQop+/ADMrBHC3n5lYGiIWKRgvkkPFky6flaQ7NTT/+RM+TI0ZPmvpXKFZdenRpJxvRphQAxIa3KtQgggAACCCCAAAII2EeAANE+bUlNELgdAbsFiF9XuDkC0cKwMsREfv3kN6yBeDv9j2tE5NDhf6RGgx4mMHy+5uPyx59/y4+7f5WeHRtKjQavywOF7pYm9arL99v3yrjpn8uCyf1MALhy3VZJnjxc8ubOLn8ePiod3hot44d0lgplH5LW3UdIeHi4dGpVR86euyALvlwnb3duIivXbZG3hk2RD0d0N9cWKpDXhI37f/vL3PPS5SvSZ/g0qViuuHRpU48AkR6KAAIIIIAAAggggAACRoAAkY6AgLMF7BYgrtIRiDdDQ2sX5v8GIoaJREYmyudV1hEgOvublIDaj526UOYuWi3fLBwdtVHJzeO7LbtMEPj1vJGSK0cW8+4zTXrK42UelO7t65vXB/44LHt+OSjHTp6WaXOWScsGtaRJ3WrS8NVBki1LRhNC5siWyXVPT1OYj504Ldt27pejx0+ZYDJD+ggZN7gTAWIC2pVLEUAAAQQQQAABBBCwkwABop1ak7og4LuA3QLE1ToCMQmOygSISaBuk0f2GDTJ1GRYrzYxavTZ0m9k1AfzZf3nY1zv9xkxTc6dvygj+75ipijrNObK5UtIvrw5ZemqTdLoharSrH4NM3X5jcGTzPTmPLmyS8sGT0vdWhXN9OXYayAuW71ZuvWfICWLFZIiBe+SX377y0x7njisKwGiTfoY1UAAAQQQQAABBBBAIKECBIgJFeR6BEJbwG4B4poK0Xdhvjng0Bp46PoZJpGRkaJjvcyARD98XnEtIxBD+5uQhKUfMXGufLNxhyyaMThGKdZs2C6v9nxfNiwaJxkzpDWf6chCDfnaNq4tTzzXUaaO6iFlShQxn7Xt8a6UKXG/CRD1uH79hvx+6Ih8tX6r6CjHJTOHyJWr1+SFlm/LtpWTJVXKFOY8HdVYvXIZad+ktnk9dc5SM12aADEJOwWPRgABBBBAAAEEEEAgyAQIEIOsQSgOAgEWsFuAuK5iuLtNkqNtp+J5k2Y3myt7fR0BYoA7rp0et+mHPdKi6zvydufG8n9Vy8uRoydkw5ZdUuupslK1fnd56dnKZmry1h9/dq1zWKJYQSlbq73ZGKVqhdKydcc+M4pQQ0ANEN+dOE/q1Kogd92Zw2zMoqMOde3Eu/PmklLVW5vg8cEiBUySrtOkC96TR7q0rit/HTkmfUdMl8yZ0hEg2qmTURcEEEAAAQQQQAABBBIoQICYQEAuRyDEBewYIHoeWehpxGHC339iDSMQQ/yrkLTFnz5vuQwfP8dViLaNn5EOzZ+XdRt3mGDw4qXL5jPrff3fU2YvlZGT5pn3dYdmHV2oOzE3fbG6dOj1vqz+brv57I7smeXl556Uli8/bV7raMQJM78w/1s3U7kRGSlvDJokJ0+fk4g0qaVwgbySPl2ETBjaWfb88ofUbd1XdqyaIsnDw0UuHk9aKJ6OAAIIIIAAAggggAACSSJAgJgk7DwUgaARsFuAuL5S0qyB+DgBYtD06ZAtiE45PnHqrGTKkFZS3pxerJXR9/8+dlKyZMogaVKnjFG/Cxcvy9nzF12brET/8PKVq3L23MUYm6hYn1+6fFWuXrsmGdNHTY3+9/p1OfLPCcmZI6ukSB7Hl4gAMWT7FwVHAAEEEEAAAQQQQCAhAgSICdHjWgRCX8BuAeK3GiCaNQ0jJcxMQJaAvH5sNSMQQ//bQA3iFyBAjN+IMxBAAAEEEEAAAQQQsKEAAaING5UqIeCDgN0CxO8qRxs8ZS1qaHkk4uvyBIg+9DpODV0BAsTQbTtKjgACCCCAAAIIIIBAAgQIEBOAx6UI2EDAbgHihsrJzMjDSIkagRion2UJEG3wbaAK8QsQIMZvxBkIIIAAAggggAACCNhQgADRho1KlRDwQcBuAeJGawSizl52t62yZePnzwkQfeh0nBrCAgSIIdx4FB0BBBBAAAEEEEAAgdsXIEC8fTuuRMAOAnYLEDdVuTkCUddADAuLWgsx+k9rZKKfPy+zijUQ7fB9oA7xCRAgxifE5wgggAACCCCAAAII2FKAANGWzUqlEPBawG4B4vdV/lsD0Zq+bGEk5utHCBC97nOcGMoCBIih3HqUHQEEEEAAAQQQQACBoBK48f3koCoPhUEAAc8CyR5p5X+eiGz+v6eXd/xeRyC6G3noacShn94v/TUjEL1sIk4LaQECxJBuPgqPAAIIIIAAAggggEAwCRAgBlNrUBYE4hawW4C49Ukdgehu8UNPix765/1SX9+wVVcLi9TJ3xwIxBYgQKRPIIAAAggggAACCCCAgJ8ECBD9BMltEAiAgN0CxB+eTCYSFiai8Ve0n9ZaiLHft14n9POHv2IEYgC6K49IcgECxCRvAgqAAAIIIIAAAggggIBdBAgQ7dKS1MMJAnYLELc95WkEotWaft5+WaLuV/IrRiA64ftCHQkQ6QMIIIAAAggggAACCCDgJwECRD9BchsEAiBgtwBx+1NJswZi8ZWMQAxAd+URgRY4fvKMbNu5X6pWKBX1aALEQDcBz0MAAQQQQAABBBBAwLYCBIi2bVoqZkMBuwWIP1ZNZpZANBupmLUQo5ZEtDZWuTlg0O+fF2cEog2/HVRJvt/+szTrPFR2r51OgEh/QAABBBBAAAEEEEAAAb8KECD6lZObIZCoAnYLEHdUTSZhEhUeBvLng4xATNR+ys2TSIAAMYngeSwCCCCAAAIIIIAAAg4QIEB0QCNTRdsI2C1A3FktPGoDFWsvZmvJQ9frmxus+PnzYitZA9E2X4pAV+TGjUiZPm+ZTJuzTE6ePidlSz0gV65ck1ljepqivNR+gLRuWEvWb94pe/cflIE9Wpj3B703SzZv3ysF8uWWV5s/75pm3HfEdClTsojUqFzGnLdmw3ZZvuZ7Gdarjet+lcoVl5XrtsrBv/6R+rUrS/umz0qa1ClFdxOauWClzJi3XP45dkoK3ZNHfvntL0YgBrpT8DwEEEAAAQQQQAABBBwgQIDogEamirYRsF2AqCMQdfpyZGRAfxZdwRqItvlSBLoiC5etl7eGTZEubepJuVIPyLLVm2XK7KWu0O6Bik1NkRo8/5TkzplVKpYrIc07D5UHCt0tTepVl++375Vx0z+XBZP7SZGC+aRRh8EmPHz5uSrmOr3/zPkrZOHUgea13k9Dx7aNa0tEmlTSfcBEGdm3vTxe5kFZuur/2bvvuCiOhw3gj4cNBEFF7Bo19pLYYxJrEqPRWBI19i5iwV7Bgg01KhYUe0/sLbHF3iJq5Bc19qixRGOMvVFU5H1n4E6q3MHt3t3es//Asrszs98Z/nk+szPHMWjsHPTs0BjVq36A3QdDsHDlNgaIag8K1kcBClCAAhSgAAUoQAE7EGCAaAedzFfUjIDWAsRzX+os0jeldiacgRjx8hXuPXgMJ8eMyOrmYnK7nj0PxevISGRxNf1ZkyuLcOju8gAAIABJREFU90CaKBHB8lBFQAR++fN4YPzQLrK++J8Ni8Bv7qQBqFaljLx+5MRZeA6agj1rA5DLI6v8W8P2PjIAHNSjhVEB4g+zfFGudBH57JDx8+CexVU+26nfJHhkz4KJPp6JtoWbqKgyJFgJBShAAQpQgAIUoAAF7EKAAaJddDNfUiMCWgsQz4sAMU3MZ8oxny/H30BFieslfok7A1FMKBMTv/RH+TJFETiuN9xcneVXqtUaeycYQYsCBuOj8iURGhaOIePmYd+Rk/KesiULy2fds7qqNuoYIKpGDTkY+nZthm/rV08yQIwd+G3cfgjT5q/D4c2BhlaOmrIEInEO8OtpcoA4fsYKvI58g1H928u29OnSFE0b1GCAqOIYYFUUoAAFKEABClCAAhSwRwEGiPbY63xnWxXQWoB4oa5lZiCW+CXuDMR5K7bg08plULRwPty5ex+te45D26Z14Nnmazx49BTVm/SWk8rExDP94eGeRS5DJ74YXbflAFYE+srz7kOnoWD+XBg7uJNqw4wBomrUwMAxc5DLIxsGeDU3KkAUaxr28pmB4J9nwzVzJvlMm17jUaJIfvj2aStnEVb/6AN0+K6uvJbYJ8yxA8nYAWJPn+ko8X4B9OrUhAGiimOAVVGAAhSgAAUoQAEKUMAeBRgg2mOv851tVUBrAeLFumINxOh9VNT8WWxH0puovHr1GrWb9YN3p2/QvGEtQ4C4dfkEGQzGP5p2HYUva1ZC19YN5KWdB35Df78gnN2/RK7rqMbBAFEN5Zg6ftp5BOOmr0D39g2RPasblq3bKTdLOXdgqbxDfMIcO/B79OQZ6rQYhJaNa6NL6wYIOXUR3sNnImhCP9So+gGClv0k10WcMdYbt+/cw4TAlXj+IjTOGohJBYgrN+2VG6hM8PFE9myumLVkE7buPso1EFUcD6yKAhSgAAUoQAEKUIAC9iLAANFeeprvqQUBrQWIf9bTQazdp998Wa2fiQWIL1++wuLVO3Dw2GmZxfgP7QrnTI6GALH2J+XgmtlZbnTbqO6ncHWJnkxWqZ6X3GhXhIjiOP/ndTTz9EPwltmGe5QeewwQlRaOVb5YLHPmwg1yt2QxDbVooXxy1uCJHXMTDRDFHw8ePS1nLorv3cXh1a6hTKjFce3mHfT3my13TxYLcFYoWwT3Hz6Vm6wkFkiKGYiRb6Iwsl873H/4BF0HTpbPiuOTSqXlmov6MJNrIKo4MFgVBShAAQpQgAIUoAAFNC7AAFHjHczX05SAFgPEt7sw62ciJrUrs/muF9mecBfmsPCX8J24EBev3JC5kJjUJfa8eP4iDDMWrpd/E8vWiaxIrG+4Zu4opEuXFqVrdTRMJhOD7er122jYwRd71kxFrhzZVBl/DBBVYY6uJDLyDXS6NIbppQt+3IpDx/7AikCfd7ZCPPfvvYfI6pZZfuse/xDXxMBK6+Bg0tuI/XP+/e8hsri5IGOGeOWG3jepLN5MAQpQgAIUoAAFKEABClAgKQEGiBwbFLAdAa0FiJe/ip2V6Oci6vtDufPEAkRDrVFR6DpoCnJmzypnFsY/xISxBu2GYVXQCLlhipiBKDbkrVOjoryVMxBt5/8pRS09efYyBowOQqmi7yEs4iWOhpzDwimDULViqRSVp+hDDBAV5WXhFKAABShAAQpQgAIUsCcBBoj21Nt8V1sX0GKAmOjah/rPmpNaGzGV19/flnAGYuyx4T/zB/x1847MheIfL0LDUfkrLyyeNgRVypWAWAOxbq3K6NKqvryVayDa+n9ZMu0XnyGLz4TvPXiCzM5OKFemCPLkdLfOt2aAaJ39wlZRgAIUoAAFKEABClDABgUYINpgp7HJdiugtQDxSoO08XZQAaIXRUxqZxXzXI8dIIpPlOf/sAVN6lVD3twecgZhlwGTZSDYre3Xcvm68IgIfFShFNKldcD0BRvkZ8x71k6VaxyKL1jXbz0od2F2cswAryEB3IXZbv9Dre3FGSBaW4+wPRSgAAUoQAEKUIACFLBZAQaINtt1bLgdCmgxQIzOCqPkknJvd2NW9rzwlteG0SNmFLbvM0FupKs/Gtf9FCP7t0eG9Omw+1AIfCYsNOx/kdXNBZNHdMdHFUrK28XzYn+MQ8dOy/PSxQoicHwfeLi7qTZCuQaiatQ2VhEDRBvrMDaXAhSgAAUoQAEKUIAC1ivAANF6+4Yto0B8Aa0FiFe/TmuRTo4dIOobIILAB4+ewD2rm5xJGPt4HRmJBw+fyj+JYFCEnfGPJ89e4NWr13IfDLUPBohqi9tKfQwQbaWn2E4KUIACFKAABShAAQpYvQADRKvvIjaQAgYBLQaIb3dhTmr3ZfP/vdDPrzQ1qhggaqo7zfgyDBDNiMmiKEABClCAAhSgAAUoQAElBN6s7alEsSyTAnYtoGs+2/zv72S5/R/+apQuZs1D/dqG6vxkgGj+YcQSrVGAAaI19grbRAEKUIACFKAABShAAQrEEmCAyOFAAfMLaDFANMxARBpEQb/2YSI/zXi94OaX5u8cC5bIGYgWxLfqqhkgWnX3sHEUoAAFKEABClCAAhSgAMAAkaOAAuYX0FqAeK1xevMjGVEiA0QjkHiLBgQYIGqgE/kKFKAABShAAQpQgAIU0LYAA0Rt9y/fzjICWgwQLbEG4nubIizTgQrVyhmICsHafLEMEG2+C/kCFKAABShAAQpQgAIU0LoAA0St9zDfzxICWgsQr3+TAYiKAsSuxgl+AhCbHStw/b1N/ITZEuOXdaZQ4Njv55HDPQsK5s/1zhLEVuDBJ86ibq3K0VuFM0BMoTgfowAFKEABClCAAhSgAAXUEmCAqJY067EnAS0GiIbsUL9/ij5LVPC8wAbOQLSn/xubf9e23v4yFGz9zefvfJdzl66jeTc/nN67CGkdHBgg2nzP8wUoQAEKUIACFKAABSigfQEGiNrvY76h+gJaCxBvfJsxJibUW8oph7FglTlngKj+2GWNqRBggJgKPD5KAQpQgAIUoAAFKEABCli1AANEq+4eNs5GBbQXIGaQX1pGRb1j92UFrudfH26jIyDxZnMNRCvozomzVsJBp8PVG7dx+PgZVK1YCkN7tsKClVux79eTqFyuOHp3/hbFCueTrd0ffBLT5q3D1Rv/oHyZohjRrx2KFsorr928/R/GTV+OIyfOokDeHLj34An6dm0qZyCKf5a1P+/HsnU78ex5KL75qjpaNvkMObNnBWcgWsFAYBMoQAEKUIACFKAABShAAZMEGCCaxMWbKWCUgNYCxJtNxQxE/VqHsQiUmXhoqIABolHDjTeZItB96DSEnL6E/t2aoWC+XBg1ZQlu3bmHrq0byDBx+bqdcHF2wkQfT1y5dhuNOvrKa9U/KosfNuzGiVMXsXPVFKRPnxYN2/sgq1tmeT19urTwnbQQnVvWlwHitr3H4DdlKUYP7IiC+XNizvKf4OrijLGDOzFANKXDeC8FKEABClCAAhSgAAUoYBUCDBCtohvYCI0JaC1A/LtZRrmBipyBiDSIQqyZiNE7qChyPd/aME2NDM5AtILuFAFi+TJFZOgnjukL1uPytVuY7d9XnosZhyO/X4zDmwMxc9EGbNtzDDtXTZbXHjx6iupNemOWfx9kds6Edr39sXX5BMOmKbE/YW7Ta7ycldjm2y/ksxcu38CEwJU4unU2Ll35m2sgWsFYYBMoQAEKUIACFKAABShAAeMFGCAab8U7KWCsgNYCxFvNHVVY8fCtrn5iIwNEY0cc7zNaIH6AOP+HLTh9/qohQBQ7KXfu/z3OHViKof7zZbliNqL+qN2snwwfMzllxNhpK3Bix1zDtdgBYrXG3nByzIjs2dzitG36mF64e+8RA0Sje4w3UoACFKAABShAAQpQgALWIMAA0Rp6gW3QmoDWAsS/mzsmugYiomLNPExkDcTUXs+7JlRTQ4MzEK2gO+MHiAt+3IpT564kGiBODlqN4JCz2LR4nGz5i9BwVP7KCwF+PZAnV3Z81220DBBFUCiO2AFi066j0OjLT9C2aZ0Eb801EK1gILAJFKAABShAAQpQgAIUoIBJAgwQTeLizRQwSkBrAeLt75zkDMSkljzU/93c1xkgGjXceJMpAqYEiEdDzqHLwMkyMPy4Ymm5PmLQsp9wYMN0ZHFzQdUGPeV6h62afI4zF/6SMxb1m6iImY0r1u9C0IR+KFn0Pdz+9z7Wbz2A/t2acw1EUzqM91KAAhSgAAUoQAEKUIACViHAANEquoGN0JiA5gLEFk4yPpRrH+r7yrAmouEPZr+eZ9ULTY0MzkC0gu4UAWKFskXRpVV92Zr4MxCPn7yAXj4zDJ8mi81PZi3eJO8VMw3F58yfVSsvz1du2ovxM1bI38XOzE+evUCXVg3QqslnePnyFaYtWC9DR/1R6cPiWDp9KM7/eR3NPP1weu8ipHVwAELvW4EMm0ABClCAAhSgAAUoQAEKUCBpAQaIHB0UML+A1gLEf1pmijUDUb+Rit7t7Xn0DETzXWeAaP6xyRJTIBAe8RL3Hz5BTo+s0YFfrCM0LBzPnochR/YsiZb8OjISDx4+RWaXTHDMmD7x2hkgpqBX+AgFKEABClCAAhSgAAUooKYAA0Q1tVmXvQhoMUA0fL8c/3tlBc9zr+QMRHv5n7Hv92SAaN/9z7enAAUoQAEKUIACFKCADQgwQLSBTmITbU5AawHinVb6GYj6z5jj/xQzD8VHzua9zgDR5oY+G5wiAQaIKWLjQxSgAAUoQAEKUIACFKCAegIMENWzZk32I6C5ALG1c/QExJhdl0VcqA8Lo7dWidlgxczXc/74XFODhmsgaqo7zfgyDBDNiMmiKEABClCAAhSgAAUoQAElBBggKqHKMu1dQGsB4r9tnA1dasgIY/6i351Zf4M5r+f8gQGivf8v2cf7M0C0j37mW1KAAhSgAAUoQAEKUMCGBRgg2nDnselWK6DFADGNftflmJ9Ik0ZMSUT8v+vPzXE954pnVtvHKWkYZyCmRM0enmGAaA+9zHekAAUoQAEKUIACFKCATQswQLTp7mPjrVRAawHi3bYuCaTjzzyMf4M5rjNAtNIBzmaZWYABoplBWRwFKEABClCAAhSgAAVsQyDq39O20VC2kgIUUEQgTc4PzF+uk7v5yzSyxLvtXKLXPIy/xqHC5x7LnxrZQtu4jTMQbaOf1G8lA0T1zVkjBShAAQpQgAIUoAAFrECAAaIVdAKbQAELCmgtQPyvfea3mgkWPYzZQUV/hxmveyxjgGjBYcyqVRNggKgaNSuiAAUoQAEKUIACFKCANQkwQLSm3mBbKKC+gBYDxKTWOlTy79mXPlG/8xSskTMQFcS16aIZINp097HxFKAABShAAQpQgAIUSKkAA8SUyvE5CmhDQGsB4r0OrgCSmlqo/7v5r2dfyhmI2viP4Fu8W4ABIkcIBShAAQpQgAIUoAAF7FKAAaJddjtfmgIGAc0FiB1dkUZEiFGI3lw55qtlpc/dl3AGIv+tVBAIOX0JWVydUfi9PGarLTQsAunTp0VaB4fky2SAmLwR76AABShAAQpQgAIUoIAGBRggarBT+UoUMEFAawHi/U5uMTMQ9RMRZZwYLSLTRGXO3RczQDRh2PHWlAp0HzoN5csUQdfWDVJaRJznwsJfomJdTwSO74Pan5RLvkwGiMkb8Q4KUIACFKAABShAAQpoUIABogY7la9EARMEtBcgukLJtQ7F7s6JlZ9t0eME6hEvX+Heg8dwcsyIrG4uCa6/fPkKj548h4e7mywz/vHseSheR0Yii2vCZ03o4hTdyjUQU8Sm/EPmDhDfvInCxSs3kDe3BzI7OyX/AgwQkzfiHRSgAAUoQAEKUIACFNCgAANEDXYqX4kCJghoLUB80FnMQFT/iB8gDp+0CJt2HDY0pHyZoggc1xturs4QIeSc5T9j9pJN8roIF2f598UHJQvL89CwcAwZNw/7jpyU52VLFpbPumcV6zuqczBAVMfZ5FpEgOiYMT2ePg/F0ZBzKFe6CCb4dEW+3B54/OQ5ug+bhivXbstySxV7D8O8W6NY4XwIj3iJqXPX4Jf9vyE84hU+KFUYvr3boGD+XGjTazx8+7RBiSIFsGVXMA4eOw1Xl0z4eVcwir+fH706NUGVciWi28oA0eQ+4wMUoAAFKEABClCAAhTQggADRC30It+BAikX0GKAaIkZiFkXPorTCfNWbMGnlcugaOF8uHP3Plr3HIe2TevAs83XOHn2ssxsVgT6oEzxQpi5aCO27T2KPWsCoNOlwcKV27BuywGsCPSVWZHIjETOM3Zwp5R3tIlPMkA0EUyt28VgOHvxL/Tq2ESm0TMWbkCFssXk4Hjy7IVMrcuXLoL06dNh8art+OvmHaxfMFoOqmVrf5FJtYODDvuPnMRH5Uui0ofFUapmByyf6YMKZYti6ZpfMHnOanRsUU8O4B37juPcpeuyDAaIavUy66EABShAAQpQgAIUoID1CTBAtL4+YYsooKaA1gLEh12zxNo5Rb+DivI/4weIsfvw1avXqN2sH7w7fYPmDWth6ty1uPD/X40unDJI3vbf/ceo1bSvzGjEJLCmXUfhy5qVDMvc7TzwG/r7BeHs/iWJfuqsxHhhgKiEqhnKjP8J866DIRg7bRkObw6UpYs1Df+4cBXXb97BmYvXZKB47sBSzFq8CVt2B2PmuN4oWihvnIEUP0D89cQZw+C8dvMOGrQbhuCfZ8M1cybOQDRDH7IIClCAAhSgAAUoQAEK2KIAA0Rb7DW2mQLmE9BigGiJGYhZ5j9M0ClijcPFq3fIL0KzZ3OF/9CucM7kiIFj5siNdH37tDU8IzKcoAn9UKPqB6hUzwvjhnSWIaI4zv95Hc08/RC8Zbb8slSNgwGiGsopqCN+gPjnX7fQpNNw7F8/HU+fvUDHfhPh4uwkZxaKRTjFJ8kiQLzz30P4TliA4ycvyEU5WzauDa92jeDkmCHBDMTYAaI+3d67LgA5s2dlgJiCPuMjFKAABShAAQpQgAIU0IIAA0Qt9CLfgQIpF9BagPjIM2vKMVLxZGIBopgM5jtxodyjwsM9Cyb4eCKXR1Z4DpqCYoXzY4BXc0ONIjT0G9gBX9WugtK1OhrCRHHD1eu30bCDL/asmYpcObKlopXGP8oA0XgrVe+MHyBu33scg8bOwe+7FmD6gvW4cPkGFk0dLD9TPn3+Klr1GCsDRP1x5+4D/HbqIsZNX4Fh3q3wzVfVGSCq2oOsjAIUoAAFKEABClCAArYpwADRNvuNraaAuQS0GCC+nYEIREXhHbsym++627wHSXaJ2DSl66ApcgKXmFkoZiCKjVN8ercxPBN/BuL4oV1Qp0ZFeZ0zEM012jVQjggQ8+Zyx+AeLXHj1l2MDliGzC5OmO3fV+7Ksz/4FOZM7IfXryMxe+lmwyfMP27cLb+PFzvyvAgNl7MWB3VvgXq1qzBA1MC44CtQgAIUoAAFKEABClBAaQEGiEoLs3wKWLeA1gLEx17ZotdAlIdMD1U5f1eAKFriP/MHuZ+FWPdQrIF46epNzJ88ULYysTUQ69aqjC6t6svrXAPRuv+HVG2dCBBDTl+SW3WLQ4SCYotuMTVVfKbs7TtDzkIUR7UqZXD4+Bk5A3Hx6u1y4IlDfMIs0unRgzoirYODDBDFjj5iq/Cla39B8ImzhsF578Fj1Py2L/atm4Yc2bPwE2ZVe5uVUYACFKAABShAAQpQwHoEGCBaT1+wJRSwhIAWA0SZGeqzQ/EzOkqMzhJj/11/bobrrnPfzkB8/iIM83/Ygib1qiFvbg85g7DLgMkyEOzWNvYuzL4oU6IQZixcj+17jxl2YV7w41as33pQ7sIslqjzGhLAXZgt8c9hzXU+evIM4RGv5Dfx8Y9//r0PN1cXOXhiH68jI/Hg4VNky5pZBocpOkLvp+gxPkQBClCAAhSgAAUoQAEK2LYAA0Tb7j+2ngKpFdBagPiku7s+Doz5qRcyxIiKXHed8zZAFF+Itu8zwTARTLSgcd1PMbJ/e2RInw7ik+ZZSzZh7vKfZePEhLD5kwegXOki8lw8Lz5zPnTstDwvXawgAsf3gYe7W2q72+jnuQai0VR2diMDRDvrcL4uBShAAQpQgAIUoAAFogUYIHIkUMC+BbQYIEbPNIyKWftQP/NQ2fPMQQknZokg8MGjJ3DP6pZgMpgYdeERL/Hw0VPk9MgGnU4EnHGPJ89e4NWr13DP6qr6IGWAqDq5jVTIANFGOorNpAAFKEABClCAAhSggHkFGCCa15OlUcDWBLQWID7tmT36O2X9of9uWeHzxAJEWxsLsdvLANGWe0/JtjNAVFKXZVOAAhSgAAUoQAEKUMBqBRggWm3XsGEUUEVAiwHi212Y08SaiaifgajMT5dZ/6nSX2pVwgBRLWlbq4cBoq31GNtLAQpQgAIUoAAFKEABswgwQDQLIwuhgM0KaC1AfNbLwyJ9wQDRIuysVHUBBoiqk7NCClCAAhSgAAUoQAEKWIMAA0Rr6AW2gQKWE9BcgOidQ+y3HDPz8O32ytFrIuqdzX/dJfBfy3WiAjVzBqICqJookgGiJrqRL0EBClCAAhSgAAUooF2BN1f3aPfl+GYUoIDFBHSFPzd/3U5iJ2TLHM9653xbsVgKMfbeJAqeu8xkgGiZHmet6gowQFTXm7VRgAIUoAAFKEABClDARAEGiCaC8XYKUMAoAe0FiLnkTEOxj4qaP51n3DHK21Zu4gxEW+kptdvJAFFtcdZHAQpQgAIUoAAFKEABkwQYIJrExZspQAEjBbQWID7vk9vINzfvbc4z/jFvgRYujQGihTvAaqtngGi1XcOGUYACFKAABShAAQpQQAgwQOQ4oAAFlBDQXIDYN08Mk4LfK8sa4pbvPP22Et1jsTKNDhBv3r6LY/87j1t37iVobPf2jeGYMb3FXoIVKyDAAFEBVBZJAQpQgAIUoAAFKEAB8wkwQDSfJUuiAAXeCmguQOyX1yLd6zztlkXqVapSowLEjdsPYcT3i2UbcmTPgnRp08Zpz/oFo+Hi7KRUG1muJQQYIFpCnXVSgAIUoAAFKEABClDAaAEGiEZT8UYKUMAEAS0GiGnS6HdZVu9npoC/TVC3/luNChC/bDkIhQrkxsyx3kiXLm54aP2vyBamSIABYorY+BAFKEABClCAAhSgAAXUEmCAqJY066GAfQloLUB80T9/9OfFSe2gIrdlNv91uwwQm3Qajs8+rYBenZrY13+NPb8tA0R77n2+OwUoQAEKUIACFKCADQgwQLSBTmITKWCDApoLEAfkh34GImLCQsN5TKioxHWnKTdssPeTbrJRMxBnLd6Evb/+D+sWjEZaBwdNAWjhZf7+5z9MnLUSv528iIwZ0qFqhVIYN6Qz0qdPh5DTlzA5aDX+unkHX1SvgJZNPkeZ4gXx+MlzdB82DVeuRS/qWarYexjm3RrFCueLJmGAqIWhwXegAAUoQAEKUIACFNCwAANEDXcuX40CFhTQWoAYOvA9RMkNTqKPNEijynkmewwQw8JfonqT3qj4QTG4Z3VNMIx9erfhJioW/Of2HDQFDg4O6Nu1KZ4+e4H12w5iZL/2ePDoKeq1HowBXs1RrUpZ7Nx/Aht3HMLetQF4+jwUm3YcRvnSRWTQuHjVdhkyivUsGSBasDNZNQUoQAEKUIACFKAABYwUYIBoJBRvowAFTBLQWoD4YuB7hhmIaq6F6DT5mknu1n6zUTMQF63ajoB5a+HkmBEF8uaAg04X570WBQyGcyZHa39XzbavTa/xMtgVQa6Hu5vhPYOWbsbWPUcxdVQP+bfXryPRovsYbFg4BsXfzw8RDP9x4Squ37yDMxevyUDx3IGlDBA1O1L4YhSgAAUoQAEKUIACWhJggKil3uS7UMB6BLQWIIYOLiiXONQvdRjnp55dget2GSA2bO+D9wvmkUGUSGt5WJeA+HR5qP883L33CHlzZUeX1vXRrEFNDPWfj72Hf3/7WXJMs7u3b4Qc7lnQsd9EuXt2pQ+LI+LlK2zZFcwA0bq6lq2hAAUoQAEKUIACFKBAkgIMEDk4KEABJQS0FyAWersGomG/lOjdmN/un2L+c8dJV5XoHouVadQMxO+6jcYnlUujd+dvLdZQVvxugcjIN7h28w52Hw6BWLNy6/IJ2Lj9MK7/fQeB4/skeHjS7FW4cPkGFk0dDAcHHU6fv4pWPcYyQORAowAFKEABClCAAhSggI0IMEC0kY5iMylgYwJaCxDDhhSOtQLi24mI+m6JyRQNvWSucyd7DBCXr9uJ1T/tw+bF4+R6eTysS2Dq3LVo2qAG8ufxwMUrN9G06yi5lmFYeATaevtjoo8n6n1WBU+evsDuQyGoWLYYdh74DfuDT2HOxH7y0+bZSzfzE2br6la2hgIUoAAFKEABClCAAu8UYIDIAUIBCighoLUAMXTo+4l+vZzUV8vm+rvjxCtKdI/FyjRqBmLQsp8we8kmlCtdBFmzuCRorAioxPqIPCwj4O07A/uOnJSV58ieBa2afI4urerL843bD2FC4EqEhoXLc7GG5dxJ/ZEuXTqI58QsRHFUq1IGh4+f4QxEy3Qha6UABShAAQpQgAIUoIDJAgwQTSbjAxSggBECWgsQw4YVMeKtzX+L44TL5i/UgiUaFSDOWf4T/jj/V5LNnDqqOwNEC3aiqDo84iWePguNs4mKvkniu36xI3O6dGnh6pIpTkv/+fc+3Fxd4OSYIe4bhN638BuxegpQgAIUoAAFKEABClDgXQIMEDk+KEABJQS0FyAWhdjOI3rNQ/lLvF2Z9X827/WM/n8q0T0WK9OoAFFszhHx8iXy58lhsYayYpUFGCCqDM7qKEABClCAAhSgAAUoYJoAA0TTvHg3BShgnIDWAsRw32IiM4w59OGhfi3Et+fRN5jvuqP/JePAbeQuowLE/n6z8fDxMyydPtRGXovNTLUAA8R6AM+5AAAgAElEQVRUE7IAClCAAhSgAAUoQAEKKCnAAFFJXZZNAfsV0FqAGOZbLN6Mw/gzEJU5zzjuoqYGkVEB4vezV+G3Uxflxhw87ESAAaKddDRfkwIUoAAFKEABClDAVgUYINpqz7HdFLBuAa0FiOHDS8iZhW/3X9bvs6zsT7sMEMVGG2Jn3y3LJ6BQ/lzWPdLZOvMIMEA0jyNLoQAFKEABClCAAhSggEICDBAVgmWxFLBzAc0FiCNKIA3SyDUQ06RJgyjxmbIK5xnGntfUSDJqBuKCH7di+oL1yJsrO4q9ny8BAHdh1tSYiH4ZBoga7FS+EgUoQAEKUIACFKCAlgQYIGqpN/kuFLAeAa0FiBEjS8WEhnL/FBkiRs9IfHseHSqa93qGMXYYIHIXZuv5R1atJQwQVaNmRRSgAAUoQAEKUIACFNC6wOuZX2n9Ffl+FNCMQNre283/Lk7u5i/TyBLDR5ayyBqIGUafNbKFtnGbUTMQbeNV2EqzCjBANCsnC6MABShAAQpQgAIUoIA9CzBAtOfe57vbmoDWAsSIUaUt0gV2GyCKb8Wv//0v/v3vIQoVyI0c2bPg5u27cHLMCPesrhbpDFaqoAADRAVxWTQFKEABClCAAhSgAAXsS4ABon31N9/WtgU0FyD6lYle81D/mbLcTkX58/R+fyQYCGHhL/Ho8VPk9MgGnU58NG3a8ex5KF5HRiKLq4tpD5rhbqNmIL4IDYfXkAD8fuZPWaVY8/DrOh+j94iZuH7zX/y8zN8MTWERViXAANGquoONoQAFKEABClCAAhSggC0LMEC05d5j2+1NQGsB4svRZQ1dqN+LWf8HJc/Tj4obIHr7zsC+Iydl1VndXNC4bjUM8Gouzx8+foZqjb0TDLVFAYPxUfmSCA0Lx5Bx8wzPly1ZGIHjeqs6oc+oAHHtlgMIXLQBg3u0xA8bdqPNt1/IAPG3kxfRsd9E7F8/HR7ubvb2P6Xt92WAqO3+5dtRgAIUoAAFKEABClBARQEGiCpisyoKpFJAawFixOiyMWsgig1UxEYp0bsxy31UYp1Hb7BivuvpR56O0xOzFm9CnZqVkD+PB4797zx6+kzH6jkjUaZEITx49BTVm/TG3EkD5HX94eGeBY4Z02Phym1Yt+UAVgT6yvPuQ6ehYP5cGDu4Uyp72/jHjQoQm3Qaji9rVoZXu4bwHDQFX3/xsQwQ9Qnp6rmjUKZ4QeNr5Z3WL8AA0fr7iC2kAAUoQAEKUIACFKCAjQgwQLSRjmIzKQBAawHiy7EfGsLCmM2Xo/tZvxlz/J/6UZDK6+lHnnrneKrdrB9aNKoNzzZfGwLErcsnyGAw/tG06yh8WbMSurZuIC/tPPAb+vsF4ez+JTG7Sis/dI0KEBu290Hjep+iU4uv4gSIV6/fRsMOvti1egry5LTcjjrKM9lhDQwQ7bDT+coUoAAFKEABClCAAhRQRoABojKuLJUCSghoMUAUMw7lzEP92ocqnKcbHv25cmLHjVt38VWbIQia0A81qn5gCBBrf1IOrpmdUbRQXjSq+ylcXTLJxyvV88K4IZ1liCiO839eRzNPPwRvmW24R4mxELtMowLEsdOW49ffzmDZzGEY+f1iOQPxs2oVMGjsHPxx/ioObJgBBwed0m1l+WoKMEBUU5t1UYACFKAABShAAQpQQNMCDBA13b18OY0JaC1AfDWuvKGHojdSebt5iZLn6Yb/nujIEPuMtOk1Ds6ZnLB0+lCZpz1/EYYZC9dDfLIsNkrZtOOwXN9wzdxRSJcuLUrX6mgIG0Wh+gl9e9ZMRa4c2VQZgUkGiE+fh+L8peuoULYonoeG4dsuI3H33iPZqLy5ssvPl8UijrP8+6DWx+VUaay9V7Ji/S4sWbND9oNYcLNl48/QvX0jOV11y65gnDp3BR+UKoytu4+iSMG8GNSjBUJOX8LkoNX46+YdfFG9Alo2+Vx+bv74yXN0HzYNV67dlqylir2HYd6tUaxwvmhmBoj2Ptz4/hSgAAUoQAEKUIACFDCbAANEs1GyIAooLqDJADF6cUP9Ioeq/Ezn+78EfSV2Ye4zYib+/e8hls/0gZurc6L9ee3mHTRoNwyrgkZAbJgiZiCOH9oFdWpUlPdb1QzE389cRlvv8Ti4cYZMPcVLrt2yH+cuXsOzF2EomC8nmnxVTQZVPNQR2HUwBGnTOiBf7uz4+/Z/8B4+05BAL13zCybPWS0H1ufVKiCXRzaULl4Q9VoPlrv6VKtSFjv3n8DGHYewd20AREAsEu3ypYsgffp0WLxquwwZ1y8YzQBRne5kLRSgAAUoQAEKUIACFLAbAQaIdtPVfFENCGguQPSv+DY8fLtzSuIhohmvxw8QRQ7Te/hMhIVFYN73A5IMD8UQErMUK3/lhcXThqBKuRIQayDWrVUZXVrVlyPMqtZAjB8gauB/QBOvIKapnv/zBu49fIwlq3egS+sGaN/sS4gAcefBE/hx1nDodNHTcYOWbsbWPUcxdVQPef76dSRadB+DDQvHoPj7+WUo/MeFq7h+8w7OXLwmA8VzB5YyQNTESOFLUIACFKAABShAAQpQwHoEGCBaT1+wJRRITkCLAaJhDUT92ocq/Ew77ISBOjQsAi28RuN1ZCSmje4F50yO8ppOp0Muj6w4ePQ0wiMi8FGFUkiX1gHTF2yQGc2etVPlGocLftyK9VsPyl2YnRwzwGtIgPXswswAMbl/KfWvT5y1EuIzZrGoZoF8ObF97zG0/bYOOraoJwPEX0+cwcIpgwwNG+o/H3sP//72s+SYK+Kz5xzuWdCx30S4ODuh0ofFEfHylfwMmgGi+v3KGilAAQpQgAIUoAAFKKB1AQaIWu9hvp+WBLQWIL6eEL3xiDiiYjZfTqq/zHk9doAolqITuy7HP8TydIc3B2L3oRD4TFgolwoUh/j75BHd8VGFkvJczEgcOGYODh07Lc9LFyuIwPF94OHuptrQS3INRH2A+GXNynDMmP6dDRret12y96j2Rhqt6MGjp6jepLdh+qp4Ta8hU1GlXMkkA8Spc9fi+t935KCKf0yavQoXLt/AoqmD5YKdp89fRaseYxkganT88LUoQAEKUIACFKAABShgSQEGiJbUZ90UME1AcwHixMoxsaE+HhQ/9Yf4gjP23/Xnqb+eduhvJsGL2YkPHj6Vz4hgUMyajH88efYCr169lksNqn0kGyCKDVOS22FZ7AojZrLxUE5AfCtftUEPuW13nRqV5OYoIn3u0b5RkgHi72f+RFtvf0z08US9z6rgydMXMtWuWLaY/F5+f/ApzJnYT37aPHvpZn7CrFz3sWQKUIACFKAABShAAQrYtQADRLvufr68jQloLkCcVMUiPZB2yHGL1KtUpckGiPpNVJRqAMs1XmDRqu0ImLdWPlC4QG752bHYibnDd3WxdO0vCD5xFvMnD4xT4MbthzAhcKVhGmyBvDkwd1J/pEuXDt6+M+QsRHFUq1IGh4+f4QxE47uDd1KAAhSgAAUoQAEKUIACRgowQDQSirdRwAoEtBcgfgQLbMIMh8HHrKA3zdcEBojms1SlJPHdu5iNKBbZNPaIioqC+AQ6Xbq0cvHN2Mc//96Hm6uLXIQzzhF639jieR8FKEABClCAAhSgAAUoQIF3CjBA5AChgO0IaC1AjJxcNforZf2R1FfKZr7uMPio7XS6ES1lgGgEkl3ewgDRLrudL00BClCAAhSgAAUoQAElBBggKqHKMimgjIAmA0QLTEF0GBisTAdZqNQkA8R/7z3Etj3H0KrJ59wgxUKdY9FqGSBalJ+VU4ACFKAABShAAQpQQEsCDBC11Jt8F60LaC5AnPKJRbrMYeARi9SrVKVJBohKVchybUSAAaKNdBSbSQEKUIACFKAABShAAesXYIBo/X3EFlJAL6C5AHHqJ0iDNBDLu4mdjRP8RJQi13UDftXUoGKAqKnuNOPLMEA0IyaLogAFKEABClCAAhSggH0LMEC07/7n29uWgNYCxDcB1d52QFQU5I4qclHENIDhPOYWM17X9WeAaFsjn61NmQADxJS58SkKUIACFKAABShAAQpQIIEAA0QOCgrYjoAmA0QLrIGo63fIdjrdiJZyBqIRSHZ5CwNEu+x2vjQFKEABClCAAhSgAAXerOhIBApQwI4FdG2XmP/tndzNX6aRJb6ZVuPtjEP9zEMVfjJANLKDeJuNCzBAtPEOZPMpQAEKUIACFKAABSiQMgEGiClz41MU0IqA5gLE6SJAjP25cvzPl5U51/U9oJUhId+DMxA11Z1mfBkGiGbEZFEUoAAFKEABClCAAhSwHQEGiLbTV2wpBZQQ0FyAOKPWW6Yk1zyMucWM13V99ivRPRYrkwGixeitvGIGiFbeQWweBShAAQpQgAIUoAAFlBFggKiMK0ulgK0IaDJAtMQaiL332UqXG9VOBohGMdnhTQwQ7bDT+coUoAAFKEABClCAAhQAGCByFFDAvgU0FyDO/MwiHarrvdci9SpVKQNEpWQVLvfZ81AEh5yFTqfDF9Urmr82BojmN2WJFKAABShAAQpQgAIUsAEBBog20ElsIgUUFNBcgBj4ebSW/vPk+D/1lma+rvPeo2AvqV80A0T1zVNd4+Vrt9C443BU+rA4srplRoBfj1SXmaAABojmN2WJFKAABShAAQpQgAIUsAEBBog20ElsIgUUFNBcgDjrC6iw6bLcpyV2PTrv3Qr2kvpFM0BU3zzVNc5Z/hMu/3ULAX49U11WkgUwQFTOliVTgAIUoAAFKEABClDAigUYIFpx57BpFFBBQJMBolwDMXoz5rczEZU91/XcpUJvqVcFA0SVrI/97zymzV+Hv27eQfZsrmhSrxq6tm6AdVsP4Oat/zDAq7lsyZ3/HqLviEAsChgM50yOaNljLDzbNMDh42dw4fINfP15VUxbsB4ZM6RD7hzuaNG4NvLl9sCYgGXyWXHU+vhD+PZtC1eXTIiKisKGbYfw48bduHXnPoq/nx/9uzVDudJFEHL6EiYHrZZt+qJ6BbRs8jnKFC8YLcIAUaWRwWooQAEKUIACFKAABShgXQIMEK2rP9gaCqgtoLkAcfaXahPK+nQ9d1qkXqUqZYColGyscsMjXqLCl57o1vZr1P/sI1z/+y6O/X4Ovn3aImjZT7h45QZmju0tn7h5+y7qtR6C4C2zZQBYqmYH+ffW33yB3DmzoeIHxRAwdy2yZsmM7xrWQp6c7nj45JmckViiSAGEhUdg1OQlqPnxh+jfrTm27ArGUP/56N35W1StUBLBIeeQ2SUTPq1cBvVaD5bBZbUqZbFz/wls3HEIe9cGII1I5hkgqjAyWAUFKEABClCAAhSgAAWsT4ABovX1CVtEATUFtBcg1k3weXHiMxH1MxKTmplo2nVdj1/U7DbF62KAqDgx8PxFGKrU7y5DvLZNv4CTY0ZDrcYEiHMnDUC1KmUMzwweOxe5cmRDP89mhr/de/AYv5+5jP/uP8KugyHI7OKE2f590abXeOTNnR0TfTzjvGnQ0s3Yuucopo6KXj/x9etItOg+BhsWjpGzFBkgqjAwWAUFKEABClCAAhSgAAWsUIABohV2CptEARUFNBcgzqkX/fmy/tCvVajwua7HDhV7TfmqGCAqbyxrWLlpL8bPWCF/F58P9+3aVM4mNCZA/GGWr3xGf8QPEHfsO46BY+agfJmiKFEkP/7865b8xFkEj5XqeWFor1b4tn71OG8qZiXuPfw7ihXOF+fv3ds3wieVSjNAVGlcsBoKUIACFKAABShAAQpYmwADRGvrEbaHAuoKaC9A/CqJGYjxZxSa91zntV3djlO4NgaICgPHLl58ynzp6t9YtnYnTpy6gAMbZmDhym04de4K5kzsJ29N7BPm5ALEhu19ULd2FfRo30iWsXj1dvx28oIMEJt0Go4q5UvKEDH2MXXuWlz/+w4Cx/dJXICfMKs4MlgVBShAAQpQgAIUoAAFrEeAAaL19AVbQgFLCGguQJxb3xKM0Hlts0i9SlXKAFEp2Vjl3rn7AD/tPILvGtWCq4szVv+0T26oEvzzLJw8ewU9fabLT4cdHHQyUFz78/44ayAmFyCKz5SLFMqL/p7NcOvOPfhNWYosbs4yQJy9ZJOsz39YV1StWAohpy7heWgYsrq5oK23v/y0ud5nVfDk6QvsPhSCimWL4f2CeTgDUYVxwSooQAEKUIACFKAABShgjQIMEK2xV9gmCqgnoL0AsQEgd2GOijUTUflzXbct6nWaCjUxQFQBWaxP2L7PBNy4dVfWJjY78e70DWpU/QCvXkei78hAHAg+Ja99WbMSdh448c4Accj4ecjlkU1+Bi2OIyfOYuj4eXj4+JlcX1F8luzi7CRnNYaFv8S46cux+Zdf5b3i+qTh3VD7k3LYuP0QJgSuRGhYuLxWIG8OzJ3UH/nz5GCAqMK4YBUUoAAFKEABClCAAhSwRgEGiNbYK2wTBdQT0FyAOP9ry6yByABRvUGrtZqePg9FZGQksri6JHi1B4+eynDPMWP6FL3268hIiJmOOT2yIV1ahwRlvHz5Co+fvkC2LJnlTEf9ERUVBVF3unRp5a7PhoOfMKeoH/gQBShAAQpQgAIUoAAFbF2AAaKt9yDbT4HUCWgvQGyY9OKG+pmJ0dsyJ7wvFdd1nj+lriOs7GnOQLSyDrGa5jBAtJquYEMoQAEKUIACFKAABSigpgADRDW1WRcFrE9AcwHiguj9ItQ+dF0ZIKptzvosIcAA0RLqrJMCFKAABShAAQpQgAIWF2CAaPEuYAMoYFEB7QWIjWOtgahf+1D5n7oumyzaj+aunDMQzS2qlfIYIGqlJ/keFKAABShAAQpQgAIUMEmAAaJJXLyZApoT0FyAuOib6A1U9Ifhs+SYPyh0zgBRc/8afKFEBRggcmBQgAIUoAAFKEABClDALgUYINplt/OlKWAQ0GSAmNQahwr+Xdd5Q4JRJTa6ffT4qdy/QqcT6y7GPcT+FY+ePIeHuxvSiGAz3vHseSjEHhiJ7a2h9BDmDESlhW21fAaIttpzbDcFKEABClCAAhSgAAVSJcAAMVV8fJgCNi+guQBxcdPEN0hRMDwUG7LoOsUNEL19Z2DfkZNyfGR1c0HjutUwwKu5PBcb3M5Z/jNmL4n+7Flcn+XfFx+ULCzPQ8PCMWTcPMPzZUsWRuC43nDP6qraeGOAqBq1jVXEANHGOozNpQAFKEABClCAAhSggP0JvAmeZX8vzTemgMICuo97mb8GJ3fzl2lkiW9EgKj/TFnFn7qO6+K0cNbiTahTsxLy5/HAsf+dR0+f6Vg9ZyTKlCiEk2cvo02v8VgR6IMyxQth5qKN2Lb3KPasCZAzFReu3IZ1Ww5gRaAvHDOmR/eh01Awfy6MHdzJSIXU38YAMfWG2iyBAaI2+5VvRQEKUIACFKAABShAAQ0JMEDUUGfyVaxGQHMB4pLoWX7Rh1gLUXwaHP+n+a/HDxDjd3DtZv3QolFteLb5GlPnrsWFKzewcMogedt/9x+jVtO+WL9gNEoUKYCmXUfhy5qV0LV1A3l954Hf0N8vCGf3L0n0U2clBhMDRCVUtVAmA0Qt9CLfgQIUoAAFKEABClCAApoWYICo6e7ly1lIQHMB4lIRICYWGiYVJprn77oOa5LswRu37uKrNkMQNKEfalT9AAPHzEEWV2f49mlreKZUzQ6G65XqeWHckM4yRBTH+T+vo5mnH4K3zIarSyZVRgoDRFWYbbASBog22GlsMgUoQAEKUIACFKAABexLgAGiffU331YdAc0FiMtaJAKnDwmTMk39dV37xAPEF6HhaNNrHJwzOWHp9KFwcNDBc9AUFCuc37AmomiVCA39BnbAV7WroHStjoYwUVy7ev02GnbwxZ41U5ErRzZVBgYDRFWYbbASBog22GlsMgUoQAEKUIACFKAABexLgAGiffU331YdAU0GiCqufYioKLnmoq7dqgQdJnZh7jNiJv797yGWz/SBm6uzvEfMQBQbp/j0bmN4Jv4MxPFDu6BOjYryOmcgqvO/kKCWY7+fRw73LHIByuSOV68j8dvJC3jw6Ak+r1YBTo4Zk3tEteuRkW8Q8fIVnBwzpL5OBoipN2QJFKAABShAAQpQgAIUoICiAgwQFeVl4XYqoLkAcXmrd/Rk6mcaRn8enfDQtVsZ549Pn4ei9/CZCAuLwLzvBxjCQ3GTWAPx0tWbmD95oHwmsTUQ69aqjC6t6svrXAPRQv+cbb39ITqi9Tefv7MFryMjUafFQDg7OaJA3hwY5t0auXNabiehIePnycFTpGBe2e6jIefQZeBkHPlpVpyBmCJWBogpYuNDFKAABShAAQpQgAIUoIB6AgwQ1bNmTfYjoMkAUcxATGoDlZgZg+a+rmv7o2HQhIZFoIXXaIhcadroXnDO5Civ6XQ65PLIGmsXZl+5K/OMheuxfe8xwy7MC37civVbD8pdmMWkMa8hAdyF2RL/ksYGiCdOXUSPYdNxbGuQ/Ebd0oeYzrpk2lBULldcNuX5izCIhTiLvZ8PaR0cUtc8Boip8+PTFKAABShAAQpQgAIUoIDiAgwQFSdmBXYooLkA8Yc2b7NBfX/Gn3iowLmu7Q+G0XP33iOIXZfjH+Kz5cObAxEVFYVZSzZh7vKf5S3ia9f5kwegXOki8lysmyg+cz507LQ8L12sIALH94GHu5tqI9Qu10C8efs/jJu+HEdOnJUzCe89eIK+XZvKGYii09b+vB/L1u3Es+eh+Oar6mjZ5DO8iXyDNt7jITpddFSp4gUxsl877A8+iWnz1uHqjX9QvkxRjOjXDkULRc8IbNljLDzbNMDh42dw4fINuWPOui0H4KDT4eqN2/LvVSuWwtCerbBg5Vbs+/WkDAN7d/4WxQrnk2WIWYbBJ87i4eNnKFwgN3p2bCJ33QmYtxaLVm1H3lzZ4ZbZGU2+qoZKHxaHj/8CrAwaIQNO0abx01fg+MkL8tlenb4xfC+/ZVcwDh47LXfr+XlXMIq/nx+9OjVBlXIlogcfA0TV/glZEQUoQAEKUIACFKAABSiQMgEGiClz41MUeJeA9gLEtjGbMEevTfh2IqKy57rWK0weaOERL/Hw0VPk9MgGnS7hp9FPnr3Aq1ev4Z7V1eSyU/uA3QWIYrpow/Y+yOqWGV1bN0D6dGnhO2khOresLwPEbXuPwW/KUowe2BEF8+fEnOU/wdXFWS5kGbhoA/b++rsMAl2cneQsv0YdfWU51T8qix827IaYpbhz1RQ5pVTMEBRH62++QO6c2fBlzcoYE7AMIacvoX+3ZiiYLxdGTVmCW3fuyTJEmLh83U5Z9kQfT/nsjxt34/2CeZHNLTMOHD2FafPXIfjn2fjvwSM07jgcg3u2RMkiBZDTIyuePgtF825+OL13EcR6iPVaD0apou+hffO6ct3G2Us3Y/2C0ShRpACWrvkFk+esRscW9fBp5TLYse84zl26Lq8zQEztvxWfpwAFKEABClCAAhSgAAXUEGCAqIYy67A3Ac0FiD+2s0gX6lovt0i9SlVqdwHi//74E+16+2Pr8gmGTVNif8Lcptd4OSuxzbdfSHMxc3BC4Eoc3Tobm3f8KmcQrpk3Sl6buWgDtu05hp2rJsvzB4+eonqT3pjl3we1Pi4nA8S5kwagWpUyhv7rPnQaypcpIgNDcUxfsB6Xr93CbP++8lzMaBz5/WI5hVUcIggUC2levHJTLqIZuHijrF/Ogoz3CbMIAPUB4vHfL8htwPesDZDf04tDBKfVqpTFoB4tZID464kzWDhlkLx27eYdNGg3TIaTrpkzcQaiUv9xLJcCFKAABShAAQpQgAIUMJsAA0SzUbIgChgEtBcgto+ZgYi4P/VvrP98Of7PVF7XtVqmqVFldwHiz7uOYOy0FTixY66hI2MHiNUae8tvzbNni/sd+fQxvXAg+FScAHGo/3xZhn62oPhdfNMuwsGWjT+TAd8Ps3wN36yL6/EDxPk/bMHp81cNAaLYEbpz/+9x7sBS+Y27WBhThIe1Py2HXB7ZIBbOXBU0AmVLFn5ngPjzziNytqI+iBR1i9mO4rPsAL+eCQJE/Q4/e9cFIGf2rAwQNfVvzpehAAUoQAEKUIACFKCANgUYIGqzX/lWlhXQXIC4qkOS+6Mkta+KOf6ua7XUsh1p5trtLkA8e+kavus2WgaIIigUR+wAsWnXUWj05Sdo27ROAmqx403sGYiTg1YjOOQsNi0eJ+8VgV/lr7wQ4NdDfq5sTIAoAsFT564kGiDuPfw7eo+YieAts+VaheIQZcYOEBcFDMZH5UvKa7FnIB4+/gd6+cx4O6MQgJhdWaJIfvj2acsA0cz/SCyOAhSgAAUoQAEKUIACFFBfgAGi+uasUfsC2gsQOyYyA1GshSjWQExqc+bUX9e1WKypwWJ3AaJYA7Fqg55yvcNWTT7HmQt/Qcwk1G+iImYErli/C0ET+qFk0fdw+9/7WL/1APp3ay63zI4dIB4NOYcuAyfLwPDjiqXl+oVBy37CgQ3T5QzG1AaIx/53Hp0HfI+Ni8bKWYFifcbxM1YYAsRO/SahUrni6NKqAUJDw+VaivpPmMVMwzotBqFl49ro0roBQk5dhPfwmfK9alT9gAGipv6N+TIUoAAFKEABClCAAhSwTwEGiPbZ73xrZQU0FyCu7qQsWBKlM0C0CLt5K125aa8M4sQhdkwWu9iIEK5Vk8/w8uUrTFuwXoaB+kPsbrx0+lBs2HZI7tCsXwNRXBebrMxavEneKmY0is+ZP6tWXp4nFSBWKFsUXVrVl/fEn4EodkwWMwfFDMk3b6LQ3282dh8KkffW/qQc9h05idVzRqJMiUIQMxT9pi6ROzR3b9dIfubczDN6ExWxwcvBo6flNt+hYeHyea92DeHd6Rv5+9K1v8jdnedPHijP7z14jJrf9sW+ddOQI3sWfsJs3iHH0ihAAQpQgAIUoAAFKEABBfzrRd0AACAASURBVAQYICqAyiLtXkB7AWLnWDMN488sVO5c990iTY0lu5uBqO89Eao9ex4WHZYlcoiZig8ePkVml0xwzJj+nZ0uttm+//CJ3AlZBHfmPkTZDg46ZHF1SVC02GTl0ZNnyJYlM9KI7cjjHeL6v/ceyl2nk3uPOI+G3jf3a7A8ClCAAhSgAAUoQAEKUIACZhVggGhWThZGASmguQBxbRfLrIH43UJNjSi7DRA11YtKvAwDRCVUWSYFKEABClCAAhSgAAUoYEYBBohmxGRRFIgR0F6A2BUQE67Emof6Q38e/6cZr+uaRW+8q5WDAaJWetLc78EA0dyiLI8CFKAABShAAQpQgAIUMLMAA0Qzg7I4CmhxBuI6T4v0KwNEi7CzUtUFGCCqTs4KKUABClCAAhSgAAUoQAHTBBggmubFuylgjIDmZiCu6/Z2BmJSMw8V+Luu6VxjuG3mHs5AtJmuUrmhDBBVBmd1FKAABShAAQpQgAIUoICpAgwQTRXj/RRIXkBzAeIGryTWQIy/gQri3Ze66wwQkx9rvEMLAgwQtdCLfAcKUIACFKAABShAAQpoWoABoqa7ly9nIQHtBYjdY62BKDafjVLlXPdNkIV6UJlqOQNRGVfbL5UBou33Id+AAhSgAAUoQAEKUIACGhd4c/2Qxt+Qr0cB9QV071U3f6VO7uYv08gS32zsGTO1UP9ATIhoeF6ZcwaIRnYQb7NxAQaINt6BbD4FKEABClCAAhSgAAW0L8AAUft9zDdUX0CTAaICaxzKXZ3fUa6uySz1O0/BGjkDUUFcmy6aAaJNdx8bTwEKUIACFKAABShAAXsQYIBoD73Md1RbQHMB4ibvWJ8t6zX14Z9y5wwQ1R65rM8yAgwQLePOWilAAQpQgAIUoAAFKEABowUYIBpNxRspYLSA5gLEzb0B+ZVyFKJ/ifmh8Lmu0UyjzW3hRs5AtIVeskQbGSBaQp11UoACFKAABShAAQpQgAImCDBANAGLt1LASAHNBYg/9THyzc17m67RDPMWaOHSGCBauAOSqz4y8g0iXr6Ck2OG5G6FKfcmWxgDxGSJeAMFKEABClCAAhSgAAUoYFkBBoiW9Wft2hTQXoDY951rFSa3lmFKr+saTtPUAGGAaOXdeTTkHLoMnIwjP82Cm6vzO1tryr3JvjYDxGSJeAMFKEABClCAAhSgAAUoYFkBBoiW9Wft2hTQXIC4pX/058v6DU+iv2dOOlQ003UGiNr8/7Dat3r+Igw3bt1FsffzIa2Dwzvbacq9yb4wA8RkiXgDBShAAQpQgAIUoAAFKGBZAQaIlvVn7doU0GSAmFR4GD8sNOO5rsFUTQ0QzkC0QHe27DEW1T8qi10HTuDWnftoUu9TfP3Fx5g6by3OXbqOr+t8jN6dvpEzDq/e+Ac+/guwMmgEHBx0EM/W+vhD7DoYIoPFFo1qo0eHxnDMmN6ke9+8icLStTuwZPUOPHz8DFUrlkJExCusCPSJFmGAaIGRwSopQAEKUIACFKAABShAAVMEGCCaosV7KWCcgOYCxK0DjXtxM9+lazDFzCVatjgGiBbwL1WzA4oWyguvdo3ktNn+fkFwcsyIgV7NkT9PDvhOWoheHZvgm6+qy0CxeTc/nN67SM5AFM8WLpBbPivWRRw0di4C/HqgWpWyJt27acdhDJ+0CP27NcfHFUthx77jWLRqO84dWMoA0QJjglVSgAIUoAAFKEABClCAAqYLMEA03YxPUCA5Ae0FiIMsswZi/e+To7ap6wwQLdBdIgT8YZYvypUuImv/rtto1P/8I7Rr9qU8nxy0Gg8eP8VEH89EQ8HYzw4ZPw/uWVwxqEcLk+5t6+2P/Hk8MH5oF1nnbycvomO/iQwQLTAeWCUFKEABClCAAhSgAAUokDIBBogpc+NTFHiXgOYCxO2D5ZKHhjUQY5ZAVPpcxwCR/2ipFYgfIHbqNwk1Pv4Q7WMCxKClm/HnX7cwfUyvZEPB8TNW4HXkG4zq396ke6s19kbfrs3wbf3qDBBT26F8ngIUoAAFKEABClCAAhSwiAADRIuws1KNC2gvQByS9AYqZlzzUG7UEqs8Xb2JmhopnIFoge6MHyCKXZbFJ8hqBogDx8xBLo9sGODVnAGiBcYAq6QABShAAQpQgAIUoAAFUi/AADH1hiyBAvEFNBcg7hhmkU7W1ZtgkXqVqpQBolKy7yjXGgLEn3YewbjpK9C9fUNkz+qGZet24sLlG/yE2QLjgVVSgAIUoAAFKEABClCAAikTYICYMjc+RYF3CWgvQPQxTAzUTxBU46eurr+mBhoDRAt0Z7IB4rKfcOXaLQT49cT5P6+jmWfcTVRir4EoPmGOfBOFkf3amXRvxMtXmLlwA/YHn4SHexYULZQPYmOVEzvmRotwF2YLjAxWSQEKUIACFKAABShAAQqYIsAA0RQt3ksB4wQ0FyDu9I1ZA1HuY6tamKirO944cBu5iwGijXSUuZsZGfkGOl0apEkjVg8FFvy4FYeO/YEVgT4MEM2NzfIoQAEKUIACFKAABShAAUUEGCAqwspC7VxAewHicMusgVhnrKZGEgNETXWn8S9z8uxlDBgdhFJF30NYxEscDTmHhVMGoWrFUgwQjWfknRSgAAUoQAEKUIACFKCABQUYIFoQn1VrVkBzAeKuke/oK/2UxKRuSfl1XZ0xiRb65k0UoqKi4OCgM3kMPXseiteRkcji6mLys6l9gAFiagVt9PnQsHAcOXEW9x48QWZnJ5QrUwR5crq/fRt+wmyjPctmU4ACFKAABShAAQpQwH4EGCDaT1/zTdUT0F6AOEq1z5Zjr62o+2J0gk4TwaHf1KXy76MHdjRcf/j4Gao19k5w/6KAwfiofEmIDGfIuHnYd+SkvKdsycIIHNcb7lldVRsYDBBVo7axihgg2liHsbkUoAAFKEABClCAAhSwPwEGiPbX53xj5QU0FyDu8VN17UN9iKj7wi9OZ+088JvczFaEhU0b1IgTID549BTVm/TG3EkDkD+Ph+E5sWeFY8b0WLhyG9ZtOYAVgb7yvPvQaSiYPxfGDu6k/ICIqYEBomrUNlYRA0Qb6zA2lwIUoAAFKEABClCAAvYnwADR/vqcb6y8gPYCxNFJr4Eo9oX4/1mB0P/Up3/x/56C67rP4n46HRoWgafPX2Da/HXImCF9ogHi1uUTZDAY/2jadRS+rFkJXVs3kJdEGNnfLwhn9y8x7G2h9MhggKi0sK2WzwDRVnuO7aYABShAAQpQgAIUoIDdCDBAtJuu5ouqKKC5AHFv7M1M4q9pqNy57rMRifbamGnLERkZmWiAWPuTcnDN7IyihfKiUd1P4eqSSZZRqZ4Xxg3pLENEcZz/8zqaefoheMtswz1KDxEGiEoL22r5DBBttefYbgpQgAIUoAAFKEABOxF4s3mAnbwpX5MCFFBTQNd4qvmrc4q154L5S39niW9EgJjojML/X4gwDeLOQIxzX+qu62oPNzpAfP4iDDMWrof4ZFlslLJpx2G5vuGauaOQLl1alK7VEUET+qFG1Q9kmVev30bDDr7Ys2YqcuXIpoooA0RVmG2wEgaINthpbDIFKEABClCAAhSggD0JMEC0p97mu1JAPQHNBYj7x1tmDcTavkYHiPFvvHbzDhq0G4ZVQSPkhiliBuL4oV1Qp0ZFeStnIKr3/8CakhNggJicEK9TgAIUoAAFKEABClDAogIMEC3Kz8opoFkB7QWI/knMQIy19mFSax6m4u+6msNSHCC+CA1H5a+8sHjaEFQpVwJiDcS6tSqjS6v6skyugajZfz8bfDEGiDbYaWwyBShAAQpQgAIUoIA9CTBAtKfe5rtSQD0BzQWIByaK75T13yOr9jN+gBgZ+QZv3rzBuBkr8Pp1JPwGdICDgwN0ujQ4ePQ0wiMi8FGFUkiX1gHTF2yQnzHvWTtVrnG44MetWL/1oNyF2ckxA7yGBHAXZvX+JVjTOwUYIHKAUIACFKAABShAAQpQwKoFGCBadfewcRSwWQFNBoipmEkYZ5dmE8rR1RgSZwys/Xk/Rgcsi/O3sYM74ZuvqmP3oRD4TFiI0LBweT2rmwsmj+iOjyqUlOdiRuLAMXNw6NhpeV66WEEEju8DD3c31cYZ10BUjTp1Fe08cAIVPyiGbFkyp66gJJ5+8uwFgk+clVNi04h/CAaIijizUApQgAIUoAAFKEABCphLgAGiuSRZDgUoEFtAcwHioe8tMQERuhqDTRpYryMj8eDhU/mMCAZlNhPvENnNq1ev5QYrah8MENUWT2F9pWp2wPKZPqhQtmgKS3j3Y+cuXUfzbn44vXcR0jo4MEBURJmFUoACFKAABShAAQpQwHwCDBDNZ8mSKECBtwLaCxAnx1oDUf81c5qY3ZeVO9dVG6ipYcUA0Ua6kwGijXQUm0kBClCAAhSgAAUoQAGVBBggqgTNaihgZwKaCxAPT41JCfUdKWb2iTURlT1ngGhn/zjW8rqxA8SoqCiIb+eXrduJZ89D5ffyLZt8BscMGeA5aApG9G8nv4cXx70Hj9HLZwYmj/RCvtweiT6XM3tWcAaitfQ020EBClCAAhSgAAUoQAHjBBggGufEuyhAAdMENBkgmrB2YUrXPIz/nO7T/qbBW/ndnIFo5R2kb17sAHHb3mPwm7IUowd2RMH8OTFn+U9wdXGGWHxTBIh5c3tgZL928tHl63bKnXp+XuaPdz3HANFGBgKbSQEKUIACFKAABShAgRgBBogcChSggBICmgsQj0yzzBqIn/ZTonssViYDRIvRm1Zx7ACxTa/xKJA3B9p8+4Us5MLlG5gQuBJHt87G/iMn0XfkLIT8Mh+OGdOjYXsfOTuxZePP8K7nLl35m2sgmtYlvJsCFKAABShAAQpQgAIWFWCAaFF+Vk4BzQpoL0Ccbpk1ED/uo6kxwgDRRrozdoBYrbE3nBwzInu2uNt1Tx/TC5mdnfBJI2/4DewgQ8bvuo1G8JbZcHXJhHc9d/feIwaINjIW2EwKUIACFKAABShAAQoIAQaIHAcUoIASApoLEINnxGOKvwZifEXzXNcxQFRieLLM5ARiB4hNu45Coy8/QdumdRJ9LGDeWpy9eA3vF8yDF6HhGD+0i7zvXc/xE+bkeoDXKUABClCAAhSgAAUoYF0CDBCtqz/YGgpoRUB7AeLMWDMQ9bsvK/9TV9VbK0NCvgdnINpId8YOEOf/sAUr1u9C0IR+KFn0Pdz+9z7Wbz2A/t2ay7e5dvMOGrQbJn9fEeiL8mWKyN/f9RwDRBsZCGwmBShAAQpQgAIUoAAFYgQYIHIoUIACSghoLkA8NssyayBW7aVE91isTAaIFqM3rWIRIK4I9EH5MkXx8uUrTFuwXm6Qoj8qfVgcS6cPNZy39fbHg0dPsG3FRKQRuw0B73zu/J/X0czTD6f3LkJaBwcg9L5pDeTdFKAABShAAQpQgAIUoICqAgwQVeVmZRSwGwHtBYizAflVclTMTEQxnU75c12VnpoaMwwQbbg7X0dG4sHDp8jskklumGLsYdRzDBCN5eR9FKAABShAAQpQgAIUsIgAA0SLsLNSCmheQHMB4vEgi/SZrkoPi9SrVKUMEJWStfVyGSDaeg+y/RSgAAUoQAEKUIACGhdggKjxDubrUcBCAtoLEOckvgYioqKnIhpmJupnKMb8TOV1XWUvC/WgMtUyQFTG1fZLZYBo+33IN6AABShAAQpQgAIU0LQAA0RNdy9fjgIWE9BcgHhi3jtCwhjmJEPElF9ngGixIcyKVRVggKgqNyujAAUoQAEKUIACFKCAqQIMEE0V4/0UoIAxAtoLEOersuZh/DUWdRU9jeG2mXs4A9FmukrlhjJAVBmc1VGAAhSgAAUoQAEKUMA0AQaIpnnxbgpQwDgBzQWIIQuMe3Ez36Wr2NXMJVq2OAaIlvW33toZIFpv37BlFKAABShAAQpQgAIUAMAAkcOAAhRQQkB7AeLC6DUQU7mmoanP6yp0VqJ7LFYmA0SL0Vt5xQwQrbyD2DwKUIACFKAABShAAQooI/Dm2FxlCmapFKCATQjoPlJg8w8nd4u9+5vfF79dA1HFEJEBosW6nBWrKsAAUVVuVkYBClCAAhSgAAUoQAFrEWCAaC09wXZQwDICmgwQxQzEpDZKUejvunIdLdOBCtXKGYgKwdp8sQwQbb4L+QIUoAAFKEABClCAAhRIiQADxJSo8RkKaEdAcwHiyaUW6RxduQ4WqVepShkgKiVr6+UyQLT1HmT7KUABClCAAhSgAAUokCIBBogpYuNDFNCMgPYCxGXRayDGmWmo766kZiam/rruw/aaGRPiRRggaqo7zfgyDBDNiMmiKEABClCAAhSgAAUoYDsCDBBtp6/YUgooIaC5APH0inhrIOr3U0lkYxW5RqJ5rus+bKdE91isTKsMEJ88e4HgE2dRt1ZlpJE75ZjnePMmCjsP/IaPK5WGq0sm8xQKIOT0JWRxdUbh9/KYVGZoWATSp0+LtA4OCZ67//AJfj9zGXVqVDSpTLPdzADRbJQsiAIUoAAFKEABClCAArYkwADRlnqLbaWA+QU0GSBaYg3Esm3M3zkWLNEqA8Rzl66jeTc/nN67KNFwLaVer169xodfdMH6BaNRokiBlBaT4LnuQ6ehfJki6Nq6gdFlhoW/RMW6nggc3we1PymX4LnfTl5Ex34Tce6AZb7VBwNEo/uSN1KAAhSgAAUoQAEKUEBLAgwQtdSbfBcKmC6guQDxjx/1Uwrj/dTbxJqJqN+lWT8TUd6Ssus6BoimDz5Tn7CHAFHMhrx45Qby5vZAZmcnBoimDhLeTwEKUIACFKAABShAAQooIsAAURFWFkoBmxHQZIBoiRmIZVrZTJ8b01CbmIE4ZPw8+Unzw8fPULhAbvTs2ARf1qwk32/irJVIm9YBV6//Iz8lrvXxh/Du/A3y5faQ14+GnMOEwB9x9cY/KFuyMP44fzXJGYh///OfLE/M/suYIR2qViiFcUM6489rtzBp1iqsCPQxmHoNmYqurb9GhbJFIWYgOmZMj6fPQ2V95UoXwQSfrrIN4REvMXXuGvyy/zeER7zCB6UKw7d3GxTMnwtteo2Hb582cjZkVFQUlq/fhWVrf8Hde49QtFBe/PnXLcMMRPFuk4NW46+bd/BF9Qpo2eRzlCleULanZY+x8r13HQzBjVt30aJRbfTo0Fi2SZS7Ydsh/LhxN27duY/i7+dH/27NZBvfVSZnIBrz78N7KEABClCAAhSgAAUooD0BBoja61O+EQVMEdBcgHhmVRIzEJOaWWiev+sYIJoy7FJ2b/wZiCL8er9gXmRzy4wDR09h2vx1CP55NlwzZ5LhnQjC+nb9Vt4TMHctqpQvgf7dmkMEgnVbDUajLz9B0wY18e9/DzFo7JwkA0TPQVPg4OCAvl2b4umzF1i/7SBG9muPs5euoVO/SXE+J67W2BtjB3dGzY8/lG04e/Ev9OrYBG6uzpixcAMqlC2GsYM7YeHKbTIUnOXfFw4OOuw/chIflS+JSh8WR6maHbB8po8MIbfvPS7b1rNDY1Sv+gF2HwyRz4pPmG/e/g/1Wg/GAK/mqFalLHbuP4GNOw5h79oAuUakKEcEq17tGsHJMQMGjZ2LAL8e8t4tu4Ix1H8+enf+FlUrlERwyDlkdsmETyuXeWeZDBBTNnb5FAUoQAEKUIACFKAABWxdgAGirfcg20+B1AloMUAU2YmYYKX/HNlwHjMzUYnraUq3SF1HWNnTNjEDMTLyDS5dvYmLV27iv/uPEbh4I9bMG4XSxQrK8C72+oNitt0PG3Zh0+JxmLdii/z90KaZMmhLbg1EMSPQPasrfHq3gYe7m6Grjp+8kGyAGLsNYibg2GnLcHhzIGYt3oQtu4Mxc1xvOasw9qYwsQNEEVB6ZM+CiT6est7YayAGLd2MrXuOYuqoHvLa69eRaNF9DDYsHCNnFIpyfpjlK2cVikPM2HTP4opBPVrIWY55c2c3lKt/qeTKZIBoZf+pbA4FKEABClCAAhSgAAVUEmCAqBI0q6GAlQpoLkA8uyZmBmL83ZWVPdcxQFR+hMeegRgR8QpeQwJkeFj703LI5ZENC37cilVBI+QnyfEDRLHLcsC8ddi5ajKGT1qEly9f4fsRXrLRyQWIIrQb6j9PfkKcN1d2dGldH80a1ISpAaL49LhJp+HYv346It+8ge+EBbIMJ8eMaNm4tmGmYOwAUcxo7NOlKZo2qJEgQBQzCPce/h3FCueLg9+9fSN8Uql0ggBx/IwVeB35BqP6t0elel4Y2qsVvq1fPc6zyZXJAFH5cc4aKEABClCAAhSgAAUoYI0CDBCtsVfYJgqoJ6DFAFE/wzCpmYZKXNeVbq5ep6lQk9XPQDwYfBq9R8xE8JbZcHXJJElE8GZMgLhs3U75KbCYnWdMgCjuEbMdr928g92HQ+Tswa3LJ8i1F9v19n/nJ8yxZyDqP0f+fdcCZEifTtZ95+4D/HbqIsZNX4Fh3q3wzVfV43zC3NNnOkq8XwC9OjVJECBOnbsW1/++I3dsTuyIPwMxdoAogswq5UvKEDH2kVyZDBBV+O9jFRSgAAUoQAEKUIACFLBCAQaIVtgpbBIFVBTQXIB4bm1CPfk1s36tw0RwzXA9qQBRbKorPqcWy9zFP8QkuEdPnsuvYmN/waq/79nzULyOjEQWVxcVR0R0VVYfIIacuoTOA77HxkVjkTN7VmzbewwiIDMmQBSzFr/tMhKTR3RH5XLFsXX3UUyeszrJNRBFqCZmAObP4yFnPDbtOkreWyBvDjmTb7Z/X7kJyo59v8k2iHP9Goh5c7ljcI+WchOT0QHLkNnFSV4X6zeKTVLEbMkXoeFyZuKg7i1Qr3aVOAHiyk175VqJE3w8kT2bK2Yt2STbK9ZA/P3Mn2jr7S8/Q673WRU8efoCuw+FoGLZYni/YJ53zkCcvWQTVv+0D/7DuqJqxVIQns9Dw5DVzeWdZTJAVP1/kRVSgAIUoAAFKEABClDAKgQYIFpFN7ARFLCYgPYCxHUyjEt0DUREh4hKXE9TsmmCPhRt8Ju6VP599MCOhuvi73OW/wyR4YhDZDZiL40PShaW56Fh4Rgybh72HTkpz0XGFDiut1yGT63DKgPE839eRzNPP5zeuwi6NDr095stAzNx1P6knARbPWckypQoJD9hFpuQdGlVX17feeAEAuatlZ8wi1R38Li52LHvuLwmwr4DwacMawfGR/b2nWHojBzZs6BVk88N5Yo1A2cv3RynnKAJ/VCj6geGjVxEh4pDBIaiI3PlyIbFq7dDBJPiEJ8w16lREaMHdURaBwcZ/ImdncuXKYr7D5+g68DJcudlcYhPk4+cOGuY9bhx+yFMCFwpB404RKg5d1J/5M+TI9EAMfJNFEb2a4ew8JcYN305Nv/yq6ENk4Z3k47vKpMBolr/gqyHAhSgAAUoQAEKUIAC1iXAANG6+oOtoYDaApoLEM+vf0sYf2ahfl8V/R1mvK6LFyCKJffEV6niK1cxeS12gHjy7GW5h4XIiMoUL4SZizZi296j2LMmADpdGrnJ7rotB7Ai0BeOGdPLHKpg/lz/196dwNlY/v8ff89M9mWMXYkkS5aKkigl9VVKodQXRbJliexEQvavLWu2EH1TUipSKZFKm1+rbylJRPZ9X2bm/78uzunMmOXMmXPOnHOf1/14fB++x7nv677u5+fcHfOe674uu3hvsLaQDBBTungTsJnhnb4M0zTHZst2ifsR6LRwT50+oyNHTyRZRMW1vxlBaBYwMas/p7QdPHxUp06fVYmiBZO8bYaX7j9wRIUK5rfBYWqbSZzNStFxBfIpZ47sF+1m3t9/8IjX1+LZgBkGe+jIcRWKy59kmGyqbZ7YF6zPIOdBAAEEEEAAAQQQQACBEBIgQAyhYtAVBLJAwHkB4huKUpQSPUYaBuN1VKUHklTvxMnTOnLsuCbOet1mPp4Bohl49svvWzVnXB97jFlA+Pam3d1P0JonZO+qW0PtH2lo3zdhZM8h07Vh9bwUH3UOxMcmbALEQFw8baYhQIDIxwMBBBBAAAEEEEAAgYgUIECMyLJz0Qi4BRwXIP7ypnlKWfZpZdcWhNfRyQJE16mfm7hA8fHxSQLE3s+9oLjYvBr4VEt3F81Tq64nX820esP7tbUhotlcT+56rhcS6I8wAWKghcO1fQLEcK0c/UYAAQQQQAABBBBAIFMCBIiZ4uNgBMJewHkB4lL3HIeuZVPcqy5fNDLxQtZ4Yc7Ei0cqev9+VMXGKX4WUgoQO/QZpwplS6lXx39Wbjah4ZDerXVPvZqqcvvj7jDRNLr5zx26v/VAffTaeDt9XjA2AsRgKIfjOQgQw7Fq9BkBBBBAAAEEEEAAgUwLECBmmpAGEAhrAacFiIkb37IDEO2WwshDsxhzIN6PzkCAaEYgmoVTBnR71P3ZST4CcUT/dnZdDbMxAjGsbzGHdZ4A0WEF5XIQQAABBBBAAAEEEPBOgADROyf2QsCpAk4LEBM2vv3PHIiuEYeuLDH5SEM/vh9VsVGKH5GURiCaORB/3bxNs8b2tsekNAfi3bff6LGAMHMgOvX+C7/rIkAMv5rRYwQQQAABBBBAAAEE/CBAgOgHRJpAIIwFHBcg/vrOPyMPXSMQE6UURx768f3oivcn+RTExycoISFBwycttAv0DunVWjExMXaV5X9WYR6oqldfqUlzlmjFqi/dqzDP/u9yLVn+iV2FOXeuHOrYbwKrMIfxPeasrhMgOqueXA0CCCCAAAIIIIAAAl4KJCzu4uWe7IYAAk4UiH54mv8vK3dh/7fpZYsJvy5LYw7E5HMa+u91VPnzKya7tsXvrNbQCS8l+bthfdvogXtuVWJioqbOW6oZC96x7+fOlVOzxvZStSrl7OvjJ07JPOa89ssf7OsqFcpoyoinVLRwAS8VMr8bcyBm3tCZLRAgOrOuXBUClQU1YgAAIABJREFUCCCAAAIIIIAAAukIECDyEUEgsgWcFiAm/rY8yRyHyUceBup1dLIA0ZtP1anTZ3Tg4BEVL1rIjkxMvh0+elxnz55T4YKx3jTn130IEP3K6aDGCBAdVEwuBQEEEEAAAQQQQAAB7wUIEL23Yk8EnCjgtAAx4bd35V59Ofkchx7rqpx/ejlKiUr02D/508/evx9V/l5HfTwIEB1VTj9eDAGiHzFpCgEEEEAAAQQQQACB8BEgQAyfWtFTBAIh4LQAMXHTCiW65jwM4p/R5e8JRHmyrE0CxCyjD/ETEyCGeIHoHgIIIIAAAggggAACgREgQAyMK60iEC4CTgsQEza9d/EciMlHHrpXY05xvRWfjo8q1yBcSu5VPwkQvWKKwJ0IECOw6FwyAggggAACCCCAAAISASKfAgQiW8BpAWLi7+9fPAdi8pGIrpKnNkLRh/ejy93tqA8SAaKjynn+YvqNmKl2Le5VuTIlfb86AkTf7TgSAQQQQAABBBBAAIEwFiBADOPi0XUE/CDgtAAx4fcP0pjTMJURhxfNjZjx/aKuussP1QidJggQQ6cWfutJ5bqtNW9if91YraLvbRIg+m7HkQgggAACCCCAAAIIhLEAAWIYF4+uI+AHAacFiImbV2bNHIhX1fdDNUKnCQLE0KmFlq1cp8/Xb9DoAR1sr8bNeE2F42LV+t/nh7227j5aT7V7UGUuL6FOT0/U71t22L+vXOEKPd31EVUoe7kmzFysFxetUMkSRVQgf141uaeOmjWqp/U//Kqx01/VH9t26l+3Xq/mTe5U1YplbBsDR89R/64ttHDJSu3Zd0gvTx0oESCG0CeDriCAAAIIIIAAAgggEDwBAsTgWXMmBEJRwGkBYsLmDy+MQDSrK5tVlM1qy2ZEYWBfR5X9VyiW1+c+ESD6TOf/A7/96Te17DpS69+fpejoKFWv317FisRp1eIJ2r5zr+5u0Vefvz1VUdFRWvrep6pepZyyZ8+muYtW2GBwyeyh2rRluxo//oz6dmmuSuVKq3jRgjZpb/BIX/Xq+LDq1LxGH6z+Rm++t9a2u2HjFjXr9Jw9z4P33KqcOXOobfN7CBD9X15aRAABBBBAAAEEEEAgLAQIEMOiTHQSgYAJOC1ATPzjI/fzx67VmF2TItow8XyaaFNFf74fVfbOgNUoKxomQMwK9VTOeebMWVWr314LpwzU8ROnNHPhO/p1819aMPlpbduxWzMWvKOlc4fbo0+eOqMff9msP7ft1E8bt9hA8X9r5tv3kj/CPH3+W1r+0RcaP7izff/cuXgbGr4x5zmdPXvO/v+vV8xQntw5/+kZIxBD6JNBVxBAAAEEEEAAAQQQCJ4AAWLwrDkTAqEo4LQAMeGPj5KNPHSNQExtJKJ/3o+68o5QLK/PfSJA9JkuMAd26DNOta6vrG1/77GPJP/2x3YVjsuvY8dPKiYmxo4iNI8dP95jtPLlza0a11XU6TNn7ePPqQWI/UfO0qpPv7XteW6dHmuk/Hlz2wBxw+p5dlly90aAGJgC0yoCCCCAAAIIIIAAAiEuQIAY4gWiewgEWMBpAWLilo+TjixMPtIwQK+jr6wX4EoFt3kCxOB6p3u2ea++p6+/32jnLFy+YJT+2Pq3hj2/QPny5NaTbZrYR5DHTFukXzZt1Yvj+yomJlo//LxZLToPSxIgvjihr26qXsmeb/yMxfrzr52aMuKpi87/0y9/ECCmWxV2QAABBBBAAAEEEEAgcgQIECOn1lwpAikJOC1ATNiy+vych4mJduCUew7EAL+OKnO7oz5gBIghVk7zOHKzjkN1TaWyWjR9kM6ei1ft+7roxMlT+mL5dDticNq8pVq97nu9MLqHfRx52vy3kjzC3KbHGNWoVlHtWjTUiROntHnrDju3olmcpcEdNXX4yHF9uHa9brimgk6eOk2AGGKfAbqDAAIIIIAAAggggEBWChAgZqU+50Yg6wWcFiAm/rnm/ByHrqkOXXMeBvh1VJm6WV9MP/aAANGPmP5o6lx8vGo17KJubR9Qy6bnl/we9J+5+m3zX3pt5mD7eueeA+o6cJIdhWi2OjWr6tOvfnKPQDSPKw8ZP08HDh1Vp1aN7MjFN1es1agpr9gg0mylSxbTjDE9dfjoCRtY8gizP6pHGwgggAACCCCAAAIIhL8AAWL415ArQCAzAk4LEBP+XHNhDsQLqy4nH3noXo3Zv+9HXXFbZsoQcscSIIZcSbzv0N+79qlAbD7lzpXjooPi4xN08PBRFYrL757b0AzX3X/wiLJlu0Sx+fKkfSLmQPS+EOyJAAIIIIAAAggggICDBAgQHVRMLgUBHwScFiAmbl3rXmXZNRLRsnisvvzPc80eYJl8P+qKW33QD91DCBBDtzZZ2zMCxKz15+wIIIAAAggggAACCGSRAAFiFsFzWgRCRMBpAWLC1rXnRyCmOudhKiMP3fv79n5U6TohUlH/dIMA0T+OzmuFANF5NeWKEEAAAQQQQAABBBDwQoAA0QskdkHAwQJOCxATt33mngPRls1jDsRAvo4qfYujPiUEiI4qpx8vhgDRj5g0hQACCCCAAAIIIIBA+AgQIIZPregpAoEQcFqAmLD1swurLycfSRjY11Glbg5EebKsTQLELKMP8RMTIIZ4gegeAggggAACCCCAAAKBESBADIwrrSIQLgJOCxATt607Tx+kkYeuOkeVqh0uJfeqnwSIXjFF4E4EiBFYdC4ZAQQQQAABBBBAAAGJAJFPAQKRLeC4APEvEyD+Mweia0UV15yIgXoddXktR32QCBAdVU4/XgwBoh8xaQoBBBBAAAEEEEAAgfARSJjbPHw6S08RQMDvAtFtFvm9TeUu7P82vWwxcfuXHnMgJkpRUUF5HXX5TV72MDx2I0AMjzoFv5cEiME354wIIIAAAggggAACCISAAAFiCBSBLiCQhQJOCxAT/vry/ByIrlWVL/yZfOShv9+PKlkzC6vo/1MTIPrf1BktEiA6o45cBQIIIIAAAggggAACGRQgQMwgGLsj4DABpwWIidu/liss/KdUiRcmRXT9jf9fEyA67MbgclIRIEDko4EAAggggAACCCCAQEQKECBGZNm5aATcAo4LEHeYADEL5kC8rIajPlWMQHRUOf14MQSIfsSkKQQQQAABBBBAAAEEwkeAADF8akVPEQiEgPMCxPUeTCnMgWiXZ3Zt/ns/6rIbAlGeLGuTADHL6H078ZZtO7Vn/yHVrHa1bw2kctS+A4f17U+bVP+2Cx9wAkS/+tIYAggggAACCCCAAALhIkCAGC6Vop8IBEbAkQGiXTjFFQ4G58+oS68PTIGyqFUCxCyC9/W0C17/QGvWfa+5E/v52kSKx3393UY93mO0/rdm/vn3CRD96ktjCCCAAAIIIIAAAgiEiwABYrhUin4iEBgBxwWIf3/rMQeia67DwP9JgBiYzyeteilAgOglFLshgAACCCCAAAIIIICATwIEiD6xcRACjhFwZIDosQpzaiMRXasw++v9qBLVHPOZMBfCCMQwK6dngHjo8DF1enqift+yw15F5QpX6Omuj6hC2cvt6+adh+n22tdp5SfrtXX7bjVrVE+dWzdWrpzZ7fLlC5as1EuL39fuvQdV/sqS+u2P7YxADLPPA91FAAEEEEAAAQQQQMDfAgSI/halPQTCS8BxAeLO7y+MQHTVwcx5aEYgBvY1AWJ4fe4d11vPAPHw0eNa+t6nql6lnLJnz6a5i1boj207tWT20POBYt3WKlv6UnVs1Ui5c+VQn2EzNGFIZ9WpeY1WrPpKfYa9oC6tG+vWWtfqw0/Wa84r7xIgOu4TwwUhgAACCCCAAAIIIJAxAQLEjHmxNwJOE3BkgOjKDIP4Z1Tx6zL80Thz5qwOHj6mooULKMrM2xhCGyMQQ6gY3nQl+SPMJ0+d0Y+/bNaf23bqp41bbKDomsfQBIgvTx2oalXK2ab7jZipwnGx6tO5mdr0GKOiReI0ekAH+x5zIHqjzz4IIIAAAggggAACCDhfgADR+TXmChFIS8BxAeKuH9KYA9ElkdqciL6/nzxAHDNtkUym47mZvMbkNuYp0RcWvKNp85batwsWyKepI7vr2kplQ+bDSoAYMqXwriOeAaJ5dNksfJIvb27VuK6iTp85q2Ur16UaII6YtFDn4hM0uOdjqtO4q55q11RNG95GgOgdPXshgAACCCCAAAIIIBARAgSIEVFmLhKBVAWcGCCa0XwmpJPOD0EMxmsVuyaJ8eipr+ivv/eob+fm7r/PkSObihcpqO82bNKjT47QwikDVLXilZr84pt6d9UX+ui1CYqODo2RiASIYfYfDc8A0aTXv2zaqhfH91VMTLR++HmzWnQe5lWA2GXA87r6qtJ6sk0TAsQw+wzQXQQQQAABBBBAAAEEAilAgBhIXdpGIPQFHBcg7v7x/JSHQXx82ZwvqvjFAeKhI8fcT4J6fhLGz1isX37fqjnj+ti/3rPvkG5v2t1OUXd1udIh8aEhQAyJMnjfCc8A0QxtXb3ue70wuofOnYvXtPlvpfkIs+cIxFeWrrILqIwa0EFFCsVq6rylWv7hF8yB6H0p2BMBBBBAAAEEEEAAAUcKECA6sqxcFAJeCzguQNz1o2TmE0z0GHkYhNdRxaomMTcjEFd+8o1uql5JcbH5VO+W6rr+mvJ2n97PvaC42Lwa+FRL9zFmWrrpo3rotlrXel27QO5IgBhI3QC0vXDJSq3+/DvNndhPO/ccUNeBk+woRLPVqVlVn371U5ojEOMTEvVsj1bad+Cw2vcea1deNtvNNaro8282ECAGoGY0iQACCCCAAAIIIIBAOAkQIIZTtegrAv4XcFyAuPsn/yN50WLyANFMOffn9l3KkT2bNvy6Ras+/dYudHtX3RvVoc84VShbSr06PuxuuUaDjhrSu7XuveMmL84W+F0IEANvHPAz/L1rnwrE5rMrLWdkM8//79pzQHEF8ilnjuxJDz2xLyNNsS8CCCCAAAIIIIAAAgg4RIAA0SGF5DIQ8FHAeQHihgsDEM1zxWYkomtAYmBfq2iVNCvQf+QsHTp8VDPG9LIjEM3CKQO6Peo+hhGIPn6AOSzIAgSIQQbndAgggAACCCCAAAIIhIYAAWJo1IFeIJBVAo4LEPf878IqzC7R1CZD9O/7UekEiM/PXqL/+/E3u3CKmQPx183bNGtsb9sJ5kDMqk8/5824AAFixs04AgEEEEAAAQQQQAABBwgQIDqgiFwCApkQcGKAeGHKQ/fqy+4I8cLqzIF4X0UrJ6nCxFmv6/76tVWqZHEbFj7efYzatbhXT7S8z2MV5oGqevWVmjRniVas+pJVmDPxOebQYAkQIAZLmvMggAACCCCAAAIIIBBSAgSIIVUOOoNA0AUcFyDu/TlrVmEuWilJ7f79xFA796Fra3z3LRrUo5WdUs5MMWcWt52x4B37du5cOTVrbC9Vq1Iu6PVP7YTMgRgypQixjhAghlhB6A4CCCCAAAIIIIAAAsERIEAMjjNnQSBUBZwYIEYpyoZ0UWbEoRIVjNcqcvVFJT567IQOHj6qIoXilCtnsrUoJJ06fUYHDh5R8aKFFB1txkmGzkaAGDq1CK2eECCGVj3oDQIIIIAAAggggAACQRIgQAwSNKdBIEQFnBcg/pJE2vX4cmr8/no/KoUAMURL7lW3CBC9YorAnQgQI7DoXDICCCCAAAIIIIAAAlLC60/CgAACESwQ/dBU/1997sL+b9PLFhP3/vLP3IfuOQ89RiS6Rib6+U8VruhlD8NjNwLE8KhT8HtJgBh8c86IAAIIIIAAAggggEAICBAghkAR6AICWSjguABx38YLmv4aW5hacZK2H0WAmIWfYk4dPAECxOBZcyYEEEAAAQQQQAABBEJIgAAxhIpBVxDIAgHnBYi/6vwqyxfmPpQuvJb+WY3Z/++rUIUsqF7gTskIxMDZhnfLBIjhXT96jwACCCCAAAIIIICAjwIEiD7CcRgCDhFwWoCo/b8pMdGkhjIp4YXHmQP/OqpweYd8Is5fBgGio8rpx4shQPQjJk0hgAACCCCAAAIIIBA+AgSI4VMreopAIAScFiAm7vst5TkQk6/GnHwOxEy+r0LlAlGeLGuTADHL6M+feN+Bw/r2p02qf9sNWdyTZKcnQAytetAbBBBAAAEEEEAAAQSCJECAGCRoToNAiAo4LUDU/k1yD0D0MHc/vmwGJroGJPrzfQLEEP2Eh2m3vv5uox7vMVr/WzPfb1fQb8RMtWtxr8qVKel7mwSIvttxJAIIIIAAAggggAACYSxAgBjGxaPrCPhBwGkBYuL+TVmzCnPBq/xQjdBpghGIWVyLQASIleu21ryJ/XVjtUwsGU6AmMWfDE6PAAIIIIAAAggggEDWCBAgZo07Z0UgVAScFiDqwO92hKFrS74Wc6BeRxEghspH2vt+JCQkav7i9zTv1fd04NBR1bqhsk6fPquFUwbYRpp3HqYOjzbUp1/9pF82bdXwfm1VuGCs/jNtkVZ+sl758uZS04Z17T6XxMTo9eVrtG37HvXq+LA9fueeA+o+aIpenNBXefPksu3dXvs6e+zW7bvVrFE9dW7dWLlyZrer/ixYslIvLX5fu/ceVPkrS+q3P7bbEYjLVq7T5+s3aPSADrbdcTNeU+G4WLX+9932devuo/VUuwdV5vIS6vT0RP2+ZYf9+8oVrtDTXR9RhbKXa8LMxXpx0QqVLFFEBfLnVZN76tjzr//hV42d/qr+2LZT/7r1ejVvcqeqViyT6vWXLZLde2D2RAABBBBAAAEEEEAAAccIECA6ppRcCAI+CTgtQEw88Lt74RT3asxRUXZhlUC+VlxZn/xD9aCIGIG49L1P9cyYF9XziYdV+4bKeu/jr2zI5nps2IzYM9sjD/xLlxYvpLvq3qiJMxdr4+/b7DEHDh3RqCmvqHv7pnrkgTs1/aW3tfH3rZo8rJs9btuO3WrwSD+tWzZNsfnyyLRXtvSl6tiqkXLnyqE+w2ZowpDOqlPzGq1Y9ZX6DHtBXVo31q21rtWHn6zXnFfetX359qff1LLrSK1/f5aio6NUvX57FSsSp1WLJ2j7zr26u0Vfff72VEVFR8lcU/Uq5ZQ9ezbNXbTCBoNLZg/Vpi3b1fjxZ9S3S3NVKldaxYsWtDdFg0f62sDT9OGD1d/ozffW2najoqJsf5Nff4m8CaH6maVfCCCAAAIIIIAAAgggEEABAsQA4tI0AmEg4LQAUQf/sIO5TP4R1D8LEiCGwcc9aRdNKFfqsqIa0b+dfSP5Y8MmQJsxppfq1Kxq3z9x8pRqNOiosYM66Z47atq/Gz31FX317c9aOne4VwHiy1MHqlqV8yvumDkJzUjCPp2bqU2PMSpaJM49ytCzL2fOnFW1+u21cMpAHT9xSjMXvqNfN/+lBZOftiHljAXv2POb7eSpM/rxl836c9tO/bRxiw0UPQNRz0eYp89/S8s/+kLjB3e2x547F69mnZ7TG3OeU8WrStkA0fP6zyPsC7s602EEEEAAAQQQQAABBBDIvAABYuYNaQGBcBZwWoCYePAPRckVHroWTAn8a8Wdf+rTKVtEjECs07irurd/SA/ee6utW0oBomfgt2XbTjVs9bRWvDxGpUsWs8cs//ALDZ3wkr55b0aGA8QRkxbqXHyCBvd8TKYvT7VrqqYNb0uxLx36jFOt6ytr29977CPJ5vHmwnH5dez4ScXExNhRhObRZbPwSr68uVXjuoo6feasffw5tQCx/8hZWvXpt7Y9z63TY410c40qNkD0vH4CRKfc3lwHAggggAACCCCAAAIZFyBAzLgZRyDgJAGnBYg6uMVdHs/Vlj1XYXbt4Nf3CRDD77bo/dwLKlG0kHvOwvQCxMNHjqv2/V00bWR31a19nb3gqXOXasXHX9pQcebCZfr+f7/rhdE97HspPcLsGch5BohdBjyvq68qrSfbNEkxQDTzNH79/UY7Z+HyBaP0x9a/Nez5BcqXJ7c9xjyCPGbaIjtX44vj+yomJlo//LxZLToPSxIgmvkYb6peyZ5j/IzF+vOvnZoy4qkUi0eAGH6faXqMAAIIIIAAAggggECgBAgQAyVLuwiEh4DTAsTEg1uC//iymVyxwBXhUXAvexkRIxDf/uBzDX9+oTo9dr+KFCygl17/wAZwniP2ko/Ae/TJEcqbJ6cG92ytg4ePqsfgaap/Ww0bQpoA0gSB5hFgE+CZOQwXv7M6yRyIqQWIryxdZRdQGTWgg4oUitXUeUvt6EZXX8zjyM06DtU1lcpq0fRBOnsuXrXv62Ifq/5i+XTlz5tb0+Yt1ep139sA0zyOPG3+W0keYTaPSdeoVlHtWjTUiROntHnrDju3olmcpcEdNWUC0g/XrtcN11TQVWUuYwSilzcLuyGAAAIIIIAAAgggEAkCBIiRUGWuEYHUBZwWIOrQn3YVZs8Rh67XLoWAvE+AGH63mXnEd/KcN7R63XcqWjhO5a+83AZu5nFks6U0As88xvzUoCnavPVvu48ZiWgCOPPYsAn1uj87RWvWfW/fu6tuDX2w5ps0A8T4hEQ926OV9h04rPa9x9pHk81mHiH+/JsN7gDxXHy8ajXsom5tH1DLpvXtPoP+M1e/bf5Lr80cbF+bVZ+7DpxkQ1CzmbkbzQrSrhDSPK48ZPw8u+J0p1aN7MjFN1estQvBmCDSbObR7BljeqrUZcUIEMPvI02PEUAAAQQQQAABBBAImAABYsBoaRiBsBBwWoCYeOjPf0YgmrkQ5bGgSgBfK7Z0WNTb205GxAjE+PgEu6qxWXHHbLP/u1xrv/xRC6cMSNdpz75DypEjm11dOfm2/+AR5c6VU7lyZk+3Hc8dzKo/u/YcUFyBfMqZI2PHerbz9659KhCbz670nHwz12xGThaKy+++bnNe0+ds2S5J8XqStMEiKhmqKTsjgAACCCCAAAIIIOAUAQJEp1SS60DANwGnBYg6vE1KTLRDEO0fJhsKxusCBIi+fQKz8KjvNmxSr6HTVbn8FTp5+oy+WP8/zRnXR7VuqJyFvQrxUxMghniB6B4CCCCAAAIIIIAAAoERIEAMjCutIhAuAk4LEBMPbZM7M3SHiK4wMXB/KrZUuJTcq35GxAhE89iueUx47/7Ddg7BalXL6bLihb0CitidCBAjtvRcOAIIIIAAAggggEBkCxAgRnb9uXoEnBYg6vBfknsWxNTqm3xWxOT7+fB+7OWO+jBFRIDoqIoF62IIEIMlzXkQQAABBBBAAAEEEAgpAQLEkCoHnUEg6AJOCxATD/+VNasw5y8Z9NoF8oQEiIHUDee2CRDDuXr0HQEEEEAAAQQQQAABnwUIEH2m40AEHCHgtABRR84vYms2H8YRJqlpho4nQHTE/cBFpCdAgJieEO8jgAACCCCAAAIIIOBIAQJER5aVi0LAawGnBYiJR7ZfPAIx+erLUVEyC8+aBVbsn354X/kv89o8HHZkBGI4VCkr+kiAmBXqnBMBBBBAAAEEEEAAAa8FhlQv4fW+7IgAAgh4KzDk253e7ur9frmzcB2Kozv+GXroGkIYjD8JEL3/fLBnGAsQIIZx8eg6AggggAACCCCAQCQIECBGQpW5RgSCL+C0ADHx6N+KMo8vXxhh6BJ1jziUAvK+8l0a/OIF8IyMQAwgblg3TYAY1uWj8wgggAACCCCAAALOFyBAdH6NuUIEskLAaQGijpoRlcEYcmhjSlccKQLErPj0cs7gCxAgBt+cMyKAAAIIIIAAAgggkAEBAsQMYLErAgh4LeC0ADHx6M6sWYU5b3GvzcNhR0YghkOVsqKPBIhZoc45EUAAAQQQQAABBBDwWoAA0WsqdkQAgQwIOC1A1LFdGbh6P+5KgOhHTIc0tf6HXxUXm1dlr8jYCjsnTp5W9uyX6JKYmIskNm3ZrqPHTqh61fJZo0SAmDXunBUBBBBAAAEEEEAAAS8FCBC9hGI3BBDIkIAjA0SP1ZXtY8ZBeK08xVJ033fgsPLkzqVcObNnqC5ZvTMjEP1QgU79J6p61XJq/0hDr1s7eeqMbri7g6aMeEr1bq520XHTX3pbG3/fqsnDunndpl93JED0KyeNIYAAAggggAACCCDgbwECRH+L0h4CCBgBxwWIx3dnyRSIyps0QNy2Y7c69pugrdt32w/aA/fcqmd7PqZsl1w8qCwUP4kEiH6oii8BYkJCog0IS15aVPnz5iZA9EMdaAIBBBBAAAEEEEAAgUgSIECMpGpzrQgET8BxAeKx3XbEoRI9Rh6a164FTy78vd/fz1M0SdE69BmnvHlyaUT/9tq1Z78efmKonu3RSvfVrx284mbiTASIHnh//b1Ho6e+oq+/26icObKp1vWVNbxfW/22ZbvGTF2khVMGuPfu2G+82j9yn66/prxMgGiGnh45dkJfrP+fqlUpp1ED2uvyS4vq1OkzGj/jNb2/+mudOn1W11Yuq4HdHlWZUiX06JMjNPCpR3V1udI6cfKU/jPtVb276kt77ty5cqrCVZe7RyCax6THTn9Vf2zbqX/der2aN7lTVSuWsf1p3nmYbq99nVZ+st4m2c0a1VPn1o1tn8yy5G+8u1b/ffNDbd+5TxWvKqWeTzxk+5hWm2IEYiZuKw5FAAEEEEAAAQQQQCDwAgSIgTfmDAhEooDjAsTje5OtwuyqarJVk+V67af3PQLEw0ePq/Z9XfTy1IE2jzHbiEkLtWvPAftkajhsBIgeVTJpcExMjLq3b6ojR49rybuf6Nkej2nDr1vUpscY/W/NfPfedRp31bC+bVW39nU2QNyw8Q89+XgTFYjNq0lz3tD111TQsL5tNOeVd/XS4vc1dWR3xcREa/Xn3+mm6pVU47qKqly3tRZMHmBDyKETXtInX3yvLq2b6Koyl2nGgneULVuMDRC37dijBo/0Va+OD6tOzWv0wepv9OZ7a7Vq8QS7kpBpp2zpS9WxVSMjDgYCAAAgAElEQVTlzpVDfYbN0IQhne2+y1auU/+Rs9St7YOqdX0lrVv/P+XPl0e33Fg1zTYJEMPh9qWPCCCAAAIIIIAAApEsQIAYydXn2hEInIAjA8SLssIoO+DKcyCia0Di+T8z/75yF3YXafOfO3R/64Fa88bzKlKogP37hUtW6u0PPteS2UMDV0w/tkyA6IFpRgQWLhirAd0eVdHC5wtqtq+++yXdANFzDkQzEnDYxJf06VtTNHXuUi37cJ0mD++m8leWtIGfa3MFiNdcfaWu+1c7GziaZ+DN5jkH4vT5b2n5R19o/ODO9r1z5+LVrNNzemPOc3ZEoWnHM8XuN2KmCsfFqk/nZnaUY8lLi2j0gA5JPjbptUmA6Me7jKYQQAABBBBAAAEEEAiAAAFiAFBpEgEEnDcHYpInLG066FHlAL72CBC/27DJ5jPrlk1TbL489vyLl63RjAVv6+PXJ4bFp44A0aNM5tHl/iNnavfegypZoojaPXKvHmpYN8MB4m9/bFeTNs9o9ZLnFZ+QoIGjZts2zGPJzRvXc48UdAWIJqy8u0VfLVswSleWKnFRgGhGEK769FtVKHt5kg9Vp8ca6eYaVS4KEM0w2HPxCRrc8zHVaNBR/Z9soQfvPR9Murb02iRADIv7l04igAACCCCAAAIIRLAAAWIEF59LRyCAAo4bgRhAK2+bdo1A/OTNSXbgmtkYgeitXojuFx+foC3bdurDT9fb0YPLF4zSgUNH1arbyDQfYfYcgbhi1VfqM+wFfbtytnJkz2avdOfu/fr6+40a/vxCPd21hR1p6B6BWKmsrruzreaM66NaN1S+KEAcP2Ox/vxrZ6rPxScfgegZIJogs2b1SjZE9NzSa5MAMUQ/oHQLAQQQQAABBBBAAIELAgSIfBQQQCAQAgSI/ldNaQ7EYRMXaM++g8yB6H/uwLdoQrWmDW9TqcuKauPv29S0/WD7LHrpksXsSL5pI7vbRVDe+/hrO9mlee2aA7FkicLq27m5XcTEzGeYP19u+75ZvMQsknJNpbI6fuKUHZnYp1MzNahXM8kciN0GTZYJL/t1aa7DR47bNi4tXsjOgfjtT7+pZdeR9jHkBnfUtO9/uHa9brimgp0vMa0Acdq8pXr17Y818un2Npxc//2vOnbipAoWyJdmmwSIgf+8cQYEEEAAAQQQQAABBDIjQICYGT2ORQCB1AQIEAPz2WjXe6zy582jEf3bsQpzYIiD12rXgZP08eff2RMWKxKnFk3uVLsW99rXZs7AafPfsv/fhIZr1n2v6aN66LZa19pFVMyKxmYlZbOZwHDK8G4qUayQ5r66QiaYNJt5hLn+bTdoaJ/HdUlMjA3+zMrO1auWl3kevkOf8bYNs58JBosUinWvwvzmirUaNeUV9zlMqDljTE+VuqxYigFifEKiXQ785KkzGv78Ar31/mfuPox55gnVu7ma0mqTADF4nzvOhAACCCCAAAIIIICALwIEiL6ocQwCCKQnQICYnpBv75unXTv2m6DtO82q0FLju2/RkF6tlS3bJb41GOSjmAMxGfip02d05OiJJIuouHYxIwjNAiax+c9PeJl8O3j4qE6dPqsSRQsmeetcfLz2HziiQgXz2+Awte3suXg7fLV4kYJ2xebkm1khaP/BI/bD5Zp009vPy5kzZ3XoyHEVisufpO1U20wyyai3Z2E/BBBAAAEEEEAAAQQQCJYAAWKwpDkPApElQIAY2HqbdTfy5smlPLlzBvZEfm6dANHPoI5pjgDRMaXkQhBAAAEEEEAAAQScKUCA6My6clUIZLUAAWJWVyA0z0+AGJp1yfpeESBmfQ3oAQIIIIAAAggggAACaQgQIPLxQACBQAgQIAZCNfzbJEAM/xoG5goIEAPjSqsIIIAAAggggAACCPhJgADRT5A0gwACSQQIEPlApCRAgMjnImUBAkQ+GQgggAACCCCAAAIIhLQAAWJIl4fOIRC2AgSIYVu6gHacADGgvGHcOAFiGBePriOAAAIIIIAAAghEggABYiRUmWtEIPgCBIjBNw+HMxIghkOV6CMCCCCAAAIIIIAAAggggAACCCCAAAJZJECAmEXwnBYBBBBAAAEEEEAAAQQQQAABBBBAAIFwECBADIcq0UcEEEAAAQQQQAABBBBAAAEEEEAAAQSySIAAMYvgOW3oCpyLj1d0VLSio6Mu6uT+g0cUEx2tArF5U7yAfQcOK2+eXMqZI/tF7585c1YHDx9T0cIFFBV1cduhKxK6PQtUrUL3isO3Z2nV6viJUzL/K1Io9qJ7IyEhUXv2H1RsvrzKlfPi+8r1fuGCsbokJiZ8gUKo54GqVQhdoiO6cvjocZ0+fdZ+p6S0pfedY76v8uTOxX0VhE/DyVNndPDQERUvWijFf1ukV6ujx07I3JdxsfmC0NvIPkVma5WYmKj4hIQUv4/4vvLvZyuQtfJvT2ktvVqldW+Y//bt3X9YBQvkU47s2VL9+Su17zP0MyYQ6FplrDfsHYoCBIihWBX6lGUC5j+a/35iiDo8ep8a/quWux87du1Tz8HTtOHXLfbvalxXUeMHd1ahuPz29effbNDUeUu1/e89OnX6rGrdUEkj+7e3YaL5x+QLC97RtHlL7b7mC3DqyO66tlLZLLtOJ5w4ELU6cOio6jTuehHPixP66qbqlZzAliXXkFqtzA/Ng8bO1fIPv7D9KlYkTpOGdVPVimXc91X/ETNl6mK2Fk3u0NNdH3X/AP7JFz+o93Mv6MTJU/b9wb1a6+H76mbJNTrlpIGq1f2PDdDmrX8nYerSurE6t27sFLqgXYcJ/lp1G6mt23fbc5YtfanaP9JQ99WvbV+n952zbcdudew3wX38A/fcqmd7PqZsl5wP4Lmv/FvKrgMn6ePPv3N//ze+u456dXzYq1qZ/7b1Gz7Tffw1lcpqyvBuMr8wMRv3VejUytWTZSvXaeLs1/Xx6xOTdI77KnxqxX0VvFql950z+7/L9fzsJe4O3VW3hgb3bK3Y/Hns36X3febfK3F+a2l9X2W2VmOmLdKC1z9IglitSjm9PHWg82EddoUEiA4rKJfju8C4Ga9p3qvv2QbGDHwiSYA4ZNx87dq7X0N7t7G//Xqi73iVveJSjXy6vR0VcO0dbfVkmybq2PJ+nTx1Wk3bD1bThrepTbN79N2GTXr0yRFaOGWAqla8UpNffFPvrvpCH702IcWRCL5fQeQcGahamRGmtzbpphljeqnUZUXdoEULx6U4SidyxH2/0rRq9e6qLzV6yn81a2xvXVWmpEZOflnrvtmgd14aKTPi5rYHnrLBSIdHG2r7zn1q0uYZDe/XVk0a1JEJukytzH33yAN3as267/XUoCn6YNFYlSxRxPcOR/CRgaqVK+i4985auvv2G93CsfnypDqaO4LLkO6l79l3SG+9/6nuv+tm5cmVUwuXrNS8197X2qWT7X+n0vvO6dBnnP3l1oj+7bVrz349/MRQPdujlQ0gua/S5c/wDlPnLlX9ujXsd8qX//ezugx4Xq++8KyqXn1lurWa88q7en3ZGi2cMtDWtlP/iSpTqoSG9W1j+2GCDu6rDJck1QMyUysTZLTvPU7bd+61vwzzDBC5r/xXI1dLgaoV91Vwa5XevfH68jW6/NKiurbSVfrr7z1q23OM2ja/V63/fbftaFrfZ/6/Eue3mNZ9ldlajZ76iq1h387N3ZA5cmRT8SIFnQ/rsCskQHRYQbkc3wUOHT6mU2fOqEXnYerZ4WF3gHjk2AnVathZ00f10G21rrUn+Pizb9X1mcnasHqe/YGrRoMn3MGGeX/AqNmKiYmx/8gfP2Oxfvl9q+aM62OPNT/83d60u5bMHqqry5X2vcMRfGSgauUKEJcvGGV/SGPLvEBqtTItm2C9etVy6vnE+dE4Bw8f1S2NumrxzCHavfeAvcfWLZsmEzSZzfz20vyQNm1kdztKqvPTE/XdytnKfuGRlnse7WfDxEce+FfmOx6BLQSqVq4fyMw/+M1oNzb/CpjA4q7mfewvqapXLZ/md86lxQur9n1d7G/8zW/+zTZi0kLt2nNAU0Y8xX3l39Kk2Fq9h3qoWaN69kmH9P59YH4ZaUbcmF+kmO2DNV+r55Dp9t8eZioUEyByXwWuaBmplfllshkd/PFn32nOK8uTBIh8XwWuRq6W/VUrvq+CW6uM3huD/jNXO3bu1dyJ/WSm8Ujr+yzwV+L8M3jeV5mplZEyAeKhI8c0ekAH58M5/AoJEB1eYC4v4wLmB7GubR5wB4jHjp9UzXs72VFpdWpWtQ1u/H2bHmz3rB3xYR5jnjBzsV5ctEKPN2tgQ0HXqCrz/80jlnGxeTXwqZbuzlSu2zpJIJnxXnKEEfB3rVwBYr2bqyk2f16Vv7KkGt19izvAQt13geS1Mi39+4mhqnVDZXVv3zTJvTFpWFflyJ5dHfuN15fLpytf3tz2/VeWrtKrb62yIxQXL1uj+a+9pxUvj3Efax69uOLyEu7HA33vbWQf6e9auX4gy5Mnl33c9tJihex/X0tdViyyof109Uvf+1TPjHlRn741xU6RkdZ3TskShXV/64Fa88bzKlLo/NyJZgTj2x98bn+pxX3lp6Kk0ox57Nz8osP1C8n0/n1Qo0FH+8tJEyKa7eff/tRDHYa4f7FiAkTuq8DULKO1cvXivY+/0tgXXk0SIHJfBaZGrlb9WSu+r4Jbq4zcG2fPxeuu5r117x217L/zNv+5I83vs8BeifNbT35fZaZWRssEiCs/+cZOCWXm8613S3Vdf01550M68AoJEB1YVC4pcwIp/fBsHhv6dfM2Gyxmu+QSrVz7jVZ9+q07QPzy25/V57kXZOYnMo9S3lyjisY+28kGT2Z4fYWypZKEGuaHgiG9W+veO27KXGcj/Gh/18qExZPmLJF5ZNk8Qmt+MDdzTb02Y7B7lFuEk/t8+SnVav7i9zV2+qvq06mZShQrqB9//kPm70yAaOYZbdiyv8qVKal/N7pdh4+e0KKlHyk+PsEGiObRvvdXf21DD9dmfhjPmzuXvbfYfBfwd61MT8wcsNEx0UpMPD+C2/zD9I05QwkRfS+TPXLTlu1q0Xm4HnvoLvs4v9nS+s4x4a0Z+es5stf8UDBjwds28OC+ymRB0jjcLBT16JPDlTdPbs1/vr9iYqLTrNU99Wqqyu2PJ/llo+sH5o9eG68SxQpxXwWoXBmtlee/5VIKELmvAlQoyS7AlpH7Kr1a8X0V3Fpl5N4YPG6eVqz6Su8uHG0XD3NN15Ha91ngrsT5Lad0X2WmVkbMzA/75/Zddiows6aA+Tl6wpDOuqvuP1PbOF/WGVdIgOiMOnIVfhRI6YdnEyaZ/3D+8PNm5cuTS2fPndOnX/1kHyMyjzibIfRmOH3NalfbRyyfHDDJzulm/sNoQg0zKmRAt0fdvWQEon8K5u9aJe/Vlm071bDV01o0fZANh9l8F0ipVmaxh9feWW0DJbNdVqKIFr+z2j7CXLnCFTZkMhNomz8vK1FYP//6py6/rKh9hDkjvwn1vdeReaS/a5Vc8ezZc7qrRR+1fLC+HbXN5puAWdyrZdcRNmw3i3aZQMpsaX3nuEYgfvLmJPdCHIxA9M0/I0eZqU6eGjTZPiq+YPIA99yf6f37wPyycUT/dqp/2w32dMlHIHr2gfsqIxVJfV9fa+VqkRGI/qmDN60EolZ8X3kjn/F9UquVt/+Wmz7/LU2b/5ZenTHYvdCe6xcqqX2fZbyXHGEEAlGrlGT7j5ylQ4eP2if82MJLgAAxvOpFb4MgkNIPz8lP26bHGOXJndPOGWWCRPOo5edvT3X/UGBWmZoyd6m+eW+GnePIjF40C0WYjTkQ/VdEf9cqec/Mb+BuvKejOxz2X88jryVvajXr5WWa/d933YtBeCqZ1UjND9P9n2yhlk3ru+dq+/7DOcqW7RK7qzlHq4fqMwdiJj9e/q5VSt0xj6/fVvs6dX6sUSZ7G5mH/75lhx7vMdo+AjSoRytdEnN+BWWzpfWdk9IciMMmLtCefQeTzIHIfeW/z5X5JWO3Zybr5MnTmvmfXkkWDkrv3wdmDkSz8FC7FvfaDiWfAzF5L7mvMle3zNTKdeaUAkTX3GHcV5mrj+fRgaoV31f+q5GrpbRqld69kZCQqPEzXrO/NH5pUn9VKn+Fu4MpzYHo+X3m/ytxfouBqlVKcmZ17f/78Tc7fzNbeAkQIIZXvehtAAXMBNiJCYl2xFnHVver4Z213MGEebTVTFhu9ln+4TqNnPxf92/BzCiQ+s162x+EzaToJ0+fUef+E+28bS+M7uGxyuJAu+qieUR2xaovWYU5E7UMVK3MP2ROnT6tm66vrGyXxOj52W/Yx5g/WjyeeRB9rFdatTL/MNx/8LC9V775fqNdHKBb2wdsQGg2Mydl/nx57D5TXnxTa7/8wa6ynDtXTp04edouXtSvS3O1YBVmH6uT9LBA1cqMyv748+9sEFIoLlYfrP5a/UbMtCOxmP8m46X7dfNfeqDtIDsFRte2Dyg6+vzIw9y5cth5hf5ZhTnl75x2vccqf948dmRb8lWYua8yXo+0jjCezToOtf92mDj0Sbv6tdlMzUoULZhurcwI7CXLP7GrMJv6duw3wb0KM/dVaNXKjKg/dy7eTq0xcfbr+uCVsYqKjrLhPvdV+NSK+yq4tUrv3jDz+5p/h5tRaleW/mdxQ7PSubm30vo+8++VOL+19L6vMluribNe1/31a6tUyeJ2YM3j3cfYX4490fI+5+M67AoJEB1WUC7HdwETXpjf7nturtV4P/9mg52ryGxmEYChfR53r2Bp/s4ct3DJhzI/2JnNPG5kfrAzS9Obf1ROnbdUMxa8c+GHvJyaNbZXkuN973VkHhmoWn24dr0GjJojM9rNbObR87GDOumm6ytFJrQfrjqtWrlGFbqsu7Z9UA/fV9d9VvPbSfMDtNnMAkZDe7eR+UejazOhlFk4xbU9072lmje+ww+9jswmAlUr8wNZ6+6jtXvvQTesCX5bPXRXZEJn8qrNCCfz6Gvy7b76te3qhul955ipGUwQZVZvNlvju2/RkF6t3b8w477KZIE8DjefebOKZfLNfLeYRW/Sq5UZBW9qbX55YrYqFcrYkaJm/i/uK//VybSU2VqZUcGNHh+YpFOue9L8JfeV/+oVyFpxX/mvTt7cV+ndG+aJCNd3lWfPzAJ6pUsWU3rfZ/69Gme3lt59ldlamRHyZu5D12b+7WGeoMiZI7uzYR14dQSIDiwql+R/ATN6YOfu/XbFZTP6KbVt7/5DdsSUmSA2+Xbq9BkdOHhExYsWUnR0lP87SYtWILO1MsfvP3DEtmV+SDMjT9kCJ7Br7wG7MJG5t5Jv5redZvShuWfMiNCUNrOoimmjaKEC7gAkcL2N7JYzUysTlBw4dNSG82bxB89HbiNbNXBXn953jvlhwYyIM9NxJN+4rwJXl5RaTq9W5lE9M8ehWdTLc+O+Cm6dzNnSq1VaPeK+Cm69fK0V91Vw62TOltl7I63vs+BfjbPPmJlamTUFDh4+qiKF4pQrJ8FhuH5SCBDDtXL0GwEEEEAAAQQQQAABBBBAAAEEEEAAgSAIECAGAZlTIIAAAggggAACCCCAAAIIIIAAAgggEK4CBIjhWjn6jQACCCCAAAIIIIAAAggggAACCCCAQBAECBCDgMwpEEAAAQQQQAABBBBAAAEEEEAAAQQQCFcBAsRwrRz9RgABBBBAAAEEEEAAAQQQQAABBBBAIAgCBIhBQOYUCCCAAAIIIIAAAggggAACCCCAAAIIhKsAAWK4Vo5+I4AAAggggAACCCCAAAIIIIAAAgggEAQBAsQgIHMKBBBAAAEEEEAAAQQQQAABBBBAAAEEwlWAADFcK0e/EUAAAQQQQAABBBBAAAEEEEAAAQQQCIIAAWIQkDkFAggggAACCCCAAAIIIIAAAggggAAC4SpAgBiulaPfCCCAAAIIIIAAAggggAACCCCAAAIIBEGAADEIyJwCAQQQQAABBBBAAAEEEEAAAQQQQACBcBUgQAzXytFvBBBAAAEEEEAAAQQQQAABBBBAAAEEgiBAgBgEZE6BAAIIIIAAAggggAACCCCAAAIIIIBAuAoQIIZr5eg3AggggAACCCCAAAIIIIAAAggggAACQRAgQAwCMqdAAAEEEEAAgcgVOHrshL75fuN5gKgo5cqRXRWuKqWCBfJ5jXLy1Bm16TlGTz7eRDfXqJLicT/+vFmjpr6iycO6qkihAtq2Y4/+78dfVbf2dYqL9f5cXndKCso5MtIf9kUAAQQQQAABBBAIjAABYmBcaRUBBBBAAAEEELACv2zaqqbtB1+k0aV1Y3Vu3dgrJRNC3tSws8YO6qR77qiZ4jGff7NBHfqM0we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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ Saved: /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults/burst_terms.html\n" ] } ], "source": [ "N_BURST_TERMS = 20\n", "\n", "def doc_year_series(df_local):\n", " \"\"\"Return only rows that have a parseable year, with year as int.\"\"\"\n", " sub = df_local[df_local[\"year\"].notna()].copy()\n", " sub[\"year\"] = sub[\"year\"].astype(int)\n", " return sub\n", "\n", "\n", "def term_year_counts(docs, term):\n", " \"\"\"Count of docs (per year) whose text_content contains `term` as a word.\"\"\"\n", " pat = re.compile(rf\"\\b{re.escape(term.lower())}\\b\")\n", " mask = docs[\"text_content\"].str.lower().apply(lambda t: bool(pat.search(t)))\n", " return docs.loc[mask].groupby(\"year\").size().to_dict()\n", "\n", "\n", "def top_corpus_terms(docs, stopwords, n=N_BURST_TERMS):\n", " vec = TfidfVectorizer(\n", " max_features=3000, stop_words=list(stopwords),\n", " ngram_range=(1, 2), min_df=5,\n", " )\n", " X = vec.fit_transform(docs[\"text_content\"].tolist())\n", " means = np.asarray(X.mean(axis=0)).ravel()\n", " vocab = np.array(vec.get_feature_names_out())\n", " # Filter out terms that are basically year strings or single chars\n", " keep = [(t, m) for t, m in zip(vocab, means) if not t.isdigit() and len(t) > 2]\n", " keep.sort(key=lambda x: -x[1])\n", " return [t for t, _ in keep[:n]]\n", "\n", "\n", "docs_year = doc_year_series(df)\n", "if len(docs_year):\n", " year_min = int(docs_year[\"year\"].min())\n", " year_max = int(docs_year[\"year\"].max())\n", " years_axis = list(range(year_min, year_max + 1))\n", "\n", " burst_terms = top_corpus_terms(docs_year, ALL_STOPWORDS, n=N_BURST_TERMS)\n", " print(f\" Window: {year_min}–{year_max} ({len(years_axis)} years)\")\n", " print(f\" Top {len(burst_terms)} distinctive terms identified\")\n", "\n", " matrix = np.zeros((len(burst_terms), len(years_axis)))\n", " for ti, term in enumerate(burst_terms):\n", " yc = term_year_counts(docs_year, term)\n", " bursts = kleinberg_bursts_annual(yc)\n", " for b_start, b_end, intensity in bursts:\n", " for y in range(b_start, b_end + 1):\n", " if year_min <= y <= year_max:\n", " j = y - year_min\n", " matrix[ti, j] = max(matrix[ti, j], intensity)\n", "\n", " fig_term_burst = go.Figure(go.Heatmap(\n", " z=matrix, x=years_axis, y=burst_terms,\n", " colorscale=\"Oranges\",\n", " colorbar=dict(title=\"burst
intensity\"),\n", " hovertemplate=\"Year %{x}
Term: %{y}
Intensity: %{z:.2f}\",\n", " ))\n", " fig_term_burst.update_layout(\n", " title=\"Term-level bursts in the SUBSIDE literature (annual)\",\n", " xaxis_title=\"Publication year\",\n", " yaxis_title=\"Term\",\n", " height=600, plot_bgcolor=\"white\",\n", " )\n", " fig_term_burst.write_html(str(OUTPUT_DIR / \"burst_terms.html\"),\n", " include_plotlyjs=\"cdn\")\n", " fig_term_burst.show()\n", " print(f\"✓ Saved: {OUTPUT_DIR / 'burst_terms.html'}\")\n", "else:\n", " print(\"(no entries with valid year — skipping term burst heatmap)\")\n" ] }, { "cell_type": "markdown", "id": "2dd74329", "metadata": {}, "source": [ "### 8.3 **Domain-level** burst heatmap\n", "\n", "For each backbone domain, count the number of documents per year whose `text_content` matches *any* of that domain's terms (from §5). Run the burst detector on those annual counts. This produces the *DOMAINS through time* view the user asked for.\n", "\n", "This is the most informative view for a 30-second skim of the corpus: it shows the rise of *Geodesy & Remote Sensing* alongside InSAR maturation, the recent surge in *Computational & Data Science*, and so on.\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "e80d834c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_3873435/1550925816.py:58: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n", "/tmp/ipykernel_3873435/1550925816.py:58: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n", "/tmp/ipykernel_3873435/1550925816.py:58: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n", "/tmp/ipykernel_3873435/1550925816.py:58: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n", "/tmp/ipykernel_3873435/1550925816.py:58: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "colorbar": { "title": { "text": "burst
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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ Saved: /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults/burst_domains.html\n" ] } ], "source": [ "def domain_year_counts(docs, domain_terms):\n", " \"\"\"Annual count of docs matching ANY term in domain_terms.\"\"\"\n", " if not domain_terms:\n", " return {}\n", " pat = re.compile(r\"\\b(\" + \"|\".join(re.escape(t.lower()) for t in domain_terms) + r\")\\b\")\n", " mask = docs[\"text_content\"].str.lower().apply(lambda t: bool(pat.search(t)))\n", " return docs.loc[mask].groupby(\"year\").size().to_dict()\n", "\n", "\n", "if len(docs_year):\n", " domain_names = list(SCIENCE_BACKBONE.keys())\n", " matrix_d = np.zeros((len(domain_names), len(years_axis)))\n", " raw_counts_d = np.zeros_like(matrix_d)\n", "\n", " for di, domain in enumerate(domain_names):\n", " terms = _node_terms(SCIENCE_BACKBONE[domain])\n", " yc = domain_year_counts(docs_year, terms)\n", " # Record raw counts for the hover (so users see what fed the burst calc)\n", " for y, c in yc.items():\n", " if year_min <= y <= year_max:\n", " raw_counts_d[di, y - year_min] = c\n", " # Burst calc\n", " bursts = kleinberg_bursts_annual(yc)\n", " for b_start, b_end, intensity in bursts:\n", " for y in range(b_start, b_end + 1):\n", " if year_min <= y <= year_max:\n", " j = y - year_min\n", " matrix_d[di, j] = max(matrix_d[di, j], intensity)\n", "\n", " custom = np.stack([raw_counts_d], axis=-1)\n", "\n", " fig_domain_burst = go.Figure(go.Heatmap(\n", " z=matrix_d, x=years_axis, y=domain_names,\n", " colorscale=\"Viridis\",\n", " colorbar=dict(title=\"burst
intensity\"),\n", " customdata=custom,\n", " hovertemplate=(\"Year %{x}
Domain: %{y}\"\n", " \"
Burst intensity: %{z:.2f}\"\n", " \"
Papers in year: %{customdata[0]:.0f}\"\n", " \"\"),\n", " ))\n", " fig_domain_burst.update_layout(\n", " title=\"DOMAIN-level bursts in the SUBSIDE literature (annual)\",\n", " xaxis_title=\"Publication year\",\n", " yaxis_title=\"Backbone domain\",\n", " height=520, plot_bgcolor=\"white\",\n", " margin=dict(l=240), # room for long domain labels\n", " )\n", " fig_domain_burst.write_html(str(OUTPUT_DIR / \"burst_domains.html\"),\n", " include_plotlyjs=\"cdn\")\n", " fig_domain_burst.show()\n", " print(f\"✓ Saved: {OUTPUT_DIR / 'burst_domains.html'}\")\n", "else:\n", " print(\"(no entries with valid year — skipping domain burst heatmap)\")\n" ] }, { "cell_type": "markdown", "id": "477fc120", "metadata": {}, "source": [ "### 8.4 Key data-resource burst heatmap\n", "\n", "A curated list of named **data resources** relevant to subsidence work. Edit `DATA_RESOURCES` to add (e.g.) sensor families, model codes, or named datasets specific to your study. Each resource is matched as a regex word boundary so `\"GNSS\"` doesn't accidentally hit `\"gnssr\"`.\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "10fd01e0", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "colorbar": { "title": { "text": "burst
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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ Saved: /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults/burst_resources.html\n" ] } ], "source": [ "DATA_RESOURCES = {\n", " # Geodetic / remote sensing instruments\n", " \"InSAR\": [\"insar\", \"interferometric synthetic aperture radar\"],\n", " \"Sentinel-1\": [\"sentinel-1\", \"sentinel 1\"],\n", " \"ALOS\": [\"alos\", \"palsar\"],\n", " \"Envisat\": [\"envisat\"],\n", " \"TerraSAR-X\": [\"terrasar\", \"terrasar-x\"],\n", " \"GNSS / GPS\": [\"gnss\", \"gps\"],\n", " \"LiDAR\": [\"lidar\"],\n", " \"Extensometer\": [\"extensometer\", \"extensometers\"],\n", " \"Leveling\": [\"leveling\", \"levelling\"],\n", " # Model codes & data products\n", " \"MODFLOW\": [\"modflow\"],\n", " \"DEM / DTM\": [\"digital elevation model\", \"dem\", \"dtm\"],\n", " \"SRTM\": [\"srtm\"],\n", " # Aggregating data sources\n", " \"GRACE\": [\"grace\"],\n", " \"PSInSAR\": [\"psinsar\", \"persistent scatterer\"],\n", "}\n", "\n", "if len(docs_year):\n", " res_names = list(DATA_RESOURCES.keys())\n", " matrix_r = np.zeros((len(res_names), len(years_axis)))\n", " raw_counts_r = np.zeros_like(matrix_r)\n", "\n", " for ri, res in enumerate(res_names):\n", " terms = DATA_RESOURCES[res]\n", " yc = domain_year_counts(docs_year, terms) # same matcher works here\n", " for y, c in yc.items():\n", " if year_min <= y <= year_max:\n", " raw_counts_r[ri, y - year_min] = c\n", " bursts = kleinberg_bursts_annual(yc)\n", " for b_start, b_end, intensity in bursts:\n", " for y in range(b_start, b_end + 1):\n", " if year_min <= y <= year_max:\n", " j = y - year_min\n", " matrix_r[ri, j] = max(matrix_r[ri, j], intensity)\n", "\n", " custom_r = np.stack([raw_counts_r], axis=-1)\n", "\n", " fig_res_burst = go.Figure(go.Heatmap(\n", " z=matrix_r, x=years_axis, y=res_names,\n", " colorscale=\"Cividis\",\n", " colorbar=dict(title=\"burst
intensity\"),\n", " customdata=custom_r,\n", " hovertemplate=(\"Year %{x}
Resource: %{y}\"\n", " \"
Burst intensity: %{z:.2f}\"\n", " \"
Papers in year: %{customdata[0]:.0f}\"\n", " \"\"),\n", " ))\n", " fig_res_burst.update_layout(\n", " title=\"Key DATA RESOURCE bursts in the SUBSIDE literature (annual)\",\n", " xaxis_title=\"Publication year\",\n", " yaxis_title=\"Data resource\",\n", " height=520, plot_bgcolor=\"white\",\n", " margin=dict(l=180),\n", " )\n", " fig_res_burst.write_html(str(OUTPUT_DIR / \"burst_resources.html\"),\n", " include_plotlyjs=\"cdn\")\n", " fig_res_burst.show()\n", " print(f\"✓ Saved: {OUTPUT_DIR / 'burst_resources.html'}\")\n", "else:\n", " print(\"(no entries with valid year — skipping data-resource burst heatmap)\")\n" ] }, { "cell_type": "markdown", "id": "61a8cc1f", "metadata": {}, "source": [ "### 8.5 Combined dashboard view\n", "\n", "Three burst panels stacked, sharing the same year axis, for at-a-glance comparison. Hover any cell to see what year and what intensity it represents.\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "bbff8f2e", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "colorscale": [ [ 0, "rgb(255,245,235)" ], [ 0.125, "rgb(254,230,206)" ], [ 0.25, "rgb(253,208,162)" ], [ 0.375, "rgb(253,174,107)" ], [ 0.5, "rgb(253,141,60)" ], [ 0.625, "rgb(241,105,19)" ], [ 0.75, "rgb(217,72,1)" ], [ 0.875, "rgb(166,54,3)" ], [ 1, "rgb(127,39,4)" ] ], "hovertemplate": "Year %{x}
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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ Saved: /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults/burst_dashboard.html\n" ] } ], "source": [ "if len(docs_year):\n", " fig_dash = make_subplots(\n", " rows=3, cols=1, shared_xaxes=True,\n", " vertical_spacing=0.07,\n", " row_heights=[0.30, 0.40, 0.30],\n", " subplot_titles=(\"Terms — top distinctive vocabulary\",\n", " \"DOMAINS — backbone aggregates\",\n", " \"Data resources — named sensors / models / products\"),\n", " )\n", "\n", " fig_dash.add_trace(go.Heatmap(\n", " z=matrix, x=years_axis, y=burst_terms,\n", " colorscale=\"Oranges\", showscale=False,\n", " hovertemplate=\"Year %{x}
Term: %{y}
Intensity: %{z:.2f}\",\n", " ), row=1, col=1)\n", "\n", " fig_dash.add_trace(go.Heatmap(\n", " z=matrix_d, x=years_axis, y=domain_names,\n", " colorscale=\"Viridis\", showscale=False,\n", " hovertemplate=\"Year %{x}
Domain: %{y}
Intensity: %{z:.2f}\",\n", " ), row=2, col=1)\n", "\n", " fig_dash.add_trace(go.Heatmap(\n", " z=matrix_r, x=years_axis, y=res_names,\n", " colorscale=\"Cividis\", showscale=False,\n", " hovertemplate=\"Year %{x}
Resource: %{y}
Intensity: %{z:.2f}\",\n", " ), row=3, col=1)\n", "\n", " fig_dash.update_layout(\n", " title=\"SUBSIDE literature — burst dashboard (terms · DOMAINS · resources)\",\n", " height=1100, plot_bgcolor=\"white\",\n", " margin=dict(l=200, r=40, t=80, b=60),\n", " )\n", " fig_dash.update_xaxes(title_text=\"Publication year\", row=3, col=1)\n", " fig_dash.write_html(str(OUTPUT_DIR / \"burst_dashboard.html\"),\n", " include_plotlyjs=\"cdn\")\n", " fig_dash.show()\n", " print(f\"✓ Saved: {OUTPUT_DIR / 'burst_dashboard.html'}\")\n" ] }, { "cell_type": "markdown", "id": "58bf10a6", "metadata": {}, "source": [ "## 9. Export\n", "\n", "Writes the analysis artefacts to `OUTPUT_DIR`:\n", "\n", "- `bib_entries_tagged.csv` — every entry with all `group_*` boolean flags\n", "- `keyword_groups.json` — the editable keyword-group definitions used in this run\n", "- `group_mapping.csv` — keyword group → primary backbone domain table\n", "- `lda_topics.csv` — LDA topics surfaced in §3.3\n", "- Plus the four interactive HTML figures already saved in §7–§8.5\n", "- `summary.md` — one-page Markdown digest\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "15693f9f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tabulate in /work/01813/sawp33/ls6/miniconda3/envs/semantic-SVO/lib/python3.10/site-packages (0.10.0)\n", "✓ All exports written to /work/01813/sawp33/ls6/cookbooks/SUBSIDE SciMap/ScienceBackboneResults\n", " · UCSDmap_with_disciplines.net.txt\n", " · bib_entries_tagged_20260511_012348.csv\n", " · bib_entries_tagged_20260511_012437.csv\n", " · bib_entries_tagged_20260511_013008.csv\n", " · bib_entries_tagged_20260512_063128.csv\n", " · burst_dashboard.html\n", " · burst_domains.html\n", " · burst_resources.html\n", " · burst_terms.html\n", " · group_mapping_20260511_012348.csv\n", " · group_mapping_20260511_012437.csv\n", " · group_mapping_20260511_013008.csv\n", " · group_mapping_20260512_063128.csv\n", " · keyword_groups_20260511_012348.json\n", " · keyword_groups_20260511_012437.json\n", " · keyword_groups_20260511_013008.json\n", " · keyword_groups_20260512_063128.json\n", " · lda_topics_20260511_012348.csv\n", " · lda_topics_20260511_012437.csv\n", " · lda_topics_20260511_013008.csv\n", " · lda_topics_20260512_063128.csv\n", " · science_backbone.json\n", " · science_backbone_network.html\n", " · summary_20260511_012437.md\n", " · summary_20260511_013008.md\n", " · summary_20260512_063128.md\n" ] } ], "source": [ "!pip install tabulate\n", "from datetime import datetime as _dt\n", "stamp = _dt.now().strftime(\"%Y%m%d_%H%M%S\")\n", "\n", "# Tagged entries CSV (flatten list cols)\n", "export_df = df.copy()\n", "export_df[\"group_matches\"] = export_df[\"group_matches\"].str.join(\"|\")\n", "export_df[\"kw_list\"] = export_df[\"kw_list\"].apply(lambda lst: \"|\".join(lst))\n", "export_df.to_csv(OUTPUT_DIR / f\"bib_entries_tagged_{stamp}.csv\",\n", " index=False, encoding=\"utf-8-sig\")\n", "\n", "# Keyword groups JSON\n", "with open(OUTPUT_DIR / f\"keyword_groups_{stamp}.json\", \"w\") as fh:\n", " json.dump(KEYWORD_GROUPS, fh, indent=2)\n", "\n", "# Group mapping CSV\n", "gm = group_mapping_df.copy()\n", "gm[\"backbone_mapping\"] = gm[\"backbone_mapping\"].apply(\n", " lambda hits: \" | \".join(f\"{d}>{s}({sc})\" for d, s, sc in hits))\n", "gm.to_csv(OUTPUT_DIR / f\"group_mapping_{stamp}.csv\", index=False)\n", "\n", "# LDA topics CSV\n", "lda_export = lda_topics_df.copy()\n", "lda_export[\"top_terms\"] = lda_export[\"top_terms\"].apply(lambda lst: \", \".join(lst))\n", "lda_export.to_csv(OUTPUT_DIR / f\"lda_topics_{stamp}.csv\", index=False)\n", "\n", "# Markdown summary\n", "summary_md = f\"\"\"# SUBSIDE Science Backbone — Run Summary\n", "\n", "- **BibTeX source**: `{BIB_PATH}`\n", "- **Entries loaded**: {len(df):,}\n", "- **Entries with year**: {int(df['year'].notna().sum()):,}\n", "- **Year range**: {int(df['year'].min()) if df['year'].notna().any() else 'n/a'} – {int(df['year'].max()) if df['year'].notna().any() else 'n/a'}\n", "- **Backbone source**: {BACKBONE_SOURCE}\n", "- **Domains**: {len(SCIENCE_BACKBONE)}\n", "- **Keyword groups**: {len(KEYWORD_GROUPS)}\n", "- **LDA topics surfaced**: {N_TOPICS}\n", "\n", "## Top 5 keyword groups by document matches\n", "{match_summary.head().to_markdown(index=False)}\n", "\n", "## Outputs\n", "- `science_backbone_network.html`\n", "- `burst_terms.html`\n", "- `burst_domains.html`\n", "- `burst_resources.html`\n", "- `burst_dashboard.html`\n", "- `bib_entries_tagged_{stamp}.csv`\n", "- `group_mapping_{stamp}.csv`\n", "- `lda_topics_{stamp}.csv`\n", "- `keyword_groups_{stamp}.json`\n", "\"\"\"\n", "with open(OUTPUT_DIR / f\"summary_{stamp}.md\", \"w\") as fh:\n", " fh.write(summary_md)\n", "\n", "print(f\"✓ All exports written to {OUTPUT_DIR}\")\n", "for p in sorted(OUTPUT_DIR.iterdir()):\n", " print(f\" · {p.name}\")\n" ] }, { "cell_type": "markdown", "id": "ce5e2f91", "metadata": {}, "source": [ "---\n", "\n", "## What this notebook does, at a glance\n", "\n", "| Section | Role | Source |\n", "|---|---|---|\n", "| 1 | Setup, paths, imports | new |\n", "| 2 | BibTeX ingest → tidy DataFrame, corpus stats | new |\n", "| 3 | Auto-extract keywords (author tags + TF-IDF/LDA) | adapted from `semantic_bridge_cookbook` §3 |\n", "| 4 | User-editable keyword groups + per-doc tagging | adapted from EPH §3.1 |\n", "| 5 | Three-tier science backbone (ETO → sbp → SUBSIDE inline) | adapted from EPH §5.1 |\n", "| 6 | Map keyword groups → backbone domains/subdisciplines | adapted from EPH §5.1 mapper |\n", "| 7 | Interactive backbone network (Plotly) | adapted from EPH §5.2 |\n", "| 8 | Annual Kleinberg bursts for terms, **DOMAINS**, data resources | adapted from EPH §6 |\n", "| 9 | Exports — CSV + HTML + Markdown summary | new |\n", "\n", "### Where to customize\n", "\n", "- **§1.1** — point `BIB_PATH` at a different bibliography or set `ETO_EXPORT_PATH` to a real CSV to force Tier 1 of the backbone.\n", "- **§3.1** — extend `CUSTOM_STOPWORDS` with corpus-specific noise terms.\n", "- **§4** — the `KEYWORD_GROUPS` dict is the single biggest analytical lever. Add a group, drop a group, refine the term list.\n", "- **§5** — the Tier-3 fallback backbone is also editable: rename a domain, add a subdiscipline, extend the `terms` list.\n", "- **§8.4** — `DATA_RESOURCES` is curated; add the sensors and model codes you actually care about.\n", "\n", "### Next steps (training-institute exercises)\n", "\n", "1. *Replace* the SUBSIDE `.bib` with a different domain bibliography (e.g., a Texas water-resources `.bib`) and see how the same scaffolding produces a different backbone shape and burst pattern.\n", "2. *Export* an ETO Map-of-Science CSV for the LDA-discovered topics in §3.3 and re-run with `ETO_EXPORT_PATH` set — verify the Tier-1 backbone surfaces the same primary domains as the inlined fallback.\n", "3. *Cross-link* the `bib_entries_tagged.csv` with the CKAN registry's data-resource records and produce a backbone diagram that includes named SUBSIDE datasets as a fourth node tier.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.20" } }, "nbformat": 4, "nbformat_minor": 5 }