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A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security
منشور في 2023"…Among other methodologies, the CSOM has the unique characteristics of increased searching efficiency, high convergence, and fast processing speed. …"
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Habitat in flames: How climate change will affect fire risk across koala forests
منشور في 2023"…</p><p><br></p><h3>Method:</h3><p dir="ltr">The Decision Tree machine learning algorithm was applied to generate a fire susceptibility index (a measure of the potential for a given area or region to experience wildfires) using a dataset of conditioning factors, namely: altitude, aspect, rainfall, distance from rivers, distance from roads, forest type, geology, koala presence and future dietary sources, land use-land cover (LULC), normalized difference vegetation index (NDVI), slope, soil, temperature, and wind speed.</p><p><br></p><h3>Results:</h3><p dir="ltr">We found a general increase in susceptibility of Australian vegetation to bushfires overall. …"
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Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2...
منشور في 2024"…Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. …"
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Basin of Attraction through Invariant Curves and Dominant Functions
منشور في 2015احصل على النص الكامل
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Road deaths and injuries in India: Action required on the five pillars of road safety
منشور في 2015"…<p dir="ltr">Road deaths and injuries are increasing in India due to unprecedented motorization and expansion of infrastructure amidst absence of strong road safety policies and programmes. …"
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Global, regional, and national burden of household air pollution, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
منشور في 2025"…We first estimated the mean fuel type-specific concentrations (in μg/m3) of fine particulate matter (PM2·5) pollution to which individuals using solid fuels for cooking were exposed, categorised by fuel type, location, year, age, and sex. …"
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