Results 201 to 210 of about 9,678 (263)

Explainable Machine Learning for Predicting Time‐to‐Failure of Horizontal Pressurized Storage Tanks in Fire Scenarios

open access: yesSafety Science and Technology, EarlyView.
An interpretable machine learning framework with SHAP analysis achieves accurate time‐to‐failure prediction for horizontal pressurized tanks under pool fires while identifying fire intensity, filling level, and wall thickness as dominant drivers of model explanation and uncertainty explanation, providing transparent and actionable insights for ...
Yubin Mu   +5 more
wiley   +1 more source

Autism spectrum disorder burden across 798 locations, 1990-2023: frontier and inequality mapping in the context of child injury prevention and safety promotion. [PDF]

open access: yesFront Public Health
Liang X   +14 more
europepmc   +1 more source

Jaguar Densities and Land Cover Change in a Multi‐Use Landscape: A 10‐Year Comparison in the Colombian Llanos 美洲豹种群密度与复合用地景观下的土地覆被变化:哥伦比亚亚诺斯地区10年比较研究

open access: yesWildlife Letters, EarlyView.
We used spatially explicit capture‐recapture to estimate jaguar densities in a private reserve in the Colombian Llanos and quantified land cover change in and around the reserve from 2014 to 2024. Densities increased 22% since 2014, while disturbed land cover increased by 39%–54% around the reserve depending on buffer distance. Our study indicates that
Valeria Boron   +7 more
wiley   +1 more source

Future Challenges and Opportunities for the Viability of Animal Populations in Zoos and Aquariums

open access: yesZoo Biology, EarlyView.
ABSTRACT Maintaining sustainable animal populations is fundamental to zoo and aquarium missions in education, visitor engagement, entertainment, scientific advancement, public health and wellbeing, advocacy, and conservation impact. Yet, sustaining viable populations remains a persistent challenge.
Lisa J. Faust   +18 more
wiley   +1 more source

Evolutionary Dynamic Multiobjective Optimisation Assisted by Inverse Regression Tree Predictor

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Dynamic multiobjective optimisation problems (DMOPs) are optimisation problems with multiple conflicting objectives that can change over time. Most dynamic multiobjective optimisation evolutionary algorithms (DMOEAs) attempt to estimate Pareto‐optimal sets (PS) directly in the decision space.
Kai Gao, Lihong Xu
wiley   +1 more source

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