Results 21 to 30 of about 2,178 (195)

Evolutionary Game Theory Use in Healthcare: A Synthetic Knowledge Synthesis

Information
Background: Evolutionary game theory (EGT), originating from Darwinian competition studies, offers a powerful framework for understanding complex healthcare interactions where multiple stakeholders with conflicting interests evolve strategies over time ...
Peter Kokol, Jernej Završnik, Helena Blažun Vošner, Bojan Žlahtič   +3 more
doaj   +1 more source

Information Transmission Strategies for Self‐Organized Robotic Aggregation

Advanced Robotics Research, EarlyView.
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng, Zihan Li, Yifeng Shao, Jinhao Liu, Li Yu, Shuguang Li   +5 more
wiley   +1 more source

DRIVE‐SAFE: Data‐Driven Robustness and Informed Validation for Evolving Specifications via Formal Evaluation

Advanced Robotics Research, EarlyView.
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu   +3 more
wiley   +1 more source

Collective action and rationality models

Estudios Fronterizos, 2004
The Olsonian theory of collective action (Olson, 1965) assumes a model of economic rationality, based on a simple calculus between costs and benefits, that can be hardly hold at present, given the models of rationality proposed recently by several fields
Luis Miguel Miller Moya
doaj  

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Privacy-preserving energy scheduling for data center clusters based on evolutionary game theory

Energy Informatics
The substantial energy consumption of data centers challenges power grid stability and sustainable power development. While coordinating data centers for demand response is a viable solution, existing centralized approaches face scalability bottlenecks ...
Liwen Guo, Jingsi Yang, Zhenhai Li, Ge Yang, Qiang Wang, Yiwei Cui, Zhuangxi Tan   +6 more
doaj   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Stress‐to‐Light Conversion in an Earth‐Abundant Oxide Semiconductor

Advanced Science, EarlyView.
Stress‐to‐light conversion in solids represents a unique photonic functionality, yet it has never been realized in a chemically simple and sustainable material. Here, we show that sustainable semiconductor ZnO exhibits strong near‐infrared (NIR) luminescence under elastic stress when defect‐engineered to stabilize the p‐type state.
Tomoki Uchiyama, Koki Otonari, Reona Omori, Guangfa Yang, Eiji Nishibori, Ying Chen, Xu‐Guang Zheng, Chao‐Nan Xu   +7 more
wiley   +1 more source

A Skull Bone Marrow‐to‐Brain Axis Links Osteoblastic Activity to Myeloid Cell Trafficking, Cerebral Blood Flow, and Cognition in Alzheimer's Progression

Advanced Science, EarlyView.
This study reveals that Alzheimer's disease–linked APP expression in bone‐forming cells drives skull bone marrow remodeling and alters its vascular connections to the brain. These changes disrupt immune cell trafficking, cerebral blood flow, and cognition. Targeting bone marrow macrophages restores brain function, highlighting a previously unrecognized
Lei Xiong, Dong Sun, Hao‐Han Guo, Daehoon Lee, Zhipeng Liu, Lin Mei, Wen‐Cheng Xiong   +6 more
wiley   +1 more source

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source