Results 231 to 240 of about 10,350,910 (281)

A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities. [PDF]

Sci Rep
Adjei PK, Zhiguang Q, Obiri IA, Badjie A, Cobblah CNA, Alqahtani A, Gu YH, Al-Antari MA.   +7 more
europepmc   +1 more source

Safety of Sodium‐Ion Batteries: Evaluation and Perspective from Component Materials to Cells, Modules, and Packs

Advanced Energy Materials, EarlyView.
This review provides a bottom‐up evaluation of sodium‐ion battery safety, linking material degradation mechanisms, cell engineering parameters, and module/pack assembly. It emphasizes that understanding intrinsic material stability and establishing coordinated engineering control across hierarchical levels are vital for preventing degradation coupling ...
Won‐Gwang Lim, Seoa Kim, Erik A. Wu, Darren H. S. Tan, Shawn Peng, Xiaolin Li   +5 more
wiley   +1 more source

Comparative Insights and Overlooked Factors of Interphase Chemistry in Alkali Metal‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review presents a comparative analysis of Li‐, Na‐, and K‐ion batteries, focusing on the critical role of electrode–electrolyte interphases. It especially highlights overlooked aspects such as SEI/CEI misconceptions, binder effects, and self‐discharge relevance, emphasizing the limitations of current understanding and offering strategies for ...
Changhee Lee, Zachary T. Gossage, Shinichi Kumakura, Shinichi Komaba   +3 more
wiley   +1 more source

Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning. [PDF]

Commun Eng
Suárez P, Alcántara-Ávila F, Rabault J, Miró A, Font B, Lehmkuhl O, Vinuesa R.   +6 more
europepmc   +1 more source

Degradation Pathways of Silicon‐Based Anodes in Lithium‐Ion Batteries

Advanced Energy Materials, EarlyView.
Silicon‐based anodes undergo degradation through five primary pathways: (1) mechanical and structural deterioration of the active material, (2) loss of electrode integrity and electrical contact, (3) mechanical instability of the solid electrolyte interphase (SEI), characterized by repetitive fracture and deformation, (4) chemical instability of the ...
Yoon Jeong Choi, Ji‐Youn Bae, Ga‐On Park, Seung‐Ho Yu   +3 more
wiley   +1 more source

Author Correction: A multi-agent reinforcement learning based approach for automatic filter pruning. [PDF]

Sci Rep
Li Z, Zuo X, Song Y, Liang D, Xie Z.
europepmc   +1 more source

Characterizing Biopolymer Electrolytes in Zinc–Air Batteries: Challenges, Best Practices, and a Robust Workflow Guiding Future Research Paths

Advanced Energy Materials, EarlyView.
Bio‐based gel polymer electrolytes promise sustainable, mechanically adaptable zinc–air batteries, yet their progress is constrained by inconsistent characterization. This review critically links formulation, structure, interfaces, and cell performance, identifies methodological gaps under alkaline operating conditions, and proposes application ...
Matteo Milanesi, Hiba Ali, Barbara Belza, Mattia F. Palermo, Claudio Gerbaldi, Giuseppe Antonio Elia, Nagore Ortiz‐Vitoriano   +6 more
wiley   +1 more source

A multi-agent reinforcement learning based approach for automatic filter pruning. [PDF]

Sci Rep
Li Z, Zuo X, Song Y, Liang D, Xie Z.
europepmc   +1 more source

Low Participation and Risk Reduction Potential of Supplemental Crop Insurance in the United States

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Federally subsidized crop insurance is a cornerstone of U.S. farm risk management, yet policies with the greatest share of participation only trigger indemnities after losses exceed 15%. Supplemental insurance was introduced to cover part of this deductible, but participation remains largely unchanged.
Francis Tsiboe, Hunter Biram, Amy Hagerman   +2 more
wiley   +1 more source

PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control. [PDF]

Sensors (Basel)
Bokade R, Jin X.
europepmc   +1 more source