Results 171 to 180 of about 137,924 (252)

Aqueous Zinc‐Based Batteries: Active Materials, Device Design, and Future Perspectives

Advanced Energy Materials, EarlyView.
This review conducts a comprehensive analysis of aqueous zinc‐based batteries (AZBs) based on their intrinsic mechanisms, including redox reactions, ion intercalation reactions, alloying reactions, electrochemical double‐layer reactions, and mixed mechanisms, systematically discussing recent advancements in each type of AZBs.
Yan Ran, Fang Dong, Shuhui Sun, Yong Lei
wiley   +1 more source

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

DTDE: A new cooperative multi-agent reinforcement learning framework. [PDF]

Innovation (Camb), 2021
Wen G, Fu J, Dai P, Zhou J.
europepmc   +1 more source

From Materials to Systems: Challenges and Solutions for Fast‐Charge/Discharge Na‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review systematically analyzes the key characteristics limiting the fast‐charge/discharge capability of Na‐ion batteries (SIBs) from a multi‐scale perspective encompassing electrode materials, the electrode‐electrolyte interface, and the system. Furthermore, it presents practical solution strategies for the fundamental issues arising at each scale,
Bonyoung Ku, Sangbin Park, Sunha Hwang, Sunghyun Lim, Won‐Sub Yoon, Jongsoon Kim   +5 more
wiley   +1 more source

Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation. [PDF]

Sensors (Basel), 2022
Salimibeni M, Mohammadi A, Malekzadeh P, Plataniotis KN.   +3 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

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

Multi-agent reinforcement learning approach for hedging portfolio problem. [PDF]

Soft comput, 2021
Pham U, Luu Q, Tran H.
europepmc   +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

Correction: MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management. [PDF]

PLoS One, 2022
Huang Z, Tanaka F.
europepmc   +1 more source