Results 161 to 170 of about 1,052,376 (334)

Portfolio optimization in cryptocurrencies: a comparison of deep reinforcement learning and traditional approaches

, 2022
The thesis focused on comparing traditional portfolio optimization approaches with deep reinforcement learning for cryptocurrency investments. Python was used to develop a code that optimized a portfolio of cryptocurrencies.
García Pardo, César Camilo
core  

Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence‐Driven Design Paradigms

Advanced Materials, EarlyView.
This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao, Chongyang Hao, Karol Viviana Mejia‐Centeno, Malik Dilshad Khan, Shang Wang, Longqiu Li, Yanhong Tian, Andreu Cabot, Qing Sun   +8 more
wiley   +1 more source

Reinforcement learning-based energy management for hybrid electric vehicles: A comprehensive up-to-date review on methods, challenges, and research gaps

Energy and AI
Reinforcement learning is widely used for control applications and has also been successfully implemented for efficient energy management within hybrid electric vehicles.
Mohamed Nadir Boukoberine, Muhammad Fahad Zia, Tarek Berghout, Mohamed Benbouzid   +3 more
doaj   +1 more source

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, 2018
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core  

Advances in Magnesium‐Based Thermoelectrics: A Critical Review

Advanced Materials, EarlyView.
Magnesium‐based thermoelectric materials have emerged as promising candidates for low‐to‐mid‐temperature energy conversion due to their abundance, low cost, and competitive performance. This review summarizes recent advances in Mg3X2, MgAgSb, and Mg2X systems, covering transport mechanisms, fabrication strategies, stability challenges, and device ...
Li‐Min Zhang, Li Zhang, Nan‐Hai Li, Yan‐Ling Yang, Xiao‐Lei Shi, Zhi‐Gang Chen   +5 more
wiley   +1 more source

Privacy-Preserving Deep Reinforcement Learning based on Differential Privacy

Deep reinforcement learning, with its extensive applications and remarkable performance, is emerging as a pivotal technology garnering researchers' attention.
Tian, H, Zhao, W, Sang, Y, Xiong, N
core   +1 more source

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning Shixiang

, 2018
Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques. On the other hand, on-policy algorithms are often more stable and easier to use. This paper
Gu, S., Schölkopf, B., Lillicrap, T., Ghahramani, Z., Turner, R., Levine, S.   +5 more
core   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Interface‐Engineered Binary Framework Composites: Advancing Porous Materials for Precision Medicine

Advanced Materials Interfaces, EarlyView.
Binary framework composites integrate two complementary porous architectures into a unified platform, enabling multifunctional design, enhanced structural tunability, and improved physicochemical performance. By combining high surface area, ordered porosity, interfacial synergy, and versatile functionalization, these hybrid materials offer new ...
Navid Rabiee, Mohammad Rabiee, Mohammad Akrami‐Hasan‐Kohal, Sidi A. Bencherif   +3 more
wiley   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source