Results 141 to 150 of about 44,296 (265)

Leading the Pack: Next‐Generation Batteries for Humanoid Robotics

Advanced Science, EarlyView.
Humanoid robots challenge traditional and anticipated energy technologies. Humanoid robot benchmarks of runtimes and energy demands for industry applications inform humanoid robot battery design choices at the cell level, pack level, and system level.
Matthew Bergschneider, Jiaqi Ke, Jin Luo, Laisuo Su, Thomas Campbell, Yue Zhou, Kyeongjae Cho   +6 more
wiley   +1 more source

Similarity-guided swarm of models: enhancing semi-supervised learning in computational pathology. [PDF]

Sci Rep
Weng Z, Pryalukhin A, Hulla W, Bychkov A, Fukuoka J, Schallenberg S, Buchstab O, Klauschen F, Büttner R, Tolkach Y.   +9 more
europepmc   +1 more source

Computation and Structure‐Guided Arginine Scanning Engineers a Hyperactive AP Endonuclease for Multiplex Viral RNA Sensing

Advanced Science, EarlyView.
Computational and structure‐guided arginine scanning rewires the DNA‐binding interface of APE1 to create APE1‐Evo, a hyperactive yet specific AP endonuclease. Integrated into the NAPTUNE‐V2.0 cascade, APE1‐Evo enables amplification‐free, multiplex viral RNA sensing for dengue virus and influenza A/B, highlighting a general strategy for engineering ...
Junlan Wang, Ting Wu, Feizuo Wang, Boyang Wang, Xin Yu Wong, Senfeng Zhang, Shengsheng Ma, Yao Liu, Fangyao Yao, Zhifeng Zeng, Siyuan Yang, Kunming Liu, Qi Wang, Yilong Wang, Yunxiang Zang, Xiaolong Wang, Xingxu Huang, Shao Li, Quanquan Ji, Tao Hu, Chunyi Hu   +20 more
wiley   +1 more source

Flexoelectric Suppression of Interfacial Carrier Loss in BaTiO3 Thin‐Film Bulk Photovoltaic Systems

Advanced Science, EarlyView.
In single‐crystalline BaTiO3 thin films, flexoelectric strain gradients strongly enhance photocurrent by suppressing interfacial carrier loss. Loading experiments reveal a ∼35‐fold photocurrent increase with applied force. Self‐consistent electrostatic modeling shows that strain gradients reshape the interfacial potential landscape, strengthen local ...
Minwoo Jang, Hyunkyu Lim, Sanghoon Yeom, Jaewhan Oh, Yongsoo Yang, Hyungwoo Lee   +5 more
wiley   +1 more source

Source-Free Domain-Adaptive Semi-Supervised Learning for Object Detection in CCTV Images. [PDF]

Sensors (Basel)
Shin H, Kim GY.
europepmc   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Semi-supervised learning in prostate MRI tumor detection approaches fully supervised performance on external validation. [PDF]

Eur Radiol
Pooch EHP, Agrotis G, Cai L, Emberton M, Shah TT, Ahmed HU, Beets-Tan RGH, Benson S, Janssen T, Schoots IG.   +9 more
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

Semi-Supervised Learning to Improve Generalizability of Cancer Associated-Venous Thromboembolism Risk Prediction Models. [PDF]

Clin Appl Thromb Hemost
Jin S, Wang C, Qin D, Liang B, Zhang L, Gao W, Wang X, Jiang B, Rao B, Shi H, Liu L, Lu Q.   +11 more
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source