Results 201 to 210 of about 15,173 (265)

A Wearable Multi-Modal Measurement System with Self-Developed IMUs and Plantar Pressure Sensors for Real-Time Gait Recognition. [PDF]

Micromachines (Basel)
Li X, Gao Y, Chen G, Zhang M, Liao J, Wang Z, Sun J.   +6 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Simultaneous learning of static and dynamic charges.

Phys Chem Chem Phys
Stärk P, Stooß H, Langer MF, Rumiantsev E, Schlaich A, Ceriotti M, Loche P.   +6 more
europepmc   +1 more source

Safe and Accurate Sustentaculum Screw Placement in Minimally Invasive Surgery for Calcaneal Fractures: The "Sustentaculum View" Technique. [PDF]

J Clin Med
Rodemund C, Katzensteiner M, Ortmaier R, Vogel M, Recheis S, Rodemund N, Mattiassich G.   +6 more
europepmc   +1 more source

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +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

The wavefront set: bounds for the Langlands parameter. [PDF]

Math Ann
Ciubotaru D, Kim JL.
europepmc   +1 more source

Farmers' Preferences for Gene Editing Crops and Influencing Factors

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Gene editing (GE) is gaining momentum worldwide, but limited data on UK farmers' preferences hinders our understanding of its potential impact amid deregulation debates. Based on a survey of 200 English arable farmers, we employ a Latent Class Analysis and Multinomial Logit regressions to investigate current preferences for GE crops.
Bertolozzi‐Caredio Daniele, Vigani Mauro   +1 more
wiley   +1 more source

Hierarchical secure key assignment scheme. [PDF]

PLoS One
Celiktas B, Çelikbilek I, Guzey S, Ozdemir E.   +3 more
europepmc   +1 more source