Results 171 to 180 of about 243,099 (282)

Ductility Tuning via Cluster Network Characteristics of Porous Components

Advanced Materials Technologies, EarlyView.
Network optimization via cluster characteristics induced by interaction of stress concentration is proposed, demonstrating increased cluster size and dispersion in non‐uniform porous components. The optimized structures exhibit, for the first time, that enhanced ductility and damage progression is controllable through zigzag cluster network designed by
Ryota Toyoba, Yuichi Otsuka, Ian J Davies, Naritoshi Aoyagi, Yukio Miyashita   +4 more
wiley   +1 more source

Completing the hierarchy of rotational defects in monolayer MoS₂ through symmetry-aware evolutionary search. [PDF]

Phys Chem Chem Phys
Adel A, Wanzenböck R, Madsen GKH.
europepmc   +1 more source

Flow‐Adaptive Gas Sensing Enabled Using a Uniform Au Nanosheet Sensor Array and a Neural Network Inference

Advanced Materials Technologies, EarlyView.
Integrated Au nanosheet sensor array enables simultaneous inference of gas concentration and flow rate via deep neural network analysis, without external flow control. ABSTRACT Gas sensor responses are considerably affected by gas flow rates, thereby inhibiting the accurate detection of target gas concentrations in variable‐flow applications such as ...
Taro Kato, Takahisa Tanaka, Yusuke Hamanaka, Ryo Toyoshima, Ken Uchida   +4 more
wiley   +1 more source

Artificial intelligence-assisted personal protective equipment donning and doffing training for health professions students and healthcare workers: a scoping review. [PDF]

BMC Med Educ
Lv Y, Cai M, Xiang Q, Wang P.
europepmc   +1 more source

End‐to‐End Sensing Systems for Breast Cancer: From Wearables for Early Detection to Lab‐Based Diagnosis Chips

Advanced Materials Technologies, EarlyView.
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane, Lyn I. Jones, Richard M. Martin, Helen Winter, Faezeh Arab Hassani   +4 more
wiley   +1 more source

Hundred-layer photonic deep learning. [PDF]

Nat Commun
Zhou T, Jiang Y, Xu Z, Xue Z, Fang L.
europepmc   +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

PLXFPred: interpretable cross-attention networks with hierarchical fusion of multi-modal features for predicting protein-ligand interactions and affinities. [PDF]

Bioinformatics
Li J, Cai R, Wang Z, Sun Y, Yang W, Hu Y.   +5 more
europepmc   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

Advanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding, Danny E. P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, Wim Deferme   +5 more
wiley   +1 more source

A sparrow search algorithm-optimized LSTM framework with EMD denoising for rolling element bearing remaining useful life prediction. [PDF]

Sci Rep
Li Q, Zhang B, Fang X.
europepmc   +1 more source