Results 211 to 220 of about 424,647 (298)

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring

Advanced Science, EarlyView.
This study presents a semantic representation framework for clinically interpretable cardiac monitoring from contactless radio signals. It formulates radio semantic learning as an information‐bottleneck problem and approximates the objective via intra‐modal compression and cross‐modal alignment, structuring radio measurements into meaningful semantic ...
Jinbo Chen, Haoyu Wang, Guixin Xu, Yuqin Yuan, Xinmeng Cai, Zhengwu Wei, Dongheng Zhang, Qibin Sun, Tianwei Zhang, Cuntai Guan, Yan Chen   +10 more
wiley   +1 more source

Non-HDL to HDL cholesterol ratio and coronary outcomes in U.S. adults: a cross-sectional and prospective NHANES analysis. [PDF]

Eur J Med Res
Xu H, Yao B.
europepmc   +1 more source

Recent Advances in Sustainable Single‐Atom Catalysts from Biomass and Solid Waste: Design, Synthesis and Applications

Advanced Science, EarlyView.
Biomass‐ and solid waste‐derived sustainable single‐atom catalysts (Sus‐SACs) provide a cost‐effective and renewable approach to catalyst design. This review summarizes precursor selection, including AI‐assisted screening, synthesis strategies with emphasis on ultrafast methods, and advanced characterization techniques.
Hongzhe He, Yuhang Qiu, Ruoqun Zhang, Sasha Yang, Jingwei Wang, Jianglong Yu, Lian Zhang, Ning Chen, Baiqian Dai   +8 more
wiley   +1 more source

Integrating Machine Learning With Constant‐Potential Simulation to Unravel Charge‐Transfer Mechanisms in Electrochemical Nitrogen Fixation

Advanced Science, EarlyView.
Integrating interpretable machine learning with the fixed‐potential method reveals a novel mechanism: the catalytic activity of the electrochemical nitrogen reduction reaction is governed by partial charge transfer, induced by variations in the intermediate potential of zero charge under constant potential.
Yufei Xue, Dushuo Feng, Yuefei Zhang, Yang Zhang, Yalong Jiao, Aijun Du, Guoping Gao   +6 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Systemic inflammation response index and Parkinson's disease risk: A cross-sectional study. [PDF]

Medicine (Baltimore)
Wu W, Liu Y, Gong S, Wang S, Lei W, Tao Y.   +5 more
europepmc   +1 more source

Deep Volumetric Super‐Resolution Imaging in Thick Biological Specimens With Sparse Scanning SIM

Advanced Science, EarlyView.
Sparse scanning structured illumination microscopy (SS‐SIM) extends the axial depth in super‐resolution fluorescence imaging by combining rapid laser scanning, pixel‐addressed intensity modulation, and sCMOS camera detection. SS‐SIM yields 1.6× lateral and 1.7× axial resolution gains over wide‐field microscopy and enables imaging through 300–600 µm ...
Sha An, Xuhong Guo, Zhongxia Cai, Yunze Lei, Kai Wen, Hongfei Suo, Xiaoyu Kong, Gerd Ulrich Nienhaus, Peng Gao   +8 more
wiley   +1 more source

Leveraging Rank Information for Robust Regression Analysis: A Nomination Sampling Approach. [PDF]

Stat Med
Loewen N, Jafari Jozani M.
europepmc   +1 more source

Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges

Advanced Science, EarlyView.
This review offers a comprehensive comparison between perovskites and perovskite‐inspired materials (PIMs), focusing on their crystal structures, electronic properties, and chemical compositions. It evaluates the applicability of machine learning (ML) descriptors and models across both material classes.
Yangfan Zhang, Yiming Xia, Ali Shakiba, Hongrui Zhang, Xiaojing Hao, Priyank V. Kumar, Mahesh P. Suryawanshi   +6 more
wiley   +1 more source