Results 131 to 140 of about 2,475,679 (305)

Redefining the Health Risk of Battery Materials Through a Biologically Transformed Metal Mixture

Advanced Science, EarlyView.
Inhaled NCM particles undergo lysosomal degradation, releasing complex ion mixtures that induce systemic impact. The impact is determined by a critical balance between antagonistic Ni‐Co interactions and synergistic Mn effects. To capture these complexities in risk assessment, we develop an IAI model, ensuring a more accurate quantitative risk ...
Ze Zhang, Gan Miao, Xueyu Zhang, Zhao Shu, Dawei Lu, Yujie Song, Shanfa Yu, Qian Liu, Yang Song, Rong Zhang, Xiaoting Jin, Yuxin Zheng   +11 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Comparative evaluation of score criteria for dynamic Bayesian Network structure learning.

PLoS ONE
Dynamic Bayesian Networks (DBNs) are probabilistic models with a directional structure employed to model temporal processes. Three approaches to DBN structure learning are constraint-based, score-based, and hybrid.
Aslı Yaman, Mehmet Ali Cengiz
doaj   +1 more source

Analysis of Gas Pipeline Failure Factors Based on the Novel Bayesian Network by Machine Learning Optimization

IEEE Access
Assessing the failure of urban gas pipelines is crucial for identifying risk factors and preventing gas accidents that result in economic losses and casualties.
Shuangqing Chen, Shun Zhou, Zhe Xu, Yongbin Liu, Bing Guan, Xiaoyu Jiang, Wencheng Li   +6 more
doaj   +1 more source

Customizing Tactile Sensors via Machine Learning‐Driven Inverse Design

Advanced Science, EarlyView.
ABSTRACT Replicating the sophisticated sense of touch in artificial systems requires tactile sensors with precisely tailored properties. However, manually navigating the complex microstructure‐property relationship results in inefficient and suboptimal designs.
Baocheng Wang, Depeng Kong, Zhiao He, Jikai Liang, Yuyao Lu, Zikang Deng, Honghe Li, Mengke Wang, M. Jamal Deen, Zhiqiu Ye, Shuyao Zhou, Huayong Yang, Honghao Lyu, Jun Chen, Kaichen Xu, Geng Yang   +15 more
wiley   +1 more source

A smart headband for multimodal physiological monitoring in human exercises

Advanced Science, EarlyView.
A novel smart headband incorporating a thermal‐sensation‐based electronic skin is presented for continuous and accurate multimodal physiological monitoring, including pulse waveforms, total metabolic energy expenditure, heart rate, and forehead temperature, across both static and dynamic daily activities.
Shiqiang Liu, Guanxing Chen, Jingyi Peng, Shuo Tian, Qian Mao, Caise Wei, Jinfeng Yuan, Rong Zhu   +7 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Bayesian network structure learning by opposition-based learning

Scientific Reports
As a classical basic model for causal inference, Bayesian networks are of vital importance both in artificial intelligence with uncertainty and interpretability.
Baodan Sun, Xinyi Zhang, Junhui Jiang, Jianguang Gong, Dan Lin   +4 more
doaj   +1 more source

Maneuver strategy recognition technology for enemy combat aircraft based on Bayesian deep learning

Shenzhen Daxue xuebao. Ligong ban
Enhancing identification of enemy combat aircraft maneuver strategies is a critical factor in improving air combat decision-making capabilities. As traditional deep learning models often show overconfidence in complex and variable combat environment, and
YUAN Yinlong, ZHANG Sijie, CHENG Yun, HUA Liang   +3 more
doaj   +1 more source