Results 81 to 90 of about 4,682,633 (203)

Nonlinear support vector machines through iterative majorization and I-splines [PDF]

To minimize the primal support vector machine (SVM) problem, wepropose to use iterative majorization. To do so, we propose to use it-erative majorization. To allow for nonlinearity of the predictors, we use(non)monotone spline transformations.
Groenen, P.J.F., Bioch, J.C., Nalbantov, G.I.   +2 more
core  

Autonomous High‐Throughput Characterization of Liquid‐Liquid Phase Behavior

Advanced Science, EarlyView.
This study introduces an automated dual modality platform, combining asymmetric capacitance deviation and multiangle turbidimetry, for high‐throughput characterization of liquid‐liquid phase behavior across chemically diverse fluid systems. The platform enables miscibility classification, resolution of phase separation kinetics and emulsion stability ...
Tarek Eid, Maryam Ebrahimiazar, Mohammad Zargartalebi, David Sinton   +3 more
wiley   +1 more source

Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization

Advanced Science, EarlyView.
A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley   +1 more source

Deep Learning Network‐Tailored Microenvironment Matching of 4D Bioprinting Bioactive Scaffolds for Bone Regeneration

Advanced Science, EarlyView.
A DLN dataset was built to analyze MABS composition versus in vitro/in vivo osteogenesis and angiogenesis. An MLP neural network, taking BG morphological parameters as input, extracts bioactive features from these datasets. A rabbit tibial defect model then validates 4D‐printed MABS for adaptability and bone regeneration in critical defects.
Xiongjie Liang, Yuechi Zhang, Weifeng Hu, Shiyan Lv, Fan Jia, Yan Zhang, Feng Wu, Changjiang Yang, Guanqi Zhen, Jinglong Yan, Xu Cui, Wei Zhao, Guanghua Chen   +12 more
wiley   +1 more source

Thirtieth Illinois Custom Spray Operators Training School: summaries of presentations [PDF]

, 1978
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Illinois Custom Spray Operators' School
core  

Discovering Early‐Stage Gas Generation Kinetics Enables Thermal Runaway Early Warning in Lithium‐Ion Batteries

Advanced Science, EarlyView.
Operando gas diagnostics reveal a previously unrecognized chemical activation (CA) stage during thermal runaway in lithium‐ion batteries. A physics‐informed gas generation kinetics network (GGKNet) is developed to reconstruct reaction pathways and physical models automatically.
Jiabo Zhang, Qianzhen Guo, Shuaiqi Liu, Shuai Li, Xianghao Kong, Dong Han, Zhen Huang   +6 more
wiley   +1 more source

Multiscale Spatial Fusion Feature‐Driven Characterization of Gastric Cancer Invasive Margins: A Multicenter Cohort Study for Preoperative Accurate Differentiation Between T4a and T4b Subtypes

Advanced Science, EarlyView.
This study develops a multi‐dimensional vision Transformer‐based model, GAVR, to accurately distinguish gastric cancer T4a/b stages preoperatively. Validated across multi‐center cohorts, it achieves excellent performance and significantly improves radiologists’ diagnostic accuracy, offering a promising tool for clinical decision‐making.
Guoliang Zheng, Xiaomiao Chai, Peng Jin, Xin Xin, Yingjie Li, Chun Yang, Jia Wei, Zishuo Yan, Jingyu Zhang, Qianning Zhao, Yingchen Han, Ning Zhang, Fuze Li, Bo Qiao, Huan Wang, Huachuan Zheng, Yan Li, Xin Zhang, Yan Zhao, Wenjun Mao, Jing Zhang   +20 more
wiley   +1 more source

Physics‐Guided Descriptors Enable Data‐Efficient Prediction of Battery Coulombic Efficiency

Advanced Science, EarlyView.
This work integrates multiscale simulations with data‐driven approaches to predict Coulombic efficiency (CE). Multiscale simulations of battery systems are performed to extract Physics‐Guided descriptors and construct a dataset. Machine learning models trained on this dataset are then subjected to interpretable analysis to identify the most influential
Qintao Sun, Xuewei Gu, Yulin Jie, Hao Yang, Shuhong Jiao, Chao Tang, Yangping Sheng, Yuhao Lu, Ruiguo Cao, Tao Cheng   +9 more
wiley   +1 more source

scTIDE: Deciphering Critical Transitions Through Cell‐Perturbed Manifold Graphs and Optimal Transport Conditional Flow Matching

Advanced Science, EarlyView.
scTIDE identifies single‐cell tipping points by combining manifold‐based graph representations with optimal‐transport conditional flow matching, which preserves intrinsic topology and models distributional dynamics. It supports critical‐transition detection at individual‐cell resolution, prediction of unseen cells, and dimensionality reduction and ...
Jiayuan Zhong, Bowen Niu, Yongbo Yu, Shiyang Nie, Xuerong Gu, Pei Chen, Rui Liu   +6 more
wiley   +1 more source

Human‐Guided Bayesian Optimization Enables High‐Throughput Laser Annealing of Mesoporous SiOx Anodes for Lithium‐Ion Batteries

Advanced Science, EarlyView.
An empirical‐aided active learning framework is developed to optimize high‐throughput laser‐induced photothermal annealing of silicon suboxide anodes. By integrating probabilistic machine learning with empirical domain knowledge, this approach achieves optimal electrochemical performance using limited experiments.
Chaeyoung Park, Yeongje Lee, Sunho Jeong, Eungkyu Lee   +3 more
wiley   +1 more source