Results 221 to 230 of about 3,398,718 (314)

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

Predicting Impact Loads on Polymer Materials from Laser-Induced Cavitation Bubble Collapse Using Random Forest Regression. [PDF]

Micromachines (Basel)
Abdillah MF, Inaba K.
europepmc   +1 more source

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source

A Random Forest-Based Risk Prediction Model for Non-Response to Methylphenidate in Children with Attention Deficit Hyperactivity Disorder. [PDF]

Neuropsychiatr Dis Treat
Lei Y, Li F, Xiao L, Lei C, Tang Y, Zou D.   +5 more
europepmc   +1 more source

Multitask Deep Learning Model for Predicting Lymph Node Metastasis and Disease‐Free Survival From Cytology Images in Bladder Cancer: A Multicenter Study With Pan‐Cytology Validation

Advanced Science, EarlyView.
Through self‐supervised pretraining and supervised fine‐tuning on large‐scale cytology image datasets, CytoNet predicts lymph node metastasis and survival. Across independent cohorts, this multitask framework demonstrates robust performance, improves risk stratification beyond radiological assessment, and provides interpretable evidence to support ...
Guibin Hong, Fan Jiang, Ye Xie, Xiaowei Huang, Runnan Shen, Yun Wang, Mengyi Zhu, Haoxuan Wang, Hongkun Yang, Yanxiao Nie, Zhen Lin, Rui Chen, Nengtai Ouyang, Tianxin Lin, Shaoxu Wu   +14 more
wiley   +1 more source

Monitoring seasonal and long-term forest canopy dynamics using Sentinel-2 imagery and Random Forest on the Google Earth Engine platform. [PDF]

PLoS One
Kumar P, Kumar R, Sahu S, Dobriyal M, Lavania P, Srivastav M, Pandey PC, Turyasingura B.   +7 more
europepmc   +1 more source

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Advanced Science, EarlyView.
Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and ...
P. Ciais, S. Peng, J. Chang, F. Li, Q. Zhu, K. Yuan, G. Hugelius, H. Li, Y. Cai, F. Chevallier, K. Tibrewal, E. A. Kort, K. Arndt, J. Watts, B. Buma, N. Besic, P. I. Palmer, H. Cadillo‐Quiroz, E. Euskirchen, M. J. Gondwe, A. Hoyt, R. Jackson, S. Malone, D. Monteverde, S. Natali, M. Ramonet, C. Rey‐Sanchez, L. B. Sagang, E. A. G. Schuur, R. Vargas, R. Varner, Z. Zhang, B. Poulter   +32 more
wiley   +1 more source

Development and validation of a diagnostic model for malignant pleural effusion based on random forest. [PDF]

J Thorac Dis
Guo X, Wei XL, Yin SM, Li YP, Zhai K, Shi HZ, Wang Z.   +6 more
europepmc   +1 more source

Artificial Intelligence‐Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae

Advanced Science, EarlyView.
Integrated artificial intelligence (AI) ensembles identify highly potent repurposed drugs against Streptococcus pneumoniae. Prospective virtual screening of 6747 drugs uncovers nine active candidates, including thiostrepton. This drug exhibits low picomolar potency and bypasses established resistance in multidrug‐resistant strains, demonstrating an ...
Joshua S. Fitch, Muhammad D. Ariadi, Min Jung Kwun, Leonie Howells, Saiveth Hernandez‐Hernandez, Nicholas J. Croucher, Pedro J. Ballester   +6 more
wiley   +1 more source