Results 71 to 80 of about 24,298 (249)

SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis

Machine Learning and Knowledge Extraction
This study introduces an efficient methodology for addressing fault detection, classification, and severity estimation in rolling element bearings. The methodology is structured into three sequential phases, each dedicated to generating distinct machine ...
Mailson Ribeiro Santos, Affonso Guedes, Ignacio Sanchez-Gendriz   +2 more
doaj   +1 more source

Groundwater Rise Sustains the World's Largest Alpine Water System Under Global Warming

Advanced Science, EarlyView.
Shallow groundwater depth (SGWD) across the non‐permafrost plains of the Qinghai–Xizang Plateau decreased at averagely 0.02 m year−1, adding 31.44 Gt of freshwater storage from 2000 to 2020 and sustaining ∼53 500 km2 of alpine ecosystems. A vadose‐zone capacity of 426.6 Gt reveals these aquifers as promising reservoirs, highlighting groundwater's ...
Jianqing Du, Zhixiang Niu, Yanfen Wang, Shirui Zhou, Youqing Yang, Xiang Wei, Haishan Niu, Yitao Zhang, Anquan Xia, Yali Liu, Yanbin Hao, Kai Xue, Bingfang Wu, Xinquan Zhao, Tandong Yao, Yiyu Chen   +15 more
wiley   +1 more source

Architecture‐Engineered Semi‐Crystalline Microporous Polymer Membranes for Precise Molecular Separation

Advanced Science, EarlyView.
Guided by a Random Forest model, a π–π‐driven ordered stacking strategy deploys functionally distinct substructures to restrict chain segment motion and increase free volume, while preserving the intermolecular interactions that maintain structural integrity.
Zi‐Meng Xu, Hao Zhuo, Song‐Hui Zhi, Xiao‐Feng Zhong, Pan‐Pan Zhang, Si‐Yuan Yang, Wen‐Yong Yang, Guang‐Yong Shen, Ya‐Zhao Li, Yi Li, Ming Xue   +10 more
wiley   +1 more source

Trustworthy Multimodal Attention Framework for Creep Rupture Life Prediction Under Data‐Scarce Conditions: A Case Study on IN718

Advanced Science, EarlyView.
An attention‐based multimodal deep learning framework is developed to predict the creep life of Ni‐based superalloys by fusing processing parameters with microstructural micrographs. The model achieves high accuracy (R2 = 0.92), aligns with metallurgical principles by capturing δ‐phase influence, and incorporates uncertainty quantification, offering a ...
Haopeng Lv, Dayong Wu, Ziyuan Rao, Qian Wang, Haikun Ma, Jie Kang, Wang Li, Huicong Dong, Yandong Wang, Zhinan Yang, Ru Su   +10 more
wiley   +1 more source

Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations

Известия высших учебных заведений: Проблемы энергетики
RELEVANCE of the study lies in the development of system for the short-term forecasting of power consumption by the enterprise of the oil and gas industry with consideration of technological factors and interpretation of their influence on the result of ...
A. I. Stepanova, A. I. Khalyasmaa, P. V. Matrenin   +2 more
doaj   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source

The Visualization of the Importance of Covariance Importance in a Machine Learning Model for Advanced Liver Fibrosis in a Nationally Representative Sample

JGH Open
Introduction Accurate prediction of liver disease is vital for early intervention, given its potential severity. This study aims to improve the prediction of advanced liver fibrosis and investigate its associations with factors, ultimately contributing ...
Alexander A. Huang, Samuel Y. Huang
doaj   +1 more source

Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing

Advanced Electronic Materials, EarlyView.
A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun, S. M. Rifatur Rana, Md. Golam Morshed, Imrose Jahan, F. A. Sabbir Ahamed, Md. Jahedul Islam   +5 more
wiley   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 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