Results 211 to 220 of about 1,208,657 (304)

LSTM-Transformer-Based Mine Pressure Prediction Using Hydraulic-Support Monitoring Data. [PDF]

Sensors (Basel)
Tao R, Lei X, Wan L, Wang Y, Xu N.
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Dynamic tensile mechanical properties and energy dissipation characteristics of sandstone with varying degrees of initial damage. [PDF]

Sci Rep
Shi W, Shi H, Li C, Qi C, Chang J, Yan A, Ren F.   +6 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Quantitative assessment of china's policies on the online sale of prescription drugs. [PDF]

PLoS One
Zhang Y, Zhang Y, Zhao C, Xu X, Wang G, Xie X.   +5 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Study on the design of section coal pillars and support technology for roadways along gob in inclined thick coal seams. [PDF]

Sci Rep
Jiang B, Ren X, Li W, Gu S, Guo Z, Zhang H.   +5 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Computer Vision-Based Techniques for Conveyor Belt Condition Monitoring: A Systematic Review. [PDF]

Sensors (Basel)
Rios-Colque P, Rios-Colque V, Rios-Colque L, Robles PA.   +3 more
europepmc   +1 more source