Results 231 to 240 of about 6,285,273 (279)

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Intelligent neural network-based framework for Maxwell nanofluid flow with Soret-Dufour coupling and nonlinear thermal radiation. [PDF]

Discov Nano
Ishaq M, Ashraf MB, Fantaye AK, Zayani HM, Alshehery S, Alsawah GA, Khedher NB.   +6 more
europepmc   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Influence of dominant sequence artifacts on generative AI for virtual contrast-enhancement in breast MRI. [PDF]

BMC Med Imaging
Brock L, Markale A, Nguyen TT, Metz M, George A, Heidarikahkesh S, Schreiter H, Pfann Hossbach J, Kapsner LA, Ohlmeyer S, Wenkel E, Uder M, Laun FB, Knoll F, Bickelhaupt S.   +14 more
europepmc   +1 more source

TAMNet: Temporal and adaptive‐frequency network with MixStyle for cross‐region oil and fluid production forecasting

Deep Underground Science and Engineering, EarlyView.
This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang, Leifeng Meng, Qinghu Fan, Ziang Guo, Ting Bao, Pingdong Zhong, Fulin Luo   +6 more
wiley   +1 more source

Comparing Ensemble and Standalone Machine Learning Models for Optimizing Biodiesel Production via Esterification. [PDF]

ACS Omega
Şenoymak Tarakçı Mİ.
europepmc   +1 more source

Experimental and smoothed particle hydrodynamics‐finite element method simulation investigation of synergistic erosion mechanism of gangue‐based abrasive water jets

Deep Underground Science and Engineering, EarlyView.
This study proposes an eco‐friendly coal permeability enhancement technique by utilizing mine waste gangue as an abrasive in high‐pressure water jets. Integrating erosion experiments with smoothed particle hydrodynamics‐finite element method coupled simulations, the research study elucidates the flow field dynamics and a nonlinear solid–liquid ...
Qingxiang Wang, Jiaxu Tang, Qiheng Ren, Ding Wang, Tong Wang   +4 more
wiley   +1 more source

Generative adversarial networks for enhanced performance prediction of square CFST members under axial tension. [PDF]

PLoS One
Zhang H, Liu Y, Yan J.
europepmc   +1 more source

Multi‐factor coupling effects in hydraulic fracturing of laminated shale: Experimental insights and physics‐informed neural network‐driven optimization

Deep Underground Science and Engineering, EarlyView.
This study establishes a multi‐factor coupling framework for predicting breakdown pressure in laminated shale by integrating experimental hydraulic fracturing tests, physics‐informed neural networks (PINNs), and Sobol sensitivity analysis. It reveals how differential stress, the bedding dip angle, and the injection rate interact to influence fracture ...
Tao Wang, Guoyao Xu, Liyuan Liu, Yao Zhang, Sitao Zhu, Hongyang Chu, Kaikai Zhao   +6 more
wiley   +1 more source

Simulations on Scintillator Thicknesses for Bone Mineral Density Measurement Based on Dual-Layer Flat-Panel Detectors. [PDF]

Diagnostics (Basel)
Kim J, Kim DS, Lee E.
europepmc   +1 more source