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| http://dx.doi.org/10.1007/s00259-024-06781-z | DOI Listing |
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Prediction correction modeling of water-conducting fracture zones height due to repeated mining in close distance coal seams.
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School of Minging and Geomatics Engineering, Hebei University of Engineering, Handan, 10076, China.
The height of the water-conducting fracture zones (WCFZ) is crucial for ensuring safe coal mining beneath aquifers, particularly considering the secondary development of the WCFZ in upper seams due to repeated mining in close distance coal seams. Accurately predicting this height is essential for mine safety, groundwater protection, and optimal coal resource use. This study compiles extensive measured data from various mining areas in China to analyze the coupling relationship between the WCFZ development height and six influencing factors: mining thickness, mining depth, coal seam spacing, hard rock lithology ratio, and the slope length of working face.
View Article and Find Full Text PDFMachine Learning-Corrected Simplified Time-Dependent DFT for Systems With Inverted Single-t-o-Triplet Gaps of Interest for Photocatalytic Water Splitting.
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Department of Chemistry, University of Nevada Reno, Reno, Nevada, USA.
Hydrogen gas (H) can be produced via entirely solar-driven photocatalytic water splitting (PWS). A promising set of organic materials for facilitating PWS are the so-called inverted singlet-triplet, INVEST, materials. Inversion of the singlet (S) and triplet (T) energies reduces the population of triplet states, which are otherwise destructive under photocatalytic conditions.
View Article and Find Full Text PDFVision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans.
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Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Alzheimer's disease (AD) is a neurodegenerative ailment that is becoming increasingly common, making it a major worldwide health concern. Effective care depends on an early and correct diagnosis, but traditional diagnostic techniques are frequently constrained by subjectivity and expensive costs. This study proposes a novel Vision Transformer-equipped Convolutional Neural Networks (VECNN) that uses three-dimensional magnetic resonance imaging to improve diagnosis accuracy.
View Article and Find Full Text PDFInvestigating the cognitive architecture of verbal fluency: evidence from an interference design on 487 controls.
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Introduction: Numerous studies have explored the linguistic and executive processes underlying verbal fluency using association designs, which provide limited evidence. To assess the validity of our model, we aimed to refine the cognitive architecture of verbal fluency using an interference design.
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