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Hydraulic performance of a pre-aerated stilling basin: Experimental study.
PLoS One
January 2025
School of Hydraulic Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou, Zhejiang, China.
Spillway chutes are critical in dam flood control, particularly in high dams where high water heads and large discharge in narrow canyons amplify the demand for safe discharging. For large unit discharges in spillways, aeration protection is essential to prevent cavitation erosion, but challenges arise from air duct choking in the traditional spillway and nonaerated regions in the stepped spillway. This paper introduces a novel spillway called the pre-aerated stilling basin spillway (PSBS).
View Article and Find Full Text PDFA novel feature extraction method based on dynamic handwriting for Parkinson's disease detection.
PLoS One
January 2025
School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China.
Parkinson's disease (PD) is a common disease of the elderly. Given the easy accessibility of handwriting samples, many researchers have proposed handwriting-based detection methods for Parkinson's disease. Extracting more discriminative features from handwriting is an important step.
View Article and Find Full Text PDFMaize quality detection based on MConv-SwinT high-precision model.
PLoS One
January 2025
Engineering Research Center of Hydrogen Energy Equipment& Safety Detection, Universities of Shaanxi Province, Xijing University, Xi'an, China.
The traditional method of corn quality detection relies heavily on the subjective judgment of inspectors and suffers from a high error rate. To address these issues, this study employs the Swin Transformer as an enhanced base model, integrating machine vision and deep learning techniques for corn quality assessment. Initially, images of high-quality, moldy, and broken corn were collected.
View Article and Find Full Text PDFVistla: identifying influence paths with information theory.
Bioinformatics
January 2025
Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, 02-106, Poland.
Motivation: It is a challenging task to decipher the mechanisms of a complex system from observational data; especially in biology, where systems are sophisticated, measurements coarse and multi-modality is a common trait. The typical approaches of inferring a network of relationships between system's components struggle with the quality and feasibility of estimation, as well as with the interpretability of the results they yield.Said issues can be avoided, however, when dealing with a simpler problem of tracking only the influence paths, defined as circuits relying the information of an experimental perturbation as it spreads through the system.
View Article and Find Full Text PDFAutomating alloy design and discovery with physics-aware multimodal multiagent AI.
Proc Natl Acad Sci U S A
January 2025
Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA 02139.
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.
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