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As seen in: Frontiers, Physical Review B
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Articles

Correction: Predicting college students' exercise dependence: a machine learning approach

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By Neijiang Normal, Hiyoshi Campus, Nanyang Technological
| Frontiers
A Correction on Predicting college students´ exercise dependence: a machine learning approach by Deng, Y., Lan, W., Si, M., and Ren, Y. L. (2026). Front. Psychol 17:1743725.
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Predicting college students’ exercise dependence: a machine learning approach

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By Neijiang Normal, Hiyoshi Campus, Nanyang Technological, Yi Lin
| Frontiers
ORIGINAL RESEARCH article Volume 17 - 2026 | https://doi.org/10.3389/fpsyg.2026.1743725 This article is part of the Research TopicPsychological Factors in Physical Education and Sport - Volume VIIView all 16 articles Highlights • This study applies advanced machine learning algorithms to predict exercise dependence among Chinese college students. • Deviation regulation theory and exercise psychology provide the theoretical foundation for identifying key psychological predictors.
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Analysis of anisotropic subgrid-scale stress for coarse large-eddy simulation

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By Hiyoshi Campus
| Physical Review B
This study discusses the necessity of anisotropic subgrid-scale (SGS) stress in large-eddy simulations (LESs) of turbulent shear flows using a coarse grid resolution. We decompose the SGS stress into two parts to observe the role of SGS stress in turbulent shear flows in addition to the energy transfer between grid-scale (GS or resolved scale) and SGS.
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