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The quasi-geostrophic two-layer model is a widely used tool to study baroclinic instability in the ocean. One instability criterion for the inviscid two-layer model is that the potential vorticity (PV) gradient must change sign between the layers. This has a well-known implication if the model includes a linear bottom slope: for sufficiently steep retrograde slopes, instability is suppressed for a flow parallel to the isobaths. This changes in the presence of bottom friction as well as when the PV gradients in the layers are not aligned. We derive the generalised instability condition for the two-layer model with nonzero friction and arbitrary mean flow orientation. This condition involves neither the friction coefficient nor the bottom slope; even infinitesimally weak bottom friction destabilises the system regardless of the bottom slope. We then examine the instability characteristics as a function of varying slope orientation and magnitude. The system is stable across all wavenumbers only if friction is absent and if the planetary, topographic and stretching PV gradients are aligned. Strong bottom friction decreases the growth rates but also alters the dependence on bottom slope. Thus the often mentioned stabilisation by steep bottom slopes in the two-layer model only holds in very specific circumstances and thus probably plays only a limited role in the ocean.
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http://dx.doi.org/10.1017/jfm.2025.10172 | DOI Listing |
Front Public Health
September 2025
Institute of Physical Education, Sichuan University, Chengdu, China.
Objective: This study aimed to examine the relationship between physical activity volume and sleep duration in older adults, using objective monitoring data to investigate their non-linear association and threshold effects, thereby providing references for developing exercise programs to improve sleep duration.
Methods: The study used two consecutive waves of NHANES cross-sectional data (2011-2014) as the derivation cohort and NHANES 2005-2006 data as the validation cohort. Analysis of the derivation cohort included weighted univariate analysis, weighted multivariate logistic regression, and interpretable machine learning analysis.
J Sci Comput
August 2025
Department of Mathematics, The Pennsylvania State University, Pennsylvania, USA.
Neural network-based approaches have recently shown significant promise in solving partial differential equations (PDEs) in science and engineering, especially in scenarios featuring complex domains or incorporation of empirical data. One advantage of the neural network methods for PDEs lies in its automatic differentiation (AD), which necessitates only the sample points themselves, unlike traditional finite difference (FD) approximations that require nearby local points to compute derivatives. In this paper, we quantitatively demonstrate the advantage of AD in training neural networks.
View Article and Find Full Text PDFJ Biomater Appl
September 2025
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Mechanotransduction plays a pivotal role in shaping cellular behavior including migration, differentiation, and proliferation. To investigate this mechanism more accurately further, this study came up with a novel elastomeric substrate with a stiffness gradient using a sugar-based replica molding technique combined with a two-layer PDMS system. The efficient water solubility of candy allows easy release, creating a smooth substrate.
View Article and Find Full Text PDFDespite promising results in using deep learning to infer genetic features from histological whole-slide images (WSIs), no prior studies have specifically applied these methods to lung adenocarcinomas from subjects who have never smoked tobacco (NS-LUAD) - a molecularly and histologically distinct subset of lung cancer. Existing models have focused on LUAD from predominantly smoker populations, with limited molecular scope and variable performance. Here, we propose a customized deep convolutional neural network based on ResNet50 architecture, optimized for multilabel classification for NS-LUAD, enabling simultaneous prediction of 16 molecular alterations from a single H&E-stained WSI.
View Article and Find Full Text PDFSci Rep
September 2025
Sports Psychology, School of Arts and Sports, Hanyang University, Seoul, 04763, South Korea.
The growing use of artificial intelligence (AI) in physical education (PE) has led to an urgent need to develop robust methodologies that can be used to choose the most suitable algorithms in uncertain and vague environments. This paper introduces a new hybrid decision-making (DM) model that incorporates the weighted aggregated sum product assessment (WASPAS) technique into the q-rung linear Diophantine fuzzy set (q-RLDFS) framework. The primary objective is to address the gap in the lack of structured and uncertainty-resistant methods for assessing AI models based on multiple, frequently conflicting criteria in the domain of PE.
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