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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701243 | PMC |
| http://dx.doi.org/10.1007/s40614-017-0091-z | DOI Listing |
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Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals.
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Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.
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School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China.
Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters.
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This study presents a methodology for characterizing the constituent properties of composite materials by back-calculating from the laminate behavior under fatigue loading. Composite materials consist of fiber reinforcements and a polymer matrix, with the fatigue performance of the laminate governed by the interaction between these constituents. Due to the challenges in directly measuring the properties of individual fibers and the polymer matrix, a reverse-engineering approach was employed.
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View Article and Find Full Text PDFThe Role of IL-6 and TNF-Alpha Biomarkers in Predicting Disability Outcomes in Acute Ischemic Stroke Patients.
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Faculty of Medicine, Lucian Blaga University of Sibiu, Izvorului Street, 550169 Sibiu, Romania.
Introduction: Inflammatory biomarkers, including Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-alpha), play a significant role in influencing stroke outcomes, particularly in the progression of post-stroke disability. While numerous studies have suggested a correlation between elevated levels of these cytokines and poor functional recovery, further investigation is needed to understand their prognostic value in acute ischemic stroke.
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