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Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.

Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection.

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Background/objectives: The use of digital media, and especially social media, has been increasing over recent years. Previous research has reported a negative impact of media use on headaches; however, most of these studies are cross-sectional. Therefore, we conducted a longitudinal study to explore the relationship between different types of media usage (watching videos, gaming, and social media) and headache frequency and headache intensity over time.

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Background: In today's society, low levels of physical activity are observed in the child and adolescent population, which can cause numerous pathologies, such as obesity and mental health problems.

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Introduction: Pathogenic variants in the gene are linked to a spectrum of syndromes that exhibit partial clinical overlap. Hemizygous loss-of-function variants are considered lethal in males, while heterozygous loss-of-function variants generally result in oro-facial-digital syndrome type 1. A reported phenotype, Simpson-Golabi-Behmel syndrome type 2, was published once but remains controversial, with many specialists questioning its validity and arguing about its continued listing in the OMIM database.

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