To determine the prevalence of computer vision syndrome (CVS) and the relationship between CVS and depression, anxiety, insomnia, stress, and aggression among a sample of Lebanese male adolescents. This cross-sectional observational study enrolled 389 male Lebanese students (aged 13-17 years) between October and December 2019. After adjusting for the covariates (age, body mass index, and House Crowding Index), those with CVS had significantly higher mean depression ( < .001), anxiety ( = .003), and insomnia ( = .007) scores compared to those without CVS. The presence of CVS was associated with significantly higher depression (B = 3.25), anxiety (B = 4.11), and insomnia (B = 4.49), but not aggression. Stress mediated the association between CVS and depression, anxiety, insomnia, and aggression ( < .001 for all). The findings of this study indicate the importance of recognizing CVS in adolescents and raising awareness of time spent daily using computers and other electronic devices.
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http://dx.doi.org/10.4088/PCC.21m03180 | DOI Listing |
Sci Rep
January 2025
State Key Laboratory of Baiyunobo Rare Earth Resource Researches and Comprehensive Utilization, Baotou Research Institute of Rare Earths, Baotou, 014030, China.
This study introduces a deep learning-based automatic evaluation method for analyzing the microstructure of steel with scanning electron microscopy (SEM), aiming to address the limitations of manual marking and subjective assessments by researchers. By leveraging advanced computer vision algorithms, specifically a suitable model for long-term dendritic solidifications named Tang Rui Detect (TRD), the method achieves efficient and accurate detection and quantification of microstructure features. This approach not only enhances the training process but also simplifies loss function design, ultimately leading to a proper evaluation of surface modifications in steel materials.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Computer Science and Technology, East China Normal University, 200062, Shanghai, China.
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Musical, Plastic and Corporal Expression, University of Jaén, 23071 Jaén, Spain.
: Eye-foot coordination is essential in sports and daily life, enabling the synchronization of vision and movement for tasks like ball control or crossing obstacles. This study aimed to examine both the validity and reliability of an innovative eye-foot coordination (EFC) test in a dual-task paradigm in children aged 6-11 years and the capacity of this test to discriminate between sex and age. : A total of 440 schoolchildren aged 6-11 years participated in this cross-sectional study.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia.
Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fallible, as it depends on the user's perception.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns.
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