Introduction: The increased reliance on computer results in crucial health issues among the users. This research aimed to study the prevalence and factors associated with Computer-related health problems among University students in Majmaah region, Saudi Arabia. Materials and methods: 146 students were selected for this cross-sectional study using conveniencesampling technique. Data regarding personal characteristics, computer usage and prevalence of Musculoskeletal disorders (MSDs), Visual symptoms and sleep disorders were collected by a valid, reliable and self-administered questionnaire. Results: The prevalence of MSDs (any one body region), Visual symptoms (any one symptom) and sleep disorders was 52.7%, 54.8% and 56.8% respectively. Female gender, Laptop use without external mouse and inadequate breaks were associated with MSDs (P<0.05). Extensive smart phone use was associated with sleep disorders (P<0.05). Conclusion: The measures to promote the awareness about health and safety issues related to computer use among the university students should be given utmost priority. Moreover, the culture of reporting injuries and relevant issues should be encouraged among the student community to enhance early detection and intervention.

The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003-2025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003-2020 with a rate ranging from −5.88 ± 1.2 mm/year to −14.12 ± 1.2 mm/year and −3.5 ± 1.5 to −10.7 ± 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from −7.78 ± 1.2 to −15.6 ± 1.2 for TWSC and −4.97 ± 1.5 to −12.21 ± 1.5 for GWSC from 2020-2025. An interesting observation was a minor increase in rainfall during the study period for three basins.

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An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy cmeans clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05% defect classification accuracies. The novelty of this research lies in the concurrent use of CHT, MR, and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location, area, and nature of defects. This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process. Moreover, the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process, improve the PCB quality, and lower the production cost.