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Al Khobar

As seen in: Nature, Elsevier, Frontiers, Global Finance Journal, Agriculture & Food Security, Biomed Central (BMC), International Business Times (U.S.), Journal of Medical Case Reports, The Minnesota Star Tribune, Royal Society of Chemistry and
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Articles

Human-Centric Digital Twins in Industry 5.0: Technologies, Applications, and Future Directions

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By Al Khobar
| Frontiers
REVIEW article Volume 13 - 2026 | doi: 10.3389/frobt.2026.1983733 Reviewed by REVIEW article SK Sagheer Khan 1 NV Nguyen Van Nam 1 AK Ashfaq Khan 2 1. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia 2. Sheffield Hallam University, Sheffield, United Kingdom 3. Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia Article metrics View details Abstract Industry 5.0 (I5.0) emphasizes industrial technologies that are human-centric, sustainable, and resilient.
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Residence Time Distribution in Tubular Flow Systems Using Machine Learning and Deep Learning Techniques. An Experimental and Predictive Modeling

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By Al Khobar, Gyanendra Kumar
| Frontiers
Abstract While the axial dispersion model can greatly simplify the intricacies of flow behavior in small-diameter tubes, its efficacy decreases with complex systems having heterogeneous properties. The proposed study assesses machine learning and deep learning for predictive analysis of complex nonlinear systems governing residence time distribution (RTD).
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Prevalence and Factors Associated With Post-Stroke Depression: A Cross-Sectional Analysis of a Prospectively Assembled Cohort in Eastern Saudi Arabia

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By Al Khobar, Al Qarni
| Frontiers
Abstract Abstract Objective: To estimate the prevalence and severity of post-stroke depression (PSD) and identify factors independently associated with PSD among stroke survivors. Methods: This cross-sectional analysis was conducted within a prospectively assembled hospital-based cohort at King Fahd University Hospital between January 2021 and November 2025.
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