Ahmed El-Sherbeeny | Human Factors Engineering | Best Researcher Award

Dr. Ahmed El-Sherbeeny | Human Factors Engineering | Best Researcher Award

Assistant Professor | King Saud University  | Saudi Arabia

Dr. Ahmed M. El-Sherbeeny is an Assistant Professor of Industrial Engineering at King Saud University, specializing in safety engineering, human factors and ergonomics, and environmental engineering. With an extensive interdisciplinary research portfolio, he has contributed significantly to high-impact domains including public health, intelligent systems, machine learning, environmental remediation, and industrial sustainability. His scholarly impact is reflected in 456 publications 7,784 citations, an h-index of 44, and an i10-index of 135, positioning him as a highly influential researcher within and beyond his primary discipline.Dr. El-Sherbeeny has collaborated on several landmark global studies most notably the Recovery trials published in The Lancet, which transformed clinical decision-making for COVID-19 treatment. His co-authored works on Tocilizumab Casirivimab/Imdevimab Azithromycin Baricitinib and corticosteroid therapy collectively account for thousands of citations underscoring his role in research that shaped international health policies. Beyond clinical research he has made notable contributions to intelligent security frameworks for electric vehicles machine-learning-driven medical diagnostics and blockchain-based IoT access control demonstrating strong engagement with emerging technological frontiers.In the environmental and materials engineering domains his publications address photocatalytic degradation nanocomposites for pollutant removal drug-delivery materials groundwater quality assessment and energy-efficient systems reflecting an impressive breadth of problem-solving across sustainability and environmental protection. His collaborative works span multiple continents involving international teams from engineering medical sciences environmental chemistry and computational intelligence.Dr. El-Sherbeeny’s research outputs appearing in top-tier journals such as The Lancet Chemical Engineering Journal Journal of Hazardous Materials ACS Omega and IEEE Access highlight both methodological rigor and societal impact. His contributions advance safer industrial systems enhance public health resilience support data-driven environmental management and promote technologically integrated solutions for global sustainability challenges.

Profiles : ORCID | Scopus | Google Scholar

Featured Publications

1.Avatefipour, O., Al-Sumaiti, A. S., El-Sherbeeny, A. M., Awwad, E. M., & Elmeligy, M. A. (2019). An intelligent secured framework for cyberattack detection in electric vehicles’ CAN bus using machine learning. Cited By : 185

2.Nadeem, A., Ahmad, S. F., Al-Harbi, N. O., Fardan, A. S., & El-Sherbeeny, A. M. (2017). IL-17A causes depression-like symptoms via NFκB and p38MAPK signaling pathways in mice: Implications for psoriasis associated depression.
Cited By : 172

3.Nadeem, A., Al-Harbi, N. O., Al-Harbi, M. M., El-Sherbeeny, A. M., & Ahmad, S. F. (2015). Imiquimod-induced psoriasis-like skin inflammation is suppressed by BET bromodomain inhibitor in mice through RORC/IL-17A pathway modulation. Pharmacological Research, 99, 248–257.  Cited By : 132

4.Mahum, R., Rehman, S. U., Meraj, T., Rauf, H. T., Irtaza, A., & El-Sherbeeny, A. M. (2021). A novel hybrid approach based on deep CNN features to detect knee osteoarthritis. Cited By : 117

5.Yang, X., Wang, J., El-Sherbeeny, A. M., AlHammadi, A. A., & Park, W. H. (2022). Insight into the adsorption and oxidation activity of a ZnO/piezoelectric quartz core-shell for enhanced decontamination of ibuprofen: Steric, energetic, and oxidation studies. Cited By : 98

Dr. Ahmed M. El-Sherbeeny’s interdisciplinary research integrates industrial engineering, machine learning, and environmental sciences to advance public health, industrial safety, and sustainable technologies. His work informs global healthcare strategies, enhances intelligent systems, and drives innovative solutions with broad societal and industrial impact.

Dr. Vahideh Bafandegan Emroozi | Maintenance | Women Researcher Award

Dr. Vahideh Bafandegan Emroozi | Maintenance | Women Researcher Award

Author , Ferdowsi university of Mashhad , Iran

Vahideh Bafandegan Emroozi is a passionate Iranian researcher specializing in industrial management and optimization. 🎓 With a Ph.D. from Ferdowsi University of Mashhad, her work bridges technology and human-centric approaches. 📊 Her research spans supply chain innovation, IoT applications, and human error analysis. 🤖🧠 She has published in esteemed journals and held research fellowships at Ferdowsi and Sanabad Universities. 📚✍️ Known for her analytical skills and academic dedication, Vahideh continues to contribute significantly to industrial systems and decision sciences. 🔍📈 Her collaborative spirit and teaching experience further highlight her dynamic role in academia. 👩‍🏫🌐

Professional Profile:

SCOPUS

Education & Experience:

Vahideh earned her Ph.D. in Industrial Management (2019–2024) 🎓 from Ferdowsi University, where her thesis focused on IoT-based maintenance and human error modeling. 📡🛠️ She also completed an M.Sc. in Industrial Management (2014–2017) with a high GPA of 18.96/20 📚 and a B.Sc. in Industrial Engineering (2008–2012). 🏗️ Her academic journey led to research fellow roles at Ferdowsi University (2021–2023) and Sanabad University (2023–2024). 🔬🏛️ In addition to research, she has taught Operations Research, Strategic Management, and Multi-Criteria Decision Making. 👩‍🏫 Her experience reflects a strong foundation in both theory and application. 💼🧮

Professional Development:

Vahideh continually builds her academic and technical skills through professional development. 📈💡 She has mastered analytical and modeling tools such as Python, MATLAB, GAMS, LINGO, LaTeX, and Vensim. 💻📐 Her commitment to research excellence is evident in her publications in Scopus-indexed journals 📄🔍 and her work on complex topics like green supply chain management and pandemic response strategies. 🌍📦 She actively contributes to knowledge dissemination through teaching, collaborative research, and methodological innovation. 📊🧠 Her engagement with multidisciplinary topics ensures she remains at the forefront of industrial and systems engineering. 🚀📘

Research Focus:

Vahideh’s research spans across multiple domains in industrial management. 🏭🔍 Her core interests include supply chain management, optimization, and maintenance planning. 🧾🛠️ She also explores the effects of human error, reliability analysis, and inventory control systems. ⚙️🧠📦 A significant part of her work integrates the Internet of Things (IoT) 🌐 with system dynamics and mathematical modeling 📊📉 to improve industrial decision-making. Her goal is to create smarter, more resilient, and sustainable industrial systems. 🌱💡 Her innovative contributions are driving progress in operational efficiency and risk reduction. 🚚📈

Awards & Honors:

While specific awards were not listed, Vahideh’s academic record speaks to her excellence. 🌟 She achieved outstanding GPAs in both her Ph.D. (19.49/20) and M.Sc. (18.96/20) programs. 🥇📘 Her research has been recognized with publications in high-impact international journals like Process Integration and Optimization for Sustainability and Journal of Industrial and Management Optimization. 📚✨ She has contributed novel methodologies in green supplier selection, VIKOR optimization, and system dynamics during COVID-19. 🧪🌐 Her roles as research fellow at top Iranian universities also reflect her academic merit and potential. 🏛️🔬

Publication Top Notes

1. Markov Chain-Based Model for IoT-Driven Maintenance Planning with Human Error and Spare Part Considerations

Authors: Bafandegan Emroozi, Vahideh; Doostparast, Mahdi
Journal: Reliability Engineering and System Safety
Year: 2025
Access: Open Access
Citations: 0 (as of now)

🔍 Summary:
This article introduces a novel Markov chain-based framework that integrates the Internet of Things (IoT) into industrial maintenance planning. The model accounts for human error probabilities and spare part availability, creating a dynamic and realistic approach to predictive maintenance. 📈 The use of Markov chains enables the system to model stochastic transitions between equipment states, improving decision-making accuracy. 🤖📦 The study enhances reliability and safety in industrial systems by aligning IoT data with probabilistic risk and resource planning, offering a scalable tool for real-time maintenance strategy optimization. 🛠️📊

2. Enhancing Industrial Maintenance Planning: Optimization of Human Error Reduction and Spare Parts Management

Authors: Bafandegan Emroozi, Vahideh; Kazemi, Mostafa; Doostparast, Mahdi
Journal: Operations Research Perspectives
Year: 2025
Access: Open Access
Citations: 0 (as of now)

🔍 Summary:
This paper proposes an optimization model aimed at improving maintenance planning by focusing on human error mitigation and efficient spare parts management. 👷⚙️ It applies advanced operations research techniques to identify cost-effective strategies for minimizing failures and delays due to incorrect human actions or resource shortages. The model bridges the gap between human factors engineering and logistical planning, integrating real-time data and decision analysis. 🧠📦 It offers a comprehensive framework suitable for modern industries aiming to balance cost, reliability, and safety. 🧾📉

Conclusion

Vahideh Bafandegan Emroozi exemplifies the qualities celebrated by Women in Research Awards: innovation, impact, leadership, and academic excellence. 🌟 Her work addresses critical industrial challenges through smart technologies and rigorous modeling, while her dedication to teaching and mentoring amplifies her influence. As a pioneering female researcher in a highly technical and traditionally male-dominated field, she is not only technically accomplished but also a role model for aspiring women in STEM. 🧠🔬👩‍🏫 She is highly deserving of recognition through a Women Researcher Award.