Mohammad Reza Gharib | Optimization | Research Excellence Award

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Assoc. Prof. Dr. Mohammad Reza Gharib | Optimization | Research Excellence Award

Assoc. Prof. Dr. Mohammad Reza Gharib | University of Torbat Heydarieh | Iran

Assoc. Prof. Dr. Mohammad Reza Gharib is an established researcher whose work spans advanced control engineering, intelligent systems, and sensor modeling, contributing significantly to the development of robust, efficient, and high-precision engineering solutions. His research focuses on sensor error characterization, robust control techniques such as QFT, MIMO, and H-infinity methods, MEMS-NEMS technologies, intelligent control frameworks using fuzzy logic and neural networks, and the modeling and control of actuators, robots, and power plant systems. Dr. Gharib’s publication record reflects both depth and breadth, with impactful studies addressing energy-efficient building design, photovoltaic panel cooling through porous medium optimization, SCARA robot modeling using quantitative feedback theory, and quadrotor dynamic control. His work also includes comparative analyses of advanced control strategies, providing valuable insights into optimal controller design for complex multi-input multi-output systems. Through a combination of theoretical innovation and applied engineering, his research has advanced the understanding of nonlinear dynamics, sensor–actuator integration, and intelligent automation. His contributions to building energy optimization demonstrate his interdisciplinary capability to merge control engineering with sustainable systems, while his robotics and unmanned systems research highlights precision modeling and stability enhancement under real-world constraints. The citation performance of his publications underscores the relevance of his work to key technological domains such as renewable energy, autonomous robotics, and next-generation sensor systems. With a strong focus on solving practical engineering challenges through rigorous analytical methods and intelligent algorithms, Dr. Gharib has established a notable research footprint characterized by innovation, applicability, and high technical merit.

Profile: Google Scholar | ResearchGate

Featured Publications

Heydari, A., Sadati, S. E., & Gharib, M. R. (2021). Effects of different window configurations on energy consumption in building: Optimization and economic analysis. Journal of Building Engineering, 35, 102099.

Gharib, M. R. (2020). Comparison of robust optimal QFT controller with TFC and MFC controller in a multi-input multi-output system. Reports in Mechanical Engineering, 1(1), 151–161.

Mesgarpour, M., Heydari, A., Wongwises, S., & Gharib, M. R. (2021). Numerical optimization of a new concept in porous medium considering thermal radiation: Photovoltaic panel cooling application. Solar Energy, 216, 452–467.

Amiri-M, A. A., Gharib, M. R., Moavenian, M., & Torabiz, K. (2009). Modelling and control of a SCARA robot using quantitative feedback theory. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 223(7), 865–875.

Gharib, M. R., & Moavenian, M. (2016). Full dynamics and control of a quadrotor using quantitative feedback theory. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 29(3), 476–488.*

Demmelash Mollalign Moges | Mathematics | Editorial Board Member

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Dr. Demmelash Mollalign Moges | Mathematics | Editorial Board Member

Dr. Demmelash Mollalign Moges | Hawassa University | Ethiopia

Dr. Demmelash Mollalign Moges is a dedicated researcher in the field of optimization, with extensive contributions to intuitionistic fuzzy systems, multi-objective decision models, and fractional programming. His work focuses on developing advanced mathematical frameworks that address complex real-world optimization challenges, particularly those involving uncertainty, multi-level decision structures, and competing objectives. He has produced influential research on intuitionistic fuzzy multi-objective linear fractional optimization, proposing new methods that have been successfully applied to agricultural land allocation and transportation systems. His studies extend to multi-objective multilevel programming, where he has introduced two-phase intuitionistic fuzzy goal programming approaches capable of handling hierarchical decision environments. Dr. Moges has also advanced the application of neutrosophic parameters within multilevel multi-objective fractional optimization, offering efficient algorithms that improve model robustness under indeterminate conditions. His contributions include compensatory intuitionistic fuzzy mathematical techniques for solving decentralized bi-level decision-making problems, broadening the applicability of fuzzy-based optimization in distributed systems. Beyond theoretical developments, he has collaborated on interdisciplinary research, including the design of an optimized YOLO NAS-based framework for real-time object detection, demonstrating his versatility and ability to apply analytical principles to modern computational challenges. His published works in leading journals such as Information Sciences, Journal of Computational Science, and Expert Systems with Applications highlight his consistent engagement with high-impact research that enhances the understanding and applicability of fuzzy optimization, multi-objective fractional models, and advanced decision-making methodologies. Through his continuous efforts, Dr. Moges contributes to shaping the evolving landscape of optimization theory and its practical implementation across diverse problem domains.

Profile: Scopus | ORCID | Google Scholar

Featured Publications

Moges, D. M., Mushi, A. R., & Wordofa, B. G. (2023). A new method for intuitionistic fuzzy multi-objective linear fractional optimization problem and its application in agricultural land allocation problem. Information Sciences, 625, 457–475.

Mollalign, D., Mushi, A., & Guta, B. (2022). Solving multi-objective multilevel programming problems using two-phase intuitionistic fuzzy goal programming method. Journal of Computational Science, 63, 101786.

Moges, D. M., & Wordofa, B. G. (2024). An efficient algorithm for solving multilevel multi-objective linear fractional optimization problem with neutrosophic parameters. Expert Systems with Applications, 257, 125122.

Moges, D. M., Wordofa, B. G., & Mushi, A. R. (2023). Solving multi-objective linear fractional decentralized bi-level decision-making problems through compensatory intuitionistic fuzzy mathematical method. Journal of Computational Science, 71, 102075.

Gupta, C., Gill, N. S., Gulia, P., Kumar, A., Karamti, H., & Moges, D. M. (2025). An optimized YOLO NAS based framework for realtime object detection. Scientific Reports, 15(1), 32903.