Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

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Assist. Prof. Dr. Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

Assistant Professor | Ferdowsi University of Mashhad | Iran

Assist. Prof. Dr. Amir R. Masoodi is a highly accomplished structural engineering researcher whose work spans nonlinear mechanics, composite structures, vibration analysis, finite element modeling, and advanced material systems. With 1,789 Scopus citations, 77 publications, and an h-index of 29, he has established a strong international research footprint in computational mechanics, structural stability, soil–structure interaction, wave propagation, and multiscale modeling of advanced composites. His research contributions include developing novel finite element formulations for beams, plates, and shells, particularly for functionally graded materials (FGMs), carbon nanotube (CNT)-reinforced composites, graphene nanocomposites, and porous structural systems. Assist. Prof. Dr. Amir R. Masoodi’s work on nonlinear dynamic analysis, thermal–mechanical coupling, shell instability, and multiscale behavior of nano-engineered materials has been widely cited and influential in advancing modern structural design methodologies. He has published extensively in leading journals such as Composite Structures, Engineering Structures, Mechanics of Advanced Materials and Structures, Aerospace Science and Technology, Scientific Reports, and Applied Sciences. His publications address cutting-edge topics including vibration of hybrid nano-reinforced shells, multiscale characterization of nanocomposites, nonlinear buckling behavior of tapered beams, thermomechanical modeling of composite cables, and smart materials incorporating shape-memory alloys. Assist. Prof. Dr. Amir R. Masoodi has presented his findings at numerous international conferences and contributed several book chapters, including work on nanofillers and thermal properties in advanced materials. His research output extends to R&D projects, predictive modeling, and computational innovations in structural and nano-engineered systems. He has been recognized with multiple distinguished researcher awards, national elite recognitions, and research excellence honors. His expertise is further reflected in his editorial board memberships and contributions as a reviewer for reputable journals. Overall, Assist. Prof. Dr. Amir R. Masoodi’s research stands at the intersection of computational mechanics, smart materials, and multiscale structural engineering, offering impactful advances for next-generation civil, mechanical, and aerospace systems.

Profiles: Scopus | ORCID | Google Scholar | Sci Profiles | Web of Science

Featured Publications

1. Sobhani, E., Masoodi, A. R., & Ahmadi-Pari, A. (2021). Vibration of FG-CNT and FG-GNP sandwich composite coupled conical–cylindrical–conical shell. Composite Structures, 273, 114281.

2. Sobhani, E., Masoodi, A. R., Civalek, O., & Ahmadi-Pari, A. R. (2021). Agglomerated impact of CNT vs. GNP nanofillers on hybridization of polymer matrix for vibration of coupled hemispherical–conical–conical shells. Aerospace Science and Technology, 120, 107257.

3. Rezaiee-Pajand, M., Sobhani, E., & Masoodi, A. R. (2020). Free vibration analysis of functionally graded hybrid matrix/fiber nanocomposite conical shells using multiscale method. Aerospace Science and Technology, 105, 105998.

4. Rezaiee-Pajand, M., Arabi, E., & Masoodi, A. R. (2019). Nonlinear analysis of FG-sandwich plates and shells. Aerospace Science and Technology, 87, 178–189.

5. Rezaiee-Pajand, M., Masoodi, A. R., & Mokhtari, M. (2018). Static analysis of functionally graded non-prismatic sandwich beams. Advances in Computational Design, 3(2), 165–190.

Wei Zhang | Robotics and Automation | Breakthrough Research Award

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Prof. Dr. Wei Zhang | Robotics and Automation | Breakthrough Research Award

Doctor of Engineering, Professor, Doctoral Supervisor, School of Aeronautical Engineering, Research Institute of Science and Technology, Civil Aviation University of China, China

Prof. Dr. Wei Zhang is a leading scholar in aviation ground operation safety, intelligent systems, and AI-driven mechanical dynamics. His research focuses on integrating robotics, artificial intelligence, and mechanical engineering to advance airport operational safety, efficiency, and equipment innovation. Over his career, Prof. Dr. Wei Zhang has authored 68 peer-reviewed journal articles and holds 17 patents, demonstrating significant scientific impact and contributions to both theoretical and applied civil aviation research. He has a Scopus h-index of 8 with 279 citations, reflecting the influence of his work in aviation engineering and intelligent systems. Prof. Dr. Wei Zhang’s key contributions span several areas of aviation technology. He has developed advanced algorithms for aircraft skin defect detection (FC-YOLO) and adaptive surface fitting of aircraft point clouds, enabling improved aircraft inspection and maintenance processes. In mechanical dynamics, he has performed in-depth modeling and vibration analysis of scissor-like lift mechanisms and flapping-wing micro air vehicles. He has also designed and optimized control strategies for towbarless aircraft taxiing, collision avoidance systems, and chassis suspension and vibration isolation for airport vehicles, bridging the gap between theory and practical application. His methodological expertise combines AI-based algorithm design, multi-scale feature fusion, predictive modeling, and nonlinear dynamics analysis. His recent high-impact publications include work on FC-YOLO for defect detection (Measurement Science and Technology, 2024), aircraft point cloud surface fitting (Robotics and Autonomous Systems, 2025), and vibration isolation performance of scissor-like structures (Nonlinear Dynamics, 2016). Prof. Dr. Wei Zhang’s research has been implemented in collaboration with major industry partners such as COMAC and Beijing Capital International Airport, leading to measurable improvements in aviation ground safety and operational efficiency. Through leadership in national and industry-funded projects, contributions to intelligent airport systems, and pioneering work in AI-enabled mechanical systems, Prof. Dr. Wei Zhang has established himself as a prominent figure in civil aviation research, advancing both scientific understanding and practical applications in autonomous ground vehicles, robotics, and airport equipment design.

Profile: Scopus | Google Scholar | ResearchGate | Sci Profiles

Featured Publications

  • Zhang, W., Xiong, A., & Zhang, B. (2026). Research on surface fitting technology for aircraft point cloud feature region based on adaptive complete natural segmentation. Robotics and Autonomous Systems, 195, 105221.

  • Qin, J., Ma, J., Lin, Q., Wu, H., & Zhang, W. (2025). Model predictive control with real-time variable weight for civil aircraft towing taxi-out control systems. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. Advance online publication.

  • Wang, X., Liu, J., & Zhang, W. (2025). A novel attitude-variable high acceleration motion planning method for the pallet-type airport baggage handling robot. Machines, 13(5), 343.

  • Huang, W., Xu, P., Zhao, T., Zhang, W., & Zhao, Y. (2025). Robust adaptive cascade trajectory tracking control for an aircraft towing and taxiing system. Actuators, 14(3), 105.

  • Xu, P., Huang, W., Zhao, T., Zhang, W., Lu, T., Qiang, G., Zhang, L., & Zhao, Y. (2025). Adaptive coordinated control for an under-actuated airplane–tractor system with parameter uncertainties. Engineering Science and Technology, an International Journal, 62, 101938.

 

Soufiane Bacha | Artificial Intelligence | Best Researcher Award

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Mr. Soufiane Bacha | Artificial Intelligence | Best Researcher Award

PhD Student, University of Science and Technology Beijing, Algeria

Mr. Soufiane Bacha is a promising young researcher in Artificial Intelligence and Data Quality with a strong academic background and growing international exposure. He is currently pursuing a Ph.D. in Data Quality at the University of Science and Technology Beijing (2023–ongoing) and a Ph.D. in Cancer Epidemiology at the Department of Computer Science, Ibn Khaldoun University of Tiaret, Algeria (2021–2025). He also holds a Master’s degree in Software Engineering (2019–2021), where he ranked first in his class and completed a thesis on imbalanced datasets and boosting methods, and a B.Sc. in Computer Science (2016–2019) with strong foundations in algorithms, cryptography and programming. Professionally, Mr. Soufiane Bacha gained valuable international research experience through an internship at the Faculty of Polytechnic Mons, UMONS University in Belgium, where he worked on Internet of Things (IoT) applications involving Raspberry Pi, Arduino and sensor technologies. He has served as a part-time lecturer in Graph Theory and as an ICT trainer in web development, demonstrating strong teaching, leadership, and communication skills. His research interests span artificial intelligence, data quality, machine learning for imbalanced datasets, cancer epidemiology, distributed applications and business analytics. He is proficient in Python, C/C++, Java, SQL and data analysis tools, with expertise in OLAP, data mining, and deep learning frameworks. His achievements include an NVIDIA Deep Learning Institute Certificate, participation in AI workshops, and a Scopus-indexed publication. With a dual doctoral training and interdisciplinary focus, Mr. Soufiane Bacha is well-positioned to make impactful contributions to AI-driven data quality research and healthcare analytics on a global scale.

Profile: ORCID | Google Scholar | ResearchGate

Featured Publications

1. Bacha, S., Ning, H., Mostefa, B., Sarwatt, D. S., & Dhelim, S. (2025). A novel double pruning method for imbalanced data using information entropy and roulette wheel selection for breast cancer diagnosis (arXiv:2503.12239).