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 Pan | Artificial Intelligence | Best Researcher Award

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Dr. Wei Pan | Artificial Intelligence | Best Researcher Award

Researcher | OPT Machine Vision | Japan

Dr. Wei Pan is an accomplished researcher specializing in machine vision, 3D imaging, computational geometry, and optical metrology, currently contributing to OPT Machine Vision Corporation in Japan. His research is positioned at the intersection of machine learning, geometric learning, and computer-aided design, with applications in precision manufacturing, intelligent inspection, and automation. With 26 Scopus-indexed publications, 146 citations, and an h-index of 7, Dr. Wei Pan’s research has advanced computational methodologies for 3D reconstruction, point cloud processing, mesh denoising, phase-shifting profilometry, and surface metrology. His works have appeared in leading journals including Advanced Photonics, Optics Express, Computer-Aided Design, Automation in Construction, and The Visual Computer. Notably, his 2024 publications explore deep-learning-embedded structured light imaging and topology-aware transformers for point cloud registration, reflecting his pioneering integration of AI and optical engineering. Dr. Wei Pan has demonstrated exceptional innovation through 39 patents across domains such as 3D data filtering, surface defect detection, structured light reconstruction, and intelligent robotic calibration. These inventions strengthen industrial imaging precision and automation efficiency. His patent WO-2022057250-A1 on mesh denoising and CN-118397020-A on image segmentation and contour extraction exemplify impactful R&D contributions to intelligent vision systems. Beyond publications and patents, Dr. Wei Pan actively engages in collaborative research and R&D leadership, driving algorithmic innovation in structured-light metrology and computer vision. His research excellence has been recognized with multiple distinctions, including the President’s Graduate Fellowship (Singapore) and the Kuang-Chi Young Talents Award (China). Through his interdisciplinary expertise bridging optical design, machine learning, and computational modeling, Dr. Wei Pan continues to advance the frontiers of intelligent manufacturing and vision-based automation technologies.

Profiles: Scopus | ORCID | Google Scholar | ResearchGate

Featured Publications

  • Liu, J., Hao, J., Lin, H., Pan, W., Yang, J., Feng, Y., Wang, G., Li, J., Jin, Z., Zhao, Z., & Liu, Z. (2023). Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction. Patterns, 4(9), Article 100953.

  • Lu, L., Bu, C., Su, Z., Guan, B., Yu, Q., Pan, W., & Zhang, Q. (2024). Generative deep-learning-embedded asynchronous structured light for three-dimensional imaging. Advanced Photonics, 6(4), 046004–046004.

  • Chen, S., Wang, J., Pan, W., Gao, S., Wang, M., & Lu, X. (2023). Towards uniform point distribution in feature-preserving point cloud filtering. Computational Visual Media, 9(2), 249–263.

  • Lu, L., Jia, Z., Pan, W., Zhang, Q., Zhang, M., & Xi, J. (2020). Automated reconstruction of multiple objects with individual movement based on PSP. Optics Express, 28(18), 28600–28611.

  • Si, G. Y., Leong, E. S. P., Pan, W., Chum, C. C., & Liu, Y. J. (2014). Plasmon-induced transparency in coupled triangle-rod arrays. Nanotechnology, 26(2), 025201.