Mitsuru Endo | Computational Theory | Best Researcher Award

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Prof. Dr. Mitsuru Endo | Computational Theory | Best Researcher Award

Professor Emeritus| Tokyo Institute of Technology | Japan

Mitsuru Endo has made distinguished contributions to applied mechanics and vibration engineering, focusing on the dynamic behavior of continua and structures and the development of advanced noise and vibration control systems. His work bridges theoretical mechanics and practical applications in acoustic control, offering innovative solutions for vibration reduction in engineering systems. Endo has pioneered the extension of Southwell-Dunkerley methods for synthesizing frequencies, contributing to a deeper understanding of vibrational modes in complex systems. His research on flexural vibrations of rotating rings and deformation theories for beams, plates, and cylindrical shells has advanced modeling precision in mechanical structures. By introducing alternative formulations for Timoshenko beam and Mindlin plate models, Endo improved computational accuracy in vibration analysis. His innovative “one-half order shear deformation theory” redefined how transverse shear deformation is represented in structural mechanics, influencing global research on elasticity and composite structures. Endo’s extensive publications in leading journals such as the Journal of Sound and Vibration and the International Journal of Mechanical Sciences have established a strong foundation for future explorations in vibration modeling, acoustic optimization, and structural mechanics. His studies integrate both analytical and experimental perspectives, driving advancements in passive and active noise control technologies essential to aerospace, automotive, and civil engineering applications. The recognition of his work through multiple prestigious awards underscores his impact in mechanical sciences and engineering research, with 440 citations, 64 documents, and an h-index of 8.

Profiles: Scopus | ORCID
Featured Publication

Endo, M. (2013). Study on direct sound reduction structure for reducing noise generated by vibrating solids. Journal of Sound and Vibration, 332, 2643–2658. 5 citations

Endo, M. (2015). Study on an alternative deformation concept for the Timoshenko beam and Mindlin plate models. International Journal of Engineering Science, 87, 32–56. 34 citations

Endo, M. (2016). An alternative first-order shear deformation concept and its application to beam, plate and cylindrical shell models. Composite Structures, 146, 50–61. 17 citations

Endo, M. (n.d.). Study on the characteristics of noise reduction effects of a sound reduction structure. Conference Paper. 1 citation

Arif Basgumus | Mobile Computing | Best Researcher Award

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Dr. Arif Basgumus | Mobile Computing | Best Researcher Award

Associate Professor | Bursa Uludag University | Turkey

Dr. Arif Basgumus is a distinguished Associate Professor at Bursa Uludag University, whose research profoundly advances wireless communication, signal processing, and next-generation network systems. His extensive contributions encompass cognitive radio networks, non-orthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RIS), cooperative communications, integrated sensing and communication (ISAC), and physical layer security. Dr. Arif Basgumus has developed robust models for interference alignment, hybrid RF/VLC systems, and UAV-assisted network architectures, contributing significantly to 5G and 6G technology evolution. His studies integrate theoretical modeling with artificial intelligence applications, enhancing the efficiency and reliability of communication frameworks. Actively collaborating with industrial partners such as ASELSAN, HAVELSAN, and TUSAŞ, he bridges academic innovation with practical defense and aerospace applications. His authorship spans influential journals including IEEE Access, IET Communications, and Digital Signal Processing, reflecting a consistent research impact in signal optimization, deep learning-aided communications, and security enhancement in RIS-assisted systems. He has guided numerous graduate theses, emphasizing interdisciplinary approaches across electrical, electronics, and computer engineering. His projects funded by TUBITAK and other research councils explore UAV communication, smart vehicle systems, and optical sensor networks, fostering sustainable and intelligent connectivity. Dr. Arif Basgumus has also co-authored several books and chapters on communication systems, cognitive networks, and artificial intelligence in engineering. His long-standing involvement in international collaborations and IEEE activities highlights a leadership role in shaping the technological foundations of future communication infrastructures, with 256 citations, 48 documents, and an h-index of 10 (View h-index).

Profiles:  Google Scholar | Scopus | ORCID
Featured Publication

Alakoca, H., Namdar, M., Aldirmaz-Colak, S., Basaran, M., & Basgumus, A. (2022). Metasurface manipulation attacks: Potential security threats of RIS-aided 6G communications. IEEE Communications Magazine, 61(1), 24–30. Citations: 43

Bayhan, E., Ozkan, Z., Namdar, M., & Basgumus, A. (2021). Deep learning-based object detection and recognition of unmanned aerial vehicles. In Proceedings of the 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications. Citations: 41

Ozkan, Z., Bayhan, E., Namdar, M., & Basgumus, A. (2021). Object detection and recognition of unmanned aerial vehicles using Raspberry Pi platform. In Proceedings of the 5th International Symposium on Multidisciplinary Studies and Innovative Technologies. Citations: 34

Altuncu, A., & Basgumus, A. (2005). Gain enhancement in L-band loop EDFA through C-band signal injection. IEEE Photonics Technology Letters, 17(7), 1402–1404. Citations: 27

Basgumus, A., Durak, F. E., Altuncu, A., & Yilmaz, G. (2015). A universal and stable all-fiber refractive index sensor system. IEEE Photonics Technology Letters, 28(2), 171–174. Citations: 26

Umakoglu, I., Namdar, M., Basgumus, A., Kara, F., Kaya, H., & Yanikomeroglu, H. (2021). BER performance comparison of AF and DF assisted relay selection schemes in cooperative NOMA systems. In Proceedings of the 2021 IEEE International Black Sea Conference on Communications and Networking. Citations: 22

Ying Yi Tan | Smart Manufacturing | Best Researcher Award

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Dr. Ying Yi Tan | Smart Manufacturing | Best Researcher Award

Research Fellow | Singapore University of Technology and Design | Singapore

Dr. Tan Ying Yi is a Research Fellow at the Singapore University of Technology and Design (SUTD) whose research lies at the intersection of digital fabrication, smart textiles, and computational design. The focus of his work is the development of digital knitting technologies and multi-material additive manufacturing methods for creating functional, mechanically graded, and intelligent textile-based systems. His investigations explore how knitted fabrics can be engineered with integrated electrical and mechanical properties, transforming traditional textiles into high-performance materials applicable to both architectural and biomedical domains. Ying Yi has played a significant role in advancing customized technical textiles for applications such as structural membranes, façade systems, prosthetic interfaces, and wearable technologies. His leadership in projects involving smart garments for body joint monitoring has contributed to innovations in digital health and human–machine interaction, demonstrating the potential of computational design and materials research to improve quality of life. Collaborative projects with institutions like SingHealth Polyclinics, Tan Tock Seng General Hospital, and Hyundai Motor Group have led to impactful real-world solutions such as smart knee braces for gait assessment and smart shirts for motion tracking. His work is characterized by an interdisciplinary approach, blending engineering precision, material science, and architectural design principles to create responsive systems that interact dynamically with users and environments. Recognized with awards for excellence in architectural membranes and advanced manufacturing, Ying Yi continues to contribute to the integration of digital fabrication, computational modeling, and soft robotics in technical textile research. His studies have been featured by major media outlets for their innovation and societal relevance, showcasing how fabric-based systems can bridge the gap between engineering and design. Citations 19 Documents 5 h-index View.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Weeger, O., Sakhaei, A. H., Tan, Y. Y., Quek, Y. H., Lee, T. L., Yeung, S. K., & Kaijima, S. (2018). Nonlinear multi-scale modelling, simulation and validation of 3D knitted textiles. Applied Composite Materials, 25(4), 797–810. Citations: 43

Sakhaei, A. H., Kaijima, S., Lee, T. L., Tan, Y. Y., & Dunn, M. L. (2018). Design and investigation of a multi-material compliant ratchet-like mechanism. Mechanism and Machine Theory, 121, 184–197. Citations: 31

Gupta, S. S., Tan, Y. Y., Chia, P. Z., Pambudi, C. P., Quek, Y. H., Yogiaman, C., & Tracy, K. J. (2020). Prototyping knit tensegrity shells: A design-to-fabrication workflow. SN Applied Sciences, 2(6), 1062. Citations: 25

Do, H., Tan, Y. Y., Ramos, N., Kiendl, J., & Weeger, O. (2020). Nonlinear isogeometric multiscale simulation for design and fabrication of functionally graded knitted textiles. Composites Part B: Engineering, 202, 108416. Citations: 20

Gupta, U., Lau, J. L., Chia, P. Z., Tan, Y. Y., Ahmed, A., Tan, N. C., Soh, G. S., & Low, H. Y. (2023). All knitted and integrated soft wearable of high stretchability and sensitivity for continuous monitoring of human joint motion. Advanced Healthcare Materials, 12(21), 2202987. Citations: 17

Pal, A., Chan, W. L., Tan, Y. Y., Chia, P. Z., & Tracy, K. J. (2020). Knit concrete formwork. Proceedings of the 25th CAADRIA Conference, 1, 213–222. Citations: 7

Ye Tao | Machine Learning | Best Researcher Award

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Dr. Ye Tao | Machine Learning | Best Researcher Award

PhD Student | China University of Petroleum, Beijing| China

Dr Ye Tao is a dedicated researcher focusing on sedimentology, unconventional oil and gas exploration, and the integration of artificial intelligence into geological studies. His work emphasizes fine characterization and sweet spot evaluation of shale gas reservoirs, tectonic evolution, sedimentary system reconstruction, and deepwater hydrocarbon accumulation models. Ye Tao has served as principal investigator and key researcher on multiple funded projects, including studies on shale reservoir heterogeneity in the Wufeng–Longmaxi Formations, tectonic evolution of the North Uscult Basin, and migration and accumulation mechanisms in the Guyana Basin. His expertise spans seismic data interpretation, fracture classification, mechanical modeling, and stress field simulation, contributing to accurate prediction of reservoir sweet spots and caprock sealing capacity. Ye Tao has actively published in peer-reviewed journals, presenting significant contributions such as deep learning-aided shale reservoir analysis, isotope-based sea-level reconstructions, and machine learning-based carbonate fossil recognition. His interdisciplinary approach bridges geology with computer vision and artificial intelligence, providing innovative methodologies for improving exploration accuracy. Ye Tao has been awarded multiple national and institutional prizes, including first prizes at China University of Petroleum’s Graduate Academic Forum and the National Doctoral Student Academic Forum, showcasing his academic excellence and leadership. His skillset includes seismic processing, petrographic thin section analysis, carbon and oxygen isotope testing, and restoration of paleoenvironments, enabling comprehensive understanding of sedimentary processes. By applying deep learning techniques to geological data, Ye Tao is contributing to next-generation exploration strategies that enhance prediction of hydrocarbon distribution and optimize resource development. His work demonstrates strong potential for advancing both theoretical sedimentology and applied petroleum exploration, making significant impact on energy resource evaluation and development strategies in complex geological settings.

Profile:  ORCID
Featured Publication

Tao, Y., Bao, Z., & Ma, F. (2025). Analyzing key controlling factors of shale reservoir heterogeneity in “thin” stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou. Applied Computing and Geosciences, 100293.

Tao, Y., Bao, Z., Yu, J., & Li, Y. (2025). The petrophysical characteristics and controlling factors of the Wufeng Formation–Longmaxi Formation shale reservoirs in the Fuyan Syncline, Northern Guizhou. Geological Journal.

Tao, Y., Gao, D., He, Y., Ngia, N. R., Wang, M., Sun, C., Huang, X., & Wu, J. (2023). Carbon and oxygen isotopes of the Lianglitage Formation in the Tazhong area, Tarim Basin: Implications for sea-level changes and palaeomarine conditions. Geological Journal, 58, 967–980.

Tao, Y., He, Y., Zhao, Z., Wu, D., & Deng, Q. (2023). Sealing of oil-gas reservoir caprock: Destruction of shale caprock by micro-fractures. Frontiers in Earth Science, 10, 1065875.