Dr. Sekhar Reddy Kola | Best Researcher Award

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Dr. Sekhar Reddy Kola | Best Researcher Award

National Yang Ming Chiao Tung University | Taiwan

Dr. Sekhar Reddy Kola is a Postdoctoral Fellow in the Department of Electrical and Computer Engineering at National Yang Ming Chiao Tung University (NYCU), Taiwan. He earned his Ph.D. in Semiconductor Devices from NYCU, where he focused on the process variation effect and intrinsic parameter fluctuation of vertically stacked gate-all-around (GAA) silicon nanosheet complementary field-effect transistors (CFETs) under the supervision of Prof. Yiming Li. With a solid academic foundation, including an M.Sc. in Electronics and a B.S. in Mathematics, Electronics, and Computer Science from Sri Venkateswara University, India, Dr. Kola has made significant contributions to the field of semiconductor device physics and modeling. His research interests encompass GAA nanosheet and nanowire FETs, CFET design, statistical process variation modeling, reliability analysis, and machine learning applications in nanoelectronics. He has published 33 documents with over 311 citations and an h-index of 10, reflecting his impactful scientific contributions. Dr. Kola has received several honors, including the Best Paper Award at IEDMS 2018 and the Outstanding Foreign Student Scholarship from NYCU. Through his innovative research on nanoscale device modeling and variability analysis, Dr. Kola continues to advance the development of next-generation semiconductor technologies for sub-1-nm nodes.

Profiles : Google Scholar | Scopus | Orcid

Featured Publications

Kola, S. R., & Li, Y. (2025). Effects of bottom channel coverage ratio on leakage current and static power consumption of vertically stacked GAA Si NS FETs. ECS Journal of Solid State Science and Technology, 14(2), 025001.

Kola, S. R., Li, Y., & Butola, R. (2024). Statistical device simulation and machine learning of process variation effects of vertically stacked gate-all-around Si nanosheet CFETs. IEEE Transactions on Nanotechnology, 23, 386–392.

Kola, S. R., & Li, Y. (2023). Electrical characteristic and power fluctuations of GAA Si NS CFETs by simultaneously considering six process variation factors. IEEE Open Journal of Nanotechnology, 4, 112–120.

Sreenivasulu, V. B., Kumari, N. A., Kola, S. R., Singh, J., & Li, Y. (2023). Exploring the performance of 3-D nanosheet FET in inversion and junctionless modes: Device and circuit-level analysis and comparison. IEEE Access, 11, 42256–42265.

Kola, S. R., Li, Y., & Thoti, N. (2020). Effects of a dual spacer on electrical characteristics and random telegraph noise of gate-all-around silicon nanowire p-type MOSFETs. Japanese Journal of Applied Physics, 59(SGGA02).

Prof. Nicolas Lori | Best Researcher Award

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Prof. Nicolas Lori | Best Researcher Award

University of Azores | Portugal

Dr. Nicolas F. Lori is a distinguished physicist and computer scientist, presently serving as an Assistant Professor at the University of the Azores and a researcher at the Centre Algoritmi, University of Minho, Portugal. He earned a Ph.D. in Physics from Washington University in St. Louis in 2001 and a Ph.D. in Informatics from the University of Minho in 2020. With 44 research papers, over 2,495 citations, and an h-index of 13, his work spans theoretical physics, computer science, and neuroscience. Dr. Lori has made pioneering contributions to diffusion MRI, brain connectivity mapping, and theoretical models bridging physics and computation. His studies have appeared in top-tier journals such as PNAS, Radiology, and Annals of Physics. He has successfully led national research projects totaling over €1.5 million and supervised multiple graduate students. His current research interests include MRI data processing, quantum gravity, artificial intelligence, theoretical neuroscience, and computational modeling. A Fulbright Fellow and former Vice-President of Fulbrighters Portugal, Dr. Lori’s career demonstrates a strong commitment to advancing interdisciplinary research that integrates physics, computation, and cognitive science to understand the complexity of the brain and the universe.

Profiles : Google Scholar | Scopus | Orcid

Featured Publications

Lori, N. F., Akbudak, E., Shimony, J. S., Cull, T. S., Snyder, A. Z., Guillory, R. K., … & Conturo, T. E. (2002). Diffusion tensor fiber tracking of human brain connectivity: Acquisition methods, reliability analysis, and biological results. NMR in Biomedicine, 15(7–8), 459–477.

Seehaus, A., Roebroeck, A., Bastiani, M., Fonseca, L., Bratzke, H., Lori, N., … & Galuske, R. (2015). Histological validation of high-resolution DTI in human post mortem tissue. Frontiers in Neuroanatomy, 9, 98.

Sedeno, L., Piguet, O., Abrevaya, S., Desmaras, H., García-Cordero, I., Baez, S., … & Lori, N. F. (2017). Tackling variability: A multicenter study to provide a gold‐standard network approach for frontotemporal dementia. Human Brain Mapping, 38(8), 3804–3822.

Lori, N. F. (2023). Mass creation in superconductors by Physics-cells quantum gravity. Physica C: Superconductivity and Its Applications, 611, 135722.

Lori, N. F. (2025). Darwinian quantum gravity dynamics of small particles. Annals of Physics, 449, 169553.

Lori, N. F. (2025). Varying Newton gravitational “constant” cosmology. Annals of Physics, 451, 170012.

Mr. Ulrich Ngnassi Nguelcheu | Best Researcher Award

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Mr. Ulrich Ngnassi Nguelcheu | Best Researcher Award

Researcher at University of Ngaoundéré | Cameroon

Dr. Ulrich Ngnassi Nguelcheu is a researcher at the University of Ngaoundéré, Cameroon, specializing in artificial intelligence applications in renewable energy systems. With 11 publications, 1,302 reads, and 5 citations (h-index: 2), he has made valuable contributions to intelligent control and data-driven optimization for sustainable energy technologies. He holds a doctorate in Artificial Intelligence applied to Renewable Energies, focusing on enhancing the performance and reliability of electromechanical systems through AI-based modeling and simulation. His research experience covers wind energy systems, maintenance optimization, reliability analysis, and composite material development. Among his notable works are studies on the use of artificial neural networks for improved wind turbine control and the optimization of preventive maintenance using genetic algorithms, published in leading journals such as Engineering Applications of Artificial Intelligence. Dr. Nguelcheu actively collaborates with researchers across Cameroon and internationally, emphasizing sustainable and intelligent energy management. His research interests include machine learning for energy systems, renewable energy integration, and smart maintenance strategies. He is dedicated to advancing innovative and eco-efficient technologies that support the global shift toward clean and sustainable energy.

Profiles : Research Gate | Scopus

Featured Publications

Nguelcheu, U. N., Ndjiya, N., Kenmoe Fankem, E. D., Ngnassi Djami, A. B., Guidkaya, G., & Effa, J. Y. (2025). Harnessing artificial neural networks for improved control of wind turbines based on brushless doubly fed induction generator. Engineering Applications of Artificial Intelligence, 154, 110925.

Nguelcheu, U. N., Ndjiya, N., Kenmoe Fankem, E. D., Ngnassi Djami, A. B., Guidkaya, G., & Dountio, T. (2023). Literature review on the control of brushless doubly-fed induction machines. Global Journal of Engineering and Technology Advances, 16(3), 51–69.

Ngnassi Djami, A. B., Nguelcheu, U. N., & Yamigno, S. D. (2023). Formulation, characterization and future potential of composite materials from natural resources: The case of kenaf and date palm fibers. Online Journal of Mechanical Engineering.

Mr. Asif Khan | Best Researcher Award

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Mr. Asif Khan | Best Researcher Award

University of Science and Technology Bannu KPK  | Pakistan

Dr. Asif Nawaz Khan is a Pakistani physicist and lecturer at the University of Science and Technology Bannu (USTB), specializing in computational materials science. He is currently pursuing a Ph.D. in Physics at USTB, after completing an M.Phil. from Gomal University and an M.Sc. from Kohat University. Since 2009, he has been actively involved in teaching, supervising BS and M.Phil students, and guiding research in computational simulations and solar cell device modeling. His research focuses on the design and analysis of lead-free perovskite materials (3D and 2D) and their structural, optical, thermoelectric, elastic, thermodynamic, and phonon properties, along with molecular dynamics studies. He employs advanced simulation tools including WIEN2k, Quantum Espresso, CASTEP, and SCAPS-1D, and applies machine learning techniques for material property prediction. Dr. Khan has co-authored multiple high-impact publications, currently holding an h-index of 3 with 38 citations, reflecting his contributions to clean energy materials and sustainable photovoltaics. His work advances the understanding and development of efficient, stable, and multifunctional energy materials. Overall, Dr. Khan is committed to advancing computational materials research and training the next generation of scientists in energy and optoelectronic applications.

Profile : Google Scholar  

Featured Publications

Khan, A., Khan, N. U., Nawaz, A., Ullah, K., & Manan, A. (2024). A DFT study to explore structural, electronic, optical and mechanical properties of lead-free Na₂MoXO₆ (X= Si, Ge, Sn) double perovskites for photovoltaic and optoelectronic applications. Computational and Theoretical Chemistry, 1240, 114834. https://doi.org/10.1016/j.comptc.2024.114834

Hosen, A., Mousa, A. A., Nemati-Kande, E., Khan, A. N., Abu-Jafar, M. S., … (2025). Systematic computational screening and design of double perovskites Q₂LiMH₆ (Q= K, Rb; M= Ga, In, Tl) for efficient hydrogen storage: A DFT and AIMD approach. Surfaces and Interfaces, 106608. https://doi.org/10.1016/j.surfin.2025.106608

Khan, A. N., Rabhi, S., Jehangir, M. A., Charif, R., Khan, N. U., Begagra, A., … (2025). Evaluating A₂SrGeI₆ (A= K and Rb) lead-free double perovskites: Structural, elastic, and optoelectronic insights for clean energy. Inorganic Chemistry Communications, 174, 113949. https://doi.org/10.1016/j.inoche.2025.113949

Khan, N. U., Ghani, U., Khan, A., Khan, A. N., Ullah, K., Ali, R., & Fadhali, M. M. (2025). Theoretical insight into stabilities and optoelectronic properties of RbZnX₃ (X=Cl, Br) halide perovskites for energy conversion applications. Optical and Quantum Electronics, 57(1), 109. https://doi.org/10.1007/s11082-025-04789-1

Rabhi, S., Khan, A. N., Chinoune, O., Charif, R., Bouri, N., Al-Qaisi, S., … (2025). Insight into NaSiCl₃: A lead-free perovskite for the next generation revealed by DFT and SCAPS-1D. Physical Chemistry Chemical Physics, 27(25), 13490–13507. https://doi.org/10.1039/D5CP02345A

Assoc. Prof. Dr. Shuai Zheng | Best Researcher Award

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Assoc. Prof. Dr. Shuai Zheng | Best Researcher Award

Dalian Jiaotong University | China

Dr. Shuai Zheng is an accomplished researcher and Associate Professor at Dalian Jiaotong University, specializing in intelligent transportation infrastructure, geotechnical safety, and computational modeling. He earned his Ph.D. in Civil Engineering and has extensive experience in bridge and tunnel stability, BIM-based digital construction, and AI-driven reliability analysis. His research integrates theoretical modeling with data-driven engineering to improve the safety and resilience of transportation systems. Dr. Zheng has authored 22 peer-reviewed publications with 94 citations and an h-index of 6, demonstrating consistent scholarly impact. His key works include “Research and Application of Reliability Evaluation Model for Water Inrush Risk during Tunnel Construction” (Tunnelling and Underground Space Technology, 2026) and “ZSI-R Method for Evaluating Foundation Pit Stability in Karst Regions” (Geotechnical and Geological Engineering, 2025). He collaborates with over 50 co-authors worldwide, contributing to multidisciplinary studies in digital and sustainable construction. Dr. Zheng has received provincial-level Science and Technology Progress Awards (2020 & 2022) for his innovative contributions to digital infrastructure development. His research advances the understanding of smart, resilient, and sustainable engineering systems, positioning him as a promising leader in next-generation intelligent construction technologies.

Profiles : Orcid | Scopus

Featured Publications

Zhang, Q., Zheng, S., Zhao, J., Liu, X., Du, E., & Yang, Y. (2025). ZSI-R method for evaluating foundation pit stability in karst regions. Geotechnical and Geological Engineering. https://doi.org/10.1007/s10706-025-03358-x

Ma, C., Yang, Y., Wang, X., Zhang, Y., Wang, H., & Zheng, S. (2025). A new DEM calibration method for the adhesion and shear behavior of clay materials based on response surface methodology. Engineering Research Express. https://doi.org/10.1088/2631-8695/ae0411

Jia, B., Yang, Y., Wang, X., Li, L., Zhang, Y., & Zheng, S. (2025). Real-time prediction method of shield tunneling attitude under complex geological conditions. Engineering Research Express. https://doi.org/10.1088/2631-8695/ae0b30

Zhang, H., Dong, S., Li, S., & Zheng, S. (2025). Sensitivity analysis and optimization of urban roundabout road design parameters based on CFD. Eng, 6(7), 156. https://doi.org/10.3390/eng6070156

Jiang, T., Jiang, A., Zheng, S., Xu, M., & Nguyen-Xuan, H. (2021). Prediction of blast-induced ground vibration (BIGV) of metro construction using differential evolution algorithm-optimized Gaussian process (DE-GP). Shock and Vibration, Article 2847180. https://doi.org/10.1155/2021/2847180

Dr. Patrick Arnaud Wandji Zoumb | Best Researcher Award

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Dr. Patrick Arnaud Wandji Zoumb | Best Researcher Award

National Advanced School of Public Works | Cameroon

Dr. Patrick Arnaud Wandji Zoumb, Ph.D., is a distinguished researcher in civil and structural engineering, specializing in train–bridge interaction, structural dynamics, and machine learning applications for railway infrastructure. He earned his Ph.D. in Bridge and Tunnel Engineering from Southwest Jiaotong University, following a Master’s degree in Bridge and Tunnel Engineering from Wuhan University of Technology and a Bachelor’s in Public Works from the National Advanced School of Public Works. Dr. Zoumb currently serves as an Assistant at the National Advanced School of Public Works, where he contributes to teaching, research, and faculty development, focusing on railway design, structural design, fluid mechanics, and transportation infrastructure. His prior professional experience includes engineering roles in public works projects, providing expertise in bridge assessment, road safety, and infrastructure planning. With an h-index of 8, over 25 indexed publications, and more than 350 citations, his research is internationally recognized. His work integrates advanced computational methods such as Fourier regression, fuzzy random uncertainty, Kalman filters, and neural networks to investigate the dynamic behavior of train–bridge systems under wind, wave, and seismic loads. Dr. Zoumb is a recipient of the prestigious Arthur Wellington Prize (2023) awarded by the American Society of Civil Engineers (ASCE). His ongoing research continues to advance resilient, intelligent, and sustainable railway infrastructure systems.

Profiles : Orcid | Research Gate

Featured Publications

Zoumb, P. A. W., Bwemba, C., Mbessa, M., Moussus, T. W., & Kemta, L. P. (2025). Train-bridge interaction under correlated wind and rain using machine learning. Advances in Structural Engineering, 28(9).

Zoumb, P. A. W., Wang, M., & Kouame, A. R. (2025). Fourier series-based reliability analysis of train-bridge interaction under crosswind action using fuzzy random uncertainty. Structures, 81, 110148.

Li, X., & Zoumb, P. A. W. (2022). Extraction of the unknown hydrodynamic pressure from stochastic responses of the train-bridge system under wind and wave actions using iterative least square estimation and Kalman filter model. Journal of Wind Engineering and Industrial Aerodynamics, 231, 105202.

Zoumb, P. A. W., Li, X., & Wang, M. (2022). Effects of earthquake-induced hydrodynamic force on train–bridge interactions. Journal of Bridge Engineering.

Zoumb, P. A. W., & Li, X. (2022). Influence of earthquake-induced hydrodynamic pressure on train-bridge interactions based on back-propagation neural network. Advances in Structural Engineering.

Zoumb, P. A. W., et al. (2022). Fourier regression model predicting train-bridge interactions under wind and wave actions. Structure and Infrastructure Engineering.