Prof. Nicolas Lori | Best Researcher Award

Published on by Physics and Quantum Physics

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

Published on by Physics and Quantum Physics

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.

Dr. Patrick Arnaud Wandji Zoumb | Best Researcher Award

Published on by Physics and Quantum Physics

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.