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

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.

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.