Mr. Shehzad Khan | Best Researcher Award

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Mr. Shehzad Khan | Best Researcher Award

Nanjing University of Science and Technology | China

Mr. Shehzad Khan is a promising Pakistani quantum physicist with a growing research profile in the fields of quantum optics, quantum information, plasmonics, and nonlinear optics. With an h-index of 2, 3 published documents, and 7 citations, he has contributed to several high-impact journals, including Results in Physics, The European Physical Journal Plus, International Journal of Theoretical Physics, Journal of Magnetism and Magnetic Materials, and Physics Letters A. He completed his Bachelor’s degree in Physics from the University of Malakand (2019–2023), where his thesis focused on “Manipulation of Spectral Hole Burning in Atomic Medium by Doppler Broadening Effect.” His research expertise includes density matrix formalism, optical solitons, Goos-Hänchen shift, photonic spin Hall effect, and surface plasmon polaritons. Shehzad has demonstrated strong analytical and computational skills using Mathematica, MATLAB, and LaTeX, coupled with proficiency in data analysis and technical writing. Recognized for his academic excellence, he received the Higher Education Commission (HEC) Laptop Award for outstanding performance and an HEC Merit and Need-Based Scholarship. With a clear vision to advance the understanding of light-matter interaction and quantum systems, Shehzad Khan aspires to make impactful contributions to modern quantum science and optical physics.

Profile : Scopus

Featured Publications

Khan, S., Bilal, M., Uddin, S., Akgül, A., & Riaz, M. B. (2024). Spherical manipulation of lateral shifts in reflection and transmission through chiral medium. Results in Physics, 107647.

Khan, S., Saeed, M., Khan, M. A., Aldosary, S. F., & Ahmad, S. Coherent manipulation of optical solitons in four-level N-type atomic medium. International Journal of Theoretical Physics.

Ullah, R., Khan, S., Amina, S., & Javaid, S. Tunable cratering of lateral Goos–Hänchen shift in reflection and transmission of structured light in a chiral atomic medium. The European Physical Journal Plus.

Ullah, H., Khan, S., & Bilal, M. Localized electric and magnetic tangent loss via parity-time symmetry in induced high magneto-optical atomic medium. Journal of Magnetism and Magnetic Materials.

Ahmad, M., Khan, S.*, Shah, S. M. H., Salman, M., & Yousaf, M. (2025). Coherent manipulation of sensitivity of structure plasmon polariton waves. The European Physical Journal Plus.

Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

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Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

Anurag University | India

Dr. Shravan Kumar Rudrabhatla is an Assistant Professor at Anurag University, Hyderabad, specializing in fluid dynamics and artificial neural networks. He earned his Ph.D. in Applied Mathematics from the National Institute of Technology (NIT), Warangal in 2023 under the supervision of Prof. D. Srinivasacharya, focusing on the artificial neural network treatment of Casson fluid flow over a radially stretching sheet. His research integrates deep learning, computational fluid dynamics, and heat and mass transfer modeling, contributing to the understanding of complex non-Newtonian flows. Dr. Rudrabhatla has authored 6 research articles, accumulated 49 citations from 43 documents, and achieved an h-index of 4, as indexed by Scopus. His recent works include publications in European Journal of Mechanics B/Fluids, Physics of Fluids, Mathematical Models and Computer Simulations, and Journal of Thermal Analysis and Calorimetry. He has participated in numerous faculty development programs, workshops, and GIAN courses focused on machine learning and computational modeling. His academic journey is complemented by strong technical skills in Python, MATLAB, and C++, and a teaching background spanning over a decade. Dr. Rudrabhatla’s work continues to advance the intersection of mathematics, fluid mechanics, and artificial intelligence, contributing significantly to modern computational sciences.

Profiles : Orcid | Google Scholar | Scopus

Featured Publications

Srinivasacharya, D., & Kumar, R. S. (2022). Artificial neural network modeling of the Casson fluid flow over unsteady radially stretching sheet with Soret and Dufour effects. Journal of Thermal Analysis and Calorimetry, 147, 14891–14903. https://doi.org/10.1007/s10973-022-11694-w

Srinivasacharya, D., & Shravan Kumar, R. (2023). Neural network analysis for bioconvection flow of Casson fluid over a vertically extending sheet. International Journal of Applied and Computational Mathematics, 9(5), 80. https://doi.org/10.1007/s40819-023-01556-w

Srinivasacharya, D., & Kumar, R. S. (2023). An artificial neural network solution for the Casson fluid flow past a radially stretching sheet with magnetic and radiation effect. Mathematical Models and Computer Simulations, 15(5), 944–955. https://doi.org/10.1134/S2070048223050101

Nallapu, S., Sneha, G. S., & Kumar, S. R. (2018). Effect of slip on Jeffrey fluid flow through an inclination tube. Journal of Physics: Conference Series, 1000(1), 012041. https://doi.org/10.1088/1742-6596/1000/1/012041

Rudrabhatla, S. K., & Srinivasacharya, D. (2025). Deep learning framework for Casson fluid flow: A PINN approach to heat and mass transfer with chemical reaction and viscous dissipation. European Journal of Mechanics – B/Fluids, 204401. https://doi.org/10.1016/j.euromechflu.2025.204401

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.