Mr. Harish Verma | Best Researcher Award

Published on by Physics and Quantum Physics

Mr. Harish Verma | Best Researcher Award

Indian Institute of Technology (Banaras Hindu University) Varanasi | India

Dr. Harish Verma holds a B.Sc (UG), B.Ed, M.Sc (PG), and M.Phil in Physics and has qualified the CSIR-NET JRF examination. He is currently pursuing a Ph.D. in energy storage, dielectric materials, density functional theory (DFT), artificial intelligence (AI), and machine learning (ML) at the Indian Institute of Technology (BHU), Varanasi. His research focuses on the synthesis and characterization of advanced functional materials such as oxide perovskites, spinels, and graphene-based nanocomposites for dielectric and electrochemical energy storage applications. Dr. Verma integrates computational DFT analysis with AI- and ML-assisted materials modeling to accelerate the design and optimization of high-performance materials. His recent works include studies on dielectric and conductivity behavior of SrCeO₃, Ru-doped CNT/graphene-oxide supercapacitors, and MgAl₀.₅Fe₁.₅O₄ spinel ferrite systems. With over 20 scientific publications, an h-index of 6, and more than 90 citations, he has contributed significantly to understanding charge transport, dielectric relaxation, and structure–property relationships in multifunctional ceramics. His research aims to bridge experimental materials science and computational intelligence for developing sustainable, next-generation energy storage technologies and smart functional materials with enhanced performance and stability.

Profile : Google Scholar

Featured Publications

Verma, H., Tripathi, A., & Upadhyay, S. (2024). A comprehensive study of dielectric, modulus, impedance, and conductivity of SrCeO₃ synthesized by the combustion method. International Journal of Applied Ceramic Technology, 21(4), 3032–3047.

Verma, S., Das, T., Verma, S., Pandey, V. K., Pandey, S. K., Verma, H., & Verma, B. (2025). Hierarchically architecture of Ru-doped multichannel carbon nanotubes embedded with graphene oxide for supercapacitor material with long-term cyclic stability. Fuel, 381, 133517.

Verma, S., Maurya, A., Verma, H., Singh, R., & Bhoi, B. (2024). Unveiling the characteristics of MgAl₀.₅Fe₁.₅O₄ spinel ferrite: A study of structural, optical, and dielectric properties. Chemical Physics Impact, 9, 100674.

Nirala, G., Katheriya, T., Yadav, D., Verma, H., & Upadhyay, S. (2023). The evolution of coil-less inductive behaviour in La-doped Sr₂MnO₄. Emergent Materials, 6(6), 1951–1962.

Verma, H., Kumar, P., Satyarthi, S. K., Bhattacharya, B., Singh, A. K., & Upadhyay, S. (2025). Investigation of La₂FeO₄–rGO nanocomposite electrode material for symmetric and asymmetric supercapacitor. Journal of Energy Storage, 114, 115849.

Assist. Prof. Dr. Fayzullo Nazarov | Best Researcher Award

Published on by Physics and Quantum Physics

Assist. Prof. Dr. Fayzullo Nazarov | Best Researcher Award

Samarkand State University | Uzbekistan

Dr. Fayzullo Nazarov is a distinguished researcher at Sharof Rashidov Samarkand State University, Uzbekistan, recognized for his contributions to artificial intelligence, data science, and computational optimization. With an h-index of 10, 28 publications, and 217 citations across 94 documents, his research demonstrates significant scholarly impact and growing recognition in the field. Dr. Nazarov earned his advanced degrees in computer science and applied mathematics, where he developed a strong foundation in algorithmic modeling, neural networks, and machine learning applications. His academic and professional experience centers on the intelligent management of data systems, optimization of distribution mechanisms, and neural network ensemble methodologies. Dr. Nazarov’s recent works, including studies on effective distribution determination using neural network ensembles and machine learning-based data storage optimization, highlight his innovative approach to integrating AI with intelligent system design. He actively collaborates with international researchers to advance computational intelligence and smart data technologies. Throughout his career, Dr. Nazarov has received multiple academic recognitions for excellence in research and publication. His dedication to advancing AI-driven optimization techniques positions him as a leading researcher in intelligent systems and computational innovation, with ongoing contributions to the digital transformation of data management and predictive modeling.

Profiles : Google Scholar | Scopus

Featured Publications

khatov, A. R., Nazarov, F. M., & Rashidov, A. (2021). Mechanisms of information reliability in big data and blockchain technologies. In Proceedings of the 2021 International Conference on Information Science and Communications.

Akhatov, A. R., Nazarov, F. M., & Rashidov, A. (2021). Increasing data reliability by using big data parallelization mechanisms. In Proceedings of the 2021 International Conference on Information Science and Communications.

Rashidov, A., Akhatov, A., & Nazarov, F. (2023). The same size distribution of data based on unsupervised clustering algorithms. In The International Conference on Artificial Intelligence and Logistics.

Akhatov, A. R., Sabharwal, M., Nazarov, F. M., & Rashidov, A. (2022). Application of cryptographic methods to blockchain technology to increase data reliability. In 2nd International Conference on Advance Computing and Innovative Technologies in Engineering.

Dagur, A., Shukla, D. K., Makhmadiyarovich, N. F., & Rustamovich, A. A. (2024). Artificial intelligence and information technologies: Proceedings of the 1st International Conference on Artificial Intelligence and Information Technologies (ICAIIT 2023). CRC Press.

Assoc. Prof. Dr. Ateke Goshvarpour | Best Researcher Award

Published on by Physics and Quantum Physics

Assoc. Prof. Dr. Ateke Goshvarpour | Best Researcher Award

Imam Reza International University | Iran

Ateke Goshvarpour is a distinguished researcher at Imam Reza International University, renowned for her extensive contributions to biomedical signal processing, emotion recognition, and neurophysiological data analysis. With an impressive h-index of 20, over 1,093 citations, and 70 research publications, her work has significantly advanced the integration of computational intelligence and physiological signal modeling. She has co-authored several influential studies on electroencephalography (EEG), electrocardiography (ECG), photoplethysmography (PPG), and galvanic skin response (GSR) for emotion and mental disorder recognition. Dr. Goshvarpour earned her higher education in biomedical engineering and has accumulated years of academic and research experience focusing on nonlinear analysis, chaos theory, and machine learning applications in healthcare. Her recent works explore quantum-inspired models, graph-based EEG analysis, and spectral–spatiotemporal fusion for diagnosing schizophrenia and cognitive disorders. She has been recognized for developing innovative feature fusion techniques that enhance accuracy in automated emotion recognition and neurodiagnostic systems. Her publications in high-impact journals such as Cognitive Neurodynamics, Chaos, Solitons & Fractals, and Biomedical Signal Processing and Control underscore her leadership in the field. Through her pioneering research, she continues to shape the future of computational neuroscience and affective computing, bridging the gap between biomedical engineering and mental health diagnostics.

Profiles : Scopus | Google Scholar

Featured Publications

Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2017). An accurate emotion recognition system using ECG and GSR signals and matching pursuit method. Biomedical Journal, 40(6), 355–368. https://doi.org/10.1016/j.bj.2017.10.001

Goshvarpour, A., & Goshvarpour, A. (2019). EEG spectral powers and source localization in depressing, sad, and fun music videos focusing on gender differences. Cognitive Neurodynamics, 13(2), 161–173. https://doi.org/10.1007/s11571-018-9510-8

Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2017). Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged Poincaré plots. Australasian Physical & Engineering Sciences in Medicine, 40(3), 617–629. https://doi.org/10.1007/s13246-017-0560-7

Goshvarpour, A., & Goshvarpour, A. (2020). Schizophrenia diagnosis using innovative EEG feature-level fusion schemes. Physical and Engineering Sciences in Medicine, 43(1), 227–238. https://doi.org/10.1007/s13246-019-00853-8

Goshvarpour, A., & Goshvarpour, A. (2018). Poincaré’s section analysis for PPG-based automatic emotion recognition. Chaos, Solitons & Fractals, 114, 400–407. https://doi.org/10.1016/j.chaos.2018.07.009

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.