Dr. Fan Zhang | Energy Technologies | Best Researcher Award

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Dr. Fan Zhang | Energy Technologies | Best Researcher Award

Research Associate | Queensland University of Technology | Australia

Dr. Fan Zhang is a distinguished researcher at the Queensland University of Technology whose work focuses on the advancement of next-generation aqueous zinc-ion batteries and sustainable energy storage technologies. Their research integrates bioinspired materials design, electrolyte optimization, and interfacial engineering to address key challenges such as dendrite formation, hydrogen evolution, and low reversibility in Zn-based systems. With significant contributions to materials science and electrochemistry, Dr. Zhang has established a strong reputation for innovative approaches that enhance the safety, energy density, and long-term stability of aqueous batteries. Their studies combine experimental synthesis with advanced characterization techniques, leading to impactful findings published in high-impact journals such as Advanced Materials, Journal of the American Chemical Society, National Science Review, and Nano Energy. Dr. Zhang’s scholarly influence is evidenced by a Scopus citation count of 370 (h-index: 12, 17 documents) and a Google Scholar citation count of 352 (h-index: 11, i10-index: 11). Their research continues to drive progress in electrochemical energy storage, contributing to the global shift toward sustainable and environmentally friendly power solutions.

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Featured Publications

Zhang, F., Liao, T., Liu, C., Peng, H., Luo, W., Yang, H., Yan, C., & Sun, Z. (2022). Biomineralization-inspired dendrite-free Zn-electrode for long-term stable aqueous Zn-ion battery. Nano Energy, 103, 107830.

Zhang, F., Liao, T., Peng, H., Xi, S., Qi, D. C., Micallef, A., Yan, C., Jiang, L., & Sun, Z. (2024). Outer sphere electron transfer enabling high-voltage aqueous electrolytes. Journal of the American Chemical Society, 146(15), 10812–10821.

Zhang, F., Liao, T., Qi, D. C., Wang, T., Xu, Y., Luo, W., Yan, C., Jiang, L., & Sun, Z. (2024). Zn-ion ultrafluidity via bioinspired ion channel for ultralong lifespan Zn-ion battery. National Science Review, 11(8), nwae199.

Zhang, F., Liao, T., Yan, C., & Sun, Z. (2024). Bioinspired designs in active metal-based batteries. Nano Research, 17(2), 587–601.

Zhang, F., Liao, T., Zhou, Q., Bai, J., Li, X., & Sun, Z. (2025). Advancements in ion regulation strategies for enhancing the performance of aqueous Zn-ion batteries. Materials Science and Engineering: R: Reports, 165, 101012.

Assist. Prof. Dr. Lotfi Jlali | Mathematics | Best Researcher Award

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Assist. Prof. Dr. Lotfi Jlali | Mathematics | Best Researcher Award

Imam Mohammad Ibn Saud Islamic University | Tunisia

Dr. Lotfi Mohamed Alhosine Jlali is an accomplished Tunisian mathematician and Assistant Professor at Imam Mohammad Ibn Saud Islamic University, Saudi Arabia. His research primarily focuses on Partial Differential Equations (PDEs) and Nonlinear Analysis, with particular expertise in the mathematical modeling of fluid dynamics, including the Navier–Stokes, Euler, and Magnetohydrodynamic (MHD) systems. Dr. Jlali’s work delves into the local and global existence, uniqueness, and regularity of solutions for incompressible fluid equations, as well as the asymptotic behavior of problems influenced by small or large parameters, such as rotating and anisotropic fluid systems. His studies also address the blow-up criteria for non-regular solutions and the stability of global solutions, applying advanced mathematical tools like Strichartz inequalities, energy estimates, and Sobolev embeddings. Dr. Jlali has made significant contributions to understanding the long-term dynamics of fluid equations, particularly in Sobolev–Gevrey and Fourier–Lei–Lin spaces. His research output includes 13 indexed publications with 51 citations in Scopus (h-index: 4) and 85 citations on Google Scholar (h-index: 5, i10-index: 3), reflecting the growing impact of his work in mathematical fluid mechanics.

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Featured Publications:

Benameur, J., & Jlali, L. (2016). Long time decay for 3D Navier-Stokes equations in Sobolev-Gevrey spaces. Electronic Journal of Differential Equations.

Benameur, J., & Jlali, L. (2016). On the blow-up criterion of 3D-NSE in Sobolev–Gevrey spaces. Journal of Mathematical Fluid Mechanics.

Jlali, L. (2017). Global well posedness of 3D-NSE in Fourier–Lei–Lin spaces. Mathematical Methods in the Applied Sciences.

Benameur, J., & Jlali, L. (2020). Long time decay of 3D-NSE in Lei-Lin-Gevrey spaces. Mathematica Slovaca.

Jlali, L., & Benameur, J. (2024). Long time decay of incompressible convective Brinkman-Forchheimer in L2(R3). Demonstratio Mathematica.

Dr. Yonglin Ren | Computer Science | Innovative Research Award

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Dr. Yonglin Ren | Computer Science | Innovative Research Award

Senior Project Engineer & Researcher | Concordia University | Canada

Dr. Yonglin Ren is a distinguished Senior Project Engineer and Researcher at Concordia University, recognized for his interdisciplinary expertise in mathematical modeling, logistics optimization, and sustainable engineering systems. His research bridges theoretical optimization frameworks and industrial applications, focusing on metaheuristic algorithms, CAD/CAE-based modeling, and supply chain design for humanitarian and sustainable logistics. Dr. Ren’s contributions have advanced methodologies for capacitated location allocation problems, high-speed rail freight transport, and dynamic mechanical system modeling. His work integrates computational intelligence with real-world challenges in water resource management, transportation networks, and crisis logistics, making a significant impact in both academia and industry. His publications are widely cited, reflecting his influence in the fields of operational research and applied optimization, with a Scopus record of 3 indexed documents, 6 citations, and an h-index of 1, alongside a Google Scholar citation count of 26. Dr. Ren has collaborated on multiple international engineering and research projects, driving innovations that contribute to sustainable development and global resource optimization.

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Featured Publications 

Ren, Y., & Awasthi, A. (2014). Investigating metaheuristics applications for capacitated location allocation problem on logistics networks. Chaos Modeling and Control Systems Design, 213–238.

Ren, Y., & Awasthi, A. (2012). Location allocation planning of logistics depots using genetic algorithm. Research in Logistics & Production, 2, 247–257.

Ren, Y. (2011). Metaheuristics for multiobjective capacitated location allocation on logistics networks. Concordia University.

Ren, Y., Hajiebrahimi, S., Azad, M., Awasthi, A., & Salah, S. (2020). Humanitarian aid for Wuhan with crisis logistics management approach. Proceedings of the International Conference on Industrial Engineering and Operations Management.

Ren, Y., & Awasthi, A. (2025). Logistics hub location for high-speed rail freight transport—Case Ottawa–Quebec City corridor. Logistics, 9(4), 158.

Dr. Malaya Nath | Signal Processing | Best Researcher Award

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Dr. Malaya Nath | Signal Processing | Best Researcher Award

Assistant Professor | National Institute of Technology Puducherry | India

Dr. Malaya Kumar Nath is an accomplished researcher and academician in the field of Electronics and Communication Engineering, specializing in Biomedical Signal and Image Processing, Pattern Recognition, Deep Learning, and Computational Neuroscience. His research primarily focuses on developing advanced computational models for medical image analysis, disease diagnosis, and intelligent healthcare systems using signal and image processing techniques integrated with artificial intelligence. Dr. Nath has significantly contributed to diagnostic automation through the application of deep learning architectures such as CNNs and EfficientNet for skin cancer, glaucoma, and retinal image analysis. His scholarly contributions have earned him recognition among the Top two percentage most influential scientists worldwide, as reported by Stanford University and Elsevier in 2025. He has an extensive publication record, with 69 Scopus-indexed documents and over 1,291 citations by 902 documents, achieving an h-index of 21 on Scopus. On Google Scholar, he has accumulated 2,185 citations with an h-index of 24 and an i10-index of 47, reflecting his impactful research influence. His interdisciplinary research integrates biomedical data analytics with machine learning and deep neural frameworks, addressing challenges in medical imaging and healthcare informatics.

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Featured Publications

Keerthana, D., Venugopal, V., Nath, M. K., & Mishra, M. (2023). Hybrid convolutional neural networks with SVM classifier for classification of skin cancer. Biomedical Engineering Advances, 5, 100069.

Anbalagan, T., Nath, M. K., Vijayalakshmi, D., & Anbalagan, A. (2023). Analysis of various techniques for ECG signal in healthcare, past, present, and future. Biomedical Engineering Advances, 6, 100089.

Elangovan, P., & Nath, M. K. (2021). Glaucoma assessment from color fundus images using convolutional neural network. International Journal of Imaging Systems and Technology, 31(2), 955–971.

Vijayalakshmi, D., & Nath, M. K. (2020). A comprehensive survey on image contrast enhancement techniques in spatial domain. Sensing and Imaging, 21(1), 40.

Venugopal, V., Raj, N. I., Nath, M. K., & Stephen, N. (2023). A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images. Decision Analytics Journal, 8, 100278.

Dr. Jan Muhammad | Mathematics | Best Researcher Award

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Dr. Jan Muhammad | Mathematics | Best Researcher Award

Shanghai University | China

Dr. Jan Muhammad is a distinguished mathematician and postdoctoral researcher specializing in nonlinear partial differential equations (PDEs), soliton theory, and mathematical physics. His research primarily focuses on analytical and semi-analytical approaches for exploring nonlinear dynamical systems, fractional calculus, and optical wave propagation in applied mathematics and engineering contexts. With over 50 peer-reviewed publications in prestigious international journals, Dr. Muhammad has significantly contributed to advancing the theoretical and applied aspects of nonlinear PDEs and fractional models. His studies often explore the mathematical structures governing complex fluid mechanics, magnetohydrodynamics, and fractional optical systems, offering new insights into the behavior of nonlinear waves, stability, and multistability phenomena. His scholarly impact is reflected by 294 Scopus citations across 149 documents with an h-index of 11, showcasing the depth and reach of his contributions. His work is also widely recognized on Google Scholar, emphasizing his growing influence within the global mathematical community. Dr. Muhammad’s ongoing research bridges mathematical theory with real-world physical systems, demonstrating excellence in mathematical modeling, analytical methods, and interdisciplinary applications.

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Featured Publications

Muhammad, J., Fang, L., & Guo, Z. (2020). Global weak solutions to a class of compressible non-Newtonian fluids with vacuum. Mathematical Methods in the Applied Sciences, 43, 5234–5249.

Zhu, H., Fang, L., Muhammad, J., & Guo, Z. (2020). Global weak solutions to a Vlasov–Fokker–Planck/compressible non-Newtonian fluid system of equations. ZAMM, 100, e201900091.

Muhammad, J., Ali, Q., & Younas, U. (2024). Three component coupled fractional nonlinear Schrödinger equations: Diversity of exact optical solitonic structures. Modern Physics Letters B, 2450373.

Muhammad, J. (2024). On the global existence for a class of compressible non-Newtonian fluids with inhomogeneous boundary data. Russian Journal of Mathematical Physics, 31, 276–298.

Muhammad, J., Younas, U., & Nasreen, N. (2024). Multicomponent nonlinear fractional Schrödinger equation: Optical wave propagation in fiber optics. Partial Differential Equations in Applied Mathematics, 100805.

Prof. Joongrock Kim | Computer Vision | Best Researcher Award

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Prof. Joongrock Kim | Computer Vision | Best Researcher Award

Associate Professor | Changwon National University | South Korea

Prof. Joongrock Kim is an accomplished researcher and Associate Professor in Artificial Intelligence Convergence Engineering at Changwon National University, Republic of Korea. His expertise spans computer vision, 3D scene understanding, deep learning-based perception, and intelligent systems for automotive and consumer applications. Over his distinguished career, he has contributed significantly to the development of advanced AI technologies, including driver monitoring systems, 3D reconstruction, food recognition, and smart V2X perception systems. His research focuses on integrating multimodal sensing, neural rendering, and adaptive feature extraction for robust real-world perception, bridging academia and industry to advance AI deployment in smart vehicles and appliances. Dr. Kim’s prolific output includes numerous high-impact publications and international patents on AI-based sensing and perception systems. According to Scopus, he has achieved 212 citations across 207 documents with an h-index of 7, while his Google Scholar profile reflects broader academic engagement and influence. His work continues to drive innovation in perception AI, human–machine interaction, and computational imaging, establishing him as a leading figure in applied artificial intelligence and computer vision research.

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Featured Publications

Park, M., Do, M., Shin, Y. J., Yoo, J., Hong, J., Kim, J., & Lee, C. (2024). H2O-SDF: Two-phase learning for 3D indoor reconstruction using object surface fields. International Conference on Learning Representations (ICLR).

Kim, J., Yu, S., Kim, D., Toh, K.-A., & Lee, S. (2017). An adaptive local binary pattern for 3D hand tracking. Pattern Recognition.

Kim, J., Yoon, C. (2016). Three-dimensional head tracking using adaptive local binary pattern in depth images. International Journal of Fuzzy Logic and Intelligent Systems.

Kim, K., Kim, J., Choi, J., Kim, J., & Lee, S. (2015). Depth camera-based 3D hand gesture controls with immersive tactile feedback for natural mid-air gesture interactions. Sensors.

Kim, J., Yu, S., & Lee, S. (2014). Random-profiles-based 3D face recognition system. Sensors.

Mr. Siddhant Srinivas | Cyber Security | Best Researcher Award

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Mr. Siddhant Srinivas | Cyber Security | Best Researcher Award

California State University | United States

Siddhant Srinivas is an emerging researcher in Artificial Intelligence and Cybersecurity, currently contributing to the advancement of AI-augmented Security Operations Centers (SOC) through the integration of Large Language Models (LLMs) and AI agents. His research primarily focuses on developing intelligent frameworks that enhance the efficiency, scalability, and trustworthiness of SOC workflows. As the first author of a peer-reviewed publication in MDPI, Siddhant has presented a comprehensive taxonomy of AI-driven applications across SOC processes, highlighting their potential in transforming traditional alert triage, threat detection, and incident response systems. His work introduces a capability-maturity model that outlines the evolution from manual to autonomous SOC operations while addressing the challenges of explainability, safety, and reliability in AI deployments. Siddhant’s contributions emphasize bridging the gap between theoretical AI models and their practical implementation in cybersecurity domains. He has been recognized for his scholarly excellence through published research and active involvement in Dr. Alzahrani’s AI Research Lab. His published works are cited in indexed databases such as Scopus and Google Scholar, reflecting a growing academic footprint and influence in the emerging intersection of AI and security research. His citation records and h-index metrics from both Scopus and Google Scholar demonstrate his contributions to advancing secure, transparent, and automated AI systems.

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Featured Publication

Srinivas, S., Kirk, B., Zendejas, J., Bari, A., Dajani, K., & Alzahrani, N. (2025). AI-Augmented SOC: A survey of LLMs and agents for security automation. MDPI Informatics, 5(4), 95.

Prof. Piotr Kaminski | Biological Sciences | Best Researcher Award

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Prof. Piotr Kaminski | Biological Sciences | Best Researcher Award

Nicolaus Copernicus University | Poland

Dr. Piotr Kamiński, PhD, is a distinguished Professor and Head of the Division of Ecology and Environmental Protection at the Department of Medical Biology and Biochemistry, Nicolaus Copernicus University in Toruń, Poland. He also serves at the Department of Biotechnology, University of Zielona Góra, contributing significantly to interdisciplinary studies that bridge environmental sciences and medical biology. His scientific expertise lies in environmental ecophysiology, with research encompassing the physiological responses of humans, birds, mammals, fish, and plants to varying environmental conditions. Dr. Kamiński has made impactful contributions to understanding how ecological and biochemical factors influence organismal health, oxidative stress, and adaptive mechanisms in diverse ecosystems. His collaborations span numerous countries, reflecting a global approach to ecophysiological and environmental safety studies. He has supervised over 180 master’s and bachelor’s theses, and 23 doctoral dissertations, fostering the next generation of scientists. With over 190 peer-reviewed publications, his work appears in reputable journals and edited volumes, often addressing bioenergetics, antioxidant mechanisms, and ecological physiology. His academic excellence is reflected in Scopus metrics with 63 documents, 782 citations from 713 sources, and an h-index of 16. On Google Scholar, his citations exceed 1200, with an h-index of 19, underscoring his international scientific influence and recognition.

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Featured Publications

Dietrich-Muszalska, A., Kamiński, P., Kontek, B., & Gorzelańczyk, E. J. (2025). Curcumin as an antioxidant against ziprasidone-induced lipid peroxidation in human plasma: Potential relevance to cortico-subcortical circuit function. International Journal of Molecular Sciences.

Kurhaluk, N., Buyun, L., Kołodziejska, R., Kamiński, P., & Tkaczenko, H. (2025). Effect of phenolic compounds and terpenes on the flavour and functionality of plant-based foods. Nutrients.

Tkaczenko, H., Buyun, L., Kołodziejska, R., Kamiński, P., & Kurhaluk, N. (2025). Neuroactive phytochemicals as multi-target modulators of mental health and cognitive function: An integrative review. International Journal of Molecular Sciences.

Kurhaluk, N., Kamiński, P., Bilski, R., Kołodziejska, R., Woźniak, A., & Tkaczenko, H. (2025). Role of antioxidants in modulating the microbiota–gut–brain axis and their impact on neurodegenerative diseases. International Journal of Molecular Sciences.

Bilski, R., Kamiński, P., Kupczyk, D., Jeka, S., Baszyński, J., Tkaczenko, H., & Kurhaluk, N. (2024). Environmental and genetic determinants of ankylosing spondylitis. International Journal of Molecular Sciences.

Prof. Dr. Bogdan Kulig | Crop Production | Innovative Research Award

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Prof. Dr. Bogdan Kulig | Crop Production | Innovative Research Award

Agricultural University in Krakow | Poland

Prof. Dr. Bogdan Kulig is a distinguished Professor of Agricultural Sciences at the University of Agriculture in Krakow, specializing in agronomy, crop production, and agroecology. His extensive research focuses on enhancing the productivity and sustainability of legume and oilseed crop cultivation, improving cereal production through precision agriculture, and applying deterministic and mathematical models to plant growth and development. He has authored over 200 scholarly works, including 151 peer-reviewed journal articles and 42 popular science publications, along with several academic textbooks. His research contributions have significantly advanced modern crop science, particularly in developing improved cultivation technologies for large- and small-seeded legumes and oilseed crops such as Abyssinian crambe, oilseed flax, and winter rapeseed. Prof. Kulig has also contributed to academic leadership through mentoring graduate and doctoral students and participating in numerous scientific and organizational committees. His scholarly impact is reflected in his citation metrics, with a Scopus h-index of 12 (464 citations from 41 documents) and a Google Scholar h-index of 17 (1,501 citations and 44 i10-index). His innovative research combining agronomic science and modeling approaches continues to shape sustainable agricultural practices and academic discourse in plant production systems.

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Featured Publications

Dacko, M., Oleksy, A., Synowiec, A., Klimek-Kopyra, A., Kulig, B., & Zając, T. (2023). Plant-architectural and environmental predictors of seed mass of winter oilseed rape in southern Poland based on the CART trees regression model. Industrial Crops and Products, 192, 1–8.

Kulig, B., Waga, J., Oleksy, A., Rapacz, M., Kołodziejczyk, M., Wężyk, P., Klimek-Kopyra, A., Witkowicz, R., Skoczowski, A., Podolska, G., & Grygierzec, W. (2023). Forecasting of hypoallergenic wheat productivity based on unmanned aerial vehicles remote sensing approach – Case study. Agriculture, 13, null.

Kulig, B., & Klimek-Kopyra, A. (2023). Sowing date and fertilization level are effective elements increasing soybean productivity in rainfall deficit conditions. Agriculture, 13, null.

Kulig, B., Oleksy, A., & Zając, T. (2010). Mathematical modeling of plant growth and development. University of Agriculture Press.

Kulig, B., Klimek-Kopyra, A., & Oleksy, A. (2020). Plant cultivation. University of Agriculture Press.

Assist. Prof. Dr. Yasser Almoteri | Biomathematics | Best Researcher Award

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Assist. Prof. Dr. Yasser Almoteri | Biomathematics | Best Researcher Award

Assistance Professor | Imam Mohammad Ibn Saud Islamic University | United States

Dr. Yasser Almoteri is an accomplished scholar in applied mathematics, specializing in biomathematics, complex fluid dynamics, and the modeling of microswimmer behavior in porous environments. His research focuses on the mathematical modeling and computational analysis of bacterial motion, chemotaxis, and collective dynamics of micro-swimmers in confined or impure flow conditions. By integrating analytical, numerical, and experimental perspectives, Dr. Almoteri’s work provides deeper insights into how microorganisms interact with complex media, contributing to advancements in biofluid mechanics, environmental biophysics, and medical microbiology. His recent studies explore bacterial chemotactic aggregation and motion within Brinkman flows, shedding light on fluid-mediated collective dynamics that influence microbial dispersion. Dr. Almoteri’s scholarly output includes multiple peer-reviewed publications and conference presentations at major international scientific meetings such as the APS March Meeting and ICIAM. His research has been cited in both Scopus and Google Scholar databases, reflecting growing recognition in the fields of applied and biological mathematics. Currently, his citation count stands at 5, with an h-index of 2 on Google Scholar, signifying the early but impactful trajectory of his academic contributions.

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Featured Publications

Almoteri, Y., & Lushi, E. (2025). Chemotactic aggregation dynamics of micro-swimmers in Brinkman flows. arXiv preprint arXiv:2504.20925.

Almoteri, Y. (2023). Bacterial motion and spread in porous environments. New Jersey Institute of Technology.

Almoteri, Y., & Lushi, E. (2025). Microswimmer collective dynamics in Brinkman flows. Physical Review Fluids, 10(8), 083102.

Chiu, S. H., Almoteri, Y., & Lushi, E. (2024). Run-and-tumble bacterial chemotaxis in confinement. APS March Meeting Abstracts, G27.006.

Almoteri, Y., & Lushi, E. (2023). Collective dynamics of micro-swimmers in Brinkman flows. Bulletin of the American Physical Society.