Ashiraf Kategaya | Emerging Technologies | Best Researcher Award

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Mr. Ashiraf Kategaya | Emerging Technologies | Best Researcher Award

Erciyes University | Uganda

Ashiraf Kategaya is a researcher and Ph.D. candidate at Erciyes University with expertise in circular economy, circular supply chain management, sustainable development, and digital technologies. His research includes high-impact publications and reviews in leading indexed journals, focusing on circular supply chains, Industry 4.0, and meta-analysis. He has led and contributed to numerous consultancy and industry projects in AI adoption, ERP systems, GovTech, smart mobility, digital transformation, and public sector innovation. His contributions bridge research and practice through data-driven insights, frameworks, and innovation strategies that support organizational growth and sustainable development.

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Julie Leignadier | Scientific Breakthroughs | Research Excellence Award

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Dr. Julie Leignadier | Scientific Breakthroughs | Research Excellence Award

Lucas Meyer Cosmetics by Clariant | France

Dr. Julie Leignadier is an accomplished immunologist and biologist with extensive experience in both fundamental research and applied biotechnology. She earned her PhD in Montreal, where over five years she studied the mechanisms underlying the strength of the memory T cell receptor, gaining deep expertise in adaptive immunity and molecular signaling. Her doctoral work provided critical insights into immune memory and receptor dynamics, establishing a solid foundation for her future research. Following her PhD, Dr. Leignadier spent eight years as a postdoctoral researcher in leading Swiss and French laboratories, where she led multiple projects at the intersection of immunology and oncology. During this period, she honed her skills in experimental design, cellular and molecular biology, and translational research, contributing to high-impact publications and fostering international collaborations. Her work in cancer immunology emphasized understanding complex immune responses and translating fundamental findings into potential therapeutic strategies. In 2020, Dr. Leignadier joined Lucas Meyer Cosmetics by Clariant as Head of the Biology Laboratory, where she oversees the development of innovative and sustainable active ingredients for the cosmetics industry. In this role, she leverages her expertise in cellular biology and immunology to create biologically effective compounds that meet both consumer needs and environmental sustainability standards. Her leadership drives research initiatives that bridge cutting-edge science with industrial application, positioning the company at the forefront of sustainable cosmetic innovation. Dr. Leignadier remains committed to integrating scientific excellence with industrial innovation, applying her deep knowledge of immunology and molecular biology to deliver products that are both effective and environmentally responsible. Her career reflects a unique blend of academic rigor and practical expertise, making her a recognized leader in biologically informed cosmetic research.

 


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Shreedhar Sahoo | Emerging Technologies | Best Researcher Award

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Mr. Shreedhar Sahoo | Emerging Technologies | Best Researcher Award

Mr. Shreedhar Sahoo | Indian Institute of Technology Kharagpur | India

Shreedhar Sahoo is a Prime Minister’s Research Fellow (PMRF) and Ph.D. candidate in Mechanical Engineering at the Indian Institute of Technology Kharagpur, specializing in rail–wheel interaction, traction, and slip dynamics. His doctoral research focuses on the investigation of traction and slip at the rail–wheel contact using wheel tread temperature monitoring, contributing to improved safety, efficiency, and predictive maintenance in railway systems. He holds a Dual Degree (B.Tech + M.Tech) in Mechanical Engineering from IIT Kharagpur with an excellent CGPA of 9.26/10. His academic foundation spans advanced mechanical engineering, railway vehicle dynamics, finite element methods, vibration analysis, thermodynamics, and applied mathematics. His M.Tech project involved the active control of functionally graded shells using piezoelectric fiber-reinforced composites, while his B.Tech project explored personality trait prediction from Twitter data using SVM, achieving an accuracy of 80.1%. Shreedhar has completed two technical internships at Transenigma, Kolkata, where he worked on automation in motion graphics and 3D human-prototype modeling using Adobe and Autodesk Maya platforms. He has also obtained OCA Java certification with a 91% score and participated in specialized workshops, including SIMPACK training on railway vehicle dynamics. His research work has led to publications in reputed journals such as the Journal of Rail and Rapid Transit and Tribology International. He has presented at major conferences, including the 4th International Conference on Friction-based Processes, where he won the 2nd prize for oral presentation in 2025. He is also a co-inventor of a provisional patent on room-temperature deposition of nanoparticle-based coatings. Alongside technical expertise, he has served as a student coordinator for courses on text analytics and modeling tools. Overall, Shreedhar Sahoo’s academic excellence, research contributions, and interdisciplinary skills highlight his strong potential as a researcher and innovation-driven engineer in rail transport and tribology.

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Featured Publications

Sahoo, S., Kushan, D. S., Ronith, G. S. P. J., & Racherla, V. (2026). Nano-scale friction modifier coatings: Application methodology, friction characteristics, and surrogate models. Tribology International, Article 111429. https://doi.org/10.1016/j.triboint.2025.111429

Yunsong Mu | Emerging Technologies | Best Researcher Award

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Prof. Yunsong Mu | Emerging Technologies | Best Researcher Award

Prof. Yunsong Mu | Renmin University | China

Professor Yunsong Mu is an accomplished environmental toxicologist and academic leader serving as Vice Dean at the School of Chemistry and Life Resources, Renmin University of China. His pioneering research integrates computational toxicology and risk assessment to address the health impacts of emerging environmental pollutants. With over 50 Scopus-indexed publications, two authored books, and 20 patents, he has made significant contributions to environmental science innovation and policy. His groundbreaking GNN-based immunotoxicity prediction framework offers transformative tools for pollutant risk evaluation. Recognized by national and international bodies, Professor Mu exemplifies excellence in environmental research and scientific leadership.

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Featured Publications

Mu, Y., et al. (2025). Machine learning-driven 3D-QSAR models facilitated rapid on-site broad-spectrum immunoassay of (fluoro)quinolones using evanescent wave fiber-embedded optofluidic biochip. Biosensors and Bioelectronics.

Mu, Y., et al. (2025). Advances and perspectives on the life-cycle impact assessment of personal protective equipment in the post-COVID-19 pandemic.

Mu, Y., et al. (2025). Application of machine learning in nanotoxicology: A critical review and perspective.

Mu, Y., et al. (2024). Predicting the water ecological criteria of copper using machine learning and multiple linear regression approaches. Zhongguo Huanjing Kexue (China Environmental Science).

Bao Liu | Emerging Technologies | Best Researcher Award

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Dr. Bao Liu | Emerging Technologies | Best Researcher Award

Dr. Bao Liu | Xi’an University of Science and Technology | China

Dr. Liu Bao is an Associate Professor and Academic Leader in the field of Pattern Recognition and Intelligent Systems at the School of Electrical and Control Engineering, Xi’an University of Science and Technology, where he also serves as a Graduate Supervisor and Project-based Ph.D. Supervisor. He earned his doctorate in engineering from Xi’an Jiaotong University, completed postdoctoral research at Xi’an University of Science and Technology, and broadened his academic experience as a visiting fellow at Macquarie University in Australia. Recognized as a Senior Data Analyst by the Ministry of Industry and Information Technology of China, Dr. Liu is an active member of several national academic societies and professional committees. His research focuses on multi-source information fusion and intelligent technologies for coal fire disaster prevention and control, integrating advanced computational and automation techniques to address complex industrial challenges. Throughout his career, he has led diverse national, provincial, and industry-based research projects and contributed extensively to scientific publications and technological innovation through patents and software developments. As a committed educator and mentor, Dr. Liu has inspired students to excel in academic and professional pursuits and has been honored with multiple awards recognizing his dedication to teaching, research, and academic service.

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Featured Publications

Liu, B., Liu, Q., & Wu, Z. (2026, February). A novel robust Student’s t scale mixture distribution based Kalman filter. Signal Processing. https://doi.org/10.1016/j.sigpro.2025.110296

Liu, B., Wu, Z., & Liu, Q. (2025). Gaussian mixture model-based variational Bayesian approach for extended target tracking. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2025.3565347

Liu, B., & Jiang, W. (2024). DFKD: Dynamic focused knowledge distillation approach for insulator defect detection. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2024.3485446

Liu, B., & Jiang, W. (2024, December). LA-YOLO: Bidirectional adaptive feature fusion approach for small object detection of insulator self-explosion defects. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2024.3467915

Liu, B., Zhou, N., & Wang, Z. (2024, December 27). DFI-YOLOv8 based defect detection method for fan blades. In Proceedings of the 2024 Conference on [Insert Conference Name]. https://doi.org/10.1145/3722405.3722437

Alexander Migdal | Scientific Breakthroughs | Best Researcher Award

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Prof. Alexander Migdal | Scientific Breakthroughs | Best Researcher Award 

Prof. Alexander Migdal | Institute for Advanced Study | United States

Alexander A. Migdal is a renowned theoretical physicist with a lifetime of pioneering contributions to mathematical and theoretical physics. Currently a Member of the School of Mathematics at the Institute for Advanced Study, Princeton, he has advanced key areas of physics including quantum field theory, gauge theory, turbulence, and quantum gravity. Educated at the Landau Institute for Theoretical Physics, Migdal has held leading academic positions at prestigious institutions such as Princeton University and New York University. His groundbreaking work includes the Migdal–Kadanoff recursion equations, the Makeenko–Migdal loop equations in large-N QCD, the matrix model solution of two-dimensional quantum gravity, and recent advances in the exact solution of turbulence. Internationally recognized for his achievements, he has received distinguished honors such as the Landau–Weizmann Award and has delivered invited lectures across the globe, continuing to shape modern physics and inspire new generations of researchers.

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Featured Publications

Migdal, A. (2025). Spontaneous quantization of the Yang–Mills gradient flow. Nuclear Physics B. Advance online publication.

Migdal, A. (2025). Duality of Navier–Stokes to a one-dimensional system. International Journal of Modern Physics A. Advance online publication.

Migdal, A. (2024, December 23). Fluid dynamics duality and solution of decaying turbulence. Preprints.

Migdal, A. (2024, November 12). Duality of the Navier–Stokes dynamics and lack of finite-time explosion (Version 2). Preprints.

Migdal, A. (2024, November 5). Duality of the Navier–Stokes dynamics and lack of finite-time explosion (Version 1). Preprints.

Migdal, A. (2024). Quantum solution of classical turbulence: Decaying energy spectrum. Physics of Fluids, 36(9), 095117.

Migdal, A. (2024, August 4). Quantum solution of classical turbulence: Decaying energy spectrum (Version 3). Qeios.

Migdal, A. (2024, July 9). Quantum solution of classical turbulence: Decaying energy spectrum (Version 14). Preprints.

Migdal, A. (2024, July 9). Quantum solution of classical turbulence: Decaying energy spectrum (Version 2). Qeios.

Migdal, A. (2024, July 3). Quantum solution of classical turbulence: Decaying energy spectrum. Qeios.

Migdal, A. (2024, June 3). Quantum solution of classical turbulence: Decaying energy spectrum (Version 12). Preprints.

Migdal, A. (2024, May 6). Quantum solution of classical turbulence: Decaying energy spectrum (Version 11). Preprints.