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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Sonia Abdennadher | Emerging Technologies | Research Excellence Award

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Dr. Sonia Abdennadher | Emerging Technologies | Research Excellence Award

The Higher Colleges of Technology | United Arab Emirates

Dr. Sonia Abdennadher, PhD, CMA, is an accomplished Associate Professor of Finance and Accounting at the Higher Colleges of Technology (HCT), Al Ain, United Arab Emirates, and a tenured Associate Professor at the University of Rouen, France. With over two decades of academic and professional experience across Europe and the Middle East, she has established a strong international reputation in corporate governance, auditing, financial reporting, fintech adoption, and ESG practices. Dr. Abdennadher earned her PhD in Business Administration from Paris-Saclay University, where her doctoral research pioneered the study of technology intermediation in corporate governance, with a particular focus on Internet voting in shareholders’ general meetings. Her academic background is further strengthened by dual master’s degrees in Networks Management and Economics and International Finance, Trading, and Capital Markets, complemented by a solid undergraduate foundation in finance. At HCT, Dr. Abdennadher plays a key leadership role, serving as Chair of Promotion Committees, member of the Higher Faculty Promotion Committee, Applied Research Coordinator, and System Course Team Leader in Sustainable Finance. She has taught a wide range of undergraduate and postgraduate courses in finance, accounting, auditing, corporate governance, sustainable finance, and investment analysis, and has extensive experience in executive education and capstone research supervision. Her research portfolio includes numerous high-impact publications in leading Q1 journals such as Journal of Business Ethics, Finance Research Letters, Corporate Governance, Corporate Social Responsibility and Environmental Management, and International Journal of Finance & Economics. Her work bridges theory and practice by examining blockchain, artificial intelligence, fintech, and ESG measurement within financial markets and governance systems, particularly in the UAE and MENA region. Dr. Abdennadher has successfully led and co-led multiple competitive research grants exceeding AED 600,000, and actively collaborates with regulators, stock exchanges, and Big Four audit firms. Through her scholarship, leadership, and policy-relevant research, she continues to contribute significantly to the modernization of corporate governance and sustainable finance globally.

 

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The Effects of Blockchain Technology on the Accounting and Assurance Profession in the UAE: An Exploratory Study


– Journal of Financial Reporting and Accounting, Vol. 20(1), pp. 53–71, 2022

Corporate Social Responsibility Antecedents and Practices as a Path to Enhance Organizational Performance: Evidence from SMEs


– Corporate Social Responsibility and Environmental Management, Vol. 28(6), pp. 1647–1663, 2021

The Effectiveness of E-Corporate Governance: An Exploratory Study of Internet Voting at Shareholders’ Annual Meetings in France


– Corporate Governance: The International Journal of Business in Society, Vol. 20(4), 2020

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.

Profile: Scopus

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

Sayed Abdul Majid Gilani | Emerging Technologies | Best Researcher Award

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Dr. Sayed Abdul Majid Gilani | Emerging Technologies | Best Researcher Award

Dr. Sayed Abdul Majid Gilani | Birmingham City University | United Kingdom

Dr. Sayed Abdul Majid Gilani is an accomplished researcher in electrical and electronic engineering, specializing in embedded systems, automation, and control engineering. His multidisciplinary research integrates hardware design, sensor networks, and artificial intelligence to develop innovative and energy-efficient solutions for real-world challenges. With over a decade of experience in academia and applied research, Dr. Gilani has contributed significantly to the advancement of embedded control technologies, renewable energy optimization, and industrial automation systems. His work emphasizes intelligent system design, IoT-based automation, and the integration of machine learning algorithms for enhanced performance and sustainability. Dr. Gilani has published extensively in high-impact journals and presented at leading international conferences, reflecting his global engagement and scientific rigor. He has also supervised numerous research projects and guided students in developing practical applications of emerging technologies. His research outputs demonstrate a strong commitment to technological innovation that bridges the gap between theory and application. Recognized for his academic excellence and collaborative research initiatives, Dr. Gilani continues to advance cutting-edge developments that contribute to the evolution of smart, adaptive, and efficient engineering systems—making him a deserving candidate for the Best Researcher Award.

Profiles: Google Scholar | Scopus | LinkedIn | Research Gate

Featured Publications 

Gilani, S. A. M., & Faccia, A. (2021). Broadband connectivity, government policies, and open innovation: The crucial IT infrastructure contribution in Scotland. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 1. https://doi.org/10.3390/joitmc8010001

Gilani, S. A. M., Copiaco, A., Gernal, L., Yasin, N., Nair, G., & Anwar, I. (2023). Savior or distraction for survival: Examining the applicability of machine learning for rural family farms in the United Arab Emirates. Sustainability, 15(4), 3720. https://doi.org/10.3390/su15043720

Gilani, S., Gernal, L., Tantry, A., Yasin, N., & Sergio, R. (2022). Leadership styles adopted by Scottish micro-businesses during the COVID-19 pandemic. In Proceedings of the International Conference on Business and Technology (pp. 144–156). Springer.

Al Jaghoub, J., Suleiman, A., Takshe, A. A., Moussa, S., Gilani, S. A. M., Sheikh, S., & others. (2024). The role of innovation in waste management for enterprises: A critical review of the worldwide literature. In Technology-Driven Business Innovation (pp. 453–464). Springer.

Gernal, L., Tantry, A., Gilani, S. A. M., & Peel, R. (2024). The impact of online learning and soft skills on college student satisfaction and course feedback. In Technology-Driven Business Innovation: Unleashing the Digital Advantage (pp. 42–54). Springer.

Gilani, S. A. M., Tantry, A., Askri, S., Gernal, L., & Sergio, R. (2023). Adoption of machine learning by rural farms: A systematic review. In Proceedings of the International Conference on Computing and Informatics (pp. 324–335). Springer.

Shadi Shayan | Emerging Technologies | Best Researcher Award

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Dr. Shadi Shayan | Emerging Technologies | Best Researcher Award

Dr. Shadi Shayan | Adelaide University | Australia

Dr. Shadi Shayan’s research lies at the intersection of project management, smart technologies, and social sustainability, focusing on how large-scale urban innovation programs can effectively manage social risks and deliver equitable outcomes. His scholarly work explores the dynamic relationships between technological transformation, social response, and governance frameworks in smart city development. By integrating change management models with risk management processes, Dr. Shayan has developed innovative frameworks that address the socio-demographic dimensions of smart city transitions—bridging theory, practice, and policy. His doctoral research, “Integrating change models and risk management processes: A framework to manage social risks in smart city programs”, provides a comprehensive model for mitigating community resistance and fostering inclusive participation in technologically driven urban initiatives. Dr. Shayan’s publications in leading journals such as Smart Cities, Sustainable Cities and Society, and International Journal of Construction Management advance understanding of how social factors, demographic variables, and stakeholder perceptions influence the success of smart city and infrastructure projects. A consistent theme in his research is the application of systems thinking and socio-technical analysis to enhance decision-making in project and program management. He also examines the evolving roles of professionals, including engineers and quantity surveyors, in adapting to emerging technological and societal challenges. Dr. Shayan’s work has significant implications for both academia and industry. It informs policy frameworks for smart urban governance, supports organizational strategies for managing social risk, and contributes to sustainable and resilient infrastructure planning. Through interdisciplinary collaborations and active engagement with the Smart Cities Council Australia New Zealand, he connects research with real-world impact—helping shape cities that are not only technologically advanced but also socially responsible and inclusive.

Profiles: Google Scholar | LinkedIn

Featured Publications 

Shayan, S., & Kim, K. P. (2025). Social responses and change management strategies in smart city transitions: A socio-demographic perspective. Smart Cities, 8(6), 188. https://doi.org/10.3390/smartcities8060188

Shayan, S., & Kim, K. P. (2023). Understanding correlations between social risks and sociodemographic factors in smart city development. Sustainable Cities and Society, 89, 104320. https://doi.org/10.1016/j.scs.2022.104320

Shayan, S., & Kim, K. P. (2022). A conceptual framework to manage social risks for smart city development programs. In Resilient and responsible smart cities (pp. 77–86). Springer, Cham. https://doi.org/10.1007/978-3-030-98423-6_6

Shayan, S., Kim, K. P., Ma, T., & Nguyen, T. H. D. (2020). The first two decades of smart city research from a risk perspective. Sustainability, 12(21), 9280. https://doi.org/10.3390/su12219280

Shayan, S., & Kim, K. P., & Tam, V. (2019). Critical success factor analysis for effective risk management at the execution stage of a construction project. International Journal of Construction Management, 22(3), 379–386. https://doi.org/10.1080/15623599.2019.1624678

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.

Profile: Scopus

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.

Profile: Orcid

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

Zahra Amini | Emerging Technologies | Best Researcher Award

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Assist. Prof. Dr. Zahra Amini | Emerging Technologies | Best Researcher Award

Assist. Prof. Dr. Zahra Amini | Sharif University of Technology | Iran

Dr. Zahra Amini is an Assistant Professor of Civil Engineering at Sharif University of Technology, specializing in intelligent transportation systems, sustainable mobility, and operations research for urban traffic management. She earned her Ph.D. in Civil and Environmental Engineering from the University of California, Berkeley, and has led impactful projects in Iran and the U.S., ranging from corridor management to urban traffic resilience. With several high-impact publications, awards, and ongoing research on data-driven and machine learning approaches to transportation systems, she is recognized for advancing reliable, sustainable, and intelligent urban mobility solutions.

Academic Profile 

Google Scholar

Education

Dr. Zahra Amini completed her higher education in Civil and Environmental Engineering at the University of California, Berkeley, where she advanced her expertise in transportation systems and sustainable infrastructure. Alongside her major studies, she pursued minors in Industrial Engineering, Operations Research, and City and Regional Planning, which provided her with a strong interdisciplinary foundation. Her doctoral thesis focused on data-driven approaches for developing robust signal plans in urban transportation networks, reflecting her commitment to integrating advanced analytical methods with practical engineering challenges. This academic background equipped her with the technical knowledge and research skills that now define her scholarly and professional contributions.

Professional Experience

Dr. Amini serves as an Assistant Professor in the Department of Civil Engineering at Sharif University of Technology, where she teaches both undergraduate and graduate courses. Her teaching portfolio includes systems engineering, traffic engineering, and advanced transportation analysis, complemented by the design of a specialized course in Intelligent Transportation Systems. In addition to her academic role, she manages several research and development projects aimed at improving urban transportation and logistics planning. She has also gained international experience through her work at California Partners for Advanced Transportation Technology, contributing to projects focused on corridor management and traffic system optimization.

Research Interests

Dr. Amini’s research is centered on intelligent transportation systems, sustainable mobility solutions, and operations research applications in transportation system design. She focuses on integrating data-driven and machine learning methods to analyze and improve urban traffic flow, evaluate network resilience, and design reliable transport strategies. Her work also explores the operational sustainability of urban traffic networks and the role of intelligent systems in shaping resilient cities. By bridging theoretical models with applied research, she contributes innovative solutions to the pressing challenges of modern transportation.

Awards and Honors

Dr. Amini has received recognition for her teaching and research contributions through multiple awards. She has been honored for designing innovative courses in Intelligent Transportation Systems and has secured research grants supporting the development of advanced mobility solutions. Her scholarly excellence has been acknowledged with distinctions such as best paper and best presentation awards at international conferences. Additionally, she has been awarded prestigious fellowships that supported her academic journey and research endeavors, reflecting her status as a promising leader in the field of transportation engineering.

Publications

Agent-Based Modeling for Sustainable Urban Passenger Vehicle Mobility: A Case of Tehran
Author: M.E. Doraki, A. Avami, M. Boroushaki, Z. Amini
Journal: Transportation Research Part D: Transport and Environment
Year: 2024

Optimizing offsets in signalized traffic networks: A case study
Author: Z. Amini, S. Coogan, C. Flores, A. Skabardonis, P. Varaiya
Journal: IEEE Conference on Control Technology and Applications (CCTA)
Year: 2018

The Impact of Network Indices Integration on Traffic Flow Imputation Accuracy: A Machine Learning Approach
Author:  S. Sabzekar, A. Roudbari, A. Dehghani, A. Safaeiestalkhzir, Z. Amini
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2025

Using neural network for predicting hourly origin-destination matrices from trip data and environmental information
Author: E. Hassanzadeh, Z. Amini
Journal: Scientia Iranica
Year: 2024

Unsupervised learning for topological classification of transportation networks
Author: S. Sabzekar, M.R.V. Malakshah, Z. Amini
Journal: arXiv preprint –
Year: 2023

Data-Driven Approaches for Robust Signal Plans in Urban Transportation Networks
Author: Z. Amini
Journal:  University of California, Berkeley (Thesis)
Year: 2018

Spatial network-wide traffic flow imputation with graph neural network
Author: S. Sabzekar, R. Bahmani, M. Ghasemi, Z. Amini
Journal:  International Journal of Intelligent Transportation Systems Research
Year: 2025

Analyzing the impacts of gasoline price change on nationwide trip demand and drivers’ behavior using regression discontinuity design
Author: S. Saeidi, Z. Amini
Journal: Scientia Iranica
Year: 2024

Conclusion

Dr. Zahra Amini’s distinguished academic background, impactful research in intelligent and sustainable transportation systems, and recognized leadership in advancing innovative solutions firmly establish her as an outstanding candidate for the Best Researcher Award. Her dedication to bridging research, education, and real-world applications makes her not only deserving of this recognition but also a valuable contributor to the global scientific community.