Mahbouba Nasraoui | Regression Analysis | Research Excellence Award

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Dr. Mahbouba Nasraoui | Regression Analysis | Research Excellence Award

University of Burgundy | France

Dr. Mahbouba Nasraoui is a researcher and lecturer in finance whose work examines the intersections of economic policy uncertainty, ESG practices, market behavior, and corporate decision-making. She holds advanced academic qualifications, including a doctorate in finance, and has taught at institutions such as IAE Dijon and the Faculty of Economics and Management, where she has delivered courses in corporate finance, financial principles, management control, and derivatives. Her professional background spans higher education, professional training, and corporate financial management, offering her a multidisciplinary perspective that enriches her research and teaching. Dr. Nasraoui has published in reputable journals, including Economic Modelling and The Journal of Risk Finance, with studies exploring investment inefficiency, stock market liquidity, and investor sentiment. She has presented her work at several international conferences in France and Tunisia, contributing to discussions on sustainability, ethics, monetary policy, and financial markets. Her research interests include economic policy uncertainty, behavioral finance, ESG integration, financial decision-making, and market microstructure. In recognition of her academic engagement, she continues to collaborate with research teams and participate in scientific events. Dr. Nasraoui strives to advance knowledge in finance while fostering rigorous, engaged learning environments for her students.

Profile : Orcid

Featured Publications

Nasraoui, M., Ajina, A., & Herve, F. (2025). Economic Policy Uncertainty, ESG Practices, and Investment Inefficiency in U.S. Firms. Economic Modelling. https://doi.org/10.1016/j.econmod.2025.107414

Nasraoui, M., Ajina, A., & Kahloul, A. (2024). The influence of economic policy uncertainty on stock market liquidity? The mediating role of investor sentiment. The Journal of Risk Finance. https://doi.org/10.1108/JRF-06-2023-0129

Rozana Ghandy | Digital Data | Best Researcher Award

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Dr. Rozana Ghandy | Digital Data | Best Researcher Award

 Islamic Azad University, Tehran | Iran

Dr. Rozana Ghandy is an academic researcher in physical oceanography, pursuing doctoral studies at the Science and Research Branch of Islamic Azad University, where her work focuses on analyzing heat fluxes over the North Indian Ocean and the Persian Gulf using advanced weather reanalysis datasets. She holds a master’s degree in oceanography from Islamic Azad University, North Tehran Branch, where she investigated geostrophic currents through field measurements and satellite observations, and a bachelor’s degree in science from Isfahan University. With extensive teaching experience across multiple educational levels—including physics instruction at pre-university, high-school, and laboratory settings—she has contributed significantly to academic development and educational leadership. Her research interests include air–sea interactions, ocean circulation, climate variability, regional sea dynamics, and the application of remote sensing in marine studies. She has presented and published studies on physical parameters in coastal regions, surface flux behavior, and standardized approaches to air–sea heat flux assessment across key marine basins. Her academic achievements include competitive rankings in national entrance examinations and scholarships recognizing her scientific potential. Overall, her work reflects a dedication to advancing understanding of ocean–atmosphere processes and contributing to the broader field of marine and climate science.

Profile : Orcid

Featured Publication

Study of surface fluxes over the Northern Indian Ocean Seas, 2023

Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

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Mr. Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

Chongqing University of Technology | China

Dr. Xiaoxia Yu is a scholar in mechanical engineering whose work advances intelligent diagnostics and predictive maintenance for large-scale rotating machinery, particularly wind turbines. With a Ph.D. in Mechanical Engineering and earlier degrees in Vehicle Engineering and Armored Vehicle Engineering, she has built a strong interdisciplinary foundation that integrates mechanical systems knowledge with advanced computational modeling. Her research spans fault diagnosis, health assessment, digital twin systems, graph neural networks, reinforcement learning, and signal processing, supported by a growing publication record that includes 29 documents, 477 citations by 448 documents, and an h-index of 7. As a Lecturer, she leads research projects funded by regional scientific agencies and has contributed to national-level R&D initiatives related to machinery health management. Her work appears in high-impact journals, and she has secured patents focused on structural health monitoring, image recognition, and intelligent fault detection. Recognized with competitive grants and academic honors, she continues to influence the fields of renewable energy reliability and smart manufacturing. Through her commitment to innovation, research leadership, and engineering application, she is emerging as a key contributor to the development of intelligent, data-driven mechanical health monitoring systems.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Yu, X., Tang, B., & Zhang, K. (2021). Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks. IEEE Transactions on Instrumentation and Measurement, 70, 1–14.

Yu, X., Tang, B., & Deng, L. (2023). Fault Diagnosis of Rotating Machinery Based on Graph Weighted Reinforcement Networks Under Small Samples and Strong Noise. Mechanical Systems and Signal Processing, 186, 109848.

Zhang, K., Tang, B., Deng, L., & Yu, X. (2021). Fault Detection of Wind Turbines by Subspace Reconstruction‑Based Robust Kernel Principal Component Analysis. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

Li, B., Tang, B., Deng, L., & Yu, X. (2020). Multiscale Dynamic Fusion Prototypical Cluster Network for Fault Diagnosis of Planetary Gearbox Under Few Labeled Samples. Computers in Industry, 123, 103331.

Xiong, P., Tang, B., Deng, L., Zhao, M., & Yu, X. (2021). Multi‑block Domain Adaptation with Central Moment Discrepancy for Fault Diagnosis. Measurement, 169, 108516.

Ann-marie Moiwo | Government and Public Policy Analysis | Best Research Article Award

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Ms. Ann-marie Moiwo | Government and Public Policy Analysis | Best Research Article Award

Njala University | Sierra Leone

Ann-Marie Moiwo is a dynamic professional with interdisciplinary training in international relations, public administration, and the social sciences. She holds a Master’s degree in International Relations from Renmin University and a Master of Public Administration from Njala University, supported by a strong academic foundation in History and Sociology from Fourah Bay College. Her professional experience spans administrative management, organizational coordination, research operations, and executive support across institutions in the public, private, and development sectors. She has contributed to nationwide research initiatives, supported high-level decision-making processes, and provided strategic and logistical support within academic and corporate environments. Her research interests include governance, public policy, international development, gender studies, and sociopolitical systems in West Africa. Passionate about evidence-based policy and institutional strengthening, she aims to advance impactful research and contribute to solutions addressing national and global development challenges. Ann-Marie has been recognized for her diligence, discipline, and strong work ethic, consistently demonstrating the capacity to thrive in fast-paced, multicultural, and high-responsibility environments. She remains committed to continuous learning, professional growth, and service within institutions that value integrity, innovation, and social transformation.

Profile : Orcid

Featured Publications

Genealogy as Analytical Framework of Cultural Evolution of Tribes, Communities, and Societies Ann-Marie Moiwo, Massaquoi, D., Moiwo, T. W., Sam, M., & Moiwo, J. P. (2025). Genealogy as analytical framework of cultural evolution of tribes, communities, and societies. Genealogy, 9(4).

Comparative Analysis of Neglected Attenuation and Negotiated Intervention Pathways to Conflict Resolution for Lasting Peace Juana, J., Moiwo, J. P., Challay, S., Bebeley, S. J., Mbavai, J. J., Yeganegi, K., & Sanguiné, L. (2025). Comparative analysis of neglected attenuation and negotiated intervention pathways to conflict resolution for lasting peace. Journal of Global Peace and Conflict, 12(3).

Hydro-economic Model Framework for Achieving Groundwater, Food, and Economy Trade-offs by Optimizing Crop Patterns Ma, Q., Yang, Y., Sheng, Z., Han, S., Yang, Y., & Moiwo, J. P. (2022). Hydro-economic model framework for achieving groundwater, food, and economy trade-offs by optimizing crop patterns. Water Research, 119, 119199.

Rainwater Harvesting for Supplemental Irrigation Under Tropical Inland Valley Swamp Conditions Blango, M. M., Cooke, R. A. C., Moiwo, J. P., Sawyerr, P. A., & Kangoma, E. (2020). Rainwater harvesting for supplemental irrigation under tropical inland valley swamp conditions. Irrigation and Drainage, 69(5).

Effect of Biochar Application Depth on Crop Productivity Under Tropical Rainfed Conditions Moiwo, J. P., Wahab, A., Kangoma, E., Blango, M. M., Ngegba, M. P., & Suluku, R. (2019). Effect of biochar application depth on crop productivity under tropical rainfed conditions. Applied Sciences, 9(13), 2602.

Marco Gaiotti | Shipbuilding | Best Researcher Award

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Mr. Marco Gaiotti | Shipbuilding | Best Researcher Award

Università di Genova | Italy

Marco Gaiotti is an associate professor in ship structures and marine engineering whose work focuses on the structural behavior, fabrication effects, and ultimate strength of ships and offshore structures. Holding a Bachelor (2005), Master (2008), and PhD (2012) in Naval Architecture and Marine Engineering from the Università degli Studi di Genova, he has developed expertise in composite materials, fabrication-induced imperfections, and advanced simulation techniques. His academic career includes progressive research appointments leading to his current professorship, along with coordination roles for the Nautical Engineering and Yacht Design programs. Gaiotti has contributed internationally as a visiting researcher at NAOE–Osaka University and through long-standing service within the ISSC, where he has served as specialist member, award-winning expert, and Chairman of Committee III.1 on Ultimate Strength. He has led competitive research initiatives, notably the EU-funded LeaderSHIP project (2023–2027), promoting innovation, skills development, and collaboration in the maritime sector. His work also extends to technology transfer, including a patented method for validating robotic inspection technologies in naval environments. With 454 citations by 341 documents, 67 documents, and an h-index of 12, his research continues to advance materials, structural performance, and safety in marine engineering.

Profiles : Scopus | Orcid

Featured Publications

Gaiotti, M., Brubak, L., Chen, B.-Q., Darie, I., Georgiadis, D., Shiomitsu, D., Kõrgesaar, M., Lv, Y., Nahshon, K., Paredes, M., et al. (2026). “Evaluating numerical simulation accuracy for full-scale high-strength steel ship structures: Insights from the ISSC 2025 Ultimate Strength Committee benchmark on transversely stiffened panels” in Marine Structures.

Aguiari, M., Gaiotti, M., & Rizzo, C.M. (2022). “Ship weight reduction by parametric design of hull scantling” in Ocean Engineering.

Aguiari, M., Gaiotti, M., & Rizzo, C.M. (2022). “A design approach to reduce hull weight of naval ships” in Ship Technology Research.

Poggi, L., Gaggero, T., Gaiotti, M., Ravina, E., & Rizzo, C.M. (2022). “Robotic inspection of ships: inherent challenges and assessment of their effectiveness” in Ships and Offshore Structures.

Ringsberg, J.W., Darie, I., Nahshon, K., Shilling, G., Vaz, M.A., Benson, S., Brubak, L., Feng, G., Fujikubo, M., Gaiotti, M., et al. (2021). “The ISSC 2022 Committee III.1–Ultimate strength benchmark study on the ultimate limit state analysis of a stiffened plate structure subjected to uniaxial compressive loads” in Marine Structures.

Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

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Dr. Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Bishop’s University | Canada

Dr. Mebarka Allaoui dedicated computer science researcher with a strong background in machine learning, manifold learning, and computer vision, this scholar holds a PhD in Computer Science focused on embedding techniques and their applications to visual data analysis. Their academic journey includes a master’s degree in industrial computer science and a bachelor’s degree in information systems, all completed with high distinction. Professionally, they have served as a Postdoctoral Fellow contributing to industry-funded research on anomaly detection, developing novel embedding, deep learning, and clustering methods to enhance the interpretability of latent representations and improve fraud detection in real-world financial datasets. Prior experience includes working as a computer engineer supporting system administration, software development, data analysis, and network configuration, alongside several teaching appointments delivering practical courses in software engineering, algorithmics, and web development. Their research contributions span dimensionality reduction, clustering, optimization, document analysis, and scientific information retrieval, with publications in reputable journals and conferences. Collaborative work further extends to studies on optimizers, object detection, and embedding initialization strategies. Recognized for high-quality academic performance and impactful research outputs, they continue to advance data-driven methodologies, aiming to bridge theoretical innovation with practical applications in intelligent systems and decision-support technologies.

Profile : Google Scholar

Featured Publications

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “Considerably improving clustering algorithms using UMAP dimensionality reduction technique” in International Conference on Image and Signal Processing, 317–325.

Drid, K., Allaoui, M., & Kherfi, M. L. (2020). “Object detector combination for increasing accuracy and detecting more overlapping objects” in International Conference on Image and Signal Processing, 290–296.

Allaoui, M., Belhaouari, S. B., Hedjam, R., Bouanane, K., & Kherfi, M. L. (2025). “t-SNE-PSO: Optimizing t-SNE using particle swarm optimization” in Expert Systems with Applications, 269, 126398.

Allaoui, M., Kherfi, M. L., Cheriet, A., & Bouchachia, A. (2024). “Unified embedding and clustering” in Expert Systems with Applications, 238, 121923.

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “International Conference on Image and Signal Processing” in Springer.

Saralah Devi Mariamdaran Chethiyar | Smarter Analytics With Ai | Best Researcher Award

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Assoc. Prof. Dr. Saralah Devi Mariamdaran Chethiyar | Smarter Analytics With Ai | Best Researcher Award

Universiti Utara Malaysia | Malaysia

Prof. Madya Dr. Saralah Devi A/P Mariamdaran is an Associate Professor at the School of Applied Psychology, Social Work, and Policy, Universiti Utara Malaysia, specializing in counselling, psychology, and correctional science. She holds a Ph.D. in Correctional Counselling from Universiti Utara Malaysia, a Master’s in Counselling from Universiti Malaya, and a Bachelor’s degree with Honours in Industrial and Organisational Psychology from Universiti Malaysia Sabah. Her academic and professional experience spans counselling for children, adolescents, women, and prison inmates, with a focus on social psychology, violence, aggression, and psychotherapy. Dr. Saralah Devi has an extensive publication record in both national and international journals and has presented at numerous conferences, earning recognition for innovative teaching, research, and community engagement. She has received multiple awards including Best Innovative Research, Best Conference Presenter, and accolades from governmental and educational institutions for her contributions to education, research, and social development. Actively involved in professional associations such as PERKAMA and APECA, she also serves as a state judge for archery and is recognized for her social activism. Her work bridges academic research, applied psychology, and community development, reflecting a commitment to advancing mental health, counselling, and social well-being in diverse populations.

Profile : Google Scholar

Featured Publications

Asad, M., Chethiyar, S.D.M., & Ali, A. (2020). Total quality management, entrepreneurial orientation, and market orientation: Moderating effect of environment on performance of SMEs. Paradigms, 14(1), 102–108.

Chethiyar, S.D.M., Asad, M., Kamaluddin, M.R.U., Ali, A., & Sulaiman, M.A.B.A. (2019). Impact of information and communication overload syndrome on the performance of students. Opción: Revista de Ciencias Humanas y Sociales, 390–405.

Asad, M., Muhammad, R., Rasheed, N., Chethiyar, S.D., & Ali, A. (2020). Unveiling antecedents of organizational politics: An exploratory study on science and technology universities of Pakistan. International Journal of Advanced Science and Technology, 29(6s), 2057–2066.

Rathakrishnan, M., Raman, A., Haniffa, M.A.B., Mariamdaran, S.D., & Haron, A.B. (2018). The drill and practice application in teaching science for lower secondary students. International Journal of Education, Psychology and Counseling, 3(7), 100–108.

Kashif, M., Asif, M.U., Ali, A., Asad, M., Chethiyar, S.D.M., & Vedamanikam, M. (2020). Managing and implementing change successfully with respect to COVID-19: A way forward for SMEs. PEOPLE: International Journal of Social Sciences, 6(2), 609–624.

Naima Rahiel | Public Health Analytics | Women Researcher Award

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Mrs. Naima Rahiel | Public Health Analytics | Women Researcher Award

QARTZ, Université Paris 8 | France

Naima Rahiel is a doctoral researcher in Industrial Engineering and Productics at the University of Paris 8, specializing in the modeling and optimization of complex systems, with a particular focus on hospital logistics and supply chain resilience. She holds a Master’s and a Bachelor’s degree in Industrial Engineering from the University of Oran 2, Algeria, where she built strong foundations in probabilistic analysis, production systems, and decision-making under uncertainty. Her professional experience includes academic teaching at IUT de Montreuil and practical research in industrial and healthcare environments, such as Tosyali Algeria and the Canastel Pediatric Hospital in Oran. Her research explores the resilience of healthcare supply chains through analytical and simulation-based approaches, leading to several international conference presentations and peer-reviewed publications, including contributions to Springer’s book series and the journal Environmental Systems and Decision. Passionate about innovation, data analysis, and system reliability, she aims to bridge theoretical modeling with real-world decision support tools for sustainable and adaptive supply chain management. Her academic achievements and active participation in scientific events demonstrate her commitment to advancing research on healthcare logistics and resilience engineering, contributing valuable insights to the industrial and operational research community.

Profile : Google Scholar

Featured Publication

Rahiel, N., El Mhamedi, A., & Hachemi, K. (2024). Healthcare Supply Chain: Resilience Qualitative Evaluation. In Hospital Supply Chain: Challenges and Opportunities for Improving Healthcare.

Rahiel, N., El Mhamedi, A., Hachemi, K., Aouffen, N., & Rahiel, I. (2025). Resilience of the hospital supply chain: a case study-based approach on safety stock. Environment Systems and Decisions, 45 (4), 56.

Rahiel, N., Addouche, S.A., El Mhamedi, A., & Hachemi, K. (2025). Function-Based Modeling for Reactive Optimization of Healthcare Resource Reallocation. In Proceedings of the 16th International Conference on Logistics and Supply Chain Management (LOGISTIQUA 2025).

Decheng Li | Engineering | Best Researcher Award

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Mr. Decheng Li | Engineering | Best Researcher Award

Lanzhou University of Technology | China

Dr. Decheng Li is a dedicated scholar and researcher at the School of Automation and Electrical Engineering, Lanzhou University of Technology, China. He obtained his academic training in electrical engineering and automation, focusing on intelligent control systems, robotics, and power electronics. With extensive teaching and research experience, Dr. Li has contributed significantly to the advancement of automation technologies and intelligent systems applications in industrial environments. His research interests encompass intelligent control theory, optimization algorithms, renewable energy integration, and advanced signal processing techniques for control systems. Dr. Li has authored and co-authored numerous papers in leading international journals and conferences, reflecting his commitment to academic excellence and technological innovation. He has been involved in several national and provincial research projects, fostering collaboration between academia and industry. In recognition of his contributions, Dr. Li has received multiple academic awards and honors for his outstanding research and teaching performance. He continues to mentor graduate students and promote interdisciplinary research to solve real-world engineering challenges. Dr. Li remains committed to advancing automation and intelligent systems for sustainable industrial development and the future of smart technologies.

Profile: Orcid

Featured Publication

Liu, J., Li, D., & Chen, H. (2025). “Robust hybrid decentralized controller design for Voice Coil Actuator-Fast Steering Mirror system in high-precision optical measurements.