Deqian Fu | Data Science and Analytics | Best Researcher Award

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Prof. Dr. Deqian Fu | Data Science and Analytics | Best Researcher Award

Professor | Linyi University | China

Prof. Dr. Deqian Fu is a prominent researcher at Linyi University, China, with a strong focus on logistics, data exchange, and trust management in supply chain and intermodal transport systems. His research explores innovative methods for secure and efficient data sharing in the logistics industry, integrating advanced technologies such as blockchain, edge computing, and ontology-based frameworks. Fu has made notable contributions in developing trusted data access control mechanisms and non-intrusive data exchange models that enhance collaboration and operational efficiency across complex logistics networks. He has authored 39 publications, which have collectively garnered 127 citations, reflecting the growing impact of his work in the fields of applied sciences and industrial informatics. His research outputs demonstrate a commitment to advancing the intersection of information technology and logistics, emphasizing both theoretical development and practical applications. With an h-index of 7, Fu’s scholarly contributions have been recognized for their relevance and innovation, particularly in promoting secure and intelligent data-sharing frameworks within the logistics sector. Selected works include “Trusted Data Access Control Based on Logistics Business Collaboration Semantics” in Applied Sciences (2024), alongside conference papers such as “Data Exchange and Sharing Framework for Intermodal Transport Based on Blockchain and Edge Computing” and “Trusted Non-intrusive Data Exchange based on Ontology in Logistics Industry,” underscoring his focus on reliable, technology-driven logistics solutions.

Profiles : ORCID | Scopus 

Featured Publications

1. Wang, W., Li, Q., Jiang, Z., Fu, D., & Camacho, D. (2025). An efficient framework for general long-horizon time series forecasting with Mamba and diffusion probabilistic models. Engineering Applications of Artificial Intelligence.

2.Liu, Z., Shi, Z., Wang, W., Kong, R., Fu, D., & Qiu, J. (2025). Research on data ownership and controllable sharing schemes in the process of logistics data flow.

3.Wang, L., Zhang, X., Xu, L., Fu, D., & Qiu, J. (2024). Data exchange and sharing framework for intermodal transport based on blockchain and edge computing. In Communications in Computer and Information Science. Springer.

4.Zhang, X., Jing, C., Chen, Y.-C., Wang, L., Xu, L., & Fu, D. (2024). Trusted data access control based on logistics business collaboration semantics.

5.Zhang, X., Wang, L., Xu, L., & Fu, D. (2023). A distributed logistics data security sharing model based on semantics and CP-ABE. In Proceedings of the ACM International Conference (pp. 1–8).

Jun Peng | Big Data Analysis | Best Researcher Award

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Prof. Jun Peng | Big Data Analysis | Best Researcher Award

Professor | Ningbo University | China

Prof. Jun Peng is a distinguished scholar in the field of educational technology with expertise in big data in education, artificial intelligence in learning, blended learning, and curriculum design. He earned his PhD in Education from the University of Hong Kong and has built a strong academic career through teaching, research, and international collaborations. Currently serving as a professor and doctoral supervisor at Ningbo University, he has also contributed to the University of Hong Kong and the City University of Macau in research and teaching capacities. His professional experience includes leading several funded projects across China and Macau, with a focus on AI integration in education and innovative digital learning models. His research interests span online learning environments, project-based education, and sustainable approaches to technology-enhanced learning, reflected in numerous publications in leading SSCI, SCI, and Scopus-indexed journals such as Computers & Education, Education and Information Technologies, and Sustainability. Recognized with multiple commendations for research excellence, he has also received awards for educational innovation and course design. Prof. Peng is active in academic service as an editorial board member, peer reviewer for reputed journals, and keynote speaker at international conferences. His research skills include quantitative and qualitative analysis, big data applications, machine learning for education, and curriculum development. With a proven record of impactful research, leadership, and mentoring, he continues to advance the field of educational technology while contributing to the global academic community.

Profile: ORCID

Featured Publications

1.Shu, X., Peng, J., & Wang, G. (2023). Deciding alone or with others: Employment anxiety and social distance predict intuitiveness in career decision making. International Journal of Environmental Research and Public Health, 20(2), 1484.
2. Su, B., & Peng, J. (2023). Sentiment analysis of comment texts on online courses based on hierarchical attention mechanism. Applied Sciences, 13(7), 4204.
3. Zhou, J., Ran, F., Li, G., Peng, J., Li, K., & Wang, Z. (2022). Classroom learning status assessment based on deep learning. Mathematical Problems in Engineering, 2022, 7049458.
4. Peng, J., Yuan, B., Sun, M., Jiang, M., & Wang, M. (2022). Computer-based scaffolding for sustainable project-based learning: Impact on high- and low-achieving students. Sustainability, 14(19), 12907.
5. Li, Y., & Peng, J. (2022). Evaluation of expressive arts therapy on the resilience of university students in COVID-19: A network analysis approach. International Journal of Environmental Research and Public Health, 19(13), 7658.