Haonan Xu | Business Intelligence | Best Researcher Award

Assoc. Prof. Dr. Haonan Xu | Business Intelligence | Best Researcher Award

Dr. Haonan Xu is a distinguished scholar in the field of management science and engineering, specializing in multimodal transport, shipping economics, and supply chain management. His academic and research contributions have significantly advanced knowledge in these areas, particularly regarding AI integration in port operations, emission reduction in shipping supply chains, and multimodal transportation strategies. With a robust publication record in high-impact journals and recognition through prestigious awards, Dr. Xu continues to influence both academic and industrial landscapes. His work is characterized by a strong analytical approach, incorporating mathematical modeling, empirical analysis, and policy evaluation to address real-world transportation and logistics challenges.

Profile

Scopus

Education

Dr. Xu obtained his Ph.D. in Management Science and Engineering from Dalian Maritime University, where he was recognized as an excellent postgraduate. Prior to this, he completed his Master of Engineering in Logistics from Chongqing Jiaotong University, earning accolades for his volunteer work and research excellence. His undergraduate studies in Economics at the same university provided him with a solid foundation in economic theories and quantitative analysis. Throughout his academic journey, Dr. Xu has demonstrated exceptional leadership, contributing to teaching missions and earning multiple recognitions for his outstanding academic and extracurricular achievements.

Experience

Dr. Xu has a rich background in both academia and practical research. His teaching experience includes serving at Chongqing Jiaotong University, where he currently leads research initiatives. Earlier in his career, he contributed to educational development at Garden Primary School, successfully securing national funding for information technology projects. His work has extended to collaborative research on port operations and supply chain management, engaging with international scholars and policymakers to address critical global challenges in transportation and logistics.

Research Interests

Dr. Xu’s research primarily focuses on multimodal transport, shipping economics, and supply chain management. His studies explore the integration of AI in port decision-making, the impact of carbon reduction policies on shipping logistics, and strategies for optimizing rail-water multimodal transportation. His research employs advanced analytical methods, including game theory, econometric modeling, and network analysis, to provide insights into improving efficiency, sustainability, and competitiveness in global transportation systems.

Awards

Dr. Xu has received multiple prestigious awards for his academic and research excellence. These include:

  • First Prize for Thesis Award from the Chongqing Municipal Education Commission.
  • First Prize for Creative Education from the Chongqing Education Information Technology and Equipment Center.
  • Recognition as an Excellent Student Leader and recipient of the Outstanding Graduation Thesis award.
  • Chair of the Social Science Planning Program of Chongqing, leading a significant funded research project. These accolades highlight his contributions to both theoretical advancements and practical implementations in logistics and transportation research.

Selected Publications

Dr. Xu has published extensively in top-tier journals, with several of his works being highly cited. Below are some of his key publications:

“The impact of AI technology adoption on operational decision-making in competitive heterogeneous ports,” published in Transportation Research Part E (2024). This study examines the role of AI in enhancing port service quality and its implications for social welfare. (Cited by: Highly Cited in ESI)

“Emission reduction technologies for shipping supply chains under carbon tax with knowledge sharing,” published in Ocean & Coastal Management (2024). This paper explores the influence of carbon tax policies on green shipping technologies. (Cited by: High-impact environmental studies)

“Incentive policy for rail-water multimodal transport: Subsidizing price or constructing dry port,” published in Transport Policy (2024). It analyzes policy incentives for optimizing multimodal transportation efficiency. (Cited by: Notable transportation policy papers)

“The effects and conflicts of co-opetition in a rail-water multimodal transportation system,” published in Annals of Operations Research (2023). This study develops a multi-party game model for conflict resolution in multimodal transport. (Cited by: High-impact logistics research)

“International container intermodal competitiveness: an empirical study from Chinese hub ports,” published in Ocean & Coastal Management (2024). This empirical study evaluates the competitiveness of China’s multimodal transport hubs. (Cited by: Global trade logistics research)

“Economic-environmental coordination and influencing factors under dual-carbon goals: A spatial empirical analysis,” published in Environment, Development, and Sustainability (2024). The study uses network DEA to analyze sustainable transportation policies. (Cited by: ESI Highly Cited)

“The study of OEM/ODM supply chain decision-making considering supply risk,” published in China Management Science (2024). It develops a multi-party game model to address supply chain risk management. (Cited by: Supply chain strategy scholars)

Conclusion

Dr. Haonan Xu’s academic and research journey exemplifies his commitment to advancing the fields of transportation, logistics, and supply chain management. His work integrates innovative methodologies with real-world applications, influencing both policy and industry practices. With a strong publication record, notable awards, and active participation in funded research projects, he continues to contribute significantly to the academic and professional community. His research not only enhances theoretical knowledge but also provides actionable insights for improving efficiency and sustainability in global transport systems.

xiaoyu Zhu | Data Mining | Best Researcher Award

Dr. xiaoyu Zhu | Data Mining | Best Researcher Award

Shandong Second Medical University | School of Public Health | China

Dr. Xiaoyu Zhu, is a prominent academic and researcher specializing in social network analysis and applied computational methods. Zhu’s academic journey led him through Shandong Normal University, where he pursued his undergraduate, master’s, and doctoral studies, obtaining a Bachelor’s in Science, a Master’s in Engineering, and a Doctorate in Management. His extensive training in various fields of study, combined with his passion for technological applications, has contributed significantly to his work in social networks, specifically on topics like centrality measures, community detection, and network analysis. Since 2020, Zhu has been a lecturer at Shandong Second Medical University, where he teaches a variety of courses, including “SPSS Software and Applications,” to both undergraduate and postgraduate students. His academic and professional journey showcases a strong commitment to advancing knowledge in the intersection of technology and management.

Profile

Scopus

Education

Xiaoyu Zhu’s educational background is rooted in the rigorous academic environment of Shandong Normal University. He completed his Bachelor’s degree in Science in 2008, followed by a Master’s degree in Engineering in 2013. In 2019, he earned his Doctorate in Management, a culmination of years of dedicated study and research. His doctoral work, which delved into advanced methods in social network analysis and computational algorithms, laid the foundation for his future research endeavors. Zhu’s academic path reflects a blend of disciplines, where scientific methods, engineering principles, and management theory converge to address complex issues in social networks and data science.

Experience

After completing his education, Xiaoyu Zhu transitioned into academia, starting his career at Shandong Second Medical University in March 2020. As a lecturer, he is responsible for teaching courses related to data analysis and statistical software, including “SPSS Software and Applications,” to students across various levels. His role involves both undergraduate and postgraduate instruction, providing a bridge between theoretical concepts and practical applications. His deep knowledge in the fields of network science, computational algorithms, and applied statistics makes him a valuable educator, equipping students with skills needed to analyze and interpret complex data. Zhu’s professional experience also extends to research, where he continues to publish impactful papers on topics such as social network analysis and community detection.

Research Interests

Xiaoyu Zhu’s primary research interests lie in the fields of social network analysis, data mining, and computational algorithms. His work focuses on understanding the structure and dynamics of networks, particularly in the context of signed social networks. Zhu’s studies often explore advanced techniques for identifying key nodes and detecting communities within networks. His research extends to improving the efficiency of centrality measures, such as the Laplacian centrality, and developing evolutionary algorithms for community detection. These interests are informed by the desire to solve real-world problems, particularly in areas where network-based data can be leveraged to make informed decisions. Zhu’s work is an intersection of computational methods, network theory, and applied statistics, pushing the boundaries of how network data can be analyzed and utilized.

Awards

Throughout his academic career, Xiaoyu Zhu has garnered recognition for his research contributions. His innovative work on social network analysis and computational algorithms has earned him accolades within academic circles. In particular, his groundbreaking papers, including those on improving Laplacian centrality and community detection in signed networks, have been widely cited and acknowledged for their contribution to the field. While specific awards and nominations were not listed, the significant impact of his research and the consistent publication in respected journals speaks to his recognition in the academic community. His continued work is expected to bring further accolades as it influences future research and applications in network science.

Publications

Xiaoyu Zhu has made substantial contributions to the field through his published research papers. Below are some of his key publications:

Identifying influential nodes in social networks via improved Laplacian centrality

  • Authors: Zhu, X.; Hao, R.
  • Publication Year: 2024
  • Citations: 0

Identify Coherent Topics for Short Text Data by Eliminating Background Words via Topic Attention

  • Authors: Zhu, X.; Sun, X.
  • Publication Year: 2024
  • Citations: 0

Sign Prediction on Social Networks Based Nodal Features

  • Authors: Zhu, X.; Ma, Y.
  • Publication Year: 2020
  • Citations: 4

Partition signed social networks by spectral features and structural balance

  • Authors: Zhu, X.; Ma, Y.
  • Publication Year: 2019
  • Citations: 0

Clusters detection based leading eigenvector in signed networks

  • Authors: Ma, Y.; Zhu, X.; Yu, Q.
  • Publication Year: 2019
  • Citations: 7

A novel evolutionary algorithm on communities detection in signed networks

  • Authors: Zhu, X.; Ma, Y.; Liu, Z.
  • Publication Year: 2018
  • Citations: 10

These works, published in highly regarded journals, have contributed to the development of new methods for network analysis, particularly in the realm of social networks. Each publication addresses different aspects of network dynamics, from centrality measures to community detection, and has been cited in various other research papers, reflecting their influence in the academic community.

Conclusion

Xiaoyu Zhu’s academic and professional journey is marked by his dedication to advancing the field of social network analysis through innovative computational methods. His education in science, engineering, and management, coupled with his extensive research in network science, positions him as an influential figure in his field. As a lecturer at Shandong Second Medical University, he has been instrumental in educating the next generation of researchers and practitioners, instilling in them the tools necessary for tackling complex data-related problems. His research contributions, especially in the areas of centrality measures and community detection, have garnered attention from both academics and professionals. With a solid track record of publications in high-impact journals, Zhu continues to push the boundaries of knowledge in the analysis of social networks. His continued research promises to influence the way networks are understood and analyzed, particularly in applied settings where network data plays a crucial role.

jizhou Cao | Data-Driven Decision Making | Best Scholar Award

Mr. jizhou Cao | Data-Driven Decision Making | Best Scholar Award

Student | Xinjiang University | China

Mr. Jizhou Cao is a dedicated academic and researcher currently serving at Xinjiang University. With a background in civil engineering and machine learning, he has significantly contributed to the understanding of reinforced concrete (RC) column shear behaviour, integrating advanced machine learning techniques into structural engineering. His work has explored the initial failure process in RC columns and prediction methods for shear capacity, demonstrating a unique synergy between civil engineering and machine learning. Mr. Cao’s research has been published in well-respected journals, furthering the application of machine learning to solve real-world engineering problems.

Profile

Scopus

Education

Mr. Cao earned his master’s degree from Hainan University, where he gained a solid foundation in civil engineering. He continued his academic journey by pursuing further studies at Xinjiang University, which has fostered his research interests in the intersection of civil engineering and machine learning. His educational path reflects a blend of practical expertise and theoretical understanding, particularly in the realm of structural analysis and innovative technologies such as machine learning.

Experience

With years of academic and research experience, Mr. Cao has engaged in multiple projects that apply cutting-edge technologies to civil engineering problems. His work has focused on developing predictive models for the shear capacity of RC columns and understanding the failure processes in concrete structures using machine learning techniques. He has also been involved in consultancy projects, contributing his expertise to real-world applications. His professional journey highlights his commitment to advancing both the scientific understanding and practical application of structural engineering.

Research Interest

Mr. Cao’s primary research interests lie in the integration of machine learning with civil engineering, particularly in structural analysis and the failure mechanisms of reinforced concrete structures. His research aims to bridge the gap between computational techniques and practical engineering solutions, with a special focus on the prediction of shear failure in RC columns. His work seeks to improve the accuracy of structural safety evaluations and enhance the resilience of concrete structures under various loading conditions.

Award

Mr. Cao has been recognized for his contributions to the field of civil engineering and machine learning. His research has garnered attention from leading academic institutions, with multiple nominations for prestigious awards such as the Young Scientist Award and the Excellence in Innovation Award. These accolades reflect his impactful contributions to advancing engineering practices, particularly in the realm of structural safety and the application of machine learning.

Publications

Mr. Cao has authored several influential articles, contributing to the academic discourse on machine learning applications in civil engineering. Some of his key publications include:

“Exploring the initial state of the shear failure process in RC columns based on machine learning,” Journal of Structural Engineering, 2024.

“Prediction of shear capacity of RC columns and discussion on shear contribution via the explainable machine learning,” Structural Safety Journal, 2023. These works have been cited by numerous researchers, highlighting the significance of his research in the field.

His publications have addressed critical aspects of structural engineering and have demonstrated the potential of machine learning to revolutionize the field.

Conclusion

Mr. Jizhou Cao’s work stands as a testament to the potential of machine learning in reshaping civil engineering practices. His academic background, coupled with a strong research focus on shear failure prediction in RC columns, underscores his commitment to advancing both theoretical and applied knowledge in structural engineering. As he continues to explore innovative solutions through machine learning, Mr. Cao is poised to make lasting contributions to the safety and efficiency of civil infrastructure, enhancing the way engineers approach complex structural challenges. His dedication to research and innovation makes him a valuable asset to both academia and the engineering community.