Andrea Reisdorf | Transport and Logistics | Young Scientist Award

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Young Scientist Award

Andrea Reisdorf
Vienna University of Economics and Business

Andrea Reisdorf
Affiliation Vienna University of Economics and Business
Country Germany
Scopus ID 60089064700
Documents 4
Citations 9
h-index 2
Subject Area Transport and Logistics
Event Global Innovation Technologist Awards

Andrea Reisdorf is a researcher affiliated with Vienna University of Economics and Business, whose scholarly work contributes to discussions surrounding transport, logistics, industrial competitiveness and the strategic management of critical raw materials. Her research profile includes an internationally published contribution examining the relationship between government intervention, corporate competitiveness and resource-dependent industries. Her collaborative research reflects the growing importance of understanding how geopolitical and economic developments influence industrial operations and supply chain resilience. [1]

Abstract

Andrea Reisdorf’s research addresses the evolving relationship between public authorities, private enterprises and industrial competitiveness in sectors dependent on critical raw materials. Her 2026 publication in Resources Policy develops a competitiveness framework that considers how government strategies, geopolitical relationships and access to essential resources influence business positioning. The research highlights the importance of examining industrial competitiveness beyond conventional market interactions, particularly where strategic resources and international dependencies shape corporate decision-making. [1]

Keywords

Critical Raw Materials; Industrial Competitiveness; Government–Business Relations; Geopolitics; Supply Chain Resilience; Transport and Logistics; Resource Dependency; Strategic Management.

Introduction

Critical raw materials have become strategically important to contemporary industries because of their role in manufacturing, infrastructure, renewable energy and advanced technologies. Their uneven geographical distribution and concentrated supply chains create economic and geopolitical challenges for businesses and governments. Research investigating these relationships is increasingly relevant to industrial policy, international trade and logistics management. Reisdorf’s collaborative publication contributes to this discussion by examining competitiveness through the interaction of corporate activities and government interests. [1]

Research Profile

Reisdorf’s documented research activity includes interdisciplinary engagement with industrial competitiveness and resource-dependent economic systems. Her work connects business perspectives with broader policy and geopolitical considerations, providing an analytical perspective on how external institutional conditions influence enterprises. The research is particularly relevant to transport and logistics because critical material availability, international sourcing and supply chain dependencies are important considerations in industrial operations. Her publication demonstrates engagement with a subject that requires collaboration across business, economics and resource governance.

Research Contributions

The publication, Blurred lines between governments and firms: A competitiveness framework for critical raw material dependent industries, co-authored with Leon Zacharias and Sebastian Kummer, presents a framework for understanding how relationships between governments and companies influence competitiveness. Its central contribution is the extension of traditional firm-level competition perspectives to include state interests, geopolitical power relations and strategic resource access. The accompanying discussion highlights the importance of considering political and regulatory conditions when evaluating industrial performance, including challenges affecting European hydropower industries. [1]

Publications

A notable publication associated with Reisdorf is:

  • Reisdorf, A. R., Zacharias, L., and Kummer, S. (2026). Blurred lines between governments and firms: A competitiveness framework for critical raw material dependent industries.

Research Impact

According to the supplied Scopus profile information, Reisdorf has four indexed documents, nine citations and an h-index of two. These indicators provide a quantitative overview of her recorded scholarly output and citation activity. Her research also addresses an important international issue: the influence of strategic material dependencies on industrial competitiveness. Its relevance extends to supply chain planning, business strategy, international resource governance and policy discussions concerning economic resilience.

Award Suitability

Reisdorf’s research profile demonstrates relevance to the Young Scientist Award category under the Global Innovation Technologist Awards. Her collaborative publication addresses a contemporary industrial challenge through an interdisciplinary competitiveness framework. The integration of government–business relationships, geopolitical considerations and critical resource dependencies represents a meaningful research contribution to current discussions on industrial transformation. Her documented publication activity and developing citation record provide additional indicators for consideration in an academic recognition assessment.

Conclusion

Andrea Reisdorf’s research contributes to the understanding of industrial competitiveness in an increasingly interconnected and resource-dependent global economy. Her 2026 publication highlights the importance of incorporating geopolitical and institutional relationships into business and supply chain analysis. Through interdisciplinary collaboration and engagement with strategically significant research questions, she demonstrates a scholarly contribution relevant to the Young Scientist Award category.

References

  1. Reisdorf, A. R., Zacharias, L., & Kummer, S. (2026). Blurred lines between governments and firms: A competitiveness framework for critical raw material dependent industries. Resources Policy, 122, Article 106086. Elsevier.
    https://doi.org/10.1016/j.resourpol.2026.106086
  2. Elsevier. (2026). Scopus author details: Andrea Reisdorf, Author ID 60089064700. Scopus.
    Scopus Author Profile
  3. Elsevier. (2026). Resources Policy. Journal publication and article information.
  4. Kummer, S. (2026). Research announcement: Blurred lines between governments and firms. Publication overview and discussion of research contributions.
  5. Global Innovation Technologist Awards. (2026). Official awards website.
    innovationtechnologist.com

Jianfeng Qiao | Accident Analysis | Best Researcher Award

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Mr. Jianfeng Qiao | Accident Analysis | Best Researcher Award

Associate Professor at Capital University of Economics and Business, China

Dr. Jianfeng Qiao is an Associate Professor at the School of Management and Engineering, Capital University of Economics and Business (CUEB), Beijing. His research focuses on accident analysis and safety risk early warning using advanced machine learning and artificial intelligence technologies. He has authored influential publications in top-tier international journals and has collaborated globally, including serving as a visiting scholar at Rutgers University, USA. Dr. Qiao’s work bridges engineering management, data science, and safety technology, contributing significantly to the fields of construction safety, risk optimization, and intelligent decision support systems.

Publication Profile 

Scopus

Educational Background 🎓

  • Doctor of Engineering
    Beijing University of Posts and Telecommunications, Beijing, China
    September 2011 – July 2015

  • Visiting Scholar
    Rutgers, The State University of New Jersey, USA
    March 2019 – May 2022
    (Focus: Machine learning applications in accident analysis and safety systems)

Professional Experience 💼

  • Associate Professor
    School of Management and Engineering, Capital University of Economics and Business, Beijing, China
    Dates: Ongoing

    • Teaching courses related to engineering management and safety systems

    • Conducting research in machine learning-driven safety risk assessment and early warning models

    • Supervising graduate students in safety engineering and AI applications

  • Research Collaborator
    Rutgers University, USA (as Visiting Scholar)

    • Conducted collaborative research projects focused on AI in construction accident prevention

    • Developed methods for classifying narrative accident data using deep learning techniques

Research Interests 🔬

  • Accident analysis and prediction using machine learning

  • Early warning systems for safety risks

  • Text mining and natural language processing in safety narratives

  • Entropy-based methods in project management

  • AI applications in construction and industrial safety

  • Resource optimization and scheduling

Conclusion🌟

Dr. Jianfeng Qiao is a committed academic and researcher whose work advances the intersection of machine learning and safety engineering. Through his academic publications and international collaborations, particularly in the area of construction accident prevention, he continues to impact both scholarly communities and practical safety management systems. His expertise and contributions make him a valued expert in AI-driven safety solutions.

Publications 📚

  • 🏗️ Qiao, J., Wang, C., Guan, S., Lv, S. (2022).
    Construction-Accident Narrative Classification Using Shallow and Deep Learning.
    Journal of Construction Engineering and Management, 148(9), 04022088.
    🔗 https://doi.org/10.1061/(ASCE)CO.1943-7862.0002354
    ✨ Used machine learning techniques to classify construction accident narratives.

  • ⚙️ Qiao, J., Li, Y. (2018).
    Resource Leveling Using Normalized Entropy and Relative Entropy.
    Automation in Construction, 87, 263–272.
    🔗 https://doi.org/10.1016/j.autcon.2017.12.022
    📊 Introduced entropy-based methods for efficient resource scheduling in construction.

 

 

 

 

Ke Wang | Travel Demand Modeling | Best Researcher Award

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Assoc. Prof. Dr. Ke Wang | Travel Demand Modeling | Best Researcher Award

Associate Professor at University of Shanghai for Science and Technology, China

Ke Wang is an Associate Professor at the University of Shanghai for Science and Technology, specializing in travel behavior analysis, travel demand modeling, sustainable mobility, and data mining. His research contributions focus on advanced econometric models and machine learning techniques for understanding travel behavior and transportation systems. With over 190 citations, an H-index of 7, and an i-10 index of 6, Ke Wang has published extensively in high-impact transportation journals and conferences.

Publication Profile 

Scopus

Educational Background 🎓

  • Joint Ph.D. in Transportation Engineering (2018.09 – 2019.09)

    • University of Texas at Austin

    • Supervisor: Prof. Chandra R. Bhat

  • Ph.D. in Transportation Engineering (2015.09 – 2021.01)

    • Tongji University

    • Supervisor: Prof. Xin Ye

  • B.E. in Transportation Engineering (2011.09 – 2015.06)

    • Chang’an University

Professional Experience 💼

  • Associate Professor (2024.07 – Present)

    • University of Shanghai for Science and Technology

  • Assistant Professor (2021.06 – 2024.06)

    • University of Shanghai for Science and Technology

  • Research Assistant (2018.09 – 2019.09)

    • Center for Transportation Research, University of Texas at Austin

  • Research Assistant (2013.12 – 2015.06)

    • Institute of Regional and Urban Transportation Economics, Chang’an University

Research Interests 🔬

  • Travel Behavior Analysis & Travel Demand Modeling

  • Sustainable Mobility & Urban Transportation Planning

  • Advanced Econometric & Machine Learning Models for Transportation

  • Shared Autonomous Vehicles & Ride-Hailing Services

  • Public Transport Accessibility & Mode Choice Analysis

Conclusion🌟

Ke Wang is a rising scholar in transportation engineering, contributing innovative methodologies to travel behavior modeling and sustainable mobility. His research integrates statistical modeling, artificial intelligence, and urban transportation systems to inform policy decisions. His extensive publication record in top-tier journals (SSCI, SCI, JCR Q1-Q3) demonstrates his influence in the field.

Publications 📚

📄 Article | ✈️ Urban Air Mobility & Flying Cars
Title: Modeling the adoption of urban air mobility based on technology acceptance and risk perception theories: A case study on flying cars
Authors: S. Hu, Z. Huang, K. Wang, H. Lin, M. Pei
Journal: Multimodal Transportation (2025)
📌 Citations: 0

📄 Article | 🚲 E-Bike Helmet Usage & Safety
Title: Deconstructing the barriers and facilitators of e-bike helmet usage: A structural equation modeling approach
Authors: M. Pei, Z. Huang, K. Wang, X. Ye
Journal: Journal of Transport and Health (2025)
📌 Citations: 0

📄 Article | 🚗 Mobility as a Service (MaaS) & Face Consciousness
Title: The effects of face consciousness on young travelers’ intention to adopt mobility as a service (MaaS): A case study in Shanghai, China
Authors: J. Wen, H. Gan, K. Wang, Y. Huang, H. Lu
Journal: Research in Transportation Economics (2025)
📌 Citations: 0

📄 Article | ⚡ EV Range Anxiety & Willingness to Pay
Title: Range anxiety and willingness to pay: Psychological insights for electric vehicles
Authors: M. Pei, Z. Huang, Z. Zhang, K. Wang, X. Ye
Journal: Journal of Renewable and Sustainable Energy (2025)
📌 Citations: 0

📄 Article | 🌍 Carbon Emissions & Power Sector Transformation
Title: Carbon emission drivers of China’s power sector and its transformation for global decarbonization contribution
Authors: L. Zhao, K. Wang, H. Yi, J. Zhen, H. Hu
Journal: Applied Energy (2024)
📌 Citations: 4

Jingcai Yu | Transportation | Best Researcher Award

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Dr. Jingcai Yu | Transportation | Best Researcher Award

lecturer at Xihua University, China

Dr. Jingcai Yu is a dedicated transportation engineer and researcher with expertise in public transportation and traffic safety. He earned his Doctor of Engineering degree from Southeast University’s School of Transportation, where he specialized in travel behavior modeling and transit adoption studies. Currently based at Xihua University in Chengdu, China, Dr. Yu has published extensively in peer-reviewed journals, contributing valuable insights into the effects of attitudes, sociodemographic factors, and traffic conditions on transit and mobility choices. His work addresses emerging challenges in urban mobility, particularly through studies on flexible transit routes and shared autonomous vehicles, highlighting his commitment to advancing sustainable and efficient transportation solutions.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Dr. Jingcai Yu completed his Doctorate in Engineering in Transportation at the School of Transportation, Southeast University, China. His doctoral research focused on public transportation, travel behavior modeling, and traffic safety, areas in which he has since made numerous academic contributions.

💼 Professional Experience :

Dr. Yu is currently based at Xihua University in Chengdu, China, where he holds an academic position. He engages in teaching, research, and academic mentorship, primarily within the field of transportation engineering. Through his role at Xihua University, Dr. Yu applies his expertise in public transit systems, travel behavior analysis, and transportation safety to help address urban mobility challenges. His work includes investigating the factors influencing the adoption of flexible route transit systems, carsharing, and shared autonomous vehicles. Dr. Yu’s research outputs, published in prestigious journals, have significantly contributed to the fields of transportation safety, autonomous vehicle studies, and public transit utilization.

📚 Research Interests : 

Dr. Jingcai Yu’s research interests encompass public transportation systems, travel behavior modeling, and traffic safety. He focuses on innovative transit solutions such as flex-route transit systems, shared autonomous vehicles, and carsharing. His work also investigates factors affecting crash severity and traffic violations, as well as advanced prediction models for urban traffic flow.

📝 Publication Top Notes :

  1. Yu, J., Wang, S., Wang, B., Li, W., & Feng, T. (2024). Effects of COVID-19 on Flex Route Transit Utilization: An Interrupted Time Series Analysis. Research in Transportation Business & Management, 50, 101230.
  2. Yu, J., Li, W., Yin, Z., Zheng, Y., & Guo, R. (2024). Segmenting and exemplifying potential Flex route transit adopters. Transportation Research Record, 2678(9), 791-806. (SCI)
  3. Yu, J., Lin, Q., Ding, H., Li, W., & Feng, T. (2024). Examining individuals’ adoption of flex route transit. Transportation Planning and Technology, 47(7), 996-1021. (SCI)
  4. Yu, J., Li, W., Song, Z., Wang, S., Ma, J., & Wang, B. (2023). The role of attitudinal features on shared autonomous vehicles. Research in Transportation Business & Management, 50, 101032. (SCI)
  5. Yu, J., Wang, S., Ma, J., Song, Z., & Li, W. (2023). Roles of attitudinal factors on the adoption stages of carsharing. Transportation Letters, 16(6), 542-553. (SCI)
  6. Yu, J., Zheng, Y., Li, W., Zhang, J., Guo, R., & Wu, L. (2023). Understanding Flex-Route Transit Adoption from a Stage of Change Perspective. Transportation Research Record, 2677(6), 743-758. (SCI)
  7. Yu, J., Li, W., Zhang, J., Guo, R., & Zheng, Y. (2023). Understanding the effect of sociodemographic and psychological latent characteristics on flex-route transit acceptance. PLOS ONE, 18(2), e0279058. (SCI)
  8. Wang, S., Yu, J.*, & Ma, J. (2023). Identifying the heterogeneous effects of road characteristics on Motorcycle-Involved crash severities. Travel Behaviour and Society, 33, 100636. (SCI)
  9. Ma, J., Ren, G., Li, H., & Yu, J. (2022). Characterizing the differences of injury severity between single-vehicle and multi-vehicle crashes in China. Journal of Transportation Safety & Security, 1-21. (SCI)
  10. Wang, S., Li, Z., Wang, B., & Yu, J. (2022). Velocity obstacle-based collision avoidance and motion planning framework for connected and automated vehicles. Transportation Research Record, 2676(5), 748-766. (SCI)
  11. Zheng, Y., Wang, S., Li, W., & Yu, J. (2022). Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism. Physica A: Statistical Mechanics and its Applications, 608, 128274. (SCI)
  12. Ma, J., Ren, G., Wang, S., & Yu, J. (2022). Characterizing the effects of contributing factors on crash severity involving e-bicycles: A study based on police-reported data. International Journal of Injury Control and Safety Promotion, 29(4), 463-474. (SCI)
  13. Ma, J., Ren, G., Fan, H., Wang, S., & Yu, J. (2021). Determinants of traffic violations in China: A case-study with a partial proportional odds model. Journal of Transportation Safety & Security, 1-21. (SCI)