Prof. Chi Wei Su, Qingdao University, China

Bio: Professor Chi Wei Su is a distinguished professor and doctoral supervisor at Qingdao University. He is also the dean of the Capital Market Research Institute and the deputy director of the China Center for Financial Inclusion Research. He has been recognized as one of the world’s top 2% scientists in 2021 to 2024 by Stanford University, and was listed in the World’s Most Influential Researchers list (1% of the World, Clarivate). In addition, he was awarded the title of the International Engineering and Technology Institute Fellow. He is a guest editor of Energy Economics, Economic and Analysis Policy, Romanian Journal of Economic Forecasting, Economic Research- Ekonomska Istrazivanja, Frontiers in Public Health, Environmental Science and Pollution, among others. His research interests focus on the theoretical and practical issues related to green finance, regional economic development, energy, carbon neutrality, and climate change. He has published more than 380 papers (WOS) as the first or corresponding author and dozens of highly cited papers. His work has been featured in various top-tier academic journals, including Energy Policy, Technological Forecasting & Social Change, Pacific Basin Finance Journal, Resources Policy, Energy, Urban Studies, Economic Modelling, International Review Economic and Finance.

Title: Can artificial intelligence become an accelerator for achieving carbon neutrality?

Abstract: A common worldwide goal in the fight against climate change is becoming carbon neutral, and emerging markets are crucial to this effort. This paper focuses on China and India as emerging markets, employing the quantile-on-quantile wavelet analysis method to assess the comprehensive impact of artificial intelligence (AI) on carbon neutrality, and how it accelerates the achievement of carbon neutrality objectives. The results show that the direction and magnitude of the impact between these two variables vary across countries. Overall, AI has both positive and negative effects on carbon neutrality, but in most cases, AI has advanced the carbon neutrality process in emerging markets. Nonetheless, in the short term, the substantial energy consumption of AI training may temporarily hinder the achievement of carbon neutrality goals. These results are consistent with the stochastic impact analysis based on population, affluence, and technology models. Based on this information, it is recommended that emerging markets actively promote AI research and applications in emission reduction and low-carbon technologies to advance carbon neutrality goals.

Prof. Long Chen, University of Shanghai for Science and Technology, China

Introduction: Chen Long, Ph.D., Professor, and doctoral supervisor. He graduated with a bachelor's degree in 2002 from the School of Logistics Engineering at Wuhan University of Technology and earned his Ph.D. in 2008 from Zhejiang University. Since then, he has been employed at Shanghai University of Science and Technology. From 2012 to 2013, he conducted a one-year visiting research at Illinois Institute of Technology in the United States. He serves as a committee member for multiple specialized committees, including the Mechanical Design Branch of the Chinese Society of Mechanical Engineering, the Computer-Aided Design and Computer Graphics Committee of the China Computer Federation, and the Chinese Society of Graph. His current research focuses on industrial software, intelligent robot design and applications. He has led two projects  of National Natural Science Foundation of China and over 20 projects  from company. He has published more than 50 SCI/EI-indexed papers in domestic and international academic journals such as CMAME, CS, SMO, CAD, JCAM, RAL, the Journal of Mechanical Engineering, and the Journal of Computer-Aided Design and Graphics. Additionally, he has obtained over 10 authorized invention patents.

Title: Fusion of Industrial Hand-Eye-Brain-Object ---- Implementation and Practice of Industrial Embodied Intelligence

Abstract: Driven by the breakthrough progress of the new generation of artificial intelligence, embodied intelligence, as an important branch of the field of artificial intelligence, is accelerating its penetration into industrial manufacturing scenarios. Industrial scenarios, due to their semi-structured manufacturing environment, relatively stable working conditions, and relatively standardized process flow, are more likely to achieve rapid implementation of embodied intelligence technology and are likely to become the first large-scale application field of embodied intelligence. In industrial embodied intelligence systems, robots serve as the hands of industry, machine vision as the eyes of industry, artificial intelligence models as the brains of industry, and work objects as industrial objects. Researching industrial hand eye brain object fusion systems ultimately achieves industrial embodied intelligence systems. This report focuses on the field of intelligent manufacturing, researching key technologies such as machine vision, artificial intelligence, and robot design to achieve vision based recognition, detection, positioning, measurement, and other functions. The machine vision system is integrated into various robot carriers to achieve functions such as robot grasping, human-machine cooperation, and robot inspection, thereby forming an intelligent mechanical system that integrates perception, decision-making, execution, and other functions, and is applied in intelligent manufacturing scenarios such as new energy vehicles. This report provides some references for the development of industrial embodied intelligent systems.

Assoc. Prof. Adnan Safi, Qingdao University, China

Bio: Adnan Safi is Associate Professor of Finance at Qingdao University in China. He holds a PhD in Applied Economics, majoring in Finance from Qingdao University, China, and has extensive research experience in various fields of finance. Safi’s primary research interests include behavioral finance, green finance, environmental economics, financial and resource markets, investment and financial analysis, corporate finance, and risk management. He has published numerous articles in top-tier academic journals, including Energy Economics, Risk Management, Sustainable Production and Consumption, and Journal of competitiveness. Safi has actively participated in various international competitions and has won awards for his contributions. In addition to his academic achievements, Safi is deeply committed to promoting sustainable and socially responsible financial systems and believes that finance can be used to create a positive impact on society.

Title: Leveraging Digital Technologies for Environmental Sustainability: ICT, Institutional Quality, and Trade-Adjusted CO2 Emissions

Abstract: This study examines the linkage between digital transformation, institutional quality, and consumption-based CO2 emissions. Unlike previous research utilizing production-based metrics, this study employs trade-adjusted carbon emissions to provide more accurate insights for policymakers. The findings reveal that institutional quality significantly reduces carbon emissions in OECD countries. ICT implementation demonstrates substantial positive effects on environmental quality through enhanced efficiency and innovation. The study also uncovers complex relationships between economic indicators and emissions: while economic growth generally increases emissions, trade dynamics show nuanced outcomes, with exports significantly reducing emissions and imports increasing them. This research contributes to understanding how institutional frameworks and digital transformation can be strategically leveraged to achieve carbon neutrality, providing actionable insights for policymakers addressing global warming and climate change.

Prof. Wenjuan Hao, Nanhang Jincheng College, China

Bio: Wenjuan Hao (IEEE member), outstanding young teacher of Jiangsu QingLan project. Since 2008, She has been with in Nanhang Jin Cheng College , where currently she is a professor.

She has authored or coauthored more than 20 technical papers, and she is the holder of 10 China Invention Patents. Her research interests include design, control of PM machines. 

Title: Methods for Reducing Cogging Force in Permanent Magnet Machines: A Review

Abstract: Permanent magnet (PM) machines inevitably suffer from cogging force, which does not contribute to the average output torque (force) but contributes as a type of torque (force) ripple. We provides an overview of the cogging force reduction methods for different types of PM machines. First, a systematic and comprehensive categorization of different kinds of cogging force reduction methods is given according to the reduction principle. Then, the cogging force reduction methods for different types of PM machines are analyzed and discussed based on the categorization. Finally, according to the versatility and feasibility of the cogging force reduction methods, practical methods are recommended for different types of PM machines. The categorization, analyses, and recommendations presented in the speech are useful for the design of different types of PM machines with the requirement of cogging force reduction or output torque (force) ripple suppression.