2026 2nd International Conference on Simulation, Modeling and Big Data (SMBD 2026)

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Qinxi Dong

Shandong Jiaotong University

Biography: Dr. Qinxin Dong is a professor at Hainan University, PhD supervisor, Foreign Fellow of the Engineering Academy of Japan, Fellow of the Japan Society of Civil Engineers. He received his Bachelor’s degree in Solid Mechanics from the School of Aerospace at Beijing Institute of Technology in 1986, a Master’s degree in Computational Mechanics from the same university in 1989, and a PhD degree in Science and Technology in Computational Mechanics from the University of Innsbruck in Austria in 1997. From 1998 to 2000, he conducted postdoctoral research at the Japan Society for the Promotion of Science (JSPS).Dr. Dong has over 35 years of experience in the research and development of CAE (Computer-Aided Engineering) industrial software across multi-field coupling systems. He is proficient in the core algorithms and data storage formats of scientific computing industrial software from Europe and the United States, capable of interpreting and modifying industrial software source codes. He has long been dedicated to research on supercomputing algorithms and large-scale data storage in supercomputing environments. He develops a supercomputing support software platform with independent intellectual property rights and has successfully applied his research findings to the analysis and simulation of seismic engineering, engineering geology, fluid mechanics, multi-field coupling, intelligent manufacturing, and the formation and effects of new composite materials, resulting in a comprehensive suite of engineering application systems, with supercomputing algorithm optimization, supercomputing software support, and big data analysis. The exascale supercomputing software developed by Prof. Dong has become one of the irreplaceable support tools in Japan for disaster prevention and mitigation engineering.
Title: Domestic High-End Industrial Software to Empower Intelligent Manufacturing
Abstract:With supercomputing entering the exascale era (1018 Floating-point Operations Per Second，FLOPS), developed countries have formulated strategic measures to engage in related research, and at the same time supercomputing is facing a new wave of huge challenges. Based on supercomputing algorithms and the supporting software they generate, this talk provides a brief introduction to data structures, parallel processing methods, data exchange, simulation implementation, etc. It also explores the application of these elements in the research and development of high-end industrial software for intelligent manufacturing-related fields.

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Teng Zhou

Hainan University

Biography: Dr. Teng Zhou, is a Professor and Ph.D. Supervisor at the School of Mechanical and Electrical Engineering, Hainan University. He concurrently serves as the Associate Director of the Editorial Department of Journal of Hainan University. His research focuses on multiphysics coupling in micro/nanofluidics, with related findings published in prestigious journals such as Nature Communications, Energy & Environmental Science, and National Science Review. He has authored over 100 SCI-indexed papers and has an h-index of 28 on Google Scholar. He was awarded the 10th Hainan Provincial Youth Science and Technology Award and the Third Prize in Natural Science of Hainan Province. Under his supervision, two graduate students have received the Outstanding Master’s Thesis Award of Hainan Province. He currently holds several academic roles, including Council Member of the Micro-Nano Optical Devices and Systems Branch of the Chinese Society of Micro-Nano Technology, Member of the Fluid Control Professional Committee of the Chinese Society of Theoretical and Applied Mechanics, and Editorial Board Member of Optics and Precision Engineering, Machine Tool & Hydraulics, and Micromachines. He also serves as Associate Director of the Youth Editorial Board of International Journal of Hydromechatronics.
Title:Design, Fabrication, and Energy Harvesting Mechanism of Multiphysics-Coupled Composite Membranes for Salinity Gradient Energy in the South China Sea
Abstract:Salinity gradient energy (SGE), as an abundant renewable ocean energy resource with a theoretical power potential of 2.6 TW, holds significant importance for advancing the sustainable development of the marine economy. However, existing technologies face substantial challenges, including pronounced concentration polarization in nanochannel membranes, high costs of two-dimensional (2D) materials, and the difficulty in balancing power density with energy efficiency. To address these challenges, a collaborative research team from Hainan University and the Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, leveraged the specific salinity gradient characteristics at the estuaries of Hainan Island, conducting a comprehensive study spanning fundamental design, composite membrane fabrication, to device assembly. Utilizing the principles of continuum mechanics and the Poisson-Nernst-Planck (PNP) equations, the research systematically elucidated the ion transport dynamics within nanochannels, revealing the correlation mechanism between key channel parameters and the power density of energy capture. Employing core micro/nano-fabrication technologies, high-performance biomimetic nanochannel membranes based on MXene/CMC-Na and GO/sodium alginate composites were successfully fabricated. Experimental validation demonstrated their exceptional ion transport capability, achieving a high power density of 19.39 W/m² under a seawater/river water gradient. Through the innovative coupling of multiple physical fields—the light field (photothermal effect), temperature field (asymmetric temperature gradient), and chemical field (proton diffusion driven by acid-base reactions)—synergistic light-heat-ion co-transport was realized. This approach broke through the energy conversion limits imposed by traditional methods and enabled enhanced efficiency via multi-physics field synergy. Building on this foundation, advanced material integration and device assembly techniques were applied to optimize and construct high-efficiency SGE nanochannel membranes and energy harvesting devices. Starting from the fundamental flow control mechanisms at the micro/nano scale, this study provides a novel composite material system, a precise numerical computational model, and an efficient multi-field synergistic enhancement strategy—collectively offering a pathway to surpass the industrial application power density threshold for SGE (5 W/m²). The research outcomes possess significant practical value for promoting the development and utilization of marine renewable energy in the South China Sea region and the industrial applications of SGE technology. Furthermore, they directly contribute to advancements in precision manufacturing, micro/nano manufacturing, novel sensor materials & manufacturing technologies, as well as sensor and detection technologies.

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Xiangjie Kong

Zhejiang University of Technology

Biography:Dr. Xiangjie Kong is currently a Full Professor and Acacemic Associate Dean in the College of Computer Science & Technology, Zhejiang University of Technology (ZJUT), China. Previously, he was an Associate Professor in School of Software, Dalian University of Technology (DUT), China, where he was the Head of the Department of Cyber Engineering. He is the Founding Director of City Science of Social Computing Lab (The CSSC Lab) (http://cssclab.cn/). He is/was on the Editorial Boards of 6 International journals. He has served as the General Chair or Program Chair of more than 10 conferences. Dr. Kong has authored/co-authored over 200 scientific papers in international journals and conferences including IEEE TKDE, IJCAI, ACL, IEEE TMC, ACM CSUR, ACM TKDD, IEEE TNSE, IEEE TII, IEEE TITS, IEEE NETW, IEEE COMMUN MAG, IEEE TVT, IEEE IOJ, IEEE TSMC, IEEE TETC, IEEE TASE, IEEE TCSS, ACM TSON, ACM TSAS, WWWJ, etc.. 5 of his papers is selected as ESI- Hot Paper (Top 1‰), and 20 papers are ESI-Highly Cited Papers (Top 1%). His research has been reported by Nature Index and other medias. He has been invited as Reviewers for numerous prestigious journals including IEEE TKDE, IEEE TMC, IEEE TNNLS, IEEE TNSE, IEEE TII, IEEE IOTJ, IEEE COMMUN MAG, IEEE NETW, IEEE TITS, TCJ, JASIST, etc.. Dr. Kong has authored/co-authored three books (in Chinese). He has contributed to the development of 14 copyrighted software systems and 30 filed patents. He has an h-index of 51 and i10-index of 122, and a total of more than 8600 citations to his work according to Google Scholar. He is named in the 2019 - 2023 world's top 2% of Scientists List published by Stanford University. He is named in the 2022-2024 Best Computer Science Scientists List published by Research.com. Dr. Kong received IEEE Vehicular Technology Society 2020 Best Land Transportation Paper Award, IEEE CSCWD 2024 Best Paper Award, and The Natural Science Fund of Zhejiang Province for Distinguished Young Scholars. He has been invited as Keynote Speaker at more thant 10 international conferences, and delivered a number of Invited Talks at international conferences and many universities worldwide. His research interests include big data, network science, and computational social science. He is a Distinguished Member of CCF, a Senior Member of IEEE, a Full Member of Sigma Xi, and a Member of ACM.
Title:Spatio-Temporal Graph Learning based Urban Big Data Analysis and Applications

Abstract:A modern city is a ternary space that contains the physical world, human society, and information space. Urban big data is the foundation of urban travel intelligence. Based on urban big data, the accurate description of travel information in cities is the premise of forecasting/warning and decision-making assistance. Spatio-temporal graph learning having been extensively used in urban travel profilling in recent years, proves effective for many tasks in real-world applications, such as regression, classification, clustering, matching, and ranking. Spatio-temporal graph learning brings new idea to solve the challenges for smart transportation, improve the efficiency of urban resource utilization, optimize urban management and services, and improve residents' lives quality towards smart cities. This report will explore the research frontiers of spatio-temporal graph learning-based urban travel profiling, traffic data mining and analysis and its application in intelligent transportation systems, and introduce some related work.

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Jie Chen

Huaan Detection Group Co., Ltd.

Biography: The Civil Engineering major at Shandong Jianzhu University has been engaged in the research and management of engineering testing technology for 20 years, focusing on the fields of engineering quality and urban safety risk management. It has developed and applied multiple platform technology solutions such as building health monitoring, smart fire protection, and urban lifeline. It has led the construction of a full lifecycle urban safety management platform, which integrates offline technical team operation and maintenance, IoT monitoring, data analysis, insurance compensation and other modules. The Hua'an Testing Group it created is a leading comprehensive testing institution and urban safety management service provider in China.
Title:From Precise Detection to Intelligent Warning, Building a Safety Barrier for the Entire Lifecycle of Urban Infrastructure
Abstract:The engineering testing industry faces pain points such as long reporting cycles, difficulty in cross departmental collaboration, data silos, and extensive resource scheduling.
Hua'an Testing Group proposes innovative solutions:
1. Engineering testing: Throughout the entire construction cycle, ensuring the authenticity of data (such as government approval support and customized testing by owners).
2. Assessment and consultation: Reduce risks such as hollowing, cracking, and leakage, and provide specialized services for leak prevention and safety.
3. Health monitoring: Real time monitoring of building structural safety (such as bridge and subway tunnel monitoring systems).
4. Construction insurance: The "insurance+service+big data" model assists the government in establishing a long-term risk management mechanism.
Future direction of Hua'an Testing Group
1. Technical research and development: deepen the application of AI, big data and edge computing (such as structural damage prediction and risk early warning).
2. Urban lifeline: Expand safety monitoring of infrastructure such as water supply, heating, drainage, and gas.

3. Ecological Co construction: Collaborate with universities, insurance companies, and governments to build a full lifecycle security system.

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Peiran Li

Shandong Inspur Intelligent Energy Technology Co., Ltd.

Biography: Graduated from the College of Electrical Engineering, Zhejiang University in 2013 with a Doctor of Engineering degree. Recipient of the National Scholarship for Postgraduates awarded by the Ministry of Education in 2012. Postdoctoral Fellow at the Australian Defence Force Academy, University of New South Wales, Australia. With 4 years of working experience in overseas listed energy companies. Research interests focus on Energy Industrial Internet, dispatching optimization, coordinated control, unmanned inspection of new energy, and cluster control. Currently, serves as an External Master's Supervisor at the College of Electrical Engineering, Shandong University. In terms of patents, 6 authorized patents have been obtained (including 2 invention patents and 4 utility model patents), with another 3 invention patents under review. In terms of publications, 13 papers have been published, including 1 in SCI Zone 1, 3 in SCI Zone 2, 8 EI papers, and 2 Chinese core papers. Participated in compiling 1 monograph. In terms of vertical projects, 10 projects have been participated in, including: 1 Major Project of the Ministry of Science and Technology (2018AAA0101703); 3 National Natural Science Foundation projects (52277191 <General Program>, 51107135, 60804045); 3 National 863 Program projects (2006AA04Z185, 2007AA05Z232, 2012AA051704); 1 Shandong Provincial Excellent Youth and Middle-aged Project (2007BS01015); 1 Shandong Provincial Natural Science Foundation project (G0568); and 1 Zhejiang Provincial Natural Science Foundation project (Y1110229).
Title:Computing Power, Artificial Intelligence, and Energy
Abstract: The rise of generative artificial intelligence and the expansion of data centers have driven a surge in computing power. Coupled with growing user scale, this has significantly increased electricity demand. To address energy consumption constraints and accelerate the low-carbon transition, China has implemented advanced models such as microgrids, integrated generation-grid-load-storage systems, and virtual power plants. These are coupled with mechanisms like direct green power procurement and green electricity-certificate trading, substantially increasing the share of green power used in data centers and enhancing system resilience. Meanwhile, artificial intelligence can provide the power sector with intelligent forecasting, load dispatching, and equipment optimization services, thereby improving the coordination efficiency of generation-grid-load-storage, reducing energy consumption, and enabling the flexible integration of renewables. This establishes a synergistic mechanism of computing power, algorithms, and energy.