Lei Guan | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Mr. Lei Guan | Machine Learning | Research Excellence Award

Director | China Academy of Safety Science and Technology | China

Lei Guan is a Director and Professor at the Risk Monitoring and Early Warning Center, China Academy of Safety Science and Technology, with expertise in risk monitoring, early warning systems, artificial intelligence, and industrial safety engineering. He holds a Bachelor’s degree in Materials Science and Master’s and Doctoral degrees in Mechanical Engineering with specialization in precision instruments and safety-related systems. He has led major national and ministerial research programs, directed key laboratories and professional committees, supervised graduate researchers, and provided technical leadership for large-scale industrial and governmental safety initiatives. His research focuses on intelligent work safety systems, industrial internet applications, digital twins, data-driven risk modeling, and emergency management, with sustained contributions through peer-reviewed publications, patents, and standards development. His scholarly impact is reflected in 18 citations, an h-index of 3, and 13 published articles.

Citation Metrics (Google Scholar)

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View  Google Scholar  View ResearchGate View ORCID Profile

Featured Publications

Numerical simulation of the double pits stress concentration in a curved casing inner surface
W. Yan, L. Guan, Y. Xu, J.G. Deng – Advances in Mechanical Engineering, 9(1) (3 citations)

Safety monitoring and management system for fluid catalytic cracking (FCC) process
L. Fang, Z. Wu, L. Wei, R. Kang, L. Guan – International Conference on Information and Automation (3 citations)

Study on SVM-based Flame Recognition and Fire Warning for Cotton and Linen Warehouses
X. Zhao, S. Hao, L. Guan, Y. Wang, Q. Zhao, D. Lv – IEEE Conference on Advances in Electrical Engineering (2 citations)

Industrial Internet of Things (IIoT) Identity Resolution Techniques: A Review
C. Dai, H. Li, L. Guan, M. Chi – IEEE BigDataSecurity (1 citation)

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Associate Professor | Shanghai Electric Power University | China

Dr. Li Xiaohua, a distinguished Professor at Sichuan University and leading expert in materials science and structural engineering, is renowned for advancing high-performance composite materials and sustainable structural systems. She holds advanced degrees in materials engineering with specialization in composite behavior and structural performance, complemented by extensive experience in academic leadership, project supervision, and collaborative research initiatives. Her professional portfolio includes directing major institutional projects, mentoring interdisciplinary teams, and contributing to engineering innovations that strengthen the reliability and resilience of modern structures. Dr. Li’s research focuses on composite structures, fire-resistant materials, mechanical behavior, and performance optimization, supported by 297 citations, 34 scholarly documents, and an h-index of 11, reflecting her growing global impact. She has authored influential publications, contributed to high-level research panels, and advanced knowledge dissemination through editorial responsibilities and membership in professional engineering societies. Recognized for excellence in research, innovation, and service, she also holds relevant professional certifications that underscore her commitment to scientific rigor and continued advancement in the engineering sciences.

Profile: Scopus

Featured Publications

Li Xiaohua*, Probabilistic forecasting of coal consumption for power plants under deep peak shaving conditions using Informer with DDPM-based uncertainty modeling. Int. J. Electr. Power Energy Syst., 2025.

Li Xiaohua*, Electromagnetic vibration characteristics of permanent magnet synchronous motors with segmented grain-oriented electrical steel teeth–yoke.

Li Xiaohua, Research on core loss prediction of low-frequency transformer based on Grey Wolf optimisation algorithm optimised Back Propagation neural network. IET Electr. Power Appl., 2025.

 










































































 




	


	

	






	
	

		


Mr. Jufeng Han | Materials Informatics | Best Researcher Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Mr. Jufeng Han | Materials Informatics | Best Researcher Award


Master | Institute of Semiconductors | China










Dr. Jufeng Han, currently pursuing a Master’s degree in Artificial Intelligence for Science at the Institute of Semiconductors, Chinese Academy of Sciences, is an emerging researcher specializing in materials informatics and semiconductor and optoelectronic materials. His academic foundation combines advanced studies in artificial intelligence with applications in materials science, focusing on the integration of data-driven modeling with physical principles. Professionally, he has contributed to innovative research projects, most notably the development of a symbolic–neural hybrid modeling framework for perovskite bandgap prediction—an approach that enhances accuracy and interpretability in photovoltaic material screening. His work has been recognized with a Best Paper Candidate nomination and publication acceptance in Materials Today Energy. Beyond research, he collaborates within interdisciplinary teams at the Institute of Semiconductors, demonstrating leadership in bridging AI methodologies with materials discovery. His research contributions have strengthened the role of AI in accelerating semiconductor innovation, particularly in energy-efficient and sustainable technologies. He is a member of the Association for the Advancement of Artificial Intelligence (AAAI) and maintains a strong academic presence through his Google Scholar profile. Jufeng Han’s combination of technical expertise, academic excellence, and forward-looking research vision positions him as a promising scholar in AI-driven materials science and a deserving nominee for the Best Researcher Award.


Profile: Scopus


Featured Publications


Han, Jufeng*, Bandgap prediction for perovskite materials based on symbolic–neural hybrid modeling. Materials Today Energy, Accepted.


Han, Jufeng*, Symbolic–neural hybrid framework for enhanced interpretability and accuracy in perovskite bandgap prediction. Institute of Semiconductors, Chinese Academy of Sciences, In production.


Han, Jufeng, AI-driven modeling approaches for semiconductor and optoelectronic material discovery. AI for Science Research Series, Under review.
































	


	

	






	
	

		


Adil Sultan | Environmental Modeling | Best Researcher Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Mr. Adil Sultan | Environmental Modeling | Best Researcher Award


PhD Student | National Yunlin University of Science and Technology | Taiwan
















Adil Sultan is a dynamic researcher and postgraduate scholar in Computer Science and Information Engineering at the National Yunlin University of Science and Technology, Taiwan, with a Bachelor’s degree in Geophysics from Bahria University Islamabad, Pakistan. His interdisciplinary expertise bridges artificial intelligence, computational modeling, and earth sciences, emphasizing intelligent predictive frameworks for environmental and marine ecosystem dynamics. Adil’s research integrates machine learning, fractional calculus, and neurocomputational modeling to address complex ecological and climatological phenomena, with publications in high-impact Q1 journals such as Water Research, Engineering Applications of Artificial Intelligence, and Process Safety and Environmental Protection. He has contributed to advancing predictive neural architectures for modeling plankton dynamics, environmental toxin propagation, and climate-induced marine variations. His professional experience includes seismic data processing at Oil and Gas Development Company Limited, where he applied geophysical modeling and data analytics for subsurface evaluations, alongside earlier roles in communication and technical support at IBEX Global. Recognized for academic excellence and innovation, he ranked among the top three in his master’s program and earned a Bronze Medal for his undergraduate thesis. Adil has presented his work at international conferences, authored multiple manuscripts under review, and actively engages in interdisciplinary research collaborations. His memberships, scholarly achievements, and leadership in applied machine learning for environmental intelligence underscore his commitment to sustainable scientific innovation and global research excellence. His Scopus profile reflects 17 citations, 6 indexed documents, and an h-index of 2.


Profile: Scopus


Featured Publications


Adil Sultan*, Design of a Fractional-Order Environmental Toxin-Plankton System in Aquatic Ecosystems: A Novel Machine Predictive Expedition with Nonlinear Autoregressive Neuroarchitectures. Water Research, Q1, 12.4 I.F.


Adil Sultan*, Bayesian-Regularized Cascaded Neural Networks for Fractional Asymmetric Carbon-Thermal Nutrient-Plankton Dynamics under Global Warming and Climatic Perturbations. Engineering Applications of Artificial Intelligence, Q1, 8.0 I.F.


Adil Sultan*, Intelligent Predictive Networks for Nonlinear Oxygen-Phytoplankton-Zooplankton Coupled Marine Ecosystems under Environmental and Climatic Disruptions. Process Safety and Environmental Protection, Q1, 7.8 I.F.


Adil Sultan*, Prognostication of Zooplankton-Driven Cholera Pathoepidemiological Dynamics: Novel Bayesian-Regularized Deep NARX Neuroarchitecture. Computers in Biology and Medicine, Q1, 6.3 I.F.


Adil Sultan*, Predictive Modeling of Fractional Plankton-Assisted Cholera Propagation Dynamics Using Bayesian Regularized Deep Cascaded Exogenous Neural Networks. Process Safety and Environmental Protection, Q1, 7.8 I.F.



























	


	

	






	
	

		


Ms. Siqin Liao | Control Systems & Optimization | Best Researcher Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Ms. Siqin Liao | Control Systems & Optimization | Best Researcher Award


Student | The School of Computer Science, Guangdong University of Technology | China






























Siqin Liao is a Ph.D. candidate at the School of Computer Science, Guangdong University of Technology, specializing in control theory and intelligent systems. He holds a Master of Engineering in Control Engineering from Guangdong University of Technology and a Bachelor of Engineering in Electrical Engineering and Automation from Xinyu University, and has completed a visiting research program at the University of Adelaide, Australia. His research focuses on asynchronous control of Markov jump systems, cooperative control of multi-agent systems, and network security control. Liao has published seven SCI-indexed papers in leading journals such as IEEE Transactions on Cybernetics and Systems & Control Letters, contributing novel control methodologies for stochastic and networked systems under uncertainty and cyberattacks. He has participated in provincial-level projects funded by the Guangdong Basic and Applied Basic Research Foundation and collaborates internationally on control design research. His scholarly impact includes over 100 citations, with recognition for his first-author and corresponding-author publications. As a student member of IEEE, he upholds academic excellence and innovation in control system theory. His achievements have earned him awards such as the First Prize in the Siemens Cup China Intelligent Manufacturing Challenge and the Third Prize in the China Robowork Competition, highlighting both his technical expertise and leadership in engineering research. His research record includes 80 citations across 7 publications with an h-index of 2, reflecting his growing academic influence.
















Profile: Scopus


Featured Publications


Siqin Liao*, Zheng-Guang Wu, Yuanqing Wu. Constrained quadratic control for Markov jump systems with multiplicative noise. Syst. Control Lett., 2025, 203:06–27.


Siqin Liao*, Yuanqing Wu, Zheng-Guang Wu, Peng Shi. Design on dynamic event and self-triggered control for systems with Markov jumps and denial-of-service attacks. J. Franklin Inst., 2025, doi:10.1016/j.jfranklin.2025.108006.


Siqin Liao, Zheng-Guang Wu, Yuanqing Wu*. Control for Markov jump systems with partially unknown transition probabilities under denial-of-service attacks. Int. J. Robust Nonlinear Control., 2025, doi:10.1002/rnc.70142.