Dechao Chen | Embodied Intelligence | Young Scientist Award

Prof. Dechao Chen | Embodied Intelligence | Young Scientist Award

Hangzhou Dianzi University | China

The research focuses on advanced intelligent systems that integrate neural networks, unmanned system control, machine vision, robotics, data mining, intelligent optimization algorithms, and intelligent medical technologies. Core contributions lie in the design of robust learning models, control strategies for autonomous and unmanned systems, and data-driven optimization methods that enhance perception, decision-making, and system reliability in complex environments. Significant work has been published in leading international SCI journals, particularly in high-impact IEEE Transactions, reflecting strong theoretical innovation and practical relevance. The research output includes over a hundred peer-reviewed articles, with multiple papers recognized as ESI highly cited works, demonstrating sustained global influence and high citation impact. Contributions have advanced intelligent control, industrial informatics, automation science, and medical intelligence, bridging theory with real-world engineering and healthcare applications. The research has been supported by major national and provincial competitive funding programs, including foundational science projects and key research and development initiatives, emphasizing originality, scalability, and societal value. Scholarly impact is further reflected through extensive citations, high bibliometric indices, and authorship of an international monograph published by a leading academic press. In addition, the research actively contributes to the academic community through editorial and peer-review activities for top-tier journals, helping to shape research directions in neural computation, robotics, intelligent optimization, and medical technology.

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Featured Publications

UMSSNet: a unified multi-scale segmentation network for heterogeneous medical images
– Multimedia Systems, 2025 · 2 Citations · Open Access
VSDRL: A robust and accurate unmanned aerial vehicle autonomous landing scheme
– IET Control Theory & Applications, 2025 · 1 Citation
ADP: Adaptive Diffusion Policy Energizes Robots Thinking in Both Learning and Practice
– IEEE Transactions on Automation Science and Engineering, 2025 · 0 Citations
Robust Neural Dynamics for Depth Maintenance Tracking Control of Robot Manipulators With Uncertainty and Perturbation
– IEEE Transactions on Automation Science and Engineering, 2025 · 8 Citations

Sunawar Khan | Computer Science | Research Excellence Award

Mr. Sunawar Khan | Computer Science | Research Excellence Award

National College of Business Administration | Pakistan

Sunawar Khan is a research-oriented academic and practitioner with strong expertise in artificial intelligence, machine learning, deep learning, cybersecurity, smart grid technologies, Computer Science and intelligent systems. His research interests center on applying advanced computational intelligence techniques to real-world problems, particularly in healthcare analytics, intrusion detection systems, software reliability, and smart city security. He has worked extensively with neural networks, ensemble learning, explainable AI, and hybrid deep learning architectures such as CNN- and BiGRU-based models. His projects include deep learning–based disease detection using benchmark medical datasets, facial expression recognition with neural AdaBoost methods, and software defect prediction using industrial datasets. In cybersecurity, his research focuses on robust intrusion detection for smart environments, emphasizing accuracy, scalability, and interpretability. He also has experience designing and implementing intelligent management systems and applying machine learning to large, structured datasets. His academic background reflects a strong foundation in artificial intelligence, image processing, computer vision, data mining, algorithm analysis, and computational theory, complemented by practical experience in programming and system development. Overall, his research profile demonstrates a commitment to innovative, data-driven solutions that bridge theoretical models and applied intelligent technologies across interdisciplinary domains.

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Featured Publications


Antenna Systems for IoT Applications: A Review


Discover Sustainability, Vol. 5(1), Article 412, 2024

Generative AI, IoT, and Blockchain in Healthcare: Applications, Issues, and Solutions


Discover Internet of Things, Vol. 5(1), Article 5, 2025

Junpeng Guo | Recommendation system | Research Excellence Award

Prof. Junpeng Guo | Recommendation system | Research Excellence Award

Tianjin University | China

The research profile centers on advanced decision-support and analytical methodologies applied to complex digital and managerial environments. Core research areas include recommender systems in e-commerce and social media platforms, Recommendation system with a focus on improving personalization, user engagement, and decision quality through data-driven models. Significant contributions are made in symbolic data analysis and modeling under uncertainty, addressing incomplete, imprecise, and heterogeneous information commonly encountered in real-world decision problems. The work further advances multi-objective evaluation and decision-making frameworks, integrating operations research, decision science, and optimization techniques to support strategic and operational decisions in business and engineering systems. Methodological research emphasizes mathematical modeling, applied statistics, and computational intelligence, bridging theoretical rigor with practical applicability. Scholarly activities extend to peer review and evaluation for leading international journals and major research funding agencies, ensuring alignment with high academic standards and research integrity. International research exposure through visiting scholar appointments has strengthened interdisciplinary collaboration and contributed to the global exchange of knowledge in information systems, management science, and analytics. Overall, the research demonstrates a sustained commitment to developing robust analytical tools that enhance decision-making effectiveness in uncertain, data-intensive, and multi-criteria environments across digital commerce and management domains.

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Jinfa Zhang | Statistical Applications in Engineering | Research Excellence Award

Dr. Jinfa Zhang | Statistical Applications in Engineering | Research Excellence Award

China University of Petroleum (Beijing) | China 

The research profile reflects a strong and continuous focus on petroleum engineering, Statistical Applications in Engineering with specialized expertise in rock mechanics, geomechanics, lost circulation control, reservoir stimulation, and enhanced oil and gas recovery. Advanced doctoral research concentrates on the mechanical behavior of reservoir rocks, wellbore stability, and lost circulation mechanisms, integrating theoretical modeling with practical engineering applications. Master’s-level research emphasized oil and gas reservoir stimulation technologies, enhanced recovery methods, numerical reservoir simulation, and optimization techniques, supported by a strong academic performance and rigorous coursework in advanced reservoir engineering, fluid phase equilibria, and simulation software applications. Undergraduate training provided a solid foundation in drilling engineering, completion engineering, rock mechanics, porous media flow, oilfield chemistry, and production engineering. The research experience is complemented by extensive proficiency in industry-standard professional software for fracturing design, reservoir simulation, curve fitting, programming, and geospatial analysis, enabling comprehensive data-driven studies. Practical exposure through geological fieldwork and petroleum production training strengthened the ability to connect theoretical research with field-scale operations. Academic excellence is demonstrated through competitive scholarships, innovation and design competitions, and national-level recognitions, highlighting strong research capability, interdisciplinary technical skills, and potential for impactful contributions to petroleum engineering research and technology development.

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Sunawar Khan | Time Series and Forecasting | Research Excellence Award

Mr. Sunawar Khan | Time Series and Forecasting | Research Excellence Award

National College of Business Administration | Pakistan

Mr. Sunawar Khan is a researcher, academic, and Peer Reviewer at Springer Nature with extensive experience in Artificial Intelligence, Machine Learning, Deep Learning, Smart Grids, Cyber Security, and intelligent data-driven systems. He has served in government education services and held academic roles as a Visiting Lecturer at the Islamia University of Bahawalpur, International Islamic University Islamabad, and as a Lab Administrator at the University of Central Punjab, Lahore. He holds an MS in Computer Science from International Islamic University Islamabad, along with degrees in Computer Science, Education, and Commerce. His research contributions span smart grids, renewable energy forecasting, intrusion detection systems, explainable AI, IoT, blockchain, healthcare analytics, and sustainable smart cities. Dr. Khan has authored and co-authored numerous high-impact journal articles published in leading outlets such as Scientific Reports, Discover Sustainability, Future Internet, IET Software, Measurement: Energy, Digital Communications and Networks, and Peer-to-Peer Networking and Applications. His work emphasizes applied AI solutions for sustainability, security, and performance optimization across diverse domains including energy, education, healthcare, and communication systems. With strong programming expertise in Java and Python, he combines analytical rigor with practical implementation, contributing to innovative research, interdisciplinary collaboration, and the advancement of intelligent technologies for societal benefit.

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Xin Wang | Artificial Intelligence in Statistics | Best Paper Award

Ms. Xin Wang | Artificial Intelligence in Statistics | Best Paper Award

Chang’an University | China

Ms. Xin Wang is a dedicated researcher in transportation engineering with a strong interdisciplinary background in engineering and economics. She is currently pursuing her PhD in Engineering at the College of Transportation Engineering, Chang’an University, where she specializes in travel behaviour research, traffic network modelling, optimal network toll problems, and econometric modelling of transport costs and efficiency. She previously earned a Master of Economics degree in Regional Economics and a Bachelor of Economics degree in Finance from Xi’an International Studies University, establishing a solid academic foundation that connects transportation systems with economic analysis. Her professional background includes serving as an Account Manager at the Bank of China, working as a Teaching Assistant at Xi’an International Studies University, and completing an internship as a Lobby Manager at the Bank of China, roles that strengthened her analytical capability, communication skills, and leadership experience. Throughout her academic journey, she has received multiple honors, including several College First-class and Second-class academic scholarships in recognition of her outstanding performance. Xin Wang has also participated in numerous key research projects and academic meetings, such as the World Transportation Convention, the Intelligent Operation and Management Evaluation of the Hong Kong-Zhuhai-Macao Bridge, the Higher Education Discipline Innovation Project 111 on sustainable transportation development in Western China, a research project on a MaaS-based public transportation integrated travel platform in Xi’an, and a study on financing and lending challenges faced by small and medium-sized enterprises. Her scholarly contributions include publications in respected journals such as IEEE Transactions on Intelligent Transportation Systems, Journal of Transport Geography, and Special Zone Economy. She is proficient in SPSS, ArcGIS, Endnote, Origin, EViews, Python, Photoshop, and Microsoft Office applications, supporting her research excellence. Fluent in English and Chinese with basic knowledge of French, Xin Wang is supervised by Professor Wei Zhou and is affiliated with the College of Transportation Engineering at Chang’an University in Xi’an, China.

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Jian Xia | Artificial Intelligence in Statistics | Research Excellence Award

Dr. Jian Xia | Artificial Intelligence in Statistics | Research Excellence Award

Hubei University of Automotive Industry | China

Dr. Jian Xia is a dedicated materials scientist specializing in next-generation electronic and photonic devices, with a strong academic foundation and a growing record of impactful research. He obtained his Ph.D. degree from the School of Materials Science and Engineering at Huazhong University of Science and Technology, where he developed expertise in resistive switching devices, phase-change materials, and advanced optical memory technologies. After completing his doctoral studies, he joined the Hubei University of Automotive Technology as a lecturer, contributing actively to both teaching and research in the field of electronic materials and integrated circuit design. Dr. Xia’s research interests encompass memristors, phase-change memory, and photonic neuromorphic devices, all of which hold promising applications in high-performance computing, data storage, and artificial intelligence hardware. He has undertaken notable research projects, including the Open Fund of the Hubei Key Laboratory of Energy Storage and Power Battery and the Doctoral Scientific Research Foundation of Hubei University of Automotive Technology. With a citation index of 361 and a research portfolio of 20 SCI-indexed publications, Dr. Xia has contributed articles to leading international journals such as Nature Communications, Laser & Photonics Reviews, ACS Photonics, Applied Physics Letters, IEEE Electron Device Letters, and Science China Materials. His innovative contributions are further demonstrated by nine patents that are either published or under review, highlighting his commitment to advancing practical and technologically significant developments in electronic device engineering. Although he has yet to hold editorial appointments or professional memberships, his scholarly influence continues to grow through strong research visibility and future collaboration potential. Dr. Xia maintains an active academic presence on platforms such as ResearchGate and continues to advance pioneering research aimed at developing energy-efficient, high-density, and neuromorphic computing devices to meet the evolving demands of modern information technology.

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Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

Dr. Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

Jinling Institute of Technology | China

Dr. Yuying Chen is a dedicated materials scientist in the Department of Materials Engineering at the School of Materials Engineering, Jinling Institute of Technology, Nanjing, China, where she contributes extensively to research, teaching, and the advancement of materials innovation. She earned her Ph.D. in Materials Science from the Harbin Institute of Technology and enriched her international academic profile through a visiting Ph.D. appointment at the Department of Mining and Materials Engineering at McGill University in Montreal, Canada. Her academic background also includes a Master’s degree in Materials Science from the Harbin Institute of Technology and a Bachelor’s degree in Metal Materials Engineering from Shenyang University of Technology. Dr. Chen’s research expertise encompasses first-principles calculations, hydrogen storage materials, interface engineering, alloying effects, metal hydrides, and computational modeling of welding processes. She has authored 8 documents that investigate hydrogen adsorption and desorption mechanisms, Mg/Ni and Mg/Ti interface stability, alkali- and alkaline-earth-metal-doped hydrides, Zn-induced embrittlement behavior in steels, and advanced modeling techniques for underwater wet welding and duplex stainless-steel welding under acoustic and vibrational fields. Her scholarly contributions have accumulated 90 citations and reflect an impactful research profile with an h-index of 5, demonstrating the academic significance and visibility of her work within the materials science community. Over the course of her academic journey, Dr. Chen has received numerous accolades, including Merit Student awards, multiple University Fellowships, Outstanding Student Leader recognition, and acknowledgment as an Excellent League Member at Harbin Institute of Technology. She has presented her research findings at major scientific gatherings, including the International Conference on Computational Design and Simulation of Materials and the Chinese Materials Conference. With a strong record in computational materials science and interface behavior, Dr. Chen continues to advance innovative methodologies and scientific understanding toward the design, optimization, and reliability of next-generation materials systems.

Profiles: Scopus Orcid

Featured Publications

Chen, Y., Dai, J., & Song, Y. Catalytic mechanisms of TiH2 thin layer on dehydrogenation behavior of fluorite-type MgH2: A first principles study.

Chen, Y. Y., Dai, J. H., Xie, R. W., & Song, Y. A first-principles study on interaction of Mg/Ni interface and its hydrogen absorption characteristics.

Chen, Y. Y., Dai, J. H., Xie, R. W., Song, Y., & Bououdina, M. First principles study of dehydrogenation properties of alkali and alkali-earth metal doped Mg₇TiH₁₆.

Chen, Y. Y., Dai, J. H., & Song, Y. Stability and hydrogen adsorption properties of Mg/Mg₂Ni interface: A first principles study.

Dai, J. H., Chen, Y. Y., Xie, R. W., & Song, Y. Influence of alloying elements on the stability and dehydrogenation properties of Y(BH₄)₃ by first principles calculations.

Liangcan He | Design of Experiments (DOE) | Research Excellence Award

Dr. Liangcan He | Design of Experiments (DOE) | Research Excellence Award

Harbin Institute of Technology | China

Dr. Liangcan He is a distinguished Professor at the School of Medicine and Health, Harbin Institute of Technology, China, where he leads advanced interdisciplinary research programs focused on biomaterials, nanomedicine, multimodal imaging, and bio-nano interface engineering. His scientific work spans the design, synthesis, and biomedical translation of organic–inorganic nanoclusters, functional nanomaterials, therapeutic hydrogels, DNA nanotechnology, and stimuli-responsive nanosystems for diagnostic and therapeutic applications. Before joining his current institution, he completed a highly productive postdoctoral fellowship at the National Institutes of Health under the mentorship of Dr. Xiaoyuan (Shawn) Chen, where he developed cutting-edge nanoplatforms for NIR-II imaging, X-ray-mediated dynamic immunotherapy, responsive nanoparticle assemblies, and cascade-reaction-based photodynamic therapy. He also served as a Senior Research Associate and Research Associate at the University of Colorado Boulder’s Soft Materials Research Center, working closely with leading investigators across materials science, chemical engineering, optical engineering, and nanobiotechnology. During this period, he contributed significantly to DNA-programmable nanoparticle crystallization, MOF-based hybrid therapeutic systems, plasmon-enhanced upconversion luminescence, single-particle photophysics, engineered UCNP–Au nanostructure assemblies, and DNA-origami-directed enzymatic cascade architectures. Dr. He received his Ph.D. in Chemistry from Tsinghua University and conducted parallel research at the National Center for Nanoscience and Technology under the supervision of Prof. Zhiyong Tang and Prof. Yadong Li, where he worked extensively on plasmonic nanoparticle@MOF core–shell systems, catalytic nanomaterials, graphene-oxide-integrated hybrid structures, yolk–shell nanostructures for controlled release, supraparticle-based photocatalysis, and chiral coordination polymer assemblies. His current research continues to advance critical frontiers such as photothermal therapy, radiosensitization, microenvironment-responsive nanoreactors, injectable biomaterials for tissue repair, bioinspired catalytic systems, and multifunctional hydrogel-based therapeutic platforms. Dr. He has authored more than 76 peer-reviewed publications, achieving over 6,130 citations and an H-index of 33, underscoring his global impact in nanobiotechnology, functional materials, and translational biomedical engineering. He has also contributed as Guest Editor for the Special Issue “Research Progress of Bioimaging Materials” in the International Journal of Molecular Sciences. With a strong interdisciplinary foundation bridging chemistry, nanoscience, materials engineering, and biomedical translation, he is recognized as a leading innovator in the development of functional nanomaterials for precision imaging, sensing, biosystem modulation, and advanced therapeutic strategies.

Profiles: Scopus Orcid

Featured Publications

Zhang, R., Ma, Q., Zheng, N., Wang, R., Visentin, S., He, L., & Liu, S. (2025). Plant polyphenol-based injectable hydrogels: Advances and biomedical applications.

Huihui Hong | Health Statistics and Public Health Analysis | Research Excellence Award

Dr. Huihui Hong | Health Statistics and Public Health Analysis | Research Excellence Award

Chongqing University | China

Dr. Huihui Hong is an Assistant Research Fellow and Postdoctoral Fellow at the School of Medicine, Chongqing University, whose research spans environmental toxicology, occupational health, and molecular mechanisms underlying pollutant-induced diseases. With academic training that includes a doctoral degree in Occupational and Environmental Health from Zhejiang University and a bachelor’s degree in Preventive Medicine from Hangzhou Normal University, she has built a strong foundation in toxicological mechanisms, environmental exposure assessment, and cellular pathology. Dr. Hong’s research focuses on the biological consequences of exposure to heavy metals and environmental pollutants, such as cadmium, manganese, paraquat, and dissolved organic matter, and she explores how these toxicants interfere with metabolic regulation, β-cell function, lipid homeostasis, mitochondrial integrity, neuronal signaling, and inflammatory responses. Her work has revealed critical mechanistic insights, including pollutant-induced ferroptosis through axes such as Gpx4/Ager/p65, mitochondrial dysfunction-dependent β-cell impairment, lipidomic disturbances contributing to diabetes progression, and neuroinflammatory pathways leading to Parkinson-like phenotypes. She has also contributed to understanding the molecular impact of chlorination disinfection on dissolved organic matter in surface water sources and how such transformations influence cytotoxicity, advancing environmental health risk assessment. Dr. Hong has authored more than twenty scientific publications, many as first author or corresponding author, in prominent international journals covering environmental science, toxicology, and public health. According to current bibliometric sources, her scholarly output has accumulated approximately 466 citations, and based on citation distribution, her estimated h-index is in the mid-teens, demonstrating strong research visibility and influence at an early stage in her career. In addition to elucidating pollutant-driven metabolic and neurological injury, she frequently collaborates across multidisciplinary teams to integrate metabolomics, lipidomics, molecular biology, and in vivo toxicology, strengthening the translational relevance of her findings. Through this combination of methodological rigor, mechanistic depth, and sustained productivity, Dr. Hong continues to expand her impact within environmental toxicology, contributing valuable knowledge toward understanding how environmental exposures shape human health risks and guiding future intervention or prevention strategies within public health and environmental safety domains.

Profile: Scopus

Featured Publication

Hong, H., Liu, S., Yang, T., Lin, J., Luo, K., Xu, Y., Li, T., Xi, Y., Yang, L., Lu, Y.-Q., Yuan, W., & Zhou, Z. (2025). Manganese exposure induces parkinsonism-like symptoms by Serpina3n–TFEB–v/p-ATPase signaling mediated lysosomal dysfunction. Cell Biology and Toxicology, 41, Article 34.