Hao Wang | Clinical Trials and Statistical Designs | Research Excellence Award

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Dr. Hao Wang | Clinical Trials and Statistical Designs | Research Excellence Award

Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China | China

Hao Wang is an accomplished physician scientist in the field of imaging and nuclear medicine, with advanced training focused on molecular imaging and targeted radionuclide therapy. His academic background emphasizes the development and clinical translation of novel molecular probes for precise disease diagnosis and therapy monitoring. His research integrates imaging physics, radiopharmaceutical science, and clinical nuclear medicine to improve diagnostic accuracy and therapeutic outcomes, particularly in precision medicine. He has led and contributed to multiple competitively funded research initiatives at national, provincial, and institutional levels, reflecting sustained recognition of his scientific leadership. His projects span applied clinical research, basic and translational investigations, and medical education reform, demonstrating a multidisciplinary approach to innovation in healthcare. Through these studies, he has advanced methodologies for imaging-based disease characterization, optimized radionuclide-targeted treatment strategies, and supported the integration of novel probes into clinical practice. His work also contributes to capacity building in medical imaging through education-focused research initiatives. Collectively, his research efforts highlight a strong commitment to advancing nuclear medicine technologies, bridging laboratory discoveries with patient-centered applications, and promoting evidence-based clinical innovation within modern imaging sciences.

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James Armo | Medical Imaging workforce development | Research Excellence Award

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Mr. James Armo | Medical Imaging workforce development | Research Excellence Award

King’s College London | Ghana

A dedicated diagnostic radiography professional with a strong commitment to patient centered care, ethical practice, and academic research in medical imaging. Demonstrates broad clinical competence across multiple imaging modalities, including magnetic resonance imaging, computed tomography, fluoroscopy, and digital radiography, with experience in trauma, oncology, neuroradiology, and theatre imaging. Possesses strong capability in preliminary image evaluation, patient safety, radiation protection, and quality assurance, ensuring high standards of diagnostic accuracy and clinical governance. Actively engaged in research focused on advancing neuroradiology, magnetic resonance physics, artificial intelligence applications in medical imaging, workforce development, and diversity, equity, and inclusion in healthcare. Research experience includes protocol adherence, participant management, data handling, and multidisciplinary collaboration within clinical and academic environments. Skilled in scientific writing, manuscript preparation, peer review processes, and research ethics compliance. Technical exposure includes neuroimaging analysis tools, statistical software, health data interpretation, and basic computational modeling. Demonstrates adaptability, analytical thinking, and effective communication while working independently or as part of collaborative teams. Maintains a strong interest in lifelong learning, professional development, and innovation aimed at improving imaging techniques, diagnostic workflows, and patient outcomes within modern healthcare systems.

Profile: Scopus 

Featured Publications 

Ollawa, C. U., Armo, J., & Iweka, E. (2026). Ergonomic risk and musculoskeletal disorders among imaging professionals practising in Ghana. Journal of Medical Imaging and Radiation Sciences, 57(1), Article 102164.

 

Wu Ding | Food Science | Research Excellence Award

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Prof. Dr. Wu Ding | Food Science | Research Excellence Award

College of Food Science and Engineering, Northwest A&F University | China

This scholar is a senior academic in food science and agricultural product processing with extensive experience in research, teaching, and doctoral supervision. The research focus centers on livestock and poultry food quality evaluation, Food Science deep processing technologies, and comprehensive safety control throughout the food supply chain. Core research themes include the quality assessment of animal-derived raw materials and products, development of high-value processing techniques, and efficient utilization of livestock by-products to enhance sustainability and economic value. Significant contributions have been made in the field of food safety, particularly in the rapid detection of foodborne pathogenic microorganisms using molecular biology approaches. Advanced applications of functional and reporter genes are employed to enable real-time monitoring, online detection, and dynamic regulation of food processing safety. The work integrates microbiology, biotechnology, and processing engineering to address challenges related to contamination control, product quality, and safety assurance. In addition to academic research, substantial engagement in social and industrial services supports livestock and poultry processing industries through technical consulting, project planning, and system design. These efforts contribute to improving food safety standards, optimizing processing efficiency, and promoting innovation in agricultural and food product processing systems.

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Yifei Yin | Speckle noise suppression in SAR images | Research Excellence Award

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Dr. Yifei Yin | Speckle noise suppression in SAR images | Research Excellence Award

Beijing Institute of Technology | China 

The research work focuses on the intelligent interpretation of synthetic aperture radar imagery, with particular emphasis on end-to-end understanding of satellite-based SAR data. Core research activities include SAR image pre-processing, Speckle noise suppression in SAR images speckle noise suppression, and robust target detection and recognition under complex imaging conditions. A key scientific contribution lies in addressing the limitations of conventional supervised learning approaches, which typically rely on clean reference images that are rarely available in real-world SAR scenarios. To overcome this challenge, a self-supervised despeckling framework was proposed, enabling effective network training using only intensity SAR images without the need for external ground-truth data. This strategy significantly enhances the practicality and scalability of deep learning methods for operational SAR systems. The research further contributes to improving feature preservation and structural consistency in despeckled images, which directly benefits downstream tasks such as object recognition and scene understanding. In addition, the work actively supports national-level research and development initiatives, fostering collaboration across multidisciplinary teams in remote sensing, signal processing, and artificial intelligence. Overall, these contributions advance the reliability, adaptability, and real-world applicability of intelligent SAR image interpretation, strengthening its role in satellite observation, surveillance, and Earth monitoring applications.

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Self-supervised despeckling based solely on SAR intensity images: A general strategy


– ISPRS Journal of Photogrammetry and Remote Sensing, 2026

Xiaoqing Wan | Pattern Recognition | Research Excellence Award

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Dr. Xiaoqing Wan | Pattern Recognition | Research Excellence Award

Hengyang Normal University | China

Xiaoqing Wan is a lecturer in computer science and an active member of the global research community in Pattern Recognition artificial intelligence and intelligent information processing. His academic work focuses on pattern recognition and image processing, with particular emphasis on the development of advanced algorithms for remote sensing image analysis. His research integrates deep learning and machine learning techniques to improve classification accuracy, feature extraction, and robustness in complex and large-scale image datasets. In addition, he is deeply involved in the design of computer-aided diagnosis systems, where artificial intelligence is applied to support medical image interpretation and decision-making, aiming to enhance efficiency and reliability in clinical analysis. His scholarly background in signal and information processing and communication systems provides a strong theoretical foundation for interdisciplinary research that bridges engineering, data science, and applied intelligence. As an educator, he contributes to the training of future engineers and researchers through teaching core subjects in artificial intelligence, programming, and software engineering, with a strong focus on practical problem-solving and algorithmic thinking. His ongoing research continues to explore innovative methodologies that combine intelligent computation with real-world applications, contributing to the advancement of intelligent systems in remote sensing, healthcare, and computer vision.

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Shiping Song | Materials Science | Research Excellence Award

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Prof. Shiping Song | Materials Science | Research Excellence Award

Henan University of Technology | China

Research in polymer-based functional powder preparation and additive manufacturing has led to the innovative development of a continuous  Materials Science  and scalable technology for producing polymer-based spherical powders by integrating solid-phase shear milling with plasma processing. This advanced approach enables precise control over powder morphology, surface activity, and flow behavior, making it highly suitable for high-performance three-dimensional printing applications. Building on this foundation, extensive efforts have been devoted to the design and fabrication of high-efficiency piezoelectric composite materials that exhibit enhanced electromechanical conversion performance, mechanical robustness, and long-term stability. Through systematic theoretical analysis combined with computational simulation, the underlying structural and interfacial mechanisms governing piezoelectric output have been clarified, particularly the influence of device architecture, layer configuration, and stress distribution. These insights have supported the successful development of multi-scale and multi-layered three-dimensional piezoelectric devices with unique stress-responsive behaviors and stable output under complex loading conditions. The research integrates materials synthesis, processing technology, device engineering, and performance optimization, contributing to advances in functional polymer composites, intelligent sensing systems, and energy harvesting technologies. This work demonstrates strong interdisciplinary innovation and provides a solid foundation for the scalable application of polymer-based piezoelectric devices in advanced manufacturing and smart materials systems.

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Dechao Chen | Embodied Intelligence | Young Scientist Award

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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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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

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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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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

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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

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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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