Genwei Ma | Technology | Best Research Article Award

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Mr. Genwei Ma | Technology | Best Research Article Award

Mr. Genwei Ma | Technology | Associate Research Fellow​ at Capital Normal University | China

Dr. Genwei Ma is a highly regarded researcher in the domain of medical imaging, particularly in computed tomography (CT) image reconstruction. Known for his expertise in advanced imaging algorithms, Dr. Ma integrates deep learning with physics-based modeling to address critical challenges in spectral CT and limited-angle tomography. His recent contributions reflect a dynamic blend of theoretical innovation and real-world clinical application. With numerous publications in leading journals, he continues to advance the frontiers of computational imaging and healthcare diagnostics.

Academic Profile

ORCID

Education

Dr. Ma holds a Ph.D. in the field of medical imaging and computational modeling, awarded by a reputed institution in the early 2020s. His doctoral research focused on the development of high-resolution image reconstruction techniques using machine learning and optimization strategies. This academic foundation has enabled him to delve deeply into AI-powered medical imaging technologies, paving the way for his postdoctoral and independent research achievements.

Experience

Following his Ph.D., Dr. Ma gained extensive experience through academic research and collaborative projects across medical imaging labs. He has worked in interdisciplinary teams alongside physicists, data scientists, and clinical experts to develop practical solutions for image enhancement and diagnostic efficiency. His contributions span several domains, including image reconstruction algorithms, signal processing, spectral analysis, and neural networks. Dr. Ma’s hands-on engagement in research and technical development equips him to bridge theoretical research with real-world diagnostic applications.

Research Interest

Dr. Ma’s research interests lie in advancing computed tomography through intelligent reconstruction models that reduce data dependency while enhancing image quality. He specializes in: Limited-angle CT reconstruction, Spectral CT imaging, Deep learning and dual-domain algorithms, Physics-guided neural networks, Total variation and projection-based techniques His work focuses on improving diagnostic accuracy while minimizing radiation exposure, making a direct impact on patient safety and imaging efficiency. Dr. Ma’s innovations aim to overcome limitations in conventional CT systems through data-efficient and AI-enhanced solutions.

Awards

While formal award recognitions have not yet been publicly listed, Dr. Ma’s research excellence is evident in his selection for high-quality journal publications and growing academic citations. His work is frequently referenced in computational imaging literature and used in ongoing international studies, signaling a strong reputation among peers. His trajectory suggests upcoming accolades as he continues to expand his research influence and visibility in global imaging communities.

Publications

“Dual-Domain Joint Learning Reconstruction Method (JLRM) Combined with Physical Process for Spectral Computed Tomography (SCT)”
Journal: Symmetry, Year: 2025
Cited by: 6 articles

“Multi-domain Information Fusion Diffusion Model (MDIF-DM) for Limited-Angle Computed Tomography”
Journal: Journal of X-Ray Science and Technology, Year: 2025
Cited by: 5 articles

“Fourier-Enhanced High-Order Total Variation (FeHOT) Iterative Network for Interior Tomography”
Journal: Physics in Medicine & Biology, Year: 2025
Cited by: 8 articles

“Projection-to-Image Transform Frame: A Lightweight Block Reconstruction Network for Computed Tomography”
Journal: Physics in Medicine & Biology, Year: 2022
Cited by: 10 articles

“A Neural Network with Encoded Visible Edge Prior for Limited-Angle Computed Tomography Reconstruction”
Journal: Medical Physics, Year: 2021
Cited by: 12 articles

Conclusion

In summary, Dr. Genwei Ma is an outstanding researcher whose work significantly improves computational tomography and medical diagnostics. Through a powerful combination of AI, physical modeling, and collaborative research, he has contributed novel methodologies that are already influencing imaging science. His strong academic record, multi-authored international collaborations, and growing publication impact position him as a rising leader in the medical imaging community. With a clear vision for innovation and public health advancement, Dr. Ma is an ideal nominee for the Best Researcher Award.

Xiaole Han | Blockchain | Best Researcher Award

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Mr. Xiaole Han | Blockchain | Best Researcher Award

Mr. Xiaole Han | Blockchain – PHD student at SEGi University, Malaysia

Han Xiaole is a dedicated researcher and innovator in the field of blockchain technology and its application in supply chain management. With a solid background in engineering and a research-driven mindset, Han has been instrumental in advancing the practical implementation of blockchain systems to improve operational efficiencies across industries. His work bridges technical engineering with management science, reflecting a unique interdisciplinary strength that sets him apart. Han combines his hands-on experience in blockchain R&D with academic rigor, aiming to promote sustainable technological adoption and policy development in emerging economies.

Profile verified 

ORCID | Google Scholar

Education

Han is currently pursuing a Ph.D. in Management Science at SEGi University, where his academic journey is focused on the study of blockchain adoption within supply chain management, particularly for small and medium-sized enterprises (SMEs). His thesis, titled “Blockchain Adoption in Supply Chain Management: Multi-Level Determinants and Policy Implications”, explores the intersection of technology, organizational behavior, and policy. Under the supervision of Dr. Gooi Leong Mow, Han has employed sophisticated methodologies such as Partial Least Squares Structural Equation Modeling (PLS-SEM) and multi-level modeling to investigate the barriers and enablers of blockchain adoption from individual, organizational, and societal perspectives.

Experience

From 2018 to 2020, Han worked as a Blockchain R&D Engineer at the HPB Foundation, where he led several core initiatives. He played a key role in the development of High-Performance Blockchain (HPB) hardware, specifically the BOE acceleration engine, which significantly improved transaction throughput, achieving over 5,000 transactions per second with latency below 100 milliseconds. Additionally, he designed a dual-layer consensus mechanism that balanced decentralization with performance, and created smart contract protocols to enhance data traceability and integrity within supply chains. His technical contributions not only improved blockchain performance but also aligned with real-world industry needs, making his work highly impactful in the enterprise context.

Research Interest

Han’s research interests lie at the intersection of blockchain technology, supply chain management, and innovation policy. He is particularly interested in how blockchain can be sustainably adopted across different levels of an organization and society, including technological readiness, organizational capability, user perception, and regulatory frameworks. His research investigates how SMEs can overcome barriers to adopting emerging technologies and leverage blockchain to gain competitive advantage through enhanced transparency, traceability, and trust. Han’s academic work contributes to filling critical gaps in technology adoption models by integrating multi-level analytical frameworks with blockchain-specific trust and governance mechanisms.

Award

Han Xiaole has been nominated for the [Award Name Placeholder] based on his contributions to blockchain research and innovation. His work exemplifies a blend of theoretical depth and practical application, making a significant contribution to the advancement of supply chain resilience through technology. This nomination reflects his exceptional research impact, technological creativity, and potential for future leadership in the field of management science and digital innovation.

Publications 📚

“Multi-Level Determinants of Sustainable Blockchain Adoption in SCM” – Sustainability, 2025. 🌱🔗DOI: 10.3390/su17062621 | Cited by 23 articles (Google Scholar, 2025) | Integrates individual, organizational, and societal frameworks with blockchain trust models to explain adoption patterns.

Conclusion

Han Xiaole’s career reflects a powerful synergy between advanced research and real-world application. His contributions to blockchain technology, particularly in the context of supply chain management, demonstrate both visionary thinking and technical acumen. Through rigorous academic inquiry and practical innovation, he has helped shape the conversation around sustainable and scalable blockchain adoption. With a strong publication record, technical expertise, and a deep understanding of industry dynamics, Han continues to make a lasting impact in the fields of digital transformation and management science. His work not only advances academic theory but also provides actionable insights for businesses and policymakers navigating the digital economy.