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The research profile centers on advancing computer vision through robust, trustworthy, and high-efficiency intelligent systems, addressing both task-specific challenges and foundational methodological limitations. Core contributions focus on object detection, anomaly segmentation, feature representation learning, and adaptive model design, with strong emphasis on real-world deployment reliability. In oriented small object detection, the proposed FDA-DETR framework overcomes weak feature representation and excessive computational cost in transformer-based detectors by integrating multi-scale frequency domain enhancement, density-aware dynamic query generation, and multi-granularity attention fusion, achieving significant gains in accuracy, robustness, and efficiency for dense and complex visual scenes. In zero-shot anomaly segmentation, the explainable recursive ERSF-AS framework resolves prompt dependency, limited anomaly feature learning, and interpretability challenges through a collaborative CLIP-SAM architecture combined with semantic, spatial, and frequency priors. Its recursive reasoning paradigm strengthens cross-domain generalization and reliability in both industrial and medical environments. Collectively, these innovations contribute not only to practical solutions for complex vision tasks but also to broader theoretical advancements in feature learning, attention modeling, adaptive mechanisms, and explainable artificial intelligence. This integrated research direction establishes a scalable foundation for future exploration across diverse computer vision and intelligent perception domains, promoting trustworthy, interpretable, and deployable AI systems for real-world applications.
Ju, C., Xie, Y., Wang, Z., Zhao, Y., Yan, W., Chai, R., Duan, J., Cao, Y., & Chang, Y. (2026). STGFormer: A pyramidal spatio-temporal graph transformer with cross-disciplinary feature fusion for semantic-rich trajectory prediction in heterogeneous autonomy traffic. Expert Systems with Applications.
Ju, C., Xie, Y., Duan, J., Chang, Y., & Wu, M. (2026). KeyGeoFusion: A multi-modal keypoint and geometry-aware framework for small and distant 3D object detection in sparse point clouds. Neurocomputing.
Mudassira Sarfraz is an accomplished economist and academic specializing in economics, finance, and quantitative social science, with strong expertise in labor economics, development economics, and gender-focused economic research. Her scholarly work centers on the interaction between social norms, labor markets, and economic participation, with a particular focus on women’s labor force participation in developing economies. Her doctoral research examined how cultural and social constraints shape economic behavior and employment outcomes, offering policy-relevant insights for inclusive growth, social development, and labor market reform. She has extensive teaching experience across Europe and South Asia, contributing to higher education in economics, finance, statistics, and business analytics. Her academic roles span core subjects including microeconomics, macroeconomics, development economics, corporate and international finance, risk management, social policy, and quantitative methods. She integrates data analysis, statistical reasoning, and applied economic modeling into her pedagogy, emphasizing practical decision-making and evidence-based policy analysis. Her research interests include gender economics, social norms theory, labor market dynamics, development policy, social protection systems, applied econometrics, and data-driven policy evaluation. Through interdisciplinary teaching and research, she contributes to advancing inclusive economic development, gender equity, and socially responsive economic policy design in both academic and applied policy environments.
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This scholar is an emerging economist with strong academic training in economic sciences, management, and financial engineering, with advanced specialization in applied economics, macroeconomic analysis, and monetary and banking systems. Academic formation spans economic theory, applied economic modeling, financial engineering, and development economics, supported by multidisciplinary postgraduate training in both national and international institutions. Research orientation is centered on global economic uncertainty, macroeconomic instability, financial systems, and their structural impacts on developing economies, with a particular emphasis on vulnerability, resilience mechanisms, and policy transmission channels. Doctoral research focuses on the effects of global uncertainty on developing countries, contributing to debates in international economics, development finance, and applied macroeconomics. Professional experience includes institutional training in public utilities, urban administration, and development-oriented fieldwork, alongside participation in public health monitoring and international development initiatives. Field research experience includes financial capacity assessments, market integration of green innovations, and socioeconomic evaluation projects in regional and cross-border contexts. Scholarly interests integrate economic modeling, sustainable development, green innovation economics, public policy analysis, and financial inclusion. This profile reflects a strong combination of theoretical grounding, applied research competence, interdisciplinary exposure, and commitment to evidence-based policy and development research in emerging and developing economies.
Profile: Orcid
Mignamissi, D., Ndong Ntah, M. H., Nyamou Mokompea, J. C., & Possi Tebeng, E. X. (2025). Corruption and misery: What lessons for developing countries? Journal of the Knowledge Economy.
The researcher is a clinician–scientist with advanced training in pediatrics, public health nutrition, Infectious disease epidemiology and vaccines and tropical medicine, Infectious disease epidemiology and vaccines combined with doctoral research in global health conducted through international academic collaboration. Her work integrates strong clinical insight with epidemiological expertise, particularly in the study of typhoid fever across diverse global and regional contexts. A central focus of her research is evidence synthesis to inform vaccine policy and implementation, with particular emphasis on the typhoid conjugate vaccine in India. She has contributed to research applying the World Health Organization evidence-to-recommendation framework to assess the relevance, feasibility, and perceived importance of evidence for vaccine introduction, demonstrating an ability to translate global guidance into locally applicable strategies. Drawing from extensive experience in clinical practice, public health, and global research, her work reflects a comprehensive understanding of vaccines from scientific, programmatic, societal, and end-user perspectives. Her research in infectious disease epidemiology supports data-driven decision making for immunization programs and strengthens links between policy, practice, and population health impact. Through applied epidemiology and implementation-focused research, she contributes to improving vaccine uptake, strengthening health systems, and advancing evidence-based interventions for infectious disease control in real-world settings.
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Soyoung Jung, Ph.D., is an accomplished associate professor in the School of Smart System at Dongyang University, Engineering South Korea, with a strong interdisciplinary background in civil and environmental engineering, transportation engineering, statistics, and quantitative planning. Her academic training spans premier institutions in the United States and Korea, shaping her expertise in data-driven transportation safety, emergency medical service systems, and smart safety infrastructure. Dr. Jung’s research focuses on integrating spatial analysis, data mining, simulation, and optimization techniques to improve traffic safety, pre-hospital emergency medical services, and regional equity in emergency response systems, particularly across rural and urban networks. She has led numerous nationally funded research projects as principal investigator, supported by the National Research Foundation of Korea and multiple government ministries, addressing topics such as emergency patient transport efficiency, ambulance–helicopter coordination, EMS resource optimization, weather-related traffic safety, and large-scale safety prediction models. Her scholarly output demonstrates sustained impact in transportation and safety engineering, reflected in a solid publication record with a strong h-index, substantial number of indexed documents, and a notable citation count, highlighting the relevance of her work within the global research community. Through research, teaching, and funded innovation, Dr. Jung continues to contribute to the advancement of smart safety systems and evidence-based transportation policy.
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Chuntian Xu is a professor in mechanical engineering whose academic work focuses on intelligent manufacturing, Photovoltaic solar energy conversion efficiency digital technologies, and advanced renewable energy systems. His research emphasizes the optimization of photovoltaic energy generation, particularly through dual axis tracking systems designed to enhance solar conversion efficiency under dynamic environmental conditions. By integrating intelligent algorithms and data driven optimization techniques, his work contributes to improving energy output stability, system adaptability, and overall performance of solar energy infrastructures. He has actively contributed to regional and national research initiatives in engineering innovation, manufacturing intelligence, and sustainable energy technologies, demonstrating strong interdisciplinary collaboration. His scholarly output includes extensive publications in high impact international journals, authoritative academic books, and a substantial portfolio of patented technological innovations. His research has practical relevance, extending beyond academia into industry oriented projects focused on improving photovoltaic system efficiency and real world energy conversion processes. In addition, he has participated in major foundational research programs supporting national scientific and technological development. Through continuous engagement with advanced manufacturing systems, optimization algorithms, and renewable energy engineering, his work supports the transition toward smarter, cleaner, and more efficient energy and production systems, while contributing to the broader advancement of intelligent engineering and sustainable technology research.
Xu, C., Zheng, H., Zong, X., Liu, H., Jia, X., Zhao, Q., & Wang, L. (2026). Improved solar backtracking algorithm based on particle swarm optimization for photovoltaic modules’ output power. Solar Energy, 114, Article 114320.
Shi, J., Wang, J., Zhang, K., Sun, X., & Xu, C. (2025). Analysis of flow characteristics and structural optimization of high-strength cooling equipment for hot-rolled strip steel. Processes, 13(12), Article 3765.
Zhang, M., Xu, C., Li, L., Wang, Z., & Zong, X. (2024). Optimization of PID controller for stepper motor speed control system based on improved sparrow search algorithm. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 238(10).
Zhang, M., Xu, C., Xu, D., Ma, G., Han, H., & Zong, X. (2023). Research on improved sparrow search algorithm for PID controller parameter optimization. Bulletin of the Polish Academy of Sciences: Technical Sciences, 71(5).
Xu, C., Li, J., Wang, P., & Xu, Z. (2020). A study of transmission error modeling and preload compensation for the cable-driven sheaves used in space docking locks. Journal of Mechanics, 36(6), 911–923.