Cheng Ju | 3D object detection | Research Excellence Award

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Dr. Cheng Ju | 3D object detection | Research Excellence Award

Xi'an Innovation College of Yan'an University | China 

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.

Profile: Orcid 

Featured Publications 

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 | Labour Economics | Research Excellence Award

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Dr. Mudassira Sarfraz | Labour Economics | Research Excellence Award

University of Warsaw, Poland | Poland 

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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JOSE CLAUDE NYAMOU MOKOMPEA | Econometrics and Statistical Economics | Research Excellence Award

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Mr. JOSE CLAUDE NYAMOU MOKOMPEA | Econometrics and Statistical Economics | Research Excellence Award

Université de Douala (FSEGA) | Cameroon

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 

Featured Publications 

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.

Vijayalaxmi Mogasale | Infectious disease epidemiology and vaccines | Research Excellence Award

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Dr. Vijayalaxmi Mogasale | Infectious disease epidemiology and vaccines | Research Excellence Award

London School of Hygiene and Tropical Medicine & Nagasaki University | India 

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 | Engineering | Research Excellence Award

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Dr. Soyoung Jung | Engineering | Research Excellence Award

Dongyang University |  South Korea

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 | Photovoltaic solar energy conversion efficiency | Excellence in Research Award

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Prof Dr. Chuntian Xu | Photovoltaic solar energy conversion efficiency | Excellence in Research Award

University of Science and Technology Liaoning | China 

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.

Profile: ORCID

Featured Publications 

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.

Pratibha Pareek | Process capability index | Best Researcher Award

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Dr. Pratibha Pareek | Process capability index | Best Researcher Award

Chandigarh group of colleges, Chandigarh college of technology, Mohali | India

Dr. Pratibha Pareek is an accomplished statistician and academician with specialized expertise in statistical quality control, statistical inference, Process capability index and reliability analysis. She holds a doctoral degree in statistical quality control from the Central University of Rajasthan, Ajmer, and has completed her postgraduate studies in statistics from the Central University of Punjab, Bathinda, with a strong academic foundation developed during her undergraduate education in mathematics, statistics, and computer applications at Panjab University, Chandigarh. Dr. Pareek is currently serving as an Assistant Professor at CGC Landran, Mohali, where she is actively engaged in teaching, research, and mentoring students in core and applied statistics. Her scholarly profile reflects a growing research impact, supported by peer-reviewed research documents, an emerging h-index, and increasing citations within the academic community. She actively participates in national and international academic events, including workshops and seminars focused on regression analysis, statistical optimization techniques, econometrics, and data-driven national development. Through her engagement with interdisciplinary statistical applications and software-based methodologies, Dr. Pareek continues to contribute meaningfully to research, education, and capacity building in statistics and data science, strengthening her role as a promising researcher and dedicated educator.

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Applications of reliability test plan for logistic Rayleigh distributed quality characteristic


M. Saha, H. Tripathi, A. Devi, P. Pareek – Annals of Data Science, 11(5), 1687–1703, 2024

Applications of process capability indices for supplier selection problems using generalized confidence interval


M. Saha, A. Devi, P. Pareek – Communications in Statistics: Case Studies, Data Analysis and Applications, 9, 2023

Time truncated attribute control chart for the generalized Rayleigh distributed quality characteristics and beyond


M. Saha, P. Pareek, H. Tripathi, A. Devi – International Journal of Quality & Reliability Management, 41(5), 1400–1416, 2024

Shaopeng Che | Public Opinion Simulation and Algorithmic Fidelity in Social Contexts | Research Excellence Award

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Dr. Shaopeng Che | Public Opinion Simulation and Algorithmic Fidelity in Social Contexts | Research Excellence Award

Chang'an University | China

This scholarly profile represents an advanced researcher working at the intersection of computational communication and large language model studies, with a strong foundation in human–artificial intelligence interaction. The research agenda centers on understanding how algorithmic systems simulate, shape, Public Opinion Simulation and Algorithmic Fidelity in Social Contexts and respond to public communication across complex sociopolitical environments, particularly within non-Western and regulated information contexts. Core contributions include large-scale empirical investigations into language model–generated social data, offering critical insights into the reliability, bias, and cultural embeddedness of algorithmic outputs. A key theoretical advancement lies in redefining algorithmic fidelity as a context-dependent concept, moving beyond surface-level accuracy toward a multidimensional framework that evaluates response behavior, distributional alignment, and subgroup representation. Methodologically, the work integrates survey-based validation, robustness testing across multiple model architectures, and comparative analysis to uncover stable structural patterns as well as systemic limitations in simulated public opinion. These findings provide practical guidance for the responsible application of generative models in communication research, policy analysis, and media studies. With extensive publication experience in high-impact academic journals and active engagement in international scholarly communities, this body of work contributes to advancing ethical, culturally aware, and empirically grounded approaches to artificial intelligence–mediated communication research.

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Featured Publications

Simulating the People’s Voice: Leveraging Algorithmic Fidelity to Assess ChatGPT’s Performance in Modeling Public Opinion on Chinese Government Policies

S. P. Che, M. Zhu, S. Zhang, H. S. Jung, H. Lee, Z. Wang, L. Miller –
Information Processing & Management, 63(3), 104567, 2026
A Clinical Prediction Model for Short-Term Prognosis in Patients with Non–Acute Myocardial Infarction–Related Cardiogenic Shock

X. Wang, X. Fan, T. Wu, S. P. Che, X. Shi, P. Liu, J. Liu, Y. Luo, Y. Wu, B. Lan –
Shock, 2025
Communicating Climate Change to Young Adults in China: Examining Predictors of User Engagement on Chinese Social Media

S. P. Che, K. Kuang, L. Liu, S. Liu –
International Journal of Climate Change Strategies and Management, 2025
Exploring China’s Climate Innovation: Insights from Outlier Patents Using BERT-LOF and LDA

S. P. Che, L. Miller –
19th International Conference on Ubiquitous Information Management and Communication, 2025

Lianhui Liang | Deep learning or Image processing | Excellence in Research Award

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Dr. Lianhui Liang | Deep learning or Image processing | Excellence in Research Award

Guangxi University | China 

Lianhui Liang is an academic researcher in electrical and information engineering with a strong focus on intelligent remote sensing analysis and advanced signal interpretation. His work centers on hyperspectral, multispectral, and LiDAR data understanding, Deep learning or Image processing  with applications spanning environmental monitoring, land surface analysis, and complex scene interpretation. His research integrates signal processing, pattern recognition, and deep learning techniques to enhance feature extraction, classification accuracy, and information fusion in high-dimensional remote sensing imagery. He has contributed significantly to spectral intelligence, including algorithm development for image inversion, target detection, and data fusion across heterogeneous sensors. His interdisciplinary approach bridges theoretical modeling with practical engineering applications, particularly in optical and microwave remote sensing. Through sustained collaboration with international research groups, his work reflects a global perspective on emerging challenges in remote sensing and artificial intelligence. He has actively engaged in advanced training programs related to remote sensing data processing, deep learning frameworks, and intelligent interpretation systems, strengthening the transfer of cutting-edge methods into applied research. His scholarly contributions include peer-reviewed publications, intellectual property development, and participation in research and development projects supported by public and industrial partners. Overall, his research advances intelligent remote sensing systems and contributes to the broader fields of geospatial analytics and artificial intelligence-driven Earth observation.

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Featured Publications


Prototype Similarity-Constraint Enhancement Network: A Few-Shot Class-Incremental Learning for Hyperspectral Image Classification

– Expert Systems with Applications, 2025
L. Yang, Y. Tan, L. Liang, H. Xu, T. Wu, Z. Huang, X. Li, Y. Tang

Cross-Stage Attention Edge Enhancement and Fourier-Wavelet Transformer Integrated Network for Hyperspectral Image Classification

– IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025
L. Liang, S. Yuan, Y. Zeng, Y. Lin, Y. Zhang, P. Xie, T. X. Wu

LHCF: Liquid Neural Network with Hierarchical Collaborative Fusion for Hyperspectral Image Classification

– Authorea Preprints, 2025
L. Liang, J. Zhang, S. Zhang, B. Tu, L. Yang, J. Li, A. Plaza

LKMA: Learnable Kernel and Mamba with Spatial-Spectral Attention Fusion for Hyperspectral Image Classification

– IEEE Transactions on Geoscience and Remote Sensing, 2025
L. Liang, J. Zhang, P. Duan, X. Kang, T. X. Wu, J. Li, A. Plaza

Cross-Domain Few-Shot Hyperspectral Image Classification with Local Entropy Adaptation Metric

– Authorea Preprints, 2025
Y. Zhang, Z. Liu, P. Duan, L. Lian