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.

Citation Metrics (Google Scholar)

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Featured Publications


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.

Citation Metrics (Google Scholar)

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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

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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Featured Publications

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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Featured Publications


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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Featured Publications

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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Featured Publications