Peng Su | Machine Learning on Databases | Research Excellence Award

Published on by Database Scientist

Prof. Peng Su | Machine Learning on Databases | Research Excellence Award

Hebei University of Technology | China

Prof. Peng Su’s research centers on the advanced design, electromagnetic modeling, and performance optimization of permanent-magnet (PM) electrical machines, with a primary emphasis on flux-switching machine topologies for electric and hybrid-electric vehicle applications. With a citation record of 563 citations in total (431 since 2020), an h-index of 12 (11 since 2020), and an i10-index of 16 (12 since 2020), his contributions are well recognized within the electrical machine research community. His work significantly advances understanding of rotor-PM and stator-PM flux-switching architectures through rigorous analyses of operating principles, air-gap field modulation, hybrid-excitation mechanisms, and multi-phase configurations, enabling improved torque density, efficiency, and thermal robustness. Prof. Peng Su has delivered influential findings on PM eddy-current losses, stator-slot and rotor-pole selection, cogging-torque reduction strategies, and magnetization effects, offering practical design paths for minimizing parasitic losses and enhancing reliability under high-speed and vector-controlled drive conditions. His portfolio extends across diverse machine types—including axial-modular machines, multitooth structures, tubular PM generators, and toroidally wound direct-drive motors—demonstrating comprehensive expertise in advanced electromagnetic machine architectures. He also contributes to loss modeling in soft magnetic composites, fault behavior characterization, and performance evaluation methodologies tailored to transportation electrification requirements. Through systematic comparative studies, innovative structural proposals, and refined analytical models, Prof. Peng Su continues to shape the development of next-generation PM machines and high-efficiency energy-conversion technologies, reinforcing his position as a leading contributor to modern electrical machine engineering.

Profiles: Google Scholar | Orcid

Featured Publications

  • Hua, W., Su, P., Tong, M., & Meng, J. (2016). Investigation of a five-phase E-core hybrid-excitation flux-switching machine for EV and HEV applications. IEEE Transactions on Industry Applications, 53(1), 124–133.

  • Su, P., Hua, W., Wu, Z., Han, P., & Cheng, M. (2017). Analysis of the operation principle for rotor-permanent-magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 65(2), 1062–1073.

  • Su, P., Hua, W., Wu, Z., Chen, Z., Zhang, G., & Cheng, M. (2018). Comprehensive comparison of rotor permanent magnet and stator permanent magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 66(8), 5862–5871.

  • Su, P., Hua, W., Hu, M., Chen, Z., Cheng, M., & Wang, W. (2019). Analysis of PM eddy current loss in rotor-PM and stator-PM flux-switching machines by air-gap field modulation theory. IEEE Transactions on Industrial Electronics, 67(3), 1824–1835.

  • Su, P., Hua, W., Hu, M., Wu, Z., Si, J., Chen, Z., & Cheng, M. (2019). Analysis of stator slots and rotor pole pairs combinations of rotor-permanent magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 67(2), 906–918.*

Sukumar Letchmunan | Computer Science | Best Researcher Award

Published on by Database Scientist

Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

University Sains Malaysia | Malaysia

Dr. Sukumar Letchmunan is an accomplished researcher and academic at Universiti Sains Malaysia, specializing in Software Engineering, Machine Learning, Software Metrics, and Service-Oriented Software Engineering. His research primarily focuses on developing intelligent computational models and data-driven frameworks that address complex real-world problems in domains such as crime prediction, healthcare analytics, uncertainty modeling, and sustainable digital systems. Over the years, Dr. Sukumar Letchmunan has made significant contributions to artificial intelligence applications through the integration of fuzzy logic, evidential theory, and deep learning, as reflected in his high-impact publications in leading journals including IEEE Access, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based Systems, and Applied Soft Computing. His collaborations have produced novel methods for multi-view evidential clustering, belief function-based uncertainty representation, and medical decision-making systems, demonstrating his expertise in handling imprecision in data-centric environments. Notably, his works on AI-driven crime forecasting, machine learning-based diabetes prediction, and deep neural network analysis for human activity recognition have been widely cited, influencing interdisciplinary research across computer science and healthcare informatics. With a strong record of international collaboration, particularly with researchers from China, Turkey, and Saudi Arabia, Dr. Sukumar Letchmunan continues to advance the frontier of trustworthy and interpretable AI systems. His recent works in fuzzy similarity measures, transformer-based spatiotemporal modeling, and green decision-making frameworks exemplify his commitment to enhancing computational intelligence for sustainable and socially relevant applications. With 1,298 citations, an h-index of 19, and an i10-index of 22, Dr. Sukumar Letchmunan research impact underscores his influential role in advancing next-generation AI methodologies and their meaningful application to global scientific and societal challenges.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

1. Butt, U. M., Letchmunan, S., Ali, M., Hassan, F. H., Baqir, A., & Sherazi, H. H. R. (2021). Machine learning based diabetes classification and prediction for healthcare applications. Journal of Healthcare Engineering, 2021(1), 9930985.

2. Khaw, T. Y., Teoh, A. P., Abdul Khalid, S. N., & Letchmunan, S. (2022). The impact of digital leadership on sustainable performance: A systematic literature review. Journal of Management Development, 41(9–10), 514–534.

3. Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553–166574.

4. Liu, Z., & Letchmunan, S. (2024). Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Transactions on Knowledge Discovery from Data, 18(3), 1–20.

5. Liu, Z., Huang, H., Letchmunan, S., & Deveci, M. (2024). Adaptive weighted multi-view evidential clustering with feature preference. Knowledge-Based Systems, 294, 111770.

Ivica Kopriva | Pattern Recognition | Best Researcher Award

Published on by Database Scientist

Dr. Ivica Kopriva | Pattern Recognition | Best Researcher Award

Rudjer Boskovich Institute | Croatia

Dr. Ivica Kopriva is a distinguished Senior Scientist at the Ruđer Bošković Institute, Croatia, internationally recognized for his pioneering contributions to signal processing, machine learning, and blind source separation. Dr. Ivica Kopriva’s interdisciplinary research integrates statistical learning, low-rank sparse modeling, and nonlinear decomposition methods to address complex challenges in biomedical imaging, remote sensing, and hyperspectral data analysis. His highly influential publications, including “Multi-view Low-Rank Sparse Subspace Clustering” (Pattern Recognition, 2018) and “l₀-Motivated Low-Rank Sparse Subspace Clustering” (IEEE Transactions on Cybernetics, 2018), have significantly advanced subspace clustering and unsupervised learning, with the former ranked among the top 1% of highly cited papers in Engineering. Dr. Ivica Kopriva’s research further encompasses image co-segmentation, tumor detection, and signal demixing, contributing to innovations in medical imaging and AI-based diagnostics. With over 2,546 citations, an h-index of 21, and an i10-index of 50, Dr. Ivica Kopriva’s scholarly impact extends across multiple disciplines of computational and biomedical sciences. His exceptional achievements have been recognized through numerous awards, including the State Award of the Republic of Croatia for Scientific Achievement (2009), multiple Director’s Awards for Scientific Excellence (2010–2021), and MICCAI Outstanding Reviewer Awards. As a Senior Member of IEEE and OSA and an affiliated faculty member at Virginia Commonwealth University, Dr. Ivica Kopriva continues to contribute extensively to international scientific communities, shaping the global research landscape in computational imaging, data-driven signal analysis, and intelligent biomedical systems.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Brbić, M., & Kopriva, I. (2018). Multi-view low-rank sparse subspace clustering. Pattern Recognition, 73, 247–258.

  • Huang, T. M., Kecman, V., & Kopriva, I. (2006). Kernel based algorithms for mining huge data sets: Supervised, semi-supervised, and unsupervised learning. Springer Berlin Heidelberg.

  • Ju, W., Xiang, D., Zhang, B., Wang, L., Kopriva, I., & Chen, X. (2015). Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Transactions on Image Processing, 24(12), 5854–5867.

  • Brbić, M., & Kopriva, I. (2020). l₀-Motivated low-rank sparse subspace clustering. IEEE Transactions on Cybernetics, 50(4), 1711–1725.

  • Tolić, D., Antulov-Fantulin, N., & Kopriva, I. (2018). A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering. Pattern Recognition, 82, 40–55.

Norma Aurea Vazquez | Computational Modelling | Best Researcher Award

Published on by Database Scientist

Dr. Norma Aurea Vazquez | Computational Modelling | Best Researcher Award

Technological Institute of Aguascalientes | Mexico

Dr. Norma Aurea Vazquez is a distinguished Mexican professor and researcher specializing in polymer science, molecular modeling, and advanced materials. She is a full-time faculty member at the Technological Institute of Aguascalientes, where she has significantly contributed to postgraduate education, research leadership, and program accreditation. With over two decades of experience, Dr. Vazquez has worked extensively on polymer nanocomposites, drug delivery systems, and sustainable material design. Her academic journey includes postdoctoral research at the University of Minnesota (USA) and the Technological Institute of Aguascalientes (Mexico), as well as a Ph.D. in Polymer Science. Recognized by CONACYT as a Level 1 researcher, she has published in leading journals, authored book chapters, and collaborated internationally. Her career reflects excellence in research, teaching, and applied innovation for biomedical and environmental challenges.

Professional Profile

Scopus

Orcid

Education 

Dr. Norma Aurea Vazquez holds a Ph.D. in Polymer Science from the Technological Institute of Madero City, where she graduated with an exceptional average. She earned her Bachelor’s degree in Chemical Engineering from the same institution, achieving an outstanding academic record. To expand her expertise, she completed postdoctoral training in Polymer Science at the Technological Institute of Aguascalientes, followed by advanced research at the School of Chemistry, University of Minnesota, USA. Her academic formation was complemented by specialized training in spectroscopy, rheology, bioinformatics, molecular simulation, and polymer characterization. These experiences provided her with strong theoretical and practical foundations for interdisciplinary research in polymer chemistry, nanocomposites, and sustainable materials. Her international education reflects her commitment to global scientific collaboration and innovation.

Professional Experience 

Dr. Norma Aurea Vazquez has been a full-time Professor-Researcher at the Technological Institute of Aguascalientes since 2008, contributing to research, teaching, and postgraduate program coordination. She served as coordinator of the Master’s in Chemical Engineering, managed CONACYT scholarships, and led program accreditation under PNPC-CONACYT standards.  she was also a part-time professor at CIATEQ, teaching postgraduate courses in advanced manufacturing. Her leadership roles include Head of the Metal-Mechanical Department, President of the Industrial Advisory Board, and leader of ABET accreditation processes in Mechanical Engineering. She has developed academic programs, evaluation frameworks, and guided students through advanced research projects. With expertise spanning molecular simulation, advanced coatings, and sustainable materials, Dr. Vazquez has built a career dedicated to innovation, academic quality, and applied research collaborations.

Awards

Dr. Norma Aurea Vazquez has received multiple awards and recognitions for her outstanding contributions to polymer science, education, and research leadership. She has been recognized as a Level 1 member of Mexico’s National System of Researchers (SNI-CONACYT), reflecting her sustained scientific impact. She earned first place in the National Thesis Competition in Polymers for her innovative project on controlled-release drug delivery systems, and earlier received third place in the National Graduate Thesis Contest for her research on polyurethane-hydroxyapatite composites for biomedical applications. She has also been honored as Best Professor at CIATEQ and served as leader of the Advanced Materials research line at her institution. Additionally, she has been awarded the desirable academic profile distinction across multiple evaluation periods. These honors highlight her excellence in science, teaching, and mentoring.

Research Interests 

Dr. Norma Aurea Vazquez’s research interests lie at the intersection of polymer science, molecular modeling, and sustainable materials. She applies computational methods such as DFT, Monte Carlo, and molecular dynamics to study adsorption, thermodynamics, and structural behavior of polymers, hydrogels, and nanocomposites. A major focus of her work is the design of controlled drug delivery systems using chitosan, polyurethane, and hydrogel-based materials. She has also investigated adsorption of heavy metals, dyes, and pharmaceuticals for water purification, advancing sustainable environmental technologies. Her studies on renewable biopolymer composites, nanostructured materials, and hybrid polymers contribute to applications in energy, healthcare, and advanced manufacturing. By combining theoretical simulations with experimental validation, Dr. Vazquez continues to bridge chemistry, engineering, and applied sciences, driving innovation in materials design and biomedical applications.

Publication Top Notes

Title: PM3 semi-empirical method and Monte Carlo simulation application on pesticides adsorption on SWCNT
Year:2023
Citations: 11

Title: Adsorption process of pesticide on SWCNT functionalized and crosslinked with chitosan using PM3 semi-empirical method and Monte Carlo simulation
Year: 2025

Title: Analysis of the adsorption of Hg²⁺, Ni²⁺ and Cu²⁺ on chitosan hydrogels
Year: 2024

Title: Modeling of artificial neural networks for the adsorption of synthetic dyes in an aqueous solution using double layer hydroxides
Year: 2023

Conclusion 

Dr. Norma Aurea Vazquez is an accomplished academic, researcher, and leader in polymer science and advanced materials. Her career exemplifies dedication to both fundamental and applied research, spanning biomedical engineering, environmental sustainability, and nanocomposites. Through her teaching, she has shaped the next generation of engineers and scientists, while her leadership in accreditation processes has strengthened academic programs at national and international levels. Recognized with awards and national research distinctions, she has built a strong record of publications, collaborations, and contributions to global scientific advancement. Dr. Vazquez continues to integrate computational chemistry with experimental approaches to address modern challenges in medicine, materials design, and environmental protection. She remains a role model and inspiration for future researchers in Mexico and beyond.