Tag: Engineering Development Award

Ching-Yuan Lin | Engineering | Research Excellence Award

Published on by World Science Awards

Research Excellence Award

Ching-Yuan Lin – Ten-Chen Medical Group Ten Chan General Hospital, Taiwan

Ching-Yuan Lin
Affiliation Ten-Chen Medical Group Ten Chan General Hospital
Country Taiwan
Scopus ID 57219406802
Documents 4
Citations 13
h-index 2
Subject Area Engineering
Event World Science Awards

Ching-Yuan Lin is a Taiwanese medical technologist, biomedical engineering researcher, and healthcare administrator affiliated with Ten-Chen Medical Group Ten Chan General Hospital. His work primarily focuses on cold atmospheric plasma technologies for sterilization and infection control in medical environments. With a professional background combining laboratory medicine, healthcare management, and biomedical engineering research, Lin contributes to clinical innovation by developing sterilization systems that enhance patient safety and hospital hygiene practices. His work addresses real clinical challenges such as the sterilization of sensitive medical instruments and the control of multidrug-resistant bacteria in healthcare environments [1].

Abstract

The research work of Ching-Yuan Lin focuses on the development and application of cold atmospheric plasma systems for medical sterilization and infection control. His investigations explore plasma-based technologies capable of eliminating microorganisms while preserving the integrity of delicate medical devices. Through interdisciplinary research combining biomedical engineering and laboratory medicine, Lin contributes to the design of plasma sterilization systems that enhance hospital hygiene and improve clinical safety. His work particularly addresses challenges in sterilizing medical ultrasound probes and managing multidrug-resistant bacterial contamination in clinical environments [2].

Keywords

Cold atmospheric plasma; dielectric barrier discharge; ultrasound probe sterilization; infection control; plasma sterilization; multidrug-resistant bacteria; biomedical engineering; clinical sterilization technologies.

Introduction

Advancements in biomedical engineering have enabled the development of innovative sterilization technologies designed to improve clinical hygiene and patient safety. Cold atmospheric plasma has emerged as a promising technology capable of effectively neutralizing pathogens while operating at temperatures suitable for delicate medical instruments. Ching-Yuan Lin’s research integrates plasma science with clinical laboratory applications, focusing on sterilization methods that address the growing challenge of multidrug-resistant bacteria in healthcare environments. His work contributes to the development of sterilization systems that combine efficiency, safety, and compatibility with modern medical equipment [3].

Research Profile

Ching-Yuan Lin holds a Bachelor of Science in Medical Technology from Chung Shan Medical University and a Master of Science in Medical Administration from Taipei Medical University. He is currently pursuing doctoral studies in Biomedical Engineering at Chung Yuan Christian University. Professionally, he has served as Director of the Department of Laboratory Medicine at Ten Chan General Hospital since 2011 and is scheduled to assume the role of Vice Superintendent in 2025. His leadership and professional service include acting as Secretary General of the Taiwan Association of Medical Technologists, contributing to the advancement of medical laboratory science and professional development in Taiwan [1].

Research Contributions

Ching-Yuan Lin’s research contributions focus on plasma sterilization technologies designed for real clinical applications. His work includes the development of a dual-mode plasma sterilization system that balances rapid antimicrobial action with the biological requirements of wound healing. Additionally, his research explores the application of plasma sterilization to sensitive medical equipment such as ultrasound probes, ensuring that sterilization procedures do not damage the devices while maintaining effective microbial elimination. These innovations address practical challenges encountered in hospital environments and support improved infection prevention protocols [2].

Publications

  • Peer-reviewed articles indexed in Scopus focusing on plasma sterilization and biomedical engineering applications.
  • Research studies addressing infection control using cold atmospheric plasma technologies.
  • Collaborative clinical research related to sterilization technologies and multidrug-resistant bacteria.

Research Impact

The research conducted by Ching-Yuan Lin contributes to the advancement of plasma-based sterilization technologies in healthcare environments. By developing systems capable of sterilizing delicate medical instruments without causing structural damage, his work supports safer clinical procedures and improved infection prevention strategies. The integration of plasma technology with hospital sterilization practices represents an important step toward addressing antimicrobial resistance and enhancing healthcare quality. His contributions also support interdisciplinary collaboration between biomedical engineering and clinical laboratory medicine [3].

Award Suitability

Ching-Yuan Lin’s work aligns with the objectives of the Research Excellence Award by demonstrating meaningful contributions to applied biomedical engineering and healthcare innovation. His research emphasizes practical medical applications that directly improve patient safety and clinical hygiene. Through the development of plasma sterilization technologies and leadership in laboratory medicine, Lin exemplifies interdisciplinary research that bridges engineering science and healthcare practice. Such contributions highlight the relevance of his work within global scientific and medical innovation communities.

Conclusion

The research and professional contributions of Ching-Yuan Lin demonstrate the integration of biomedical engineering innovation with practical clinical implementation. His work on plasma sterilization technologies contributes to improved infection control strategies and safer healthcare practices. By addressing challenges related to sterilization efficiency and equipment safety, his research supports the broader goals of medical engineering and public health advancement. These efforts position his work within the evolving landscape of healthcare technology and scientific recognition.

References

  1. Elsevier. (n.d.). Scopus author details: CHING-YUAN LIN, Author ID 57219406802. Scopus.https://www.scopus.com/authid/detail.uri?authorId=57219406802
  2. ORCID. (n.d.). ORCID record for CHING-YUAN LIN.https://orcid.org/0009-0009-4293-8076
  3. Laroussi, M. (2018). Cold atmospheric plasma in biomedical applications. Plasma Processes and Polymers.https://pmc.ncbi.nlm.nih.gov/articles/PMC13077500/

Feyyaz Alpsalaz | Engineering | Research Excellence Award

Published on by World Science Awards

Research Excellence Award

Feyyaz Alpsalaz
Department of Artificial Intelligence and Machine Learning, Faculty of Science and Arts, Amasya University

Feyyaz Alpsalaz
Affiliation Amasya University
Country Turkey
Scopus ID 59221704100
Documents 16
Citations 141
h-index 7
Subject Area Engineering
Event World Science Awards

Feyyaz Alpsalaz is an academic researcher affiliated with the Department of Artificial Intelligence and Machine Learning at Amasya University in Türkiye. His research integrates advanced computational intelligence with engineering systems, focusing on machine learning applications in energy systems, predictive maintenance, explainable artificial intelligence, and intelligent fault detection. His scholarly work contributes to the development of robust AI-based analytical models that enhance the reliability, monitoring, and predictive capabilities of modern technological infrastructures. His research outputs have appeared in international journals including Scientific Reports, IEEE Access, and IET Renewable Power Generation, reflecting interdisciplinary engagement across artificial intelligence, electrical engineering, and environmental monitoring systems [1].

Abstract

This article summarizes the research profile and academic contributions of Dr. Feyyaz Alpsalaz, a researcher specializing in artificial intelligence and machine learning applications in engineering systems. His work focuses on predictive analytics, hybrid machine learning models, explainable artificial intelligence, and intelligent diagnostics for power systems and environmental monitoring. Through interdisciplinary collaboration and data-driven methodologies, his studies contribute to advancements in predictive fault detection, renewable energy monitoring, and intelligent agricultural disease detection systems. The integration of deep learning, ensemble learning, and signal processing techniques within his work highlights the growing importance of AI-driven solutions in complex engineering infrastructures [1].

Keywords

  • Artificial Intelligence
  • Machine Learning
  • Explainable Artificial Intelligence
  • Fault Detection Systems
  • Renewable Energy Monitoring
  • Predictive Maintenance

Introduction

The rapid development of artificial intelligence has transformed the analysis and management of complex technological systems. Researchers across engineering and computational sciences are increasingly integrating machine learning algorithms to enhance predictive capabilities and optimize system performance. Dr. Feyyaz Alpsalaz contributes to this evolving domain by applying machine learning methodologies to energy infrastructure monitoring, environmental prediction systems, and biomedical data analysis. His research emphasizes robust hybrid models and explainable AI techniques designed to improve interpretability and reliability in high-stakes decision-making environments [2].

Research Profile

Dr. Alpsalaz conducts research at the intersection of artificial intelligence, electrical engineering, and environmental monitoring. His work explores the design of hybrid machine learning frameworks capable of identifying anomalies, forecasting environmental parameters, and diagnosing mechanical faults in complex engineering systems. His research integrates deep neural networks, ensemble learning strategies, signal processing methods, and explainable AI models to improve predictive accuracy and system interpretability. These approaches have been applied across multiple domains including renewable energy performance monitoring, power transformer diagnostics, acoustic motor fault detection, and crop disease identification using computer vision technologies [3].

Research Contributions

  • Development of hybrid machine learning models for photovoltaic power prediction and fault detection systems.
  • Application of explainable artificial intelligence methods to interpret complex deep learning models in engineering diagnostics.
  • Implementation of acoustic signal processing combined with convolutional neural networks for electric motor fault diagnosis.
  • Machine learning frameworks for environmental forecasting, particularly air quality prediction using ensemble models.
  • Deep learning-based image classification models for agricultural disease detection and plant pathology research.

Publications

  1. Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems. IET Renewable Power Generation. DOI: https://doi.org/10.1049/rpg2.70153
  2. Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using IoT Monitoring and Explainable AI. IEEE Access. DOI: https://doi.org/10.1109/access.2025.3583773
  3. Classification of Maize Leaf Diseases with Deep Learning. Chemometrics and Intelligent Laboratory Systems. DOI: https://doi.org/10.1016/j.chemolab.2025.105412
  4. Air Quality Forecasting Using Machine Learning. Water, Air, & Soil Pollution. DOI: https://doi.org/10.1007/s11270-025-08122-8
  5. Optimized ANN–RF Hybrid Model for Fault Detection in Power Transmission Systems. Scientific Reports. DOI: https://doi.org/10.1038/s41598-025-31008-y
  6. Fault Detection in Power Transmission Lines Using Machine Learning Models. Maintenance & Reliability. DOI: https://doi.org/10.17531/ein/203949
  7. Acoustic-Based Fault Diagnosis of Electric Motors Using CNNs. Scientific Reports. DOI: https://doi.org/10.1038/s41598-025-33269-z
  8. Hybrid Deep Learning with Attention Fusion for Colon Cancer Detection. Scientific Reports. DOI: https://doi.org/10.1038/s41598-025-29447-8
  9. Hybrid Deep Learning Model for Maize Leaf Disease Classification. New Zealand Journal of Crop and Horticultural Science.
  10. Detection of Arc Faults in Transformer Windings via Transient Signal Analysis. Applied Sciences. DOI: https://doi.org/10.3390/app14209335

Research Impact

The research contributions of Dr. Alpsalaz demonstrate the growing relevance of artificial intelligence in predictive engineering systems and sustainable infrastructure management. His studies integrate machine learning techniques with engineering diagnostics to improve reliability and predictive maintenance capabilities. Through publications in peer-reviewed international journals and interdisciplinary collaboration, his work supports advancements in intelligent monitoring technologies across renewable energy, agriculture, and industrial systems. These contributions illustrate the practical impact of AI-driven analytical methods in modern scientific and engineering research environments [1].

Award Suitability

Dr. Alpsalaz’s scholarly activities demonstrate interdisciplinary innovation within artificial intelligence applications for engineering systems. His work combines computational intelligence, predictive analytics, and explainable AI frameworks to address real-world challenges in energy infrastructure and environmental monitoring. The development of hybrid AI models and their implementation in applied engineering contexts highlight the relevance of his research to contemporary technological challenges. Such contributions align with the evaluation criteria commonly associated with international research recognition programs focused on artificial intelligence innovation and technological impact [3].

Conclusion

The academic profile of Dr. Feyyaz Alpsalaz reflects the integration of artificial intelligence techniques with complex engineering applications. His research emphasizes hybrid machine learning architectures, explainable AI methodologies, and predictive diagnostic systems designed to enhance reliability across multiple technological domains. As artificial intelligence continues to transform modern engineering research, contributions such as these provide valuable insights into the development of intelligent monitoring and forecasting systems capable of supporting sustainable and resilient infrastructure.

References

  1. Elsevier. (n.d.). Scopus author details: Feyyaz Alpsalaz, Author ID 59221704100. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59221704100
  2. Google Scholar. (n.d.). Scholar profile of Feyyaz Alpsalaz.
    https://scholar.google.com.tr/citations?user=EP2ybTEAAAAJ&hl=tr&oi=ao
  3. ORCID. (n.d.). ORCID record for Feyyaz Alpsalaz.
    https://orcid.org/0000-0002-7695-6426

Obuya Oloo | Engineering | Research Excellence Award

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Mr. Obuya Oloo | Engineering | Research Excellence Award

Durban University of Technology | Kenya

Mr. Obuya Oloo Tryphone is an emerging interdisciplinary researcher and engineer specializing in mechatronics, mechanical and industrial engineering, with a strong focus on design, simulation, fracture mechanics, and advanced manufacturing technologies. He holds a Bachelor’s degree in Mechanical Engineering and a Master of Engineering in Industrial Engineering, and is currently pursuing dual master’s programs in Mechatronics at ETH Zurich and Ashesi University under prestigious ETH4D and Tetra Pak scholarships. His research contributions include a peer-reviewed master’s thesis published with Wiley, a book chapter with Elsevier on additive manufacturing for energy storage applications, and applied engineering projects adopted in academic and industrial settings. He has collaborated with international faculty across Europe and Africa and contributed to infrastructure safety, materials research, and laboratory innovation. His work demonstrates clear societal impact through sustainable engineering, education mentorship, and climate-positive initiatives aligned with the SDGs.

ORCID Profile

Featured Publications

Tryphone Obuya Oloo, Oludolapo Akanni Olanrewaju, Samson Oluropo Adeosun, Mohammad Rezwan Habib (2026).
Experimental Analysis of Fracture Mechanics of Aluminum 7075 Alloy Plate With an Edge Crack Using MATLAB Software. Advances in Materials Science and Engineering • Journal Article 

 

Wei Huang | Engineering | Research Excellence Award

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Prof. Dr. Wei Huang | Engineering | Research Excellence Award

SINOMACH Research Center of Engineering Vibration Control Technology | China

Prof. Dr. Wei Huang is a senior researcher in engineering vibration control, vibration isolation, and intelligent structural control, with a strong focus on integrating optimization algorithms and deep learning into vibration analysis and mitigation. His research spans active, semi-active, and passive vibration control, magnetorheological dampers, low-frequency isolation systems, and vibration recognition and prediction using CNNs, ResNet, LSTM, Transformer, and reinforcement learning. He has authored more than 35 peer-reviewed journal papers, including SCI/EI-indexed publications, and contributed to 10+ academic monographs published by Springer Nature and leading Chinese publishers. He has played key roles in the development of national and group standards for engineering vibration control and holds over 25 granted patents, with many more under review. His work has been widely applied in precision equipment, industrial buildings, nuclear and seismic engineering, delivering significant societal and engineering impact.

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Changsen Sun | Engineering | Research Excellence Award

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Prof. Changsen Sun | Engineering | Research Excellence Award

College of Optoelectronic Engineering and Instrumentation Science | China

Professor Changsen Sun is a senior scholar in Optical Engineering and a long-standing faculty member at Dalian University of Technology (DUT), China, where he currently serves as Professor in the College of Optoelectronic Engineering and Instrumentation Science. He earned his bachelor’s and master’s degrees in Electrical Engineering from Jilin University of Technology and completed his Ph.D. in Optical Engineering at Dalian University of Technology. With more than three decades of academic experience, Professor Sun has built a distinguished career integrating fundamental optical science with engineering-oriented applications. Professor Sun’s primary field of expertise lies in optical fiber sensing technologies and their engineering applications, with particular emphasis on precision measurement, instrumentation, and real-world deployment of fiber-optic sensor systems. His research has contributed to advancements in high-sensitivity sensing, system reliability, and the integration of optical fiber sensors into complex engineering environments. He has led and completed more than 20 competitive research projects, securing over 15 million RMB in research funding, reflecting strong national-level recognition of his scientific and technical capabilities. His scholarly output includes over 30 peer-reviewed journal articles, published in leading international journals such as Optics Letters and IEEE Transactions on Instrumentation and Measurement, demonstrating sustained contributions to both theoretical development and applied innovation in optical sensing and measurement science. In addition to research productivity, Professor Sun has played a significant academic leadership role, notably serving as Director of the Doctoral Program in Optical Engineering (2019–2022), where he contributed to talent cultivation, curriculum development, and doctoral training quality.

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Guang Feng | Engineering | Best Research Article Award

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Dr. Guang Feng | Engineering | Best Research Article Award

Taiyuan University of Technology | China

Professor Guang Feng is a distinguished scholar and research leader in mechanical engineering, currently serving as a Center Director at Taiyuan University of Technology, China. He received his Ph.D. from Dalian University of Technology and further broadened his international research perspective as a Visiting Scholar at the University of Nottingham. His expertise lies in advanced metal forming and manufacturing technologies, with a particular focus on metal laminate rolling processes, ultra-precision machining technologies and equipment, and the processing of complex structural components for high-performance engineering applications. Professor Feng has led or participated in 30 completed and ongoing research projects, including 8 consultancy and industry-oriented projects, demonstrating strong integration of fundamental research with industrial application. His scholarly output includes 38 peer-reviewed journal publications, one academic book (ISBN registered), and an impressive portfolio of 33 patents granted or under process, reflecting sustained innovation and strong translational impact. His research contributions are widely recognized through citations documented on international academic platforms, underscoring his influence in the field of metal processing and advanced manufacturing. Among his most significant contributions are the establishment of a novel lattice severe deformation rolling principle for metallic laminates, the development of a theoretical framework for predicting bonding strength in roll-bonded heterogeneous metal composites, and the construction of a high-accuracy mathematical model for predicting plate warpage in rolled metal laminates.

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Featured Publications

Asma Alfergani | Engineering | Best Researcher Award

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Ms. Asma Alfergani | Engineering | Best Researcher Award

University of Benghazi | Libya

Dr. Asma Mohamed Najem Alfergani is an accomplished researcher and emerging leader in electrical and electronics engineering, with research focus areas spanning microgrid control, renewable energy systems, communication-delay modeling, optimization techniques, and intelligent control strategies. With a scholarly record that includes 88 Scopus-indexed publications, 287 citations, and an h-index of 8, she has made notable contributions to advancing theoretical and applied research in microgrid stability, distributed control systems, and smart energy technologies. Her work demonstrates strong technical rigor, experimental validation, and interdisciplinary integration spanning renewable energy engineering, power electronics, communication networks, and computational intelligence. Dr. Alfergani has received multiple recognitions including the Libyan Innovation Prize, a Best Paper Award at IREC 2021, and top academic standing during both undergraduate and postgraduate studies, demonstrating a sustained trajectory of excellence. Beyond research, she has contributed significantly to academic leadership through curriculum development, quality assurance coordination, laboratory establishment, and supervision of numerous student research projects, further strengthening engineering education and research capacity in her institution and region. Her strengths include a strong research output trajectory, impactful publications in Q1 journals, mobility across domains such as optimization, microgrid modeling, and smart control systems, and a demonstrated ability to translate complex systems theory into implementable engineering solutions. She also shows strong collaboration potential with national and international partners, evidenced by participation in IEEE-indexed conferences and cross-institution academic engagements. Areas of improvement include expanding participation in large-scale international research consortia, increasing interdisciplinary industry partnerships, and enhancing visibility through keynote roles, invited talks, and cross-continental collaborations to amplify global research influence. Looking ahead, Dr. Alfergani possesses substantial potential to become a leading scientific voice in renewable energy systems, next-generation distributed control, and resilient microgrid architectures. With continued expansion of research networks, broader project leadership, and further engagement in policy-driven energy transformation initiatives, her research is well positioned to shape sustainable energy technologies, support energy security in developing regions, and contribute meaningfully to the global transition toward intelligent, carbon-neutral power systems.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

Khalil, A., Rajab, Z., Alfergani, A., & Mohamed, O. (2017). The impact of the time delay on the load frequency control system in microgrid with plug-in-electric vehicles.

Alfergani, A., Alfaitori, K. A., Khalil, A., & Buaossa, N. (2018). Control strategies in AC microgrid: A brief review.

Alfergani, A., Khalil, A., & Rajab, Z. (2018). Networked control of AC microgrid.

Alfergani, A., & Khalil, A. (2017). Modeling and control of master-slave microgrid with communication delay.

Alfergani, A., Khalil, A., Rajab, Z., Zuheir, M., Khan, S., & Aboadla, E. H. (2017). Control of Master-Slave Microgrid Based on CAN Bus.

Kasye Shitu Mulat | Engineering | Editorial Board Member

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Mr. Kasye Shitu Mulat | Engineering | Editorial Board Member

Anhui University | Ethiopia

Mr. Kasye Shitu Mulat is an accomplished Irrigation Engineer and GIS & Remote Sensing Specialist with extensive academic, research, and professional experience in water resources, climate change impacts, hydrological modeling, and sustainable agricultural development. Born on 02 October 1991 in Borena Mekane Selam, Ethiopia, he currently serves as a Lecturer and Researcher at Assosa University, where he contributes to teaching, scientific research, and community-centered development initiatives. Mr. Kasye earned his Bachelor of Science in Water Resource and Irrigation Management from Aksum University with Very Great Distinction (CGPA 3.64). He later completed his MSc in Irrigation Engineering at Haramaya University, achieving an “A” thesis grade and a CGPA of 3.79. Further expanding his scientific expertise, he obtained a second Master of Science in GIS and Remote Sensing from Wollo University. His multidisciplinary background positions him at the forefront of research linking climate dynamics, hydrological systems, and agricultural water management. He has authored more than 11 peer-reviewed publications and has four additional manuscripts under review in reputable international journals. His research outputs span topics such as climate change impacts on irrigation potential, hydrological modeling of river basins, statistical downscaling, kriging-based spatial analyses, land use/land cover dynamics, and soil–water interactions. His works have contributed to improving understanding of the Upper Blue Nile Basin, Borkena Catchment, and other key Ethiopian watersheds. In addition to academic research, Mr. Kasye has led impactful community engagement projects, including free-energy garden irrigation initiatives and wheat production enhancement programs across Benishangul-Gumuz. These interventions have strengthened food security, improved smallholder livelihoods, and promoted climate-resilient agricultural practices. With advanced skills in ArcGIS, SWAT, HBV, R, GAMS, CropWat, and hydrological modeling software, he collaborates with agricultural offices, university research committees, and interdisciplinary teams. His contributions continue to influence regional water resource planning, climate adaptation strategies, and sustainable development efforts in Ethiopia.

Profile: Scopus

Featured Publications

  1. (2025). Assessing drought dynamics in a semi-arid basin: A multi-index approach using hydrological and remote-sensing indicators. Environmental Sciences Europe.

Fan Feng | Engineering | Best Researcher Award

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Assist. Prof. Dr. Fan Feng | Engineering | Best Researcher Award

Peking University, China

Assist. Prof. Dr. Fan Feng is a distinguished scholar in mechanics and materials science, currently serving as Assistant Professor at the School of Mechanics and Engineering Science, Peking University, China. He earned his B.Sc. in Mathematics and Physics from Tsinghua University and obtained his Ph.D. in Solid Mechanics from the University of Minnesota under the guidance of Prof. Richard D. James. Following his doctoral studies, he pursued postdoctoral research at the University of Minnesota and later at the University of Cambridge, working with leading experts Prof. Mark Warner and Prof. John Biggins. Dr. Feng’s research interests lie in the geometric mechanics approach to the rational design of functional and phase-transforming materials and structures, covering martensitic phase transformations, elastocaloric cooling, liquid crystal elastomers, soft robotics, origami and kirigami structures, and mechanics of surfaces and interfaces under extreme conditions. His research skills span advanced mathematical modeling, continuum mechanics, material design, and interdisciplinary applications that integrate physics, mechanics, and engineering. He has authored 18 publications, cited 376 times with an h-index of 11, in reputed journals such as Physical Review Letters, Journal of the Mechanics and Physics of Solids, Soft Matter, and Proceedings of the Royal Society A, and has also contributed to international conferences and workshops with invited talks. Dr. Feng has been the recipient of significant research grants, including funding from the National Natural Science Foundation of China and Peking University. His commitment to mentoring students, organizing international symposiums, and serving as a reviewer for leading journals demonstrates his academic leadership and dedication to advancing science. His awards and honors include the SIAM Travel Award for ICIAM 2019, the John and Jane Dunning Copper Fellowship at the University of Minnesota, and multiple scholarships from Tsinghua University. In conclusion, Dr. Fan Feng exemplifies an innovative and impactful researcher whose contributions to geometric mechanics and functional materials hold immense promise for sustainability, robotics, aerospace engineering, and advanced material design, marking him as a future global leader in his field.

Profile: Scopus | ORCID

Featured Publications

  1. Wen, Z., Yu, T., & Feng, F. (2025). Geometry and mechanics of non-Euclidean curved-crease origami (arXiv preprint arXiv:2502.20147).

  2. Gu, H., & Feng, F. (2025). Simplified cofactor conditions for cubic to tetragonal, orthorhombic, and monoclinic phase transformations (arXiv preprint arXiv:2503.24224).

  3. Wang, L., & Feng, F. (2025). A continuum mechanics approach for the deformation of non-Euclidean origami generated by piecewise constant nematic director fields (arXiv preprint arXiv:2506.01309).

  4. Feng, F. (2025). Objective moiré patterns. Journal of Applied Mechanics, 92(8), 081002.

Le Chang | Engineering | Best Researcher Award

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Assist. Prof. Dr. Le Chang | Engineering | Best Researcher Award

Xi’an Jiaotong University | China

Dr. Le Chang is an Assistant Professor at the College of Electric Power Engineering, Shanghai University of Electric Power, China, specializing in networked control systems and nonlinear dynamics. He earned his Ph.D. from Shandong University, focusing on control theory and its applications. His professional experience includes serving as a Research Associate at the College of Electric Power Engineering, where he contributes to the development of advanced control strategies for complex systems. Dr. Chang’s research interests encompass the analysis and design of control systems in the presence of network-induced delays and nonlinearities, aiming to enhance the stability and performance of interconnected systems. His research skills are demonstrated through his work on cascade control for post-chlorine dosage during drinking water treatment under cyber attacks, published in the IEEE Transactions on Automation Science and Engineering. Additionally, he has contributed to the global stabilization of strict-feedback nonlinear systems with applications to circuits, employing an intermittent impulsive control approach, as detailed in the IEEE Control Systems Letters. Dr. Chang’s work on global output regulation for uncertain feedforward nonlinear systems with unknown nonlinear growth rates has been published in the International Journal of Robust and Nonlinear Control. His contributions to global output feedback stabilization for nonlinear systems via a switching control gain approach are featured in the International Journal of Control. Furthermore, his research on global sampled-data output feedback stabilization for nonlinear systems via intermittent hold has been published in the IEEE/CAA Journal of Automatica Sinica. Dr. Chang’s innovative approaches to stabilization and regulation in nonlinear systems have significantly advanced the field of control engineering. In conclusion, Dr. Le Chang’s academic background, professional experience, and research contributions underscore his expertise in control systems, particularly in addressing challenges posed by networked and nonlinear dynamics. His work continues to influence the development of robust control strategies in various engineering applications.

Profile: Scopus

Featured Publications

1. Liu, D., Chang, L., He, W., Wei, K., & Zhang, A. (2025). Wideband low-directivity cavity-backed Yagi-Uda dipole antenna for electrically large laptops. IEEE Transactions on Antennas and Propagation, in press.

2. Zhang, H., Chang, L., Chen, X., Chen, J., & Zhang, A. (2025). Ultra-low-profile and ultra-wideband microstrip patch antenna based on hybrid coupling for mobile Wi-Fi 6/6E and UWB channels 5–11 applications. IEEE Transactions on Antennas and Propagation, in press.

3. Wang, S., Bu, H., Zhang, Y., Chang, L., Chen, X., Wei, K., & Li, Y. (2025). Active antenna hub: A multi-port shared-antenna architecture for scalable internet of things devices. IEEE Internet of Things Journal, in press.

4. Zhao, Z., Chang, L., Cui, Y., & Zhang, A. (2025). Miniaturized and wideband metasurface antenna sensor for breast tumor detection. Sensors and Actuators: A. Physical, in press.

5. Chen, M., Chang, L., Cao, Y., Yan, S., & Zhang, A. (2025). Simultaneous enhancements of bandwidth and isolation of frame monopoles utilizing elongated back cover patches for smartphones. IEEE Transactions on Antennas and Propagation, in press.