Fei HE | Engineering | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Fei He
Affiliation Anhui University of Technology
Country China
Subject Area Engineering
Event Best Paper Awards
Research Gate Fei-He-21

Fei He

Fei He of Anhui University of Technology, China, is recognized in connection with the Best Paper Award for research concerning machine vision and its application to steelmaking processes. The associated publication, “Advances in the Application of Machine Vision in Steelmaking Processes,” presents a review of machine-vision technologies used across multiple stages of steel production, including hot metal pretreatment, converter steelmaking, secondary refining, and continuous casting. The article was published in steel research international, volume 97, issue 4, pages 1813–1830, in 2026, and was first published online on 8 November 2025. The article has DOI 10.1002/srin.202500769. [1]

Abstract

The Best Paper Award recognition highlights research by Fei He and co-authors on the application of machine vision within steelmaking processes. The recognized review examines the development of machine-vision hardware and analytical algorithms, covering the progression from traditional image-processing techniques and machine learning to contemporary deep-learning approaches. It further surveys applications across hot metal pretreatment, converter steelmaking, secondary refining, continuous casting, and related steel-production operations. The publication identifies machine vision as a technological component supporting process optimization, abnormal-condition prediction, product classification and grading, and increased automation in steel manufacturing. [1]

Keywords

Machine Vision, Steelmaking, Artificial Intelligence, Deep Learning, Machine Learning, Image Processing, Intelligent Steelmaking, Converter Steelmaking, Continuous Casting, Secondary Refining, Hot Metal Pretreatment, Industrial Automation, Metallurgical Engineering, Digital Twin.

Introduction

Machine vision has become an increasingly important technology in industrial automation because it enables visual information to be acquired and processed for monitoring, classification, measurement, and decision support. In steelmaking, where production involves high temperatures, complex physical environments, continuous material movement, and demanding quality requirements, machine-vision systems can provide additional sources of information for process monitoring and control. The recognized publication examines the development and application of these systems throughout the steelmaking production chain. [1]

Research Profile

Fei He is affiliated with Anhui University of Technology in China and is associated with research in engineering and metallurgical applications of computational and machine-vision technologies. The recognized publication lists Fei He as a corresponding author and identifies the School of Metallurgical Engineering at Anhui University of Technology as the institutional affiliation. [1]

Research Contributions

The principal contribution of the recognized publication is its systematic overview of machine-vision development in steelmaking. The authors describe the evolution from conventional image-processing methods to machine-learning and deep-learning algorithms and examine how these technologies can be incorporated into industrial steel-production environments. [1]

Publications

The principal publication associated with this recognition is a peer-reviewed review article published in steel research international:

  • Wang, X., He, F., Xu, C., Wu, T., Zhuang, X., Zhang, R., Xie, W., Liu, Y., & Li, H. (2026). Advances in the Application of Machine Vision in Steelmaking Processes. steel research international, 97(4), 1813–1830. DOI: https://doi.org/10.1002/srin.202500769. [1]

Research Impact

The recognized publication contributes to research on intelligent steel manufacturing by consolidating evidence concerning the use of machine vision across different stages of steel production. By connecting visual sensing with machine-learning and deep-learning algorithms, the review provides a structured account of how visual information can support monitoring, prediction, classification, and automation. [1]

Award Suitability

The Best Paper Award recognition is associated with scholarly work demonstrating relevance, methodological organization, research value, and contribution to an academic or professional field. The publication associated with Fei He addresses a clearly defined engineering topic and provides a broad review of machine-vision technologies and their applications within steelmaking. [1]

Conclusion

Fei He is associated with engineering research at Anhui University of Technology, with the recognized publication focusing on machine vision and intelligent steelmaking. The article provides a comprehensive review of the development of machine-vision hardware and algorithms and examines their applications throughout several stages of steel production. [1]

External Links

References

    1. Wang, X., He, F., Xu, C., Wu, T., Zhuang, X., Zhang, R., Xie, W., Liu, Y., & Li, H. (2026). Advances in the Application of Machine Vision in Steelmaking Processes. steel research international, 97(4), 1813–1830. First published online 8 November 2025. DOI: https://doi.org/10.1002/srin.202500769.

Huimin Wang | Engineering | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Huimin Wang
Affiliation Southwest Jiaotong University
Country China
Documents 62
Citations 1,567
h-index 22
Subject Area Engineering
Event Best Paper Awards

Huimin Wang

Southwest Jiaotong University, China, is recognized for significant contributions in engineering research and electrical machine systems. This article highlights the academic profile, research influence, and award recognition of Huimin Wang, focusing on the paper titled Guest Editorial: Reliability Oriented Electrical Machine Systems: Topology, Design, Monitoring, Diagnostic Techniques, and Control.

Abstract

This article recognizes Huimin Wang for receiving the Best Paper Award and highlights the importance of the publication focused on reliability-oriented electrical machine systems. The research explores topology design, monitoring systems, diagnostic methods, and advanced control strategies to improve system reliability, efficiency, and performance in engineering applications.

Keywords

Electrical Machine Systems, Reliability Engineering, System Design, Monitoring, Diagnostics, Control Systems, Engineering Innovation.

Introduction

Electrical machine systems play a vital role in modern engineering applications, requiring high reliability and efficiency. Advances in system topology, monitoring techniques, and intelligent control methods contribute significantly to improving system performance and operational safety.

Research Profile

Huimin Wang has authored 62 academic publications with 1,567 citations and an h-index of 22. The research demonstrates consistent contributions in engineering, particularly in electrical machine systems, diagnostics, and system reliability.

Research Contributions

The awarded paper emphasizes reliability-focused design and advanced diagnostic strategies in electrical machine systems. It integrates monitoring techniques and control mechanisms to enhance operational stability and long-term system efficiency in engineering applications.

Research Impact

The research has contributed to advancements in engineering systems by improving reliability and performance standards. Citation metrics indicate growing recognition within the scientific and engineering community, supporting further research and innovation.

Award Suitability

The Best Paper Award recognizes outstanding research contributions demonstrating innovation, technical excellence, and practical impact. This work aligns with these criteria by presenting advanced methodologies for reliable electrical machine system design and control.

Conclusion

Huimin Wang’s research contributes significantly to the field of engineering by advancing reliable electrical machine systems. The awarded publication reflects innovation, technical expertise, and strong academic impact within the global research community.

References

  1. Sliding-mode observer-based speed-sensorless vector control of linear induction motor with a parallel secondary resistance online identification.
    https://digital-library.theiet.org/doi/10.1049/iet-epa.2018.0049
  2. Google Scholar. (n.d.). Huimin Wang research profile. Retrieved from https://scholar.google.com

External Links

1.Best Paper Awards Official Website
2.Google Scholar

ALIAS PAUL | Engineering | Research Excellence Award

Published on by Best Paper Awards

 

Best Researcher Award

ALIAS PAUL
Affiliation Viswajyothi College of Engineering and Technology
Country India
Scopus ID 57211407905
Documents 7
Citations 191
h-index 3
Subject Area Engineering
Event Best Paper Awards

Researcher: ALIAS PAUL
Institution: Viswajyothi College of Engineering and Technology

The Best Researcher Award recognizes sustained academic excellence, impactful scholarly publications, and meaningful research contributions within engineering disciplines. ALIAS PAUL has established a measurable research profile through peer-reviewed publications, citations, and scholarly engagement. The available bibliometric indicators demonstrate academic participation suitable for recognition in research-oriented award programs.[1]

Abstract

The Best Researcher Award recognizes scholars demonstrating measurable academic achievement through quality publications, citation performance, research innovation, and contributions to scientific advancement. ALIAS PAUL has developed an engineering research profile supported by indexed publications and scholarly citations recorded in Scopus. These indicators reflect active participation in academic research and dissemination of knowledge. Recognition through the Best Paper Awards aligns with evaluation criteria emphasizing publication quality, research relevance, academic integrity, citation influence, and continuing contribution to engineering research communities while encouraging future interdisciplinary collaboration and sustainable technological innovation.[1]

Keywords

Best Researcher Award, Engineering Research, Scopus Publications, Citation Analysis, Academic Recognition, Research Excellence, Scholarly Impact, Best Paper Awards.

Introduction

Academic awards acknowledge researchers who consistently contribute to scientific knowledge through peer-reviewed publications, innovation, and collaboration. Engineering research recognition commonly considers publication quality, citation metrics, and research significance while encouraging continued excellence across academic and industrial research environments.[1]

Research Profile

ALIAS PAUL is affiliated with Viswajyothi College of Engineering and Technology, India. According to the available Scopus profile, the researcher has published seven indexed documents with 191 citations and an h-index of three, demonstrating active scholarly engagement in engineering research.[1]

Research Contributions

The research contributions emphasize engineering innovation through peer-reviewed publications supporting scientific understanding and technological development. Citation performance indicates that published work has received attention within the academic community, contributing to knowledge dissemination and future research activities.[1]

Publications

The Scopus database records seven indexed publications associated with ALIAS PAUL. These publications represent scholarly output evaluated through recognized indexing standards and provide measurable evidence of academic productivity within engineering disciplines.[1]

Research Impact

Research impact is reflected through citation counts, publication visibility, and academic influence. The available citation record demonstrates that published research has been referenced by subsequent studies, indicating continuing scholarly relevance and contribution to engineering literature.[1]

Award Suitability

The documented publication record, citation metrics, and engineering research activity correspond with common evaluation criteria applied in research recognition programs. These achievements support consideration for the Best Researcher Award while reflecting measurable academic performance and scholarly engagement.[1]

Conclusion

ALIAS PAUL’s documented scholarly profile demonstrates continued participation in engineering research through indexed publications and measurable citation performance. These academic indicators provide an objective basis for recognition within research award programs that value publication quality, scientific contribution, and sustained academic excellence.[1]

External Links

References

  1. Elsevier. (n.d.). Scopus Author Details: ALIAS PAUL, Author ID 57211407905. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57211407905
  2. Best Paper Awards. Official Award Website.
    https://bestpaperawards.com/

 

Tianyu Sun | Engineering | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Tianyu Sun
Xi’an Technological University
China

Tianyu Sun
Affiliation Xi’an Technological University
Country China
Scopus ID 58571661300
Documents 9
Citations 30
h-index 3
Subject Area Engineering
Event Best Paper Awards
Paper Title Inverter Open Circuit Fault Diagnosis Method Based on Residual Evaluation and Machine Learning

Tianyu Sun is recognized for the research contribution entitled “Inverter Open Circuit Fault Diagnosis Method Based on Residual Evaluation and Machine Learning”. The work represents an engineering-focused investigation into diagnostic methodologies using residual analysis and machine learning techniques for inverter fault identification. [1]

Abstract

This article presents the academic recognition of Tianyu Sun through the Best Paper Award for research on inverter open circuit fault diagnosis using residual evaluation and machine learning. The study focuses on improving fault identification accuracy and supporting reliable power electronic system operation. By combining analytical evaluation methods with intelligent computational approaches, the research contributes to engineering applications involving inverter monitoring, reliability enhancement, and predictive maintenance. The award highlights the relevance of this contribution within engineering research communities and acknowledges methodological development supported by systematic investigation, experimental analysis, and scholarly communication practices. [2]

Keywords

Inverter diagnosis, machine learning, residual evaluation, power electronics, fault detection, engineering research, predictive maintenance, intelligent systems.

Introduction

Power electronic converters require effective diagnostic methods to maintain operational stability and efficiency. Research on inverter fault detection has increasingly incorporated machine learning methods because of their ability to process complex signals and identify abnormal operating conditions. [3]

Research Profile

Tianyu Sun is affiliated with Xi’an Technological University and conducts research in the engineering domain. The academic profile indicates contributions in areas related to electrical systems, diagnostic technologies, and intelligent analysis approaches, supported by indexed research outputs and citation records. [1]

Research Contributions

The recognized paper develops an inverter open circuit fault diagnosis method based on residual evaluation and machine learning. The contribution combines signal-based assessment with computational classification techniques to improve fault recognition processes in modern power electronic applications. [2]

  • Development of diagnostic approaches for inverter fault identification.
  • Application of machine learning techniques in engineering analysis.
  • Integration of residual evaluation methods for system monitoring.

Publications

The researcher has produced nine indexed documents with thirty citations and an h-index of three according to available academic indexing information. These publications reflect ongoing engagement with engineering research topics and demonstrate participation in scholarly communication through peer-reviewed scientific outputs. [1]

Research Impact

The research contributes to the broader field of engineering by addressing challenges associated with inverter reliability and automated fault diagnosis. Its methods may support future developments in intelligent monitoring systems, industrial applications, and improved operational decision-making. [3]

Award Suitability

The Best Paper Award recognition is associated with the originality, technical relevance, and practical importance of the presented research. The work demonstrates a structured approach to solving engineering problems through analytical evaluation and machine learning methodologies. [2]

Conclusion

Tianyu Sun’s awarded research represents a contribution to inverter fault diagnosis and intelligent engineering analysis. Through the integration of residual evaluation and machine learning, the study provides a relevant example of contemporary approaches for improving reliability in power electronic systems.

References

  1. Elsevier. (n.d.). Scopus author details: Tianyu Sun, Author ID 58571661300. Scopus.
    https://www.scopus.com/pages/authors/60146797200

Hongchen Liu | Engineering | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Researcher: Hongchen Liu
Institution: Harbin Institute of Technology

Hongchen Liu
Affiliation Harbin Institute of Technology
Country China
Scopus ID 60146797200
Paper Title A Novel Three-Phase Passive Auxiliary Resonant Pole Soft-Switching Inverter With Symmetrical Auxiliary Networks and Electric Energy Feedback Function
Documents 9
Citations 62
h-index 3
Subject Area Engineering
Event Best Paper Awards

This article presents an academic overview recognizing Hongchen Liu for research associated with the paper A Novel Three-Phase Passive Auxiliary Resonant Pole Soft-Switching Inverter With Symmetrical Auxiliary Networks and Electric Energy Feedback Function. The profile summarizes scholarly contributions, publication impact, research significance, and award suitability using publicly available bibliographic indicators and standard academic references.[1]

Abstract

Hongchen Liu has contributed to engineering research through investigations into power electronic converter technologies emphasizing efficient inverter operation and soft-switching techniques. The featured publication introduces a three-phase passive auxiliary resonant pole soft-switching inverter incorporating symmetrical auxiliary networks and electric energy feedback functionality. This design aims to improve switching efficiency, reduce power losses, minimize electromagnetic interference, and enhance overall converter performance. Scholarly indicators including Scopus-indexed publications, citation activity, and research influence demonstrate measurable academic visibility. Collectively, these achievements provide a meaningful basis for evaluating the research within the context of Best Paper Awards recognition.[1][2]

Keywords

Soft-switching inverter, power electronics, resonant converter, auxiliary network, energy feedback, engineering, Scopus, Best Paper Award.

Introduction

Modern power electronics continually seek higher efficiency and improved reliability. Research on resonant soft-switching technologies addresses switching losses while supporting stable converter operation. Hongchen Liu’s publication contributes to this field by proposing a practical inverter architecture suitable for engineering applications and academic investigation.[2]

Research Profile

Affiliated with Harbin Institute of Technology, Hongchen Liu has published research indexed by Scopus within engineering disciplines. Available bibliometric indicators include nine indexed documents, sixty-two citations, and an h-index of three, reflecting recognized scholarly activity in power electronics research.[1]

Research Contributions

The featured research presents symmetrical auxiliary resonant networks combined with electric energy feedback mechanisms for three-phase inverter systems. These engineering concepts aim to reduce switching stress, improve efficiency, and support practical converter implementation through carefully designed passive circuit configurations.[2]

Publications

The highlighted publication, titled A Novel Three-Phase Passive Auxiliary Resonant Pole Soft-Switching Inverter With Symmetrical Auxiliary Networks and Electric Energy Feedback Function, represents a notable contribution within Hongchen Liu’s indexed engineering publications and demonstrates continued engagement with advanced power conversion technologies.[2]

Research Impact

Citation activity indicates that the research has received scholarly attention within engineering literature. The publication contributes to discussions surrounding efficient inverter topologies, offering reference material for subsequent investigations into converter optimization, soft-switching strategies, and practical electrical energy conversion systems.[1]

Award Suitability

The publication demonstrates technical originality through innovative inverter architecture emphasizing efficiency and operational performance. Considering its engineering relevance, measurable scholarly impact, and contribution to power electronics research, the work represents an appropriate candidate for consideration within Best Paper Awards evaluation processes.[2]

Conclusion

Hongchen Liu’s published engineering research contributes to the advancement of soft-switching inverter technologies through practical circuit innovations and measurable academic influence. Bibliographic evidence and citation performance support recognition of the featured publication as a meaningful contribution within contemporary power electronics scholarship.[1]

References

    1. Elsevier. (n.d.). Scopus author details: Hongchen Liu, Author ID 60146797200. Scopus.
      https://www.scopus.com/pages/authors/60146797200

Xiaomin Zhao | Engineering | Best Researcher Award

Published on by Best Paper Awards

Best Researcher Award

Xiaomin Zhao — Hefei University of Technology

Xiaomin Zhao
Affiliation Hefei University of Technology
Country China
Documents 13
Subject Area Engineering
Event Best Paper Awards
ORCID 0000-0002-7300-5457

Xiaomin Zhao is an engineering researcher affiliated with Hefei University of Technology, recognized for contributions to applied engineering research. The Best Paper Award acknowledges scholarly impact and research quality demonstrated through published work. This page provides a structured academic overview of Zhao’s research profile, contributions, and recognition within the engineering domain.[1]

Abstract

This article presents a comprehensive overview of Xiaomin Zhao’s academic contributions within the field of engineering, focusing on research productivity, scholarly impact, and recognition through the Best Paper Award. The study highlights publication outputs, thematic research directions, and measurable indicators such as document count and citation performance. Emphasis is placed on methodological rigor, innovation, and relevance to contemporary engineering challenges. By synthesizing available academic data and scholarly records, this profile illustrates Zhao’s role in advancing engineering research and contributing to scientific discourse, offering insights into the broader implications of award-based academic recognition in global research ecosystems.[1]

Keywords

Engineering research, Best Paper Award, academic recognition, research productivity, scholarly impact, Hefei University of Technology, innovation, applied engineering.

Introduction

Engineering research continues to shape technological progress and industrial development. Xiaomin Zhao’s work contributes to this domain through focused academic outputs. Recognition through the Best Paper Award reflects scholarly merit and research quality within competitive academic environments, highlighting the importance of impactful research dissemination.

Research Profile

Xiaomin Zhao is affiliated with Hefei University of Technology in China, specializing in engineering research. With a documented portfolio of thirteen publications, the researcher demonstrates consistent academic engagement. The profile reflects contributions across engineering subfields, emphasizing methodological application and interdisciplinary collaboration.

Research Contributions

The research contributions of Xiaomin Zhao include applied engineering studies addressing practical challenges. Work focuses on advancing technical methodologies, improving system performance, and contributing to theoretical understanding. Publications demonstrate integration of analytical techniques with real-world applications, supporting innovation in engineering practices.

Publications

The publication record includes thirteen academic documents indexed within scholarly databases. These works encompass journal articles and conference papers. The research outputs reflect engagement with engineering challenges and contribute to ongoing scientific discussions, supporting knowledge advancement and academic collaboration.

Research Impact

Research impact is evaluated through publication metrics and scholarly visibility. Zhao’s work contributes to engineering knowledge dissemination and supports innovation. The presence in indexed databases enhances accessibility and citation potential, reinforcing academic influence within the global research community.

Award Suitability

Eligibility for the Best Paper Award is determined by originality, research depth, and contribution to the field. Xiaomin Zhao’s work aligns with these criteria through structured methodologies and impactful findings. The award recognition underscores the academic merit and relevance of the research contributions.[3]

Conclusion

This profile summarizes Xiaomin Zhao’s academic contributions and recognition within engineering research. The Best Paper Award highlights scholarly excellence and research quality. Continued academic engagement is expected to further strengthen contributions and expand impact within the global engineering community.[3]

References

  1. Best Paper Awards. (n.d.). Award criteria and evaluation standards.
    https://bestpaperawards.com/

  2. State-of-Charge Estimation by Backstepping Observer Based on Voltage–Current Dynamics Model for Lithium-Ion Battery.
    https://www.researchgate.net/publication/405905511_State-of-charge_estimation_by_backstepping_observer_based_on_voltage-current_dynamics_model_for_lithium-ion_battery

  3. SGTP: A Safety-Guaranteed Trajectory Planning Algorithm for Autonomous Vehicles Using Gap-Oriented Spatio-Temporal Corridor.
    https://www.researchgate.net/publication/397820803_SGTP_A_Safety-Guaranteed_Trajectory_Planning_Algorithm_for_Autonomous_Vehicles_Using_Gap-Oriented_Spatio-Temporal_Corridor

  4. A Fuzzy-Theoretic Cooperative Game Framework for Adaptive Robust Control of Air–Ground Vehicle Systems.
    https://oipub.com/papers/400465355

Priscilla Nelson | Engineering | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

The Body Underground: A Biological Framework for Infrastructure Health, Regulation and Resilience
Priscilla Nelson
Affiliation Colorado School of Mines
Country United States
Article Title The Body Underground: A Biological Framework for Infrastructure Health, Regulation and Resilience
Scopus ID 7402246675
Article Type Research Article
Article Views 673
Reference Count 24
Award Category Best Paper Award
Event International Research Excellence and Best Paper Awards
Google Scholar 3hezpIkAAAAJ&hl

The Best Paper Award recognizes scholarly contributions that advance disciplinary knowledge through originality, methodological rigor, and measurable academic impact. This recognition highlights the work of Priscilla Nelson of the Colorado School of Mines for her article, The Body Underground: A Biological Framework for Infrastructure Health, Regulation and Resilience. Published in MDPI in 2026, the study explores infrastructure systems through a biologically inspired framework that integrates resilience, regulation, and long-term performance evaluation, contributing to contemporary engineering research and interdisciplinary infrastructure science.[1]

Abstract

This award-recognized article presents an interdisciplinary framework that interprets infrastructure systems through biological principles of health, adaptation, regulation, and resilience. The study examines how engineering networks can be assessed similarly to living systems, emphasizing continuous monitoring, response mechanisms, and long-term sustainability. By integrating concepts from biology, systems engineering, and resilience science, the research offers a novel perspective on infrastructure management. The framework supports improved understanding of infrastructure behavior under stress and changing environmental conditions while encouraging proactive maintenance and adaptive governance strategies. The work contributes to emerging discussions surrounding resilient infrastructure planning and engineering innovation.[2]

Keywords

Infrastructure Health; Urban Systems; Community Resilience; Underground Systems.

Introduction

Modern infrastructure systems face increasing demands arising from urbanization, environmental variability, aging assets, and technological complexity. Traditional engineering approaches often evaluate infrastructure through isolated performance metrics, whereas contemporary resilience research emphasizes interconnected and adaptive system behavior. The article investigates how biological concepts can provide a useful analogy for understanding infrastructure health and long-term functionality, creating a foundation for more integrated approaches to engineering management and policy development.[2]

Research Profile

Priscilla Nelson is an engineering scholar associated with the Colorado School of Mines whose research interests encompass infrastructure systems, resilience engineering, sustainability, and interdisciplinary approaches to complex societal challenges. With a Scopus Author ID of 7402246675, 63 indexed documents, 793 citations, and an h-index of 12, her scholarly record reflects substantial engagement with infrastructure-related research and engineering innovation across multiple domains.[3]

Scientific Background

Biological systems maintain functionality through regulation, adaptation, feedback mechanisms, and recovery processes. Infrastructure networks similarly require monitoring, maintenance, and adaptive responses to disturbances. Previous resilience research has explored system dynamics and risk management, but fewer studies have directly employed biological frameworks to conceptualize infrastructure health. This article builds upon interdisciplinary scholarship by connecting biological theory with engineering practice, thereby expanding the conceptual tools available for infrastructure assessment and governance.[4]

Methodology

The study employs a conceptual and analytical methodology that synthesizes biological principles with engineering resilience literature. Through comparative examination of living organisms and infrastructure systems, the research identifies common characteristics related to health assessment, regulation, adaptation, and recovery. The framework is developed through interdisciplinary integration of theoretical sources and engineering perspectives, enabling the formulation of a structured model for interpreting infrastructure performance under changing conditions and external stresses.[2]

Key Findings

The article demonstrates that infrastructure systems can be understood more effectively when viewed as dynamic entities possessing characteristics comparable to biological organisms. The framework highlights the importance of continuous monitoring, adaptive management, and systemic feedback mechanisms. It further suggests that infrastructure resilience depends not only on physical robustness but also on regulatory capacity and organizational adaptability. These findings encourage broader adoption of interdisciplinary approaches within infrastructure planning and engineering decision-making processes.[2]

Scientific Contributions

A significant contribution of the research lies in its development of a biological framework for infrastructure health that bridges conceptual boundaries between engineering and life sciences. The work advances resilience theory by introducing new interpretative models for infrastructure assessment and management. It also encourages researchers and policymakers to consider infrastructure systems as adaptive networks requiring ongoing regulation, learning, and recovery mechanisms, thereby enriching discussions surrounding sustainable engineering and resilient urban development.[4]

Conclusion

The recognition of this publication through the Best Paper Award reflects its scholarly value and interdisciplinary significance within engineering research. By integrating biological concepts into infrastructure science, the article provides a distinctive framework for understanding resilience, health, and long-term system sustainability. Its conceptual contributions support future research, policy discussions, and practical applications aimed at enhancing infrastructure performance in increasingly complex and uncertain environments.[1]

References

  1. MDPI. (2026). The Body Underground: A Biological Framework for Infrastructure Health, Regulation and Resilience.
    https://doi.org/10.3390/urbansci10040201
  2. MDPI. (2026). Buildings Journal: Urban Science.
    https://www.mdpi.com/journal/urbansci
  3. Elsevier. (n.d.). Scopus author details: Priscilla Nelson, Author ID 7402246675. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=7402246675
  4. Wiley Online Library. (2025). Beyond Equations: From Models to Materials to Society: Reframing the Future of Underground Engineering.
    https://doi.org/10.1002/jci3.70012
  5. Google Scholar. (n.d.). Scholar profile and citation metrics for Priscilla Nelson.
    https://scholar.google.com/citations?user=3hezpIkAAAAJ&hl=en

Jianbin Chen | Engineering | Best Researcher Award

Published on by Best Paper Awards

Mr. Jianbin Chen | Engineering | Best Researcher Award

Chief Technology Officer | Guangdong Titans Intelligent Power Company Ltd | China

Mr. Jianbin Chen is a distinguished engineering professional with more than 11 years of expertise in digital signal design, iterative coding, data storage and communication systems, and the integration of power and wireless communication technologies in the Internet of Things (IoT). He currently serves as the Executive Vice President and R&D Director at Guangdong Titan Intelligent Power Co., Ltd., in addition to holding roles as a Senior Engineer, IEEE member, off-campus master’s mentor at Nanchang Institute of Technology, and Visiting Associate Professor at Guangdong Polytechnic of Science and Technology. Mr. Chen earned his bachelor’s degree from North Central University in 2011 and completed his Ph.D. at the University of Macau in 2021. Throughout his career, he has led and executed numerous high-impact projects, including intelligent air conditioning energy control systems, IoT-based smart street lighting systems, and advanced energy consumption control platforms for major infrastructure. He has overseen several provincial and municipal innovation programs, demonstrating strong leadership in research and technology development. Mr. Chen has secured 30 patents and 28 software copyrights, with many of his innovations being successfully commercialized and widely recognized. His outstanding contributions have earned him multiple prestigious honors such as the Zhuhai Talent Program, the Best Software Technology Innovation Product Awards, and national innovation competition prizes. Academically, he has published influential research papers and a book, with his work featured in SCI-indexed journals, covering topics like power electronics, intelligent control systems, and smart cities. His ability to combine advanced research with industrial applications has significantly contributed to the development of smart energy and IoT technologies in China. Mr. Chen’s visionary leadership, technical excellence, and dedication to innovation position him as a key figure in advancing intelligent infrastructure and sustainable technology solutions for the future.

Profile: ORCID
Featured Publications

  1. Chen, J., Yang, C., Zou, J., & Chen, K. (2025). Multiplier operated controller for CCM boost PFC converter with regulated input impedance and improved power factor. IEEE Access. DOI: 10.1109/ACCESS.2025.3548096

  2. Chen, J., Yang, C., & Zou, J. (2025). Optimization control strategy of wide ZVS range and automatic Euler angle for bi-directional wireless power transfer system by TPS. International Journal of Electrical Power & Energy Systems. DOI: 10.1016/j.ijepes.2025.111133

  3. Chen, J., Yang, C., & Zou, J. (2022). Robust enhanced voltage range control for industrial robot chargers. IEEE Access. DOI: 10.1109/ACCESS.2022.3229688

  4. Chen, J., Yang, C., Tang, S., & Zou, J. (2021). A high power interleaved parallel topology full-bridge LLC converter for off-board charger. IEEE Access. DOI: 10.1109/ACCESS.2021.3130051

  5. Chen, J. (2017). SMT物料种类与标准. 电子工业出版社. ISBN: 978-7-121-31740-8

Xingyu Zhou | Engineering | Best Researcher Award

Published on by Best Paper Awards

Prof. Dr. Xingyu Zhou | Engineering | Best Researcher Award 

Assistant Professor | Beijing Institute of Technology | China

Dr. Zhou Xingyu, Assistant Professor at the Beijing Institute of Technology, is an accomplished researcher specializing in renewable energy and electric vehicles. He earned his Ph.D. in Vehicle Engineering from Chongqing University in 2020, following a Bachelor’s degree in Mechanical Design, Manufacturing, and Automation from the same institution. Dr. Zhou has extensive professional experience, including his current role as Assistant Professor at the School of Mechanical Engineering and Vehicle Engineering, Beijing Institute of Technology since March 2023, and a postdoctoral fellowship at the same institute from 2020 to 2023, where he contributed to the National Engineering Research Center for Electric Vehicles. His research interests focus on vehicle powertrain optimization, intelligent energy management, stochastic and data-driven modeling, and electric vehicle motion planning for enhanced energy efficiency. He has demonstrated expertise in multi-objective optimization, machine learning applications for powertrain design, and integration of fuel cell and hybrid electric vehicle systems. Dr. Zhou has led and participated in multiple high-impact research projects, including a National Natural Science Foundation of China Youth Project and key provincial and national projects on electric vehicle energy optimization and system integration. He has published 27 Scopus-indexed documents with 448 citations and an h-index of 11, in reputed journals such as Applied Energy, Journal of Power Sources, Journal of Cleaner Production, and IEEE Transactions on Vehicular Technology, serving frequently as corresponding author. His awards and honors include the Best Student Paper Award at the 2018 Italian Conference on Machines and Mechanisms. In addition, he contributes to the academic community as a reviewer for top journals and Guest Editor of Sustainability. Dr. Zhou Xingyu’s strong technical expertise, leadership in research projects, international collaborations, and commitment to sustainable innovation make him a highly deserving candidate for the Best Researcher Award, reflecting both outstanding academic achievements and meaningful contributions to advancing green mobility and energy-efficient transportation solutions globally.

Profiles: Scopus | ORCID

Featured Publications

Sun, C., Zhang, C., Sun, F., & Zhou, X. (2022). Stochastic co-optimization of speed planning and powertrain control with dynamic probabilistic constraints for safe and ecological driving. Applied Energy, 35, 119874.

Zhou, X., Sun, C., Sun, F., & Zhang, C. (2022). Commuting-pattern-oriented optimal sizing of electric vehicle powertrain based on stochastic optimization. Journal of Power Sources, 545, 23178.

Zhou, X., Sun, F., Zhang, C., & Sun, C. (2022). Stochastically predictive co-optimization of speed planning and powertrain controls for electric vehicles driving in random traffic environment safely and efficiently. Journal of Power Sources, 528, 231200.

Zhou, X., Sun, F., Sun, C., & Zhang, C. (2022). Predictive co-optimization of speed planning and powertrain energy management for electric vehicles driving in traffic scenarios: Combining strengths of simultaneous and hierarchical methods. Journal of Power Sources, 523, 230910.

Zhou, X., Sun, F., & Sun, C. (2021). Machine learning aided methods for reducing the dimensionality of the comprehensive energy economy optimization of fuel cell powertrains. Journal of Cleaner Production, 327, 129250.

Naghi Rostami | Engineering | Best Researcher Award

Published on by Best Paper Awards

Assoc. Prof. Dr. Naghi Rostami | Engineering | Best Researcher Award 

Faculty of Electrical and Computer Engineering | University of Tabriz | Iran

Dr. Naghi Rostami is an accomplished academic and researcher in electrical power engineering, currently serving as Associate Professor and Head of the Electric Power Engineering Department at the University of Tabriz, Iran, where he has held leadership responsibilities from 2018 to 2024. He completed his B.Sc. in Electrical Engineering at K. N. Toosi University of Technology in 2006, his M.Sc. at the University of Tehran in 2008, and earned his Ph.D. from the University of Tabriz in 2013. He also gained international exposure through a research opportunity at Lappeenranta University of Technology, Finland, in 2012 under the supervision of Prof. Juha Pyrhönen. His primary research interests include permanent magnet machine design, particularly axial flux and radial flux configurations, hybrid electric vehicle energy management, modeling and optimization of electrical machines, and the integration of renewable energy systems with electric vehicles and storage technologies. Dr. Rostami’s research skills span analytical and numerical design methods, genetic algorithms, particle swarm optimization, and advanced co-simulation approaches, which he has applied to the design and performance improvement of permanent magnet machines and energy systems. He has an impressive record of publications in reputable journals such as IEEE Transactions on Magnetics, IET Electric Power Applications, Sustainable Cities and Society, and COMPEL, with many works indexed in Scopus and IEEE Xplore. His Google Scholar profile records more than 1,500 citations with an H-index of 19, highlighting the international recognition of his work. While his CV does not list specific awards and honors, his achievements in leadership, international collaborations, and sustained scholarly contributions stand as testaments to his professional excellence. In conclusion, Dr. Rostami is a dedicated scholar whose expertise, impactful publications, and leadership in academia and research make him a strong candidate for recognition through distinguished awards and honors in the field of electrical power engineering.

Profiles: Google Scholar | LinkedIn | ResearchGate

Featured Publications

  1. Jalilzadeh, T., Rostami, N., Babaei, E., & Maalandish, M. (2018). Nonisolated topology for high step-up DC–DC converters. IEEE Journal of Emerging and Selected Topics in Power Electronics, 11(1), 137–150.(Cited 137 times)

  2. Rostami, N., Feyzi, M. R., Pyrhonen, J., Parviainen, A., & Niemela, M. (2012). Lumped-parameter thermal model for axial flux permanent magnet machines. IEEE Transactions on Magnetics, 49(3), 1178–1184.(Cited 136 times)

  3. Zeynali, S., Rostami, N., Ahmadian, A., & Elkamel, A. (2020). Two-stage stochastic home energy management strategy considering electric vehicle and battery energy storage system: An ANN-based scenario generation methodology. Sustainable Energy Technologies and Assessments, 39, 100722.(Cited 104 times)

  4. Marzang, V., Hosseini, S. H., Rostami, N., Alavi, P., & Mohseni, P. (2020). A high step-up nonisolated DC–DC converter with flexible voltage gain. IEEE Transactions on Power Electronics, 35(10), 10489–10500.(Cited 96 times)

  5. Zeynali, S., Rostami, N., & Feyzi, M. R. (2020). Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering uncertainties. International Journal of Electrical Power & Energy Systems, 119, 105885.(Cited 93 times)