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.

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