Shui Yu | Reliability analysis and design optimization | Best Researcher Award

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Dr. Shui Yu | Reliability analysis and design optimization | Best Researcher Award

Yu Shui is an Associate Researcher at the University of Electronic Science and Technology of China, with a Ph.D. in Engineering and extensive academic and research experience in reliability analysis, robust design, and AI-driven robotics. He has previously held postdoctoral and lecturer roles at UESTC and Southwest Jiaotong University, respectively. His research spans intelligent systems, robust optimization, and reliability engineering, with publications in top-tier journals like Reliability Engineering & System Safety. His academic path reflects a strong commitment to developing advanced models and frameworks for time-variant reliability design and intelligent algorithms. He is an active researcher contributing to the frontiers of artificial intelligence in engineering systems.

Profile

Education 🎓

Yu Shui completed both his Bachelor’s (2009.09–2013.06) and Ph.D. (2013.09–2019.06) degrees at the University of Electronic Science and Technology of China (UESTC), majoring in engineering fields related to system reliability and optimization. His academic training provided a rigorous foundation in theoretical modeling, numerical simulations, and intelligent systems. During his doctoral studies, he focused on reliability design and probabilistic modeling under uncertainty, incorporating machine learning techniques into engineering optimization. He worked under distinguished mentors, gaining expertise in both the practical and theoretical aspects of engineering reliability. His Ph.D. research laid the groundwork for innovative solutions to complex, real-world reliability issues using AI methods.

Experience 👨‍🏫

Yu Shui started his academic career with a postdoctoral position (2019.07–2021.07) at UESTC, focusing on intelligent algorithms in reliability systems. From 2021.07 to 2024.03, he worked as a Lecturer at Southwest Jiaotong University, where he led courses and supervised research in design optimization and AI applications. In March 2024, he returned to UESTC as an Associate Researcher, contributing to high-impact projects in robotics and reliability engineering. Throughout his career, he has collaborated on interdisciplinary projects involving surrogate modeling, dynamic pruning methods, and AI-driven design optimization, earning recognition for both teaching and research contributions.

Research Interests 🔬

Yu Shui’s research centers on reliability analysis, robust design, intelligent robotics, and artificial intelligence. He develops optimization frameworks and surrogate models to improve the performance and resilience of complex engineering systems. His work incorporates Bayesian regression, dynamic pruning, and demand-objective frameworks for time-variant reliability-based design. His interdisciplinary focus bridges engineering with machine learning, pushing the boundaries of how intelligent systems can manage uncertainty in design and operations. He is particularly interested in integrating AI techniques into robust mechanical systems to enhance reliability in real-world applications.

Publications

  • Empirical Examination of the Interactions Between Healthcare Professionals and Patients Within Hospital Environments—A Pilot Study

    Hygiene
    2025-05-08 | Journal article
    DOI: 10.3390/hygiene5020020
    CONTRIBUTORS: Dimitris Charalambos Karaferis; Dimitris A. Niakas

  • Digitalization and Artificial Intelligence as Motivators for Healthcare Professionals

    Japan Journal of Research
    2025-01-01 | Journal article
    DOI: 10.33425/2690-8077.1170
    CONTRIBUTORS: Karaferis Dimitris; Balaska Dimitra; Pollalis Yanni

  • Workplace Violence in Healthcare: Effects and Preventive Measures and Strategies

    SunText Review of Case Reports & Images
    2024 | Journal article
    DOI: 10.51737/2766-4589.2024.141
    Part ofISSN: 2766-4589
    CONTRIBUTORS: Karaferis D; Balaska D

  • Enhancement of Patient Engagement and Healthcare Delivery Through the Utilization of Artificial Intelligence (AI) Technologies

    Austin Journal of Clinical Medicine
    2024-11-15 | Journal article
    DOI: 10.26420/austinjclinmed.2024.1053
    Part of ISSN: 2381-9146
    CONTRIBUTORS: Department of Economic Science, University of Piraeus, Piraeus, Greece; Dimitris Karaferis; Dimitra Balaska; Department of Economic Science, University of Piraeus, Piraeus, Greece; Yannis Pollalis; Department of Economic Science, University of Piraeus, Piraeus, Greece

Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

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Prof. Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

Mingshun Jiang is a professor at the School of Control Science and Engineering, Shandong University 🎓. He is a doctoral supervisor and a young expert of Mount Taishan Scholars 🌟. He serves as the director of the Shandong Engineering Research Center for Intelligent Sensor and Detection Technology 🔬 and deputy director of the Institute of Intelligent Perception 🏛️. His research primarily focuses on intelligent sensors and detection technologies, with over 20 funded projects, including the National Natural Science Foundation and the National Key R&D Program 🏆. He has authored 60+ high-level academic papers in renowned journals 📑. His innovative contributions aim at monitoring complex structural states in high-end equipment 🚀. With extensive industry collaborations, his work has applications in aerospace, rail transit, and military technology 🛰️🚆.

Profile

Education 🎓

Mingshun Jiang earned his doctoral degree in Control Science and Engineering from Shandong University 🎓. His academic journey focused on developing intelligent sensor systems and detection methodologies 📡. His research expertise was cultivated through interdisciplinary learning, integrating control science, artificial intelligence, and structural health monitoring 🤖. His doctoral research emphasized advanced ultrasonic-guided wave detection and probabilistic diagnostic imaging techniques 🏗️. Jiang’s educational background provided him with expertise in designing smart sensor networks, optimizing detection mechanisms, and enhancing structural health monitoring systems ⚙️. With strong mathematical and engineering foundations, he developed novel algorithms for real-time damage localization and predictive maintenance 📊. His continuous learning and research efforts have been instrumental in bridging technological gaps in aerospace, rail transit, and high-end industrial applications 🚆✈️.

Experience 👨‍🏫

Mingshun Jiang has extensive research and academic experience, currently serving as a professor at Shandong University 🏛️. He has led over 20 major research projects, including the National Natural Science Foundation and National Key R&D Program 🌍. As the director of the Shandong Engineering Research Center, he focuses on intelligent sensor development and detection technologies 🔍. His research has been successfully applied in aerospace, rail transit, and high-end industrial monitoring 🚀🚆. He has supervised numerous doctoral students and collaborated with various enterprises on engineering solutions 🏗️. Jiang has also played a key role in technical verification and real-world applications of his research findings 📡. His leadership in academia and industry-driven research has established him as a leading expert in intelligent perception and structural health monitoring 🏆.

Awards & Recognitions 🏅

Mingshun Jiang has received multiple prestigious recognitions, including being a young expert of Mount Taishan Scholars in Shandong Province 🌟. His work has been supported by national and provincial funding agencies, highlighting his contributions to intelligent sensor technology 🏆. He has been awarded numerous grants under the National Natural Science Foundation and National Key R&D Program 🎖️. Jiang’s research achievements have been recognized through invited talks at leading academic conferences and industry collaborations 🤝. He has served as an executive director of the China Inspection and Testing Society, further solidifying his reputation in the field 🔬. His high-impact publications in top-tier journals have earned him accolades for innovation and research excellence 📑. Jiang continues to receive recognition for his contributions to the monitoring of complex structural states in high-end equipment 🚀.

Research Interests 🔬

Mingshun Jiang’s research focuses on intelligent sensors, structural health monitoring, and detection technology 📡. His work integrates artificial intelligence, probabilistic diagnostic imaging, and ultrasonic-guided wave techniques for real-time damage localization and predictive maintenance 🏗️. Jiang has developed innovative methodologies for monitoring key structural indicators such as boundary loads, damage detection, and component failures 🚆. His research aims to bridge the gap between technological innovation and application in aerospace, rail transit, and industrial monitoring 🛰️. His team has successfully engineered high-end monitoring systems that have undergone technical validation and real-world implementation 🔍. Jiang’s expertise extends to developing smart sensing layers for structural health monitoring, contributing to safer and more efficient industrial systems ⚙️. Through his interdisciplinary research, he continues to advance intelligent perception systems for next-generation monitoring applications 🚀.

Publications 
  • Ruijie Song, Lingyu Sun, Yumeng Gao, Juntao Wei, Chang Peng, Longqing Fan andMingshun Jiang*. Unsupervised temperature-compensated damage localization method based on damage to baseline autoencoder and delay-based probabilistic imaging. Mechanical Systems and Signal Processing, 230: 112649, 2025.
  • Hong Zhang ,Feiyu Teng , Juntao Wei , Shanshan Lv , Lei Zhang , Faye Zhang  and Mingshun Jiang*. Damage Location Method of Pipeline Structure by Ultrasonic Guided Wave Based on Probability Fusion.  IEEE Transactions on Instrumentation and Measurement, 73, 9504914, 2024.
  • . LingyuSun , Juntao Wei , Chang Peng , Wei Hao , Feiyu Teng , Longqing Fan , Lei Zhang , Qingmei Sui  and Mingshun Jiang. Ultrasonic guided wave-based probabilistic diagnostic imaging method with Single-Path-Scattering sparse reconstruction for Multi-Damage detection in composite structures.  Mechanical Systems and Signal Processing, 223, 111858, 2024.
  • XiaoshuQin , Shanshan Lv , Changhang Xu , Jing Xie , Lei Jia , Qingmei Sui  and Mingshun Jiang*. Implications of liquid impurities filled in breaking cracks on nonlinear acoustic modulation response: Mechanisms, phenomena and potential applications.  Mechanical Systems and Signal Processing, 200, 110550, 2023.
  • Shanshan Lv , Juntao Wei  and Mingshun Jiang*. Damage localization method for plate-like composite structure based on valid path optimization and search point matching.  Mechanical Systems and Signal Processing, 182, 109562, 2023.

Partha Sengupta | Structural Health Monitoring | Best Researcher Award

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Dr.Partha Sengupta | Structural Health Monitoring | Best Researcher Award

 

AECOM,India

Profile

Education

He holds a Ph.D. in Civil Engineering from the Indian Institute of Engineering Science and Technology (IIEST), Shibpur (2018–2023), with a perfect CGPA of 10/10. His doctoral research focused on “Finite Element Model Updating of Structures in Bayesian Framework and Enhanced Model Reduction Techniques.” Prior to this, he completed his M.Tech in Civil Engineering from IIEST, Shibpur (2014–2016), earning a CGPA of 9.03/10, with a thesis on the “Application of Ground Penetrating Radar in Concrete Evaluation, Pavement Profile, and Utility Detection.” He obtained his B.Tech in Civil Engineering from West Bengal University of Technology (2010–2014) with a CGPA of 9.02/10.

Professional Experience

Dr. Partha Sengupta conducts research in Structural Health Monitoring, focusing on model updating within a Bayesian framework using an enhanced model reduction technique with incomplete modal and time history response data. His work involves developing an iterative model reduction technique in the frequency domain by eliminating stiffness terms from the transformation equation, effectively mapping the full model and predicting its dynamic responses. The modified equation depends on measured modal responses and invariant mass matrices, eliminating the need for repeated evaluations of stiffness terms typically required in structural health monitoring (SHM) updating algorithms. Furthermore, this model reduction approach is integrated with a sub-structuring scheme, making it applicable to large finite element models. Additionally, Dr. Sengupta has developed an improved Bayesian model updating technique within the Transitional Markov Chain Monte Carlo (TMCMC) framework in the frequency domain, incorporating modifications to enhance the TMCMC algorithm.

AWARDS & ACHIEVEMENTS:

He received the Professor Amiya K. Basu Research Award in Structural Dynamics from the Department of Civil Engineering, IIEST Shibpur, and an additional monthly stipend of ₹10,000 from MHRD in 2022, along with his institute fellowship. He was also awarded the Best Paper Award in “Control and Health Monitoring” at the International Conference on Materials, Mechanics, and Structures (ICMMS 2020) in Kozhikode, India. As a reviewer, he has contributed to the G20 C20 International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development, published by Springer Nature, as well as Engineering Structures and Computer Methods in Applied Mechanics and Engineering, prestigious SCI journals published by Elsevier. Additionally, he received the Ministry of Human Resource and Development (MHRD) Institute Fellowship for pursuing his Ph.D. and M.Tech, having qualified GATE 2014 with a 99 percentile.

Publications