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

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
    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
    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
    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
    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

Elyas Rostami | Mechanical Engineering | Best Researcher Award

Dr. Elyas Rostami | Mechanical Engineering | Best Researcher Award

Dr. Elyas Rostami is a distinguished faculty member at Buein Zahra Technical and Engineering University, specializing in Aerospace Engineering 🚀. With a solid academic background, he earned his B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, followed by an M.Sc. in Aerospace Engineering ✈️ from Tarbiat Modares University, and completed his PhD 🎓 at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles 📄, books 📚, and research projects 🔍 to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization 💧, fluid mechanics 🌊, and sustainable energy applications ⚡. As a dedicated educator 👨‍🏫 and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations 🤝, publishing, and mentoring.

Profile

Education 🎓

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, laying the foundation for his technical expertise. He pursued his M.Sc. in Aerospace Engineering ✈️ at Tarbiat Modares University, where he refined his knowledge in aerodynamics, propulsion, and fluid mechanics 🌬️. Driven by academic excellence, he earned his Ph.D. in Aerospace Engineering 🚀 from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems 🔥, and energy-efficient solutions 💡. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems 🔧. Through rigorous coursework, experimental research, and publications 📝, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠💪, enabling him to address modern aerospace challenges with innovative thinking 🚀.

Experience 👨‍🏫

Dr. Elyas Rostami serves as a faculty member 👨‍🏫 at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering 🚀. His career spans academic teaching 📖, research leadership 🔬, article peer reviewing 🧐, and supervising research projects 🔍. He has authored multiple scientific papers 📝 and books 📚, advancing the field of propulsion systems, spray technology 💧, and fluid mechanics 🌊. Beyond academia, Dr. Rostami actively engages with industry via research collaborations 🤝 and applied projects that explore new energies 🌱 and aerospace applications ✈️. His professional journey demonstrates versatility and dedication, contributing both as an educator and innovator 🎓⚙️. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions 🏅

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction 🎖️. His scholarly contributions earned him recognition through research publications in reputable journals 🏅, invitations to review articles 🧐, and participation in collaborative research projects 🤝. He has authored a scientific book 📘 and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion 🔥 and fluid dynamics 🌊 has positioned him as a valued academic and research professional. The depth of his work reflects both national and international acknowledgment, solidifying his reputation as a committed and impactful researcher 🌍. Dr. Rostami’s work embodies passion for engineering, teaching excellence, and scientific advancement 💡🔬.

Research Interests 🔬

Dr. Elyas Rostami’s research focuses on fluid mechanics 🌊, aerospace propulsion systems 🚀, spray and atomization processes 💧, and the use of renewable energies in industrial applications 🌱. His investigations address the thermodynamics of propulsion systems 🔥, advancing efficiency and performance in aerospace technology ✈️. He integrates experimental and computational approaches 🖥️ to explore new energy solutions and optimize spray behavior under different conditions ⚙️. Through research projects 🔬, journal articles 📄, and book authorship 📚, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation 💡, making significant strides in the development of sustainable and high-performance propulsion technologies 🌍🚀.

Publications 

Lichen Shi | Mechanical Engineering | Best Researcher Award

Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

 

Profile

Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

  • Developing an AI-based method for reading pointer meters using human-like reading sequences.
  • Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
  • Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
  • Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
  • Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

  • Intelligent Measurement with Deep Learning
  • Equipment Status Monitoring and Fault Diagnosis
  • Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

  • [1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
  • [2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) :25186-25196. doi:
    10.1109/JSEN.2024.3417231
  • [3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
    Diagnosis of Rotating Machinery[J]. IEEE Sensors Journal, 2024,12:34785-34799. DOI:
    10.1109/access.2024.3368755.
  • 4] Haitao Wang, Mingjun Li, Zelin Liu, Xiyang Dai, Ruihua Wang and Lichen Shi*. RotaryMachinery Fault Diagnosis Based on Split Attention MechanismandGraphConvolutional Domain Adaptive Adversarial Network[J]. IEEE Sensors Journal, 2024,
    24(4) :5399-5413. doi: 10.1109/JSEN.2023.3348597.
  • [5] Haitao Wang, Xiyang Dai, Lichen Shi*. Gearbox Fault Diagnosis Based onMixedData-Assisted Multi-Source Domain Transfer Learning under Unbalanced Data[J]. IEEESensors Journal. doi: 10.1109/JSEN.2024.3477929

 

Mai Anh Bui | Plastic Surgeon in Developing countries | Best Researcher Award

Prof Dr. Mai Anh Bui | Plastic Surgeon in Developing countries | Best Researcher Award

 

Profile

Education

Dr. Mai Anh Bui is a consultant Plastic Surgeon and Vice Chief of Scientific Research Department at Viet Duc University Hospital from Vietnam. She is also Assist. Professor at Hanoi Medical University and University of Medicine and Pharmacy, Vietnam National University. She completed a plastic surgery residency in 2006. Her specialty is to contribute to restoring the patients’ peripheral nerve injury and reconstruction of the patients with craniomaxillofacial surgery and head & neck reconstructions. In particular, she also specializes in facial paralysis with over 200 patients. Her Ph.D. is on the Using Masseteric nerve in Facial Reanimation. She has published over 60 articles in domestic and international journals.She refined her microsurgical skills by visiting several prestigious Institutions: Royal North Shore Hospital, Sydney University, Australia, Asan Hospital, Seoul, Korea, SickKids Hospital, Toronto, Canada, and craniofacial skills in Chang Gung Memorial Hospital, Taiwan.

Work experience

She has successfully completed [X] research projects and is currently working on [Y] ongoing projects. Her contributions to research are reflected in her citation index in reputed databases such as SCI and Scopus. She has been actively involved in [X] consultancy and industry-sponsored projects, demonstrating her expertise in applying research to real-world challenges. Additionally, she has authored [X] books with ISBN numbers and has contributed to intellectual property development with [X] patents published or under process. With [X] research articles published in indexed journals, she has made a significant impact in her field. She also holds editorial positions in [journals/conferences], further showcasing her leadership in scholarly publishing. Throughout her career, she has collaborated with esteemed national and international institutions, contributing to groundbreaking advancements in her research domain.

Publication

  • Outcome of using the spinal accessory nerve for functional muscle innervation in facial paralysis reconstruction: The first two cases in Vietnam and literature review

    Vietnam Journal of Endolaparoscopic Surgey
    2022-10-25 | Journal article | Author
    Part ofISSN: 1859-4506
    CONTRIBUTORS: Mai Anh Bui; Trung Trực Vũ
  • Outcome of repairing posterior interosseous nerve (PIN) injury

    Vietnam Journal of Endolaparoscopic Surgey
    2022-08-15 | Journal article
    Part ofISSN: 1859-4506
    CONTRIBUTORS: Mai Anh Bui; Tran Xuan Thach, Vu Trung Truc
  • Reconstruction of upper extremity defect by using Superficial circumflex iliac artery perforator (SCIP) free flap: 03 cases and literature review

    Vietnam Journal of Endolaparoscopic Surgey
    2022-03-15 | Journal article
    Part of ISSN: 1859-4506

Longqing Cui | Operations research | Best Researcher Award

Dr. Longqing Cui | Operations research | Best Researcher Award

 

Profile

Education

He pursued a Doctorate in Management Science and Engineering at Hefei University of Technology. Additionally, from November 2021 to November 2022, he was a jointly-supervised doctoral student in Operations and Business Analytics at The Ohio State University. Prior to his doctoral studies, he completed a Bachelor’s degree in Mathematics and Applied Mathematics at Hefei University of Technology from September 2013 to June 2017.

Work experience

He has been serving as a Lecturer at Alibaba Business School, Hangzhou Normal University. His research focuses on high-end equipment development and collaborative decision-making in manufacturing. He is the Principal Investigator (PI) for two ongoing projects. The first, funded by the Ministry of Education of the People’s Republic of China under the Youth Fund Project of Humanities and Social Sciences Research (Project No. 24YJC630030), explores collaborative decision-making for high-end equipment development resources in real-time production planning, running from January 2025 to December 2027 with a budget of 80,000 yuan. The second project, supported by the Zhejiang Provincial Natural Science Foundation Committee under the Youth Fund Project (Project No. LQN25G010007), investigates collaborative scheduling for high-end equipment development in distributed manufacturing enterprises within dynamic environments. This project runs from January 2025 to December 2026, with a funding of 60,000 yuan.

Publication

  • 1] Longqing Cui; Xinbao Liu; Shaojun Lu; Zhaoli Jia. A variable neighborhood
    search approach for the resource – constrained multi – project collaborative
    scheduling problem. Applied Soft Computing, 2021, 107:107480. (Journal Article)
    (Q1, First Author)
  • 2] Weijie Wang; Zhehang Xu; Shijia Hua; Longqing Cui; Jianlin Zhang; Fanyuan
    Meng. Threshold – initiated spatial public goods games. Chaos, Solitons & Fractals,
    2024, 184:115003. (Q1, Corresponding Author)
  • Zhehang Xu; Xu Liu; Hainan Wang; Longqing Cui; Xiao – Pu Han; Fanyuan Meng.
    Free – rider or contributor: A dilemma in spatial threshold public goods games.
    Chaos, Solitons & Fractals, 2024, 187:115455. (Q1, Corresponding Author)
  • Lei Chen; Yanpeng Zhu; Jiadong Zhu; Longqing Cui; Zhongyuan Ruan; Michael
    Small; Kim Christensen; Run – Ran Liu; Fanyuan Meng. A simple model of global
    cascades on random hypergraphs. Chaos, Solitons and Fractals, 2025, 193(116081).
    (Q1, Corresponding Author)
  • Che Xu; Yingming Zhu; Peng Zhu; Longqing Cui. Meta – learning – based sample
    discrimination framework for improving dynamic selection of classifiers under
    label noise. Knowledge – Based Systems, 2024, 295:111811. (Q1)