Yu Cheng Wang | Engineering | Best Research Article Award

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Assoc. Prof. Dr. Yu Cheng Wang | Engineering | Best Research Article Award

Aeronautical Engineering Of Chair at Chaoyang University of Technology | Taiwan

Prof. Dr. Yu Cheng Wang  is a distinguished academic and researcher, currently serving as Associate Professor and Chair of the Department of Aeronautical Engineering at Chaoyang University of Technology, Taiwan. He holds a Ph.D. in Industrial Engineering from Feng Chia University and has built a reputation for advancing research at the intersection of aeronautical systems, intelligent manufacturing, and explainable artificial intelligence. With more than publications in SCI and Scopus-indexed journals, his contributions have made significant impact in manufacturing optimization and decision-support systems. He has an h-index of  with over citations, reflecting the scholarly influence of his work. Prof. Wang also collaborates extensively with colleagues across Taiwan and internationally, bridging academic research and industry practice. His work on Industry 4.0 applications in semiconductor manufacturing showcases his commitment to developing transparent and human-centered AI systems that directly address real-world industrial challenges.

Professional Profile

ORCID Profile | Scopus Profile

Education 

Prof. Dr. Yu Cheng Wang pursued his academic journey with a focus on engineering, systems, and innovation. He earned his Ph.D. in Industrial Engineering from Feng Chia University, Taiwan, where his doctoral research laid the foundation for his expertise in intelligent systems and complex manufacturing processes. His educational background reflects a strong balance between theoretical modeling and applied problem-solving. Dr. Wang’s training emphasized operations research, production systems, and the integration of artificial intelligence into industrial applications, which later expanded into explainable AI frameworks for decision support. His solid grounding in industrial engineering principles has allowed him to extend his research into aeronautical systems and semiconductor manufacturing. With this interdisciplinary academic foundation, he has successfully bridged domains such as fuzzy theory, optimization, and smart manufacturing, enabling him to pursue pioneering research in Industry 4.0. His educational journey demonstrates a commitment to combining engineering rigor with innovative technological applications.

Experience 

Prof. Dr. Yu-Cheng Wang has extensive academic and professional experience that combines leadership, research, and industry collaboration. As Department Chair and Associate Professor at Chaoyang University of Technology, he oversees curriculum development, research strategy, and faculty mentorship in aeronautical engineering. His leadership extends to managing cross-disciplinary projects that integrate aeronautical engineering with intelligent manufacturing and artificial intelligence applications. He has spearheaded major research initiatives, including the Industry 4.0 XAI project for wafer-fab output forecasting, a groundbreaking effort that combines machine learning with interpretability for industrial decision-making. His experience also spans consultancy projects that provide practical solutions for semiconductor manufacturing, aligning academic research with industry needs. Prof. Wang’s editorial contributions over appointments demonstrate his recognition as a peer reviewer and thought leader in his field. Through collaborations with colleagues such as Tin-Chih Toly Chen and Chi-Wei Lin, he has broadened his international research presence and strengthened academia-industry knowledge exchange.

Research Interest

Prof. Dr. Yu-Cheng Wang’s research interests lie at the intersection of aeronautical engineering, smart manufacturing, and artificial intelligence. His primary focus is on explainable AI (XAI), where he develops models that not only achieve predictive accuracy but also provide transparency and interpretability for industrial decision-makers. He applies these methods to semiconductor manufacturing, Industry 4.0 environments, and production planning, ensuring that complex systems are optimized while remaining human-understandable. His work extends to fuzzy theory and decision analytics, particularly in contexts where uncertainty and complexity are critical, such as aerospace systems and large-scale industrial operations. Beyond manufacturing, Dr. Wang also explores applications of XAI in training and maintenance, including VR-based approaches for sustainable engineering education. By linking advanced computational models with practical engineering needs, his research contributes to both academic advancement and industry transformation, ensuring technological innovation supports efficiency, sustainability, and human factors integration.

Award and Honor

Prof. Dr. Yu-Cheng Wang has earned recognition for his scholarly contributions and leadership in the fields of aeronautical engineering and artificial intelligence. His publications in high-impact international journals such as The International Journal of Advanced Manufacturing Technology, Complex & Intelligent Systems, and Decision Analytics Journal highlight his academic influence and earned him strong citation metrics, with an h-index of 15 and more than citations. These achievements reflect his standing in the research community. His editorial appointments  across SCI and Scopus-indexed journals demonstrate the trust placed in him as a global reviewer and evaluator of cutting-edge research. He has also been actively involved in industry-driven projects, bridging academia and practical innovation, which further highlights his leadership. Recognition through research funding, collaborations, and invitations to contribute to international projects underscores his role as a thought leader. Collectively, these honors validate his impact as a forward-looking scientist and educator.

Research Skill

Prof. Dr. Yu Cheng Wang possesses a robust set of research skills that combine technical depth with interdisciplinary application. He is proficient in developing explainable AI frameworks, integrating advanced machine learning models with interpretability methods such as SHAP and rule-based surrogates to improve transparency in industrial decision systems. His expertise extends to fuzzy theory, production planning, and smart manufacturing analytics, making him adept at tackling complex and uncertain problems in both aeronautical and industrial domains. He has successfully applied these skills to semiconductor manufacturing, leading research on wafer-fab output forecasting that directly supports industry needs. In addition to computational modeling, Dr. Wang demonstrates strong skills in data analytics, simulation, and optimization, enabling him to bridge theory with real-world application. His experience with large-scale collaborations and consultancy projects further reflects his ability to integrate technical innovation with industry practices, positioning him as both a problem solver and research leader.

Publication Top Notes

Title: An explainable decision model for selecting facility locations in supply chain networks
Authors: Tin-Chih Toly Chen; Yu-Cheng Wang; Yi-Chi Wang
Journal: Supply Chain Analytics
Year: 2025

Title: Enhancing the effectiveness of output projection in wafer fabrication using an Industry 4.0 and XAI approach
Authors: Tin-Chih Toly Chen; Yu-Cheng Wang; Chi-Wei Lin
Journal: The International Journal of Advanced Manufacturing Technology
Year: 2024

Title: Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling
Authors: Yu-Cheng Wang; Toly Chen
Journal: Expert Systems with Applications
Year: 2024

Title: Evaluating innovative future robotic applications in manufacturing using a fuzzy collaborative intelligence approach
Authors: Tin-Chih Toly Chen; Yu-Cheng Wang
Journal: The International Journal of Advanced Manufacturing Technology
Year: 2024

Title: A heterogeneous fuzzy collaborative intelligence approach: Air quality monitor selection study
Authors: Tin-Chih Toly Chen; Yu-Cheng Lin; Yu-Cheng Wang
Journal: Applied Soft Computing
Year: 2023

Title: Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network
Authors: Yu-Cheng Wang
Journal: Journal of Ambient Intelligence and Humanized Computing
Year: 2023

Title: Improving people’s health by burning low-pollution coal to improve air quality for thermal power generation
Authors: Tin-Chih Toly Chen; Teng Chieh Chang; Yu-Cheng Wang
Journal: Digital Health
Year: 2023

Title: A selectively calibrated derivation technique and generalized fuzzy TOPSIS for semiconductor supply chain localization assessment
Authors: Toly Chen; Yu-Cheng Wang; Pin-Hsien Jiang
Journal: Decision Analytics Journal
Year: 2023

Title: New XAI tools for selecting suitable 3D printing facilities in ubiquitous manufacturing
Authors: Yu-Cheng Wang; Toly Chen
Journal: Complex & Intelligent Systems
Year: 2023

Title: A modified random forest incremental interpretation method for explaining artificial and deep neural networks in cycle time prediction
Authors: Toly Chen; Yu-Cheng Wang
Journal: Decision Analytics Journal
Year: 2023

Title: 3D Printer Selection for Aircraft Component Manufacturing Using a Nonlinear FGM and Dependency-Considered Fuzzy VIKOR Approach
Authors: Yu-Cheng Wang; Tin-Chih Toly Chen; Yu-Cheng Lin
Journal: Aerospace
Year: 2023

Title: An efficient approximating alpha-cut operations approach for deriving fuzzy priorities in fuzzy multi-criterion decision-making
Authors: Tin-Chih Toly Chen; Yu-Cheng Wang; Min-Chi Chiu
Journal: Applied Soft Computing
Year: 2023

Title: A novel auto-weighting deep-learning fuzzy collaborative intelligence approach
Authors: Yu-Cheng Wang; Tin-Chih Toly Chen; Hsin-Chieh Wu
Journal: Decision Analytics Journal
Year: 2023

Title: An explainable deep-learning approach for job cycle time prediction
Authors: Yu-Cheng Wang; Toly Chen; Min-Chi Chiu
Journal: Decision Analytics Journal
Year: 2023

Conclusion

Dr. Yu-Cheng Wang has consistently demonstrated academic excellence and research innovation across aeronautical engineering, explainable AI, and smart manufacturing systems. In publications in leading SCI/Scopus-indexed journals, an h-index of , and more than  citations, his work bridges theory with impactful industrial applications, particularly in semiconductor manufacturing and Industry 4.0 transformations. His leadership as Department Chair, coupled with collaborations with renowned scholars, highlights his influence on both research and education. Recognized for advancing interpretable AI for real-world adoption, Dr. Wang’s contributions embody the spirit of innovation, making him a strong and deserving candidate for the Best Researcher Award.

Aixia Yuan | Engineering | Best Researcher Award

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Dr. Aixia Yuan | Engineering | Best Researcher Award

Teacher at Dalian Polytechnic University, China

Dr. Aixia Yuan is a passionate educator and researcher currently serving as a Teacher at Dalian Polytechnic University, China . She received her Ph.D. in Communication and Information Systems from Dalian Maritime University . With a strong background in wireless communication, filters, and radio frequency technology, she has authored and co-authored  research papers in international journals and conferences . Dr. Yuan teaches “Communication Principles”, a course recognized as an “Excellent Course” award-winning program . Her research explores negative group delay circuits, passive filters, RFID, and advanced communication systems . She has actively participated in provincial-level research projects and collaborated with industry partners to translate research into practical innovations . With three patents filed and multiple impactful contributions, Dr. Yuan continues to inspire her students and peers through her dedication to scientific excellence, innovation, and applied communication systems .

Professional Profile

ORCID Profile

Education 

Dr. Aixia Yuan’s educational journey reflects her deep commitment to communication engineering and information systems . She earned her Ph.D. in Communication and Information System from Dalian Maritime University, China.. Her doctoral work focused on advanced communication structures, filters, and negative group delay circuits, laying a strong foundation for her later research and teaching endeavors. Prior to her Ph.D., she built her academic base with solid training in electronics and wireless communication . Beyond her doctoral studies, Dr. Yuan has expanded her expertise through provincial-level education and science planning projects and collaborative academic programs . As a faculty member at Dalian Polytechnic University, she integrates her academic knowledge into teaching, mentoring, and curriculum development, especially in her recognized “Communication Principles” course . Her academic path demonstrates a perfect blend of rigorous theoretical training, applied research skills, and continuous innovation in communication systems .

Experience 

Dr. Aixia Yuan has accumulated extensive teaching and research experience as a faculty member at Dalian Polytechnic University . She has taught the “Communication Principles” course, which earned the prestigious “Excellent Course” award for its innovation in teaching and relevance in communication studies . On the research front, Dr. Yuan has completed five funded research projects and participated in multiple Liaoning provincial education and science initiatives . She has also worked on two projects under the Liaoning Province Department of Education, contributing to applied research in communication and radio frequency systems . Her professional expertise extends to industry collaborations, with five consultancy projects, bridging the gap between academia and practical technological development . publications and three patents filed, her professional journey demonstrates her dedication to advancing communication technologies while nurturing the next generation of engineers and researchers .

Research Interest

Dr. Aixia Yuan research interests lie at the intersection of communication systems and applied circuit design . She is particularly passionate about radio frequency communication (RFID), negative group delay circuits, and lumped parameter passive filters . Her innovative work in designing multifunctional circuits enables the realization of band-pass, high-pass, and low-pass filters with negative group delays without altering circuit structures . This flexibility offers promising applications in broadband communication and signal processing. She also focuses on wireless communication systems, addressing both theoretical foundations and real-world implementations . Dr. Yuan’s interest in goal-oriented design and low-loss broadband solutions reflects her vision of creating practical, scalable, and efficient solutions for next-generation communication systems . Her active engagement in provincial and international projects and multiple journal publications demonstrates her strong commitment to advancing the frontiers of communication science and engineering .

Award and Honor

Dr. Aixia Yuan has earned recognition for her outstanding contributions in research and teaching . Her course, “Communication Principles”, was honored as an “Excellent Course”, highlighting her commitment to academic excellence and innovative pedagogy . She has successfully completed five competitive research projects and contributed to provincial education and science initiatives at both local and national levels . Her groundbreaking research on negative group delay circuits has been widely recognized in international journals and conferences, showcasing her as a rising scholar in communication engineering . Additionally, Dr. Yuan has filed three patents, emphasizing her ability to transform research into practical, innovative solutions . Her impactful publication, “A Novel Multifunctional Negative Group Delay Circuit…” indexed in WOS, has been cited internationally, strengthening her academic reputation . These accomplishments reflect her dedication to advancing communication technologies while inspiring students and peers through excellence in research and teaching .

Research Skill

Dr. Aixia Yuan possesses a diverse skill set in advanced communication systems and circuit design . Her expertise includes designing negative group delay circuits, lumped parameter passive filters, and RFID-based wireless communication systems . She has hands-on experience in theoretical modeling, equation derivation, simulation, and experimental validation . Dr. Yuan is adept at using simulation tools and analytical methods to optimize circuit design for broadband characteristics and low-loss performance . She has successfully authored  publications and filed three patents, demonstrating strong research writing, innovation, and problem-solving skills . Additionally, her experience in provincial projects and industry consultancy highlights her ability to collaborate across academia and industry . Dr. Yuan’s strengths also include mentorship, teaching, and curriculum development, proven through her award-winning course on Communication Principles . Altogether, her research skills combine technical innovation, scientific rigor, and educational excellence, positioning her as a leader in communication science .

Publication Top Note

Title: A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
Authors: Enze Shi, Aixia Yuan, Junzheng Liu, Niannan Chang, Xinqi Guo
Journal: Chips (MDPI)
Year: 2025

Conclusion

Dr. Aixia Yuan has demonstrated remarkable dedication to advancing the fields of communication systems and radio frequency technology . With a Ph.D. in Communication and Information Systems , her academic contributions include over  research papers,  patents, and several impactful projects that have advanced understanding in negative group delay circuits, RFID, and wireless communication. Her published works, such as the  Chips article “A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics  reflect her commitment to high-quality, innovative, and practical research. Through her teaching at Dalian Polytechnic University, where she has also been recognized for excellence in course delivery , she continues to inspire the next generation of engineers and researchers. Her research bridges theoretical innovation with real-world application, enhancing the future of wireless communication and circuit design. Dr. Yuan’s outstanding achievements, integrity, and community impact make her a deserving candidate for the Best Researcher Award .

Vahideh Bafandegan Emroozi| Engineering | Women Researcher Award

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Dr. Vahideh Bafandegan Emroozi| Engineering | Women Researcher Award

Corresponding Author, Ferdowsi university of Mashhad,Iran

Dr. Vahideh Bafandegan Emroozi is a rising academic with a robust publication record, collaborative outlook, and applied interdisciplinary focus. Her work on supply chain optimization, IoT integration, and human reliability is timely and contributes to both industrial efficiency and sustainable development.

Professional Profile:

Scopus

Google scholar

🎓 Education

Vahideh Bafandegan Emroozi holds a Ph.D. in Industrial Management from Ferdowsi University of Mashhad, Iran (2019–2024), with a remarkable GPA of 19.49 out of 20. Her doctoral thesis focuses on developing a maintenance planning model using the Internet of Things (IoT) while accounting for human error. She earned her M.Sc. in Industrial Management from the same university in 2017, graduating with a GPA of 18.96. She began her academic journey with a B.Sc. in Industrial Engineering at Ferdowsi University, graduating in 2012.

Professional Experience

Dr. Bafandegan Emroozi has served as a research fellow at Sanabad University (2023–2024) and Ferdowsi University of Mashhad (2021–2023). In these roles, she has contributed to various multidisciplinary projects focusing on optimization, reliability, and maintenance strategies within industrial systems.

Skills

She brings a strong technical toolkit that includes programming and modeling in Python, MATLAB, GAMS, Vensim, LINGO, LaTeX, UCINET, and Minitab. She is also proficient in MICMAC and Microsoft Office applications, reflecting a solid foundation in both qualitative and quantitative analysis.

Research Interests

r research spans multiple domains, including supply chain management, optimization, maintenance and reliability, human error analysis, inventory control, system dynamics, and mathematical modeling. Her work often explores the intersection of advanced technologies (e.g., IoT) with human-centered decision-making.

✅ Conclusion

Women Researcher Award: Strongly Recommended. Her academic output, innovative scope, and relevance to modern global challenges make her an excellent candidate. Best Researcher Award: Recommended with Reservations. She is well on her way, but continued growth in citations, funding, and global recognition would strengthen her case in the future.

Publication Top Notes:

  • Modares, A., Kazemi, M., Bafandegan Emroozi, V., & Roozkhosh, P. (2023). A new supply chain design to solve supplier selection based on internet of things and delivery reliability. Journal of Industrial and Management Optimization, 19(11), 7993–8028. Cited by: 39

  • Modares, A., Motahari Farimani, N., & Bafandegan Emroozi, V. (2023). A vendor-managed inventory model based on optimal retailers selection and reliability of supply chain. Journal of Industrial and Management Optimization, 19(5), 3075–3106. Cited by: 32

  • Modares, A., Motahari Farimani, N., & Bafandegan Emroozi, V. (2023). A new model to design the suppliers portfolio in newsvendor problem based on product reliability. Journal of Industrial and Management Optimization, 19(6), 4112–4151. Cited by: 25

  • Bafandegan Emroozi, V., Roozkhosh, P., Modares, A., & Roozkhosh, F. (2023). Selecting green suppliers by considering the internet of things and CMCDM approach. Process Integration and Optimization for Sustainability, 7(5), 1167–1189. Cited by: 19

  • Bafandegan Emroozi, V., & Fakoor, A. (2023). A new approach to human error assessment in financial service based on the modified CREAM and DANP. Journal of Industrial and Systems Engineering, 14(4), 95–120. Cited by: 19

  • Bafandegan Emroozi, V., Kazemi, M., Doostparast, M., & Pooya, A. (2024). Improving industrial maintenance efficiency: A holistic approach to integrated production and maintenance planning with human error optimization. Process Integration and Optimization for Sustainability, 8(2), 539–564. Cited by: 18

  • Modares, A., Motahari, N., & Bafandegan Emroozi, V. (2022). Developing a newsvendor model based on the relative competence of suppliers and probable group decision-making. Industrial Management Journal, 14(1), 115–142. Cited by: 18

  • Bafandegan Emroozi, V., Modares, A., & Roozkhosh, P. (2024). A new model to optimize the human reliability based on CREAM and group decision making. Quality and Reliability Engineering International, 40(2), 1079–1109. Cited by: 16

  • Modares, A., Motahari Farimani, N., & Bafandegan Emroozi, V. (2023). Applying a multi-criteria group decision-making method in a probabilistic environment for supplier selection (Case study: Urban railway in Iran). Journal of Optimization in Industrial Engineering, 16(1), 129–140. Cited by: 15

  • Emroozi, V. B., Kazemi, M., Modares, A., & Roozkhosh, P. (2024). Improving quality and reducing costs in supply chain: the developing VIKOR method and optimization. Journal of Industrial and Management Optimization, 20(2), 494–524. Cited by: 1