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.

Daniel Salomone Gonzalez | Engineering | Best Researcher Award

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Dr.Daniel Salomone Gonzalez |Engineering| Best Researcher Award

Phd. Ingeniero Consultor,Segingenieria ,Uruguay

Daniel Salomone González  is a distinguished Consultant Engineer in Energy Efficiency with SEG Ingeniería and serves as an independent expert across Latin America, the Caribbean, and Africa. With over 14 years of experience in the energy sector , Daniel has made significant contributions to the optimization of thermal systems, renewable energy integration, and large-scale efficiency audits . He holds a Ph.D. in Energy Engineering and has published cutting-edge research in leading journals . Daniel is also a lecturer at UNIT, where he trains the next generation of energy professionals. His career is marked by international collaborations, notably with institutions in Spain , Argentina , and Brazil . His passion lies in transforming energy systems toward a sustainable future through innovative, science-based solutions . He combines academic rigor with hands-on industrial experience, making him a leading voice in energy efficiency across developing regions.

Professional Profile

ORCID

Education & Experience 

Daniel is a Chemical Engineer  by training, with a Master’s and Ph.D. in Energy Engineering  from the University of the Republic, Uruguay . With more than 14 years of multidisciplinary expertise, he has specialized in energy audits, thermodynamic modeling, and renewable systems integration . His hands-on experience spans over 100 consultancy projects, including public, commercial, and industrial sectors. A dynamic educator , he lectures at UNIT, where he teaches future engineers about energy management and sustainability. His expertise is regularly sought after by governments and research institutions across the Caribbean, Africa, and Latin America . Whether leading a national-level energy audit or advising on heat storage modeling, Daniel blends academic precision with practical implementation, making him a trusted leader in energy systems engineering. His technical insights and field-tested solutions are vital for regions seeking energy transition and carbon reduction .

Professional Development 

Daniel has earned several distinguished professional certifications that reinforce his credibility in the energy sector. He is a Certified Measurement & Verification Professional (CMVP)® from EVO , a recognized ISO 50001 Energy Auditor , and an active instructor at UNIT , Uruguay’s national technical institute. He continuously enhances his professional scope through international collaborations with institutions like the University of Salamanca , ITCL Research Center, and the Saint Lucia Government . His leadership in energy audits and consultancy projects for public and industrial clients has contributed to transformative energy-saving strategies . As a lifelong learner, Daniel remains engaged in the latest energy innovations, such as heat pump energy storage systems and biomass integration . His role as a bridge between academia, industry, and policy not only elevates his career but also amplifies the impact of sustainable technologies across global regions. He exemplifies continual growth in the clean energy sector .

Research Focus 

Daniel’s research is rooted in the science and application of sustainable energy systems. His primary focus areas include pumped heat energy storage systems , thermodynamic modeling , biomass heating solutions , and energy management frameworks . With six completed or ongoing research projects and 5 peer-reviewed publications in high-impact journals like Energy Conversion and Management and Journal of Energy Storage, his work blends innovation with practicality. Daniel develops simulation-based models to optimize energy storage and consumption, a critical area for regions with intermittent energy supplies . His deep learning in system optimization enables industries and municipalities to reduce their carbon footprint effectively . Collaborating with universities and government bodies, Daniel ensures his research has both academic and on-ground impact. He consistently brings theoretical advancements to life through real-world applications, ensuring his work contributes to a cleaner, more efficient energy future .

Awards & Honors 

Daniel’s excellence in the energy field has earned him notable recognition. He was awarded first prize for both his master’s and Ph.D. theses , reflecting his academic brilliance and innovative contributions to energy systems. His practical work in consultancy has led to significant energy savings across over 100 major projects, earning acclaim from regional governments and international institutions . His expertise has positioned him as a preferred consultant and lecturer in both academic and industry circles. While he has not yet published books or registered patents, his published works in SCI-indexed journals and his role in transformative energy audits  make him a standout in applied energy research. His certification as a CMVP and ISO 50001 auditor adds further distinction . These honors highlight his dedication to both innovation and implementation, making him a strong candidate for the Best Researcher Award  at international forums.

Publication of Top Notes

1.Title: Advanced strategies for the efficient optimization and control of industrial compressed air systems

Authors: D. Salomone-González
Year: 2025
Citation: Results in Engineering, DOI: 10.1016/j.rineng.2025.10542

2.Title: Pumped heat energy storage with liquid media: Thermodynamic assessment by a transcritical Rankine-like model

Authors: D. Salomone-González, P.L. Curto-Risso, A. Calvo Hernández, A. Medina, J.M.M. Roco, J. Gonzalez-Ayala
Year: 2022
Citation: Journal of Energy Storage, DOI: 10.1016/j.est.2022.10596

3.Title: Modeling of heat leak effect in round trip efficiency for Brayton pumped heat energy storage with liquid media, by cooling and heating of the reservoirs tanks

Authors: D. Salomone-González
Year: 2022
Citation: Journal of Energy Storage, DOI: 10.1016/j.est.2021.103793

4.Title: Multicriteria optimization of Brayton-like pumped thermal electricity storage with liquid media

Authors: D. Salomone-González
Year: 2021
Citation: Journal of Energy Storage, DOI: 10.1016/j.est.2021.103242

5.Title: Pumped heat energy storage with liquid media: Thermodynamic assessment by a Brayton-like model

Authors: D. Salomone-González
Year: 2020
Citation: Energy Conversion and Management, DOI: 10.1016/j.enconman.2020.113540

6.Title: Redes de distribución de calor y frío a partir de biomasa para pequeñas comunidades en Uruguay

Authors: D. Salomone-González
Year: 2020
Citation: Memoria Investigaciones en Ingeniería, DOI: 10.36561/ing.18.2

Conclusion

Daniel Salomone González seamlessly bridges academic excellence and practical engineering innovation. His research has not only been published in top-tier journals but has also delivered measurable energy savings across developing regions. With a solid record of international collaboration, technical leadership, and recognized innovation, Daniel exemplifies the best qualities of a high-impact researcher. He stands out as a leading figure in applied energy research and is therefore an ideal 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