Xingyu Zhou | Engineering | Research Excellence Award

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Prof. Xingyu Zhou | Engineering | Research Excellence Award

Assistant Professor | Beijing Institute of Technology | China

Prof. Xingyu Zhou is an Associate Professor at the School of Mechanical Engineering, Beijing Institute of Technology, China, specializing in renewable energy systems and electric vehicle (EV) powertrain optimization. His research integrates deep learning, stochastic modeling, and co-optimization techniques to enhance EV efficiency, safety, and ecological performance. He has authored 30 publications, cited over 494 Citations, and maintains collaborations with 36 co-authors, contributing to high-impact journals such as Applied Energy and Journal of Power Sources. Prof. Zhou’s work on predictive speed planning, powertrain energy management, and renewable energy integration advances sustainable transportation solutions, promoting environmentally responsible and technologically innovative mobility systems globally.

Citation Metrics (Scopus)

800

600

400

200

0

Citations
494

Documents
30

h-index
11

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications

Sabitha Vemula | Engineering | Best Researcher Award

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Mrs. Sabitha Vemula | Engineering | Best Researcher Award

Associate Professor | Vaagdevi College Of Engineering | India

Mrs. Sabitha Vemula  is an Associate Professor at Vaagdevi College of Engineering with expertise in signal and image processing, medical image analysis, machine learning, and VLSI-related systems. Her research is strongly focused on MRI-based brain tumor detection and classification, integrating kernel methods, support vector machines, convolutional neural networks, and transformer architectures. She has authored over 7 peer-reviewed publications in international journals, IEEE/Elsevier conference proceedings, and edited volumes, accumulating more than 37 citations. Her work reflects sustained interdisciplinary collaboration and contributes to improved diagnostic accuracy, healthcare decision support, and intelligent imaging systems with clear societal and clinical relevance.

Citation Metrics (Google Scholar)

80

60

40

20

0

Citations
37

Documents
7

h-index
2

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications


MRI brain tumor detection and classification using KPCA and KSVM.

– Materials Today: Proceedings. (2021). Citations: 21

Transformer-enhanced MRI analysis for brain tumor detection with kernel-based PCA and support vector techniques.

– International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC) . (2024). 

Tzu Wen Kuo | Engineering | Editorial Board Member

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Assist. Prof. Dr Tzu Wen Kuo | Engineering | Editorial Board Member

Architect | Private Chinese Culture University  | Taiwan

Assist. Prof. Dr Tzu Wen Kuo is a dedicated scholar and practitioner in the field of technological disaster prevention, currently serving as a Lecturer in the Department of Architecture and Urban Design at the Chinese Culture University in Taipei, Taiwan. He is also a practicing architect and an active instructor for architectural license examination preparation, demonstrating a strong commitment to bridging academic knowledge with professional practice. Kuo is presently pursuing his PhD in the Department of Architecture at the National Taiwan University of Science and Technology, where his doctoral research focuses on enhancing safety, resilience, and emergency response mechanisms in built environments.Kuo’s research centers on integrating advanced technologies into disaster prevention frameworks, particularly with respect to fire safety, emergency evacuation, and smart building systems. His scholarly contributions reflect a strong emphasis on simulation-based analysis, digital tools, and mobile-assisted evacuation strategies. He has authored multiple peer-reviewed journal articles, including recent works published in Fire and the International Journal of Environmental Research and Public Health. His studies ranging from QR code-enabled fire rescue notification systems to smartphone-based evacuation guidance and stadium evacuation efficiency—highlight his interdisciplinary approach that combines architecture, information technology, and public safety engineering.Through collaborations with academic and industry experts, Kuo contributes to practical solutions that strengthen building safety management and emergency preparedness across various public infrastructures. His work provides empirical insights that support policymakers, architects, and safety professionals in developing more efficient, technology-enhanced disaster response strategies. With growing citations and recognition in the field, Kuo’s research continues to advance the integration of smart technologies into architectural planning and urban safety systems.

Profile : ORCID 

Featured Publications

1.Yang, C.-H., Lin, C.-Y., & Kuo, T.-W. (2025). Simulation-based assessment of evacuation efficiency in sports stadiums: Insights from case studies. Fire, 8(6), 210.

2.Kuo, T.-W., & Lin, C.-Y. (2025). Smart building technologies for fire rescue: A QR code-enabled notification system. Fire, 8(3), 114..

3.Kuo, T.-W., Lin, C.-Y., Chuang, Y.-J., & Hsiao, G. L.-K. (2022). Using smartphones for indoor fire evacuation. International Journal of Environmental Research and Public Health, 19(10), 6061.

Mohammad Taghilou | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Mohammad Taghilou | Engineering | Best Researcher Award

Associate professor | University of Zanjan | Iran

Dr. Mohammad Taghilou is an Associate Professor in the Department of Mechanical Engineering at the University of Zanjan, Iran. His research expertise lies in heat transfer, phase change problems, energy storage, and porous media, with an emphasis on the lattice Boltzmann method (LBM) and its applications in thermal systems. Over his academic career, he has authored impactful studies published in leading journals such as Applied Thermal Engineering, Computers & Mathematics with Applications, and the International Journal of Thermal Sciences. His most cited works explore PCM solidification, nanofluid-based heat exchangers, and the thermal behavior of energy storage systems. Dr. Taghilou’s studies significantly contribute to advancing thermal management technologies, including applications in lithium-ion batteries, heat sinks, and double-pipe exchangers, with an aim to enhance energy efficiency and system reliability. His collaborations with international scholars from institutions such as the University of Tehran, Aalto University (Finland), and the University of Technology Sydney have expanded the interdisciplinary reach of his research. With 541 total citations, an h-index of 15, and an i10-index of 16, his work demonstrates both academic impact and global relevance. Through innovative numerical modeling and experimental approaches, Dr. Taghilou continues to advance understanding in phase-change thermal systems and nanomaterial-enhanced heat transfer, fostering sustainable energy applications and modern engineering solutions.

Profiles: ORCID | Scopus | Google Scholar | ResearchGate | LinkedIn

Featured Publications

1.Sajedi, R., Osanloo, B., Talati, F., & Taghilou, M. (2016). Splitter plate application on the circular and square pin fin heat sinks.  Cited by : 70

2.Taghilou, M., & Rahimian, M. H. (2014). Investigation of two-phase flow in porous media using lattice Boltzmann method. Cited By : 49

3.Talati, F., & Taghilou, M. (2015). Lattice Boltzmann application on the PCM solidification within a rectangular finned container. Cited By : 44

4.Taheri, A. A., Abdali, A., Taghilou, M., Alhelou, H. H., & Mazlumi, K. (2021). Investigation of mineral oil-based nanofluids effect on oil temperature reduction and loading capacity increment of distribution transformers.
Cited By : 40

5.Taghilou, M., & Khavasi, E. (2020). Thermal behavior of a PCM filled heat sink: The contrast between ambient heat convection and heat thermal storage. Cited By : 36

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.

Amir Hossein Asadi | Engineering | Top Researcher Award

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Mr. Amir Hossein Asadi | Engineering | Top Researcher Award

MSC Graduated at University of Guilan, Iran

Mr.  Amir Hossein Asadi is a dedicated researcher and innovator in civil engineering, specializing in road and transportation systems . He is the Founder and Head of the Road & Transportation Group at Rah Sazan Segal Gharn Consulting Engineers Company and holds an MSc in Road & Transportation Engineering from the University of Guilan . His work primarily focuses on enhancing bitumen properties to improve the durability and resilience of asphalt mixtures. With over seven published research papers in high-impact Q1 journals such as Elsevier, ASCE, and Nature, he is recognized for his valuable contributions to infrastructure development . Amir is also a peer reviewer and an industry consultant, involved in major road safety and construction projects across Iran. His innovative approach, integrating artificial intelligence  and nanotechnology into civil material studies, has positioned him as a forward-thinking leader in his field.

Professional Profile

ORCID Profile | Google Scholar

Education

Mr.  Amir Hossein Asadi pursued his academic journey at the University of Guilan, earning his MSc in Road and Transportation Engineering . With a foundational background in civil engineering , his academic focus revolved around transportation systems, highway design, and materials engineering. His postgraduate studies deepened his expertise in the mechanical and thermodynamic behavior of bituminous mixtures, emphasizing the need for enhanced material performance and longevity. Throughout his studies, he demonstrated exceptional research aptitude by contributing to both experimental and AI-driven modeling approaches . His thesis and project work laid the groundwork for his future innovations in bitumen modification and asphalt durability. Amir’s academic path reflects a strong balance of theoretical depth and applied knowledge, equipping him to bridge the gap between laboratory research and practical engineering solutions .

Experience

Mr.  Amir Hossein Asadi brings substantial experience in both research and field consultancy . As the Founder and Head of the Road & Transportation Group at Rah Sazan Segal Gharn Consulting Engineers Co., he has led multidisciplinary teams on critical infrastructure projects, including the design of highways, interchanges in Tabriz, and safety enhancements in Bojnourd . His work extends beyond design, involving construction supervision of major structures like university buildings. With  completed research projects and 2 ongoing, Amir integrates academic rigor with real-world challenges. He also actively contributes to the scientific community through peer reviewing for high-impact journals like the Journal of Materials in Civil Engineering (ASCE) . His hands-on experience in bitumen characterization and the application of AI in road material design makes him a rare blend of researcher and practitioner, dedicated to safe, sustainable, and high-performing transportation networks .

Research Interest

Mr.  Amir Hossein Asadi research interests are firmly rooted in bitumen modification, asphalt mixture performance, and advanced failure analysis of road materials . He focuses on improving the thermal and fatigue resistance of hot mix asphalt (HMA) by incorporating nanomaterials and using thermodynamic and mechanical analysis. His investigations also explore surface free energy (SFE) methods to evaluate adhesive properties and moisture susceptibility under varying environmental conditions . Moreover, he integrates artificial intelligence and machine learning models like neural networks to predict and validate failure mechanisms . Amir has contributed significant insights into low-temperature cracking, anti-freezing agents, and the chemical behavior of mixtures exposed to acidic environments. His interdisciplinary approach, merging material science with predictive analytics, aims to enhance the longevity and reliability of road infrastructures making his research vital for sustainable urban development.

Award and Honor

Mr.  Amir Hossein Asadi is a multi-award-winning researcher recognized for his outstanding work in transportation engineering . He is a registered member of the Petroleum Engineering Awards  and has been nominated for prestigious accolades such as the Best Researcher Award, Best Innovation Award, Excellence in Research, and Best Research Scholar Award . His papers have been published in top-tier journals like Scientific Reports, Construction and Building Materials, and Journal of Materials in Civil Engineering all Q1 ranked and with high impact factors . Asadi’s innovative use of nanotechnology and AI in asphalt engineering has earned acclaim both academically and in professional practice. His role as a peer reviewer and conference speaker further underscores his commitment to advancing knowledge in the field . His blend of academic excellence and industry relevance marks him as a leading figure in infrastructure innovation.

Research Skill

Mr.  Amir Hossein Asadi possesses a robust set of research skills across computational modeling, materials testing, and engineering design . He excels in thermodynamic and mechanical analysis of asphalt mixtures, especially under extreme temperature and moisture conditions . His proficiency with surface free energy (SFE) analysis, fatigue testing, and AI-based modeling particularly artificial neural networks makes his work highly predictive and data-driven . He is adept at laboratory experimentation involving nanomaterials, anti-freezing agents, and modified bitumen formulations. Amir also shows competence in interpreting and publishing findings in high-impact journals, ensuring scientific rigor and relevance . In consultancy, he applies these skills to real-world projects like highway safety enhancements and structural supervision. His balanced command of both theory and application enhances the impact and accuracy of his research contributions, making him a versatile and skilled transportation engineer .

Publication Top Notes

Title: Investigating the effect of fundamental properties of materials on the mechanisms of thermal cracking of asphalt mixtures
Authors: GH Hamedi, AH Asadi, J Zarrinfam
Journal: Construction and Building Materials, Vol. 411, Article 134426
Year: 2024
Cited by: 13

Title: Using mechanical and thermodynamic methods to evaluate the effects of nanomaterials on thermal cracking mechanisms of asphalt mixtures under the influence of different moisture conditions
Authors: AH Asadi, GH Hamedi, A Azarhoosh
Journal: Case Studies in Construction Materials, Vol. 22, Article e04310
Year: 2025
Cited by: 7

Title: Evaluating the effects of nanomaterials on thermal cracking of HMA in the presence of moisture with different degrees of acidity
Authors: AH Asadi, GH Hamedi, A Azarhoosh
Journal: Journal of Materials in Civil Engineering, Vol. 37(5), Article 04025096
Year: 2025
Cited by: 4

Title: Effect of surface free energy and mix design parameters on low-temperature cracking of hot mix asphalt
Authors: GH Hamedi, J Zarrinfam, AH Asadi
Journal: Journal of Traffic and Transportation Engineering (English Edition)
Year: 2025
Cited by: 2

Title: Evaluating mechanical and thermodynamic properties of asphalt mixtures containing anti-freezing agents against low temperature cracking
Authors: GH Hamedi, MH Dehnad, AH Asadi
Journal: Scientific Reports, Vol. 15(1), Article 26143
Year: 2025
Cited by: 1

Title: Effects of calcined dolomite on the fatigue performance of asphalt concrete affected by water with variable acidity
Authors: M Mehdinazar, GH Hamedi, AH Asadi
Journal: Scientific Reports, Vol. 15(1), Article 24359
Year: 2025
Cited by: 1

Conclusion

Mr. Amir Hossein Asadi represents an exemplary blend of academic insight and engineering application. His research has already influenced how thermal cracking, moisture damage, and nanomaterials are approached in asphalt engineering. With multiple peer-reviewed contributions, growing citations, and a leadership role in both research and practice, he is highly deserving of the Top Researcher Award.