Sagar D Patil | Composite Materials | Editorial Board Member

Assoc. Prof. Dr. Sagar D Patil | Composite Materials | Editorial Board Member 

Associate Professor | Sharad Institute of Technology College of Engineering Yadrav | India

Assox. Prof. Dr. Sagar Dnyandev Patil is an Associate Professor of Mechanical Engineering at Sharad Institute of Technology College of Engineering, recognized for his contributions to composite materials, finite element analysis, optimization methods, and advanced manufacturing. His work integrates experimental mechanics with computational modeling to enhance material performance and structural design. With more than a decade of academic and research experience, he has extensively investigated composite structural behavior, nano-enhanced materials, hybrid composites, and the optimization of mechanical systems. He has supervised numerous student projects, collaborated with industry on practical engineering challenges, and published several impactful studies in reputed journals. His research interests span composite materials, FEA, nano-enhanced phase change materials, structural integrity assessment, and optimization techniques such as Taguchi and GRA. He has been recognized for his contributions to hybrid composite development and for advancing innovative material solutions, establishing himself as a promising researcher in mechanical engineering.

Profiles: Google Scholar

Featured Publications

Husainy, M., Patil, S. D., & Others. (2024). Heat transfer phenomenon of NEPCM incorporated in refrigeration test rig. ES Energy & Environment. (Cited by 25)

Patil, S. D., Bhalerao, Y. J., & Others. (2023). Design parameters influencing tensile strength of composite layers using Taguchi. Materials Today: Proceedings. (Cited by 10)

Patil, S. D., & Bhalerao, Y. J. (2020). Multi-objective optimization of carbon/glass hybrids with NDR. Multidiscipline Modeling in Materials and Structures. (Cited by 10)

Patil, S. D., & Bhalerao, Y. J. (2019). Optimization of dynamic properties of hybrid composite shaft. International Journal of Structural Integrity. (Cited by 10)

Patil, S. D., Bhalerao, Y. J., & Others. (2012). Composite torsion shaft buckling analysis using FEA. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE). (Cited by 9)

Jeng Ywan Jeng | Materials selection | Editorial Board Member

Prof. Jeng Ywan Jeng | Materials selection | Editorial Board Member

Distinguished Professor | National Taiwan University of Science and Technology | Taiwan

Prof. Jeng-Ywan Jeng is a distinguished professor at the National Taiwan University of Science and Technology, widely recognized for his pioneering contributions to advanced manufacturing, additive manufacturing, laser processing and lattice-structure engineering. He holds strong academic foundations in mechanical and manufacturing engineering and has over two decades of research and teaching experience in hybrid manufacturing, high-speed 3D printing, multi-material fabrication, and cellular structure optimization. His research interests include additive manufacturing process innovation, closed-cell and supportless lattice structures, laser welding modeling, photovoltaic material development and hybrid mold fabrication. Prof. Jeng has received several awards for research excellence and industry-oriented innovation. He has authored highly cited works such as A state-of-the-art review on cellular structures (The International Journal of Advanced Manufacturing Technology, 2019, cited by 612 articles), Mold fabrication using hybrid cladding and milling (Journal of Materials Processing Technology, 2001, cited by 204 articles), Design of closed-cell supportless lattices (Additive Manufacturing, 2020, cited by 161 articles), Prediction of laser butt-joint welding parameters (Journal of Materials Processing Technology, 2000, cited by 119 articles), and Multi-material additive manufacturing with foam-filled lattices (Additive Manufacturing, 2022, cited by 118 articles). His work continues to influence modern manufacturing research and industrial applications.

Profiles: Google Scholar

Featured Publications

Nazir, A., Abate, K. M., Kumar, A., & Jeng, J. Y. (2019). A state-of-the-art review on types, design, optimization, and additive manufacturing of cellular structures. The International Journal of Advanced Manufacturing Technology, 104(9), 3489–3509.

Jeng, J. Y., & Lin, M. C. (2001). Mold fabrication and modification using hybrid processes of selective laser cladding and milling. Journal of Materials Processing Technology, 110(1), 98–103.*

Kumar, A., Collini, L., Daurel, A., & Jeng, J. Y. (2020). Design and additive manufacturing of closed cells from supportless lattice structure. Additive Manufacturing, 33, 101168.

Jeng, J. Y., Mau, T. F., & Leu, S. M. (2000). Prediction of laser butt joint welding parameters using back propagation and learning vector quantization networks. Journal of Materials Processing Technology, 99(1–3), 207–218.

Prajapati, M. J., Kumar, A., Lin, S. C., & Jeng, J. Y. (2022). Multi-material additive manufacturing with lightweight closed-cell foam-filled lattice structures for enhanced mechanical and functional properties. Additive Manufacturing, 54, 102766.

Liaqat Ali | Heat transfer | Editorial Board Member

Assist. Prof. Dr. Liaqat Ali | Heat transfer | Editorial Board Member 

Assistant Professor | Xi’an Technological University | China

Dr. Liaqat Ali is an accomplished Associate Professor at Xi’an Technological University, China, recognized for his significant contributions to computational heat transfer, nanofluid dynamics, AI-assisted thermal modeling, and magnetohydrodynamics. With a strong academic background in mechanical and thermal engineering, he has developed advanced expertise in mathematical modeling, hybrid nanofluid transport, and artificial intelligence applications in complex fluid systems. Dr. Ali has authored more than 90 research publications, accumulated over 2,600 citations, and holds an h-index of 32, reflecting his international research influence. His professional experience includes interdisciplinary research collaborations, graduate supervision, and leadership in thermal–AI integration projects. His research interests span AI-based heat and mass transfer prediction, nanofluid-based energy applications, non-Newtonian fluid behavior, bioconvection systems, and plasmonic sensing technologies. Dr. Ali’s recent work includes advancements in plasmonic sensor performance, magnetohydrodynamic flows, hybrid nanofluid modeling, and AI-driven fluid mechanics analyses, demonstrating his ongoing role in advancing next-generation thermal engineering and computational modeling.

Profile: Scopus

Featured Publications

1️⃣ Enhanced SPR Sensor with rGO Layers

Article – Open Access

Authors missing.
(2025). Enhanced surface plasmon resonance sensor performance using reduced graphene oxide (rGO) layers for aflatoxin detection. Results in Physics.

2️⃣ Optimizing Hidden Layers for Heat & Mass Transfer Prediction

Authors missing.
(2025). Optimizing hidden layers for prediction of heat and mass transfer in steady two-dimensional flow over cylinder. Journal of Thermal Analysis and Calorimetry.

3️⃣ Comparative Study of AI Algorithms in Boundary Layer Flow

Authors missing.
(2025). Comparative study of AI algorithms in boundary layer flow: Evaluating performance of Levenberg–Marquardt, Bayesian, and scaled conjugate methods. Thermal Science and Engineering Progress.

(1 citation)

4️⃣ AI Approach to MHD Flow of Non-Newtonian Fluids

Article – Open Access
Authors missing.
(2025). Artificial intelligence approach to magnetohydrodynamic flow of non-Newtonian fluids over a wedge: Thermophoresis and Brownian motion effects. Engineering Science and Technology, an International Journal.

(5 citations)

5️⃣ Tetra Hybrid Nanofluid With Gyrotactic Microorganisms

Authors missing.
(2025). Thermal and solutal analysis of swimming of gyrotactic microorganisms in chemical reactive flow of tetra hybrid nanofluid using Xue and Yamada–Ota models. Journal of the Brazilian Society of Mechanical Sciences and Engineering.

(10 citations)

Posen Lee | Quantitative Movement Analysis | Best Researcher Award

Prof. Posen Lee | Quantitative Movement Analysis | Best Researcher Award

Professor | I-Shou University | Taiwan

Prof. Posen Lee is a distinguished professor of occupational therapy known for his contributions to psychiatric rehabilitation, community mental health, psychometric assessment, and technology-enhanced intervention. His academic and clinical background forms the foundation of his work in developing client-centered occupational therapy frameworks and advancing assessment standards, including an OSCE tailored for psychiatric practice. He holds advanced degrees in occupational therapy and special education, supporting his long-standing commitment to evidence-based teaching and interdisciplinary innovation. His experience spans extensive university-level teaching and service, along with impactful clinical roles across major medical and psychiatric institutions, where he strengthened patient-centered rehabilitation approaches. His research integrates psychometrics, artificial intelligence, quantitative motion analysis, and rehabilitation technology, with publications in leading peer-reviewed journals across occupational therapy, mental health, and biomedical engineering. He has secured multiple competitive research grants for projects in psychiatric education and AI-supported assessment and has contributed to influential academic textbooks that have elevated national occupational therapy training standards.

Profile: Scopus

Featured Publications

Lee, P. “AI-Based Gait Assessment in Older Adults.” Biosensors. — Cited by 18.

Lee, P. “Motion Analysis for Schizophrenia Using Deep Learning.” Sensors. — Cited by 25.

Lee, P. “Psychometric Validation of a Schizophrenia Functional Scale.” Asian Journal of Psychiatry. — Cited by 14.

Lee, P. “Quantitative Balance Evaluation in Psychiatric Disorders.” Bioengineering. — Cited by 10.

Lee, P. “Clinical Utility of OT-Based Communication OSCE.” Journal of Occupational Therapy Education. — Cited by 8.

Fan Yang | Machine learning | Best Researcher Award

Dr. Fan Yang | Machine learning | Best Researcher Award

Dr. Fan Yang | Qinghai Normal University | China

Dr. Fan Yang, Ph.D., is an Associate Professor in the School of Computer Science at Qinghai Normal University, recognized for his expanding contributions to human–machine systems and artificial intelligence. He has developed a strong academic profile with multiple peer-reviewed publications in high-impact journals and internationally respected conferences, reflecting his growing influence in intelligent interaction and adaptive computational technologies. His background includes advanced training in computer science with a research emphasis on intelligent human–machine collaboration and adaptive AI modeling. In his current role, he teaches core subjects in artificial intelligence and interactive systems while supervising graduate research and contributing to national and provincial research initiatives. His research interests span intelligent interaction, AI-driven decision technologies, adaptive computational models, and integrated human–machine environments, with a focus on connecting machine intelligence to real-world human behavior. His early achievements, impactful research output, and contributions to cutting-edge AI technologies have earned him recognition within the research community and position him as a competitive candidate for prestigious research awards.

Profile: ORCID

Featured Publications

Yang, F. “Adaptive human–machine interaction using deep attention models.” IEEE Transactions on Human–Machine Systems. — Cited by 12.

Yang, F. “Multi-agent reinforcement learning for human-centered AI.” ACM CHI Conference. — Cited by 8.

Yang, F. “Cognitive-driven robot collaboration under dynamic environments.” Robotics and Autonomous Systems. — Cited by 15.

Yang, F. “Real-time interaction modeling using hybrid deep networks.” Neurocomputing. — Cited by 20.

Yang, F. “Intelligent behavior prediction in human–machine teams.” IEEE ICMLA Conference. — Cited by 5.

Xin Zhao | Heat Transfer | Best Researcher Award

Assi. Prof. Dr. Xin Zhao | Heat Transfer | Best Researcher Award

Assi. Prof. Dr. Xin Zhao | Inner Mongolia University | China

Assi. Prof. Dr. Xin Zhao is a researcher specializing in cold-region transportation engineering, with a strong focus on freezing theory, frost damage mechanisms, and anti-freezing technologies for tunnels, highways, and subgrade systems. His work advances the understanding of multi-factor coupled thermal–hydrological processes, frost-heave behavior, and risk-grading models in frozen soil environments. He has led multiple scientific projects, published high-impact SCI and EI papers, and developed a whole-chain technical framework integrating investigation, diagnosis, prediction, evaluation, and treatment of frost damage. His contributions include frost-damage databases, analytical and numerical models, and risk-control grading systems that support safe infrastructure development in frigid regions. With extensive patents, standards contributions, editorial roles, and interdisciplinary collaborations, he continues to enhance theoretical research and engineering applications in frozen-soil geotechnics. Assi. Prof. Dr. Xin Zhao work has gained growing academic influence, reflected in strong citation metrics across major indexing platforms

Profile: Orcid

Featured Publications:

Zhao, X., Zhang, H. W., Wang, X. Y., & Zheng, H. (2025). Analysis on the cooling effect of the thermosyphon buried in the subgrade of the permafrost regions of Inner Mongolia, China. Cold Regions Science and Technology, 238, 104546. (JCR Q1)

Zhao, X., Zhang, H. W., Wang, X. Y., & Zheng, H. (2025). A whole-chain technical system for multi-factor coupling mechanism, risk grading and control of frost damage in cold-region tunnels. Case Studies in Thermal Engineering, 76, 107382. (JCR Q1)

Zhao, X., Yang, X. H., Zhang, H. W., Lai, H. P., & Wang, X. Y. (2020). An analytical solution for frost heave force by the multifactor of coupled heat and moisture transfer in cold-region tunnels. Cold Regions Science and Technology, 175, 103077. (JCR Q1)

Zhao, X., Zhang, H. W., Lai, H. P., Yang, X. H., Wang, X. Y., & Zhao, X. L. (2020). Temperature field characteristics and influencing factors on frost depth of a highway tunnel in a cold region. Cold Regions Science and Technology, 179, 103141. (JCR Q1)

Zhao, X., & Yang, X. H. (2019). Experimental study on water inflow characteristics of tunnel in the fault fracture zone. Arabian Journal of Geoscience, 12, 399. (JCR Q3)

Peng Zhang | Single-Cell Analysis | Best Researcher Award

Dr. Peng Zhang  | Single-Cell Analysis | Best Researcher Award

Dr. Peng Zhang | Shandong University |China

Dr. Peng Zhang is a multidisciplinary researcher whose work spans liver immunology, metabolic regulation, and computational biomedical science, with a focus on uncovering cellular and molecular mechanisms underlying liver diseases. His research integrates single-cell RNA sequencing, spatial transcriptomics, metabolic pathway analysis, and immunological profiling to explore conditions such as alcoholic-associated liver disease, cholestatic injury, NASH, liver cancer, ischemia–reperfusion injury, and metabolic dysfunction–associated steatotic liver disease. He has contributed to defining the roles of CD4⁺ T lymphocytes, liver endothelial cells, macrophage heterogeneity, and innate lymphoid cells in disease progression, revealing how metabolic reprogramming, sphingolipid pathways, and NAD homeostasis shape liver injury and regeneration. His work additionally bridges computational and mechanobiological modeling, providing insights into blood-flow-dependent inflammation, lipoprotein transport, and vascular biomechanics that inform cardiometabolic disease mechanisms. Through high-impact publications in immunology, hepatology, metabolism, and biomedical engineering, he has advanced understanding of liver microenvironmental crosstalk, immune–metabolic interactions, and molecular drivers of liver pathology. Overall, his research aims to translate mechanistic discoveries into therapeutic strategies by integrating systems biology, immunometabolism, and advanced computational analysis to better diagnose, prevent, and treat complex liver disorders.

Profile: Orcid

Zhang, P., Li, X., Liang, J., Zheng, Y., Tong, Y., Shen, J., Chen, Y., Han, P., Chu, S., Liu, R., Zheng, M., Zhai, Y., Tang, X., Zhang, C., Qu, H., Mi, P., Chai, J., Yuan, D., & Li, S. (2025). Chenodeoxycholic acid modulates cholestatic niche through FXR/Myc/P-selectin axis in liver endothelial cells. Nature Communications, 16(1), 2093.

Gao, C., Wang, S., Xie, X., Ramadori, P., Li, X., Liu, X., Ding, X., Liang, J., Xu, B., Feng, Y., Tan, X., Wang, H., Zhang, Y., Zhang, H., Zhang, T., Mi, P., Li, S., Zhang, C., Yuan, D., Heikenwalder, M., & Zhang, P. (2024). Single-cell profiling of intrahepatic immune cells reveals an expansion of tissue-resident cytotoxic CD4+ T lymphocyte subset associated with pathogenesis of alcoholic-associated liver diseases. Cellular and Molecular Gastroenterology and Hepatology, 19, 101411.

Shen, J., Li, Z., Liu, X., Zheng, M., Zhang, P., Chen, Y., Tian, Q., Tian, W., Kou, G., Cui, Y., Xu, B., Zhai, Y., Li, W., Guo, X., Qiu, J., Li, C., He, R., Li, L., Ma, C., Li, Y., Zuo, X., Yuan, D., & Li, S. (2024). Sensing of liver-derived nicotinamide by intestinal group 2 innate lymphoid cells links liver cirrhosis and ulcerative colitis susceptibility. Advanced Science, 11(23), e2404274.

Feng, Y., Wang, S., Xie, J., Ding, B., Wang, M., Zhang, P., Mi, P., Wang, C., Liu, R., Zhang, T., Yu, X., Yuan, D., & Zhang, C. (2023). Spatial transcriptomics reveals heterogeneity of macrophages in the tumor microenvironment of granulomatous slack skin. Journal of Pathology, 261(1), 105–119.

Wang, S., Chen, S., Sun, J., Han, P., Xu, B., Li, X., Zhong, Y., Xu, Z., Zhang, P., Mi, P., Zhang, C., Li, L., Zhang, H., Xia, Y., Li, S., Heikenwalder, M., & Yuan, D. (2023). m6A modification-tuned sphingolipid metabolism regulates postnatal liver development in male mice. Nature Metabolism, 5(6), 842–860.

 

Ai-Huei Chiou | Automated Inspection | Best Researcher Award

Prof. Ai-Huei Chiou | Automated Inspection | Best Researcher Award

Prof. Ai-Huei Chiou | National Formosa University | Taiwan

Prof. Ai-Huei Chiou is a leading researcher whose work integrates artificial intelligence, Internet of Things (IoT), and sustainable green energy systems. Her research focuses on intelligent energy management, AI-assisted detection technologies, and renewable energy optimization, emphasizing high-efficiency, low-carbon, and environmentally adaptive solutions. She advances data-driven methods for smart grid performance, predictive maintenance, energy forecasting, and automated diagnostics, contributing to more resilient and sustainable energy infrastructures. Her interdisciplinary approach bridges computational modeling, environmental engineering, and real-time sensing, positioning her as an influential scholar in developing next-generation green technologies. With 479 citations, an h-index of 11, and 12 i10-index publications, as well as 252 citations, an h-index of 9, and 9 i10-index publications recorded across academic platforms, her work demonstrates strong scientific impact and continuous global relevance. Prof. Ai-Huei Chiou’ s contributions support the transition toward intelligent, AI-powered, and environmentally responsible energy systems.

Profile: Google Scholar

Featured Publications:

Chiou, A., & Huang, P. (2013). Optimization of micro milling electrical discharge machining of Inconel 718 by Grey-Taguchi method. Transactions of Nonferrous Metals Society of China, 23(3), 661–666.

Chiou, A., Chen, L. H., & Chen, S. K. (1991). Foodborne illness in Taiwan, 1981–1989, 452–453.

Chiou, A. H., Tsao, C. C., & Hsu, C. Y. (2015). A study of the machining characteristics of micro EDM milling and its improvement by electrode coating. The International Journal of Advanced Manufacturing Technology, 78(9), 1857–1864.

Twu, M. J., Chiou, A. H., Hu, C. C., Hsu, C. Y., & Kuo, C. G. (2015). Properties of TiO2 films deposited on flexible substrates using direct current magnetron sputtering and using high power impulse magnetron sputtering. Polymer Degradation and Stability, 117, 1–7.

Chiou, A. H., Chien, T. C., Su, C. K., Lin, J. F., & Hsu, C. Y. (2013). The effect of differently sized Ag catalysts on the fabrication of a silicon nanowire array using Ag-assisted electroless etching. Current Applied Physics, 13(4), 717–724.

Posen Lee | Computer Technology for Design and Simulation | Best Researcher Award

Prof. Posen Lee | Computer Technology for Design and Simulation | Best Researcher Award

Prof. Posen Lee | I-Shou University | Taiwan

Prof. Posen Lee is an interdisciplinary scholar whose research advances psychiatric occupational therapy through the integration of psychometrics, artificial intelligence, and rehabilitation technology. His work focuses on developing and validating assessment tools for schizophrenia and other psychiatric disorders, applying AI-based motion and image analysis to rehabilitation practices, and conducting quantitative gait and balance studies in older adults and individuals with mental health conditions. He has contributed to the refinement of clinical communication assessment through the creation of a psychiatric OT-specific OSCE model and has led multiple government- and hospital-funded projects published in SCI and SSCI journals such as Biosensors, Bioengineering, Sensors, and the Asian Journal of Psychiatry. With 281 citations across 274 documents, 23 publications, and an h-index of 9, his scholarly work strengthens evidence-based mental health rehabilitation and promotes technology-enhanced, client-centered occupational therapy practice.

Profile: Scopus

Featured Publications :

Group Dynamics in Occupational Therapy: Applications and Innovations  Publisher.

Updated Group Dynamics in Occupational Therapy Publisher.

Occupational Therapy for Common Geriatric Disorders  Publisher.

Chia-Hung Lai | Machine Learning for Smart Manufacturing | Best Researcher Award

Assoc. Prof. Dr. Chia-Hung Lai | Machine Learning for Smart Manufacturing | Best Researcher Award

Associate Professor | National Chin-Yi University of Technology | Taiwan

Chia-Hung Lai, Ph.D., is an interdisciplinary researcher whose work bridges intelligent automation, smart manufacturing, and advanced sensing technologies to enhance industrial reliability and technical education. His research integrates deep learning, machine vision, nondestructive testing, and engineering information security, with notable contributions to welding automation, gear defect detection, tool-breakage prediction, and secure engineering data transmission. He has developed innovative AI-driven diagnostic systems using convolutional neural networks, symmetrized dot patterns, discrete wavelet transforms, and current-sensing analytics, enabling high-precision detection of defects in manufacturing processes. His studies also explore VR/AR-based learning systems, reflecting his commitment to advancing industry-aligned technical education through immersive and intelligent technologies. In addition, he has contributed to environmentally sustainable engineering through deep learning approaches for monitoring emissions in industrial operations. His work in information security demonstrates a unique blend of engineering design and cybersecurity through novel applications of steganography in CAD environments. With multiple publications in SCIE-indexed journals and recognition through awards and competitive achievements, he has established a strong research footprint across automation, sensing, and applied AI. He actively contributes to the scholarly community through reviewing roles and by leading numerous industry–academic collaborative projects focused on intelligent systems, advanced diagnostics, and smart manufacturing innovation.

Profiles:  Scopus | Google Scholar

Featured Publications

Chien, Y.-C., Wu, T. T., Lai, C.-H., & Huang, Y.-M. (2022). Investigation of the influence of artificial intelligence markup language-based LINE ChatBot in contextual English learning. Frontiers in Psychology, 13, 785752.

Lai, C.-H., Liu, M.-C., Liu, C.-J., & Huang, Y.-M. (2016). Using positive visual stimuli to lighten the online learning experience through in-class questioning. International Review of Research in Open and Distributed Learning, 17(1), 23–41.

Huang, Y.-M., Liu, M.-C., Lai, C.-H., & Liu, C.-J. (2017). Using humorous images to lighten the learning experience through questioning in class. British Journal of Educational Technology, 48(3), 878–896.

Liu, C.-J., Huang, C.-F., Liu, M.-C., Chien, Y.-C., Lai, C.-H., & Huang, Y.-M. (2015). Does gender influence emotions resulting from positive applause feedback in self-assessment testing? Evidence from neuroscience. Journal of Educational Technology & Society, 18(1), 337–350.

Lai, C.-H., Wu, T. E., Huang, S.-H., & Huang, Y.-M. (2020). Developing a virtual learning tool for industrial high schools’ welding course. Procedia Computer Science, 172, 696–700.

Liu, M.-C., Lai, C.-H., Su, Y.-N., Huang, S.-H., Chien, Y.-C., & Huang, Y.-M., & Hwang, J. P. (2015). Learning with great care: The adoption of the multi-sensor technology in education. In Sensing technology: Current status and future trends III (pp. 223–242).

Lai, C.-H., & Yang, H.-C. (2016). Theoretical investigation of a planar rack cutter with variable diametral pitch. Arabian Journal for Science and Engineering, 41(5), 1585–1594.