Huan Wang | Smart Manufacturing | Best Researcher Award

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Dr. Huan Wang | Smart Manufacturing | Best Researcher Award

Dr. Huan Wang | sun yat-sen university | China

Huan Wang is a dedicated researcher currently pursuing a Ph.D. at the School of Advanced Manufacturing, Sun Yat-sen University, building upon a strong academic foundation established during his master’s studies in aerospace engineering at the same institution. His research focuses on advancing sensor technology through innovative approaches in temperature compensation, fault diagnosis, and the reliability assessment of pressure scanners—key components in precision measurement and industrial instrumentation. Over the years, he has contributed significantly to national and industry-driven scientific efforts, including participation in one National Key R&D Program, one National Natural Science Foundation project, and three important commissioned projects involving electronic pressure scanning valves. His expertise extends to instrumentation and measurement consultancy, allowing him to bridge academic research with practical engineering applications. Dr. Wang’s scholarly output includes more than eight peer-reviewed research articles, several of which he authored as first author in highly regarded SCI-indexed journals such as Measurement, Measurement Science and Technology, Micromachines, Instrumentation Science and Technology, and Metrology and Measurement Systems. His research demonstrates a strong commitment to integrating intelligent optimization algorithms with sensor systems to improve accuracy, stability, and reliability in real-world applications. Alongside his research achievements, he is a professional member of AAAS and IEEE, showcasing his active engagement with the global scientific community. Through his interdisciplinary skills, academic rigor, and industry collaborations, Huan Wang continues to make meaningful contributions to the fields of sensor technology, advanced manufacturing, and applied measurement science. His growing body of work reflects not only technical depth but also a forward-looking approach aimed at enhancing next-generation intelligent measurement systems. With a strong commitment to innovation, integrity, and scientific excellence, he stands out as a promising researcher who significantly contributes to the advancement of engineering research and instrumentation technologies.

Profile: Orcid

Featured Publications

Wang, H., Chen, X., Xia, J., Zhao, H., & Maddaiah, P. N. (2026). Newton-Raphson-based optimizer combined with LSSVM: Temperature compensation applied to small-range electronic pressure scanners. Flow Measurement and Instrumentation. https://doi.org/10.1016/j.flowmeasinst.2025.103127

Wang, H., Chen, X., Xia, J., Liu, P., & Zhao, H. (2025). A novel model fusing ALA and integrated learning: Temperature compensation for 700 kPa pressure scanners. International Journal of Thermophysics. https://doi.org/10.1007/s10765-025-03638-x

Wang, H. (2025). Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR. Journal article. https://doi.org/10.1007/s40747-025-01899-5

Wang, H., Wu, T., Liu, P., Zou, Y., & Zeng, Q. (2025). Kernel extreme learning machine combined with gray wolf optimization for temperature compensation in pressure sensors. Metrology and Measurement Systems. https://doi.org/10.24425/mms.2025.152773

Wu, T., Wang, H., Huang, Z., & Maddaiah, P. N. (2025). Optimal tracking differentiator algorithm for accurate pressure scanner measurements. Instrumentation Science and Technology. https://doi.org/10.1080/10739149.2025.2556107

Liu, C., Wang, H., Zhu, H., Zhou, W., & Zhao, H. (2025). Optimized design of support points in solar panels based on thermal deformation analysis. Journal of Physics: Conference Series, 3039(1), 012004. https://doi.org/10.1088/1742-6596/3039/1/012004

Sayed Abdul Majid Gilani | Emerging Technologies | Best Researcher Award

Published on by Scientist Day

Dr. Sayed Abdul Majid Gilani | Emerging Technologies | Best Researcher Award

Dr. Sayed Abdul Majid Gilani | Birmingham City University | United Kingdom

Dr. Sayed Abdul Majid Gilani is an accomplished researcher in electrical and electronic engineering, specializing in embedded systems, automation, and control engineering. His multidisciplinary research integrates hardware design, sensor networks, and artificial intelligence to develop innovative and energy-efficient solutions for real-world challenges. With over a decade of experience in academia and applied research, Dr. Gilani has contributed significantly to the advancement of embedded control technologies, renewable energy optimization, and industrial automation systems. His work emphasizes intelligent system design, IoT-based automation, and the integration of machine learning algorithms for enhanced performance and sustainability. Dr. Gilani has published extensively in high-impact journals and presented at leading international conferences, reflecting his global engagement and scientific rigor. He has also supervised numerous research projects and guided students in developing practical applications of emerging technologies. His research outputs demonstrate a strong commitment to technological innovation that bridges the gap between theory and application. Recognized for his academic excellence and collaborative research initiatives, Dr. Gilani continues to advance cutting-edge developments that contribute to the evolution of smart, adaptive, and efficient engineering systems—making him a deserving candidate for the Best Researcher Award.

Profiles: Google Scholar | Scopus | LinkedIn | Research Gate

Featured Publications 

Gilani, S. A. M., & Faccia, A. (2021). Broadband connectivity, government policies, and open innovation: The crucial IT infrastructure contribution in Scotland. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 1. https://doi.org/10.3390/joitmc8010001

Gilani, S. A. M., Copiaco, A., Gernal, L., Yasin, N., Nair, G., & Anwar, I. (2023). Savior or distraction for survival: Examining the applicability of machine learning for rural family farms in the United Arab Emirates. Sustainability, 15(4), 3720. https://doi.org/10.3390/su15043720

Gilani, S., Gernal, L., Tantry, A., Yasin, N., & Sergio, R. (2022). Leadership styles adopted by Scottish micro-businesses during the COVID-19 pandemic. In Proceedings of the International Conference on Business and Technology (pp. 144–156). Springer.

Al Jaghoub, J., Suleiman, A., Takshe, A. A., Moussa, S., Gilani, S. A. M., Sheikh, S., & others. (2024). The role of innovation in waste management for enterprises: A critical review of the worldwide literature. In Technology-Driven Business Innovation (pp. 453–464). Springer.

Gernal, L., Tantry, A., Gilani, S. A. M., & Peel, R. (2024). The impact of online learning and soft skills on college student satisfaction and course feedback. In Technology-Driven Business Innovation: Unleashing the Digital Advantage (pp. 42–54). Springer.

Gilani, S. A. M., Tantry, A., Askri, S., Gernal, L., & Sergio, R. (2023). Adoption of machine learning by rural farms: A systematic review. In Proceedings of the International Conference on Computing and Informatics (pp. 324–335). Springer.