Mr. Ridwan Yusuff | Engineering | Research Excellence Award

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Mr. Ridwan Yusuff | Engineering | Research Excellence Award

University of Waterloo | Canada

Mr. Ridwan Olayinka Yusuff is a civil and environmental engineering researcher specializing in geotechnical engineering, sustainable infrastructure, and foundation optimization for energy systems. His research focuses on soil-structure interaction, wind energy infrastructure design, and risk-informed geotechnical analysis to improve performance and constructability. He contributes to advancing resilient infrastructure through applied engineering studies and technical publications. His emerging scholarly record is reflected in developing metrics across Scopus and Google Scholar, including growing citation counts, indexed documents, and an early-stage h-index, indicating increasing academic impact and research visibility within civil, geotechnical, and environmental engineering domains.

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Featured Publications

Prof. Dr. Kai Wang | Engineering | Research Excellence Award

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Prof. Dr. Kai Wang | Engineering | Research Excellence Award

Professor | Hydrological Bureau (Information Center), Huaihe River Commission | China

Prof. Dr. Kai Wang, is Senior Engineer and Vice Director at the Hydrologic Bureau of the Huaihe River Commission, Ministry of Water Resources, China. He specializes in hydrological modeling, integrated water resources management, flood forecasting, and basinscale water planning. He has led and directed major national projects on probabilistic flood forecasting, water resources simulation, and drought relief systems. Dr. Wang has authored 17 Scopus-indexed documents with 224 citations and an h-index of 8, reflecting strong research impact.

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Featured Publications


Comparative Analysis of Extreme Flood Characteristics in the Huai River Basin: Insights from the 2020 Catastrophic Event

– Water (Switzerland), 2025


Hydrological Monitoring and Forecasting Mechanisms in the Huai River Basin

– Environmental Monitoring Research, 2024


Extreme Precipitation Events and Basin-Scale Flood Response Analysis

– Hydrology Research Journal, 2023


Climate Change Impact Assessment on Regional River Basin Flood Risks

– Water Resources Management, 2022


Basin-Wide Hydrological Modeling and Early Warning Systems for Flood Disaster Prevention

– Journal of Hydrologic Engineering, 2021

Prof. Saad Aljlil | Engineering | Best Researcher Award

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Prof. Saad Aljlil | Engineering | Best Researcher Award

Prof. Saad Aljlil | Chief Researcher | King Abdulaziz City for Science and Technology | Saudi Arabia

Prof. Saad A. Aljlil is a distinguished research professor at the King Abdulaziz City for Science and Technology (KACST), Saudi Arabia, specializing in sustainability, membrane technology, and water and environmental engineering. His research focuses on advanced desalination and water treatment technologies, wastewater purification, membrane synthesis, adsorption processes, and the integration of renewable energy systems for sustainable water management. Prof. Aljlil has made significant contributions to developing ceramic and polymeric membranes, nanocomposite materials, and hybrid desalination systems that enhance water purification efficiency while minimizing environmental impact. His work extends to smart water networks, solar-driven desalination, greywater reuse, and innovative applications of artificial intelligence in membrane distillation and water resource optimization. A highly cited researcher, Prof. Aljlil has achieved 1,079 citations in Scopus across 44 documents with an h-index of 19, and 1,397 citations on Google Scholar with an h-index of 20 and i10-index of 23. His interdisciplinary approach bridges chemical engineering, nanotechnology, and sustainability to address critical challenges in clean water access and environmental preservation.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Ali, A., Macedonio, F., Drioli, E., Aljlil, S. A., & Alharbi, O. A. (2013). Experimental and theoretical evaluation of temperature polarization phenomenon in direct contact membrane distillation. Chemical Engineering Research and Design, 91(10), 1966–1977.

Quist-Jensen, C. A., Macedonio, F., Conidi, C., Cassano, A., Aljlil, S. A., Alharbi, O. A., & Drioli, E. (2016). Direct contact membrane distillation for the concentration of clarified orange juice. Journal of Food Engineering, 187, 37–43.

Fontananova, E., Bahattab, M. A., Aljlil, S. A., Alowairdy, M., Rinaldi, G., Vuono, D., & Drioli, E. (2015). From hydrophobic to hydrophilic polyvinylidenefluoride (PVDF) membranes by gaining new insight into material properties. RSC Advances, 5(69), 56219–56231.

Park, C. H., Tocci, E., Fontananova, E., Bahattab, M. A., Aljlil, S. A., & Drioli, E. (2016). Mixed matrix membranes containing functionalized multiwalled carbon nanotubes: Mesoscale simulation and experimental approach for optimizing dispersion. Journal of Membrane Science, 514, 195–209.

Sedighi, M., Aljlil, S. A., Alsubei, M. D., Ghasemi, M., & Mohammadi, M. (2018). Performance optimisation of microbial fuel cell for wastewater treatment and sustainable clean energy generation using response surface methodology. Alexandria Engineering Journal, 57(4), 4243–4253.

 

Dr. Shengfei Ji | Mechanical | Best Researcher Award

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Dr. Shengfei Ji | Mechanical | Best Researcher Award

Dr. Shengfei Ji , China University of Mining and Technology, China

Shengfei Ji is a dedicated Ph.D. candidate in Mechatronic Engineering at the School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, P.R. China. With a strong academic and technical background, he focuses on developing intelligent systems to enhance the operational reliability of hydraulic machinery. His passion for research and innovation in fault diagnosis and predictive modeling has led to several impactful publications in renowned journals.

Publication Profile

Scopus

ORCID

🎓 Education Background

Shengfei Ji is currently pursuing his Ph.D. in Mechatronic Engineering at China University of Mining and Technology, where he is engaged in advanced studies on intelligent condition monitoring systems. His academic foundation includes rigorous training in machine learning, system dynamics, and hydraulic machinery.

💼 Professional Experience

As a Ph.D. researcher, Shengfei has collaborated with a multidisciplinary team on projects involving construction machinery and intelligent fault detection. His work involves both theoretical research and practical application, integrating AI technologies like graph convolutional networks and LSTM models with mechanical systems. He has co-authored research with industry and academic experts, further expanding his expertise in smart diagnostics.

🏆 Awards and Honors

While formal awards and grants are not currently listed, Shengfei Ji’s work has gained recognition in the academic community with citations in 19 documents and a Scopus h-index of 2, reflecting growing interest in his innovative contributions to intelligent machinery diagnostics.

🔬 Research Focus

Shengfei Ji’s core research interests lie in intelligent fault diagnosis 🛠️, anomaly detection 🚨, and condition monitoring 📡 of hydraulic systems used in construction machinery. His work primarily applies deep learning and graph-based methods to create predictive models that enhance machine efficiency and reliability.

📝 Conclusion

With a strong commitment to integrating AI with mechanical systems, Shengfei Ji is emerging as a promising researcher in the field of mechatronic engineering. His scientific contributions reflect a unique intersection of engineering insight and computational intelligence, positioning him for continued academic and industrial impact 🌐.

📚 Top Publications

  1. Multivariate Prediction Soft Sensor Model for Truck Cranes Based on Graph Convolutional Network and Random Forest
    Published in: Actuators, 2024

  2. A Soft Sensor Model for Predicting the Flow of a Hydraulic Pump Based on Graph Convolutional Network–Long Short-Term Memory
    Published in: Actuators, 2024

  3. Bucket Teeth Detection Based on Faster Region Convolutional Neural Network
    Published in: IEEE Access, 2021
    Cited by: 19 articles