Choubani Karim | Applied Mechanics | Best Researcher Award

Published on 07/10/202507/10/2025 by Cognitive Scientist Awards

Assoc. Prof. Dr. Choubani Karim | Applied Mechanics | Best Researcher Award

Imam Mohamed bin Saoud university | Saudi Arabia

Assoc. Prof. Dr. Karim Choubani is a dedicated academic and researcher with a strong background in mechanical engineering, combining international education, teaching, and applied research. He earned his doctorate from the Engineering School of Tunisia in collaboration with the Institute of Fluid Mechanics in Toulouse, following a master’s degree in industrial thermal systems with Marseille University and a mechanical engineering diploma through partnerships with universities in Italy. His professional journey spans roles as an assistant professor and trainer across Tunisia and Saudi Arabia, where he has taught courses such as statics, dynamics, vibration, thermodynamics, fluid mechanics, and automatic control while supervising undergraduate and graduate students. His research interests focus on heat and mass transfer in stratified flows, fluid instabilities, solar ponds, thermal and membrane desalination, nanotechnology in energy conversion, and visualization techniques for fluid systems. Over his career, he has been actively involved in research groups, project administration, and curriculum development, contributing to both academic advancement and applied technological innovation. His skills encompass experimental design, numerical modeling, energy systems, and interdisciplinary collaboration. Recognized for his commitment to education and research, he has received honors through international collaborations and institutional contributions. In conclusion, Karim Choubani exemplifies a scholar-practitioner dedicated to advancing mechanical engineering through teaching, research, and applied innovation.

Profile: ORCID

Featured Publications

Ben Rabha, M., El Haj, A., Mannai, A., Choubani, K., Almeshaal, M. A., & Dimassi, W. (2025). Enhancement of optoelectronic properties of multicrystalline silicon using Al-NPs and SiNₓ layer. Crystals, 15(4), 381.

Choubani, K., Ghriss, O., Alrasheedi, N. H., Dhaoui, S., & Bouabidi, A. (2024). Experimental investigation of a phase-change material’s stabilizing role in a pilot of smart salt-gradient solar ponds. Frontiers in Heat and Mass Transfer.

Almeshaal, M. A., Majeed, A., Ijaz, N., Alvi, H., Palaniappan, M., Choubani, K., Khan, S. U., & Kolsi, L. (2024). Numerical simulations for thermally developed double diffusion flow of nanoparticles due to elongated cylinder with multiple slip effects: Applications to energy storage system. Numerical Heat Transfer, Part A: Applications.

Rebhi, A., Amri, C., Khemissi, I., Zaghouani, R. B., Gaidi, M., Hajjaji, A., Choubani, K., Almeshaal, M. A., Papathi, M. P., & Ben Rabha, M. (2024). Porous silicon layer decorated with PbS nanoparticles by SILAR method for enhanced photocatalytic degradation of amido black dye. Journal of Nanoparticle Research.

Abbas, A., Hussanan, A., Anwar, F., Obalalu, A. M., Almeshaal, M. A., Palaniappan, M., Choubani, K., Kolsi, L., & Aslam, M. (2024). Thermal analysis of AA7075-AA7072/methanol via Williamson hybrid nanofluid model past thin needle: Effects of Lorentz force and irregular heat rise/fall. Case Studies in Thermal Engineering.

Hamoudi, A. M., Choubani, K., & Rabha, M. B. (2023). Multicrystalline silicon passivation by hydrogen and oxygen-rich porous silicon layer for photovoltaic cells applications. Iranian Journal of Materials Science and Engineering.

Almeshaal, M. A., Abdouli, B., Choubani, K., Khezami, L., & Rabha, M. B. (2023). Study of porous silicon layer effect in optoelectronics properties of multi-crystalline silicon for photovoltaic applications. Silicon.

Elyas Rostami | Mechanical Engineering | Best Researcher Award

Published on 19/04/202519/04/2025 by Cognitive Scientist Awards

Dr. Elyas Rostami | Mechanical Engineering | Best Researcher Award

Dr. Elyas Rostami is a distinguished faculty member at Buein Zahra Technical and Engineering University, specializing in Aerospace Engineering 🚀. With a solid academic background, he earned his B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, followed by an M.Sc. in Aerospace Engineering ✈️ from Tarbiat Modares University, and completed his PhD 🎓 at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles 📄, books 📚, and research projects 🔍 to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization 💧, fluid mechanics 🌊, and sustainable energy applications ⚡. As a dedicated educator 👨‍🏫 and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations 🤝, publishing, and mentoring.

Profile

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Education 🎓

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, laying the foundation for his technical expertise. He pursued his M.Sc. in Aerospace Engineering ✈️ at Tarbiat Modares University, where he refined his knowledge in aerodynamics, propulsion, and fluid mechanics 🌬️. Driven by academic excellence, he earned his Ph.D. in Aerospace Engineering 🚀 from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems 🔥, and energy-efficient solutions 💡. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems 🔧. Through rigorous coursework, experimental research, and publications 📝, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠💪, enabling him to address modern aerospace challenges with innovative thinking 🚀.

Experience 👨‍🏫

Dr. Elyas Rostami serves as a faculty member 👨‍🏫 at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering 🚀. His career spans academic teaching 📖, research leadership 🔬, article peer reviewing 🧐, and supervising research projects 🔍. He has authored multiple scientific papers 📝 and books 📚, advancing the field of propulsion systems, spray technology 💧, and fluid mechanics 🌊. Beyond academia, Dr. Rostami actively engages with industry via research collaborations 🤝 and applied projects that explore new energies 🌱 and aerospace applications ✈️. His professional journey demonstrates versatility and dedication, contributing both as an educator and innovator 🎓⚙️. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions 🏅

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction 🎖️. His scholarly contributions earned him recognition through research publications in reputable journals 🏅, invitations to review articles 🧐, and participation in collaborative research projects 🤝. He has authored a scientific book 📘 and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion 🔥 and fluid dynamics 🌊 has positioned him as a valued academic and research professional. The depth of his work reflects both national and international acknowledgment, solidifying his reputation as a committed and impactful researcher 🌍. Dr. Rostami’s work embodies passion for engineering, teaching excellence, and scientific advancement 💡🔬.

Research Interests 🔬

Dr. Elyas Rostami’s research focuses on fluid mechanics 🌊, aerospace propulsion systems 🚀, spray and atomization processes 💧, and the use of renewable energies in industrial applications 🌱. His investigations address the thermodynamics of propulsion systems 🔥, advancing efficiency and performance in aerospace technology ✈️. He integrates experimental and computational approaches 🖥️ to explore new energy solutions and optimize spray behavior under different conditions ⚙️. Through research projects 🔬, journal articles 📄, and book authorship 📚, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation 💡, making significant strides in the development of sustainable and high-performance propulsion technologies 🌍🚀.

Publications

Experimental and numerical investigation of the welding impact on residual stresses of the joint region of ASTM-A36 marine steel

S., Nickabadi, Saeid, H., Rostami, Hosein, M.R., Hadavi, Mohammad Reza, E., Rostami, Elyas, S., Hamzeh, Siroos

Materials Chemistry and Physics, 2025

Lichen Shi | Mechanical Engineering | Best Researcher Award

Published on 14/02/202514/02/2025 by Cognitive Scientist Awards

Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

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Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

Developing an AI-based method for reading pointer meters using human-like reading sequences.
Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

Intelligent Measurement with Deep Learning
Equipment Status Monitoring and Fault Diagnosis
Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

[1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
[2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) ：25186-25196. doi:
10.1109/JSEN.2024.3417231
[3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
Diagnosis of Rotating Machinery[J]. IEEE Sensors Journal, 2024,12:34785-34799. DOI:
10.1109/access.2024.3368755.
4] Haitao Wang, Mingjun Li, Zelin Liu, Xiyang Dai, Ruihua Wang and Lichen Shi*. RotaryMachinery Fault Diagnosis Based on Split Attention MechanismandGraphConvolutional Domain Adaptive Adversarial Network[J]. IEEE Sensors Journal, 2024,
24(4) :5399-5413. doi: 10.1109/JSEN.2023.3348597.
[5] Haitao Wang, Xiyang Dai, Lichen Shi*. Gearbox Fault Diagnosis Based onMixedData-Assisted Multi-Source Domain Transfer Learning under Unbalanced Data[J]. IEEESensors Journal. doi: 10.1109/JSEN.2024.3477929