Dr. Narcisse Defo | Experimental Methods | Research Excellence Award

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Dr. Narcisse Defo | Experimental Methods | Research Excellence Award

Teacher | University of Douala | Cameroon

Dr. Narcisse Defo is an accomplished researcher whose expertise lies in materials chemistry, composite materials, and sustainable materials valorization, with strong emphasis on Experimental Methods applied to advanced engineering materials. His research integrates Experimental Methods for composite development, Experimental Methods for physicochemical characterization, Experimental Methods for mechanical and tribological evaluation, Experimental Methods for thermal and microstructural analysis, and Experimental Methods for performance optimization. Through extensive use of Experimental Methods, his work supports industrial innovation, environmental sustainability, and materials recycling for societal benefit. He has authored multiple peer reviewed scientific publications and a patented innovation, demonstrating consistent application of Experimental Methods in bio based composites and abrasive materials. His collaborations with international research laboratories and industry partners strengthen knowledge transfer and applied research impact, while his Experimental Methods driven approach contributes to sustainable manufacturing, waste valorization, and technological advancement. His scientific output reflects rigor, reliability, and global relevance supported by standardized Experimental Methods. Scopus profile of 12 Citations, 5 Documents, 2 h- index.

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Featured Publications


Effect of alkaline treatment on hard vegetable shells on the properties of biobased abrasive wheels

Composites Part A: Applied Science and Manufacturing, 2024
Cited by 5

Assoc. Prof. Dr. Osama Hussein Galal | Stochastic Fluid Dynamics | Excellence in Research

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Assoc. Prof. Dr. Osama Hussein Galal | Stochastic Fluid Dynamics | Excellence in Research

Associated Professor | Fayoum University | Egypt

Assoc. Prof. Dr. Osama Hussein Galal is a distinguished academic specializing in Stochastic Fluid Dynamics, whose professional journey reflects exceptional expertise in Engineering Mathematics and Physics. His academic and research trajectory demonstrates profound engagement with Stochastic Fluid Dynamics in analyzing uncertainty quantification, fractional-order systems, and fluid flow modeling. Over his extensive academic tenure, he has served as an educator, researcher, consultant, and supervisor, contributing significantly to Stochastic Fluid Dynamics applications in non-Newtonian fluid analysis, stochastic differential equations, and advanced computational mechanics. His professional experience extends to engineering consultancy and structural design, where he integrated Stochastic Fluid Dynamics methodologies for enhanced prediction accuracy in complex engineering systems. Assoc. Prof. Dr. Osama Hussein Galal has guided numerous postgraduate dissertations focusing on Stochastic Fluid Dynamics and uncertainty modeling in power systems, beam analysis, and transmission lines. His research interest revolves around integrating Stochastic Fluid Dynamics with machine learning, renewable energy modeling, and fractional calculus applications. Recognized for his scholarly contributions, he has received several awards for excellence in teaching, research supervision, and scientific publications. His research skills encompass analytical modeling, stochastic simulation, and the mathematical treatment of Stochastic Fluid Dynamics in engineering contexts, establishing him as a leading voice in the field. Through his numerous publications in reputed international journals, he has advanced global understanding of Stochastic Fluid Dynamics and its engineering implications. His career exemplifies the fusion of theoretical rigor and practical innovation, positioning him as a prominent figure in modern computational and stochastic analysis. Google Scholar profile of 102 Citations, 6 h-index, 5 i10-index.

Profiles: Google Scholar | ORCID

Featured Publications

1. Hatata, A., Galal, O. H., Said, N., & Ahmed, D. (2021). Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network. Renewable Energy, 178, 226–240.

2. Galal, O. H., El-Tahan, W., El-Tawil, M. A., & Mahmoud, A. A. (2008). Spectral SFEM analysis of structures with stochastic parameters under stochastic excitation. Structural Engineering and Mechanics: An International Journal, 28(3), 281–294.

3. Galal, O. H., El-Tawil, M. A., & Mahmoud, A. A. (2002). Stochastic beam equations under random dynamic loads. International Journal of Solids and Structures, 39(4), 1031–1040.

4. Galal, O. H. (2013). A proposed stochastic finite difference approach based on homogenous chaos expansion. Journal of Applied Mathematics, 2013(1), 950469.

5. El-Beltagy, M. A., Wafa, M. I., & Galal, O. H. (2012). Upwind finite-volume solution of Stochastic Burgers’ equation. Scientific Research Publishing.

Sathya Arumugam Thirumalai | Computational Methods | Young Scientist Award

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Mr. Sathya Arumugam Thirumalai | Computational Methods | Young Scientist Award

Mr. Sathya Arumugam Thirumalai | Indian Institute of Technology Roorkee | India

Mr. Sathya Arumugam Thirumalai is a highly motivated researcher whose work integrates Computational Methods with experimental nanomaterial science, emphasizing sustainability, environmental protection, and advanced detection technologies. His academic journey, from IIT Roorkee to TU Dresden, reflects an enduring commitment to merging experimental nanotechnology with Computational Methods for the synthesis and characterization of perovskite, MXene, and 2D materials. Mr. Sathya’s professional experience spans renowned institutions like IISc Bengaluru, BARC Mumbai, and IIT Roorkee, where he utilized Computational Methods in density functional theory (DFT) simulations, material modeling, and radiation detector design. His research, grounded in Computational Methods, has contributed to multiple journal publications addressing gas sensing, field emission, and radiation detection. He applies Computational Methods to optimize nanomaterial performance, enhance photonic properties, and improve the efficiency of radiation detectors. Recognized with several awards and fellowships, including the National Talent Search Fellowship and the Saxon Student Mobility Grant, he has demonstrated excellence in both theoretical and practical domains. His technical mastery extends to Python, MATLAB, COMSOL, and VASP, emphasizing his strength in applying Computational Methods across interdisciplinary fields. Mr. Sathya’s skill in Computational Methods enables him to bridge theoretical simulations with experimental validation, ensuring scientific precision and innovation. His collaborative engagements with global research groups highlight his leadership and cross-disciplinary adaptability. In conclusion, Mr. Sathya exemplifies how Computational Methods can revolutionize material science, fostering technological advancements that align with sustainability and human welfare.

Profiles: Google Scholar | ORCID

Featured Publications

1. Sathya, A. T., Jethawa, U., Sarkar, S. G., & Chakraborty, B. (2025). Pd-decorated MoSi₂N₄ monolayer: Enhanced nitrobenzene sensing through DFT perspective. Journal of Molecular Liquids, 427, 127310.

2. Sathya, A. T., Kandasamy, M., & Chakraborty, B. (2024). Strain induced nitrobenzene sensing performance of MoSi₂N₄ monolayer: Investigation from density functional theory. Surfaces and Interfaces, 55, 105386.

3. Sanyal, G., Vaidyanathan, A., Sathya, A. T., & Chakraborty, B. (2025). Efficient catechol sensing in newly synthesized 2D material Ti₂B MBene: Insights from density functional theory simulations. Langmuir, 41(33), 22525–22534.

4. Sathya, A. T., Sarkar, S. G., Bakhtsingh, R. I., & Mondal, J. (2024). Suppression of shielding effect of large area field emitter cathode in radio frequency gun environment. Physica Scripta, 99(12), 125301.