Lourdes Casas Cardoso | Green Technology | Best Researcher Award

Published on by Natural Scientist Awards

Prof. Dr. Lourdes Casas Cardoso | Green Technology | Best Researcher Award

Professor, Universidad de Cádiz, Spain

Prof. Dr. Lourdes Casas Cardoso is a distinguished academic and researcher specializing in supercritical fluids, advanced separation processes, and supercritical impregnation, with a strong focus on sustainable applications in the agri-food and biomedical sectors. She earned her Bachelor’s Degree in Chemistry from the Marta Abreu Central University of Las Villas (UCLV), Cuba, in 1996, followed by a Master’s in Organic Chemistry with a specialization in Natural Products from the University of Havana in 2001, a Diploma in Advanced Studies from the University of Cádiz in 2004, and a PhD from the same university in 2006. She began her academic career as a professor at UCLV, Cuba, where she served for a decade, before joining the University of Cádiz, Spain, in 2007, where she advanced from researcher to full professor. Since May 2023, she has held the position of University Professor at UCA. Her research has concentrated on the reutilization of byproducts and industrial waste using innovative separation techniques, including supercritical carbon dioxide and pressurized liquids, leading to significant advancements in green technologies. She has published 80 indexed articles, six book chapters, and participated in over 80 international conferences, accumulating 1,420 citations and an h-index of 26. She has supervised four doctoral theses, directed funded research projects, coordinated multiple industry collaborations, and co-authored three patents. Additionally, she has held leadership roles such as Coordinator of the Chemical Engineering Degree at UCA and has received recognition for excellence in teaching. Her career reflects a remarkable integration of research, teaching, and innovation.

Profile:  Scopus  |  ORCID  |  Google Scholar

Featured Publications

Otero-Pareja, M. J., Casas, L., Fernández-Ponce, M. T., Mantell, C., & de la Ossa, E. M. (2015). Green extraction of antioxidants from different varieties of red grape pomace. Molecules, 20(6), 9686–9702.

Fernández-Ponce, M. T., Casas, L., Mantell, C., Rodríguez, M., & de la Ossa, E. M. (2012). Extraction of antioxidant compounds from different varieties of Mangifera indica leaves using green technologies. The Journal of Supercritical Fluids, 72, 168–175.

Fernández-Ponce, M. T., Casas, L., Mantell, C., & de la Ossa, E. M. (2015). Use of high pressure techniques to produce Mangifera indica L. leaf extracts enriched in potent antioxidant phenolic compounds. Innovative Food Science & Emerging Technologies, 29, 94–106.

Fernández-Ponce, M. T., Parjikolaei, B. R., Lari, H. N., Casas, L., Mantell, C., & de la Ossa, E. M. (2016). Pilot-plant scale extraction of phenolic compounds from mango leaves using different green techniques: Kinetic and scale up study. Chemical Engineering Journal, 299, 420–430.

El Marsni, Z., Torres, A., Varela, R. M., Molinillo, J. M. G., Casas, L., Mantell, C., & de la Ossa, E. M. (2015). Isolation of bioactive compounds from sunflower leaves (Helianthus annuus L.) extracted with supercritical carbon dioxide. Journal of Agricultural and Food Chemistry, 63(28), 6410–6421.

Pouya Ghadesi | Earth Science | Best Researcher Award

Published on by Natural Scientist Awards

Mr. Pouya Ghadesi | Earth Science | Best Researcher Award

Mr. Pouya Ghadesi | University of Mohaghegh Ardabili | Iran

Mr. Pouya Ghadesi is a dedicated researcher in the field of mechatronics and artificial intelligence. He earned his Bachelor of Science in Mechanical Engineering and Master of Science in Mechatronics from the University of Mohaghegh Ardabili, Iran. His research interests include deep learning, neural networks, image processing, machine learning, and object detection. As an independent researcher, he has contributed to projects involving advanced frameworks such as MobileNetV3 and optimization algorithms, with applications in land-use classification. His work emphasizes innovation, model design, data validation, and publication of impactful research in high-quality scientific journals.

Professional Profile

Google Scholar

Academic and Professional Background

Mr. Pouya Ghadesi is a passionate researcher specializing in mechatronics and artificial intelligence with a strong academic foundation in mechanical engineering and mechatronics from the University of Mohaghegh Ardabili, Iran. His scholarly interests span deep learning, neural networks, image processing, machine learning, and object detection. As an independent researcher, he has actively contributed to the development of advanced frameworks and optimization methods, particularly focusing on applications in land-use classification. His efforts in model design, data analysis, experimental validation, and scholarly writing highlight his dedication to advancing scientific knowledge and producing impactful research within the global academic community.

Research Focus

Mr. Pouya Ghadesi is an emerging researcher whose work centers on the integration of mechatronics and artificial intelligence, with a particular emphasis on deep learning, machine learning, neural networks, and image processing. His research explores advanced approaches to machine vision and object detection, aiming to improve accuracy and efficiency in practical applications. He has contributed to the design and implementation of innovative frameworks that combine optimization methods with intelligent models, demonstrating his ability to address complex computational challenges. His focus reflects a commitment to advancing intelligent systems that can support real-world technological and scientific development.

Publication Top Notes

Improving accuracy of land-use classification through MobileNetV3 and Greedy Osprey Optimization

Year: 2025 | Cited by: 1

Conclusion

Mr. Pouya Ghadesi is a promising researcher with a strong background in mechanical engineering and mechatronics, focusing on artificial intelligence, machine learning, and image processing. His innovative work on the MobileNetV3 framework with the Greedy Osprey Optimization algorithm demonstrates both creativity and technical skill in land-use classification. Although at an early career stage, he shows great potential for impactful contributions, with opportunities to expand publications, international collaborations, and industrial applications, making him a strong candidate for the Best Researcher Award.