Mawia Osman | Quantum Chemistry | Research Excellence Award

Dr. Mawia Osman | Quantum Chemistry | Research Excellence Award

China West Normal University, China

Dr. Mawia Osman is a dedicated researcher in advanced mathematical sciences, recognized for her work at the intersection of fuzzy systems, nonlinear dynamics, and computational analysis. She built a strong academic foundation through a BSc in Science and Technology, followed by qualifying studies in mathematical sciences, an MSc in pure and applied sciences, and a PhD in Mathematics and Statistics from Northwest Normal University, China. Her doctoral journey was supported by the prestigious Chinese Government Scholarship (CSC), awarded for excellence and potential in research. Throughout her academic progression, she has developed expertise in fuzzy analysis, fuzzy differential and fractional differential equations, nonlinear partial differential equations, fuzzy integral equations, and numerical analysis. Her broader research interests also extend to fuzzy mathematical modeling, quantum calculus, fractal calculus, nonlinear analysis, and biomedical research, reflecting a multidisciplinary vision. Dr. Osman’s scholarly work contributes to solving complex real-world problems using advanced mathematical frameworks, positioning her as a promising researcher in modern applied mathematics. Her commitment to research, continuous learning, and innovative thinking defines her professional identity, and she remains focused on expanding the theoretical and practical applications of fuzzy and nonlinear systems in scientific and technological advancements.

Profiles: Orcid | Google Scholar

Featured Publications

Shah, S.O., Tikare, S., & Osman, M. (2023). Ulam Type Stability Results of Nonlinear Impulsive Volterra–Fredholm Integro-Dynamic Adjoint Equations on Time Scale. Mathematics, 11(21).

Ali, U., & Osman, M. (2023). On Consequences of Carreau Nanofluid Model with Dufour–Soret Effects and Activation Energy Subject to New Mass Flux Condition: A Numerical Study. Mathematics, 11(11).

Osman, M., Xia, Y., Omer, O.A., & Hamoud, A. (2022). On the Fuzzy Solution of Linear–Nonlinear Partial Differential Equations. Mathematics, 10(13).

Omer, O.A., Saibi, K., Abidin, M.Z., & Osman, M. (2022). Parametric Marcinkiewicz Integral and Its Higher-Order Commutators on Variable Exponents Morrey–Herz Spaces. Journal of Function Spaces, Article 7209977.

Osman, M., Almahi, A., Omer, O.A., Mustafa, A.M., & Altaie, S.A. (2022). Approximation Solution for Fuzzy Fractional-Order Partial Differential Equations. Fractal and Fractional, 6(11).

Osman, M., Xia, Y., Marwan, M., & Omer, O.A. (2022). Novel Approaches for Solving Fuzzy Fractional Partial Differential Equations. Fractal and Fractional, 6(11).

Osman, M., Gong, Z., Mustafa, A.M., & Yang, H. (2021). Solving Fuzzy (1+n)-Dimensional Burgers’ Equation. Advances in Difference Equations, Article 3376.

Dr. Abhijeet Das | Water Resource Engineering | Distinguished Scientist Award

Dr. Abhijeet Das | Water Resource Engineering | Distinguished Scientist Award

C.V. Raman Global University (CGU), Bhubaneswar | India

Dr. Abhijeet Das is an accomplished water resource engineer specializing in watershed hydrology, hydrological modeling, water quality control, and climate change impact assessment. He holds a B.Tech in Civil Engineering from ABIT College (BPUT, Rourkela), an M.Tech in Water Resource Engineering from the College of Engineering and Technology (BPUT, Bhubaneswar), and a Ph.D. in Water Resource Engineering from C.V. Raman Global University, Bhubaneswar. With extensive experience in GIS, remote sensing, and geoinformatics, he has contributed significantly to hydrological modeling, flood and drought assessment, and the integration of machine learning techniques for water resource management. Dr. Das has held positions as a GIS Analyst, Teaching Assistant, and Research Consultant, leading spatial data management, hydrological and hydraulic modeling, flood risk analysis, and multi-criteria decision support. His expertise spans numerical modeling, optimization techniques, artificial neural networks, and fuzzy logic for environmental and water systems. He has successfully guided GIS projects, developed dashboards for real-time monitoring, and collaborated with government agencies for watershed and crop management planning. Dr. Das’s research focuses on sustainable water management, climate resilience, and the Food-Energy-Water (FEW) nexus. His contributions have advanced environmental impact assessment and decision-making tools. He continues to drive innovation in water resource engineering and hydrological research, fostering a data-driven approach to sustainable resource management.

Profile: Scopus

Featured Publications

Drinking water resources suitability assessment in Brahmani River, Odisha, based on pollution index of surface water utilizing advanced water quality methods. Scientific Reports.

An optimization-based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance. Discover Environment.

A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India. Discover Sustainability.

Water quality assessment and geospatial techniques for the delineation of surface water potential zones: A data-driven approach using machine learning models. Desalination and Water Treatment

Evaluation and prediction of surface water quality status for drinking purposes using an integrated water quality indices, GIS approaches, and machine learning techniques. Desalination and Water Treatment