Ms. Ekaterina Sadikova | Heat Transfer Analysis | Best Researcher Award

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Ms. Ekaterina Sadikova | Heat Transfer Analysis | Best Researcher Award

Ms. Ekaterina Sadikova | Peter the Great St.Petersburg Polytechnic University | Russia

Ms. Ekaterina Sadikova is a postgraduate student at Peter the Great St. Petersburg Polytechnic University, specializing in Thermo physics and Theoretical Heat Engineering, and currently working as a Research Scientist at Aurora Chemicals LLC. She earned her Bachelor’s degree in Applied Mathematics and Physics and a Master’s degree with honors in 2023 from the same institution, where she also received recognition for her strong academic achievements. Her research interests lie at the intersection of computational modeling, thermophysical processes, and applied mathematics, with an emphasis on validating theoretical models through experimental methods. She has contributed to both fundamental and applied research, completing projects such as her Bachelor’s thesis on the propagation of laminar flames over combustible surfaces and her Master’s thesis on computational analysis of the temperature state of fuel assemblies in the spent fuel pool of the Beloyarskaya Nuclear Power Plant under beyond-design accident conditions. Her expertise has been applied to consultancy and industry projects with organizations such as Atom Energy Project and Aurora Chemicals LLC, where she is engaged in developing a laboratory-scale prototype for synthesizing hexafluoroethane. She was recognized with the Best Presentation Award at the Minsk International Heat and Mass Transfer Forum.

Profile: Google Scholar

Featured Publications

  • Sadikova, E. V. (2023). Computational analysis of the temperature state of fuel rods in the spent fuel pool of power unit No. 4 at the Beloyarsk Nuclear Power Plant under beyond-design basis accidents (Master’s thesis). Peter the Great St. Petersburg Polytechnic University.

  • Sadikova, E. V., & Snegiryov, A. Yu. (2022). Propagation of a laminar flame over the surface of a combustible material. Science Week, PhysMech, 291–293.

 

Mr. Talal Taha | Fluid Dynamics | Best Researcher Award

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Mr. Talal Taha | Fluid Dynamics | Best Researcher Award

Mr. Talal Taha , International Islamic University Islamabad , Pakistan.

Mr. Talal Taha is a passionate researcher in applied mathematics and fluid dynamics, with a strong interest in the intersection of artificial intelligence and engineering 🌊🤖. Currently completing his MS in Applied Mathematics from International Islamic University Islamabad 🎓, he has authored multiple publications in top-tier journals by 2025 📚. His research explores machine learning applications to complex flow systems, using tools like MATLAB, Python, and COMSOL Multiphysics 💻. With teaching experience in Islamabad and active participation in scientific conferences, Talal remains committed to academic excellence and collaborative growth 🌟.

Professional Profile

Scopus
Google Scholar

Education and Experience 

🎓 Education:

  • M.S. in Applied Mathematics, International Islamic University Islamabad (2022–2024) – GPA: 3.85/4.00

  • B.S. in Mathematics, Khwaja Fareed University of Engineering and IT (2018–2022) – GPA: 3.55/4.00

💼 Work Experience:

  • 👨‍🏫 Mathematics Teacher, The Smart School Capital Campus, G13-4, Islamabad (Mar 2023 – Sep 2023)

  • 📘 Mathematics Lecturer, Seneta Vision School and College, G-13, Islamabad (Oct 2023 – Nov 2024)

Summary Suitability

Mr. Talal Taha stands as a highly promising researcher and a deserving candidate for the Best Researcher Award, demonstrating outstanding contributions in the fields of Applied Mathematics, Fluid Dynamics, and Artificial Intelligence-driven modeling techniques. His exceptional academic track record, advanced computational expertise, and impactful research publications in international journals reflect his commitment to advancing scientific understanding in engineering and thermal sciences.

Professional Development

Mr. Talal Taha continuously advances his skills through academic training and software mastery 🔍💡. He is proficient in MATLAB for fluid simulations and neural network modeling, Python for data analysis and predictive modeling, and COMSOL for Multiphysics simulations 🔧📊. He has actively participated in international conferences and seminars, including the 9th International Conference on Fluid Mechanics hosted by the Pakistan Academy of Sciences 🌐📅. Additionally, Talal has completed courses in time management and financial literacy, reinforcing both technical and soft skills necessary for academic and professional success 📈🎯.

Research Focus

Talal Taha’s research is primarily focused on fluid dynamics, applied mathematics, and machine learning 🔬🌊🧠. His work involves magnetohydrodynamics (MHD), bioconvection in non-Newtonian fluids, and thermal radiation using artificial intelligence and numerical techniques 🧪🔥. With tools like neural networks and regression modeling, he explores how complex boundary layer flows react to thermal and chemical influences, aiming to optimize real-world engineering systems 🛠️📉. His interdisciplinary approach connects physics, AI, and mathematics to simulate and solve challenges in engineering fluid systems efficiently and innovatively 🤖📐.

Honors and Awards 

🏅 Awarded certificate for short course “Time Management Skills” – 2020
🏆 Certificate of participation in Mathematics Quiz Competition – KFUEIT
💰 Certificate in “National Financial Literacy Program for Youth” – 2021

Publication Top Notes

  • Advanced intelligent computing ANN for momentum, thermal, and concentration boundary layers in plasma electro hydrodynamics Burgers fluid
    Authors: M.I. Khan, R. Ghodhbani, T. Taha, F.A.M. Al-Yarimi, A. Zeeshan, N. Ijaz, N. Ben Khedher
    Journal: International Communications in Heat and Mass Transfer
    Volume: 159, Article ID: 108195, Year: 2024
    Citations: 22

  • Machine learning-based prediction of heat transfer enhancement in Carreau fluids with impact of homogeneous and heterogeneous reactions
    Authors: N. Fatima, R. Ghodhbani, N. Khalid, M.I. Khan, T. Taha, N. Ijaz, N. Saleem
    Journal: Multiscale and Multidisciplinary Modeling, Experiments and Design
    Volume: 8(2), Pages: 145–158, Year: 2025
    Citations: 12

  • Novel investigation of Burger fluid with gyrotactic microorganisms over a sheet using Levenberg–Marquardt backpropagation (LMBP)
    Authors: S. Abdal, T. Taha, N.A. Shah, S.J. Yook
    Journal: Alexandria Engineering Journal
    Volume: 117, Pages: 403–417, Year: 2025
    Citations: 7

  • Non-Newtonian fluid flow with bioconvection: The role of Rayleigh number and buoyancy over a sheet
    Authors: J.A. Jugnoo, A. ur Rehman, T. Taha
    Journal: Journal of Innovative Research in Mathematical and Computational Sciences
    Volume: 3, Issue TBD, Year: 2024
    Citations: 6

  • Neural networking-based approach for examining heat transfer and bioconvection in Non-Newtonian fluid with chemical reaction over a stretching sheet
    Authors: S. Abdal, T. Taha, L. Ali, R.M. Zulqarnain, S.J. Yook
    Journal: Case Studies in Thermal Engineering
    Volume: 69, Article ID: 106047, Year: 2025
    Citations: 2

  • Machine learning study for three-dimensional magnetohydrodynamic Casson fluid flow with Cattaneo–Christov heat flux using linear regression technique: Application in porous media
    Authors: S. Abdal, T. Taha, N.A. Shah, S.J. Yook
    Journal: Engineering Applications of Artificial Intelligence
    Volume: 156, Article ID: 111159, Year: 2025

  • Effects of thermal radiation on MHD bioconvection flow of non-Newtonian fluids using linear regression-based machine learning and artificial neural networks
    Authors: S.M. Sait, R. Ellahi, N. Khalid, T. Taha, A. Zeeshan
    Journal: International Journal of Numerical Methods for Heat & Fluid Flow
    Volume: 35(5), Year: 2025

  • Artificial intelligence approach to magnetohydrodynamic flow of non-Newtonian fluids over a wedge: Thermophoresis and Brownian motion effects
    Authors: T. Taha, S. Abdal, L. Ali, R.M. Zulqarnain, S.J. Yook
    Journal: Engineering Science and Technology, an International Journal
    Volume: 66, Article ID: 102071, Year: 2025

Conclusion

Mr. Talal Taha exemplifies the qualities sought in a Best Researcher Award recipient. His work seamlessly integrates theoretical mathematics, fluid mechanics, and modern machine learning approaches to solve complex engineering problems. With a consistent publication record, advanced modeling skills, international collaboration, and a forward-looking research agenda, Talal’s contributions have already made a measurable impact in computational science and are poised to influence future developments across interdisciplinary domains.