Ebenezer Aniyom | Environmental Biotechnology | Best Researcher Award

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Ms. Ebenezer Aniyom | Environmental Biotechnology | Best Researcher Award

Graduate Engineer at Hydroserve Oil Services, Nigeria

Engr. Aniyom Ebenezer Ananiyom is a petroleum engineer and data scientist whose expertise bridges engineering innovation and data-driven technologies in the oil and gas industry. He earned a Bachelor’s degree in Petroleum Engineering from the Federal University of Technology Owerri, graduating with a 4.41 CGPA, and currently serves as a Graduate Engineer at Hydroserve Oil Services. His professional experience spans reservoir management, coiled tubing operations, and predictive modeling for optimizing oilfield productivity. He has conducted research in areas including reservoir characterization, flood susceptibility mapping, and machine learning applications for environmental and production systems. His published works appear in reputable journals such as Science Direct (Elsevier), Improved Oil and Gas Recovery Journal, and Engineering World Journal. Aniyom’s research contributions demonstrate the integration of artificial intelligence with petroleum engineering to enhance decision-making, efficiency, and sustainability in energy systems. He has authored seven peer-reviewed papers, completed eight research projects, and contributed to one consultancy project. His citation record reflects a growing influence in data-based petroleum research, supported by an H-index of 1 in the Scopus database. A member of professional societies including the Nigerian Society of Engineers (NSE), the Society of Petroleum Engineers (SPE), and the International Association of Engineers (IAENG), Aniyom exemplifies a new generation of engineers committed to advancing energy technology through interdisciplinary research and innovation.

profile: Google Scholar | Orcid

Featured Publications

Okoli, E. A., Josephine, K. M., Agoha, C. C., Ikoro, D. O., Oyinebielador, D. O., Aniyom, E. E. A., Oladipupo, J. T., & Emenyonu, U. D. (n.d.). Integrated flood susceptibility mapping using machine learning and geospatial techniques: A case study of Imo State, Southeastern Nigeria. Science Direct (Elsevier).

Chikwe, A. O., Aniyom, E. E., & Mbah, S. (n.d.). Enhancing well productivity through acidizing using coiled tubing – Case study of the Niger Delta. Improved Oil and Gas Recovery Journal, 9.

Anyadiegwu, C. I., Okalla, C. E., Kerunwakerunwa, A., Uzor, C. D., Uzohuzoh, J. C., Aniyom, E. E. A., & Dike, C. F. D. (n.d.). Data-driven modeling and analysis of reservoir fluid behavior: A machine learning approach to PVT characterization in heterogeneous reservoirs. Engineering World Journal, 7(11).

Aniyom, E. E., & Chikwe, A. O. (n.d.). Prediction of leak on gas pipeline using a hybrid machine learning model. Improved Oil and Gas Recovery Journal, 9.

Nmesoma, L. W., Aniyom, E. E. A., & Okere, N. (n.d.). Optimizing bubble point pressure prediction in petroleum reservoirs through ensemble voting regressors. Society of Petroleum Engineers – SPE Nigeria Annual International Conference and Exhibition, NAIC.

Chikwe, A. O., Aniyom, E. E. A., Nwanwe, O. I., & Odo, J. E. (n.d.). Comparative analysis of leak prediction in gas pipelines using physical models versus machine learning regression models. Journal of Petroleum and Mining Engineering, 0(0), 1–6.

Ihenetu, V. N., Aniyom, E. E., Jean Claude, W., Ewuzie, U., & Okoli, E. A. (n.d.). Prediction of quality groundwater availability using a hybrid machine learning model. Nigerian Association of Petroleum Explorationists Bulletin.

Aniyom, E. E., Chikwe, A. O., & Odo, J. E. (n.d.). Hybridization of optimized supervised machine learning algorithms for effective lithology prediction. Society of Petroleum Engineers – SPE Nigeria Annual International Conference and Exhibition, NAIC.

Mahboubeh Molavi-Arabshahi | Environmental Biotechnology | Women Researcher Award

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Assist. Prof. Dr. Mahboubeh Molavi-Arabshahi | Environmental Biotechnology | Women Researcher Award

Assistant professor at Iran university of science and technology, Iran

Mahboubeh Molavi-Arabshahi is an accomplished researcher and educator in applied mathematics whose academic contributions span numerical methods, climate modeling, and partial differential equations. Holding a solid academic background and a proven record in both theoretical and applied aspects of mathematics, she has established herself as a dedicated scholar with significant impact in academia and collaborative research circles. Her commitment to bridging advanced mathematical techniques with real-world applications is reflected in her long-term teaching, leadership, and diverse publication portfolio.

Profile

Orcid

Scopus

Google Scholar

Education

She earned her Ph.D. in Applied Mathematics from Iran University of Science & Technology between 2007 and 2011 under the supervision of Dr. A. Golbabai, with a dissertation focusing on compact high-order methods in partial differential equations. Her educational path began with a Bachelor’s degree in Applied Mathematics from Mazandaran University, Babolsar, from 1999 to 2003, where she built her foundational skills in numerical computation and linear algebra. She then pursued her Master’s degree at Amirkabir University of Technology from 2003 to 2005, researching optimal preconditioning techniques for solving linear systems associated with partial differential equations under the guidance of Dr. M. Dehghan. This rigorous academic training laid the groundwork for her expertise in numerical solutions and computational modeling.

Experience

In her professional career, Mahboubeh Molavi-Arabshahi has accumulated substantial experience as an Assistant Professor and academic leader. Since January 2016, she has been serving as an Assistant Professor and Deputy of the Applied Mathematics Group at Iran University of Science & Technology, where she teaches courses ranging from General Mathematics and Numerical Analysis to Wavelet Theory and Advanced Modeling. Prior to this role, she contributed as an Assistant Professor at the Iranian National Institute for Oceanography and Atmospheric Science from 2011 to early 2016, where she expanded her research scope to climate data analysis, oceanographic applications, and environmental modeling. Early in her career, she also managed the Mathematics Group at the Ghalamchi Institute, which gave her valuable experience in educational leadership and the development of learning materials.

Research Interest

Her main research interests focus on the numerical solution of partial differential equations, high-order compact difference schemes, preconditioning methods, numerical linear algebra, and climate modeling with a special emphasis on the Caspian Sea region. She has managed multiple projects analyzing climate data for Iranian coastal regions and the Persian Gulf, contributing valuable insights into regional climate variability, sea surface temperature trends, and large-scale teleconnections. Her work frequently integrates advanced computational methods with environmental sciences, demonstrating her interdisciplinary strength and her capacity to address complex real-world problems with rigorous mathematical frameworks.

Award

Her scholarly contributions have been recognized with academic honors and active participation in national and international mathematical competitions during her early years. She was a member of the Mathematics Competition Team at Mazandaran University in 2002 and 2003, representing her institution in national events and showcasing her analytical abilities and dedication to problem-solving. She has since continued to share her expertise through workshops, conferences, and collaborative projects with diverse institutions and scientific communities.

Publication

Her impactful research is evident in her peer-reviewed publications, with several notable works cited in reputable journals. Key examples include:

  1. “A simple form for the fourth-order difference method for 3-D elliptic equations” in Applied Mathematics and Computation (2007), cited for advancing computational methods in multi-dimensional problems.
  2. “Comparison of preconditioning techniques for solving linear systems arising from the fourth-order approximation of the 3-D elliptic equation” in Applied Mathematics and Computation (2007), recognized for practical numerical solutions.
  3. “Preconditioned techniques for solving large sparse linear systems arising in the 2-D elliptic partial differential equations” in Applied Mathematics and Computation (2007), demonstrating robust algorithm design.
  4. “A Numerical method for diffusion–convection equation using high-order difference schemes” in Computer Physics Communications (2010), contributing to computational physics applications.
  5. “On the behavior of high-order compact approximations in the one-dimensional sine–Gordon equation” in Physica Scripta (2011), exploring advanced modeling of wave equations.
  6. “Precipitation and temperature of the Southwest Caspian Sea during the last 56 years, their trends and teleconnections with large-scale atmospheric phenomena” in International Journal of Climatology (2016), bridging numerical methods with climatology.
  7. “An efficient approach for solving the fractional model of the human T-cell lymphotropic virus I by the spectral method” in Journal of Mathematical Modeling (2023), expanding her work to mathematical biology and health applications.

Conclusion

In conclusion, Mahboubeh Molavi-Arabshahi’s career exemplifies dedication to advancing numerical analysis and its real-world applications in environmental sciences and engineering. With a steadfast commitment to academic excellence, collaborative research, and effective teaching, she continues to contribute meaningfully to the global mathematics community. Her body of work, which combines deep theoretical knowledge with practical problem-solving, highlights her as a researcher who remains passionate about leveraging mathematics to better understand and address complex scientific challenges.