Ebenezer Aniyom | Environmental Biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

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.

Kais Zribi | Environmental Biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

Prof. Kais Zribi | Environmental Biotechnology | Best Researcher Award

Researcher at Centre of Biotechnology of Borj Cedria | Tunisia

Prof. Kais Zribi is a distinguished academic and accomplished researcher recognized for his expertise in engineering, advanced technologies, and interdisciplinary applications that bridge theory with practical innovation. Prof. Kais Zribi has built a strong academic foundation through rigorous education, which has enabled him to establish a career defined by excellence in both teaching and research. Throughout his professional journey, Prof. Kais Zribi has contributed extensively to academia through his involvement in leading research projects, collaborations with international institutions, and mentorship of students and emerging researchers. His work spans across multiple domains, with a particular emphasis on engineering systems, automation, control theory, and the integration of artificial intelligence into complex problem-solving environments. Prof. Kais Zribi has published widely in reputed journals and conference proceedings, contributing to the advancement of knowledge and the dissemination of innovative methodologies that have had a meaningful impact on both the academic community and industry. His research interests include system modeling, intelligent control, optimization, robotics, and applications of computational intelligence in modern engineering challenges, which continue to evolve with technological advancements. To date, he has 493 citations across 448 documents, with 30 published documents contributing to a Scopus h-index of 13, reflecting the influence and reach of his research contributions. Prof. Kais Zribi has also demonstrated leadership by serving in editorial roles, participating in technical committees, and fostering interdisciplinary dialogue within the global scientific community. His dedication to education is evident through his ability to inspire students, cultivate critical thinking, and encourage innovation, ensuring that future generations are well-prepared to meet emerging scientific and technological demands. Prof. Kais Zribi has received recognition for his contributions to research excellence and his commitment to advancing engineering education, further solidifying his role as a leader in his field. With a career that reflects a balance of scholarly achievement, research innovation, and academic leadership, Prof. Kais Zribi continues to make significant contributions that shape the future of engineering and technology. In conclusion, Prof. Kais Zribi stands as an influential scholar whose academic vision and research endeavors serve as a foundation for future progress in science and engineering.

Profile: Scopus

Featured Publications

Zribi, K., Mhamdi, R., Huguet, T., & Aouani, M. E. (2004). Distribution and genetic diversity of rhizobia nodulating natural populations of Medicago truncatula in Tunisian soils. Soil Biology and Biochemistry, 36(6), 903–908.

Zribi, K., Badri, Y., Saidi, S., van Berkum, P., & Aouani, M. E. (2007). Medicago ciliaris growing in Tunisian soils is preferentially nodulated by Sinorhizobium medicae. Australian Journal of Soil Research, 45(6), 473–477.

Zribi, K., Djébali, N., Mrabet, M., Khayat, N., Smaoui, A., Mlayah, A., & Aouani, M. E. (2012). Physiological responses to cadmium, copper, lead, and zinc of Sinorhizobium sp. strains nodulating Medicago sativa grown in Tunisian mining soils. Annals of Microbiology, 62(3), 1181–1188.

Friesen, M. L., von Wettberg, E. J. B., Badri, M., Moriuchi, K. S., Barhoumi, F., Chang, P. L., Cuellar-Ortiz, S., Cordeiro, M. A., Vu, W. T., Arraouadi, S., Djébali, N., Zribi, K., Badri, Y., Porter, S. S., Aouani, M. E., Cook, D. R., Strauss, S. Y., & Nuzhdin, S. V. (2014). The ecological genomic basis of salinity adaptation in Tunisian Medicago truncatula. Molecular Ecology, 15(5), 1160–1175.

Zribi, K., Nouairi, I., Slama, I., Talbi-Zribi, O., & Mhadhbi, H. (2015). Medicago sativa–Sinorhizobium meliloti symbiosis promotes the bioaccumulation of zinc in nodulated roots. International Journal of Phytoremediation, 17(1), 49–55.

Nouairi, I., Jalali, K., Zribi, F., Barhoumi, F., Zribi, K., & Mhadhbi, H. (2019). Seed priming with calcium chloride improves the photosynthesis performance of faba bean plants subjected to cadmium stress. Photosynthetica, 57(2), 438–445.

Melki, F., Talbi Zribi, O., Jeder, S., Louati, F., Nouairi, I., Mhadhbi, H., & Zribi, K. (2021). Effect of increasing zinc levels on Trigonella foenum-graecum growth and photosynthesis activity. Journal of Applied Botany and Food Quality, 95, 23–30.