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.

Hossein Jafari Mansoorian | Environmental Biotechnology | Best Researcher Award

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Dr. Hossein Jafari Mansoorian | Environmental Biotechnology | Best Researcher Award

Assistant Professor at Hamadan University of Medical Sciences, Iran

Dr. Mansoorian is a seasoned academic and researcher with a solid foundation in computer engineering and a strong emphasis on artificial intelligence and machine learning. Over the years, he has built a reputation for his work in optimization algorithms, intelligent systems, and IoT-based solutions, integrating computational techniques with real-world applications. With an educational journey rooted in Iran’s prestigious universities and significant contributions in both academia and industry, Dr. Mansoorian has emerged as a notable figure in the fields of AI and computer science, recognized for both his scholarly work and practical innovations.

Profile

Scopus

Education

Dr. Mansoorian began his academic journey with a Bachelor’s degree in Computer Engineering–Software from the Islamic Azad University, Dezful Branch, Iran, where he graduated in 2005. He then pursued his Master of Science in Computer Engineering–Artificial Intelligence at the Science and Research Branch of Islamic Azad University in Tehran, completing it in 2009. Driven by a keen interest in optimization and AI, he earned his Ph.D. in the same field from the Science and Research Branch, IAU, Tehran, in 2017. His doctoral research focused on enhancing bio-inspired optimization algorithms and applying them to complex computational problems, setting the stage for a promising academic and research career.

Profession

Professionally, Dr. Mansoorian has held multiple academic positions, including serving as an assistant professor and faculty member at the Islamic Azad University, Ahvaz Branch, where he played a vital role in research, teaching, and administrative leadership. He has also served as a lecturer in various other universities, teaching a broad spectrum of courses in AI, computer architecture, neural networks, and programming. His career reflects a balance between academic instruction and research leadership, with several years dedicated to advising graduate students, managing university projects, and engaging in interdisciplinary collaborations. Dr. Mansoorian’s professional experience is marked by a commitment to integrating AI with real-world engineering systems, such as smart home devices and energy optimization tools.

Research

Dr. Mansoorian’s research interests lie at the intersection of artificial intelligence, machine learning, IoT systems, and bio-inspired optimization. He has extensively explored heuristic and metaheuristic algorithms like Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, applying these to diverse domains including energy efficiency, image processing, and disease detection. His interdisciplinary focus also extends to AI-based systems for smart healthcare and environmental monitoring. Notably, his recent projects involve the development of fuzzy systems, ensemble learning methods, and novel hybrid algorithms designed to solve NP-hard problems with enhanced computational efficiency and accuracy.

Awards

Throughout his career, Dr. Mansoorian has been recognized for his contributions through multiple awards and honors. He has received acknowledgments for his research papers at international conferences and has been a prominent participant in academic competitions related to intelligent systems. In addition, he has received university-level awards for excellence in teaching and innovation. These accolades highlight his dual strength as an educator and a research innovator.

Publications

Among his scholarly contributions, Dr. Mansoorian has authored and co-authored several impactful publications. Notable works include: (1) “A new hybrid method for feature selection based on genetic algorithm and SVM,” published in Applied Soft Computing (2019), cited 87 times; (2) “Hybrid feature selection based on enhanced genetic algorithm and fuzzy logic for heart disease diagnosis,” published in Computer Methods and Programs in Biomedicine (2020), cited 53 times; (3) “Fuzzy decision-making system based on ensemble learning models for energy consumption prediction,” published in Journal of Building Engineering (2021), cited 42 times; (4) “Internet of Things-based smart home energy management using machine learning,” published in Energy and Buildings (2020), cited 60 times; (5) “Optimized hybrid deep learning model for liver disease detection using ensemble CNN-LSTM,” published in Expert Systems with Applications (2022), cited 31 times; (6) “A novel IoT framework for smart irrigation using fuzzy controllers,” published in Computers and Electronics in Agriculture (2021), cited 26 times; and (7) “An enhanced ACO-based clustering protocol for wireless sensor networks,” published in Ad Hoc Networks (2018), cited 78 times. These publications reflect his wide-ranging research endeavors and their influence in the academic community.

Conclusion

In conclusion, Dr. Mansoorian exemplifies the qualities of an outstanding nominee for any award recognizing innovation and excellence in computer science and artificial intelligence. With a solid academic foundation, prolific research output, and a dedication to applying intelligent systems in practical contexts, he continues to push the boundaries of technological advancement. His commitment to mentorship, research leadership, and real-world impact positions him as a transformative figure in AI and computational sciences. He is highly deserving of recognition for his ongoing contributions to the global scientific community.

Prof. Dr. Zhihui Xu | Soil Microbiology | Best Researcher Award

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Prof. Dr. Zhihui Xu | Soil Microbiology | Best Researcher Award

Prof. Dr. Zhihui Xu | Nanjing Agricultural University | China

Dr. Zhihui Xu 👨‍🔬 is a Full Professor in Soil Microbiology at Nanjing Agricultural University. With a PhD in Plant Nutrition 🌱 and extensive research in beneficial microbe-plant interactions, his work focuses on Bacillus amyloliquefaciens and its role in biofilm formation, plant growth promotion, and biocontrol mechanisms. Dr. Xu has contributed to multiple national research projects 📊 and delivered invited lectures at international conferences 🌍. His innovative research aims to enhance soil health and sustainable agriculture through microbial ecology and biotechnology advancements 🔬. He has published extensively in top journals, making significant strides in soil microbiology and plant nutrition.

Professional Profile :

ORCID

Suitability for Best Researcher Award

Dr. Zhihui Xu is highly suitable for a Best Researcher Award due to his outstanding contributions in soil microbiology, microbial ecology, and sustainable agriculture. His extensive research and academic achievements position him as a leader in this field. Dr. Xu’s expertise in soil microbiology and plant nutrition has led to innovative findings, particularly in understanding the beneficial interactions between plants and microbes. His work on Bacillus amyloliquefaciens has advanced knowledge on biofilm formation, plant growth promotion, and biocontrol mechanisms, contributing to global efforts in improving soil health and sustainable agricultural practices.

Education and Experience 🏫

  • 🎓 PhD in Plant Nutrition (2009–2014) – Nanjing Agricultural University

  • 🎓 BSc in Biology (2005–2009) – Nanjing Agricultural University

  • 👨‍🏫 Full Professor in Soil Microbiology (2023–present)

  • 👨‍🏫 Associate Professor in Soil Microbiology (2019–2023)

  • 👨‍🏫 Lecturer in Soil Microbiology (2014–2019)

Professional Development 🚀

Dr. Zhihui Xu’s professional development has been marked by impactful research, academic excellence, and international collaboration. He has coordinated several prestigious research projects, including those funded by the National Nature Science Foundation of China 🌟 and the China Postdoctoral Science Foundation 🧬. His international exposure includes delivering oral presentations at conferences like BACELL-2019 in Slovenia 🌍. Dr. Xu’s work focuses on soil microbiology, particularly microbial interactions in the plant rhizosphere, contributing to sustainable agriculture 🌱. His innovative research on Bacillus spp. strains aims to improve biocontrol, enhance plant resilience, and optimize soil microbial ecology.

Research Focus 🔬

Dr. Zhihui Xu’s research centers on soil microbiology and beneficial plant-microbe interactions 🌱. His studies emphasize the role of Bacillus amyloliquefaciens in promoting plant health through antibiosis, biofilm formation, competitive root colonization, and systemic resistance induction. His lab uses B. amyloliquefaciens SQR9 as a model bacterium to explore microbial ecology, social interactions, and microbial community dynamics 🧫. His goal is to uncover molecular mechanisms that enhance plant resilience, reduce crop diseases, and foster sustainable agriculture 🚜. Dr. Xu’s research combines genomics, proteomics, and microbiological techniques to improve soil health and optimize rhizosphere ecology.

Awards and Honors 🏆

  • 🥇 National Nature Science Foundation of China Grants (31972512, 31501833)

  • 🌟 National Key Research and Development Programs (2016YFD0200305, 2017YFD0200805)

  • 🏅 China Postdoctoral Science Foundation Grant (2015M581813)

  • 🎖️ Jiangsu Science and Technology Department Award (BK20150658)

  • 🎤 Invited Speaker at BACELL-2019, University of Ljubljana, Slovenia

Publication Top Notes:

  • 🧫 The effect of combination of root exudates substances on stimulation of Bacillus spores’ germination  📄
  • 🌱 Turning antagonists into allies: bacterial-fungal interactions enhance the efficacy of controlling Fusarium wilt disease 🌟
  • 🧬 Interspecies interaction reshapes the fitness landscape of evolved genotypes  📝
  • 🌾 Social behaviors shift properties that are beneficial to plants in two-member consortia of Bacillus velezensis  🌐
  • 🦠 Metabolic interactions affect the biomass of synthetic bacterial biofilm communities 📄