Mohammad Silani | Engineering | Editorial Board Member

Assoc Prof Dr. Mohammad Silani | Engineering | Editorial Board Member

Isfahan University of Technology | Iran

Dr. Mohammad Silani is an accomplished Associate Professor in the Department of Mechanical Engineering at Isfahan University of Technology (IUT), Iran, where he currently serves as the Head of International Scientific Cooperation. His academic career reflects a continuous trajectory of excellence in multiscale modeling, computational mechanics, and advanced materials research. Since joining IUT as an Assistant Professor in 2015, Dr. Silani has made significant contributions in micromechanics, multiscale simulation, fracture mechanics, and computational materials science. From 2022 to 2023, he was awarded the prestigious MSCA Seal of Excellence Fellowship at the Free University of Bozen-Bolzano, Italy, where he advanced adaptive concurrent multiscale methods for wear modeling and developed coarse-grained molecular dynamics tools for fatigue crack propagation. His international research engagements also include visiting fellowships at the University of New South Wales in Australia, Qatar University, the National University of Singapore, and multiple research positions at Bauhaus University Weimar, Germany, where he contributed to the development of open-source multiscale finite element codes and advanced modeling techniques for nanocomposites. Dr. Silani earned all three of his degrees—B.Sc., M.Sc., and Ph.D.—from IUT, specializing in solid mechanics, fracture mechanics, vibrations, FEM, and multiscale analysis. He possesses strong programming expertise in Python, Fortran, MATLAB, and Abaqus scripting and has advanced proficiency in leading finite element software including Abaqus, ANSYS, and LS-DYNA. His research achievements include more than 2,400 citations with an H-index of 20, reflecting his impactful contributions to computational mechanics, phase-field modeling, stochastic analysis, XFEM, SBFEM, and machine-learning-assisted material design. He has supervised over 70 postgraduate students, taught a wide range of undergraduate and graduate courses, and reviewed for leading journals such as Materials & Design, International Journal of Fatigue, Composite Structures, and Scientific Reports. His extensive publication record includes high-impact works in Advanced Materials, International Journal of Fracture, Computational Mechanics, Nanotechnology, Acta Mechanica Sinica, and Journal of Mechanical Behavior of Biomedical Materials. Dr. Silani’s honors include the Distinguished Young Professor Award from Iran’s National Elites Foundation (2022, 2023), multiple national science grants, a DAAD Research Grant, and project funding from the German Research Foundation (DFG). His current research spans phase-field modeling of nanowires, fracture and wear simulations, machine learning for materials design, nano- and micro-scale damage analysis, bone tissue mechanics, and Industry 4.0-based mechanical monitoring. Dr. Silani maintains active collaborations with leading researchers worldwide, reinforcing his position as a distinguished scholar in computational mechanics and multiscale material modeling.

Profile: Google Scholar

Featured Publications

A computational library for multiscale modeling of material failure
Talebi, H., Silani, M., Bordas, S. P. A., Kerfriden, P., & Rabczuk, T. (2014). A computational library for multiscale modeling of material failure. Computational Mechanics, 53(5), 1047–1071.

Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions
Hamdia, K. M., Silani, M., Zhuang, X., He, P., & Rabczuk, T. (2017). Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 206(2), 215–227.

First-principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine-learning interatomic potentials
Mortazavi, B., Silani, M., Podryabinkin, E. V., Rabczuk, T., Zhuang, X., & Shapeev, A. V. (2021). First-principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine-learning interatomic potentials. Advanced Materials, 33(35), 2102807.

Yonas Gezahegn | Engineering | Best Researcher Award

Dr. Yonas Gezahegn | Engineering | Best Researcher Award

Nestle Purina/Washington State University | United States

Dr. Yonas A. Gezahegn is a distinguished research and development engineer specializing in thermal and food process engineering, with extensive expertise in microwave-assisted thermal sterilization and pasteurization, heat and mass transfer, biochemical engineering, and food safety. With over 15 years of academic and industry experience, Dr. Gezahegn has developed a strong reputation for integrating engineering principles with advanced experimental and computational methods to optimize food processing and thermal treatment technologies. His research bridges the gap between fundamental engineering science and industrial applications, ensuring both efficiency and safety in food production systems. Dr. Gezahegn’s academic training includes a PhD in Biological Systems Engineering (Food Engineering) from Washington State University, where he focused on optimization of microwave-assisted thermal sterilization and pasteurization processes using analytical models and computer simulations. His prior degrees include a Master’s in Chemical Engineering from Addis Ababa University, and a Bachelor’s in Food and Biochemical Technology from Bahir Dar University, where his research addressed critical challenges in oil and fat extraction, fermentation, and food quality assessment. Currently serving as R&D Process Engineer – Thermal Process Expert at Nestle Purina, Dr. Gezahegn leads projects on process improvement, thermal sterilization validation, and retort commissioning for low-acid and acidified food products. He has successfully managed large-scale research projects, including microwave-assisted thermal processing of breaded meats, temperature distribution studies, and process optimization for commercial food production. His work also encompasses pilot-plant scale-up, analytical characterization, and data-driven modeling to ensure precise control of thermal processing conditions. Dr. Gezahegn has published over 12 peer-reviewed journal articles in top-tier journals, including the Journal of Food Engineering, Current Research in Food Science, Innovative Food Science & Emerging Technologies, Food Science and Nutrition, and LWT – Food Science and Technology. His publications focus on microwave-assisted processing, dielectric properties of foods, thermal pasteurization optimization, and oil extraction technologies. Notably, his research has led to multiple patents, including a utility model for screw expeller-based shea butter extraction and pending patents on gluten-free pizza crust and crispy breaded food processes. His work has been widely cited in the food engineering and process optimization communities, highlighting his influence in both academic and industrial research. In addition to research, Dr. Gezahegn has contributed extensively to industry-academic collaborations, securing competitive grants such as the USDA-NIFA and WSU Hatch projects totaling over USD 4 million, and Ethiopian national projects on drying and fermentation of plant-based products. Dr. Gezahegn published 12+ peer-reviewed articles, 550 Citations and 10 H-index.  His projects integrate  analytical modeling, simulation, experimental validation, and process design to improve efficiency, safety, and nutritional quality in food production. Dr. Gezahegn has served as a reviewer for journals including Applied Food Research, Journal of Food Engineering, and the International Journal for Vitamin and Nutrition Research, reflecting his standing in the research community. His leadership extends to professional societies, including IFT, IMPI, SoFE, and ASABE, and he has held roles such as President of the Food Engineering Club and departmental representative in the Graduate and Professional Student Association. Overall, Dr. Gezahegn’s work demonstrates a sustained commitment to advancing food engineering, thermal process optimization, and industrial innovation, making significant contributions to improving food safety, process efficiency, and product quality. His research portfolio combines rigorous academic scholarship with practical applications, establishing him as a leading expert in thermal food processing and microwave-assisted sterilization technologies.

Profiles: Scopus | Orcid

Featured Publications

Gezahegn, Y., Tang, J., et al. (2024). Development and validation of engineering charts: Heating time and optimal salt content prediction for microwave assisted thermal sterilization. Journal of Food Engineering, 369, 111909. https://doi.org/10.1016/j.jfoodeng.2023.111909

Gezahegn, Y., Yoon-Ki, H., Tang, J., et al. (2023). Development and validation of analytical charts for microwave assisted thermal pasteurization of selected food products. Journal of Food Engineering, 349, 111434. https://doi.org/10.1016/j.jfoodeng.2023.111434

Zhou, X., Gezahegn, Y., et al. (2023). Theoretical reasons for rapid heating of vegetable oils by microwaves. Current Research in Food Science, 7, 100641. https://doi.org/10.1016/j.crfs.2023.100641

Gezahegn, Y., Tang, J., Sablani, S., et al. (2021). Dielectric properties of water relevant to microwave assisted thermal pasteurization and sterilization of packaged foods. Innovative Food Science & Emerging Technologies, 74, 102837. https://doi.org/10.1016/j.ifset.2021.102837

Gezahegn, Y., Emire, S., & Asfaw, S. (2016). Optimization of Shea (Vitellaria paradoxa) butter quality using screw expeller extraction. Food Science & Nutrition, 4(6), 840–847. https://doi.org/10.1002/fsn3.351

Gezahegn, Y., Emire, S., & Asfaw, S. (2016). Effect of processing factors on Shea (Vitellaria paradoxa) butter extraction. LWT – Food Science and Technology, 66, 172–178. https://doi.org/10.1016/j.lwt.2015.10.036

 

Dandan Zhu | Engineering | Best Researcher Award

Assoc. Prof. Dr. Dandan Zhu | Engineering | Best Researcher Award

Assoc. Prof. Dr. Dandan Zhu,China University of Petroleum, Beijing,China

Dr. Dandan Zhu, Associate Professor at China University of Petroleum, Beijing, is a leading researcher in integrating artificial intelligence with petroleum engineering. Her work on intelligent drilling technologies and real-time trajectory control has advanced automation in complex subsurface environments. With over 40 research projects, 39 journal publications, and multiple patents, she bridges theory and field application. Her innovative learning frameworks and strong industry collaborations have significantly contributed to the development of smart drilling systems, reinforcing her candidacy for the Best Researcher Award.

Author Profile

Google  Scholar

🎓 Early Academic Pursuits

Dr. Dandan Zhu’s academic journey reflects a deep-rooted passion for engineering and innovation. Her pursuit of excellence began at Beihang University, one of China’s leading institutions in aerospace and engineering, where she earned her Master’s degree in Aircraft Design. This foundational training laid the groundwork for her precision-oriented approach and problem-solving mindset. Driven by a keen interest in cutting-edge technologies and global research exposure, she went on to pursue a Ph.D. in Precision Engineering at the University of Tokyo, Japan. Her doctoral research refined her expertise in high-accuracy systems and complex mechanical processes—skills that would later fuel her contributions in artificial intelligence (AI) and petroleum engineering.

🧑‍💼 Professional Endeavors

Since 2015, Dr. Zhu has served as an Associate Professor at the College of Artificial Intelligence, China University of Petroleum, Beijing (CUPB). In this role, she has emerged as a thought leader and mentor in the field of intelligent energy systems. Her work involves teaching, supervising postgraduate students, and leading several high-impact research initiatives. Dr. Zhu has also built a bridge between academia and industry by actively participating in national-level science and technology programs, NSFC–enterprise joint funding projects, and technical consultations with leading energy companies. Her professional portfolio boasts 40 completed and ongoing research projects and 27 consultancy or industry-driven assignments. These efforts are deeply rooted in real-world challenges, ensuring that her research not only advances academic knowledge but also meets the evolving demands of energy exploration and production sectors.

🧠 Contributions and Research Focus

Dr. Zhu’s core research area lies at the intersection of artificial intelligence and petroleum engineering. Her pioneering work focuses on intelligent drilling systems, real-time wellbore trajectory control, reinforcement learning, and geological modeling. She has developed a robust learning framework that combines offline training, real-time geosteering decision-making, and post-drilling strategy optimization. By leveraging reinforcement learning algorithms and generative simulation environments, Dr. Zhu’s research enhances the adaptability and robustness of drilling operations in geologically uncertain environments. Her research contributions extend beyond theory. Integrated software platforms developed under her leadership have been field-tested in collaboration with major Chinese oil and gas companies, such as CNPC, Sinopec, and CNOOC. These platforms facilitate intelligent automation in subsurface operations, ensuring improved safety, efficiency, and cost-effectiveness.

🏅 Accolades and Recognition

Although Dr. Zhu maintains a modest public profile, her work has earned substantial recognition within academic and professional circles. She has authored 39 papers in reputed journals indexed by SCI and Scopus, and her publications have collectively received over 60 citations since 2020—a testament to their relevance and influence. Her book, published under ISBN: 978-7-3025-3524-9, further underscores her authority in the domain of intelligent drilling technologies. She holds five patents, reflecting her commitment to innovation and practical impact. While she has not yet served on editorial boards, her active participation in international conferences and professional associations such as IEEE, ACM, and SPE demonstrates her ongoing contribution to the global scientific community through peer review and scholarly discourse.

🌍 Impact and Influence

Dr. Zhu’s interdisciplinary collaborations have significantly influenced both academia and industry. Her work has helped develop more intelligent, data-driven petroleum engineering systems, contributing to the broader push for digital transformation in energy exploration. Through partnerships with research institutions and enterprises, she has been instrumental in advancing the application of AI in areas such as hydraulic fracturing, electromagnetic exploration, and 3D geological visualization. Beyond technical outcomes, her projects have delivered impactful results such as enhanced resource recovery, reduced environmental impact, and optimized operational costs—outcomes highly valued by industrial stakeholders. Furthermore, her mentorship of students and young researchers ensures the continuity of innovation and excellence in the field.

🔮 Legacy and Future Contributions

Looking forward, Dr. Zhu is poised to further advance the integration of AI with traditional engineering practices. Her vision includes the development of autonomous drilling systems that can self-optimize and self-correct in real time, even in highly unpredictable geological conditions. She also plans to expand research into simulation-based control frameworks and digital twins, providing a virtual testing ground for future subsurface technologies. With her continued dedication, Dr. Zhu is expected to leave a lasting legacy as a trailblazer in intelligent energy systems. She not only represents the new era of AI-driven engineering but also serves as an inspiration for the next generation of researchers aiming to solve the world’s most pressing energy challenges.

✍️Publication Top Notes


📘End-to-end multiplayer violence detection based on deep 3D CNN

Author: C Li, L Zhu, D Zhu, J Chen, Z Pan, X Li, B Wang

Journal: international conference on network …

Year: 2018


📘PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning

Author: K Zhang, DD Zhu, Q Xu, H Zhou, C Zheng

Journal: international conference on network …arXiv preprint arXiv:2403.02635

Year:  2024


📘An intelligent drilling guide algorithm design framework based on high interactive learning mechanism

Author: Y Zhao, DD Zhu, F Wang, XP Dai, HS Jiao, ZJ Zhou

Journal: Petroleum Science

Year:  2025