Mohammad Silani | Engineering | Editorial Board Member

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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.

Sedighe Mirbolouk | Engineering | Editorial Board Member

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Dr. Sedighe Mirbolouk | Engineering | Editorial Board Member 

Iran National Science Foundation | Iran

Dr. Sedighe Mirbolouk is a dedicated postdoctoral researcher and advanced machine learning specialist with strong expertise in communication systems, data science, and artificial intelligence. She is affiliated with the Iran National Science Foundation and has built a diverse research portfolio spanning deep learning, wireless communication optimization, image processing, and intelligent sensing systems. Her technical proficiency covers a wide spectrum of tools and programming environments, including Python, MATLAB, LATEX, and advanced libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, SciPy, Pandas, and Matplotlib. With a strong theoretical foundation in data telecommunication networks, convex optimization, communication theory, and signal and image processing, she integrates computational intelligence with modern communication challenges. In her role as a Postdoctoral Researcher (2024–2025) at the Iran National Science Foundation, Dr. Mirbolouk focuses on cutting-edge topics in graph learning and federated learning, particularly designing machine learning approaches for predictive beamforming in Reconfigurable Intelligent Surface (RIS)-aided Integrated Sensing and Communication (ISAC) systems. Her work aims to improve efficiency, adaptability, and intelligence in next-generation wireless communication networks. Previously, she served as a Visiting Researcher (2022) at the University of Oulu in Finland, where she explored advanced deep reinforcement learning methods to enhance ISAC designs. These research experiences have positioned her at the frontier of combining AI with communication technologies. During her doctoral studies at the University of Urmia (2018–2021), Dr. Mirbolouk contributed significantly to satellite–UAV cooperative network optimization. She developed innovative solutions involving UAV selection and power allocation for CoMP-NOMA transmissions, introducing both Lagrangian and heuristic algorithms that advanced energy-efficient communication frameworks. Alongside communications research, she proposed image processing solutions such as fuzzy histogram weighting methods and contrast enhancement techniques. Her academic involvement includes teaching core engineering subjects—Digital Communication, Probability and Statistics, and Signals and Systems—and assisting courses on Stochastic Processes and Digital Signal Processing. Her work at the National Elite Foundation (2020–2022) expanded her portfolio into biomedical machine learning applications, where she designed systems for automatic breast cancer detection using histopathology images and cardiac arrhythmia recognition using ECG signals through deep learning approaches. Dr. Mirbolouk holds a Ph.D. in Electrical Engineering, with earlier B.Sc. and M.Sc. degrees from the University of Guilan, where she studied SAR radar Doppler ambiguity for moving targets. Her scholarly contributions include high-impact publications in journals such as IEEE Transactions on Vehicular Technology, Physical Communication, and Multimedia Tools and Applications. Collectively, her research reflects an outstanding integration of machine learning, optimization, sensing, and communication technologies.

Profile: Google Scholar

Featured Publications

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Ali, S. (2022). Relay selection and power allocation for energy efficiency maximization in hybrid satellite-UAV networks with CoMP-NOMA transmission. IEEE Transactions on Vehicular Technology, 71(5), 5087–5100.

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Choukali, M. A. (2021). A fuzzy histogram weighting method for efficient image contrast enhancement. Multimedia Tools and Applications, 80(2), 2221–2241.

Mirbolouk, S., Choukali, M. A., Valizadeh, M., & Amirani, M. C. (2020). Relay selection for CoMP-NOMA transmission in satellite and UAV cooperative networks. 2020 28th Iranian Conference on Electrical Engineering (ICEE), 1–5.

Choukali, M. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2023). A desired histogram estimation accompanied with an exact histogram matching method for image contrast enhancement. Multimedia Tools and Applications, 82(18), 28345–28365.

Hussein, A. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2025). Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework. Scientific Reports, 15(1), 25071.

Choukali, M. A., Mirbolouk, S., Valizadeh, M., & Amirani, M. C. (2024). Deep contextual bandits-based energy-efficient beamforming for integrated sensing and communication. Physical Communication, 68, 102576.

Mujahid Aziz | Engineering | Best Researcher Award

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Prof. Mujahid Aziz | Engineering | Best Researcher Award

Cape Peninsula University of Technology | South Africa

Professor Mujahid Aziz is a distinguished academic and research leader serving as the Assistant Dean: Learning & Teaching in the Faculty of Engineering and the Built Environment (FEBE) at the Cape Peninsula University of Technology (CPUT) in South Africa. In this leadership role, he oversees and enhances academic excellence across eight departments within the faculty, which collectively serve nearly 10,000 students, including a growing cohort of postgraduate scholars. As a champion of academic transformation, Prof. Aziz is deeply committed to promoting innovative teaching practices, curriculum modernization, and student-centered learning within engineering education. His leadership is instrumental in aligning the faculty’s academic strategies with industry relevance, sustainability goals, and the national development agenda. With over 23 years of experience as an Associate Professor of Chemical Engineering, Prof. Aziz has established himself as a transformative educator, researcher, and mentor. His academic journey reflects a sustained dedication to advancing both the theoretical and practical dimensions of environmental and chemical engineering. Throughout his career, he has supervised numerous postgraduate students and contributed to the development of engineering curricula that integrate sustainability, innovation, and applied research. His pedagogical philosophy emphasizes experiential learning and the development of problem-solving skills essential for addressing real-world engineering challenges, particularly in water and environmental systems. As the Principal Investigator of the Environmental Engineering Research Group (EERG), Prof. Aziz leads multidisciplinary research focused on sustainable water and wastewater treatment technologies. His work is internationally recognized, with publications in high-impact journals such as Desalination, MDPI Membranes, MDPI Water, and Environmental Processes. Recent research endeavors have explored cutting-edge methods for biofouling mitigation in polyamide thin-film composite reverse osmosis membranes, particularly through polymer grafting and nanoparticle coating. These innovations are pivotal for improving the treatment of municipal bioreactor secondary effluent and enhancing the efficiency and longevity of membrane systems used in desalination and wastewater reuse. Prof. Aziz’s research portfolio is characterized by a strong interdisciplinary approach that bridges materials science, chemical process design, and environmental sustainability. His areas of specialization encompass membrane technology, wastewater reuse, electrochemical and adsorption processes, brine management, and zero liquid discharge (ZLD) systems. His work addresses critical environmental challenges associated with water scarcity and industrial pollution, offering viable pathways for circular water economies and resource recovery. His pursuit of innovation in micropollutant removal, membrane fouling control, and electro-oxidation for water reuse reflects his vision of achieving sustainable and intelligent environmental engineering solutions. Through his academic leadership, pioneering research, and commitment to mentorship, Prof. Mujahid Aziz continues to make a profound impact on the future of engineering education and sustainable water technology development in South Africa and beyond.

Profiles: Orcid | Google Scholar

Featured Publications

Aziz, M., & Ojumu, T. (2020). Exclusion of estrogenic and androgenic steroid hormones from municipal membrane bioreactor wastewater using UF/NF/RO membranes for water reuse application. Membranes, 10(3), 37. https://doi.org/10.3390/membranes10030037

Aziz, M., & Kasongo, G. (2021). The removal of selected inorganics from municipal membrane bioreactor wastewater using UF/NF/RO membranes for water reuse application: A pilot-scale study. Membranes, 11(2), 1–14. https://doi.org/10.3390/membranes11020104

Myburgh, D. P., Aziz, M., Roman, F., Jardim, J., & Chakawa, S. (2019). Removal of COD from industrial biodiesel wastewater using an integrated process: Electrochemical oxidation with IrO₂–Ta₂O₅/Ti anodes and chitosan powder. Environmental Processes, 6(4), 819–840. https://doi.org/10.1007/s40710-019-00393-5

Kasongo, G., Steenberg, C., Morris, B., Kapenda, G., Jacobs, N., & Aziz, M. (2019). Surface grafting of polyvinyl alcohol (PVA) cross-linked with glutaraldehyde (GA) to improve resistance to fouling of aromatic polyamide thin film composite reverse osmosis membranes. Water Practice & Technology, 14(3), 614–624. https://doi.org/10.2166/wpt.2019.042

Chakawa, S., & Aziz, M. (2021). Investigating the result of current density, temperature, and electrolyte concentration on COD subtraction of petroleum refinery wastewater using response surface methodology. Water, 13(6), 835. https://doi.org/10.3390/w13060835

Aziz, M., & Kasongo, G. (2019). Scaling prevention of thin film composite polyamide reverse osmosis membranes by Zn ions. Desalination, 464, 76–83. https://doi.org/10.1016/j.desal.2019.04.006

Chinedu Okere | Engineering | Best Researcher Award

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Dr. Chinedu Okere | Engineering | Best Researcher Award 

University of Houston | United States

Dr. Chinedu (Junior) Okere is a dynamic early-career researcher whose interests span subsurface hydrogen generation, large-scale hydrogen storage in geological formations, experimental and numerical modelling of CO₂ capture, utilisation and storage (CCUS), methane leakage from orphaned wells, and drilling/fracturing fluid design and formation-damage mitigation in petroleum reservoirs. His professional trajectory has taken him from graduate research at the China University of Petroleum (Beijing) (M.Eng., 2022) to doctoral studies at the Texas Tech University (Ph.D., 2025) and onward to a post-doctoral appointment in the Department of Petroleum Engineering at the University of Houston (from mid-2025). In these roles he has supervised PhD students, managed a U.S. Department of Energy-funded CarbonSAFE project on CO₂ storage, and led the development of grant proposals, patents and peer-reviewed publications. According to his Google Scholar profile he has to date achieved 659 citations and an h-index of 15, with an i10-index of 19. His publication record includes a broad spectrum of articles (20+, depending on counting method) covering topics from “clean hydrogen generation from petroleum reservoirs” to fuzzy-ball fluid‐induced damage in tight reservoirs, reservoir suitability for hydrogen storage, and methane leakage from abandoned wells. Most recently, his first‐author papers (2024-2025) address techno-economic feasibility of in-situ hydrogen production from petroleum reservoirs, SARA-based experimental and numerical investigations of in-situ hydrogen generation, and comparative numerical studies for optimisation of hydrogen production and CCUS strategies. In recognition of his impact he has received numerous honours including the 2024 International Inventions Awards – Hydrogen Energy Best Researcher Award, and the Society of Petroleum Engineers Permian Basin Scholarship. With strong interdisciplinary credentials spanning petroleum engineering, energy systems, reservoir simulation, and hydrogen/CCUS technologies, Dr. Okere stands out as an emerging scholar bridging the conventional oil-&-gas domain with the clean/hydrogen energy transition. His h-index of 15 reflects a solid early‐career impact: it means he has at least 15 publications each cited at least 15 times. (The h-index concept was originally proposed by J. E. Hirsch as a simple measure of productivity and citation impact. Going forward, his strong publication momentum, growing citation base and leadership in grant/industry-adjacent projects suggest that he is well-positioned to further increase both his research output and influence in the hydrogen/CCUS engineering community.

Profiles: Scopus | Orcid | Google Scholar 

Featured Publications

Okere, C. J., & Sheng, J. J. (2023). Review on clean hydrogen generation from petroleum reservoirs: Fundamentals, mechanisms, and field applications. International Journal of Hydrogen Energy, 101.

Edouard, M. N., Okere, C. J., Ejike, C., Dong, P., & Suliman, M. A. M. (2023). Comparative numerical study on the co-optimization of CO₂ storage and utilization in EOR, EGR, and EWR: Implications for CCUS project development. Applied Energy, 347, 121448.

Eyitayo, S. I., Okere, C. J., Hussain, A., Gamadi, T., & Watson, M. C. (2024). Synergistic sustainability: Future potential of integrating produced water and CO₂ for enhanced carbon capture, utilization, and storage (CCUS). Journal of Environmental Management, 351, 119713.

He, J., Okere, C. J., Su, G., Hu, P., Zhang, L., Xiong, W., & Li, Z. (2021). Formation damage mitigation mechanism for coalbed methane wells via refracturing with fuzzy-ball fluid as temporary blocking agents. Journal of Natural Gas Science and Engineering, 90, 103956.

Okere, C. J., Su, G., Zheng, L., Cai, Y., Li, Z., & Liu, H. (2020). Experimental, algorithmic, and theoretical analyses for selecting an optimal laboratory method to evaluate working fluid damage in coal bed methane reservoirs. Fuel, 282, 118513.

Tao, X., Okere, C. J., Su, G., & Zheng, L. (2022). Experimental and theoretical evaluation of interlayer interference in multi-layer commingled gas production of tight gas reservoirs. Journal of Petroleum Science and Engineering, 208, 109731.

Okere, C. J., & Sheng, J. J. (2024). A new modelling approach for in-situ hydrogen production from heavy oil reservoirs: Sensitivity analysis and process mechanisms. Energy, 302, 131817.

Opara, S. U., & Okere, C. J. (2024). A review of methane leakage from abandoned oil and gas wells: A case study in Lubbock, Texas, within the Permian Basin. Energy Geoscience, 5(3), 100288.

Yonas Gezahegn | Engineering | Best Researcher Award

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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

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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

Dayeong An | Engineering | Women Researcher Award | 13446

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Dr. Dayeong An | Engineering | Women Researcher Award

Dr. Dayeong An, Medical College of Wisconsin, United States

Dr. Dana (Dayeong) An is a Postdoctoral Fellow in the Department of Radiology at Northwestern University with a strong interdisciplinary background in biomedical engineering, computational sciences, and statistics. Her research focuses on machine learning and probabilistic modeling for multimodal biomedical data integration, particularly in neurovascular and cardiac imaging. She has developed advanced AI frameworks for stroke outcome prediction, perfusion analysis, and cardiac strain estimation. With multiple peer-reviewed publications and awards, Dr. An brings expertise in deep learning, medical image processing, and translational AI for precision medicine.

Profile

ORCID

🎓 Early Academic Pursuits

Dr. Dana (Dayeong) An’s academic journey is rooted in a solid foundation of mathematics, statistics, and computational sciences. She began her higher education at Minnesota State University, earning a B.S. in Mathematics with a minor in Economics in 2012. Her strong mathematical background laid the groundwork for advanced study, leading her to pursue dual M.S. degrees in Mathematics and Statistics (2014) and Computational Sciences (2018). These degrees reflect a growing interest in data analysis, modeling, and algorithmic thinking—skills that would become central to her future research. Her academic path culminated in a Ph.D. in Biomedical Engineering from the Medical College of Wisconsin in 2024. During her doctoral training, Dr. An fused her analytical skills with biomedical applications, working at the intersection of medical imaging and machine learning. Her education reflects a rare combination of quantitative rigor and domain-specific insight, enabling her to tackle complex problems in healthcare and precision medicine.

🧠 Professional Endeavors

Dr. An currently serves as a Postdoctoral Fellow in the Department of Radiology at Northwestern University, where she applies advanced machine learning techniques to neurovascular and cardiac imaging data. Her professional roles have spanned research, teaching, and clinical applications. At the Medical College of Wisconsin, she worked as a Research Assistant, refining deep learning algorithms for myocardial strain analysis, MRI-based diagnostics, and experimental studies on cardiotoxicity in animal models. Earlier in her career, she served as an Adjunct Professor and Teaching Assistant at multiple institutions, including Marquette University, Globe University, and South Central College, where she taught a variety of math and statistics courses. This teaching experience showcases her commitment to education and her ability to communicate complex topics to diverse audiences.

🧪 Contributions and Research Focus

Dr. An’s research is centered on machine learning and probabilistic modeling for multimodal biomedical data integration. Her contributions span multiple domains:

  • Neurovascular Imaging: She has developed frameworks using Bayesian priors and transformer models to estimate physiological parameters from perfusion MRI data. She also works with large-scale databases such as NVQI-QOD to predict stroke outcomes and recurrence risks in intracranial atherosclerotic disease (ICAD).

  • Cardiac MRI and Strain Analysis: Dr. An fine-tuned U-Net and GAN architectures to automate strain generation and displacement field analysis from cine MRI images. These tools enhance early detection of cardiotoxicity and improve diagnostic accuracy.

  • Image Processing and Simulation: She built deep learning-based deformable registration tools to reduce motion artifacts in angiography and improve vascular fidelity. Additionally, she contributed to differentiable projection modeling for fluoroscopic pose estimation.

  • Translational AI: Her work aims to bridge the gap between algorithm development and clinical implementation, with models designed for real-time, patient-specific analysis.

Her research is not only technical but also translational, addressing real-world challenges in healthcare delivery and diagnostics.

🏆 Accolades and Recognition

Dr. An has received numerous honors for her research excellence and academic contributions:

  • Poster Competition Winner at Marquette University and the Medical College of Wisconsin.

  • Scholarship and Travel Grants from prestigious societies such as the Global Cardio Oncology Summit, ISMRM, and Marquette University.

  • Kayoko Ishizuka Award and Graduate Student Association Awards at MCW.

  • Recognition for conference presentations at RSNA, ISMRM, SCMR, and ASNR.

Her work has been published in well-regarded journals including Radiology and Oncology, Journal of Imaging Informatics in Medicine, and Tomography, reflecting her influence across multiple disciplines.

🌍 Impact and Influence

Dr. An’s interdisciplinary expertise positions her as a valuable contributor to both the academic and clinical communities. Her collaborations with leading institutions such as Cleveland Clinic and Purdue University demonstrate the broader impact of her research. Whether improving stroke outcome prediction or refining cardiac diagnostics, her contributions are making real-world differences in how clinicians approach patient care. She is also actively involved in professional societies like RSNA, ISMRM, IEEE, and the American Statistical Association, fostering knowledge exchange and staying at the forefront of innovation.

🌱 Legacy and Future Contributions

Looking ahead, Dr. An aspires to expand her impact by continuing to develop explainable, reliable, and patient-specific AI tools for medical imaging. Her future work will likely delve deeper into probabilistic deep learning, longitudinal outcome modeling, and integrated diagnostics using multi-modal data sources such as imaging, genomics, and electronic health records. She is poised to be a leader in translational AI, driving innovations that not only push the boundaries of computational medicine but also enhance patient outcomes and healthcare efficiency.

🔗 Final Thoughts

Dr. Dana (Dayeong) An exemplifies a new generation of biomedical engineers—fluent in mathematics, passionate about clinical impact, and committed to advancing the future of precision medicine through data-driven innovation. Her legacy is being built at the nexus of technology, healthcare, and humanity.

📄 Publication Top Notes

Radiation-Induced Cardiotoxicity in Hypertensive Salt-Sensitive Rats: A Feasibility Study

Author: Dayeong An; Alison Kriegel; Suresh Kumar; Heather Himburg; Brian Fish; Slade Klawikowski; Daniel Rowe; Marek Lenarczyk; John Baker; El-Sayed Ibrahim

Journal: Life

Year: 2025

Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI

Author: Dayeong An; El-Sayed Ibrahim

Journal: Journal of Imaging

Year: 2024

Jun Liu | Engineering | Best Researcher Award | 13444

Published on by Research Awards

Assoc. Prof. Dr. Jun Liu | Engineering | Best Researcher Award

Assoc. Prof. Dr. Jun Liu, North China University of Water Resources and Electric Power, China

Assoc. Prof. Dr. Jun Liu is an Assistant Professor and Master’s Supervisor in the Department of Thermal Engineering at North China University of Water Resources and Electric Power. He holds a Ph.D. in Engineering Thermophysics from Zhejiang University and specializes in CO₂ capture and utilization, solid waste treatment, multiphase flow and combustion simulation, and pollutant removal technologies. Dr. Liu has led multiple provincial-level research projects and published extensively in SCI and EI-indexed journals. His teaching focuses on boiler principles, operations, and clean combustion technologies.

Profile

Scopus

🎓 Early Academic Pursuits

Assoc. Prof. Dr. Jun Liu began his academic journey with a solid foundation in engineering and technology. In 2005, he enrolled at Shanxi University, where he pursued a Bachelor’s degree in Automation under the Department of Information Engineering. This initial exposure to systems control and engineering principles cultivated his interest in energy systems and laid the groundwork for his future endeavors in thermal engineering and environmental research. In 2009, he took a decisive step toward specializing in energy technologies by pursuing a Master’s degree in Fluid Machinery and Engineering at the School of Electric Power, North China University of Water Resources and Electric Power. Here, he honed his understanding of energy conversion systems, power plant operations, and machinery critical to thermal power generation. His passion for research and academic excellence led him to earn a Ph.D. in Engineering Thermophysics at the prestigious Zhejiang University from 2012 to 2016. This phase of his education sharpened his expertise in combustion processes, thermodynamic systems, and pollutant control, which later became key pillars of his professional and research identity.

💼 Professional Endeavors

Following the completion of his doctorate, Dr. Liu began his professional career at the Xi’an Thermal Power Research Institute Co., Ltd., Suzhou Branch in 2016. In this applied research environment, he gained hands-on experience in industrial-scale power systems and thermal processes, translating academic knowledge into practical solutions. In April 2019, Dr. Liu returned to academia, joining the College of Energy and Power Engineering at the North China University of Water Resources and Electric Power as an Assistant Professor and Master’s Supervisor. His return marked a blend of academic vigor and industrial insight, enriching the university’s teaching and research capabilities.

🔬 Contributions and Research Focus

Dr. Liu’s research spans several crucial areas within energy and environmental engineering:

  1. CO₂ Capture and Resource Utilization – He leads studies on innovative adsorbent materials and absorption technologies aimed at mitigating greenhouse gas emissions.

  2. Solid Waste Treatment – His work on incinerator systems and waste-to-energy solutions contributes to sustainable waste management practices.

  3. Multiphase Flow and Combustion Simulation – By modeling combustion processes, he aims to optimize energy efficiency and reduce emissions.

  4. Pollutant Removal – His research explores integrated technologies for removing NOx, SOx, and other harmful emissions from combustion systems.

He has presided over several key provincial research projects, including studies on CO₂ adsorption kinetics, microencapsulated absorbents, and waste heat boiler performance. His work reflects a deep commitment

🏅 Accolades and Recognition

to both scientific innovation and environmental sustainability.

Dr. Liu’s contributions have been recognized with several prestigious awards:

  • 🥇 First Prize, Boiler Science and Technology Award (2023), for his contributions to power generation technology for large mechanical grates.

  • 🥈 Second Prize, Henan Provincial Science and Technology Progress Award (2022), for his role in developing low-temperature waste heat recovery systems.

  • 🏆 First Prize, Excellent Scientific and Technological Paper Award by the Henan Province Office of Education (2021).

These accolades underscore his impactful research and its relevance to both academia and industry.

🌍 Impact and Influence

Dr. Liu has authored multiple peer-reviewed papers in SCI and EI indexed journals, reflecting the scientific merit and practical application of his research. His publication in Waste Management on flue gas recirculation and NOx emission control is especially noteworthy in the context of sustainable waste-to-energy practices. Moreover, his work influences not only fellow researchers but also policymakers and industry professionals seeking advanced environmental solutions. As a committed educator, he imparts knowledge through courses like Boiler Principle, Boiler Operation, and Clean Combustion and Pollutant Control. His teaching integrates the latest research findings, ensuring that students are prepared for real-world energy challenges.

🌱 Legacy and Future Contributions

Looking ahead, Dr. Liu is poised to continue making substantial contributions to the fields of clean energy and environmental protection. His interdisciplinary approach, combining engineering thermophysics, environmental science, and applied technology, equips him to tackle emerging challenges such as carbon neutrality, smart power systems, and circular economy strategies for waste management. He is also likely to mentor the next generation of researchers, fostering innovation through student supervision, collaborative projects, and academic outreach. As climate concerns and energy demands rise globally, Dr. Liu’s expertise will remain critical in shaping sustainable technological pathways for the future.

📄 Publication Top Notes

Research Progress on the Occurrence Characteristics of AAEM Elements in Zhundong Coal

Author: W., Wang, Wei, X., Guo, Xinwei, X., Wu, Xiaojiang, … C., Fan, Cunjiang, L., Zhuo, Lanting

Journal: Dongli Gongcheng Xuebao /Journal of Chinese Society of Power Engineering

Year: 2025

The effect of air distribution on the characteristics of waste combustion and NO generation in a grate incinerator

Author:  J., Liu, Jun, Z., Xie, Zheng, B., Guo, Bingyu, … L., Bai, Li, J., Long, Jisheng

Journal: Journal of the Energy Institute .,

Year: 2024

Adnan Abu-Mahfouz | Engineering | Best Researcher Award

Published on by Research Awards

Prof. Adnan Abu-Mahfouz, Engineering, Best Researcher Award

Professor at Council for Scientific and Industrial Research (CSIR), South Africa

Prof. Abu-Mahfouz is a distinguished researcher and academic in computer engineering. He holds a PhD and MEng from the University of Pretoria, South Africa, and a BSc in Information Engineering from the University of Baghdad, Iraq. Currently, he is the Centre Manager for Emerging Digital Technologies at CSIR NextGen Enterprises and Institutions and holds professorial roles at the University of Pretoria, Tshwane University of Technology, and the University of Johannesburg. Prof. Abu-Mahfouz excels in industrial-based R&D, particularly in the Internet of Things (IoT) and smart systems. He has secured over USD 4.5 million in research funding and has published extensively, with 139 journal articles and 136 conference papers. His work has earned him numerous awards, and his high H-index scores reflect his impactful research. Additionally, he has mentored over 70 postgraduate and postdoctoral researchers, contributing significantly to the academic and scientific community.

Professional Profiles:

Education

Prof. Abu-Mahfouz has an extensive and distinguished educational background in computer engineering. He earned his PhD in Computer Engineering from the University of Pretoria, South Africa, from 2007 to 2011. Prior to that, he completed his MEng in Computer Engineering, also at the University of Pretoria, from 2003 to 2005, graduating Cum Laude. His foundational education in engineering began with a BSc in Information Engineering from the University of Baghdad, Iraq, where he graduated in 2000 with an impressive 84.18%, earning the “First” rank in his class. Throughout his academic journey, Prof. Abu-Mahfouz has demonstrated consistent excellence, receiving numerous accolades such as Academic Honorary Colours from the Student Representative Council at the University of Pretoria for both his PhD and MEng degrees. His educational achievements have laid a strong foundation for his prolific research and professional career.

Professional Experience

Prof. Abu-Mahfouz is currently the Centre Manager for the CSIR NextGen Enterprises and Institutions (EDT4IR) since July 2020 and holds multiple professorial positions. He is an Extraordinary Professor at the University of Pretoria, a Professor Extraordinaire at Tshwane University of Technology, and a Visiting Professor at the University of Johannesburg. His previous roles include Principal Research Engineer and Senior Research Engineer at CSIR, and Chair of the Water Resource Management Network at the City of Tshwane. His early career includes positions as a Software Engineer, Network Administrator, and Faculty Coordinator at the Emirates College of Technology.

Research Interest

Professor Adnan Abu-Mahfouz’s research interests encompass a broad spectrum of topics in computer engineering and technology. His primary focus is on the Internet of Things (IoT), where he explores the design, implementation, and application of IoT systems, with a particular interest in smart infrastructure and smart cities. He is also deeply invested in network security, investigating methods to enhance the security and reliability of network systems, including cybersecurity measures and protocols. Additionally, he studies the development and optimization of wireless sensor networks (WSNs), which are crucial for monitoring and data collection in various environments. His work in cyber-physical systems (CPS) involves integrating physical processes with networked digital systems to ensure seamless interaction between the physical and virtual worlds. Prof. Abu-Mahfouz is also focused on industrial informatics, applying advanced informatics techniques to improve efficiency, productivity, and automation in industrial systems. Furthermore, he leverages artificial intelligence (AI) and machine learning (ML) algorithms to enhance the functionalities of technological systems, particularly in IoT and CPS. His interest in smart water management involves developing innovative solutions for water resource management using advanced technologies and smart systems. Lastly, he utilizes data analytics to extract meaningful insights from large datasets, particularly in the context of IoT and smart systems, addressing real-world challenges and promoting sustainable development through his research.

Award and Honors

Prof. Abu-Mahfouz has received numerous awards and honors in recognition of his contributions to research, innovation, and academic excellence. Among these accolades, he was awarded the “Career Growth CSIR Excellence Award” and the “Outstanding Contribution by a Team – Electronic Monitoring Team – Excellence Award” by the Council for Scientific and Industrial Research in 2022. He also received the “Outstanding Associate Editor of 2018” award from the IEEE Access Journal. His research excellence earned him the “Established Researcher CSIR Excellence Award” in 2018 and the “Emerging Leader Excellence Award” in 2015 from CSIR’s Meraka Institute. Furthermore, Prof. Abu-Mahfouz has been recognized by the University of Pretoria for his academic achievements, being listed in the top 15% of academic achievers and invited to join the Golden Key International Honour Society. These awards highlight his leadership, significant research contributions, and dedication to advancing science and technology.

Research Skills

Prof. Abu-Mahfouz possesses exceptional research skills that have significantly advanced his field. He has a proven track record in developing and implementing robust research and development (R&D) strategies, particularly in emerging digital technologies and the Internet of Things (IoT). His expertise in formulating large, multidisciplinary R&D proposals has secured over USD 4.5 million in funding, demonstrating his ability to attract substantial investment and drive impactful research projects. Prof. Abu-Mahfouz’s research skills are evidenced by his extensive publication record, which includes 139 journal articles, 136 conference papers, and 6 book chapters. His work is highly regarded, reflected in his H-index of 48 on Google Scholar and 39 on Scopus. Additionally, he has successfully supervised and mentored numerous postgraduate and postdoctoral researchers, further underscoring his leadership and mentorship in the research community. His role as an associate editor for several high-impact IEEE journals also highlights his expertise in evaluating and guiding cutting-edge research in his field.

Publications

A survey on 5G networks for the Internet of Things: Communication technologies and challenges

  • Authors: GA Akpakwu, BJ Silva, GP Hancke, AM Abu-Mahfouz
  • Year: 2017
  • Citations: 1473
  • Journal: IEEE Access
  • Volume: 6
  • Pages: 3619-3647

From industry 4.0 to agriculture 4.0: Current status, enabling technologies, and research challenges

  • Authors: Y Liu, X Ma, L Shu, GP Hancke, AM Abu-Mahfouz
  • Year: 2020
  • Citations: 620
  • Journal: IEEE Transactions on Industrial Informatics
  • Volume: 17
  • Issue: 6
  • Pages: 4322-4334

A survey on software-defined wireless sensor networks: Challenges and design requirements

  • Authors: HI Kobo, AM Abu-Mahfouz, GP Hancke
  • Year: 2017
  • Citations: 541
  • Journal: IEEE Access
  • Volume: 5
  • Pages: 1872-1899

Software defined wireless sensor networks application opportunities for efficient network management: A survey

  • Authors: KM Modieginyane, BB Letswamotse, R Malekian, AM Abu-Mahfouz
  • Year: 2018
  • Citations: 268
  • Journal: Computers & Electrical Engineering
  • Volume: 66
  • Pages: 274-287

A review of machine learning approaches to power system security and stability

  • Authors: OA Alimi, K Ouahada, AM Abu-Mahfouz
  • Year: 2020
  • Citations: 266
  • Journal: IEEE Access
  • Volume: 8
  • Pages: 113512-113531

IoT devices and applications based on LoRa/LoRaWAN

  • Authors: O Khutsoane, B Isong, AM Abu-Mahfouz
  • Year: 2017
  • Citations: 249
  • Conference: IECON 2017 – 43rd Annual Conference of the IEEE Industrial Electronics Society
  • Pages: Not specified

Software defined networking for improved wireless sensor network management: A survey

  • Authors: M Ndiaye, GP Hancke, AM Abu-Mahfouz
  • Year: 2017
  • Citations: 243
  • Journal: Sensors
  • Volume: 17
  • Issue: 5
  • Article Number: 1031

IoT in the wake of COVID-19: A survey on contributions, challenges and evolution

  • Authors: M Ndiaye, SS Oyewobi, AM Abu-Mahfouz, GP Hancke, AM Kurien, …
  • Year: 2020
  • Citations: 193
  • Journal: IEEE Access
  • Volume: 8
  • Pages: 186821-186839

Towards achieving a reliable leakage detection and localization algorithm for application in water piping networks: An overview

  • Authors: KB Adedeji, Y Hamam, BT Abe, AM Abu-Mahfouz
  • Year: 2017
  • Citations: 180
  • Journal: IEEE Access
  • Volume: 5
  • Pages: 20272-20285

Smart water meter system for user-centric consumption measurement

  • Authors: MJ Mudumbe, AM Abu-Mahfouz
  • Year: 2015
  • Citations: 167
  • Conference: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN)