Size Ai | Engineering | Research Excellence Award

Mr. Size Ai | Engineering | Research Excellence Award 

Harbin Institute of Technology | China

Dr. Size Ai, currently a PhD researcher at the Harbin Institute of Technology, is an emerging scholar in the field of mechanical metamaterials and advanced structural design. Holding a PhD in Mechanics from Harbin Institute of Technology, Dr. Ai has developed strong expertise in the design, modeling, and steady-state analysis of mechanical metamaterials, focusing particularly on negative stiffness structures, multi-stable metastructures, and pneumatic actuators with tunable mechanical responses. His academic journey reflects a commitment to high-quality research, having published three SCI-indexed papers in top-tier JCR Q1 journals such as Thin-Walled Structures and Engineering Structures. His works include: “Analysis of Negative Stiffness Structures with B-spline Curved Beams” (Thin-Walled Structures, 2024), “Design and Analysis of an Origami-Embedded Multi-Stable Metastructure with Shape Reconfiguration” (Engineering Structures, 2025), and “Deep Learning-Based Structural Design and Mechanical Properties Analysis of Pneumatic Actuators with Tunable Multistability” (Thin-Walled Structures, 2025). These publications highlight Dr. Ai’s ability to integrate theoretical modeling, simulation optimization, deep learning techniques, and experimental validation to solve complex challenges in structural mechanics. He has contributed significantly to ongoing national research through his involvement in the National Natural Science Foundation of China project (Grant No. 12372041), which further demonstrates his active engagement in advancing scientific knowledge. One of Dr. Ai’s major contributions includes developing a configuration parameterization method based on B-spline curves to customize negative stiffness characteristics in metamaterials. Additionally, he proposed a steady-state switching strategy using reconfigurable energy barrier elements, enabling precise control over multi-stability and shape transformation in engineered structures. His work successfully demonstrates, through combined theory, simulations, and experiments, the feasibility of programmable mechanical behavior after forming—an advancement with promising applications in soft robotics, adaptive structures, vibration isolation, and smart materials. Dr. Ai’s research continues to attract academic attention, with citations indexed in the Web of Science database. He maintains a strong ethical commitment to research integrity, with no consultancy projects, patents, or books yet undertaken. While he currently holds no editorial appointments, professional memberships, or formal collaborations, his research trajectory shows excellence, independence, and innovation, positioning him as a competitive candidate for the Research Excellence Award. Dr. Ai affirms that all submitted information is accurate, verifiable, and supported by relevant research links, including: 10.1016/j.tws.2025.114287 and 10.1016/j.tws.2023.111418. He fully agrees to the terms, policies, and responsibilities associated with this award nomination and submits this application with the highest level of integrity.

Profile: Scopus

Featured Publications

Ai, S., Xie, Z., & Wei, J. (2025, November). Deep learning-based structural design and mechanical properties analysis of pneumatic actuators with tunable multistability.

Ai, S., Hou, S., Wei, J., & Xie, Z. (2025, October). Design and analysis of an origami-embedded multi-stable metastructure with shape reconfiguration.

Hou, S., Wei, J., Ai, S., & Tan, H. F. (2025, March). Broadband nonlinear vibration isolation for a friction dynamic system via quasi-zero stiffness isolator.

Bian, S., Ai, S., Wei, J., & Qingxiang, J. (2025, March). Structural design and performance analysis of large inflatable solar membrane reflector.

Ai, S., Wei, J., Xie, Z., & Tan, H. F. (2023, November). Analysis of negative stiffness structures with B-spline curved beams.

Sedighe Mirbolouk | Engineering | Editorial Board Member

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.

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