Zina Boussada | Engineering | Research Excellence Award

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Dr. Zina Boussada | Engineering | Research Excellence Award

Company for Petroleum Research and Operations | Tunisia

Dr. Zina Boussada is an emerging researcher in electrical engineering whose work bridges advanced control systems, intelligent automation, renewable energy technologies, and high-performance power electronics. Her scientific contribution focuses on the modeling, optimization, and control of induction motors, photovoltaic systems, and microgrid energy management using intelligent and hybrid computational approaches. She has contributed extensively to sensorless motor control through ANFIS-based strategies, multilevel NPC inverter topologies, stator-flux orientation techniques, and advanced inverter modulation methods, enhancing system efficiency, stability, and predictive performance in industrial and renewable energy applications. Her research extends to photovoltaic cell modeling, hybrid optimization frameworks, exponential smoothing forecasting, diode-clamped inverter strategies, and comparative inverter control techniques, addressing key challenges in modern smart-grid and clean-energy systems. She has collaborated with several research groups and contributed to journals and international conferences in the areas of energy systems, green technologies, and intelligent electrical drives. Her publication record reflects steady scholarly growth, supported by contributions in peer-reviewed journals such as Symmetry, WSEAS Transactions on Systems and Control, the International Journal of Environmental Sciences, and various high-impact conference proceedings. She has also published multiple studies on photovoltaic modeling and multilevel inverter technologies, reinforcing her position within the renewable-energy research community. Her citation metrics indicate increasing academic visibility, with Scopus reporting approximately 490 citations from 482 citing documents, 23 indexed documents, and an h-index of 9. Google Scholar metrics show comparable academic impact, reflecting a growing global readership and recognition for her work in intelligent control and energy-system optimization. Overall, her research trajectory demonstrates strong potential for continued advancement in sustainable energy technologies, intelligent control methodologies, and high-performance electrical systems, positioning her as a promising candidate for the Research Excellence Award.

Publication Profile

Scopus | Orcid 

Featured Publications

Boussada, Z., Omri, B., & Ben Hamed, M. (2025). High-performance sensorless control of induction motor via ANFIS and NPC inverter topology. Symmetry.

Boussada, Z., Omri, B., & Ben Hamed, M. (2025). Data-driven optimization for efficient integration of photovoltaic agents in residential microgrid systems. Euro-Mediterranean Journal for Environmental Integration.

Xingjian Huang | Engineering | Best Research Article Award

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Dr. Xingjian Huang | Engineering | Best Research Article Award

Huaihua University | China

Xingjian Huang is a distinguished food‑science researcher whose work integrates protein chemistry, food structure and functionality, biopolymer‑based materials, and the nutritional evaluation of plant proteins. His research has significantly advanced understanding of how soy proteins and other plant‑derived proteins behave under various processing conditions, including proteolysis, gelation, hydrolysis, and complex formation, and how these behaviors influence texture, gel strength, nutritional quality, and functional properties. Among his notable contributions is the study of amyloid‑fibril formation from selectively hydrolyzed soy protein hydrolysates, which provided key insights into protein aggregation, fibrillation mechanisms, and structural modification. He has also conducted extensive research on exopolysaccharide production by lactic acid bacteria, improving yields through strain screening and optimization of fermentation and extraction conditions, linking microbial fermentation to food‑biopolymer applications. In addition, Huang has investigated the nutritional value and amino acid composition of various plant proteins, such as the protein subunits of the Chinese chestnut (Castanea mollissima), enhancing understanding of plant protein quality and potential functional applications. His work further explores the practical implications of protein interactions in food systems, including mixed‑protein gels, soy‑protein/corn‑starch composites, and the interplay of lipids and proteins in gel networks, bridging fundamental biochemical insights with industrial food processing relevance. Huang’s research has contributed valuable knowledge for improving food texture, nutrition, and the scalable processing of plant‑based proteins, supporting both academic research and applied food technology. According to his ResearchGate profile, he has published over 20 peer‑reviewed papers with more than 1,800 reads, demonstrating significant influence in the field and a substantial citation record that reflects his impact on food science research worldwide. For his outstanding contributions, Xingjian Huang has been recognized with the Best Research Article Award, highlighting his innovative work and high impact in the field of food science and technology.

Publication Profile

Orcid

Featured Publications

Yang, F., Huang, X., Zhang, C., … Hao, Y. (2018). Amino acid composition and nutritional value evaluation of Chinese chestnut (Castanea mollissima Blume) and its protein subunit. RSC Advances.

Xie, D., Liu, X., Zhang, H., … Pan, S., Huang, X. (2017). Textural properties and morphology of soy 7S globulin–corn starch (amylose, amylopectin). International Journal of Food Properties.

Xia, W., … Pan, S., Huang, X. (2017). Formation of amyloid fibrils from soy protein hydrolysate: Effects of selective proteolysis on β‑conglycinin. Food Research International.

Qi, L., … Pan, S., Huang, X. (2016). Yield improvement of exopolysaccharides by screening of the Lactobacillus acidophilus ATCC and optimization of the fermentation and extraction conditions. EXCLI Journal.

Pan, Y., Huang, X., Shi, X., … Du, Y. (2015). Antimicrobial application of nanofibrous mats self-assembled with quaternized chitosan and soy protein isolate. Carbohydrate Polymers.

 

Mehran Pourgholi | Engineering | Best Researcher Award

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Assist. Prof. Dr. Mehran Pourgholi | Engineering | Best Researcher Award

Islamic Azad University | Iran

Assist. Prof. Dr. Mehran Pourgholi, an accomplished researcher in Civil and Structural Engineering, has made notable contributions to the fields of system identification, structural health monitoring, inverse problems, and stochastic subspace methods. His research focuses on enhancing the reliability of modal analysis and vibration-based damage detection in large-scale structures such as dams and steel buildings. Dr. Pourgholi integrates advanced computational approaches including entropy-based model selection, optimization algorithms, and error analysis frameworks to improve accuracy in structural system modeling. His collaborative work with experts from the University of Tabriz and Islamic Azad University has produced high-impact studies published in leading international journals such as the Journal of Vibration and Control, Mechanical Systems and Signal Processing, and Engineering Reports. According to Google Scholar, Dr. Pourgholi has 77 citations (68 since 2020), an h-index of 4, and an i10-index of 2, while Scopus records 51 citations across 46 documents with an h-index of 3. His influential publications on stochastic subspace identification and modal analysis have advanced understanding of dynamic behavior in civil structures. Recognized for his scholarly excellence, he has been honored with the Best Researcher Award for his significant impact in the field of structural system identification and vibration analysis.

Publication Profile

Scopus | Orcid | Google Scholar 

Featured Publications

Tarinejad, R., & Pourgholi, M. (2018). Modal identification of arch dams using balanced stochastic subspace identification. Journal of Vibration and Control, 24(10), 2030–2044.

Pourgholi, M., Mohammadzadeh Gilarlue, M. M., Vahdaini, T., & Azarbonyad, M. (2023). Influence of Hankel matrix dimension on system identification of structures using stochastic subspace algorithms. Mechanical Systems and Signal Processing, 186, 109893.

Pourgholi, M., Tarinejad, R., Khabir, M. E., & Mohammadzadeh Gilarlue, M. M. (2023). System identification of Karun IV Dam using balanced stochastic subspace algorithm considering the uncertainty of results. Journal of Vibration and Control, 29(23–24), 5342–5356.

Tarinejad, R., Pourgholi, M., & Yaghmaei-Sabegh, S. (2016). Signal processing of dynamic tests results using subspace identification based on orthogonal decomposition technique (SI-ORT). Modares Mechanical Engineering, 15(10), 104–116.

Pourgholi, M., Ghannadi, M., & Gavgani, S. S. (2024). Modal analysis of earthquake records for dams using stochastic subspace based on error analysis. Engineering Reports, 6(8), e12822

 

Reza Alimardani | Design | Outstanding Educator Award

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Prof. Reza Alimardani | Design | Outstanding Educator Award

Professor at university of tehran, Iran

Dr. Reza Alimardani  is a distinguished professor in Agricultural Machinery at the University of Tehran, renowned for his contributions to agricultural engineering and precision farming technologies 🚜🌾. With a career spanning over three decades, he specializes in agricultural machinery design, power systems, and smart farming solutions. Dr. Alimardani is deeply involved in advancing automation in agriculture, leveraging AI, IoT, and machine learning for improving productivity and sustainability in farming practices 🌱🤖. His research outputs have earned numerous citations, reflecting his significant impact in the academic community 📚📈. Besides teaching and mentoring, he has been actively contributing to various national and international projects focused on sustainable agriculture and environmental management 🌍. He is also an influential voice in conferences and journals related to agricultural systems and smart technologies, continuing to inspire new generations of engineers and researchers globally 🌐🎓.

Professional Profile

ORCID

Education 

Dr. Reza Alimardani completed his Ph.D. in Agricultural Engineering (Power & Machinery) from Iowa State University between 1985 and 1988 🎓🚜. His doctoral research equipped him with advanced knowledge in agricultural mechanization, power machinery, and system optimization. Prior to his Ph.D., he earned his Master of Applied Science (M.A.Sc) in Agricultural Engineering (Power & Machinery) from Oklahoma State University from 1983 to 1985 📘⚙️, focusing on mechanical systems in agriculture and energy efficiency in farming tools. Remarkably, he also completed his Bachelor of Science (B.Sc) in Agricultural Engineering (Power & Machinery) at Oklahoma State University in 1983 🎓🔧. His academic journey across top U.S. universities has laid a solid foundation for his lifelong contributions to agricultural engineering, emphasizing power systems, mechanization, and technological innovation in farming equipment and processes.

Research Focus 

Dr. Reza Alimardani’s research focuses on agricultural engineering innovations, specifically within precision farming, smart machinery, and biosystems engineering 🚜📡. He investigates the integration of deep learning algorithms, machine learning, and IoT technologies to optimize agricultural processes, enhance crop yields, and ensure environmental sustainability 🌾🔬. His work on greenhouse microclimatic parameter prediction using AI aims to revolutionize controlled environment agriculture, making farming more data-driven and efficient 📊🌿. He is also pioneering research in precision beekeeping, employing acoustic analysis and IoT sensors to improve hive health and productivity 🐝📈. His multidisciplinary approach bridges engineering with biological sciences to develop advanced machinery tailored for modern farming needs. By focusing on automation, robotics, and data analytics in agriculture, Dr. Alimardani contributes to creating intelligent systems that support sustainable agricultural practices in Iran and beyond

Publications to Noted

On‑line separation and sorting of chicken portions using a robust vision‑based intelligent modelling approach 

Authors: Nima Teimouri, Mahmoud Omid, Kaveh Mollazade, Hossein Mousazadeh, Reza Alimardani, Henrik Karstoft

Year: 2018

Citations: 42 

A novel application of stand‑alone photovoltaic system in agriculture: solar‑powered Microner sprayer 

Authors: Meysam Karami Rad*, Mahmoud Omid, Reza Alimardani, Hossein Mousazadeh

Year: 2015

Citations: 4

Application of hyperspectral imaging and acoustic emission techniques for apple quality prediction

Year: 2017

Design a new cutter‑bar mechanism with flexible blades and its evaluation on harvesting of lentil

Year: 2017

Hyperspectral imaging for detection of codling moth infestation in GoldRush apples

Year: 2017

Artificial neural network based modeling of tractor performance at different field conditions

Year: 2016

Fuel consumption models of MF285 tractor under various field conditions

Year: 2016

A numerical and an analytical method for optimum planting date determination

Year: 2015

Design, construction and evaluation of a sprayer drift measurement system

Year: 2015

Design, construction and evaluation of a sprayer drift measurement system

Year: 2015

Conclusion

Based on his research excellence, commitment to advancing agricultural engineering, and integration of modern technologies like AI and IoT, Dr. Reza Alimardani stands as a strong candidate for the Research for Outstanding Educator Award 🏆. His scientific achievements, particularly in precision agriculture, reflect an educator deeply connected to evolving industry needs. However, for a more robust alignment with the Outstanding Educator profile, additional emphasis on educational leadership, teaching innovations, and student impact metrics would enhance his nomination. Nevertheless, his profile exemplifies a researcher-educator model advancing both science and education in agricultural engineering.

Yair Rivera | Engineering | Best Researcher Award

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