Seyyedmorteza Ghamari | Engineering | Best Researcher Award

Published on by Top Teachers

Best Researcher Award

Seyyedmorteza Ghamari
Edith Cowan University

Seyyedmorteza Ghamari
Affiliation Edith Cowan University
Country Australia
Scopus ID 57220131139
Documents 32
Citations 645
h-index 15
Subject Area Engineering
Event Top Teachers Awards
Google Scholar ID IUT6xloAAAAJ

Seyyedmorteza Ghamari is an engineering researcher affiliated with Edith Cowan University, Australia, whose scholarly activities focus on intelligent control systems, power electronics, electric vehicle technologies, machine learning applications, and advanced optimization methodologies. His body of work demonstrates sustained contributions to robust controller design, adaptive control frameworks, reinforcement learning integration, and hardware-in-the-loop validation techniques for industrial and energy applications.[1] Through a combination of theoretical development and practical implementation, his research addresses challenges related to efficiency, stability, and reliability in modern electrical and electromechanical systems.[2]

Abstract

This article presents an overview of the academic achievements and research contributions of Seyyedmorteza Ghamari. His research portfolio emphasizes intelligent control systems for power electronics, electric drives, and energy conversion technologies. Through the integration of transfer learning, reinforcement learning, fractional-order control, optimization algorithms, and hardware validation methodologies, he has contributed to the advancement of reliable and adaptive engineering solutions.[3]

Keywords

Power Electronics, Intelligent Control Systems, Reinforcement Learning, Transfer Learning, Electric Vehicles, Brushless DC Motors, Optimization Algorithms, Engineering Research.

Introduction

The increasing complexity of modern energy systems has created demand for adaptive and intelligent control strategies. Researchers in this field seek solutions capable of maintaining stability and efficiency under varying operating conditions. Seyyedmorteza Ghamari’s research addresses these challenges through innovative control architectures that combine artificial intelligence techniques with advanced engineering principles.[2]

Research Profile

According to available scholarly metrics, Ghamari has produced 32 indexed publications, accumulated approximately 645 citations, and achieved an h-index of 15. His research activities span engineering disciplines involving power conversion systems, motor control, adaptive algorithms, optimization techniques, and machine learning-assisted control design.[1]

Research Contributions

  • Development of hybrid deep quantum-transfer learning controllers for DC-DC boost converters.
  • Integration of Grey Wolf Optimization and reinforcement learning algorithms into adaptive control frameworks.
  • Advancement of fractional-order super-twisting sliding mode control methodologies.
  • Hardware-in-the-loop validation of power electronic systems and electric vehicle applications.
  • Design of robust cascade controllers for brushless DC motor speed regulation and power factor correction systems.

Publications

  • A Universal Hybrid Model-Free Deep Quantum–Transfer Learning Controller Enhanced By Grey Wolf Optimization for DC–DC Boost Converters With Hardware-in-Loop Validation (2026).
  • A Novel Hybrid Robust Transfer Learning-Based Adaptive Fractional-Order Super-Twisting Sliding Mode Controller for Brushless DC Motors (2026).
  • Deep Transfer Learning-Based Adaptive Cascade PI Controller Enhanced by Reinforcement Learning and Snake Optimization (2026).
  • Robust Cascade Fractional-Order PI-Sliding Mode Controller for Boost Rectifier Power Factor Correction (2025).
  • Adaptive Cascade Fractional-Order PID Controller Enhanced by Reinforcement Learning for Speed Regulation (2025).

Research Impact

The research impact of Ghamari is reflected in citation performance, publication activity, and the practical relevance of his engineering solutions. His studies contribute to the growing body of literature on intelligent control systems while providing experimentally validated approaches applicable to renewable energy systems, electric vehicles, and industrial automation.[4]

Award Suitability

Based on documented publication output, citation metrics, and demonstrated innovation in engineering research, Seyyedmorteza Ghamari presents a strong profile for consideration within the Best Researcher Award category at the Top Teachers Awards. His work illustrates a commitment to methodological rigor, interdisciplinary innovation, and real-world applicability, characteristics commonly associated with scholarly excellence and research leadership.[5]

Conclusion

Seyyedmorteza Ghamari has established a notable research profile through contributions to advanced control systems, power electronics, and intelligent engineering methodologies. His scholarly output, citation record, and focus on experimentally validated innovations support recognition within competitive research award programs and demonstrate ongoing contributions to engineering science.[6]

References

  1. Elsevier. (n.d.). Scopus author details: Seyyedmorteza Ghamari, Author ID 57220131139. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57220131139
  2. Ghamari, S.M., Aziz, A. (2026). Hybrid Deep Transfer Learning Controllers for Power Electronics Applications.
  3. IET Power Electronics. (2026). Deep Quantum–Transfer Learning Controller Enhanced by Grey Wolf Optimization.
  4. IEEE Conference Proceedings. (2025). Power Factor Correction and Hardware-in-the-Loop Validation for Electric Vehicles.
  5. Ghamari, S.M., Ghahramani, M., Habibi, D., Aziz, A. (2025). Energies, 18(19), 5056
  6. Top Teachers Awards. (n.d.). Best Researcher Award Evaluation Framework and Recognition Criteria.
    topteachers.net

Houqing Wang | Engineering | Best Researcher Award

Published on by Top Teachers

Best Researcher Award

Houqing Wang
Anhui University of Science and Technology, China
Houqing Wang
Affiliation Anhui University of Science and Technology
Country China
Scopus ID 57190811711
Documents 39
Citations 365
h-index 10
Subject Area Engineering
Event Top Teachers Awards
Google Scholar 1TYHoFkAAAAJ

Houqing Wang is a Chinese researcher specializing in power electronics, renewable energy systems, advanced converter topologies, energy storage technologies, electric drones, robotic systems, and intelligent power conversion. His academic and industrial contributions span research, teaching, technology development, patents, and international collaborations across leading institutions in China, Hong Kong, and the United States. His scholarly profile demonstrates sustained engagement with high-impact engineering research and innovation.[1]

Abstract

This article evaluates the academic achievements, research profile, technological innovations, publications, patents, industrial collaborations, and scholarly impact of Houqing Wang. His work focuses on power electronics, renewable energy integration, converter topology development, intelligent control systems, energy storage technologies, and advanced grid-connected applications. Through contributions spanning academia and industry, Wang has established a record of interdisciplinary engineering research and innovation.[1]

Keywords

Power Electronics, Renewable Energy, Energy Storage Systems, Converter Topology, Grid-Connected Inverters, LCL Filters, Electric Drones, Smart Energy Systems, Control Strategy, Power Quality.

Introduction

Houqing Wang earned a B.S. degree in Marine Electronics and Electrical Engineering and subsequently completed M.S. and Ph.D. degrees in Power Electronics and Power Drives at Shanghai Maritime University. His career includes appointments at City University of Hong Kong, Tsinghua University, Florida State University, the University of Arkansas, and Anhui University of Science and Technology. These positions enabled engagement with advanced research in power conversion, renewable energy integration, energy management, and intelligent electrical systems.[1]

Research Profile

Houqing Wang’s research profile emphasizes converter topology innovation, renewable energy integration, motor drives, intelligent control, grid-connected power systems, energy storage technologies, machine-learning-assisted power electronics, electromagnetic interference mitigation, photovoltaic systems, and high-efficiency power conversion. His research activities combine theoretical modeling, hardware validation, industrial deployment, and multidisciplinary collaboration across universities, government agencies, and private-sector partners.[1] [2]

Research Contributions

Major contributions include development of buck–boost converters, coupled-inductor inverter architectures, DC nanogrid converters, bipolar voltage balancing techniques, renewable energy power interfaces, energy storage converter systems, drone power management platforms, grid harmonic compensation methods, machine-learning-assisted power electronics optimization, and advanced fault diagnosis techniques. He has also served as principal investigator and collaborator on projects funded by provincial programs, utilities, industry partners, and international research initiatives.[1]

Publications

Houqing Wang’s publications advance DC nanogrid converters, coupled-inductor inverters, buck–boost AC–DC conversion, grid-tied inverter topologies, and network defense technologies. Key themes include power electronics, renewable energy integration, voltage balancing, power quality enhancement, energy regulation, control strategies, converter efficiency, harmonic reduction, common-mode voltage suppression, intelligent energy management, grid connectivity, high-power-density systems, advanced inverter architectures, and practical industrial applications.[3][4][5][6][7]

  • A Dual-Buck–Boost AC/DC Converter for DC Nanogrid With Three Terminal Outputs. IEEE Transactions on Industrial Electronics. DOI: 10.1109/TIE.2016.2598804
  • Coupled-Inductor-Based Aalborg Inverter With Input DC Energy Regulation. IEEE Transactions on Industrial Electronics. DOI: 10.1109/TIE.2017.2760849
  • A Coupled-Inductor-Based Buck–Boost AC–DC Converter With Balanced DC Output Voltages. IEEE Transactions on Power Electronics. DOI: 10.1109/TPEL.2018.2820173
  • A Novel Dual Buck and Boost Transformer-Less Single-Phase Grid-Tied Inverter. IEEE Transactions on Power Electronics. DOI: 10.1109/TPEL.2021.3121365
  • The Art of Defense: Letting Networks Fool the Attacker. IEEE Transactions on Information Forensics and Security. DOI: 10.1109/TIFS.2023.3278458

Research Impact

The research output of Houqing Wang has contributed to advancements in converter efficiency, renewable energy integration, power quality enhancement, intelligent energy management, and grid-connected systems. His work is reflected through peer-reviewed publications, patented technologies, industrial collaborations, and service as a reviewer for leading IEEE journals. The combination of academic output, practical implementation, and technology transfer demonstrates measurable impact within the engineering community.[1]

Award Suitability

The candidate demonstrates strong qualifications for recognition through the Best Researcher Award. His profile combines scholarly productivity, international research experience, successful project leadership, patented innovations, interdisciplinary collaborations, industrial engagement, teaching responsibilities, and contributions to emerging energy technologies. These achievements align with commonly recognized criteria for academic excellence and research leadership.[1]

Conclusion

Houqing Wang has established a significant research profile in power electronics and renewable energy engineering through publications, patents, project leadership, and international collaboration. His sustained contributions to converter technologies, energy systems, and intelligent control applications support consideration for academic recognition within engineering research communities.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Houqing Wang, Author ID 57190811711. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57190811711
  2. Google Scholar. (n.d.). Research profile and citation metrics of Houqing Wang. https://scholar.google.com/citations?user=1TYHoFkAAAAJ&hl=en&oi=sra
  3. Wu, W., Wang, H., Liu, Y., Huang, M., & Blaabjerg, F. A Dual-Buck–Boost AC/DC Converter for DC Nanogrid With Three Terminal Outputs. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2016.2598804
  4. Wang, H., Wu, W., Chung, H.S., & Blaabjerg, F. Coupled-Inductor-Based Aalborg Inverter With Input DC Energy Regulation. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2017.2760849
  5. Wang, H., Wu, W., Li, Y., & Blaabjerg, F. A Coupled-Inductor-Based Buck–Boost AC–DC Converter With Balanced DC Output Voltages. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TPEL.2018.2820173
  6. Wang, H., Wu, W., Zhu, J., Koutroulis, E., Chung, H.S.H., & Blaabjerg, F. A Novel Dual Buck and Boost Transformer-Less Single-Phase Grid-Tied Inverter. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TPEL.2021.3121365
  7. Zhang, J., Dong, Y., Kuang, M., Liu, B., Ouyang, B., Zhu, J., Wang, H., & Meng, Y. The Art of Defense: Letting Networks Fool the Attacker. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2023.3278458

Yongxi Zhang | Engineering | Best Researcher Award

Published on by Top Teachers

Best Researcher Award

Yongxi Zhang
Changsha University of Science and Technology, China

Yongxi Zhang
Affiliation Changsha University of Science and Technology
Country China
Scopus ID 16246642100
Documents 45
Citations 1,124
h-index 13
Subject Area Engineering
Event Top Teachers Awards
ORCID 0000-0002-4609-6189

Yongxi Zhang is a Chinese electrical engineering researcher and Associate Professor at Changsha University of Science and Technology whose academic work focuses on energy storage systems, power system planning, intelligent transportation electrification, and sustainable energy management. Her publication record demonstrates contributions to battery energy storage optimization, electric vehicle infrastructure planning, and renewable energy integration, supporting her recognition as a candidate for the Best Researcher Award.[1]

Abstract

This article summarizes the academic achievements, educational background, research activities, and scholarly output of Yongxi Zhang. Her work emphasizes energy storage technologies, electric transportation systems, and power system operation, while contributing to practical and theoretical developments in sustainable engineering and renewable energy integration.[1]

Keywords

Energy Storage Systems, Electric Vehicles, Power System Planning, Battery Energy Storage, Renewable Energy, Smart Grids, Intelligent Transportation Systems, Photovoltaic Energy, Power Engineering, Sustainable Infrastructure.

Introduction

Yongxi Zhang received engineering degrees from Changsha University of Science and Technology, The Hong Kong Polytechnic University, and The University of Sydney. Since joining academia, she has developed an interdisciplinary research profile connecting electrical engineering, energy storage technologies, and transportation electrification. Her scholarly activities have contributed to the advancement of efficient energy management strategies and resilient power infrastructure systems.[1]

Research Profile

As an Associate Professor in the School of Electrical and Information Engineering, Yongxi Zhang conducts research on energy storage system operation and control, power system planning, electric vehicle charging infrastructure, microgrid optimization, and renewable energy integration. She is an IEEE Member and participates in international professional communities dedicated to power and energy engineering.[1]

Research Contributions

  • Development of planning methodologies for battery energy storage systems in built environments.
  • Research on coordinated deployment of electric vehicle charging stations and mobile energy storage vehicles.
  • Investigation of second-life battery applications for residential and community energy systems.
  • Optimization of photovoltaic-powered transportation and sustainable mobility solutions.
  • Advancement of hierarchical energy management frameworks for microgrids and distributed energy resources.

Publications

Yongxi Zhang has published influential studies on battery energy storage systems, electric vehicle infrastructure planning, renewable energy integration, and intelligent transportation optimization, advancing sustainable engineering solutions and power system resilience.[2][3][4][5][6]

Selected publications highlight contributions to intelligent transportation systems, renewable energy engineering, battery storage optimization, and microgrid management.

Research Impact

With 45 indexed documents, more than 1,124 citations, and an h-index of 13, Yongxi Zhang has established a measurable research presence within engineering and energy-related disciplines. Her studies have supported emerging approaches for energy storage deployment, sustainable transportation, and renewable power system integration.[1]

Award Suitability

The combination of international academic training, sustained publication activity, professional society engagement, and impactful engineering research provides evidence supporting Yongxi Zhang’s suitability for recognition through the Best Researcher Award. Her contributions address contemporary challenges associated with clean energy systems, transportation electrification, and grid modernization.[1]

Conclusion

Yongxi Zhang represents an active researcher in electrical engineering whose work bridges energy storage technologies, intelligent transportation systems, and renewable energy applications. Her publication record, citation impact, and professional engagement collectively demonstrate continuing contributions to engineering research and innovation.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Yongxi Zhang, Author ID 16246642100. Scopus. https://www.scopus.com/authid/detail.uri?authorId=16246642100
  2. Zhang, Y., et al. (2025). Multi-Objective Route Optimization for Photovoltaic Solar-Powered Electric Waste Collection Vehicles. IEEE Transactions on Intelligent Transportation Systems. DOI: https://doi.org/10.1109/TITS.2025.3639053
  3. Zhang, Y., et al. Optimal Planning of Battery Energy Storage System in a Built Environment With Hybrid Thermal Management System and Temperature-Induced Battery Degradation. IET Renewable Power Generation. DOI: https://doi.org/10.1049/rpg2.70256
  4. Zhang, Y., et al. (2024). Coordinated Planning of EV Charging Stations and Mobile Energy Storage Vehicles in Highways With Traffic Flow Modeling. IEEE Transactions on Intelligent Transportation Systems. DOI: https://doi.org/10.1109/TITS.2024.3472755
  5. Zhang, Y., et al. (2022). Two-stage Capacity Determination Framework for Residential Second-Life BESSs Considering Cloud Energy Storage Service. IEEE Systems Journal. DOI: https://doi.org/10.1109/JSYST.2022.3232732
  6. Deng, Y., Zhang, Y., et al. Hierarchical Energy Management for Community Microgrids With Integration of Second-Life Battery Energy Storage Systems and Photovoltaic Solar Energy. IET Energy Systems Integration. DOI:https://doi.org/10.1049/esi2.12055

Ziyoda Adilova | Engineering | Innovative Research Award

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Innovative Research Award

Ziyoda Adilova
Tashkent State Transport University, Uzbekistan
Ziyoda Adilova
Affiliation Tashkent State Transport University
Country Uzbekistan
Scopus ID 57192575499
Documents 30
Citations 131
h-index 7
Subject Area Engineering
Event Top Teachers Awards
ORCID 0000-0002-1825-2447
Google Scholar m5hPvtMAAAAJ

Ziyoda Adilova is an Uzbek engineering researcher and academic affiliated with Tashkent State Transport University. Her scholarly work focuses on railway transport systems, freight transportation, railcar dynamics, maintenance optimization, transport logistics, and railway safety engineering. Through research on dynamic modelling, service-life extension technologies, and freight transport innovations, she has contributed to the advancement of transportation engineering and railway operational efficiency.[1]

Abstract

This article summarizes the academic achievements, research activities, professional experience, scientific projects, and scholarly contributions of Ziyoda Adilova. Her work primarily addresses railway transport engineering, railcar dynamics, freight transportation systems, transport logistics, and maintenance optimization. The combination of theoretical modelling and practical engineering applications demonstrates sustained contributions to transportation research and railway operational safety.[1]

Keywords

Railway Engineering, Freight Transport Systems, Railcar Dynamics, Transport Logistics, Maintenance Optimization, Railway Safety, Engineering Research, Rail Service Vehicles, Transportation Technology, Infrastructure Engineering.

Introduction

Railway transportation remains a critical component of economic development and logistics infrastructure. Research directed toward improving operational safety, freight efficiency, and rolling-stock performance plays an important role in modern transportation systems. Ziyoda Adilova’s academic activities have focused on these areas through investigations of railcar oscillations, maintenance strategies, freight flow optimization, and innovative transport technologies.[1]

Research Profile

Adilova completed her bachelor’s and graduate studies at the Tashkent Institute of Railways and Engineers between 2003 and 2009. She subsequently progressed through research and academic positions, including trainee researcher, assistant lecturer, senior researcher, researcher, doctoral candidate, and professor. Her academic development reflects long-term engagement with transport engineering education and railway research.[1]

  • Bachelor and graduate education in railway engineering.
  • PhD and DSc doctoral training in transport logistics and freight transport systems.
  • Professor, Department of Freight Transport Systems.
  • Research interests in rail vehicle dynamics and transport logistics.
  • Member of Science Slam.

Research Contributions

The research contributions of Adilova are centered on mathematical modelling of railcar structures, oscillation analysis of rolling stock, maintenance planning, railway safety, and freight transport optimization. Her investigations have supported the development of analytical tools for understanding rail vehicle performance under operational conditions and have contributed to extending service life and reliability of transport assets.[2]

  • Dynamic modelling of railcar bearing frames.
  • Oscillation analysis of rail service vehicles.
  • Preventive maintenance optimization methodologies.
  • Freight flow enhancement technologies.
  • Railway safety-oriented engineering innovations.
  • Development of container block-train logistics technologies.
  • Projects addressing service-life extension of transport vehicles.

Publications

Selected publications demonstrate sustained research activity in railway engineering and transport systems, particularly in railcar dynamics and maintenance analysis.[2]

  1. Modelling of Fluctuations in the Main Bearing Frame of Railcar.
  2. Mathematical Model for Calculation of Oscillations in the Main Bearing Frame of Railcar with Changing Stiffness and Physical Parameters.
  3. Mathematical Model of Oscillations of Bearing Body Frame of Emergency and Repair Railcars.
  4. Development of Generalized Dynamic Model of Oscillations of the Main Frame and Running Gear of Rail Service Cars.
  5. Analysis of Optimal Periodicity of Preventive Maintenance of Rail Service Car Taking into Account Operational Technology.

Research Impact

The research output associated with Adilova has contributed to engineering knowledge related to rolling-stock reliability, transport safety, and freight transportation efficiency. Her scholarly profile includes 30 indexed documents, 131 citations, and an h-index of 7, indicating measurable academic visibility within transportation and engineering research communities.[1]

Beyond publication activity, she has led scientific and industrial projects addressing logistics technologies and service-life extension methods for railway vehicles. These initiatives demonstrate engagement with practical transportation challenges and knowledge transfer between academia and industry.[1]

Award Suitability

Ziyoda Adilova’s academic profile aligns with the objectives commonly associated with innovation and teaching recognition programs. Her record includes scholarly publications, leadership of applied research projects, university-level teaching responsibilities, and national recognition through scientific competitions. Notable distinctions include participation as a winner in the “100 Best Innovative Projects of Uzbekistan Women” competition and recognition in the national “Young Scientist” competition. These achievements provide evidence of sustained contributions to engineering research, innovation, and higher education.[1]

Conclusion

Ziyoda Adilova has established a research profile focused on railway engineering, freight transportation systems, transport logistics, and rail vehicle dynamics. Through academic leadership, engineering research, scientific project management, and publication activity, she has contributed to both theoretical and applied developments in transportation engineering. Her achievements support consideration for professional and academic recognition within the engineering and higher-education sectors.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Ziyoda Adilova, Author ID 57192575499. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57192575499
  2. Mukhamedova, Z., and collaborators. Selected publications in railway engineering, railcar dynamics, transport logistics, and maintenance optimization. https://scholar.google.com/citations?user=m5hPvtMAAAAJ&hl=en&oi=sra
  3. ORCID. (n.d.). Researcher Profile: Ziyoda Adilova. https://orcid.org/0000-0002-1825-2447
  4. Top Teachers Awards. (n.d.). Award and recognition platform. https://topteachers.net/
  5. Evaluating skill acquisition in primary education in Uzbekistan through STEAM-based learning approaches. G Mukhamedov, Z Mukhamedova, D Mukhamedova, G Fuzailova, Z Dineeva, OD Akanji. Discover Education. https://link.springer.com/article/10.1007/s44217-026-01442-9

Giovanni Maria Ferraris | Engineering | Research Excellence Award

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Dr. Giovanni Maria Ferraris | Engineering | Research Excellence Award

University of Genoa | Italy

Dr. Giovanni Maria Ferraris is an interdisciplinary engineering researcher specializing in occupational health and safety, fire prevention, risk analysis, and industrial project management, with contributions spanning energy systems, environmental protection, and critical infrastructure. His research integrates applied engineering solutions with safety, sustainability, and innovation in complex industrial and public systems. He has authored 6 Scopus-indexed documents with 3 citations and an h-index of 1, reflecting emerging scholarly impact. His profile is further strengthened by academic engagement in engineering, security, and decision-making systems. Ferraris’s work bridges research, policy, and practice in high-risk and technologically advanced environments.

Citation Metrics (Scopus)

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Featured Publications

Chen Yang | Engineering | Research Excellence Award

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Prof. Chen Yang | Engineering | Research Excellence Award

School of Energy and Power, Chongqing University  |  China

Prof. Chen Yang  research centers on advanced energy systems, renewable energy utilization, and thermal power engineering, with strong emphasis on modeling, optimization, and dynamic control of complex thermo-energy systems, supported by a research record of 1,004 citations across 868 documents, 98 publications, and an h-index of 18. His contributions span ultra-supercritical circulating fluidized bed boilers, nuclear power reactor secondary systems, compressed air energy storage, and hybrid solid oxide fuel cell–gas turbine systems, advancing the efficiency, reliability, and safety of large-scale power generation. He has developed multi-physics and multi-scale reduced-order modeling techniques to address nonlinear dynamics, uncertainty, cooperative simulation, and system stability challenges, enabling enhanced operational performance under transient and abnormal working conditions. His work integrates mechanistic models with artificial intelligence, including neural networks and time-series methods, to achieve online simulation, intelligent prediction, fault early warning, and predictive control in energy systems. He has also contributed to thermodynamic coupling analysis, waste heat utilization strategies, and multi-objective optimization frameworks for green energy systems. Through these innovations, his research significantly supports sustainable power technology development, promotes intelligent and resilient energy infrastructures, and contributes to low-carbon energy transformation and modern energy system advancement.

Citation Metrics (Scopus)

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🟦 Citations    🟥 Documents    🟩 h-index


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Featured Publications

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.

 

Vasso Papadimitriou | Engineering | Best Researcher Award

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Ms. Vasso Papadimitriou | Engineering | Best Researcher Award

Aristotle University of Thessaloniki | Greece

Ms. Vasso Papadimitriou is an accomplished researcher and academic affiliated with the Aristotle University of Thessaloniki and the Region of Central Macedonia, Greece. Her research primarily focuses on construction project management, cost estimation models, and the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly Artificial Neural Networks (ANNs), in the field of building renovation and project planning. She has contributed significantly to the development of predictive and hybrid models that enhance accuracy in early-stage and final cost estimation for construction and renovation projects. Dr. Papadimitriou’s innovative work combines ANN methodologies, including Radial Basis Function (RBF) and Multilayer Perceptron (MLP) networks, with Multi-Criteria Decision-Making (MCDM) approaches such as the TOPSIS Methodology to create efficient, data-driven tools for project assessment and optimization. Her research also aligns with Sustainable Development Goals (SDG 9 and SDG 17), focusing on promoting innovation, infrastructure, and partnerships for sustainable growth. She has published in international peer-reviewed journals indexed in Scopus, Web of Science (SCI-Expanded, ESCI), and other scientific databases. According to Scopus, she has 6 publications, 3 citations, and an h-index of 1. On Google Scholar, she holds 14 total citations, an h-index of 3, and an i10-index of 1, while ResearchGate records 6 publications, 11 citations, and an h-index of 2. Her interdisciplinary approach bridges civil engineering, computer science, and digital construction, contributing to advancements in cost modeling and sustainable infrastructure management. Through her publications and research collaborations, Dr. Papadimitriou continues to make impactful contributions to the field of engineering innovation and AI-driven construction technology. Her outstanding achievements and innovative contributions to predictive modeling and sustainable construction management make her a deserving nominee for the Best Researcher Award.

Publication Profile

Scopus | Google Scholar

Featured Publications

Papadimitriou, V. E., & Aretoulis, G. N. (2024). A final cost estimating model for building renovation projects. Buildings, 14(4), 1072.

Papadimitriou, V. E., Aretoulis, G. N., & Papathanasiou, J. (2024). Radial Basis Function (RBF) and Multilayer Perceptron (MLP) comparative analysis on building renovation cost estimation: The case of Greece. Algorithms, 17(9), 390.

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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