Miroslaw Wcislik | Electric Engineering | Research Excellence Award

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Prof. Dr. Miroslaw Wcislik | Electric Engineering | Research Excellence Award

Department of Electric Engineering, Automatics and Computer Science | Kielce University Of Technology | Poland

Prof. Mirosław Wcislik is a distinguished scholar in electrical and control engineering, internationally recognized for his pioneering contributions to dynamic systems, power engineering, and advanced control modeling. His academic career reflects decades of excellence in research, teaching, and scientific leadership, with strong impact on both theoretical development and industrial application. His expertise spans control systems, power quality, dynamic modeling, and computer-aided design, supported by advanced simulation methodologies. His scholarly impact includes 177 citations across 150 citing documents, 77 published works, and an h-index of 6, demonstrating sustained academic influence. He has also contributed significantly to international standardization, professional societies, and high-impact scientific dissemination across global engineering communities.

Citation Metrics (Scopus)

200

150

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Citations
177

Documents
77

h-index
6

📘 Citations 📕 Documents 📗 h-index


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Featured Publications

Konstantinos Blazakis | Machine Learning | Research Excellence Award

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Dr. Konstantinos Blazakis | Machine Learning | Research Excellence Award

Adjunct Professor | Hellenic Mediterranean University | Greece

Dr. Konstantinos Blazakis is an accomplished researcher specializing in advanced artificial intelligence applications for energy systems, with strong expertise in smart grids, renewable energy integration, and intelligent power system analytics. His research bridges theoretical modeling and real-world deployment, contributing to sustainable and intelligent energy infrastructures. He holds advanced training in electrical and computer engineering, with strong foundations in applied mathematics, physics, and intelligent systems. His work spans quantum machine learning, deep learning for renewable forecasting, electricity theft detection, and AI-driven energy optimization. His scholarly impact includes 345 citations from 336 documents, 11 publications, and an h-index of 6, reflecting consistent academic influence and research excellence.

Citation Metrics (Scopus)

400

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Citations
345

Documents
11

h-index
6

        🟦 Citations    🟥 Documents    🟩 h-index

Featured Publications

Alexandra Moshou | Quality control | Research Excellence Award

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Dr. Alexandra Moshou | Quality control | Research Excellence Award

Civil Engineering | Civil Engineering Department, Polytechnic School, DUTH, Vas. Sofias 12, 67100, Xanthi Greece | Greece

Dr. Alexandra Moshou is a Greece-based seismology researcher whose work combines applied mathematics, seismic waveform analysis, and computational seismology. She received advanced academic training in seismology and applied mathematics from leading Greek universities, building a strong interdisciplinary foundation. Her research focuses on seismic source parameter determination, fault plane characterization, seismic hazard assessment, GNSS–seismology integration, and data-driven modeling of subsurface fault systems using modern computational and machine learning approaches. She has contributed significantly to the scientific understanding of earthquake processes in the Hellenic Arc and adjacent regions and has actively participated in international research collaborations. Her scholarly outputs appear in high-impact geophysics and remote sensing journals and have gained sustained recognition through citations in major academic databases.

Citation Metrics (Google Scholar)

500

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100

0

Citations
500

Documents
37

h-index
12


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Featured Publications

The July 20, 2017 Mw 6.6 Kos Earthquake: Seismic and Geodetic Evidence for an Active North-Dipping Normal Fault
– Pure and Applied Geophysics, 2019 (Cited by 63)

The Cephalonia Earthquake Sequence of January–February 2014: A First Report
– Research in Geophysics, 2014 (Cited by 45)

The 25 October 2018 Mw 6.7 Zakynthos Earthquake: GNSS-Based Low-Angle Fault Modeling
– Journal of Geodynamics, 2020 (Cited by 43)

Coseismic Displacements from Moderate Earthquakes Mapped by Sentinel-1 DInSAR
– Remote Sensing, 2018 (Cited by 38)

Monthly Migration of a Tectonic Seismic Swarm Detected by DInSAR
– Geophysical Journal International, 2013 (Cited by 32)

Ekrem Ozkaya | Manufacturing processes | Best Industrial Research Award

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Assoc. Prof. Dr. Ekrem Ozkaya | Manufacturing processes | Best Industrial Research Award 

Associate Research | Faculty of Engineering and Architecture – Department of Mechanical Engineering, Recep Tayyip Erdoğan University | Turkey

Assoc. Prof. Dr. Ing. Ekrem Özkaya is a distinguished mechanical engineering scholar known for advancing high-performance materials, manufacturing processes, and simulation-driven engineering. With extensive industrial and academic experience, he contributes to innovation in material processing, machining optimization, and computational modeling. His academic background spans multiple doctorate degrees in mechanical engineering, informatics, applied mathematics, and fluidics, supported by additional qualifications in energy engineering, product development, and management. Professionally, he has served as a researcher, associate professor, project leader, and head of R&D initiatives, collaborating closely with industry to integrate advanced simulation technologies into manufacturing. His research focuses on FEM, CFD, CAE, FSI, and mathematical optimization for the development and processing of modern aerospace-grade and difficult-to-machine materials, as well as modeling erosion, cavitation, and structural behaviors. His scholarly output includes 37 publications with 524 citations across 371 citing documents and an h-index of 14, reflecting his impact in the field. Recognized for multidisciplinary expertise and leadership in engineering innovation, he has supervised numerous research projects and significantly contributed to advancing industrial R&D through simulation-based methods.

Citation Metrics (Scopus)

600

500

400

300

200

100

0

Citations
524

Documents
37

h-index
14

Citations
Documents
h-index


View Scopus Profile

Featured Publications

Dogan Celik | Power Electronics | Editorial Board Member

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Assist. Prof.  Dr. Dogan Celik | Power Electronics | Editorial Board Member 

Professor | Van Yuzuncu Yıl University | Turkey

Assist. Prof.  Dr. Doğan Çelik is a prominent researcher known for his significant contributions to microgrids, distributed generation, power quality improvement, and electric vehicle (EV) systems. His work integrates advanced control strategies with modern smart grid and renewable energy technologies, supporting the transition toward efficient, reliable, and sustainable power networks. With strong academic experience and active involvement in research and supervision, he has contributed to the advancement of electrical power systems, distributed generation coordination, power electronics, and EV charging and grid-integration methods. His research focuses on microgrid optimization, power quality enhancement, active filtering, smart grid digitalization, EV charging strategies including V2G, and advanced control methods such as sliding mode control, PR controllers, Lyapunov-based techniques, and Kalman filtering. His publications in reputable international journals have received notable citations, reflecting the technical relevance and practical value of his work. Through ongoing research collaborations and innovative contributions, he continues to support global efforts aimed at strengthening modern energy infrastructure and accelerating the deployment of intelligent, sustainable electrical systems.

Profiles: Google Scholar

Featured Publications

1. Mahmud, K., Town, G. E., Morsalin, S., & Hossain, M. J. (2022). Investigation and analysis of effective approaches, opportunities, bottlenecks and future potential capabilities for digitalization of energy systems and sustainable … Electric Power Systems Research, 212, 108251.

2. Abujubbeh, A., Mossa, M. A., & Chaari, O. (2019). A comprehensive survey on control strategies of distributed generation power systems under normal and abnormal conditions. Annual Reviews in Control, 47, 102–117.

3. Çelik, D., Niazi, K. A. K., & Ahmad, M. W. (2022). Lyapunov-based harmonic compensation and charging with three-phase shunt active power filter in electrical vehicle applications. International Journal of Electrical Power & Energy Systems, 137, 107564.

4. Darwish, M. K., Abdelaziz, A. Y., & El-Naggar, K. M. (2019). Current control based power management strategy for distributed power generation system. Control Engineering Practice, 85, 90–104.

5. Çelik, D., Khan, M. A., & Hossain, M. J. (2023). Kalman Filter-Based Super-Twisting Sliding Mode Control of Shunt Active Power Filter for Electric Vehicle Charging Station Applications. IEEE Transactions on Power Delivery, 38(2), 1547–1556.

Sagar D Patil | Composite Materials | Editorial Board Member

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Assoc. Prof. Dr. Sagar D Patil | Composite Materials | Editorial Board Member 

Associate Professor | Sharad Institute of Technology College of Engineering Yadrav | India

Assox. Prof. Dr. Sagar Dnyandev Patil is an Associate Professor of Mechanical Engineering at Sharad Institute of Technology College of Engineering, recognized for his contributions to composite materials, finite element analysis, optimization methods, and advanced manufacturing. His work integrates experimental mechanics with computational modeling to enhance material performance and structural design. With more than a decade of academic and research experience, he has extensively investigated composite structural behavior, nano-enhanced materials, hybrid composites, and the optimization of mechanical systems. He has supervised numerous student projects, collaborated with industry on practical engineering challenges, and published several impactful studies in reputed journals. His research interests span composite materials, FEA, nano-enhanced phase change materials, structural integrity assessment, and optimization techniques such as Taguchi and GRA. He has been recognized for his contributions to hybrid composite development and for advancing innovative material solutions, establishing himself as a promising researcher in mechanical engineering.

Profiles: Google Scholar

Featured Publications

Husainy, M., Patil, S. D., & Others. (2024). Heat transfer phenomenon of NEPCM incorporated in refrigeration test rig. ES Energy & Environment. (Cited by 25)

Patil, S. D., Bhalerao, Y. J., & Others. (2023). Design parameters influencing tensile strength of composite layers using Taguchi. Materials Today: Proceedings. (Cited by 10)

Patil, S. D., & Bhalerao, Y. J. (2020). Multi-objective optimization of carbon/glass hybrids with NDR. Multidiscipline Modeling in Materials and Structures. (Cited by 10)

Patil, S. D., & Bhalerao, Y. J. (2019). Optimization of dynamic properties of hybrid composite shaft. International Journal of Structural Integrity. (Cited by 10)

Patil, S. D., Bhalerao, Y. J., & Others. (2012). Composite torsion shaft buckling analysis using FEA. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE). (Cited by 9)

Jeng Ywan Jeng | Materials selection | Editorial Board Member

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Prof. Jeng Ywan Jeng | Materials selection | Editorial Board Member

Distinguished Professor | National Taiwan University of Science and Technology | Taiwan

Prof. Jeng-Ywan Jeng is a distinguished professor at the National Taiwan University of Science and Technology, widely recognized for his pioneering contributions to advanced manufacturing, additive manufacturing, laser processing and lattice-structure engineering. He holds strong academic foundations in mechanical and manufacturing engineering and has over two decades of research and teaching experience in hybrid manufacturing, high-speed 3D printing, multi-material fabrication, and cellular structure optimization. His research interests include additive manufacturing process innovation, closed-cell and supportless lattice structures, laser welding modeling, photovoltaic material development and hybrid mold fabrication. Prof. Jeng has received several awards for research excellence and industry-oriented innovation. He has authored highly cited works such as A state-of-the-art review on cellular structures (The International Journal of Advanced Manufacturing Technology, 2019, cited by 612 articles), Mold fabrication using hybrid cladding and milling (Journal of Materials Processing Technology, 2001, cited by 204 articles), Design of closed-cell supportless lattices (Additive Manufacturing, 2020, cited by 161 articles), Prediction of laser butt-joint welding parameters (Journal of Materials Processing Technology, 2000, cited by 119 articles), and Multi-material additive manufacturing with foam-filled lattices (Additive Manufacturing, 2022, cited by 118 articles). His work continues to influence modern manufacturing research and industrial applications.

Profiles: Google Scholar

Featured Publications

Nazir, A., Abate, K. M., Kumar, A., & Jeng, J. Y. (2019). A state-of-the-art review on types, design, optimization, and additive manufacturing of cellular structures. The International Journal of Advanced Manufacturing Technology, 104(9), 3489–3509.

Jeng, J. Y., & Lin, M. C. (2001). Mold fabrication and modification using hybrid processes of selective laser cladding and milling. Journal of Materials Processing Technology, 110(1), 98–103.*

Kumar, A., Collini, L., Daurel, A., & Jeng, J. Y. (2020). Design and additive manufacturing of closed cells from supportless lattice structure. Additive Manufacturing, 33, 101168.

Jeng, J. Y., Mau, T. F., & Leu, S. M. (2000). Prediction of laser butt joint welding parameters using back propagation and learning vector quantization networks. Journal of Materials Processing Technology, 99(1–3), 207–218.

Prajapati, M. J., Kumar, A., Lin, S. C., & Jeng, J. Y. (2022). Multi-material additive manufacturing with lightweight closed-cell foam-filled lattice structures for enhanced mechanical and functional properties. Additive Manufacturing, 54, 102766.

Liaqat Ali | Heat transfer | Editorial Board Member

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Assist. Prof. Dr. Liaqat Ali | Heat transfer | Editorial Board Member 

Assistant Professor | Xi’an Technological University | China

Dr. Liaqat Ali is an accomplished Associate Professor at Xi’an Technological University, China, recognized for his significant contributions to computational heat transfer, nanofluid dynamics, AI-assisted thermal modeling, and magnetohydrodynamics. With a strong academic background in mechanical and thermal engineering, he has developed advanced expertise in mathematical modeling, hybrid nanofluid transport, and artificial intelligence applications in complex fluid systems. Dr. Ali has authored more than 90 research publications, accumulated over 2,600 citations, and holds an h-index of 32, reflecting his international research influence. His professional experience includes interdisciplinary research collaborations, graduate supervision, and leadership in thermal–AI integration projects. His research interests span AI-based heat and mass transfer prediction, nanofluid-based energy applications, non-Newtonian fluid behavior, bioconvection systems, and plasmonic sensing technologies. Dr. Ali’s recent work includes advancements in plasmonic sensor performance, magnetohydrodynamic flows, hybrid nanofluid modeling, and AI-driven fluid mechanics analyses, demonstrating his ongoing role in advancing next-generation thermal engineering and computational modeling.

Profile: Scopus

Featured Publications

1️⃣ Enhanced SPR Sensor with rGO Layers

Article – Open Access

Authors missing.
(2025). Enhanced surface plasmon resonance sensor performance using reduced graphene oxide (rGO) layers for aflatoxin detection. Results in Physics.

2️⃣ Optimizing Hidden Layers for Heat & Mass Transfer Prediction

Authors missing.
(2025). Optimizing hidden layers for prediction of heat and mass transfer in steady two-dimensional flow over cylinder. Journal of Thermal Analysis and Calorimetry.

3️⃣ Comparative Study of AI Algorithms in Boundary Layer Flow

Authors missing.
(2025). Comparative study of AI algorithms in boundary layer flow: Evaluating performance of Levenberg–Marquardt, Bayesian, and scaled conjugate methods. Thermal Science and Engineering Progress.

(1 citation)

4️⃣ AI Approach to MHD Flow of Non-Newtonian Fluids

Article – Open Access
Authors missing.
(2025). Artificial intelligence approach to magnetohydrodynamic flow of non-Newtonian fluids over a wedge: Thermophoresis and Brownian motion effects. Engineering Science and Technology, an International Journal.

(5 citations)

5️⃣ Tetra Hybrid Nanofluid With Gyrotactic Microorganisms

Authors missing.
(2025). Thermal and solutal analysis of swimming of gyrotactic microorganisms in chemical reactive flow of tetra hybrid nanofluid using Xue and Yamada–Ota models. Journal of the Brazilian Society of Mechanical Sciences and Engineering.

(10 citations)

Posen Lee | Quantitative Movement Analysis | Best Researcher Award

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Prof. Posen Lee | Quantitative Movement Analysis | Best Researcher Award

Professor | I-Shou University | Taiwan

Prof. Posen Lee is a distinguished professor of occupational therapy known for his contributions to psychiatric rehabilitation, community mental health, psychometric assessment, and technology-enhanced intervention. His academic and clinical background forms the foundation of his work in developing client-centered occupational therapy frameworks and advancing assessment standards, including an OSCE tailored for psychiatric practice. He holds advanced degrees in occupational therapy and special education, supporting his long-standing commitment to evidence-based teaching and interdisciplinary innovation. His experience spans extensive university-level teaching and service, along with impactful clinical roles across major medical and psychiatric institutions, where he strengthened patient-centered rehabilitation approaches. His research integrates psychometrics, artificial intelligence, quantitative motion analysis, and rehabilitation technology, with publications in leading peer-reviewed journals across occupational therapy, mental health, and biomedical engineering. He has secured multiple competitive research grants for projects in psychiatric education and AI-supported assessment and has contributed to influential academic textbooks that have elevated national occupational therapy training standards.

Profile: Scopus

Featured Publications

Lee, P. “AI-Based Gait Assessment in Older Adults.” Biosensors. — Cited by 18.

Lee, P. “Motion Analysis for Schizophrenia Using Deep Learning.” Sensors. — Cited by 25.

Lee, P. “Psychometric Validation of a Schizophrenia Functional Scale.” Asian Journal of Psychiatry. — Cited by 14.

Lee, P. “Quantitative Balance Evaluation in Psychiatric Disorders.” Bioengineering. — Cited by 10.

Lee, P. “Clinical Utility of OT-Based Communication OSCE.” Journal of Occupational Therapy Education. — Cited by 8.

Fan Yang | Machine learning | Best Researcher Award

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Dr. Fan Yang | Machine learning | Best Researcher Award

Dr. Fan Yang | Qinghai Normal University | China

Dr. Fan Yang, Ph.D., is an Associate Professor in the School of Computer Science at Qinghai Normal University, recognized for his expanding contributions to human–machine systems and artificial intelligence. He has developed a strong academic profile with multiple peer-reviewed publications in high-impact journals and internationally respected conferences, reflecting his growing influence in intelligent interaction and adaptive computational technologies. His background includes advanced training in computer science with a research emphasis on intelligent human–machine collaboration and adaptive AI modeling. In his current role, he teaches core subjects in artificial intelligence and interactive systems while supervising graduate research and contributing to national and provincial research initiatives. His research interests span intelligent interaction, AI-driven decision technologies, adaptive computational models, and integrated human–machine environments, with a focus on connecting machine intelligence to real-world human behavior. His early achievements, impactful research output, and contributions to cutting-edge AI technologies have earned him recognition within the research community and position him as a competitive candidate for prestigious research awards.

Profile: ORCID

Featured Publications

Yang, F. “Adaptive human–machine interaction using deep attention models.” IEEE Transactions on Human–Machine Systems. — Cited by 12.

Yang, F. “Multi-agent reinforcement learning for human-centered AI.” ACM CHI Conference. — Cited by 8.

Yang, F. “Cognitive-driven robot collaboration under dynamic environments.” Robotics and Autonomous Systems. — Cited by 15.

Yang, F. “Real-time interaction modeling using hybrid deep networks.” Neurocomputing. — Cited by 20.

Yang, F. “Intelligent behavior prediction in human–machine teams.” IEEE ICMLA Conference. — Cited by 5.