Elly Ogutu Isaya | Mechanical Engineering | Research Excellence Award

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Mr. Elly Ogutu Isaya | Mechanical Engineering | Research Excellence Award

Budapest University of Technology and Economics | Hungary

Ogutu Isaya Elly is a mechanical engineering researcher and PhD candidate at the Géza Pattantyús-Ábrahám Doctoral School of Mechanical Sciences, Budapest University of Technology and Economics (BME), specializing in advanced manufacturing and micromachining. He earned a Master’s degree in Mechanical Engineering from Huazhong University of Science and Technology (HUST), China and a Bachelor’s degree in Mechanical Engineering from Jomo Kenyatta University of Agriculture and Technology (J.K.U.A.T), Kenya. His academic experience includes active participation in nationally funded Hungarian research projects focused on AI-based predictive modeling for machining quality and on transient deformation, thermal, and tribological phenomena in fine machining of high-hardness metals. His research interests center on micromachining processes, burr formation mechanisms, and the integration of machine learning within Industry 4.0 manufacturing frameworks. His recent work on burr-size prediction and optimization provides industry-ready solutions to reduce deburring costs and improve surface integrity. He is currently a nominee for the Best Research Article Award, reflecting the applied impact and relevance of his research to modern manufacturing systems.

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Featured Publications

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN Tool
U.L. Adizue, A.D. Tura, E.O. Isaya, B.Z. Farkas, M. Takács,
The International Journal of Advanced Manufacturing Technology, 129(3), 1375–1389, 2023. (Citations: 34)


Analysis, Modelling, and Optimization of Force in Ultra-Precision Hard Turning of Cold Work Hardened Steel Using the CBN Tool
O.I. Elly, U.L. Adizue, A.D. Tura, B.Z. Farkas, M. Takács,
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46, 2024. (Citations: 8)


Optimization of Ultra-Precision CBN Turning of AISI D2 Using Hybrid GA-RSM and Taguchi-GRA Statistical Tools
A.D. Tura, E.O. Isaya, U.L. Adizue, B.Z. Farkas, M. Takács,
Heliyon, 10(11), 2024. (Citations: 5)


Feed Optimization Based on Force Modelling and TLBO Algorithm in Milling Al 7075
O.I. Elly, Y. Yin,
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2024. (Citations: 2)


Burr Size Minimization Using a Surrogate Artificial Neural Network (ANN) Assisted Multi-Objective Genetic Algorithm (MOGA) in Micromilling Hardened AISI H13
O.I. Elly, M. Takács, B.Z. Balázs,
The International Journal of Advanced Manufacturing Technology, 2026. (In Press)

Jingyuan Zhao | Energy and Sustainability | Research Excellence Award

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Dr. Jingyuan Zhao | Energy and Sustainability | Research Excellence Award

University of California Davis | United States

Jingyuan (Andy) Zhao, Ph.D., is an Assistant Professional Researcher and independent principal investigator at the University of California, Davis, internationally recognized for pioneering work in AI-enabled battery and energy systems. His research integrates multiphysics and multiscale modeling with advanced artificial intelligence to address battery safety, diagnostics, prognostics, and system-level optimization for electrified transportation.  He has led or co-led major U.S. and international research projects supported by USDOT, Caltrans, CEC, CARB, and national science foundations in China, while also translating research into industrial impact through prior leadership roles in electric vehicle battery intelligence. His academic training spans mechanical and vehicle engineering, complemented by extensive postdoctoral research in the U.S. and China. Dr. Zhao has received numerous honors, including Elsevier–Stanford World’s Top 2% Scientist and ScholarGPS Top 0.5% Scholar distinctions. Through interdisciplinary scholarship, global collaboration, and mentorship, his work advances safe, intelligent, and scalable battery energy systems, bridging laboratory innovation with real-world deployment and shaping the future of sustainable mobility.

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Review on Supercapacitors: Technologies and Performance Evaluation
J. Zhao, A.F. Burke, Journal of Energy Chemistry, 59, 276–291, 2021. (Citations: 602)


Autonomous Driving System: A Comprehensive Survey
J. Zhao, W. Zhao, B. Deng, Z. Wang, F. Zhang, et al., Expert Systems with Applications, 242, 122836, 2024. (Citations: 330)


Electrochemical Capacitors: Materials, Technologies and Performance
J. Zhao, A.F. Burke, Energy Storage Materials, 36, 31–55, 2021. (Citations: 202)


Electrochemical Capacitors: Performance Metrics and Evaluation by Testing and Analysis
J. Zhao, A.F. Burke, Advanced Energy Materials, 11(1), 2002192, 2021. (Citations: 182)


Machine Learning for Predicting Battery Capacity for Electric Vehicles
J. Zhao, H. Ling, J. Liu, J. Wang, A.F. Burke, Y. Lian, eTransportation, 15, 100214, 2023. (Citations: 171)

Amir Hossein akbari | Engineering and Technology | Research Excellence Award

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Dr. Amir Hossein akbari | Engineering and Technology | Research Excellence Award

Iran University of Science and Technology | Iran

Amir Hosein Akbari is an accomplished researcher in industrial engineering with a strong record of scholarly impact his academic background is grounded in advanced industrial engineering education, complemented by progressive research experience spanning optimization, decision sciences, and intelligent systems. His professional experience includes active involvement in high-quality research collaborations and contributions to applied and theoretical studies addressing complex industrial and societal problems. His core research interests focus on supply chain management, optimization, meta-heuristic and evolutionary algorithms, scheduling, decision support systems, and artificial intelligence–driven industrial applications, with several influential works in expert systems, soft computing, and manufacturing systems. His publications have appeared in high-impact venues such as Expert Systems with Applications, Soft Computing, and Neural Computing and Applications, reflecting both methodological rigor and practical relevance. Recognition of his work is demonstrated through strong citation performance and collaborations with well-established scholars in operations research and industrial engineering. Overall, his research portfolio highlights a consistent commitment to advancing intelligent optimization methods and decision-making frameworks, contributing valuable insights to academia and industry while strengthening the scientific foundations of modern industrial engineering.

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Florin Popister | Mechanical Engineering | Engineering Research Excellence Award

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Assoc. Prof. Dr. Florin Popister | Mechanical Engineering | Engineering Research Excellence Award

Technical University of Cluj-Napoca | Romania

Assoc. Prof. Dr. Eng. Florin Popișter is an accomplished researcher and academic in industrial engineering, mechanical design, and robotics. He earned his Ph.D. in Industrial Engineering from the Technical University of Cluj-Napoca, where he currently serves as an Associate Professor, contributing to teaching, research, and supervision in areas such as CAD/CAM systems, industrial robots, reverse engineering, and advanced manufacturing. His professional experience spans both academia and industry, including significant contributions as a design engineer at Gühring Romania, where he developed customized PCD/PCBN tools for the automotive sector. His research includes 3D printing technologies, robotic mechanisms, toolpath generation, workspace optimization, and digital manufacturing, with multiple Q1 journal publications in Polymers, Applied Sciences, and Mathematics. He has led and contributed to national and international research contracts, including H2020 Smart2 and projects focused on automation systems for the pharmaceutical industry. His achievements include a Best Paper Award, participation in Prototypes for Humanity, and invited professorships in Europe. His work continues to advance intelligent manufacturing, Industry 4.0 applications, and smart robotic systems.

Profile : Orcid

Featured Publications

Popescu, D., Dragomir, D., Popișter, F., & Dragomir, M. (2025). AI alignment to enhance production processes performance and resilience. In Book chapter. Springer.

Dragomir, M., Apolțan, D., Cioșan, A., & Popișter, F. (2025). Assessing the potential of emerging digital technologies to transform the production sector. Annals of the Academy of Romanian Scientists Series on Economy, Law and Sociology.

Popișter, F., Ciudin, P., Dragomir, M., & Goia, H. Ș. (2025). From assistive to intelligent: The development of a low-cost smart crutch system. In Book chapter. Springer.

Popișter, F. (2025). Experimental study of comprehensive performance analysis regarding the dynamical/mechanical aspects of 3D-printed UAV propellers and sound footprint. Polymers, 17(11), 1466.

Popișter, F., Goia, H. Ș., & Ciudin, P. (2025). Influence of polymers surface roughness on noise emissions in 3D-printed UAV propellers. Polymers, 17(8), 1015.

Teresa Dieguez | Social Sciences | Excellence in Research Award

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Prof. Teresa Dieguez | Social Sciences | Excellence in Research Award

Universidade Portucalense | Portugal

Teresa Dieguez is a distinguished academic and researcher at the Polytechnic Institute of Cávado and Ave (IPCA), Portugal, specializing in strategy, entrepreneurship, innovation, and management.  she has contributed extensively to advancing knowledge in managerial sciences and sustainable development. She holds a degree in Economics, a Master’s in Innovation and Technological Transfer, a Specialist title in Strategy and Entrepreneurship , an MBA in Social Sustainability and Development, and is currently pursuing her PhD in Managerial Sciences. Teresa’s professional journey began in the automotive sector, where she led international projects promoting innovation and competitiveness, including initiatives under the EU’s ESPRIT and RECITE programs. Her current research interests include digital transformation, social entrepreneurship, Industry 4.0, sustainable leadership, and innovation ecosystems. As a professor and project leader, she has mentored over 30 master’s theses and collaborated with research centers such as INESC TEC, CiTUR, and REMIT. A recipient of various academic recognitions, Teresa remains committed to fostering creativity, critical thinking, and entrepreneurial mindsets to drive sustainable change and global competitiveness in education and industry.

Profiles : Google Scholar | Orcid

Featured Publications

Sivam, A., Dieguez, T., Ferreira, L. P., & Silva, F. J. G. (2019). Key settings for successful open innovation arena. Journal of Computational Design and Engineering, 6(4), 507–515.

Mourato, J., Ferreira, L. P., Sá, J. C., Silva, F. J. G., Dieguez, T., & Tjahjono, B. (2021). Improving internal logistics of a bus manufacturing using the lean techniques. International Journal of Productivity and Performance Management, 70(7).

Dieguez, T., Ferreira, L. P., Silva, F. J. G., & Tjahjono, B. (2020). Open innovation and sustainable development through industry-academia collaboration: A case study of automotive sector. Procedia Manufacturing, 51, 1773–1778.

Alves, D., Dieguez, T., & Conceição, O. (2019). Retaining talents: Impact on innovation. In Proceedings of the ECMLG 2019 15th European Conference on Management, Leadership and Governance (p. 36).

Dieguez, T., Loureiro, P., & Ferreira, I. (2021). Entrepreneurship and leadership in higher education to develop the needed 21st century skills. In Proceedings of the ECMLG 2021 17th European Conference on Management, Leadership and Governance.

Marius Šumanas | Computer Vision | Best Researcher Award

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Assist. Prof. Dr Marius Šumanas | Computer Vision | Best Researcher Award

Vilnius Tech | Lithuania

Dr. Marius Šumanas is an Associate Professor in the Department of Mechatronics, Robotics, and Digital Manufacturing at Vilnius Gediminas Technical University (VILNIUS TECH).  His research interests encompass robotics, machine learning, and digital manufacturing. Dr. Šumanas has authored several publications, including a study on improving positioning accuracy of articulated robots using deep Q-learning algorithms. He has also participated in various conferences, presenting topics related to machine learning applications in robotics. In addition to his academic roles, Dr. Šumanas has practical experience in the industry. Since 2018, he has been working as a Process Engineer and ERP Systems Business Analyst at MB Pramones algoritmas. He has also held positions in marketing and sales management in previous years. Dr. Šumanas’ work bridges the gap between theoretical research and practical application, contributing to advancements in robotics and digital manufacturing.

Profile : Orcid

Featured Publications

Andrijauskas, I., Šumanas, M., Dzedzickis, A., Tanaś, W., & Bučinskas, V. (2025). Computer vision-based optical odometry sensors: A comparative study of classical tracking methods for non-contact surface measurement. Sensors.

Šumanas, M., Treciokaite, V., Čerškus, A., Dzedzickis, A., Bučinskas, V., & Morkvenaite-Vilkonciene, I. (2022). Sitting posture monitoring using Velostat based pressure sensors matrix. In Smart Sensor Systems (pp. 1–12). Springer.

Bučinskas, V., Dzedzickis, A., Šumanas, M., Sutinys, E., Petkevicius, S., Butkiene, J., Virzonis, D., & Morkvenaite-Vilkonciene, I. (2022). Improving industrial robot positioning accuracy to the microscale using machine learning method. Machines, 10(10), 940.

Šumanas, M., Urbonis, D., Petronis, A., Stankaitis, S., Januškevičius, T., Iljin, I., & Dzedzickis, A. (2021). Evaluation of motion characteristics using absolute sensors. In Advances in Mechatronics and Intelligent Robotics (pp. 1–12). Springer.

Šumanas, M., Petronis, A., Urbonis, D., Januskevicius, T., Rasimavicius, T., Morkvenaite-Vilkonciene, I., Dzedzickis, A., & Bučinskas, V. (2021, April 22). Determination of excavator tool position using absolute sensors. In 2021 Open Conference of Electrical, Electronic and Information Sciences (eStream). IEEE.