Urszula Zielenkiewicz | Industry Collaboration | Outstanding Contribution Award

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Dr. Urszula Zielenkiewicz | Industry Collaboration | Outstanding Contribution Award

Dr. Urszula Zielenkiewicz | Institute of Biochemistry and Biophysics PAS | Poland

Dr. Urszula Zielenkiewicz is an accomplished biologist and biochemist at the Institute of Biochemistry and Biophysics of the Polish Academy of Sciences, where she serves as an adjunct researcher and long-standing member of the scientific community. She holds a D.Sc. (Habilitation, 2015) in Biology from the University of Warsaw, a Ph.D. in Biochemistry (2001) from the same Institute, and an M.Sc. in Biology with a specialization in microbiology from the University of Warsaw (1980). Her early academic experience includes advanced training at the Department of Microbiology, Faculty of Pharmacy, Universidad Autónoma de Barcelona, Spain. Dr. Zielenkiewicz’s research career spans over three decades, beginning as a biologist in 1993 and evolving through roles as a research assistant and later adjunct scientist. Her work has significantly advanced the understanding of bacterial toxin–antitoxin systems, mobile genetic elements, and microbial biodiversity. Since 2007, she has led the research group “Microorganisms Potentially Useful in Bioremediation,” conducting influential projects in metagenomics, environmental microbiology, and microbial communities inhabiting metal-polluted soils, agricultural ecosystems, hydrogen-producing bioreactors, and methanogenic sludge. She has authored 41 original research articles, three review papers, and two book chapters, achieving a cumulative impact factor exceeding 123 and over 1,000 citations, with an h-index of 17. Her publications span high-impact journals such as Journal of Evolutionary Biology, Scientific Reports, Genome Biology and Evolution, Frontiers in Microbiology, Microbial Ecology, and International Journal of Molecular Sciences. Her scholarly contributions also include pioneering insights into apoptosis evolution, soil microbial ecology, extremophilic biofilms, and protein–protein interaction inhibitors relevant to SARS-CoV-2. Widely recognized for her interdisciplinary expertise bridging molecular biology, microbiology, biochemistry, and environmental biotechnology, Dr. Zielenkiewicz continues to shape contemporary understanding of microbial adaptation, metabolic diversity, and biotechnological applications of microorganisms.

Profile: Google Scholar

Featured Publications

Wolińska, A., Kuźniar, A., Zielenkiewicz, U., Izak, D., & Szafranek-Nakonieczna, A. (2017). Bacteroidetes as a sensitive biological indicator of agricultural soil usage revealed by a culture-independent approach. Applied Soil Ecology, 119, 128–137.

Sikora, A., Błaszczyk, M., Jurkowski, M., & Zielenkiewicz, U. (2013). Lactic acid bacteria in hydrogen-producing consortia: On purpose or by coincidence? In Lactic acid bacteria – R & D for food, health and livestock purposes (pp. 488–514).

Zielenkiewicz, U., & Cegłowski, P. (2001). Mechanisms of plasmid stable maintenance with special focus on plasmid addiction systems. Acta Biochimica Polonica, 48(4), 1003–1023.

Tomczyk-Żak, K., & Zielenkiewicz, U. (2016). Microbial diversity in caves. Geomicrobiology Journal, 33(1), 20–38.

Zielenkiewicz, U., & Cegłowski, P. (2005). The toxin–antitoxin system of the streptococcal plasmid pSM19035. Journal of Bacteriology, 187(17), 6094–6105.

Chojnacka, A., Szczęsny, P., Błaszczyk, M. K., Zielenkiewicz, U., Detman, A., & others. (2015). Noteworthy facts about a methane-producing microbial community processing acidic effluent from sugar beet molasses fermentation. PLoS ONE, 10(5), e0128008.

Fengzhou Wang | Industry Collaboration | Best Researcher Award

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Mr. Fengzhou Wang | Industry Collaboration | Best Researcher Award

Mr. Fengzhou Wang | Zhe Jiang University | China

The nominee is an emerging researcher in the field of industrial engineering, currently engaged in advanced studies with a strong focus on traffic big data analytics, machine learning, and large-scale AI models. Their academic journey began with foundational training in system modeling and optimization algorithms, which laid the groundwork for their present research direction. Through this evolving academic path, the nominee has developed a growing interest in applying computational intelligence to transportation systems, contributing to the interdisciplinary space where engineering, data science, and artificial intelligence converge. Despite being at an early stage of their research career, the nominee has participated in scholarly work that includes publishing in reputable indexed journals. Their contribution to the article on segmented parabolic adjustment of the FAST reflector demonstrates proficiency in programming computation, data visualization, and scientific writing—skills essential for modern research environments. While the nominee has not yet undertaken formal research projects, consultancy assignments, patents, or editorial responsibilities, they remain committed to expanding their expertise and research footprint. The nominee possesses a strong sense of scientific curiosity and expresses an aspiration to contribute meaningfully to their field in the future. They acknowledge current limitations related to resources, knowledge, and experience but emphasize a forward-looking mindset rooted in creativity and innovative thinking. Their research interests reflect an alignment with emerging global priorities, particularly the integration of AI and big data for intelligent transportation systems. Through academic participation and continued skill development, the nominee aims to build a foundation for impactful research and real-world innovation. By engaging with the research community and enhancing technical competencies, they seek to evolve into a contributor capable of influencing advancements in transportation engineering, machine learning applications, and AI-driven solutions.

Profiles: Scopus | Orcid 

Featured Publications

Wang, F., Kang, Y., & Guo, F. (2024). Segmented parabolic adjustment of the FAST reflector utilizing spatial coordinate rotation transformation. Measurement Science and Technology, 35(10). https://doi.org/10.1088/1361-6501/ad5c93

Raffaele Marotta | Industry Collaboration | Young Innovator Award

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Dr. Raffaele Marotta | Industry Collaboration | Young Innovator Award

University of Naples Federico II | Baker Hughes | Italy

Dr. Raffaele Marotta is an accomplished researcher in vehicle dynamics, control systems, and AI-driven estimation, with proven academic and industrial impact. He earned his Ph.D. in Industrial Engineering (Mechatronics) with honors from the University of Naples Federico II, focusing on AI-enhanced vehicle dynamics. His career includes key roles at the Italian National Research Council (CNR), TU Ilmenau, Tenneco, ZF Group, and currently Baker Hughes, where he leads the development of advanced control algorithms for sustainable energy systems. He has contributed significantly to the European OWHEEL project, developing active chassis control and virtual sensing strategies. His research integrates Kalman filtering, neural networks, reinforcement learning, and digital twins into practical solutions for automotive and energy applications. He has published 22 documents, with 83 citations across 42 sources and an h-index of 6, reflecting strong scientific visibility and influence. His works, published in IEEE and SAE journals, include pioneering studies on wheel displacement estimation, traction force prediction, and vehicle mass estimation. International collaborations across Italy, Germany, Belgium, and Lithuania highlight his global network and impact. Recognized by Nova Talent’s top  global talent network, he also mentors young engineers in STEM leadership programs. With his blend of theoretical innovation, experimental validation, and industrial application, Dr. Marotta stands out as a promising candidate for global research excellence awards.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

“Multi-output physically analyzed neural network for the prediction of tire–road interaction forces”

“Deep learning for the estimation of the longitudinal slip ratio”

“Estimation of the tire-road interaction forces by using Pacejka’s formulas with combined slips and camber angles”

“Active control of camber and toe angles to improve vehicle ride comfort”

“Improvement of traction force estimation in cornering through neural network”

“Camber angle estimation based on physical modelling and artificial intelligence”

“Electric vehicle corner architecture: driving comfort evaluation using objective metrics”

“A PID-Based Active Control of Camber Angles for Vehicle Ride Comfort Improvement”

“A strain-based estimation of tire-road forces through a supervised learning approach”

“On the prediction of the sideslip angle using dynamic neural networks”

“Neural Network-Based Virtual Measurement of Road Vehicle Wheel Displacements”

“Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator with Artificial Intelligence”

“On the measurement of unsprung mass displacement of road vehicles through a model-based virtual sensor”

“Model-Based Vehicle Mass Estimation for Enhanced Adaptive Cruise Control Performance”