GUANGYAO FAN – Sustainable Development – Best Researcher Award

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GUANGYAO FAN - Sustainable Development - Best Researcher Award

Shandong university - China

AUTHOR PROFILE

SCOPUS

RESEARCH FOCUS ON HYDROGEN-BASED MULTI-ENERGY SYSTEMS

GUANGYAO FAN is currently pursuing a PhD in Control Theory and Control Engineering at Shandong University. Their research interests primarily center around the intelligent optimization of hydrogen-based regional multi-energy systems and the hydrogen economy.

EDUCATIONAL JOURNEY

Prior to their doctoral studies, FAN completed an M.S. in Energy Power at North China Electric Power University (NCPU) from September 2020 to July 2023.

PUBLICATIONS AND RESEARCH CONTRIBUTIONS

FAN has made significant contributions to the field, publishing four SCI papers as first author or corresponding author. These publications include research on multi-time-space scale optimization for hydrogen-based regional multi-energy systems, collaborative optimization for renewable energy systems, energy management strategies for near-zero energy community energy supply systems, and multi-time scale operation optimization for community energy systems.

RESEARCH PUBLICATIONS

Notably, their papers have been published in high-impact journals such as Applied Energy, Energy, and Sustainable Cities and Society, showcasing their expertise in energy optimization and management.

ACADEMIC AFFILIATION

FAN is affiliated with the School of Control Science and Engineering at Shandong University, where they continue to advance research in the field of energy systems optimization.

CONTACT INFORMATION

For further inquiries, please contact GUANGYAO FAN via their academic institution at the School of Control Science and Engineering, Shandong University, China.

NOTABLE PUBLICATION

Multi-time-space scale optimization for a hydrogen-based regional multi-energy system

Adaptive energy optimization strategy of island renewable power-to-hydrogen system with hybrid electrolyzers structure 2024 (1)

Collaborative optimization method and energy-saving, carbon-abatement potential evaluation for nearly-zero energy community supply system with different scenarios 2023 (8)

Two-layer collaborative optimization for a renewable energy system combining electricity storage, hydrogen storage, and heat storage 2022 (24)

A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings 2022 (84)

Yi Di – Sustainable development strategy – Best Researcher Award

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Yi Di - Sustainable development strategy - Best Researcher Award

Hubei Key Laboratory of Digital Finance Innovation - China

AUTHOR PROFILE

Scopus

EARLY ACADEMIC PURSUITS

Yi Di embarked on his academic journey by obtaining a B.Sc. degree in Information Science from West Anhui University in 2009. He continued his academic pursuit with an M.Sc. degree in Intelligent Signal Processing from Guizhou University in 2013, laying the foundation for his subsequent research endeavors.

PROFESSIONAL ENDEAVORS

Currently serving as an associate professor at the Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Yi Di has immersed himself in various aspects of intelligent information processing and applications. His professional journey has been characterized by a keen focus on target recognition and tracking, intelligent systems, machine learning and deep neural networks, and big data analytics.

CONTRIBUTIONS AND RESEARCH FOCUS

Yi Di's research interests span a wide array of topics, including intelligent information processing, target recognition and tracking, and machine learning. His contributions to these fields have been significant, with a particular emphasis on developing sustainable development strategies through innovative research methodologies.

IMPACT AND INFLUENCE

As an associate professor, Yi Di's work has made a tangible impact on the academic community. His research outputs have contributed to the advancement of knowledge in intelligent systems and machine learning, influencing the development of sustainable development strategies in various domains.

ACADEMIC CITES

Yi Di's publications in reputable academic journals and conferences have garnered citations, reflecting the relevance and impact of his research in the academic community. His work serves as a testament to his expertise and contributions to the field of intelligent information processing and applications.

LEGACY AND FUTURE CONTRIBUTIONS

Yi Di's legacy is marked by excellence and innovation in intelligent systems and machine learning. His future contributions are poised to further advance research in these areas, with a continued focus on developing sustainable development strategies to address contemporary societal challenges.

NOTABLE PUBLICATION

Computer-aided resilience: Advanced techniques for disaster management in smart urban environments 2024

Nonlinear simulating of the Proton Exchange Membrane Fuel Cells utilizing Ridgelet Neural Network optimized using a hybrid form of Northern Goshawk Optimizer 2024

Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems 2023 (3)

A model-driven approach for fast modeling of three-dimensional laser point cloud in large substation 2023 (2)

A novel hermit crab optimization algorithm 2023 (5)

Mattias Simons – Nuclear Decomissioning – Best Researcher Award

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Mattias Simons - Nuclear Decomissioning - Best Researcher Award

Hasselt University - Belgium

AUTHOR PROFILE

Scopus

EARLY ACADEMIC PURSUITS

Mattias Simons began his academic journey at Hasselt University, where he pursued a Bachelor of Science in Engineering, specializing in Nuclear Technology, from 2016 to 2019. His early academic pursuits laid the groundwork for his future endeavors in nuclear technology and radiation detection.

PROFESSIONAL ENDEAVORS

As a dedicated Ph.D. researcher at the Research Foundation Flanders – Hasselt University since 2020, Mattias Simons has been actively involved in advancing nuclear decomissioning processes. His work involves developing innovative measurement setups using various detectors, such as Lidar and Compton camera technology, to enhance the visualization and localization of hotspots within decommissioning environments.

CONTRIBUTIONS AND RESEARCH FOCUS

Mattias Simons' research primarily focuses on nuclear decomissioning, with a specialization in radiation detection and measurement automation. His contributions include the development of novel measurement setups and the analysis of complex datasets to improve efficiency and safety in nuclear facility dismantlement.

IMPACT AND INFLUENCE

Through his research efforts, Mattias Simons has made a significant impact on the field of nuclear decomissioning. His work has the potential to revolutionize current practices by introducing advanced measurement techniques and automation technologies, thereby enhancing the overall safety and effectiveness of decommissioning processes.

ACADEMIC CITES

Mattias Simons' research findings have been widely cited in academic literature, underscoring the relevance and significance of his contributions to the field of nuclear technology and radiation detection. His work serves as a valuable resource for researchers, practitioners, and policymakers involved in nuclear decomissioning efforts.

LEGACY AND FUTURE CONTRIBUTIONS

As Mattias Simons continues his research endeavors, his legacy in the field of nuclear decomissioning is characterized by his dedication to innovation and excellence. His future contributions hold promise for further advancements in measurement automation, radiation detection, and safety protocols, shaping the future of nuclear decomissioning practices.

NOTABLE PUBLICATION

Towards a Semi-Autonomous Robot Platform for the Characterisation of Radiological Environments  2022 (2)

Minimal Detection Time for Localization of Radioactive Hot Spots in Low and Elevated Background Environments Using a CZT Gamma-Ray Spectrometer 2022 (1)

Learning Multiple Radiation Source Distribution Models using Gaussian Processes  2023