Yuan Xiaolin | Machine Learning | Editorial Board Member

Published on by Computer Science Engineering

Dr. Yuan Xiaolin | Machine Learning | Editorial Board Member

Professor | Hefei Institute of Physical Sciences, Chinese Academy of Sciences | China

Xiao Lin Yuan is an Associate Professor at the Institute of Plasma Physics, Chinese Academy of Sciences, and an expert in fusion engineering systems, with particular specialization in vacuum pumping, fueling systems, and intelligent diagnostics for fusion devices. He earned a doctoral degree in Nuclear Science and Engineering, following comprehensive academic training that laid a strong foundation in plasma physics and large-scale scientific instrumentation. His professional experience includes long-term research and technical roles at a national fusion research institute, where he has contributed to the design, integration, and optimization of critical subsystems for advanced tokamak facilities, as well as participation in nationally and internationally funded collaborative projects. His research focuses on vacuum system design, leak detection technologies, molecular pump fault diagnosis, and the application of artificial intelligence methods such as support vector machines and deep learning models to enhance reliability and predictive maintenance in fusion devices. He has published extensively in leading peer-reviewed journals and international conference proceedings in the fields of fusion engineering, nuclear science, and vacuum technology, demonstrating both methodological rigor and practical impact. Through his sustained research output, project involvement, and academic leadership, he has earned professional recognition within the fusion research community and actively contributes to the advancement of intelligent control and diagnostic technologies for next-generation fusion systems.

Profile : ORCID

Featured Publications

Yuan, X.-L., Chen, Y., Hu, J.-S., et al. (2016). Development and implementation of flowing liquid lithium limiter control system for EAST. Fusion Engineering and Design, 112, 332–337.

Yuan, X.-L., Chen, Y., Hu, J.-S., et al. (2018). 10 Hz pellet injection control system integration for EAST. Fusion Engineering and Design, 126, 130–138.

Yuan, X.-L., Chen, Y., et al. (2018). Development and implementation of supersonic molecular beam injection for EAST tokamak. Fusion Engineering and Design, 134, 62–67.

Yuan, X.-L., Chen, Y., et al. (2023). A support vector machine framework for fault detection in molecular pump. Journal of Nuclear Science and Technology, 60, 72–82.

Zhou, Y., Jiang, M., Yuan, X.-L., et al. (2024). Fault prediction of molecular pump based on DE-Bi-LSTM. Fusion Science and Technology, 80, 1001–1011.

Marco Capogni | Data Science | Best Researcher Award

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Prof. Dr. Marco Capogni | Data Science | Best Researcher Award

Researcher | ENEA – National Institute for Ionizing Radiation Metrology | Italy

Prof. Dr. Marco Capogni’s research focuses on the precise measurement and standardization of radionuclides, with a strong emphasis on ionizing radiation metrology and its applications in medicine, industry, and environmental monitoring. He has developed and maintained primary national standards for radioactivity, collaborating with international institutions such as the Bureau International des Poids et Mesures (BIPM) and contributing to interlaboratory comparisons to ensure global consistency in radionuclide measurements. His work includes the implementation of absolute measurement techniques and computational codes such as GEANT, MCNP, Penelope, and Fluka for both direct and indirect activity determination. Marco has been actively involved in projects producing medical radionuclides like Mo-99 and Cu-64, utilizing neutron activation and absolute or relative measurement systems, and has contributed to the development of innovative sources of fusion neutrons for radioisotope production under the Sorgentina-RF project. His expertise spans gamma spectrometry, liquid scintillation counting, and coincidence counting methods, and he has served as a member of international working groups including the International Committee for Radionuclide Metrology (ICRM) and the European Metrology Network for Radiation Protection (EURAMET). Marco has led and coordinated numerous European research projects funded by EMRP and EMPIR, focusing on robust production chains for medical radionuclides, radiological early warning networks, and metrology for decommissioning nuclear facilities. He has also contributed to the training of students at the master’s and doctoral levels in physics, engineering, and medical physics, supervising multiple theses on radionuclide metrology and measurement techniques. His work has resulted in significant publications, patents, and participation in international conferences, reflecting his leadership in metrological science and nuclear applications. Marco Capogni’s contributions demonstrate a blend of experimental expertise, computational proficiency, and collaborative engagement with international metrology and research networks, addressing challenges in nuclear measurement, radioprotection, and medical isotope production. He has achieved 1,882citations, authored 133 documents, and holds an h-index of 21.

Profiles: Scopus | ORCID
Featured Publication

Capogni, M., … (2024). Assessment of impurity production upon 14 MeV fusion neutron irradiation of both natural and isotopically enriched 100Mo samples. European Physical Journal Plus.
Citations: 1

Capogni, M., … (2024). Measurements of the absolute gamma-ray emission intensities from the decay of 166Ho. Applied Radiation and Isotopes.
Citations: 2

Capogni, M., … (2024). Future of 99Mo reactor-independent supply. Nature Reviews Physics.
Citations: 3

Capogni, M., … (2023). Analytical study of low energy proton interactions in the SORGENTINA’s fusion ion source-Part II: beam-gas: SORGENTINA ion beam interactions. European Physical Journal Plus.
Citations: 2

Capogni, M., … (2023). The international reference system for beta-particle emitting radionuclides: Validation through the pilot study CCRI(II)-P1.Co-60. Applied Radiation and Isotopes.
Citations: 5

Capogni, M., … (2023). The importance of uncertainty analysis and traceable measurements in routine quantitative 90Y-PET molecular radiotherapy: A multicenter experience. Pharmaceuticals.
Citations: 1

Capogni, M., … (2023). Experimental campaign on ordinary and baritic concrete samples for the SORGENTINA-RF plant: The SRF-bioshield tests. European Physical Journal Plus.
Citations: 3

Vandana Rajput | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Ms. Vandana Rajput | Machine Learning | Best Researcher Award

Research Scholar| Netaji Subhas University of Technology | India

Ms. Vandana Rajput, currently a Research Scholar at Netaji Subhas University of Technology, am pleased to nominate myself for the Best Researcher Award. I received my B.E. (2015) and M.Tech (2017) in Information Technology from MITS, Gwalior, and gained valuable industry experience as a Senior Research Analyst at TechieShubhdeep Itsolution Pvt. Ltd. in 2019. Additionally, I served as guest faculty at MNNIT Allahabad and SRCEM colleges, sharing knowledge and guiding students. I have worked as a Junior Research Fellow (JRF) on the prestigious IIT Mandi iHub research project, which helped strengthen my expertise in machine learning and research methodology. My work involves designing innovative concepts, developing methodologies, conducting experiments, and validating results to ensure accuracy and scientific rigor. I have authored one Scopus-indexed publication and continue to contribute to research through original manuscripts. My areas of research focus on machine learning and its applications in solving real-world challenges. I remain committed to advancing research excellence and innovation, collaborating with peers, and producing high-quality, impactful work. I hereby declare that the information provided is accurate to the best of my knowledge and agree to abide by all rules, terms, and conditions of the award nomination process.

Profile:  Scopus

Featured Publication

1. Rajput, V., Jain, A., & Jain, M. (2025). An Automatic Approach for Detecting Cognitive Distortion from Spontaneous Thinking. Procedia Computer Science, 260, 768-775 Citations: 2