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.

Amir Reza Rahimi | Artificial Intelligence | Best Research Article Award

Published on by Computer Science Engineering

Mr. Amir Reza Rahimi | Artificial Intelligence | Best Research Article Award

University of Valencia | Spain 

Amir Reza Rahimi is distinguished for his unwavering dedication to research excellence, demonstrated through his rigorous scientific investigations, analytical depth, and meaningful contributions to advancing knowledge in his field. His research is grounded in systematic inquiry, where he applies advanced methodologies, precise data interpretation, and comprehensive theoretical perspectives to address complex scientific problems with clarity and a strong sense of academic responsibility. Rahimi consistently integrates interdisciplinary approaches, enabling him to explore scientific questions from multiple angles and generate insights that hold both scholarly value and real world relevance. His body of work, which includes peer reviewed publications, collaborative projects, and active participation in academic discussions, reflects originality, innovation, and a clear commitment to producing high quality evidence based outcomes. These contributions not only enrich scientific literature but also support practical applications in policy development, environmental management, and broader scientific decision making. Rahimi’s engagement with emerging research trends, utilization of modern analytical tools, and strict adherence to ethical principles further highlight his professionalism and commitment to responsible scholarship. His ability to collaborate with international researchers, secure research opportunities, and share knowledge across diverse academic platforms showcases his growing influence and leadership within the scientific community. Additionally, his involvement in mentoring students and early career researchers demonstrates his dedication to nurturing scientific talent and promoting a culture of curiosity, critical thinking, and continuous learning. Through his sustained efforts, Rahimi exemplifies the highest standards of research excellence, characterized by intellectual rigor, scientific creativity, and societal relevance. His work continues to contribute significantly to the advancement of his discipline and supports the development of future research directions that address both present and emerging scientific challenges.

Profiles : Google Scholar | ORCID

Featured Publications

Rahimi, A. R., & Sevilla-Pavon, A. (2025). The role of design thinking skills in artificial-intelligence language learning (DEAILL) in shaping language learners’ L2 grit: The mediator and moderator role of artificial intelligence L2 motivational self-system. Computer Assisted Language Learning.

Rahimi, A. R., & Daneshvar Ghorbani, B. (2025). Developing and validating the scale of language teachers’ computational thinking competency in Computer Assisted Language Learning (LTCCTCALL): Empowering language teaching by cultivating the heart of the 21st-century digital skill. Asian-Pacific Journal of Second and Foreign Language Education.

Rahimi, A. R., & Sevilla-Pavón, A. (2025). Modeling the relationship between online L2 motivational self-system and EFL learners’ virtual exchange self-regulations: The mediator and moderator roles of L2 grit. ReCALL.

Rahimi, A. R., & Sevilla-Pavón, A. (2025). The role of interactive, constructive, active, and passive learning activities (ICAPCALL) in shaping students’ online engagement and learning approaches to virtual exchange (SAVE): A bisymmetric approach. Smart Learning Environments.

Rahimi, A. R., & Teimouri, R. (2025). Advancing language education with ChatGPT: A path to cultivate 21st-century digital skills. Research Methods in Applied Linguistics.

Rahimi, A. R. (2025). Developing and validating the scale of language teachers’ design thinking competency in artificial intelligence language teaching (LTDTAILT). Computers and Education: Artificial Intelligence.

Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Published on by Computer Science Engineering

Dr. Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Libero professionista | Consiglio Nazionale delle RicercheThis link is disabled | Italy

Dr. Christian Peluso is a researcher specializing in artificial intelligence with expertise in federated learning, deep learning, and cybersecurity, focusing on privacy-preserving systems for mobile and distributed environments. His research aims to develop intelligent models capable of processing complex and varied data while safeguarding user privacy and ensuring compliance with data protection regulations. Christian earned his Master’s degree in Artificial Intelligence from the University of Pisa with the highest distinction, presenting a thesis titled PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection, which introduced an innovative federated learning approach for mobile malware analysis using convolutional neural networks optimized for image-based data. He has actively collaborated with the Consiglio Nazionale delle Ricerche (CNR) and several academic and research institutions, contributing to projects that merge AI, cybersecurity, and data privacy. His publications, including “PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection” and “An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning,” reflect his deep involvement in advancing secure and decentralized AI solutions. He has also contributed to research on explainability-driven malware analysis using deep learning, aimed at improving model interpretability and aiding analysts in identifying malicious software components efficiently. Christian’s technical proficiency covers Python, machine learning frameworks, and reverse engineering methodologies, enabling him to design intelligent systems with strong analytical and practical impact. His academic achievements and professional experiences in software engineering, mobile application security, and AI-driven analysis demonstrate a consistent pursuit of excellence and innovation. His work not only strengthens theoretical understanding in federated machine learning but also delivers practical tools for protecting digital ecosystems. Through his commitment to research, collaboration, and ethical AI development, he continues to contribute meaningfully to the evolving landscape of artificial intelligence and data security. 17 Citations 3 Documents 2 h-index View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Iadarola, G., Casolare, R., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2021). A semi-automated explainability-driven approach for malware analysis through deep learning. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. Cited by: 19

Ciaramella, G., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2024). An approach for privacy-preserving mobile malware detection through federated machine learning. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024). SciTePress.
Cited by: 5

Peluso, C., Ciaramella, G., Mercaldo, F., Santone, A., & Martinelli, F. (2024). A federated learning-based Android malware detector through differential privacy. In International Conference on Computer Aided Systems Theory (EUROCAST 2024) (pp. 307–319).