Yuanzhi Zhang | Machine learning | Research Excellence Award

Prof. Dr. Yuanzhi Zhang | Machine learning | Research Excellence Award

Nanjing University of Info. Sci. Techn. & Nantong Institute of Technology | China

Dr. Zhang Yuanzhi is a distinguished researcher at the University of Chinese Academy of Sciences, Beijing, China, specializing in advanced materials science and nanotechnology. He holds advanced degrees in materials science and engineering with a strong academic foundation in nanomaterials and electronic materials. Throughout his professional career, he has contributed extensively to academic research, collaborative scientific projects, and scholarly leadership within the materials science community. His research primarily focuses on nanomaterials, energy materials, and functional electronic materials, with significant contributions reflected in a large body of peer-reviewed publications and high citation impact. Dr. Zhang has authored numerous scientific articles and maintains a strong global research presence through collaborations with international scholars. His scholarly excellence is reflected in his high h-index, extensive citation record, and active participation in the scientific community through editorial contributions, peer-review activities, and professional memberships, demonstrating sustained commitment to advancing materials science research.

Citation Metrics (Scopus)

7193
6000
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Citations

7193

Documents

264

h-index

44

Citations

Documents

h-index

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Featured Publications

Two-Decadal Estimation of Sixteen Phytoplankton Pigments from Satellite Observations in Coastal Waters
International Journal of Applied Earth Observation and Geoinformation – Journal Article

Different Mechanisms for the Seasonal Variations of the Mesoscale Eddy Energy in the South China Sea
Deep Sea Research Part I: Oceanographic Research Papers – Journal Article

Effect of Melt Ponds Fraction on Sea Ice Anomalies in the Arctic Ocean
International Journal of Applied Earth Observation and Geoinformation – Journal Article

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Associate Professor | Shanghai Electric Power University | China

Dr. Li Xiaohua, a distinguished Professor at Sichuan University and leading expert in materials science and structural engineering, is renowned for advancing high-performance composite materials and sustainable structural systems. She holds advanced degrees in materials engineering with specialization in composite behavior and structural performance, complemented by extensive experience in academic leadership, project supervision, and collaborative research initiatives. Her professional portfolio includes directing major institutional projects, mentoring interdisciplinary teams, and contributing to engineering innovations that strengthen the reliability and resilience of modern structures. Dr. Li’s research focuses on composite structures, fire-resistant materials, mechanical behavior, and performance optimization, supported by 297 citations, 34 scholarly documents, and an h-index of 11, reflecting her growing global impact. She has authored influential publications, contributed to high-level research panels, and advanced knowledge dissemination through editorial responsibilities and membership in professional engineering societies. Recognized for excellence in research, innovation, and service, she also holds relevant professional certifications that underscore her commitment to scientific rigor and continued advancement in the engineering sciences.

Profile: Scopus

Featured Publications

Li Xiaohua*, Probabilistic forecasting of coal consumption for power plants under deep peak shaving conditions using Informer with DDPM-based uncertainty modeling. Int. J. Electr. Power Energy Syst., 2025.

Li Xiaohua*, Electromagnetic vibration characteristics of permanent magnet synchronous motors with segmented grain-oriented electrical steel teeth–yoke.

Li Xiaohua, Research on core loss prediction of low-frequency transformer based on Grey Wolf optimisation algorithm optimised Back Propagation neural network. IET Electr. Power Appl., 2025.