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Yingying Fan

As seen in: Nature, Wiley Online Library, arXiv, Journal of Clinical Pathology, MDPI, Taylor & Francis Online, ACS Publications, Royal Society of Chemistry, Canadian Journal of Infectious Diseases & Medical Microbiology, Dove Medical Press and
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Articles

Relación entre inteligencia emocional y percepción de apoyo académico entre internos de enfermería: El papel moderador de los comportamientos de acoso en la educación de enfermería

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By Yi Huang, Yingying Fan, Chaofeng Li
| Dialnet - Universidad de La Rioja
Objetivo: Este estudio tiene como objetivo investigar el estado de la percepción de apoyo académico entre internos de enfermería y explorar la correlación entre esta percepción, la inteligencia emocional y los comportamientos de acoso en la educación de enfermería, especialmente el papel moderador de los comportamientos de acoso en la relación entre la inteligencia emocional y la percepción de apoyo académico.
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SOFARI: High-Dimensional Manifold-Based Inference

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By Zemin Zheng, Xin Zhou, Yingying Fan, Jinchi Lv
| Taylor & Francis Online
Abstract Multi-task learning is a widely used technique for harnessing information from various tasks. Recently, the sparse orthogonal factor regression (SOFAR) framework, based on the sparse singular value decomposition (SVD) within the coefficient matrix, was introduced for interpretable multi-task learning, enabling the discovery of meaningful latent feature-response association networks across different layers.
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Dynamic prediction of carbon prices based on the multi-frequency combined model

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By Yonghui Duan, Zhengzhou City, Henan Province, Yingying Fan
| PeerJ
Maintaining the robust growth of China’s carbon trading market requires the development of a high-precision prediction model that can reliably forecast the price of carbon trading in China. In this study, the CEEMDAN decomposition method is introduced into the problem of correction of residual series, and further individual prediction of the eigenmode function generated by CEEMDAN is performed, and finally, the COA-XGBoost-CEEMDAN integrated learning combination prediction model is established.
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