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Akademický článek

Analysis of clinical outcome risk factors in glioma

Autor: Wang Yuru

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

In this paper, survival curves and Cox regression models were used to form an integrated disease risk factor model. Stepwise Cox regression models using the AIC criterion and the BIC criterion were included in the comparative models, penalties were i

Externí odkaz: https://doaj.org/article/d07778ac63b54eb2b5cd615248286a9d

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Akademický článek

Image and Video Style Transfer Based on Transformer

Autor: Sun Fengxue, Sun Yanguo, Lan Zhenping, Wang Yanqi, Zhang Nianchao, Wang Yuru, Li Ping

Publikováno v: IEEE Access, Vol 11, Pp 56400-56407 (2023)

The essence of image style transfer is to generate images that both maintain in the original content image and present the effect with artistic features under the guidance of style images. Deep learning’s quick rise has resulted in even more achiev

Externí odkaz: https://doaj.org/article/425688a033b1402f887fd15df5b63205

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Akademický článek

Research progress on the correlation between hepcidin and vitamin D levels in children with iron deficiency anemia

Autor: Wang Yuru, Zou Jingwen, Wang Yun, Yuan Bo, Bai Jinxiu

Publikováno v: Xin yixue, Vol 53, Iss 9, Pp 625-629 (2022)

Iron deficiency anemia （IDA） is a common type of anemia in childhood caused by iron deficiency due to varying causes, which can result in a decline in the synthesis of hemoglobin. IDA will adversely affect the growth and development of children.

Externí odkaz: https://doaj.org/article/355e4cf534914bb280a2a79eb962f6b6

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Akademický článek

Rejecting unfairness enhances the implicit sense of agency in the human brain.

Autor: Wang, Yuru¹ (AUTHOR) yuruwang2012@outlook.com, Zhou, Jiaxin¹ (AUTHOR)

Publikováno v: Scientific Reports. 10/1/2024, Vol. 14 Issue 1, p1-11. 11p.

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Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data

Autor: Cui, Zijun, Yin, Naiyu, Wang, Yuru, Ji, Qiang

Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications. Existing causal discovery methods assume data sufficiency, which may not be the case in many real world datasets.

Externí odkaz: http://arxiv.org/abs/2206.08448

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