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3,184 results on '"Zhang, Junying"'

111. A new Gini correlation between quantitative and qualitative variables

Author

Dang, Xin, Nguyen, Dao, Chen, Yixin, and Zhang, Junying

Subjects
Statistics - Methodology
Abstract

We propose a new Gini correlation to measure dependence between a categorical and numerical variables. Analogous to Pearson $R^2$ in ANOVA model, the Gini correlation is interpreted as the ratio of the between-group variation and the total variation, but it characterizes independence (zero Gini correlation mutually implies independence). Closely related to the distance correlation, the Gini correlation is of simple formulation by considering the nature of categorical variable. As a result, the proposed Gini correlation has a lower computational cost than the distance correlation and is more straightforward to perform inference. Simulation and real applications are conducted to demonstrate the advantages., Comment: 24 + 3 Pages, 4 figures

Published
2018

118. High performance selective light absorber based on nickel nanopillar array for photothermal conversion.

Author

Cai, Jianing, Li, Linhao, Chen, Zhao, Zhang, Junying, and Hou, Zhi-Ling

Subjects
PHOTOTHERMAL conversion, PHOTOTHERMAL effect, SOLAR energy conversion, RADIATION absorption, SURFACE plasmon resonance, LIGHT absorption, EMISSIVITY
Abstract

Efficient absorption of solar radiation holds the key to photothermal utilization; however, realizing solar absorber designs with high absorption efficiency remains challenging. Herein, a nickel-based metamaterial selective solar absorber with a nanopillar array structure was proposed to realize nearly perfect optical absorption over a broad spectrum. The average absorbance is up to 96% in the 300–2033 nm wavelength range. Notably, a reasonably detailed analysis of the physical mechanism of the proposed absorber was performed in this paper, attributing the exceptional broadband absorption to the concurrent interaction with surface plasmon resonance, quarter-wavelength resonance, and electric dipole resonance. The absorption efficiency declines significantly when λ > 2.5 μm, with only 20% absorptivity at λ = 6 μm in the radiation-absorbing transition region. This decline is desirable, as it contributes to reducing the emissivity in the mid-infrared range and, therefore, prevents self-radiation. The results demonstrate that the selective absorber possesses the potential to capture solar energy within a broadband, while avoiding undesirable self-radiation, thereby enhancing the integral efficiency of the solar energy conversion system. Moreover, the absorption spectrum shows insensitivity to polarization and angle of incidence. The selective solar absorber proposed here offers excellent performance with a simple structure, showing great promise in the field of photothermal conversion. [ABSTRACT FROM AUTHOR]

Published
2023
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134. A Prospective Study on Risk Prediction of Preeclampsia Using Bi-Platform Calibration and Machine Learning.

Author

Zhao, Zhiguo, Dai, Jiaxin, Chen, Hongyan, Lu, Lu, Li, Gang, Yan, Hua, and Zhang, Junying

Subjects
PLACENTAL growth factor, RANDOM forest algorithms, MACHINE learning, LONGITUDINAL method, PREECLAMPSIA, PREGNANCY, MISSING data (Statistics)
Abstract

Preeclampsia is a pregnancy syndrome characterized by complex symptoms which cause maternal and fetal problems and deaths. The aim of this study is to achieve preeclampsia risk prediction and early risk prediction in Xinjiang, China, based on the placental growth factor measured using the SiMoA or Elecsys platform. A novel reliable calibration modeling method and missing data imputing method are proposed, in which different strategies are used to adapt to small samples, training data, test data, independent features, and dependent feature pairs. Multiple machine learning algorithms were applied to train models using various datasets, such as single-platform versus bi-platform data, early pregnancy versus early plus non-early pregnancy data, and real versus real plus augmented data. It was found that a combination of two types of mono-platform data could improve risk prediction performance, and non-early pregnancy data could enhance early risk prediction performance when limited early pregnancy data were available. Additionally, the inclusion of augmented data resulted in achieving a high but unstable performance. The models in this study significantly reduced the incidence of preeclampsia in the region from 7.2% to 2.0%, and the mortality rate was reduced to 0%. [ABSTRACT FROM AUTHOR]

Published
2024
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144. Genomic Disorders in CKD across the Lifespan

Author

Verbitsky, Miguel, Krishnamurthy, Sarathbabu, Krithivasan, Priya, Hughes, Daniel, Khan, Atlas, Marasà, Maddalena, Vena, Natalie, Khosla, Pavan, Zhang, Junying, Lim, Tze Y., Glessner, Joseph T., Weng, Chunhua, Shang, Ning, Shen, Yufeng, Hripcsak, George, Hakonarson, Hakon, Ionita-Laza, Iuliana, Levy, Brynn, Kenny, Eimear E., Loos, Ruth J.F., Kiryluk, Krzysztof, Sanna-Cherchi, Simone, Crosslin, David R., Furth, Susan, Warady, Bradley A., Igo, Robert P., Jr., Iyengar, Sudha K., Wong, Craig S., Parsa, Afshin, Feldman, Harold I., and Gharavi, Ali G.

Published
2023
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