Hong Kong Statistical Society

Abstract The study on active binaries can provide detailed insights to understand how binarity affects dynamo actions inside stellar interiors. Here we report two active eclipsing binaries, USNO-B1.0 0743-0306638 and 0763-0282368, discovered in the Yunnan–Hong Kong wide-field photometric (YNHK) survey. Both of the two systems are detached binaries and show spot activities, while USNO-B1.0 0763-0282368 is more close to its Roche lobe. By means of the Wilson–Devinney (WD) code, their photometric solutions are determined based on parts of the YNHK photometric light curves. Furthermore, the spot distribution and evolution are investigated using time series of the YNHK light curves from 2016 to 2023. The results show that the spot activities appeared on the primary components for the two binary systems. There exist two kinds of spots: one is polar spots that lead to the variation of overall luminosity of the binaries, while the other is low-latitude spots that cause the rotational modulation. The polar spots behave more long-lived than the low-latitude spots on both binaries. For the low-latitude spot, its longitudinal distribution becomes more stable and closer to the substellar point as the binary system is close to the Roche lobe.

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.