BackgroundTo curb the spread of COVID-19, most countries have adopted measures such as banning shore leave at ports and placed restrictions on crew change. Seafarers may bear an excess pressure during the COVID-19 pandemic. This study aimed to investigate the prevalence and risk factors associated with depression symptoms among Chinese seafarers during the COVID-19 pandemic.DesignCross-sectional study.MethodsThis field survey-based study was conducted at Rongcheng Port, Shandong Province, China, from 10 June 2020 to 25 July 2020. Sociodemographic and occupational characteristics and health-related behaviours were collected through a face-to-face questionnaire. The Self-Rating Depression Scale was used to evaluate depression status during the preceding week. Logistic regression models were used to explore factors related to depression.Results441 male Chinese seafarers were enrolled. Overall, the proportions of seafarers with low, moderate and severe depression symptoms were 23.35%, 9.30% and 9.07%, respectively. Compared with those with good self-rated health (SRH), seafarers with poor SRH had higher odds of depression (OR, 2.24, 95% CI 1.22 to 4.11). Less leisure time or physical exercise was associated with more severe self-reported depression symptoms (1–3 per week vs ≥4 per week: OR, 1.72, 95% CI 0.71 to 4.14; none vs ≥4 per week: OR, 3.93, 95% CI 1.67 to 9.26). Poor sleep quality was associated with higher likelihood of reporting severe depression (fair vs good: OR, 2.78, 95% CI 1.54 to 5.01; poor vs good: OR, 4.30, 95% CI 1.65 to 11.24). The more frequent seafarers worked overtime a week, the higher the likelihood of reporting severe depression symptoms (1–2 per week vs none: OR, 1.82, 95% CI 1.04 to 3.18; ≥3 per week vs none: OR, 2.49, 95% CI 1.05 to 5.92). Also, high perceived work stress was linked to higher odds of being depressed (intermediate vs low: OR, 2.06, 95% CI 0.78 to 5.46; high vs low: OR, 3.83, 95% CI 1.35 to 10.90).ConclusionsThere is a high burden of depression associated with COVID-19 among seafarers. Special interventions that protect the mental health of seafarers are more critical than ever in the context of the pandemic.

Inspired by the importance of distance metrics and the structure-preserving ability of features, a novel recognition method for underwater targets, called generalized robust principal component analysis (GRPCA), is proposed in this paper. Several advantages of GRPCA are summarized as follows. First, GRPCA employs the l 2,p -norm as the distance metric for calculating the reconstruction error and variance of projected data and attempts to minimize the sum of the ratios between the reconstruction error and the variance for each data sample. This approach allows it to extract the feature information of an image more accurately, which is important for recognition and representation. Second, the proposed GRPCA algorithm not only is robust but also retains the desirable properties of PCA, such as rotational invariance. Moreover, we present a simple yet efficient iterative update algorithm to solve the GRPCA problem. Finally, on the basis of GRPCA, underwater target recognition technology is developed. The extensive experiments on several underwater optical image databases show that our method is more effective and advantageous than other subspace learning algorithms are.INDEX TERMS Distance metric, generalized robust principal component analysis (GRPCA), underwater target recognition.

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