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Background The outbreak of Coronavirus disease (COVID-19) in 2019 and the resulting quarantine may have increased the prevalence of mental health problems in adolescents. The aim of this study was to explore the association between the effects of home-based learning during the pandemic and the risks of depression, anxiety, and suicidality among junior and senior high school students. Methods An online survey using Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) was conducted between 12 to 30 April 2020, on a total of 39,751 students. Multivariable logistic regression analysis was used to analyze the risk factors of associated depression, anxiety and suicidality during the pandemic. Results Prevalence of depression, anxiety symptoms and suicidality found was 16.3% (95% CI: 16.0, 16.7), 10.3% (95% CI: 10.0, 10.6) and 20.3% (95% CI: 19.9, 20.7), respectively. Participants with female gender and in junior high school, with poor overall sleep quality and poor academic performance and very worried about being infected during COVID-19 were highly associated with the risk of depression, anxiety symptoms and suicidal ideation (all P

Background: This study examines the trajectories of the mental health conditions of 13,494 new undergraduate students who enrolled in 2019 in China from the beginning of the pandemic to the local recurrence of the pandemic, and found factors which may be associated with diverse trajectories. Methods: The trajectories of depression–anxiety outcomes were modeled using the growth mixture model. The multinomial logistic regression model was used to identify variables associated with different trajectory groups. Results: Both depression and anxiety in the new college students slightly increased during the 16-month period. The slopes of depression and anxiety were lower after the local outbreak. From the trajectories of depression and anxiety, five heterogeneous groups were identified: low–stable (64.3%), moderate–increased (18.2%), high–stable (11.1%), recovery (4.5%), and rapid–increased (1.8%). Environmental, somatic, and social factors were used to differentiate the low–stable group from the other groups. We found that college students with female gender, more conflict with parents, and feelings of loneliness during the pandemic were more likely to enter a high stability trajectory compared to a recovery trajectory. Conclusion: Most participants showed a stable mental health status, while others experienced deteriorating or chronic mental health problems, especially those who had sleep disturbances, less social support before the pandemic, or conflicts with parents during the pandemic. These students may need additional support and monitoring from college mental health providers to improve their wellbeing.

This study aimed to characterize job burnout in longitudinal trajectories among bus drivers and examine the impact of variables related to job burnout for trajectories. A longitudinal study was conducted in 12,793 bus drivers in Guangdong province, China, at 3-year follow-up assessments. Growth mixture modeling (GMM) was used to estimate latent classes of burnout trajectories and multinomial logistic regression models were applied to predict membership in the trajectory classes. In general, there was a decrease in job burnout in 3 years [slope = −0.29, 95%CI = (−0.32, −0.27)]. Among those sub-dimensions, reduced personal accomplishment accounted for the largest proportion. GMM analysis identified five trajectory groups: (1) moderate-decreased (n = 2870, 23%), (2) low-stable (n = 5062, 39%), (3) rapid-decreased (n = 141, 1%), (4) moderate-increased (n = 1504, 12%), and (5) high-stable (n = 3216, 25%). Multinomial logistic regression estimates showed that depression symptoms, anxiety symptoms, and insomnia were significant negative predictors, while daily physical exercise was a significantly positive predictor. We found an overall downward trend in bus drivers’ burnout, particularly in the sub-dimension of personal accomplishment. Mentally healthier drivers and those who were usually exercising were more resilient to occupational stress and less likely to suffer burnout.

Background During the COVID-19 pandemic, college students were required to stay at home and maintain social distancing the entire spring semester of 2020. There is little research on how family function influenced mental health problems and how coping styles moderated the relationship between family function and mental health problems among college students during their stay-at-home period. Methods We carried out four evaluations between February and October 2020 through an online survey. A total of 13,462 college students (age = 16–29 years) participated. Family function, coping styles, depression symptoms, and anxiety symptoms were studied. To evaluate the associations between variables, a generalized estimating equation was used. Results Findings indicate that the incidence rates of depression rose during stay-at-home period from 33.87%, 95% CI (29.88%, 38.10%) to 40.08% 95% CI (35.76%, 44.55%) after schools reopened, χ2 = 193.68, p 2 = 195.74, p

Developing renewable energy generation such as wind power and photovoltaics is an effective way to cope with energy shortage and environmental problems. The virtual power plant (VPP) realizes the grid-connected operation of intermittent distributed renewable energy by aggregating multiple types of distributed energy resources. In order to achieve the energy optimization problem of the VPP, an optimization method of VPP considering coordination and interaction of source-load-storage is proposed in this paper. First, models of components in the VPP are established. Next, an energy optimization model of the VPP considering coordination and interaction of source-load-storage is constructed. Finally, a case is studied to verify the correctness and effectiveness of the method. The results show that the coordinated consideration of distributed generation, energy storage and demand response loads contributes to reducing the operating costs and carbon emissions of the VPP.

Power demand is constant increasing, so power networks are push to the operation limits. Traditional presentation of SCADA/EMS HMI(Human-Machine-Interface) is not more suitable to this situation. Various technology such as AI base alarming, dynamic coloring, contour, map based diagrams, new presentation methods are purposed. New method for indication and measurement alarm processing, measurement presentation, topology presentation, risk assessment presentation, KPI presentation, map based presentation are implemented. Through these presentation techniques, the operators can comprehend the system information easily and more accurate, then more proper action can be promptly taken to ensure the power networks operate more safely and more economically.