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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

Energy Internet is a hot spot of research and practice in the field of energy at home and abroad. DC power distribution technology has the characteristics of bidirectional controllable power flow, convenient access to distributed resources, improved power quality, transmission capacity and reliability of power supply, and reduced system loss, which has a wide application prospect in energy Internet. The safety and reliability of ubiquitous distributed generation (DG) and multi load access to DC distribution network is an important problem to be studied and solved in the planning and operation of energy Internet. In this paper, the different ways and operation characteristics of AC-DG and DC-DG connected to DC distribution network are analyzed, in which AC-DG is connected through voltage source inverter, and DC-DG is connected through full bridge DC / DC converter. Based on PSCAD / EMTDC, and taken Shenzhen flexible DC distribution network demonstration project as an example, the dynamic operation characteristics of DGs under different modes of connecting to the DC distribution network are analyzed and verified.

Energy Internet is a hot spot of research and practice in the field of energy at home and abroad. DC power distribution technology is an important element of energy Internet. How to plan the type and capacity of energy storage system (ESS) is a problem worthy of attention when designing the optimal energy conversion path in the energy Internet, especially in the DC distribution network. The proper type and capacity of ESS can effectively suppress DC voltage flicker and improve the system operation stability and fault crossing ability. In this paper, the hybrid energy storage scheme of energy storage battery and super capacitor is adopted in DC distribution network, and the discrete Fourier spectrum analysis of power demand sample data is carried out to obtain the basic power in low frequency and fluctuating power in high frequency. The energy storage battery with slow dynamic response speed is used to match the power demand of low-frequency segment, and the super capacitor with slow dynamic response speed is used to match the power demand of high-frequency segment. The influence index of SOC over limit times of energy storage system is introduced to seek the critical value of ESS capacity planning. Finally, the proposed hybrid energy storage planning method is verified by the case studying.

Most research on drone detection and classification focus on using features from micro-Doppler signatures with blade flashes. However, these methods are limited in range and require radars with high pulse repetition frequency (PRF)–at least twice the maximum tip velocity. A different method to detect and classify drones at longer ranges using a low PRF radar is desired. In the literature, the cepstrum method was shown to be able to estimate the rotation rate when the PRF is insufficient. An alternative way of analyzing micro-Doppler is by using a long windowed Short-time Fourier transform (STFT) to generate HElicopter Rotation Modulation (HERM) lines. HERM lines exhibit similar behavior to a cepstrogram, with spectral lines separated in frequency by a value related to the rotation rate. In this paper, the separation frequency of HERM lines was estimated using a log harmonic summation algorithm. The proposed algorithm was tested on a simple HERM line model and also on real data obtained from two blade single rotor micro-helicopter drone. The algorithm was shown to be more resilient than cepstrum under Gaussian noise.

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.