No abstract
ObjectiveTo systematically review the prevalence of anxiety and depression among frontline healthcare workers during the coronavirus disease 2019 (COVID-19) pandemic.MethodsComputers were used to search CNKI, VIP, WanFang Data, PubMed, and other Chinese and English databases. The search period was limited to December 2019 to April 2022. Cross-sectional studies collected data on the prevalence of anxiety and depression among frontline healthcare workers since the onset of COVID-19. The STATA 15.1 software was used for the meta-analysis of the included literature.ResultsA total of 30 studies were included, with a sample size of 18,382 people. The meta-analysis results showed that during the COVID-19 pandemic, the total prevalence of anxiety among frontline healthcare workers was 43.00%, with a 95% confidence interval (CI) of 0.36–0.50, and the total prevalence of depression was 45.00%, with a 95% CI of 0.37–0.52. The results of the subgroup analysis showed that prevalence of anxiety and depression in women, married individuals, those with children, and nurses was relatively high. Frontline healthcare workers with a bachelor's degree or lower had a higher prevalence of anxiety. The prevalence of depression was higher among frontline healthcare workers with intermediate or higher professional titles.ConclusionDuring the COVID-19 pandemic, the prevalence of anxiety and depression among frontline healthcare workers was high. In the context of public health emergencies, the mental health status of frontline healthcare workers should be given full attention, screening should be actively carried out, and targeted measures should be taken to reduce the risk of COVID-19 infection among frontline healthcare workers.Systematic review registrationhttp://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022344706.
Abstract:Although traditional fault diagnosis methods can qualitatively identify the failure modes for power equipment, it is difficult to evaluate the failure probability quantitatively. In this paper, a failure probability calculation method for power equipment based on multi-characteristic parameters is proposed. After collecting the historical data of different fault characteristic parameters, the distribution functions and the cumulative distribution functions of each parameter, which are applied to dispersing the parameters and calculating the differential warning values, are calculated by using the two-parameter Weibull model. To calculate the membership functions of parameters for each failure mode, the Apriori algorithm is chosen to mine the association rules between parameters and failure modes. After that, the failure probability of each failure mode is obtained by integrating the membership functions of different parameters by a weighted method, and the important weight of each parameter is calculated by the differential warning values. According to the failure probability calculation result, the series model is established to estimate the failure probability of the equipment. Finally, an application example for two 220 kV transformers is presented to show the detailed process of the method. Compared with traditional fault diagnosis methods, the calculation results not only identify the failure modes correctly, but also reflect the failure probability changing trend of the equipment accurately.
Abstract.As an important kind of data for device status evaluation, the increasing infrared image data in electrical system puts forward a new challenge to traditional manually processing mode. To overcome this problem, this paper proposes a feasible way to automatically process massive infrared fault images. We take advantage of the imaging characteristics of infrared fault images and detect fault regions together with its belonging device part by our proposed algorithm, which first segment images into superpixels, and then adopt the state-of-the-art convolutional and recursive neural network for intelligent object recognition. In the experiment, we compare several unsupervised pre-training methods considering the importance of a pretrain procedure, and discuss the proper parameters for the proposed network. The experimental results show the good performance of our algorithm, and its efficiency for infrared analysis.
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