There is an increasing amount of evidence exploring the adverse effects of perceived stress or anxiety and depression independently on sleep quality during the COVID-19 outbreak, although the underlying mechanisms are unclear. The aim of the current study was to explore the role of anxiety and depression as a potential mediator between perceived stress and sleep quality among health care workers. Methods: Data were collected through an online survey using the snowball sampling method and comprised 588 current health care workers in Zhejiang and Hubei provinces, China, from February to March 2020. We administered the Sleep Quality Questionnaire (SQQ), the Perceived Stress Scale (PSS-10), the Patient Health Questionnaire (PHQ-4) and the sociodemographic characteristics and COVID-19-related characteristics questionnaire. Structural equation modelling (SEM) was used to examine the direct and indirect relationships between perceived stress, anxiety and depression, and sleep quality. Results:The average scores for sleep quality and perceived stress were 16.01 (95% CI [15.40, 16.57]) and 15.46 (95% CI [15.05, 15.87]), respectively. The positive rates of anxiety and depression symptom tests were 9.86% and 10.37%, respectively. The SEM results indicated that the original relationship between perceived stress and sleep quality was beta = 0.52 (P < 0.001) and reduced to beta = 0.25 (P = 0.045) while introducing anxiety and depression as mediating variables. Perceived stress was positively associated with anxiety and depression (beta = 0.78, P = 0.014), and anxiety and depression were positively associated with sleep quality (beta = 0.42, P < 0.001). Conclusion:Poor sleep quality and high perceived stress were common during the COVID-19 crisis. Reducing perceived stress could help reduce anxiety and depression symptoms, thereby improving sleep quality among health care workers. In an attempt to promote psychological resources, we should perhaps take multiple measures, including personal tailored intervention and organizational humanistic concern.

There are hundreds of millions of internal migrants in China, and tuberculosis (TB) is an important health threat to them. However, the mental health problems of internal migrants with TB in China have been ignored. The present study aimed to determine the prevalence of depressive symptoms and its associated risk factors among internal migrants with TB in China. A cross-sectional survey was conducted between June 2018 and March 2019 in Shenzhen, southern China. Data were collected from 1,057 internal migrants with TB using a structured questionnaire. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale. Multinomial logistic regression analysis was used to identify risk factors for depressive symptoms. Of the 1,057 participants included in this study, 53.8% had depressive symptoms. Of these, 38.9% had mild, whereas 14.9% had moderate-to-severe depressive symptoms. Multinomial logistic regression analysis suggested that higher likelihoods of depressive symptoms were associated with female gender, lower education, family dysfunction, poor doctor-patient communication, and TB-related stigma. This study shows that the prevalence of depressive symptoms among internal migrants with TB is high in China. Targeting interventions and treatment of depressive symptoms among internal migrants with TB are needed.

With the increase in the installed capacity of wind power systems, the fault diagnosis and condition monitoring of wind turbines (WT) has attracted increasing attention. In recent years, machine learning (ML) has played a crucial role as an emerging technology for fault diagnosis in wind power systems has played a crucial role. Even though ML methods have shown great potential in dealing with the issues related to the fault diagnosis of WT, there are still some challenges encountered in many aspects. In this paper, typical fault diagnosis methods based on ML methods for wind power systems are thoroughly reviewed in terms of both theoretical fundamentals and industrial applications, including traditional machine learning (TML), artificial neural networks (ANN), deep learning (DL) and transfer learning (TL), in the development line of ML technologies. The advantages and disadvantages of various methods are analyzed and discussed. Meanwhile, a distribution diagram is provided for the discussions of ML methods applied for WT fault diagnosis, and the existing challenges on the applications for fault diagnosis based on ML for wind power generation systems are presented. Moreover, some prospects for future research directions are provided. INDEX TERMSwind turbines, machine learning, fault diagnosis, review Nomenclature AE autoencoder AI artificial intelligence ANN artificial neural networks ART adaptive resonance theory BPNN back propagation neural network CA clustering algorithm CNN convolutional neural network DAE denoising autoencoder DBN deep belief network FCM fuzzy C-means clustering HMM hidden Markov model LSSVM least squares support vector machine LSTM long short-term memory network ML machine learning RBFNN radial basis function neural network RF random forest RLM extreme learning machine RNN recurrent neural network RVM relevance vector machine SAE stacked autoencoder SCADA supervisory control and data acquisition SDAE stacked denoising autoencoders 23 SOM self-organizing map 24 SVM support vector machine 25 t-SNE t-distributed logistic neighbor embedding 26 TL transfer learning 27 TML traditional machine learning 28 VMD variational mode decomposition 29 WT wind turbines 30 I. INTRODUCTION 31 With the increasing consumption of fossil fuels and the 32 gradual deterioration of environmental problems, there is an 33 urgent need to find a clean and renewable energy source. 34 Wind energy is irreplaceable in energy structures owing 35 to its rapid growth. Wind power accounts for 20% of the 36 world's total electricity, and WT are receiving increasing 37 attention as the core components of wind power generators [1], 38 [2]. Usually, wind power generators are installed in remote 39 areas or offshore areas where traffic is inconvenient, and 40 the gearbox is generally installed in a sky above tens or 41even hundreds of meters from the ground. In addition, the 42 blades are often subjected to complex alternating impact loads

Objectives: In recent years, the harm of smoking has attracted more and more public attention. Creating a healthy smoke-free environment has been widely favored and supported by the public. Smokeless environment has a certain positive effect on the rehabilitation of dementia patients. Sustained smoking cessation is associated with significantly decreased the future prevalence of dementia. Therefore, hospitals often establish a special organizational structure and management model for tobacco control, and carry out training and assessment for all staff. The nursing group also took it as the basic nursing standard. The 2019 disease pandemic has posed unique health threats to people living with dementia (PLWD). Therefore, a strict smoke-free environment is more necessary for the nursing group. One of the key challenges is scaling up long-term care services to meet the needs of the rapidly growing population of PLWD in developing countries. The aim of the study is to explore the care service needs, utilization and build an essential care service package (ECSP) for PLWD under the disease pandemic in China. Methods: From July 2018 to October 2019, a total of 1255 elderly with dementia in six cities in China by a cluster sampling were investigated with the self-designed questionnaire. Care service needs and utilization for PLWD with different levels of cognitive impairment were summarized. Results: The ECSP for PLWD was composed of 30 service items (7 for core care) in order to guarantee that all PLWD enjoy equal care services, basing on public financing and implementing strategies and taking China's situations into account. The selection plan for ECSP at different levels is designed as 'General Care Services + Selective Care Services ', in which respective service items for low, mid, and high-level care for PLWD are 7+3, 7+6, and 7+10, and requires 151.41 hours, 201.88 hours, and 252.35 hours per month, respectively. Conclusion: The present study provides the first large-scale data on care service needs and utilization for PLWD in mainland China. The ECSP for PLWD based on the needs advanced in the paper was a practicable and effective quantitative management means that deserves a large-scale application. Some safeguard mechanisms and operational implementing pathways of ECSP for PLWD during and after the disease pandemic in China are proposed.

Recently, with the rise of deep reinforcement learning model, robot navigation based on this method has a huge advantage compared with traditional slam method, which has attracted extensive attention. However, when the navigation algorithm trained in the virtual environment is transferred to the real environment, the navigation performance of the robot will decline sharply because of the great difference between the virtual environment and the real environment. In order to improve the navigation ability of mobile robot, this paper implements a mobile robot navigation system based on deep reinforcement learning without environment map and only visual input. At the same time, in order to solve the problem of poor generalization ability of deep reinforcement learning from virtual environment to real environment, this paper proposes a preprocessing layer with knowledge and combines it with deep reinforcement learning module. The combined algorithm model alleviates the performance fault problem caused by the migration algorithm and the performance difference between virtual sensor and real sensor. At the end of this paper, a navigation experiment based on the turtlebot is designed, which proves that the deep reinforcement learning algorithm with the preprocessing layer can alleviate the performance fault problem caused by the migration algorithm, and have a certain ability of obstacle avoidance and avoidance without the environment map.