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Background Effects of parental marital status on nonsuicidal self-injury (NSSI) behaviors and negative emotions in adolescents have been rarely reported, and the existing research sample size is relatively small. Objective To explore the association of parental marital status with negative emotions and NSSI in middle school students, to provide a reference for the prevention and control of NSSI in them. Methods A stratified random sampling method was used to conduct the questionnaire survey on students from 20 townships and 67 middle schools in Nanchong City and its surrounding areas from March to April 2020. The investigators input the questionnaire into the WJX.cn to generate QR codes, which were distributed to parents or student guardians by schools. The questionnaire, consisting of the General Information Questionnaire, Ottawa Self-injury Inventory (OSI) and the Chinese version of the Depression Anxiety Stress Scale-21 (DASS-21), was used to calculate the detection rates of NSSI, depression, anxiety and stress in middle school students to analyze the relevant factors affecting NSSI, depression, anxiety and stress in middle school students. Results A total of 8 785 students from 23 middle schools were surveyed, and 8 606 valid questionnaires were finally obtained, with a valid recovery rate of 98.0%. Among the 8 606 middle school students, there were 4 540 (52.8%) males, 4 066 (47.2%) females; 5 937 (69.0%) junior high school students, 1 028 (11.9%) senior high school students and 1 641 (19.1%) vocational high school students; 7 274 (84.5%) students in two-parent families, 787 (9.1%) students in divorced single-parent families, and 545 (6.4%) students in families with divorced and remarried parents. The detection rates of NSSI, depression, anxiety and stress in middle school students were 5.6% (486/8 606), 21.9% (1 884/8 606), 26.8% (2 305/8 606) and 12.3% (1 060/8 606), respectively. The results of multivariate Logistic regression analysis showed that among all middle school students, compared with boys, girls had a higher risk of NSSI (OR=2.363, 95%CI=1.946-2.868) and anxiety (OR=1.195, 95%CI=1.086-1.315) ; compared with junior high school students, vocational high school students had a higher risk of NSSI (OR=1.847, 95%CI=1.494-2.284), depression (OR=1.886, 95%CI=1.667-2.134), anxiety (OR=1.857, 95%CI=1.141-1.532) and stress (OR=1.527, 95%CI=1.309-1.781) ; compared with middle school students in two-parent family, students in divorced single-parent family had a higher risk of NSSI (OR=2.194, 95%CI=1.669-2.834), depression (OR=1.563, 95%CI=1.326-1.843), anxiety (OR=1.573, 95%CI=1.346-1.839) and stress (OR=1.417, 95%CI=1.153-1.741), students in families with divorced and remarried parents also had a higher risk of NSSI (OR=2.207, 95%CI=1.638-2.975), depression (OR=1.863, 95%CI=1.543-2.249), anxiety (OR=1.796, 95%CI=1.499-2.153) and stress (OR=1.821, 95%CI=1.453-2.282) ; compared to middle school students who are taken care of by their parents for a long time, those who are taken care of by their father alone, mother alone or grandparents alone for a long time had a higher risk of NSSI, depression and anxiety (by father alone: OR=2.045, 95%CI=1.366-3.062; OR=1.633, 95%CI=1.289-2.145; OR=1.373, 95%CI=1.069-1.762; by mother alone: OR=1.772, 95%CI=1.141-2.751; OR=1.435, 95%CI=1.095-1.882; OR=1.513, 95%CI=1.174-1.951; by grandparents alone: OR=2.465, 95%CI=1.918-3.168; OR=1.783, 95%CI=1.513-2.100; OR=1.843, 95%CI=1.577-2.154), students who were taken care of by mother alone or by grandparents alone had a higher risk of stress (OR=1.604, 95%CI=1.166-2.207; OR=1.678, 95%CI=1.375-2.049) (P

Moral ethic prevails in the science and education of chemistry. These issues are unique to chemistry and arise from between the theoretical and the practical behavior in laboratory. The study reported in the paper investigated the impact of personal moral philosophies on the safety practices of students in chemistry and related majors. The results showed that lab anxiety, safety attitude, safety knowledge, and idealism were significant predictors of safety practice, with the last item being the strongest predictor. According to Forsyth's four distinct personal moral philosophies, among Chinese students, situationism was the most common, followed by exceptionism and then absolutism, with subjectivism as the least common. All lowest scores are from exceptionism. Idealism was more common among males than females, more among higher grade students than lower, and more among students from eastern as compare to western regions of China. All the results showed that the content of idealism should be incorporated into safety training to improve safety practices. Attention to idealistic education in all aspects of university education can help to improve safety behavior.

BackgroundChronic obstructive pulmonary disease (COPD) is a common chronic disease of the respiratory tract, and lung function is necessary for the diagnosis of COPD. However, conventional pulmonary function meters are not suitable for a large number of physical examinations in the community. In recent years, it is recommended to use spirometry for COPD screening and management both at home and abroad. However, there is still a lack of relevant data on its consistency and effectiveness with conventional lung function.ObjectiveTo explore the value of handheld COPD-6 spirometry for early COPD detection in high risk elderly population in community.MethodsFrom January 2018 to December 2019 at Dongshan Community Health Service Center, Jiangning District, Nanjing City, a free physical examination was performed on the elder population over 60 years who had high risk factors of COPD. Handheld COPD-6 spirometry was performed pre- and post-bronchodilator, the forced expiratory volume in one second (FEV1) , the forced expiratory volume in one second as a percentage of the predicted value (FEV1%prep) , the forced expiratory volume in six second (FEV6) , the percentage of forced expiratory volume in six second to the predicted value (FEV6%prep) , and the value of FEV1/FEV6 were evaluated and recorded. With FEV1/FEV6

The difficulty in fault diagnosis of mine hoist inverters lies in extracting the features that characterize faults. At present, signal processing methods are mainly used to obtain fault statistical features, or the fault depth features are extracted by neural networks. In the actual working environment, the hoist inverter is affected by factors such as background noise and load changes. Therefore, it is difficult to obtain features that can characterize the faults effectively by using a single feature extraction method, resulting in low fault diagnosis accuracy of the hoist inverter. In order to solve the above problems, a fault diagnosis method of hoist inverter based on the fusion of statistical features and depth features is proposed. Firstly, the Hilbert-Huang transform(HHT) is used to conduct modified ensemble empirical mode decomposition(MEEMD) of the inverter output current signal so as to obtain the fault statistical features. At the same time, the squeeze and excitation with densely connected convolutional network(SE-DenseNet) is used to extract the depth features of the output current signal. Secondly, the local fisher discriminant analysis(LFDA) is used to perform fusion and dimensionality reduction processing on the combination of the two features to obtain low-dimensional fusion features of statistical features and depth features. Finally, the low-dimensional fusion features are input to the extreme learning machine to obtain inverter fault classification. Experiments are conducted for a single IGBT open-circuit fault in the hoist inverter. The results show that the low-dimensional fusion features obtained by this method are more capable of fault characterization than single features, which improves the fault recognition accuracy effectively.

In view of problems that existing neutral point clamped (NPC) three-level inverter fault diagnosis method needs large amount of calculation and its accuracy is affected by noise and load, an open-circuit fault diagnosis method of three-level inverter based on Park transformation was proposed. Park transformation is carried out on three-phase output current of the three-level inverter to calculate average current Park vector;sliding window is used to obtain a single-cycle current signal, and output current change is analyzed in case of IGBT fault according to inverter circuit structure; iterative method is used to determine threshold value of average current Park vector modulus, and 13 kinds of open-circuit faults of three-level inverter power tube are identifid and located combining phase angle and modulus.Simulation results show that the fault diagnosis accuracy of the method reaches 98.31%. Compared with traditional open-circuit fault diagnosis method, the proposed method does not need to learn and train the data of 13 kinds of open-circuit faults, which greatly reduces calculation amount and improves efficiency and accuracy of fault diagnosis.

In view of problems of complicated calculation and low accuracy existed in traditional open-circuit fault diagnosis methods of three-level inverter, an open-circuit fault diagnosis method of three-level inverter based on wavelet analysis and particle swarm optimization support vector machine (WT-PSO-SVM) was proposed. On the basis of analyzing the characteristics of the three-phase current signal of the three-level inverter, the current signal is decomposed by using the three-layer wavelet, and the energy of each frequency band is extracted as the fault feature. After the energy was extracted by wavelet transform, the extracted energy under partial faults is very close and cannot be distinguished effectively, and then the positive half-cycle proportional coefficient is introduced as auxiliary feature. The normalized energy and the positive half-cycle proportional coefficient are used as feature vectors to input support vector machines for classification training, and the parameters of support vector machine are optimized by particle swarm optimization algorithm to achieve the best classification effect, so as to realize fault diagnosis. The experimental results show that the WT-PSO-SVM method can effectively identify open-circuit faults of the three-level inverter, which has higher diagnostic accuracy and speed than other fault diagnosis methods, and still has a higher fault identification accuracy of 97.3% in the case of variable load and noise.