Metal roof enclosure system is an important part of steel structure construction. In recent years, it has been widely used in large-scale public or industrial buildings such as stadiums, airport terminals, and convention centers. Affected by bad weather, various types of accidents on metal roofs frequently occurred, causing huge property losses and adverse effects. Because of wide span, long service life and hidden fault of metal roof, the manual inspection of metal roof has low efficiency, poor real-time performance, and it (...) is difficult to find hidden faults. On the basis of summarizing the working principle of metal roof and cause of accidents, this paper classifies the fault types of metal roofs in detail and establishes a metal roof monitoring and fault diagnosis system using distributed multisource heterogeneous sensors and Zigbee wireless sensor networks. Monitoring data from strain gauge, laser ranging sensor, and ultrasonic ranging sensor is utilized comprehensively. By extracting time domain feature, the data trend characteristics and correlation characteristics are analyzed and fused to eliminate erroneous data and find superficial faults such as sensor drift and network interruption. Aiming to the hidden faults including plastic deformation and bolt looseness, an SVM fault diagnosis algorithm based on RBF kernel function is designed and applied to diagnose metal roof faults. The experimental results show that the RBF-SVM algorithm can achieve high classification accuracy. (shrink)

This study via the sensemaking perspective examines whether spiritual leadership can influence employee workplace spirituality and employee corporate social responsibility (CSR) participation. We also examine the joint effects of spiritual leadership and employee Machiavellianism on employee workplace spirituality. Using a sample of 556 employees from four commercial banks in China, analyses demonstrate that employee workplace spirituality mediates the relationship between spiritual leadership and employee CSR participation and that the indirect effect of spiritual leadership on employee CSR participation is dependent on (...) the level of employee Machiavellianism. These results shed light on how and why spiritual leadership and employee Machiavellianism influence employee workplace spirituality and employee CSR participation. The theoretical and practical implications of these finding are discussed along with directions for future research. (shrink)

A product harm crisis undermines a firm’s reputation as well as its managers’ career outlook. To shake off the stigmatization resulting from the PHC and regain a firm’s legitimacy among stakeholders, managers usually face an ethical dilemma as they choose to be transparent about the crisis’ financial implications or to obfuscate them to neutralize the negative impact of the PHC. We find evidence that managers engage in income-increasing earnings management when their firms experience PHCs. Moreover, while income-increasing earnings management in (...) PHCs reduces the likelihood of customer loss and CEO forced turnover in the short run, such behavior can be deemed opportunistic and unethical as it carries long-run negative consequences in terms of a higher likelihood of accounting restatement and weaker future operating performance. Finally, managers in firms that are subject to stricter external monitoring and managers in firms with proactive ethical policies are less likely to engage in upward earnings management in PHCs. (shrink)

Generalized anxiety disorder (GAD) is characterized by excessive and uncontrollable worry about everyday life. Prior neuroimaging studies have demonstrated that GAD is associated with disruptions in specific brain regions; however, little is known about the global functional connectivity maps in adolescents with GAD. Here, first-episode, medication-naive, adolescent GAD patients ( N = 36) and healthy controls ( N = 28) (HCs) underwent resting-state functional MRI (R-fMRI) and completed a package of questionnaires to assess clinical symptoms. Functional connectivity strength and seed-based (...) functional connectivity were employed to investigate the functional connectivity architecture. GAD patients showed reduced functional connectivity strength in right supramarginal gyrus (SMG) and right superior parietal gyrus (SPG) compared with HCs. Further seed-based functional connectivity analysis revealed that GAD patients displayed decreased functional connectivity between right SMG and left fusiform gyrus, inferior temporal gyrus, parahippocampal gyrus, bilateral precuneus and cuneus, and between right SPG and bilateral supplementary motor area and middle cingulate gyrus, as well as between the SMG-based network and the SPG-based network. Moreover, the disrupted intra-network connectivity (i.e., the SMG-based network and the SPG-based network) and inter-network connectivity between the SMG-based network and the SPG-based network accounted for 25.5% variance of the State and Trait Anxiety Inventory (STAI) and 39.5% variance of the trait subscale of STAI. Our findings highlight the abnormal functional architecture in the SMG-based network and the SPG-based network in GAD, providing novel insights into the pathological mechanisms of this disorder. (shrink)

In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm based on elite heuristic (...) operation and migration strategy and the improved coyote optimization algorithm based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search, and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology. (shrink)

As an important component to promote the development of affective brain–computer interfaces, the study of emotion recognition based on electroencephalography (EEG) has encountered a difficult challenge; the distribution of EEG data changes among different subjects and at different time periods. Domain adaptation methods can effectively alleviate the generalization problem of EEG emotion recognition models. However, most of them treat multiple source domains, with significantly different distributions, as one single source domain, and only adapt the cross-domain marginal distribution while ignoring the (...) joint distribution difference between the domains. To gain the advantages of multiple source distributions, and better match the distributions of the source and target domains, this paper proposes a novel multi-source joint domain adaptation (MSJDA) network. We first map all domains to a shared feature space and then align the joint distributions of the further extracted private representations and the corresponding classification predictions for each pair of source and target domains. Extensive cross-subject and cross-session experiments on the benchmark dataset, SEED, demonstrate the effectiveness of the proposed model, where more significant classification results are obtained on the more difficult cross-subject emotion recognition task. (shrink)

本研究得到国家社会科学基金“法制与法治国家”重大项目、教育部“社会变迁中的中国法治的理论与实践”项目以及北京大学“985”项目的资助.

Adherence to ethical principles in clinical research and practice is becoming topical issue in China, where the prevalence of mental illness is rising, but treatment facilities remain underdeveloped. This paper reports on a study aiming to understand the ethical knowledge and attitudes of Chinese mental health professionals in relation to the process of diagnosis and treatment, informed consent, and privacy protection in clinical trials.

Government communication has been playing an important role in mass vaccination to conduct the largest vaccination campaign of the world for COVID-19 and to counter vaccine hesitancy. This study employs the health belief model to examine the association between government communication and the COVID-19 vaccination intention. A survey of Chinese adults was conducted in March 2021, and partial least squares structural equation modeling was employed to estimate the multi-construct relationships. The findings indicate that government communication has both direct positive association (...) with vaccination intention and indirect association with vaccination intention through the mediation of perceived severity, benefits, and barriers. Multi-group comparisons suggest that individuals from private sectors are more easily mobilized to receive COVID-19 vaccination by government communication than those from public sectors. Similarly, the correlation between government communication and the vaccination intention of individuals with a good health status was stronger than that of those with a poor health status. The theoretical and practical implications of these findings are further discussed. (shrink)

本书共分为4编，共12章，具体内容包括：司法制度、司法知识与技术、法官与法律人、研究方法的反思。.

This paper focuses on the characteristics of immersive media technology and the advantages, problems and solutions in applying this technology to improve the teaching effectiveness of ideological and political theory courses in colleges and universities. Firstly, it introduces the current development and characteristics of immersive media technology. Secondly, it analyzed the outstanding advantages of immersive media technology in teaching from the following perspectives: virtual reality and augmented reality; sensory stimulation and emotional experience; and human-computer interaction and self-harmony. Thirdly, it puts (...) forward the ways to improve the teaching effectiveness based on immersive media technology from the aspects of theoretical study, history study, and practical study of ideological and political theory courses in colleges and universities. Finally, it discusses the problems in applying immersive media technology to teaching the above courses and puts forward some solutions. (shrink)

Common spatial pattern is an effective algorithm for extracting electroencephalogram features of motor imagery ; however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor—even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels, wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass (...) filtered EEG into multiple time–frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time–frequency components and the original EEG signal. Furthermore, phase space reconstruction was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster–Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II, which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels. (shrink)