Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 subjects, was constructed. The electroencephalogram signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman (...) derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed thatK-Nearest Neighbor had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis. (shrink)

This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement learning and fusion (...) algorithms; the paper closes with concluding observations and a summary of the principal work and contributions of this paper. (shrink)

Given the importance of data safety for psychology, the present study investigated the influence of data leaking scandal on campus customers’ financial consumption behaviors at intelligent tourism platforms in China, and explored the roles that individual characteristics play in this process by focusing on a set of participants from colleges. Data were collected through sending out an online questionnaire, where respondents were asked to finish a series of questions about their background information, their trust, future consuming intention, and defensive behaviors (...) toward intelligent platforms. After they finished these questions, a short description about an online tourism platform leaking customers’ personal information was presented to the respondents, following which they were asked to report about their future consuming intentions and defensive behaviors again. In total, 236 participants of college students and teachers were recruited. Paired samples mean comparison showed that after the stimulus was presented, the respondents had a significant decrease in future financial consumption intention, and a significant increase in defensive behaviors toward online tourism platforms due to risks perceived. Multiple regression analysis was conducted subsequently to investigate individual characteristics that may account for part of the decrease in consuming intention. Results showed that, customers with higher level of trust and monthly income, as well as older customers, tend to experience higher level of decrease in consuming intention, and increase in defensive behaviors. These findings highlighted the importance of online tourism platforms guaranteeing data security of their customers. (shrink)

This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are (...) proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis. (shrink)

Previous studies have shown that social categorization can induce an own-group face recognition bias. However, similar and better other-group face recognition emerged recently. In this research, we aimed to examine whether competitive cues and group status accompanied by social categorization can modulate the inter-group face recognition bias. Moreover, we investigated how the group identification of individuals with different statuses affected the inter-group face recognition bias. The results indicated that an own-group face recognition bias emerged for targets with in-group labels compared (...) to out-group labels. Moreover, when the group labels signaled competitive cues, the own-group face recognition bias was reversed. Furthermore, low-status and similar-status individuals exhibited out-group face recognition bias, but high-status individuals did not. In addition, the higher the in-group identification scores of participants from the low-status group, the stronger the out-group face recognition bias. These results suggested that competitive cues would reverse the own-group face recognition bias and the group status would play a modulating role in face recognition bias. (shrink)

In order to demonstrate the existence of the god, Plato introduces an investigation into the kinds of movement in the Laws X. This research begins obviously with the circular movement and the local movement of the Platonic Polyhedrons, follows with their combination, separation, growth and decay that result in the generation and destruction of the four elements and all perceptible things, and ends with the corporeal movement that encompasses the preceding eight elemental motion-types, and the motion of the soul, which (...) is proclaimed to be the self-motion. Given that the self-motion is caused by the intellect and functions as the principle and reason of all physical movements in the χώρα, the soul is deemed to be divine, in this sense the existence of the god is demonstrated. (shrink)

This work aims to increase the consumption of online tourism products and promote the development of the tourism economy. Based on this, it first analyses the Internet market under the guidance of consumer psychology. Then, the influencing factors of online product decision-making for the tourism economy are discussed. Finally, based on the above analysis, it discusses and evaluates the main factors affecting the consumption of online travel products. The research method of this work is set based on psychology so that (...) it can analyze the psychological state of consumers more deeply and promote the development of the consumer market. The results show that the main factors affecting the consumption of online travel products include online travel platforms and user characteristics. Specifically, approximately 80% of users consume online travel products based on platform reviews, approximately 10% of users consume online travel products based on platform recommended content, and approximately 5% of users consume online travel products based on platform search content. Users vary mainly by age, gender, and region and have different preferences for different platforms. Among the four major platforms, Ctrip occupies the most consumers. The conclusion is that the main way to develop the tourism economy is to build a better online travel platform. At the same time, it is necessary to promote online tourism according to the characteristics of users and increase the marketing of online tourism products. This work not only provides a reference for promoting online tourism product marketing but also helps to promote the development of the tourism economy. (shrink)

Since December 2019, the COVID-19 has continued to rage, and epidemic prevention policies have limited contact between individuals, which may has a great influence on the income of individuals, exacerbate anxiety and depression, and cause serious mental health problems. The current study aims to examine the association between income and mental health during the COVID-19 pandemic by using the data of 9,296 observations from the 2020 China Family Panel Studies. Employing ordinary least squares regression and two-stage least squares regression, we (...) find the significant positive effect of income on Chinese mental health during this pandemic. In addition, the number of cigarettes smoked per day has significant negative effects on mental health. Education levelˎmarriage and exercise frequency have significant positive correlation with mental health. Furthermore, the impact of income on individuals of different groups is heterogeneous during this pandemic. The impact of income for well-educated individuals is less strong than their less-educated counterparts. People who exercise regularly respond less strongly to changes in income than those who do not exercise. Finally, individuals’ salary satisfaction and interpersonal relationship are shown to be the potential mechanism for the effect of income on Chinese mental health. (shrink)

ObjectiveThis paper studies the mediating and interactive effects of social capital on psychological capital and the feeling of happiness from the impact of COVID-19. Since its emergence, the COVID-19 pandemic has taken a toll on people’s mental health and affected their hopes for the future. Lifestyle and economic conditions have also been affected and have subsequently impacted people’s sense of confidence in life. This could increase the likelihood of many people developing mental health issues, such as anxiety or depression. Therefore, (...) it is vital to study the influence of psychological capital and social capital on people’s subjective psychology and happiness experiences.Materials and MethodsUsing an ordered probit model, this paper studied the independent influence and interaction between psychological capital and social capital on people’s happiness. The ordered probit model was chosen because subjective well-being is an ordered variable. We further used structural equation modeling to study the mediating effects of social capital on psychological capital and happiness.ResultsThe regression results showed that both psychological capital and social capital were significantly positively correlated with happiness when controlling for other factors. In addition, psychological and social capital significantly interacted, in which the psychological capital promotes the effect of social capital on happiness. Moreover, the effect of psychological capital on happiness was greater than that of social capital, demonstrating that happiness is more greatly influenced by subjective psychological experience. The interaction coefficient of psychological and social capital was also significant, showing that the two have mutually reinforcing effects on happiness. Finally, health, income class, real estate, stranger trust, age, and urban household registration had significant positive effects on happiness, while the view of money, being female, education had a negative relationship with happiness. The SEM results showed that the mediating effect of psychological capital on happiness was partly transmitted through social capital: the total effect of psychological capital on happiness was highly significant, as was the total effect of social capital on happiness ; however, the coefficient for psychological capital was greater than that for social capital. Through heterogeneity analysis, we found that the relationship between psychological capital, social capital, and happiness was significantly positive in each sub-sample group. There was also a significant interaction between psychological and social capital for men, women, urban and rural residents, and higher education background sample groups. However, the interaction was not significant in the sample group without higher education. In addition, the relationship between the happiness of rural residents and their educational background and gender was not significant.ConclusionWe found that psychological and social capital have significant positive relationships and effects on happiness. Psychological capital demonstrated both direct and indirect influences on happiness, and further strengthens the influence of social capital on happiness. These results support a scheme to emphasize psychological support during the COVID-19 pandemic period to enhance the mental health of citizens. (shrink)