IntroductionDuring the COVID-19 outbreak, many citizens were asked to stay at home in self-quarantine, which can pose a significant challenge with respect to remaining physically active and maintaining mental health. This study aimed to evaluate the prevalence of inadequate physical activity, anxiety, and depression and to explore the relationship of physical activity with anxiety and depression symptoms among Chinese college students during quarantine.MethodUsing a web-based cross-sectional survey, we collected data from 1,396 Chinese college students. Anxiety and depression were assessed with (...) the Self-Rating Anxiety Scale and the Self-Rating Depression Scale, respectively. The data on physical activity were collected by types of physical activity and the International Physical Activity Questionnaire.ResultsDuring the COVID-19 outbreak, about 52.3% of Chinese college students had inadequate physical activity. The rates of anxiety and depression symptoms were 31.0 and 41.8%, respectively. A high level of physical activity was significantly closely associated with low anxiety, while a moderate, or high level of physical activity was significantly closely associated with reduced depression after adjusting confounding demographic factors. Moreover, specific types of physical activity, such as stretching and resistance training, were negatively correlated with both anxiety and depression; doing household chores was negatively correlated with depression.ConclusionOur findings highlight specific levels and types of home-based physical activities that need to be taken into consideration to protect the mental health of college students during the COVID-19 epidemic. (shrink)

With the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable development capacity is very important. An index system of urban low-carbon sustainable development capability is constructed in this paper, and a TOPSIS-BP neural network model is established to evaluate the low-carbon sustainable development capability of Beijing, Shanghai, Shenzhen, and Guangzhou in (...) China. At the same time, the difference degree of low-carbon sustainable development level in these four cities is analyzed by standard deviation and coefficient of variation, and the influencing factors of urban low-carbon sustainable development ability are extracted by grey correlation analysis. The results show that the capability of low-carbon sustainable development in four cities is rising and the difference of low-carbon sustainable development capability is decreasing; the general view that the higher the general investment in low-carbon sustainable development, the higher the level of low-carbon sustainable development in cities has not been verified; with the change of time series, the factors affecting the capability of low-carbon sustainable development in the same city are different and the influence of the same factor on the capability of low-carbon sustainable development in different cities is different. (shrink)

Study of Suddhadvaita philosophy with reference to other system of Hindu philosophy.

In recent years, the network has become more complex, and the attacker’s ability to attack is gradually increasing. How to properly understand the network security situation and improve network security has become a very important issue. In order to study the method of extracting information about the security situation of the network based on cloud computing, we recommend the technology of knowledge of the network security situation based on the data extraction technology. It converts each received cyber security event into (...) a standard format that can be defined as multiple brochures, creating a general framework for the cyber security situation. According to the large nature of network security situation data, the Hadoop platform is used to extract aggregation rules, and perform model extraction, pattern analysis, and learning on a network security event dataset to complete network security situation rule mining, and establish a framework for assessing the state of network security. According to the results of the federal rule extraction, the level of network node security risk is obtained in combination with signal reliability, signal severity, resource impact, node protection level, and signal recovery factor. A simulation test is performed to obtain the intrusion index according to the source address of the network security alarm. Through the relevant experiments and analysis of the results, the attack characteristics obtained in this study were obtained after manually reducing the network security event in the 295 h window. The results show that after the security event is canceled, the corresponding window attack index decreases to 0, indicating that this method can effectively implement a network security situation awareness. The proposed technique allows you to accurately sense changes in network security conditions. (shrink)

Emotion recognition plays an important part in human-computer interaction. Currently, the main challenge in electroencephalogram -based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are then passed through (...) TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high: sadness, anger, and fear on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high: sadness, anger, and fear on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition. (shrink)

Grounded in Bandura’s social cognitive theory of moral thought and action, we develop a conceptual model linking supervisors’ perceptions of organizational injustice and abusive supervision with moral disengagement mechanisms acting as the underlying process. Specifically, we elaborate why and how supervisors’ experiences of each type of injustice would trigger their adoption of distinctive moral disengagement mechanisms, which in turn lead to their abusive supervisory conduct. The present conceptual model sheds new light on linking organizational injustice to abusive supervision from a (...) moral perspective. In addition, it also provides important theoretical and managerial implications to our current understanding of why and how abusive supervision happens. (shrink)

One of the most significant challenges in the application of brain-computer interfaces is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern and support vector (...) machine algorithms to measure the MI performance of each subject and session. Relative Power values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance. (shrink)

In this study, we investigated the integration between neuroscience and entrepreneurship. First, we explored the concept of neuroentrepreneurship and the investigation of neuroentrepreneurship using scientific research methods. Second, we constructed a road map for entrepreneurial researchers interested in conducting neuroentrepreneurship-related research. This is an emerging research area; therefore, to more clearly analyze the dynamics of the research trends, we used a bibliometric method to capture patterns in current publications on subjects related to neuroentrepreneurship, examining papers published between 1999 and 2021 (...) using the keywords “neuroscience” and “entrepreneurship” or “neuroentrepreneurship.” To identify the keywords, we used two academic databases—the Social Science Citation Index and Science Citation Index—accessed through the Web of Science website. The three keywords were identified from studies integrating neuroscience with entrepreneurship. After carefully reviewing the research papers, we identified neuroentrepreneurship as a novel research area. The outcomes of this study provide a guide for describing the theoretical connection between neuroscience and entrepreneurship. In the future, this field of study should be empirically investigated. (shrink)

BackgroundAt present, tic disorder has attracted the attention of medical researchers in many countries. More clinicians choose non-drug therapy, especially cognitive-behavioral therapy because of the cognitive side effects of drug therapy. However, few studies had assessed its efficacy. It is necessary to have a more comprehensive understanding of the literature quality of CBT and its intervention effect.MethodsIn this study, MEDLINE, Embase, and Cochrane were searched from the beginning to June 15, 2021 to study the efficacy of -CBT on tic disorder. (...) Only studies using the Yale Global Tic Severity Scale and the control group were included.ResultsA total of 12 randomized controlled trials, including 536 patients with tic disorders, were identified. The results showed that the effect of CBT was better than that of the control group. The pooled standardized mean difference was −0.34. The effect size of CBT differs from different intervention conditions. In seven studies, the subjects’ motor tic scores were counted. The sample size of the experimental group was 224 and that of the control group was 218. The pooled SMD was −0.43. Seven studies counted the vocal tic scores of subjects, 224 in the experimental group and 218 in the control group. The pooled SMD was −0.22. Seven studies counted the tic impairment scores of subjects, 220 in the experimental group and 214 in the control group. The pooled SMD was −0.48.ConclusionThe literature shows that different CBTs can significantly reduce the total score of tic disorder and the score of motor tic, but cannot significantly reduce the score of vocal tic. In the future, more new interventions were needed to improve the symptoms of different patients, especially vocal tic. (shrink)

Crowdsourcing innovation community has become an important platform for enterprises to gather group wisdom. However, how the crowdsourcing innovation community plays a reference role in creative crowdsourcing participation is unclear. Based on the reference group theory, taking online impression management as the explanatory framework, this study explores the impact of crowdsourcing innovation community reference on the creative territory behavior, and the differences in the crowdsourcing innovation community reference effect among members of different community age groups. A total 524 valid two-stage (...) questionnaires were collected. The empirical analysis results show that under the influence of informational reference and utilitarian reference of the crowdsourcing innovation community, community members are significantly more likely to adopt acquired impression management than defensive impression management ; under the influence of value expressive reference of the crowdsourcing innovation community, the possibility of adopting DIM behavior is significantly greater than that of adopting AIM behavior; compared with DIM behavior, AIM behavior has a more inhibitory effect on creative territory behavior. Interestingly, there are different community reference effects among members of different community age groups. In particular, the positive contribution of the elder members is not as good as that of the newcomers. The above research conclusions not only confirm the influence of crowdsourcing community reference on crowd participation decision making but also provide theoretical and practical enlightenment for exploring the cooperation mechanism of crowdsourcing innovation. (shrink)

As coronavirus disease 2019 swept the world in early 2020, all the Chinese universities and colleges adopted online learning to fulfill the directive saying “classes suspended but learning continues.” Understanding the impact of this large-scale online learning experience on the future online learning intention of Chinese university students can help design better blended-learning activities. This study applies flow experience and theory of planned behavior to construct a theoretical framework for assumption making and the assumptions made are validated by data gained (...) from questionnaires. A total of 6,933 students from 54 institutions in China participated in the investigation, with 5,456 valid questionnaires returned. This study employs partial least squares regression and confirmative factor analysis to analyze and estimate the measurement model and the structural model. The results indicate that the experience of home-based learning significantly influenced the attitudes of Chinese university students, which in turn had a positive influence on their intention to continue online learning. The research findings provide a theoretical framework and practical guidelines on building a scientific online learning platform with appropriate online learning environments and tasks for a post-COVID-19 era blended-learning in Chinese universities. (shrink)

We present several new studies focusing on “salience effects”—the decreased tendency to attribute knowledge to someone when an unrealized possibility of error has been made salient in a given conversational context. These studies suggest a complicated picture of epistemic universalism: there may be structural universals, universal epistemic parameters that influence epistemic intuitions, but that these parameters vary in such a way that epistemic intuitions, in either their strength or propositional content, can display patterns of genuine cross-cultural diversity.

In order to avoid the motor damage caused by excessive temperature rise of armature winding of the walking motor during braking of high-voltage inspection robot in complexity microgrid networks, an unpowered downhill speed and energy recovery optimization control strategy is proposed based on temperature rise characteristics of the walking motor. Firstly, the thermal equivalent circuit model of the walking motor is established, and the mapping relationship between the armature winding temperature of the walking motor and ambient temperature is solved; secondly, (...) the influence of armature winding temperature on unpowered downhill speed and energy recovery control strategy of robot is analyzed; thirdly, according to the temperature of front and rear wheel walking motors and the temperature difference between them, the optimal control strategy of unpowered downhill speed and energy recovery of robot is put forward; fourthly, by adjusting the duty ratio u of the energy consumption speed control system and the front wheel feedback brake distribution factor λ, the temperature difference between the front and rear wheel walking motors is reduced. Through the experimental analysis on the simulated line, it is verified that the unpowered downhill speed and energy recovery optimization control method based on the temperature rise characteristics of the walking motor can effectively reduce the temperature difference between the front and rear wheels. (shrink)

The present study explored the role of emotion regulation and emotion lability/negativity as a moderator in the relation between child social avoidance and social adjustment in Chinese culture. Participants were N = 194 children recruited from nine classrooms in two public kindergartens in Shanghai, People’s Republic of China. Multi-source assessments were employed with mothers rating children’s social avoidance and teachers rating children’s emotion regulation, emotion lability/negativity and social adjustment outcomes. The results indicated that the relations between social avoidance and social (...) adjustment difficulties were more negative among children lower in emotion regulation, but not significant for children with higher emotion regulation. In contrast, the relations between social avoidance and social adjustment difficulties were more positive among children higher in emotion lability/negativity, but not significant for children with lower emotion lability/negativity. This study informs us about how emotion regulation and emotion lability/negativity are jointly associated with socially avoidant children’s development. As well, the findings highlight the importance of considering the meaning and implication of social avoidance in Chinese culture. (shrink)

The South-to-North Water Diversion Project consists of long-distance water delivery channels and a complicated geological environment along the way. To deal with the operation safety of the water conveyance channels in the middle route of the South-to-North Water Diversion Project, this study analyzes six failure modes: structural cracks, poor water delivery during ice periods, instability of canal slopes, material aging, abnormal leakage, and foundation defects. Based on FMEA, a multigranularity language evaluation method that can be converted into interval intuitionistic fuzzy (...) numbers is used to evaluate the severity, occurrence, and detection difficulty of the six failure modes. Interval intuitionistic fuzzy entropy is used to calculate the weights of the risk factors. Finally, a ranking model of each failure mode is built based on the TODIM method. The final ranking results show that the risk of abnormal leakage is the largest, and the risk of poor water delivery during ice periods is the smallest. The feasibility and validity of the calculation results are verified by comparing them with the ranking results of the traditional RPN and TOPSIS methods. The TODIM-FMEA risk assessment model offers a new solution to the problem of risk assessment for water transfer projects. (shrink)

We study a joint pricing and inventory control problem for perishables with positive lead time in a finite horizon periodic-review system. Unlike most studies considering a continuous density function of demand, in our paper the customer demand depends on the price of current period and arrives according to a homogeneous Poisson process. We consider both backlogging and lost-sales cases, and our goal is to find a simultaneously ordering and pricing policy to maximize the expected discounted profit over the planning horizon. (...) When there is no fixed ordering cost involved, we design a deep reinforcement learning algorithm to obtain a near-optimal ordering policy and show that there are some monotonicity properties in the learned policy. We also show that our deep reinforcement learning algorithm achieves a better performance than tabular-based Q-learning algorithms. When a fixed ordering cost is involved, we show that our deep reinforcement learning algorithm is effective and efficient, under which the problem of “curse of dimension” is circumvented. (shrink)

The Negative Problem Orientation Questionnaire is a widely used tool for assessing negative problem orientation. However, its construct and measurement invariance has not been adequately tested in adolescents. The present study explored the possible construct of the NPOQ and its measurement invariance in a sample of 754 Chinese adolescents. The results supported a bifactor model of the NPOQ that consists of a general factor NPO and three domain-specific factors including perceived threat, self-inefficacy, and negative outcome expectancy. A multiple-group CFA indicated (...) that the bifactor model showed strict invariance across gender and age. The general and domain factors showed unique variance in indexes of worry, depression, anxiety, and stress, which supported well incremental validity of them. This study confirms for a bifactor conceptualization of the NPOQ and its measurement invariance across gender and age in Chinese adolescents. Additionally, it is recommended that the total score should be used to assess NPO in Chinese adolescents. (shrink)

In recent years, foreign language learning has been proposed as a possible cognitive intervention for older adults. However, the brain network and cognitive functions underlying FLL has remained largely unconfirmed in older adults. In particular, older and younger adults have markedly different cognitive profile—while older adults tend to exhibit decline in most cognitive domains, their semantic memory usually remains intact. As such, older adults may engage the semantic functions to a larger extent than the other cognitive functions traditionally considered the (...) most important. Using anatomical measurements and a cognitive test battery, the present study examined this hypothesis in twenty cognitively normal older adults, who participated in a two-month Italian learning programme. Results showed that the immediate learning success and long-term retention of Italian vocabularies were most consistently predicted by the anatomical measures of the left pars orbitalis and left caudal middle frontal cortex, which are implicated in semantic and episodic memory functions. Convergent evidence was also found based on the pattern of cognitive associations. Our results are consistent with a prominent role of semantic and episodic memory functions in vocabulary learning in older learners. (shrink)

The economic globalization process has integrated different competitive markets and pushes firms in different countries to improve their managerial and operational efficiencies. Given the recent empirical evidence for the benefits to firms and stakeholders of good corporate governance (CG) practice, it is expected that good CG practice would be a common strategy for firms in different countries to meet the increasingly intense competition; however, this is not the case. This study examines the differences in CG practices in firms across different (...) countries using the concept of ethical sensitivity. Through the regression analysis of 271 firms in 12 countries and regions, it is found that Hofstede’s cultural dimensions can explain the differences in CG practices. Furthermore, the results demonstrate the influence of culture on ethical sensitivity, which eventually determines the CG practices in different regions. (shrink)

Understanding the implication of point cloud is still challenging in the aim of classification or segmentation for point cloud due to its irregular and sparse structure. As we have known, PointNet architecture as a ground-breaking work for point cloud process can learn shape features directly on unordered 3D point cloud and has achieved favorable performance, such as 86% mean accuracy and 89.2% overall accuracy for classification task, respectively. However, this model fails to consider the fine-grained semantic information of local structure (...) for point cloud. Then, a multiscale receptive fields graph attention network by means of semantic features of local patch for point cloud is proposed in this paper, and the learned feature map for our network can well capture the abundant features information of point cloud. The proposed MRFGAT architecture is tested on ModelNet datasets, and results show it achieves state-of-the-art performance in shape classification tasks, such as it outperforms GAPNet model by 0.1% in terms of OA and compete with DGCNN model in terms of MA. (shrink)

Extant literature has underlined the importance of newcomer proactive socialization to the organization. However, the effect of coworker ostracism on newcomers’ proactive behaviors has not been noticed. Drawing on the conservation of resources theory, we proposed a model exploring how coworker ostracism impacted newcomers’ proactive behaviors via the mediation of psychological availability. Through an empirical study with a sample of 263 newcomers and three waves of longitudinal data, we found that coworker ostracism had a negative effect on newcomers’ information seeking (...) and guanxi developing. In addition, emotional intelligence enhanced the negative effect of coworker ostracism on newcomers’ psychological availability and the indirect influence of coworker ostracism on newcomers’ proactive behaviors via psychological availability. Important theoretical and practical implications are discussed. (shrink)

BackgroundThe effects of psychological interventions on symptoms and psychology of functional dyspepsia remain unclear. We aimed to comprehensively evaluate the effects of psychological interventions on symptoms and psychology of FD.MethodsWe searched the PubMed, Cochrane Library, and Embase electronic databases for randomized controlled trials evaluating the role of psychological interventions in FD patients published before July 2021. Standardized mean differences, risk ratios and 95% confidence intervals were calculated by a random effects model. Subgroup analyses and sensitivity analyses were also performed.ResultsFourteen RCTs (...) with a total of 1,434 FD patients were included. Compared with the control group, psychological interventions were significantly more likely to symptom improvement [RR = 1.74, 95% CI, p = 0.01], relieve gastrointestinal symptoms scores at follow up [SMD = −1.06, 95% CI, p < 0.0001], relieve gastrointestinal symptoms scores at end of treatment [SMD = −0.98, 95% CI, p < 0.001], decrease anxiety [SMD = −0.8, 95% CI, p = 0.006] and depression levels [SMD = −1.11, 95% CI, p < 0.001]. The results of the subgroup analysis showed that psychotherapy was more likely to symptom improvement, relieve gastrointestinal symptoms scores and decreased depression levels compared to the control.ConclusionsPsychological interventions may be effective in alleviating the symptoms and psychology of FD, but the effect appears to be limited to psychotherapy with fewer trials for other psychological interventions. More data from high-quality RCTs are needed to confirm their use in the treatment of FD. (shrink)

This study aimed to explore the association between self-oriented empathy and compassion fatigue, and examine the potential mediating roles of dispositional mindfulness and the counselor’s self-efficacy. A total of 712 hotline psychological counselors were recruited from the Mental Health Service Platform at Central China Normal University, Ministry of Education during the outbreak of Corona Virus Disease 2019, then were asked to complete the questionnaires measuring self-oriented empathy, compassion fatigue, dispositional mindfulness, and counselor’s self-efficacy. Structural equation modeling was utilized to analyze (...) the possible associations and explore potential mediations. In addition to reporting confidence intervals, we employed a new method named model-based constrained optimization procedure to test hypotheses of indirect effects. Results showed that self-oriented empathy was positively associated with compassion fatigue. Dispositional mindfulness and counselor’s self-efficacy independently and serially mediated the associations between self-oriented empathy and compassion fatigue. The findings of this study confirmed and complemented the etiological and the multi-factor model of compassion fatigue. Moreover, the results indicate that it is useful and necessary to add some training for increasing counselor’s self-efficacy in mindfulness-based interventions in order to decrease compassion fatigue. (shrink)

Empathy is sharing and understanding others’ emotions. Recently, researchers identified a culture–sex interaction effect in empathy. This phenomenon has been largely ignored by previous researchers. In this study, the culture–sex interaction effect was explored with a cohort of 129 participants (61 Australian Caucasians and 68 Chinese Hans) using both self-report questionnaires (i.e., Empathy Quotient and Interpersonal Reactivity Index) and computer-based empathy tasks. In line with the previous findings, the culture–sex interaction effect was observed for both trait empathy (i.e., the generalized (...) characteristics of empathy, as examined by the self-report questionnaires) and state empathy (i.e., the on-spot reaction of empathy for a specific stimulus, as evaluated by the computer-based tasks). Moreover, in terms of state empathy, the culture–sex interaction effect further interacted with stimulus traits (i.e., stimulus ethnicity, stimulus sex, or stimulus emotion) and resulted in three- and four-way interactions. Follow-up analyses of these higher-order interactions suggested that the phenomena of ethnic group bias and sex group favor in empathy varied among the four culture–sex participant groups (i.e., Australian female, Australian male, Chinese female, and Chinese male). The current findings highlighted the dynamic nature of empathy (i.e., its sensitivity toward both participant traits and stimulus features). Furthermore, the newly identified interaction effects in empathy deserve more investigation and need to be verified with other Western and Asian populations. (shrink)

Discussion around the increasing use of AI in healthcare tends to focus on the technical aspects of the technology rather than the socio-technical issues associated with implementation. In this paper, we argue for the development of a sustained societal dialogue between stakeholders around the use of AI in healthcare. We contend that a more human-centred approach to AI implementation in healthcare is needed which is inclusive of the views of a range of stakeholders. We identify four key areas to support (...) stakeholder involvement that would enhance the development, implementation, and evaluation of AI in healthcare leading to greater levels of trust. These are as follows: (1) aligning AI development practices with social values, (2) appropriate and proportionate involvement of stakeholders, (3) understanding the importance of building trust in AI, (4) embedding stakeholder-driven governance to support these activities. (shrink)

For understanding and controlling spreading in complex networks, identifying the most influential nodes, which can be applied to disease control, viral marketing, air traffic control, and many other fields, is of great importance. By taking the effect of the spreading rate on information entropy into account, we proposed an improved information entropy method. Compared to the benchmark methods in the six different empirical networks, the IIE method has been found with a better performance on Kendall’s Tau and imprecision function under (...) the Susceptible Infected Recovered model. Especially in the Facebook network, Kendall’s Tau can grow by 120% as compared with the original IE method. And, there is also an equally good performance in the comparative analysis of imprecise functions. The imprecise functions’ value of the IIE method is smaller than the benchmark methods in six networks. (shrink)

Background: With the outbreak of the coronavirus disease 2019 epidemic in China, the general public but also medical staff were confronted with psychological challenges, suffering from the highly infectious and unknown characteristics of COVID-19. In this study, we surveyed psychological symptoms including anxiety, depression, and sleep disorders in medical staff.Method: A questionnaire star/WeChat link-based survey assessing the Generalized Anxiety Disorder 7-item scale, Patient Health Questionnaire-9 depression, the Insomnia Severity Index, Social Support scales in addition to lifestyle, and income level was (...) conducted and included 8,288 medical staff from 24 provinces in China. Pearson Chi-square and Mann-Whitney U-tests were used to evaluate single risk factors and significant differences in psychological symptoms before and during the outbreak of COVID-19. Binary logistic regression analyses were conducted for the risk factors of anxiety, depression, and sleep disorder symptoms.Results: Medical staff had a high incidence of psychological symptoms, which was more prominent during the COVID-19 epidemic. Comparatively, females, nurses, first-line department, never exercised, and low income were risk factors for psychological symptoms. Social support including objective support, subjective support, support utility, and regular sports over 3 times per week were protective and manageable elements that could protect from and manage the psychological symptoms of medical staff.Conclusion: The susceptibility of psychological symptoms among medical staff should be of concern to policymakers and the public in the long-term, and the aggravation of mental health problems of medical staff could be eased by providing adequate social support during and after the COVID-19 outbreak. (shrink)

On the fundamentals of Vedanta philosophy and Hindu spiritual life.

None of the studies published in the extant literature has discussed the role of green innovation climate and green autonomy concerning green creativity and this study aims to offer these two novel constructs. By introducing the componential theory of creativity, this study explores green transformational leadership, green innovation climate, and green autonomy as antecedents of green creativity. The authors employed structural equation modeling to analyze survey-based data collected from automotive firms in China. Data were collected from employee-supervisor working in the (...) automotive industry located in Liaoning province, China. The findings reveal that GTL directly and indirectly via green innovation climate partially mediates the green creativity of employees in China. Moreover, green autonomy moderates the relationship between green innovation climate and green creativity. This pivotal contribution suggests that automotive business enterprises should develop GTL to nurture a green innovation climate and offer green autonomy for the green creativity of employees. The above antecedents of green creativity may enable business firms to gain a competitive advantage by innovating green products and practices. (shrink)

Current development in a deep neural network has given an opportunity to a novel framework for the reconstruction of a holographic image and a phase recovery method with real-time performance. There are many deep learning-based techniques that have been proposed for the holographic image reconstruction, but these deep learning-based methods can still lack in performance, time complexity, accuracy, and real-time performance. Due to iterative calculation, the generation of a CGH requires a long computation time. A novel deep generative adversarial network (...) holography framework is proposed for hologram reconstruction. This novel framework consists of two phases. In phase one, we used the Fresnel-based method to make the dataset. In the second phase, we trained the raw input image and holographic label image data from phase one acquired images. Our method has the capability of the noniterative process of computer-generated holograms. The experimental results have demonstrated that the proposed method outperforms the existing methods. (shrink)

Facial expressions are a vital way for humans to show their perceived emotions. It is convenient for detecting and recognizing expressions or micro-expressions by annotating a lot of data in deep learning. However, the study of video-based expressions or micro-expressions requires that coders have professional knowledge and be familiar with action unit coding, leading to considerable difficulties. This paper aims to alleviate this situation. We deconstruct facial muscle movements from the motor cortex and systematically sort out the relationship among facial (...) muscles, AU, and emotion to make more people understand coding from the basic principles: We derived the relationship between AU and emotion based on a data-driven analysis of 5,000 images from the RAF-AU database, along with the experience of professional coders.We discussed the complex facial motor cortical network system that generates facial movement properties, detailing the facial nucleus and the motor system associated with facial expressions.The supporting physiological theory for AU labeling of emotions is obtained by adding facial muscle movements patterns.We present the detailed process of emotion labeling and the detection and recognition of AU.Based on the above research, the video's coding of spontaneous expressions and micro-expressions is concluded and prospected. (shrink)