Due to the arrival of positive psychology in the development of teaching, the construct of engagement has been thrived and got a notable function in the educational arena. Alternatively, numerous individual differences, containing ambiguity of tolerance, have been taken into consideration as a result of the key role they can play in the process of learning, and thus, on different facets of the learners’ engagement. Furthermore, resilience is recommended to be an alternate and effective way of engaging English as a (...) foreign language learners. Also, it is a significant feature of the human adaptation system in which students can efficaciously manage and tackle stressful involvements despite their troubles and disasters. Given the eminence of both ambiguity tolerance and resilience in educational settings and the fact that little attention has been given to these constructs in foreign language learning, the present review makes an effort to scrutinize the impact of ambiguity of tolerance and resilience on EFL learners’ engagement. Succinctly, the fundamental roles of ambiguity tolerance and resilience in learners’ engagement were confirmed, and consistent with the conclusions drawn from the present review, some suggestions are set forth concerning the implications of this paper. (shrink)

Classification and gradation system adopts different security protection schemes for different types of data by implementing classification and gradation management of data, which is an important pretechnical means for data security protection and prevention of data leakage. This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. The generated training model can intelligently identify and classify file streams, improving work efficiency and accuracy of classification and (...) gradation. At the same time, the differences, advantages, and disadvantages of K-NN, DT, and LinearSVC algorithms are compared. The experimental data shows that LinearSVC algorithm is applicable to high-dimensional data, with discrete, sparse data features and large number of features, which is more suitable for classification of sensitive data of enterprises. (shrink)

Students’ active learning behavior determines learning performance. In post-COVID-19 period, Online Merging Offline method become a common way of university students’ learning. However, at present, there are few studies in active learning behavior in the OMO mode. Combined with learning satisfaction and Technology Acceptance Model, this paper proposes an Online Active Learning Model to predict the influencing factors of college students’ active learning behavior and then analyzes the differences between OMO model and pure online model by multi-group analysis based on (...) the model. The designed questionnaire was distributed, and a total of 498 valid questionnaires were collected. Using SmartPLS to analyze partial least squares structural equation modeling and MGA, it is found that: there are differences in the influencing factors of active learning between OMO and pure online model; the moderating effect of learning complaint in OMO mode is not established, and social isolation and age does not affect active learning in OMO mode; learning quality, perceived ease of use, expectation, perceived usefulness, and social isolation indirectly affect active learning through learning satisfaction in both OMO model and pure online model; learning satisfaction is an important mediating variable affecting active learning; and learning complaints will negatively regulate the relationship between learning satisfaction and active learning only in pure online model. According to these findings, the paper provides theoretical and practical implementation suggestions implications for OMO teaching and OAL to ensure the expected learning outcome. (shrink)

Professional values are standards for action and provide a framework for evaluating behavior. This study examined changes in the professional values of nursing students between their entrance to and graduation from an undergraduate nursing program. A pre- and post-test design was employed. A convenience sample of 94 students from a university in Taiwan was surveyed. Data were collected from students during the sophomore and senior years. Total scores obtained for the revised Nurses Professional Values Scale during the senior year of (...) the nursing program were significantly higher than upon program entry. The ‘caring’ subscale was scored highest at both program entry and graduation, but the pre- and post-test scores were not significantly different from each other. The students scored significantly higher on the ‘professionalism’ and ‘activism’ subscales at post-test than they did at pre-test. Professional values changed in a positive direction between the beginning of the student nurses’ educational experience and their graduation. The results supported the premise that education had a positive effect on these students’ professional values but causality could not be assumed. (shrink)

© The Author, 2017. Published by Oxford University Press. All rights reserved. In this article, we introduce a lightweight dynamic epistemic logical framework for automated planning under initial uncertainty. We generalize the standard conformant planning problem in AI in two crucial aspects: first, the planning goal can be any formula expressed in an epistemic propositional dynamic logic ; second, procedural constraints of the desired plan specified by regular expressions can be imposed. We then reduce the problem of generalized conformant planning (...) to the model checking problem of our logic. Although our conformant planning problem is much more general than the standard one with Boolean goals and no procedural constraints, the complexity is still PSPACE-complete which is equally hard as standard conformant planning over explicit transition systems. (shrink)

Background Social responsibility can motivate disaster relief nurses to devote themselves to safeguarding rights and interests of people when facing challenges that threaten public health. However, few studies focused on the relationship of moral courage, job-esteem, and social responsibility among disaster relief nurses. Objective To explore the influence of moral courage and job-esteem on the social responsibility in disaster relief nurses and clarify the relationship model between them. Methods A cross-sectional study was conducted among 716 disaster relief nurses from 14 (...) hospitals in central China through an online survey, including moral courage scale, job-esteem scale, and social responsibility questionnaire. The data were analyzed by Pearson’s correlation, and the mechanism of the effect of moral courage and job-esteem on social responsibility was completed. Ethical considerations This study was approved by the Medical Ethics Committee of the Second Xiangya Hospital of Central South University (Approval Number: 2019016). Results Disaster relief nurses’ moral courage positively impacted social responsibility (r = 0.677, p < 0.01), and moral courage could affect social responsibility through the mediating role of job-esteem. Conclusion Job-esteem mediated between moral courage and social responsibility among disaster relief nurses. Nursing managers regular assessment of nurses’ moral courage and interventions such as meetings and workshops can reduce moral distress, foster morally courageous behavior, enhance job-esteem, and improve social responsibility performance among disaster relief nurses. (shrink)

Biobanks are vital for advancing medical research, and public participation is a crucial determinant of their success. This study uses a survey to assess the awareness, attitudes, and motivation of the public in China with regard to participating in biobanks. We conducted an online survey that yielded 616 responses from participants with diverse demographic backgrounds. The survey included questions on the respondents’ awareness of biobanks, their attitudes toward them, their preferences with regard to consent, and their concerns. The results of (...) the survey revealed that 57.95% of the respondents were aware of biobanks. Altruism was the respondents’ primary motivation for participation in biobanks. Their preferences for models of consent varied. The respondents raised concerns about the commercialization of biobanks (56.66%) and data privacy (55.84%). Notably, only 37.01% of the respondents were concerned about the risk of discrimination in biobanks, where this was lower compared with the results for populations in Western countries. This study provides valuable insights into the Chinese public’s awareness of and attitudes toward biobanks. To foster public trust and enhance participation, biobanks should prioritize transparent and continual communication to ensure that the participants are well informed about the use and protection of the samples that they have donated. Future research should explore the influence of cultural nuances to develop strategies that address specific concerns and ethical challenges in the context of public participation in biobanks. (shrink)

The genetic basis for animal social organization is poorly understood. Fire ants provide one of the rare cases where variation in social organization has been demonstrated to be under genetic control, which amazingly, segregates as a single Mendelian locus. A recent genetic, genomic, and cytological analysis revealed that this locus actually consists of over 600 genes locked together in a supergene that possesses many characteristics of sex chromosomes. The fire ant social supergene also behaves selfishly, and an interesting evolutionary question (...) is whether the genes incorporated first into the social supergene were those for social adaptation, selfish genetic drive, or something else. In depth, functional molecular genetic analysis in fire ants and comparative genomics in other closely related socially polymorphic species will be required to resolve this question.Editor's suggested further reading in BioEssays Social supergenes of superorganisms: Do supergenes play important roles in social evolution? Abstract. (shrink)

Globalization is a part of modern life. Sharing a common set of professional nursing values is critical in this global environment. The purpose of this research was to examine the professional values of nursing students from two distinct cultural perspectives. Nurse educators in Taiwan partnered with nurse educators in the United States to compare professional values of their respective graduating nursing students. The American Nurses Association Code of Ethics served as the philosophical framework for this examination. The convenience sample comprised (...) 94 Taiwanese students and 168 US students. Both groups reported high scores on an overall measure of values. They did differ substantially on the relative importance of individual items related to advocacy, competence, education, self-evaluation, professional advancement, and professional associations. Global implications for the collaborative practice of nurses from different cultures working together can be improved by first recognizing and then attending to these differences in value priorities. (shrink)

This study aimed to explore the association between the functioning of family environment and academic burnout of Chinese college students as well as the mediating effects of the interpersonal resource and intrapersonal resource [i.e., positive psychological capital ] in this relationship. A total of 1971 Chinese undergraduates were involved in an online questionnaire survey and data analysis. It was found that family cohesion and adaptability was negatively related to academic burnout. Mediation analyses demonstrated that family cohesion and adaptability did not (...) directly affect academic burnout, but indirectly through increasing PsyCap, and through enhancing peer support and then increasing PsyCap in serial. Meanwhile, the relationship between family cohesion and adaptability and academic burnout was not mediated by peer support alone. These findings highlight the family environment’s crucial role in youth mental health and positive development in the college context. (shrink)

Protein disulfide isomerase (PDI) is one of the most abundant and critical protein folding catalysts in the endoplasmic reticulum of eukaryotic cells. PDI consists of four thioredoxin domains and interacts with a wide range of substrate and partner proteins due to its intrinsic conformational flexibility. PDI plays multifunctional roles in a variety of pathophysiological events, both as an oxidoreductase and a molecular chaperone. Recent studies have revealed that the conformation and activity of PDI can be regulated in multiple ways, including (...) posttranslational modification and substrate/ligand binding. Here, we summarize recent advances in understanding the function and regulation of PDI in different pathological and physiological events. We propose that the multifunctional roles of PDI are regulated by multiple mechanisms. Furthermore, we discuss future directions for the study of PDI, emphasizing how different regulatory modes are linked to the conformational changes and biological functions of PDI in the context of diverse pathophysiologies. (shrink)

BackgroundThe number of students enrolled in higher education in China accounts for more than one-fifth of the world, and universities, as a community of faculty, staff and scholars, currently do not have a scale that specifically assesses the well-being of the population in the environment of Chinese universities. However, the University of Pittsburgh has developed a comprehensive well-being scale, referred to as the Pitt Wellness Scale, specifically to measure people’s well-being in a university environment.AimsInvestigate the psychometric properties of the Pitt (...) Wellness Scale in Chinese university environmental samples.MethodsThe original scale was culturally adapted and modified through expert consultation, a random sample of 1870 current faculty, staff, and students were selected for the questionnaire survey. Exploratory factor analysis and confirmatory factor analysis were used to investigate the potential factor structure of the Chinese Revision of the Pitt Wellness Scale and to measure its reliability and validity. Finally, the factors that affect people’s well-being in the Chinese university environment were explored.ResultsThe Chinese Revision of the Pitt Wellness Scale retained 30 items, and the EFA supports a five-factor structure, which differed from the results of the original scale, and the CFA results showed that the model fitted well. The discriminant validity of the modified Chinese scale was excellent. The overall Omega coefficient of the scale was 0.958, and the reliability of the retest after 4 weeks was 0.821.ConclusionThe Chinese Revision of the Pitt Wellness Scale possesses satisfactory psychometric properties, and it can be considered an instrument for assessing personal well-being in Chinese university environment. (shrink)

This study investigates the volatility risk premium on the emerging financial market. We also consider the expected return and ESG sentiment. Based on the SSE 50 ETF 5-minute high-frequency spots and daily options data from 2016 to 2021, we adopt nonparametric model-free approaches to calculate realized and implied volatilities. And the volatility risk premium is constructed by subtracting these volatility series. We examine the relations between the volatility risk premium and future excess returns as well as ESG sentiment through multifactor (...) specifications. We find that the volatility risk premium also exists in the Chinese market and is significantly negative. In addition, the statistically positive correlation between the volatility risk premium and aggregate returns is an outlier compared to the empirically negative pattern in developed markets. At last, ESG sentiment is positively associated with the volatility risk premium, especially the impact of environmental and social. This evidence supports the agency theory, which indicates that investors perceive ESG investments as waste resources in a short term and become potentially risky. (shrink)

BackgroundPrehospital delay is associated with non-modifiable factors such as age, residential region, and disease severity. However, the impact of psychosocial factors especially for job burnout on prehospital decision delay is still little understood.MethodThis internet-based survey was conducted between 14 February 2021 and 5 March 2021 in China through the Wechat platform and web page. Self-designed questionnaires about the expected and actual length of prehospital decision time and the Chinese version of Maslach Burnout Inventory-General Survey, Type D Personality Scale-14, and Social (...) Support Rating Scale were applied. A total of 1,039 general participants with a history of perceptible but tolerable body discomfort were included.ResultsThe top six reasons for prehospital decision delay were: endure until self-healing, too busy to ask for leave, process for seeing a doctor too complicated, too tired after work, worry about the expenditure, and fear of being identified as with serious problem. The univariate analyses revealed that older age, type D personality, job burnout, and worrying about expenditure were associated with prolonged prehospital decision time, while engaged in medical-related job and with more social support would shorten the delay. The multivariate analysis using logistic regression model with forward selecting method showed that age [per 10 years, odds ratio 1.19, p 7 days). Mediating effects were analyzed by using bias-corrected percentile bootstrap methods. Social support was found partially mediated the relationship between the determinants and prehospital decision time. The partial mediating effect of social support accounted for 24.0% of the total effect for job burnout and 11.6% for worrying about expenditure.ConclusionPsychosocial factors have a non-negligible impact on prehospital decision delay. The crucial part of prehospital decision delay may be the lack of motivation inside. Job burnout and lack of social support, as two commonly seen features in the modern world, should be given enough consideration in disease prevention and treatment. (shrink)

Background:When the contagious COVID-19 spread worldwide, the frontline staff faced unprecedented excessive work pressure and expectations of all of the society.Objective:The aim was to explore healthcare workers’ stress and influencing factors when caring for COVID-19 patients from an altruistic perspective.Methods:A cross-sectional, descriptive study was conducted in a tertiary hospital during the outbreak of COVID-19 between February and March 2020 in Wuhan, the capital city of Hubei province in China. Data were collected from 1208 healthcare workers. Descriptive statistics and multiple linear (...) regression were used to analyze the data.Ethical considerations:Research ethics approval (with the code of TJ-IRB20200379) was obtained from Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology. Written informed consent was also received from participants.Results:Less than 60% of participants chose moderate or severe stress on all stressors, indicating a low stress level among healthcare workers. The main source of stress among frontline healthcare workers caring for COVID-19 patients came from the fear of being infected, the fear of family members being infected, and the discomfort caused by protective equipment. Frontline staff who were nurses, were married, and had worked more than 20 days suffered higher stress, whereas rescue staff showed lower stress.Conclusion:The healthcare workers caring for patients with COVID-19 had low stress level, although they still had the fear of being infected or uncomfortable feeling caused by personal protective equipment. A low stress level among healthcare workers indicated their professional devotion and altruism during COVID-19 epidemic. Medical institutions and the government should continue to strengthen infection prevention measures and provide more comprehensive care involving families of frontline healthcare workers, especially nurses and married staff. It will be a lesson to other countries that awaking healthcare workers’ inside motivation and providing necessary support from government and society were significant. (shrink)

Background Ethical dilemmas that arise in the clinical setting often require the collaboration of multiple disciplines to be resolved. However, medical and nursing curricula do not prioritize communication among disciplines regarding this issue. A common teaching strategy, problem-based learning, could be used to enhance communication among disciplines. Therefore, a university in southern Taiwan developed an interprofessional ethics education program based on problem-based learning strategies. This study described tutors’ experience teaching in this program. Aim To explore the phenomenon of teaching and (...) learning in interprofessional ethics education for medical and nursing students from the perspectives of tutors. Design Phenomenological qualitative research. Methods Medical and nursing students completed a 6-week interprofessional ethics education program moderated by either physician or nurse tutors. At the conclusion of the ethics education program, all 14 tutors were invited to participate in focus group interviews. Among them, six tutors (three nursing tutors and three physician tutors) participated in additional individual interviews. All of the contents from the focus group interviews and individual interviews were recorded and transcribed. Using the phenomenological approach, the phenomenon of teaching and learning in interprofessional ethics education were generated. Ethical consideration The study was approved by the Institutional Review Board. Findings Three themes emerged from the tutors' teaching perspectives, including the instructor’s motivation to teach, the use of narrative case scenarios, and the emphasis on improving interprofessional ethics communication. Discussion Problem-based learning creates an interprofessional communication platform in interprofessional ethics education. The phenomenon of value convergence between tutors and students, between different students’ professions, and between different students’ professional maturities is observed. Conclusion Problem-based learning is an effective teaching strategy for creating a communication platform for interprofessional ethics education. Ethic curriculum should emphasize motivating instructor, use narrative case scenarios, and focus on interprofessional communication. (shrink)

The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides (...) the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology. (shrink)

Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature (...) combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data. (shrink)

Because of the lack of interaction between seeking mode cats and tracking mode cats in cat swarm optimization, its convergence speed and convergence accuracy are affected. An information interaction strategy is designed between seeking mode cats and tracking mode cats to improve the convergence speed of the CSO. To increase the diversity of each cat, a top-N learning strategy is proposed during the tracking process of tracking mode cats to improve the convergence accuracy of the CSO. On ten standard test (...) functions, the average values, standard deviations, and optimal values of the proposed algorithm with different N values are compared with the original CSO algorithm and the adaptive cat swarm algorithm based on dynamic search. Experimental results show that the global search ability and the convergence speed of the proposed algorithm are significantly improved on all test functions. The proposed two strategies will improve the convergence accuracy and convergence speed of CSO greatly. (shrink)

BackgroundIndividuals with mind-wandering experience their attention decoupling from their main task at hand while others with flow experience fully engage in their task with the optimum experience. There seems to be a negative relationship between mind-wandering and flow. However, it remains unclear to what extent mind-wandering exerts an impact on flow. And it is also elusive whether physical activity and mindfulness, which are as important factors that affected individuals’ attentional control and psychological health, are beneficial in explaining the association between (...) mind-wandering and flow. The current study investigated the relationship between mind-wandering and flow, and the potential mediation effects of physical activity and mindfulness in this association.MethodsA cross-sectional exploratory study design, including multiple scales such as the Mind-Wandering Questionnaire, the International Physical Activity Questionnaire Short Form, Mindfulness Attention and Awareness Scale, and the Short Dispositional Flow Scale was applied. Descriptive statistics and bivariate correlation coefficients were applied in the analysis of these data. A multiple mediation model was used to examine the relationships between mind-wandering, flow, physical activity, and mindfulness.ResultsMind-wandering was inversely associated with physical activity, mindfulness and flow, respectively; and flow was positively related to physical activity and mindfulness, respectively. Moreover, multiple mediation results demonstrated that physical activity and mindfulness, respectively, mediated the relationship between mind-wandering and flow.ConclusionThese findings are helpful to understand how our minds attend to the present moment, and the crucial roles of physical activity and mindfulness in the association between mind-wandering and flow. An implication of these is the possibility that the effective strategies aimed at enhancing both the levels of physical activity and mindfulness are needed to reduce the negative impact of mind-wandering on flow. (shrink)

Irresponsible research practices damaging the value of science has been an increasing concern among researchers, but previous work failed to estimate the prevalence of all forms of irresponsible research behavior. Additionally, these analyses have not included articles published in the last decade from 2011 to 2020. This meta-analysis provides an updated meta-analysis that calculates the pooled estimates of research misconduct and questionable research practices, and explores the factors associated with the prevalence of these issues. The estimates, committing RM concern at (...) least 1 of FFP and QRPs concern 1 or more QRPs, were 2.9% and 12.5%, respectively. In addition, 15.5% of researchers witnessed others who had committed at least 1 RM, while 39.7% were aware of others who had used at least 1 QRP. The results document that response proportion, limited recall period, career level, disciplinary background and locations all affect significantly the prevalence of these issues. This meta-analysis addresses a gap in existing meta-analyses and estimates the prevalence of all forms of RM and QRPs, thus providing a better understanding of irresponsible research behaviors. (shrink)

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense mission planning is described as a sequential decision-making problem and formalized as a Markov decision process. Then, (...) the SEAD intelligent planning model based on the proximal policy optimization algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy. (shrink)

In recent years, somatosensory interaction technology, represented by Microsoft’s Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the (...) decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis. (shrink)

Experts play a crucial role in underpinning decision-making in most management situations. While recent studies have disclosed the impacts of individuals’ inherent cognition and the external environment on expert performance, these two-dimensional mechanisms remain poorly understood. In this study, we identified 14 factors that influence expert performance in a bid evaluation and applied cross-impact matrix multiplication to examine the interdependence of the factors. The results indicate that the two dimension-related factors affect each other within a person–environment system, and a poor (...) situation perception gives rise to the deviation of expert performance. Expert performance can be improved if external supervision and expertise are strengthened through deliberate practices. The study proposes a new expert performance research tool, elucidates its mechanism in bid evaluation from a cognitive psychology perspective, and provides guidelines for its improvement in workplace contexts. (shrink)

Mental health issues are becoming increasingly prevalent amongst university students. However, research on the psychological profile of the general university population is relatively limited. Thus, this study analyses the current state of university students’ psychological conditions; the demographic differences in depression, anxiety, and stress and the influencing factors. The objectives are to provide additional appropriate guidance in mental health for university students with different demographic characteristics. A cross-sectional study of 6,032 university students nationwide was conducted from October 2020 to January (...) 2021. A randomized whole-group sampling method was used to select the study participants, and the 21-item Depression, Anxiety, and Stress Scale was used. P < 0.05 in the final model were considered statistically significant. The number of university students with no complain of depression, anxiety, or stress was 3,751. The odds of developing complain of depression were higher amongst anxious respondents and senior year than their counterparts. Students with “myopia” were 1.263 times more likely to be anxious. In terms of “impaired” or not, impaired is defined as any injury, such as sprain, strain, and fracture, “impaired” university students were 1.321 times more likely to be anxious. Furthermore, history of impairment and myopia increased the odds of stress by 1.305 and 1.305, respectively. Myopia, physical-activity-related injury and irrational eating habits are risk factors for complain of anxiety and stress. Males, upper grades, low parental education, and irrational eating habits are risk factors for complain of depression. Low physical activity levels are also an influential factor for complain of depression. DASS consists of interchangeable risk factors and multiple complains of DASS may coexist. (shrink)

With more and more investors exerting their voices through network forums or social media platforms, the relationships between online investor sentiment and stock movements have drawn more and more attention. In this paper, we crawl stock comments from China’s most popular online stock forum, East Money, and then develop a sentiment classifier using the LSTM method. Using the online investor sentiment of the stock forum, we explore the effect of online investor sentiment on the stock movements of CSI300. The results (...) show that online investor sentiment has a significant positive impact on both stock return and trading volume and remains significant after controlling book-to-market ratio, BETA, and market value. Moreover, investor sentiment has a significant positive impact on order imbalance of big trade, which represents the main flow of money in the market. As a result, investor sentiment has a positive impact on the major fund flows in the market. In other words, an increase in investor sentiment can boost the major money flows in the market to some extent. From a practical point of view, investor sentiment can assist investors to make investment decisions and help the government to regulate the stock market. (shrink)

Smart homes represent an effective approach to improve one’s quality of life. Developing user interfaces that are both comfortable and understandable can assist users, particularly the elderly, embrace smart home technologies. It’s critical to concentrate on the characteristics of smart home interface design and their impact on people of various ages. Since sliders are one of the most common components utilized in the smart home user interface, this article aimed to investigate the effects of slider design characteristics on user performance (...) and preference. Thirty-four participants were recruited for the experiment. Our results revealed that both groups had shorter task completion time, less fixation time, and saccades on horizontal sliding orientation and larger buttons, which means better user performance. For the older group, the slider with color gradient track led to better user performance, while the track color only had less effect on the performance of the younger group. In terms of user preference, the results and performance of the older group were basically consistent, while the younger group had no significant difference in sliding orientation and track color. (shrink)

To better reflect the precise clustering results of the data samples with different shapes and densities for affinity propagation clustering algorithm, an improved integrated clustering learning strategy based on three-stage affinity propagation algorithm with density peak optimization theory was proposed in this paper. DPKT-AP combined the ideology of integrated clustering with the AP algorithm, by introducing the density peak theory and k-means algorithm to carry on the three-stage clustering process. In the first stage, the clustering center point was selected by (...) density peak clustering. Because the clustering center was surrounded by the nearest neighbor point with lower local density and had a relatively large distance from other points with higher density, it could help the k-means algorithm in the second stage avoiding the local optimal situation. In the second stage, the k-means algorithm was used to cluster the data samples to form several relatively small spherical subgroups, and each of subgroups had a local density maximum point, which is called the center point of the subgroup. In the third stage, DPKT-AP used the AP algorithm to merge and cluster the spherical subgroups. Experiments on UCI data sets and synthetic data sets showed that DPKT-AP improved the clustering performance and accuracy for the algorithm. (shrink)

A scientific paper’s citation represents its influence, which is the most intuitive indicator to access the quality of papers. This paper mainly adopts the social network analysis method, using the authors and the keywords of sports industry papers in China to constitute the networks of collaboration and knowledge, to explore effects of the degree centrality of authors and keywords and the structural hole of authors and keywords on the citation of papers in the collaboration and knowledge networks and draw the (...) following conclusions: as for collaboration networks, the degree centrality at the paper level is positively correlated with citations; in the collaboration network, the positive correlation between the structural hole at the paper level and citations does not exist; within knowledge networks, an inverted- U shape was found between degree centrality and paper’s citation; and within knowledge networks, a positive correlation is in existence between the structural hole of papers and their citation. This study synthesizes the already widely used collaboration network with the knowledge network constructed through keywords, distinguishes from the previous network features focusing at the author level, and explores research projects of Chinese Sports Industry from the paper level, providing a new perspective for the research of sports industry in China, complementing the methods and ideas of sports industry research, as well as providing a reference for the research in other disciplinary fields. (shrink)

Climate change presents a fundamental challenge to the current global energy regime. Under the Framework Convention on Climate Change, the international community is developing the architecture of a policy response. Three serious flaws are examined: (a) the potential sacrifice of small island states, (b) the use of market-based policy measures to commodify the atmospheric commons, and (c) the substitution of carbon sequestration for meaningful reductions in energy use. The authors’ analysis of the politics of climate change, based on these issues, (...) suggests a new understanding of ecology is emerging—what they term postmodern ecology—in which a global environmental crisis is risked to secure the future of the world energy regime. An alternative, based on principles of sustainability and equity, is proposed that would require abandoning the global energy status quo. (shrink)

Our knowledge about the functional diversity and importance of RNA in biology has grown enormously over the past three decades and has driven efforts to develop better tools to characterize RNAs. Amongst these tools are methods for preparing specifically labeled or chemically modified RNAs, which are essential for basic research, biomedical, and clinical applications. Understanding the potential and limits of these different RNA synthesis and labeling strategies is important in deciding how to approach the preparation of a particular RNA molecule. (...) Here, we review these various labeling methods and future directions of the field. (shrink)

Numerous studies have focused on the understanding of rapid automatized naming, which can be applied to predict reading abilities and developmental dyslexia in children. Eye tracking technique, characterizing the essential ocular activities, might have the feasibility to reveal the visual and cognitive features of RAN. However, traditional measures of eye movements ignore many dynamical details about the visual and cognitive processing of RAN, and are usually associated with the duration of time spent on some particular areas of interest, fixation counts, (...) revisited fixation counts, saccadic velocities, or saccadic amplitudes. To cope with this drawback, we suggested an entropy-based method to measure eye movements for the first time, which first mapped eye movements during RAN in a time-series and then analyzed the time-series by a proper definition of entropy from the perspective of information theory. Our findings showed that the entropy was more sensitive to reflect small perturbation of eye movements, and thus gained better performance than traditional measures. We also verified that the entropy of eye movements significantly deceased with the age and the task complexity of RAN, and significantly correlated with traditional eye-movement measures [e.g., total time of naming ] and the RAN-related skills [e.g., selective attention, cognitive speed, and visual-motor integration]. Our findings may bring some new insights into the understanding of both RAN and eye tracking technique itself. (shrink)

In recent years, supply chain finance is exploited to solve the financing difficulties of small- and medium-sized enterprises. SME credit risk assessment is a critical part in the SCF system. The diffusion of SME credit risk may cause serious consequences, leading the whole supply chain finance system unstable and insecure. Compared with traditional credit risk assessment models, the supply chain relationship, credit condition of SME, and core enterprises should all be considered to rate SME credit risk in SCF. Traditional methods (...) mix all indicators from different index systems. They cannot give a quantitative result on how these index systems work. Furthermore, traditional credit risk assessment models are heavily dependent on the number of annotated SME data. However, it is implausible to accumulate enough credit risky SMEs in advance. In this paper, we propose an adaptive heterogenous multiview graph learning method to tackle the small sample size problem for SMEs’ credit risk forecasting. Three graphs are constructed by using indicators from supply chain operation, SME financial indicator, and nonfinancial indicator individually. All the graphs are integrated in an adaptive manner, providing a quantitative explanation on how the three parts cooperate. The experimental analysis shows that the proposed method has good performance for determining whether SME is risky or nonrisky in SCF. From the perspective of SCF, SME financing ability is still the main factor to determine the credit risk of SME. (shrink)

Slagging-off is an important preprocessing operation of steel-making to improve the purity of iron. Current manual-operated slag removal schemes are inefficient and labor-intensive. Automatic slagging-off is desirable but challenging as the reliable recognition of iron and slag is difficult. This work focuses on realizing an efficient and accurate recognition algorithm of iron and slag, which is conducive to realize automatic slagging-off operation. Motivated by the recent success of deep learning techniques in smart manufacturing, we introduce deep learning methods to this (...) field for the first time. The monotonous gray value of industry images, poor image quality, and nonrigid feature of iron and slag challenge the existing fully convolutional networks. To this end, we propose a novel spatial and feature graph convolutional network module. SFGCN module can be easily inserted in FCNs to improve the reasoning ability of global contextual information, which is helpful to enhance the segmentation accuracy of small objects and isolated areas. To verify the validity of the SFGCN module, we create an industrial dataset and conduct extensive experiments. Finally, the results show that our SFGCN module brings a consistent performance boost for a wide range of FCNs. Moreover, by adopting a lightweight network as backbone, our method achieves real-time iron and slag segmentation. In the future work, we will dedicate our efforts to the weakly supervised learning for quick annotation of big data stream to improve the generalization ability of current models. (shrink)

We analyze and study the tracking of nonrigid complex targets of sports video based on mean shift fusion color histogram algorithm. A simple and controllable 3D template generation method based on monocular video sequences is constructed, which is used as a preprocessing stage of dynamic target 3D reconstruction algorithm to achieve the construction of templates for a variety of complex objects, such as human faces and human hands, broadening the use of the reconstruction method. This stage requires video sequences of (...) rigid moving target objects or sets of target images taken from different angles as input. First, the standard rigid body method of Visuals is used to obtain the external camera parameters of the sequence frames as well as the sparse feature point reconstruction data, and the algorithm has high accuracy and robustness. Then, a dense depth map is computed for each input image frame by the Multi-View Stereo algorithm. The depth reconstruction with a too high resolution not only increases the processing time significantly but also generates more noise, so the resolution of the depth map is controlled by parameters. The multiple hypothesis target tracking algorithms are used to track multiple targets, while the chunking feature is used to solve the problem of mutual occlusion and adhesion between targets. After finishing the matching, the target and background models are updated online separately to ensure the validity of the target and background models. Our results of nonrigid complex target tracking by mean shift fusion color histogram algorithm for sports video improve the accuracy by about 8% compared to other studies. The proposed tracking method based on the mean shift algorithm and color histogram algorithm can not only estimate the position of the target effectively but also depict the shape of the target well, which solves the problem that the nonrigid targets in sports video have complicated shapes and are not easy to track. An example is given to demonstrate the effectiveness and adaptiveness of the applied method. (shrink)

Public value facilitates the democratization of public policy making, it should be the common-sense and bottom-line for policymakers. However, it has not been paid enough attention by the practical and theoretical circles. How to realize the transformation from individual value to public value and make public policies be in line with citizens’ preferences, is vital for the democratic politics. How individual value becomes public value in the agenda-setting stage during the policy-making process? What are the key influencing factors? Using the (...) case of Yangzhou Chemical Co. Youjia Program and interview data, this paper studies the mechanisms that could make individual value be transformed to public value through deeply observing and measuring individuals’ attitudes, demands, motivations, beliefs, and preferences. We argue that (1) public value is an essential foundation for agenda setting and guidance to policy making, whereas individual value cannot naturally substitute value orientation in the public sphere. (2) Transferring individual value to public value can reduce divergence appearing in the subsequent stage in policy activities. (3) Factors that can influence this transformation are policy subjects, relations, rule and expression channels with various determinants and conditions. (4) Public value can terminate irrational and unreasonable policies or drag the policy issues back into the agenda-setting stage. This paper provides unique insights into why individual citizens enter the public sphere to take public actions so that the Chinese government can recognize additional public opinions, which makes cooperative governance be realized. (shrink)

A growing body of research has started to examine how individuals from different countries may differ in their use of ethically questionable tactics during business negotiations. Whereas prior research focused on the main effect of the national culture or nationality of the negotiator, we add a new factor, which is the nationality of the counterpart. Looking at both these variables allows us to examine whether and how people may change their likelihood of using ethically questionable tactics in inter-cultural negotiations as (...) opposed to intra-cultural ones. Results of an experiment show that overall, American participants were less likely than Chinese participants to use ethically questionable tactics in negotiations. However, American participants were more likely to use ethically questionable tactics, particularly those related to false promises and inappropriate information gathering, in inter-cultural negotiations with Chinese counterparts, than in intra-cultural negotiations with American counterparts. By contrast, Chinese participants were less likely to use ethically questionable tactics, particularly those related to false promises and attacking opponent’s network, in inter-cultural negotiations with American counterparts, than in intra-cultural negotiations with Chinese counterparts. Implications and future directions are discussed. (shrink)

This paper provides an in-depth analysis and discussion of the key issues of quality management, evaluation, and detection contained in big data for automotive application scenarios. A generalized big data quality management model and programming framework are proposed, and a series of data quality detection and repair interfaces are built to express the processing semantics of various data quality issues. Through this data quality management model and detection and repair interfaces, users can quickly build custom data quality detection and repair (...) tasks for different data quality requirements. To improve the operational efficiency of complex data quality management algorithms in large-scale data scenarios, corresponding parallelization algorithms are studied and implemented for detection and repair algorithms with long computation time, including priority-based multiconditional function-dependent detection and repair algorithms, entity detection, and extraction algorithms based on semantic information and chunking techniques, and plain Bayesian-based missing value filling algorithms, and this paper proposes a data validity evaluation algorithm and enhances the validity of the original data in practical applications by adding temporal weights, and finally it passed the experimental validation. Through the comprehensive detection process of data importance, network busyness, duration of transmission process, and failure situation, the efficiency has been increased by 20%, and an adaptive data integrity detection method based on random algorithm and encryption algorithm is designed. After experimental verification, this method can effectively detect the integrity of the data transmission process and improve the application of data value, and the final effect is increased by 30.5%. (shrink)

An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- optimized kernel extreme learning machine model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions using the complete (...) ensemble empirical mode decomposition based on adaptive noise. Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings. (shrink)

With the troubles of core losses and all-state confined to certain limitations which are the innate traits of permanent magnet synchronous motors, this article develops a command filtered adaptive backstepping approach to follow the track of PMSM’s desired rotor position. To begin with, the RBF neural network technique is utilized to get close to the uncharted nonlinear terms which existed in PMSM’s mathematical model. Meanwhile, an advanced adaptive command filter control methodology is constructed to avoid the computing explosion during the (...) process of backstepping design. Furthermore, to make sure that all the state variables are confined into certain ranges, we employed the barrier Lyapunov function at every step of the controllers construction. In addition, an error compensating mechanism is proposed to neutralize filtering errors and only one adaptive law is required. At last, simulation results bear out the superiority of the aforementioned control scheme. (shrink)

Cognitive and neural processes underlying visual creativity have attracted substantial attention. The current research uses a critical time point analysis to examine how spontaneous activity in the primary visual area is related to visual creativity. We acquired the functional magnetic resonance imaging data of 16 participants at the resting state and during performing a visual creative synthesis task. According to the CTPA, we then classified spontaneous activity in the PVA into critical time points, which reflect the most useful and important (...) functional meaning of the entire resting-state condition, and the remaining time points. We constructed functional brain networks based on the brain activity at two different time points and then subsequently based on the brain activity at the task state in a separate manner. We explore the relationship between resting-state and task-fMRI functional brain networks. Our results found that: the pattern of spontaneous activity in the PVA may associate with mental imagery, which plays an important role in visual creativity; in comparison with the RTPs-based brain network, the CTP-network showed an increase in global efficiency and a decrease in local efficiency; the regional integrated properties of the CTP-network could predict the integrated properties of the creative-network while the RTP-network could not. Thus, our findings indicated that spontaneous activity in the PVA at CTPs was associated with a visual creative task-evoked brain response. Our findings may provide an insight into how the visual cortex is related to visual creativity. (shrink)