With the rapid development of Internet of Things technology, RFID technology has been widely used in various fields. In order to optimize the RFID system hardware deployment strategy and improve the deployment efficiency, the prediction of the RFID system identification rate has become a new challenge. In this paper, a neighborhood rough set and random forest combination model is proposed to predict the identification rate of an RFID system. Firstly, the initial influencing factors of the RFID system identification rate are (...) reduced using neighborhood rough set theory combined with the principle of heuristic attribute reduction of neighborhood weighted dependency, thus obtaining a kernel factor subset. Secondly, a random forest prediction model is established based on the kernel factor subset, and a confusion matrix is established using out-of-bag data to evaluate the prediction results. The test is conducted under the constructed RFID experimental environment, whose results showed that the model can predict the identification rate of the RFID system in a fast and efficient way, and the classification accuracy can reach 90.5%. It can effectively guide the hardware deployment and communication parameter protocol setting of the system and improve the system performance. Compared with BP neural network and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. Finally, the validity of the proposed model was verified by the RFID intelligent archives management platform. (shrink)

With the spread of the COVID-19 pandemic worldwide, university teachers are coping with and adjusting to online teaching platforms. In this concurrent mixed-methods study, 10 science and technology universities as the research sites were first chosen, and educational planning in these sites during the pandemic was examined; then, eight selected teacher participants in these sites were interviewed to report how their beliefs and practices changed during the pandemic echoing the examined educational planning. The results show that educational planning and policies (...) assisted teachers in accommodating the new demands and changes during the pandemic; teachers' beliefs and practices generally echoed the educational planning and policies, with certain tensions still existing. The discussion part of the study is centered around emergency remote teaching and planning, tensions between teacher beliefs and practices, and the shift from emergency remote teaching to regular, sustainable online schooling. The study provides administrators and teacher educators with insights on how emergency remote teaching can be planned and implemented during an unprecedented time. (shrink)

Since the coming of Positive Psychology in the field of second language acquisition, the significance of emotion, especially positive emotion, has been well recognized by researchers. Educational research has indicated that both emotion and engagement play fundamental roles in learning process and psychological wellbeing, but research on their relationship is scant in SLA. The present study contributed to the development of Positive Psychology in SLA by investigating the relationships between achievement emotions, behavioral engagement, and self-reported English achievements. 633 students from (...) four senior high schools in China participated in the study. Data collected through questionnaires revealed: Participants reported a medium to high level of foreign language enjoyment, foreign language classroom anxiety, and behavioral engagement; females had a higher level of FLE and behavioral engagement than males; the participants reported more anxiety and less enjoyment than western samples; significant correlations were found between FLE, FLCA, behavioral engagement, and self-reported achievement; significant mediating effects of behavioral engagement were found between both achievement emotions and self-reported achievement, and the mediating effect of engagement was stronger between FLCA and self-reported achievement. The findings extended the nomological network of achievement emotions, developed our insights into the complex relationships between emotions, learner engagement and language achievement, and revealed the mediating effects of behavioral engagement. Finally, directions for future study and implications for foreign language educators were provided. (shrink)

Since the sudden outbreak of the coronavirus disease 2019 epidemic in 2020, the second language learning patterns of students in mainland China have encountered new challenges that have had a psychological impact on mainland Chinese students. The epidemic has not only inconvenienced students’ normal second language learning but also greatly affected the second language learning patterns of mainland Chinese students. In the post-epidemic era, more and more students are becoming accustomed to studying and learning a second language online. The level (...) of informatization of second language learning patterns of students in mainland China has increased significantly. This study first analyses the mechanisms of change in second language learning patterns and further analyses the influence of knowledge background on the perception of second language learning patterns on this basis. To design the influencing factors of second language learning patterns, a questionnaire was used to investigate the influence of knowledge background on the perception of second language learning patterns. The survey was conducted on students who were learning a second language in mainland China. Then, the survey data were statistically analyzed. In analyzing the influence of effect on second language learning behaviors of students in mainland China, observed variables were designed, including observed variables of affective factors and learning behaviors. After that, the findings of the experiment were summarized based on the results of the questionnaire survey, and the positive influence of emotional factors on second language learning behaviors of mainland Chinese students in the post-development era was concluded. (shrink)

A series of our experimental studies show that some actions which consequentialists would treat as morally right or wrong were not regarded by ordinary people as a matter of morality. These results...

Background: Previous studies have found that alexithymia plays an important role in the pathogenesis of Internet addiction. However, the effect of alexithymia on both metacognition and Internet addiction has yet to be examined.Methods: The Toronto Alexithymia Scale, Metacognition Questionnaire, and Internet Addiction Test were used to assess a sample of 356 college students. A parallel mediator effect analysis was applied to test the hypothesis that metacognition mediates the relationship between alexithymia and Internet addiction.Results: The parallel multiple mediator models showed that (...) alexithymia predicted the five dimensions of metacognition and Internet addiction, and that three dimensions—cognitive confidence, positive beliefs about worry, and the need to control thoughts—partially mediated this relationship.Conclusion: Alexithymia could directly and indirectly predict Internet addiction via metacognition. (shrink)

本书内容包括:立法的准则、司法的尺度、司法推理的艺术、最好的论证、最好的反驳五部分。.

Research on abusive supervision is imbalanced in two ways. First, with most research attention focused on the destructive consequences of abusive supervision, there has been relatively little work on subordinate-related predictors of perceptions of abusive supervision. Second, with most research on abusive supervision centered on its main effects and the moderating effects of supervisor-related factors, there is little understanding of how subordinate factors can moderate the main effects of perceptions of abusive supervision on workplace outcomes. The current study aims to (...) advance knowledge of the roles of subordinates in the formation of and reactions to perceptions of abusive supervision. Specifically, based on victim precipitation theory, the authors examined subordinates’ personality traits and self-reports of task performance as antecedents of perceptions of abusive supervision. The results show that subordinates high in neuroticism or low in conscientiousness had high levels of perceived abusive supervision partially through their self-reported deleterious job performance. In addition, the authors investigated the moderating effect of subordinates’ personality on the relationship between perceptions of abusive supervision and subordinates’ interpersonal deviance. Consistent with trait activation theory, subordinates low in both agreeableness and extraversion were more likely to engage in deviant behaviors in response to perceptions of abusive supervision than subordinates high in either or both agreeableness and extraversion. (shrink)

The popularization of intelligent machines such as service robot and industrial robot will make human–machine interaction, an essential work mode. This requires employees to adapt to the new work content through learning. However, the research involved human–machine interaction that how influences the employee’s learning is still rarely. This paper was to reveal the relationship between human–machine interaction and employee’s learning from the perspective of job characteristics and competence perception of employees. We sent questionnaire to 500 employees from 100 artificial intelligence (...) companies in China and received 319 valid and complete responses. Then, we adopted a hierarchical regression for the test. Empirical results show that human–machine interaction has a U-shaped curvilinear relationship with employee learning, and employee’s vitality mediates the curvilinear relationship. In addition, job characteristics moderate the U-shaped curvilinear relationship between human–machine interaction and employee’s vitality, especially the results of moderating effects varying with employee’s competence perception. Exploring the mechanism of the effect of human–machine interaction on employee’s learning enriches the socially embedded model. Moreover, it provides managerial implications how to enhance individual adaptability with the introduction of AI into firms. However, our research focuses more on the impact of human–machine interaction on employees at the initial stage of AI development, and the level of machine intelligence in various industries will reach a high degree of autonomy in the future. The future research can explore the impact of human–machine interaction on individual’s behavior at different stages, and the results may vary depending on the technologies mastered by different individuals. The study has theoretical and practical significance to human–machine interaction literature by underscoring the important of individual’s behavior among individuals with different skills. (shrink)

This study investigated Chinese university students’ technology-assisted self-regulated learning strategies and whether the technology-based SRL strategies mediated the associations between English language self-efficacy, English enjoyment, and learning outcomes. Data were collected from 525 undergraduate students in mainland China through three self-report questionnaires and the performance on an English language proficiency test. While students reported an overall moderate level of SRL strategies, they reported a high level of technology-based vocabulary learning strategies. A statistically significant positive relationship was noted between the use (...) of technology-based SRL strategies and students’ English learning outcomes. English language self-efficacy and English language enjoyment were both related to technology-based SRL strategies. Furthermore, SRL strategies fully mediated the relationship between English enjoyment and English learning outcomes, but the association between English enjoyment and SRL strategies was only partially mediated by English language self-efficacy. Pedagogically, findings of this study suggest that training and instruction aimed at promotion of modern educational technology among students need to give attention to developing their strategic awareness of motivation regulation in optimizing effectiveness of their technology use in learning the target language. (shrink)

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the (...) development of the community-based Coronavirus Infectious Disease Ontology in early 2020. -/- As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. -/- CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. (shrink)

BackgroundAthletes will increase their state anxiety under stress situations, which will lead to the decline of sports performance. The improvement of anxiety by probiotics has been reported, but there is a lack of research in the athlete population. The purpose of the current study is to explore the effectiveness of probiotics in improving athletes’ state anxiety and sports performance under stress situations.MethodsWe conducted this single-arm study in Chongqing Institute of Sports Technology. In the 8-week study, 21 Chongqing young divers were (...) given probiotic Bifidobacterium animalis subsp. lactis BB-12 daily. The state anxiety and sports performance of athletes were measured before, during, and after the intervention, and the gut microbiota of athletes was measured before and after the intervention.ResultsThe intervention results showed that cognitive state anxiety, somatic state anxiety, and anxiety emotion were improved. In terms of gut microbiota, the intervention did not change the gut microbial composition but increased the abundance of Bifidobacteriaceae. At the 8th week, the performance of athletes under stress was significantly improved.LimitationsFirst of all, due to the restriction of the number of subjects in this study, there was no control group. Secondly, although the athletes’ diet was recorded in this study, the influence of this factor on gut microbiota was not eliminated. Finally, the anxiety level of the athletes in this study was obtained through a self-report, lacking physiological data in state anxiety.ConclusionThe results show that probiotics intervention can improve the state anxiety of athletes under stress situation and improve the performance of athletes under stress situation. (shrink)

According to the reports in the past decade, some Asian subcontractors, mainly Taiwan, Hong Kong and Korea transnational corporations, tend to be labor abusive in their overseas investment destinations like China or Southeast Asia. Taking Vietnam as an example, this paper raises questions as to why Taiwanese transnational companies can control workplace unions in a trade-union-supportive regime. Given the government s constraint of political rights, and the individualized workplace unions, the function of trade unions in Vietnam is destined to (...) be limited. The trade unions turn out be an arm of management, rather than representing workers interests in these transnational companies. This article also explores the influence of the newly developed codes of conducts from Western buyers. In the survey of three companies which are required to follow the codes of conduct by buyers, trade unions had no more freedom than those in companies without codes of conduct. The paper discusses the implications of this research, offering strategies for labor rights improvements. (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)

The COVID-19 pandemic has caused tremendous loss starting from early this year. This article aims to investigate the change of anxiety severity and prevalence among non-graduating undergraduate students in the new semester of online learning during COVID-19 in China and also to evaluate a machine learning model based on the XGBoost model. A total of 1172 non-graduating undergraduate students aged between 18 and 22 from 34 provincial-level administrative units and 260 cities in China were enrolled onto this study and asked (...) to fill in a sociodemographic questionnaire and the Self-Rating Anxiety Scale twice, respectively, during February 15 to 17, 2020, before the new semester started, and March 15 to 17, 2020, 1 month after the new semester based on online learning had started. SPSS 22.0 was used to conduct t-test and single factor analysis. XGBoost models were implemented to predict the anxiety level of students 1 month after the start of the new semester. There were 184 and 221 students who met the cut-off of 50 and were screened as positive for anxiety, respectively, in the two investigations. The mean SAS scores in the second test was significantly higher than those in the first test. Significant differences were also found among all males, females, and students majoring in arts and sciences between the two studies. The results also showed students from Hubei province, where most cases of COVID-19 were confirmed, had a higher percentage of participants meeting the cut-off of being anxious. This article applied machine learning to establish XGBoost models to successfully predict the anxiety level and changes of anxiety levels 4 weeks later based on the SAS scores of the students in the first test. It was concluded that, during COVID-19, Chinese non-graduating undergraduate students showed higher anxiety in the new semester based on online learning than before the new semester started. More students from Hubei province had a different level of anxiety than other provinces. Families, universities, and society as a whole should pay attention to the psychological health of non-graduating undergraduate students and take measures accordingly. It also confirmed that the XGBoost model had better prediction accuracy compared to the traditional multiple stepwise regression model on the anxiety status of university students. (shrink)

The force of globalization has greatly challenged people’s conceptualization of national identity. The traditional definition of national identity as being distinct, stable and generated by such internal factors as ethnic, religion, citizenship and so on, has been replaced by the understanding that national identity is invested with more dynamic and complex features and is actually constructed differently in different situations. By following Van Dijk’s socio-cognitive perspective in critical discourse analysis and drawing on the 47 news reports collected on the websites (...) of People’s Daily Online and Xinhuanet, this study explores how China’s national identities are negotiated in its news reports of the Sino-US trade war. It is found that China’s news reports have constructed three types of Sino-US relations, namely, antagonistic, negotiatory and cooperative relations. Out of these relations, China has been constructed as victim of the trade war, defender of free trade, driver for negotiations and beneficiary of mutual cooperation. The construction of these identities and relations is realized through constructive, destructive, justification and transformation strategies. It is further found that the diversity of both China’s national identities and Sino-US relations in China’s news reports is the result of the political, social and economic differences between the two countries. (shrink)

The category \(\mathbb {DRDL}{'}\), whose objects are c-differential residuated distributive lattices satisfying the condition \(\textbf{CK}\), is the image of the category \(\mathbb {RDL}\), whose objects are residuated distributive lattices, under the categorical equivalence \(\textbf{K}\) that is constructed in Castiglioni et al. (Stud Log 90:93–124, 2008). In this paper, we introduce weak monadic residuated lattices and study some of their subvarieties. In particular, we use the functor \(\textbf{K}\) to relate the category \(\mathbb {WMRDL}\), whose objects are weak monadic residuated distributive lattices, (...) and the category \(\mathbb {WMDRDL}{'}\), whose objects are pairs formed by an object of \(\mathbb {DRDL}{'}\) and a center weak universal quantifier. (shrink)

The integrated scheduling algorithm of process sequence time-selective strategy is an advanced algorithm in the field of integrated scheduling. The proposed algorithm points out the shortcomings of the process sequence time-selective strategy. Generally, there are too many “trial scheduling” times. The authors propose that there is no need to make “trial scheduling” at every “quasi-scheduling time point.” In fact, the process scheduling scheme can be obtained by trial scheduling on some “quasi-scheduling time points.” The scheduling result is the same as (...) that of the sequence timing strategy. The proposed algorithm reduces the runtime of scheduling algorithm and improves the performance of the algorithm without reducing the optimization effect. (shrink)

Conventional wisdom suggests mid-task rest as a potential approach to relieve the time-on-task (TOT) effect while accumulating evidence indicated that acute exercise might also effectively restore mental fatigue. However, few studies have explored the neural mechanism underlying these different break types, and the results were scattered. This study provided one of the first looks at how different types of fatigue-recovery break exerted influence on the cognitive processes by evaluating the corresponding behavioral improvement and neural response (EEG power spectral) in a (...) sustained attention task. Specifically, 19 participants performed three sessions of psychomotor vigilance tasks (PVT), with one session including a continuous 30-min PVT while the other two sessions additionally inserted a 15-min mid-task cycling and rest break, respectively. For behavioral performance, both types of break could restore objective vigilance transiently, while subjective feeling was only maintained after mid-task rest. Moreover, divergent patterns of EEG change were observed during post-break improvement. In detail, relative theta decreased and delta increased immediately after mid-task exercise, while decreased delta was found near the end of the rest-inserted task. Meanwhile, theta and delta could serve as neurological indicators to predict the reaction time change for exercise and rest intervention, respectively. In sum, our findings provided novel evidence to demonstrate divergent neural patterns following the mid-task exercise and rest intervention to counter TOT effects, which might lead to new insights into the nascent field of neuroergonomics for mental fatigue restoration. (shrink)

Introduction: Few studies are available on the prevalence and sleep-related factors of dementia with Lewy bodies (DLB) in Chinese older adults, aiming to explore the associations between sleep characteristics and DLB.Methods: A cross-sectional study with 7,528 individuals aged ≥65 years in 106 communities in Northern China was conducted from April 2019 to January 2020. Questionaries (including demographic characteristics, comorbidities, lifestyles, and sleep characteristics) were administered, and neuropsychological assessments and physical examination were conducted in phase I; screening for probable DLB was (...) done in phase II. Logistic regressions were used to assess associations.Results: A total of 919 (12.2%, 919/7,528) participants had dementia, and 101 (1.3%, 101/7,528) participants were diagnosed with DLB. The prevalence of dementia and DLB were slightly higher or equal in women, increased with age, and roughly decreased with nighttime sleep duration. Of the 101 participants, all of them (100.0%) had cognitive impairment, 46 (44.54%) displayed fluctuating cognition, 72 (71.29%) of them showed visual hallucination, 22 (21.78%) individuals reported RBD, and 27.71% showed Parkinsonism. Sleeping for 8 h had combined diagnostic value for DLB (AUC = 0.783, 95%CI: 0.734–0.831, p < 0.001).Conclusions: The current prevalence of DLB is 1.3% in Northern China. Short or long nighttime sleep duration is independently associated with the occurrence of dementia and DLB. (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)

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved (...) in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed. (shrink)