B. Zhang

Artificial intelligence technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology and the theory of perceived risk, this study identified six factors that influence students' willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed “weak rejection” of the construction of AI-assisted learning environments. Effort expectancy, performance expectancy, and social influence were all positively related to college students' WTA AI-assisted learning environments. Psychological risk significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments.

BackgroundCoronary heart disease is closely associated with cognitive impairment, especially in severe cases of heart failure. However, it is unclear whether cardiac systolic function plays a role in the relationship between pre-existing CHD and cognitive impairment in subjects without clinical heart failure.MethodsIn total, 208 subjects from the First Affiliated Hospital of Xi’an Jiaotong University were recruited from June 2014 to January 2015, and were divided into CHD and non-CHD groups according to the inclusion and exclusion criteria. The global cognitive function of all subjects was assessed by the Mini-Mental State Examination and cognitive impairment was defined as the score lower than the cutoff value. Left ventricular ejection fraction was measured using transthoracic echocardiograms. The relationship among pre-existing CHD, LVEF, and cognitive impairment was analyzed by multivariate logistic regression.ResultsIn total, 34 subjects met the criteria of cognitive impairment. Univariate analysis showed that the cognitive impairment prevalence in the CHD group was significantly higher than that in the non-CHD group. Multivariate logistic analysis revealed that CHD was significantly associated with a higher risk of cognitive impairment after adjusting for confounds except for LVEF. However, the OR of CHD decreased when LVEF was further corrected as a continuous variable, and LVEF was negatively associated with the risk of cognitive impairment.ConclusionPre-existing CHD is associated with a higher risk of cognitive impairment, and such an association can be considerably explained by reduced LVEF. An impaired cardiac systolic function may play a key role in the relationship between CHD and cognitive impairment among patients with pre-heart failure conditions.

Industry 4.0 has changed the paradigm in the business practice and business model, and digital technology has brought radical transformations to enterprises. To support this transformation, digital leaders are required to help enterprises transform and lead them to a more promising future. Based on job demands-resources model and person-organization fit theory, this study examines the relationship between digital leadership and employee creativity. Based on a sample of 357 employees from various Chinese companies, this study used SPSS 22.0 and MPLUS 7.0 to examine the hypotheses. The findings indicate the following digital leadership has a positive effect on employee creativity. employee job crafting mediate the relationship between digital leadership and employee creativity. person-organization fit positively moderates the relationship between digital leadership and employee job crafting. person-organization fit positively moderates the indirect effect of digital leadership on employee creativity via employee job crafting. The findings reveal the effect mechanism of digital leaders on employee creativity and enrich the literature on antecedents of employee creativity. Practical implications and future research are also discussed.

With the quickly rising popularity of smartphone among adolescents over the past decade, studies have begun to investigate the relationship between smartphone addiction and Eysenck's personality traits. Despite numerous studies on this topic, however, findings have been mixed and there is a lack of consensus regarding this relationship. Thus, this meta-analysis aimed to explore the relationship between smartphone addiction and Eysenck's personality traits in Chinese adolescents, as well as its possible moderators. Through literature search and screening, 33 studies were included, comprising 79 independent effect sizes with a total of 17, 737 subjects. A random effects model was selected, and it was found that smartphone addiction was positively associated with psychoticism and neuroticism, but not significantly associated with extroversion. The moderating effect test showed that sex and year of study publication had significant influences on the relationship between smartphone addiction and psychoticism, and the year of study publication had a significant influence on the relationship between smartphone addiction and neuroticism. This study is the first meta-analysis on the relationship between smartphone addiction and Eysenck's personality traits among adolescents in China, and the results have helped to clarify the controversy of previous studies regarding this relationship.

Schizophrenia is considered as a self-disorder with disordered local synchronous activation. Previous studies have reported widespread dyssynchrony of local activation in patients with SZ, which may be one of the crucial physiological mechanisms of SZ. To further verify this assumption, this work used a surface-based two-dimensional regional homogeneity approach to compare the local neural synchronous spontaneous oscillation between patients with SZ and healthy controls, instead of the volume-based regional homogeneity approach described in previous study. Ninety-seven SZ patients and 126 HC were recruited to this study, and we found the SZ showed abnormal 2dReHo across the cortical surface. Specifically, at the global level, the SZ patients showed significantly reduced global 2dReHo; at the vertex level, the foci with increased 2dReHo in SZ were located in the default mode network, frontoparietal network, and limbic network ; however, foci with decreased 2dReHo were located in the somatomotor network, auditory network, and visual network. Additionally, this work found positive correlations between the 2dReHo of bilateral rectus and illness duration, as well as a significant positive correlation between the 2dReHo of right orbital inferior frontal gyrus with the negative scores of the positive and negative syndrome scale in the SZ patients. Therefore, the 2dReHo could provide some effective features contributed to explore the pathophysiology mechanism of SZ.

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.

Neurite orientation dispersion and density imaging is a diffusion model specifically designed for brain magnetic resonance imaging. Despite recent studies suggesting that NODDI modeling might be more sensitive to brain development than diffusion tensor imaging, these studies were limited to a relatively small age range and mainly based on the manually operated region of interest analysis. Therefore, this study applied NODDI to investigate brain development in a large sample size of 214 subjects ranging in ages from 0 to 14. The whole brain was automatically segmented into 122 regions. The maturation trajectory of each region was characterized by the time course of diffusion metrics and further quantified using nonlinear regression. The NODDI-derived metrics, neurite density index and orientation dispersion index, increased with age. And these two metrics were superior to the DTI-derived metrics in SVM regression models of age. The NDI in white matter exhibited a more rapid growth than that in gray matter. These diffusion indicators experienced conspicuous increases during early childhood and the growth speed slowed down in adolescence. Region-specific maturation patterns were described throughout the brain, including white matter, cortical and deep gray matter. These development patterns were evaluated and discussed on the basis of NODDI’s model assumptions. To summarize, this study verified the high sensitivity of NODDI to age over a crucial developmental period from newborn to adolescence. Moreover, the existing knowledge of brain development has been complemented, suggesting that NODDI has a potential capability in the investigation of brain development.

In user cluster analysis, users with the same or similar behavior characteristics are divided into the same group by iterative update clustering, and the core and larger user groups are detected. In this paper, we present the formulation and data mining of the correlation rules based on the clustering algorithm through the definition and procedure of the algorithm. In addition, based on the idea of the K-mode clustering algorithm, this paper proposes a clustering method combining related rules with multivalued discrete features. In this paper, we construct a method to calculate the similarity between users using Jaccard distance and combine correlation rules with Jaccard distances to improve the similarity between users. Next, we propose a clustering method suitable for MDF. Finally, the basic K-mode algorithm is improved by the similarity measure method combining the correlation rule with the Jaccard distance and the cluster center update method which is the ARMDKM algorithm proposed in this paper. This method solves the problem that the MDF cannot be effectively processed in the traditional model and demonstrates its theoretical correctness. This experiment verifies the correctness of the new method by clustering purity, entropy, contour, and other indicators.

In response to the lack of research on the online social network structure of athletes, elements of research on the online social network structure of athletes were constructed based on the whole network perspective and through the study of the characteristics of the whole online social network structure of athletes, in order to provide reference for the physical and mental health development of athletes from a new perspective. Data were collected through questionnaires, and several software programs were used to preprocess and analyse the collected data. Through the analysis of the online whole network structure, it was found that the network density of the online support network was generally greater than that of the online discussion network, and athletes still showed stronger practical support demands and behaved more rationally in the process of training and learning life, while from the perspective of the relationship structure, the athletes’ family and classmates’ online support is weaker than that in previous studies; in terms of the whole network, strong relationships still dominate in this population, while attention should be paid to the impact of weak relationships.

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems.

To protect energy resources and alleviate environmental pollution, many countries attach great importance to the transformation of traditional industries into clean energy industries. In this paper, fuel vehicles, hybrid vehicles, and electric vehicles are included in the research. Then, based on the aging chain and coflow theory of SDs, we construct a dynamic matching model of the auto industry upgrade process and its energy consumption attributes. The simulation results of China’s auto industry show that the upgrading of the auto industry is an evolutionary process from high energy consumption and high pollution to low energy consumption and no pollution and the transition from FVs and HVs to EVs will undergo two adjustments; simply reducing energy supply does not have the expected impact on vehicle size and vehicle energy consumption intensity and only by adjusting the energy supply and upgrade ratios together, energy utilization efficiency can be improved; market screening time has an impact on auto industry upgrade speed by affecting vehicle market share and dwell time; China’s auto industry upgrade process should adhere to “problem-oriented” and strengthen consumer guidance, technology innovation, and infrastructure construction. The conclusions can provide references for industrial policy adjustment and industrial structure optimization.

We describe a corpus of speech taking place between 30 Korean mother–child pairs, divided in three groups of Prelexical (M= 0;08), Early-Lexical (M= 1;02), and Advanced-Lexical (M= 2;03). In addition to the child-directed speech (CDS), this corpus includes two different formalities of adult-directed speech (ADS), i.e., family-directed ADS (ADS_Fam) and experimenter-directed ADS (ADS_Exp). Our analysis of the MLU in CDS, family-, and experimenter-directed ADS found significant differences between CDS and ADS_Fam, and between ADS_Fam and ADS_Exp, but not between CDS and ADS_Exp. Our finding suggests that researchers should pay more attention to controlling the level of formality in CDS and ADS when comparing the two registers for their speech characteristics. The corpus was transcribed in the CHAT format of the CHILDES system, so users can easily extract data related to verbal behavior in the mother–child interaction using the CLAN program of CHILDES.

Early and later Confucians, known in Chinese as the “ruists” school of ancient origins, perceived the idea of “harmony” as a fundamental concept that lies at the basis of self-cultivation, society and governance. In modern times this idea still plays in one or another form a dominant note in Chinese politics and social life. The article attempts to search for causes of the significance of “harmony” by focusing on analyzing two pivotal Confucian texts compiled in the Han dynasty, namely, Records of Music [Yue ji 樂記] and Divination of Music [Yue wei 樂緯]. The analysis shows that ruists belonging to Zhou dynasty’s imperial class of music officials, gradually developed the aesthetics of music into a complex idea of "harmony" that contains the highest aesthetical way—“Dao”—which guides both the whole universe as well as the evolution of human society.

Functionally graded materials (FGMs) have been used in many different kinds of applications in recent years and have attracted significant research attention. However, we do not yet have a commonly accepted way of representing the various aspects of FGMs. Lack of standardised vocabulary creates obstacles to the extraction of useful information relating to pertinent aspects of different applications. A standard resource is needed for describing various elements of FGMs, including existing applications, manufacturing techniques, and material characteristics. This motivated the creation of the FGM Ontology (FGMO) in 2016. Here, we present a revised and expanded version of the FGM Ontology, which includes enrichments along four dimensions: (1) documenting recent FGMs applications; (2) reorganising the framework to incorporate an updated representation of types of manufacturing processes; (3) enriching the axioms of the ontology; and (4) importing mid-level ontologies from the Common Core Ontologies (CCO) and Product Life Cycle (PLC) Ontologies. The work is being carried out within the framework of the Industry Ontology Foundry (IOF), and the ontology is conformant to Basic Formal Ontology (BFO).

The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organizations. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end of life. We call this suite the Product Life Cycle (PLC) Ontologies. The suite extends proximately from The Common Core Ontologies (CCO) used widely in defense and intelligence circles, and ultimately from the Basic Formal Ontology (BFO), which serves as top level ontology for the CCO and for some 300 further ontologies. The PLC Ontologies were developed together, but they have been factored to cover particular domains such as design, manufacturing processes, and tools. We argue that these ontologies, when used together with standard public domain alignment and browsing tools created within the context of the Semantic Web, may offer a low-cost approach to solving increasingly costly problems of data management in the manufacturing industry.

This co-edited volume compares Chinese and Western experiences of engineering, technology, and development. In doing so, it builds a bridge between the East and West and advances a dialogue in the philosophy of engineering. Divided into three parts, the book starts with studies on epistemological and ontological issues, with a special focus on engineering design, creativity, management, feasibility, and sustainability. Part II considers relationships between the history and philosophy of engineering, and includes a general argument for the necessity of dialogue between history and philosophy. It continues with a general introduction to traditional Chinese attitudes toward engineering and technology, and philosophical case studies of the Chinese steel industry, railroads, and cybernetics in the Soviet Union. Part III focuses on engineering, ethics, and society, with chapters on engineering education and practice in China and the West. The book’s analyses of the interactions of science, engineering, ethics, politics, and policy in different societal contexts are of special interest. The volume as a whole marks a new stage in the emergence of the philosophy of engineering as a new regionalization of philosophy. This carefully edited interdisciplinary volume grew out of an international conference on the philosophy of engineering hosted by the University of the Chinese Academy of Sciences in Beijing. It includes 30 contributions by leading philosophers, social scientists, and engineers from Australia, China, Europe, and the United States.

To understand the qualities of Western civilization and its modernity, to think about the future of humanity, and to understand how modern science was gestated in Western civilization: in the author’s view, these are the most important reasons to do history of science research in China. Study of the history of Western science in China is in its infancy, and there are great deficiencies leading to its lagging behind the international world of scholarship. In this situation, the most urgent task is to translate as soon as possible a batch of high quality and classic books in the field, to establish a basic academic platform for research in the history of science, and, once that foundation has been laid, to deepen our researches further. Given these considerations, this essay discusses why the author translates works on the history of science, how he chooses books for translation, and his experiences as a translator.