Y Li

This study aimed to develop and test the reliability and validity of a multi-item nurses’ presenteeism behaviour questionnaire. Study 1 administered the Nurse Presenteeism Questionnaire to 250 Chinese nurses. Study 2, surveyed 650 nurses with the NPQ, the Sickness Presenteeism Questionnaire, the Stanford Presenteeism Scale, the General Health Questionnaire, and the Emotional Exhaustion Scale using convenience sampling. After item analysis, the subjects were randomly divided into two groups to verify the questionnaire structure. Study 1 revealed the nurses’ core symptoms when they go to work with illness, and the NPQ with 11 items was developed. Study 2’s item analysis revealed that 11 NPQ items had good discrimination and high homogeneity. Besides, the scale had good reliability and external criterion validity. Thus, the NPQ can be used to measure presenteeism behaviour in nursing.

In this paper, through research and analysis of the communication network of the physical activity monitoring system, we combine wearable technology and identification technology and design a physical health monitoring bracelet that integrates multifaceted physical data collection and effective identity matching function. We match the identity through the chip and collect the physical fitness data generated in the process of exercise and centralized test by the sensor in real-time. Finally, the data transmission is realized through the WIFI communication function to achieve the purpose of monitoring physical exercise and improving physical quality. To ensure the continuity and stability of information transmission, the joint transmission method of direct transmission and indirect transmission is essential. Besides, considering the energy causality limitation of sensor nodes and relay nodes, a collaborative transmission model of wireless body area network based on wireless cognitive network is constructed. And, a power allocation algorithm based on maximum ratio merging and wireless cognitive network is proposed, which puts forward a new idea for the future research of wireless body area network resource allocation.

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function neural model to estimate the state-of-charge of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm. Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.

As the most frequently used tool for measuring empathy, the Interpersonal Reactivity Index (IRI) is often scored by researchers arbitrarily and casually. Many commonly used IRI scoring approaches and their corresponding measurement models are unverified, which may make the conclusions of subsequent variable relation studies controversial and even misleading. We make the first effort to summarize these measurement models and to evaluate rationality of the common scoring methods of the IRI by confirmatory factor analysis, focusing on model fitting, factor loading, and model-based reliability simultaneously. The results show that most of these models do not fit well, indicating that the scoring approaches of the IRI corresponding to these models may be problematic. Relatively speaking, better scoring approaches of the IRI include summing empathic concern and perspective taking as the total score of the IRI and reporting the score of perspective taking as cognitive empathy.

This article provides current Schwartz Values Survey (SVS) data from samples of business managers and professionals across 50 societies that are culturally and socioeconomically diverse. We report the society scores for SVS values dimensions for both individual- and societal-level analyses. At the individual-level, we report on the ten circumplex values sub-dimensions and two sets of values dimensions (collectivism and individualism; openness to change, conservation, self-enhancement, and self-transcendence). At the societal-level, we report on the values dimensions of embeddedness, hierarchy, mastery, affective autonomy, intellectual autonomy, egalitarianism, and harmony. For each society, we report the Cronbach’s α statistics for each values dimension scale to assess their internal consistency (reliability) as well as report interrater agreement (IRA) analyses to assess the acceptability of using aggregated individual level values scores to represent country values. We also examined whether societal development level is related to systematic variation in the measurement and importance of values. Thus, the contributions of our evaluation of the SVS values dimensions are two-fold. First, we identify the SVS dimensions that have cross-culturally internally reliable structures and within-society agreement for business professionals. Second, we report the society cultural values scores developed from the twenty-first century data that can be used as macro-level predictors in multilevel and single-level international business research.

People often judge trustworthiness based on others’ faces (e.g., facial expression and facial gender). However, it is unclear whether social context plays a moderating role in forming trustworthiness judgments. Based on the emotions as social information (EASI) model, differing contexts may impact the effect of facial expression; however, there is no evidence demonstrating that differing contexts will or will not influence the effect of facial gender. In this study, we used two experiments to examine how facial expression and facial gender affect facial trustworthiness judgments and whether the effects on facial trustworthiness judgments are consistent in cooperative and competitive settings. Twenty-seven undergraduates (14 female; Mage = 21.81 years, SD = 2.66) participated in experiment 1. The results showed significant main effects of facial expression and facial gender as well as the interaction between them. To examine the social context effect, 28 undergraduates (18 female; Mage = 20.93 years, SD = 2.94) participated in experiment 2. The results showed the main effects of facial expression, facial gender, and social context. Moreover, there was a significant interaction between facial gender and facial expression and a marginally significant interaction between social context and facial expression. These results suggest that in the process of judging facial trustworthiness, individuals’ judgments are affected by both facial expression and facial gender. Furthermore, the effect of facial gender on facial trustworthiness judgments presents cross-situational stability, and the role of facial expression is influenced by the settings. These findings support and expand the EASI model.

The development of biotechnologies has broadly interfered with a number of life processes, including human birth. An important moral question arises from the application of such medical technologies to birth: do biotechnological advancements violate human dignity? Many valid arguments have been raised. Yet bioethicists are still far from reaching a consensus on how best to protect the dignity of human birth. Confucianism is an influential ethical theory in China and presents a distinctive understanding of human dignity. In this paper, we reconstruct the two Confucian concepts of dignity—namely, universal dignity and personal dignity. We then apply these concepts to human birth in the context of biotechnologies involving embryos and genetic enhancement. We conclude that the dual Confucian understanding of dignity contributes a valuable perspective to the question of whether biotechnologies related to human birth violate human dignity.

This study investigated the effects of culture and collaborative discussion on Chinese and American children’s moral reasoning in reflective essays that they composed about a moral and practical dilemma. In contrast to American children who frequently expressed egocentric concerns, Chinese children exhibited altruistic tendencies and expressed more concern for maintaining in-group harmony, which are the core values advocated in collectivist culture. Collaborative discussion promoted children’s moral reasoning in both cultures, leading to significantly more consideration of the principles of being honest, having empathy for others, keeping promises, honoring friendship, being trustworthy, and not betraying others by tattling.

Leader behavior significantly influences employees’ safety performance. This study aimed to examine the effect of abusive supervision on the safety behaviors of subordinates. By drawing on the strength model of self-control, we predicted that abusive supervision would negatively affect safety behaviors through emotional exhaustion, and trait self-control and attentional bias toward safety would moderate the relationship between abusive supervision, emotional exhaustion, and safety behaviors. Our hypothesized model was supported by results from a sample of 159 workers at a chemical product manufacturing enterprise in China. Emotional exhaustion mediated the link between abusive supervision and safety behaviors. Moreover, trait self-control moderated the relationship between abusive supervision and emotional exhaustion, and attentional bias toward safety moderated the relationship between emotional exhaustion and safety compliance. This study elucidates the effects of abusive supervision on safety behaviors through the resource depletion process. Likewise, the importance of trait self-control and attentional bias toward safety in mitigating the potentially harmful effects of abusive supervision in workplace safety is highlighted. Minimizing abusive supervision, providing self-control training, and implementing safety-specific implicit cognition intervention can effectively improve employees’ safety behaviors.

We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the proposed matrix factorization method can respect the intrinsic graph structure and provide the sparse representation. Different from some existing traditional methods, the inertial neural network was developed, which can be used to optimize our proposed matrix factorization problem. By adopting one parameter in the neural network, the global optimal solution can be searched. Finally, simulations on numerical examples and clustering in real-world data illustrate the effectiveness and performance of the proposed method.

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives, CombineFileInputFormat, and Sequence Files, to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth algorithm in efficiency and scalability.

As an imitation of the biological nervous systems, neural networks, which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

This chapter examines Zhuangzi, an influential Daoist text written by Zhuang Zhou and named after the Daoist thinker Zhuang Zi. It discusses Zhuangzi’s emphasis on the philosopher’s wisdom regarding political and social management, its concept of personal realization, and its concern with social and political order. It also considers Daoism’s notion of wuwei in relation to wuwo, wudai, wugong, and wuming. Furthermore, it looks at the wholeness, equality, relatedness, and relativity of wanwu, along with the organizational implications of Zhuangzi, and the connection between Daoist thinking and organizational management.