Background Altruism is the core of nursing professionalism. Graduate nursing education in China started late and is still developing, exploring the current state of altruistic behavior and the perceived experience of altruism among graduate nursing students may have important implications for nursing education. Objective Explore the current state of altruistic behavior and the perceived experience of altruism among graduate nursing students in China. Research design This is a descriptive phenomenological qualitative research study, semi-structured in-depth interviews were conducted. Seventeen graduate nursing (...) students from three schools were selected to participate in the study. Colaizzi’s analysis method was performed with NVivo software to develop common themes from the data. Ethical considerations The research proposal was approved by the Research Ethic Committee of Yangzhou University, China. Results Four themes emerged from the analysis of the interviews of the 17 participants: “Definition of altruism,” “Altruism in the nursing profession,” “Altruism applied in practice,” and “Factors influencing altruistic behavior.” Conclusions Although participants indicated that the concept of “altruism” was relatively new to them, altruistic behavior is common in both their work and life. Many factors influence the altruistic behavior of graduate nursing students, including the environment, personal factors, education, recipient factors, occupational factors, and gains and losses. Families, schools, and hospitals should create favorable environments to foster altruistic tendencies in students. (shrink)

At present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning. First, the encoders are used to extract the features of the (...) behaviour. At the same time, in order to reduce the computational complexity, the feature extraction is restricted to the space-time volume of the dense trajectory. Second, the neural network model is used to transform features into behavioural visual word representations, and feature fusion is performed using weighted correlation methods to improve feature classification capabilities. Finally, sparse reconstruction errors are used to judge and detect financial fraud. This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. Experimental results show that this method can effectively learn the essential characteristics of the data, and significantly improve the detection rate of fraud detection algorithms. (shrink)

This article considers how networked large urban screens can act as a platform for the creation of an experimental transnational public sphere. It takes as a case study a specific Australia-Korea cultural event that linked large screens in Federation Square, Melbourne, and Tomorrow City, Incheon,1through the presentation of SMS-based interactive media art works. The article combines theoretical analyses of global citizenship, mobility, digital technologies, and networked public space with empirical analyses of audience response research data collected during the screen event. (...) The central argument is that large public screens can offer a strategic site for examining transformations in the constitution of public agency in a digitized, globalized environment. The idea of ‘aesthetic cosmopolitanism’ is finally proposed as a conceptual framework for understanding how new forms of transnational public agency in mediated public spaces might operate. (shrink)

The impact of industrial peers' donations on firms' charitable practices has been tested and verified in existing literatures. This paper further studies the motivations and scenarios of non-listed companies to imitate their listed counterparts in the same industry to formulate charitable policies. Deeply rooted in institutional isomorphism theory, uncertainty and professional networks are employed as philanthropic motives for unlisted companies to mimic their listed peers. Managerial decision mechanisms and network status perceptions enhance imitation by reinforcing decision uncertainty and network effect. (...) Institutional environment heterogeneity can amplify or reduce the imitative stimulus of managers' decision mechanisms and status perceptions. Our empirical tests are based on a unique dataset of 14,873 unlisted firms and their corresponding listed counterparts in China from 2006–2016, and use Tobit regression methods. The results show a significantly positive association between listed peers' giving and non-listed firms' donation. The peer effects are intensified when non-listed firms' decentralized decision-making and higher managers' status identification are present. Furthermore, when the institutional environment changes from weak to strong, the impetus of decentralized decision-making and status identification will be weakened. These findings contribute to the corporate philanthropy literature and facilitate the development of effective corporate charity policies and government promotion of philanthropic responsibility. (shrink)