C-E Translation of Buzzwords From the Perspective of Eco-Translatology

Buzzwords, the popular words and expressions, spread widely among the public, as the comprehensive product of society, culture and language, and it has become a new research subject in recent years. This paper analyzes the C-E translation of buzzwords from the perspective of Eco-translatology. Three-dimensional transformations of Eco-translatology provide a new aspect for us to improve the translation of buzzwords. Targeted readers will understand the translated version of buzzwords as much as possible if transformations were made from linguistic, cultural and communicative dimensions

The Influencing Factors Model of Cross-Border E-commerce Development: A Theoretical Analysis

Cross-border e-commerce (CBEC) is the future trend of cross-border trade. Although China is at the forefront of CBEC development, its transaction volume is still not satisfactory. The purpose of this paper is to study factors influencing the development of CBEC industry from the macro-environment perspective. First, we commented and summarized relevant literature at home and abroad about business ecosystem and factors determining the development of CBEC, then proposed a model of factors influencing CBEC development by combining business ecosystem theory with PEST framework, followed by interpretation and discussion. The model consists of core species, key species, supporting species, parasitic species in the CBEC ecosystem, and they are affected by external environmental factors from political, economic, social and technological perspectives

Near field communication system development

NFC is the acronym for Near Field Communication which is a short-range wireless communication technology that enables the exchange of data between devices. NFC technology is being grown up at enormous speed. It has been promoted by major technology companies such as Google and Apple. Therefore, it has great potential to become the most popular wireless technology in near future. This report focus on the NFC application programming on Android phone and the NFC system developed with a micro-controller and a reader board. Details of NFC technology will be studied as wellBachelor of Engineering (Computer Engineering

Experimental immunology The effects of EGb761 on lipopolysaccharide-induced depressive-like behaviour in C57BL/6J mice

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Relation between Shyness and Music Academic Engagement: The Mediation of Achievement Goals-A Cross-Sectional Survey Study.

Peer reviewed: TrueMusic discipline that emphasizes expression, performance and collaboration may cause difficulties for shy students who are prone to anxiety about social interaction, which might cause low music academic engagement and achievement. According to Models of Personality and Affect regarding the role of psychological constructs in educational contexts, shyness and academic engagement are the first and third-level variables, respectively. We hypothesized that achievement goals might be the second-level variable between shyness and academic engagement. Two hypotheses were proposed in the study: (1) shyness is negatively related to music academic engagement; (2) the music achievement goals mediate shyness and music academic engagement. The research was conducted in May 2022. A total of 515 college students who major in music were randomly recruited from a public university in Shanxi province, China. A 20 min self-report questionnaire was conducted as the data collection method. The research results revealed the following: (1) shyness was negatively associated with musical academic engagement; (2) the music mastery goals and the music performance avoidance goals (excluding the performance approach goal) partially mediated the association between shyness and music academic engagement in music learning. These findings have implications for the research and practice of music academic engagement of shyness

Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network

The accurate forecasting of urban taxi demands, which is a hot topic in intelligent transportation research, is challenging due to the complicated spatial-temporal dependencies, the dynamic nature, and the uncertainty of traffic. To make full use of the global and local correlations between traffic flows on road sections, this paper presents a deep learning model based on a graph convolutional network, long short-term memory (LSTM), and multitask learning. First, an undirected graph model was formed by considering the spatial pattern distribution of taxi trips on road networks. Then, LSTMs were used to extract the temporal features of traffic flows. Finally, the model was trained using a multitask learning strategy to improve the model’s generalizability. In the experiments, the efficiency and accuracy were verified with real-world taxi trajectory data. The experimental results showed that the model could effectively forecast the short-term taxi demands on the traffic network level and outperform state-of-the-art traffic prediction methods