South China Business College Guangdong University of Foreign Studies

The utilization of digital educational infrastructure in schools has propelled digital educational games to the forefront of educational innovation. Despite an abundance of empirical studies on the relationship between digital educational games and student's motivation for learning, a consensus has yet to be reached. This study aims to bridge existing research gaps by adopting a mixed-methods approach grounded in behaviorist learning theory and contextual cognitive theory. A detailed questionnaire was disseminated to students from three distinct university in Thailand. After the exclusion of invalid responses, a robust sample of 434 valid responses was curated and utilized for analysis. Utilizing SPSS and MPLUS software, empirical analyses were conducted to explore the impact of digital educational games on student's motivation for learning. Research results indicate that: First, digital educational games positively influence student's motivation for learning; Second, learning engagement serves as a mediator between digital educational games and student's motivation for learning; Third, the digital environment moderates the relationship between digital educational games and student's learning engagement. Notably, the positive impact of digital educational games on student learning engagement is amplified in a more immersive digital environment. This study contributes to behaviorist theory and social cognition theory by elucidating how digital educational games affect student's motivation for learning through their engagement and by highlighting the moderating role of the digital environment. Practically, these findings underscore the significance of digital educational games and the digital environments in schools to enhance student's motivation for learning.

The energy industry is one of the industries with high energy consumption, high pollution and high emissions in China. Low-carbon policies in the energy sector have a vital impact on China’s carbon reduction, climate change response and social development. In 2021, China’s energy status is mainly coal, accounting for about 56% of the total energy consumption. After 20 years of rapid development of clean energy in China since the 21st century, China’s wind power generation currently ranks first in the world, and the installed capacity of newly added wind power generation equipment is the largest. In terms of solar energy, China has more than 400 photovoltaic (PV) companies, and China-made solar PV panels have accounted for more than 70% of the global market share, with its own total installed capacity reaching 253.3GW. A total of 52 nuclear power units have been put into commercial operation in Mainland China, with an installed capacity of 53,485.95MWE. In terms of nuclear energy, the accumulative power generation of commercial nuclear power units accounts for about 4.99% of China’s accumulative power generation.1 In addition, this paper takes the power industry as an example to study the low-carbon policy of China’s energy industry. By using the policy text mining method to analyze the original text types, high-frequency words and implementation effects of 96 selected policies, the social impact of low-carbon policy on carbon reduction effect, green finance development, residents’ health, job creation and some energy-consuming enterprises is obtained. This paper puts forward targeted suggestions to provide effective reference for China’s relevant policy making.

This article presents an overview of audiovisual translation (AVT) as an area of enquiry within Translation Studies. It starts with a brief reference to its historical appearance in the early twentieth century before introducing the main translation modes associated with AVT and its related research. The next sections offer a panorama of some of the themes and methods that have attracted the attention of scholars in the past two decades with reference to the articles included in this special issue, which include contributions delving into subtitling and media accessibility, among other modes, and which use a variety of methods, including mixed-methods approaches and reception studies. The contributors are interested in a variety of themes, i.e. pragmatics, humor and ideology, and are based in Britain, China, Iran, Ireland, Italy, Poland, Spain, Turkey and the United States.

Stress-induced anisotropy has been applied for FeSiBCuNb nanocrystalline alloy ribbons in transformers with large direct current (DC) component. However, the physical mechanism of anisotropy induced by stress annealing during crystallization remains controversial. Here, a systematic study was conducted on the microstructure, residual stress state, dynamic domain structure and properties of the stress-annealed FeSiBCuNb nanocrystalline ribbons. Present results demonstrate that, the increase of the applied stress greatly increases the residual tensile stress and induces lattice distortion, which acts as pinning site during magnetization, resulting in enhanced domain-wall energy and narrower wall width. Meanwhile, stress annealing promotes the flattening of hysteresis loops and decreases the effective permeability, which improves the DC-bias characteristics of the ribbons, thereby promoting the transition from “hard saturation” to “soft saturation” behavior. Also, stress-induced grain refinement is beneficial to obtaining high hardness. Owing to the stress-induced anisotropy, the stress-annealed sample with applied stress of 113 MPa exhibits excellent combined properties, including low constant effective permeability of 438 at wide frequency range (1 kHz–8 MHz), high DC-bias capability of 74% (30 Oe), low power loss of 49 kW/m3 (100 mT, 100 kHz), and high hardness of 644 HV. This discovery provides an insightful understanding of stress-induced anisotropy mechanism on the high-performance Fe-based nanocrystalline alloys.

Local government financing vehicles (LGFVs) significantly influence enterprise sustainability and growth. However, the extent to which LGFVs debt affects corporate risk-taking remains unclear. Utilizing data from A-share listed companies from 2010 to 2020, this study examines LGFVs debt's influence on corporate risk-taking and the modulatory effect of land transfer income. Findings indicate that LGFVs debt generally curtails corporate risk-taking, but this effect is moderated by land transfer income. Notably, the impact of LGFVs debt on risk-taking varies across enterprises, exerting a more pronounced effect on smaller firms than on larger ones.

This article discusses the difficulties that the divergent conceptualizations of translation in journalism and translation studies pose to conduct inter-disciplinary research into the role of translation practices in journalistic production. It is divided into four sections that review four concepts, namely domestication (in connection with localization), transediting, gatekeeping and convergence. The first two have been widely discussed in translation studies in relation to a variety of genres, while the latter have been central to journalism studies research. The article also discusses the usefulness of these four concepts for the study of journalistic translation practices from the perspective of both translation and journalism studies.

Speaker verification, as one of the most convenient methods in biometric systems, has been widely applied. However, most automatic speaker verification systems are vulnerable to a variety of attacks, especially audio-replay attacks. Therefore, this paper focuses on the detection method of audio-replay attacks. It is found that using a hybrid feature can achieve better results than a single feature. As for the classifier in the backend, the use of the Gaussian mixture model cannot obtain convincing results, and the traditional convolution neural network architecture can easily lead to the model overfitting. Therefore, this paper chooses DenseNet architecture. The experimental results show that the hybrid feature and DenseNet architecture can achieve 46.06% relative improvement than the baseline system.

Aiming at the deficiency of losing texture information in image filtering process, an image filtering algorithm based on uncorrelated dictionary learning is proposed. Firstly, the noisy image is divided into overlapping image blocks, and the image blocks are randomly extracted. The uncorrelated redundant dictionary is obtained by using uncorrelated dictionary learning technology. Finally, the sparse representation coefficients of each image block under the redundant dictionary are obtained by sparse coding algorithm, and the original image is restored by using the sparse representation coefficients. The experimental results show that the irrelevant redundant dictionary has a strong ability to express the texture information of the image. It can keep the details and texture information of the image better and improve the visual effect.

The difference between traditional classroom and flipped classroom was analyzed in view of the characteristics of micro-learning video. Graphics and Image Processing was selected as the course to be studied. The way to design micro-learning videos specific to key and difficult points in teaching during the reform of flipped classroom was discussed. The application effect of micro-learning video was analyzed. Practice has proven that micro-learning video plays an important role in helping teachers and students solve the problems in teaching. It improves the autonomous learning and collaborative learning of students as well as their ability to find and solve problems.

Abstract Drawing on the concepts of agenda-setting and framing, this article aims to examine the role played by translation in the selection of articles of the New York Times for the Spanish and Chinese versions. It analyses whether the three versions focus on similar topics and therefore follow a similar agenda, identifies the topics that receive more salience via translation, and how these are complemented with texts specifically written for the translated/foreign language versions, as well as the framing mechanisms used by the writers and/or translators to create, suppress or accentuate ideological positionings. For that purpose, a constructed week methodology was used in order to collect a total of seventy articles per language. The analysis, based on Baker's adaptation of narrative theory and Kress and van Leeuwen's study of non-verbal signs, shows that the three versions of the New York Times vary in terms of format and content. Thus, while the English and Chinese versions focus on political and economic issues, the Spanish version undergoes a process of tabloidization.

This article serves to introduce the papers in this special issue, devoted to interpreting studies. Over the past decades, interpreting has gained recognition as an academic field, typically as a branch of translation studies. The paper starts with a brief historical overview of this practice, with a focus on the early modern and the modern periods, and provides references to some of the research conducted in other fields as well. The next section offers a very brief survey of the modes and settings in which interpreting currently takes place, including simultaneous, consecutive and community interpreting. The article also serves to highlight the diversity of themes, languages, theoretical and methodological approaches, and geographical origin of the authors.

Abstract This article aims to problematize the role of translation in news production as a result of the invisibility of indirect translation (ITr). In the first section, I argue that in journalistic translation ITr is not merely ‘hidden translation’ but rather ‘ignored translation’ as a consequence of the traditional status of the translational activity in journalism and because researchers can hardly find traces of ITr in news production, such as the name of sources, attributions, or paratexts. I then move on to discuss the importance of the various forms of translation in the emergence of journalism in the early modern period. Human conflicts and movement meant that news texts were recycled across Europe, often via ITr. News writers used various sources from different languages and adapted the texts taking into account political and cultural considerations. This establishes a link with contemporary journalism, as news articles are characterized by their multi-authored nature. In addition, translations can be embedded and are often circular rather than linear. In the concluding discussion, I suggest that journalistic translation research, including research into ITr, can benefit not only from interdisciplinary approaches, but also from incorporating historical aspects.

This article aims to analyse the role of translation in the tabloidization of the New York Times in Spanish. Drawing on the findings of a previous study that compared the English, Chinese and Spanish versions of the online newspaper, I hypothesise that the Spanish version focuses on human-interest news items translated from English originals and, therefore, translation may be used in a tabloidization process of the source texts in order to cater for the needs of the target audiences, that is, Spanish speakers of Spanish-speaking origin living in the United States or in Latin America. To study the process, I gathered two constructed weeks, a methodology widely used in communication studies, to analyse the differences between the two versions both in terms of content and format. The analysis, shows a tendency towards the ‘diminution of seriousness’, which can be explained as the result of the attempts to attract a readership belonging to different news traditions.

Abstract In the domestic fresh market, agricultural products are one of the major consumer goods. The rational optimization of the fresh agricultural products cold chain distribution center location is of great significance in enhancing the efficiency of the entire cold chain logistics. Therefore, in the context of big data, this paper chooses the grey forecasting method with a higher prediction accuracy, applied GM(1,1) prediction model for fresh agricultural products demand forecast under the premise of comparing different forecast methods’ accuracy; Basing on the demand forecast, this paper formulates fresh agricultural products distribution center location model with the minimum total cost objective. In this objective, the thesis considers the cost of cargo damage and the penalty cost of violating the time window in order to this order to improve customer satisfaction and loyalty, which is considered by a few researchers. Finally, a specific case study is designed for Q enterprise to solve the optimal the result and test the validity of the model algorithm.

This article introduces a thematic issue devoted to the translation of multimodal texts as well as some of the theoretical approaches used by researchers. It provides an overview of some of the most recent publications on the interaction between multimodality and translation as well as some of the challenges that both translators and researchers encounter when dealing with multimodal texts. Drawing on functional linguistics, Kress and van Leeuwen proposed a model of the analysis of ‘visual grammar’ that has been widely used in several disciplines, including Translation Studies. Some of the articles included in this thematic issue also draw on Kress and van Leeuwen’s approach, while others explore the modes of the source texts (including films, opera, museum texts, comics, and video games) by considering linguistic, cultural, and technical aspects from various theoretical standpoints.

In the field of image recognition, the issue of face age recognition has attracted the attention of many scholars, and a lot of outstanding algorithms have been proposed, but the correctness rate of age recognition is not high. To improve the AGE identification accuracy, this paper proposes a face AGE recognition based on convolutional neural network (CNN) --the AGE model, the IMDB -WIKI database and Caffe framework for training and testing, and the AGE recognition has the highest accuracy of 52%. Through experiments, this model is proved to be scientific and provides new ideas and methods for the study of face age recognition.

This article provides an overview of the research devoted to journalistic translation in the past five years and serves as an introduction to a thematic issue comprising twelve articles on diverse topics, using various methodologies and representing the various geographical areas where news translation has received greater attention, namely Europe, North America and China. It complements the introductory article of the thematic issue of Perspectives published in 2020. The final section mentions several possible avenues of research for the future, including the study of translation in the history of news production, news translation and ideological issues, and translation and constructive news amongst others.

Abstract With the rapid development of computer technology, the personalized needs of users become more and more prominent. The complexity of online learning resources and social networks lead to sparse data sets and low recommendation efficiency. In this paper, the existing collaborative filtering recommendation algorithm does not distinguish the degree of user’s trust when integrating the social impact theory. Therefore, in order to establish more accurate characteristics of user’s social interaction, this paper integrates the trust mechanism in the social impact theory, brings the establishment, dissemination and socialization factors of trust into the research scope, and introduces them into personalized recommendation in the recommendation process of recommendation model, it is used to solve the problem of low recommendation effect and low quality of recommendation system on sparse data set. Experimental results show that the proposed personalized recommendation method can improve the recommendation effect and quality on sparse data sets.

This article aims to test the vulgarization hypothesis in audiovisual translation (AVT) with regards to possible differences between male and female translators’ practices. It starts with an overview of the most recent publications on swearing and AVT, and of studies that have analyzed the use of swearwords by male and female speakers. Two seasons of these police dramas were used for the study. Both were produced by the same team at approximately the same time. After some methodological and theoretical considerations, the article presents and analyzes the translational choices made by the female translators (F) of Chicago PD and the male translator (M) of FBI . The results confirm the vulgarization hypothesis in AVT in both series, although the tendency in (F) Chicago PD is to use a greater number of swearwords than in (M) FBI . • The vulgarization hypothesis applies to both male and female translators. • Intensification strategies occur in the translation practices of both male and female translators. • Female translators opt for a greater number of stronger swearwords than the male translator. • Translators do not follow any conventions or guidelines when they opt for any of three strategies used.

Since the 21st century, people have put forward higher requirements for information security technology in many fields such as society and economy. The requirement has promoted the development of biometric identification technology. Face recognition technology has become a research hotspot in biometric identification technology with its unique advantages. In recent years, with the development of deep learning, face recognition technology has made breakthroughs. Face recognition technology based on deep learning has been widely used in various fields such as finance, education, security, transportation, and new retail. In the process of face recognition technology becoming popular, some comprehensive literatures are urgently needed to summarize the methods of face recognition technology. Based on this, this paper first introduces the principle and existing problems of traditional face recognition methods, and then introduces in detail two typical face recognition methods based on deep learning—face recognition methods based on convolutional neural networks and face recognition methods based on deep belief networks. Finally, we provide an overview of common face datasets.