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Celebrity endorsement on microblogging platforms (such as Twitter or Weibo) has become a widely adopted marketing practice. Compared to its counterpart on traditional mass media, celebrity endorsement on microblogging platforms has some unique characteristics. For example, the endorsement information is usually more implicit and the endorsers tend to use different tactics so as to maximize the impact on their followers. However, these new practices have not been thoroughly investigated and the underlying mechanism by which the endorsers influence potential information receivers is not well understood. Anchored on the theory of parasocial interaction borrowed from the mass communication literature, this paper aims to reveal the underlying mechanism of celebrity endorsement on microblogging platforms. More specifically, it examines the relationship between the intensity of microbloggers’ various celebrity-following activities and endorsement effectiveness.

The authors designed and conducted a Web-based survey containing scales for all focal constructs and demographic and control variables. Through online and offline campus advertisement, undergraduate and graduates students who have used microblogging for at least three months were recruited.

First of all, the survey results show that the intensity of users’ celebrity-following activities on microblogging platforms has a positive effect on the effectiveness of celebrity endorsement. Second, the positive effect of the intensity of microbloggers’ celebrity-following activities on the effectiveness of celebrity endorsement is mediated by the perceived parasocial interaction with the endorsers.

Firstly, most respondents of the survey are university students. Second, because of the intrinsic disadvantage of the survey method, the causal relationship between constructs cannot be examined directly. Last, parasocial interaction/relationship is a complex theoretical construct whose influence is unveiled partially in this research.

First, this study found that the effectiveness of celebrity endorsement on microblogging platforms are largely affected by celebrity-followers’ online involvement. Second, this study revealed that celebrity-following activities that help enhance followers’ perceptions of parasocial interactions are particularly beneficial for endorsement effectiveness. Last, the exploratory analysis further revealed that followers’ perceptions of ingenuousness and companionship are two key sub-dimensions of parasocial interaction.

First, the authors verified the positive relationship between information receiver’s involvement and the effectiveness of celebrity endorsement in the context of microblogging platforms. Second, this study found that parasocial interaction fully mediates the relationship between celebrity-following intensity and endorsement effectiveness. Last, through an exploratory factor analysis, the authors further decomposed the construct of parasocial interaction into three sub-dimensions, namely, ingenuousness, empathy and companionship.

Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

The purpose of this paper is to investigate the inequality in career constructions among freshmen in an elite university in Shanghai, China. The authors first investigated whether rural students and those from municipalities (zhi xia shi) and provincial capitals differ in their career awareness when arriving at college. After finding the difference, the authors explored how this initial difference in career awareness evolves and influences the career construction process in the freshman year.

This study used a complementary mixed-methods approach to monitor a cohort of students’ career construction process and the evolvement of their career awareness throughout the freshman year (n=210). Data collection included two surveys: students’ self-reflections and in-depth interviews to capture a holistic story.

The findings revealed that students differed in career awareness when arriving at college. This initial difference further evolved in the first year of college: students from municipalities and provincial capitals considered college a part of their career paths and began timely to construct their careers, whereas students from rural areas lagged behind. This study suggests that college maintains inequality, reinforcing the initial gap in career construction based on students’ family background.

College students differ in career prospects and associated skills when transitioning from school to work. Only a few studies have explored the role of college in shaping the career construction process during the college years. By exploring the process of career construction among freshmen, this study contributes to the growing literature on school-to-work transition and educational inequality in China.

This study aims to review the recent advancements in high entropy alloys (HEAs) called high entropy materials, including high entropy superalloys which are current potential alternatives to nickel superalloys for gas turbine applications. Understandings of the laser surface modification techniques of the HEA are discussed whilst future recommendations and remedies to manufacturing challenges via laser are outlined.

Materials used for high-pressure gas turbine engine applications must be able to withstand severe environmentally induced degradation, mechanical, thermal loads and general extreme conditions caused by hot corrosive gases, high-temperature oxidation and stress. Over the years, Nickel-based superalloys with elevated temperature rupture and creep resistance, excellent lifetime expectancy and solution strengthening L12 and γ´ precipitate used for turbine engine applications. However, the superalloy’s density, low creep strength, poor thermal conductivity, difficulty in machining and low fatigue resistance demands the innovation of new advanced materials.

HEAs is one of the most frequently investigated advanced materials, attributed to their configurational complexity and properties reported to exceed conventional materials. Thus, owing to their characteristic feature of the high entropy effect, several other materials have emerged to become potential solutions for several functional and structural applications in the aerospace industry. In a previous study, research contributions show that defects are associated with conventional manufacturing processes of HEAs; therefore, this study investigates new advances in the laser-based manufacturing and surface modification techniques of HEA.

The AlxCoCrCuFeNi HEA system, particularly the Al0.5CoCrCuFeNi HEA has been extensively studied, attributed to its mechanical and physical properties exceeding that of pure metals for aerospace turbine engine applications and the advances in the fabrication and surface modification processes of the alloy was outlined to show the latest developments focusing only on laser-based manufacturing processing due to its many advantages.

It is evident that high entropy materials are a potential innovative alternative to conventional superalloys for turbine engine applications via laser additive manufacturing.

This study investigates the correlation between economic development and scientific knowledge production indicators in the BRICS countries from 2000 to 2020, highlighting the importance of human resources, natural resources, and innovation. Addressing a gap in the existing literature, this study aims to contribute significantly to understanding this relationship.

Employing a descriptive statistical approach, this study utilizes GDP and per capita income as economic indicators and scientific data from WoS and SCOPUS databases, focusing on scientific document production and citations per document.

The analysis reveals a strong correlation between economic development and scientific performance within the BRICS nations during the specified period. It emphasizes the interdependence of economic progress and scientific prowess, underscoring that they cannot be considered independently.

However, limitations exist, notably the reliance on specific databases that might not cover the entire scientific output and the inability to capture all factors influencing economic and scientific development.

Understanding this interdependence has crucial originality. Policymakers and stakeholders in BRICS countries can leverage these insights to prioritize investments in human capital development and scientific research. This approach can foster sustainable economic growth by reducing reliance on natural resources.

In this research, neural network (NN) and genetic algorithm (GA) are used together to design optimal NN structure. The proposed approach combines the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications in design and manufacturing. Genetic input selection approach is introduced to obtain optimal NN topology. Experimental results are given to evaluate the performance of the proposed system.

The primary objective of this paper is to illustrate the development of an integrated product development environment using template‐based methodology. This system would thus act as an effective tool to reduce cost by foreseeing manufacturability constraints during concept generation phase. Concurrent engineering approach is applied to product and process development and thus the different phases of manufacturing are integrated together. The developed template system is then used for the design of a prototype using an electric motor.

In this paper, implementation of an integrated design based on templates is presented. The developed system components such as design calculations, component search, machine selection, process template retrieval and cost estimation. These components are integrated to relational databases. These databases contain all information required for integrated product and process development. Concepts used in developing the system are independent of the software used.

By using templates, the time required for new product development is drastically reduced. At the same time incorporating computer‐aided process planning into the system gives the designer a better understanding of the cost implications of the modified design with respect to manufacturing. The major challenge in implementing of such system is that any changes in the manufacturing facility have to be incorporated in the process plans stored. This can be a tedious job but can be overcome by using hybrid process planning approach instead of variant based approach.

The proposed template systems can be further developed to be considered as infrastructure necessary for design within a collaborative engineering environment. Further research and development is necessary for implementation. Though, the result from this case is promising.

The paper proposes the structure necessary for the implementation of a template‐based system. The approach used in the development of the integrated systems could be used by small and mid segment industries which do not have enough resources for costly software upgrading and complex computer technologies. By using templates, the time required for new product development is drastically reduced.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

In this paper I investigate the nature of the differential in poverty by ethnicity in rural China using data from the Chinese Household Income Project in 2002. For that, I compare observed poverty with that in a counterfactual distribution in which ethnic minorities are given a set of relevant village and household characteristics of the Han majority. In particular, I investigate the importance of the location of minorities in explaining their higher poverty levels. The ethnic poverty differential does not change after equalizing the distribution of the population by geographical region (unless we use a higher poverty line). However, it is reduced after equalizing other locational characteristics of minorities (such as them living in less developed and mountainous areas), their larger number of children, their low education, and their fewer skilled non-agriculture workers. Finally, the ethnic per capita (log) income differential is shown to be higher for higher percentiles, with an increasing role of the geographical region as the main driver of these higher differentials.