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The purpose of this paper is to propose a method to remit or mitigate deterioration resulting from the influence of short data length to existing signal extracting methods.

Careful design of the pre-filtering circuits to refrain most of the noise and disturbance and remove the influence of operation speed of the concerned balancing machine. Based on the analysis on the spectral feature of the unbalance vibration signal, a pre-filtering circuit is designed, then the signal extension method based on AR prediction model are discussed and used to prolong sampled signal.

With the extension method, sampled signal can be extended to required length to enhance the performance of refraining nearby frequency disturbance. The results of simulation and field experiments demonstrate the feasibility of the presented extension method.

Improved measurement efficiency of balancing machine and provided a method to trade off between measurement accuracy and measurement efficiency.

The paper presents a way to improve extraction accuracy and frequency resolution with limited cycles of unbalance vibration signal.

The purpose of this paper is to integrate social capital theory and motivation theory to identify the factors that affect the intention of users to share mobile coupons (m-coupons) via social network sites (SNS). Social capital includes social ties, trust, and perceived similarity, whereas motivation comprises sense of self-worth and socializing.

A research model that integrates three social capital factors, two motivations, and m-coupon sharing is developed. Quantitative data from 297 users who had coupon usage experience are collected via offline and online survey. Partial least squares is used to conduct data analysis and test hypotheses.

Social ties, trust, and perceived similarity are positively related to m-coupon sharing intention and positively affect sense of self-worth and socializing, which have significant positive effects on m-coupon sharing intention and mediate the relationships between social capital factors and sharing intention.

This study highlights the integrated effects of social capital and motivations on m-coupon sharing intention in SNS. While social capital factors (i.e. social ties, trust, and perceived similarity) and motivations (i.e. sense of self-worth and socializing) positively affect m-coupon sharing, motivations are more directly associated with m-coupon sharing than social capital factors.

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.

This paper aims to contribute to the performance improvement and the broader application of hot-dip galvanized coating.

First, the ability to provide barrier protection, galvanic protection, and corrosion product protection provided by hot-dip galvanized coating is introduced. Then, according to the varying Fe content, the growth process of each sublayer within the hot-dip galvanized coating, as well as their respective microstructures and physical properties, is presented. Finally, the electrochemical corrosion behaviors of the different sublayers are analyzed.

The hot-dip galvanized coating is composed of η-Zn sublayer, ζ-FeZn13 sublayer, δ-FeZn10 sublayer, and Γ-Fe3Zn10 sublayer. Among these sublayers, with the increase in Fe content, the corrosion potential moves in a noble direction.

There is a lack of research on the corrosion behavior of each sublayer of hot-dip galvanized coating in different electrolytes.

It provides theoretical guidance for the microstructure control and performance improvement of hot-dip galvanized coatings.

The formation mechanism, coating properties, and corrosion behavior of different sublayers in hot-dip galvanized coating are expounded, which offers novel insights and directions for future research.

This paper reviews the extant research on Web3.0 published between 2003 and 2022.

This study uses a topic modeling procedure latent Dirichlet allocation to uncover the research themes and the key phrases associated with each theme.

This study uncovers seven research themes that have been featured in the existing research. In particular, the study highlights the interaction among the research themes that contribute to the understanding of a number of solutions, applications and use cases, such as metaverse and non-fungible tokens.

Despite the relatively small data size of the study, the results remain significant as they contribute to a more profound comprehension of the relevant field and offer guidance for future research directions. The previous analysis revealed that the current Web3.0 technology is still encountering several challenges. Building upon the pioneering research in the field of blockchain, decentralized networks, smart contracts and algorithms, the study proposes an exploratory agenda for future research from an ecosystem approach, targeting to enhance the current state of affairs.

Although topics around Web3.0 have been discussed intensively among the crypto community and technological enthusiasts, there is limited research that provides a comprehensive description of all the related issues and an in-depth analysis of their real-world implications from an ecosystem perspective.

The purpose of this study is to examine the intricate dynamics within the hospitality service sector, which predominantly employs low-wage and low-skilled workers. These employees frequently encounter challenges related to breaches in their psychological contracts. Despite their critical role in customer service, their experiences are often overlooked in organizational inclusion research. We investigate the relationship between employees’ perceptions of organizational inclusion and their assessments of organizational ethical virtues (OEVs), considering the potential moderating effect of psychological contract breach. Furthermore, it explores how these factors influence customer- and organizationdirected organizational citizenship behaviors (OCBs).

Our research design incorporates a two-stage moderated-mediation model to test our proposed hypotheses empirically. A two-source sample of 451 European hotel managers and employees extends the inquiry with the proposed model. Structural Equation Modeling (SEM) was employed to analyze the proposed relationships.

Findings reveal that organization inclusion is positively associated with OEV and employee-perceived OEV mediates an indirect link between organization inclusion on customer-oriented OCB. Moreover, psychological contract breach (P CB) attenuates the association between organization inclusion and OEV at the first stage and OEV, and OCB at the second stage.

The findings robustly corroborate our proposed model. The study findings culminate in a discussion accentuating the extensive implications of our findings for both research and practicality within the hospitality sector. Anchored in empirical revelations, we delineate avenues for future exploration in this pivotal domain.

The purpose of this paper is to investigate the influence of two intrinsic (i.e. sense of self-worth and socializing) and two extrinsic motivations (i.e. economic reward and reciprocity) on mobile coupon (m-coupon) sharing by users in social network sites (SNSs). Moreover, this study examines how coupon proneness moderates the relationship between motivations and m-coupon sharing in SNSs.

A research model that integrates four motivations, coupon proneness, and m-coupon sharing is developed. Quantitative data from 247 users are collected via online and offline survey. Partial least squares technique is employed to evaluate the measurement model, and hypotheses are tested through hierarchical regression analysis.

Sense of self-worth, socializing, economic reward and reciprocity have positive effects on m-coupon sharing in SNSs. Furthermore, coupon proneness positively moderates the relationship of socializing and reciprocity with m-coupon sharing, whereas the moderating effects of coupon proneness on the relationship of sense of self-worth and economic reward with m-coupon sharing are insignificant.

The findings highlight the integrated effects of coupon proneness and motivations on m-coupon sharing in SNS. The impact of socializing and reciprocity on m-coupon sharing is higher for users with higher coupon proneness. However, the effect of sense of self-worth and economic reward on m-coupon sharing is the same regardless of coupon proneness of users. Therefore, although users with different motivations should be identified, SNSs and merchants should develop different incentive mechanisms to promote m-coupon sharing among various users.

This study explores the potential impact of enterprise social media (ESM) communication visibility on knowledge sabotage to reduce knowledge sabotage within organizations.

We collected data from 389 Chinese employees across three stages and used hierarchical regression analysis and the bootstrap method to test our hypotheses.

Communication visibility negatively affects knowledge sabotage, and the loss of knowledge power mediates the relationship between communication visibility and knowledge sabotage. Digital work connectivity strengthens the negative relationship between message transparency and loss of knowledge power but weakens the negative relationship between network translucence and loss of knowledge power. Therefore, digital work connectivity plays a dual role.

Managers can encourage employees to share their knowledge advantages through ESM and seek cross-disciplinary knowledge cooperation, which helps restrain knowledge sabotage from the source. At the same time, maintaining appropriate digital work connectivity enables employees to leverage their knowledge interaction advantages of ESM, thereby fostering their knowledge competitiveness.

This study is the first to reveal the internal mechanism (loss of knowledge power) through which ESM communication visibility affects knowledge sabotage and explores the boundary condition (digital work connectivity) impacting the effectiveness of communication visibility. It contributes to a deeper understanding of the inherent nature of knowledge sabotage from an information technology perspective and offers novel technical insights into its management.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

Online business can be an attractive career choice but bears gendered implications under China’s market economy. This study aims to examine how highly educated young women negotiate their career choice of online business, given their enhanced career ambitions and the persisting conservative views of their parents. It is to be examined how these factors interact in shaping women’s strategies and commitment to their nonconventional careers.

The study draws on 23 interviews with 18 women involved in online business. The interviews were coded by themes about how women perceive and make career choices to enter online business, the influence of various motivations (e.g., economic security, decency, autonomy and earning potential) and women’s interactions with their parents under gendered social expectations. The study analyzes and categorizes women’s varied strategies and levels of commitment in taking up online business, an attractive but suspicious career for highly educated young women.

The study finds that some women chose to conduct online business as a secondary job behind their stable and formal primary job; their compromise under parents’ preferences and social expectations as “compliant daughters,” willingly or forced, coexisted with their persisting interest in online entrepreneurship. Other women, or “self-determined daughters,” embraced online business as their primary job; some benefited from parents’ tolerant views, but others needed to handle the pressure of parental disapproval by hiding or proving their nonconventional career choices worthwhile.

This study speaks to the gendered opportunity-necessity framework of entrepreneurship by illustrating women’s multiple motivations in China’s market reforms and the rising online entrepreneurship dynamics. The findings contextualize women’s career choices in different family dynamics and suggest how social expectations and gender norms are imposed and transformed, with a focus on the shifting gendered concerns of opportunity, security and decency in an era of digital economy.