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We consider the problem of estimating a varying coefficient panel data model with fixed-effects (FE) using a local linear regression approach. Unlike first-differenced estimator, our proposed estimator removes FE using kernel-based weights. This results a one-step estimator without using the backfitting technique. The computed estimator is shown to be asymptotically normally distributed. A modified least-squared cross-validatory method is used to select the optimal bandwidth automatically. Moreover, we propose a test statistic for testing the null hypothesis of a random-effects varying coefficient panel data model against an FE one. Monte Carlo simulations show that our proposed estimator and test statistic have satisfactory finite sample performance.

Identification and inference are central to applied analysis, and two papers examine these issues, the first being theoretical in nature and the second being simulation based.

This paper aims to study the wear properties of electron beam melted Ti6Al4V (EBM-Ti6Al4V) in simulated body fluids for orthopedic implant biomedical applications compared with wrought Ti6Al4V (Wr-Ti6Al4V).

Wear properties of EBM-Ti6Al4V compared with Wr-Ti6Al4V against ZrO₂ and Al₂O₃ have been investigated under dry friction and the 25 Wt.% newborn calf serum (NCS) lubricated condition using a ball-on-disc apparatus reciprocating motion. The microstructure, composition and hardness of the samples were characterized using scanning electron microscopy (SEM), x-ray diffraction and a hardness tester, respectively. The contact angles with 25 Wt.% NCS were measured by a contact angle apparatus. The wear parameters, wear 2D and 3D morphology were obtained using a 3D white light interferometer and SEM.

EBM-Ti6Al4V yields a higher contact angle than the Wr-Ti6Al4V with the 25 Wt.% NCS. EBM-Ti6Al4V couplings exhibit lower coefficients of friction compared with the Wr-Ti6Al4V couplings under both conditions. There is only a slight difference in the wear resistance between the Wr-Ti6Al4V and EBM-Ti6Al4V alloys. Both Wr-Ti6Al4V and EBM-Ti6Al4V suffer from similar friction and wear mechanisms, i.e. adhesive and abrasive wear in dry friction, while abrasive wear under the NCS condition. The wear depth and wear volume of the ZrO₂ couplings are lower than those of the Al₂O₃ couplings under both conditions.

This paper helps to establish baseline bio-tribological data of additively manufactured Ti6Al4V by electron beam melting in simulated body fluids for orthopedic applications, which will promote the application of additive manufacturing in producing the orthopedic implant.

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 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 study aims to answer two questions: (a) what obstacles and opportunities do Chinese female entrepreneurs face when doing business? And (b) how do they negotiate their entrepreneurial careers and gender identities in different gender-segregated markets?

This study uses qualitative research methods of participant observation and in-depth interviews with 41 female entrepreneurs in China and the theoretical lenses of gender role theory and doing gender in entrepreneurship.

The study findings reveal that Chinese female entrepreneurs face different obstacles and opportunities in gender-segregated industries. Their experiences vary in industries that are mainly occupied by males and females. On the one hand, women in female-dominated industries may be supported by a feminine working environment that is coherent with their domestic roles. However, they may also be questioned on the cultural impurity implied in some industries, which harms their class-based feminine virtue. On the other hand, women in male-dominated industries may be challenged and marginalized due to their gender. However, some find ways to turn the disadvantaged feminine characters into favourable conditions and break out of the stereotypical gender constraints in doing business.

This study contributes to the literature on gender and entrepreneurship in general. More specifically, it contributes to the study of doing gender in gender-segregated markets, and it also illustrates women’s gendered opportunities and constraints in Chinese society that are affected by the long-lasting traditional gender norms.

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.

This study explores the factors influencing the value of enterprise social media (ESM) from the perspective of compatibility. Establishing a theoretical model based on compatibility theory, the authors examine the effects of two dimensions of compatibility and the mediating effects of employees' intrinsic motivations. ESM is an important tool that helps companies to enhance knowledge sharing and cross-department collaboration. Thus, it is important to understand factors that can facilitate the role of ESM in improving enterprise operating performance.

The authors conducted a survey among 353 users of a leading ESM platform and empirically investigated how compatibility influences ESM value through employees' intrinsic motivations. Structural equation modeling (SEM) was applied to study the relationship among compatibility, employees' intrinsic motivations and ESM value.

The empirical research results indicate that compatibility of self-interest with group interest influences the value of ESM, and intrinsic motivations toward collaboration and toward knowledge management partially mediate the effects of the two dimensions of compatibility on ESM value.

First, the empirical analysis relies on data from surveying employees of Chinese companies. Therefore, one direction for future research is to reexamine the model using data from other countries. Second, the effects of compatibility identified in the study may vary among different ESM platforms. In addition, the findings may change for organizations having different sizes.

This finding suggests that managers should pay close attention to potential conflicts of interest when implementing ESM to enhance group communication and collaboration. This study also highlights the importance of compatibility of new working processes with experience in practice. In addition, intrinsic motivations towards both cooperation and knowledge management in ESM are important factors influencing the value creation of ESM. Therefore, to cultivate employees' intrinsic motivation, managers and organizations need to facilitate the formation of a collaborative atmosphere and habits of cooperative adoption.

Although previous studies show that compatibility is a strong belief salient to technology acceptance and continuance usage behavior, the operational definition of compatibility developed by prior studies has generally been limited to the technology perspective and the individual level. However, the primary benefit of ESM is enabling online team collaboration and knowledge sharing across various departments. Thus, the level of compatibility between employees' self-interests and group interests may influence their intrinsic motivations toward ESM usage. From this perspective of individual–group interest conflicts, the authors propose a conceptual research model based on the theory of compatibility in innovation diffusion.

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.