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Library data are often hard to analyze because these data come from unconnected sources, and the data sets can be very large. Furthermore, the desire to protect user privacy has prevented the retention of data that could be used to correlate library data to non-library data. The research team used data mining to determine library use patterns and to determine whether library use correlated to students’ grade point average.

A research team collected and analyzed data from the libraries, registrar and human resources. All data sets were uploaded into a single, secure data warehouse, allowing them to be analyzed and correlated.

The analysis revealed patterns of library use by academic department, patterns of book use over 20 years and correlations between library use and grade point average.

Analysis of more narrowly defined user populations and collections will help develop targeted outreach efforts and manage the print collections. The data used are from one university; therefore, similar research is needed at other institutions to determine whether these findings are generalizable.

The unexpected use of the central library by those affiliated with law resulted in cross-education of law and central library staff. Management of the print collections and user outreach efforts will reflect more nuanced selection of subject areas and departments.

A model is suggested for campus partnerships that enables data mining of sensitive library and campus information.

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.

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 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.

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.

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.

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.

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.