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The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

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 review the topic of “luxury fashion consumption”, a field of recent interest for academics and practitioners. However, a literature review that can map the existing knowledge and aggregate it into relevant topics and offers a research agenda for future research is still lacking.

This paper uses a systematic review and a text mining approach to analyse 73 articles on luxury fashion consumption aiming to clarify, rationalise and critically interpret the literature on luxury fashion consumption; identify the core topic, create an integrative framework of core constructs; and offer research gaps and suggest a research agenda for future studies.

From this analysis, eight major research topics are found and analysed (brand desire, authenticity, luxury markets, value perceptions, luxury retail experience, luxury brands communication, responsible consumption and sustainability and status signalling). Based on these topics and following the TCM framework, this review offers directions for future research.

This research offers a text-mining review of luxury fashion consumption to help scholars and managers further develop this field, as there is no comprehensive review on the topic exploring the themes, theories, constructs and methods used in prior studies.

Este artículo pretende revisar el “consumo de moda de lujo”, un tema de reciente interés para académicos y profesionales. Sin embargo, sigue faltando una revisión de la literatura que pueda ordenar el conocimiento existente y aglutinarlo en temas relevantes y que ofrezca una agenda de investigación futura.

Este trabajo emplea una revisión sistémica de la literatura y la minería de textos para analizar 73 artículos sobre el consumo de moda de lujo con el objetivo de (i) aclarar, racionalizar e interpretar críticamente la literatura sobre el consumo de moda de lujo, (ii) identificar el tema central, crear un marco integrador de constructos clave y (iii) presentar las lagunas de la investigación y sugerir una agenda de investigación para futuros estudios.

A partir de este análisis, se identifican y analizan ocho temas principales de investigación (el deseo de marca, la autenticidad, los mercados de lujo, las percepciones de valor, la experiencia de la venta al por menor de lujo, la comunicación de las marcas de lujo, el consumo responsable y la sostenibilidad, y la señalización de estatus). Sobre la base de estos temas y siguiendo el marco del TCM, esta revisión propone líneas para futuras investigaciones.

Esta investigación ofrece una revisión de la minería de textos sobre el consumo de moda de lujo para ayudar a los académicos y gestores a seguir desarrollando este campo, ya que no existe una revisión exhaustiva sobre el tema que explore los conceptos, teorías, constructos y métodos utilizados en estudios previos.

Revisión de la literatura

本文旨在回顾 “奢侈时尚消费”, 这是学术界和从业人员最近关注的一个话题。然而, 目前仍然未能将现有知识分类并为未来研究提供议程的文献回顾。

本文使用系统的文献综述和文本挖掘, 分析了73篇关于奢侈时尚消费的文章。此文目的是：（1）批判性地解释关于奢侈时尚消费的文献; （2）确定中心主题, 建立综合框架; （3）提出研究缺憾, 为未来的研究提出议程。

从这个分析中, 我们发现并分析了八个主要的研究主题（品牌欲望、真实性、奢侈品市场、价值认知、奢侈品零售体验、奢侈品品牌传播、负责任的消费和可持续性、以及地位信号）。基于这些主题并遵循TCM框架, 本评论提出了未来研究的方向。

目前还没有关于该主题的全面文献回顾, 以探索以前研究中使用的概念、理论、构造和方法。本研究对奢侈时尚消费的文本挖掘进行了回顾, 以帮助学者和管理者进一步发展该领域。

文献评论

This study aims to explore the holistic context of organisational staff retention in small, medium and large organisations. It also aims to identify the factors affecting the retention of organisations of different sizes.

The study implements an empirical test of a model created during previous research with the participation of 511 employees. The responses to the online questionnaire and the modelling were analysed using the partial least squares structural equation modelling method. The models were tested for internal consistency reliability, convergent and discriminant validity, multicollinearity and model fit.

Two models were tested by organisation size, which revealed a total of 62 significant correlations between the latent variables tested. Identical correlations were present in both models in 22 cases. After testing the hypotheses, critical variables (nature of work, normative commitment, benefits, co-workers and organisational commitment) were identified that determine employees’ organisational commitment and intention to leave, regardless of the size of the organisation.

As a result of this research, the models developed are suitable for identifying differences in organisational staffing levels, but there is as yet no empirical evidence on the use of the scales for homogeneous groups of employees.

The results show that employees’ normative commitment and organisational commitment are critical factors for retention. Of the satisfaction factors examined, the nature of work, benefits and co-workers have a significant impact on retention in organisations, so organisational retention measures should focus on improving satisfaction regarding these factors.

The readers of the journal would appreciate the work, which highlights the significance of employee psychology and retention for organisational success.

The study is based on primary data and, to the best of the authors’ knowledge, is one of the few studies that take a holistic approach to organisational staff retention in the context of the moderating effect of organisational size. This study contributes to a comprehensive understanding of the phenomenon of employee retention and in contrast to previous research, examines the combined effect of several factors.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

With the recognition that generating economic growth is not the same as sustaining it, the challenge to catch-up and growth literature is discerning between these processes. Recent research suggests that the decline in the frequency of “shrinking” episodes is more important for long-term development than higher growth rates. By using a framework centred around social capabilities, this study aims to investigate the effects of income inequality and poverty on economic shrinking frequency, as opposed to previous literature that has exclusively had a growth focus. The aim is to investigate how and why some societies might be more resilient to economic shrinking.

The research is a quantitative study, and the authors build a longitudinal data set including 23 developing countries throughout 42 years to test the paper’s purpose. This study uses country and period fixed-effects specifications as well as cross-sectional graphical representations to investigate the relationship between proxies of economic inclusivity and the frequency of shrinking episodes.

The authors demonstrate that while inclusive societies are more resilient to shrinking overall, it is changes in poverty levels, but not changes in income inequality, that appear to be correlated with economic shrinking frequency. Inequality, while still an important element to explain countries’ growth potential as an initial condition, does not seem to make the sample more resilient to shrinking. The authors conclude that the mechanisms in which poverty and inequality are correlated with the catch-up process must run through different channels. Ultimately, processes that explain growth may intersect but not always overlap with the ones that explain resilience to shrinking.

The need for inclusive growth in long-term development has been championed for decades, yet inclusion has seldom been explored from the shrinking perspective. Though poverty reduction is already an important mainstream political objective, this paper differentiates itself by providing an alternate viewpoint of why this is important. Income inequality could have more of an economic growth limiting effect, while poverty reduction could be required to build resilience to economic shrinking. Developing countries will need both growth and resilience to shrinking, to catch-up with higher-income economies, which policymakers might need to balance carefully.

In Brazil, over 4.7 million women enrolled in university in the year 2017. However, Brazilian women have been consistently overrepresented in humanities and care majors and underrepresented in science, technology, engineering and mathematics (STEM). Given that observed gender differences in math-intensive fields have lasting effects on gender inequality in the labor market, and that observed gender variations do not necessarily associate with differences in innate ability, in this paper we explore the paths of societal gender bias and gender differences in a Brazilian university.

We conduct a social experiment at a University in Southeastern Brazil, applying the gender-STEM Implicit Association Test.

We found that women in STEM are less likely to show gender-STEM implicit stereotypes, compared to women in humanities. The results indicate a negative correlation between implicit gender stereotyping and the choice of math-intensive majors by women.

The stereotype-congruent results are indicative of the gender bias in Brazilian society, and suggest that stereotypes created at early stages in life are directly related to future outcomes that reinforce gender disparities in Brazil, which can be observed in career choices.

This paper aims to explore the roles of psychologists in seclusion in adult mental health services in Ireland, their perspectives on seclusion and its use in recovery-oriented practice and related professional practice issues.

A qualitative hermeneutic phenomenological study was conducted from a social constructivist perspective. Semi-structured interviews with 17 psychologists were analysed using reflexive thematic analysis.

Twenty-four themes were identified, which were clustered into four overarching themes. Participants viewed themselves and psychology in Ireland more broadly as peripheral to seclusion. They believed that seclusion possessed no inherent therapeutic value but viewed it as an uncomfortable and multi-faceted reality. Participants regarded seclusion and recovery as largely inconsistent and difficult to reconcile, and they perceived systemic factors, which had a pervasive negative impact on seclusion and recovery in practice.

The findings highlight the perceived complexity of seclusion and its interface with recovery, and the need to conscientiously balance conflicting priorities that cannot be easily reconciled to ensure ethical practice. The findings suggest psychologists are well-suited to participate in local and national discussions on using seclusion in recovery-oriented practice.

This study offers a unique insight into psychologists’ perceptions of seclusion and considers the implications of these views. Participants’ nuanced views suggest that psychologists can make valuable contributions to local and national discussions on these topics.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This study aims to analyze the precursors of high and low intrapreneurial intentions among fitness center employees, considering various variables (gender, age, organization size and job satisfaction).

The study involved 166 fitness center employees of the Portuguese fitness center. The study used a two-part questionnaire to gather sociodemographic data and assess variables related to intrapreneurial intentions and job satisfaction among fitness employees. The first part collected basic demographic information, while the second used validated scales to measure intrapreneurial intentions (innovation and risk-taking) and job satisfaction (intrinsic and extrinsic).

This study underscores intrapreneurship as key for the evolving global fitness sector, highlighting job satisfaction as critical for fostering intrapreneurial intentions. Age, organizational size and gender diversity are also significant, suggesting that fostering a diverse and satisfied workforce under transformational leadership can enhance fitness organizations’ adaptability and growth.

This research supports the growth of the fitness sector by demonstrating how intrapreneurship, propelled by job satisfaction, can resolve challenges, benefiting fitness centers regardless of size, age or gender diversity.

The study highlights the vital role of intrapreneurs in the fitness industry, advocating a nongender-biased approach to intrapreneurship and identifying job satisfaction as key to fostering intrapreneurial intentions, beneficial for all fitness centers.