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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 (1) examine the effect of customer awareness of restaurant philanthropic activities on customer loyalty; (2) investigate the mediating roles of customer social benevolence trust, perceived restaurant reputation and affective commitment on the relationship between their awareness of restaurant philanthropic activities and customer loyalty; and (3) test the path effect differences between the directed and general philanthropic activities during the COVID-19 pandemic period.

This study used online scenario-based surveys to collect data. Based on 293 useable surveys, partial least squares structural equation modeling was applied for data analysis.

This study finds that customer awareness of restaurant philanthropic activities positively relates to customer loyalty. Moreover, customer social benevolence trust, perceived restaurant reputation and affective commitment have positive mediating effects on the relationship between their awareness of restaurant philanthropic activities and customer loyalty. There is no significant path effect difference between the directed and general philanthropic activities.

This study suggests that restaurant decision-makers should conduct either directed or general philanthropic activities as a marketing tool to sustain customers during the COVID-19 recovery.

This study is the first study that discusses the marketing role of corporate philanthropy in the restaurant industry during the COVID-19 pandemic and stresses the importance of proactive strategic donations that helps restaurants' recovery.

The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.

This paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.

The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.

This paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

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.

Mental illness presents a huge individual, societal and economic challenges, currently accounting for 20% of the worldwide burden of disease. There is a gap between the need for and access to services. Digital technology has been proven effective in e-mental health for preventing and treating mental health problems. However, there is a need for cross-disciplinary efforts to increase the impact of e-mental health services. This paper aims to report key challenges and possible solutions for cross-disciplinary and cross-sectorial research teams within the domain of e-mental health.

The key challenges and possible solutions will be discussed in light of the literature on effective cross-disciplinary research teams.

Six topics have been key challenges in our cross-disciplinary and cross-sectorial research team: to develop a shared understanding of the domain; to establish a common understanding of key concepts among the project participants; to involve the end-users in the research and development process; to collaborate across sectors; to ensure privacy and security of health data; and to obtain the right timing of activities according to project dependencies.

This study focuses to increase knowledge and training in cross-disciplinary and cross-sectorial research, as this is often referred to as an important tool when developing sustainable solutions for major societal challenges.

This study needs to include theory and skills training in cross-disciplinary research in research training.

Cross-disciplinary teams have the potential to address major societal challenges, including more perspectives and more stakeholders than single disciplinary research teams.

Major societal challenges require complex and sustainable solutions. However, there is a lack of knowledge about how cross-disciplinary and cross-sectorial research teams may work productively to solve these challenges. This paper shares experiences regarding the challenges and possible solutions for productive collaboration in cross-disciplinary and cross-sectorial research teams within the domain of e-mental health services.

The purpose of this paper seeks to develop a motivation-based complementary framework for temporally dynamic user preferences to facilitate optimal timing in web personalisation. It also aims to highlight the benefits of considering user motivation when addressing issues in temporal dynamics.

Through theory, a complementary framework and propositions for motivation-based temporal dynamics for further testing are created. The framework is validated by feeding back findings, whereas some of the propositions are validated through an experiment.

The suggested framework distinguishes two ways (identifying/learning and shifting) of using a motive-based approach to temporal dynamics in web personalisation. The suggested outcomes include enhanced timing in matching current preferences and improved conversion. Validation measures predominantly support both the framework and the tested propositions. The theoretical basis for the approach paves a path towards refined psychological user models; however, currently on a complementary level.

While the framework is validated through feeding back findings, and some of the propositions are validated through basic experimentation, further empirical testing is required.

A generalised approach for complementing personalisation procedures with motivation-based temporal dynamics is offered, with implications for both user modelling and preference matching.

This paper offers novel insights to web personalisation by considering the in-depth effects of user motivation.

The study aims to take a step back and take the big picture of how digital capitalism is changing people's ways of living and production. On that basis, China should enhance its digital governance rationally and develop the digital economy efficiently, thereby bringing its socialist economy to new heights.

The rise of digital capitalism in the 1990s has profoundly changed the ways of consumption, employment, production organization and investment in the realm of capitalism.

Digital Capitalism has not changed the nature of capitalism, that is, exploitation and capital accumulation, which continue only in a more profound, extensive and covert way.

For the economy of socialist China to grow in the new era, China should tap into digital economy platforms, take a people-centered approach and let the people jointly develop the digital economy, share the fruits of development and participate in the governance of the digital economy. The government should leverage its modern digital governance and a high-quality digital economy to meet people's ever-growing demand for a better life.

Since the outbreak of COVID-19, tremendous changes have taken place in the US economy – the economic growth in the whole year of 2020 was negative, and though it enjoyed a significant rebound for the first half of 2021, the growth rate began to decline rapidly by the third quarter, and inflation suddenly rises rapidly, which after came the all-time highs of the “misery index” consisted of the inflation rate and unemployment rate. All signs indicate that the US economy will likely enter a “stagflation” crisis.

This paper analyzes the institutional and social contradictions in the United States during the neoliberal era from the perspectives of domestic social structure of accumulation (SSA) and international SSA based on the SSA theory.

The current risk of stagflation in the US economy is a concentrated outbreak of the long-term accumulated contradictions in neoliberal SSA under the impact of the epidemic, which is the product of the irreconcilable contradictions inherent in the capitalist mode of production.

Based on this analysis, the paper points out that with the deepening of the crisis, the neoliberal SSA is likely to end and a new SSA will be established gradually.

This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.

The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.

This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.

Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train, and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence, the study analyzes aerodynamic parameters with multi-objective optimization design.

The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics. Then the modified vehicle modeling function (VMF) parameterization method and surface discretization method are adopted for the parametric design of the nose. For the 12 key design parameters extracted, combined with computational fluid dynamics (CFD), support vector machine (SVR) model and multi-objective particle swarm optimization (MPSO) algorithm, the multi-objective aerodynamic optimization design of high-speed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint. The engineering improvement and wind tunnel test verification of the optimized shape are done.

Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train. The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.

Compared with the original shape, the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%, and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%, respectively, after adopting the optimized shape modified according to engineering design requirements.