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Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station.

This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train.

A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow.

This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

This chapter examines the similarities and differences between the concepts of transformational leadership as developed within North America and the Confucian idea of transformation. It argues that Confucian tradition encompasses the essential elements embedded in the concept of transformational leadership. The former differentiates from the latter in its deeper degree of transformation, emphasis on morality and culture, and its focus on transformation from the inside outwards. The two greatest educators in Chinese history, Confucius and Cai Yuanpei, are evaluated in terms of their transformational leadership qualities in the Western sense. By looking at Confucius and Cai Yuanpei as successful transformational leaders, the chapter identifies four important factors from Chinese cases that may contribute to the success of this type of leadership. Implications of this comparison are discussed as they may inform the knowledge, research and practices of transformational leadership.

Although an important facet of modernist architecture in which function plays a prominent role, building flexibility is not entirely a new concept. Its relevance transcends generations, allowing space and structure to evolve through time. This paper investigates the relationship among main building structures, infill elements, and space by studying examples in ancient Chinese architecture. It reveals the role of building owners, users, and craftsmen from a survey of historical documentation. In studying these examples, it is concluded that craftsmen in ancient China were involved not only during the construction phase but throughout the period of use as well. Thus, in select cases, the relationship between craftsmen and owners or users had been preserved for generations. Finally, this paper suggests potential strategies for the building industry and technology in the move towards sustainable development.

Hand, foot and mouth disease (HFMD) is a common infectious disease in infants and children. HFMD has caused millions of cases and a large epidemic worldwide. A number of studies have shown that the incidence of HFMD is closely related to various factors such as meteorological factors, environmental air pollution factors and socio-economic factors. However, there are few studies that systematically consider the impact of various factors on the incidence of HFMD. The paper aims to discuss these issues.

This study used grey correlation analysis and principal component analysis (PCA) method to systematically analyse the impact of meteorological factors, health resource factors, socio-economic factors and environmental air pollution factors on the incidence of HFMD in Shenzhen.

The incidence of HFMD in Shenzhen was affected by multiple factors. Grey correlation analysis found eight influencing factors which are as follows: volume of industrial waste gas emission; the days of air quality equal to or above grade; the volume of industrial nitrogen oxide emission; precipitation; the mean air temperature; the gross domestic product; the expenditure for medical and health care; and the gross domestic product per capita. PCA found that the gross domestic product, the volume of industrial soot emission, the relative humidity, and the days of air quality equal to or above grade have a higher load value.

This study is the one of the first studies that apply the grey correlation analysis to analyse the influencing factors of HFMD in the English literature, which to some extent fills up the blank in this field.

The conventional textile dyeing process requires various operational characteristics, and determining the most reliable factor in dyeing performance has always been a challenge for the textile industry. Thus, the present paper aimed to evaluate the process sensitivity of C. I. Reactive Blue 194 dyeing of cotton fabric using a statistical technique.

An L₂₇ orthogonal array-based Taguchi's methodology was used with six parameters and three levels of each parameter. The signal-to-noise (S/N) ratio and analysis of variance were studied using total fixation efficiency (T%) as the response of the process sensitivity.

Results showed that dyebath pH was the most influential factor on the process and total fixation efficiency (p-value = 0.00 and contribution percentage 45.03%), followed by dye-fixing temperature, dye mass, electrolyte concentration, dye-fixing time and material to liquor ratio.

Overall this study provides a foundation for the determination of dyeing process sensitivity that will be useful in textile industries toward further development.

The purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.

Two methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.

The causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.

The obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.

This paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.

Based on social information processing (SIP) theory, this study explores the cross-level effect of high-involvement work practices (HIWPs) on employee innovative behavior by studying the mediating role of self-reflection/rumination and the moderating role of transactive memory system (TMS).

This study collects data from 452 employees and their direct supervisors in 94 work units, and tests a cross-level moderated mediation model using multilevel path analysis.

The results suggest that HIWPs significantly contribute to employee innovative behavior. Both self-reflection and self-rumination mediate the above relationship. TMS not only positively moderates the relationship between HIWPs and self-reflection, but also reinforces the linkage of HIWPs. →self-reflection→employee innovative behavior. Furthermore, TMS negatively moderates the relationship between HIWPs and self-rumination, and attenuates the mediating effect of self-rumination.

The study suggests that enterprises should invest more in promoting HIWPs and TMS in the workplace. Furthermore, managers should provide employees training programs to enhance their self-reflection, as well as lower self-rumination, in order to facilitate employee innovative behavior.

This research identifies self-reflection and self-rumination as key mediators that link HIWPs to employee innovative behavior and reveals the moderating role of TMS in the process.

Technology-enhanced learning (TEL), particularly in science, technology, engineering, arts, and math (STEAM), revolutionizes educational approaches by fostering active, transformative learning and expediting the learning process. TEL employs various tools like online courses, artificial intelligence (AI) technologies, virtual reality (VR), simulations, makerspaces, visual learning, and project-based learning, all contributing to accelerated learning in STEAM. A notable TEL innovation is the emergence of Large Language Models (LLMs) and AI chatbots, exemplified by the release of GPT-3 in December 2022. These tools utilize extensive parameters to generate natural language and perform tasks such as classification and prediction, thereby offering personalized and collaborative learning experiences essential for STEAM education. The generative pre-training transformer (GPT), a leading model in natural language processing (NLP), excels in generating human-like text and handling complex tasks like translation, summarization, and question answering. This chapter explores TEL environments that support transformative learning in STEAM, focusing on AI models. It reviews research on TEL’s impact on STEAM education, discussing the constructionism theory and emphasizing TEL’s role in creating engaging, student-centered learning experiences. However, challenges like technology access, instructor training, infrastructure, internet connectivity, and hardware resources are crucial. Additionally, the rise of AI brings ethical concerns regarding privacy, security, and potential biases in AI algorithms. Despite these hurdles, TEL’s potential to enhance STEAM learning experiences and accelerate the educational process is significant. By effectively implementing TEL strategies and leveraging LLMs and AI tools, educators can substantially improve learning outcomes in STEAM education.

This paper considers the problem of estimating a partially linear varying coefficient fixed effects panel data model. Using the series method, we establish the root N normality for the estimator of the parametric component; and we show that the unknown function can be consistently estimated at the standard nonparametric rate. Furthermore, we extend the model to allow endogeneity in the parametric component and establish the asymptotic properties of the semiparametric instrumental variable estimators.

This study aims to testify the effects of green marketing on peer-to-peer (P2P) accommodation consumers’ repurchase intention and positive word-of-mouth (WOM) towards P2P accommodation platforms through the mediation of trust and consumer identification.

A questionnaire survey was applied to collect data from a sample of 488 consumers in China who had used P2P accommodation platforms in the past six months. Partial least squares structural equation modelling was used to analyse the data.

The study found that consumers’ perception of green marketing orientation of P2P accommodation platforms significantly increased consumer trust in the platform and consumer identification with the platform, which in turn each positively influenced repurchase intention and positive WOM to the platform. Furthermore, consumer trust had a positive effect on consumer identification and both trust and identification mediated the relationship between green marketing and repurchase intention and that between green marketing and positive WOM.

The findings of this study provide P2P accommodation platform operators with important insights to apply green marketing and focus on consumer trust and identification in sustaining their business and coping with the intense market competition.

This study contributes to better understanding of the impact of green business practices on consumers in sharing economy and offers practical implications on sustainable P2P accommodation practices.