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The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

The advent of online live streaming platforms (OLSPs) and online health communities (OHCs) has expedited the integration of traditional medical services with Internet new media technology. Since the practice of physicians conducting live streaming is a relatively new phenomenon, the potential cross-platform effects of such physicians’ live streaming have not received adequate attention.

This study collected data from 616 physicians specializing in cardiology, obstetrics and gynecology and neurology between April and November 2022 on Live.Baidu.com and WeDoctor.com. It constructed a panel data set comprising a total of 4,928 observations over an 8-month period and validated the model using empirical analysis with the fixed-effects method.

We find evidence of cross-platform influence in online healthcare. Physicians’ live streaming behavior (whether live or not and the heat of their streams) on OLSPs positively impacts both their consultation and reputation on OHCs. Additionally, physicians’ ability positively moderates the relationships between live streaming heat and their performance (in terms of consultation volume and reputation) on OHCs. However, ability does not moderate the relationship between physicians’ live streaming status (live or not) and their performance (in terms of consultation and reputation) on OHCs. Furthermore, the attractive appearance of the physicians also significantly moderates the impact in a positive way.

This is one of the pioneering studies on physicians’ live streaming. The study offers vital guidance for physicians and patients utilizing dual platforms and holds significant reference value for platform operators (such as OLSPs and OHCs) aiming to optimize platform operations and for the government in policy formulation and industry regulation.

This study aims to examine the relationship between a team’s value diversity (VD) and creativity and investigate the moderating effect of emotional intelligence (EI) to explain inconsistent results regarding this relationship.

We conducted a cross-sectional sequential study with 340 employees and tested the hypothesis in a laboratory setting with 180 undergraduate students.

EI had a moderating effect on the relationship between a team’s VD and creativity in that the relationship was positive among teams with high EI. However, the relationship tended to be negative in the long term among teams with low EI.

Managers should pay attention to how teams benefit from VD because it can help or harm team performance. By assigning people with different EI levels into suitable teams and providing EI interventions, organizations may manage affective consequences and enjoy more benefits of cognitive consequences resulting from VD.

No previous study has investigated the effect of a team’s EI in the relationship between VD and team creativity. Drawing on the categorization-elaboration model of diversity and affective events theory, through the present two-study design, we obtained data from multiple sources and improved limitations in measurements of previous studies, thereby broadening the literature by highlighting the dynamic relationship between a team’s EI, VD and creativity in the Vietnamese context.

Safety training can effectively facilitate workers’ safety awareness and prevent injuries and fatalities on construction sites. Traditional training methods are time-consuming, low participation, and less interaction, which is not suitable for students who are born in Generation Z (Gen Z) and expect to be positively engaged in the learning process. With the characteristic of immersive, interaction, and imagination, virtual reality (VR) has become a promising training method. The purpose of this study is to explore Gen Z students’ learning differences under VR and traditional conditions and determine whether VR technology is more suitable for Gen Z students.

This paper designed a comparison experiment that includes three training conditions: VR-based, classroom lecturing, and on-site practice. 32 sophomore students were divided into four groups and received different training methods. The eye movement data and hazard-identification index (HII) scores from four groups were collected to measure their hazard-identification ability. The differences between the participants before and after the test were tested by paired sample t-test, and the differences between the groups after the test were analyzed by one-way Welch’s analysis of variance (ANOVA) test.

The statistical findings showed that participants under VR technology condition spent less time finding and arriving at the Areas of Interest (AOIs). Both the eye movement data and HII scores indicated that VR-based safety training is an alternative approach for Gen Z students to traditional safety training methods.

These findings contribute to the theoretical implications by proving the applicability of VR technology to Gen Z students and empirical implications by guiding colleges and universities to design attractive safety training lessons.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

The role of corporate social responsibility (CSR) fulfillment is critical when building resilience of project-based organizations (PBOs). However, fulfilling CSR to build a highly resilient PBO remains a black box problem. This study explores the different CSR combinations that enhance PBO resilience.

This study defines CSR in terms of shareholder, employee, and social CSR, and analyzes corporate characteristics in terms of corporate scale and nature. Data are collected from Hexun.com and the China Stock Market and Accounting Research Database (CSMAR). The qualitative comparative analysis (QCA) method is used to analyze 48 listed construction and engineering companies from China to explore the CSR configurations for PBOs in enhancing organizational resilience.

A large firm size is a necessary condition for high organizational resilience. We find six paths to build high and non-high resilience in PBOs, and the driving mechanisms of high and non-high resilience exhibit an asymmetric relationship.

This study cracks the black box of CSR fulfillment and PBO resilience. It reveals the CSR configurations that enhance or inhibit the resilience of PBOs. It also provides scientific basis for PBOs in their fulfillment of CSR in response to crises, and the enhancement of organizational resilience. Future research can be expanded to other industries, as the study sample is only limited to civil engineering construction companies. Since this study uses cross-sectional data, time series can be introduced in the future to further explore the relationship between CSR and organizational resilience.

This study provides targeted suggestions that can help decision-makers of construction companies to determine how they can fulfill CSR to enhance organizational resilience. At the same time, it can provide intellectual support for PBOs to cope with systemic crises and promote the fulfillment of CSR.

In terms of theoretical value, on the one hand, this study verifies the relationship between CSR fulfillment and PBO resilience, revealing its mechanism of action and multiple paths; on the other hand, it provides a new way of thinking for management research methods and enriches the theoretical study of organizational resilience.

This study collected the bibliographic data of 2034 journal articles published in 2000–2021 from Web of Science (WoS) core collection database and adopted two bibliometric analysis methods, namely historiography and keyword co-occurrence, to identify the evolution trend of construction risk management (CRM) research topics.

CRM has been a key issue in construction management research, producing a big number of publications. This study aims to undertake a review of the global CRM research published from 2000 to 2021 and identify the evolution of the research topics relating to CRM.

This study found that risk analysis methods have shifted from simply ranking risks in terms of their relative importance or significance toward examining the interrelationships among risks, and that the objects of CRM research have shifted from generic construction projects toward specified types of construction projects (e.g. small projects, underground construction projects, green buildings and prefabricated projects). In addition, researchers tend to pay more attention to an individual risk category (e.g. political risk, safety risk and social risk) and integrate CRM into cost, time, quality, safety and environment management functions with the increasing adoption of various information and communication technologies.

This study focused on the journal articles in English in WoS core collection database only, thus excluding the publications in other languages, not indexed by WoS and conference proceedings. In addition, the historiography focused on the top documents in terms of document strength and thus ignored the role of the documents whose strengths were a little lower than the threshold.

This review study is more inclusive than any prior reviews on CRM and overcomes the drawbacks of mere reliance on either bibliometric analysis results or subjective opinions. Revealing the evolution process of the CRM knowledge domain, this study provides an in-depth understanding of the CRM research and benefits industry practitioners and researchers.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.

The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.

The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

We have witnessed an evolution in the use of smartphones in recent years. We have been aware for some time of the potentially deleterious impact of smartphones on users' lives and their propensity for user addiction, as reflected in the large and growing body of work on this topic. One modern phenomenon – the distracted mobile phone user in public, or “smartphone zombie” – has received limited research attention. The purpose of the present study is to develop a robust measure of smartphone zombie behaviour.

The research deign comprises three studies: A round of focus groups (n = 5) and two online surveys (survey one n = 373, survey two n = 386), in order to develop and validate a three-factor, 15-item measure named the Smartphone Zombie Scale (SZS).

Following the round of focus groups conducted, Exploratory Factor Analysis and a Confirmatory Factor Analysis, the SZS measure (Cronbach's α = .932) is demonstrated to be robust and comprises three factors: Attention Deficit (Cronbach's α = .922), Jeopardy (Cronbach's α = .817) and Preoccupation (Cronbach's α = .835), that is shown to be distinct to existing closely related measures (Smartphone Addiction scale and Obsessive Compulsive Use).

The present study represents the first extant attempt to produce a measure of smartphone zombie behaviour, and provides us with a reliable and valid measure with which we can study this growing phenomenon.