Search results

This study aims to provide a reference basis for waterproofing for the long-term safe operation of shield tunnels. Shielding subways in the long-term operation of tunnel tube seams leads to opening, dislocation and other issues, which in turn cause the tube sealing gasket to break and ultimately cause water seepage, and the existing symmetrical sealing gasket arrangement cannot meet the waterproofing requirements of the tunnel structure.

First, we carry out an indoor “one-seam” hydrostatic test to quantitatively determine the waterproofing performance of symmetric and four asymmetric arrangements of gaskets. And the arrangement with the best stability and waterproofing performance is selected. Second, we establish a three-dimensional numerical seepage model for the waterproof failure of gaskets with different arrangements, which mechanistically explains the whole course of the gradual failure of the waterproof performance of gaskets with the wedging of water. Finally, we compare and analyze the experimental results with the numerical results to verify the reliability of the different analysis methods.

The results of the research show that the gasket will undergo four stages: the initial stage, deformation stage, wedging stage, and breakthrough stage during the continuous wedging process of the water body. Compared with the symmetric arrangement of the gasket, the asymmetric arrangement of the effective contact part of the gasket stress wave peaks and troughs is smaller, the deformation stage of the ability to resist the deformation of the water pressure is stronger, and the role of the water pressure between the two sealing gaskets of the stress path is less likely to be damaged.

The current test can't fully reproduce real engineering site conditions as it ignores factors like temperature, time and aging during waterproofing tests and lacks tests based on actual application. Only one – seam test is done, lacking research on other seams. The current seepage model has difficulty reflecting some details and needs refinement.

The study focuses on the tube sheet joint problem in underground tunnels and proposes four asymmetric gasket arrangements, which are tested and analysed using a variety of methods. The results show that the asymmetric arrangement has a slower decline in waterproofing capacity and better stability, providing a new method and basis for solving tunnel waterproofing problems.

The study focuses on the tube sheet joint problem in underground tunnels and proposes four asymmetric gasket arrangements, which are tested and analysed using a variety of methods. The results show that the asymmetric arrangement has a slower decline in waterproofing capacity and better stability, providing a new method and basis for solving tunnel waterproofing problems.

The launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas.

Through an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare.

The subject experts first share an overview of the use of GAI, followed by the relevant opportunities and challenges in implementing GAI in each identified area. Afterwards, based on the opportunities and challenges, the subject experts propose several research agendas for the stakeholders to consider.

This paper serves as a frontier in exploring the opportunities and challenges implicated by the GAI in six identified areas that this emerging technology would considerably influence. It is believed that the viewpoints offered by the subject experts would enlighten the stakeholders in the identified areas.

Based on affective events theory, this research attempted to investigate how negative gossip about organizational change drives employees to experience negative emotions and direct their aggression toward customers.

We conducted a scenario-based experiment (Study 1) and a multiwave field survey (Study 2) to test our hypotheses.

The results show that (1) negative emotions mediate the relationship between change-related negative gossip and displaced aggression toward customers; (2) perceived organizational constraints strengthen the relationship between change-related negative gossip and negative emotions; (3) future work self-salience weakens the relationship between change-related negative gossip and negative emotions; and (4) change-related negative gossip has a strengthened (weakened) indirect effect on displaced aggression via negative emotions when employees have high perceived organizational constraints (future work self-salience).

The study expands research on organizational change and displaced aggression and provides practical implications for managing organizational change.

This study aims to explore the impacts of service industry employees’ AI awareness on their voice behavior while also considering the dual mediating roles of voice efficacy and job insecurity, as well as the moderating role of trait competitiveness.

The sample comprises data from a two-wave longitudinal survey of 203 employees in the service sector. This study examined all the hypotheses using Mplus 8.0.

This study confirms that service sector employees’ AI awareness has significant negative effects on both promotive and prohibitive voice behaviors. Voice efficacy can mediate the negative impact of AI awareness on promotive voice. Both voice efficacy and job insecurity can mediate the negative impact of AI awareness on prohibitive voice. Furthermore, employees’ trait competitiveness can weaken the negative impact of employees’ AI awareness on their voice efficacy.

Managers should first investigate employees’ AI awareness and then adopt targeted managerial strategies to promote their voice behavior.

This study contributes to the literature related to the consequences of AI awareness by linking AI awareness to employee voice behavior. Furthermore, this study deepens our understanding of how AI awareness affects employee voice behavior by proposing voice efficacy (i.e. the efficacy pathway) and job insecurity (i.e. the safety pathway) as key mediating mechanisms. Moreover, this study advances our understanding of when AI awareness influences employee voice behavior by identifying the moderating role of trait competitiveness.

This investigation endeavors to examine the routes by which environmental–social–governance (ESG) performance influences the competitive landscape for logistics enterprises, with a particular emphasis on the function of digitalization in this complex process. The research underscores the significance of the ESG context in the realm of digitalization, providing valuable insights into its impact on the overall competitiveness of logistics enterprises.

This research gathers information from a total of 90 logistics enterprises that are publicly traded on the Shanghai and Shenzhen A-share stock markets for analysis and model testing. Due to the multiple pathways of influence and the constrained size of the sample, it has been decided that the Piecewise structural–equation–modeling (SEM) approach will be employed.

The research reveals that ESG factors positively impact enterprises' competitiveness (EC). The augmentation of competitiveness is attributed to the moderating role of green technology innovation (GTI) and agency costs between ESG and EC. In the context of digitalization, the level of digitalization of logistics enterprises may create a capital squeeze effect on environmental performance, weakening competitiveness. Conversely, the level of digitalization positively regulates the promoting effect of governance performance on competitiveness.

This research provides a sound theoretical foundation for understanding how ESG contributes to boosting the competitiveness of Chinese logistics enterprises and extends the application of Piecewise SEM in the research field of logistics enterprise competitiveness. Furthermore, it offers a practical pathway for companies to implement ESG practices and foster competitiveness in digital environments.

This study aims to explore the relationship between facilities managers’ human values, organizational pride and work outcomes, with the aim of discovering ways to boost their work outcomes.

A two-pronged research design comprising an online survey followed by in-depth interviews was adopted. Partial least square structural equation modeling (PLS-SEM) was used to model the data.

All 18 human values and 7 organizational pride emotions are present to a significant extent in facilities managers. The PLS-SEM showed that human values positively affect work outcomes of facilities managers significantly. In addition, work outcomes are further boosted when organizational pride is present.

The degree of human values that an individual possesses may not be easily measured using a five-point Likert scale as there might be social desirability bias.

The implication is that organizations should select and hire facilities managers with high human values and introduce programs and initiatives to improve facilities managers’ organizational pride as these lead to higher work outcomes. Some recommendations on how to achieve these are provided.

The findings extend existing knowledge by empirically showing the mediating influence of organizational pride on the relationship between human values and facilities managers’ work outcomes. When organizational pride is present, work outcomes of facilities managers is boosted.

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.

The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.

An online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).

Both streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.

Despite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.

Though daycare centres are built to facilitate children's social and physical growth, divergent stakeholder perceptions yield conflicts while managing risks for each space due to the minimal guidelines in the Republic of Korea. Due to the unclearly defined knowledge and experience gap between representative stakeholders, teachers have difficulties in operating it with high satisfaction. This study therefore was conducted to systematically identify which parts of performance should be increased to preferentially increase end-user satisfaction in limited resources.

This research conducted quantitative analysis gradually. Importance–performance analysis (IPA) grasped the perceptions of construction practitioners and end-users after identifying space composition and representative risks of daycare centres. Based on the factors influencing perception differences, principal component analysis and structural equation modelling (SEM) were conducted to determine which space improves the performance preferentially to increase overall end-user satisfaction.

As the utilisation of indoor spaces increases after COVID-19, improvement of indoor spaces except for indoor playroom is necessary, especially, advancement of the quality of teachers' rooms.

Outputs by reflecting the end-user experiences support to understand which spaces are managed from the limited resources.

Theoretical framework for daycare centre was established in Korea where guidelines were insufficient, and differences in perception of each risk of stakeholders were identified. Moreover, gradual statistical analysis was set to construct end-user-focused centres by extracting where the performance of space should be improved in prior.

In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.