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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.

This study aims to investigate the antecedents of social interaction among Indian retail investors and fund managers to understand how these factors influence investment decisions. By identifying and examining these antecedents, the study aims to shed light on the social dynamics that shape investment behavior in the Indian financial market.

The researchers have mainly adopted an interpretive strategy for the present study. Qualitative data elicited through semistructured interviews with six retail investors and two fund managers were subjected to qualitative thematic analysis.

Our research found several factors that make Indian retail investors and fund managers connect and make financial decisions. Peers can improve a person’s investing performance through social facilitation, and discussing investment suggestions and lessons learned can affect a group’s investment behavior. Social norms also influenced investors’ financial decisions, demonstrating compliance. Investor closeness increased information sharing. Finally, the fear of missing out (FOMO), a psychological phenomenon where people fear missing out on rewarding experiences, encouraged social engagement as investors sought appealing prospects.

The researchers interviewed eight carefully selected interviewees across the divide between retail investors and fund managers. Adopting other grouping criteria, conducting a focus group discussion with more respondents or adopting a mixed-methods approach may increase our understanding of the investment decision behaviors of Indian retail investors and fund managers.

The findings have far-reaching consequences, from deepening our knowledge of investors’ motivations and actions to directing individual savers, informing the development of financial literacy initiatives, influencing fund management practices and inspiring additional research in this study area.

This research, including retail investors and fund managers, significantly contributes to the literature on investment decisions and behavioral finance, particularly in the context of Indian investors and managers. This study’s unique perspective and comprehensive approach make it a valuable addition to the field, sparking interest and further exploration among academics, practitioners and investors alike.

Recent trends have shown the integration of Chinese word segmentation (CWS) and part-of-speech (POS) tagging to enhance syntactic and semantic parsing. However, the potential utility of hierarchical and structural information in these tasks remains underexplored. This study aims to leverage multiple external knowledge sources (e.g. syntactic and semantic features, lexicons) through various modules for the joint task.

We introduce a novel learning framework for the joint CWS and POS tagging task, utilizing graph convolutional networks (GCNs) to encode syntactic structure and semantic features. The framework also incorporates a pre-defined lexicon through a lexicon attention module. We evaluate our model on a range of public corpora, including CTB5, PKU and UD, the novel ZX dataset and the comprehensive CTB9 dataset.

Experimental results on these benchmark corpora demonstrate the effectiveness of our model in improving the performance of the joint task. Notably, we find that syntax information significantly enhances performance, while lexicon information helps mitigate the issue of out-of-vocabulary (OOV) words.

This study introduces a comprehensive approach to the joint CWS and POS tagging task by combining multiple features. Moreover, the proposed framework offers potential adaptability to other sequence labeling tasks, such as named entity recognition (NER).

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 study synthesises the self-determination theory (SDT), expectation-confirmation model (ECM), and protection motivation theory (PMT) to formulate an integrated theoretical framework that elucidates the process of shaping the intention to continue using facial recognition payment (FRP) under the conditional impact of perceived technology security.

Data from 667 Beijing Winter Olympics visitors with FRP experience were collected through an online survey and analysed using variance based-structural equation modelling (VB-SEM).

This study reveals that the intention to continue using FRP evolves through three key stages. Initially, in the expectation stage, the multidimensional concept of artificial autonomy (sensing, thought, and action), which is underpinned by self-determination, is pivotal, strongly influencing perceptions of service enhancement and fostering trust in FRP. Subsequently, the confirmation stage underscores the importance of perceived service enhancement and trust as vital drivers in maintaining FRP usage, while also contributing to subjective well-being. Crucially, perceived technology security emerges as a key moderating factor, enhancing positive perceptions and intentions towards FRP, thus influencing its sustained adoption.

This study stands out by revealing the nuanced interplay between artificial autonomy and user perceptions, particularly concerning service enhancement, technology security, and trust, as they influence well-being and the continued adoption of FRP. Robustly grounded in the integrated theoretical framework of SDT, ECM, and PMT, the study’s findings are critical for comprehending the core elements and specific drivers that promote sustained FRP use, especially as we consider its potential widespread implementation. Therefore, this study not only advances theoretical understanding but also offers practical guidance for optimising FRP deployment strategies in a rapidly evolving technological landscape.

Previous investigations into the connection between strategic orientation (SO) and firm performance (FP) have generated inconsistent outcomes. The authors aim to reconcile and explain these varying results through the application of a mediated moderation model. The purpose of this paper is to explore how SO and strategic flexibility (SF) could create the possibility of improving FP. The indirect effect of SO was also tested in this study. Furthermore, by using the moderated mediation model, the authors also investigated the moderating role of strategic human resource management (SHRM).

This study adopted the empirical research method, collecting data through questionnaires by a two-wave and multi-source data collection approach. The sample consisted of 188 firms from Shandong and Fujian Province in China. SPSS was used to test the research hypotheses.

This study examines how and when SO enhances FP by considering SF as a critical mediator and SHRM as an important contingency of the mediation effect. The results revealed that SO positively affects SF and FP in small and medium-sized enterprises (SMEs). Furthermore, the mediating role of SF between the relationships of SO and FP was also confirmed. Moreover, SHRM strengthens the connection between SO, SR and FP.

This study contributes to the SO literature by revealing how firms translate their SO-based strategies into positive FP through SF and when this indirect effect is most effective.

Small and medium enterprises (SMEs) are engines of economic growth. Research indicates that the adoption of green procurement practices (GPPs) significantly influences the sustainable growth of SMEs. However, there is a lack of understanding of factors that link the adoption of GPPs with enhanced competitiveness. The purpose of this study is two-fold: first, to identify factors that affect the competitiveness of SMEs caused by adopting GPPs, and second, to test those factors whether they serve as necessary conditions in achieving that competitiveness.

A quantitative approach was used to survey 188 manufacturing SMEs in Pakistan. Cross-sectional data was collected through online questionnaires and analyzed using structural equation modelling (PLS-SEM) and necessary condition analysis (NCA).

Results indicate a less pronounced direct association between the adoption of GPPs and firm competitiveness. However, this association becomes strongly positive with the mediating roles of SSB and GI. On the other hand, NCA results reveal that the adoption of GPPs, SSB and GI acts as necessary conditions for achieving firm competitiveness.

This research highlights the fact that simply adopting GPPs is not sufficient to guarantee true competitiveness; a multifaceted approach is required. Moreover, it offers practical insights into effective planning of green investments leading to sustainable development.

Various practical measures can be adopted to manage the social outcomes of investment in the adoption of GPPs by SMEs.

This study relates and contributes to the natural resource-based view (NRBV) theory, the stakeholder theory and the necessity theory by developing a novel analytical framework.

This study aims to identify a value co-creation framework for live streaming through tourism scenes (LStTS). It also clarifies the value attributes of LStTS and makes an empirical test.

The study used a mixed-method approach. In Study 1, a total of 12,216 pieces of viewers’ comments and ten web news reports were coded and analyzed employing a grounded theory approach. In Study 2, data were collected from 587 Douyin e-commerce users. Exploratory factor analysis and partial least squares structural equation modeling were used to test the value co-creation framework of LStTS.

In Study 1, six value attributes in three categories were identified based on a content analysis of viewers’ comments. In Study 2, a three-order factorial model of value co-creation in LStTS was identified and tested.

Our study is limited by the preponderance of female respondents in the sample and the unique nature of the research context.

Merchants and streamers should consider whether there is a fit between the merchandise and the tourism scene when selecting the tourism scene for live streaming marketing; they can select novel and beautiful natural tourism scenes to attract viewers. Detailed and comprehensive product information should be provided in the process of live streaming marketing and sharing with consumers.

The novelty of our study lies in the provision of a new value co-creation framework in LStTS, which offers a theoretical basis for analyzing the value of the tourism scene in live streaming marketing.

In today’s era, a country’s economy and society are continuously improved by entrepreneurship that utilizes digital technology. The current study, conducted among university students in Pakistan, examines the roles of digital aspects in moving toward digital innovation (DI) and digital entrepreneurship (DE).

The study is based on quantitative and cross-sectional data. A survey questionnaire was used to obtain responses from respondents recruited through the convenience sampling technique.

The results from structural equation modeling (SEM) analysis suggest significant effects of technology orientation (TO), entrepreneurial orientation (EO), and DI on DE. The impact of DI on DE is also significant. On the other hand, digital technology self-efficacy (DTSE) is not found to be a significant predictor of DI and DE. Moreover, DI is found to be a significant mediator, which mediates the relationships of TO and EO with DE. On the contrary, DI did not mediate the significant relationship between DTSE and DE.

The study’s findings would benefit policymakers and planners in developing policies to encourage the usage of digital technologies to provide solutions in entrepreneurship. The findings also support promoting DE to tackle business challenges and achieve organizational and academic goals.

The study emphasizes the roles of digital technology, innovation, and entrepreneurship in a developing context.

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.