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This study aims to propose a series of numerical and surrogate models to investigate the aerodynamic pressure inside cracks in high-speed railway tunnel linings and to predict the stress intensity factors (SIFs) at the crack tip.

A computational fluid dynamics (CFD) model is used to calculate the aerodynamic pressure exerted on two cracked surfaces. The simulation uses the viscous unsteady κ-ε turbulence model. Using this CFD model, the spatial and temporal distribution of aerodynamic pressure inside longitudinal, oblique and circumferential cracks are analyzed. The mechanism behind the pressure variation in tunnel lining cracks is revealed by the air density field. Furthermore, a response surface model (RSM) is proposed to predict the maximum SIF at the crack tip of circumferential cracks and analyze its influential parameters.

The initial compression wave amplifies and oscillates in cracks in tunnel linings, resulting from an increase in air density at the crack front. The maximum pressure in the circumferential crack is 2.27 and 1.76 times higher than that in the longitudinal and oblique cracks, respectively. The RSM accurately predicts the SIF at the crack tip of circumferential cracks. The SIF at the crack tip is most affected by variations in train velocities, followed by the depth and length of the cracks.

The mechanism behind the variation of aerodynamic pressure in tunnel lining cracks is revealed. In addition, a reliable surrogate model is proposed to predict the mechanical response of the crack tip under aerodynamic pressures.

The purpose of this paper is to develop a topology optimization algorithm considering natural frequencies.

To incorporate natural frequency as design criteria of shell-infill structures, two types of design models are formulated: (1) type I model: frequency objective with mass constraint; (2) type II model: mass objective with frequency constraint. The interpolation functions are constructed by the two-step density filtering approach to describe the fundamental topology of shell-infill structure. Sensitivities of natural frequencies and mass with respect to the original element densities are derived, which will be used for both type I model and type II model. The method of moving asymptotes is used to solve both models in combination with derived sensitivities.

Mode switching is one of the challenges faced in eigenfrequency optimization problems, which can be overcome by the modal-assurance-criterion-based mode-tracking strategy. Furthermore, a shifting-frequency-constraint strategy is recommended for type II model to deal with the unsatisfactory topology obtained under direct frequency constraint. Numerical examples are systematically investigated to demonstrate the effectiveness of the proposed method.

In this paper, a topology optimization method considering natural frequencies is proposed by the author, which is useful for the design of shell-infill structures to avoid the occurrence of resonance in dynamic conditions.

We take a state-stewardship view on corporate governance and executive compensation in economies with strong political involvement, where state-appointed managers act as responsible “stewards” rather than “agents” of the state.

We test this view on China and find that Chinese managers are remunerated not for maximizing equity value but for increasing the value of state-owned assets.

Managerial compensation depends on political connections and prestige, and on the firms’ contribution to political goals. These effects were attenuated since the market-oriented governance reform.

Economic reform without reforming the human resources policies at the executive level enables the autocratic state to exert political power on corporate decision making, so as to ensure that firms’ business activities fulfill the state’s political objectives.

As a powerful social elite, the state-steward managers in China have the same interests as the state (the government), namely extracting rents that should adhere to the nation (which stands for the society at large or the collective private citizens).

As China has been a communist country with a single ruling party for decades, the ideas of socialism still have a strong impact on how companies are run. The legitimacy of the elite’s privileged rights over private sectors is central to our question.

Chinese executive compensation stimulates not only the maximization of shareholder value but also the preservation of the state’s interests.

Using different trade indices spanning 2018–2021, this chapter investigates Afghanistan's current patterns, prospects and barriers for intra- and inter-regional trade perspectives, emphasising the different pathways by which Afghanistan trades with Central Asian economies. The empirical findings demonstrate that the anticipated significance of trade between Afghanistan and Central Asia is double compared to the existing levels of trade. Furthermore, the analysis encompasses the categorisation of sectors based on the intensity of usage of production factors like resource, labour and technology. The analysis further elaborates on Afghanistan's trade potential with Central Asia and vice versa by highlighting the comparative advantage, diversification, complementarity and similarity in trade. The findings suggest that larger economies are rated higher than smaller ones as size and development level are essential factors in regional trade development. The most effective channels of regional trade development are price competitiveness measures, intra-industry trade and trade complementarities. These findings have a substantial influence on the development of different trade policy efforts to stimulate investment and trade within and among the two regions.

Earnings management is a common term in the academic community and is likely understood by managers and professional investors, but how the large community of non-professional investors interprets this term is less clear. We examine non-professional investors’ attitudes toward earnings management and their resulting investing behaviors using a 2 × 2 mixed design. We manipulate investor role (prospective vs current) between participants and the method of earnings management within participants. We believe that different investment goals (prevention vs promotion) between current and prospective investors should lead to different investing behaviors. Consistent with our expectations, we find that current investors are more likely to maintain an equity than prospective investors are to invest in the same opportunity. Further, the consistent link between investors’ attitudes and actual investment behavior is only present for prospective investors. The prevention goal drives the current investors to maintain their investment, while the prospective investors remain more objective and focus on a goal of promotion. Importantly, prior research examining investor attitude toward earnings management has failed to link investors’ attitudes with actual investing decisions; our study attempts to fill this void by examining attitudes toward earnings management as well as subsequent investment behavior.

The aim of this research was to identify the importance of trust as a determinant of participants’ behaviour and the weight of different motivational factors that enhance the amount and the quality of contribution.

Quantitative research methods (online survey of 450 respondents and content analysis of 250 reviews) were applied on a Romanian crowdsourcing platform founded in 2008, with the mission to help potential tourists to take the most informed decision in their travel choices.

The data collected showed that the majority of the active members have a positive outlook over their experience within the community, admitting its trustworthy characteristics. The findings show that most of the top-rated members of the community were not motivated by material rewards such as money or prizes, but rather by socially related factors or by individual factors (positive feedback through comments or acquiring knowledge).

The findings cannot be generalised to other crowdsourcing models, which are subject to different task designs, outcomes, local contexts and even functionalities.

The results of this research can contribute to the design and implementation of customer-centred platforms, which might represent a way of development of organisational communication in the future.

The research posits that individuals’ experience within colloraborative crowdsourcing communities needs to be meaningful, as participants act based on a reciprocity norm, of giving something back to the community which is useful for fulfilling their own information-seeking purposes.

With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.

First, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.

The authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.

This paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

This study aims to identify and analyse critical success factors (CSFs) for the successful implementation of distributed ledger technology (DLT) in the Nigerian construction industry.

This study adopts a quantitative approach that uses snowball sampling techniques to identify professionals participating in the study. A structured questionnaire was used to collect data virtually, using Google Forms, resulting in 217 valid responses. The collected data were subjected to rigorous statistical analysis (descriptive and inferential) to identify and prioritise the CSFs and evaluate the participants’ awareness and knowledge of DLT.

This study revealed 24 key CSFs that are pivotal in ensuring the effective implementation and adoption of DLT in the Nigerian construction industry. Furthermore, the research highlights a moderate level of awareness, but significantly low knowledge of DLT among industry professionals.

The findings of this study will benefit professionals, practitioners and policymakers in the Nigerian construction industry by providing insights into the potential of DLT to improve construction operations.

This study contributes to the literature by identifying the CSFs for implementing DLT in the construction industry and shedding light on the current level of awareness and knowledge within the Nigerian context. The findings offer valuable insights for policymakers, industry practitioners and researchers, providing a solid foundation for informed decision-making and developing effective strategies to enhance DLT adoption in the construction sector.

The importance of smart tourism technologies (STTs) has been rising recently, but how STTs are related to tourists' psychological well-being has got little attention. The study intends to show how STTs increase tourists' psychological well-being by capitalizing on the theory of Tourism 4.0. The study investigates the associations between the factors of STTs with the perceived benefit and, subsequently, perceived benefit with the psychological well-being of tourists. Moreover, this study also examines the moderating impact of a growth mindset (GM) between perceived benefits (PB) and tourists' psychological well-being (TPW) in the tourism industry.

Using a structured questionnaire, 243 responses were collected through the convenience sampling method. Partial least square structural equation modeling (PLS-SEM) was used for the analysis.

Findings show that automation, security/privacy concerns, information accuracy, and personalization are significantly related to the PB of tourists. The results also confirm that a GM moderates the relationship between PB and TPW.

The results show the social and managerial importance of the perspective of tourism in developing countries. Based on the tourism 4.0 concept, this study presents a theoretical contribution by expanding practical information regarding the link between STTs and psychological well-being. Moreover, the study shows some directions to the tour operator about improving PB and psychological well-being. The notion of tourism 4.0 is a new paradigm that seeks to unlock the psychological well-being of visitors through the hyper-interconnectedness of humans and technologies.

This study contributes to prior research on technology-based tourism by exploring the uncharted STT variables, PB, GM and psychological well-being. Moreover, the study makes an empirical effort to conceptualize the tourism 4.0 concept on the proposed relationships. Tourist firms should focus on implementing this theory to develop their business.