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The number of construction dispute cases has surged in recent years. The effective exploration and management of risks associated with construction contracts helps to directly enhance the overall project performance. The existing approaches to identify the risks associated with construction project contracts have a heavy reliance on manual review techniques, which are inefficient and highly restricted by personnel experience. The existing intelligent approaches only work for the contract query and storage. Hence, it is necessary to improve the intelligence level for contract risk management. This study aims to propose a novel method for the intelligent identification of risks in construction contract clauses based on natural language processing.

This proposed method can formalize the linguistic logic and semantic information of contract clauses into multiple triples and transform the structural processing results of general clauses in a construction contract into rights and interests rules for risk review. In addition, the core semantic information of special clauses in a construction contract, rights and interests rules are used for semantic conflict detection. Finally, this study achieves the intelligent risk identification of construction contract clauses.

The method is verified by selecting several construction contracts that had been applied in engineering contracting as a corpus. The results showed a high level of accuracy and applicability of the proposed method.

This novel method can identify the risks in contract clauses with complex syntactic structures and realize rule extension according to the semantic relation network of the ontology. It can support efficient contract review and assist the decision-making process in contract risk management.

Express freight transportation is in rapid development currently. Owing to the higher speed of express freight train, the deformation of the bridge deck worsens the railway line condition under the action of wind and train moving load when the train runs over a long-span bridge. Besides, the blunt car body of vehicle has poor aerodynamic characteristics, bringing a greater challenge on the running stability in the crosswind.

In this study, the aerodynamic force coefficients of express freight vehicles on the bridge are measured by scale model wind tunnel test. The dynamic model of the train-long-span steel truss bridge coupling system is established, and the dynamic response as well as the running safety of vehicle are evaluated.

The results show that wind speed has a significant influence on running safety, which is mainly reflected in the over-limitation of wheel unloading rate. The wind speed limit decreases with train speed, and it reduces to 18.83 m/s when the train speed is 160 km/h.

This study deepens the theoretical understanding of the interaction between vehicles and bridges and proposes new methods for analyzing similar engineering problems. It also provides a new theoretical basis for the safety assessment of express freight trains.

Inspired by the high-friction performance of the soft toe pads of tree frogs, this study aims to investigate the effect of elastic deformation on the lubrication properties of squeezing films inside soft tribocontacts with microstructured surface under wet conditions.

A one-dimensional hydrodynamic extrusion model was used to study the film lubrication characteristics of conformal contact. The lubrication characteristics of the extruded film, including load-carrying capacity, liquid flow and surface elastic deformation, were obtained through the simultaneously iterative solution of the fluid-governing and deformation equations.

The results show that the hydrodynamic pressure is approximating parabolically and symmetrically distributed in the contact area, and the peak value appears in the center of the extrusion surface. Elastic deformation increases the thickness of the liquid film, weakens the bearing capacity and homogenizes the liquid flow rate of inside soft friction contact. The magnitude of this effect greatly increases as the initial liquid film thickness decreases. Moreover, the elastic deformation directly affects the average film thickness of the extrusion contact. Narrow and shallow microchannels are found to result in a more prominent elastic deformation on the microstructured soft surface.

These results present a design for soft tribocontacts suitable for submerged or wet environments involving high friction, such as wiper blades, in situ flexible electrons and underwater robots.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0049/

This study leverages an integrated framework that uses the theory of planned behavior (TPB), risk perception and sustainable behavior to investigate the COVID-19 risk perception of a potentially powerful consumer group – generation Z – on decisions to participate in the domestic tourism stimulus campaign and their willingness to practice socially responsible behaviors.

Self-administered questionnaires were used to collect data. The study adopts partial least squares-structural equation modelling (PLS-SEM) to examine the data with the final sample of 422 generation Z in Thailand.

The COVID-19 perceived risk positively affects attitudes, perceived behavioral control and the intention to join domestic tourism campaign, affecting the desire to engage in sustainable behaviors when traveling. Attitude and perceived behavioral control mediated risk perception and the decision to join the campaign. Unlike other studies, generation Z is conscious of the situation but is not risk-averse to travel.

The study offers recommendations (and domestic tourism campaign's features) for government agencies and tourism partitioners, especially developing tourism destinations, to effectively launch domestic tourism campaigns to target generation Z during and after post-pandemic crises.

This study contributes to our limited understanding of generation Z's travel behaviors. It contributes to the extended use of TPB, risk perception and socially responsible conduct of such a specific generation. It is one of the first studies integrating the COVID-19 risk perception of generation Z and their intention to utilize the stimulus campaign.

This paper aims to understand the vibration characteristics of full ceramic ball bearings under grease lubrication, reduce the vibration of the bearings and improve their service life.

The Hertz contact stiffness formula for full ceramic ball bearings is constructed; the equivalent comprehensive stiffness calculation model and vibration model of full ceramic ball bearings are established. The dynamic characteristic test of full ceramic ball bearing under grease lubrication was carried out by using the bearing life testing machine, and its vibration was measured, and its vibration acceleration root-mean-square was obtained by software calculation and compared with the simulation results.

At the rotational speed of 12,000 r/min, the root-mean-square value of vibration acceleration is maximum 10.82 m/s², and the error is also maximum 7.49%. As the rotational speed increases, the oil film stiffness decreases. In the radial load of 600 N, the vibration acceleration root-mean-square is minimum 6.40 m/s², but its error is maximum 6.56%. As the radial load increases, the vibration of the bearing decreases and then increases, so under certain conditions increasing the radial load can reduce the bearing vibration. With different types of grease, the best preload is also different; low-speed heavy load should be used when the viscosity of the grease is large, and high-speed light load should be used when the choice of smaller viscosity grease is made.

It provides a theoretical basis for the application of full ceramic ball bearings under grease lubrication, which is of great significance for reducing the vibration of bearings as well as enhancing the service life of bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0094/

Tourism and hospitality employment have long faced widely recognised challenges with regard to employment, its workforce and the workplace environment, issues that have been addressed by generations of policymakers and practitioners without evident success or solution. These wicked problems are frequently characterised by inherent paradoxes and, therefore, accepting the tenets of paradox theory provides the basis for recognising the need to accept contradictions as a reality which a search for solutions will not resolve. This chapter presents six examples of wicked problems in tourism and hospitality employment, which are underpinned by paradoxes as proxies for the much wider range of intractable problems that beset policy-making and practice in this vital area of tourism and hospitality. The chapter concludes by suggesting ways in which wicked problems can be accommodated, and stakeholders can learn to understand and live with paradoxes.

To optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. However, delay predictions often involve imprecise shifts based on known delay times. Real-time and accurate train delay predictions, facilitated by data-driven neural network models, can significantly reduce dispatcher stress and improve adjustment plans. Leveraging current train operation data, these models enable swift and precise predictions, addressing challenges posed by train delays in high-speed rail networks during unforeseen events.

This paper proposes CBLA-net, a neural network architecture for predicting late arrival times. It combines CNN, Bi-LSTM, and attention mechanisms to extract features, handle time series data, and enhance information utilization. Trained on operational data from the Beijing-Tianjin line, it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.

This study evaluates our model's predictive performance using two data approaches: one considering full data and another focusing only on late arrivals. Results show precise and rapid predictions. Training with full data achieves a MAE of approximately 0.54 minutes and a RMSE of 0.65 minutes, surpassing the model trained solely on delay data (MAE: is about 1.02 min, RMSE: is about 1.52 min). Despite superior overall performance with full data, the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals. For enhanced adaptability to real-world train operations, training with full data is recommended.

This paper introduces a novel neural network model, CBLA-net, for predicting train delay times. It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.

The purpose of the study was to investigate both the positive and negative effects of workplace loneliness on innovative behavior. By applying the unified theory on contingencies of self-worth, the study aimed to integrate these effects into a single framework, thereby confirming the presence of the double-edged sword effect of workplace loneliness on innovative behavior.

A survey was conducted among enterprises across China, involving 246 employees. Hierarchical regression analysis was utilized to test the moderating hypotheses. Additionally, the mediating effects and the moderated mediation effects were further explored using the bootstrapping method.

The results indicated that workplace loneliness positively influenced innovative behavior through the desire to prove ability, with the promotion regulatory focus enhancing this relationship. Conversely, workplace loneliness negatively influenced innovative behavior through self-handicapping, with the prevention regulatory focus intensifying this relationship.

The findings revealed that workplace loneliness exerts a double-edged effect on innovative behavior. Lonely employees can enhance their sense of self-worth by engaging in domain switching, thereby alleviating feelings of loneliness.

The research confirmed a novel perspective: workplace loneliness can promote innovative behavior by influencing employees’ desire to prove ability. It also revealed the double-edged sword effect of workplace loneliness on innovative behavior. Based on these findings, employees experiencing loneliness can enhance their self-worth and alleviate feelings of loneliness through domain switching.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

The purpose of this study is to examine the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention in the context of resource building during the socialization process of new faculty members, particularly in the context of the coronavirus disease 2019 (COVID-19) pandemic.

The study utilizes a quantitative research design and employs purposive sampling to obtain 554 valid questionnaires. The study analyzes the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention and examines the influence of strategic human resource management (SHRM) on these variables.

The results of the study reveal that SHRM positively influences positive emotion and self-efficacy, which, in turn, positively impact job satisfaction. However, positive emotion is negatively related to turnover intention.

This study contributes to the existing literature on human resource management (HRM) by examining the impact of strategic HRM on the socialization process of new faculty members. The findings of the study have significant practical implications for the implementation of HRM in research-oriented universities.