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This study aims to explore the formation mechanism of aerodynamic noise of a high-speed maglev train and understand the characteristics of dipole and quadrupole sound sources of the maglev train at different speed levels.

Based on large eddy simulation (LES) method and Kirchhoff–Ffowcs Williams and Hawkings (K-FWH) equations, the characteristics of dipole and quadrupole sound sources of maglev trains at different speed levels were simulated and analyzed by constructing reasonable penetrable integral surface.

The spatial disturbance resulting from the separation of the boundary layer in the streamlined area of the tail car is the source of aerodynamic sound of the maglev train. The dipole sources of the train are mainly distributed around the radio terminals of the head and tail cars of the maglev train, the bottom of the arms of the streamlined parts of the head and tail cars and the nose tip area of the streamlined part of the tail car, and the quadrupole sources are mainly distributed in the wake area. When the train runs at three speed levels of 400, 500 and 600 km·h⁻¹, respectively, the radiated energy of quadrupole source is 62.4%, 63.3% and 71.7%, respectively, which exceeds that of dipole sources.

This study can help understand the aerodynamic noise characteristics generated by the high-speed maglev train and provide a reference for the optimization design of its aerodynamic shape.

The purpose of this paper is to investigate the friction and wear mechanisms in copper-based self-lubricating composites with MoS₂ as the lubricating phase, which provides a theoretical basis for subsequent research on high-performance copper-based self-lubricating materials.

Friction tests were performed at a speed of 100 r/min, a load of 10 N, a friction radius of 5 mm and a sliding speed of 30 min. Friction experiments were carried out at RT-500°C. The phase composition of the samples was characterized by X-ray diffraction of Cu Ka radiation, and the microstructure, morphology and elemental distribution were characterized by scanning electron microscopy and energy dispersive spectroscopy. Reactants and valences formed during the wear process were analyzed by X-ray photoelectron spectroscopy.

The addition of MoS₂ can effectively improve friction-reducing and anti-wear action of the matrix, which is beneficial to form a lubricating film on the sliding track. After analyzing different changing mechanism of the sliding tracks, the oxides and sulfides of MoS₂, MoO₂, Cu₂O, CuO and Ni(OH)₂ were detected to form a synergetic lubricating film on the sliding track, which is responsible for the excellent tribological properties from room to elevated temperature.

For self-lubrication Cu–Sn–Ni–MoS₂ material in engineering field, there are still few available references on high-temperature application.

This paper provides a theoretical basis for the following research on copper-based self-lubricating materials with high performance.

With this statement, the authors hereby certify that the manuscript is the results of their own effort and ability. They have indicated all quotes, citations and references. Furthermore, the authors have not submitted any essay, paper or thesis with similar content elsewhere. No conflict of interest exits in the submission of this manuscript.

This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

This study examines opinion leadership's personal and social characteristics to see which one is more effective in opinion leadership in four different industries: fashion, travel and tourism, wellness and book and literature. The specific subject of this investigation is how largely openness, exhibitionism and competence in interpersonal relationships and status and attitude homophily affect the opinion leadership and the decision-making of opinion leaders' followers.

The proposed model was tested with the questionnaire shared via stories featured on Instagram among followers of four micro-influencers in different industries. For the purpose of testing the offered hypotheses of this study, the partial least squares method was used.

The findings show that openness, exhibitionism and competence in interpersonal relationships have a substantial effect on opinion leadership. It was also evident that status and attitude homophily impact opinion leadership. The model supports the effect of both personal and social characteristics on opinion leadership; however, based on the results, the effect of personal characteristics on opinion leadership is more remarkable, both in a direct relationship and through the mediating role of para-social interaction.

This study is novel in categorizing opinion leaders' attributes in two different extents of personal and social characteristics. The authors defined a model of the effectiveness of each personal and social characteristic on opinion leaders. The model investigates whether the personal or social characteristics have the most effect on opinion leadership, particularly with the mediating role of para-social interaction.

Enterprise innovation depends on the innovative behaviour of employees. The relationship between leaders and employees has a significant impact on employees' attitudes and behaviours. Therefore, it is of great practical significance to explore how a good leader–member relationship (LMR) motivates employees' innovative behaviour.

Based on 316 questionnaires completed by the members of 53 organisations, SPSS 25.0, Mplus 8.0 and HLM 6.08 were used to analyse the internal mechanisms of LMRs and employees' innovative behaviour.

The study identified the following findings: first, LMR was positively correlated with employees' innovative behaviour; second, perceived supervisor support and followership behaviour played mediating roles between leader–member relationship and employees' innovative behaviour and third, organisational political climate was negatively correlated with employees' innovative behaviour and played a moderating role in the relationship between LMR and employees' innovative behaviour.

The results of this study have clarified the transmission mechanism between LMRs and employees' innovative behaviour while providing useful references for improving the effectiveness of human resource management in organisations.

Due to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.

First, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.

The TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.

The purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.

First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.

The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.

Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

This study aims to investigate mixed lubrication performances of stern bearing in a misaligned state considering turbulence and bearing deformation impacts.

A mixed lubrication model of stern bearing is established. The generalized average Reynolds equation governing the turbulent flow of lubricant is analyzed by considering the interaction of bearing elastic deformation, asperity contact pressure and film pressure. The bearing behaviors including minimum film thickness, hydrodynamic pressure, asperity friction force and frictional coefficient are studied under different models. The correctness of this model is verified by comparing it with that of the published data.

Numerical results indicate that elastic deformation noticeably decreases the maximum film pressure, the asperity contact force and the friction coefficient in the mixed lubrication stage. The effect of elastic deformation and turbulence reduces the transition speed from mixed to liquid lubrication.

This model includes both turbulence and bearing deformation impacts on journal bearing performances. It is expected that the numerical results can provide useful information to establish a stern bearing exposed to mixed lubrication conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2022-0352/

Building supply chain resilience is increasingly recognized as an effective strategy to deal with supply chain challenges, risks and disruptions. Nevertheless, it remains unclear how to build supply chain resilience and whether supply chain resilience could achieve a competitive advantage.

By analyzing the data collected from 216 firms in China, the current study empirically examines how information technology (IT) capability and supply chain collaboration affect different forms of supply chain resilience (external resilience and internal resilience) and examines the performance implications of these two forms of supply chain resilience.

Results show that IT capability is positively related to external resilience, whereas supply chain collaboration is positively related to internal resilience. The combination of IT capability and supply chain collaboration is positively related to external resilience. In addition, internal resilience is positively related to firm performance.

This study used only cross-sectional data from China for hypothesis testing. Future studies could utilise longitudinal data and research other countries/regions.

The findings systematically assess how IT capability and supply chain collaboration contribute to supply chain resilience and firm performance. The results provide a benchmark of supply chain resilience improvement that can be expected from IT capability and supply chain collaboration.

The study findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.

The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends, hotspots, and directions for future research.

The data source for this paper is the Web of Science Core Collection, and 7,154 publications and related information have been derived. We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools (VOS Viewer and CiteSpace).

The analysis results show that China is the most productive and influential country/region. East Asia countries have strong cooperation with each other and also have cooperation with other countries. The study shows that risk management has been involved in various fields such as credit, supply chain, health emergency and disaster especially in the background of COVID-19. We also found that machine learning, especially deep learning, has been playing an increasingly important role in risk management due to its excellent performance.

This paper focuses on studying risk management in East Asia, exploring its publication's fundamental information, citation and cooperation networks, hotspots, and research trends. It provides some reference value for scholars who are interested or further research in this field.