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Aerospace assembly demands high drilling position accuracy for fastener holes. Hole position error correction is a key issue to meet the required hole position accuracy. This paper aims to propose a combined hole position error correction method to achieve high positioning accuracy.

The bilinear interpolation surface function based on the shape of the aerospace structure is capable of dealing with position error of non-gravity deformation. A gravity deformation model is developed based on mechanics theory to efficiently correct deformation error caused by gravity. Moreover, three solution strategies of the average, least-squares and genetic optimization algorithms are used to solve the coefficients in the gravity deformation model to further improve position accuracy and efficiency.

Experimental validation shows that the combined position error correction method proposed in this paper significantly reduces the position errors of fastener holes from 1.106 to 0.123 mm. The total position error is reduced by 43.49% compared with the traditional mechanics theory method.

The position error correlation method could reach an accuracy of millimeter or submillimeter scale, which may not satisfy higher precision.

The proposed position error correction method has been integrated into the automatic drilling machine to ensure the drilling position accuracy.

The proposed position error method could promote the wide application of automatic drilling and riveting machining system in aerospace industry.

A combined position error correction method and the complete roadmap for error compensation are proposed. The position accuracy of fastener holes is reduced stably below 0.2 mm, which can fulfill the requirements of aero-structural assembly.

During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data.

First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method.

The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers.

First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.

Financial technology (FinTech) has enhanced the inclusivity and accessibility of traditional finance, offering a novel pathway for rural revitalization and development. The paper aims to assess the rural revitalization development level in prefecture-level cities in China and investigate the potential impact mechanism of FinTech.

This paper develops an index system to evaluate the rural revitalization level across 279 cities in China from 2011 to 2021. In addition, multi-mediation and threshold models are employed to analyze how FinTech influences rural revitalization.

The results reveal that, first, FinTech has significantly promoted rural revitalization. Second, entrepreneurial activeness, innovation capability, and industrial structure advancement are intermediary factors within the benchmark path. Third, FinTech exhibits varied threshold effects in entrepreneurial activeness, innovation capability, and industrial structure advancement, influencing rural revitalization with diverse impacts.

First, this paper expands the rural revitalization evaluation to include 30 indexes, enhancing overall measurement comprehensiveness. Second, in contrast to previous research concentrating on provincial-level assessments, this paper explores rural revitalization across 279 cities in China from 2011 to 2021, broadening the study’s scope and timeline. Third, this paper delves into empirical evidence illustrating how FinTech contributes to rural revitalization through entrepreneurial activeness, urban innovation capability, and industrial structure advancement, thereby deepening research in this domain.

With the rapid development of the pipeline transportation and exploitation of mineral resources, it is urgent requirement for the high-performance polymer matrix composites with low friction and wear to meet the needs of solid material transportation. This paper aims to prepare high-performance ultrahigh molecular weight polyethylene (UHMWPE) matrix composites and investigate the effect of service condition on frictional behavior for composite.

In this study, UHMWPE matrix composites with different content of MoS₂ were prepared and the tribological performance of the GCr15/composites friction pair in various sliding speeds (0.025–0.125 m/s) under dry friction conditions were studied by ball-on-disk tribology experiments.

Results show that the frictional behavior was shown to be sensitive to MoS2 concentration and sliding velocity. As the MoS2 content is 2 Wt.%, composites presented the best overall tribological performance. Besides, the friction coefficient fluctuates around 0.21 from 0.025 to 0.125 m/s sliding speed, while the wear rate increases gradually. Scanning electron microscopy images, energy-dispersive spectroscopy and Raman Spectrum analysis present that the main wear mechanisms were abrasive and fatigue wear.

The knowledge obtained herein will facilitate the design of UHMWPE matrix composites with promising self-lubrication performances which used in slag transport engineering field.

This paper aims to examine the role of off-farm work in the rural residential energy transition in China.

To guide this empirical work, the authors present a simple farm-household model to explain rural energy consumption. The authors then empirically assess three main mechanisms through which off-farm work can speed up energy transition in rural China using panel data methods.

The study shows that income growth from off-farm work can reduce the consumption of traditional biomass energy and facilitate a shift to commercial energy. The losses of labor available for on-farm production raise the shadow price of non-tradable biomass energy and further dampen the demand for traditional biomass energy. More importantly, the authors find that working in service sectors can significantly promote the consumption of commercial energy by rural households. The sectoral exposure effect indicates that a new working environment may influence rural households' energy preferences and thus accelerate the transition away from traditional biomass energy.

Previous studies focus mainly on the income effect of off-farm work on rural energy consumption. The authors first identify three related but essentially different effects of off-farm work on rural energy transition in China. This study provides new insights into the process of energy consumption transition in rural China.

In China, it is prevalent for parents to share health information on WeChat and receive feedback from their children. This study aims to investigate the feedback from younger generations regarding their parents’ health information sharing. It will examine the different types of feedback, explore the factors influencing it and analyze the outcomes that result from this feedback exchange.

The empirical findings draw on the qualitative analysis using grounded theory. This study collects data from 34 participants (17 pairs of one young person and one parent) through in-depth interviews and WeChat chat records. Then, a theoretical model was developed through open, axial and selective coding.

Feedback can be classified into five types: support, correction, perfunctoriness, ostracism and rejection as well as into “Affective-Behavioral-Cognitive” dimensions. Younger generations’ feedback types are influenced by a variety of factors, including information, emotion and individual and family-related factors. Each feedback type has distinct effects, such as altering older generations’ emotional and communication responses.

This pioneering study explores how younger generations in China perceive their parents’ health information sharing on social media. It highlights the importance of feedback in this context, providing actionable insights to enhance digital literacy among older adults, strengthen family bonds and enhance the spread of valuable and scientific health information online.

This paper aims to clarify an important nuance by proposing that people attribute human mind to brands on two distinct dimensions: think and feel.

Eight studies were conducted to first develop and validate the 14-item Brand Anthropomorphism Questionnaire, and then to investigate how the two subscales, think or feel dimensions, influence consumer moral judgment of brands.

This research developed a 14-item Brand Anthropomorphism Questionnaire with two subscales, which are psychometrically sound and show discriminant validity with regard to existing brand constructs. Furthermore, think or feel brand anthropomorphism dimensions can predict consumers’ moral judgment of brands.

The present research offers preliminary evidence about the value of distinguishing between think brand and feel brand in consumer moral judgment. Further research could investigate other potential impact of the two dimensions, and possible antecedents of think/feel dimensions.

Managers can use the scale for assessment, planning, decision-making and tracking purposes. In addition, in the event of brand scandal or brand social responsibility activities, public-relations efforts can use the findings to earn or regain the trust of consumers, as this research demonstrates that marketers can shape (tailor) the feel or think dimensions of brand perception to change consumers’ moral judgment of the brands.

This research makes theoretical contribution to the brand anthropomorphism literature by differentiating the two dimensions and exploring the influence of anthropomorphism of consumer moral judgment.

Given the complexity and increasing interdependence of supply chain networks, a broader perspective beyond a simple binary relationship is needed to analyse the impact of supply chain networks on firms. Based on social network theory, this study aims to explore the relationship between firms’ network position in the supply chain network and digital transformation, as well as the moderating effects of structural holes and network partners’ digitalisation.

This study analyses a sample of Chinese A-share companies listed on the Shanghai and Shenzhen Stock Exchanges for the period 2011–2020 using regression analysis to test hypothesised relationships.

A firm’s centrality in the supply chain network is positively related to its digital transformation, and that the firm’s structural holes and the level of network partners’ digitalisation can both strengthen the positive influence of network centrality on digital transformation. Moreover, the heterogeneity analysis reveals that the positive effect of firms’ network centrality on digital transformation is more pronounced in non-SOEs and upstream firms.

In the pursuit of digital transformation, firms should recognise the significance of their position in the supply chain network and enhance their capability for information and resource acquisition by proactively strengthening their business cooperation with other network members. Moreover, firms should emphasise the value of open networks and their partners in supply chain networks.

This study applies social network theory to investigate the role of a firm’s external supply chain network in its digital transformation, clarifying the context and boundaries in which a firm’s position in the supply chain network influences digital transformation. The results extend the research and theoretical perspective on digital transformation and the consequences of supply chain networks.

The purpose of this paper is to investigate how Chinese firms’ innovation is related to their perceived incentives and pressures from the transitioning institutional environment.

A sample of 166 manufacturing firms located in Guangdong Province (China) is analyzed using binomial and moderated multiple regression models.

The results show that institutional incentives are more effective in promoting incremental innovations than radical ones, whereas institutional pressures are more pronounced in facilitating radical innovations than incremental ones. In addition, the interaction between the two divergent institutional forces is negatively related to innovation performance.

The findings inform managers and policy makers in institutional transition environments to consider and balance the effects of institutional forces. Firms should match the institutional incentives and pressures with their own innovation objectives in terms of incremental or radical goals, and take caution to deal with the divergent institutional directions, so as to avoid the negative interaction effects. Policy makers should take a systems approach when considering the incentive-based and/or command-and-control designs of innovation policies and regulations.

The study contributes to existing literature on institutions and innovation by disentangling incentive and pressure effects of institutions, regulation and innovation policies, as well as the combined and interaction effects intrinsic within institutional mixes.

This study aims to consider the influence of density difference integral and relative flow difference on traffic flow, a novel two-lane lattice hydrodynamic model is proposed. The stability criterion for the new model is obtained through the linear analysis method.

The modified Korteweg de Vries (KdV) (mKdV) equation is derived to describe the characteristic of traffic jams near the critical point. Numerical simulations are carried out to explore how density difference integral and relative flow difference influence traffic stability. Numerical and analytical results demonstrate that traffic congestions can be effectively relieved considering density difference integral and relative flow difference.

The traffic congestions can be effectively relieved considering density difference integral and relative flow difference.

Novel two-lane lattice hydrodynamic model is presented considering density difference integral and relative flow difference. Applying the linear stability theory, the new model’s linear stability is obtained. Through nonlinear analysis, the mKdV equation is derived. Numerical results demonstrate that the traffic flow stability can be efficiently improved by the effect of density difference integral and relative flow difference.