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The purpose of this paper is to propose a condition-based opportunistic maintenance policy considering economic dependence for a series–parallel hybrid system with a K-out-of-N redundant structure, where a single component in series is denoted as subsystem1, and K-out-of-N redundant structure is denoted as subsystem2.

Based on the theory of Residual Useful Life (RUL), inspection points are determined, and then different maintenance actions are adopted in the purpose of minimizing the cost rate. Both perfect and imperfect maintenance actions are carried out for subsystem1. More significantly, regarding economic dependence, condition-based opportunistic maintenance is designed for the series–parallel hybrid system: preemptive maintenance for subsystem1, and both preemptive and postponed maintenance for subsystem2.

The sensitivity analysis indicates that the proposed policy outperforms two classical maintenance policies, incurring the lowest total cost rate under the context of both heterogeneous and quasi-homogeneous K-out-of-N subsystems.

This model can be applied in series–parallel systems with redundant structures that are widely used in power transmission systems in electric power plants, manufacturing systems in textile factories and sewerage systems. Considering inconvenience and high cost incurred in the inspection of hybrid systems, this model helps production managers better maintain these systems.

In maintenance literature, much attention has been received in repairing strategies on hybrid systems with economic dependence considering preemptive maintenance. Limited work has considered postponed maintenance. However, this paper uses both condition-based preemptive and postponed maintenance on the issue of economic dependence bringing opportunities for grouping maintenance activities for a series–parallel hybrid system.

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

This study aims to investigate whether brand positioning strategies influence individuals’ conformity in product choices and identifies the mediator and boundary condition of this relationship.

To test the hypotheses, three experiments were conducted, with data collected using an online platform.

The results indicate that local (vs global) brand positioning promotes consumers’ tendencies to conform in their product choice. Furthermore, this effect is sequentially driven by their perceived similarity with such positioning and the feeling of social connectedness. The influence of local (vs global) brand positioning on consumer conformity diminishes among consumers with a focus on similarity.

This study expands the consumer conformity literature by identifying a new antecedent of consumer conformity. It also introduces a novel downstream consequence of local (vs global) brand positioning on consumer behavior and provides a broader theoretical basis for understanding the psychological connotations underlying local (vs global) brands.

This study aimed to investigate the relationship between spirituality, including religious spirituality (i.e. supernatural beliefs) and secular spirituality (i.e. social beliefs), and mental health among police trainees.

Participants in this study were police trainees of a police academy. An online survey was conducted to measure spirituality and mental health among these police trainees. The association between spirituality and mental health was analyzed using hierarchical linear regression and hierarchical logistic regression with demographic variables (i.e. gender and age) controlled for.

The results revealed that the police trainees with stronger secular spirituality tended to have better general mental health. Higher levels of secular spirituality were significantly associated with lower levels of mental illness risk and suicidal ideation. By contrast, religious spirituality was not significantly related to police trainees' mental health.

The present study is the first to empirically investigate the relationship between spirituality and mental health among police trainees. The findings may be enlightening for future research on the mental health of police officers and trainees, and provide novel perspectives and pragmatic implications for the development of spirituality-based prevention strategies and intervention programs for enhancing the mental health and well-being of the police.

This study explores the influencing factors affecting smart supply chain innovation (SSCI) performance of commodity distribution enterprises, and proposes the corresponding framework from the perspective of the application of technology to improve the SSCI performance and make up the research gap in this field.

A multi-case study method is adopted in this study. Four distribution commodity distribution enterprises A, B, C and D in China are chosen as case enterprises. The interviews with senior management team members are used to collect data. The combination of open coding and axial coding are used to process the data. By testing the reliability and validity, the theoretical framework is summarized.

First, we find that the technology application cost inhibits SSCI and that the level of technology suitable for enterprise development will promote SSCI. Second, SSCI in structure, management and services can improve the performance and innovation ability of enterprises. Third, the quality of multi-channel integration and degree of customization around customer demand can significantly modify the above effects.

Compared with previous studies, this study reveals for the first time the correlation between the SSCI performance and technology application, SSCI in structure, management and service, providing new ideas for relevant researches on SSCI, and providing new theoretical support for managers' decision-making related to SSCI.

To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small.

The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power spectrum and the pitch acceleration power spectrum, which is calculated using ADAMS finite element software. Adaboost Backward Propagation algorithm is used in each restricted Boltzmann machine deep neural network classification model for fine-tuning given its performance of global searching. The algorithm is first applied to road spectrum detection and experiments indicate that the algorithm is suitable for detecting pavement roughness.

The detection rate of RBM deep neural network algorithm based on Adaboost Backward Propagation is up to 96 per cent, and the false positive rate is below 3.34 per cent. These indices are both better than the other supervised algorithms, which also performs better in extracting the intrinsic characteristics of data, and therefore improves the classification accuracy and classification quality. Additionally, the classification performance is optimized. The experimental results show that the algorithm can improve performance of restricted Boltzmann machine deep neural networks. The system can be used for detecting pavement roughness.

This paper presents an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation for identifying the road roughness. Through the restricted Boltzmann machine, it completes pre-training and initializing sample weights. The entire neural network is fine-tuned through the Adaboost Backward Propagation algorithm, verifying the validity of the algorithm on the MNIST data set. A quarter vehicle model is used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS as the input samples. The experimental results show that the improved algorithm has better optimization ability, improves the detection rate and can detect the road roughness more effectively.

This study aims to understand Chinese consumers’ perceived barriers to using peer-to-peer (P2P) accommodation before and after the outbreak of COVID-19 and the negotiation strategies they applied in overcoming the barriers and enabling consumption.

A qualitative research design with 28 semi-structured interviews was used. Data were analysed by content analysis.

Five psychological barriers and four functional barriers were found to inhibit consumers from using P2P accommodation both before and after the COVID-19 outbreak. In overcoming the perceived barriers, consumers applied both behavioural negotiation strategies, including seeking information, behavioural adaptation, selective choice and seeking social support, and cognitive negotiation strategies, including cognitive adaptation and trusting agents. COVID-19 was found to serve as both a barrier and a facilitator for using P2P accommodation. A barriers–negotiation framework was developed in the context.

Theoretically, this study advances consumer resistance and perceived barriers literature by integrating negotiation and developing a barriers–negotiation framework of P2P accommodation usage. This study also offers insights for practitioners in the P2P accommodation industry.

This study showcases the role of negotiation in understanding barriers to using P2P accommodation, paving the way to extend relevant knowledge to advance consumer resistance research, which is an emerging topic in the broader management domain.

This study aims to build a theoretical model including intermediate-level outside-in marketing capabilities (ILOIMC), radical and incremental technological innovations and management innovation.

This research used 272 pairs of survey questionnaires from Chinese firms’ managers to examine the hypotheses.

The results indicate that ILOIMC enhance management innovation by stimulating radical technological innovation. Furthermore, the mediating effect of incremental technological innovation depends on technological turbulence.

This study may have several limitations which future research could try to overcome: cross-sectional data, Chinese samples, exclusive focus on ILOIMC, sociotechnical approach to innovation typology and measuring ILOIMC as a first-order variable.

ILOIMC can significantly improve innovations in technology and management systems by using customer value and market information.

This study proposes a new taxonomy to classify marketing capabilities into lower-level inside-out marketing capabilities, ILOIMC and higher-level outside-in marketing capabilities. It also provides an explicit discussion and examination of the influence of ILOIMC on technological and management innovations and the contingency effect of technological turbulence. Thus, it responds to Musarra and Morgan’s (2020) call for more research into the mechanism that explains when (the conditions under which) and how (the process by which) outside-in marketing capabilities could contribute to firm innovation.

The purpose of this paper is to empirically analyze the impacts of short prohibitions on stock prices.

The authors adopt event study in this paper. First, the authors match each shortable stocks with one unshortable stocks by the propensity score matching method. Second, the authors check the performance difference between treatment group and control group after the event date. Third, the authors check the performance difference among sub-groups sorted by other factors associated with stock returns.

The authors find that stocks do not decline necessarily after removal of short prohibitions; only those heavily overpriced stocks, such as small stocks, lower B/M or P/E stocks and higher turnover stocks, decline significantly.

The media falsely stated that short selling lead to market crash; otherwise, short selling is beneficial for improving market efficiency as it is helpful for keeping overpriced stocks in line with the fundamental value.

This is the first paper showing that removal of short prohibitions only impacts heavily overpriced stocks significantly, which is valuable for policy making.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.