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Brand crisis has become an increasingly common phenomenon recently. While corporate social responsibility (CSR) plays a role in mitigating the negative consequences of brand crisis, it is not always effective, especially for foreign companies. Therefore, this study aims to investigate the differential effects of CSR on brand crisis, considering the impact of country of origin and consumer ethnocentrism.

This study used a 2 (country of origins: domestic vs foreign) × 2 (consumer ethnocentrism: high vs low) × 2 (CSR: before vs after related information is presented) between-subjects experiment to simulate a brand crisis. A fictional WeChat Moment posting was used as a stimulus. Data from 210 Chinese respondents were analyzed.

When consumer ethnocentrism is high, the impact of CSR on consumer attitude toward the company undergoing a crisis was greater for domestic than for foreign companies. Conversely, for consumers with low ethnocentrism, the effectiveness of CSR in attenuating the negative impact of the brand crisis (i.e. the insurance-like effect of CSR) was insignificant across domestic and foreign companies.

This study extends the prior literature and clarifies the unclear results of previous studies on the effect of CSR on brand crisis by examining the impact of country of origin and consumer ethnocentrism. Novel insights into the insurance-like effect of CSR in brand crises were obtained.

This paper aims to focus on the tracking of a moving magnetic target by using total field magnetometers and to present a tracking method based on the gradient of a magnetic anomaly. In the tracking, it is assumed that the motion of the target is equivalent to a first-order Markov process. And the unit direction vector of the magnetic moment from the gradient of the magnetic anomaly can be obtained. According to the unit direction vector, the inverse problem is turned into an optimization problem to estimate the parameters of the target. The particle swarm optimization algorithm is used to solve this optimization problem. The proposed method is validated by the numerical simulation and real data. The parameters of the target can be calculated rapidly using the proposed method. And the results show that the estimated parameters of the mobile target using the proposed method are very close to the true values.

The authors focus on the tracking of a moving magnetic target by using total field magnetometers and present a tracking method based on the gradient of a magnetic anomaly.

The paper provides an effective method for tracking the magnetic target based on an array with total field sensors.

Comparing with a vector magnetic sensor, the measurement of the scalar magnetic sensor is almost not influenced by its orientation. In this paper, a moving magnetic target was tracked by using total field magnetometers and a tracking method presented based on the gradient of a magnetic anomaly.

Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.

To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.

The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.

This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

Between 1840s and 1940s, 27 occidental concessions have been created in several cities in China which represented difficult signs and memories for Chinese. Nowadays, these territories are experiencing a joint phenomenon of heritagization and tourismification which makes them experimental theaters for modern urban life and identity. Taking the former concessions of Tianjin as place study, the purpose of this study is to analyze the role of the heritage and tourism in the former concessions in city branding and more specifically the actors, approaches and products of this phenomenon.

This research draws on the comparison and analysis of two place studies in China. The authors base their analysis on semi-structured interviews in Chinese with previously identified stakeholders. In all, 20 individuals, including developers, public authority representatives, business owners, academics and conservation association members, were interviewed. This research was completed, updated and triangulated by content analysis of Web-based materials; official documents such as urban plans, guidelines and urban and tourism strategies collected during the fieldwork, as well as non-intrusive spatial observations of the concession and its various developments.

The results of this study show that the heritage in the former concessions has become an attractive tool for the city branding through tourism development, often led by the public actors with the participation of private entrepreneurs.

This study looks at the hybrid dimensions of the former concessions in China. It provides a better understanding of the co-action of heritage and tourism in the processes of territorial rehabilitation, which contributes to both the practitioners and researchers in this domain.

Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station.

This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train.

A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow.

This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

This study aims to comprehensively investigate the relationship between government subsidies and innovation performance in Chinese enterprises listed on the SSE STAR Market.

An unbalanced sample, covering 285 observations in 215 enterprises listed on the SSE STAR Market from 2019 to 2020, was used to explore the relationships between government subsidies, R&D investment, CEO shareholding and innovation performance. Counterfactual analysis is added for robustness testing.

Empirical evidence confirms that government subsidies have an inverted U-shaped relationship with R&D investment and innovation performance. Meanwhile, R&D investment is a mediating variable between government subsidies and innovation performance. Moreover, CEO shareholding plays a moderating role between government subsidies and R&D investment. The higher the CEO ownership, the steeper the inverted U-shaped relationship.

The government should introduce a dynamic mechanism to reasonably control subsidy amounts and strengthen the supervision of subsidy use. Enterprise managers should be aware of how incentives affect the firm’s innovation and implement a coordinated development of government subsidy policies and internal enterprise governance.

This study adds new empirical evidence for the relationship between government subsidies and enterprise innovation performance. The risk incentive provided by stock options is an important micro mechanism to compensate for the lack of government subsidies. The study identifies ways to promote firm innovation based on the synergistic effect of internal and external mechanisms.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.