Search results

In the continuous development of high-speed railways, ensuring the safety of the operation control system is crucial. Electromagnetic interference (EMI) faults in signaling equipment may cause transportation interruptions, delays and even threaten the safety of train operations. Exploring the impact of disturbances on signaling equipment and establishing evaluation methods for the correlation between EMI and safety is urgently needed.

This paper elaborates on the necessity and significance of studying the impact of EMI as an unavoidable and widespread risk factor in the external environment of high-speed railway operations and continuous development. The current status of research methods and achievements from the perspectives of standard systems, reliability analysis and safety assessment are examined layer by layer. Additionally, it provides prospects for innovative ideas for exploring the quantitative correlation between EMI and signaling safety.

Despite certain innovative achievements in both domestic and international standard systems and related research for ensuring and evaluating railway signaling safety, there’s a lack of quantitative and strategic research on the degradation of safety performance in signaling equipment due to EMI. A quantitative correlation between EMI and safety has yet to be established. On this basis, this paper proposes considerations for research methods pertaining to the correlation between EMI and safety.

This paper overviews a series of methods and outcomes derived from domestic and international studies regarding railway signaling safety, encompassing standard systems, reliability analysis and safety assessment. Recognizing the necessity for quantitatively describing and predicting the impact of EMI on high-speed railway signaling safety, an innovative approach using risk assessment techniques as a bridge to establish the correlation between EMI and signaling safety is proposed.

More people share their tourism experiences on social media today than in the past, and as a result, more people follow these posts in their trip planning. However, studies into tourists' intention to follow such posts are scarce. Therefore, this study investigates the antecedents influencing social media users' intentions to follow tourism-related posts (TRPs) when planning their trips.

Questionnaires were collected from 402 social media users who had followed TRPs for their trip planning. Data were then analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural networks.

The authors found that blogger–user fit and users' involvement with TRPs influenced behavior components (attitudes toward TRPs and intention to follow TRPs) via assessment components (bloggers' credibility and content quality), and the authors developed a framework to explain this relationship.

The findings advance prior studies by investigating (1) the antecedents of intention to follow TRPs when trip planning, (2) the two main social media elements – bloggers and posts – to understanding the role of social media on travel behavior and (3) involvement with TRPs and their impacts on travel behavior. This study contributes to the research on social media and tourism marketing and proposes practical indications for bloggers, social media platforms and destination marketing organizations.

The purpose of this paper is to explore the effect of ZnO on the structure and properties of micro-arc oxidation (MAO) coating on rare earth magnesium alloy under large concentration gradient.

The macroscopic and microscopic morphology, thickness, surface roughness, chemical composition and structure of the coating were characterized by different characterization methods. The corrosion resistance of the film was studied by electrochemical and scanning Kelvin probe force microscopy. The results show that the addition of ZnO can significantly improve the compactness and corrosion resistance of the MAO coating, but the high concentration of ZnO will cause microcracks, which will reduce the corrosion resistance to a certain extent.

When the concentration of zinc oxide is 8 g/L, the compactness and corrosion resistance of the coating are the best, and the thickness of the coating is positively correlated with the concentration of ZnO.

Too high concentration of ZnO reduces the performance of MAO coating.

The MAO coating prepared by adding ZnO has good corrosion resistance. Combined with organic coatings, it can be applied in corrosive marine environments, such as ship parts and hulls. To a certain extent, it can reduce the economic loss caused by corrosion.

The effect of ZnO on the corrosion resistance of MAO coating in electrolyte solution was studied systematically, and the conclusion was new to the common knowledge.

The purpose of this paper is to determine (1) the relationship between microstructure and fatigue cracking behavior and (2) effect of rolling on the process of crack initiation and propagation in FeCrAl alloys.

The qualitative and quantitative fracture studies were performed using scanning electron microscopy and the non-contact optical measurement system (IFMG5).

The results show that the formation of facets, rough facets and parallel stripes in the crack initiation and early crack propagation zones are closely related to the sensitivity of crack behavior to the microstructure of the material. Besides, the rolling process has a significant influence on the small crack initiation and propagation behavior. Quantitative analysis demonstrates that the size of the stress intensity factor and plastic zone size in the rough zone is associated with the rolling process.

The findings of this study have the potential to enhance the understanding of the microstructural crack formation mechanisms in FeCrAl alloys and shed light on the impact of rolling on the long-term and ultra-long fatigue behavior of these alloys. This new knowledge is vital for improving manufacturing processes and ensuring the safety and reliability of FeCrAl alloys used in nuclear industry applications.

In theory, the impact of debt liquidity risk (DLR) on the firm's future growth is ambiguous. This study aims to examine the empirical relationship between the DLR and firms' growth rate using annual data for USA companies from 1976 to 2020.

Given the longitudinal nature of the data, the author uses OLS (ordinary least squares) regression methodology with fixed effects to control for unobserved characteristics that might affect the dependent variable. Instrument variable regression is also used to address the potential endogeneity problem.

The results show that firms having higher DLR, as proxied by more short-term debt, experience lower growth rate. An increase in firms' short-term debt decreases the firms' future growth rate as evidenced by lower assets, revenue and employee growth rate. Moreover, the authors' results show that small firms or firms with more investment opportunities grow fast if the firms take higher DLR. Finally, cyclical firms with higher DLR exhibit lower growth rate during the credit tighten period. The authors' results hold for both the pre-zero lower bound (ZLB) era and ZLB period.

To the authors' best knowledge, this is one of the earliest studies to carefully examine the effects of DLR on firms' growth rate. While prior research finds that firms with higher growth potential, measured by market-to-book (MTB) ratio, use more short-term debt, the authors' research directly addresses whether DLR affects firms' future growth rate. The authors’ findings also help explain why firms with high growth potential use more short-term debt.

Drawing on the social information processing theory, this study aims to adopt a moderated mediation model to investigate the mediation role of cognitive crafting and the moderation role of regulatory focus in the relationship between perceived deviance tolerance and employee innovative behavior.

A questionnaire study with 181 employees from a state-owned communications technology company in China was conducted through a two-wave survey, with a one-month lagged design. The model is tested through confirmatory factor analysis, correlation analysis and PROCESS bootstrapping program in SPSS24.0 and AMOS22.0 software.

This study confirms that perceived deviance tolerance is positively related to innovative behavior, while cognitive crafting mediates the relationship between perceived deviance tolerance and innovative behavior. Furthermore, the promotion focus positively moderates the relationship between perceived deviance tolerance and cognitive crafting, and higher promotion focus enhances the mediating effect of cognitive crafting on the relationship between perceived deviance tolerance and innovative behavior. The prevention focus negatively moderates the relationship between perceived deviance tolerance and cognitive crafting, and higher prevention focus weakens the mediating effect of cognitive crafting on the relationship between perceived deviance tolerance and innovative behavior.

Organizations need to establish a tolerant and inclusive management system and create a harmonious working atmosphere to provide a platform basis to inspire the innovative behavior of employees. Also, regulatory focus variables are suggested to be considered in organizational human resource management processes (e.g. recruitment and training) to improve organizational person–job fit.

The primary contribution of this study is to confirm that perceived deviance tolerance has a positive impact on innovation behavior and thereby providing a new perspective to understand the impact effect of perceived deviance tolerance. Another contribution the study explores the mechanisms and boundary conditions of perceived deviance tolerance on innovative behavior fills the theoretical gap of perceived deviance tolerance.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

As university faculty faced new challenges, such as rapid digital social and the coronavirus disease 2019 (COVID-19) response, this study aimed to identify the daily changes in the interaction between the faculty and the organizational environment (colleague, policy and new issue) by exploring their recent dynamic educational efforts and the professional development.

This is a study wherein perceptions of 20 faculty from 15 universities and colleges were collected through in-depth online interviews. The authors analyzed interview data by arranging and visualizing the analyzed data using network clustering. Further, they applied the Latent Dirichlet allocation of the topic modeling to monitor the appropriate number of clusters, ultimately determined as four clusters using partial clustering.

The results showed that university faculty spontaneously tried to solve the problems through informal learning while the commitment to peer learning was deepening, reflecting the collectivist orientation nature of Chinese culture. Besides, the faculty also required support to reflect on their daily efforts for professional development. These results about their various learning routines prove the justification for the faculty's professional development to be discussed from the “learning by doing” perspective of lifelong learning.

This study proved the significance of informal learning for university faculty's professional development and the reasonable value of peer learning, and provided insights into how the Chinese context may influence university faculty's informal learning experience.

The study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.

The study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.

The study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.

The machine learning techniques could help governments make more precise policies and help producers make better investment decisions.

This is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

This study investigates the effect of product quality and price fairness on consumer cause-related marketing (CRM) participation and also the moderating role of donation-related customer predispositions (i.e. empathy and cause importance) on this relationship.

Data for the study were obtained from 240 respondents. A structural equation modelling approach was used in analysing the data.

Results from the analysis indicate that fair pricing and product quality affect CRM participation. The findings also provide insights into the moderation role of empathy and cause importance.

Prior research studied the effect of promotion on CRM participation; however, this study examines the effect of product and price. The findings offer insight into issues of research and managerial interest, offering insightful implications to the academic and practitioner communities in developing countries, respectively.