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This paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue cumulative damage theory.

A fatigue life analysis method by modifying S–N curve and considering material difference is presented, which improves the fatigue life of EMU gear based on shape modification optimization. A corrected method for stress amplitude, average stress and S–N curve is proposed, which considers low stress cycle, material difference and other factors. The fatigue life prediction of EMU gear is carried out by corrected S–N curve and transient dynamic analysis. Moreover, the gear modification technology combined with intelligent optimization method is adopted to investigate the approach of fatigue life analysis and improvement.

The results show that it is more corresponded to engineering practice by using the improved fatigue life analysis method than the traditional method. The function of stress and modification amount established by response surface method meets the requirement of precision. The fatigue life of EMU gear based on the intelligent algorithm for seeking the optimal modification amount is significantly improved compared with that before the modification.

The traditional fatigue life analysis method does not consider the influence of working condition and material. The life prediction results by using the method proposed in this paper are more accurate and ensure the safety of the people in the EMU. At the same time, the combination of intelligent algorithm and gear modification can improve the fatigue life of gear on the basis of accurate prediction, which is of great significance to the portability of EMU maintenance.

This paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.

An intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.

The fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.

In most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

While the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.

This study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.

It identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.

Our study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.

Maintaining a city’s identity and locality is an urgent problem in urban construction. Historical districts are very important parts of a city. However, non-heritage buildings, which make a significant contribution to the character and appearance of historical areas, are facing arbitrary demolition. They are more often ignored compared with buildings on the protection list. The paper aims to discuss these issues.

This paper chooses two historic blocks in Wuxi, in which 256 non-heritage buildings were examined, the SPSS method and the AHP method are used to find renewal modes for the buildings that had not finished updates, and the two blocks are then used as practical cases to validate the matching results.

In conclusion, on the one hand, according to different building categories, different renewal modes are found. On the other hand, this study finds differences in the renewal modes of different historic districts.

The study shows a renewal method for non-heritage buildings, which require attention due to their dangerous situation, as these buildings in historical districts also contribute to a city’s uniqueness and locality. These results should be helpful in the planning and practices for urban historical districts.

This paper aims to introduce the history and major achievement of the Chinese private enterprise survey (CPES), which is one of the most enduring large-scale nationwide sample surveys in China, providing important micro firm-level data for understanding and studying the development of Chinese enterprises and entrepreneurs over the past 26 years.

The main body of this paper is based on a bibliometric analysis of all literature using CPES until 2017.

This paper discusses problems that users may encounter during data mining. By doing so, it can assist other researchers to get a better understanding of what has been done (e.g. journals, topics, scholars and institutions) and do their research in a more targeted way.

As members of the survey project team, the authors also take a prospect of the future data design and use, as well as offer some suggestions about how to use the CPES data to improve high-quality development and business environment evaluation in China.

This paper is the first to provide an overall picture of academic papers in China and abroad that have used the CPES data.

Based on a contingent resource-based view, this paper explores whether and how data-driven supply chain orientation (DDSCO) affects supply chain performance (SCP).

Survey data were collected from 307 manufacturers in China, Confirmatory factor analysis confirmed the reliability and validity of measures, and bootstrapping was used to test all hypotheses.

DDSCO positively and significantly influences SCP and supply chain risk management capabilities (i.e. robustness and resilience). Additionally, supply chain risk management capabilities partially mediate the relationship between DDSCO and SCP. Furthermore, the findings provide evidence for the moderating effect of supply dynamism on the DDSCO–SCP relationship, with robustness and resilience capability functioning as mediators.

Building upon existing digital strategies and supply chain performance literature, this study provides a new perspective (supply chain risk management capabilities) on maintaining a sustainable supply chain under supply dynamism. These findings offer new theoretical and managerial contributions that add value to the supply chain management literature.

With the growing pressure to gain optimum level of quality and speed, Lean Six Sigma (LSS) practices have drawn considerable attention as a viable alternative for process improvement. However, previous studies revealed that there is very little systematic and rigorous research to validate the claims. In this regard, this paper aims to empirically examine the effect of LSS practices on quality performance in the medical device manufacturing industry.

For this study, partial least square–based structural equation modeling (PLS-SEM) was used to empirically examine the effect of LSS practices on quality performance in Malaysian medical device manufacturing industry.

The findings of this paper revealed that LSS practices have a significant and positive effect on quality performance in the medical device manufacturing industry.

This paper will serve as a valuable implication for industry practitioners in providing them with a clearer managerial direction to exploit the strength of LSS practices to achieve company’s quality goals. Moreover, this study will serve as a basis for future LSS scholars, providing them with valuable insights and directions for future research.

This paper develops a conceptual LSS framework that captures the integrated nature of two methodologies and provides empirical evidence that supports the positive influence of LSS practices on quality performance; hence, it contributes to the growing body of LSS literature in both theoretical and empirical sense.