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This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.

This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.

The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.

This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management.

The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge.

The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers.

This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

Suggests that misunderstandings frequently occur when trying to understand Chinese language and culture, and so gives the implied meaning of various Chinese expressions and sayings such as greetings, thanks, respect, age, congratulations and taboo subjects.

Compared with the fierce price competition in 1998, the well‐order price competition is witnessed in Chinese iron and steel industry now and the pricing behaviors of steel firms also follow the certain rules. Based on the methods of collecting the secondary data and interviewing, this paper examines the pricing behaviors of firms to explain the how Chinese steel firms make their pricing decisions and maintain the well‐order competitive relationship among them. The authors found out that (1) most Chinese steel companies adopt a kind of strategic perspective in their pricing decision making, in which understanding of the market trend and the close attention to their competitors are both important; (2) there obviously exists price leader and followers in Chinese iron and steel industry, and the relationship between price leader and followers is relatively stable and the factor behind this phenomenon is the existence of a kind of informal platform of communication among competitors, government and trade associations.

Under the repeated action of the construction load, opening deformation and disturbed deformation occurred at the precast box culvert joints of the shield tunnel. The objective of this paper is to investigate the effect of construction vehicle loading on the mechanical deformation characteristics of the internal structure of a large-diameter shield tunnel during the entire construction period.

The structural response of the prefabricated internal structure under heavy construction vehicle loads at four different construction stages (prefabricated box culvert installation, curved lining cast-in-place, lane slab installation and pavement structure casting) was analyzed through field tests and ABAQUS (finite element analysis software) numerical simulation.

Heavy construction vehicles can cause significant mechanical impacts on the internal structure, as the construction phase progresses, the integrity of the internal structure with the tunnel section increases. The vertical and horizontal deformation of the internal structure is significantly reduced, and the overall stress level of the internal structure is reduced. The bolts connecting the precast box culvert have the maximum stress at the initial stage of construction, as the construction proceeds the stress distribution among the bolts gradually becomes uniform.

This study can provide a reference for the design model, theoretical analysis and construction technology of the internal structure during the construction of large-diameter tunnel projects.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

The purpose of this paper is to examine the role of education in the labor market and to understand how returns to education change over time in rural China.

Using nationally representative survey data from 2004 to 2015, this study provides insights on wage determination in the labor market and examines how the returns to education in rural China differ with time and educational endowment. This study applies ordinary least squares estimation and the Heckman selection model to estimate the returns to education.

The returns to education decreased during the observed years from more than 6 percent in 2004 to only about 3 percent in 2011, rising to nearly 4 percent in 2015. The overall trend is robust and observed within groups defined by education. Additionally, the returns to education vary greatly with educational endowment. Tertiary education has always maintained a high rate of returns at nearly 10 percent, while returns to senior high school education and below have gradually diminished.

The authors believe that the results will not only enrich studies on the returns to education in rural China, but also provide a basis for diagnosing the changes of rural labor market in the early twenty-first century.

The purpose of this paper is to examine the occupational specialization in rural labor market by analyzing the nature of part-time farming in rural China and estimating the impact of off-farm experience on the individual’s persistence and exit of part-time farming as well as its heterogeneity.

Using the panel data collected in 100 villages and 2,000 households across five provinces in 2008, 2012 and 2016, this study provides insights on the nature of part-time farming in rural labor market and find the impact and mechanism of off-farm employment experience on exiting part-time farming by adopting event history analysis.

Part-time farming is a stable long-run occupation in rural labor market of China from 2008 to 2015. Off-farm employment experience generally has positive effects on long-term part-time farming and the probability of exiting part-time farming. It significantly promotes female to exit part-time farming.

Based on the two-sector model, this study builds a conceptual framework of off-farm experience and occupational specialization and sets a theoretical basis of hazard model when using event history analysis. This study contributes to identify the impact of off-farm experience on persistence and exiting part-time farming in recent years. The empirical findings support the policy of promoting off-farm employment to improve occupational specialization.

To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.