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Low levels of human capital in rural China are rooted in the poor schooling outcomes of elementary school students. The purpose of this paper is to provide insight into the distribution of academic performance in rural China and identify vulnerable groups.

The authors draw on a data set of 25,892 observations constructed from 11 school-level surveys spanning nine provinces and one municipality in China conducted from 2013 to 2015.

The authors find that the distribution of academic performance is uneven across provinces and subgroups. In general, male students, Han, living in richer counties, living with their parents and studying in rural public schools do better academically than female students, non-Han, living in poorer counties, left behind and studying in private migrant schools in cities.

Using the results of this study, policymakers should be able to better target investments into rural education focusing on at risk subpopulations.

With limited data sources, the research on the academic performance of students in rural China is largely absent. The findings of this study help to fill the gaps in the literature base.

The purpose of this paper is to measure the turnover (or stability in employment) of village clinicians in rural China over the past decade. The authors also want to provide quantitative evidence on the individual characteristics of the clinicians who provide health care to villagers in rural China and whether we should expect these individuals to be interested in continuing to supply quality health care in China’s villages in the coming years.

This paper uses data from a survey of rural China’s village clinicians conducted in five provinces, 25 counties, and 101 villages in 2005 and 2012. This paper also uses qualitative data from interviews with 31 village clinicians. Using a mixed methods approach, this study describes the turnover of village clinicians and the main factors that impact the career decisions of clinicians.

Turnover of China’s village doctors, while not trivial (about 25 percent of village doctors exited their field between 2005 and 2012), is still not overly high. Only five out of 101 villages did not have village clinicians in 2012. Of those that lost village doctors between 2005 and 2012, nearly all of them still had a village doctor in 2012 (either taken over by another local clinician or the position was taken by a newcomer). The authors find that three main sets of factors are correlated with the career decisions of village clinicians: village clinicians’ opportunity cost, the profitability of running a village clinic, and commitment to the field of medicine. In general, clinicians who left the village faced a much higher opportunity cost, had been running a clinic that was not profitable, and had fewer ties to the field of medicine. Newcomers over the same period had higher levels of education, went to higher profit clinics between 2005 and 2012, and had a stronger commitment to the field.

This study makes use of a data set with a large and nationally representative sample to provide a new perspective to better understand clinician turnover at village clinics, the career decisions of clinicians, and the implied trends for the quality and access to rural health care services in the future.

This paper aims to report on a study that used the practice theory lens to understand how Chinese ethnic culture influences restaurant workers' learning through engagement in everyday work practices.

A multiple case study approach was used. Data were collected from semi-structured interviews and site observations. Thematic analysis was conducted to identify how workers learnt the sayings, doings and relatings in their workplaces.

The findings show that the ethnic culture of the participants influences and enriches their learning in practice settings such as small Chinese restaurants.

The understandings presented here need to be verified through more research in different regions and nations. In addition, cross-cultural studies on other ethnic restaurants may contribute to deeper understandings of the influences of ethnic culture on practice-based learning.

The research contributes to understanding the influence of ethnic culture on practice-based learning.

The understandings gained from the findings of this study form a useful basis for curriculum development and instructional design of training programmes for practice-based as well as work-integrated-learning components of vocational curriculum. Furthermore, awareness of the strengths of the ethnic culture is of interest to owner/managers of small Chinese restaurants to afford supportive learning environments for workers.

The purpose of this paper is to examine the important application value of extending the concept of classification rule, so that it can describe and measure the uncertainty of classification knowledge.

The rough concept lattice (RCL), which is an effective tool for uncertain data analysis and knowledge discovery, reflects a kind of unification of concept intent and upper/lower approximation extent, as well as the certain and uncertain relations between objects and attributes.

A classification rules extraction algorithm, extraction algorithm of classification rule (EACR), based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule. The algorithm EACR is experimentally validated by taking the star spectrum data as the decision context.

An efficient way for classification rule extraction is provided.

The algorithm EACR based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule.

The applications of multi-agent systems (MASs) are considered to be among the most promising paradigms for detailed investigations and reliable problem-solving methods, and MAS applications make it possible for researchers and practitioners to better understand complex systems. Although a number of prior studies have been conducted to address complex issues that arise from construction projects, few studies have summarised the applications and discussed the capacity of MASs from the perspective of construction management. To fill the gap, this paper provides a comprehensive literature review of MAS applications from the perspective of construction management.

Web of Science and Scopus are the most commonly used international databases in conducting the literature reviews. A total of 86 relevant papers published in SCI-Expanded, SSCI and Ei Compendex journals related to the application of MASs from the perspective of construction management are selected to be analysed and discussed in this paper.

Based on the 86 collected publications, the utilisations of MASs to support the management of the supply chain and the improvement of project performance are identified from the perspective of construction management, the characteristics and barriers of current MAS applications are analysed, a framework for developing agent-based models to address complex problems is proposed, and future research directions of MAS applications are discussed.

This review can serve as a useful reference for scholars to enhance their understanding of the current research and guide future research on MASs. The proposed framework can help build agent-based models to address complex problems in construction management.

With the rapid development in the internet technologies, the applications of big data in construction have seen considerable attention. Currently, there are many input/output modes of capturing construction knowledge related to all construction stages. On the other hand, building information modelling (BIM) systems have been developed to help in storing various structured data of buildings. However, these systems cannot fully capture the knowledge and unstructured data used in the operation of building systems in a usable format that uses the intelligent capabilities of BIM systems. Therefore, this research aims to adopt the concept of big data and develop a spoken dialogue BIM system to capture buildings operation knowledge, particularly for building maintenance and refurbishment.

The proposed system integrates cloud-based spoken dialogue system and case-based reasoning BIM system.

The system acts as an interactive expert agent that seeks answers from the user for questions specific to building maintenance problems and helps searching for solutions from previously stored knowledge cases. The practices of monitoring and maintaining buildings performance can be more efficient by the retrieval of relevant solutions from the captured knowledge to new problems when maintaining buildings components. The developed system enables easier capture and search for solutions to new problems with a more comprehensive retrieval of information.

Capturing multi-modes data into BIM systems using the cloud-based spoken dialogue systems will help construction teams use the high volume of data generated over building lifecycle and search for the most suitable solutions for maintenance problems. This new area of research also contributes to the current BIM systems by advancing their capabilities to instantly capture and retrieve knowledge of operations instead of only information.

The next generation of Building Information Modelling (BIM) seeks to establish the concept of Building Knowledge Modelling (BKM). The current BIM applications in construction, including those for asset management, have been mainly used to ensure consistent information exchange among the stakeholders. However, BKM needs to utilise knowledge management (KM) techniques into building models to advance the use of these systems. The purpose of this paper is to develop an integrated system to capture, retrieve, and manage information/knowledge for one of the key operations of asset management: building maintenance (BM).

The proposed system consists of two modules; BIM module to capture relevant information and case-based reasoning (CBR) module to capture the operational knowledge of maintenance activities. The structure of the CBR module was based on analysis of a number of interviews and case studies conducted with professionals working in public BM departments. This paper discusses the development of the CBR module and its integration with the BIM module. The case retaining function of the developed system identifies the information/knowledge relevant to maintenance cases and pursues the related affected building elements by these cases.

The paper concludes that CBR as a tool for KM can improve the performance of BIM models.

As the research in BKM is still relatively immature, this research takes an advanced step by incorporating the intelligent functions of knowledge systems into BIM-based systems which helps the transformation from the conventional BIM to BKM.

Building information modelling (BIM) is increasingly being adopted during construction projects. Design and construction practices are adjusting to the new system. BIM is intended to support the entire project life-cycle: the design and construction phases, and also facility management (FM). However, BIM-enabled FM remains in its infancy and has not yet reached its full potential. The purpose of this paper is to identify major aspects of BIM in order to derive a fully BIM-enabled FM process.

In total, 207 papers were classified into main and subordinate research areas for quantitative analysis. These findings were then used to conceptualise a BIM-enabled FM framework grounded by innovation diffusion theory for adoption, and for determining the path of future research.

Through an extensive literature review, the paper summarises many benefits and challenges. Major aspects of BIM are identified in order to describe a BIM-enabled FM implementation process grounded by innovation diffusion theory. The major research areas of the proposed framework include: planning and guidelines; value realisation; internal leadership and knowledge; procurement; FM; specific application areas; data capture techniques; data integration; knowledge management; and legal and policy impact. Each element is detailed and is supported by literature. Finally, gaps are highlighted for investigation in future research.

This paper systematically classifies and evaluates the existing research, thus contributing to the achievement of the ultimate vision of BIM-enabled FM. The proposed framework informs facility managers, and the BIM-enabled FM implementation process. Further, the holistic survey identifies gaps in the body of knowledge, revealing avenues for future research.

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.