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Product configuration is considered as one of the most successful applications of knowledge‐based approaches in the past decade. Knowledge‐based configurations can be classified into three different approaches, namely, rule‐based, model‐based and case‐based approaches. Past research has mainly focused on the development of reasoning techniques for mapping requirements to configurations. Despite the success of certain conventional approaches, the acquisition of configuration knowledge is usually done manually. This paper aims to explore fundamental issues in product configuration system, and propose a novel approach based on data mining techniques to automatically discover configuration knowledge in constraint‐based configurations.

Given a set of product data comprising product requirements specification and configuration information, the paper adopted an association rule mining algorithm to discover useful patterns between requirement specification and product components, as well as the correlation among product components. A configuration was developed which takes XML‐based requirement specification as input and bases on a constraint knowledge base to produce product configuration as output consisting of a list of selected components and the structure and topology of the product. Three modules are developed, namely product data modelling, configuration knowledge generation and product configuration generation module. The proposed approach is implemented in the configuration knowledge generation module. The configuration generation module realizes a resolution of constraint satisfaction problem to generate the output configuration.

The significance and effectiveness of the proposed approach is demonstrated by its incorporation in our configuration system prototype. A case study was conducted and experimental results show that the approach is promising in finding constraints with given sufficient data.

Novel knowledge generation approach is proposed to assist constraint generation for Constraint‐based product configuration system.

This study aims to examine how higher education can overcome some of the psychological barriers to pro-environmental behavior among college students by cultivating growth mindset of nature and to investigate whether, how and when growth mindset of nature can enhance pro-environmental behavior.

A three-wave survey was conducted to collect quantitative data on growth mindset of nature, connectedness with nature, pro-environment behavior and pro-environmental school climate among 283 undergraduate college students at two universities in China. This study used the Process Macro for SPSS (models 4 and 14) to reveal the effects of direct, mediating and moderated mediation.

The findings showed that growth mindset of nature positively affected students’ pro-environment behavior. Moreover, growth mindset of nature contributed to pro-environment behavior through the beneficial effects of connectedness with nature. In particular, positive pro-environmental school climate strengthens the mediating effects of connectedness with nature on the relationship between growth mindset of nature and pro-environment behavior.

Growth mindset of nature provides a vital impetus for facilitating college students’ pro-environmental behavior so that universities can incorporate sustainable development, climate change and environmental sustainability into curricula to cultivate students’ growth mindset of nature. Furthermore, educators could develop students’ connectedness with nature through environmental education by directly exposing them to nature. Finally, the pro-environmental school climate is the essential social-environmental nourishment for college students’ pro-environmental behavior.

This study advances implicit theory by introducing implicit beliefs to the environmental realm. Meanwhile, this study solves the puzzle concerning why people are aware of increasingly serious environmental issues, yet few are really engaged in pro-environmental behavior. A second contribution lies in extending the application of the pro-environmental climates into educational contexts to uncover the “black box” of how growth mindset of nature promotes college students’ pro-environmental behavior. Finally, this study enriches the literature on connectedness with nature by exploring the mediating mechanism of connectedness with nature between growth mindset of nature and pro-environmental behavior.

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.

In gearbox fault diagnosis, identifying the fault type and severity simultaneously, as well as the compound fault containing multiple faults, is necessary.

To diagnose multiple faults simultaneously, this paper proposes a multichannel and multi-task convolutional neural network (MCMT-CNN) model.

Experiments were conducted on a bearing dataset containing different fault types and severities and a gearbox compound fault dataset. The experimental results show that MCMT-CNN can effectively extract features of different tasks from vibration signals, with a diagnosis accuracy of more than 97%.

Vibration signals at different positions and in different directions are taken as the MC inputs to ensure the integrity of the fault features. Fault labels are established to retain and distinguish the unique features of different tasks. In MCMT-CNN, multiple task branches can connect and share all neurons in the hidden layer, thus enabling multiple tasks to share information.

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

The purpose of this paper is to improve the dyeing effect of fast-growing fir wood dyed with reactive dyes.

In this study, five factors including temperature, the dosage of dye accelerator, dyeing time, the dosage of fixing agent and fixing time were investigated. Then, the color difference and light resistance of the wood surface after dyeing were used as the evaluation indicators; the best dyeing process under the two indicators was obtained through the range analysis. Finally, the two indicators were considered comprehensively, and the fuzzy comprehensive evaluation method was used to obtain the best dyeing process under the comprehensive indicators.

The results show that when the comprehensive index was used as the evaluation index, the optimal dyeing process for reactive red X-3B dyeing fast-growing fir veneer was that the dyeing temperature was 65°C; the amount of dye accelerator was 25 g L⁻¹; the dyeing time was 2 h; the amount of fixing agent was 15 g L⁻¹; and the fixing time was 35 min.

The technique of wood dyeing is an important method to increase the value of wood products. When using different kinds of dyes or dyeing substrates for wood dyeing, the dyeing process is different. This study determined the best process for reactive dye dyeing of fast-growing fir veneer and provided a solution for improving the value of fast-growing fir wood.

This is a literature survey paper. The purpose of this paper is to focus on the latest developments in textual analysis on China’s financial markets, highlighting its differences from existing works in the US markets.

The authors review the literature and carry out an experiment of sentiment analysis based on a small sample of Chinese news articles.

Based on the experiment of sentiment analysis, there is limited evidence on the association between sentiment and other contemporaneous or future returns.

The supply of financial textual information has grown exponentially in the past decades. Technological advancements in recent years make the programming-based analysis an effective tool to digest such information. The authors highlight the use of credible textual information and discuss directions of research in this important field.

Although building information modeling (BIM) has brought competitive advantages and many new jobs, the BIM-related job market is still confusing in China, which will undermine the adoption of BIM. This paper aims to show what kinds of BIM-related jobs are there in China, what employers require and whether all BIM engineers are the same kind.

A text mining approach, structural topic model, was used to process the job descriptions of 1,221 BIM-related online job advertisements in China, followed by a cluster analysis based on it.

First, 10 topics of requirements with the impact of experience and educational background to them were found, namely, rendering software, international project, design, management, personal quality, experience, modeling, relation and certificate. Then, six types were clustered, namely, BIM modeler, BIM application engineer, BIM consultant, BIM manager, BIM developer and BIM designer. Finally, different kinds of BIM engineers proved this title was an expediency leading to confusion.

This paper can provide a clear and insightful look into the confusing and unheeded BIM-related job market in China and might help to cope with the abuse of job titles. It could also benefit both employers and candidates in their recruitment for better matching.

The purpose of this paper is to provide an imprinting perspective of the organizational culture evolution at a large state-owned heavy equipment manufacturer. It aims at exploring sensitive periods and the tension between persistence and decay of imprints.

It employs the case study approach. Both qualitative (interviews) and quantitative (survey) data were collected, from the directors, middle managements, and grass-roots staffs of Dong Fang Turbine Co. Ltd. Based on the set of four scenarios, both within-scenario analysis and cross-scenario analysis were conducted following the “replication logic.”

New survival threats are more possible to develop sensitive periods with new imprints than transition periods, and the authors suggest organizational culture can be divided into two categories as the institutional sensitive and the local community sensitive.

This study is not only an exploitation of imprinting theories, but also provides a different understanding of organizational evolution, especially in terms of imprints dynamic. Meanwhile, the case shows how institutional environment and local community has shaped differently the organizational culture.