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In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

Enterprises' multichannel operations provide various avenues for customer interaction; however, existing literature investigating customer-to-customer interaction (CCI) mainly focuses on a single channel. The purpose of this paper is to investigate the spillover effect of CCI and potential underlying mediating mechanisms in different information channels.

Three between-subjects experiments with 946 participants were employed to empirically validate the proposed hypotheses in the context of an experiential product and a material product.

Results suggest the clear spillover effect of CCI, indicating that positive CCI improves focal customers' satisfaction and purchase intention, whereas negative CCI reduces focal customers' satisfaction and purchase intention. Moreover, CCI's spillover effect varies based on the CCI channel. Offline CCI has a stronger positive spillover effect than online CCI. Contrarily, online CCI has a stronger negative spillover effect than offline CCI. Customer experience and trust are demonstrated to have mediating roles in this process.

This study is the first to comprehensively understand and compare the CCI spillover effect of the two information channels. The findings add to the existing knowledge of information processing in the psychological mechanisms influencing the belief in addition to providing insights for companies engaged in multichannel operations management across different channels.

This paper aims to, from a Taoism perspective, one of Chinese inveterate cultures and mindsets, add knowledge into how Chinese indigenous cultures and mindsets will affect the way of Chinese people perceive and process guanxi. Specifically, this paper outlines the mechanism of guanxi from a culture perspective. Cultures significantly affect local people behavior.

A mixed-methods survey (Study 1) – analyzed with one-way ANOVA – and interview (Study 2) – analyzed with grounded theory – were used to answer the research questions. In total, 248 surveys were collected for Study 1, and 34 interviews for Study 2 that were transcribed into a word file, which consists of 609 pages with 327,463 Chinese characters, were processed.

The findings show that guanxi is determined by positive and negative forces between instrumental and affective components. Further, two essential conditions – fitness of personality and clear contract, which would determine when a positive or negative force would emerge in a guanxi – were identified.

The main contribution is that this paper clarifies the guanxi mechanism based upon one of the most significant Chinese cultures and mindsets. Or guanxi is viewed from a new perspective – how Taoism affects Chinese people’s perception and evaluation of guanxi. This paper also finds evidence for the main arguments based upon the two studies.

The purpose of this paper is to prepare Ti(C,N) coatings on TA15 treated and not treated by shot peening using double glow plasma alloying technique. The effect of shot peening on the wear behavior of Ti(C,N) coatings is discussed.

The Ti(C,N) coatings were prepared by double glow plasma alloying technique on two different TA15 substrate; one is shot peened and the other is not.

Ti(C,N) coating on SP-treated TA15 was thicker and denser, and the grain size was smaller compared with that on original TA15. Compared with the Ti(C,N) coating on original TA15, the wear resistance of that on SP-treated TA15 is improved. Ti(C,N) coating on SP-treated TA15 showed higher nanohardness and bearing capacity than that on original TA15.

For double glow plasma alloying technique, surface quality, surface activity and other factors will have influence on the thickness and density of the coating. The wear mechanisms of Ti(C,N) coating on original TA15 are serious abrasive wear and oxidation wear. However, the wear mechanism of Ti(C,N) coating on SP-treated TA15 is slightly oxidation wear.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0283/

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

The development of social networks enhances the interaction between people, which brings new challenges to the research of group decision-making (GDM). This study aims at the problem that the synergy and redundancy due to interaction among decision-makers are ignored in the previous GDM, a trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences is proposed.

Firstly, confidence level is introduced to improve the hesitation phenomenon that should be considered when calculating trust degree; Secondly, a new trust propagation operator is developed to deal with indirect trust relationships; Thirdly, trust degree is transformed into interaction index to quantify the synergy and redundancy in decision-making. Fuzzy capacities of decision-makers are used to replace traditional weights, and the final scores of alternatives are obtained through Choquet integral.

The proposed model using fuzzy capacity can reflect the synergy or redundancy among decision-makers and improve the accuracy of final ranking result and reduce the loss of information.

This study proposes a trust-enhanced consensus reaching model, which develops a new trust propagation operator to ensure the continuous attenuation of trust in propagation process. And the proposed model uses fuzzy capacity to improve the enhancement or attenuation on the scores of alternatives.

With a fast-growing Muslim population and consumer income, the demand for halal products by Chinese Muslims has expanded strongly. However, literature addressing Chinese Muslims’ consumption is limited, and their demand for halal products is little understood. This study aims to investigate what affects Chinese Muslims’ demand for halal products, with a focus on halal personal care products.

A survey of 500 respondents was conducted to collect cross-sectional data in northwest China. Data were processed and analysed with a logit model.

Apart from faithfulness, reliability of recommendations, product price, product availability and halal authenticity are most important determinants influencing the purchase of halal products by Chinese Muslims.

In this study, the focus is only on Muslims from China’s Northwest. Due to various constraints, the cluster and convenience sampling methods are used.

The findings are invaluable for governments and industry bodies to form policies to better meet the burgeoning demand for halal products by Chinese Muslims. They are also very invaluable for producers and exporters who intend to penetrate the halal market in non-Muslim-dominant countries like China.

Studies on understanding the needs of Muslims in non-Muslim countries are limited. Given the sheer size of the Muslim population in China, understanding their demand for halal products and influential determinants concerning such demand adds to the literature and helps the industry to better serve and capitalise on the growing market.