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Denim, a common fade fabric, can present different degrees of fade under different washing conditions. The phenomenon is similar to the washing efficiency. This study aims to discuss the relationship between impact factors and washing efficiency as well as the color fastness.

JMP software was used to design different experiments and 40 experimental groups were obtained. Then Statistical Package for the Social Sciences was used to analyze the results about washing efficiency and chromatic aberration.

Results showed that washing temperature, washing time and rotation speed have an effect on color fastness after washing in turn. In a certain range, when washing temperature, washing time or rotation speed increases, color fastness gradually increases as well. These three washing parameters work on washing efficiency as well. After setting and analyzing the mathematical model, the R² between the three washing parameters and washing efficiency is 0.855 and the R² between the three washing parameters and the post-wash chromatic is 0.849. There is a correlation between “Washing Efficiency of Sebum RM (P)” and “Post-wash Chromatic aberration (Q)”.

Denim could be used in some tests in laboratories instead of sebum standard stain cloth.

This paper provides an indirect research and feasible method for exploring a new object instead of the standard pollution cloth in the test of related textile study in the future.

This paper aims to discuss the low carbon supply chain practices in China’s textile industry. To curb greenhouse gas emissions, the Chinese government has launched restrict regulatory system and imposed the energy consumption constraint in the textile industry to guarantee the achievability of low carbon economy. The authors aim to examine how the energy consumption constraint affects the optimal decisions of the supply chain members and address the supply chain coordination issue.

The authors conduct two case studies from Chinese textile companies and examine the impact of energy consumption constraints on their production and operations management. Based on the real industrial practices, the authors then develop a simple analytical model for a low carbon supply chain in which it consists of one single retailer and one single manufacturer, and the manufacturer determines the choice of clean technology for energy efficiency improvement and emission reduction.

From the case studies, the authors find that the textile companies develop clean technologies to reduce carbon emission in production process under the energy consumption enforcement. In this analytical model, the authors derive the optimal decisions of the supply chain members and reveal that supply chain coordination can be achieved if the manufacturer properly sets the reservation wholesale price (WS) despite the production capacity can fulfill partial market demand under a WS (or cost sharing) contract. The authors also find that the cost-sharing contract may induce the manufacturer to increase the investment of clean technology and reduce the optimal WS.

This paper discusses low carbon supply chain practices in China’s textile industry and contributes toward green supply chain development. Managerial implications are identified, which are beneficial to the entire textile industry in the developing countries.

The purpose of this paper is to explore a descriptive framework for a more structured and objective evaluation of the risk situation of textile and apparel, also to find the best set of methods or optimal scientific grounds for the safety evaluation of textile and apparel.

Risk analysis theory is used to analyze potential hazard of textile and apparel, weight is given to risk indicators using subjective and objective weighting method, respectively, grading standards of safe risk of textile and apparel is made. Finally a safety risk assessment model of textile and apparel based on support vector machine (SVM) is built, and empirical analysis is also made.

Quantitative and highly reliable evaluation of textile and apparel risks, relatively easy grading classification and simplicity in operating the evaluation process are the advantages that promote the application of risk assessment model based on SVM for textile and apparel, and empirical analysis showed considerably good applicability.

The research is useful to ensure safety textile and apparel in market, also contributing to the sustainable development of textile industries in future.

SVM as a risk assessment method provided safety evaluation to toxic and harmful substance and small parts in textile and apparel, which can be an effective tool to monitor textile and apparel safety.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

The purpose of this paper is to examine the effectiveness of an improved dummy variables control grey model (DVCGM) considering the hysteresis effect of government policies in China's energy intensity (EI) forecasting.

Energy consumption is considered as an important driver of economic development. China has introduced policies those aim at the optimization of energy structure and EI. In this study, EI is forecasted by an improved DVCGM, considering the hysteresis effect of energy-saving policies of the government. A nonlinear optimization method based on particle swarm optimization (PSO) algorithm is constructed to calculate the hysteresis parameter. A one-step rolling mechanism is applied to provide input data of the prediction model. Grey model (GM) (1, N), DVCGM (1, N) and ARIMA model are applied to test the accuracy of the improved DVCGM (1, N) model prediction.

The results show that the improved DVCGM provides reliable results and works well in simulation and predictions using multivariable data in small sample size and time-lag virtual variable. Accordingly, the improved DVCGM notes the hysteresis effect of government policies and significantly improves the prediction accuracy of China's EI than the other three models.

This study estimates the EI considering the hysteresis effect of energy-saving policies in China by using an improved DVCGM. The main contribution of this paper is to propose a model to estimate EI, considering the hysteresis effect of energy-saving policies and improve forecasting accuracy.

This study aims to develop a hierarchical reliability framework to evaluate the service delivery performance of education public–private partnerships (PPPs) effectively and efficiently during long-term operations.

The research design included development and test phases. In the development phase, three performance layers, i.e. indicator, component and system, in the education service delivery system were identified. Then, service component reliability was computed through first order reliability method (FORM). Finally, the reliability of the service system was obtained using dynamic component weightings. A PPP school example in Australia was set up in the test phase, where performance indicators were collected from relevant contract documents and performance data were simulated under three assumptive scenarios.

The example in the test phase yielded good results for the developed framework in evaluating uncertainties of service delivery performance for education PPPs. Potentially underperforming services from the component to the system level at dynamic timepoints were identified, and effective preventative maintenance strategies were developed.

This research enriches reliability theory and performance evaluation research on education PPPs. First, a series of performance evaluation indicators are constructed for assessing the performance of the service delivery of the education PPP operations. Then, a reliability-based framework for service components and system is developed to predict service performance of the PPP school operations with consideration of a range of uncertainties during project delivery.

The developed framework was illustrated with a real-world case study. It demonstrates that the developed reliability-based framework could potentially provide the practitioners of the public sector with a basis for developing effective preventative maintenance strategies with the aim of prolonging the service life of the PPP schools.

Evaluating education PPPs is challenging as it involves long-term measurement of various service components under uncertainty. The developed reliability-based framework is a valuable tool to ensure that reliability is maintained throughout the service life of education PPPs in the presence of uncertainty.

This study, based on construal level theory, aims to examine the influential mechanism of leader empowerment behaviour on employee creativity. Specifically, it examines the mediating role of cognitive flexibility between leader empowerment behaviour and employee creativity, along with the moderating effect of consideration of future consequences (CFC) on this linkage.

A two time-point survey study (n = 214) was conducted to collect information from leaders and employees in terms of mutual evaluation in several Chinese industries. To effectively avoid common source bias, this survey was conducted through pairing leaders and employees. During the survey, the supervisors and subordinates were double-blinded. Correlation analysis and hierarchical regression analysis were used to test the research hypotheses.

Firstly, leader empowerment behaviour can significantly predict employee creativity. Second, cognitive flexibility plays a partial mediating role in the linkage between leader empowerment behaviour and employee creativity. Thirdly, CFC moderates the relationship between leadership empowerment behaviour and cognitive flexibility. The mediating role of cognitive flexibility underlies the overall moderating effect of CFC on the relationship between leader empowerment behaviour and employee creativity.

We used construal level theory to explain the influence of the mechanism of leader empowerment behaviour on employee creativity. In this manner, this study bridges the gap between theory and practice, as well as enriching the research on leader empowerment behaviour and employee creativity, especially in the Chinese context. Moreover, our study has several practical managerial implications, based on the importance of employee creativity. It inspires the implementation of leader empowerment behaviour, cultivation of employee creativity and introduction of several procedures.

This paper discusses the influential mechanism of leader empowerment behaviour on employee creativity from a new perspective and explains the process of encouraging employee creativity through information-processing methods. It mainly highlights the application of construal level theory to discuss employee creativity and develops a new research frame for employee creativity. Leaders can raise employee creativity through leader empowerment behaviour.

The purpose of this study is to evaluate the tea stem natural dye was extracted from tea stem waste and applied to dyeing silk fiber, after which the properties of dyed samples were tested and analyzed.

The dyeing process was optimized using the response surface methodology (RSM) approach. Dyeing temperature, pH and time were chosen as variables and the color difference value as a response. The properties of dyed samples were tested and analyzed.

The optimized dyeing process was as follows: dyeing temperature 70°C, pH 3.5 and time 110 min. The K/S and color difference value of silk fiber dyed with the optimal process dye enzymatic oxidation with laccase was 1.4 and 27.8, respectively. The silk fiber dyed has excellent color fastness, antioxidant and antibacterial property, which greatly increases the added value of the dyed products. Furthermore, the optimized dyeing process did not significantly affect the strength properties and handle of the silk fiber.

Researchers have not used statistical analysis to optimize the process of dyeing process of silk fiber by tea stem natural dye enzymatic oxidation with laccase using response surface methodology. Additionally, this dyeing process was a low-temperature dyeing process, which not only saves energy consumption and reduces silk fiber damage but also obtains superbly dyeing results and biological functional properties, achieve the effects of waste utilization and clean dyeing.