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The purpose of this paper is to identify most favorable (or quasi-preferred) industry characteristics of remanufacturing industry and most favorable (or quasi-preferred) industry factors which have an effect on these characteristics so as to improve these factors.

Grey system theory has prominent advantage of using few data and uncertainty information to analyze many factors. Therefore, it is more suited for system analysis than traditional statistical analysis methods like regression analysis, variance analysis and principal component analysis, which require massive data, certain probability distribution in the data and few variant factors. So in this paper, grey incidence analysis method, which is an important part of grey system theory, is used to identify industry characteristics and key industry factor of remanufacturing industry in China and then put forward appropriate industrial policies and countermeasures to improve these industry factors.

According to the results of this study, it reveals that there are no most favorable industry characteristics and no most favorable industry factors in remanufacturing industry of China. “Annual sale of remanufacturing industry” is identified as quasi-preferred industry characteristic, and “total number of employees with master degree or above in remanufacturing enterprise” is identified as the quasi-preferred industry factor. “Total building area of remanufacturing enterprise” is referred as the most unfavorable industry factors.

Judging from the findings of this study, four practical implications are summarized as follows: “annual sale of remanufacturing industry” should be given great importance because it is a quasi-preferred industry characteristic. “Total number of employees with master degree or above in remanufacturing enterprise” and “total number of research institution and university participated in remanufacturing” should be further strengthened by establishing an industry-university-research institute collaboration network, due to the fact that they are the top two quasi-preferred industry factors. “Total investment of remanufacturing industry” and “total annual R&D expenditures” have not played their due role in improving remanufacturing industry, so they should be moderately controlled so as to reduce waste of investment. “Total building area of remanufacturing enterprise” must be strictly controlled because of its little impact on remanufacturing industry.

In this research, grey incidence analysis is applied to identify key industry factors of remanufacturing industry for the first time. It helps in finding industry factors which are in urgent need of improvement and assists in making appropriate industrial policies and countermeasures to improve them by studying relationships between industry characteristic and industry factors.

As the grey systems theory has been widely used in the field of sustainable development (SD) research, in the following, a short literature overview will be put forward, starting from the usage of these theories in the economic development, social inclusion and environmental protection contributions to the evolving process of SD during 2011–2021. The purpose of this paper is to identify some key studies from all the SD areas in which the grey systems can be used in order to open and to bring the researchers to new domains in which they can reveal their interest and in which they can successfully use the methods offered by the grey systems theory.

Using the search engine offered by the Google Scholar and the Web of Science (WoS), a literature review has been performed for the grey systems applications on SD research on both grey relational analysis (GRA) and grey forecasting. In addition, some grey evaluation theories – clustering evaluation models and grey target decision models – have also been presented.

Many grey models are widely used in the field of SD. Compared with other methods such as grey prediction, grey evaluation and decision-making model, GRA technology is the most used method, and the research using this method is more than three times that of all other methods.

The present paper identifies some of the most representative examples in which the grey system theory (GST) has been used, but, in the same time, there are a lot of studies that have not been mentioned here due to the lack of space.

The present paper focuses on the SD applications in which GST has been successfully used, bringing to the reader a general overview on this field and, in the same time, enables new research perspectives.

The purpose of this paper is to study the modelling mechanisms of several grey incidence analysis models with great influence, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model; then analyse the problems to be solved in grey incidence analysis models; and clarify the applicable ranges of commonly used grey incidence models.

The paper comes to conclusions by means of comparable analysis. The authors compare several commonly used grey incidence analysis models, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model and give several examples to clarify the reasons why quantitative analysis results of different models are not exactly the same.

As the intension of each kind of incidence model is clear and the extension is relatively obscure, grey incidence orders calculated by different incidence models are often different. When making actual decisions, incompatible results may appear. According to different characteristics of extraction, grey incidence analysis models can be divided into three types: incidence model based on closeness perspective, incidence model based on similarity perspective and incidence model based on comprehensive perspective.

The conclusions obtained in this paper can help people avoid some defects in the process of actual selection and choose the better incidence analysis model.

The conclusions can be used as a reference and basis for the selection of grey incidence analysis models, it can help to overcome the defects and shortcomings of models caused by themselves and screen out more excellent analytical models.

The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost.

This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM.

This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved.

This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost.

This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

Drawing on attribution theory, the current paper aims to examine the effects of review content structures on online review helpfulness, focusing on three pertinent variables: review sidedness, information factuality, and emotional intensity at the beginning of a review. Moreover, the moderating roles of reviewer reputation and review sentiment are investigated.

The review sentiment of 144,982 online hotel reviews was computed at the sentence level by considering the presence of adverbs and negative terms. Then, the authors quantified the impact of variables that were pertinent to review content structures on online review helpfulness in terms of review sidedness, information factuality and emotional intensity at the beginning of a review. Zero-inflated negative binomial regression was employed to test the model.

The results reveal that review sidedness negatively affects online review helpfulness, and reviewer reputation moderates this effect. Information factuality positively affects online review helpfulness, and positive sentiment moderates this impact. A review that begins with a highly emotional statement is more likely to be perceived as less helpful.

Using attribution theory as a theoretical lens, this study contributes to the online customer review literature by investigating the impact of review content structures on online review helpfulness and by demonstrating the important moderating effects of reviewer reputation and review sentiment. The findings can help practitioners develop effective review appraisal mechanisms and guide consumers in producing helpful reviews.

This paper presents the application of grey modeling for thermal spray processing parameter analysis in less data environment.

Based on processing knowledge, key processing parameters of thermal spray process are analyzed and preselected. Then, linear and non-linear grey modeling models are integrated to mine the relationships between different processing parameters.

Model A reveals the linear correlation between the HVOF process parameters and the characterization of particle in-flight with average relative errors of 9.230 percent and 5.483 percent for velocity and temperature.

The prediction accuracies of coatings properties vary, which means that there exists more complex non-linear relationship between the identified input parameters and coating results, or more unexpected factors (e.g. factors from material side) should be further investigated.

According to the modeling case in this paper, method has potential to deal with other diverse modeling problems in different industrial applications where challenge to collecting large quantity of data sets exists.

It is the first time to apply grey modeling for thermal spray processing where complicated relationships among processing parameters exist. The modeling results show reasonable results to experiment and existing processing knowledge.

Due to the inability to directly apply an intra-oral image with esthetic restoration to restore tooth shape in the computer-aided design system, this paper aims to propose a method that can use two-dimensional contours obtained from the image for the three-dimensional dental mesh model restoration.

First, intra-oral image and smiling image are taken from the patient, then teeth shapes of the images are designed based on esthetic restoration concepts and the pixel coordinates of the teeth’s contours are converted into the vertex coordinates in the three-dimensional space. Second, the dental mesh model is divided into three parts – active part, passive part and fixed part – based on the teeth’s contours of the mesh model. Third, the vertices from the teeth’s contours of the dental model are matched with ones from the intra-oral image and with the help of matching operation, the target coordinates of each vertex in the active part can be calculated. Finally, the Laplacian-based deformation algorithm and mesh smoothing algorithm are performed.

Benefitting from the proposed method, the dental mesh model with esthetic restoration can be quickly obtained based on the intra-oral image that is the result of doctor-patient communication. Experimental results show that the quality of restoration meets clinical needs, and the typical time cost of the method is approximately one second. So the method is both time-saving and user-friendly.

The method provides the possibility to design personalized dental esthetic restoration solutions rapidly.

The purpose of this study is to advance a novel evaluation index system and evaluation approach for ability of older adults in China.

This study constructed a comprehensive older adult ability evaluation index system with 4 primary indicators and 17 secondary indicators. Grey clustering analysis and entropy weight method are combined into a robust evaluation model for the ability of older adults.

The result demonstrates that the proposed grey clustering model is readily available to calculate the disability level of elderly individuals. The constructed index system more comprehensively considers all aspects of the disability of the elderly.

This study provides a quantitative method and a more reasonable index system for the determination of the disability level of the elderly.

This paper aims to obtain a texture dental model with real images and improve the rendering effect of the dental model.

The paper proposes a semiautomatic method to construct a realistic dental model with real images based on two-dimensional/three-dimensional (2D/3D) registration. First, a 3D digital dental model and three intraoral images are obtained by a 3D scanner and digital single-lens reflex camera. Second, the camera projection poses for every intraoral images are calculated by using the single-objective optimization algorithm. Third, with camera poses, the preliminary projection texture mapping is performed; besides, the seam between two textures is marked. Finally, the marked regions are fused based on the image pyramid to eliminate obvious seams.

The paper provides a method to construct a realistic dental model. The method can map three intraoral images to the dental model. The experimental results show that the textured dental model without obvious distortion, dislocation and seams is constructed with simple interactions.

The proposed method can be applied to the digital smile design system to improve the communication efficiency between doctors, patients and technicians.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.