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Body type classification has a great influence on plate making and garment sizing system, and the accuracy of body type classification method will greatly affect the fit of garment production. The purpose of this paper is to use the decision tree algorithm to study body classification rules, develop a decision tree body recognition model and judge the body shape of middle-aged women in Xinjiang.

First, perform dimensionless processing on the collected data of 256 middle-aged women in Xinjiang, and the dimensionless data were used for K-means body clustering; Then, quantitatively analyze the effectiveness of different classification clusters based on the silhouette coefficients. Second, the decision tree algorithm is used to divide the classified sample data into a training set and a test set at a ratio of 70/30, and select the best node and the best branch based on the Gini coefficient to construct a classification tree. Last, the overall optimal decision tree is generated by means of hyperparameter pruning.

The body shape of middle-aged women in Xinjiang can be divided into three types: standard body, plump body and obese body. The decision tree model has an excellent effect on body classification of middle-aged women in Xinjiang (precision (macro), 95.46%; precision (micro), 95.95%; recall (macro), 95.46%; recall (micro), 95.95%; F1 (macro), 95.46%; F1 (micro), 95.95%).

For scientific research, this paper is conducive to increasing the regional body type theory and stimulating the establishment of a garment sizing subdivision system in Xinjiang. In terms of production practice, this paper not only establishes a model for judging the shape of middle-aged women in Xinjiang, but also provides reference data for intermediates of various sizes. In addition, to facilitate pattern-making and the establishment of a subdivision system for the size of middle-aged women's garments in Xinjiang, this paper provides the grading values of various body control parts of middle-aged women in Xinjiang.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

The study aims to provide a basis for the effective use of safety-related information data and a quantitative assessment way for the occurrence probability of the safety risk such as the fatigue fracture of the key components.

The fatigue crack growth rate is of dispersion, which is often used to accurately describe with probability density. In view of the external dispersion caused by the load, a simple and applicable probability expression of fatigue crack growth rate is adopted based on the fatigue growth theory. Considering the isolation among the pairs of crack length a and crack formation time t (a∼t data) obtained from same kind of structural parts, a statistical analysis approach of t distribution is proposed, which divides the crack length in several segments. Furthermore, according to the compatibility criterion of crack growth, that is, there is statistical development correspondence among a∼t data, the probability model of crack growth rate is established.

The results show that the crack growth rate in the stable growth stage can be approximately expressed by the crack growth control curve da/dt = Q•a, and the probability density of the crack growth parameter Q represents the external dispersion; t follows two-parameter Weibull distribution in certain a values.

The probability density f(Q) can be estimated by using the probability model of crack growth rate, and a calculation example shows that the estimation method is effective and practical.

In keeping with recent body image social trends, consumer demand for the adoption of plus-size models is increasing, although the use of thin models remains prevalent. The current study explores how consumers process information about fashion products displayed on different sizes of models in advertisements, focusing on model and consumer body sizes and both genders. As an underlying mechanism explaining how the relationship between model and consumer body sizes shapes consumer purchase intention, this study explores the role of guilt, shame and mental imagery.

The current study uses a text analytics technique to identify female consumers' general opinions of thin models in advertising. Employing a 3 (consumer body size: normal, overweight, obese) × 2 (model body size: thin, plus-size) × 2 (gender: male, female) between-subjects online experiment (n = 718), the main study comparatively analyzes the influences of plus-size and thin models on consumer responses.

The results reveal that, despite body positivity movements, thin models still generate negative emotions among female consumers. For obese female consumers, advertisements featuring plus-size models produce fewer negative emotions but not more mental imagery than advertisements featuring thin models. Conversely, for obese male consumers, advertisements featuring plus-size models generate more mental imagery but not more negative emotions than advertisements featuring thin models. The results also reveal that the relationship between consumer body size and guilt is moderated by perceived model size, which is also moderated by gender in generating mental imagery. While guilt plays a mediating role in enhancing mental imagery, resulting in purchase intention, shame does not take on this role.

This study is the first to present an integrated model that elucidates how consumers with varying body sizes respond to different sizes of models in advertising and how these responses impact purchase intentions.

Our findings only apply to contexts where consumers purchase fashion clothing in response to advertisements featuring thin versus plus-size models.

Exposing normal-size consumers to plus-size models generates less mental imagery, and thus, practitioners should seek to match the body sizes of the models featured in advertising to the body sizes of their target audience or ad campaigns that include both plus-size and thin models may help improve message persuasiveness in fashion advertising. Moreover, guilt-appeal advertising campaigns using thin models would appeal more to thin consumers of both genders than shame-appeal advertising.

The purpose of this paper is to investigate a special type of unethical behaviors among frontline service employees – unethical pro-organizational behaviors (UPB). Building on social identity theory, the paper examines how social identifications with the organization and customers interactively affect employees' engagement in UPB. The paper also explores the underlying psychological mechanisms that explain this effect.

This study uses a multistage, sequential research design to test the hypothesized model. Studies 1A and 1B use scenario-based experiments with a randomized between-subjects design. Study 2 uses a survey design to replicate and expand the findings from Study 1 by collecting survey data from frontline service employees in various service sectors.

The results across two studies reveal that high organizational identification will motivate employees to engage in UPB when the opportunity arises, while employees who also identify with customers will more likely abstain from committing UPB. Findings from the survey study also show that this interactive effect on UPB is achieved by devaluing customers as tools or placing fault upon them.

This research provides a deeper exploration of the UPB at the organizational frontline. From a social identity theoretical perspective, this research examines how identification with customers and with the organization jointly shape frontline employees' engagement in UPB. In doing so, this research provides insight into the contextual limitations of existing UPB research while also offering practically relevant implications for managing UPB in frontline service contexts.

The purpose of this research is to study the management approach of automotive industry managers and students in China.

This paper focused on comparing the leadership orientation of respondents and compared 200 working managers with 181 graduate students in the automotive industry.

It appears that these Chinese respondents from the automotive industry have significantly higher scores on the relationship orientation than task orientation. Managers have significantly higher scores on both dimensions of leadership. Similarly, the female respondents in China had similar scores as their male colleagues.

It appears that being socialized in a collectivistic or high context culture can lead to higher focus on relationship orientation. Based on the results, practical implications and application for future research are explored.

This paper is original and analyzes the leadership orientation of respondents.

The purpose of this paper is to develop a modeling and interactive operating method for virtual assembly (VA) to support assembly process generation based on interactive operation.

This paper puts forward an assembly semantic modeling method for interactive assembly and process generation after the analysis on requirements of operation process generation. Based on this semantic model, methods for semantic generation, semantic processing and assembly motion extraction from interactive operation are presented. Partial process generation of auto engine is proposed to verify the approaches in this paper.

The application shows that assembly semantic modeling and operating methods can support process generation based on VA operations.

The approaches presented in this paper improve the efficiency of assembly process, making assembly process intuitive and natural.

The purposes of this paper are to prepare the carbon nanotube (CNT) grease, to contrast the tribology properties of the CNT grease with the original grease and to find the lubricating mechanism of the CNT grease.

The CNTs (single-wall and multi-wall) are added into the polyalphaolefin oils (DURASYN_166) to form stable and homogeneous CNT grease with potential heat transfer, conductive and lubricative properties. The friction of this new type of CNT grease was determined by wear experiments under three conditions: dry friction, with the base oil grease and with the CNT grease.

The research is about the tribological properties of CNT greases; it shows better lubricating performance and wear resistance than the base oil grease. The performance improvement of CNT grease is owing to the unique hexagonal structure and the high thermal conductivity of CNTs.

The paper documents that CNTs can obviously improve the lubricating effect of grease, and the lubricating mechanism of the CNT grease is also discussed.