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Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

In view of high consumption situation of raw materials in the apparel industry due to unique and diversified style, the purpose of this paper is to investigate the application of pattern transformation between clothing and tent, explore the feasibility of extending multi-functional product and provide experimental demonstration for realizing 5R (Reduce, Reevaluate, Reuse, Recycle, Rescue) design goal of garment product.

According to the results of market research, in order to make single product have the functions of both outdoor couple clothing and simple tent, the planar patterns of clothing and tent are compared and transformed to make them compatible with each other, then the removable multi-functional design and technique processing are adopted.

It was found that outdoor clothing and tent with similar application occasions and raw materials could realize the assumption of multi-functional product. Their combination has the functions of both clothing and tent by wearing and assembling.

By means of this transformation design, the diversification and enjoyment of garment styles and functions can be realized. This helps to improve the energy efficiency of raw materials and accessories by increasing the frequency of product reuse and sharing. In addition, the enjoyment design of products also helps to lead and promote ecological consumption.

The absence of government intervention and market supervision cannot effectively promote green process innovation in manufacturing industries. As a new government regulation approach, environmental taxes provide a platform to internalize the externality of environmental pollution. This paper empirically investigates the impact of environmental taxes on green process innovation and the moderating effects of industry pollution heterogeneity and green credit.

This research collects manufacturing industry data ranging from 2008 to 2020, resulting in a total of 351 observations. Time-individual, two-way fixed effect models are constructed to examine the hypotheses.

The results indicate environmental taxes have an inverted-U effect on green process innovation in manufacturing industries. Implementation intensity of the current environmental taxes on China's manufacturing industries does not reach an inflection point. Further analysis suggests that environmental taxes exert influence on the inverted-U relationship with low-pollution industries displaying a steeper curvilinear pattern than high-pollution industries. Moreover, the analysis shows that green credit plays a moderating role in the inverted-U relationship, as low green credit provides more limited stimulus than high green credit in terms of the effect of environmental taxes on green process innovation.

This study offers empirical evidence to accommodate negative externalities of corporate production and provides new perspectives in nudging corporate green-process innovation.

This paper verifies the effect of environmental taxes on green process innovation amid industry pollution heterogeneity by introducing an industrial-level analysis unit. This study improves the means by which environmental taxes are measured. Existing literature has narrowly used pollution discharge fees as a proxy for environmental taxes. The authors have summed up the taxes on vehicle and vessels, urban land use, urban maintenance and construction, vehicle purchases, waste gas, wastewater and solid waste to measure the effect of environmental taxes in this study.

The purpose of this paper is to investigate the tribological properties of carbonized polydopamine/reduced graphene oxide (CPDA/rGO) composite coatings.

CPDA/rGO composite coatings were prepared using the spray technique and subsequent pyrolysis under argon. The transmission electron microscopy, field-emission scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and Raman spectroscopy revealed the conversion of PDA and GO into CPDA and rGO, respectively.

The results of tribological investigations show that the CPDA/rGO composite coatings with heat treatment at 300°C possess much better friction-reduction and anti-wear properties.

The worn surfaces of the PDA/GO composite films after heat treatment at 300°C were much smoother than that of the copper substrate. The tribofilms containing C, N, O and Cu played an important role on reducing friction and increasing wear resistance.

In this research, the highly dense bulk Cu-5Sn alloy specimens were fabricated using selective laser melting (SLM). This study aims to establish the relationship between laser power (LP), scanning speed (SS) and hatch space (HS) with surface roughness (Ra) and density. To obtain Cu-5Sn alloy formed parts with high strength and low surface roughness. The microstructure and mechanical properties of SLMed Cu-5Sn were investigated.

The relative density (RD) was optimized using the response surface method (RSM) and analysis of variance. First, the Ra of SLMed formed specimens was studied to optimize the forming process parameters with a good surface. Then, the dense specimens were studied by ANOVA and the RSM to obtain dense specimens for mechanical property analysis.

Dense specimens were obtained by RSM and ANOVA. The tensile properties were compared with the casted specimens. The yield and ultimate strengths increased from 71 and 131 MPa for the cast specimens to 334 and 489 MPa for the SLMed specimens, respectively. The ductility increased significantly from 11% to 23%, due to the refined microstructure of the SLMed specimens, as well as the formation of many twin crystals.

The Ra, RD and mechanical properties of SLM specimens Cu-5Sn were systematically studied, and the influencing factors were analyzed together. This study provides a theoretical and practical example to improve the surface quality and RD.

The purpose of this paper is to investigate the relationship between potential affecting factors and the local communities’ willingness to pay (WTP) for housing earthquake insurance (HEI) in the context of ethnic minority communities.

A literature review was done to identify possible factors affecting WTP for HEI. Fieldwork was conducted in 2017 in Dali Minority Autonomous Prefecture, where the first Chinese HEI was launched in 2015. Interviews were done in two earthquake-prone counties, as the main ethnic minority communities in the area. A total of 536 questionnaires were collected and used as empirical data for testing the impacts mechanism.

Respondents’ risk perception, risk exposure, self-prevention behaviors, government aid, insurance experience and sociodemographic characteristics were hypothesized as theoretical indicators correlated to WTP for HEI. Empirical analysis results predict that WTP for HEI is significantly influenced by risk perception, insurance experience, government aid, and age and out-migrating labors. It is evident that higher risk perception and more insurance experience lead to stronger desire for HEI coverage. However, dependency on government aid negatively affects WTP for HEI. Moreover, WTP for HEI is negative in relation to age and out-migrating labors. Surprisingly, ethnic-culture factors were not statistically significant to WTP for HEI.

This paper is an attempt to identify and verify factors affecting WTP for HEI, bridging the gap of inadequate research on WTP for HEI in ethnic minority communities.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

This paper aims to select an appropriate contact force model and apply it to the interaction model between the balls and the cage in the rolling bearings to describe the elastic–plastic collision phenomena between the two.

Taking the ball–disk collision mode as an example, several main contact force models were compared and analyzed through simulation and experiment. In addition, based on the consideration of yield strength of materials and initial collision velocity, a variable recovery coefficient model was proposed, and its validity and accuracy were verified by the ball–disk collision experiments. Then, respectively, the Flores model and the Hertz model were applied to the interaction between the balls and the cage, and the dynamics simulation results were compared.

The results indicate that the Flores model has good regression of recovery coefficient, indicating good applicability for both elastic and elastic–plastic contacts and can be applied to the contact collision situations of various materials. Under certain working conditions, there are significant differences in the dynamics results of rolling bearings simulated using the Flores model and Hertz model, respectively.

This paper applies the Flores model with variable recovery coefficients to the dynamics simulation analysis of ball bearings to solve the elastic–plastic collision problem between the rolling elements and the cage that cannot be reasonably handled by the Hertz model.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0138/

This study aims to investigate how individual career management (ICM) affects career success in Chinese organizations. Leader emergence was examined through the theoretical lens of implicit leadership theory as a mediating mechanism of this relationship. In addition, leadership self-efficacy and organizational warmth were analyzed jointly as boundary conditions strengthening the relationship between ICM and leader emergence.

To avoid common method bias, the authors adopted a three-wave data collection with a one-month lagged design. A total of 765 questionnaires were distributed and 424 usable questionnaires were collected. Mplus version 8.3 was used to test the hypothesized relationships.

Findings indicated that ICM is positively related to subjective career success and objective career success via leader emergence. Moreover, leadership self-efficacy and organizational warmth jointly moderate the relationship between ICM and leader emergence.

Based on implicit leadership theory, this study identifies leader emergence as a critical mechanism explaining the positive impact of ICM on career success in the Chinese context. Lastly, results stress the simultaneous need for leadership self-efficacy and organization warmth, which can promote high-ICM employees to emerge as leaders.

This paper aims to study the sampling methods (or design of experiments) which have a large influence on the performance of the surrogate model. To improve the adaptability of modelling, a new sequential sampling method termed as sequential Chebyshev sampling method (SCSM) is proposed in this study.

The high-order polynomials are used to construct the global surrogated model, which retains the advantages of the traditional low-order polynomial models while overcoming their disadvantage in accuracy. First, the zeros of Chebyshev polynomials with the highest allowable order will be used as sampling candidates to improve the stability and accuracy of the high-order polynomial model. In the second step, some initial sampling points will be selected from the candidates by using a coordinate alternation algorithm, which keeps the initial sampling set uniformly distributed. Third, a fast sequential sampling scheme based on the space-filling principle is developed to collect more samples from the candidates, and the order of polynomial model is also updated in this procedure. The final surrogate model will be determined as the polynomial that has the largest adjusted R-square after the sequential sampling is terminated.

The SCSM has better performance in efficiency, accuracy and stability compared with several popular sequential sampling methods, e.g. LOLA-Voronoi algorithm and global Monte Carlo method from the SED toolbox, and the Halton sequence.

The SCSM has good performance in building the high-order surrogate model, including the high stability and accuracy, which may save a large amount of cost in solving complicated engineering design or optimisation problems.