Fashion and Textiles最新文献

Herein, we investigate the effects of 3D printed electrodes on electrophysiological signals and identify the important design elements required for manufacturing better electrodes for high body adhesion for smart wear. Ten electrodes of different shapes (plain, check, stripe, circular, radial cut-out) and thicknesses (0.5 mm and 1.0 mm) were manufactured. The electrodes were evaluated by testing on 20 healthy individuals (10 men and 10 women). To measure the electroencephalogram (EEG) of the participants, we used BIOS-S8 (BioBrain Inc., Korea), an 8-channel polygraph for multibody signal measurement. Data were analyzed using the SPSS 26.0 statistical program. The EEG values were significantly activated according to gender. For the male participants, relative alpha (RA), relative slow theta (RST), relative mid theta (RMT), and the ratio of SMR-mid beta to theta (RSMT) values were highly activated and for the female participants, RA, relative fast alpha (RFA), and relative slow theta (RSA) values were highly activated. There were no significant gendifferences in the EEG of both genders for the 10 types of electrodes. However, for the female participants, the ‘RA’ indices showed a significant difference based on electrode shape on the right temporal lobe (T4), but there was no significant difference based on the thickness. There was a significant difference in the subjective preference of the electrodes also. In the subjective evaluation, it was found that the differences based on the shape and thickness of the electrodes were sensitively recognized.

This study aimed to improve the flame retardancy and mechanical properties of bacterial cellulose (BC) by introducing cereal proteins, namely zein and gluten. The production conditions were determined by observing residual masses of samples at 1000 ℃ using thermogravimetric analysis (TGA). According to the TGA results, the optimized production conditions for the BCs with zein and gluten were combined solvent exchange and entrapment of 20 weight% (wt.%) of zein, and entrapment of 40 wt.% of gluten, respectively. Surface characterization of BC prepared with zein and gluten under the optimal conditions confirmed that the cereal proteins were incorporated into the BC nanostructures via solvent exchange and/or entrapment and the original chemical and crystal structures of BC were not significantly changed. Limiting oxygen index (LOI) analysis confirmed that cereal proteins improved the flame retardancy of BC. In particular, the LOI of BC entrapped with gluten was 50%, which was better than that of cowhide leather. Char morphology analysis confirmed that the as-produced BCs with cereal proteins exhibited condensed-phase flame-retardant mechanism by forming intumescent chars. Analysis of the mechanical properties confirmed that compared with cowhide leather, as-produced BCs with cereal proteins possessed high tensile strength and dimensional stability, making them suitable leather substitutes.

To realize the three-dimensional structure simulation and dress simulation of the fully-fashioned knitted skirt, based on the study of the structural characteristics of the fully-fashioned knitted skirt, the mathematical model of the knitted skirt pattern, the loop geometry mathematical model and the loop mesh model were established by the method of mathematical modelling. The spatial transformation relationship of each pair of triangles in the plate model and the three-dimensional surface model was calculated by using the spatial matrix. To draw a loop on the surface model's triangular patch, spatial transformation was used to calculate the grid point positions and obtain three-dimensional coordinates of the loop-type value points. Based on C# programming language, the human body model in the database was matched with the knitted skirt surface model, and the three-dimensional graphics library Open-GL and loop drawing method were used to realize the simulation of the knitted skirt dress based on the loop structure. The simulation method was tested by the dressing effects of hip skirts and umbrella skirts with different postures. The results showed that the spatial transformation could realize the fast transformation and calculate the coordinates of the loop-type value points. The C# programming language and Open-GL technologies realized the visual simulation of the fully-fashioned knitted skirt and the multi-pose dressing simulation based on the loop structure.

Smart technology has become an increasingly prominent feature in the fashion apparel industry. However, small retailers still face challenges while trying to adopt such innovative smart technologies to increase consumer interaction and sales. By applying the Technology, Organization, and Environment (TOE) framework, the aim of this study is to understand small independent fashion retail owners’ and employees’ thoughts on innovative retail technologies and their reactions to the currently available new technology. A qualitative research method of in-depth interviews with 11 participants working in fashion companies was used. The themes that emerged in this study represent criteria to be met prior to adopting new technology for small fashion retailers, including transparency, consistency, and integration of technology. While there were concerns regarding the cost and timing of adopting smart technology, they all expressed unanimous agreement that these advancements would become the next major trend in fashion retail, enhancing consumer connectivity. In particular, the smart technology they plan to adopt must possess the capability for reciprocity between consumers and the company. This will motivate a resurgence of innovative technologies in the less advanced fragment of small independent fashion retailers. Future research can focus on analyzing how the implementation of new smart technologies affects these types of businesses and their customer satisfaction.

The Color Garments Encouragement Banner was designated a Korean Heritage in 2014 to recognize it as the most significant object of the color garments encouragement campaign. However, despite its significance, nothing is known about its manufacture. Therefore, this study attempted to analyze the materials of the banners to estimate when they were manufactured and repaired. The investigation of materials on the banner involved visual examination, literature review, microscopy, SEM–EDS, FT-IR, Py-GC–MS, ICP-MS, and LC–MS. The fabric, patch, and threads comprising the artifact were identified as cotton. FT-IR and Py-GC–MS confirmed that the repair patch was a woven blend of polyester and cotton yarns. EDS analysis indicated that the polyester was treated with titanium delustering. ICP-MS detected high concentrations of chromium that were not used in traditional dyeing techniques. The azo and sulfur compounds were identified by LC–MS analysis. The material layered on the grommet patch was thought to be a mixture of Pb, Ti with CaCO₃ and BaSO₄. Based on the overall results, the production date of the banner was narrowed down to the late 1920s, and the repair date to the mid-1950s. Although the materials used could not be identified owing to the limitations of the applicable analysis. Nonetheless, it is hoped that the analyses conducted in this study can serve as a scientific foundation for dating modern cultural heritage objects with limited handed-down record and historical documentation.

With the rise of online shopping due to the COVID-19 pandemic, Recommender Systems have become increasingly important in providing personalized product recommendations. Recommender Systems face the challenge of efficiently extracting relevant items from vast data. Numerous methods using deep learning approaches have been developed to classify fashion images. However, those models are based on a single model that may or may not be reliable. We proposed a deep ensemble classifier that takes the probabilities obtained from five pre-trained models such as MobileNet, DenseNet, Xception, and the two varieties of VGG. The probabilities obtained from the five pre-trained models are then passed as inputs to a deep ensemble classifier for the prediction of the given item. Several similarity measures have been studied in this work and the cosine similarity metric is used to recommend the products for a classified product given by a deep ensemble classifier. The proposed method is trained and validated using benchmark datasets such as Fashion product images dataset and Shoe dataset, demonstrating superior accuracy compared to existing models. The results highlight the potential of leveraging transfer learning and deep ensemble techniques to enhance fashion recommendation systems. The proposed model achieves 96% accuracy compared to the existing models.

This study forged an interdisciplinary design partnership between an apparel designer, biomechanical kinesiologist, and a professional female athlete to design and test a novel new upper body performance garment for improved fit, mobility, comfort, donning/doffing and throwing performance in female professional athletes. Researchers collaborated to address garment functionality and performance during the multi-phase prototyping design process. The final designed performance top was tested against a control for satisfaction differences with fit, mobility, comfort, donning, and doffing, along with throwing performance utilizing a mixed methods questionnaire and motion capture equipment. Open-ended questions were analyzed with content analysis, while quantitative measures utilized ANOVA and independent t-tests. Results indicated the designed performance top had higher fit satisfaction and more optimal positioning during the overhead throw when compared to the control garment, with no difference in comfort or mobility, and the designed top had lower donning and doffing satisfaction. The designed performance top illustrates the need for further research in increasing satisfaction in donning and doffing with bust/bra satisfaction in performance garments and future research to better understand the psychological sensory cues in posture cueing garments.

This study aimed to investigate the determinants of transaction satisfaction and intention to use local second-hand marketplace platforms, focusing on the impact of negotiation costs and the moderating influence of users’ sense of their neighborhood. Building on transaction cost theory, we conceptualized negotiation costs as a multidimensional construct that encompassed economic, performance, time, and psychological costs. Our findings revealed that economic and time costs had a significant negative effect on transaction satisfaction, whereas performance and psychological costs did not exhibit such an impact. Furthermore, transaction satisfaction positively influenced users’ intention to use the platform, thereby mediating the relationship between negotiation costs and intention to use. Additionally, we found that users’ sense of their neighborhood enhanced transaction satisfaction and acted as a moderator, attenuating the negative impact of negotiation costs on satisfaction. For users with a high sense of my neighborhood, the negative impact of economic, performance, and time costs on transaction satisfaction was attenuated. These findings contribute to a deeper understanding of consumer behavior on local second-hand trading platforms and emphasize the importance of social cues in shaping transaction satisfaction and intention to use. The implications of our study offer valuable insights for second-hand platform operators.

With the rapid development of technology and media, people’s experiences of other cultures have shifted from direct only to a combination of direct and virtual experiences. This study investigated how global consumers directly and indirectly experience other cultures, mediated by YouTube. It focused on the consumer experience of the Korean traditional costume, the Hanbok. YouTube content focusing on Hanbok experiences was collected to understand the direct Hanbok experiences, while viewers’ reactions to YouTube content (i.e., views, likes, and comments) were collected to understand indirect Hanbok experiences. The analysis involved two steps. First, the latent Dirichlet allocation topic modeling algorithm in Python was used to extract topics from direct and indirect Hanbok experiences. Second, simple regression analysis was conducted using numerical data derived from textual data through linguistic inquiry and word count to examine the relationship between direct Hanbok experiences via YouTube and viewer engagement as an indicator of indirect Hanbok experiences. The results showed that YouTubers’ Hanbok experiences were categorized into seven topics, namely novelty, sensory, daily, cultural, trial, pleasing, and intellectual experiences. Indirect Hanbok experiences were identified based on dimensions of consumer engagement, such as social, cognitive, learning, behavioral, and affective engagement. The regression analysis results revealed that cultural and affective Hanbok experiences significantly impacted consumer engagement. This study expanded the scope of research by exploring indirect cultural experiences mediated by a media platform.

This paper presents research into the parameters affecting the real and virtual drapability of silk fabrics for traditional Hanbok. The KES-FB (Kawabata evaluation system of fabric) and the CLO (3D fashion design software) fabric kit system were used to evaluate the physical properties. The relationships between physical properties and the drape coefficient of silk fabrics were statistically analyzed. It was determined whether the objectified real fabric data could be used and applied as a database when implementing virtual clothing using a 3D virtual clothing program. The KES-FB properties and CLO fabric kit results indicated that the bending property and weight were significantly correlated with the drape coefficient. The bending rigidity and weight per unit area were influential parameters of the drape coefficient. The regression analysis results of both the KES-FB and the CLO fabric kit revealed that bending rigidity and thickness were statistically significantly correlated with the drape coefficient. The bending property was the most determining parameter for the drape coefficient of silk fabrics. A statistically significant difference between the real and virtual drape coefficients of stiffer silk fabrics was found. In the CLO 7.1 program, the accuracy of drape implementation of virtual fabrics was found to be useful when the bending stiffness of the actual fabric was less than 0.089 gf·cm²/cm and the elasticity was more than 1.03%. The results revealed that more research on the program modeling method considering the mechanical and physical properties of the real fabric and its structure is needed.