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The concept of developing clothing sizes has taken importance in recent years due to increasing expectations of consumers for branded clothing and its value in terms of fit and durability. The kids’ ready-to-wear brands are expected to pose the least fit issues, thereby covering a wider population of a particular size. This necessitates the standardization of measurements. The lack of standardized approaches has caused fit issues like mismatching of sizes and alterations, in a heterogenous consumer market, like India. The performance of branded apparel depends on the quality of the measurements considered in developing sizes and the approach for standardization. There is a lacuna in the measurements used by the kids’ apparel domestic brands. This study aims to propose an anthropometric approach for deriving quality measurements that can be used effectively in developing kids’ sizes to fit a wider population of kids, thereby reducing the need for alterations.

The measurement data was gathered through the quantitative method. An anthropometric survey was conducted by measuring school kids. A total of 544 kids (girls and boys) of age group 6–8 years were measured to obtain prime anthropometric measurements required for ready-to-wear apparel production. WHO manual and ISO 8559, 1998 meant for anthropometry survey for garment industry was referred for accurate measuring following the landmarks for measuring.

The findings revealed differences in the anthropometric measurements based on gender and age. The anthropometric measurements showed variations within the same body mass index (BMI) range. S, M and L sizes were identified within an age group. The apparel manufacturers and designers need to focus on the discrepancies occurring in the body measurements of an age group to address and control fit issues in kids ready to wear apparel.

The anthropometric approach can be significantly used to control undesired fit and comfort issues in kids’ ready-to-wear apparel.

This study helped to understand the importance of scientific measuring practices to arrive at standardized measurements to develop sizes in ready-to-wear apparel manufacturing.

This paper aims to present investigations of the influence of sewing and adhesive bonding technology on the aesthetic, mechanical and conductive properties of the e-textile package. Commercially available conductive textiles are tested for the production of e-textile package by most common cut-and-sewn clothing production technologies.

Sewing, adhesive bonding and seam sealing technologies used to obtain e-textile packages with woven and knitted conductive textiles. Produced e-textile packages described in terms of thickness, bending rigidity and general appearance. Exploitation properties of prepared samples tested by cycle tensile experiment and discussed on the basis of variation of linear electrical resistance property.

Research has shown that a reliable e-textile package can be obtained by applying cut-and-sew technology for conductive tracks of silver coated woven and knitted material. Seam sealing by thermoplastic polymer layer has an impact on the electrical and deformation properties of the samples. To create attractive smart clothing design, the appropriate joining method and its technological parameters must be chosen to ensure the durability and safety of e-textile packages.

The findings of the research are of substantial value for the production of e-textiles by cut-and-sewn technologies. The required shape of the conductive textile element for various applications can be cut and joined to the garment parts using traditional sewing or adhesive bonding techniques.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

To explore the influence of various factors on the adhesion strength of 3D printing materials and chiffon fabrics, and to develop an original design clothing prototype with an extended functionality that would be compatible with the specifics of the circular design.

Four different chiffon fabrics and four 3D printed materials were chosen as the research subjects to determine the influence of various factors on the adhesion strength and ductility. The uniaxial tensile test was used to determine pull-out force and the pull-out elongation from the interlayer.

3D printed TPU elements can be used to join clothing parts made from low-elasticity chiffon fabrics to improve wearing comfort. In order to comply with the circular economy concept, it is important to select such adhesion parameters of the 3D printed elements and the material system that would ensure wear comfort and withstand wear-level loads; and at the end of the life cycle of a garment, the 3D printed elements could be separated from the product and recycled.

The systems developed can be used to renew and repair products, adding originality, individual touch or additional decorative features, while extending the functional possibilities of clothing items in accordance with circular design principles.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

The research evaluated the feasibility of 3D dynamic fit utilizing female compression tops by comparatively analyzing the virtual and actual dynamic fit.

Six female participants were 3D body-scanned and photographed in compression tops in four types of athletic movements (pull-up, kettlebell swing, circle-crunch and sit-up). Fit measurements, waist cross-sectional areas, waist width, waist depth, numerical simulation of clothing pressure (kPa) and objective pressure measurements (kPa) were collected from 3D virtual animation, 3D fit scan data and actual photos with the four types of athletic motions. The data were comparatively investigated between virtual and actual dynamic fit.

The 3D-animated body was not reflected with human body deformation because only bone structure was changed while maintaining the constant forms of muscle and body surface in athletic movements. Due to this consistency of virtual dynamic fit, there were significant differences with the actual dynamic fit at the top length, shoulder width and waist cross-sectional areas. Also, the virtual dynamic pressure indicated significantly higher levels than the objective dynamic pressure while presenting no significant correlations at the front neckline, breast, lateral waist, upper back, back armhole and back waist.

This study is the first to verify multiple aspects of virtual dynamic fit using 3D digital technology. This study provided useful information about which aspects of the current virtual animation need to be improved to apply in the dynamic fit evaluation.

The objective of this study was to develop wearable suit platforms with various anchoring structure designs with the intention of improving wearability and enhancing user satisfaction.

This study selected fabrics and materials for the suit platform through material performance tests. Two anchoring structure designs, 11-type and X-type are compared with regular clothing under control conditions. To evaluate the comfort level of the wearable suit platform, a satisfaction survey and electroencephalogram (EEG) measurements are conducted to triangulate the findings.

The 11-type exhibited higher values in comfort indicators such as α, θ, α/High-β and lower values in concentration or stress indicators such as β, ϒ, sensorimotor rhythm (SMR)+Mid-β/θ, and a spectral edge frequency of 95% compared to the X-type while walking. The 11-type offers greater comfort and satisfaction compared to the X-type when lifting based on the EEG measurements and the participants survey.

It is recommended to implement the 11-type when designing wearable suit platforms. These findings offer essential data on wearability, which can guide the development of soft wearable robots.

This study focused on how to quantify the similarities of body shape based on the front and side images, and a shape comprehensive index (I_SC) of female upper body shape based on 2D images was proposed.

In total, 190 young women were shot for front and side images, and 18 shape parameters were automatically extracted, including seven angles and 11 ratio parameters. The coefficient of variation method was used to assign different weights for related parameters, and the I_SC was calculated to describe the body shape of each subject. Five cross-sectional curves of the upper body (e.g. shoulder, chest, waist, abdomen and hip) were selected for exploring the range of shape similarity.

According to the value of I_SC, if the difference among the subjects is within the range of ±0.02, their body shapes can be regarded as similar, and the subject with the minimum distance is considered as the most similar. Error results show that the error range of the angle parameter is from 0.2° to 3.6° and the ratio range is from 0.001 to 0.119. Moreover, the t-test value among the parameters of the similar body is above 0.05, indicating that there is no significant difference for the upper body shape of the similar groups.

This method can quantify body shapes with the upper body characteristics of young women instead of subjective judgment. The study can be extended to other parts of the body and can also provide a new thought for shape similarity retrieval based on 2D images.

The purpose of this paper is to present a fit prediction case study of virtual twins of women jacket. For this reason, several basic principles of clothes fit prediction were developed and verified.

To develop the principles of fit prediction, the women's jacket sleeve was selected as the study object. The study objects were categorized into three types: the patterns and two virtual sleeves generating on full avatar from Clo3D and on dummy (arm partly removed). Through series of subjective and objective evaluation experiments, the relationship between the similar indexes of the patterns and the virtual sleeves was built, including fit criteria range, the categorization of the indexes in terms of its sensitiveness, and the linear regressions to predict several indexes of virtual sleeves after its pattern parameterization. The results obtained were verified in case study by the virtual and real sleeves generating.

The proposed principles of clothing fit prediction based on parallel research of the flat patterns and its virtual 3D shapes. The principles include the choosing of virtual twins of human body for virtual try-on, the establishing of indexes common schedule for patterns and virtual sleeves, the creation of criteria and its ranges for perfect fit and poor fit evaluation, and the application of existing relations between the patterns and the sleeves for predicting indexes responsible for fit.

The authors propose and verify the validity of the principles to predict several parameters of virtual 3D sleeve of women's jacket which are forming the level of fit. The result of this study can be used to convenient fit prediction and to find the misfit reasons.

This study developed basic principles for predicting the fit of the clothing through the virtual simulation and the statistical analysis. Through studying the jacket sleeve, the several related ranges, the row of more sensitive indexes, and the equations were presented and verified, which certified the validity of proposed principles.

The results can effectively predict the sleeve fit before sewing, which reduce the time and materials cost and the operator's skill requirements.

The authors propose and verify the validity of the principles to predict several parameters of virtual 3D sleeve of women's jacket which form the level of fit. The result of this study can be used for convenient fit prediction and to find the misfit reasons.

The purpose of the study is to measure the performance of Indian hospitals, both operationally and financially, by using hospital KPI's. The assessment is predominantly done by linking it to the existing Lean practices in Indian hospitals.

An empirical study based on cross-sectional survey of hospital managers and specialists in various private healthcare facilities across India was conducted to validate the proposed Lean framework. From an extensive literature survey, the authors identified quality, delivery, efficiency, accessibility and patient centeredness to be the main operational performance (OP) indicators for hospitals. Business or financial performance was measured based on parameters which are average revenue per occupied bed (ARPOB), earnings before interest, tax, depreciation and amortization (EBITDA) and operating revenue. Confirmatory Factor Analysis (CFA) was carried out using a specialized technique, called Structural Equation Modelling(SEM) and an explicit factor structure was hypothesized.

Management commitment towards Lean in hospitals is statistically proven to have impacted operational and financial performance. However, leanness in technology and business processes showed no statistical significance on either operational or financial performance parameters. Hospital stakeholders showed statistical significance on though it had no impact on the financial performance. Results obtained from the statistical analysis indicate a positive impact of hospital Lean practices on timely delivery of services and improved service quality. Efficiency, accessibility of services and patient centered behavior in hospital operations could not be statistically proven to have impacted the financial performance.

Effectiveness of Lean management (LM) principles in improving hospital operations is largely dependent on patient centered behavior. Empowered employees who are trained to add value from a customer view point, make hospital operations safe and improved. Properly trained and communicated employees who are committed to quality improvements can make a positive impact on patients' quality of life and thus positively impact the society. The study lists ways to attain the required outcomes.

This paper is among the very few that has attempted to suggest ways to link implementation of Lean practices more effectively in Indian hospitals to improve hospital performance at operational and financial levels.