In the context of upcoming EU legislation, this article assesses the physical durability of a specific textile product – a laid-in knit fabric suitable for the manufacturing of sweatshirts or sweatpants. Five key parameters – dimensional stability in the length and width directions, color fastness (both across the fabric surface and at the seams), and pilling – were monitored in relation to the number of washing cycles (1, 5, 10, 20, and 30 cycles). Our study demonstrates that when evaluating the environmental performance of textiles, assessing “initial quality” based on a new or once-washed sample may be insufficient; instead, it is essential to carefully establish a simulated deterioration procedure. Significant differences between the tested samples may only become apparent after the 20th washing cycle; furthermore, our results showed that the sample that appeared best after the first wash gradually deteriorated, ultimately yielding the poorest results after the 30th washing cycle. Our experiment and its results demonstrate, using a specific product example, that assessing the physical durability of clothing is a complex, multifaceted issue involving numerous variables, and that determining the parameters relevant to testing is merely the starting point of the entire process.
In textile waste management and the circular economy, reliable discrimination between natural and synthetic fabrics can support sorting and recycling. Manual inspection is labor-intensive and subjective, while many automated approaches rely on opaque image-based models. This paper presents a reproducible and interpretable method for binary natural-versus-synthetic fabric discrimination from microimages acquired under controlled conditions. The proposed approach represents each image by a fixed numerical profile of 111 interpretable microtexture features rather than raw pixels. Each input image is resized to 256×256 pixels, converted to grayscale, and represented at three automatically defined centered fields of view (96×96, 160×160, and 256×256). The profile includes GLCM descriptors, local binary pattern features, photometric intensity characteristics, Shannon entropy, and gradient-based edge measures. Statistical tests and effect sizes are used for post-hoc characterization and ranking of interpretable indicators; they are not used to select the input variables of the classifier. The source dataset contains 3,107 images (1,547 natural and 1,560 synthetic). The experiment reported in this paper uses a reproducible balanced subset of 400 images, with 200 images per class selected using a fixed random seed (42). The subset is divided by stratified imagelevel splitting into 280 training images and a held-out test set of 120 images (60 per class). The predictive pipeline is fitted using the training partition only; S63/C15 statistics are reported separately as post-hoc descriptive characterization and do not determine the MLP inputs or the reported test metrics. Of the 111 predefined features, 63 show significant class-distribution differences after FDR correction in this descriptive analysis. A multilayer perceptron trained on the complete 111-feature profile achieves Accuracy = 0.9917 and Macro-F1 = 0.9917 on the held-out 120-image test set. Median imputation and feature standardization are fitted within the training pipeline only. The resulting confusion matrix contains 59 correctly classified natural images, one natural image classified as synthetic, and all 60 synthetic images correctly classified. The threshold-based E14 module is a separate post-hoc interpretation layer; its class ranges and thresholds are calculated from training data and do not constitute or modify the MLP decision boundary. The results demonstrate strong image-level discrimination within the studied acquisition protocol, but they should not be interpreted as specimen-level validation or as a complete industrial textile-identification system. The published image metadata do not preserve physical-specimen identifiers, so specimen-level independence between partitions cannot be verified retrospectively. In addition, photometric features are highly discriminative and may partly encode illumination/reflectance differences. The current task is binary and does not address blends, fibre-content estimation, or multi-class sorting. External validation across independent specimens and imaging conditions is therefore required.
The confluence of increasing global sustainability demands and the imperative for real-time quality control in advanced manufacturing systems provides the foundation for this study. We address this by initially conceptualizing an active robotic noise-mitigation panel based on flax fiber designed for autonomous deployment in landscaping and urban noise control environments. The feasibility of this active system, which relies on the dynamic manipulation of a passive flax core integrated with AI-driven sensing and actuation, is fundamentally dependent on precise and rapid assessment of the raw fiber’s acoustic potential and physicomechanical quality. Confronting the scarcity of suitable non-destructive pre-assessment techniques, this paper details the subsequent development and comprehensive validation of a novel methodology utilizing the sound absorption effect to characterize flax fiber quality. A specialized device was engineered and rigorously optimized, establishing critical operational parameters: a 10 g sample mass, an emitter frequency of 1750 Hz (optimally aligned with the λ/4 thickness), and a reference moisture content of 12%. The research successfully established a robust statistical correlation between the acoustic attenuation measurements and key industrial indicators, specifically linear density, breaking load, and flexibility. Statistical validation using the Student’s t-test confirmed a high degree of agreement with established standards (DSTU 4015-2001), demonstrating excellent reproducibility and high precision (relative expanded uncertainty below 5 %). Furthermore, empirical power and logarithmic regression formulas were derived to enable the direct calculation of quality parameters from the acoustic data. This integrated approach not only provides a reliable, rapid, and objective tool for industrial quality control, but also furnishes the essential material assessment capability required to transition sustainable flax materials into the demanding domain of smart, active noise-mitigation technologies.
This study aims to identify the key functional and material determinants influencing user satisfaction with mastectomy bras and external breast prostheses among Vietnamese breast cancer survivors living in a tropical climate. A cross-sectional survey was conducted with 157 post-mastectomy women recruited from breast cancer support organizations across Vietnam. The questionnaire collected demographic information, product usage characteristics, and satisfaction ratings using a five-point Likert scale covering functional, ergonomic, and aesthetic attributes. Data were analyzed using descriptive statistics, Gaussian distribution analysis, Pearson correlation, and multiple linear regression, while multicollinearity was assessed through the variance inflation factor (VIF). The regression models explained a substantial proportion of the variance in overall satisfaction for both product categories. For mastectomy bras (R2 = 0.642), fit (β = 0.428, p < 0.001) and physical comfort (β = 0.365, p < 0.001) emerged as the strongest predictors of satisfaction, while material performance showed a smaller but statistically significant contribution (p < 0.05). In contrast, tactile softness alone was not a significant independent predictor (p = 0.106). For external breast prostheses, thermal comfort (β = 0.328, p < 0.001), weight (β = 0.312, p < 0.001), and positional stability (β = 0.245, p = 0.002) were identified as the primary determinants of user satisfaction. Silicone-based prostheses were frequently associated with thermal discomfort under hot and humid conditions. Although the study is limited by its reliance on self-reported data and a cross-sectional design, the findings provide evidence-based insights for developing climate-responsive mastectomy apparel and prostheses. The research contributes to a user-centered design framework emphasizing adaptive fit, pressure reduction, thermal regulation, and moisture management for post-mastectomy women in tropical environments.
Magnetisable fibres, among others, can be used for targeted fibrearrangement or fixing by use of a magnetic fields, antistatic properties or shielding against electromagnetic fields. In some cases the application requires to keep the functional, magnetic component separate from the environment. Liquid-filled hollow fibres are a promising candidate for this purpose, as the fibre hull material can be chosen to keep optimal interaction with the surrounding environment, while the liquid-phase allows for introduction of large fractions of fillers. In this study, a novel strategy to achieve magnetisable fibres was investigated. By melt spinning, hollow polyethylene fibres were produced and in situ filled with a liquid containing iron oxide particles. As reference, polypropylene monofilaments were melt spun, where the iron oxide particles were compounded into the polymer prior to melt spinning. Both processes were successfully implemented, however in the first strategy the ferrofluid caused deficient process robustness due to nozzle clogging and thermal instability. For rough access of the achievable magnetisability, hollow fibres were filled manually with a ferrofluid. To evaluate magnetic functionality, a custom-built measurement unit was developed for quantifying the magnetic attraction force of fibre samples. The measured magnetic detachment forces of the ferrofluid-filled samples were 17.0 ±0.8 mN and therefore in the range of the monofilaments, where values from 11.4 ±0.7 mN to 26.6 ±1.8 mN were measured, depending on fibre diameter and iron content.
