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This article presents studies on the evaluation of the impact of surface modification of cotton, viscose, and polyester fabrics using three techniques (flocking, layer by layer, and screen printing) with materials with electrically conductive properties on their structural, biophysical, and conductive properties. Each tested fabric is characterized by specific biophysical properties. which can be disturbed by various modification methods, therefore, the following tests were carried out in the article: optical microscopy, micro-computed tomography, guarded perspiration heating plate, air permeability, sorption and electrical conductivity tester. The use of screen printing increased the thermal resistance of the cotton woven fabric by 119%, the polyester woven fabric by 156%, and the viscose fabric by 261%. The smallest changes in thermal resistance compared to unmodified textiles were observed in layer by layer modified fabrics and are as follows: −15% (cotton woven fabric), +77% (PES woven fabric), and +80% (viscose woven fabric). [ABSTRACT FROM AUTHOR]

This paper describes methods for evaluating the thermal properties of textile materials, clothing composites, and clothing using an integrated measurement system that includes a hot plate, a multi-purpose differential conductometer, a thermal manikin, a temperature gradient measurement device, and a device for measuring the physiological parameters of the human body during the exact evaluation of garment thermal comfort. In practice, measurements were taken on four types of materials widely used in the production of conventional and protective clothing. The measurements were carried out using a hot plate and a multi-purpose differential conductometer, determining the thermal resistance of the material both in its uncompressed form and when a force was applied that was ten times greater than that needed to determine its thickness. Using a hot plate and a multi-purpose differential conductometer, thermal resistances of textile materials were assessed at different levels of material compression. On hot plates, both conduction and convection had an impact on thermal resistance, but in the multi-purpose differential conductometer, only conduction did. Moreover, a reduction in thermal resistance was observed as a result of compressing textile materials. [ABSTRACT FROM AUTHOR]

Since gait can be utilized to identify individuals from a far distance without their interaction and coordination, recently many gait recognition methods have been proposed. However, due to a real-world scenario of clothing changes, a degradation occurs for most of these methods. Thus in this paper, a more efficient gait recognition method is proposed to address the problem of clothing variances. First, part-based gait features are formulated from two different perspectives, i.e., the separated body parts that are more robust to clothing changes and the estimated human skeleton key-point regions. It is reasonable to formulate such features for cloth-changing gait recognition, because these two perspectives are both less vulnerable to clothing changes. Given that each feature has its own advantages and disadvantages, a more efficient gait feature is generated in this paper by assembling these two features together. Moreover, since local features are more discriminative than global features, in this paper more attention is focused on the local short-range features. Also, unlike most methods, in our method we treat the estimated key-point features as a set of word embeddings, and a transformer encoder is specifically used to learn the dependence of each correlative key-points. The robustness and effectiveness of our proposed method are certified by experiments on CASIA Gait Dataset B, and it has achieved the state-of-the-art performance on this dataset. [ABSTRACT FROM AUTHOR]

In this paper, we tackle the problem of static 3D cloth draping on virtual human bodies. We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes. Our network learns to mimic a physics-based simulation (PBS) method while requiring two orders of magnitude less computation time. To train the network, we introduce loss terms inspired by PBS to produce plausible results and make the model collision-aware. To increase the details of the draped garment, we introduce two loss functions that penalize the difference between the curvature of the predicted cloth and PBS. Particularly, we study the impact of mean curvature normal and a novel detail-preserving loss both qualitatively and quantitatively. Our new curvature loss computes the local covariance matrices of the 3D points, and compares the Rayleigh quotients of the prediction and PBS. This leads to more details while performing favorably or comparably against the loss that considers mean curvature normal vectors in the 3D triangulated meshes. We validate our framework on four garment types for various body shapes and poses. Finally, we achieve superior performance against a recently proposed data-driven method. [ABSTRACT FROM AUTHOR]

The influence of the drying position and time on the persistence of gunshot residues (GSR) on fabrics was investigated. Powder tattooing from a 40 cm shooting distance on targeted fabrics were slightly wetted using a spray and dried for 3 h or 48 h in horizontal or vertical positions. The GSR particles were visualised using IR fluorescence with a Video Spectral Comparator (VSC) and automatically counted with a software before the wetting and after the drying stages. While a significant rate of GSR particles (up to 22%) were lost, it was mainly due to the wetting procedure rather than the drying stage. No statistically significant difference was observed between the drying positions, indicating that both drying positions can be used in practice to dry slightly wet fabrics. The drying time had no influence on the loss, and the GSR distribution was not significantly influenced by either factor (i.e., time and position). • Gunshot residue loss was studied on fabrics shot at a 40 cm distance and sprayed with water. • The drying position for slightly wet clothes had little to no influence on the GSR loss. • No significant particle displacement was observed between the upper and lower half of the targets. • Drying will not significantly influence the estimation of the shooting distance. • No contamination was optically detected on blank fabrics during the drying process. [ABSTRACT FROM AUTHOR]

• Systematic review of application of ML and SC in textile and clothing supply chain. • Yarn and fabric manufacturing stages have most of the applications of ML and SC. • ANN, genetic algorithm and fuzzy logic are the most explored ML and SC techniques. • Lack of collaborative research on ML and SC applications in textile and clothing supply chain. • ML aided fibre development, clothing engineering and fashion forecasting need attention. Machine learning (ML) and soft computing (SC) techniques have contributed immensely towards improvisation of manufacturing, process and quality control, automation of operations, and decision making in the textile and clothing supply chain. The current study systematically reviews 312 research articles published in Scopus indexed journals in last two decades (2000–2020). Bibliometric analysis revealed a drastic rise in number of studies since 2015. Operations related to manufacturing and quality control of yarns and fabrics are found to be benefitted most from ML techniques, as deduced from keyword and thematic content analyses. On the other hand, with respect to ML and SC techniques, artificial neural network, genetic algorithm and fuzzy logic have preponderance over the others. Nevertheless, over the years, many novel ML and SC techniques have emerged in accomplishing diverse objectives of the textile and clothing supply chain. A detailed analysis of their evolution has been documented. Unfortunately, the extent of knowledge sharing and collaboration within the scientific community working in this domain has been very low. This review also unearths lack of integrated focus on complete value chain of textiles and clothing. Fibre development, fuzzy inference and control in textile machines, predictive analytics for clothing engineering, and fashion forecasting have been identified as areas for future research. [ABSTRACT FROM AUTHOR]