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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]