The global textile industry faces a critical inflexion point as circular economy mandates intensify and waste volumes soar beyond 100 million tonnes annually. Central to realising circularity is the efficiency and fidelity of textile waste sorting, a longstanding bottleneck dominated by manual, low-throughput, and error-prone methods. This paper investigates the deployment of an AI-enabled robotic sorting system integrating hyperspectral imaging (HSI) and deep learning algorithms within the context of India’s fragmented textile recycling ecosystem. We demonstrate that spectral imaging combined with convolutional neural networks (CNNs) achieves over 95% classification accuracy across heterogeneous, post-consumer Indian textile waste streams, including multi-fibre blends that typically confound manual sorters. Drawing from industrial benchmarks such as Sweden’s SipTex and U.S.-based Refiberd, we design a prototype that integrates conveyor automation, real-time classification, and robotic actuation. Comparative analysis reveals that the AI system achieves throughput rates exceeding 1,000 garments per hour, representing a 20× gain over manual processes while reducing misclassification rates by more than 60%. A techno-economic model suggests payback periods under four years when scaled to medium-sized facilities, with significant reductions in labour dependency and waste-to-landfill ratios. Our findings have strong implications for policy and industry: AI sorting systems not only align with India’s National Textile Policy and MITRA initiatives but also represent an enabling infrastructure for chemical recycling, extended producer responsibility, and traceable material flows. By bridging technological innovation with operational scalability, this study advances the industrial feasibility of circular textiles in the Global South.

The need for natural clothing is increasing day by day. To meet this demand, the apparel industry is developing new systems to enhance production and raw material usage. Using healthy products is essential for a healthy life, which increases the need for natural raw materials. Cotton is the ideal natural raw material for a renewable and sustainable production line. Despite the growing production, it cannot fully meet the demand. Therefore, new systems are being developed to improve the quality of cotton production. The foundation of the textile industry is yarn, and yarn production lines consist of systematically operated machines. These production systems include carded, combed, and open-end methods. In combed production, high-quality and long fibres are used to produce yarns with counts such as Ne 30 or Ne 50. In combed yarn production, fibre length and ratio can be adjusted through machine settings. Lap feeding cylinder gaps in combed yarn machines are critical for this adjustment. In this study, experimental results were obtained using 4 different yarn counts produced from the same blend and 5 different combed feeding jaw settings. These results were optimised using artificial neural networks. In the analysis, yarn count and combing cylinder gap were used as input data, while the physical properties of the yarn were used as output data.

In the process of apparel design, understanding consumers’ emotional demand is crucial to creating satisfactory garment styles. To solve the problem of the mismatch between consumers’ personalised needs and the design of trench coat styles. This paper focuses on women’s trench coats and proposes a style design research method that combines Quantification Theory I and Kansei Engineering. Initially, it employs the Semantic Difference Analysis Method to extract consumers’ emotional evaluations of trench coat samples. Using SPSS software, it analyses the emotional ratings and identifies key emotional factors, constructing a two-dimensional emotional distribution map for trench coat styles. Simultaneously, it analyses style characteristics to extract the main design elements. Building on this, it integrates Quantification Theory I and performs linear regression, predicting relationships between emotional factors and design elements and establishing a mathematical model. This model exhibits a high degree of fit between measured and predicted values and adheres to normal distribution requirements, demonstrating its effectiveness. Ultimately, the study validates the mathematical model through real consumer design cases, further confirming that it can effectively translate consumers’ emotional needs into trench coat design elements, thus providing significant insights and references for women’s trench coat style design.

Conductive knitted fabrics can function as humidity sensors, detecting the presence of liquids on their surface through changes in electrical resistance. This property can be leveraged for automatic hemostasis systems, where the detection of blood at a wound site triggers real-time intervention. In this study, conductive yarns including Shieldex (Statex: 60- 440 W/m), AgSiS (Lib-40: 5 W/m), and stainless steel (60 W/m) were integrated into knitted fabrics using a Shima Seiki machine. The fabrics were characterised for mechanical strength, abrasion resistance (1,000 and 5,000 cycles), washing durability (1 and 5 cycles), and resistance to acidic and alkaline perspiration. Electrical resistance was measured under exposure to four aqueous media simulating physiological and wound conditions: deionised water (pH 6, 244 mS/cm), acidic perspiration (pH 5.5, 10.73 mS/cm), alkaline perspiration (pH 8, 11.35 mS/cm), and 20% saline solution (pH 5.0, 9.5 mS/cm). Morphological and compositional analyses were conducted using SEM, EDX, and FTIR spectroscopy. The results demonstrated that all fabrics exhibited measurable and repeatable resistance variations, with the strongest response observed for the 20% saline solution and Lib-40 conductive yarn, highlighting their potential as humidity sensors for real-time detection of bleeding events in automatic hemostasis systems.

Protecting natural resources for future generations has become an important concern in the debates among policymakers and institutions. Sindh is the third largest province by geography and the second most urbanised province, located in the southeast of Pakistan. This study aims to empirically analyse the role of green human resource management practices on environmental sustainability in higher educational institutions in Sindh, Pakistan, particularly those offering degrees and responsible for research in textile engineering and fashion design. Further, we explore whether green innovation as a potential mediator stimulates the relationship between green human resource management and environmental sustainability. We employed a quantitative research technique and retrieved data from 376 respondents who are employees of higher education institutions. This study for analysis used SPSS 26 and partial least squares based on structural equation modelling (SEM). Our outcomes suggested that green human resource management promotes environmental sustainability. Notably, green recruitment and selection (0.384) exerts the strongest influence on environmental sustainability, followed by green training and development (0.341), green compensation and reward (0.232) lastly green performance management (0.184) also contribute positively to environmental sustainability. All relationships observed in the study were statistically significant (p<0.05). The current study finds that green innovation partially mediates the relationships between all constructs and plays a crucial role in enhancing environmental sustainability. This study provides insightful recommendations for educational institutions currently operating in Pakistan and other emerging economies to achieve sustainability objectives. It also promotes eco-friendly practices and raises awareness among stakeholders, contributing to the achievement of environmental sustainability.

The yarn spinning method and the utilised raw material play a significant role in determining the comfort properties of fabrics. Spinning methods, such as conventional ring, mechanical compact, and Siro spinning, influence the yarn’s structure, uniformity, and surface characteristics, which in turn affect fabric properties like moisture management and thermal comfort. This study explores the moisture management and thermal comfort properties of knitted fabrics produced from different blended yarns spun on three distinct spinning techniques: Conventional ring, mechanical compact, and Siro spinning. For analysing how different spinning methods and yarn types influence some comfort properties, Moisture Management Test (MMT), Alambeta Tests and air permeability tests were performed in the context of this research. For the statistical analyses, a Two-way ANOVA test was performed in order to investigate the effect of yarn spinning method and yarn type on moisture management, thermal comfort and air permeability properties of knitted samples. The findings revealed that spinning methods and fibre blends significantly impact the properties of the fabric. The research aims to provide insights into the relationship between yarn structure and fabric behaviour, offering valuable guidance for textile development and innovation.

Textiles at the Moghadam Museum (Tehran, Iran) are considered a valuable heritage collection that has a historical legacy of more than 2,200 years. They comprise a variety of weaving and stitching techniques, textures, materials, patterns and designs, and they are from a wide range of national and ethnic people who lived in the Persian plateau during the above-mentioned two millennia. The valuable collection was gathered by its founders, Dr. Mohsen Moghadam and his wife, Mrs. Selma Kiyoomjian. They utilised different sustainable preservation techniques and restoration methods, based on the historical value, size, and material of the textiles. After their demise, facilities limitations and poor maintenance gave way to the occurrence of irreparable damage to the collection. High fluctuations of the environmental conditions, in humidity and temperature, accompanied by low ventilation, resulted in the growth of fungi and the attack of insects on some of the textiles. Exposure to light also paled the colours in some of the textiles. In terms of weaving technique, three textile types are discussed in the present research study, including mixed-weaving technique (the Parthian Empire, 247 BC till 224 AD), Ghalamkaar textiles and textiles with metal-threads (both during the Safavid dynasty, 1501 AD till 1722 AD).

The article addresses a highly relevant topic: the application of artificial intelligence (AI) to the design of Minoan-inspired clothing by transforming fashion illustrations into photorealistic visualisations of physical, wearable garments. Advanced AI systems have been used to develop innovative and practical fashion design solutions that capture the timeless elegance of Minoan costumes while giving them contemporary flair. The study includes an application-based analysis of five affordable AI systems that can transform fashion drawings into photorealistic images. A comparative analysis highlights the observed differences in colours and shapes between the original fashion drawings and the AI-generated models. The effectiveness of these AI systems was validated through a survey and principal component analysis. The results obtained have practical implications in areas such as fashion design, custom clothing production, sustainable fashion, marketing and the training of professionals in this field.

The textile printing and dyeing industry, with huge chemical demand, has a negative impact on the ecosystem. Chemical footprint quantifies the toxic impacts of chemical pollutants by assessing their behaviour in the environment. In this paper, four methods were used to calculate and evaluate the chemical footprint of a polyester dress printing and dyeing process. The chemical footprint of the printing and dyeing process of a polyester dress, calculated with USEtox, Assessment of Mean Impact, Score System, and Strategy Tool, was 1585.51 PAF×m3 ×day, 14089.04 l, 331, and 75, respectively. Scouring, colouring, pretreatment, and printing were identified as the major procedures contributing, with the antifoaming agents and the chelating disperse agents as the major auxiliaries contributing. The results of the Strategy Tool are limited in their representativeness of environmental load. Compared to other methods, AMI ensures that the evaluation results are scientific while maintaining user-friendliness.

Rapport formats, a fundamental element of textile design, significantly influence the aesthetic appeal of patterned fabrics. Despite their importance in shaping visual perception, limited research has systematically investigated the impact of different rapport formats on user preferences. This study addresses this gap by exploring the aesthetic preferences for various rapport formats applied to floral and geometric patterns in home textiles, employing a Kansei Engineering approach to understand and quantify user perceptions. The research investigates the impact of five commonly used rapport formats -straight, half-drop, diagonal half-drop, mirror, and turned- on both floral and geometric patterns. A survey of 115 participants, comprising textile industry professionals and design academics, was conducted to evaluate the designs. Participants rated the patterns on a semantic differential scale, assessing their emotional and aesthetic responses. Descriptive statistics and exploratory factor analysis were employed to analyse the collected data, revealing patterns and relationships between rapport formats and perceived aesthetic qualities. The findings indicate that straight and mirror rapports consistently emerged as the most preferred formats across both floral and geometric designs. This preference stemmed from their visual balance, simplicity, and modern appeal, suggesting a desire for order and clarity in textile patterns. In contrast, more complex rapports, such as turned and diagonal half-drop, while perceived as visually intriguing, lacked the same level of order and clarity favoured by participants. These findings provide textile designers with evidence-based guidance for selecting rapport formats that enhance the aesthetic appeal and user acceptance of their designs, ultimately contributing to more user-centred and appealing textile products.