To prepare wearable personal thermal management fabric, conductive yarn was synthesised by in-situ polymerisation of polyaniline (PANI) and electroless silver plated nanoparticles (AgNPs) using acrylic (PAN) yarn as substrate. Subsequently, AgNPs/PANI/PAN conductive yarns of different structures are woven. SEM, XRD and FT-IR characterised the structure and properties of AgNPs/PANI/PAN yarns. The resistance and temperature of fabric at different voltages were measured by four probe testers and a thermal infrared imager. The results show that when ${\rm AgNO}_3$ concentration is 10 g/L, the resistance of AgNPs/PANI/PAN conductive yarn is 0.81 Ω/cm. At the same warp and weft density, the resistance of the satin fabric is 0.272 Ω/sq, and the resistance of the plain fabric is 0.404 Ω/sq. When the voltage is 0.8 V, the equilibrium temperature of the satin fabric reaches $77.6 ^◦C,$ and that of the plain fabric reaches $63.4^◦C.$ With the increase of applied voltage, the heat loss of satin fabric decreases during the heating process.

Fabric flammability is influenced by factors such as the composition of the fibre material, the manufacturing process and additional chemical treatments that are applied. These variables make it difficult to predict the nature and intensity of a clothing fire and the risk of injury associated with flame exposure. The severity of an injury will depend on the duration of the exposure and the temperature of the flames. This investigation aimed to identify the flaming characteristics of multi-layered fabric samples, which have not been reported extensively in the literature. Ninety fabric samples were used in this study, including cotton, wool, polyester, nylon and silk. The tests were conducted inside a controlled laboratory environment. The results revealed that fabric flame temperatures and burn times for wool, polyester, nylon and silk decreased significantly with adding one and two fabric layers. This appears to represent a “protective factor” against burn injury. However, the Cotton samples exhibited the opposite. The maximum flame temperature and combustion times were increased when additional layers were applied. Therefore, it is necessary to perform flammability tests that include multiple layers of fabric material when assessing the potential for fire injury in clothing.

As a visual symbol of information, brand identity influences consumers’ perception of brand image and their purchase intention. However, few literatures have examined the influence of brand identity color on consumers’ purchase intention. The present study takes the green brand logo of clothing as a stimulus material to explore the green symbol of its color and the influence of consumers’ green consumption intention. The results show that (1) the color of clothing brand logo has a significant impact on consumers’ green consumption intention. Consumers have the strongest purchase intention aroused by green clothing brand logos, and the weakest purchase intention aroused by red and purple clothing brand logos. (2) Consumers have the strongest purchase intention provoked by dark-colored clothing brand logos, and the weakest purchase intention provoked by light-colored clothing brand logos; (3) Green purchase intention is highly correlated with the symbol of clothing brand logo color.

Supportive waist protection is the most used among various functional waist protection products. It achieves its supportive effect primarily through elastic-banded supportive strips, with their shape, number, and material influencing the level of support. This study assesses the subjective and objective pressure comfort of waist protection with different support strip designs, utilizing a combination of subjective pressure numerical representation and the Novel-appliance pressure testing system. The findings indicate that pressure from supportive waist protection is primarily concentrated in the posterior and lateral lumbar regions, with the lateral region experiencing greater pressure than the anterior and posterior centre regions. Moreover, pressure along the abdominal circumference line generally exceeds that along the waist circumference line. The comfortable pressure value in the posterior waist region exceeds that in the lateral and frontal regions. Finally, varying shapes, numbers, and materials of support strips result in different pressure intensities in the posterior waist region, with 4 strips of bionic material offering the best waist comfort and 4 strips of steel plate providing the least comfort.

Analysing human body shapes requires a substantial amount of anthropometric data. However, traditional manual measurement methods are often inefficient, while 3D scanning devices are expensive and inconvenient. To address these challenges, this study presents a method based on the U2-Net neural network model for extracting human body contours in complex backgrounds, feature point extraction, and circumference fitting analysis. Using data enhancement techniques, we utilized a dataset comprising 2 560 frontal and lateral images of individuals against various backgrounds, which was augmented to 42 800 images. Subsequently, a deep learning network model was trained to accurately fit chest, waist, and hip circumference measurements. Finally, 20 samples were selected for validation, and the predicted values were compared with manually measured values, showing that the errors fall within an acceptable range. The effectiveness and accuracy of this method have been validated, providing a practical solution for anthropometric data collection and body shape research in remote areas.