Thermal and moisture properties of the textile materials worn in close contact with the skin greatly contribute to the comfort of the workwear and of the personal protective clothing (PPC) assemblies they are part of. This study examines in depth the thermoregulatory properties of eighteen knitted fabrics used in polo shirts and T-shirts, which function as thermal underwear, standard workwear compliant with various regulations, or as base layers in PPC systems. Most of the fabrics specifically engineered for heat protection demonstrated superior air permeability (ranging from 700 to 1200 mm/s) and efficient moisture management (OMMC 0.5–0.7). Their drying time varied between 12 and 18 min, comparable to most commodity fibre blend fabrics investigated. Generally, the heat-protective fabrics were heavier and exhibited greater thermal and vapour resistance. However, despite minor variations in predicted thermal comfort, seventeen of the fabrics were classified in the same cluster. These findings offer valuable insights into the thermal and moisture management properties of knitted fabrics with various levels of protection, and the correlations found between their thermoregulatory and physical properties, such as mass and thickness, provide guidance for the development of innovative knitted materials for workwear that enhance wearer comfort.

In this study the influence of fabric weave (plain, twill, and panama) and weft type (flax and hemp yarns) on selected mechanical and comfort properties of six fabrics was analyzed. The results showed that tear and abrasion properties were most affected by the weave. The tensile properties of the linen fabrics were not significantly different when weft hemp yarns were used instead of flax. Fabrics with the same weave seemed to be equally resilient to abrasion regardless of the type of weft. By contrast, the hemp weft yarns favorized the physical and comfort properties of the investigated fabrics. For the same weave, the hemp–linen fabrics were slightly lighter and exhibited lower bulk density, significantly larger air permeability, and improved moisture management properties. Although the results of maximum thermal flux (Qmax) suggested a cooler sensation of the linen fabrics with panama and twill, the hemp–linen fabric with a plain weave seemed to be the optimal choice when a cool sensation was desired. Higher thermal conductivity values also suggested slightly better heat transfer properties of the hemp–linen fabrics, and these were significantly influenced by the weave type. The results clearly indicated the advantages of using hemp for improving physical and specific comfort properties of linen fabrics.

In this study two elastic polyester fabrics differentiated by a graphene-printed pattern, called honeycomb (HC) and spider web (SW), were analyzed with a focus on their thermal, mechanical, moisture management and sensorial properties, aiming to identify the fabric with the most elevated heat dissipation and comfort for sportswear. The shape of the graphene-printed circuit did not lead to significant difference between the mechanical properties of the fabrics SW and HC assessed by the Fabric Touch Tester (FTT). Fabric SW outperformed fabric HC with respect of drying time, air permeability, moisture, and liquid management properties. On the other hand, both the Infrared (IR) thermography and FTT-predicted warmness clearly showed that fabric HC dissipates heat faster on its surface along the graphene circuit. This fabric was also predicted by the FTT as smoother and softer than fabric SW and had a better overall fabric hand. The results revealed that both graphene patterns resulted in comfortable fabrics with great potential applications in sportswear fields, in specific use scenario’s.

A library of knowledge that contains a fabric database, 3D human body models database, and 3D garment database has been developed in the framework of the Erasmus+ project DigitalFashion – “Collaborative Online International Learning in Digital Fashion”, which is a collaboration of 6 European partners who would like to strengthen their working together through collaboration in online international teaching. The fabric database contains 49 physical textile fabrics, both woven and knitted fabrics. Each partner provided at least eight fabrics. The library contains also 3D human body models of different sizes (male, and female), and 48 pattern designs for women’s and men’s garments. The fabric’s physical and visual parameters have been determined using known textile standards, including their draping images taken using a Cusick Drape Tester. These fabric parameters have been used to predict the equivalent digital twin fabric based on Lectra digital fabrics, thereby completing the fabric database with their matching digital twin fabric. The digital twin fabrics have been used to simulate the 3D garments for the 3D garment database, based on the selected patterns and styles. To test the robustness of the fabric digitization process, a woman A-line skirt (real) was made by each partner using their fabric. These skirts were then compared with their simulated (virtual) skirts. The investigation has shown that the digital twin fabrics represent the real fabrics well. The virtual training library will support the specially developed training platform for virtual fashion design.

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient $\eta$ using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast $\eta$ values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter $\eta$, achieving an $R^2$ of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast $\eta$ with an $R^2$ of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.

Industrial hemp (Cannabis sativa L.) is gaining renewed interest as a sustainable source of natural fibres, particularly in regions like Belgium, where well-established flax processing infrastructure exists. However, region-specific data on varietal performance for long fibre production remain limited, hindering large scale adoption by both farmers and processors. This study aimed to assess the agronomic performance of early- and late-flowering hemp varieties under temperate maritime conditions through a three-year field trial at two sites in Flanders (Belgium). The evaluation focused on key parameters including emergence rate, plant morphology, long fibre yield and quality. Results showed that successful crop establishment is critical, as poor emergence influenced stem diameter (increasing with decreasing emergence) and leads to reduced long fibre yields by up to 50% between tested sites. Significant yield differences between trial years were present at both sites, with the biomass yield ranging from 10.7 to 14.5 and from 7.8 to 9.6 t ha⁻¹ for Bottelare and Beitem, respectively. Under favourable conditions, long fibre yields reached up to 2.4 t ha⁻¹ for late-flowering and 2.1 t ha⁻¹ for early- and mid-late-flowering varieties. In Western Europe, early to mid-late flowering varieties are generally favoured. Among these, the mid-late flowering variety Bialobrzeskie demonstrated strong yield potential. Overall, fibre quality across varieties was comparable to that of flax.