Search Results (481)

Infant clothing represents a critical yet overlooked exposure pathway for heavy metals, with significant implications for child health and sustainable consumption. This study investigates cadmium (Cd) and chromium (Cr) contamination in 33 textile samples, integrating in vitro bioaccessibility assays, cytotoxicity analysis, and risk assessment models to evaluate dermal exposure risks. Results reveal that 80% of samples exceeded OEKO-TEX Class I limits for As (mean 1.01 mg/kg), Cd (max 0.25 mg/kg), and Cr (max 4.32 mg/kg), with infant clothing showing unacceptable hazard indices (HI = 1.13) due to Cd (HQ = 1.12). Artificial sweat extraction demonstrated high bioaccessibility for Cr (37.8%) and Ni (28.5%), while keratinocyte exposure triggered oxidative stress (131% ROS increase) and dose-dependent cytotoxicity (22–59% viability reduction). Dark-colored synthetic fabrics exhibited elevated metal loads, linking industrial dye practices to health hazards. These findings underscore systemic gaps in textile safety regulations, particularly for low- and middle-income countries reliant on cost-effective apparel. We propose three policy levers: (1) tightening infant textile standards for Cd/Cr, (2) incentivizing non-toxic dye technologies, and (3) harmonizing global labeling requirements. By bridging toxicological evidence with circular economy principles, this work advances strategies to mitigate heavy metal exposure while supporting Sustainable Development Goals (SDGs) 3 (health), 12 (responsible consumption), and 12.4 (chemical safety). Full article

This study investigates the impact of teacher decisions and other contextual factors on indoor air quality (IAQ) in mechanically ventilated elementary school classrooms using low-cost air quality sensors. Four classrooms at a K–8 school in San Jose, California, were monitored for airborne particulate matter (PM), carbon dioxide (CO₂), temperature, and humidity over seven weeks. Each classroom was equipped with an HVAC system and a portable air cleaner (PAC), with teachers having full autonomy over PAC usage and ventilation practices. Results revealed that teacher behaviors, such as the frequency of door/window opening and PAC operation, significantly influenced both PM and CO₂ levels. Classrooms with more active ventilation had lower CO₂ but occasionally higher PM_2.5 due to outdoor air exchange, while classrooms with minimal ventilation showed the opposite pattern. An analysis of PAC filter material and PM morphology indicated distinct differences between indoor and outdoor particle sources, with indoor air showing higher fiber content from clothing and carpets. This study highlights the critical role of teacher behavior in shaping IAQ, even in mechanically ventilated environments, and underscores the potential of low-cost sensors to support informed decision-making for healthier classroom environments. Full article

This paper presents research in the field of computer-aided 3D clothing design, focusing on an investigation of three methods for determining the mechanical properties of woven fabrics and their impact on 3D clothing simulations in the context of sustainable apparel development. Five mechanical parameters were analyzed: tensile elongation in the warp and weft directions, shear stiffness, bending stiffness, specific weight, and fabric thickness. These parameters were integrated into the CLO3D CAD software v.2025.0.408, using data obtained via the KES-FB system, the Fabric Kit protocol, and the AI-based tool, SEDDI Textura 2024. Simulations of women’s blouse and trousers were evaluated using dynamic tests and validated by real prototypes measured with the ARAMIS optical 3D system. Results show average differences between digital and real prototype deformation data up to 6% with an 8% standard deviation, confirming the high accuracy of 3D simulations based on the determined mechanical parameters of the real fabric sample. Notably, the AI-based method demonstrated excellent simulation results compared with real garments, highlighting its potential for accessible, sustainable, and scalable fabric digitization. Presented research is entirely in line with the current trends of digitization and sustainability in the textile industry. It contributes to the advancement of efficient digital prototyping workflows and emphasizes the importance of reliable mechanical characterization for predictive garment modeling. Full article

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article

Ensuring proper sizing and fit for U.S. female firefighters’ personal protective clothing and equipment (PPE) is a crucial challenge for researchers and manufacturers. The National Fire Protection Association (NFPA) establishes design and performance standards in the U.S., with NFPA 1977 specifying sizing guidelines for wildland firefighting gear. However, the absence of an anthropometric database representing female firefighters limits the effectiveness of these standards. This research evaluates the effectiveness of NFPA 1977 sizing system by investigating whether correlated body measurements maintain internal consistency and provide data-driven recommendations for improvement. Anthropometric data from 187 U.S. female firefighters were analyzed to assess the 2016 and 2022 NFPA 1977 upper and lower torso sizing systems. Correlation analysis was performed between body measurements and corresponding sizes. Contingency tables presented proportion of participants accommodated. Results indicated significant correlations between chest and wrist measurements and sizes in the upper torso, though these were the only available measurements. In the lower torso, hip size strongly correlated with thigh and knee sizes. However, the system inadequately accommodates female firefighters with larger waist and hip measurements. Furthermore, rise sizes demonstrated inconsistent, weak relationships with hip circumference. Overall, the NFPA 1977 sizing requires revision to better serve U.S. female firefighters. Full article

Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios. Full article

Background/Objectives: Canine intervertebral disc herniation (IVDH) is an important musculoskeletal pathology. Unlike in humans, IVDH mechanisms in dogs are underinvestigated from a system-level integrative omics point of view. The aim of this study was to identify key serum molecular players in canine IVDH. Methods: An integrative multi-omics approach combining high-resolution LC-MS-based untargeted metabolomics and tandem mass tag (TMT)-based proteomics was applied. Additionally, serum zinc concentration was determined by spectrophotometry. Results: Nineteen serum metabolites were differentially abundant in IVDH dogs. Metabolite analysis highlighted dysregulation in lipoic acid and branched-chain amino acid (BCAA) metabolism, with elevated levels of valine, leucine, and isoleucine in IVDH. These findings suggest disrupted energy, nitrogen, and neurotransmitter metabolism, potentially contributing to the IVDH pathophysiology. Additionally, lower serum uridine, possibly influenced by BCAA accumulation, was observed, indicating altered neuroinflammatory responses. ELISA validation confirmed elevated serum levels of zinc-α2-glycoprotein (ZAG), alpha-1-microglobulin/bikunin precursor (AMBP), and vitronectin (VTN) in IVDH, supporting immune modulation and neuroprotective mechanisms. Serum prekallikrein (KLKB1) and Protein C inhibitor (SERPINA5), involved in fibrin cloth formation, were found to be lowered in IVDH patients. Pathway enrichment revealed disturbances in aromatic amino acid biosynthesis, with elevated phenylalanine, tyrosine, and tryptophan influencing neurotransmission and inflammation. In addition, elevated serum Zn concentration emphasized its antioxidant importance in immune response, wound healing, and neuropathic pain signaling. Conclusions: Integration with our prior CSF multi-omics data reinforced the relevance of identified molecules in IVDH-associated neurodegeneration, inflammation, and repair processes. This study offers insight into potential diagnostic biomarkers and therapeutic targets for canine IVDH through serum-based molecular profiling. Full article

This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. The proposed system achieves real-time performance by implementing efficient collision detection and response handling with complex environmental meshes (RoomMesh) and dynamic hand meshes (HandMesh), as well as capsule colliders based on hand skeleton tracking (OVRSkeleton). Performance evaluations were conducted for both single-sided and double-sided cloth configurations across multiple resolutions. At a 32 × 32 resolution, both configurations maintained stable frame rates of approximately 72 FPS. At a 64 × 64 resolution, the single-sided cloth achieved around 65 FPS, while the double-sided configuration recorded approximately 40 FPS, demonstrating scalable quality adaptation depending on application requirements. Functionally, the GPU-PBD system significantly surpasses Unity’s built-in Cloth component by supporting double-sided cloth rendering, fine-grained constraint control, complex mesh-based collision handling, and real-time interaction with both hand meshes and capsule colliders. These capabilities enable immersive and physically plausible XR experiences, including natural cloth draping, grasping, and deformation behaviors during user interactions. The technical advantages of the proposed system suggest strong applicability in various XR fields, such as virtual clothing fitting, medical training simulations, educational content, and interactive art installations. Future work will focus on extending the framework to general deformable body simulation, incorporating advanced material modeling, self-collision response, and dynamic cutting simulation, thereby enhancing both realism and scalability in XR environments. Full article

Advancements in thermoregulating textiles have been propelled by innovations in nanotechnology, composite materials, and smart fiber engineering. This article reviews recent scholarly papers on experimental passive and active thermoregulating textiles to present the latest advancements in these fabrics, their mechanisms of thermoregulation, and their feasibility for use. The review underscores that phase-change materials enhanced with graphene, boron nitride, and carbon nanofibers offer superior thermal conductivity, phase stability, and flexibility, making them ideal for wearable applications. Shape-stabilized phase-change materials and aerogel-infused fibers have shown promising results in outdoor, industrial, and emergency settings due to their durability and high insulation efficiency. Radiative cooling textiles, engineered with hierarchical nanostructures and Janus wettability, demonstrate passive temperature regulation through selective solar reflection and infrared emission, achieving substantial cooling effects without external energy input. Thermo-responsive, shape-memory materials, and moisture-sensitive polymers enable dynamic insulation and actuation. Liquid-cooling garments and thermoelectric hybrids deliver precise temperature control but face challenges in portability and power consumption. While thermoregulating textiles show promise, the main challenges include achieving scalable manufacturing, ensuring material flexibility, and integrating multiple functions without sacrificing comfort. Future research should focus on hybrid systems combining passive and active mechanisms, user-centric wearability studies, and cost-effective fabrication methods. These innovations hold significant potential for applications in extreme environments, athletic wear, military uniforms, and smart clothing, contributing to energy efficiency, health, and comfort in a warming climate. Full article

In this work, the effect of the palladium precursor on the Oxygen Reduction Reaction (ORR) performance of lignin-based electrospun carbon fibers was studied. The fibers were spun from a lignin-ethanol solution free of any binder, where different Pd salts were added at two concentration levels. The system implemented to perform the spinning was a coaxial setup in which the internal flow contains the precursor dispersion with the metallic precursor, and ethanol was used as external flow to help fiber formation and prevent drying before generating the Taylor cone. The obtained cloths were thermostabilized in air at 200 °C and carbonized in nitrogen at 900 °C. The resulting carbon fibers were characterized by physicochemical and electrochemical techniques. The palladium precursor significantly affects nanoparticle distribution and size, fiber diameter, pore distribution, surface area and electrochemical behavior. The fibers prepared with palladium acetylacetonate at high Pd loading and carbonized at 900 °C under a CO₂ atmosphere showed high mechanical stability and the best ORR activity, showing near total selectivity towards the 4-electron path. These features are comparable to those of the commercial Pt/C catalyst but much lower metal loading (10.6 wt.% vs. 20 wt.%). The most promising fibers have been evaluated as cathodes in a zinc–air battery, delivering astonishing stability results that surpassed the performance of commercial Pt/C materials in both charging and discharging processes. Full article

When developing fabrics for applications in which evaporative cooling and drying play an important role, e.g., sports or occupational applications, the drying performance of fabrics is commonly determined using fast and easy-to-perform benchmark methods. The measurement conditions in these methods, however, differ significantly from the drying conditions on the human body surface, where drying is obstructed on one side of the fabric through contact with the skin and at the same time enhanced due to contact with the heated surface (skin). The aims of this study were to understand and quantify the fabric drying process at the skin interface considering these real-use effects based on tests applying two-sided drying, one-sided drying, one-sided drying on a heated surface, and one-sided drying on a heated surface in the stretched state, and to relate these to existing standard methods. The findings showed that contact with a solid heated surface such as the skin and the stretched state of the fabric both make a significant contribution (p < 0.05) to the drying rate compared to two-sided drying in standard climatic conditions. The corresponding drying rates observed for a range of typical fabrics used in leisure and sports as a first layer next to the skin were found to be 1.6 (±0.2), 1.1 (±0.2), 7.9 (±2.1), and 10.6 (±0.8) g/m² min for two-sided drying, one-sided drying, one-sided drying on a heated surface, and one-sided drying on a heated surface in the stretched state, respectively. These findings are of great importance for human thermal modelling, including clothing models, where the drying process significantly contributes to the heat and mass transfer in the skin–clothing–environment system. Full article

Human gait identification is a biometric technique that permits recognizing an individual from a long distance focusing on numerous features such as movement, time, and clothing. This approach in particular is highly useful in video surveillance scenarios, where biometric systems allow people to be easily recognized without intruding on their privacy. In the domain of computer vision, one of the essential and most difficult tasks is tracking a person across multiple camera views, specifically, recognizing the similar person in diverse scenes. However, the accuracy of the gait identification system is significantly affected by covariate factors, such as different view angles, clothing, walking speeds, occlusion, and low-lighting conditions. Previous studies have often overlooked the influence of these factors, leaving a gap in the comprehensive understanding of gait recognition systems. This paper provides a comprehensive review of the most effective gait recognition methods, assessing their performance across various image source databases while highlighting the limitations of existing datasets. Additionally, it explores the influence of key covariate factors, such as viewing angle, clothing, and environmental conditions, on model performance. The paper also compares traditional gait recognition methods with advanced deep learning techniques, offering theoretical insights into the impact of covariates and addressing real-world application challenges. The contrasts and discussions presented provide valuable insights for developing a robust and improved gait-based identification framework for future advancements. Full article

This paper examines the philosophy of “Dao follows nature” (道法自然) and investigates how Daoist clothing transforms abstract cosmological concepts into a “wearable interface for spiritual practice” through the use of materials, colors, and patterns. By integrating symbol system analysis, material culture theory, and the philosophy of body practice, this study uncovers three layers of symbolic mechanisms inherent in Daoist attire. First, the materials embody the tension between “nature and humanity”, with the intentional imperfections in craftsmanship serving as a critique of technological alienation. Second, the color coding disrupts the static structure of the Five Elements system by dynamically shifting between sacred and taboo properties during rituals while simultaneously reconstructing symbolic meanings through negotiation with secular power. Third, the patterns (such as star constellations and Bagua) employ directional arrangements to transform the human body into a miniature cosmos, with dynamic designs offering a visual path for spiritual practice. This paper introduces the concept of a “dynamic practice interface”, emphasizing that the meaning of Daoist clothing is generated through the interaction of historical power, individual experience, and cosmological imagination. This research fills a critical gap in the symbolic system of Daoist art and provides a new paradigm for sustainable design and body aesthetics, framed from the perspective of “reaching the Dao through objects”. Full article

Artificial intelligence (AI) is revolutionizing the fashion, textile, and clothing industries by enabling automated assessment of garment quality, condition, and recyclability, addressing key challenges in sustainability. This systematic review explores the applications of AI in evaluating clothing quality and condition within the framework of a circular economy, with a focus on supporting second-hand clothing resale, charitable donations by NGOs, and sustainable recycling practices. A total of 135 research resources were identified through searching academic databases including Google Scholar, Springer, ScienceDirect, IEEE, Taylor and Francis, and Sage journals. These publications were subsequently refined down to 49 based on selected inclusion criteria. The selection of these sources from diverse databases was undertaken to mitigate any potential bias in the selection process. By analyzing the effectiveness and challenges of related peer-reviewed articles, conference papers, and technical reports, this study highlights state-of-the-art methodologies such as convolutional neural networks (CNNs), hybrid models, and other machine vision systems. A critical aspect of this review is the examination and analysis of datasets used for model development, categorized and detailed in a comprehensive table to guide future research. Whilst the findings emphasize the potential of AI to enhance quality assurance in second-hand clothing markets, streamline textile sorting for donations and recycling, and reduce waste in the fashion industry, they also highlight gaps in the available datasets, often due to limited size and scope. The types of textiles captured were most commonly swatches of fabric, with 20 studies examining these, whereas whole garments were less frequently studied, with only 7 instances. This review concludes with insights into future research directions and the promising use of AI within fashion and textiles to facilitate a transition to a circular economy. This project was supported through RMIT University’s School of Fashion and Textiles internal seed funding (2024). Full article

A significant proportion of waste generated by the fashion industry is either landfilled or incinerated, primarily due to the high cost and complexity of collecting and separating mixed textile materials. While research in textile recycling often emphasizes post-consumer waste, less attention is given to pre-consumer waste, particularly cutting surpluses generated during apparel manufacturing—a labour-intensive sector with low automation and operational inefficiencies. This study addresses this gap by presenting a case study on the implementation of an automated system for collecting, transporting, sorting, and storing textile surpluses in an apparel manufacturing environment. The research aims to identify the barriers, benefits, and sustainability impact of such automation. Using both qualitative and quantitative data, the system is evaluated through key performance indicators including time reduction, ergonomic improvement, and process reliability. Results suggest that automation enhances intralogistics, reduces non-value-added labour, and enables better utilization of human resources. This case study offers a practical framework for apparel manufacturers to assess the potential of automating textile-waste handling, helping to justify such investments based on labour use, process variability, and environmental benefits. The study contributes to the broader discourse on sustainable manufacturing and supports the apparel industry’s shift toward digital transformation and circular economy practices. Full article