Search Results (225)

The textile and clothing industry exerts a significant environmental impact in the EU, contributing heavily to water, land, and resource depletion, with waste generation expected to rise sharply due to fast fashion trends. Accelerating circularity and closed-loop production is critical to reduce the sector’s ecological footprint. This study investigates newer approaches for the removal of indigo prints from cotton (CO) and polyester (PES) textiles using aqueous-based solutions and/or ozone treatment. Aqueous alkaline solutions containing reducing agents and surfactants were evaluated, as well as dry and wet ozone treatments. The efficacy of colour removal was assessed via spectrophotometric analysis [colour strength (K/S) and colour difference (ΔE)] and the fabrics were tested for dimensional stability and tensile strength before and after treatment. Results reveal that surfactant-assisted aqueous treatments enable effective pigment removal and maintain textile properties, supporting subsequent reprinting for textile upcycling. Wet ozone treatment also promoted substantial decolourisation, particularly in cellulosic substrates. Although PES samples exhibited better mechanical resistance, they revealed limited pigment extraction upon ozone treatment. These findings demonstrate the potential of chemical treatments using aqueous-based solutions and surfactants for circular textile applications, facilitating pigment removal without compromising substrate integrity, and boosting the upcycling. Full article

In the essay we introduce present-day systems concepts, such as resilience, tipping points, and hysteresis effects, via the concept of fast–slow dynamical systems (whether explicit in the models or implicit through bifurcation and stability behaviours). These lead naturally to ideas first propagated within catastrophe theory, fifty years ago. We discuss the historical catastrophe (the backlash) that befell such an abstract yet mathematically grounded (and thus inescapable) theory within economics and also its subsequent re-appraisal and re-adoption. Finally, we discuss some of the challenges inherent in anticipating tipping points from live systems data (observations), within systems-theoretic interpretations, and whether methods from topological data analysis might respond to them. While it is fashionable for national, governmental and policy institutions to speak of “resilience” in all manner of national systems contexts, we aver that it is foolishly inadequate to do so without an understanding and consideration of tipping points and hysteresis (sometimes termed “path dependence”), giving rise to “lock-in”. Full article

The fashion industry’s business is becoming increasingly complicated and active. This industry is expected to be highly competitive, particularly in the retail sector. Therefore, this research aims to examine the impact of supply chain information sharing and quality management on firm performance, with supply chain agility as a mediating variable, in the Asian fashion industry. A total of 169 participants from the fashion sector in a developing country were surveyed. The proposed hypotheses were examined using a quantitative approach, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to assess and validate the measurement model. The results indicate that supply chain information sharing and quality management have a significant impact on a firm’s performance. Similarly, the sharing of supply chain information and quality management has a significant impact on firm performance by mediating supply chain agility. The study offers actionable insights for managers in volatile fashion supply chains. Firms can enhance performance by sharing real-time demand and inventory information, strengthening key quality practices, and adopting flexible, data-driven production processes. Integrating information sharing, quality management, and agility enables faster responses to shifting consumer trends, thereby improving overall competitiveness in fast-fashion environments. This study offers valuable guidance for supply chain professionals seeking to enhance practices within their networks. The results underscore the strategic importance of information sharing and quality management in promoting agility, an essential capability for achieving a competitive advantage. Additionally, the insights generated are relevant to practitioners, policymakers, and industry leaders aiming to strengthen supply chain responsiveness and resilience. Full article

The fashion industry is one of the most resource-intensive sectors, generating major environmental impacts such as greenhouse gas emissions, excessive water and land use, and pollution from waste and microplastics. Fast fashion intensifies these issues through overproduction and overconsumption. However, growing consumer awareness and regulatory pressure are pushing brands to adopt Circular Economy (CE) and sustainability strategies, including resale platforms, recycling programs, and sustainability frameworks. Despite these efforts, their real effectiveness remains uncertain. This study investigates which CE and sustainability strategies are most common among fast fashion companies and how they can mitigate key environmental impacts. Using a Fuzzy Cognitive Maps (FCM) model, the research quantitatively evaluates the effects of various circular and sustainable strategies across the supply chain. Ten key strategies were identified, revealing that isolated actions are often ineffective. Instead, an integrated, systemic approach combining multiple initiatives is essential to achieve meaningful sustainability improvements. Full article

Climate change education requires innovative, action-oriented methodologies to foster student engagement and reflection on sustainable behaviors. This study explores an integrated educational program implemented within the Pathways for Transversal Skills and Orientation (PCTO) framework in three Italian upper secondary schools. The program combined immersive virtual reality experiences, GIS-based image analysis, traditional instruction, and two behavior-oriented web applications. A total of 181 students completed a post-activity questionnaire assessing satisfaction, perceived learning, prior knowledge, and self-reported intentions toward behavioral change. Results show that technology-enhanced and interactive modules were associated with higher levels of perceived engagement and perceived learning (with over 80% of students reporting at least moderate learning in immersive, GIS-based, and carbon footprint activities) compared to theory-only sessions. Modules explicitly linked to everyday behaviors, such as carbon footprint estimation and fast fashion consumption, were more frequently associated with self-reported intentions to adopt more sustainable practices (approximately 70% of positive responses). Given the post-only, perception-based design, findings should be interpreted as exploratory evidence of students’ perceived educational value rather than objective measures of learning outcomes. Overall, the study highlights the potential of experiential and digitally enhanced approaches in climate change education, while underscoring the need for future research incorporating objective and longitudinal assessments. Full article

The clothing and textile industry is under increasing pressure to comply with European sustainability directives, including the European Strategy for Sustainable and Circular Textiles, the Circular Economy Action Plan, and the revised Waste Framework Directive, effective October 2025. While global interest in sustainable textile practices grows, limited research has examined clothing consumption and disposal behaviors in Greece, particularly through the lens of practice theory. This study addresses that gap by exploring the dynamics of these practices using a structured questionnaire distributed online via Google Forms in 2024 with 250 valid responses. Chi-square (χ²) tests and regressions analyses were used to assess associations among certain categorical variables. Our findings reveal that older consumers tend to spend more on clothing and show a preference for fast fashion. Frequent shoppers also lean toward fast fashion, yet they demonstrate greater concern for material composition. Higher sustainability awareness is associated with a preference for purchasing fewer garments or opting for higher-quality items. Notably, discomfort with recycled materials predicts reluctance toward industrial recycling and reinforces the tendency to choose durable clothing that lasts longer. Full article

In recent decades, the global fashion and textile market has been facing an unprecedented sector-wide crisis. The growing demand for clothing, combined with continuously decreasing prices and driven by the constant availability of new quantities and styles, has allowed fast fashion and super-fast fashion business models to flood the market with low-quality, short-lived, and high environmental impact products. Starting from 1 January 2025, the separate collection of textile waste came into force in the European Union. However, under current conditions, this regulatory change has generated an imbalance between collection capacity and the availability of effective sorting and recycling channels. Furthermore, due to the low market demand for recycled fibers, warehouses and landfills are increasingly filling with post-consumer textile waste, materials that could potentially serve as secondary raw materials but currently remain unsold. Moreover, the fast fashion business model promotes the use of short fibers and complex fiber blends that are resource-intensive and generate large volumes of low-quality waste. This material profile further limits reuse and recycling options, exacerbating inefficiencies within existing waste management systems. This review aims to identify and discuss available opportunities to address textile waste containing low-quality fibers through the examination of scientific literature, technical publications, and market-ready products that utilize regenerated textile materials. The results highlight open-loop applications and processes, such as those in the automotive, building, and design sectors, thereby opening to new end-of-life scenarios for waste textiles. Full article

The consumption of clothes creates paradoxes in which values, motives, and emotions interact to generate consumption experiences. To test some of these interactions, we conducted three correlational studies, studies 1, 2, and 3, one experiment, study 4, and one qualitative study, study 5. Study 1 found negative relationships between sustainability values and materialism and positive relationships between sustainable values and the preference for experiential purchases. Study 2 found positive relationships between two components of the slow-fashion movement, equity and exclusiveness, and guilt, and a negative relationship with functionality, another component of slow fashion. Study 3 found an indirect relationship between sustainable values and guilt through their positive and significant relationship with increased awareness of the environmental impact of the fast-fashion industry, supporting a mediation model. Study 4 found that participants were was more likely, regardless of whether the purchase of clothing was labeled as fast fashion or not, to experience pride than guilt when recalling recent past purchases. Last, in study 5, we found that consumers buy clothes to look good and pay attention to quality and value without significant concerns for environmental issues. The implications for consumer behavior were discussed. Full article

Emerging research indicates that neuronal activity is maintained by an architectural system of protons in a multi-scale fashion. Proton architecture is formed when organelles (such as mitochondria, endoplasmic reticulum, lysosomes, synaptic vesicles, etc.) are coupled together to produce dynamic energy domains. Techniques have been developed to visualize protons in neurons; recent advances include near-atomic structural imaging of organelle interfaces using cryo-tomography and nanoscale resolution imaging of organelle interfaces and proton tracking using ultra-fast spectroscopy. Results of these studies indicate that protons in neurons do not diffuse randomly throughout the neuron but instead exist in organized geometric configurations. The cristae of mitochondrial cells create oscillating proton micro-domains that are influenced by the curvature of the cristae, hydrogen bonding between molecules, and localized changes in dielectric properties that result in time-patterned proton signals that can be used to determine the metabolic load of the cell and the redox state of its mitochondria. These proton patterns also communicate to the rest of the cell via hydrated aligned proton-conductive pathways at the mitochon-dria-endoplasmic reticulum junctions, through acidic lipid regions, and through nano-tethered contact sites between mitochondria and other organelles, which are typically spaced approximately 10–25 nm apart. Other proton architectures exist in lysosomes, endosomes, and synaptic vesicles. In each of these organelles, the V-ATPase generates steep concentration gradients across their membranes, controlling the rate of cargo removal from the lumen of the organelle, recycling receptors from the surface of the membrane, and loading neurotransmitters into the vesicles. Recent super-resolution pH mapping has indicated that populations of synaptic vesicles contain significant heterogeneity in the amount of protons they contain, thereby influencing the amount of neurotransmitter released per vesicle, the probability of vesicle release, and the degree of post-synaptic receptor protonation. Additionally, proton gradients in each organelle interact with the cytoskeleton: the protonation status of actin and microtubules influences filament stiffness, protein–protein interactions, and organelle movement, resulting in the formation of localized spatial structures that may possess some type of computational significance. At multiple scales, it appears that neurons integrate the proton micro-domains with mechanical tension fields, dielectric nanodomains, and phase-state transitions to form distributed computing elements whose behavior is determined by the integration of energy flow, organelle geometry, and the organization of soft materials. Alterations to the proton landscape in neurons (e.g., due to alterations in cristae structure, drift in luminal pH, disruption in the hydration-structure of the cell, or imbalance in the protonation of cytoskeletal components) could disrupt the intracellular signaling network well before the onset of measurable electrical or biochemical pathologies. This article will summarize evidence indicating that proton–organelle interaction provides a previously unknown source of energetic substrate for neural computation. Using an integrated approach combining nanoscale proton energy, organelle interface geometry, cytoskeletal mechanics, and AI-based multiscale models, this article outlines current principles and unresolved questions related to the subject area as well as possible new approaches to early detection and precise intervention of pathological conditions related to altered intracellular energy flow. Full article

Children’s skin is highly sensitive and prone to irritation, allergies, and infections, requiring special consideration in textile selection. Although clothing serves as a protective barrier, it can also pose a risk when dyed with toxic chemical colourants. This study explores the potential of multifunctional natural dyes as safer alternatives for children’s clothing, particularly for those with dermatological conditions. Cotton knitted fabrics were dyed through exhaustion with extracts of madder root (Rubia tinctorum L.), pomegranate peel (Ppe, Punica granatum L.), oxidised logwood (Logox, Haematoxylum campechianum L.), and tannin from quebracho (Schinopsis lorentzii Griseb.), both individually and in various combinations with or without potassium aluminium sulphate dodecahydrate (alum). The combination of madder and Ppe demonstrated the most promising multifunctional performance, being classified as a weak disinfectant against S. aureus (3.7 log reduction) and showing the highest antioxidant activity (92.6 ± 2.56% 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical reduction), while maintaining excellent results after washing. Moreover, these natural formulations expanded the achievable colour palette from each dye while maintaining moderate wash fastness. The results highlight the relevance of these findings to textile and fashion designers, offering sustainable tools for creating health-conscious, visually appealing garments. This research reinforces the potential of natural dyes and biomordants in developing functional textiles that support children’s wellbeing and environmental responsibility. Full article

This study investigates how Australian Zennials (born 1993–1999) navigate slow fashion consumption in a market dominated by fast fashion and affordability challenges. Using semi-structured interviews with 20 participants, it explores their motivations, barriers, and adaptive strategies. Findings reveal that Zennials are driven by ethical values, environmental awareness, and a preference for quality design, yet face constraints such as cost, limited access to sustainable brands, and skepticism toward greenwashing. Rather than a simple value–action gap, participants demonstrate creative solutions, most notably, strategic engagement with the second-hand market. This enables them to practice slow fashion ideals of durability, longevity, and mindful consumption in a cost-effective way. The study reframes the attitude–behavior gap by identifying Perceived Behavioral Control (PBC) as a key enabler, supported by knowledge, repair skills, and peer norms. These insights offer practical implications for brands, designers, and policymakers, positioning the second-hand economy as the central mechanism that operationalizes Zennial engagement with sustainable fashion. Full article

The phenomenon of fast fashion has resulted in high yarn consumption and growing textile waste from both manufacturing and consumers. Rising environmental awareness and evolving legislation, including landfill restrictions, have prompted the search for sustainable recycling methods to manage textile end-of-life. This study investigates the mechanical recycling of polyamide 6.6 (PA66) yarn using a chain extender (Joncryl) and antioxidant (Irganox). Thermogravimetric analysis (TGA) confirmed that thermal stability in recycled PA66 was maintained compared to the original yarn, and the presence of Joncryl further enhanced this stability. Oxidative-onset temperature (OOT), measured by differential scanning calorimetry (DSC), supported these improvements. Gas chromatography–mass spectrometry (GC/MS) identified key degradation products, which were correlated with changes in the polymer matrix. Mechanical testing showed a 31% decrease in Young’s modulus after initial recycling, which was reversed with further processing. This behavior suggests the formation of shortened semi-crystalline chains and new linkages promoted by Joncryl. Viscosity and limiting viscosity number increased by up to 50%, depending on both additive concentrations. Overall, Joncryl and Irganox enhanced viscosity, mechanical strength, and notably thermal stability, confirming their suitability for recyclable textile-grade PA66 yarns. Full article

With the increasing sophistication of Artificial Intelligence (AI), traditional digital steganography methods face a growing risk of being detected and compromised. Adversarial attacks, in particular, pose a significant threat to the security and robustness of hidden information. To address these challenges, this paper proposes a novel AI-based steganography framework designed to enhance the security of concealed messages within digital images. Our approach introduces a multi-stage embedding process that utilizes a sequence of encoder models, including a base encoder, a residual encoder, and a dense encoder, to create a more complex and secure hiding environment. To further improve robustness, we integrate Wavelet Transforms with various deep learning architectures, namely Convolutional Neural Networks (CNNs), Bayesian Neural Networks (BNNs), and Graph Convolutional Networks (GCNs). We conducted a comprehensive set of experiments on the FashionMNIST and MNIST datasets to evaluate our framework’s performance against several adversarial attacks. The results demonstrate that our multi-stage approach significantly enhances resilience. Notably, while CNN architectures provide the highest baseline accuracy, BNNs exhibit superior intrinsic robustness against gradient-based attacks. For instance, under the Fast Gradient Sign Method (FGSM) attack on the MNIST dataset, our BNN-based models maintained an accuracy of over 98%, whereas the performance of comparable CNN models dropped sharply to between 10% and 18%. This research provides a robust and effective method for developing next-generation secure steganography systems. Full article

The continuous rise in textile waste, driven by global population growth and the proliferation of fast fashion, has raised concerns about its efficient recycling and sustainable management. This study aims to assess the feasibility of recycling textile waste by incorporating recycled cotton fibres as reinforcement in polypropylene-based composites. Specifically, it examines the mechanical, thermal, and chemical properties of composites composed of 50% recycled polypropylene and 50% reinforcing fibres (either virgin or recycled cotton), with and without the addition of 5% maleic anhydride-grafted polypropylene as a compatibilizer to enhance fibre-matrix adhesion. Although the use of recycled cotton as reinforcement reduced the mechanical properties of the composite material, the addition of 5% compatibilizer improved these properties to levels comparable to those of composite reinforced with virgin cotton. Full article

Predictive Coding (PC) frameworks claim to model recognition via prediction–error loops, but they often lack explicit biological implementation of fast familiar recognition and impose latency that limits real-time robotic control. We begin with Experience-Based Pattern Matching (EBPM), a biologically grounded mechanism inspired by neural engram reactivation, enabling near-instantaneous recognition of familiar stimuli without iterative inference. Building upon this, we propose Dynamic Integrative Matching and Encoding (DIME), a hybrid system that relies on EBPM under familiar and low-uncertainty conditions and dynamically engages PC when confronted with novelty or high uncertainty. We evaluate EBPM, PC, and DIME across multiple image datasets (MNIST, Fashion-MNIST, CIFAR-10) and on a robotic obstacle-course simulation. Results from multi-seed experiments with ablation and complexity analyses show that EBPM achieves minimal latency (e.g., ~0.03 ms/ex in MNIST, ~0.026 ms/step in robotics) but poor performance in novel or noisy cases; PC exhibits robustness at a high cost; DIME delivers strong trade-offs—boosted accuracy in familiar clean situations (+4–5% over EBPM on CIFAR-10), while cutting PC invocations by ~50% relative to pure PC. Our contributions: (i) formalizing EBPM as a neurocomputational algorithm built from biologically plausible principles, (ii) developing DIME as a dynamic EBPM–PC integrator, (iii) providing ablation and complexity analyses illuminating component roles, and (iv) offering empirical validation in both perceptual and embodied robotic scenarios—paving the way for low-latency recognition systems. Full article