Search Results (567)

The fashion industry is facing increasing challenges related to textile waste and environmental pollution, driving the need for sustainable material innovations. Bacterial cellulose (BC), a biodegradable and non-polluting biomaterial, has emerged as a promising alternative for the sustainable transformation of fashion materials. Investigations into printing and dyeing techniques are expected to provide methodological frameworks for the design and functional application of BC materials, promoting their adoption and development in the fashion sector. This study, using the kombucha culture method, systematically investigated the cultivation, purification, plasticization, and drying processes of BC as a fashion material, examined its color characteristics using plant and reactive dyeing, and evaluated the effects of pattern printing and the feasibility of traditional plant pigment stencil printing, digital printing, and cyanotype printing on BC. Based on these printing and dyeing methods, digital printing combined with reactive dyeing—offering richer print effects, a wider color gamut, and higher rubbing fastness—was selected to realize the fashion design series Photosynthesis using BC as the primary material. This research contributes methodological insights into the integration of bio-based materials in fashion design and promotes the advancement of sustainable practices within the textile and apparel industries. Full article

Modern culture, strongly influenced by the growth of sectors such as the fashion and textile industries, has generated an environmental trend that is difficult to reverse. It is estimated that between 60 and 70% of the dyes used in these sectors are synthetic, which offer great versatility, a low cost, and a broad spectrum of colors, making them indispensable in many sectors. Among these synthetic dyes, azo dyes stand out due to their excellent chromophoric properties, structural stability, and ease of synthesis. However, these compounds are considered xenobiotics with a strong recalcitrant potential. This review article comprehensively examines the biodegradation potential of azo contaminants by microorganisms, including bacteria, fungi, microalgae, and consortia, using the PRISMA 2020 methodology. In this regard, this study identified 720 peer-reviewed articles on this topic that are outstanding. The analysis of these studies focused on the effect of parameters such as pH, temperature, and exposure time, as well as the enzymatic degradation pathways associated with the degradation efficiency of these contaminants. For example, the results identified that microorganisms such as Meyerozyma guilliermondii, Trametes versicolor, Pichia kudriavzevi, Chlorella vulgaris, and Candida tropicalis possess significant potential for degrading azo dyes (up to 90%). This degradative efficiency was attributed to the high enzymatic activity that cleaves the azo bonds of these contaminants through specialized enzymes, such as azoreductases, laccases, and peroxidases. Furthermore, the results highlight synergistic effects or metabolic cooperation between species that enhance the biodegradation of these contaminants, suggesting an eco-friendly alternative for environmental remediation. Full article

This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A late fusion approach was applied using four state-of-the-art convolutional neural networks (CNNs): InceptionV3, ResNet50_V2, DenseNet, and VGG-19. Excellent results were obtained, with the ResNet50_V2 achieving a precision of 0.929, recall of 0.914, and F1 score of 0.913. Notably, the integration of multimodal inputs allowed the models to effectively distinguish challenging fabric types, such as cotton–polyester and satin–silk pairs, which exhibit overlapping texture characteristics. This research not only enhances the accuracy of textile classification but also provides a robust methodology for material analysis, with significant implications for industrial applications in fashion, quality control, and robotics. Full article

The purpose of this study is to synthesize existing peer-reviewed literature on fashion renting and provide insights into its role within the broader framework of sustainable consumption and business practices within different cultural contexts, while also guiding future research efforts. This review includes only peer-reviewed journal articles and book chapters in English, with the search conducted up to 31 March 2025. A total of 95 academic papers published between 2010 and 2025 were analyzed to explore the evolving landscape of fashion rental consumption and business practices. NVivo 14 was used for the analysis of the collected literature. The findings revealed six key motivating benefits and six significant barriers that influence consumer participation in fashion renting. Additionally, five success factors and four critical challenges were identified as shaping the development of the fashion rental market. This research represents the first attempt to synthesize literature from both the consumer and business perspectives of fashion renting. The findings provide a comprehensive understanding of market dynamics related to fashion rental consumption and business practices, shedding light on the key factors that support the sustainability of fashion rental businesses as well as the challenges they face. Both theoretical and practical implications are discussed, offering valuable guidance for researchers and fashion industry stakeholders. Full article

The Moroccan garment industry faces challenges in scheduling small order batches, often hindered by traditional product family-based methods that increase downtime by 15–20%. This study proposes a clustering-based scheduling approach, grouping garments by technological times rather than product families to reduce changeovers and downtime by 30–35%. A case study in a Moroccan factory with 50–100-unit batches showed a 20% lead time reduction and a 15% productivity boost. Using methods like K-Means, the approach enhances planning flexibility and resource use. This methodology offers a scalable solution for optimizing production and maintaining competitiveness in fast fashion markets. Full article

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains. Full article

This study addresses the long-standing difficulty of predicting the remaining useful life (RUL) of rolling bearings from highly non-stationary vibration signals by proposing WaveAtten, a symmetry-aware deep learning framework. First, mirror-symmetric and bi-orthogonal Daubechies wavelet filters are applied to decompose each raw signal into multi-scale approximation/detail pairs, explicitly preserving the left–right symmetry that characterizes periodic mechanical responses while isolating asymmetric transient faults. Next, a bidirectional sparse-attention module reinforces this structural symmetry by selecting query–key pairs in a forward/backward balanced fashion, allowing the network to weight homologous spectral patterns and suppress non-symmetric noise. Finally, the symmetry-enhanced features—augmented with temperature and other auxiliary sensor data—are fed into a long short-term memory (LSTM) network that models the symmetric progression of degradation over time. Experiments on the IEEE PHM2012 bearing dataset showed that WaveAtten achieved superior mean squared error, mean absolute error, and R² scores compared with both classical signal-processing pipelines and state-of-the-art deep models, while ablation revealed a 6–8% performance drop when the symmetry-oriented components were removed. By systematically exploiting the intrinsic symmetry of vibration phenomena, WaveAtten offers a robust and efficient route to RUL prediction, paving the way for intelligent, condition-based maintenance of industrial machinery. Full article

This paper examines the strategic decisions of fashion brands to develop and sell non-fungible tokens (NFTs) within the metaverse. We construct two operational models based on whether NFTs are adopted: the traditional fashion model without NFT (Scenario T) and the digital fashion model with NFT (Scenario D). By comparing the equilibrium outcomes of fashion brands in Scenarios T and D, we derive valuable insights into the implementation of digital strategies in the fashion industry. Our analysis reveals three key findings. First and foremost, the proportion of fashion customers to conventional customers, as well as the digital value and cost of NFTs, are direct factors influencing the adoption of digital strategies by fashion brands. Secondly, opportunistic pricing by manufacturers is an indirect factor influencing fashion brands’ strategic choices, and a fixed production price contract can effectively avoid this case. Finally, we find that personalized pricing and a free NFT strategy are effective tools to boost fashion brands’ digital revenues. Full article

Background/Objectives: More than 33 billion chickens are industrially raised for meat and egg production globally and vaccinated against Marek’s disease virus (MDV). The antigenically related herpesvirus of turkey (HVT) is used as a live-attenuated vaccine, commonly provided as a recombinant vector to protect chickens against additional unrelated pathogens. Because HVT replicates in a strictly cell-associated fashion to low levels of infectious units, adherent primary chicken or duck embryo fibroblasts are infected, dislodged from the cultivation surface and distributed as cryocultures in liquid nitrogen to the site of application. Although viable cells are complex products, application of infected cells in ovo confers protection even in presence of maternal antibodies. Methods/Results: The aim of our study was to determine whether a continuous cell line in a scalable cultivation format can be used for production of HVT-based vaccines. The AGE1.CR cell line (from Muscovy duck) was found to be highly permissive in adherent cultures. Propagation in suspension, however, initially gave very low yields. The induction of cell-to-cell contacts in carrier-independent suspensions and a metabolic shock improved titers to levels suitable for vaccine production (>10⁵ infectious units/mL after infection with multiplicity of 0.001). Conclusions: Production of HVT is challenging to scale to large volumes and the reliance on embryonated eggs from biosecure facilities is complex. We demonstrate that a cell-associated HVT vector can be propagated in a carrier-independent suspension culture of AGE1.CR cells in chemically defined medium. The fed-batch production is independent of primary cells and animal-derived material and can be scaled to large volumes. Full article

Elevators are a vital part of urban infrastructure, playing a key role in smart cities where increasing population density has driven the rise in taller buildings. As an essential means of vertical transportation, elevators have become an integral part of daily life, making their design, construction, and maintenance crucial to ensuring safety and compliance with evolving industry standards. The safety of elevator systems depends on the continuous monitoring and fault-free operation of Permanent Magnet Synchronous Motor (PMSM) drives, which are critical to their performance. Furthermore, the fault-free operation of PMSM drives reduces operating costs, increases service life, and improves reliability. The PMSM drive components may be susceptible to electrical, mechanical, and thermal faults that, if undetected, can lead to operational disruptions or safety risks. The integration of artificial intelligence and Internet of Things (IoT) technologies can enhance fault prediction, reducing downtime and improving efficiency. Ongoing challenges such as managing machine thermal load and developing more durable materials for PMSMs require the development of suitable models that are adapted to existing drive systems. The proposed framework for fault prediction is validated on a real residential elevator equipped with a PMSM drive. Multimodal signal data is processed through a Generative Adversarial Network (GAN)-enhanced Positive Unlabeled (PU) classifier and a Reinforcement Learning (RL)-based adaptive decision engine, enabling robust and scalable fault prediction in a non-intrusive fashion. Full article

The fashion industry represents a dynamic expression of cultural diversity and plays a crucial role in national economic health. This research designs strategic management guidance for BANU, a sustainable clothing startup in Senegal aimed at empowering local families to improve their lifestyles. Utilizing an exploratory research strategy, the study develops a comprehensive strategic plan for BANU as a natural textile dyes company, examining factors influencing its development at the macro, micro, and organization layers to identify key strategic issues and strategic options as a comprehensive strategic management plan for BANU to grow. A multifaceted strategic approach is recommended, including tailored operational strategies aligned with local traditions, sustainability, and customer engagement. Collaborations with local businesses, suppliers, and educational institutions are advised to strengthen BANU’s market presence. Additionally, differentiation through unique natural dye clothing and partnerships are encouraged. As BANU evolves, a shift towards corporate strategy, diversification, and international market expansion is suggested to enhance strategic management and ensure sustainable growth. Full article

The fashion industry is widely recognized for its environmental challenges, but the health impacts related to textile toxicity remain significantly underexplored. Beyond the well-known issues of pollution and resource depletion, modern clothing often harbors a hidden threat: hazardous chemicals embedded within fabrics. These include dyes containing heavy metals, antimicrobial agents that foster bacterial resistance, and synthetic fibers that release microplastics. Unlike environmental discussions, the dialogue around the direct and long-term health effects of these substances is still limited. This entry addresses critical yet often-overlooked concerns, such as how chemicals in textiles contribute to chronic skin conditions, hormonal disruptions, and even carcinogenic risks. It also examines the proliferation of bacteria in synthetic garments, leading to dermatological infections and rapid fabric degradation. Furthermore, the globalized nature of production masks the contamination risks transferred from producer to consumer countries. Through an interdisciplinary approach, this entry highlights the urgent need for integrating scientific innovation, stringent regulation, and consumer awareness to mitigate health hazards in fashion. It calls for the adoption of safer textile technologies, sustainable materials, and transparent production practices, paving the way for a fashion future that prioritizes human health as much as environmental sustainability. Full article

The fashion industry is facing increasing pressure to adopt sustainable practices given its significant impact on the environment. This research aims to analyze the implications of marketing strategies in the fashion industry’s transition towards a sustainable and responsible business model. The study starts from the premise that marketing can influence consumer behavior and turn sustainability into a competitive advantage. To investigate this aspect, SmartPLS software was used and hypotheses were tested on the relationship between marketing strategies to educate and sensitize consumers on sustainability issues in the fashion industry and the creation of a sustainably engaged community. Moreover, it becomes essential to collaborate with non-governmental organizations and other brands that share their sustainability values. The research was based on a sample of 227 respondents, and the data were analyzed using structural equation modeling. The results indicate that marketing strategies that promote transparency in supply chain and production processes, enhance brand reputation and credibility and, promote innovation in sustainable materials and production processes through marketing strategies contributing to creating an engaged community, as well as through brand commitment to sustainability through concrete actions and access to new markets and growth opportunities. Marketing strategies to educate and sensitize consumers on sustainability issues in the fashion industry contribute to increasing consumer interest in sustainable products. The implications of the study highlight the need for coherent marketing approaches to support the sustainable transformation of the fashion industry. Full article

This study focuses on the shift to a circular economy (CE) in the textile, apparel, and fashion (TAF) sectors, which generate tons of waste annually. Thus, embracing CE practices is essential for contributing to UN Sustainable Development Goals. This study employs a mixed-methods approach, integrating PRISMA for systematic literature selection, BERTopic modeling and AI-driven SDG mapping, and case study analysis to explore emerging CE themes, implemented circular practices, and systemic barriers. Machine-learning-based SDG mapping reveals strong linkages to SDG 9 and SDG 12, emphasizing technological advancements, industrial collaborations, and circular business models. Moderately explored SDGs, namely, SDG 8, SDG 6, and SDG 7, highlight research on labor conditions, water conservation, and clean energy integration. Reviewing 655 peer-reviewed papers, the BERTopic modeling extracted six key themes, including sustainable recycling, circular business models, and consumer engagement, whereas case studies highlighted regulatory frameworks, stakeholder collaboration, and financial incentives as critical enablers. The findings advance institutional theory by demonstrating how certifications, material standards, and regulations drive CE adoption, reinforcing SDG 12 and SDG 16. The natural resource-based view is extended by showing that technological resources alone are insufficiently aligned with SDG 9. Using the Antecedents–Decisions–Outcomes framework, this study presents a structured, AI-driven roadmap for scaling CE in the TAF industry, addressing systemic barriers, and supporting global sustainability goals, highlighting how multistakeholder collaboration, digital traceability, and inclusive governance can improve the impact of CE. The results recommend that CE strategies should be aligned with net-zero targets, carbon credit systems, and block-chain-based supply chains. Full article

The fashion industry is undergoing significant transformation through blockchain technology, which enhances product traceability, authenticity, and transactional transparency. This study explores blockchain’s potential to revolutionize the fashion supply chain by enabling detailed monitoring from design and manufacturing to certification, quality control, storage, transportation, and delivery. To assess customers’ readiness to adopt these authenticated products, an innovative model for fashion product traceability and authenticity based on blockchain was proposed. Since the adoption of blockchain models relies on widespread user involvement, it is crucial to examine the factors that motivate individuals to take part. To this end, an acceptance study was conducted using the modified UTAUT2 (Unified Theory of Acceptance and Use of Technology) framework, with data analyzed using SMART PLS software. The results indicate that the proposed blockchain model can improve transparency, authenticity, and customer trust in fashion products. Furthermore, the findings identify expected effort, perceived efficiency, and social influence as key factors influencing blockchain adoption in the fashion industry. These insights show the importance of targeted education and customer engagement strategies for successful implementations of blockchain technology in the fashion industry. Full article