Search Results (4,796)

The development of high-performance flame-retardant (FR) polypropylene (PP) with high mechanical integrity remains a challenge. Herein, we demonstrate a synergistic flame retardancy system for PP achieved via partial substitution of piperazine pyrophosphate (PAPP) with 1 wt.% magnesium borate whiskers (MBW) for improved flame retardancy, and thermal and mechanical properties. The optimized PP/24PAPP/1MBW exhibits exceptional FR performance, driven by the formation of a highly ordered, continuous phosphorus–boron hybrid char in the condensed phase. Cone calorimetry test results reveal an 80% reduction in peak heat release rate, a 54% reduction in total heat release, and a 33% reduction in total smoke production compared to neat PP, while the UL-94 test confirms a V-0 rating with complete suppression of flaming drips. Morphological study of the char residue using Raman spectroscopy and SEM attributes this performance to enhanced char graphitization and structural coherence enabled by boron-mediated cross-linking. More importantly, this transformative flame retardancy performance is achieved without severe compromise to mechanical properties, retaining over 89% of the original tensile strength. This work confirms the PAPP/MBW system as a highly efficient, low-additive approach to creating advanced fire-safe polymer composites for engineering applications. Full article

Personalized federated learning (PFL) faces significant challenges in resource-constrained edge environments, where strict communication budgets and severe system heterogeneity must be jointly addressed. Although one-shot federated learning reduces communication overhead, existing methods typically impose unified model architectures or rely on coarse manual selection strategies, limiting their adaptability to highly heterogeneous data distributions and restricting personalized representation capability. To overcome these limitations, we propose Personalized Federated Zero-shot Knowledge Distillation (pFedZKD), a data-free one-shot federated learning framework designed for structurally heterogeneous scenarios. The framework follows a decouple-and-reconstruct collaborative paradigm. On the client side (decoupling stage), we introduce Particle Swarm Optimization-based Federated Neural Architecture Search (PSO-FedNAS), a gradient-free neural architecture search method that enables each client to autonomously discover a customized convolutional architecture aligned with its local data distribution, eliminating the need for architectural consistency across clients. On the server side (reconstruction stage), to address parameter-space incompatibility caused by structural heterogeneity, we develop an architecture-agnostic multi-teacher zero-shot knowledge distillation mechanism (Multi-ZSKD). This method synthesizes pseudo-samples in latent space to extract semantic consensus from heterogeneous client models and transfers the aggregated knowledge to a unified global student model without accessing real data. The entire collaborative process is completed within a single communication round, substantially reducing communication cost while enhancing privacy preservation. Extensive experiments on MNIST, FashionMNIST, SVHN, and CIFAR-10 under heterogeneous data settings demonstrate that pFedZKD consistently achieves superior personalization accuracy, global generalization performance, and communication efficiency compared with state-of-the-art PFL methods. Full article

A sense of school belonging predicts NEET outcomes for adolescents. However, young people from marginalized groups often have a lower sense of school belonging than their majority peers. Emerging understandings of belonging as a complex, agentic process shaped by multiple relational, contextual, cultural and structural factors have posed problems for real-world applications of belonging. NEET young people tend to be viewed through a lens of risk factors, with a lack of research accounting for their experiences and feelings. While recent research recognizes the intersectional effects of disadvantage, or ‘compound disadvantage’, on NEET outcomes for young people from certain social groups, there is a lack of viable alternatives for educators and policymakers to account for these differential experiences of belonging in order to be able to respond to them. Connected Belonging is a relational and identity-building approach to enhancing young people’s wellbeing through supporting their connectedness and sense of self across the eight social domains of their lives. This paper outlines the development and validation of a young people’s survey, which enables education professionals to attend to and respond to the differing belonging experiences of diverse groups, operationalizing an intersectional lens on school belonging. After introducing the views of young people about systemic priorities to better support their engagement in education, training or work (EET), gathered through a youth voice event as part of a parallel research project, the paper outlines the process of developing, piloting and validating the tool. We argue that this survey tool has the potential to support improved attention to the views and experiences of diverse young people in a systematic, regular fashion. Furthermore, it offers potential for the evaluation of supportive actions grounded in youth voice. Full article

This study examines how Saudi consumers evaluate the commodification of cultural symbols in fashion amid intensified heritage branding and symbolic market expansion. It addresses a gap in the literature on internal cultural commodification, where tensions surrounding authenticity, legitimacy, and commercialization emerge within the same cultural community rather than across clearly separate cultural groups. Drawing on a culturally grounded application of the Theory of Planned Behavior and related literature on consumer ethnocentrism and moral evaluation, the study investigates how perceived authenticity, perceived cultural appropriation, ethical sense, and consumer ethnocentrism shape attitudes toward cultural commodification and purchase intention in the Saudi fashion context. Data were collected through an Arabic-language questionnaire-based survey of Saudi consumers (N = 552) using a non-probability purposive sampling approach. The measurement model employed reflective scales adapted from prior literature and was assessed for reliability and validity. To strengthen methodological rigor, the analysis also considered common method bias diagnostics. The proposed relationships were tested using partial least squares structural equation modeling (PLS-SEM) with bootstrapping. The findings indicate that perceived authenticity is positively associated with attitudes toward cultural commodification and relates to purchase intention primarily through attitudes. Perceived cultural appropriation is negatively associated with both attitudes and purchase intention, suggesting both a direct deterrent effect and an indirect pathway via attitudes. Consumer ethnocentrism shows a negative association with purchase intention and a weaker negative association with attitudes, while its moderating role appears statistically significant but limited in magnitude. Ethical sense displays a more complex pattern, combining negative indirect effects through evaluative pathways with a positive direct association with intention, consistent with qualified rather than purely restrictive participation in symbolic consumption. The study contributes to the literature by clarifying how consumer responses to heritage-based fashion commercialization are shaped by representational, ethical, and normative evaluations in a non-Western setting. Practically, it suggests that fashion brands operating in Saudi heritage markets should manage authenticity claims, symbolic legitimacy, and appropriation risk with greater cultural and ethical sensitivity. Full article

As digital transformation accelerates, artificial intelligence (AI) has become a central driver of social media ecosystems. Drawing on Uses and Gratifications Theory (UGT) and Consumer–Brand Relationship Theory, this study employs a quantitative research design based on questionnaire data and Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine how AI-empowered social media functionalities influence the sustainability of consumer–brand relationships among Generation Z. The analysis incorporates key constructs, including AI-empowered functionalities, user engagement behaviors (information seeking, social interaction, and content co-creation), flow experience, user satisfaction, brand loyalty, and brand value co-creation. The results indicate that AI-empowered social media applications significantly enhance user engagement behaviors. However, user engagement does not directly affect consumer–brand relationship outcomes; instead, its influence operates indirectly through flow experience and user satisfaction. Notably, flow experience emerges as a critical mediating mechanism linking AI-empowered user engagement to both brand loyalty and brand value co-creation. This research provides empirical evidence for the development of sustainable consumer–brand relationships in AI-empowered social media environments and offers practical insights for fashion brands to optimize their social media strategies when targeting Generation Z users. Full article

Activewear has become a common component of women’s everyday clothing, yet emerging evidence suggests that exposure to activewear imagery may adversely affect body image. This study aimed to describe women’s activewear engagement across contexts and to investigate how different markers of engagement correspond with positive outcomes such as fitness behavior, body appreciation, and self-esteem, as well as negative outcomes including media pressure, idealized appearance aspirations, and self-objectification. We collected survey data from student (N = 455) and community (N = 37) samples to assess activewear-related behaviors, including online browsing, social media following, purchasing, and wearing, as well as measures of fitness behaviors, body appreciation, self-esteem, idealized body aspirations, appearance comparisons, perceived media pressure, and self-objectification. Across samples, 40–87% of women engaged with activewear in some form, and 30% reported feeling self-conscious at least half the time they wore it; notably, activewear was worn for exercise less than 50% of the time. Activewear engagement showed positive correlations with fitness behaviors but also with idealized body aspirations, appearance comparisons, media pressure, and self-objectification, while showing no associations with body appreciation or self-esteem. These findings highlight the growing cultural prominence of activewear and suggest that engagement with this clothing trend is linked to both adaptive and risk-related psychological factors, underscoring the need for further research into its broader psychological implications. Full article

This study investigates the fabrication of microcapsules using Nigella sativa (N.S.) oil as the core and alginate as the shell material. The N.S. oil microcapsules were prepared using the sol–gel method with different oil concentrations. The microcapsules were applied to the cotton fabric by the pad–dry–cure method, and their attachment was evidenced by scanning electron microscopy (SEM). Air permeability measurements were conducted for all developed samples, revealing that the sample with 8 g loading of N.S. oil and 4.5 g alginate exhibited a 43% reduction compared to the pristine sample. To further investigate the comfort characteristics of the samples, the functionalized cotton samples were subjected to the water vapor permeability index test. The results yielded an index value of 90, indicating that the encapsulation process preserved the comfort characteristics of the samples. Among the samples, the specimen with an oil concentration of 8 mL displayed the maximum antibacterial performance, achieving a 90% reduction in colony-forming units (CFUs) following quantitative testing protocol. However, the qualitative antibacterial assessment indicates no clear zone of inhibition, but no bacterial growth was observed on the samples. Furthermore, the fabric incorporating the maximum loadings of N.S. oil and alginate capsules exhibited the maximum antioxidant activity of 86.5%. These results underscore the critical role of N.S. oil microcapsules in enhancing the antibacterial and antioxidant properties of cotton fabric, while also revealing a harmony between functional performance and comfort characteristics. Full article

Despite the success of artificial neural networks in solving numerous tasks, they face significant challenges, including difficulties in online adaptation and rapidly increasing energy consumption. As a biologically plausible alternative, spiking neural networks offer promising capabilities for efficient cognitive computing. Recently, a three-element spiking neuron model consisting of a threshold selector, a tunnel diode, and a capacitor was proposed. In this work, we experimentally validate this model using a threshold selector hardware emulator and demonstrate its dynamical equivalence to the biologically plausible Izhikevich neuron model. To evaluate the novel neuron’s applicability for cognitive computing, we implement a liquid state machine (LSM) reservoir architecture with spatially dependent random topology for synaptic weight distribution. Our simulations on the MNIST and Fashion-MNIST benchmarks demonstrate competitive classification accuracy (97.9% and 89.5%, respectively) while offering estimated energy efficiency and processing speed enhancements compared to existing FPGA-based and memristor-based spiking reservoir implementations. The developed reservoir is feasible for processing neuromorphic sensors output, including visual perception tasks. Full article

This study compared the wound-healing response and osteogenic gene expression profile of osteoblasts exposed to pediatric-equivalent concentrations of dexmedetomidine (DXMT) and propofol (POF). Human osteoblast-like SAOS-2 cells were assigned to control, low- and high-dose DXMT and POF groups based on pharmacokinetically derived free-drug levels. Scratch-wound closure was quantified over 24 h, and expression of osteogenesis- and cytoskeleton-related genes (RANKL, RUNX2, SP7, BMP2, VIM, VCL, OCN, ALP) was measured by SYBR Green quantitative Polymerase Chain Reaction (qPCR). Normality was assessed using the Shapiro–Wilk test, and group differences were analyzed with two-way ANOVA followed by Tukey’s multiple comparisons test (p < 0.05). All groups demonstrated complete scratch closure by 24 h, with no differences at 6 h. At 18 h, POF did not differ from the control, whereas DXMT significantly accelerated closure at both doses in a dose-dependent fashion. High-dose DXMT significantly increased VIM (3.95 ± 3.12, p = 0.0144) and BMP2 (2.28 ± 0.70, p = 0.0002) expression, while RUNX2, SP7, and RANKL remained comparable to controls. ALP (1.68 ± 0.40, p = 0.0005) and OCN (3.31 ± 0.35, p = 0.0108) were significantly elevated only in the high-dose DXMT group, whereas POF showed no significant effects. At clinically relevant concentrations, DXMT was associated with enhanced scratch closure and increased expression of selected osteogenesis- and cytoskeleton-related genes in SAOS-2 cells, whereas POF showed limited effects under the tested conditions. These findings suggest that DXMT may influence early in vitro cellular responses relevant to bone healing and should be further validated in functional differentiation models and in vivo studies. Full article

The rising computational demands of deep learning models have intensified concerns regarding their energy consumption and environmental impact, motivating the development of Green Artificial Intelligence (Green AI) approaches. This paper proposes a multi-objective Green AI optimization framework based on the Grey Wolf Optimizer (GWO) to design efficient multilayer perceptron (MLP) architectures. Unlike conventional strategies that focus solely on maximizing accuracy, the proposed method jointly optimizes validation accuracy, training time, number of trainable parameters, and estimated floating-point operations (FLOPs). Evaluated on the Fashion-MNIST dataset and compared against a baseline MLP and Random Search, the GWO-based approach achieves competitive predictive performance while drastically reducing model size, computational complexity, and training time. Pareto front analysis confirms that GWO consistently identifies non-dominated architectures that offer superior trade-offs between accuracy and efficiency. Additional equal-accuracy evaluations demonstrate improved convergence efficiency and stability despite reduced model complexity. The results provide empirical evidence, within the MLP design setting considered in this study, that bio-inspired multi-objective optimization can support Green AI by identifying more compact and efficient architectures with competitive predictive performance. Full article

This study investigates the influence of conventional textile pretreatment and periodate oxidation parameters on the structural modifications and functional properties of woven cotton fabrics. Unlike most studies focused on cellulose pulps or isolated textile fibers, the present work examines how the initial structural state of the textile substrate, determined by its pretreatment history, governs the oxidation pathways. Cotton fabrics were subjected to alkaline scouring (SC), hydrogen peroxide bleaching (BC), and combined scouring–bleaching (SBC), followed by sodium periodate oxidation under controlled conditions. Carbonyl species were quantified analytically and identified by ATR-FTIR spectroscopy, while structural changes were evaluated by X-ray diffraction (XRD). Mechanical properties were assessed using the normalized parameters (Fa/Fa₀ and E/E₀), hydrophilicity by water absorption capacity (WAC), and optical stability by the yellowness index (YI). The results demonstrated that the pretreatments influence the oxidant accessibility and the balance between carbonyl speciation. XRD analysis shows a moderate decrease in crystallinity, indicating partial preservation of the crystalline domains, whereas mechanical properties decrease significantly (35–65%), concomitant with a 25–45% reduction in WAC. These results suggest that the impairment in mechanical and hydrophilic properties is primarily governed by localized C2–C3 bond scission, secondary oxidative reactions, and supramolecular rearrangements, rather than by bulk crystalline loss. The oxidized SC series exhibits higher YI values associated with an increased free aldehyde content, while the BC and SBC fabrics show improved optical stability. Overall, these results demonstrate that pretreatment history governs periodate oxidation pathways and establishes clear structure–property relationship relevant for the controlled functionalization of woven cotton fabrics. Full article

Deploying artificial intelligence models on low-cost edge devices requires balancing predictive accuracy with strict constraints on computational resources, such as inference latency and memory footprint. Despite growing interest in TinyML systems, limited empirical evidence exists on how these factors interact across different embedded hardware platforms. This study presents a systematic multi-objective evaluation of three lightweight AI architectures—multinomial logistic regression (MLR), multilayer perceptron (MLP), and a reduced convolutional neural network (CNN)—implemented natively on three representative platforms: ESP32-S3, Raspberry Pi Pico, and Raspberry Pi Zero W. The models were evaluated on three image classification datasets of increasing complexity (Synthetic Geometric Figures, MNIST, and Fashion-MNIST), measuring classification accuracy, inference latency, and peak memory footprint under real execution conditions. Pareto-front analysis was used to identify efficient model–platform configurations and characterize the trade-offs between predictive performance and computational resources. The results provide quantitative insight into accuracy–resource trade-offs and establish a reproducible framework for evaluating lightweight AI models on resource-constrained edge devices, supporting informed design decisions in TinyML applications. Full article

The Stueckelberg–Horwitz–Piron (SHP) formalism describes particles and fields traced out as spacetime events functionally dependent on an external evolution parameter $\tau$. This approach addresses a number of difficulties associated with the problem of time. In SHP general relativity, the state of the unconstrained phase space variables $\{x^{\mu }\left(\tau \right),p_{\nu }\left(\tau \right)\}$ specifies a 4D block spacetime $\mathcal{M}\left(\tau \right)$ that evolves to an infinitesimally close 4D block spacetime $\mathcal{M}(\tau +\delta \tau )$ under a scalar Hamiltonian. As the configuration of matter and energy evolves with $\tau$ it induces changes in the spacetime metric ${\gamma }_{\mu \nu }(x,\tau )$, leading to $\tau$-dependent geodesic equations for the phase space variables. The 4+1 approach in gravitation generalizes the 3+1 formalism of Arnowitt, Deser, and Misner (ADM) to construct $\tau$-dependent Einstein field equations, a canonical Hamiltonian formalism, and an initial value problem for ${\gamma }_{\mu \nu }(x,\tau )$. To conform to known gravitational phenomenology, we must respect the 5D symmetries associated with the free fields—the geometrical constructs relevant to $\mathcal{M}\left(\tau \right)$ as an embedded hypersurface—and the O(3,1) symmetries of 4D matter. The 4+1 formalism has been discussed in a series of publications. The goal of this paper is to provide a systematic review of the subject, make a few corrections and some significant additions, and present the theory in a concise and orderly fashion. Full article

Articular fractures require precise anatomical reduction and rigid fixation to heal appropriately. In veterinary cases that involve fracturing of the lateral humeral condyle, cortical bone screws inserted in lag fashion with Kirschner wire are the preferred method for surgical fixation. However, relatively high complication rates associated with cortical lag screws (CLSs) highlights the need to investigate alternate screw designs. Variable-pitch headless compression screws (VPHCSs) are unique as they advance beneath the cortical surface. Although the use of VPHCSs are widely utilised in human orthopaedics, the current use in veterinary orthopaedics is limited. This study aimed to evaluate the peak interfragmentary force (PIF) and area of compression (AOC) generated by a 3.5 mm self-tapping cortical screw placed in lag fashion and a 3.5 mm VPHCS inserted to four depths. PIF and AOC were measured using a pressure-sensitive film placed between two blocks of polyurethane foam (0.24 g/cm³), simulating a transverse fracture. CLSs were inserted by hand into predrilled 2.5 mm pilot holes. PIF and AOC were measured at full insertion. VPHCSs were placed into predrilled 2.5 mm pilot holes, followed by a 3.5 mm tapered countersink. The screw was inserted until the head was level with the surface. PIF and AOC were measured between the two blocks. The screw was continued until the head was at a depth of 2, 5, and 9 mm below the surface, and the PIF and AOC were measured again at each stage. There was no detectable difference in PIF and AOC between CLSs and VPHCSs countersunk to −2 mm (PIF–CLS: Mean = 12.886, SD = 2.370; 2 mm: Mean = 17.301, SD = 8.858, p = 0.319; AOC–CLS: Mean = 0.936, SD = 0.291; 2 mm: Mean = 0.925, SD = 0.447, p = 0.872). VPHCSs countersunk to −5 mm and −9 mm produced significantly greater PIF compared to CLSs (5 mm: Mean = 16.086, SD = 6.799, p = 0.002; 9 mm: Mean = 34.987, SD = 4.015, p < 0.001). VPHCSs countersunk to −5 and −9 mm produced significantly greater PIF and AOC compared to CLSs in this model. Further investigation is required to produce recommendations for clinical use. Full article

Virtual try-on is a key enabling technology for online fashion retail and digital garment visualization. It aims to realistically render a target garment on a person while preserving geometric alignment and fine texture details. Appearance flow-based approaches provide explicit deformation modeling but often suffer from texture squeezing and boundary artifacts in challenging scenarios, such as long sleeves and tucked-in garments, especially under high-resolution settings. In this work, we propose StageAttn-VTON (Stage-wise Attentive Virtual Try-On), an appearance flow-based framework that improves structural coherence and visual fidelity through stage-wise deformation modeling. Specifically, garment warping is decomposed into three stages—coarse alignment, local refinement, and non-target region removal—which mitigates the coupling between competing objectives, such as smooth texture preservation and accurate structural alignment. Furthermore, we introduce a self-attention module in the image synthesis stage to enhance global dependency modeling and capture long-range garment–body interactions. Experiments on VITON-HD and the upper-body subset of DressCode demonstrate that StageAttn-VTON achieves consistently strong performance against representative warping-based and diffusion-based baselines. In addition, qualitative comparisons show that the proposed method better alleviates deformation artifacts in challenging regions such as sleeves and waist areas. Full article