Search Results (26,085)

Sustainability assessment of textile materials has traditionally relied on origin-based classifications and indicator-driven life cycle assessment (LCA), often treating sustainability as an inherent or material-intrinsic property. However, materials sharing similar biological origins or “bio-based” labels frequently exhibit substantially different sustainability outcomes when processing pathways, composite structures, and end-of-life (EoL) compatibility are taken into account. To address this limitation, this study develops a qualitative, multidimensional analytical framework that conceptualizes textile material sustainability as a pathway-dependent and system-mediated outcome rather than an inherent material attribute. The framework integrates four interrelated dimensions—renewability, process sustainability, EoL options, and material source—derived from a structured review of academic, policy, and technical literature. To demonstrate the analytical scope and internal logic of the framework, a selected set of 65 innovative textile materials was systematically analyzed using a three-tier qualitative coding scheme (favorable, conditional, and unfavorable) under conservative data validation criteria. The analysis shows that sustainability performance is primarily shaped by pathway configurations—particularly processing intensity, binder chemistry, and EoL compatibility—rather than material origin alone and that similar bio-based materials can exhibit fundamentally different sustainability profiles depending on these factors. By reframing sustainability from a material-centered perspective to a pathway-oriented and system-based perspective, the proposed framework provides a structured basis for integrating material innovation, process design, and end-of-life planning in sustainability-oriented textile research and development and establishes a conceptual foundation for future empirical and quantitative extensions. Full article

Background/Objectives: Cancer-related terminology is not merely descriptive and plays a critical role in shaping emotional responses, personal identity, and communication across clinical, social, and public spheres. Despite growing interest in the psychosocial dimensions of illness language, few studies have centered the lived experiences of individuals navigating cancer through the lens of terminology. This study explores how people living with and beyond cancer perceive, interpret, and emotionally respond to cancer-related language, focusing on the way terminology influences identity, stigma, and communicative interaction. Methods: A sequential mixed-methods design was employed. The quantitative phase involved 146 participants with a cancer diagnosis completing a structured questionnaire on preferred terminology and emotional impact. The qualitative phase followed, using open-ended questionnaires with 11 participants to deepen understanding of linguistic experiences. Thematic content analysis was used to identify patterns across narratives. Results: These findings reveal that labels such as “cancer patient” evoke strong negative emotional reactions, associated with stigma, fear, and identity reduction. Person-first and context-sensitive language was perceived as more respectful and empowering. Emotional responses to language varied widely, from fear to neutrality, shaped by speaker role, context, and time since diagnosis. Media representations were often seen as dramatizing or moralizing, reinforcing the need for communicative clarity, empathy, and education in both clinical and public discourse. Conclusions: Cancer-related language is a powerful psychosocial force. It shapes how individuals are seen and see themselves and can either reinforce stigma or foster dignity and resilience. This study highlights the urgent need for person-centered, context-aware communication practices across healthcare, media, and society. Full article

Antimicrobial resistance in microorganisms associated with fermented foods is increasingly recognized, yet rapid methods to characterize antibiotic response dynamics remain limited. This study evaluates antibiotic susceptibility and physiological response patterns of kombucha-associated acetic acid bacteria and motile Escherichia coli using optical nanomotion detection (ONMD), a label-free technique that quantifies single-cell mechanical activity. Two cellulose-producing species (Komagataeibacter xylinus and K. rhaeticus), one non-cellulose-producing species (K. melaceti), and E. coli were exposed to ampicillin, ciprofloxacin, and chloramphenicol. Minimum inhibitory concentrations (MICs) were determined prior to time-resolved ONMD analysis. Susceptible strains exhibited progressive suppression of confined nanomotion consistent with MIC-defined susceptibility, whereas resistant profiles maintained sustained mechanical activity. Chloramphenicol initially induced persistent or increased nanomotion at 120 min; however, extending the observation to 180 min revealed delayed suppression in susceptible strains, demonstrating that bacteriostatic antibiotics require longer observation windows for accurate ONMD classification. In motile E. coli, ONMD revealed both intracellular nanomotion puncta and swimming trajectories, which were progressively attenuated following antibiotic exposure. These findings demonstrate that ONMD complements conventional susceptibility testing by resolving time-dependent suppression of both translational motility and intracellular nanomechanical activity at the single-cell level. Full article

Inverse synthetic aperture radar (ISAR) image classification plays a crucial role in remote sensing, traffic monitoring, and maritime surveillance. However, existing methods often suffer from limited labeled data, degraded image quality, and the insufficient adaptability of conventional loss functions. To address these issues, this paper proposes AdaProtoNet, a few-shot ISAR image classification framework based on a ResNet10 backbone and a combined adaptive and cross-entropy loss function. The model adopts a Prototypical Network architecture that balances feature extraction and class discrimination. A customized multicategory ISAR dataset is constructed through 3D target modeling and simulated radar imaging to support few-shot learning. Within the meta-learning paradigm, AdaProtoNet generates class prototypes by averaging support features and performs classification via Euclidean distance measurement. Experimental results demonstrate that AdaProtoNet achieves higher overall accuracy (OA) and stronger generalization than conventional ISAR classification methods. These findings highlight the effectiveness of adaptive-margin optimization in few-shot learning and provide guidance for the development of next-generation remote sensing recognition systems. Full article

Knowledge distillation (KD) transfers knowledge from large language models (LLMs) to smaller or similarly sized models in order to obtain efficient yet capable systems. However, performing distillation over all tokens is computationally expensive and may weaken the transfer signal. To address this limitation, Knowledge-Filtered Distillation (KFD) is introduced as a selective distillation approach in which tokens are filtered according to the divergence $KL({\mathcal{M}}_2\mid {\mathcal{M}}_0)$ between a teacher model (${\mathcal{M}}_2$) and a base model (${\mathcal{M}}_0$), while the student model (${\mathcal{M}}_1$) is also derived from the same base model. Only tokens whose divergence exceeds a predefined threshold are distilled. For the selected tokens, the teacher distribution is normalized over the Top-5 predictions, whereas tokens outside this case receive a label-ranking bonus. The proposed conditional Top-5/bonus target design is shown theoretically to yield a lower label-focused target error than using only Top-5 normalization or only the bonus across all tokens. In addition, the KL and cross-entropy (CE) losses are balanced through a dynamically computed batch-level coefficient $\alpha$. Experiments on multiple Turkish text datasets show that KFD consistently outperforms CE-only training, achieving higher accuracy with less data and shorter training time. KFD also outperforms entropy-based token selection methods and highlights the role of student initialization in effective knowledge transfer, thereby providing an efficient and scalable distillation framework for teacher–student models of equal size. Full article

Background/Objective: Immunoglobulin G (IgG) N-glycosylation is an important regulator of immune function and systemic inflammation and has been associated with cardiometabolic diseases. However, little is known about how IgG glycosylation changes during the course of acute coronary syndrome (ACS) and whether these alterations relate to lipid-lowering response after the initiation of statin therapy. The primary aim of this study was to investigate IgG N-glycosylation following ACS and evaluate its association with response to atorvastatin therapy defined as baseline LDL cholesterol reduction of ≥50%. Methods: In this prospective cohort study, 79 statin-naïve patients hospitalized for the first episode of ACS and treated with atorvastatin 80 mg daily after percutaneous coronary intervention were followed longitudinally. Plasma samples were collected at admission (acute phase), discharge (subacute phase), and follow-up (chronic phase). A control group of 21 individuals received atorvastatin for primary prevention. IgG was isolated from plasma, and N-glycans were released, fluorescently labeled with 2-aminobenzamide, and analyzed using hydrophilic interaction-based ultra-high-performance liquid chromatography with fluorescence detection. Derived glycan traits were calculated, including agalactosylated (G0), monogalactosylated (G1), digalactosylated (G2), core fucosylated (F), bisected (B), and sialylated (S) glycans. Results: No significant differences in derived IgG glycan traits were observed between ACS patients and controls at baseline or follow-up. Within the ACS group, a longitudinal analysis revealed significant increases in G0 and F and a decrease in G2 between the acute and chronic phases. A total of 65% of patients achieved ≥50% reduction in LDL cholesterol (LDL-C), whereas only 22% reached the guideline-recommended LDL-C target of <1.4 mmol/L. Patients achieving ≥50% LDL-C reduction exhibited consistently higher G0 and lower G2 and S across disease phases. In a subgroup of patients with baseline LDL-C >3.9 mmol/L, those who failed to achieve ≥50% LDL-C reduction had significantly lower G0 and higher S across all time points. Conclusions: Specific glycan traits are associated with the degree of LDL-C reduction achieved during statin therapy, particularly in patients with high baseline LDL-C. These findings suggest that IgG glycosylation patterns may reflect biological phenotypes associated with differential lipid-lowering responsiveness after ACS. Full article

Social media platforms have become critical communication environments during disasters, where individuals express emotions, share information, and engage in public discourse. These platforms also reflect heterogeneous communication patterns shaped by different actor groups. However, existing studies predominantly focus on emotion classification and often overlook the combined role of actor identity and conflict dynamics. To address this gap, this study proposes an integrated AI-based analytical framework for actor-aware emotion and conflict analysis in post-disaster social media. An expert-annotated Turkish tweet dataset was constructed based on Ekman’s emotion model, including anger, fear, sadness, happiness, and surprise, along with an additional irrelevant/off-topic category and conflict-level labels. A Transformer-based model (BERTurk) was fine-tuned for multi-class emotion classification. Experimental results show that the proposed model achieves strong classification performance, with an accuracy of 0.931 and an F1-score of 0.912, outperforming conventional machine learning and deep learning baselines. Actor-based analysis reveals systematic differences in emotional and conflict patterns across groups. Scientists, journalists, and individual users exhibit higher levels of conflict and more pronounced negative emotional expressions, whereas institutionally oriented actors display comparatively balanced and supportive communication patterns. In addition, a web-based decision support system was developed to enable interactive visualization and actor-level exploration of emotional and conflict dynamics. Overall, the proposed framework provides a scalable, analytically robust approach to understanding social media discourse in disaster contexts and offers practical implications for AI-driven crisis communication and decision-support systems. Full article

Foreign plastic objects (FPOs) in poultry products present significant food safety risks and cause economic losses for the industry. Conventional detection methods, including X-rays and color imaging, often struggle to identify small or low-density plastics. Hyperspectral imaging (HSI) offers both spatial and spectral information but suffers from high computational cost when applied for FPO identification in industrial environments. This study introduces a parameter-efficient and computationally efficient spatial–spectral transformer framework for pixel-level classification of FPOs on broiler meat using NIR-HSI (1000–1700 nm). The framework integrates three innovations: (1) center-focused linear attention (CFLA) to reduce computational complexity from $O\left(n^2\right)$ to $O\left(n\right)$; (2) patch-local mixed-axis 2D rotary position embedding to preserve geometric relationships within hyperspectral patches; and (3) low-rank factorized projection (LRP) matrices to reduce parameters by approximately 50% within projection weight matrices. The framework was trained and evaluated on a dataset of 52 chicken fillets, comprising 295,340 labeled target hyperspectral pixels from 12 common polymer types and 1 fillet class. The model achieved 99.39% overall accuracy, 99.57% average accuracy, and a 99.31 Kappa coefficient across 248,540 test pixels. Per-class precision, recall, and F1-score exceeded 98.05%, 98.59%, and 98.76%, respectively, across all classes. Efficiency analyses showed an 83% reduction in multiply–accumulate operations (MACs), a 22% reduction in trainable parameters, and a model size reduction from 1.72 MB to 1.35 MB relative to the baseline configuration. These gains also translated into practical inference benefits, with the final model achieving a throughput of 212,971.5 hyperspectral patch cubes/s and a 4.19× speedup over the baseline. These results demonstrate that the proposed framework combines strong classification performance with high efficiency, supporting high-throughput inference for real-time monitoring and enabling contamination source traceability and preventive quality control in industrial poultry processing. The approach provides a benchmark for applying transformer-based models to food safety inspection tasks. Full article

Legal element recognition, which identifies discrete factual elements in Chinese court judgments to support judicial analysis and case retrieval, faces a severe long-tail challenge: head-to-tail label-frequency ratios exceed 100:1, and over 60% of sentences carry no label, starving rare elements of training signal. Static reweighting methods assign fixed weights prior to training and cannot respond to the model’s evolving confidence; sample-level meta-learning couples all co-occurring label gradients to a single scalar, preventing independent tail-label amplification. We propose BML-Trans, a boundary-aware meta-learning framework that addresses both limitations. A label-wise meta-weighting mechanism maintains per-label gradient weights updated via bilevel hypergradient descent, decoupling tail-label amplification from co-occurring head labels. A boundary-aware meta-set concentrates calibration signal on high-uncertainty, tail-triggering sentences rather than on easy negatives, and a lightweight Multi-Scale Adapter sharpens the warm-up probability estimates on which boundary selection depends. Concretely, BML-Trans achieves an average Avg-F1 of 82.5% on CAIL2019 across the labor, divorce, and loan domains, outperforming the strongest baseline by 1.2 percentage points overall and by up to 5.7 percentage points on tail-label Macro-F1, at only 14% additional training cost. Ablation confirms a cascade dependency among the three components, establishing that the gains are structural rather than incidental to threshold selection or initialization. Full article

The instability of pigments and non-quantitative indication limit the application of intelligent labels in food freshness monitoring. Natural anthocyanins face challenges including photodegradation and difficulty in quantifying shrimp freshness. To solve these problems, this study prepared a colorimetric intelligent label with UV-shielding and real-time monitoring functions. Carbon-coated nano-TiO₂ (C-TiO₂) was synthesized by the hydrothermal method and combined with blueberry anthocyanins (BAs) in an agarose (AG)/gellan gum (GG)/glycerol matrix. The label properties were characterized and a remaining shelf-life prediction model was established based on the correlation between label color difference (ΔE) and shrimp total volatile basic nitrogen (TVB-N). The results demonstrated that C-TiO₂ could enhance compatibility and color stability, and maintain mechanical properties. After 24 h of ultraviolet irradiation, the BA degradation rate was 98.4% in the GAB group and 62.8% in the GABT-0.05 group, representing a reduction of 35.6% compared to the former. This indicates that the addition of C-TiO₂ significantly enhanced photostability. The predictive model demonstrated an error below 10% at both 10 °C and 20 °C conditions, indicating its potential for shelf-life prediction applications. This dual-functional label provides a reliable method for visual and quantitative evaluation of shrimp freshness. Full article

In the fault diagnosis of complex industrial systems, labeled samples are expensive to obtain, which leads to insufficient training data for the belief rule base (BRB) model. Although unlabeled samples are abundant, the uncertainty of their pseudo-labels may undermine semi-supervised learning and hinder accurate parameter optimization of the BRB model. To address these issues, a dual-source evidence-driven semi-supervised BRB method (SS-BRB) is proposed for fault diagnosis. The proposed method makes effective use of unlabeled samples while preserving the interpretability and inference transparency of the BRB model. To improve the reliability of pseudo-labels in semi-supervised learning, a dual-source evidence-driven pseudo-labeling mechanism is designed. In this mechanism, local similarity information is combined with the global inference results of the BRB model. An entropy factor and a feature distance factor are introduced to adaptively adjust the confidence of pseudo-labels. In this way, the quality of pseudo-labels is improved, and the influence of noisy samples is reduced. Based on this mechanism, high-confidence pseudo-labeled samples are incorporated into the training set to further optimize the model. Experimental results show that the proposed method achieves good diagnostic performance on both the gearbox dataset and the WD615 diesel engine dataset. Even with limited labeled data, the proposed method still achieves high accuracy, robustness, and good generalization performance. Full article

With the rapid proliferation of large language models (LLMs), distinguishing machine-generated text from human-authored content has become increasingly critical for ensuring content authenticity, academic integrity, and trust in information systems. However, detecting text generated by LLMs remains a challenging problem, particularly in zero-shot settings where labeled data and domain-specific tuning are unavailable. To address this challenge, in this paper, we propose a novel Collaborative Multi-Agent Zero-Shot Detection framework (CMA-ZSD). In contrast to existing methods based on watermarking, statistical heuristics, or neural classifiers, our CMA-ZSD employs three functionally heterogeneous agents that perform differentiated perturbations of the input text. By jointly modeling semantic consistency, grammatical normalization, and feature-level reconstruction, our method captures intrinsic asymmetries between human-authored and LLM-generated text. A semantic similarity evaluation mechanism, combined with majority voting, enables robust and interpretable detection decisions that balance individual agent autonomy with collective consensus. Extensive experiments across 11 domains demonstrate the effectiveness of our method, with its zero-shot detection achieving accuracy comparable to domain-finetuned models in specific domains such as Finance and Reddit-dli5. Full article

Multi-label deep learning models are widely used in real-world applications where predictions depend on the joint presence of several semantically correlated labels. However, existing adversarial attacks largely overlook these inter-label dependencies, often perturbing outputs indiscriminately and producing structurally implausible or easily detectable changes. This paper presents CAPG (Context-Aware Perturbation Generation), a white-box, label-space targeted adversarial framework for generating selective and contextually consistent perturbations in multi-label settings. CAPG incorporates correlation-weighted regularization into the adversarial objective, enabling targeted manipulation of specific labels while preserving the contextual integrity of non-target outputs. Using the Pascal VOC 2012 dataset and a ResNet-101 multi-label classifier, we show that CAPG achieves higher Attack Success Rates (ASR) and substantially improved Contextual Consistency Scores (CCSs) than FGSM, PGD, CW, and DeepFool under identical perturbation budgets. CAPG also produces lower perceptual distortion, yielding adversarial examples that better preserve contextual structure. These results highlight the importance of correlation-aware adversarial evaluation for assessing the robustness of modern multi-label deep learning systems. Full article

Globally, 5.94 million accessions are conserved across 867 genebanks, of which 41.5% (2.47 million) are cereal crop accessions. Only a small portion of global germplasm diversity has been marker-genotyped or genome-sequenced. Accurate identification of genebank accessions is essential to improve the efficiency and effectiveness of global genebanking. It is crucial for preserving the legacy knowledge associated with the germplasm and for maintaining its value to current plant science and breeding efforts. Existing practices generally fall into two categories: either expensive and complex, or inefficient, labour-intensive, and inaccurate. The first relies on high-resolution genomic sequences or saturated markers, while the second relies on morphological comparisons of regenerated plants with historical records. We propose a genotyping method based on a minimal set of Single Nucleotide Polymorphism (SNP) markers and exemplify its use on a genebank scale. We identified a small, effective set of SNPs that can differentiate between the global diversity of genebank accessions of barley (Hordeum vulgare and Hordeum spontaneum) and tetraploid wheat collections (Triticum turgidum) maintained at the Germplasm Resources National Capability at the John Innes Centre, UK. This approach offers a straightforward, automatable, and inexpensive alternative to traditional genebank crop descriptors used during seed regeneration and distribution. By establishing the minimal genomic resolution needed to distinguish genetically distinct accessions, we show that as few as 24 and 25 carefully chosen SNP markers for barley and durum wheat, respectively, can effectively differentiate individual accessions. Unlike morphology-based identification, which can detect mislabelling or contamination but often cannot prevent or correct such errors, our SNP-based molecular labelling enables error correction and the retrieval of lost germplasm identity. This study highlights how accuracy and reliability in germplasm management can be improved without costly whole-genome sequencing or resource-intensive analysis. We discuss the impact of this method on enhancing quality assurance in genebanks and its broader usefulness for the user community. Full article

The NP-hard coordination of heterogeneous robots for time-windowed warehouse tasks remains challenging: metaheuristics are precise but slow, whereas neural methods cannot handle heterogeneous constraints, leading to infeasible allocations. This paper presents the Constraint-Aware Heterogeneous Transformer (CAHT), a lightweight encoder–decoder architecture that performs end-to-end task assignment and sequencing in a single forward pass. The central innovation is a dynamic feasibility masking mechanism that enforces capacity and energy constraints directly within the softmax computation, eliminating infeasible allocations at the architectural level. This is complemented by a spatial-bias Transformer encoder and a two-stage supervised–reinforcement learning training paradigm using ALNS-generated labels. Experiments across four problem scales (5–20 robots, 50–200 tasks) demonstrate that CAHT achieves objective values within 7–13% of the ALNS reference while being 29–91× faster (23–104 ms vs. 2–3 s). Constraint violation rates remain below 6%, with time-window satisfaction above 94%. Ablation analysis identifies dynamic masking as the dominant contribution (+213% degradation upon removal), and cross-scale generalization reveals that the optimality gap decreases from 13.0% to 10.7% as the problem scale grows. With only 0.91 M parameters, CAHT occupies a new trade-off point on the Pareto frontier, offering a practical path toward real-time autonomous warehouse coordination. Full article