Search Results (2,716)

Fault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural network based on the attention mechanism is put forward for the fault diagnosis of rotating machinery. The method incorporates channel attention and spatial attention simultaneously, implementing channel-wise recalibration for frequency-dependent feature adjustment and performing spatial context aggregation across receptive fields. Subsequently, a residual module is introduced to address the vanishing gradient problem of the model in deep network structures. In addition, LSTM is used to realize spatiotemporal feature fusion. Finally, label smoothing regularization (LSR) is proposed to balance the distributional disparities among labeled samples. The effectiveness of the method is evaluated by its application to the vibration signal data from the safe injection pump and the Case Western Reserve University (CWRU). The results show that the method has superb diagnostic accuracy and strong robustness. Full article

Eosinophilic Granulomatosis with Polyangiitis (EGPA) is a rare systemic vasculitis with eosinophilic inflammation and variable clinical presentations. Although skin manifestations are frequent, current classification criteria do not include them, which may underestimate their diagnostic value. This prospective observational study aimed to assess systemic and skin involvement as well as eosinophilia, anti-neutrophil cytoplasmic antibody (ANCA), and Anti-nuclear antibodies (ANA) serum levels in 20 EGPA patients followed for one year at the University Hospital of Messina, Italy, before starting Mepolizumab, 300 mg. Eosinophilia, ANCA status, systemic and skin involvement were also evaluated at 6 and 12 months; a literature review on these data supplements our findings. Skin involvement was present in 55% of patients, including purpura, urticarial vasculitis, angioedema, maculopapular rash, and nodules, mostly in ANCA-negative patients, though purpura was more frequent in ANCA-positive cases but without any statistically significant correlation. ANAs were present in 50% of patients, together with ANCA in two subjects and without in eight. Mepolizumab significantly reduced eosinophil levels, BVASs, and corticosteroid dependence, with notable improvement in skin symptoms. In conclusion, skin manifestations are common in EGPA and may represent useful indicators of disease activity. Their integration with ANCA status, eosinophil counts, and positivity to other autoantibodies could enhance diagnostic and monitoring strategies identifying different clusters of EGPA patients even if the small sample size limits the generalizability of the findings. Full article

Basalt-fiber-reinforced polymer (BFRP) composites, utilizing a natural high-performance inorganic fiber, exhibit excellent weathering resistance, including tolerance to high and low temperatures, salt fog, and acid/alkali corrosion. They also possess superior mechanical properties such as high strength and modulus, making them widely applicable in aerospace and shipbuilding. This study experimentally investigated the mechanical properties of BFRP plates under various strain rates (10⁻⁴ s⁻¹ to 10³ s⁻¹) and directions using an electronic universal testing machine and a split Hopkinson pressure bar (SHPB).The results demonstrate significant strain rate dependency and pronounced anisotropy. Based on experimental data, relationships linking the strength of BFRP composites in different directions to strain rate were established. These relationships effectively predict mechanical properties within the tested strain rate range, providing reliable data for numerical simulations and valuable support for structural design and engineering applications. The developed strain rate relationships were successfully validated through finite element simulations of low-velocity impact. Full article

Cyclic triaxial tests are widely used in laboratory studies to assess the liquefaction susceptibility of soils. Although standardized procedures exist for conducting these tests, there is no universally accepted criterion for defining liquefaction. The choice of a liquefaction criterion significantly influences the interpretation of test results and subsequent engineering analyses. This study evaluates the impact of different liquefaction criteria by analyzing 42 cyclic triaxial tests performed on soil mixtures containing plastic fines. Both stress-based and strain-based liquefaction criteria were applied to assess their influence on test outcomes. The analyses focused on two key parameters: the number of loading cycles required to initiate liquefaction and the normalized dissipated energy per unit volume needed for liquefaction to occur. Results indicate that for soils susceptible to liquefaction failures, these parameters remain relatively consistent across different failure criteria. However, for soils prone to cyclic mobility failures, the number of loading cycles and the dissipated energy required for liquefaction vary significantly depending on the selected failure criterion. These findings highlight the importance of carefully selecting a liquefaction criterion, as it directly affects the assessment of soil behavior under cyclic loading. A better understanding of these variations can improve the accuracy of liquefaction susceptibility evaluations and inform geotechnical design and hazard mitigation strategies. Full article

Improving the green innovation quality (GIQ) of center cities is crucial to achieve sustainable urban agglomeration development. Utilizing data on green patent citations and economic indicators across cities in the Yangtze River Delta urban agglomeration (YRD) from 2003 to 2022, this research examines the influence of center cities’ GIQ on the economic performance of peripheral municipalities. The results show the following: (1) Center cities’ GIQ exerts a significant suppressive effect on peripheral cities’ economic growth overall. Heterogeneity analysis uncovers a distance-dependent duality. GIQ stimulates growth in proximate cities (within 300 km) but suppresses it beyond this threshold. This spatial siphoning effect is notably amplified in national-level center cities. (2) Mechanisms suggest that GIQ accelerates the outflow of skilled labor in peripheral cities through factor agglomeration and industry transfer mechanisms. Concurrently, it impedes the gradient diffusion of urban services, collectively hindering peripheral development. (3) Increased government green attention (GGA) and industry–university–research cooperation (IURC) in centers can mitigate these negative impacts. This paper contributes to the theoretical discourse on center cities’ spatial externalities within agglomerations and offers empirical support and policy insights for the exertion of spillover effects of high-quality green innovation from center cities and the sustainable development of urban agglomeration. Full article

This study presents the first systematic empirical comparison of neural architectures for Universal Dependencies (UD) parsing in Old English, thus addressing central questions in computational historical linguistics and low-resource language processing. We evaluate three approaches—a baseline spaCy pipeline, a pipeline with a pretrained tok2vec component, and a MobileBERT transformer-based model—across datasets ranging from 1000 to 20,000 words. Our results demonstrate that the pretrained tok2vec model consistently outperforms alternatives, because it achieves 83.24% UAS and 74.23% LAS with the largest dataset, whereas the transformer-based approach substantially underperforms despite higher computational costs. Performance analysis reveals that basic tagging tasks reach 85–90% accuracy, while dependency parsing achieves approximately 75% accuracy. We identify critical scaling thresholds, with substantial improvements occurring between 1000 and 5000 words and diminishing returns beyond 10,000 words, which provides insights into scaling laws for historical languages. Technical analysis reveals that the poor performance of the transformer stems from parameter-to-data ratio mismatches (1250:1) and the unique orthographic and morphological characteristics of Old English. These findings defy assumptions about transformer superiority in low-resource scenarios and establish evidence-based guidelines for researchers working with historical languages. The broader significance of this study extends to enabling an automated analysis of three million words of extant Old English texts and providing a framework for optimal architecture selection in data-constrained environments. Our results suggest that medium-complexity architectures with monolingual pretraining offer superior cost–benefit trade-offs compared to complex transformer models for historical language processing. Full article

As an integral component of humanity’s cultural heritage, traditional villages universally confront challenges such as population loss and cultural discontinuity amid rapid urbanization. Cluster-based protection models have increasingly become the international consensus for addressing the survival crisis of such settlements. This study selects the Central Yunnan region of Southwest China—characterized by its complex geography and multi-ethnic habitation—as the research area. Employing ArcGIS spatial analysis techniques alongside clustering algorithms, we examine the spatial distribution characteristics and clustering patterns of 251 traditional villages within this region. The findings are as follows. In terms of spatial distribution, traditional villages in Central Yunnan are unevenly dispersed, predominantly aggregating on mid-elevation gentle slopes; their locations are chiefly influenced by rivers and historical courier routes, albeit with only indirect dependence on waterways. Regarding single-cluster attributes, the spatial and geomorphological features exhibit a composite “band-and-group” pattern shaped by river valleys; culturally, two dominant modes emerge—“ancient-route-dependent” and “ethnic-symbiosis”—reflecting an economy-driven cultural mechanism alongside latent marginalization risks. Concerning construction characteristics, the “Qionglong-Ganlan” and Han-style “One-seal” residential features stand out, illustrating both adaptation to mountainous environments and the cumulative effects of historical culture. Based on these insights, we propose a three-tiered clustering classification framework—“comprehensive-element coordination”, “feature-led”, and “potential-cultivation”—to inform the development of contiguous and typological protection strategies for traditional villages in highland, multi-ethnic regions. Full article

Aramid fibre has become a critical material for individual soft body armour due to its lightweight nature and exceptional impact resistance. To investigate its energy absorption mechanism, quasi-static and dynamic tensile experiments were conducted on Kevlar^® 29 plain-woven fabric using a universal material testing machine and a Split Hopkinson Tensile Bar (SHTB) apparatus. Tensile mechanical responses were obtained under various strain rates. Fracture morphology was characterised using scanning electron microscopy (SEM) and ultra-depth three-dimensional microscopy, followed by an analysis of microstructural damage patterns. Considering the strain rate effect, a viscoelastic constitutive model was developed. The results indicate that the tensile mechanical properties of Kevlar^® 29 plain-woven fabric are strain-rate dependent. Tensile strength, elastic modulus, and toughness increase with strain rate, whereas fracture strain decreases. Under quasi-static loading, the fracture surface exhibits plastic flow, with slight axial splitting and tapered fibre ends, indicating ductile failure. In contrast, dynamic loading leads to pronounced axial splitting with reduced split depth, simultaneous rupture of fibre skin and core layers, and fibrillation phenomena, suggesting brittle fracture characteristics. The modified three-element viscoelastic constitutive model effectively captures the strain-rate effect and accurately describes the tensile behaviour of the plain-woven fabric across different strain rates. These findings provide valuable data support for research on ballistic mechanisms and the performance optimisation of protective materials. Full article

Healthy food service guidelines (HFSG) comprise food, nutrition, behavioral design, and other standards to guide the purchasing, preparation, and offering of foods and beverages in worksites and institutional food service. To date, there have been few attempts to synthesize evidence for HFSG effectiveness in non-K-12 or early childhood education sectors, particularly at worksites and institutional food services. We conducted a scoping review to achieve the following: (1) characterize the existing literature on the effectiveness of HFSG for improving the institution’s food environment, financial outcomes, and consumers’ diet quality and health, and (2) identify gaps in the literature. The initial search in PubMed and Web of Science retrieved 10,358 articles; after screening and snowball searching, 68 articles were included for analysis. Studies varied in terms of HFSG implementation settings, venues, and outcomes in both U.S. (n = 34) and non-U.S. (n = 34) contexts. The majority of HFSG interventions occurred in venues where food is sold (e.g., worksite cafeterias, vending machines). A diversity of HFSG terminology and measurement tools demonstrates the literature’s breadth. Literature gaps include quasi-experimental study designs, as well as interventions in settings that serve dependent populations (e.g., universities, elderly feeding programs, and prisons). Full article

Fe³⁺-incorporated hydrogels are particularly valuable for wearable devices due to their tunable mechanical properties and ionic conductivity. However, conventional immersion-based fabrication fundamentally limits hydrogel performance because of heterogeneous ion distribution, ionic leaching, and scalability limitations. To overcome these challenges, we report a novel one-pot strategy where controlled amounts of Fe³⁺ are directly added to polyacrylamide-sodium acrylate (PAM-SA) precursor solutions, ensuring homogeneous ion distribution. Combining this with Photoinduced Electron/Energy Transfer Reversible Addition–Fragmentation Chain Transfer (PET-RAFT) polymerization enables efficient hydrogel fabrication under open-vessel conditions, improving its scalability. Fe³⁺ concentration achieves unprecedented modulation of mechanical properties: Young’s modulus (10 to 150 kPa), toughness (0.26 to 2.3 MJ/m³), and strain at break (800% to 2500%). The hydrogels also exhibit excellent compressibility (90% strain recovery), energy dissipation (>90% dissipation efficiency at optimal Fe³⁺ levels), and universal adhesion to diverse surfaces (plastic, metal, PTFE, and cardboard). Finally, these Fe³⁺-incorporated hydrogels demonstrated high effectiveness as strain sensors for monitoring finger/elbow movements, with gauge factors dependent on composition. This work provides a scalable, oxygen-tolerant route to tunable hydrogels for advanced wearable devices. Full article

Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. Methods: Our retrospective, single-center study involved 351,019 patients consulting in APUH’s EDs between 2015 and 2018. Various models (including a cross-validation artificial neural network (ANN), a k-nearest neighbors (KNN) model, a logistic regression (LR) model, and a random forest (RF) model) were trained and assessed for performance with regard to the area under the receiver operating characteristic curve (AUROC). The best model was validated internally with a test set, and the F1 score was used to determine the best threshold for recall, precision, and accuracy. XAI techniques, such as Shapley additive explanations (SHAP) and partial dependence plots (PDP) were employed, and the clinical explanations were evaluated by emergency physicians. Results: The ANN gave the best performance during the training stage, with an AUROC of 83.1% (SD: 0.2%) for the test set; it surpassed the RF (AUROC: 71.6%, SD: 0.1%), KNN (AUROC: 67.2%, SD: 0.2%), and LR (AUROC: 71.5%, SD: 0.2%) models. In an internal validation, the ANN’s AUROC was 83.2%. The best F1 score (0.67) determined that 0.35 was the optimal threshold; the corresponding recall, precision, and accuracy were 75.7%, 59.7%, and 75.3%, respectively. The SHAP and PDP XAI techniques (as assessed by emergency physicians) highlighted patient age, heart rate, and presentation with multiple injuries as the features that most specifically influenced the admission from the ED to a hospital ward. These insights are being used in bed allocation and patient prioritization, directly improving ED operations. Conclusions: The 3P-U model demonstrates practical utility by reducing ED crowding and enhancing decision-making processes at APUH. Its transparency and physician validation foster trust, facilitating its adoption in clinical practice and offering a replicable framework for other hospitals to optimize patient flow. Full article

This research analyzed university students’ perceptions of the use of generative artificial intelligence (hereafter Gen-AI) in a higher education context. Specifically, it addressed the potential benefits and challenges related to the application of these web-based resources. A mixed method was adopted and the sample consisted of 407 teacher training students enrolled in the Early Childhood and Primary Education Degrees in the Region of Murcia in Spain. The results indicated a clear recognition of the relevance of these technological tools for teaching and learning. Respondents highlighted the potential to engage them in academic tasks, increase their motivation, and personalize their learning pathways. However, participants identified some challenges related to technology dependency, ethical issues, and privacy concerns. By understanding learners’ beliefs and assumptions, educators and educational administrations can adapt Gen-AI according to learners’ needs and preferences to improve their academic performance. In learning practice, these adaptations could involve evidence-based interventions, such as AI literacy modules or hybrid assessment frameworks, to translate findings into practice. In addition, it is necessary to adjust materials, methodologies, and the assessment of the academic curriculum to facilitate student learning and ensure that all students have access to quality education and the adequate development of digital skills. Full article

Heart rate (HR) is strongly affected by the autonomic nervous system (ANS), while its spontaneous fluctuations, called heart rate variability (HRV), report about the dynamics of the complex, vegetative regulation of the heart rhythm. Hence, HRV is widely considered an important marker of the ANS effects on the cardiac system, and as such, a crucial diagnostic tool in cardiology. In order to obtain nontrivial results from HRV analysis, it would be desirable to establish exact, universal interrelations between the typical HRV parameters and HR itself. That, however, has not yet been fully accomplished. Hence, our aim was to perform a comparative statistical analysis of ECG recordings from a public database, with a focus on the HR dependence of typical HRV parameters. We revealed their fundamental connections, which were substantiated by basic mathematical considerations, and were experimentally demonstrated via the analysis of 24 h of ECG recordings of more than 200 healthy individuals. The large database allowed us to perform unique age-cohort analyses. We confirmed the HR dependence of typical time-domain parameters, such as RMSSD and SDNN, frequency-domain parameters such as the VLF, LF, and HF components, and nonlinear indices such as sample entropy and DFA exponents. In addition to shedding light on their relationship, we are the first, to our knowledge, to identify a new, diffuse structure in the VHF regime as an important indicator of SNS activity. In addition, the demonstrated age dependence of the HRV parameters gives important new insight into the long-term changes in the ANS regulation of the cardiac system. As a possible molecular physiological mechanism underlying our new findings, we suggest that they are associated with Piezo2 channel function and its age-related degradation. We expect our results to be utilized in HRV analysis related to both medical research and practice. Full article

In this work we study the spectral dimensionality of spacetime around a radiating Schwarzschild black hole using a recently introduced formalism of quantum gravity, where the alterations of the gravitational field produced by the radiation are represented on an extended manifold, and describe a non-commutative and nonlinear quantum algebra. The relation between classical and quantum perturbations of spacetime can be measured by the parameter $z\ge 0$. In this work we have found that when $z=(1+\sqrt{3})/2\simeq 1.3660$, a relativistic observer approaching the Schwarzschild horizon perceives a spectral dimension $N\left(z\right)=4\left[\theta \left(z\right)-1\right]\simeq 2.8849$, which is related to quantum gravitational interference effects in the environment of the black hole. Under these conditions, all studied Schwarzschild black holes with masses ranging from the Planck mass to ${10}^{46}$ times the Planck mass present the same stability configuration, which suggests the existence of a universal property of these objects under those particular conditions. The difference from the spectral dimension previously obtained at cosmological scales leads to the conclusion that the spacetime dimensionality is scale-dependent. Another important result presented here is the fundamental alteration of the effective gravitational potential near the horizon due to Hawking radiation. This quantum phenomenon prevents the potential from diverging to negative infinity as the observer approaches the Schwarzschild horizon. Full article

Hyperspectral image classification faces challenges with high-dimensional spectral data and complex dependencies between bands. This paper proposes HyperspectralMamba, a novel architecture for hyperspectral image classification that integrates state space modeling with adaptive recalibration mechanisms. The method addresses limitations in existing techniques through three key innovations: (1) a novel dual-stream architecture that combines SSM global modeling with parallel convolutional local feature extraction, distinguishing our approach from existing single-stream SSM methods; (2) a band-adaptive feature recalibration mechanism specifically designed for hyperspectral data that adaptively adjusts the importance of different spectral band features; and (3) an effective feature fusion strategy that integrates global and local features through residual connections. Experimental results on three benchmark datasets—Indian Pines, Pavia University, and Salinas Valley—demonstrate that the proposed method achieves overall accuracies of 95.31%, 98.60%, and 96.40%, respectively, significantly outperforming existing convolutional neural networks, attention-enhanced networks, and Transformer methods. HyperspectralMamba demonstrates an exceptional performance in small-sample class recognition and distinguishing spectrally similar terrain, while maintaining lower computational complexity, providing a new technical approach for high-precision hyperspectral image classification. Full article