Search Results (17,846)

Background: Carotid intima-media thickness (IMT) is a well-established surrogate marker of subclinical atherosclerosis and a predictor of cardiovascular risk. Carotenoids, particularly lycopene and β-carotene, have been proposed as protective antioxidants against vascular damage, but evidence from population-based studies is inconsistent. Objective: We aim to perform a systematic review and meta-analysis of the associations between circulating levels of lycopene and β-carotene and carotid IMT in the general adult population, including potential sex-specific effects. Methods: A systematic search was conducted in PubMed, Scopus, and Web of Science up to March 2025, following PRISMA guidelines (PROSPERO registration: CRD420251003810). Observational and experimental studies reporting cross-sectional associations between plasma carotenoids and IMT were included. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated via random effects models. Subgroup and meta-regression analyses explored potential modifiers, including sex and cardiovascular risk factors. Results: Thirteen studies (n = 9131; mean age 46.4–71.6 years) met the inclusion criteria, eight of which were eligible for meta-analysis. High circulating lycopene levels were significantly associated with low IMT (pooled OR = 0.70; 95% CI: 0.59–0.84; I² = 65.7%). The association was stronger in men (OR = 0.62; 95% CI: 0.45–0.84) than in women (OR = 0.74; 95% CI: 0.58–0.95). In contrast, β-carotene was only marginally associated with IMT (pooled OR = 0.96; 95% CI: 0.92–0.99; I² = 72.6%). Meta-regression suggested that systolic blood pressure modified the lycopene-IMT relationship, whereas body mass index and low-density lipoprotein cholesterol influenced the β-carotene-IMT association. No evidence of publication bias was found. Conclusions: Increased serum lycopene concentrations, and to a lesser extent β-carotene concentrations, are inversely associated with carotid IMT, suggesting a protective role of lycopene in vascular health. The effect appears more pronounced in men, highlighting potential sex-specific differences in carotenoid metabolism and cardiovascular risk modulation. Full article

Metagenomic and targeted next-generation sequencing (NGS) technologies are rapidly transforming diagnosis and management for infectious diseases. This review comprehensively examines the current applications of metagenomic NGS (mNGS) and targeted NGS (tNGS) in clinical microbiology, highlighting their roles in pathogen detection, antimicrobial resistance profiling, virulence characterization, and outbreak investigation—particularly in complex cases such as pneumonia, critical illness with pulmonary infections, and pediatric acute respiratory illnesses. We discuss the diagnostic performance, advantages, and limitations of these approaches, including challenges related to sensitivity, specificity, standardization, bioinformatic complexity, and cost-effectiveness. Furthermore, we explore emerging opportunities for integrating NGS-based surveillance with public health strategies, such as wastewater epidemiology, to monitor healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) at the population level. Finally, we outline key steps needed to translate these powerful genomic tools from research settings into routine clinical and public health practice. Full article

Background: Brucella canis is an emerging zoonotic pathogen responsible for canine reproductive disorders and public health concerns. This study assessed the seroprevalence of B. canis in dogs from Campania, Southern Italy (2022–2025). Methods: Serum samples (n = 400) were retrospectively screened using an indirect immunofluorescence assay (IFAT), performed according to the manufacturer’s instructions. Screening was conducted at a 1:40 cut-off, followed by serial dilutions to determine endpoint titres. Statistical analysis included chi-square tests for univariable screening, followed by nominal logistic regression models to evaluate the association between IFAT positivity and predictive factors (year, province, and sex of dogs). Additionally, a general linear model (GLM) was applied to the seropositive subset (n = 69) to analyse the magnitude of the antibody response, expressed as geometric mean titres (GMTs). Results: The overall seroprevalence was 17.3% (95% CI: 13.6–21.0%). Dog’s sex, year of sampling, and province were not significant independent predictors of infection (p > 0.05), but GLM analysis showed that sampling year (p = 0.0024) and province (p = 0.0490) significantly influenced antibody intensity. A significant temporal increase in antibody intensity was observed towards 2025 (p = 0.037), suggesting an intensification of infection pressure. Conclusions: Our results confirm that Brucella canis is an endemic pathogen in the shelter dog population of southern Italy. The high seroprevalence and significant increase in antibody magnitude (GMT) over the study period indicate rising infection pressure, highlighting the urgent need for mandatory screening and a coordinated One Health surveillance strategy to manage zoonotic risk effectively. Full article

Fake news has caused significant harm and disruption across various sectors of society. With the rapid advancement of the Internet and social media platforms, both academic and industrial communities have shown growing interest in multimodal fake news detection. In this work, we propose MERF (Multimodal Evidence Retrieval and Forensics with LVLM), a unified framework for multimodal fake news detection that leverages the reasoning capabilities of Large Vision-Language Models (LVLMs). While LVLMs outperform traditional Large Language Models (LLMs) in processing multimodal content, our study reveals that their reasoning abilities remain limited in the absence of sufficient supporting evidence. MERF addresses this challenge by integrating web-based content retrieval, reverse image search, and image manipulation detection into a coherent pipeline, enabling the model to generate informed and explainable veracity judgments. Specifically, our approach performs cross-modal consistency checking, retrieves corroborative information for both textual and visual content, and applies forensic analysis to detect potential visual forgeries. The aggregated evidence is then fed into the LVLM, facilitating comprehensive reasoning and evidence-based decision-making. Experimental results on two public benchmark datasets—Weibo and Twitter—demonstrate that MERF consistently outperforms state-of-the-art baselines across all major evaluation metrics, achieving substantial improvements in accuracy, robustness, and interpretability. Full article

(1) Background: The scarcity and imbalance of real fault data significantly limit the development of data-driven fault diagnosis methods for liquid rocket engines (LREs), especially under few-shot conditions. (2) Methods: To address this issue, this study proposes a GAN-based fault data augmentation framework for multivariate LRE time-series signals and a hybrid diagnostic classifier combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and multi-head attention (MHA). The GAN component is introduced to alleviate fault-data scarcity and class imbalance by generating additional fault-like samples, while the classifier is designed to capture local features, long-range temporal dependencies, and diagnostically informative temporal regions. (3) Results: A multidimensional evaluation based on temporal similarity, statistical consistency, and global distribution discrepancy indicates that the generated samples preserve important characteristics of the original signals under the current evaluation protocol. On the augmented LRE dataset, the proposed classifier achieved strong diagnostic performance. In addition, supplementary experiments on the public HIT aero-engine dataset further support the effectiveness of the classifier architecture, its component-wise contribution, and its behavior under imbalanced few-shot settings, while also demonstrating the value of uncertainty-aware prediction. (4) Conclusions: The results provide encouraging evidence that the proposed framework can improve LRE fault diagnosis under data-scarce conditions. However, the present findings should be interpreted within the scope of the available data and evaluation setting. More comprehensive generator-side ablation, broader external validation, and physics-oriented assessment of the generated signals are still needed before stronger conclusions can be made. Full article

Driven by rapid global urbanization and expanding urban footprints, air pollution, particularly from industrial emissions and vehicular exhaust, has intensified, with rising concentrations of inhalable particulate matter (PM) posing direct threats to public health. To address this challenge, we conducted field measurements of ambient PM concentrations across diverse urban plant communities and quantitatively compared their capacity to mitigate four key size-fractionated pollutants: total suspended particles (TSPs), PM₁₀, PM_2.5, and PM₁. Our objective was to identify the most effective plant community type for PM abatement in urban settings. Results demonstrate that: (1) evergreen broad-leaved forests exhibit the highest overall PM removal efficiency among all studied communities; (2) removal efficacy declines markedly with decreasing particle size, indicating limited capacity to capture ultrafine particles (e.g., PM₁); and (3) seasonal performance peaks in summer, especially for deciduous broad-leaved forests attributable to maximal leaf area index, enhanced stomatal activity, and favorable meteorological conditions. By rigorously evaluating species composition, canopy structure, and seasonal dynamics, this study provides empirically grounded guidance for evidence-based urban greening strategies aimed at optimizing airborne particulate mitigation worldwide. Full article

Urbanization significantly enhances socio-economic conditions, health, and well-being for many by improving access to services, education, and economic opportunities. However, socio-economic and public health disparities are also being exacerbated by urbanization. The reliable data required to monitor these conditions are often unavailable, outdated, or inconsistent. This study introduces a multimodal deep learning framework that integrates satellite imagery with street network datasets to predict urban socio-economic indicators and public health determinants at the sector level as a political administrative unit of public health planning in Rwanda. We extracted latent visual and topological embeddings of the urban built environment, using a Convolutional Neural Network (CNN) and Graph Neural Network (GNN). These embeddings were fused through an attentional mechanism to train a multi-task regression model that simultaneously predicts multiple socio-economic indicators and public health determinants. This framework was applied to the City of Kigali in Rwanda. Overall, the multimodal fusion model achieved the best average performance across targets, with an average correlation of 0.68 and MAE of 1.26 for socio-economic indicators, and 0.68 and 1.46 for public health determinants, demonstrating the benefit of integrating visual and topological information. The learned fused embedding space arranges socio-economic indicators and public health determinant deciles along a continuous morphological gradient from sparsely built rural settings to dense urban settings, demonstrating that the urban form encodes latent signals that capture socio-economic indicators and health determinants. Moreover, the study reveals a strong relationship between socio-economic indicators and the public health index, with education, cooking materials, and floor materials exhibiting a correlation above 0.96. This work demonstrates the utility of an integrated framework for socio-economic indicator profiling and public health planning in data-scarce urban contexts, offering a scalable approach for monitoring the indicators of Sustainable Development Goals in rapidly changing urban environments. Full article

Hyperspectral image (HSI) classification has achieved substantial progress with deep learning. However, existing methods still underexploit frequency-domain information, particularly the complementary roles of high- and low-frequency components. The recently proposed Kolmogorov-Arnold Network (KAN) shows strong nonlinear feature extraction ability for HSI classification, but its lack of frequency-domain learning and reliance on B-spline activation functions often causes unstable training and convergence issues. To address these limitations, this study introduces a Frequency-aware Filtering Activation-based KAN (FFAKAN) for HSI classification. In this framework, the conventional B-spline activation functions in KAN are replaced with learnable high-pass and low-pass spatial filters, enabling explicit frequency decomposition while preserving spectral sequence modeling capacity. Specifically, the proposed framework includes three modules: spectral-spatial feature embedding (S2FE), adaptive filtering KAN (AFKAN), and sequence feature extraction (SeqFE) modules. First, the S2FE module generates highly discriminative feature representations, providing a strong foundation for subsequent processing. Second, the AFKAN module, serving as the core component, employs learnable cutoff frequencies together with cosine-based smooth transition functions to achieve physically interpretable high-low frequency separation, adaptively capturing fine-grained details and structural characteristics in HSI data. Finally, the SeqFE module leverages multi-layer stacked 3D convolutions to perform deep spectral-spatial correlation modeling, extracting high-level discriminative joint features for the classification task. Experiments on four public HSI datasets demonstrate that FFAKAN consistently outperforms state-of-the-art methods. Overall, the proposed method achieves significant performance gains, with maximum improvements of 6.82%, 1.83%, 4.35%, and 5.93% compared with conventional approaches. Moreover, compared with strong baseline models, FFAKAN further improves the overall accuracy by 1.70%, 0.10%, 0.02%, and 0.37%, respectively. These results clearly demonstrate the effectiveness, robustness, and superior generalization capability of the proposed method across different datasets. This study introduces a new paradigm that incorporates physically interpretable frequency-domain priors, showing strong adaptability and promising potential in complex land-cover scenarios. Full article

Hyperspectral image (HSI) classification faces significant challenges due to the spatial–spectral heterogeneity of land covers and the geometric rigidity of standard convolutions. Although Transformers offer powerful global modeling capabilities, their quadratic computational complexity limits practical efficiency. To address these limitations, this paper proposes a novel hierarchical framework named MDS³-Net (Multiscale Deformable Spectral–Spatial Sequence Network). Specifically, we design a Multiscale Spectral-Deformable Convolution (MSDC) module that adopts a cascaded strategy to sequentially extract discriminative spectral features and adaptively align spatial receptive fields with irregular object boundaries. To capture long-range dependencies efficiently, a Spectral–Spatial Sequence (S³) Encoder is introduced based on a gated large-kernel convolution mechanism, achieving global context modeling with linear complexity. Furthermore, a Dual-Path Feature Extraction (DPFE) module is proposed to perform semantics-preserving dimension reduction via spectral reorganization and spatial attention. Experimental results on four public datasets demonstrate that the proposed MDS³-Net achieves state-of-the-art classification performance and exhibits superior robustness under limited training samples compared to existing methods. Full article

The rapid integration of renewable generation, distributed energy resources, and advanced monitoring infrastructures has increased the demand for data-driven methods in modern power systems. Artificial neural networks (ANNs) have become widely adopted for load forecasting, fault diagnosis, state estimation, stability assessment, and energy management. Despite substantial publication growth, large-scale operational deployment of ANN-based solutions remains limited. This study presents a bibliometric and engineering assessment of ANN applications in power systems between 2020 and 2024, based on 1511 SCI-Expanded journal articles retrieved from the Web of Science. Beyond conventional science mapping, the study integrates an engineering-oriented deployment-readiness evaluation that systematically links ANN architectures with core operational problem classes. The results reveal a significant imbalance between reported algorithmic performance and operational validation rigor. Forecasting and energy management applications demonstrate relatively higher readiness due to real-world dataset usage, whereas fault diagnosis and state estimation remain predominantly simulation-driven and lack explainability and robustness validation. A deployment-readiness matrix is applied to quantitatively evaluate dataset realism, interpretability integration, and reliability considerations across domains. The findings indicate that the primary barriers to ANN integration in power systems stem from insufficient validation protocols and resilience-oriented design rather than algorithmic limitations, highlighting key engineering priorities for reliable real-world implementation. Full article

RGB-D salient object detection (RGB-D SOD) aims to accurately localize and segment visually salient objects by jointly leveraging RGB images and depth maps. Some existing methods rely on static fusion strategies with fixed paths and weights, which treat all regions equally and fail to capture the varying importance of different regions and modalities. Although some attention-based methods alleviate the limitations of static fusion by assigning adaptive weights to different regions and modalities, the quality of RGB and depth data may degrade in real-world scenarios due to sensor noise, illumination changes, or environmental interference. These attention-based methods often overlook inter-modality quality differences and complementarity, making them prone to over-relying on a certain modality, which can lead to noise introduction, feature conflicts, and performance degradation. To address these limitations, this paper proposes a novel dynamic feature routing and fusion framework for RGB-D SOD, which adaptively adjusts the fusion strategy according to the quality of input modalities. To enable modality quality awareness, the proposed method characterizes the modality complementarity between RGB and depth features in a task-driven manner inspired by information-theoretic principles. We introduce a task-relevance scoring function which is integrated with a mutual information estimator to quantify such complementarity, and emphasizes task-relevant features while suppressing redundancy. A dynamic routing module is then designed to perform feature selection guided by the captured complementarity. In addition, we propose a novel cross-modal fusion module to adaptively fuse the features selected by the dynamic routing module, which effectively enhances complementary representations while suppressing redundant features and noise interference. Extensive experiments conducted on seven public RGB-D SOD benchmark datasets demonstrate that the proposed method consistently achieves competitive performance, outperforming existing methods by an average of approximately 1% across multiple evaluation metrics. Notably, in challenging scenarios with severe modality quality degradation, the proposed method outperforms existing best-performing methods by up to 1.8%, demonstrating strong robustness against cluttered backgrounds, complex object structures, and diverse object scales. Overall, the proposed dynamic fusion framework provides a novel solution to modality quality imbalance in RGB-D salient object detection. Full article

To meet the requirement of real-time bridge crack detection, this paper proposes a lightweight detection model based on YOLOv7-tiny. First, an edge-preserved image enhancement method is proposed. It effectively enhances the image contrast and preserves the structural features of crack edges. This provides a high-quality data foundation for the detection network. Second, a LWCSP module is introduced. This module integrates hybrid convolution and shuffle operations. It reduces the model’s parameter count and computation. Simultaneously, it maintains strong feature representation capability. A good balance between detection performance and efficiency is achieved. Finally, an improved SWise-IoU is proposed to optimize the bounding box regression in YOLOv7-tiny. This method dynamically evaluates sample quality. It enables differentiated gradient adjustment for samples of different qualities. This promotes sufficient learning of sample features by the model, thereby improving detection accuracy. Experimental results show that the proposed model delivers strong performance on a public bridge crack dataset. Compared to the baseline, the mAP@0.5 is 12.1 higher, and model size, parameter count, and FLOPs are reduced by 7.3%, 8.03%, and 10%, respectively. The final model size is only 11.4 MB, and mAP@0.5 is 86.1%, suitable for a real-time crack detection task. Full article

Background: Many SCOR performance measures rely on internal operational data, which limits empirical work using public information. Methods: This study evaluates a small set of publicly auditable, SCOR-linked ratios (SCOR_E) in a panel of 12 publicly traded firms across four sectors from 2000 to 2022. Using firm- and year-fixed-effects panel models, the paper examines whether these candidate proxies show pre-specified directional associations within firms and whether the same ratios are associated with operating margin in parallel models. Instrumental-variable (IV) specifications are reported only as sensitivity analyses, and nearly all are weak by the paper’s reported first-stage diagnostics. Results: Accordingly, most findings are interpreted as associative rather than causal. After false-discovery-rate adjustment and weak-instrument-robust inference, only four firm–proxy pairs meet the paper’s detection criterion; all remaining estimates are treated as non-robust. Conclusions: The contribution is therefore narrow: this is a constrained exploratory screening exercise showing which candidate mappings survive the paper’s inferential filters in this sample and which do not. The results do not establish a validated cross-industry scorecard, a scalable benchmarking framework, or a basis for policy claims. Full article

The histopathological classification of ovarian carcinoma is fundamental for patient management. While microscopic evaluation by pathologists is the current diagnostic standard, it is known to be subject to interobserver variability, which can affect consistency in treatment decisions. This study addresses this clinical need by developing and validating a deep learning-based diagnostic support tool designed to enhance the objectivity and reproducibility of this classification. In this work, we address a key challenge in computational pathology—the tendency of attention mechanisms to overfit by concentrating on limited features—by systematically evaluating a direct regularization method within multiple instance learning (MIL) models. The models were trained and validated using 10-fold cross-validation on a public training set of 538 whole-slide images and further tested on an independent public dataset for the more challenging task of molecular subtype classification. We utilized features from a foundational model pre-trained on histopathology data to represent tissue morphology. Our findings demonstrate that directly regularizing the attention mechanism with a stochastic approach provides a statistically significant improvement in accuracy and generalization, highlighting its power as a robust technique to mitigate overfitting for this clinical task. In direct contrast to the reported variability in manual assessment, our final model achieved high consistency and accuracy, with a balanced accuracy of 0.854 and a Cohen’s Kappa of 0.791. The model also demonstrated strong generalization on the molecular classification task. Its attention mechanism provides visual heatmaps for pathologist review, fostering interpretability and trust. We have developed a highly accurate and generalizable artificial intelligence tool that directly addresses the challenge of interobserver variability in ovarian cancer classification. Its performance highlights the potential for artificial intelligence to serve as a decision support system, standardizing histopathological assessment. Full article

Subway public spaces have been identified as a vital medium for showcasing urban culture. The design quality of these spaces has been shown to have a profound influence on passengers’ spatial perception and cultural experience. However, amid rapid urbanization, subway stations commonly face issues such as homogeneous spatial interfaces and unclear cultural themes, resulting in diminished station identifiability. This study integrates post-use evaluation with Importance–Performance Analysis (IPA) to establish an assessment and optimization pathway aimed at systematically identifying and prioritizing key design elements for enhancing cultural identifiability. Taking Tianjin Gulou Station as a case study, user feedback collected through questionnaires identified 12 indicators influencing identifiability satisfaction. The reliability and validity of the questionnaire were confirmed through validity analysis and paired-sample t-tests, while IPA was employed to clarify improvement priorities. The results indicate that the overall perceived importance of cultural identifiability at Gulou Station significantly exceeds satisfaction levels. Landmark installations, art walls, and vertical transportation fall within the “high importance-low satisfaction” quadrant, which is identified as a primary area of focus for enhancement. Basic interface elements such as flooring and ceilings require enhancement, while transfer entrances and station name walls constitute advantageous designs warranting preservation. Based on the findings of the present study, three targeted design strategies are proposed: enhancing spatial perception, constructing cultural continuity, and integrating multidimensional experiences. These approaches seek to address the “spatial-cultural” perception gap, providing actionable pathways for the distinctive renewal of subway spaces. Full article