Search Results (20,986)

Wind turbine blade surface defect detection remains challenging due to large variations in defect scales, blurred edge textures, and severe interference from complex backgrounds, which often lead to insufficient detection accuracy and high false and missed detection rates. To address these issues, this paper proposes an improved RTDETR-based detection framework, termed CEA-DETR, for wind turbine blade surface defect inspection. First, a Cross-Scale Multi-Edge feature Extraction (CSME) backbone is designed by integrating multi-scale pooling and edge-enhancement units with a dual-domain feature selection mechanism, enabling effective extraction of fine-grained texture and edge features across different scales. Second, an Efficient Multi-Scale Feature Fusion Network (EMSFFN) is constructed to facilitate deep cross-level feature interaction through adaptive weighted fusion and multi-scale convolutional structures, thereby enhancing the representation of multi-scale defects. Furthermore, an adaptive sparse self-attention mechanism is introduced to reconstruct the AIFI module, strengthening global dependency modeling and guiding the network to focus on critical defect regions under complex background conditions. Experimental results demonstrate that CEA-DETR achieves mAP50 and mAP50:95 of 89.4% and 68.9%, respectively, representing improvements of 3.1% and 6.5% over the RT-DETR-r18 baseline. Meanwhile, the proposed model reduces computational cost (GFLOPs) by 20.1% and parameter count by 8.1%. These advantages make CEA-DETR more suitable for deployment on resource-constrained unmanned aerial vehicles (UAVs), enabling efficient and real-time autonomous inspection of wind turbine blades. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Despite the widespread adoption of BIM, information exchange across disciplines remains hindered by heterogeneous structures at the tabular data level, particularly when integrating data across multiple discipline-specific models. Manual mapping, rigid templates, or one-off programming scripts are labor-intensive and difficult to scale, limiting automated querying, cross-model aggregation, and schedule-level analytics. This study proposes a lightweight, workflow-driven approach for semantic normalization and cross-model integration of BIM schedule data, with optional script-supported workflow configuration used only to assist the configuration of deterministic, rule-guided mapping logic, rather than serving as a core analytical method. By introducing a customizable subcategory layer, the workflow enables fine-grained semantic alignment and efficient normalization across diverse schedule datasets, implemented through lightweight Python scripting and rule-guided semantic matching used solely as a supporting mechanism for deterministic field mapping. Using structural, architectural, and HVAC models, we demonstrate a stepwise process including data cleaning, hierarchical classification, consistency checking, batch analytics, and automated computation of cross-model metrics such as opening-to-wall ratios. Sample-based validation confirms the workflow’s reliability, achieving semantic mapping agreement rates above 95% and reducing manual processing time by more than 85%. The workflow is readily extensible to other disciplines and modeling conventions, supporting high-throughput data integration for tasks such as design coordination, semantic alignment, RFI reduction, accelerated design reviews, and data-driven decision making. Overall, rather than introducing a new algorithm, the contribution of this work lies in formalizing a reusable, schedule-level workflow abstraction that enables consistent semantic alignment and automated cross-model aggregation without relying on rigid ontologies or training-intensive learning-based models. Any optional tooling used during workflow configuration is auxiliary and does not constitute a standalone learning-based method requiring model training or performance benchmarking. This provides a reusable methodological foundation for scalable, schedule-level BIM data integration and cross-model analytics. Full article

The tractor road, as the core scene for autonomous driving of grain transport vehicles, is unstructured, complex, and obstacle-rich, leading to poor real-time performance and accuracy of joint road and obstacle detection with existing YOLOv5s. Furthermore, the reliability of passable area evaluation is low solely based on environmental factors. Therefore, YOLOv5s-C2S is proposed, fusing multi-scale features, attention mechanism, and dynamic features for joint detection. Firstly, YOLOv5s-CC is proposed for road detection by fusing context and spatial details and introducing Criss-Cross attention. Secondly, YOLOv5s-SGA is proposed for obstacle detection by grouped and spatial convolution, parameter-free attention, and adaptive feature fusion. By reusing YOLOv5s-CC weights, YOLOv5s-C2S shares low-level features and decouples high-level specificity. Based on the tractor road and obstacle information, combined with vehicle factors, a weighted scoring–based comprehensive method for passable area evaluation is proposed. Finally, the method was verified through experiments with an intelligent tracked grain transport vehicle using self-constructed datasets, including VOC_Road (11,927 images) and VOC_Obstacle (21,779 images). Compared with existing YOLOv5s, Deeplabv3+, FCN, Unet and SegNet, the mAP₅₀ of road detection by YOLOv5s-CC increased by over 1.2%. Compared with existing YOLOv5s, R-CNN, YOLOv7, SSD and YOLOv8n, the mAP₅₀ of obstacle detection by YOLOv5s-SGA increased by over 2%. Compared with YOLOv5s-SD, the mAP₅₀ of joint detection by YOLOv5s-C2S increased by 9.3%, and the frame rate increased by 7.0 FPS. The proposed passable area evaluation method exhibits strong robustness and reliability in complex environments, meeting the accuracy and real-time requirements in autonomous driving of grain transport vehicles. Full article

Cervical cytology screening remains dependent on manual analysis, which is time-consuming and subject to variability. This study proposes a leakage-free hybrid deep learning framework for multiclass classification of cervical cells extracted from whole-slide Papanicolaou images. A fine-tuned DenseNet121 feature extractor was combined with three classifiers: Support Vector Machine (SVM), Stacked Extreme Learning Machine (SELM), and Cascaded Deep Forest (CDF). Experiments were conducted on the CRIC Cervix Collection dataset using slide-level data partitioning and group-aware stratified 7-fold cross-validation. Model comparison followed a paired non-parametric protocol (Friedman test with Wilcoxon post hoc and Holm correction). DenseNet121 + CDF achieved the highest cross-validation Accuracy (0.7370 ± 0.0357), significantly outperforming SVM (0.6644 ± 0.0287) and SELM (0.6431 ± 0.0471) (χ²(2) = 11.14, p = 0.0038; Kendall’s W = 0.79). Independent testing showed competitive generalization across models. These results support the statistical robustness of the Cascaded Deep Forest-based hybrid architecture for multiclass cervical cytology classification under realistic slide-level conditions. Full article

Purpose: Anemia remains a major public health concern among vulnerable rural adolescent girls in many countries, including India. This study aimed to assess the prevalence of anemia, nutritional status, and anemia-related knowledge, attitudes, and practices (KAP) among school-going rural adolescent girls, along with predictors of KAP score, complemented by stakeholder perspectives. Methods: A mixed-methods cross-sectional study was conducted among 553 school-going adolescent girls aged 14–19, selected through a multi-stage stratified random sampling technique from three rural districts of Andhra Pradesh, India. Quantitative data were collected using a structured questionnaire assessing KAP, anthropometric measurements to collect Body Mass Index (BMI) and middle upper arm circumference (MUAC), dietary assessments using a dietary diversity score, and hemoglobin estimation using standardized procedures. Qualitative insights were obtained through focus group discussions (FGDs) with teachers, parents, frontline health workers, and community leaders and analyzed thematically. Logistic regression analysis was performed to identify predictors of KAP. Results: The prevalence of anemia among the participants was 55.3%, and 30.7% were underweight. Although over half of the girls demonstrated adequate knowledge of anemia, only 39.6% reported good anemia-preventive practices, indicating a significant gap between knowledge and practice. Dietary scores indicated micronutrient-deficient diet consumption by participants (36.2%), which might be contributing to anemia. Multivariable analysis revealed that maternal education, hemoglobin status, diet patterns, and type of school attended were significantly associated with KAP scores. Qualitative findings highlighted challenges related to health-seeking behavior, cultural misconceptions, gaps in awareness and implementation of existing adolescent health programs. Conclusions: Anemia remains highly prevalent among rural school-going adolescent girls in Andhra Pradesh, with suboptimal anemia-preventive practices despite moderate levels of knowledge. Strengthening school-based nutritional education, improving dietary diversity, and enhancing the reach and effectiveness of adolescent health programs through community engagement may help combat anemia. Full article

Background/Objectives: Midwifery workforce sustainability faces critical challenges including high burnout and turnover rates threating the service quality and the maternal health outcomes. While self-leadership and self-compassion represent promising psychological resources, their roles relative to organizational factors remain underexplored. This study examined associations between self-leadership, self-compassion, and workforce outcomes (job satisfaction, turnover intention, performance) among Turkish midwives. Methods: A cross-sectional survey was conducted with 346 midwives working in diverse healthcare settings across Turkey from May 2021 to April 2022. Data were collected through an online self-report questionnaire using validated scales for self-leadership and self-compassion as well as measures of job satisfaction, turnover intention, and job performance, and including demographic and organizational items. Descriptive statistics, one-way ANOVA (with Eta-squared [η²] calculated to determine effect size), and correlation analyses were conducted, followed by hierarchical multiple regression and binary logistic regression to examine predictive relationships, with organizational factors entered before psychological resources. Results: Self-leadership and self-compassion demonstrated a moderate positive correlation (r = 0.342, p < 0.01). Self-leadership strongly predicted job performance (OR = 2.497, p = 0.001), particularly through natural reward strategies emphasizing intrinsic motivation (OR = 1.970, p < 0.001). However, neither psychological resource significantly predicted job satisfaction or turnover intention when organizational factors were included. Work schedule, healthcare setting, professional position, and income emerged as primary predictors of satisfaction and retention. Work experience predicted increased psychological distress (OR = 1.073, p = 0.003). Conclusions: Psychological resources demonstrate domain-specific effects on workforce outcomes in midwifery: self-leadership strategies strongly enhance job performance, whereas job satisfaction and turnover intention are influenced primarily by organizational conditions. These findings highlight the need for multi-level strategies to support the sustainability of the midwifery workforce. Full article

Background: The COVID-19 pandemic has been associated with increased psychological distress globally. However, the independent psychological impact of prior COVID-19 infection remains heterogeneous, particularly in primary healthcare populations. This study aimed to examine differences in anxiety and depressive symptoms between individuals with and without a history of COVID-19 infection in a primary healthcare setting. Methods: A cross-sectional study was conducted in April 2022 in five family medicine practices in the primary health care facility of Sarajevo Canton. A total of 279 participants without previously diagnosed mental disorders completed an online questionnaire. Anxiety and depressive symptoms were assessed using the GAD-7 and PHQ-9 scales. Multivariable regression models were performed, and propensity score matching (1:1 nearest-neighbor matching, caliper = 0.2) was conducted to address baseline imbalance. Results: No statistically significant independent association was detected between prior COVID-19 infection and anxiety or depressive symptoms in multivariable models. Propensity score matching yielded 84 well-balanced pairs. In the matched sample, no significant differences were observed in GAD-7 (p = 0.229) or PHQ-9 scores (p = 0.139), nor in clinically relevant cut-offs. Female sex and chronic disease were independently associated with higher anxiety levels. Conclusions: In this primary healthcare population, we did not observe an independent association between prior COVID-19 infection and anxiety or depressive symptoms after covariate adjustment and propensity score matching. These findings should be interpreted cautiously given the cross-sectional design, possible exposure misclassification, and residual confounding. Full article

Background and Objectives: Red cell distribution width (RDW) has been associated with adverse cardiometabolic outcomes; however, whether RDW—particularly RDW standard deviation (RDW-SD)—represents an independent determinant of metabolic syndrome (MetS) or reflects cumulative metabolic burden remains unclear. This study evaluated the association between RDW-SD and MetS presence and examined its relationship with the quantitative accumulation of MetS components. Materials and Methods: In this single-center observational study, 222 adults undergoing evaluation for MetS were consecutively recruited. Participants with overt anemia, extreme mean corpuscular volume values, or acute inflammation were excluded. MetS was defined according to revised NCEP ATP-III criteria. Associations between RDW-SD and MetS were assessed using hierarchical multivariable logistic regression models. The relationship between RDW-SD and the number of MetS components was examined using multivariable linear regression. Discriminative performance was evaluated by receiver operating characteristic (ROC) curve analysis. Results: MetS was present in 68.0% of participants. RDW-SD levels were significantly higher in individuals with MetS and increased progressively across quartiles. RDW-SD was independently associated with the number of MetS components (standardized β = 0.226, p < 0.001). However, RDW-SD was not independently associated with MetS presence in fully adjusted logistic models (OR = 1.07, 95% CI: 0.97–1.18, p = 0.198). The addition of RDW-SD provided minimal incremental explanatory value (Nagelkerke R² increase from 0.348 to 0.356). ROC analysis demonstrated poor discriminatory ability (area under the curve [AUC] = 0.611, 95% CI: 0.535–0.687), supporting limited standalone diagnostic utility. Conclusions: RDW-SD was independently associated with cumulative metabolic burden but not with the independent presence of MetS after adjustment for established cardiometabolic factors. Given the cross-sectional design, these findings should be interpreted as associative rather than causal. Full article

This study investigates the relationship between general and problematic social media use, and young adults’ future time perspectives during their school-to-work-transition. It also explores how parents perceive the influence of their children’s media use on career perspectives. Utilizing longitudinal data from a quantitative study, 443 parent–youth dyads at t1 and 355 at t2 were surveyed on their practices concerning daily social media use, problematic social media engagement, transition and moratorium orientations, and parental assessments. Open-ended responses from parents indicated that the majority perceived either a positive effect or no influence of media use on career opportunities, while a smaller proportion reported negative impacts. Adolescents whose parents expressed positive views demonstrated significantly stronger transition orientations. Cross-sectional analyses demonstrated that problematic social media use was negatively associated with transition orientation and positively related to moratorium orientation. General usage time, however, showed no meaningful associations. Longitudinal regression analyses indicated that neither general nor problematic social media use predicted subsequent levels of transition or moratorium orientation after controlling for baseline orientations, pointing to substantial stability in these dispositions. The findings suggest that problematic social media engagement coincides with less future-oriented mindsets, while future orientations remain stable over time. Full article

To meet the requirements of quantitative elemental analysis in the ultraviolet (UV) spectrum, a UV single-photon imaging system was developed, integrating a digital micromirror device (DMD) and a single photon-counting imaging detector, enabling high sensitivity, high resolution, and a wide dynamic range. However, intrinsic geometric distortion poses a significant challenge to accurate spectral calibration. A hybrid correction framework is proposed, cascading polynomial coarse correction with multilayer perceptron (MLP) fine regression, improving calibration accuracy. The method utilizes a full-field dot-array mask projected by the DMD to acquire distortion-reference image pairs. The polynomial model rapidly captures the dominant high-order distortion, while a lightweight MLP performs non-parametric fine regression of residual displacements, achieving a mean error of 0.84 pixels. This approach reduces the root mean square (RMS) error to 1.01 pixels, outperforming traditional direct linear transformation (5.35 pixels) and pure polynomial models (1.33 pixels), while the nonlinearity index decreases from 0.35° to 0.05°. In addition, the method demonstrates stable performance across multi-scale checkerboard patterns ranging from 128 to 280 pixels, with RMS errors remaining around the 1-pixel level. These results validate the high-precision distortion suppression and robust cross-scale performance of the proposed framework. By leveraging DMD-generated patterns for self-calibration, this method eliminates the need for external targets, offering a scalable solution for high-end spectrometer calibration. Full article

The common carp (Cyprinus carpio) kidney uniquely integrates excretory nephrons, renal hematopoietic tissue, and hormonally active thyroid follicles, positioning it as a candidate “multipurpose biomarker organ” for pollutants like perfluorooctanoic acid (PFOA), a prototype long-chain PFAS and persistent organic pollutant exhibiting nephrotoxic, immunotoxic, and thyroid-disrupting effects. Building on prior histological, ultrastructural, and morphometric analyses from carp exposed to waterborne PFOA (0, 200 ng L⁻¹, 2 mg L⁻¹ for 56 days), a hierarchical multipurpose index comprising nephrotoxic, immunotoxic, and thyrotoxic subindices was developed from z-scored light-, electron-microscopy, and morphometric features, enabling cross-scale integration; proximal tubule vesiculations and effete rodlet cells (RCs) were newly quantified from archival electron micrographs. The subindices captured PFOA-induced glomerular hyperfiltration with proximal protein reabsorption and collecting duct RCs recruitment (nephrotoxic); hematopoietic tissue RCs recruitment, clustering, and exocytosis (immunotoxic); and increased thyroid follicle abundance/vesiculation, cross-sectional area, and perimeter (thyrotoxic). Quantification of previously only qualitatively assessed features provided statistical validation, while radar plot integration rendered results more intuitively evident—particularly highlighting the non-monotonic thyroid response—condensing organ-level complexity into a coherent framework supporting carp kidney as a translational One Health model for multi-endpoint waterborne pollutant assessment. Full article

Multi-agent and distributed dynamical systems can exhibit coordinated behavior that is difficult to summarize in terms of independent parts. Integrated Information Theory (IIT) provides one influential notion of system-level irreducibility, but exact computation of causal $\mathrm{\Phi }$ remains intractable except in very small systems. In this work, we introduce ${\mathrm{\Phi }}_{\mathrm{spectral}}$, a scalable observer-relative statistic defined on pairwise mutual information networks extracted from multivariate time-series data. A normalized graph Laplacian and its Fiedler vector identify a bipartition of the mutual information graph, and ${\mathrm{\Phi }}_{\mathrm{spectral}}$ reports the normalized weight of informational coupling crossing that cut. The measure is inspired by IIT’s concern with irreducibility but is not equivalent to intrinsic causal $\mathrm{\Phi }$: it is pairwise, undirected, and functional/statistical rather than intervention-based. We evaluate it on four exploratory simulation regimes: random oscillators, a transitional Kuramoto-like synchronization regime, a perfectly synchronized regime, and a combinatorial threshold-linear network (CTLN). Across these cases, ${\mathrm{\Phi }}_{\mathrm{spectral}}$ is most useful as a measure of observer-relative integration under second-order dependencies, separating redundancy-dominated from transiently differentiated regimes. The current results should be read as a proof-of-concept rather than as a formal validation against exact IIT. We discuss relations to weak IIT, Integrated World Modeling Theory (IWMT), and the perturbational complexity index (PCI), and we outline the stationary benchmarking and small-system validation needed for stronger causal claims. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Background/Objectives: Population aging has been accompanied by increased institutionalization of older adults and a high prevalence of vitamin D deficiency in this group. Although the literature suggests a possible relationship between vitamin D and cognition, findings remain inconsistent, particularly in institutional settings. This cross-sectional study aimed to investigate factors associated with vitamin D deficiency in institutionalized older adults, emphasizing the role of vitamin D supplementation and length of institutionalization, as well as to evaluate the association between serum vitamin D levels, cognitive decline, and dementia. Methods: A total of 104 older adults living in different long-term care institutions (LTCFs) in the city of Pelotas, RS, Brazil, were evaluated. Sociodemographic, clinical, and nutritional data were collected via interviews and medical record review. Serum 25-hydroxyvitamin D levels were categorized according to the Institute of Medicine cutoffs (<20 ng/mL and ≥20 ng/mL). Cognitive decline was assessed using the Mini-Mental State Examination, and dementia was evaluated with the Clinical Dementia Rating scale. Analyses included bivariate tests and binary logistic regression. Results: A high prevalence of vitamin D deficiency (52.9%), cognitive decline (83.6%), and questionable or mild dementia (79.4%) was observed. In multivariate analysis, vitamin D supplementation remained independently associated with vitamin D deficiency, whereas no significant association was observed between vitamin D levels and cognitive decline or dementia. Conclusions: Vitamin D deficiency in institutionalized older adults is predominantly associated with contextual and care-related factors rather than cognitive impairment, highlighting the importance of systematic nutritional monitoring and vitamin D supplementation strategies in institutional settings. Full article