Search Results (15,434)

Pesticide residue violations continue to challenge the compliance of Turkish horticultural exports with European Union food safety regulations. This study examined 1138 RASFF alerts (1660 detections) issued between 2020 and 2025 using statistical enrichment, time-series modeling, and unsupervised machine learning. Pepper was the most frequently rejected commodity, with strong enrichments of formetanate (71-fold), pyridaben (35-fold), and acetamiprid (5-fold). Notably, chlorpyrifos and chlorpyrifos-methyl remained among the most commonly detected residues despite EU bans, suggesting continued use of stockpiled or illicit products and prolonged environmental persistence. Rejections peaked during winter and spring, particularly for citrus and greenhouse-grown crops. Clustering and association rule mining revealed modular commodity–pesticide structures and recurrent co-detection patterns. Anomaly detection further identified discrete periods of irregular contamination. Overall, the results indicate that violations are seasonally patterned and structurally embedded. Targeted monitoring aligned with crop calendars and stricter enforcement of legacy pesticide phase-outs could significantly improve compliance and reduce export rejections. Full article

Conducting structural monitoring of highway subgrades is crucial for investigating damage evolution mechanisms under dynamic load-temperature coupling effects. However, existing sensing technologies struggle to achieve distributed, long-term, and high-precision measurements of subgrade structures. Therefore, this study employs next-generation fiber-optic array sensing technology to construct a distributed monitoring system based on weak reflection grating arrays. A dual-parameter sensing network for strain and temperature was designed and installed during the expansion and renovation of a highway in Fujian Province, enabling high-precision monitoring of the entire continuous strain field and temperature field of the subgrade structure. Through a comprehensive analysis of dynamic loading test data and long-term monitoring records, the system revealed the dynamic response patterns of subgrade structures under the interaction of modulus differences, burial depth effects, temperature gradients, and load parameters. It elucidated the mechanical sensitivity of flexible base layers and the interlayer stress redistribution mechanism. The study validated that grating array sensors not only offer advantages such as easy installation, a high survival rate, and excellent durability but also enable high-capacity, long-distance, and high-precision measurements of subgrade structures. This provides a new technical approach for full lifecycle monitoring of expressways. Full article

Accurate extraction of building features from remotely sensed data is essential for supporting research and applications in urban planning, land management, transportation infrastructure development, and disaster monitoring. Despite the prominence of deep learning as the state-of-the-art (SOTA) methodology for building extraction, substantial challenges remain, largely stemming from the diversity of building structures and the complexity of background features. To mitigate these issues, this study introduces DeepSwinLite, a lightweight architecture based on the Swin Transformer, designed to extract building footprints from very high-resolution (VHR) imagery. The model integrates a novel local–global attention module to enhance the interpretation of objects across varying spatial resolutions and facilitate effective information exchange between different feature abstraction levels. It comprises three modules: multi-scale feature aggregation (MSFA), improving recognition across varying object sizes; multi-level feature pyramid (MLFP), fusing detailed and semantic features; and AuxHead, providing auxiliary supervision to stabilize and enhance learning. Experimental evaluations on the Massachusetts and WHU Building Datasets reveal the superior performance of DeepSwinLite architecture when compared to existing SOTA models. On the Massachusetts dataset, the model attained an OA of 92.54% and an IoU of 77.94%, while on the WHU dataset, it achieved an OA of 98.32% and an IoU of 92.02%. Following the correction of errors identified in the Massachusetts ground truth and iterative enhancement, the model’s performance further improved, reaching 94.63% OA and 79.86% IoU. A key advantage of the DeepSwinLite model is its computational efficiency, requiring fewer floating-point operations (FLOPs) and parameters compared to other SOTA models. This efficiency makes the model particularly suitable for deployment in mobile and resource-constrained systems. Full article

The Mediterranean soft-shell turtle (Trionyx triunguis) is classified as critically endangered by the IUCN. Effective conservation requires a clear understanding of its reproductive strategies and population structure. By combining mitochondrial DNA tandem repeat-region profiling with genome-wide SNP data obtained through 3RADseq, we gained high-resolution insights into the genetic composition and breeding behavior of Mediterranean populations. Our results revealed complex reproductive dynamics, including multiple paternity, sperm storage, and repeated nesting within a single season—strategies that enhance genetic diversity in small, fragmented populations. Using SNP-based kinship inference, we estimated the number of breeding females and identified full and half-sibling groups, offering a robust genomic framework for assessing population size and structure. Genetic similarity patterns highlighted moderate differentiation among Israeli river populations, suggesting some connectivity, while samples from Türkiye were clearly distinct, reflecting long-term geographic and genetic separation. This integrative approach provides a scalable, repeatable tool for long-term monitoring. The combined use of maternal and biparental markers enables detailed tracking of genetic diversity, breeding contributions, and demographic trends—key elements for designing informed, adaptive conservation strategies. Full article

Railway service interruptions and electrical hazards often arise due to bird nests concealed within the intricate, highly symmetric overhead catenary networks of high-speed lines. These nests are difficult to pinpoint automatically, not only because they are diminutive and often merge visually with the surroundings but also due to occlusions and the persistent lack of substantial labeled datasets. To address this bottleneck, this work presents the High-Speed Railway Catenary Nest Dataset (HRC-Nest), merging 800 authentic images and 1000 synthetic samples to capture a spectrum of scenarios. Building on the symmetry of catenary structures—where nests appear as localized asymmetries—the Symmetry-Aware Railway Nest Detection Framework (RNDF) is proposed, an enhanced YOLOv12 system for accurate and robust nest detection in symmetric high-speed railway catenary environments. With the A2C2f_HRAMi design, the RNDF learns from multi-level features by unifying residual and hierarchical attention strategies. The SCSA component boosts the recognition in visually cluttered or obstructed settings further by jointly processing spatial and channel-wise signals. To sharpen the detection accuracy, particularly for subtle, hidden nests, the Focaler-GIoU loss guides bounding box optimization. Comparative studies show that the RNDF consistently outperforms recent detectors, surpassing the YOLOv12 baseline by 5.95% mAP@0.5 and 26.16% mAP@0.5:0.95, underscoring its suitability for symmetry-aware, real-world catenary anomaly monitoring. Full article

A barrier pillar between the surface and underground mining sections provides a critical buffer zone in the transition from the boxcut highwall to underground sections by isolating stress fields from underground sections and preventing them from affecting the boxcut highwall slope. In this study, an empirical scaled span method and Rocscience RS2 software were used to conduct parametric studies on key parameters for designing barrier pillars and analyzing the room and pillar design for a planned underground mine on the Great Dyke, Zimbabwe. The approach included analyzing the effect of barrier pillar width, assuming a 10° dipping angle of the orebody, with room and pillar dimensions of 7 m and 6 m, respectively. The impact on boxcut slope stability and the roof of the first stope was monitored. The stability of the barrier pillar was analyzed for varying widths (6 m, 10 m, 20 m, 30 m, and 40 m) and orebody dipping angles (0°, 10°, 20°, 30°, and 40°). The effect of deteriorated rock mass conditions, represented by Geological Strength Index (GSI) values from 30 to 50, was assessed. The optimum room and pillar design was evaluated against the planned 6 m pillar sizes. This comprehensive study aims to support the integrity and longevity of the critical structures of the mining operation. Full article

Background/Objectives: Syncope is a common clinical problem often requiring pharmacological treatment, yet evidence-based therapies remain limited. Midodrine, a vasopressor agent, is frequently used, though its autonomic effects over time remain unclear. This study aimed to assess autonomic nervous system changes and blood pressure response in syncope patients treated with Midodrine, placebo, or their combination. Additionally, the structural properties of the Midodrine placebo were analyzed using nanotechnological methods. Methods: A total of 67 patients with syncope were randomized to receive Midodrine, sucrose placebo, or their combination over three weeks. All participants underwent 24 h Holter ECG with heart rate variability (HRV) analysis and ambulatory blood pressure monitoring before and after therapy. Structural analysis of Midodrine tablets, sucrose, and Midodrine placebo was performed using Raman spectroscopy and X-ray diffraction (XRD). Results: Patients receiving the Midodrine–placebo combination showed a significant reduction in HRV markers of parasympathetic activity (RMSSD, pNN50, HF) and an increase in sympathetic dominance (LF/HF ratio) compared to the other groups. Only this group showed a statistically significant rise in average systolic and diastolic blood pressure. Raman and XRD analyses revealed structural alterations in the sucrose-based placebo compared to its original form, indicating subtle changes in crystalline structure. Conclusions: In this exploratory study, the combination of Midodrine and placebo was associated with autonomic imbalance and modest increases in blood pressure, which may indicate a potential effect in patients with hypotensive syncope phenotypes. These preliminary findings should be interpreted with caution, and the structural modifications observed in the placebo formulation are presented as hypotheses requiring further investigation rather than established mechanisms. Full article

Background: Healthcare workers face multiple, interlinked occupational health risks—burnout, post-COVID-19 sequelae (Long COVID), and extended medical leave. These outcomes often share predictors, contribute to each other, and, together, impact workforce capacity. Yet, existing tools typically address them in isolation. Objective: The objective of this study to develop and deploy an integrated, explainable artificial intelligence (AI) framework that predicts these three outcomes using the same structured occupational health dataset, enabling unified workforce risk monitoring. Methods: We analyzed data from 1244 Romanian healthcare professionals with 14 demographic, occupational, lifestyle, and comorbidity features. For each outcome, we trained a separate predictive model within a common framework: (1) a lightweight transformer neural network with hyperparameter optimization, (2) a transformer with multi-head attention, and (3) a stacked ensemble combining transformer, XGBoost, and logistic regression. The data were SMOTE-balanced and evaluated on held-out test sets using Accuracy, ROC-AUC, and F1-score, with 10,000-iteration bootstrap testing for statistical significance. Results: The stacked ensemble achieved the highest performance: ROC AUC = 0.70 (burnout), 0.93 (Long COVID), and 0.93 (extended leave). The F1 scores were >0.89 for Long COVID and extended leave, whereas the performance gains for burnout were comparatively modest, reflecting the multidimensional and heterogeneous nature of burnout as a binary construct. The gains over logistic regression were statistically significant (p < 0.0001 for Long COVID and extended leave; p = 0.0355 for burnout). The SHAP analysis identified overlapping top predictors—tenure, age, job role, cancer history, pulmonary disease, and obesity—supporting the value of a unified framework. Conclusions: We trained separate models for each occupational health risk but deployed them in a single, real-time web application. This integrated approach improves efficiency, enables multi-outcome workforce surveillance, and supports proactive interventions in healthcare settings. Full article

This study proposes a novel structural deflection measurement method using a single smartphone with an innovative scale factor (SF) calibration technique, eliminating reliance on laser rangefinders and industrial cameras. Conventional off-axis digital image correlation (DIC) techniques require laser rangefinders to measure discrete points for SF calculation, suffering from high hardware costs and sunlight-induced ranging failures. The proposed approach replaces physical ranging by deriving SF through geometric relationships of known structural dimensions (e.g., bridge length/width) within the measured plane. A key innovation lies in developing a versatile SF calibration framework adaptable to varying numbers of reference dimensions: a non-optimized calculation integrates smartphone gyroscope-measured 3D angles when only one dimension is available; a local optimization model with angular parameters enhances accuracy for 2–3 known dimensions; and a global optimization model employing spatial constraints achieves precise SF resolution with ≥4 reference dimensions. Indoor experiments demonstrated sub-0.05 m ranging accuracy and deflection errors below 0.30 mm. Field validations on Beijing Subway Line 13′s bridge successfully captured dynamic load-induced deformations, confirming outdoor applicability. This smartphone-based method reduces costs compared to traditional setups while overcoming sunlight interference, establishing a hardware-adaptive solution for vision-based structural health monitoring. Full article

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article

Driven by the rapid advancement of smart grid frameworks, the volume of visual data collected from power system diagnostic equipment has surged exponentially. A substantial portion of these images (30–40%) are redundant or highly similar, primarily due to periodic monitoring and repeated acquisitions from multiple angles. Traditional redundancy removal methods based on manual screening or single-feature matching are often inefficient and lack adaptability. In this paper, we propose a two-stage redundancy removal paradigm for power inspection imagery, which integrates abstract semantic priors with fine-grained perceptual details. The first stage combines an improved discrete cosine transform hash (DCT Hash) with the multi-scale structural similarity index (MS-SSIM) to efficiently filter redundant candidates. In the second stage, a Vision Transformer network enhanced with a hierarchical sparse attention mechanism precisely determines redundancy via cosine similarity between feature vectors. Experimental results demonstrate that the proposed method achieves an algorithm sensitivity of 0.9243, surpassing ResNet and VGG by 5.86 and 8.10 percentage points, respectively, highlighting its robustness and effectiveness in large-scale power grid redundancy detection. These results underscore the paradigm’s capability to balance efficiency and precision in complex visual inspection scenarios. Full article

I examine how the association between the current period’s budget variance and the subsequent period’s budget is affected by various governmental monitoring mechanisms. Specifically, I consider the following governance and monitoring mechanisms: governance structure, state/city budget-limiting regulations, and voter-initiated monitoring. I find that city budgets ratchet in the top 50 populous cities in the US. I also document evidence of asymmetric ratcheting—the current period’s favorable budget variances result in budget increases in the following year that are larger than the decreases associated with unfavorable variances of the same magnitude. Consistent with the political budget cycle hypothesis that budget pattern alters during pre-election periods, I find the asymmetric ratcheting pattern becomes invisible in times of election, particularly when an incumbent runs for re-election. Given this evidence of the opportunistic budgetary pattern, I hypothesize and find that some monitoring mechanisms mitigate the sensitivity of the subsequent period’s budget with respect to the current period’s budget variance. Full article

The construction of a wireless detection network for bridge inspection is important in intelligent infrastructure management. Advanced wireless communication technology and a sensor network enable the real-time remote and accurate monitoring of bridge structure health. We designed a protocol and implemented it in a wireless detection network to overcome the limitations of traditional bridge health monitoring methods. The network improves the efficiency and accuracy of monitoring and ensures safe bridge maintenance. We analyzed the requirements of bridge monitoring, including the strict requirements for high-precision data acquisition, low delay transmission, energy efficiency and network reliability. Full article

Electrocardiogram reports are an important component of cardiovascular diagnostics, routinely generated in hospitals and clinical settings to monitor cardiac activity and guide medical decision-making. ECG reports often consist of structured waveform data accompanied by free-text interpretations written by clinicians. Although the waveform data can be analyzed using signal processing techniques, the unstructured text component contains rich, contextual insights into diagnoses, conditions, and patient-specific observations that are not easily captured by conventional methods. Extracting meaningful patterns from clinical narratives poses significant challenges. In this work, we present an unsupervised framework for exploring and analyzing ECG diagnostic reports using transformer-based language modeling and clustering techniques. We use the domain-specific language model BioBERT to encode text-based ECG reports into dense vector representations that capture the semantics of medical language. These embeddings are subsequently standardized and subjected to a series of clustering algorithms, including KMeans, hierarchical clustering, DBSCAN, and K-Medoids, to uncover latent groupings within the data. Full article

Accurate estimation of tree structural and morphological parameters is essential in precision fruit farming, supporting optimised irrigation management, biomass estimation and carbon stock assessment. While traditional field-based measurements remain widely used, they are often time-consuming and subject to operator-induced errors. In recent years, Terrestrial Laser Scanning (TLS) and UAV-based photogrammetry have been successfully employed to generate high-resolution 3D reconstructions of plants; however, their cost and operational constraints limit their scalability in routine field applications. This study investigates the performances of a low-cost, consumer-grade device—the iPhone 13 Pro equipped with an integrated LiDAR sensor and RGB camera—for 3D scanning of fruit tree structures. Cylindrical targets with known geometric dimensions were scanned using both the LiDAR and photogrammetric (Photo) modes of the Polycam© application, with accuracy and precision assessed by comparing extracted measurements to reference values. Field applicability was also tested on hazelnut trees, assessing height, stem diameter and leaf area: the Photo mode delivered the highest accuracy (systematic error of 0.007 m and R² = 0.99) and strong agreement with manual leaf measurements (R² = 0.93). These results demonstrate that smartphone-based 3D scanning can provide a practical, low-cost approach for structural characterisation in fruit orchards, supporting more efficient crop monitoring. Full article