Search Results (92)

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

Accurate and rapid identification of bacterial species is essential for public health, clinical diagnostics, and environmental monitoring. Although Raman spectroscopy offers a powerful, non-invasive alternative, reliance solely on spectral data often fails to distinguish species with highly similar signatures, particularly when the discriminating features are subtle. This difficulty is frequently compounded by a lack of integrated biological prior knowledge, which can hinder model performance. To address these challenges, we introduce BactoRamanBioNet, a novel multimodal neural network architecture. Our model employs a synergistic approach that utilizes a ResNet-Transformer architecture to capture complex spectral patterns and a CLIP text encoder to incorporate descriptive biological information, thereby enabling highly accurate multimodal classification of bacterial species. Empirical results demonstrate that BactoRamanBioNet achieves a classification accuracy of 98.2% and an F1-score of 98.0%. This performance surpasses the current state-of-the-art deep learning model, ResNet-1D, by 2.4% in accuracy and 2.0% in F1-score. Moreover, our model outperforms traditional classifiers, such as Support Vector Machine (SVM) and Random Forest (RF), by 9.8% and 7.9% in accuracy, respectively, while also exhibiting significant improvements in precision and recall. By establishing a new benchmark in performance and robustness, BactoRamanBioNet offers a powerful and reliable framework for automated microbiological analysis, paving the way for next-generation diagnostic systems. Full article

Background/Objectives: Pressure pain thresholds (PPTs) are commonly used to quantify mechanical hyperalgesia in migraine and tension-type headache (TTH), but the discriminatory performance of PPTs across neural and muscular sites remains unclear. This study compared nerve- and muscle-related PPTs between migraine and frequent episodic TTH and explored site-specific ROC-derived cut-off values as complementary classification markers. Methods: In this cross-sectional case-group discrimination study, participants with migraine (n = 33) and frequent episodic TTH (n = 31) underwent bilateral PPT assessment (electronic algometry) over the temporalis and tibialis anterior muscles, C5/C6 zygapophyseal joints, peripheral nerves (greater occipital, median, ulnar, radial, posterior tibial, common peroneal), and the second metacarpal region. Results: PPTs were generally lower in the migraine group than in the TTH group. After adjustment for sex and age, the most consistent between-group differences remained at the temporalis muscles bilaterally (left: adjusted mean difference 0.49 kg/cm², 95% CI 0.10 to 0.89, p = 0.015; right: 0.53 kg/cm², 95% CI 0.13 to 0.93, p = 0.011) and at the left tibialis anterior muscle (0.90 kg/cm², 95% CI 0.03 to 1.78, p = 0.044). In the main ROC analysis, the temporalis muscles showed the strongest discriminatory performance (left AUC = 0.733; right AUC = 0.707), whereas tibialis anterior and left posterior tibial nerve sites showed modest, below-threshold discrimination (AUCs < 0.70 despite statistical significance in some cases). Women-only ROC analyses showed a broadly similar pattern, with slightly improved metrics at some sites, particularly the temporalis muscles. Across most sites, likelihood ratios indicated only small-to-moderate shifts in post-test probability. Conclusions: Participants with migraine showed lower PPTs than those with frequent episodic TTH across most assessed sites, with the clearest differences at the temporalis muscles. ROC and PR analyses suggest that PPTs (especially at temporalis sites) may provide complementary, hypothesis-generating discriminatory information, but their overall stand-alone discriminative utility is modest. PPT assessment should therefore be interpreted as an adjunct to clinical evaluation rather than a replacement diagnostic test. Full article

Sustainability monitoring relies on environmental indicator systems that integrate heterogeneous multivariate measurements across space and time; however, collinearity, non-Gaussian variability, and influential observations frequently destabilize classical multiblock methods and may bias indicator-based assessment and decision support. This study proposes a robust extension of the STATICO (STATIS–CO-inertia) framework to model common structures among paired environmental indicator blocks under realistic data contamination. The approach preserves the original triadic algebraic formulation while incorporating robust covariance estimation and adaptive weighting to reduce the influence of outliers and structurally unstable blocks. Robustification is implemented at the interstructure stage through a reformulated Escoufier’s RV coefficient and in the construction of the compromise space via robust distances. The RV coefficient, a multivariate generalization of the squared Pearson correlation computed between cross-product matrices, is used to quantify structural similarity between paired data blocks and to evaluate the stability of the compromise structure. Performance is evaluated using simulated datasets calibrated to represent Ecuadorian coastal monitoring conditions. The results show that Robust STATICO increases compromise dominance and stability, redistributes inter-block similarities more coherently, and improves discriminative representation in the factorial space, yielding more interpretable and environmentally plausible structures. Overall, the proposed method provides a reliable analytical tool for sustainability-oriented environmental monitoring by supporting stable identification of persistent multivariate patterns and robust comparison of indicator structures in complex systems. Full article

Background/Objectives: Ventricular repolarization abnormalities (VRA) represent a grey area in athlete screening: some patterns are physiological, while others are precursors to heart disease. Objective: to clarify the natural history of VRA and the associated factors of structural diagnosis. Methods: Retrospective observational single-center study of athletes with resting or stress VRA at the first evaluation, with normal echocardiography; minimum follow-up of 2 years. Clinical data, resting and stress ECG, echocardiography, and selective advanced imaging throughout follow-up were collected. Primary outcome: cardiovascular diagnosis at follow-up; time-to-event analysis and associations between ECG characteristics and diagnosis. Results: Fifty-three athletes (mean age 22.2 ± 9.2 years; 92.5% male) were included; 60.4% had resting VRA, and 100% had exercise-induced VRA at baseline. Over 7.3 ± 4.5 years, 28/53 (52.8%) received a diagnosis; median time-to-detection was 7.0 years (95% CI 6.0–not reached); RMST10 was 6.7 years (95% CI 5.7–7.7). Diagnoses included hypertrophic cardiomyopathy (24.5%), non-ischaemic left-ventricular scar (11.3%), myocardial bridging (7.5%), hypertensive remodelling (5.7%), coronary anomaly (1.9%), and ventricular pre-excitation (1.9%). Persistence of resting VRA from baseline to follow-up was more frequent in athletes with a final diagnosis (p = 0.01), whereas topography and exercise-induced abnormalities did not discriminate groups. Advanced imaging contributed substantially to case ascertainment. No major adverse cardiovascular events have been identified throughout follow-up. Conclusions: In athletes with screening-detected VRA and normal echocardiography, persistence of resting VRA was associated with higher detection of a cardiovascular diagnosis, while exercise-induced changes alone show limited diagnostic yield. The long median time-to-detection supports prolonged, pre-planned surveillance, with priority for advanced imaging in profiles with persistent abnormalities. These findings align with a risk-adapted, personalized management strategy in sports cardiology. Full article

Background and Objectives: Spinal infections represent a heterogeneous group of diseases where primary or secondary etiological classification is fundamental for diagnosis and clinical decision-making. The aim is to present multicenter data evaluating etiological patterns associated with comorbidity. This study investigated the etiological distribution of spinal infections in a multicenter cohort and examined the relationships between chronic kidney disease (CKD) and diabetes mellitus (DM) and primary and secondary spinal infection etiologies, which emerged in the study and are thought to contribute to the literature. Materials and Methods: For this early-phase exploratory modelling study, a ridge-penalized logistic regression (L2) model was trained using repeated nested cross-validation (outer 5-fold stratified CV ×10 repetitions; inner 5-fold CV) to generate out-of-fold (OOF) probabilities. The penalty parameter (C) was optimized by minimizing log-loss. All preprocessing was performed within the CV pipeline to prevent data leakage. A supplementary Firth-penalized analysis was conducted as a plausibility check, using the CKD0/DM0 group as reference. Results: The model demonstrated effective discrimination between spinal infection probabilistic profiles (OOF AUC 0.762; conditional OOF bootstrap 95% CI 0.608–0.885). A contrasting probabilistic profile concordance was observed: DM-only patients had a high likelihood of secondary infection (observed secondary risk 93.3%; mean OOF estimated probability 84.4%), compared to a higher likelihood of primary infection in CKD-only patients (observed secondary risk 15.4%, which translates to a primary risk of 84.6%; mean OOF estimated probability 21.8%). Calibration was near-ideal (intercept 0.069; slope 1.028). Decision curve analysis showed a clear utility between the thresholds of 0.15 and 0.84. There were no CKD+DM+ cases (n = 0); analyses were restricted to supported strata. Conclusions: In this multicenter analysis of spine infections, CKD was predominantly related to primary spine infection etiology, whereas DM was more frequently related to secondary spine infections. These findings emphasize the potential role of comorbidity profiles in etiologic classification and need to be confirmed in larger multicenter cohorts. Full article

This systematic review examines how gender shapes exposure to and experiences of psychosocial risks in the workplace. Drawing on 89 empirical studies published between 2010 and 2024, the review synthesizes evidence from occupational health psychology, gender studies, and organizational research. Searches were conducted in PubMed, Web of Science, Scopus, CINAHL, and PsycINFO, and included empirical studies published in English and Spanish. Following PRISMA guidelines, a qualitative thematic synthesis was conducted to integrate findings across diverse sectors, populations, and methodological approaches. The evidence reveals persistent gendered patterns in psychosocial risk exposure and outcomes: women are more frequently exposed to emotionally demanding and relational forms of work and report poorer mental health outcomes; men experience performance-driven strain linked to workload, competition, and reward insecurity more often; and transgender and non-binary workers face additional psychosocial burdens associated with stigma, discrimination, and minority stress. Across the literature, structural and cultural determinants—such as occupational segregation, unequal recognition, and gendered organizational norms—emerge as central mechanisms underlying these disparities. Theoretical frameworks including effort–reward imbalance, demand–control, work–family conflict, organizational climate, and minority stress collectively contribute to explaining how gendered psychosocial risks are produced and sustained. Overall, the review underscores the need to move beyond individualistic and binary models of psychosocial risk toward gender-responsive approaches that account for structural, relational, and identity-based dimensions of work, thereby informing research and organizational strategies aimed at promoting equitable and sustainable well-being at work. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article

Monitoring soil contamination from petroleum products is vital for protecting human health and the environment. In forestry, hydraulic oil spills frequently result from leaks in equipment such as harvesters. This study evaluates a custom-built, inexpensive electronic nose, equipped with a Figaro TGS gas sensor array, for discriminating between pristine and contaminated soil samples. Two oil types and three pollution intensities were analyzed. The constructed electronic nose applied two sensor operation modes: (i) response to change of sensor operation condition from clean air to target odors and (ii) response to sensor heater temperature modulation. Classification was performed using Random Forest and Support Vector Machine (SVM) algorithms, and Linear Discriminant Analysis (LDA) was used to explore multidimensional data patterns. The sensor heater temperature modulation mode provided superior classification performance. Measurements at room temperature achieved an accuracy of 97%, clearly outperforming measurements on samples heated to 60 °C (75%). While the system successfully identified biodegradable oil contamination, standard mineral oil was more challenging to detect. Among the sensors tested, TGS 2602 was the most effective. These findings indicate that portable electronic noses can provide a statistically robust and cost-effective tool for assessing the severity of soil pollution. Full article

Stroke frequently results in persistent upper limb impairments, which are often accompanied by compensatory movement strategies that are not fully captured by conventional clinical assessment scales. Quantitative kinematic analyses may provide more objective and sensitive measures of motor dysfunction. In this study, we propose a probabilistic, distribution-based analysis of upper limb kinematics to quantify motor disability in post-stroke patients. We analyzed reaching movement data acquired with a markerless Kinect V2 system from 36 post-stroke patients and age-matched healthy controls. Wrist velocity profiles were characterized using distribution metrics, including variance, skewness, kurtosis, and entropy, and divergence measures (Hellinger distance, Kullback–Leibler divergence, and Jensen–Shannon divergence). Group differences between patients and controls, as well as across impairment levels stratified by the Fugl-Meyer (FM) score, were evaluated. Several distribution metrics significantly discriminated patients from controls and scaled with motor impairment severity. In particular, divergence-based measures showed a strong association with FM scores, indicating increasing deviation from normative movement patterns with greater impairment. These findings demonstrate that distribution-based metrics focusing on kinematic analysis provide a clinically meaningful, objective descriptor of motor dysfunction and complement conventional biomechanical assessments, offering a sensitive framework for quantifying motor disability after stroke. Full article

The current state-centric analysis of Official Variety Trials (OVTs) restricts the identification of stable performance zones across political boundaries. This study employed multivariate statistical learning techniques to delineate soybean (Glycine max L.) “mega-environments” using yield data from 2269 varieties collected across seven U.S. states (2019–2022). Utilizing Quadratic Discriminant Analysis (QDA), Principal Component Analysis (PCA), and Agglomerative Hierarchical Clustering (AHC), we examined the edaphoclimatic factors influencing yield stability. QDA classified over 79% of environments into distinct temporal categories, highlighting significant inter-annual climatic variability driven by Growing Degree Days (GDD) and latitude. PCA distinguished broad climatic drivers (PC1) from localized soil texture constraints (PC2). AHC identified optimal production clusters that frequently diverged from geographic proximity, indicating that distant sites often share more critical yield-determining factors than neighboring counties. By operationalizing these latent environmental patterns, this study provides a data-driven framework for cross-state environmental zoning that can support more precise variety placement once genotype performance has been evaluated within these zones. Full article

Defects on large-scale structural surfaces can compromise integrity and pose safety hazards, highlighting the need for efficient automated inspection. UAVs provide a flexible and effective platform for such inspections, yet traditional vision-based methods often require high computational resources and show limited sensitivity to small defects, restricting practical UAV deployment. To address these challenges, we propose ESS-DETR, a lightweight and high-precision detection model designed for UAV-based surface inspection, built upon core modules: EMO-inspired lightweight backbone that integrates convolution and efficient attention mechanisms to reduce parameters; Scale-Decoupled Loss that adaptively balances targets of various sizes to enhance accuracy and robustness for small and irregular defect patterns frequently encountered in UAV imagery; and SPPELAN multi-scale fusion module that improves feature discrimination under complex reflections, shadows, and lighting variations typical of aerial inspection environments. Experimental results demonstrate that ESS-DETR reduces computational complexity from 103.4 to 60.5 GFLOPs and achieves a Precision of 0.837, Recall of 0.738, and mAP of 79, outperforming Faster R-CNN, RT-DETR, and YOLOv11, particularly for small-scale defects, confirming that ESS-DETR effectively balances accuracy, efficiency, and onboard deployability, providing a practical solution for intelligent UAV-based surface inspection. Full article

Accurate monitoring of fish movement is essential for understanding behavioral patterns and group dynamics in aquaculture systems. Underwater scenes—characterized by dense populations, frequent occlusions, non-rigid body motion, and visually similar appearances—present substantial challenges for conventional multi-object tracking methods. We propose an improved CenterTrack-based framework tailored for multi-fish tracking in such environments. The framework integrates three complementary components: a multi-branch feature extractor that enhances discrimination among visually similar individuals, occlusion-aware output heads that estimate visibility states, and a three-stage cascade association module that improves trajectory continuity under abrupt motion and occlusions. To support systematic evaluation, we introduce a self-built dataset named Multi-Fish 25 (MF25), continuous video sequences of 75 individually annotated fish recorded in aquaculture tanks. The experimental results on MF25 show that the proposed method achieves an IDF1 of 82.5%, MOTA of 85.8%, and IDP of 84.7%. Although this study focuses on tracking performance rather than biological analysis, the produced high-quality trajectories form a solid basis for subsequent behavioral studies. The framework’s modular design and computational efficiency make it suitable for practical, online tracking in aquaculture scenarios. Full article

Sarcopenia is highly prevalent among patients with chronic kidney disease (CKD) and chronic heart failure (CHF), yet the underlying immunometabolic mechanisms remain insufficiently understood. Short-chain fatty acids (SCFAs), inflammatory cytokines, and body-composition alterations may jointly contribute to the development of muscle dysfunction in this population. In this cross-sectional study, 80 patients with CKD and CHF underwent comprehensive clinical, biochemical, bioimpedance, inflammatory, and SCFA profiling. Sarcopenia was diagnosed according to EWGSOP2 criteria. Multivariable logistic regression, LASSO feature selection, correlation analysis, PCA, and Random Forest modeling were used to identify key determinants of sarcopenia. Sarcopenia was present in 39 (49%) participants. Patients with sarcopenia exhibited significantly lower body fat percentage, reduced ASM, and slower gait speed. Hexanoic acid (C6) showed an independent positive association with sarcopenia (OR = 2.24, 95% CI: 1.08–5.37), while IL-8 showed an inverse association with sarcopenia (OR = 0.38, 95% CI: 0.13–0.94), indicating that lower IL-8 levels were more frequently observed in individuals with sarcopenia. Correlation heatmaps revealed distinct SCFA–cytokine coupling patterns depending on sarcopenia status, with stronger pro-inflammatory clustering in C6-associated networks. The final multivariable model integrating SCFAs, cytokines, and body-composition metrics achieved excellent discrimination (AUC = 0.911) and good calibration. Sarcopenia in CKD–CHF patients represents a systemic immunometabolic disorder characterized by altered body composition, chronic inflammation, and dysregulated SCFA signaling. Hexanoic acid (C6) and IL-8 may serve as informative biomarkers of muscle decline. These findings support the use of multidimensional assessment and highlight potential targets for personalized nutritional, microbiota-modulating, and rehabilitative interventions. Full article

Persistent cloud cover during the growing season and mosaic cropping patterns introduce temporal gaps and mixed pixels, undermining the reliability of large-scale crop identification and acreage statistics. To address these issues, we develop a high spatiotemporal-resolution remote-sensing approach tailored to heterogeneous farmlands. First, an improved Spatiotemporal Adaptive Reflectance Fusion Model (STARFM) is used to fuse Landsat, Sentinel-2, and MODIS observations, reconstructing a continuous Normalized Difference Vegetation Index (NDVI) time series at 30 m spatial and 8-day temporal resolution. Second, at the field scale, we derive phenological descriptors from the reconstructed series—key phenophase timing, amplitude, temporal trend, and growth rate—and use a Random Forest (RF) classifier for detailed crop discrimination. We further integrate SHapley Additive exPlanations (SHAP) to quantify each feature’s class-discriminative contribution and signed effect, thereby guiding feature-set optimization and threshold refinement. Finally, we generate a 2024 crop distribution map and conduct comparative evaluations. Relative to baselines without fusion or without phenological variables, the fused series mitigates single-sensor limitations under frequent cloud/rain and irregular acquisitions, enhances NDVI continuity and robustness, and reveals inter-crop temporal phase shifts that, when jointly exploited, reduce early-season confusion and improve identification accuracy. Independent validation yields an overall accuracy (OA) of 90.78% and a Cohen’s kappa(κ) coefficient of 0.882. Coupling dense NDVI reconstruction with phenology-aware constraints and SHAP-based interpretability demonstrably improves the accuracy and reliability of cropping-structure extraction in complex agricultural regions and provides a reusable pathway for regional-scale precision agricultural monitoring. Full article