Search Results (5,841)

Background: The clinical meaning of hip joint synovial cavity thickness (HJSCT) on ultrasound (US) in juvenile idiopathic arthritis (JIA) without effusion is uncertain. Methods: In this cross-sectional study, we analyzed 369 children (187 JIA; 182 controls) undergoing hip US at a referral center in Kraków, Poland. JIA examinations were performed upon initial referral, early in the care pathway. We excluded patients with hip effusion and pre-existing inflammatory, traumatic or degenerative hip pathology. HJSCT was defined as the distance from the outer capsule margin to the femoral neck cortex. We used a Toshiba Aplio 400 system with a 12 MHz probe to measure and derive mean bilateral HJSCT. Bilateral concordance was assessed. Iterative multivariable linear regression modeling was used to compare groups, adjusting for non-linear age effects (natural splines) and WHO height-for-age z-scores (HAZ). Results: Left–right HJSCT agreement was high (ICC 0.947; mean difference 0.03 mm; 95% limits of agreement −0.64–0.70). In unadjusted analysis, mean (SD) HJSCT was similar in JIA versus controls: 5.83 (1.09) vs. 5.95 (0.99) mm, respectively (p = 0.25). In the final model (adj. R2 0.656), HJSCT was strongly associated with age (non-linear, p < 0.001) but not significantly associated with HAZ (β = 0.04; p = 0.11) or JIA status (β = 0.07; p = 0.30). Predicted HJSCT showed a steep increment in childhood and plateau in adolescence. Conclusions: In children without hip effusion, HJSCT mainly reflects physiological growth and does not differ significantly between early JIA patients and healthy controls. These findings suggest that capsular thickening is not a reliable standalone marker for early disease in the absence of effusion. Full article

Nowadays, biowaste valorization is a key point in the circular economy. Digestate and compost from organic waste treatment can be used as nutrient-rich fertilizers. In Europe, the use of biowaste-derived fertilizers is promoted by the European Fertilizer Regulation (EU) 2019/1009, which requires verification of their biological stability through regulated indices; however, it is not clear whether the proposed indices and threshold values indicate the same level of stability and what correlations there are between them. This study compared four biological stability indices, namely Oxygen Uptake Rate (OUR), Self-Heating (SH), Residual Biogas Potential (RBP), and Dynamic Respirometric Index (DRI), which were tested on 50 samples of compost and digestate. Overall, the results revealed that most of the compost and digestate samples were quite far from European standards. On the contrary, the RBP test seemed to be less stringent than the other indices, since a much larger number of samples was closer to or in compliance with the established threshold. Data analysis using Pearson’s coefficients showed a strong linear correlation between the indices. Nevertheless, the linear regression predictive model based on experimental data demonstrated that the indices could not represent the same level of stability, providing poor consistency and variability in the predicted values and established threshold. In particular, the DRI test appeared to be more severe than the other aerobic indices. This work could provide valuable support in improving evaluation criteria and promoting a sustainable use of compost and digestate as organic fertilizers from a circular economy perspective. Full article

This study employed data-driven machine learning models to analyze the effects of filler material composition and other process parameters on mechanical properties during the crack repair of nickel-based superalloys such as K417G using wide-gap brazing technology. First, a linear regression model was used to analyze the influence of independent variables (filler material composition and other process parameters) on the dependent variables (tensile strength and elongation). The regression results indicated that temperature and filler composition significantly affected tensile strength and elongation. Subsequently, a TabNet machine learning model was applied to simulate the relationship between parameters such as composition and mechanical properties. The experimental results showed that when four parameters, namely, the filler composition, temperature, holding time, and pressure, were used as input features, the deviation between the actual and predicted values of elongation was minimal, with a value of only 1.5650. Full article

Background/Objectives: Improper landing mechanics in Taekwondo can lead to non-contact injuries such as ankle sprains and knee ligament tears, highlighting the necessity for objective methods to evaluate landing stability and injury risk. Electromyography (EMG) enables the examination of muscle activation patterns; however, conventional analyses based on simple averages have limited predictive value. Methods: This study analyzed EMG signals recorded during single-leg landings (45 cm height) in 30 elite male Taekwondo athletes. Participants were divided into regular exercise groups (REG, n = 15) and non-exercise groups (NEG, n = 15). Signals were segmented into two phases. Eight features were extracted per muscle per phase. Classification models (Random Forest, XGBoost, Logistic Regression, Voting Classifier) were used to classify between groups, while regression models (Ridge, Random Forest, XGBoost) predicted continuous muscle activation changes as injury risk indicators. Results: The Random Forest Classifier achieved an accuracy of 0.8365 and an F1-score of 0.8547. For regression, Ridge Regression indicated high performance (R² = 0.9974, MAE = 0.2620, RMSE = 0.4284, 5-fold CV MAE: 0.2459 ± 0.0270), demonstrating strong linear correlations between EMG features and outcomes. Conclusions: The AI-enabled EMG analysis can be used as an objective measure of the study of the individual landing stability and risk of injury in Taekwondo athletes, but its clinical application has to be validated in the future by biomechanical injury indicators and prospective cohort studies. Full article

High-dimensional regression with multivariate responses poses significant challenges when data are collected across multiple platforms, each with potentially correlated outcomes. In this paper, we introduce a multi-platform multivariate high-dimensional linear regression (MM-HLR) model for simultaneously modeling within-platform correlation and cross-platform information fusion. Our approach incorporates a mixture of Lasso and group Lasso penalties to promote both individual predictor sparsity and cross-platform group sparsity, thereby enhancing interpretability and estimation stability. We develop an efficient computational algorithm based on iteratively reweighted least squares and block coordinate descent to solve the resulting regularized optimization problem. We establish theoretical guarantees for our estimator, including oracle bounds on prediction error, estimation accuracy, and support recovery under mild conditions. Our simulation studies confirm the method’s strong empirical performance, demonstrating low bias, small variance, and robustness across various dimensions. The analysis of real financial data further validates the performance gains achieved by incorporating multivariate responses and integrating data across multiple platforms. Full article

Background: The relationship between comorbidity burden, as measured by the American Society of Anesthesiologists (ASA) classification, and functional recovery after total hip replacement (THR) remains uncertain. This study aimed to clarify whether ASA grade independently predicts postoperative patient-reported outcomes. Methods: We conducted a retrospective analysis of 218 consecutive patients from a prospectively maintained institutional registry who underwent primary unilateral THR between March 2021 and March 2024 in a single center. Patients were stratified into ASA 1–2 and ASA 3 groups. The Oxford Hip Score (OHS, 0–48) was collected preoperatively and at 1 week, 3 months, and 6 months postoperatively. Between-group differences were assessed, and multivariable linear regression was used to identify predictors of 6-month OHS. Results: Compared with ASA 1–2 patients, ASA 3 patients had lower preoperative OHS and longer hospital stay, but both groups showed substantial improvement over time and achieved excellent mean OHS at 6 months. In the adjusted model, higher ASA grade remained an independent negative predictor of 6-month OHS, whereas higher preoperative OHS and BMI were positive predictors. Conclusions: Despite presenting with worse baseline function and requiring longer hospitalization, ASA 3 patients experienced clinically meaningful recovery and achieved favorable 6-month outcomes after THR. Higher ASA status should therefore inform perioperative optimization rather than preclude surgery. Full article

Unsafe behaviors at construction sites often originate from cognitive failures such as lapses in memory and attention. This study proposes a novel, hybrid framework to systematically identify and predict the key contributors of cognitive failures among construction workers. First, a detailed literature review was conducted to identify 30 candidate factors related to cognitive failures and unsafe behaviors at construction sites. Thereafter, 10 construction safety experts ranked these factors to prioritize the most influential variables. A questionnaire was then developed and field surveys were conducted across various construction sites. A total of 500 valid responses were collected from construction workers involved in residential, highway, and dam projects in Pakistan. The collected data was first analyzed using conventional statistical analysis techniques like correlation analysis followed by multiple linear and binary logistic regression to estimate factor effects on cognitive failure outcomes. Thereafter, machine-learning models (including support vector machine, random forest, and gradient boosting) were implemented to enable a more robust prediction of cognitive failures. The findings consistently identified fatigue and stress as the strongest predictors of cognitive failures. These results extend unsafe behavior frameworks by highlighting the significant factors influencing cognitive failures. Moreover, the findings also imply the importance of targeted interventions, including fatigue management, structured training, and evidence-based stress reduction, to improve safety conditions at construction sites. Full article

This study evaluates the performance of several machine learning models in predicting dissolved oxygen concentration in the surface layer of the Mar Menor coastal lagoon. In recent years, this ecosystem has suffered a continuous process of eutrophication and episodes of hypoxia, mainly due to continuous influx of nutrients from agricultural activities, causing severe water quality deterioration and mortality of local flora and fauna. In this context, monitoring the ecological status of the Mar Menor and its watershed is essential to understand the environmental dynamics that trigger these dystrophic crises. Using field data, this study evaluates the performance of eight predictive modelling approaches, encompassing regularised linear regression methods (Ridge, Lasso, and Elastic Net), instance-based learning (k-nearest neighbours, KNN), kernel-based regression (support vector regression with a radial basis function kernel, SVR-RBF), and tree-based ensemble techniques (Random Forest, Regularised Random Forest, and XGBoost), under multiple experimental settings involving spatial variability and varying time lags applied to physicochemical and meteorological predictors. The results showed that incorporating time lags of approximately two weeks in physicochemical variables markedly improves the models’ ability to generalise to new data. Tree-based regression models achieved the best overall performance, with eXtreme Gradient Boosting providing the highest evaluation metrics. Finally, analysing predictions by sampling point reveals spatial patterns, underscoring the influence of local conditions on prediction quality and the need to consider both spatial structure and temporal inertia when modelling complex coastal lagoon systems. Full article

In populations with limited genotyping, single-step genomic best linear unbiased predictions (ssGBLUP) can produce biased or less accurate genomic predictions due to incompatibilities between genomic and pedigree relationship matrices. The study evaluated the impact of five alternative ssGBLUP models for genomic predictions of milk, fat, and protein yield production traits in South African Holstein cattle. The dataset included 696,413 milk production records and pedigrees of 541,325 animals. Production traits were 305-day lactation yields for milk, protein, and fat. Genotype data were based on the Illumina 50K chip v3, with 53,218 SNPs. A total of 1221 animals with genotypes and 41,407 SNP markers were in the final dataset. The five models used to estimate genomic estimated breeding values (GEBVs) were the single-step method (ssGBLUP), ssGBLUP accounting for inbreeding (ssGBLUP_Fx), ssGBLUP with unknown parent groups (ssGBLUP_upg), and two ssGBLUP models with blending, tuning, and scaling parameters set to optimum values in constructing the inverse of the unified relationship matrix (ssGBLUP_adjusted). Realized prediction accuracies were highest for ssGBLUP_adjusted models (6–7% improvements compared to ssGBLUP). Accuracy of GEBVs for milk, protein, and fat yields ranged from 0.23, 0.29, and 0.30 for both ssGBLUP and ssGBLUP_Fx, 0.26, 0.32, and 0.34 for ssGBLUP_upg, and 0.29, 0.35, and 0.37 for ssGBLUP_adjusted models, respectively. Corresponding bias, expressed as regression coefficients, ranged from 0.30, 0.31, and 0.36 for ssGBLUP; 0.31, 0.32, and 0.37 for ssGBLUP_Fx; 0.41, 0.44, and 0.49 for ssGBLUP_upg; and 0.44, 0.47, and 0.53 for ssGBLUP_adjusted models, respectively. The improved accuracy and reduced bias observed with the ssGBLUP_adjusted underscores the importance of optimizing the blending of pedigree- and genome-based relationships to achieve more reliable GEBVs, thereby improving selection decisions in Holstein dairy cattle. Full article

The increasing penetration of solar photovoltaic (PV) generation into power systems poses significant operational and planning integration challenges due to the high variability in solar irradiance, which makes PV power forecasting difficult—particularly in the short term. These fluctuations originate from atmospheric dynamics that are only partially captured by numerical weather prediction (NWP) models. In this context, probabilistic forecasting has emerged as a state-of-the-art approach, providing central estimates and additional quantification of uncertainty for decision-making under risk conditions. This work proposes a novel hybrid methodology for day-ahead, hourly resolution point, and probabilistic PV power forecasting. The approach integrates a multiple linear regression (LM) model to predict global tilted irradiance (GTI) from NWP-derived variables, followed by support vector quantile regression (SVQR) applied to the residuals to correct systematic errors and derive GTI quantile forecasts and a linear mapping to PV power quantiles. Robust data preprocessing procedures—including outlier filtering, smoothing, gap filling, and clustering—ensured consistency. The hybrid model was applied to a 960 kWp PV plant in southern Italy and outperformed benchmarks in terms of interval coverage and sharpness while maintaining accurate central estimates. The results confirm the effectiveness of hybrid risk-informed modeling in capturing forecast uncertainty and supporting reliable, data-driven operational planning in renewable energy systems. Full article

High-mountain areas with sensitive ecosystems are experiencing a steady increase in visitation, with visitors progressively moving outside designated trails, generating pressures on the natural environment. In extensive areas with numerous access points, it is difficult to monitor visitors’ movement and resulting impacts. This article describes a method for combining various data sources and approaches to determine affected areas, including their locations and extent. The method combines (1) field-mapping, (2) remote-sensing data display analysis, and (3) processing of publicly available GNSS tracks from sports applications, using 46 test plots along a selected trail to Mount Triglav in Slovenia. Affected-area surfaces and their spatial overlap were compared across the three approaches. The usefulness of remote-sensing displays and GNSS tracks for determining and predicting affected areas was assessed by reference to field measurements. A linear regression model showed that the display-analysis approach can explain 52.7% of the variability in field-mapping approach, while GNSS tracks do not provide enough information nor the accuracy comparable to field surveys. This study can help other researchers and nature-protection managers in selecting most suitable data derived from non-traditional sources to improve delineation of hiking trails and estimation of potential pressures on fragile environments. Full article

Background/Objectives: Although numerous studies have examined associations between screen time and early language development, less is known about how screen exposure interacts with developmental stage, digital content quality, and parental mediation across childhood and adolescence, particularly with respect to pragmatic communication. This study aimed to address these gaps by examining the joint associations of screen time, content composition, and parental mediation with multiple linguistic and pragmatic domains across a broad age range. Methods: A cross-sectional study was conducted with 286 Romanian participants aged 5–19 years, grouped into four developmental stages. Measures included daily screen time, proportion of educational versus recreational content, parental mediation practices, and standardized assessments of vocabulary, verbal fluency, grammatical competence, and pragmatic communication. Analyses included descriptive statistics, Pearson correlations, 4 × 3 factorial ANOVAs (age group × screen-time category), and multiple linear regression. Results: Higher levels of screen exposure were consistently associated with lower performance across all linguistic and pragmatic domains (r = −0.19 to −0.28, all p < 0.01). Participants viewing >2 h/day showed significantly weaker outcomes than those with ≤1 h/day, particularly in semantic and phonemic fluency and pragmatic communication (p < 0.001). Educational content correlated positively with linguistic scores, whereas recreational content showed negative associations. Parental mediation emerged as a significant positive predictor. In the regression model (R² = 0.42), age (β = 0.47), parental mediation (β = 0.21), and educational content (β = 0.18) predicted better linguistic performance, while screen time (β = −0.29) predicted lower performance. Conclusions: The findings indicate that associations between digital media use and linguistic and pragmatic performance vary across developmental stages and contextual factors. Rather than screen time alone, digital content quality and parental mediation are associated with differences in communicative performance. These results highlight the value of a nuanced, developmentally informed perspective when considering children’s digital media environments. Full article

Background and Objectives: Contrast-associated acute kidney injury (CA-AKI) is a frequent and clinically significant complication in patients with acute myocardial infarction (AMI) undergoing coronary angiography. Early and accurate risk stratification remains challenging with conventional models that rely on linear assumptions and limited variable integration. This study aimed to evaluate and compare the predictive performance of multiple machine learning (ML) algorithms with traditional logistic regression and the Mehran risk score for CA-AKI prediction and to explore key determinants of risk using explainable artificial intelligence methods. Materials and Methods: This retrospective, single-center study included 1741 patients with AMI who underwent coronary angiography. CA-AKI was defined according to KDIGO criteria. Multiple ML models, including gradient boosting machine (GBM), random forest (RF), XGBoost, support vector machine, elastic net, and standard logistic regression were developed using routinely available clinical and laboratory variables. A weighted ensemble model combining the best-performing algorithms was constructed. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Model interpretability was evaluated using feature importance and SHapley Additive exPlanations (SHAP). Results: CA-AKI occurred in 356 patients (20.4%). In multivariable logistic regression, lower left ventricular ejection fraction, higher contrast volume, lower sodium, lower hemoglobin, and higher neutrophil-to-lymphocyte ratio (NLR) were independently associated with CA-AKI. Among ML approaches, the weighted ensemble model demonstrated the highest discriminative performance (AUC 0.721), outperforming logistic regression and the Mehran risk score (AUC 0.608). Importantly, the ensemble model achieved a consistently high NPV (0.942), enabling reliable identification of low-risk patients. Explainability analyses revealed that inflammatory markers, particularly NLR, along with sodium, uric acid, baseline renal indices, and contrast burden, were the most influential predictors across models. Conclusions: In patients with AMI undergoing coronary angiography, interpretable ML models, especially ensemble and gradient boosting-based approaches, provide superior risk stratification for CA-AKI compared with conventional methods. The high negative predictive value highlights their clinical utility in safely identifying low-risk patients and supporting individualized, risk-adapted preventive strategies. Full article

Background: Dietary transitions toward Westernized patterns (WDPs) (high in processed foods, sugars, and fats) pose a global public health challenge. The Westernized Diet Index (WDI) measures adherence to these patterns. However, its validity with respect to metabolic biomarkers warrants thorough evaluation for use in epidemiological and clinical research. Objectives: This study validates the WDI using metabolic biomarkers (including anthropometrics, blood pressure, fasting blood glucose (FBG), triglycerides, HDL-c, LDL-c, and total cholesterol), examines its association with metabolic syndrome (MetS), and compares scoring methods to identify the most effective measure of WDPs adherence. Methods: Data from 10,146 participants in the Fasa Adult Cohort Study (FACS) were used. We calculated the WDI using global (WDI-G) and population (WDI-P) Z scores and food group (WDI-FG)-based algorithms. Validation employed logistic and linear regression, ROC (receiver operating characteristic) curves, Youden’s index, and k-means clustering. Results: All WDI scoring methods (across all methods, higher scores indicated lower adherence to WDPs) demonstrated a strong, significant association with all three MetS definitions (WHO, NCEP: ATPIII, and IDF) and nearly all investigated metabolic biomarkers. In fully adjusted logistic models, WDI Global (WDI-G) (OR: 0.23) and WDI Food Groups (WDI-FG) (OR: 0.26) were significantly associated with MetS (based on the WHO definition). Also, in fully adjusted linear regression models, a 10% increase (reflecting lower adherence to WDPs) in the WDI-G score (range: −2.03 to 1.11) was significantly associated with a 3.96 mg/dL reduction in FBG and a 2.61 cm reduction in waist circumference. Additionally, ROC curves (AUC: 0.57–0.61) demonstrated that WDI predicts MetS with moderate accuracy. The strongest associations were observed with population-based scoring. In addition, based on comparative performance, WDI-G, WDI-P, and WDI-FG appear most suitable for cross-population, within-cohort, and mechanistic or intervention-focused research, respectively. Conclusions: The WDI shows promise as a nutritional tool for assessing adherence to WDPs and exploring associations with metabolic health outcomes, including MetS. These findings suggest that the WDI may be useful in future dietary, public health, and clinical research, although further validation in diverse populations is warranted. Full article

An accurate in situ stress field is a prerequisite for evaluating the stability of surrounding rock in underground caverns of a pumped-storage power station (PSPS) and ensuring the long-term safe operation of underground powerhouses. However, in situ stress measurements in the field are typically characterized by a limited number of measurement points, strong data randomness, and high testing costs. Meanwhile, conventional regression inversion methods often yield stress fields with insufficient accuracy or unstable spatial distributions. To address these issues, this paper proposes an in situ stress field inversion method based on the particle swarm optimization–support vector regression (PSO-SVR) algorithm. Stress boundary conditions are formulated in terms of lateral stress coefficients combined with shear stresses, and PSO is employed to optimize the hyperparameters of the SVR model. The stress boundary conditions predicted by the PSO-SVR algorithm are then imposed on a numerical model to compute the stresses at the measurement points, and the optimal boundary conditions are identified by minimizing the root mean square error (RMSE) between the inverted and measured in situ stresses. On this basis, the stress components at the measurement points and the in situ stress field in the study area are obtained. The results demonstrate that the inverted in situ stresses agree well with the field measurements, exhibiting good consistency and spatial regularity. Specifically, compared with the traditional multiple linear regression (MLR) method, the PSO-SVR algorithm reduces the RMSE and mean absolute error (MAE) of the in situ stress measurement data by 48.21% and 47.01%, respectively, and produces inversion results with higher accuracy, more stable spatial patterns, and markedly fewer anomalous zones. Consequently, the PSO-SVR algorithm is well suited for in situ stress inversion in PSPSs and provides a reliable stress-field basis for subsequent optimization of underground cavern excavation and support. Full article