Search Results (466)

Large-scale population datasets are rarely generated via simple random sampling; instead, they reflect complex designs involving stratification, clustering, and unequal inclusion probabilities. While survey weights are provided to recover population-representative estimates, standard Bayesian Kernel Machine Regression (BKMR), a flexible nonlinear model for high-dimensional exposure mixtures, does not explicitly accommodate these design features. We present a simulation-based framework that evaluates performance under complex sampling by comparing two analytic strategies applied to identical survey-like data: (i) a naïve, unweighted BKMR implementation and (ii) a design-aware workflow that can be executed using existing software without modifying the BKMR algorithm itself. Finite populations are generated with correlated exposures and a known nonlinear data-generating function. Stratified two-stage cluster samples are then drawn under both non-informative and exposure-dependent (informative) selection mechanisms, with controlled intra-class correlation (ICC). The design-aware approach incorporates sampling weights through resampling of the dataset while preserving primary sampling unit structure, followed by standard BKMR fitting. Methods are evaluated using bias, interval width, and empirical 95% coverage relative to the known truth. Across simulation scenarios, naïve BKMR exhibits bias and systematic under-coverage under informative sampling, with empirical 95% coverage often dropping to approximately 0–40%, whereas the design-aware workflow improves coverage to approximately 40–60%, moving results closer to nominal levels. These findings provide a practical, implementation-ready strategy for integrating survey design considerations into BKMR analyses and delineate conditions under which accounting for sampling design affects inference. While the proposed approach improves inferential performance relative to naïve BKMR, it does not fully achieve nominal coverage, indicating that further methodological development is required for fully valid uncertainty quantification under complex survey designs. Full article

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

Lactarius hatsudake is an ecologically and economically significant wild edible mushroom in southern China. To elucidate its population genetic diversity, differentiation, and evolutionary history, we analyzed 172 fruiting bodies from eight geographic populations (AQ, BS, DZ, JS, NC, PT, SG, YX) across seven provinces in the western and eastern regions of southern China using five highly polymorphic simple sequence repeat (SSR) markers. Combined with STRUCTURE clustering, discriminant analysis of principal components (DAPC), unweighted pair group method with arithmetic mean (UPGMA), and analysis of molecular variance (AMOVA), the results revealed high polymorphism across the studied loci (mean PIC = 0.842). A total of 75 alleles were identified, averaging 15 alleles per locus. At the population level, the mean effective number of alleles (Ne) was 4.023, and the mean unbiased gene diversity (uH) was 0.768. The NC population exhibited the highest genetic diversity (uH = 0.796), whereas the BS population showed relatively lower diversity (uH = 0.647). Clustering analyses (STRUCTURE, DAPC, and UPGMA) consistently identified two distinct genetic clusters (K = 2). Cluster I consisted of populations AQ, PT, BS, and SG, while Cluster II was composed of the remaining four populations. Notably, individuals from AQ and NC displayed significant genetic admixture, suggesting a transitional zone. AMOVA revealed that the majority of genetic variation (83%) resided within populations and 17% among populations. Moderate population differentiation (ENA-corrected global Fst = 0.102) and admixture signals suggest non-negligible connectivity among populations. Full article

Background: Depressive symptoms have been linked to systemic inflammation, yet estimates in population-representative data vary by symptom severity and analytic specifications. We quantified the association between depressive symptom severity and high-sensitivity C-reactive protein (hs-CRP) in U.S. adults using design-based inference. Methods: We analysed pooled NHANES 2015–2018 data for adults aged ≥ 20 years (unweighted n = 9164; complete-case adjusted models n = 8173). Depressive symptom severity was categorised using the Patient Health Questionnaire-9 (PHQ-9) with 0–4 as the reference group and a pre-specified primary contrast of 10–14 versus 0–4. Outcomes were (i) continuous hs-CRP modelled on the log scale, reported as geometric mean ratios (GMR), and (ii) elevated inflammation defined as hs-CRP > 3 mg/L, modelled using a log-link to obtain prevalence ratios (PR). Models incorporated NHANES complex sampling and adjusted for a pre-specified core covariate set (age, sex, race/ethnicity, education, poverty-income ratio, and smoking). Sensitivity analyses excluded hs-CRP > 10 mg/L and added BMI. Results: After adjustment, the geometric mean hs-CRP was 1.43 mg/L (95% CI 1.21–1.70) for PHQ-9 0–4 and 1.63 mg/L (95% CI 1.29–2.08) for PHQ-9 10–14. For the primary contrast (10–14 vs. 0–4), the adjusted GMR was 1.14 (0.96–1.35) and the PR was 1.15 (0.95–1.39). Using a clinically relevant dichotomy (PHQ-9 ≥ 10 vs. <10), depressive symptoms were associated with higher hs-CRP (GMR 1.24 (1.07–1.43)) and a higher prevalence of hs-CRP > 3 mg/L (PR 1.19 (1.01–1.39)). Associations were strongest for PHQ-9 15–19 (GMR 1.62 (1.20–2.19); PR 1.49 (1.15–1.92)). In sensitivity analyses for the primary contrast, GMR estimates ranged from 1.01 to 1.14 and PR estimates ranged from 1.05 to 1.15, with attenuation towards the null after excluding hs-CRP > 10 mg/L and after additional adjustment for BMI. Conclusions: Higher depressive symptom severity was associated with higher hs-CRP and a higher prevalence of low-grade systemic inflammation in U.S. adults, with the clearest elevations observed among those with moderately severe symptoms. For the pre-specified moderate-symptom contrast, point estimates were modest and sensitive to handling of high hs-CRP values and adiposity-related adjustment. Full article

This study addresses virtual metrology (VM) for the taping process of chip electronic components, in which partial observability, unmeasured disturbances, and severe label imbalance make direct batch-wise yield prediction unstable. Rather than proposing a new standalone learning algorithm, we develop a data-centric VM framework that reformulates the task as the prediction of operating-condition-level typical yield. First, physically relevant features are retained based on process knowledge and analyzed using Pearson correlation, Spearman correlation, and mutual information. We then perform multidimensional equal-frequency binning to partition the observable feature space into locally homogeneous operating condition groups, and define the within-bin median yield as the typical yield, thereby constructing an operating condition dictionary. Based on this dictionary-based representation, low-yield-oriented sample weighting is combined with nested cross-validation and Bayesian optimization for model comparison and hyperparameter tuning. Using desensitized production data from an electronic component taping process, the results under this representation show more stable prediction than direct modeling on unbinned batch samples while also improving tail-oriented fitting relative to unweighted baselines. These findings suggest that, for partially observable manufacturing data, operating condition stratification provides a practical basis for stabilizing VM prediction, while low-yield-oriented sample weighting further improves sensitivity to the low-yield tail, supporting picture yield early warning and process-level decision making. Full article

Physical inactivity remains a major modifiable risk factor for non-communicable diseases and continues to exhibit marked socioeconomic and gender disparities in Latin America. Identifying robust and interpretable predictors of inactivity in nationally representative datasets is essential for informing public health strategies. This study compared a survey-weighted logistic regression model and an explainable machine learning approach (XGBoost) to predict physical inactivity among Chilean adults using data from the 2024 National Physical Activity and Sports Survey (ENAFyD; n = 5248). Models were evaluated on a stratified held-out test set (n = 1050) using weighted and unweighted area under the ROC curve (AUC), Brier scores, and calibration curves. Survey-weighted logistic regression achieved a weighted AUC of 0.801, while XGBoost achieved 0.797, demonstrating comparable discrimination. XGBoost showed marginally lower Brier scores, indicating slightly improved probabilistic calibration. Low socioeconomic status, female sex, lower monthly physical activity expenditure, limited facility access, and lower engagement with digital resources were consistently associated with higher inactivity risk. SHAP-style contribution analysis provided additional insight into feature-level influence within the machine learning framework. Overall, both approaches demonstrated similar predictive capacity, supporting the complementary use of classical regression and explainable machine learning for population-level physical inactivity research. Full article

Background/Objectives: Type II superior labrum anterior–posterior (SLAP) lesions of the long head of the biceps (LHB) tendon are associated with excessive tendon loading and are commonly treated surgically using SLAP repair, tenotomy, or tenodesis. These procedures alter musculotendinous length and loading and may affect functional outcomes, including forearm supination strength. Appropriate restoration of tendon tension is critical for favorable muscle adaptation and recovery. Shear wave elastography (SWE) is a non-invasive imaging technique capable of quantifying tissue stiffness as a surrogate for in vivo musculotendinous tension. This study aimed to characterize LHB tendon tension across forearm positions and loading conditions to improve the understanding of functional tendon loading relevant to postoperative activation and rehabilitation. Methods: In this controlled laboratory study, thirteen healthy female volunteers without shoulder pathology were assessed using SWE with the elbow positioned at 90° flexion. LHB tendon tension was measured in forearm pronation and supination under passive, active (unresisted), and weighted conditions. Paired t-tests were used to compare forearm positions within each loading condition. Results: LHB tendon tension was significantly greater during active and weighted conditions compared with passive loading in the pronated position (p < 0.05). During active contraction, tendon tension was significantly lower in supination than pronation (p < 0.05), whereas no positional differences were observed under passive or weighted conditions. Relative to passive loading, tendon tension increased by approximately 18.2% and 89.2% in supination, and 67.0% and 97.9% in pronation during active and weighted conditions, respectively. Conclusions: Forearm position selectively influences LHB tendon tension during active, unresisted contraction. Forearm orientation affected LHB tendon stiffness primarily during active, unweighted contraction, where pronation resulted in higher stiffness than supination. On the other hand, stiffness outcomes measured during passive and weighted positions were comparable between forearm orientations, indicating that positional effects are most evident when tendon loading is primarily muscle-driven. These findings highlight the relevance of forearm positioning during early postoperative activation and provide normative in vivo reference data to inform personalized rehabilitation strategies and future investigations of postoperative tendon loading following SLAP lesion treatment. Full article

Background/Objectives: Genome-wide association studies (GWAS) based on single-step genomic BLUP (ssGBLUP) commonly assume equal single nucleotide polymorphism (SNP) variances, which may not reflect the biological architecture of complex traits. Alternative weighting strategies can increase detection power but may affect stability. This study evaluated how different SNP weighting approaches influence genomic region detection and biological interpretation of ribeye area (REA) and subcutaneous fat thickness (SFT) in Guzerá cattle. Methods: Phenotypic records from 2729 animals and genotypes from 1405 individuals (43,039 SNPs after quality control) were analyzed. Heritabilities were estimated using Restricted Maximum Likelihood (REML), and GWAS were conducted under five approaches: unweighted method (UM), quadratic method (QM), and three Non-Linear A strategies with weighting constants (1.125, 1.2, and 1.5). Genomic windows of 20 adjacent SNPs explaining ≥0.5% of the additive genetic variance (AGV) were considered significant. Recurrent regions were prioritized, and functional enrichment analyses (KEGG, GO, and MeSH) were performed. Results: Heritability estimates were moderate for REA (0.26 ± 0.05) and SFT (0.22 ± 0.04). Weighted approaches increased detection sensitivity. For REA, UM identified 10 windows, whereas QM and A_1.5 detected 24 and 31 windows. For SFT, UM identified 8 windows, while QM and A_1.5 detected 30 and 23 windows. Recurrent chromosomes included 2, 4, 6, 12, 16, 19, and 22 for REA, and 2, 3, 5, 7, 11, 17, and 22 for SFT. Key genes included AKT3, NOS2, and MSTN. Enrichment highlighted pathways related to muscle growth and lipid metabolism. Conclusions: SNP-weighted GWAS increased detection sensitivity but involved trade-offs between signal amplification and stability. Integrating weighting strategies improves biological interpretation and supports robust candidate gene identification for genomic selection. Full article

Background/Objectives: Hip displacement is a common problem in children with cerebral palsy (CP). Typically, the recommended hip surveillance imaging for these children consists of an anteroposterior pelvic radiograph, from which we calculate the migration percentage (MP) to determine treatment plans (conservative/preventive therapy, femoral osteotomy, femoral and pelvic osteotomies, and salvage surgery). However, little is known about the accuracy of MP for treatment planning. We aim to compare treatment plans based on MP thresholds with plans determined by an orthopedic surgeon following review of the hip CTs. Methods: We retrospectively identified hip CTs performed in children who were ≤18 years old with CP (11/2018—07/2024). The inclusion criteria were: (1) a pelvic radiograph performed 6 months prior to the hip CT; and (2) no surgeries between the pelvic radiograph and the hip CT. These hip CTs were randomized and blindly reviewed by an orthopedic surgeon to determine each child’s treatment plan (CT-treatment). Separately, a pediatric radiologist blindly reviewed the randomized pelvic radiographs and measured each hip’s MP to determine each child’s treatment plan (XR-treatment). We used kappa-agreement and Bland–Altman analyses to compare XR- and CT-treatments. Results: Our study cohort consisted of 139 children (mean age = 9.3 ± 3.8 years; male = 90) with 278 hips. The proportion of agreement and unweighted kappa between XR- and CT-treatment were both low: 0.532 (148/278) and 0.339, respectively. Bland–Altman analyses showed that XR-treatment and CT-treatment were exchangeable when MP ≤ 10% but were not exchangeable otherwise. Conclusions: We should be cautious about relying exclusively on pelvic radiographs and subsequent MP calculation in making treatment decisions for hip displacement in children with CP since many anatomic details become evident on 3D imaging. Full article

The rapid digital transformation of education systems has profoundly changed teachers’ working conditions, intensified administrative demands, and highlighted territorial and organizational inequalities. In this context, understanding how these dynamics influence teacher engagement is essential for promoting healthy educational organizations. This study examined the factor structure of the UWES-17 and analyzed the relationship between engagement levels and sociodemographic variables in a sample of 314 elementary school teachers from four regions of Chile. Descriptive analyses, exploratory factor analysis with polychoric correlations and unweighted least squares, and confirmatory factor analysis using robust ULS and the Hull method were performed. The results showed a robust two-factor structure—Inspired Vitality and Challenging Commitment—with excellent fit indices. Freeman–Halton exact tests showed that Inspired Vitality was significantly associated with age, gender, region, location, administrative dependency, and professional experience, while Challenging Commitment was associated with gender, region, context, and professional experience. These findings indicate that teacher engagement is influenced by both structural inequalities and individual trajectories. The results underscore the need to strengthen organizational resources, regulate digital intensification, and reduce territorial gaps to promote teacher well-being. Full article

The well-known Heath–Jarrow–Morton (HJM) framework provides a universal and efficacious instrument for modeling the stochastic evolution of an entire yield curve by explaining the interest rate dynamics in continuous time under no-arbitrage conditions. Existing implementations involve exponentially weighted function spaces as theoretical settings for the former stochastic evolution. While the choice of weight can have a drastic effect on model calibration and subsequent forecasting, it cannot be estimated from market data and does not allow for any objective interpretation. The proposed approach does not have this shortcoming as it adopts a suitably designed unweighted function space. The HJM equation is discretized using a finite difference approach. The resulting semiparametric model is then calibrated on real-world yield data with a new type of functional principal component analysis (PCA)-based approach. Backtesting and benchmarking are conducted against the one-factor Vasicek model using historical data to illustrate its simulation capabilities for prediction and uncertainty quantification. Additionally, in contrast to widely studied US treasuries, negative interest rates are observed for AAA Euro Bonds during the sample period employed for this study. Accordingly, the framework allows for the possibility of negative yields. Full article

Metabolic syndrome (MetS) is common in later life and shaped by modifiable lifestyle and clinical factors, yet data specific to rural older adults are limited. This cross-sectional study analyzed rural Koreans aged ≥65 years (unweighted n = 467) from the 2023 Korea National Health and Nutrition Examination Survey, incorporating the complex survey design (strata, clusters, and weights). MetS was defined using National Cholesterol Education Program Adult Treatment Panel III criteria with Asian-specific waist cutoffs (≥3 of 5 components). Sociodemographic, behavioral, and clinical characteristics were compared by MetS status using design-based tests, and complex-sample logistic regression estimated adjusted odds ratios (aORs) with 95% confidence intervals (CIs). The survey-weighted prevalence of MetS was 42.8%. Compared with those without MetS, participants with MetS had higher body mass index (BMI) and waist circumference, more hypertension and diabetes, higher triglycerides, and lower high-density lipoprotein cholesterol; low-density lipoprotein cholesterol did not differ meaningfully. In multivariable models, BMI ≥25 kg/m² (aOR 9.08; 95% CI 6.01–13.71, p ≤ 0.001), hemoglobin A1c ≥ 7.0% (aOR 4.42; 95% CI 1.75–11.16, p = 0.003), and vitamin D deficiency <20 ng/mL (aOR 2.32; 95% CI 1.23–4.35, p = 0.012) were independently associated with higher odds of MetS, whereas meeting the World Health Organization physical activity guideline was inversely associated (aOR 0.50; 95% CI 0.26–0.96, p = 0.039). These findings highlight adiposity, suboptimal glycemic control, and vitamin D deficiency as key, potentially modifiable correlates of MetS in rural older adults and support promotion of guideline-level physical activity as part of integrated cardiometabolic risk management in rural settings. Full article

Objective: This study set out to develop a Chinese version of the Attitude Survey towards Inclusive Education–Students and to examine its psychometric properties among Grade 4–6 primary students. Method: Rasch analysis was conducted using a convenience sample of 295 students from two primary schools in Chongqing and Chengdu to investigate the psychometric properties of the instrument, including dimensionality, validity, and reliability. Results: Both sub-scales of the Chinese version of the Attitude Survey towards Inclusive Education-Students are unidimensional; the reliability of the affective and behavioral sub-scales is 0.85 and 0.89, respectively. Except for the negatively worded items, all the other items have acceptable model-data fit indices (weighted and unweighted), ranging from 0.5 to 1.5; both sub-scales can be used to distinguish students with moderate to low levels of inclusive educational attitudes, rather than those with upper levels. Conclusions: The Chinese version of the Attitude Survey towards Inclusive Education-Students has good reliability and validity, making it a suitable tool for research on inclusive educational attitudes among Grade 4–6 students. Full article

This study proposes a topic-modeling guided framework that enhances image classification by introducing semantic clustering prior to CNN training. Images are processed through two key-point extraction pipelines: Scale-Invariant Feature Transform (SIFT) with Sobel edge detection and Block Gabor Filtering (BGF), to obtain local feature descriptors. These descriptors are clustered using K-means to build a visual vocabulary. Bag of Words histograms then represent each image as a visual document. Latent Dirichlet Allocation is applied to uncover latent semantic topics, generating coherent image clusters. Cluster-specific CNN models, including AlexNet, GoogLeNet, and several ResNet variants, are trained under identical conditions to identify the most suitable architecture for each cluster. Two topic guided integration strategies, the Maximum Proportion Topic (MPT) and the Weight Proportion Topic (WPT), are then used to assign test images to the corresponding specialized model. Experimental results show that both the SIFT-based and BGF-based pipelines outperform non-clustered CNN models and a baseline method using Incremental PCA, K-means, Same-Cluster Prediction, and unweighted Ensemble Voting. The SIFT pipeline achieves the highest accuracy of 95.24% with the MPT strategy, while the BGF pipeline achieves 93.76% with the WPT strategy. These findings confirm that semantic structure introduced through topic modeling substantially improves CNN classification performance. Full article

In this study, an improved particle swarm optimization (PSO) algorithm is designed to construct a weighting model for line-of-sight (LOS) and non-line-of-sight (NLOS) channels in an ultra-wideband (UWB) indoor positioning system. In the proposed algorithm, the particle position represents candidate weight vectors, and the fitness function is defined by the 3D positioning error over multiple test points. An optimized weight modeling framework is proposed for a multi-anchor, three-dimensional UWB indoor positioning system under LOS and NLOS channels. First, the three-dimensional positioning problem is formulated as a multilateration model, and the tag coordinates are estimated via a linearized matrix equation solved by the least-squares method, which explicitly links anchor geometry and ranging errors to the positioning accuracy. To evaluate the proposed method, extensive ranging and positioning experiments are conducted in a realistic indoor environment using up to eight anchors with different LOS/NLOS configurations, including dynamic scenarios with varying numbers of NLOS anchors. The results show that, compared with the conventional unweighted multi-anchor scheme, the PSO-based weighting model can reduce the average 3D positioning error by more than 30% in typical LOS-dominant settings and significantly suppress error bursts in severe NLOS conditions. These findings demonstrate that the combination of mathematical modeling, least-squares estimation, and swarm intelligence optimization provides an effective tool for designing intelligent engineering positioning systems in complex indoor environments, which aligns with the development of smart factories and industrial Internet-of-Things (IIoT) applications. Full article