Search Results (2,252)

This study presents an integrated statistical framework for identifying representative large-scale climate teleconnection indices associated with total burned area and for supporting the selection of climate predictors in wildfire-related statistical models. A large set of seasonally resolved climate indices, including the North Atlantic Oscillation (NAO—two versions), the Arctic Oscillation (AO), the Atlantic Multidecadal Oscillation (AMO), the Mediterranean Oscillation (MO—two versions), the East Atlantic–West Russia pattern (EAWR), the Tropical North Atlantic (TNA), and the Atlantic Meridional Mode (AMM), was examined. Because many of these indices describe related atmospheric and oceanic processes, dimensionality reduction and predictor selection were required to limit multicollinearity. Principal component analysis (PCA) was first used to identify groups of interrelated climate indices, followed by partial correlation analysis to distinguish redundant predictors from those retaining independent information with respect to total burned area. Finally, LASSO regression was applied to evaluate the relative explanatory contribution of candidate indices and to perform automatic variable selection. The PCA solution identified ten rotated components explaining 82.31% of the total variance. The results indicate that several seasonal NAO and MO indices contain highly overlapping information, whereas selected indices, particularly MOI2 spring and MOI2 summer, retain comparatively stronger independent associations with total burned area. The integrated PCA–partial correlation–LASSO framework provides a systematic approach for reducing redundant climate predictors and identifying large-scale climate signals that may be informative for understanding variability in total burned area and for supporting statistical analyses of wildfire–climate relationships. Full article

Economic growth and the effectiveness of official development assistance (ODA) have long been a topic of debate, with the current concern being that there are high levels of aid dependency in Sub-Saharan Africa, despite low levels of structural transformation. The study analyses the growth impacts of multidimensional ODA dependency, the moderating effect of institutional quality and the possible thresholds of aid dependency in an unbalanced panel of 48 Sub-Saharan African countries over the period 2000–2025. Two-way fixed effects, System Generalized Method of Moments, Difference Generalized Method of Moments, and Common Correlated Effects Pooled are used to analyze a principal component-based ODA Dependency Index, and least-squares threshold regression is used to investigate multiple thresholds. These results show that there is a strong negative correlation between aid dependency and economic growth, while institutional quality is consistently positive to growth, but does not significantly moderate the aid–growth relationship. There are no stable thresholds for aid dependency as a function of the specification of the index. The findings highlight the need to improve domestic resource mobilization, productive investment and institutional capacity to decrease reliance on structural aid and ensure sustainable economic growth in the long term. Full article

This study analyzes the relationship between economic inequality, social capital, and crime across Mexican municipalities in 2020, adopting a mediation approach and an exploratory territorial analysis. Using data from the National Council for the Evaluation of Social Development Policy (CONEVAL), the National Institute of Statistics and Geography (INEGI), the Executive Secretariat of the National Public Security System (SESNSP), the National Statistical Directory of Economic Units (DENUE), and the National Electoral Institute (INE), a structural social capital index was constructed through Principal Component Analysis (PCA), a structural social capital index was constructed through Principal Component Analysis (PCA), incorporating indicators of civic participation, associational density, and local social infrastructure. The empirical strategy combines ordinary least squares (OLS) regression models, Exploratory Spatial Data Analysis (ESDA), bootstrap mediation models with 5000 resamples, and spatial regression robustness checks. The results show that inequality is positively and significantly associated with municipal crime rates; the Gini index is associated with vehicle theft (β = 0.115, p < 0.001) and homicide (β = 0.073, p < 0.001). After structural covariates are included, inequality loses statistical significance for property crime but remains associated with homicide (β = 0.098, p < 0.001). Social capital shows a significant negative association with both crime outcomes. The mediation analysis indicates a statistically significant indirect pathway linking inequality and crime through structural social capital; however, the form of mediation differs by crime type. For homicide, the evidence is consistent with complementary or partial mediation, whereas the covariate-adjusted vehicle theft model shows a competitive mediation or suppression pattern. The spatial analysis reveals territorial autocorrelation in all variables. Spatial regression robustness checks further show that structural social capital remains negatively associated with both crime outcomes after accounting for spatial dependence. The findings suggest that the inequality–crime relationship depends on contextual factors and local social structures and should be interpreted as statistical associations rather than causal effects. Full article

Szechuan pepper pungency is traditionally assessed by subjective sensory panels or laboratory instruments, hindering on-site detection. Perceived numbing sensation does not always correspond directly to alkylamide content because multiple electroactive constituents, including polyphenols and sanshools, may contribute to the overall sensory response. Chromatographic methods quantify individual compounds but may not fully reflect integrated human pungency perception. Electrochemical detection bypasses separation, as the voltammetric response integrates oxidative signals from multiple electroactive species. We developed a portable electrochemical system with a custom programmable-gain potentiostat, three-electrode detector, and STM32-controlled software for differential pulse voltammetry (DPV) measurement. Coupled with machine learning, it assessed Zanthoxylum bungeanum (red peppers) and Zanthoxylum schinifolium (green peppers). Fifteen replicate scans from each of 22 origins yielded 330 DPV curves calibrated against general Labeled Magnitude Scale (gLMS) scores from a trained panel. An artificial neural network (ANN) achieved R² = 0.937 for red peppers, while principal component analysis–support vector regression (PCA–SVR) achieved R² = 0.860 for green peppers. Competitive adaptive reweighted sampling (CARS) identified three characteristic potential intervals for each type: 0.17–0.21, 0.57–0.62, and 0.69–0.80 V for red peppers, and 0.24–0.27, 0.56–0.68, and 0.69–0.77 V for green peppers. These intervals indicate that pungency-related electrochemical information is distributed across multiple potential regions. The system shows potential for rapid and objective quality assessment of Szechuan pepper. Full article

Machine learning (ML) integrated with metabolomics has emerged as a promising strategy to advance precision pharmacotherapy, enabling data-driven prediction of drug response. This review provides an overview of commonly applied ML methodologies in metabolomics-based pharmacological studies, including supervised models (Random Forest, Extreme Gradient Boosting, Support Vector Machine, Logistic Regression, K-Nearest Neighbors), unsupervised models (K-Means Clustering, Principal Component Analysis), and deep learning approaches. We summarize recent progress in the application of metabolomics-driven ML to personalized medication, with a focus on drug dosage optimization, therapeutic efficacy prediction, and adverse drug reaction assessment. Despite these advances, significant challenges remain, including limited explainability, insufficient prospective clinical validation, lack of standardization and reproducibility, and data dimensionality and quality issues. Addressing these issues will be essential for the clinical translation of ML-metabolomics integration. Looking ahead, continued methodological innovation, large-scale multi-center prospective validation, and integration with other omics platforms will be key to unlocking the full potential of metabolomics combined with ML in precision healthcare. Full article

Cow milk adulteration in plant-based milk alternatives (PBMAs) raises authenticity and safety concerns, particularly for consumers with dairy allergies or lactose intolerance. This study evaluated the laboratory-scale feasibility of excitation–emission matrix (EEM) fluorescence spectroscopy combined with chemometric and machine-learning approaches for detecting and quantifying cow milk adulteration in almond and oat milk alternatives used as model matrices. Three independent preparation batches were produced for each PBMA matrix using one almond source, one oat source, and one commercial cow milk product, with cow milk concentrations ranging from 0 to 100% (v/v) and 2.5% as the lowest non-zero adulteration level. Parallel factor analysis identified 270 and 350 nm as informative excitation wavelengths, and the corresponding emission profiles were analyzed using principal component analysis, data-driven soft independent modeling of class analogy (DD-SIMCA), partial least squares regression (PLSR), random forest regression (RFR), and a one-dimensional convolutional neural network (1D-CNN). DD-SIMCA effectively rejected most adulterated samples, although target-class sensitivity was based on resubstitution because only three authentic spectra were available per condition. Under batch-grouped cross-validation, the best performance within the 0–50% adulteration range was obtained by PLSR for oat milk at 350 nm ($R_{\mathrm{GCV}}^2=0.877$, ${\mathrm{RMSE}}_{\mathrm{GCV}}=6.12\%$, and ${\mathrm{RPD}}_{\mathrm{GCV}}=2.91$), while RFR showed more consistent performance across matrix–wavelength combinations. The 1D-CNN exhibited greater variability and did not consistently outperform conventional models. These findings support the laboratory-scale feasibility of fluorescence-based screening for the investigated almond and oat matrices, while further validation using additional product sources, independently prepared batches, commercial samples, and lower adulteration levels is required before broader application to PBMA products can be established. Full article

Digital transformation is reshaping the mining industry by improving resource efficiency and environmental performance. However, empirical evidence explaining how, why, and under which organizational and institutional conditions digitalisation enhances sustainable operational performance (SOP) in Sub-Saharan African mining remains limited, despite the region’s strategic role in global mineral supply. This study examines the effect of digitalisation on sustainable operational performance using longitudinal panel data from 48 mining companies operating in Sub-Saharan Africa between 2013 and 2022. Digitalisation is conceptualized as a multidimensional organizational capability and measured through a Digitalisation Index. The index was systematically derived from corporate annual environmental, social and governance reports using transparent coding procedures and Principal Component Analysis, enhancing measurement transparency and reproducibility. SOP is measured using a composite index encompassing operational efficiency, equipment utilization and maintenance effectiveness, resource utilization and environmental sustainability, and occupational health and safety. Fixed effects panel regression serves as the primary estimator, while the two-step System Generalized Method of Moments addresses endogeneity and dynamic persistence, with robustness analyses confirming result stability. The findings show that digitalisation significantly enhances sustainable operational performance by transforming digital resources into organizational capabilities that strengthen operational resilience, optimize resource allocation, and improve sustainability outcomes. By integrating the Resource-Based View, Dynamic Capabilities Theory, the TOE framework, and the Natural Resource-Based View into a unified explanatory framework, this study advances theory while providing practical guidance for digital capability development and Industry 4.0 investment and informing policies that strengthen digital infrastructure, institutional readiness, and regulatory support for sustainable mining in Sub-Saharan Africa. Full article

Background/Objectives: Congenital heart defects (CHDs) are among the most common congenital anomalies worldwide, and maternal nutrition during early pregnancy may influence their development. Although previous studies have mainly evaluated individual B-complex vitamins, the contribution of their combined dietary intake to CHD risk remains poorly understood. This study investigated the association between maternal dietary B-complex vitamin intake and the risk of complex CHDs in a Mexican case–control study. Methods: Maternal dietary intake during pregnancy was assessed using a validated food frequency questionnaire, and nutrient intakes were energy-adjusted. Principal component analysis was used to construct a B-Complex Index based on the combined intake of vitamins B1, B2, B3, B6, and B12. Associations were evaluated using multivariable logistic regression adjusted for maternal age, total energy intake, multivitamin supplementation during pregnancy, and periconceptional folic acid supplementation. Results: Mothers of children with complex CHDs had significantly lower dietary intakes of B-complex vitamins than control mothers. The B-Complex Index was independently associated with lower odds of complex CHDs (adjusted OR = 0.707, 95% CI: 0.586–0.843; p = 0.0001). Conclusions: These findings suggest that evaluating the overall maternal dietary pattern of selected B-complex vitamins may provide complementary information regarding the nutritional environment relevant to early cardiac development. Prospective studies integrating dietary assessment, biomarkers, and genetic susceptibility are warranted to confirm these findings and further elucidate the underlying biological mechanisms. Full article

Objectives: To examine the association between premorbid personality in people with Alzheimer’s disease and caregiver well-being, while accounting for caregiver personality, sociodemographic characteristics, and behavioural and psychological symptoms of dementia. Methods: This cross-sectional observational study included 56 dyads consisting of people with mild-to-moderate Alzheimer’s disease and their informal caregiver. Caregiver well-being was operationalised using a continuous index derived from a principal component analysis integrating depressive and anxiety symptoms, burden, loneliness, perceived social support, quality of life and happiness. Caregiver personality and the premorbid personality of people with Alzheimer’s disease (assessed retrospectively by an informant) were assessed according to the Big Five model. Behavioural and psychological symptoms of dementia were assessed using the Neuropsychiatric Inventory. Associations were examined using correlation analyses and hierarchical multiple linear regression models. Results: Caregivers had a mean age of 55.2 years (SD = 11.0), and 82.1% were women. Higher premorbid extraversion of people with Alzheimer’s disease was significantly associated with better caregiver well-being (β = 0.123; p < 0.05), and this association remained significant after adjustment for caregiver personality and other covariates. The final model explained 57.3% of the variance in caregiver well-being (R² = 0.573; p < 0.001). Conclusions: Premorbid extraversion of people with Alzheimer’s disease was independently associated with caregiver well-being. These findings suggest that personality characteristics of both may be relevant for understanding caregiver well-being within a relational perspective of caregiving. Full article

Background: Subjective experience is increasingly recognised as an important component of brain–computer interface (BCI) performance in extended reality (XR) environments. Although embodiment and simulator sickness are known to influence user experience, their relationships with cortical activity during XR–BCI operation remain poorly understood. Building upon our previous investigations of embodiment and simulator sickness in XR–BCIs, the present study examined whether these subjective dimensions are associated with distinct neurophysiological patterns during repeated XR–BCI use in a participant with chronic spinal cord injury (SCI). Methods: Seventeen XR–BCI sessions performed by a participant with chronic complete SCI were analysed. Bayesian correlation analyses examined associations among embodiment, simulator sickness, BCI performance, and EEG activity. Multiple linear regression was used to identify variables independently associated with sensorimotor beta activity, and the robustness of the regression findings was evaluated using bootstrap estimation and leave-one-out sensitivity analyses. Results: Bayesian analyses identified two principal patterns of association. Sense of embodiment was positively associated with frontal theta activity (F3), whereas simulator sickness showed a negative association with sensorimotor beta activity (C3–C4). As expected, classifier acquisition accuracy was strongly associated with subsequent BCI performance. Multiple regression demonstrated that simulator sickness was the only variable independently associated with C3–C4 beta activity after accounting for embodiment and BCI performance. This association remained robust following bootstrap estimation and leave-one-out sensitivity analyses. Conclusions: Although limited to a single participant, these findings suggest that different dimensions of subjective experience during XR–BCI operation are associated with partially distinct neurophysiological correlates. In particular, simulator sickness was the variable most consistently associated with sensorimotor beta activity across all analyses. These findings provide a foundation for future longitudinal investigations of the neural mechanisms linking subjective experience and cortical dynamics during XR–BCI use. Full article

This study investigated how detection distance affects spectral models for soluble solids content (SSC) and firmness evaluation in Korla fragrant pears and provides a reference for calibrating standardized indoor non-destructive detection equipment. Two hundred visually intact fruit samples at the early-ripening stage were collected from the Korla production area in Xinjiang. An FS-640 multispectral camera system equipped with a VS-SWR fixed-focus industrial lens (16 mm focal length, F1.8 maximum aperture, 1/2-inch sensor format) was used to acquire fruit reflectance spectra at seven vertical lens-to-fruit-surface distances of 90, 100, 110, 120, 130, 140, and 150 cm. A 625-pixel region of interest (ROI) was selected using ENVI at an undamaged equatorial or near-equatorial position of each fruit, and the regional mean spectrum was used as the spectral feature of one fruit sample. The sample-set partitioning based on joint X–Y distances (SPXY) algorithm was used to divide the calibration and prediction sets at a 3:1 ratio after outlier removal via a residual-threshold method. Four preprocessing methods, namely LOESS smoothing, standardization, vector normalization, and Savitzky–Golay (SG) smoothing, were compared. Competitive adaptive reweighted sampling (CARS) was performed with 50 Monte-Carlo sampling runs, a maximum of 30 principal components, and 10-fold cross-validation, yielding 99 characteristic wavelengths. Partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and artificial neural network (ANN) models were then established using identical input variables and sample partitions. Model performance was evaluated using the coefficient of determination for calibration ($R_c^2$), coefficient of determination for prediction ($R_P^2$), root-mean-square error of calibration (RMSEC), root-mean-square error of prediction (RMSEP), relative prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). Under the static laboratory acquisition conditions in this work, the SSC model achieved the best prediction performance at 110 cm with SG smoothing ($R_P^2$) = 0.8949, RPD = 3.0633, RPIQ = 5.8661), whereas the firmness model obtained optimal prediction performance at 140 cm with standardization ($R_P^2$) = 0.7460, RPD = 1.9425, RPIQ = 3.2867). Changes in detection distance altered illumination uniformity, effective reflected signal, photon-scattering paths, and background-noise proportion. These effects may partially explain why the chemical-absorption-dominated SSC index and the tissue-scattering-dominated firmness index responded differently to detection distance. The results provide a reference for setting spectral detection parameters for Korla fragrant pears; however, samples were obtained from only a single producing region, harvest season, and maturity stage, and no independent external validation dataset was used. Therefore, the generalization ability of the developed models needs to be further verified using cross-season and cross-orchard sample sets. Full article

Feed efficiency classification based on residual feed intake (RFI) and residual intake and gain (RIG) is widely used to identify biologically efficient animals, yet it remains unclear whether this classification reflects consistent differences in digestive, fermentative, and metabolic processes. This study evaluated the effects of RFI and RIG classification on nutrient intake, apparent digestibility, ruminal fermentation, nitrogen metabolism, microbial protein synthesis, and gaseous emissions in confined lambs. Thirty-eight weaned Texel ewe lambs underwent a 60-day performance test using an automated feed intake system and were classified as high-efficiency, neutral, or low-efficiency based on both indices. Animals were individually housed in respirometric chambers where emissions of methane, carbon dioxide, nitrous oxide, and ammonia were assessed by cavity ring-down spectroscopy; apparent digestibility was determined from total collections of feed, orts, faeces, and urine; microbial protein synthesis was estimated from urinary purine derivatives; and ruminal short-chain fatty acid profiles were determined by gas chromatography. Feed efficiency classification did not significantly affect body weight, nutrient intake, apparent digestibility, ruminal fermentation parameters, nitrogen balance, microbial protein synthesis, or greenhouse gas emissions. Principal component analysis revealed two major biological gradients related to nutrient intake and utilisation (42.9%) and ruminal fermentation and gaseous emissions (23.3%), together explaining 66.2% of total variance, but showing no clear separation among efficiency groups. These findings indicate that the digestive, fermentative, and nitrogen metabolism variables evaluated in this study did not account for the observed variation in feed efficiency. Because the regression underlying RIG explained little additional variation (R² = 0.01), these conclusions primarily reflect feed efficiency as classified by RFI, suggesting that other physiological mechanisms may play a more important role in determining feed efficiency in confined Texel ewe lambs. Full article

We introduce a robust nonparametric regression framework for functional covariates that combines functional principal component analysis (FPCA), marginal copula-scale normalization, bounded-score M-estimation, and multivariate Bernstein smoothing. The proposed procedure reduces the infinite-dimensional functional predictor to a low-dimensional score representation, transforms the retained scores onto the compact unit cube, and estimates a conditional M-functional through a smoothly aggregated system of local estimating equations. This construction is designed to accommodate nonlinear regression structure, heavy-tailed score distributions, and response contamination while limiting the influence of extreme observations. Under suitable regularity and undersmoothing conditions, we establish pointwise and uniform consistency, derive explicit convergence rates, and prove asymptotic normality. The limiting variance contains an explicit Bernstein concentration factor that plays a role analogous to the integrated squared kernel in classical nonparametric regression. The analysis also clarifies the interaction among the projection dimension, the Bernstein resolution, the empirical copula transformation, and the effective local sample size. The finite-sample performance of the method is examined through simulations involving heavy-tailed functional scores, Student-t errors, nonlinear regression effects, and increasing response contamination. The proposed estimator exhibits strong overall predictive performance and good robustness, with particularly favorable behavior under absolute-error criteria. Full article

Data-driven surrogate methods are increasingly applied to real-time structural health monitoring of critical engineering systems, but their use is often limited by the computational cost of high-fidelity finite element (FEM) simulations. This work proposes an FEM-based machine learning framework combining FEM, Principal Component Analysis (PCA), and Random Forest regression to reconstruct full-field von Mises stress distributions of a hydropower regulating ring from a reduced set of proximity sensor measurements. A calibrated 3D FEM model generated a representative dataset of operating conditions using a Design of Experiments (DOE) sampling strategy, reducing the simulation space. The resulting stress fields were reduced using a single global PCA model, and the retained principal components were predicted by a single Random Forest model trained on the guide vane opening and four displacement sensors installed on the turbine unit. Stress reconstruction was obtained via inverse PCA transformation and validated against FEM results through a leave-one-opening-out cross-validation, in which each guide vane opening was entirely withheld from training. The method achieved an average PSNR of 17.4 dB and SSIM of 0.837 across the eleven withheld openings, with 8 to 9 PCA components sufficient to preserve over 95% of the cumulative explained variance. A sensitivity analysis of the ensemble size showed that 100 decision trees provide accuracy comparable to larger ensembles at lower computational cost, and a feature importance analysis revealed the guide vane opening as the dominant predictor, with the four sensors providing complementary, fine-grained corrections. The framework enables near real-time reconstruction, requiring approximately 1.5 s per condition versus several hours for FEM. These results show that combining physics-based modeling with machine learning enables efficient structural monitoring for predictive maintenance and operational decision-making in hydroelectric systems. Full article

Wildfires are increasingly recognized as environmental disturbances associated with socio-economic transformations in agrarian systems. This study examines the associations between reported wildfire exposure, land-market outcomes, and agrarian inequality in the Malakand Division of northern Pakistan, a region characterized by forest–agriculture interfaces and livelihood dependence on land. The study aims to analyze how different levels of wildfire exposure are associated with land values, ownership patterns, market transactions, inequality, and coping strategies among farming households. A quantitative cross-sectional design was employed using a sample of 400 households selected through multistage sampling. Data were collected through structured questionnaires and analyzed using ANOVA, chi-square tests, multiple and logistic regression, hierarchical regression, and principal component analysis. Results show that reported land values differed significantly across wildfire-exposure categories (F = 48.72, p < 0.001), with directly exposed households reporting the lowest values. Regression analysis identified direct wildfire exposure as the strongest negative statistical predictor of reported land value (β = −0.468, p < 0.001), while directly exposed households had substantially higher odds of reporting land sales (Exp(B) = 6.35). Chi-square results indicate a significant association between wildfire exposure and land transactions (χ² = 64.82, p < 0.001). Retrospectively reported landholding data show an increase in the Gini coefficient from 0.41 before the reported fire period to 0.53 afterward. The addition of land-transaction variables increased the explained variance in agrarian inequality to 72%, which is consistent with a potential land-market pathway but does not constitute evidence of causal mediation. Coping strategies such as land sale, migration, and borrowing emerged as dominant reported responses among affected households. The study concludes that wildfire exposure is strongly associated with land devaluation, land sales, and greater agrarian inequality. Because the study is cross-sectional and lacks an independently observed pre-fire baseline or causal identification strategy, these findings should not be interpreted as definitive causal effects. Full article