Search Results (41)

Italy is among the world’s leading producers and exporters of chestnut. Over the past two decades, however, the sector has undergone significant structural changes driven by phytosanitary shocks and evolving market conditions. This study examines the structural and economic heterogeneity of Italian commercial chestnut farms over the period 2019–2023, aiming to identify recurrent production configurations and assess their economic performance and territorial distribution within the Farm Sustainability Data Network (FSDN) field of observation. The analysis is based on a balanced panel of 96 farms, from which a subsample of 77 inliers was identified through robust multivariate diagnostic tests. Farm-level indicators were aggregated over five years to capture medium-term positioning. Principal Component Analysis (PCA) was used to identify the main latent dimensions of variability, and fuzzy k-means clustering was subsequently performed on the resulting component scores. A five-cluster configuration was selected on the basis of internal validity indices, bootstrap stability, fuzzifier sensitivity and leave-one-variable-out robustness checks. The results reveal pronounced multidimensional differentiation within the observed sample. High economic intensity does not necessarily translate into greater margin stability, the effects of structural maturity vary according to cost exposure and labor organization. Territorial differentiation is statistically significant but not deterministic. Overall, the analysis provides an empirical characterization of structural profiles and their associated trade-offs within the observed commercial segment, offering insights into differentiated policy responses for perennial Mediterranean farming systems. Full article

Depopulation constitutes a fundamental challenge for territorial governance, particularly within the framework of sustainable development; however, its representation in local strategic documents remains insufficiently explored. This study aims to identify and compare strategic responses to depopulation across two contrasting Polish regions—Dolnośląskie Voivodeship and Podlaskie Voivodeship—and three governance levels: regional, county, and municipal. An abductive mixed-methods approach was applied, combining discourse analysis with latent semantic analysis (LDA), and principal component analysis (PCA). The findings reveal a clear dominance of the pro-growth paradigm, while the adaptive approach associated with managed shrinkage remains marginal. Regional differences are primarily observed in problem framing—functional in Dolnośląskie and demographic in Podlaskie—but these distinctions do not significantly affect the types of policy responses. Structural instruments, particularly those related to consolidation, prevail, whereas functional and competency-based measures are less prominent. The results suggest the existence of a standardized model of strategic response to depopulation across regions and governance levels. This indicates limited diversification of policy approaches and highlights the need to more fully integrate adaptive strategies into territorial policy, especially in the context of long-term demographic change. Full article

Population growth and changing urban activity increase pressure on public transport to be efficient and equitable. This study examines how specific socioeconomic conditions around Athens Metro stations influence ridership patterns, with particular emphasis on employment structure, education levels, and household characteristics such as parking availability. Using 2021 census data and monthly station ridership for 2021, 10 min walking isochrone catchments are delineated for each station, and socioeconomic indicators are spatially aggregated to these zones. We screen variables through correlation analysis and estimate month-specific Ordinary Least Squares (OLS) models to capture seasonal effects. The best-performing month is then analyzed to examine spatial non-stationarity, while Principal Component Analysis (PCA) reduces multicollinearity and highlights the most influential latent socioeconomic dimensions. The results indicate strong spatial disparities: central interchange stations show consistently high demand, whereas peripheral stations exhibit lower and more variable ridership. Localized relationships link ridership to employment structure, educational profiles, and indicators of car availability, such as household parking, suggesting uneven accessibility and mobility opportunities across the metropolitan area. The proposed GIS-spatial econometric workflow supports targeted, equity-oriented interventions and transit-oriented development and is transferable to other cities with comparable open ridership and census datasets. Full article

Background: To investigate the effects of probiotics and their postbiotics on mouse health, this study utilized healthy mice randomly assigned to a control group (CK, n = 6), a probiotic group (L, n = 6, oral gavage 200 μL Pediococcus lactis), and a postbiotic group (PL, n = 6, oral gavage 200 μL Pediococcus lactis postbiotic). Methods: Following 21 days of continuous intervention, changes in gut metabolic profiles, microbial community structure, tissue morphology, and tight junction protein expression were systematically analyzed using metabolomics, 16S rRNA sequencing, hematoxylin and eosin (HE) staining, and immunohistochemistry techniques. Results: The results revealed that screening for significantly altered endogenous metabolites identified core differences concentrated in metabolites related to intestinal barrier repair, anti-inflammation, and antioxidant activity (e.g., 3-indolepropionic acid, astaxanthin, hydroxybenzoic acid). 16S rRNA sequencing revealed that the overall community structure was relatively stable according to principal component analysis, although differences were detected in specific taxa. However, LEfSe analysis identified significantly enriched functional microbial groups at multiple taxonomic levels in the PL group: phylum: Actinomycetota; class: Coriobacteriia; order: Coriobacteriales, Erysipelotrichales; family: Erysipelotrichaceae, Eggerthellaceae; genus: norank_Erysipelotrichaceae, Intestinimonas. These results suggest that although the overall community structure remained relatively stable, specific taxa may have differed between groups. Hematoxylin and eosin staining revealed no pathological lesions in intestinal tissues from either group, with intact mucosal architecture. Immunohistochemistry demonstrated significantly elevated expression of intestinal tight junction proteins Claudin 1, MUC-2, Occludin, and ZO-1 in the PL group compared to the CK group (p < 0.001). Conclusions: In summary, this probiotic (Pediococcus lactis) and its postbiotic showed promising effects, which may be related to changes in specific microbiota taxa, intestinal metabolic profiles, and tight junction protein expression. Beyond maintaining gut microbiota and tissue homeostasis, it enhances intestinal barrier function, suppresses latent inflammation, and boosts antioxidant capacity. Postbiotics may exhibit superior efficacy compared to probiotics. This provides robust experimental evidence for its development and application in gut health products for healthy populations. However, these findings still require further validation in studies with longer intervention periods and in disease models. Full article

This study evaluates the ability of three classification procedures to distinguish areas with different levels of atmospheric pollution, based on biomonitoring carried out by analyzing the color and spectral characteristics of mulberry (Morus L.) and linden (Tilia L.) leaves. Sampling was carried out in areas that were grouped into four classes according to the concentrations of fine particulate matter (PM2.5, PM10) and gaseous pollutants (TVOC, NOx, SOx, CO, and eCO₂), measured using a specialized multisensor device. A total of 57 informative features were analyzed, representing indices obtained from two color models (RGB and Lab), as well as from VIS and NIR spectral characteristics measured for the adaxial and abaxial leaf surfaces. The data processing methodology includes feature selection using the ReliefF method and a comparative analysis between two approaches to dimensionality reduction—principal components (PC) and latent variables (LV). The results indicate that data reduction using PC provides significantly higher accuracy and better class separability, regardless of the classifier used, compared to LV, where errors exceed 40%. The comparison between classifiers shows a clear superiority of nonlinear models. While linear discriminant analysis demonstrates low efficiency, quadratic discriminant analysis (Q and DQ) and SVM with radial basis function (RBF) achieve high accuracy of class separability, reaching 100% in the SVM-RBF model for both tree species. The study also reveals functional asymmetry: the adaxial side of the leaves is more informative for spectral indices, while the abaxial side is more sensitive to color changes. The results confirm that the combined optical characteristics obtained from the leaf surface of bioindicators form a reliable method for ecological monitoring of air quality in urban areas. Full article

This study proposes an explainable machine learning framework for estimating the total project cost (TPC) of AI training-data construction, where cost information is difficult to structure due to heterogeneous workflows and quality requirements. Using 386 public AI training-data projects conducted between 2020 and 2022, we derive 24 numerical predictors from standardized final reports and construct three input tracks: a baseline feature set, a principal component analysis (PCA)-enhanced set, and a factor analysis (FA)–enhanced set capturing latent cost structures. Four regression models (Ridge, Random Forest, XGBoost, and LightGBM) are evaluated using nested cross-validation. XGBoost achieves the best overall performance across all three tracks (Baseline, PCA-enhanced, and FA-enhanced). Among them, PCA-enhanced XGBoost attains the highest predictive accuracy (R² = 0.868; RMSE = 1084.9; MAE = 746.9; MAPE = 0.358; pooled out-of-fold), while Baseline XGBoost yields the lowest MAE (731.4; R² = 0.863). To support transparent decision-making, Shapley Additive exPlanations (SHAP)-based attribution and scenario-based sensitivity analyses are conducted. Results show that project scale and process-level unit costs are dominant cost-drivers, while cloud usage, expert participation, and de-identification requirements exhibit secondary effects. The proposed framework provides an interpretable, data-driven approach to cost information management and decision support for data-intensive AI projects. Full article

Ischemic stroke remains a major cause of mortality and long-term disability worldwide, and improved strategies for identifying individuals at elevated vascular risk are needed. Serum autoantibodies have emerged as potential biomarkers reflecting vascular injury and immune activation; however, their integrative biological significance and incremental predictive value beyond established clinical risk factors remain unclear. We analyzed 833 participants, including patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA) and healthy controls. Serum levels of anti-PDCD11 antibody (Ab), anti-DNAJC2 antibody, and anti-PAI-1 (SERPINE1) antibody were quantified, and multivariable logistic regression and machine-learning (ML) models (logistic regression and random forest) were constructed using clinical variables with and without antibody markers. Model performance was evaluated using cross-validation, bootstrap-derived confidence intervals, calibration metrics, and reclassification indices. Model interpretability analyses, principal component analysis (PCA), unsupervised clustering, and propensity score matching were performed to explore latent biological structures. Clinical-only models demonstrated excellent discrimination (bootstrap Area Under the Curve (AUC) 0.917 for random forest and 0.919 for logistic regression). The addition of antibody markers yielded similar performance (AUC 0.913 and 0.923, respectively) without evidence of meaningful improvement in reclassification. However, SHapley Additive exPlanations (SHAP) analysis identified antibody markers as influential contributors following major clinical risk factors. PCA revealed a dominant antibody component explaining approximately 79% of the variance, which remained independently associated with stroke after age adjustment. Unsupervised clustering further identified a high-risk subgroup characterized by consistently elevated antibody levels. These findings support the presence of a latent antibody axis associated with vascular vulnerability. Although antibody markers did not substantially enhance global predictive performance, they captured integrated biological signals reflecting cumulative vascular and immunological stress. Autoantibody profiling may complement conventional risk assessment by improving biological characterization of stroke susceptibility. Prospective validation in independent cohorts is required prior to clinical implementation. Full article

Assessing regional sustainability is challenged by the multidimensional, non-linear, and highly correlated nature of socio-economic and environmental indicators. Conventional composite indices often rely on linear aggregation and fixed weighting schemes, which can obscure structural interdependencies and amplify scale dominance. To address these limitations, this study proposes the Knowledge-Aware Sustainability Variational Assessment (KASVA), a deep-learning-based framework that integrates variational representation learning, latent-space clustering, and robustness analysis to construct a composite sustainability index. Using a comprehensive set of demographic, economic, social, and environmental indicators for Turkish Nomenclature of Territorial Units for Statistics level 2 (NUTS2) regions, KASVA learns a compact latent representation that captures non-linear interactions among indicators exhibiting strong multicollinearity, with pairwise correlations frequently exceeding 0.8. The resulting Global Territorial Variational Sustainability Index (GTVSI) reveals substantial regional heterogeneity and pronounced spatial inequality. Latent-space clustering identifies distinct regional sustainability regimes, with silhouette scores predominantly in the range 0.4–0.5, indicating stable and well-separated clusters. Robustness analysis based on 1000 bootstrap resamples demonstrates high ranking stability, with a median Spearman rank correlation of approximately 0.69 and the majority of correlations exceeding 0.6. Compared with conventional equal-weight and principal component analysis (PCA)-based indices, the proposed framework yields more coherent and stable regional rankings. Overall, KASVA provides a data-driven, robust approach to sustainability assessment, offering improved interpretability and reliability for regional policy analysis and evidence-based decision-making. Full article

Environmental element monitoring is essential for assessing environmental quality, identifying pollution sources, evaluating ecological risks, and understanding long-term contamination trends. Modern monitoring campaigns routinely generate large volumes of complex data that require advanced analytical strategies. This study applied chemometric techniques to analyze elements and BVOCs (biogenic volatile organic compounds) measured from Posidonia oceanica and related environmental matrices (seawater, sediment, and rhizomes) during three sampling campaigns in the Tremiti Islands (Italy). Twenty-two trace elements were quantified, and BVOC profiles were obtained from the leaf samples. The dataset was analyzed using a combination of univariate visualizations, unsupervised and supervised multivariate techniques, and multi-way methods. PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) revealed distinct spatial (leaf section) and temporal (sampling period) trends, supported by consistent elemental markers. A low-level data fusion approach integrating BVOC and element data improved group discrimination and interpretability. PARAFAC (PARAllel FACtor analysis) applied to a three-way array successfully separated background trends from meaningful compositional changes, uncovering latent structures across chemical, spatial, and temporal dimensions. This work illustrates the usefulness of chemometrics in environmental monitoring and the effectiveness of combining multivariate tools and data fusion to improve the interpretability of complex environmental datasets. The methodology used in this study is fully generalizable and applicable to other environmental multi-way datasets. Full article

Background: Health disparities research increasingly relies on complex survey data to understand survival differences between population subgroups. While Peters–Belson decomposition provides a principled framework for distinguishing disparities explained by measured covariates from unexplained residual differences, traditional approaches face challenges with complex data patterns and model validation for counterfactual estimation. Objective: To develop validated Peters–Belson decomposition methods for survival analysis that integrate ensemble machine learning with transfer learning while ensuring logical validity of counterfactual estimates through comprehensive model validation. Methods: We extend the traditional Peters–Belson framework through ensemble machine learning that combines Cox proportional hazards models, cross-validated random survival forests, and regularized gradient boosting approaches. Our framework incorporates a transfer learning component via principal component analysis (PCA) to discover shared latent factors between majority and minority groups. We note that this “transfer learning” differs from the standard machine learning definition (pre-trained models or domain adaptation); here, we use the term in its statistical sense to describe the transfer of covariate structure information from the pooled population to identify group-level latent factors. We develop a comprehensive validation framework that ensures Peters–Belson logical bounds compliance, preventing mathematical violations in counterfactual estimates. The approach is evaluated through simulation studies across five realistic health disparity scenarios using stratified complex survey designs. Results: Simulation studies demonstrate that validated ensemble methods achieve superior performance compared to individual models (proportion explained: 0.352 vs. 0.310 for individual Cox, 0.325 for individual random forests), with validation framework reducing logical violations from 34.7% to 2.1% of cases. Transfer learning provides additional 16.1% average improvement in explanation of unexplained disparity when significant unmeasured confounding exists, with 90.1% overall validation success rate. The validation framework ensures explanation proportions remain within realistic bounds while maintaining computational efficiency with 31% overhead for validation procedures. Conclusions: Validated ensemble machine learning provides substantial advantages for Peters–Belson decomposition when combined with proper model validation. Transfer learning offers conditional benefits for capturing unmeasured group-level factors while preventing mathematical violations common in standard approaches. The framework demonstrates that realistic health disparity patterns show 25–35% of differences explained by measured factors, providing actionable targets for reducing health inequities. Full article

Introduction: In recent decades, the burden of mental disorders has become a major determinant of population health in the European Union, generating profound clinical, socioeconomic, and institutional consequences. Despite political recognition of this silent crisis, substantial methodological challenges persist in the transnational monitoring of mental health and in linking disease burden with the resources allocated to address it. The present analysis develops a multivariate taxonomy of EU Member States from a psychosocial perspective, using an integrative quantitative approach. Methods: This cross-sectional, comparative study follows international standards for transparent and reproducible quantitative reporting and is based on 18 harmonized clinical, epidemiological, and institutional indicators collected for 27 EU Member States over the period 2014–2023. The indicators used in this study were grouped according to their position along the care continuum. Hospital-based indicators refer to inpatient activity and institutional capacity, including total hospital discharges, psychiatric admissions (affective disorders, schizophrenia, dementia, alcohol- and drug-related disorders), and hospital bed availability. Outpatient and community-level indicators reflect the capacity of systems to provide non-hospital psychiatric care and consist primarily of psychiatrist density and total specialist medical workforce. Finally, subjective perception indicators capture population-level self-assessed health status, complementing clinical and institutional measures by integrating a psychosocial perspective. After harmonization and standardization, Principal Component Analysis (PCA) with Varimax rotation was applied to identify latent dimensions of mental health. Model adequacy was confirmed using the Kaiser–Meyer–Olkin coefficient (0.747) and Bartlett’s test of sphericity (p < 0.001). Results: Three latent dimensions explaining 77.7% of the total variance were identified: (1) institutionalized psychiatric burden, (2) functional capacity of the health care system, and (3) suicidal vulnerability associated with problematic substance use. Standardized factor scores allowed for the classification of Member States, revealing distinct patterns of psychosocial risk. For example, Germany and France display profiles marked by high levels of institutionalized psychiatric activity, while the Baltic and Southeast European countries exhibit elevated suicidal vulnerability in the context of limited medical resources. These results highlight the deep heterogeneity of psychiatric configurations in Europe and reveal persistent gaps between population needs and institutional response capacity. Conclusions: The analysis provides an empirical foundation for differentiated public policies aimed at prevention, early intervention, and stigma reduction. It also supports the case for institutionalizing a European mental health monitoring system based on harmonized indicators and common assessment standards. Overall, the findings clarify the underlying structure of mental health across the European Union and underscore the need for coherent, evidence-based strategies to reduce inequalities and strengthen system performance at the continental level. Full article

This paper investigates the systemic macroeconomic determinants of Pillar 2 Requirements (P2R) imposed by the European Central Bank (ECB) under the Single Supervisory Mechanism (SSM). While P2R is formally calibrated at the individual bank level through the Supervisory Review and Evaluation Process (SREP), we explore the extent to which common macro-financial shocks influence supervisory capital expectations across banks. Using a panel dataset covering euro area banks between 2021 and 2025, we match bank-level P2R data with country-level macroeconomic indicators. Those variables include real GDP growth, HICP inflation and index levels, government fiscal balance, euro yield curve spreads, net turnover, FDI inflows, construction and industrial production indices, the price-to-income ratio in real estate, and trade balance measures. We apply Principal Component Analysis (PCA) to extract latent variables related to the macroeconomic factors from a broad set of variables, which are then introduced into a Bayesian Vector Autoregression (BVAR) model to assess their dynamic impact on P2R. Our results identify three principal components that capture general macroeconomic cycles, sector-specific real activity, and financial/external imbalances. The impulse response analysis shows that sectoral and external shocks have a more immediate and statistically significant influence on P2R adjustments than broader macroeconomic trends. These findings clearly support the use of systemic macro-financial conditions in supervisory decision-making and support the integration of anticipating macro-prudential analysis into capital requirement frameworks. Full article

Background: The PRO-CTCAE provides patient-reported data on symptomatic AEs. A summary metric—the ACS—reflecting total AE burden can be calculated by averaging AE-level composite scores at a given timepoint for each participant. This study investigated the psychometric properties and interpretability of this PRO-CTCAE ACS in patients with breast, lung, or head/neck cancers. Methods: We conducted a secondary analysis of a PRO-CTCAE validation dataset comprising 940 adults undergoing chemotherapy or radiation therapy (clinicaltrials.gov: NCT02158637). We focused on empirically recommended symptom terms for three cancer sites. Analyses included Spearman’s correlations, coefficient alpha, and eigenvalues from the correlation matrices, confirmatory factor analysis (CFA), and principal component analysis (PCA). Latent profile analysis (LPA) was used to assess ACS interpretability in the lung cohort. Results: Mean composite score inter-correlations were moderate (0.30–0.35), and coefficient alphas were high (0.81–0.91). Eigenvalue ratios and CFA supported retention of a single factor/component, with suitable model fit indices. ACS correlated highly with factor scores and the first principal component from the PCA. Reduced sets of terms produced reliable scores that closely approximated the full set scores and aligned with external criteria. LPA in the lung subgroup identified four latent classes; ACS differentiated high vs. low symptom burden groups but did not distinguish the two groups expressing distinct symptom profiles. Conclusion: The ACS demonstrated structural validity through adequately fitting linear factor models and effectively summarized symptomatic AE burden. However, similar ACS values may mask clinically distinct symptomatic AE profiles, underscoring the value of both summary metrics and profile-based approaches. Full article

Plate food waste (PFW) in early childhood education is a critical yet understudied issue in Eastern Europe, with implications for nutrition, sustainability, and food security. This study examined PFW in a kindergarten in the Republic of Moldova, encompassing all 58 enrolled children and generating 14,292 meal-level observations through direct weighing of served meals and leftovers. Variance analysis (ANOVA) was used to test the influence of weekday, meal type, age, and gender, while Principal Component Analysis (PCA) explored latent structures of waste determinants. Results showed significant effects of weekday and meal type on PFW, with lunch consistently generating the highest waste levels and snacks the lowest. Gender differences were modest, while the interaction between age and gender indicated heterogeneous developmental patterns in waste behavior. PCA reduced the dataset to three main components: Portion Control, Menu Design, and Serving Strategy, explaining 84.7% of the total variance. These findings provide novel evidence for understanding how contextual and nutritional variables shape children’s PFW in early education and offer a replicable framework for reducing PFW and improving dietary adequacy in kindergartens. The study’s implications extend to sustainable nutrition planning and early behavioral interventions in preschool settings. Full article

Sustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels—including sentence length, punctuation density, word length, and type–token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT’s communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation—though often subtle—may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability—key pillars of sustainable development. Full article