Search Results (110)

High-frequency monitoring of manufacturing agility and Environmental, Social, and Governance (ESG) responsiveness is increasingly required in data-rich operations, yet many practical indices remain low-frequency, weakly decomposable, or difficult to interpret in weekly control settings. This study presents a single-enterprise methodological demonstration of a weekly fuzzy monitoring model with dual benchmarks for explainable operational control. The empirical panel covers 156 weeks from 2023 to 2025 across three plants, five product families, and 27 KPIs grouped into Operational Agility, Sustainable Responsiveness, and Socio-Market Adaptability. KPI and benchmark weights were elicited through a two-round Delphi process with 24 experts. The model combines fixed target-centered B1 compliance thresholds with percentile-calibrated B2 thresholds for direction-adjusted week-to-week adaptation. In the calibrated specification, the overall index mean is 0.618 with a range of 0.489 to 0.741, while the mean B1 and B2 values are 0.619 and 0.617. Matched-level validation at the plant–product–week level (N = 2340) shows a positive association with EBIT (Pearson r = 0.222, 95% CI [0.200, 0.245]), and time-safe calibration checks preserve the substantive interpretation of the index. The results support the model as an explainable, human-in-the-loop instrument for within-case weekly monitoring and diagnosis rather than as a broadly validated predictive model. Full article

Advances in probability distributions are important for modelling complex data across fields such as actuarial science, environmental science, biomedical science, economics, finance, and insurance. Classical distributions often have limitations when dealing with highly skewed data, heavy tails, or unusual failure patterns. To address these challenges, this study introduces the Sine Inverse Lomax Burr III distribution, a new flexible model that combines the tail behaviour of the Burr III distribution with the skewness-control properties of the sine inverse transformation. Statistical properties, including quantiles, moments, moment generating functions, and order statistics, are derived. Some risk measures, including the value at risk, tail value at risk, and tail variance, are derived and studied. Parameter estimation is performed using five different estimation techniques: maximum likelihood estimation, least squares, weighted least squares, percentile matching, and Anderson–Darling. The usefulness of the proposed model is demonstrated using monthly tax revenue data. The results show that the SILBIII distribution performs better than the competing distributions. The proposed model is an alternative model suitable for modeling data in finance, actuarial, and related fields. Full article

Kernel-combination procedures used in classification often return only a combined kernel matrix on the training sample, rather than a kernel function that can be evaluated consistently at new points. This limitation is especially important for supervised or label-aware combinations, whose entries may depend on training labels and therefore have no immediate out-of-sample meaning. We study the problem of constructing an inductive, finite-rank kernel extension from such empirical matrices. The proposed framework makes the non-uniqueness of this extension explicit: it is determined by empirical coordinates, a positive-semidefinite coefficient matrix, and a continuation model for the coordinates. Experiments on vector, tabular, and relational classification problems give a deliberately diagnostic picture. Smooth direct combinations are stable: on Synthetic, the direct mean gives error $0.0793\pm 0.0227$, essentially matching the best individual RBF kernel ($0.0809\pm 0.0231$), and on Telco it remains close to the best individual polynomial kernel ($0.2061\pm 0.0154$ versus $0.2045\pm 0.0154$). In the controlled Synthetic oracle diagnostic, reconstructing a smooth sum/mean gives relative Frobenius error $4.13\times {10}^{-6}\pm 9.41\times {10}^{-6}$ and functional MSE at numerical scale. By contrast, abrupt label-aware matrix-only rules are less robust: the Synthetic percentile_inout_auto rule has error $0.1404\pm 0.1198$, Telco matrix-only supervised rules are around $0.307$–$0.326$ error, and the Chickenpieces pickout_auto rule fails under strict out-of-sample reconstruction ($0.3545\pm 0.2666$ error), whereas direct relational combinations match the best individual relational kernel within ${10}^{-3}$. Overall, the empirical evidence supports the method as a bridge from finite matrix-level information fusion to deployable kernels, while also identifying abrupt label-aware geometries as the main limitation for stable generalization. Full article

Decentralized compute markets require autonomous agents to negotiate heterogeneous resources under budget constraints, stochastic supply, and strategic interaction. We present Agora-RL, a reproducibility-first benchmark for repeated negotiation of GPU, memory, and bandwidth through token-denominated double auctions. The study asks two questions: how standard MARL baselines rank when reward, social welfare, and inequality are evaluated jointly; and whether a transparent benchmark protocol can make such comparisons auditable. PPO, MAPPO, MADDPG, and IQL are evaluated with matched 300-episode training budgets, 30 deterministic evaluation episodes, and 12 random seeds. Using percentile-bootstrap 95% confidence intervals, MAPPO achieves the highest reward (0.0140 [0.0124, 0.0154]) and social welfare (0.0952 [0.0854, 0.1045]), whereas IQL yields the lowest Gini coefficient (0.4477 [0.4360, 0.4613]). Secondary diagnostics show that reward leadership does not imply fairness, equilibrium closeness, or communication robustness. The contribution is an empirical benchmark and audit protocol rather than a new auction theorem or blockchain settlement layer. Full article

Stand-level canopy base height (Cbh) is a key variable controlling crown fire initiation, yet it is commonly computed as the mean of tree height to the base of the crown (hbc), which does not reflect the lower portion of the hbc distribution governing the transition from surface to crown fire. This study investigates the relationship between physically based Cbh definitions and the hbc distribution. We develop a general multi-percentile modeling framework to estimate hbc percentiles at the stand level. Using a dataset of Pinus pinaster Aiton trials in Portugal, percentile-specific models (5th to 50th) were fitted and synthesized into a nonlinear multi-percentile formulation. Results show that the height at which canopy bulk density exceeds the critical threshold does not match mean hbc, but instead corresponds to lower percentiles, typically around the 10th percentile, varying with stand structure and age. Mean-based Cbh tends to overestimate the lower canopy boundary, reflecting its inability to capture structural variability. The final model predicts hbc at any percentile and incorporates effects of stand height, basal area, tree density, and age, ensuring positive predictions and high predictive accuracy (adjusted R² = 0.9770; RSE = 0.4073 m; PRESS R² = 0.9769). The framework provides a consistent representation of canopy base height for fire behavior modelling. Full article

This study aims to estimate the effect of disability on household income in Thailand using different econometric approaches and to examine heterogeneity across the income distribution. We analyze data from Thailand’s 2021 Socioeconomic Survey covering 46,775 households, of which 4255 (9.1%) report having at least one disabled member. Employing three complementary methods—ordinary least squares regression, propensity score matching, and quantile regression—we find that households with disabled members experience significant income penalties. The OLS estimate with full controls shows a 10.0% income penalty, while propensity score matching yields 17.4%, suggesting that standard regression underestimates the true effect. Quantile regression reveals striking heterogeneity: the disability effect ranges from 4.8% at the 10th percentile to 30.5% at the 90th percentile. This pattern suggests that among Thailand’s poorest households, both disabled and non-disabled families face universal constraints; leaving minimal scope for disability is strongly associated with reduced household income, while at higher income levels disability creates barriers to advancement. Decomposition analysis indicates disability affects income through both reduced hourly wages (8.2% lower) and fewer work hours (12.4% reduction). These findings reveal that Thailand’s disability allowance of 800 baht per month—representing only 29% of the poverty line—is grossly inadequate, covering merely 17% of the observed income gap. The results highlight urgent needs for allowance increases with inflation indexation, differentiated support across the income distribution, improved employment quota enforcement, and streamlined registration to address the 46% unregistered rate. Policymakers should prioritize raising the monthly allowance to a level commensurate with the national poverty line, implementing tiered benefit structures based on disability severity, and strengthening employment quota enforcement mechanisms to reduce disability-related income inequality. Full article

Background: The impact of Type 1 Diabetes Mellitus (T1DM) on linear growth in children remains unclear, with conflicting evidence regarding the roles of glycemic control, disease duration, and nutritional factors. This study aimed to assess growth patterns and identify factors independently associated with short stature among Saudi school-age children and adolescents with T1DM, comparing them to a healthy control group. Methods: A retrospective cross-sectional study was conducted at an endocrinology clinic, including 250 patients with T1DM aged 5–18 years and 267 healthy controls. After propensity score matching, 231 patients were matched to 231 controls. Data were extracted from electronic medical records using a standardized form. Anthropometric measurements were converted to Z-scores and percentiles using validated Saudi growth charts. Short stature was defined as height below the third percentile for age and gender. Univariate and multivariable logistic regression analyses were performed to identify factors associated with short stature among T1DM participants. Results: The median age of the 250 T1DM participants was 13.0 [10.0–15.0] years, with a slight male predominance (58.0%). Of them, 6 (2.4%) children were tall, 30 (12.0% were short), and 214 (85.6%) were normal. A significantly higher proportion of short stature was observed in the T1DM group compared with the control group (6.1% vs. 1.7%; p = 0.016). Among T1DM participants, the proportion of short stature increased progressively with diabetes duration: 3.0% in new-onset disease, 14.9% in intermediate duration (2–5 years), and 25.0% in long-standing disease (>5 years) (p = 0.001). Age at onset of T1DM was also significantly associated with having short stature (p = 0.036). In multivariable analysis, intermediate duration (adjusted odds ratio [aOR] = 5.72, 95% CI (1.02–32.1); p = 0.047) and long-standing duration (aOR = 11.03, 95% CI: (1.09–111.45); p = 0.042) remained significant independent predictors of short stature. In contrast, glycemic control (HbA1c), vitamin D status, time in range, and treatment adherence were not significantly associated with short stature after adjustment. Conclusions: Children and adolescents with T1DM have significantly lower height percentiles and a higher prevalence of short stature compared to their controls. Diabetes duration is a strong, independent predictor of short stature in this population, with progressively higher risk as disease duration lengthens, regardless of glycemic control status. These findings underscore the necessity of systematic longitudinal growth monitoring, particularly in patients with disease duration exceeding five years, to enable early identification and intervention for those at highest risk of growth impairment. Full article

This study assessed phenotypic variation among open-pollinated half-sib families from a single relict population. Autochthonous silver fir (Abies alba Mill.) preserved in the Tisovik Reserve of Białowieża Forest represents the northeasternmost isolated relict population of the species in Europe. To secure its genetic resources and evaluate its breeding potential, a conservation plantation of open-pollinated half-sib families was established in the Hajnówka Forest District outside the natural species range. This study assessed the effects of half-sib family affiliation on the growth and phenotypic performance of almost two thousand 28–31-year-old trees representing 20 half-sib families and compared them with age-matched managed stands in the State Forests of Poland. Significant within- and among-family variation was observed for diameter at breast height (DBH) and height (H), while environmental factors had only marginal influence under the uniform site conditions of the plantation. Several half-sib families produced disproportionately high numbers of individuals with exceptional phenotypic performance, including DBH values exceeding 25 cm and height values surpassing those of managed stands. Based on a combined assessment of qualitative traits, selection differential, and 95th percentile values, 30 prototype plus trees were selected as sources of scions for establishing a future seed orchard. The outstanding growth parameters of these individuals correspond to stand ages of 40–65 years according to yield tables, despite their biological age of only 28–31 years. The results confirm the high breeding value and substantial genetic variability of the Tisovik population and demonstrate its potential for producing genetically diverse planting material adapted to lowland sites under changing climatic conditions. Full article

This work considers admissibility-enforcing reconstruction of reaction-channel subgroup levels on prescribed total-subgroup support and probabilities, a setting in which conventional exact reconstruction may produce negative reaction-channel levels. The proposed reconstruction relaxes conventional full matching by retaining selected low-order channel quantities associated with limiting dilution responses exactly, while fitting the remaining matching conditions in a constrained least-squares sense under nonnegativity. The exact-retention constraints are embedded through a null-space parametrization, which reduces the reconstruction to a convex optimization problem over the remaining degrees of freedom. Two variants are examined: a single-retention formulation, which is automatically feasible for nonnegative retained data, and a two-retention formulation, which is more restrictive and depends on compatibility with the fixed total-subgroup rule. Numerical tests for ²³⁸U capture data show that the proposed reconstruction removes the negative reaction-channel levels observed in the violating groups. Restoring admissibility entails deterioration in response accuracy relative to the unconstrained full-matching baseline, reflecting the trade-off between exact matching and nonnegativity on the fixed rule. Of the two variants considered, the single-retention formulation shows more stable overall behavior in the present comparison. In particular, for all violating cases at orders $N\ge 10$, it restores nonnegativity, with the reported 95th-percentile relative errors in the folded effective cross section not exceeding $8.90\times {10}^{-7}$. Full article

Background and aim: Chest CT is a major contributor to population radiation exposure. Conventional, pooled diagnostic reference levels (DRLs) do not account for inter-individual variability in body habitus and are typically used retrospectively. We evaluated dose behavior in adult chest CT, derived BMI-stratified local DRLs, and developed models to enable AI-assisted, prescan dose prediction. Methods: Consecutive adult chest CT examinations from a single center were analyzed. Dose indices (CTDIvol, DLP) and patient factors (BMI, weight, height, age, sex; scan length and planned technical parameters where available) were extracted. DRLs were defined as the 75th percentile overall and within BMI categories (underweight, normal, overweight, and obese). Group differences were assessed using non-parametric tests; associations were examined using correlation analysis. Supervised learning (e.g., Random Forest, Gradient Boosting) was trained to predict CTDIvol and DLP from routinely available variables. Results: BMI-stratified DRLs increased monotonically with habitus: underweight 444.95 mGy·cm/9.60 mGy; normal 513.00/11.55; overweight 756.08/14.65; obese 931.60/20.25 (DLP/CTDIvol). Differences across BMI groups were significant for DLP (H = 31.53, p < 0.001) and CTDIvol (H = 33.61, p < 0.001). DLP correlated moderately with weight and BMI (r ≈ 0.54–0.56, p < 0.001), with a weaker association for age; height was not a meaningful predictor. No sex-based differences in CTDIvol or DLP were observed. Predictive models estimated CTDIvol and DLP with high performance (R² up to ~0.79 and ~0.77, respectively), enabling comparison of predicted dose against BMI-matched DRLs before acquisition. Conclusions: Size-aware, BMI-stratified DRLs provide clinically interpretable investigation levels that avoid pitfalls of pooled benchmarks. Coupled with robust prediction of individualized dose from routine variables, this framework supports a shift from retrospective audit to prospective, point-of-care dose governance and protocol optimization in chest CT. Full article

The automated production of large-scale labeled datasets from concrete X-ray computed tomography (CT) images is a fundamental prerequisite for training and validating deep learning-based segmentation models. However, existing methods either require extensive manual annotation or rely on domain-specific deep learning models that themselves demand labeled data—a circular dependency. This paper presents a parameter-driven three-class segmentation framework that automatically classifies each pixel in a concrete CT slice into one of three material phases: void (air pores and cracks), coarse aggregate, and mortar matrix, generating annotation masks suitable for large-scale dataset production without manual labeling. The proposed method combines: (1) fixed-threshold void detection calibrated to concrete CT grayscale characteristics; (2) adaptive percentile-based initial segmentation responsive to image-specific statistics; (3) multi-criteria connected component scoring based on area, shape descriptors (circularity, solidity, compactness, extent, aspect ratio), intensity distribution, and boundary gradient; (4) material science-informed size constraints aligned with concrete phase volume fractions; and (5) a material continuity enforcement module that applies topological hole-filling and conditional convex-hull consolidation to eliminate internal contamination within accepted aggregate regions, reducing boundary roughness by 7.6% and recovering misclassified boundary pixels. All parameters are centralized in a configuration file, enabling reproducible batch processing of 224 × 224 pixel CT slices at 0.07–1.12 s per image. Evaluated on 1007 224 × 224 concrete CT patches cropped from 200 representative scan frames, the framework produces three-class segmentation masks with physically consistent void fractions (mean 3.2%), aggregate fractions (mean 32.4%), and mortar fractions (mean 64.4%), all within ranges reported in the concrete CT literature (used as a dataset-scale QC screen, not a validation metric). Primary outputs and the archived image–mask pairs for this work are provided as an 8-bit patch archive. For pixel-wise validation, we report IoU, Dice, and pixel accuracy on an independently labeled subset that can be unambiguously paired with the released predictions: averaged over 57 matched patches, mean pixel accuracy is 88.6%, macro-mean IoU is 74.7%, and macro-mean Dice is 84.9%. The framework provides a fully automated annotation pipeline for dataset production, eliminating manual labeling costs for concrete CT image collections. The generated datasets are suitable for training semantic segmentation networks such as U-Net and its variants. Full article

Background: Efficient parking navigation in large and dynamic parking areas requires systems that can adapt to real-time conditions and provide precise vehicle localization. Methods: This paper presents a smart car parking navigation module that integrates camera-based vehicle perception, homography-based ground-plane localization, mobile GNSS positioning, and dynamic route planning into a unified framework. Instance segmentation (YOLOv8n-seg) is used to detect vehicles and extract ground-contact regions, which are associated with parking slots defined in a GeoJSON-based site model. Mobile GNSS data are fused with visual observations via spatio-temporal proximity scoring to enable robust user–vehicle matching without optical identification. An A* routing algorithm dynamically computes and updates navigation paths, adapting to lane obstructions and slot availability in real time. Results: Experimental evaluation on a real six-camera parking facility shows that the proposed segmentation-based localization reduces mean error from 0.732 m to 0.283 m (61.3% improvement), with the 95th-percentile error dropping from 1.892 m to 0.908 m, and outperforming the bounding-box baseline in 85.3% of detections. Conclusions: These results demonstrate that sub-meter vehicle localization and reliable user–vehicle association are achievable using standard surveillance cameras without specialized infrastructure, offering a scalable and cost-effective solution for intelligent parking navigation. Full article

Background/Objectives: Epiblepharon is a common congenital eyelid anomaly in East Asian children, often associated with redundant skin and orbicularis oculi muscle overriding the eyelid margin. Recent studies have suggested that systemic factors such as body mass index (BMI) may contribute to its development. This study aimed to investigate the relationship between BMI and epiblepharon and to analyze the correlation between BMI and skin-fold height as a marker of eyelid structural redundancy. Methods: This retrospective comparative study included 100 pediatric patients (54 males, 46 females) aged 3–13 years who underwent surgical correction for lower eyelid epiblepharon and 100 age-matched controls without the condition. Preoperative height, weight, and skin-fold height were analyzed. Intergroup comparisons were performed using independent t-tests, and correlations between BMI and skin-fold height were evaluated using Spearman correlation. Results: There were no significant differences in overall BMI, obesity index, or prevalence of obesity defined as BMI ≥ 95th percentile between groups. Boys aged 7–8 years demonstrated significantly higher BMI in the epiblepharon group, and boys aged 9–10 years showed a significantly higher obesity index in the epiblepharon group, whereas boys aged 3–4 years showed significantly lower BMI. No significant differences were observed in girls. BMI was not independently associated with epiblepharon in multivariate logistic regression analysis (OR 1.06, 95% CI 0.96–1.16, p = 0.278). Among patients with epiblepharon, BMI showed a significant negative correlation with skin-fold height (r = −0.410, p < 0.001), suggesting increased orbicularis muscle redundancy in obese children. Conclusions: BMI was not independently associated with the presence of epiblepharon; however, age-specific differences were observed in certain male subgroups. Higher BMI was correlated with lower skin-fold height among affected patients, suggesting that adiposity may influence eyelid morphology in specific developmental stages. Further longitudinal studies are warranted to clarify the age-dependent relationship between obesity and epiblepharon. Full article

Background/Objectives: Translating brain volumetric biomarkers to individual-level Alzheimer’s disease (AD) diagnosis remains challenging due to difficulty interpreting raw volumes without longitudinal monitoring or matched controls. We tested a classification model using population-referenced volumetric percentiles to distinguish AD from cognitively normal (CN) subjects and evaluated its generalization across independent cohorts. Methods: Brain volumes from 95 regions were extracted using an automated segmentation pipeline and converted to age and sex adjusted percentiles using a reference population (N = 1833). A logistic regression classifier was trained on ADNI subjects (N = 873; AD = 183, CN = 690) split into training (60%), validation (20%), and test (20%) sets. The model was evaluated on two independent validation datasets: the held-out ADNI validation set and an external Korean cohort (N = 72; AD = 36, CN = 36) acquired with different scanner protocols and demographic characteristics. Results: The model achieved excellent discrimination across all evaluation sets: ADNI validation (AUC = 0.963, accuracy = 90.3%), ADNI test (AUC = 0.960, accuracy = 89.7%), and Korean external validation (AUC = 0.981, accuracy = 87.5%). The minimal validation gap (0.018) demonstrated robust generalization. Positive coefficients for ventricular regions reflected AD-associated atrophy patterns, while negative coefficients for medial temporal structures indicated their contribution within multivariate patterns distinguishing AD from normal aging. Conclusions: Population-referenced brain volumetric percentiles enable accurate AD classification with robust generalization across populations and scanner protocols. By contextualizing individual brain structure relative to normative populations while accounting for age and sex, this approach demonstrates potential for clinical translation as an accessible neuroimaging-based diagnostic tool. Full article

All existing Satellite-Based Augmentation Systems (SBAS) are regional, leading to discontinuous global coverage and significant service gaps. To overcome the inherent coverage limitations of SBAS caused by regional ground station distribution, this study integrates Low Earth Orbit (LEO) satellites with ground-based networks to enable global SBAS coverage. Using real observational data from eight LEO satellites, we demonstrate their feasibility as space-based monitoring stations. The results show that incorporating LEO satellites reduces the dual-frequency range error (DFRE) and significantly increases the number of available augmented satellites within the service region. Compared with Standard Point Positioning (SPP), this augmentation reduces the 95th-percentile horizontal and vertical positioning errors by approximately 51.4% and 44.2%, respectively. Furthermore, all stations satisfy the Approach with Vertical guidance I (APV-I) availability requirements, and no Hazardous Misleading Information (HMI) events are observed. Based on the observation data from eight LEO satellites, we construct an eight-satellite simulated constellation that matches the real satellites’ orbital characteristics, thereby validating the consistency between real-data findings and simulation-based assessments. Subsequently, we built a hybrid LEO constellation (108 Walker + 60 polar) as space-based monitoring stations integrated with ground stations to evaluate global SBAS service performance. The results show that with LEO satellite augmentation, the global number of available augmented satellites remains above nine. The 95th-percentile horizontal and vertical positioning accuracies are better than 0.75 m and 1.6 m. All global evaluation stations achieve APV-I availability above 99%. In addition, sensitivity analysis reveals that dissemination delay is a critical factor affecting protection levels and service availability, particularly at high latitudes. Overall, both real-data experiments and global simulations validate the significant benefit of LEO augmentation in improving global SBAS service performance. Full article