Search Results (354)

Background and Objectives: The relative prognostic information provided by different nodal metrics and the independent role of primary tumor size remain unclear in gastric cancer. This study evaluated the association of metastatic lymph node count and primary tumor size with disease-free survival (DFS) in stage II–III gastric cancer (GC) treated with a surgery-first approach. Materials and Methods: This retrospective single-center study included 128 patients who underwent curative-intent gastrectomy between 2003 and 2019 without neoadjuvant treatment. A multivariable Cox model with clinically prespecified covariates was fitted. Metastatic lymph node count, pathological N (pN) category, lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) were each added separately to the same base clinical model and compared using the Akaike information criterion (AIC). Results: During a median follow-up of 114.3 months, 86 DFS events occurred. Each additional metastatic lymph node was independently associated with a higher hazard of a DFS event (HR, 1.049; 95% CI, 1.022–1.076; p < 0.001), and this association persisted across four sensitivity analyses. Primary tumor size was not independently associated with DFS. All four nodal metrics improved model fit, with LNR yielding the lowest AIC. Conclusions: Metastatic lymph node count was independently and robustly associated with DFS, whereas primary tumor size was not. Although LNR provided the best relative model fit, these within-cohort comparisons do not establish clinical superiority, and external validation is required. Full article

Climate change, increasing aridification, and water scarcity are intensifying the vulnerability of irrigated agroecosystems in Central Asia to drought and secondary soil salinization. In this study, we assessed the spatio-temporal vulnerability of irrigated land in the Turkestan Region (Kazakhstan) using SPEI-3, NDVI, NDSI, Landsat imagery (2000–2025), spatial analysis, and copula modeling. A strong positive relationship was found between SPEI-3 and NDVI (Pearson’s r = 0.861; Spearman’s r = 0.851), indicating vegetation is highly sensitive to moisture availability. Moderate and severe droughts reduced NDVI values by 15–25%, exceeding 30% in the most vulnerable areas, while persistent salinity hotspots were identified in the southern and southeastern irrigated zones. Copula modeling quantified the relationship between SPEI-3 and NDVI using 23 paired annual observations (2000–2022). The best-fitting model, selected using the Akaike Information Criterion (AIC = −31.074) and Bayesian Information Criterion (BIC = −29.939), revealed a nonlinear and asymmetric dependence between drought conditions and vegetation response. The probability of concurrent drought and vegetation degradation reached 0.55–0.70 in the most vulnerable areas. Integrated vulnerability mapping identified the Maktaaral, Zhetysay, Shardara, and Otyrar districts as the most vulnerable territories. The proposed framework supports drought vulnerability assessment and sustainable management of irrigated agroecosystems under climate change. Full article

Modeling count data using generalized linear mixed models (GLMMs) is a common practice in ecological research. However, datasets such as bird vocalization counts often display overdispersion, where the variance exceeds the mean, violating Poisson assumptions and potentially leading to biased inference. This study investigates the call counts of owl nestlings by comparing Poisson and Negative Binomial (NB) GLMMs to assess the impact of overdispersion on model performance. The models incorporate a random intercept for nest identity, thereby accounting for the hierarchical structure of the data and the correlation among observations within the same nest. Parameter estimation was performed using the Laplace approximation and the Adaptive Gauss–Hermite Quadrature (AGHQ) method to evaluate likelihood-based inference under different estimation approaches. Although the overdispersion diagnostic indicated extra-Poisson variation, model comparison based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood showed that the Poisson GLMM provided a better overall fit than the NB-GLMM. These findings demonstrate that the presence of overdispersion alone does not necessarily justify replacing a Poisson GLMM with a NB-GLMM and highlight the importance of combining distributional diagnostics with likelihood-based model selection when analyzing hierarchical ecological count data. Full article

In the Neotropical Savanna domain, few equations are available for estimating biomass stocks in Cerrado sensu stricto (CSS), despite the importance of such tools for estimating carbon stocks, understanding ecosystem functioning, and supporting conservation actions. We conducted forest inventories in 40 temporary 1000 m² plots in southeastern Brazil to estimate total and compartmental biomass stocks and to model biomass using structural and edaphic predictors. Total biomass (TB) included aboveground woody biomass (AGWB), necromass, litter, and belowground biomass (BGB). AGWB was estimated for trees with basal diameter ≥ 5 cm using a previously fitted regional equation. Root biomass was sampled using a 1 m³ trench excavated at a single point adjacent to each plot. Necromass was quantified using the line-intersect method along a 50 m transect, considering debris with diameter ≥ 3 cm. Litter was sampled using a 0.25 m² frame placed at the center of each plot. Biomass was modeled on an area basis using a hierarchical approach for TB, total tree biomass (TTB = AGWB + BGB), and AGWB. Mean stocks (Mg ha⁻¹ ± s.d.) were 45.24 ± 17.32 (TB), 20.47 ± 11.26 (AGWB), 18.47 ± 10.88 (BGB), 5.49 ± 4.18 (litter), and 0.81 ± 1.62 (necromass). Models selected using the Akaike Information Criterion (AIC) and validated by repeated k-fold cross-validation achieved rŷy = 0.76, 0.72, and 0.94 and RMSE = 24.40%, 27.06%, and 18.10% for TB, TTB, and AGWB. As the equations were calibrated for CSS under the environmental conditions of the Brazilian semiarid region, their transferability to other Cerrado regions should be considered with caution. The inclusion of soil variables (e.g., Al, Mg, and sand) improved predictions, reducing relative costs and taxonomic dependence, while incorporating nutritional adaptations to the acidic soils characteristic of the biome. Full article

Natural fiber-reinforced polymer composites are attractive for lightweight and sustainable engineering applications; however, property scatter remains a major barrier to reliable design. Mean tensile properties alone are insufficient when material selection depends on repeatability and lower-tail performance. This study presents a statistical variability and lower-tail reliability assessment of flax, jute, and carbon fiber composite laminates using 590 open-access tensile test records from a published natural-fiber composite dataset. Flax and jute were selected as representative bast-fiber systems covering a range of woven, unidirectional, and short-fiber architectures; carbon fiber was included as a synthetic-fiber reference system. Three mechanically important properties were analyzed: the recalculated tensile modulus, tensile strength, and axial failure strain. Normal, lognormal, and two-parameter Weibull distributions were screened for each material–property combination using the Akaike information criterion (AIC); empirical fifth percentiles (P5) and bootstrap 95% confidence intervals (CI) were computed as lower-tail descriptors. The results show that Carbon-0 has the highest lower-tail modulus and strength, with empirical fifth percentiles of 104.95 GPa and 989.64 MPa, respectively. Among the natural fiber systems, Flax-0 and Flax-VE-0 provided the highest lower-tail strengths, whereas Flax-Twill and Flax-CP showed the highest lower-tail failure strains. The lowest tensile strength coefficient of variation was observed for Flax-90 (2.41%), followed by Flax-Twill (3.43%), Flax-0 (4.50%), Jute-Satin (4.83%), and Jute-Plain (4.92%). A balanced reliability ranking that combined lower-tail property ranks and coefficient of variation ranks identified Flax-0, Flax-VE-0, Flax-Twill, Flax-CP, and Jute-Satin as the most favorable natural-fiber systems. The lower coefficient of variation values observed in aligned and satin-weave architectures relative to short-fiber and plain-weave systems reflect the role of fiber orientation uniformity in moderating property scatter at the laminate scale. This study provides a reproducible statistical framework based on lower-tail performance descriptors for comparative screening purposes, not on formal design allowables for distinguishing high mean performance from reliable minimum-level performance in natural fiber composite laminates. Full article

This study investigates and explores the volatility dynamics of the South African rand against the US dollar (ZAR/USD) using the Family GARCH (fGARCH) model and the First-Order Beta-Skew-T-Generalised Autoregressive Conditional Heteroskedasticity (Beta-Skew-T-EGARCH) model. Currency volatility across the globe, uncertainties, and instability in emerging markets have become increasingly consequential for trade flows, investment allocation, and macroeconomic management. The ZAR/USD serves as a benchmark of South Africa’s economic wealth and vulnerability to external shocks and is one of the most valued, significant, and heavily traded pairings of emerging market currencies. Simple standard GARCH (sGARCH) is one of the most useful models for exchange rate volatility; however, the sGARCH model has some limitations: it fails to accommodate or allow the long memory effects, skewness distribution, and leverage dynamics consistently observed in emerging-market currency returns. This study addresses these limitations by using the fGARCH model, which includes the most popular GARCH models and Beta-Skew-T-EGARCH for daily ZAR/USD returns ranging from 5 January 2000 to 1 October 2024. Five innovation distributions are used for evaluation and comparison under fGARCH and sGARCH, namely generalised hyperbolic (GH), generalised error (GED), skewed Student’s t (SSTD), skewed generalised error (SGED), and Student’s t (STD), with model fitness criteria assessed using the Shibata criterion (SIC), Hannan–Quinn criterion (HQ), Bayesian information criterion (BIC), and Akaike information criterion (AIC), choosing the specification with the lowest overall penalty. It is found that the fGARCH(1,1) model fitted to return-frequency data under the SSTD achieves the lowest AIC, outperforming sGARCH. The study also includes an analysis among covariates, which are day, month, trend, oil, and platinum; the trend variable is a statistically significant predictor, with p = 0.007, showing a positive influence on ZAR/USD volatility. The Beta-Skew-T-EGARCH model with two components divides volatility into long-run and short-run components, which is found to deliver a superior fit over the one-component variant, evidenced by a lower BIC (3.068435) and a higher log-likelihood (−748.464826). The two components confirm that the model captures declining conditional volatility, whereas the one-component model sustains persistence in the evaluated estimates. Full article

This study examines the modelling and forecasting of South African macroeconomic and financial time series using a comparative framework based on Vector Autoregressive (VAR), Vector Autoregressive Moving Average (VARMA), and GARCH-type models. Quarterly data spanning 1970 to 2024 were analysed to determine GDP growth, exchange rates, interest rates, and household consumption expenditure. VAR and VARMA models were employed to capture conditional mean dynamics, while GARCH, EGARCH, and GJR-GARCH models, including ARMA-GARCH extensions, were used to model volatility behaviour. Optimal model specifications were selected using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan–Quinn Criterion (HQ), and the Extended Cross-Correlation Matrix (ECCM), resulting in the estimation of VAR (4) and VARMA (1,1) models. The results reveal strong dynamic interdependencies among the variables. However, diagnostic tests indicate that the VAR (4) and VARMA (1,1) models do not fully capture the underlying data-generating process, as evidenced by residual autocorrelation, heteroskedasticity, and non-normality. Although the VARMA (1,1) model improved forecasting performance relative to the VAR (4) model, important nonlinear and higher-order dynamics remained unexplained. Volatility modelling revealed substantial persistence and clustering, particularly in exchange rates and interest rates. Initial GARCH, EGARCH, and GJR-GARCH specifications exhibited residual autocorrelation and remaining ARCH effects, suggesting model misspecification. The incorporation of an ARMA (1,1) term into the asymmetric GARCH models significantly improved model adequacy by eliminating residual autocorrelation and heteroskedasticity. Limited evidence of asymmetric volatility effects was found. Overall, the findings demonstrate that GARCH-ARMA specifications provide a more robust framework for modelling South Africa’s macroeconomic and financial dynamics. This study recommends future research incorporating nonlinear, regime-switching, and exogenous-variable models to enhance forecasting accuracy and policy relevance. Full article

The complex environment in metal mines introduces substantial noise into microseismic signals, which reduces the accuracy of arrival time picking and affects source localization. When the Cumulative Sum (CUSUM) method is directly applied, it often leads to inaccurate arrival time picking with low precision and high false alarm rates. To address these limitations, this paper proposes a novel arrival time picking method (V-CUSUM) that combines Variational Mode Decomposition (VMD) with the CUSUM algorithm. The microseismic signal is first decomposed into band-limited intrinsic mode functions (IMFs) using VMD with fractal box dimension-optimized parameters (K = 5; α = 2000). Effective IMFs are selected based on the Pearson correlation coefficient (threshold: |r| > 0.5), and the CUSUM method is independently applied to each selected component for arrival time detection. The final arrival time is determined by averaging the individual picking results. Simulation experiments demonstrate that the proposed method achieves a root mean square error (RMSE) of 0.0024 s and a mean absolute error (MAE) of 0.0018 s, substantially outperforming the Short-Term Average/Long-Term Average Method (STA/LTA) (RMSE = 0.0347 s) and Akaike information criterion, AIC (RMSE = 0.061 s), methods. Validation with field microseismic data from a metal mine shows that V-CUSUM achieves an average picking accuracy of 99.86% with a standard deviation of 0.007 s, compared with 97.85% for STA/LTA and 99.58% for AIC. These results confirm that the V-CUSUM method provides a robust, accurate, and physically interpretable framework for microseismic arrival time picking under complex noise conditions. Full article

Background: Routine full blood count (FBC) testing is part of the haematological workup in diabetes management. There is limited information regarding the contributions of individual haematological parameters to regression models for glycated haemoglobin (HbA1c), estimated whole-blood viscosity (eWBV) and the resulting blood viscosity complications. Importantly, because association and prediction represent distinct concepts, this study extends previous work with a focus on comparative and exploratory relationships. The objective was to compare FBC parameters between higher and lower HbA1c and eWBV groups and identify variables contributing to the Akaike Information Criterion (AIC)-based regression model among diabetics. Methods: This laboratory-based mixed quantitative study involved cross-sectional and regression analyses. Fifteen parameters were evaluated, including the following: red blood cell count (RBC) and indices (MCV, MCH, MCHC); platelet count and derived ratios (PRR, PWR, RPR); and white blood cell count (WBC) with lymphocyte ratios (MLR, NLR, PLR). HbA1c and eWBV data were used to create dichotomous subgroups for univariate comparison, followed by exploratory AIC-based model identification of variables. Results: HbA1c, RDW, MCV, and RPR, differed significantly between HbA1c groups (p < 0.1). Regression analysis identified RDW, MCV, RPR, MCH and RBC as contributors to the HbA1c model. For eWBV, five out of seven parameters (HCT, HB, RBC, WBC, and MLR) showed a significant association. Conclusions: These findings highlight haematological parameters with potential values for future predictive model development. Overall, the study supports the usefulness of selected FBC variables as adjuncts in diabetes monitoring with potential utility in understanding glycaemia control and blood viscosity-related complications. Full article

Objective: To evaluate the incremental value of immediate postpartum point-of-care ultrasound (POCUS) parameters for risk stratification of adverse maternal outcomes (AMO) in women with hypertensive disorders of pregnancy (HDP). Methods: This prospective observational cohort study was conducted between January 2024 and March 2025 in the Labor Ward of the Second Affiliated Hospital of Fujian Medical University. Women diagnosed with HDP after 20 weeks of gestation underwent standardized lung and cardiac POCUS examinations within 2 h after delivery. Maternal demographic, laboratory, and ultrasound variables were compared between women with and without AMO during the 42-day postpartum follow-up period. A baseline clinical model was constructed using conventional clinical and laboratory variables. Ultrasound parameters were subsequently added individually to assess their incremental value for risk stratification. Model performance was evaluated using the Brier score, Akaike Information Criterion (AIC), and area under the receiver operating characteristic curve (AUC). Results: A total of 160 women were included, of whom 35 (21.88%) experienced AMO. Compared with women without AMO, those with AMO showed significantly higher echo comet score (ECS), left atrial volume index (LAVI), and left ventricular index of myocardial performance (LIMP), while left ventricular E/A ratio was significantly lower (all p < 0.05). The baseline clinical model yielded an AUC of 0.88. Addition of ECS, LAVI, or LIMP individually improved model discrimination, with corresponding AUCs increasing to 0.93. These ultrasound-enhanced models also demonstrated lower Brier scores and AIC values compared with the baseline clinical model alone. Conclusions: In women with HDP, immediate postpartum lung and cardiac ultrasound parameters differed significantly according to postpartum outcome status. Incorporation of ultrasound-derived maternal hemodynamic indicators provided incremental value beyond conventional clinical variables for risk stratification of AMO. Immediate postpartum POCUS may therefore serve as a practical bedside adjunct for early identification of high-risk women with HDP and may help guide individualized postpartum monitoring. To our knowledge, this is the first prospective study evaluating the incremental value of immediate postpartum lung and cardiac POCUS for risk stratification in women with HDP. Full article

Background/Objectives: Neoadjuvant cisplatin-based chemotherapy (NAC) followed by radical cystectomy (RC) is a standard treatment for cisplatin-eligible patients with muscle-invasive bladder cancer (MIBC), yet baseline tools to refine prognostic stratification remain limited. We aimed to develop and internally validate a clinicopathological nomogram integrating the neutrophil-to-lymphocyte ratio (NLR) to estimate event-free survival (EFS) in patients with MIBC treated with NAC. Methods: We retrospectively analyzed 210 patients with cT2–T4aN0–1M0 MIBC treated with cisplatin-based NAC at two Spanish institutions between 2010 and 2021. Candidate predictors included demographic, clinicopathological, and routine laboratory variables. A multivariable Cox model with backward selection based on the Akaike information criterion (AIC) was used to derive the final model, and internal validation was performed using 1000 bootstrap resamples. Results: Sex, age, prior non–muscle-invasive bladder cancer (NMIBC), and NLR were retained in the final nomogram. The model showed moderate discrimination, with a Harrell’s c-index of 0.60 and an optimism-corrected c-index of 0.58. The nomogram stratified patients into low-, intermediate-, and high-risk groups, with median EFS not reached, 47.5 months, and 18.0 months, respectively. High-risk patients also showed lower pathological complete response (pCR) rates. Conclusions: This exploratory nomogram integrates an accessible systemic inflammatory marker with baseline clinical variables to identify patients with poorer outcomes despite NAC. External validation in contemporary cohorts is warranted before clinical implementation. Full article

Background and Objectives: Patients undergoing incisional hernia repair constitute a clinically heterogeneous population with variable postoperative outcomes. Conventional risk models based on isolated risk factors may inadequately capture this complexity. This study aimed to identify data-driven clinical phenotypes and evaluate their association with surgical outcomes. Methods and Materials: A retrospective cohort of 1262 patients undergoing retromuscular incisional hernia repair (Rives–Stoppa technique) was analyzed. Unsupervised clinical phenotyping was performed using latent class analysis based on seven preoperative variables. Model selection was guided by Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and clinical interpretability. Postoperative outcomes were compared across phenotypes. Results: Three distinct phenotypes were identified: metabolic (34.6%), structural (33.9%), and frailty (31.5%). The structural phenotype showed the highest complication (22.7%) and recurrence rates (8.6%), while the frailty phenotype had the lowest complication burden (14.6%). The metabolic phenotype was characterized by obesity and diabetes, consistent with increased wound-related morbidity. Cluster robustness was supported by internal validation metrics and sensitivity analyses. Conclusions: In this retrospective single-center cohort, distinct clinical phenotypes with different outcome profiles were identified among patients undergoing incisional hernia repair, supporting the concept that this population comprises clinically heterogeneous subgroups with distinct patterns of vulnerability. These findings should be considered preliminary and hypothesis-generating. Further external validation and prospective studies are required to determine the clinical utility of phenotype-based risk stratification. Full article

Vapor Pressure Deficit (VPD) is a critical determinant of atmospheric evaporative demand and plant water stress in tropical agricultural systems. This study applied a Gaussian Mixture Model (GMM) and K-Means clustering to 36,528 hourly meteorological observations collected from Eastern Thailand between August 2021 and September 2025, with the objective of identifying distinct atmospheric moisture regimes relevant to precision irrigation management in durian cultivation. Two input configurations were evaluated: a multivariate feature space comprising air temperature, relative humidity, wind speed, solar radiation, and VPD; and a univariate input consisting of VPD alone. Model selection for GMM was guided by the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), while K-Means performance was assessed using the Elbow method, Silhouette Coefficient, Calinski–Harabasz Index, and Davies–Bouldin Index. For the multivariate input, GMM identified K = 7 as the optimal number of clusters, supported by the largest single-step reduction in both AIC and BIC at this transition point. For the univariate VPD input, K = 5 was selected as the most parsimonious and agriculturally interpretable solution. The seven clusters derived from the multivariate GMM were organized into four atmospheric moisture regimes, such as very low, moderate, high, and very high evaporative demand, capturing the full spectrum of diurnal and seasonal VPD variability characteristic of Eastern Thailand. The results demonstrate that GMM-based probabilistic clustering applied to multivariate meteorological inputs provides a more comprehensive characterization of atmospheric moisture dynamics than univariate or geometric clustering approaches, offering a practical framework for tiered irrigation scheduling and drought stress early warning systems in tropical fruit cultivation. Full article

Objectives: Bone mineral density (BMD) assessment, the gold standard for diagnosing osteoporosis, does not account for bone quality and fracture susceptibility. Trabecular bone score (TBS) adds value to traditional densitometry. No studies have been conducted in the Polish population to date to confirm the association between TBS and fracture occurrence. This study aimed to evaluate the TBS derived from lumbar spine (L1–L4) dual-energy X-ray absorptiometry (DXA) scans in Polish women aged 40–76 years, both with and without osteoporotic fractures. The relationship between TBS, fracture risk (assessed by FRAX and TBS-adjusted FRAX), and BMD at the lumbar spine, femoral neck, and total hip was investigated. Methods: A total of 933 Caucasian women (760 without fracture and 173 with fracture) who underwent DXA examinations (Hologic Discovery A) between 2022 and 2024 were included. Lumbar TBS, BMD, and clinical fracture risk factors were analyzed, excluding subjects with scan artefacts or extreme BMI. Group differences were assessed using t-tests and chi-square tests. Pearson correlation was used to evaluate associations between TBS, age, and BMI. Logistic regression models assessed TBS and BMD as fracture discrimination, and model performance was compared using the Akaike Information Criterion (AIC) and the area under the receiver operating characteristic (ROC) curve (AUC). Results: TBS values were significantly lower in the fracture group (p < 0.001). TBS demonstrated negative correlations with age (r ≈ −0.36) and BMI (r ≈ −0.14). Low TBS values (≤1.23) were associated with the highest fracture prevalence (28.8%) and a threefold increased risk compared to high TBS (odds ratio = 3.0). Each one standard-deviation decrease in BMD or TBS T-score increased fracture risk by 56–67% (both p < 0.001). Models combining TBS and BMD improved discrimination, as indicated by higher AUC and lower AIC, with TBS remaining an independent predictor. In subgroups with osteopenia or osteoporosis, TBS retained statistical significance. Conclusions: TBS combined with BMD effectively discriminates fracture risk in Polish women and offers superior diagnostic accuracy compared to BMD alone. Integrating TBS with BMD enhances fracture accuracy. Routine assessment of TBS may improve clinical management of osteoporosis. Prospective studies are needed to confirm its long-term predictive value. Full article

The Poisson exponentially weighted moving average (PEWMA) control chart rests upon the equidispersion assumption of the pure Poisson distribution, a structural symmetry condition stipulating that the process mean and variance are equal. In manufacturing environments characterized by latent process heterogeneity, this assumption is systematically violated: the resulting distributions are inherently asymmetric, heavily right-skewed, and overdispersed. This structural asymmetry renders standard PEWMA control limits artificially narrow, inducing a substantial inflation of false alarm rates. This paper introduces the Poisson mixture EWMA (PM-EWMA) control chart, which models the latent heterogeneous structure of count data as a finite Poisson mixture distribution, with parameters estimated via the Expectation–Maximization (EM) algorithm without requiring prior labeling of process states. The optimal number of components is determined via the Bayesian Information Criterion (BIC) as the primary criterion, supplemented by the Akaike Information Criterion (AIC), its bias-corrected variant (AICc), and the log-likelihood ratio diagnostic. The PM-EWMA chart incorporates the exact mixture variance, accounting for both within-component and between-component variability, into the EWMA control limit structure, thereby providing a theoretically justified correction under the fitted Poisson mixture assumption. A Monte Carlo simulation study comprising 495 factorial configurations benchmarks the PM-EWMA chart against both the standard PEWMA chart and the negative binomial EWMA (NB-EWMA) chart with oracle dispersion calibration, confirming stable in-control ARL performance and demonstrating improved discrimination relative to the misspecified PEWMA baseline. Empirical validation using fabric defect count data from two textile manufacturers in Türkiye, with Overdispersion Indices of 6.01 and 2.74, respectively, demonstrates false alarm reductions ranging from 40.9% to 89.2% relative to the standard PEWMA chart, depending on the smoothing parameter and degree of overdispersion. Full article