Search Results (43)

The zero-inflated two-parameter Rayleigh (ZITR) distribution provides a flexible framework for modeling data with excess zeros and positive observations following a two-parameter Rayleigh distribution. It is particularly suitable for right-skewed data and has applications in areas such as road traffic mortality and survival analysis. This study develops and compares several methods for constructing confidence intervals for the difference and ratio of variances from two independent ZITR populations. The considered methods include Bayesian approaches based on Markov Chain Monte Carlo (MCMC) and highest posterior density (HPD) intervals, as well as the generalized confidence interval (GCI), method of variance estimates recovery (MOVER), approximate normal (AN), percentile bootstrap (PB), and bootstrap with standard error (BS). The performance of these methods is evaluated via Monte Carlo simulations under various parameter settings and sample sizes, using coverage probability and expected interval length as performance criteria. The results indicate that the Bayesian HPD method generally performs well across a wide range of scenarios. A real-data application using road traffic mortality data from January 2025 in Chanthaburi and Narathiwat provinces is also presented, demonstrating the practical usefulness of the proposed approaches for comparing the variance structure between the two regions. Full article

The paper introduces a computationally efficient semantic-aware route planning framework for indoor mobile robots, designed for real-time execution on resource-constrained edge hardware (Raspberry Pi 5, CPU-only). The proposed architecture fuses monocular object detection with 2D LiDAR-based range estimation and integrates the resulting semantic annotations into the Nav2 Route Server for penalty-weighted route selection. Object localization in the map frame is achieved through the Angular Sector Fusion (ASF) pipeline, a deterministic geometric method requiring no parameter tuning. The ASF projects YOLO bounding boxes onto LiDAR angular sectors and estimates the object range using a 25th-percentile distance statistic, providing robustness to sparse returns and partial occlusions. All intrinsic and extrinsic sensor parameters are resolved at runtime via ROS 2 topic introspection and the URDF transform tree, enabling platform-agnostic deployment. Detected entities are classified according to mobility semantics (dynamic, static, and minor) and persistently encoded in a GeoJSON-based semantic map, with these annotations subsequently propagated to navigation graph edges as additive penalties and velocity constraints. Route computation is performed by the Nav2 Route Server through the minimization of a composite cost functional combining geometric path length with semantic penalties. A reactive replanning module monitors semantic cost updates during execution and triggers route invalidation and re-computation when threshold violations occur. Experimental evaluation over 115 navigation segments (legs) on three heterogeneous robotic platforms (two single-board RPi5 configurations and one dual-board setup with inference offloading) yielded an overall success rate of 97% (baseline: 100%, adaptive: 94%), with 42 replanning events observed in 57% of adaptive trials. Navigation time distributions exhibited statistically significant departures from normality (Shapiro–Wilk, p < 0.005). While central tendency differences between the baseline and adaptive modes were not significant (Mann–Whitney U, p = 0.157), the adaptive planner reduced temporal variance substantially (σ = 11.0 s vs. 31.1 s; Levene’s test W = 3.14, p = 0.082), primarily by mitigating AMCL recovery-induced outliers. On-device YOLO26n inference, executed via the NCNN backend, achieved 5.5 ± 0.7 FPS (167 ± 21 ms latency), and distributed inference reduced the average system CPU load from 85% to 48%. The study further reports deployment-level observations relevant to the Nav2 ecosystem, including GeoJSON metadata persistence constraints, graph discontinuity (“path-gap”) artifacts, and practical Route Server configuration patterns for semantic cost integration. Full article

This study develops and evaluates simultaneous confidence interval procedures for all pairwise differences of coefficients of variation under delta-inverse Gaussian distributions. The objective is to provide reliable comparative inference for relative variability in zero-inflated and highly skewed data, where standard normal-based methods may be unreliable. Five approaches were studied and compared in terms of coverage probabilities and average widths: generalized confidence interval, adjusted generalized confidence interval, fiducial confidence interval, method of variance estimates recovery, and normal approximation. A Monte Carlo simulation study was conducted under varying shape parameters, zero-inflation probabilities, sample sizes, and numbers of populations ($k$ = 3, 6, and 10). Although most methods produced CPs near the nominal 0.95 level, meaningful differences emerged when both coverage accuracy and interval efficiency were considered. The AGCI method consistently delivered stable coverage across parameter settings and remained robust as dimensionality increased. The MOVER approach achieved competitive coverage while frequently yielding narrower intervals. In contrast, GCI occasionally showed mild undercoverage, and FCI tended to produce overly wide intervals. An empirical application to zero-inflated mortality data supports the simulation findings. Overall, AGCI and MOVER provide reliable and practical tools for simultaneous inference on differences in CVs across delta-IG populations. Full article

This paper develops simultaneous confidence intervals (SCIs) for pairwise differences of means with zero-inflated Rayleigh (ZIR) distributions, a flexible framework for modeling positively skewed data with excess zeros. Closed-form expressions for the ZIR mean are derived, and several competing interval estimation procedures are investigated, including generalized confidence interval (GCI), parametric bootstrap (PB), method of variance estimates recovery (MOVER), delta-method normal approximation, and highest posterior density (HPD) intervals. The finite-sample performance of the proposed SCIs is examined via extensive Monte Carlo simulations, focusing on empirical coverage probabilities (CPs) and average interval lengths (ALs) over a broad range of parameter configurations and zero-inflation levels. A real data application to road accident fatality counts demonstrates the practical utility of the proposed methodology. The results show that the HPD method consistently achieves the most favorable balance between coverage accuracy and interval efficiency. Overall, this study advances reliable simultaneous inference for zero-inflated models commonly encountered in environmental, biomedical, and reliability studies. Full article

Background/Objectives: Intraoperative bleeding remains one of the major challenges in pediatric liver transplantation (PLT), contributing significantly to perioperative morbidity, transfusion-related complications, and prolonged recovery. Although viscoelastic testing has improved intraoperative hemostatic management, there are currently no validated preoperative tools capable of predicting bleeding risk in this vulnerable population. Methods: We conducted a prospective, single-center observational study including 43 consecutive pediatric patients who underwent orthotopic liver transplantation between May 2008 and August 2009. A comprehensive dataset encompassing demographic, clinical, biochemical, and surgical variables was collected. A multivariable linear regression model was developed to predict intraoperative blood loss (IBL). Variable selection was guided by Mallows’ Cp criterion to ensure optimal model fit and clinical interpretability. Model performance was assessed using adjusted R², diagnostic residual analysis, and internal validation to verify regression assumptions. Results: Six independent predictors of IBL were identified: presence of ascites, prior abdominal surgery, operative time, baseline fibrinogen concentration, platelet count, and recipient weight. The final model explained 35.2% of IBL variance (adjusted R² = 0.352; F = 7.68; p < 0.001). Model diagnostics confirmed linearity, normal distribution of residuals, and homoscedasticity, supporting its robustness and reliability. Conclusions: This multivariable model provides an interpretable, clinically applicable framework for individualized preoperative estimation of blood loss in PLT. It may assist in planning perioperative patient blood management strategies and serve as a foundation for future decision-support systems. Limitations include the single-center design and modest sample size; however, internal validation supported the stability and reliability of the model. Full article

Accurate statistical inference for zero-inflated and highly skewed data requires confidence interval procedures with a strong finite-sample performance. The delta–inverse Gaussian distribution provides a flexible framework for modeling such data by combining a point mass at zero with an inverse Gaussian distribution for positive observations, making it suitable for application in various fields such as traffic mortality, insurance, and environmental studies. This paper develops and compares several confidence interval estimation methods for the difference and the ratio of two variances from independent delta–IG distributions. The proposed approaches include adjusted generalized confidence intervals, fiducial confidence intervals, Bayesian credible intervals, the method of variance estimates recovery, and normal approximation methods used as benchmarks. The finite-sample performance of these methods is evaluated through Monte Carlo simulations under various parameter configurations and both balanced and unbalanced sample sizes, with an emphasis on coverage probability and interval width. The simulation results show that AGCI and MOVER generally achieve coverage probabilities close to the nominal level while producing relatively narrow intervals. The MOVER performs particularly well when zero-inflation probabilities are equal, whereas AGCI is more effective when they differ. Illustrative real-data examples are provided to demonstrate practical implementations. Full article

High-dimensional regression with multivariate responses poses significant challenges when data are collected across multiple platforms, each with potentially correlated outcomes. In this paper, we introduce a multi-platform multivariate high-dimensional linear regression (MM-HLR) model for simultaneously modeling within-platform correlation and cross-platform information fusion. Our approach incorporates a mixture of Lasso and group Lasso penalties to promote both individual predictor sparsity and cross-platform group sparsity, thereby enhancing interpretability and estimation stability. We develop an efficient computational algorithm based on iteratively reweighted least squares and block coordinate descent to solve the resulting regularized optimization problem. We establish theoretical guarantees for our estimator, including oracle bounds on prediction error, estimation accuracy, and support recovery under mild conditions. Our simulation studies confirm the method’s strong empirical performance, demonstrating low bias, small variance, and robustness across various dimensions. The analysis of real financial data further validates the performance gains achieved by incorporating multivariate responses and integrating data across multiple platforms. Full article

This study applies spectral analysis and singular spectrum analysis (SSA) to mean annual runoff of the Dniester River for 1950–2024 to identify dominant periodic components governing the hydrological regime of this transboundary basin shared by Ukraine and Moldova. The novelty lies in a basin-specific integration in the first systematic application of a combined spectral–SSA framework to the Dniester River, enabling consistent characterization of runoff variability and assessment of large-scale natural drivers. Time series from three gauging stations are analysed to develop data-driven runoff models and medium-term forecasts. Four stable groups of periodic variability are identified, with characteristic timescales of approximately 30, 11, 3–5.8, and 2 years, corresponding to major atmospheric–oceanic oscillations (AMO, NAO, PDO, ENSO, QBO) and the 11-year solar cycle. Cross-spectral and coherence analyses reveal a statistically significant relationship between solar activity and river discharge, with an estimated lag of about 2 years. SSA reconstructions explain more than 80% of discharge variance, indicating high model reliability. Forecast comparisons show that spectral methods tend to amplify long-term trends, CNN–LSTM models produce conservative trajectories, while a hybrid ensemble approach provides the most balanced and physically interpretable projections. Ensemble forecasts indicate reduced runoff during 2025–2028, followed by recovery in 2029–2034, supporting long-term water-resources planning and climate adaptation. Full article

The analysis of road traffic accidents often reveals asymmetric patterns, providing insights that support the development of preventive measures, reduce fatalities, and improve road safety interventions. The Rayleigh distribution, a continuous distribution with inherent asymmetry, is well suited for modeling right-skewed data and is widely used in scientific and engineering fields. It also shares structural characteristics with other skewed distributions, such as the Weibull and exponential distributions, and is particularly effective for analyzing right-skewed accident data. This study considers several approaches for constructing confidence intervals, including the percentile bootstrap, bootstrap with standard error, generalized confidence interval, method of variance estimates recovery, normal approximation, Bayesian Markov Chain Monte Carlo, and Bayesian highest posterior density methods. Their performance was evaluated through Monte Carlo simulation based on coverage probabilities and expected lengths. The results show that the HPD method achieved coverage probabilities at or above the nominal confidence level while providing the shortest expected lengths. Finally, all proposed confidence intervals were applied to fatalities recorded during the seven hazardous days of Thailand’s Songkran festival in 2024 and 2025. Full article

Background: Clostridium perfringens is an infectious agent of concern in wild/farmed shellfish. Hence, this study assessed shellfish-borne Clostridium perfringens (ShbCp) prevalence. Methods: A total of 1469 ShbCp from 2336 shellfish were modelled using hierarchical generalized linear and 1000-permutation-based-mixed-effects, meta-regression models. Results: The overall ShbCp prevalence was 54.12% (19.73–84.99) with a 32.02% (14.52–56.64) toxigenic rate and a higher estimate in 2020–2025 (41.01%, 17.00–70.23) versus 1970–2019 (20.01%, 4.49–57.08). Culture media significantly affect ShbCp recovery, with cooked meat medium and thioglycollate medium registering higher estimates (77% and 25.15%, respectively) than selective agars (<7%). The molluscans had a higher ShbCp rate (60.68%) than crustaceans (1.57%) and cephalopods (0.14%); oysters (85.97%) than mussels (71.81%), clams (50.38%), slug/snails (48.23%), scallops (16.24%), crabs (11.91%), shrimps (1.05%), and squids (0.42%); and Crassostrea gigas (89.27%) versus Ruditapes philippinarum (45.92%) versus Mytilus galloprovincialis (30.14%). ShbCp differed significantly by nations but not by continent with Spain (87.79%) having the highest rate, then China (47.01%), Japan (43.91%), the USA (10.44%), and Greece (0.00%); South America (51.36%), then Asia (44.77%), Europe (21.97%), and North America (10.44%). Sample size, growth medium, nation, and shellfish class significantly explained 27.58%, 72.30%, 67.52%, and 28.51% ($R^2$) variance in ShbCp prevalence, respectively. Conclusions: The present study estimated a high ShbCp prevalence, suggesting a significant public health risk. It recommends that C. perfringens should be incorporated as a supplemental indicator into shellfish safety/shellfish water quality monitoring alongside traditional indicators. Also, geographical data gaps from Africa, Latin America, the Middle East, and Oceania underline the need for national and global monitoring attention and priority on C. perfringens in shellfish/shellfish beds. Full article

Objectives: Neuronavigation may improve the precision and reproducibility of repetitive transcranial magnetic stimulation (rTMS) by aligning stimulation with individualized targets. Whether navigation-guided rTMS benefits post-stroke upper-limb recovery is unclear. We conducted a PRISMA-compliant systematic review and meta-analysis to estimate the effect of navigated rTMS, added to standard rehabilitation, versus sham. Methods: The protocol was registered in PROSPERO (CRD420251165052). Two reviewers independently searched CENTRAL, MEDLINE, Embase, CINAHL, Web of Science, and Google Scholar (October 2025), screened records, extracted data, and assessed risk of bias (Cochrane RoB-1). The prespecified primary endpoint was changed in Fugl–Meyer Assessment of the upper extremity (FMA-UE) from baseline to end of treatment. Effects were pooled as mean differences under random-effects models. When change-score standard deviations (SDs) were unavailable, they were derived from pre/post SDs assuming within-person correlation r = 0.5; sensitivity analyses used r = 0.7 and r = 0.9. Multi-arm trials were combined to avoid double counting. Results: four randomized, sham-controlled trials (n = 297) contributed end-of-treatment change in FMA-UE. The pooled effect favored navigated rTMS but was not statistically significant (MD 3.65, 95% CI −1.84 to 9.13; I² = 73%). Sensitivity analyses with higher r produced directionally consistent estimates. A subgroup of 2-week (10-session) protocols (k = 3) showed a significant benefit (MD 7.09, 95% CI 4.14 to 10.05; I² = 0%). Most risk-of-bias domains were low risk. Conclusions: Navigated rTMS did not show a consistent short-term advantage over sham on FMA-UE across heterogeneous protocols. A positive signal in standardized 2-week courses supports further adequately powered multicenter randomized controlled trials (RCTs) with harmonized protocols and complete variance reporting. Full article

Objectives: Motor imagery (MI) may enhance post-stroke recovery, but evidence of its benefit over conventional rehabilitation therapy (CRT) is inconsistent. This study evaluated the effect of MI combined with CRT on upper-limb recovery, accounting for methodological quality and publication bias. Methods: A systematic review and meta-analysis was conducted following PRISMA guidelines. Searches were performed in multiple databases up to July 2025. Methodological quality and risk of bias were assessed using the PEDro scale and Cochrane RoB 2 tool, respectively. Analyses included the calculation of effect sizes (ES), heterogeneity, sensitivity, publication bias, and GRADE-based certainty assessment. Results: From 4074 records, 10 randomized controlled trials (n = 255) were included. The initial pooled analysis showed a small-to-moderate effect of MI + CRT versus CRT alone (ES = 0.45; 95% CI: 0.16–0.74). However, the overall ES calculated with a robust variance estimator was −0.06 (95% CI: −0.21, 0.08). Most trials had methodological limitations (mean PEDro = 6.0; high risk of bias in 7/10 studies). The GRADE evaluation indicated a very low certainty of evidence. Conclusions: The initially observed positive effect of MI combined with CRT is not robust. When accounting for statistical dependencies and potential biases, the effect vanishes and is no different from zero. Current evidence does not support the use of MI as a standalone adjunct to CRT. Larger, high-quality RCTs with standardized protocols are required to establish any potential clinical relevance. Full article

Hymenaea martiana is a species native to Brazil. It has ecological value, contributes to forest restoration, and is economically important because of the use of its wood and fruits. However, it is frequently exploited. Therefore, understanding genetic diversity becomes essential for guiding conservation strategies as well as ecological restoration actions in the face of climate change and anthropogenic pressures. Thus, this study aimed to evaluate the intraspecific diversity of 160 H. martiana mother plants on the basis of morphological descriptors of fruits and seeds and physiological indicators of seed quality, identifying the most discriminating characters. Eighteen traits were analyzed and subjected to analysis of variance and the Scott–Knott test (p < 0.05), with estimates of heritability and the ratio between genetic and environmental coefficients of variation. Phenotypic divergence was obtained via the Mahalanobis distance (D²) and grouped via UPGMA, whereas the relative contribution of the traits was estimated via the Singh method. The results revealed that seed length and weight, emergence speed index, and shoot dry mass were the most effective descriptors for discriminating parent plants. Multivariate analysis revealed the formation of eleven phenotypically distinct groups, demonstrating high variability. These findings support the selection of superior genotypes and representative seed collection, as well as practical initiatives such as the formation of germplasm banks, the selection of breeding stock for forest nurseries, and reintroduction programs. Thus, the data obtained offer technical and scientific support for biodiversity conservation and ecosystem recovery in the semiarid region of Brazil. Full article

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details. Full article

This study developed diameter distribution models using the Weibull function for Korean red pine (Pinus densiflora), Korean white pine (P. koraiensis), and Japanese larch (Larix kaempferi). The study data were collected from 49 Korean red pine stands, 54 Korean white pine stands, and 49 Japanese larch stands located in national forests in Gangwon and North Gyeongsang Provinces, South Korea. To identify the optimal method for modeling the diameter distribution of these three species, parameter recovery methods and parameter prediction methods were analyzed. To identify the optimal parameter recovery method for presenting the diameter distribution of these three species, ten parameter recovery methods were compared using moment-based, percentile-based, and hybrid approaches. For parameter prediction methods, major stand characteristics were used as independent variables to develop the models for the parameters a, b, and c of the Weibull function. For estimating the Weibull parameters, two methods—the estimated parameter recovery method and the parameter prediction method—were compared and analyzed. The optimal parameter recovery method was the one using the minimum DBH, the mean DBH, and the DBH variance. The coefficient of determination (R²) for the models predicting the minimum DBH, the mean DBH, and the DBH variance ranged from 0.7186 to 0.9747, and the R² for the models directly predicting parameters ranged from 0.7032 to 0.9374, indicating high explanatory power and unbiased results. When comparing the two methods, the parameter prediction method showed higher accuracy and lower bias. In addition, paired t-tests were conducted to assess differences from the observed Weibull parameters. The results showed a significant difference for the estimated parameter recovery method, whereas no significant difference was found for the parameter prediction method, further supporting its reliability. Full article