Search Results (1,034)

Effect-size estimation for Mood’s median test has received relatively little methodological attention despite the test’s widespread use in robust and nonparametric analysis. This study evaluates four candidate effect-size estimators: the median absolute deviation-based estimator (Delta–MAD), the probability of superiority (PS), Cramér’s V, and a newly proposed bootstrap-standardized median difference (Delta-Boot) across simulation settings involving normal data with equal variances, log-normal skewness, and heteroscedasticity with a twofold variance difference. Under equal variances, PS achieved the highest classification accuracy for moderate and large effects, with Delta–MAD and Delta–Boot close behind and Cramér’s V performing worst. Performance under log-normal skewness was nearly unchanged, demonstrating the robustness of median- and rank-based methods to heavy right-tailed distributions. Notably, Delta–Boot began to show improved performance for moderate effect sizes in the log-normal setting. Under heteroscedasticity, estimator behaviour diverged sharply: PS remained highly effective for distinguishing no and large effects but showed reduced accuracy for moderate effects due to its sensitivity to spread differences; Cramér’s V degraded substantially across all effect sizes; and the two median-standardized estimators—especially Delta–Boot—were more resilient, stabilizing more rapidly with increasing sample size and achieving the highest accuracy for moderate and large shifts at larger n. These patterns indicate that PS (or Delta–MAD) is most appropriate when variances are equal or nearly so, whereas Delta–Boot provides the most reliable performance in settings where variance imbalance is likely. Finally, a real-world application to fasting glucose data from the 2024 WHO STEPS survey in Trinidad and Tobago illustrates the practical utility of these approaches. Full article

Bayesian network-based causal structure learning provides an effective framework for uncovering causal relationships among continuous variables. However, many existing methods for continuous data still rely on strong parametric distribution assumptions, which may introduce information loss and reduce Bayesian network modeling accuracy. Kernel density estimation (KDE), a non-parametric statistical method that is more flexible in density estimation form, offers a versatile framework for conducting conditional independence (CI) tests. This approach enables the estimation of mutual information and conditional mutual information, thereby facilitating the identification of underlying structural relationships. Nevertheless, the high computational cost of KDE-based CI testing restricts its practical application in continuous-variable causal learning. To address this issue, this study introduces a radial symmetric kernel-based acceleration scheme within a Fast Fourier Transform (FFT) framework to improve the efficiency of density estimation. On this basis, an enhanced Bayesian network structure learning method is developed for continuous variables, enabling more efficient estimation of mutual information and conditional mutual information while improving the computational efficiency and empirical stability of variable dependency discovery. With proper bandwidth and grid resolution, the proposed MMHC-FFTKDE framework achieves a reduction in computational runtime and improves efficiency compared to MMHC-KDE in the ablation setting, while maintaining competitive F1-scores and SHD for causal structure discovery. Full article

Partially liquid-filled rotor systems subjected to lateral excitation exhibit pronounced fluid–structure interaction, leading to complex and highly sensitive vibration responses. To enable efficient probabilistic prediction under parametric uncertainty, this study develops a deterministic–data-driven framework for a rigid hollow rotor partially filled with liquid. Based on small-perturbation flow theory, the liquid-induced feedback forces are analytically derived and incorporated into the coupled rotor–liquid dynamic equations, yielding a closed-form steady-state solution. The results reveal that lateral excitation in one direction induces coupled vibration in the orthogonal direction, resulting in an elliptical whirl trajectory of the rotor center. The vibration characteristics depend jointly on excitation frequency and rotor angular velocity, and for a given angular velocity, two critical excitation frequencies are identified at which the response amplitude increases sharply. Surrogate models based on a backpropagation neural network (BPNN) and a support vector machine (SVM) are constructed and validated, with the BPNN demonstrating superior predictive accuracy. Uncertainty analysis further shows that the maximum vibration amplitude exhibits asymmetric, non-Gaussian distributions even under normally distributed inputs, and excessive amplification may occur beyond certain uncertainty levels. The proposed framework provides a robust tool for probabilistic vibration assessment and uncertainty-informed design of partially liquid-filled rotor systems. Full article

The foaming of hydrogels presents a promising strategy for tailoring mechanical and radiological properties to replicate biological soft tissues for biomedical phantom applications. A computational fluid dynamics (CFD) framework is developed to predict void fraction distribution in alginate hydrogel precursor solutions aerated by air injection through a bottom nozzle. The objective is to use the framework for the design of the foaming system to match the desired gas-fraction distribution and radiological property. Seven parametric cases are investigated, varying inlet air velocity, alginate concentration, and surface tension. Results show that higher inlet velocities promote stronger jet penetration and greater gas accumulation, while increasing alginate concentration confines the bubble plume, with quasi-steady gas fractions displaying a non-monotonic trend with concentration. Elevated surface tension yields broader plume coverage and improved gas distribution uniformity at the expense of peak void fraction. The predicted void fractions map to Hounsfield Unit (HU) values of −34 to −103, corresponding to adipose and fatty breast tissue attenuation (−50 to −150 HU). The peak gas fraction at 5.0 wt% alginate yields −307 HU, approaching published experimental CT measurements for the same formulation (−460 to −233 HU). Full article

Quantitative trading systems require predictive models that simultaneously deliver accurate forecasts, calibrated uncertainty quantification, and actionable risk measures. This paper proposes an information-theoretic semiparametric regression framework combining a convolutional neural network–Transformer (CNN–Transformer) network for nonlinear temporal dependencies with a Gaussian process (GP) prior for residual autocorrelation and calibrated predictive distributions. Three theoretical results are established: an identifiability theorem guarantees joint recoverability of the nonparametric and GP components; a consistency theorem showing that the penalised maximum likelihood estimator converges at a rate $n^{-1/(2+d_{\mathrm{eff}})}$; and a coverage theorem proving asymptotic nominal coverage of the GP’s credible intervals. The framework enables an entropy-regulated trading module where predictive differential entropy informs position sizing via an uncertainty-penalised Kelly criterion, Kullback–Leibler divergence quantifies model uncertainty, and CVaR-constrained optimisation controls the tail risk. Simulations show the method outperforms the CNN, long short-term memory (LSTM), Transformer, XGBoost, random forest, least absolute shrinkage and selection operator (LASSO), and standard GP regression approaches. Backtesting on four Chinese A-share stocks yielded annualised returns of 15.9–22.4% with Sharpe ratios of 0.49–0.62, maximum drawdowns below 15%, and daily 95% CVaR reductions of 28–31% relative to a full-Kelly baseline, confirming both predictive accuracy and risk management effectiveness. Full article

Accurately characterizing the statistical fluctuations of vehicle radar cross sections (RCSs) across polarization states and azimuthal sectors is essential for evaluating detection performance, conducting probabilistic simulations, and analyzing target features in millimeter-wave radar systems. Existing one-dimensional RCS statistical models, including Weibull, Chi-square, Lognormal, Rice, and Gaussian distributions, are often limited by their restricted functional expressiveness, making it difficult to simultaneously capture skewness, tail thickness, and azimuthal dependence under narrow angular-domain conditions. In addition, purely nonparametric approaches tend to produce spurious modes under finite-sample conditions and lack interpretable structural priors. To address these limitations, this paper proposes a Unimodal RCS Semiparametric Density Estimator (URCS-SDE) tailored for ground-vehicle targets. The proposed approach adopts kernel density estimation (KDE) as a data-driven baseline representation and incorporates physically plausible structural constraints through unimodal shape projection. Then a beta-type tail template is further introduced in the normalized amplitude domain to regulate boundary decay behavior. Finally, weighted least-squares calibration is performed on the histogram grid of the empirical probability density function (PDF), achieving a balanced trade-off between fitting accuracy and stability in both the peak and tail regions. Using multi-azimuth RCS measurements of two representative ground vehicles, the URCS-SDE is systematically compared with five classical parametric distributions and a representative regularized mixture density network (MDN) baseline. Performance is evaluated under both full-azimuth and directional-window conditions using the sum of squared errors (SSE), root mean squared error (RMSE), coefficient of determination (R-square) and held-out negative log-likelihood (NLL). The results show that the URCS-SDE consistently provides the most accurate and stable density estimates, especially in narrow angular windows. In addition, a threshold-based detection-support example derived from the fitted PDFs demonstrates that the advantage of the URCS-SDE transfers from density reconstruction to a directly engineering-relevant downstream quantity. Full article

The concentration of tourist flows along short urban links caused by cruise stops in medium-sized Mediterranean ports exacerbates traffic and localized environmental externalities. This study evaluates the willingness to pay (WTP) of cruise passengers for an electric tram that would connect the Gaženica Port with Zadar’s historic center, an intervention designed to cut travel time and reduce on-street congestion and emissions. Over the course of two seasons, a two-wave, two-site, in-person survey was conducted at the port and in the city center. The instrument adopts a double-bounded dichotomous choice (DBDC) contingent valuation design with randomized starting bids that were calibrated using a pre-test that benchmarked prevailing transport pricing. Primary WTP estimates are obtained from a binary choice model with socio-demographic and environmental covariates; whereby inference relies on cluster-robust errors. Robustness is assessed through three complementary checks that do not require additional data: (i) a bivariate specification to account for within-respondent correlation between first and follow-up bids; (ii) Turnbull nonparametric bounds for the interval-censored WTP distribution; and (iii) starting-point tests using split-sample estimation and bid-set indicators. A spike adjustment based on “no–no at the lowest bid” responses is explored where appropriate. Beyond its methodological contribution, this research advances the sustainable tourism development discourse by quantifying visitors’ financial support for low-emission urban mobility infrastructure that mitigates environmental stresses while preserving residential life quality. The results integrate cruise tourist management with the more general goals of resilient and sustainable urban destinations by offering a decision-ready value input for port-city mobility planning in historic Mediterranean centers. Full article

Wind dynamics in urban environments exhibit non-stationarity and marked spatial variability, complicating stochastic modeling when a single global distribution is assumed. This article discusses the estimation of wind density under quasi-stationary regimes at the local level using SICABI, a two-phase framework: (i) Stationary Region Identification (ISR) estimates, through spectral power analysis, a specific dominant period for each location and validates the induced subsampling using the Augmented Dickey–Fuller (ADF) test, and (ii) RAPID adjusts an adaptive parametric density by recursively updating the mixture parameters and creating new components when a normalized membership distance exceeds a threshold. The analysis uses wind speed records collected from eight stations in the Metropolitan Area of Queretaro, Mexico, during the period from 1 January 2023 to 31 December 2023, aggregated at a 10 min resolution, from which $X_{\delta ,s}$ is constructed for each site. RAPID is compared against Gaussian Kernel Density Estimation (KDE) with Silverman bandwidth and EM-fitted Gaussian mixtures with BIC-based selection ($K_{max}=12$). The resulting densities were compared with an empirical density estimated from a histogram over a fixed grid ($m=50$) using the MISE and RMSE metrics. The results reveal marked site-dependent differences in dominant periodicity and residual behavior, including asymmetry and heavy tails. ISR identified dominant periods ranging from 37 to 166 days, and RAPID adapted its complexity with $K_s\in [5,10]$ without fixing the number of mixture components in advance. Quantitatively, RAPID achieved the lowest RMSE at 6/8 sites and the lowest MISE at 5/8 sites, while also exhibiting shorter execution times than KDE and MoG under the same input $X_{\delta ,s}$. The results support RAPID as a competitive adaptive method for site-specific density estimation in non-stationary urban climate signals. In this context, local regimes can be viewed as approximate invariants under time translation in the weak stochastic sense, while deviations from this assumption are reflected in increased distributional complexity across sites. Full article

Additive manufacturing (AM) requires process-comparison tools that remain practical when sample generation and testing are costly. We propose a univariate, nonparametric workflow for comparing a candidate AM process to a stable reference process by testing distributional equivalency for a single response variable. The method discretizes the reference distribution into empirical percentile-defined bins and combines this representation with a sequential sampling protocol designed to reduce unnecessary sampling when evidence for equivalency or non-equivalency becomes sufficient. Simulation studies were used to evaluate operating characteristics across experimental settings, and a validation case study based on geometric measurements of laser based powder bed fusion plate scans correctly classified a candidate process expected to be equivalent to the reference while identifying a non-equivalent process at the first sampling step. The workflow is most appropriate for low-sample, high-cost, or throughput-constrained settings, and is best viewed as a tool for process comparability, change control, calibration, and requalification support rather than as a standalone replacement for qualification standards. The full workflow is implemented in the open-source AMEquivalency package to support reproducible analysis. Full article

Floodplain forests in central Amazonia are structured along a marked flooding gradient that influences species distribution, performance, and survival. This study evaluated the demographic structure, survival, and growth responses of two co-occurring tree species across contrasting várzea environments differing in inundation regimes. Field surveys quantified seedlings, juveniles, and adults in low- and high-floodplain forests, while a field experiment assessed survival and growth under conditions with and without interspecific interaction. Repeated-measures ANOVA revealed that temporal variation and forest type significantly affected growth parameters, with species-specific responses to flooding intensity. In the field experiment, mortality of Crateva tapia L. differed significantly among treatments (χ² = 24.96, p < 0.001), with the highest mortality observed in high-várzea (up to 75% under interspecific interaction), while Hura crepitans L. showed 100% survival across all treatments. Non-parametric analyses detected no significant treatment effects on selected morphological traits. The results support the stress-gradient hypothesis, suggesting that plant–plant interactions may shift along the flooding gradient, with facilitative processes becoming more relevant under higher stress conditions. Overall, differential flood tolerance appears to be a key driver of habitat preference and population structure in these Amazonian wetlands. Full article

Water distribution networks are part of critical infrastructure, and ensuring a rapid return to service after failures is vital for public health and economic resilience. While numerous studies have quantified failure rates and examined factors that influence the duration of repairs, the seasonal variability of repair time itself has received little attention. This study analyses 264 monthly observations (January 2004–December 2025) from a large urban water supply system in south-eastern Poland. We evaluate the seasonality of failure counts, average repair time per event, and the total labour hours needed to restore service. Methods include descriptive statistics, seasonal indices, non-parametric tests, kernel density estimation, parametric distribution fitting, empirical exceedance curves of monthly mean repair duration, and time-series decomposition. The results show a pronounced seasonal pattern in the number of failures and total labour hours, with peaks in winter and minima in spring, whereas the monthly mean repair duration remained stable at approximately 8 h and showed no significant seasonal variation. Among the positive-support candidate distributions, the log-normal model provided a slightly better fit than the Weibull model. Empirical exceedance analysis and non-parametric tests confirmed no significant differences in monthly mean repair duration between seasons or calendar months. Decomposition reveals a small downward trend in total repair hours after 2010. These findings provide new insights for maintenance planning and indicate that winter workload peaks are driven primarily by higher failure counts rather than by longer mean repair duration. Full article

This research investigates the influence of environmental regulation on subjective evaluations of video quality within the Quality of Experience (QoE) paradigm. This work presents a supplementary experiment conducted in a controlled laboratory setting, building on our previous crowdsourcing studies carried out in uncontrolled, web-based conditions using the Prolific platform. Both tests utilized the identical crowdsourcing platform and complied with the International Telecommunication Union Telecommunication (ITU-T) P.910 Recommendations, ensuring external validity and methodological consistency. Participants assessed a collection of processed video sequences (PVS) comprising 46 distinct video clips utilizing the 5-point Absolute Category Rating (ACR) scale, while their response times were documented in milliseconds as measures of cognitive exertion and decision delay. The comparison analysis employs nonparametric tests (Mann–Whitney U and Kolmogorov–Smirnov) and a hierarchical Linear Mixed-Effects Model (LMM) to examine disparities in reaction time distributions, rating consistency, and the incidence of outliers across both environments. The results indicate that controlled settings produce statistically significantly less response variability and enhanced data reliability, whereas uncontrolled settings encompass greater external diversity and real-world unpredictability. These findings offer significant insights into the balance between experimental control and external validity in crowdsourced video quality assessment, advancing the development of scalable approaches for Quality of Experience research. Full article

Competence-oriented Education for Sustainable Development requires evidence that immersive and gamified learning experiences elicit sustainability-relevant change beyond short pre–post windows. This study examines the Art Nouveau Path, a location-based mobile augmented reality heritage game implemented in Aveiro, Portugal, using a four-wave repeated cross-sectional design with anonymous student samples: baseline (S1-PRE, N = 221), immediate post-activity (S2-POST, N = 439, validated n = 438), follow-up (S3-FU, N = 434), and distant follow-up (S4-DFU, N = 69, validated n = 67). Analyses were anchored in a shared 25-item GreenComp-based questionnaire (GCQuest) block targeting Embodying Sustainability Values (ESVs; scale of 1 to 6) and combined distribution-aware descriptives, nonparametric omnibus, and pairwise tests with Holm correction, and planned robustness checks including equal-n downsampling and alternative scoring. Results displayed a pronounced post-activity peak (S2-POST), partial attenuation at follow-up (S3-FU), and convergence toward baseline at distant follow-up (S4-DFU), accompanied by loss of the high-agreement tail. Item-level contrasts suggested that later-wave declines concentrated in effortful self-regulation and critical appraisal items, whereas value endorsement items were more stable. These findings indicate that field-deployable mobile AR heritage paths may generate strong proximal competence-aligned signals; nevertheless, durable enactment-oriented change is likely to require structured reinforcement and integration into broader curricular sequences. Full article

Background: Organic cation transporter 2 (OCT2) mediates the renal uptake of cisplatin and is a principal contributor to its dose-limiting nephrotoxicity. Despite reports of OCT2 inhibition by various phytochemicals, the structure–activity relationships (SARs) governing inhibition and their functional implications remain poorly understood. Methods: We systematically evaluated OCT2 inhibitory activity across a structurally diverse library of 146 phytochemicals, including anthraquinones, flavanols, stilbenes, and isoflavones, using Madin–Darby canine kidney (MDCK) cells stably overexpressing OCT2. Structure–activity relationships were analyzed using non-parametric statistics and multivariate logistic regression, and functional relevance was assessed via cisplatin-induced cytotoxicity assays. Results: Inhibitory activity varied widely across the library, with potent inhibitors identified across multiple chemical scaffolds. Non-parametric statistical analyses revealed no significant differences in overall activity distributions among scaffold classes. Notably, chemical substituent patterns, rather than core scaffold identity, were the primary drivers of OCT2 inhibitory potency. Methoxylation was consistently associated with enhanced OCT2 inhibition, particularly within isoflavones, although its impact varied across structural scaffolds. The selected OCT2 inhibitors markedly reduced cisplatin-mediated cell death in OCT2-expressing cells but not in mock-transfected controls, confirming an OCT2-dependent mechanism of protection. Conclusions: This study establishes a structure-guided framework linking phytochemical OCT2 inhibition to nephroprotective potential and identifies methoxylation as a major determinant of OCT2-targeted intervention strategies. Full article

Urban coastal wetlands constitute important ecological interfaces where human activities, wildlife, and arthropod vectors interact, potentially increasing the risk of pathogen transmission. In the city of Veracruz, Mexico, several interdunal lagoons have been incorporated into urban areas and are intensively used for recreational activities; however, information on their mosquito fauna remains limited. This study aimed to characterize mosquito species composition, abundance, and diversity in three urban recreational interdunal lagoons in Veracruz. Adult mosquitoes were collected weekly during the rainy season (June–September) 2023 using CDC light traps. Specimens were identified based on morphological characters using standard taxonomic keys, including genitalia dissections for male specimens when necessary. Species richness, sampling completeness, and community structure were evaluated using non-parametric richness estimators, diversity indices, species accumulation curves, and similarity analyses. A total of 1465 adult mosquitoes belonging to 11 species and five genera were collected. Mosquito assemblages were characterized by low species richness and a marked dominance of Culex panocossa and Culex quinquefasciatus across all lagoons. Diversity indices were low, and species composition showed a high degree of similarity among sites. Notably, Uranotaenia apicalis was recorded for the first time in the state of Veracruz, expanding its known geographical distribution. These findings indicate that urban interdunal lagoons support simplified mosquito communities dominated by disturbance-tolerant species, highlighting their potential epidemiological relevance and the need for targeted vector surveillance in urban coastal environments. Full article