Search Results (263)

Aging river levees are heterogeneous flood-defense earthworks whose reliability is affected by limited site data, interparameter dependence, and spatial variability. This study develops an integrated framework combining normal information diffusion (NID), Copula-based dependence identification, cross-correlated random fields (CCRFs), and Monte Carlo reliability analysis. Reliability was evaluated using Monte Carlo simulation based on a simplified infinite-slope benchmark model. Data were obtained from the authors’ own site investigations and laboratory tests. NID yields the lowest Kolmogorov–Smirnov statistics for all documented variables and better preserves local fluctuations and lower-strength tails than four classical marginals. Gaussian Copula is preferred for the representative bivariate cases and slightly outperforms the multivariate t Copula for Hongpaihe. For strongly dependent Beiwei data, the correlated model gives a mean factor of safety of 1.235 and a failure probability of 0.153, whereas independence gives a similar mean factor of safety of 1.238 but increases failure probability to 0.200. The weakly dependent main-dike case changes negligibly, while the φ–γ pair dominates the trivariate response. Illustrative CCRF realizations show how pointwise dependence can propagate into spatially continuous weak zones. Failure probability is therefore more diagnostic than mean factor of safety when stochastic model form is uncertain. The main novelty lies in the consistent integration and propagation of marginal, dependence, and spatial uncertainties within a unified levee reliability framework. Full article

This publication presents the results of studies on exhaust emissions and fuel consumption from spark-ignition engines in the NEDC test, which applies to the vast majority of passenger cars in use in the European Union. This emissions testing procedure was selected based on an analysis of the number of existing engines meeting specific emission standards. A statistical analysis of the studied processes was conducted. Additionally, the product of speed and acceleration modulus was examined as quantities characterizing the dynamic characteristics of the vehicle speed process during testing. Correlation studies of vehicle speed and exhaust emission rates, particle number rates, and mass fuel consumption rates were presented. Average distance-specific emissions, average distance-specific particulate number, and average distance-specific fuel consumption were determined in the NEDC test. The probability density distributions of the investigated processes in the NEDC test were analyzed. Based on an assessment of the conformity of the studied sets with the normal distribution using the Kolmogorov–Smirnov, Lilliefors, and Shapiro–Wilk hypotheses, it was concluded that there was no basis for accepting the hypotheses that the sets conform to the normal distribution. Moreover, the power spectral density of the investigated processes was evaluated. Significant variation in spectral characteristics, especially at high frequencies, was identified, revealing substantial dynamic differences between the processes. The average road emission values in the NEDC test were significantly lower than even the limits for Euro 7. Full article

In the era of big data, the demand for vast quantities of diverse and representative datasets has surged across various domains, from healthcare and finance to artificial intelligence and machine learning. Synthetic data generation offers a promising solution by enabling the creation of data with specific properties that closely mimic real-world data while avoiding privacy concerns and regulatory limitations. However, generating high-quality synthetic data that accurately preserves complex dependencies remains a significant challenge. This paper addresses this gap by exploring a novel approach: Inverse Copula Sampling for Multi-Dimensional Data Synthesis. Utilizing copulas, which are powerful tools for modeling dependencies between variables, our method generates synthetic data that maintains intricate interdependencies. We demonstrate the effectiveness of this approach through various experiments and case studies, showing high fidelity in preserving dependencies and minor discrepancies in marginal distributions. The method’s robustness was validated through comparative analysis and statistical checks, including the Kolmogorov–Smirnov test. Our research contributes to the field by introducing a flexible and efficient method for synthetic data generation that is applicable to a wide range of data distributions and practical applications. Future work will explore the application of other copula types and the further refinement of the method to enhance its versatility. Full article

Introduction: Verbal fluency (VF) is a neuropsychological measure sensitive to lexical–semantic organization and prefrontal circuits. Its link with social cognition—specifically with the recognition of mental states from the eyes—remains scarcely explored in Latin American contexts. Objective: To examine the association between performance on the Verbal Fluency Test (phonological and semantic) and mental-state recognition, as measured by the Reading the Mind in the Eyes Test (RMET) in Ecuadorian adults, controlling for sociodemographic variables. Method: A cross-sectional correlational study with 397 healthy adults (Mage = 36.8 years, SD = 11.5; range: 18–83; 219 women, 178 men) assessed in Quito, Ecuador, using the multidomain ECUACOG neuropsychological battery. Pearson’s correlations with 95% CIs, Spearman’s correlations, the Kolmogorov–Smirnov test, one-way ANOVA with η², Tukey’s HSD post hoc comparisons, and simple linear regressions were computed. Results: Total VF was significantly associated with RMET score (r = 0.227, 95% CI [0.131, 0.319], p < 0.001, N = 390). Semantic VF showed a higher correlation than phonological VF (r = 0.239, 95% CI [0.144, 0.331] vs. r = 0.163, 95% CI [0.065, 0.258]; Steiger’s Z = 1.86, p = 0.063, non-significant trend). Educational level was associated with both VF (semantic VF: η² = 0.093, p < 0.001) and RMET (η² = 0.045, p < 0.001) scores. The semantic VF–RMET association remained significant after statistically controlling for TMT-B and for educational level (partial r = 0.202, p < 0.001). Conclusions: Lexical–semantic richness is significantly associated with mental-state recognition in Ecuadorian adults. These findings contribute to the generation of neuropsychological normative data in Ecuador. Full article

Background/Objective: Vaccine hesitancy continues to pose a challenge to public health, and health literacy has been suggested as a potential factor influencing vaccination attitudes. However, the nature of this relationship and the role of sociodemographic characteristics remain to be clarified. This study aimed to examine the association between health literacy and vaccine refusal and to explore whether this relationship is moderated by education level, age, income, place of residence, and gender. Methods: A cross-sectional nationwide online survey was conducted among 413 adults living in Türkiye. Data was analyzed using SPSS version 22. Distributional assumptions were evaluated using the Kolmogorov–Smirnov test together with skewness and kurtosis values. Moderation analyses were performed using Hayes’ PROCESS macro (Model 1), and statistical significance was set at p < 0.05. Results: The study included 413 participants, most of whom were aged 18–30 years (62.7%) and female (69.3%). Health literacy showed a statistically significant negative association with vaccine refusal (β ranging from −0.30 to −0.72, p < 0.001). Education level was identified as a significant moderator (R² = 0.14), indicating that the strength of this association varied across educational groups. Specifically, the inverse relationship appeared more pronounced in individuals with lower educational attainment and attenuated at higher education levels, becoming non-significant in the postgraduate group. In contrast, age, income, place of residence, and gender did not demonstrate consistent or statistically robust moderating effects. Although rural residence was associated with higher vaccine refusal levels, it did not significantly modify the relationship between health literacy and vaccine refusal. Conclusions: The findings suggest that higher health literacy is associated with lower vaccine refusal; however, this relationship appears to vary by educational level. The modest explanatory power of the models indicates that health literacy alone may not fully account for vaccine-related attitudes. Further research incorporating additional behavioral and contextual factors is warranted to better understand the determinants of vaccine refusal. Full article

The Terziali is a shear-hosted orogenic gold prospect located in the Central Anatolian Crystalline Complex, Türkiye. This study focuses on soil geochemistry, element correlations, background and threshold values, and evaluates exploration implications over a survey area of 35.5 km². A total of 1826 soil samples were collected from the B horizon using a grid of 100 × 50 m and were analyzed using ICP-AES, ICP-MS, and fire assay techniques. Statistical techniques of median + 2MAD threshold calculations, descriptive statistics, Kolmogorov–Smirnov tests, correlation analysis, hierarchical clustering, and Q–Q plots were carried out to identify geochemical anomalies. The data demonstrate Au threshold (28 ppb) and peak concentration (460 ppb), non-normal distributions characterized by strong positive skewness, revealing the outliers linked to mineralization. Soil geochemistry indicates a moderate association between Au and As in the four-acid dataset (r = 0.465), although the correlations between Au and Sb and Ag and W are relatively weak. The spatial analysis indicates that Au anomalies are predominantly linked to the NW–SE-oriented Demirli Thrust Fault. As displays extensive dispersion halos surrounding the gold anomalies; it establishes itself as an efficient pathfinder element. Conversely, Sb and W exhibit unique anomaly patterns, whereas Ag patterns are weak and dispersed. The Terziali prospect provides a substantial geochemical framework for identifying structurally controlled orogenic gold systems in Central Anatolia and the western Tethyan metallogenic belt. Full article

In many orographically complex monsoon basins, rain gauge networks are sparse and lack the long-term continuous records required for reliable precipitation probability analysis. Traditional regional frequency analysis assumes spatially uniform precipitation across the analysis zone, which is inadequate for basins with steep rainfall gradients and strong seasonal variability. Gridded precipitation products (GPPs) provide spatially continuous, long-term records that enable grid-cell-level probability distribution fitting. However, GPPs may exhibit local biases and errors, and statistical evaluation against gauge observations is necessary before application. This study was conducted in the Phetchaburi–Prachuap Khiri Khan River Basin, western Thailand, a region with steep orographic and coastal rainfall gradients. Four GPPs, namely CHIRPS, CHELSA, WorldClim, and PERSIANN-CCS-CDR, were evaluated against gauge observations. The best-performing product, after monthly bias correction, was then used to generate spatially continuous monthly exceedance probability maps using grid-cell gamma distribution fitting. CHELSA showed the best overall performance across all evaluation metrics (correlation coefficient (r) = 0.908, percent bias (PBIAS) = 7.0%, root mean square error (RMSE) = 48.3 mm), passing the Kolmogorov–Smirnov (KS) goodness-of-fit test at all 96 station-months. CHIRPS and WorldClim showed satisfactory overall performance but exhibited localized biases in complex terrain, whereas PERSIANN-CCS-CDR substantially overestimated wet-season rainfall, limiting its suitability for this basin. Spatial precipitation patterns varied markedly between monsoon regimes, shifting from a dominant west-to-east orographic gradient during the southwest monsoon to a less differentiated advective pattern during the northeast monsoon. Furthermore, analysis at the 75% exceedance probability level showed that mean-based effective rainfall overestimated reliable water supply in high-variance months, leading to underestimation of supplemental irrigation demand. The generated maps provide spatially explicit dependable rainfall estimates across the basin, supporting probabilistic agricultural water management at multiple planning scales in orographically complex monsoon basins. Full article

Floods remain one of the most frequent and destructive natural phenomena, the scale and consequences of which are exacerbated by climate variability and anthropogenic pressure on river systems. The Yertis water basin (Kazakhstan) is an area with high exposure to flood risks, where the dense concentration of settlements and infrastructure is within floodplain areas. This study applies an integrated approach based on the integration of statistical methods for hydrological analysis and GIS-based spatial modeling to assess and delimit potential flood zones. Long-term series of maximum water levels and discharges from hydrological stations for the period 1974–2025 were analyzed using probability distribution functions, including the log-normal, Pearson Type III, and Gumbel distributions. The optimal distribution model for each station was selected based on the Kolmogorov–Smirnov goodness-of-fit test and the Akaike information criterion. Exceedance-probability curves for extreme hydrological events were constructed for 0.1%, 1%, and 10% probabilities. Spatial flood modeling was performed in the ArcGIS 10.8 environment using a hydrologically corrected digital elevation model and interpolated flood levels. The resulting flood zone maps allow for the identification of the highest-risk areas and serve as a tool for scientifically based planning of emergency prevention measures and floodplain area management. The study contributes to the methodological development of probabilistic floodplain mapping through the integration of statistical frequency analysis and GIS technologies and demonstrates the applicability of this approach for flood hazard assessment in large transboundary river systems under conditions of climatic and hydrological variability. Full article

Stock price forecasting remains an extremely challenging problem due to the non-stationary nature of financial markets. Recent deep learning approaches model complex stock correlations by learning temporal patterns from individual stock series and then aggregating cross-stock information. However, existing methods select which alpha factors to trust using static projections of market features, ignoring how market regimes evolveover the lookback window—a “recovering from a crash” regime and a “new bull market” produce similar instantaneous statistics but require different factor selections. Moreover, standard MSE training objectives weight all stocks equally, wasting gradient signal on mid-ranked stocks that never enter a long–short portfolio. To address these issues, we introduce StockMamba, a State-Space Gated Stock Transformer with Rank-Aware Optimization. StockMamba replaces static market gating with a Mamba-2 state-space model that scans market regime dynamics in linear time and produces time-varying factor gates via temperature-controlled softmax. For training, StockMamba pairs cross-stock attention and temporal distillation with a U-shaped Rank-Position Loss that concentrates gradients on the head and tail stocks where portfolio P&L is determined. Experiments on CSI-300 and CSI-800 with the Qlib pipeline show that StockMamba achieves 12.1% higher IC and 15.0% higher Rank IC over the MASTER baseline on CSI-300 (13.5% and 14.8% on CSI-800), with ablation studies confirming the contribution of each proposed module. A cross-market evaluation on S&P 500 further confirms that the gains generalize to a structurally different market (9.5% higher IC over MASTER), and a Kolmogorov–Smirnov test on the learned factor gates provides statistical evidence that the gating mechanism is genuinely regime-dependent. Full article

Consensus sequences at sites such as exon–intron boundaries or branch points are displayed with sequence logos. Implicit in this representation is a presumption of independence of nucleic acids at distinct sites; consequently, sequence logos fail to elicit higher-order statistical characteristics within nucleic acid sequences. We introduce a graphical approach to display such features. Probability distribution functions on these point-sets are used to highlight correlations at exon–intron boundaries and at branch points. Point-sets provide a more intuitive view of the differences than quantitative tests like the Kolmogorov–Smirnov test. Differences in density functions at normal exon–exon boundaries and cancer fusion junctions can be used to highlight the distinctions between the two classes of junctions. The fractal structure of point-sets for sites within exons and within introns emerges as the neighborhood used for its construction is enlarged. The two sets can be differentiated using their singularity spectra. Full article

This study evaluates the wind energy potential of Polokwane, South Africa, using statistical distribution models to estimate wind power density (WPD) and assess turbine performance under low-wind inland conditions. Hourly wind speed and direction data (2015–2024) measured at a 10 m height above ground level (AGL) were analysed to characterise wind behaviour and assess energy availability. Four probability distributions, namely generalised logistic (GLD), generalised extreme value (GEVD), Gumbel (GD), and Weibull (WD), were fitted using the maximum likelihood (ML) method. Model performance was evaluated using Kolmogorov–Smirnov (KS), Anderson–Darling (AD), and Chi-square $\left({\chi }^2\right)$ tests, while wind power density accuracy was assessed using wind power density error (WPDE). The results showed that Polokwane is characterised by low wind speeds, with an overall mean wind speed of 2.72 m/s at 10 m AGL, reaching a low of 3.88 m/s at a hub height of 125 m. The GEVD model produced the most accurate wind power density estimate of 32.37 W/m², classifying the site within the poor wind resource category. Wind direction analysis revealed a dominant northeast sector with seasonal shifts toward the south. Wind turbine performance analysis showed improved energy generation at higher hub heights, with the Gamesa G136-4.5 MW turbine identified as the most suitable option for the site, achieving the highest net annual energy production (AEP) of 10.82 GWh/yr and the highest net capacity factor (CF) of 27.44%. These results indicate that the Polokwane site is suitable for low-to-moderate wind energy applications and small-scale distributed wind generation rather than large-scale commercial wind farm development. Full article

The intralaminar and interlaminar mode I initiation fracture toughness of unidirectional IM7/8552 carbon/epoxy composites were re-evaluated using only the published experimental data. Classical statistics, two-parameter Weibull analysis (location fixed at zero), non-parametric kernel density estimation (KDE), bootstrap resampling (10,000 replications), and bootstrap-based uncertainty quantification were applied to the fatigue-precracked (FPC) initiation values (n = 12) and the corresponding R-curves. The pooled FPC mean initiation toughness was 0.1982 kJ/m² (COV = 8.50%). Weibull fitting yielded a shape parameter β = 12.33 and scale η = 0.2058 kJ/m², providing a B-basis value of 0.1715 kJ/m² (90% reliability) and an A-basis value of 0.1417 kJ/m² (99% reliability). The Kolmogorov–Smirnov test confirmed statistical equivalence between intralaminar and interlaminar groups (p > 0.05), validating the use of a single initiation toughness for both crack planes when sharp fatigue-precracked starter cracks are employed. Intralaminar R-curves exhibited significantly steeper propagation, rising to approximately 0.385 kJ/m² at Δa = 30 mm due to extensive fiber bridging, whereas interlaminar R-curves reached a near-plateau after 12–15 mm. Bootstrap 95% confidence bands quantified the higher uncertainty associated with the intralaminar R-curve. Teflon-insert data produced artificially high initiation values and unstable growth, confirming that only fatigue-precracked results are suitable for design allowables. This study demonstrates that a single, statistically robust initiation toughness (B-basis = 0.1715 kJ/m²) can be used interchangeably for intra- and interlaminar cracking in progressive-damage models and preliminary design analysis of IM7/8552 structures. The open-source statistical workflow (KDE + bootstrap) developed here is transferable to other small-sample composite datasets, though the numerical B-basis value (0.1715 kJ/m²) is specific to IM7/8552 and should not be generalized without validation. Full article

Background: Ischemic stroke remains a major cause of disability and reduced quality of life (QoL). This study aimed to identify factors associated with QoL after ischemic stroke, with particular emphasis on independence, illness acceptance, social support, comorbidity status, and the timeliness of diagnosis and treatment. Methods: This single-center cross-sectional study included 100 consecutively recruited patients after ischemic stroke hospitalized in the Department of Neurology with the Stroke Unit at the S. Żeromski Specialist Hospital in Krakow. Data were collected using an author-designed questionnaire and standardized instruments: the World Health Organization Quality of Life-BREF (WHOQOL-BREF), the Multidimensional Scale of Perceived Social Support (MSPSS), the Lawton Instrumental Activities of Daily Living Scale (IADL), and the Acceptance of Illness Scale (AIS). Statistical analysis included Spearman’s rank correlation coefficient and the Mann–Whitney U, Friedman, and Kolmogorov–Smirnov tests. Results: Significant positive correlations were found between all WHOQOL-BREF domains and IADL, AIS, and MSPSS scores. The strongest correlations were observed between IADL and the physical and psychological QoL domains. A strong positive correlation was also found between IADL and AIS (rho = 0.88; p < 0.001). Better QoL and greater independence were observed in patients with fewer comorbidities. Patients who received timely diagnosis and treatment achieved better outcomes in terms of QoL, IADL, and AIS. Perceived social support was comparable across MSPSS subscales (p = 0.56) but positively correlated with all QoL domains (rho = 0.55–0.64; p < 0.001). Conclusions: Better QoL after ischemic stroke was associated with greater independence, higher illness acceptance, stronger perceived social support, and timely diagnosis and treatment, suggesting that post-stroke QoL is related to both functional and psychosocial factors. Full article

Gaseous emissions from coal combustion during electricity generation continue to be a challenge in South Africa. To meet the regulatory limits, it is crucial to understand the statistical distribution of such emissions from the power generating plants. The current paper characterises the nitrogen dioxide (NO₂) emissions from Eskom’s Majuba coal-fired power station by making use of the quantile–quantile (QQ) plots and derivative plots of three statistical parent distributions, namely, the Weibull, Lognormal, and Pareto distributions. These distributions are fitted and compared according to their tail heaviness as they cater for data that may have tails lighter or heavier than that of the Exponential distribution. Of the three distributions evaluated here, the Lognormal gave the best fit for the full body of the data according to the QQ and derivative plots, and the goodness-of-fit tools (bootstrap Kolmogorov–Smirnov (KS), Anderson–Darling (AD), Akaike Information Criterion (AIC), Schwarz’s Bayesian Information Criterion (BIC), and the BIC-corrected Vuong test for non-nested distributions). The Lognormal distribution also gave the best fit for the overall upper tail, while at the very top six largest NO₂ emission observations in the upper tail, a Pareto-type tail was observed. The practical implication of a heavy tail like the Pareto is that it models more frequent larger sized NO₂ emissions compared to lighter tails like the Weibull and Lognormal tails. The methods used in this study give a framework on how emissions of NO₂ from a coal-fired power station can be modelled using statistical parent distributions whilst also taking into account the distribution of the data in the tails which is mostly ignored when fitting statistical parent distributions. Understanding the distribution of the upper tail is very important since higher and rare emissions are of the most concern and are dangerous to human health and the environment. 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