Search Results (81)

This study proposes a framework, the RA_WLTE_River model, for quantifying the reliability of flood-altering water-level thresholds, considering rainfall and runoff-related uncertainties. The Keelung River in northern Taiwan is selected as the study area, and associated hydrological data from 2008 to 2016 are applied in the development and application of the model. According to the results from the model development and demonstration, the average and maximum rainfall intensities, roughness coefficients, and maximum tide depths exhibit a significant contribution to the reliability quantification of the estimated water-level thresholds. In addition, empirically based water-level thresholds can achieve the goal of rainfall-induced flood early warning, with a high likelihood of nearly 0.95. Additionally, the probabilistically based water-level thresholds derived from the described reliability can efficiently ensure consistent flood early warning performance at all control points along the river. Full article

Soil contamination by heavy metals (HMs) threatens crop productivity, food safety, and ecosystem health, especially in intensively cultivated Mediterranean regions. This study investigated the influence of soil HM contamination on nutrient uptake, photosynthetic traits, and metal bioaccumulation in avocado (Persea americana Mill.) orchards. Twenty orchard sites were sampled, collecting paired soil and mature leaf samples. Soil physicochemical properties and HM concentrations were determined, while leaves were analyzed for macro- and micronutrients, photosynthetic pigments, and metal contents. Bioaccumulation Factors (BAFs) were computed, and multivariate analyses (Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR)) were applied to assess soil–plant relationships, complemented by Monte Carlo simulations to quantify probabilistic contamination risks. Results revealed substantial inter-site variability, with leaf Cd and Pb concentrations reaching 0.92 and 3.54 mg/kg, and BAF values exceeding 1 in several orchards. PLSR models effectively predicted leaf Cd (R² = 0.789) and Pb (R² = 0.772) from soil parameters. Monte Carlo simulations indicated 15–25% exceedance of FAO/WHO safety limits for Cd and Pb. These findings demonstrate that soil metal accumulation substantially alters avocado nutrient balance and photosynthetic efficiency, highlighting the urgent need for site-specific soil monitoring and sustainable remediation strategies in contaminated orchards. Full article

We propose a distribution-aware framework for unsupervised outlier detection that transforms multivariate data into one-dimensional neighborhood statistics and identifies anomalies through fitted parametric distributions. This directly addresses central difficulties of high-dimensional data—including sparsity of observations, the concentration of pairwise distances, hubness phenomena in nearest-neighbor graphs, and general effects of the curse of dimensionality that degrade classical distance-based scoring. Supported by the Cumulative Distribution Function (CDF) Superiority Theorem and validated through Monte Carlo simulations, the method connects distributional modeling with Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) consistency and produces interpretable, probabilistically calibrated scores. Across 23 real-world datasets, the proposed parametric models demonstrate competitive or superior detection accuracy with strong stability and minimal tuning compared with baseline non-parametric approaches. The framework is computationally lightweight and robust across diverse domains, offering clear probabilistic interpretability and substantially lower computational cost than conventional non-parametric detectors. These findings establish a principled and scalable approach to outlier detection, showing that statistical modeling of neighborhood distances can achieve high accuracy, transparency, and efficiency within a unified parametric framework. Full article

This paper presents three different approaches for generating points along the interaction diagram corresponding to design load effects—shear, bending moment, and axial force—to achieve optimal shear strength adequacy with the Australian bridge design standard AS 5100.5. The methodology targets the optimal shear condition by matching the design shear $V^{*}$ with the capacity $\varphi V_u$, which represents achieving a load rating factor of unity within the specified tolerance limits. The first typical approach for generating points for two load effects is by increasing the moment–shear ratio ${\eta }_m$ in small increments from zero to a large value (theoretically infinity), and for each increment, to goal-seek the condition. The other approaches investigated are the use of increasing factored moment $M^{*}$ and the use of Monte Carlo simulation. A pretensioned bridge I-girder section reported in the literature was used in the study. The Monte Carlo simulation method was found to be the simplest to program. It allows an interaction surface for the influence of three load effects for optimal shear adequacy to be obtained with minimal program coding and outperforms the goal–seeking approaches for multi-variable interactions. It can create 2-D interaction lines for various levels of shear adequacy for the interaction of $M^{*}$ and $V^{*}$, and 3-D interaction surfaces for $M^{*}$, $V^{*}$, and $N^{*}$. The potential use of interaction diagrams was explored, and the advantages and limitations of using each method are presented. The interaction curves of two typical pretensioned concrete sections of a plank girder, one next to an end support and the other close to mid-span, were created to show the distinguishing features resulting from their reinforcement. Full article

This study investigated the spatial distribution of HMs in agricultural and urban soils near the largest oil refining complex in Serbia, identified pollution sources, and assessed ecological and human health risks. A large fraction of soil samples showed elevated Hg (40% of samples), Pb (53%), Cd (90%), and As (93%) concentrations compared to the background levels. Hotspots for Pb, As, Hg, Cd, and Zn were observed in the industrial area, indicating significant anthropogenic input. Multivariate analysis, including PMF, revealed four contamination sources: emissions from the oil refining industry, agricultural activities, traffic emissions, and natural background. The pollution indices mostly fell into the moderate pollution range, with As, Hg, and Cd showing the highest enrichment. The potential ecological risk index (RI) indicated that about one-third of the samples had moderate ecological risk and determined a major RI hotspot near the refinery. The health risk assessment identified As and Cr as the largest contributors to non-carcinogenic risk, although the average HI was below one. Monte Carlo simulation confirmed that adults and children had negligible health risks at the 95th percentile and highlighted exposure frequency and body weight as the most influential exposure parameters. Based on source-specific risk, the oil refining industry emissions had the highest impact on HI and TCR values. Full article

This study investigated the occupational hazard effects of heavy metal(loid)s (HMs) from soil in several critical mining activity areas at the Majdanpek copper mine in Serbia. Soil contamination and associated ecological and health risks to workers were evaluated through an apportionment of sources and a quantitative evaluation of ecological and health risks. The majority of soil samples had increased concentrations of Cd, Cu, Pb, Zn, Hg, As, Mo, and Sb. The results of the multivariate statistical analysis suggested the existence of multiple sources. The positive matrix factorization further explained these associations between HMs and defined three main pollution sources: natural (Factor 1), mixed source (Factor 2), and mining pollution (Factor 3). According to the RI, the average value was 1215, with more than half of the samples (57.4%) showing very high pollution levels, while 3.3% of the samples had an RI lower than 150. The ecological risk was dominated by Cd, Cu, and Hg, with Factor 3 contributing the most to the RI values. Assessment of worker exposure to soil revealed that outdoor workers had a higher potential for adverse health effects, with mean HI and TCR being 0.18 and 2.9 × 10⁻⁵, respectively. The identified sources had similar impacts on non-carcinogenic and carcinogenic risks, with a decreasing trend: Factor 3 > Factor 2 > Factor 1. Indoor workers were exposed to neither non-carcinogenic or carcinogenic risks, whereas outdoor workers suffered from possible health issues regarding TCR. Source-specific health risk assessment indicated mining pollution as the only risk contributing factor. A Monte Carlo simulation of risks revealed that the probability of developing carcinogenic issues for outdoor workers was within the safety threshold (TCR < 10⁻⁴). The findings of this study emphasize the need for regulation and control strategies for worker health risks from HM-contaminated soil in mining areas. Full article

Statistical significance varies depending on the sample size. Therefore, when the sample size is sufficient, even differences that affect the total variation very little may be statistically significant. For this reason, it is very important to report effect size measures that estimate the share of the difference between groups of samples in the total variation. This study aims to determine the most reliable effect size measures that can be used when evaluating data obtained from ecological studies. The three most popular effect size measures used in practice were compared in terms of their performance in 2700 different experimental conditions. For this purpose, random numbers generated from the multivariate Poisson distribution were used with the Monte Carlo simulation technique. As a result of the simulations, it was determined that Epsilon-squared and Omega-squared were quite unbiased estimators. Therefore, it was concluded that one of these two effect size measures should be reported in addition to the p-value when evaluating ecological studies. Full article

This study explores asymmetric volatility structures within multivariate hysteretic autoregressive (MHAR) models that incorporate conditional correlations, aiming to flexibly capture the dynamic behavior of global financial assets. The proposed framework integrates regime switching and time-varying delays governed by a hysteresis variable, enabling the model to account for both asymmetric volatility and evolving correlation patterns over time. We adopt a fully Bayesian inference approach using adaptive Markov chain Monte Carlo (MCMC) techniques, allowing for the joint estimation of model parameters, Value-at-Risk (VaR), and Marginal Expected Shortfall (MES). The accuracy of VaR forecasts is assessed through two standard backtesting procedures. Our empirical analysis involves both simulated data and real-world financial datasets to evaluate the model’s effectiveness in capturing downside risk dynamics. We demonstrate the application of the proposed method on three pairs of daily log returns involving the S&P500, Bank of America (BAC), Intercontinental Exchange (ICE), and Goldman Sachs (GS), present the results obtained, and compare them against the original model framework. Full article

This study aims to assess the spatial distribution and health risk potential of nitrate (NO₃⁻) contamination in groundwater resources of the Harran Plain, a semi-arid agricultural region in Southeastern Türkiye. Groundwater samples were collected from 20 locations during pre- and post-irrigation periods and analyzed for a range of hydrochemical parameters. A probabilistic risk assessment framework, based on the U.S. Environmental Protection Agency (USEPA) guidelines, was employed to evaluate non-carcinogenic health risks across different demographic groups. The integration of Geographic Information Systems (GIS), multivariate statistical analyses, and Monte Carlo simulation enabled a comprehensive evaluation of exposure scenarios and contributing factors. This research contributes to the scientific understanding of groundwater vulnerability in intensively farmed areas, provides a decision-support framework for water quality management, and emphasizes the importance of protecting sensitive populations in nitrate-affected regions. Full article

Soil potentially toxic element (PTE) contamination remains a global concern, particularly in rural agricultural regions. This study collected 157 agricultural topsoil samples within a rural area in SW China. Combined with multivariate statistical analysis in the compositional data analysis (CoDa) perspective, the PMF model was applied to identify key contamination sources and quantify their contributions. Potential ecological risk assessment and Monte Carlo simulation were employed to estimate ecological-health risks associated with PTE exposure. The results revealed that the main exceeding PTEs (Mercury—Hg and Cadmium—Cd) are rich in urbanized areas and the GFGP (Grain for Green Program) regions. Source apportionment indicated that soil parent materials constituted the dominant contributor (32.48%), followed by traffic emissions (28.31%), atmospheric deposition (21.48%), and legacy agricultural effects (17.86%). Ecological risk assessment showed that 60.51% of soil samples exhibited higher potential ecological risk (PERI > 150), with moderate-risk areas concentrated in the GFGP regions. The elements Cd and Hg from legacy agricultural effects and atmospheric deposition contributed the most to ecological risk. Health risk assessment demonstrated that most risk indices fell within acceptable ranges for all populations, while only children showed elevated non-carcinogenic risk (THI_max > 1.0). Among PTEs, the element As, mainly from traffic emissions, was identified as a priority control element due to its significant health implications. Geospatial distributions showed significant risk enrichment in the GFGP regions (legacy agricultural areas). These findings present associated risk levels in sustainable agricultural regions, providing valuable data to support soil environmental management in regions requiring urgent intervention worldwide. Full article

A Bayesian network offers powerful knowledge representations for independence, conditional independence and causal relationships among variables in a given domain. Despite its wide application, the detection limits of modern measurement technologies make the use of the Bayesian networks theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper, we introduce the censored Gaussian Bayesian network (GBN), an extension of GBNs designed to handle left- and right-censored data caused by instrumental detection limits. We further propose the censored Structural Expectation-Maximization (cSEM) algorithm, an iterative score-and-search framework that integrates Monte Carlo sampling in the E-step for efficient expectation computation and employs the iterative Markov chain Monte Carlo (MCMC) algorithm in the M-step to refine the network structure and parameters. This approach addresses the non-decomposability challenge of censored-data likelihoods. Through simulation studies, we illustrate the superior performance of the cSEM algorithm compared to the existing competitors in terms of network recovery when censored data exist. Finally, the proposed cSEM algorithm is applied to single-cell data with censoring to uncover the relationships among variables. The implementation of the cSEM algorithm is available on GitHub. Full article

High-purity distillation columns typically give rise to multi-variable, strongly coupled nonlinear systems with substantial time delay and significant inertia. The control performance of high-purity distillation columns crucially influences the purity of the final product. Taking into account the process of a high-purity distillation column, this article puts forward a dual-loop modified active disturbance rejection control (MADRC) scheme to improve the control of product purity. During the stable operation of the distillation process, the structures of two control loops are, respectively, approximated by two linear transfer function models via open-loop experiments. Subsequently, the compensation part of the MADRC scheme is designed, respectively, for each approximate model. Furthermore, this paper employs singular perturbation theory to prove the stability of MADRC. The performance of the dual-loop MADRC scheme (MADRC) is compared with that of a proportional–integral–derivative (PID) control scheme, a cascade PID control scheme (CPID), and a regular ADRC scheme (ADRC). The simulations demonstrate that the dual-loop MADRC scheme is capable of efficiently tracking the reference value and exhibits optimal disturbance rejection capabilities. Additionally, the superiority of the dual-loop MADRC scheme is validated through Monte Carlo trials. Full article

This paper presents a novel multivariate mean-reverting jump-diffusion model that incorporates correlated jumps and seasonal effects to capture the complex dynamics of commodity prices. The model also accounts for the interplay between price volatility and convenience yield, offering a comprehensive framework for commodity futures pricing. By leveraging the Feynman–Kac theorem, we derive a partial integro-differential equation for the conditional moment generating function of the log price, enabling an analytical solution for pricing commodity futures. This solution is validated against Monte Carlo simulations, demonstrating high accuracy and computational efficiency. The model is empirically applied to historical futures prices of natural rubber from the Thailand Futures Exchange. Key parameters—including commodity price dynamics, convenience yields, and seasonal factors—are estimated, revealing the critical role of jumps and seasonality in influencing market behavior. Notably, our findings show that convenience yields are negative, reflecting higher inventory costs, and tend to increase with rising spot prices. These results provide actionable insights for traders, risk managers, and policymakers in commodity markets, emphasizing the importance of correlated jumps and seasonal patterns in pricing and risk assessment. Full article

Water pollution is a significant problem stemming from several causes, with the contamination of heavy metal(oid)s being a primary concern. This is especially concerning because of the poisonous characteristics of these metal(oid)s and their effects on the aquatic ecosystem. This research is distinguished by its unique methodology for assessing metal(oid)s in the surface water of Bangladeshi rivers over a period of sixteen years, from 2007 to 2022. This work seeks to elucidate recent results on metal(oid) concentrations, contamination levels, multivariate statistical analyses, source identification using positive matrix factorization models, and probabilistic health risks. The findings reveal that the concentrations of chromium, nickel, arsenic, cadmium, and lead exceeded the acceptable limits for drinking water established by the World Health Organization (WHO) by factors of 4.64, 2.25, 22.51, 45.60, and 10.13, respectively. Our meta-analysis, subsequent to a Principal Component Analysis, indicated that increased concentrations of hazardous metals account for 85.47% of the variation from both anthropogenic and natural causes. Ecological risk indicators, including the metal index (84.06) and the Nemerow pollution index (10.55), indicated significant metal contamination. Ecological risk indicators, like the metal index (84.06) and the Nemerow pollution index (10.55), indicate substantial metal contamination. The positive matrix factorization (PMF) model detected the following sources of metals in water: industrial (22%), mixed (32%), agricultural activities (27%), and natural sources (19%). Furthermore, Monte Carlo-simulation-based assessments of health hazards indicated that the mean hazard index (HI) and cancer risk values for adults (301.89 and 422.76) and children (51.56 and 39.45) significantly exceeded the recommended limits, suggesting that both adults and children are vulnerable to potential non-carcinogenic and carcinogenic health risks. The immediate execution of control measures and regulations is essential to avert escalating pollution in surface water, protect ecosystems, and mitigate health hazards. Full article

This study employed the dynamic conditional correlation algorithm and incorporated the temporal dynamics of spillover effect to enhance the Multivariate Stochastic Volatility (MSV) model. Consequently, a DGC-t-MSV model (multiple stochastic volatility model of dynamic correlation coefficient with Granger causality test) was constructed to simulate and examine the volatility spillover effects between China’s carbon market and the traditional energy market. The findings reveal the following: (1) A significant spillover effect in price volatility exists between China’s carbon and traditional energy markets, with a notably fluctuating spillover index. The traditional energy market in China exerts a stronger unidirectional volatility spillover effect on the carbon market. Price fluctuations in the traditional energy market impact carbon market prices through mechanisms such as cost transmission and market expectations. (2) In the initial stages, the dynamic correlation between China’s carbon and traditional energy markets showed an overall downward trend, underscoring the positive influence of policy incentives and technological advancements on the growth of alternative energy. A mutual weakening effect exists between the carbon and traditional energy markets. (3) Price fluctuations in China’s carbon and traditional energy markets display a high degree of interdependence and short-term persistence, with evidence of a long memory and significant inertia in these price movements. Integration of the DGC-t-MSV model with the Bayesian approach and the Markov Chain Monte Carlo (MCMC) method and the introduction of a time-varying factor enabled the efficient measurement of the volatility spillover effect between China’s carbon and traditional energy markets. Full article