Search Results (348)

The increasing global reliance on wind and solar energy underscores the critical vulnerability of renewable systems to extreme weather, which can severely disrupt power generation. Accurately modelling the complex, multivariate dependencies of weather extremes is essential for building grid resilience, yet conventional statistical models often fail to capture critical tail dependencies. This study aims to develop a robust framework using vine copulas to model the tail dependencies among key meteorological variables, extreme temperature, wind speed, and relative humidity, across the Eastern Cape province, South Africa, in order to identify optimal seasons for renewable energy production. We first clustered weather stations across the province into five distinct groups using Partitioning Around Medoids (PAM), based on geographical features (elevation, longitude, and latitude). This study explored an automatic selection of the optimal vine copula structure that adequately describes the dependence structure of the meteorological variables employed. The analysis demonstrated that R-vine copulas successfully captured the multivariate tail behaviour of temperature and relative humidity, while D-vine copulas were highly effective for wind speed. The models revealed significant tail dependencies, indicating a high potential for concurrent extreme weather events that impact energy generation. Our findings confirm that vine copulas offer a superior framework for assessing the risks associated with extreme weather to renewable energy systems. The results provide critical insights for regional energy policy and grid resilience planning, highlighting the importance of advanced risk assessment to safeguard renewable energy production against climate extremes. Full article

Background/Objectives: The transition to higher education is a critical phase of human development that makes adolescents and young adults particularly vulnerable to mental health problems, such as depression, anxiety, and stress. This study aimed to evaluate the psychometric properties of the Portuguese version of the Depression, Anxiety and Stress Scale-21 Items (DASS-21) among first-year undergraduate nursing students. Methods: A methodological study was conducted with 225 undergraduate nursing students, aged 17 to 18 years, from a higher education institution in central Portugal. Data were collected using the Google Forms platform. Confirmatory factor analysis was conducted to test three competing models: a single-factor model, a three-factor correlated model, and a second-order factor model. Reliability was assessed using composite reliability, and validity was evaluated using average variance extracted and the Fornell–Larcker criterion for discriminant validity. Results: Factor analyses revealed that the three-factor correlated model fit the data best overall, showing superior fit indices compared to the competing models (χ²/df = 2.37; CFI = 0.90; and RMSEA = 0.08; TLI = 0.88 and SRMR = 0.04). Composite reliability was high across all tested models, ranging from 0.84 to 0.94. The analysis of score distributions by category revealed a high prevalence of severe or extremely severe symptoms of anxiety, stress, and, to a lesser extent, depression. A statistically significant association was found between higher symptom severity and prior familiarity with mental illness. Conclusions: The DASS-21 proved to be a valid and reliable instrument for assessing psychological distress in higher education students. These findings underscore the urgent need for mental health programs in higher education institutions that focus on early detection and intervention, particularly for students initiating their studies and those with a history of mental health problems. Full article

This study reviews advanced extreme value theory techniques and applies them to extreme rainfall events recorded at two meteorological stations, Port Edward and Virginia, in the KwaZulu-Natal province of South Africa. The study aims to provide a comparative analysis of the performance of three extreme value theory models—the generalised extreme value distribution (GEVD), the generalised extreme value distribution for r-largest order statistics (GEVDr), and the blended generalised extreme value distribution (bGEVD)—in modelling extreme rainfall events. The monthly maximum rainfall data used in the study was obtained from the South African Weather Service. The Shapiro–Wilk test demonstrated the non-normality of the rainfall datasets. Parameter estimation was performed using maximum likelihood estimation and Bayesian estimation methods, both yielding positive shape parameters consistent with the Fréchet class of distributions. The goodness-of-fit tests confirmed the suitability of the GEVD model for the data. The results of both the standard GEVD and GEVDr models provided consistent return level estimates, suggesting strong model performance. The bGEVD model produced lower return level estimates compared to the GEVD and GEVDr models. Overall, the findings of the study offer valuable insights into the behaviour of extreme rainfall in KwaZulu-Natal province, with significant implications for risk management, infrastructure planning, and disaster preparedness. This study will add value to the literature and knowledge of statistics. Full article

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R², RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R² from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications. Full article

Background: Peripheral artery disease (PAD) is a manifestation of atherosclerosis that affects the extremities, leading to reduced perfusion and functional impairment. Non-contrast magnetic resonance angiography (NC-MRA) provides a safe and quantitative approach for early detection of PAD without the risks associated with contrast agents. The purpose of this study was to demonstrate the application of standard and centric ky-kz FBI techniques for rapid three-station NC-MRA of the entire lower extremity. Methods: This prospective cross-sectional study compared standard three-station fresh blood imaging (sFBI) with centric ky-kz ordered fresh blood imaging (cFBI) sequences in 10 healthy subjects and 3 patients with PAD (age range: 23–79 years; 7 females) using a 3-Tesla magnetic resonance imaging (MRI) system. Both sequences were acquired at the iliac, femoral, and tibial stations. Image quality (0–4 scale), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated. Statistical analysis was performed using repeated-measures analysis of variance (ANOVA) with significance set at α = 0.05. Results: Image quality did not differ significantly between sFBI and cFBI (p = 1.0). The iliac station exhibited lower image quality than the femoral station (p < 0.01). In a PAD patient with an iliac stent, cFBI preserved good image quality in the femoral and tibial stations, whereas sFBI was affected by N/2 aliasing artifacts. Both methods failed to visualize the stented iliac segment. Compared to sFBI, cFBI yielded significantly lower SNR (p < 0.01) and CNR (p < 0.001) but reduced total scan time by approximately 40% (468 s vs. 291 s). Conclusions: Three-station non-contrast FBI MRA of the peripheral arteries is feasible. The cFBI sequence substantially shortens scan time without compromising diagnostic image quality, offering practical advantages for clinical implementation, improved patient comfort, and reduced motion artifacts. Full article

Climate change represents a critical challenge for viticulture worldwide, primarily through increased heat stress, more frequent and severe drought periods, and unseasonal rainfall events. There is increasing evidence of its negative effects on both thermal regimes—potentially leading to accelerated phenology and unbalanced sugar-to-acid ratios—and hydric regimes—causing water stress that impacts berry development and final yield. The use of plastic covering in vineyards is a widespread technique, particularly in regions with high climatic variability such as the Mediterranean Basin (e.g., Southern Italy, Spain, Greece), aimed at protecting both vegetation and grapes from external factors such as hail, heavy rainfall, wind, and extreme solar radiation, which can cause physical damage, promote fungal diseases, and lead to berry sunburn. This study explores the impact of six distinct commercial plastic films, with varying optical properties, on the retrieval and accuracy of vegetation indices derived from Sentinel-2 imagery in a mid-season table grape vineyard (Autumn Crisp^®) in Southern Italy during the 2024 growing season. Laboratory spectroradiometric analyses were conducted to measure film-specific transmittance and reflectance factors from 200 to 1500 nm, enabling the development of a first-order linear spectral correction model applied to Sentinel-2 imagery. Vegetation indices (NDVI, CVI, GNDVI, LWCI) were corrected for plastic interference and analysed through univariate statistics and Principal Component Analysis. Results showed that after applying the spectral correction model, film T2 displayed the higher NDVI value (0.73). Films T3 and T4—characterised by high visible light transmittance (>39%) and low reflectance (<11% in the Red/NIR)—resulted in lower vine vigour and photosynthetic activity, with mean corrected NDVI values equal to 0.70, though still significantly higher than those of films T1 (0.65) and T5 (0.67). Films T6 and T1 were associated with greater water conservation, as indicated by the highest mean LWCI values (T6: 0.59; T1: 0.52), but lower chlorophyll-related signals, evidenced by the lowest mean CVI values (T6: 1.31; T1: 1.74) and GNDVI values (T6: 0.46; T1: 0.48). Among the corrected indices, NDVI demonstrated strong positive correlations with yield (r = 0.900) and total soluble solids per vine (TSS*vine, in kg), a key quality parameter representing the total sugar yield (r = 0.883), supporting its suitability as an index for vine productivity and fruit quality. The proposed correction method significantly improves the reliability of remote sensing in covered vineyards, as demonstrated by the strong correlations between corrected NDVI and yield (R² = 0.810) and sugar content (R² = 0.779), relationships that were not analysable with the uncorrected data; may guide film selection—opting for high-transmittance films (e.g., T2, T3) for yield or water-conserving films (e.g., T6) for stress mitigation—and irrigation strategies, such as using the corrected LWCI for precision scheduling. Future efforts should include angular effects and ground-truth validation to enhance correction accuracy and operational relevance. Full article

To address the limitations of traditional groundwater quality assessment and prediction methods, this study integrates game theory and machine learning to investigate the drinking quality of groundwater in the southwestern Qinghai–Tibet Plateau. The results showed that the groundwater in the study area is generally weakly alkaline (mean pH: 8.08) and dominated by freshwater (mean TDS: 302.58 mg/L), with hardness levels mostly ranging from soft to medium. Major cations follow the concentration order: Ca²⁺ > Na⁺ > Mg²⁺ > K⁺; anions are in the sequence of HCO₃⁻ > SO₄²⁻ > Cl⁻. The hydrochemical type is mainly Ca-HCO₃. A few samples exceed the limit values specified in the Groundwater Quality Standard. Through multivariate statistical analysis, ion ratio analysis, and saturation index calculations, water-rock interaction is identified as the primary factor influencing groundwater chemistry. It consists of carbonate dissolution and silicate weathering, accompanied by cation exchange. The water quality index improved based on game theory, integrated subjective weights (from analytic hierarchy process) and objective weights (from entropy-weighted method), shows that the overall groundwater quality in the study area is good: 95.97% of the samples are high-quality water (WQI ≤ 50), more than 99% of the samples have a WQI < 150, which is suitable as drinking water sources; only 0.81% of the samples are of extremely poor quality, presumably related to local pollution. Linear regression achieved the best performance (R² = 0.99, RMSE≈0.00) with strong stability, followed by support vector machines (test R² = 0.98), while the extreme gradient boosting model showed overfitting. This study provides a scientific basis for groundwater management in river basins. Full article

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to evaluate the drought indices. These indices were computed across six timescales: 1, 3, 4, 6, 9, and 12 months. A Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was employed to detect temporal volatility in precipitation, followed by a second-order geospatial autocorrelation eigenfunction eigendecomposition using Global Moran’s Index statistics to geolocate both aggregated and non-aggregated precipitation locations. The performance of drought indices was assessed using non-parametric Spearman’s correlation to identify the strength, direction, and similarity of regional-specific drought events. The temporal lag interdependence between meteorological and agricultural droughts was assessed using a non-parametric Spearman’s cross correlation function (SCCF). The findings revealed that the GARCH model with a skewed Student’s t distribution effectively captured conditional temporal volatility and asymptotic behavior in the precipitation series. The model’s sensitivity enabled the incorporation of temporal fluctuations related to droughts and extreme meteorological events. The Bhalme and Mooley Drought Index (BMDI-6) and Z-Score Index (ZSI-6) were the most applicable indices for drought monitoring. Spearman’s cross-correlation analysis revealed that meteorological droughts influenced agricultural droughts with a time lag of up to 4 months. Full article

Droughts are becoming more frequent, severe, and impactful across the globe. Agroecosystems, which are human-made ecosystems with high water demand that provide essential ecosystem services, are vulnerable to extreme droughts. Although water use efficiency in agriculture has increased in rec ent decades, drought management should be based on long-term, proactive strategies rather than crisis management. The AgrHyS network of sites in French Brittany collects high-resolution soil moisture data from agronomic stations and catchments to improve understanding of temporal soil moisture dynamics and enhance water use efficiency. Frequent mapping of soil moisture and plant water stress is crucial for assessing water stress risk in the context of global warming. Although satellite remote sensing provides reliable, periodic global data on surface soil moisture, it does so at a very coarse spatial resolution. The intrinsic spatial heterogeneity of surface soil moisture requires a higher spatial resolution in order to address upcoming challenges on a local scale. Drones are an excellent tool for upscaling point measurements to catchment level using different onboard cameras. In this study, we evaluated the potential of multispectral images, thermal images and LiDAR data captured in several concurrent drone flights for high-resolution mapping of soil moisture spatial variability, using in situ point measurements of soil water content and plant water stress in both agricultural areas and natural ecosystems. Statistical models were fitted to map soil water content in two areas: a natural marshland and a grassland-covered agricultural field. Our results demonstrate the statistical significance of topography, land surface temperature and red band reflectance in the natural area for retrieving soil water content. In contrast, the grasslands were best predicted by the transformed normalised difference vegetation index (TNDVI). Full article

Several decades after the development of FEM in computer-based form, which is a milestone in the evaluation of mechanical systems, the method has been adopted to analyze the biomechanical response of human skeletal structures. This innovative technique has generated new questions, but also new results, and, at the same time, competitive environments with explosive development, in the recent period. This research is focused on analyzing, using FEM, the extreme thermal variations produced at the level of two oro-facial systems (one control and one subjected to orthodontic therapy using a fixed metallic orthodontic appliance). The objective of the study was to determine the temperature evolution in different dental structures subjected to extreme temperatures given by variations between very cold and very hot foods. Each system was exposed to a succession of extreme thermal regimes (70…−18…70… °C and −18…70…−18… °C). In order to conduct this research, we used the case of a 14-year-old female patient. Following an orthodontic evaluation, we discovered that the patient had dento-alveolar disharmony with crowding. The straight-wire method of applying a fixed metallic orthodontic appliance was chosen. As complementary examinations, the patient was subjected to a bimaxillary CBCT. Using a series of programs (InVesalius, Geomagic, SolidWorks, and AnsysWorkbench), a three-dimensional model was obtained. This model contained jaws and teeth. Also, brackets, tubes, and orthodontic wires can be incorporated into the model. Following the simulations carried out in this study, it was found that thermal variations from the dental pulp are more severe for the oro-facial system with a fixed metallic orthodontic appliance (regardless of the type of thermal stimulus used). Thus, even today, with all the facilities available in the dental materials industry, metallic orthodontic devices present significant thermal conductivity, generating harmful effects on the dental structures. The reading of the results was performed on the virtual model, more precisely, on the internal dental structures (enamel, dentin, and pulp). A statistical study was not performed because it was considered that, in other patients, the results would be similar. Full article

River flow forecasting remains a critical yet challenging task in hydrological science, owing to the inherent trade-offs between physics-based models and data-driven methods. While physics-based models offer interpretability and process-based insights, they often struggle with real-world complexity and adaptability. Conversely, purely data-driven models, though powerful in capturing data patterns, lack physical grounding and often underperform in extreme scenarios. To address this gap, we propose PESTGCN, a Physical-Enhanced Spatio-Temporal Graph Convolutional Network that integrates hydrological domain knowledge with the flexibility of graph-based learning. PESTGCN models the watershed system as a Heterogeneous Information Network (HIN), capturing various physical entities (e.g., gauge stations, rainfall stations, reservoirs) and their diverse interactions (e.g., spatial proximity, rainfall influence, and regulation effects) within a unified graph structure. To better capture the latent semantics, meta-path-based encoding is employed to model higher-order relationships. Furthermore, a hybrid attention mechanism incorporating both local temporal features and global spatial dependencies enables comprehensive sequence learning. Importantly, key variables from the HEC-HMS hydrological model are embedded into the framework to improve physical interpretability and generalization. Experimental results on four real-world benchmark watersheds demonstrate that PESTGCN achieves statistically significant improvements over existing state-of-the-art models, with relative reductions in MAE ranging from 5.3% to 13.6% across different forecast horizons. These results validate the effectiveness of combining physical priors with graph-based temporal modeling. Full article

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

This study evaluates six parameter estimation methods for the generalized extreme value (GEV) distribution: maximum likelihood estimation (MLE), two probability-weighted moments (PWM-UE and PWM-PP), and three robust two-stage order statistics estimators (TSOS-ME, TSOS-LMS, and TSOS-LTS). Their performance was assessed using simulation experiments under varying tail behaviors, represented by three types of GEV distributions: Weibull (short-tailed), Gumbel (light-tailed), and Fréchet (heavy-tailed) distributions, based on the mean squared error (MSE) and mean absolute percentage error (MAPE). The results showed that TSOS-LTS consistently achieved the lowest MSE and MAPE, indicating high robustness and forecasting accuracy, particularly for short-tailed distributions. Notably, PWM-PP performed well for the light-tailed distribution, providing accurate and efficient estimates in this specific setting. For heavy-tailed distributions, TSOS-LTS exhibited superior estimation accuracy, while PWM-PP showed a better predictive performance in terms of MAPE. The methods were further applied to real-world monthly maximum PM2.5 data from three air quality stations in Bangkok. TSOS-LTS again demonstrated superior performance, especially at Thon Buri station. This research highlights the importance of tailoring estimation techniques to the distribution’s tail behavior and supports the use of robust approaches for modeling environmental extremes. Full article

Background/Objectives: Unilateral hearing loss (UHL), particularly single-sided deafness (SSD), disrupts spatial hearing in children, leading to academic and social challenges. This study aimed to (1) compare azimuthal sound-localization accuracy and compensatory strategies between children with single-sided deafness (SSD) and their normal-hearing (NH) peers within a virtual reality environment, and (2) investigate sound-localization performance across various azimuths by contrasting left-SSD (L-SSD) and right-SSD (R-SSD) groups. Methods: A cohort of 44 participants (20 NH, 24 SSD) performed sound localization tasks in a 3D virtual environment. Unsigned azimuth error (UAE), unsigned elevation error (UEE), and head movement distance were analyzed across six azimuthal angles (−75° to 75°) at 0°elevation. Non-parametric statistics (Mann–Whitney U tests, Holm–Bonferroni correction) compared performance between NH and SSD groups and within SSD subgroups (L-SSD vs. R-SSD). Results: The SSD group exhibited significantly higher UAE (mean: 22.4° vs. 3.69°, p < 0.0001), UEE (mean: 5.95° vs. 3.77°, p < 0.0001) and head movement distance (mean: 0.35° vs. 0.12°, p < 0.0001) compared with NH peers, indicating persistent localization deficits and compensatory effort. Within the SSD group, elevation performance was superior to azimuthal accuracy (mean UEE: 3.77° vs. mean UAE: 22.4°). Participants with R-SSD exhibited greater azimuthal errors at rightward angles (45°and 75°) and at −15°, as well as increased elevation errors at 75°. Hemifield-specific advantages were strongest at extreme lateral angles (75°). Conclusions: Children with SSD rely on insufficient compensatory head movements to resolve monaural spatial ambiguity in order to localize sounds. Localization deficits and the effort associated with localization task call for action in addressing these issues in dynamic environments such as the classroom. L-SSD subjects outperformed R-SSD peers, highlighting hemispheric specialization in spatial hearing and the need to study its neural basis to develop targeted rehabilitation and classroom support. The hemifield advantages described in this study call for further data collection and research on the topic. Full article

This paper introduces a new extension of exponentiated standard logistic distribution. Some important statistical properties of the novel family of distributions are discussed. A simulation study is also conducted to observe the behavior of the estimated parameter using several estimation methods. The adaptability as well as the flexibility of the new model is checked through two real-life applications. A comprehensive financial risk assessment is conducted using multiple actuarial risk measures: Peaks Over Random Threshold Value-at-Risk, Value-at-Risk, Tail Value-at-Risk, the risk-adjusted return on capital and the Mean of Order P. These indicators offer a nuanced view of risk by capturing different aspects of tail behavior, which are critical in understanding potential extreme losses. These risk indicators are applied to analyze actuarial financial claims data, providing a robust framework for assessing financial stability and decision-making in the face of uncertainty. Full article