Search Results (6,121)

This paper investigates whether water-related climate stress predicts tail movements in food system assets and whether these spillovers vary across market regimes and investment horizons. Using daily data from January 2012 to January 2026, we examine the relationships among a water-risk proxy, agricultural commodities, agribusiness, and food supply-chain equities, and a fertilizer-related proxy. The analysis combines the cross-quantilogram with quantile spillover analysis in the frequency domain, allowing us to capture directional dependence in the tails of the distribution and short- and long-run connectedness. To account for structural change, we employ data-driven break detection and identify three major regimes: a pre-disruption period, a COVID-related adjustment phase, and a broader food system stress regime from early 2022 onward. The findings indicate that water-related climate stress has its strongest predictive power in the tails, especially for agribusiness and fertilizer-related assets, while the broad agricultural commodity basket is comparatively less sensitive. Lower-tail dependence is predominantly negative and often significant, whereas upper-tail dependence is generally positive, indicating asymmetric transmission under extreme market conditions. The spillover results further show that connectedness in the water–food system is mainly short-run, with agribusiness and fertilizer channels acting as the primary conduits of transmission. From a practical perspective, these findings suggest that investors and risk managers can use water-related market signals as early warning indicators of stress in food system assets, while policymakers can strengthen food system resilience through integrated water management, input market monitoring, and supply chain adaptation measures. The findings suggest that water-related climate stress is not merely an environmental constraint but a systemic source of food system risk with implications for resilience, risk monitoring, and integrated water-agriculture governance. Full article

This study recognizes education as an investment and estimates the private rates of return to upper secondary education in Greece, overall, by type (general or vocational) and by gender. Earnings data were collected through primary research using stratified sampling from the private sector of the economy. The analysis is based on the Mincer method and is complemented by machine learning methods, including Support Vector Regression, Random Forests, and Extreme Gradient Boosting. The empirical analysis shows that investing in upper secondary education (general and vocational) is profitable. The private rates of return in upper general secondary education are higher than those in vocational education, and female graduates exhibit higher returns than male graduates. Machine learning models achieve modest improvements in predictive performance, as reflected in higher adj. R² values and lower prediction errors. However, the estimated rates of return remain broadly consistent with those obtained from the Mincer method. This convergence suggests that the Mincer specification captures the core structural relationship between education and earnings, while machine learning models primarily enhance predictive accuracy without substantially altering the estimated economic returns. This finding highlights the robustness of the traditional econometric framework and clarifies the complementary role of machine learning techniques in empirical labor economics. Full article

Bottom sediments in anthropogenically impacted freshwater systems represent a dynamic and poorly constrained source of secondary pollution, where heavy metal mobility, rather than total concentration, controls the release of contaminants into the water column under changing physicochemical conditions. This issue is particularly pronounced in small and medium-sized freshwater systems subjected to sustained anthropogenic pressure, where local hydrochemical conditions and sediment composition strongly influence metal speciation and remobilization dynamics. This study aims to quantitatively assess heavy metal speciation, mobility, and associated ecological risk in bottom sediments of anthropogenically impacted freshwater systems using complementary analytical approaches. The data obtained indicate a pronounced spatial heterogeneity in the total metal content, due to varying degrees of anthropogenic impact on the water bodies. The highest level of pollution was recorded in the bottom sediments of the Chizhovskoye reservoir, where Zn concentrations reach 755 mg/kg, Cr—379 mg/kg, Ni—106 mg/kg, and Cu—158 mg/kg, indicating intense technogenic influence. The bottom sediments of the Loshitsa River are characterized by elevated, but less extreme values: the content of Cu is up to 77 mg/kg, Zn—up to 263 mg/kg, and Mn—up to 418 mg/kg. In contrast to urbanized water bodies, the background site—Lake Sergeevskoye—is characterized by significantly lower concentrations of heavy metals, which confirms its representativeness as a control object. Analysis of the fractional composition showed that Zn and Mn have the largest share of mobile forms, with their concentrations in the mobile phase reaching 12–92 mg/kg and 60–116 mg/kg, respectively, especially under conditions of increased anthropogenic load. A significant portion of Cu and Zn (up to 60–70% of the total content) is associated with organic matter, indicating the important role of the organic matrix in retaining metals and their potential mobilization under changing environmental conditions. Calculation of the geoaccumulation index showed that most of the studied bottom sediments belong to the from uncontaminated to moderately contaminated class, while for Cr and Ni in the Chizhovskoye reservoir, I_geo values up to 1.9 are characteristic, corresponding to a moderate level of pollution. The results obtained indicate a significant impact of anthropogenic load on the forms of occurrence and mobility of heavy metals and highlight the role of bottom sediments as an active factor in the secondary pollution of freshwater ecosystems. Full article

This research article explores whether the climate transition risk (CTR) and climate physical risk (CPR) transmit greater shocks towards the sustainable, gold-backed, energy-related and Sharia-compliant cryptocurrencies during bullish market conditions as compared with the normal and bearish market conditions. We employ the novel quantile vector auto-regression (QVAR)-based connectivity framework. Overall findings suggested that CPR and CTR transmitted greater shocks towards cryptocurrency classes during extremely high and lower quantiles as compared with the median quantile. This U-shaped and non-linear climate risks shock transmission indicates that Sharia-compliant, energy-related and gold-backed cryptocurrencies become more vulnerable during extreme market conditions (higher and lower quantiles) and may not consistently serve as reliable hedging or diversification instruments, particularly during periods of heightened climate uncertainty. Overall findings suggested that both the CPR and CTR transmitted greater shocks towards energy-related, gold-backed, and Sharia-compliant cryptocurrencies as compared with the sustainable cryptocurrencies, across all the quantiles. Therefore, sustainable cryptocurrencies, particularly those with energy-efficient consensus mechanisms such as Stellar, Cardano and Ripple, exhibited resilience to climate risks and can therefore function as stabilizing core holdings in diversified portfolios. Fund managers should incorporate a rebalancing strategy that increases allocation to these climate-resilient, sustainable digital assets during periods of elevated climate risk. Fund managers should integrate CPR and CTR into the quantile-domain forecasting frameworks for predicting digital asset market returns to enhance financial stability. Portfolio managers should undertake dynamic and quantile-contingent climate risk hedging strategies that account for tail-risk exposure rather than relying on average market behavior. Full article

This paper presents a collaborative optimization design methodology aimed at improving heat dissipation efficiency through the modulation of microstructural variations. The approach addresses the thermal protection requirements of high-temperature components, such as ceramic matrix composite turbine blades, which are subjected to complex and elevated thermal loads. Through the integration of numerical simulation and experimental validation, a bidirectional mapping model linking carbon nanotube (CNT) content with the macroscopic anisotropic thermal conductivity of the material was developed. Furthermore, a thermal conduction analysis and optimization framework for Ceramic Matrix Composite (CMC) high-temperature components under non-uniform thermal loads was established. This study expands the adjustable range of the material’s thermal conductivity by allowing flexible modulation of carbon nanotube content. The results demonstrate that this methodology effectively enhances the heat dissipation capacity of CMC materials in extreme thermal environments: the maximum surface temperature of the optimized flat plate is reduced by 8.96%, the peak temperature gradient is lowered by 46.64%, and the maximum thermal stress is decreased by 38.17%. This research provides new insights into the comprehensive integration of thermal dissipation requirements for CMC hot components. Full article

Background: Diabetic foot ulcers (DFUs) and lower extremity amputations are major contributors to morbidity and mortality in individuals with diabetes. Among patients undergoing active cancer treatment, the risks are compounded by immunosuppression, peripheral neuropathy, and vascular complications. Even minor foot infections or wounds in these patients can necessitate the suspension of cancer therapy, with potentially lifethreatening consequences. This study evaluated the impaqt of integrating symptom-focused patient education with coordinated podiatric care to reduce DFUs and amputations in this highrisk population with concurrent cancer and diabetes. Methods: A five-year retrospective review was conducted at a National Cancer Institute (NCl)designated comprehensive cancer center as part of the Novel Limb Preservation Initiative. The cohort included patients with Type II diabetes undergoing treatment for prostate, breast, colorectal, lymphoma, leukemia, thyroid, or lung cancers. Patients were assigned targeted educational modules based on self-reported diabetic foot symptoms. Podiatric care was individualized according to each patient's signs and symptoms, including routine diabetic foot examinations and close, timely monitoring when indicated. Results: The intervention yielded a DFU incidence of 2. 8% and an amputation rate of 0. 43%, both lower than national benchmarks. Enhanced patient engagement through diabetic foot symptom-focused education and earlier detection of foot complications-including diabetic foot ssues that may appear minor to laypersons-contributed to these improved outcomes. Conclusion: Integrating diabetic foot symptom-focused education with proactive podiatric monitoring significantly reduced DFUs and amputations in this high-risk population. This model, developed under the Novel Limb Preservation Initiative, offers a scalable strategy for broader implementation, particularly in high-risk communities, including Hispanic, African American, low socioeconomic, and rural populations across the United States. Full article

The calculation for the local bearing capacity of stirrup-confined concrete is an important issue in structural design. Due to the coupling effects of multiple factors, there is no unified calculation method recognized by scholars. The improved backpropagation neural network model based on the particle swarm optimization algorithm (PSO-BPNN) is used in this research to conduct a systematic analysis. The results of 40 stirrup-confined concrete specimens from the tests conducted by ourselves and an additional 92 similar test data points from references were combined; the calculation efficiency and accuracy of the PSO-BPNN model were verified. Compared with the BPNN model, the training iterations of the PSO-BPNN model were reduced by 74.23% with the condition of same training effect. The mean squared error (MSE) is reduced by 33.9%, and the coefficient of determination (R²) is increased by 5.5% with the condition of the same number training iterations. In addition, compared with the calculation stability and accuracy of Random Forest Regression (RFR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models, the PSO-BPNN model also shows better results. Within the applicable range of the codes, the average ratio of the predicted values to the calculated values for GB50010-2010, MC2020 and ACI318-25 are 1.988, 1.719, and 5.387, respectively. A higher evaluation for the contribution of stirrup is considered in the MC2020 code; the predicted values of some specimens are lower than the calculated values when A_cor/A_l is less than 1.35. The brittleness effect is not adequately considered: the predicted values of some specimens are also lower than the calculated values with the active powder concrete (RPC) is used. The sensitivity ranking of the model with coupling effect for parameters is A_l, A_b, f_c,k, s, d, d_cor, and f_y,k. It is slightly different from the sensitivity ranking obtained by analyzing individual parameters, but the calculation logic is consistent. The research results can provide a theoretical basis for practical engineering. Full article

The latest H266/VVC standard has received numerous praises for its excellent compression efficiency. However, its extremely high computational complexity has become a hindrance to the VVC adaptation industry ecosystem, while also increasing the difficulty of hardware design and application costs. To address this issue, we designed an efficient intra-coding scheme based on neural networks, which consists of three parts: Firstly, we designed a neural network-based reverse prediction algorithm that uniquely utilizes the CNN’s prediction results for lower-level blocks to determine the QTMT partitioning of upper-level blocks, cleverly solving the adaptation problem of existing models to complex VVC partitioning patterns—a decision-making logic that has not been fully explored. Secondly, we designed a pruning algorithm, which is the first to dynamically couple the real-time RDO cost of BT segmentation with the TT segmentation direction, achieving adaptive decision-making. Finally, we designed a complexity pre-screening module. On the basis of analyzing whether the CU texture is smooth, this module designs four sets of adaptive thresholds for non-square CUs introduced in VVC. These thresholds can dynamically adjust local and global thresholds based on CU size, enabling size sensitive texture evaluation to determine whether the current block needs further partitioning. The experimental results show that, compared with traditional VTM4.0, our method reduces the average encoding time by 49.21%, while the BD-BR increase is 1.61%, and the BD-PSNR decreases by 0.06 dB, fully demonstrating its superiority and performance balance. Full article

Background and objectives: Mental health assessment of healthcare workers (HCWs) is essential to inform prevention-oriented policies and interdisciplinary support strategies to strengthen HCWs’ mental health and optimize patient care. Therefore, the present study assessed mental health status and associated factors of HCWs using the DASS-21. Methods: Using a cross-sectional study design and the standardized DASS-21 questionnaire, we assessed the mental health status of HCWs of different categories from various healthcare settings of northern Saudi Arabia. A binomial logistic regression analysis was performed to examine the factors associated with each DASS-21 domain. Finally, Spearman’s correlation test was done to find the correlation across the domains. Results: Of the 385 participants, some forms of depression, anxiety, and stress were found in 49.6%, 49.4%, and 39.0% of the participants, respectively. Extremely severe symptoms were observed in depression and anxiety (9.6% each), and the lowest were observed for stress (3.9%). Depression was significantly associated with female gender (p = 0.017) and being single (p = 0.043), while anxiety was associated with nurses (p = 0.002) and non-Saudi nationality (p = 0.037). Stress was higher among HCWs working in specialty hospitals (p = 0.045) and lower among those aged > 40 years (p = 0.003). Furthermore, a positive correlation was noted within each DASS-21 domain (p < 0.001). Conclusions: Given the high prevalence of mental health issues, the relevant authorities should consider implementing preventative measures, including regular screening, psychoeducation workshops, interdisciplinary care, and proper referral pathways for the HCWs who screen positive for any of the mental health domains. Full article

This study proposes a novel transformation method for analyzing block minima using the generalized extreme value distribution (GEVD). The negative power transformation (NPT), which includes a tunable hyperparameter and reduces to the reciprocal transformation (RT) when set to 1, improves the accuracy and robustness in estimating long-term return levels (RL). Compared to traditional methods, the NPT-GEVD demonstrates lower bias, standard errors, and root mean square errors in Monte Carlo simulations. Furthermore, the NPT-GEVD provides consistent RL estimates with improved robustness across varying parameterizations and sample sizes, mainly when using L-moments for small datasets. The application of the NPT-GEVD to rainfall data from South Korea revealed that the RLs for detecting hourly cumulative rainfall threshold levels varied from 30 min to over 4 h, depending on the location and threshold. This research underscores the value of advanced transformation techniques in environmental risk management, offering critical insights for flood prediction and mitigation strategies in climate change. Full article

Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, and model resolution. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as promising data-driven alternatives for improving TC forecasts. This study presents a comparative evaluation of six ML and DL models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)—for forecasting TC track and intensity in the North Atlantic basin. The models are trained using the National Hurricane Center’s (NHC) HURDAT2 best-track dataset for storms from 1990 to 2019 and evaluated on an independent test set from the 2020 season. Model performance is compared across all models and benchmarked against the 2020 mean Decay-SHIFOR5 intensity error, CLIPER5 track errors, and the NHC official forecast (OFCL) errors. Forecast skill is assessed using mean absolute error (MAE) with 95% bootstrap confidence intervals and the coefficient of determination (R²) across lead times of 6, 12, 18, 24, 48, and 72 h. The results show that: (1) several ML and DL models achieve intensity forecast performance that is broadly comparable in magnitude to the 2020 mean OFCL benchmarks, with an average error reduction of 5–11% at the 24 h lead time; (2) among the ML models, XGBoost and CatBoost slightly outperform LightGBM and RF in accuracy, while LightGBM demonstrates the highest computational efficiency; and (3) among the DL models, CNNs outperform ANNs in predictive accuracy and intensity forecasting efficiency, while ANNs exhibit lower computational cost for track forecast. Bootstrap confidence intervals indicate relatively low variability in model errors, supporting the statistical stability of the results within the 2020 season. However, these results reflect within-season variability and do not necessarily generalize across different years or climatological conditions. Overall, the findings demonstrate the potential of ML/DL-based approaches to complement existing operational forecast systems and enhance TC track and intensity forecasting in the North Atlantic basin. Full article

The Tibetan Plateau (TP) atmospheric heat source crucially modulates East Asian summer monsoon precipitation, yet its synergy with upstream oceanic signals remains elusive. Using observations (1971–2020) and CMIP6 simulations, we investigate mechanisms coupling the summer TP heating and precipitation over the Middle and Lower Yangtze River (MLYR). SVD analysis reveals a robust positive coupling between them. Mechanistically, TP heating triggers a quasi-stationary Rossby wave train, inducing a “saddle-like” circulation that drives intense MLYR moisture convergence (contributing >90% to precipitation changes). Crucially, we re-examine the upstream oceanic precursor to propose a “dual-pathway superposition” framework. Contrary to the assumed linear causal chain, four-quadrant analysis reveals the spring Indian Ocean Basin Warming (IOBW) and summer TP heating are largely independent drivers (R = 0.24). While IOBW thermodynamically excites an Anomalous Anticyclone supplying abundant MLYR moisture, it lacks robust control over TP heating, which is dominated by internal atmospheric dynamics. However, our findings reveal a critical non-linear synergy: extreme MLYR rainfall strictly requires the coincidental phase overlap of these independent pathways (strong dynamic lifting coupled with oceanic moisture). CMIP6 simulations corroborate this independence, further emphasizing that extreme MLYR rainfall results from phase superposition rather than a single causal chain. Full article

Sandy shorelines respond to variability in boundary conditions over a wide range of time and spatial scales. While recent studies show that climate modes may affect shoreline evolution at interannual scales, such relationships remain unclear in the South Atlantic Ocean. Here, we investigate whether climate mode-driven variability in wave climate influences shoreline evolution using Ilha Comprida, a barrier island on the southeastern Brazilian coast, as a case study. Offshore wave conditions from the ERA5 reanalysis were analyzed over the last four decades and propagated to the nearshore using wave modeling. Shoreline change was quantified from satellite-derived shoreline positions, and relationships with interannual climate modes were evaluated using climate indices. Results show that the wave climate is bimodal and dominated by swell, with strong seasonality and no significant long-term trend in storminess. The El Niño–Southern Oscillation (ENSO) influences wave energy and extremes, with La Niña phases associated with higher wave power without a change in wave direction. No significant signal of the Southern Annular Mode (SAM) was found. At the coast, shoreline evolution is controlled by long-term sediment redistribution driven by alongshore transport gradients. ENSO-related shoreline signals are weak and spatially limited, occurring only in lower Empirical Orthogonal Function (EOF) modes of variability. These results suggest that, at Ilha Comprida, ENSO mainly modulates episodic wave-driven events rather than long-term shoreline patterns, emphasizing the need to distinguish between short-term energetic variability and longer-term morphodynamic response. This distinction is important for coastal management because even where climate modes do not produce persistent long-term shoreline trends due to site-specific aspects, they may still modulate event-scale risk, which can vary independently of the long-term average shoreline behavior. Full article

Rivers are a key pathway for the transport of plastics into the ocean. Studies of plastic pollution in Arctic rivers remain limited due to the inaccessibility of sampling sites and work in extreme weather conditions. This work presents the results of a three-year (2019–2021) survey of floating large microplastics (0.5–5 mm) and meso/macroplastics (>5 mm) in the Northern Dvina River, an actively navigated river that drains a densely populated region into the White Sea. Sampling was conducted during the ice-free periods (May–October) along a ∼3.5 km transect using a Neuston net, providing a multi-year dataset spanning three ice-free seasons. A critical methodological advancement was the calculation of plastic river–sea flux using the discharge of the sampled surface layer (upper 20 cm), which constitutes only ∼3% of the river’s total discharge, rather than the total discharge itself. Observed microplastic concentrations (average 0.003 items ${\mathrm{m}}^{-3}$) were low compared to many European rivers, and lower than those reported in the adjacent Barents and Kara Seas. Microplastic abundance was significantly lower during the high-water season than during the low-water season, which resulted in practically no seasonal variability in microplastic fluxes from the river to the White Sea (average 0.3 items ${\mathrm{s}}^{-1}$). A notable finding was that in some cases, meso/macroplastics outnumbered microplastics by item count, underscoring the river’s role as a significant source of larger plastic debris. A geospatial assessment of Arctic rivers’ pollution potential was performed, using socio-economic indicators such as near-delta population density and port activity. This study identified the Northern Dvina River as a major contributor of microplastics among the Arctic rivers. Full article

Given limited evidence on temporal changes in pathogen detection patterns and hospital-based inflammatory burden across the pandemic transition, this study examined their long-term relationship using respiratory multiplex PCR positivity and concurrent C-reactive protein (CRP) levels. We analyzed 19,002 episodes linking respiratory multiplex PCR (mPCR) results and concurrent CRP from October 2008 to December 2024. Pre-pandemic, pandemic, and post-pandemic changes in monthly testing volume, positivity rate, median CRP, high and extreme inflammation by mPCR status, and the correlation between positivity rate and median CRP were assessed. mPCR positivity decreased from 60.62% (pre-pandemic) to 22.45% (pandemic) and remained low at 25.95% thereafter, whereas the median CRP increased from 0.94 to 3.35 and 5.97 mg/dL, respectively. After January 2020, testing volume and positivity rate decreased, whereas the median CRP increased. High inflammation increased from 13.78% to 27.93% and 38.98% in mPCR-negative episodes, and from 4.61% to 7.20% and 27.66% in mPCR-positive episodes, remaining consistently lower in the latter. Monthly positivity rate was strongly negatively correlated with median CRP. Overall, respiratory virus positivity declined, whereas CRP-based inflammatory burden increased, indicating divergent temporal trends across the pandemic transition. These findings should be interpreted descriptively, not causally, as reflecting divergent temporal trajectories of pathogen detection and inflammatory burden. Full article