Search Results (5,746)

We adapt previous conceptual and numerical models of ciguateric food chains for the bioaccumulation of Pacific-ciguatoxin-1 (P-CTX-1) to a general model for bioaccumulation of P-CTX3C by parrotfish (Scarus frenatus, S. niger, and S. psittacus) that feed by scraping turf algae, and surgeonfish (Naso unicornis) that mostly feed on macroalgae. We also include the Indian Ocean parrotfish Chlorurus sordidus as a model for an excavator feeding parrotfish and include comparisons with the detritivorous surgeonfish Ctenochaetus striatus that brush-feeds on turf algae. Our food chain model suggests that, of the Gambierdiscus and Fukuyoa species so far analysed for ciguatoxin (CTX) production from the Pacific, only G. polynesiensis produces sufficient P-CTX3C to consistently produce parrotfish or N. unicornis with poisonous flesh. Our model suggests that insufficient CTX would accumulate into the flesh of parrotfish or N. unicornis to become poisonous from ingesting benthic dinoflagellates producing ≤0.03 pg P-CTX3C eq./cell, except from extended feeding times on high-density blooms and in the absence of significant depuration of CTX. Apart from G. polynesiensis, only G. belizeanus and possibly G. silvae and G. australes are thought to produce >0.03 pg P-CTX3C eq./cell in the Pacific. However, with relatively low maximum concentrations of ≤0.1 pg P-CTX3C eq./cell it is likely that their contribution is minimal. Our model also suggests that the differences between the area of turf algae grazed by parrotfish and similar sized C. striatus results in greater accumulation of CTX by this surgeonfish. This makes C. striatus a higher ciguatera risk than similar sized parrotfish, either directly for human consumption or as prey for higher trophic level fishes, consistent with poisoning data from Polynesia. It also suggests the possibility that C. striatus could bioaccumulate sufficient CTX to become mildly poisonous from feeding on lower toxicity Gambierdiscus or Fukuyoa species known to produce ≥0.02 P-CTX3C eq./cell. This indicates the potential for at least two food chain pathways to produce ciguateric herbivorous fishes, depending on the CTX concentrations produced by resident Gambierdiscus or Fukuyoa on a reef and the grazing capacity of herbivorous fish. However, only G. polynesiensis appears to produce sufficient P-CTX3C to consistently accumulate in food chains to produce higher trophic level fishes that cause ciguatera in the Pacific. We incorporate CTX depuration into our model to explore scenarios where mildly poisonous parrotfish or N. unicornis ingest CTX at a rate that is balanced by depuration to estimate the Gambierdiscus/Fukuyoa densities and CTX concentrations required for these fish to remain poisonous on a reef. Full article

Global estimates show a 17.9% prevalence of neuropsychiatric disorders in individuals recently hospitalized with COVID-19. Cognitive behavioral therapy (CBT) has been proposed as a nonpharmacological strategy to mitigate these effects. This study examined the potential effects of CBT on anxiety, depression, post-traumatic stress disorder (PTSD), and quality of life (QoL) in post-COVID-19 patients. This prospective, nonrandomized, single-center clinical trial involved 15 patients (mean age 53.4 years) who underwent weekly CBT sessions for six weeks. Between-group differences in anxiety and depression scores were non-significant (p > 0.05); however, significant intragroup improvements were observed in anxiety (p = 0.01), depression (p = 0.01), and PTSD (p = 0.01) after the intervention. Thus, CBT was associated with reduced anxiety, depression, and PTSD as well as improved quality of life in post-COVID-19 patients. Improvements in QoL were noted mainly in the domains of functional capacity, vitality, emotional aspects, and mental health. While these findings suggest that CBT may be beneficial for post-COVID-19 patients, the small sample size, absence of a control group, and short follow-up period limit the strength of our conclusions. Therefore, the results should be considered preliminary, and further randomized controlled trials with larger sample sizes are warranted. Full article

Achieving carbon neutrality requires a comprehensive understanding of terrestrial carbon dynamics, particularly the capacity of ecosystems to act as carbon sinks. This study quantified the temporal and spatial variability of net primary production (NPP) and net ecosystem production (NEP) across South Korea from 2010 to 2024, assessing long-term carbon sink trends and their implications for carbon neutrality and nature-based solutions (NbSs). Using the Carnegie–Ames–Stanford Approach (CASA) model driven by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data and climate variables, we estimated ecosystem carbon fluxes at high spatial and temporal resolutions. In 2024, national NPP totaled 78.63 Mt CO₂ yr⁻¹, with a mean value of 1956.63 t CO₂ ha⁻¹ yr⁻¹. High productivity was concentrated in upland forests of Gangwon-do, Mt. Jirisan, and northern Gyeongsangbuk-do, where favorable vegetation indices and climatic conditions enhanced photosynthesis. Lower productivity occurred in urbanized areas and intensively farmed lowlands. Heterotrophic respiration (RH) was estimated at 15.35 Mt CO₂ yr⁻¹, with elevated rates in warm, humid lowlands and reduced values in high-elevation forests. The resulting NEP in 2024 was 63.29 Mt CO₂ yr⁻¹, with strong sinks along the Baekdudaegan Range and localized negative NEP pockets in lowlands dominated by urban development or agriculture. From 2010 to 2024, the spatially averaged NPP increased from 1170 to 1543 g C m⁻² yr⁻¹, indicating a general upward trend in ecosystem productivity. However, interannual variability was influenced by climatic fluctuations, land-cover changes, and data masking adjustments. These findings provide critical insights into the spatiotemporal dynamics of terrestrial carbon sinks in South Korea, offering essential baseline data for national greenhouse gas inventories and the strategic integration of NbSs into carbon-neutral policies. Full article

The photocatalytic activity of Bi₂WO₆ Aurivillius phase has been widely exploited for the degradation of a wide range of gaseous and aqueous molecules, as well as microorganisms, under the influence of visible irradiation. Strategies for the development of doped and co-doped bismuth tungstate materials require the thermodynamic data on this phase. The heat capacity of bismuth tungstate, Bi₂WO₆, was investigated using a DSC microcalorimeter on polycrystalline powder samples in the temperature range from 313 to 1103 K (40–830 °C) in two separate runs. The samples were synthesized by solid-state reaction from pure binary oxides at 1033 K (760 °C) in a platinum crucible with cover. The high temperature C_p(T) data were fitted by the Maier–Kelley equation and, from this relation, the standard molar heat capacity of γ-Bi₂WO₆ polymorph was estimated to be at 298.15 K 176.8 ± 3.9 J·K⁻¹·mol⁻¹. A reversible second-order transition of Bi₂WO₆ phase was observed in the experimental temperature range, with a peak close to 940 K (667 °C). Additionally, the extrapolation of C_p(T) to 0 K was proposed using a method based on the multiple Einstein model. Thermodynamic properties (heat capacity C_p(T), entropy S°(T), enthalpy H°(T), Gibbs energy G°(T)) of crystalline γ-Bi₂WO₆ were calculated in the temperature range of 298.15–1123 K (25–850 °C). Full article

Marine sediments enriched with rare earth elements (REEs) serve as a significant reservoir, particularly for heavy REEs. Conventional lab-based REE exploration restricts rapid and large-scale assessment, whereas hyperspectral imaging provides a promising approach for quantitative evaluation. This study evaluates the capacity of hyperspectral data for the quantitative determination of REEs in marine sediments. A total of 53 samples from various locations were analyzed to determine their chemical composition and spectral characteristics within the 380–1000 nm range under natural light. The influence of surface conditions on spectral integrity was evaluated, and multiple preprocessing and spectral feature extraction methods were applied to enhance data reliability. This study proposes a novel approach, termed Feature Importance within Pearson Correlation Coefficient-Based High-Correlation Spectral Range (PCCR-FI), designed for the identification of characteristic spectral bands associated with REEs. Machine learning models were subsequently constructed to estimate REE concentrations, and the following key findings were observed: (a) technical adjustments effectively addressed variations in sediment surface conditions, ensuring data reliability. (b) The PCCR-FI technique identified characteristic REEs spectral bands, enhancing processing efficiency and prediction accuracy. (c) The integration of the reciprocal logarithmic first derivative (TLOG-FD) technique with a multilayer perceptron (MLP) model, termed TLOG-FD-MLP, efficiently captured critical spectral features, resulting in improved prediction accuracy. For light REEs, the model achieved coefficient of determination (R²) values exceeding 0.60 and relative performance deviation (RPD) values exceeding 1.60, with some elements demonstrating R² values as high as 0.81 with RPD values surpassing 2.00. Furthermore, several heavy REEs exhibited moderate prediction performance, with R² values consistently exceeding 0.60. When considering the total REE content, an R² of 0.73 and an RPD of 1.97 were achieved. These findings demonstrate the use of hyperspectral imaging as a viable tool for quantitative evaluation of REE concentrations in marine sediments, providing valuable guidance for resource mapping and the exploration of seafloor polymetallic deposits. Full article

Soil organic carbon (SOC) is a critical component of soil health, influencing soil structure, soil water retention capacity, and nutrient cycling while playing a key role in the global carbon cycle. Accurate SOC estimation over croplands is essential for sustainable land management and climate change mitigation. This study explores a novel approach to SOC estimation using multi-frequency synthetic aperture radar (SAR) data, specifically Sentinel-1 and ALOS-2/PALSAR-2 imagery, combined with advanced machine learning techniques for cropland SOC estimation. Diverse agricultural practices, with major crop types such as rice (Oryza sativa), finger millet (Eleusine coracana), Niger (Guizotia abyssinica), maize (Zea mays), and vegetable cultivation, characterize the study region. By integrating C-band (Sentinel-1) and L-band (ALOS-2/PALSAR-2) SAR data with key polarimetric features such as the C2 matrix, entropy, and degree of polarization, this study enhances SOC estimation. These parameters help distinguish variations in soil moisture, texture, and mineral composition, reducing their confounding effects on SOC estimation. An ensemble model incorporating Random Forest (RF) and neural networks (NNs) was developed to capture the complex relationships between SAR data and SOC. The NN component effectively models complex non-linear relationships, while the RF model helps prevent overfitting. The proposed model achieved a correlation coefficient (r) of 0.64 and a root mean square error (RMSE) of 0.18, demonstrating its predictive capability. In summary, our results offer an efficient approach for enhanced SOC mapping in diverse agricultural landscapes, with ongoing work targeting challenges in data availability to facilitate large-scale SOC mapping. Full article

Public CVE feeds add tens of thousands of entries each year, overwhelming patch-management capacity. We model the CWE–CVE–CPE triad and, for each CWE, build count-weighted product co-exposure graphs by projecting CVE–CPE links. Because native graphs are highly fragmented, we estimate graph-distance box-counting dimensions component-wise on the fragmented graphs using greedy box covering on unweighted shortest paths, then assess significance on the largest component of reconnected graphs. Significance is evaluated against degree-preserving nulls, reporting null percentiles, a z-score–based p-value, and complementary KS checks. We further characterise meso-scale organisation via normalized rich-club coefficients and k-core structure. Additionally, we quantify percolation sensitivity on the reconnected graphs by contrasting targeted removals with random failures for budgets of 1%, 5%, 10%, and 20%. This quantification involves tracking changes in largest-component size, average shortest-path length on the LCC, and global efficiency, and an amplification factor at 10%. Our corpus covers the MITRE CWE Top 25; we report high-level summaries for all 25 and perform the deepest null-model and sensitivity analyses on a subset of 12 CWEs selected on the basis of CVE volume. This links self-similar topology on native fragments with rich-club/core organisation and disruption sensitivity on reconnections, yielding actionable, vendor/software-type-aware mitigation cues. Structural indices are used descriptively to surface topological hotspots within CWE-conditioned product networks and are interpreted alongside, not in place of, EPSS/KEV/CVSS severity metrics. Full article

Medicinal plants have long been used for therapeutic purposes in the mountainous Hakkâri region of southeastern Türkiye. This study presents an integrated toxicological risk and therapeutic assessment of four ethnomedicinal species—Daphne mucronata Royle, Ferula communis L., Heracleum persicum Desf., and Tragopogon coloratus C.A.Mey—based on their flavonoid and phenolic composition, elemental content, and antioxidant capacity. To the best of our knowledge, this is the first study to integrate multiple analytical platforms—including HPLC, ICP-OES, AAS, UV-Vis spectrophotometry, and SEM/EDX—to assess both the therapeutic potential and toxicological risks of these ethnomedicinal species. Although a complete phytochemical profile was not the objective of this study, selected phenolic compounds and antioxidant capacity were evaluated to highlight bioactivity, while heavy metal-based risk assessment was prioritized given public health relevance. Antioxidant capacity was measured using DPPH, ABTS, and CUPRAC assays, while human health risks were quantified through Estimated Daily Consumption (EDC), Target Hazard Quotient (THQ), Hazard Index (HI), and Carcinogenic Risk (CR). The results revealed a dual nature: Heracleum persicum exhibited the strongest antioxidant activity, correlating with its high phenolic content, while Daphne mucronata showed elevated toxic metals exceeding WHO/FAO thresholds. Overall, the findings emphasize the importance of combining ethnobotanical knowledge with robust analytical tools for safe medicinal plant usage. Full article

Rainfall deficits and erratic dry spells pose major challenges in rainfed ecosystem. In-situ moisture conservation practices (MCP) like ridge–furrow methods, improve soil moisture but are inadequate during 2–3 week dry spells at critical crop stages (flowering and maturity), leading to yield loss. Supplemental irrigation (SI) using an ex-situ rainwater harvesting (RWH) pond can mitigate these effects, but optimizing the pond design is challenging due to limited runoff and storage losses. This study aims to design RWH pond for small farm holders with a 1.0 ha area and evaluate its efficient use for SI during intermittent dry spells and critical crop stages. The design volume was estimated using the SCS-CN method based on daily rainfall data (1974–2010) for the northeast monsoon. A pond with a capacity of 487.5 m³, constructed for a 1 ha micro-watershed, was used to observe the runoff for design validation. The harvested runoff can be used as SI for a cultivable area of 0.4 ha, based on the watershed-to-cultivable area ratio. Statistical analysis of observed and estimated runoff data from 2011 to 2023 revealed a strong correlation (r = 0.87), confirming the pond design. Harvested rainwater, applied through micro-irrigation (rain gun) at a depth of 50 mm during moisture stress periods, significantly improved cotton productivity. The combined use of harvested rainwater and MCP increased yield in the range of 3.8 to 25.3%, improved rainwater use efficiency (1.52 to 3.13 kg ha⁻¹ mm⁻¹), and had a higher benefit-cost ratio (1.15 to 2.43) over a 13-year period. This study concludes that integrating in-situ MCP with ex-situ RWH with micro-irrigation significantly improves rainfed crop productivity in vertisols. Full article

In battery energy storage systems, accurately estimating battery capacity and state of health is crucial to ensure satisfactory operation and system efficiency and reliability. However, these tasks present particular challenges under irregular charge–discharge conditions, such as those encountered in renewable energy integration and electric vehicles, where heterogeneous cycling accelerates degradation. This study introduces a hybrid deep learning framework to address these challenges. It combines convolutional layers for localized feature extraction, bidirectional recurrent units for sequential learning and a temporal attention mechanism. The proposed hybrid deep learning model, termed CBATE-Net, uses ensemble averaging to improve stability and emphasizes degradation-critical intervals. The framework was evaluated using voltage, current and temperature signals from four benchmark lithium-ion cells across complete life cycles, as part of the NASA dataset. The results demonstrate that the proposed method can accurately track both smooth and abrupt capacity fade while maintaining stability near the end of the life cycle, an area in which conventional models often struggle. Integrating feature learning, temporal modelling and robustness enhancements in a unified design provides the framework with the ability to make accurate and interpretable predictions, making it suitable for deployment in real-world battery energy storage applications. Full article

Pavement performance prediction serves as a core basis for maintenance decision-making. Although numerous studies have been conducted, most focus on road segments and aggregate indicators such as IRI and PCI, with limited attention to the daily deterioration of individual distresses. Subject to the combined influence of multiple factors, pavement distress deterioration exhibits pronounced nonlinear and time-lag characteristics, making distress-level predictions prone to disturbances and highly uncertain. To address this challenge, this study investigates the distress-level deterioration of three representative distresses—transverse cracks, alligator cracks, and potholes—with causal analysis and uncertainty quantification. Based on two years of high-frequency road inspection data, a continuous tracking dataset comprising 164 distress sites and 9038 records was established using a three-step matching algorithm. Convergent cross mapping was applied to quantify the causal strength and lag days of environmental factors, which were subsequently embedded into an encoder–decoder framework to construct a BayesLSTM model. Monte Carlo Dropout was employed to approximate Bayesian inference, enabling probabilistic characterization of predictive uncertainty and the construction of prediction intervals. Results indicate that integrating causal and time-lag characteristics improves the model’s capacity to identify key drivers and anticipate deterioration inflection points. The proposed BayesLSTM achieved high predictive accuracy across all three distress types, with a prediction interval coverage of 100%, thereby enhancing the reliability of prediction by providing both deterministic results and interval estimates. These findings facilitate the identification of high-risk distresses and their underlying mechanisms, offering support for rational allocation of maintenance resources. Full article

Land use change strongly influences ecosystem carbon services. This study evaluates long-term variations in carbon storage resulting from land use transitions in the Southeast region of Vietnam during 1990–2020. The analysis uses ALOS (JAXA) land use data in combination with QGIS-based spatial analysis to estimate carbon stocks. Land use trajectories were classified according to their dominant driving processes (urbanization, restoration, succession, reclamation, and reverse succession) to assess how each process affects carbon storage. The results indicate that total carbon stock increased from 475 million tons in 1990 to 502 million tons in 2010, before declining to 462 million tons in 2020. Carbon loss was mainly caused by urban expansion and ecological degradation, while ecological succession and forest restoration only partially compensated for these losses. This study develops a spatial framework for analyzing land use trajectories based on natural and anthropogenic dynamics, reflecting the region’s current administrative boundaries to improve management relevance. These findings underscore the need for sustainable land management, controlled urbanization, and ecosystem restoration to maintain carbon sequestration capacity and support Vietnam’s net-zero emission goals. Full article

This study evaluates the estimation of Weibull distribution parameters (shape, k; scale, c) for wind speed modeling in wind energy potential assessments. Traditional empirical methods—Justus Moment Method (JEM), Power Density Method (PDM), Energy Pattern Factor Method (EPFM), Lysen Moment Method (LAM), and Standard Deviation Empirical Method (SEM)—are compared with advanced artificial intelligence optimization algorithms (AIOAs), including Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Sine Cosine Algorithm (SCA), Teaching-Learning-Based Optimization (TLBA), Grey Wolf Optimizer (GWA), Red Fox Algorithm (RFA), and Red Panda Optimization Algorithm (RPA). Using hourly wind speed data from Foça, Urla, Karaburun, and Çeşme in Turkey, the analysis demonstrates that AIOAs, particularly GA, GSA, SCA, TLBA, and GWA, outperform empirical methods, achieving low RMSE (0.0071) and high R² (0.9755). SEM and LAM perform competitively among empirical methods, while PDM and EPFM show higher errors, highlighting their limitations in complex wind speed distributions. The study also conducts a techno-economic analysis, assessing capacity factors, unit energy costs, and payback periods. Foça and Urla are identified as optimal investment sites due to high energy yields and economic efficiency, whereas Çeşme is unviable due to low production and long payback periods. This research provides a robust framework for Weibull parameter estimation, demonstrating AIOAs’ superior accuracy and offering a decision-support tool for sustainable wind energy investments. Full article

Water and soil resources (WSRs) determine the healthy development of the socio-economic systems. This research seeks to clarify the spatiotemporal evolution characteristics, spatial spillover effects, and key constraint factors influencing the comprehensive carrying capacity (CCC) of WSR in the Yellow River (YR) Basin from 2012 to 2023, thereby supporting the healthy development of the river basin. Based on the structural relationships among the internal elements of this system, the entropy method and an extensible cloud model are employed in this study to evaluate the WSR-CCC. Based on the estimation theory and spatial econometrics methods, the temporal and spatial evolution process of WSR-CCC was explored, and the obstructive factors were analyzed. We made the following discoveries: (1) The WSR-CCC demonstrates a fluctuating upward tendency, gradually moving from critical overload level IV to sustainable level II, but inter-provincial disparities expand. (2) The spatial pattern exhibits a gradient of higher levels in the western region, lower levels in the eastern region, stronger intensity in the northern region, and weaker intensity in the southern region, with weak spatial correlation. However, the spatial spillover effect is significant, with club convergence and the Matthew effect coexisting. (3) The obstacle factors exhibit a drive–influence–state three-stage dominant characteristic. The findings provide actionable insights for coordinating WSR optimization and ecological conservation. Full article

Tourism performance often depends on the joint provision of built (“hard”) and environmental (“green”) infrastructure, yet their combined effects are not well established. Using official data for Saudi Arabia’s 13 regions (2023–2024), this study constructs composite hard and green indices, estimates ordinary least squares models with heteroskedasticity-consistent inference, and probes spatial heterogeneity using geographically weighted regression (exploratory) alongside k-means/hierarchical clustering. Hard infrastructure is the strongest and most consistent correlate of overnight visitors and spending, whereas green infrastructure exhibits non-positive marginal effects over the observed range of hard capacity; a negative, statistically significant Hard × Green interaction indicates diminishing returns to greening as built capacity increases. Clustering differentiates metropolitan hubs from nature-oriented regions, underscoring place-specific policy needs. Practically, results support sequencing prioritizing foundational access and basic accommodation in under-served regions, quality upgrades and public-realm enhancement in mature centers, and targeted green interventions where marginal gains are greatest. Key limitations (cross-sectional design; coarse green metrics) motivate richer environmental indicators and longitudinal data to clarify dynamics and thresholds over time. Full article