Search Results (2,843)

The Loess Plateau has experienced persistent vegetation greening over the past two decades, yet this recovery has occurred under a concurrent intensification of atmospheric evaporative demand and drying. This raises a key land–atmosphere question: which hydroclimatic processes most strongly constrain greening, and where do vegetation responses shift across environmental regimes? To address this issue, we integrated spatiotemporal trend analysis, Geographical Convergent Cross Mapping (GCCM)-based directional attribution, and an interpretable machine-learning framework combining Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to diagnose the dominant controls and threshold-like response patterns of vegetation activity. Using 1 km kernel Normalized Difference Vegetation Index (kNDVI) and eight hydroclimatic variables during 2000–2019, we found that regionally averaged kNDVI increased from 0.099 in 2000 to 0.164 in 2019, with a significant trend of 0.003 year⁻¹, and greening trends covered 65.503% of the Loess Plateau. Over the same period, Vapor Pressure Deficit (VPD) increased from 0.142 to 0.275 kPa (+0.133 kPa), indicating that vegetation recovery did not occur under a more humid atmospheric background. GCCM results consistently showed stronger directional influence from hydroclimatic drivers to kNDVI than the reverse, with evaporation and thermal conditions, especially Tmin, emerging as the dominant constraints, followed by Tmax, VPD, and wind speed, whereas precipitation showed comparatively weaker recoverable influence. The tuned XGBoost model achieved strong out-of-sample performance (R² = 0.9611, RMSE = 0.0188, MAE = 0.0131), and SHAP revealed clear nonlinear thresholds: evaporation and Tmin shifted into persistently positive contribution regimes beyond 302 mm and −17.6 °C, respectively; Tmax became predominantly inhibitory beyond −1.9 °C, and Palmer Drought Severity Index (PDSI) exhibited a multi-stage non-monotonic transition around −0.7. These results provide a coherent evidence chain linking directional influence, relative contribution, and threshold boundaries, offering quantitative support for identifying climate-sensitive zones and restoration risk regimes under continued warming and rising atmospheric dryness. Full article

Background: Hemoglobin (Hb) and hematocrit (Hct) reference intervals (RIs) are critical for diagnosing hematological disorders. However, existing reference values often do not account for demographic and environmental variability. Particularly in countries with altitude gradients, such as Colombia, the absence of locally adjusted intervals may lead to the misclassification of anemia and polycythemia. Therefore, this study aims to establish sex-, age-, and altitude-specific reference intervals for Hb and Hct within the Colombian adult population via an indirect, big-data-based methodology. Methods: This retrospective cross-sectional study used 3.1 million Hb and Hct test results nationwide between 2022 and 2024. After applying the exclusion criteria, Hb data from 667,857 individuals and Hct data from 662,024 individuals were included. The population was stratified by sex, age, and altitude into <1100 m above sea level (m.a.s.l.), 1100–2000 m.a.s.l., and 2000–3000 m.a.s.l. Reference intervals (RIs) were estimated via the refineR algorithm, and the results were compared across altitude categories and against World Health Organization (WHO) anemia and polycythemia thresholds. Results: Hb and Hct concentrations increased with altitude in all sexes and age groups. Compared with women, men presented higher mean values and narrower RIs, whereas older adults presented greater variability. Compared with WHO thresholds, a significant proportion of individuals living above 2000 m exceeded polycythemia cutoffs without clinical evidence of disease, suggesting the need for altitude-adjusted diagnostic criteria. Conclusions: This study provides the first large-scale, data-driven reference intervals for Hb and Hct in Colombia, adjusted for altitude, sex, and age. The implementation of locally derived RIs may improve diagnostic accuracy and prevent the over- or underdiagnosis of hematological disorders, with direct implications for clinical decision-making and public health policy. Full article

Marine energy technologies offer renewable alternatives to conventional energy sources by harnessing ocean-based resources such as wave motion, tides, temperature, and salinity gradients. They are particularly promising for coastal and island regions. This paper presents a literature-based assessment of the technical potential and limitations of these resources, with a focus on the Adriatic Sea as a model for low-energy, semi-enclosed basins. Resource availability and technological maturity are systematically reviewed. Results indicate that wave energy offers the highest regional potential, with peak annual mean wave power reachig up to 2.784 kW/m near the southern offshore regions of the Adriatic. However, current resource levels limit feasibility to down-scaled, modular installations. Tidal and thermal energy are constrained by the Adriatic’s microtidal regime and limited temperature gradients. Although still in early development, salinity gradient systems may become viable near major river mouths such as those of the Po and Neretva. In addition to technical analysis, broad environmental and socio-economic considerations are reviewed to inform responsible marine energy development. These findings help define strategic development and research priorities for marine renewables in enclosed seas and other resource-constrained marine environments. Full article

Urban waterlogging has become a critical challenge to urban sustainability under the combined pressures of rapid urbanization and increasingly frequent extreme weather events. However, traditional predictive models struggle to achieve real-time, point-specific early warning effectively, primarily due to the interference of redundant high-dimensional data and the inability to handle severe data imbalance. This study proposes a lightweight and interpretable machine learning framework for real-time waterlogging hotspot prediction, based on a multi-dimensional feature space. Specifically, we implement a Lasso-based mechanism to distill 37 multi-source variables into five core determinants. This process effectively isolates dominant environmental drivers while filtering noise. To further overcome the recall bottleneck, we propose a Synthetic Minority Over-sampling Technique based on Weighted Distance and Cleaning (SMOTE-WDC) algorithm that incorporates weighted feature distances and density-based noise cleaning. Validating the framework on datasets from Shenzhen (2023–2024), we demonstrate that the integrated Gradient Boosting Decision Tree (GBDT) model integrated with this strategy achieves optimal performance using only five features, yielding an F1-score of 0.808 and an Area Under the Precision-Recall Curve (AUC-PR) of 0.895. Notably, a Recall of 0.882 is attained, representing a 4.6% improvement over the baseline. This study contributes a cost-effective, high-sensitivity approach to disaster risk reduction, advancing predictive urban waterlogging management. Full article

This study presents an improved methodology for assessing the environmental sensitivity of the host rock in Huashan art paintings. A hygroscopic experiment was first designed to determine the moisture diffusion coefficient of the rock mass preserving the Huashan rock paintings, as verified by hygroscopic kinetics. Additionally, variations in color difference values were simultaneously used to quantitatively evaluate moisture absorption characteristics. Subsequently, a finite element (FE) simulation was conducted to assess potential damage to the rock art system with respect to varying environmental conditions. Regarding the correlated functions with consideration of the influencing factors, the environmental sensitivity of the host rock in Huashan art paintings was clarified to illustrate the deterioration process resulting from the combined effects of temperature and humidity. It is found that the deformation gradient (F) and maximum tensile stress (σ_max) exhibit a linear relationship with ambient temperature (T_a), and an exponential relationship with heat transfer coefficient (h). The ambient humidity (H_en) and surface humidity exchange coefficient (f) primarily influence the water content of the rock mass. This insight into the host rock in Huashan art paintings provides a valuable approach to highlight the active role of environmental conditions and offers an additional methodology to understand the detachment of large superficial rock flakes and the granular disintegration of the rock. Full article

Pondweeds, an important component of macrophyte vegetation, are influenced by various ecological factors of the aquatic ecosystem. In turn, pondweeds affect the nutrient and sediment dynamics and provide food and shelter for other organisms. As different species have specific environmental preferences and tolerances, they can serve as indicators of the ecological status of water bodies. Here, the ecological preference of the seven most frequent pondweeds in Croatia (Potamogeton berchtoldii, P. crispus, P. lucens, P. natans, P. nodosus, P. perfoliatus and Stuckenia pectinata) for chemical and physical water parameters was studied using 218 vegetation relevés and the accompanying water parameters. CCA revealed the main environmental gradients described by six parameters (chemical oxygen demand, total nitrogen, total phosphorus, electrical conductivity, dissolved oxygen and pH), while ecological responses of the species were further explored by GAMs. Potamogeton berchtoldii, P. lucens, P. natans and P. perfoliatus prefer clean, oxygenated, oligo- to mesotrophic water, and P. crispus and S. pectinata thrived in eutrophic water with low oxygen levels, while P. nodosus is a widespread generalist. The results of this study explain the distribution patterns of Potamogeton and Stuckenia species in Croatia, and add to the general knowledge on their role as bioindicators. Full article

The insect threat to heritage objects can increase with climate change, increased travel, movement of goods and loan exhibitions. This study used catch from 30 heritage environments across Austria. Overall arthropod catch rate in storerooms was lower than in museums and libraries. Taxonomic richness of the ecosystem in the buildings was a product of building size, perhaps paralleling island biogeography. Heritage pests are distributed independently and follow environmental gradients, perhaps aligning with Henry Gleason’s continuum theory of ecological communities. Catch rates for some abundant pests are evenly distributed among buildings (e.g., Psocoptera booklice, Lepisma saccharinum common silverfish), but Tineola bisselliella, the webbing clothes moth, is unevenly distributed because some locations have large infestations. Rare species are unevenly distributed, as these are found in only a few buildings. A characteristic set of insect pests appear to dominate indoor heritage environments in Austria: Psocoptera, Lepismatidae silverfish, Tineola bisselliella webbing clothes moth and carpet beetles like Anthrenus spp. and Attagenus spp. These pests are also common in the interiors of heritage buildings in some other European countries. Full article

The Urban Heat Island (UHI) effect represents a major barrier to sustainable urban development, amplifying energy demand, public health risks, and climate vulnerability. This study provides an advanced geospatial assessment of UHI dynamics in Southampton, UK, using Landsat 8 and 9 imagery (2017–2023) to evaluate seasonal and interannual variations relevant to climate-resilient urban planning. This study integrates spatial techniques, including Land Surface Temperature estimation, NDVI-based emissivity modelling, hotspot analysis, and urban–rural gradient profiling, to identify persistent UHI hotspots concentrated in high-density commercial and industrial zones, with intensities reaching 2–3 °C above the citywide mean. It combines seasonal UHI mapping, hotspot analysis, and urban–rural gradient profiling to provide a comprehensive assessment of Southampton’s thermal landscape. The findings reveal persistent UHI hotspots in the city centre and industrial zones, with intensity peaks of 2–3 °C above the mean. Temporal analysis reveals winter-intensified UHI patterns, consistent with climate-sensitive processes observed in temperate coastal environments. Green spaces demonstrate measurable cooling benefits (up to ~1 °C), underscoring their role as sustainable nature-based mitigation strategies. By delivering a replicable, data-driven framework for continuous environmental monitoring, the research directly supports sustainable urban design, targeted greening interventions, and climate-adaptation policies. The findings provide practical tools for reducing heat stress, enhancing energy efficiency, and strengthening long-term urban resilience in medium-sized coastal cities. Full article

Accurate prediction of contact temperature in disconnectors is critical for early fault detection. However, purely physics-based models face difficulties in parameter identification, while purely data-driven models often suffer from error accumulation in long-term forecasting. To address these challenges, this paper proposes a novel framework named Hybrid Physics-Informed Long Short-Term Memory (Hybrid-PI-LSTM). Firstly, this paper mathematically formulates the transient heat transfer process as a constrained optimization problem governed by a nonlinear ordinary differential equation (ODE), embedding physical laws into the loss function as a regularization term to promote dynamic consistency. Secondly, to address the inverse problem of parameter drift caused by environmental changes, an Adaptive Parameter Updating (APU) mechanism is introduced. This algorithm utilizes a gradient-based iterative approach to dynamically estimate equivalent physical coefficients (e.g., heat capacity) from observational residuals during inference. Finally, numerical experiments on a real-world dataset demonstrate that the proposed framework significantly outperforms baseline models. Specifically, it achieves a Root Mean Squared Error (RMSE) of 0.283 at a 720-step forecasting horizon, reducing the prediction error by over 35% compared to static-parameter physical models. The results indicate that the proposed adaptive constraint mechanism contributes to enhanced long-term numerical stability and physics-guided parameter tracking. Full article

Freshwater ostracods have considerable potential as indicators of environmental conditions, yet their ecology remains poorly documented in many large river floodplains of Southeast Europe. This study examines samples collected from ten aquatic habitats located along the Danube floodplain in Serbia’s Banat and Podunavlje regions. Monthly sampling was conducted over a twelve-month period (July 2023–June 2024), with concurrent measurements of water temperature, pH, dissolved oxygen, electrical conductivity, and turbidity. Ostracods were recorded at seven sites, yielding 19 taxa belonging to 13 genera and four families within all three non-marine superfamilies of Podocopida. Eight recorded taxa represent new additions to the Serbian fauna. Species richness was highest in semi-isolated floodplain habitats. Canonical correspondence analysis (CCA) showed that seasonal environmental variation, especially water temperature, turbidity, and conductivity, strongly structured assemblages. Hierarchical cluster analysis (UPGMA) grouped samples primarily by species composition, with seasonality exerting a strong secondary influence. Seasonal patterns revealed pronounced interspecific differences in temporal persistence and ecological tolerance of recorded species. Findings highlight the Danube floodplain’s role as a dispersal corridor, while also revealing that the river itself acts as a partial barrier, restricting faunal exchange to widespread, tolerant species. The results emphasize the importance of habitat heterogeneity and year-round sampling and support the integration of ostracods into long-term floodplain monitoring programs. Full article

Reinforcement learning (RL) has demonstrated considerable promise in the field of autonomous driving. However, decision-making processes in real-world environments are inevitably influenced by measurement noise and perception inaccuracies, which can result in suboptimal or even unsafe actions. To mitigate these risks, it is imperative for autonomous vehicles to model and account for such perception uncertainties effectively. This study focuses on the ramp merging scenario in autonomous driving, where environmental uncertainties are modeled as adversarial perturbations to the states observed by RL agents. We propose a novel adversarial attack framework that combines RL with momentum-based projected gradient descent (MPGD), aiming to simulate worst-case perception errors by perturbing the sensory inputs of the driving agent. Experimental evaluations across varying traffic densities and multiple RL algorithms for autonomous driving demonstrate that our approach outperforms three baseline adversarial attack strategies in simulating the worst-case perception errors. Additionally, adversarial training of the driving agent with our attack model significantly enhances the robustness of the autonomous vehicle, improving its performance in the presence of such worst-case perception uncertainties. Full article

Rapid search for evasive targets using multiple Unmanned Aerial Vehicles (UAVs) presents significant challenges, as it requires real-time target-motion prediction, multi-agent coordination, and adherence to kinematic constraints. Existing cooperative search methods often assume non-adversarial target behavior or model target motion independently of UAV actions, which reduces their effectiveness against targets that actively evade based on UAV positions. To address these limitations, this study introduces the Cooperative Rapid Search Algorithm for Evasive Targets (CRS-AET). The proposed framework utilizes graph-theoretic modeling to represent spatial-temporal relationships among UAVs, targets, and environmental grids. A directional gradient-based motion prediction (DG-Prediction) method first estimates probable movement areas of dynamic targets within the graph-structured environment. An improved multi-round auction algorithm with graph-based utility propagation (IMRAA) then optimizes UAV resource allocation. Finally, Dubins-Constrained Trajectory Optimization (DC-RTO) is integrated within a distributed model predictive control (DMPC) scheme to ensure kinematic feasibility. Simulation results across three representative scenarios indicate that CRS-AET enables faster target detection, enhanced area coverage, and more efficient coordination than baseline methods. Hardware-in-the-loop (HIL) experiments further confirm the robustness and practical applicability of the framework in realistic operational environments. Full article

Accurate deflection prediction is vital for structural health monitoring of large-span bridges yet remains challenging due to complex nonlinear environmental couplings. This paper proposes a hybrid deep learning framework, BGCO-PIC-DA-LSTM, for precise bridge deflection prediction. First, a Prior-Informed Correlation (PIC) strategy incorporating temperature lag terms is introduced to enhance the statistical consistency of input features. Second, a dual-stream residual Bi-LSTM network integrating adaptive temporal attention is developed to simultaneously capture long-term evolutionary trends and instantaneous dynamic fluctuations. Furthermore, a Bayesian-Gradient Cooperative Optimization (BGCO) strategy is employed to automatically configure optimal hyperparameters. Validation using in situ data from a large-span cable-stayed bridge demonstrates that the proposed method significantly outperforms baseline algorithms in prediction accuracy and robustness. Additionally, the prediction residuals exhibit characteristics approximating zero-mean Gaussian white noise, establishing a reference baseline for structural state evolution and providing a certain basis for identifying potential performance shifts. Full article

Afrotropical inland waters remain poorly studied for rotifer diversity. Here, we provide new distribution data from Botswana and connect these local patterns to continental-scale biogeography using an Africa–Europe occurrence dataset. In Botswana, we analyzed rotifer species richness, functional traits, and environmental drivers using 37 samples from 15 water bodies spanning natural and anthropogenic habitats. We recorded 107 rotifer taxa: 92 identified to species or subspecies level and 14 to genus. Seventy taxa (~65%) are new records for Botswana, and one species, Donneria sudzukii, is reported for the first time in Africa. Physicochemical gradients explained community structure, with the first two constrained RDA axes accounting for 40.7% and 23.7% of variation. Axis 1 captured a mineralization gradient linked to total dissolved solids and temperature, whereas Axis 2 reflected oxygen concentration and pH. Traits tracked these gradients: warmer, more mineralized waters were associated with specific trophi types, compact body shapes, and intermediate body sizes, whereas less mineralized, better oxygenated sites were related to smaller taxa and alternative feeding morphologies. To place these trait–environment relationships in a broader geographic context, we then analyzed an Africa–Europe dataset (67,170 records) to quantify latitudinal patterns in thermal classes and morphological traits (geometric body shape and trophi type). Diversity showed clear latitudinal structuring: warm-water genera clustered at low latitudes, only Kellicottia and Didymodactylos had mean distributions above 50° N, and bdelloid families were associated with higher latitudes. Morphological traits also varied with latitude, with trilateral truncated pyramid body shapes and malleoramate trophi occurring closest to the equator. Overall, by combining new species-level data from Botswana with continent-scale occurrence patterns, we link local community assembly to macroecological structure in rotifer functional and biogeographical organization. Full article

Marine ranching, as a pivotal strategy for enhancing the ocean’s carbon sequestration potential, offers significant potential to mitigate nearshore fishery depletion and restore marine ecosystems amid the global carbon neutrality agenda. However, the mechanistic pathways linking sediment total organic carbon (TOC) to various environmental factors in tropical marine ranches remain insufficiently quantified. This study selected the Wuzhizhou Island Marine Ranch in Hainan Province—a representative tropical marine ranch—as the research site. Field investigations and sampling were conducted during the dry (March 2024) and wet (September 2024) seasons to quantify TOC in surface sediments and associated environmental variables. A two-step analytical framework, integrating Principal Component Analysis (PCA) and Generalized Additive Models (GAM), was employed to elucidate the environmental drivers governing the spatiotemporal dynamics of TOC. The results show that the surface sediment TOC at Wuzhizhou Island Marine Ranch exhibits a distinct spatial gradient—Core Reef > Atoll > Control > Estuarine, and a pronounced seasonal pattern with elevated concentrations in the dry season relative to the wet season. The spatiotemporal differentiation of TOC is mainly driven by a gradient (explaining 52.1% of variation) that encompasses processes related to carbon accumulation from terrestrial inputs and primary production, as well as organic matter degradation promoted by nutrients and higher water temperatures. Sediment total nitrogen (TN) emerges as the primary environmental driver of TOC distribution, contributing up to 46.9% of the variance at an extremely significant level (p < 0.001). Furthermore, total phosphorus (TP), pH, and water temperature (WT) have relatively minor influences on the distribution of sedimentary TOC. Our study offers a crucial reference for elucidating the key processes governing the carbon cycle in tropical marine ranches and provides essential theoretical support for optimizing ocean carbon sink strategies in the context of global climate change. Full article