Search Results (46,322)

Data uncertainty and limited model generalization remain critical bottlenecks in large-scale remote sensing of soil salinization. Although the integration of multi-source data has improved predictive potential, conventional deterministic feature selection methods often overlook stochastic noise inherent in environmental variables, leading to models that overfit spurious correlations rather than learning stable physical signals. To address this limitation, this study proposes a Bootstrap–Boruta feature stability assessment framework that shifts feature selection from deterministic “feature importance” ranking to probabilistic “feature stability” evaluation, explicitly accounting for uncertainty induced by data perturbations. The proposed framework is evaluated by integrating stability-driven feature sets with multiple machine learning models, including a Back-Propagation Neural Network (BPNN) optimized using the Red-billed Blue Magpie Optimization (RBMO) algorithm as a representative optimization strategy. Using the Minqin Lake region as a case study, the results demonstrate that the stability-based framework effectively filters unstable noise features, reduces systematic estimation bias, and improves predictive robustness across different modeling approaches. Among the tested models, the RBMO-optimized BPNN achieved the highest accuracy. Under a rigorous bootstrap validation framework, the quality-controlled ensemble model yielded a robust mean R² of 0.657 ± 0.05 and an RMSE of 1.957 ± 0.289 dS/m. The framework further identifies eleven physically robust predictors, confirming the dominant diagnostic role of shortwave infrared (SWIR) indices in arid saline environments. Spatial mapping based on these stable features reveals that 30.7% of the study area is affected by varying degrees of soil salinization. Overall, this study provides a mechanism-driven, promising, within-region framework that enhances the reliability of remote-sensing-based soil salinity inversion under heterogeneous environmental conditions. Full article

Background: This study investigates the global burden of early-onset bladder cancer (EOBC) from 1990 to 2021, highlighting regional disparities and the growing role of metabolic risk factors. Early-onset bladder cancer (EOBC), diagnosed before age 50, is an emerging global health concern. While less common than kidney cancer, EOBC contributes substantially to mortality and disability-adjusted life years (DALYs), with marked sex disparities. Its global epidemiology remains unassessed systematically. Methods: Using GBD 1990–2021 data, we analyzed EOBC incidence, prevalence, mortality, and DALYs across 204 countries in individuals aged 15–49. Trends were examined via segmented regression, EAPC, and Bayesian age-period-cohort modeling. Inequality was quantified using SII and CI. Decomposition and SDI-efficiency frontier analyses were introduced. Results: From 1990 to 2021, EOBC incidence rose 62.2%, prevalence 73.1%, deaths 15.3%, and DALYs 15.8%. Middle-SDI regions bore the highest burden. Aging drove trends in high-SDI areas and population growth in low-SDI regions. Over 25% of high-SDI countries underperformed in incidence/prevalence control. Smoking remained the leading risk factor, with rising hyperglycemia burdens in high-income areas. Males carried over twice the female burden, peaking at age 45–49. Conclusions: EOBC shows sustained global growth with middle-aged concentration and significant regional disparities. Structural inefficiencies highlight the need for enhanced screening, early warning, and tailored resource allocation. Full article

While extant literature has thoroughly investigated carbon mitigation in grain production and agricultural infrastructure’s yield effects, significant knowledge gaps remain regarding their synergistic pathways for emission reduction. This empirical study examines how agricultural infrastructure contributes to carbon emission reduction in grain production across 30 Chinese provinces from 2009 to 2023. Using two-way fixed-effects and mediation-effect models, we demonstrate that agricultural infrastructure significantly inhibits carbon emissions intensity, with effects varying by type of infrastructure: agricultural water infrastructure, digital infrastructure, agricultural power infrastructure and rural transportation infrastructure, in descending order. We identify three key mechanisms: planting structure optimization, technological progress, and disaster incidence reduction. Specifically, agricultural water infrastructure and digital infrastructure operate through structural improvement and technological advancement, while agricultural water infrastructure and rural transportation infrastructure function through disaster mitigation. Heterogeneity analysis reveals distinct regional patterns: northern regions benefit more from agricultural water infrastructure and rural transportation infrastructure, while southern regions show stronger effects from agricultural water infrastructure and digital infrastructure. In major grain-producing areas, agricultural water infrastructure and agricultural power infrastructure demonstrate significant emissions reduction, whereas non-core production regions rely more on agricultural water infrastructure and digital infrastructure. Additionally, infrastructure generates greater yield-enhancing effects for rice and wheat versus corn. Policy implications include strengthening investments in agricultural water infrastructure, promoting digital agriculture, and developing region-specific infrastructure strategies. Full article

This study presents an analysis of the Infrastructure Leakage Index (ILI) variability for two District Metered Areas (DMAs) in the Silesian Region (Poland), based on 2024 data. The objective of the study was to evaluate whether high-frequency AMI data can be used to reliably identify and remove distorted measurement periods, thereby improving the credibility of the annual ILI value for each individual DMA. ILI_T values were calculated for daily, weekly, and monthly intervals using synchronized hourly data from an Advanced Metering Infrastructure (AMI) system and water network monitoring platforms. A key methodological advantage was the use of fully synchronous inflow–outflow–consumption data, enabling diagnostic reconstruction of hourly water balances and validation of the representativeness of data segments used for ILI_T estimation. The study applied statistical measures of variability (standard deviation, variance, coefficient of variation) and graphical methods (histograms, boxplots) to evaluate ILI_T behavior across time resolutions. Rather than comparing leakage performance between DMAs—which is performed exclusively using normalized indicators such as ILI—the analysis examined how hourly diagnostic information explains short-term distortions in the ILI and how filtering such periods affects the stability of the annual value for each DMAs. The results confirm that ILI_T interpretation is highly dependent on temporal resolution. Daily data is more responsive to anomalies and operational events, while monthly data provides more stable values suitable for benchmarking. The findings demonstrate that daily and hourly data should be used diagnostically to detect non-representative periods, whereas monthly aggregation provides the most robust basis for reporting and inter-DMA comparison. Overall, the study proposes a practical procedure for ILI validation using AMI data and demonstrates its application on two real DMAs. Full article

With the continuous proliferation of radio frequency devices, electromagnetic environments in various regions are becoming increasingly complex. Effective monitoring of the electromagnetic environment and identification of interference sources have thus become critical tasks for maintaining order in the electromagnetic spectrum. In recent years, rapid advances in UAV technology have spurred exploration of UAV-based electromagnetic spectrum monitoring as a novel approach. However, the limited payload capacity and endurance of UAVs constrain their monitoring capabilities. To address these challenges, we propose HMUDRL, a distributed heterogeneous multi-agent deep reinforcement learning algorithm. By leveraging cooperative operation between cluster-head UAVs (CH) and cluster-monitoring UAVs (CM) within a heterogeneous UAV swarm, HMUDRL enables high-precision detection and wide-area localization of UHF radiation source. Furthermore, we integrate a minimum-gap localization algorithm that exploits the spatial distribution of multiple CM to accurately pinpoint anomalous radiation sources. Simulation results validate the effectiveness of HMUDRL: in the later stages of training, the success rate of localizing target radiation sources converges to 96.1%, representing an average improvement of 1.8% over baseline algorithms; localization accuracy, measured by root mean square error (RMSE), is enhanced by approximately 87.3% compared to baselines; and communication overhead is reduced by more than 80% relative to homogeneous architectures. These results demonstrate that HMUDRL effectively addresses the challenges of data transmission control and sensing-localization performance faced by UAVs in UHF spectrum monitoring. Full article

Ecosystem health assessment is essential for informing ecological protection and sustainable management, yet current evaluation frameworks often overlook the foundational role of natural background conditions and struggle with methodological uncertainties in indicator weighting, particularly in ecologically fragile regions. To address these dual challenges, this study proposes a novel Base–Pressure–State–Response (BPSR) framework that systematically integrates key natural background factors as a fundamental “Base” layer. Focusing on the Qinling Mountains—a critical ecological barrier in China—we implemented this framework at the county scale using multi-source data (2000–2023) and introduced a Monte Carlo simulation with triangular probability distributions to quantify and synthesize weight uncertainties from multiple methods, thereby enhancing assessment robustness. Furthermore, the Geodetector method was employed to quantitatively identify the driving forces behind the spatiotemporal heterogeneity of ecosystem health. Supported by 3S technology, our analysis demonstrates a sustained improvement in ecosystem health: the composite index rose from 0.723 to 0.916, healthy areas expanded from 60.17% to 68.48%, and nearly half of the region achieved a higher health grade. Spatially, a persistent “low–south, high–north” pattern was observed, shaped by human disturbance gradients, while temporally, the region evolved from localized improvement (2000–2010) to broad-scale recovery (2010–2023), despite lingering degradation in human-dominated zones. Driving force analysis revealed a shift from early dominance by natural and land use factors to a later complex interplay where urbanization pressure and climatic conditions jointly shaped the health pattern. The BPSR framework, combined with probabilistic weight optimization and driving force quantification, offers a methodologically robust and spatially explicit tool that advances ecosystem health evaluation and supports targeted ecological governance, policy formulation, and sustainable management in fragile mountain ecosystems, with transferable insights for similar regions globally. Full article

Particulate matter is widely known as a significant air pollutant due to its proven detrimental impact on human health. Furthermore, ultrafine particles (UFPs) are those with diameters smaller than 100 nm, which can cause numerous serious health effects. Thus, identifying the sources of UFPs is essential for formulating effective mitigation strategies. Quantifying the contributions of particle sources can be performed by measuring particle number size distributions (PNSDs) for specific size ranges. This study was conducted in the city of Belgrade, the capital of Serbia, and one of the largest cities in the Balkans peninsula, which, within the European framework, belongs to a region and urban area characterized by high levels of atmospheric particulate matter pollution. In addition, there is a lack of studies addressing UFP levels and their sources in Serbia, including Belgrade. Several criteria pollutants were measured, together with the UFPs and equivalent black carbon (BC) at the urban background site in the city of Belgrade, Serbia, for the period from February to August 2024. The particle sources were analyzed using Positive Matrix Factorization (PMF) of PNSDs along with equivalent BC, PM₁₀, PM_2.5, O₃, SO₂, NO, NO₂ and NO_x. Seven source types were identified, characterized, and quantified, including two traffic sources (separated into traffic 1 and traffic 2), mixed traffic, an urban diffuse source, nucleation and nucleation growth sources, and a biomass burning source. Traffic-related sources were found to have the most significant contribution at around 40% of total particles emitted, followed by nucleation-related sources (24%) and biomass burning (20%). This is the first study performed in Serbia and Belgrade that addresses source apportionment of PNSD, for particles in the range 10–400 nm. Full article

Global environmental changes significantly alter ecosystem services (ESs), particularly in fragile regions like the Tibetan Plateau. While methodological advances have improved spatial assessment capabilities, understanding of how multiple drivers interact to shape ecosystem service heterogeneity remains limited to regional scales, especially across complex alpine landscapes. This study aims to clarify whether multi-factor interactions produce nonlinear enhancements in ES explanatory power and how these driver–response relationships vary across heterogeneous terrains. We quantified spatiotemporal patterns of four key ecosystem services—water yield (WY), soil conservation (SC), carbon sequestration (CS), and habitat quality (HQ)—across the southeastern Tibetan Plateau from 2000 to 2020 using multi-source remote sensing data and spatial econometric modeling. Our analysis reveals that SC increased by 0.43 t·hm⁻²·yr⁻¹, CS rose by 1.67 g·m⁻²·yr⁻¹, and HQ improved by 0.09 over this period, while WY decreased by 3.70 mm·yr⁻¹. ES variations are predominantly shaped by potent synergies, where interactive explanatory power consistently surpasses individual drivers. Hydrothermal coupling (precipitation ∩ potential evapotranspiration) reached 0.52 for WY and SC, while climate–vegetation synergy (precipitation ∩ normalized difference vegetation index) achieved 0.76 for CS. Such climate–restoration synergies now fundamentally shape the region’s ESs. Geographically weighted regression (GWR) further revealed distinct spatial dependencies, with southeastern regions experiencing strong negative effects of land use type and elevation on WY, while northwestern areas showed a positive elevation associated with WY but negative effects on SC and HQ. These findings highlight the critical importance of accounting for spatial non-stationarity in driver–ecosystem service relationships when designing conservation strategies for vulnerable alpine ecosystems. Full article

Over the past several centuries, many lakes in the Yunnan Plateau disappeared or are disappearing due to climate change and human activities; the developments of these lakes are thus crucial for understanding their evolutions and underlying causes. Here we present a near 260-year history of water area changes in Lake Zhehai, a disappearing lake in northeastern Yunnan of Southwest China, based on historical documents such as local and regional annals and gazetteers, water conservancy records, and old maps using GIS and remote sensing techniques, to identify the dominant drivers of the lake disappearing. Results show that the reconstructed water area of Lake Zhehai was ca. 1500, 710, 370, 340, and 110 ha in 1761, 1912, 1935, 1950, and 1975 AD; this indicates that Lake Zhehai experienced three-phase lake evolution over the past 260 years, i.e., large lake in 1761–1920 AD, shrinking lake in 1921–1980 AD, and disappearing lake since 1981. Significant changes in the water area of Lake Zhehai were mainly attributed to both climate change and human activities, especially human activities as dominant drivers during the last two phases of lake evolution. Our findings provide a reference for both understanding the driving mechanisms of large shallow lake evolution during historical times in Yunnan, as well as assessing strategies of lake environmental protection under global warming and increasing human activities. Full article

Understanding the spatiotemporal evolution of forest biomass energy potential is essential for supporting low-carbon land-use planning and regional energy transitions. China, characterized by pronounced spatial heterogeneity in forest resources and ecological conditions, provides an ideal case for examining how biophysical endowments and management factors shape biomass energy potential. This study constructs a province-level panel dataset for China covering the period from 1998 to 2018 and investigates long-term spatial patterns, regional disparities, and driving mechanisms using spatial visualization, Dagum Gini decomposition, and fixed-effects estimation. The results reveal a gradual spatial reorganization of forest biomass energy potential, with the national center of gravity shifting westward and northwestward, alongside a moderate dispersion of high-potential clusters from coastal areas toward the interior. Interregional transvariation is identified as the dominant source of regional inequality, indicating persistent structural differences among major regions. To explore future dynamics, a random forest model is employed to project provincial forest biomass energy potential from 2018 to 2028. The projections suggest moderate overall growth, smoother distributional structures, and a partial reduction in extreme provincial disparities. Central, southwestern, and northwestern provinces are expected to emerge as important contributors to future growth, reflecting ecological restoration efforts, expanding plantation forests, and improved forest management. The findings highlight a continued upward trend in national forest biomass energy potential, accompanied by a spatial shift toward inland regions and evolving regional disparities. This study provides empirical evidence to support region-specific development strategies, optimized spatial allocation of forest biomass resources, and integrated policies linking ecological sustainability with renewable energy development. Full article

Wildfires are increasing globally and pose major challenges for assessing post-fire vegetation recovery and ecosystem resilience. We analyzed long-term Landsat time series in two contrasting fire-prone ecosystems in the United States and Australia. Vegetation area was extracted using the Enhanced Vegetation Index (EVI) with Otsu thresholding. Recovery to pre-fire baseline levels was modeled using linear, logistic, locally estimated scatterplot smoothing (LOESS), and generalized additive models (GAM), and their performance was compared using multiple metrics. The results indicated rapid recovery of Australian forests to baseline levels, whereas this was not the case for forests in the United States. Among climatic factors, temperature was the dominant parameter in Australia (Spearman ρ = 0.513, p < 10⁻⁸), while no climatic variable significantly influenced recovery in California. Methodologically, GAM consistently performed best in both regions due to its success in capturing multiphase and heterogeneous recovery patterns, yielding the lowest values of AIC (United States: 142.89; Australia: 46.70) and RMSE_cv (United States: 112.86; Australia: 2.26). Linear and logistic models failed to capture complex recovery dynamics, whereas LOESS was highly sensitive to noise and unstable for long-term prediction. These findings indicate that post-fire recovery is inherently nonlinear and ecosystem-specific and that simple models are insufficient for accurate estimation, with GAM emerging as an appropriate method for assessing vegetation recovery using remote sensing data. This study provides a transferable approach using remote sensing and GAM to monitor forest resilience under accelerating global fire regimes. Full article

In Guangxi, China, the area used to plant sugarcane is growing in order to meet the Fourteenth Five-Year Plan’s objective of sugar self-sufficiency (2021–2025). Comprehensive soil heavy metal data are necessary for growing area expansion in order to inform farmers and policymakers. Here, we analyzed soil samples from ten sugarcane-growing counties/districts of Guangxi by employing four different risk assessment indices. Our results indicate that the studied soils are moderately to strongly acidic and are deficient in soil organic matter (<6 g/kg). Single-factor pollution index evaluation revealed detectable heavy metal pollution, with Cd present above reference levels in all ten areas, Cr in six, Pb in four, As in two, and Hg in two areas. The Nemerow comprehensive pollution index indicated that the overall soil pollution level was mild, except for Jiangzhou district (moderate). The geo-accumulation index revealed significant anthropogenic enrichment, with severe Cr pollution (Igeo > 3) across all regions and Pb and As contamination ranging from moderate to severe, particularly in Jiangzhou district. Contrastingly, Cd and Hg showed no significant enrichment (Igeo < 0) relative to the local background, though their sources require further investigation. The potential ecological risk assessment showed a high risk, specifically from As in Jiangzhou district, which was the only area showing a moderate comprehensive potential ecological risk. A significant positive correlation was found between the total and bioavailable contents of all five heavy metals, whereas soil pH and organic matter were significantly negatively correlated with the bioavailability of Cr and Pb, but positively correlated with As and Hg. The availability of Cd, however, was independent of pH and OM, suggesting the influence of other, unmeasured geochemical factors. These results highlight specific and localized environmental risks that may require targeted management to ensure agricultural safety, ecosystem health, and sustainable sugarcane production. Full article

River ice is a common natural phenomenon in cold regions during winter, and it is also one of the key factors that must be considered in the development and utilization of water resources in these areas. In this paper, based on a two-dimensional hydrodynamic model and ice dynamics model coupled with a linear thermodynamic process, this study simulates and validates the formation, decay, transport, and accumulation of river ice at the Toudaoguai reach of the Yellow River in Inner Mongolia during the winters of 2019–2020 and 2020–2021. The influence of different parameters on backwater level variations caused by ice jams is further investigated using a modified Morris sensitivity analysis method. The results show that (1) the coupled thermal-dynamic model can accurately simulate the formation, transport, and accumulation process of river ice in natural river, as well as the freeze-up patterns and corresponding hydraulic characteristics. (2) Due to the influence of river topography, flow rate, and flow density, the freeze-up form is slightly different in different years, and the low discharge process favor a more stable freeze-up. (3) According to the modified Morris screening method, discharge (Q) and ice concentration (N) are the most sensitive to the change in the backwater water level after the ice jam, and the sensitivity is more than 50%. The next most sensitive factor is the ice-cover roughness (n_i), whereas ice porosity (e_f) exhibits a negative sensitivity to the water level after ice jam. Thus, this study provides effective tools to reproduce the process of river ice transport and accumulation in the reach of the Yellow River (Inner Mongolia section) and offers technical support and insights for ice-flood prevention and mitigation in this section. Full article

Infrared spectroscopic analysis of palygorskite clay from the Dashkovskoye and Borshchevskoye deposits yielded key insights into the sedimentation conditions prevailing in the study area. In this paper, the composition, structure, and adsorption properties of smectite–palygorskite clays from the Steshevian sub-stage of the Lower Carboniferous (Russia) are investigated. The study applied X-ray diffraction, infrared spectroscopy, scanning electron microscopy, assessment of cation exchange capacity by adsorption of [Cu(trien)²⁺], assessment of Cs sorption, and particle size analysis. It is demonstrated that the Al–palygorskite of the Dashkovskoye deposit was formed by sedimentation from suspended matter in a shallow-water basin in the Steshevian sub-age, despite a different genesis (chemogenic in the case of the palygorskites, clastic/redeposited in the case of the smectites). The palygorskites of the Borschovskoye deposit have a complex terrigenous genesis and were formed from redeposited chemogenic Al–palygorskites transported into the basin from the surrounding region of the Dashkovskoye deposit. With increasing depth of the basin in the Steshevian sub-age, the volume of incoming palygorskite material decreases, and the proportion of smectite material increases. The Fe–palygorskites entered the Borschovskoye deposit due to the redeposition of sediments from soils upstream of water flows. All the studied clays have considerable adsorption properties (32–49 mg-eq/100 g) and can be used in various industries. Full article

Medical image segmentation acts as a foundational element of medical image analysis. Yet its accuracy is frequently limited by the scale fluctuations of anatomical targets and the intricate contextual traits inherent in medical images—including vaguely defined structural boundaries and irregular shape distributions. To tackle these constraints, we design a multi-scale feature attention network (MFA-Net), customized specifically for thyroid nodule, skin lesion, and breast lesion segmentation tasks. This network framework integrates three core components: a Bidirectional Feature Pyramid Network (Bi-FPN), a Slim-neck structure, and the Convolutional Block Attention Module (CBAM). CBAM steers the model to prioritize boundary regions while filtering out irrelevant information, which in turn enhances segmentation precision. Bi-FPN facilitates more robust fusion of multi-scale features via iterative integration of top-down and bottom-up feature maps, supported by lateral and vertical connection pathways. The Slim-neck design is constructed to simplify the network’s architecture while effectively merging multi-scale representations of both target and background areas, thus enhancing the model’s overall performance. Validation across four public datasets covering thyroid ultrasound (TNUI-2021, TN-SCUI 2020), dermoscopy (ISIC 2016), and breast ultrasound (BUSI) shows that our method outperforms state-of-the-art segmentation approaches, achieving Dice similarity coefficients of 0.955, 0.971, 0.976, and 0.846, respectively. Additionally, the model maintains a compact parameter count of just 3.05 million and delivers an extremely fast inference latency of 1.9 milliseconds—metrics that significantly outperform those of current leading segmentation techniques. In summary, the proposed framework demonstrates strong performance in thyroid, skin, and breast lesion segmentation, delivering an optimal trade-off between high accuracy and computational efficiency. Full article