Search Results (3,492)

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

In the ecosystem, birds are important indicators that can sensitively reflect changes in the ecological environment and its health. However, bird monitoring has challenges due to species diversity, variable behaviors, and distinct morphological characteristics. Therefore, we propose a parallel dual-branch hybrid CNN–Transformer architecture for feature extraction that simultaneously captures local and global image features to address the “local feature similarity” issue in dual tasks of bird species and behaviors. The dual-task framework comprises three main components: the Token Re-segmentation Module (TRM), the Multi-scale Adaptive Module (MAM), and the Feature Interleaving Structure (FIS). The designed MAM fuses hybrid attention to address the problem of different-scale birds. MAM models the interdependencies between spatial and channel dimensions of features from different scales. It enables the model to adaptively choose scale-specific feature representations, accommodating inputs of different scales. In addition, we designed an efficient feature-sharing mechanism, called FIS, between parallel CNN branches. FIS interleaving delivers and fuses CNN feature maps across parallel layers, combining them with the features of the corresponding Transformer layer to share local and global information at different depths and promote deep feature fusion across parallel networks. Finally, we designed the TRM to address the challenge of visually similar but distinct bird species and of similar poses with distinct behaviors. TRM adopts a two-step approach: first, it locates discriminative regions, and then performs fine segmentation on them. This module enables the network to allocate relatively more attention to key areas while merging non-essential information and reducing interference from irrelevant details. Experiments on the self-made dataset demonstrate that, compared with state-of-the-art classification networks, the proposed network achieves the best performance, achieving 79.70% accuracy in bird species recognition, 76.21% in behavior recognition, and the best performance in dual-task recognition. Full article

Invasive alien plant species pose significant threats to biodiversity and the ecological functioning of ecosystems, necessitating targeted clearing strategies. This study investigated the short-term recovery of native vegetation following the control of Lantana camara and Chromolaena odorata in communal lands of Manzini, Eswatini. Nineteen sites were sampled across cleared and uncleared areas to assess changes in species diversity and veld condition. Cleared sites showed slightly reduced heterogeneity (D′ = 0.722) and higher diversity (H′ = 2.081) compared to uncleared sites (D′ = 0.732) and diversity (H′ = 2.032). Sites free from invasive alien plants had higher species richness (EXP (H′) = 35.693) than invaded sites (EXP (H′) = 28.237). Although statistical analyses showed no significant differences in stem counts between cleared and uncleared sites, effect sizes indicated potential practical significance for C. odorata. The Veld Condition Index (VCI) revealed high spatial variability with no consistent trend associated with clearing. Findings emphasise the complexity of early post-clearing dynamics and the importance of site-specific follow-up and monitoring. Full article

Landfills serve as a primary disposal method for municipal solid waste in China, with over 20,000 operational sites nationwide; however, long-term operations risk leachate leakage and groundwater contamination. Amid intensifying climate change and human activities, understanding contaminant evolution mechanisms in landfills has become critically urgent. Focusing on a representative plain-based landfill in North China, this study integrated field investigations and groundwater monitoring to establish a monthly coupled groundwater flow–solute transport model (using MODFLOW and MT3DMS codes) based on site-specific hydrogeological boundaries and multi-year monitoring data, analyzing spatiotemporal plume evolution under the coupled impacts of precipitation variability (climate change) and intensive groundwater extraction (human activities), spanning the historical period (2021–2024) and future projections (2025–2040). Historical simulations demonstrated robust model performance with satisfactory calibration against observed water levels and chloride concentrations, revealing that the current contamination plume exhibits a distinct distribution beneath the site. Future projections indicate nonlinear concentration increases: in the plume core zone, concentrations rise with precipitation, whereas at the advancing front, concentrations escalate with extraction intensity. Spatially, high-risk zones (>200 mg/L) emerge earlier under wetter conditions—under the baseline scenario (S0), such zones form by 2033 and exceed site boundaries by 2037. Plume expansion scales positively with extraction intensity, reaching its maximum advancement and coverage under the high-extraction scenario. These findings demonstrate dual drivers—precipitation accelerates contaminant accumulation through enhanced leaching, while groundwater extraction promotes plume expansion via heightened hydraulic gradients. This work elucidates coupled climate–human activity impacts on landfill contamination mechanisms, proposing a transferable numerical modeling framework that provides a quantitative scientific basis for post-closure supervision, risk assessment, and regional groundwater protection strategies, thereby aligning with China’s Standard for Pollution Control on the Landfill Site of Municipal Solid Waste and the Zero-Waste City initiative. Full article

Atherosclerosis remains a leading cause of cardiovascular mortality despite advances in pharmacological and interventional therapies. Current treatment approaches face limitations including systemic side effects, inadequate local drug delivery, and restenosis following vascular interventions. Gel-based technologies offer unique advantages through tunable mechanical properties, controlled degradation kinetics, high drug-loading capacity, and potential for stimuli-responsive therapeutic release. This review examines gel platforms across multiple scales and applications in atherosclerosis research and intervention. First, gel-based in vitro models are discussed. These include hydrogel matrices simulating plaque microenvironments, three-dimensional cellular culture platforms, and microfluidic organ-on-chip devices. These devices incorporate physiological flow to investigate disease mechanisms under controlled conditions. Second, therapeutic strategies are addressed through macroscopic gels for localized treatment. These encompass natural polymer-based, synthetic polymer-based, and composite formulations. Applications include stent coatings, adventitial injections, and catheter-delivered depots. Natural polymers often possess intrinsic biological activities including anti-inflammatory and immunomodulatory properties that may contribute to therapeutic effects. Third, nano- and microgels for systemic delivery are examined. These include polymer-based nanogels with stimuli-responsive drug release responding to oxidative stress, pH changes, and enzymatic activity characteristic of atherosclerotic lesions. Inorganic–organic composite nanogels incorporating paramagnetic contrast agents enable theranostic applications by combining therapy with imaging-guided treatment monitoring. Current challenges include manufacturing consistency, mechanical stability under physiological flow, long-term safety assessment, and regulatory pathway definition. Future opportunities are discussed in multi-functional integration, artificial intelligence-guided design, personalized formulations, and biomimetic approaches. Gel technologies demonstrate substantial potential to advance atherosclerosis management through improved spatial and temporal control over therapeutic interventions. Full article

The ESTIMET (Enhanced and Spatial-Temporal Improvement of MODIS EvapoTranspiration algorithm) model provides continuous, spatially distributed daily ET, essential for model calibration in data-scarce environments where conventional hydrological monitoring is unavailable. The challenge of applying SWAT in arid regions without ground observations, this study proposes a remote-sensing-based calibration approach using ESTIMET to overcome data scarcity. Daily satellite-derived evapotranspiration (ET) data to assess the performance of the Soil and Water Assessment Tool (SWAT) was used to evaluate the performance of the SWAT in a desertified watershed in Brazil, aiming to assess ESTIMET’s effectiveness in supporting SWAT calibration, quantify sediment yield, and examine the influence of land-use changes on environmental quality over 21-years period. The results highlight a distinct hydrological response in SWAT initially underestimated ET, contrasting with patterns typically observed in other semi-arid applications and demonstrating that desertified environments require distinct calibration strategies. Performance indicators showed strong agreement between observed and simulated ET (R² = 0.94; NSE = 0.76), supporting satellite-based ET as a valuable source for improving SWAT performance in watersheds where empirical hydrometeorological data are sparse or unevenly distributed. Sediment yield was generally low to moderate, with degradation concentrated in bare-soil areas associated with deforestation. Full article

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments. Full article

The causes of agricultural drought are complex, and its actual occurrence process is often characterized by rapid onset in terms of time and small scale in terms of space. Monitoring agricultural drought using satellite remote sensing with low spatial resolution makes it difficult to accurately capture the details of small-scale drought events. High-resolution satellite remote sensing has relatively long revisit cycles, making it difficult to capture the rapid evolution of drought conditions. Furthermore, the occurrence of agricultural drought is linked to multiple factors including precipitation, evapotranspiration, soil properties, and crop physiological characteristics. Consequently, relying on a single variable or indicator is insufficient for multidimensional monitoring of agricultural drought. This study takes Hebi City, Henan Province as the research area. It uses Sentinel-1 satellite data (HV, VV), Sentinel-2 data (NDVI, B2, B11), elevation, slope, aspect, and GPM precipitation data from 2019 to 2024 as independent variables. Three machine learning algorithms—Random Forest (RF), Random Forest-Recursive Feature Elimination (RF-RFE), and eXtreme Gradient Boosting (XGBoost)—were employed to construct a multi-dimensional agricultural drought monitoring model at the field scale. Additionally, the study verified the sensitivity of different environmental variables to agricultural drought monitoring and analyzed the accuracy performance of different machine learning algorithms in agricultural drought monitoring. The research results indicate that under the condition of full-factor input, all three models exhibit the optimal predictive performance. Among them, the XGBoost model performs the best, with the smallest Relative Root Mean Square Error (RRMSE) of 0.45 and the highest Correlation Coefficient (R) of 0.79. The absence of Digital Elevation Model (DEM) data impairs the models’ ability to capture the patterns of key features, which in turn leads to a reduction in predictive accuracy. Meanwhile, there is a significant correlation between model performance and sample size. Ultimately, the constructed XGBoost model takes the lead with an accuracy of 89%, while the accuracies of Random Forest (RF) and Random Forest-Recursive Feature Elimination (RF-RFE) are 88% and 86%, respectively. Based on these three drought monitoring models, this study further monitored a drought event that occurred in Hebi City in 2023, presented the spatiotemporal distribution of agricultural drought in Hebi City, and applied the Mann–Kendall test for time series analysis, aiming to identify the abrupt change process of agricultural drought. Meanwhile, on the basis of the research results, the feasibility of verifying drought occurrence using irrigation signals was discussed, and the potential reasons for the significantly lower drought occurrence probability in the western mountainous areas of the study region were analyzed. Full article

Healthcare has been fundamentally changed by the expansion of IoT, which enables advanced diagnostics and continuous monitoring of patients outside clinical settings. Frequently interconnected medical devices often encounter resource limitations and lack comprehensive security safeguards. Therefore, such devices are prone to intrusions, with DDoS attacks in particular threatening the integrity of vital infrastructure. To safe guard sensitive patient information and ensure the integrity and confidentiality of medical devices, this article explores the critical importance of robust security measures in healthcare IoT systems. In order to detect DDoS attacks in healthcare networks supported by WBSN-enabled IoT devices, we propose a hybrid detection model. The model utilizes the advantages of Long Short-Term Memory (LSTM) networks for modeling temporal dependencies in network traffic and Convolutional Neural Networks (CNNs) for extracting spatial features. The effectiveness of the model is demonstrated by simulation results on the CICDDoS2019 datasets, which indicate a detection accuracy of 99% and a loss of 0.05%, respectively. The evaluation results highlight the capability of the hybrid model to reliably detect potential anomalies, showing superior performance over leading contemporary methods in healthcare environments. Full article

Invasions by alien plants are major global drivers of ecosystem changes and loss of biodiversity. Guangxi is an ecological barrier in southern China that is increasingly being affected by invasive alien plant species. We comprehensively reviewed the literature, compiling and analyzing the long-term changes in species composition, native range, life forms, municipal-scale patterns, and correlates of invasive alien plant richness in Guangxi at three time points (1973, 2010, and 2023). Over the 50-year period, the number of invasive alien plant species markedly increased from 31 species in 1973 to 84 in 2010 and 158 in 2023; the number of families, genera, and species increased 2.05-, 3.75-, and 5.10-fold, respectively. Species native to North America consistently dominated the invasive flora, followed by those native to Africa. The number of species native to South America and Asia increased in the records from 2010 to 2023. Annual herbaceous plants accounted for the largest proportion of invasive species throughout the study period and showed the largest absolute increase in species number. However, no substantial temporal shifts in the overall life-form composition were detected. At the municipal scale, the invasive alien plant richness exhibited pronounced spatial heterogeneity. The invasive alien plant richness was highest in Guilin and Baise in 1973, in Guilin in 2023, followed by Nanning and Baise. Correlation analyses based on 2023 data revealed a significant positive association between invasive alien plant richness and tourism intensity, whereas relationships between population size, gross domestic product, and climatic variables were weak or nonsignificant. Overall, our results document the continued expansion and the spatial differentiation of invasive alien plants in Guangxi over the 50-year period of 1973–2023. These patterns primarily reflect the accumulation in the number of recorded invasive species under a consistent classification framework and should be interpreted with caution given the potential variation in survey effort among periods and cities. The results provide a descriptive baseline for the provincial-scale monitoring, risk assessment, and management of invasive alien plants. Full article

Mountain Excavation and Land Creation Projects (MELCPs) have emerged as a critical strategy for expanding urban development space in mountainous regions facing land scarcity. Dynamic monitoring and risk management of these projects are essential for promoting sustainable urban development. This study develops an integrated monitoring framework for MELCPs by combining ascending and descending Sentinel-1 SAR data, Sentinel-2 optical imagery, SRTM digital elevation models (DEM), and field survey data. The framework incorporates multi-temporal change detection, random forest classification, and time-series InSAR analysis to systematically capture the spatiotemporal evolution and subsidence mechanisms associated with MELCPs. Key findings include: (1) The use of dual-orbit SAR data significantly improves the detection accuracy of excavation areas, achieving an overall accuracy of 87.1% (Kappa = 0.85) and effectively overcoming observation limitations imposed by complex terrain. (2) By optimizing the combination of spectral, texture, topographic, and polarimetric features using a random forest algorithm, the classification accuracy of MELCPs is enhanced to 91.2% (Kappa = 0.889). This enables precise annual identification of MELCP progression from 2017 to 2022, revealing a three-stage evolution pattern: concentrated expansion, peak activity, and restricted slowdown. Specifically, the reclaimed area increased from 2.66 km² (pre-2018) to a peak of 12.61 km² in 2021, accounting for 34.56% of the total area of the study region, before decreasing to 2.69 km² in 2022. (3) InSAR monitoring from 2017 to 2023 indicates that areas with only filling experience minor shallow subsidence (<50 mm), whereas subsequent building loads and underground engineering activities lead to continuous deep soil consolidation, with maximum cumulative subsidence reaching 333.8 mm. This study demonstrates that subsidence in MELCPs follows distinct spatiotemporal patterns and is predictable, offering important theoretical insights and practical tools for engineering safety management and territorial spatial optimization in mountainous cities. Full article

Efficient and reliable road damage detection is a critical component of intelligent transportation and infrastructure control systems that rely on visual sensing technologies. Existing road damage detection models are facing challenges such as missed detection of fine cracks, poor adaptability to lighting changes, and false positives under complex backgrounds. In this study, we propose an enhanced YOLO-based framework, YOLO-ERCD, designed to improve the accuracy and robustness of sensor-acquired image data for road crack detection. The datasets used in this work were collected from vehicle-mounted and traffic surveillance camera sensors, representing typical visual sensing systems in automated road inspection. The proposed architecture integrates three key components: (1) a residual convolutional block attention module, which preserves original feature information through residual connections while strengthening spatial and channel feature representation; (2) a channel-wise adaptive gamma correction module that models the nonlinear response of the human visual system to light intensity, adaptively enhancing brightness details for improved robustness under diverse lighting conditions; (3) a visual focus noise modulation module that reduces background interference by selectively introducing noise, emphasizing damage-specific features. These three modules are specifically designed to address the limitations of YOLOv10 in feature representation, lighting adaptation, and background interference suppression, working synergistically to enhance the model’s detection accuracy and robustness, and closely aligning with the practical needs of road monitoring applications. Experimental results on both proprietary and public datasets demonstrate that YOLO-ERCD outperforms recent road damage detection models in accuracy and computational efficiency. The lightweight design also supports real-time deployment on edge sensing and control devices. These findings highlight the potential of integrating AI-based visual sensing and intelligent control, contributing to the development of robust, efficient, and perception-aware road monitoring systems. Full article

The rapid shrinkage of the climate-regulating cryosphere, driven by global warming and anthropogenic activities, underscores the urgency of understanding its impact on regional ecological vulnerability. This study develops a Sensitivity–Resilience–Pressure (SRP) model-based framework comprising 21 natural and socio-economic indicators, employs spatial autocorrelation and center of gravity migration to characterize spatiotemporal patterns in the Qilian Mountains region, and integrates Random Forests (RF) with Shapley Additive Explanations (SHAP) to identify key drivers. Results reveal a downward trend in the Ecological Vulnerability Index (EVI) from 2000 to 2020, with areas of very heavy vulnerability declining from 21.05% to 14.73%, indicating gradual ecological recovery. The study area exhibits moderate vulnerability, with the western region dominated by heavy and very heavy vulnerability, whereas the eastern region is characterized by potential and light vulnerability, indicating a high-west, low-east spatial pattern. A significant positive spatial autocorrelation is observed, revealing that areas with high vulnerability are highly clustered and primarily overlap with regions of high elevation and sparse vegetation. The RF–SHAP analysis demonstrates that natural factors dominate the EVI, with fractional vegetation cover, biological abundance, glacial meltwater volume, annual precipitation, and the landscape diversity index emerging as the main drivers, and the EVI changing sequentially as each indicator approaches its threshold: 0.16, 56.57, 2.23 mm, 400.73 mm, and 0.39. In conclusion, although ecological vulnerability in the Qilian Mountains has declined, future management strategies should leverage these threshold effects to implement precise, indicator-based monitoring and regulation. Full article

The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and supporting sustainable land management under increasing fire frequency. This study aims to develop a high-resolution detection framework specifically calibrated for Mediterranean environmental conditions, ensuring the production of consistent and accurate annual BA maps. Using Sentinel-2 MSI time series over Sardinia (Italy), the research objectives were to: (i) integrate field surveys with high-resolution photointerpretation to build a robust, locally tuned training dataset; (ii) evaluate the discriminative power of multi-temporal spectral indices; and (iii) implement a Random Forest classifier capable of providing higher spatial precision than current operational products. Validation results show a Dice Coefficient (DC) of 91.8%, significantly outperforming the EFFIS Burnt Area product (DC = 79.9%). The approach proved particularly effective in detecting small and rapidly recovering fires, often underrepresented in existing datasets. While inaccuracies persist due to cloud cover and landscape heterogeneity, this study demonstrates the effectiveness of a machine learning approach for long-term monitoring, for generating multi-year wildfire inventories, offering a vital tool for data-driven forest policy, vegetation recovery assessment and land-use change analysis in fire-prone regions. Full article

Remote sensing building change detection (RSBCD) is critical for land surface monitoring and understanding interactions between human activities and the ecological environment. However, existing deep learning-based RSBCD methods often result in mis-detected pixels concentrated around object boundaries, mainly due to ambiguous object shapes and complex spatial distributions. To address this problem, we propose a new Multiscale Edge-Aware change detection Network (MEANet) that accurately locates edge pixels of changed objects and enhances the separability between changed and unchanged pixels. Specifically, a high-resolution feature fusion network is adopted to preserve spatial details while integrating deep semantic information, and a multi-scale supervised contrastive loss (MSCL) is designed to jointly optimize pixel-level discrimination and embedding space separability. To further improve the handling of difficult samples, hard negative sampling is adopted in the contrastive learning process. We conduct comparative experiments on three benchmark datasets. Both Visual and quantitative results demonstrate that our new MEANet significantly reduces misclassified pixels at object boundaries and achieve superior detection accuracy compared to existing methods. Especially on the GZ-CD dataset, MEANet improves F1-Score and mIoU by more than 2% compared with ChangeFormer, demonstrating strong robustness in complex scenarios. It is worth noting that the performance of MEANet may still be affected by extremely complex edge textures or highly blurred boundaries. Future work will focus on further improving robustness under such challenges and extending the method to broader RSBCD scenarios. Full article