Search Results (13,288)

The rapid urban expansion (UE) in the Yangtze River Economic Belt (YREB) in China has profoundly reshaped landscape patterns and ecosystem functions. Understanding the impact of UE on ecosystem health (EH) across different urban agglomerations is crucial for informing effective ecological governance and sustainability strategies. However, whether UE ultimately promotes or constrains EH across urban agglomerations under multi-scenario remains unclear. This study aims to address this gap by employing the Patch-generating Land Use Simulation model and the Vigor–Organization–Resilience–Service framework to simulate UE and EH in three major urban agglomerations of the YREB, while also examining the mechanisms through which UE influences EH. The results revealed substantial UE under all scenarios, with the Yangtze River Delta urban agglomerations exhibiting the most pronounced growth. The EH index showed a downward trend, from 0.621 in 2010 to 0.613 in 2020. Bivariate spatial autocorrelation and spatial regression analyses revealed a significant negative correlation between UE and EH. The study identified land fragmentation and occupation due to UE as the primary factors contributing to the deterioration of EH. The findings indicated the necessity of strategic urban planning to mitigate potential ecosystem risks while promoting sustainable urban development. Furthermore, regional cooperation is critical for addressing transboundary ecological challenges and ensuring the long-term sustainability and resilience of the YREB ecosystem. Full article

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease targeted by the World Health Organization (WHO) for global elimination as a public health problem. Sixteen Pacific Island countries and territories were historically endemic, and eight have now met the WHO criteria for elimination as a public health problem. Elimination as a public health problem does not imply zero transmission. Rather, it denotes that LF prevalence has been reduced below a defined threshold at which community transmission can be sustained. Following validation of elimination, the WHO recommends post-validation surveillance (PVS) to detect potential re-emergence of LF as a public health problem. However, implementing PVS is challenging in Small Island Developing States with dispersed populations, limited workforce capacity, resource constraints, and competing health priorities. The ‘Voices and Visions: Building Partnerships for Integrated Serosurveillance of LF and Other Infectious Diseases in the Pacific Islands’ meeting was held in Brisbane, Australia, from 8–10 July 2025. Fifty-one delegates, including Pacific LF programme managers, WHO representatives, global health partners, and academic researchers, reviewed regional PVS progress, discussed the newly released WHO guidelines for the implementation, monitoring, and evaluation of PVS, planned for PVS implementation, and explored novel multiplex bead assay (MBA) serological analysis methods to strengthen regional coordination for its development as a public health tool. Five broad themes emerged. First, the new WHO Monitoring and Epidemiological Assessment of Mass Drug Administration in the Global Programme to Eliminate Lymphatic Filariasis: A Manual for National Elimination Programmes, 2nd edn needs to be operationalised to meet decision-making needs across diverse Pacific settings. Second, integrating LF-PVS with existing surveys and health service activities could improve efficiency and long-term sustainability. Third, regional coordination and alignment of funding cycles will require high-level collaboration. Fourth, community engagement is essential to strengthen demand for PVS. Finally, while at an early stage and with further evidence needed, MBA laboratory methods hold promise for cost-effective, feasible integrated multi-pathogen serosurveillance. Full article

Maize/peanut intercropping is increasingly promoted as a climate-smart strategy for enhancing resource use efficiency and reducing environmental impacts in dryland cropping systems. However, its effects on multi-gas greenhouse emissions and yield-scaled climate performance remain insufficiently understood in semi-arid regions with sandy soil. Here, a two-year field experiment was conducted in western Liaoning, Northeast China, to quantify soil CO₂, CH₄, and N₂O fluxes, cumulative emissions, crop yield, global warming potential (GWP), and greenhouse gas intensity (GHGI) under sole maize (SM), sole peanut (SP), and two maize/peanut intercropping systems. SM produced the highest cumulative CO₂ emissions, whereas SP generated the highest CH₄ uptake and the highest N₂O emissions. Compared with peanut monoculture, maize/peanut intercropping significantly reduced soil N₂O emissions, indicating that the introduction of maize in the intercropping system provided an effective regulatory pathway for reducing N₂O emissions. Peanut yields declined by approximately 47.29–49.41%, leading to total land equivalent ratio (LER) values of 0.83–0.99. Although no significant land use advantage was observed for maize/peanut intercropping at the field scale, when crop yields were taken into account for assessment, the global warming potential (GWP) and greenhouse gas emission intensity (GHGI) were lower than those of monoculture uniformity. CO₂, CH₄ and N₂O fluxes were strongly correlated with soil temperature and moisture, underscoring the dominant role of microclimate rather than soil structure in regulating greenhouse gas (GHG) fluxes in monoculture, while in the intercropping system, the microclimate and the soil stucture together regulate the GHG fluxes. Overall, maize/peanut intercropping has the potential of reducing the climate cost per unit of production and represents a promising strategy for enhancing GHG mitigation potential in semi-arid agroecosystems. Full article

Wildfire monitoring in tropical regions requires robust frameworks capable of transforming heterogeneous satellite detections into consistent, event-level information suitable for decision support. This study presents the DescrEVE Fogo (Descrição de Eventos de Fogo) framework, a relational and scalable system that models daily fire events in Brazil by integrating Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections within a unified Structured Query Language (SQL)/PostGIS environment. The framework formalizes a mathematical and computational model that defines and tracks fire fronts and multi-day fire events based on explicit spatio-temporal rules and geometry-based operations. Using database-native functions, DescrEVE Fogo aggregates daily fronts into events and computes intrinsic and environmental descriptors, including duration, incremental area, Fire Radiative Power (FRP), number of fronts, rainless days, and fire risk. Applied to the 2003–2025 archive of the Brazilian National Institute for Space Research (INPE) Queimadas Program, the framework reveals that the integration of VIIRS increases the fraction of multi-front events and enhances detectability of larger and longer-lived events, while the overall regime remains dominated by small, short-lived occurrences. A simple, prototype fire-type rule distinguishes new isolated fire events, possible incipient wildfires, and wildfires, indicating that fewer than 10% of events account for more than 40% of the area proxy and nearly 60% of maximum FRP. For the 2025 operational year, daily ignition counts show strong temporal coherence with the Global Fire Emissions Database version 5 (GFEDv5), albeit with a systematic positive bias reflecting differences in sensors and event definitions. A case study of the 2020 Pantanal wildfire illustrates how front-level metrics and environmental indicators can be combined to characterize persistence, spread, and climatic coupling. Overall, the database-native design provides a transparent and reproducible basis for large-scale, near-real-time wildfire analysis in Brazil, while current limitations in sensor homogeneity, typology, and validation point to clear avenues for future refinement and operational integration. Full article

This study uses a globally coupled climate framework to examine how regional differences in sulfate emissions, through both direct and indirect aerosol effects, regulate interactions between clouds and radiation and drive nonlinear thermodynamic and hydrological responses in the East Asia and South Asia summer monsoon region. We employ the Community Earth System Model to compare the Shared Socioeconomic Pathways 1–2.6 and 5–8.5 against the historical scenario with perturbations of anthropogenic sulfate. The results reveal regional contrasts in sulfate concentration and aerosol optical depth: direct shortwave radiation increases in East Asia, while South Asia experiences radiation weakening due to higher aerosol optical depth. Indirect aerosol effects induce cloud adjustments, with East Asia developing more low clouds and higher cloud droplet number concentrations and liquid water paths, leading to greater attenuation of surface shortwave radiation and changes in precipitation and convection. Over the Tibetan Plateau, a higher fraction of high clouds and changes in cloud-top heights jointly drive warming, raising net radiation and strengthening both latent-heat and sensible-heat release. South Asia exhibits a north–south oriented precipitation pattern, with intensified warm advection but a distribution shaped by upper and mid-tropospheric circulations. Overall, the coupling of cloud macro-distribution and cloud microphysics emerges as the principal driver, with direct and indirect effects amplifying nonlinear regional responses. To improve predictability, we advocate multi-model comparisons, observational constraints, tighter bounds on cloud-droplet size distributions, liquid water paths, and cloud droplet number concentrations. Full article

During long-term service, concrete structures are exposed to various adverse factors, which often lead to the formation of numerous surface cracks. These cracks pose serious threats to structural safety and durability. Therefore, accurately identifying crack characteristics is essential for evaluating the service performance of concrete structures. A two-stage concrete crack segmentation method is presented in this study. The crack is initially located by the improved YOLOv11 that integrates three novel modules, namely Multi-scale Edge Information Enhancement, Efficient-Detection, and P2-Level Feature Integration, to form the MEP-YOLOv11 model. Then, the detected region is taken as input prompts for Segment Anything Model (SAM) to achieve precise crack segmentation. This approach eliminates the need for manual prompting in SAM, enabling automatic crack feature identification. The average Accuracy, precision, and Intersection over Union (IoU) for crack segmentation are 95.98%, 92.60%, and 0.77, respectively. To further enhance the robustness of the two-stage segmentation method under non-uniform illumination conditions, a mask re-input strategy is introduced. The crack mask generated by SAM using bounding-box prompts is fed back into SAM to guide a second round of segmentation. Experimental results demonstrate that the improved method maintains high segmentation performance, with an average Accuracy of 92.38%, precision of 85.70%, and IoU of 0.64. Overall, the proposed method meets engineering requirements for high-precision and efficient crack detection and segmentation, showing strong potential for practical inspection tasks. Full article

Cyclonic extreme events have recently undergone an important boost over the Mediterranean Sea, a trend closely linked to ongoing strong climate variations. Several studies are explaining the combination of many different effects that increase the frequency of mesoscale vortices’ intensification, namely Mediterranean tropical-like cyclones (TLCs), until the stage of Medicanes. Among these effects, processes like sea–atmosphere energy exchanges, baroclinic instability, and the release of latent heat lead to the intensification of these systems into fully tropical-like structures. This study investigates the formation and development of Ianos, the most intense Mediterranean tropical-like cyclone recorded in recent years, which affected the Ionian Sea and surrounding regions in September 2020. Using satellite observations and remote sensing data, the study applies a dual approach to characterise the system evolution across the spatial and temporal scales. Firstly, proper orthogonal decomposition (POD) is exploited to assess temperature and pressure fluctuations derived from the geostationary database of Meteosat Second Generation (MSG-11)/SEVIRI. POD allows for the identification of dominant modes of variability and the quantification of energy distribution across different spatial structures during the cyclone’s lifecycle. The decomposition reveals that a small number of orthogonal modes capture a significant proportion of the total variance, highlighting the emergence and persistence of coherent structures associated with the cyclone’s core and peripheral convection. To support scale-dependent energy organisation and dissipation within Ianos, total-period and three-period analyses were carried out, in addition to early-stage intensification patterns and implications for meteorological scale assessments. From the study on the temperatures’ spatio-temporal evolution, a comparison in the POD spectra and of the structures during the peak of intensity was carried out between the Ianos TLC and the Faraji and Freddy tropical cyclones. Additional multi-sensor data from Suomi NPP and Sentinel-3 satellites were integrated to analyse the evolution of the same parameters, also taking into account an evaluation of the vertical temperature gradient, over a 4-day period encompassing the full life cycle of Ianos. The study of the daily evolution helps investigate the spatial trends around the warm core regions, identifying the pressure minima for a comparison with the BOLAM and ERA5 databases of the mean sea level pressure. Overall, this study demonstrates the value of combining dynamic decomposition methods with high-resolution satellite datasets to gain insight into the multiscale structure and convective energetics of Mediterranean tropical-like cyclones. Some significant patterns come out from the spatial organisation of deep convection that seem to be linked to the permanent structures of atmospheric fluctuations near the warm core centre. Full article

Coal mining in plain regions and its related surface subsidence and geological hazards have been extensively studied, whereas research on mining-induced hazards in mountainous areas remains limited. This knowledge gap has contributed to the frequent occurrence of mining disasters, particularly under steep ridge-type mountain geometry, where deformation characteristics, large-scale slope failure risks, and mining-induced hazard mechanisms remain poorly understood. In this study, a mining area in Zhenxiong, Zhaotong, Yunnan Province, China, is investigated using SBAS-InSAR, GNSS observations, UAV surveys, optical satellite imagery, and detailed field investigations. Surface hazards triggered by coal extraction are identified, and the response relationship between surface subsidence and mining activities is analyzed to reveal the development mechanisms of surface deformation beneath steep ridge-type mountain geometry. The results show that: (1) deep coal mining can still induce significant surface deformation due to the combined amplification effects of steep slopes and lithological conditions; (2) mining-induced deformation does not necessarily evolve into large-scale slope collapse and may gradually stabilize through natural adjustment processes; (3) SBAS-InSAR, validated by GNSS and field observations, provides an effective approach for detecting mining-related subsidence; (4) surface deformation in the study area is jointly influenced by multiple working faces; and (5) strong coupling between the unique steep ridge-type mountain geometry and underlying coal extraction leads to a compound disaster chain under multi-source interactions. These findings offer a critical scientific understanding of mining-induced deformation beneath steep ridge-type mountain geometry and provide important guidance for geological hazard prevention and control in similar mountainous mining areas. Full article

The condition of technical surfaces strongly influences the functionality and lifetime of many components. In particular, the performance of aero-engines can be impaired by increased roughness of the turbine blade surfaces. In this work, an LED- and camera-based illumination sensor is presented for reflection-based characterisation of turbine blade surfaces, with a focus on rapid, wide-area assessment rather than direct roughness measurement. Traditional roughness measurements (e.g., profilometry, confocal microscopy) provide micrometre-scale height information but are limited in working distance and measurement volume, making complete surface coverage time-consuming. The proposed sensor acquires multi-illumination image data, from which an anisotropic BRDF (bidirectional reflectance distribution function) model is fitted on a per-pixel basis to obtain reflectance parameters. Independently, surface roughness parameters (Sa, Sq, Sz, Ssk, Sku) are measured using a confocal laser scanning microscope in accordance with ISO 25178 and used as reference data. Using two turbine blades with contrasting surface conditions (comparatively smooth vs. visibly rough), the study qualitatively investigates whether there are indications of relationships between BRDF model parameters and roughness characteristics. The results show weak relationships with height-based parameters (Sa, Sq, Sz), but clearer trends for distribution parameters (Ssk, Sku) and a good qualitative agreement between directional BRDF parameters and texture orientation. These findings indicate that the illumination sensor provides a complementary, reflectance-based approach for surface condition triage in MRO and QA contexts, highlighting regions that warrant more detailed roughness measurements. Extension of the approach to other component geometries and a comprehensive quantitative analysis of BRDF–roughness relationships are planned for follow-up studies. Full article

General-purpose large language models excel at open-domain question answering, but in railway operation and maintenance (O&M) scenarios they still suffer from hallucinated knowledge and poor domain adaptation. In practice, railway O&M knowledge mainly arises from two heterogeneous sources: spatio-temporal data such as train trajectories, which are organized along the spatial layout of railway lines, and domain documents such as operating rules, which exhibit varying degrees of structural regularity. Traditional retrieval-augmented generation (RAG) systems usually flatten these multi-source data into a single unstructured text space and perform global retrieval in one embedding space, which easily introduces noisy context and makes it difficult to precisely target knowledge for specific lines, sections, or equipment states. To overcome these limitations, we propose CARAG, a context-aware RAG framework tailored to railway O&M data. CARAG treats domain documents and spatial data as a unified knowledge substrate and builds a spatial knowledge graph with concept and instance levels. On top of this knowledge graph, a GraphReAct-based multi-turn interaction mechanism guides the LLM to reason and act over the concept knowledge graph, dynamically navigating to spatially and semantically relevant candidate regions, within which vector retrieval and instance-level graph retrieval are performed. Experiments show that CARAG significantly outperforms baseline RAG methods on RAGAS metrics, confirming the effectiveness of structure-guided multi-step reasoning for question answering over multi-source heterogeneous railway O&M data. Full article

The rapid growth of artificial intelligence-generated content (AIGC) has created serious challenges for image copyright protection, since semantic edits and deep-fake manipulations can easily erase or distort embedded watermarks. Traditional robust watermarking methods, which are mainly designed to resist pixel-level distortions such as noise, compression or filtering, often fail when faced with content-level transformations generated by AIGC models. This paper presents PatchSeal, a robust and intangible image watermarking framework that combines multi-targeted and attention-oriented embedding with a focus-oriented masking. The proposed framework introduces a segmentation-assisted embedding strategy that distributes watermark bits across several prominent regions to improve resilience to semantic changes. An attention-based module, composed of a subject extraction branch and a channel weighting branch, adapts to the encoder towards texture-rich and semantically stable regions, improving both invisibility and robustness. Experiments conducted in three public object data sets show that PatchSeal achieves an average PSNR of 43.13 dB and a bit precision of 92.98 percent under various AIGC editing conditions, surpassing representative methods such as MBRS and FIN. These results demonstrate the effectiveness of the proposed method in resisting AIGC-driven manipulations and provide new practical paths and methodological insights for the design of robust watermarks in the AIGC era. Full article

The Three-River Connectivity Zone in the lower Tarim River Basin (TRCZ) is a typical area that has experienced decades of river cut-off, followed by artificial ecological water transfers and vegetation restoration. However, the long-term patterns of ecological water requirements and their response mechanisms to ecosystem services in this region remain unclear. This study aims to quantify the spatiotemporal dynamics and driving factors of ecological water requirements in the TRCZ from 1990 to 2020. We integrated multi-temporal remote sensing land cover data with the FAO Penman–Monteith equation to estimate vegetation evapotranspiration (as a proxy for ecological water requirement) and coupled the InVEST model with Random Forest modeling to identify key climatic and hydrological drivers. Unlike previous studies that focused primarily on precipitation inputs, our approach explicitly considers the ecosystem’s water yield function alongside water demand, offering new insights into the constraints on ecosystem services. Key findings reveal: (1) During the period of 2005–2010, the land cover types underwent significant changes, characterized by a marked expansion of sparse forest (14–21%) and a pronounced decline in forest land, which fundamentally reconfigured the ecosystem’s water demand structure. (2) Accordingly, the multi-year average ecological water requirement quota in the study area is 2.95 × 10⁷ m³, and the total ecological water requirement exhibited a fluctuating decline at a rate of −1.39 × 10⁵ m³/yr, yet sparse forest persisted as the dominant water-consuming component. (3) The Random Forest model (R² = 0.942) identified water yield (importance: 0.527) and precipitation (0.255) as the primary drivers, establishing the ecosystem’s water yield function rather than precipitation input alone as the critical constraint. (4) A widespread increase in the unit area ecological water requirement across vegetation types signaled escalating pressures from climate change. This research provides a quantitative framework and a transferable methodology for adaptive water resource management and ecological restoration in arid regions, emphasizing the balance between ecosystem water demand and supply functions. Full article

Amid accelerating global urbanization, the Qinghai–Tibet Plateau, as a repository of multi-ethnic architectural heritage, plays a crucial role in preserving plateau cultural diversity and sustaining harmonious human–environment relationships. A critical research gap persists, however, in the systematic, comparable, and quantitative assessment of urban architectural character across plateau towns, particularly in high-altitude, ecologically sensitive, and multi-ethnic regions such as Haixi Mongol and Tibetan Autonomous Prefecture. This study takes the Haixi Mongol and Tibetan Autonomous Prefecture as a case to address the specific paradox between the homogenization of urban architectural styles and the erosion of cultural authenticity in plateau towns. We develop and apply an innovative three-dimensional evaluation model—encompassing natural substrate, built environment, and cultural context—to 22 towns. For the first time in research on this region, a chained methodological approach integrating descriptive statistics, principal component analysis (PCA), and cluster analysis is employed to systematically examine the spatial differentiation of architectural character. The analysis reveals three key findings. First, it delineates a regional composite landscape characterized by mountain-basin enclosures, seasonal arid rivers and lakes, small-scale towns with expansive layouts, and multi-ethnic cultural fusion. Second, it identifies a clear ternary differentiation in urban style dominance: nine towns are nature-dominated, nine are human-made (built environment) dominated, and only four are culture-dominated, quantitatively highlighting a significant weakness in the cultural dimension. Third, cluster analysis objectively classifies the towns into eight distinct character groups—for instance, Category I towns exhibit strong architectural regionalism and traditional continuity, whereas Category V towns integrate modern relics with adjacent mountain-water features. Methodologically, this study contributes by providing a replicable, chained quantitative framework that addresses a critical gap in comparative urban studies of high-altitude, underdeveloped regions. Empirically, it reveals the specific “nature > human-made > culture” dominance pattern in Haixi and offers a scientific foundation for formulating differentiated conservation and development strategies tailored to distinct town types in the ecologically fragile areas of western China. Full article

Multi-temporal remote sensing image cloud removal aims to reconstruct land surface information in regions obscured by clouds and their shadows, thereby mitigating a major constraint on the application of remote sensing imagery. However, existing multi-temporal deep learning methods for cloud removal often fail to model complex spatio-temporal dynamics, leading to suboptimal performance. To address this challenge, we propose a novel framework for multi-temporal cloud removal. In this architecture, the most critical component is the Spatio-Temporal Alternating Iterative Transformer (STAIT), which primarily consists of temporal and spatial attention mechanisms. STAIT is engineered to refine spatio-temporal feature representation by establishing an effective interplay between spatial details and temporal dynamics. Our framework is enhanced by an efficient image token generator with group convolution-based multi-level feature extraction to manage complexity, and a pixel reconstruction decoder with a shared progressive upsampling network to improve reconstruction by learning time-invariant features. Experimental results demonstrate that by explicitly modeling spatio-temporal feature dependencies, our approach achieves superior performance in restoring high-fidelity, cloud-free imagery. Full article

Accurate and real-time vehicle detection is a fundamental requirement for smart urban traffic monitoring, particularly in densely populated cities where heterogeneous traffic, frequent occlusion, and severe scale variation challenge lightweight vision systems deployed at the edge. To address these issues, this paper proposes BDNet, a lightweight YOLOv12-based vehicle detection framework designed to enhance feature preservation, contextual modeling, and multi-scale representation for intelligent transportation systems. BDNet integrates three complementary architectural components: (i) HyDASE, a hybrid detail-preserving downsampling module that mitigates information loss during resolution reduction; (ii) C3k2_MogaBlock, which strengthens long-range contextual interactions through multi-order gated aggregation; and (iii) an A2C2f_FRFN neck, which refines multi-scale features by suppressing redundancy and emphasizing discriminative responses. To support evaluation under realistic developing-region traffic conditions, we introduce the Bangladeshi Road Vehicle Dataset (BRVD), comprising 10,200 annotated images across 13 native vehicle categories captured under diverse urban scenarios, including daytime, nighttime, fog, and rain. On BRVD, BDNet achieves 85.9% ${m\mathcal{A}\mathcal{P}}_{50}$ and 67.3% ${m\mathcal{A}\mathcal{P}}_{50-95}$, outperforming YOLOv12n by +1.4 and +0.7 percentage points, respectively, while maintaining a compact footprint of 2.5 M parameters, 6.0 GFLOPs, and a real-time inference speed of 285.7 FPS. Cross-dataset evaluation on VisDrone-DET2019, using models trained exclusively on BRVD, further demonstrates improved generalization, achieving 31.9% ${m\mathcal{A}\mathcal{P}}_{50}$ and 17.9% ${m\mathcal{A}\mathcal{P}}_{50-95}$. These results indicate that BDNet provides an effective and resource-efficient vehicle detection solution for smart city–scale urban traffic monitoring. Full article