Search Results (4,479)

The uneven distribution of populations within urban and rural built-up areas restricts balanced regional development. This study employs statistical data from 296 cities in China covering the years 2010, 2015, and 2020 to analyze the coupling coordination relationship of population density in urban and rural built-up land and its influencing factors. The findings reveal the following: (1) Throughout the study period, population density in urban built-up areas (PUL) experienced a slow increase, whereas population density in rural built-up areas (PRL) declined rapidly. (2) Spatially, high levels of coupling coordination in population density between urban and rural built-up areas are primarily concentrated in the southwestern region, particularly in Sichuan, demonstrating a trend of gradual diffusion from the core to the periphery. Overall, a southwest-high to northeast-low pattern emerged. (3) The regression results indicate that economic development, agricultural structure, public services, and urbanization significantly affect the coupling coordination of population densities in urban and rural built-up areas. Among natural conditions, both elevation and temperature show significantly positive effects. This research provides theoretical foundations and policy recommendations for promoting urban-rural integrated development and achieving regional sustainable development. Full article

CPVG (Utility-scale photovoltaic generation) is expanding rapidly worldwide, yet its cumulative ecological effects remain insufficiently quantified. This review synthesizes current evidence to clarify how CPVG influences ecosystems through linked mechanisms of energy redistribution, biogeochemical cycling disturbance, and ecological responses. CPVG alters surface radiation balance, modifies microclimate, and disrupts carbon–nitrogen–water fluxes, thereby driving vegetation shifts, soil degradation, and biodiversity decline. These impacts accumulate across temporal scales—from short-term construction disturbances to long-term operational feedbacks—and propagate spatially from local to regional and watershed levels. Ecological outcomes differ substantially among deserts, grasslands, and agroecosystems due to contrasting resilience and limiting factors. Based on these mechanisms, we propose a multi-scale cumulative impact assessment framework integrating indicator development, multi-source monitoring, coupled modelling, and ecological risk tiering. A full-chain mitigation pathway is further outlined, emphasizing optimized siting, disturbance reduction, adaptive management, and targeted restoration. This study provides a systematic foundation for evaluating and regulating CPVG’s cumulative ecological impacts, supporting more sustainable solar deployment. Full article

Cyclone Global Navigation Satellite System (CYGNSS) has emerged as an effective technique for inland water body detection due to its high sensitivity to inland waters. However, existing methods for inland water body detection using CYGNSS are limited by the difficulty in balancing high spatiotemporal resolution with strong generalization capability. Moreover, the limited spatial redundancy in short-term CYGNSS data restricts its capacity for high-precision inland water detection on its own. To address these issues, this study proposed a novel dual-branch model, termed STRUE. The model integrated a Swin Transformer and ResNet within a U-Net-enhanced student-teacher framework. This framework was developed through the fusion of multi-source data, including CYGNSS, SMAP, FABDEM, MODIS, and GSWE. The results showed that, for inland water body detection, the model attained a spatial resolution of 0.01° and a temporal resolution of 7 days. In terms of performance, it achieved an F1-score (F1) of 0.914, a mean Intersection over Union (mIoU) of 0.880, a Matthews Correlation Coefficient (MCC) of 0.873, and a Recall (R) of 0.963. Additionally, compared with traditional methods and models, the proposed model demonstrated a better performance in spatial continuity, structural integrity, and detail recovery, while mitigating common limitations such as cloud obscuration, spatial incoherence, and overestimation artifacts. These results further enhance the capacity of spaceborne GNSS-R for inland water body detection. Full article

Background: This study evaluates an additive manufacturing (AM) network designed to balance economic performance, lead time, and environmental impact within the healthcare logistics and supply chain. Methods: An integrated framework is proposed that identifies optimal AM facility locations using spatial K-means clustering and optimizes delivery routes through a multi-objective vehicle routing problem with time windows (MOVRPTW). This framework was applied to a case study in Phra Nakhon Si Ayutthaya, Thailand, utilizing hospital geocoordinates, demand profiles, and CO₂ emission factors to evaluate centralized versus decentralized network configurations. Results: Findings demonstrate that hub structures derived from K-means clustering achieve the highest economic efficiency, reducing the AM part cost per unit to 698.51 Baht. In contrast, a fully centralized network resulted in a significantly higher unit cost of 4759.79 Baht, while clustering based on hospital types yielded a unit cost of 959.34 Baht. Quantitative results indicate that the multi-objective approach provides a superior trade-off, achieving lead time requirements while maintaining operational costs and emissions. Conclusions: The results indicate that the proposed framework, particularly through spatial clustering, offers a practical decision-support tool for designing AM networks that achieve a balance between operational efficiency and sustainability objectives in healthcare logistics. Full article

Industrial Control Systems (ICSs), critical to infrastructure, face escalating cyber threats under Industry 4.0, yet existing intrusion detection methods are hindered by attack sample scarcity, spatiotemporal heterogeneity of industrial protocols, and resource constraints of embedded devices. This paper proposes a four-stage closed-loop intrusion detection framework for ICSs, with its core innovations integrating the following key components: First, a protocol-conditioned Conditional Generative Adversarial Network (CTGAN) is designed to synthesize realistic attack traffic by enforcing industrial protocol constraints and validating syntax through dual-path discriminators, ensuring generated traffic adheres to protocol specifications. Second, a three-tiered sliding window encoder transforms raw network flows into structured RGB images, capturing protocol syntax, device states, and temporal autocorrelation to enable multiresolution spatiotemporal analysis. Third, an Efficient Multiscale Attention Visual State Space Model (EMA-VSSM) is developed by integrating gate-enhanced state-space layers with multiscale attention mechanisms and contrastive learning, enhancing threat detection through improved long-range dependency modeling and spatial–temporal correlation capture. Finally, a lightweight EMA-VSSM student model, developed via hierarchical distillation, achieves a model compression rate of 64.8% and an inference efficiency enhancement of approximately 30% relative to the original model. Experimental results on a real-world ICS dataset demonstrate that this lightweight model attains an accuracy of 98.20% with a False Negative Rate (FNR) of 0.0316, outperforming state-of-the-art baseline methods such as XGBoost and Swin Transformer. By effectively balancing protocol compliance, multi-resolution feature extraction, and computational efficiency, this framework enables real-time deployment on resource-constrained ICS controllers. Full article

Alpacas thrive in Andean ecosystems, efficiently converting natural pasture into products such as fiber and meat, making their breeding a production alternative in Guamote. Intensive grazing and the shift in the spatial distribution of plants due to climate change negatively impact the moorlands. In this context, this study analyzed the influence of floristic composition on the productivity and quality of natural pastures. The methodology included a floristic inventory in a sample of 98 cells in four communities, collecting flora data using the Parker method to measure species composition, density, and cover. In addition, soil fertility and nutritional quality of desirable pastures were assessed through physical and chemical analyses. Principal component and cluster analyses were then applied to correlate the variables. The results showed 26 species, with Poaceae and Asteraceae standing out as dominant and abundant. Tablillas and Pull Quishuar stood out for their productivity and carrying capacity (4.83 t/ha), while Galte Bisñag showed high protein and plant vitality in their pastures. Component 1 stood out for its high production (3.71 t/ha) and carrying capacity in fertile soils; Axis 2 linked Galte Bisñag with high nutritional quality and vegetation cover, while Axis 3 related Asaraty with compacted soils and an intermediate balance. The direct influence between floral species and the productivity of natural pastures leads to the exploration and implementation of measures for sustainable grazing. Full article

Accurate solar forecasting is critical for photovoltaic integration, particularly in regions with complex terrain and limited observations. This study benchmarks five deep learning architectures—MLP, RNN, LSTM, CNN, and a Grey Wolf Optimizer–enhanced MLP (MLP–GWO)—to evaluate the accuracy–computational efficiency trade-off for generating daily solar potential maps from ERA5 reanalysis over Mexico. Models were trained using a strict temporal split on a high-dimensional grid (85 × 129 points, flattened to 10,965 outputs) and evaluated in terms of predictive skill and hardware cost. The RNN achieved the best overall performance (RMSE ≈ 32.3, MAE ≈ 27.8, R² ≈ 0.96), while the MLP provided a competitive lower-complexity alternative (RMSE ≈ 54.8, MAE ≈ 46.0, R² ≈ 0.88). In contrast, the LSTM and CNN showed poorer generalization, and the MLP–GWO failed to converge. For the CNN, this underperformance is linked to the intentionally flattened spatial representation. Overall, the results indicate that within a specific ERA5-based, daily-resolution, and resource-constrained experimental framework, lightweight architectures such as RNNs and MLPs offer the most favorable balance between accuracy and computational efficiency. These findings position them as efficient surrogates of ERA5-derived daily solar potential suitable for large-scale, preliminary energy planning applications. Full article

The preservation of cultural heritage within museum environments requires systematic control and monitoring of indoor microclimatic conditions. Over the past four decades, scientific evidence has established the critical role of environmental parameters, including air temperature, relative humidity, light, and airborne pollutants, in the preventive conservation of artifacts. International standards and national guidelines mandate continuous, non-invasive monitoring protocols that integrate conservation requirements with the architectural and operational constraints of historic buildings. Effective implementation necessitates a multidisciplinary approach balancing artifact preservation, human comfort, and building energy efficiency. Recent international recommendations further promote adaptive approaches wherein microclimate thresholds are calibrated to site-specific “historical climate” conditions, derived from minimum one-year baseline datasets. While essential for long-term conservation management, the design and implementation of such monitoring systems present significant technical and logistical challenges. This study presents a replicable methodological approach wherein preliminary surveys and three short-term monitoring campaigns (duration: 2 to 5 weeks) supported design, sensor selection, and spatial deployment and will allow the validation of a long-term continuous monitoring infrastructure (at least one year). These preliminary investigations enabled the following: (1) identification of priority environmental parameters; (2) optimization of sensor placement relative to exhibition layouts and maintenance protocols; and (3) preliminary assessment of microclimate risks in naturally ventilated spaces in the absence of HVAC systems. Full article

Building extraction from remote sensing imagery plays a pivotal role in applications such as smart cities, urban planning, and disaster assessment. Although deep learning has significantly advanced this task, existing methods still struggle to strike an effective balance among global semantic understanding, local detail recovery, and multi-scale contextual awareness—particularly when confronted with challenges including extreme scale variations, complex spatial distributions, occlusions, and ambiguous boundaries. To address these issues, we propose TriadFlow-Net, an efficient end-to-end network architecture. First, we introduce the Multi-scale Attention Feature Enhancement Module (MAFEM), which employs parallel attention branches with varying neighborhood radii to adaptively capture multi-scale contextual information, thereby alleviating the problem of imbalanced receptive field coverage. Second, to enhance robustness under severe occlusion scenarios, we innovatively integrate a Non-Causal State Space Model (NC-SSD) with a Densely Connected Dynamic Fusion (DCDF) mechanism, enabling linear-complexity modeling of global long-range dependencies. Finally, we incorporate a Multi-scale High-Frequency Detail Extractor (MHFE) along with a channel–spatial attention mechanism to precisely refine boundary details while suppressing noise. Extensive experiments conducted on three publicly available building segmentation benchmarks demonstrate that the proposed TriadFlow-Net achieves state-of-the-art performance across multiple evaluation metrics, while maintaining computational efficiency—offering a novel and effective solution for high-resolution remote sensing building extraction. Full article

Indoor localization finds wide-ranging applications in user navigation and intelligent building systems. Nevertheless, signal interference within complex indoor environments and challenges regarding localization generalization in multi-building and multi-floor scenarios have restricted the performance of traditional localization methods based on Wi-Fi fingerprinting. To tackle these issues, this paper presents the SE-MTCAELoc model, a multi-task convolutional autoencoder approach that integrates a squeeze-excitation (SE) attention mechanism for indoor positioning. Firstly, the method preprocesses Wi-Fi Received Signal Strength (RSSI) data. In the UJIIndoorLoc dataset, the 520-dimensional RSSI features are extended to 576 dimensions and reshaped into a 24 × 24 matrix. Meanwhile, Gaussian noise is introduced to enhance the robustness of the data. Subsequently, an integrated SE module combined with a convolutional autoencoder (CAE) is constructed. This module aggregates channel spatial information through squeezing operations and learns channel weights via excitation operations. It dynamically enhances key positioning features and suppresses noise. Finally, a multi-task learning architecture based on the SE-CAE encoder is established to jointly optimize building classification, floor classification, and coordinate regression tasks. Priority balancing is achieved using weighted losses (0.1 for building classification, 0.2 for floor classification, and 0.7 for coordinate regression). Experimental results on the UJIIndoorLoc dataset indicate that the accuracy of building classification reaches 99.57%, the accuracy of floor classification is 98.57%, and the mean absolute error (MAE) for coordinate regression is 5.23 m. Furthermore, the model demonstrates exceptional time efficiency. The cumulative training duration (including SE-CAE pre-training) is merely 9.83 min, with single-sample inference taking only 0.347 milliseconds, fully meeting the requirements of real-time indoor localization applications. On the TUT2018 dataset, the floor classification accuracy attains 98.13%, with an MAE of 6.16 m. These results suggest that the SE-MTCAELoc model can effectively enhance the localization accuracy and generalization ability in complex indoor scenarios and meet the localization requirements of multiple scenarios. Full article

To address the problem of insufficient detection accuracy, high false negative rate of small targets, and large positioning errors of ships in complex marine environments and foggy conditions, an improved DBL-YOLO method based on YOLOv11 is proposed. This method customizes and optimizes modules according to the characteristics of foggy scenes—the C3k2-MDSC module is designed to efficiently extract and fuse multi-scale spatial features, and a dynamic weight allocation mechanism is adopted to balance the contributions of features at different scales in the foggy and blurred environment; a lightweight BiFPN structure is introduced to enhance the efficiency of cross-scale feature transmission and solve the problem of feature attenuation in foggy conditions; a novel fusion of the Deformable-LKA attention mechanism is innovated, which combines a large receptive field and spatial adaptive adjustment capabilities to focus on the key contour features of blurred ships in foggy conditions; an Inner-SIoU regression loss function is proposed, which optimizes the positioning accuracy of dense and small targets through an auxiliary bounding box dynamic scaling strategy. Experimental results show that in foggy scenes, the recall rate is increased by 3.4%, the F1 score is increased by 1%, and mAP@0.5 and mAP@0.5:0.95 are increased by 1.4% and 3.1%, respectively. The final average precision reaches 98.6%, demonstrating excellent detection accuracy and robustness. Full article

Identifying crops at the plot level is essential for developing effective agricultural management policies across diverse scales. The agricultural landscape of the Comunitat Valenciana (CV) region in Spain is characterized by a high density of small plots and a wide variety of crops, ranging from rice fields to vine and tree orchards, the latter being the predominant type. This fragmentation poses challenges for current crop monitoring using satellite imagery provided by the Sentinel-2 (S2) mission, largely because its relatively low spatial resolution results in pixels overlapping field boundaries. However, this study proposes a methodological approach that exploits the high temporal resolution of S2 to help overcome these limitations and automatically classify the six most representative crop types in this fragmented landscape. The study analyzed temporal variations in the correlation structure of common spectral indices over the year, leading to the selection of the Normalized Difference Moisture Index (NDMI), Normalized difference Red Edge Index (NDRE), and Plant Senescence Reflectance Index (PSRI) for complementary information. Fourier coefficients of a year time series of these indices served as inputs for a random forest classifier. Relative importance of indices for the classification was also assessed. Additionally, a new metric for classification confidence at plot level is introduced. This metric enables strategies to balance between classification precision and the proportion of classified plots. The model achieved an overall accuracy of 86.85% and a kappa index of 0.82 without considering classification confidence levels. Applying a 70% confidence threshold increased overall accuracy to 93.44% and the kappa index to 0.91 at a cost of 16.19% of plots unclassified. Full article

Ensuring equitable and inclusive access to children’s extracurricular activity facilities represents a profound manifestation of educational equity and is crucial for promoting children’s holistic development and societal sustainability. However, the underlying spatial mechanisms shaping their equity remain insufficiently explored. Using Tianjin’s central urban area as a case study, this study examines the spatial accessibility and equity of such facilities under dual government–market provision systems. The multi-mode Huff two-step floating catchment area model (MM-Huff-2SFCA) was employed to assess accessibility across walking, e-bike, public transport, and private car modes, integrating facility quality, household preference, and time-based distance decay. Equity was further evaluated using Lorenz curves and Gini coefficients across multiple spatial scales, while geographically weighted regression (GWR) identified spatial heterogeneity in factors such as child population density, transport infrastructure, household economic status, and basic education coverage. Results indicate that macro-level spatial balance masks substantial micro-scale inequities, particularly among transport-disadvantaged groups. Government and market systems exhibit contrasting spatial logics, forming a compensation–complementarity pattern across urban space. These findings underscore the need for refined and differentiated governance in extracurricular activity facilities planning, integrating spatial planning, transport accessibility, and social equity to advance child-friendly urban development and equitable public service provision. Full article

Multi-platform aerial remote sensing supports critical applications including wide-area surveillance, traffic monitoring, maritime security, and search and rescue. However, constrained by observation altitude and sensor resolution, targets inherently exhibit small-scale characteristics, making small object detection a fundamental bottleneck. Aerial remote sensing faces three unique challenges: (1) spatial heterogeneity of modality reliability due to scene diversity and illumination dynamics; (2) conflict between precise localization requirements and progressive spatial information degradation; (3) annotation ambiguity from imaging physics conflicting with IoU-based training. This paper proposes RAPT-Net with three core modules: MRAAF achieves scene-adaptive modality integration through two-stage progressive fusion; CMFE-SRP employs hierarchy-specific processing to balance spatial details and semantic enhancement; DS-STD increases positive sample coverage to 4× through spatial tolerance expansion. Experiments on VEDAI (satellite) and RGBT-Tiny (UAV) demonstrate mAP values of 62.22% and 18.52%, improving over the state of the art by 4.3% and 10.3%, with a 17.3% improvement on extremely tiny targets. Full article

In the past decade, non-native suckermouth armored catfish, Pterygoplichthys spp., have spread throughout the highland rivers of Lam Dong province, Vietnam. We examined spatial and temporal variation in endocrine and biochemical profiles across different river reaches, river systems, and between two sampling years (2020 and 2022). Seven blood parameters related to metabolism and energy balance were measured: total and free triiodothyronine, cholesterol, triglycerides, total protein, creatinine, and direct bilirubin. Concentrations of thyroid hormones and cholesterol did not differ significantly across sites or years. Multivariate analyses indicated that thyroid-related pathways were only weakly influenced by the environmental variation, suggesting preserved thyroid homeostasis. In contrast, triglycerides, total protein, creatinine, and direct bilirubin varied among rivers and between years at the same site, likely reflecting differences in food availability and energy balance. These results suggest that biochemical variation in non-native armored catfish is primarily expressed through lipid metabolism and protein turnover, while thyroid function remains comparatively conserved across invaded river habitats. Full article