Search Results (3,036)

Geographical knowledge graphs (GeoKGs) have long experienced several fundamental challenges in representing complex spatial relationships, such as limited dimensionality, insufficient quantification of relationship strength, and weak reasoning capabilities. To address these issues, this study presents the multidimensional spatial relation knowledge graph (MDSR-KG) framework. The novelty of this framework lies in advancing the shift toward spatial relation node-based representation, thereby elevating the spatial relations from edge structures to independent, computable, and inferable structured nodes. This approach was complemented by a parametric method aimed at quantifying the relation strength between nodes, thereby facilitating an advancement from discrete relations to continuous and interpretable association weighting. In experiments conducted in this study using the Berlin OpenStreetMap data, we noted that for complex spatial queries, the MDSR-KG framework significantly outperformed the baseline models in accuracy and completeness. The framework also exhibited advanced reasoning capabilities, such as ranking and recommendation, which are lacking in traditional methods. Thus, the framework lays a theoretical foundation for advancing from geographic feature recognition to spatial relationship comprehension. Full article

Medical resources are primary public goods, but the nature of their distribution across different-sized cities is unclear. Here, we examined the nonlinear scaling relationship between urban populations and medical resources in China, moving beyond the limitations of traditional linear evaluation metrics. Taking 296 Chinese cities as samples, we constructed scaling law models between population size and three medical resource indicators: the numbers of hospital beds, doctors, and hospitals. The results show that the number of doctors maintained a linear scaling relationship on the whole (scaling exponent $\beta$: 0.98–1.06), while the numbers of hospitals ($\beta$: 0.79–0.91) and hospital beds ($\beta$: 0.91–0.99) both exhibited sublinear scaling (2000–2022), confirming the existence of economies of scale in basic medical facilities. The Scale-Adjusted Metropolitan Indicator (SAMI) further reveals spatial agglomeration characteristics: the northern and southwestern regions of China perform notably better than expected in hospital availability, while provincial cites show advantages in terms of the numbers of beds and doctors. This study quantifies the nonlinear allocation of medical resources across Chinese cities and advocates for a reasonable allocation mechanism to promote medical equity. Full article

Bridge structural health monitoring (SHM) systems often suffer from large-scale data missing due to sensor faults, communication interruptions and other reasons during long-term operation, which seriously restricts the reliability of structural state assessment and maintenance decision-making. Compared with conventional single-channel independent modeling strategies commonly used for data imputation, their inherent neglect of spatial correlations and cross-modal causal associations among multi-source heterogeneous monitoring data such as displacement, wind speed, and temperature constrain the imputation capability, particularly when the target channel suffers from long-term continuous data loss. To address the above problems, this paper proposes a collaborative imputation framework integrating a graph attention network (GAT), a modal-aware cross-attention (MACA) mechanism and temporal encoder–decoder architecture (ITimeGAN). Firstly, the sensor feature topological graph is constructed based on the Pearson correlation coefficient, and the spatial dependency among multi-source features is adaptively learned through GAT. Then, the MACA module is introduced, which takes the target displacement as Query and environmental loads as Key/Value, and dynamically aggregates cross-modal driving information through multi-head attention. Finally, a bidirectional LSTM encoder and a unidirectional LSTM decoder are adopted to capture long-range temporal dependencies, so as to realize the accurate reconstruction of missing displacement data. Validated on the 9-dimensional real-world monitoring data from the GeoSHM system of the Forth Road Bridge (UK) under both random missing (10–50%) and continuous long-term missing (1–10 days) scenarios, ITimeGAN achieves an R² of 0.9950 (MAE = 4.25 mm) for longitudinal displacement and 0.9759 (MAE = 6.70 mm) for vertical displacement even under 10 consecutive days of complete data absence. Ablation analysis further reveals that the incorporation of graph attention and cross-modal attention modules reduces the longitudinal displacement MAE by 57% over the baseline, with the imputation performance ranking across three displacement directions being fully consistent with the underlying physical correlation strengths, thereby confirming the effectiveness of the proposed cross-modal collaborative strategy. Full article

Coalbed methane (CBM) is an important unconventional natural gas resource, and coal seam gas content is a key parameter for CBM resource evaluation and favorable-zone prediction. Taking the Jiulongchuan exploration area in Gansu Province as the study area, this study integrated drilling, well-logging, and measured gas content data to establish a multivariate regression model for coal seam gas content prediction. On this basis, three-dimensional geological modeling and variogram analysis were applied to characterize the spatial distribution of gas content in the main mineable coal seams (Nos. 5, 6, and 8). The results indicate that the regression model constructed using acoustic transit time, natural gamma-ray values, density logging parameters, and burial depth shows generally reasonable predictive capability for coal seam gas content. Cross-validation results suggest that the predicted gas contents are generally consistent with measured values. Spatial modeling results show that gas content in Seam No. 8 is generally higher than that in Seams No. 5 and No. 6, and gas content tends to increase with burial depth and coal seam thickness. In addition, relatively high gas contents are commonly observed along synclinal zones, whereas lower values occur near anticlinal areas. The integrated application of well-log interpretation and three-dimensional geological modeling provides a reasonable characterization of the spatial variation in coal seam gas content in the study area. The results may provide useful references for CBM resource evaluation and favorable-zone prediction in the Jiulongchuan exploration area. Full article

Motor imagery (MI)-based brain–computer interfaces (BCIs) have led to great interest as a result of their potential use in neurorehabilitation, assistive robotics, and human–computer interaction. However, decoding electroencephalographic (EEG) signals with high accuracy continues to be a difficult task due to the weak signal-to-noise ratio, differences among subjects, and the complicated temporal–spectral–spatial neural dynamics. Deep learning methods recently developed, such as convolutional neural networks, recurrent architectures, graph neural networks, and adversarial transfer learning, have enhanced MI decoding performance, yet many models are still concentrating on a single representation domain or they need costly adaptation phases in terms of computation. To tackle these shortcomings, we present NeuroCrossNet, a unified tri-modal deep learning model that is able to learn the temporal, spectral, and spatial EEG features jointly for robust and calibration-free MI decoding. The suggested network combines a Temporal HyperMixer Block for capturing long-range temporal dependencies, a wavelet transformer for learning localized time–frequency representation, and a Graph Attention Network for EEG topology-aware spatial reasoning. Additionally, a Dynamic Residual Attention Gate (DRAG) has been developed to adaptively merge heterogeneous feature streams, and a compact subject-aware normalization (SAN) method enhances cross-subject generalization without the use of labeled target-domain calibration data. Our proposed model was tested following the rigorous leave-one-subject-out (LOSO) approach on BCI Competition IV-2a and High-Gamma datasets. NeuroCrossNet reached a classification accuracy of 91.30%, surpassing several strong benchmark methods, including CNN-LSTM, EEGNet, DeepConvNet, spectral CNN, and graph-based EEG decoding frameworks. Furthermore, a large number of ablation studies reveal that the integration of temporally, spectrally, and spatially complementary representations considerably boosts robustness and inter-subject consistency. Full article

Digital camera sensors are designed to capture a wide range of incident illuminants, enabling the creation of high-quality images. However, these sensors lack the capability to differentiate between the color of the source illuminant and the actual color (or original color) of the object being captured. For this reason, the computational color constancy (CCC) was introduced and has been developed over decades. The CCC is an approach to modeling the color perception of the human visual system (HVS) by ensuring accurate object color determination under varying source illuminant conditions. At the core of human visual perception (HVP)-based CCC is attaining higher accuracy in scene illuminant estimation. The emergence of deep convolutional neural networks (DCNNs) was a recent innovation in accurate illuminant estimation, fundamentally transforming the CCC research landscape. Nevertheless, accurate illuminant estimation still remains a huge challenge for both traditional and state-of-the-art (SOTA) approaches. To further advance precision in illuminant estimation, this article presents a novel learning-based illumination color cast estimation approach to HVP-based CCC. Most importantly, the proposed approach is intended to integrate informative features into both channel and spatial regions while preserving long-term dependency feature information with the use of dense skip connections. To achieve these objectives, the proposed Dense Dual Connection Aggregated Transform Network (DDCATNet) architecture is designed to comprise several modules: shallow feature extraction, channel-wise and spatial feature-based Dense Dual Connection (DDC), fusion of the dense channel-wise attention (CA) and spatial attention (SA) branches through a gate mechanism (GM) unit, and aggregate transform. It is worth noting that both the CA blocks and the SA blocks in the DDC module are characterized by dense and cascading connections, meant to preserve long-term feature information and modulate different-level feature information at both global and local scales. The densely connected CA branch (DCA) and the densely connected SA branch (DSA) are also highly effective in securing high-contribution information while suppressing redundant data. The GM unit is integrated at the back of the DDC module, fusing the two DCA and DSA branches to ensure the adaptive merging of useful hierarchical feature information and the extraction of more valuable feature information. As a result, the proposed DDCATNet architecture significantly enhanced precision in illuminant estimation, thereby improving performance. In rigorous experiments on a wide range of datasets, the proposed DDCATNet approach outperformed its SOTA counterparts, validating the efficacy and generalization capabilities, as well as robust camera-invariance, across diverse, single- and multi-illuminant datasets and model architectures. Full article

In the contemporary field of geographic information, place name services serve as a core application support in geographic information science, widely applied in public services, cultural tourism, emergency management, and other scenarios. Place name service composition is a critical link in the integration of spatiotemporal knowledge and intelligent services for place names, determining the ability to rapidly solve complex place name problems. Traditional case-based reasoning methods are primarily rule-driven, making it difficult to deeply integrate semantic and graph structural features, and they also lack precision in measuring the similarity of multi-type place name service cases. To address this, this paper integrates knowledge graphs and case-based reasoning to propose a place name service composition method that balances semantic and graph structural similarity, aiming to enhance the response efficiency and recognition accuracy of complex natural language queries. The method consists of two steps: the first is constructing a knowledge graph case base. Semantic feature extraction is performed on the standard geographic question-answering standard dataset GeoQuery corpus to build a place name service knowledge graph case base that integrates semantic associations and spatial attributes. The second step is constructing a similarity model. The method combines four similarity measures—DeBERTa, TF-IDF, SimHash, and maximum common subgraph—and employs the Analytic Hierarchy Process for weighting to develop a novel similarity evaluation model for case-based reasoning. Experiments demonstrate that this method achieves a 21% improvement in F1-score compared to traditional rule-based methods. Furthermore, the developed prototype system for the intelligent recommendation of place name service composition achieves a recommendation accuracy of 92.64%. This research holds significant practical implications and application value for advancing the geographic information field toward intelligent and precision-based development. Full article

In this paper, we propose a new digital filtering approach based on the FIR-Median Hybrid (FMH) structure, which incorporates an Unbiased Finite Impulse Response (UFIR) filter as its core component. The proposed filter employs spatially symmetric window configurations to reduce Gaussian noise while preserving edges in images. Although the scientific community is rapidly adopting machine-learning- and deep-learning-based filters, there are several reasons to continue developing filters based on traditional methods. For example, these methods are well understood and rely on a strong mathematical foundation. Moreover, the structure of the proposed filter is simple; thus, this type of filter may be appealing to engineers unfamiliar with the machine-learning field. The performance of the proposed filter was assessed using two datasets: the first consisted of a set of artificial binary images, and the second comprised a subset of the BOWS image dataset. We conducted three main experiments. In the first experiment, we fine-tuned the filter considering three window-shape configurations. In the second experiment, Gaussian noise was added to the images, and the proposed filter was compared against other filters using edge-preservation-oriented metrics such as the Structural Similarity Index Measure (SSIM), the Normalized Step Edge Response (NSER), and the Gradient Conduction Mean Square Error (GcMSE), among others. The third experiment evaluated the performance of the best-performing window-shape configurations. This final test was assessed quantitatively using the Friedman test to identify the best-performing structure, whereas qualitative assessment was conducted using a Mean Opinion Score (MOS) test. The results show that the proposed filter achieved improved performance according to the PSNR, SNR, RMSE, and GcMSE metrics. These findings suggest that the proposed filter can be used in practical applications such as image enhancement, computer vision, and edge-detection-based preprocessing. Full article

Tumor acidity is a hallmark of the tumor microenvironment (TME) and has been widely regarded as a promising therapeutic target due to its ubiquity, functional relevance, and apparent selectivity for malignant tissues. Extensive preclinical studies have demonstrated that targeting tumor acidity—through inhibition of lactate production, blockade of proton transport, systemic buffering, and pH-responsive drug delivery—can suppress tumor growth, reduce metastasis, and enhance antitumor immunity. However, despite strong mechanistic rationale and consistent preclinical efficacy, these strategies have failed to achieve meaningful and durable clinical success. In this review, we examine the underlying reasons for this translational discrepancy. We highlight key mechanistic and systemic barriers, including spatial heterogeneity of tumor pH, temporal dynamics and adaptive evolution, metabolic plasticity, redundancy of pH-regulating systems, systemic physiological constraints, and drug delivery limitations in hypoxic and acidic regions. We further argue that tumor acidity is not a sufficient standalone driver of tumor progression but rather a feature of a complex and adaptive system shaped by metabolic and microenvironmental interactions. Finally, we discuss emerging strategies that may overcome these limitations, including combination therapies integrating metabolic targeting with immunotherapy, pH-responsive drug delivery systems, microenvironment reprogramming, and biomarker-guided patient stratification. Overall, current evidence suggests that future therapeutic approaches may benefit more from exploiting tumor acidity as a feature of the tumor microenvironment rather than attempting to directly neutralize it. Full article

Urban color plays a fundamental role in shaping the visual character and cultural identity of cities. Yet in many contexts, current practices remain fragmented, with color analysis often disconnected from planning implementation and governance. To address this issue, this study proposes a decision-support framework and a method for urban color evaluation and planning that integrates street view imagery, machine learning algorithms, and a parameter-based decision-support system. Using 430,000 street view images of Jiading District, Shanghai, we developed a computational model to systematically map building color characteristics in terms of hue, saturation, and brightness at both building and neighborhood scales. A multi-dimensional criteria framework encompassing the macro-environment, building characteristics, and micro-context is developed to guide automatic color scheme generation and evaluation for both existing and new buildings. The findings extract dominant color features and reveal spatial clustering patterns across Jiading District. The platform evaluates color schemes for new developments and generates color schemes for existing buildings, thereby linking urban color analysis with planning recommendations. This study presents a digital decision-support tool for urban color governance that integrates SVI, semantic segmentation, and rule-based reasoning. It shows how large-scale visual data can be organized and translated into structured references for planning practice, offering a more systematic and measurable support tool for urban color assessment. Full article

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

Spatial inequality in productivity, closely related to the spatial discontinuity of regional markets, presents a constraint on sustainable development. This study proposes an analytical framework of structural market segmentation, based on the process of urban agglomeration development and the heterogeneity of regional integration in both time and space, offering a novel perspective for understanding the intricate relationship between the spatial distribution of productivity and the spatial structure of regional markets. Applying city and firm-level data, this study utilizes a fixed-effects model and instrumental variables method to reveal how structural market segmentations contribute to spatial inequalities in productivity. The results indicate that structural commodity market segmentation negatively impacts productivity growth, and structural labor market segmentation exerts both growth and distributional effects on productivity, providing a reasonable explanation for spatial inequalities in productivity. And it is further amplified by associated scale effects, agglomeration economies, and the spatial distribution of industries. The government should evaluate potential side effects of policies to establish a regional development pattern of mutual benefit and win-win outcomes. Full article

Recent YOLO-based object detection methods have demonstrated strong performance in real-time applications due to their efficient end-to-end architecture. However, in complex scenarios such as VisDrone2019, existing methods still face limitations in small object detection and multi-scale feature modeling capability. These performance bottlenecks are not only attributed to model-level constraints, such as the loss of low-level spatial details during progressive downsampling and the insufficient preservation of fine-grained structural information in high-level semantic representations during feature propagation, which consequently limits multi-scale feature representation and fusion, but are also influenced by data-level factors, including long-tailed distributions and spatial distribution bias. To address these limitations, this paper proposes an improved model named MD-YOLO. First, a Multi-scale Adaptive Channel (MAC) module is introduced into the backbone to replace conventional stride-based downsampling, enhancing multi-scale feature representation while preserving fine-grained information. Second, a Dual Attention Feature Fusion (DAFA) module is designed to align features across different resolutions and further enhance fused representations using both channel and spatial attention mechanisms. Furthermore, a high-resolution P2 detection head is incorporated to enhance the detection capability for dense small objects. Experimental results on the VisDrone2019 dataset demonstrate that the proposed method substantially outperforms the YOLOv11s baseline, improving mAP@0.5 from 38.5% to 45.6% and mAP@0.5:0.95 from 22.8% to 27.1%, while maintaining a reasonable computational cost. Full article

Subsidence is the lowering of the ground surface caused by both natural processes, such as geological and tectonic dynamics, and anthropogenic activities related to land and resource use. Identifying and monitoring this phenomenon is essential for several reasons, including ensuring public safety, supporting the sustainable management of subsurface resources, and mitigating potential economic impacts. This study investigates ground deformation in an underexplored sector of the Calabria Region (Southern Italy), namely the Sant’Eufemia Plain. To this end, long-term Sentinel-1 datasets were processed using multi-temporal Synthetic Aperture Radar Interferometry techniques. Significant subsidence, reaching locally up to −17 mm/yr, was detected in the industrial area of San Pietro Lametino. Historical SAR datasets (ERS, ENVISAT) and optical imagery were used to reconstruct the long-term evolution of deformation since the 1990s. Satellite observations were integrated with rainfall records, piezometric data, and geotechnical modelling. A spatially distributed comparison between groundwater level variations and InSAR-derived deformation, supported by local time-series analysis, highlights weak and inconsistent correlations, indicating that groundwater fluctuations alone do not linearly control subsidence. The results suggest that subsidence is primarily associated with long-term consolidation processes affecting highly compressible Holocene deposits, likely enhanced by anthropogenic loading, while groundwater variations may contribute by modifying effective stress conditions within the subsoil. The relative contribution of these processes remains unquantified, highlighting the need for coupled hydro-mechanical investigations. Full article