Search Results (138)

Roadside LiDAR is a key sensing technology for intelligent transportation systems (ITSs) due to its high-precision spatial information and reliable monitoring of traffic environments. However, extracting traffic information from LiDAR point cloud data remains challenging because measurements are produced through angular sampling, causing the spacing between adjacent points to depend on radius and beam distribution. This study proposes a geometry-aware framework that incorporates LiDAR sampling geometry into the neighborhood criterion used to determine point-to-point association. The formulation defines neighborhood tolerance as a function of radial distance and vertical angular separation, enabling clustering decisions that are consistent with the sensing mechanism. In addition, the approach integrates deployment constraints based on sensor mounting height and region-of-interest limits to maintain physically meaningful connectivity under roadside sensing conditions. A systematic calibration procedure is conducted to estimate the scaling factor and radial spacing parameters and evaluate the method using both controlled and real-world datasets. Experimental results reveal that the proposed approach improves clustering accuracy and stability by reducing false negatives in sparse regions while avoiding excessive cluster merging in dense areas. The method demonstrates robust performance across varying sensing conditions and achieves higher accuracy than baseline approaches without parameter retuning, while introducing negligible computational overhead. Full article

Façades play a decisive role in shaping the visual and symbolic character of historic urban environments. Recent European funding schemes promoting energy-efficient retrofitting have accelerated interventions on building envelopes. Although aligned with decarbonization objectives, these processes are generating significant chromatic and material transformations that risk eroding the visual coherence and cultural sustainability of consolidated urban areas. In the historic Ensanches of San Sebastián, the replacement of traditional envelope systems with new cladding solutions is leading to the loss of the architectural style of some facades and altering their materials, textures, and colors. A progressive “contagion effect” has been identified, whereby dissonant chromatic schemes—often associated with the proliferation of so-called “zebra blocks”, residential buildings with façades clad in alternating black and white stripes that have proliferated in recent urban developments—are replicated across adjacent buildings, gradually weakening spatial continuity and the genius loci of the neighborhood. In response to this phenomenon, this research develops a systematic methodology to analyze, quantify, and anticipate chromatic transformation in consolidated urban fabrics. The study combines historical morphological analysis, classification of architectural periods, and chromatic mapping of recent façade interventions. Based on this framework, a CARI, Chromatic Alteration Risk Index is proposed to evaluate the potential impact of façade alterations on urban chromatic coherence. Drawing on an epidemiological framework, the methodology enables the identification of critical transformation clusters, the assessment of contagion dynamics, and the definition of regulatory thresholds for color and material interventions. By integrating perceptual criteria, urban morphology, and spatial distribution patterns, the study moves beyond descriptive diagnosis and offers a transferable tool for municipal planning. The proposed approach supports the proactive regulation of façade rehabilitation processes, balancing energy efficiency objectives with the preservation of collective memory, material identity, and urban sensory quality. This study proposes a quantitative model of “urban chromatic contagion” to assess how façade color interventions propagate within a neighborhood. We define the Chromatic Integration Percentage (CIP) and the Chromatic Alteration Risk Index (CARI) of the analyzed area. Results indicate that poorly regulated façades show higher chromatic dissonance (low CIP) and act as contagion hotspots, while a clear risk gradient emerges: highly protected buildings present lower risk, whereas mixed typologies and recent rehabilitations concentrate higher CARI values. The model supports preventive urban color management by identifying areas at risk before visible alteration. Full article

Effectively integrating graph topology and node attributes, while assigning nodes with both semantic similarity and structural closeness to the same community, remains a key challenge in attributed graph community detection. To address this challenge, this study proposes TVHCL-CD, a two-view hierarchical contrastive learning-driven method for community detection. The proposed method constructs an attribute view and a modularity view from the node attribute matrix and the modularity matrix, respectively, to model attribute semantics and high-order community structure priors. Structure-aware two-view representations are then learned in parallel through dual-view graph attention encoders incorporating multi-order neighborhood priors. Furthermore, a structure-enhanced Graph Transformer fusion module is designed to achieve node-level adaptive fusion of the two-view representations by introducing a learnable adjacency bias into global self-attention and a view-aware gating mechanism into the feed-forward network. To align the optimization objective with community semantics, a hierarchical contrastive learning strategy is further developed. Specifically, view-level consistency contrastive learning constructs modularity-guided augmented views to improve representation robustness, while community-level semantic contrastive learning incorporates partial ground-truth labels to enhance intra-community compactness and inter-community separation. Finally, clustering is performed on the fused representations to obtain community partitions. Experimental results on eight real-world attributed graphs and the generated tree-like attributed graph Tree-2500 indicate that TVHCL-CD achieves competitive performance under the semi-supervised transductive setting, while ablation results support the contributions of its main components. Full article

Degree-based topological indices play a central role in characterizing graph structures and their chemical applications. Among these, multiplicative Zagreb indices have attracted considerable attention due to their strong discriminative power and relevance in chemical graph theory. Neighborhood versions of these indices extend the classical concept by incorporating the aggregate degree information of adjacent vertices, capturing more subtle structural effects related to local branching. Trees, as connected acyclic graphs, provide a natural and tractable class for studying the extremal behaviors of these indices, while molecular trees—trees with a maximum degree of at most four—serve as chemically meaningful models of acyclic organic compounds. Investigating extremal values on these structures offers both theoretical insight into the indices’ behavior and identification of molecular graphs that maximize or minimize them. In this work, we determine the maximal and minimal values of the neighborhood-based multiplicative Zagreb indices for trees of fixed order and prescribed maximum degree, and we provide a complete structural characterization of all extremal graphs. Special attention is given to molecular trees, for which explicit extremal bounds are derived and all optimal structures are identified. These results provide efficient tools for evaluating the indices and illuminate the structural principles governing their extremal behavior. Full article

Accurate prediction of slope displacement from high-frequency GNSS monitoring data is critical for early warning of landslides and tailings dam failures. However, existing deep learning approaches often neglect the spatial coordination imposed by geological structures and fail to decouple abrupt deformation signals from background noise, leading to non-physical oscillations and inconsistent long-term predictions. To address these limitations, this paper proposes a Symmetry-Aware Physics-Guided Spatio-Temporal Graph Network (PG-STGN). First, a geological hierarchy-aware graph is constructed by integrating geometric proximity with prior knowledge of exploration levels, where the resulting adjacency matrix is symmetric by design and reflects the physical symmetry of deformation interactions among monitoring points at the same elevation. A hierarchical masking mechanism restricts feature aggregation to physically connected neighborhoods while preserving this symmetry. Second, an improved dual-path temporal convolutional network (iTCN) decouples high-frequency abrupt variations from low-frequency evolutionary trends, enabling both sensitive detection of sudden deformation and stable tracking of long-term creep. Third, a physics-consistent loss function combining first-order temporal differencing and graph Laplacian regularization enforces kinematic smoothness and spatial coordination; the Laplacian itself is derived from the symmetric adjacency matrix, ensuring symmetric regularization across the monitoring network. Evaluated on a real-world slope GNSS dataset from a large-scale mining project, PG-STGN reduces mean squared error (MSE) by approximately 23.7% and achieves a global R² of 0.924, outperforming state-of-the-art spatio-temporal models. Ablation studies confirm that the symmetric physics-guided graph, dual-path decoupling, and consistency loss are each essential for suppressing spurious correlations and maintaining physically plausible predictions. The proposed framework provides a robust, interpretable, and symmetry-constrained solution for automated slope monitoring under complex geological conditions. Full article

Accurate corrosion-rate prediction for buried pipelines is fundamental to sustainable integrity management, yet industrial corrosion datasets are typically small and heterogeneous, making reliable model training challenging. This study proposes CARE-Boost (Context-Aware Restrained-Ensemble Boosting), a compact method designed for exactly this setting. The algorithm fuses three complementary components: a practical-variable gradient-boosting branch trained on directly measurable pipeline predictors; a spatial-neighborhood context branch that encodes short-range continuity from adjacent stake-point predictors; and a restrained regime-focused augmentation scheme stabilized by fixed convex blending. The engineering dataset was collected from a natural-gas pipeline in Central Asia and organized as a one-dimensional spatial sequence. Under repeated $5\times 2$ cross-validation, CARE-Boost achieves RMSE $=0.0577\mathrm{mm}/\mathrm{year}$, MAE $=0.0314\mathrm{mm}/\mathrm{year}$, and $R^2=0.472$, outperforming XGBoost ($0.0599$, $0.0320$, $0.432$) and LightGBM ($0.0618$, $0.0333$, $0.385$); the improvement over XGBoost is statistically significant ($p=0.0068$, splitwise Wilcoxon). Split-conformal intervals achieve $95.0\%$ empirical coverage at the nominal $90\%$ level. SHAP attribution identifies soil aggressiveness, pH, water content, and bicarbonate as the dominant corrosion drivers, and the mean fit–predict cycle completes in $1.80$ s, supporting deployment in routine integrity workflows. These findings position CARE-Boost as a practically viable uncertainty-aware corrosion predictor for sustainable integrity management under small-sample conditions, with its primary evidence lying in improved point prediction, calibrated uncertainty, and interpretable spatially informed inference. Full article

The Western Chagai Belt of Pakistan hosts major porphyry Cu-Au systems, but prospectivity mapping in this arc remains difficult because favorable lithology, intrusive bodies, fault corridors, hydrothermal alteration, and Cu geochemical anomalies are spatially heterogeneous across a structurally complex and arid terrain. These conditions create a scientific need for an integrated mapping framework that can combine remote sensing alteration evidence, geology, structure, and geochemistry within a unified and reproducible workflow. This study presents GeoHybridGNN, a hybrid deep learning framework for porphyry copper prospectivity mapping in the Western Chagai Belt. The framework integrates multi-source raster evidence, including remote sensing-derived spectral alteration indices, a Cu geochemical raster, and distance-to-fault information, with graph-based node representations that combine regular neighborhood adjacency on retained grid cells with node attributes derived from lithology and aligned geoscientific raster summaries. All predictors were harmonized to a common 30 m reference raster grid and evaluated using five-fold spatial block cross-validation to provide a more spatially realistic assessment than ordinary random splitting. The implemented model combines a CNN-based raster patch encoder with a GraphSAGE-based graph classifier. Raster patches extracted around graph nodes are encoded into 64-dimensional embeddings, and these embeddings are concatenated with node-level graph features before full-batch graph learning and prediction. Copper occurrences were used only for supervised label assignment and evaluation and were not used as predictive inputs. The results show that GeoHybridGNN produces spatially coherent prospectivity maps, stable fold-wise prediction patterns, and improved target delineation relative to the tested comparison models. Cu geochemical integration produces only a limited change in global discrimination but provides modest local target sharpening in selected zones. These results indicate that GeoHybridGNN can serve as an uncertainty-aware and geologically constrained decision support workflow for porphyry copper targeting. More broadly, the framework provides a transparent strategy for exploration screening in structurally complex and data-heterogeneous metallogenic belts where remote sensing, geological, structural, and geochemical evidence must be integrated consistently. Full article

Small unmanned aerial vehicle (UAV)-borne distributed tomographic synthetic aperture radar (TomoSAR) systems offer flexible baseline configurations and low deployment cost, making them attractive for rapid and high-resolution three-dimensional (3D) reconstruction. However, the distance between adjacent channels placed on different UAVs is relatively large due to the flight safety spacing considerations. This leads to high sidelobes in the elevation point spread function (PSF) within the reconstruction range. Meanwhile, atmospheric turbulence may cause UAVs to deviate from their predefined trajectories, making it difficult to suppress sidelobes through baseline optimization. Large baselines may also introduce spatial decorrelation between channels, which gives rise to random phase noise in the interferometric phase and further aggravates elevation ambiguity by increasing the sidelobe level of the PSF. To address this problem, this paper proposes an elevation ambiguity resolution method based on neighborhood-adaptive elevation priors. In the proposed method, a window function is constructed from reconstruction results of neighboring pixels and incorporated into the reconstruction process to suppress the interference caused by high sidelobes. In this way, the probability of correct target reconstruction is improved. The effectiveness and robustness of the proposed method are validated using both simulations and real measured data. Experimental results obtained with a C-band small UAV-borne distributed TomoSAR system show that the proposed method effectively suppresses ambiguity and enables ambiguity-free reconstruction of target buildings. Statistical analysis further demonstrates that the number of ambiguous points produced by the proposed algorithm is only one-fifth of that produced by the conventional OMP method. Full article

Advancements in infrared imaging technology have introduced a novel perspective for inspecting power transmission lines. Nevertheless, the inherent low contrast and indistinct edges of infrared images present significant challenges, rendering the direct application of traditional semantic segmentation algorithms unsatisfactory. To mitigate this problem, we propose a multi-neighborhood densely connected network architecture. This framework incorporates two pivotal modules: the Multi-Head Squeeze-and-Excitation (MHSE) module and the Multi-Neighborhood Feature Fusion (MNFF) module. The MHSE enhances local feature representations by capturing nuanced feature interactions, thereby alleviating the issue of imbalanced global feature weight distribution. The MNFF aggregates feature data from multiple adjacent nodes at each node’s input, which not only facilitates the integration of multi-scale target features but also leverages neighborhood information to precisely localize and amplify features within specific regions. Furthermore, we have built the first Infrared Dataset of Power Transmission Line Suspension Clamp (CLAMPTISS) to substantiate our approach. Empirical evidence demonstrates that our proposed network surpasses state-of-the-art networks across three key metrics: the mean Intersection over Union (mIoU) and localization accuracy (Pd) have increased by 8.3% and 13.3%, respectively, while the false alarm rate (Fa) has decreased by 38.2%. Full article

In distribution networks, traditional fault location methods have insufficient anti-interference capability and low accuracy in locating high-resistance grounding faults. To address these issues, a distribution network fault location method on the basis of SimAM-GraphSAGE-GAT is proposed. Firstly, the distribution network topology structure is converted into an adjacency matrix, and the electrical parameters of the faulty line are incorporated as node features into the graph structure of the network. Subsequently, the sampling and aggregation mechanism of GraphSAGE is used for learning node representation. Features are refined using SimAM. As a parameter-free attention mechanism, SimAM improves the ability of the model to capture important fault information. Then, the multi-head attention mechanism of GAT is introduced to enhance the representation of neighborhood relationships. Finally, GraphSAGE is utilized once again for deep aggregation, with a view to localizing faults by node classification. An IEEE 33-node distribution network model is adopted to verify the effectiveness of the algorithm in the experiment. The results show that this method can maintain high positioning accuracy even under the tested conditions, such as high-resistance grounding, noise interference, and data loss. Full article

Classic machine learning and regime identification methods applied to financial time series lack theoretical guarantees and exhibit systematic failure modes: heavy-tails invalidate moment-based geometry, rendering distances and centroids dominated by extremes or unstable; jumps violate smoothness, destabilizing local regressions, kernel methods, and gradient-based learning; and non-stationarity disrupts neighborhood relations, so distances in classical feature spaces no longer reflect meaningful proximity. To address these challenges, we propose a topology-based machine-learning framework grounded on probabilistic reconstruction of state-space geometry, which replaces moment- and smoothness-dependent representations with deformation-stable summaries of state-space geometry, preserving neighborhoods, adjacency, and topology. The finite-sample validity of homeomorphic state-space reconstruction, required for topology-based machine learning, is assessed through numerical studies on synthetic data with heavy tails, jumps, and known ground-truth regimes. Further diagnostics of local invertibility and bounded geometric distortion quantify when embedding windows are consistent with local diffeomorphic behavior, enabling metric-sensitive, geometry-aware learning. Clustering of Hilbert-space summaries accurately recovers underlying market tail-risk regimes with robust results across selected filtrations. Temporal, feature-space, and cluster-label null tests confirm that topology-based clustering captures genuine topological structure rather than noise or artifacts, and encodes temporal dependencies at local, mesoscopic, and network levels associated with market regimes. Full article

Since the 1970s, accelerated urban development in Doha has contributed to a disjointed and inefficient city structure. While the Doha Metro has begun to address spatial and mobility-related challenges, planners continue to call for a more integrated, strategic approach to ensure safe, accessible, and efficient transit connectivity. In response, the Qatar National Development Framework provides a long-term vision for sustainable urban transformation, with a central aim of embedding the Metro system within the existing urban context and aligning expansion with Transit-Oriented Development (TOD), which promotes dense, multifunctional, pedestrian-oriented neighborhoods along transit corridors. Within this context, this study investigates how TOD strategies can enhance quality of life in mixed-use environments, focusing on the area surrounding Al Mansoura metro station and the adjacent Najma and Al Mansoura districts. Using the Integrated Modification Methodology (IMM), the analysis assesses spatial structure across density, spatial diversity, and connectivity, and derives evidence-based recommendations to improve livability and support sustainable revitalization. To broaden regional applicability, the study also compares Al Mansoura with Olaya in Riyadh—two mid-to-late 20th-century, high-density mixed-use districts undergoing TOD-driven transition—highlighting how spatial form, infrastructure legacy, and urban governance shape TOD outcomes and inform adaptable TOD frameworks for Gulf cities. Full article

The virtual reassembly of fragmented cultural relics remains a challenging task due to incomplete contours, complex fracture geometries, and the lack of reliable accuracy evaluation when ground-truth models are unavailable. To address these issues, this study proposes an automated virtual reassembly framework based on multi-feature extraction and hierarchical fragment matching. First, contour points are extracted from fragment point clouds using neighborhood roughness analysis and further refined through a Cylinder Box-based completion strategy to recover missing contour segments. Then, multiple complementary features, including Fast Point Feature Histograms (FPFHs), Heat Kernel Signatures (HKSs), and a spatial cube-based contour shape descriptor, are jointly constructed to characterize both local geometric details and global structural properties of fragments. To improve matching efficiency and robustness, a tree-based fragment retrieval strategy combined with a coarse-to-fine registration scheme is employed to identify adjacent fragments while reducing computational complexity. In addition, a pseudo-ground-truth accuracy evaluation method is introduced to quantitatively assess cumulative reassembly errors in the absence of reliable reference data. Experiments conducted on the public Buddha head dataset demonstrate that the proposed method achieves stable and visually consistent reassembly results, with a cumulative error as low as 1.58%, while significantly reducing retrieval computations compared with exhaustive matching strategies. These results indicate that the proposed framework provides a practical and verifiable solution for the automated digital restoration of fragmented cultural relics. Full article

Rapid urbanization has significantly exacerbated the Urban Heat Island (UHI) effect in high-density megacities, driven by the intensifying competition between built-up morphology and natural cooling infrastructure. Current research, however, often fails to accurately predict land surface temperatures (LST) because traditional models frequently overlook the complex spatial dependencies and neighborhood spillover effects inherent in urban environments. Existing studies often ignore the spatial dependence of heat transfer. This study proposes an explainable machine learning framework incorporating spatial lag variables to capture the thermal spillover from adjacent neighborhood context—such as green space cooling diffusion or built-up heat accumulation—which is frequently treated as noise in traditional models. Taking Shenzhen as a case study, we integrated multi-source data (Landsat 8, building vectors, DEM) and developed an XGBoost regression model (R² = 0.806) augmented with SHAP (Shapley Additive exPlanations) to quantify the contributions of local and contextual features. The results revealed that: (1) Non-linear Thresholds: Vegetation cooling exhibits a saturation effect, with the highest marginal benefit observed in the NDVI range of 0.2–0.4, while building warming effects converge at extremely high densities due to mutual shading; (2) Neighborhood Spillovers: Spatial interaction analysis confirms significant cool island synergy (where clustered green spaces provide amplified cooling) and heat island agglomeration effects—e.g., green spaces surrounded by high ecological backgrounds provide amplified cooling benefits; (3) Spatial Antagonism: A novel Interaction Balance Index (IBI) based on game-theoretic SHAP contributions was constructed to map the source-sink competition patterns, identifying distinct heat-dominated (West) and cool-dominated (East) zones. Unlike traditional area-weighted source-sink landscape metrics, IBI enables a pixel-level additive decomposition of warming and cooling factors, quantifying the net thermal outcome of local morphology and neighborhood spillover. By explicitly encoding spatial context into non-linear modeling, this study provides a more mechanistically robust understanding of urban thermal environments. The identified thresholds and dominant driver maps offer precise, spatially differentiated guidance for urban climate-adaptive planning and ecological restoration. Full article

Behavioral deviations from subjective expected utility theory, most famously captured by the Allais paradox and the Ellsberg paradox, have inspired extensive theoretical and experimental research into risk and ambiguity preferences. While the existing analyze these paradoxes independently, little work explores how such heterogeneously biased agents interact in networked strategic environments. Our paper fills this gap by modeling a convergent Markov–network game between Allais-type and Ellsberg-type players, each endowed with fully enriched loss matrices that reflect their distinct probabilistic and ambiguity attitudes. We define convergent priors as those inducing a spectral radius of <1 in iterated enriched matrices, ensuring iterative convergence under a matrix-based update rule. Players minimize their losses under these priors in each iteration, converging to an equilibrium where no further updates are feasible. We analyze this convergence under three learning regimes—homophily, heterophily, and type-neutral randomness—each defined via distinct neighborhood learning dynamics. To validate the equilibrium, we construct a risk-neutral measure by transforming losses into payoffs and derive a riskless rate of return representing players’ subjective indifference to risk. This applies risk-neutral pricing logic to behavioral matrices, which is novel. This framework unifies paradox-type decision makers within a networked Markovian environment (stochastic adjacency matrix), extending models of dynamic learning and providing a novel equilibrium characterization for heterogeneous, ambiguity-averse agents in structured interactions. Full article