Search Results (892)

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

Topological signal processing is emerging alongside Graph Signal Processing (GSP) in various applications, incorporating higher-order connectivity structures—such as faces—in addition to nodes and edges, for enriched connectivity modeling. Rich point clouds acquired by multi-camera systems in Visual Simultaneous Localization and Mapping (V-SLAM) are typically processed using graph-based methods. In this work, we introduce a topological signal processing (TSP) framework that integrates texture information extracted from V-SLAM; we refer to this framework as TSP-SLAM. We show how TSP-SLAM enables the extension of graph-based point cloud processing to more advanced topological signal processing techniques. We demonstrate, on real stereo data, that TSP-SLAM enables a richer point cloud representation by associating signals not only with vertices but also with edges and faces of the mesh computed from the point cloud. Numerical results show that TSP-SLAM supports the design of topological filtering algorithms by exploiting the mapping between the 3D mesh faces, edges and vertices and their 2D image projections. These findings confirm the potential of TSP-SLAM for topological signal processing of point cloud data acquired in challenging V-SLAM environments. Full article

When a city decides to undertake a certain urban project, is it modifying just the physical environment or the social fabric that dwells within? This work investigates the relationship between the geometric configuration of urban space (geometry–city) and the topology of the networks of encounters of its inhabitants (network–city) that form through daily interactions. The research departs from the hypothesis that changes in geometry–city would not significantly alter the topology of the network–city, testing this proposition conceptually through abstract computational simulations developed specifically for this study. In this simulator, abstract maps with buildings distributed over different primary geometries are generated and have activities (use: home or work) and a population assigned. Encounters of the “inhabitants” are registered while daily commute routines, enough to achieve differentiation and stability, are run. The initial results revealed that the geometry description was not enough, and definitions regarding activity attribution were also necessary. Thus, we could not confirm nor reject the original hypothesis exactly, but it had to be complemented, including the idea of an activity–city dimension. We found that despite the geometry–city per se not determining the structure of the network–city, the spatial (geometric) distribution of activities directly impacts the resulting topology. Urban geometry influences networks–city only insofar as it conforms to activity–city, defining areas for activities or restricting routing between them. But it is the geometry of localization of the activities that has a direct impact on the topology of the network–city. This conceptual discovery can have significant implications for urban planning if corroborated in real-world situations. It could suggest that land use policies may be more effective for intervening in network-based characteristics, like social cohesion and resilience, than purely morphological interventions. Full article

Highly Hazardous Pesticides (HHPs) pose severe risks to human health and the environment, making it essential to understand their molecular stability and degradation pathways. In this study, the Quantum Theory of Atoms in Molecules (QTAIM) was applied to four representative organophosphate pesticides, allowing the identification of electronically weak bonds as intrinsic sites of lability. These findings are consistent with reported hydrolytic, oxidative, enzymatic, and microbial degradation routes. Importantly, QTAIM descriptors proved largely insensitive to solvation, confirming their intrinsic character within the molecular electronic structure. To complement QTAIM, conceptual DFT (Density Functional Theory) reactivity indices were analyzed, revealing that solvent effects induce more noticeable variations in global and local descriptors than in topological parameters. In addition, a Topological Analysis of the Fukui Function (TAFF) was performed, which mapped nucleophilic, electrophilic, and radical susceptibilities directly onto QTAIM basins. The TAFF analysis confirmed that bonds identified as weak by QTAIM (notably P–O, P–S, and P–N linkages) also coincide with the most reactive sites, thereby reinforcing their mechanistic role in degradation pathways. This integrated framework highlights the robustness of QTAIM, the sensitivity of global and local reactivity descriptors to solvation revealed by conceptual DFT, and the complementary insights provided by TAFF, contributing to risk assessment, remediation strategies, and the rational design of safer pesticides. Full article

Partial Differential Equation (PDE)-based hydrologic models demand extensive preprocessing, creating a bottleneck and slowing down the model setup process. Mesh generation typically lacks integration with hydrological features like river networks. We present GHOST Mesh (GMesh), an automated, watershed-oriented mesh generator built within the Watershed Modeling Framework (WMF), to address this. While primarily designed for the GHOST hydrological model, GMesh’s functionalities can be adapted for other models. GMesh enables rapid mesh generation in Python by incorporating Digital Elevation Models (DEMs), flow direction maps, network topology, and online services. The software creates Voronoi polygons that maintain connectivity between river segments and surrounding hillslopes, ensuring accurate surface–subsurface interaction representation. Key features include customizable mesh generation and variable refinement to target specific watershed areas. We applied GMesh to Iowa’s Bear Creek watershed, generating meshes from 10,000 to 30,000 elements and analyzing their effects on simulated stream flows. Results show that higher mesh resolutions enhance peak flow predictions and reduce response time discrepancies, while local refinements improve model performance with minimal additional computation. GMesh’s open-source nature streamlines mesh generation, offering researchers an efficient solution for hydrological analysis and model configuration testing. Full article

We study the dynamics of von Neumann entropy driven by the Gorini–Kossakowski–Sudarshan–Lindblad (GKSL) equation, focusing on its camel-like behavior—a hump-like entropy evolution reflecting the system’s adaptation to its environment. Within this framework, we analyze quantum correlations under decoherence and environmental interaction for three sets of quantum states. Our results show that the sign of the entanglement entropy’s derivative serves as an indicator of the system’s drift toward either classical or quantum information exchange—an insight relevant to quantum error correction and dissipation in quantum thermal machines. We parameterize quantum states using both single-parameter and Bloch-sphere representations, where the angle $\theta$ on the Bloch sphere corresponds to the state’s position. On this sphere, we construct gradient and basin maps that partition the dynamics of quantum states into stable and unstable regions under decoherence. Notably, we identify a Braiding ring of decoherence-unstable states located at $\theta ={\displaystyle \frac{3\pi }{4}}$; these states act as attractors under a constructed Lyapunov function, illustrating the topological and dynamical complexity of quantum evolution. Finally, we propose a testable experimental setup based on camel-like entropy and discuss its connection to the theoretical framework of this entropy behavior. Full article

We propose a strictly causal early–warning framework for financial crises based on topological signal extraction from multivariate return streams. Sliding windows of daily log–returns are mapped to point clouds, from which Vietoris–Rips persistence diagrams are computed and summarised by persistence landscapes. A single, interpretable indicator is obtained as the $L_2$ norm of the landscape and passed through a causal decision rule (with thresholds $\alpha ,\beta$ and run–length parameters $s,t$) that suppresses isolated spikes and collapses bursts to time–stamped warnings. On four major U.S. equity indices (S&P 500, NASDAQ, DJIA, Russell 2000) over 1999–2021, the method, at a fixed strictly causal operating point $(\alpha =\beta =3.1,s=57,t=16)$, attains a balanced precision–recall ($F_1\approx 0.50$) with an average lead time of about 34 days. It anticipates two of the four canonical crises and issues a contemporaneous signal for the 2008 global financial crisis. Sensitivity analyses confirm the qualitative robustness of the detector, while comparisons with permissive spike rules and volatility–based baselines demonstrate substantially fewer false alarms at comparable recall. The approach delivers interpretable topology–based warnings and provides a reproducible route to combining persistent homology with causal event detection in financial time series. Full article

Urban morphology has long been studied through typologies, spatial configurations, and historical change, yet cities are not static artifacts but dynamic environments continually reshaped by people, infrastructures, and politics. This article brings Actor–Network Theory (ANT) into dialogue with Aldo Rossi’s notion of the locus to rethink urban design as both enduring form and relational process. Building on Manuel Lima’s taxonomy, the study develops a methodological workflow that translates street networks into visualizations, pairing embeddings with topographic maps to highlight structural patterns. Applied to a comparative set of cities, the analysis distinguishes three broad morphological tendencies—archetypal, geometrical, and relational—each reflecting different logics of urban organization. The results show how scale and connectivity condition the interpretability of embeddings, revealing both alignments and divergences between cartographic and topological representations. Beyond empirical findings, the article frames network literacy as a meeting ground for design theory, science and technology studies, and information visualization. It concludes by proposing that advancing urban morphology today requires not only new computational tools but also sustained interdisciplinary collaboration across design, urban studies, and data science. Full article

This study introduces SOMTreeNet, a novel hybrid neural model that integrates Self-Organizing Maps (SOMs) with BIRCH-inspired clustering features to address structured learning in a scalable and interpretable manner. Unlike conventional deep learning models, SOMTreeNet is designed with a recursive and modular topology that supports both supervised and unsupervised learning, enabling tasks such as classification, regression, clustering, anomaly detection, and time-series analysis. Extensive experiments were conducted using various publicly available datasets across five analytical domains: classification, regression, clustering, time-series forecasting, and image classification. These datasets cover heterogeneous structures including tabular, temporal, and visual data, allowing for a robust evaluation of the model’s generalizability. Experimental results demonstrate that SOMTreeNet consistently achieves competitive or superior performance compared to traditional machine learning and deep learning methods while maintaining a high degree of interpretability and adaptability. Its biologically inspired hierarchical structure facilitates transparent decision-making and dynamic model growth, making it particularly suitable for real-world applications that demand both accuracy and explainability. Overall, SOMTreeNet offers a versatile framework for learning from complex data while preserving the transparency and modularity often lacking in black-box models. Full article

This paper presents an integrated approach for optimizing the performance of a 36-pulses converter system by using artificial intelligence (AI) techniques to be included in a Supervisory Control and Data Acquisition (SCADA) environment. The focus of the proposal is on enhancing harmonic reduction through intelligent adjustment of switching angles and coordinated control of the reinjection transformer included in the power converter topology. A key component of the proposed methodology involves a simulation-based process to determine optimal firing angles (${\mathit{\alpha }}_1$, ${\mathit{\alpha }}_2$, and ${\mathit{\alpha }}_3$), based on Selective Harmonic Elimination (SHE) theory, that minimize Total Harmonic Distortion (THD). Using MATLAB with Simulink and PLECS models, a parametric sweep of the firing angles, generating a comprehensive dataset of THD outcomes. This dataset, consisting of THD evaluations across fine-grained angle variations, serves as the training foundation for supervised machine learning models—specifically, neural network regressors—that approximate the nonlinear mapping between firing angles and harmonic distortion. These predictive models are then employed as surrogates to estimate THD rapidly and guide the selection of optimal switching angles in real time without requiring iterative numerical solvers. Optimization heuristics and predictive models are then deployed to dynamically adapt system parameters in real time under varying load conditions. The proposed method demonstrates significant improvements in power quality and operational reliability, highlighting the potential of AI-assisted SCADA systems in advanced power electronics applications. Implementation results performed on a 36-pulses voltage source converter prototype are included to illustrate the appropriateness of the proposal. Full article

The characterization and mapping of urban landscape spatial form are critical for advancing sustainable planning and informed environmental management. From a morphometric perspective, this study introduces a novel, data-driven framework for typo-morphological analysis. First, morphological cells (MCs) are defined as objectively and universally applicable spatial units for morphometric investigation. Second, by integrating a multi-dimensional cognition of full-scale morphological and associated landscape elements, we construct a set of 48 spatial form indicators and attach them to morphological cells, enabling a precise description of each unit. Third, a Gaussian mixture model (GMM) is employed to cluster the metrical information within the spatially lagged context derived from the topological structure of the morphological cells, resulting in the delineation of distinct typo-morphological zones (TMZs). We then adopt Ward’s algorithm to establish a hierarchical relationship among identified urban landscape types. Using Wuxi City, China, as a case study, our results demonstrate the effectiveness of the proposed framework in capturing the heterogeneity and underlying connotation of urban landscape spatial characteristics. Building upon the unsupervised clustering results, we further apply the classification and regression tree (CART) to provide a supervised interpretation of the key spatial form conditions driving typological decisions. It facilitates the systematic identification of the components and formative mechanisms of spatial form. The findings contribute a scalable, reproducible, and interpretable typo-morphometric approach for analyzing urban landscape spatial characteristics, thereby providing a robust quantitative foundation for integrated decision-making in landscape planning, socio-ecological assessment, and urban design practices. More broadly, the study carries both applied and theoretical significance for advancing refined urban governance and fostering interdisciplinary research related to urban sustainable development. Full article

Accurate correspondence of complex neuroanatomical surfaces under non-rigid deformations remains a formidable challenge in computational neuroimaging, owing to inter-subject topological variability, partial occlusions, and non-isometric distortions. Here, we introduce the Dynamic Graph Analyzer (DGA), a unified hybrid framework that integrates simplified structural descriptors with spatial constraints and formulates matching as a global linear assignment. Structurally, the DGA computes node-level metrics, degree weighted by betweenness centrality and local clustering coefficients, to capture essential topological patterns at a low computational cost. Spatially, it employs a two-stage scheme that combines global maximum distances and local rescaling of adjacent node separations to preserve geometric fidelity. By embedding these complementary measures into a single cost matrix solved via the Kuhn–Munkres algorithm followed by a refinement of weak correspondences, the DGA ensures a globally optimal correspondence. In benchmark evaluations on the FAUST dataset, the DGA achieved a significant reduction in the mean geodetic reconstruction error compared to spectral graph convolutional netwworks (GCNs)—which learn optimized spectral descriptors akin to classical approaches like heat/wave kernel signatures (HKS/WKS)—and traditional spectral methods. Additional experiments demonstrate robust performance on partial matches in TOSCA and cross-species alignments in SHREC-20, validating resilience to morphological variation and symmetry ambiguities. These results establish the DGA as a scalable and accurate approach for brain shape correspondence, with promising applications in biomarker mapping, developmental studies, and clinical morphometry. Full article

This paper proposes a methodology for the generation and registration of three-dimensional data sets to support an adaption of Teach and Repeat path following for an Autonomous Underwater Vehicle (AUV) equipped with a multibeam sonar system. The goal of this system is to enable an AUV to generate a topological map of a path consisting of locally consistent sub maps and to re-follow this path using newly collected data. For AUVs traversing long distances without external navigational aids, this methodology would allow robust return-to-home capability, specifically in remote and harsh environments such as beneath ice. Full article

Rapid advancements in peer-to-peer (P2P) streaming technologies have significantly impacted digital communication, enabling scalable, decentralized, and real-time content distribution. Despite these advancements, challenges persist, including dynamic topology management, high latency, security vulnerabilities, and unfair resource sharing (e.g., free rider). While software-defined networking (SDN) and blockchain individually address aspects of these limitations, their combined potential for comprehensive optimization remains underexplored. This study proposes a distributed SDN (DSDN) architecture enhanced with blockchain support to provide secure, scalable, and reliable P2P video streaming. We identified research gaps through critical analysis of the literature. We systematically compared traditional P2P, SDN-enhanced, and hybrid architectures across six performance metrics: latency, throughput, packet loss, authentication accuracy, packet delivery ratio, and control overhead. Simulations with 200 peers demonstrate that the proposed hybrid SDN–blockchain framework achieves a latency of 140 ms, a throughput of 340 Mbps, an authentication accuracy of 98%, a packet delivery ratio of 97.8%, a packet loss ratio of 2.2%, and a control overhead of 9.3%, outperforming state-of-the-art solutions such as NodeMaps, the reinforcement learning-based routing framework (RL-RF), and content delivery networks-P2P networks (CDN-P2P). This work establishes a scalable and attack-resilient foundation for next-generation P2P streaming. Full article

With the vigorous development of deep learning technology, lane detection tasks have achieved phased results. However, existing lane detection models do not consider the unique geometric and visual features of lanes when dealing with some challenging scenarios, resulting in many difficulties and limitations. To this end, we propose a lane detection network based on full-dimensional convolutional Transformer (OTVLD-Net) to improve the adaptability of the model under extreme road conditions and better handle complex lane topology. In order to extract richer contextual features, we designed ODVT-Net, which uses full-dimensional dynamic convolution combined with improved feature flip fusion layer and non-local network layer, and aggregates lane symmetry features by utilizing the horizontal symmetry of lanes. A feature weight generation mechanism based on Transformer is designed, and a cross-attention mechanism between feature maps and lane requests is added in the decoding stage to enable the network to aggregate global feature information. At the same time, a vanishing point detection module is introduced, and a joint weighted loss function is designed to be trained in coordination with the lane detection task to improve the generalization ability of the lane detection model. Experimental results on the OpenLane and CurveLanes datasets show that the detection effect of the OTVLD-Net model has reached the current advanced level. In particular, the accuracy on the OpenLane dataset is 6.4% higher than the F1 score of the second-ranked model, and the average performance in different challenging scenarios is also improved by 8.9%. At the same time, when ResNet-18 is used as the template feature extraction network, the model achieves a speed of 103FPS and a computing power of 14.2 GFlops, achieving good performance while ensuring real-time performance. Full article