Search Results (182)

Roman cities represent the Empire’s broader approach to urban planning, characterized by geometric precision and a strategic layout. Their spatial organization reflects the underlying military and administrative objectives, which can be better understood through new analytical tools. This research investigates the Roman military strategy behind the establishment of Timgad, a Roman archeology in Algeria, using Space Syntax Analysis (SSA) to examine its spatial and urban structure. This study highpoints how its spatial configuration was intricately linked to military tactics aimed at asserting control and dominance by analyzing the city’s grid-like layout and applying SSA indicators, such as Connectivity, Integration, Entropy, Control, Controllability and Through Vision (via Axial Map and Visibility Graph Analysis). The results show high value in these indicators, especially in areas where military structures were strategically located along main roads and key urban nodes, demonstrating a careful exertion to maintain surveillance and authority over space. This spatial configuration reveals a deep synergy connecting military logic and urban design, sustaining the idea that Roman town planning supported both functional and symbolic roles in establishing imperial authority. This study concludes that Roman military strategy was not only central to their territorial expansion but also instrumental in shaping long-lasting urban models, influencing the structure of colonial cities far beyond their time. Timgad thus serves as an influential case of how military requirements shaped the built environment in the Roman Empire. Full article

Efficient autonomous exploration in unknown obstacle cluttered environments with interior obstacles remains a challenging task for mobile robots. In this work, we present a novel exploration process for a non-holonomic agent exploring 2D spaces using onboard LiDAR sensing. The proposed method generates velocity commands based on the calculation of the solution of an elliptic Partial Differential Equation with Dirichlet boundary conditions. While solving Laplace’s equation yields collision-free motion towards the free space boundary, the agent may become trapped in regions distant from free frontiers, where the potential field becomes almost flat, and consequently the agent’s velocity nullifies as the gradient vanishes. To address this, we solve a Poisson equation, introducing a source point on the free explored boundary which is located at the closest point from the agent and attracts it towards unexplored regions. The source values are determined by an exponential function based on the shortest path of a Hybrid Visibility Graph, a graph that models the explored space and connects obstacle regions via minimum-length edges. The computational process we apply is based on the Walking on Sphere algorithm, a method that employs Brownian motion and Monte Carlo Integration and ensures efficient calculation. We validate the approach using a real-world platform; an AmigoBot equipped with a LiDAR sensor, controlled via a ROS-MATLAB interface. Experimental results demonstrate that the proposed method provides smooth and deadlock-free navigation in complex, cluttered environments, highlighting its potential for robust autonomous exploration in unknown indoor spaces. Full article

The main anthropogenic sources of air pollution in big cities are vehicular traffic and industrial activities. The emissions of primary pollutants are produced directly from the combustion of fossil fuels of vehicles and industry, whilst the secondary pollutants, such as tropospheric ozone ($O_3$), are produced from precursors like Carbon monoxide ($CO$), among others, and meteorological factors such as radiation. In this study, we analyze the time series of $\mathrm{CO}$ and ${\mathrm{O}}_3$ concentrations monitored by the RAMA program between 2019 and 2023 in the southwest of the Mexico City Metropolitan Area, encompassing the COVID-19 lockdown period declared from March to September–October 2020. After removing cyclic patterns and normalizing the data, we applied informational and topological methods to investigate variability changes in the concentration time series, particularly in response to the lockdown. Following the onset of lockdown measures in March 2020—which led to a significant reduction in industrial activity and vehicular traffic—the informational quantities $N_X$ and Fisher Information Measure (FIM) for $\mathrm{CO}$ revealed significant shifts during the lockdown, while these metrics remained stable for ${\mathrm{O}}_3$. Also, the coefficient of variation of the degree $C{V}_k$, which was defined for the network constructed for each series by the Visibility Graph, showed marked changes for $\mathrm{CO}$ but not for ${\mathrm{O}}_3$. The combined informational and topological analysis highlighted distinct underlying structures: $\mathrm{CO}$ exhibited localized, intermittent emission patterns leading to greater structural complexity, while ${\mathrm{O}}_3$ displayed smoother, less organized variability. Also, the temporal variation of the FIM and $N_X$ provides a means to monitor the evolving statistical behavior of the $CO$ and $O_3$ time series over time. Finally, the Visibility Graph (VG) method shows a behavioral trend similar to that shown by the informational quantifiers, revealing a significant change during the lockdown for $CO$, although remaining almost stable for $O_3$. Full article

Visible-Infrared Person Re-Identification (VI-ReID) is of crucial importance in applications such as monitoring and security. However, challenges faced from intra-class variations and cross-modal differences are often exacerbated by inaccurate infrared analysis and insufficient structural modeling. To address these issues, we propose Parsing-guided Differential Enhancement Graph Learning (PDEGL), a novel framework that learns discriminative representations through a dual-branch architecture synergizing global feature refinement with part-based structural graph analysis. In particular, we introduce a Differential Infrared Part Enhancement (DIPE) module to correct infrared parsing errors and a Parsing Structural Graph (PSG) module to model high-order topological relationships between body parts for structural consistency matching. Furthermore, we design a Position-sensitive Spatial-Channel Attention (PSCA) module to enhance global feature discriminability. Extensive evaluations on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our PDEGL method achieves competitive performance. Full article

In industrial systems, sensors often classify collected time series data for incipient fault diagnosis. However, time series data from sensors during the initial stages of a fault often exhibits minor fluctuation characteristics. Existing time series classification (TSC) methods struggle to achieve high classification accuracy when these minor fluctuations serve as the primary distinguishing feature. This limitation arises because the low-amplitude variations of these fluctuations, compared with trends, lead the classifier to prioritize and learn trend features while ignoring the minor fluctuations crucial for accurate classification. To address this challenge, this paper proposes a novel graph-based time series classification framework, termed MFSI-TSC. MFSI-TSC first extracts the trend component of the raw time series. Subsequently, both the trend series and the raw series are represented as graphs by extracting the “visible relationship” of the series. By performing a subtraction operation between these graphs, the framework isolates the differential information arising from the minor fluctuations. The subtracted graph effectively captures minor fluctuations by highlighting topological variations, thereby making them more distinguishable. Furthermore, the framework incorporates optimizations to reduce computational complexity, facilitating its deployment in resource-constrained sensor systems. Finally, empirical evaluation of MFSI-TSC on both real-world and publicly available datasets demonstrates its effectiveness. Compared with ten benchmark methods, MFSI-TSC exhibits both high accuracy and computational efficiency, making it more suitable for deployment in sensor systems to complete incipient fault detection tasks. Full article

The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the “preclinical” stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using t-tests to reduce noise. Subsequent statistical analysis reveals significant disparities in the structure of WVG networks between preAD and normal subjects. Furthermore, Principal Component Analysis (PCA) was applied to condense the input data into its principal features. Leveraging these PCA components as input features, several machine learning algorithms are used to classify preAD vs. normal subjects. To enhance classification accuracy and robustness, an ensemble method is employed alongside the classifiers. Our framework achieved an accuracy of up to 92% discriminating preAD from normal old using both linear and non-linear classifiers, signifying the efficacy of combining WVG and ensemble learning in identifying very early AD from EEG signals. The framework can also improve clinical efficiency by reducing the amount of data required for effective classification and thus saving valuable clinical time. Full article

Ventricular arrhythmias (VAs) are critical cardiovascular diseases that require rapid and accurate detection. Conventional approaches relying on multi-lead ECG or deep learning models have limitations in computational cost, interpretability, and real-time applicability on wearable devices. To address these issues, a lightweight and interpretable framework based on multiple complex networks was proposed for the detection of life-threatening VAs using short-term single-lead ECG signals. The input signals were decomposed using the fixed-frequency-range empirical wavelet transform, and sub-bands were subsequently analyzed through multiscale visibility graphs, recurrence networks, cross-recurrence networks, and joint recurrence networks. Eight topological features were extracted and input into an XGBoost classifier for VA identification. Ten-fold cross-validation results on the MIT-BIH VFDB and CUDB databases demonstrated that the proposed method achieved a sensitivity of 99.02 ± 0.53%, a specificity of 98.44 ± 0.43%, and an accuracy of 98.73 ± 0.02% for 10 s ECG segments. The model also maintained robust performance on shorter segments, with 97.23 ± 0.76% sensitivity, 98.85 ± 0.95% specificity, and 96.62 ± 0.02% accuracy on 2 s segments. The results outperformed existing feature-based and deep learning approaches while preserving model interpretability. Furthermore, the proposed method supports mobile deployment, facilitating real-time use in wearable healthcare applications. Full article

In the vernacular architectural history of the Arabian Gulf region (the Gulf), the courtyard house is a common domestic typology. Islamic and sociological precepts regulate the design and the function of vernacular courtyard dwellings, resulting in homogeneity and harmony in the entire urban fabric of historic settlements. In this research endeavor, the aim is to investigate the degree to which the shaping of the spatial form in a sample of vernacular courtyard houses in the Gulf inscribes socio-cultural factors. It sheds light on visibility graph analysis, human behavior, and the system of activities in the domestic space. As a hypothesis, visibility and connectivity analysis could prove the existence of common spatial patterns among courtyards in the vernacular courtyard houses of the Gulf, attributing it to the similar socio-cultural context, the climatic effect, and the architectural and construction knowledge of the region. This study utilizes a collection of courtyard houses from the Gulf as a pilot study to investigate the emerging patterns in the spatial analysis and room layout, or in the distribution of activities or functions in the domestic space. It, therefore, offers a visual analysis (VGA) of six regional courtyard houses from each Gulf country that were built during the period 1850–1950. This study anticipates an affirmation of a direct inscription of socio-cultural factors in the spatial form of the courtyard house in the Gulf. Conclusively, a sustainable, organic linkage between architecture and society exists where the three criteria of housing spatial form, socio-cultural factors, and system of activities interact. Full article

In this study, we analyzed the microearthquake seismicity in the Enguri area (Georgia) recorded between 2020 and 2023 using a newly installed seismic network developed within the DAMAST project. The high sensitivity of the network allowed the detection of even very small seismic events, enabling a detailed investigation of the temporal dynamics of local seismicity. Statistical analyses suggest that the seismic activity around the Enguri Dam is influenced by a combination of natural tectonic processes and subtle reservoir-induced stress changes. While the dam does not appear to exert strong seismic forcing, the observed ≈7-month delay between water level variations and seismicity may indicate a triggering effect. Localized stress variations and temporal clustering further support the hypothesis that water level fluctuations modulate seismic activity. Additionally, the mild persistence in interoccurrence times is consistent with a stress accumulation and delayed triggering mechanism associated with reservoir loading. Full article

Surface-guided tram systems are increasingly being recognised not only as mobility instruments but also as agents of urban regeneration that reshape spatial and social dynamics. This study evaluates the configurational impact of the Trambesòs tram in Barcelona on accessibility, integration, and urban cohesion within the Levante del Besòs area. A Space Syntax analysis was conducted in UCL DepthmapX for axial map analysis and visual graph analysis within a 500 m radius around each station. Three typologies of intervention (site-specific, articulation axes, and saturation pieces) guided the assessment. This analysis shows that Avinguda Diagonal and Avinguda Meridiana are primary structural corridors, while stations Glòries, Ca l’Aranyó, and Pere IV recorded the highest accessibility and visual openness. The results indicate that targeted interventions have positive impacts on the Space Syntax metrics regardless of their spatial centrality, highlighting the critical role of this diverse intervention typology in shaping the study area’s spatial configuration and influencing a hierarchy of social appropriation and use. It is concluded that the Trambesòs tram and associated urban interventions have jointly enhanced centrality and permeability in key sectors, and specific peripheral enclaves have local functioning. These findings, focused on spatial and morphological patterns, may support future interventions in urban design and mobility planning. Although the analysis centres on spatial configuration, future research may integrate socioeconomic variables to broaden the understanding of regeneration processes. Full article

This study introduces a novel forecasting framework that identifies and predicts recurrently emerging structural patterns in stock trends using rising visibility graphs (RVGs) and the Weisfeiler–Lehman (WL) subtree kernel. The proposed method, RVGWL, addresses a key limitation of traditional visibility graphs, namely the structural indistinguishability between rising and falling trends, by selectively constructing edges only along upward price movements. This approach produces graph representations that capture direction-sensitive market dynamics and facilitate the extraction of meaningful topological features from price data. By applying the WL kernel, RVGWL quantifies structural similarities between graph-transformed time series, enabling the identification of structurally similar preceding patterns and the probabilistic forecasting of their subsequent trajectories based on nine canonical trend templates. Experiments on time series data from four major stock indices and their constituent stocks during the year 2023—characterized by diverse market regimes across the U.S., Japan, the U.K., and China—demonstrate that RVGWL consistently outperforms classical rule-based strategies. These results support the predictive value of recurring topological structures in financial time series and higight the potential of structure-aware forecasting methods in quantitative analysis. Full article

Elevator fault diagnosis is critical for ensuring operational safety and reliability in modern vertical transportation systems. Traditional approaches, which rely on time- and frequency-domain signal analysis, often struggle with the issues such as noise sensitivity, inadequate feature extraction, and limited adaptability to complex scenarios. To address these challenges, this paper proposes a Graph Attention Recurrent Network (GARN) which integrates graph-structured signal representation with spatiotemporal feature learning. The GARN employs a limited penetrable visibility graph to transform raw vibration signals into noise-robust graph topologies, preserving critical patterns while suppressing high-frequency noise through controlled edge penetration. An adaptive attention mechanism dynamically fuses triaxial features to prioritize the most relevant information for fault diagnosis. The GARN combines a graph convolutional network to extract spatial correlations and a gated recurrent unit to capture temporal fault progression, enabling holistic and accurate fault classification. Experimental results based on real-world elevator datasets demonstrate the superior performance of the GARN, showcasing its strong noise resistance, adaptability to complex fault conditions, and ability to provide reliable and timely fault diagnosis, making it a robust solution for modern elevator systems. Full article

A new multi-robot path planning (MRPP) algorithm for 2D static environments was developed and evaluated. It combines a roadmap method, utilising the visibility graph (VG), with the algebraic connectivity (second smallest eigenvalue (λ₂)) of the graph’s Laplacian and Dijkstra’s algorithm. The paths depend on the planning order, i.e., they are in sequence path-by-path, based on the measured values of algebraic connectivity of the graph’s Laplacian and the determined weight functions. Algebraic connectivity maintains robust communication between the robots during their navigation while avoiding collisions. The algorithm efficiently balances connectivity maintenance and path length minimisation, thus improving the performance of path finding. It produced solutions with optimal paths, i.e., the shortest and safest route. The devised MRPP algorithm significantly improved path length efficiency across different configurations. The results demonstrated highly efficient and robust solutions for multi-robot systems requiring both optimal path planning and reliable connectivity, making it well-suited in scenarios where communication between robots is necessary. Simulation results demonstrated the performance of the proposed algorithm in balancing the path optimality and network connectivity across multiple static environments with varying complexities. The algorithm is suitable for identifying optimal and complete collision-free paths. The results illustrate the algorithm’s effectiveness, computational efficiency, and adaptability. Full article

Art exhibition spaces increasingly emphasize visitor experience, yet the relationships among spatial structure, visitor behavior, and emotional response remain unclear. Traditional space syntax analyses typically focus on physical spatial structures, insufficiently capturing visitors’ emotional and cognitive experiences. To address these gaps, this study presents an integrative evaluation framework that combines space syntax theory with multimodal physiological measurements to systematically assess spatial design performance in art exhibition environments. Eye-tracking and heart rate variability (HRV) experiments were conducted to investigate how spatial configuration affects visual attention and emotional responses. Visibility graph analysis, spatial integration metrics, and regression modeling were applied using the third-floor temporary exhibition hall of the Pudong Art Museum in Shanghai as a case study. The results revealed that HRV levels (β = −7.92) were significantly predicted via spatial integration, and the relationship between spatial integration and the number of fixations was partially mediated by HRV (indirect effect: β = −0.36; direct effect: β = 8.23). Additionally, zones with higher occlusivity were associated with more complex scanpaths (mean complexity: 0.14), whereas highly integrated regions triggered more fixations (mean = 10.54) and longer total fixation durations (mean = 2946.98 ms). Therefore, spatial syntax, when coupled with physiological indicators, provides a robust and actionable method for evaluating and optimizing exhibition space design. Full article

In the context of evolving Smart Cities, the integration of drone technology and distributed computing paradigms presents significant potential for enhancing urban infrastructure and services. This paper proposes a comprehensive approach to optimizing urban delivery logistics through a cloud-based model that employs Ant Colony Optimization (ACO) for planning and Model Predictive Control (MPC) for trajectory tracking within a broader Computing Continuum framework. The proposed system addresses the Capacitated Vehicle Routing Problem (CVRP) by considering both drone capacity constraints and autonomy, using the ACO-based algorithm to efficiently assign delivery destinations while minimizing travel distances. Collision-free paths are computed by using a Visibility Graph (VG) based approach, and MPC controllers enable drones to adapt to dynamic obstacles in real time. Additionally, this work explores how clusters of drones can be deployed as edge devices within the Computing Continuum, seamlessly integrating with IoT sensors and fog computing infrastructure to support various urban applications, such as traffic management, crowd monitoring, and infrastructure inspections. This dual-architecture approach, combining the optimization capabilities of ACO with the flexible, distributed nature of the Computing Continuum, allows for scalable and efficient urban drone deployment. Simulation results validate the effectiveness of the proposed model in enhancing delivery efficiency and collision avoidance while demonstrating the potential of integrating drone technology into Smart City environments for improved data collection and real-time response. Full article