Search Results (105)

This work investigates methods for predicting low-cycle fatigue life by employing new energy-based fatigue damage measures. The primary goal of this research is to evaluate whether fatigue life can be predicted based on an energy accumulation graph, proposed as a generalization of the isodamage lines concept. The efficiency of fatigue life predictions using this approach, derived from the empirical linear Palmgren–Miner hypothesis, is compared against the physically grounded Unified Mechanics Theory thermodynamic approach, which allows for general understanding of material degradation, in contrast to empirical approaches. The study also accounts for the influence of anisotropy resulting from the sheet rolling process on the fatigue response of S420M steel. Samples were tested in orientations both parallel to the rolling direction and perpendicular to the sheet surface. Microstructural analysis revealed a visible banded structure in the perpendicular samples, which is a consequence of anisotropy. The fatigue life of samples taken perpendicular to the sheet surface was lower than that of parallel samples. Verification of the linear Palmgren–Miner damage summation hypothesis, using both the classical fatigue chart and the cumulative energy chart, resulted in calculated fatigue life consistently higher than the experimental fatigue life in all cases. The reduction in fatigue life ranged from 40% (for total strain amplitude equal to 1.0%) to almost 290% for a strain amplitude of 0.25%. A comparative analysis of the unit loop energy shows that at all tested levels of strain amplitude, the unit loop energy of parallel samples is higher than that of samples perpendicular to the surface. Full article

Historic religious cities are living examples of cultural landscapes where spiritual traditions, heritage, and visitor experiences combine to demonstrate a timeless experience. It is very challenging to achieve balance among the demands of mass pilgrimage, heritage preservation, and urbanization. Govardhan, India is a Hindu religious town with historical significance. Millions of pilgrims travel to Govardhan every year to perform parikrama and take a holy dip in kunds. The quality of the visitor experience, spatial coherence, and heritage conservation are all at risk due to increasing urbanization and tourism. The study intends to create a paradigm for the sustainable management of religious heritage towns by evaluating the factors involving visitor perception, historical significance, and spatial visibility, employing a combination of computational methods and cognitive assessments. The study employed space syntax tools (visibility graph analysis and isovist area analysis) to quantify spatial significance (SS) and identify patterns of openness, congestion, and visibility along the parikrama route of Govardhan. By examining pilgrims’ cognitive surveys for openness, orientation, congestion, and spiritual impression, a cognitive index (CI) and heritage importance scores (HIS) have been developed. The computed spatial significance (SS) has been correlated with cognitive index (CI) and heritage importance (HIS) scores to create an experiential priority index (EPI). The study employs a mixed-method approach that incorporates heritage significance scoring, cognitive surveys, and spatial analytics, including methods such as the isovist area analysis and visibility graph analysis. In order to assess how spatial arrangement and intangible perceptions together influence visitor experience, these statistics are further combined using a composite experiential priority index (EPI). The findings show a strong correlation between spiritual orientation, visual connectivity, and spatial openness; locations such as ‘punchari ka lota temple’ and ‘kusum sarovar’ are high-priority nodes. In accordance with United Nation Sustainable Development Goals (SDGs) (11, 9, 12, 4.7, and 8.9), this research proposes a heritage impact assessment (HIA) framework that provides workable solutions for ecological restoration, heritage-sensitive zoning, sustainable pilgrimage management, and enhanced tourism. Full article

We present a quantitative and qualitative evaluation of despeckling filters based on a set of Haralick-derived features and the Jensen–Shannon Divergence obtained from ratio images. To that aim, we propose a normalized composite index, called the Texture-Divergence Measurement ($TDM$), that describes the statistical and structural behavior of the filtered images. Complementary qualitative analysis using Image Horizontal Visibility Graphs (IHVGs) confirms the results of the proposed metric. The combination of the proposed $TDM$ metric and IHVG visualization provides a robust framework for assessing despeckling performance from both statistical and structural perspectives. Full article

Traditional Environmental, Social, and Governance (ESG) assessments rely heavily on corporate disclosures and third-party ratings, which are often delayed, inconsistent, and prone to bias. These limitations leave stakeholders without timely visibility into rapidly evolving ESG events. These assessment frameworks also fail to capture the dynamic nature of ESG issues reflected in public news media. This research addresses these limitations by proposing and implementing an automated framework utilising Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Knowledge Graphs (KG), to analyse ESG news data for companies listed on major stock indices. The methodology involves several stages: collecting a registry of target companies; retrieving relevant news articles; applying Named Entity Recognition (NER), sentiment analysis, and ESG domain classification; and constructing a linked property knowledge graph to structure the extracted information semantically. The framework culminates in an interactive dashboard for visualising and querying the resulting graph database. The resulting knowledge graph supports comparative inferential analytics across indices and sectors, uncovering divergent ESG sentiment profiles and thematic priorities that traditional reports overlook. The analysis also reveals comparative insights into sentiment trends and ESG focus areas across different exchanges and sectors, offering perspectives often missing from traditional methods. Findings indicate differing ESG sentiment profiles and thematic focuses between the UK (FTSE) and Australian (ASX) indices within the analysed dataset. This study confirms AI/KG’s potential for a modular, dynamic, and semantically rich ESG intelligence approach, transforming unstructured news into interconnected insights. Limitations and areas for future work, including model refinement and integration of financial data, are also discussed. This proposed framework augments traditional ESG evaluations with automated, scalable, and context-rich analysis. Full article

This article presents a novel methodology for assessing the visibility and reachability of cultural heritage objects within urban structures, tested through a pilot study in Kaunas New Town (Naujamiestis), Lithuania. While heritage protection policies usually emphasize architectural composition, details, and external visual protection zones, interior urban views and functional spatial dynamics remain underexplored. Building upon Space Syntax theory and John Peponis’s concepts of distributive and correlative situational codes, this study integrates detailed visibility analysis with graph-based accessibility modeling. Visibility was quantified through a raster-based viewshed analysis of building footprints and street-based observation points, producing a normalized visibility index. Reachability was examined using a new graph indicator based on the ratio of reachable polygon area to perimeter (A²/P), further weighted by the area of adjacent buildings to reflect the potential for urban activity. Validation against independent datasets (population, companies, and points of interest) confirmed the superior explanatory power of the proposed indicator over traditional centralities. By combining visibility and reachability in a bivariate matrix, the model provides insights into heritage objects’ dual roles as landmarks, everyday hubs, or hidden sites, and offers predictive capacity for evaluating urban transformations and planning interventions. Full article

Effective safety management in air traffic is essential for operational reliability and risk reduction. We propose a multi-channel fusion framework to predict intervals between consecutive air traffic incidents by combining structural, semantic, and temporal information. Inter-incident time series are transformed into complex networks via visibility graphs to learn node embeddings capturing structural recurrence. Semantic features are derived through latent Dirichlet allocation (LDA) and bidirectional encoder representations from Transformers (BERT) embeddings to reveal latent risk-related topics, and an adaptive spectral filter enhances temporal features. These are processed through three modules: a gravity-inspired visibility graph model (GVG), a semantic-aware LSTM (Sem-LSTM), and a spectral-enhanced temporal convolutional network (Spec-TCN). An attention mechanism fuses all modules to predict incident intervals. Using 1298 real-world incidents from China’s Central and Southern Region for validation, the model achieves a mean absolute error of 1.42 h and sMAPE of 17.5%. SHAP analysis indicates that structural similarity and incident topics jointly drive prediction. By integrating interval predictions with topic cues, we construct a safety management framework enabling proactive decision-making. This framework delivers a practical bridge from interval predictions to proactive air traffic control (ATC) decisions. Full article

Industrial tourism has become an increasingly important means of promoting corporate identity and fostering public engagement, yet many factory tours suffer from fragmented layouts, congestion, and low visitor engagement. This study addresses these challenges by developing a hybrid framework that integrates expert-driven spatial zoning with bottom-up visitor analytics. Using an optical lens manufacturer in Taiwan as a case study, we applied a three-step process: (1) Delphi-based zoning of key subareas into functional zones, (2) empirical analysis of visitor movement, feedback, and shadowing data, and (3) computational spatial evaluation through Visibility Graph Analysis (VGA). The findings revealed thematic inconsistencies, overlooked exhibits, and bottlenecks that disrupted narrative flow and reduced engagement. Spatial reorganization—such as relocating interactive subareas to visually integrated zones—enhanced circulation, storytelling alignment, and experiential coherence. A complementary service blueprint linked spatial redesign to operational delivery, ensuring consistency between frontstage activities and backstage support. The data-driven spatial analytics validated the effectiveness of this study’s hybrid approach—combining expert-driven insights with grounded visitor behavior data—to optimize factory tours. Spatial efficiency contributes to reduced energy use and congestion, participatory experiences enhance education and inclusivity, and improved visitor satisfaction strengthens brand resilience and economic viability. The framework thus provides a replicable and sustainable model for industrial tourism development across diverse manufacturing sectors. Full article

This article develops an interdisciplinary framework that applies topological and graph-theoretical methods to public procurement markets and digital platform economies. Conceptualizing legal–economic interactions as dynamic networks of nodes and edges, we show how structural properties—centrality, clustering, connectivity, and boundary formation—shape contestability, resilience, and compliance. Using EU-relevant contexts (public procurement directives and the Digital Markets Act), we formalize network representations for buyers, suppliers, platforms, and regulators; define operational indicators; and illustrate an empirical, value-weighted buyer → supplier network to reveal a sparse but highly modular architecture with a high-value backbone. We then map these structural signatures to concrete legal levers (lotting and framework design, modification scrutiny, interoperability and data-access duties) and propose dashboard-style diagnostics for proactive oversight. The findings demonstrate how topological modelling complements doctrinal analysis by making hidden architectures visible and by linking measurable structure to regulatory outcomes. We conclude with implications for evidence-informed regulatory design and a research agenda integrating graph analytics, comparative evaluation across jurisdictions, and machine-learning-assisted anomaly detection. Full article

This research offers a fresh lens on Iranian cultural heritage houses by interrogating the overlooked role of Orosi windows in shaping socio-spatial accessibility and visual permeability. While these decorative stained-glass features are traditionally appreciated for their artistry and environmental performance, their functional impact on visibility and spatial interaction remains underexplored. The study aims to assess how window visual permeability influences socio-spatial accessibility within the hierarchical layouts of historic houses in Iran. To this end, a quantitative approach was adopted, applying convex space analysis to examine socio-spatial dynamics and visibility graph analysis (VGA) to study visual permeability within the space syntax framework. Fifteen heritage houses were analysed under two conditions using VGA: their current status quo, and a hypothetical model in which windows were treated as fully transparent, allowing unobstructed sightlines. The analyses demonstrated that removing window barriers enhanced visual integration and connectivity across all cases. Statistical t-tests further confirmed that these differences were significant, establishing that Orosi windows exert a profound influence on visual permeability. Beyond their ornamental and climatic roles, this study redefines Orosi windows as dynamic cultural devices that actively script human visibility, privacy, and interaction, revealing how historical design intelligence can inform sustainable, culturally responsive architectural practices. Full article

The influence of traditional Chinese ritual culture on courtyard spatial sequences is widely acknowledged. However, quantitative analytical methods, such as space syntax, have rarely been applied in studies of ritual–residential space relations. This study uses space syntax, specifically Visibility Graph Analysis (VGA) and axial maps, to conduct a quantitative study of the spatial relationship between ritual and residential areas in Prince Kung’s Mansion. The VGA results indicate a distinct gradient of visual integration, which decreases progressively from the outward-oriented ritual areas, such as the palace gate and halls, through the transitional domestic ritual areas to the inward-oriented residential areas, such as Xijin Zhai and Ledao Tang. This pattern demonstrates a positive correlation between spatial visibility and ritual hierarchy. The axial map results confirm that the central axis and core ritual spaces exhibit the highest spatial connectivity, reflecting their supreme ritual status. More importantly, spatial connectivity is intensified during ritual activities compared to in daily life, indicating that enhanced spatial connectivity is required during rituals. Ritual spaces are characterized by extroversion, high visibility, and connectivity, while residential spaces prioritize introversion and minimal exposure. The deliberately designed ritual–residential architectural spatial sequence of Prince Kung’s Mansion articulates Confucian ideological principles, such as centrality as orthodoxy, gender segregation, and hierarchy. This study visually and quantitatively illustrates the harmony between ritual and residential spaces in Prince Kung’s Mansion. It enhances our understanding of the mechanisms of expression of courtyard ritual cultural spaces, providing evidence-based guidance for functional adaptive transformations in heritage conservation practices. It also offers a fresh perspective on the analysis of courtyard ritual spaces. Full article

In response to the growing relevance of shared housing models such as co-living and co-housing, this study investigates how spatial configuration affects the experience and negotiation of privacy in shared domestic environments. While privacy is often treated as a subjective or cultural concern, this research adopts a spatial perspective to examine its morphological underpinnings. Using space syntax methods, the study analyses contemporary shared housing models, focusing on three shared housing developments in Barcelona. Through Visual Graph Analysis (VGA), spatial parameters, including integration, through vision, control, and controllability values, are applied to assess the degree of accessibility, visibility, and spatial separation within and between private and communal areas. The results reveal distinct configurational patterns that correlate with different privacy gradients, identifying how spatial arrangement enables or restricts autonomy and co-presence among residents. The study concludes that privacy in shared housing is not only a matter of design intention but is embedded in the spatial logic of dwelling morphology: exposed and controlled spaces provide less privacy but enhance sociability, while spatial elements such as boundaries and transitions play an important role in managing privacy gradation and degrees. These findings offer a framework for understanding and designing shared living environments that are better attuned to the complexities of everyday privacy needs. Full article

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

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

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