Search Results (1,092)

This research aims to investigate the Italian architectural heritage in Brazil through the analysis of the ‘Santa Casa de Misericórdia’ hospital in São Carlos, in the state of São Paulo. As part of the KNOW.IT national project, the work aims to recover and digitally enhance Italian heritage abroad from the 19th and 20th centuries. The buildings analysed were either designed or built by Italian architects who emigrated to South America or constructed using materials and techniques typical of Italian architecture of those years. The hospital, designed by the Italian architect Samuele Malfatti in 1891, was chosen for its historical value and its role in the urban context of the city of São Carlos, which, moreover, continues to perform its function even today. The study aims to create a digital archive with 3D models and two-dimensional graphical drawings. The methodology includes historical analysis, photogrammetric survey, and digital modelling using Agisoft Metashape and 3DF Zephyr software. A total of 636 images were processed, with the maximum resolution achieved in the models being 3526 × 2097 pixels. The results highlight the influence of Italian architecture on late 19th-century São Carlos and promote its virtual accessibility and wide-ranging knowledge. Full article

Air pollution source tracing is vital for effective pollution prevention and control, yet traditional methods often require large amounts of manual data, have limited cross-regional generalizability, and present challenges in capturing complex pollutant interactions. This study introduces AirTrace-SA (Air Pollution Tracing for Source Attribution), a novel hybrid deep learning model designed for the accurate identification and quantification of air pollution sources. AirTrace-SA comprises three main components: a hierarchical feature extractor (HFE) that extracts multi-scale features from chemical components, a source association bridge (SAB) that links chemical features to pollution sources through a multi-step decision mechanism, and a source contribution quantifier (SCQ) based on the TabNet regressor for the precise prediction of source contributions. Evaluated on real air quality datasets from five cities (Lanzhou, Luoyang, Haikou, Urumqi, and Hangzhou), AirTrace-SA achieves an average $R^2$ of 0.88 (ranging from 0.84 to 0.94 across 10-fold cross-validation), an average mean absolute error ($MAE$) of 0.60 (ranging from 0.46 to 0.78 across five cities), and an average root mean square error ($RMSE$) of 1.06 (ranging from 0.51 to 1.62 across ten pollution sources). The model outperforms baseline models such as 1D CNN and LightGBM in terms of stability, accuracy, and cross-city generalization. Feature importance analysis identifies the main contributions of source categories, further improving interpretability. By reducing the reliance on labor-intensive data collection and providing scalable, high-precision source tracing, AirTrace-SA offers a powerful tool for environmental management that supports targeted emission reduction strategies and sustainable development. Full article

As China shifts from rapid to high-quality development, urban growth has exhibited allometric patterns. This study evaluated land use efficiency from the perspective of architectural space, focusing on 41 cities in the Yangtze River Delta urban agglomeration from 2010 to 2020. A land development intensity index was constructed at both the provincial and municipal levels using the entropy weight method, integrating floor area ratio, building density, and functional mix. The spatiotemporal characteristics of land development intensity and population density were analyzed, and a coordination coupling model was applied to identify mismatches between land and population. The results reveal: (1) Temporally, the imbalance of “more people, less land” in the Yangtze River Delta diminished. Spatially, leading regions exhibit a diffusion effect. Shanghai showed a decline in both population density and development intensity; Zhejiang maintained balanced development; Jiangsu experienced accelerated growth; and Anhui showed signs of catching up. (2) Although the two indicators showed a high coupling degree and strong correlation, the coordination degree remained low, indicating poor quality of correlation. The land-population relationship demonstrated a fluctuating pattern of “strengthening–weakening” over time. Shanghai exhibited the highest coordination, while more than half of the cities in Jiangsu, Zhejiang, and Anhui still needed optimization. (3) Unlike previous findings that linked such patterns to shrinking cities, in this transformation stage, the number of cities where land development intensity exceeded population density continued to grow in advanced regions. This study first applied 3D building data at the macro scale to support differentiated spatial policies. Full article

This study examines the impact of the Beijing–Tianjin–Hebei (BTH) coordinated development policy on the regional industrial network structure, with a focus on the significance of electricity consumption data in social network analysis (SNA). Utilizing a gravity model integrated with electricity consumption data, this research employs centrality analysis and Lambda analysis to compare changes in the steel industry network before and after policy implementation. The findings reveal that traditional models relying solely on indicators such as population and Gross Domestic Product (GDP) fail to comprehensively capture regional economic linkages, whereas incorporating electricity consumption data enhances the model’s accuracy in identifying core nodes and latent connections. Post policy implementation, the centrality of Beijing and Tianjin increased significantly, reflecting their transition from production hubs to centers for research and development (R&D) and management, while Shijiazhuang’s pivotal role diminished. This study also uncovers a “core–periphery” structure in the BTH urban network, where core cities (Beijing, Tianjin, and Shijiazhuang) dominate resource allocation and information flow, while peripheral cities exhibit uneven development. These results provide a scientific basis for optimizing regional coordinated development policies and underscore the critical role of electricity consumption data in refining regional economic analysis. Incorporating electricity consumption data into the gravity model significantly enhances its explanatory power by capturing hidden economic ties and improving policy evaluation, offering a more accurate and dynamic assessment of regional industrial linkages. Full article

During heavy rainfalls, overflowing sewage water flows from the Combined Sewer Overflow (CSO) chambers and pollutes the Trnávka River in Trnava, Slovakia. This paper aims to propose water retention measures for the Trnávka River as a remediation technique for CSO-affected watercourses, which can contribute to the ‘flushing’ of the riverbed. During heavy rainfalls, the Trnávka River is polluted by solid, non-soluble materials, which produce unpleasant odors and are the subject of numerous complaints by citizens, particularly during low water levels. Three inflatable rubber weirs were designed, and their design was verified using a 1D numerical model of the Trnávka River. The simulations of the proposed measures performed in the HEC-RAS 5.0 software excluded the adverse effect of the backwater on the functioning of the CSO chambers in the city of Trnava during normal flow rates and confirmed that, even after installation of the weirs, the transition of the flood wave will pass in the riverbed, not causing the flooding of the adjacent area. The chemical–physical study of the Trnávka River confirmed our assumption that higher flow rates, which can be secured by the regulation of the proposed weirs, can contribute to the purity of the watercourse in the city of Trnava. Full article

The rapid urbanization and technological advancements in the United Arab Emirates (UAE) have placed its modern architectural heritage from the 1970s and 1980s at increasing risk of being unrecognized and lost, particularly in Dubai following the discovery of oil. This research addresses the critical need for the documentation and heritage representation of Dubai’s modern heritage, a city undergoing rapid transformation within a globalized urban landscape. Focusing on the Nasser Rashid Lootah Building (Toyota Building), an iconic early 1970s residential high-rise representing the modern architecture of Dubai and a significant milestone in its architectural history, this study explores a replicable and cost-effective approach to digitally document and conserve urban heritage under threat. The existing building was meticulously documented and analyzed to highlight its enduring value within the fast-changing urban fabric. Through the innovative combination of drone photography, ground-based photography, and HBIM, a high-resolution 3D model and a semantically organized HBIM prototype were generated. This research demonstrates a replicable measure for identifying architectural values, understanding modernist design typologies, and raising local community awareness about Dubai’s modern heritage. Ultimately, this study contributes toward developing recognition criteria and guiding efforts in documenting modern high-rise buildings as vital heritage worthy of recognition, documentation, and future conservation in the UAE. Full article

Background: Adolescents with a chronological age of less than 15 years or a gynecological age of less than 2 years may have a higher probability of complications because they are more likely to conceive within 1 to 2 years of menarche and, therefore, are still growing and maturing. This could impair their ability to adapt to the physiological demands of pregnancy. Objective: To evaluate the relationship between chronological age and gynecological age with low birth weight and small for gestational age among adolescent mothers in Mexico City. Methods: A retrospective cohort design of adolescent mother–child dyads was conducted. The study followed 1242 adolescents under 19 years of age and their children, collecting data on physical, socioeconomic, and clinical characteristics, including hemoglobin levels. Low birth weight was assessed using the Intergrowth-21st project standards and categorized as above or below 2500 g. The mothers were grouped by chronological age (<15 years and ≥15 years) and gynecological age (<3 years and ≥3 years). Adjusted odds ratios were calculated using binary logistic regression models. The outcome variables were low birth weight and small for gestational age. The independent variables included gynecological age, chronological age, age at menarche, hemoglobin concentration, and gestational weight gain, among others. All independent variables were converted to dummy variables for analysis. Calculations were adjusted for the following variables: marital status, maternal education, occupation, educational lag, family structure, socioeconomic level, pre-pregnancy body mass index, and initiation of prenatal care. Results: The average age of the participants was 15.7 ± 1 years. The frequency of small for gestational age and low birth weight was 20% and 15.3%, respectively. Factors associated with small for gestational age included gynecological age < 3 years [aOR = 2.462, CI 95%; 1.081–5.605 (p = 0.032)], hemoglobin < 11.5 g/dL [aOR = 2.164, CI 95%; 1.08–605 (p = 0.019)], insufficient gestational weight gain [aOR = 1.858, CI 95%; 1.059–3.260 (p = 0.031)], preterm birth [aOR = 1.689, CI 95%; 1.133–2.519 p = 0.01], and living more than 50 km from the care center [aOR = 2.256, CI 95%; 1.263–4.031 (p = 0.006)]. An early age of menarche [aOR = 0.367, CI 95%; 0.182–0.744 (p = 0.005)] showed a protective effect against small for gestational age. Factors associated with low birth weight included gynecological age < 3 years [aOR = 3.799, CI 95%; 1.458–9.725 (p = 0.006)], maternal age < 15 years [aOR = 5.740, CI 95%; 1.343–26.369 (p = 0.019)], preterm birth [aOR = 54.401, CI 95%; 33.887–87.335, p = 0.001], living more than 50 km from the care center [aOR = 1.930, CI 95%; 1.053–3.536 (p = 0.033)], and early age of menarche [aOR = 0.382, CI 95%; 0.173–0.841 (p = 0.017), which demonstrated a protective effect, respectively. Conclusions: The study concludes that biological immaturity, particularly early gynecological age, significantly contributes to adverse birth outcomes during adolescent pregnancies. Interestingly, early menarche appeared to have a protective effect, whereas chronological age was not a significant predictor of small for gestational age. Chronological age has an even greater impact: women younger than 15 years are 5.7 times more likely to have low birth weight infants. However, chronological age did not increase the likelihood of having an SGA newborn. Full article

This paper outlines a methodology for creating an educational and informative communication system for non-specialised audiences in order to preserve and pass on the heritage of ideas and practices adopted in the medieval political and administrative sphere. Through the combined use of digital technologies (such as GISs, 3D modelling and virtual tours), historical sources can potentially reveal how political and administrative aspects affected different areas within the medieval city, not just the main seats of power. Bologna, a prestigious medieval university metropolis, is chosen as a case study because of the remarkable wealth of documentation in its archives from the city’s political culture in the Middle Ages. Written historical sources, including documentary and narrative texts, are among the primary tools employed in the study of European medieval urban communities in general. Documentary sources help us understand and reconstruct the complexities of civic administration, urban policies and the economy, as well as how citizens experience them daily. The involvement of citizens in the political and administrative life of late medieval cities is explored through the management and digital processing of historical documentation. Digital humanities tools can facilitate this analysis, offering a perspective that sheds light on the formation of the pre-modern state. Although digital databases and repositories have significantly contributed to preserving and digitally archiving historical sources, these are often aimed exclusively at the academic level and remain underutilised as privileged didactic and educational tools for a broad audience. Full article

Against the backdrop of global climate change and increasing ecological pressure, the refined monitoring of forest resources and accurate tree species identification have become essential tasks for sustainable forest management. Hyperspectral remote sensing, with its high spectral resolution, shows great promise in tree species classification. However, traditional methods face limitations in extracting joint spatial–spectral features, particularly in complex forest environments, due to the “curse of dimensionality” and the scarcity of labeled samples. To address these challenges, this study proposes a synergistic classification approach that combines the spatial feature extraction capabilities of deep learning with the generalization advantages of machine learning. Specifically, a 2D convolutional neural network (2DCNN) is integrated with a support vector machine (SVM) classifier to enhance classification accuracy and model robustness under limited sample conditions. Using UAV-based hyperspectral imagery collected from a typical plantation area in Fuzhou City, Jiangxi Province, and ground-truth data for labeling, a highly imbalanced sample split strategy (1:99) is adopted. The 2DCNN is further evaluated in conjunction with six classifiers—CatBoost, decision tree (DT), k-nearest neighbors (KNN), LightGBM, random forest (RF), and SVM—for comparison. The 2DCNN-SVM combination is identified as the optimal model. In the classification of Masson pine, Chinese fir, and eucalyptus, this method achieves an overall accuracy (OA) of 97.56%, average accuracy (AA) of 97.47%, and a Kappa coefficient of 0.9665, significantly outperforming traditional approaches. The results demonstrate that the 2DCNN-SVM model offers superior feature representation and generalization capabilities in high-dimensional, small-sample scenarios, markedly improving tree species classification accuracy in complex forest settings. This study validates the model’s potential for application in small-sample forest remote sensing and provides theoretical support and technical guidance for high-precision tree species identification and dynamic forest monitoring. Full article

Global climate change has altered precipitation patterns, leading to an increased frequency and intensity of extreme rainfall events and introducing greater uncertainty to flood risk in river basins. Traditional assessments often rely on static indicators and single-design scenarios, failing to reflect the dynamic evolution of floods under varying intensities. Additionally, oversimplified topographic representations compromise the accuracy of high-risk-zone identification, limiting the effectiveness of precision flood management. To address these limitations, this study constructs multi-return-period flood scenarios and applies a coupled 1D/2D hydrodynamic model to analyze the spatial evolution of flood hazards and extract refined hazard indicators. A multi-source weighting framework is proposed by integrating the triangular fuzzy analytic hierarchy process (TFAHP) and the entropy weight method–criteria importance through intercriteria correlation (EWM-CRITIC), with game-theoretic strategies employed to achieve optimal balance among different weighting sources. These are combined with the technique for order preference by similarity to an ideal solution (TOPSIS) to develop a continuous flood risk assessment model. The approach is applied to the Georges River Basin in Australia. The findings support data-driven flood risk management strategies that benefit policymakers, urban planners, and emergency services, while also empowering local communities to better prepare for and respond to flood risks. By promoting resilient, inclusive, and sustainable urban development, this research directly contributes to the achievement of United Nations Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

Urban regulations in Amman, Jordan, enforce uniform building setbacks irrespective of topography, exacerbating shading effects and compromising daylight access in residential areas—a critical factor for occupant health and psychological well-being. This study evaluates the interplay between standardized setbacks, slope variations (0–30%), and shadow patterns in Amman’s dense, mountainous urban fabric. Focusing on the Al Jubayhah district, a mixed-methods approach was used, combining field surveys, 3D modeling (Revit), and seasonal shadow simulations (March, September, December) to quantify daylight deprivation. The results reveal severe shading in winter (78.3% site coverage in December) and identify slope-dependent setbacks as a key determinant: for instance, a 15 m building on a 30% slope requires a 26.4 m rear setback to mitigate shadows, compared to 13.8 m on flat terrain. Over 39% of basements in the study area remain permanently shaded due to retaining walls, correlating with poor living conditions. The findings challenge Amman’s one-size-fits-all regulatory framework (Building Code No. 67, 1979), and we propose adaptive guidelines, including slope-adjusted setbacks, restricted basement usage, and optimized street orientation. This research underscores the urgency of context-sensitive urban policies in mountainous cities to balance developmental density with daylight equity, offering a replicable methodology for similar Mediterranean climates. Full article

LiDAR-captured 3D point clouds are widely used in self-driving cars and smart cities. Point-based semantic segmentation methods allow for more efficient use of the rich geometric information contained in 3D point clouds, so it has gradually replaced other methods as the mainstream deep learning method in 3D point cloud semantic segmentation. However, existing methods suffer from limited receptive fields and feature misalignment due to hierarchical downsampling. To address these challenges, we propose PSNet, a novel patch-based self-attention network that significantly expands the receptive field while ensuring feature alignment through a patch-aggregation paradigm. PSNet combines patch-based self-attention feature extraction with common point feature aggregation (CPFA) to implicitly model large-scale spatial relationships. The framework first divides the point cloud into overlapping patches to extract local features via multi-head self-attention, then aggregates features of common points across patches to capture long-range context. Extensive experiments on Toronto-3D and Complex Scene Point Cloud (CSPC) datasets validate PSNet’s state-of-the-art performance, achieving overall accuracies (OAs) of 98.4% and 97.2%, respectively, with significant improvements in challenging categories (e.g., +32.1% IoU for fences). Experimental results on the S3DIS dataset show that PSNet attains competitive mIoU accuracy (71.2%) while maintaining lower inference latency (7.03 s). The PSNet architecture achieves a larger receptive field coverage, which represents a significant advantage over existing methods. This work not only reveals the mechanism of patch-based self-attention for receptive field enhancement but also provides insights into attention-based 3D geometric learning and semantic segmentation architectures. Furthermore, it provides substantial references for applications in autonomous vehicle navigation and smart city infrastructure management. Full article

Decades of migration and refugee influxes have driven Amman’s rapid urban growth, yet newer neighborhoods increasingly grapple with fragmented social cohesion. This study examines whether walkable design can strengthen community bonds, focusing on Deir Ghbar, a car-centric district in West Amman. Using GIS and mixed-methods analysis, we assess how walkability metrics (residential density, street connectivity, land-use mix, and retail density) correlate with sense of community. The results reveal that street connectivity and residential density enhance social cohesion, while land-use mix exhibits no significant effect. High-density, compact neighborhoods foster neighborly interactions, but major roads disrupt these connections. A critical mismatch emerges between quantitative land-use metrics and resident experiences, highlighting the need to integrate spatial data with community insights. Amman’s zoning policies, particularly the stark contrast between affluent low-density Zones A/B and underserved high-density Zones C/D, perpetuate socio-spatial segregation—a central critique of this study. We urge the Greater Amman Municipality’s 2025 Master Plan to prioritize mixed-density zoning, pedestrian retrofits (e.g., traffic calming and sidewalk upgrades), and equitable access to amenities. This study provides a replicable GIS and survey-based framework to address urban socio-spatial divides, aligning with SDG 11 for inclusive cities. It advocates for mixed-density zoning and pedestrian-first interventions in Amman’s Master Plan. By integrating a GIS with social surveys, this study offers a replicable model for addressing socio-spatial divides in cities facing displacement and inequality. Full article

Reliable source term prediction for hazardous pollutant puffs in urban microenvironments is challenging, especially for risk management under strict time constraints. Puff movement is highly stochastic due to atmospheric turbulence, intensified by complex urban canopies. This complexity, combined with time limitations, makes advanced computational modeling impractical. A more efficient approach is leveraging past and present data using Machine Learning (ML) techniques. This study proposes an ML-based method, enriched with simplified physical modeling, for source term estimation of unforeseen hazardous air releases in monitored urban areas. The Random Forest Regression, commonly used in meteorology and air quality studies, has been selected. A novel variable selection method is introduced, including the following: (a) a model-derived Exposure Burden Index (EBI) reflecting plume–morphology interactions; (b) a plume travel time indicator; (c) the standard deviation of input variables capturing stochastic behavior; and (d) the total dosage-to-mass released ratio at sensor locations as the target variable. The case study examines JU2003 field experiments involving SF₆ puffs released at street level in Oklahoma City’s urban core, a challenging scenario due to the limited number of sensors and historical data. Results demonstrate the approach’s effectiveness, offering a promising, realistic alternative to traditional computationally intensive methods. Full article

This study investigates the potential of reality-based 3D digital modeling, acquired for scientific purposes, to enhance the understanding and accessibility of ballistic imprints on Pompeii’s city walls. These impact marks, attributed to the Sullan siege of 89 BC, were caused by projectiles launched by Roman elastic torsion weapons. High-resolution models were acquired through integrated 3D survey techniques to create both virtual and physical replicas. These assets enhance museum accessibility, offering interactive digital content and tactile 3D-printed replicas for visually impaired and mobility-restricted visitors. The findings highlight the role of digital heritage in archaeological research, conservation, and public engagement, bridging the gap between academic study and inclusive cultural dissemination. Full article