Search Results (57,278)

The frequency of urban fires has grown in recent years everywhere, especially in historic districts, including in Portugal, due to the existence of sensitive igniting materials, the proximity of buildings, the complex urban layout, and the presence of many people. The current study proposes a technique, applied in the Baixa Pombalina (downtown) area in Lisbon, to undertake an appropriate evaluation of the post-earthquake fire cascading effect, which may cause major damage. The earthquake vulnerability and damage scenario were carried out using the Risk-UE method. An empirical fire ignition model was then applied to determine the quantity and location of fire ignitions for different return periods. Furthermore, the simple fire spread Hamada’s model was applied to both the equally spaced grid buildings, as in the original Hamada procedure, and the current study area layout for different time thresholds. Finally, the risk assessment for both models was carried out, allowing for the estimation of earthquake and fire losses, respectively. The results demonstrated that the models are comparable, showing that the Hamada model might be a useful tool for large-scale evaluations aimed at disaster-risk reduction and management since it gives useful information for managing and reducing natural and anthropogenic hazards. Full article

Background: Electroconvulsive therapy (ECT) is a well-established treatment for various psychiatric disorders. This retrospective study evaluates anesthesia practices, patient characteristics, and outcomes in ECT over a two-year period at the “Dr. Gheorghe Preda” Clinical Psychiatry Hospital in Sibiu, Romania. Methods: From March 2022 to July 2024, the Neuroscience Scientific Research Collective at our institution carried out a retrospective observational study on patients who underwent ECT. The evaluation and treatment protocol involved patients from all over the country. Results: The study involved 30 patients aged between 22 and 67 years and a mean age of 39.4 years; among them, 57% were male. The majority of the patients (68%) lived in urban areas, and 80% came from a different county. Schizophrenia was the most prevalent diagnosis (56.6%), followed by depression (40%) and bipolar disorder (3.4%). Common comorbidities included obesity/overweight, high blood pressure, and sinus tachycardia. A total of 330 ECT sessions were conducted, with an average of 11 sessions per patient, and 10 patients underwent multiple treatment courses. The reported adverse events included arterial hypertension, agitation, tachycardia, and shivering. Conclusions: This study underlines the safety and effectiveness of ECT when patients are closely monitored. Our results are consistent with the global data, suggesting that ECT is a good treatment option for severe psychiatric conditions with a manageable incidence of adverse events. Full article

Cargo terminals, as pivotal hubs of mechanical activities, maritime shipping, and land transportation, are significant sources of air pollutants, exhibiting considerable spatiotemporal heterogeneity due to the complex and irregular nature of emissions. This study employed a high-density air sensor network with 17 sites across four functional zones in two Shanghai cargo terminals to monitor NO and NO₂ concentrations with high spatiotemporal resolution post sensor data validation against regulatory monitoring stations. Notably, NO and NO₂ concentrations within the terminal surged during the night, peaking at 06:00 h, likely due to local regulations on heavy-duty diesel trucks. Spatial analysis revealed the highest NO concentrations in the core operational areas and adjacent roads, with significantly lower levels in the outer ring, indicating strong emission sources and limited dispersion. Employing the lowest percentile method for baseline extraction from high-resolution data, this study identified local emissions as the primary source of NO, constituting over 80% of total emissions. Elevated background concentrations of NO₂ suggested a gradual oxidation of NO into NO₂, with local emissions contributing to 32–70% of the total NO₂ concentration. These findings provide valuable insights into the NO and NO₂ emission characteristics across different terminal areas, aiding decision-makers in developing targeted emission control policies. Full article

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level. Full article

In continuity with the research on social segregation and the phenomenon of urban gentrification, this article examines the cohabitation patterns of populations with diametrically opposed incomes within the same neighborhood, typically observed in the city center. This phenomenon is defined here as internal socio-economic polarization. It is measured through the combination of two original indexes (poverty and wealth indexes) constructed based on income deciles per consumption unit for the year of 2017. The analysis focuses on the municipality of Nice, characterized by a low demographic dynamic, a relative concentration of seniors, and a strong tourist attractiveness, particularly in the highly polarized neighborhoods that occupy almost the entire city center. This study is complemented by a principal component analysis summarizing the characteristics of the population and housing stock in the neighborhoods of Nice. The main objective of this research is to identify and locate polarized neighborhoods within the urban context of Nice, to analyze the distinctive traits of their population and housing stock, and, finally, to highlight potential trends in the population’s socio-economic status. Moreover, the economic trajectories of polarized neighborhoods, in connection with their population and housing characteristics (such as the secondary use of a portion of the housing stock, often low-quality old buildings, social housing, and the overrepresentation of retirees), help explain the forms of socio-economic polarization observed in these neighborhoods (such as the indications of gentrification, unfinished gentrification, and sustainable cohabitation). Full article

Access to clean and safe drinking water is a fundamental human right. Despite global efforts, including the UN’s “Water for Life” program, a significant portion of the population in developing countries, including Somaliland, continues to rely on unimproved water sources. These unimproved sources contribute to poor health outcomes, particularly for children. This study aimed to investigate the factors associated with the use of unimproved drinking water sources in Somaliland by employing supervised machine learning models to predict patterns and determinants based on data from the 2020 Somaliland Demographic and Health Survey (SHDS). Secondary data from SHDS 2020 were used, encompassing 8384 households across Somaliland. A multilevel logistic regression model was applied to analyze the individual- and community-level factors influencing the use of unimproved water sources. In addition, machine learning models, including logistic regression, decision tree, random forest, support vector machine (SVM), and K-nearest neighbor (KNN), were compared in terms of accuracy, sensitivity, specificity, and other metrics using cross-validation techniques. This study uses supervised machine learning models to analyze unimproved drinking water sources in Somaliland, providing data-driven insights into the complex determinants of water access. This enhances predictive accuracy and informs targeted interventions, offering a robust framework for addressing water-related public health issues in Somaliland. The analysis identified key determinants of unimproved water source usage, including socioeconomic status, education, region, and household characteristics. The random forest model performed the best with an accuracy of 93.57% and an area under the curve (AUC) score of 98%. Decision tree and KNN also exhibited strong performance, while SVM had the lowest predictive accuracy. This study highlights the role of socioeconomic and community factors in determining access to clean drinking water in Somali Land. Factors such as age, education, gender, household wealth, media access, urban or rural residence, poverty level, and literacy level significantly influenced access. Local policies and resource availability also contribute to variations in access. These findings suggest that targeted interventions aimed at improving education, infrastructure, and community water management practices can significantly reduce reliance on unimproved water sources and improve the overall public health. Full article

Many countries are adopting renewable energy (RE) and electric vehicles (EVs) to achieve net-zero emissions by 2050. The indicators of RE and EV potentials are different. Decision-makers want to introduce RE and EVs; however, they need a method to find suitable areas. In addition, this is required in the time-series analysis to provide a detailed resolution. In this study, we conducted a time-series analysis in Japan to evaluate suitable areas for the combined use of RE and EVs. The results showed the surplus RE areas and shortage RE urban areas. The time-series analysis has quantitatively shown that it is not enough to charge EV batteries using surplus RE. Moreover, a ranking methodology was developed for the evaluation based on electric demand and vehicle numbers. This enables the government’s prioritization of prefectures and the prefectures’ prioritization of municipalities according to their policies. Full article

This article explores the application of procedural design methods in urban morphology, drawing inspiration from the innovative work of the Architectural Workshop of Miskolc in Hungary during the late 20th century. This study presents a generative approach termed “Simulated Morphogenesis” (or the “Miskolc Method”), which models organic city growth by analysing historical urban tissues and applying recursive algorithms to simulate natural urban development. The method leverages advanced generative tools, such as Rhinoceros 3D and Grasshopper, to model the step-by-step growth of Central European cities, with a particular focus on Miskolc. By incorporating controlled randomness into the algorithmic processes, the method captures the complexity of organic urban growth while maintaining structured development. The Miskolc Method emphasizes the importance of continuity and context, allowing for the “healing” of urban fabric discontinuities or the generation of new urban structures. This article demonstrates how this approach, while rooted in geometrical analysis, offers a valuable foundation for preliminary urban planning. The findings are relevant for understanding the morphogenesis of cities and provide a flexible framework applicable to various urban contexts globally. Full article

With the advancement of urbanization, the complexity and diversity of urban spatial forms have become increasingly prominent, profoundly and widely affecting aspects such as urban spatial layout and planning, as well as residents’ quality of life. This paper focuses on the buildings in Harbin City, comprehensively reflecting the spatial form of Harbin through multiple dimensions including building height, volume, and area. This research precisely quantifies three key indicators of urban buildings: building coverage, building expandability, and building staggeredness. Subsequently, these indicators are intertwined with the main driving factors of urban development (including economic development and resident population) to conduct a multidimensional spatial form analysis. The results indicate that the diversity of Harbin’s urban spatial form is the result of the interplay of multiple factors, including economic and demographic influences. These analytical outcomes not only reveal the evolution mechanism of Harbin’s current urban spatial form but also provide data support and theoretical basis for future urban planning and management. Full article

Coastal forests in the eastern United States are increasingly threatened by hurricanes; however, monitoring their initial impacts and subsequent recovery is challenging across scales. Understanding disturbance impacts and responses is essential for sustainable forest management, biodiversity conservation, and climate change adaptation. Using Sentinel-2 imagery, we calculated the annual Normalized Difference Vegetation Index change (∆NDVI) of forests before and after Hurricane Michael (HM) in Florida to determine how different forest use types were impacted, including the initial wind damage in 2018 and subsequent recovery or reactive management for two focal areas located near and far from the coast. We used detailed parcel data to define forest use types and characterized multi-year impacts using sampling and k-means clustering. We analyzed five years of timberland logging activity up to the fall of 2023 to identify changes in logging rates that may be attributable to post-hurricane salvage efforts. We found uniform impacts across forest use types near the coast, where winds were the most intense but differences inland. Forest use types showed a wide range of multi-year responses. Urban forests had the fastest 3-year recovery, and the timberland response was delayed, apparently due to salvage logging that increased post-hurricane, peaked in 2021–2022, and returned to the pre-hurricane rate by 2023. The initial and secondary consequences of HM on forests were complex, as they varied across local and landscape gradients. These insights reveal the importance of considering forest use types to understand the resilience of coastal forests in the face of potentially increasing hurricane activity. Full article

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing. Full article

Urban energy systems planning presents significant challenges, requiring the integration of multiple objectives such as economic feasibility, technical reliability, and environmental sustainability. Although previous studies have focused on optimizing renewable energy systems, many lack comprehensive decision frameworks that address the complex trade-offs between these objectives in urban settings. Addressing these challenges, this study introduces a novel Multi-Criteria Decision Analysis (MCDA) framework tailored for the evaluation and prioritization of energy scenarios in urban contexts, with a specific application to the city of Bozen-Bolzano. The proposed framework integrates various performance indicators to provide a comprehensive assessment tool, enabling urban planners to make informed decisions that balance different strategic priorities. At the core of this framework is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is employed to systematically rank energy scenarios based on their proximity to an ideal solution. This method allows for a clear, quantifiable comparison of diverse energy strategies, facilitating the identification of scenarios that best align with the city’s overall objectives. The flexibility of the MCDA framework, particularly through the adjustable criteria weights in TOPSIS, allows it to accommodate the shifting priorities of urban planners, whether they emphasize economic, environmental, or technical outcomes. The study’s findings underscore the importance of a holistic approach to energy planning, where trade-offs are inevitable but can be managed effectively through a structured decision-making process. Finally, the study addresses key gaps in the literature by providing a flexible and adaptable tool that can be replicated in different urban contexts to support the transition toward 100% renewable energy systems. Full article

Background: Environmental surroundings reduce the rate of several diseases, especially those related to stressful events. Ischemic stroke can be affected by such events, either directly or through its risk factors. Therefore, the present study evaluates the effects of green spaces and noise exposure on the risk of ischemic stroke. Methods: A case–control study was carried out, including 200 ischemic stroke cases within the first 48 h of diagnosis and 200 controls, divided equally into hospitalized and non-hospitalized participants. Controls were matched to cases based on age and gender. Socio-demographic characteristics were assessed, in addition to environmental surroundings and noise exposure at home and at workplaces. Results: Living in a house, having a house garden, and taking care of the garden were associated with a lower risk of suffering an ischemic stroke (p < 0.001, p < 0.001, and p = 0.009, respectively). However, having buildings as the view from home led to a higher stroke rate (p < 0.001). Working in an urban area, the workplace being surrounded by buildings, and the workplace not being surrounded by green spaces were also associated with a higher risk of suffering an ischemic stroke (p = 0.002, p = 0.001, and p = 0.03, respectively). As for noise exposure, being exposed to traffic noise, human noise, and other types of noise was significantly associated with a higher risk of ischemic stroke, while being exposed to higher levels of natural noise was significantly associated with a lower risk of ischemic stroke. Higher levels of noise were also associated with higher risks of ischemic stroke in homes and workplaces (p < 0.001 and p = 0.008, respectively). Conclusions: Environmental surroundings and noise exposure were found to affect the risk of ischemic stroke. Greater green spaces and lower noise exposure play a protective role against ischemic stroke, suggesting a possible prevention strategy through environmental modifications at home and workplaces. Full article

The urban knowledge network in China has undergone in-depth development in recent decades, intimately connecting the position characteristics of cities in the knowledge network to their knowledge production performance. While existing research focuses predominantly on the unidirectional relationship between network position and the knowledge production of cities, there is a notable dearth of studies exploring the bidirectional relationship between the two constructs. By proposing a conceptual framework, this paper empirically examines the interplay between network position and knowledge production of cities through simultaneous equation models. The results revealed a mutually reinforcing relationship between network position and knowledge production, and this relationship exhibits heterogeneous characteristics and spillover effects. Specifically, cities in the periphery block and the central-western region benefit more from the effect of network position on knowledge production, while cities in the core block and the eastern region benefit more from the effect of knowledge production on network position. Moreover, the interactive effect between network position and knowledge production of cities is significantly affected by the network position characteristics and knowledge production performance of their neighboring cities in geographically adjacent regions and relationally adjacent regions. These findings enhance the understanding of urban network externalities and the connotations of the knowledge production function. Full article

Invasive alien plants represent one of the five major threats to biodiversity and the disruption of ecosystems. They are introduced through various routes, starting with commercial trade. Preventing their introduction is essential to avoid the spread of new invasive plants. In this paper, we propose a new early warning DNA barcoding tool for invasive plant detection. Eight invasive alien species of European Union concern (i.e., Ludwigia grandiflora, Elodea nuttallii, Myriophyllum aquaticum, Pontederia crassipes, Ailanthus altissima, Heracleum mantegazzianum, Impatiens glandulifera, Pueraria montana) were selected and analysed. A unique DNA marker for each species was identified and amplified using species-specific primers capable of identifying the presence of alien species. To verify whether the approach could detect the presence of alien plants in urban areas from lawn clippings, mixes with typical urban spontaneous plants and invasive species were tested. In all mixes, only the invasive species was identified. This rapid detection capability will enable environmental operators to intervene promptly to contain the spread of invasive plants before they can cause significant damage to the local ecosystem. This tool could have a significant impact on the protection of local biodiversity and the integrity of urban habitats. Full article