Search Results (6,185)

Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, naïve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed. Full article

The high seismic vulnerability of unreinforced masonry buildings urgently calls for researchers to develop sustainable reinforcing methods and materials. This paper presents an innovative lime-based mortar reinforced with randomly oriented basalt fibers for the reinforcement of masonry heritage. The main aim of this study is to understand the effect of the content and the length of basalt fibers on the mortar’s mechanical behavior. As a cementitious material made mostly out of lime, the mortar is chemically compatible with the historical substrate and therefore suitable in cases of restoration works on architectural heritage. Moreover, the chopped basalt fibers are randomly oriented, and this characteristic makes the overall layer effective in all directions, as the state of stress induced by seismic action is directionally undetermined. The newly proposed reinforcement system is characterized by a twofold aspect related to sustainability: 30% of the aggregates composing the mortar mix design is a recycled result of the ruins of the 2009 L’Aquila earthquake, and the chopped fibers are made out of basalt, widely known for its environmentally supportable peculiarity. The study consists of testing samples characterized by two fiber lengths and six fiber contents, along with one set of plain mortar samples. Specimens measuring 160 mm × 40 mm × 40 mm are first tested in a three-point bending (TPB) configuration, aiming to determine the flexural strength and the post-peak capacity through the calculation of the fracture energy. Then, the two broken pieces resulting from the TPB tests, each measuring 80 mm × 40 mm × 40 mm, are tested in splitting and compression, respectively, aiming to compute the tensile and compressive strengths. Finally, to provide a trend for the mortar’s mechanical properties, a regression analysis is performed by fitting the experimental data with simple linear, polynomial, and exponential regression models. Results show that: (i) both fiber content and fiber length are responsible for a linear increase of the flexural strength and the fracture energy; (ii) for both short- and long-fiber mortar samples, the tensile strength and the compressive strength parabolically increase with the fiber content; (iii) the increase in fiber content and fiber length always generates a reduction in the conglomerate workability. The fiber content (FC) optimization with respect to the mechanical properties leads to a basalt FC equal to 1.2% for long-fiber samples and an FC equal to 1.9% for short-fiber ones. Full article

Socially assistive robots are embodied technological artifacts that can interact socially with people. These devices are increasingly investigated as a means of mental health support in different populations, especially for alleviating loneliness, depression, and anxiety. While the number of available, increasingly sophisticated social robots is growing, their adoption is slower than anticipated. There is much effort to determine the effectiveness of social robots in various settings, including healthcare; however, little is known about the acceptability of these devices by the following distinct user groups: healthcare providers, parents, and children. To better understand the priorities and attitudes of social robot users, we carried out (1) a survey of parents and children who have previously been admitted to a hospital and (2) a series of three modified focus group meetings with healthcare providers. The online survey (n = 71) used closed and open-ended questions as well as validated measures to establish the attitudes of children and parents towards social human–robot interaction and identify any potential barriers to the implementation of a robot intervention in a hospital setting. In the focus group meetings with healthcare providers (n = 10), we identified novel potential applications and interaction modalities of social robots in a hospital setting. Several concerns and barriers to the implementation of social robots were discussed. Overall, all user groups have positive attitudes towards interactions with social robots, provided that their concerns regarding robot use are addressed during interaction development. Our results reveal novel social robot application areas in hospital settings, such as rapport-building between patients and healthcare providers and fostering patient involvement in their own care. Healthcare providers highlighted the value of being included and consulted throughout the process of child–robot interaction development to ensure the acceptability of social robots in this setting and minimize potential harm. Full article

Research on polarization is a contested issue in itself. One key question in this context is how strongly attitude patterns must be linked to socio-structural characteristics in order to justify speaking of polarized attitudes. Moreover, evidence of an association between attitudes may reveal ways of triggering clusters of attitudes, which can then easily be turned into affective polarization by political leaders. We investigate the prevalence of different, potentially polarizing attitude patterns among social groups in four European countries (Sweden, Austria, Italy, and Poland) using data from the European Social Survey 10. We link two sets of attitudes, namely attitudes towards climate change and attitudes towards non-heteronormative ways of life, to build four ideational types of attitude patterns. Our findings point to some associations between the two scales, which leads us to examine influence factors by Multinomial regression in the next step. High education of respondents and being female raise awareness for climate change and increase tolerance for non-heteronormative ways of life. Moreover, we find some evidence of a rural-urban divide in that residents of urban areas seem to be more open towards non-heteronormative ways of life. We argue that the analysis of mixed attitude types can be especially instructive since country effects and personal experience prove to be influential in these cases. It is precisely these cases that can tell us much about means of tackling polarization. Full article

Background/Objectives: Alexithymia is a condition marked by difficulties in identifying and expressing emotions, rooted in both physiological and behavioral mechanisms. The aim of this study was to investigate the relationship between parental alexithymia and family functioning in families of children with Autism Spectrum Disorder (ASD) compared to families of typically developing children (TD). Methods: The study sample included parents of children with ASD (n = 120) and a control group of parents of typically developing children (n = 120). A comprehensive set of self-report instruments was used to evaluate alexithymia levels, parental stress, family experience, resilience, cognitive emotion regulation, social support, and family flexibility and cohesion. Results: The analysis revealed that parental alexithymia in families of children with ASD was directly associated with lower levels of family flexibility and cohesion, independent of increased stress or reduced family resilience. Furthermore, the findings indicate that alexithymia in parents is directly linked to reduced family cohesion in ASD families. Conclusions: These results highlight the significant role of parental alexithymia in shaping family dynamics and underscore the necessity for targeted interventions that emphasize emotional skill-building, adaptive coping mechanisms, and resilience to stressful events. This research enhances the understanding of parental alexithymia’s effect on family functioning in the context of ASD. Full article

Urban areas serve as critical habitats for numerous plant species. Existing studies suggest that, due to human-mediated introductions, urban environments often harbor a greater variety of plant species compared to suburban areas, potentially becoming focal points for biodiversity. Consequently, investigating the driving forces and complex mechanisms by which urban environmental factors influence plant species distribution is essential for establishing the theoretical foundation for urban biodiversity conservation and future urban planning and management. Solar radiation, among these factors, is a critical determinant of plant growth, development, and reproduction. However, there is a notable lack of research on how this factor affects the distribution of urban plant species and influences species’ richness and composition within plant communities. We present for the first time an analysis of how solar radiation drives the spatial distribution of plant species within the built-up areas of Nanchang City, China. Based on three years of monitoring and survey data from experimental sites, this study employs three evaluation models—Species Richness Index (R), Simpson’s Diversity Index (D), and Shannon–Wiener Index (H)—to analyze and validate the survey results. Additionally, MATLAB and ArcGIS Pro software are utilized for the numerical simulation and visualization of spatial data. Our study shows that areas with low solar radiation exhibit higher plant species richness, while plots with high plant diversity are primarily concentrated in regions with strong solar radiation. Moreover, the Diversity Index D proves to be more sensitive than the Shannon–Wiener Index (H) in evaluating the spatial distribution of plant species, making it a more suitable metric for studying urban plant diversity in our study area. Among the 18 plant species analyzed, Mulberry and Dandelion are predominantly dispersed by birds and wind, showing no significant correlation with solar radiation. This finding indicates that the spatial distribution of urban plant species is influenced by multiple interacting factors beyond solar radiation, highlighting the critical need for long-term observation, monitoring, and analysis. This study also suggests that shaded urban areas may serve as hubs of high species richness, while regions with relatively strong solar radiation can sustain greater plant diversity. These findings underscore the practical significance of this research, offering essential insights to guide urban planning and management strategies. Additionally, this study offers valuable insights for the future predictions of plant species distribution and potential areas of high plant diversity in various urban settings by integrating computational models, building data, Digital Elevation Models (DEMs), and land cover data. Full article

In this paper, we propose sketch-based video object segmentation (SKVOS), a novel task that segments objects consistently across video frames using human-drawn sketches as queries. Traditional reference-based methods, such as photo masks and language descriptions, are commonly used for segmentation. Photo masks provide high precision but are labor intensive, limiting scalability. While language descriptions are easy to provide, they often lack the specificity needed to distinguish visually similar objects within a frame. Despite their simplicity, sketches capture rich, fine-grained details of target objects and can be rapidly created, even by non-experts, making them an attractive alternative for segmentation tasks. We introduce a new approach that utilizes sketches as efficient and informative references for video object segmentation. To evaluate sketch-guided segmentation, we introduce a new benchmark consisting of three datasets: Sketch-DAVIS16, Sketch-DAVIS17, and Sketch-YouTube-VOS. Building on a memory-based framework for semi-supervised video object segmentation, we explore effective strategies for integrating sketch-based references. To ensure robust spatiotemporal coherence, we introduce two key innovations: the Temporal Relation Module and Sketch-Anchored Contrastive Learning. These modules enhance the model’s ability to maintain consistency both across time and across different object instances. Our method is evaluated on the Sketch-VOS benchmark, demonstrating superior performance with overall improvements of 1.9%, 3.3%, and 2.0% over state-of-the-art methods on the Sketch-YouTube-VOS, Sketch-DAVIS 2016, and Sketch-DAVIS 2017 validation sets, respectively. Additionally, on the YouTube-VOS validation set, our method outperforms the leading language-based VOS approach by 10.1%. Full article

The construction industry accounts for approximately 28% of global CO₂ emissions, and emission management at the building demolition stage is important for achieving carbon neutrality goals. Systematic studies on the demolition stage, however, are still lacking. In this study, research on the development of optimal machine learning (ML) models was conducted to predict CO₂ emissions at the demolition stage. CO₂ emissions were predicted by applying various ML algorithms (e.g., gradient boosting machine [GBM], decision tree, and random forest), based on the information on building features and the equipment used for demolition, as well as energy consumption data. GBM was selected as a model with optimal prediction performance. It exhibited very high accuracy with R² values of 0.997, 0.983, and 0.984 for the training, test, and validation sets, respectively. The GBM model also showed excellent results in generalization performance, and it effectively learned the data patterns without overfitting in residual analysis and mean absolute error (MAE) evaluation. It was also found that features such as the floor area, equipment, wall type, and structure significantly affect CO₂ emissions at the building demolition stage and that equipment and the floor area are key factors. The model developed in this study can be used to support decision-making at the initial design stage, evaluate sustainability, and establish carbon reduction strategies. It enables efficient data collection and processing and provides scalability for various analytical approaches compared to the existing life cycle assessment (LCA) approach. In the future, it is deemed necessary to develop ML tools that enable comprehensive assessment of the building life cycle through system boundary expansion. Full article

The environment continues to erode the roofs of ancient buildings in Longmen Ancient Town, posing a threat to the safety of villagers. Scientific detection and diagnosis are important steps in the repair and protection of historical buildings. In order to effectively protect cultural heritage, this study uses the YOLOv8 deep learning model to automatically detect damage on images of traditional residential roofs. The researchers constructed image data sets for the four categories of green vegetation, dry vegetation, missing tiles, and repaired tiles and then perform model training. The results show that the model is generally accurate for missing tiles (0.94 for missing tiles and 0.93 for repaired tiles), and it has a low false detection rate and a low missed detection rate. It does make some mistakes when it comes to green and dry vegetation in complex backgrounds, but the overall detection coverage and F1 score are better. This practical application shows that the model can accurately mark most target areas, especially for the recognition of high-contrast damage types. This study provides efficient and accurate technical support for the diagnosis of traditional roof structures and protection of cultural heritage. Full article

The schedule is a critical factor in the development of prefabricated buildings. This paper establishes the schedule risk influencing factors for prefabricated building projects across five dimensions—design, production, transportation, installation, and others—encompassing a total of 14 factors. By integrating DEMATEL and ISM, it constructs a hierarchical network model using expert knowledge and maps it to Bayesian networks (BN), and the node probabilities were calculated using fuzzy set theory combined with the noisy-OR gate model. This DEMATEL-ISM-BN model not only infers the probability of schedule risk occurrence in prefabricated construction projects through causal reasoning and controls the schedule risk of prefabricated construction projects, but it also deduces the posterior probabilities of other influencing factors when a schedule risk occurs through diagnostic reasoning. This approach identifies the key factors contributing to schedule risk and pinpoints the final influencing factors. Research has shown that the three influencing factors of “tower crane worker lifting level”, “construction worker component installation technology”, and “design changes” significantly affect project progress, providing a new risk assessment tool for prefabricated building project progress, effectively helping enterprises identify potential risks, formulate risk control strategies, improve project success rates, and overall benefits. Full article

The paper will focus on the topic of adapting digital humanities methods from architectural history to technical history, considering mapping and image analysis for increasing disaster resilience in Art Nouveau and Modernist buildings in different geographical areas—including lessons from Europe to the USA. The project proposes the transformation of the collection of photographs of early 20th-century architecture gathered by the applicant over about 30 years of travel into a database by answering the research question on how threats from the hazards of earthquakes, floods, and fires can be answered by taking into account the local culture in the European countries covered, for buildings from a period when the architecture styles were already global at that time. For this purpose, digital humanities methods of image annotation (including architectural volumetric analysis) and mapping are employed. From the knowledge gathered and the resulting database, a prototyping ontology and taxonomy is derived. This outcome can be further developed into a set of evaluation criteria, considering the decisions that can be taken to prioritize the retrofit interventions depending on the geographic positions of the buildings. Full article

This research proposes a ground-breaking technique for protecting agricultural fields against animal invasion, addressing a key challenge in the agriculture industry. The suggested system guarantees real-time intrusion detection and quick reactions by combining cutting-edge sensor technologies, image processing capabilities, and the Internet of Things (IoT), successfully safeguarding crops and reducing agricultural losses. This study involves a thorough examination of five models—Inception, Xception, VGG16, AlexNet, and YoloV8—against three different datasets. The YoloV8 model emerged as the most promising, with exceptional accuracy and precision, exceeding 99% in both categories. Following that, the YoloV8 model’s performance was compared to previous study findings, confirming its excellent capabilities in terms of intrusion detection in agricultural settings. Using the capabilities of the YoloV8 model, an IoT device was designed to provide real-time intrusion alarms on farms. The ESP32cam module was used to build this gadget, which smoothly integrated this cutting-edge model to enable efficient farm security measures. The incorporation of this technology has the potential to transform farm monitoring by providing farmers with timely, actionable knowledge to prevent possible threats and protect agricultural production. Full article

Due to the high-density coverage of vegetation, the complexity of terrain, and occlusion issues, ground point extraction faces significant challenges. Airborne Light Detection and Ranging (LiDAR) technology plays a crucial role in complex mountainous areas. This article proposes a method for constructing fine terrain in high vegetation coverage areas based on implicit neural representation. This method consists of data preprocessing, multi-scale and multi-feature high-difference point cloud initial filtering, and an upsampling module based on implicit neural representation. Firstly, preprocess the regional point cloud data is preprocessed; then, K-dimensional trees (K-d trees) are used to construct spatial indexes, and spherical neighborhood methods are applied to capture the geometric and physical information of point clouds for multi-feature fusion, enhancing the distinction between terrain and non-terrain elements. Subsequently, a differential model is constructed based on DSM (Digital Surface Model) at different scales, and the elevation variation coefficient is calculated to determine the threshold for extracting the initial set of ground points. Finally, the upsampling module using implicit neural representation is used to finely process the initial ground point set, providing a complete and uniformly dense ground point set for the subsequent construction of fine terrain. To validate the performance of the proposed method, three sets of point cloud data from mountainous terrain with different features are selected as the experimental area. The experimental results indicate that, from a qualitative perspective, the proposed method significantly improves the classification of vegetation, buildings, and roads, with clear boundaries between different types of terrain. From a quantitative perspective, the Type I errors of the three selected regions are 4.3445%, 5.0623%, and 5.9436%, respectively. The Type II errors are 5.7827%, 6.8516%, and 7.3478%, respectively. The overall errors are 5.3361%, 6.4882%, and 6.7168%, respectively. The Kappa coefficients of the measurement areas all exceed 80%, indicating that the proposed method performs well in complex mountainous environments. Provide point cloud data support for the construction of wind and photovoltaic bases in China, reduce potential damage to the ecological environment caused by construction activities, and contribute to the sustainable development of ecology and energy. Full article

Since people spend most of their time in indoor environments, they are continuously exposed to various contaminants that threaten human health. The air quality in these settings is therefore a crucial factor in maintaining health safety. In order to reduce the concentration of indoor air pollutants and improve air quality, photocatalytic oxidation has drawn the attention of researchers. This study aims to provide a comprehensive view of the nanomaterials used in the photocatalytic oxidation of the most common pollutants in indoor environments. The effects of various parameters like humidity, airflow, deposition time, and light intensity were also evaluated, as they can significantly influence photocatalytic reactions. The most common nanomaterials used in photocatalysis are TiO₂-based and, in this study, they were classified and examined based on their morphology. TiO₂ doping with metals and non-metals has demonstrated an enhancement of its adsorption properties and photocatalytic efficiency for the removal of several pollutants. The role of carbon-based nanomaterials in photocatalysis was also evaluated due to their adsorption capabilities towards various pollutants. In addition, other less common photocatalysts such as ZnO, MnO₂, WO₃, CeO₂, and CdS also exhibited high photocatalytic activity for pollutant degradation. Applications of these photocatalysts in air purifiers, paints, and building materials e.g., concrete, glass, and wallpapers, lead to efficient reduction of pollutants in indoor settings. Full article

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments. Full article