Search Results (1,912)

Forest fires are recurrent in Spain and affect tree species in different ways. Fire incidence in the main Spanish forest species, both native and alien, is estimated in this study based on actual fire occurrences. Indices of presence, burned area, fire extent, frequency, and recurrence were calculated for each species, and with them, fire incidence indices were obtained. Significant fire incidence was detected in Pinus canariensis, P. pinaster, Eucalyptus globulus, Quercus robur, Betula spp., Castanea sativa, Pinus radiata, and Quercus pyrenaica. Most of the species with the highest fire incidence are not located in the areas with the highest climatic hazard. There is limited correlation between flammability and fire extension, and this is not significant when considering fire incidence. The relationship between fire incidence and conifers is valid in absolute terms, but only partially in relative terms. Similarly, there is no general relationship between relative fire incidence and species with a natural or reforested origin. Some native hardwood species have unexpectedly high incidence, probably due to collateral damage caused by fires in nearby pine and eucalyptus stands. The fire incidence index of forest species is useful for forest management and for protecting species that are suffering severely from fire effects. Full article

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining. Full article

Precise odor source localization in turbulent and sparse environments plays a vital role in enabling robotic systems for hazardous chemical monitoring and effective disaster response. To address this, we propose Cooperative Gravitational-Rényi Infotaxis (CGRInfotaxis), a distributed decision-optimization framework that combines multi-agent collaboration with hybrid cognitive strategy to improve search efficiency and robustness. The method integrates a gravitational potential field for rapid source convergence and Rényi divergence-based probabilistic exploration to handle sparse detections, dynamically balanced via a regulation factor. Particle filtering optimizes posterior probability estimation to autonomously refine search areas while preserving computational efficiency, alongside a distributed interactive-optimization mechanism for real-time decision updates through agent cooperation. The algorithm’s performance is evaluated in scenarios with fixed and randomized odor source locations, as well as with varying numbers of agents. Results demonstrate that CGRInfotaxis achieves a near-100% success rate with high consistency across diverse conditions, outperforming existing methods in stability and adaptability. Increasing the number of agents further enhances search efficiency without compromising reliability. These findings suggest that CGRInfotaxis significantly advances multi-agent odor source localization in turbulent, sparse environments, offering practical utility for real-world applications. Full article

To study the generation and dissipation process of agglomerate fog in mountainous expressways and deeply understand the hazard mechanisms of agglomerate fog sections in mountainous expressways, based on the analysis of the geographical location characteristics of mountainous expressways and the spatial and temporal distribution characteristics of agglomerate fog, the airspace constitutive model of agglomerate fog in mountainous expressways was constructed based on Newton constitutive theory. Firstly, the properties of the Newtonian fluid and cluster fog were compared and analyzed, and the influence mechanism of environmental factors such as the altitude difference, topography, water system, valley effect, and vegetation on the generation and dissipation of agglomerate fog in mountainous expressways was analyzed. Based on Newton’s constitutive theory, the constitutive model of temperature, humidity, wind speed, and agglomerate fog points in the foggy airspace of the mountainous expressway was established. Then, the time and spatial distribution of fog in Chongqing and Guizhou from 2021 to 2023 were analyzed. Finally, the model was verified by using the meteorological data and fog warning data of Liupanshui City, Guizhou Province in 2023. The results show that the foggy airspace of mountainous expressways can be defined as “the space occupied by the agglomerate fog that occurs above the mountain expressway”; The temporal and spatial distribution of foggy airspace on expressways in mountainous areas is closely related to the topography, water system, vegetation distribution, and local microclimate formed by thermal radiation. The horizontal and vertical movements of the atmosphere have little influence on the foggy airspace on expressways in mountainous areas. The specific manifestation of time distribution is that the occurrence of agglomerate fog is concentrated from November to April of the following year, and the daily occurrence time is mainly concentrated between 4:00–8:00 and 18:00–22:00. The calculation results of the foggy airspace constitutive model of the expressway in the mountainous area show that when there is low surface radiation or no surface radiation, the fogging value range is [90, 100], and the fogging value range is [50, 70] when there is high surface radiation (>200), and there is generally no fog in other intervals. The research results can provide a theoretical basis for traffic safety management and control of mountainous expressway fog sections. Full article

Local site conditions can significantly influence the amplitude, duration, and frequency content of seismic recordings, making the characterization of subsoil properties a critical component in seismic hazard assessment. However, despite extensive research, standardized methodologies for assessing site effects are still lacking. This study presents preliminary steps in the site characterization of a small area of Campi Flegrei caldera (Italy), with the aim of enhancing understanding of local lithology and seismic wave propagation. The analysis focuses on the broad-band seismic station CSTH, installed in 2021, and incorporates data from a temporary 2D array of five short-period sensors deployed around the station. These sensors recorded both ambient noise and seismic events associated with caldera dynamics. To improve the robustness of the characterization, data from two additional permanent broad-band stations (CPIS and CSOB) of the Istituto Nazionale di Geofisica e Vulcanologia—Osservatorio Vesuviano’s monitoring network, also located nearby a hydrothermal field, were included. Spectral analyses such as Power Spectral Density (PSD), Horizontal-to-Vertical (H/V) spectral ratios, and f-k array technique were performed to evaluate the frequency-dependent response of the site and to support the development of a comprehensive seismic site model. Full article

This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article

The NW–SE-trending Firenze-Pistoia Basin (FPB) is an intermontane tectonic depression in the Northern Apennines (Italy) bounded to the northeast by a SW-dipping normal fault system. Although it has moderate historical seismicity (maximum estimated Mw 5.5 in 1895), the FPB lacks detailed characterization of its recent tectonic structures, unlike those of nearby basins that have produced Mw > 6 events. This study focuses on the southeastern sector of the basin, including the urban area of Florence, using tectonic geomorphology derived from remote sensing, in particular LiDAR data, field verification, and high-resolution geophysical surveys such as electrical resistivity tomography and seismic reflection profiles. The integration of these techniques enabled interpretation of the subdued and anthropogenically masked tectonic structures, allowing the identification of Holocene activity and significant, although limited, surface vertical offset for three NE–SW-striking normal faults, the Peretola, Scandicci, and Maiano faults. The Scandicci and Maiano faults appear to segment the southeasternmost strand of the master fault of the FPB, the Fiesole Fault, which now shows activity only along isolated segments and cannot be considered a continuous active fault. From empirical relationships, the Scandicci Fault, the most relevant among the three active faults, ~9 km long within the basin and with an approximate Late Quaternary slip rate of ~0.2 mm/year, might source Mw > 5.5 earthquakes. These findings highlight the need to reassess the local seismic hazard for more informed urban planning and for better preservation of the cultural and architectural heritage of Florence and the other artistic towns located in the FPB. Full article

Roadway deformation failure is often related to the presence of weak structural planes (WSPs) in the surrounding rock mass. Especially in coastal mining environments, WSP-induced deformation can create pathways that connect faults with seawater, accelerating groundwater seepage and inrush hazards. This study employs an optimized Finite–Discrete Element Method (Y-Mat) to simulate WSP-driven fracture evolution, introducing an elastoplastic failure criterion and enhanced contact force calculations. The results show that the farther the WSP is from the roadway, the lower its influence; its existence alters the shape of the plastic zone by lengthening the failure zone along the fault direction, while its angle changes the shape and location of the failure zone and deflects fracture directions, with the surrounding rock between the roadway and WSP suffering the most severe failure. The deformation failure of roadway surrounding rock is influenced by WSPs. Excavation unloading reduces the normal stress and shear strength in the weak structural plane of surrounding rock, resulting in slip and deformation. Additionally, WSP-induced fractures act as groundwater influx conduits, especially in fault-proximal roadways or where crack angles align with hydraulic gradients, so mitigation in water-rich mining environments should prioritize sealing these pathways. The results provide a theoretical basis for roadway excavation and support engineering under the influence of WSPs. Full article

The precise characterization of surface rupture zones and associated co-seismic displacement fields from large earthquakes provides critical insights into seismic rupture mechanisms, earthquake dynamics, and hazard assessments. Stereo-photogrammetric digital elevation models (DEMs), produced from high-resolution satellite stereo imagery, offer reliable global datasets that are suitable for the detailed extraction and quantification of vertical co-seismic displacements. In this study, we utilized pre- and post-event WorldView-2 stereo images of the 2024 Ms7.1 Wushi earthquake in Xinjiang to generate DEMs with a spatial resolution of 0.5 m and corresponding terrain point clouds with an average density of approximately 4 points/m². Subsequently, we applied the Iterative Closest Point (ICP) algorithm to perform differencing analysis on these datasets. Special care was taken to reduce influences from terrain changes such as vegetation growth and anthropogenic structures. Ultimately, by maintaining sufficient spatial detail, we obtained a three-dimensional co-seismic displacement field with a resolution of 15 m within grid cells measuring 30 m near the fault trace. The results indicate a clear vertical displacement distribution pattern along the causative sinistral–thrust fault, exhibiting alternating uplift and subsidence zones that follow a characteristic “high-in-center and low-at-ends” profile, along with localized peak displacement clusters. Vertical displacements range from approximately 0.2 to 1.4 m, with a maximum displacement of ~1.46 m located in the piedmont region north of the Qialemati River, near the transition between alluvial fan deposits and bedrock. Horizontal displacement components in the east-west and north-south directions are negligible, consistent with focal mechanism solutions and surface rupture observations from field investigations. The successful extraction of this high-resolution vertical displacement field validates the efficacy of satellite-based high-resolution stereo-imaging methods for overcoming the limitations of GNSS and InSAR techniques in characterizing near-field surface displacements associated with earthquake ruptures. Moreover, this dataset provides robust constraints for investigating fault-slip mechanisms within near-surface geological contexts. Full article

The investigations examined a potential reduction in discrepancies between the values of the unperturbed radiofrequency (RF) electromagnetic field (EMF) and values of the EMF measured by wearable equipment (personal exposure meters) impacted by the proximity of the human body. This was done by modelling distributed wearable (multi-location, with up to seven simultaneously locations) measurements. The performed numerical simulations mimicked distributed measurements in 24 environmental exposure scenarios (recognized as virtual measurements) covered: the horizontal or vertical propagation of the EMF and electric field vector polarization corresponding to typical conditions of far-field exposure from wireless communication systems (at a frequency of 100–3600 MHz). Physical tests using three EMF probes for simultaneous measurements have been also performed. Studies showed that the discrepancy in assessing EMF exposure by an on-body equipment and the parameters of the unperturbed EMF in the location under inspection (mimicking the contribution to measurement uncertainty from the human body proximity) may be significantly reduced by the appropriate use of a distributed measurement system. The use of averaged values, from at least three simultaneous measurements at relevant locations on the body, may reduce the uncertainty approximately threefold. Full article

Historical buildings are exposed to an increased risk of fire. The direct influence comes from the buildings’ structural design and the fire protection level. The fundamental principle for reducing the loss of heritage value in historical buildings due to fire is fire protection, as part of crisis management. This article focuses on selected castle buildings from Slovakia. Three castle buildings were selected based on their location in the country. All of them are currently used for museum purposes. Using an analytical form, we assessed fire hazards and fire safety measures in two parts, calculated the fire risk index, and proposed solutions. Qualitative research, which is more suitable for the issue at hand, was used to evaluate the selected objects. The main methods used in the research focused on visual assessment of the current condition of the objects and analysis of fire documentation and its comparison with currently valid legal regulations. Based on the results, we can conclude that Kežmarok Castle (part of the historical city center) has a small fire risk (fire risk index = 13 points). Trenčín Castle (situated on a rock above the city) and Stará Ľubovňa Castle (situated on a limestone hill outside the city, surrounded by forest) have an increased risk of fire (fire risk index = 50–63). Significant risk sources identified included surrounding forest areas, technical failures related to outdated electrical installations, open flames during cultural events, the concentration of highly flammable materials, and complex evacuation routes for both people and museum collections. Full article

Background/Objectives: The COVID-19 pandemic disrupted global cancer care. This study compared gastric cancer surgical outcomes before and during the pandemic in Turkey. We also aimed to analyze the impact of the pandemic and factors on survival and mortality in gastric cancer patients. Materials and Methods: This retrospective, multicenter cohort study included 324 patients from three tertiary centers in Turkey who underwent gastric cancer surgery between January 2018 and December 2022. Patients were stratified into Pre-COVID-19 (n = 150) and COVID-19 Era (n = 174) groups. Comprehensive demographic, surgical, pathological, and survival data were analyzed. To identify factors independently associated with postoperative mortality, a multivariable logistic regression model was applied. For evaluating predictors of long-term survival, multivariable Cox proportional hazards regression analysis was conducted. Results: The median time from diagnosis to surgery was comparable between groups, while the time from surgery to pathology report was significantly prolonged during the pandemic (p = 0.012). Laparoscopic surgery (p = 0.040) and near-total gastrectomy (p = 0.025) were more frequently performed in the Pre-COVID-19 group. Although survival rates between groups were similar (p = 0.964), follow-up duration was significantly shorter in the COVID-19 Era (p < 0.001). Comparison between survivor and non-survivor groups showed that several variables were significantly associated with mortality, including larger tumor size (p < 0.001), greater number of metastatic lymph nodes (p < 0.001), elevated preoperative CEA (p = 0.001), CA 19-9 (p < 0.001), poor tumor differentiation (p = 0.002), signet ring cell histology (p = 0.003), lymphovascular invasion (p < 0.001), and perineural invasion (p < 0.001). Multivariable logistic regression identified total gastrectomy (OR: 2.14), T4 tumor stage (OR: 2.93), N3 nodal status (OR: 2.87), and lymphovascular invasion (OR: 2.87) as independent predictors of postoperative mortality. Cox regression analysis revealed that combined tumor location (HR: 1.73), total gastrectomy (HR: 1.56), lymphovascular invasion (HR: 2.63), T4 tumor stage (HR: 1.93), N3 nodal status (HR: 1.71), and distant metastasis (HR: 1.74) were independently associated with decreased overall survival. Conclusions: Although gastric cancer surgery continued during the COVID-19 pandemic, some delays in pathology reporting were observed; however, these did not significantly affect the timing of adjuvant therapy or patient outcomes. Importantly, pandemic timing was not identified as an independent risk factor for mortality in multivariable logistic regression analysis, nor for survival in multivariable Cox regression analysis. Instead, tumor burden and aggressiveness—specifically advanced stage, lymphovascular invasion, and total gastrectomy—remained the primary independent determinants of poor prognosis. While pandemic-related workflow delays occurred, institutional adaptability preserved oncologic outcomes. Full article

This study presents a scenario-based, two-dimensional flood modeling approach to assess the potential downstream impacts of a piping-induced dam failure triggered by seismic activity. The case study focuses on the Doğantepe Dam in northwestern Türkiye, located near an active branch of the North Anatolian Fault. Critical deformation zones were previously identified through PLAXIS 2D seismic analyses, which served as the physical basis for a dam break scenario. This scenario was modeled using the HEC-RAS 2D platform, incorporating high-resolution topographic data, reservoir capacity, and spatially varying Manning’s roughness coefficients. The simulation results show that the flood wave reaches downstream settlements within the first 30 min, with water depths exceeding 3.0 m in low-lying areas and flow velocities surpassing 6.0 m/s, reaching up to 7.0 m/s in narrow sections. Inundation extents and hydraulic parameters such as water depth and duration were spatially mapped to assess flood hazards. The study demonstrates that integrating physically based seismic deformation data with hydrodynamic modeling provides a realistic and applicable framework for evaluating flood risks and informing emergency response planning. Full article

In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article

Vegetables and fruits grown in urban areas pose a potential threat to human health due to contamination with heavy metals (HMs). This study aimed to identify and quantify the concentrations of heavy metals (Fe, Mn, Zn, Cu, Pb, Cd) in tomatoes, leafy vegetables, and fruits collected from 16 allotment gardens (AGs) located in Warsaw. A total of 112 samples were analyzed (72 vegetable and 40 fruit samples). Vegetables from AGs accumulated significantly higher levels of HMs than fruits. Leafy vegetables, particularly those cultivated near high-traffic roads, exhibited markedly elevated levels of Pb, Cd, and Zn compared to those grown in peripheral areas. Lead concentrations exceeded permissible limits by six to twelve times, cadmium by one to thirteen times, and zinc by 0.7 to 2.4 times. Due to high levels of Pb and Cd, tomatoes should not be cultivated in urban environments. Regardless of location, only trace amounts of HMs were detected in fruits. The greatest health risk is associated with the consumption of leafy vegetables. Lettuce should be considered an indicator plant for assessing environmental contamination. The obtained Hazard Index (HI) values indicate that only the tested fruits are safe for consumption. Meanwhile, the values of the Hazard Quotient (HQ) indicate no health risk associated with the consumption of lettuce, cherries, and red currants. Among the analyzed elements, Pb showed a higher potential health risk than other metals. This study emphasizes the need for continuous monitoring of HM levels in urban soils and the establishment of baseline values for public health purposes. Remediation of contaminated soils and the implementation of safer agricultural practices are recommended to reduce the exposure of urban populations to the risks associated with the consumption of contaminated produce. In addition, the safety of fruits and vegetables grown in urban areas is influenced by the location of the AGs and the level of industrialization of the agglomeration. Therefore, the safety assessment of plant products derived from AGs should be monitored on a continuous basis, especially in vegetables. Full article