Search Results (242)

Background: The development of brain metastases (BM) is a relatively uncommon but significantly adverse event in the spread of colorectal cancer (CRC). Although management of CRC BM has improved with advances in imaging and systemic therapies, clinical outcomes remain poor. Methods: This retrospective cohort study used the U.S. National Cancer Database to evaluate survival outcomes, treatment patterns, and prognostic factors in CRC patients diagnosed with BM between 2010 and 2020. Patients with isolated brain-only metastases formed the primary analytic cohort, while those with additional extracranial metastases were included for descriptive comparison. Multivariable Cox proportional hazards and logistic regression models were used to assess factors associated with of survival. Proportional hazards assumptions were tested using Schoenfeld residuals. Accelerated failure time models were also employed. Results: From a cohort of 1,040,877 individuals with CRC, 795 had metastatic disease present along with relevant data, of which 296 had isolated BM. Median overall survival (mOS) in BM-only metastatic disease group was 7.82 months (95% CI: 5.82–9.66). The longest survival was observed among patients treated with stereotactic radiosurgery combined with systemic therapy (SRS+Sys), with a median OS of 23.26 months (95% CI: 17.51–41.95) and a 3-year survival rate of 35.8%. In adjusted Cox models, SRS, systemic therapy, and definitive surgery of the primary site were each independently associated with reduced hazard of death. Rectal cancer patients had longer survival than those with colon primaries (mOS: 10.35 vs. 6.08 months). Age, comorbidity burden, and insurance status were not associated with survival in adjusted analyses. Conclusions: SRS+Sys was associated with longer survival compared to other treatment strategies. However, treatment selection is highly dependent on individual clinical factors such as performance status, comorbidities, and disease extent; therefore, these findings must be interpreted with caution Future prospective studies incorporating molecular and biomarker data are warranted to better guide care in this rare and high-risk group. Full article

Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km² of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered. Full article

Empty container depot (ECD) design significantly impacts maritime terminal efficiency, yet traditional evaluation approaches assess limited operational factors, constraining comprehensive performance optimization. This study develops an integrated discrete event simulation (DES) framework that simultaneously evaluates lifting equipment utilization, truck turnaround times, and potential collisions to support terminal decision-making. This study combines operational efficiency metrics with safety analytics for non-automated ECDs using Top Lifters and Reach Stackers. Additionally, a regression analysis examines efficiency metrics’ effect on safety risk. A case study at a Chilean multipurpose terminal reveals performance trade-offs between indicators under different operational scenarios, identifying substantial efficiency disparities between dry and refrigerated container operations. An analysis of four distinct collision zones with varying historical risk profiles showed the gate area had the highest potential collisions and a strong regression correlation with efficiency metrics. Similar models showed a poor fit in other conflict zones, evidencing the necessity for dedicated safety indicators complementing traditional measures. This integrated approach quantifies interdependencies between safety and efficiency metrics, helping terminal managers optimize layouts, expose traditional metric limitations, and reduce safety risks in space-constrained maritime terminals. 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

This paper presents a meta-analysis of social research on water, offering a novel methodological contribution to the study of emerging interdisciplinary research fields. We propose and implement a mixed methods framework that integrates quantitative network analysis with qualitative research, aiming to enhance both to give access to new emerging empirical fields and enhance the analytical depth of empirical social research. Drawing on a dataset of publications from the Web of Science over four distinct time intervals, we identify thematic clusters through keyword co-occurrence networks that reveal the evolving structure and internal dynamics of the field. Our findings show a clear trend toward increasing interdisciplinarity, responsiveness to global events, and contemporary challenges such as the emergence of COVID-19 and the continued centrality of topics related to water management and evaluation. By uncovering latent structures, our approach not only maps the field’s development but also lays the foundation for targeted qualitative analysis of articles representative of identified clusters. This methodological design contributes to the broader discourse on mixed methods research in the social sciences by demonstrating how computational tools can enhance the transparency and reliability of qualitative inquiry without sacrificing its interpretive richness. Furthermore, this study opens new avenues for critically reflecting on the epistemic culture of social water research, particularly in relation to its proximity to applied science and governance-oriented perspectives. The proposed method holds potential relevance for both academic researchers and decision makers in the water sector, offering a means to systematically access dispersed knowledge and identify underrepresented subfields. Overall, the study showcases the potential of mixed methods designs for navigating and structuring complex interdisciplinary research landscapes. Full article

The city of Uvira, located in the eastern Democratic Republic of Congo (DRC), is increasingly experiencing flood events with devastating impacts on human life, infrastructure, and livelihoods. This study evaluates flood susceptibility in Uvira using Geographic Information Systems (GISs), and an Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Making approach. It integrates eight factors contributing to flood occurrence: distance from water bodies, elevation, slope, rainfall intensity, drainage density, soil type, topographic wetness index, and land use/land cover. The results indicate that proximity to water bodies, drainage density and slope are the most influential factors driving flood susceptibility in Uvira. Approximately 87.3% of the city’s land area is classified as having high to very high flood susceptibility, with the most affected zones concentrated along major rivers and the shoreline of Lake Tanganyika. The reliability of the AHP-derived weights is validated by a consistency ratio of 0.008, which falls below the acceptable threshold of 0.1. This research provides valuable insights to support urban planning and inform flood management strategies. Full article

Background: Fish escape events from aquaculture facilities are increasing and pose significant ecological, economic, and traceability concerns. Accurate methods to differentiate between wild, cultured, and escaped fish are essential for fishery management and seafood authentication. Methods: This study analyzed muscle tissue from Sparus aurata, Dicentrarchus labrax, and Argyrosomus regius using a multiomics approach. Heavy metals were quantified by ICP-MS, fatty acid profiles were assessed via GC-MS, and metabolomic and lipidomic signatures were identified using 1H NMR spectroscopy. Multivariate statistical models (MDS and PLS-LDA) were applied to classify fish origins. Results: Wild seabream showed significantly higher levels of arsenic (9.5-fold), selenium (3.5-fold), and DHA and ARA fatty acids (3.2-fold), while cultured fish exhibited increased linoleic and linolenic acids (6.5-fold). TMAO concentrations were up to 5.3-fold higher in wild fish, serving as a robust metabolic biomarker. Escaped fish displayed intermediate biochemical profiles. Multivariate models achieved a 100% classification accuracy across species and analytical techniques. Conclusions: The integration of heavy metal analysis, fatty acid profiling, and NMR-based metabolomics enables the accurate differentiation of fish origin. While muscle tissue provides reliable biomarkers relevant to human exposure, future studies should explore additional tissues such as liver and gills to improve the resolution of traceability. These methods support seafood authentication, enhance aquaculture traceability, and aid in managing the ecological impacts of escape events. Full article

The increasing number of natural and man-made disasters registered at the global level is causing a significant amount of damage. This represents one of the main sustainability challenges at the global level. The collapse of the Twin Towers, Hurricane Katrina, and the nuclear accident at the Fukushima power plant are some of the most representative disaster events that occurred at the beginning of the third millennium. These relevant disasters need an enhanced level of preparedness to reduce the gaps between the plan and its implementation. Among these actions, training and exercises play a relevant role because they increase the capability of planners, managers, and the people involved. By focusing on the exposure risk component, the general objective of the research is to obtain quantitative evaluations of the exercise’s contribution to risk reduction through evacuation. The paper aims to analyze serious games using a set of methods and models that simulate an urban risk reduction plan. In particular, the paper proposes a transparent framework that merges transport risk analysis (TRA) and transport system models (TSMs), developing serious game activities with the support of emerging information and communication technologies (e-ICT). Transparency is possible through the explicitation of reproducible analytical formulations and linked parameters. The core framework of serious games is constituted by a set of models that reproduce the effects of players’ choices, including planned actions of decisionmakers and travel users’ choices. The framework constitutes the prototype of a digital platform in a “non-stressful” context aimed at providing more insights about the effects of planned actions. The proposed framework is characterized by transparency, a feature that allows other analysts and planners to reproduce each risk scenario, by applying TRA and relative effects simulations in territorial contexts by means of TSMs and parameters updated by e-ICT. A basic experimentation is performed by using a game, presenting the main results of a prototype test based on a reproducible exercise. The prototype experiment demonstrates the efficacy of increasing preparedness levels and reducing exposure by designing and implementing a serious game. The paper’s methodology and results are useful for policymakers, emergency managers, and the community for increasing the preparedness level. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article

The management of public health projects in a BANI (brittle, anxious, non-linear, incomprehensible) environment, exemplified by the ongoing war in Ukraine, presents unprecedented challenges due to fragile systems, heightened uncertainty, and complex socio-political dynamics. This study proposes an AI-driven framework to enhance the resilience and effectiveness of public health interventions under such conditions. By integrating a coupled SEIR–Infodemic–Panicdemic Model with war-specific factors, we simulate the interplay of infectious disease spread, misinformation dissemination, and panic dynamics over 1500 days in a Ukrainian city (Kharkiv). The model incorporates time-varying parameters to account for population displacement, healthcare disruptions, and periodic war events, reflecting the evolving conflict context. Sensitivity and risk–opportunity analyses reveal that disease transmission, misinformation, and infrastructure damage significantly exacerbate epidemic peaks, while AI-enabled interventions, such as fact-checking, mental health support, and infrastructure recovery, offer substantial mitigation potential. Qualitative assessments identify technical, organisational, ethical, regulatory, and military risks, alongside opportunities for predictive analytics, automation, and equitable healthcare access. Quantitative simulations demonstrate that risks, like increased displacement, can amplify infectious peaks by up to 28.3%, whereas opportunities, like enhanced fact-checking, can reduce misinformation by 18.2%. These findings provide a roadmap for leveraging AI to navigate BANI environments, offering actionable insights for public health practitioners in Ukraine and other crisis settings. The study underscores AI’s transformative role in fostering adaptive, data-driven strategies to achieve sustainable health outcomes amidst volatility and uncertainty. Full article

Extreme wildfire events (EWEs) are becoming increasingly frequent in Mediterranean regions, posing significant threats to ecosystems. This study aimed to support post-fire restoration planning by developing a prioritization framework that categorizes areas according to different levels of vulnerability to the adverse impacts of EWEs. We developed a multi-criteria decision analysis (MCDA) approach to classify these areas within a fire perimeter. The process begins with the collection of available spatial data to assess the pre- and post-fire conditions. Following this, a set of criteria and sub-criteria was established through a participatory approach with local stakeholders. The analytic hierarchy process (AHP) was used to determine stakeholders’ preferences, which were then processed using the Criterium Decision Plus (CDP) version 4 software to support problem modeling. A combined consistency check was applied to ensure both individual coherence and group agreement. Finally, the methodology was integrated using the Ecosystem Management Decision Support (EMDS) software version 9, resulting in a spatial prioritization map that visually represents the levels of restoration priority and serves as a decision-support tool for post-fire restoration planning. Both the process and its results are discussed for an application to a large fire perimeter in the Vale do Sousa forested landscape. Full article

Background: Obstructive sleep apnea (OSA) is a prevalent but frequently underdiagnosed comorbidity in patients undergoing cardiac surgery, including coronary artery bypass grafting (CABG), aortic valve replacement (AVR), and mitral valve repair or replacement (MVR). This systematic review and meta-analytic synthesis investigates the relationship between OSA and postoperative morbidity and mortality, with particular attention to the predictive utility of established screening instruments. Methods: A systematic search of the PubMed database was conducted (April 2025), identifying 724 articles published in the last ten years. Seventeen primary studies met the inclusion criteria for qualitative synthesis, and four additional studies were included in the meta-analyses. Outcomes assessed included atrial fibrillation, major adverse cardiac and cerebrovascular events (MACCE), acute kidney injury (AKI), respiratory complications, pneumonia, hospital length of stay (LOS), and mortality. Risk of bias was assessed qualitatively based on study design and reporting limitations. This review was registered in the PROSPERO database under registration number CRD420251049574. Results: Meta-analyses demonstrated significantly elevated odds of atrial fibrillation (OR = 2.44, 95% CI: 1.46–4.07), major adverse cardiac and cerebrovascular events (OR = 2.06, 95% CI: 1.61–2.63), acute kidney injury (OR = 2.24, 95% CI: 1.67–3.01), and respiratory complications (OR = 1.15, 95% CI: 1.05–1.25) among patients with OSA. Additionally, OSA was associated with a significantly prolonged hospital length of stay (standardized mean difference [SMD] = 0.62, 95% CI: 0.46–0.78) and a marginal increase in pneumonia risk (OR = 1.07, 95% CI: 1.00–1.15). Evidence regarding stroke, intensive care unit (ICU) stay, and mortality was inconsistent or underpowered. Conclusions: Across core outcomes, findings were consistent across multiple studies involving a large patient population. Obstructive sleep apnea is a clinically consequential risk factor in cardiac surgery, associated with increased perioperative complications and prolonged hospitalization. These findings support the integration of routine OSA screening into preoperative risk assessment protocols. Further prospective, multicenter trials are warranted to assess the efficacy of perioperative management strategies, including continuous positive airway pressure (CPAP) therapy, in improving surgical outcomes. 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

This study applied data analysis techniques to analyze work-related accidents in Brazil’s mining sector from 2019 onward, identifying key risks and patterns. Using public datasets from governmental sources, it categorized accidents by the type of injury, causal agents, and affected body parts. The methodology employed included data cleaning, processing, and the development of interactive visualizations using advanced analytical tools, such as Python and Power BI, to facilitate data interpretation. Among the most significant events, the Brumadinho tailings dam collapse in 2019 emerged as a major outlier, substantially affecting multiple aspects of the analysis. This single incident accounted for 71.7% of all work-related fatalities recorded during the four-year period under study, highlighting its disproportionate impact on the dataset. This study also examined the main causes and consequences of mining accidents and facilitated the creation of victim profiles based on gender and age group, incorporating psychological theories regarding risk perception. It was concluded that, although the mining sector represents a small fraction of all work-related accidents in Brazil, the proportion of accidents relative to the number of workers in the sector is substantial, highlighting the need for stricter occupational safety management. The results can guide regulations and help companies and institutions to create safer, more sustainable mining policies. The methodology proved to be highly suitable, indicating its potential for application in safety analysis across other sectors. Full article

In the context of progressing climate change and the increasing frequency of extreme weather events, there is a growing need for effective strategies to mitigate their impacts. One such strategy involves the implementation of tools aimed at sustainable rainfall management at the site of precipitation. This study focuses on assessing the state of the water environment as a prerequisite for introducing sustainable Managed Aquifer Recharge (MAR) practices in urban areas. The research was conducted in the historic district of Warsaw, Poland. A comprehensive methodological approach was employed, including field and laboratory measurements of soil moisture and electrical conductivity (EC), vadose zone hydraulic conductivity, spring discharge rates, and analytical calculations based on climatic data. These were supplemented by groundwater flow modeling to estimate infiltration rates. The study showed that the infiltration rate in the aquifer is low—only 4.4% of the average annual precipitation. This is primarily due to limited green space coverage and high surface runoff, as well as high potential evaporation rates and low soil permeability in the vadose zone. A positive water balance and infiltration were observed only in December and January, as indicated by increased soil moisture and decreased EC values. A multi-criteria spatial analysis identified priority zones for the installation of retention infrastructure aimed at enhancing effective infiltration and improving the urban water balance. These findings underscore the need for targeted interventions in urban water management to support climate resilience and sustainable development goals. Full article