Search Results (268)

Objectives: EuroSCORE II is widely used to predict perioperative and 30-day mortality in cardiac surgery, yet data on its ability to predict long-term outcomes remain limited. This study investigates whether EuroSCORE II is associated with one-year and long-term mortality in a heterogeneous population undergoing major cardiac surgery with cardiopulmonary bypass. Methods: A retrospective cohort study was conducted including 2179 patients who underwent elective or urgent cardiac surgery with cardiopulmonary bypass between 2017 and 2021 at the University Hospital Salzburg. Data were extracted from the Salzburg Intensive Care database (SICdb) and supplemented with mortality information from Statistik Austria. EuroSCORE II values were compared between survivors and non-survivors. Kaplan–Meier analyses, Cox regression and logistic regression with ROC analysis were performed to evaluate the predictive association of EuroSCORE II with mortality. Results: EuroSCORE II was significantly higher in patients who died within one year and in those who died during a mean follow-up period of 1152.67 ± 521.39 days. Patients who survived at least one year had a median EuroSCORE II of 2.2, whereas those who died within one year had a median of 7.0. Cox regression demonstrated a hazard ratio of 1.062 for one-year mortality and 1.058 for long-term mortality. Kaplan–Meier curves showed significantly reduced survival with increasing EuroSCORE II quartiles. Logistic regression for one-year mortality yielded an AUC of 0.773, indicating good discriminative ability. Conclusions: EuroSCORE II is significantly associated with long-term mortality after major cardiac surgery, demonstrating good discriminatory performance. These findings support its potential utility not only as a short-term but also as a long-term prognostic indicator in cardiac surgery populations. Full article

The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish National Geographic Institute creates significant challenges due to differences in formats, update frequencies, and access methods. To overcome these limitations, this paper presents a modular and automated framework for the scheduled near-real-time ingestion of global seismic data using open APIs and semi-structured web data. The system, implemented using a Docker-based architecture, automatically retrieves, harmonizes, and stores seismic information from heterogeneous sources at regular intervals using a cron-based scheduler. Data are standardized into a unified schema, validated to remove duplicates, and persisted in a relational database for downstream analytics and visualization. The proposed framework adheres to the FAIR data principles by ensuring that all seismic events are uniquely identifiable, source-traceable, and stored in interoperable formats. Its lightweight and containerized design enables deployment as a microservice within emerging data spaces and open environmental data infrastructures. Experimental validation was conducted using a two-phase evaluation. This evaluation consisted of a high-frequency 24 h stress test and a subsequent seven-day continuous deployment under steady-state conditions. The system maintained stable operation with 100% availability across all sources, successfully integrating 4533 newly published seismic events during the seven-day period and identifying 595 duplicated detections across providers. These results demonstrate that the framework provides a robust foundation for the automated integration of multi-source seismic catalogs. This integration supports the construction of more comprehensive and globally accessible earthquake datasets for research and near-real-time applications. By enabling automated and interoperable integration of seismic information from diverse providers, this approach supports the construction of more comprehensive and globally accessible earthquake catalogs, strengthening data-driven research and situational awareness across regions and institutions worldwide. Full article

Floods rank among the most frequent and destructive natural hazards, threatening ecosystems, human settlements, and national economies. This study delineates flood-prone areas across Kosovo by developing a national-scale Flood Risk Database (FRDB) and a comprehensive mapping framework integrating Geographic Information Systems (GIS), Multi-Criteria Decision-Making (MCDM), and the Analytical Hierarchy Process (AHP). Eight hydrological and topographic conditioning factors—slope, elevation, flow accumulation, distance to rivers, land use/land cover, soil type, precipitation, and drainage density—were analyzed. AHP was employed to assign factor weights based on their relative influence on flood susceptibility, while MCDM aggregated these weighted spatial layers to generate a national flood risk map. Model validation, based on historical flood points, achieved an AUC of 0.909, confirming its high predictive accuracy. The resulting flood risk map classifies Kosovo’s territory into five risk levels: very high (0.56%), high (14.44%), moderate (36.68%), low (46.46%), and very low (1.88%). This research provides the first systematic national-scale FRDB for Kosovo, offering a reliable scientific basis for flood management, spatial planning, and climate resilience policy. Full article

Background: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are a major cause of intensive care unit (ICU) admissions and are associated with substantial short-term mortality. Diabetes mellitus is a frequent comorbidity in patients with COPD, yet its impact on short-term outcomes in critically ill AECOPD patients remains uncertain. Aim: The aim of this study was to investigate whether diabetes mellitus is independently associated with 30-day mortality in critically ill adult patients admitted to the ICU with AECOPD. Methods: We conducted a retrospective cohort study of adult ICU patients with AECOPD using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. All eligible adult patients with a documented diagnosis of AECOPD during the study period were included. Patients were categorized according to the presence or absence of diabetes mellitus. Diabetes mellitus was identified based on documented diagnostic codes and clinical records at the time of ICU admission. Demographic variables, laboratory parameters obtained within the first 24 h of ICU admission, and mechanical ventilation requirements were assessed. Mechanical ventilation was initiated according to standard clinical indications, including acute respiratory failure, hypoxemia, or hypercapnia. The primary outcome was 30-day all-cause mortality. Kaplan–Meier survival analysis, multivariable logistic regression, and Cox proportional hazards models were applied to identify independent predictors of mortality. Results: A total of 5874 ICU patients were included, of whom 2489 (42.3%) had diabetes. Patients with diabetes were slightly younger, more frequently male, and more often received mechanical ventilation than non-diabetic patients. Unadjusted 30-day mortality was lower among diabetic patients (15.3% vs. 17.5%; p = 0.032). However, after adjustment for relevant covariates, diabetes was not an independent predictor of 30-day mortality (HR = 0.80; p = 0.46). Age, male sex, and elevated lactate levels were associated with increased mortality, while early mechanical ventilation showed an association with improved short-term survival. Conclusions: Diabetes mellitus was not independently associated with 30-day mortality in critically ill patients with AECOPD. Short-term outcomes were primarily influenced by age, markers of metabolic stress, and timely ventilatory support. Due to limitations of the database, reliable differentiation between type 1 and type 2 diabetes mellitus and detailed assessment of COPD severity or phenotype were not consistently feasible. Further prospective studies are warranted to clarify the long-term implications of diabetes in this patient population. Full article

Timely and accurate inspection of concrete bridges is critical to ensuring structural integrity and public safety. Traditional visual inspections conducted by human inspectors are labour-intensive, inconsistent, and often limited in their ability to access all structural components, particularly in hazardous or inaccessible areas. Image-based inspection techniques have emerged as a safer and more efficient alternative, and recent advancements in deep learning have significantly enhanced their diagnostic capabilities. This systematic review critically evaluates 77 studies that applied deep learning approaches to the detection and classification of surface defects in concrete bridges using 2D images. Relevant publications were retrieved from major scientific databases, screened for eligibility, and analyzed in terms of model type, training strategies, and evaluation metrics. The reviewed works encompass a wide spectrum of algorithms—spanning classification, object detection, and image segmentation models—highlighting their architectural features, strengths, and trade-offs in terms of accuracy, computational complexity, and real-time applicability. Key findings reveal that transfer learning, data augmentation, and careful dataset composition are pivotal in improving model performance. Moreover, the review identifies emerging research trajectories, such as integrating deep learning with Building Information Modeling (BIM), leveraging edge computing for real-time monitoring, and developing rich annotated datasets to enhance model generalizability. By mapping the current state of knowledge and outlining future research directions, this study provides a foundational reference for researchers and practitioners aiming to deploy deep learning technologies in bridge inspection and infrastructure monitoring. Full article

With the trend of internationalization, maritime traffic density has gradually increased. Since 2002, the International Maritime Organization (IMO) has required various types of vessels to be equipped with the Automatic Identification System (AIS). Through AIS static and dynamic data, more complete navigational information of vessels can be obtained. As the Port of Kaohsiung is currently transitioning into a smart port, this study focuses on inbound and outbound vessels of the Second Port of Kaohsiung. It considers both the safety monitoring of the smart port and environmental security, integrating a big data database to provide early warnings for abnormal navigation conditions. This study builds an integrated database based on vessel AIS data, conducts AIS big data analysis to extract useful information, and establishes a random forest model to predict whether a vessel’s course and speed during port navigation deviate from normal patterns, thereby achieving the goal of early warning. This study also helps reduce the risk of collisions caused by abnormal vessel operations and thus prevents marine pollution in the port area due to oil spills or hazardous substance leakage. Through real-time monitoring and early warning of navigation behavior, it not only enhances navigation safety but also serves as the first line of defense against marine pollution, contributing significantly to the protection of the port’s ecological environment and the promotion of sustainable development. Full article

The construction industry remains among the most hazardous sectors globally, facing persistent safety challenges despite advancements in occupational health and safety OHS) measures. The objective of this study is to systematically analyze the use of Artificial Intelligence (AI) in construction safety management and to identify the most effective techniques, data modalities, and validation practices. The method involved a systematic review of 122 peer-reviewed studies published between 2016 and 2025 and retrieved from major academic databases. The selected studies were classified by AI technologies including Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), and the Internet of Things (IoT), and by their applications in real-time hazard detection, predictive analytics, and automated compliance monitoring. The results show that DL and CV models, particularly Convolutional Neural Network (CNN) and You Only Look Once (YOLO)-based frameworks, are the most frequently implemented for personal protective equipment recognition and proximity monitoring, while ML approaches such as Support Vector Machines (SVM) and ensemble algorithms perform effectively on structured and sensor-based data. Major challenges identified include data quality, generalizability, interpretability, privacy, and integration with existing workflows. The paper concludes that explainable, scalable, and user-centric AI integrated with Building Information Modeling (BIM), Augmented Reality (AR) or Virtual Reality (VR), and wearable technologies is essential to enhance safety performance and achieve sustainable digital transformation in construction environments. Full article

Background and Aim: Natural hazard-related disasters such as wildfires, hurricanes, earthquakes, and floods pose significant risks to older adults, individuals with disabilities, and those with chronic health conditions. Transportation-related challenges during and after evacuation can severely impact their safety, mobility, and recovery. This scoping review examines the current evidence to identify research gaps and inform strategies to improve evacuation outcomes and long-term resilience for these at-risk groups. The STEPS framework (Spatial, Temporal, Economic, Physiological, Social) was applied to guide the analysis and interpretation of findings. Methods: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines and searched five databases, including PubMed, APA PsycINFO, CINAHL Complete, EMBASE, and Web of Science for primary studies on transportation and disaster evacuation among vulnerable populations. Results: Twenty studies were included. Four key areas of concern were identified: (1) immediate transportation barriers during evacuation, (2) prolonged transportation disruptions post-disaster, (3) anticipated logistical challenges in future evacuation planning, and (4) inconsistent and inaccessible communication of transportation-related information during emergencies. These challenges intersected with all five STEPS dimensions. Conclusions: Transportation barriers remain a persistent and under-addressed risk factor in disaster contexts for vulnerable groups. The STEPS framework helped reveal the multidimensional nature of these issues, emphasizing the need for integrated planning, assistive transport options, inclusive communication systems, and stronger public–private coordination. Future research should focus on collecting disaggregated data, evaluating interventions, and supporting inclusive policy reforms tailored to each type of disaster. Full article

The availability of reliable and spatially distributed rainfall data is a key element flood and landslide risk assessment, both for forecasting and post-event analysis. In this context, this study evaluates the contribution of radar-based rainfall estimates to enhancing the spatial accuracy of precipitation fields with respect to those derived from rain gauge networks alone. The analysis was conducted over a ~100 km² area in the Liguria Region, north-western Italy, characterized by a dense rain gauge network, with an average density of one gauge per 10 km², and covers seven years of hourly rainfall observations. Radar-derived rainfall fields, available at a 1 × 1 km² spatial resolution, were locally corrected across the study area by interpolating gauge-based local correction factors through an Inverse Distance Weighting (IDW) scheme. The corrected radar fields were then assessed through Leave-P-Out Cross-Validation and rainfall-intensity-based classification, also simulating scenarios with progressively reduced gauge density. The results demonstrate that radar-corrected estimates systematically provide a more accurate spatial representation of rainfall, especially for high-intensity events and in capturing the actual magnitude of local rainfall peaks, even in areas covered by a dense rain gauge network. Regarding the implications for rainfall-induced landslide hazard assessment, the analysis of 56 landslides from the ITALICA (Italian Rainfall-Induced Landslides Catalogue) database showed that including radar information can lead to significant differences in the estimation of rainfall thresholds for landslide initiation compared with gauge-only data. Full article

The presence of microorganisms in fish and fish products is a relevant factor in spoilage and food safety. Fish is considered a nutritious staple of the human diet and is produced, processed, and marketed worldwide. To describe the role of microorganisms in regard to the safety of fish and fish products, we conducted this narrative review to present information on fish production; various pollution hazards; and the causes, control, and prevention of diseases caused by food consumption. It also explores documented cases of foodborne illnesses in Mexico associated with microorganisms. Furthermore, microbiological evaluations of products that are offered for consumption in different areas and cities of Mexico are reviewed, as is the regulatory framework that has been developed regarding the safety of produced and marketed products. This was achieved through the searching for, compilation of, and analysis of information in various databases (Redalyc, Scielo, Scopus, Web of Science, ScienceDirect, Google Scholar, among others). The knowledge obtained indicates that bacteria and parasites are frequently associated with illnesses caused by the consumption of raw products or products subjected to inadequate cooking and hygiene practices. Meanwhile, a microbiological evaluation of fish and marketed products reveals contamination that compromises food safety. Therefore, it is necessary to strengthen microbiological surveillance of products and hygiene education throughout the food chain by government, industry, researchers, and end consumers to promote the availability of safe, nutritious foods for the population. Full article

Background/Purpose: Reproductive health and fertility outcomes are essential but often overlooked aspects of occupational well-being. Organizational dysfunction, demanding workloads, and limited workplace accommodations may negatively affect fertility, while supportive policies and inclusive cultures can mitigate risks. This review aimed to map current evidence on these relationships and their economic consequences. Methodology/Approach: A scoping review was conducted using the PCC (Population–Concept–Context) framework. Systematic searches across multiple databases identified 30 eligible studies, including quantitative, qualitative, and mixed-method designs, spanning different sectors and international contexts. Findings: Four main domains emerged: shift work and circadian disruption, organizational stress and burnout, workplace flexibility and accommodations, and fertility-related policies and organizational support. Hazardous working conditions, long hours, and psychosocial stressors were consistently associated with impaired fertility, reduced fecundability, and pregnancy complications. Conversely, flexible scheduling, fertility benefits, and supportive organizational cultures were linked to improved well-being, retention, and productivity. Originality/Value: This review integrates evidence across occupational health, organizational psychology, and labor economics, offering a comprehensive overview of workplace influences on reproductive health. It highlights gaps in equity and representation—particularly for men, LGBTQ+ employees, and workers in precarious jobs—and calls for longitudinal, interdisciplinary, and intervention-based studies to inform effective workplace policies. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

(1) Background: Invasive micropapillary carcinoma (IMPC) is a rare, aggressive breast cancer subtype marked by high lymph node metastasis rates. While Oncotype DX recurrence score (RS) offers prognostic information for patients with hormone-receptor-positive (HR+) breast cancer, its utility in IMPC—a histology with distinct biologic behavior—remains unvalidated. This study evaluates whether Oncotype DX offers prognostic information with respect to overall survival (OS) in non-metastatic, early-stage patients with IMPC of the breast. (2) Methods: The National Cancer Database (2004–2020) was queried to select for women with ER+/HER2−, T1-T2N0-N1 IMPC who underwent Oncotype DX testing and received no neoadjuvant therapy. Patients were stratified by RS: low (≤11), intermediate (12–25), and high (>25). Kaplan–Meier survival curves and log-rank tests compared 5-year OS between groups. Multivariable Cox proportional hazards models assessed RS as an independent predictor, adjusting for age, race, comorbidities, grade, radiation, and insurance status. (3) Results: A total of 1325 women met the selection criteria. The cohort demonstrated significant survival disparities by RS (log-rank p = 0.017). Five-year OS rates were 97.5%, 97.5%, and 93.7% for low, intermediate, and high-risk patients, respectively. Adjusted multivariate analysis confirmed RS as an independent prognosticator: low (HR = 0.31, 95% CI: 0.15–0.75) and intermediate (HR = 0.32, 95% CI: 0.15–0.75) scores correlated with reduced mortality versus high RS. Omission of radiation therapy (HR = 2.68, 95% CI: 1.05–6.86) and higher comorbidity burden (0 comorbidities vs. ≥2: HR = 0.25, 95% CI: 0.10–0.61) were significantly associated with worse survival. (4) Conclusions: Oncotype DX is predictive for OS in IMPC, with high RS (>25) portending poorer outcomes. The survival detriment associated with RT omission aligns with prior studies demonstrating RT benefit in higher-risk cohorts. These findings validate RS as a prognostic tool in IMPC and underscore its potential to refine adjuvant therapy, particularly RT utilization. Future studies should explore RS-driven treatment personalization in IMPC, including comorbidity management and adjuvant radiation to improve outcomes in this distinct patient population. Full article

Background and Objectives: Children and adolescents with intellectual disability (ID) experience substantial health disparities, yet their skeletal health has been overlooked. Early-onset osteoporosis and fracture remain underrecognized in this population. Hence, this study assessed the risk of osteoporosis with concomitant fracture in this population using nationwide cohort data. Materials and Methods: This population-based retrospective cohort study examined data from South Korea’s National Health Insurance Service-National Health Information Database (2004–2021). In all, 75,790 individuals with ID and 922,921 control individuals aged 2–18 years were included. The primary outcome was osteoporosis with concomitant fracture occurring within 1 year before or after the osteoporosis diagnosis. The secondary outcome was osteoporosis with a pathological fracture. Results: The ID group had a significantly higher risk of developing osteoporosis with concomitant fracture than the control group (unadjusted hazard ratio [HR], 6.821; 95% confidence interval [CI], 5.065–9.187; p < 0.001). This association remained significant after adjusting for demographic factors and medical comorbidities as a composite variable (HR, 4.385; 95% CI, 3.080–6.245; p < 0.001) and after additional adjustment for cerebral palsy (HR, 3.331; 95% CI, 2.252–4.926; p < 0.001). Subgroup analyses showed stronger associations in males (HR, 7.597), younger ages (7–11 years: HR, 9.501), and rural areas (HR, 8.882). Conclusions: Children and adolescents with ID have a high risk of osteoporosis with concomitant fracture. Early assessment and targeted strategies are needed to promote bone health in this population. Full article

Objectives: This study aims to evaluate the association between antibiotic prophylaxis (particularly cephalosporins) and clinical outcomes in elderly hip fracture patients. Methods: We analyzed 4044 elderly hip fracture patients (2008–2022) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database using inverse probability treatment weighting (IPTW). Cox proportional hazards models assessed mortality risk, while logistic regression evaluated infection and Intensive Care Unit (ICU) admission risks. Dose–response and subgroup analyses were performed for significant findings. Results: In total, 166 patients received no antibiotics, 2589 received Cephalosporin monotherapy, 403 received non-cephalosporin therapy, and 886 received Cephalosporin combination therapy. After IPTW adjustment, monotherapy showed significantly lower mortality risk versus combination therapy at all timepoints (hazard ratio (HR) for 28-day mortality: 0.46, 95% confidence interval (95% CI): 0.28–0.75; HR for 90-day mortality: 0.60, 95% CI: 0.44–0.82; HR for 180-day mortality: 0.67, 95% CI: 0.51–0.87; HR for 1-year mortality: 0.71, 95% CI: 0.57–0.89). The SII cut-off values were 1310.1 for 28-day mortality, 2077.5 for both 90-day and 180-day mortality, 1742.2 for 1-year mortality, 2199.7 for ICU admission, and 1930.7 for infection. Subgroup analyses showed that males and internal fixation patients derived more benefits after cephalosporin monotherapy treatment at all time nodes. Patients with multiple injuries had a lower risk of 28-day mortality, while high-comorbidity patients (CCI ≥ 5) and those with osteoporosis exhibited particular advantages with cephalosporin monotherapy. Conclusions: Cephalosporin monotherapy appears non-inferior to combination therapy for elderly hip fracture patients, potentially reducing long-term mortality risk, especially in males, internal fixation cases, and patients with CCI ≥ 5 and osteoporosis. Full article