Search Results (673)

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Background and Objectives: Patients with hip fractures have a high mortality rate, highlighting the need for a reliable prognostic tool. Although the prognostic nutritional index (PNI) is a well-established predictor in patients with cancer, its utility has not been thoroughly investigated in patients with hip fractures. Therefore, this study aims to evaluate the association between PNI and mortality in patients undergoing hip fracture surgery. Materials and Methods: A retrospective review was conducted on all patients aged ≥65 years who underwent surgery for hip fracture between January 2014 and February 2018. Quartile stratification was chosen because no universally accepted clinical cut-off exists for PNI; this approach enables comparison of equally sized groups and exploration of potential non-linear risk patterns. The primary endpoints were 1-year and overall mortality in older adults undergoing hip fracture surgery. Multivariable Cox proportional-hazards models adjusted for age, sex, ASA class and comorbidities. Results: A total of 815 patients were analyzed. One-year and overall mortality rates were highest in the Q1 group (26.6%, 14.2%, 6.9%, 6.4% [p < 0.001] and 56.7%, 36.3%, 27.0%, 15.2% [p < 0.001], respectively). In Cox regression analysis, a lower preoperative PNI was significantly associated with an increased risk of overall mortality (Q1: HR 3.25, 95% confidence interval [CI] 2.11–5.01, p < 0.001; Q2: HR 1.85, 95% CI 1.19–2.86, p = 0.006; Q3: HR 1.52, 95% CI 0.97–2.38, p = 0.065; Q4 as reference), indicating a stepwise, dose–response increase in mortality risk as PNI decreases. Conclusions: The findings demonstrate that a lower preoperative PNI is significantly associated with higher 1-year and overall mortality in older adults undergoing hip fracture surgery. Although further prospective validation is needed, preoperative PNI may help predict mortality in frail patients undergoing hip fracture surgery and identify those who could benefit from nutritional assessment and optimization before surgery. Full article

Objectives: Patients with acute heart failure (AHF) often receive initial non-invasive ventilation (NIV). This study aimed to evaluate the prognostic role of NIV in patients hospitalized for AHF. Methods: This was a retrospective cohort study. We enrolled patients admitted to our cardiac intensive care unit with a diagnosis of AHF. Anthropometric, clinical, pharmacological, and instrumental assessments were collected. Both in-hospital and 180-day post-discharge mortality were evaluated. Results: Among 200 patients (mean age 81 ± 9 years; 52% male), NIV was applied in 80 cases (40%). These patients had more severe NYHA functional class, a higher prevalence of de novo AHF, required higher diuretic doses, and had longer hospital stays. In multivariate analysis, NIV remained significantly associated with length of stay (LOS) (r = 0.26; p = 0.0004). In-hospital mortality was 5% overall and significantly higher in the NIV group compared to non-NIV patients (10% vs. 1.6%, p < 0.001). At 180 days, mortality was also significantly higher in the NIV group [hazard ratio (HR) 1.84; 95% confidence interval (CI): 1.18–2.85; p = 0.006]. After adjusting for age, BNP, CRP, arterial blood gas parameters, renal function, and LVEF, NIV remained an independent predictor of 180-day mortality (HR 1.61; 95% CI: 1.01–2.54; p = 0.04). Conclusions: Patients with AHF who required NIV exhibited more severe disease and longer hospital stays. NIV use was independently associated with both in-hospital and post-discharge mortality, suggesting its potential role as a prognostic marker in AHF. Full article

Background and Objectives: Cancer patients are particularly susceptible to infections caused by multidrug-resistant Gram-negative bacteria (MDR GNB) due to chemotherapy- or radiation therapy-induced immunosuppression. Colistin is often prescribed as a last-resort agent for MDR GNB infection, but its clinical benefit in oncology patients remains unclear. This study aims to evaluate the mortality risk associated with colistin versus non-colistin regimens in cancer patient with MDR GNB infections, stratified by resistance profiles, infection sites, and concomitant medication use. Materials and Methods: A retrospective cohort study was conducted in adult cancer patients with MDR GNB infections that are resistant to at least three antibiotic classes and identified from at least two anatomical sites at a tertiary care hospital in Korea. Propensity score-matched in a 1:3 ratio either to the colistin group or non-colistin group and multivariate Cox hazard regression analyses were used to evaluate mortality in cancer patients with MDR GNB infections, primarily Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Results: A total of 85 patients (29 patients in the colistin and 56 patients in the non-colistin group) were included in the analysis. Overall, colistin use did not show a statistically significant mortality benefit compared to non-colistin regimens (hazard ratio (HR) 0.93, 95% CI 0.47–1.87). However, the subgroup analysis revealed that colistin had a potential association with significantly lower mortality in pneumonia patients with aminoglycoside-resistant infections (HR 0.04, 95% CI 0.002–0.69). Concomitant use of antipsychotics and benzodiazepines in selected resistance profiles also correlated with improved outcomes. In contrast, a potential association was found between concomitant macrolide use and increased mortality in patients with fluoroquinolone- or penicillin-resistant profiles. Conclusions: Colistin may offer survival benefits in selected high-risk cancer patients with MDR GNB pneumonia. Treatment outcomes are influenced by resistance profiles, infection sites, and concomitant medications, indicating the significant importance of individualized antimicrobial therapy and antimicrobial stewardship in oncology patients. Full article

Infant clothing represents a critical yet overlooked exposure pathway for heavy metals, with significant implications for child health and sustainable consumption. This study investigates cadmium (Cd) and chromium (Cr) contamination in 33 textile samples, integrating in vitro bioaccessibility assays, cytotoxicity analysis, and risk assessment models to evaluate dermal exposure risks. Results reveal that 80% of samples exceeded OEKO-TEX Class I limits for As (mean 1.01 mg/kg), Cd (max 0.25 mg/kg), and Cr (max 4.32 mg/kg), with infant clothing showing unacceptable hazard indices (HI = 1.13) due to Cd (HQ = 1.12). Artificial sweat extraction demonstrated high bioaccessibility for Cr (37.8%) and Ni (28.5%), while keratinocyte exposure triggered oxidative stress (131% ROS increase) and dose-dependent cytotoxicity (22–59% viability reduction). Dark-colored synthetic fabrics exhibited elevated metal loads, linking industrial dye practices to health hazards. These findings underscore systemic gaps in textile safety regulations, particularly for low- and middle-income countries reliant on cost-effective apparel. We propose three policy levers: (1) tightening infant textile standards for Cd/Cr, (2) incentivizing non-toxic dye technologies, and (3) harmonizing global labeling requirements. By bridging toxicological evidence with circular economy principles, this work advances strategies to mitigate heavy metal exposure while supporting Sustainable Development Goals (SDGs) 3 (health), 12 (responsible consumption), and 12.4 (chemical safety). Full article

Air pollution caused by pesticide residues is an emerging concern in urban environments influenced by nearby agricultural activities. In this study, weekly air samples were collected between May 2018 and March 2020 in Strasbourg, France, to quantify 104 pesticides in both gas and particle phases using GC-MS/MS and LC-MS/MS. Herbicides and fungicides were the most frequently detected classes, appearing in 98% of both phases followed by insecticides. Key compounds such as metalaxyl-M, diphenylamine, and bifenthrin were present in over 90% of samples. Concentrations ranged from 2.5 to 63 ng m⁻³ weekly, with cumulative annual loads exceeding 1200 ng m⁻³. Gas–particle partitioning revealed that highly volatile compounds like azinphos-ethyl favored the gas phase, while less volatile ones like bifenthrin and tebuconazole partitioned >95% into particles. A third-degree polynomial regression (R² of 0.74) revealed a nonlinear relationship between Kₚ and particle-phase concentrations, highlighting a threshold above Kₚ of 0.025 beyond which compounds accumulate disproportionately in the particulate phase. Seasonal variability showed that 36% of the annual pesticide load occurred in autumn, with total airborne levels peaking near 400 ng m⁻³, while the lowest load occurred during summer. Principal component analysis identified rainfall and total suspended particles as major drivers of pesticide phase distribution. The inhalation health risk assessed yielded hazard index values < 1 × 10⁻⁷ for all population groups, suggesting negligible non-cancer risk. This study highlights the prevalence, seasonal dynamics, and partition behavior of airborne pesticides in urban air and underscores the need for regulatory attention to this overlooked exposure route. Full article

Modern energy storage systems for both power and transportation are highly related to lithium-ion batteries (LIBs). However, their safety depends on a potentially hazardous failure mode known as thermal runaway (TR). Predicting and classifying TR causes can widely enhance the safety of power and transportation systems. This paper presents an advanced machine learning method for forecasting and classifying the causes of TR. A generative model for synthetic data generation was used to handle class imbalance in the dataset. Hyperparameter optimization was conducted using Optuna for four classifiers: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), tabular network (TabNet), and Extreme Gradient Boosting (XGBoost). A three-fold cross-validation approach was used to guarantee a robust evaluation. An open-source database of LIB failure events is used for model training and testing. The XGBoost model outperforms the other models across all TR categories by achieving 100% accuracy and a high recall (1.00). Model results were interpreted using SHapley Additive exPlanations analysis to investigate the most significant factors in TR predictors. The findings show that important TR indicators include energy adjusted for heat and weight loss, heater power, average cell temperature upon activation, and heater duration. These findings guide the design of safer battery systems and preventive monitoring systems for real applications. They can help experts develop more efficient battery management systems, thereby improving the performance and longevity of battery-operated devices. By enhancing the predictive knowledge of temperature-driven failure mechanisms in LIBs, the study directly advances thermal analysis and energy storage safety domains. Full article

Oil spills on the water surface pose a significant environmental hazard, underscoring the critical need for developing Artificial Intelligence (AI) detection methods. Utilizing Unmanned Aerial Vehicles (UAVs) can significantly improve the efficiency of oil spill detection at early stages, reducing environmental damage; however, there is a lack of training datasets in the domain. In this paper, LADOS is introduced, an aeriaL imAgery Dataset for Oil Spill detection, classification, and localization by incorporating both liquid and solid classes of low-altitude images. LADOS comprises 3388 images annotated at the pixel level across six distinct classes, including the background. In addition to including a general oil class describing various oil spill appearances, LADOS provides a detailed categorization by including emulsions and sheens. Detailed examination of both instance and semantic segmentation approaches is illustrated to validate the dataset’s performance and significance to the domain. The results on the test set demonstrate an overall performance exceeding 66% mean Intersection over Union (mIoU), with specific classes such as oil and emulsion to surpass 74% of IoU part of the experiments. Full article

The bimodal generalized extreme value (BGEV) distribution was first introduced in 2023. This distribution offers greater flexibility than the generalized extreme value (GEV) distribution for modeling extreme and heterogeneous (bimodal) events. However, applying this model requires a data-centering technique, as it lacks a location parameter. In this work, we investigate the properties of the BGEV distribution as redefined in 2024, which incorporates a location parameter, thereby enhancing its flexibility in practical applications. We derive explicit expressions for the probability density, the hazard rate, and the quantile function. Furthermore, we establish the identifiability property of this new class of BGEV distributions and compute expressions for the moments, the moment-generating function, and entropy. The applicability of the new model is illustrated using climate data. Full article

This paper introduces a novel and flexible class of continuous probability distributions, termed the Alpha Power Survival Ratio-X (APSR-X) family. Unlike many existing transformation-based families, the APSR-X class integrates an alpha power transformation with a survival ratio structure, offering a new mechanism for enhancing shape flexibility while maintaining mathematical tractability. This construction enables fine control over both the tail behavior and the symmetry properties, distinguishing it from traditional alpha power or survival-based extensions. We focus on a key member of this family, the two-parameter Alpha Power Survival Ratio Exponential (APSR-Exp) distribution, deriving essential mathematical properties including moments, quantile functions and hazard rate structures. We estimate the model parameters using eight frequentist methods: the maximum likelihood (MLE), maximum product of spacings (MPSE), least squares (LSE), weighted least squares (WLSE), Anderson–Darling (ADE), right-tailed Anderson–Darling (RADE), Cramér–von Mises (CVME) and percentile (PCE) estimation. Through comprehensive Monte Carlo simulations, we evaluate the estimator performance using bias, mean squared error and mean relative error metrics. The proposed APSR-X framework uniquely enables preservation or controlled modification of the symmetry in probability density and hazard rate functions via its shape parameter. This capability is particularly valuable in reliability and survival analyses, where symmetric patterns represent balanced risk profiles while asymmetric shapes capture skewed failure behaviors. We demonstrate the practical utility of the APSR-Exp model through three real-world applications: economic (tax revenue durations), engineering (mechanical repair times) and medical (infection durations) datasets. In all cases, the proposed model achieves a superior fit over that of the conventional alternatives, supported by goodness-of-fit statistics and visual diagnostics. These findings establish the APSR-X family as a unique, symmetry-aware modeling framework for complex lifetime data. Full article

In the present era, achieving sustainability requires the development of planning strategies to develop a safer urban infrastructure. This study examines the realistic aspects of cyclist safety by analysing cyclists’ fields of view, using Geographic Information Systems (GIS) and spatial data analysis. The research introduces novel geoprocessing tools-based GIS techniques that mathematically simulate cyclists’ angles of view and the distances to nearby environmental features. It provides precise insights into some potential hazards and infrastructure challenges encountered while cycling. This research focuses on managing and analysing the data collected, utilising OpenStreetMap (OSM) as vector-based supporting data. It integrates cyclists’ behavioural data with the urban environmental features encountered, such as intersections, road design, and traffic controls. The analysis is categorised into specific classes to evaluate the impacts of these aspects of the environment on cyclists’ behaviours. The current investigation highlights the importance of integrating the objective environmental elements surrounding the route with subjective perceptions and then determining the influence of these environmental elements on cyclists’ behaviours. Unlike previous studies that ignore cyclists’ visual perspectives in the context of real-world data, this work integrates objective GIS data with cyclists’ field of view-based modelling to identify high-risk areas and highlight the need for enhanced safety measures. The proposed approach equips urban planners and designers with data-informed strategies for creating safer cycling infrastructure, fostering sustainable mobility, and mitigating urban congestion. Full article

Background: A single measurement or a summary of a limited number of measurements of CA125 was considered in the prediction of clinical outcomes for patients with ovarian cancer. We aimed to identify the classes of patients with advanced ovarian cancer based on their CA125 trajectory and to investigate the heterogeneity of clinical outcomes among the patients in the different classes. Methods: CA125 trajectory classes were identified by latent-class mixed models based on values collected within 3 and 6 months post-treatment for 819 women with advanced ovarian cancer enrolled in a randomized trial. Results: Based on their CA125 values during the first 6 months of treatment, the patients with low CA125 levels at baseline that remained low during treatment had the best clinical outcome (a median survival of 83 months and a progression-free survival of 34 months). In contrast, the patients with high CA125 values at baseline with a modest decrease during treatment had the highest risk of death and progression (hazard ratio [95% confidence interval]: 4.83 [3.56, 6.54] for overall survival and 5.15 [3.87, 6.87] for progression-free survival). Conclusions: Longitudinal trajectories of CA125 may provide more direct information for the prognoses of patients with advanced ovarian cancer undergoing chemotherapy treatment. Full article

Background: Transarterial radioembolization (TARE) with Yttrium-90 microspheres is an established therapy for unresectable hepatocellular carcinoma (HCC). However, its clinical efficacy compared to transarterial chemoembolization (TACE) remains unclear. Methods: We retrospectively reviewed 279 consecutive patients undergoing TARE (n = 104) or TACE (n = 175) at four tertiary centers. Patients with metastatic disease, locally advanced HCC, or Child–Pugh (CP) C were excluded. Data on treatment, adverse events, survival outcomes (median overall survival [mOS], and objective response rates [by modified Response Evaluation Criteria in Solid Tumors; mRECIST]) were collected. Results: The median follow-up of the cohort was 27 months (IQR 13–50), the mean age was 67.6 ± 10.1 years, and 207 (74.2%) were male. The cohort was balanced in age, performance status, CP class, and HCC etiology. Maximum tumor diameter was significantly larger in the TARE cohort compared to the TACE cohort (4.4 vs. 3.1 cm, p < 0.001), including within the BCLC 0/A (4.2 vs. 2.7 cm, p = 0.001) and BCLC B (5.0 vs. 4.0 cm, p = 0.049) subgroups. The mOS was longer with TACE (37 vs. 22 months; hazard ratio [HR] 1.65, 95% CI: 1.19–2.29, p = 0.002). In BCLC 0/A patients, TACE yielded longer mOS (60 vs. 25 months; HR 2.35, 95% CI: 1.17–4.69; p = 0.016). In BCLC B, mOS was longer with TACE (32 vs. 20 months), but was not statistically significant (HR 1.39, 95% CI: 0.96–2.03, p = 0.080). In BCLC 0/A, complete response rates were higher with TACE (43.2% vs. 34.3%, p = 0.012). Hepatic decompensation was more frequent with TARE- (26.0%) than with TACE-treated patients (13.7%, p = 0.010). Conclusions: TACE demonstrated superior survival outcomes over TARE, particularly in early-stage disease. These results advocate for a more nuanced selection of embolization therapies in these patients. Full article

Neonicotinoids (NEOs) are a class of systemic insecticides widely used in agriculture owing to their high efficacy and selectivity. As one of the most globally consumed beverages, tea may represent a potential dietary source of pesticide residues. However, limited research has examined NEO contamination in tea and its implications for human exposure, highlighting the need for further investigation. Therefore, this study comprehensively evaluated the residue characteristics, processing effects, and human exposure risks of six NEOs—dinotefuran (DIN), imidacloprid (IMI), acetamiprid (ACE), thiamethoxam (THM), clothianidin (CLO), and thiacloprid (THI)—in Chinese tea products. According to the findings, the primary pollutants, ACE, DIN, and IMI, accounted for 95.65% of the total NEO residues in 137 tea samples, including green, oolong, white, black, dark, and herbal teas. The highest total target NEO (∑₆NEOs) residue level was detected in oolong tea (mean: 57.86 ng/g). Meanwhile, IMI exhibited the highest residue level (78.88 ng/g) in herbal tea due to the absence of high-temperature fixation procedures. Concentrations of DIN in 61 samples (44.5%) exceeded the European Union’s maximum residue limit of 10 ng/g. Health risk assessment indicated that both the chronic hazard quotient (cHQ) and acute hazard quotient (aHQ) for adults and children were below the safety threshold (<1). However, children required special attention, as their exposure risk was 1.28 times higher than that of adults. The distribution of NEO residues was significantly influenced by tea processing techniques, such as full fermentation in black tea. Optimizing processing methods (e.g., using infrared enzyme deactivation) and implementing targeted pesticide application strategies may help mitigate risk. These results provide a scientific foundation for enhancing tea safety regulations and protecting consumer health. Full article

Antarctic navigation encounters substantial challenges due to the dynamic and perilous characteristics of sea ice, which pose threats to vessel safety and operational efficiency. Existing risk assessment methodologies frequently lack real-time adaptability, while strategies for icebreaker convoys remain insufficiently quantified. To address these deficiencies, this study introduces an integrated framework that combines satellite-based sea ice monitoring, operational risk prediction, and icebreaker escort optimization. First, polar research routes and hydrographic conditions are systematically analyzed to enhance navigation planning. Second, a risk assessment system is developed by leveraging satellite-derived sea ice density and thickness data, facilitating a near-real-time hazard assessment (subject to satellite data latency) evaluation with 96.3% accuracy in ice type classification and a 15% improvement in risk prediction precision compared to conventional methods. Finally, kinematic safety criteria for icebreaker-escorted convoys are established, specifying speed-dependent distance thresholds to minimize collision risks, achieving optimal speeds of 1.4–2.3 knots for PC3-class vessels and 10–20% speed improvements for escorted vessels in cleared channels. The findings offer actionable insights into polar route optimization, risk mitigation, and safe ice navigation protocols, thereby directly supporting operational decision making in Antarctic waters. Full article