Search Results (215)

The association between COVID-19 vaccination and thromboembolic events has garnered significant research attention, particularly with the advent of vaccines based on adenoviral vectors, including AstraZeneca’s and Johnson & Johnson’s vaccines. This review underscores the uncommon occurrence of venous thromboembolism (VTE), arterial thromboembolism (ATE), and vaccine-induced thrombotic thrombocytopenia (VITT) following COVID-19 vaccination. Although these complications are extremely rare compared to the heightened risk of thrombosis from COVID-19 infection, elements like age, biological sex, type of vaccine and underlying health conditions may contribute to their development. In addition, rare renal complications such as acute kidney injury and thrombotic microangiopathy have been documented, broadening the spectrum of potential vaccine-associated thrombotic manifestations. Current guidelines emphasize early detection, individualized risk assessment, and use of anticoagulation therapy to mitigate risks. Despite these events, the overwhelming majority of evidence supports the continued use of COVID-19 vaccines, given their proven efficacy in reducing severe illness and mortality. In addition, recent comparative data confirm that mRNA-based vaccines are associated with a significantly lower risk of serious thrombotic events compared to adenoviral vector platforms. Ongoing research is essential to further refine preventive and therapeutic strategies, particularly for at-risk populations. Full article

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest $R^2$ (0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article

River embankments are critical flood defense structures, stretching for thousands of kilometers across alluvial plains. They often originated as natural levees resulting from overbank flows and were later enlarged using locally available soils yet rarely designed according to modern engineering standards. Substantially under-characterized, their performance to extreme events provides an invaluable opportunity to highlight their vulnerability and then to improve monitoring, management, and reinforcement strategies. In May 2023, two extreme meteorological events hit the Emilia-Romagna region in rapid succession, causing numerous breaches along river embankments and therefore widespread flooding of cities and territories. These were followed by two additional intense events in September and October 2024, marking an unprecedented frequency of extreme precipitation episodes in the history of the region. This study presents the methodology adopted to create a regional database of 66 major breaches and damages that occurred during May 2023 extensive floods. The database integrates multi-source information, including field surveys; remote sensing data; and eyewitness documentation collected before, during, and after the events. Preliminary interpretation enabled the identification of the most likely failure mechanisms—primarily external erosion, internal erosion, and slope instability—often acting in combination. The database, unprecedented in Italy and with few parallels worldwide, also supported a statistical analysis of breach widths in relation to failure mechanisms, crucial for improving flood hazard models, which often rely on generalized assumptions about breach development. By offering insights into the real-scale behavior of a regional river defense system, the dataset provides an important tool to support river embankments risk assessment and future resilience strategies. Full article

This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may not adequately capture the behavior of extreme events. The study focuses on four hydrometric stations in Romania, analyzing maximum discharges associated with rare and very rare events. The research employs seven parameter estimation methods: the method of ordinary moments (MOM), the maximum likelihood estimation (MLE), the L-moments, the LH-moments, the probability-weighted moments (PWMs), the least squares method (LSM), and the weighted least squares method (WLSM). Results indicate that the GEV distribution, particularly when using L-moments, consistently provides more reliable predictions for extreme events, reducing biases compared to MOM. Compared to the Wakeby distribution for an extreme event (T = 10,000 years), the GEV distribution produced smaller deviations than the Pearson III distribution, namely +7.7% (for the Danube River, Giurgiu station), +4.9% (for the Danube River, Drobeta station), and +35.3% (for the Ialomita River). In the case of the Siret River, the Pearson III distribution generated values closer to those obtained by the Wakeby distribution, being 36.7% lower than those produced by the GEV distribution. These results support the use of L-moments in national hydrological guidelines for critical infrastructure design and highlight the need for further investigation into non-stationary models and regionalization techniques. Full article

The performance of monthly prediction of extreme precipitation from the BCC-CPSv3-S2Sv2 model over the Yellow River Basin (YRB) using historical hindcast data from 2008 to 2022 was evaluated in this study, mainly from three aspects: overall performance in predicting daily precipitation rates, systematic biases, and monthly prediction of extreme precipitation metrics. The results showed that the BCC-CPSv3-S2Sv2 model demonstrates approximately 10-day predictive skill for summer daily precipitation over the YRB. Relatively higher skill regions concentrate in the central basin, while skill degradation proves more pronounced in downstream areas compared to the upper basin. After correcting model systematic biases, prediction skills for total precipitation-related metrics significantly surpass those of extreme precipitation indices, and metrics related to precipitation amounts demonstrate relatively higher skill compared to those associated with precipitation days. Total precipitation (TP) and rainy days (RD) exhibit comparable skills in June and July, with August showing weaker performance. Nevertheless, basin-wide predictions within 10-day lead times remain practically valuable for most regions. Prediction skills for extreme precipitation amounts and extreme precipitation days share similar spatiotemporal patterns, with high-skill regions shifting progressively south-to-north from June to August. Significant skills for June–July are constrained within 10-day leads, while August skills rarely exceed 1 week. Further analysis reveals that the predictive capability of the model predominantly originates from normal or below-normal precipitation years, whereas the accurate forecasting of extremely wet years remains a critical challenge, which highlights limitations in capturing mechanisms governing exceptional precipitation events. Full article

Extremely powerful flares releasing energy well above ${10}^{32}$ erg are rare compared to the typical manifestations of solar activity, which are already being routinely monitored by the existing Space Weather network—with some level of predictability. However, much less is known about the mechanisms behind such rare events (like the well-documented Carrington event of 1859) or about hypothetical superflares that could exceed current energy estimates by several orders of magnitude. We propose a model based on the nonlinear suppression of turbulent diffusion with increasing magnetic field, which ultimately leads to the random occurrence of regions with a magnetic field amplitude significantly exceeding the magnetic field amplitude in a regular cycle. This is similar to the mechanism of a local “explosion of an overheated boiler”. Such regions can be correlated with flares. In our model, flares have different powers. Full article

Given the global rise in magnitude and frequency of extreme events due to climate change, accurately determining these values—typically through frequency analysis—is especially important. The article analyzes the particular aspects of three probability distributions of 4 and 5 parameters in flood frequency analysis (FFA) using the L-moments as a parameter estimation method. Aspects regarding the behavior of the five-parameter Wakeby, four-parameter generalized Pareto and four-parameter Burr distributions are highlighted in generating the maximum flow values in the area of low annual exceedance probabilities characteristic of rare and very rare events. After applying these distributions to four case studies, it was found that for the 10,000-year return period event, the relative error between multi-parameter distributions is under 20%—a more than acceptable margin given the extremely low exceedance probability. However, its importance depends on the use of the generated values, which in some cases can lead to excessive costs in establishing structural flood protection measures (urban planning), which can be avoided. It also highlights possible negative consequences (material and human lives) regarding the risk associated with these analyses that can lead to an under-dimensioning of this infrastructure. Full article

Background: Lumbar disc herniation (LDH) recurrence remains a pressing clinical challenge, with limited predictive tools available to support early identification and personalized intervention. Predicting recurrence after lumbar disc herniation (LDH) remains clinically important but algorithmically difficult due to extreme class imbalance and low signal-to-noise ratio. Objective: This study proposes a hybrid machine learning framework that integrates supervised classifiers, unsupervised anomaly detection, and decision threshold tuning to predict LDH recurrence using routine clinical data. Methods: A dataset of 977 patients from a Romanian neurosurgical center was used. We trained a deep neural network, random forest, and an autoencoder (trained only on non-recurrence cases) to model baseline and anomalous patterns. Their outputs were stacked into a meta-classifier and optimized via sensitivity-focused threshold tuning. Evaluation was performed via stratified cross-validation and external holdout testing. Results: Baseline models achieved high accuracy but failed to recall recurrence cases (0% sensitivity). The proposed ensemble reached 100% recall internally with a threshold of 0.05. Key predictors included hospital stay duration, L4–L5 herniation, obesity, and hypertension. However, external holdout performance dropped to 0% recall, revealing poor generalization. Conclusions: The ensemble approach enhances detection of rare recurrence cases under internal validation but exhibits poor external performance, emphasizing the challenge of rare-event modeling in clinical datasets. Future work should prioritize external validation, longitudinal modeling, and interpretability to ensure clinical adoption. Full article

Background: Reports of primary cutaneous lymphomas (CLs) following COVID-19 vaccines are extremely rare. Nevertheless, clinicians should be aware of a potential association between these events. Here, we report a case of the development and rapid progression of mycosis fungoides (MF) with lymph node involvement after COVID-19 vaccination. Case presentation: A 75-year-old female developed disseminated plaques and patches shortly after receiving the first dose of the SARS-CoV-2 mRNA vaccine. Within one month following the second dose of the mRNA vaccine, she additionally experienced rapid progression, leading to the development of tumors and inguinal lymphadenopathy. Blood and visceral involvement were excluded. The clinicopathological findings were consistent with the diagnosis of MF, and systemic methotrexate with topical treatment was implemented, resulting in remission of the lesions. Conclusions: The presented case of the development and rapid progression of MF after the SARS-CoV-2 mRNA vaccine raises the question of the possible immunomodulatory or oncomodulatory effects of mRNA vaccines. It prompted us to conduct a review outlining the mechanisms potentially causing the mRNA vaccine-associated CLs. We have performed an extensive literature search to determine an explanation for the observed phenomenon. Accumulated evidence suggests a link between CL occurrence and immunization with an mRNA vaccine. The proposed hypothesis revolves around shared signaling pathways that are enhanced by SARS-CoV-2 mRNA vaccines, thus driving the pathogenesis of MF. We want to raise clinicians’ attention to the rare side effects of COVID-19 vaccines and emphasize the need for thorough monitoring of patients with altered immunity in the course of various lymphoproliferative disorders. Full article

The reduction in disaster risk in urban regions due to natural hazards (e.g., earthquakes, landslides, floods, and tropical cyclones) is primarily a development matter that must be treated within the scope of a broader urban development framework. Natural hazard assessment is one of the turning points in mitigating disaster risk, which typically contributes to stronger urban resilience and more sustainable urban development. Regarding this challenge, our research proposes a new approach in the signal processing chain and feature extraction from microtremor data that focuses mainly on the Horizontal-to-Vertical Spectral Ratio (HVSR) so as to assess liquefaction potential as a natural hazard using AI. The key raw seismic features of site amplification and resonance are extracted from the data via bandpass filtering, Fourier Transformation (FT), the calculation of the HVSR, and smoothing through the use of moving averages. The main novelty is the integration of machine learning, particularly stacked ensemble learning, for liquefaction potential classification from imbalanced seismic datasets. For this approach, several models are used to consider class imbalance, enhancing classification performance and offering better insight into liquefaction risk based on microtremor data. Then, the paper proposes a liquefaction detection method based on deep learning with an autoencoder and stacked classifiers. The autoencoder compresses data into the latent space, underlining the liquefaction features classified by the multi-layer perceptron (MLP) classifier and eXtreme Gradient Boosting (XGB) classifier, and the meta-model combines these outputs to put special emphasis on rare liquefaction events. This proposed methodology improved the detection of an imbalanced dataset, although challenges remain in both interpretability and computational complexity. We created a synthetic dataset of 1000 samples using realistic feature ranges that mimic the Rif data region to test model performance and conduct sensitivity analysis. Key seismic and geotechnical variables were included, confirming the amplification factor (Af) and seismic vulnerability index ($K_g$) as dominant predictors and supporting model generalizability in data-scarce regions. Our proposed method for liquefaction potential classification achieves 100% classification accuracy, 100% precision, and 100% recall, providing a new baseline. Compared to existing models such as XGB and MLP, the proposed model performs better in all metrics. This new approach could become a critical component in assessing liquefaction hazard, contributing to disaster mitigation and urban planning. Full article

Statistical distributions play a crucial role in water resources management and civil engineering, particularly for analyzing data variability and predicting rare events with extremely long return periods (e.g., T = 1000 years, T = 10,000 years). Among these, the Pearson III (PE3) distribution is widely used in hydrology and flood frequency analysis (FFA). This study aims to provide a comprehensive guide to the practical application of the PE3 distribution in FFA. It explores five parameter estimation methods, presenting both exact and newly developed approximate relationships for calculating distribution parameters and frequency factors. The analysis relies on data from four rivers with varying morphometric characteristics and record lengths. The results highlight that the Pearson III distribution, when used with the L-moments method, offers the most reliable quantile estimates, characterized by the smallest biases compared to other methods (e.g., 31% for the Nicolina River and, respectively, 5% for the Siret and Ialomita Rivers) and the highest confidence in predicting rare events. Based on these findings, the L-moments approach is recommended for flood frequency analysis to improve the accuracy of extreme flow forecasts. Full article

The Atacama Desert, the driest and oldest desert on Earth, hosts a unique floral phenomenon known as the Desierto Florido (Flowering Desert), which occurs sporadically in response to rare rainfall events. Bomarea ovallei (Phil.) Ravenna is an endemic and endangered species of the Atacama Desert. However, its populations are geographically restricted and potentially vulnerable to genetic erosion due to isolation and extreme environmental conditions. This study aims to assess the genetic diversity of B. ovallei populations and develop microsatellite markers using next-generation sequencing (NGS) technology. A total of 268 microsatellite loci were identified, and 34 co-dominant markers were successfully developed for the first time in B. ovallei. Genetic diversity analysis using eight fluorescently labeled SSR markers revealed low genetic diversity across four populations, with the highest diversity observed in the QCA population, located within Llanos de Challe National Park, and the lowest in the TOTO population, which is highly exposed to anthropogenic activities. UPGMA and STRUCTURE analyses revealed three genetic clusters and high admixture among populations, suggesting historical or ongoing gene flow despite geographical separation. The presence of non-polymorphic loci and low PIC values in some markers further supports limited genetic variation. The newly developed microsatellite markers offer a valuable tool for future genetic studies, enabling the monitoring of genetic diversity and informing strategies for the preservation of this rare and ecologically significant species. Full article

The rupture of the contact wire (CW) of a railway overhead contact line (OCL or catenary) is expected to be a rare event. However, when it occurs, and a pantograph transits under the already broken section of the CW, this can have catastrophic consequences for the pantograph which in turn can cause a further extension of the damaged portion on the OCL with a consequent disruption in the service and cause there to be a long time before the operating condition can be restored. Therefore, the prevention of such events through effective catenary monitoring is gaining significant attention. The purpose of this work is to investigate the feasibility of a monitoring system that can be installed at each end of an OCL section which is able to detect the occurrence of a broken CW event, sending an alert to the management traffic system, so as to stop the train traffic before the damaged catenary is reached by other trains. A nonlinear dynamic analysis is employed to model the OCL’s response following a simulated CW rupture and identify a set of variables that can be measured at the line’s extremities related to the occurrence of breakage in the CW. Several locations of the rupture of a CW section along the line are simulated to investigate the influence on the time pattern of the measured variables and consequently on the extraction of a signature. Finally, a proposed measurement setup is presented, combining accelerometers and displacement transducers, instead of the direct measurement of the axial load of the OCL conductors. Full article

As a critical infrastructure, the transportation network impacts health, safety, comfort, and the economy, making it highly vulnerable to disruptions that significantly affect social and economic well-being. To maintain optimal service during such disruptions, the critical links that are vulnerable to disruptions must be identified and their impact on network performance must be understood. This study proposes a method for identifying network vulnerabilities by targeting critical links based on topological parameters, assessing worst-case scenarios under severe conditions. These parameters serve as proxies for performance and are utilized to generate critical-link attacks to assess the network vulnerability. In addition, this study proposes a straightforward and simplistic modeling framework using topological parameters to assess the impact of such attacks on traffic flow changes. To characterize network performance and traffic volume changes under critical-link attacks, this study utilizes the complementary cumulative distribution function (CCDF), which highlights the upper tail of the distribution where extreme or rare events occur. The proposed method was applied to a real network in the Colombo Municipal Council (CMC) area in Sri Lanka. The findings of this study will help us understand the impact of critical-link attacks on transportation network performance and traffic flow and develop proactive policies to address vulnerabilities and improve overall network performance. Full article

Background/Objectives: Composite hemangioendothelioma (CHE) is a rare vascular endothelial tumor with borderline malignancy. This study presents a case of CHE and an updated systematic review of previously reported cases, providing insights into recurrence patterns and survival outcomes. Methods: A comprehensive electronic search was conducted across PubMed, Scopus, the Cochrane Library, and Web of Science up to 31 December 2024, to identify eligible case reports. Kaplan–Meier curves were used to estimate event-free survival. Results: We report a 61-year-old man with a splenic lesion associated with weight loss and abdominal pain persisting for 1 year. Intraoperative findings revealed an enlarged spleen and multiple hepatic deposits. Splenectomy and liver biopsy revealed a well-demarcated, nodular tumor measuring 160 × 145 × 100 mm, with histological and immunohistochemical findings consistent with CHE, complicated by hepatic metastasis. Of 405 potentially eligible studies, 59 were included in the review, covering cases from 2000 to 2024, with a peak in 2020 and 2023. The median age of patients was 42 years, with the most common tumor sites being the lower extremities (30.48%), followed by the face, head, and neck (20.95%), and upper extremities (18.1%). Surgical intervention was the most common treatment (60.95%). Recurrence-free survival was observed in 42.86% of cases, while 15.24% experienced recurrence with or without metastasis. Two patients (1.90%) died from the disease. The median recurrence-free survival was 48 months (95% CI: 7.3–88.7). Conclusions: CHE exhibits significant morphological variation and can mimic other vascular tumors. Accurate diagnosis is crucial for proper prognosis and avoiding overtreatment due to misdiagnosis as more aggressive neoplasms. Patients with high-risk CHE should undergo closer surveillance to ensure timely detection of progression. Full article