Application of the k-epsilon-v(exp 2) model to multi-component airfoils

Flow computations around two-element and three-element configurations are presented and compared to detailed experimental measurements. The k-epsilon-v(exp 2)(bar) model has been applied and the ability of the model to capture streamline curvature effects, wake-boundary layer confluence, and laminar/turbulent transition is discussed. The numerical results are compared to experimental datasets that include mean quantities (velocity and pressure coefficient) and turbulent quantities (Reynolds normal and shear stresses)

Cerebrovascular complications and infective endocarditis. impact of available evidence on clinical outcome

Infective endocarditis (IE) is a life-threatening disease. Its epidemiological profile has substantially changed in recent years although 1-year mortality is still high. Despite advances in medical therapy and surgical technique, there is still uncertainty on the best management and on the timing of surgical intervention. The objective of this review is to produce further insight intothe short- and long-term outcomes of patients with IE, with a focus on those presenting cerebrovascular complications

Characterization of four typical calabrian cured meat products: Spicy sausage, soppressata, ’nduja and capocollo

Typical Calabrian cured meat products, produced with meat of local and commercial pig breeds were evaluated and characterized for their quality and homogeneity. Sensory, microbiological and physicochemical analyses were carried out at the end of cured meat products ripening. A wide statistical variability was observed in these commercial products due to both company and different productions. The mineral composition was similar to that observed by other authors in similar cured meat products; the statistical analysis revealed only a difference among the samples for magnesium (P<0.01) and for calcium (P<0.05) contents. According to the performed sensory analysis, the meat products were acceptable with some differences due to both production and company variables. About microbial populations, the most abundant were lactic acid bacteria and total aerobic bacteria, while enterobacteria were less represented

Earthquake Early Warning System for Structural Drift Prediction Using Machine Learning and Linear Regressors

In this work, we explored the feasibility of predicting the structural drift from the first seconds of P-wave signals for On-site Earthquake Early Warning (EEW) applications. To this purpose, we investigated the performance of both linear least square regression (LSR) and four non-linear machine learning (ML) models: Random Forest, Gradient Boosting, Support Vector Machines and K-Nearest Neighbors. Furthermore, we also explore the applicability of the models calibrated for a region to another one. The LSR and ML models are calibrated and validated using a dataset of ∼6,000 waveforms recorded within 34 Japanese structures with three different type of construction (steel, reinforced concrete, and steel-reinforced concrete), and a smaller one of data recorded at US buildings (69 buildings, 240 waveforms). As EEW information, we considered three P-wave parameters (the peak displacement, Pd, the integral of squared velocity, IV2, and displacement, ID2) using three time-windows (i.e., 1, 2, and 3 s), for a total of nine features to predict the drift ratio as structural response. The Japanese dataset is used to calibrate the LSR and ML models and to study their capability to predict the structural drift. We explored different subsets of the Japanese dataset (i.e., one building, one single type of construction, the entire dataset. We found that the variability of both ground motion and buildings response can affect the drift predictions robustness. In particular, the predictions accuracy worsens with the complexity of the dataset in terms of building and event variability. Our results show that ML techniques perform always better than LSR models, likely due to the complex connections between features and the natural non-linearity of the data. Furthermore, we show that by implementing a residuals analysis, the main sources of drift variability can be identified. Finally, the models trained on the Japanese dataset are applied the US dataset. In our application, we found that the exporting EEW models worsen the prediction variability, but also that by including correction terms as function of the magnitude can strongly mitigate such problem. In other words, our results show that the drift for US buildings can be predicted by minor tweaks to models

Post-COVID-19 arthritis: a case report and literature review

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is the novel pathogen responsible for the coronavirus disease 19 (COVID-19) outbreak. Researchers and clinicians are exploring the pathogenetic mechanisms of the viral-induced damage and growing interest is focusing on the short-term and long-term immune-mediated consequences triggered by the infection. We will focus on post-SARS-CoV2 infection arthritis which may arise as a new pathological condition associated with COVID-19. In this article, we describe a case of acute oligoarthritis occurring 13 days after a SARS-CoV2 severe pneumonia in a middle-aged Caucasian man and we go over a brief review of the current available literature. We hypothesize that molecular mimicry might be the basic immunological mechanism responsible for the onset of COVID-19-related arthritis based on the current knowledge of SARS-CoV2 and on the known pathogenetic mechanism of viral-induced arthritis