Stability analysis of a hyperbolic stochastic Galerkin formulation for the Aw-Rascle-Zhang model with relaxation

We investigate the propagation of uncertainties in the Aw-Rascle-Zhang model, which belongs to a class of second order traffic flow models described by a system of nonlinear hyperbolic equations. The stochastic quantities are expanded in terms of wavelet-based series expansions. Then, they are projected to obtain a deterministic system for the coefficients in the truncated series. Stochastic Galerkin formulations are presented in conservative form and for smooth solutions also in the corresponding non-conservative form. This allows to obtain stabilization results, when the system is relaxed to a first-order model. Computational tests illustrate the theoretical results

CPC-135 The Effect of Gender on the Use of Medicines in Multiple Sclerosis Patients

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Biomarkers in the prediction and management of acute coronary syndromes: current perspectives

Emanuele Gilardi, Paolo Iacomini, Davide Marsiliani, Guido De Marco, Marcello CovinoDepartment of Emergency Medicine, Catholic University of the Sacred Heart, A Gemelli Hospital, Rome, ItalyAbstract: A large branch of research has focused on the search for biomarkers for early detection of myocardial cell injuries. Most of these studies have evaluated patients presenting to the emergency department, underlining the need for an ideal biomarker for rapid recognition of acute coronary syndrome (ACS). In the recent past, diagnosis of ACS in the emergency department has been based mostly on clinical information and electrocardiographic findings, and markers of generic cell damage have been used to support clinical suspicion. Over the last few years, the role of markers has taken up increasingly more space in non-life-threatening conditions, confining the clinical examination of the patient to the mere waiting for results of blood tests after the electrocardiograph. Currently, the biomarkers most widely used for the diagnosis of ACS are cardiac troponins. Since their introduction into clinical practice, several generations of commercial cardiac troponin assays have been validated in analytical and clinical trials. Development of newer high-sensitivity assays seems to have improved the value of cardiac troponin as both a diagnostic and risk indicator. Several other biomarkers of ACS apart from cardiac troponin have been investigated, but most still require validation in further studies. Among these, pregnancy-associated plasma protein-A, ischemia-modified albumin, and heart-type fatty acid binding protein seem to be the most promising markers under investigation for their possible usefulness in the emergency department setting for early diagnosis of ACS. In conclusion, a multimarker approach could be the future of research. In this review, we highlight the old and new markers, especially the most studied and widely used in clinical practice in recent years, particularly those that can help the clinician to make a rapid and confident diagnosis of ACS.Keywords: biomarkers, acute coronary syndrome, myocardial infarction, emergency departmen

Modeling Of Metabolic Heat Regenerated Temperature Swing Adsorption (MTSA) Subassembly For Prototype Design

This paper describes modeling methods for the three core components of a Metabolic heat regenerated Temperature Swing Adsorption (MTSA) subassembly: a sorbent bed, a sublimation (cooling) heat exchanger (SHX), and a condensing icing (warming) heat exchanger (CIHX). The primary function of the MTSA, removing carbon dioxide from a space suit Portable Life Support System (PLSS) ventilation loop, is performed via the sorbent bed. The CIHX is used to heat the sorbent bed for desorption and to remove moisture from the ventilation loop while the SHX is alternately employed to cool the sorbent bed via sublimation of a spray of water at low pressure to prepare the reconditioned bed for the next cycle. This paper describes subsystem heat a mass transfer modeling methodologies relevant to the description of the MTSA subassembly in Thermal Desktop and SINDA/FLUINT. Several areas of particular modeling interest are discussed. In the sorbent bed, capture of the translating carbon dioxide (CO2) front and associated local energy and mass balance in both adsorbing and desorbing modes is covered. The CIHX poses particular challenges for modeling in SINDA/FLUINT as accounting for solids states in fluid submodels are not a native capability. Methods for capturing phase change and latent heat of ice as well as the transport properties across a layer of low density accreted frost are developed. This extended modeling capacity is applicable to temperatures greater than 258 K. To extend applicability to the minimum device temperature of 235 K, a method for a mapped transformation of temperatures from below the limit temperatures to some value above is given along with descriptions for associated material property transformations and the resulting impacts to total heat and mass transfer. Similar considerations are given for the SHX along with functional relationships for areal sublimation rates as limited by flow mechanics in t1he outlet duct

Design and Assembly of an Integrated Metabolic Heat Regenerated Temperature Swing Adsorption (MTSA) Subassembly Engineering Development Unit

Metabolic heat regenerated Temperature Swing Adsorption (MTSA) technology is being developed for thermal and carbon dioxide (CO2) control for a Portable Life Support System (PLSS), as well as water recycling. The core of the MTSA technology is a sorbent bed that removes CO2 from the PLSS ventilation loop gas via a temperature swing. A Condensing Icing Heat eXchanger (CIHX) is used to warm the sorbent while also removing water from the ventilation loop gas. A Sublimation Heat eXchanger (SHX) is used to cool the sorbent. Research was performed to explore an MTSA designed for both lunar and Martian operations. Previously the sorbent bed, CIHX, and SHX had been built and tested individually on a scale relevant to PLSS operations, but they had not been done so as an integrated subassembly. Design and analysis of an integrated subassembly was performed based on this prior experience and an updated transient system model. Focus was on optimizing the design for Martian operations, but the design can also be used in lunar operations. An Engineering Development Unit (EDU) of an integrated MTSA subassembly was assembled based on the design. Its fabrication is discussed. Some details on the differences between the as-assembled EDU and the future flight unit are considered

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home

Overview on Uncertainty Quantification in Traffic Models via Intrusive Method

We consider traffic flow models at different scales of observation. Starting from the well known hierarchy between microscopic, kinetic and macroscopic scales, we will investigate the propagation of uncertainties through the models using the stochastic Galerkin approach. Connections between the scales will be presented in the stochastic scenario and numerical simulations will be performed

An interface-free multi-scale multi-order model for traffic flow

In this paper we present a new multi-scale method for reproducing traffic flow which couples a first-order macroscopic model with a second-order microscopic model, avoiding any interface or boundary conditions between them. The multi-scale model is characterized by the fact that microscopic and macroscopic descriptions are not spatially separated. On the contrary, the macro-scale is always active while the micro-scale is activated only if needed by the traffic conditions. The Euler-Godunov scheme associated to the model is conservative and it is able to reproduce typical traffic phenomena like stop & go waves

Contrarian effect in opinion forming: insights from Greta Thunberg phenomenon

In recent months, the figure of Greta Thunberg and the theme of climate changings quickly became the focus of the debate. This has led to a polarization effect in opinion forming about the climate subject. Starting from the analysis of this phenomenon, we develop an opinion dynamics model in which several types of contrarian agents are considered. Each agent is supposed to have an opinion on several topics related to each other; thus, the opinions being formed on these topics are also mutually dependent. The aim of the paper is to investigate the indirect effects of contrarian agents on the collective opinion about these topics. Several numerical tests are presented in order to highlight the main features of the model

Stop & Go waves: a microscopic and a macroscopic description

In this paper we investigate a typical phenomenon of congested traffic: the stop-and-go waves. Since modelling properly this phenomenon is crucial for developing techniques aimed at reducing it, we present two different models: a microscopic and a macroscopic one, both of them able to reproduce stop-and-go waves. In the former, vehicles’ dynamics are described by a second-order microscopic Follow-the-Leader model, which is calibrated and validated by real measurements. Data are analysed and compared with the numerical solutions computed by the microscopic model. The latter provides a description of traffic dynamic via the macroscopic second-order CGARZ model. With the numerical implementation, by means of the 2CTM scheme, we test the ability of the model of capturing stop-and-go waves. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG