Quaderni MOX
Pubblicazioni
del Laboratorio di Modellistica e Calcolo Scientifico MOX. I lavori riguardano prevalentemente il campo dell'analisi numerica, della statistica e della modellistica matematica applicata a problemi di interesse ingegneristico. Il sito del Laboratorio MOX è raggiungibile
all'indirizzo mox.polimi.it
Trovati 1349 prodotti
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06/2021 - 27/01/2021
Negrini, G.; Parolini, N.; Verani, M.
A diffuse interface box method for elliptic problems | Abstract | | We introduce a diffuse interface box method (DIBM) for the numerical approximation on complex geometries of elliptic problems with Dirichlet boundary conditions. We derive a priori H1 and L2 error estimates highlighting the role of the mesh discretization parameter and of the diffuse interface width. Finally, we present a numerical result assessing the theoretical findings. |
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05/2021 - 25/01/2021
Antonietti, P.F.; Mazzieri, I.; Migliorini, F.
A discontinuous Galerkin time integration scheme for second order differential equations with applications to seismic wave propagation problems | Abstract | | In this work, we present a new high order Discontinuous Galerkin time integration scheme for second-order (in time) differential systems that typically arise from the space discretization of the elastodynamics equation.
By rewriting the original equation as a system of first order differential equations we introduce the method and show that the resulting discrete formulation is well-posed, stable and retains super-optimal rate of convergence with respect to the discretization parameters, namely the time step and the polynomial approximation degree. A set of two- and three-dimensional numerical experiments confirm the theoretical bounds. Finally, the method is applied to real geophysical applications. |
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04/2021 - 25/01/2021
Orlando, V.; Rea, F.; Savaré, L.; Guarino, I; Mucherino, S.; Perrella, A.; Trama, U.; Coscioni, E.; Menditto, E.; Corrao, G.
Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy | Abstract | | Background
The novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the people at high risk of infection. Hence, we aimed (i) to identify clinical predictors of SARSCoV-2 infection risk, (ii) to develop and validate a score predicting SARS-CoV-2 infection risk, and (iii) to compare it with unspecific scores.
Methods
Retrospective case-control study using administrative health-related database was carried
out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each person with SARS-CoV-2 confirmed infection (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS) was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. Discriminant power was used to compare SIS performance with that of other comorbidity scores.
Results
Subjects suffering from diabetes, anaemias, Parkinson’s disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARSCoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS = 4) 1.74 times higher than those unaffected by any SIS contributing conditions (SIS = 1).
Conclusion
Conditions and diseases making people more vulnerable to SARS-CoV-2 infection were identified by the current study. Our results support decision-makers in identifying high-risk people and adopting of preventive measures to minimize the spread of further epidemic waves.
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03/2021 - 23/01/2021
Torti, A.; Marika, A.; Azzone, G.; Secchi, P.; Vantini S.
Bridge closure in the road network of Lombardy: a spatio-temporal analysis of the socio-economic impacts | Abstract | | This paper introduces a methodology to evaluate the socio-economic impacts of closure for maintenance of one or more infrastructures of a large and complex road network. Motivated by a collaboration with Regione Lombardia, we focus on a subset of bridges in the region, although we aim at developing a method scalable to all road infrastructures of the regional network, consisting of more than 10000 tunnels, bridges and overpasses. The final aim of the endeavor is to help decision-makers in prioritizing their interventions for maintaining and repairing infrastructure segments. We develop two different levels of impact assessment, both providing a unique global score for each bridge closure and investigating its spatio-temporal effects on mobility. We take advantage of a functional data analysis approach enhanced by a complex network theory perspective, thus modelling the roads of Lombardy as a network in which weights attributed to the edges are functional data. Results reveal the most critical bridges of Lombardy; moreover, for each bridge closure, the most impactful hours of the day and the most impacted municipalities of the region are identified. The proposed approach develops a flexible and scalable method for monitoring infrastructures of large and complex road networks.
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02/2021 - 23/01/2021
Calissano, A.; Feragen, A; Vantini, S.
Graph-Valued Regression: Prediction of unlabelled networks in a Non-Euclidean Graph-Space | Abstract | | Understanding how unlabeled graphs depend on input values or vectors is of extreme interest in a range of applications. In this paper, we propose a regression model taking values in Graph Space, representing unlabeled graphs which can be weighted or unweighted, one or multi-layer, and have same or different numbers of nodes, as a function of real valued regressor. As Graph Space is not a manifold, well-known manifold regression models are not applicable. We provide flexible parameterized regression models for Graph Space, along with precise and computationally efficient estimation procedures given by the introduced Align All and Compute regression algorithm. We show the potential of the proposed model for two real datasets: a time dependent cryptocurrency correlation matrices and a set of bus mobility usage network in Copenhagen (DK) during the Covid-19 pandemic. |
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01/2021 - 17/01/2021
Pegoraro, M.; Beraha, M.
Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric | Abstract | | We present a novel class of projected methods, to perform statistical analysis on a data set of probability distributions on the real line, with the 2-Wasserstein metric. We focus in particular on Principal Component Analysis (PCA) and regression. To define these models, we exploit a representation of the Wasserstein space closely related to its weak
Riemannian structure, by mapping the data to a suitable linear space and using a metric projection operator to constrain the results in the Wasserstein space. By carefully choosing the tangent point, we are able to derive fast empirical methods, exploiting a constrained B-spline approximation. As a byproduct of our approach, we are also able to derive faster routines for previous work on PCA for distributions. By means of simulation studies, we compare our approaches to previously proposed methods, showing that our projected PCA has similar performance for a fraction of the computational cost and that the projected regression is extremely flexible even under misspecification. Several theoretical properties of the models are investigated and asymptotic consistency is proven. Two real world applications to Covid-19 mortality in the US and wind speed forecasting are discussed.
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85/2020 - 23/12/2020
Cavinato, L.; Sollini, M.; Kirienko, M.; Biroli, M.; Ricci, F.; Calderoni, L.; Tabacchi, E.; Nanni, C.; Zinzani, P. L.; Fanti, S.; Guidetti, A.; Alessi, A.; Corradini, P.; Seregni, E.; Carlo-Stella, C.; Chiti, A.; Ieva, F.;
PET radiomics-based lesions representation in Hodgkin lymphoma patients | Abstract | | As medical image analysis has been proven to entail tumor-specific in- formation, the so-called radiomics paradigm holds the promise to characterize the disease and infer long term outcomes of chemotherapy. In this work, we propose an insightful framework for disease characterization in Hodgkin lymphoma which could inform future research. Particularly, an intra-patient similarity index (ISI) was built to represent the homogeneity of the patients’ disease, while a radiomics-based fingerprint was create for local lesion description. Through descriptive statistics and classification algorithms, ISI-weighted fingerprint has been showed to be discriminatory between responders and relapsing patients. |
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84/2020 - 23/12/2020
Vergara, C.; Stella, S.; Maines, M.; Catanzariti, D.; Demattè, C.; Centonze, M.; Nobile, F.; Quarteroni, A.; Del Greco, M.
Computational electrophysiology to support the mapping of coronary sinus branches for cardiac resynchronization therapy | Abstract | | BACKGROUND
This work dealt with the assessment of a computational tool to estimate the latest electrically activated segment (LEAS) of the left ventricle during cardiac resynchronization therapy (CRT). OBJECTIVE
The aim of the work was to show that for patients with left bundle branch block (LBBB), possibly in presence of fibrosis, the proposed computational tool was able to accurately reproduce the epicardial activation maps and in particular LEAS location in the epicardial veins, often used as a target site for the left lead placement.
METHODS
We considered a computational tool based on Finite Elements used to recover the activation maps in all the myocardium. The model was calibrated by using activation times acquired in the epicardial veins with an electroanatomic mapping system (EAMS).
RESULTS
We applied our computational tool to predict LEAS in the epicardial veins of ten patients. We found an excellent accordance with LEAS measured by EAMS, the discrepancy being less than 4mm. We also calibrated our model using only the activation maps of the coronary sinus (CS), still obtaining an excellent agreement with the measured LEAS.
CONCLUSION
We showed that our computational tool is able to accurately predict the location of LEAS, even when information only at CS were used for calibration. This could be of utmost importance in view of CRT implantation, since LEAS could be determined by mapping only CS, saving time and avoiding the exposition of the patient to a deeper invasive procedure. |
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