225 research outputs found
Hierarchical Space-time Modelling of PM10 Pollution in the Emilia-Romagna Region
In questo lavoro si propone un modello gerarchico per lo studio della distribuzione spazio-temporale dell’inquinamento da PM10 in Emilia-Romagna. L’obiettivo è quello di fornire una prima caratterizzazione della variabilitàspaziale e temporale delle concentrazioni e di misurarne la dipendenza dalle principali grandezze meteorologiche. I risultati mostrano come la variabilitàtemporale sia largamente dominante rispetto all’eterogeneitàspaziale ed alla variabilitànon spiegat
Assessing the evolution of territorial disparities in health
The paper investigates spatio-temporal trends in health disparities through an empirical example. We deal with
geographical health pattern in Italy from 1991 to 2010, starting from infant mortality data available at the
provincial level and assessing the existent disparity among macro-regions (the conventional Northern, Central and
Southern macro-regions). After a discussion concerning suitable inequality indices and their decompositions when
dealing with small area data, we propose a model-based approach that allows to properly tackle sampling
variability. Results give evidences of persisting spatial disparity in infant mortality along time
Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic
If understanding sentiments is already a difficult task in human‐human communication,
this becomes extremely challenging when a human‐computer interaction happens, as for
instance in chatbot conversations. In this work, a machine learning neural network‐based
Speech Emotion Recognition system is presented to perform emotion detection in a
chatbot virtual assistant whose task was to perform contact tracing during the COVID‐19
pandemic. The system was tested on a novel dataset of audio samples, provided by the
company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID‐19. The dataset provided was
unlabelled for the emotions associated to the conversations. Therefore, the work was
structured using a sort of transfer learning strategy. First, the model is trained using the
labelled and publicly available Italian‐language dataset EMOVO Corpus. The accuracy
achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact
tracing, shedding lights towards the importance of the use of such techniques in virtual
assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SE
Non-parametric regression on compositional covariates using Bayesian P-splines
Methods to perform regression on compositional covariates have recently
been proposed using isometric log-ratios (ilr) representation of compositional parts.
This approach consists of first applying standard regression on ilr coordinates and
second, transforming the estimated ilr coefficients into their contrast log-ratio counterparts.
This gives easy-to-interpret parameters indicating the relative effect of each
compositional part. In this work we present an extension of this framework, where compositional
covariate effects are allowed to be smooth in the ilr domain. This is achieved
by fitting a smooth function over the multidimensional ilr space, using Bayesian Psplines.
Smoothness is achieved by assuming random walk priors on spline coefficients
in a hierarchical Bayesian framework. The proposed methodology is applied to spatial
data from an ecological survey on a gypsum outcrop located in the Emilia Romagna
Region, Italy
lifex-fiber: an open tool for myofibers generation in cardiac computational models
Background: Modeling the whole cardiac function involves the solution of several complex multi-physics and multi-scale models that are highly computationally demanding, which call for simpler yet accurate, high-performance computational tools. Despite the efforts made by several research groups, no software for whole-heart fully coupled cardiac simulations in the scientific community has reached full maturity yet.Results: In this work we present life(x)-fiber, an innovative tool for the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, which are the essential building blocks for modeling the electrophysiological, mechanical and electromechanical cardiac function, from single-chamber to whole-heart simulations. life(x)-fiber is the first publicly released module for cardiac simulations based on life(x), an open-source, high-performance Finite Element solver for multi-physics, multi-scale and multi-domain problems developed in the framework of the iHEART project, which aims at making in silico experiments easily reproducible and accessible to a wide community of users, including those with a background in medicine or bio-engineering.Conclusions: The tool presented in this document is intended to provide the scientific community with a computational tool that incorporates general state of the art models and solvers for simulating the cardiac function within a high-performance framework that exposes a user-and developer-friendly interface. This report comes with an extensive technical and mathematical documentation to welcome new users to the core structure of life(x)-fiber and to provide them with a possible approach to include the generated cardiac fibers into more sophisticated computational pipelines. In the near future, more modules will be successively published either as pre-compiled binaries for x86-64 Linux systems or as open source software
Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations
Since myocardial fibers drive the electric signal propagation throughout the
myocardium, accurately modeling their arrangement is essential for simulating
heart electrophysiology (EP). Rule-Based-Methods (RBMs) represent a commonly
used strategy to include cardiac fibers in computational models. A particular
class of such methods is known as Laplace-Dirichlet-Rule-Based-Methods (LDRBMs)
since they rely on the solution of Laplace problems. In this work we provide a
unified framework, based on LDRBMs, for generating full heart muscle fibers.
First, we review existing ventricular LDRBMs providing a communal mathematical
description and introducing also some modeling improvements with respect to the
existing literature. We then carry out a systematic comparison of LDRBMs based
on meaningful biomarkers produced by numerical EP simulations. Next we propose,
for the first time, a LDRBM to be used for generating atrial fibers. The new
method, tested both on idealized and realistic atrial models, can be applied to
any arbitrary geometries. Finally, we present numerical results obtained in a
realistic whole heart where fibers are included for all the four chambers using
the discussed LDRBMs
A comprehensive and biophysically detailed computational model of the whole human heart electromechanics
While ventricular electromechanics is extensively studied, four-chamber heart
models have only been addressed recently; most of these works however neglect
atrial contraction. Indeed, as atria are characterized by a complex physiology
influenced by the ventricular function, developing computational models able to
capture the physiological atrial function and atrioventricular interaction is
very challenging. In this paper, we propose a biophysically detailed
electromechanical model of the whole human heart that considers both atrial and
ventricular contraction. Our model includes: i) an anatomically accurate
whole-heart geometry; ii) a comprehensive myocardial fiber architecture; iii) a
biophysically detailed microscale model for the active force generation; iv) a
0D closed-loop model of the circulatory system; v) the fundamental interactions
among the different core models; vi) specific constitutive laws and model
parameters for each cardiac region. Concerning the numerical discretization, we
propose an efficient segregated-intergrid-staggered scheme and we employ
recently developed stabilization techniques that are crucial to obtain a stable
formulation in a four-chamber scenario. We are able to reproduce the healthy
cardiac function for all the heart chambers, in terms of pressure-volume loops,
time evolution of pressures, volumes and fluxes, and three-dimensional cardiac
deformation, with unprecedented matching (to the best of our knowledge) with
the expected physiology. We also show the importance of considering atrial
contraction, fibers-stretch-rate feedback and suitable stabilization
techniques, by comparing the results obtained with and without these features
in the model. The proposed model represents the state-of-the-art
electromechanical model of the iHEART ERC project and is a fundamental step
toward the building of physics-based digital twins of the human heart
lifex-ep: a robust and efficient software for cardiac electrophysiology simulations
Background: Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. Results: This work introduces lifex-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. lifex-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, lifex-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within lifex-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying lifex-ep, along with comprehensive implementation details and instructions for users. lifex-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of lifex-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. Conclusions: lifex-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. lifex-ep represents a valuable tool for conducting in silico patient-specific simulations
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