208 research outputs found
Preconditioned fully implicit PDE solvers for monument conservation
Mathematical models for the description, in a quantitative way, of the
damages induced on the monuments by the action of specific pollutants are often
systems of nonlinear, possibly degenerate, parabolic equations. Although some
the asymptotic properties of the solutions are known, for a short window of
time, one needs a numerical approximation scheme in order to have a
quantitative forecast at any time of interest. In this paper a fully implicit
numerical method is proposed, analyzed and numerically tested for parabolic
equations of porous media type and on a systems of two PDEs that models the
sulfation of marble in monuments. Due to the nonlinear nature of the underlying
mathematical model, the use of a fixed point scheme is required and every step
implies the solution of large, locally structured, linear systems. A special
effort is devoted to the spectral analysis of the relevant matrices and to the
design of appropriate iterative or multi-iterative solvers, with special
attention to preconditioned Krylov methods and to multigrid procedures.
Numerical experiments for the validation of the analysis complement this
contribution.Comment: 26 pages, 13 figure
An Activity Classifier based on Heart Rate and Accelerometer Data Fusion
The European project ProeTEX realized a novel set of prototypes based on smart garments
that integrate sensors for the real-time monitoring of physiological, activity-related and environmental
parameters of the emergency operators during their interventions. The availability of these parameters
and the emergency scenario suggest the implementation of novel classification methods aimed at
detecting dangerous status of the rescuer automatically, and based not only on the classical activityrelated
signals, rather on a combination of these data with the physiological status of the subject. Here
we propose a heart rate and accelerometer data fusion algorithm for the activity classification of
rescuers in the emergency context
Analysis of Multigrid Preconditioning for Implicit PDE Solvers for Degenerate Parabolic Equations
Predictive ability of the estimate of fat mass to detect early-onset metabolic syndrome in prepubertal children with obesity
Body mass index (BMI), usually used as a body fatness marker, does not accurately dis-criminate between amounts of lean and fat mass, crucial factors in determining metabolic syndrome (MS) risk. We assessed the predictive ability of the estimate of FM (eFM) calculated using the following formula: FM = weight â exp(0.3073 Ă height2 â10.0155Ăd-growth-standards/standards/body-mass-index-for-age-bmi-for-age weightâ1 +0.004571Ăweightâ 0.9180Ăln(age) + 0.6488Ăage0.5 + 0.04723Ămale + 2.8055) (exp = exponential function, score 1 if child was of black (BA), south Asian (SA), other Asian (AO), or other (other) ethnic origin and score 0 if not, ln = natural logarithmic transformation, male = 1, female = 0), to detect MS in 185 prepubertal obese children compared to other adiposity parameters. The eFM, BMI, waist circumference (WC), body shape index (ABSI), tri-ponderal mass index, and conicity index (C-Index) were calculated. Patients were classified as hav-ing MS if they met â„ 3/5 of the following criteria: WC â„ 95th percentile; triglycerides â„ 95th percen-tile; HDL-cholesterol †5th percentile; blood pressure â„ 95th percentile; fasting blood glucose â„ 100 mg/dL; and/or HOMA-IR â„ 97.5th percentile. MS occurred in 18.9% of obese subjects (p < 0.001), with a higher prevalence in females vs. males (p = 0.005). The eFM was correlated with BMI, WC, ABSI, and Con-I (p < 0.001). Higher eFM values were present in the MS vs. non-MS group (p < 0.001); the eFM was higher in patients with hypertension and insulin resistance (p <0.01). The eFM shows a good predictive ability for MS. Additional to BMI, the identification of new parameters determi-nable with simple anthropometric measures and with a good ability for the early detection of MS, such as the eFM, may be useful in clinical practice, particularly when instrumentation to estimate the body composition is not available
Contralateral cortico-ponto-cerebellar pathways reconstruction in humans in vivo: implications for reciprocal cerebro-cerebellar structural connectivity in motor and non-motor areas
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Contralateral cortico-ponto-cerebellar pathways reconstruction in humans in vivo: implications for reciprocal cerebro-cerebellar structural connectivity in motor and non-motor areas
Fulvia Palesi, Andrea De Rinaldis, Gloria Castellazzi, Fernando Calamante, Nils Muhlert, Declan Chard, J. Donald Tournier, Giovanni Magenes, Egidio DâAngelo & Claudia A. M. Gandini Wheeler-Kingshott
Scientific Reports 7, Article number: 12841 (2017)
doi:10.1038/s41598-017-13079-8
Download Citation
BrainNeuroscience
Received:
11 May 2017
Accepted:
18 September 2017
Published online:
09 October 2017
Abstract
Cerebellar involvement in cognition, as well as in sensorimotor control, is increasingly recognized and is thought to depend on connections with the cerebral cortex. Anatomical investigations in animals and post-mortem humans have established that cerebro-cerebellar connections are contralateral to each other and include the cerebello-thalamo-cortical (CTC) and cortico-ponto-cerebellar (CPC) pathways. CTC and CPC characterization in humans in vivo is still challenging. Here advanced tractography was combined with quantitative indices to compare CPC to CTC pathways in healthy subjects. Differently to previous studies, our findings reveal that cerebellar cognitive areas are reached by the largest proportion of the reconstructed CPC, supporting the hypothesis that a CTC-CPC loop provides a substrate for cerebro-cerebellar communication during cognitive processing. Amongst the cerebral areas identified using in vivo tractography, in addition to the cerebral motor cortex, major portions of CPC streamlines leave the prefrontal and temporal cortices. These findings are useful since provide MRI-based indications of possible subtending connectivity and, if confirmed, they are going to be a milestone for instructing computational models of brain function. These results, together with further multi-modal investigations, are warranted to provide important cues on how the cerebro-cerebellar loops operate and on how pathologies involving cerebro-cerebellar connectivity are generated
Vitis vinifera - a chemotaxonomic approach: Seed storage proteins
The IEF pattern of the constituent peptides for the storage protein from Viris vinifera endosperm is used for the construction of a dendrogram relating 74 seed specimens
A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VDâAD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a âmixed VDâAD dementiaâ (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations
Seismic behaviour of traditional timber frame walls: experimental results on unreinforced walls
Timber frame buildings are well known as an efficient seismic resistant structure
and they are used worldwide. Moreover, they have been specifically adopted in codes and
regulations during the XVIII and XIX centuries in the Mediterranean area. These structures
generally consist of exterior masonry walls with timber elements embedded which tie the
walls together and internal walls which have a timber frame with masonry infill and act as
shearwalls. In order to preserve these structureswhich characterizemany cities in theworld it
is important to better understand their behaviour under seismic actions. Furthermore, historic
technologies could be used even in modern constructions to build seismic resistant buildings
using more natural materials with lesser costs. Generally, different types of infill could be
applied to timber frame walls depending on the country, among which brick masonry, rubble
masonry, hay and mud. The focus of this paper is to study the seismic behaviour of the walls
considering different types of infill, specifically: masonry infill, lath and plaster and timber
frame with no infill. Static cyclic tests have been performed on unreinforced timber frame
walls in order to study their seismic capacity in terms of strength, stiffness, ductility and
energy dissipation. The tests showed how in the unreinforced condition, the infill is able to
guarantee a greater stiffness, ductility and ultimate capacity of the wall.The authors would like to acknowledge Eng. Filipe Ferreira and A.O.F. (Augusto Oliveira Ferreira &
C Lda.) for their expertise and collaboration in the construction of the wall specimens.
The first author would also like to acknowledge the Portuguese Science and Technology
Foundation (FCT) for its financial support through grant SFRH / BD / 61908 / 2009
INVESTIGATING THE SEISMIC RESPONSE OF URM WALLS WITH IRREGULAR OPENING LAYOUT THROUGH DIFFERENT MODELING APPROACHES
The façade and internal walls of unreinforced masonry (URM) buildings often present an irregular opening layout, due to architectural reasons or modifications to the structure, which make the expected seismic damage pattern less predictable a priori. Therefore, the discretization of the walls in structural components is not standardized, conversely to cases with a regular opening layout for which the available modeling methods are corroborated by seismic damage surveys reporting recurrent failure patterns. The structural component discretization is a relevant step for the code-conforming seismic assessment, typically based on comparing the internal forces and drifts of each component to strength criteria and drift thresholds. Therefore, the lack of well-established approaches can significantly influence the assessment. The issue is even more evident when the structural components must be identified a priori in the modeling stage, namely for equivalent frame models. The applicability of available methods for discretization of URM walls with irregular opening layout has been already investigated in literature, but a conclusive judgment requires further studies. In this context, this paper presents an overview of the preliminary results addressing the numerical modeling of this type of walls within the framework of the DPC-ReLUIS 2022-2024 project (Subtask 10.3), funded by the Italian Department of Civil Protection. The Subtask aims to propose consensus-based recommendations for researchers and practitioners which can contribute to harmonize the use of different modeling approaches. Seven research groups are involved in the research, adopting different modeling approaches and computer codes, but similar assumptions and the same analysis method (pushover) are used. The benchmark URM structure illustrated in the paper is a two-story wall from which four configurations with increasing irregularity of opening layout were derived. The results of four modeling approached are presented. Three of them reproduce the mechanical response of masonry at the material scale by means of FE models implemented in OpenSees, DIANA and Abaqus software, while the remaining approach describes the mechanical response of masonry at the macro-element scale in 3DMacro software. Results were compared in terms of capacity curves, predicted failure mechanisms and evolution of internal forces in piers. The adoption of consistent assumptions among the different approaches led to an overall agreement of predictions at both wall and pier scales, particularly in terms of damage pattern with higher concentration of damage at the ground story. Despite that, differences on the pushover curves have been highlighted. They are mainly due to some deviations of the internal forces in squat piers deriving from a complex load flow in these elements
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