Search results for "parametric"

Post‐COVID ‐19 Liver Injury: Comprehensive Imaging With Multiparametric Ultrasound

2021

OBJECTIVES: This study aimed to define patterns of liver injury after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using multiparametric ultrasound (mpUS) in a variable patient population with differing severities of COVID-19. METHODS: Ninety patients were enrolled into the study: 56 had SARS-CoV-2 3-9 months prior to enrolment; 34 served as a clinically healthy control group. All patients underwent an mpUS evaluation of the liver (elastography, dispersion and attenuation imaging). Seventy-six patients had abdominal magnetic resonance (MR) and noncontrast enhanced thoracic computed tomography (CT) scans performed at the same day. All patients were screened for bioc…

Assessment of the Inter-Batch Variability of Microstructure Parameters in Topical Semisolids and Impact on the Demonstration of Equivalence

2019

Demonstration of similar microstructure is essential for demonstrating the equivalence of generic topical products since the microstructure of semisolids may affect the drug release. The objective of this study was to compare the microstructure-defining physical parameters of different batches of a reference ointment containing calcipotriol and betamethasone (Daivobet 50 &micro

Transcriptional profiles from patients with dystrophinopathies and limb girdle muscular dystrophies as determined by qRT-PCR.

2003

Mutations in genes coding for the dystrophin-glycoprotein complex (DGC) cause inherited muscular dystrophies (MD), including Morbus Duchenne (DMD) and M. Becker (BMB) as well as limb-girdle muscular dystrophies (LGMD). New insights into the pathophysiology of the dystrophic muscle, the identification of compensatory mechanisms and additional proteins interacting with dystrophin are essential for developing new treatments. In order to define molecular mechanisms induced by lack of dystrophin and the subsequent counter-regulatory transcriptional response of degenerating muscle fibres, we have investigated the mRNA expression of 19 functionally linked genes in biopsies of patients with MD by m…

STUDIO DELLE PROPRIETÀ MECCANICHE ED ELETTROMECCANICHE DI NANOCOMPOSITI E NANOFIBRE MEDIANTE APPROCCI NUMERICI

Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algo…

2019

Self-Organising Feature Map (SOFM) neural models and the Learning Vector Quantization (LVQ) algorithm were used to produce a classifier identifying the quality classes of compost, according to the degree of its maturation within a period of time recorded in digital images. Digital images of compost at different stages of maturation were taken in a laboratory. They were used to generate an SOFM neural topological map with centres of concentration of the classified cases. The radial neurons on the map were adequately labelled to represent five suggested quality classes describing the degree of maturation of the composted organic matter. This enabled the creation of a neural separator classify…

CALIBRATION OF LÉVY PROCESSES USING OPTIMAL CONTROL OF KOLMOGOROV EQUATIONS WITH PERIODIC BOUNDARY CONDITIONS

2018

We present an optimal control approach to the problem of model calibration for L\'evy processes based on a non parametric estimation procedure. The calibration problem is of considerable interest in mathematical finance and beyond. Calibration of L\'evy processes is particularly challenging as the jump distribution is given by an arbitrary L\'evy measure, which form a infinite dimensional space. In this work, we follow an approach which is related to the maximum likelihood theory of sieves. The sampling of the L\'evy process is modelled as independent observations of the stochastic process at some terminal time $T$. We use a generic spline discretization of the L\'evy jump measure and selec…

Solutions with sign information for nonlinear Robin problems with no growth restriction on reaction

2019

We consider a parametric nonlinear Robin problem driven by a nonhomogeneous differential operator. The reaction is a Carathéodory function which is only locally defined (that is, the hypotheses concern only its behaviour near zero). The conditions on the reaction are minimal. Using variational tools together with truncation, perturbation and comparison techniques and critical groups, we show that for all small values of the parameter λ > 0, the problem has at least three nontrivial smooth solutions, two of constant sign and the third nodal.

Common functional component modelling

2005

Functional data analysis (FDA) has become a popular technique in applied statistics. In particular, this methodology has received considerable attention in recent studies in empirical finance. In this talk we discuss selected topics of functional principal components analysis that are motivated by financial data.

Signal processing in photonic crystals and nanostructures

2006

International audience; Optical devices employing photonic crystals and novel nanostructure materials may exhibit useful properties for applications to all-optical signal processing. In this work we analyze as a first example four-wave mixing of polarized beams in photonic crystal fibers. We show that by properly tuning the pump wavelength and the linear dispersion properties of the fiber one may obtain broadband parametric amplification and frequency conversion. Next we consider the in-line periodic amplification of short optical pulses by means of quantum-dot semiconductor optical amplifiers. We show by numerical simulations that pattern-free amplification of a 40 Gbit/s soliton signal at…

Mixed estimation technique in semi-parametric space-time point processes for earthquake description

2013

An estimation approach for the semi-parametric intensity function of a particular space-time point process is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or one belonging to a seismic sequence is therefore estimated.