Statistical evidences of cyclic changes in volcanic gas chemistry composition by inverse modelling

The identification of compositional changes in fumarolic gases of active and quiescent volcanoes is one of the most important targets in monitoring programs. From a general point of view, many systematic (often cyclic) and random processes control the chemistry of gas discharges, making difficult to produce a convincing mathematical-statistical modelling. Changes in the chemical composition of volcanic gases sampled at Vulcano Island (Aeolian Arc, Sicily, Italy) from eight different fumaroles located in the northern sector of the summit crater (La Fossa) have been analysed by considering their dependence from time in the period 2000-2007. Each intermediate chemical composition has been considered as potentially derived from the contribution of the two temporal extremes represented by the 2000 and 2007 samples, respectively, by using inverse modelling methodologies for compositional data. Data pertaining to fumaroles F5 and F27, located on the rim and in the inner part of La Fossa crater, respectively, have been used to achieve the proposed aim. The statistical approach has allowed us to highlight the presence of random and not random fluctuations, features useful to understand how the volcanic system works, opening new perspectives in sampling strategies and in the evaluation of the natural risk related to a quiescent volcano

Modelling scenarios of diffusion and control of pandemic influenza, italy

En aquest article es resumeixen els resultats publicats en un informe de l' ISS (Istituto Superiore di Sanità) del desembre de 2006, sobre un model matemàtic desenvolupat per un grup de treball que inclou a investigadors de les Universitats de Trento, Pisa i Roma, i els Instituts Nacionals de Salut (Istituto Superiore di Sanità, ISS), per avaluar i mesurar l'impacte de la transmissió i el control de la pandèmia de grip

Artur Gas, editor de Josep Pla: breu història d'Ediciones Alfa

Repàs de la trajectòria professional com a editor, començant com a editor de caire socialista i acabant col·laborant amb la dictadura de Primo de Rivera. Es fa especial menció al seu paper com a editor de Josep Pla i a la seva editorial, Ediciones Alfa

Gas collisions and pressure quenching of the photoluminescence of silicon nanopowder grown by plasma-enhanced chemical vapor deposition

The quenching of the photoluminescence of Si nanopowder grown by plasma-enhanced chemical vapor deposition due to pressure was measured for various gases ( H2, O2, N2, He, Ne, Ar, and Kr) and at different temperatures. The characteristic pressure, P0, of the general dependence I(P)=I0exp(-P/P0) is gas and temperature dependent. However, when the number of gas collisions is taken as the variable instead of pressure, then the quenching is the same within a gas family (mono- or diatomic) and it is temperature independent. So it is concluded that the effect depends on the number of gas collisions irrespective of the nature of the gas or its temperature

Atomic diffusion induced by stress relaxation in InGaAs/GaAs epitaxial layers

The origin of the microscopic inhomogeneities in InxGa1-xAs layers grown on GaAs by molecular beam epitaxy is analyzed through the optical absorption spectra near the band gap. It is seen that, for relaxed thick layers of about 2.8μm, composition inhomogeneities are responsible for the band edge smoothing into the whole compositional range (0.05diffusion at the surface during growth, induced by the strain inhomogeneities that arise from stress relaxation. In consequence, the strain variations present in the layer are converted into composition variations during growth. This process is energetically favorable as it diminishes elastic energy. An additional support to this hypothesis is given by a clear proportionality between the magnitude of the composition variations and the mean strain

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Visualitzation and processing of diffusion tensor MRI

Diffusion Tensor Imaging (DTI) is a new magnetic resonance imaging modality capable of producing quantitative maps of microscopic natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. This technique has become a powerful tool in the investigation of brain structure and function because it allows for in vivo measurements of white matter fiber orientation. The application of DTI in clinical practice requires specialized processing and visualization techniques to extract and represent acquired information in a comprehensible manner. Tracking techniques are used to infer patterns of continuity in the brain by following in a step-wise mode the path of a set of particles dropped into a vector field. In this way, white matter fiber maps can be obtained.