Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

Kinematic models of interseismic deformation from inversion of GPS and InSAR measurements to estimate fault parameters and coupling degree

We have used kinematic models in two Italian regions to reproduce surface interseismic velocities obtained from InSAR and GPS measurements. We have considered a Block modeling, BM, approach to evaluate which fault system is actively accommodating the occurring deformation in both considered areas. We have performed a study for the Umbria-Marche Apennines, obtaining that the tectonic extension observed by GPS measurements is explained by the active contribution of at least two fault systems, one of which is the Alto Tiberina fault, ATF. We have estimated also the interseismic coupling distribution for the ATF using a 3D surface and the result shows an interesting correlation between the microseismicity and the uncoupled fault portions. The second area analyzed concerns the Gargano promontory for which we have used jointly the available InSAR and GPS velocities. Firstly we have attached the two datasets to the same terrestrial reference frame and then using a simple dislocation approach, we have estimated the best fault parameters reproducing the available data, providing a solution corresponding to the Mattinata fault. Subsequently we have considered within a BM analysis both GPS and InSAR datasets in order to evaluate if the Mattinata fault may accommodate the deformation occurring in the central Adriatic due to the relative motion between the North-Adriatic and South-Adriatic plates. We obtain that the deformation occurring in that region should be accommodated by more that one fault system, that is however difficult to detect since the poor coverage of geodetic measurement offshore of the Gargano promontory. Finally we have performed also the estimate of the interseismic coupling distribution for the Mattinata fault, obtaining a shallow coupling pattern. Both of coupling distributions found using the BM approach have been tested by means of resolution checkerboard tests and they demonstrate that the coupling patterns depend on the geodetic data positions.

Biochar characterization for its environmental and agricultural utilization. Occurrence, distribution and fate of labile organic carbon and polycyclic aromatic hydrocarbons

In this thesis the potential risks associated to the application of biochar in soil as well the stability of biochar were investigated. The study was focused on the potential risks arising from the occurrence of polycyclic aromatic hydrocarbons (PAHs) in biochar. An analytical method was developed for the determination of the 16 USEPA-PAHs in the original biochar and soil containing biochar. The method was successfully validated with a certified reference material for the soil matrix and compared with methods in use in other laboratories during a laboratory exercise within the EU-COST TD1107. The concentration of 16 USEPA-PAHs along with the 15 EU-PAHs, priority hazardous substances in food, was determined in a suite of currently available biochars for agricultural field applications derived from a variety of parent materials and pyrolysis conditions. Biochars analyzed contained the USEPA and some of the EU-PAHs at detectable levels ranging from 1.2 to 19 µg g-1. This method allowed investigating changes in PAH content and distribution in a four years study following biochar addition in soils in a vineyard (CNR-IBIMET). The results showed that biochar addition determined an increase of the amount of PAHs. However, the levels of PAHs in the soil remained within the maximum acceptable concentration for European countries. The vineyard soil performed by CNR-IBIMET was exploited to study the environmental stability of biochar and its impact on soil organic carbon. The stability of biochar was investigated by analytical pyrolysis (Py-GC-MS) and pyrolysis in the presence of hydrogen (HyPy). The findings showed that biochar amendment significantly influence soil stable carbon fraction concentration during the incubation period. Moreover, HyPy and Py-GC-MS were applied to biochars deriving from three different feedstock at two different pyrolysis temperatures. The results evidenced the influence of feedstock type and pyrolysis conditions on the degree of carbonisation.