The Time-Of-Flight detector of ALICE at LHC: construction, test and commissioning with cosmic rays

The Time-Of-Flight (TOF) detector of ALICE is designed to identify charged particles produced in Pb--Pb collisions at the LHC to address the physics of strongly-interacting matter and the Quark-Gluon Plasma (QGP). The detector is based on the Multigap Resistive Plate Chamber (MRPC) technology which guarantees the excellent performance required for a large time-of-flight array. The construction and installation of the apparatus in the experimental site have been completed and the detector is presently fully operative. All the steps which led to the construction of the TOF detector were strictly followed by a set of quality assurance procedures to enable high and uniform performance and eventually the detector has been commissioned with cosmic rays. This work aims at giving a detailed overview of the ALICE TOF detector, also focusing on the tests performed during the construction phase. The first data-taking experience and the first results obtained with cosmic rays during the commissioning phase are presented as well and allow to confirm the readiness state of the TOF detector for LHC collisions.

Use of sub-lethal high pressure homogenization (HPH) treatments to enhance functional properties of lactic acid bacteria probiotic strains

The aim of this PhD thesis was to evaluate the effect of a sub-lethal HPH treatment on some probiotic properties and on cell response mechanisms of already-known functional strains, isolated from Argentinean dairy products. The results achieved showed that HPH treatments, performed at a sub-lethal level of 50 MPa, increased some important functional and technological characteristics of the considered non intestinal probiotic strains. In particular, HPH could modify cell hydrophobicity, autoaggregation and resistance to acid gastric conditions (tested in in vitro model), cell viability and cell production of positive aroma compounds, during a refrigerate storage in a simulated dairy product. In addition, HPH process was able to increase also some probiotic properties exerted in vivo and tested for two of the considered strains. In fact, HPH-treated cells were able to enhance the number of IgA+ cells more than other not treated cells, although this capacity was time dependent. On the other hand, HPH treatment was able to modify some important characteristics that are linked to the cell wall and, consequently, could alter the adhesion capacity in vivo and the interaction with the intestinal cells. These modifications, involving cell outermost structures, were highlighted also by Trasmission Electron Microscopy (TEM) analysis. In fact, the micrographs obtained showed a significant effect of the pressure treatment on the cell morphology and particularly on the cell wall. Moreover, the results achieved showed that composition of plasma membranes and their level of unsaturation are involved in response mechanisms adopted by cells exposed to the sub-lethal HPH treatment. Although the response to the treatment varied according to the characteristics of individual strains, time of storage and suspension media employed, the results of present study, could be exploited to enhance the quality of functional products and to improve their organoleptic properties.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.