7 resultados para system of analysis
em AMS Tesi di Dottorato - Alm@DL - Università di Bologna
Resumo:
During recent years a consistent number of central nervous system (CNS) drugs have been approved and introduced on the market for the treatment of many psychiatric and neurological disorders, including psychosis, depression, Parkinson disease and epilepsy. Despite the great advancements obtained in the treatment of CNS diseases/disorders, partial response to therapy or treatment failure are frequent, at least in part due to poor compliance, but also genetic variability in the metabolism of psychotropic agents or polypharmacy, which may lead to sub-therapeutic or toxic plasma levels of the drugs, and finally inefficacy of the treatment or adverse/toxic effects. With the aim of improving the treatment, reducing toxic/side effects and patient hospitalisation, Therapeutic Drug Monitoring (TDM) is certainly useful, allowing for a personalisation of the therapy. Reliable analytical methods are required to determine the plasma levels of psychotropic drugs, which are often present at low concentrations (tens or hundreds of nanograms per millilitre). The present PhD Thesis has focused on the development of analytical methods for the determination of CNS drugs in biological fluids, including antidepressants (sertraline and duloxetine), antipsychotics (aripiprazole), antiepileptics (vigabatrin and topiramate) and antiparkinsons (pramipexole). Innovative methods based on liquid chromatography or capillary electrophoresis coupled to diode-array or laser-induced fluorescence detectors have been developed, together with the suitable sample pre-treatment for interference removal and fluorescent labelling in case of LIF detection. All methods have been validated according to official guidelines and applied to the analysis of real samples obtained from patients, resulting suitable for the TDM of psychotropic drugs.
Resumo:
In this thesis the performances of the CMS Drift Tubes Local Trigger System of the CMS detector are studied. CMS is one of the general purpose experiments that will operate at the Large Hadron Collider at CERN. Results from data collected during the Cosmic Run At Four Tesla (CRAFT) commissioning exercise, a globally coordinated run period where the full experiment was involved and configured to detect cosmic rays crossing the CMS cavern, are presented. These include analyses on the precision and accuracy of the trigger reconstruction mechanism and measurement of the trigger efficiency. The description of a method to perform system synchronization is also reported, together with a comparison of the outcomes of trigger electronics and its software emulator code.
Resumo:
The ALICE experiment at the LHC has been designed to cope with the experimental conditions and observables of a Quark Gluon Plasma reaction. One of the main assets of the ALICE experiment with respect to the other LHC experiments is the particle identification. The large Time-Of-Flight (TOF) detector is the main particle identification detector of the ALICE experiment. The overall time resolution, better that 80 ps, allows the particle identification over a large momentum range (up to 2.5 GeV/c for pi/K and 4 GeV/c for K/p). The TOF makes use of the Multi-gap Resistive Plate Chamber (MRPC), a detector with high efficiency, fast response and intrinsic time resoltion better than 40 ps. The TOF detector embeds a highly-segmented trigger system that exploits the fast rise time and the relatively low noise of the MRPC strips, in order to identify several event topologies. This work aims to provide detailed description of the TOF trigger system. The results achieved in the 2009 cosmic-ray run at CERN are presented to show the performances and readiness of TOF trigger system. The proposed trigger configuration for the proton-proton and Pb-Pb beams are detailed as well with estimates of the efficiencies and purity samples.
Resumo:
The present work is a collection of three essays devoted at understanding the determinants and implications of the adoption of environmental innovations EI by firms, by adopting different but strictly related schumpeterian perspectives. Each of the essays is an empirical analysis that investigates one original research question, formulated to properly fill the gaps that emerged in previous literature, as the broad introduction of this thesis outlines. The first Chapter is devoted at understanding the determinants of EI by focusing on the role that knowledge sources external to the boundaries of the firm, such as those coming from business suppliers or customers or even research organizations, play in spurring their adoption. The second Chapter answers the question on what induces climate change technologies, adopting regional and sectoral lens, and explores the relation among green knowledge generation, inducement in climate change and environmental performances. Chapter 3 analyzes the economic implications of the adoption of EI for firms, and proposes to disentangle EI by different typologies of innovations, such as externality reducing innovations and energy and resource efficient innovations. Each Chapter exploits different dataset and heterogeneous econometric models, that allow a better extension of the results and to overcome the limits that the choice of one dataset with respect to its alternatives engenders. The first and third Chapter are based on an empirical investigation on microdata, i.e. firm level data extracted from innovation surveys. The second Chapter is based on the analysis of patent data in green technologies that have been extracted by the PATSTAT and REGPAT database. A general conclusive Chapter will follow the three essays and will outline how each Chapter filled the research gaps that emerged, how its results can be interpreted, which policy implications can be derived and which are the possible future lines of research in the field.