The changing politics of preference formation in international trade negotiations: the European Union in the Doha Round

This research seeks to provide an explanation for variations of “politics” of preference formation in international trade negotiations. Building on the ‘policy determines politics’ argument, I hypothesize the existence of a causal relationship between issue-characteristics and their variations with politics dynamics and their variations. More specifically, this study seeks to integrate into a single analytical framework two dimensions along which variations in the “politics of preference formation” can be organized: configurations of power relationships among the relevant actors in the structures within which they interact as well as the logic and the motivations of the actors involved in the policy making process. To do so, I first construct a four-cell typology of ‘politics of preference formation’ and, then, I proceed by specifying that the type of state-society configurations as well as the type of actors’ motivations in the “politics of preference formation” depend, respectively, on the degree to which a policy issue is perceived as politically salient and on the extent to which the distributional implications of such an issue can be calculated by the relevant stakeholders in the policy making process. The empirical yardstick against which the validity of the theoretical argument proposed is tested is drawn from evidence concerning the European Union’s negotiating strategy in four negotiating areas in the context of the so-called WTO’s Doha Development Round of multilateral trade negotiations: agriculture, competition, environment and technical assistance and capacity building.

Fatigue Crack Propagation in Pressurized metallic Fuselages

On the basis of well-known literature, an analytical tool named LEAF (Linear Elastic Analysis of Fracture) was developed to predict the Damage Tolerance (DT) proprieties of aeronautical stiffened panels. The tool is based on the linear elastic fracture mechanics and the displacement compatibility method. By means of LEAF, an extensive parametric analysis of stiffened panels, representative of typical aeronautical constructions, was performed to provide meaningful design guidelines. The effects of riveted, integral and adhesively bonded stringers on the fatigue crack propagation performances of stiffened panels were investigated, as well as the crack retarder contribution using metallic straps (named doublers) bonded in the middle of the stringers bays. The effect of both perfectly bonded and partially debonded doublers was investigated as well. Adhesively bonded stiffeners showed the best DT properties in comparison with riveted and integral ones. A great reduction of the skin crack growth propagation rate can be achieved with the adoption of additional doublers bonded between the stringers.

Tracing AGN accretion and star formation in Herschel galaxies

In this Thesis, we study the physical properties and the cosmic evolution of AGN and their host galaxies since z∼3. Our analysis exploits samples of star forming galaxies detected with Herschel at far-IR wavelengths (from 70 up to 500 micron) in different extragalactic surveys, such as COSMOS and the deep GOODS (South and North) fields. The broad-band ancillary data available in COSMOS and the GOODS fields, allows us to implement Herschel and Spitzer photometry with multi-wavelength ancillary data. We perform a multicomponent SED-fitting decomposition to decouple the emission due to star formation from that due to AGN accretion, and to estimate both host-galaxy parameters (such as stellar mass, M* and star formation rate, SFR), and nuclear intrinsic bolometric luminosities. We use the individual estimates of AGN bolometric luminosity obtained through SED-fitting decomposition to reconstruct the redshit evolution of the AGN bolometric luminosity function since z∼3. The resulting trends are used to estimate the overall AGN accretion rate density at different cosmic epochs and to trace the first ever estimate of the AGN accretion history from an IR survey. Later on, we focus our study on the connection between AGN accretion and integrated galaxy properties. We analyse the relationships of AGN accretion with galaxy properties in the SFR-M* plane and at different cosmic epochs. Finally, we infer what is the parameter that best correlates with AGN accretion, comparing our results with previous studies and discussing their physical implications in the context of current scenarios of AGN/galaxy evolution.

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.