Permutation-based stochastic ordering using pairwise comparisons

The topic of this work concerns nonparametric permutation-based methods aiming to find a ranking (stochastic ordering) of a given set of groups (populations), gathering together information from multiple variables under more than one experimental designs. The problem of ranking populations arises in several fields of science from the need of comparing G>2 given groups or treatments when the main goal is to find an order while taking into account several aspects. As it can be imagined, this problem is not only of theoretical interest but it also has a recognised relevance in several fields, such as industrial experiments or behavioural sciences, and this is reflected by the vast literature on the topic, although sometimes the problem is associated with different keywords such as: "stochastic ordering", "ranking", "construction of composite indices" etc., or even "ranking probabilities" outside of the strictly-speaking statistical literature. The properties of the proposed method are empirically evaluated by means of an extensive simulation study, where several aspects of interest are let to vary within a reasonable practical range. These aspects comprise: sample size, number of variables, number of groups, and distribution of noise/error. The flexibility of the approach lies mainly in the several available choices for the test-statistic and in the different types of experimental design that can be analysed. This render the method able to be tailored to the specific problem and the to nature of the data at hand. To perform the analyses an R package called SOUP (Stochastic Ordering Using Permutations) has been written and it is available on CRAN.

Reasoning with incomplete and imprecise preferences

Preferences are present in many real life situations but it is often difficult to quantify them giving a precise value. Sometimes preference values may be missing because of privacy reasons or because they are expensive to obtain or to produce. In some other situations the user of an automated system may have a vague idea of whats he wants. In this thesis we considered the general formalism of soft constraints, where preferences play a crucial role and we extended such a framework to handle both incomplete and imprecise preferences. In particular we provided new theoretical frameworks to handle such kinds of preferences. By admitting missing or imprecise preferences, solving a soft constraint problem becomes a different task. In fact, the new goal is to find solutions which are the best ones independently of the precise value the each preference may have. With this in mind we defined two notions of optimality: the possibly optimal solutions and the necessary optimal solutions, which are optimal no matter we assign a precise value to a missing or imprecise preference. We provided several algorithms, bases on both systematic and local search approaches, to find such kind of solutions. Moreover, we also studied the impact of our techniques also in a specific class of problems (the stable marriage problems) where imprecision and incompleteness have a specific meaning and up to now have been tackled with different techniques. In the context of the classical stable marriage problem we developed a fair method to randomly generate stable marriages of a given problem instance. Furthermore, we adapted our techniques to solve stable marriage problems with ties and incomplete lists, which are known to be NP-hard, obtaining good results both in terms of size of the returned marriage and in terms of steps need to find a solution.

The incomplete ionization of substitutional dopants in Silicon Carbide

This thesis analyzes theoretically and computationally the phenomenon of partial ionization of the substitutional dopants in Silicon Carbide at thermal equilibrium. It is based on the solution of the charge neutrality equation and takes into account the following phenomena: several energy levels in the bandgap; Fermi-Dirac statistics for free carriers; screening effects on the dopant ionization energies; the formation of impurity bands. A self-consistent model and a corresponding simulation software have been realized. A preliminary comparison of our calculations with existing experimental results is carried out.

The Architecture of the Persian Period in the Levant

The earliest scholars were not concerned about preparing extensive investigations linking the Persian-period building remains excavated in the entire Levant together. Moreover, the research interests of scholars caused some impediments to the study of this period viz in the last decades; the Achaemenid period has been neglected by the scholars who -in turn- focused on the earlier and later periods for religious reasons. Too, while some regions have been studied abundantly, but it was not the case in other areas, which makes our knowledge is incomplete. From the explanation side, some scholars try to interpret the architectural remains from an ethnic perspective or unsubstantiated personal fancies, so their arguments were utterly lacking any objectivity. This thesis explores what are the Persian architectural and ornamental impacts on the Levantine architecture and the relations between Persian-period sites in Syria-Palestine region. Too, the architectural remains and their contents benefited us to clarify the settlement patterns in the regions being discussed. The author analyzed the ground plans of the buildings and their architectural features and ornamental motifs by conducting a descriptive, analytical, and interpretative study. He also conducted comparisons with similar buildings outside the Levant, especially in Fars to obtain a more comprehensive and systematic study, and then extracting any direct or indirect Persian influences. This has given us a better understanding of the nature of the social, political, and religious life in the entire Levant and the knowledge gap has been bridged to a satisfying extent. This study has demonstrated a few of the Achaemenid impacts, especially on the northern coastline of the Levant.