The aim of this project was to record the Traditional Local Knowledge (TLK) concerning the traditional uses of wild food plants together with all the practices linked as gathering, processing, cooking, including the therapeutic uses, to re-discover plant species often under-utilized or neglected and to identify those with new or underestimated healthy effects for human people. This research was performed in two areas belonging to the province of the city of Bologna (Emilia-Romagna region, Northern Italy) and in the Middle Agri Valley (Potenza province, Basilicata region, Southern Italy). Up to now no research has been carried out on the use of wild edibles in these territories and, therefore, this study represents the first attempt to collect and save from oblivion an important part of the cultural heritage preserved by these populations. Using an ethnobotanical approach, people still retaining TLK about wild food plants were interviewed recording the edible species and related practices used. By means of the Relative Frequency of Citation index (RFC), it was possible to state that Crepis vesicaria subsp. taraxacifolia (Thuill) Thell and Cichorium intybus L. were the most recognised and important wild food species respectively in the area of Bologna and in the Middle Agri Valley. The results of the two investigated areas were compared. The ethnobotanical study was followed by a metabolic screening of 34 plants samples mentioned by the informants (13 collected in Bologna’s area and 21 in Middle Agri Valley). Sanguisorba minor Scop. showed the highest antioxidant activity and polyphenol content; Mentha spicata L. exhibited the highest flavonoid content; Sinapis arvensis L. revealed the highest content of chlorophylls and carotenoids and Clematis vitalba L. the highest protein content. Finally, the most considerable and cited species in the area of Bologna, Crepis vesicaria subsp. taraxacifolia (Thuill) Thell, was analysed by an untargeted metabolomics approach.

Many problems arising in several and different areas of human knowledge share the characteristic of being intractable in real cases. The relevance of the solution of these problems, linked to their domain of action, has given birth to many frameworks of algorithms for solving them. Traditional solution paradigms are represented by exact and heuristic algorithms. In order to overcome limitations of both approaches and obtain better performances, tailored combinations of exact and heuristic methods have been studied, giving birth to a new paradigm for solving hard combinatorial optimization problems, constituted by model-based metaheuristics. In the present thesis, we deepen the issue of model-based metaheuristics, and present some methods, belonging to this class, applied to the solution of combinatorial optimization problems.

In this thesis, it is examined the issue of equivalence between linear terms in higher order languages, that is, in languages which allow to use functions as variables, and where variables which appear in the terms must be used exactly once. The work is developed focusing on the bisimulation method, with the purpose to compare this technique with that which has become the standard for the comparison between the terms of a language, i.e. the context equivalence. The thesis is divided into three parts: in the first one, the introduction of the bisimulation and context equivalence techniques takes place within a deterministic linear and typed language. In the second part, the same techniques are reformulated for a language that, while preserving the linearity, loses the deterministic connotation, allowing the terms to evaluate to a set of values each one having a certain probability to appear in the end of calculation. In the last part, a quantum language is examined, discussing the advantages of quantum computation, which allows to speed-up many of the algorithms of computation. Here one gives the concept of quantum program, which is inextricably linked to the (quantum) register where the qubits used in the computation are stored, entailing a more complex notion of equivalence between terms. The techniques to demonstrate that bisimulation is a congruence are not standard and have been used for the first time by Howe for untyped languages: within the thesis, one shows that bisimulation is a congruence in all considered languages but it coincides with the context equivalence relation only for the deterministic one. Indeed, extending the techniques already used by Howe to the probabilistic and quantum environment, it is shown, as non trivial result, that in probabilistic and quantum linear languages the bisimulation is contained in context equivalence relation.

Next-generation sequencing (NGS) has rapidly become the current standard in genetic related analysis. This switch from microarray to NGS required new statistical strategies to address the research questions inherent to the considered phenomena. First and foremost, NGS dataset usually consist of discrete observations characterized by overdispersion - that is, discrepancy between expected and observed variability - and an abundance of zeros, measured across a huge number of regions of the genome. With respect to chromatin immunoprecipitation sequencing (ChIP-Seq), a class of NGS data, it is of primary focus to discover the underlying (unobserved) pattern of `enrichment': more particularly, there is interest in the interactions between genes (or broader regions of the genome) and proteins, as they describe the mechanism of regulation under different conditions such as healthy or damaged tissue. Another interesting research question involves the clustering of these observations into groups that have practical relevance and interpretability, considering in particular that a single unit could potentially be allocated into more than one of these clusters, as it is reasonable to assume that its participation is not exclusive to one and only biological function and/or mechanism. Many of these complex processes, indeed, could also be described by sets of ordinary differential equations (ODE's), which are mathematical representations of the changes of a system through time, following a dynamic that is governed by some parameters we are interested in. In this thesis, we address the aforementioned tasks and research questions employing different statistical strategies, such as model-based clustering, graphical models, penalized smoothing and regression. We propose extensions of the existing approaches to better fit the problem at hand and we elaborate the methodology in a Bayesian environment, with the focus on incorporating the structural dependencies - both spatial and temporal - of the data at our disposal.

This thesis concerns the statistical analysis of open quantum systems subject to an external and non-stationary perturbation. In the first paper, a generalization of the explicit-duration hidden Markov models (EDHMM) which takes into account the presence of sparse data is presented. Introducing a kernel estimator in the estimation procedure increases the accuracy of the estimates, and thus allows one to obtain a more reliable information about the evolution of the unobservable system. A generalization of the Viterbi algorithm to EDHMM is developed. In the second paper, we develop a Markov Chain Monte Carlo (MCMC) procedure for estimating the EDHMM. We improve the flexibility of our formulation by adopting a Bayesian model selection procedure which allows one to avoid a direct specification of the number of states of the hidden chain. Motivated by the presence of sparsity, we make use of a non-parametric estimator to obtain more accurate estimates of the model parameters. The formulation presented turns out to be straightforward to implement, robust against the underflow problem and provides accurate estimates of the parameters. In the third paper, an extension of the Cramér-Rao inequality for quantum discrete parameter models is derived. The latter are models in which the parameter space is restricted to a finite set of points. In some estimation problems indeed, theory provides us with additional information that allow us to restrict the parameter space to a finite set of points. The extension presented sets the ultimate accuracy of an estimator, and determines a discrete counterpart of the quantum Fisher information. This is particularly useful in many experiments in which the parameters can assume only few different values: for example, the direction which the magnetic field points to. We also provide an illustration related to a quantum optics problem.

This thesis addresses the statistical modeling of turbulence, focusing on three main aspects: the critical transition from laminarity to turbulence, the effects of the so-called intermittency and the energy dynamics of a turbulent flow. The central part of the thesis consists of six papers, divided into two parts. In Part I we develop two new indices to quantify the proximity to critical transitions in stochastic dynamical systems, with particular attention to the transition from laminarity to turbulence in fluids (Paper A). The two indices are tested on two toy models and then applied to the detection of critical events in a magnetised fluid and in financial time series. We define a third index Y, which quantifies the effects of intermittency and does not require very long time series. This index turns out to be effective in recovering the structure of the turbulent flow (Papers B, C). In Paper D we show that Y is also sensitive to the turbulent behavior of financial markets, providing a possible early warning indicator of the proximity to critical events. In Part II we introduce a new local observable as the arrival times of tracer particles at a particular point in the fluid as a proxy of the turbulent velocity field. We model the universal self-organising structure of this observable in an effective and parsimonious way. In the second paper of Part II, we model the continuous-time dynamics of the energy budget of the turbulent field. We show that this observable can be characterised as the exponential of a stochastic integral on a Lévy basis, under the assumption that the energy transmission across time scales is a multiplicative cascade process.

With an increasing number of people who live in cities, urban mobility becomes one of the most important research fields in the so-called smart city environments. Urban mobility can be defined as the ability of people to move around the city, living and interacting with the space. For these reasons, urban accessibility represents a primary factor to keep into account for social inclusion and for the effective exercise of citizenship. In this thesis, we researched how to use crowdsourcing and participative sensing to effectively and efficiently collect data about aPOIs (accessible Point Of Interests) with the aim of obtaining an updated, trusted and completed accessible map of the urban environment. The data gathered in such a way, was integrated with data retrieved from external open dataset and used in computing personalized accessible urban paths. In order to deeply investigate the issues related to this research, we designed and prototyped mPASS, a context-aware and location-based accessible way-finding system.

No description available.

La tesi analizza il rapporto tra rischio e liberalismo economico nel contratto e la qualificazione giuridica dei contratti in prodotti finanziari derivati. / The thesis analyzes the relationship between risk and economic liberalism in the contract and the legal classification of financial derivatives.

Graphene excellent properties make it a promising candidate for building future nanoelectronic devices. Nevertheless, the absence of an energy gap is an open problem for the transistor application. In this thesis, graphene nanoribbons and pattern-hydrogenated graphene, two alternatives for inducing an energy gap in graphene, are investigated by means of numerical simulations. A tight-binding NEGF code is developed for the simulation of GNR-FETs. To speed up the simulations, the non-parabolic effective mass model and the mode-space tight-binding method are developed. The code is used for simulation studies of both conventional and tunneling FETs. The simulations show the great potential of conventional narrow GNR-FETs, but highlight at the same time the leakage problems in the off-state due to various tunneling mechanisms. The leakage problems become more severe as the width of the devices is made larger, and thus the band gap smaller, resulting in a poor on/off current ratio. The tunneling FET architecture can partially solve these problems thanks to the improved subthreshold slope; however, it is also shown that edge roughness, unless well controlled, can have a detrimental effect in the off-state performance. In the second part of this thesis, pattern-hydrogenated graphene is simulated by means of a tight-binding model. A realistic model for patterned hydrogenation, including disorder, is developed. The model is validated by direct comparison of the momentum-energy resolved density of states with the experimental angle-resolved photoemission spectroscopy. The scaling of the energy gap and the localization length on the parameters defining the pattern geometry is also presented. The results suggest that a substantial transport gap can be attainable with experimentally achievable hydrogen concentration.