Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

The Allocation of Asylum Responsibilities in the EU: a Law & Economics Analysis

This dissertation focuses on how the design of the EU asylum allocation system, the system that allocates the EU’s asylum duties to its member states, relates to the development of asylum crises. The current EU asylum allocation system, the Dublin system, has in the literature frequently been blamed as an important factor that contributed to the events that occurred during the 2015/2016 EU Asylum Crisis. In the first part of this dissertation, I use a Law & Economics methodology based on rational choice theory to study how the Dublin system creates behavioural incentives for both asylum seekers and member states and how this relates to the events during the 2015/2016 EU Asylum Crisis. In the second part, I analyse how behavioural incentives for asylum seekers and member states would change if the EU would replace the Dublin system with a so-called (tradable) quota system. By comparing the outcomes of the first and the second part of the dissertation I make some normative recommendations on desirable features for an EU asylum allocation system that provides better incentives for asylum seekers and member states.

Multiscale comparative analysis of marine biominerals and their ecological implications

Marine biomineralizing organisms provide a fundamental link between biology and environment. Calcified structure are important archives that can provide us main means of understanding organism adaptation, habits, environmental characteristics, and to look back in time and explore the past climate and their evolutionary history. In fact, biomineralized structures retain an unparalleled record of current and past ocean conditions through the investigation of their microchemistry and isotopes. This thesis considers aspects of two different biomineralization systems: fish otolith and coral skeletons at macro-, micro- and nanoscale, with the aim to understand how their morphology, structural characteristics and compositions can provide information of their functionality, and the environmental, behavioural, and evolutionary context in which organisms are framed. To this end, I applied a multidisciplinary approach in the scope to investigate calcified structures as “information recorders” and as models to study the phenotypic plasticity.

Modelling spillover effects in spatial stochastic frontier analysis

In the last two decades, authors have begun to expand classical stochastic frontier (SF) models in order to include also some spatial components. Indeed, firms tend to concentrate in clusters, taking advantage of positive agglomeration externalities due to cooperation, shared ideas and emulation, resulting in increased productivity levels. Until now scholars have introduced spatial dependence into SF models following two different paths: evaluating global and local spatial spillover effects related to the frontier or considering spatial cross-sectional correlation in the inefficiency and/or in the error term. In this thesis, we extend the current literature on spatial SF models introducing two novel specifications for panel data. First, besides considering productivity and input spillovers, we introduce the possibility to evaluate the specific spatial effects arising from each inefficiency determinant through their spatial lags aiming to capture also knowledge spillovers. Second, we develop a very comprehensive spatial SF model that includes both frontier and error-based spillovers in order to consider four different sources of spatial dependence (i.e. productivity and input spillovers related to the frontier function and behavioural and environmental correlation associated with the two error terms). Finally, we test the finite sample properties of the two proposed spatial SF models through simulations, and we provide two empirical applications to the Italian accommodation and agricultural sectors. From a practical perspective, policymakers, based on results from these models, can rely on precise, detailed and distinct insights on the spillover effects affecting the productive performance of neighbouring spatial units obtaining interesting and relevant suggestions for policy decisions.

Analysis of urban infrastructure for sustainable mobility through instrumented bicycles for students

In Europe almost 80% of the continent's population lives in cities. It is estimated that by 2030 most regions in Europe which contain major cities will have even more inhabitants on 35–60% more than now. This process generates a consequent elevate human pressure on the natural environment, especially around large urban agglomerations. Cities could be seen as an ecosystem, represented by the dominance of humans that re-distribute organisms and fluxes and represent the result of co-evolving human and natural systems, emerging from the interactions between humans, natural and infrastructures. Roads have a relevant role in building links between urban components, creating the basis on which it is founded the urban ecosystem itself. This thesis is focused on the research for a comprehensive model, framed in European urban health & wellbeing programme, aimed to evaluate the determinants of health in urban populations. Through bicycles, GPS and sensor kits, specially developed and produced by University of Bologna for this purpose, it has been possible to conduct on Bologna different direct observations that oriented the novelty of the research: the categorization of university students cyclists, connection among environmental data awareness and level of cycling, and an early identification of urban attributes able to impact on road air quality and level of cycling. The categorization of university students’ cyclist has been defined through GPS analysis and focused survey, that both permit to identify behavioural and technical variables and attitudes towards urban cycling. The statistic relationship between level of cycling, seen as number of bicycles passages per lane and pollutants level, has been investigated through an inverse regression model, defined and tested through SPSS software on the basis of the data harvest. The research project that represents a sort of dynamic mobility laboratory on two wheels, that permits to harvest and study detected parameters.