Development of oil spill detection techniques for satellite optical sensors and their application to monitor oil spill discharge in Mediterranean sea

Satellite remote sensing has proved to be an effective support in timely detection and monitoring of marine oil pollution, mainly due to illegal ship discharges. In this context, we have developed a new methodology and technique for optical oil spill detection, which make use of MODIS L2 and MERIS L1B satellite top of atmosphere (TOA) reflectance imagery, for the first time in a highly automated way. The main idea was combining wide swaths and short revisit times of optical sensors with SAR observations, generally used in oil spill monitoring. This arises from the necessity to overcome the SAR reduced coverage and long revisit time of the monitoring area. This can be done now, given the MODIS and MERIS higher spatial resolution with respect to older sensors (250-300 m vs. 1 km), which consents the identification of smaller spills deriving from illicit discharge at sea. The procedure to obtain identifiable spills in optical reflectance images involves removal of oceanic and atmospheric natural variability, in order to enhance oil-water contrast; image clustering, which purpose is to segment the oil spill eventually presents in the image; finally, the application of a set of criteria for the elimination of those features which look like spills (look-alikes). The final result is a classification of oil spill candidate regions by means of a score based on the above criteria.

Essays on Regional Development and Internationalisation

This collection of essays examines various aspects of regional development and the issues of internationalization. The first essay investigates the implications of the impressive growth of China from a rural-urban perspective and addresses the topic of convergence in China by employing a non-parametrical approach to study the distribution dynamics of per capita income at province, rural and urban levels. To better understand the degree of inequality characterizing China and the long-term predictions of convergence or divergence of its different territorial aggregations, the second essay formulates a composite indicator of Regional Development (RDI) to benchmark development at province and sub-province level. The RDI goes beyond the uni-dimensional concept of development, generally proxied by the GDP per capita, and gives attention to the rural-urban dimension. The third essay “Internationalization and Trade Specialization in Italy. The role of China in the international intra-firm trade of the Italian regions” - deals with another aspect of regional economic development: the progressive de-industrialisation and de-localization of the local production. This essay looks at the trade specialization of selected Italian regions (those regions specialized in manufacturing) and the fragmentation of the local production on a global scale. China represents in this context an important stakeholder and the paper documents the importance of this country in the regional intra-firm trade.

Development of newly conceived biomimetic nano-structured biomaterials as scaffolds for bone and osteochondral regeneration

The present research thesis was focused on the development of new biomaterials and devices for application in regenerative medicine, particularly in the repair/regeneration of bone and osteochondral regions affected by degenerative diseases such as Osteoarthritis and Osteoporosis or serious traumas. More specifically, the work was focused on the synthesis and physico-chemical-morphological characterization of: i) a new superparamagnetic apatite phase; ii) new biomimetic superparamagnetic bone and osteochondral scaffolds; iii) new bioactive bone cements for regenerative vertebroplasty. The new bio-devices were designed to exhibit high biomimicry with hard human tissues and with functionality promoting faster tissue repair and improved texturing. In particular, recent trends in tissue regeneration indicate magnetism as a new tool to stimulate cells towards tissue formation and organization; in this perspective a new superparamagnetic apatite was synthesized by doping apatite lattice with di-and trivalent iron ions during synthesis. This finding was the pin to synthesize newly conceived superparamagnetic bone and osteochondral scaffolds by reproducing in laboratory the biological processes yielding the formation of new bone, i.e. the self-assembly/organization of collagen fibrils and heterogeneous nucleation of nanosized, ionically substituted apatite mimicking the mineral part of bone. The new scaffolds can be magnetically switched on/off and function as workstations guiding fast tissue regeneration by minimally invasive and more efficient approaches. Moreover, in the view of specific treatments for patients affected by osteoporosis or traumas involving vertebrae weakening or fracture, the present work was also dedicated to the development of new self-setting injectable pastes based on strontium-substituted calcium phosphates, able to harden in vivo and transform into strontium-substituted hydroxyapatite. The addition of strontium may provide an anti-osteoporotic effect, aiding to restore the physiologic bone turnover. The ceramic-based paste was also added with bio-polymers, able to be progressively resorbed thus creating additional porosity in the cement body that favour cell colonization and osseointegration.

Development of new tools and devices for CMB and foreground data analysis and future experiments

The discovery of the Cosmic Microwave Background (CMB) radiation in 1965 is one of the fundamental milestones supporting the Big Bang theory. The CMB is one of the most important source of information in cosmology. The excellent accuracy of the recent CMB data of WMAP and Planck satellites confirmed the validity of the standard cosmological model and set a new challenge for the data analysis processes and their interpretation. In this thesis we deal with several aspects and useful tools of the data analysis. We focus on their optimization in order to have a complete exploitation of the Planck data and contribute to the final published results. The issues investigated are: the change of coordinates of CMB maps using the HEALPix package, the problem of the aliasing effect in the generation of low resolution maps, the comparison of the Angular Power Spectrum (APS) extraction performances of the optimal QML method, implemented in the code called BolPol, and the pseudo-Cl method, implemented in Cromaster. The QML method has been then applied to the Planck data at large angular scales to extract the CMB APS. The same method has been applied also to analyze the TT parity and the Low Variance anomalies in the Planck maps, showing a consistent deviation from the standard cosmological model, the possible origins for this results have been discussed. The Cromaster code instead has been applied to the 408 MHz and 1.42 GHz surveys focusing on the analysis of the APS of selected regions of the synchrotron emission. The new generation of CMB experiments will be dedicated to polarization measurements, for which are necessary high accuracy devices for separating the polarizations. Here a new technology, called Photonic Crystals, is exploited to develop a new polarization splitter device and its performances are compared to the devices used nowadays.

Building Peace and/or Gender Equality. Changing Attitudes around Peace, Development and Security in International Cooperation in Rwanda.

The times following international or civil conflicts but also violent revolutions often come with unequal share of the peace dividend for men and women. Delusions for women who gained freedom of movement and of roles during conflict but had to step back during reconstruction and peace have been recorded in all regions of the world. The emergence of peacebuilding as a modality for the international community to ensure peace and security has slowly incorporated gender sensitivity at the level of legal and policy instruments. Focusing on Rwanda, a country that has obtained significant gender advancement in the years after the genocide while also obtaining to not relapse into conflict, this research explores to what extent the international community has contributed to this transformation. From a review of evaluations, findings are that many of the interventions did not purse gender equality, and overall the majority understood gender and designed actions is a quite superficial way which would hardly account for the significative advancement in combating gender discrimination that the Government, for its inner political will, is conducting. Then, after a critique from a feminist standpoint to the concept of human security, departing from the assumption (sustained by the Governemnt of Rwanda as well) that domestic violence is a variable influencing level of security relevant at the national level, a review of available secondary data on GBV is conducted an trends over the years analysed. The emerging trends signal a steep increase in prevalence of GBV and in domestic violence in particular. Although no conclusive interpretation can be formulated on these data, there are elements suggesting the increase might be due to augmented reporting. The research concludes outlining possible further research pathways to better understand the link in Rwanda between the changing gender norms and the GBV.

Topics in Local Economic Development

This thesis contributes to the current debate in literature about local economic development by considering two different topics: quality of institutions, and the role of clusters in innovation and productivity growth. The research is built upon three papers. The first paper deals with the analysis of the effect of administrative continuity on administrative efficiency. The analysis underlines the importance of different typologies of social capital. Findings reveal a positive impact on administrative efficiency (AE) by administrative continuity (AC) when it is coupled by bridging and linking social capital. On the contrary, bonding social capital influences negatively the effect by AC on AE. The second paper investigates the spatial interaction in levels of quality of government (QoG) among European regions. Notwithstanding the largely recognised role by institutions in the design of regional policies, no study has been conducted about the mechanisms of interaction and diffusion of QoG at regional level. This research wants to overcome this knowledge gap in literature. Findings reveal a heterogeneity in spatial interaction among groups of regions, i.e. ‘leader regions’ (Northern regions) and ‘lagging regions’ (Southern regions), when considering different mechanisms of interaction (learning / imitating competition and pure competition). Moreover, the effect of wealth on the levels of QoG is nonlinear. Finally, the third paper analyses the relation among specialization and productivity within the agricultural sector. In literature, the study of clusters dynamics has long neglected agriculture. The analysis describes the changes in sectorial specialization for eight main crop groups in Italian regions (NUTS 3), assessing the existence of spatial autocorrelations by using an exploratory data analysis. Furthermore, the effect of specialization on productivity is analysed within the main crop groups using a spatial panel data model. Findings reveal a marked tendency to specialization in the Italian agriculture, and a heterogeneous effect by specialization on productivity.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Development of sexing systems functional to mass production of Aedes albopictus sterile males

Aedes albopictus is a vector able to transmit several arboviruses. Due to its high impact on human health, it is important to develop an efficient control strategy for this pest. Nowadays, control based on chemical insecticides is limited by the number of available active principles and the occurrence of resistance. A valuable alternative to the conventional control strategies is the sterile insect technique (SIT) which relies on releasing sterile males of the target insect. Mating between wild females and sterile males results in no viable offspring. A crucial aspect of SIT is the production of a large number of sterile males with a low presence of females that can bite and transmit viruses. The present thesis aimed to find, implement and study the most reliable mechanical sex sorter and protocol to implement male productivity and reduce female contamination. In addition, I evaluated different variables and sorting protocols to enable female recovery for breeding purposes. Furthermore, I studied the creation of a hyper-protandric strain potentially able to produce only males. I also assessed the integration of artificial intelligence with an optical unit to identify sexes at the adult stage. All these applications helped to realise a mass production model in Italy with a potential weekly production of 1 million males. Moreover, I studied and applied for aerial sterile male release in an urban environment. This technology could allow the release of males in a wide area, overcoming environmental and urban obstacles. However, the development and application of drone technologies in a metropolitan area close to airports, such as in Bologna area, must fit specific requirements. Lastly, at Réunion Island, during a Short Term Scientific Mission France (AIM-COST Action), Indian Ocean, I studied the Boosted SIT application. Coating sterile males with Pyriproxyfen may help spread the insecticide into the larval breeding sites.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.