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Optimization of the power electronics system associated with ocean wave electric generators
Get PDFThe present thesis is focused on wave energy, which is a particular kind of ocean energy, and is based on the activity carried out during the EU project SEA TITAN. The main scope of this work is the design of a power electronic section for an innovative wave energy extraction system based on a switched-reluctance machine.
In the first chapter, the general features of marine wave energy harvesting are treated. The concept of Wave Energy Converter (WEC) is introduced as well as the mathematical description of the waves, their characterization and measurement, the WEC classification, the operating principles and the standardization framework. Also, detailed considerations on the environmental impact are presented. The SEA TITAN project is briefly described.
The second chapter is dedicated to the technical issues of the SEA TITAN project, such as the operating principle, the performance optimization carried out in the project, the main innovations as well as interesting demonstrations on the behavior of the generator and its control.
In the third chapter, the power electronics converters of SEA TITAN are described, and the design choices, procedures and calculations are shown, with a further insight into the application given by analyzing the MATLAB Simulink model of the system and its control scheme.
Experimental tests are reported in the fourth chapter, with graphs and illustrations of the power electronic apparatus interfaced with the real machine.
Finally, the conclusion in the fifth chapter offers a global overview of the project and opens further development pathways
A distributed middleware for IT/OT convergence in modern industrial environments
Get PDFThe modern industrial environment is populated by a myriad of intelligent devices that collaborate for the accomplishment of the numerous business processes in place at the production sites. The close collaboration between humans and work machines poses new interesting challenges that industry must overcome in order to implement the new digital policies demanded by the industrial transition.
The Industry 5.0 movement is a companion revolution of the previous Industry 4.0, and it relies on three characteristics that any industrial sector should have and pursue: human centrality, resilience, and sustainability. The application of the fifth industrial revolution cannot be completed without moving from the implementation of Industry 4.0-enabled platforms. The common feature found in the development of this kind of platform is the need to integrate the Information and Operational layers. Our thesis work focuses on the implementation of a platform addressing all the digitization features foreseen by the fourth industrial revolution, making the IT/OT convergence inside production plants an improvement and not a risk. Furthermore, we added modular features to our platform enabling the Industry 5.0 vision. We favored the human centrality using the mobile crowdsensing techniques and the reliability and sustainability using pluggable cloud computing services, combined with data coming from the crowd support.
We achieved important and encouraging results in all the domains in which we conducted our experiments. Our IT/OT convergence-enabled platform exhibits the right performance needed to satisfy the strict requirements of production sites. The multi-layer capability of the framework enables the exploitation of data not strictly coming from work machines, allowing a more strict interaction between the company, its employees, and customers
Valorization of by-products from olive oil industry and their utilization for innovative food formulation
Get PDFThe purpose of this thesis work was the valorization of the main by-products obtained from olive oil production chain (wastewater and pomace) and their utilization in innovative food formulation. In the first part of the thesis, an olive mill wastewater extract rich in phenols were used in the formulation of 3 innovative meat products: beef hamburgers, cooked ham and würstels. These studies confirms that olive mill wastewaters extract rich in phenols could be an alternative for the reduction/total replacement of additives (i.e., nitrites) in ground and cooked meat preparations, which would promote the formulation of healthier clean label products and improve the sustainability of the olive oil industry with a circular economy approach, by further valorizing this olive by-product. In the second part of the thesis, the lipid composition and oxidative stability of a spreadable product obtained from a fermented and biologically de-bittered olive pomace, was assessed during a shelf-life study. This study confirmed that olive pomace represents an excellent ingredient for the formulation of functional foods
In the third and last part of the thesis, carried out at the Universidad de Navarra (Pamplona, Spain), during a period abroad (3 months), three extracts obtained from purification of olive mill wastewaters, were subjected to in-vitro digestion and characterized. From the analysis of the three phenolic extracts, it emerged that the most promising extract to be used in the food field is the spry-dried one. Thanks to its formulation containing maltodextrins it manages to maintain its antioxidant capacity even after being underwent to in-vitro digestion.
This thesis work is a part of the PRIN 2015 project (PROT: 20152LFKAT) "Olive phenols as multifunctional bioactives for healthier food: evaluation of simplified formulation to obtain safe meat products and new foods with higher functionality", coordinated by University of Perugia
Applications of network-based approaches in drug research
Get PDFRecent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks.
Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs.
The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases.
To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information
Characterization of first sorption cycle of white Portland cement by 1H NMR
Get PDFThe work carried out is focused on the exploration of processes occurring in cement materials during sorption cycles by using Nuclear Magnetic Resonance (NMR) relaxometry. Long (months) and short (days-weeks) sorption cycles of cement materials were explored. The long cycle consists of around 6 months of drying and re-wetting cement samples of different sizes and water-to-cement (w/c) ratios in a homemade relative humidity (RH) chamber. Short cycles were performed by drying samples of different sizes and w/c ratios in the oven at 60 ˚C and re-wetting underwater. Different NMR techniques, such as one- and two-dimensional relaxometry and solid-signal analyses, were used to study the samples.
Firstly, by the interpretation of quasi-continuous distributions of T2 relaxation time, we demonstrated that some reversible and irreversible changes concerning smaller porosity happened during the first sorption cycle.
Secondly, using 2D NMR and a new 2D NMR inversion algorithm we showed preliminary results on the cement T1-T2 maps. Data obtained during sorption processes indicated possible water exchange between different pore populations inside the cement samples.
Thirdly, the solid structure of cement samples was qualitatively investigated with T1 measurements and, as far as we know, for the first time interpreted with the Pake-Doublet theory. Changes in the solid structure were observed. Precisely variations of the amount of Ettringite during drying/wetting were proposed to take place.
Finally, a work on NMR single-sided equipment design for in situ cement investigation was shown. The multi-cubic-blocks magnet structure design was performed using different specific CAD software, and the magnetic fields generated by RF coils of different geometries were investigated using a customized Matlab script.
The single-sided NMR instrument equipped with the designed single-sided magnet and coil was built by the ERICA partner company MR Solutions (Abingdon, UK), and the preliminary results resultsated the correctness of the developed design
Holocaust postmemory in contemporary anglophone children's literature
Get PDFThis dissertation discusses contemporary Anglophone children’s literature representing the Holocaust and it claims that, through the reading of historical novels, children can acquire a specific kind of postmemory, which I call ‘attitudinal postmemory’. The works analyzed have been written by ‘non-related’ authors, meaning writers who are not witnesses nor their descendants. Attitudinal postmemory is based on the readers’ establishment of a personal-emotional link with the Holocaust by means of narrative empathy towards the characters; it is an ‘active’ kind of memory because it will hopefully convert into an informed, respectful attitude towards peers that opposes the Nazi ideology.
The dissertation is structured into two main parts. Part One provides an overview of the origins and development of Holocaust memory in Western countries. Chapter 1 introduces two major historiographical-literary debates and the following chapter discusses three main issues concerning the representation of the Holocaust (naming, the need to represent, and the ‘right to’ represent) while considering the forms and genres traditionally used and considered ‘appropriate’. Focusing on the scope of literary narratives, Chapter 3 explains how the presence of a personal-emotional link is essential to acquire Holocaust postmemory and, in particular, attitudinal postmemory. The criteria adopted with regard to the case studies are described in Chapter 4.
Part Two discusses the process of interweaving historical truth with fiction and how historical fiction helps child readers acquire attitudinal postmemory. After a brief overview of the genre in Chapter 5, Chapter 6 probes how it is possible to meet the two main expectations of historical fiction while avoiding a disrespectful stance towards the Holocaust. Chapter 7 discusses the idea of empathy and some issues in the representation of Nazi evil, while Chapter 8 offers a comparative analysis of the case studies proposed, including authors from the UK, Ireland, Australia, and the USA
Evaluation of the pathogenetic role of cellular subsets of the Follicular Lymphoma Microenvironment
Get PDFFollicular lymphoma (FL) is a B cell neoplasm, composed of follicle center cells, that accounts for about 20% of all lymphomas, with the highest incidence reported in the USA and western Europe. FL has been considered a virtually incurable disease, with a high response rate alternated with frequent post-therapy relapses or progression towards more aggressive lymphomas. Due to the extreme variability in outcome, many efforts were made to predict prognosis, the need for therapy, and the likelihood of evolution.
Even if clinical scores turned out to be robust and easy to use in clinical practice for patient risk stratification, marked heterogeneity in outcome remains within each group and further insights into the biology of FL are needed. The genome-wide approach underscored the pivotal role of the FL microenvironment in the evolution of the disease. In 2004, a landmark study by Dave et al. first described the microenvironment impact on tumor biology. By gene expression profiling they identified two different immune response signatures, involving T-cells and macrophages which seemed to independently predict FL outcome, but their exact is not completely understood and different studies led to variable results. Subsequently, many workgroups identified in amount and distribution pattern of these different cell subsets features which can impact prognosis, this leading to hypothesizing the use of these parameters as surrogate markers of the molecular signature.
We aimed to assess the possible contributions of micro-environmental components to FL transformation or progression, its relevance as a prognostic/predictive tool, and its potential role as an innovative therapeutic target. We used immunohistochemical techniques, focusing specifically on macrophages and T-cells subsets, and then found correlations between the presence, proportions, and distribution of these reactive cells and the clinical outcomes leading to the future development of a reliable tool for upfront risk stratification of patients affected by FL
Development and characterization of deep learning techniques for neuroimaging data
Get PDFDeep 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
Towards eco-friendly batteries: concepts for lithium and sodium ion batteries
Get PDFSeveral possibilities are arising aiming the development of “greener”, more sustainable energy storage systems. One point is the completely water-based processing of battery electrodes, thus being able to renounce the use of toxic solvents in the preparation process. Despite its advantage of lower cost and eco-friendlyness, there is the need of similar mechanical and electrochemichal behavior for boosting this preparation mode.
Another point – accompanying the water-based processing - is the replacement of solvent-based polymer binders by water-based ones. These binders can be based on fluorinated, crude-oil based polymers on the one side, but also on naturally abundant and economic friendly biopolymers.
The most common anode materials, graphite and lithium titanate (LTO), have been subjected a water-based preparation route with different binder systems. LTO is a promising anode material for lithium ion batteries (LIBs), as it shows excellent safety characteristics, does not form a significant SEI and its volume change upon intercalation of lithium ions is negligible. Unfortunately, this material suffers from a rather low electric conductivity - that is why an intensive study on improved current collector surfaces for LTO electrodes was performed.
In order to go one step ahead towards sustainable energy storage, anode and cathode active materials for a sodium ion battery were synthesized. Anode active material resulted in a successful product which was then subjected to further electrochemical tests.
In this PhD work the development of “greener” energy storage possibilities is tested under several aspects. The ecological impact of raw materials and required battery components is examined in detail
Models and Simulations of Diamond-like Carbon for large-area high voltage power diodes
Get PDFSilicon-based discrete high-power devices need to be designed with optimal performance up to several thousand volts and amperes to reach power ratings ranging from few kWs to beyond the 1 GW mark. To this purpose, a key element is the improvement of the junction termination (JT) since it allows to drastically reduce surface electric field peaks which may lead to an earlier device failure. This thesis will be mostly focused on the negative bevel termination which from several years constitutes a standard processing step in bipolar production lines. A simple methodology to realize its counterpart, a planar JT with variation of the lateral doping concentration (VLD) will be also described. On the JT a thin layer of a semi insulating material is usually deposited, which acts as passivation layer reducing the interface defects and contributing to increase the device reliability. A thorough understanding of how the passivation layer properties affect the breakdown voltage and the leakage current of a fast-recovery diode is fundamental to preserve the ideal termination effect and provide a stable blocking capability. More recently, amorphous carbon, also called diamond-like carbon (DLC), has been used as a robust surface passivation material. By using a commercial TCAD tool, a detailed physical explanation of DLC electrostatic and transport properties has been provided. The proposed approach is able to predict the breakdown voltage and the leakage current of a negative beveled power diode passivated with DLC as confirmed by the successfully validation against the available experiments. In addition, the VLD JT proposed to overcome the limitation of the negative bevel architecture has been simulated showing a breakdown voltage very close to the ideal one with a much smaller area consumption. Finally, the effect of a low junction depth on the formation of current filaments has been analyzed by performing reverse-recovery simulations