Analysis of optimal control problems for the incompressible MHD equations and implementation in a finite element multiphysics code

This thesis deals with the study of optimal control problems for the incompressible Magnetohydrodynamics (MHD) equations. Particular attention to these problems arises from several applications in science and engineering, such as fission nuclear reactors with liquid metal coolant and aluminum casting in metallurgy. In such applications it is of great interest to achieve the control on the fluid state variables through the action of the magnetic Lorentz force. In this thesis we investigate a class of boundary optimal control problems, in which the flow is controlled through the boundary conditions of the magnetic field. Due to their complexity, these problems present various challenges in the definition of an adequate solution approach, both from a theoretical and from a computational point of view. In this thesis we propose a new boundary control approach, based on lifting functions of the boundary conditions, which yields both theoretical and numerical advantages. With the introduction of lifting functions, boundary control problems can be formulated as extended distributed problems. We consider a systematic mathematical formulation of these problems in terms of the minimization of a cost functional constrained by the MHD equations. The existence of a solution to the flow equations and to the optimal control problem are shown. The Lagrange multiplier technique is used to derive an optimality system from which candidate solutions for the control problem can be obtained. In order to achieve the numerical solution of this system, a finite element approximation is considered for the discretization together with an appropriate gradient-type algorithm. A finite element object-oriented library has been developed to obtain a parallel and multigrid computational implementation of the optimality system based on a multiphysics approach. Numerical results of two- and three-dimensional computations show that a possible minimum for the control problem can be computed in a robust and accurate manner.

Aging of nuclear power plant cables: in search of non-destructive diagnostic quantities

The safety systems of nuclear power plants rely on low-voltage power, instrumentation and control cables. Inside the containment area, cables operate in harsh environments, characterized by relatively high temperature and gamma-irradiation. As these cables are related to fundamental safety systems, they must be able to withstand unexpected accident conditions and, therefore, their condition assessment is of utmost importance as plants age and lifetime extensions are required. Nowadays, the integrity and functionality of these cables are monitored mainly through destructive test which requires specific laboratory. The investigation of electrical aging markers which can provide information about the state of the cable by non-destructive testing methods would improve significantly the present diagnostic techniques. This work has been made within the framework of the ADVANCE (Aging Diagnostic and Prognostics of Low-Voltage I\&C Cables) project, a FP7 European program. This Ph.D. thesis aims at studying the impact of aging on cable electrical parameters, in order to understand the evolution of the electrical properties associated with cable degradation. The identification of suitable aging markers requires the comparison of the electrical property variation with the physical/chemical degradation mechanisms of polymers for different insulating materials and compositions. The feasibility of non-destructive electrical condition monitoring techniques as potential substitutes for destructive methods will be finally discussed studying the correlation between electrical and mechanical properties. In this work, the electrical properties of cable insulators are monitored and characterized mainly by dielectric spectroscopy, polarization/depolarization current analysis and space charge distribution. Among these techniques, dielectric spectroscopy showed the most promising results; by means of dielectric spectroscopy it is possible to identify the frequency range where the properties are more sensitive to aging. In particular, the imaginary part of permittivity at high frequency, which is related to oxidation, has been identified as the most suitable aging marker based on electrical quantities.

Teaching informatics to novices: big ideas and the necessity of optimal guidance

This thesis reports on the two main areas of our research: introductory programming as the traditional way of accessing informatics and cultural teaching informatics through unconventional pathways. The research on introductory programming aims to overcome challenges in traditional programming education, thus increasing participation in informatics. Improving access to informatics enables individuals to pursue more and better professional opportunities and contribute to informatics advancements. We aimed to balance active, student-centered activities and provide optimal support to novices at their level. Inspired by Productive Failure and exploring the concept of notional machine, our work focused on developing Necessity Learning Design, a design to help novices tackle new programming concepts. Using this design, we implemented a learning sequence to introduce arrays and evaluated it in a real high-school context. The subsequent chapters discuss our experiences teaching CS1 in a remote-only scenario during the COVID-19 pandemic and our collaborative effort with primary school teachers to develop a learning module for teaching iteration using a visual programming environment. The research on teaching informatics principles through unconventional pathways, such as cryptography, aims to introduce informatics to a broader audience, particularly younger individuals that are less technical and professional-oriented. It emphasizes the importance of understanding informatics's cultural and scientific aspects to focus on the informatics societal value and its principles for active citizenship. After reflecting on computational thinking and inspired by the big ideas of science and informatics, we describe our hands-on approach to teaching cryptography in high school, which leverages its key scientific elements to emphasize its social aspects. Additionally, we present an activity for teaching public-key cryptography using graphs to explore fundamental concepts and methods in informatics and mathematics and their interdisciplinarity. In broadening the understanding of informatics, these research initiatives also aim to foster motivation and prime for more professional learning of informatics.

[In search of] Digital biomarkers for objective pain assessment

Pain is a highly complex phenomenon involving intricate neural systems, whose interactions with other physiological mechanisms are not fully understood. Standard pain assessment methods, relying on verbal communication, often fail to provide reliable and accurate information, which poses a critical challenge in the clinical context. In the era of ubiquitous and inexpensive physiological monitoring, coupled with the advancement of artificial intelligence, these new tools appear as the natural candidates to be tested to address such a challenge. This thesis aims to conduct experimental research to develop digital biomarkers for pain assessment. After providing an overview of the state-of-the-art regarding pain neurophysiology and assessment tools, methods for appropriately conditioning physiological signals and controlling confounding factors are presented. The thesis focuses on three different pain conditions: cancer pain, chronic low back pain, and pain experienced by patients undergoing neurorehabilitation. The approach presented in this thesis has shown promise, but further studies are needed to confirm and strengthen these results. Prior to developing any models, a preliminary signal quality check is essential, along with the inclusion of personal and health information in the models to limit their confounding effects. A multimodal approach is preferred for better performance, although unimodal analysis has revealed interesting aspects of the pain experience. This approach can enrich the routine clinical pain assessment procedure by enabling pain to be monitored when and where it is actually experienced, and without the involvement of explicit communication,. This would improve the characterization of the pain experience, aid in antalgic therapy personalization, and bring timely relief, with the ultimate goal of improving the quality of life of patients suffering from pain.

Study of the X(3872) state with the CMS experiment at LHC

The surprising discovery of the X(3872) resonance by the Belle experiment in 2003, and subsequent confirmation by BaBar, CDF and D0, opened up a new chapter of QCD studies and puzzles. Since then, detailed experimental and theoretical studies have been performed in attempt to determine and explain the proprieties of this state. Since the end of 2009 the world’s largest and highest-energy particle accelerator, the Large Hadron Collider (LHC), started its operations at the CERN laboratories in Geneva. One of the main experiments at LHC is CMS (Compact Muon Solenoid), a general purpose detector projected to address a wide range of physical phenomena, in particular the search of the Higgs boson, the only still unconfirmed element of the Standard Model (SM) of particle interactions and, new physics beyond the SM itself. Even if CMS has been designed to study high energy events, it’s high resolution central tracker and superior muon spectrometer made it an optimal tool to study the X(3872) state. In this thesis are presented the results of a series of study on the X(3872) state performed with the CMS experiment. Already with the first year worth of data, a clear peak for the X(3872) has been identified, and the measurement of the cross section ratio with respect to the Psi(2S) has been performed. With the increased statistic collected during 2011 it has been possible to study, in bins of transverse momentum, the cross section ratio between X(3872) and Psi(2S) and separate their prompt and non-prompt component.

Application of Deep Learning techniques in the search for BSM Higgs bosons in the $\mu\mu$ final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range.

Preclinical development of cancer vaccines

Triplex cell vaccine is a cancer immunopreventive cell vaccine that can prevent almost completely mammary tumor onset in HER-2/neu transgenic mice. A future translation of cancer immunoprevention from preclinical to clinical studies should take into account several aspects. The work reported in this thesis deals with the study of three of these aspects: vaccine schedule, activity in a therapeutic set-up and second-generation DNA vaccines. An important element in determining human acceptance and compliance of a treatment protocol is the number of vaccinations. In order to improve the vaccination schedule a minimal protocol was searched, i.e. a schedule consisting of a lower number of administrations than standard protocol but with a similar efficacy. A candidate optimal protocol was identified by the use of an in silico model, SimTriplex simulator. The in vivo test of this schedule in HER-2/neu transgenic mice only partially confirmed in silico predictions. This result shows that in silico models have the potential ability to aid in searching of optimal treatment protocols, provided that they will be further tuned on experimental data. As a further result this preclinical study highlighted that kinetic of antibody response plays a major role in determining cancer prevention, leading to the hypothesis of a threshold that must be reached rapidly and maintained lifetime. Early clinical trials would be performed in a therapeutic, rather than preventive, setting. Thus, the activity of Triplex vaccine was investigated against experimental lung metastases in HER-2/neu transgenic mice in order to evaluate if the immunopreventive Triplex vaccine could be effective also against a pre-existing tumor mass. This preclinical model of aggressive metastatic development showed that the vaccine was an efficient treatment also 4 for the cure of micrometastases. However the immune mechanisms activated against tumor mass were not antibody dependent, i.e. different from those preventing the onset of primary mammary carcinoma. DNA vaccines could be more easily used than cellular ones. A second generation of Triplex vaccine based on DNA plasmids was evaluated in an aggressive preclinical model (BALBp53neu female mice) and compared with the preventive ability of cellular Triplex vaccine. It was observed that Triplex DNA vaccine was as effective as Triplex cell vaccine, exploiting a more restricted immune stimulation.

Molecular approach to Neurodegenerative Disorders: Role of Nociceptin/Orphanin FQ - NOP System in Parkinson and Epigenetic Mechanism in Alzheimer's Disease

With life expectancies increasing around the world, populations are getting age and neurodegenerative diseases have become a global issue. For this reason we have focused our attention on the two most important neurodegenerative diseases: Parkinson’s and Alzheimer’s. Parkinson’s disease is a chronic progressive neurodegenerative movement disorder of multi-factorial origin. Environmental toxins as well as agricultural chemicals have been associated with PD. Has been observed that N/OFQ contributes to both neurotoxicity and symptoms associated with PD and that pronociceptin gene expression is up-regulated in rat SN of 6-OHDA and MPP induced experimental parkinsonism. First, we investigated the role of N/OFQ-NOP system in the pathogenesis of PD in an animal model developed using PQ and/or MB. Then we studied Alzheimer's disease. This disorder is defined as a progressive neurologic disease of the brain leading to the irreversible loss of neurons and the loss of intellectual abilities, including memory and reasoning, which become severe enough to impede social or occupational functioning. Effective biomarker tests could prevent such devastating damage occurring. We utilized the peripheral blood cells of AD discordant monozygotic twin in the search of peripheral markers which could reflect the pathology within the brain, and also support the hypothesis that PBMC might be a useful model of epigenetic gene regulation in the brain. We investigated the mRNA levels in several genes involve in AD pathogenesis, as well DNA methylation by MSP Real-Time PCR. Finally by Western Blotting we assess the immunoreactivity levels for histone modifications. Our results support the idea that epigenetic changes assessed in PBMCs can also be useful in neurodegenerative disorders, like AD and PD, enabling identification of new biomarkers in order to develop early diagnostic programs.

Statistical Physics and Modeling of Human Mobility

In this thesis, we extend some ideas of statistical physics to describe the properties of human mobility. By using a database containing GPS measures of individual paths (position, velocity and covered space at a spatial scale of 2 Km or a time scale of 30 sec), which includes the 2% of the private vehicles in Italy, we succeed in determining some statistical empirical laws pointing out "universal" characteristics of human mobility. Developing simple stochastic models suggesting possible explanations of the empirical observations, we are able to indicate what are the key quantities and cognitive features that are ruling individuals' mobility. To understand the features of individual dynamics, we have studied different aspects of urban mobility from a physical point of view. We discuss the implications of the Benford's law emerging from the distribution of times elapsed between successive trips. We observe how the daily travel-time budget is related with many aspects of the urban environment, and describe how the daily mobility budget is then spent. We link the scaling properties of individual mobility networks to the inhomogeneous average durations of the activities that are performed, and those of the networks describing people's common use of space with the fractional dimension of the urban territory. We study entropy measures of individual mobility patterns, showing that they carry almost the same information of the related mobility networks, but are also influenced by a hierarchy among the activities performed. We discover that Wardrop's principles are violated as drivers have only incomplete information on traffic state and therefore rely on knowledge on the average travel-times. We propose an assimilation model to solve the intrinsic scattering of GPS data on the street network, permitting the real-time reconstruction of traffic state at a urban scale.

De Novo Design of Foldamers for Preparation of Nanostructured Materials

This thesis deals with the synthesis and the conformation analysis of hybrid foldamers containing the 4-carboxyoxazolidin-2-one unit or related molecules, in which an imido-type function is obtained by coupling the nitrogen of the heterocycle with the carboxylic acid moiety of the next unit. The imide group is characterized by a nitrogen atom connected to an endocyclic and an exocyclic carbonyl, which tend always to adopt the trans conformation. As a consequence of this locally constrained disposition effect, these imide-type oligomers are forced to fold in ordered conformations. The synthetic approach is highly tuneable with endless variations, so, simply by changing the design and the synthesis, a wide variety of foldamers with the required properties may be prepared “on demand”. Thus a wide variety of unusual secondary structures and interesting supramolecular materials may be obtained with hybrid foldamers. The behaviour in the solid state of some of these compounds has been analyzed in detail, thus showing the formation of different kinds of supramolecular materials that may be used for several applications. A winning example is the production of a bolaamphiphilic gelators that may also be doped with small amounts of dansyl containing compounds, needed to show the cellular uptake into IGROV-1 cells, by confocal laser scanning microscopy. These gels are readily internalized by cells and are biologically inactive, making them very good candidates in the promising field of drug delivery. In the last part of the thesis, a particular attention was directed to the search of new scaffolds that behave as constrained amino acid mimetics, showing that tetramic acids derivatives could be good candidates for the synthesis and applications of molecules having an ordered secondary structure.

Analysis and modelling of the performance of technologies for flue gas treatment in Waste-to-Energy processes

Waste management represents an important issue in our society and Waste-to-Energy incineration plants have been playing a significant role in the last decades, showing an increased importance in Europe. One of the main issues posed by waste combustion is the generation of air contaminants. Particular concern is present about acid gases, mainly hydrogen chloride and sulfur oxides, due to their potential impact on the environment and on human health. Therefore, in the present study the main available technological options for flue gas treatment were analyzed, focusing on dry treatment systems, which are increasingly applied in Municipal Solid Wastes (MSW) incinerators. An operational model was proposed to describe and optimize acid gas removal process. It was applied to an existing MSW incineration plant, where acid gases are neutralized in a two-stage dry treatment system. This process is based on the injection of powdered calcium hydroxide and sodium bicarbonate in reactors followed by fabric filters. HCl and SO2 conversions were expressed as a function of reactants flow rates, calculating model parameters from literature and plant data. The implementation in a software for process simulation allowed the identification of optimal operating conditions, taking into account the reactant feed rates, the amount of solid products and the recycle of the sorbent. Alternative configurations of the reference plant were also assessed. The applicability of the operational model was extended developing also a fundamental approach to the issue. A predictive model was developed, describing mass transfer and kinetic phenomena governing the acid gas neutralization with solid sorbents. The rate controlling steps were identified through the reproduction of literature data, allowing the description of acid gas removal in the case study analyzed. A laboratory device was also designed and started up to assess the required model parameters.

Cognitive Assessment and Rehabilitation of subjects with Traumatic Brain Injury

This thesis regards the study and the development of new cognitive assessment and rehabilitation techniques of subjects with traumatic brain injury (TBI). In particular, this thesis i) provides an overview about the state of art of this new assessment and rehabilitation technologies, ii) suggests new methods for the assessment and rehabilitation and iii) contributes to the explanation of the neurophysiological mechanism that is involved in a rehabilitation treatment. Some chapters provide useful information to contextualize TBI and its outcome; they describe the methods used for its assessment/rehabilitation. The other chapters illustrate a series of experimental studies conducted in healthy subjects and TBI patients that suggest new approaches to assessment and rehabilitation. The new proposed approaches have in common the use of electroencefalografy (EEG). EEG was used in all the experimental studies with a different purpose, such as diagnostic tool, signal to command a BCI-system, outcome measure to evaluate the effects of a treatment, etc. The main achieved results are about: i) the study and the development of a system for the communication with patients with disorders of consciousness. It was possible to identify a paradigm of reliable activation during two imagery task using EEG signal or EEG and NIRS signal; ii) the study of the effects of a neuromodulation technique (tDCS) on EEG pattern. This topic is of great importance and interest. The emerged founding showed that the tDCS can manipulate the cortical network activity and through the research of optimal stimulation parameters, it is possible move the working point of a neural network and bring it in a condition of maximum learning. In this way could be possible improved the performance of a BCI system or to improve the efficacy of a rehabilitation treatment, like neurofeedback.