Media activism on and off line by Italian adolescents and young adults

From several researchers it appears that Italian adolescents and young people are grown up with commercial television which is accused to contain too much violence, sex, reality shows, advertising, cartoons which are watched from 1 to 4 hours daily. Adolescents are also great users of mobile phones and spend a lot of time to use it. Their academic results are below the average of Ocse States. However the widespread use of communication technology and social networks display also another side of adolescents who engage in media activism and political movement such as Ammazzateci tutti!, Indymedia, Movimento 5 Stelle, Movimento No Tav. In which way does the world economic crisis -with the specific problems of Italy as the cutting founds for school, academic research and welfare, the corruption of political class, mafia and camorra organisation induce a reaction in our adolescents and young people? Several researches inform us about their use of internet in terms of spending time but, more important, how internet, and the web 2.0, could be an instrument for their reaction? What do they do online? How they do it? Which is the meaning of their presence online? And, has their online activity a continuity offline? The research aims are: 1. Trough a participant observation of Social Network profiles opened by 10 young active citizens, I would seek to understand which kind of social or political activities they engage in online as individuals and which is the meaning of their presence online. 2. To observe and understand if adolescents and young people have a continuity of their socio-political engagement online in offline activities and which kind of experiences it is. 3. Try to comprehend which was (or which were) the significant, learning experiences that convinced them about the potential of the web as tool for their activism.

Impact of Protein Induced by Vitamin K Absence (PIVKA-II) in diagnosis and monitoring of patients with hepatocellular carcinoma

Background and Aims: Hepatocellular carcinoma (HCC) represents the second leading cause of cancer deaths worldwide. Protein induced by vitamin K absence (PIVKA-II) has been proposed as potential screening biomarker for HCC.This study has been designed to evaluate the role of PIVKA-II as diagnostic HCC marker, through the comparison between PIVKA-II and alpha-fetoprotein (AFP) serum levels on HCC patients and the two control groupsof patients with liver disease and without HCC. Methods: In an Italian prospective cohort, PIVKA-II levels were assessed on serum samplesby an automated chemiluminescent immunoassay (Abbott ARCHITECT). The study population included 65 patients with HCC (both “de novo” and recurrent), 111 with liver cirrhosis (LC) and 111 with chronic hepatitis C (CHC). Results: PIVKA-II levels were increased in patients with HCC (median 63.75, range: 12-2675 mAU/mL) compared to LC (median value: 30.95, range: 11.70–1251mAU / mL, Mann Whitney test p < 0.0001) and CHC (median value: 24.89, range: 12.98-67.68mAU / mL, p < 0.0001).The area under curve (AUC) for PIVKA-II was 0.817 (95% Confidence Interval(CI), 0.752-0.881). At the optimal threshold of 37 mAU / mL, identified by the Youden Index, the sensitivity and specificity were 79% and 76%, respectively. PIVKA-II was a better biomarker than AFP for the diagnosis of HCC, since the AUC for AFP was 0.670 (95% CI 0.585-0.754, p<0.0001) and at the best cutoff of 16.4 ng / mL AFP yielded 98% specificity but only 34% sensitivity. Conclusions:These initial data suggest the potential utility of this tool in the diagnosis of HCC.PIVKA-II alone or in combination may help to an early diagnosis of HCC and a significant optimization of patient management.

New target tracking and monitoring guidance laws for UAV

A pursuer UAV tracking and loitering around a target is the problem analyzed in this thesis. The UAV is assumed to be a fixed-wing vehicle and constant airspeed together with bounded lateral accelerations are the main constraints of the problem. Three different guidance laws are designed for ensuring a continuos overfly on the target. Different proofs are presented to demonstrate the stability properties of the laws. All the algorithms are tested on a 6DoF Pioneer software simulator. Classic control design methods have been adopted to develop autopilots for implementig the simulation platform used for testing the guidance laws.

Comparison between analytical and numerical methods for the assessment of masonry arch bridges: Case study of Clemente Bridge on Savio river

This research has focused on the study of the behavior and of the collapse of masonry arch bridges. The latest decades have seen an increasing interest in this structural type, that is still present and in use, despite the passage of time and the variation of the transport means. Several strategies have been developed during the time to simulate the response of this type of structures, although even today there is no generally accepted standard one for assessment of masonry arch bridges. The aim of this thesis is to compare the principal analytical and numerical methods existing in literature on case studies, trying to highlight values and weaknesses. The methods taken in exam are mainly three: i) the Thrust Line Analysis Method; ii) the Mechanism Method; iii) the Finite Element Methods. The Thrust Line Analysis Method and the Mechanism Method are analytical methods and derived from two of the fundamental theorems of the Plastic Analysis, while the Finite Element Method is a numerical method, that uses different strategies of discretization to analyze the structure. Every method is applied to the case study through computer-based representations, that allow a friendly-use application of the principles explained. A particular closed-form approach based on an elasto-plastic material model and developed by some Belgian researchers is also studied. To compare the three methods, two different case study have been analyzed: i) a generic masonry arch bridge with a single span; ii) a real masonry arch bridge, the Clemente Bridge, built on Savio River in Cesena. In the analyses performed, all the models are two-dimensional in order to have results comparable between the different methods taken in exam. The different methods have been compared with each other in terms of collapse load and of hinge positions.

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.

Hybrid constraint programming and metaheuristic methods for large scale optimization problems

This work presents hybrid Constraint Programming (CP) and metaheuristic methods for the solution of Large Scale Optimization Problems; it aims at integrating concepts and mechanisms from the metaheuristic methods to a CP-based tree search environment in order to exploit the advantages of both approaches. The modeling and solution of large scale combinatorial optimization problem is a topic which has arisen the interest of many researcherers in the Operations Research field; combinatorial optimization problems are widely spread in everyday life and the need of solving difficult problems is more and more urgent. Metaheuristic techniques have been developed in the last decades to effectively handle the approximate solution of combinatorial optimization problems; we will examine metaheuristics in detail, focusing on the common aspects of different techniques. Each metaheuristic approach possesses its own peculiarities in designing and guiding the solution process; our work aims at recognizing components which can be extracted from metaheuristic methods and re-used in different contexts. In particular we focus on the possibility of porting metaheuristic elements to constraint programming based environments, as constraint programming is able to deal with feasibility issues of optimization problems in a very effective manner. Moreover, CP offers a general paradigm which allows to easily model any type of problem and solve it with a problem-independent framework, differently from local search and metaheuristic methods which are highly problem specific. In this work we describe the implementation of the Local Branching framework, originally developed for Mixed Integer Programming, in a CP-based environment. Constraint programming specific features are used to ease the search process, still mantaining an absolute generality of the approach. We also propose a search strategy called Sliced Neighborhood Search, SNS, that iteratively explores slices of large neighborhoods of an incumbent solution by performing CP-based tree search and encloses concepts from metaheuristic techniques. SNS can be used as a stand alone search strategy, but it can alternatively be embedded in existing strategies as intensification and diversification mechanism. In particular we show its integration within the CP-based local branching. We provide an extensive experimental evaluation of the proposed approaches on instances of the Asymmetric Traveling Salesman Problem and of the Asymmetric Traveling Salesman Problem with Time Windows. The proposed approaches achieve good results on practical size problem, thus demonstrating the benefit of integrating metaheuristic concepts in CP-based frameworks.

Melanocytes: a new potential tool to study and monitoring Duchenne muscular dystrophy

Dystrophin is a subsarcolemmal protein critical for the integrity of muscle fibers by linking the actin cytoskeleton to the extracellular matrix via the dystroglycan complex. It is reported that dystroglycans are also localized in the skin, at dermal-epidermal junction. Here we show that epidermal melanocytes express dystrophin at the interface with the basement membrane. The full-length muscle isoform mDp427 was clearly detectable in epidermis and in melanocyte cultures as assessed by RNA and western blot analysis. Dystrophin was absent in Duchenne Muscular Dystrophy (DMD) patients melanocytes, and the ultrastructural analysis revealed mitochondrial alterations, similar to those occurring in myoblasts from the same patients. Interestingly, mitochondrial dysfunction of DMD melanocytes reflected the alterations identified in dystrophin-deficient muscle cells. In fact, mitochondria of melanocytes from DMD patients accumulated tetramethylrhodamine methyl ester but, on the contrary of control donor, mitochondria of DMD patients readily depolarized upon the addition of oligomycin, suggesting either that they are maintaining the membrane potential at the expense of glycolytic ATP, or that they are affected by a latent dysfunction unmasked by inhibition of the ATP synthase. Melanocyte cultures can be easily obtained by conventional skin biopsies, less invasive procedure than muscular biopsy, so that they may represent an alternative cellular model to myoblast for studying and monitoring dystrophinopathies also in response to pharmacological treatments.

Effect of training systems and pruning methods on fruit quality in apple

The introduction of dwarfed rootstocks in apple crop has led to a new concept of intensive planting systems with the aim of producing early high yield and with returns of the initial high investment. Although yield is an important aspect to the grower, the consumer has become demanding regards fruit quality and is generally attracted by appearance. To fulfil the consumer’s expectations the grower may need to choose a proper training system along with an ideal pruning technique, which ensure a good light distribution in different parts of the canopy and a marketable fruit quality in terms of size and skin colour. Although these aspects are important, these fruits might not reach the proper ripening stage within the canopy because they are often heterogeneous. To describe the variability present in a tree, a software (PlantToon®), was used to recreate the tree architecture in 3D in the two training systems. The ripening stage of each of the fruits was determined using a non-destructive device (DA-Meter), thus allowing to estimate the fruit ripening variability. This study deals with some of the main parameters that can influence fruit quality and ripening stage within the canopy and orchard management techniques that can ameliorate a ripening fruit homogeneity. Significant differences in fruit quality were found within the canopies due to their position, flowering time and bud wood age. Bi-axis appeared to be suitable for high density planting, even though the fruit quality traits resulted often similar to those obtained with a Slender Spindle, suggesting similar fruit light availability within the canopies. Crop load confirmed to be an important factor that influenced fruit quality as much as the interesting innovative pruning method “Click”, in intensive planting systems.

Numerical and Analytical Methods for Laser-Plasma Acceleration Physics

Theories and numerical modeling are fundamental tools for understanding, optimizing and designing present and future laser-plasma accelerators (LPAs). Laser evolution and plasma wave excitation in a LPA driven by a weakly relativistically intense, short-pulse laser propagating in a preformed parabolic plasma channel, is studied analytically in 3D including the effects of pulse steepening and energy depletion. At higher laser intensities, the process of electron self-injection in the nonlinear bubble wake regime is studied by means of fully self-consistent Particle-in-Cell simulations. Considering a non-evolving laser driver propagating with a prescribed velocity, the geometrical properties of the non-evolving bubble wake are studied. For a range of parameters of interest for laser plasma acceleration, The dependence of the threshold for self-injection in the non-evolving wake on laser intensity and wake velocity is characterized. Due to the nonlinear and complex nature of the Physics involved, computationally challenging numerical simulations are required to model laser-plasma accelerators operating at relativistic laser intensities. The numerical and computational optimizations, that combined in the codes INF&RNO and INF&RNO/quasi-static give the possibility to accurately model multi-GeV laser wakefield acceleration stages with present supercomputing architectures, are discussed. The PIC code jasmine, capable of efficiently running laser-plasma simulations on Graphics Processing Units (GPUs) clusters, is presented. GPUs deliver exceptional performance to PIC codes, but the core algorithms had to be redesigned for satisfying the constraints imposed by the intrinsic parallelism of the architecture. The simulation campaigns, run with the code jasmine for modeling the recent LPA experiments with the INFN-FLAME and CNR-ILIL laser systems, are also presented.

Untangle sustainable development goal 8 through data visualization and HCI methods

Following the approval of the 2030 Agenda for Sustainable Development in 2015, sustainability became a hotly debated topic. In order to build a better and more sustainable future by 2030, this agenda addressed several global issues, including inequality, climate change, peace, and justice, in the form of 17 Sustainable Development Goals (SDGs), that should be understood and pursued by nations, corporations, institutions, and individuals. In this thesis, we researched how to exploit and integrate Human-Computer Interaction (HCI) and Data Visualization to promote knowledge and awareness about SDG 8, which wants to encourage lasting, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. In particular, we focused on three targets: green economy, sustainable tourism, employment, decent work for all, and social protection. The primary goal of this research is to determine whether HCI approaches may be used to create and validate interactive data visualization that can serve as helpful decision-making aids for specific groups and raise their knowledge of public-interest issues. To accomplish this goal, we analyzed four case studies. In the first two, we wanted to promote knowledge and awareness about green economy issues: we investigated the Human-Building Interaction inside a Smart Campus and the dematerialization process inside a University. In the third, we focused on smart tourism, investigating the relationship between locals and tourists to create meaningful connections and promote more sustainable tourism. In the fourth, we explored the industry context to highlight sustainability policies inside well-known companies. This research focuses on the hypothesis that interactive data visualization tools can make communities aware of sustainability aspects related to SDG8 and its targets. The research questions addressed are two: "how to promote awareness about SDG8 and its targets through interactive data visualizations?" and "to what extent are these interactive data visualizations effective?".

Diabete mellito nel cane: terapia, monitoraggio e aspetti prognostici

Il diabete mellito (DM) è una delle malattie endocrine più comuni nel cane. Una volta raggiunta la diagnosi di DM, è necessario iniziare un trattamento insulinico nonché una dieta specifica, al fine di controllare i livelli di glucosio nel sangue e di conseguenza i segni clinici. Inoltre, al fine di ottenere un buon controllo glicemico, è essenziale garantire uno stretto monitoraggio terapeutico. Nella presente tesi sono riportati numerosi studi relativi a trattamento, monitoraggio e prognosi dei cani con DM. Il capitolo 2 è una review che illustra i principali aspetti terapeutici e di monitoraggio del DM. Il capitolo 3 riporta uno studio che confronta l'efficacia e la sicurezza dell'insulina Lenta e dell'insulina Neutra Protamine Hagedorn (NPH). I metodi di monitoraggio per cani con DM possono essere classificati in diretti od indiretti. I metodi di monitoraggio diretto includono misurazioni serali della glicemia o monitoraggio continuo del glucosio interstiziale tramite appositi dispositivi (Continuous Glucose Monitoring System, CGMS). Le modalità indirette comprendono la valutazione dell'assunzione di acqua e del peso corporeo, la quantificazione del glucosio/chetoni nelle urine e la misurazione delle concentrazioni di proteine glicate. Il capitolo 4 mostra uno studio volto a valutare l'accuratezza e la precisione di un glucometro e un glucometro/chetometro nel cane. Il Flash Glucose Monitoring system è un CGMS recentemente validato per l'uso nel cane; la sua utilità clinica nel monitoraggio del DM canino è esaminata nel capitolo 5. Il capitolo 6 descrive uno studio in cui si validano 2 metodi analitici per la misurazione delle fruttosamine sieriche e dell'emoglobina glicata nel cane e confronta l’utilità delle due proteine glicate nel definire il controllo glicemico. Infine, il capitolo 7 riporta uno studio finalizzato a determinare il tempo di sopravvivenza e ad identificare il valore prognostico di diverse variabili cliniche e clinico-patologiche nei cani con DM.

Hybrid Methods for Resource Allocation and Scheduling Problems in Deterministic and Stochastic Environments

This work presents exact, hybrid algorithms for mixed resource Allocation and Scheduling problems; in general terms, those consist into assigning over time finite capacity resources to a set of precedence connected activities. The proposed methods have broad applicability, but are mainly motivated by applications in the field of Embedded System Design. In particular, high-performance embedded computing recently witnessed the shift from single CPU platforms with application-specific accelerators to programmable Multi Processor Systems-on-Chip (MPSoCs). Those allow higher flexibility, real time performance and low energy consumption, but the programmer must be able to effectively exploit the platform parallelism. This raises interest in the development of algorithmic techniques to be embedded in CAD tools; in particular, given a specific application and platform, the objective if to perform optimal allocation of hardware resources and to compute an execution schedule. On this regard, since embedded systems tend to run the same set of applications for their entire lifetime, off-line, exact optimization approaches are particularly appealing. Quite surprisingly, the use of exact algorithms has not been well investigated so far; this is in part motivated by the complexity of integrated allocation and scheduling, setting tough challenges for ``pure'' combinatorial methods. The use of hybrid CP/OR approaches presents the opportunity to exploit mutual advantages of different methods, while compensating for their weaknesses. In this work, we consider in first instance an Allocation and Scheduling problem over the Cell BE processor by Sony, IBM and Toshiba; we propose three different solution methods, leveraging decomposition, cut generation and heuristic guided search. Next, we face Allocation and Scheduling of so-called Conditional Task Graphs, explicitly accounting for branches with outcome not known at design time; we extend the CP scheduling framework to effectively deal with the introduced stochastic elements. Finally, we address Allocation and Scheduling with uncertain, bounded execution times, via conflict based tree search; we introduce a simple and flexible time model to take into account duration variability and provide an efficient conflict detection method. The proposed approaches achieve good results on practical size problem, thus demonstrating the use of exact approaches for system design is feasible. Furthermore, the developed techniques bring significant contributions to combinatorial optimization methods.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.