3D Si radiation sensors came along with extreme radiation hard properties, primarily owing to the geometrical advantages over planar sensors where electrodes are formed penetrating through the active substrate volume. Among them: reduction of the inter-electrode distance, lower depletion voltage requirement, inter-columnar high electric field distribution, lower trapping probability, faster charge collection capability, lower power dissipation, and lower inter-pitch charge sharing. Since several years, FBK has developed 3D sensors with a double-sided technology, that have also been installed in the ATLAS Insertable B-Layer at LHC. However, the future High-Luminosity LHC (HL-LHC) upgrades, aimed to be operational by 2024, impose a complete swap of current 3D detectors with more radiation hard sensor design, able to withstand very large particle fluences up to 2×1016 cm-2 1-MeV equivalent neutrons. The extreme luminosity conditions and related issues in occupancy and radiation hardness lead to very dense pixel granularity (50×50 or 25×100 μm2), thinner active region (~100 μm), narrower columnar electrodes (~5μm diameter) with reduced inter-electrode spacing (~30 μm), and very slim edges (~100 μm) into the 3D pixel sensor design. This thesis includes the development of this new generation of small-pitch and thin 3D radiation sensors aimed at the foreseen Inner Tracker (ITk) upgrades at HL-LHC.

The scarcity of freshwater is becoming a global challenge worldwide due to limited resources availability and increasing demand both for manufacturing and household use. For this reason, there is an important need to develop efficient, economic and sustainable desalination technologies able to take advantage of unconventional sources of water (seawater, brackish groundwater and wastewater) in order to produce freshwater. Sun is considered as the most promising abundant renewable (and free) energy source that can be employed in steam and vapor generation processes, which has a great importance in many applications such as: water desalination, domestic water heating, and power generation. This doctoral dissertation presents a study on the efficiency of different carbon based systems - nanofluids and hybrid natural composites - for the improvement of direct-solar evaporation systems, for the production of freshwater. The two main goals of this work consist of: (i) the synthesis and characterization of stable carbon-based nanofluids in water and of re-usable, economical and ecological hybrid composite materials, and (ii) the comparison of such carbon-based systems applied to water evaporation, understanding mechanisms, advantages and limitations. Carbon based materials (carbon black, graphene and multi-walled carbon nanotubes) were chosen because of their high sunlight absorption ability, unique thermal properties, as well as low cost and abundant availability. However, the hydrophobic character of such materials makes necessary to find efficient strategies to overcome this problem when dealing with water. In this work, the suspension stability of graphene-based nanofluids in water - a key parameter for the application of nanofluids in any field - was effectively improved by combining physical (by RF Sputtering coating) or chemical (by NaClO-NaBr solution) graphene surface modification treatments, and the use of common additives (Triton X-114, SDBS and gum arabic) showing different stabilization mechanisms. The best strategy to obtain long-time graphene suspension stability in water (both deionized water and saline solution with 3.5 wt% NaCl) turned out to be the combination of the easy chemical treatment with the electro-steric stabilization effect of gum arabic. In addition to nanofluids, a re-usable devices based on gum arabic cross-linked gelatin hydrogel were synthesized and characterized. Hydrophobic carbon-based materials were easily and uniformly embedded into the porous hydrogel matrix, thanks to the amphiphilic character of both gelatin and gum arabic. The effect of carbon-nanoparticles nature, morphology and concentration on the measured effective thermal conductivity of the composite material was studied and the thermal conductivity of the nanoparticles was evaluated applying several models based on the effective medium approach. The values obtained for the nanoparticles were far from the tabulated thermal conductivity values because of the combination of the composite features (such as nanoparticles concentration, Kapitza resistance) and the particles characteristics (such as aspect ratio, crystalline structure). The performance of carbon-based nanofluids and hybrid hydrogels on direct-solar evaporation of water was tested and compared to that of carbon-wood bilayer composite (which presents both hydrophilic character and natural channels for water transportation) under solar simulator. The effect of surface temperature, light-to-heat conversion efficiency of carbon-based materials, heat losses, water transport through a porous medium and suspension stability (in the case of nanofluids) were investigated in order to understand the advantages and limitations of such systems. All the tested systems were able to improve water evaporation rate and evaporation efficiency up to 70% and 82% under 1 sun and 2 suns respectively using a small amount of nanoparticles: the same amount of particles dispersed in nanofluid (0.01 wt%) was embedded into hydrogels or deposited onto wood. The high sunlight absorption ability of carbon-based nanoparticles appeared as a dominant parameter for the improvement of water evaporation rate. In fact, enhanced light absorption was directly related to a high photothermal conversion efficiency, which caused an improvement in the surface temperature, leading to a consequent enhancement in evaporation rate. It has been found that an adequate supply of water to the evaporation surface represents a fundamental parameter as well considering floating systems.

Condizioni meteorologiche e clima influenzano fortemente il settore delle produzioni animali. Lo stress da caldo determina una significativa riduzione dell'attività metabolica, della produzione, della capacità riproduttiva e una maggiore predisposizione alle malattie. Nel contesto biologico, la temperatura dell’aria è sicuramente considerata il principale fattore di stress, tuttavia, un’elevata umidità peggiora l'effetto della temperatura riducendo le perdite di calore per evaporazione e ostacolando quindi l’eliminazione del calore in eccesso. Scopi principali della ricerca sono stati la caratterizzazione dell’area del Mediterraneo in termini di Temperature Humidity Index (THI) e stabilire le relazioni tra stagione/THI e mortalità nella bovina da latte allevata in un’area geografica Italiana altamente vocata per questo tipo di allevamento. I risultati suggeriscono che, allevatori e politici che operano nell’area Mediterranea, dovrebbero tenere nella dovuta considerazione variabilità e scenari del THI nella pianificazione degli investimenti nel settore delle produzioni animali. Conoscere in anticipo il verificarsi di condizioni climatiche avverse permetterebbe agli allevatori di metter in atto misure di contrasto sugli effetti negativi delle condizioni climatiche. Inoltre, dovrebbero venire sviluppate misure di adattamento appropriate per contesti specifici in termini di cultura, società, o sistemi politici, che possano contribuire alla sostenibilità ambientale, nonché allo sviluppo economico e alla lotta alla povertà. / Weather and climate strongly influence the field of animal production. Heat stress causes a significant reduction in metabolic activity, production, reproductive capacity, and increases susceptibility to diseases. In the biological context, the air temperature is definitely considered the main factor of stress, however, high humidity worsens the effect of temperature by reducing the evaporation heat loss and thus preventing the removal of excess heat. The main purposes of the research were the characterization of the Mediterranean basin in terms of Temperature Humidity Index (THI) and to establish relationships between season/THI and mortality in dairy cattle bred in an Italian geographic area with a high concentration of dairy farms. THI variability and scenarios should be taken into careful consideration by farmers and policy makers operating in Mediterranean countries when planning investments. Investments should at least partially be directed to implementation of adaptation measures, which may support farmers in the transition to climate-smart agriculture and help them to alleviate the impact of hot extremes on animal welfare, performance and health. In addition, measures should be developed appropriate adaptation to the specific contexts in terms of culture, society, or political systems, which can contribute to environmental sustainability and economic development and fighting poverty.

This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals.

This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals with Bayes and Lavenberg-Marquardt neural networks. Bayes neural network with 23 neurons in hidden layer is best with F1 measure = 83,6 %, Sensitivity = 88,1 % and Specificity 94,5 %.

A new approach to obtain visible luminescence from sol-gel derived SiOC films is proposed. This novel method is based on a simple processing route to produce nanostructured multicomponent ceramics. According to this route, hybrid sol-gel derived precursors are converted to ceramic materials by a pyrolysis process in controlled atmosphere at 800-1000°C. Higher temperatures lead to formation of Si-rich SiOC, C-rich SiOC or stoichiometric SiOC according to the starting composition. The final composition, which is relevant to line emission, can be easily controlled through a number of processing parameters like the composition of the preceramic gel and the heat treatment conditions. Thus, this new processing method seems very well suited for the production of white emitting materials since the Si- and C-based emission can be tuned across the visible spectral range from UV-blue to red by controlling film composition. A further advantage of this method is that the thin films can be formed on Si or quartz wafers and this can serve as starting material to process more complex photonic devices such as waveguides or LEDs. In the amorphous state (800-100°C), all SiOC films showed UV-blue luminescence peaking at about 410 nm, which is attributed to defect states present in the matrix such as dangling bonds. The increase of the pyrolysis temperature (≥1100°C) led to the partition of SiOC and formation of SiC, C and Si phases. The intense green-yellow luminescence observed in stoichiometric SiOC films caused by the presence of SiC and very low amount of free C. On the other hand, Si rich SiOC film showed a very broad and extremely intense white luminescence peak centred at 620 nm covering almost all visible range (430 nm-900 nm) at 1200 °C. This behaviour is explained by the simultaneous presence of SiC, C and Si in the film. External quantum efficiency measurements yielded 11.5% and 5% efficiencies in Si rich SiOC and stoichiometric SiOC films, respectively, pyrolysed at 1200°C. On the other hand, C rich SiOC films did not show any noticeable improvement in PL, indicating that C excess in the SiOC system is detrimental for the luminescence behaviour. Solutions which used in thin film production have been characterized extensively by means of several characterization properties. Moreover, the related powders and bulks have been characterized for the sake of coherency and widen the study. In addition, a study on volumetric shrinkage of films and powders has been done. The results showed that the shrinkage in films happens almost 200°C earlier than powder and higher amount of siloxane release due to the low dimension, the shrinkage is higher than powders. The last part of the study dedicated to two different systems, SiBOCs and SiOCNs, in order to understand the effect of the boron addition on SiOC system and study the optical properties of the SiOCN. Tunable (color emission change) SiOC films is obtained with high quantum efficiency by adding very few amount of boron in SiOC. Moreover, the processing temperature is decreased and very broad emission is obtained. Finally, results showed that SiOCN PDC gives very high emission in UV range and they are promising materials for UV-LEDs.

Real-time neuroscience can be described as the use of neuroimaging techniques to extract and evaluate brain activations during their ongoing development. The possibility to track these activations opens the doors to new research modalities as well as practical applications in both clinical and everyday life. Moreover, the combination of different neuroimaging techniques, i.e. multimodality, may reduce several limitations present in each single technique. Due to the intrinsic difficulties of real-time experiments, in order to fully exploit their potentialities, advanced signal processing algorithms are needed. In particular, since brain activations are free to evolve in an unpredictable way, data-driven algorithms have the potentials of being more suitable than model-driven ones. In fact, for example, in neurofeedback experiments brain activation tends to change its properties due to training or task eects thus evidencing the need for adaptive algorithms. Blind Source Separation (BSS) methods, and in particular Independent Component Analysis (ICA) algorithms, are naturally suitable to such kind of conditions. Nonetheless, their applicability in this framework needs further investigations. The goals of the present thesis are: i) to develop a working real-time set up for performing experiments; ii) to investigate different state of the art ICA algorithms with the aim of identifying the most suitable (along with their optimal parameters), to be adopted in a real-time MRI environment; iii) to investigate novel ICA-based methods for performing real-time MRI neuroimaging; iv) to investigate novel methods to perform data fusion between EEG and fMRI data acquired simultaneously. The core of this thesis is organized around four "experiments", each one addressing one of these specic aims. The main results can be summarized as follows. Experiment 1: a data analysis software has been implemented along with the hardware acquisition set-up for performing real-time fMRI. The set-up has been developed with the aim of having a framework into which it would be possible to test and run the novel methods proposed to perform real-time fMRI. Experiment 2: to select the more suitable ICA algorithm to be implemented in the system, we investigated theoretically and compared empirically the performance of 14 different ICA algorithms systematically sampling different growing window lengths, model order as well as a priori conditions (none, spatial or temporal). Performance is evaluated by computing the spatial and temporal correlation to a target component of brain activation as well as computation time. Four algorithms are identied as best performing without prior information (constrained ICA, fastICA, jade-opac and evd), with their corresponding parameter choices. Both spatial and temporal priors are found to almost double the similarity to the target at not computation costs for the constrained ICA method. Experiment 3: the results and the suggested parameters choices from experiment 2 were implemented to monitor ongoing activity in a sliding-window approach to investigate different ways in which ICA-derived a priori information could be used to monitor a target independent component: i) back-projection of constant spatial information derived from a functional localizer, ii) dynamic use of temporal , iii) spatial, or both iv) spatial-temporal ICA constrained data. The methods were evaluated based on spatial and/or temporal correlation with the target IC component monitored, computation time and intrinsic stochastic variability of the algorithms. The results show that the back-projection method offers the highest performance both in terms of time course reconstruction and speed. This method is very fast and effective as far as the monitored IC has a strong and well defined behavior, since it relies on an accurate description of the spatial behavior. The dynamic methods oer comparable performances at cost of higher computational time. In particular the spatio-temporal method performs comparably in terms of computational time to back-projection, offering more variable performances in terms of reconstruction of spatial maps and time courses. Experiment 4: finally, Higher Order Partial Least Square based method combined with ICA is proposed and investigated to integrate EEG-fMRI data acquired simultaneously. This method showed to be promising, although more experiments are needed.

The physics of ultracold atomic gases has been the subject of a long standing theoretical and experimental research over the last half century. The development of evaporative cooling techniques and the realization of the first Bose-Einstein Condensate (BEC) in 1995 gave a great advantage to the field. A great experimental knowledge of the fundamental properties of BECs, such as long-range coherence, superfluidity and topological excitations, has now been acquired. On top of these advances, current research on ultracold atoms is also focusing on quantum simulations, which aim at building analogue models of otherwise difficult to compute physical systems in the lab. In this context, BECs, with their enhanced coherence, many-body dynamics and superfluid character offer a powerful platform for advances in the field. Shortly after the first realization of a BEC, research started also investigating the physics of quantum mixtures of a BECs, either composed of different atomic species or isotopes, or of atoms occupying different hyperfine states. The latter are known as spin mixtures, or spinor condensates. The presence of multiple components interacting through mutual contact interactions enriches the physics of the condensate, introducing ground states with magnetic ordering as well as spin dynamics, which can be order of magnitudes less energetic than the density one. On top of this, hyperfine states can be coherently coupled with an external resonant radiation. Interesting physics arises when the strength of the coupling is comparable with the energy of spin excitations, an example of which is given by the emergence of the internal Josephson effect. This regime has been the subject of intense theoretical studies in the past twenty years, however its experimental realization on ultracold atomic platforms have been proven to be challenging, with experiments strongly limited by coherence times of few tens of milliseconds. In fact, the small energy scale of spin excitations reflects in a high sensitivity coupling to environmental magnetic noise, which affects the resonant condition. The experimental apparatus on which I worked during my Ph.D. solve this problem employing a magnetic shield that surrounds the science chamber, attenuating external magnetic fields by 6 orders of magnitudes. During my Ph.D., I investigated the properties of a coherently coupled mixture of BEC of Sodium 23, performing different experiments in two atomic configurations. The first configuration consist of a mixture of hyperfine states, namely the |F=1, mF = -1> and |F=1, mF = +1>, coupled by a two-photon transition, which is characterized by miscibility in the ground state. Another configuration was instead realized working with a strongly immiscible mixture of |F=1, mF=-1> and |F=2, mF = -2>, realized through with a one photon transition. My first experiment was devoted to the characterization of different methods of manipulation of the coupled miscible mixture in an elongated quasi-1D geometry. In Local Density Approximation (LDA), The dynamics of the system, depends on the atom number difference, the relative phase, and coupling to mean field energy ratio, can be fully described as an internal Josephson junction. We characterized this dynamics on a sample an inhomogeneous spatial profile, developing three different protocols for state manipulations. In a second experiment, I developed a protocol to generate Faraday waves in an unpolarized miscible mixture. Faraday waves are classical non-linear waves characterized by a regular pattern, that originate in classical and quantum fluids via a parametric excitation in the fluid. Interestingly enough, this process resembles the phase of reheating of the early universe, where the oscillation of the inflaton field is thought to have excited particles out of the vacuum. In analogy with this phenomenon, the oscillation of the inflaton field can be simulated with the periodic modulation of the trapping potential. On top of this, in a spin mixture, the parametric modulation can excite either in-phase (density) modes or out-of-phase (spin) modes, as two possible elementary excitations are present in the system. By extracting the spatial periodicity of the generated pattern at different modulation frequencies, I was then able to measure the dispersion relations for both density and spin modes of the system. In the presence of the coherent coupling, when spin excitations becomes gapped, we further demonstrate the scaling of the gap with the strength of the coupling radiation. The third experiment I realized concerned the characterization of the magnetic ground state of a spatially extended immiscible mixture in the presence of the coherent coupling. The Hamiltonian of such a system is formally equivalent to a continuous version of the transverse field Ising model, which describes magnetic materials at zero temperature. In this mapping, a nonlinear interaction term arises from the ratio between the self-interaction energy and the strength of the coupling, which acts as the transverse field. As the ratio between the two quantities is varied above and below one, the ground state of the system spontaneously changes from a paramagnetic phase to an ordered ferromagnetic phase, featuring two equivalent and opposite magnetizations, a signature of the occurrence of a second order quantum phase transition (QPT). Furthermore, in the magnetic model, the degeneracy between the two ferromagnetic ground states can be broken by introducing an additional longitudinal field. In the atomic case, the role of this additional field is taken by the detuning between the coupling radiation and the resonant transition frequency of non-interacting atoms. I characterized the QPT developing protocols to manipulate the spin mixture in its spatially extended ground state, varying the longitudinal field. Leveraging on the inhomogeneity of a BEC trapped in the harmonic potential, a smooth variation of the spin self-interaction energy occurs spontaneously in space, introducing different magnetic regimes at fixed coupling strength. These protocols gave access to a characterization of static properties typical of magnetic materials, such as the presence of an hysteresis cycle. The occurrence of the phase transition was instead validated by a measurement of the magnetic susceptibility and corresponding fluctuations, which both show a divergence when crossing the QPT critical point. At last, I developed a protocol to smoothly manipulate the position of magnetic domain walls, the least energetic excitations in a ferromagnet. While the previous study focused on static properties, the last experimental investigation presented in this thesis was devoted to the study of the dynamics of the metastable ferromagnetic region of the BEC. As a result of the presence of an hysteresis cycle, it is possible to engineer states of the ferromagnetic energy landscape that are homogeneously prepared either in the global minimum, with trivial dynamics, or in the metastable, higher energy, local minima. In the latter case, a classical system should eventually decay towards the global minimum, driven by temperature fluctuations which overtop the energy barrier separating the two minima. For a quantum system described by a field theory, such as a ferromagnetic BEC, the decay towards the global minimum occurs by tunneling through the barrier, triggered by quantum fluctuations. The event of tunneling is known as False Vacuum Decay (FVD), and is of outstanding relevance also for high energy physics and cosmology, were the first theoretical models were developed. In the FVD model, the decay towards the global minimum, the true vacuum, is a stochastic process that occurs only if a resonant bubble of true vacuum is formed. Once formed, the bubble will eventually expand throughout the whole system, as the true vacuum is energetically favorable. The probability for such a bubble to form can be approximately calculated analytically in 1D, and should depend exponentially on the height of the barrier the field has to tunnel through. Due to the exponentially long time scale of the process, experimental observations of FVD were still lacking. Thanks to the enhanced coherence time of the superfluid ferromagnetic mixture, and to the precise control of the barrier height through the detuning from atomic resonance, we were able to observe the event of bubble nucleation in a ferromagnetic BEC. To corroborate the observation, I measured the characteristic timescale of the decay for different values of the control parameters. Results were successfully compared first with numerical simulation, and then validated by instanton theory.

This thesis aims at evaluating, in two separate studies, strategies for physiological noise and head motion correction in resting state brain FC-fMRI. In particular, as a general marker of noise correction performance we use the test-retest reproducibility of the DMN. The guiding hypothesis is that methods that improve reproducibility should reflect more efficient corrections and thus be preferable in longitudinal studies. The physiological denoising study evaluated longitudinal changes in a 3T harmonized multisite fMRI study of healthy elderly participants from the PharmaCog Consortium (Jovicich et al., 2016). Retrospective physiological noise correction (rPNC) methods were here implemented to investigate their influence on several DMN reliability measures within and between 13 MRI sites. Each site involved five different healthy elderly participants who were scanned twice at least a week apart (5 participants per site). fMRI data analysis was performed once without rPNC and then with WM/CSF regression, with physiological estimation by temporal ICA (PESTICA) (Beall & Lowe, 2007) and FMRIB's ICA-based Xnoiseifier (FSL-FIX) (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014). These methods differ for their data-based computational approach to identify physiological noise fluctuations and need to be applied at different stages of data preprocessing. As a working hypothesis, physiological denoising was in general expected to improve DMN reliability. The head motion study evaluated longitudinal changes in the DMN connectivity from a 4T single-site study of 24 healthy young volunteers who were scanned twice within a week. Within each scanning session, RS-fMRI scans were acquired once using interleaved and then sequential slice-order acquisition methods. Furthermore, brain volumes were corrected for motion using once rigid-body volumetric and then slice-wise methods. The effects of these choices were then evaluated computing multiple DMN reliability measures and investigating single regions within the DMN to assess the existence of inter-regional effects associated with head-motion. In this case, we expected to find slice-order acquisition effects in reliability estimates under standard volumetric motion correction and no slice-order acquisition effect under 2D slice-based motion correction. Both studies used ICA to characterize the DMN using group-ICA and dual regression procedures (Beckmann et al., 2009). This methodology proved successful at defining consistent DMN connectivity metrics in longitudinal and clinical RS-fMRI studies (Zuo & Xing, 2014). Automatic DMN selection procedures and other quality assurance analyses were made to supervise ICA performance. Both studies considered several test-retest (TRT) reliability estimates (Vilagut, 2014) for some DMN connectivity measurements: absolute percent error between the sessions, intraclass correlation coefficients (ICC) between sessions and multiple sites, the Jaccard index to evaluate the degree of voxel-wise spatial pattern actiavtion overlap between sessions.

The research activity of this thesis deals with the modelling of mechanical systems and two applications are analyzed by means of the same approach to modelling process. The first application is about the wind energy conversion systems. The modelling activity aimed at representing the behaviour of wind turbines operating in turbulent wind in terms of power conversion performances. The characterization of the test site has been thoroughly presented. Firstly, the concepts and the state of the art of both power curve and turbulence have been examined in detail. Secondly, a model based on a steady instantaneous power curve has been developed starting from the Reynolds approach at turbulence and a procedure to estimate the steady power curve from experimental data has been presented. The model proposed has been used on experimental data collected at the Trento test site to small wind turbines. Finally the model has been validated on experimental data through the comparison on energy capture forecasting. The second application presented deals with the measurement of cylinder roundness by means of multi-point method. The purpose of the modelling activity was the representation of the measurement process of cylinders surface through multi sensor measurement systems. The model has been developed considering step by step the architecture of the measurement system. Firstly, the generic cylinder shape and axis has been modelled in a parametric way. Then the irregular motion during the measurement process has been modelled thanks to a few parameters and finally the sensor disposition and theirs error has been implemented in the model. The parametric model obtained has been used to demonstrate the importance of consider the positioning errors of the sensors and the motion of the cylinder in 3D domain. Finally a three point method for radius and motion reconstruction has been implemented in the model and same Monte Carlo simulation has been carried out to demonstrate the effect of 3D disturbances on shape reconstruction.