This thesis examines various methods for estimating the spatial distribution of solar radiation, and in particular its diffuse and direct components in mountainous regions. The study area is the Province of Bolzano (Italy). The motivation behind this work is that radiation components are an essential input for a series of applications, such as modeling various natural processes, assessing the effect of atmospheric pollutants on Earth's climate, and planning technological applications converting solar energy into electric power. The main mechanisms that should be considered when estimating solar radiation are: absorption and scattering by clouds and aerosols, and shading, reflections and sky obstructions by terrain. Ground-based measurements capture all these effects, but are unevenly distributed and poorly available in the Italian Alps. Consequently they are inadequate for assessing spatially distributed incoming radiation through interpolation. Furthermore conventional weather stations generally do not measure radiation components. As an alternative, decomposition methods can be applied for splitting global irradiance into the direct and diffuse components. In this study a logistic function was developed from the data measured at three alpine sites in Italy and Switzerland. The validation of this model gave MAB = 51 Wm^-2, and MBD = -17 Wm^-2 for the hourly averages of diffuse radiation. In addition, artificial intelligence methods, such as artificial neural networks (ANN), can be applied for reproducing the functional relationship between radiation components and meteorological and geometrical factors. Here a multilayer perceptron ANN model was implemented which derives diffuse irradiance from global irradiance and other predictors. Results show good accuracy (MAB in [32,43] Wm^-2, and MBD in [-7,-25] Wm^-2) suggesting that ANN are an interesting tool for decomposing solar radiation into direct and diffuse, and they can reach low error and high generality. On the other hand, radiative transfer models (RTM) can describe accurately the effect of aerosols and clouds. Indeed in this study the RTM libRadtran was exploited for calculating vertical profiles of direct aerosol radiative forcing, atmospheric absorption and heating rate from measurements of black carbon, aerosol number size distribution and chemical composition. This allowed to model the effect of aerosols on radiation and climate. However, despite their flexibility in including as much information as available on the atmosphere, RTM are computationally expensive, thus their operational application requires optimization strategies. Algorithms based on satellite data can overcome these limitations. They exploit RTM-based look up tables for modeling clear-sky radiation, and derive the radiative effect of clouds from remote observations of reflected radiation. However results strongly depend on the spatial resolution of satellite data and on the accuracy of the external input. In this thesis the algorithm HelioMont, developed by MeteoSwiss, was validated at three alpine locations. This algorithm exploits high temporal resolution METEOSAT satellite data (1 km at nadir). Results indicate that the algorithm is able to provide monthly climatologies of both global irradiance and its components over complex terrain with an error of 10 Wm^-2. However the estimation of the diffuse and direct components of irradiance on daily and hourly time scale is associated with an error exceeding 50 Wm^-2, especially under clear-sky conditions. This problem is attributable to the low spatial and temporal resolution of aerosol distribution in the atmosphere used in the clear-sky scheme. To quantify the potential improvement, daily averages of accurate aerosol and water vapor data were exploited at the AERONET stations of Bolzano and Davos. Clear-sky radiation was simulated by the RTM libRadtran, and low values of bias were found between RTM simulations and ground measurements. This confirmed that HelioMont performance would benefit from more accurate local-scale aerosol boundary conditions. In summary, the analysis of different methods demonstrates that algorithms based on geostationary satellite data are a suitable tool for reproducing both the temporal and the spatial variability of surface radiation at regional scale. However better performances are achievable with a more detailed characterization of the local-scale clear-sky atmospheric conditions. In contrast, for plot scale applications, either the logistic function or ANN can be used for retrieving solar radiation components.

The mutation/deletion of the hereditary material in the cell nuclei is a chronic biochemical hazard; in fact, nuclear DNA faces tens of lesions from metabolic intermediates, hydrolytic reactions and external vectors a minute. The canonical lesions of DNA involve the DNA backbone as well as the nucleic bases and are mostly associated with reversible chemical modifications. However, the radiation field from beams of accelerated ions accounts for a dense streak of collisions and reactions with the DNA molecule, thereby achieving lethal clusters of elemental lesions. Double strand breaks (DSB), i.e., the cleft of the DNA backbone over both strands, are hazardous fractures of the chromatin fold associated with the radiation field, underlying cytotoxic outcomes and chromosomal aberrations. Eukaryotic cells, however, rejoin the fractured DNA moieties from DSB events via an apt enzymatic machinery, or the DDR. Prior to the deployment of enzymatic effectors, host enzyme sensors engage the DNA termini in reversible supramolecular assemblies, which requires that the fractured DNA moieties be fully exposed. The in silico assessments of the early layout of DNA lesions by radiations have defined DSBs as the closely associated modifications of the DNA backbone by means of “coarse” criteria, that is, within an arbitrary distance of the two clefts. However, the diverse DSB motifs, i.e. at a strand break distance of zero to several nucleotides, account for a different contact interface between the DNA termini, thus modulating the dynamics of the lesion sites. Moreover, it is reckoned that in the absence of excess external stimuli, far-distanced DSBs may not fracture the broken DNA moieties by thermal dissociation, within the characteristic timescales of the DDR activity. This thesis elaborate tackles the in silico assessment of the distribution of DSBs in a chromatin-like fold and the local mechanical strain enforced by blunt DSBs, by means of state-of-the-art Monte-Carlo track structure tools and classical molecular dynamics. We infer that i) a Poisson fit describes the spectrum of DSB motifs by the direct effect of accelerated hydrogen ions (H+) at a Bragg peak relevant energy range (500 keV - 5 MeV) and, notably, we observe a bias towards short-distanced, staggered DSBs; ii) the nucleosome fold, i.e. the elemental unit of the chromatin hierarchical framework, exerts an excess kinetic barrier on the disruption of DSBs, which is not observed in linear DNA, mediated by the contact interface between DNA and the core histone fold. In conclusion, we remark that in the absence of further data from in vitro and in vivo assessments, the (kinetic, thermodynamic) inferences about the thermal and mechanical resilience of broken DNA frameworks are as reliable as the force fields underneath; in fact, it is debated whether all-atom force fields and water models overestimate the force of the intermolecular contacts and over-stabilize the DNA double helix.

No description available.

Looking at state of the art of optical devices, it is evident that glass-based rare-earth-activated optical structures represent the technological pillar of a huge number of photonic applications covering Health and Biology, Structural Engineering, Environment Monitoring Systems, Lighting, Laser sources and Quantum Technologies. Among different glass-based systems, a strategic place is assigned to transparent glass-ceramics, nanocomposite materials, which offer specific characteristics of capital importance in photonics. Following this strategy, this PhD thesis exploits tin dioxide (SnO2)-based glass-ceramic activated by erbium ions (Er3+) to put the basis for the fabrication of solid state and integrated lasers. The research discussed in my PhD thesis gives a possible solution to two crucial and decisive points in the development of an optically pumped rare-earth-based laser: (i) the low absorption cross section of the rare-earth ions; (ii) the writing of channels and mirrors in the case of waveguide integrated laser, thanks to the demonstration of two innovative and unique characteristics of SnO2-based transparent glass-ceramics, i.e. luminescence sensitizing and photorefractivity. The role of SnO2 nanocrystals as rare-earth ion luminescence sensitizers allows to overcome the low absorption cross section of the Er3+ ion. The photorefractivity in range of 10-3 of SiO2-SnO2:Er3+ glass-ceramics allows applying the robust direct laser photoinscription technique on the systems to fabricate Bragg gratings and channel waveguides for waveguide integrated lasers. Based on an application-oriented approach, a comprehensive study on SiO2-SnO2:Er3+ glass-ceramic planar waveguides and monoliths, has been carried out. The work covers different research stages and aspects from the material preparation to a complete assessment of systems for the applications employing a rich number and variety of experimental techniques. The energy transfer from SnO2 to Er3+ and the efficient pumping scheme exploiting SnO2 as Er3+ luminescence sensitizers were demonstrated. The relaxation dynamic of the electronic states as well as the location of the dopant and density of states are discussed, and a specific modeling has been developed to the proof of concept realization of the considered devices. The obtained photorefractivity in range of 10-3 allowed the inscription of gratings on the fabricated SiO2-SnO2:Er3+ planar waveguides using UV laser direct writing technique. Exploiting the robust femtosecond laser micromachining, the optical waveguides were inscribed in the fabricated SiO2-SnO2:Er3+ monolithic squares. Another important outcome of this research is the design of a solid state laser with lateral pumping scheme and of an integrated waveguide laser in two different distributed feedback structures using all the parameters measured during the experimental activity.

Biological materials such as wood show outstanding properties due to the self assembly of components from molecular to macroscopic size. An emerging nanotechnology-based strategy consists of the isolation of biological components with size in the range from nanometers to micrometers and of the design of human-driven assembly processes to obtain multifunctional materials. The aim of this thesis was to isolate cellulose nanocrystals, with dimensions of around 4-5 nm in width and some hundred nanometers in length, and investigate their assembly processes through weak interactions among them and with small molecules, like water or ions. Knowing their interaction properties and self-assembly is indeed fundamental in order to fully exploit the potential of nanocellulose in its recently emerging applications. In particular, I focused on cellulose nanocrystals supramolecular self-organization both in absence and presence of water, studying cellulose nanocrystals-based films and hydrogels. In dry conditions, the self-assembly of cellulose nanocrystals on a polylactic support was demonstrated to form few micrometers thick films, characterized by a densely packed arrangement of the crystals leaving elongated cavities of about 0.31 nm cross section between them. These cavities provide the pathway for gaseous 2H2 diffusion. Conversely, these films are impermeable barriers for the transport of gaseous molecules such as O2 and CO2. In aqueous solution, instead, cellulose nanocrystals undergo sonication- or cation-assisted entanglement, forming soft hydrogels. Na+, Ca2+ and Al3+ crosslink the nanocrystals and produce stable hydrogels with structurally ordered domains in which water is confined. Since the gelation process is diffusion controlled, small hydrogel objects with different size and shape have been designed by the coordination-driven assembly of supramolecular rod-like cellulose crystallites, using ionotropic gelation as a methodological approach and Ca2+ as a gelling agent. In parallel to material characterization, particular attention was devoted to the possible exploitation of cellulose nanocrystals-based materials in the biomedical field. In this regard, toxicity studies were performed both on the individual nanocrystals and on the films and hydrogels resulting from their assembly. Moreover, a hybrid cellulose-nanocrystals/chitosan material was developed and characterized, which shows some potential to be used as therapeutic delivery system in the gastrointestinal tract. Indeed, though a mould assisted gelation process, composite hydrogels can be produced, which are degraded by human digestive enzymes and release a model protein according to a biphasic kinetic profile.

Negli ultimi decenni si è vista la nascita di un grande interesse verso l'utilizzo dell'idrogeno come vettore energetico. Le ragioni sono da ricercarsi nelle problematiche sorte in tempi recenti in relazione alle emissioni inquinanti ed al rapido esaurimento delle fonti energetiche. Per poter concepire un'economia basata sull'idrogeno è tuttavia necessario risolvere una serie di problematiche connesse alla sua produzione, immagazzinamento e filtraggio. Il presente lavoro di tesi ha riguardato gli ultimi due punti. In relazione allo stoccaggio del gas, uno dei sistemi più promettenti risulta quello dell'utilizzo d'idruri metallici. Tali materiali presentano ancora una serie di problematiche che ne limitano l'utilizzo pratico, sia in ambito stazionario, che per applicazioni al settore della mobilità. Per superare questi ostacoli è necessaria la comprensione dei meccanismi microscopici con cui avviene la formazione e la dissociazione della fase idruro. Il presente lavoro è stato quindi incentrato sull'approfondimento di tale aspetto, concentrandosi in particolare sul ruolo giocato dai difetti di tipo vacanza in sistemi a base magnesio. Per quanto concerne l'aspetto del filtraggio, le tecniche positroniche sono state impiegate, in concomitanza con altre ad esse complementari, per ottenere una caratterizzazione della porosità, in termini di dimensioni, distribuzione ed interconnettività dei pori, in film di silice da utilizzare per la realizzazione di membrane selettive.

The problem of the relationship between brain function, characterized by functional magnetic resonance imaging, and physiological fluctuations by means of cardiac / respiratory oscillations is one of the most debated topics in the last decade. In recent literature, a great number of studies are found that focus on both practical and conceptual aspects about this topic. In this work, we start with reviewing two distinct approaches in considering physiology - related sequences with respect to functional magnetic resonance imaging: one treating physiology - related fluctuations as generators of noise, the other considering them as carriers of cognitively relevant information. In chapter 2 – “Physiology – related effects in the BOLD signal at rest at 4T”, we consider physiological quantities as generators of noise, and discuss conceptual flaws researchers have to face when dealing with data de-noising procedures. We point out that it can be difficult to show that the procedure has achieved its stated aim, i.e. to remove only physiology - related components from the data. As a practical solution, we present a benchmark for assessing whether correction for physiological noise has achieved its stated aim, based on the principle of permutation testing. In chapter 3 – “Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition”, on the other hand, we will consider autonomic indicants derived from physiological time - series as meaningful components of the BOLD signal. There, we describe a FMRI experiment building on this, where the goal was to localize brain areas whose activity is directionally related to autonomic one, in a top - down modulation fashion. In chapter 4 we recap the conclusions we found from the two approaches and we summarize the general contributions of our findings. We point out that bringing together the distinct approaches we reviewed lead us to mainly two contributions. On one hand we thought back the validity of almost established procedures in FMRI resting - state pre-processing pipelines. On the other we were able to say something new about general relationship between BOLD and autonomic activity, resting state fluctuations and deactivation theory.

The goal of this thesis is the discovery of a bioinformatics solution for network-based predictive analysis of NGS data, in which network structures can substitute gene lists as a more rich and complex signature of disease. I have focused on methods for network stability, network inference and network comparison, as additional components of the pipeline and as methods to detects outliers in high-throughput datasets. Besides a first work on GEO datasets, the main application of my pipeline has been on original data from the FDA SEQC (Sequencing Quality Control)project. Here I will report some initial findings to which I have contributed with methods and analysis: as the corresponding papers are being submitted. My goal is to provide a comprehensive tool for network reconstruction and network comparison as an R package and user-friendly web service interface available on-line at https://renette.fbk.eu The goal of this thesis is the discovery of a bioinformatics solution for network-based predictive analysis of NGS data, in which network structures can substitute gene lists as a more rich and complex signature of disease. I have focused on methods for network stability, network inference and network comparison, as additional components of the pipeline and as methods to detects outliers in high-throughput datasets. Besides a first work on GEO datasets, the main application of my pipeline has been on original data from the FDA SEQC (Sequencing Quality Control)project. Here I will report some initial findings to which I have contributed with methods and analysis: as the corresponding papers are being submitted. My goal is to provide a comprehensive tool for network reconstruction and network comparison as an R package and user-friendly web service interface available on-line at https://renette.fbk.eu.

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.

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.