The dynamics of systems out of equilibrium, such as the phase transition process, are very rich, and related to largely scalable problems, from very small ultracold gases to large expanding galaxies. Quantum low-dimensional systems show interesting features, notably different from the canonical three-dimensional case. Bose-Einstein condensates are very good platforms to study macroscopic quantum phenomena. These three points describe well the motivation behind the study presented in this work. In this thesis, some dynamical problems of trapped and uniform condensates are studied, both at zero and finite temperature. In particular, we focus on the analysis of the propagation of linear and nonlinear excitations in a quasi-1D and in quasi-2D systems. In the first case, we are able to correctly describe the dynamics of a solitonic vortex in an elongated condensate, as measured by Serafini et al. [Phys. Rev. Lett. 115, 170402 (2015)]. In the second case, we reproduce the decay rate of a phase-imprinted soliton (collaboration with Birmingham), and assess its dependence on the temperature. We also replicate the propagation speed of sound waves over a wide range of temperatures as in Ville et al. [arXiv:1804.04037] (collaboration with Collà ̈ge de France). The result of this analysis is included in Ota et al. [arXiv:1804.04032], which is currently under revision. In uniform low-dimensional systems Bose-Einstein condensation is technically not possible, and in two dimensions it is replaced by the Berezinskii-Kosterlitz-Thouless superfluid phase transition. We study its critical properties by analysing the spontaneous generation of vortices during a quench, produced via the Kibble-Zurek mechanism. This procedure predicts, for any dimension, the scaling for the density of defects formed during a fast transition, when the system is not adiabatically following the control parameter, and regions of phase inhomogeneity are formed. We address the role of reduced dimensionality on this process. All finite temperature simulations are performed by means of the stochastic (projected) Gross-Pitaevskii equation, a model fully incorporating density and phase fluctuations for weakly interacting Bose gases.

La tesi è una raccolta di tre saggi sulla soddisfazione sul posto di lavoro. Il primo capitolo utilizza il dataset panel BHPS per analizzare le differenze di soddisfazione lavorativa dei lavoratori impiegati nel settore nonprofit rispetto agli altri settori. Il secondo considera l'effetto del salario -sia quello del lavoratore che di un gruppo rilevante di riferimento- e degli schemi incentivanti di retribuzione sulla soddisfazione lavorativa. L'analisi si basa sul round 5 dello European Working Conditions Survey (EWCS). Il terzo capitolo analizza e critica la relazione da tempo condivisa in letteratura che i membri di organizzazioni sindacali riportano minore soddisfazione lavorativa rispetto ai non iscritti, mediante l'analisi dei dati dello European Social Survey (ESS),round 3 e 5. / The thesis provides a detailed examination of job satisfaction through three distinct essays. The first chapter uses UK household panel data (the BHPS) to consider differences in job satisfaction in the non-profit sector, compared with other sectors. The second looks at the effect of earnings – that of the respondent and of relevant other people – and of payment systems on job satisfaction. This is based on data from round 5 of the European Working Conditions Survey (EWCS). The third essay seeks to examine and to challenge the long-established finding that union members tend to have lower job satisfaction than non-members, and this is addressed with two rounds of data from the European Social Survey (ESS).

La presente tesi intende esplorare gli effetti della resistenza parziale sulle epidemie di peronospora della vite (Plasmopara viticola). È stato sviluppato un modello di simulazione teorico che comprende lo sviluppo della pianta ospite e le fasi principali della malattia, dalla mobilizzazione dell’inoculo, alla moltiplicazione della malattia sulle foglie, all’infezione dei grappoli. Attraverso esperimenti (monociclici) di inoculazione è stata studiata la risposta alle infezioni di P. Viticola di 16 varietà parzialmente resistenti, analizzando le seguenti componenti: frequenza d’infezione, durata del periodo di latenza, dimensione delle lesioni, produzione di sporangi, durata del periodo infezioso e infettività degli sporangi prodotti sulle lesioni. Queste componenti di resistenza sono state incorporate nel modello, attraverso cui sono stati studiati i loro effetti sull’epidemia (policiclica) in diversi scenari. Le componenti di resistenza hanno mostrato diversi livelli di efficacia nel sopprimere l’epidemia: l’efficienza di infezione e la produzione di sporangi risultano avere un maggiore impatto nella resistenza espressa a livello di pieno campo. Questo approccio è utile per guidare lo studio fenotipico della resistenza dell’ospite e per anticipare le prestazioni di un genotipo a livello di pieno campo, che risulterebbe difficile e dispendioso considerando la natura perenne della vite. / The present dissertation aims to explore the effects of partial resistance on grapevine downy mildew (Plasmopara viticola) epidemics. A theoretical simulation model was developed including host dynamics and main phases of the disease, from inoculum mobilisation to disease multiplication on foliage, and to infection of clusters. The response to P. Viticola infection was studied for 16 grapevine varieties through (monocyclic) inoculation experiments, by measuring components of partial resistance: infection frequency, duration of latent period, size of lesions, production of sporangia, duration of infectious period, and infectivity of sporangia produced on lesion. Components of partial resistance were incorporated into the model and their effects on the (polycyclic) epidemic were investigated accross different scenarios. Components of partial resistance showed different effectiveness on the suppression of epidemics, infection efficiency and spore production having the strongest impact on the overall field resistance response. This approach is an useful tool for phenotyping studies on host plant resistance and for anticipating the performance of a genotype at the field scale, that otherwise is difficult and time requiring due to the perennial nature of grapevine.

La tesi ripercorre le linee di sviluppo del genere poliziesco nella televisione italiana e americana per coglierne i tratti fondamentali e le tendenze in termini di formati, strategie narrative ed evoluzione dei contenuti. In seguito si analizza nel dettaglio il caso della serie Distretto di Polizia, dall'ideazione fino alla sua evoluzione nei sei anni della messa in onda, con particolare attenzione al confronto con i precedenti modelli di racconto italiani e stranieri. La tesi si concentra poi sui diversi modelli di organizzazione del lavoro creativo legati ad alcuni dei maggiori titoli seriali italiani, individuando pregi e limiti di ogni struttura in rapporto alle esigenze produttive e alle particolarità di formato e mantenendo per quanto possibile valido il confronto con analoghi team autoriali americani. / The first part of the research goes over the lines of development of the detective story's genre in Italian and American TV, grasping its fundamental traits and tendencies in terms of formats, narrative strategies and contents' evolution. The following chapters focus on a case study. TV show Distretto di Polizia is an excellent example of this crime story series; this TV series is examined from the moment of the creation trough the six years of its broadcasting, focusing especially on the confrontation with the previous Italian and foreign story models. The last part of the research presents the different models of internal organisation of the creative work teams responsible for the most important titles among Italian TV shows. The aim of the research is to grasp the advantages and the limits of each model in relation to the different production requirements and the special characters of the various formats, trying to confront, as often as possible, the Italian management models with similar American models.

The study of experimental recording of dynamical systems often consists in the analysis of signals produced by that system. Time series analysis consists of a wide range of methodologies ultimately aiming at characterizing the signals and, eventually, gaining insights on the underlying processes that govern the evolution of the system. A standard way to tackle this issue is spectrum analysis, which uses Fourier or Laplace transforms to convert time-domain data into a more useful frequency space. These analytical methods allow to highlight periodic patterns in the signal and to reveal essential characteristics of linear systems. Most experimental signals, however, exhibit strange and apparently unpredictable behavior which require more sophisticated analytical tools in order to gain insights into the nature of the underlying processes generating those signals. This is the case when nonlinearity enters into the dynamics of a system. Nonlinearity gives rise to unexpected and fascinating behavior, among which the emergence of deterministic chaos. In the last decades, chaos theory has become a thriving field of research for its potential to explain complex and seemingly inexplicable natural phenomena. The peculiarity of chaotic systems is that, despite being created by deterministic principles, their evolution shows unpredictable behavior and a lack of regularity. These characteristics make standard techniques, like spectrum analysis, ineffective when trying to study said systems. Furthermore, the irregular behavior gives the appearance of these signals being governed by stochastic processes, even more so when dealing with experimental signals that are inevitably affected by noise. Nonlinear time series analysis comprises a set of methods which aim at overcoming the strange and irregular evolution of these systems, by measuring some characteristic invariant quantities that describe the nature of the underlying dynamics. Among those quantities, the most notable are possibly the Lyapunov ex- ponents, that quantify the unpredictability of the system, and measure of dimension, like correlation dimension, that unravel the peculiar geometry of a chaotic system’s state space. These methods are ultimately analytical techniques, which can often be exactly estimated in the case of simulated systems, where the differential equations governing the system’s evolution are known, but can nonetheless prove difficult or even impossible to compute on experimental recordings. A different approach to signal analysis is provided by information theory. Despite being initially developed in the context of communication theory, by the seminal work of Claude Shannon in 1948, information theory has since become a multidisciplinary field, finding applications in biology and neuroscience, as well as in social sciences and economics. From the physical point of view, the most phenomenal contribution from Shannon’s work was to discover that entropy is a measure of information and that computing the entropy of a sequence, or a signal, can answer to the question of how much information is contained in the sequence. Or, alternatively, considering the source, i.e. the system, that generates the sequence, entropy gives an estimate of how much information the source is able to produce. Information theory comprehends a set of techniques which can be applied to study, among others, dynamical systems, offering a complementary framework to the standard signal analysis techniques. The concept of entropy, however, was not new in physics, since it had actually been defined first in the deeply physical context of heat exchange in thermodynamics in the 19th century. Half a century later, in the context of statistical mechanics, Boltzmann reveals the probabilistic nature of entropy, expressing it in terms of statistical properties of the particles’ motion in a thermodynamic system. A first link between entropy and the dynamical evolution of a system is made. In the coming years, following Shannon’s works, the concept of entropy has been further developed through the works of, to only cite a few, Von Neumann and Kolmogorov, being used as a tool for computer science and complexity theory. It is in particular in Kolmogorov’s work, that information theory and entropy are revisited from an algorithmic perspective: given an input sequence and a universal Turing machine, Kolmogorov found that the length of the shortest set of instructions, i.e. the program, that enables the machine to compute the input sequence was related to the sequence’s entropy. This definition of the complexity of a sequence already gives hint of the differences between random and deterministic signals, in the fact that a truly random sequence would require as many instructions for the machine as the size of the input sequence to compute, as there is no other option than programming the machine to copy the sequence point by point. On the other hand, a sequence generated by a deterministic system would simply require knowing the rules governing its evolution, for example the equations of motion in the case of a dynamical system. It is therefore through the work of Kolmogorov, and also independently by Sinai, that entropy is directly applied to the study of dynamical systems and, in particular, deterministic chaos. The so-called Kolmogorov-Sinai entropy, in fact, is a well-established measure of how complex and unpredictable a dynamical system can be, based on the analysis of trajectories in its state space. In the last decades, the use of information theory on signal analysis has contributed to the elaboration of many entropy-based measures, such as sample entropy, transfer entropy, mutual information and permutation entropy, among others. These quantities allow to characterize not only single dynamical systems, but also highlight the correlations between systems and even more complex interactions like synchronization and chaos transfer. The wide spectrum of applications of these methods, as well as the need for theoretical studies to provide them a sound mathematical background, make information theory still a thriving topic of research. In this thesis, I will approach the use of information theory on dynamical systems starting from fundamental issues, such as estimating the uncertainty of Shannon’s entropy measures on a sequence of data, in the case of an underlying memoryless stochastic process. This result, beside giving insights on sensitive and still-unsolved aspects when using entropy-based measures, provides a relation between the maximum uncertainty on Shannon’s entropy estimations and the size of the available sequences, thus serving as a practical rule for experiment design. Furthermore, I will investigate the relation between entropy and some characteristic quantities in nonlinear time series analysis, namely Lyapunov exponents. Some examples of this analysis on recordings of a nonlinear chaotic system are also provided. Finally, I will discuss other entropy-based measures, among them mutual information, and how they compare to analytical techniques aimed at characterizing nonlinear correlations between experimental recordings. In particular, the complementarity between information-theoretical tools and analytical ones is shown on experimental data from the field of neuroscience, namely magnetoencefalography and electroencephalography recordings, as well as mete- orological data.

The work presents the recent developments in Quantum Monte Carlo calculations for nuclear systems including strange degrees of freedom. The Auxiliary Field Diffusion Monte Carlo algorithm has been extended to the strange sector by the inclusion of the lightest among the hyperons, the Λ particle. This allows to perform detailed calculations for Λ hypernuclei, providing a microscopic framework for the study of the hyperon-nucleon interaction in connection with the available experimental information. The extension of the method for strange neutron matter, put the basis for the first Diffusion Monte Carlo analysis of the hypernuclear medium, with the derivation of neutron star observables of great astrophysical interest.

The present work analyzes and describes a method for the direct numerical solution of the Maxwell's equations of classical electromagnetism. This is the FDTD (Finite-Difference Time-Domain) method, along with its implementation in an "in-house" computing code for large parallelized simulations. Both are then applied to the modelization of photonic and plasmonic structures interacting with light. These systems are often too complex, either geometrically and materially, in order to be mathematically tractable and an exact analytic solution in closed form, or as a series expansion, cannot be obtained. The only way to gain insight on their physical behavior is thus to try to get a numerical approximated, although convergent, solution. This is a current trend in modern physics because, apart from perturbative methods and asymptotic analysis, which represent, where applicable, the typical instruments to deal with complex physico-mathematical problems, the only general way to approach such problems is based on the direct approximated numerical solution of the governing equations. Today this last choice is made possible through the enormous and widespread computational capabilities offered by modern computers, in particular High Performance Computing (HPC) done using parallel machines with a large number of CPUs working concurrently. Computer simulations are now a sort of virtual laboratories, which can be rapidly and costless setup to investigate various physical phenomena. Thus computational physics has become a sort of third way between the experimental and theoretical branches. The plasmonics application of the present work concerns the scattering and absorption analysis from single and arrayed metal nanoparticles, when surface plasmons are excited by an impinging beam of light, to study the radiation distribution inside a silicon substrate behind them. This has potential applications in improving the eciency of photovoltaic cells. The photonics application of the present work concerns the analysis of the optical reflectance and transmittance properties of an opal crystal. This is a regular and ordered lattice of macroscopic particles which can stops light propagation in certain wavelenght bands, and whose study has potential applications in the realization of low threshold laser, optical waveguides and sensors. For these latters, in fact, the crystal response is tuned to its structure parameters and symmetry and varies by varying them. The present work about the FDTD method represents an enhacement of a previous one made for my MSc Degree Thesis in Physics, which has also now geared toward the visible and neighboring parts of the electromagnetic spectrum. It is organized in the following fashion. Part I provides an exposition of the basic concepts of electromagnetism which constitute the minimum, although partial, theoretical background useful to formulate the physics of the systems here analyzed or to be analyzed in possible further developments of the work. It summarizes Maxwell's equations in matter and the time domain description of temporally dispersive media. It addresses also the plane wave representation of an electromagnetic field distribution, mainly the far field one. The Kirchhoff formula is described and deduced, to calculate the angular radiation distribution around a scatterer. Gaussian beams in the paraxial approximation are also slightly treated, along with their focalization by means of an approximated diraction formula useful for their numericall FDTD representation. Finally, a thorough description of planarly multilayered media is included, which can play an important ancillary role in the homogenization procedure of a photonic crystal, as described in Part III, but also in other optical analyses. Part II properly concerns the FDTD numerical method description and implementation. Various aspects of the method are treated which globally contribute to a working and robust overall algorithm. Particular emphasis is given to those arguments representing an enhancement of previous work.These are: the analysis from existing literature of a new class of absorbing boundary conditions, the so called Convolutional-Perfectly Matched Layer, and their implementation; the analysis from existing literature and implementation of the Auxiliary Differential Equation Method for the inclusion of frequency dependent electric permittivity media, according to various and general polarization models; the description and implementation of a "plane wave injector" for representing impinging beam of lights propagating in an arbitrary direction, and which can be used to represent, by superposition, focalized beams; the parallelization of the FDTD numerical method by means of the Message Passing Interface (MPI) which, by using the here proposed, suitable, user dened MPI data structures, results in a robust and scalable code, running on massively parallel High Performance Computing Machines like the IBM/BlueGeneQ with a core number of order 2X10^5. Finally, Part III gives the details of the specific plasmonics and photonics applications made with the "in-house" developed FDTD algorithm, to demonstrate its effectiveness. After Chapter 10, devoted to the validation of the FDTD code implementation against a known solution, Chapter 11 is about plasmonics, with the analytical and numerical study of single and arrayed metal nanoparticles of different shapes and sizes, when surface plasmon are excited on them by a light beam. The presence of a passivating embedding silica layer and a silicon substrate are also included. The next Chapter 12 is about the FDTD modelization of a face-cubic centered (FCC) opal photonic crystal sample, with a comparison between the numerical and experimental transmittance/reflectance behavior. An homogenization procedure is suggested of the lattice discontinuous crystal structure, by means of an averaging procedure and a planarly multilayered media analysis, through which better understand the reflecting characteristic of the crystal sample. Finally, a procedure for the numerical reconstruction of the crystal dispersion banded omega-k curve inside the first Brillouin zone is proposed. Three Appendices providing details about specific arguments dealt with during the exposition conclude the work.

This thesis investigates two biological systems using atomistic modelling and molecular dynamics simulation. The work is focused on: (a) the study of the interaction between a segment of a DNA molecule and a functionalized surface; (b) the dynamical modelling of protein tau, an intrinsically disordered protein. We briefly describe here the two problems; for their detailed introduction we refer respectively to chapter DNA and chapter TAU. The interest in the study of the adsorption of DNA on functionalized surfaces is related to the considerable effort that in recent years has been devoted in developing technologies for faster and cheaper genome sequencing. In order to sequence a DNA molecule, it has to be extracted from the cell where it is stored (e.g. the blood cells). As a consequence any genomic analysis requires a purification process in order to remove from the DNA molecule proteins, lipids and any other contaminants. The extraction and purification of DNA from biological samples is hence the first step towards an efficient and cheap genome sequencing. Using the chemical and physical properties of DNA it is possible to generate an attractive interaction between this macromolecule and a properly treated surface. Once positioned on the surface, the DNA can be more easily purified. In this work we set up a detailed molecular model of DNA interacting with a surface functionalized with amino silanes. The intent is to investigate the free energy of adsorption of small DNA oligomers as a function of the pH and ionic strength of the solution. The tau protein belongs to the category of Intrinsically Disordered Proteins (IDP), which in their native state do not have an average stable structure and fluctuate between many conformations. In its physiological state, tau protein helps nucleating and stabilizing the microtubules in the axons of the neurons. On the other hand, the same tau - in a pathological aggregation - is involved in the development of the Alzheimer disease. IDPs do not have a definite 3D structure, therefore their dynamical simulation cannot start from a known list of atomistic positions, like a protein data bank file. We first introduce a procedure to find an initial dynamical state for a generic IDP, and we apply it to the tau protein. We then analyze the dynamical properties of tau, like the propensity of residues to form temporary secondary structures like beta-sheets or alpha-helices.

In this work we apply simulation techniques, namely Monte Carlo simulations and a path integral based method called Dominant Reaction Pathways (DRP) approach, in order to study aspects of dynamics and thermodynamics in three different families of peculiar proteins. These proteins are, for reasons such as the presence of an intermediate state in the folding path or topological constraints or large size, different from ideal systems, as may be considered small globular proteins that fold in a two state manner. The first treated topic is represented by the colicin immunity proteins IM9 and IM7, very similar in structure but with an apparently different folding mechanism. Our simulations suggest that the two proteins should fold with a similar folding mechanism via a populated on-pathway intermediate state. Then, two classes of pheromones that live in temperate and arctic water respectively are investigated. The two types of pheromones, despite the high structural similarity, show a different thermodynamic behavior, that could be explained, according to our results, by considering the role played by the location of CYS-CYS bonds along the chain. Finally, the conformational changes occurring in serpin proteins are studied. The serpins are very flexible, with a large size, more than 350 residues, and slow dynamics, from hours to weeks, completely beyond the possibilities of the simulation techniques to date. In this thesis we present the first all-atom simulations, obtained with the DRP approach, of the mechanism related to serpins and a complete characterization of the serpin dynamics is performed. Moreover, important implications for what concerns medical research field, in particular in drug design, are drown from this detailed analysis.

In this thesis an innovative approach to assess connectivity in a complex network was proposed. In network connectivity studies, a major problem is to estimate the links between the elements of a system in a robust and reliable way. To address this issue, a statistical method based on Pearson’s correlation coefficient was proposed. The former inherits the versatility of the latter, declined in a general applicability to any kind of system and the capability to evaluate cross–correlation of time series pairs both simultaneously and at different time lags. In addition, our method has an increased “investigation power”, allowing to estimate correlation at different time scale–resolutions. The method was tested on two very different kind of systems: the brain and a set of meteorological stations in the Trentino region. In both cases, the purpose was to reconstruct the existence of significant links between the elements of the two systems at different temporal resolutions. In the first case, the signals used to reconstruct the networks are magnetoencephalographic (MEG) recordings acquired from human subjects in resting–state. Zero–delays cross–correlations were estimated on a set of MEG time series corresponding to the regions belonging to the default mode network (DMN) to identify the structure of the fully–connected brain networks at different time scale resolutions. A great attention was devoted to test the correlation significance, estimated by means of surrogates of the original signal. The network structure is defined by means of the selection of four parameter values: the level of significance α, the efficiency η0, and two ranking parameters, R1 and R2, used to merge the results obtained from the whole dataset in a single average behav- ior. In the case of MEG signals, the functional fully–connected networks estimated at different time scale resolutions were compared to identify the best observation window at which the network dynamics can be highlighted. The resulting best time scale of observation was ∼ 30 s, in line with the results present in the scientific liter- ature. The same method was also applied to meteorological time series to possibly assess wind circulation networks in the Trentino region. Although this study is pre- liminary, the first results identify an interesting clusterization of the meteorological stations used in the analysis.