La migración infantil indocumentada de Centroamérica, un desafío a la reformulación de la política migratoria de Estados Unidos. Estudio de caso de Honduras (2010-2014)

El siguiente estudio de caso analiza la influencia de la llegada de la migración infantil indocumentada centroamericana en la reformulación de la política migratoria de Estados Unidos en el período 2010-2014. Enfocándose en el caso de Honduras para dar a conocer y analizar las causas que crean las dinámicas migratorias por parte de los menores, este trabajo analiza como la llegada de estas poblaciones genera ciertos efectos en el proceso de toma de decisión de las políticas internas de los Estados Unidos. Por un lado, para resaltar las características del fenómeno migratorio, se utilizan las teorías de redes sociales y la teoría push and pull. Por otro, mediante los conceptos de Sensibilidad y Vulnerabilidad expuestos en la teoría de la Interdependencia Compleja de las Relaciones Internacionales, como también el concepto de Seguridad Societal propuesto por Barry Buzan se estudia el nivel de influencia del fenómeno infantil en el gobierno norteamericano.

Simulation of photoinduced processes in organic chromophores: mapping photochemically relevant states for accurate dynamics

The simulation of ultrafast photoinduced processes is a fundamental step towards the understanding of the underlying molecular mechanism and interpretation/prediction of experimental data. Performing a computer simulation of a complex photoinduced process is only possible introducing some approximations but, in order to obtain reliable results, the need to reduce the complexity must balance with the accuracy of the model, which should include all the relevant degrees of freedom and a quantitatively correct description of the electronic states involved in the process. This work presents new computational protocols and strategies for the parameterisation of accurate models for photochemical/photophysical processes based on state-of-the-art multiconfigurational wavefunction-based methods. The required ingredients for a dynamics simulation include potential energy surfaces (PESs) as well as electronic state couplings, which must be mapped across the wide range of geometries visited during the wavepacket/trajectory propagation. The developed procedures allow to obtain solid and extended databases reducing as much as possible the computational cost, thanks to, e.g., specific tuning of the level of theory for different PES regions and/or direct calculation of only the needed components of vectorial quantities (like gradients or nonadiabatic couplings). The presented approaches were applied to three case studies (azobenzene, pyrene, visual rhodopsin), all requiring an accurate parameterisation but for different reasons. The resulting models and simulations allowed to elucidate the mechanism and time scale of the internal conversion, reproducing or even predicting new transient experiments. The general applicability of the developed protocols to systems with different peculiarities and the possibility to parameterise different types of dynamics on an equal footing (classical vs purely quantum) prove that the developed procedures are flexible enough to be tailored for each specific system, and pave the way for exact quantum dynamics with multiple degrees of freedom.

Wireless Network Analytics for Next Generation Spectrum Awareness

The importance of networks, in their broad sense, is rapidly and massively growing in modern-day society thanks to unprecedented communication capabilities offered by technology. In this context, the radio spectrum will be a primary resource to be preserved and not wasted. Therefore, the need for intelligent and automatic systems for in-depth spectrum analysis and monitoring will pave the way for a new set of opportunities and potential challenges. This thesis proposes a novel framework for automatic spectrum patrolling and the extraction of wireless network analytics. It aims to enhance the physical layer security of next generation wireless networks through the extraction and the analysis of dedicated analytical features. The framework consists of a spectrum sensing phase, carried out by a patrol composed of numerous radio-frequency (RF) sensing devices, followed by the extraction of a set of wireless network analytics. The methodology developed is blind, allowing spectrum sensing and analytics extraction of a network whose key features (i.e., number of nodes, physical layer signals, medium access protocol (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to estimate the number of sources and separate the traffic patterns. After the separation, we put together a set of methodologies for extracting useful features of the wireless network, i.e., its logical topology, the application-level traffic patterns generated by the nodes, and their position. The whole framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. The numerical results obtained by extensive and exhaustive simulations show that the proposed framework is consistent and can achieve the required performance.

RICORDACI: Research on the Conservation, Restoration and Diagnosis of Cinematographic Films

This thesis is the result of the RICORDACI project, a three-year European-funded initiative involving the collaboration between the University of Bologna and the restoration laboratory of the Cineteca di Bologna, L'immagine Ritrovata, which aimed to develop innovative solutions and technologies for the preservation of cinematographic film heritage. In particular, this thesis presents new analytical methodologies to exploit two types of portable miniaturized Near Infrared spectrometers working in Diffuse Reflectance over the Short Wave Infrared (SWIR) range, to study the near infrared (NIR) spectral behavior of film base materials for an accurate, non-invasive and fast characterization of the polymer type; and for films with cellulose acetate supports, they can be employed as a diagnostic tool for monitoring the Degree of substitution (DS) affected by the loss of acetyl groups. The proposed methods offer non-invasive, fast, inexpensive and simple alternatives for the characterization and diagnosis of film bases to help the strategic planning and decision-making regarding storage, digitalization and intervention of film collections. Secondly, the thesis includes the evaluation of new green cleaning systems and solvents for the effective, fast and innocuous removal of undesired substances from degraded cinematographic films bases; these tests compared the efficiency of traditional systems and solvents against the new proposals. Firstly, the use of Deep Eutectic Solvent formulations for removing softened gelatin residues from cellulose nitrate bases; and secondly, the employment of green volatile solvents with different application methods, including the use of new electrospun nylon mats, for avoiding the dangerous use of friction for the removal of Triphenyl Phosphate blooms from the surface of cellulose acetate bases. The results obtained will help improving the efficiency of the interventions needed before the digitalization of historical cinematographic films and will pave the way for further investigation on the use of green solvents for cleaning polymeric heritage objects.

Towards a full cosmological exploitation of cosmic voids

A stately fraction of the Universe volume is dominated by almost empty space. Alongside the luminous filamentary structures that make it up, there are vast and smooth regions that have remained outside the Cosmology spotlight during the past decades: cosmic voids. Although essentially devoid of matter, voids enclose fundamental information about the cosmological framework and have gradually become an effective and competitive cosmological probe. In this Thesis work we present fundamental results about the cosmological exploitation of voids. We focused on the number density of voids as a function of their radius, known as void size function, developing an effective pipeline for its cosmological usage. We proposed a new parametrisation of the most used theoretical void size function to model voids identified in the distribution of biased tracers (i.e. dark matter haloes, galaxies and galaxy clusters), a step of fundamental importance to extend the analysis to real data surveys. We then applied our built methodology to study voids in alternative cosmological scenarios. Firstly we exploited voids with the aim of breaking the degeneracies between cosmological scenarios characterised by modified gravity and the inclusion of massive neutrinos. Secondly we analysed voids in the perspective of the Euclid survey, focusing on the void abundance constraining power on dynamical dark energy models with massive neutrinos. Moreover we explored other void statistics like void profiles and clustering (i.e. the void-galaxy and the void-void correlation), providing cosmological forecasts for the Euclid mission. We finally focused on the probe combination, highlighting the incredible potential of the joint analysis of multiple void statistics and of the combination of the void size function with different cosmological probes. Our results show the fundamental role of the void analysis in constraining the fundamental parameters of the cosmological model and pave the way for future studies on this topic.

Analytical methods for modeling elastic metasurfaces

This dissertation aims at developing advanced analytical tools able to model surface waves propagating in elastic metasurfaces. In particular, four different objectives are defined and pursued throughout this work to enrich the description of the metasurface dynamics. First, a theoretical framework is developed to describe the dispersion properties of a seismic metasurface composed of discrete resonators placed on a porous medium considering part of it fully saturated. Such a model combines classical elasticity theory, Biot’s poroelasticity and an effective medium approach to describe the metasurface dynamics and its coupling with the poroelastic substrate. Second, an exact formulation based on the multiple scattering theory is developed to extend the two-dimensional classical Lamb’s problem to the case of an elastic half-space coupled to an arbitrary number of discrete surface resonators. To this purpose, the incident wavefield generated by a harmonic source and the scattered field generated by each resonator are calculated. The substrate wavefield is then obtained as solutions of the coupled problem due to the interference of the incident field and the multiple scattered fields of the oscillators. Third, the above discussed formulation is extended to three-dimensional contexts. The purpose here is to investigate the dynamic behavior and the topological properties of quasiperiodic elastic metasurfaces. Finally, the multiple scattering formulation is extended to model flexural metasurfaces, i.e., an array of thin plates. To this end, the resonant plates are modeled by means of their equivalent impedance, derived by exploiting the Kirchhoff plate theory. The proposed formulation permits the treatment of a general flexural metasurface, with no limitation on the number of plates and the configuration taken into account. Overall, the proposed analytical tools could pave the way for a better understanding of metasurface dynamics and their implementation in engineered devices.

Phenotypic and molecular investigation of circulating tumor cells (CTCs) isolated from breast cancer patients at single cell resolution

Despite the paramount advances in cancer research, breast cancer (BC) still ranks one of the leading causes of cancer-related death worldwide. Thanks to the screening campaign started in developed countries, BC is often diagnosed at early stages (non-metastatic BC, nmBC), but disease relapse occurrence even after decades and at distant sites is not an uncommon phenomenon. Conversely, metastatic BC (mBC) is considered an incurable disease. The major perpetrators of tumor spread to secondary organs are circulating tumor cells (CTCs), a rare population of cells detectable in the peripheral blood of oncologic patients. In this study, CTCs from patients diagnosed with luminal nmBC and mBC (hormone receptor positive, Human Epidermal Growth Factor Receptor 2 (HER2) negative) were characterized at both phenotypic and molecular levels. To better understand the molecular mechanisms underlying their biology and their metastatic potential, next-generation sequencing (NGS) analyses were performed at single-cell resolution to assess copy number aberrations (CNAs), single nucleotide variants (SNVs) and gene expression profiling. The findings of this study arise hints in CTC detection, and pave the way to new application in CTC research.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Variational Quantum Splines: Moving Beyond Linearity for Quantum Activation Functions

Activation functions within neural networks play a crucial role in Deep Learning since they allow to learn complex and non-trivial patterns in the data. However, the ability to approximate non-linear functions is a significant limitation when implementing neural networks in a quantum computer to solve typical machine learning tasks. The main burden lies in the unitarity constraint of quantum operators, which forbids non-linearity and poses a considerable obstacle to developing such non-linear functions in a quantum setting. Nevertheless, several attempts have been made to tackle the realization of the quantum activation function in the literature. Recently, the idea of the QSplines has been proposed to approximate a non-linear activation function by implementing the quantum version of the spline functions. Yet, QSplines suffers from various drawbacks. Firstly, the final function estimation requires a post-processing step; thus, the value of the activation function is not available directly as a quantum state. Secondly, QSplines need many error-corrected qubits and a very long quantum circuits to be executed. These constraints do not allow the adoption of the QSplines on near-term quantum devices and limit their generalization capabilities. This thesis aims to overcome these limitations by leveraging hybrid quantum-classical computation. In particular, a few different methods for Variational Quantum Splines are proposed and implemented, to pave the way for the development of complete quantum activation functions and unlock the full potential of quantum neural networks in the field of quantum machine learning.

Design, preparazione e studio applicativo di nuovi metallopolimeri luminescenti a base di complessi ciclometallati di Ir(III)

The research project of my experimental thesis deals with the design, synthesis and characterization of a new series of luminescent metallapolymers to be exploited for their peculiar photophysical and opto-electronic properties. To this end, our design strategy consisted in the incorporation of brightly luminescent and colour tuneable Ir(III) cyclometalated complexes with general formula [Ir(C^N)2(N^N)]+, where C^N represents various phenyl piridine based cyclometalating ligands and N^N is an aromatic chelating N-heterocyle, into methyl methacrylate (MMA) based copolymers. Whereas the choice of the cyclometalating ligands was driven by the possibility to obtain different emission colours, the design of the N^N ligands was aimed to obtain a molecule capable of providing the chelate coordination to the metal centre and, at the same time, of being susceptible to polymerisation reactions. To fulfil these requirements, a new molecule (abbreviated as L) consisting in an alkylated 2-pyrydyl tetrazole structure equipped with a styryl unit was designed and successfully prepared. The preparation of the target cationic metallapolymers was accomplished by the complexation of the preformed MMA-L copolymers with different amounts of an appropriate Ir(III) dimeric precursor [(Ir(C^N)2Cl)2]. The investigation of the photophysical features of the new hybrid compounds in the solid state at r.t. suggested how these metallapolymers displayed brightly intense phosphorescent emissions, whose colour was found to span from blue to yellow according to the nature of the cyclometalating ligands. In all cases, the emissive performances were superior to those displayed by the corresponding mononuclear “model” complexes. These promising results pave the way for the application of this new class of metallapolymers as Luminescent Solar Concentrators for the photovoltaic technology and/or to solid state lighting.