METODOLOGÍA PARA LA GESTIÓN DE CUENCAS HIDROGRÁFICAS SIGUIENDO EL ENFOQUE DEL RIESGO INTEGRAL

Se presenta metodología general del enfoque de riesgo a través de la tecnología de los Sistemas de Información Geográfcos, la evolución de los conceptos del enfoque integral de riesgo como apoyo a la Gestión de las Cuencas Hidrográfcas y su evolución. Mediante ejemplos de proyectos realizados con base en una metodología integradora en la variable de riesgo, que es útil, para realizar un ordenamiento integral de los recursos ambientales (biofísicos, socioeconómico-culturales) a nivel de la Cuenca Hidrográfca.El grado de riesgo se determina, partiendo de que el riesgo se entiende como una evaluación cognoscitiva de las pérdidas que pueden ocurrirle a un elemento expuesto, de acuerdo con sus características, su situación y un contexto particular de tiempo y de espacio, el análisis, cuantifca espacialmente el grado de riesgo, utilizando la tecnología SIG.El modelo general de ordenamiento territorial con énfasis en prevención en el que se considere el riesgo como una función de la amenaza y de la vulnerabilidad, esto es, r = f(A,V) Finalmente, se  introduce  la evolución de  los conceptos con el aporte de  los escenarios de amenazas y el manejo de la vulnerabilidad socioeconómica que permite aplicar el enfoque de riesgo integral, por medio de la tecnología SIG.Palabras claves: Amenaza, vulnerabilidad, riesgo, manejo integrado de recursos naturales,ordenamiento territorial, Sistemas de Información Geográfcos.Abstract General methodology of focusing on risk is presented through the technology of the Geographic Information System (GIS), the evolution of the concepts of the integral focus of risk as a support to the management of Hydrographic Watershed and its evolution.Through  examples of  completed projects,  an  integrative methodology  is made of  the  risk variable that is useful to achieving an integral ordering of the environmental resources (biophysics, socioeconomic-cultural) at the Hydrographic Watershed level.The risk degree is determined, starting form the premise that the risk can be understood as a cognitive evaluation from the losses that can happen to an exposed element, according to their characteristics, situation and a particular context of time and space, the quantifed analysis, especially of the degree of risk, using GIS technology.The general model of territorial ordering, with emphasis on prevention and what is considered the risk as a function of the hazard and the vulnerability, that is: r = f(A, V).Finally, evolution of the concepts is introduced, with the contribution of the hazard scenarios and management of the socioeconomic vulnerability that allows application of the risk focus, by GIS technology.Key words: hazards, vulnerability, risk, integrated management of natural resources, territorial ordering, GIS, Geographic Information Systems.

Simulating the aggregation of DNA oligonucleotides

In this work we have studied, by means of Molecular Dynamics simulations, the process of denaturation and self-assembly of short oligonucleotides. Supramolecular ordering of DNA short strands is a promising field which is constantly enriched with new findings. Examples are provided by micellar and fibrils formations and due to the selectivity of DNA bindings, "intelligent" devices have been developed to perform simple logic operations. It is worth to notice that computer simulations of these DNA nanosystems would complement experiments with detailed insight into processes involved in self-assembly. In order to obtain an accurate description of the interactions involved in the complex structure of DNA we used oxDNA, a coarse-grained model developed by Ouldridge. We simulated the melting transition of 4, 6, and 8 base pair sequences. Sequence and length dependence were analyzed, specifically we compared thermodynamic parameters DeltaH, DeltaS and the melting temperature with literature results. Moreover, we have attempted to reproduce liquid crystal ordering of the ultrashort sequence GCCG at relatively high saline concentration, until now only experimentally observed in Bellini's works. We found that our simple model successfully reproduces the experimental phase sequence (isotropic, nematic, columnar) at T= 5 °C as a function of oligonucleotide concentration, and we fully characterized the microscopic structure of the three phases.

Study of the tracking performance of a liquid Argon detector based on a novel optical imaging concept

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline accelerator experiment designed to make a significant contribution to the study of neutrino oscillations with unprecedented sensitivity. The main goal of DUNE is the determination of the neutrino mass ordering and the leptonic CP violation phase, key parameters of the three-neutrino flavor mixing that have yet to be determined. An important component of the DUNE Near Detector complex is the System for on-Axis Neutrino Detection (SAND) apparatus, which will include GRAIN (GRanular Argon for Interactions of Neutrinos), a novel liquid Argon detector aimed at imaging neutrino interactions using only scintillation light. For this purpose, an innovative optical readout system based on Coded Aperture Masks is investigated. This dissertation aims to demonstrate the feasibility of reconstructing particle tracks and the topology of CCQE (Charged Current Quasi Elastic) neutrino events in GRAIN with such a technique. To this end, the development and implementation of a reconstruction algorithm based on Maximum Likelihood Expectation Maximization was carried out to directly obtain a three-dimensional distribution proportional to the energy deposited by charged particles crossing the LAr volume. This study includes the evaluation of the design of several camera configurations and the simulation of a multi-camera optical system in GRAIN.

L'opera alchemica del Filosofo Cristiano. Edizione, traduzione e note di commento

Questa tesi è l’edizione critica, con traduzione italiana e note di commento, dell’opera alchemica di un autore bizantino, detto il Filosofo Cristiano. Nella parte introduttiva si esaminano le testimonianze dirette e indirette dell’opera, senza tralasciare l’analisi di alcuni problemi storici concernenti il nome e la datazione del nostro autore. L’edizione critica, con traduzione a fronte in italiano, è il nucleo della tesi. Seguono alcuni capitoli sulle varianti recenziori e le note di commento più estese, concernenti specifici punti dell’opera. Concludono il presente lavoro le appendici e le tabelle riguardanti l’ordinamento dei capitoli del Cristiano e la posizione dell’opera all’interno del corpus alchemico greco, secondo i principali manoscritti.

Joint value at risk: a new conditional risk measure

In this PhD thesis a new firm level conditional risk measure is developed. It is named Joint Value at Risk (JVaR) and is defined as a quantile of a conditional distribution of interest, where the conditioning event is a latent upper tail event. It addresses the problem of how risk changes under extreme volatility scenarios. The properties of JVaR are studied based on a stochastic volatility representation of the underlying process. We prove that JVaR is leverage consistent, i.e. it is an increasing function of the dependence parameter in the stochastic representation. A feasible class of nonparametric M-estimators is introduced by exploiting the elicitability of quantiles and the stochastic ordering theory. Consistency and asymptotic normality of the two stage M-estimator are derived, and a simulation study is reported to illustrate its finite-sample properties. Parametric estimation methods are also discussed. The relation with the VaR is exploited to introduce a volatility contribution measure, and a tail risk measure is also proposed. The analysis of the dynamic JVaR is presented based on asymmetric stochastic volatility models. Empirical results with S&P500 data show that accounting for extreme volatility levels is relevant to better characterize the evolution of risk. The work is complemented by a review of the literature, where we provide an overview on quantile risk measures, elicitable functionals and several stochastic orderings.

Study of the calorimetric performance of a liquid Argon detector based on a novel optical imaging concept

DUNE is a next-generation long-baseline neutrino oscillation experiment. It aims to measure the still unknown $ \delta_{CP} $ violation phase and the sign of $ \Delta m_{13}^2 $, which defines the neutrino mass ordering. DUNE will exploit a Far Detector composed of four multi-kiloton LArTPCs, and a Near Detector (ND) complex located close to the neutrino source at Fermilab. The SAND detector at the ND complex is designed to perform on-axis beam monitoring, constrain uncertainties in the oscillation analysis and perform precision neutrino physics measurements. SAND includes a 0.6 T super-conductive magnet, an electromagnetic calorimeter, a 1-ton liquid Argon detector - GRAIN - and a modular, low-density straw tube target tracker system. GRAIN is an innovative LAr detector where neutrino interactions can be reconstructed using only the LAr scintillation light imaged by an optical system based on Coded Aperture masks and lenses - a novel approach never used before in particle physics applications. In this thesis, a first evaluation of GRAIN track reconstruction and calorimetric capabilities was obtained with an optical system based on Coded Aperture cameras. A simulation of $\nu_\mu + Ar$ interactions with the energy spectrum expected at the future Fermilab Long Baseline Neutrino Facility (LBNF) was performed. The performance of SAND was evaluated, combining the information provided by all its sub-detectors, on the selection of $ \nu_\mu + Ar \to \mu^- + p + X $ sample and on the neutrino energy reconstruction.

Modelling and classification with quantile-based distributions

In this work, we explore and demonstrate the potential for modeling and classification using quantile-based distributions, which are random variables defined by their quantile function. In the first part we formalize a least squares estimation framework for the class of linear quantile functions, leading to unbiased and asymptotically normal estimators. Among the distributions with a linear quantile function, we focus on the flattened generalized logistic distribution (fgld), which offers a wide range of distributional shapes. A novel naïve-Bayes classifier is proposed that utilizes the fgld estimated via least squares, and through simulations and applications, we demonstrate its competitiveness against state-of-the-art alternatives. In the second part we consider the Bayesian estimation of quantile-based distributions. We introduce a factor model with independent latent variables, which are distributed according to the fgld. Similar to the independent factor analysis model, this approach accommodates flexible factor distributions while using fewer parameters. The model is presented within a Bayesian framework, an MCMC algorithm for its estimation is developed, and its effectiveness is illustrated with data coming from the European Social Survey. The third part focuses on depth functions, which extend the concept of quantiles to multivariate data by imposing a center-outward ordering in the multivariate space. We investigate the recently introduced integrated rank-weighted (IRW) depth function, which is based on the distribution of random spherical projections of the multivariate data. This depth function proves to be computationally efficient and to increase its flexibility we propose different methods to explicitly model the projected univariate distributions. Its usefulness is shown in classification tasks: the maximum depth classifier based on the IRW depth is proven to be asymptotically optimal under certain conditions, and classifiers based on the IRW depth are shown to perform well in simulated and real data experiments.

Study of charge, spin, and structural orderings in quantum materials using nuclei and muons as local probes

Quantum Materials are many body systems displaying emergent phenomena caused by quantum collective behaviour, such as superconductivity, charge density wave, fractional hall effect, and exotic magnetism. Among quantum materials, two families have recently attracted attention: kagome metals and Kitaev materials. Kagome metals have a unique crystal structure made up of triangular lattice layers that are used to form the kagome layer. Due to superconductivity, magnetism, and charge ordering states such as the Charge Density Wave (CDW), unexpected physical phenomena such as the massive Anomalous Hall Effect (AHE) and possible Majorana fermions develop in these materials. Kitaev materials are a type of quantum material with a unique spin model named after Alexei Kitaev. They include fractional fluctuations of Majorana fermions and non-topological abelian anyons, both of which might be used in quantum computing. Furthermore, they provide a realistic framework for the development of quantum spin liquid (QSL), in which quantum fluctuations produce long-range entanglements between electronic states despite the lack of classical magnetic ordering. In my research, I performed several nuclear magnetic resonance (NMR), nuclear quadrupole resonance (NQR), and muon spin spectroscopy (µSR) experiments to explain and unravel novel phases of matter within these unusual families of materials. NMR has been found to be an excellent tool for studying these materials’ local electronic structures and magnetic properties. I could use NMR to determine, for the first time, the structure of a novel kagome superconductor, RbV3Sb5, below the CDW transition, and to highlight the role of chemical doping in the CDW phase of AV3Sb5 superconductors. µSR has been used to investigate the effect of doping on kagome material samples in order to study the presence and behaviour of an anomalous phase developing at low temperatures and possibly related to time-reversal symmetry breaking.

Neutrino interactions in the SAND LAr volume at the DUNE Near Detector site

The neutrino mass ordering and the leptonic CP violation phase are key parameters of the three-neutrino flavour mixing still to be determined. Measuring these parameters is the main goal of DUNE, a next generation Long Baseline neutrino experiment under construction in the United States. DUNE will feature a Near and a Far Detector site. An important component of the Near detector complex is the SAND apparatus, which will include GRAIN, a novel liquid Argon detector that aims at imaging neutrino interactions using scintillation light. For this purpose, an innovative optical readout system based on Coded Aperture Masks is under study. This thesis work is aimed at a first quantitative assessment of a 3D neutrino event reconstruction algorithm for GRAIN. The processing procedure is optimized and the reconstruction performance is evaluated. Promising results are obtained.

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.

Calorimetric efficiency of the DUNE experiment

The Deep Underground Neutrino Experiment is a long-baseline neutrino experiment which is under construction in the United States. It will be composed of a Near Detector system located a few hundred meters from the neutrino source at Fermilab and a far detector system composed of four multi-kt LArTPCs at Sanford Underground Research Facility in South Dakota. The experiment will measure the leptonic CP violation phase of the PMNS matrix and discriminate the ordering of neutrino masses. Additional physics goals include detection of neutrinos from supernovae collapse and search for possible proton decay. One component of the Near detector complex is the System for on-Axis Neutrino Detection apparatus, which includes GRanular Argon for Interaction of Neutrinos, a novel liquid Argon detector that aims at imaging neutrino interactions using scintillation light collected by optical system and read-out by SIPM matrix. This thesis work aims at studying the GRAIN performances as a homogeneous calorimeter, able to measure the energy deposited by charged particles in LAr through scintillation photons emitted along their path inside the vessel. The energy calibration of the liquid argon volume required to write (and validate) an efficient software for the detector response simulation to the arrival of scintillation photons.