Dipartimento di Fisica - Tesi di Dottorato
Permanent URI for this collectionhttps://lisa.unical.it/handle/10955/35
Questa collezione raccoglie le Tesi di Dottorato afferenti al Dipartimento di Fisica dell'Università della Calabria.
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Item Identification of Brain Structures and Functional Cortico - Muscular Networks: Machine Learning Object Recognition and Network Physiology Approach(Università della Calabria, 2020-03-05) Rizzo, Rossella; Critelli, Salvatore; Pantano, Pietro; Ivanov, PlamenThe brain is the most complex part in the human body. This organ is responsible for our intelligence, interpreting sensation, initiating body movement, and controlling all of our behaviors. Over hundreds of years, scientists have learned much about the brain, from a microscopic and macroscopic point of view. We now know the general rules under which information is transferred from neuron to neuron and we can differentiate between various brain structures and brain areas, each of them responsible of a particular function in the human organism. However, due to the vast complexity of the brain, much remains to be discovered. Researchers continue to explore the mechanics regulating a healthy brain that functions quickly and automatically, but we are still at the point where much work remains to identify the key differences between a physiological and a pathological situation in anatomic brain structures and functionality of the brain. The lack of information in this sense affects the diagnostic process of many neurodegenerative disorders, that can be discovered only from the symptoms shown by the subject and that, therefore, can be treated to reduce the pain and to give better conditions of life. The present research aims to better understand anatomic brain structures and functional interactions networks in the brain in order to early diagnose the most common neurodegenerative diseases. In the framework of the investigation of the anatomic brain structures the Neuroimaging is the most powerful tool used in basic research and clinical field. The Magnetic Resonance Imaging (MRI) is one of the most recent techniques of brain imaging and largely used for its low degree of invasion in the human body. It can provide valuable information in the detection of morphological markers that can highlight on the healthy status of the subject. A fundamental step in the pre-processing and analysis of magnetic resonance images is the individuation of the Mid-Sagittal Plane (MSP), where the mid brain is located, in order to set a coordinate reference system for the MRI scan images, and to precisely measure small changes in the surfaces, volumes and distances between different brain areas, which are used as biomarkers in the diagnostic process of certain diseases, such as Parkinson, Alzheimer, Progressive Supra-Nuclear Palsy. In this regard, part of the present research involves the improvement of brain MRIs analysis, with the use of machine learning techniques applied for the automatic identification of the MSP. In particular, the proposed method, Image Pixel Intensity (IPI) algorithm, is implemented in MatLab and is based on the k-mean, which allow to automatically segment the 2D MRIs in different brain tissues, and automatically identifies the slice where the brain tissues are most distinct from each other exploiting the intensity of the resonance signal expressed in the MRI by the color of the grayscale pixels. The results of this algorithm have been compared with the evaluation of four medical experts who manually identified the Mid-Sagittal, providing an average percentage error of 1.84%, and demonstrating that the proposed algorithm is promising and could be directly incorporated into larger diagnostic support systems. Following the main aim of the present research, the early diagnosis of neurodegenerative diseases, another machine learning technique, elastic net, has been implemented in Matlab in order to automatically predict the brain age, exploiting relationships involving the amount of gray matter present in the brain of the subjects analyzed, through a structural MRI study. The outcome of this work is the identification of profound correlations between the expected brain age and the general cognitive state: semantic verbal fluidity, processing speed, visual attention and cognitive flexibility, and visual attention and cognitive flexibility. Among the neurodegenerative diseases Parkinson lately acquired particular interest, due to its growing diffusion even within forty years old patients. This led to the study of functional interactions networks between the brain and the locomotor system during different sleep stages. Electroencephalography (EEG) and electromyography (EMG) data of healthy subjects and Parkinson's patients have been analyzed highlighting the correlations between different frequency bands present in the electrical signals emitted in the different brain areas and in the muscles of the chin and leg. Synchronous bursts in electrical activity signals in the brain and muscles have been analyzed, using the innovative method of Time Delay Stability (TDS), based on the cross-correlation function in consecutive time windows between two different signals. Links between the different frequency bands of different brain areas and the muscles with a long stable delay of the peak in the cross-correlation function are considered more stable, then stronger. The same analysis has been conducted on healthy and Parkinson's subjects, showing substantial differences in the networks of cortico-muscular interactions involving different frequencies between a physiological situation and a pathological one. Each sleep stage is uniquely identified by a particular pattern in the brain-muscle interactions. For Parkinson’s subjects these functional patterns change during each sleep stage; moreover, in general the strength of the links decreases during wake and light sleep but increases or remains the same during REM and deep sleep, especially for the brain-leg interactions, showing that during the waking phase the brain is not able to adequately control the muscles of the lower limbs. Analyzing in details the behavior of muscles the electric activity of different muscle fibers has been studied, considering subjects of different age groups (children, young adults and elderly subjects) in situations of stress or rest. In particular, EMG signals from the muscles of the leg and the back have been taken into account. The analysis shows that rest and stress have very different patterns, due to the different types of muscle fibers involved and how they behave during muscle relaxation and contraction; these relationships also change with age, identifying patterns that uniquely identify the age of the subjects analyzed and also vary during the same exercise by marking the precise point at which the subject reaches fatigue first and exhaustion afterwards.Item Applicazione dei big data nel turismo, marketing ed education(Università della Calabria, 2020-03-18) Giglio, Simona; Critelli, Salvatore; Pantano, PietroIl mondo è attualmente inondato da dati e l’avanzare delle tecnologie digitali amplifica questo fenomeno in modo esponenziale. Tale fenomeno viene etichettato con il concetto di Big Data ovvero le tracce digitali che le nostre attività quotidiane lasciano per effetto dell’uso massiccio dei sistemi ICT (Information Communication Technologies). I Big Data sono diventati il nuovo microscopio che rende “misurabile” la società. Per tali ragioni, la ricerca è incentrata sull’analisi dei Big data, estratti dai social media, da indagini online, da piattaforme di recensioni e da database, attraverso l’applicazione di tecniche e strumenti sviluppati nell’ambito dell’Intelligenza Artificiale. Algoritmi di machine learning, analisi semantica ed analisi statistica sono stati utilizzati per estrarre, dai Big Data, informazione sotto forma di “conoscenza” e “valore”, dimostrando come dati di grandi dimensioni possano fungere da ricca fonte di informazione, da un lato, per comprendere il comportamento dell’utente, parte integrante di una società complessa (conoscenza), e dall’altro, per sostenere i processi decisionali e i servizi forniti agli utenti/consumatori (valore). Il lavoro si caratterizza per un approccio multidisciplinare tra settori differenti quali le scienze sociali, le scienze statistiche e l’informatica. Questo ha permesso di fondare la ricerca sui Big Data nella teoria, e fornire un efficace recupero e analisi dei dati nella pratica. Le tecniche di machine learning sono state applicate per (i) il riconoscimento delle immagini, (ii) per la creazione di cluster, (iii) per l’analisi del testo (sentiment analysis) e (iv) per la profilazione di classi di utenti. Per il riconoscimento delle immagini l’approccio ha richiamato le reti neurali artificiali (deep artificial neural networks), algoritmi e sistemi computazionali ispirati al cervello umano utilizzando le potenzialità del programma Wolfram Mathematica e la disponibilità di dati estratti da social network quali Flickr, Twitter, Instagram ed altre piattaforme come TripAdvisor. Gli strumenti utilizzati nella ricerca hanno permesso di indagare e di rilevare in modo oggettivo dall’analisi di immagini e di testi condivisi sul web, alcuni comportamenti cognitivi degli utenti/consumatori alla base delle loro scelte nonché l’attrattività di una destinazione turistica e la qualità dell’esperienza dell’utente. Lo studio del significato delle parole nel testo ha aperto la strada al web semantico che permette ad un utente di acquisire informazioni approfondite durante una ricerca attraverso un sistema formato da una rete di relazioni e connessioni tra documenti. Partendo dalle ricerche di Ogden e Richards sullo studio del significato e di Jakobson che studiò i processi comunicativi, si è cercato di strutturare e sistematizzare un processo che riflette un atto comunicativo ed informativo tale che un simbolo (immagine) attraverso l’applicazione di un significante (machine learning che si sostituisce al processo mentale proprio dell’uomo) permettesse l’esplicitazione di un referente (oggetto\etichetta) che opportunatamente porta alla trasmissione di un messaggio sotto forma di conoscenza. Il tutto coordinato da un sistema in grado di coniugare fattori differenti in un’ottica interdisciplinare dove l’analisi dei dati combacia perfettamente con la linguistica. Attingendo da studi precedenti, i risultati raggiunti dimostrano che gli algoritmi di analisi dei Big Data quali l’apprendimento automatico contribuiscono da un lato alla comprensione sull’esperienza dell’utente verso un luogo, una destinazione; d’altra parte, la loro analisi fornisce una conoscenza sistematica delle valutazioni dei consumatori su un determinato prodotto o servizio e verso lo sviluppo di una sorta di “intelligenza sociale”. Inoltre i risultati della ricerca propongono come, un approccio più sofisticato al monitoraggio dei social media nel contesto turistico e nel marketing, nonché nel settore dell’education, possa contribuire a migliorare le decisioni strategiche e le politiche operative degli stakeholder nonché ad avere una visione psicologica sugli atteggiamenti e sul comportamento di un ampio spettro di utenti.Item Development of advanced systems for energy conversion based on innovative two- dimensional materials(Università della Calabria, 2021-09-27) Zappia, Marilena Isabella; Critelli, Salvatore; Chiarello, Gennaro; Cupolillo, AnnaThe even growing energy demand due to the demographic growth and the consequent economic expansion has led to the search for innovative technologies available for energy production and conversion from green and renewable sources such as solar energy. In this context, twodimensional (2D) materials, including either single- and few-layer flake forms, are constantly attracting more and more interest as potential advanced photo(electro)catalysts for redox reactions leading to green fuel production. Recently, layered semiconductors of group-III and group-IV, which can be exfoliated in their 2D form due to low cleavage energy (typically < 0.5 J m-2), have been theoretically predicted as water splitting photocatalysts for hydrogen production. For example, their large surface-to-volume ratio intrinsically guarantees that the charge carriers are directly photogenerated at the interface with the electrolyte, where redox reactions take place before they recombine. Moreover, their electronic structure can be tuned by controlling the number of layers, fulfilling the fundamental requirements for water splitting photocatalysts, i.e.: 1) conduction band minimum (CBM) energy (ECBM) > reduction potential of H+/H2 (E(H+/H2)); 2) valence band maximum (VBM) energy (EVBM) < reduction potential of O2/H2O (E(O2/H2O)). A requirement for large-scale applications is the development of low-cost, reliable industrial production processes. In this scenario, liquid-phase exfoliation (LPE) methods provide scalable production of 2D materials in form of liquid dispersions, enabling their processing in thin-film through low‐cost and industrially relevant deposition techniques. This thesis investigates, for the first time, the photoelectrochemical (PEC) activity of single-/fewlayer flakes of GaS, GaSe, and GeSe produced through ultrasound-assisted LPE in environmentally friendly solvents (e.g., 2-propanol) in aqueous media. Our results are consequently used to design proof-of-concept PEC water splitting photoelectrodes, as well as PEC-type photodetectors. Moreover, structural and electronic properties of PtTe2 have been investigated, being this material a potential catalyst for the hydrogen evolution reaction (HER) and other fuel-producing electrochemical reactions.Item Synthesis and cherization of low-dimensional materials(Università della Calabria, 2020-04-16) Alessandro, Francesca; Critelli, Salvatore; Caputi, Lorenzo; Cupolillo, AnnaThe main aim of this thesis is to synthesize and study low-dimensional materials, with special focus on: silicene, PtTe2, carbon nano-onions and activated carbon. The first section of this work describes the study of the collective modes in silicene and PtTe2. Silicene, the silicon equivalent of graphene, is attracting increasing scientific and technological interest in view of the exploitation of its exotic electronic properties. This material has been theoretically predicted to exist as a free-standing layer in a low-buckled, stable form, and can be synthesized by the deposition of Si on appropriate crystalline substrates. Using a combined experimental (High-Resolution Electron-Energy-Loss Spectroscopy, HR-EELS) and theoretical (Time Dependent Density Functional Theory, TDDFT) approach the electronic excitations of two phases of silicene growth on silver were studied showing that silicene grown in a mixed phase on Ag(111), preserves part of the semimetallic character of its freestanding form, exhibiting an interband π-like plasmon. Recently, the PtTe2 has emerged as one of the most promising among layered materials ―beyond graphene‖. In this work, the electronic excitations of the bulk PtTe2 were investigated by means of EELS and DFT detecting a sequence of modes at 3.9, 7.5 and 19.0 eV. The comparison of the excitation spectrum with the calculated density of states (DOS) allowed to ascribe spectral features to transitions between specific electronic states. Moreover, it has been observed that, in contrast to graphene, the high-energy plasmon in PtTe2 gets red-shifted by 2.5 eV with increasing thickness. The second section of this thesis reports the synthesis of polyhedral carbon nano-onions by arc discharge in water and the electrochemical performance of activated carbon in aqueous electrolytes. CNOs, in their spherical or polyhedral forms, represent an important class of nanomaterials, due to their peculiar physical and chemical properties. In this work, polyhedral carbon nano-onions (CNOs) were obtained by underwater arc discharge of graphite electrodes using an innovative experimental arrangement. Dispersed nanomaterials and a black hard cathodic deposit were generated during the discharges and studied by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman spectroscopy and thermogravimetric analysis (TGA). A model for the formation of the deposit was proposed, in which the crystallization is driven by an intense temperature gradient in the space very close to the cathode surface. Electric double layer capacitors (EDLC) are gaining increasing popularity in high power energy storage applications. Novel carbon materials with high surface area, high electrical conductivity, as well as a range of shapes, sizes and pore size distributions are being constantly developed and tested as potential supercapacitor electrodes. In this thesis, the electrochemical behavior of a highly microporous activated carbon was studied as electrode for symmetric and asymmetric capacitors in acid and neutral media. The highest capacity and energy density values were obtained in the case of the activated carbon in acid solution.Item Construction and test of high resolution and radiation hard particle detectors for fundamental physics at the LHC experiments and search for signal of new physics through displaced and collimated jet-like structures with ATLAS experiment(Università della Calabria, 2021-11-08) Carducci, Giovandomenico; Critelli, Salvatore; Schioppa, Marco