A study of air-sea interaction processes on water mass formation and upwelling in the Mediterranean sea

Air-sea interactions are a key process in the forcing of the ocean circulation and the climate. Water Mass Formation is a phenomenon related to extreme air-sea exchanges and heavy heat losses by the water column, being capable to transfer water properties from the surface to great depth and constituting a fundamental component of the thermohaline circulation of the ocean. Wind-driven Coastal Upwelling, on the other hand, is capable to induce intense heat gain in the water column, making this phenomenon important for climate change; further, it can have a noticeable influence on many biological pelagic ecosystems mechanisms. To study some of the fundamental characteristics of Water Mass Formation and Coastal Upwelling phenomena in the Mediterranean Sea, physical reanalysis obtained from the Mediterranean Forecating System model have been used for the period ranging from 1987 to 2012. The first chapter of this dissertation gives the basic description of the Mediterranean Sea circulation, the MFS model implementation, and the air-sea interaction physics. In the second chapter, the problem of Water Mass Formation in the Mediterranean Sea is approached, also performing ad-hoc numerical simulations to study heat balance components. The third chapter considers the study of Mediterranean Coastal Upwelling in some particular areas (Sicily, Gulf of Lion, Aegean Sea) of the Mediterranean Basin, together with the introduction of a new Upwelling Index to characterize and predict upwelling features using only surface estimates of air-sea fluxes. Our conclusions are that latent heat flux is the driving air-sea heat balance component in the Water Mass Formation phenomenon, while sensible heat exchanges are fundamental in Coastal Upwelling process. It is shown that our upwelling index is capable to reproduce the vertical velocity patterns in Coastal Upwelling areas. Nondimensional Marshall numbers evaluations for the open-ocean convection process in the Gulf of Lion show that it is a fully turbulent, three-dimensional phenomenon.

Multitemporal analysis of satellite imagery: implementation of a land evolution model in ALCS (ATSR LAND CLASSIFICATION SYSTEM)

In this report it was designed an innovative satellite-based monitoring approach applied on the Iraqi Marshlands to survey the extent and distribution of marshland re-flooding and assess the development of wetland vegetation cover. The study, conducted in collaboration with MEEO Srl , makes use of images collected from the sensor (A)ATSR onboard ESA ENVISAT Satellite to collect data at multi-temporal scales and an analysis was adopted to observe the evolution of marshland re-flooding. The methodology uses a multi-temporal pixel-based approach based on classification maps produced by the classification tool SOIL MAPPER ®. The catalogue of the classification maps is available as web service through the Service Support Environment Portal (SSE, supported by ESA). The inundation of the Iraqi marshlands, which has been continuous since April 2003, is characterized by a high degree of variability, ad-hoc interventions and uncertainty. Given the security constraints and vastness of the Iraqi marshlands, as well as cost-effectiveness considerations, satellite remote sensing was the only viable tool to observe the changes taking place on a continuous basis. The proposed system (ALCS – AATSR LAND CLASSIFICATION SYSTEM) avoids the direct use of the (A)ATSR images and foresees the application of LULCC evolution models directly to „stock‟ of classified maps. This approach is made possible by the availability of a 13 year classified image database, conceived and implemented in the CARD project (http://earth.esa.int/rtd/Projects/#CARD).The approach here presented evolves toward an innovative, efficient and fast method to exploit the potentiality of multi-temporal LULCC analysis of (A)ATSR images. The two main objectives of this work are both linked to a sort of assessment: the first is to assessing the ability of modeling with the web-application ALCS using image-based AATSR classified with SOIL MAPPER ® and the second is to evaluate the magnitude, the character and the extension of wetland rehabilitation.

Phage therapy: A software system for phage quantification and kinetic model inference.

One of the most serious problems of the modern medicine is the growing emergence of antibiotic resistance among pathogenic bacteria. In this circumstance, different and innovative approaches for treating infections caused by multidrug-resistant bacteria are imperatively required. Bacteriophage Therapy is one among the fascinating approaches to be taken into account. This consists of the use of bacteriophages, viruses that infect bacteria, in order to defeat specific bacterial pathogens. Phage therapy is not an innovative idea, indeed, it was widely used around the world in the 1930s and 1940s, in order to treat various infection diseases, and it is still used in Eastern Europe and the former Soviet Union. Nevertheless, Western scientists mostly lost interest in further use and study of phage therapy and abandoned it after the discovery and the spread of antibiotics. The advancement of scientific knowledge of the last years, together with the encouraging results from recent animal studies using phages to treat bacterial infections, and above all the urgent need for novel and effective antimicrobials, have given a prompt for additional rigorous researches in this field. In particular, in the laboratory of synthetic biology of the department of Life Sciences at the University of Warwick, a novel approach was adopted, starting from the original concept of phage therapy, in order to study a concrete alternative to antibiotics. The innovative idea of the project consists in the development of experimental methodologies, which allow to engineer a programmable synthetic phage system using a combination of directed evolution, automation and microfluidics. The main aim is to make “the therapeutics of tomorrow individualized, specific, and self-regulated” (Jaramillo, 2015). In this context, one of the most important key points is the Bacteriophage Quantification. Therefore, in this research work, a mathematical model describing complex dynamics occurring in biological systems involving continuous growth of bacteriophages, modulated by the performance of the host organisms, was implemented as algorithms into a working software using MATLAB. The developed program is able to predict different unknown concentrations of phages much faster than the classical overnight Plaque Assay. What is more, it gives a meaning and an explanation to the obtained data, making inference about the parameter set of the model, that are representative of the bacteriophage-host interaction.

Keller-Segel system, chemotaxis and applications to a mathematical model for Alzheimer's disease

In questa tesi viene presentato il modello di Keller-Segel per la chemiotassi, un sistema di tipo parabolico-ellittico che appare nella descrizione di molti fenomeni in ambito biologico e medico. Viene mostrata l'esistenza globale della soluzione debole del modello, per dati iniziali sufficientemente piccoli in dimensione N>2. La scelta di dati iniziali abbastanza grandi invece può causare il blow-up della soluzione e viene mostrato sotto quali condizioni questo si verifica. Infine il modello della chemiotassi è stato applicato per descrivere una fase della malattia di Alzheimer ed è stata effettuata un'analisi di stabilità del sistema.

Development of a polyplexes-based miRNA delivery system in a 3D-bioplotted osteoarthritis cellular model.

L'osteoartrite (OA) è una patologia infiammatorio/degenerativa ossea per la quale non sono disponibili terapie causali efficaci ma solo approcci palliativi per la riduzione del dolore cronico. E’ quindi giustificato un investimento per individuare nuove strategie di trattamento. In quest’ottica, lo scopo di questa tesi è stato quello di indagare l’efficacia di polyplexi a base di chitosano o di PEI-g-PEG in un modello cellulare 3D in vitro basato su un hydrogel di Gellan Gum Metacrilato (GGMA) con a bordo condrociti in condizioni simulate di OA. Inizialmente sono state studiate la dimensione e il potenziale-Z di un pool di formulazioni di poliplexi. Quindi se ne è valutata la citocompatibilità utilizzando cellule staminali mesenchimali immortalizzate Y201. Infine, una miscela di GGMA, cellule e polyplexi è stata utilizzata per la stampa 3D di campioni che sono stati coltivati fino a 14 giorni. La condizione OA è stata simulata trattando le cellule con una miscela di citochine implicate nello sviluppo della malattia. Tutte le formulazioni a base di chitosano e due basate su PEI-g-PEG si sono dimostrate citocompatibili e sono hanno veicolato i miRNA nelle cellule (come mostrato dai risultati di analisi in fluorescenza). I risultati delle colorazioni H&E e AlcianBlue hanno confermato che il terreno condizionato ha ben ricreato le condizioni di OA. I polyplexi a base di chitosano e PEI-g-PEG hanno controbilanciato gli effetti delle citochine. Risultati incoraggianti, anche se da approfondire ulteriormente, provengono anche dall’analisi di espressione (RT-PCR) di cinque geni specifici della cartilagine. Concludendo, questo modello ha ben riprodotto le condizioni di OA in vitro; il chitosano ha mostrato di essere un adeguato veicolo per un trattamento a base di miRNA; il PEI-g-PEG si propone come un'alternativa più economica e ragionevolmente affidabile, sebbene il rischio di citotossicità alle concentrazioni più elevate richieda una più esteva validazione sperimentale.

Model-Based validation of Driver Drowsiness Detection System for ADAS

The work described in this Master’s Degree thesis was born after the collaboration with the company Maserati S.p.a, an Italian luxury car maker with its headquarters located in Modena, in the heart of the Italian Motor Valley, where I worked as a stagiaire in the Virtual Engineering team between September 2021 and February 2022. This work proposes the validation using real-world ECUs of a Driver Drowsiness Detection (DDD) system prototype based on different detection methods with the goal to overcome input signal losses and system failures. Detection methods of different categories have been chosen from literature and merged with the goal of utilizing the benefits of each of them, overcoming their limitations and limiting as much as possible their degree of intrusiveness to prevent any kind of driving distraction: an image processing-based technique for human physical signals detection as well as methods based on driver-vehicle interaction are used. A Driver-In-the-Loop simulator is used to gather real data on which a Machine Learning-based algorithm will be trained and validated. These data come from the tests that the company conducts in its daily activities so confidential information about the simulator and the drivers will be omitted. Although the impact of the proposed system is not remarkable and there is still work to do in all its elements, the results indicate the main advantages of the system in terms of robustness against subsystem failures and signal losses.

Modeling and Model Predictive Control of an Industrial Hydraulic System

This thesis aims to illustrate the construction of a mathematical model of a hydraulic system, oriented to the design of a model predictive control (MPC) algorithm. The modeling procedure starts with the basic formulation of a piston-servovalve system. The latter is a complex non linear system with some unknown and not measurable effects that constitute a challenging problem for the modeling procedure. The first level of approximation for system parameters is obtained basing on datasheet informations, provided workbench tests and other data from the company. Then, to validate and refine the model, open-loop simulations have been made for data matching with the characteristics obtained from real acquisitions. The final developed set of ODEs captures all the main peculiarities of the system despite some characteristics due to highly varying and unknown hydraulic effects, like the unmodeled resistive elements of the pipes. After an accurate analysis, since the model presents many internal complexities, a simplified version is presented. The latter is used to linearize and discretize correctly the non linear model. Basing on that, a MPC algorithm for reference tracking with linear constraints is implemented. The results obtained show the potential of MPC in this kind of industrial applications, thus a high quality tracking performances while satisfying state and input constraints. The increased robustness and flexibility are evident with respect to the standard control techniques, such as PID controllers, adopted for these systems. The simulations for model validation and the controlled system have been carried out in a Python code environment.

A mathematical model of the early visual system and border completion via subriemannian Hamiltonian geodesics

In this thesis, we aim to discuss a simple mathematical model for the edge detection mechanism and the boundary completion problem in the human brain in a differential geometry framework. We describe the columnar structure of the primary visual cortex as the fiber bundle R2 × S1, the orientation bundle, and by introducing a first vector field on it, explain the edge detection process. Edges are detected through a lift from the domain in R2 into the manifold R2 × S1 and are horizontal to a completely non-integrable distribution. Therefore, we can construct a subriemannian structure on the manifold R2 × S1, through which we retrieve perceived smooth contours as subriemannian geodesics, solutions to Hamilton’s equations. To do so, in the first chapter, we illustrate the functioning of the most fundamental structures of the early visual system in the brain, from the retina to the primary visual cortex. We proceed with introducing the necessary concepts of differential and subriemannian geometry in chapters two and three. We finally implement our model in chapter four, where we conclude, comparing our results with the experimental findings of Heyes, Fields, and Hess on the existence of an association field.

Integrating an Emotion Recognition Model for the Flobi System

This thesis investigates if emotional states of users interacting with a virtual robot can be recognized reliably and if specific interaction strategy can change the users’ emotional state and affect users’ risk decision. For this investigation, the OpenFace [1] emotion recognition model was intended to be integrated into the Flobi [2] system, to allow the agent to be aware of the current emotional state of the user and to react appropriately. There was an open source ROS [3] bridge available online to integrate OpenFace to the Flobi simulation but it was not consistent with some other projects in Flobi distribution. Then due to technical reasons DeepFace was selected. In a human-agent interaction, the system is compared to a system without using emotion recognition. Evaluation could happen at different levels: evaluation of emotion recognition model, evaluation of the interaction strategy, and evaluation of effect of interaction on user decision. The results showed that the happy emotion induction was 58% and fear emotion induction 77% successful. Risk decision results show that: in happy induction after interaction 16.6% of participants switched to a lower risk decision and 75% of them did not change their decision and the remaining switched to a higher risk decision. In fear inducted participants 33.3% decreased risk 66.6 % did not change their decision The emotion recognition accuracy was and had bias to. The sensitivity and specificity is calculated for each emotion class. The emotion recognition model classifies happy emotions as neutral in most of the time.