Genetic characterisation and global comparison of ancient Mediterranean Great White Sharks population (Carcharodon carcharias, L.1758)

This study poses as its objective the genetic characterization of the ancient population of the Great White shark, Carcharodon carcharias, L.1758, present in the Mediterranean Sea. Using historical evidence, for the most part buccal arches but also whole, stuffed examples from various national museums, research institutes and private collections, a dataset of 18 examples coming from the Mediterranean Sea has been created, in order to increase the informations regarding this species in the Mediterranean. The importance of the Mediterranean provenance derives from the fact that a genetic characterization of this species' population does not exist, and this creates gaps in the knowledge of this species in the Mediterranean. The genetic characterization of the individuals will initially take place by the extraction of the ancient DNA and the analysis of the variations in the sequence markers of the mitochondrial DNA. This approach has allowed the genetic comparison between ancient populations of the Mediterranean and contemporary populations of the same geographical area. In addition, the genetic characterization of the population of white sharks of the Mediterranean, has allowed a genetic comparison with populations from global "hot spots", using published sequences in online databases (NCBI, GenBank). Analyzing the variability of the dataset, both in terms space and time, I assessed the evolutionary relationships of the Mediterranean population of Great Whites with the global populations (Australia/New Zealand, South Africa, Pacific USA, West Atlantic), and the temporal trend of the Mediterranean population variability. This method based on the sequencing of two portions of mitochondrial DNA genes, markers showed us how the population of Great White Sharks in the Mediterranean, is genetically more similar to the populations of the Australia Pacific ocean, American Pacific Ocean, rather than the population of South Africa, and showing also how the population of South Africa is abnormally distant from all other clusters. Interestingly, these results are inconsistent with the results from tagging of this species. In addition, there is evidence of differences between the ancient population of the Mediterranean with the modern one. This differentiation between the ancient and modern population of white shark can be the result of events impacting on this species occurred over the last two centuries.

Graphite for lithium-ion batteries: mineral analysis and global Material Flow Analysis

Graphite is a mineral commodity used as anode for lithium-ion batteries (LIBs), and its global demand is doomed to increase significantly in the future due to the forecasted global market demand of electric vehicles. Currently, the graphite used to produce LIBs is a mix of synthetic and natural graphite. The first one is produced by the crystallization of petroleum by-products and the second comes from mining, which causes threats related to pollution, social acceptance, and health. This MSc work has the objective of determining compositional and textural characteristics of natural, synthetic, and recycled graphite by using SEM-EDS, XRF, XRD, and TEM analytical techniques and couple these data with dynamic Material Flow Analysis (MFA) models, which have the objective of predicting the future global use of graphite in order to test the hypothesis that natural graphite will no longer be used in the LIB market globally. The mineral analyses reveal that the synthetic graphite samples contain less impurities than the natural graphite, which has a rolled internal structure similar to the recycled one. However, recycled graphite shows fractures and discontinuities of the graphene layers caused by the recycling process, but its rolled internal structure can help the Li-ions’ migration through the fractures. Three dynamic MFA studies have been conducted to test distinct scenarios that include graphite recycling in the period 2022-2050 and it emerges that - irrespective of any considered scenario - there will be an increase of synthetic graphite demand, caused by the limited stocks of battery scrap available. Hence, I conclude that both natural and recycled graphite is doomed to be used in the LIB market in the future, at least until the year 2050 when the stock of recycled graphite production will be enough to supersede natural graphite. In addition, some new improvement in the dismantling and recycling processes are necessary to improve the quality of recycled graphite.

Local and nonlocal integro-differential reaction-diffusion models for protein dynamics in Alzheimer's disease

In this work, integro-differential reaction-diffusion models are presented for the description of the temporal and spatial evolution of the concentrations of Abeta and tau proteins involved in Alzheimer's disease. Initially, a local model is analysed: this is obtained by coupling with an interaction term two heterodimer models, modified by adding diffusion and Holling functional terms of the second type. We then move on to the presentation of three nonlocal models, which differ according to the type of the growth (exponential, logistic or Gompertzian) considered for healthy proteins. In these models integral terms are introduced to consider the interaction between proteins that are located at different spatial points possibly far apart. For each of the models introduced, the determination of equilibrium points with their stability and a study of the clearance inequalities are carried out. In addition, since the integrals introduced imply a spatial nonlocality in the models exhibited, some general features of nonlocal models are presented. Afterwards, with the aim of developing simulations, it is decided to transfer the nonlocal models to a brain graph called connectome. Therefore, after setting out the construction of such a graph, we move on to the description of Laplacian and convolution operations on a graph. Taking advantage of all these elements, we finally move on to the translation of the continuous models described above into discrete models on the connectome. To conclude, the results of some simulations concerning the discrete models just derived are presented.

Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.