Synthetic data generation for the assessment of antimicrobial resistance through machine learning

As a consequence of the diffusion of next generation sequencing techniques, metagenomics databases have become one of the most promising repositories of information about features and behavior of microorganisms. One of the subjects that can be studied from those data are bacteria populations. Next generation sequencing techniques allow to study the bacteria population within an environment by sampling genetic material directly from it, without the needing of culturing a similar population in vitro and observing its behavior. As a drawback, it is quite complex to extract information from those data and usually there is more than one way to do that; AMR is no exception. In this study we will discuss how the quantified AMR, which regards the genotype of the bacteria, can be related to the bacteria phenotype and its actual level of resistance against the specific substance. In order to have a quantitative information about bacteria genotype, we will evaluate the resistome from the read libraries, aligning them against CARD database. With those data, we will test various machine learning algorithms for predicting the bacteria phenotype. The samples that we exploit should resemble those that could be obtained from a natural context, but are actually produced by a read libraries simulation tool. In this way we are able to design the populations with bacteria of known genotype, so that we can relay on a secure ground truth for training and testing our algorithms.

Ephemeris generation of the Galilean moons by using VLBI observations in support to stellar occultations

Stellar occultations are the most accurate Earth-based astronomy technique to obtain the lateral position of celestial bodies, in the case of natural satellites, their accuracy also depends on the central body to which the satellite orbits. The main goal of this thesis work is to analyze if and how very long baseline interferometry (VLBI) measurements of a body like Jupiter can be used in support to stellar occultations of its natural satellites by reducing the planetary uncertainty at the time of the occultation. In particular, we analyzed the events of the stellar occultations of Callisto (15.01.2024) and Io (02.04.2021). The stellar occultation of Callisto has been predicted and simulated using the stellar occultation reduction analysis (SORA) toolkit while the stellar occultation of Io has been already studied by Morgado et al. We then simulated the VLBI data of Jupiter according to the current JUNO trajectories. The required observation were then used as input of an estimation to which then we performed a covariance analysis on the estimated parameters to retrieve the formal errors (1 − σ uncertainties) at each epoch of the propagation. The results show that the addition of the VLBI slightly improves the uncertainty of Callisto even when Jupiter knowledge is worse while for Io we observed that the VLBI data is especially crucial in the scenario of an a priori uncertainty in Jupiter state of about 10km. Here we can have improvements of the estimated initial states of Io of about 70m, 230m and 900m to the radial, along-track and cross-track directions respectively. Moreover, we have also obtained the propagated errors of the two moons in terms of right ascension and declination which both show uncertainties in the mas level at the occultation time. Finally, we simulated Io and Europa together and we observed that at the time of the stellar occultation of Europa the along-track component of Io is constrained, confirming the coupling between the two inner moons.

Random Access Procedures in 5G IoT via NTN: System level performances

The Internet of Things (IoT) is a critical pillar in the digital transformation because it enables interaction with the physical world through remote sensing and actuation. Owing to the advancements in wireless technology, we now have the opportunity of using their features to the best of our abilities and improve over the current situation. Indeed, the Internet of Things market is expanding at an exponential rate, with devices such as alarms and detectors, smart metres, trackers, and wearables being used on a global scale for automotive and agriculture, environment monitoring, infrastructure surveillance and management, healthcare, energy and utilities, logistics, good tracking, and so on. The Third Generation Partnership Project (3GPP) acknowledged the importance of IoT by introducing new features to support it. In particular, in Rel.13, the 3GPP introduced the so-called IoT to support Low Power Wide Area Networks (LPWAN).As these devices will be distributed in areas where terrestrial networks are not feasible or commercially viable, satellite networks will play a complementary role due to their ability to provide global connectivity via their large footprint size and short service deployment time. In this context, the goal of this thesis is to investigate the viability of integrating IoT technology with satellite communication (SatCom) systems, with a focus on the Random Access(RA) Procedure. Indeed, the RA is the most critical procedure because it allows the UE to achieve uplink synchronisation, obtain the permanent ID, and obtain uplink transmission resources. The goal of this thesis is to evaluate preamble detection in the SatCom environment.