Characterization of microplastics ingested by marine benthos - a methodological and field-experimental study

Microplastics have become ubiquitous pollutants in the marine environment. Ingestion of microplastics by a wide range of marine organisms has been recorded both in laboratory and field studies. Despite growing concern for microplastics, few studies have evaluated their concentrations and distribution in wild populations. Further, there is a need to identify cost-effective standardized methodologies for microplastics extraction and analysis in organisms. In this thesis I present: (i) the results of a multi-scale field sampling to quantify and characterize microplastics occurrence and distribution in 4 benthic marine invertebrates from saltmarshes along the North Adriatic Italian coastal lagoons; (ii) a comparison of the effects and cost-effectiveness of two extraction protocols for microplastics isolation on microfibers and on wild collected organisms; (iii) the development of a novel field- based technique to quantify and characterize the microplastic uptake rates of wild and farmed populations of mussels (Mytilus galloprovincialis) through the analysis of their biodeposits. I found very low and patchy amounts of microplastics in the gastrointestinal tracts of sampled organisms. The omnivorous crab Carcinus aestuarii was the species with the highest amounts of microplastics, but there was a notable variation among individuals. There were no substantial differences between enzymatic and alkaline extraction methods. However, the alkaline extraction was quicker and cheaper. Biodeposit traps proved to be an effective method to estimate mussel ingestion rates. However their performance differed significantly among sites, suggesting that the method, as currently designed, is sensible to local environmental conditions. There were no differences in the ingestion rates of microplastics between farmed and wild mussels. The estimates of microplastic ingestion and the validated procedures for their extraction provide a strong basis for future work on microplastic pollution.

Multi-mode communications in near field exploiting OAM properties

This dissertation analyzes the exploitation of the orbital angular momentum (OAM) of the electromagnetic waves with large intelligent surfaces in the near-field region and line-of-sight conditions, in light of the holographic MIMO communication concept. Firstly, a characterization of the OAM-based communication problem is presented, and the relationship between OAM-carrying waves and communication modes is discussed. Then, practicable strategies for OAM detection using large intelligent surfaces and optimization methods based on beam focusing are proposed. Numerical results characterize the effectiveness of OAM with respect to other strategies, also including the proposed detection and optimization methods. It is shown that OAM waves constitute a particular choice of communication modes, i.e., an alternative basis set, which is sub-optimum with respect to optimal basis functions that can be derived by solving eigenfunction problems. Moreover, even the joint utilization of OAM waves with focusing strategies led to the conclusion that no channel capacity achievements can be obtained with these transmission techniques.

Methods for monitoring Machine Learning models

The following thesis aims to investigate the issues concerning the maintenance of a Machine Learning model over time, both about the versioning of the model itself and the data on which it is trained and about data monitoring tools and their distribution. The themes of Data Drift and Concept Drift were then explored and the performance of some of the most popular techniques in the field of Anomaly detection, such as VAE, PCA, and Monte Carlo Dropout, were evaluated.