Spatial based approach to fisheries management. An application to bottom trawling in the strait of Sicily

At the beginning of my thesis project, considering that some stocks are in overfishing status due to both high fishing effort and high level of juveniles in the catch, my main purpose was to understand how to contribute to improving the state of the fishery resources of the Mediterranean Sea. To mitigate the overfishing, the General Fisheries Commission for the Mediterranean (GFCM) adopted several Fishery Restricted Areas, which are geographically defined areas where some specific fishing activities are temporarily or permanently banned or restricted in order to reduce the exploitation patterns and conservation of specific stocks as well as of habitats and deep-sea ecosystems, including the Essential Fish Habitats (EFH) and the Vulnerable Marine Ecosystems (VME). Considering that GFCM established 3 Fisheries Restricted Areas (FRAs) in the Strait of Sicily (SoS) in 2016 aimed at protecting the nursery areas of the deep-water rose shrimp (DPS, Parapenaeus longirostris – Lucas, 1846) and the European hake (HKE, Merluccius merluccius – Linnaeus, 1758) to reduce the exploitation pattern of undersized species, in my thesis project I devoted myself to evaluate the effect of the FRAs on the status stock and the fishery performance using a spatial bio-economic model.

A Unified Model for XVA, including Interest Rates and Rating

We start in Chapter 2 to investigate linear matrix-valued SDEs and the Itô-stochastic Magnus expansion. The Itô-stochastic Magnus expansion provides an efficient numerical scheme to solve matrix-valued SDEs. We show convergence of the expansion up to a stopping time τ and provide an asymptotic estimate of the cumulative distribution function of τ. Moreover, we show how to apply it to solve SPDEs with one and two spatial dimensions by combining it with the method of lines with high accuracy. We will see that the Magnus expansion allows us to use GPU techniques leading to major performance improvements compared to a standard Euler-Maruyama scheme. In Chapter 3, we study a short-rate model in a Cox-Ingersoll-Ross (CIR) framework for negative interest rates. We define the short rate as the difference of two independent CIR processes and add a deterministic shift to guarantee a perfect fit to the market term structure. We show how to use the Gram-Charlier expansion to efficiently calibrate the model to the market swaption surface and price Bermudan swaptions with good accuracy. We are taking two different perspectives for rating transition modelling. In Section 4.4, we study inhomogeneous continuous-time Markov chains (ICTMC) as a candidate for a rating model with deterministic rating transitions. We extend this model by taking a Lie group perspective in Section 4.5, to allow for stochastic rating transitions. In both cases, we will compare the most popular choices for a change of measure technique and show how to efficiently calibrate both models to the available historical rating data and market default probabilities. At the very end, we apply the techniques shown in this thesis to minimize the collateral-inclusive Credit/ Debit Valuation Adjustments under the constraint of small collateral postings by using a collateral account dependent on rating trigger.

Deep learning and spatial transcriptomics to investigate thyroid tumors

There are many diseases that affect the thyroid gland, and among them are carcinoma. Thyroid cancer is the most common endocrine neoplasm and the second most frequent cancer in the 0-49 age group. This thesis deals with two studies I conducted during my PhD. The first concerns the development of a Deep Learning model to be able to assist the pathologist in screening of thyroid cytology smears. This tool created in collaboration with Prof. Diciotti, affiliated with the DEI-UNIBO "Guglielmo Marconi" Department of Electrical Energy and Information Engineering, has an important clinical implication in that it allows patients to be stratified between those who should undergo surgery and those who should not. The second concerns the application of spatial transcriptomics on well-differentiated thyroid carcinomas to better understand their invasion mechanisms and thus to better comprehend which genes may be involved in the proliferation of these tumors. This project specifically was made possible through a fruitful collaboration with the Gustave Roussy Institute in Paris. Studying thyroid carcinoma deeply is essential to improve patient care, increase survival rates, and enhance the overall understanding of this prevalent cancer. It can lead to more effective prevention, early detection, and treatment strategies that benefit both patients and the healthcare system.