In recent years, technological innovations have resulted in manifold applications using rare earth elements (REEs), leading to a dramatic increase in demand for them. Because of their unique physicochemical properties, REEs are considered indispensable in modern industry. They are extensively used in new materials, energy conservation, environmental protection and IT devices as well as in military weapon systems. They have also significantly contributed to the miniaturisation of electronic components, such as, for example, cell phones and laptop computers. REEs are essential for green technologies such as wind turbines. They are widely applied in the automotive industry for catalysts, hybrid vehicle batteries, motors and generators, etc. (Hurst, 2010). Due to the similarity of the chemical characteristics of each individual REE, the production processes for REEs with high purity are very complex: the processing and separation can be technically challenging. Furthermore, the chemical extraction processes involved have generated severe environmental problems. Currently, the supply of REEs is concentrated in China. To reduce the dependence on China, many countries have started to search for alternative REE sources, which can be classified into “primary sources” and “secondary sources”. Many REE exploration projects outside China and REE recycling projects have been launched. However, the success of the development of these projects is impacted by various risks, such as political risks, technical risks, environmental risks and social risks. The main research aim of this thesis is to establish a model for the evaluation of REE projects and to provide a basis for investment decision making. In order to complete this task, an analysis of REE deposits and the supply chain for REEs is provided. As results, a data base of potential REEs project is compiled, while an overview of the supply chain for REEs and an analysis of risks across the supply chain are presented. In order to assess potential REE production projects, a new real options valuation (ROV) model using a multi-dimensional binomial lattice approach is developed. For the application of the new real options model, a range of risk parameters and the expected production output of REE products are estimated using the Monte Carlo simulation method. The application of the new real options model is presented for the evaluation of the Bayan Obo mine in China, the Kvanefjeld REE project in Greenland, and a REE recycling project from magnetic scrap.

<p dir="ltr">Tuberculosis (TB), caused by <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>), and human immunodeficiency virus-1 (HIV) are major public health concerns, individually and in combination. The status of the host immune system, previous <i>Mtb</i> infection and HIV-mediated T cell exhaustion, can have significant impacts on immune dynamics during reinfection. Individuals with asymptomatic latent TB infection (LTBI) may be protected against <i>Mtb </i>reinfection, as demonstrated by animal and <i>in vitro </i>studies. However, the underlying dynamics and protective mechanisms of LTBI are poorly understood. In HIV, long-term infection in children and associated T cell exhaustion leads to weakened immune responses to HIV reinfection. The complexity of these infections, particularly in the context of the heightened vulnerability of HIV+ individuals to TB, underscores the need for novel investigative approaches to study host-pathogen and pathogen-pathogen interactions. To this, we have developed an agent-based model (ABM) as a mechanistic computational tool to simulate the immune response to <i>Mtb </i>and HIV, separately and during coinfection. Our ABM integrates clinical and experimental data; simulates immune cell dynamics between macrophages, CD4+ and CD8+ T cells; and produces emergent granuloma-like structures – a critical response to <i>Mtb</i>. This <i>in silico</i> approach allows us to efficiently explore host-pathogen interactions and their clinical implications. By unraveling the complex interplay of immune cell activation, T cell exhaustion, and pathogen dynamics, our model offers insights that could guide the development of targeted therapies. By quantifying the multifaceted nature of these diseases and their interactions, we highlight the potential of computational approaches in understanding and treating complex diseases, individually and in combination.</p>

<p dir="ltr">Tuberculosis (TB) remains a global public health crisis, causing over 10 million new infections and 1.3 million deaths in 2022 alone. TB is caused by <i>Mycobacterium tuberculosis </i>(<i>Mtb</i>), which initiates heterogeneous pathology in the lungs, including granulomas and cavities. Granulomas are organized structures of immune cells, traditionally thought to contain bacteria. Cavities are pathological spaces caused by the destruction of extracellular matrix (ECM), which can worsen disease outcomes and cause long-lasting pulmonary impairment.<i> In vitro </i>methods are commonly used to study host-pathogen interactions in <i>Mtb</i> infection, and recent developments have led to models that represent the TB granuloma environment more closely than traditional cell culture. These advances include the development of 3D models and the inclusion of physiological ECM components like collagen. Increasing complexity has been accomplished in a piece-wise manner – minimally necessary components are included to minimize cost while maintaining throughput and tractability. This creates a need for tools to analyze these systems and, more importantly, integrate the independent data created. We developed an agent-based model to characterize multiple <i>in vitro</i> models of TB and apply it to 1) separate the contributions of dimension and structure to bacterial control in granuloma-like spheroids and 2) explore how the interactions of collagen and matrix metalloproteinases (MMP) contribute to clinically relevant outputs such as bacterial load and ECM destruction. The model provides insights into the role of granuloma structure and the conflicting results of MMP inhibition, generating new hypotheses to be tested in tandem with <i>in vitro</i> models.</p>

Questa tesi di dottorato analizza l’utilizzo del sorgo (Sorghum bicolour (L.) Moench) al fine di produrre energia elettrica, tramite combustione diretta della biomassa. Il focus della tesi è stato quello di sottolineare i benefici ed i rischi associati all’uso di tre genotipi di sorgo caratterizzati da diversa lunghezza del ciclo culturale (precoce, medio-tardivo e tardivo). La dinamica e la durata del processo di essicazione in campo sono state simulate attraverso un modello ad hoc (“sorghum haying model”), il quale integrato a CropSyst, è stato utilizzato per realizzare un’analisi del rischio produttivo stimando le perdite di biomassa (respirazione e meccanizzazione), ed i mancati affienamenti. Nell’analisi del rischio vengono stimati il numero di ettari necessari e la probabilità di eccedere la soglia di 64.000 ton ss anno-1 necessari per l’alimentazione di una centrale nell’Oltrepò pavese . Inoltre uno studio di Life Cycle Assessment è stato condotto per la valutazione dell’impatto ambientale dell’utilizzo del sorgo integrato a quello della paglia per il completamento del fabbisogno totale della centrale 94.000 ton ss anno-1. Particolare attenzione inoltre è stata data alla variazione del contenuto di C organico del suolo dovuto alla rimozione della paglia ed all’interramento dei mancati affienamenti di sorgo. Il genotipo precoce mostra le migliori performance produttive ed energetiche oltre che la più alta probabilità di eccedere la soglia di 64.000 ton ss anno-1. Lo studio di LCA non ha mostrato differenze significative tra i genotipi anche se il minor impatto ambientale, è stato evidenziato dal genotipo tardivo conseguenza dell’interramento della più alta quantità di mancati affienamenti. / This PhD thesis explores the use of sorghum (Sorghum bicolour (L.) Moench) as a dedicated bio-energy crop and highlights the benefits and risks associated with the use of early, medium-late and late sorghum genotypes to generate electricity by direct combustion in a biomass power plant. The dynamics and duration of the field drying process were simulated through the development of a specific model ("sorghum haying model"), which integrated with CropSyst, was used to perform a production risk assessment analysis estimating the biomass losses (respiration and mechanical), the haymaking failures and consequently to quantify the amount of dry baled biomass available for the power plant. In addition, the number of hectares needed to plant sorghum and the probability to exceed the threshold of 64000 Mg DM y-1, necessary to feed a biomass power plant in Oltrepò Pavese, were estimated. A complete Life Cycle Assessment (LCA) study was carried out in order to evaluate the environmental impact of the three sorghum genotypes involved in this study. The LCA study takes into consideration the use of winter wheat straw as an additional biomass source to satisfy the total biomass power plant needs (94000 Mg DM y-1). Particular attention was given to the soil organic C change (ΔSOC) due to straw removal and haymaking failures soil incorporation. Early genotype showed the best biomass production and energy performance as well as the highest probability to exceed the threshold of 64000 Mg DM y-1. The LCA results did not show significant differences between genotypes although the lower environmental impact, has been achieved by the late genotype due to the highest amount of haymaking failures incorporated in the soil.