Brexit, Trade Disruptions, and Export Recommendations

This doctoral dissertation is structured into three chapters, each ad- dressing aspects related to policy evaluation, with a focus on events car- rying substantial implications for the economy and international trade. These events include the Brexit referendum, trade disruptions, and the development of export recommendations. The Economic Cost of a Referendum. The Case of Brexit. This paper esti- mates how GDP would have behaved in the United Kingdom after the Brexit referendum in the absence of the mentioned poll using the Syn- thetic Control Method. We contribute to the research on the effects of Brexit by quantifying the macroeconomic cost of this referendum before the actual Brexit has taken place. We find a large and significant negative effect of the Brexit referendum on the GDP of UK. This loss is increasing in time representing, in 2017 Q4, 1.71% of the observed GDP of UK. Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms. This paper investigates the impact of COVID-19 on Colombian exports, revealing a substantial de- cline in survival probabilities during 2020. On average, we find that the COVID-19 shock decreased a firm’s probability of surviving in the export market by about 20 percentage points in April 2020. Importantly, ex- porters more integrated into Global Value Chains (GVCs) and importing higher value emerged as pivotal in bolstering exporter resilience during the crisis, emphasizing the need for policies supporting varied import networks, as well as international trade facilitation. Methodologically, this research innovates by utilizing causal Machine Learning (ML) tools in scenarios where the pervasive nature of the shock hinders the identi- fication of a control group unaffected by the shock, as well as the ex-ante definition of the intensity of the shock’s exposure of each unit, making a traditional control group identification unfeasible. This approach effec- tively predicts firms’ trade and uses these predictions to reconstruct the counterfactual distribution of firms’ trade under different scenarios and to study treatment effect heterogeneity. Exports’ Survival in New Markets: A firm-level export recommendation model. This paper investigates the factors that better predict a firm’s trade status of exporters after expanding to a new destination, specif- ically whether they continue exporting after two years. Using Colom- bian customs data, I show that market-level information is crucial for understanding export survival rates, beyond traditional firm-level char- acteristics like export experience. The paper introduces a novel Machine Learning-based export market entry recommendation tool, designed at the firm-product market level. While firms in the sample did not have access to this tool, the analysis observes which firms chose new destina- tions that align with the recommendations generated by the tool. Sim- ulated back-testing indicates that firms selecting destinations consistent with the tool’s guidance would have experienced a 5 percentage point higher survival rate compared to those choosing other destinations. Ad- ditionally, product growth would have been 34 percentage points higher for products where at least one firm followed the tool’s suggested market entry, compared to those that did not. The findings suggest that incom- plete market insights may lead to sub-optimal export decisions and that exporters incur temporary trade as a way of experimentation and to re- solve incomplete market information

Numerical Modeling and Optimization of Fractured Structures via Machine Learning and Topology Optimization

During the continuous development of science and technol- ogy, optimization plays a tremendous role in improving our resources without compromising the quality of performance. This thesis work investigates the application of the phase- field method for fracture (PFF) in brittle materials, focusing on the understanding of the influence of the model parame- ters, both for the isotropic and the anisotropic cases, in cap- turing the mechanical response of experimental results. For the PFF isotropic case, an experimental investigation was car- ried out on an ABS co-polymers. A MATLAB-based algo- rithm combining particle swarm optimization (PSO) with PFF has been utilized to determine optimal values of Young’s mod- ulus (E), fracture toughness (Gc), and the PFF internal length scale (lc) through uni-axial tensile and three-point bending tests. To understand the potential of bio-polymers in vari- ous industrial applications, 3D printed PLA materials were fabricated via fusion deposition modeling, and due to their anisotropic behavior, an anisotropic PFF approach was ex- ploited. A metaheuristic machine learning algorithm coupled with PFF demonstrates robustness in estimating fracture pa- rameters (Gc, lc, β) and a strong influence of β the penalty parameter on the predicted force-displacement curves. The thesis examine also the critical issue of delamination at internal interfaces/adhesive joints and internal cracks in com- posite and multi-material components, which can lead to catas- trophic failures. Existing structural topology optimization (TO) methods typically assumes perfect bonding, which urges the development of approaches that explicitly optimize struc-tures against delamination. The proposed data-driven heuris- tic optimization strategy has been applied to identify optimal cohesive interface properties with linear grading, enhancing the composite structure’s resistance to peeling. Additionally, it explored the application of the Solid Isotropic Material with Penalty (SIMP) topology optimization approach to optimize substrate internal structures affected by interface delamina- tion. The integration of a phase-field for fracture (PFF) approach with TO has been highlighted as a robust mathematical frame- work to mitigate crack progression in structures compromised by initial damage under operational loads. Employing the SIMP technique and optimality criteria (OC) method, the re- search validated its effectiveness through numerical exam- ples, demonstrating potential improvements in fracture re- sistance for damaged structures crucial in aerospace, marine, automotive, and civil engineering industries

Dynamical systems reduction through approximate lumping techniques

Model reduction is a fundamental technique utilized across various disciplines, such as engineering, physics, and compu- tational sciences, to simplify complex mathematical models while retaining essential dynamics. This thesis introduces two novel approaches for model reduc- tion, particularly focusing on dynamical systems described by polynomial ordinary differential equations (ODEs). The pro- posed techniques aim to reduce ODE systems while providing formal error bounds for the resultant reduced models. The first approach, based on backward and forward differen- tial equivalence (BDE/FDE), partitions the set of variables in an ODE system to construct a reduced model, incorporating a tolerance parameter ε to capture perturbations in polynomial coefficients. In the second approach, we present an algorithm to transform an ODE system into a so-called differential hull. This is a construction whereby variables with structurally sim- ilar dynamics but originally different parameters may be rep- resented by the same lower and upper bounds and reduced through the backward differential equivalence. Furthermore, the thesis explores the application of these tech- niques in discovering regular equivalences on networks. An iterative scheme, called iterative ε-BDE, is introduced to com- pute regular equivalences, allowing for the analysis of roles in networks. Experimental evaluations demonstrate the effectiveness and efficiency of the proposed approaches compared to existing methods in the literature

Stratification of first episode psychosis based on clinical and neurobiological features: from single-center studies to big data

Psychosis is a common and functionally disruptive clinical syndrome that might be present in many psychiatric, neurodevelopmental, neurologic, and medical conditions. Rather than a nosological entity, psychosis is a syndrome characterized by different symptoms and domains. Therefore, an increasing amount of pointed out the importance of recognising and treating a first episode as soon as possible. For these reasons, first episode psychosis (FEP) rapidly became a very important population of study and assessment. More than just the first symptomatic presentation of a disease, FEP often shows already some of the features of the advanced psychiatric illnesses, although to a minor extent. On the other hand, great efforts are being made in order to establish an effective intervention, given the fact that early treatment has been proved to ameliorate the course of the disease, ranging from symptoms, relapse, and number of hospitalisations to quality-of-life measures such as involvement in school or work and global functioning. Given the multifactorial nature of FEP and the different trajectories it can follow (e.g., affective vs. non-affective psychosis), the possibility of predicting future trajectories and to obtain clear and valid biomarkers is becoming of paramount importance. Prediction modelling has the potential to revolutionize medicine by predicting individual patient outcome. Early identification of those with good and poor outcomes would allow for a more personalised approach to care, matching interventions and resources to those most at need. Through a series of studies, we explored: 1) the possibility to stratify FEP patients based on neuroimaging and biological measures; 2) the possibility of use cutting edge machine learning techniques to improve classification and cluster subtypes of FEP patients; 3) the presence of autoimmune features in FEP in a multi-site study I had the opportunity to coordinate as Co-PI (namely the PHLAMES study). Specifically, in single-site studies we showed that neuroimaging and biological variables can be predictive of the course of the disease. Moreover, in large multi-site bid data analyses we presented how machine learning can improve the prediction of the disorder and help in stratify the risk, using both clinical and neuroimaging data. Finally, in the first results of PHLAMES study emerged that a subsample of FEP with autoimmune characteristics might be defined. This subsample shows some unique features in terms of neurological symptoms, cognitive deficits, and brain imaging alterations. The studies presented in this dissertation point out that it is possible to dissect the clinical and biological heterogeneity of psychosis at the beginning of its disease course, by defining meaningful groups of patients and therefore tailor personalized management. In conclusion, these data foster the research for subtypes of FEP and the definition of disease trajectories. These advances might have a great impact on patients’ lives, by defining specific subgroups or progression that benefit of tailored interventions

An Essay on the Emergence of Non-pathological Aggressive-like Behaviors in the Context of Social Interactions

Aggressivity is a type of widespread behavior in our society, yet its outcomes are everything but desirable. If evolutionary, being aggressive might have given some clear advantages to one to prevail (e.g., seizing resources, better mating options); nowadays, aggressive behavior held none of those advan- tages, being a form of prevarication where the ultimate goal is to harm another individual, either physically, emotionally, morally or materially. Why, then, is aggressive behavior still persistent despite the rise of cooperative societies? In this doctoral Thesis, with the aid of three controlled experiments and the expertise of Behavioral Economics and Neuroscience, I aim to shed more light on non-pathological aggressiveness, its genetic underpinnings, and cognitive mechanisms. Specif- ically, we found that some genetic variants of dopamine and serotonin are highly connected with actions and beliefs re- garding cooperation and punishment, where having a par- ticular variant makes one more prone to act and think pes- simistically toward the behaviors of others or to free-ride more. In another experiment, we demonstrate that extreme exertion of self-control makes it more probable to behave aggressively in a subsequent social situation. Frontal areas dedicated to impulse control regulation are, in fact, extremely vulnerable to functional fatigue, showing signs of local sleep. In this neu- ronal phenomenon, groups of neurons fire at frequencies typ- ical of sleep states instead of the ones of wake. Our exper- iment associated the prolonged exertion of self-control with the emergence of delta waves in frontal areas dedicated to impulse and emotion regulation and subsequent aggressive choices in a series of proxied social situations

A RIGHT TO DESTROY? THE LEGAL BOUNDARIES OF CULTURAL MEMORY. An Examination of the Role of the International Community in the Protection of National Heritage Sites against Deliberate Destruction.

The dissertation explores the deliberate destruction of cultural heritage under international law. The main thread of theresearch concerns the legal boundaries of cultural memory by examining when a duty to remember cultural heritage can translate into a legal obligation to preserve it - a right to remember- and when, at the same time, there may be a symmetrical legal duty to forget it. In other terms, the dissertation seeks to study not only the “pathological” dimension of destruction but also tracetheborders of destruction by verifying lawful situations in which is possible to recognize “a right to destroy”, b comparing different casesof destruction, or, as the case may be, removal of heritage. Thestudy explores cases of the destruction of heritage in national contexts authorized by domestic governments in light of applicable international norms in both peacetime and wartime contexts. The scope of the research includes mainly examples of tangible cultural heritage (more specifically, public monuments and buildings), which are characterized as contested heritage with accompanying issues of memory and divisive identity narratives. The research will focus on iconoclastic episodes driven by ideological reasons, rooted in three case-studies: Soviet monuments in Ukraine, Confederate iconography in the United States, and the situation of the Rohingya in Myanmar. It will exclude cases of peacetime threats to cultural heritage caused by economic development. The study seeks to enrich the interdisciplinary literature on memory and heritage studies in connection with law

Multi-field and multi-scale modeling of fracture for renewable energy applications

This thesis is mainly focused on the computational model- ing of solar cell cracking, multiphyics phenomena, and re- cycling of photovoltaic (PV) modules through the finite ele- ment method. Specifically, it consists of three parts. In the first part, a comprehensive hygro-thermo-mechanical com- putational framework in the 3D setting is proposed to model the coupled degradation phenomena in the PV modules for the durability analysis, and it is applied to the simulation of three international standard tests of PV modules, namely the damp heat test, the humidity freeze test, and the ther- mal cycling test. The second part is focused on the crack modeling of very thin and brittle silicon solar cells in the PV modules, and a reliable computational framework integrat- ing solid shell element formulation with phase field fracture modeling is developed using the efficient quasi-Newton so- lution scheme and global local approach. The excellent per- formance is showcased through the simulation of different boundary value problems, and then applied to predict the crack growth of silicon solar cells when the PV modules are subjected to different external loadings. The third part ad- dresses the efficient recycling of PV modules through the nu- merical modeling method by the development of 3D interface finite element with humidity-dose enhanced cohesive zone model for the peeling simulation to separate different layers, and diffusion-swelling large deformation continuum theory for the nondestructive recovery of silicon cells in the PV recy- cling using the solvent method. With these tools at hand, it is possible to design suitable virtual testing procedures for PV durability and recyclability analysis

On the conceptualisation and forecasting of emotion dynamics in healthy and psychiatric individuals

Throughout the day, individuals experience a variety of feelings, such as amusement, relaxation, and envy. By observing the regularities in the stream of affect, individuals learn to predict future emotions from current ones and develop accurate mental models of emotion transitions. This thesis aims to explore the cognitive architecture underlying these affective forecasts. Through a series of experiments involving both healthy and psychiatric individuals, we investigate i) the temporal boundaries of affective predictions and their evolution over time, ii) the influence of the conceptual knowledge about emotions on transition judgements at various timescales, iii) the impact of dysfunctional affective dynamics on the forecast of future emotions. Results indicate that people trust more their predictions in the near future, with confidence dropping after 24 hours. We identified nine prototypes in the temporal profiles of affective forecasts and mapped their trajectories in a two-dimensional space defined by transition plausibility and slope. Also, we characterise emotions as starting states (e.g., surprise) or end-points (e.g., irritation) based on transition judgments, and reveal asymmetry in forecasts for specific transitions (e.g., relief → fear). Analysis of the scaffolding of affective forecasts confirms the relevance of conceptual knowledge about emotions in shaping mental models of emotion transitions. Our findings indicate that similarities between emotions in certain dimensions (e.g., valence) inform predictions regardless of the time interval, while others (e.g., arousal) exert influence only within specific timescales. We demonstrate that psychiatric disorders such as depression and bipolar disorder significantly affect the architecture of affective forecasts, although these adjustments do not undermine the core predictive structure. Findings suggest that patients use their internal emotion dynamics as a reference to construct (or refine) their predictive models of emotion transitions

Computational Mechanics Framework for Simulations and Prediction of Wear in Frictional Contacts

A computational fnite element modelling of a mechanical model to predict wear, including friction, is proposed in this work. As an ex- pansion of the interface fnite element with an embedded profle for joint roughness (MPJR interface fnite element), it is designed to solve the frictional contact problem between rigid indenters of any complex shape and elastic bodies. In the formulation, the non-linearity due to contact is considered for predicting contact traction, frictional effects, and wear. This formulation interfaces with FEM software and can em- bed roughness or general deviations from the planarity as a correction to the normal gap function. The model employs a regularized version of the Coulomb friction law for the tangential contact response while introducing a penalty approach in the normal contact direction. The present framework enables the comprehensive investigation of the tan- gential and normal tractions via the computation of displacements and the displacement gaps in the model. These tangential and normal trac- tions can be used to calculate the wear rate via the wear law. The model defnes wear by contact force and gaps. Due to this, contact pressure develops wear and the normal gap changes. Model parameters related to the constitutive equations of the interface where two bodies come in contact: regularized coulomb friction law and Archard’s wear law out- lined. In conclusion, this model predicts the wear and wear rate at the micro-scale level and explains how to formulate and predict wear at the macro-scale level

Perception, Cognition and Ayahuasca A Multidimensional Analysis of Alter States of Consciousness

My Ph.D. thesis comprises a series of experiments aimed at investigating the impact of a novel ayahuasca analog, pharmahuasca (PHA), on face perception and creative cognition. These studies were executed with a within-subject, double-blind, placebo-controlled design involving 30 healthy male participants. Chapter 2 centers on the effects of psychedelics on face perception, utilizing electroencephalography (EEG) during a visual oddball task with self, familiar, and unknown faces as stimuli. Notable changes induced by PHA in early visual processing, such as increased P1 and reduced N170 across all face categories, were observed. In late visual processing, a decrease in neural activation in response to the self-face, as indicated by the P300 wave, highlights the significance of psychedelics in altering self-referential information processing. Additionally, the impact of psychedelics on face discrimination was explored through a two-alternative forced choice (2-AFC) task, where faces are incrementally morphed to each other, revealing a decreased sensitivity for discrimination during psychedelic experiences across all face categories. Chapter 3 shifts focus to understanding how psychedelics influence creative cognition. Through task-based methodologies, the findings unveil a reduction in convergent thinking without affecting on divergent thinking. Next, we investigate how utilization of different thinking modes during the artistic creation, specifically in the domain of painting, under the influence of psychedelics. Importantly, there was a significant reduction in transitions between different creative thinking modes during the psychedelic-induced creative process, particularly affecting stages traditionally requiring convergent thinking, offered valuable insights into the phenomenological nuances of the interplay between psychedelics and the dynamics of creative thinking