Creep and damage models for the service behaviour of structural members

Deformability is often a crucial to the conception of many civil-engineering structural elements. Also, design is all the more burdensome if both long- and short-term deformability has to be considered. In this thesis, long- and short-term deformability has been studied from the material and the structural modelling point of view. Moreover, two materials have been handled: pultruded composites and concrete. A new finite element model for thin-walled beams has been introduced. As a main assumption, cross-sections rigid are considered rigid in their plane; this hypothesis replaces that of the classical beam theory of plane cross-sections in the deformed state. That also allows reducing the total number of degrees of freedom, and therefore making analysis faster compared with twodimensional finite elements. Longitudinal direction warping is left free, allowing describing phenomena such as the shear lag. The new finite-element model has been first applied to concrete thin-walled beams (such as roof high span girders or bridge girders) subject to instantaneous service loadings. Concrete in his cracked state has been considered through a smeared crack model for beams under bending. At a second stage, the FE-model has been extended to the viscoelastic field and applied to pultruded composite beams under sustained loadings. The generalized Maxwell model has been adopted. As far as materials are concerned, long-term creep tests have been carried out on pultruded specimens. Both tension and shear tests have been executed. Some specimen has been strengthened with carbon fibre plies to reduce short- and long- term deformability. Tests have been done in a climate room and specimens kept 2 years under constant load in time. As for concrete, a model for tertiary creep has been proposed. The basic idea is to couple the UMLV linear creep model with a damage model in order to describe nonlinearity. An effective strain tensor, weighting the total and the elasto-damaged strain tensors, controls damage evolution through the damage loading function. Creep strains are related to the effective stresses (defined by damage models) and so associated to the intact material.

The role of the Rocky Mountains in shaping the atmospheric mean state and the response to tropical forcing in idealised simulations.

Understanding the natural and forced variability of the atmospheric general circulation and its drivers is one of the grand challenges in climate science. It is of paramount importance to understand to what extent the systematic error of climate models affects the processes driving such variability. This is done by performing a set of simulations (ROCK experiments) with an intermediate complexity atmospheric model (SPEEDY), in which the Rocky Mountains orography is increased or decreased to influence the structure of the North Pacific jet stream. For each of these modified-orography experiments, the climatic response to idealized sea surface temperature anomalies of varying intensity in the El Niño Southern Oscillation (ENSO) region is studied. ROCK experiments are characterized by variations in the Pacific jet stream intensity whose extension encompasses the spread of the systematic error found in Coupled Model Intercomparison Project (CMIP6) models. When forced with ENSO-like idealised anomalies, they exhibit a non-negligible sensitivity in the response pattern over the Pacific North American region, indicating that the model mean state can affect the model response to ENSO. It is found that the classical Rossby wave train response to ENSO is more meridionally oriented when the Pacific jet stream is weaker and more zonally oriented with a stronger jet. Rossby wave linear theory suggests that a stronger jet implies a stronger waveguide, which traps Rossby waves at a lower latitude, favouring a zonal propagation of Rossby waves. The shape of the dynamical response to ENSO affects the ENSO impacts on surface temperature and precipitation over Central and North America. A comparison of the SPEEDY results with CMIP6 models suggests a wider applicability of the results to more resources-demanding climate general circulation models (GCMs), opening up to future works focusing on the relationship between Pacific jet misrepresentation and response to external forcing in fully-fledged GCMs.

The gender wage gap in developing and transition countries

The aim of my dissertation is to study the gender wage gap with a specific focus on developing and transition countries. In the first chapter I present the main existing theories proposed to analyse the gender wage gap and I review the empirical literature on the gender wage gap in developing and transition countries and its main findings. Then, I discuss the overall empirical issues related to the estimation of the gender wage gap and the issues specific to developing and transition countries. The second chapter is an empirical analysis of the gender wage gap in a developing countries, the Union of Comoros, using data from the multidimensional household budget survey “Enquete integrale auprès des ménages” (EIM) run in 2004. The interest of my work is to provide a benchmark analysis for further studies on the situation of women in the Comorian labour market and to contribute to the literature on gender wage gap in Africa by making available more information on the dynamics and mechanism of the gender wage gap, given the limited interest on the topic in this area of the world. The third chapter is an applied analysis of the gender wage gap in a transition country, Poland, using data from the Labour Force Survey (LSF) collected for the years 1994 and 2004. I provide a detailed examination of how gender earning differentials have changed over the period starting from 1994 to a more advanced transition phase in 2004, when market elements have become much more important in the functioning of the Polish economy than in the earlier phase. The main contribution of my dissertation is the application of the econometrical methodology that I describe in the beginning of the second chapter. First, I run a preliminary OLS and quantile regression analysis to estimate and describe the raw and conditional wage gaps along the distribution. Second, I estimate quantile regressions separately for males and females, in order to allow for different rewards to characteristics. Third, I proceed to decompose the raw wage gap estimated at the mean through the Oaxaca-Blinder (1973) procedure. In the second chapter I run a two-steps Heckman procedure by estimating a model of participation in the labour market which shows a significant selection bias for females. Forth, I apply the Machado-Mata (2005) techniques to extend the decomposition analysis at all points of the distribution. In Poland I can also implement the Juhn, Murphy and Pierce (1991) decomposition over the period 1994-2004, to account for effects to the pay gap due to changes in overall wage dispersion beyond Oaxaca’s standard decomposition.

Interpreting Judgements using Knowledge Representation Methods and Computational Models of Argument

The goal of the present research is to define a Semantic Web framework for precedent modelling, by using knowledge extracted from text, metadata, and rules, while maintaining a strong text-to-knowledge morphism between legal text and legal concepts, in order to fill the gap between legal document and its semantics. The framework is composed of four different models that make use of standard languages from the Semantic Web stack of technologies: a document metadata structure, modelling the main parts of a judgement, and creating a bridge between a text and its semantic annotations of legal concepts; a legal core ontology, modelling abstract legal concepts and institutions contained in a rule of law; a legal domain ontology, modelling the main legal concepts in a specific domain concerned by case-law; an argumentation system, modelling the structure of argumentation. The input to the framework includes metadata associated with judicial concepts, and an ontology library representing the structure of case-law. The research relies on the previous efforts of the community in the field of legal knowledge representation and rule interchange for applications in the legal domain, in order to apply the theory to a set of real legal documents, stressing the OWL axioms definitions as much as possible in order to enable them to provide a semantically powerful representation of the legal document and a solid ground for an argumentation system using a defeasible subset of predicate logics. It appears that some new features of OWL2 unlock useful reasoning features for legal knowledge, especially if combined with defeasible rules and argumentation schemes. The main task is thus to formalize legal concepts and argumentation patterns contained in a judgement, with the following requirement: to check, validate and reuse the discourse of a judge - and the argumentation he produces - as expressed by the judicial text.

The mesophotic zone of the Mediterranean Sea: spatial definition, biodiversity, and predictive models

The mesophotic zone is frequently defined as ranging between 30-40 and 150 m depth. However, these borders are necessarily imprecise due to variations in the penetration of light along the water column related to local factors. Moreover, density of data on mesophotic ecosystems vary along geographical distance, with temperate latitudes largely less explored than tropical situations. This is the case of the Mediterranean Sea, where information on mesophotic ecosystems is largely lower with respect to tropical situations. The lack of a clear definition of the borders of the mesophotic zone may represent a problem when information must be transferred to the policy that requires a coherent spatial definition to plan proper management and conservation measures. The present thesis aims at providing information on the spatial definition of the mesophotic zone in the Mediterranean Sea, its biodiversity and distribution of its ecosystems. The first chapter analyzes information on mesophotic ecosystems in the Mediterranean Sea to identify gaps in the literature and map the mesophotic zone in the Mediterranean Sea using light penetration estimated from satellite data. In the second chapter, different visual techniques to study mesophotic ecosystems are compared to identify the best analytical method to estimate diversity and habitat extension. In the third chapter, a set of Remotely Operated vehicles (ROV) surveys performed on mesophotic assemblages in the Mediterranean Sea are analyzed to describe their taxonomic and functional diversity and environmental factors influencing their structure. A Habitat Suitability Model is run in the fourth chapter to map the distribution of areas suitable for the presence of deep-water oyster reefs in the Adriatic-Ionian area. The fifth chapter explores the mesophotic zone in the northern Gulf of Mexico providing its spatial and vertical extension of the mesophotic zone and information on the diversity associated with mesophotic ecosystems.

Comprehensive characterization of SDH-deficient GIST using NGS data and iPSC models

Gastrointestinal stromal tumors (GIST) are the most common di tumors of the gastrointestinal tract, arising from the interstitial cells of Cajal (ICCs) or their precursors. The vast majority of GISTs (75–85% of GIST) harbor KIT or PDGFRA mutations. A small percentage of GIST (about 10‐15%) do not harbor any of these driver mutations and have historically been called wild-type (WT). Among them, from 20% to 40% show loss of function of the succinate dehydrogenase complex (SDH), also defined as SDH‐deficient GIST. SDH-deficient GISTs display distinctive clinical and pathological features, and can be sporadic or associated with Carney triad or Carney-Stratakis syndrome. These tumors arise most frequently in the stomach with predilection to distal stomach and antrum, have a multi-nodular growth, display a histological epithelioid phenotype, and present frequent lympho-vascular invasion. Occurrence of lymph node metastases and indolent course are representative features of SDH-deficient GISTs. This subset of GIST is known for the immunohistochemical loss of succinate dehydrogenase subunit B (SDHB), which signals the loss of function of the entire SDH-complex. The overall aim of my PhD project consists of the comprehensive characterization of SDH deficient GIST. Throughout the project, clinical, molecular and cellular characterizations were performed using next-generation sequencing technologies (NGS), that has the potential to allow the identification of molecular patterns useful for the diagnosis and development of novel treatments. Moreover, while there are many different cell lines and preclinical models of KIT/PDGFRA mutant GIST, no reliable cell model of SDH-deficient GIST has currently been developed, which could be used for studies on tumor evolution and in vitro assessments of drug response. Therefore, another aim of this project was to develop a pre-clinical model of SDH deficient GIST using the novel technology of induced pluripotent stem cells (iPSC).

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Graphene-based bioelectronic interfaces and devices and interpretative models targeting glial cells

Bioelectronic interfaces have significantly advanced in recent years, offering potential treatments for vision impairments, spinal cord injuries, and neurodegenerative diseases. However, the classical neurocentric vision drives the technological development toward neurons. Emerging evidence highlights the critical role of glial cells in the nervous system. Among them, astrocytes significantly influence neuronal networks throughout life and are implicated in several neuropathological states. Although they are incapable to fire action potentials, astrocytes communicate through diverse calcium (Ca2+) signalling pathways, crucial for cognitive functions and brain blood flow regulation. Current bioelectronic devices are primarily designed to interface neurons and are unsuitable for studying astrocytes. Graphene, with its unique electrical, mechanical and biocompatibility properties, has emerged as a promising neural interface material. However, its use as electrode interface to modulate astrocyte functionality remains unexplored. The aim of this PhD work was to exploit Graphene-oxide (GO) and reduced GO (rGO)-coated electrodes to control Ca2+ signalling in astrocytes by electrical stimulation. We discovered that distinct Ca2+dynamics in astrocytes can be evoked, in vitro and in brain slices, depending on the conductive/insulating properties of rGO/GO electrodes. Stimulation by rGO electrodes induces intracellular Ca2+ response with sharp peaks of oscillations (“P-type”), exclusively due to Ca2+ release from intracellular stores. Conversely, astrocytes stimulated by GO electrodes show slower and sustained Ca2+ response (“S-type”), largely mediated by external Ca2+ influx through specific ion channels. Astrocytes respond faster than neurons and activate distinct G-Protein Coupled Receptor intracellular signalling pathways. We propose a resistive/insulating model, hypothesizing that the different conductivity of the substrate influences the electric field at the cell/electrolyte or cell/material interfaces, favouring, respectively, the Ca2+ release from intracellular stores or the extracellular Ca2+ influx. This research provides a simple tool to selectively control distinct Ca2+ signals in brain astrocytes in neuroscience and bioelectronic medicine.

Correlations and Quantum Dynamics of 1D Fermionic Models: New Results for the Kitaev Chain with Long-Range Pairing

In the first part of the thesis, we propose an exactly-solvable one-dimensional model for fermions with long-range p-wave pairing decaying with distance as a power law. We studied the phase diagram by analyzing the critical lines, the decay of correlation functions and the scaling of the von Neumann entropy with the system size. We found two gapped regimes, where correlation functions decay (i) exponentially at short range and algebraically at long range, (ii) purely algebraically. In the latter the entanglement entropy is found to diverge logarithmically. Most interestingly, along the critical lines, long-range pairing breaks also the conformal symmetry. This can be detected via the dynamics of entanglement following a quench. In the second part of the thesis we studied the evolution in time of the entanglement entropy for the Ising model in a transverse field varying linearly in time with different velocities. We found different regimes: an adiabatic one (small velocities) when the system evolves according the instantaneous ground state; a sudden quench (large velocities) when the system is essentially frozen to its initial state; and an intermediate one, where the entropy starts growing linearly but then displays oscillations (also as a function of the velocity). Finally, we discussed the Kibble-Zurek mechanism for the transition between the paramagnetic and the ordered phase.