Preference inference based on Pareto models

In this paper, we consider Preference Inference based on a generalised form of Pareto order. Preference Inference aims at reasoning over an incomplete specification of user preferences. We focus on two problems. The Preference Deduction Problem (PDP) asks if another preference statement can be deduced (with certainty) from a set of given preference statements. The Preference Consistency Problem (PCP) asks if a set of given preference statements is consistent, i.e., the statements are not contradicting each other. Here, preference statements are direct comparisons between alternatives (strict and non-strict). It is assumed that a set of evaluation functions is known by which all alternatives can be rated. We consider Pareto models which induce order relations on the set of alternatives in a Pareto manner, i.e., one alternative is preferred to another only if it is preferred on every component of the model. We describe characterisations for deduction and consistency based on an analysis of the set of evaluation functions, and present algorithmic solutions and complexity results for PDP and PCP, based on Pareto models in general and for a special case. Furthermore, a comparison shows that the inference based on Pareto models is less cautious than some other types of well-known preference model.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Damage Identification of Structures through Vibration-Based Structural Monitoring Systems

The thesis has been carried out within the “SHAPE Project - Predicting Strength Changes in Bridges from Frequency Data Safety, Hazard, and Poly-harmonic Evaluation” (ERA-NET Plus Infravation Call 2014) which dealt with the structural assessment of existing bridges and laboratory structural reproductions through the use of vibration-based monitoring systems, for detecting changes in their natural frequencies and correlating them with the occurrence of damage. The main purpose of this PhD dissertation has been the detection of the variation of the main natural frequencies as a consequence of a previous-established damage configuration provided on a structure. Firstly, the effect of local damage on the modal feature has been discussed mainly concerning a steel frame and a composite steel-concrete bridge. Concerning the variation of the fundamental frequency of the small bridge, the increasing severity of two local damages has been investigated. Moreover, the comparison with a 3D FE model is even presented establishing a link between the dynamic properties and the damage features. Then, moving towards a diffused damage pattern, four concrete beams and a small concrete deck were loaded achieving the yielding of the steel reinforcement. The stiffness deterioration in terms of frequency shifts has been reconsidered by collecting a large set of dynamic experiments on simply supported R.C. beams discussed in the literature. The comparison of the load-frequency curves suggested a significant agreement among all the experiments. Thus, in the framework of damage mechanics, the “breathing cracks” phenomenon has been discussed leading to an analytical formula able to explain the frequency decay observed experimentally. Lastly, some dynamic investigations of two existing bridges and the corresponding FE Models are presented in Chapter 4. Moreover, concerning the bridge in Bologna, two prototypes of a network of accelerometers were installed and the data of a few months of monitoring have been discussed.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

The main topic of this thesis is confounding in linear regression models. It arises when a relationship between an observed process, the covariate, and an outcome process, the response, is influenced by an unmeasured process, the confounder, associated with both. Consequently, the estimators for the regression coefficients of the measured covariates might be severely biased, less efficient and characterized by misleading interpretations. Confounding is an issue when the primary target of the work is the estimation of the regression parameters. The central point of the dissertation is the evaluation of the sampling properties of parameter estimators. This work aims to extend the spatial confounding framework to general structured settings and to understand the behaviour of confounding as a function of the data generating process structure parameters in several scenarios focusing on the joint covariate-confounder structure. In line with the spatial statistics literature, our purpose is to quantify the sampling properties of the regression coefficient estimators and, in turn, to identify the most prominent quantities depending on the generative mechanism impacting confounding. Once the sampling properties of the estimator conditionally on the covariate process are derived as ratios of dependent quadratic forms in Gaussian random variables, we provide an analytic expression of the marginal sampling properties of the estimator using Carlson’s R function. Additionally, we propose a representative quantity for the magnitude of confounding as a proxy of the bias, its first-order Laplace approximation. To conclude, we work under several frameworks considering spatial and temporal data with specific assumptions regarding the covariance and cross-covariance functions used to generate the processes involved. This study allows us to claim that the variability of the confounder-covariate interaction and of the covariate plays the most relevant role in determining the principal marker of the magnitude of confounding.

Mainstreaming of Nature-Based Solutions for the mitigation of hydro-meteorological hazard: governance analysis of a socio- technical change

The Nature-Based Solutions (NBS) concept and approach were developed to simultaneously face challenges such as risk mitigation and biodiversity conservation and restoration. NBSs have been endorsed by major International Organizations such as the EU, the FAO and World Bank that are pushing to enable a mainstreaming process. However, a shift from traditional engineering “grey” solutions to wider and standard adoption of NBS encounters technical, social, cultural, and normative barriers that have been identified with a qualitative content analysis of policy documents, reports and expert interviews. The case of the region Emilia-Romagna was studied by developing an analytical framework that brought together the social-ecological context, the governance system and the characteristics of specific NBSs.

Data-driven multivariate flood hazard modeling

The present Dissertation shows how recent statistical analysis tools and open datasets can be exploited to improve modelling accuracy in two distinct yet interconnected domains of flood hazard (FH) assessment. In the first Part, unsupervised artificial neural networks are employed as regional models for sub-daily rainfall extremes. The models aim to learn a robust relation to estimate locally the parameters of Gumbel distributions of extreme rainfall depths for any sub-daily duration (1-24h). The predictions depend on twenty morphoclimatic descriptors. A large study area in north-central Italy is adopted, where 2238 annual maximum series are available. Validation is performed over an independent set of 100 gauges. Our results show that multivariate ANNs may remarkably improve the estimation of percentiles relative to the benchmark approach from the literature, where Gumbel parameters depend on mean annual precipitation. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across space and time-aggregation intervals. In the second Part, decision trees are used to combine a selected blend of input geomorphic descriptors for predicting FH. Relative to existing DEM-based approaches, this method is innovative, as it relies on the combination of three characteristics: (1) simple multivariate models, (2) a set of exclusively DEM-based descriptors as input, and (3) an existing FH map as reference information. First, the methods are applied to northern Italy, represented with the MERIT DEM (∼90m resolution), and second, to the whole of Italy, represented with the EU-DEM (25m resolution). The results show that multivariate approaches may (a) significantly enhance flood-prone areas delineation relative to a selected univariate one, (b) provide accurate predictions of expected inundation depths, (c) produce encouraging results in extrapolation, (d) complete the information of imperfect reference maps, and (e) conveniently convert binary maps into continuous representation of FH.

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.

A performance analysis of mesh models for cloud-based workflows

Nell'ambito della loro trasformazione digitale, molte organizzazioni stanno adottando nuove tecnologie per supportare lo sviluppo, l'implementazione e la gestione delle proprie architetture basate su microservizi negli ambienti cloud e tra i fornitori di cloud. In questo scenario, le service ed event mesh stanno emergendo come livelli infrastrutturali dinamici e configurabili che facilitano interazioni complesse e la gestione di applicazioni basate su microservizi e servizi cloud. L’obiettivo di questo lavoro è quello di analizzare soluzioni mesh open-source (istio, Linkerd, Apache EventMesh) dal punto di vista delle prestazioni, quando usate per gestire la comunicazione tra applicazioni a workflow basate su microservizi all’interno dell’ambiente cloud. A questo scopo è stato realizzato un sistema per eseguire il dislocamento di ognuno dei componenti all’interno di un cluster singolo e in un ambiente multi-cluster. La raccolta delle metriche e la loro sintesi è stata realizzata con un sistema personalizzato, compatibile con il formato dei dati di Prometheus. I test ci hanno permesso di valutare le prestazioni di ogni componente insieme alla sua efficacia. In generale, mentre si è potuta accertare la maturità delle implementazioni di service mesh testate, la soluzione di event mesh da noi usata è apparsa come una tecnologia ancora non matura, a causa di numerosi problemi di funzionamento.

Computational Analysis Based on Parametric Models of Historic Masonry Vaulted Structures Strengthened by Fiber-Reinforced Composite Materials

Historic vaulted masonry structures often need strengthening interventions that can effectively improve their structural performance, especially during seismic events, and at the same time respect the existing setting and the modern conservation requirements. In this context, the use of innovative materials such as fiber-reinforced composite materials has been shown as an effective solution that can satisfy both aspects. This work aims to provide insight into the computational modeling of a full-scale masonry vault strengthened by fiber-reinforced composite materials and analyze the influence of the arrangement of the reinforcement on the efficiency of the intervention. At first, a parametric model of a cross vault focusing on a realistic representation of its micro-geometry is proposed. Then numerical modeling, simulating the pushover analyses, of several barrel vaults reinforced with different reinforcement configurations is performed. Finally, the results are collected and discussed in terms of force-displacement curves obtained for each proposed configuration.

Development of a relationship between physical parameters of tsunami waves and tsunami intensity based on macroscopic effects and its application to the Italian tsunami database

Tsunamis are rare events. However, their impact can be devastating and it may extend to large geographical areas. For low-probability high-impact events like tsunamis, it is crucial to implement all possible actions to mitigate the risk. The tsunami hazard assessment is the result of a scientific process that integrates traditional geological methods, numerical modelling and the analysis of tsunami sources and historical records. For this reason, analysing past events and understanding how they interacted with the land is the only way to inform tsunami source and propagation models, and quantitatively test forecast models like hazard analyses. The primary objective of this thesis is to establish an explicit relationship between the macroscopic intensity, derived from historical descriptions, and the quantitative physical parameters measuring tsunami waves. This is done first by defining an approximate estimation method based on a simplified 1D physical onshore propagation model to convert the available observations into one reference physical metric. Wave height at the coast was chosen as the reference due to its stability and independence of inland effects. This method was then implemented for a set of well-known past events to build a homogeneous dataset with both macroseismic intensity and wave height. By performing an orthogonal regression, a direct and invertible empirical relationship could be established between the two parameters, accounting for their relevant uncertainties. The target relationship is extensively tested and finally applied to the Italian Tsunami Effect Database (ITED), providing a homogeneous estimation of the wave height for all existing tsunami observations in Italy. This provides the opportunity for meaningful comparison for models and simulations, as well as quantitatively testing tsunami hazard models for the Italian coasts and informing tsunami risk management initiatives.

Ihwg-μnir: A Miniaturised Near-infrared Gas Sensor Based On Substrate-integrated Hollow Waveguides Coupled To A Micro-nir-spectrophotometer.

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

Factors Associated With The Age Of The Onset Of Diabetes In Women Aged 50 years Or More: A Population-based Study.

Investigate factors associated with the onset of diabetes in women aged more than 49 years. Cross-sectional, population-based study using self-reports with 622 women. The dependent variable was the age of occurrence of diabetes using the life table method. Cox multiple regression models were adjusted to analyse the onset of diabetes according to predictor variables. Sociodemographic, clinical and behavioural factors were evaluated. Of the 622 women interviewed, 22.7% had diabetes. The mean age at onset was 56 years. The factors associated with the age of occurrence of diabetes were self-rated health (very good, good) (coefficient=-0.792; SE of the coefficient=0.215; p=0.0001), more than two individuals living in the household (coefficient=0.656, SE of the coefficient=0.223; p=0.003), and body mass index (BMI) (kg/m(2)) at 20-30 years of age (coefficient= 0.056, SE of the coefficient=0.023; p=0.014). Self-rated health considered good or very good was associated with a higher rate of survival without diabetes. Sharing a home with two or more other people and a weight increase at 20-30 years of age was associated with the onset of type 2 diabetes.

Augmented Mixed Models For Clustered Proportion Data.

Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.