Probabilistic approaches in civil engineering: generation of random fields and structural identification with genetic algorithms

The inherent stochastic character of most of the physical quantities involved in engineering models has led to an always increasing interest for probabilistic analysis. Many approaches to stochastic analysis have been proposed. However, it is widely acknowledged that the only universal method available to solve accurately any kind of stochastic mechanics problem is Monte Carlo Simulation. One of the key parts in the implementation of this technique is the accurate and efficient generation of samples of the random processes and fields involved in the problem at hand. In the present thesis an original method for the simulation of homogeneous, multi-dimensional, multi-variate, non-Gaussian random fields is proposed. The algorithm has proved to be very accurate in matching both the target spectrum and the marginal probability. The computational efficiency and robustness are very good too, even when dealing with strongly non-Gaussian distributions. What is more, the resulting samples posses all the relevant, welldefined and desired properties of “translation fields”, including crossing rates and distributions of extremes. The topic of the second part of the thesis lies in the field of non-destructive parametric structural identification. Its objective is to evaluate the mechanical characteristics of constituent bars in existing truss structures, using static loads and strain measurements. In the cases of missing data and of damages that interest only a small portion of the bar, Genetic Algorithm have proved to be an effective tool to solve the problem.

Measurement of the muon-induced neutron flux at LNGS with the LVD experiment

In this thesis we describe in detail the Monte Carlo simulation (LVDG4) built to interpret the experimental data collected by LVD and to measure the muon-induced neutron yield in iron and liquid scintillator. A full Monte Carlo simulation, based on the Geant4 (v 9.3) toolkit, has been developed and validation tests have been performed. We used the LVDG4 to determine the active vetoing and the shielding power of LVD. The idea was to evaluate the feasibility to host a dark matter detector in the most internal part, called Core Facility (LVD-CF). The first conclusion is that LVD is a good moderator, but the iron supporting structure produce a great number of neutrons near the core. The second conclusions is that if LVD is used as an active veto for muons, the neutron flux in the LVD-CF is reduced by a factor 50, of the same order of magnitude of the neutron flux in the deepest laboratory of the world, Sudbury. Finally, the muon-induced neutron yield has been measured. In liquid scintillator we found $(3.2 \pm 0.2) \times 10^{-4}$ n/g/cm$^2$, in agreement with previous measurements performed at different depths and with the general trend predicted by theoretical calculations and Monte Carlo simulations. Moreover we present the first measurement, in our knowledge, of the neutron yield in iron: $(1.9 \pm 0.1) \times 10^{-3}$ n/g/cm$^2$. That measurement provides an important check for the MC of neutron production in heavy materials that are often used as shield in low background experiments.

The effect of osmolytes on protein fold stability at the single-molecule level

By pulling and releasing the tension on protein homomers with the Atomic Force Miscroscope (AFM) at different pulling speeds, dwell times and dwell distances, the observed force-response of the protein can be fitted with suitable theoretical models. In this respect we developed mathematical procedures and open-source computer codes for driving such experiments and fitting Bell’s model to experimental protein unfolding forces and protein folding frequencies. We applied the above techniques to the study of proteins GB1 (the B1 IgG-binding domain of protein G from Streptococcus) and I27 (a module of human cardiac titin) in aqueous solutions of protecting osmolytes such as dimethyl sulfoxide (DMSO), glycerol and trimethylamine N-oxide (TMAO). In order to get a molecular understanding of the experimental results we developed an Ising-like model for proteins that incorporates the osmophobic nature of their backbone. The model benefits from analytical thermodynamics and kinetics amenable to Monte-Carlo simulation. The prevailing view used to be that small protecting osmolytes bridge the separating beta-strands of proteins with mechanical resistance, presumably shifting the transition state to significantly higher distances that correlate with the molecular size of the osmolyte molecules. Our experiments showed instead that protecting osmolytes slow down protein unfolding and speed-up protein folding at physiological pH without shifting the protein transition state on the mechanical reaction coordinate. Together with the theoretical results of the Ising-model, our results lend support to the osmophobic theory according to which osmolyte stabilisation is a result of the preferential exclusion of the osmolyte molecules from the protein backbone. The results obtained during this thesis work have markedly improved our understanding of the strategy selected by Nature to strengthen protein stability in hostile environments, shifting the focus from hypothetical protein-osmolyte interactions to the more general mechanism based on the osmophobicity of the protein backbone.

Modeling the spatial dynamics of economic models

The advances that have been characterizing spatial econometrics in recent years are mostly theoretical and have not found an extensive empirical application yet. In this work we aim at supplying a review of the main tools of spatial econometrics and to show an empirical application for one of the most recently introduced estimators. Despite the numerous alternatives that the econometric theory provides for the treatment of spatial (and spatiotemporal) data, empirical analyses are still limited by the lack of availability of the correspondent routines in statistical and econometric software. Spatiotemporal modeling represents one of the most recent developments in spatial econometric theory and the finite sample properties of the estimators that have been proposed are currently being tested in the literature. We provide a comparison between some estimators (a quasi-maximum likelihood, QML, estimator and some GMM-type estimators) for a fixed effects dynamic panel data model under certain conditions, by means of a Monte Carlo simulation analysis. We focus on different settings, which are characterized either by fully stable or quasi-unit root series. We also investigate the extent of the bias that is caused by a non-spatial estimation of a model when the data are characterized by different degrees of spatial dependence. Finally, we provide an empirical application of a QML estimator for a time-space dynamic model which includes a temporal, a spatial and a spatiotemporal lag of the dependent variable. This is done by choosing a relevant and prolific field of analysis, in which spatial econometrics has only found limited space so far, in order to explore the value-added of considering the spatial dimension of the data. In particular, we study the determinants of cropland value in Midwestern U.S.A. in the years 1971-2009, by taking the present value model (PVM) as the theoretical framework of analysis.

Essays in Macroecometrics: methodological aspects and empirical applications

In the first chapter, I develop a panel no-cointegration test which extends Pesaran, Shin and Smith (2001)'s bounds test to the panel framework by considering the individual regressions in a Seemingly Unrelated Regression (SUR) system. This allows to take into account unobserved common factors that contemporaneously affect all the units of the panel and provides, at the same time, unit-specific test statistics. Moreover, the approach is particularly suited when the number of individuals of the panel is small relatively to the number of time series observations. I develop the algorithm to implement the test and I use Monte Carlo simulation to analyze the properties of the test. The small sample properties of the test are remarkable, compared to its single equation counterpart. I illustrate the use of the test through a test of Purchasing Power Parity in a panel of EU15 countries. In the second chapter of my PhD thesis, I verify the Expectation Hypothesis of the Term Structure in the repurchasing agreements (repo) market with a new testing approach. I consider an "inexact" formulation of the EHTS, which models a time-varying component in the risk premia and I treat the interest rates as a non-stationary cointegrated system. The effect of the heteroskedasticity is controlled by means of testing procedures (bootstrap and heteroskedasticity correction) which are robust to variance and covariance shifts over time. I fi#nd that the long-run implications of EHTS are verified. A rolling window analysis clarifies that the EHTS is only rejected in periods of turbulence of #financial markets. The third chapter introduces the Stata command "bootrank" which implements the bootstrap likelihood ratio rank test algorithm developed by Cavaliere et al. (2012). The command is illustrated through an empirical application on the term structure of interest rates in the US.

Monte Carlo Simulations of Novel Scintillator Detectors and Dosimetry Calculations

Monte Carlo (MC) simulation techniques are becoming very common in the Medical Physicists community. MC can be used for modeling Single Photon Emission Computed Tomography (SPECT) and for dosimetry calculations. 188Re, is a promising candidate for radiotherapeutic production and understanding the mechanisms of the radioresponse of tumor cells "in vitro" is of crucial importance as a first step before "in vivo" studies. The dosimetry of 188Re, used to target different lines of cancer cells, has been evaluated by the MC code GEANT4. The simulations estimate the average energy deposition/per event in the biological samples. The development of prototypes for medical imaging, based on LaBr3:Ce scintillation crystals coupled with a position sensitive photomultiplier, have been studied using GEANT4 simulations. Having tested, in the simulation, surface treatments different from the one applied to the crystal used in our experimental measurements, we found out that the Energy Resolution (ER) and the Spatial Resolution (SR) could be improved, in principle, by machining in a different way the lateral surfaces of the crystal. We have then studied a system able to acquire both echographic and scintigraphic images to let the medical operator obtain the complete anatomic and functional information for tumor diagnosis. The scintigraphic part of the detector is simulated by GEANT4 and first attempts to reconstruct tomographic images have been made using as method of reconstruction a back-projection standard algorithm. The proposed camera is based on slant collimators and LaBr3:Ce crystals. Within the Field of View (FOV) of the camera, it possible to distinguish point sources located in air at a distance of about 2 cm from each other. In particular conditions of uptake, tumor depth and dimension, the preliminary results show that the Signal to Noise Ratio (SNR) values obtained are higher than the standard detection limit.