Dynamic identification of structures: experimental assessment of modal parameteres through methods in frequency domain, in time-frequency domain and model updating

Among the experimental methods commonly used to define the behaviour of a full scale system, dynamic tests are the most complete and efficient procedures. A dynamic test is an experimental process, which would define a set of characteristic parameters of the dynamic behaviour of the system, such as natural frequencies of the structure, mode shapes and the corresponding modal damping values associated. An assessment of these modal characteristics can be used both to verify the theoretical assumptions of the project, to monitor the performance of the structural system during its operational use. The thesis is structured in the following chapters: The first introductive chapter recalls some basic notions of dynamics of structure, focusing the discussion on the problem of systems with multiply degrees of freedom (MDOF), which can represent a generic real system under study, when it is excited with harmonic force or in free vibration. The second chapter is entirely centred on to the problem of dynamic identification process of a structure, if it is subjected to an experimental test in forced vibrations. It first describes the construction of FRF through classical FFT of the recorded signal. A different method, also in the frequency domain, is subsequently introduced; it allows accurately to compute the FRF using the geometric characteristics of the ellipse that represents the direct input-output comparison. The two methods are compared and then the attention is focused on some advantages of the proposed methodology. The third chapter focuses on the study of real structures when they are subjected to experimental test, where the force is not known, like in an ambient or impact test. In this analysis we decided to use the CWT, which allows a simultaneous investigation in the time and frequency domain of a generic signal x(t). The CWT is first introduced to process free oscillations, with excellent results both in terms of frequencies, dampings and vibration modes. The application in the case of ambient vibrations defines accurate modal parameters of the system, although on the damping some important observations should be made. The fourth chapter is still on the problem of post processing data acquired after a vibration test, but this time through the application of discrete wavelet transform (DWT). In the first part the results obtained by the DWT are compared with those obtained by the application of CWT. Particular attention is given to the use of DWT as a tool for filtering the recorded signal, in fact in case of ambient vibrations the signals are often affected by the presence of a significant level of noise. The fifth chapter focuses on another important aspect of the identification process: the model updating. In this chapter, starting from the modal parameters obtained from some environmental vibration tests, performed by the University of Porto in 2008 and the University of Sheffild on the Humber Bridge in England, a FE model of the bridge is defined, in order to define what type of model is able to capture more accurately the real dynamic behaviour of the bridge. The sixth chapter outlines the necessary conclusions of the presented research. They concern the application of a method in the frequency domain in order to evaluate the modal parameters of a structure and its advantages, the advantages in applying a procedure based on the use of wavelet transforms in the process of identification in tests with unknown input and finally the problem of 3D modeling of systems with many degrees of freedom and with different types of uncertainty.

Study of Candidate Genes for production and carcass traits in Italian Heavy Pigs: identification of a new DNA Markers, Expression Studies and Association Analysis using Different Experimental Design

Heavy pig breeding in Italy is mainly oriented for the production of high quality processed products. Of particular importance is the dry cured ham production, which is strictly regulated and requires specific carcass characteristics correlated with green leg characteristics. Furthermore, as pigs are slaughtered at about 160 kg live weight, the Italian pig breeding sector faces severe problems of production efficiency that are related to all biological aspects linked to growth, feed conversion, fat deposition and so on. It is well known that production and carcass traits are in part genetically determined. Therefore, as a first step to understand genetic basis of traits that could have a direct or indirect impact on dry cured ham production, a candidate gene approach can be used to identify DNA markers associated with parameters of economic importance. In this thesis, we investigated three candidate genes for carcass and production traits (TRIB3, PCSK1, MUC4) in pig breeds used for dry cured ham production, using different experimental approaches in order to find molecular markers associated with these parameters.

Identification of a molecular signature as potential biomarker in blood of symptomatic and asymptomatic patients with carotid atherosclerosis

Background and Objectives: Carotid revascularization to prevent future vascular events is reasonable in patients with high-grade carotid stenosis. Currently, several biomarkers to predict carotid plaque development and progression have been investigated, among which microRNAs (miRs) are promising tools for the diagnosis of atherosclerosis. Methods and Results: A total of 49 participants were included in the study, divided into two main populations: Population 1 comprising symptomatic and asymptomatic inpatients, and Population 2 comprising asymptomatic outpatients. The study consisted of two main phases: a preliminary discovery phase and a validation phase, applying different techniques. MiR-profiles were performed on plasma and plaque tissue samples obtained from 4 symptomatic and 4 asymptomatic inpatients. MiRs emerging from profiling comparisons, i.e. miR-126-5p, miR-134-5p, miR-145-5p, miR-151a-5p, miR-34b, miR-451a, miR-720 and miR-1271-5p, were subjected to validation through RT-qPCR analysis in the total cohort of donors. Comparing asymptomatic and symptomatic inpatients, significant differences were reported in the expression levels of c-miRs for miR-126-5p and miR-1271-5p in blood, being more expressed in symptomatic subjects. In contrast, simultaneous evaluation of the selected miRs in plaque tissue samples did not confirm data obtained by the miR profiling, and no significant differences were observed. Using Receiver-Operating Characteristic (ROC) analysis, a circulating molecular signature (mir-126-5p, miR-1271-5p, albumin, C-reactive protein, and monocytes) was identified, allowing the distinction of the two groups in Population 1 (AUC = 0.795). Conclusions: Data emerging from this thesis suggest that c-miRs (i.e. miR-126-5p, miR-1271-5p) combined with selected haemato-biochemical parameters (albumin, C-reactive protein, and monocytes) produced a good molecular 'signature' to distinguish asymptomatic and symptomatic inpatients. C-miRs in blood do not necessarily reflect the expression levels of the same miRs in carotid plaque tissues since different mechanism can influence their expression.

Identification of sunflower genotypes suitable for organic agriculture and development of analytical tools for their traceability

Thanks to the development and combination of molecular markers for the genetic traceability of sunflower varieties and a gas chromatographic method for the determination of the FAs composition of sunflower oil, it was possible to implement an experimental method for the verification of both the traceability and the variety of organic sunflower marketed by Agricola Grains S.p.A. The experimental activity focused on two objectives: the implementation of molecular markers for the routine control of raw material deliveries for oil extraction and the improvement and validation of a gas chromatographic method for the determination of the FAs composition of sunflower oil. With regard to variety verification and traceability, the marker systems evaluated were the following: SSR markers (12) arranged in two multiplex sets and SCAR markers for the verification of cytoplasmic male sterility (Pet1) and fertility. In addition, two objectives were pursued in order to enable a routine application in the industrial field: the development of a suitable protocol for DNA extraction from single seeds and the implementation of a semi-automatic capillary electrophoresis system for the analysis of marker fragments. The development and validation of a new GC/FID analytical method for the determination of fatty acids (FAME) in sunflower achenes to improve the quality and efficiency of the analytical flow in the control of raw and refined materials entering the Agricola Grains S.p.A. production chain. The analytical performances being validated by the newly implemented method are: linearity of response, limit of quantification, specificity, precision, intra-laboratory precision, robustness, BIAS. These parameters are used to compare the newly developed method with the one considered as reference - Commission Regulation No. 2568/91 and Commission Implementing Regulation No. 2015/1833. Using the combination of the analytical methods mentioned above, the documentary traceability of the product can be confirmed experimentally, providing relevant information for subsequent marketing.

Inside standard and hyperspectral low-dose CT variational imaging: parameter identification and material decomposition

The main contribution of this thesis is the proposal of novel strategies for the selection of parameters arising in variational models employed for the solution of inverse problems with data corrupted by Poisson noise. In light of the importance of using a significantly small dose of X-rays in Computed Tomography (CT), and its need of using advanced techniques to reconstruct the objects due to the high level of noise in the data, we will focus on parameter selection principles especially for low photon-counts, i.e. low dose Computed Tomography. For completeness, since such strategies can be adopted for various scenarios where the noise in the data typically follows a Poisson distribution, we will show their performance for other applications such as photography, astronomical and microscopy imaging. More specifically, in the first part of the thesis we will focus on low dose CT data corrupted only by Poisson noise by extending automatic selection strategies designed for Gaussian noise and improving the few existing ones for Poisson. The new approaches will show to outperform the state-of-the-art competitors especially in the low-counting regime. Moreover, we will propose to extend the best performing strategy to the hard task of multi-parameter selection showing promising results. Finally, in the last part of the thesis, we will introduce the problem of material decomposition for hyperspectral CT, which data encodes information of how different materials in the target attenuate X-rays in different ways according to the specific energy. We will conduct a preliminary comparative study to obtain accurate material decomposition starting from few noisy projection data.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.