Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

The topological trigger system of the TOF detector for the ALICE experiment at the LHC

The ALICE experiment at the LHC has been designed to cope with the experimental conditions and observables of a Quark Gluon Plasma reaction. One of the main assets of the ALICE experiment with respect to the other LHC experiments is the particle identification. The large Time-Of-Flight (TOF) detector is the main particle identification detector of the ALICE experiment. The overall time resolution, better that 80 ps, allows the particle identification over a large momentum range (up to 2.5 GeV/c for pi/K and 4 GeV/c for K/p). The TOF makes use of the Multi-gap Resistive Plate Chamber (MRPC), a detector with high efficiency, fast response and intrinsic time resoltion better than 40 ps. The TOF detector embeds a highly-segmented trigger system that exploits the fast rise time and the relatively low noise of the MRPC strips, in order to identify several event topologies. This work aims to provide detailed description of the TOF trigger system. The results achieved in the 2009 cosmic-ray run at CERN are presented to show the performances and readiness of TOF trigger system. The proposed trigger configuration for the proton-proton and Pb-Pb beams are detailed as well with estimates of the efficiencies and purity samples.

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.

Congenital Nystagmus eye movements analysis and oculomotor system models evaluation

The term Congenital Nystagmus (Early Onset Nystagmus or Infantile Nystagmus Syndrome) refers to a pathology characterised by an involuntary movement of the eyes, which often seriously reduces a subject’s vision. Congenital Nystagmus (CN) is a specific kind of nystagmus within the wider classification of infantile nystagmus, which can be best recognized and classified by means of a combination of clinical investigations and motility analysis; in some cases, eye movement recording and analysis are indispensable for diagnosis. However, interpretation of eye movement recordings still lacks of complete reliability; hence new analysis techniques and precise identification of concise parameters directly related to visual acuity are necessary to further support physicians’ decisions. To this aim, an index computed from eye movement recordings and related to the visual acuity of a subject is proposed in this thesis. This estimator is based on two parameters: the time spent by a subject effectively viewing a target (foveation time - Tf) and the standard deviation of eye position (SDp). Moreover, since previous studies have shown that visual acuity largely depends on SDp, a data collection pilot study was also conducted with the purpose of specifically identifying eventual slow rhythmic component in the eye position and to characterise in more detail the SDp. The results are presented in this thesis. In addition, some oculomotor system models are reviewed and a new approach to those models, i.e. the recovery of periodic orbits of the oculomotor system in patients with CN, is tested on real patients data. In conclusion, the results obtained within this research consent to completely and reliably characterise the slow rhythmic component sometimes present in eye position recordings of CN subjects and to better classify the different kinds of CN waveforms. Those findings can successfully support the clinicians in therapy planning and treatment outcome evaluation.

Molecular approach to Neurodegenerative Disorders: Role of Nociceptin/Orphanin FQ - NOP System in Parkinson and Epigenetic Mechanism in Alzheimer's Disease

With life expectancies increasing around the world, populations are getting age and neurodegenerative diseases have become a global issue. For this reason we have focused our attention on the two most important neurodegenerative diseases: Parkinson’s and Alzheimer’s. Parkinson’s disease is a chronic progressive neurodegenerative movement disorder of multi-factorial origin. Environmental toxins as well as agricultural chemicals have been associated with PD. Has been observed that N/OFQ contributes to both neurotoxicity and symptoms associated with PD and that pronociceptin gene expression is up-regulated in rat SN of 6-OHDA and MPP induced experimental parkinsonism. First, we investigated the role of N/OFQ-NOP system in the pathogenesis of PD in an animal model developed using PQ and/or MB. Then we studied Alzheimer's disease. This disorder is defined as a progressive neurologic disease of the brain leading to the irreversible loss of neurons and the loss of intellectual abilities, including memory and reasoning, which become severe enough to impede social or occupational functioning. Effective biomarker tests could prevent such devastating damage occurring. We utilized the peripheral blood cells of AD discordant monozygotic twin in the search of peripheral markers which could reflect the pathology within the brain, and also support the hypothesis that PBMC might be a useful model of epigenetic gene regulation in the brain. We investigated the mRNA levels in several genes involve in AD pathogenesis, as well DNA methylation by MSP Real-Time PCR. Finally by Western Blotting we assess the immunoreactivity levels for histone modifications. Our results support the idea that epigenetic changes assessed in PBMCs can also be useful in neurodegenerative disorders, like AD and PD, enabling identification of new biomarkers in order to develop early diagnostic programs.

Optical Design for Automatic Identification and Portable Systems

This thesis proposes design methods and test tools, for optical systems, which may be used in an industrial environment, where not only precision and reliability but also ease of use is important. The approach to the problem has been conceived to be as general as possible, although in the present work, the design of a portable device for automatic identification applications has been studied, because this doctorate has been funded by Datalogic Scanning Group s.r.l., a world-class producer of barcode readers. The main functional components of the complete device are: electro-optical imaging, illumination and pattern generator systems. For what concerns the electro-optical imaging system, a characterization tool and an analysis one has been developed to check if the desired performance of the system has been achieved. Moreover, two design tools for optimizing the imaging system have been implemented. The first optimizes just the core of the system, the optical part, improving its performance ignoring all other contributions and generating a good starting point for the optimization of the whole complex system. The second tool optimizes the system taking into account its behavior with a model as near as possible to reality including optics, electronics and detection. For what concerns the illumination and the pattern generator systems, two tools have been implemented. The first allows the design of free-form lenses described by an arbitrary analytical function exited by an incoherent source and is able to provide custom illumination conditions for all kind of applications. The second tool consists of a new method to design Diffractive Optical Elements excited by a coherent source for large pattern angles using the Iterative Fourier Transform Algorithm. Validation of the design tools has been obtained, whenever possible, comparing the performance of the designed systems with those of fabricated prototypes. In other cases simulations have been used.

Identification and functional characterization of human cytomegalovirus Fc binding proteins

ABSTRACT Human cytomegalovirus (HCMV) employs many different mechanisms to escape and subvert the host immune system surveillance. Among these different mechanisms the role of human IgG Fc receptors (FcγR) in HCMV pathogenesis is still unclear. In mammalians, FcγRs are expressed on the surface of all haematopoietic cells and have a multifaceted role in regulating the activity of antibodies to generate a well-balanced immune response. Viral proteins with Fcγ binding ability are highly diffuse among herpesviruses. They interfere with the host receptors functions in order to counteract immune system recognition. So far, two human HCMV Fcγ binding proteins have been described: UL119 and RL11. This work was aimed to the identification and characterization of HCMV Fcγ binding proteins. The study is divided in two parts: first the characterization of UL119 and RL11; second the identification and characterization of novel HCMV Fcγ binding proteins. Regarding the first part, we demonstrated that both UL119 and RL11 internalize Fcγ fragments from transfected cells surface through a clathrin dependent pathway. In infected cells both proteins were found in the viral assembly complex and on virions surface as envelope associated glycoproteins. Moreover, internalized Fcγ in infected cells do not undergo lysosomal degradation but rather traffic in early endosomes up to the viral assembly complex. Regarding the second part, we were able to identify two novels Fcγ binding protein coded by CMV: RL12 and RL13. The latter was also further characterized as recombinant protein in terms of cellular localization, Fc binding site and IgG internalization ability. Finally binding specificity of both RL12 and RL13 seems to be confined to human IgG1 and IgG2. Taken together, these data show that HCMV codes for up to 4 FcγR and that they could have a double role both on virus and on infected cells.

Evaluation of Antitumoral activity of bone targeted drugs/conventional chemotherapies and identification of biomarkers for the selection of patients with breast cancer for the bone targeted therapy in adjuvant setting

Bone metastases are responsible for different clinical complications defined as skeletal-related events (SREs) such as pathologic fractures, spinal cord compression, hypercalcaemia, bone marrow infiltration and severe bone pain requiring palliative radiotherapy. The general aim of these three years research period was to improve the management of patients with bone metastases through two different approaches of translational research. Firstly in vitro preclinical tests were conducted on breast cancer cells and on indirect co-colture of cancer cells and osteoclasts to evaluate bone targeted therapy singly and in combination with conventional chemotherapy. The study suggests that zoledronic acid has an antitumor activity in breast cancer cell lines. Its mechanism of action involves the decrease of RAS and RHO, as in osteoclasts. Repeated treatment enhances antitumor activity compared to non-repeated treatment. Furthermore the combination Zoledronic Acid + Cisplatin induced a high antitumoral activity in the two triple-negative lines MDA-MB-231 and BRC-230. The p21, pMAPK and m-TOR pathways were regulated by this combined treatment, particularly at lower Cisplatin doses. A co-colture system to test the activity of bone-targeted molecules on monocytes-breast conditioned by breast cancer cells was also developed. Another important criticism of the treatment of breast cancer patients, is the selection of patients who will benefit of bone targeted therapy in the adjuvant setting. A retrospective case-control study on breast cancer patients to find new predictive markers of bone metastases in the primary tumors was performed. Eight markers were evaluated and TFF1 and CXCR4 were found to discriminate between patients with relapse to bone respect to patients with no evidence of disease. In particular TFF1 was the most accurate marker reaching a sensitivity of 63% and a specificity of 79%. This marker could be a useful tool for clinicians to select patients who could benefit for bone targeted therapy in adjuvant setting.

Automatic risk evaluation in elderly patients based on Autonomic Nervous System assessment

Dysfunction of Autonomic Nervous System (ANS) is a typical feature of chronic heart failure and other cardiovascular disease. As a simple non-invasive technology, heart rate variability (HRV) analysis provides reliable information on autonomic modulation of heart rate. The aim of this thesis was to research and develop automatic methods based on ANS assessment for evaluation of risk in cardiac patients. Several features selection and machine learning algorithms have been combined to achieve the goals. Automatic assessment of disease severity in Congestive Heart Failure (CHF) patients: a completely automatic method, based on long-term HRV was proposed in order to automatically assess the severity of CHF, achieving a sensitivity rate of 93% and a specificity rate of 64% in discriminating severe versus mild patients. Automatic identification of hypertensive patients at high risk of vascular events: a completely automatic system was proposed in order to identify hypertensive patients at higher risk to develop vascular events in the 12 months following the electrocardiographic recordings, achieving a sensitivity rate of 71% and a specificity rate of 86% in identifying high-risk subjects among hypertensive patients. Automatic identification of hypertensive patients with history of fall: it was explored whether an automatic identification of fallers among hypertensive patients based on HRV was feasible. The results obtained in this thesis could have implications both in clinical practice and in clinical research. The system has been designed and developed in order to be clinically feasible. Moreover, since 5-minute ECG recording is inexpensive, easy to assess, and non-invasive, future research will focus on the clinical applicability of the system as a screening tool in non-specialized ambulatories, in order to identify high-risk patients to be shortlisted for more complex investigations.

Robust Nonlinear Output Regulation by Identification Tools

The present thesis focuses on the problem of robust output regulation for minimum phase nonlinear systems by means of identification techniques. Given a controlled plant and an exosystem (an autonomous system that generates eventual references or disturbances), the control goal is to design a proper regulator able to process the only measure available, i.e the error/output variable, in order to make it asymptotically vanishing. In this context, such a regulator can be designed following the well known “internal model principle” that states how it is possible to achieve the regulation objective by embedding a replica of the exosystem model in the controller structure. The main problem shows up when the exosystem model is affected by parametric or structural uncertainties, in this case, it is not possible to reproduce the exact behavior of the exogenous system in the regulator and then, it is not possible to achieve the control goal. In this work, the idea is to find a solution to the problem trying to develop a general framework in which coexist both a standard regulator and an estimator able to guarantee (when possible) the best estimate of all uncertainties present in the exosystem in order to give “robustness” to the overall control loop.

Interspecies study of the enteric nervous system and related pathologies

The enteric nervous system (ENS) modulates a number of digestive functions including well known ones, i.e. motility, secretion, absorption and blood flow, along with other critically relevant processes, i.e. immune responses of the gastrointestinal (GI) tract, gut microbiota and epithelial barrier . The characterization of the anatomical aspects of the ENS in large mammals and the identification of differences and similarities existing between species may represent a fundamental basis to decipher several digestive GI diseases in humans and animals. In this perspective, the aim of the present thesis is to highlight the ENS anatomical basis and pathological aspects in different mammalian species, such as horses, dogs and humans. Firstly, I designed two anatomical studies in horses:  “Excitatory and inhibitory enteric innervation of horse lower esophageal sphincter”.  “Localization of 5-hydroxytryptamine 4 receptor (5-HT4R) in the equine enteric nervous system”. Then I focused on the enteric dysfunctions, including:  A primary enteric aganglionosis in horses: “Extrinsic innervation of the ileum and pelvic flexure of foals with ileocolonic aganglionosis”.  A diabetic enteric neuropathy in dogs: “Quantification of nitrergic neurons in the myenteric plexus of gastric antrum and ileum of healthy and diabetic dogs”.  An enteric neuropathy in human neurological patients: “Functional and neurochemical abnormalities in patients with Parkinson's disease and chronic constipation”. The physiology of the GI tract is characterized by a high complexity and it is mainly dependent on the control of the intrinsic nervous system. ENS is critical to preserve body homeostasis as reflect by its derangement occurring in pathological conditions that can be lethal or seriously disabling to humans and animals. The knowledge of the anatomy and the pathology of the ENS represents a new important and fascinating topic, which deserves more attention in the veterinary medicine field.

System innovation and life cycle thinking in packaging value chain: the circularity of plastics.

Compared to other, plastic materials have registered a strong acceleration in production and consumption during the last years. Despite the existence of waste management systems, plastic_based materials are still a pervasive presence in the environment, with negative consequences on marine ecosystem and human health. The recycling is still challenging due to the growing complexity of product design, the so-called overpackaging, the insufficient and inadequate recycling infrastructure, the weak market of recycled plastics and the high cost of waste treatment and disposal. The Circular economy package, the European Strategy for plastics in a circular economy and the recent European Green Deal include very ambitious programmes to rethink the entire plastic value chain. As regards packaging, all plastic packaging will have to be 100% recyclable (or reusable) and 55% recycled by 2030. Regions are consequently called upon to set up a robust plan able to fit the European objectives. It takes on greater importance in Emilia Romagna where the Packaging valley is located. This thesis supports the definition of a strategy aimed to establish an after-use plastics economy in the region. The PhD work has set the basis and the instruments to establish the so-called Circularity Strategy with the aim to turn about 92.000t of plastic waste into profitable secondary resources. System innovation, life cycle thinking and participative backcasting method have allowed to deeply analyse the current system, orientate the problem and explore sustainable solutions through a broad stakeholder participation. A material flow analysis, accompanied by a barrier analysis, has supported the identification of the gaps between the present situation and the 2030 scenario. Eco-design for and from recycling (and a mass _based recycling rate (based on the effective amount of plastic wastes turned into secondary plastics), valorized by a value_based indicator, are the key-points of the action plan.

Study of genes involved in her2-positive breast cancer progression for identification of new therapeutic targets

HER2 overexpression is observed in 20-30% of invasive breast carcinomas and it is correlated with poor prognosis. Although targeted therapies have revolutionized the treatment of HER2-positive breast cancer, a high number of patients presented primary or acquired resistance to monoclonal antibodies and tyrosine kinase inhibitors. Tumor heterogenicity, epithelial to mesenchymal transition (EMT) and cancer stem cells are key factors in target therapy resistance and tumor progression. The aim of this project was to discover alternative therapeutic strategies to over-come tumor resistance by harnessing immune system and looking for new targetable molecules. The results reported introduce a virus-like particles-based vaccine against HER2 as promising therapeutic approach to treat HER2-positive tumors. The high and persistent anti-HER2 antibody titers elicited by the vaccine significantly inhibited tumor growth and metastases onset. Furthermore, the polyclonal response induced by the vaccine also inhibited human HER2-positive breast cancer cells resistant to trastuzumab in vitro, suggesting its efficacy also on trastuzumab resistant tumors. To identify new therapeutic targets to treat progressed breast cancer, we took advantage from a dynamic model of HER2 expression obtained in our laboratory, in which HER2 loss and cancer progression were associated with the acquisition of EMT and stemness features. Targeting EMT-involved molecules, such as PDGFR-β, or the induction of epithelial markers, like E-cadherin, proved to be successful strategy to impair HER2-negative tumor growth. Density alterations, which might be induced by anti-HER2 target therapies, in cell culture condition of a cell line with a labile HER2 expression, caused HER2 loss probably as consequence of more aggressive subpopulations which prevail over the others. These subpopulations showed an increased EMT and stemness profile, confirming that targeting EMT-involved molecules or antigen expressed by cancer stem cells together with anti-HER2 target therapies is a valid strategy to inhibit HER2-positive cells and simultaneously prevent selection of more aggressive clone.

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.

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.