Neural network modelling of the wave-structure interaction processes

This thesis presents a new Artificial Neural Network (ANN) able to predict at once the main parameters representative of the wave-structure interaction processes, i.e. the wave overtopping discharge, the wave transmission coefficient and the wave reflection coefficient. The new ANN has been specifically developed in order to provide managers and scientists with a tool that can be efficiently used for design purposes. The development of this ANN started with the preparation of a new extended and homogeneous database that collects all the available tests reporting at least one of the three parameters, for a total amount of 16’165 data. The variety of structure types and wave attack conditions in the database includes smooth, rock and armour unit slopes, berm breakwaters, vertical walls, low crested structures, oblique wave attacks. Some of the existing ANNs were compared and improved, leading to the selection of a final ANN, whose architecture was optimized through an in-depth sensitivity analysis to the training parameters of the ANN. Each of the selected 15 input parameters represents a physical aspect of the wave-structure interaction process, describing the wave attack (wave steepness and obliquity, breaking and shoaling factors), the structure geometry (submergence, straight or non-straight slope, with or without berm or toe, presence or not of a crown wall), or the structure type (smooth or covered by an armour layer, with permeable or impermeable core). The advanced ANN here proposed provides accurate predictions for all the three parameters, and demonstrates to overcome the limits imposed by the traditional formulae and approach adopted so far by some of the existing ANNs. The possibility to adopt just one model to obtain a handy and accurate evaluation of the overall performance of a coastal or harbor structure represents the most important and exportable result of the work.

Evaluation of a multi variated analysis for the selection of t-tbar multijet events in the current CMS implemented environment at LHC

In the present study we are using multi variate analysis techniques to discriminate signal from background in the fully hadronic decay channel of ttbar events. We give a brief introduction to the role of the Top quark in the standard model and a general description of the CMS Experiment at LHC. We have used the CMS experiment computing and software infrastructure to generate and prepare the data samples used in this analysis. We tested the performance of three different classifiers applied to our data samples and used the selection obtained with the Multi Layer Perceptron classifier to give an estimation of the statistical and systematical uncertainty on the cross section measurement.

Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.

High-Speed Laser Processing of Thin Single and Multi-Layer Films

Theoretical models are developed for the continuous-wave and pulsed laser incision and cut of thin single and multi-layer films. A one-dimensional steady-state model establishes the theoretical foundations of the problem by combining a power-balance integral with heat flow in the direction of laser motion. In this approach, classical modelling methods for laser processing are extended by introducing multi-layer optical absorption and thermal properties. The calculation domain is consequently divided in correspondence with the progressive removal of individual layers. A second, time-domain numerical model for the short-pulse laser ablation of metals accounts for changes in optical and thermal properties during a single laser pulse. With sufficient fluence, the target surface is heated towards its critical temperature and homogeneous boiling or "phase explosion" takes place. Improvements are seen over previous works with the more accurate calculation of optical absorption and shielding of the incident beam by the ablation products. A third, general time-domain numerical laser processing model combines ablation depth and energy absorption data from the short-pulse model with two-dimensional heat flow in an arbitrary multi-layer structure. Layer removal is the result of both progressive short-pulse ablation and classical vaporisation due to long-term heating of the sample. At low velocity, pulsed laser exposure of multi-layer films comprising aluminium-plastic and aluminium-paper are found to be characterised by short-pulse ablation of the metallic layer and vaporisation or degradation of the others due to thermal conduction from the former. At high velocity, all layers of the two films are ultimately removed by vaporisation or degradation as the average beam power is increased to achieve a complete cut. The transition velocity between the two characteristic removal types is shown to be a function of the pulse repetition rate. An experimental investigation validates the simulation results and provides new laser processing data for some typical packaging materials.

Development, degeneration and neural network of the bodily self

The question addressed by this dissertation is how the human brain builds a coherent representation of the body, and how this representation is used to recognize its own body. Recent approaches by neuroimaging and TMS revealed hints for a distinct brain representation of human body, as compared with other stimulus categories. Neuropsychological studies demonstrated that body-parts and self body-parts recognition are separate processes sub-served by two different, even if possibly overlapping, networks within the brain. Bodily self-recognition is one aspect of our ability to distinguish between self and others and the self/other distinction is a crucial aspect of social behaviour. This is the reason why I have conducted a series of experiment on subjects with everyday difficulties in social and emotional behaviour, such as patients with autism spectrum disorders (ASD) and patients with Parkinson’s disease (PD). More specifically, I studied the implicit self body/face recognition (Chapter 6) and the influence of emotional body postures on bodily self-processing in TD children as well as in ASD children (Chapter 7). I found that the bodily self-recognition is present in TD and in ASD children and that emotional body postures modulate self and others’ body processing. Subsequently, I compared implicit and explicit bodily self-recognition in a neuro-degenerative pathology, such as in PD patients, and I found a selective deficit in implicit but not in explicit self-recognition (Chapter 8). This finding suggests that implicit and explicit bodily self-recognition are separate processes subtended by different mechanisms that can be selectively impaired. If the bodily self is crucial for self/other distinction, the space around the body (personal space) represents the space of interaction and communication with others. When, I studied this space in autism, I found that personal space regulation is impaired in ASD children (Chapter 9).

A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition.

The predictive role of the human gut microbiota in covid-19: implications for disease severity and post-covid syndrome

The COVID-19 pandemic, sparked by the SARS-CoV-2 virus, stirred global comparisons to historical pandemics. Initially presenting a high mortality rate, it later stabilized globally at around 0.5-3%. Patients manifest a spectrum of symptoms, necessitating efficient triaging for appropriate treatment strategies, ranging from symptomatic relief to antivirals or monoclonal antibodies. Beyond traditional approaches, emerging research suggests a potential link between COVID-19 severity and alterations in gut microbiota composition, impacting inflammatory responses. However, most studies focus on severe hospitalized cases without standardized criteria for severity. Addressing this gap, the first study in this thesis spans diverse COVID-19 severity levels, utilizing 16S rRNA amplicon sequencing on fecal samples from 315 subjects. The findings highlight significant microbiota differences correlated with severity. Machine learning classifiers, including a multi-layer convoluted neural network, demonstrated the potential of microbiota compositional data to predict patient severity, achieving an 84.2% mean balanced accuracy starting one week post-symptom onset. These preliminary results underscore the gut microbiota's potential as a biomarker in clinical decision-making for COVID-19. The second study delves into mild COVID-19 cases, exploring their implications for ‘long COVID’ or Post-Acute COVID-19 Syndrome (PACS). Employing longitudinal analysis, the study unveils dynamic shifts in microbial composition during the acute phase, akin to severe cases. Innovative techniques, including network approaches and spline-based longitudinal analysis, were deployed to assess microbiota dynamics and potential associations with PACS. The research suggests that even in mild cases, similar mechanisms to hospitalized patients are established regarding changes in intestinal microbiota during the acute phase of the infection. These findings lay the foundation for potential microbiota-targeted therapies to mitigate inflammation, potentially preventing long COVID symptoms in the broader population. In essence, these studies offer valuable insights into the intricate relationships between COVID-19 severity, gut microbiota, and the potential for innovative clinical applications.

Structural changes and dynamic behaviour of vanadium oxide-based catalysts for gas-phase selective oxidations

Selective oxidation is one of the simplest functionalization methods and essentially all monomers used in manufacturing artificial fibers and plastics are obtained by catalytic oxidation processes. Formally, oxidation is considered as an increase in the oxidation number of the carbon atoms, then reactions such as dehydrogenation, ammoxidation, cyclization or chlorination are all oxidation reactions. In this field, most of processes for the synthesis of important chemicals used vanadium oxide-based catalysts. These catalytic systems are used either in the form of multicomponent mixed oxides and oxysalts, e.g., in the oxidation of n-butane (V/P/O) and of benzene (supported V/Mo/O) to maleic anhydride, or in the form of supported metal oxide, e.g., in the manufacture of phthalic anhydride by o-xylene oxidation, of sulphuric acid by oxidation of SO2, in the reduction of NOx with ammonia and in the ammoxidation of alkyl aromatics. In addition, supported vanadia catalysts have also been investigated for the oxidative dehydrogenation of alkanes to olefins , oxidation of pentane to maleic anhydride and the selective oxidation of methanol to formaldehyde or methyl formate [1]. During my PhD I focused my work on two gas phase selective oxidation reactions. The work was done at the Department of Industrial Chemistry and Materials (University of Bologna) in collaboration with Polynt SpA. Polynt is a leader company in the development, production and marketing of catalysts for gas-phase oxidation. In particular, I studied the catalytic system for n-butane oxidation to maleic anhydride (fluid bed technology) and for o-xylene oxidation to phthalic anhydride. Both reactions are catalyzed by systems based on vanadium, but catalysts are completely different. Part A is dedicated to the study of V/P/O catalyst for n-butane selective oxidation, while in the Part B the results of an investigation on TiO2-supported V2O5, catalyst for o-xylene oxidation are showed. In Part A, a general introduction about the importance of maleic anhydride, its uses, the industrial processes and the catalytic system are reported. The reaction is the only industrial direct oxidation of paraffins to a chemical intermediate. It is produced by n-butane oxidation either using fixed bed and fluid bed technology; in both cases the catalyst is the vanadyl pyrophosphate (VPP). Notwithstanding the good performances, the yield value didn’t exceed 60% and the system is continuously studied to improve activity and selectivity. The main open problem is the understanding of the real active phase working under reaction conditions. Several articles deal with the role of different crystalline and/or amorphous vanadium/phosphorous (VPO) compounds. In all cases, bulk VPP is assumed to constitute the core of the active phase, while two different hypotheses have been formulated concerning the catalytic surface. In one case the development of surface amorphous layers that play a direct role in the reaction is described, in the second case specific planes of crystalline VPP are assumed to contribute to the reaction pattern, and the redox process occurs reversibly between VPP and VOPO4. Both hypotheses are supported also by in-situ characterization techniques, but the experiments were performed with different catalysts and probably under slightly different working conditions. Due to complexity of the system, these differences could be the cause of the contradictions present in literature. Supposing that a key role could be played by P/V ratio, I prepared, characterized and tested two samples with different P/V ratio. Transformation occurring on catalytic surfaces under different conditions of temperature and gas-phase composition were studied by means of in-situ Raman spectroscopy, trying to investigate the changes that VPP undergoes during reaction. The goal is to understand which kind of compound constituting the catalyst surface is the most active and selective for butane oxidation reaction, and also which features the catalyst should possess to ensure the development of this surface (e.g. catalyst composition). On the basis of results from this study, it could be possible to project a new catalyst more active and selective with respect to the present ones. In fact, the second topic investigated is the possibility to reproduce the surface active layer of VPP onto a support. In general, supportation is a way to improve mechanical features of the catalysts and to overcome problems such as possible development of local hot spot temperatures, which could cause a decrease of selectivity at high conversion, and high costs of catalyst. In literature it is possible to find different works dealing with the development of supported catalysts, but in general intrinsic characteristics of VPP are worsened due to the chemical interaction between active phase and support. Moreover all these works deal with the supportation of VPP; on the contrary, my work is an attempt to build-up a V/P/O active layer on the surface of a zirconia support by thermal treatment of a precursor obtained by impregnation of a V5+ salt and of H3PO4. In-situ Raman analysis during the thermal treatment, as well as reactivity tests are used to investigate the parameters that may influence the generation of the active phase. Part B is devoted to the study of o-xylene oxidation of phthalic anhydride; industrially, the reaction is carried out in gas-phase using as catalysts a supported system formed by V2O5 on TiO2. The V/Ti/O system is quite complex; different vanadium species could be present on the titania surface, as a function of the vanadium content and of the titania surface area: (i) V species which is chemically bound to the support via oxo bridges (isolated V in octahedral or tetrahedral coordination, depending on the hydration degree), (ii) a polymeric species spread over titania, and (iii) bulk vanadium oxide, either amorphous or crystalline. The different species could have different catalytic properties therefore changing the relative amount of V species can be a way to optimize the catalytic performances of the system. For this reason, samples containing increasing amount of vanadium were prepared and tested in the oxidation of o-xylene, with the aim of find a correlations between V/Ti/O catalytic activity and the amount of the different vanadium species. The second part deals with the role of a gas-phase promoter. Catalytic surface can change under working conditions; the high temperatures and a different gas-phase composition could have an effect also on the formation of different V species. Furthermore, in the industrial practice, the vanadium oxide-based catalysts need the addition of gas-phase promoters in the feed stream, that although do not have a direct role in the reaction stoichiometry, when present leads to considerable improvement of catalytic performance. Starting point of my investigation is the possibility that steam, a component always present in oxidation reactions environment, could cause changes in the nature of catalytic surface under reaction conditions. For this reason, the dynamic phenomena occurring at the surface of a 7wt% V2O5 on TiO2 catalyst in the presence of steam is investigated by means of Raman spectroscopy. Moreover a correlation between the amount of the different vanadium species and catalytic performances have been searched. Finally, the role of dopants has been studied. The industrial V/Ti/O system contains several dopants; the nature and the relative amount of promoters may vary depending on catalyst supplier and on the technology employed for the process, either a single-bed or a multi-layer catalytic fixed-bed. Promoters have a quite remarkable effect on both activity and selectivity to phthalic anhydride. Their role is crucial, and the proper control of the relative amount of each component is fundamental for the process performance. Furthermore, it can not be excluded that the same promoter may play different role depending on reaction conditions (T, composition of gas phase..). The reaction network of phthalic anhydride formation is very complex and includes several parallel and consecutive reactions; for this reason a proper understanding of the role of each dopant cannot be separated from the analysis of the reaction scheme. One of the most important promoters at industrial level, which is always present in the catalytic formulations is Cs. It is known that Cs plays an important role on selectivity to phthalic anhydride, but the reasons of this phenomenon are not really clear. Therefore the effect of Cs on the reaction scheme has been investigated at two different temperature with the aim of evidencing in which step of the reaction network this promoter plays its role.

A New Prediction Model for Slope Stability Analysis

The instability of river bank can result in considerable human and land losses. The Po river is the most important in Italy, characterized by main banks of significant and constantly increasing height. This study presents multilayer perceptron of artificial neural network (ANN) to construct prediction models for the stability analysis of river banks along the Po River, under various river and groundwater boundary conditions. For this aim, a number of networks of threshold logic unit are tested using different combinations of the input parameters. Factor of safety (FS), as an index of slope stability, is formulated in terms of several influencing geometrical and geotechnical parameters. In order to obtain a comprehensive geotechnical database, several cone penetration tests from the study site have been interpreted. The proposed models are developed upon stability analyses using finite element code over different representative sections of river embankments. For the validity verification, the ANN models are employed to predict the FS values of a part of the database beyond the calibration data domain. The results indicate that the proposed ANN models are effective tools for evaluating the slope stability. The ANN models notably outperform the derived multiple linear regression models.