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A comprehensive experimental and numerical approach for assessing the hydro-mechanical behaviour of river embankments
Get PDFIn this doctoral dissertation, a comprehensive methodological approach for the assessment of river embankments safety conditions, based on the integrated use of laboratory testing, physical modelling and finite element (FE) numerical simulations, is proposed, with the aim of contributing to a better understanding of the effect of time-dependent hydraulic boundary conditions on the hydro-mechanical response of river embankments. The case study and materials selected for the present research project are representative for the riverbank systems of Alpine and Apennine tributaries of the main river Po (Northern Italy), which have recently experienced various sudden overall collapses.
The outcomes of a centrifuge test carried out under the enhanced gravity field of 50-g, on a riverbank model, made of a compacted silty sand mixture, overlying a homogeneous clayey silt foundation layer and subjected to a simulated flood event, have been considered for the definition of a robust and realistic experimental benchmark.
In order to reproduce the observed experimental behaviour, a first set of numerical simulations has been carried out by assuming, for both the embankments and the foundation unit, rigid soil porous media, under partially saturated conditions. Mechanical and hydraulic soil properties adopted in the numerical analyses have been carefully estimated based on standard saturated triaxial, oedometer and constant head permeability tests.
Afterwards, advanced suction-controlled laboratory tests, have been carried out to investigate the effect of suction and confining stresses on the shear strength and compressibility characteristics of the filling material and a second set of numerical simulations has been run, taking into account the soil parameters updated based on the most recent tests.
The final aim of the study is the quantitative estimation of the predictive capabilities of the calibrated numerical tools, by systematically comparing the results of the FE simulations to the experimental benchmark
Understanding 3D structures from sketches
Get PDFSketches are a unique way to communicate: drawing a simple sketch does not require any training, sketches convey information that is hard to describe with words, they are powerful enough to represent almost any concept, and nowadays, it is possible to draw directly from mobile devices. Motivated from the unique characteristics of sketches and fascinated by the human ability to imagine 3D objects from drawings, this thesis focuses on automatically associating geometric information to sketches. The main research directions of the thesis can be summarized as obtaining geometric information from freehand scene sketches to improve 2D sketch-based tasks and investigating Vision-Language models to overcome 3D sketch-based tasks limitations.
The first part of the thesis concerns geometric information prediction from scene sketches improving scene sketch to image generation and unlocking new creativity effects.
The thesis proceeds showing a study conducted on the Vision-Language models embedding space considering sketches, line renderings and RGB renderings of 3D shape to overcome the use of supervised datasets for 3D sketch-based tasks, that are limited and hard to acquire.
Following the obtained observations and results, Vision-Language models are applied to Sketch Based Shape Retrieval without the need of training on supervised datasets. We then analyze the use of Vision-Language models for sketch based 3D reconstruction in an unsupervised manner. In the final chapter we report the results obtained in an additional project carried during the PhD, which has lead to the development of a framework to learn an embedding space of neural networks that can be navigated to get ready-to-use models with desired characteristics
Learning representations for graph-structured socio-technical systems
Get PDFThe recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism.
The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased
emphasis on addressing the limitations of interpretability in graph representation learning.
This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations
The role of technology at school: new perspectives on distance education and cyberbullying
Get PDFThe integration of digital technology in school is a complex phenomenon that affects both teaching and peer relationships. Accordingly, the main aim of this dissertation was to investigate the implementation of distance education among Italian teachers during the COVID-19 pandemic and analyze peer relationships concerning cyberbullying and bullying. While the theoretical section provided an overview of the phenomena, four empirical studies were presented. The first one tested a moderated moderation model among 178 secondary teachers on the interactions among perceived usefulness, perceived ease of use of technology and online teaching self-efficacy. Findings showed that each variable significantly predicted the intention to use technology. In addition, a moderation effect of online teaching self-efficacy on perceived usefulness was found. The second study analyzed the differences in factors promoting the integration of digital technology among 357 teachers of different levels and subjects and their positive and negative experiences with distance education. Results revealed several differences in the function of the grade and subjects taught. Moreover, four main themes emerged from the content analysis. The third study investigated the dyadic perception of bullying and cyberbullying among 50 students using the eye-tracker. Findings showed that, despite differences among different kinds of bullying and cyberbullying, the victim and bully were the most observed roles. Finally, the last study tested two multiple mediation models among 563 students on the association between bullying, cyberbullying, and well-being, considering three different variables related to the school context (peer network, teacher support and school connectedness). The results highlighted the importance of peer networks and school connectedness in mediating the association between victimization, cybervictimzation and well-being. Taken together, the findings provided a rich overview of digital technology integration in schools, highlighting positive and negative aspects and its implications for future research and school policies
Portfolio optimization in the energy market
Get PDFLet’s put ourselves in the shoes of an energy company. Our fleet of electricity production plants mainly includes gas, hydroelectric and waste-to-energy plants. We also sold contracts for the supply of gas and electricity. For each year we have to plan the trading of the volumes needed by the plants and customers: better to fix the price of these volumes in advance with the so-called forward contracts, instead of waiting for the delivery months, exposing ourselves to price uncertainty. Here’s the thing: trying to keep uncertainty under control in a market that has never shown such extreme scenarios as in recent years: a pandemic, a worsening climate crisis and a war that is affecting economies around the world have made the energy market more volatile than ever. How to make decisions in such uncertain contexts? There is an optimization problem: given a year, we need to choose the optimal planning of volume trading times, to meet the needs of our portfolio at the best
prices, taking into account the liquidity constraints given by the market and the risk constraints imposed by the company. Algorithms are needed for the generation of market scenarios over a finite time horizon, that is, a probabilistic distribution that allows a view of all the dates between now and the end of the year of interest. Algorithms are needed to solve the optimization problem: we have proposed more than one and compared them; a very simple one, which avoids considering part of the complexity, moving on to a scenario approach and finally a reinforcement learning approach
Kinetic and mechanistic studies on peroxidation and stabilization of lipids with industrial and biological relevance
Get PDFLipid peroxidation is a complex mechanism that causes the degradation of lipid material of both industrial and biological significance. During processing, it is known that thermal stress produces oxidation and polymerization of oils. Additionally, biological lipids with both structural and bioactive roles are prone to peroxidation, which can have pathogenic effects including cancer and long-term degenerative disorders. To create innovative strategies to slow down the deterioration of lipids, it is crucial to improve our understanding of oxidation reactions and kinetics. To this purpose, Chapter II of this thesis focuses on the kinetic study of the oxidation reactions that take place during the thermal processing of bio-oils for industrial application. Through a new method it was possible to evaluate the kinetic parameters of oxidation of various lipid materials. This allowed us to distinguish between the different lipid materials based on their intrinsic properties. The effect of 18 antioxidants from the major families of natural and synthetic phenols were studied using the same methodology in order to acquire crucial data for enhancing the antioxidant activity of phenols based on structure-activity at high temperatures. Finally, it has been described how the antioxidant activity of α-tocopherol, revealed to be scarce in our conditions, can be improved in the presence of gamma-terpinene, through a synergistic action. Chapter III describes the synthesis and study of the antioxidant activity of polydopamine nanoparticles, in order to clarify the unclear mechanism of action of this material. Finally, in Chapter IV it was reported how the gamma-terpinene strongly inhibits the peroxidation of unsaturated lipids in heterogeneous model systems (micelles and liposomes) by forming hydroperoxyl radicals which diffuse outside the lipid nucleus, blocking the propagation of the chain radical. Furthermore, gamma-terpinene shows a very potent protective activity against ferroptosis being effective in the nanomolar range in the human neuroblastoma cell model
Radio frequency communication and fault detection for railway signalling
Get PDFThe continuous and swift progression of both wireless and wired communication technologies in today's
world owes its success to the foundational systems established earlier. These systems serve as the building
blocks that enable the enhancement of services to cater to evolving requirements. Studying the
vulnerabilities of previously designed systems and their current usage leads to the development of new
communication technologies replacing the old ones such as GSM-R in the railway field. The current industrial
research has a specific focus on finding an appropriate telecommunication solution for railway
communications that will replace the GSM-R standard which will be switched off in the next years.
Various standardization organizations are currently exploring and designing a radiofrequency technology
based standard solution to serve railway communications in the form of FRMCS (Future Railway Mobile
Communication System) to substitute the current GSM-R. Bearing on this topic, the primary strategic
objective of the research is to assess the feasibility to leverage on the current public network technologies
such as LTE to cater to mission and safety critical communication for low density lines. The research aims
to identify the constraints, define a service level agreement with telecom operators, and establish the
necessary implementations to make the system as reliable as possible over an open and public network,
while considering safety and cybersecurity aspects.
The LTE infrastructure would be utilized to transmit the vital data for the communication of a railway system
and to gather and transmit all the field measurements to the control room for maintenance purposes. Given
the significance of maintenance activities in the railway sector, the ongoing research includes the
implementation of a machine learning algorithm to detect railway equipment faults, reducing time and
human analysis errors due to the large volume of measurements from the field
Italian bibliographical periodicals as a source for cultural and scientific communication in the 17th and 18th centuries
Get PDFLa ricerca prende in esame la produzione della stampa periodica bibliografica italiana nel corso del Seicento e Settecento. Da un lato mira a ricostruirne il percorso storico attraverso la raccolta, la selezione e l’analisi delle principali testimonianze; dall’altro a indagarne le diverse forme e fisionomie assunte nel corso del tempo, nonché le modalità attraverso le quali fu somministrata la notitia librorum. A questo primo piano di indagine se ne è affiancato un secondo, per mezzo dell’elaborazione di due modelli descrittivi. Il primo è finalizzato alla raccolta delle principali generalità ed evidenze formali di una testata. Il secondo, invece, rappresenta un tentativo di spoglio e analisi approfondita dei contributi offerti da due campioni periodici presi come modelli di riferimento: La Galleria di Minerva, relativamente al biennio 1696-1697, e il Giornale della letteratura italiana (Mantova, 1793-1795). L’intento è quello di ricostruire, anche attraverso un processo di formulazione di keywords, le principali tematiche e i principali interessi emersi dalle esperienze menzionate. E mostrare, pertanto, il valore rappresentativo e identificativo del periodico bibliografico relativamente al contesto erudito di riferimento, nella sua veste di fonte informativa all’interno della quale si rispecchiarono le principali istanze scientifico-culturali del periodo.The research examines the production of the Italian bibliographic periodical press during the seventeenth and eighteenth centuries. On the one hand, it aims at reconstructing its historical course through the collection, selection and analysis of the main testimonies; on the other hand, it investigates the different forms and physiognomies it assumed over time, as well as the ways in which the notitia librorum was delivered. This first plan of investigation was flanked by a second, through the elaboration of two descriptive models. The first is aimed at collecting the main generalities and formal evidence of a headline. The second, on the other hand, represents an attempt at perusal and in-depth analysis of the contributions offered by two periodical samples taken as reference models: La Galleria di Minerva, for the two-year period 1696-1697, and the Giornale della letteratura italiana (Mantua, 1793-1795). The intention is to reconstruct, also through a process of keyword formulation, the main themes and interests that emerged from the experiences mentioned. And to show, therefore, the representative and identifying value of the bibliographical periodical in relation to the scholarly context of reference, in its capacity as an information source within which the main scientific-cultural instances of the period are reflected
Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures
Get PDFIn this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed
Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning
Get PDFThe design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model