Per un atlante dell'architettura storica bolognese. Caratteri costruttivi e conservazione consapevole

La ripresa degli studi sulla manualistica del recupero ha contribuito, attraverso una lettura tecnica vista in prospettiva storica, a diffondere sensibilità conoscitiva e consapevolezza del patrimonio premoderno. Tuttavia l’esigenza di superare il tracciato delineato dall’uso dei manuali di recupero – da molti intesi, semplicisticamente, come cataloghi per soluzioni architettoniche di ripristino e ricostruzione – ha reso indispensabile una riflessione sul reale bisogno di questi strumenti e sulle loro ripercussioni operative. Se i manuali, spesso, esprimono una visione statica e totalizzante dell’edilizia storica, l’atlante dichiara una concezione dinamica e “sempre aperta”, in cui ogni elemento rilevato è caso a sé. L’atlante fa, quindi, riferimento ad una concezione “geografica” in cui la catalogazione non è esaustiva e dogmatica ma, contrariamente, dà luogo ad un repertorio di casi criticamente analizzati nell’ottica della conoscenza e della conservazione. L’obiettivo della ricerca non è consistito, pertanto, nel descrivere la totalità dei caratteri costruttivi e delle loro combinazioni, ma nell’individuare casi singoli che sono letti ed interpretati all’interno del loro contesto storico-costruttivo e che valgono quale monito per un’azione progettuale consapevole, orientata al minimo intervento e alla compatibilità fisico-meccanica, figurativa e filologica. Nello specifico la ricerca, collocata in un riferimento temporale compreso tra il XIII e il XIX secolo, ha approfondito i seguenti caratteri: solai lignei, appartato decorativo in cotto e portali. Attraverso un approccio interdisciplinare lo studio si è proposto di contribuire alla costituzione di una metodologia di ricerca sulle tecniche costruttive storiche, ravvisando nel momento conoscitivo la prima fase del progetto di conservazione. È indiscusso, infatti, il solido legame che esiste tra conoscenza, progetto ed operatività. Solo attraverso la consapevolezza storica e architettonica del manufatto è possibile individuare scelte conservative criticamente vagliate ed operare in funzione della specificità del caso in esame e delle sue reali necessità.

Advanced polymeric materials for applications in technical equipment for snow sports

The thesis is divided in three chapters, each one covering one topic. Initially, the thermo-mechanical and impact properties of materials used for back protectors have been analysed. Dynamical mechanical analysis (DMTA) has shown that materials used for soft-shell protectors present frequency-sensitive properties. Furthermore, through impact tests, the shock absorbing characteristics of the materials have been investigated proving the differences between soft and hard-shell protectors; moreover it has been demonstrated that the materials used for soft-shell protectors maintain their protective properties after multi-impacts. The second chapter covers the effect of the visco-elastic properties of the thermoplastic polymers on the flexural and rebound behaviours of ski boots. DMTA analysis on the materials and flexural and rebound testing on the boots have been performed. A comparison of the results highlighted a correlation between the visco-elastic properties and the flexural and rebound behaviour of ski boots. The same experimental methods have been used to investigate the influence of the design on the flexural and rebound behaviours. Finally in the third chapter the thermoplastic materials employed for the construction of ski boots soles have been characterized in terms of chemical composition, hardness, crystallinity, surface roughness and coefficient of friction (COF). The results showed a relation between material hardness and grip, in particular softer materials provide more grip with respect to harder materials. On the contrary, the surface roughness has a negative effect on friction because of the decrease in contact area. The measure of grip on inclined wet surfaces showed again a relation between hardness and grip. The performance ranking of the different materials has been the same for the COF and for the slip angle tests, indicating that COF can be used as a parameter for the choice of the optimal material to be used for the soles of ski boots.

Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance

In my PhD thesis I propose a Bayesian nonparametric estimation method for structural econometric models where the functional parameter of interest describes the economic agent's behavior. The structural parameter is characterized as the solution of a functional equation, or by using more technical words, as the solution of an inverse problem that can be either ill-posed or well-posed. From a Bayesian point of view, the parameter of interest is a random function and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of inconsistency, from a frequentist point of view, of the posterior distribution (i.e. problem of ill-posedness). The contribution of this essay is to propose new methods to deal with this problem of ill-posedness. The first one consists in adopting a Tikhonov regularization scheme in the construction of the posterior distribution so that I end up with a new object that I call regularized posterior distribution and that I guess it is solution of the inverse problem. The second approach consists in specifying a prior distribution on the parameter of interest of the g-prior type. Then, I detect a class of models for which the prior distribution is able to correct for the ill-posedness also in infinite dimensional problems. I study asymptotic properties of these proposed solutions and I prove that, under some regularity condition satisfied by the true value of the parameter of interest, they are consistent in a "frequentist" sense. Once I have set the general theory, I apply my bayesian nonparametric methodology to different estimation problems. First, I apply this estimator to deconvolution and to hazard rate, density and regression estimation. Then, I consider the estimation of an Instrumental Regression that is useful in micro-econometrics when we have to deal with problems of endogeneity. Finally, I develop an application in finance: I get the bayesian estimator for the equilibrium asset pricing functional by using the Euler equation defined in the Lucas'(1978) tree-type models.

Definition, realization and evaluation of a software reference architecture for use in space applications

A recent initiative of the European Space Agency (ESA) aims at the definition and adoption of a software reference architecture for use in on-board software of future space missions. Our PhD project placed in the context of that effort. At the outset of our work we gathered all the industrial needs relevant to ESA and all the main European space stakeholders and we were able to consolidate a set of technical high-level requirements for the fulfillment of them. The conclusion we reached from that phase confirmed that the adoption of a software reference architecture was indeed the best solution for the fulfillment of the high-level requirements. The software reference architecture we set on building rests on four constituents: (i) a component model, to design the software as a composition of individually verifiable and reusable software units; (ii) a computational model, to ensure that the architectural description of the software is statically analyzable; (iii) a programming model, to ensure that the implementation of the design entities conforms with the semantics, the assumptions and the constraints of the computational model; (iv) a conforming execution platform, to actively preserve at run time the properties asserted by static analysis. The nature, feasibility and fitness of constituents (ii), (iii) and (iv), were already proved by the author in an international project that preceded the commencement of the PhD work. The core of the PhD project was therefore centered on the design and prototype implementation of constituent (i), a component model. Our proposed component model is centered on: (i) rigorous separation of concerns, achieved with the support for design views and by careful allocation of concerns to the dedicated software entities; (ii) the support for specification and model-based analysis of extra-functional properties; (iii) the inclusion space-specific concerns.

Learning representations for graph-structured socio-technical systems

The 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.