Arricchimento semantico nel contesto dell'Existing Building Information Modelling: aspetti metodologici e applicazione al caso della Rocca Estense di San Felice sul Panaro.

(ITA) L’industria mondiale odierna nel campo dell’architettura e dell’ingegneria si esprime quasi esclusivamente mediante l’approccio BIM, Building Information Modeling. Anche se sviluppato pensando alle nuove costruzioni ed ancora in via di perfezionamento, è entrato prepotentemente nei capitoli normativi di molti stati all’urlo dell“interoperability”. Su questo tema è recente l’interesse e la possibilità di adozione per l’intervento sul costruito, ovvero di Existing Building Information Modelling, eBIM. Gli studi applicativi-sperimentali in questo ambito sono sempre più numerosi e convergono, purtroppo, sulla delicata correlazione tra la gestione del contenuto semantico e la perdita di interoperabilità. Questa tesi si incentra sull’analisi di tale correlazione valutando in particolare l’aspetto metodologico-applicativo dell’arricchimento semantico adottando come caso studio la Torre Nord della Rocca Estense di San Felice sul Panaro. (ENG)Today's global industry in architecture and engineering fields, expresses itself almost entirely focusing on BIM, Building Information Modeling. Even though it was developed taking in consideration new buildings and the ones that are in the process of improvement, it has entered the regulatory chapters of many states in the hymn of "interoperability". Concerning this topic is recent the interest and possibility of adopting a process to intervene on the already built constructions, Existing Building Information Modeling, eBIM. Application-experimental studies in this area are increasingly numerous and unfortunately converge, on the delicate correlation between the management of the semantic content and the loss of interoperability. This thesis focuses on the analysis of this correlation by evaluating in particular the methodological-applicative aspect of semantic enrichment by adopting the North Tower of the Rocca Estense in San Felice sul Panaro as a case study.

Computational design of a new type of sandwich cross-section for a defined solution of a tree-like column produced by wire-and-arc additive manufacturing

When it comes to designing a structure, architects and engineers want to join forces in order to create and build the most beautiful and efficient building. From finding new shapes and forms to optimizing the stability and the resistance, there is a constant link to be made between both professions. In architecture, there has always been a particular interest in creating new shapes and types of a structure inspired by many different fields, one of them being nature itself. In engineering, the selection of optimum has always dictated the way of thinking and designing structures. This mindset led through studies to the current best practices in construction. However, both disciplines were limited by the traditional manufacturing constraints at a certain point. Over the last decades, much progress was made from a technological point of view, allowing to go beyond today's manufacturing constraints. With the emergence of Wire-and-Arc Additive Manufacturing (WAAM) combined with Algorithmic-Aided Design (AAD), architects and engineers are offered new opportunities to merge architectural beauty and structural efficiency. Both technologies allow for exploring and building unusual and complex structural shapes in addition to a reduction of costs and environmental impacts. Through this study, the author wants to make use of previously mentioned technologies and assess their potential, first to design an aesthetically appreciated tree-like column with the idea of secondly proposing a new type of standardized and optimized sandwich cross-section to the construction industry. Parametric algorithms to model the dendriform column and the new sandwich cross-section are developed and presented in detail. A catalog draft of the latter and methods to establish it are then proposed and discussed. Finally, the buckling behavior of this latter is assessed considering standard steel and WAAM material properties.

Variational Quantum Splines: Moving Beyond Linearity for Quantum Activation Functions

Activation functions within neural networks play a crucial role in Deep Learning since they allow to learn complex and non-trivial patterns in the data. However, the ability to approximate non-linear functions is a significant limitation when implementing neural networks in a quantum computer to solve typical machine learning tasks. The main burden lies in the unitarity constraint of quantum operators, which forbids non-linearity and poses a considerable obstacle to developing such non-linear functions in a quantum setting. Nevertheless, several attempts have been made to tackle the realization of the quantum activation function in the literature. Recently, the idea of the QSplines has been proposed to approximate a non-linear activation function by implementing the quantum version of the spline functions. Yet, QSplines suffers from various drawbacks. Firstly, the final function estimation requires a post-processing step; thus, the value of the activation function is not available directly as a quantum state. Secondly, QSplines need many error-corrected qubits and a very long quantum circuits to be executed. These constraints do not allow the adoption of the QSplines on near-term quantum devices and limit their generalization capabilities. This thesis aims to overcome these limitations by leveraging hybrid quantum-classical computation. In particular, a few different methods for Variational Quantum Splines are proposed and implemented, to pave the way for the development of complete quantum activation functions and unlock the full potential of quantum neural networks in the field of quantum machine learning.

Analisi della qualità dei dati in un’enterprise architecture utilizzando un sistema d’inferenza.

Lo scopo della tesi è definire un modello e identificare un sistema d’inferenza utile per l’analisi della qualità dei dati. Partendo da quanto descritto in ambito accademico e business, la definizione di un modello facilita l’analisi della qualità, fornendo una descrizione chiara delle tipologie di problemi a cui possono essere soggetti i dati. I diversi lavori in ambito accademico e business saranno confrontati per stabilire quali siano i problemi di qualità più diffusi, in modo da realizzare un modello che sia semplice e riutilizzabile. I sistemi d’inferenza saranno confrontati a livello teorico e pratico per individuare lo strumento più adatto nell’analisi della qualità dei dati in un caso applicativo. Il caso applicativo è caratterizzato da requisiti funzionali e non; il principale requisito funzionale è l’individuazione di problemi di qualità nei dati, mentre quello non funzionale è l’ usabilità dello strumento, per permettere ad un qualunque utente di esprimere dei controlli sulla qualità. Il caso applicativo considera dati di un’enterprise architecture reale ed è stato fornito dall’azienda Imola Informatica.

Scalable and Seamless Discovery and Selection of Services in Mobile Cloud Computing

Mobile devices are now capable of supporting a wide range of applications, many of which demand an ever increasing computational power. To this end, mobile cloud computing (MCC) has been proposed to address the limited computation power, memory, storage, and energy of such devices. An important challenge in MCC is to guarantee seamless discovery of services. To this end, this thesis proposes an architecture that provides user-transparent and low-latency service discovery, as well as automated service selection. Experimental results on a real cloud computing testbed demonstrated that the proposed work outperforms state of-the-art approaches by achieving extremely low discovery delay.

Energy consumption of parallel algorithms for solving linear systems on HPC architecture

Modern High-Performance Computing HPC systems are gradually increasing in size and complexity due to the correspondent demand of larger simulations requiring more complicated tasks and higher accuracy. However, as side effects of the Dennard’s scaling approaching its ultimate power limit, the efficiency of software plays also an important role in increasing the overall performance of a computation. Tools to measure application performance in these increasingly complex environments provide insights into the intricate ways in which software and hardware interact. The monitoring of the power consumption in order to save energy is possible through processors interfaces like Intel Running Average Power Limit RAPL. Given the low level of these interfaces, they are often paired with an application-level tool like Performance Application Programming Interface PAPI. Since several problems in many heterogeneous fields can be represented as a complex linear system, an optimized and scalable linear system solver algorithm can decrease significantly the time spent to compute its resolution. One of the most widely used algorithms deployed for the resolution of large simulation is the Gaussian Elimination, which has its most popular implementation for HPC systems in the Scalable Linear Algebra PACKage ScaLAPACK library. However, another relevant algorithm, which is increasing in popularity in the academic field, is the Inhibition Method. This thesis compares the energy consumption of the Inhibition Method and Gaussian Elimination from ScaLAPACK to profile their execution during the resolution of linear systems above the HPC architecture offered by CINECA. Moreover, it also collates the energy and power values for different ranks, nodes, and sockets configurations. The monitoring tools employed to track the energy consumption of these algorithms are PAPI and RAPL, that will be integrated with the parallel execution of the algorithms managed with the Message Passing Interface MPI.