Computer aided design and finite element modeling to predict bulk mechanical properties of bone scaffolds using triply periodic minimal surfaces (progettazione assistita al calcolatore ed analisi con il metodo degli elementi finiti per predire il comportamento meccanico di scaffolds ossei creati con l'uso di superfici minime periodiche in tre direzioni)

The aim of Tissue Engineering is to develop biological substitutes that will restore lost morphological and functional features of diseased or damaged portions of organs. Recently computer-aided technology has received considerable attention in the area of tissue engineering and the advance of additive manufacture (AM) techniques has significantly improved control over the pore network architecture of tissue engineering scaffolds. To regenerate tissues more efficiently, an ideal scaffold should have appropriate porosity and pore structure. More sophisticated porous configurations with higher architectures of the pore network and scaffolding structures that mimic the intricate architecture and complexity of native organs and tissues are then required. This study adopts a macro-structural shape design approach to the production of open porous materials (Titanium foams), which utilizes spatial periodicity as a simple way to generate the models. From among various pore architectures which have been studied, this work simulated pore structure by triply-periodic minimal surfaces (TPMS) for the construction of tissue engineering scaffolds. TPMS are shown to be a versatile source of biomorphic scaffold design. A set of tissue scaffolds using the TPMS-based unit cell libraries was designed. TPMS-based Titanium foams were meant to be printed three dimensional with the relative predicted geometry, microstructure and consequently mechanical properties. Trough a finite element analysis (FEA) the mechanical properties of the designed scaffolds were determined in compression and analyzed in terms of their porosity and assemblies of unit cells. The purpose of this work was to investigate the mechanical performance of TPMS models trying to understand the best compromise between mechanical and geometrical requirements of the scaffolds. The intention was to predict the structural modulus in open porous materials via structural design of interconnected three-dimensional lattices, hence optimising geometrical properties. With the aid of FEA results, it is expected that the effective mechanical properties for the TPMS-based scaffold units can be used to design optimized scaffolds for tissue engineering applications. Regardless of the influence of fabrication method, it is desirable to calculate scaffold properties so that the effect of these properties on tissue regeneration may be better understood.

Computer assisted synthesis and in-vitro cytotoxic evaluation of new pyrazole-fused isoquinolinoquinones derivatives as PI3K receptor antagonist with promising antitumoral activity

The importance of pyrazole and isoquinoline-5,8-dione scaffolds in medical chemistry is underlined by the high number of drugs currently on trading that contains these active ingredients. Due to their cytotoxic capability, the interest of medicinal chemists in these heterocyclic rings has grown exponentially especially, for cancer therapy. In this project, the first synthesis of pyrazole-fused isoquinoline-5,8-diones has been developed. 1,3-Dipolar cycloaddition followed by oxidative aromatization, established by our research group, has been employed. Screening of reaction conditions and characterization studies about the regioselectivity have been successfully performed. A remote control of regioselectivity, to achieve the two possible regioisomers has been accomplished. Through Molecular Docking studies, Structure-Activity relationship of differently substituted scaffolds containing our central core proved that a family of PI3K inhibitors have been discovered. Finally, in order to verify the promising antitumor activity, a first test of cell viability in vitro on T98G cell line of a solid brain tumor, the Glioblastoma Multiforme, showed cytotoxic inhibition comparable to currently trade anticancer drugs.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Traduzione tecnica e internazionalizzazione d'impresa: Il caso di Technologica S.r.l.

This dissertation is part of the Language Toolkit project which is a collaboration between the School of Foreign Languages and Literature, Interpreting and Translation of the University of Bologna, Forlì campus, and the Chamber of Commerce of Forlì-Cesena. This project aims to create an exchange between translation students and companies who want to pursue a process of internationalization. The purpose of this dissertation is demonstrating the benefits that translation systems can bring to businesses. In particular, it consists of the translation into English of documents supplied by the Italian company Technologica S.r.l. and the creation of linguistic resources that can be integrated into computer-assisted translation (CAT) software, in order to optimize the translation process. The latter is claimed to be a priority with respect to the actual translation products (the target texts), since the analysis conducted on the source texts highlighted that the company could streamline and optimize its English language communication thanks to the use of open source CAT tools such as OmegaT. The work consists of five chapters. The first introduces the Language Toolkit project, the company (Technologica S.r.l ) and its products. The second chapter provides some considerations about technical translation, its features and some misconceptions about it. The difference between technical translation and scientific translation is then clarified and an overview is offered of translation aids such as those used for computer-assisted translation, machine translation, termbases and translation memories. The third chapter contains the analysis of the texts commissioned by Technologica S.r.l. and their categorization. The fourth chapter describes the translation process, with particular attention to terminology extraction and the creation of a bilingual glossary based on a specialized corpus. The glossary was integrated into the OmegaT software in order to facilitate the translation process both for the present task and for future applications. The memory deriving from the translation represents a sort of hybrid resource between a translation memory and a glossary. This was found to be the most appropriate format, given the specific nature of the texts to be translated. Finally, in chapter five conclusions are offered about the importance of language training within a company environment, the potentialities of translation aids and the benefits that they would bring to a company wishing to internationalize itself.