Design of an innovative electric vehicle simulator for Charging Systems' End-of-Line Testing

The trend related to the turnover of internal combustion engine vehicles with EVs goes by the name of electrification. The push electrification experienced in the last decade is linked to the still ongoing evolution in power electronics technology for charging systems. This is the reason why an evolution in testing strategies and testing equipment is crucial too. The project this dissertation is based on concerns the investigation of a new EV simulator design. that optimizes the structure of the testing equipment used by the company who commissioned this work. Project requirements can be summarized in the following two points: space occupation reduction and parallel charging implementation. Some components were completely redesigned, and others were substituted with equivalent ones that could perform the same tasks. In this way it was possible to reduce the space occupation of the simulator, as well as to increase the efficiency of the testing device. Moreover, the possibility of conjugating different charging simulations could be investigated by parallelly launching two testing procedures on a unique machine, properly predisposed for supporting the two charging protocols used. On the back of the results achieved in the body of this dissertation, a new design for the EV simulator was proposed. In this way, space reduction was obtained, and space occupation efficiency was improved with the proposed new design. The testing device thus resulted to be way more compact, enabling to gain in safety and productivity, along with a 25% cost reduction. Furthermore, parallel charging was implemented in the proposed new design since the conducted tests clearly showed the feasibility of parallel charging sessions. The results presented in this work can thus be implemented to build the first prototype of the new EV simulator.

Computational study on the asymmetric aminocatalysed Michael addition reaction of cyclohexanone to trans–β–nitrostyrene

Asymmetric organocatalysed reactions are one of the most fascinating synthetic strategies which one can adopt in order to induct a desired chirality into a reaction product. From all the possible practical applications of small organic molecules in catalytic reaction, amine–based catalysis has attracted a lot of attention during the past two decades. The high interest in asymmetric aminocatalytic pathways is to account to the huge variety of carbonyl compounds that can be functionalized by many different reactions of their corresponding chiral–enamine or –iminium ion as activated nucleophile and electrophile, respectively. Starting from the employment of L–Proline, many useful substrates have been proposed in order to further enhance the catalytic performances of these reaction in terms of enantiomeric excess values, yield, conversion of the substrate and turnover number. In particular, in the last decade the use of chiral and quasi–enantiomeric primary amine species has got a lot of attention in the field. Contemporaneously, many studies have been carried out in order to highlight the mechanism through which these kinds of substrates induct chirality into the desired products. In this scenario, computational chemistry has played a crucial role due to the possibility of simulating and studying any kind of reaction and the transition state structures involved. In the present work the transition state geometries of primary amine–catalysed Michael addition reaction of cyclohexanone to trans–β–nitrostyrene with different organic acid cocatalysts has been studied through different computational techniques such as density functional theory based quantum mechanics calculation and force–field directed molecular simulations.

Computational Investigation of the Molecular Mechanisms Regulating Nucleic Acid Processing Metalloenzymes

Polymerases and nucleases are enzymes processing DNA and RNA. They are involved in crucial processes for cell life by performing the extension and the cleavage of nucleic acid chains during genome replication and maintenance. Additionally, both enzymes are often associated to several diseases, including cancer. In order to catalyze the reaction, most of them operate via the two-metal-ion mechanism. For this, despite showing relevant differences in structure, function and catalytic properties, they share common catalytic elements, which comprise the two catalytic ions and their first-shell acidic residues. Notably, recent studies of different metalloenzymes revealed the recurrent presence of additional elements surrounding the active site, thus suggesting an extended two-metal-ion-centered architecture. However, whether these elements have a catalytic function and what is their role is still unclear. In this work, using state-of-the-art computational techniques, second- and third-shell elements are showed to act in metallonucleases favoring the substrate positioning and leaving group release. In particular, in hExo1 a transient third metal ion is recruited and positioned near the two-metal-ion site by a structurally conserved acidic residue to assist the leaving group departure. Similarly, in hFEN1 second- and third-shell Arg/Lys residues operate the phosphate steering mechanism through (i) substrate recruitment, (ii) precise cleavage localization, and (iii) leaving group release. Importantly, structural comparisons of hExo1, hFEN1 and other metallonucleases suggest that similar catalytic mechanisms may be shared by other enzymes. Overall, the results obtained provide an extended vision on parallel strategies adopted by metalloenzymes, which employ divalent metal ion or positively charged residues to ensure efficient and specific catalysis. Furthermore, these outcomes may have implications for de novo enzyme engineering and/or drug design to modulate nucleic acid processing.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Orchestration of multiscale model for computational oncology

Cancer is a challenging disease that involves multiple types of biological interactions in different time and space scales. Often computational modelling has been facing problems that, in the current technology level, is impracticable to represent in a single space-time continuum. To handle this sort of problems, complex orchestrations of multiscale models is frequently done. PRIMAGE is a large EU project that aims to support personalized childhood cancer diagnosis and prognosis. The goal is to do so predicting the growth of the solid tumour using multiscale in-silico technologies. The project proposes an open cloud-based platform to support decision making in the clinical management of paediatric cancers. The orchestration of predictive models is in general complex and would require a software framework that support and facilitate such task. The present work, proposes the development of an updated framework, referred herein as the VPH-HFv3, as a part of the PRIMAGE project. This framework, a complete re-writing with respect to the previous versions, aims to orchestrate several models, which are in concurrent development, using an architecture as simple as possible, easy to maintain and with high reusability. This sort of problem generally requires unfeasible execution times. To overcome this problem was developed a strategy of particularisation, which maps the upper-scale model results into a smaller number and homogenisation which does the inverse way and analysed the accuracy of this approach.