Fighting child marriage in developing countries: design and development of a mobile application

Child marriage is still a great issue in developing countries and even if the interventions to prevent it are having results, they are not enough to eliminate the problem. Among the strategies that seem to work most to fight child marriage, there is the empowerment of girls with information combined with education of parents and community. As smartphones are more accessible year after year in developing countries, this thesis wants to investigate if a mobile app could be effective in fighting child marriage and which characteristics such an app should have. The research was organized in four phases and used design and creation and case study methodologies. Firstly, the literature was analyzed and an initial design was proposed. Secondly, expert interviews were performed to gain feedback on the proposed design, and afterwards prototype was built. Thirdly, a case study in the Democratic Republic of Congo (DRC) was performed to test the prototype, gaining insights and improvements through group interviews with 26 girls aged 15-19. Finally, a first version of the app was developed and a second phase of the case study was run in the DRC to understand if the girls were able to use the app. This phase included 14 girls of which 6 had participated in the prototype testing and used questionnaires as a data generation method. The app was built following the Principles for Digital Development. Even if this app is built based on the case study in DRC is modular and easily adaptable to other contexts as it is not content-specific. It was shown that is worth continuing to study this topic and it was defined a conceptual framework for designing learning apps for developing countries, in particular, to fight child, early, and forced marriage.

Rapid Control Prototyping of an L2+ Highway Assist System

Advanced Driver Assistance Systems (ADAS) are proving to have huge potential in road safety, comfort, and efficiency. In recent years, car manufacturers have equipped their high-end vehicles with Level 2 ADAS, which are, according to SAE International, systems that combine both longitudinal and lateral active motion control. These automated driving features, while only available in highway scenarios, appear to be very promising towards the introduction of hands-free driving. However, as they rely only on an on-board sensor suite, their continuative operation may be affected by the current environmental conditions: this prevents certain functionalities such as the automated lane change, other than requiring the driver to keep constantly the hands on the steering wheel. The enabling factor for hands-free highway driving proposed by Mobileye is the integration of high-definition maps, thus leading to the so-called Level 2+. This thesis was carried out during an internship in Maserati's Virtual Engineering team. The activity consisted of the design of an L2+ Highway Assist System following the Rapid Control Prototyping approach, starting from the definition of the requirements up to the real-time implementation and testing on a simulator of the brand new compact SUV Maserati Grecale. The objective was to enhance the current Level 2 highway driving assistance system with hands-free driving capability; for this purpose an Autonomous Lane Change functionality has been designed, proposing a Model Predictive Control-based decision-maker, in charge of assessing both the feasibility and convenience of performing a lane-change maneuver. The result is a Highway Assist System capable of driving the vehicle in a traffic scenario safely and efficiently, never requiring driver intervention.

Introducing CHIMeRA: from the development of a comprehensive biomedical database to the analysis of its sub-components.

In this thesis we discuss the expansion of an existing project, called CHIMeRA, which is a comprehensive biomedical network, and the analysis of its sub-components by using graph theory. We describe how it is structured internally, what are the existing databases from which it retrieves information and what machine learning techniques are used in order to produce new knowledge. We also introduce a new technique for graph exploration that is aimed to speed-up the network cover time under the condition that the analyzed graph is stellar; if this condition is satisfied, the improvement in the performance compared to the conventional exploration technique is extremely appealing. We show that the stellar structure is highly recurrent for sub-networks in CHIMeRA generated by queries, which made this technique even more interesting. Finally, we describe the convenience in using the CHIMeRA network for research purposes and what it could become in a very near future.