Blockchain technology offers unique features, such as transparency, the immutability of data, and the capacity to establish trust without a central authority. Such characteristics can be leveraged to support the collaboration among several different software systems operating within the healthcare ecosystem, while ensuring data integrity and make electronic health records (EHRs) more easily accessible. To provide a solution based on blockchain technology, this paper has evaluated the main issues that arise when large amounts of data are expected, i.e., mainly cost and performance. A balanced approach that maximizes the benefits and mitigates the constraints of the blockchain has been designed. The proposed decentralized application (dApp) architecture employs a hybrid storage strategy that involves storing medical records locally, on users’ devices, while utilizing blockchain to manage an index of these data. The dApp clients facilitate interactions among participants, leveraging a smart contract to enable patients to set authorization policies, thereby ensuring that only designated healthcare providers and authorized entities have access to specific medical records. The blockchain data-immutability property is used to validate data stored externally. This solution significantly reduces the costs related to the utilization of the blockchain, while retaining its advantages, and improves performance, since the majority of data are available off-chain.

Physical surveys in many fields use segmented detectors to sense some physical quantities. In many cases, physical phenomena produce raw data with a rate higher than that possible to identify interesting events and organize data when using a single host. This paper proposes a distributed and parallel processing system that greatly reduces the time needed for identifying events and organizing data. Moreover, our solution can be scaled according to the experiment at hand. This opens the possibility to analyse a wider range of physical phenomena, increases the accuracy of observations, and makes it possible to organize widely distributed experiments involving different laboratories or facilities at the same time. The proposed solution consists of a parallel algorithm able to quickly process patterns sensed by a segmented detector, hence achieving identification and data cataloguing. Moreover, a distributed software architecture properly taps into cloud-based resources to handle the massive amount of raw data generated by an experiment. The results in terms of performances for the proposed distributed and parallel solution have shown a relevant speed up for the required processing.

The BitTorrent mechanism effectively spreads file fragments by copying the rarest fragments first. We propose to apply a mathematical model for the diffusion of fragments on a P2P in order to take into account both the effects of peer distances and the changing availability of peers while time goes on. Moreover, we manage to provide a forecast on the availability of a torrent thanks to a neural network that models the behaviour of peers on the P2P system. The combination of the mathematical model and the neural network provides a solution for choosing file fragments that need to be copied first, in order to ensure their continuous availability, counteracting possible disconnections by some peers.

Developing large systems exhibiting a high degree of modularity can be a difficult task even for experienced developers. Hindering modularity has several armful effects, such as decreased readability, higher complexity and difficulties to reuse and evolve components. This paper assists developers to achieve modularity of components by providing a way to automatically characterise the concerns within components according to the APIs they are based on. This allows finding the degree of tangling and scattering of concerns over methods and classes. Moreover, by means of the proposed approach developers are given suggestions on how to reduce tangling of some components, thanks to the use of a metric and refactoring techniques. For systems comprising thousand of classes this is a valuable support, since unassisted developers could miss appropriate refactoring opportunities, due to the large number of details they should take into account.

Surface Plasmon Polaritons are collective oscillations of electrons occurring at the interface between a metal and a dielectric. The propagation phenomena in plasmonic nanostructures is not fully understood and the interdependence between propagation and metal thickness requires further investigation. We propose an ad-hoc neural network topology assisting the study of the said propagation when several parameters, such as wavelengths, propagation length and metal thickness are considered. This approach is novel and can be considered a first attempt at fully automating such a numerical computation. For the proposed neural network topology, an advanced training procedure has been devised in order to shun the possibility of accumulating errors. The provided results can be useful, e.g., to improve the efficiency of photocells, for photon harvesting, and for improving the accuracy of models for solid state devices.