ALBERT

Description: TED (Tethered Electromagnetic Docking) is a system proposed by a group of researchers and students of the University of Padova for close rendezvous and docking between spacecraft. It consists in a small tethered probe ejected by the chaser, reaching the proximity of the target with a controlled deployment, and then magnetically guided by a receiving electromagnet mounted on it. Because of the generated magnetic field, alignment and mating are possible; then, as the tether is rewound, the chaser is able to dock with the target. To perform a preliminary verification of TED, three groups of students have been involved in the project and contributed to the evaluation of its critical technologies in reduced gravity: in the framework of ESA “Drop your Thesis!” 2014 and 2016 campaigns the experiments FELDs and STAR focused on the test of the tether deployment and control, while PACMAN, in the framework of ESA “Fly Your Thesis! 2017” parabolic flights campaign, tested proximity operations by means of electromagnetic interactions. In this paper, a description of TED concept and its development roadmap is presented, introducing the critical technologies tested by FELDs, STAR, and PACMAN experiments. The second part of the paper focuses on the educational outcomes of the three experiments, introducing statistics on (1) student participation, (2) scientific publication production, and (3) influence of the educational programs on the students’ career.

Description: Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions.

Description: Psychological stress is known to activate the autonomic nervous system (ANS), thus representing a useful target to be monitored to understand the physiological, unconscious effect of stress on the human body. However, little is known about how differently the ANS responds to cognitive and sensory stimulations in healthy subjects. To this extent, we enrolled 23 subjects and administered a stress protocol consisting of the administration of sensory (olfactory) and cognitive (mathematical) stressors. Autonomic parameters were unobtrusively monitored through wearable sensors for capturing electrocardiogram and skin conductance signals. The results obtained demonstrated an increase of the heart rate during both stress protocols, with a similar decrease of the heart rate variability. Cognitive stress test appears to affect the autonomic parameters to a greater extent, confirming its effects on the human body. However, olfactory stimulation could be useful to study stress in specific experimental settings when the administration of complex cognitive testing is not feasible.