Abstract
This paper presents the results from search and rescue missions performed with the aerial robot OTUS in the the context of the ISEP/INESC TEC aerial robotics team participation on the euRathlon 2015 robotics competition. The multi-domain (land, sea and air) search and rescue scenario is described and technical solution adopted is presented with emphasis on the perception system. The calibration of the image based system is addressed. Results from the operational missions performed are also discussed. The aerial autonomous vehicle was able to successfully perform multiple tasks from the aerial reconnaissance and 3D mapping to the identification of leaking pipes, obstructed passages and missing workers. The system was validated a realistic operational scenario and won the Grand Challenge in cooperation with land and marine robotics partner teams. This challenge was the first time that a real time collaborative team of aerial, land and marine robots was deployed successfully in a search and rescue mission.

Abstract
Solving programming exercises is the best way to promote practice in computer programming courses and, hence, to learn a programming language. Meanwhile, programming courses continue to have an high rate of failures and dropouts. The main reasons are related with the inherent domain complexity, the teaching methodologies, and the absence of automatic systems with features such as intelligent authoring, profile-based exercise sequencing, content adaptation, and automatic evaluation on the student's resolution. At the same time, gamification is being used as an approach to engage learners' motivations. Despite its success, its implementation is still complex and based on ad-hoc and proprietary solutions. This paper presents PROud as a framework to inject gamification features in computer programming learning environments based on the usage data from programming exercises. This data can be divided into two categories: generic data produced by the learning environmentsuch as, the number of attempts and the duration that the students took to solve a specific exerciseor code-specific data produced by the assessment toolsuch as, code size, use memory, or keyword detection. The data is gathered in cloud storage and can be consumed by the learning environment through the use of a client library that communicates with the server through an established Application Programming Interface (API). With the fetched data, the learning environment can generate new gamification assets (e.g., leaderboards, quests, levels) or enrich content adaptations and recommendations in the inner components such as the sequencing tools. The framework is evaluated on its usefulness in the creation of a gamification asset to present dynamic statistics on specific exercises.

Abstract

Abstract
Abstract Background Heart Rate Variability (HRV) is an increasing area of interest in patients with epilepsy. The effects of epilepsy on the autonomic control of the heart are not completely understood and that autonomic dysfunction has been implicated in some cases of Sudden Unexpected Death in Epilepsy (SUDEP). Objective To study the influence of generalized tonic-clonic seizures (GTCS) on HRV of patients with focal refractory epilepsy. Method We prospectively evaluated (January 2015 to July 2018) 121 patients admitted to our institution's Epilepsy Monitoring Unit. All patients performed a 48-hour Holter recording. Patients who had GTCS during the recording were included and we selected the first GTCS as the index seizure. HRV (AVNN, SDNN, RMSSD, pNN50, and LF/HF) was evaluated by analyzing 5-min-ECG epochs during inter-ictal and post-ictal periods: baseline, pre-ictal (5 min before the GTCS seizure), post-ictal (5 min after the seizure), and late post-ictal (>5 hours after the seizure). We compared HRV data from these patients with normative values for a healthy population (controlling age and gender). The study was approved by our Institution Ethics Committee and all patients gave informed consent. Results Twenty three patients were included (mean age: 38.61±11.58; 70% Female). Thirty percent presented cardiovascular risk factors without known cardiac disease. We found significant differences between the analyzed periods for all but one (LF/HF) HRV metrics (using Friedman test, p<0.05, two-tailed). Specifically during the post-ictal period, we found a significant reduction for AVNN, SDNN, RMSSD and pNN50 (Wilcoxon test, p<0.05; two-tailed). LF/HF was increased during this period, but changes were not statistically significant. There was also a tendency for a reduction of AVNN, SDNN, RMSSD and pNN50 and an increase of LF/HF in our patients during all the analyzed periods when compared to normative healthy population values. Conclusion Our work shows reduced HRV after a GTCS in patients with focal resistant epilepsy, both in inter-ictal and post-ictal periods, when compared to normative healthy population values. These results might reflect long term structural changes in autonomic centers. The HRV changes were significant particularly during the post-ictal period, and should prompt further investigation, giving this period is critical for SUDEP.

Abstract
The thermocline is a relatively narrow vertical region that separates the mixed layer at the surface from the deep-water layer. In this region, the gradient of temperature with respect to depth is higher than in the rest of the water column. The characteristics of the thermocline have strong impact in marine biology, since it may trap high-nutrient organisms, and it also affects sound propagation, with direct impact on underwater acoustic communications and military operations. Under adaptive sampling, Autonomous Underwater Vehicles are practical tools for efficient ocean observation. In this work, we describe an implementation of an Extremum Seeking Controller that performs identification and tracking of thermoclines at its point of highest temperature gradient in a completely autonomous way. The vehicle chosen to perform this tracking was an autonomous vertical profiler, and the algorithms were validated using both real and simulated data.

Abstract
The current algorithm to support platelets stock management assumes that there are always sufficient whole blood donations (WBD) to produce the required amount of pooled platelets. Unfortunately, blood donation rate is uncertain so there is the need to backup pooled platelets productions with single-donor (apheresis) collections to compensate periods of low WBD. The aim of this work was to predict the daily number of WBD to a tertiary care center to preemptively account for a decrease of platelets production. We have collected 62,248 blood donations during 3 years, the daily count of which was used to feed (standalone and ensemble versions of) six prediction models, which were evaluated using the Mean Absolute Error (MAE). Forecast models have shown better performances with a MAE of about 8.6 donations, 34% better than using means or medians alone. Trend lines of donations are better modeled by autoregressive integrated moving average (ARIMA) using a frequency of 365 days, the trade-off being the need for at least two years of data.