Abstract
With the recent technological developments new possibilities arise for the use of wearables and medical monitoring devices by patients and their respective integration into the digital health ecosystem. FHIRbox is a distributed system under development by the authors for integrating data from various diagnosis devices, complying with FHIR-the latest HL7 standard for exchanging clinical information. The innovative aspects of FHIRbox constitute a reference to drive a paradigm shift in terms of access to health information; as it is a solution that places the patient as the true owner of his clinical data. In this work the authors present the project requirements and the system architecture. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the CENTERIS-International Conference on ENTERprise Information Systems / ProjMAN-International Conference on Project MANagement / HCist-International Conference on Health and Social Care Information Systems and Technologies.

Abstract

Abstract
Knowledge of the atmospheric turbulence in the telescope line-of-sight is crucial for widefield observations assisted by adaptive optics (AO), particularly tomodel how the point spread function (PSF) elongates across the field of view(FOV) owing to the anisoplanatism effect. The extraction of key astronomical parameters accounts on an accurate representation of the PSF, which call for an accurate anisoplanatism characterisation . This one is, however, a function of the Cn2(h) profile, which is not directly accessible from single-conjugate AO telemetry. It is possible to rely on external profilers, but recent studies have highlighted discrepancies of more than 10 per cent with AO internal measurements, while we aim at better than 1 per cent accuracy for PSF modelling. In order to tackle this limitation, we present focal-plane profiling (FPP) as a Cn2(h) profiling method that relies on post-AO focal-plane images.We demonstrate that such an approach complies with a 1 per cent level of accuracy on the Cn2(h) estimation and establish how this accuracy varies regarding the calibration star magnitudes and their positions in the field. We highlight the fact that photometry and astrometry errors caused by PSF mis-modelling reach respectively 1 per cent and 50 µas using FPP on a Keck baseline, with a preliminary calibration using a star of magnitude H = 14 at 20 arcsec. We validate this concept using Canada's NRC-Herzberg HeNOS testbed images by comparing FPP retrieval with alternative Cn2 (h) measurements on HeNOS. The FPP approach allows the Cn2(h) to be profiled using the SCAO systems and significantly improves the PSF characterization. Such a methodology is also ELT-size-compliant and will be extrapolated to tomographic systems in the near future.

Abstract
This paper presents the development of an Escargot Nursery by a multinational and multidisciplinary team of 3rd year undergraduates within the framework of EPS@ISEP - the European Project Semester (EPS) offered by the Instituto Superior de Engenharia do Porto (ISEP). The challenge was to design, develop and test a snail farm compliant with the applicable EU directives and the given budget. The Team, motivated by the desire to solve this multidisciplinary problem, embarked on an active learning journey, involving scientific, technical, marketing, sustainable and ethical development studies, brainstorming and decision-making. Based on this project-based learning approach, the Team identified the lack of innovative domestic snail farm products and, consequently, proposed the development of "EscarGO", a stylish solution for the domestic market. The paper details the proposed design and control system, including materials, components and technologies. This learning experience, which was focussed on the development of multicultural communication, multidisciplinary teamwork, problem-solving and decision-making competencies in students, produced as a tangible evidence the proof of concept prototype of "EscarGO", an Escargot Nursery designed for families to easily grow snails at home.

Abstract

Abstract
Facial expression analysis has a wide area of applications including health, psychology, sports etc. In this study, we explored different methods of automatic classification of exercise intensities using facial image processing of a subject performing exercise on a cycloergometer during an incremental standardized protocol. The method can be implemented in real time using facial video analysis. The experiments were done with images extracted from a 12 min HD video collected in laboratorial normalized settings (TechSport from the University of Trás-os-Montes e Alto Douro) with a static camera (90° angle with face and camera). The time slot for video to extract images for a particular class of exercise intensity is correspondence to the incremental heart rate. The facial expression recognition has been performed mainly in two steps: facial landmark detection and classification using the facial landmarks. Luxand application was used to detect 70 landmarks were detect using the adaptation of code available in Luxand application and we applied machine learning classification algorithms including discriminant analysis, KNN and SVM to classify the exercise intensities from the facial images. KNN algorithms presents up to 100% accuracy in classification into 2 and 3 classes. The distances between a lowermost landmark of the faces, which is indicated in landmark number 11 in the Luxand application, and the 26 landmarks around mouth were calculated and considered as features vector to train and test the classifier. Separate experiments were done for classification into two, three, and four classes and the accuracy of each algorithm was analyzed. From the overall results, classification into two and three classes was easy and resulted in very good classification performance whereas the classification with four classes had poor classification performance in each algorithm. Preliminary results suggest that distinguishing more levels of exertion, might require additional feature variables. © 2018 IEEE.