Abstract

Abstract
This work presents an interferometric study of the massive-binary fraction in the Orion Trapezium cluster with the recently comissioned GRAVITY instrument. We observed a total of 16 stars of mainly OB spectral type. We find three previously unknown companions for theta(1) Ori B, theta(2) Ori B, and theta(2) Ori C. We determined a separation for the previously suspected companion of NU Ori. We confirm four companions for theta(1) Ori A, theta(1) Ori C, theta(1) Ori D, and theta(2) Ori A, all with substantially improved astrometry and photometric mass estimates. We refined the orbit of the eccentric high-mass binary theta(1) Ori C and we are able to derive a new orbit for theta(1) Ori D. We find a system mass of 21.7 M-circle dot and a period of 53 days. Together with other previously detected companions seen in spectroscopy or direct imaging, eleven of the 16 high-mass stars are multiple systems. We obtain a total number of 22 companions with separations up to 600 AU. The companion fraction of the early B and O stars in our sample is about two, significantly higher than in earlier studies of mostly OB associations. The separation distribution hints toward a bimodality. Such a bimodality has been previously found in A stars, but rarely in OB binaries, which up to this point have been assumed to be mostly compact with a tail of wider companions. We also do not find a substantial population of equal-mass binaries. The observed distribution of mass ratios declines steeply with mass, and like the direct star counts, indicates that our companions follow a standard power law initial mass function. Again, this is in contrast to earlier findings of flat mass ratio distributions in OB associations. We excluded collision as a dominant formation mechanism but find no clear preference for core accretion or competitive accretion.

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

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.