Abstract
This submission describes the analysis of an evaluation of 155 teenagers (15-19 years old) who took part in a co-design session centred around how mobile technology might enhance their own experiences in a natural history museum. At the end, participants were required to make a word association to evaluate the session. An analysis of how teen participants responded to the design session was conducted using thematic analysis to show the different categories of adjectives used by participants in their evaluations. The goal for the evaluation was mainly to pilot the design session process and if teens enjoyed participating in it. We believe this is of interest to designers and cultural heritage professionals.

Abstract
The project VIRTUAL TUTORING - the virtual tutor as learning mediating artifact in online university education, is an ongoing project, with the main goal of analyzing the pedagogic impact of an anthropomorphic user interface on a typical distance learning environment targeted to support online higher education. It implies the development of 3D rigged avatars that should perform typical online tutor activities. The virtual tutor should mimic a human tutor, being a kind of emphatic interface between the student and the course module in Moodle. But more than this, the virtual tutor should give support in the learning process of the student, working as much as a guide inside the contents offered by the e-learning course. This paper gives an overview of the project present development status.

Abstract
Water from mountainous regions is a strategic natural resource. In Mediterranean mountainous regions, which, in many cases, correspond to protected areas, high-altitude roads are often the main threat to the sustainability of water resources. In these regions, the regular socioeconomic functioning requires frequent road de-icing operations which normally consist of spreading NaC1 and other chemicals, such as CaCl2, in pavements. The main purpose of this research is to assess the environmental impact of road de-icing on groundwater resources in a Mediterranean mountainous region and to describe it by means of a hydrogeological conceptual model. The research focused in a cross-sectional sector located in Serra da Estrela (Central Portugal), where a hydrogeological inventory was carried out, followed by hydrogeochemical and hydrogeophysical studies. The results clearly identify different hydrogeo-chemical signatures in polluted (Cl-Na facies and higher EC) and unpolluted (HCO3-Na, Cl-Na, and very low EC). The relation of hydrogeochemistry and altitude is complex and depends on both natural processes (namely, water-rock interaction) and anthropic processes (de-icing operations). The hydrogeophysical survey systematically identified the presence of a pollution plume migrating downstream from roads.

Abstract
This study presents and applies combined phase and magnitude metrics for validation of multibody dynamics (MBD) estimated muscle actions with simultaneous registered sEMG of lower limb muscles. Subject-specific tests were performed for acquisition of ground reaction forces and kinematic data from joint reflective markers during NG, SKG and SR. Inverse kinematics and dynamics was performed using AnyBody musculoskeletal personalized modeling and simulation. MBD estimated muscle activity (MA) of soleus medialis (SM) and tibialis anterior (TA) were compared on phase, magnitude and combined metric with simultaneous acquisition of sEMG for the same muscles. Results from quantitative metrics presented better agreement between MDB MA and sEMG on phase (P) than on magnitude (M) with combined (C) metric following the same pattern as the magnitude. Soleus medialis presented for specific subject lower P and M error on NG and SKG than at SR with similar P errors for tibialis anterior and higher error on M for TA at NG and SKG than SR. Separately and combined quantitative metrics of phase and magnitude presents as a suitable tool for comparing measured sEMG and MBD estimated muscle activities, contributing to overcome qualitative and subjective comparisons, need for intensive observer supervision, low reproducibility and time consuming.

Abstract
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

Abstract
The localization algorithms have different errors which can impair the robot's navigation. In this way, we propose an approach that will supervise the localization while the robot navigate. Our approach is based on another work present in the literature, where we detected a problem during its analysis. Therefore, this article will present a new method based on the RLS algorithm, to solve the identified problem. Besides, we propose the supervision of two more localization algorithms, being now four the supervised algorithms, namely: Augmented Monte Carlo Localization, Extended Kalman Filter with Beacons, Perfect Match and Odometry. The results show that the robustness and reliability of the system were increased.