Abstract
Elite swimmers and swimming pool employees are likely to be at greater health risk due to their regular and intense exposure to air stressors in the indoor swimming pool environment. Since data on the real long-term exposure is limited, a long-term monitoring and sampling plan (22 non-consecutive days, from March to July 2017) was carried out in an indoor Olympic-size pool with a chlorine-based disinfection method to characterize indoor environments to which people involved in elite swimming and maintenance staff may be exposed to. A comprehensive set of parameters related with comfort and environmental conditions (temperature, relative humidity (RH), carbon dioxide (CO2) and monoxide and ultrafine particles (UFP)) were monitored both indoors and outdoors in order to determine indoor-to-outdoor (I/O) ratios. Additionally, an analysis of volatile organic compounds (VOC) concentration and its dynamics was implemented in three 1-hr periods: early morning, evening elite swimmers training session and late evening. Samplings were simultaneously carried out in the air layer above the water surface and in the air surrounding the pool, selected to be representative of swimmers and coaches/employees' breathing zones, respectively. The results of this work showed that the indoor climate was very stable in terms of air temperature, RH and CO 2 . In terms of the other measured parameters, mean indoor UFP number concentrations (5158 pt/cm(3)) were about 50% of those measured outdoors whereas chloroform was the predominant substance detected in all samples collected indoors (13.0-369.3 mu g/m(3)), among a varied list of chemical compounds. An I/O non-trihalomethanes (THM) VOC concentration ratio of 2.7 was also found, suggesting that, beyond THM, other potentially hazardous VOC have also their source(s) indoors. THM and non-THM VOC concentration were found to increase consistently during the evening training session and exhibited a significant seasonal pattern. Compared to their coaches, elite swimmers seemed to be exposed via inhalation to significantly higher total THM levels, but to similar concentrations of non-THM VOC, during routine training activities. Regarding swimming employees, the exposure to THM and other VOC appeared to be significantly minimized during the early morning period. The air/water temperature ratio and RH were identified as important parameters that are likely to trigger the transfer processes of volatile substances from water to air and of their accumulation in the indoor environment of the swimming pool, respectively.

Abstract
A dense and accurate disparity map is relevant for a large number of applications, ranging from autonomous driving to robotic grasping. Recent developments in machine learning techniques enable us to bypass sensor limitations, such as low resolution, by using deep regression models to complete otherwise sparse representations of the 3D space. This article proposes two main approaches that use a single RGB image and sparse depth information gathered from a variety of sensors/techniques (stereo, LiDAR and Light Stripe Ranging (LSR)): a Convolutional Neural Network (CNN) and a cascade architecture, that aims to improve the results of the first. Ablation studies were conducted to infer the impact of these depth cues on the performance of each model. The models trained with LiDAR sparse information are the most reliable, achieving an average Root Mean Squared Error (RMSE) of 11.8 cm on our own Inhouse dataset; while the LSR proved to be too sparse of an input to compute accurate predictions on its own. © Springer Nature Switzerland AG 2020.

Abstract
This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics; learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino – Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. Throughout the course, participants were introduced to programming languages (including Python and C++), advanced programming concepts such as ROS, basic API development, system concepts such as I2C and UART serial interfaces, PWM motor control and sensor fusion to improve robotic navigation and localization. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions. © 2018 The Authors

Abstract

Abstract
Nowadays, many drone models are available, designed for the most diverse applications. However, the various models fall into one of two types of drone: multi-copter or fixed-wing. The first type of drone consumes a lot of energy, since motors have to turn during all the flight. The former type of drone, in general needs a runway to take-off and landing. Moreover, they fly fast and cannot be motionless, which is unsuitable for many applications. In this paper we present a new paramotor drone, conceived and designed to overcome the highlighted limitations and to be a low-cost solution adapted for most applications. The selection of the various components of the presented prototype was based on a very thorough study, considering aerodynamic and efficiency criteria.

Abstract
The performance of secondary schools is usually assessed based on students' results on national exams at the end of secondary education. This research uses data on academic achievements by first-year university students to benchmark secondary schools on their ability to lead students to success in higher education. The analysis is conducted using data of University of Porto and Catholic University of Porto, Portugal, for a three-year period, corresponding to more than 10.000 students from 65 degrees, for which the school of origin is known. A number of variables representing students' success in Higher education were constructed for each school in our sample and aggregated through a Benefit of the Doubt indicator. Results suggest that the schools' ranking based on schools' ability to prepare students for university success is quite different from the ranking based on results on national exams. Given these findings, we propose complementing schools' performance assessments (traditionally based on national exam results or indicators of value added) with indicators that account for the preparation of students for success in future challenges, which is indisputably a key objective of secondary education. We propose a composite indicator for the analysis of these complementary aims as well, and results show that frontier units indeed exhibit trade offs between traditional measures of performance and our new measure of performance.