Abstract
This study analyzed the accuracy of a BGL predictive model (BGL-PM) for type 1 diabetes mellitus patients (T1DM) in a real-world environment. The study population consisted of 10 individuals with T1DM, half of them were female, age 33 (SD:11.2), BMI of 26.1 (4.2) and 60% were under carbohydrate-count treatment. After consent, patients underwent a medical evaluation and registered their daily activities using a smartphone application (GlucoTrends) for 28 days, with BGL and heart rate continuously monitored. BGL-PM was developed using a Deep Learning architecture, based on Recurrent Neural Networks. Models were trained for each patient using different training sets sizes (7, 14, 21 days). Prediction accuracy was evaluated by Mean Absolute Percentage Error (MAPE) on the last 5 days for different Prediction Horizons (PH): 30, 60, 120, 180 and 360 minutes, comparing full day and nocturnal period. The model predicted BGL with relevant accuracy for the dataset with 21 training days up to 60 minutes in both periods: full day (median MAPE 22.5%) and nocturnal (14.3%) (Figure). The BGL-PM was able to provide useful BGL predictions, especially during the night period, which can be improved by increasing the training period. Consequently, this BGL-PM poses as a complementary tool for the prevention of acute complications such as hypoglycemia and hyperglycemia in the management of DM. Disclosure M. Foss-Freitas: None. G.S. Moreira: Stock/Shareholder; Self; GlucoGear Tecnologia. V.P. Antloga: Stock/Shareholder; Self; GlucoGear Tecnologia. C.R. Neto: Research Support; Self; University of Sao Paulo. E.M. Rodrigues: Consultant; Self; GlucoGear Tecnologia. M.F. da Costa: Research Support; Self; GlucoGear. A.P. dos Santos: None. Y.K. Matsumoto: Board Member; Self; GlucoGear. Stock/Shareholder; Self; GlucoGear. Other Relationship; Self; GlucoGear.

Abstract
Precision agriculture nowadays has great importance as it brings together the knowledge acquired through traditional cultivation techniques with precision and technological automation. One of the inherent techniques of precision agriculture is hydroponics, with plants growing using aqueous solutions and without soil availability. Although NFT (Nutrient Film Technique) systems are already well-developed systems, there is a big difference between home projects and highly automated processes, which in turn require high investment values. Among other things, in this work, the aim was to study and developed algorithms that allow the efficient recirculation of water, allowing electricity savings to be around 40% compared to more traditional systems. © 2019 IEEE.

Abstract
In obstructive sleep apnea, respiratory effort is maintained but ventilation decreases/disappears due to upper-airway partial/total occlusion. This condition affects about 4% of men and 2% of women worldwide. This study aimed to define an auxiliary diagnostic method that can support the decision to perform polysomnography, based on risk and diagnostic factors. Our sample performed polysomnography between January and May 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes and Tree Augmented Naïve Bayes, using 38 variables identified by literature review or just a selection of 6. Area under the ROC curve, sensitivity, specificity and predictive values were evaluated using leave-one-out and cross-validation techniques. From a total of 241 patients, only 194 fulfilled the inclusion criteria, 123 (63%) were male, with a mean age of 58 years, 66 (34%) patients had a normal result and 128 (66%) a diagnosis of obstructive sleep apnea. The cross-validated AUCs for each model were: NB38: 69.2%; TAN38: 69.0%; NB6: 74.6% and TAN6: 63.6%. Regarding risk matrix, female gender presented a starting rate of 8%, comparing to 20% in male gender, almost 3 times higher. The high (34%) proportion of normal results confirms the need for a pre-evaluation prior to polysomnography, making the search for a validated model to screen patients with suspicion of obstructive sleep apnea essential, especially at primary care level.

Abstract

Abstract
Line-following robots can recognize and follow a line drawn on a surface. Their operating principles have elements that could be used in the development of numerous autonomous technologies, with applications in education and industry. This class of robots usually represent the first contact students have with educational robotics, being used to develop students' logic thinking and programming skills. The cost of robotic platforms is still prohibitive in low-budget schools and universities, which makes almost impossible having a platform for each small group of students in a classroom, harming the learning process. This work proposes a 3D web-based open-source simulator for Pololu's 3Pi line-following robots, making such technology more accessible and available even for distance learning courses. The developed software simulates the robot's physical structure, behavior, and operations-as being able to read surfaces-, enabling the user to observe the robot following the line as the code commands. The simulator was validated based on experiments that included motion analysis and time measurements of pre-stablished tasks so that its execution could be more coherently based on what happens in reality.

Abstract
Aims. To date, infrared interferometry at best achieved contrast ratios of a few times 10(-4) on bright targets. GRAVITY, with its dual-field mode, is now capable of high contrast observations, enabling the direct observation of exoplanets. We demonstrate the technique on HR 8799, a young planetary system composed of four known giant exoplanets. Methods. We used the GRAVITY fringe tracker to lock the fringes on the central star, and integrated off-axis on the HR 8799 e planet situated at 390 mas from the star. Data reduction included post-processing to remove the flux leaking from the central star and to extract the coherent flux of the planet. The inferred K band spectrum of the planet has a spectral resolution of 500. We also derive the astrometric position of the planet relative to the star with a precision on the order of 100 mu as. Results. The GRAVITY astrometric measurement disfavors perfectly coplanar stable orbital solutions. A small adjustment of a few degrees to the orbital inclination of HR 8799 e can resolve the tension, implying that the orbits are close to, but not strictly coplanar. The spectrum, with a signal-to-noise ratio of approximate to 5 per spectral channel, is compatible with a late- type L brown dwarf. Using Exo-REM synthetic spectra, we derive a temperature of 1150 +/- 50 K and a surface gravity of 10(4.3 +/- 0.3) cm s(2). This corresponds to a radius of 1.17(-0.11)(+0.13) R-Jup and a mass of 10(-4)(+7) M-Jup, which is an independent confirmation of mass estimates from evolutionary models. Our results demonstrate the power of interferometry for the direct detection and spectroscopic study of exoplanets at close angular separations from their stars.