Abstract

Abstract
This article describes a comparison of two neural networks, the multilayer perceptron and Neural Network, for the detection and classification of pneumonia. The database used was the Chest-X-Ray data set provided by (Kermany et al., 2018) with a total of 5840 images, with two classes, normal and with pneumonia. to validate the models used, cross-validation of k-fold was used. The classification models were efficient, resulting in an average accuracy of 92.16% with the Multilayer Perceptron and 94.40% with the Convolution Neural Network.

Abstract
Regional contribution to left ventricular (LV) ejection is of much clinical importance but its assessment is notably challenging. While deformation imaging is often used, this does not take into account loading conditions. Recently, a method for intraventricular pressure estimation was proposed, thus allowing for loading conditions to be taken into account in a non-invasive way. In this work, a method for 3D automatic myocardial performance mapping in echocardiography is proposed by performing 3D myocardial segmentation and tracking, thus giving access to local geometry and strain. This is then used to assess local LV stress-strain relationships which can be seen as a measure of local myocardial work. The proposed method was validated against F-18-fluorodeoxyglucose positron emission tomography, the reference method to clinically assess local metabolism. Averaged over all patients, the mean correlation between FDG-PET and the proposed method was 0.67 +/- 0.18. In conclusion, stress-strain loops were, for the first time, estimated from 3D echocardiography and correlated to the clinical gold standard for local metabolism, showing the future potential of real-time 3D echocardiography ( RT3DE) for the assessment of local metabolic activity of the heart.

Abstract
Steep slope vineyards are a complex scenario for the development of ground robots due to the harsh terrain conditions and unstable localization systems. Automate vineyard tasks (like monitoring, pruning, spraying, and harvesting) requires advanced robotic path planning approaches. These approaches usually resort to Simultaneous Localization and Mapping (SLAM) techniques to acquire environment information, which requires previous navigation of the robot through the entire vineyard. The analysis of satellite or aerial images could represent an alternative to SLAM techniques, to build the first version of occupation grid map (needed by robots). The state of the art for aerial vineyard images analysis is limited to flat vineyards with straight vine’s row. This work considers a machine learning based approach (SVM classifier with Local Binary Pattern (LBP) based descriptor) to perform the vineyard segmentation from public satellite imagery. In the experiments with a dataset of satellite images from vineyards of Douro region, the proposed method achieved accuracy over 90%. © Springer Nature Switzerland AG 2019.

Abstract
Autonomous underwater vehicles (AUVs) are becoming increasingly ubiquitous due to the growing needs in exploring Ocean resources. One of the most challenging tasks in this domain relates to the energy these vehicles require, given the increase in the number of scientific payloads and on the mission complexity. One way to potentially reduce the amount of energy consumed during vertical motion is to replace or complement the thruster action with a controlled change of the vehicles floatation, using a variable buoyancy system (VBS). This paper presents the development of an electromechanical VBS for shallow depths, up to 100 m, to be included in an existing AUV. A preliminary mechanical design is presented, along with a mathematical model allowing the calculation of the energy spent by this device, based on the components manufacturers' data. A comparison between the energy consumption using thrusters and the designed VBS is presented. © 2019 IEEE.

Abstract
This study focuses on the impact of technology and on the planning of a trip by a Portuguese tourist. From the literature review, studies done at the European level, the technology inherent to tourism is provided to the entire age range, and fundamentally to the planning of accommodation, transport and tourist packs. The absorption of information is always highlighted by the easy and fast access to the Internet increasingly accessible to anyone, promoting behavioral changes in tourism planning. Thus, an online survey (with 180 valid answers) was carried out, which focused mainly on university students, and was restricted to Portuguese citizens. The survey determined that online platforms are most used for accommodation (87.2%), transportation (87,2%) and the demand for information (68.8%) to the detriment of catering (7.3%), and these are mainly being used because of their speed (79.4%) and simplicity (80.7%). To conclude, our study confirms the bibliographic review, and it was determined that practically all respondents assume the influence of digital platforms in the planning of their trips (99.1%), with 89% going further and affirming that in the future all planning will be carried out in an exclusive way online. © 2019 AISTI.