Abstract

Abstract

Abstract
In the context of SaaS (Software as a Service) where software has to be up and miming 7 days a week and 24 hours a day, keeping the requirements specification and related test cases up to date can be difficult. Managing requirements in this context has additional challenges that need to be taken into account, for instance, re-prioritize requirements continuously and identify/update new dependencies among them. When requirements change, related test cases need to be updated accordingly. We claim that extracting and analyzing the usage of the SaaS can help to maintain requirements and test cases updated and contribute to improve the overall quality of the services provided. This paper presents an extension to REQAnalytics. REQAnalytics is a recommendation system that collects information about the usage of a SaaS and generates recommendations to improve the SaaS provided. The evolution involves extending the analysis performed over the sequences of functionalities (requirements) and refining the data provided for Software Requirements Specification, with the purpose of helping the requirements engineers in the requirement maintenance activities, and to improve the overall quality of the services. Furthermore, the extension presented in this paper is able to generate test cases in a regression testing context. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract
It is known that academic performance is not correlated with the way people understand and deal with their own emotions and other peoples’ emotions. Active methodologies allow students to be constantly involved in the learning process and thus allow Higher Education students to cognitively develop Emotional Intelligence (EI). This study is guided by the following research question: what are the learning strategies for developing EI skills in Higher Education students? This is a qualitative study and two focus groups were held with two institutions of Public and Private Higher Education, in which 10 students and 4 Professors participated. The content of the interviews was analyzed using the qualitative analysis software webQDA®. One concludes that the active methodologies, Problem Based Learning and Inverted Classroom, can contribute to develop EI skills in Higher Education students, as they enable mental skills such as reasoning and problem solving, from the perception and knowledge of emotion patterns. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In Portugal, labour availability has been revealed as a key factor for the activity, particularly in mountain viticulture. The latest statistics present worrying values that could undermine the production of quality wine and the attractive set of wine landscapes considered as a potential resource for tourism development. The Douro Region is one of the main Portuguese wine regions, characterized by a prominent and accentuated mountain viticulture. This paper aims to simulate the behaviour of its farms about the changes in the price of labour, through Agent-Based Models (ABM). The MATLAB software was used to obtain periodic functions adjusted to the data that characterize the relevant variables, obtained from face-to-face surveys of 110 farms, and taking into account the data provided by PTFADN. Subsequently, the ABM software (NETLOGO) was selected to simulate the next 100 years, familiarizing the real dynamics based on the previously considered data. Depending on the price of labour at the end of the simulation horizon, with a grape price of 0,77 €/kg, from the 300 initially existing farms survive between 127 and 231 (42,3 - 77%). In a more optimistic scenario, with a grape price of 1,17 €/kg, the survival rate ranges between 72.1 and 93.2%. © 2021

Abstract
Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.