Abstract
INTRODUCTION AND OBJECTIVES. The knowledge and skills that enable pregnant women to adopt healthy lifestyles is broad and complex. In addition to factual knowledge, the adoption of health behaviours also implies a set of emotional, cognitive and behavioural skills that allow the use of this knowledge in the context of pregnancy. METHODOLOGY. Cross-sectional, quantitative, descriptive-correlational study with non-probabilistic, intentional sample by convenience (n = 404 pregnant women) with a mean age of 32 years. They answered the sociodemographic, obstetrical and HLS-EU-PT questionnaire (National School of Public Health, 2014). Following the methodology used in the European Survey, four ways of dealing with relevant health information were recognized. RESULTS AND DISCUSSION. Overall, 36.9% of pregnant women presented a problematic level of health literacy, 40.1%, 39.9% and 38.4%, a sufficient level of health literacy in the area of Health Care, Disease Prevention and Health Promotion, respectively. The factorial analysis demonstrates the validity of its framework. The alpha values of the items are above 0.9.The correlations between the different domains and the overall value are all positive and above 0.8. All dimensions of the scale correlate with each other in a statistically significant way, with values for the different domains. The split-half coefficient was alpha = 0.939 in the first half and alpha = 0.930 in the second half. CONCLUSIONS. The results of the present study support the psychometric adequacy of the European Questionnaire on Health Literacy - (HLS-EU-PT) for the population of pregnant women, indicating that it could be used in future trials.

Abstract
Compared to general purpose programming languages, domain specific languages (DSLs) targeting adaptive embedded software development provide a very promising alternative for developing clean and error free run-time adaptations. However, the ability to use several rules in a single adaptation strategy, as allowed by some DSLs, may lead to conflicts and reachability issues, which can eventually lead to functional bugs. Traditionally, such conflict analysis is done using software testing or manual manipulation of automata based models of rules. However, both of these techniques are error-prone and thus can lead to unwanted situations. As an accurate alternative, we propose to use model checking for rule conflict and reachability analysis in DSL adaptation. In particular, this paper provides an approach to formally model DSL adaptation specifications, along with their rules, and identifies a set of generic temporal properties to check for reachability and other rule conflicts, using the finite and infinite state-space based model checking capabilities of nuXmv model checker. For illustration, formal analyses of an energy aware CPU scheduling algorithm, i.e, PAST, for adaptivity rules for a stereo navigation system and for a context aware application are presented.

Abstract
Despite connections and common traits between geopolitics and international business based on geography and location, literature on this matter is somewhat scarce. This chapter aims to contribute to this literature gap. Using the Russian Federation as a case study and by framing its geopolitical situation, it seeks to answer the following question: What is the importance of geopolitical factors in international location decisions? Applying a hybrid methodology which combines qualitative and quantitative analyses, the chapter concludes though Russia has an innately favorable geopolitical situation, its full potential is not being exploited, remaining latent and underutilized. Additionally, the pragmatic standing of Russia's foreign policy, the permanence of some structures recalling the USSR and the persistence of corruption, and an unsteady business environment place constraints on improving the open market and raises obstacles to FDI.

Abstract
The early classification of university students according to their potential academic performance can be a useful strategy to mitigate failure, to promote the achievement of better results and to better manage resources in higher education institutions. This paper proposes a two-stage model, supported by data mining techniques, that uses the information available at the end of the first year of students' academic career (path) to predict their overall academic performance. Unlike most literature on educational data mining, academic success is inferred from both the average grade achieved and the time taken to conclude the degree. Furthermore, this study proposes to segment students based on the dichotomy between the evidence of failure or high performance at the beginning of the degree program, and the students' performance levels predicted by the model. A data set of 2459 students, spanning the years from 2003 to 2015, from a European Engineering School of a public research University, is used to validate the proposed methodology. The empirical results demonstrate the ability of the proposed model to predict the students' performance level with an accuracy above 95%, in an early stage of the students' academic path. It is found that random forests are superior to the other classification techniques that were considered (decision trees, support vector machines, naive Bayes, bagged trees and boosted trees). Together with the prediction model, the suggested segmentation framework represents a useful tool to delineate the optimum strategies to apply, in order to promote higher performance levels and mitigate academic failure, overall increasing the quality of the academic experience provided by a higher education institution.

Abstract
Rheumatic heart disease is the serious consequence of repeated episodes of acute rheumatic fever. It is the major cause of heart valve damage resulting in morbidity and mortality. Its early detection is considered vital to control the disease's progression. The key manifestations that are visible in the early stages of this disease are changes in the thickness, shape and mobility of the mitral valve leaflets. Echocardiography based screening is sensitive enough to identify these changes in early stages of the disease. In this work, an automatic approach is proposed to measure, quantify and analyze the thickness of the anterior mitral leaflet, in an echocardiographic video. The shape of the anterior mitral leaflet is simplified via morphological skeletonization and spline modelling to get the central line of the leaflet. To analyze the overall thickness from the tip to its base, the anterior mitral leaflet is divided into four quartiles. In ach quartile the thickness is measured as the length of the line segment resulting from the intersection of the contour with the normal direction of the central point of each quartile. Finally, the thickness is analyzed by measuring the variance per quartile, divided by leaflet position (open, straight and closed). The comparison between the normal and pathological leaflets are also presented, exhibiting statistical significant differences in all quartiles, especially near the tip of the leaflet.

Abstract
In a global market characterized by strong competition and quickly changing boundary conditions, flexible and reconfigurable production systems can rapidly react to both endogenous and exogenous drivers. To this extent, it is necessary to define a new production system model, which can combine the most significant key business factors (KBFs), in order to meet the specified objectives and the relevant KPIs and to control the system. The model can be used within a cyber-physical system, to properly support the different functions and take the right decisions through simulation ICT tools. This research task is part of PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery), a European funded project, which aims at developing an innovative manufacturing system based on a new agile concept introducing the implementation of methods, methodologies and strategies for transforming existing production systems into plug-and-produce production ones based on Cyber-Physical Systems technologies. In particular, this paper aims at describing the relationships among the KBFs (Key Business Factors), namely the drivers of the production system, and the relevant KPIs. The model has been validated through an industrial use case, in order to gain important information about constraints and opportunities for improvement in other contexts. © 2017 IEEE.