Abstract
Rheumatic heart disease remains a major burden in the developing countries. The World Heart Federation proposed guidelines for the echocardiographic detection of the disease, in which the mitral leaflets' morphology assessment is a key indicator. The drawback is that these guidelines are dependent on the clinician experience. To overcome this limitation, we propose an automatic segmentation of the mitral leaflets using a new method based on convolutional neural network, specifically the UNet architecture. The results indicate a median DICE coefficient of 0.74 in PLAX and 0.79 in A4C for the anterior mitral leaflet segmentation, while median DICE of 0.60 in PLAX and 0.69 A4C are met for the posterior leaflet. A visual evaluation of this segmentation approach by two cardiologists is in line with the numerical results. The false detection due to overestimation and artifacts remains an issue to be addressed in the future.

Abstract
The longevity of the population is the result of important scientific breakthroughs in recent years. However, living longer with quality, also brings new challenges to governments, and to the society as a whole. One of the most significant consequences will be the increasing pressure on the healthcare services. Ambient Assisted Living (AAL) systems can greatly improve healthcare scalability and reach while keeping the user in their home environment. The work presented in this paper specifies, implements, and validates a smart environment system that aggregates Automation and Artificial Intelligence (AI). The specification includes a reference architecture, composed by three modules, whose tasks are to automate and standardize the collection of data, to relate and give meaning to that data and to learn from it. The system is able to identify daily living activities with different levels of complexity using a temporal logic. It enables a real time response to emergency situations and also a long term analysis of the user daily routine useful to induce healthier lifestyles. The implementation addresses the applications and techniques used in the development of a functional prototype. To demonstrate the system operation three use cases with increasing levels of complexity are proposed and validated. A discussion on related projects is also included, specifically on automation applications, Knowledge Representation (KR) and Machine Learning (ML). © Springer Nature Switzerland AG 2019.

Abstract
Increasing scientific evidence supports an association between alexithymia and psychoactive substance use. This study explores alexithymia´s expression in sample of long-term drug users, undergoing outpatient treatment in public health units in Oporto, Portugal, as well as its´ association with social demographic risk factors. Data was collected from a sample of 90 adults, participants, mainly men (n=90; 87%), considered to be old consumers (81% with a age>40 years), with a mean age of 46.1 years (SD=8.3; range=21–64).Two instruments were used: a sociodemographic questionnaire and the 20-item Toronto Alexithymia Scale (TAS-20). More than 51.1% of the individuals were alexithymic, indicating a high prevalence of deficits in emotional awareness. The treatment period varied from 0 to 15 years, included a medication in 55% of cases, mostly methadone (83%). This profile illustrates the gradual aging of the long-term users of illicit drugs and alcohol with a clear diagnosis of an emotional disorder. Therefore, clinicians who develop treatment strategies may want to take into account the likelihood that many of their patients may be alexithymic; in being so, they should integrate specific psychotherapeutic techniques that promote both the identification and the differentiation in emotionally dysfunctional patients.

Abstract
While highlighting the significance of exposure to ads to explain consumer behavior, extant literature has so far disregarded the potential impact of dreams. Linking the current-concerns theory and the model of cognitive response to advertising, this study focuses on the impact of dreaming of ads on purchase intentions. To test the 3 research hypotheses proposed, a quantitative study was conducted with Iranian consumers, using individuals' retrospective self-assessment on the 3 variables of the study: exposure to ads, dreams of ads, and purchase intentions. Results were obtained using structural equation modeling analysis. The findings confirm that exposure to ads has a positive impact on purchase intention, comprising both direct and indirect effects through dreams of ads. In addition, it is shown that also dreaming about ads has a positive impact on purchase intentions. The article provides insights for researchers and practitioners interested in the effectiveness of advertising strategies and in the role of dreams for individuals.

Abstract
The issue of climate change has received considerable attention in recent decades. Therefore, renewable energies and especially wind units have become a central point of attention. The stochastic nature of wind power production is modeled by means of a scenario-based method to show the possible events in the real time. Based on the Monte-Carlo simulation method and employing constructed Rayleigh probability distribution function (PDF), several scenarios that demonstrate the behavior of wind farms in real time are generated. To this end, a uniform random variable is generated and assigned to the mentioned PDF. Afterwards, a wind speed with a probability is achieved followed by the amount of wind power generation. Also, with a scenario reduction method (forward method), the desired number of scenarios can be obtained. To cope with the uncertainties of wind power generation, resulting from the intermittent nature of this kind of energy, this paper proposes a demand response (DR)-based operation approach. In other words, unlike the previous models in the literature that considered a supplementary role for the DR, this paper introduces the main role for the DR in the operation of future electricity markets. This approach focuses on a comprehensive modeling of the DR programs (DRPs) for the operational scheduling of electricity markets, considering the uncertainties of the generation of wind turbines, aiming at increasing the network security and decreasing the operation cost. The incorporation of market-based DRPs, such as demand bidding and ancillary service DR, is also considered. Two novel quantitative indices are introduced to analyze the success of DRPs regarding efficiency and wind integration. Numerical results obtained on two IEEE test systems indicate the effectiveness of the proposed model.

Abstract
VirtuosoNext (TM) is a distributed real-time operating system (RTOS) featuring a generic programming model dubbed Interacting Entities. This paper focuses on these interactions, implemented as so-called Hubs. Hubs act as synchronisation and communication mechanisms between the application tasks and implement the services provided by the kernel as a kind of Guarded Protected Action with a well defined semantics. While the kernel provides the most basic services, each carefully designed, tested and optimised, tasks are limited to this handful of basic hubs, leaving the development of more complex synchronization and communication mechanisms up to application specific implementations. In this work we investigate how to support a programming paradigm to compositionally build new services, using notions borrowed from the Reo coordination language, and relieving tasks from coordination aspects while delegating them to the hubs. We formalise the semantics of hubs using an automata model, identify the behaviour of existing hubs, and propose an approach to build new hubs by composing simpler ones. We also provide tools and methods to analyse and simplify hubs under our automata interpretation. In a first experiment several hub interactions are combined into a single more complex hub, which raises the level of abstraction and contributes to a higher productivity for the programmer. Finally, we investigate the impact on the performance by comparing different implementations on an embedded board.