Abstract
One of the current trends in the luxury market is the development of offers targeted to the millennial generation. In fact, this generation is seen as a potentiator of market growth, considering that the future of the luxury sector will depend on the capacity to reach this generation. This topic is particularly relevant for the marketing area, namely for the tourism sector. Another of the major trends for the tourism sector is the sustainability issue, with an emphasis on ecotourism. In this work we discuss the main contributions of the literature that allow to interlink the domains of luxury marketing, millennial generation, and ecotourism, proposing a set of hypotheses for future research.

Abstract
Today's power systems are subject to various challenges arising from the large-scale integration of renewable energy sources (RES), especially wind energy production. System flexibility, or the capability of a system to address deviations in variable RES production, is becoming more and more relevant. This paper aims to provide a systematic approach to evaluate the level of flexibility of a power system by unequivocally considering fast-ramping units (FRU), hourly demand response (DR) and energy storage (ES). In addition, to research the flexibility role in power system operation, an "online" index is considered to evaluate the technical aptitude of the FRU, hourly DR and ES system to deliver the required flexibility. The mathematical representation of day-ahead scheduling, with the added modeling of an online flexibility index, is a mixed-integer nonlinear program (MINLP). This paper presents a method to convert this MINLP into a mixed-integer linear program without loss of accuracy. The adapted 6-bus and IEEE 118-bus systems are employed to assess the suggested models and flexibility metric, demonstrating the proficiency of the online flexibility index.

Abstract
Heart sounds are difficult to interpret due to events with very short temporal onset between them (tens of milliseconds) and dominant frequencies that are out of the human audible spectrum. Computer-assisted decision systems may help but they require robust signal processing algorithms. In this paper, we propose a new algorithm for heart sound segmentation using a hidden semi-Markov model. The proposed algorithm infers more suitable sojourn time parameters than those currently suggested by the state of the art, through a maximum likelihood approach. We test our approach over three different datasets, including the publicly available PhysioNet and Pascal datasets. We also release a pediatric dataset composed of 29 heart sounds. In contrast with any other dataset available online, the annotations of the heart sounds in the released dataset contain information about the beginning and the ending of each heart sound event. Annotations were made by two cardiopulmonologists. The proposed algorithm is compared with the current state of the art. The results show a significant increase in segmentation performance, regardless the dataset or the methodology presented. For example, when using the PhysioNet dataset to train and to evaluate the HSMMs, our algorithm achieved average an F-score of 92% compared to 89% achieved by the algorithm described in [D.B. Springer, L. Tarassenko, and G. D. Clifford, "Logistic regressionHSMM-based heart sound segmentation," IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 822-832, 2016]. In this sense, the proposed approach to adapt sojourn time parameters represents an effective solution for heart sound segmentation problems, even when the training data does not perfectly express the variability of the testing data.

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.