Abstract
Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.

Abstract
We report the first findings of an empirical study aimed at investigating how COVID-19 pandemic has impacted the work practices and lifestyles of digital nomads (DN). To do this, we analyzed messages, questions and comments posted by digital nomads in a specific online discussion community of the Reddit social network. Preliminary findings indicate COVID-19 as an opportunity to test DN lifestyle by aspiring digital nomads who want to plan their careers and also present evidence of an overload of online channels for actual DNs. On the other hand, we found that much of the literature on digital nomadism is fragmented and scattered through different disciplines and perspectives, with a strong focus on digital nomads' lifestyles. In order to obtain a holistic and unified understanding of digital nomads, we conducted a comprehensive literature review to further conceptualize the phenomenon under study.

Abstract
Sustainable agricultural production requires a controlled usage of water, nutrients, and minerals from the environment. Different strategies of plant irrigation are being studied to control the quantity and quality balance of the fruits. Regarding efficient irrigation, particularly in deficit irrigation strategies, it is essential to act according to water stress status in the plant. For example, in the vine, to improve the quality of the grapes, the plants are deprived of water until they reach particular water stress before re-watered in specified phenological stages. The water status inside the plant is estimated by measuring either the Leaf Potential during the Predawn or soil water potential, along with the root zones. Measuring soil water potential has the advantage of being independent of diurnal atmospheric variations. However, this method has many logistic problems, making it very hard to apply along all the yard, especially the big ones. In this study, the Predawn Leaf Water Potential (PLWP) is daily predicted by Machine Learning models using data such as grapes variety, soil characteristics, irrigation schedules, and meteorological data. The benefits of these techniques are the reduction of the manual work of measuring PLWP and the capacity to implement those models on a larger scale by predicting PLWP up to 7 days which should enhance the ability to optimize the irrigation plan while the quantity and quality of the crop are under control.

Abstract
The combination of consumer owned Distributed Energy Resources, new Information and Communication Technologies (ICT), as well as changes to the national electricity regulations have created new opportunities for consumer engagement in the electricity sector. In this paper, this combination of technologies and regulations is examined in the Portuguese context. The new regulations dealing with self-consumption from prosumers are combined with smart contracts and distributed ledger technology to formulate an automated energy trading system for residential end-users in local energy markets. Results show that including prosumers in the local energy market brings significant benefits to all market participants. Additionally, results show that the newly created regulatory role of a Market Facilitator is beneficial to these type of local energy exchanges.

Abstract
A novel algorithm to separate S2 heart sounds into their aortic and pulmonary components is proposed. This approach is based on the assumption that, in different heartbeats of a given recording, aortic and pulmonary components maintain the same waveform but with different relative delays, which are induced by the variation of the thoracic pressure at different respiration phases. The proposed algorithm then retrieves the aortic and pulmonary components as the solution of an optimization problem which is approximated via alternating optimization. The proposed approach is shown to provide reconstructions of aortic and pulmonary components with normalized root mean-squared error consistently below 10% in various operational regimes.

Abstract
High penetration of distributed energy resources in distribution networks is facilitated through the microgrids (MGs) structure. From the technical point of view, the MG operator (MGO) is responsible for the internal operation of the MG regarding which the distribution system operator (DSO) cannot take any decision. From the market viewpoint, the MGO participates in the wholesale markets regarding which the scheduling of the MG's resources is monitored. Therefore, the operation problem of the MGO considering its participation in the wholesale markets under uncertainty has been investigated in many studies. In this paper, a two-stage stochastic optimization approach is developed to model the MGO's bidding strategies in the day-ahead energy and reserve markets considering its stochastic decisions in a real-time market. In this model, the uncertainties of demand, wind speed, and solar radiation are modeled through different scenarios using the probability distribution functions (PDFs) of these parameters. Moreover, the uncertainty of the real-time energy price is modeled using the information gap decision theory (IGDT) method. To show the effectiveness of the model, it is applied on a MG test system.