Abstract
In rural destinations like Douro region, tourism activities may be important to territories’ development because they use some of their natural resources and endogenous products, develop another related local economy business, and contributes to population fixation. Technologies can support the promotion and distribution of these destinations, namely digital marketing tools. The present study aims to analyze the influence that some of these tools perceived usefulness, from the tourist point of view, have on a rural destination image. To accomplish this, it was used a sample of 555 tourists visiting Douro destination and data collected were analyzed using structural equation modeling. The results demonstrated that tourist’s trust in tourism digital marketing tools and their attitude toward these tools have an indirect effect on destination image. Moreover, the perceived usefulness of some digital marketing tools utilized to search, plan or purchase Douro destination, as website, booking and mobile devices, has also a positive effect on this destination image. On another hand, the perceived usefulness of a conventional channel, as travel agencies, has a negative effect on this rural destination image. This research provided some interesting insights for practitioners working in tourism destination marketing.

Abstract
In research carried out by the Brazilian Institute of Geography and Statistics (IBGE), alarming data on motor diseases are disclosed, in which it is shown that 7% of the Brazilian population has some type of motor deficiency. The gaming industry is the most profitable entertainment industry today, yet the area of gaming adapted for people with motor disabilities is under-explored. Thinking about it, this work proposes to develop a work environment (Framework) to facilitate the creation of accessible games using eye tracking, thus giving the possibility of playing with just the eyes. © Springer Nature Singapore Pte Ltd 2020.

Abstract
Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ° C.

Abstract
Voltage imbalances are one of the most severe challenges in electrical networks, which negatively affect their loads and other connected equipment. This paper proposes a voltage support control strategy to mitigate the voltage imbalance in inverter-based low voltage distribution networks. The control scheme is derived taking in mind the following control objectives: a) to increase the positive sequence voltage as much as possible, b) to decrease the negative sequence voltage as much as possible, c) to inject the power generated by the primary source, and d) to minimize the output current of the inverter. The innovative contribution of the proposed solution is based on the design of a control algorithm that meets the aforementioned objectives without resorting to communications with other grid components. The theoretical results are experimentally validated by selected tests on a laboratory setup with X/R ratio close to one.

Abstract
The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% <= CV <= 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (-0.50 <= r <= 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.

Abstract
Artificial Intelligence (AI) has seen numerous applications in the area of Education. Through the use of educational technologies such as Intelligent Tutoring Systems (ITS), learning possibilities have increased significantly. One of the main challenges for the widespread use of ITS is the ability to automatically generate questions. Bearing in mind that the act of questioning has been shown to improve the students learning outcomes, Automatic Question Generation (AQG) has proven to be one of the most important applications for optimizing this process. We present a tool for generating factual questions in Portuguese by proposing three distinct approaches. The first one performs a syntax-based analysis of a given text by using the information obtained from Part-of-speech tagging (PoS) and Named Entity Recognition (NER). The second approach carries out a semantic analysis of the sentences, through Semantic Role Labeling (SRL). The last method extracts the inherent dependencies within sentences using Dependency Parsing. All of these methods are possible thanks to Natural Language Processing (NLP) techniques. For evaluation, we have elaborated a pilot test that was answered by Portuguese teachers. The results verify the potential of these different approaches, opening up the possibility to use them in a teaching environment.