Abstract
Portugal is a country in which the tourism sector assumes great importance, contributing around 15% of the national GDP and with more than 1 million jobs. In this sense, COVID-19 is expected to have a dramatic impact on the Portuguese economy aggravated by the existence of a reduced internal market with low purchasing power and in which there is a high dependence on the external market. This perspective study explores the challenges and opportunities that Portuguese companies in the field of tourism are facing due to the emergence of this pandemic. The challenges faced by companies are both short-term and long-term. In the short term, it is essential to ensure sufficient liquidity to reopen activities and in the long term, it is necessary to be prepared and reactive to disruptive movements that may arise in tourist demand. However, it is also important to recognize that some opportunities can be exploited, such as the quality of the health response, the exploitation of a less mass tourism supply based on the components of social and environmental sustainability, the increase of tourism among the elderly population from countries with greater purchasing power and the acceleration of the digitalization of tourism operations.

Abstract
Tourism is an industry, very focused on economic growth, with significant negative environmental and social impacts. Consequently, the tourism industry faces major challenges related to sustainability. Sustainable innovation is a tool that contributes not only to increased business competitiveness but can also play an important role in mitigating the negative impacts that such growth can generate. Recognizing the opportunity that this innovation can have in the tourism industry, this chapter analyzes the state of the art and systematizes the knowledge and evolution of the academic debate about this relationship between sustainable innovation and tourism from 1992 to 2018. This chapter indicates that sustainable tourism is focused on seven major areas of research and predominantly analyzed through quantitative methods. It is still an embryonic topic with scarce research done in several areas, such as the monitoring of its impacts, the effects felt by the communities of tourist destinations, and the impacts that sustainable innovation may have on other tourism subsectors. © 2020, IGI Global.

Abstract
The requirements of Machine Learning applications are changing rapidly. Machine Learning models need to deal with increasing volumes of data, and need to do so quicker as responses are expected more than ever in real-time. Plus, sources of data are becoming more and more dynamic, with patterns that change more frequently. This calls for new approaches and algorithms, that are able to efficiently deal with these challenges. In this paper we propose the use of a Genetic Algorithm to Optimize a Stacking Ensemble specifically developed for streaming scenarios. A pool of solutions is maintained in which each solution represents a distribution of weights in the ensemble. The Genetic Algorithm continuously optimizes these weights to minimize the cost function. Moreover, new models are added at regular intervals, trained on more recent data. These models eventually replace older and less accurate ones, making the ensemble adapt continuously do changes in the distribution of the data.

Abstract
This study offers an overview of the H2020 InterConnect project, which targets the relation between smart homes and distribution grids. The project vision is to produce a digital marketplace, using an interoperable marketplace toolbox and Smart appliances REference Ontology (SAREF) compliant Internet of Things (IoT) reference architecture as the main backbone, through which all SAREF-ized services, compliant devices, platform enablers and applications can be downloaded onto IoT and smart grid digital platforms. Energy users in buildings, either residential or non-residential, manufacturers, distribution grid operators and the energy retailers will work together towards the demonstration of the smart energy management solutions in seven connected large-scale test-sites in Portugal, Belgium, Germany, the Netherlands, Italy, Greece and France. This study depicts how InterConnect project will enhance the relation and the interconnectivity between smart buildings and grids safeguarding the definition of the role of each stakeholder in energy and non-energy services. © 2020 Institution of Engineering and Technology. All rights reserved.

Abstract
Portugal has witnessed some of its largest wildfires in the last decade, due to the lack of forestry management and valuation strategies. A cost-effective biomass collection tool/approach can increase the forest valuing, being a tool to reduce fire risk in the forest. However, cost-effective forestry machinery/solutions are needed to harvest this biomass. Most of bigger operations in forests are already highly mechanized, but not the smaller operations. Mobile robotics know-how combined with new virtual reality and remote sensing techniques paved the way for a new robotics perspective regarding work machines in the forest. Navigation is still a challenge in a forest. There is a lot of information, trees consist of obstacles while lower vegetation may hide danger for robot trajectory, and the terrain in our region is mostly steep. The existence of accurate information about the environment is crucial for the navigation process and for biomass inventory. This paper presents a prototype forest robot for biomass collection. Besides, it is provided a dataset of different forest environments, containing data from different sensors such as 3D laser data, thermal camera, inertial units, GNSS, and RGB camera. These datasets are meant to provide information for the study of the forest terrain, allowing further development and research of navigation planning, biomass analysis, task planning, and information that professionals of this field may require.

Abstract
One of the most difficult scenarios for unsupervised segmentation of moving objects is found in nighttime videos where the main challenges are the poor illumination conditions resulting in low-visibility of objects, very strong lights, surface-reflected light, a great variance of light intensity, sudden illumination changes, hard shadows, camouflaged objects, and noise. This paper proposes a novel method, coined COLBMOG (COLlinearity Boosted MOG), devised specifically for the foreground segmentation in nighttime videos, that shows the ability to overcome some of the limitations of state-of-the-art methods and still perform well in daytime scenarios. It is a texture-based classification method, using local texture modeling, complemented by a color-based classification method. The local texture at the pixel neighborhood is modeled as an..-dimensional vector. For a given pixel, the classification is based on the collinearity between this feature in the input frame and the reference background frame. For this purpose, a multimodal temporal model of the collinearity between texture vectors of background pixels is maintained. COLBMOG was objectively evaluated using the ChangeDetection.net (CDnet) 2014, Night Videos category, benchmark. COLBMOG ranks first among all the unsupervised methods. A detailed analysis of the results revealed the superior performance of the proposed method compared to the best performing state-of-the-art methods in this category, particularly evident in the presence of the most complex situations where all the algorithms tend to fail.