Abstract

Abstract
Informal learning can have an important role in today’s Education but, to be effective, it should be contextualized individually for each learner, and situated to enhance experience. Museums have invaluable collections of assets that are in display and curators use their knowledge to engage the audience. Museums are places where informal learning can be fostered to engage the students and provide opportunities for situated learning. Pervasive systems, that take into account context, from both the learner and the location, have a good potential to promote this effectiveness in a gamified process that transforms the regular museum exploration into an engaging experience that provides learning opportunities at the appropriate time and place. In this paper, we propose a gamified approach based on the concept of sticker album collection and its integration in an Augmented Reality (AR) mobile application. The concept of sticker album collection is quite familiar to most people, mainly from their youth, and is the main dynamic of the gamification design, engaging the learner to collect more stickers and progress in the exploration of the museum. As a pervasive solution, we do not use physical support, but instead, a mobile application to provide the learning experiences by uncovering the stickers using AR over the museum collection, in order to enhance the knowledge transfer and rewarding. We present a prototype developed for a boat museum where, digital stickers are obtained by overcoming challenges in the context of the exploration of the boats in the museum. Furthermore, we provide two evaluations fromexperts: a preliminary evaluation of user experienceand a gamification evaluation using the Octalsysframework. © Springer International Publishing AG 2017.

Abstract
The operation of Wireless Sensor Networks (WSNs) is subject to multiple constraints, among which one of the most critical is available energy. Sensor nodes are typically powered by electrochemical batteries. The stored energy in battery devices is easily influenced by the operating temperature and the discharge current values. Therefore, it becomes difficult to estimate their voltage/charge behavior over time, which are relevant variables for the implementation of energy-aware policies. Nowadays, there are hardware and/or software approaches that can provide information about the battery operating conditions. However, this type of hardware-based approach increases the battery production cost, which may impair its use for sensor node implementations. The objective of this work is to propose a software-based approach to estimate both the state of charge and the voltage of batteries in WSN nodes based on the use of a temperature-dependent analytical battery model. The achieved results demonstrate the feasibility of using embedded analytical battery models to estimate the lifetime of batteries, without affecting the tasks performed by the WSN nodes.

Abstract
The information provided by online traveler reviews is becoming a key element in the decision-making process of hotel customers, reducing the uncertainty and the perceived risk of a traveler. Therefore, a careful analysis of the content provided by online customers' reviews might give invaluable information concerning the key determinants, from a user's perspective, of the quality of the service provided, justifying the attributed service rating. The objectives of this study are twofold: (1) use text-mining techniques to analyze the user's generated content automatically collected from hotels in Porto in a certain period of time and, from this analysis, derive the most frequent terms used to describe the service; (2) understand whether it is possible to predict the aggregated rating assigned by reviewers based on the terms used and, at the same time, identify the terms showing high predictive capacity. Our study attempts to support hotel service managers in achieving their strategic and tactical goals by using innovative text- and data-mining tools to explore the wealth of information provided by user generated content in an easy and timely way.

Abstract
In recent years, in addition to the traditional aspects concerning efficiency and profitability, the energy sector is facing new challenges given by environmental issues, security of supply, and the increasing role of the local demand. Therefore, the researchers have developed new decision-making frameworks enabling higher local integration of distributed energy resources (DER). In this context, new energy players appeared in the retail markets, increasing the level of competition on the demand side. In this paper, a multienergy player (MEP) is defined, which behaves as a DER aggregator between the wholesale energy market and a number of local energy systems (LES). The MEP and the LES have to find a long-term equilibrium in the multienergy retail market, in which they are interrelated through the price signals. To achieve this goal, in this paper the decision-making conflict between the market players is represented through a bilevel model, in which the decision variables of the MEP at the upper level are parameters for the decision-making problem at the lower level (for the individual LES). The problem is transformed into a mathematical program with equilibrium constraints by implementing duality theory, which is solved with the CPLEX 12 solver. The numerical results show the different MEP behavior in various conditions that impact on the total flexibility of the energy system.

Abstract
The incidence of chronic diseases is increasing and monitoring patients in a home environment is recommended. Noncompliance with prescribed medication regimens is a concern, especially among older people. Heart failure is a chronic disease that requires patients to follow strict medication plans permanently. With the objective of helping these patients managing information about their medicines and increasing adherence, the personal medication advisor CARMIE was developed as a conversational agent capable of interacting, in Portuguese, with users through spoken natural language. The system architecture is based on a language parser, a dialog manager, and a language generator, integrated with already existing tools for speech recognition and synthesis. All modules work together and interact with the user through an Android application, supporting users to manage information about their prescribed medicines. The authors also present a preliminary usability study and further considerations on CARMIE.