Abstract
This paper presents a systematic mapping literature review about the mobile robotics competitions that took place over the last few decades in order to obtain an overview of the main objectives, target public, challenges, technologies used and final application area to show how these competitions have been contributing to education. In the review we found 673 papers from 5 different databases and at the end of the process, 75 papers were classified to extract all the relevant information using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. More than 50 mobile robotics competitions were found and it was possible to analyze most of the competitions in detail in order to answer the research questions, finding the main goals, target public, challenges, technologies and application area, mainly in education.

Abstract
This research aims to analyze Corporate Social Responsibility (CSR) in the Sports Industry, specifically in the universe of sports clubs in Portugal. The main goal was to understand what the fans' perceptions on CSR practices of Portuguese sports clubs are, and how these perceptions influence their brand attitude and e-WOM intention. A questionnaire survey was conducted, obtaining a total of 159 responses. The research results show that the fans' perception of CSR initiatives positively influences their identification with the club, pride, word-ofmouth (WOM), electronic word-of-mouth (e-WOM) and the intention to remain members. The intention to become paid members of a sports club is not influenced by the fan's perception of CSR. However, their familiarity with the clubs' CSR initiatives positively influences their identification with the club, WOM and eWOM. In addition, relevant managerial implications are provided for sports club's managers as to how to improve CSR communication and create long-term benefits for the clubs.

Abstract
A new sensing platform based on long-period fiber gratings (LPFGs) for direct, fast, and selective detection of human immunoglobulin G (IgG; Mw = 150 KDa) was developed and characterized. The transducer's high selectivity is based on the specific interaction of a molecularly imprinted polymer (MIPs) design for IgG detection. The sensing scheme is based on differential refractometric measurements, including a correction system based on a non-imprinted polymer (NIP)-coated LPFG, allowing reliable and more sensitive measurements, improving the rejection of false positives in around 30%. The molecular imprinted binding sites were performed on the surface of a LPFG with a sensitivity of about 130 nm/RIU and a FOM of 16 RIU-1. The low-cost and easy to build device was tested in a working range from 1 to 100 nmol/L, revealing a limit of detection (LOD) and a sensitivity of 0.25 nmol/L (0.037 mu g/mL) and 0.057 nm.L/nmol, respectively. The sensor also successfully differentiates the target analyte from the other abundant elements that are present in the human blood plasma.

Abstract
In-situ calibration of marine sensors requires close-range positioning. In turn, localization relative to a given object of interest is necessary. This paper deals with the detection of a vertical cable hanging from a marine observatory implemented by means of a moored buoy. An algorithm composed of sequential image filtering, segmentation and template matching is proposed. Two approaches for generating the cable's acoustic image template are introduced. The performance of the approaches, obtained by comparison with ground-truth measurements, are illustrated over challenging cluttered acoustic images collected in a test tank. The results indicate a performance better than 74% of the best candidate to match the actual cable.

Abstract
As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy Consumption Data in the London Households dataset, provided by UK Power Networks, containing half-hourly readings from an original sample of 5567 households (71 households were hereby carefully selected after a justified filtering process). The main aim is to forecast the day-ahead load profile, based only on previous load values and some auxiliary variables. During this research different forecasting models are applied, tested and compared to allow comprehensive analyses integrating forecasting accuracy, processing times and the interpretation of the most influential features in each case. The selected models are based on Multivariate Adaptive Regression Splines, Random Forests and Artificial Neural Networks, and the accuracies resulted from each model are compared and confronted with a baseline (Naive model). The different forecasting approaches being evaluated have been revealed to be effective, ensuring a mean reduction of 15% in Mean Absolute Error when compared to the baseline. Artificial Neural Networks proved to be the most accurate model for a major part of the residential consumers.

Abstract
The Internet of Things (IoT) emerged to describe a network of connected things on a large scale to offer services to a large number of applications in different environments and domains. Middleware is software that seeks to facilitate the management and communication of all these things, providing the necessary functionalities to manage things, to discover, to compose services, and perform communication. For this reason, several proposals for middleware solutions for IoT have been developed. In this article, we conducted a systematic review of the literature to bring together middleware solutions for IoT, identifying the requirements and communication protocols used. In addition, we present some gaps and directions for future research in the development of IoT middleware.