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

Abstract
The use of industrial by-products to produce new types of cement-substitute binders is gaining significant momentum, particularly through the alkaline activation technique. However, the exact curing conditions that should be considered with each binder variation have not been fully understood yet. The aim of the present work is thus the statistical analysis of the effects of curing conditions (humidity and temperature) on the mechanical response (uniaxial compression strength and elastic modulus) of different aggregate/binder weight ratios. Five blends of solid mine tailings (as an aggregate) and fly ash (as a precursor), both collected from the Portuguese industry, were activated with sodium hydroxide solution and cured under nine different temperature and humidity combinations. The data analysis was performed using multivariate analysis of variance and analysis of variance, followed by Tukey's post hoc test, whenever appropriate. Results show that the curing humidity factor showed a lower impact than the curing temperature. Although the increase in temperature and decrease in humidity produced higher compression strengths, the best results were obtained with a specific combination of both (60 degrees C and 50% relative humidity). In general, the increase in tailings' content produced a reduction in compression strength, but only for values above 20% (by weight of the sum of solids).

Abstract
Intrapreneurship is becoming a key factor in the growth of a company in a highly dynamic and progressively more competitive business environment. The idea of intrapreneurship is to encourage greater employee involvement within the organisation in which they work, giving them the freedom to innovate and experiment in a proactive, creative, and innovative way. In the startups, the role of intrapreneurship is of great relevance knowing that startups are designed to scale and grow exponentially in a short time and with few resources. Innovation is at the core of a startup and intrapreneurship initiatives allow leveraging this capacity in startups. Accordingly, this study seeks to explore the phenomenon of intrapreneurship in startups, seeking to understand how formal and informal intrapreneurship initiatives are taken on by startups, and also exploring the role played by existing resources to support these initiatives. The results of the study allow us to conclude that startups value intrapreneurship initiatives despite financial constraints that overlap with time constraints that affect what can be allocated to these activities. Finally, medium-sized startups and those with more qualified human capital tend to value and support intrapreneur initiatives more intensely. In contrast, startups with less academically qualified human capital offer worse conditions and support to intrapreneur activities.

Abstract
The increasing need to innovate to keep or create competitive advantage and the difficulty in retaining resources that enable innovation, force companies to look for partnerships. Partnering with the appropriate and leading R&D (Research and Development) institutions is critical to be able to innovate. One of the first challenges that emerges when building a collaboration is the selection of organizations to form the consortium. Technology foresight might be an interesting process to start building such partnership, as it helps identify technology opportunities and the entities that lead technology development. This paper uses case research to study the creation and use of a technology foresight process to build new R&D collaborations, while identifying of technology development opportunities. Findings from the case study led to the identification of how the technology foresight process was used to create a new R&D collaboration in the company under study, while identifying what motivated such collaboration and how it was managed. Furthermore, important characteristics of the team responsible for that process and of the process management are highlighted.

Abstract
Effective testing of message-oriented software requires describing the expected behaviour of the system and the causality relations between messages. This is often achieved with formal specifications based on temporal logics that require both first-order and metric temporal constructs - to specify constraints over data and real time. This paper proposes a technique to automatically generate tests for metric first-order temporal specifications that match well-understood specification patterns. Our approach takes in properties in a high-level specification language and identifies test schemas (strategies) that are likely to falsify the property. Schemas correspond to abstract classes of execution traces, that can be refined by introducing assumptions about the system. At the low level, concrete traces are successively produced for each schema using property-based testing principles. We instantiate this approach for a popular robotic middleware, ROS, and evaluate it on two systems, showing that schema-based test generation is effective for message-oriented software.