Abstract
Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, the challenges of PMG's uncertainties such as stochastic behavior of load data and weather conditions (solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated. In this paper, a new energy management systems (EMS) based on weather and load forecasting is proposed for PMG's optimal scheduling and operation. Developing a novel hybrid machine learning-based method using adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) artificial neural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG's operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.

Abstract
Use error due to user interface design defects is a major concern in many safety critical domains, for example avionics and health care. Early detection of latent user interface problems can be facilitated by user-centered design methods that integrate formal verification technologies. This article considers the role that formal verification technologies can play in the context of user-centered design by considering the following three existing tools: CIRCUS, PVSio-web, and IVY. These tools have been developed to support the model based analysis of critical user interfaces. They have their foundations in existing formal verification technologies, but each of them is focused towards particular issues relating to user interface design. The article explores the different phases of the user-centered design process and the extent to which each of these tools supports these phases. Criteria are developed for assessing their role at each stage of the design process. The results of the evaluation provide guidance to developers to help choose the most appropriate tool based on their analysis needs while at the same time setting challenges for future developments.

Abstract
Nowadays, companies are demanding better-prepared professionals to succeed in a digital society, and the acquisition of Science, Technology, Engineering, Arts, and Mathematics (STEAM)-related competencies is a key issue. One of the main problems in this sense is how to integrate STEAM into the current educational curricula. This is not something related to a subject or educational trend but rather to new methodological approaches that can engage students. In this sense, such active methodologies that apply mechatronics and robotics could be an interesting path to pursue. Given this context, the first necessary task in evaluating the potential of this approach is to understand the landscape of the application of robotics and mechatronics in STEAM Education and how active methodologies are applied in this sense. To carry out this analysis in a systematic and replicable manner, it is necessary to follow a methodology. In this case, the researchers employ a systematic mapping review. This paper presents this process and its main findings. Fifty-four studies have been selected out of 242 total studies analyzed. From these, beyond obtaining a clear vision of the STEAM landscape regarding project topics, we can also conclude that robotics and physical devices have been applied successfully with collaborative methodologies in STEAM Education. Regarding conclusions, this paper shows that robotics and mechatronics applied with active methodologies is to be a good means to engage students in STEAM disciplines and thus aid the acquisition of what is commonly known as "21st-century skills."

Abstract
This chapter is based on consumer behavior theories and analyses consumers' perspectives about camping as a tourism alternative. It explores motivations and several relevant factors that influence the attitudes and behaviors of tourists regarding camping activities. The methodology was qualitative and used focus groups as a data collection tool. A content and thematic analysis was adopted as data mining technique. Results provide empirical support to the influence of subjective norms, relevant others' preference for camping, and sustainable consumer profile on attitudes toward camping which influence camping intention. Moreover, camping intention, motivations, relevant others' preference for camping, perceived control, and past experience affect camping behavior. Overall, this chapter shows that consumer behavior theories and models provide very interesting cues on campers' decision process, offering alternative and complementing views to the extant literature, namely to the studies using the popular push-pull approach.

Abstract
This paper proposes a "grey-box" aggregated dynamic model for active distribution networks, taking into account a heterogeneous fleet of generation technologies alongside their expected behavior when taking into account the latest European grid codes requirements in terms of voltage support services. The main goal of the proposed model and underlying methodology for its identification is to represent the transient behavior of the active distribution system following large voltage disturbances occurring at the transmission side. The proposed aggregated model is composed by three main components: an equivalent power converter for generation and battery energy storage systems portfolio representation; an equivalent synchronous generation unit; and an equivalent composite load model. The model's parameters are estimated by an evolutionary particle swarm optimization algorithm, by comparing a fully-detailed model of a distribution network with the aggregated model's frequency domain's responses of active and reactive power flows, at the boundary of transmission-distribution interface substation.

Abstract