Abstract
Load profiles are a crucial tool for power system planning and operation, and also in several operations of electricity markets. This article proposes a new methodology for the determination of load profiles based on a two-step approach. The first phase employs a neural network autoencoder to reduce the dimensionality of the input vectors. The second phase is a clustering process based on the Kohonen Self- Organizing Maps, to identify cohesive consumers' classes. The implemented approach produces classes based on load diagrams and, simultaneously, a class identification based on consumers' billing data.

Abstract
In order to increase the efficiency in the use of energy resources, the electrical grid is slowly evolving into a smart(er) grid that allows users' production and storage of energy, automatic and remote control of appliances, energy exchange between users, and in general optimizations over how the energy is managed and consumed. One of the main innovations of the smart grid is its organization over an energy plane that involves the actual exchange of energy, and a data plane that regards the Information and Communication Technology (ICT) infrastructure used for the management of the grid's data. In the particular case of the data plane, the exchange of large quantities of data can be facilitated by a middleware based on a messaging bus. Existing messaging buses follow different data management paradigms (e.g.: request/response, publish/subscribe, data-oriented messaging) and thus satisfy smart grids' communication requirements at different extents. This work contributes to the state of the art by identifying, in existing standards and architectures, common requirements that impact in the messaging system of a data plane for the smart grid. The paper analyzes existing messaging bus paradigms that can be used as a basis for the ICT infrastructure of a smart grid and discusses how these can satisfy smart grids' requirements.

Abstract
In face of growing global competition, the ability of organizations to effectively use information technologies to deliver innovation and creativity is widely recognized as an important competitive advantage. In this context, knowledge of how to apply creativity techniques to information systems planning becomes particularly relevant. This chapter presents a framework for the introduction of creativity in Information Systems Planning. The framework aims at promoting the development of innovative Information Systems, which traditional methods of requirements elicitation fail to address. Finally, we discuss how the framework was implemented at a public organization to identify information systems opportunities.

Abstract
Radon monitoring at different levels of the cover of the Urgeirica tailings shows that the sealing is effective and performing as desired in terms of containing the strongly radioactive waste resulting from uranium ore processing. However, the analysis of the time series of radon concentration shows a very complex temporal structure, particularly at depth, including very large and fast variations from a few tens of kBq m(-3) to more than a million kBq m(-3) in less than one day. The diurnal variability is strongly asymmetric, peaking at 18 h/19 h and decreasing very fast around 21 h/22 h. The analysis is performed for summer and for a period with no rain in order to avoid the potential influence of precipitation and related environmental conditions on the radon variability. Analysis of ancillary measurements of temperature, relative humidity, wind speed and wind direction, as well as atmospheric pressure reanalysis data shows that the daily averaged radon concentration in the taillings material is anti-correlated with the atmospheric pressure and that the diurnal amplitude is associated with the magnitude of atmospheric pressure daily oscillations.

Abstract
The context in which service is delivered and experienced has, in many respects, fundamentally changed. For instance, advances in technology, especially information technology, are leading to a proliferation of revolutionary services and changing how customers serve themselves before, during, and after purchase. To understand this changing landscape, the authors engaged in an international and interdisciplinary research effort to identify research priorities that have the potential to advance the service field and benefit customers, organizations, and society. The priority-setting process was informed by roundtable discussions with researchers affiliated with service research centers and networks located around the world and resulted in the following 12 service research priorities: stimulating service innovation, facilitating servitization, service infusion, and solutions, understanding organization and employee issues relevant to successful service, developing service networks and systems, leveraging service design, using big data to advance service, understanding value creation, enhancing the service experience, improving well-being through transformative service, measuring and optimizing service performance and impact, understanding service in a global context, and leveraging technology to advance service. For each priority, the authors identified important specific service topics and related research questions. Then, through an online survey, service researchers assessed the subtopics' perceived importance and the service field's extant knowledge about them. Although all the priorities and related topics were deemed important, the results show that topics related to transformative service and measuring and optimizing service performance are particularly important for advancing the service field along with big data, which had the largest gap between importance and current knowledge of the field. The authors present key challenges that should be addressed to move the field forward and conclude with a discussion of the need for additional interdisciplinary research.

Abstract
Accurate measurements of global solar radiation and atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors algorithm and artificial neural network models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to three atmospheric variables, over a prediction horizon of 48-steps-ahead.