Abstract
The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques are suitable to deal with spatio-temporal data, they have received little attention in the energy domain. In this paper, we propose a new method based on the Tucker tensor decomposition, capable of extracting a new feature space for the learning task. For evaluation purposes, we have investigated the performance of predictive clustering trees with the new feature space, compared to the original feature space, in three renewable energy datasets. The results are favorable for the proposed method, also when compared with state-of-the-art algorithms.

Abstract
Low loss optical waveguides are the key component for the fabrication of more complex integrated optics devices. In most works related to femtosecond laser written waveguides, the values presented give results at a single wavelength or in a narrow wavelength band; but some applications in optical sensing, for example, would benefit from waveguides having good propagation properties in a larger wavelength range. This paper presents results that allow one to gain insight into the major loss mechanisms present in laser written waveguides in two different types of glasses (fused silica and Eagle 2000 glass) and the dependence of those on the fabrication parameters. Finally, an example of application of broadband operating waveguides is given.

Abstract
There has been a significant effort by the research community to address the problem of providing methods to organize documentation, with the help of Information Retrieval methods. In this paper, we present several experiments with stream analysis methods to explore streams of text documents. This paper also presents possible architectures of the Text Document Stream Organization, with the use of incremental algorithms like Incremental Sparse TF-IDF and Incremental Similarity. Our results show that with this architecture, significant improvements are achieved, regarding efficiency in grouping of similar documents. These improvements are important since it is of general knowledge that great amounts of text analysis are a high dimensional and complex subject of study, in the data analysis area.

Abstract
This paper focuses primarily on the flexibility of active prosumers in an islanded microgrid operation. The main objective is finding the best strategy to implement on an existing medium voltage grid, with several consumers, with the capability of producing some power for the grid operation, via Renewable Energy Resources (RES), or thermal Units, generally gas turbines, also there is the capability of some energy storage through batteries. Since power output of RES has a cost per kw of zero, it is greatly important to find the best combination of these resources who best suit the test system. For the purposes of these tests, the available investment funds are unlimited, although, there are some constraints regarding maximum RES penetration and ESS capacity.

Abstract
Customer relationship management is critical for organizations. Public institutions, in particular municipalities, are no exception to this. Since the process of implementing a CRM system is not risk-free, it is important to know the factors that influence its success. From studies conducted, it was possible to verify that there is a gap in the literature regarding the influential factors of the successful adoption of CRM systems in public institutions (CzRM). Also, through interviews conducted in some municipalities and CRM suppliers, it was possible to identify the relevant factors for the adoption of CRM systems. The purpose of this article is to present the influence factors of the success of the implementation of CzRM systems. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Abstract
Peer-to-Peer (P2P) energy trading (ET) is a paradigm of energy trading between consumers without intermediaries. This model of ET allows the commercialization of energy resources produced through renewable sources that do not need other local consumers. This energy trading between consumers is able to improve the local balance of energy generation and consumption. Currently, this paradigm is being evaluated to show the suitability of its application in today’s society, significantly increasing the number of projects in this area worldwide. This article reviews the main models of application of this paradigm in smart cities, presenting the main characteristics of these approaches. This paper proposes an architectural model for P2P energy trading that solves the main deficiencies detected. The designed system allows the simulation of P2P processes using a novel negotiation model. This energy trading system is based on a Multi-Agent System (MAS) using the Robot Operating System (ROS). The system allows representing using independent agents each one of the zones that intervene in the process of negotiation of the energy of a city, being already representing a consumer’s role or a producer’s role of energy. The system has been tested on a model in which each zone uses real data about the role it simulates over a period of two and a half years. The preliminary results show how the energy trading market allows a smart city to evolve towards a high degree of sustainability. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.