Abstract
This work deals with the design of a smart balancing system for e-mobility applications. Low-cost bi-directional DC/DC converters, based on cell-to-cell shared energy transfer configuration, are used to connect battery cells to the balancing bus, which also includes a supercapacitor bank. This system can be seen as a hybrid battery management system (HBMS), since, in addition to traditional BMS features, it also enables hybridization of batteries and supercapacitors. A convex optimization problem is formulated to control the HBMS, focusing on the minimization of energy losses, while considering safety and balancing constraints. Simulation results demonstrate that, in comparison with state-of-the-art BMS solutions, the proposed HBMS reduces energy losses in up to 15%.

Abstract
In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.

Abstract
The increasing amount of scholarly literature and the diversity of dissemination channels are challenging several fields and research communities. A continuous interplay between researchers and citizen scientists creates a vast set of possibilities to integrate hybrid, crowd-machine interaction features into crowd science projects for improving knowledge acquisition from large volumes of scientific data. This paper presents SciCrowd, an experimental crowd-powered system under development “from the ground up” to support data-driven research. The system combines automatic data indexing and crowd-based processing of data for detecting topic evolution by fostering a knowledge base of concepts, methods, and results categorized according to the particular needs of each field. We describe the prototype and discuss its main implications as a mixed-initiative approach for leveraging the analysis of academic literature.

Abstract

Abstract
Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

Abstract
Smart buildings will play a fundamental role in ensuring comfort while reducing the energy required. However, due to the lack of knowledge about the operation of the smart controllers, the occupants can unintentionally increase the energy spent. Nevertheless, there is evidence that the informed and motivated user will actually cooperate with the system. Some of the issues associated with researching control systems in the context of building automation are difficult to address, because of the chronic lack of effective laboratory settings for experimentation. In this paper, we describe a system representative of the usual complexity found in cyber-physical systems, whose purpose is to address the needs for experimenting with building automation, with a focus on control systems and gamification. Designed with pragmatic concerns, this system presents a unique set of challenges and opportunities to research a new generation of software control systems, and supporting interfaces, that leverage the occupants' behaviour.