Abstract
Futures contracts are a valuable market option for electricity negotiating players, as they enable reducing the risk associated to the day-ahead market volatility. The price defined in these contracts is, however, itself subject to a degree of uncertainty; thereby turning price forecasting models into attractive assets for the involved players. This paper proposes a model for futures contracts price forecasting, using artificial neural networks. The proposed model is based on the results of a data analysis using the spearman rank correlation coefficient. From this analysis, the most relevant variables to be considered in the training process are identified. Results show that the proposed model for monthly average electricity price forecast is able to achieve very low forecasting errors.

Abstract
Disinfection by-products (DBP) such as trihalomethanes (THM) are formed when chlorine and bromine interact with natural organic materials in chlorine-treated swimming pools. Epidemiological evidence demonstrated an association between exposure to swimming pool environment and adverse health effects. Therefore, this study aimed to assess carcinogenic and non-carcinogenic risk of long-term exposure of elite swimmers and their coaches. In an Olympic-size indoor chlorinated swimming pool, THM levels were determined in water (21-69 mu g/L), in the boundary layer above the water surface (59-397 mu g/m(3)), and in the air surrounding the pool (28-390 mu g/m(3)). These values were used to predict multi-pathway chronic daily intake (CDI), cancer risk (CR) and hazard index (HI). Oral and dermal CDI for swimmers were 2.4 x 10(-6) and 2.0 x 10(-8), respectively. The swimmers' inhalation CDI (1.9 x 10(-3) mg/kg/day) was estimated to be sixfold higher than levels obtained for coaches (3.3 x 10(-4) mg/kg/day). According to guidelines, the HI was acceptable, but CR exceeded the recommended limit for both, coaches (CR: 5.5 x 10(-7)-8.5 x 10(-5); HI: 6.5 x 10(-4)-1 x 10(-1)) and swimmers (CR: 1.4 x 10(-5)-3.6 x 10(-4) HI: 1.6 x 10(-2)-4.3 x 10(-1)). Our findings provide further support to the need to develop comprehensive guidelines to safeguard the health of individuals involved in elite swimming.

Abstract
Face-to-face telepresence promotes the sense of "being there" and can improve collaboration by allowing immediate understanding of remote people's nonverbal cues. Several approaches successfully explored interactions with 2D content using a see-through whiteboard metaphor. However, with 3D content, there is a decrease in awareness due to ambiguities originated by participants' opposing points-of-view. In this paper, we investigate how people and content should be presented for discussing 3D renderings within face-to-face collaborative sessions. To this end, we performed a user evaluation to compare four different conditions, in which we varied reflections of both workspace and remote people representation. Results suggest potentially more benefits to remote collaboration from workspace consistency rather than people's representation fidelity.We contribute a novel design space, the Negative Space, for remote face-to-face collaboration focusing on 3D content.

Abstract
During recent decades with the power system restructuring process, centralized energy sources are being replaced with decentralized ones. This phenomenon has resulted in a novel concept in electric power systems, particularly in distribution systems, known as Distributed Generation (DG). On one hand, utilizing DG is important for secure power generation and reducing power losses. On the other hand, widespread use of such technologies introduces new challenges to power systems such as their optimal location, protection devices' settings, voltage regulation, and Power Quality (PQ) issues. Another key point which needs to be considered relates to specific DG technologies based on Renewable Energy Sources (RESs), such as wind and solar, due to their uncertain power generation. In this regard, this paper provides a comprehensive review of different types of DG and investigates the newly emerging challenges arising in the presence of DG in electrical grids.

Abstract
Inertial measurement units (IMU) are, typically, a cluster of accelerometers, gyroscopes and magnetometers. Its use was introduced with military applications, being, nowadays, widely common on industrial applications, namely robot navigation. Since there are a lot of units in different cost ranges, it is proposed, in this paper, a procedure to compare their performance in tracking tasks. Once IMU samples are unavoidably corrupted by systematic and stochastic errors, a calibration procedure (without any external equipment) to identify sensors’ error models and a Kalman filter implementation to remove white noise are suggested. Then, the comparison is carried out over two trajectories, square and circular paths, respectively, being described by a robotic arm, which acts as reference. The results show that different manufacturing quality units can track, with success, orientation references but are incapable to perform position tracking activities.

Abstract
Old documents tend to be difficult to be analyzed and understood, not only for average users but oftentimes for professionals as well. This is due to the context shift, vocabulary evolution and, in general, the lack of precise knowledge about the writing styles in the past. We propose a concept of positioning document in the context of its time, and develop an interactive system to support such an objective. Our system helps users to know whether the vocabulary used by an author in the past were frequent at the time of text creation, whether the author used anachronisms or neologisms, and so on. It also enables detecting terms in text that underwent considerable semantic change and provides more information on the nature of such change. Overall, the proposed tool offers additional knowledge on the writing style and vocabulary choice in documents by drawing from data collected at the time of their creation or at other user-specified time.