Abstract
Rumination is a psychological process that is featured by a repetitive pattern of intrusive thoughts that, by focusing the person into painful cognitive contents, elicits suffering. Usually people try to deal with these ruminative thoughts by trying to avoid thinking, which paradoxically makes the thoughts even more intense. Several researchers proposed that ruminative thoughts are central in anxiety and depressive disorders and that the treatment of rumination is also pivotal in the treatment of these disorders. This study is a first attempt to construct a game that trains players in shifting the attention from ruminative thoughts to problem-solving tasks, illustrating the use of the game. We expect that training such an ability will reduce the intensity of that thoughts. It is our expectation that the continuous use of games with these kind of features will reduce the intensity of ruminative thoughts, improving the wellbeing and reducing the psychological suffering of players.

Abstract

Abstract
Walking robots are well known for being able to walk over rough terrain and adapt to various environments. Hexapod robots are chosen because of their better stability and higher number of different gaits. However, having to hold the whole weight of the body and a large number of actuators makes all walking robots less energetically efficient than wheeled machines. Special methods for energy consumption optimization must be found. In this paper, hexapod robot energy consumption dependence on body elevation and step height is presented. Three main hexapod gaits are used: tripod, tetrapod and wave. Experimental results show that energy consumption does not depend on body elevation or gait. Although, higher steps increases the power consumption. Therefore, when walking over even terrain, lower step heights along with higher body elevation must be selected for tripod or tetrapod gait in order to surpass ground irregularities but still maintain maximum energetic efficiency.

Abstract

Abstract
Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks.

Abstract
The main motivation of this work was to provide a valid contribution for the assessment of the cardiovascular condition by the analysis of several Arterial Pressure Waveform (APW) parameters collected by a new non-invasive device. Three sets of recordings for the carotid pressure waveform at left and right carotid arteries were performed, under standardized conditions, in 20 volunteers by three trained operators. The mean of the inter-operator differences were higher for the right artery, comparatively to the left artery. In this case, an Augmentation Index (AIx) value of -2.31 ± 7.29 % and a Systolic Wave Transit Time (SWTT) value of -12.94 ± 31.46 ms were observed, which are higher than the left measurements, 0.94 ± 7.52 % and -2.96 ± 22.67 ms, respectively. Intra-operator differences were calculated for each of the three sets of measurements and showed good reproducibility. The pulse-by-pulse variability analysis gives very good markers for the Left Ventricular Ejection Time (LVET), Dicrotic Wave Amplitude (DWA), Reflection Wave Amplitude (RWA), Coefficient of Variation (CV) < 10 %, and satisfactory values for the AIx (CV< 30 %). The SWTT and Reflected Wave Transit Time (RWTT) also presented satisfactory results (10 %