Abstract

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
Wireless Sensor Networks (WSNs) are made of a large amount of small devices that are able to sense changes in the environment, and communicate these changes throughout the network. An example of such network is a photo voltaic (PV) power plant, where there is a sensor connected to each solar panel. Because such a network covers a large area, the number of sensors can be very large. The task of each sensor is to sense the output of the panel which is then sent to a central node for processing. As the network grows, it becomes impractical and even impossible to configure all these nodes manually. And so, the use of self-organization and auto-configuration algorithms becomes essential. In this paper, two algorithms are proposed that can be used to allow each node in the network to automatically identify its closest neighbors as well as its relative location in the network using the value of the Received Signal Strength indicator (RSSI) of the messages sent back and forth during the setup phase. Results show that the error in neighbor identification decreases as we increase the number of RSSI values used for decision making. Additionally, the number of nodes in the network affects the setup error greatly. However, the value of the error is still acceptable even for high number of simulated columns.

Abstract
Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are most frequently used. However, in some cases, these meshes cannot cope accurately with nonlinear behaviour unless a very large number of mesh nodes is used. One way to improve the solution involves adaptive mesh refinement algorithms which allow a non uniform node collocation. In the method presented in this paper, a time mesh refinement strategy based on the local error is developed. The technique was applied to solve two problems involving nonholonomic vehicles and it led to results with higher accuracy and yet with lower overall computational time when compared to a mesh having equidistant nodes. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Abstract