Abstract
The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

Abstract

Abstract
Frequency blueshifting was recently observed in light pulses propagating on gas-filled hollow-core photonic crystal fibers where a plasma has been produced due to photoionization of the gas. One of the propagation models that is adequate to describe the actual experimental observations is here investigated. It is a nonlinear Schrodinger equation with an extra term, to which we applied a self-similar change of variables and found its accelerating solitons. As in other NLS-related models possessing accelerating solitons, there exist asymmetrical pulses that decay as they propagate in some parameter region that was here well defined.

Abstract
In this work, a remote curvature sensor using a standard OTDR as the interrogation system is presented. This approach uses a core diameter mismatch sensor which is formed by a short section of a multimode fiber, with a length of 3 mm, sandwiched between two singlemode fibers. In this case, the attenuation of the optical signal will vary as the fiber is bent allowing interrogating the sensor with OTDR technology. Preliminary results indicate a resolution range of similar to 0.0003 cm(-1), sensitivity in the range of similar to-208.46 dB/cm(-1) and a variation of 2.67 dB in the OTDR trace within the bend radius range.

Abstract
Breast Cancer is the most common type of cancer in women worldwide. In spite of this fact, there are insufficient studies that, using data mining techniques, are capable of helping medical doctors in their daily practice. This paper presents a comparative study of three ensemble methods (TreeBagger, LPBoost and Subspace) using a clinical dataset with 25% missing values to predict the overall survival of women with breast cancer. To complete the absent values, the k-nearest neighbor (k-NN) algorithm was used with four distinct neighbor values, trying to determine the best one for this particular scenario. Tests were performed for each of the three ensemble methods and each k-NN configuration, and their performance compared using a Friedman test. Despite the complexity of this challenge, the produced results are promising and the best algorithmconfiguration (TreeBagger using 3 neighbors) presents a prediction accuracy of 73%. © Springer International Publishing Switzerland 2014.

Abstract
Organizations are increasingly seeking to improve their efficiency and effectiveness to obtain reliable and high quality product at a lower cost. To achieve this objective, the use of tools that facilitate the acquisition of knowledge about processes is a valuable support. In maintenance, recording data about corrective actions is essential to define proactive actions that aim to avoid or mitigate the effects of future failures. Several computer systems are commercially available for maintenance management. However, the degree of suitability of these systems is low, once they do not consider organizations particularities. This paper presents a computer system for collecting and analyzing data from the machines of the production lines of a semiconductor company. This project aims to provide the company with a tool adapted to its needs, able to perform a behavioral analysis of all the machines of the production lines based on the systematic record of failures occurrence and maintenance activities.