Abstract

Abstract
Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents' behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Network, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Abstract
This study provides a review of all-optical flip-flops (AOFFs) technologies, and their possible experimental implementation solutions, for a variety of applications in optical communication networks. A description of the state-of-the-art experimental implementations and validation testing of the technologies used in different AOFFs schemes is made, presenting to the interested reader an overview of the up to date AOFFs design schemes. Some of the research results presented in this study were performed under the EU NoE EURO-FOS project consortium. This study also provides researchers working on this topic with interesting trends that are worth considering in their own research studies.

Abstract
Sampling techniques play a key role in achieving efficient network measurements by reducing the amount of traffic processed while trying to maintain the accuracy of network statistical behavior estimation. Despite the evolution of current techniques regarding the correctness of network parameters estimation, the overhead associated with the volume of data involved in the sampling process is still considerable. In this context, this paper proposes a new technique for multiadaptive traffic sampling based on linear prediction, which allows to reduce significantly the traffic under analysis, keeping the representativeness of samples in capturing network behavior. A proof-of-concept, evaluating this technique for real traffic traces representing distinct traffic profiles, demonstrates the effectiveness of the proposal, outperforming classic techniques both in accuracy and data volumes processed. © 2012 Springer-Verlag.

Abstract
The evacuation of complex buildings is a challenge under any circumstances. Fire drills are a way of training and validating evacuation plans. However, sometimes these plans are not taken seriously by their participants. It is also difficult to have the financial and time resources required. In this scenario, serious games can be used as a tool for training, planning and evaluating emergency plans. In this paper a prototype of a serious games evacuation simulator is presented. To make the environment as realistic as possible, 3D models were made using Blender and loaded onto Unity3D, a popular game engine. This framework provided us with the appropriate simulation environment. Some experiences were made and results show that this tool has potential for practitioners and planners to use it for training building occupants.

Abstract
In this paper, two hybrid multimode/single mode fiber Fabry-Perot (FP) cavities were compared. The cavities fabricated by chemical etching are presented as high temperature and strain sensors. In order to produce this FP cavity a single mode fiber was spliced to a graded index multimode fiber with 62.5 mu m core diameter. The Fabry-Perot cavities were tested as a high temperature sensor in the range between room temperature and 700 C and as strain sensors. A reversible shift of the interferometric peaks with temperature allowed to estimate a sensitivity of 0.75 +/- 0.03 pm/degrees C and 0.98 +/- 0.04 pm/degrees C for the sensor A and B respectively. For strain measurement sensor A demonstrated a sensitivity of 1.85 +/- 0.07 pm/mu and sensor B showed a sensitivity of 3.14 +/- 0.05 pm/mu. The sensors demonstrated the feasibility of low cost fiber optic sensors for high temperature and strain.