Abstract
This paper presents a method used to validate a spatial load forecasting model based on fuzzy systems implemented in a Geographical Information System. The validation process confirms the adequacy of the rule base, and also it is strictly necessary to define the confidence intervals associated to the predicted spatial demand.

Abstract
Ambitious targets for renewable power production have been defined for the electric power systems in Europe. The accomplishment of these targets requires the increase in renewable energy production, namely from wind power generation. However, the intermittent nature of wind creates several problems to the power system operation and new approaches based on the combined use of wind power and energy storage technologies need to be developed. In this paper, the concept of the combined use of wind power production and hydro storage/production is exploited, through the development of an operational optimisation approach applied to a wind generator park with little water storage ability. The optimisation model defines the operational strategy to be followed for the hours ahead by a pump station and an hydraulic generator embedded in a wind/hydro pumping facility, using the Portuguese energy remuneration rules. The proposed methodology leads to considerable yearly profits for the wind generator production.

Abstract

Abstract
Finding one's way around an unfamiliar city can be a major challenge. While maps can provide a very good abstract representation of our world, and a simple and efficient way to navigate within that world, they are of little use when, for example, the absence of road signs prevents us from locating where we are on the map. Mobile devices offer the potential for providing relevant 3D information to enable us to locate ourselves, rapidly navigate around an unfamiliar environment and explore it interactively. However, mobile devices are constrained by resources such as bandwidth, storage and small displays. In this paper we investigate which is the most important visual information for position location within an unfamiliar urban environment and show how we can use this knowledge to provide a perceptually high quality 3D virtual environment on existing mobile devices. Copyright 2004 ACM.

Abstract
In experimental peripheral nerve studies, the rat sciatic nerve model is widely used to examine functional changes after different surgical repairs or pharmacological treatments, following nerve injury. The number and diversity of tests which have been used to assess functional recovery after experimental interventions often makes it difficult to recommend any particular indicator of nerve regeneration. Functional assessment after sciatic nerve lesion has long been focused on walking track analysis, therefore, this article describes in more detail the method to obtain and measure the walking tracks in order to calculate the sciatic functional index (SFI). However, it is important to note that the validity of the SFI has been questioned by several researchers. In addition, the present review includes other traditional tests described in the experimental peripheral nerve literature regarding the rate of return of motor function and sensation, such as the extensor postural thrust (EPT), nociceptive function, and the gastrocnemius-soleus weight parameters. In the last decade, several authors have designed a series of sensitive quantitative methods to assess the recovery of hind limb locomotor function using computerized rat gait analysis. This study aims to review kinematic measures that can be gathered with this technology, including calculation of sciatic functional index, gait-stance duration, ankle kinematics and toe out angle (TOA). A combination of tests, each examining particular components of recovered sensorimotor function is recommended for an overall assessment of rat sciatic nerve regeneration.

Abstract
This paper describes a method that can be seen as an improvement of, the standard progressive sampling. The standard method uses samples of data of increasing size until accuracy of the learned concept cannot be further improved. The issue we have addressed here is how to avoid using some of the samples in this progression. The paper presents a method for predicting the stopping point using a meta-learning approach. The method requires just four iterations of the progressive sampling. The information gathered is used to identify the nearest learning curves, for which the sampling procedure was carried out fully. This in turn permits to generate the prediction regards the stopping point. Experimental evaluation shows that the method can lead to significant savings of time without significant losses of accuracy.