Abstract
The development of automated individualized drug dosage regimens, namely in general anaesthesia environment, has been a subject of interest in the last decades. The use of continuous intravenous drug administration aims at, accurately, maintaining the system at a desired target effect concentration level. Different methods have been proposed for the design of individualized dosage regimens. In this study individual drug dose design is achieved through the characterization of transient initial response induced by a bolus administration of drug. This approach is based on the statistical analysis of the data using Walsh-Fourier spectral analysis which provides information about patient dynamics, allowing the on-line drug dose design using multiple linear least squares and quantile regression technics. The proposed methodology is illustrated in the case where the effect measured on the patient corresponds to the neuromuscular blockade (NMB) level and the drug to the muscle relaxant atracurium.

Abstract
This paper presents the participation of distributed generation and interruptible loads for Demand Side Management (DSM) in market environment. This contribution has two aspects, first, the technical requirements that should be implemented on these applications. Then the way DGs and ILs are offered in market and what should be included in their contracts. In this paper the above mentioned issues will be discussed and a database system will be presented by which a Disco can deal with interruptible loads and distributed generations simultaneously in its contracts.

Abstract
Loads estimation is becoming each time more fundamental for an efficient management and planning of electric distribution systems. Among the factors that contribute to this need of more efficiency are the increasing complexity of these networks, the deregulation process and the competition in an open energy market, and environment preservation requirements. However, the only information generally available at MV and LV levels is essentially of commercial nature, i.e., monthly energy consumption, hired power contracts and activity codes. In consequence, distribution utilities face the problem of estimating load diagrams to be used in planning and operation studies. The typical procedure uses measurements in typical classes of consumers defined by experts to construct inference engines that, most of the times, only estimate peak loads. In this paper, the definition of classes was performed by clustering the collected load diagrams. Artificial Neural Networks (ANN) were then used for load Curve estimation. This article describes the adopted methodology and presents some representative results. Performance attained is discussed as well as a method to achieve confidence intervals of the main predicted diagrams.

Abstract
This paper presents a new methodology for assessing both reliability and well-being indices for composite generation and transmission systems. Firstly, a transmission network reduction is applied to find an equivalent for assessing composite reliability for practical large power systems. After that, in order to classify the operating states, Artificial Neural Networks (ANNs) based on Group Method Data Handling (GMDH) techniques are used to capture the patterns of the operating states, during the beginning of the non-sequential Monte Carlo simulation (MCS). The idea is to provide the simulation process with an intelligent memory, based only on polynomial parameters, to speed up the evaluation of the operating states. For the conventional reliability assessment, the ANNs are used to classify the operating states into success and failure. However, for the well-being analysis, only success states are classified into healthy and marginal by the ANNs. The proposed methodology is applied to the IEEE Reliability Test System 1996 and to a configuration of the Brazilian South-Southeastern System.

Abstract
This paper reports new results in adopting entropy concepts to the training of mappers such as neural networks to perform wind power prediction as a function of wind characteristics (mainly speed and direction) in wind parks connected to a power grid. It also addresses the differences relevant to power system operation between off-line and on-line training of neural networks. Real case examples are presented.

Abstract
The integration in electric power networks of new renewable energy facilities is the final result of a complex planning process. One of the important objectives of this process is the selection of suitable geographical locations where such facilities can be built. This selection procedure can be a difficult task because of the initially opposing positions of the different agents involved in this procedure, such as, for example, investors, utilities, governmental agencies or social groups. The conflicting interest of the agents can delay or block the construction of new facilities. This paper presents a new decision support system, based on Geographic Information Systems, designed to overcome the problems posed by the agents and thus achieve a consensual selection of locations and overcome the problems deriving from their preliminary differing preferences. This paper presents the description of the decision support system, as well as the results obtained for two groups of agents useful for the selection of locations for the construction of new wind farms in La Rioja (Spain).