Abstract
This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

Abstract
Partner selection in virtual enterprises (VE) can be viewed as a multi-criteria decision making problem that involves assessing trade-offs between conflicting tangible and intangible criteria. In general, this is a very complex problem due to the dynamic topology of the network, the large number of alternatives and the different types of criteria. In this paper we propose an exploratory process to help the decision-maker obtain knowledge about the network in order to identify the criteria and the companies that best suit the needs of each particular project. This process involves a multi-objective tabu search metaheuristic designed to find a good approximation of the Pareto front, and a fuzzy TOPSIS algorithm to rank the alternative VE configurations. In the exploratory phase we apply clustering analysis to confine the search according to the decision-maker beliefs, and case base reasoning, an artificial intelligence approach, to totally or partially construct VEs by reusing past experiences. Preliminary computational results clearly demonstrate the potential of the approach for practical application.

Abstract
Access control models describe frameworks that dictate how subjects (e.g. users) access resources. In the Role-Based Access Control (RBAC) model access to resources is based on the role the user holds within the organization. RBAC is a rigid model where access control decisions have only two output options: Grant or Deny. Break The Glass (BTG) policies on the other hand are flexible and allow users to break or override the access controls in a controlled and justifiable manner. The main objective of this paper is to integrate BTG within the NEST/ANSI RBAC model in a transparent and secure way so that it can be adopted generically in any domain where unanticipated or emergency situations may occur. The new proposed model, called BTG-RBAC, provides a third decision option BTG, which grants authorized users permission to break the glass rather than be denied access. This can easily be implemented in any application without major changes to either the application code or the RBAC authorization infrastructure, apart from the decision engine. Finally, in order to validate the model, we discuss how the BTG-RBAC model is being introduced within a Portuguese healthcare institution where the legislation requires that genetic information must be accessed by a restricted group of healthcare professionals. These professionals, advised by the ethical committee, have required and asked for the implementation of the BTG concept in order to comply with the said legislation.

Abstract
Heart rate variability (HRV) data display nonstationary characteristics, exhibit long-range correlations (memory) and instantaneous variability (volatility). Recently, we have proposed fractionally integrated autoregressive moving average (ARFIMA) models for a parametric alternative to the widely-used technique detrended fluctuation analysis, for long memory estimation in HRV. Usually, the volatility in HRV studies is assessed by recursive least squares. In this work, we propose an alternative approach based on ARFIMA models with generalized autoregressive conditionally heteroscedastic (GARCH) innovations. ARFIMA-GARCH models, combined with selective adaptive segmentation, may be used to capture and remove long-range correlation and estimate the conditional volatility in 24 hour HRV recordings. The ARFIMA-GARCH approach is applied to 24 hour HRV recordings from the Noltisalis database allowing to discriminate between the different groups.

Abstract
This paper presents an automated design procedure of radio-frequency integrated CMOS discrete timing varactors (RFDTVs). This new method use the maximin and the particle swarm optimization (PSO) algorithms to promote well distributed non-dominated fronts in the parameters space when a single-objective function is optimized. The fitness function used in the search tool is proportional to the RFDTV quality factor. The outcome of the automated design method comprises a set of RFDTV circuits, all having the same maximum performance. Each solution, which corresponds to one RFDTV circuit, is defined by the number of cells and by the circuit components values. This approach allows the designer to choose among several possible circuits the one that is easier to implement in a given CMOS process. To validate the effectiveness of the synthesis procedure proposed in this paper (PSO-method) comparisons with a design method based on genetic algorithms (GA-method) are presented. A 0.18 mu m CMOS radio-frequency binary-weighted differential switched capacitor array (RFDSCA) was designed and implemented (the RFDSCA is one of the possible topologies of the RFDTVs). The results show that both design methods are in very good agreement. However, the PSO technique outperforms the GA-method in the design procedure run time taken to accomplish the same performance results.

Abstract
The digital equalization of fibre impairments in coherent optical communications allows for ultimate limits of spectral efficiency to be achieved, while reducing the cost and complexity compared to optical based solutions. Digital backpropagation (BP) was proposed recently as a reduced complexity approach for jointly compensating linear and non-linear impairments in single polarization coherent optical systems. Furthermore, polarization division multiplexed quadrature phase-shift keying (PDM-QPSK) has emerged as a promising format to increase spectral efficiency. Here we assess the performance of the simplified backpropagation equalization in both single channel and multiple channel transmission in combination with polarization multiplexing for different dispersion map configurations. It is shown that polarization multiplexing induces a degradation in performance due to cross-phase modulation polarization scattering, the backpropagation algorithm still overperforming linear equalization.