Abstract
This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level a is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Abstract
This work describes the development and implementation of a grid of self-powered multi-functional probes (MFPz) for small-scale measurements of different soil properties, as being part of a wireless sensor network. The measurement principle is based on the heat-pulse method for soil moisture and water flux measurements and in a Wenner array for soil electrical conductivity. To promote the deployment of these sensing devices across large areas, such as irrigation fields, the ZigBee standard has been adopted as a multi-hop, ad-hoc network enabler. The core of the MTPz device is a wireless microcontroller (with a built-in ZigBee stack) that builds upon an IEEE 802.15.4 radio device. A 7.2Ah NiHM battery that is charged by a solar panel powers the MFPz device. Experimental results have proofed the reliability of the MFPz, regarding power consumption, connectivity and data agreement with known soil samples, as a cost-effective solution for environment monitoring.

Abstract
In this paper we study a heat diffusion system on a fractional calculus perspective. Bearing theses ideas in mind, several fractional PID tuning methodologies are investigated and compared. The simulations demonstrate the good performance of the proposed fractional-order algorithm.

Abstract
This paper presents two evolutionary schemes and a swarm intelligence algorithm for the design of combinational logic circuits. A Genetic and a Memetic schemes as the evolutionary algorithms. The Particle Swarm Optimization as the swarm algorithm. The fitness function used in these three algorithms is sequential, that is, divided in two parts. The first part of the fitness function f(1) evaluates the circuit functionality, while the second part f(2) deals with the circuit complexity. The experiments consist in applying the algorithms in the design of two arithmetic circuits: the one-bit full adder and the one-bit full subtractor. We also present a scalability analysis using the parity checker family of circuits.

Abstract
The internal impedance of a wire is the function of the frequency. In a conductor, where the conductivity is sufficiently high, the displacement current density can be neglected. In this case, the conduction current density is given by the product of the electric field and the conductance. One of the aspects of the high-frequency effects is the skin effect (SE). The fundamental problem with SE is it attenuates the higher frequency components of a signal. The SE was first verified by Kelvin in 1887. Since then many researchers developed work on the subject and presently a comprehensive physical model, based on the Maxwell equations, is well established. The Maxwell formalism plays a fundamental role in the electromagnetic theory. These equations lead to the derivation of mathematical descriptions useful in many applications in physics and engineering. Maxwell is generally regarded as the 19th century scientist who had the greatest influence on 20th century physics, making contributions to the fundamental models of nature. The Maxwell equations involve only the integer-order calculus and, therefore, it is natural that the resulting classical models adopted in electrical engineering reflect this perspective. Recently, a closer look of some phenomenas present in electrical systems and the motivation towards the development of precise models, seem to point out the requirement for a fractional calculus approach. Bearing these ideas in mind, in this study we address the SE and we re-evaluate the results demonstrating its fractional-order nature.

Abstract
This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity Evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical. evaluation. The dynamic-fitness function results from an analogy with control systems where it is possible to benefit the proportional algorithm by including a differential scheme. It is investigated the CA performance when adopting each concept separately. The experiments reveal superior results, in terms of speed and convergence of the number of iterations required to achieve a, solution. In a final phase the two concepts are integrated in the GA fitness function leading to the best performance.