Abstract
This paper presents the implementation of a real-time adaptive controller to regulate the air temperature, humidity and C02 concentration for a greenhouse located in the north of Portugal. The controller outputs are computed in real-time with the aim of achieving1, adequate set-point tracking, minimization of the actuators efforts and minimization of the energy inputs for heating, cooling and CO2injection. This is done by computing a sequence of future control actions to minimize a cost function over a prediction horizon of one hour. The cost function is proportional to the sum of the squared errors between the predicted and desired greenhouse climates plus the square of the energy inputs computed over the prediction horizon. The prediction climate models employ data from the air temperature, relative humidity and carbon dioxide concentration, inside and outside the greenhouse, solar radiation, wind speed and control actuating signals. In this way the performance of the adaptive controller depends largely on the accuracy of the prediction models. Since the greenhouse-crop system is time-variant, recursive identification techniques were applied to adapt in real-time the models parameters. Several simulations and experiments were realized with the proposed real-time adaptive controller and the results achieved proved to be suitable for this application. Also, the controller performance was compared with the one achieved using common PID, Proportional-Integrative-Derivative, controllers. With the proposed algorithm the set-point tracking and the minimization of actuator effort and energy consumption are significantly improved.

Abstract
Agricultural Engineers and Biologists often need to develop and use functions in order to explain observed events, perform predictions under different scenarios, find relevant characteristics of the data, such as growth rates, etc.. The purpose of curve fitting is to specify an appropriate function and adjust its parameters in a way that it matches, as close as possible, an experimental or historical data set. Fitting functions can be derived by applying regression techniques, interpolation, and spline curves. This paper deals with the development and application of linear and nonlinear regression techniques for fitting functions to agricultural and biological data sets. The application and utility of these fitting techniques are illustrated using experimental sets of data. The examples cover from simple linear curve fitting techniques, which use least squares algorithms, to complex real world problems, which must be described by functions that are nonlinear in the parameters and so needs the use of nonlinear regression techniques. In these illustrative examples, a particular attention is paid to the validation and adequacy of the fitting functions, using graphical inspection and numerical performance criteria's, such as: statistical analysis of the residuals, the R-square, adjusted R-square, the RMSE-Root mean squared errors, confidence bounds for the estimated function parameters and prediction bounds for the fitted functions.

Abstract
A genetic algorithm (GA) is a search technique based on the natural selection process. The GAs provide further flexibility and robustness that are unique for signal process. Recently, a closer look of some phenomena present in electrical systems, such as motors, transformers and lines, and the motivation towards the development of comprehensive models, seem to point out the requirement for a fractional calculus approach. Bearing these ideas in mind, we address the analysis and the synthesis of fractional-order multipoles, based in a GA optimization scheme. © 2006 Civil-Comp Press.

Abstract
The Maxwell equations constitute a formalism for the development of models describing electromagnetic phenomena. The four Maxwell laws have been adopted successfully in many applications and involve only the integer order differential calculus. Recently, a closer look for the cases of transmission lines, electrical motors and transformers, that reveal the socalled skin effect, motivated a new perspective towards the replacement of classical models by fractional-order mathematical descriptions. Bearing these facts in mind this paper addresses the concept of static fractional electric potential. The fractional potential was suggested some years ago. However, the idea was not fully explored and practical methods of implementation were not proposed. In this line of thought, this paper develops a new approximation algorithm for establishing the fractional order electrical potential and analyzes its characteristics.

Abstract
This paper describes the goals and some of the results obtained in the project entitled "Computerised Management of Greenhouses", that has started in November 1995. This project has a duration of three years and is sponsored by JNICT (Portuguese Institute for Scientific Research Funding). At present, the experimental set-up is composed of a greenhouse with a floor area of 210m(2) equipped with several actuators and sensors that are connected to an acquisition and control system developed at the Engineering Department of UTAD University. The set-points for the air temperature and humidity, CO2 concentration, soil moisture and ambient radiation inside the greenhouse are calculated on-line as a function of the greenhouse climate, outside weather and the specified optimal set of values for the plants, in this case tomato. The results show that the climate control system performs well and that has been achieved a reduction of the energy consumption compared with the case when the set-points are fixed. In the second year it will be implemented a distributed control system for monitoring and control the environment of four greenhouses located at the University campus. Also, in one of the greenhouses, will be used renewable energies such as the solar photovoltaic and the solar thermal energies. The first is to deliver power to the electronic equipment and to some of the actuators and the second for heating the greenhouses. During this period and the third year it will be implemented and compared different real-time control strategies with respect to set-point accuracy and energy consumption.

Abstract