Abstract
The detailed study of folding and unfolding events in proteins is becoming central to develop rational therapeutic strategies against maladies such as Alzheimer and Parkinson disease. A promising approach to study the unfolding processes of proteins is through computer simulations. However, these computer simulations generate huge amounts of data that require computational methods for their analysis. In this paper we report on the use of Inductive Logic Programming (ILP) techniques to analyse the trajectories of protein unfolding simulations. The paper describes ongoing work on one of several problems of interest in the protein unfolding setting. The problem we address here is that of explaining what makes secondary structure elements to break down during the unfolding process. We tackle such problem collecting examples of contexts where secondary structures break and (automatically) constructing rules that may be used to suggest the explanations.

Abstract
Organ transplantation is a highly complex decision process that requires expert, decisions. The major problem ill a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straightforward task because a complex network of relations exist's between the immunological and the clinical variables that, influence the receivers acceptance of the transplanted organ. Currently the process of analyzing these variables involves a careful study by the clinical transplant team. The number and complexity of the relations between variables make the manual process very slow. Ill this paper we propose and compare two Machine Learning algorithms that might help the transplant team ill improving and Speeding up their decisions. We achieve that objective by analyzing past real cases and constructing models as set, of rules. Such models are accurate and understandable by experts.

Abstract
To get the most out of powerful tools, expert knowledge is often required. Experts are the ones with the suitable knowledge to tune the tools' parameters. In this paper we assess several techniques which can automatically fine-tune ANN parameters. Those techniques include the use of GA and stratified sampling. The fine-tuning includes the choice of the best ANN structure and the best network biases and their weights. Empirical results achieved in experiments performed using nine heterogeneous data sets show that the use of the proposed Stratified Sampling technique is advantageous. © 2009 Springer Science+Business Media, LLC.

Abstract
Climatic change emphasize the importance of biodiversity maintenance, Suggesting that germplasm adapted to organic, low input, or conventional conditions is needed to face future demands. This Study presents: I - The two steps genesis of the synthetic maize population 'Fandango', A) 'NUTICA' creation: in 1975, Miguel Mota and Silas Pego, initiated a new type of polycross method involving 77 yellow elite inbred lines (dent and flint; 20% Portuguese and 80% North American germplasm) from the NUMI programme (NUcleo de melhoramento de Milho, Braga, Portugal). These inbreds were intermated in natural isolation and progenies submitted to intensive selection for both parents during continued cycles; B) From 'NUTICA' to 'Fandango': Tandango' was composed of all the crosses that resulted from a North Carolina Design I matting design (1 male crossed with 5 females) applied to 'NUTICA'. II - The diversity evolution of 'Fandango' under a Participatory Breeding project at the Portuguese Sousa Valley region (VASO) initiated in 1985 by Pego, with CIMMYT support. Morphological, fasciation expression, and yield trials were conducted in Portugal (3 locations, 3 years) and in the USA (4 locations, I year) using seeds obtained from five to seven cycles of mass selection (MS). The selection across cycles wits clone by the breeder (until cycle 5) and farmer (before cycle II in present). ANOVA and regression analysis on the rate of direct response to selection were performed when the assumption of normality was positively confirmed. Otherwise the non parametric Multivariate Adaptive Regression Splines (MARS) was performed. Response to mass selection in lowa showed significant decrease in yield, while in Portugal a significant increase for time of silking, plant and ear height, ear diameters 2, 37 4, kernel number, cot) diameters, and rachis was observed. At this location also a significant decrease was observed for thousand kernel weight and ear length. These results showed that mass selection were not effective for significant yield increase, except when considered Lousada with breeder selection (3.09% of gain per cycle per year). Some non-para metric methods (MARS, decision trees and random forests) were used to get insights on the causes that explain yield in Fandango. Kernel weight and ear weight were the most important traits, although row numbers, number of kernels per row, ear length, and ear diameter were also of some importance influencing 'Fandango' yield.

Abstract
We propose a two-layer scheme to control a set of vehicles moving in a formation. The first; layer, file trajectory controller, is a nonlinear controller since most vehicles are nonholonomic systems and require a nonlinear, even discontinuous, feedback to stabilize them. The trajectory controller, a model predictive controller, computes centrally a bang-bang control law and only a small set of parameters need to be transmitted to each vehicle at each iteration. The second layer, the formation controller, aims to compensate for small changes around a nominal trajectory maintaining the relative positions between vehicles. We argue that; the formation control call be, in most; cases, adequately carried out, by a linear model predictive controller accommodating input, and state constraints. This has the advantage that the control laws for each vehicle are simple piecewise affine feedback laws that, call be pre-computed off-line and implemented in a, distributed way in each vehicle. Although several optimization problems have to be solved, the control strategy proposed results in a simple and efficient; implementation where no optimization problem needs to be solved in real-time at each vehicle.

Abstract
We report on the development of a Genetic Algorithm (GA), which has been integrated into a Decision Support System to plan the best assignment of the weekly self-promotion space for a TV station. The problem addressed consists on deciding which shows to advertise and when such that the number of viewers, of an intended group or target, is maximized. The GA proposed incorporates a greedy heuristic to find good initial solutions. These solutions, as well as the solutions later obtained through the use of the GA, go then through a repair procedure. This is used with two objectives, which are addressed in turn. Firstly, it checks the solution feasibility and if unfeasible it is fixed by removing some shows. Secondly, it tries to improve the solution by adding some extra shows. Since the problem faced by the commercial TV station is too big and has too many features it cannot be solved exactly. Therefore, in order to test the quality of the solutions provided by the proposed GA we have randomly generated some smaller problem instances. For these problems we have obtained solutions on average within 1% of the optimal solution value.