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
The aim of the present study was to develop a hierarchical bioclimatic model for forecasting olive crop yields in the Alentejo region of south-eastern Portugal. The model was estimated for three different developmental stages: (1) at flowering, using only the regional pollen index (RPI); (2) at fruit growth using RPI and a plant water requirements index (PWRI) and (3) at fruit maturing using RPI plus a water requirements index plus a phytopathological index (PPI). Olive airborne pollen was sampled from 1999 to 2007, using a Cour trap installed in Reguengos de Monsaraz. The meteorological parameters used in the calculation of the post-flowering indices corresponded to data from a meteorological station located near the airborne sampling point. At the flowering stage, 0.66 of the regional olive yield can be explained by the RPI with an average deviation between observed and predicted production of 0.15 for the forecast model internal validation and of 0.19 for the cross-validation. The addition of the variable PWRI to the forecasting model explained an additional 026 of the variation, while the PPI explained an additional 0.05. The final bioclimatic model, with all the three variables tested, explained 0.97 of the regional olive fruit yield being the average deviation between observed and predicted production of 0.04 for the internal validation of the model and of 0.07 for the external validation. The hierarchical nature of this bioclimatic model, along three different development stages, enabled the prediction to be updated as the growing season progressed.

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Abstract
Cutting and packing problems have been a core area of research for many decades. Irregular shape packing is one of the most recent variants to be widely researched and its history extends over 40 years. The evolution of solution approaches to this problem can be attributed to increased computer power and advances in geometric techniques as well as more sophisticated and insightful algorithm design. In this paper we will focus on the latter. Our aim is not to give a chronological account or an exhaustive review, but to draw on the literature to describe and evaluate the core approaches. Irregular packing is combinatorial and as a result solution methods are heuristic, save a few notable exceptions. We will explore different ways of representing the problem and mechanisms for moving between solutions. We will also propose where we see the future challenges for researchers in this area.

Abstract
Long (>30 years) monthly records of relative sea-level heights from tide gauges in the Baltic sea are analyzed. Time series clustering based on forecast densities is applied in order to describe regional sea-level variability in the Baltic Sea in terms of future relative heights. The tide gauge records are clustered on the basis of forecasts at 3-month and 6-month horizons. For the 3-month horizon, the results of the cluster analysis show a fairly spatial coherency in terms of grouping together locations from the same sub-basin, with the northern records in the Bothnian Sea and Gulf of Finland clustering together, followed by the tide gauges in the Baltic Proper and lastly the southernmost stations in the western Baltic. For the 6-month horizon, the results show a higher degree of homogeneity between different locations, but a clear separation between the stations at the Baltic entrance and the tide gauges inside the Baltic basin. Moreover, when considering detrended records, reflecting mainly the seasonal cycle, the clustering results are more homogeneous and suggest a distinct response of coastal sea-level in spring and in summer.

Abstract