Abstract
The use of black white (BW) air photographs for the production of historic land cover maps can be done by image classification, using additional texture features. In this paper we evaluate the importance of a number of parameters in the image classification process based on texture, such as the window size, angle and distance used to produce the texture features, the number of features used, the image quantization level and its spatial resolution. The evaluation was performed using five photographs from the 1950s. The influence of the classification method, the number of classes searched for in the images and the post-processing tasks were also investigated. The effect of each of these parameters for the classification accuracy was evaluated by cross-validation. The selection of the best parameters was performed based on the validation results, and also on the computation load involved for each case and the end user requirements. The final classification results were good (average accuracy of 85.7%, k=0.809) and the method has proven to be useful for the production of historic land cover maps from BW air photographs.

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Abstract
Algae blooms are ecological events associated with extremely high abundance value of certain algae. These rare events have a strong impact in the river's ecosystem. In this context, the prediction of such events is of special importance. This paper addresses the problems that result from evaluating and comparing models at the prediction of rare extreme values using standard evaluation statistics. In this context, we describe a new evaluation statistic that we have proposed in Torgo and Ribeiro [Torgo, L., Ribeiro, R., 2006. Predicting rare extreme values. In: Ng, W, Kitsuregawa, M., Li, J., Chang, K. (Eds.), Proceedings of the loth Pacific-Asia Conference on Knowledge Discover and Data Mining (PAKDD'2006). Springer, pp. 816-820 (number 3918 in LNAI)], which can be used to identify the best models for predicting algae blooms. We apply this new statistic in a comparative study involving several models for predicting the abundance of different groups of phytoplankton in water samples collected in Douro River, Porto, Portugal. Results show that the proposed statistic identifies a variant of a Support Vector Machine as outperforming the other models that were tried in the prediction of algae blooms.

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Structural change analysis has an important tradition in economic theory. However, up to the present date, no attempt had been made to provide an overall survey on the matter. This paper aims to fill this gap. To this end, bibliometric methods were applied, combining 9703 citations from the area's 'seed journal' with a review of 910 abstracts of all theoretical and empirical articles on structural change that were published over the past 40 years in the journals indexed in the Econlit. We testify the recent rise of interest in structural change where technological issues gained increasing relevance. The 1990s witnessed a spurt in formal work, but more recently such trend was not confirmed; on the contrary, there has been a strong impetus towards empirically led work. Our analysis further reveals that most contributions put great emphasis on technology-driven growth and lack an appropriate treatment of the demand side.

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