Elisa Barros

Efeito do acipin sobre a degradabilidade e taxa de passagem de silagens de capim-elefante e de milho, em bovinos Holandês × Zebu

The effects of adding acipin to elephantgrass and corn silages on the passage rates of the solid and liquid phases of the ruminal digesta, and on degradabilities of dry matter (DM), crude protein (CP) and neutral detergent fiber (NDF) were evaluated. The animals with average live weight of 550kg, were kept in individual stalls for 90 days. The experiment was carried out in four experimental periods, according to a split plot arrangement, with 2×2 (two silages combined with absence or presence of acipin) factorial treatment combination in the plot and the time of collection of ruminal liquid in the split plot, in a completely randomized design with four replicates. The degradabilities of DM, CP and NDF in the elephantgrass silage without acipin was lower as compared to elephantgrass silage with acipin and to corn silage with or without acipin, that were similar. The passage rates of the ruminal solids were higher in animals fed on corn silage, and the ruminal liquid passage rates were higher in animals fed on elephantgrass silage. The addition of acipin improved the degradability of DM, CP and NDF of elephantgrass silage.

Clustering Techniques Applied on Cross-Sectional Unemployment Data

Using a cross-section database that observes the Portuguese labour market in two different phases of the business cycle, the present paper aims to address the issue of the segmentation of the Portuguese labour market taking into account the heterogeneity resulting from different unemployment characteristics observed along the Portuguese geographical space and applying two optimization clustering methods: the k-means and the spectral methods. The k-means is a traditional optimisation clustering method applied to cluster data observations. Spectral clustering is an alternative method based on the computation of the dominant eigenvectors of a matrix related with the distance among data points. The results obtained by the two methods are not identical but are very close and show that, apart the economic phase of the cycle, Portugal presents two very different profiles of registered unemployment. One of them can be considered problematic because it presents a higher percentage of unemployed women, long duration unemployed and unemployed with low levels of formal education—these are the groups that present more difficulties in the labour market and for which is more difficult to find a job after losing one. The segmentation of the labour market is a reality and the labour market is not adjusting to the business cycle.

Optimization Clustering Techniques on Register Unemployment Data

An important strategy for data classification consists in organising data points in clusters. The k-means is a traditional optimisation method applied to cluster data points. Using a labour market database, aiming the segmentation of this market taking into account the heterogeneity resulting from different unemployment characteristics observed along the Portuguese geographical space, we suggest the application of an alternative method based on the computation of the dominant eigenvalue of a matrix related with the distance among data points. This approach presents results consistent with the results obtained by the k-means.