Identification of descendants of an extinct bovine population from the Algarve region of Portugal using numerical taxonomy analysis of morphological traits

The morphology of a sample of four bulls and 43 cows, presumed to be descendants of the extinct cattle breed ‘Algarvia’ (AG), was used to assign their relationship with animals from other Portuguese autochthonous breeds – Arouquesa (AR), Barrosa˜ (BA), Cachena (CA), Marinhoa (MA), Maronesa (MO), Minhota (MN), Mirandesa (MI), (only bulls), Alentejana (AL), Garvonesa (GA), Mertolenga (ME) and Preta (PR). Standard numerical taxonomic methods were applied to a set of 183 (cows) and 170 (bulls) traits, to derive average pairwise taxonomic distances among the sample of 257 cows and 76 bulls. Distance coefficients (morphological index of distance) ranged from 0.22 to 2.62 (cows) and from 0.49 to 2.13 (bulls). Unweighted pair group method using arithmetic averages (UPGMA)-based phenograms and a principal coordinate analysis showed that bulls were highly clustered and cows showed a tendency to cluster according to their geographical and breed origin. The AG population grouped together with GA, AL, ME and MN breeds in the Red Convex group. The average taxonomic distance among breeds was 1.02, the highest being 1.39 (ME versus BA) and the lowest being 0.64 (MA versus AR). The approach allowed for the identification of a phenotypically differentiated set of animals, comprising 19 cows and four bulls representative of the AG breed, and which can be targeted in further studies aiming at the recovery of this extinct breed.

A neural network seismic detector

This experimental study focuses on a detection system at the seismic station level that should have a similar role to the detection algorithms based on the ratio STA/LTA. We tested two types of neural network: Multi-Layer Perceptrons and Support Vector Machines, trained in supervised mode. The universe of data consisted of 2903 patterns extracted from records of the PVAQ station, of the seismography network of the Institute of Meteorology of Portugal. The spectral characteristics of the records and its variation in time were reflected in the input patterns, consisting in a set of values of power spectral density in selected frequencies, extracted from a spectro gram calculated over a segment of record of pre-determined duration. The universe of data was divided, with about 60% for the training and the remainder reserved for testing and validation. To ensure that all patterns in the universe of data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. The best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favorably with the ones obtained by the existing detection system, 50%.

On-line operation of an intelligent seismic detector

This study describes the on-line operation of a seismic detection system to act at the level of a seismic station providing similar role to that of a STA /LTA ratio-based detection algorithms. The intelligent detector is a Support Vector Machine (SVM), trained with data consisting of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network's stations of the Institute of Meteorology of Portugal (IM). Records' spectral variations in time and characteristics were reflected in the SVM input patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. After having been trained, the proposed system was experimented in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The same type of ANN presented 88.4 % and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM. © 2013 Springer-Verlag Berlin Heidelberg.