Improved extraction of arabinoxylans from rye bran and production of health beneficial oligosaccharides

Dissertação de mest., Engenharia Biológica, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2011

By-catch and discarding practices in five Algarve (southern Portugal) metiers

The catches and discards of trawlers, seiners and trammel netters were studied in the Algarve (southern Portugal) using observers onboard commercial fishing vessels. Species diversity was high, with 236 species recorded as occasionally, frequently or regularly discarded. Mean discard rates per trip were 0.13, 0.20, 0.27, 0.62 and 0.70, respectively, for trammel nets, demersal purse seines, pelagic purse seines, fish trawls and crustacean trawls, with high variability in terms of discard volume and discard rate, This was especially so for se ncrs where from 0 to 100% of the total catch might be discarded. No significant relationships were found between discard quantity or discard rate and characteristics of the fishing vessels sampled within each metier (total length, TAB, hp, kW). Based on the above discard rates and the official landings, it was estimated that in 1996, Algarve trawlers discarded 9000-13000 tonnes (t) while seiners discarded 5500-8200 t. Discarding practices in these fisheries are reviewed and the reasons for discarding are presented by species and metier basis. The results support the need for more studies on the factors contributing to discarding, variability in discard quantities and rates, the fate of discards and their importance to the marine ecosystem, and on ways of reducing by-catch and discarding in these fisheries.

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.