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.

Identification of reliable reference genes for quantitative gene expression in the Mozambique tilapia, Oreochromis mossambicus

Dissertação de mest., Biologia Marinha (Aquacultura), Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2010

An overview of nonlinear identification and control with neural networks.

The aim of this chapter is to introduce background concepts in nonlinear systems identification and control with artificial neural networks. As this chapter is just an overview, with a limited page space, only the basic ideas will be explained here. The reader is encouraged, for a more detailed explanation of a specific topic of interest, to consult the references given throughout the text. Additionally, as general books in the field of neural networks, the books by Haykin [1] and Principe et al. [2] are suggested. Regarding nonlinear systems identification, covering both classical and neural and neuro-fuzzy methodologies, Reference 3 is recommended. References 4 and 5 should be used in the context of B-spline networks.

Identification of greenhouse climate dynamic models

In this paper climate discrete-time dynamic models for the inside air temperature of two different greenhouses are identified, using data acquired during two different periods of the year. These models employ data from air temperature and relative humidity (both outside and inside the greenhouse), solar radiation, wind speed, and control inputs (ventialtion, etc.).

Electrical characterization of electronic circuits produced by inkjet printing

Dissertação de Mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014