DLTS investigation of acceptor states in P3MeT Schottky barrier diodes

Deep Level Transient Spectroscopy (DLTS) has been used to investigate hole traps in the depletion region of Schottky barrier diodes formed from electropolymerised poly(3-methylthiophene). The capacitance transients appear to be composed of a fast and a slow component. Analysis of the slower component using the ''rate window'' technique yields isochronal differential capacitance curves that depend on temperature in the manner predicted by theory.

Electronic levels in MEH-PPV

pn-Junctions of MEH-PPV on top of heavily doped n-type silicon were used in electrical measurements. Through deep-level transient-spectroscopy (DLTS)-like measurements, four traps (two majority and two minority traps) could be identified on top of the shallow acceptor level responsible for conduction. Furthermore, evidence is found for interface states. (C) 2000 Published by Elsevier Science S.A. All rights reserved.

Estimation of unemployment rates in small areas of Portugal: a best linear unbiased prediction approach versus a hierarchical Bayes approach

The high level of unemployment is one of the major problems in most European countries nowadays. Hence, the demand for small area labor market statistics has rapidly increased over the past few years. The Labour Force Survey (LFS) conducted by the Portuguese Statistical Office is the main source of official statistics on the labour market at the macro level (e.g. NUTS2 and national level). However, the LFS was not designed to produce reliable statistics at the micro level (e.g. NUTS3, municipalities or further disaggregate level) due to small sample sizes. Consequently, traditional design-based estimators are not appropriate. A solution to this problem is to consider model-based estimators that "borrow information" from related areas or past samples by using auxiliary information. This paper reviews, under the model-based approach, Best Linear Unbiased Predictors and an estimator based on the posterior predictive distribution of a Hierarchical Bayesian model. The goal of this paper is to analyze the possibility to produce accurate unemployment rate statistics at micro level from the Portuguese LFS using these kinds of stimators. This paper discusses the advantages of using each approach and the viability of its implementation.

Supervised training approach using spiking neural networks

One of the basic aspects of some neural networks is their attempt to approximate as much as possible their biological counterparts. The goal is to achieve a simple and robust network, easy to understand and able of simulating the human brain at a computational level. Recently a third generation of neural networks (NN) [1], called Spiking Neural Networks(SNN) was appeared. This new kind of networks use the time of a electrical pulse, or spike, to encode the information. In the first and second generation of NN analog values are used in the communication between neurons.

Applying the ecohydrology approach to the Guadiana estuary and coastal areas: lessons learned from dam impacted ecosystems

Ecohydrology is a scientific concept applied to problem- solving in environmental issues. It recognises that the present practice of relying nearly exclusively on engineering fixes to solve environmental problems is failing to restore the aquatic environment to a level that can sustain the quality of life that people are demanding. Ecohydrology is based on the ability of science to quantify and explain the relationships between hy- drological processes and biotic dynamics at a catchment scale and to manipulate these processes to increase the robustness of the aquatic system and thus its ability to cope with human- induced stresses. The concept was developed by the UNESCO International Hydrologic Programme (IHP) and the Man and Biosphere Programme (MAB).

Combined disaggregation of agricultural land uses, livestock numbers and crops' production: an entropy approach

This paper presents several combined agricultural data disaggregation models in order to recover the farms' land uses, the livestock numbers and main crops' productions. The proposed approach estimates incomplete information at disaggregated level through entropy, using an information prior, and generating information for a combined calculation use of data in the estimation of other variables. The models were applied to the region of Algarve, to some rural pilot areas (Salir-Ameixial-Cachopo and Alcoutim) for livestock data, since this data in some Algarve's inland areas is needed for a European forest fire prevention project, and to the agrarian zones in a more complex framework. The results are promising. They were validated, in cross reference to real data, having proven to be valid and reliable. The total error was small and a considerable level of information heterogeneity was recovered. The total error was about 27,9% for the counties' land uses and 21% for the agrarian zones, and for the livestock it was also acceptable. The level of heterogeneity recovered was always higher than 50%, revealing some improvements regarding previous studies.

Contamination of farmed gilthead sea bream (Sparus aurata, Linnaeus, 1758) by the parasite Amyloodinium ocellatum: a proteomic approach

Dissertação de mestrado, Aquacultura e Pescas, Faculdade de Ciências e Tecnologia, Uniersidade do Algarve, 2015