860 resultados para two-stage sampling


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High-resolution polymerase chain reaction using sequence-specific oligonucleotide probes (PCR-SSOP) typing methods for HLA-A identification have been established. The four systems, which operate independently of each other, are intended for use as secondary typing systems following HLA-A identification with a medium-resolution PCR-SSOP technique. The systems, all using digoxigenin-labelled probes, are based on group specific amplifications for resolution of: i) HLA-A*29 & -A*33; ii) HLA-A*24 & -A*30; and iii) HLA-A*26, -A*25, -A*11, -A*34, -A*66 and -A*68 alleles, respectively. The fourth system, for the detection of HLA-A*02 alleles, is a modification of a previously reported PCR-SSOP subtyping system. The methods have been applied to individuals from the local bone marrow registry and HLA-A allele frequencies for the Northern Ireland population have been established.

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This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

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The study explores the application of a two-stage electrokinetic washing system on remediation of lead (Pb) contaminated soil. The process involved an initial soil washing, followed by an electrokinetic process. The use of electrokinetic process in soil washing not only provided additional driving force for transporting the desorbed Pb away from the soil but also reduced the high usage of wash solution. In this study, the effect of NaNO3, HNO3, citric acid and EDTA as wash solutions on two-stage electrokinetic washing system were evaluated. The results revealed that a two-stage electrokinetic washing process enhanced Pb removal efficiency by 2.52-9.08% and 4.98-20.45% in comparison to a normal electrokinetic process and normal washing process, respectively. Low pH and adequate current were the most important criteria in the removal process as they provided superior desorption and transport properties. The effect of chelating by EDTA was less dominant as it delayed the removal process by forming a transport loop in anode region between Pb ion and complexes. HNO3 was not suitable as wash solution in electrokinetic washing in spite of offering highest removal efficiency as it caused pH fluctuation in the cathode chamber, corroded graphite anode and showed high power consumption. In contrast, citric acid not only yielded high Pb removal efficiency with low power consumption but also maintained a low soil: solution ratio of 1 g: <1 mL, stable pH and electrode integrity. Possible transport mechanisms for Pb under each wash solution are also discussed in this work.

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A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

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We present a new regime to generate high-energy quasimonoenergetic proton beams in a "slow-pulse" regime, where the laser group velocity vg<c is reduced by an extended near-critical density plasma. In this regime, for properly matched laser intensity and group velocity, ions initially accelerated by the light sail (LS) mode can be further trapped and reflected by the snowplough potential generated by the laser in the near-critical density plasma. These two acceleration stages are connected by the onset of Rayleigh-Taylor-like (RT) instability. The usual ion energy spectrum broadening by RT instability is controlled and high quality proton beams can be generated. It is shown by multidimensional particle-in-cell simulation that quasimonoenergetic proton beams with energy up to hundreds of MeV can be generated at laser intensities of 1021W/cm2.

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A Masters Thesis, presented as part of the requirements for the award of a Research Masters Degree in Economics from NOVA – School of Business and Economics

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I provide choice-theoretic foundations for a simple two-stage model, called transitive shortlist methods, where choices are made by sequentially by applying a pair of transitive preferences (or rationales) to eliminate inferior alternatives. Despite its simplicity, the model accommodates a wide range of choice phenomena including the status quo bias, framing, homophily, compromise, and limited willpower. I establish that the model can be succinctly characterized in terms of some well-documented context effects in choice. I also show that the underlying rationales are straightforward to determine from readily observable reversals in choice. Finally, I highlight the usefulness of these results in a variety of applications.

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Two stage processes consisting of precursor preparation by thermal evaporation followed by chalcogenisation in the required atmosphere is found to be a feasible technique for the PV materials such as n-Beta In2S3, p-CulnSe2, p-CulnS2 and p-CuIn(Sel_xSx)2. The growth parameters such as chalcogenisation temperature and duration of chalcogenisation etc have been optimised in the present study.Single phase Beta-In2S3 thin films can be obtained by sulfurising the indium films above 300°C for 45 minutes. Low sulfurisation temperatures required prolonged annealing after the sulfurisation to obtain single phase Beta-1n2S3, which resulted in high material loss. The maximum band gap of 2.58 eV was obtained for the nearly stoichiometric Beta-In2S3 film which was sulfurised at 350°C. This wider band gap, n type Beta-In2S3 can be used as an alternative to toxic CdS as window layer in photovoltaics .The systematic study on the structural optical and electrical properties of CuInSe2 films by varying the process parameters such as the duration of selenization and the selenization temperature led to the conclusion that for the growth of single-phase CuInSe2, the optimum selenization temperature is 350°C and duration is 3 hours. The presence of some binary phases in films for shorter selenization period and lower selenization temperature may be due to the incomplete reaction and indium loss. Optical band gap energy of 1.05 eV obtained for the films under the optimum condition.In order to obtain a closer match to the solar spectrum it is desirable to increase the band gap of the CulnSe2 by a few meV . Further research works were carried out to produce graded band gap CuIn(Se,S)2 absorber films by incorporation of sulfur into CuInSe2. It was observed that when the CulnSe2 prepared by two stage process were post annealed in sulfur atmosphere, the sulfur may be occupying the interstitial positions or forming a CuInS2 phase along with CuInSe2 phase. The sulfur treatment during the selenization process OfCu11 ln9 precursors resulted in Culn (Se,S)2 thin films. A band gap of 1.38 eV was obtained for the CuIn(Se,S)2.The optimised thin films n-beta 1n2S3, p-CulnSe2 and p-Culn(Sel-xSx)2 can be used for fabrication of polycrystalline solar cells.