40 resultados para Differential Item Function

em CentAUR: Central Archive University of Reading - UK


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Hidden Markov Models (HMMs) have been successfully applied to different modelling and classification problems from different areas over the recent years. An important step in using HMMs is the initialisation of the parameters of the model as the subsequent learning of HMM’s parameters will be dependent on these values. This initialisation should take into account the knowledge about the addressed problem and also optimisation techniques to estimate the best initial parameters given a cost function, and consequently, to estimate the best log-likelihood. This paper proposes the initialisation of Hidden Markov Models parameters using the optimisation algorithm Differential Evolution with the aim to obtain the best log-likelihood.

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A scale-invariant moving finite element method is proposed for the adaptive solution of nonlinear partial differential equations. The mesh movement is based on a finite element discretisation of a scale-invariant conservation principle incorporating a monitor function, while the time discretisation of the resulting system of ordinary differential equations is carried out using a scale-invariant time-stepping which yields uniform local accuracy in time. The accuracy and reliability of the algorithm are successfully tested against exact self-similar solutions where available, and otherwise against a state-of-the-art h-refinement scheme for solutions of a two-dimensional porous medium equation problem with a moving boundary. The monitor functions used are the dependent variable and a monitor related to the surface area of the solution manifold. (c) 2005 IMACS. Published by Elsevier B.V. All rights reserved.

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The effects of temperature and light integral on fruit growth and development of five cacao genotypes (Amelonado, AMAZ 15/15, SCA 6, SPEC 54/1 and UF 676) were studied in semi-controlled environment glasshouses in which the thermal regimes of cacao-growing regions of Brazil, Ghana and Malaysia were simulated. Fruit losses because of physiological will (cherelle will) were greater at higher temperatures and also differed significantly between genotypes, reflecting genetic differences in competition for assimilates between vegetative and reproductive components. Short-term measurements of fruit growth indicated faster growth rates at higher temperatures. In addition, a significant negative linear relationship between temperature and development time was observed. There was an effect of genotype on this relationship, such that time to fruit maturation at a given temperature was greatest for the clone UF 676 and least for AMAZ 15/15. Analysis of base temperatures, derived from these relationships indicated genetic variability in sensitivity of cacao fruit growth to temperature (base temperatures ranged from 7.5 degrees C for Amelonado and AMAZ 15/15 to 12.9 for SPEC 54/1). Final fruit size was a positive function of beam number for all genotypes and a positive function of light integral for Amelonado in the Malaysia simulated environment (where the temperature was almost constant). In simulated environments where temperature was the main variable (Brazil and Ghana) increases in temperature resulted in a significant decrease in final pod size for one genotype (Amelonado) in Brazil and for two genotypes (SPEC 54/1 and UF 676) in Ghana. It was hypothesised that pod growth duration (mediated by temperature), assimilation and beam number are all determinants of final pod size but that under specific conditions one of these factors may override the others. There was variability between genotypes in the response of beam size and beam lipid content to temperature. Negative relationships between temperature and bean size were found for Amelonado and UF 676. Lipid concentration was a curvilinear function of temperature for Amelonado and UF 676, with optimal temperatures of 23 degrees C and 24 degrees C, respectively. The variability observed here of different cacao genotypes to temperature highlights the need and opportunities for appropriate matching of planting material with local environments.

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A wide range of cell culture, animal and human epidemiological studies are suggestive of a role of vitamin E (VE) in brain function and in the prevention of neurodegeneration. However, the underlying molecular mechanisms remain largely unknown. In the current investigation Affymetrix gene chip technology was utilised to establish the impact of chronic VE deficiency on hippocampal genes expression. Male albino rats were fed either a VE deficient or standard diet (60 mg/kg feed) for a period of 9 months. Rats were sacrificed, the hippocampus removed and genes expression established in individual animals. VE deficiency showed to have a strong impact on genes expression in the hippocampus. An important number of genes found to be regulated by VE was associated with hormones and hormone metabolism, nerve growth factor, apoptosis, dopaminergic neurotransmission, and clearance of amyloid-beta and advanced glycated endproducts. In particular, VE strongly affected the expression of an array of genes encoding for proteins directly or indirectly involved in the clearance of amyloid beta, changes which are consistent with a protective effect of VE on Alzheimer's disease progression.

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Here we introduce a computer database that allows for the rapid retrieval of physicochemical properties, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes information about a protein or a list of proteins. We applied PIGOK analyzing Schizosaccharomyces pombe proteins displaying differential expression under oxidative stress and identified their biological functions and pathways. The database is available on the Internet at http://pc4-133.ludwig.ucl.ac.uk/pigok.html.

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Acute doses of Ginkgo biloba have been shown to improve attention and memory in young, healthy participants, but there has been a lack of investigation into possible effects on executive function. In addition, only one study has investigated the effects of chronic treatment in young volunteers. This study was conducted to compare the effects of ginkgo after acute and chronic treatment on tests of attention, memory and executive function in healthy university students. Using a placebo-controlled double-blind design, in experiment 1, 52 students were randomly allocated to receive a single dose of ginkgo (120 mg, n=26) or placebo (n=26), and were tested 4h later. In experiment 2, 40 students were randomly allocated to receive ginkgo (120 mg/day; n=20) or placebo (n=20) for a 6-week period and were tested at baseline and after 6 weeks of treatment. In both experiments, participants underwent tests of sustained attention, episodic and working memory, mental flexibility and planning, and completed mood rating scales. The acute dose of ginkgo significantly improved performance on the sustained-attention task and pattern-recognition memory task; however, there were no effects on working memory, planning, mental flexibility or mood. After 6 weeks of treatment, there were no significant effects of ginkgo on mood or any of the cognitive tests. In line with the literature, after acute administration ginkgo improved performance in tests of attention and memory. However, there were no effects after 6 weeks, suggesting that tolerance develops to the effects in young, healthy participants.

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This paper analyzes the performance of Enhanced relay-enabled Distributed Coordination Function (ErDCF) for wireless ad hoc networks under transmission errors. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduces energy consumption compared to IEEE 802.11 Distributed Coordination Function (DCF) in an ideal channel environment. However, there is a possibility that this expected gain may decrease in the presence of transmission errors. In this work, we modify the saturation throughput model of ErDCF to accurately reflect the impact of transmission errors under different rate combinations. It turns out that the throughput gain of ErDCF can still be maintained under reasonable link quality and distance.

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In this paper we evaluate the performance of our earlier proposed enhanced relay-enabled distributed coordination function (ErDCF) for wireless ad hoc networks. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduced energy consumption compared to IEEE 802.11 distributed coordination function (DCF). This is a result of. 1) using relay which helps to increase the throughput and lower overall blocking time of nodes due to faster dual-hop transmission, 2) using dynamic preamble (i.e. using short preamble for the relay transmission) which further increases the throughput and lower overall blocking time and also by 3) reducing unnecessary overhearing (by other nodes not involved in transmission). We evaluate the throughput and energy performance of the ErDCF with different rate combinations. ErDCF (11,11) (ie. R1=R2=11 Mbps) yields a throughput improvement of 92.9% (at the packet length of 1000 bytes) and an energy saving of 72.2% at 50 nodes.

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This paper analyzes the performance of enhanced relay-enabled distributed coordination function (ErDCF) for wireless ad hoc networks under transmission errors. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduces energy consumption compared to IEEE 802.11 distributed coordination function (DCF) in an ideal channel environment. However, there is a possibility that this expected gain may decrease in the presence of transmission errors. In this work, we modify the saturation throughput model of ErDCF to accurately reflect the impact of transmission errors under different rate combinations. It turns out that the throughput gain of ErDCF can still be maintained under reasonable link quality and distance.

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Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.