Purified phenolics from hydrothermal treatments of biomass: ability to protect sunflower bulk oil and model food emulsions from oxidation

The phenolic fractions released during hydrothermal treatment of selected feedstocks (corn cobs, eucalypt wood chips, almond shells, chestnut burs, and white grape pomace) were selectively recovered by extraction with ethyl acetate and washed with ethanol/water solutions. The crude extracts were purified by a relatively simple adsorption technique using a commercial polymeric, nonionic resin. Utilization of 96% ethanol as eluting agent resulted in 47.0-72.6% phenolic desorption, yielding refined products containing 49-60% w/w phenolics (corresponding to 30-58% enrichment with respect to the crude extracts). The refined extracts produced from grape pomace and from chestnut burs were suitable for protecting bulk oil and oil-in-water and water-in-oil emulsions. A synergistic action with bovine serum albumin in the emulsions was observed.

The timing and selection ability of UK property funds: an empirical investigation

This paper examines the selectivity and market timing performance of a sample of 21 UK property funds over the period Q3 1977 through to Q2 1987. The main finding of which that there is evidence of some superior selectivity performance on the part of UK property funds but that there are few funds who are able to successfully time the market.

Measuring lexical diversity among L2 learners of French: an exploration of the validity of D, MTLD and HD-D as measures of language ability

In this study two new measures of lexical diversity are tested for the first time on French. The usefulness of these measures, MTLD (McCarthy and Jarvis (2010 and this volume) ) and HD-D (McCarthy and Jarvis 2007), in predicting different aspects of language proficiency is assessed and compared with D (Malvern and Richards 1997; Malvern, Richards, Chipere and Durán 2004) and Maas (1972) in analyses of stories told by two groups of learners (n=41) of two different proficiency levels and one group of native speakers of French (n=23). The importance of careful lemmatization in studies of lexical diversity which involve highly inflected languages is also demonstrated. The paper shows that the measures of lexical diversity under study are valid proxies for language ability in that they explain up to 62 percent of the variance in French C-test scores, and up to 33 percent of the variance in a measure of complexity. The paper also provides evidence that dependence on segment size continues to be a problem for the measures of lexical diversity discussed in this paper. The paper concludes that limiting the range of text lengths or even keeping text length constant is the safest option in analysing lexical diversity.

A neurofuzzy classifier for two class problems

A neurofuzzy classifier identification algorithm is introduced for two class problems. The initial fuzzy base construction is based on fuzzy clustering utilizing a Gaussian mixture model (GMM) and the analysis of covariance (ANOVA) decomposition. The expectation maximization (EM) algorithm is applied to determine the parameters of the fuzzy membership functions. Then neurofuzzy model is identified via the supervised subspace orthogonal least square (OLS) algorithm. Finally a logistic regression model is applied to produce the class probability. The effectiveness of the proposed neurofuzzy classifier has been demonstrated using a real data set.

Elastic-net prefiltering for two-class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.

An illustrative case study of precocious reading ability

This is a longitudinal case study of a child who taught herself to read before she went to school. This case study is drawn from a wider study of a group of precocious readers, all of whom had received no explicit instruction, but who had had positive literacy experiences in their homes. The subject of this study was able to read fluently at the age of 5 years and 4 months. Her reading was at least 5 years ahead of her chronological age and her spelling was 4 years ahead. Her reading speed was also very proficient. Moreover, tests indicated that her pseudoword reading was highly accurate and that she was highly proficient on a series of measures of phonemic awareness. Her performance was also assessed at the ages of 6, 7, and 11 years. She continued to show high levels of ability in all aspects of literacy. This study contrasts with recent case studies on very precocious readers who showed poor levels of phonological awareness and who were unable to spell at an early age.