Conditional Moment Closure for Turbulent Premixed Flames

The conditional moment closure (CMC) method has been successfully applied to various non-premixed combustion systems in the past, but its application to premixed flames is not fully tested and validated. The main difficulty is associated with the modeling of conditional scalar dissipation rate of the conditioning scalar, the progress variable. A simple algebraic model for the conditional dissipation rate is validated using DNS results of a V-flame. This model along with the standard k- turbulence modeling is used in computations of stoichiometric pilot stabilized Bunsen flames using the RANS-CMC method. A first-order closure is used for the conditional mean reaction rate. The computed non reacting and reacting scalars are in reasonable agreement with the experimental measurements and are consistent with earlier computations using flamelets and transported PDF methods. Sensitivity to chemical kinetic mechanism is also assessed. The results suggest that the CMC may be applied across the regimes of premixed combustion.

Kernel conditional quantile estimation via reduction revisited

Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. Moreover, it can directly be used in combination with tools and extensions of standard Gaussian Processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. No existing approach enjoys all of these desirable properties. Experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches. © 2009 IEEE.

Using sub-word-level information for confidence estimation with conditional random field models

The task of word-level confidence estimation (CE) for automatic speech recognition (ASR) systems stands to benefit from the combination of suitably defined input features from multiple information sources. However, the information sources of interest may not necessarily operate at the same level of granularity as the underlying ASR system. The research described here builds on previous work on confidence estimation for ASR systems using features extracted from word-level recognition lattices, by incorporating information at the sub-word level. Furthermore, the use of Conditional Random Fields (CRFs) with hidden states is investigated as a technique to combine information for word-level CE. Performance improvements are shown using the sub-word-level information in linear-chain CRFs with appropriately engineered feature functions, as well as when applying the hidden-state CRF model at the word level.

Computations of turbulent lean premixed combustion using conditional moment closure

Conditional Moment Closure (CMC) is a suitable method for predicting scalars such as carbon monoxide with slow chemical time scales in turbulent combustion. Although this method has been successfully applied to non-premixed combustion, its application to lean premixed combustion is rare. In this study the CMC method is used to compute piloted lean premixed combustion in a distributed combustion regime. The conditional scalar dissipation rate of the conditioning scalar, the progress variable, is closed using an algebraic model and turbulence is modelled using the standard k-e{open} model. The conditional mean reaction rate is closed using a first order CMC closure with the GRI-3.0 chemical mechanism to represent the chemical kinetics of methane oxidation. The PDF of the progress variable is obtained using a presumed shape with the Beta function. The computed results are compared with the experimental measurements and earlier computations using the transported PDF approach. The results show reasonable agreement with the experimental measurements and are consistent with the transported PDF computations. When the compounded effects of shear-turbulence and flame are strong, second order closures may be required for the CMC. © 2013 Copyright Taylor and Francis Group, LLC.

Utility of ITS1-5.8S-ITS2 sequences for species discrimination and phylogenetic inference of two closely related bucephalid digeneans (Digenea : Bucephalidae): Dollfustrema vaneyi and Dollfustrema hefeiensis

The complete internal transcribed spacer 1 (ITS1), 5.8S ribosomal DNA, and ITS2 region of the ribosomal DNA from 60 specimens belonging to two closely related bucephalid digeneans (Dollfustrema vaneyi and Dollfustrema hefeiensis) from different localities, hosts, and microhabitat sites were cloned to examine the level of sequence variation and the taxonomic levels to show utility in species identification and phylogeny estimation. Our data show that these molecular markers can help to discriminate the two species, which are morphologically very close and difficult to separate by classical methods. We found 21 haplotypes defined by 44 polymorphic positions in 38 individuals of D. vaneyi, and 16 haplotypes defined by 43 polymorphic positions in 22 individuals of D. hefeiensis. There is no shared haplotypes between the two species. Haplotype rather than nucleotide diversity is similar between the two species. Phylogenetic analyses reveal two robustly supported clades, one corresponding to D. vaneyi and the other corresponding to D. hefeiensis. However, the population structures between the two species seem to be incongruent and show no geographic and host-specific structure among them, further indicating that the two species may have had a more complex evolutionary history than expected.

PCR-RFLP analysis of mitochondrial DNA ND5/6 region among 3 subspecies of common carp (Cyprinus carpio L.) and its application to genetic discrimination of subspecies

Mitochondrial DNA ND5/6 region was studied by PCR-RFLP analysis among ten representative strains belonging to three subspecies (Cyprinus carpio carpio, Cyprinus carpio haematopterus and Cyprinus carpio rubrofuscus) of common carp (Cyprinus carpio L.). A total of 2.4 kb fragment was amplified and subjected to restriction endonuclease analysis with nine restriction endonucleases subsequently. The results indicated that each subspecies owned one hyplotype and four restriction enzymes (Dde I, HaeIII, Taq I and Mbo I) produced diagnostic restriction sites which could be used for discriminating the three subspecies and as molecular genetic markers for assistant selective breeding of common carp.

Fast Discrimination of Commerical Corn Varieties Based on Near Infrared Spectra

The existing methods for the discrimination of varieties of commodity corn seed are unable to process batch data and speed up identification, and very time consuming and costly. The present paper developed a new approach to the fast discrimination of varieties of commodity corn by means of near infrared spectral data. Firstly, the experiment obtained spectral data of 37 varieties of commodity corn seed with the Fourier transform near infrared spectrometer in the wavenurnber range from 4 000 to 12 000 cm (1). Secondly, the original data were pretreated using statistics method of normalization in order to eliminate noise and improve the efficiency of models. Thirdly, a new way based on sample standard deviation was used to select the characteristic spectral regions, and it can search very different wavenumbers among all wavenumbers and reduce the amount of data in part. Fourthly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 99.98%. Finally, according to the first ten components, recognition models were established based on BPR. For every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 900 samples of the other varieties were used as the second testing set. Calculation results showed that the average correctness recognition rate of the 37 varieties of corn seed was 94.3%. Testing results indicate that the discrimination method had higher precision than the discrimination of various kinds of commodity corn seed. In short, it is feasible to discriminate various varieties of commodity corn seed based on near infrared spectroscopy and BPR.

A New Discrimination Method of Maize Seed Varieties Based on Near-Infrared Spectroscopy

A new discrimination method for the maize seed varieties based on the near-infrared spectroscopy was proposed. The reflectance spectra of maize seeds were obtained by a FT-NIR spectrometer (12 000-4 000 cm(-1)). The original spectra data were preprocessed by first derivative method. Then the principal component analysis (PCA) was used to compress the spectra data. The principal components with the cumulate reliabilities more than 80% were used to build the discrimination models. The model was established by Psi-3 neuron based on biomimetic pattern recognition (BPR). Especially, the parameter of the covering index was proposed to assist to discriminating the variety of a seed sample. The authors tested the discrimination capability of the model through four groups of experiments. There were 10, 18, 26 and 34 varieties training the discrimination models in these experiments, respectively. Additionally, another seven maize varieties and nine wheat varieties were used to test the capability of the models to reject the varieties not participating in training the models. Each group of the experiment was repeated three times by selecting different training samples at random. The correct classification rates of the models in the four-group experiments were above 91. 8%. The correct rejection rates for the varieties not participating in training the models all attained above 95%. Furthermore, the performance of the discrimination models did not change obviously when using the different training samples. The results showed that this discrimination method can not only effectively recognize the maize seed varieties, but also reject the varieties not participating in training the model. It may be practical in the discrimination of maize seed varieties.

Evaluation of the thermal/optical reflectance method for discrimination between char- and soot-EC

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Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields, including work in cardiovascular research and drug toxicology. In this study, metabonomics method was employed to the diagnosis of Type 2 diabetes mellitus (DM2) based on serum lipid metabolites. The results suggested that serum fatty acid profiles determined by capillary gas chromatography combined with pattern recognition analysis of the data might provide an effective approach to the discrimination of Type 2 diabetic patients from healthy controls. And the applications of pattern recognition methods have improved the sensitivity and specificity greatly. (C) 2004 Elsevier B.V. All rights reserved.

Roundness parameters in barrier-lagoon systems in northeastern Shandong Peninsula, China: limitations to environmental discrimination

Twenty-five samples from six subenvironments in the barrier-lagoon systems in northeastern Shandong province, China, are examined. A statistical method is used to study the roundness variation of grains of different sizes. Roundness of very fine pebble and very coarse sand varies significantly in different subenvironments. It is possible to discriminate among aqueous depositional environments using the roundness of grains of these sizes. Roundness of grains finer than 0.84 φ is not distinguishable in different subenvironments.