Bias factor method using random sampling technique

Toward the practical use of the bias factor method for actual light water reactor core analyses, the bias factor method using the random sampling technique is newly proposed. The bias factor method is one of the correction methods using information of E/C values in existing measurable systems, to reduce biases and uncertainties of predicted core characteristics parameters. By the aid of the random sampling technique, our proposed bias factor method can be carried out using only forward calculations without any adjoint calculations, and can easily take into account burnup and thermal-hydraulic feedback effects, which are difficult points in the practical application to actual core analyses. Although the statistical error due to the random sampling technique is inevitable in the proposed method, the statistical error can be simply quantified by the resampling technique such as the bootstrap method. As one of the feasibility studies, effectiveness of the proposed method is verified through a numerical experiment which virtually simulates a typical equilibrium pressurized water reactor core. In this verification problem, it is clarified that E/C values of control rod worth at the beginning of cycle under the hot zero power condition are useful information to reduce biases and uncertainties of predicted assembly-wise power distributions during operation of hot full power.journal articl

高機能・環境に配慮した繊維材料およびその加工法に関する研究

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Protein Family Classification with Partial Least Squares

The quality of protein function predictions relies on appropriate training of protein classification methods. Performance of these methods can be affected when only a limited number of protein samples are available, which is often the case in divergent protein families. Whereas profile hidden Markov models and PSI-BLAST presented significant performance decrease in such cases, alignment-free partial least-squares classifiers performed consistently better even when used to identify short fragmented sequences. Keywords: partial least square • physico-chemical properties • amino acid composition • profile hidden Markov model • G-protein coupled receptor

Bias factor method using random sampling technique

Toward the practical use of the bias factor method for actual light water reactor core analyses, the bias factor method using the random sampling technique is newly proposed. The bias factor method is one of the correction methods using information of E/C values in existing measurable systems, to reduce biases and uncertainties of predicted core characteristics parameters. By the aid of the random sampling technique, our proposed bias factor method can be carried out using only forward calculations without any adjoint calculations, and can easily take into account burnup and thermal-hydraulic feedback effects, which are difficult points in the practical application to actual core analyses. Although the statistical error due to the random sampling technique is inevitable in the proposed method, the statistical error can be simply quantified by the resampling technique such as the bootstrap method. As one of the feasibility studies, effectiveness of the proposed method is verified through a numerical experiment which virtually simulates a typical equilibrium pressurized water reactor core. In this verification problem, it is clarified that E/C values of control rod worth at the beginning of cycle under the hot zero power condition are useful information to reduce biases and uncertainties of predicted assembly-wise power distributions during operation of hot full power.journal articl

The maximum-likelihood phylogeny of TAAR proteins from ten representative vertebrate species.

<p>Only representative TAAR proteins are included for each species. Four biogenic amine receptors (5HT4R: serotonin receptors, and H2R: histamine receptors) are used as the outgroup. The genes newly identified in this study are shown in italics. The numbers at internal branches show the bootstrap support values (%) for the maximum-likelihood and neighbor-joining phylogenies and the posterior probability (%) for the Bayesian phylogeny in this order. Supporting values are shown only for the internal branches that have at least one method supporting higher than 70%. For TAAR V, teleost TAARs, and lamprey TAAR-like, we followed the gene names given by Hashiguchi and Nishida [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151023#pone.0151023.ref026" target="_blank">26</a>]. The inset illustrates a current consensus of the vertebrate phylogeny with their approximate divergence times (MYA) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151023#pone.0151023.ref033" target="_blank">33</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151023#pone.0151023.ref034" target="_blank">34</a>].</p

Results from the 12 CDSs collinear with the L-strand and ND6 collinear with the H-strand, with results from individual paired-sample t-tests between P_UUA and P_AUA.

<p>Results from the 12 CDSs collinear with the L-strand and ND6 collinear with the H-strand, with results from individual paired-sample t-tests between P_UUA and P_AUA.</p

The number of TAAR genes within each TAAR subfamily for each therian species.

<p>The size of bubbles denotes the number of species where the corresponding TAAR genes are found. The average ω (dN/dS) calculated by the PAML M0 model for each TAAR subfamily is also plotted (open squares).</p

Figure 1

<p>Genomic reduction of AUA codons is associated with a reduction in methionine usage. P_AUA is defined in equation (1) and arcscine-transformed; N_Met – Number of methionine codons.</p

Distributions of nonsynonymous (dN) and synonymous (dS) rates estimated between human and rhesus macaque orthologs.

Frequency distributions of dN (left panels) and dS (right panels) are shown for the 1,131 genes where the two rhesus genome annotations have different coding sequences. The top panels show the distributions obtained using the rheMac2/NCBI annotation and the bottom panels show those obtained using the MacaM annotation.</p

The 3D-structural model of the elephant TAAR7a protein (cyan) superimposed with the turkey β1-adrenergic receptor (β1AR, gray).

<p>The ligand of the β1AR, dobutamine, is shown with the stick model. Positively selected sites are indicated by red (detected by the site model in TAAR7), green (detected by the branch-site model in flying fox TAAR7c and elephant TAAR7a), purple (detected by the site model in TAAR8), and brown (detected by the branch-site model in mouse TAAR8a). The transmembranes (TM) and internal/external loop (IC1-3 and EC1-3) regions as well as N-terminal (N) are labeled. The C-terminal is invisible locating behind TM1. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151023#pone.0151023.s006" target="_blank">S6 Fig</a> for more details.</p